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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
tensorflow/docs-l10n	site/zh-cn/tutorials/generative/cyclegan.ipynb	1	29249	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "v1CUZ0dkOo_F" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "qmkj-80IHxnd" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "_xnMOsbqHz61" }, "source": [ "# CycleGAN" ] }, { "cell_type": "markdown", "metadata": { "id": "Ds4o1h4WHz9U" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a href=\"https://tensorflow.google.cn/?hl=en\" data-md-type=\"link\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/generative/cyclegan.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/generative/cyclegan.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/generative/cyclegan.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "ITZuApL56Mny" }, "source": [ "本笔记演示了使用条件 GAN 进行的未配对图像到图像转换，如[使用循环一致的对抗网络进行未配对图像到图像转换](https://arxiv.org/abs/1703.10593) 中所述，也称之为 CycleGAN。论文提出了一种可以捕捉图像域特征并找出如何将这些特征转换为另一个图像域的方法，而无需任何成对的训练样本。\n", "\n", "本笔记假定您熟悉 Pix2Pix，您可以在 [Pix2Pix 教程](https://tensorflow.google.cn/tutorials/generative/pix2pix)中了解有关它的信息。CycleGAN 的代码与其相似，主要区别在于额外的损失函数，以及非配对训练数据的使用。\n", "\n", "CycleGAN 使用循环一致损失来使训练过程无需配对数据。换句话说，它可以从一个域转换到另一个域，而不需要在源域与目标域之间进行一对一映射。\n", "\n", "这为完成许多有趣的任务开辟了可能性，例如照片增强、图片着色、风格迁移等。您所需要的只是源数据集和目标数据集（仅仅是图片目录）\n", "\n", "![输出图像 1](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/horse2zebra_1.png?raw=1) ![输出图像 2](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/horse2zebra_2.png?raw=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "e1_Y75QXJS6h" }, "source": [ "## 设定输入管线" ] }, { "cell_type": "markdown", "metadata": { "id": "5fGHWOKPX4ta" }, "source": [ "安装 [tensorflow_examples](https://github.com/tensorflow/examples) 包，以导入生成器和判别器。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bJ1ROiQxJ-vY" }, "outputs": [], "source": [ "!pip install git+https://github.com/tensorflow/examples.git" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lhSsUx9Nyb3t" }, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YfIk2es3hJEd" }, "outputs": [], "source": [ "import tensorflow_datasets as tfds\n", "from tensorflow_examples.models.pix2pix import pix2pix\n", "\n", "import os\n", "import time\n", "import matplotlib.pyplot as plt\n", "from IPython.display import clear_output\n", "\n", "AUTOTUNE = tf.data.AUTOTUNE" ] }, { "cell_type": "markdown", "metadata": { "id": "iYn4MdZnKCey" }, "source": [ "## 输入管线\n", "\n", "本教程训练一个模型，以将普通马图片转换为斑马图片。您可以在[此处](https://tensorflow.google.cn/datasets/datasets#cycle_gan)获取该数据集以及类似数据集。\n", "\n", "如[论文](https://arxiv.org/abs/1703.10593)所述，将随机抖动和镜像应用到训练集。这是一些避免过拟合的图像增强技术。\n", "\n", "这类似于 [pix2pix](https://tensorflow.google.cn/tutorials/generative/pix2pix#load_the_dataset) 中所做的工作。\n", "\n", "- 在随机抖动中，图片大小调整为 `286 x 286`，随后被随机裁剪为 `256 x 256`。\n", "- 在随机镜像中，图片会从左到右随机翻转。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iuGVPOo7Cce0" }, "outputs": [], "source": [ "dataset, metadata = tfds.load('cycle_gan/horse2zebra',\n", " with_info=True, as_supervised=True)\n", "\n", "train_horses, train_zebras = dataset['trainA'], dataset['trainB']\n", "test_horses, test_zebras = dataset['testA'], dataset['testB']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2CbTEt448b4R" }, "outputs": [], "source": [ "BUFFER_SIZE = 1000\n", "BATCH_SIZE = 1\n", "IMG_WIDTH = 256\n", "IMG_HEIGHT = 256" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Yn3IwqhiIszt" }, "outputs": [], "source": [ "def random_crop(image):\n", " cropped_image = tf.image.random_crop(\n", " image, size=[IMG_HEIGHT, IMG_WIDTH, 3])\n", "\n", " return cropped_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "muhR2cgbLKWW" }, "outputs": [], "source": [ "# normalizing the images to [-1, 1]\n", "def normalize(image):\n", " image = tf.cast(image, tf.float32)\n", " image = (image / 127.5) - 1\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fVQOjcPVLrUc" }, "outputs": [], "source": [ "def random_jitter(image):\n", " # resizing to 286 x 286 x 3\n", " image = tf.image.resize(image, [286, 286],\n", " method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n", "\n", " # randomly cropping to 256 x 256 x 3\n", " image = random_crop(image)\n", "\n", " # random mirroring\n", " image = tf.image.random_flip_left_right(image)\n", "\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tyaP4hLJ8b4W" }, "outputs": [], "source": [ "def preprocess_image_train(image, label):\n", " image = random_jitter(image)\n", " image = normalize(image)\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VB3Z6D_zKSru" }, "outputs": [], "source": [ "def preprocess_image_test(image, label):\n", " image = normalize(image)\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RsajGXxd5JkZ" }, "outputs": [], "source": [ "train_horses = train_horses.cache().map(\n", " preprocess_image_train, num_parallel_calls=AUTOTUNE).shuffle(\n", " BUFFER_SIZE).batch(BATCH_SIZE)\n", "\n", "train_zebras = train_zebras.cache().map(\n", " preprocess_image_train, num_parallel_calls=AUTOTUNE).shuffle(\n", " BUFFER_SIZE).batch(BATCH_SIZE)\n", "\n", "test_horses = test_horses.map(\n", " preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(\n", " BUFFER_SIZE).batch(BATCH_SIZE)\n", "\n", "test_zebras = test_zebras.map(\n", " preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(\n", " BUFFER_SIZE).batch(BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e3MhJ3zVLPan" }, "outputs": [], "source": [ "sample_horse = next(iter(train_horses))\n", "sample_zebra = next(iter(train_zebras))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4pOYjMk_KfIB" }, "outputs": [], "source": [ "plt.subplot(121)\n", "plt.title('Horse')\n", "plt.imshow(sample_horse[0] * 0.5 + 0.5)\n", "\n", "plt.subplot(122)\n", "plt.title('Horse with random jitter')\n", "plt.imshow(random_jitter(sample_horse[0]) * 0.5 + 0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0KJyB9ENLb2y" }, "outputs": [], "source": [ "plt.subplot(121)\n", "plt.title('Zebra')\n", "plt.imshow(sample_zebra[0] * 0.5 + 0.5)\n", "\n", "plt.subplot(122)\n", "plt.title('Zebra with random jitter')\n", "plt.imshow(random_jitter(sample_zebra[0]) * 0.5 + 0.5)" ] }, { "cell_type": "markdown", "metadata": { "id": "hvX8sKsfMaio" }, "source": [ "## 导入并重用 Pix2Pix 模型" ] }, { "cell_type": "markdown", "metadata": { "id": "cGrL73uCd-_M" }, "source": [ "通过安装的 [tensorflow_examples](https://github.com/tensorflow/examples) 包导入 [Pix2Pix](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/pix2pix/pix2pix.py) 中的生成器和判别器。\n", "\n", "本教程中使用模型体系结构与 [pix2pix](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/pix2pix/pix2pix.py) 中所使用的非常相似。一些区别在于：\n", "\n", "- Cyclegan 使用 [instance normalization（实例归一化）](https://arxiv.org/abs/1607.08022)而不是 [batch normalization （批归一化）](https://arxiv.org/abs/1502.03167)。\n", "- [CycleGAN 论文](https://arxiv.org/abs/1703.10593)使用一种基于 `resnet` 的改进生成器。简单起见，本教程使用的是改进的 `unet` 生成器。\n", "\n", "这里训练了两个生成器（G 和 F）以及两个判别器（X 和 Y）。\n", "\n", "- 生成器 `G` 学习将图片 `X` 转换为 `Y`。 $(G: X -> Y)$\n", "- 生成器 `F` 学习将图片 `Y` 转换为 `X`。 $(F: Y -> X)$\n", "- 判别器 `D_X` 学习区分图片 `X` 与生成的图片 `X` (`F(Y)`)。\n", "- 判别器 `D_Y` 学习区分图片 `Y` 与生成的图片 `Y` (`G(X)`)。\n", "\n", "![Cyclegan 模型](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/cyclegan_model.png?raw=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8ju9Wyw87MRW" }, "outputs": [], "source": [ "OUTPUT_CHANNELS = 3\n", "\n", "generator_g = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')\n", "generator_f = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')\n", "\n", "discriminator_x = pix2pix.discriminator(norm_type='instancenorm', target=False)\n", "discriminator_y = pix2pix.discriminator(norm_type='instancenorm', target=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wDaGZ3WpZUyw" }, "outputs": [], "source": [ "to_zebra = generator_g(sample_horse)\n", "to_horse = generator_f(sample_zebra)\n", "plt.figure(figsize=(8, 8))\n", "contrast = 8\n", "\n", "imgs = [sample_horse, to_zebra, sample_zebra, to_horse]\n", "title = ['Horse', 'To Zebra', 'Zebra', 'To Horse']\n", "\n", "for i in range(len(imgs)):\n", " plt.subplot(2, 2, i+1)\n", " plt.title(title[i])\n", " if i % 2 == 0:\n", " plt.imshow(imgs[i][0] * 0.5 + 0.5)\n", " else:\n", " plt.imshow(imgs[i][0] * 0.5 * contrast + 0.5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "O5MhJmxyZiy9" }, "outputs": [], "source": [ "plt.figure(figsize=(8, 8))\n", "\n", "plt.subplot(121)\n", "plt.title('Is a real zebra?')\n", "plt.imshow(discriminator_y(sample_zebra)[0, ..., -1], cmap='RdBu_r')\n", "\n", "plt.subplot(122)\n", "plt.title('Is a real horse?')\n", "plt.imshow(discriminator_x(sample_horse)[0, ..., -1], cmap='RdBu_r')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "0FMYgY_mPfTi" }, "source": [ "## 损失函数" ] }, { "cell_type": "markdown", "metadata": { "id": "JRqt02lupRn8" }, "source": [ "在 CycleGAN 中，没有可训练的成对数据，因此无法保证输入 `x` 和目标 `y` 数据对在训练期间是有意义的。所以为了强制网络学习正确的映射，作者提出了循环一致损失。\n", "\n", "判别器损失和生成器损失和 [pix2pix](https://google.tensorflow.cn/tutorials/generative/pix2pix#define_the_loss_functions_and_the_optimizer) 中所使用的类似。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cyhxTuvJyIHV" }, "outputs": [], "source": [ "LAMBDA = 10" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q1Xbz5OaLj5C" }, "outputs": [], "source": [ "loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wkMNfBWlT-PV" }, "outputs": [], "source": [ "def discriminator_loss(real, generated):\n", " real_loss = loss_obj(tf.ones_like(real), real)\n", "\n", " generated_loss = loss_obj(tf.zeros_like(generated), generated)\n", "\n", " total_disc_loss = real_loss + generated_loss\n", "\n", " return total_disc_loss * 0.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "90BIcCKcDMxz" }, "outputs": [], "source": [ "def generator_loss(generated):\n", " return loss_obj(tf.ones_like(generated), generated)" ] }, { "cell_type": "markdown", "metadata": { "id": "5iIWQzVF7f9e" }, "source": [ "循环一致意味着结果应接近原始输出。例如，将一句英文译为法文，随后再从法文翻译回英文，最终的结果句应与原始句输入相同。\n", "\n", "在循环一致损失中，\n", "\n", "- 图片 $X$ 通过生成器 $G$ 传递，该生成器生成图片 $\\hat{Y}$。\n", "- 生成的图片 $\\hat{Y}$ 通过生成器 $F$ 传递，循环生成图片 $\\hat{X}$。\n", "- 在 $X$ 和 $\\hat{X}$ 之间计算平均绝对误差。\n", "\n", "$$forward\\ cycle\\ consistency\\ loss: X -> G(X) -> F(G(X)) \\sim \\hat{X}$$\n", "\n", "$$backward\\ cycle\\ consistency\\ loss: Y -> F(Y) -> G(F(Y)) \\sim \\hat{Y}$$\n", "\n", "![循环损失](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/cycle_loss.png?raw=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NMpVGj_sW6Vo" }, "outputs": [], "source": [ "def calc_cycle_loss(real_image, cycled_image):\n", " loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))\n", " \n", " return LAMBDA * loss1" ] }, { "cell_type": "markdown", "metadata": { "id": "U-tJL-fX0Mq7" }, "source": [ "如上所示，生成器 $G$ 负责将图片 $X$ 转换为 $Y$。一致性损失表明，如果您将图片 $Y$ 馈送给生成器 $G$，它应当生成真实图片 $Y$ 或接近于 $Y$ 的图片。\n", "\n", "$$Identity\\ loss = \|G(Y) - Y\| + \|F(X) - X\|$$\n", "\n", "$$Identity\\ loss = \|G(Y) - Y\| + \|F(X) - X\|$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "05ywEH680Aud" }, "outputs": [], "source": [ "def identity_loss(real_image, same_image):\n", " loss = tf.reduce_mean(tf.abs(real_image - same_image))\n", " return LAMBDA * 0.5 * loss" ] }, { "cell_type": "markdown", "metadata": { "id": "G-vjRM7IffTT" }, "source": [ "为所有生成器和判别器初始化优化器。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iWCn_PVdEJZ7" }, "outputs": [], "source": [ "generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n", "generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n", "\n", "discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n", "discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)" ] }, { "cell_type": "markdown", "metadata": { "id": "aKUZnDiqQrAh" }, "source": [ "## Checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WJnftd5sQsv6" }, "outputs": [], "source": [ "checkpoint_path = \"./checkpoints/train\"\n", "\n", "ckpt = tf.train.Checkpoint(generator_g=generator_g,\n", " generator_f=generator_f,\n", " discriminator_x=discriminator_x,\n", " discriminator_y=discriminator_y,\n", " generator_g_optimizer=generator_g_optimizer,\n", " generator_f_optimizer=generator_f_optimizer,\n", " discriminator_x_optimizer=discriminator_x_optimizer,\n", " discriminator_y_optimizer=discriminator_y_optimizer)\n", "\n", "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n", "\n", "# if a checkpoint exists, restore the latest checkpoint.\n", "if ckpt_manager.latest_checkpoint:\n", " ckpt.restore(ckpt_manager.latest_checkpoint)\n", " print ('Latest checkpoint restored!!')" ] }, { "cell_type": "markdown", "metadata": { "id": "Rw1fkAczTQYh" }, "source": [ "## 训练\n", "\n", "注意：本示例模型比论文中训练了更少的 epoch（本示例为 40 epoch，论文中为 200 epoch），以使训练时间相对于本教程是合理的。预测的准确率可能会低一些。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NS2GWywBbAWo" }, "outputs": [], "source": [ "EPOCHS = 40" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RmdVsmvhPxyy" }, "outputs": [], "source": [ "def generate_images(model, test_input):\n", " prediction = model(test_input)\n", " \n", " plt.figure(figsize=(12, 12))\n", "\n", " display_list = [test_input[0], prediction[0]]\n", " title = ['Input Image', 'Predicted Image']\n", "\n", " for i in range(2):\n", " plt.subplot(1, 2, i+1)\n", " plt.title(title[i])\n", " # getting the pixel values between [0, 1] to plot it.\n", " plt.imshow(display_list[i] * 0.5 + 0.5)\n", " plt.axis('off')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "kE47ERn5fyLC" }, "source": [ "尽管训练循环看起来很复杂，其实包含四个基本步骤：\n", "\n", "- 获取预测。\n", "- 计算损失值。\n", "- 使用反向传播计算损失值。\n", "- 将梯度应用于优化器。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KBKUV2sKXDbY" }, "outputs": [], "source": [ "@tf.function\n", "def train_step(real_x, real_y):\n", " # persistent is set to True because the tape is used more than\n", " # once to calculate the gradients.\n", " with tf.GradientTape(persistent=True) as tape:\n", " # Generator G translates X -> Y\n", " # Generator F translates Y -> X.\n", " \n", " fake_y = generator_g(real_x, training=True)\n", " cycled_x = generator_f(fake_y, training=True)\n", "\n", " fake_x = generator_f(real_y, training=True)\n", " cycled_y = generator_g(fake_x, training=True)\n", "\n", " # same_x and same_y are used for identity loss.\n", " same_x = generator_f(real_x, training=True)\n", " same_y = generator_g(real_y, training=True)\n", "\n", " disc_real_x = discriminator_x(real_x, training=True)\n", " disc_real_y = discriminator_y(real_y, training=True)\n", "\n", " disc_fake_x = discriminator_x(fake_x, training=True)\n", " disc_fake_y = discriminator_y(fake_y, training=True)\n", "\n", " # calculate the loss\n", " gen_g_loss = generator_loss(disc_fake_y)\n", " gen_f_loss = generator_loss(disc_fake_x)\n", " \n", " total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)\n", " \n", " # Total generator loss = adversarial loss + cycle loss\n", " total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)\n", " total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)\n", "\n", " disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)\n", " disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)\n", " \n", " # Calculate the gradients for generator and discriminator\n", " generator_g_gradients = tape.gradient(total_gen_g_loss, \n", " generator_g.trainable_variables)\n", " generator_f_gradients = tape.gradient(total_gen_f_loss, \n", " generator_f.trainable_variables)\n", " \n", " discriminator_x_gradients = tape.gradient(disc_x_loss, \n", " discriminator_x.trainable_variables)\n", " discriminator_y_gradients = tape.gradient(disc_y_loss, \n", " discriminator_y.trainable_variables)\n", " \n", " # Apply the gradients to the optimizer\n", " generator_g_optimizer.apply_gradients(zip(generator_g_gradients, \n", " generator_g.trainable_variables))\n", "\n", " generator_f_optimizer.apply_gradients(zip(generator_f_gradients, \n", " generator_f.trainable_variables))\n", " \n", " discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,\n", " discriminator_x.trainable_variables))\n", " \n", " discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,\n", " discriminator_y.trainable_variables))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2M7LmLtGEMQJ" }, "outputs": [], "source": [ "for epoch in range(EPOCHS):\n", " start = time.time()\n", "\n", " n = 0\n", " for image_x, image_y in tf.data.Dataset.zip((train_horses, train_zebras)):\n", " train_step(image_x, image_y)\n", " if n % 10 == 0:\n", " print ('.', end='')\n", " n+=1\n", "\n", " clear_output(wait=True)\n", " # 使用一致的图像（sample_horse），以便模型的进度清晰可见。\n", " generate_images(generator_g, sample_horse)\n", "\n", " if (epoch + 1) % 5 == 0:\n", " ckpt_save_path = ckpt_manager.save()\n", " print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,\n", " ckpt_save_path))\n", "\n", " print ('Time taken for epoch {} is {} sec\\n'.format(epoch + 1,\n", " time.time()-start))" ] }, { "cell_type": "markdown", "metadata": { "id": "1RGysMU_BZhx" }, "source": [ "## 使用测试数据集进行生成" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KUgSnmy2nqSP" }, "outputs": [], "source": [ "# Run the trained model on the test dataset\n", "for inp in test_horses.take(5):\n", " generate_images(generator_g, inp)" ] }, { "cell_type": "markdown", "metadata": { "id": "ABGiHY6fE02b" }, "source": [ "## 下一步\n", "\n", "本教程展示了如何从 [Pix2Pix](https://tensorflow.google.cn/tutorials/generative/pix2pix) 教程实现的生成器和判别器开始实现 CycleGAN。下一步，您可以尝试使用一个来源于 [TensorFlow 数据集](https://tensorflow.google.cn/datasets/datasets#cycle_gan)的不同的数据集。\n", "\n", "您也可以训练更多的 epoch 以改进结果，或者可以实现[论文](https://arxiv.org/abs/1703.10593)中所使用的改进 ResNet 生成器来代替这里使用的 U-Net 生成器。" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "cyclegan.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
balmandhunter/jupyter-tips-and-tricks	notebooks/01-Initial_Demo.ipynb	4	238864	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Some Notebook Possibilities\n", "\n", "http://mpld3.github.io/examples/linked_brush.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# uncomment the bottom line in this cell, change the final line of \n", "# the loaded script to `mpld3.display()` (instead of show).\n", "# when it doesn't work, try to install mpld3 by: conda install mpld3\n", "# when that fails: pip install mpld3, then rerun. :)\n", "# %load http://mpld3.github.io/_downloads/linked_brush.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# %load http://mpld3.github.io/_downloads/linked_brush.py\n", "\"\"\"\n", "Linked Brushing Example\n", "=======================\n", "This example uses the standard Iris dataset and plots it with a linked brushing\n", "tool for dynamically exploring the data. The paintbrush button at the bottom\n", "left can be used to enable and disable the behavior.\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import load_iris\n", "\n", "import mpld3\n", "from mpld3 import plugins, utils" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = load_iris()\n", "X = data.data\n", "y = data.target" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# dither the data for clearer plotting\n", "X += 0.1 * np.random.random(X.shape)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div 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\"id\": \"el197564503614608\"});\n", " })\n", " });\n", "}\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, ax = plt.subplots(4, 4, sharex=\"col\", sharey=\"row\", figsize=(8, 8))\n", "fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95,\n", " hspace=0.1, wspace=0.1)\n", "\n", "for i in range(4):\n", " for j in range(4):\n", " points = ax[3 - i, j].scatter(X[:, j], X[:, i],\n", " c=y, s=40, alpha=0.6)\n", "\n", "# remove tick labels\n", "for axi in ax.flat:\n", " for axis in [axi.xaxis, axi.yaxis]:\n", " axis.set_major_formatter(plt.NullFormatter())\n", "\n", "# Here we connect the linked brush plugin\n", "plugins.connect(fig, plugins.LinkedBrush(points))\n", "\n", "# mpld3.show()\n", "mpld3.display()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
zhongyuanzhou/FCH808.github.io	Intro to Machine Learning/notebooks/.ipynb_checkpoints/Final Version 3-Copy1-checkpoint.ipynb	2	1438688	null	mit
keflavich/image_registration	examples/Cross Correlation.ipynb	1	158024	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The Cross-Correlation package is available on github: https://github.com/keflavich/image_registration.\n", "\n", "The goal is to determine the offset between two images with primarily extended structure." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings\n", "import pylab as pl\n", "# import statement (with warnings silenced). \n", "with warnings.catch_warnings():\n", " warnings.filterwarnings(\"ignore\")\n", " import image_registration\n", "errmsgs = np.seterr(all='ignore') # silence warning messages about div-by-zero" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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7My59EZGRBV7o2bTlUFlAZZl9IHOkx8yD/OV2y3yd4KaWknbWzOkqxeeU6yQp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3Chxl9Ueq7kkCul67H/CTHNM2EqAN3UdwI38R25JeF1zfPVlDXtn7Dw1kIy3jHhNe1wLYtIuZlI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\np3ukFYRfrFixYsWKXQPbHYQ/6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"text/plain": [ "<matplotlib.figure.Figure at 0x1081abf60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a simulated image by randomly sampling from a power-law power spectrum with alpha=2\n", "im1 = image_registration.tests.make_extended(100)\n", "# create an offset version corrupted by noise\n", "im2 = image_registration.tests.make_offset_extended(im1, 4.76666, -12.33333333333333333333333, noise=0.1)\n", "pl.subplot(121); img1=pl.imshow(im1, cmap='viridis', interpolation='nearest')\n", "pl.subplot(122); img2=pl.imshow(im2, cmap='viridis', interpolation='nearest')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Run the registration methods 100 times each (and hide the output)\n", "offsets_n1,eoffsets_n1 = image_registration.tests.compare_methods(im1,im2,noise=0.1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Standard Deviations: [ 0. 0. 0. 0. 0. 0. 0. 0.]\n", "Error Means: [ 0.00497512 0.00497512 0.11975409 0.1193227 0. 0.\n", " 0.00585938 0.00390625]\n", "emeans/stds: [ inf inf inf inf nan nan inf inf]\n" ] }, { "data": { "image/png": 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CxcCPgHtjjM0fC5I6seeee669m9Cgjtw2SW2Xy5VLryU9wXMB0Cfz7zLS63cA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\n+91xGTy0T/yh5oL97lrsd9d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"text/plain": [ "<matplotlib.figure.Figure at 0x115e172b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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k4CEiIklhzpw5TJs2jWbNmnHDDTewbNmyeHcpKSl4iIhIwisqKmLChAm0b98e\ngJtvvpmPP/6YtWvXcvLkSbZu3RrnHiYPBQ8REUlob7zxBv369aN3795V28477zweeOAB5s6dy/Tp\n00lJ0cehU3Q5rYhIAtjj3sNed8NW2DxafpT5zOdo/lE2ZmwE4OB7B9k4wvvz8d3HOb77+KkdKiAl\nPYV/DfkXBLl/2on9J1iXua7eZcHabpHVghZZLaqe+/evLp3pDMDG31evP4xhDGs1jI4XdaRTj8CX\n90r0KXiIiCSATq5OdHI17MPT4/EwI3cGpcWl9M3pC8DGERvpu6Jvg/sTav9I245WGxIfOrYkIiIi\njlHwEBEREccoeIiIiIhjYhY8jDH3GWPWGWOOGGO+ClB+kTHmOWPMLmPMUWPMv40xd4XR7hJjzFbf\nPnuNMa8YY3rVqLPDGHPS71FhjJkazfGJiIhI/cVycmkz4I/ABmBygPJcYA8wFvgPkAc8YYw5Ya1d\nHKLd94ESYBfQHpgDvG6M6W6ttb46FpgBPAEY37ZDkQ1HREREIhWz4GGtnQNgjJkYpHxpjU07jDF5\nwPVA0OBhrX3S7+kuY8wM4B/AOcB2v7LD1tovGtB1ERERiZHGdjltBlDrtEwwxph0vEdTtuE9auJv\nmjHmfrxHRp4Diq21FdHqqIhIoikrK6v6+Wj5UTweT4PbCrV/pG1Hqw0Jn//fRqQaTfDwHe0YBQwN\no+7twK+BdGAzMNhae8Kvyu8AD94Qkwf8CsgECqPcbRGRJq9Dhw60atWKcePGVW2bz3xm5M5ocJuh\n9o+07Wi1IfXTqlUrOnToEHE79QoexpgHgXtDVLFAtrV2Sz3bvRB4BZhtrX0rjF1KgDeAznjDxIvG\nmDxr7TcA1tqFfnU/NMZ8AzxmjPmltfbbUA3n5+eTkZFRbZvL5cLlcoU/IBGRJqRr166UlZWxb9++\nqm1H849SWlza4DZD7R9p29FqQ+qnQ4cOdO3aFbfbjdvtrlZWXl4edjv1PeKxAKg5N6OmbfVp0BjT\nB1gFLLHWPhjOPtbaQ3gni35ijHkX2A9cB7wQZJf38I71HODjUG0XFxeTk5MTXudFRBJE165d6dq1\na9XzjRkbq1YxbYhQ+0fadrTakIYJ9GXc4/GQm5sb1v71Ch7W2i+BL+uzTyjGmAuAt4Cl1tr7G9hM\nCt4rV1qEqHMxcBJo2I0MREREJCpiuY7H2caYfkA3INUY08/3SPeVXwisAV4HFhpjOvkeHfzaOMsY\nU2aM+a4ElK9RAAAXOUlEQVTveXdjzDRjTI6v/TzgReAosNJX5zJjzN2+dUK6G2PGAr8FnrXWhn8s\nSCSBrV+/vtrzPe49cepJbZV9a0x9EpHoieXKpXPxTvCcBbT2/ezBu34HwA3AGcA44L9+j/f82mgG\nnA+08j0/BlwBvIb3lIkbKAfyrLWVJyePA6OBt4EPgV8CRcDPozw+kSZp9uzZ9O/fn6KioqptDb2r\nabQVFRXRv39/Zs+e3Wj6JCLRFct1PCYBk0KUz8G7+FeoNnbid2Nka+1nwLA69vkAuLxenRVJErNn\nz2bOHO//doWF3ou8CgoK4tmlKkVFRVV9mjNnDnvO38OjPBrnXolItOleLSJJYv369VWho1JhYSFF\nfpdQOs43M94/dFRasmVJrVNCItL0KXiIJIm8vDwWLFhQa3vh8uUs+2RZHHoEuN0BQwdAQZ8C8vLy\n4tApEYklBQ+RJFJQUBAwfBRtKqo258MpRZ98EjB0LFiwgIk9A95tQUSaOAUPkSQTLHwUFhY6Gj6K\niooo3LSp1vYFCxY0mnknyayjq2PM9o+07Wi1IfGh4CGShOIdPoKdXlHoaDw6uTrFbP9I245WGxIf\nCh4iSSpe4UOhQyS5KXiIJLGCggIW9OlTa3uswodCh4goeIgkuYKePSnoU/tDP9rhQ6FDREDBQ0SA\niT0nxvS0S9DQ0aePQodIklHwEBEgdnM+Qh7p6Nmzwe2KSNOk4CEiVaIdPnR6RURqUvAQkWoiDR9b\n7twCKHSISGAxu0mciDRdlcGgZnAI58ZyX7z4BX8+588KHSISkIKHiATU0PDhPuLmkcJHam1X6BAR\nUPAQkRDqDB87dsCiRVXbi4qKeOSwQoeIBKc5HiISUsg5H08/XfVcczpEJBw64iEidQp65OPwYfBN\nOFXoEJFwKHiISFjqOu1Sk0KHiASiUy0iErZgp11qUugQkWB0xEMkwe1x72Gve2/wCu+N5CAH2Thi\nY1jtDWYwBX0KKNoUeE2P283tXP361Wx8J4z23hsJQV734Hvh9amjq6NukS7ShCh4iCQSt9v78NPJ\n9wjuPTbyAH15NeyX6cwnQcuMPcYFx3/JaWnhvL28B1wSsGQjI2v3yeXyPkSkyVLwEEkkDflgHjEC\nuARW3BxW9aKiIgr//Oeg5YtZSlbOA9xXfF94r71iRZCyjWH3SUSaDs3xEJGwBbtktqbpC6dH5a62\nIpJ4FDxEJCxB1+mYMiUmd7UVkcSkUy0iwsljJznkORS0fNGzi5i+cHqt7b/BcOt3x0B2b45POV6r\nTmFhIcc/Pc6d4+8M2G7L8hN8HeR1Tx47WY8RiEhToeAhIhzbeYzS3NKAZX/kjzzKo7W2387tXM4g\nSscdAUrJI4/bub1W3ekLp/Ppwk8ZxahabaRyOxVBXrfl+S3rPxARafQUPESEtG5p9HH3qbV90bOL\neHRh7dDxwJQHuPO7g0gfdzlHSjZAdm8AssuyYRy1wsejPEqXKV1qHflomf8Tvi5+IWCftk3b1tDh\niEgjpuAhkuyOHSPl+CHa8HG1zUXPPsv0hQtrVV8wZQoF46+BsjKggjZsAVJ9pRWMYhRdxjRn+nO/\nq7bf9IXTacEXFIwff2pjixO0yWkTsFspaZqCJpKIFDxEkt3OnXTeUgi571ZtKgICXbuyAChYuBD8\nA8m4cVU/ppNKM15k2nMP0yJAG4W+favWND3//KgMQUSaDn2lEEl23brx2ZULoLQUSkspmjIlcOiY\nMoUCXx1KS6GkxFtQUlK17UjJBr6lHUdKNlBQWsqCKVNqtVMIFE2Z4t2nW7eYDk1EGh8d8RBJdmlp\nkJYBOX29l8wGOr0S6t4r2dmQk+N7cggo9c75yGlDQU4OdOlS+8ZyCxdCly4UpKVFdywi0ugpeIgk\nuHDv1VJ4QWHA+68U9Clg8DuDa997pfwovWnN5vyjkOEtO3nsJKR6J4ZWztEIdm+XwsJCPmt9ExN1\nrxaRpKLgIZLgOrk6hf5gHjGdwk/KAoaOkEc6PB7IPUzf4laQ07dq87rMdfR7o1/1dlhA56LOtY58\nFB1+kc4/uDTga2wcsZG+K/rW2i4iTZuCh0iSK/rkE4o21b73SrRvbV/ZVq3TLr7n0XwtEWm8NLlU\nJIkVFRVRuGlTre3RDh2VCgoKtLy6SJJT8BBJUkHvvRKj0FFJ4UMkuelUi0gicbu9jzoUffJJ4CMd\nffpQ8M478M47db/WsWOQmgrTpnmvjKm0/+fe292HUADQp0+tPhQWFsIf/kBBz57w3kgYUeP+MC6X\n9yEiTZaCh0giCeODuaioiMI/R2lOR2YmvPFGjW3rYMWKOnctALjggtrhY9MmmDyZwVwCK26uX39E\npNHTqRaRJBLs9EpBn4K4TO4s6Nkz6GmXZZ8sc7w/IhJ7Ch4iSWL9+vWB53T06cPEnhOj9jpn3nRm\nveoHm/NRtKmI9evXR6tbItJIKHiIJIm8vDxmzZpVbduCBQu88ymi6PxF9b//SqDwcdv5t5GXlxet\nbolII6HgIZJEZs+eXRU+Yn31Sn35h49Zs2bxi16/iHOPRCQWNLlUJMnMnj2bwYMHR+dowk03Rd6G\nn4KCAi6//HLy8vLCWi5dRJoeHfEQSUJRO4WxaFF02vGj0ysiiU3BQ0RERByj4CEiIiKOUfAQERER\nxyh4iIiIiGMUPERERMQxCh4iIiLiGAUPERERcYyCh4iIiDhGwUNEREQco+AhInR0dYx3F2ppjH0S\nkcgpeIgInVyd4t2FWhpjn0QkcgoeIiIi4hgFD5Fk53Il52uLSFwoeIgkOwUPEXGQgoeIiIg4RsFD\nREREHKPgISIiIo5R8BARERHHKHiIiIiIYxQ8RERExDEKHiIiIuIYBQ8RERFxzGnx7kBjU/ZFGXwW\n716ISKIr21dW7V+Rpqzsi/D/jo21NiadMMbcBwwDvgMct9a2r1F+ETAN+D7QAdgOPGat/X0d7S4B\nrgbOAg4D64F7rbUf+dVpBzwMDAdOAn8C7rbWHgnRbg5Qyq2+lkVERCQ8/wUeByDXWusJVTWWRzya\nAX8ENgCTA5TnAnuAscB/gDzgCWPMCWvt4hDtvg+UALuA9sAc4HVjTHd7KkU9B3QCBgLNgaeBx4Bx\ndXW65PoSsi/KrnNwIiKRKNtXxriXx3nfczroPUeatrJ/lTHu8To/YoEYBg9r7RwAY8zEIOVLa2za\nYYzJA64HggYPa+2Tfk93GWNmAP8AzgG2G2OygSF4U9cHvj7cCbxmjCm01n4eqt/ZZ2aT0zkn5NhE\nRKIlu4PecyQB1GOKQmObXJoBfBVuZWNMOt6jKdvwHjUBuAzYXxk6fFYBFrg0Sv0UERGRBmg0wcN3\ntGMU3lMiddW93RhzCDiE9+jGYGvtCV9xJrDXv761tgJvoMmMaqdFRESkXup1qsUY8yBwb4gqFsi2\n1m6pZ7sXAq8As621b4WxSwnwBtAZKAReNMbkWWu/qc/rBpKfn09GRka1bS6XC5du3y0iIoLb7cbt\ndlfbVl5eHvb+9Z3jsQCoOTejpm31adAY0wfvqZAl1toHw9nHWlt5tOMTY8y7wH7gOuAF4HOgY43X\nSMU7ETXk/A6A4uJicnJ0vlV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"text/plain": [ "<matplotlib.figure.Figure at 0x113a917b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the simulation data\n", "# (note that the \"gaussian\" approach is hidden; it was problematic)\n", "image_registration.tests.plot_compare_methods(offsets_n1,eoffsets_n1,dx=4.76666666,dy=-12.3333333333)\n", "pl.figure(2); ax=pl.axis([4.7,4.85,-12.23,-12.43])\n", "pl.figure(1); ax=pl.axis([4.7,4.85,-12.23,-12.43])\n", "# the outputs below show the x,y standard deviations (i.e., the \"simulated error\"), \n", "# the means of the reported errors (i.e., the measured errors)\n", "# and the ratio of the measured error to the simulated error - should be ~1 if correct\n", "# the black X is the correct answer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Standard Deviations: [ 0. 0. 0. 0. 0. 0. 0. 0.]\n", "Error Means: [ 0.00497512 0.00497512 0.11975409 0.1193227 0. 0.\n", " 0.00585938 0.00390625]\n", "emeans/stds: [ inf inf inf inf nan nan inf inf]\n" ] }, { "data": { "image/png": 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NrEslVW8FZjnnXnXOfQCMxQsqlwE45/7hnPu+c+4159wW59xbwB3AJWbWpI7H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\nIqGgtRdEREQkFBRaREREJBQUWkRERCQ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"text/plain": [ "<matplotlib.figure.Figure at 0x115ef4da0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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eeWRkZER8X0OGDKFr166MHz+eX/ziFwAsXbo0Zbf6V2gREZFWYdSoUaxfv56F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\n4ALn3PsNOK+IiIj4jHlPA4uIiIi0bPr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"text/plain": [ "<matplotlib.figure.Figure at 0x114a1e160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the simulation data but zoomed in more (same as above otherwise)\n", "# (note that the \"gaussian\" approach is hidden; it was problematic)\n", "image_registration.tests.plot_compare_methods(offsets_n1,eoffsets_n1,dx=4.76666666,dy=-12.3333333333)\n", "pl.figure(2); ax=pl.axis([4.74,4.79,-12.32,-12.35])\n", "pl.figure(1); ax=pl.axis([4.74,4.79,-12.32,-12.35])\n", "# the outputs below show the x,y standard deviations (i.e., the \"simulated error\"), \n", "# the means of the reported errors (i.e., the measured errors)\n", "# and the ratio of the measured error to the simulated error - should be ~1 if correct\n", "# the black X is the correct answer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So how do these methods work?\n", "They all use the peak of the cross-correlation, which is most efficiently done via fourier transforms,\n", "to determine the offset.\n", "\n", "The \"cross_correlation_shift\" function selects the cross-correlation peak, then finds the sub-pixel shift using\n", "a second order Taylor expansion.\n", "\n", "The \"register_images\" function uses some linear algebra + fourier space tricks to upsample the image to determine \n", "sub-pixel shifts.\n", "\n", "The \"chi2_shift\" function uses the same trick, but \"automatically\" determines the upsampling factor based on the \n", "$\\Delta \\chi^2$ values. The peak is identified, as is a region within $1\\sigma$ (for 2 fitted parameters, \n", "$\\Delta \\chi^2<2.3$ , then the original image is magnified to include only the $1\\sigma$ region. \n", "The errors are determined by marginalizing over the other fitted parameter, BUT it is possible to return the full\n", "$\\Delta \\chi^2$ image if you are concerned with correlation." ] } ], "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
dib-lab/kevlar	notebook/bigsim/roc-separate-withscalpel.ipynb	1	146639	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "cannot import name 'load_scalpel_vcf'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-e9d98ac8ca3c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mevalutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIntervalForest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopulate_index_from_bed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompact\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mevalutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msubset_variants_bed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_kevlar_vcf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_scalpel_vcf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_gatk_mvf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_triodenovo_vcf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkevlar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cannot import name 'load_scalpel_vcf'" ] } ], "source": [ "%matplotlib inline\n", "import re\n", "import sys\n", "import math\n", "import matplotlib\n", "import seaborn\n", "import numpy\n", "from matplotlib import pyplot as plt\n", "from collections import defaultdict\n", "\n", "from evalutils import IntervalForest, populate_index_from_bed, compact\n", "from evalutils import subset_variants_bed, load_kevlar_vcf, load_scalpel_vcf, load_gatk_mvf, load_triodenovo_vcf\n", "import kevlar\n", "\n", "seaborn.set_context({'figure.figsize': (24, 12)})\n", "matplotlib.rcParams['axes.labelsize'] = 16\n", "matplotlib.rcParams['xtick.labelsize'] = 14\n", "matplotlib.rcParams['ytick.labelsize'] = 14" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def roc(calls, index, delta=10, fmt='vcf'):\n", " ncorrect = 0\n", " num_true_calls_per_false_call = list()\n", " for varcall in calls:\n", " if fmt == 'vcf':\n", " valid = index.query(varcall.seqid, varcall.position, delta=delta) != set()\n", " elif fmt == 'mvf':\n", " callindex, call = varcall\n", " valid = index.query(call['CHROM'], call['POS'], delta=delta) != set()\n", " else:\n", " raise ValueError('unknown format \"'+ fmt +'\"')\n", " if valid:\n", " ncorrect += 1\n", " continue\n", " num_true_calls_per_false_call.append(ncorrect)\n", " if len(num_true_calls_per_false_call) == 0 or ncorrect > num_true_calls_per_false_call[-1]:\n", " num_true_calls_per_false_call.append(ncorrect)\n", " return num_true_calls_per_false_call" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def doplot(axis, data, color, label, linestyle, symbol, msize):\n", " if len(data) == 1:\n", " axis.plot(range(len(data)), data, symbol, markersize=12, color=color, label=label)\n", " else:\n", " axis.plot(range(len(data)), data, color=color, linestyle=linestyle)\n", " rate = 1\n", " if len(data) > 10:\n", " rate = 3\n", " if len(data) > 50:\n", " rate = 6\n", " if len(data) > 200:\n", " rate = 25\n", " axis.plot(range(len(data)), data, symbol, markersize=msize, color=color, markevery=rate, label=label)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1728x864 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "delta = 10\n", "linestyle = '-'\n", "#for cov in ('10', '20', '30', '50'):\n", "for cov in (['30']):\n", " categories = [\n", " ('SNV', None, None, 'SNVs'),\n", " ('INDEL', 1, 10, 'INDELs 1-10bp'),\n", " ('INDEL', 11, 100, 'INDELs 11-100bp'),\n", " ('INDEL', 101, 200, 'INDELs 101-200bp'),\n", " ('INDEL', 201, 300, 'INDELs 201-300bp'),\n", " ('INDEL', 301, 400, 'INDELs 301-400bp'),\n", " ]\n", " fig, ((ax11, ax12, ax13), (ax21, ax22, ax23)) = plt.subplots(2, 3)\n", " axes = (ax11, ax12, ax13, ax21, ax22, ax23)\n", " seaborn.set_context({'figure.figsize': (24, 12)})\n", " \n", " for i, (category, axis) in enumerate(zip(categories, axes)):\n", " vartype, minlength, maxlength, label = category\n", " with kevlar.open('SimulatedVariants_chr17_hg38_markII.bed', 'r') as instream:\n", " variants = subset_variants_bed(instream, vartype, minlength, maxlength)\n", " index = populate_index_from_bed(variants)\n", " \n", " kevlar_truecalls = roc(\n", " load_kevlar_vcf(\n", " 'kevlar-calls-'+ cov +'x-nohomopoly.vcf.gz', index, delta=delta,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " scalpel_truecalls = roc(\n", " load_scalpel_vcf(\n", " 'scalpel.denovo.indel.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " gatk_truecalls = roc(\n", " load_gatk_mvf(\n", " 'JointCall-'+ cov +'x-PBT.mvf',\n", " vartype=vartype, minlength=minlength, maxlength=maxlength\n", " ).iterrows(),\n", " index, delta=delta, fmt='mvf'\n", " )\n", " triodenovo_truecalls = roc(\n", " load_triodenovo_vcf(\n", " 'JointCall-'+ cov +'x-TDN.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " doplot(axis, kevlar_truecalls, 'red', 'kevlar ({}x)'.format(cov), linestyle, 'o', 6)\n", " doplot(axis, scalpel_truecalls, 'purple', 'scalpel ({}x)'.format(cov), linestyle, '', 10)\n", " doplot(axis, gatk_truecalls, 'blue', 'GATK PBT ({}x)'.format(cov), linestyle, '^', 8)\n", " doplot(axis, triodenovo_truecalls, 'green', 'triodenovo ({}x)'.format(cov), linestyle, 'D', 6)\n", " \n", " nvariants = len(index.trees['chr17'])\n", " ticknums = [0, math.ceil(nvariants 0.25), int(nvariants * 0.5), math.ceil(nvariants * 0.75), nvariants]\n", " ticklabels = ['{:d}%\\n({:d})'.format(round(tn / nvariants * 100), tn) for tn in ticknums]\n", " \n", " _ = axis.set_xlabel('False calls', fontsize=16)\n", " xaxis_max = max([len(kevlar_truecalls), len(scalpel_truecalls), len(gatk_truecalls), len(triodenovo_truecalls)])\n", " if xaxis_max < 6:\n", " xticknums = list(range(xaxis_max))\n", " _ = axis.set_xticks(xticknums)\n", " _ = axis.set_yticks(ticknums)\n", " _ = axis.set_yticklabels(ticklabels)\n", " _ = axis.set_ylabel('True calls', fontsize=16)\n", " _ = axis.set_ylim((0, nvariants))\n", " _ = axis.set_title(label, fontsize=18)\n", " if i == 0:\n", " _ = axis.legend(fontsize=14)\n", " \n", " _ = plt.subplots_adjust(hspace=0.3)\n", " _ = plt.savefig('four-callers-'+ cov +'x-combined-sep.pdf', dpi=300)\n", " _ = plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[12, 23, 23, 23, 53, 54, 76, 78, 103, 106, 127, 127, 128, 146, 150, 150, 172, 175, 183, 190, 191, 198, 201, 208, 219, 221, 221, 225, 228, 229, 230, 232, 237, 241, 241, 241, 241, 241, 241, 242, 245, 245, 245, 245, 245, 245, 245, 247]\n", "48\n", "247\n", "263\n", "0.9391634980988594\n" ] } ], "source": [ "with kevlar.open('SimulatedVariants_chr17_hg38_markII.bed', 'r') as instream:\n", " variants = subset_variants_bed(instream, 'SNV')\n", " index = populate_index_from_bed(variants)\n", "\n", "triodenovo_truecalls = roc(\n", " load_triodenovo_vcf('JointCall-20x-TDN.vcf', cov='20', vartype='SNV'),\n", " index, delta=delta, fmt='vcf'\n", ")\n", "print(triodenovo_truecalls)\n", "print(len(triodenovo_truecalls))\n", "print(triodenovo_truecalls[-1])\n", "print(len(index.trees['chr17']))\n", "print(triodenovo_truecalls[-1] / len(index.trees['chr17']))" ] }, { "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
nate331/jbt_berkeley_coref_resolution	testing.ipynb	2	49362	{ "cells": [ { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--generating textfile with resolutions...\n", "-- number of resolved referenes: 712\n", "-- number of removed sents (lacking entity): 30\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import re\n", "import os, sys\n", "import nltk\n", "\n", "class CorefOutputParser:\n", " \n", " def __init__(self, file):\n", " self.entity_dict = {}\n", " self.df = pd.read_csv(file, sep='\\t', header=None)\n", " self.df.columns = [\"document_name\",\"some id\", \"sentence_word_nr\", \"word\", \"pos_tag\", \"parsing_part\", \"\", \"\", \"\", \"\", \"\", \"coref\"]\n", " \n", " def __get_entity(self, coref_num, start_index):\n", " \n", " entity = \"\"\n", " search_length = 5\n", " good_tags = [\"DT\", \"NNP\", \"NNS\", \"NN\", \"NNPS\", \"PRP\", \"PRP$\", \"JJ\", \"JJR\", \"JJS\", \"CC\", \"CD\", \"POS\", \"IN\"]\n", " important_tags = [\"NNP\", \"NNS\", \"NN\", \"NNPS\"]\n", " found_end = False\n", " end_index = None\n", " contains_important_tag = False\n", " \n", " for index in range(start_index, start_index + search_length):\n", " \n", " if index >= self.df.shape[0]:\n", " break\n", " \n", " if not isinstance(self.df.ix[index, \"coref\"], str):\n", " continue #jump to next index\n", " \n", " entity += self.df.ix[index, \"word\"] + \" \"\n", " \n", " #queck for bad pos_tags in entity\n", " if self.df.ix[index, \"pos_tag\"] not in good_tags:\n", " return (None, None)\n", " \n", " if self.df.ix[index, \"pos_tag\"] in important_tags:\n", " contains_important_tag = True\n", " \n", " #find end of entity\n", " coref = self.df.ix[index, \"coref\"]\n", " digits_in_coref = re.findall(\"\\d+\", coref)\n", " if \")\" in coref and str(coref_num) in digits_in_coref:\n", " found_end = True\n", " end_index = index\n", " break\n", " \n", " if found_end and contains_important_tag:\n", " return (entity.strip(), end_index)\n", " else:\n", " return (None, None)\n", " \n", " def __create_entity_dict(self):\n", " '''\n", " private\n", " '''\n", " current_coref_num = None\n", " \n", " for index, row in self.df.iterrows():\n", " if (isinstance(row[\"coref\"], str)):\n", " match = re.search(\"\\d+\", row[\"coref\"])\n", " if match != None:\n", " current_coref_num = int(match.group())\n", " \n", " if \"(\" in row[\"coref\"] and \")\" in row[\"coref\"]:\n", " tmp_entity = row[\"word\"]\n", " if current_coref_num not in self.entity_dict:\n", " self.entity_dict[current_coref_num] = tmp_entity\n", " elif \"(\" in row[\"coref\"]:\n", " tmp_entity, end_index = self.__get_entity(current_coref_num, index)\n", " if tmp_entity != None and current_coref_num not in self.entity_dict:\n", " self.entity_dict[current_coref_num] = tmp_entity\n", " \n", " def get_resolved_text(self):\n", " self.__create_entity_dict()\n", " \n", " self.count = 0\n", " self.count_removed_sents = 0\n", " total_text = \"\"\n", " sent_text = \"\"\n", " coref_num = None\n", " sent_contains_entity = False\n", " \n", " length = self.df.shape[0]\n", " index = 0\n", "\n", " while index < length:\n", " row = self.df.loc[index]\n", " found_entity = False\n", " if (isinstance(row[\"coref\"], str)):\n", " \n", " if row[\"sentence_word_nr\"] == 0:\n", " if sent_contains_entity:\n", " total_text += sent_text\n", " else:\n", " self.count_removed_sents += 1\n", " \n", " sent_text = \"\"\n", " sent_contains_entity = False\n", " \n", " \n", " match = re.search(\"\\d+\", row[\"coref\"])\n", " if match != None:\n", " coref_num = int(match.group())\n", " sent_contains_entity = True\n", " \n", " if \"(\" in row[\"coref\"] and \")\" in row[\"coref\"] and coref_num in self.entity_dict:\n", " sent_text += self.entity_dict[coref_num] + \" \"\n", " self.count += 1\n", " found_entity = True\n", " elif \"(\" in row[\"coref\"] and coref_num in self.entity_dict:\n", " tmp_entity, end_index = self.__get_entity(coref_num, index)\n", " if tmp_entity:\n", " sent_text += self.entity_dict[coref_num] + \" \"\n", " index = end_index\n", " self.count += 1\n", " found_entity = True\n", " \n", " if not found_entity:\n", " sent_text += row[\"word\"] + \" \"\n", " \n", " \n", " index += 1\n", " \n", " total_text = \"\\n\".join(nltk.sent_tokenize(total_text))\n", " total_text = total_text.replace(\"-RRB-\", \")\")\n", " total_text = total_text.replace(\"-LRB-\", \"(\")\n", " \n", " return total_text\n", " \n", " def get_orig_text(self):\n", " text = \"\"\n", " for index, row in self.df.iterrows(): \n", " text += row[\"word\"] + \" \"\n", " \n", " return text\n", " \n", " def print_df(self):\n", " print(self.df[[\"word\", \"coref\"]].to_string())\n", " \n", "\n", "print(\"--generating textfile with resolutions...\")\n", "corefOutputParser = CorefOutputParser(\"output_coref/jupiter-coref-raw.txt\")\n", "\n", "#df = corefOutputParser.print_df()\n", "\n", "new_text = corefOutputParser.get_resolved_text()\n", "print(\"-- number of resolved referenes: \", corefOutputParser.count)\n", "print(\"-- number of removed sents (lacking entity): \", corefOutputParser.count_removed_sents)\n", "\n", "#corefOutputParser.create_entity_dict()\n", "#corefOutputParser.entity_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#corefOutputParser.get_entity(4, 59)\n", "#corefOutputParser.entity_dict" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Jupiter is the fifth planet from the Sun and the largest planet in the Solar System .\\nJupiter is a giant planet with a mass one-thousandth of that of the Sun , but is two and a half times that of all the other planets in the Solar System combined .\\nJupiter is a gas giant , along with Saturn ( Uranus and Neptune are ice giants ) .\\nJupiter was known to astronomers of ancient times .\\nThe Romans named The Romans after Jupiter god Jupiter .\\nWhen viewed from Earth , Jupiter can reach an apparent magnitude of 2.94 , bright enough to cast shadows , and making Jupiter on average the third-brightest object in the night sky after the Moon and Venus .\\nJupiter is primarily composed of hydrogen with a quarter of its mass being helium , although helium only comprises about a tenth of the number of molecules .\\nhelium may also have a rocky core of heavier elements , but like the other giant planets , Jupiter lacks a well-defined solid surface .\\nBecause of Jupiter rapid rotation , the planet 's shape is that of an oblate spheroid ( it has a slight but noticeable bulge around the equator ) .\\nThe outer atmosphere is visibly segregated into several bands at different latitudes , resulting in turbulence and storms along several bands at different latitudes interacting boundaries .\\nA prominent result is the Great Red Spot , a giant storm that is known to have existed since at least the 17th century when it was first seen by telescope .\\nJupiter is a faint planetary ring system and a powerful magnetosphere .\\nJupiter has at least 67 moons , including the four large Galilean moons discovered by Galileo Galilei in 1610 .\\nGanymede , the largest of these , has a diameter greater than that of the planet Mercury .\\nJupiter has been explored on several occasions by robotic spacecraft , most notably during the early Pioneer and Voyager flyby missions and later by the Galileo orbiter .\\nThe most recent probe to visit Jupiter was the Pluto-bound New Horizons spacecraft in late February 2007 .\\nThe probe used the gravity from Jupiter to increase The probe speed .\\nFuture targets for exploration in the Jovian system include the possible ice-covered liquid ocean on the moon Europa .\\nA set of new super-Earths may have originally populated the inner Solar System .\\nEarth and A set of new super-Earths neighbor planets may have formed from fragments of planets after collisions with Jupiter destroyed new super-Earths near the Sun .\\nAs Jupiter came toward the inner Solar System , in what theorists call the Grand Tack Hypothesis , gravitational tugs and pulls occurred causing a series of collisions between new super-Earths as their orbits began to overlap .\\nAstronomers have discovered nearly 500 planetary systems each with multiple planets , and typically these systems include a few planets with masses several times greater than Earth ( super-Earths ) , orbiting closer to their star than the planet Mercury is to the Sun , and Jupiter-like gas giants are also often found close to their star .\\nIt appears that Jupiter is in its present orbit in the Solar System because Saturn pulled Jupiter out during Saturn migration .\\nJupiter moving out of the inner Solar System would have allowed the formation of inner planets , including A set of new super-Earths .\\nJupiter is composed primarily of gaseous and liquid matter .\\nJupiter is the largest of the four giant planets in the Solar System and hence Jupiter largest planet .\\nJupiter has a diameter of 142,984 km ( 88,846 mi ) at its equator .\\nThe density of Jupiter , 1.326 g\\\\/cm3 , is the second highest of the giant planets , but lower than those of the four terrestrial planets .\\nJupiter upper atmosphere is composed of about 88 92 % hydrogen and 8 12 % helium by percent volume of gas molecules .\\nBecause a helium atom has about four times as much mass as a hydrogen atom , the composition changes when described as the proportion of mass contributed by different atoms .\\nThus , Jupiter atmosphere is approximately 75 % hydrogen and 24 % helium by mass , with the remaining one percent of mass consisting of other elements .\\nThe interior contains denser materials , such that the distribution is roughly 71 % hydrogen , 24 % helium and 5 % other elements by mass .\\nThe atmosphere contains trace amounts of methane , water vapor , ammonia , and silicon-based compounds .\\nThe outermost layer of The atmosphere contains crystals of frozen ammonia .\\nNeon in The atmosphere only consists of 20 parts per million by mass , which is about a tenth as abundant as in the Sun .\\nHelium is also depleted , to about 80 % of the Sun helium composition .\\nThis depletion is a result of precipitation of these elements into the interior of the planet .\\nAbundances of heavier inert gases in Jupiter atmosphere are about two to three times that of the Sun .\\nBased on spectroscopy , Saturn is thought to be similar in composition to Jupiter , but the other giant planets Uranus and Neptune have relatively much less hydrogen and helium .\\nBecause of the lack of atmospheric entry probes , high-quality abundance numbers of these elements are lacking for the outer planets beyond Jupiter .\\nJupiter mass is 2.5 times that of all the other planets in the Solar System combined this is so massive that its barycenter with the Sun lies above the Sun surface at 1.068 solar radii from the Sun center .\\nAlthough with a diameter 11 times that of A set of new super-Earths , it is much larger , it is considerably less dense .\\nJupiter volume is that of about 1,321 Earths , but it is only 318 times as massive .\\nJupiter radius is about 1\\\\/10 the radius of the Sun , and its mass is 0.001 times the mass of the Sun , so the density of the two bodies is similar .\\nA '' its '' ( MJ or MJup ) is often used as a unit to describe masses of other objects , particularly extrasolar planets and brown dwarfs .\\nSo , for example , the extrasolar planet HD 209458 b has a mass of 0.69 MJ , while Kappa Andromedae b has a mass of MJ .\\nTheoretical models indicate that if Jupiter had much more mass than Jupiter does at present , Jupiter would shrink .\\nFor small changes in mass , the radius would not change appreciably , and above about 500 M ( 1.6 Jupiter masses ) The interior would become so much more compressed under the increased pressure that The interior volume would decrease despite the increasing amount of matter .\\nAs a result , Jupiter is thought to have about as large a diameter as a planet of Jupiter composition and evolutionary history can achieve .\\nThe process of further shrinkage with increasing mass would continue until appreciable stellar ignition is achieved as in high-mass brown dwarfs having around 50 Jupiter masses .\\nAlthough Jupiter would need to be about 75 times as massive to fuse hydrogen and become a star , the smallest red dwarf is only about 30 percent larger in radius than Jupiter .\\nDespite this , Jupiter still radiates more heat than Jupiter receives from the Sun ; the amount of heat produced inside Jupiter is similar to the total solar radiation Jupiter receives .\\nThis additional heat is generated by the Kelvin Helmholtz mechanism through contraction .\\nThis process causes Jupiter to shrink by about 2 cm each year .\\nWhen it was first formed , Jupiter was much hotter and was about twice Jupiter current diameter .\\nJupiter is thought to consist of a dense core with a mixture of elements , a surrounding layer of liquid metallic hydrogen with some helium , and an outer layer predominantly of molecular hydrogen .\\nThe core is often described as rocky , but The core detailed composition is unknown , as are the properties of materials at the temperatures and pressures of those depths ( see below ) .\\nIn 1997 , the existence of The core was suggested by gravitational measurements , indicating a mass of from 12 to 45 times A set of new super-Earths mass or roughly 4 % 14 % of the total mass of Jupiter .\\nThe presence of a core during at least part of Jupiter history is suggested by models of planetary formation that require the formation of a rocky or icy core massive enough to collect its bulk of hydrogen and helium from the protosolar nebula .\\nAssuming its did exist , its may have shrunk as convection currents of hot liquid metallic hydrogen mixed with the molten core and carried its contents to higher levels in the planetary interior .\\nThe uncertainty of the models is tied to the error margin in hitherto measured parameters : one of the rotational coefficients ( J6 ) used to describe the planet 's gravitational moment , Jupiter equatorial radius , and its temperature at 1 bar pressure .\\nThe Juno mission , which launched in August 2011 , is expected to better constrain the values of these parameters , and thereby make progress on the problem of The core .\\nThe core region is surrounded by dense metallic hydrogen , which extends outward to about 78 % of the radius of the planet 's .\\nRain-like droplets of helium and neon precipitate downward through this layer , depleting the abundance of these elements in The atmosphere .\\nAbove this layer lies a transparent interior atmosphere of hydrogen .\\nAt this depth , its is above its , which for hydrogen is only 33 K. In this state , there are no distinct liquid and gas phases hydrogen is said to be in a supercritical fluid state .\\nits is convenient to treat hydrogen as gas in the upper layer extending downward from this layer to a depth of about 1,000 km , and as liquid in deeper layers .\\nPhysically , there is no clear boundary the gas smoothly becomes hotter and denser as one descends .\\nThe temperature and pressure inside Jupiter increase steadily toward The core , due to the Kelvin Helmholtz mechanism .\\nAt the '' surface '' pressure level of 10 bars , its is around 340 K ( 67 C ; 152 F ) .\\nAt the phase transition region where hydrogen heated beyond its critical point becomes metallic , its is believed its is 10,000 K ( 9,700 C ; 17,500 F ) and the pressure is 200 GPa .\\nThe temperature at the core boundary is estimated to be 36,000 K ( C ; 17,500 F ) and the pressure is roughly 3,000 4,500 GPa .\\nJupiter has the largest planetary atmosphere in the Solar System , spanning over 5,000 km ( 3,107 mi ) in altitude .\\nAs Jupiter has no surface , the base of its atmosphere is usually considered to be the point at which atmospheric pressure is equal to 1 MPa ( 10 bar ) , or ten times surface pressure on A set of new super-Earths .\\nJupiter is perpetually covered with clouds composed of ammonia crystals and possibly ammonium hydrosulfide .\\nThe clouds are located in the tropopause and are arranged into bands of different latitudes , known as tropical regions .\\nThe zones have been observed to vary in width , color and intensity from year to year , but The zones have remained sufficiently stable for astronomers to give The zones identifying designations .\\nThe cloud layer is only about 50 km ( 31 mi ) deep , and consists of at least two decks of clouds : a thick lower deck and a thin clearer region .\\nThere may also be a thin layer of water clouds underlying The cloud layer , as evidenced by flashes of lightning detected in the atmosphere of Jupiter .\\nThese electrical discharges can be up to a thousand times as powerful as lightning on A set of new super-Earths .\\nThe clouds can form thunderstorms driven by the heat rising from The interior .\\nThe orange and brown coloration in The clouds are caused by upwelling compounds that change color when they are exposed to ultraviolet light from the Sun .\\nThese colorful compounds , known as chromophores , mix with the warmer , lower deck of clouds .\\nThe zones are formed when rising convection cells form crystallizing ammonia that masks out these lower clouds from view .\\nJupiter low axial tilt means that the poles constantly receive less solar radiation than at the planet 's equatorial region .\\nConvection within the interior of the planet transports more energy to the poles , balancing out the temperatures at The cloud layer .\\nThe best known feature of Jupiter is the Great Red Spot , a persistent anticyclonic storm that is larger than A set of new super-Earths , located 22 south of the equator .\\nIt is known to have been in existence since at least 1831 , and possibly since 1665 .\\nImages by the Hubble Space Telescope have shown as many as two '' red spots '' adjacent to the Great Red Spot .\\nThe storm is large enough to be visible through Earth-based telescopes with an aperture of 12 cm or larger .\\nMathematical models suggest that The storm is stable and may be a permanent feature of the planet 's .\\nthe Great Red Spot dimensions are 24 40,000 km 12 14,000 km .\\nIt is large enough to contain two or three planets of A set of new super-Earths diameter .\\nThe maximum altitude of The storm is about 8 km ( 5 mi ) above the surrounding cloudtops .\\nJupiter also has white ovals and brown ovals , which are lesser unnamed storms .\\nwhite ovals tend to consist of relatively cool clouds within The atmosphere .\\nwhite ovals are warmer and located within the '' normal cloud layer '' .\\nEven before Voyager proved that It was a storm , there was strong evidence that the spot could not be associated with any deeper feature on the planet 's surface , as the Great Red Spot rotates differentially with respect to the rest of The atmosphere , sometimes faster and sometimes more slowly .\\nDuring the Great Red Spot recorded history the Great Red Spot has traveled several times around the planet relative to any possible fixed rotational marker below the Great Red Spot .\\nIn 2000 , an atmospheric feature formed in the southern hemisphere that is similar in appearance to the Great Red Spot , but smaller .\\nan atmospheric feature was named Oval BA , and has been nicknamed Red Spot Junior .\\nan atmospheric feature has since increased in intensity and changed color from white to red .\\nJupiter has a faint planetary ring system composed of three main segments : an inner torus of particles known as the halo , a relatively bright main ring , and an outer gossamer ring .\\nThese rings appear to be made of dust , rather than ice as with Saturn rings .\\nan outer gossamer ring is probably made of material ejected from the satellites Adrastea and Metis .\\nMaterial that would normally fall back to the moon is pulled into Jupiter because of Jupiter strong gravitational influence .\\nThe orbit of the material veers towards Jupiter and new material is added by additional impacts .\\nIn a similar way , the moons Thebe and Amalthea probably produce the two distinct components of an outer gossamer ring .\\nThere is also evidence of a rocky ring strung along Amalthea orbit which may consist of collisional debris from the moon .\\nJupiter magnetic field is 14 times as strong as A set of new super-Earths , ranging from 4.2 gauss ( 0.42 mT ) at the equator to 10 14 gauss ( 1.0 1.4 mT ) at the poles , making it the strongest in the Solar System ( except for sunspots ) .\\nThis field is believed to be generated by eddy currents swirling movements of conducting materials within The core .\\nThe volcanoes on the moon Io emit large amounts of sulfur dioxide forming a gas torus along the moon orbit .\\nthe gas is ionized in the magnetosphere producing sulfur and oxygen ions .\\nthe gas , together with hydrogen ions originating from the atmosphere of Jupiter , form a plasma sheet in Jupiter equatorial plane .\\nThe plasma in the sheet co-rotates with the planet causing deformation of the dipole magnetic field into that of magnetodisk .\\nElectrons within the sheet generate a strong radio signature that produces bursts in the range of 0.6 30 MHz .\\nAt about 75 Jupiter radii from the planet 's , the interaction of the magnetosphere with the solar wind generates a bow shock .\\nJupiter magnetosphere is a magnetopause , located at the inner edge of a magnetosheath a region between it and a bow shock .\\nthe solar wind interacts with these regions , elongating it on Jupiter lee side and extending it outward until the solar wind nearly reaches the orbit of Saturn .\\nThe four largest moons of Jupiter all orbit within it , which protects them from the solar wind .\\nit is responsible for intense episodes of radio emission from the planet 's polar regions .\\nVolcanic activity on the Jovian moon Io ( see below ) injects gas into Jupiter magnetosphere , producing a torus of particles about the planet 's .\\nAs the moon Io moves through this torus , the interaction generates Alfv n waves that carry ionized matter into the polar regions of Jupiter .\\nWhen A set of new super-Earths intersects this cone , the radio emissions from Jupiter can exceed the solar radio output .\\nJupiter is the only planet that has a barycenter with the Sun that lies outside the volume of the Sun , though by only 7 % of the Sun radius .\\nThe average distance between Jupiter and the Sun is 778 million km ( about 5.2 times the average distance from A set of new super-Earths to the Sun , or 5.2 AU ) and it completes an orbit every 11.86 years .\\nThis is two-fifths the orbital period of Saturn , forming a 5:2 orbital resonance between the two largest planets in the Solar System .\\nits is inclined 1.31 compared to A set of new super-Earths .\\nBecause of an eccentricity of 0.048 , the distance from Jupiter and the Sun varies by 75 million km between perihelion and aphelion , or the nearest and most distant points of the planet along the orbital path respectively .\\nThe axial tilt of Jupiter is relatively small : only 3.13 .\\nAs a result , The axial tilt of Jupiter does not experience significant seasonal changes , in contrast to , for example , A set of new super-Earths and Mars .\\nJupiter rotation is the fastest of all the Solar System 's planets , completing a rotation on its axis in slightly less than ten hours ; this creates an equatorial bulge easily seen through an Earth-based amateur telescope .\\nthe planet 's is shaped as an oblate spheroid , meaning that the diameter across its equator is longer than the diameter measured between its poles .\\nOn Jupiter , the diameter across its equator is 9,275 km ( 5,763 mi ) longer than the diameter measured through its .\\nBecause Jupiter is not a solid body , its upper atmosphere undergoes differential rotation .\\nThe rotation of Jupiter polar atmosphere is about 5 minutes longer than that of its ; three systems are used as frames of reference , particularly when graphing the motion of atmospheric features .\\nthe inner Solar System I applies from the latitudes 10 N to 10 S ; its period is the planet 's shortest , at 9h 50m 30.0 s .\\nSystem II applies at all latitudes north and south of these ; its period is 9h 55m 40.6 s .\\nSystem II was first defined by radio astronomers , and corresponds to the rotation of the planet 's magnetosphere ; its period is Jupiter official rotation .\\nJupiter is usually the fourth brightest object in the sky ( after the Sun , the Moon and Venus and Venus ) ; at times Mars appears brighter than Jupiter .\\nDepending on Jupiter position with respect to A set of new super-Earths , Jupiter can vary in visual magnitude from as bright as 2.9 at opposition down to 1.6 during conjunction with the Sun .\\nthe diameter across its equator likewise varies from 50.1 to 29.8 arc seconds .\\nFavorable oppositions occur when Jupiter is passing through perihelion , an event that occurs once per orbit .\\nAs Jupiter approached perihelion in March 2011 , there was a favorable opposition in September 2010 .\\nA set of new super-Earths overtakes Jupiter every 398.9 days as A set of new super-Earths orbits the Sun , a duration called the synodic period .\\nAs A set of new super-Earths does so , Jupiter appears to undergo retrograde motion with respect to the background stars .\\nThat is , for a period Jupiter seems to move backward in the night sky , performing a looping motion .\\nJupiter 12-year orbital period corresponds to the dozen astrological signs of the zodiac , and may have been the historical origin of the signs .\\nThat is , Jupiter reaches opposition Jupiter has advanced eastward by about 30 , the width of a zodiac sign .\\nBecause its is outside A set of new super-Earths , the phase angle of Jupiter as viewed from A set of new super-Earths never exceeds 11.5 .\\nThat is , the planet 's always appears nearly fully illuminated when viewed through Earth-based telescopes .\\nthe planet 's was only during spacecraft missions to Jupiter that crescent views of the planet 's were obtained .\\nA small telescope will usually show Jupiter four Galilean moons and the prominent cloud belts across Jupiter atmosphere .\\nA small telescope will show Jupiter Great Red Spot when A small telescope faces A set of new super-Earths .\\nThe observation of Jupiter dates back to the Babylonian astronomers of the 7th or 8th century BC .\\nThe Chinese historian of astronomy , Xi Zezong , has claimed that Gan De , a Chinese astronomer , made the discovery of one of Jupiter moons in 362 BC with the unaided eye .\\nIf accurate , this would predate Galileo discovery by nearly two millennia .\\nIn his 2nd century work the Almagest , the Hellenistic astronomer Claudius Ptolemaeus constructed a geocentric planetary model based on deferents and epicycles to explain Jupiter motion relative to A set of new super-Earths , giving Jupiter orbital period around A set of new super-Earths as 4332.38 days , or 11.86 years .\\nIn 499 , Aryabhata , a mathematician astronomer from the classical age of Indian mathematics and astronomy , also used a geocentric model to estimate Jupiter period as 4332.2722 days , or 11.86 years .\\nIn 1610 , Galileo Galilei discovered the four largest moons of Jupiter Io , Europa , Ganymede and Callisto ( now known as the Galilean moons ) using a telescope ; thought to be the first telescopic observation of moons other than A set of new super-Earths .\\nGalileo was also the first discovery of a celestial motion not apparently centered on A set of new super-Earths .\\nGalileo was a major point in favor of Copernicus ' heliocentric theory of the motions of the planets ; Galileo outspoken support of the Copernican theory placed Copernicus ' under the threat of the Inquisition .\\nDuring the 1660s , Cassini used a new telescope to discover spots and colorful bands on Jupiter and observed that the planet 's appeared oblate ; that is , flattened at its .\\nCopernicus ' was also able to estimate the rotation period of the planet 's .\\nIn 1690 Cassini noticed that The atmosphere undergoes differential rotation .\\nThe Great Red Spot , a prominent oval-shaped feature in the southern hemisphere of Jupiter , may have been observed as early as 1664 by Robert Hooke and in 1665 by Giovanni Cassini , although this is disputed .\\nThe pharmacist Heinrich Schwabe produced the earliest known drawing to show details of the Great Red Spot in 1831 .\\nThe Red Spot was reportedly lost from sight on several occasions between 1665 and 1708 before becoming quite conspicuous in 1878 .\\nThe Red Spot was recorded as fading again in 1883 and at the start of the 20th century .\\nBoth Giovanni Borelli and Cassini made careful tables of the motions of the Jovian moons , allowing predictions of the times when the Jovian moons would pass before or behind the planet 's .\\nBy the 1670s , it was observed that when Jupiter was on the opposite side of the Sun from A set of new super-Earths , these events would occur about 17 minutes later than expected .\\nOle R mer deduced that sight is not instantaneous ( a conclusion that Cassini had earlier rejected ) , and this timing discrepancy was used to estimate the speed of light .\\nIn 1892 , E. E. Barnard observed a fifth satellite of Jupiter with the 36-inch ( 910 mm ) refractor at Lick Observatory in California .\\nThe discovery of this relatively small object , a testament to E. E. Barnard keen eyesight , quickly made E. E. Barnard famous .\\nthe moon was later named Amalthea .\\nthe moon was the last planetary moon to be discovered directly by visual observation .\\nIn 1932 , Rupert Wildt identified absorption bands of ammonia and methane in the spectra of Jupiter .\\nThree long-lived anticyclonic features termed white ovals were observed in 1938 .\\nFor several decades Three long-lived anticyclonic features remained as separate features in The atmosphere , sometimes approaching each other but never merging .\\nFinally , two of white ovals merged in 1998 , then absorbed the third in 2000 , becoming Oval BA .\\nIn 1955 , Bernard Burke and Kenneth Franklin detected bursts of radio signals coming from Jupiter at 22.2 MHz .\\nThe period of bursts of radio signals matched the rotation of the planet 's , and Bernard Burke and Kenneth Franklin were also able to use this information to refine the rotation rate .\\nRadio bursts from Jupiter were found to come in two forms : long bursts ( or L-bursts ) lasting up to several seconds , and short bursts ( or S-bursts ) that had a duration of less than a hundredth of a second .\\nScientists discovered that there were three forms of radio signals transmitted from Jupiter .\\nDecametric radio bursts ( with a wavelength of tens of meters ) vary with the rotation of Jupiter , and are influenced by interaction of the moon Io with Jupiter magnetic field .\\nThe origin of this signal was from a torus-shaped belt around Jupiter equator .\\nthis signal is caused by cyclotron radiation from electrons that are accelerated in Jupiter magnetic field .\\nThermal radiation is produced by heat in the atmosphere of Jupiter .\\nSince 1973 a number of automated spacecraft have visited Jupiter , most notably the Pioneer 10 space probe , the first spacecraft to get close enough to Jupiter to send back revelations about the properties and phenomena of the Solar System largest planet .\\nFlights to other planets within the Solar System are accomplished at a cost in energy , which is described by the net change in velocity of automated spacecraft , or delta - v. Entering a Hohmann transfer orbit from A set of new super-Earths to Jupiter from low Earth orbit requires a delta-v of 6.3 km\\\\/s which is comparable to the 9.7 km\\\\/s delta-v needed to reach low Earth orbit .\\nFortunately , gravity assists through planetary flybys can be used to reduce the energy required to reach Jupiter , albeit at the cost of a significantly longer flight duration .\\nBeginning in 1973 , several spacecraft have performed planetary flyby maneuvers that brought several spacecraft within observation range of Jupiter .\\nThe Pioneer missions obtained the first close-up images of Jupiter atmosphere and several of its moons .\\nThe Pioneer missions discovered that the radiation fields near the planet 's were much stronger than expected , but several spacecraft managed to survive in that environment .\\nThe trajectories of several spacecraft were used to refine the mass estimates of the Jovian system .\\nRadio occultations by the planet 's resulted in better measurements of Jupiter diameter and the amount of polar flattening .\\nSix years later , The Pioneer missions vastly improved the understanding of the Galilean moons and discovered Jupiter rings .\\nThey also confirmed that the Great Red Spot was anticyclonic .\\nComparison of images showed that The Red Spot had changed hue since The Pioneer missions , turning from orange to dark brown .\\nA torus of ionized atoms was discovered along the moon Io orbital path , and volcanoes were found on the moon surface , some in the process of erupting .\\nAs automated spacecraft passed behind the planet 's , automated spacecraft observed flashes of lightning in the night side atmosphere .\\nThe next mission to encounter Jupiter , the Ulysses solar probe , performed a flyby maneuver to attain a polar orbit around the Sun .\\nDuring this pass automated spacecraft conducted studies on Jupiter magnetosphere .\\nSince Ulysses has no cameras , no images were taken .\\nIn 2000 , The probe , en route to Saturn , flew by Jupiter and provided some of the highest-resolution images ever made of the planet 's .\\nOn December 19 , 2000 , automated spacecraft captured an image of the moon Himalia , but the resolution was too low to show surface details .\\nThe probe , en route to Pluto , flew by Jupiter for gravity assist .\\nThe probe closest approach was on February 28 , 2007 .\\nThe probe cameras measured plasma output from volcanoes on the moon Io and studied all four Galilean moons in detail , as well as making long-distance observations of all four Galilean moons Himalia and Elara .\\nImaging of the Jovian system began September 4 , 2006 .\\nSo far the only spacecraft to Jupiter is the Galileo orbiter , which went into orbit around Jupiter on December 7 , 1995 .\\nIt orbited the planet 's for over seven years , conducting multiple flybys of all the Galilean moons and Amalthea .\\nautomated spacecraft also witnessed the impact of Comet Shoemaker Levy 9 as automated spacecraft approached Jupiter in 1994 , giving a unique vantage point for the event .\\nWhile the information gained about the Jovian system from Galileo was extensive , its originally designed capacity was limited by the failed deployment of its high-gain radio transmitting antenna .\\nA 340-kilogram titanium atmospheric probe was released from automated spacecraft in July 1995 , entering Jupiter atmosphere on December 7 .\\nA 340-kilogram titanium atmospheric probe parachuted through 150 km ( 93 mi ) of The atmosphere at speed of about 2,575 km\\\\/h ( 1600 mph ) and collected data for 57.6 minutes before it was crushed by the pressure ( about 23 times Earth normal , at a temperature of California ) .\\nThe Galileo orbiter itself experienced a more rapid version of the same fate when The Galileo orbiter itself was deliberately steered into the planet on September 21 , 2003 , at a speed of over 50 km\\\\/s , to avoid any possibility of The Galileo orbiter itself crashing into and possibly contaminating the moon Europa a moon which has been hypothesized to have the possibility of harboring life .\\nData from this mission revealed that hydrogen composes up to 90 % of Jupiter atmosphere .\\nNASA has a mission underway to study Jupiter in detail from a polar orbit .\\nNamed Juno , automated spacecraft launched in August 2011 , and will arrive in late 2016 .\\nThe next planned mission to the Jovian system will be the European Space Agency 's Jupiter Icy Moon Explorer ( JUICE ) , due to launch in 2022 , followed by NASA Europa Clipper mission in 2025 .\\nBecause of the possibility of subsurface liquid oceans on Jupiter moons Europa , Ganymede and Callisto , there has been great interest in studying the icy moons in detail .\\nNASA JIMO ( Jupiter Icy Moons Orbiter ) was cancelled in 2005 .\\nA subsequent proposal for a joint NASA\\\\/ESA mission , called EJSM\\\\/Laplace , was developed with a provisional launch date around 2020 .\\nEJSM\\\\/Laplace would have consisted of the NASA-led Jupiter Europa Orbiter , and the ESA-led Jupiter Ganymede Orbiter .\\nHowever by April 2011 , the ESA-led Jupiter Ganymede Orbiter had formally ended the partnership citing budget issues at NASA and the consequences on the mission timetable .\\nInstead the ESA-led Jupiter Ganymede Orbiter planned to go ahead with a European-only mission to compete in the ESA-led Jupiter Ganymede Orbiter L1 Cosmic Vision selection .\\nJupiter has 67 natural satellites .\\nThe four largest moons , visible from Earth with binoculars on a clear night , known as the '' Galilean moons '' , are the moon Io , Europa , Ganymede , and Callisto .\\nThe orbits of the moon Io , Europa , and Ganymede , some of the largest satellites in the Solar System , form a pattern known as a Laplace resonance ; for every four orbits that the moon Io makes around Jupiter , the moon Europa makes exactly two orbits and Ganymede makes exactly one .\\na Laplace resonance causes the gravitational effects of the three large moons to distort their orbits into elliptical shapes , since the moon receives an extra tug from a Laplace resonance neighbors at the same point in every orbit a Laplace resonance makes .\\nThe tidal force from Jupiter , on the other hand , works to circularize their orbits .\\nThe eccentricity of their orbits causes regular flexing of the three large moons shapes , with Jupiter gravity stretching them out as them approach Jupiter and allowing them to spring back to more spherical shapes as them swing away .\\nThis tidal flexing heats the three large moons interiors by friction .\\nThis is seen most dramatically in the extraordinary volcanic activity of innermost Io ( which is subject to the strongest tidal forces ) , and to a lesser degree in the geological youth of the moon Europa surface ( indicating recent resurfacing of the moon exterior ) .\\nBefore the discoveries of The Pioneer missions , Jupiter moons were arranged neatly into four groups of four , based on commonality of their orbital elements .\\nSince then , the large number of new small outer moons has complicated this picture .\\nA basic sub-division is a grouping of the eight inner regular moons , which have nearly circular orbits near the plane of Jupiter equator and are believed to have formed with Jupiter .\\nThe remainder of new small outer moons consist of an unknown number of small irregular moons with elliptical and inclined orbits , which are believed to be captured asteroids or fragments of captured asteroids .\\nAlong with the Sun , the gravitational influence of Jupiter has helped shape the Solar System .\\nThe orbits of most of the Solar System planets lie closer to Jupiter orbital plane than the Sun equatorial plane ( the planet Mercury is the only planet that is closer to the Sun equator in orbital tilt ) , the Kirkwood gaps in the asteroid belt are mostly caused by Jupiter , and the planet 's may have been responsible for the Late Heavy Bombardment of the Solar System history .\\nAlong with its moons , Jupiter gravitational field controls numerous asteroids that have settled into the regions of the Lagrangian points preceding and following Jupiter in its orbit around the Sun .\\nThese are known as the Trojan asteroids , and are divided into Greek and Trojan '' camps '' to commemorate the Iliad .\\nMost short-period comets belong to the Jupiter family defined as comets with semi-major axes smaller than Jupiter .\\nJupiter family comets are believed to form in the Kuiper belt outside its .\\nDuring close encounters with Jupiter their orbits are perturbed into a smaller period and then circularized by regular gravitational interaction with the Sun and Jupiter .\\nJupiter has been called the Solar System vacuum cleaner , because of Jupiter immense gravity well and location near the inner Solar System .\\nJupiter receives the most frequent comet impacts of the Solar System planets .\\nJupiter was thought that the planet 's served to partially shield the Solar System from cometary bombardment .\\nRecent computer simulations suggest that Jupiter does not cause a net decrease in the number of comets that pass through the inner Solar System , as its gravity perturbs their orbits inward in roughly the same numbers that its accretes or ejects Recent computer simulations .\\nThis topic remains controversial among astronomers , as some believe This topic draws comets towards A set of new super-Earths from the Kuiper belt while others believe that Jupiter protects A set of new super-Earths from the alleged Oort cloud .\\nJupiter experiences about 200 times more asteroid and comet impacts than A set of new super-Earths .\\nA 1997 survey of historical astronomical drawings suggested that Cassini may have recorded an impact scar in 1690 .\\nThe survey determined eight other candidate observations had low or no possibilities of an impact .\\nA fireball was photographed by Voyager 1 during A fireball Jupiter encounter in March 1979 .\\nDuring the period July 16 , 1994 , to July 22 , 1994 , over 20 fragments from the comet Shoemaker Levy 9 ( SL9 , formally designated D\\\\/1993 F2 ) collided with Jupiter southern hemisphere , providing the first direct observation of a collision between two the inner Solar System objects .\\nan impact provided useful data on the composition of Jupiter atmosphere .\\nThis impact left behind a black spot in Jupiter atmosphere , similar in size to Oval BA .\\nInfrared observation showed a bright spot where an impact took place , meaning an impact warmed up the lower atmosphere in the area near Jupiter south pole .\\nA fireball , smaller than the previous observed impacts , was detected on June 3 , 2010 , by Anthony Wesley , an amateur astronomer in Australia , and was later discovered to have been captured on video by another amateur astronomer in the Philippines .\\nYet another fireball was seen on August 20 , 2010 .\\nOn September 10 , 2012 , another fireball was detected .\\nIn 1953 , the Miller Urey experiment demonstrated that a combination of lightning and the chemical compounds that existed in the atmosphere of A set of new super-Earths could form organic compounds ( including amino acids ) that could serve as the building blocks of life .\\nThe simulated atmosphere included water , methane , ammonia , and molecular hydrogen ; all molecules still found in Jupiter atmosphere .\\nJupiter atmosphere has a strong vertical air circulation , which would carry these compounds down into the lower regions .\\nThe higher temperatures within the interior of the atmosphere break down these chemicals , which would hinder the formation of Earth-like life .\\nIt is considered highly unlikely that there is any Earth-like life on Jupiter , because there is only a small amount of water in Jupiter atmosphere and any possible solid surface deep within Jupiter would be under extreme pressures .\\nIn 1976 , before The Pioneer missions , it was hypothesized that ammonia - or water-based life could evolve in Jupiter upper atmosphere .\\nThe possible presence of underground oceans on some of Jupiter moons has led to speculation that the presence of life is more likely there .\\nJupiter has been known since ancient times .\\nJupiter is visible to the naked eye in the night sky and can occasionally be seen in the daytime when the Sun is low .\\nTo the Babylonians , this object represented the Babylonians god Marduk .\\nthe Babylonians used Jupiter roughly 12-year orbit along the ecliptic to define the constellations of the Babylonians zodiac .\\nThe Romans named it after Jupiter ( Latin : Iuppiter , I piter ) ( also called Jove ) , the principal god of Roman mythology , whose name comes from the Proto-Indo-European vocative compound \\\\* Dy u-p ter ( nominative : \\\\* Dy us-p t r , meaning '' O Father Sky-God '' , or '' O Father Day-God '' ) .\\nIn turn , Jupiter was the counterpart to the mythical Greek Zeus ( ) , also referred to as Dias ( ) , the planetary name of which is retained in modern Greek .\\nThe astronomical symbol for the planet 's , , is a stylized representation of the god 's lightning bolt .\\nthe mythical Greek Zeus supplies the root zeno - , used to form some Jupiter-related words , such as zenographic .\\nJovian is the adjectival form of Jupiter .\\nThe older adjectival form jovial , employed by astrologers in the Middle Ages , has come to mean '' happy '' or '' merry , '' moods ascribed to Jupiter astrological influence .\\nThe Chinese , Korean and Japanese referred to the planet as the '' wood star '' ( Chinese : ; pinyin : m x ng ) , based on the Chinese Five Elements .\\nChinese Taoism personified Chinese Taoism as wood star .\\nThe Greeks called Chinese Taoism , Phaethon , '' blazing . ''\\nIn Vedic Astrology , Hindu astrologers named the planet 's after Brihaspati , the religious teacher of the gods , and often called the planet 's '' Guru '' , which literally means the '' Heavy One . ''\\nIn the English language , Thursday is derived from '' Thor 's day '' , with Thor in Germanic mythology being the equivalent Germanic god to the Roman god Jupiter ( mythology ) .\\nThe Roman day Jovis was renamed Thursday .\\nIn the Central Asian-Turkic myths , Jupiter called as a '' Erendiz\\\\/Erent z '' , which means '' eren ( ?\\n) + yultuz ( star ) '' .\\nAlso , these peoples calculated the period of its as 11 years and 300 days . \"" ] }, "execution_count": 87, "output_type": "execute_result", "metadata": {} } ], "source": [ "new_text" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 79, "output_type": "execute_result", "metadata": {} } ], "source": [ "corefOutputParser.count_removed_sents" ] }, { "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": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
mne-tools/mne-tools.github.io	stable/_downloads/8de61cd59c9d83353f96a413e8484686/compute_mne_inverse_raw_in_label.ipynb	1	3039	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Compute sLORETA inverse solution on raw data\n\nCompute sLORETA inverse solution on raw dataset restricted\nto a brain label and stores the solution in stc files for\nvisualisation.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import 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 = (\n 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' / f'{label_name}.label'\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), overwrite=True)" ] }, { "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
jaropolk2/python_statistics	stochastic_variables.ipynb	1	185683	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as sts\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Нормальное распределение" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вот так можно сгенерировать выборку из нормально распределённой случайной величины с параметрами $\\mu=2.0$ и $\\sigma=0.5$:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.42471807, 2.89001427, 1.5406754 , 2.218372 , 2.48622369,\n", " 2.82460422, 2.06469003, 2.46941758, 1.7389734 , 1.17062459])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu = 2.0\n", "sigma = 0.5\n", "\n", "# зададим нормально распределенную случайную величину\n", "norm_rv = sts.norm(loc=mu, scale=sigma)\n", "\n", "# сгенерируем 10 значений\n", "norm_rv.rvs(size=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Параметр ```loc``` задаёт $\\mu$, ```scale``` — среднеквадратичное отклонение $\\sigma$, ```size``` — размер выборки. Имя параметра ```size``` при вызове функции ```rvs``` можно не писать.\n", "\n", "Следующая функция возвращает значение функции распределения нормальной случайной величины в точке, соответствующей её аргументу:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.97724986805182079" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_rv.cdf(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим график функции распределения:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xa058a20>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x3cc31d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,4,100)\n", "cdf = norm_rv.cdf(x) # функция может принимать и вектор (x)\n", "plt.plot(x, cdf)\n", "plt.ylabel('$F(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "А так можно вычислить значение функции плотности вероятности нормального распределения в заданной точке:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.10798193302637613" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_rv.pdf(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим график функции плотности вероятности:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xa2c91d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6897ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,4,100)\n", "pdf = norm_rv.pdf(x)\n", "plt.plot(x, pdf)\n", "\n", "plt.ylabel('$f(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Равномерное распределение на отрезке" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вот так можно сгенерировать выборку из случайной величины, имеющей равномерное распределение на отрезке $[a,b]$:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.90068986, 1.30900927, 2.61667386, 1.82853085, 1.11278354,\n", " 1.67101276, 1.48848226, 1.74478797, 1.5155652 , 2.54059151])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = 1\n", "b = 4\n", "\n", "# обратите внимание, что в этой функции задается левая граница и масштаб, а не левая и правая границы:\n", "uniform_rv = sts.uniform(a, b-a)\n", "\n", "uniform_rv.rvs(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "А так — вычислять значения функций распределения и плотностей:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xa2f0048>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EKs+WtFQOexCqNFvSUnkMCFWWu6SlchkQqqRDh4oinLukpfJYlFPl2JKWessmtWrJlrTU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\nJEkd/X//HVGYXAQ5qgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa031780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,5,100)\n", "cdf = uniform_rv.cdf(x)\n", "plt.plot(x, cdf)\n", "\n", "plt.ylabel('$F(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xa8dd320>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qvgb8ddOxOpXLVtJ01kc7XYTHJuCpsf2nR8dmPVaSVuSyVXNndT2BWdq1a9cr24PBgMFg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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa12dba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,5,1000)\n", "pdf = uniform_rv.pdf(x)\n", "plt.plot(x, pdf)\n", "\n", "plt.ylabel('$f(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Распределение Бернулли" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Генерация выборок из распределения Бернулли с заданным параметром $p$:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 0, 0, 1, 1, 1, 1])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bernoulli_rv = sts.bernoulli(0.7)\n", "\n", "bernoulli_rv.rvs(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Биномиальное распределение" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Генерация выборок из биномиального распределения:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([13, 11, 13, 15, 13, 14, 12, 16, 14, 16])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binomial_rv = sts.binom(20, 0.7)\n", "binomial_rv.rvs(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Первый аргумент функции binom — значение параметра $n$, второй — параметра $p$.\n", "\n", "Функция распределения:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xae11390>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BlwGfB85dy/45rWGh0XthVT0PuJr+YeqLui5oBnk1yfq9C7ioqi4D7gPe1nE9UyfJE4EP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\nk2EhSWr6f3kFxYdE3u1BAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa24b278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,20,21)\n", "cdf = binomial_rv.cdf(x)\n", "plt.step(x, cdf)\n", "\n", "plt.ylabel('$F(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Функция вероятности ```pmf``` для дискретных случайных величин заменяет функцию плотности ```pdf```:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xae1d780>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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shUm9jWRqaltlNXXCNhJJqlCvtZ3Z2N7EIJGk9tmzXZJUOoNEklSIQSJJKsQgkSQVYpBI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\nVIhBIkkqxCCRJBVikEiSCvl/doOpC5tw7s8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xa8f6860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,20,21)\n", "pmf = binomial_rv.pmf(x)\n", "plt.plot(x, pmf, 'o')\n", "\n", "plt.ylabel('$P(X=x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим, как ведут себя биномиально распределенные величины при разных значениях параметров:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xb35f278>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Xc9JJJ7F69erE/u3bt1NZWZkyrvPOO4+amhqqq6uBWA2of//+OceUzblbvPzyy1x55ZUZ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\nJQsREQn0/wE7Uh5xe1Bv2gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xad68b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,45,46)\n", "for N in [20, 30]:\n", " for p in [0.2, 0.7]:\n", " rv = sts.binom(N, p)\n", " cdf = rv.cdf(x)\n", " plt.step(x, cdf, label=\"$N=%s, p=%s$\" % (N,p))\n", "plt.legend()\n", "plt.title(\"CDF (binomial)\")\n", "\n", "plt.ylabel('$F(X)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xb34b438>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb48f898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,45,46)\n", "symbols = iter(['o', 's', '^', '+'])\n", "for N in [20, 30]:\n", " for p in [0.2, 0.8]:\n", " rv = sts.binom(N, p)\n", " pmf = rv.pmf(x)\n", " plt.plot(x, pmf, next(symbols), label=\"$N=%s, p=%s$\" % (N,p))\n", "plt.legend()\n", "plt.title(\"PMF (binomial)\")\n", "\n", "plt.ylabel('$P(X=x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Распределение Пуассона" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Генерация выборок из распределения Пуассона с параметром $\\lambda$:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6, 10, 4, 4, 4, 3, 8, 4, 3, 6])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poisson_rv = sts.poisson(5)\n", "poisson_rv.rvs(10)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xb597d68>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Sx/krbLIwM7PmKWozlJmZNZGThZmZpXKyMDOzVE4WZmaWysnCzMxSOVmYmVkq\nJwszM0vlZGFmZqmcLMzMLFXT1+A2a2fJ4jJnA9OpTKN/MnBVRPwq18DMRsk1C7NszaQye+lGKpMK\n/hPwZK4RmWXAycIsQxFxX0S8BJwK3BkRpYjozTsus9FysjDLkKSTJE0BjouIX0l6T94xmWXBfRZm\n2ZpFZVW1f5d0JrA953jMMuEpys3MLJWboczMLJWThZmZpXKyMDOzVE4WZmaWysnCzMxSOVmYmVkq\nJwszM0vlZGFmZqn+P+3WUrM083q8AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb6cd5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,30,31)\n", "for l in [1, 5, 10, 15]:\n", " rv = sts.poisson(l)\n", " cdf = rv.cdf(x)\n", " plt.step(x, cdf, label=\"$\\lambda=%s$\" % l)\n", "plt.legend()\n", "plt.title(\"CDF (poisson)\")\n", "\n", "plt.ylabel('$F(x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xb9586a0>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VxkwVPJC4+14zuxZYSDAKa7a7rzSzycFunwVcbGbfAPYAu4GvdHdsoc9BRIrfihdeYMfY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\nRqmvAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb84f438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,30,31)\n", "\n", "symbols = iter(['o', 's', '^', '+'])\n", "for l in [1, 5, 10, 15]:\n", " rv = sts.poisson(l)\n", " pmf = rv.pmf(x)\n", " plt.plot(x, pmf, next(symbols), label=\"$\\lambda=%s$\" % l)\n", "plt.legend()\n", "plt.title(\"PMF (poisson)\")\n", "\n", "plt.ylabel('$P(X=x)$')\n", "plt.xlabel('$x$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Дискретное распределение общего вида" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Чтобы сгенерировать дискретную случайную величину общего вида, нужно задать множество её значений и соответствующих вероятностей и использовать функцию ```numpy.random.choice```:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 5, 12, 12, 12, 5, 12, 5, 5, 5, 5])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elements = np.array([1, 5, 12])\n", "probabilities = [0.05, 0.7, 0.25]\n", "np.random.choice(elements, 10, p=probabilities)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Другие распределения" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Существует большое количество других стандартных семейств распределений, многие из которых также можно генерировать в Питоне. \n", "Например, распределение хи-квадрат $\\chi^2_k$, имеющее наутральный параметр $k$, который называется числом степеней свободы:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xbbb8898>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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y7cPYcVue+/33a+eE/vnPvtl5ct2TrCtbx7LLl7lmYMUKuO027Z0m2liGTYPd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\nWkdX5z5+4aRl9gGVIlIuIhnARuCliTeIyCLgOeA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"text/plain": [ "<matplotlib.figure.Figure at 0xb97d630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,30,100)\n", "for k in [1, 2, 3, 4, 6, 9]:\n", " rv = sts.chi2(k)\n", " cdf = rv.cdf(x)\n", " plt.plot(x, cdf, label=\"$k=%s$\" % k)\n", "plt.legend()\n", "plt.title(\"CDF ($\\chi^2_k$)\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0xbf24160>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7g5r6Gr7N/vboHRdfjGnECO56+WXeHzeOxXv2cNe+fbhkHBohRD/Wq4N7YV0d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\n3W0NDWyw2fixqoo0m42NlZWUNjQwLjC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JRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb0cbf60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,30,100)\n", "for k in [1, 2, 3, 4, 6, 9]:\n", " rv = sts.chi2(k)\n", " pdf = rv.pdf(x)\n", " plt.plot(x, pdf, label=\"$k=%s$\" % k)\n", "plt.legend()\n", "plt.title(\"PDF ($\\chi^2_k$)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Полный список функций SciPy для работы со всеми распределениями можно найти тут: http://docs.scipy.org/doc/scipy-0.14.0/reference/stats.html" ] } ], "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 }	unlicense
dikien/personnel-study	image-processing/opencv-study/Question_1.ipynb	1	1015846	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt, cm\n", "from sklearn.cluster import MiniBatchKMeans\n", "import numpy as np\n", "import cv2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.axes._subplots.AxesSubplot at 0x10983f190>,\n", " <matplotlib.image.AxesImage at 0x10c52dc10>)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAsEAAAEcCAYAAAAm+WkYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvcuvbVmW3vUbY861z703bkRURmb5UU9jiwJZgMBI7liI\n", "DrYs2cI9LHdp0UB0bYv/wF0kaAGCBgaJpiVjmwaSQQKBECAkGz/kKiorTaWrIjNe956z15xj0PjG\n", 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image.shape[:2]\n", "\n", "image = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)\n", "\n", "image = image.reshape((image.shape[0] * image.shape[1], 3))\n", "\n", "clt = MiniBatchKMeans(n_clusters = 5)\n", "labels = clt.fit_predict(image)\n", "quant = clt.cluster_centers_.astype(\"uint8\")[labels]\n", "\n", "quant = quant.reshape((h, w, 3))\n", "image = image.reshape((h, w, 3))\n", "\n", "# convert from Lab* to RGB\n", "quant = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)\n", "image = cv2.cvtColor(image, cv2.COLOR_LAB2BGR)\n", "\n", "# display the images and wait for a keypress\n", "plt.figure(figsize=(12,8))\n", "plt.subplot(111), plt.imshow(np.hstack([image, quant]))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.axes._subplots.AxesSubplot at 0x10da124d0>,\n", " <matplotlib.image.AxesImage at 0x10e654410>)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { 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image.reshape((image.shape[0] * image.shape[1], 3))\n", "\n", "clt = MiniBatchKMeans(n_clusters = 5)\n", "labels = clt.fit_predict(image)\n", "quant = clt.cluster_centers_.astype(\"uint8\")[labels]\n", "\n", "quant = quant.reshape((h, w, 3))\n", "image = image.reshape((h, w, 3))\n", "\n", "# convert from Lab* to RGB\n", "quant = cv2.cvtColor(quant, cv2.COLOR_LAB2RGB)\n", "image = cv2.cvtColor(image, cv2.COLOR_LAB2RGB)\n", "\n", "# display the images and wait for a keypress\n", "plt.figure(figsize=(12,8))\n", "plt.subplot(111), plt.imshow(np.hstack([image, quant]))" ] } ], "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 }	bsd-2-clause
spulido99/NetworksAnalysis	camilo_torres_botero/Ejercicios 1.3 Random Networks Vs. Real Networks.ipynb	1	18880	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Ejercicios Random Networks vs Real Networks" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Ejercicios Diferencia en Distribución de Grados\n", "\n", "Compare la distribución de grados de una red real contra una red aleatoria.\n", "\n", "- Baje un red real de SNAP\n", "- Cree una red aleatoria con el mismo número de links y nodos\n", "- Compare la distribución de grados" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Camil\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]1j\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x24704f0f080>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x24702d0d898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "edges = []\n", "for line in open('CA-HepTh.txt'):\n", " if line[0] != '#':\n", " edge = line.replace('\\n','').split('\\t')\n", " edges.append((edge[0],edge[1]))\n", " \n", "G=nx.Graph()\n", "G.add_edges_from(edges)\n", "\n", "d = G.degree()\n", "#degrees = [degree for _, d.items()]\n", "#print(d)\n", "\n", "N = len(G.nodes())\n", "p = (2len(edges))/(N*(N-1))\n", "G_rand = nx.gnp_random_graph(N,p)\n", "\n", "sns.distplot(list(G.degree().values()))\n", "sns.distplot(list(G_rand.degree().values()))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Ejercicios Comparación Tamaño del componente Gigante\n", "\n", "Genere varias realizaciones de la red aleatoria y compare el tamaño del componente gigante contra el de la red real" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Ejercicio Comparación Número de componentes\n", "\n", "Genera varias realizaciones de la red aleatoria y compare la cantidad de componentes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "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" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }	mit
AssembleSoftware/IoTPy	examples/Introduction.ipynb	1	18105	{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "091df4ee", "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append(\"../\")\n", "\n", "from IoTPy.core.stream import Stream, run\n", "\n", "from IoTPy.helper_functions.recent_values import recent_values" ] }, { "cell_type": "markdown", "id": "548cbc7c", "metadata": {}, "source": [ "# f_item(func, in_stream, keyword_arguments)\n", "\n", "### func: function \n", " function that is called when a new element is appended to in_stream\n", "### func(item_of_stream, keyword_arguments)\n", "The keyword arguments are the same for func and f_item\n", "### in_stream: a Stream or StreamArray\n", "\n", "\n", "### Example\n", "Given a stream x of integers and empty streams y and z and an integer constant M. Put items that are multiples of M that appear in stream x into stream y and all other items into stream Z.\n", "\n", "## First step: define func\n", "func operates on an item of a stream and keyword arguments. In this example, func is f and the keyword arguments of f, in addition to item are:\n", "M, multiples_stream, non_multiples_stream.\n", "\n", "item is an item of the in_stream." ] }, { "cell_type": "code", "execution_count": 2, "id": "e61ea2e3", "metadata": {}, "outputs": [], "source": [ "from IoTPy.agent_types.simple import f_item\n", "\n", "def f(item, M, multiples_stream, non_multiples_stream):\n", " if item%M:\n", " multiples_stream.append(item)\n", " else:\n", " non_multiples_stream.append(item)" ] }, { "cell_type": "markdown", "id": "41614af8", "metadata": {}, "source": [ "## Second step: define streams and call f_item( )\n", "The keyword arguments of func are passed as keyword arguments of f_item.\n", "\n", "The item argument of func is not passed as an argument to f_item; instead the input stream x is passed;\n", "\n", "So, the call to f_item is:\n", "\n", "f_item(func=f, in_stream=x, M=2, multiples_stream=y, non_multiples_stream=z)\n", "\n", "with the keyword arguments and their values \n", "\n", "M=2, multiples_stream=y, non_multiples_stream=z\n", "\n", "in f and also passed in the call to f_item.\n", "\n", "When a new item is appended to stream x, function f is called." ] }, { "cell_type": "code", "execution_count": 14, "id": "ff7723bb", "metadata": {}, "outputs": [], "source": [ "x = Stream(name='input stream')\n", "y = Stream(name='even numbers in stream x')\n", "z = Stream(name='odd numbers in stream x')\n", " \n", "f_item(func=f, in_stream=x, M=2, multiples_stream=y, non_multiples_stream=z)" ] }, { "cell_type": "markdown", "id": "bdb14314", "metadata": {}, "source": [ "## Third step: Test the function\n", "\n", "### Put test values in the input streams.\n", "x.extend(list(range(5)))\n", "\n", "### Execute a step\n", "run()\n", "\n", "### Look at recent values of streams.\n", "\n", "print ('recent values of stream y are')\n", "\n", "print (recent_values(y))\n", "\n", "# Putting the steps together:" ] }, { "cell_type": "code", "execution_count": 4, "id": "2144422b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recent values of stream y are\n", "[1, 3, 5, 7, 9]\n", "recent values of stream z are\n", "[0, 2, 4, 6, 8]\n" ] } ], "source": [ "from IoTPy.agent_types.sink import sink_element\n", "from IoTPy.agent_types.simple import f_item\n", "\n", "# STEP 1: Define function\n", "def f(item, M, multiples_stream, non_multiples_stream):\n", " if item%M:\n", " multiples_stream.append(item)\n", " else:\n", " non_multiples_stream.append(item)\n", "\n", "# STEP 2: Declare streams and call f_item\n", "x = Stream(name='input stream')\n", "y = Stream(name='even numbers in stream x')\n", "z = Stream(name='odd numbers in stream x')\n", " \n", "f_item(func=f, in_stream=x, M=2, multiples_stream=y, non_multiples_stream=z)\n", "\n", "# STEP 3: test f_item()\n", "# Put test values in the input streams.\n", "x.extend(list(range(10)))\n", "\n", "# Execute a step\n", "run()\n", "\n", "# Look at recent values of streams.\n", "print ('recent values of stream y are')\n", "print (recent_values(y))\n", "print ('recent values of stream z are')\n", "print (recent_values(z))" ] }, { "cell_type": "markdown", "id": "30d62e81", "metadata": {}, "source": [ "# ANOTHER EXAMPLE of f_item\n", "\n", "#### Problem\n", "If an item on stream x is greater than the maximum (positive) item seen so far on x then put the item on stream y, and \n", "\n", "iff an item on stream x is less than the minimum (negative) item seen so far on x then put the item on stream z\n", "\n", "## First step: define func\n", "func operates on an item of a stream and keyword arguments. In this example, func is g and the keyword arguments of g (in addition to item) are:\n", "\n", "max_and_min, max_stream, min_stream.\n", "\n", "## Second step: define streams and call f_item( )\n", "The keyword arguments of func are passed as keyword arguments of f_item.\n", "\n", "\n", "## Third step: Test the function\n", "\n", "Put items in the input streams, run(), and look at the recent values of the output streams." ] }, { "cell_type": "code", "execution_count": 5, "id": "4fc50968", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recent values of stream y are\n", "[5, 8, 10, 20]\n", "recent values of stream z are\n", "[-3, -5, -12]\n" ] } ], "source": [ "max_and_min = [0, 0]\n", "\n", "# STEP 1: Define function\n", "def g(item, max_and_min, max_stream, min_stream):\n", " if item > max_and_min[0]:\n", " max_and_min[0] = item\n", " max_stream.append(item)\n", " if item < max_and_min[1]:\n", " max_and_min[1] = item\n", " min_stream.append(item)\n", "\n", "\n", "# STEP 2: Declare streams and call f_item\n", "x = Stream(name='input stream')\n", "y = Stream(name='new maxima')\n", "z = Stream(name='new minima')\n", "\n", "f_item(func=g, in_stream=x, max_and_min=max_and_min, max_stream=y, min_stream=z)\n", "\n", "# STEP 3: test f_item()\n", "# Put test values in the input streams.\n", "x.extend([5, 4, 8, -3, -1, 10, -5, 6, 20, -12])\n", "# Execute a step\n", "run()\n", "# Look at recent values of streams.\n", "print ('recent values of stream y are')\n", "print (recent_values(y))\n", "print ('recent values of stream z are')\n", "print (recent_values(z))" ] }, { "cell_type": "markdown", "id": "9690ceae", "metadata": {}, "source": [ "# SLIDING WINDOWS OF STREAMS\n", "\n", "f_item operates on the next item in a stream. f_window operates on the next sliding window of the input stream. \n", "\n", "A sliding window is specified by:\n", "1. window_size and\n", "2. step_size\n", "both of which are positive integers.\n", "\n", "If the input stream is x then the sequence of windows are:\n", "\n", "*x[0:window_size], x[step_size: step_size+window_size], ... , x[n step_size : n * step_size + window_size]\n", "\n", "For example, if window_size = 2 and step_size = 1 then the sequence of windows are:\n", "\n", "x[0,1], x[1, 2], x[2, 3], x[3, 4], .." ] }, { "cell_type": "markdown", "id": "9f94c593", "metadata": {}, "source": [ "# Example of sliding windows\n", "The call to f_window has arguments func, in_stream, window_size, and step_size, and other keyword arguments.\n", "\n", "The arguments in func** are window which is a list or array, and the same keyword arguments.\n", "\n", "In this example the items of stream y are the sums of the sliding windows multiplied by multiplier. func is g which has a first argument, window which is a list, and keyword arguments out_stream and multiplier.\n", "\n", "The call to f_window has the arguments func, in_stream, window_size, step_size and the same keyword arguments out_stream and multiplier as in g. " ] }, { "cell_type": "code", "execution_count": 15, "id": "8ffc8911", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recent values of stream y are\n", "[1, 3, 5, 7, 9, 11, 13, 15, 17]\n" ] } ], "source": [ "from IoTPy.agent_types.simple import f_window\n", "\n", "x = Stream('x')\n", "y = Stream('y')\n", "\n", "def g(window, out_stream):\n", " out_stream.append(sum(window))\n", "f_window(func=g, in_stream=x, window_size=2, step_size=1, out_stream=y)\n", "\n", "# Put test values in the input streams.\n", "x.extend(list(range(10)))\n", "# Execute a step\n", "run()\n", "# Look at recent values of streams.\n", "print ('recent values of stream y are')\n", "print (recent_values(y))" ] }, { "cell_type": "markdown", "id": "021aba44", "metadata": {}, "source": [ "# Synchronous Join\n", "\n", "in_streams stands for a list of streams (whereas in_stream represents a single stream). A synchronous join applies a function func to the list in_streams[0][i], in_streams[1][i], in_streams[2][i],... for all i.\n", "\n", "For example, suppose x is the stream of nonnegative integers, [0, 1, 2, 3, ...] and y is the stream of letters of the alphabet repeated forever, i.e. y is ['A', 'B', 'C', .....'Z', 'A', 'B', ..]. Then join_synch applies function func to the lists [0, 'A'], [1, 'B'], [2, 'C'], ....\n", "\n", "The arguments of join_synch are func, in_streams, and additional keyword arguments. The arguments of func are a list of joined items from the streams of in_streams and the same additional keyword arguments as in join_synch.\n" ] }, { "cell_type": "markdown", "id": "a08c034b", "metadata": {}, "source": [ "# Example of join_synch\n", "\n", "Given streams w, x, y, z we want to set z[i] = sum([w[i], x[i], y[i])." ] }, { "cell_type": "code", "execution_count": 7, "id": "8a07ff07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recent values of stream w are\n", "[100, 101, 102, 103, 104, 105, 106, 107, 108, 109]\n", "recent values of stream x are\n", "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n", "recent values of stream y are\n", "[0, 1, 2, 3, 4]\n", "recent values of stream z are\n", "[100, 104, 108, 112, 116]\n" ] } ], "source": [ "from IoTPy.agent_types.simple import join_synch\n", "\n", "w = Stream(name='w')\n", "x = Stream(name='x')\n", "y = Stream(name='y')\n", "z = Stream(name='z')\n", "\n", "def h(alist, out_stream): out_stream.append(sum(alist))\n", "\n", "join_synch(func=h, in_streams=[w, x, y], out_stream=z)\n", "\n", "# Put test values in the input streams.\n", "w.extend(list(range(100, 110)))\n", "x.extend(list(range(0, 20, 2)))\n", "y.extend(list(range(5)))\n", "\n", "# Execute a step\n", "run()\n", "# Look at recent values of streams.\n", "print ('recent values of stream w are')\n", "print (recent_values(w))\n", "print ('recent values of stream x are')\n", "print (recent_values(x))\n", "print ('recent values of stream y are')\n", "print (recent_values(y))\n", "print ('recent values of stream z are')\n", "print (recent_values(z))" ] }, { "cell_type": "markdown", "id": "5a44c130", "metadata": {}, "source": [ "# Asynchronous Join\n", "\n", "The function join_asynch has arguments func, in_streams - a list of streams, and additional keyword arguments.\n", "\n", "When an item v is appended to the i-th stream of in_streams, func is applied to the tuple [i, v]. The first element of the tuple indicates the stream along which the item arrived, and the second element is the item itself.\n", "\n", "Different runs of join_asynch may produce different results because items from the different streams arrive in different orders.\n", "\n", "The arguments of func are index_item and additional keyword arguments. index_item is a tuple of the stream index and the item value, \n", "\n", "## Example of asynchronous join\n", "Print items as they arrive in streams x and y and put the items in stream z.\n", "\n", "In this example func is h. Function h has an argument index_item and keyword argument out_stream which is also used in the call to join_asynch." ] }, { "cell_type": "code", "execution_count": 8, "id": "33074809", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 : 10\n", "0 : 11\n", "1 : 0\n", "1 : 1\n", "1 : 2\n", "recent values of stream z are\n", "[10, 11, 0, 1, 2]\n", "0 : 12\n", "0 : 13\n", "0 : 14\n", "1 : 3\n", "recent values of stream z are\n", "[10, 11, 0, 1, 2, 12, 13, 14, 3]\n" ] } ], "source": [ "from IoTPy.agent_types.simple import join_asynch\n", "\n", "def h(index_item, out_stream):\n", " index, item = index_item\n", " print (index,': ', item)\n", " out_stream.append(item)\n", "\n", "x = Stream('x')\n", "y = Stream('y')\n", "z = Stream('z')\n", "\n", "join_asynch(func=h, in_streams=[x, y], out_stream=z)\n", "\n", "# Put test values in the input streams.\n", "x.extend([10, 11])\n", "y.extend([0, 1, 2])\n", "# Execute a step\n", "run()\n", "# Look at recent values of streams.\n", "print ('recent values of stream z are')\n", "print (recent_values(z))\n", "\n", "# Put test values in the input streams.\n", "x.extend([12, 13, 14])\n", "y.extend([3])\n", "# Execute a step\n", "run()\n", "# Look at recent values of streams.\n", "print ('recent values of stream z are')\n", "print (recent_values(z))" ] }, { "cell_type": "markdown", "id": "d5c4fe45", "metadata": {}, "source": [ "# Time-based Join\n", "\n", "This function operates on streams in which items are timestamped. Elements in each stream consist of pairs (timestamp, item). The timestamps on each stream are monotone increasing, i.e. the timestamp of a later element on a stream must be greater than the timestamp of an earlier element on the same stream.\n", "\n", "The following situation can arise. An element [t, v] of one input stream arrives before an element [t', v'] of a different input stream, where t > t'.\n", "\n", "The arguments of join_timed are func and in_streams - a list of streams, and additional keyword arguments which appear in func.\n", "\n", "The function func operates on a tuple of the form [timestamp, list_of_items] where list_of_items is a list of N values where N is the number of streams in in_streams.\n", "\n", "Consider a tuple [T, L] where T is the timestamp and L is the list of items. If there is no element with timestamp T on stream k then L[k] is None. If there is an element [T, v] in the k-th stream then L[k] is v.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "93f24292", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, ['x[0]', None]]\n", "[3, ['x[1]', 'y[0]']]\n", "[4, ['x[2]', None]]\n", "[5, ['x[3]', 'y[2]']]\n" ] } ], "source": [ "from IoTPy.agent_types.simple import join_timed\n", "\n", "def f(timestamped_list):\n", " print (timestamped_list)\n", "\n", "x = Stream(name='x')\n", "y = Stream(name='y')\n", "\n", "join_timed(func=f, in_streams=[x, y])\n", "\n", "# Put test values in the input streams.\n", "x.extend([[1, 'x[0]'], [3, 'x[1]']])\n", "y.extend([[3, 'y[0]'], [5, 'y[2]'], [5, 'y[3]']])\n", "# Execute a step\n", "run()\n", "\n", "# Put test values in the input streams.\n", "x.append([4, 'x[2]'])\n", "run()\n", "x.append([5, 'x[3]'])\n", "run()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "1ca94da6", "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": "3.10.2" } }, "nbformat": 4, "nbformat_minor": 5 }	bsd-3-clause
kimkipyo/dss_git_kkp	Python 복습/08일차.금_정규표현식, class, 크롤링, 숙제/8일차_2T,4T_크롤링 고급 - pagination, url builder, pandas, matplotlib.ipynb	1	253837	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2T_크롤링 고급_pagination, url builder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 네이버 블로그 페이지 크롤링" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = requests.get(\"http://naver.com\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import random" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#이거 실행하면 계속 갑니다. 랜덤으로 크롤링 하는 것\n", "# for page_id in range(1, 100):\n", "# sleeping = random.randint(30, 100)\n", "# print(str(page_id) + \"를 크롤링합니다.\")\n", "# time.sleep(sleeping)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = requests.get(\"http://search.naver.com/search.naver?sm=tab_hty.top&where=post&ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Response [200]>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<!doctype html> <html class=\"\" lang=\"ko\"> <head> <meta charset=\"utf-8\"> <meta name=\"referrer\" content=\"always\"> <meta name=\"format-detection\" content=\"telephone=no,address=no,email=no\"> <meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,maximum-scale=2.0\"> <meta property=\"og:title\" content=\"아이오아이 강미나 : 네이버 블로그검색\"/> <meta property=\"og:image\" content=\"https://ssl.pstatic.net/sstatic/search/common/og_v3.png\"> <meta property=\"og:description\" content=\"\\'아이오아이 강미나\\'의 네이버 블로그검색 결과입니다.\"> <meta name=\"description\" lang=\"ko\" content=\"\\'아이오아이 강미나\\'의 네이버 블로그검색 결과입니다.\"> <title>아이오아이 강미나 : 네이버 블로그검색</title> <link rel=\"shortcut icon\" href=\"https://ssl.pstatic.net/sstatic/search/favicon/favicon_140327.ico\"> <link rel=\"search\" type=\"application/opensearchdescription+xml\" href=\"https://ssl.pstatic.net/sstatic/search/opensearch-description.https.xml\" title=\"Naver\" /><link rel=\"stylesheet\" type=\"text/css\" href=\"https://ssl.pstatic.net/sstatic/search/pc/2016/css/search1_0804.css\"> <link rel=\"stylesheet\" type=\"text/css\" href=\"https://ssl.pstatic.net/sstatic/search/pc/2016/css/search2_0602.css\"> <link rel=\"stylesheet\" type=\"text/css\" href=\"https://ssl.pstatic.net/sstatic/search/pc/2016/css/api_atcmp_0519.css\"><script> naver = window.naver \|\| {}; 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u = d ; } if (! p) return null ; var url ; if (naver.search.https) { url = (naver.search.meta_referrer ? \"/p/crd\" : \"/p/cr\") + \"/rd\"; if (m != 0) m = 1; } else { out = isOutlink(u); url = \"http://cr.naver.com\" + (g_D && out ? \"/nr\" : \"/rd\"); if (m != 0) m = g_D && out ? 2 : 1; } url += \"?m=\" + m + \"&\" + cpip() + \"&\" + nxGetCommonCRParam() + p; return url; } function tCR (a, b, c, d, e) { var l = nxGetCRURL(0, a, b, c, d, e) ; var cr = getCRRanking(l) ; if (cr > 0) l = l + \"&cr=\" + cr ; if (document.images) (new Image()).src = l ; else document.location = l ; return false ; } function gCR (u, a, r, i, e, t) { if (u) u = urlexpand(u) ; var l = nxGetCRURL(1, a, r, i, u, e) ; var o = document.createElement(\"a\") ; var cr = getCRRanking(l) ; if (cr > 0) l = l + \"&cr=\" + cr ; if (o.click) { o.href = l ; o.style.display = \"none\" ; document.body.appendChild(o) ; o.click() ; } else document.location = l ; } function goCR (o, p, t) { var u = o.href ; tt_sub_disable(o) ; if (p.indexOf(\"u=javascript\") >= 0) t = true ; var n = (o.ownerDocument==document && o.target && o.target!=\"_self\" && o.target!=\"_parent\" && o.target!=\"_top\") && !(window.location.protocol&&window.location.protocol.indexOf(\"https:\")==0) ; var cr = getCRRanking(p) ; if (cr > 0) p = p + \"&cr=\" + cr ; if (!(u && u.indexOf(\"http://cr.naver.com/\")==0) && !(o.getAttribute !== undefined && o.getAttribute(\"crurl\"))) { if (0 && u && u.indexOf(\"/search.naver?\") >= 0) { var str = \"\" ; if (0) u += \"&crcp=1\", str += urlencode(\"&crcp=1\") ; if (0) u += \"&rule=1\", str += urlencode(\"&rule=1\") ; if (0) u += \"&debug=1\", str += urlencode(\"&debug=1\") ; p = p.replace(/(((?:^\|&)u=).\\\\/search.naver%3F[^&])/, \\'$1\\' + str) ; } u = nxGetCRURL(t?0:(n?-1:1), p, u) ; } if (u && !u.match(/m=0&/)) { var a = o.innerHTML ; if (g_D && naver.search.https && naver.search.meta_referrer && o.href && isOutlink(o.href)) o.setAttribute(\"rel\", \"noreferrer\"); o.href = u ; if (o.getAttribute !== undefined) o.setAttribute(\"crurl\", \"1\"); if (o.innerHTML != a) o.innerHTML = a ; } else if (document.images) (new Image()).src = u ; return true ; } function goOtherCR (o, p) { return goCR(o, p, false) ; } function goOtherTCR (o, p) { return goCR(o, p, true) ; } function get_form_url (o) { var url = o.getAttribute(\"action\") ; if (url == null) url = \"\" ; var e, n = 0 ; for (var i=0; i<o.elements.length; i++) { e = o.elements[i] ; if (e.disabled \|\| !e.name) continue ; url += (n++>0?\"&\":url.indexOf(\"?\")<0?\"?\":url.indexOf(\"?\")<url.length-1?\"&\":\"\") + encodeURIComponent(e.name) + \"=\" + encodeURIComponent(e.value) ; } return url ; } function formCR (o, area, rank, id) { if (typeof o == \\'string\\') o = document.getElementById(o) ; var target = o.getAttribute(\"target\") ; if (target && target!=\"_self\" && target!=\"_parent\" && target!=\"_top\" \|\| /^post$/i.test(o.getAttribute(\"method\"))) { tCR(area, rank, id) ; o.submit() ; return false ; } var url = get_form_url(o) ; var a = document.createElement(\"a\") ; 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rr_list = rr_list.concat(o) ; return 1 ; } function winload (str) { w = 870, h = 651 ; ww = 880, hh = 700 ; l = (screen.availWidth/2) - (ww/2) ; t = (screen.availHeight/2) - (hh/2) ; window.open(str,\"sdsgis\",\"top=\"+t+\",left=\"+l+\",width=\"+w+\",height=\"+h+\",status=yes,resizable=yes,toolbars=no,location=no,scrollbars=no\") ; } function winload_map (str) { t = 0; l = 0; w = screen.availWidth - 10; h = screen.availHeight - 30; window.open(str,\"sdsgis\",\"top=\"+t+\",left=\"+l+\",width=\"+w+\",height=\"+h+\",status=yes,resizable=yes,toolbars=no,location=no,scrollbars=yes\") ; } function sitevalue (title, sid) { var target_name = \"value\" ; window.open(\"about:blank\", target_name, \"width=380, height=343, resizable=no, menubar=no, statusbar=no, scrollbar=no\") ; siteform.action = \"http://dir.naver.com/siteview/sitevalue.php\"; siteform.target = target_name ; siteform.title.value = title ; siteform.sid.value = sid ; siteform.pageid.value = g_puid ; siteform.sessionid.value = g_suid ; siteform.submit() ; return false ; } function open_mapbrowser (x, y, scale, title) { var host = \"http://maps.naver.com\" ; if (x == null) x = \"\" ; else x = new String(x) ; if (y == null) y = \"\" ; else y = new String(y) ; if (title == null) title = \"\" ; if (x.indexOf(\".\")>=0 && y.indexOf(\".\")>=0) { if (x>=122.8957333 && x<=133.4503243 && y>=31.1396418 && y<=43.4549896) host = \"http://map.naver.com\" ; } win = window.open(host+\"/?x=\"+x+\"&y=\"+y+\"&title=\"+title, \"_blank\") ; win.focus() ; } function open_mapbrowser_etc (args, t) { var host = \"http://maps.naver.com\" ; if (t && t==1) host = \"http://map.naver.com\" ; if (args == null) args=\"\" ; win = window.open(host+\"/?\"+args, \"_blank\") ; if (win != null) { win.focus() ; return true ; } else return false ; } function nx_open_mapbrowser (x, y, scale, title, id, type) { var host = \"http://map.naver.com\" ; var param ; if (x == null) x = \"\" ; else x = new String(x) ; if (y == null) y = \"\" ; else y = new String(y) ; if (title == null) title = \"\" ; if (arguments.length <= 4) param = host+\"/?x=\"+x+\"&y=\"+y+\"&title=\"+title ; else param = host+\"/index.nhn?enc=utf8&mapMode=0&level=2&lng=\"+x+\"&lat=\"+y+\"&pinTitle=\"+title+\"&pinId=\"+id+\"&pinType=\"+type ; win = window.open(param, \"_blank\") ; win.focus() ; } function nx_open_mapbrowser_etc (args, t) { var host = \"http://map.naver.com\" ; if (args == null) args=\"\" ; win = window.open(host+\"/?\"+args, \"_blank\") ; if (win != null) { win.focus() ; return true ; } else return false ; } function goMap (txtAddr, txtX, txtY, rAddr, rTel) { if (txtX == \"\") alert(txtAddr+\\'을 찾을 수 없습니다. 다른 조건을 가지고 검색하여 주십시요.\\') ; else winload_map(\\'http://map.naver.com/view?item=\\'+txtAddr+\\'&x=\\'+txtX+\\'&y=\\'+txtY+\\'&strAddr=\\'+rAddr+\\'&strTel=\\'+rTel) ; } function goSanghoMap (encTitle, x, y, encAddr, encTel) { var v_args = \"&x=\" + x + \"&y=\"+ y + \"&title=\" + encTitle + \"&address=\" + encAddr + \"&tel=\" + encTel ; open_mapbrowser_etc(v_args) ; } function nx_env () { var i ; for (i=0; i<document.links.length; i++) if (document.links[i].target == \"nxt\") document.links[i].target = \"_blank\"; for (i=0; i<document.forms.length; i++) if (document.forms[i].target == \"nxt\") document.forms[i].target = \"_blank\"; return true ; } function nx_onkeydown (e) { function cancel_event (event) { if (event.preventDefault) { event.preventDefault() ; event.stopPropagation() ; } else event.returnValue = false ; } function ignore_input (e, k, c) { var el = e.target \|\| e.srcElement ; var el_upper = el && el.tagName ? el.tagName.toUpperCase() : \"\" ; if (el_upper == \"INPUT\" \|\| el_upper == \"SELECT\" \|\| el_upper == \"TEXTAREA\" \|\| el_upper == \"EMBED\" \|\| el_upper == \"OBJECT\") return true ; else if (k & m.ALT) return true ; else if (k == m.CTRL && c != 86) return true ; else if (k & m.META \|\| c == 91 \|\| c == 224) return true ; return false ; } try { if (typeof(e) == \\'undefined\\') e = event ; var f = document.getElementsByName(\"search\")[0] ; var k = 0, c = 0 ; var m = { ALT:1, CTRL:2, SHIFT:4, META:8 } ; if ((typeof(e.altKey) != \\'undefined\\') ? e.altKey : (e.modifiers & Event.ALT_MASK > 0)) k \|= m.ALT ; if ((typeof(e.ctrlKey) != \\'undefined\\') ? e.ctrlKey : (e.modifiers & Event.CONTROL_MASK > 0)) k \|= m.CTRL ; if ((typeof(e.shiftKey) != \\'undefined\\') ? e.shiftKey : (e.modifiers & Event.SHIFT_MASK > 0)) k \|= m.SHIFT ; if ((typeof(e.metaKey) != \\'undefined\\') && e.metaKey) k \|= m.META ; c = e.keyCode ? e.keyCode : e.which ; if (ignore_input(e, k, c)) return true ; if (((k === 0 \|\| k === m.SHIFT) && (c === 21 \|\| c === 229)) \|\| (k === m.SHIFT && c === 32) \|\| c === 113 \|\| c === 45) { cancel_event(e) ; scrollTo(0, 0) ; f.query.focus() ; f.query.select() ; return false ; } else if (c > 44) { scrollTo(0, 0) ; f.query.value = \\'\\' ; f.query.focus() ; f.query.select() ; return true ; } } catch (err) {} return true ; } function Init () { if (document.captureEvents && Event.KEYDOWN) document.captureEvents (Event.KEYDOWN) ; document.onkeydown = nx_onkeydown ; } var msg_hidden_idle=null; function msg (id) { if(msg_hidden_idle) clearInterval(msg_hidden_idle); if(id) { var mid=document.getElementById(id); mid.style.display=\\'\\'; mid.style.visibility=\\'visible\\'; } } function msg_hidden (id, f) { if(f) hidden(id); else msg_hidden_idle=setInterval(\"hidden(\\'\"+id+\"\\')\",100); } function hidden (id) { var mid=document.getElementById(id); mid.style.display=\\'none\\'; mid.style.visibility=\\'hidden\\'; } function nx_check_basic () { return ; } function nx_set_cookie (name, value, expire, domain) { var cookie = name + \"=\" + escape(value); if (expire != null) { if (expire instanceof Date) { cookie = cookie + \"; expires=\" + expire.toUTCString(); } else { var exdate = new Date((new Date()).getTime() + expire1000); cookie = cookie + \"; expires=\" + exdate.toUTCString(); } } cookie = cookie + \"; path=/\"; if (domain != null) { cookie = cookie + \"; domain=\" + domain; } document.cookie = cookie; } function nx_get_cookie (name) { var cookie_list = document.cookie.split(/\\\\s;\\\\s/); for (var i = 0; i < cookie_list.length; i++) { var tmp_list = cookie_list[i].split(\"=\"); var c_name = trim_space(tmp_list[0]); var c_value = tmp_list[1]; if (name == c_name) { return unescape(c_value); } } return null; } var nx_default_charset = document.charset ; function nx_form_emul_charset (form) { if (/msie/i.test(navigator.userAgent) && !/opera/i.test(navigator.userAgent)) { document.charset = form.acceptCharset ; window.onbeforeunload = function () { document.charset = nx_default_charset ; } ; } return true; } function nx_get_option_status () { return (g_sos>0 ? 1:0) ; } function nx_set_option_status (val) { g_sos = (val>0 ? 1:0) ; var expires = new Date(\"Jan 1, 2050\"); var expires_string = \" ; expires=\" + expires.toGMTString(); document.cookie = \"sos=\" + g_sos + expires_string + \" ; domain=search.naver.com ;\"; return g_sos ; } Init() ; </script> <script type=\"text/javascript\" src=\"https://ssl.pstatic.net/sstatic/sdyn.js?f=/au/pc/_nx/jindo_1.5.3_160324.js+/search/js/nhn.Component.js+/search/js/jindo.Component.1.1.0.js+/search/js/jindo.component.library_120927.js+/search/js/flashObject_121025.js+/au/pc/search_option/nhn.search.search_option_160316.js+/au/pc/naver_autocomplete/nhn.common.atcmp.naver_web_160805_1.js+/search/js/component.js+/search/js/2013/nhn.thumbnail_sliding_131211.js+/au/s/pc/_common/jindo/jindo.Rolling_140526.js+/au/pc/_common/nhn.common_160707.js\"></script> <script type=\"text/javascript\"> var displayControlFuncObject ; var displayControlObject = { renderCount : 0, func : function () { if (this.renderCount > 5) return ; var elem = $Element(\\'content\\') ; if (! elem) return ; var child = elem.child() ; for (var i = 0; i < child.length; i++) { if (child[i].hasClass(\\'section\\') && !child[i].hasClass(\\'nx_no_control\\')) { if (!child[i].visible()) child[i].show() ; } } if (this.renderCount++ > 5) displayControlFuncObject.detach(window, \\'scroll\\') ; } } ; displayControlFuncObject = $Fn(displayControlObject.func, displayControlObject) ; displayControlFuncObject.attach(window, \\'scroll\\') ; </script> <script type=\"text/javascript\"> (function () { var bSVG = !!document.createElementNS && !!document.createElementNS(\\'http://www.w3.org/2000/svg\\', \"svg\").createSVGRect; if (!bSVG) { var elHtml = document.getElementsByTagName(\"html\")[0], sClass = elHtml.className; elHtml.className = sClass + \" nosvg\"; } })(); var g_site = \"\" ; function nx_form_submit (f) { var idx = 0 ; var q = trim_space(f.query.value) ; if (g_D \|\| 0) f.action = \"\" ; return true ; } function nx_search (anchorObj, where, sm, param) { var u = \\'\\' ; var q, qe ; var form = document.getElementsByName(\"search\")[0] ; if (g_D) u = \\'\\' ; else u = \\'//search.naver.com/search.naver\\' ; q = encodeURIComponent(form.query.value), qe = \\'&ie=utf8\\' ; anchorObj.href = urlexpand(u + \\'?where=\\' + where + (sm?\\'&sm=\\'+sm:\"\") + \\'\\' + qe + \\'&query=\\' + q + \\'\\' + (param?\\'&\\'+param:\\'\\')) ; } function tab_click (linkObj, tab, nso, neso) { var param = undefined ; if (nso) param = \\'&nso=\\' + nso ; if (neso) param += \\'&neso=\\' + neso ; nx_search(linkObj, tab, \"tab_jum\", param) ; } var smartSearch ; var smartSearchU ; function raise () { } var onload_post = \"\"; function nx_init () { nx_env() ; window.onerror = null, window.status = \"\" ; try { eval(onload_post) ; } catch(e) { if (g_D) raise(e) ; } ; return true ; } function document_write (s) { document.write(s) ; } function nx_social_toggle (o) { var l = $Element($(\"social_use\")) ; if (o \|\| l.hasClass(\\'on\\')) l.removeClass(\\'on\\') ; else l.addClass(\\'on\\') ; } $Fn(function() { $Fn(function(e) { var link = $$.getSingle(\"div.social_layer\", document.body); if (link) { var el = e.element; var wel = $Element(el); elLink = $Element(link).$value(); if (!(link && (el == link \|\| wel.isChildOf(link)))) { var l = $Element($(\"social_use\")) ; if (o \|\| l.hasClass(\\'on\\')) l.removeClass(\\'on\\') ; } } }).attach(document.body, \"click\"); }).attach(window, \"load\"); var nxtt_slui = { type:\"id.html\", followMouse:true, mouseontooltip:false, delay:0.01, offset:[1,7,7,1], padding:[0,190,0,10], maxWidth:400, head:\"<div id=ctnlayer><div id=ctnlayer class=bdr><span class=ct>\", tail:\"</span></div></div>\" } ; nhn.jsLazyLoad = { include: function(a, b, c) { nx_js_lazyload(a, b, 0) ; } } ; nx_js_defer_load(\"https://ssl.pstatic.net/sstatic/au/s/pc/_others/nxtt/search_om.js\", function() {NXTT.div=document.getElementById(\"nxtt_div\");}, 50); </script></head> <body class=\\'\\'> <div id=nxtt_div style=\"display:none;position:absolute;border-width:0;z-index:11000\"></div> <div id=\"u_skip\"> <a href=\"#lnb\"><span>메뉴 영역으로 바로가기</span></a> <a href=\"#content\"><span>본문 영역으로 바로가기</span></a> </div> <div id=\"wrap\"> <div id=\"header_wrap\" role=\"heading\"> <div class=\"header_group\" style=\"background:url(https://ssl.pstatic.net/sstatic/search/nlogo/201608/20160816162834989.png) no-repeat 0 0\"> <div class=\"search_area\"> <h1><a href=\"http://www.naver.com\" onclick=\"return goOtherCR(this, \\'a=sta.naver&r=&i=&u=\\'+urlencode(this.href));\"><img src=\"https://ssl.pstatic.net/sstatic/search/nlogo/201608/20160816163019965.gif\" width=\"130\" height=\"25\" alt=\"NAVER\"></a></h1> <form id=\"nx_search_form\" name=\"search\" action=\"?\" method=\"get\" role=\"search\" onsubmit=\"return nx_form_submit(this)\"> <fieldset class=\"greenwindow\"> <legend>검색</legend> <input type=\"hidden\" name=\"sm\" value=\"tab_hty.top\"> <input type=\"hidden\" name=\"where\" value=\"post\"> <input type=\"hidden\" name=\"oquery\" value=\"아이오아이 강미나\"> <input type=\"hidden\" value=\"\" name=\"acq\" disabled> <input type=\"hidden\" value=\"\" name=\"acr\" disabled> <input type=\"hidden\" value=\"\" name=\"qdt\" disabled> <input type=\"hidden\" value=\"\" name=\"acir\" disabled> <input type=\"hidden\" value=\"\" name=\"os\" disabled> <input type=\"hidden\" value=\"\" name=\"bid\" disabled> <input type=\"hidden\" value=\"\" name=\"pkid\" disabled> <input type=\"hidden\" value=\"\" name=\"eid\" disabled> <input type=\"hidden\" value=\"\" name=\"mra\" disabled> <input type=\"hidden\" name=\"ie\" value=\"utf8\"> <div class=\"greenbox\"> <span class=\"keyword\"> <input type=\"text\" id=\"nx_query\" name=\"query\" class=\"box_window\" maxlength=255 accesskey=\"s\" value=\"아이오아이 강미나\" autocomplete=\"off\" title=\"검색어 입력\"> </span> </div> <div class=\"setkr_area\"> <!-- [D] 레이어 상태에 따라 title값 변화 필요 * 닫힌 경우 : title=\"한글 입력기 열기\" * 열린 경우 : title=\"한글 입력기 닫기\" --> <button id=\"ke_kbd_btn\" type=\"button\" class=\"bt_setkr\" title=\"한글 입력기 열기\" onclick=\"nx_ime_load(this)\"><span class=\"spim\">한글 입력기</span></button> <style type=\"text/css\" id=\"_nx_kbd_style\"></style><div id=\"_nx_kbd\" style=\"display:none;\"></div> </div> <!--@code lang=\"html\" title=\"[JS] 한글입력기 레이지 로딩 하거나 열기/닫기 수행.\"--> <script type=\"text/javascript\"> function nx_ime_load_fail () { alert(\"네트워크 상태가 안 좋아 한영입력기를 불러오지 못했습니다.\\\\n잠시 후 다시 시도해 주세요.\"); } function nx_ime_load (elBtn) { if (window.nx_kbd_toggle) { nx_kbd_toggle(elBtn); return; } new $Ajax(\"https://ssl.pstatic.net/sstatic/au/s/pc/_common/ime/ime.contents_20150716.js\", { type : \"jsonp\", timeout : 3, callbackid : \"$get_ime\", onload : function (res) { var oData = res.json(); if (oData) { var elStyle = $(\"_nx_kbd_style\"); if (elStyle.styleSheet) { elStyle.styleSheet.cssText = oData.ime_css; } else { elStyle.innerHTML = oData.ime_css; } $(\"_nx_kbd\").innerHTML = oData.ime_html; nx_js_lazyload(\"https://ssl.pstatic.net/sstatic/au/s/pc/_common/ime/nhn.ime_search_140825.js\", function () { nx_kbd_toggle(elBtn); }); } else { nx_ime_load_fail(); } }, ontimeout : nx_ime_load_fail, onerror : nx_ime_load_fail }).request(); } </script> <div id=\"nautocomplete\" class=\"autocomplete\"> <span class=\"bt_atcp\"><a href=\"#\" onclick=\"return false;\"><img class=\"triangleImg\" src=\"https://ssl.pstatic.net/sstatic/search/2015/btn_atcmp_down.gif\" alt=\"자동완성 펼치기\" width=\"13\" height=\"11\"></a></span> </div> <button type=\"submit\" class=\"bt_search spim\" onmouseover=\"$Element(this).addClass(\\'over\\');\" onmouseout=\"$Element(this).removeClass(\\'over down\\');\" onmousedown=\"$Element(this).removeClass(\\'over\\');$Element(this).addClass(\\'down\\');\">검색</button> <div class=\"ly_atcmp\" id=\"nx_autoframe_top\" style=\"display:none;\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <div class=\"api_atcmp_wrap _atcmp\" style=\"display:none;\"> <div class=\"words nature\"> <h3 class=\"tit\">생각한대로 검색해 보세요 <span class=\"beta\">Beta</span></h3> <ul class=\"_nature\"> <li class=\"_item\"><a href=\"#\" onclick=\"return false;\">@txt@</a><span style=\"display:none\" id=\"rank@rank@\">@txt@</span></li> </ul> </div> <div class=\"words _words\"> <div class=\"_atcmp_result_wrap\"> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> </div> <div class=\"add_group _atcmp_answer_wrap\"></div> </div> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"smartSearch.unuse(); return false;\">자동완성 끄기</a></span></p> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip1\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap _atcmpIng\" style=\"display:none;\"> <div class=\"words\"><p class=\"msg\">현재 자동완성 기능을 사용하고 계십니다.</p></div> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"smartSearch.unuse(); return false;\">자동완성 끄기</a></span></p> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip2\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap _atcmpStart\" style=\"display:none;\"> <div class=\"words\"><p class=\"msg\">자동완성 기능이 활성화되었습니다.</p></div> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"smartSearch.unuse(); return false;\">자동완성 끄기</a></span></p> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip3\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap _atcmpOff\" style=\"display:none;\"> <div class=\"words\"><p class=\"msg\">자동완성 기능이 꺼져 있습니다.</p></div> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\">자동완성 켜기</a></span></p> </div> <div class=\"api_atcmp_wrap _keywords\" style=\"display:none;\"> <div class=\"my_words\"> <div class=\"lst_tab\"> <ul><li class=\"on _recentTab\"><a href=\"javascript:;\">최근검색어</a></li> <li class=\"_myTab\"><a href=\"javascript:;\">내 검색어</a></li></ul> </div> <div class=\"words _recent\"> <ul><li data-rank=\"@rank@\"><a class=\"t@my@ _myBtn\" title=\"내 검색어 등록\" href=\"javascript:;\">내 검색어 등록</a><a href=\"javascript:;\">@txt@</a><em class=\"date\">@date@.</em><a href=\"javascript:;\" class=\"btn_del _del\" title=\"검색어삭제\">삭제</a><span style=\"display:none\">@in_txt@</span></li></ul> <div class=\"info_words _recentNone\" style=\"display:none\">최근검색어 내역이 없습니다.</div> <p class=\"msg _offMsg\" style=\"display:none\">검색어 저장 기능이 꺼져 있습니다.</p> </div> <div class=\"words _my\" style=\"display:none\"> <ul><li data-rank=\"@rank@\"><a class=\"ton _myBtn\" title=\"내 검색어 해제\" href=\"javascript:;\">내 검색어 해제</a><a href=\"javascript:;\">@txt@</a></li></ul> <div class=\"info_words _myNone\" style=\"display:none\">설정된 내 검색어가 없습니다.<br />최근검색어에서 <span class=\"star\">내 검색어 등록</span>를 선택하여 자주 찾는 검색어를<br />내 검색어로 저장해 보세요.</div> <p class=\"msg _offMsg\" style=\"display:none\">검색어 저장 기능이 꺼져 있습니다.</p> </div> <p class=\"noti _noti\" style=\"display:none\">공용 PC에서는 개인정보 보호를 위하여 반드시 로그아웃을 해 주세요.</p> <p class=\"func _recentBtnGroup\"><span class=\"fl\"><a class=\"_delMode\" href=\"javascript:;\">기록 삭제</a></span><span><a class=\"_keywordOff\" href=\"javascript:;\">검색어저장 끄기</a> \| <a class=\"_acOff\" href=\"javascript:;\">자동완성 끄기</a></span></p> <p class=\"func _recentDelBtnGroup\" style=\"display:none\"><span class=\"fl\"><a class=\"_delAll\" href=\"javascript:;\" title=\"최근 검색어 기록을 모두 삭제합니다.\">기록 전체 삭제</a></span><span><a class=\"_delDone\" href=\"javascript:;\">완료</a></span></p> <p class=\"func _myBtnGroup\" style=\"display:none\"><span class=\"fl\"><a class=\"_delAll\" href=\"javascript:;\" title=\"설정된 내 검색어를 모두 삭제합니다.\">기록 전체 삭제</a></span><span><a class=\"_keywordOff\" href=\"javascript:;\">검색어저장 끄기</a> \| <a class=\"_acOff\" href=\"javascript:;\">자동완성 끄기</a></span></p> <span class=\"help2 _help2\" style=\"display:none\">기능을 다시 켤 때는 펼치기 버튼을 클릭하세요</span> <div class=\"ly_noti _maxLayer\" style=\"display:none\"> <span class=\"mask\"></span> <p><span class=\"ico\"></span>내 검색어는 최대 <em>10</em>개 까지 저장할 수 있습니다.<br />추가하시려면 기존 내 검색어를 지워주세요. <a href=\"javascript:;\" class=\"btn_clse _close\">닫기</a></p> </div> </div> </div> <div class=\"api_atcmp_wrap _alert\" style=\"display:none;\"> <div class=\"api_atcmp_alert\"> <span class=\"ico\"></span> <p class=\"dsc_txt\">제20대 국회의원선거 후보에 대해 4월13일 선거일까지 자동완성 기능이 제공되지 않습니다. <a target=\"_blank\" nocr href=\"http://naver_diary.blog.me/220654539456\" onclick=\"return goOtherCR(this,\\'a=sug.vote&r=&i=&u=\\'+urlencode(this.href));\">자세히보기</a></p> </div> </div> </div> </fieldset> </form> </div> <div id=\"gnb\"> <script type=\"text/javascript\"> var gnb_option = { gnb_service : \"search\", gnb_template : \"gnb_utf8\", gnb_logout : encodeURIComponent(location.href), gnb_login : encodeURIComponent(location.href), gnb_brightness : 1 , gnb_item_hide_option : 0 } ; 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return false;\" class=\"bt_help\" target=\"_blank\"><span class=\"spim\">도움말</span></a> <span class=\"bar\"></span> </li> <li class=\"lnk\"> <a href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2018\" onclick=\"return goOtherCR(this, \\'u=\\'+urlencode(this.href)+\\'&a=fno.origin&r=&i=\\'); return false;\" class=\"bt_origin\" target=\"_blank\"><span class=\"spim\">원본반영</span></a> <span class=\"bar\"></span> </li> <li class=\"share\"> <a href=\"#\" id=\"spiButton2\" class=\"naver-splugin bt_share\" onclick=\"return tCR(\\'a=fno.pplugin\\');\" data-style=\"unity-v2\" data-evkey=\"search\" data-me-display=\"off\" data-mail-display=\"off\" data-option=\"{baseElement:\\'spiButton2\\', layerPosition:\\'outside-bottom\\', align:\\'right\\', top:4, left:0, marginLeft:8, marginTop:10}\" data-service-name=\"통합검색\" data-style=\"standard\" data-blog-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=svc_pin.blog&where=post\" data-cafe-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=svc_pin.cafe&where=post\" data-cafe-source-form=\"2\" data-memo-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=svc_pin.memo&where=post\" data-calendar-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=svc_pin.cal&where=post\" data-twitter-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=twp_pin&where=post\" data-facebook-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=fbp_pin&where=post\" data-band-url=\"https://search.naver.com/search.naver?ie=utf8&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B0%95%EB%AF%B8%EB%82%98&sm=bdp_pin&where=post\" title=\"보내기 열기/닫기\"><span class=\"spim naver-splugin-c\">보내기</span></a> </li> </ul> </div><script type=\"text/javascript\"> nx_js_defer_load(\"https://ssl.pstatic.net/spi/js/release/ko_KR/splugin.js?\" + headerfooter_time_year_s + headerfooter_time_month_s + headerfooter_time_day_s, function() { g_nx_splugin = SocialPlugIn_Core({ \"evKey\" : \"search\", \"serviceName\" : \"블로그검색\" }) ; }, 150) ; function splugin_oninitialize(sTargetId) { var elTarget = document.getElementById(sTargetId) ; var sUrl = $$.getSingle(\\'a._sp_each_url\\', elTarget ).href ; var sTitle = $Element($$.getSingle(\\'._sp_each_title\\', elTarget)).text() ; var sSource = $Element($$.getSingle(\\'._sp_each_source\\', elTarget)) ; if (sSource) sSource = sSource.text() ; return { \"url\" : sUrl, \"title\" : sTitle, \"option\" : {baseElement:sTargetId + \\'_base\\', layerPosition:\\'outside-bottom\\', align:\\'right\\', top:18, left:-28, marginLeft:8, marginTop:10}, \"me\" : { display : \"off\" }, \"mail\" : { display : \"off\" }, \"sourceName\" : sSource } ; } </script><script> nx_js_defer_load(\"//search.like.naver.com/static/js/likeIt.list.js?\" + headerfooter_time_year_s + headerfooter_time_month_s + headerfooter_time_day_s, function() { nhn.LikeIt.list.util.init({ sId : \"SEARCH\", sDomain : \"//search.like.naver.com\", bMobile : false }) ; }, 150) ; </script></div> </div><div id=\"snb\" style=\"display:none;\"> <div class=\"snb_inner\"> <ul class=\"option_menu\"><li class=\"menu\"> <a href=\"#\" class=\"m\" onclick=\"return tCR(\\'a=fno.sortlink\\');\">정렬<span class=\"spim\"></span></a> <div class=\"snb_itembox\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <ul class=\"lst_choice\"> <li class=\"selected\"><a href=\"#\" onclick=\"submit_sort_option(\\'sim\\'); return false;\">관련도순<span class=\"blind\">선택됨</span></a></li> <li><a href=\"#\" onclick=\"submit_sort_option(\\'date\\'); return false;\">최신순</a></li> </ul> </div> </li> <li class=\"menu\"> <a href=\"#\" class=\"m\" onclick=\"return tCR(\\'a=fno.datelink\\');\">기간<span class=\"spim\"></span></a> <div id=\"_nx_option_date\" class=\"snb_itembox\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <ul class=\"lst_choice\"> <li class=\"selected\"><a href=\"#\" onclick=\"submit_date_option(\\'all\\'); return false;\" class=\"_btn\" title=\"2003.05.20 이후\">전체<span class=\"blind\">선택됨</span></a></li> <li><a href=\"#\" onclick=\"submit_date_option(\\'1day\\'); return false;\" class=\"_btn\">1일</a></li> <li><a href=\"#\" onclick=\"submit_date_option(\\'1week\\'); return false;\" class=\"_btn\">1주</a></li> <li><a href=\"#\" onclick=\"submit_date_option(\\'1month\\'); return false;\" class=\"_btn\">1개월</a></li> <li><a href=\"#\" onclick=\"submit_date_option(\\'6month\\'); return false;\" class=\"_btn\">6개월</a></li> <li><a href=\"#\" onclick=\"submit_date_option(\\'1year\\'); return false;\" class=\"_btn\">1년</a></li> </ul> <div class=\"item_option set_calendar\"> <p class=\"tit\">직접입력</p> <div class=\"input_box _input_box_start\"> <input id=\"blog_input_period_begin\" type=\"text\" title=\"검색기간 시작일\" value=\"\" class=\"input_txt _input_start\" maxlength=\"10\"> <a id=\"blog_btn_prod_from\" href=\"#\" class=\"spim _btn_start\" onclick=\"return tCR(\\'a=fno.datecalenderopen\\');\">달력 레이어 호출</a> </div> <div class=\"input_box _input_box_end\"> <input id=\"blog_input_period_end\" type=\"text\" title=\"검색기간 종료일\" value=\"\" class=\"input_txt _input_end\" maxlength=\"10\"> <a id=\"blog_btn_prod_to\" href=\"#\" class=\"spim _btn_end\" onclick=\"return tCR(\\'a=fno.datecalenderopen\\');\">달력 레이어 호출</a> </div> <div class=\"ly_option_alert _alert_layer\" style=\"display:none;\">잘못된 날짜형식입니다.<br> <em>(yyyy.mm.dd)</em><span class=\"spim arrow\" style=\"left: 105px;\"></span></div> <span class=\"btn_inp\"> <button type=\"button\" class=\"_btn_submit\"><span class=\"tx\">적용하기</span></button> </span> </div> <div class=\"op_calendar _date_option_calendar_layer\"> <div class=\"h_cal\"> <strong class=\"calendar-title\"></strong> <a class=\"bt_pv2 calendar-btn-prev-year\" href=\"#\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"><span>이전 년도</span></a> <a class=\"bt_pv calendar-btn-prev-mon\" href=\"#\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"><span>이전 달</span></a> <a class=\"bt_nx calendar-btn-next-mon\" href=\"#\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"><span>다음 달</span></a> <a class=\"bt_nx2 calendar-btn-next-year\" href=\"#\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"><span>다음 년도</span></a> </div> <div class=\"cal_date\"> <table cellspacing=\"0\" cellpadding=\"0\"> <caption class=\"blind\">기간 설정 달력</caption> <thead> <tr> <th scope=\"col\">일</th> <th scope=\"col\">월</th> <th scope=\"col\">화</th> <th scope=\"col\">수</th> <th scope=\"col\">목</th> <th scope=\"col\">금</th> <th scope=\"col\">토</th> </tr> </thead> <tbody> <tr class=\"calendar-week\"> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> <td><a href=\"#\" class=\"calendar-date\" onclick=\"return tCR(\\'a=fno.datecalendercheck\\');\"></a></td> </tr> </tbody> </table> </div> <p class=\"today _footer\"> <span>오늘날짜</span> <a href=\"#\" class=\"_today\" onclick=\"return tCR(\\'a=fno.datecalendertoday\\');\"></a> </p> <a class=\"ly_close _close\" href=\"#\" onclick=\"return tCR(\\'a=fno.datecalenderclose\\');\"> <img src=\"https://ssl.pstatic.net/sstatic//keypage/lifesrch/sports/img2010/bt_ly_close.gif\" width=\"11\" height=\"11\" alt=\"닫기\"> </a> </div> </div> </li> <li class=\"menu\"> <a href=\"#\" class=\"m\" onclick=\"return tCR(\\'a=fno.alink\\');\">영역<span class=\"spim\"></span></a> <div class=\"snb_itembox\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <ul class=\"lst_choice\"> <li class=\"selected\"><a href=\"#\" onclick=\"submit_srchby_option(\\'all\\'); return false;\">전체<span class=\"blind\">선택됨</span></a></li> <li><a href=\"#\" onclick=\"submit_srchby_option(\\'title\\'); return false;\">제목</a></li> </ul> </div> </li> <li class=\"menu\"> <a href=\"#\" class=\"m\" onclick=\"return tCR(\\'a=fno.biolink\\');\">유사문서<span class=\"spim\"></span></a> <div class=\"snb_itembox\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <ul class=\"lst_choice\"> <li class=\"selected\"><a href=\"#\" onclick=\"submit_dup_option(1); return false;\">제외<span class=\"blind\">선택됨</span></a></li> <li><a href=\"#\" onclick=\"submit_dup_option(0); return false;\">포함</a></li> </ul> </div> </li> <li class=\"menu\"> <a href=\"#\" class=\"m\" onclick=\"return tCR(\\'a=fno.srclink\\');\">출처<span class=\"spim\"></span></a> <div class=\"snb_itembox\" style=\"width:180px\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <a href=\"#\" class=\"tit_txt selected\" onclick=\"submit_origin_option(\\'all\\'); return false;\">전체</a> <div class=\"item_option url_info\"> <div class=\"info_choice\"> <p class=\"title_txt\">출처선택<a href=\"#\" class=\"spim ico_q\" onclick=\"onClickSrcHelp(); return false;\">출처선택 도움말</a></p> <ul class=\"data_info _src_radio\"> <li><input type=\"radio\" id=\"url_sort\" name=\"gurl\" class=\"incr\" onclick=\"tCR(\\'a=fno.srcinclude\\');\" checked><label for=\"url_sort\">특정출처만 검색</label></li> <li><input type=\"radio\" id=\"url_res\" name=\"gurl\" class=\"incr\" onclick=\"tCR(\\'a=fno.srcexclude\\');\"><label for=\"url_res\">특정출처 제외 검색</label></li> </ul> </div> <div class=\"input_box\"> <input type=\"text\" title=\"출처 URL입력\" value=\"\" class=\"input_txt\" id=\"src_input\" maxlength=\"80\" onkeydown=\"return onEnterSrc(event);\"> </div> <span class=\"btn_inp\"> <button type=\"button\" onclick=\"submit_origin_option(\\'\\'); return false;\"> <span class=\"tx\">적용하기</span> </button> </span> <p class=\"noti_tx _src_addr\" style=\"display:none;\">주소를 입력해 주세요.</p> <div class=\"tx_area _src_help\" style=\"display:none;\"> <p class=\"tx_noti\">제한해서 보고 싶거나 결과에서 빼고 싶은 블로그 / 도메인 URL을 입력하세요 <em>예) blog.naver.com/abcdef,blog.naver.com,egloos.com</em></p> </div> </div> </div> </li> <li class=\"option_keep\"> <div class=\"option_choice\"> 옵션유지 <span class=\"option\"> <button id=\"nx_option_msoff\" type=\"button\" onclick=\"blog_mson_value_switch(0); 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return tCR(\\'u=\\'+urlencode(this.href)+\\'&a=fno.dtshelp\\');\">도움말</a> <a href=\"#\" class=\"btn_initial _reset\" onclick=\"return tCR(\\'a=fno.dtsclear\\');\">초기화</a> <button class=\"btn_ft ty_green _search\" onclick=\"return tCR(\\'a=fno.dtssrch\\');\"><span>검색</span></button><button class=\"btn_ft _close\" onclick=\"return tCR(\\'a=fno.dtsclose\\');\"><span>닫기</span></button> </div> </div> </div> </li></ul> </div> </div> <script type=\"text/javascript\"> /* 검색옵션 모듈 생성 / var oNXSearchOption = new nhn.SearchOption($(\\'_search_option_btn\\'), $(\\'snb\\'), {open:false}); </script><script> / 검색옵션 - 상세검색 - !!상세 옵션이 있는 탭에서만 생성해주세요. / new nhn.SearchOption.Detail( $(\"_nx_option_detail\"), { s_base: \"아이오아이 강미나\", b_or: false, a_exact: [], a_include: [], a_exclude: [] } ); </script><script type=\"text/javascript\"> $Fn(function (we) { var el = we.element, elLnbMore = $(\"_nx_lnb_more\"); if (el != elLnbMore && !$Element(elLnbMore).isParentOf(el)) { $Element(\\'_nx_lnb_more\\').removeClass(\\'more_on\\'); } }).attach(document, \"click\"); </script></div><script type=\"text/javascript\"> var nx_location_country = \"KR\" ; var nx_location_region = \"\" ; var nx_location_city = \"\" ; var nx_location_hcode = \"41171520\" ; var nx_location_rcode = \"02171520\" ; var nx_location_r1 = \"경기도\" ; var nx_location_r2 = \"안양시 만안구\" ; var nx_location_r3 = \"안양2동\" ; </script><div id=\"container\" role=\"main\"> <div id=\"content\" class=\"pack_group\"> <h1 class=\"blind\">OOO 검색결과 시작</h1> <div id=\"main_pack\" class=\"main_pack\"><div id=\"nx_related_keywords\" class=\"sp_keyword section\"> <dl class=\"relate_area\"> <dt><span class=\"tit_relate\">연관검색어</span ><a onclick=\"return goOtherCR(this, \\'a=rsk.guide&r=&i=&u=\\'+urlencode(urlexpand(this.href)));\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1988\" title=\"새창\" target=\"_blank\" class=\"spkw ico_help\" ><span class=\"blind\">도움말</span></a></dt> <dd class=\"lst_relate\"> <ul> <li> <a href=\"?where=post&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EA%B9%80%EC%84%B8%EC%A0%95&ie=utf8&sm=tab_she&qdt=0\" data-idx=\"1\" data-area=\"q\">아이오아이 김세정</a> </li> <li> <a href=\"?where=post&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4+%EC%9C%A0%EC%97%B0%EC%A0%95&ie=utf8&sm=tab_she&qdt=0\" data-idx=\"2\" data-area=\"q\">아이오아이 유연정</a> </li> <li> <a href=\"?where=post&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4&ie=utf8&sm=tab_she&qdt=0\" data-idx=\"3\" data-area=\"q\">아이오아이</a> </li> </ul> </dd> <dd class=\"closed\" style=\"display:none\"> 닫기 후 1주일간 유지됩니다. 연관검색어를 다시 보시겠습니까? <a href=\"#\" class=\"open\" onclick=\"return false;\">열기</a></dd> </dl> <div class=\"ly_option\"> <a href=\"#\" class=\"btn_more\" onclick=\"return false;\" style=\"display:none;\"><span class=\"unfold\">더보기<span class=\"spkw ico_darr\"></span></span><span class=\"fold\">접기<span class=\"spkw ico_uarr\"></span></span></a ><a class=\"btn_report\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\" onclick=\"return goOtherCR(this, \\'a=rsk.report&r=&i=&u=\\'+urlencode(urlexpand(this.href)));\">신고</a ><a href=\"#\" class=\"btn_close\" onclick=\"return false;\"><span class=\"spkw\">검색어제안 기능 닫기</span></a> </div> </div> <script type=\"text/javascript\"> (function() { var elRoot = $Element(\"nx_related_keywords\"); 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$Fn(function(){ rearrange_list(); }).attach(window, \"load\"); init(); }) (); </script> <script>g_crt+=\"\";</script>\\n <script type=\"text/javascript\"> var blog_terms = [\"%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4\",\"%EA%B0%95%EB%AF%B8%EB%82%98\"]; function make_nso() { var st = document.getElementById(\"st\").value; var date_option = document.getElementById(\"date_option\").value; var date_from = document.getElementById(\"date_from\").value; var date_to = document.getElementById(\"date_to\").value; var srchby = document.getElementById(\"srchby\").value; var nso = \"\"; if (st == \"sim\") { nso = \"so:r\"; } else { nso = \"so:dd\"; } if (srchby == \"all\") { nso = nso + \",a:all\"; } else { nso = nso + \",a:t\"; } if (date_option == \"2\") { nso = nso + \",p:1d\"; } else if (date_option == \"3\") { nso = nso + \",p:1w\"; } else if (date_option == \"4\") { nso = nso + \",p:1m\"; } else if (date_option == \"6\") { nso = nso + \",p:6m\"; } else if (date_option == \"7\") { nso = nso + \",p:1y\"; } else if (date_option == \"8\") { nso = nso + \",p:from\" + date_from.replace(\\'.\\',\\'\\').replace(\\'.\\',\\'\\') + \"to\" + date_to.replace(\\'.\\',\\'\\').replace(\\'.\\',\\'\\') ; } else { nso = nso + \",p:all\"; } document.getElementById(\"nso\").value = nso; } function set_start_end_date() { var input_1 = document.getElementById(\"date_from\"); var input_2 = document.getElementById(\"date_to\"); var today = new Date(); var end_year = today.getFullYear(); end_year = end_year < 10 ? \"0\" + end_year: end_year; var end_month = today.getMonth()+1; end_month = end_month < 10 ? \"0\" + end_month : end_month; var end_day = today.getDate(); end_day = end_day < 10 ? \"0\" + end_day : end_day; today = end_year + \"\" + end_month + \"\" + end_day; var date_from = input_1.value.replace(\".\", \"\").replace(\".\", \"\"); var date_to = input_2.value.replace(\".\", \"\").replace(\".\", \"\"); if (date_from.length == 8) { if (date_from < \"20030520\") date_from = \"20030520\"; else if (date_from > today) date_from = today; } else date_from = \"20030520\"; if (date_to.length == 8) { if (date_to < \"20030520\") date_to = \"20030520\"; else if (date_to > today) date_to = today; } else date_to = today; if (date_from > date_to) { var temp = date_from; date_from = date_to; date_to = temp; } $(\\'date_from\\').value = date_from; $(\\'date_to\\').value = date_to; } function submit_sort_option(sort_option) { var blog_form = $Form(\\'bs_option\\'); var area = \\'sortsim\\'; if (sort_option == \\'date\\') { area = \\'sortdate\\'; } blog_form.value(\\'st\\', sort_option); blog_form.value(\\'sm\\', \\'tab_opt\\'); make_nso(); formCR(\\'bs_option\\', \\'fno.\\' + area); } function submit_date_option(period) { var blog_form = $Form(\\'bs_option\\'); var area = \\'dateperiodall\\'; var value = \\'0\\'; if (period == \"1day\") { value = \\'2\\'; area = \\'dateoneday\\'; } else if (period == \"1week\") { value = \\'3\\'; area = \\'dateoneweek\\'; } else if (period == \"1month\") { value = \\'4\\'; area = \\'dateonemonth\\'; } else if (period == \"6month\") { value = \\'6\\'; area = \\'datesixmonth\\'; } else if (period == \"1year\") { value = \\'7\\'; area = \\'dateoneyear\\'; } else if (period == \"user\") { value = \\'8\\'; blog_form.value(\\'date_from\\', $(\\'blog_input_period_begin\\').value); blog_form.value(\\'date_to\\', $(\\'blog_input_period_end\\').value); area = \\'datecustomapply\\' ; } blog_form.value(\\'date_option\\', value); blog_form.value(\\'sm\\', \\'tab_opt\\'); set_start_end_date(); make_nso(); formCR(\\'bs_option\\', \\'fno.\\' + area); } function submit_srchby_option(srchby_option) { var blog_form = $Form(\\'bs_option\\'); var area = \\'areafieldall\\'; if (srchby_option == \"title\") { area = \\'areafieldtit\\'; } blog_form.value(\\'srchby\\', srchby_option); blog_form.value(\\'sm\\', \\'tab_opt\\'); make_nso(); formCR(\\'bs_option\\', \\'fno.\\' + area); } function submit_dup_option(dup_option) { var blog_form = $Form(\\'bs_option\\'); var area = \\'\\'; if (dup_option == 0) { blog_form.value(\\'dup_remove\\', 0); area = \\'bioinclude\\'; } else { blog_form.value(\\'dup_remove\\', 1); area = \\'bioexclude\\'; } blog_form.value(\\'sm\\', \\'tab_opt\\'); make_nso(); formCR(\\'bs_option\\', \\'fno.\\' + area); } function submit_origin_option(target) { var blog_form = $Form(\\'bs_option\\'); var area = \\'\\'; if (target === \\'all\\') { blog_form.value(\\'post_blogurl\\', \\'\\'); blog_form.value(\\'post_blogurl_without\\', \\'\\'); blog_form.value(\\'sm\\', \\'tab_opt\\'); area = \\'srcall\\'; } else { var input = $(\\'src_input\\'); var value = input ? input.value : \\'\\'; value.replace(/^\\\\s/, \\'\\').replace(/\\\\s$/, \\'\\'); if (value === \\'\\' \|\| value === \\'http://\\') { $$.getSingle(\\'._src_addr\\').style.display = \\'block\\'; input.focus(); input.value = value; return; } var radio = $$.getSingle(\\'ul._src_radio input:checked\\'); if (!radio) { return; } if (radio.id === \\'url_sort\\') { blog_form.value(\\'post_blogurl\\', value); blog_form.value(\\'post_blogurl_without\\', \\'\\'); } else { blog_form.value(\\'post_blogurl\\', \\'\\'); blog_form.value(\\'post_blogurl_without\\', value); } area = \\'srcapply\\'; } document.getElementById(\"bs_option\")[\"sm\"].value = \"tab_opt\"; make_nso(); formCR(\\'bs_option\\', \\'fno.\\' + area); } var blogSrchCleared = false ; function CRinsideBlogSearchBox() { var blog_query_search = document.getElementById(\\'blog_query_search\\').value ; if (blog_query_search == \"\" \|\| (blog_query_search == \"이 블로그에서 다시 검색\" && blogSrchCleared == false) \|\| (blog_query_search == \"이 블로그 제외하고 다시 검색\" && blogSrchCleared == false)) { alert(\"검색어를 입력하세요.\"); return; } else { document.getElementById(\\'form_query\\').value = document.getElementById(\\'blog_query_search\\').value ; document.getElementById(\\'sm\\').value = \"tab_bir\" ; document.getElementById(\\'mson\\').value = 1; this.href = location.href ; goCR(this, \"rd\", \\'a=blg.inresch&r=1\\'+\\'&u=\\', 1) ; document.getElementById(\\'bs_option\\').submit() ; } event.returnValue = false; } function blog_clear_text(obj) { var label = document.getElementById(\"blog_inner_search_label\"); obj.className = \"box_input action\"; if ((label.innerHTML == \"이 블로그에서 다시 검색\" \|\| label.innerHTML == \"이 블로그 제외하고 다시 검색\") && blogSrchCleared == false ) { label.innerHTML = \"\"; blogSrchCleared = true; } } function onClickSrcHelp() { var src_help = $$.getSingle(\\'._src_help\\'); if (src_help) { $Element(src_help).toggle(); } tCR(\\'a=fno.srctip\\'); } function onEnterSrc(e) { if (e.keyCode == 13) { submit_origin_option(\\'\\'); return false; } return true; } function blog_mson_value_switch(v) { document.getElementById(\\'mson\\').value = v; } </script> <script type=\"text/javascript\"> /* 검색옵션 - 기간 / $Fn(function() { new nhn.SearchOption.Date( $(\"_nx_option_date\"), { / 적용 버튼 혹은 enter 키 입력시 호출되는 함수 / fSubmit: function(p) { if (p) { submit_date_option(\\'user\\'); } }, htCalendar: { / 시작일을 나타내는 캘린더의 초기 설정될 날짜 값 / htStartDate: {nYear : headerfooter_time_year,nMonth: headerfooter_time_month,nDate: headerfooter_time_day}, / 종료일을 나타내는 캘린더의 초기 설정될 날짜 값 / htEndDate: {nYear: headerfooter_time_year,nMonth: headerfooter_time_month,nDate: headerfooter_time_day}, / 유효한 날짜 범위중 최소 날짜 (예. 1990.1.1) / htMinDate:{nYear: 2003,nMonth: 5,nDate: 20}, / 유효한 날짜 범위중 최대 날짜 (예. 오늘날자) / htMaxDate: {nYear: headerfooter_time_year,nMonth: headerfooter_time_month,nDate: headerfooter_time_day}, / 오늘날자 / htTodayDate: {nYear: headerfooter_time_year,nMonth: headerfooter_time_month,nDate: headerfooter_time_day,nDay: headerfooter_time_wday} } } ); }).attach(window, \"load\"); nx_set_option_switch(0); </script> <form method=\"GET\" name=\"bs_option_n\" id=\"bs_option\" action=\"?\"> <input type=hidden name=\"where\" value=\"post\"> <input type=hidden name=\"query\" id=\"form_query\" value=\"아이오아이 강미나\"> <input type=hidden name=\"ie\" id=\"ie\" value=\"utf8\"> <input type=hidden name=\"st\" id=\"st\" value=\"sim\"> <input type=hidden name=\"sm\" id=\"sm\" value=\"tab_opt\"> <input type=hidden name=\"date_from\" id=\"date_from\" value=\"\"> <input type=hidden name=\"date_to\" id=\"date_to\" value=\"\"> <input type=hidden name=\"date_option\" id=\"date_option\" value=\"0\"> <input type=hidden name=\"srchby\" id=\"srchby\" value=\"all\"> <input type=hidden name=\"dup_remove\" id=\"dup_remove\" value=\"1\"> <input type=hidden name=\"post_blogurl\" id=\"post_blogurl\" value=\"\"> <input type=hidden name=\"post_blogurl_without\" id=\"post_blogurl_without\" value=\"\"> <input type=hidden name=\"nso\" id=\"nso\" value=\"\"> <input type=hidden name=\"mson\" id=\"mson\" value=\"0\"> </form> <div class=\"blog section _blogBase\"> <div class=\"section_head\"> <h2>블로그</h2> <span class=\"title_num\">1-10 / 3,143건</span> </div> <ul id=\"elThumbnailResultArea\" class=\"type01\"> <li class=\"sh_blog_top\" id=\"sp_blog_1\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\\')+\\'&a=blgi.img&r=2&i=90000003_0000000000000033680084B4\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160815_284/poalqr04_1471249425579lxQ1B_JPEG/21.jpg#498x498\" alt=\"강미나 고화질... (fe\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160815_284/poalqr04_1471249425579lxQ1B_JPEG/21.jpg#498x498\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>13<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\\')+\\'&a=blgi.tit&r=1&i=90000003_0000000000000033680084B4\\');\" title=\"강미나 고화질 직찍사진+기타움짤(feat.아이오아이 쇼케이스) 160505\"><strong class=\"hl\">강미나</strong> 고화질... (feat.<strong class=\"hl\">아이오아이</strong>...</a></dt> <dd class=\"txt_inline\">2일 전 <div class=\"scial\" id=\"sp_blog_1_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=1&i=90000003_0000000000000033680084B4&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_1\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">blog.naver.com + 보너스 움짤 + 보너스 영상 [왕뚜껑Ⅹ<strong class=\"hl\">아이오아이</strong>] <strong class=\"hl\">강미나</strong> 먹방 영상 ▲ 이렇게 맛있게 먹어도 되는 것인지 ㅋㅋ 김세정, 강미나의 귀여움이 부러워 질투...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://section.blog.naver.com/Event/SmartEditor3Promotion.nhn\" target=\"_blank\" class=\"total_origin\" onclick=\"return goOtherCR(this,\\'a=blgi.se3&r=1&i=90000003_0000000000000033680084B4&u=\\'+urlencode(urlexpand(this.href)));\" ><em class=\"spcm ico_smarteditor\">스마트에디터3.0</em></a><a href=\"http://blog.naver.com/poalqr04\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=1&i=90000003_0000000000000033680084B4\\');\">챠콜의 아이들</a><a href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220788196532\\')+\\'&a=blgi.url&r=1&i=90000003_0000000000000033680084B4\\');\" >blog.naver.com/poalqr04/220788196532</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_2\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\\')+\\'&a=blgi.img&r=2&i=90000003_000000000000003367EA2DF4\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160813_140/poalqr04_1471060257720e8KnD_JPEG/21.jpg#500x500\" alt=\"강미나 고화질... 쇼케\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160813_140/poalqr04_1471060257720e8KnD_JPEG/21.jpg#500x500\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>11<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\\')+\\'&a=blgi.tit&r=2&i=90000003_000000000000003367EA2DF4\\');\" title=\"강미나 고화질 직찍사진(feat.구구단 쇼케이스/아이오아이) 160628\"><strong class=\"hl\">강미나</strong> 고화질... 쇼케이스/<strong class=\"hl\">아이오아이</strong>)...</a></dt> <dd class=\"txt_inline\">4일 전 <div class=\"scial\" id=\"sp_blog_2_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=2&i=90000003_000000000000003367EA2DF4&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_2\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">O.I , Humiliation of Mina ▲ 비타민 출연해 대 굴욕 당한 사연! 160728 i.o.i <strong class=\"hl\">아이오아이 강미나</strong> 식탐요정 먹방 ▲ 불후의 명곡에 출연해 증명하는 먹방요정! 160611</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://section.blog.naver.com/Event/SmartEditor3Promotion.nhn\" target=\"_blank\" class=\"total_origin\" onclick=\"return goOtherCR(this,\\'a=blgi.se3&r=2&i=90000003_000000000000003367EA2DF4&u=\\'+urlencode(urlexpand(this.href)));\" ><em class=\"spcm ico_smarteditor\">스마트에디터3.0</em></a><a href=\"http://blog.naver.com/poalqr04\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=2&i=90000003_000000000000003367EA2DF4\\');\">챠콜의 아이들</a><a href=\"http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/poalqr04?Redirect=Log&logNo=220786732532\\')+\\'&a=blgi.url&r=2&i=90000003_000000000000003367EA2DF4\\');\" >blog.naver.com/poalqr04/220786732532</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_3\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\\')+\\'&a=blgi.img&r=2&i=90000003_000000000000003367D30FA5\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160811_204/youri0902_1470895086478qtUrv_JPEG/20160805_1470355815_77832200_1.jpg#520x837\" alt=\"구구단 강미나 사복패\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160811_204/youri0902_1470895086478qtUrv_JPEG/20160805_1470355815_77832200_1.jpg#520x837\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>7<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\\')+\\'&a=blgi.tit&r=3&i=90000003_000000000000003367D30FA5\\');\" title=\"구구단 강미나 사복패션 아이오아이 강미나\">구구단 강미나 사복패션 <strong class=\"hl\">아이오아이 강미나</strong></a></dt> <dd class=\"txt_inline\">6일 전 <div class=\"scial\" id=\"sp_blog_3_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=3&i=90000003_000000000000003367D30FA5&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_3\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\"><strong class=\"hl\">아이오아이</strong>에서 이젠 구구단에서 활동중인 <strong class=\"hl\">강미나</strong>양 사복패션 모아봤어요 ㅎㅎ 역시 10대답게 귀엽고 사랑스런 코디가 대부분이네요 ^^ 테니스...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://section.blog.naver.com/Event/SmartEditor3Promotion.nhn\" target=\"_blank\" class=\"total_origin\" onclick=\"return goOtherCR(this,\\'a=blgi.se3&r=3&i=90000003_000000000000003367D30FA5&u=\\'+urlencode(urlexpand(this.href)));\" ><em class=\"spcm ico_smarteditor\">스마트에디터3.0</em></a><a href=\"http://blog.naver.com/youri0902\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=3&i=90000003_000000000000003367D30FA5\\');\">이버라킴</a><a href=\"http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/youri0902?Redirect=Log&logNo=220785217445\\')+\\'&a=blgi.url&r=3&i=90000003_000000000000003367D30FA5\\');\" >blog.naver.com/youri0902/220785217445</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_4\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://mainpassive.tistory.com/93\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://mainpassive.tistory.com/93\\')+\\'&a=blgt.img&r=2&i=a00000fa_324de4a80beea0249dd8f371\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/sun?t=r80&q=http://cfile7.uf.tistory.com/image/2254CD4F579EC7CB2BBE9B#700x387\" alt=\"아이오아이 강미나 팔\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/sun?t=r80&q=http://cfile7.uf.tistory.com/image/2254CD4F579EC7CB2BBE9B#700x387\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>6<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://mainpassive.tistory.com/93\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://mainpassive.tistory.com/93\\')+\\'&a=blgt.tit&r=4&i=a00000fa_324de4a80beea0249dd8f371\\');\" title=\"아이오아이 강미나 팔뚝 어깨 몸매가 뭐 어때서?\"><strong class=\"hl\">아이오아이 강미나</strong> 팔뚝 어깨 몸매가 뭐 어때서?</a></dt> <dd class=\"txt_inline\">2016.08.02. <div class=\"scial\" id=\"sp_blog_4_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgt.pplugin&r=4&i=a00000fa_324de4a80beea0249dd8f371&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_4\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">직히 사람들이 아무리 비난을 해도 제 눈에는 참 귀엽고, 예쁜 것 같은 <strong class=\"hl\">강미나</strong>의 모습입니다. 현재는 <strong class=\"hl\">아이오아이</strong>, 구구단, 개인 활동 등등 정말 열심히 활동을 하고 있는...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://mainpassive.tistory.com/\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgt.blogname&r=4&i=a00000fa_324de4a80beea0249dd8f371\\');\">메인패시브</a><a href=\"http://mainpassive.tistory.com/93\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://mainpassive.tistory.com/93\\')+\\'&a=blgt.url&r=4&i=a00000fa_324de4a80beea0249dd8f371\\');\" >mainpassive.tistory.com/93</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=mainpassive.tistory.com%2F\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgt.similar&r=4&i=a00000fa_324de4a80beea0249dd8f371\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_5\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\\')+\\'&a=blgi.img&r=2&i=90000003_000000000000003367352819\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160729_23/superoxy_1469778049513EI1Vu_JPEG/201607291413231114_1.jpg#450x664\" alt=\"160729 아이오아이 강\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160729_23/superoxy_1469778049513EI1Vu_JPEG/201607291413231114_1.jpg#450x664\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>7<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\\')+\\'&a=blgi.tit&r=5&i=90000003_000000000000003367352819\\');\" title=\"160729 아이오아이 강미나 공항패션 밀리터리자켓 #타미힐피거\">160729 <strong class=\"hl\">아이오아이 강미나</strong> 공항패션...</a></dt> <dd class=\"txt_inline\">2016.07.29. <div class=\"scial\" id=\"sp_blog_5_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=5&i=90000003_000000000000003367352819&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_5\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">160729 <strong class=\"hl\">아이오아이 강미나</strong> 공항패션 밀리터리자켓 #타미힐피거 오늘은 구구단의 강미나가 아닌 아이오아이의 강미나로\\'KCON 2016 LA\\'에 참석하기 위해 7월 29일 오후...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://blog.naver.com/superoxy\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=5&i=90000003_000000000000003367352819\\');\">superoxy님의블로그</a><a href=\"http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/superoxy?Redirect=Log&logNo=220774869017\\')+\\'&a=blgi.url&r=5&i=90000003_000000000000003367352819\\');\" >blog.naver.com/superoxy/220774869017</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=blog.naver.com%2Fsuperoxy\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.similar&r=5&i=90000003_000000000000003367352819\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_6\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\\')+\\'&a=blgi.img&r=2&i=90000003_000000000000003364736304\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160606_20/loveneet_1465142473156auItR_JPEG/DSC00946.JPG#3000x2400\" alt=\"아이오아이(I.O.I) 강\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160606_20/loveneet_1465142473156auItR_JPEG/DSC00946.JPG#3000x2400\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>5<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\\')+\\'&a=blgi.tit&r=6&i=90000003_000000000000003364736304\\');\" title=\"아이오아이(I.O.I) 강미나 메이크업, 데일리 메이크업♬\"><strong class=\"hl\">아이오아이</strong>(I.O.I) <strong class=\"hl\">강미나</strong> 메이크업, 데일리...</a></dt> <dd class=\"txt_inline\">2016.06.06. <div class=\"scial\" id=\"sp_blog_6_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=6&i=90000003_000000000000003364736304&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_6\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">정말 간단하면서 예쁘지 않나요? 데일리로 표현한 <strong class=\"hl\">아이오아이 강미나</strong> 메이크업! 무쌍분들 도전해보세요! 다음 메이크업은 누구로 할까요-? 댓글남겨주세요!</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://section.blog.naver.com/Event/SmartEditor3Promotion.nhn\" target=\"_blank\" class=\"total_origin\" onclick=\"return goOtherCR(this,\\'a=blgi.se3&r=6&i=90000003_000000000000003364736304&u=\\'+urlencode(urlexpand(this.href)));\" ><em class=\"spcm ico_smarteditor\">스마트에디터3.0</em></a><a href=\"http://blog.naver.com/loveneet\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=6&i=90000003_000000000000003364736304\\');\">러브닛\\'s Always Be...</a><a href=\"http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/loveneet?Redirect=Log&logNo=220728615684\\')+\\'&a=blgi.url&r=6&i=90000003_000000000000003364736304\\');\" >blog.naver.com/loveneet/220728615684</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_7\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://bluechansong.tistory.com/93\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://bluechansong.tistory.com/93\\')+\\'&a=blgt.img&r=2&i=a00000fa_682a3a8eba2788e9ee9035da\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/sun?t=r80&q=http://cfile8.uf.tistory.com/image/2208303C57590E6F07F73B#330x310\" alt=\"잘 먹는 소녀들 트와이\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/sun?t=r80&q=http://cfile8.uf.tistory.com/image/2208303C57590E6F07F73B#330x310\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>5<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://bluechansong.tistory.com/93\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://bluechansong.tistory.com/93\\')+\\'&a=blgt.tit&r=7&i=a00000fa_682a3a8eba2788e9ee9035da\\');\" title=\"잘 먹는 소녀들 트와이쯔 강미나 다현 아이오아이\">잘 먹는 소녀들 트와이쯔 <strong class=\"hl\">강미나</strong> 다현 <strong class=\"hl\">아이오아이</strong></a></dt> <dd class=\"txt_inline\">2016.06.09. <div class=\"scial\" id=\"sp_blog_7_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgt.pplugin&r=7&i=a00000fa_682a3a8eba2788e9ee9035da&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_7\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">걸그룹 <strong class=\"hl\">아이오아이 강미나</strong>가 jtbc 잘 먹는 소녀들 에서 먹방을 선보인다고 합니다 9일 방송 관계자에 따르면 강미나는 트와이스 쯔위 레드벨벳 슬기, 시크릿 전효성...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://bluechansong.tistory.com/\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgt.blogname&r=7&i=a00000fa_682a3a8eba2788e9ee9035da\\');\">막시무스</a><a href=\"http://bluechansong.tistory.com/93\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://bluechansong.tistory.com/93\\')+\\'&a=blgt.url&r=7&i=a00000fa_682a3a8eba2788e9ee9035da\\');\" >bluechansong.tistory.com/93</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=bluechansong.tistory.com%2F\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgt.similar&r=7&i=a00000fa_682a3a8eba2788e9ee9035da\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_8\"> <div class=\"thumb 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display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/woobba_?Redirect=Log&logNo=220776379745\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/woobba_?Redirect=Log&logNo=220776379745\\')+\\'&a=blgi.tit&r=8&i=90000003_0000000000000033674C3561\\');\" title=\"IOI [아이오아이] 강미나 공항패션/화이트 레이스원피스 코디\">IOI [<strong class=\"hl\">아이오아이</strong>] <strong class=\"hl\">강미나</strong> 공항패션/화이트...</a></dt> <dd class=\"txt_inline\">2016.07.31. <div class=\"scial\" id=\"sp_blog_8_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=8&i=90000003_0000000000000033674C3561&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_8\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">IOI [<strong class=\"hl\">아이오아이</strong>] <strong class=\"hl\">강미나</strong> 공항패션/ 화이트 레이스원피스 코디 <strong class=\"hl\">아이오아이 강미나</strong>의공항패션을 살펴볼까요? <strong class=\"hl\">아이오아이 강미나</strong>는 공항패션으로화이트 레이스원피스를...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://blog.naver.com/woobba_\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=8&i=90000003_0000000000000033674C3561\\');\">우빠마켓</a><a href=\"http://blog.naver.com/woobba_?Redirect=Log&logNo=220776379745\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/woobba_?Redirect=Log&logNo=220776379745\\')+\\'&a=blgi.url&r=8&i=90000003_0000000000000033674C3561\\');\" >blog.naver.com/woobba_/220776379745</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=blog.naver.com%2Fwoobba_\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.similar&r=8&i=90000003_0000000000000033674C3561\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_9\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\\')+\\'&a=blgi.img&r=2&i=90000003_0000000000000033670D479B\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160726_136/jiseunga1019_1469528199068BdT0N_JPEG/PicsArt_07-26-06.51.14.jpg#900x900\" alt=\"뿌왕 구구단 아이오아\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160726_136/jiseunga1019_1469528199068BdT0N_JPEG/PicsArt_07-26-06.51.14.jpg#900x900\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>3<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\\')+\\'&a=blgi.tit&r=9&i=90000003_0000000000000033670D479B\\');\" title=\"뿌왕 구구단 아이오아이 강미나 보정\">뿌왕 구구단 <strong class=\"hl\">아이오아이 강미나</strong> 보정</a></dt> <dd class=\"txt_inline\">2016.07.26. <div class=\"scial\" id=\"sp_blog_9_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=9&i=90000003_0000000000000033670D479B&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_9\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\">구구단, <strong class=\"hl\">아이오아이 강미나</strong> 보정 헤헤 안녕하세요 시간이 돌고돌아 미나 보정을 해 봤어요♡ 구구단 노래좋더라구요 머리부터 발끝까지 모든것~~~ 크크 상큼발랄 구구단...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://blog.naver.com/jiseunga1019\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=9&i=90000003_0000000000000033670D479B\\');\">뿌왕/나는 양치질을...</a><a href=\"http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/jiseunga1019?Redirect=Log&logNo=220772255643\\')+\\'&a=blgi.url&r=9&i=90000003_0000000000000033670D479B\\');\" >blog.naver.com/jiseunga1019/220772255643</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=blog.naver.com%2Fjiseunga1019\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.similar&r=9&i=90000003_0000000000000033670D479B\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> <li class=\"sh_blog_top\" id=\"sp_blog_10\"> <div class=\"thumb thumb-rollover\"> <a href=\"http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\" target=\"_blank\" onclick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\\')+\\'&a=blgi.img&r=2&i=90000003_0000000000000033674786AA\\');\" class=\"sp_thmb thmb80\"><img src=\"https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160731_118/wodnjs4056_1469941838842LVbeE_JPEG/NaverBlog_20160731_141036_03.jpg#900x600\" alt=\"160731 아이오아이 강\" class=\"sh_blog_thumbnail\" onload=\"onThumbnailLoad(this,\\'https://tv.pstatic.net/ugc?t=r80&q=http://blogfiles.naver.net/20160731_118/wodnjs4056_1469941838842LVbeE_JPEG/NaverBlog_20160731_141036_03.jpg#900x600\\');\" onerror=\"this.parentNode.parentNode.style.display=\\'none\\';\"><span class=\"thmb_v\"></span><span class=\"mask\"></span> </a> <a href=\"#\" class=\"thumb_num num-rollover\"><span>3<span class=\"blind\">장의 이미지 더보기</span></span></a> <div class=\"thumb_slide_wrap\" style=\"width:0px; overflow:hidden; display:none;\"></div> </div> <dl> <dt><a class=\"sh_blog_title _sp_each_url _sp_each_title\" href=\"http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\\')+\\'&a=blgi.tit&r=10&i=90000003_0000000000000033674786AA\\');\" title=\"160731 아이오아이 강미나 KCON 2016 LA 출국 고화질 직찍 사진모음/구구단 강미나 고화질 직찍 사진모음\">160731 <strong class=\"hl\">아이오아이 강미나</strong> KCON 2016 LA 출국...</a></dt> <dd class=\"txt_inline\">2016.07.31. <div class=\"scial\" id=\"sp_blog_10_base\"> <span class=\"bar\"></span><a href=\"#\" class=\"bt_scial2 naver-splugin\" onclick=\"return tCR(\\'a=blgi.pplugin&r=10&i=90000003_0000000000000033674786AA&u=\\'+urlencode(this.href));\" data-style=\"unity-v2\" data-oninitialize=\"splugin_oninitialize(\\'sp_blog_10\\');\">보내기</a> </div> </dd> <dd class=\"sh_blog_passage\"><strong class=\"hl\">아이오아이 강미나</strong> KCON 2016 LA 출국 고화질 직찍 사진모음/구구단 강미나 고화질 직찍 사진모음 <strong class=\"hl\">아이오아이 강미나</strong> KCON 2016 LA 출국 고화질 직찍 사진모음/구구단...</dd> <dd class=\"txt_block\"> <span class=\"inline\"> <a href=\"http://blog.naver.com/wodnjs4056\" class=\"txt84\" target=_blank onClick=\"return goOtherCR(this, \\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.blogname&r=10&i=90000003_0000000000000033674786AA\\');\">레드벨벳 흥을 돋우...</a><a href=\"http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\" class=\"url\" target=_blank onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(\\'http://blog.naver.com/wodnjs4056?Redirect=Log&logNo=220776072874\\')+\\'&a=blgi.url&r=10&i=90000003_0000000000000033674786AA\\');\" >blog.naver.com/wodnjs4056/220776072874</a> <span class=\"bar\"></span> <a href=\"?where=post&sm=tab_brs&query=%EC%95%84%EC%9D%B4%EC%98%A4%EC%95%84%EC%9D%B4%20%EA%B0%95%EB%AF%B8%EB%82%98&st=sim&date_option=0&date_from=&date_to=&dup_remove=1&srchby=all&nso=&ie=utf8&post_blogurl=blog.naver.com%2Fwodnjs4056\" class=\"txt84\" onClick=\"goCR(this,\\'rd\\',\\'u=\\'+urlencode(urlexpand(this.href))+\\'&a=blgi.similar&r=10&i=90000003_0000000000000033674786AA\\');\">블로그 내 검색</a> </span> </dd> </dl> </li> </ul> <script type=\"text/javascript\"> var aSlidingThumbnailData = [ {\"nTotal\" : \"14\",\"aItems\" : 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\\'a=htk.14Hlist&r=1&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">1</em><span class=\"tit\">징역 8월에 집행유예 2년</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EC%B6%94%EC%84%9D+%EA%B8%B0%EC%B0%A8%ED%91%9C+%EC%98%88%EB%A7%A4\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=2&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">2</em><span class=\"tit\">추석 기차표 예매</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EB%B0%B0%EA%B5%AC+%EA%B9%80%EC%97%B0%EA%B2%BD\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=3&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">3</em><span class=\"tit\">배구 김연경</span></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EB%8C%80%ED%83%80+%EA%B9%80%ED%98%84%EC%88%98\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=4&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">4</em><span class=\"tit\">대타 김현수</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EB%8C%80%EC%A4%91%EA%B5%AD+%EC%88%98%EC%B6%9C%EB%B6%80%EC%A7%84\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=5&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">5</em><span class=\"tit\">대중국 수출부진</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EC%83%81%EB%B0%98%EA%B8%B0+%EA%B0%90%EC%9B%90+%EC%B9%BC%EB%B0%94%EB%9E%8C\" 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\\'a=htk.14Hlist&r=8&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">8</em><span class=\"tit\">조니 뎁 엠버 허드 이혼합의</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EC%9A%B0%EC%A3%BC%EC%86%8C%EB%85%80+%EB%B9%84%EB%B0%80%EC%9D%B4%EC%95%BC\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=9&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">9</em><span class=\"tit\">우주소녀 비밀이야</span></a></li><li><a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htk&ie=utf8&tab_id=h:2&query=%EC%98%AC%ED%95%B4+%EC%98%A8%EC%97%B4%EC%A7%88%ED%99%98+%EC%82%AC%EB%A7%9D%EC%9E%90+16%EB%AA%85\" class=\"lk lk_item\" onclick=\"return goOtherCR(this, \\'a=htk.14Hlist&r=10&i=&u=\\'+urlencode(urlexpand(this.href)));\"><em class=\"num\">10</em><span class=\"tit\">올해 온열질환 사망자 16명</span><em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em></a></li></ol><div class=\"realtime_srch_area\"><p class=\"dsc\"><time>2016.08.17. 14:00</time> 기준 <a href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2012\" target=\"_blank\" class=\"spim lk_rts_help\" onclick=\"return goOtherCR(this, \\'a=htk.guide&r=&i=&u=\\'+urlencode(this.href));\">도움말</a></p></div> </div> </div> <script type=\"text/javascript\"> (function() { var https_protocol = (window.location.protocol == \"https:\"); var sJavascript = https_protocol ? \"https://ssl.pstatic.net/sstatic/au/s/pc/_search/hottopic_keyword/nhn.hottopic_keyword_20140904.js\" : \"http://sstatic.naver.net/au/s/pc/_search/hottopic_keyword/nhn.hottopic_keyword_20140904.js\"; nx_js_defer_load(sJavascript, function () { new nhn.search.hottopicKeyword.Controller({ elRoot : $(\"nxfr_htp\"), sListApiUrl : https_protocol ? \"https://s.search.naver.com/n/jsonp/hottopic.js\" : \"http://n.search.naver.com/jsonp/hottopic.js\", 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target=\"_blank\" class=\"spim lk_rts_help\" onclick=\"return goOtherCR(this, \\'a=fkc_def.guide&r=&i=&u=\\'+urlencode(this.href));\">도움말</a></h3> <div class=\"realtime_srch _roling_realtime\"> <ol class=\"lst_realtime_srch\"> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%9C%A4%EC%A0%9C%EB%AC%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=1&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">1</em> <span class=\"tit _text\">윤제문</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">429</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%9A%B0%EC%A3%BC%EC%86%8C%EB%85%80\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=2&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">2</em> <span class=\"tit _text\">우주소녀</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">99</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B5%AC%EB%A5%B4%EB%AF%B8%EA%B7%B8%EB%A6%B0%EB%8B%AC%EB%B9%9B\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=3&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">3</em> <span class=\"tit _text\">구르미그린달빛</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">54</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%97%A0%EB%B2%84+%ED%97%88%EB%93%9C\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=4&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">4</em> <span class=\"tit _text\">엠버 허드</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">222</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%A1%B0%EB%8B%88+%EB%8E%81\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=5&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">5</em> <span class=\"tit _text\">조니 뎁</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">216</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B0%95%EC%A0%95%ED%98%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=6&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">6</em> <span class=\"tit _text\">강정호</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">99</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%95%84%EC%88%98%EB%9D%BC\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=7&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">7</em> <span class=\"tit _text\">아수라</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">258</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%B6%94%EC%84%9D\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=8&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">8</em> <span class=\"tit _text\">추석</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">48</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B0%95%EC%9D%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=9&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">9</em> <span class=\"tit _text\">강인</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">603</span></em> </a> </li> <li> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EB%B0%80%EC%A0%95\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=10&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">10</em> <span class=\"tit _text\">밀정</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">72</span></em> </a> </li> <li class=\"_dummy_first\"> <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%9C%A4%EC%A0%9C%EB%AC%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_def.list&r=1&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">1</em> <span class=\"tit _text\">윤제문</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">429</span></em> </a> </li> </ol> </div> <div class=\"ly_realtime_srch _tab_chart\" id=\"nxfr_brlayer\"> <iframe frameborder=\"0\" title=\"빈프레임\" 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_stext\">상승</span><span class=\"_step\">429</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%9A%B0%EC%A3%BC%EC%86%8C%EB%85%80\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=2&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">2</em> <span class=\"tit _text\">우주소녀</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">99</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B5%AC%EB%A5%B4%EB%AF%B8%EA%B7%B8%EB%A6%B0%EB%8B%AC%EB%B9%9B\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=3&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">3</em> <span class=\"tit _text\">구르미그린달빛</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">54</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%97%A0%EB%B2%84+%ED%97%88%EB%93%9C\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=4&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">4</em> <span class=\"tit _text\">엠버 허드</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">222</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%A1%B0%EB%8B%88+%EB%8E%81\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=5&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">5</em> <span class=\"tit _text\">조니 뎁</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">216</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B0%95%EC%A0%95%ED%98%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=6&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">6</em> <span class=\"tit _text\">강정호</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">99</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%95%84%EC%88%98%EB%9D%BC\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=7&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">7</em> <span class=\"tit _text\">아수라</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">258</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EC%B6%94%EC%84%9D\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=8&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">8</em> <span class=\"tit _text\">추석</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">48</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EA%B0%95%EC%9D%B8\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=9&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">9</em> <span class=\"tit _text\">강인</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">603</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_lvf&ie=utf8&query=%EB%B0%80%EC%A0%95\" class=\"lk lk_item _link\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.list&r=10&i=&u=\\'+urlencode(this.href));\"> <em class=\"num\">10</em> <span class=\"tit _text\">밀정</span> <em class=\"rank up _status\"><span class=\"spim _stext\">상승</span><span class=\"_step\">72</span></em> </a> </li> </ol> <div class=\"realtime_srch_area\"> <p class=\"dsc\"><time><span class=\"_time\">2016.08.17. 14:39</span></time> 현재 <a href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1989\" target=\"_blank\" class=\"spim lk_rts_help\" onclick=\"return goOtherCR(this, \\'a=fkc_rkl.guide&r=&i=&u=\\'+urlencode(this.href))\">도움말</a></p> </div> </div> <div class=\"tab_area _tab_area _tab2\" style=\"display: none;\"> <h3 class=\"blind\">핫토픽 키워드</h3> <ol class=\"lst_realtime_srch\"> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%A7%95%EC%97%AD+8%EC%9B%94%EC%97%90+%EC%A7%91%ED%96%89%EC%9C%A0%EC%98%88+2%EB%85%84\" class=\"lk lk_item\"> <em class=\"num\">1</em> <span class=\"tit _text\">징역 8월에 집행유예 2년</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%B6%94%EC%84%9D+%EA%B8%B0%EC%B0%A8%ED%91%9C+%EC%98%88%EB%A7%A4\" class=\"lk lk_item\"> <em class=\"num\">2</em> <span class=\"tit _text\">추석 기차표 예매</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EB%B0%B0%EA%B5%AC+%EA%B9%80%EC%97%B0%EA%B2%BD\" class=\"lk lk_item\"> <em class=\"num\">3</em> <span class=\"tit _text\">배구 김연경</span> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EB%8C%80%ED%83%80+%EA%B9%80%ED%98%84%EC%88%98\" class=\"lk lk_item\"> <em class=\"num\">4</em> <span class=\"tit _text\">대타 김현수</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EB%8C%80%EC%A4%91%EA%B5%AD+%EC%88%98%EC%B6%9C%EB%B6%80%EC%A7%84\" class=\"lk lk_item\"> <em class=\"num\">5</em> <span class=\"tit _text\">대중국 수출부진</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%83%81%EB%B0%98%EA%B8%B0+%EA%B0%90%EC%9B%90+%EC%B9%BC%EB%B0%94%EB%9E%8C\" class=\"lk lk_item\"> <em class=\"num\">6</em> <span class=\"tit _text\">상반기 감원 칼바람</span> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EA%B5%AC%EB%A5%B4%EB%AF%B8+%EA%B7%B8%EB%A6%B0+%EB%8B%AC%EB%B9%9B+%EB%B0%95%EB%B3%B4%EA%B2%80+%EA%B9%80%EC%9C%A0%EC%A0%95\" class=\"lk lk_item\"> <em class=\"num\">7</em> <span class=\"tit _text\">구르미 그린 달빛 박보검 김유정</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%A1%B0%EB%8B%88+%EB%8E%81+%EC%97%A0%EB%B2%84+%ED%97%88%EB%93%9C+%EC%9D%B4%ED%98%BC%ED%95%A9%EC%9D%98\" class=\"lk lk_item\"> <em class=\"num\">8</em> <span class=\"tit _text\">조니 뎁 엠버 허드 이혼합의</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%9A%B0%EC%A3%BC%EC%86%8C%EB%85%80+%EB%B9%84%EB%B0%80%EC%9D%B4%EC%95%BC\" class=\"lk lk_item\"> <em class=\"num\">9</em> <span class=\"tit _text\">우주소녀 비밀이야</span> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_htf&ie=utf8&query=%EC%98%AC%ED%95%B4+%EC%98%A8%EC%97%B4%EC%A7%88%ED%99%98+%EC%82%AC%EB%A7%9D%EC%9E%90+16%EB%AA%85\" class=\"lk lk_item\"> <em class=\"num\">10</em> <span class=\"tit _text\">올해 온열질환 사망자 16명</span> <em class=\"rank new\"><span class=\"spim\"></span><span>NEW</span></em> </a> </li> </ol> <div class=\"realtime_srch_area\"> <p class=\"dsc\"><time>2016.08.17. 14:00</time> 기준 <a href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2012\" target=\"_blank\" class=\"spim lk_rts_help\" onclick=\"return goOtherCR(this, \\'a=fkc_htk.guide&r=&i=&u=\\'+urlencode(this.href))\">도움말</a></p> </div> </div> <div class=\"tab_area _tab_area _tab2\" style=\"display: none;\"> <h3 class=\"blind\">직장인 인기검색어</h3> <ol class=\"lst_realtime_srch\"> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EB%84%A4%EC%9D%B4%EB%B2%84+%EC%A7%80%EB%8F%84\" class=\"lk lk_item\"> <em class=\"num\">1</em> <span class=\"tit\">네이버 지도</span> <em class=\"percent\">20.4%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EB%82%B4%EC%9D%BC+%EC%98%A4%ED%9B%84+%EB%82%A0%EC%94%A8\" class=\"lk lk_item\"> <em class=\"num\">2</em> <span class=\"tit\">내일 오후 날씨</span> <em class=\"percent\">18.0%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EB%86%8D%ED%98%91+%EC%9D%B8%ED%84%B0%EB%84%B7%EB%B1%85%ED%82%B9\" class=\"lk lk_item\"> <em class=\"num\">3</em> <span class=\"tit\">농협 인터넷뱅킹</span> <em class=\"percent\">10.9%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EC%97%AC%EC%9E%90+%EB%B0%B0%EA%B5%AC\" class=\"lk lk_item\"> <em class=\"num\">4</em> <span class=\"tit\">여자 배구</span> <em class=\"percent\">8.9%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EC%83%9D%EB%85%84%EC%9B%94%EC%9D%BC+%EC%9A%B4%EC%84%B8\" class=\"lk lk_item\"> <em class=\"num\">5</em> <span class=\"tit\">생년월일 운세</span> <em class=\"percent\">8.5%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EB%93%B1%EA%B8%B0%EB%B6%80%EB%93%B1%EB%B3%B8+%EC%97%B4%EB%9E%8C\" class=\"lk lk_item\"> <em class=\"num\">6</em> <span class=\"tit\">등기부등본 열람</span> <em class=\"percent\">8.0%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EC%BD%94%EB%A0%88%EC%9D%BC\" class=\"lk lk_item\"> <em class=\"num\">7</em> <span class=\"tit\">코레일</span> <em class=\"percent\">6.8%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EC%A0%84%EA%B8%B0%EC%9A%94%EA%B8%88+%EB%88%84%EC%A7%84%EC%84%B8+%EA%B8%B0%EC%A4%80\" class=\"lk lk_item\"> <em class=\"num\">8</em> <span class=\"tit\">전기요금 누진세 기준</span> <em class=\"percent\">6.5%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EA%B0%A4%EB%9F%AD%EC%8B%9C%EB%85%B8%ED%8A%B87+%EB%B8%94%EB%A3%A8%EC%BD%94%EB%9E%84\" class=\"lk lk_item\"> <em class=\"num\">9</em> <span class=\"tit\">갤럭시노트7 블루코랄</span> <em class=\"percent\">6.4%</em> </a> </li> <li > <a href=\"//search.naver.com/search.naver?where=nexearch&sm=tab_bcf&ie=utf8&ug_cid=mwk&query=%EA%B3%B5%EB%AC%B4%EC%9B%90+%EC%97%B0%EA%B8%88+%EC%88%98%EB%A0%B9%EC%95%A1\" class=\"lk lk_item\"> <em class=\"num\">10</em> <span class=\"tit\">공무원 연금 수령액</span> <em class=\"percent\">5.6%</em> </a> </li> </ol> <div class=\"realtime_srch_area\"> <p class=\"dsc\"><time>2016.08.17. 14:00</time> 기준 <a href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1990\" target=\"blank\" class=\"spim lk_rts_help\" onclick=\"return goOtherCR(this, \\'a=fkc_bck.guide&r=&i=&u=\\'+urlencode(this.href))\">도움말</a></p> </div> </div> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:block;position:absolute;top:0;left:0;z-index:-1;width:100%;height:100%\"></iframe> </div> </div> <script type=\"text/javascript\"> (function() { var elRoot = $Element(\"nxfr_brlayer\"); $Fn(function(e){ var oThis = e.currentElement; var eT = $Element(oThis); var r = eT.query(\"> em.num\").innerHTML; var subcr = \"htk\"; if (eT.query(\"> em.percent\")) { subcr = \"bckw\"; } return goOtherCR(oThis, \\'a=fkc_\\'+subcr+\\'.list&r=\\'+r+\\'&i=&u=\\'+urlencode(oThis.href)); }).attach(elRoot.queryAll(\"> div._tab2 > ol > li > a\"), \"click\"); $Fn(function(e){ var eTP = $Element($Element(e.currentElement).parent()); var cid, cr; cid = eTP.attr(\"data-index\"); cr = \"fkc\"; if (cid == 0) cr += \".tabrkl\"; else if (cid == 1) cr += \".tabhtk\"; else if (cid == 2) cr += \".tabbckw\"; return tCR(cr, 1, \"\"); }).attach(elRoot.queryAll(\"> div.tab_realtime_srch > ul > li > a\"), \"click\"); var footer_callback = function() { var oFooterRealTime = new nhn.FooterRealtimeKeyword($(\"_nx_footer_realtime\"), \"nexearch\", \"https://rank.search.naver.com\", null); nxrtrank.registRealtimeKeyword(\"nexearch\", oFooterRealTime); var realtime_srch_aside = $Element(\"_nx_aside_realtime\") ; if( !realtime_srch_aside ) { var update_check =$Class({ oFooterRealTime : null, scrollTimer : null, fHandleEvent : null, $init : function(a) { this.oFooterRealTime = a ; }, startScroll : function() { this.fHandleEvent = $Fn(this.handleScroll, this) ; this.fHandleEvent.attach(window, \"scroll\") ; }, handleScroll : function() { if(this.scrollTimer) { window.clearTimeout(this.scrollTimer); this.scrollTimer = null; } var e = this ; this.scrollTimer = window.setTimeout( ( function() { var result = e.check_loc() ; if(result) { e.oFooterRealTime.start() ; e.fHandleEvent.detach(window, \"scroll\") ; } } ) ,100) ; }, check_loc : function() { var loc = $Element(\"_nx_footer_realtime\"); var ax1 = loc.offset().left; var ay1 = loc.offset().top; var ax2 = ax1 + loc.width(); var ay2 = ay1 + loc.height(); var bx1 = window.pageXOffset; var by1 = window.pageYOffset; var bx2 = bx1 + window.innerWidth; var by2 = by1 + window.innerHeight; if (ay2 < by1) return false ; if (ay1 > by2) return false ; if (ax2 < bx1) return false ; if (ax1 > bx2) return false ; return true ; } } ); var o_update_check = new update_check(oFooterRealTime); if( !o_update_check.check_loc() ) { o_update_check.startScroll() ; return ; } } oFooterRealTime.start() ; } ; if (typeof(nxrtrank) == \"undefined\") nx_js_defer_load(\"https://ssl.pstatic.net/sstatic/au/pc/realtime_keyword/nhn.nxrtrank_150610.js\", function() {nx_js_lazyload(\"https://ssl.pstatic.net/sstatic/au/s/pc/_common/realtime_keyword/nhn.footer_realtime_keyword_20150827.js\", footer_callback, 1);}, 300) ; else nx_js_defer_load(\"https://ssl.pstatic.net/sstatic/au/s/pc/_common/realtime_keyword/nhn.footer_realtime_keyword_20150827.js\", footer_callback, 300) ; }) (); </script> <form id=\"nx_search_form_btm\" name=\"searchu\" method=\"get\" action=\"?\" onsubmit=\"return nx_form_submit(this);\"> <fieldset class=\"greenwindow\"> <legend>검색</legend> <div class=\"greenbox\"> <span class=\"keyword\"> <input type=\"hidden\" name=\"sm\" value=\"tab_hty.btm\"> <input type=\"hidden\" name=\"where\" value=\"post\"> <input type=\"hidden\" name=\"oquery\" value=\"아이오아이 강미나\"> <input type=\"hidden\" value=\"\" name=\"acq\" disabled> <input type=\"hidden\" value=\"\" name=\"acr\" disabled> <input type=\"hidden\" value=\"\" name=\"qdt\" disabled> <input type=\"hidden\" name=\"ie\" value=\"utf8\"> <input type=\"hidden\" value=\"\" name=\"acir\" disabled> <input type=\"hidden\" value=\"\" name=\"os\" disabled> <input type=\"hidden\" value=\"\" name=\"bid\" disabled> <input type=\"hidden\" value=\"\" name=\"pkid\" disabled> <input type=\"hidden\" value=\"\" name=\"eid\" disabled> <input type=\"hidden\" value=\"\" name=\"mra\" disabled> <input type=\"text\" title=\"검색어 입력\" name=\"query\" id=\"nx_query_btm\" maxlength=\\'255\\' class=\"box_window\" autocomplete=\"off\" value=\"아이오아이 강미나\"> </span> </div> <div id=\"nautocomplete_btm\" class=\"autocomplete\"> <span class=\"bt_atcp\"><a href=\"#\" onclick=\"return false;\"><img class=\"triangleImg\" src=\"https://ssl.pstatic.net/sstatic/search/2015/btn_atcmp_down.gif\" alt=\"자동완성 펼치기\" width=\"13\" height=\"11\"></a></span> </div> <button type=\"submit\" class=\"bt_search spim\" onmouseover=\"$Element(this).addClass(\\'over\\');\" onmouseout=\"$Element(this).removeClass(\\'over down\\');\" onmousedown=\"$Element(this).removeClass(\\'over\\');$Element(this).addClass(\\'down\\');\">검색</button> <div class=\"ly_atcmp ly_atcmp_v1\" id=\"nx_autoframe_btm\" style=\"display:none;\"> <iframe frameborder=\"0\" title=\"빈프레임\" style=\"display:none;display:block\\\\9;display:block\\\\0/;position:absolute;top:-1px;left:-1px;z-index:-1;width:100%;height:100%;padding:1px;filter:alpha(opacity=0);opacity:0\"></iframe> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _atcmp\" style=\"display:none;\"> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"parent.smartSearchU.unuse(); return false;\">자동완성 끄기</a></span></p> <div class=\"words _words\"> <div class=\"_atcmp_result_wrap\"> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> <ul class=\"_resultBox\"></ul> </div> <div class=\"add_group _atcmp_answer_wrap\"></div> </div> <div class=\"words nature\"> <ul class=\"_nature\"> <li class=\"_item\"><a href=\"#\" onclick=\"return false;\">@txt@</a><span style=\"display:none\" id=\"rank@rank@\">@txt@</span></li> </ul> <h3 class=\"tit\">생각한대로 검색해 보세요 <span class=\"beta\">Beta</span></h3> </div> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip1\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _atcmpIng\" style=\"display:none;\"> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"parent.smartSearchU.unuse(); return false;\">자동완성 끄기</a></span></p> <div class=\"words\"><p class=\"msg\">현재 자동완성 기능을 사용하고 계십니다.</p></div> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip2\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _atcmpStart\" style=\"display:none;\"> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\" onclick=\"parent.smartSearchU.unuse(); return false;\">자동완성 끄기</a></span></p> <div class=\"words\"><p class=\"msg\">자동완성 기능이 활성화되었습니다.</p></div> <img src=\"https://ssl.pstatic.net/sstatic/search/images11/img_atcmp15.gif\" alt=\"기능을 다시 켤 때는 펼치기 버튼을 클릭하세요\" width=\"218\" height=\"23\" class=\"help _help_tooltip3\" style=\"display:none;\"/> </div> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _atcmpOff\" style=\"display:none;\"> <p class=\"func\"><span class=\"fl\"><a onclick=\"__atcmpCR(event, this, \\'help\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1987\" target=\"_blank\">도움말</a> \| <a onclick=\"__atcmpCR(event, this, \\'report\\', \\'\\',\\'\\',\\'\\');\" href=\"https://help.naver.com/support/contents/contents.nhn?serviceNo=606&categoryNo=2028\" target=\"_blank\">신고</a></span><span><em><a class=\"hisoff\" href=\"javascript:;\">검색어저장 켜기</a> \|</em><a class=\"funoff\" href=\"javascript:;\">자동완성 켜기</a></span></p> <div class=\"words\"><p class=\"msg\">자동완성 기능이 꺼져 있습니다.</p></div> </div> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _keywords\" id=\"keyword\" style=\"display:none;\"> <div class=\"my_words\"> <p class=\"func _recentBtnGroup\"><span class=\"fl\"><a class=\"_delMode\" href=\"javascript:;\">기록 삭제</a></span><span><a class=\"_keywordOff\" href=\"javascript:;\">검색어저장 끄기</a> \| <a class=\"_acOff\" href=\"javascript:;\">자동완성 끄기</a></span></p> <p class=\"func _recentDelBtnGroup\" style=\"display:none\"><span class=\"fl\"><a class=\"_delAll\" href=\"javascript:;\" title=\"최근 검색어 기록을 모두 삭제합니다.\">기록 전체 삭제</a></span><span><a class=\"_delDone\" href=\"javascript:;\">완료</a></span></p> <p class=\"func _myBtnGroup\" style=\"display:none\"><span class=\"fl\"><a class=\"_delAll\" href=\"javascript:;\" title=\"설정된 내 검색어를 모두 삭제합니다.\">기록 전체 삭제</a></span><span><a class=\"_keywordOff\" href=\"javascript:;\">검색어저장 끄기</a> \| <a class=\"_acOff\" href=\"javascript:;\">자동완성 끄기</a></span></p> <span class=\"help2 _help2\" style=\"display:none\">기능을 다시 켤 때는 펼치기 버튼을 클릭하세요</span> <p class=\"noti _noti\" style=\"display:none\">공용 PC에서는 개인정보 보호를 위하여 반드시 로그아웃을 해 주세요.</p> <div class=\"words _recent\"> <ul><li data-rank=\"@rank@\"><a class=\"t@my@ _myBtn\" title=\"내 검색어 등록\" href=\"javascript:;\">내 검색어 등록</a><a href=\"javascript:;\">@txt@</a><em class=\"date\">@date@.</em><a href=\"javascript:;\" class=\"btn_del _del\" title=\"검색어삭제\">삭제</a><span style=\"display:none\">@in_txt@</span></li></ul> <div class=\"info_words _recentNone\" style=\"display:none\">최근검색어 내역이 없습니다.</div> <p class=\"msg _offMsg\" style=\"display:none\">검색어 저장 기능이 꺼져 있습니다.</p> </div> <div class=\"words _my\" style=\"display:none\"> <ul><li data-rank=\"@rank@\"><a class=\"ton _myBtn\" title=\"내 검색어 해제\" href=\"javascript:;\">내 검색어 해제</a><a href=\"javascript:;\">@txt@</a></li></ul> <div class=\"info_words _myNone\" style=\"display:none\">설정된 내 검색어가 없습니다.<br />최근검색어에서 <span class=\"star\">내 검색어 등록</span>를 선택하여 자주 찾는 검색어를<br />내 검색어로 저장해 보세요.</div> <p class=\"msg _offMsg\" style=\"display:none\">검색어 저장 기능이 꺼져 있습니다.</p> </div> <div class=\"lst_tab\"> <ul> <li class=\"on _recentTab\"><a href=\"javascript:;\">최근검색어</a></li> <li class=\"_myTab\"><a href=\"javascript:;\">내 검색어</a></li> </ul> </div> <div class=\"ly_noti _maxLayer\" style=\"display:none\"> <span class=\"mask\"></span> <p><span class=\"ico\"></span>내 검색어는 최대 <em>10</em>개 까지 저장할 수 있습니다.<br />추가하시려면 기존 내 검색어를 지워주세요. <a href=\"javascript:;\" class=\"btn_clse _close\">닫기</a></p> </div> </div> </div> <div class=\"api_atcmp_wrap api_atcmp_wrap_v1 _alert\" style=\"display:none;\"> <div class=\"api_atcmp_alert\"> <span class=\"ico\"></span> <p class=\"dsc_txt\">제20대 국회의원선거 후보에 대해 4월13일 선거일까지 자동완성 기능이 제공되지 않습니다. <a target=\"_blank\" nocr href=\"http://naver_diary.blog.me/220654539456\" onclick=\"return goOtherCR(this,\\'a=sug.vote&r=&i=&u=\\'+urlencode(this.href));\">자세히보기</a></p> </div> </div> </div> </fieldset> </form> </div> <ul class=\"lst_foot\"> <li class=\\'first\\'><a href=\"http://searchad.naver.com/\" target=\"_blank\" onclick=\"return goOtherCR(this, \\'a=fot.searchad&r=&i=&u=\\'+urlencode(this.href));\">검색광고 안내</a><span class=\"bar\"></span></li> <li><a href=\"http://localad.naver.com/\" target=\"_blank\" onclick=\"return goOtherCR(this, 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data-acir=\"@rank@\"> <a href=\"javascript:;\" class=\"opt_exchange opt_exchange_@11@\"> <span class=\"opt_nation\"> <img src=\"https://ssl.pstatic.net/sstatic/keypage/lifesrch/exchange/ico_@[email protected]\" alt=\"\" /> <span class=\"tx_nation\">@5@</span> </span> <span class=\"opt_amount\"> <span class=\"amount\"><strong>@6@</strong>원</span><span class=\"changes\">@10@ @8@ (@9@%)</span> </span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_10\"> <div class=\"add_opt _item\" data-sm=\"@2@\" data-keyword=\"@1@\" data-template_id=\"@0@\" data-acir=\"@rank@\"> <a href=\"javascript:;\" class=\"opt_weather\"> <span class=\"opt_weather_thmb\"> <img src=\"https://ssl.pstatic.net/static/m/weather/2011/im/wt170_@[email protected]\" width=\"50\" height=\"40\" alt=\"@7@\"> </span> <span class=\"opt_weather_group\"> <span class=\"opt_weather_state\">@7@</span> <span class=\"opt_weather_state\">기온 <em class=\"opt_deg\">@8@</em><span class=\"opt_unit\">℃</span></span> <span class=\"opt_weather_state\">@9@ <em>@10@</em><span class=\"opt_unit\">@11@</span></span> </span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_11\"> <div class=\"add_opt _item\" data-sm=\"@2@\" data-keyword=\"@1@\" data-template_id=\"@0@\" data-acir=\"@rank@\"> <a href=\"javascript:;\" class=\"opt_weather\"> <span class=\"opt_weather_thmb\"> <img src=\"https://ssl.pstatic.net/static/m/weather/2011/im/wt170_@[email protected]\" width=\"50\" height=\"40\" alt=\"@7@\"> </span> <span class=\"opt_weather_group\"> <span class=\"opt_weather_state\">@7@</span> <span class=\"opt_weather_state\">기온 <em class=\"opt_deg\">@8@</em><span class=\"opt_unit\">℃</span></span> <span class=\"opt_weather_state\">@9@ <em>@10@</em><span class=\"opt_unit\">@11@</span></span> </span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_16\"> <div class=\"add_opt type_context _item\" data-sm=\"@2@\" data-keyword=\"@1@\" data-template_id=\"@0@\" data-acir=\"@rank@\"> <a href=\"#\" class=\"opt_context\"> <span class=\"opt_tit\"><strong>@keyword@</strong></span> <span class=\"opt_sub\">(@type@)</span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_17\"> <div class=\"add_opt _item\" data-sm=\"@2@\" data-keyword=\"@1@\" data-template_id=\"@0@\" data-acir=\"@rank@\"> <a href=\"@5@\" target=\"_blank\" class=\"opt_shortcut\"> <span class=\"opt_url\">@display_link@</span> <span class=\"opt_txt\">사이트로 바로 이동</span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_18\"> <div class=\"add_opt _item\" data-sm=\"@2@\" data-keyword=\"@5@\" data-template_id=\"@0@\" data-acir=\"@rank@\"> <a href=\"#\" class=\"opt_happysearch\"> <span class=\"opt_happy_tit\"><span class=\"spat\"></span>행복검색</span> <span class=\"opt_happy_word\">@5@</span> </a> </div> </script> <script type=\"text/template\" id=\"_atcmp_answer_20\"></script> <script type=\"text/template\" id=\"_atcmp_result_item_tpl\"> <li class=\"_item @url_class@\" data-acr=\"@rank@\"> <a href=\"#\" class=\"atcmp_keyword\" onclick=\"return false;\" title=\"\"><span class=\"atcmp_keyword_txt\">@txt@<span class=\"spat ic_expand\"></span></span></a> <a href=\"@url@\" target=\"_blank\" class=\"mquick\">바로이동</a> <span style=\"display:none\">@in_txt@</span> </li> </script> <script type=\"text/template\" id=\"_atcmp_keyword_highlight_tpl\"> @mismatch_before@<strong>@match@</strong>@mismatch_after@ </script> <script type=\"text/template\" id=\"_atcmp_keyword_partial_match_highlight_tpl\"> @mismatch_before@<strong>@match@</strong>@mismatch_after@ </script> <script type=\"text/javascript\" src=\"https://ssl.pstatic.net/sstatic/au/pc/naver_autocomplete/nhn.common.atcmp.naver_web_160805_1.js\"></script> <script type=\"text/javascript\"> /* [PR] 서비스에서 사용하는 클릭로그함수 설정 / window.__atcmpCR = function(we, el, name, order, areaCode, rank) { var htAreaCode = __ghtAutoCompleteConfig[\"htAreaCode\"]; if(typeof htAreaCode[name] !== 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(asc/desc) / \"sFormId\" : \\'nx_search_form\\', / 폼엘리먼트 ID / \"sInputId\" : \\'nx_query\\', / 입력창 엘리먼트 ID / \"sViewId\" : \\'nx_autoframe_top\\', / 자동완성 레이어 ID / \"sViewToggleButtonId\" : \\'nautocomplete\\' / 레이어토글 버튼 ID / } ,{ \"sOrder\" : \"desc\", / 정렬방식 (asc/desc) / \"sFormId\" : \\'nx_search_form_btm\\', / 폼엘리먼트 ID / \"sInputId\" : \\'nx_query_btm\\', / 입력창 엘리먼트 ID / \"sViewId\" : \\'nx_autoframe_btm\\', / 자동완성 레이어 ID / \"sViewToggleButtonId\" : \\'nautocomplete_btm\\' / 레이어토글 버튼 ID / } ], \"htButtonImg\" : { \"show\" : \"https://ssl.pstatic.net/sstatic/search/2015/btn_atcmp_up.gif\", \"unuseShow\" : \"https://ssl.pstatic.net/sstatic/search/images11/btn_atcmp_up_off.gif\", \"hide\" : \"https://ssl.pstatic.net/sstatic/search/2015/btn_atcmp_down.gif\", \"unuse\" : \"https://ssl.pstatic.net/sstatic/search/images11/btn_atcmp_down_off2.gif\" }, / 서비스별 클릭영역코드 매핑정보 / \"htAreaCode\": { \"ansdirect\" : [\\'sug.ansdirect\\', \\'\\'], / 정답형 바로가기 (엔터/클릭) / \"go1\" : [\\'sug.go1\\', \\'\\'], 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"http://naver.com/search?query=강미나&start=21\n", "http://naver.com/search?query=강미나&start=31\n", "http://naver.com/search?query=강미나&start=41\n", "http://naver.com/search?query=강미나&start=51\n", "http://naver.com/search?query=강미나&start=61\n", "http://naver.com/search?query=강미나&start=71\n", "http://naver.com/search?query=강미나&start=81\n", "http://naver.com/search?query=강미나&start=91\n", "http://naver.com/search?query=강미나&start=101\n", "http://naver.com/search?query=강미나&start=111\n", "http://naver.com/search?query=강미나&start=121\n", "http://naver.com/search?query=강미나&start=131\n", "http://naver.com/search?query=강미나&start=141\n", "http://naver.com/search?query=강미나&start=151\n", "http://naver.com/search?query=강미나&start=161\n", "http://naver.com/search?query=강미나&start=171\n", "http://naver.com/search?query=강미나&start=181\n", "http://naver.com/search?query=강미나&start=191\n", "http://naver.com/search?query=강미나&start=201\n", "http://naver.com/search?query=강미나&start=211\n", "http://naver.com/search?query=강미나&start=221\n", "http://naver.com/search?query=강미나&start=231\n", "http://naver.com/search?query=강미나&start=241\n", "http://naver.com/search?query=강미나&start=251\n", "http://naver.com/search?query=강미나&start=261\n", "http://naver.com/search?query=강미나&start=271\n", "http://naver.com/search?query=강미나&start=281\n", "http://naver.com/search?query=강미나&start=291\n", "http://naver.com/search?query=강미나&start=301\n", "http://naver.com/search?query=강미나&start=311\n", "http://naver.com/search?query=강미나&start=321\n", "http://naver.com/search?query=강미나&start=331\n", "http://naver.com/search?query=강미나&start=341\n", "http://naver.com/search?query=강미나&start=351\n", "http://naver.com/search?query=강미나&start=361\n", "http://naver.com/search?query=강미나&start=371\n", "http://naver.com/search?query=강미나&start=381\n", "http://naver.com/search?query=강미나&start=391\n", "http://naver.com/search?query=강미나&start=401\n", "http://naver.com/search?query=강미나&start=411\n", "http://naver.com/search?query=강미나&start=421\n", "http://naver.com/search?query=강미나&start=431\n", "http://naver.com/search?query=강미나&start=441\n", "http://naver.com/search?query=강미나&start=451\n", "http://naver.com/search?query=강미나&start=461\n", "http://naver.com/search?query=강미나&start=471\n", "http://naver.com/search?query=강미나&start=481\n", "http://naver.com/search?query=강미나&start=491\n", "http://naver.com/search?query=강미나&start=501\n", "http://naver.com/search?query=강미나&start=511\n", "http://naver.com/search?query=강미나&start=521\n", "http://naver.com/search?query=강미나&start=531\n", "http://naver.com/search?query=강미나&start=541\n", "http://naver.com/search?query=강미나&start=551\n", "http://naver.com/search?query=강미나&start=561\n", "http://naver.com/search?query=강미나&start=571\n", "http://naver.com/search?query=강미나&start=581\n", "http://naver.com/search?query=강미나&start=591\n", "http://naver.com/search?query=강미나&start=601\n", "http://naver.com/search?query=강미나&start=611\n", "http://naver.com/search?query=강미나&start=621\n", "http://naver.com/search?query=강미나&start=631\n", "http://naver.com/search?query=강미나&start=641\n", "http://naver.com/search?query=강미나&start=651\n", "http://naver.com/search?query=강미나&start=661\n", "http://naver.com/search?query=강미나&start=671\n", "http://naver.com/search?query=강미나&start=681\n", "http://naver.com/search?query=강미나&start=691\n", "http://naver.com/search?query=강미나&start=701\n", "http://naver.com/search?query=강미나&start=711\n", "http://naver.com/search?query=강미나&start=721\n", "http://naver.com/search?query=강미나&start=731\n", "http://naver.com/search?query=강미나&start=741\n", "http://naver.com/search?query=강미나&start=751\n", "http://naver.com/search?query=강미나&start=761\n", "http://naver.com/search?query=강미나&start=771\n", "http://naver.com/search?query=강미나&start=781\n", "http://naver.com/search?query=강미나&start=791\n", "http://naver.com/search?query=강미나&start=801\n", "http://naver.com/search?query=강미나&start=811\n", "http://naver.com/search?query=강미나&start=821\n", "http://naver.com/search?query=강미나&start=831\n", "http://naver.com/search?query=강미나&start=841\n", "http://naver.com/search?query=강미나&start=851\n", "http://naver.com/search?query=강미나&start=861\n", "http://naver.com/search?query=강미나&start=871\n", "http://naver.com/search?query=강미나&start=881\n", "http://naver.com/search?query=강미나&start=891\n", "http://naver.com/search?query=강미나&start=901\n", "http://naver.com/search?query=강미나&start=911\n", "http://naver.com/search?query=강미나&start=921\n", "http://naver.com/search?query=강미나&start=931\n", "http://naver.com/search?query=강미나&start=941\n", "http://naver.com/search?query=강미나&start=951\n", "http://naver.com/search?query=강미나&start=961\n", "http://naver.com/search?query=강미나&start=971\n", "http://naver.com/search?query=강미나&start=981\n" ] } ], "source": [ "for page_num in range(1, 100):\n", " start = (page_num - 1) 10 + 1\n", " BASE_URL = \"http://naver.com/search?query=강미나&start=\" + str(start)\n", " print(BASE_URL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 이번에는 직방의 경우" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#https://api.zigbang.com/v1/items?detail=true&item_ids=4461545\n", "# 100001 - 101000 ( 1000개 )\n", "# requests 를 10번만 보내기 => 즉 1000번 => 100번 * 10회" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 1번째, 100001 => 100100 ( 100개 )\n", "# 2번째, 100101 => 100200 ( 100개 )\n", "# 3번쨰, 100201 => 100300 ( 100개 )\n", "# ..." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "a = [\n", " random.randint(100001, 101000)\n", " for i\n", " in range(100)\n", "]\n", "a = list(set(a)) #이렇게 set으로 하면 중복을 없앨 수 있다." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://api.zigbang.com/v1/items?detail=true&item_ids=100901&item_ids=100902&item_ids=100903&item_ids=100904&item_ids=100905&item_ids=100906&item_ids=100907&item_ids=100908&item_ids=100909&item_ids=100910&item_ids=100911&item_ids=100912&item_ids=100913&item_ids=100914&item_ids=100915&item_ids=100916&item_ids=100917&item_ids=100918&item_ids=100919&item_ids=100920&item_ids=100921&item_ids=100922&item_ids=100923&item_ids=100924&item_ids=100925&item_ids=100926&item_ids=100927&item_ids=100928&item_ids=100929&item_ids=100930&item_ids=100931&item_ids=100932&item_ids=100933&item_ids=100934&item_ids=100935&item_ids=100936&item_ids=100937&item_ids=100938&item_ids=100939&item_ids=100940&item_ids=100941&item_ids=100942&item_ids=100943&item_ids=100944&item_ids=100945&item_ids=100946&item_ids=100947&item_ids=100948&item_ids=100949&item_ids=100950&item_ids=100951&item_ids=100952&item_ids=100953&item_ids=100954&item_ids=100955&item_ids=100956&item_ids=100957&item_ids=100958&item_ids=100959&item_ids=100960&item_ids=100961&item_ids=100962&item_ids=100963&item_ids=100964&item_ids=100965&item_ids=100966&item_ids=100967&item_ids=100968&item_ids=100969&item_ids=100970&item_ids=100971&item_ids=100972&item_ids=100973&item_ids=100974&item_ids=100975&item_ids=100976&item_ids=100977&item_ids=100978&item_ids=100979&item_ids=100980&item_ids=100981&item_ids=100982&item_ids=100983&item_ids=100984&item_ids=100985&item_ids=100986&item_ids=100987&item_ids=100988&item_ids=100989&item_ids=100990&item_ids=100991&item_ids=100992&item_ids=100993&item_ids=100994&item_ids=100995&item_ids=100996&item_ids=100997&item_ids=100998&item_ids=100999&item_ids=101000\n" ] } ], "source": [ "for i in range(10):\n", " start_room_id = 100000 + (i * 100) + 1\n", " end_room_id = 100000 + ((i + 1) * 100)\n", " # print((start_room_id, end_room_id))\n", " \n", " BASE_URL = \"https://api.zigbang.com/v1/items?detail=true\"\n", " result_url = BASE_URL + \"&item_ids=\" + \"&item_ids=\".join([\n", " str(room_id)\n", " for room_id\n", " in range(start_room_id, end_room_id + 1)\n", " ])\n", "\n", "# for room_id in range(start_room_id, end_room_id + 1):\n", "# result_url = result_url + \"&item_ids=\" + str(room_id)\n", "\n", "# api.zigbang.com/v1/items?detail=true&item_ids=3430448&item_ids=3367854&item_ids=3288446&item_ids=3467204&item_ids=3150497&item_ids=3440906&item_ids=3376834&item_ids=3139708&item_ids=3331511&item_ids=3373198&item_ids=3236734&item_ids=3376434&item_ids=3322860&item_ids=3303061&item_ids=3287167&item_ids=3262172&item_ids=3228631&item_ids=3505011&item_ids=3249401&item_ids=3330951&item_ids=3494055&item_ids=3317227&item_ids=3405679&item_ids=3103384&item_ids=3418616&item_ids=3240028&item_ids=3456814&item_ids=3416910&item_ids=3429455&item_ids=3181675&item_ids=3309372&item_ids=3501566&item_ids=3240513&item_ids=3346510&item_ids=3406670&item_ids=3412596&item_ids=3214438&item_ids=3459715&item_ids=3493254&item_ids=3475114&item_ids=3501607&item_ids=3484623&item_ids=3190410&item_ids=3350050&item_ids=3495453&item_ids=3446619&item_ids=3451078&item_ids=3248718&item_ids=3345445&item_ids=3332380&item_ids=3450803&item_ids=3471049&item_ids=3438747&item_ids=3330749&item_ids=3446920&item_ids=3151682&item_ids=3501554&item_ids=3258532&item_ids=3435418&item_ids=3501551\n", "\n", "print(result_url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4T_데이터 분석 ( Data Science ) 맛보기 - pandas, matplotlib" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# pandas - pydata\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(columns=[\"address\", \"deposit\", \"rent\", \"phonenumber\"])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.loc[0] = ['주소', 1000, 100, \"01062353317\"]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " address deposit rent phonenumber\n", "0 주소 1000.0 100.0 01062353317\n" ] } ], "source": [ "print(df)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.loc[1] = ['주소1', 2000, 100, \"01062353317\"]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.loc[2] = ['주소2', 2000, 100, \"01062353317\"]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.loc[len(df)] = ['주소3', 2000, 100, \"01062353317\"] #맨 마지막 row에 추가" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>address</th>\n", " <th>deposit</th>\n", " <th>rent</th>\n", " <th>phonenumber</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>주소</td>\n", " <td>1000.0</td>\n", " <td>100.0</td>\n", " <td>01062353317</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>주소1</td>\n", " <td>2000.0</td>\n", " <td>100.0</td>\n", " <td>01062353317</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>주소2</td>\n", " <td>2000.0</td>\n", " <td>100.0</td>\n", " <td>01062353317</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>주소3</td>\n", " <td>2000.0</td>\n", " <td>100.0</td>\n", " <td>01062353317</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " address deposit rent phonenumber\n", "0 주소 1000.0 100.0 01062353317\n", "1 주소1 2000.0 100.0 01062353317\n", "2 주소2 2000.0 100.0 01062353317\n", "3 주소3 2000.0 100.0 01062353317" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[0:3]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(1):\n", " start_room_id = 100000 + (i * 100) + 1\n", " end_room_id = 100000 + ((i + 1) * 100)\n", " # print((start_room_id, end_room_id))\n", " \n", " BASE_URL = \"https://api.zigbang.com/v1/items?detail=true\"\n", " result_url = BASE_URL + \"&item_ids=\" + \"&item_ids=\".join([\n", " str(room_id)\n", " for room_id\n", " in range(start_room_id, end_room_id + 1)\n", " ])\n", " \n", " \n", "\n", "def get_df_from_zigbang(url):\n", " response = requests.get(url)\n", " zigbang_dict = json.loads(response.text)\n", " df = pd.DataFrame(columns=[\"address\", \"deposit\", \"rent\", \"phonenumber\"])\n", " for item in zigbang_dict.get('items'):\n", " room_information = item.get('item')\n", " try:\n", " address = room_information.get('agent_address1')\n", " deposit = room_information.get('deposit')\n", " rent = room_information.get('rent')\n", " phonenumber = room_information.get('original_user_phone')\n", " df.loc[len(df)] = [address, deposit, rent, phonenumber]\n", " except:\n", " pass\n", " \n", " return df" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 강남구 신사동 ( df1 )\n", "# 관악구 신림동 ( df2 )\n", "df_shinsa = get_df_from_zigbang(\"https://api.zigbang.com/v1/items?detail=true&item_ids=5495971&item_ids=5600860&item_ids=5490317&item_ids=5643763&item_ids=5636293&item_ids=5660546&item_ids=5512589&item_ids=5595091&item_ids=5593179&item_ids=5624018&item_ids=5512559&item_ids=5608602&item_ids=5628997&item_ids=5600817&item_ids=5567541&item_ids=5642163&item_ids=5169178&item_ids=5519658&item_ids=5537230&item_ids=5555431&item_ids=5441521&item_ids=5647242&item_ids=5642175&item_ids=5642023&item_ids=5575842&item_ids=5602646&item_ids=5594247&item_ids=5652932&item_ids=5470193&item_ids=5630702&item_ids=5644599&item_ids=5423901&item_ids=5645690&item_ids=5644627&item_ids=5444342&item_ids=5646998&item_ids=5607553&item_ids=5595814&item_ids=5589916&item_ids=5631474&item_ids=5661988&item_ids=5610946&item_ids=5608753&item_ids=5664279&item_ids=5519928&item_ids=5521262&item_ids=5644685&item_ids=5623803&item_ids=5644492&item_ids=5463078&item_ids=5636014&item_ids=5538016&item_ids=5662190&item_ids=5526171&item_ids=5618541&item_ids=5569798&item_ids=5641189&item_ids=5655575&item_ids=5466507&item_ids=5555613\")\n", "df_shinlim = get_df_from_zigbang(\"https://api.zigbang.com/v1/items?detail=true&item_ids=4620292&item_ids=4366382&item_ids=4566963&item_ids=4585208&item_ids=4560308&item_ids=4552724&item_ids=4344484&item_ids=4612042&item_ids=4574810&item_ids=4588687&item_ids=4387287&item_ids=4538842&item_ids=4557985&item_ids=4579464&item_ids=4607349&item_ids=4603203&item_ids=4341393&item_ids=4575315&item_ids=4350877&item_ids=4538375&item_ids=4616443&item_ids=4281504&item_ids=4556024&item_ids=4550034&item_ids=4512172&item_ids=4507118&item_ids=4606156&item_ids=4457169&item_ids=4526327&item_ids=4407071&item_ids=4582264&item_ids=4607937&item_ids=4395275&item_ids=4568603&item_ids=4569329&item_ids=4564865&item_ids=4551098&item_ids=4617261&item_ids=4536918&item_ids=4614718&item_ids=4614198&item_ids=4610604&item_ids=4578711&item_ids=4593621&item_ids=4612621&item_ids=4518874&item_ids=4533169&item_ids=4409063&item_ids=4617602&item_ids=4477945&item_ids=4249606&item_ids=4560223&item_ids=4570020&item_ids=4517907&item_ids=4530774&item_ids=4525210&item_ids=4596138&item_ids=4588994&item_ids=4612357&item_ids=4411862\")" ] }, { "cell_type": "code", "execution_count": 25, "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>address</th>\n", " <th>deposit</th>\n", " <th>rent</th>\n", " <th>phonenumber</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>서울특별시 강남구 역삼동 826-37 쌍용플래티넘밸류 비 129호</td>\n", " <td>85.0</td>\n", " <td>85.0</td>\n", " <td>010-5691-8315</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>서울특별시 강남구 논현1동 124-33</td>\n", " <td>1000.0</td>\n", " <td>68.0</td>\n", " <td>010-3487-2672</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>서울특별시 서초구 반포동 718-7 나라부동산</td>\n", " <td>1000.0</td>\n", " <td>60.0</td>\n", " <td>010-4965-5090</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>서울특별시 강남구 역삼동 657-271층 101호</td>\n", " <td>50.0</td>\n", " <td>50.0</td>\n", " <td>010-7540-8818</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>서울특별시 강남구 역삼동 657-271층 101호</td>\n", " <td>70.0</td>\n", " <td>70.0</td>\n", " <td>010-7535-0800</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " address deposit rent phonenumber\n", "0 서울특별시 강남구 역삼동 826-37 쌍용플래티넘밸류 비 129호 85.0 85.0 010-5691-8315\n", "1 서울특별시 강남구 논현1동 124-33 1000.0 68.0 010-3487-2672\n", "2 서울특별시 서초구 반포동 718-7 나라부동산 1000.0 60.0 010-4965-5090\n", "3 서울특별시 강남구 역삼동 657-271층 101호 50.0 50.0 010-7540-8818\n", "4 서울특별시 강남구 역삼동 657-271층 101호 70.0 70.0 010-7535-0800" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_shinsa.head()" ] }, { "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>address</th>\n", " <th>deposit</th>\n", " <th>rent</th>\n", " <th>phonenumber</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>서울특별시 관악구 관악로15길 23 (봉천동)</td>\n", " <td>5000.0</td>\n", " <td>20.0</td>\n", " <td>010-4107-0987</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>서울특별시 관악구 신림동 11-19</td>\n", " <td>30000.0</td>\n", " <td>0.0</td>\n", " <td>010-6329-5563</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>서울특별시 관악구 신림로 68길 29(신림동)</td>\n", " <td>300.0</td>\n", " <td>45.0</td>\n", " <td>010-8980-3144</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>서울특별시 관악구 조원로2길 55 (신림동)</td>\n", " <td>18000.0</td>\n", " <td>0.0</td>\n", " <td>010-7795-5979</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>서울특별시 관악구 신림동 1461-3 1층</td>\n", " <td>8000.0</td>\n", " <td>0.0</td>\n", " <td>010-5015-9533</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " address deposit rent phonenumber\n", "0 서울특별시 관악구 관악로15길 23 (봉천동) 5000.0 20.0 010-4107-0987\n", "1 서울특별시 관악구 신림동 11-19 30000.0 0.0 010-6329-5563\n", "2 서울특별시 관악구 신림로 68길 29(신림동) 300.0 45.0 010-8980-3144\n", "3 서울특별시 관악구 조원로2길 55 (신림동) 18000.0 0.0 010-7795-5979\n", "4 서울특별시 관악구 신림동 1461-3 1층 8000.0 0.0 010-5015-9533" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_shinlim.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "deposit 6495.0\n", "rent 23.1\n", "dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_shinlim.mean()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "deposit 4068.220339\n", "rent 75.949153\n", "dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_shinsa.mean()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000B1C77B8>]], dtype=object)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xae70588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xae70048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xaf40cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_shinsa.hist(\"deposit\")\n", "df_shinsa.hist(\"rent\")\n", "\n", "df_shinlim.hist(\"deposit\", color=\"Red\")\n", "df_shinlim.hist(\"rent\", color=\"Red\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xae70128>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb66d128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df_shinlim.plot.scatter(x=\"deposit\", y=\"rent\", color=\"Red\")\n", "df_shinsa.plot.scatter(x=\"deposit\", y=\"rent\", color=\"Blue\", ax=ax)" ] } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
knowledgeanyhow/notebooks	mlb/mlb-salaries.ipynb	2	190086	{"nbformat_minor": 0, "cells": [{"source": "# MLB Modern Era Salary Analysis\n\n[Lahman\u2019s Baseball Database](http://seanlahman.com/baseball-archive/statistics/) contains complete batting and pitching statistics from 1871 to 2013, plus fielding statistics, standings, team stats, managerial records, post-season data, and more.\n\n", "cell_type": "markdown", "metadata": {}}, {"source": "## Objective\n\nOne of the topics covered in Lahman's Baseball Database is MLB annual salaries. This notebook provides a sample analysis that explores trends in player salaries. Several questions are addressed:\n\n1. What is the average salary increase per league since 1985?\n2. What is the average salary increase per league since 1985?\n3. Can we predict future average salary increase per league?\n\n\n## Prepare Environment\nBootstrap notebook with necessary notebook and library dependencies.\n\n### Prerequisites \nThis notebook requires the installation of the following software dependencies:\n```\n!pip install statsmodels\n```", "cell_type": "markdown", "metadata": {}}, {"execution_count": 22, "cell_type": "code", "source": "# Provide the inline code necessary for loading any required libraries", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 23, "cell_type": "code", "source": "import matplotlib.pyplot as plt\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Load the Data\n\n1. Visit the [Lahman\u2019s Baseball Database](http://seanlahman.com/baseball-archive/statistics/) and download the latest data. After unzipping the package, upload the Salaries.csv file to your workbench.\n2. Import the latest MLB Salary data and take a peak at the dataset.", "cell_type": "markdown", "metadata": {}}, {"source": "<div class=\"alert\" style=\"border: 1px solid #aaa; background: radial-gradient(ellipse at center, #ffffff 50%, #eee 100%);\">\n<div class=\"row\">\n <div class=\"col-sm-1\"><img src=\"https://knowledgeanyhow.org/static/images/favicon_32x32.png\" style=\"margin-top: -6px\"/></div>\n <div class=\"col-sm-11\">In IBM Knowledge Anyhow Workbench, you can drag/drop the file on your workbench browser tab to simplify the uploading process.</div>\n</div>\n</div>", "cell_type": "markdown", "metadata": {}}, {"execution_count": 24, "cell_type": "code", "source": "import pandas as pd\ndf_mlb_Salaries = pd.read_csv('/resources/mlb_Salaries_2011.csv').dropna()\ndf_mlb_Salaries.describe()", "outputs": [{"execution_count": 24, "output_type": "execute_result", "data": {"text/plain": " yearID salary\ncount 23141.000000 23141.000000\nmean 1998.941143 1798885.680135\nstd 7.764378 2970955.921486\nmin 1985.000000 0.000000\n25% 1993.000000 240000.000000\n50% 1999.000000 500000.000000\n75% 2006.000000 2000000.000000\nmax 2012.000000 33000000.000000\n\n[8 rows x 2 columns]", "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>yearID</th>\n <th>salary</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 23141.000000</td>\n <td> 23141.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 1998.941143</td>\n <td> 1798885.680135</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 7.764378</td>\n <td> 2970955.921486</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 1985.000000</td>\n <td> 0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 1993.000000</td>\n <td> 240000.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 1999.000000</td>\n <td> 500000.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 2006.000000</td>\n <td> 2000000.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 2012.000000</td>\n <td> 33000000.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows \u00d7 2 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 25, "cell_type": "code", "source": "df_mlb_Salaries.plot()", "outputs": [{"execution_count": 25, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes.AxesSubplot at 0x7f717dbac950>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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Yvy+wPKr7bKOK6+Or2BDeyxP76vzus4BvRvt/AexQr/xayRoLg01y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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f719e1cdb50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 26, "cell_type": "code", "source": "df_mlb_Salaries.tail()", "outputs": [{"execution_count": 26, "output_type": "execute_result", "data": {"text/plain": " yearID teamID lgID playerID salary\n23136 2012 WAS NL detwiro01 485000\n23137 2012 WAS NL stammcr01 485000\n23138 2012 WAS NL marrech01 481000\n23139 2012 WAS NL matthry01 481000\n23140 2012 WAS NL lombast02 481000\n\n[5 rows x 5 columns]", "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>yearID</th>\n <th>teamID</th>\n <th>lgID</th>\n <th>playerID</th>\n <th>salary</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>23136</th>\n <td> 2012</td>\n <td> WAS</td>\n <td> NL</td>\n <td> detwiro01</td>\n <td> 485000</td>\n </tr>\n <tr>\n <th>23137</th>\n <td> 2012</td>\n <td> WAS</td>\n <td> NL</td>\n <td> stammcr01</td>\n <td> 485000</td>\n </tr>\n <tr>\n <th>23138</th>\n <td> 2012</td>\n <td> WAS</td>\n <td> NL</td>\n <td> marrech01</td>\n <td> 481000</td>\n </tr>\n <tr>\n <th>23139</th>\n <td> 2012</td>\n <td> WAS</td>\n <td> NL</td>\n <td> matthry01</td>\n <td> 481000</td>\n </tr>\n <tr>\n <th>23140</th>\n <td> 2012</td>\n <td> WAS</td>\n <td> NL</td>\n <td> lombast02</td>\n <td> 481000</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 5 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### Observations\nBased on the 2013 data:\n\n* Between 1985 and 2013 there are 23,956 Salary records.\n* The average annual Salary across all of MLB for the last 28 years is 1.8M USD.\n* The largest annual Salary across all of MLB for the last 28 years is 33M USD.", "cell_type": "markdown", "metadata": {}}, {"source": "## Question 1: Average Salary Per League\nWhat is the average salary increase per league since 1985?\n\n### Data Munging", "cell_type": "markdown", "metadata": {}}, {"source": "#### Step A: Partition the data by league", "cell_type": "markdown", "metadata": {}}, {"execution_count": 27, "cell_type": "code", "source": "american_league = df_mlb_Salaries.query('lgID == \"AL\"')\nnational_league = df_mlb_Salaries.query('lgID == \"NL\"')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step B: Create a pivot table for salaries per year per league", "cell_type": "markdown", "metadata": {}}, {"execution_count": 28, "cell_type": "code", "source": "american_league_avg_annual_salary = american_league.groupby(['yearID']).mean()[['salary']]\nnational_league_avg_annual_salary = national_league.groupby(['yearID']).mean()[['salary']]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step C: Create a Dataframe depicting average annual salaries per league per year.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 29, "cell_type": "code", "source": "al = pd.Series(american_league_avg_annual_salary.salary, name=\"American League\")\nnl = pd.Series(national_league_avg_annual_salary.salary, name=\"National League\")", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 30, "cell_type": "code", "source": "df_league_salary_history = pd.concat([al, nl], axis=1)\ndf_league_salary_history.head()", "outputs": [{"execution_count": 30, "output_type": "execute_result", "data": {"text/plain": " American League National League\nyearID \n1985 455597 500249\n1986 402337 433925\n1987 441846 427857\n1988 453901 452374\n1989 502052 511116\n\n[5 rows x 2 columns]", "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>American League</th>\n <th>National League</th>\n </tr>\n <tr>\n <th>yearID</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1985</th>\n <td> 455597</td>\n <td> 500249</td>\n </tr>\n <tr>\n <th>1986</th>\n <td> 402337</td>\n <td> 433925</td>\n </tr>\n <tr>\n <th>1987</th>\n <td> 441846</td>\n <td> 427857</td>\n </tr>\n <tr>\n <th>1988</th>\n <td> 453901</td>\n <td> 452374</td>\n </tr>\n <tr>\n <th>1989</th>\n <td> 502052</td>\n <td> 511116</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 2 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### Data Exploration\nPlot our results and compare annual league salary averages.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 31, "cell_type": "code", "source": "df_league_salary_history.describe()", "outputs": [{"execution_count": 31, "output_type": "execute_result", "data": {"text/plain": " American League National League\ncount 28.000000 28.000000\nmean 1835922.535714 1698344.607143\nstd 1149009.237987 1016000.909183\nmin 402337.000000 427857.000000\n25% 990269.750000 887920.250000\n50% 1434191.000000 1338269.000000\n75% 2783420.250000 2615311.000000\nmax 3662264.000000 3277278.000000\n\n[8 rows x 2 columns]", "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>American League</th>\n <th>National League</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 28.000000</td>\n <td> 28.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 1835922.535714</td>\n <td> 1698344.607143</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 1149009.237987</td>\n <td> 1016000.909183</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 402337.000000</td>\n <td> 427857.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 990269.750000</td>\n <td> 887920.250000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 1434191.000000</td>\n <td> 1338269.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 2783420.250000</td>\n <td> 2615311.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 3662264.000000</td>\n <td> 3277278.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows \u00d7 2 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 32, "cell_type": "code", "source": "df_league_salary_history.plot()\nplt.rcParams['xtick.major.pad']='10'\nplt.title('MLB Annual Salary Averages')\nplt.xlabel('Year')\nplt.ylabel('Average Salary')\nyvalues = american_league_avg_annual_salary.index\nplt.xticks(yvalues, rotation='vertical')\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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jKEqIMe2AC0jcZ5t7HPYEHuIrbuAFVuXlmkqdR5vRFKXgmLvAPJpaLgc4tMdh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\nwmEcbnPprcAtHAvcA/wKnIzDxMyNVhQ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"text/plain": "<matplotlib.figure.Figure at 0x7f717dc23d90>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true, "render": true}}, {"source": "### Observations\n\n* Between 1985 and 2013 the American League has paid an average of 200K USD more than the National League.\n* In 2004, the annual salaries per league diverged with a greater degree of seperation.", "cell_type": "markdown", "metadata": {}}, {"source": "\n <div class=\"alert alert-info\">In 2004, [Texas traded A-Rod to the NY Yankees](http://sports.espn.go.com/mlb/news/story?id=1735937). This transaction could have triggered the abnormal divergence.\n</div>", "cell_type": "markdown", "metadata": {}}, {"source": "## Question 2: Average Annual Salary Increases Per League\nWhat is the average salary increase per league since 1985?\n\n### Data Munging\n\n#### Step A: Define Helper Methods", "cell_type": "markdown", "metadata": {}}, {"execution_count": 33, "cell_type": "code", "source": "def annual_salary_delta(previous_year_salary, current_year_salary):\n '''Compute annual salary delta.'''\n return current_year_salary - previous_year_salary\n\ndef annual_league_salary_increases(salaries):\n '''Return list of annual salary deltas for a series of league salaries.'''\n salincperyr = []\n for idx, val in enumerate(salaries):\n if idx == 0:\n salincperyr.append(0)\n elif idx == len(salaries)-1:\n salincperyr.append(0)\n else:\n salincperyr.append(annual_salary_delta(salaries.iloc[idx-1],\n salaries.iloc[idx]))\n return salincperyr", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step B: Extend Dataset\nCompute Salary increases per year.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 34, "cell_type": "code", "source": "tablecols = []\ntablecols.append(yvalues)\ntablecols.append(df_league_salary_history[\"American League\"].tolist())\ntablecols.append(df_league_salary_history[\"National League\"].tolist())\ntablecols.append(annual_league_salary_increases(df_league_salary_history[\"American League\"]))\ntablecols.append(annual_league_salary_increases(df_league_salary_history[\"National League\"]))\ndf_league_salary_detailed_history = pd.DataFrame(tablecols).T\ndf_league_salary_detailed_history.columns = ['Year','alAvgSalary', 'nlAvgSalary','alAnnualSalaryIncrease', 'nlAnnualSalaryIncrease']\ndf_league_salary_detailed_history.set_index(df_league_salary_detailed_history['Year'])\ndf_league_salary_detailed_history", "outputs": [{"execution_count": 34, "output_type": "execute_result", "data": {"text/plain": " Year alAvgSalary nlAvgSalary alAnnualSalaryIncrease \\\n0 1985 455597 500249 0 \n1 1986 402337 433925 -53260 \n2 1987 441846 427857 39509 \n3 1988 453901 452374 12055 \n4 1989 502052 511116 48151 \n5 1990 500415 525913 -1637 \n6 1991 908126 879587 407711 \n7 1992 1017651 1085608 109525 \n8 1993 1028575 923882 10924 \n9 1994 1130703 971003 102128 \n10 1995 1039864 890698 -90839 \n11 1996 1055235 1000406 15371 \n12 1997 1267829 1169651 212594 \n13 1998 1364396 1207658 96567 \n14 1999 1503986 1468880 139590 \n15 2000 2004401 1983096 500415 \n16 2001 2333183 2232801 328782 \n17 2002 2449016 2342579 115833 \n18 2003 2524939 2614933 75923 \n19 2004 2517280 2469002 -7659 \n20 2005 2681580 2590986 164300 \n21 2006 3088941 2616445 407361 \n22 2007 3304390 2623749 215449 \n23 2008 3449574 2870790 145184 \n24 2009 3380833 3184368 -68741 \n25 2010 3431360 3142161 50527 \n26 2011 3505557 3156654 74197 \n27 2012 3662264 3277278 0 \n\n nlAnnualSalaryIncrease \n0 0 \n1 -66324 \n2 -6068 \n3 24517 \n4 58742 \n5 14797 \n6 353674 \n7 206021 \n8 -161726 \n9 47121 \n10 -80305 \n11 109708 \n12 169245 \n13 38007 \n14 261222 \n15 514216 \n16 249705 \n17 109778 \n18 272354 \n19 -145931 \n20 121984 \n21 25459 \n22 7304 \n23 247041 \n24 313578 \n25 -42207 \n26 14493 \n27 0 \n\n[28 rows x 5 columns]", "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>Year</th>\n <th>alAvgSalary</th>\n <th>nlAvgSalary</th>\n <th>alAnnualSalaryIncrease</th>\n <th>nlAnnualSalaryIncrease</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0 </th>\n <td> 1985</td>\n <td> 455597</td>\n <td> 500249</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n <tr>\n <th>1 </th>\n <td> 1986</td>\n <td> 402337</td>\n <td> 433925</td>\n <td> -53260</td>\n <td> -66324</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 1987</td>\n <td> 441846</td>\n <td> 427857</td>\n <td> 39509</td>\n <td> -6068</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 1988</td>\n <td> 453901</td>\n <td> 452374</td>\n <td> 12055</td>\n <td> 24517</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 1989</td>\n <td> 502052</td>\n <td> 511116</td>\n <td> 48151</td>\n <td> 58742</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 1990</td>\n <td> 500415</td>\n <td> 525913</td>\n <td> -1637</td>\n <td> 14797</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 1991</td>\n <td> 908126</td>\n <td> 879587</td>\n <td> 407711</td>\n <td> 353674</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 1992</td>\n <td> 1017651</td>\n <td> 1085608</td>\n <td> 109525</td>\n <td> 206021</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 1993</td>\n <td> 1028575</td>\n <td> 923882</td>\n <td> 10924</td>\n <td>-161726</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 1994</td>\n <td> 1130703</td>\n <td> 971003</td>\n <td> 102128</td>\n <td> 47121</td>\n </tr>\n <tr>\n <th>10</th>\n <td> 1995</td>\n <td> 1039864</td>\n <td> 890698</td>\n <td> -90839</td>\n <td> -80305</td>\n </tr>\n <tr>\n <th>11</th>\n <td> 1996</td>\n <td> 1055235</td>\n <td> 1000406</td>\n <td> 15371</td>\n <td> 109708</td>\n </tr>\n <tr>\n <th>12</th>\n <td> 1997</td>\n <td> 1267829</td>\n <td> 1169651</td>\n <td> 212594</td>\n <td> 169245</td>\n </tr>\n <tr>\n <th>13</th>\n <td> 1998</td>\n <td> 1364396</td>\n <td> 1207658</td>\n <td> 96567</td>\n <td> 38007</td>\n </tr>\n <tr>\n <th>14</th>\n <td> 1999</td>\n <td> 1503986</td>\n <td> 1468880</td>\n <td> 139590</td>\n <td> 261222</td>\n </tr>\n <tr>\n <th>15</th>\n <td> 2000</td>\n <td> 2004401</td>\n <td> 1983096</td>\n <td> 500415</td>\n <td> 514216</td>\n </tr>\n <tr>\n <th>16</th>\n <td> 2001</td>\n <td> 2333183</td>\n <td> 2232801</td>\n <td> 328782</td>\n <td> 249705</td>\n </tr>\n <tr>\n <th>17</th>\n <td> 2002</td>\n <td> 2449016</td>\n <td> 2342579</td>\n <td> 115833</td>\n <td> 109778</td>\n </tr>\n <tr>\n <th>18</th>\n <td> 2003</td>\n <td> 2524939</td>\n <td> 2614933</td>\n <td> 75923</td>\n <td> 272354</td>\n </tr>\n <tr>\n <th>19</th>\n <td> 2004</td>\n <td> 2517280</td>\n <td> 2469002</td>\n <td> -7659</td>\n <td>-145931</td>\n </tr>\n <tr>\n <th>20</th>\n <td> 2005</td>\n <td> 2681580</td>\n <td> 2590986</td>\n <td> 164300</td>\n <td> 121984</td>\n </tr>\n <tr>\n <th>21</th>\n <td> 2006</td>\n <td> 3088941</td>\n <td> 2616445</td>\n <td> 407361</td>\n <td> 25459</td>\n </tr>\n <tr>\n <th>22</th>\n <td> 2007</td>\n <td> 3304390</td>\n <td> 2623749</td>\n <td> 215449</td>\n <td> 7304</td>\n </tr>\n <tr>\n <th>23</th>\n <td> 2008</td>\n <td> 3449574</td>\n <td> 2870790</td>\n <td> 145184</td>\n <td> 247041</td>\n </tr>\n <tr>\n <th>24</th>\n <td> 2009</td>\n <td> 3380833</td>\n <td> 3184368</td>\n <td> -68741</td>\n <td> 313578</td>\n </tr>\n <tr>\n <th>25</th>\n <td> 2010</td>\n <td> 3431360</td>\n <td> 3142161</td>\n <td> 50527</td>\n <td> -42207</td>\n </tr>\n <tr>\n <th>26</th>\n <td> 2011</td>\n <td> 3505557</td>\n <td> 3156654</td>\n <td> 74197</td>\n <td> 14493</td>\n </tr>\n <tr>\n <th>27</th>\n <td> 2012</td>\n <td> 3662264</td>\n <td> 3277278</td>\n <td> 0</td>\n <td> 0</td>\n </tr>\n </tbody>\n</table>\n<p>28 rows \u00d7 5 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### Explore Results\nPlot comparison between annual league salary increases per year.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 35, "cell_type": "code", "source": "x = df_league_salary_detailed_history['Year']\nplt.plot(x,df_league_salary_detailed_history[\"alAnnualSalaryIncrease\"], label=\"American League\")\nplt.plot(x,df_league_salary_detailed_history[\"nlAnnualSalaryIncrease\"], label=\"National League\")\nplt.title('MLB Year-to-Year Salary Deltas')\nplt.xlabel('Year')\nplt.ylabel('Average Salary')\nxvalues = american_league_avg_annual_salary.index\nplt.xticks(xvalues, rotation='vertical')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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URVGCoyJIKSS0TpCiKIqiKIoXFUGKoiiKojRJVAQpiqIoitIkURGkKMWIvb85oaIoipIm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\nHuyekfIt4OvuNfu6MqR9hbvE2nA8kGSMP0OsDlvC4qWU6yM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h44OWA99zz90ETMR2UD8E\n6zoLvk5EREREREREREREREREREREREREREREREREREREREREREREREREpCb9P0TeUIjG3uehAAAA\nAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f717da70c90>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### Observations\n\n* Between 1985 and 2002 the leagues on average tended to offer similiar annual salary increases.\n* After 2002 it would seem that the leagues were more reactionary as they tend to handle increases in opposite manners.", "cell_type": "markdown", "metadata": {}}, {"source": "## Question 3: Future Predictions\nCan we predict future average salary increase per league?\n\n### Data Munging\n\n#### Step A: Define Helper Methods", "cell_type": "markdown", "metadata": {}}, {"execution_count": 36, "cell_type": "code", "source": "def create_season_opener_datetime_index(date_series):\n '''Return a series of datetime index objects for a series of date strings'''\n import datetime\n import pandas as pd\n dates = []\n for i in date_series:\n d = datetime.date(i,4,1)\n dates.append(d)\n return pd.DatetimeIndex(dates)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step B: Perform ETL\nWe have several data structures we need to transform in order to perform predictive analytics. Most notably is the need to have our data structures indexed using a datetime object. Our original data contains only the year for a season but our algorithms require an actual date so we will assume April 1st for each year as the season opener.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 37, "cell_type": "code", "source": "season_opener_dates = create_season_opener_datetime_index(df_league_salary_detailed_history['Year'])\ndf_al_annual_salary_inc = pd.DataFrame(\n df_league_salary_detailed_history[\"alAnnualSalaryIncrease\"].tolist(),\n index=season_opener_dates,\n columns=[\"alAnnualSalaryIncrease\"]\n )\ndf_nl_annual_salary_inc = pd.DataFrame(\n df_league_salary_detailed_history[\"nlAnnualSalaryIncrease\"].tolist(),\n index=season_opener_dates,\n columns=[\"nlAnnualSalaryIncrease\"]\n )\nprint df_al_annual_salary_inc.head()\nprint df_nl_annual_salary_inc.head()", "outputs": [{"output_type": "stream", "name": "stdout", "text": " alAnnualSalaryIncrease\n1985-04-01 0\n1986-04-01 -53260\n1987-04-01 39509\n1988-04-01 12055\n1989-04-01 48151\n\n[5 rows x 1 columns]\n nlAnnualSalaryIncrease\n1985-04-01 0\n1986-04-01 -66324\n1987-04-01 -6068\n1988-04-01 24517\n1989-04-01 58742\n\n[5 rows x 1 columns]\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step C: Create Prediction Models", "cell_type": "markdown", "metadata": {}}, {"execution_count": 38, "cell_type": "code", "source": "import statsmodels.api as sm\nal_ar_model = sm.tsa.AR(df_al_annual_salary_inc)\nal_ar_model_res = al_ar_model.fit()\nprint al_ar_model_res.params\nnl_ar_model = sm.tsa.AR(df_nl_annual_salary_inc)\nnl_ar_model_res = nl_ar_model.fit()\nprint nl_ar_model_res.params", "outputs": [{"output_type": "stream", "name": "stdout", "text": "const 114112.892576\nL1.alAnnualSalaryIncrease 0.588458\nL2.alAnnualSalaryIncrease -0.382062\nL3.alAnnualSalaryIncrease 0.063075\nL4.alAnnualSalaryIncrease -0.400611\nL5.alAnnualSalaryIncrease 0.186784\nL6.alAnnualSalaryIncrease 0.054495\nL7.alAnnualSalaryIncrease -0.177574\nL8.alAnnualSalaryIncrease 0.004017\nL9.alAnnualSalaryIncrease 0.255028\ndtype: float64\nconst 104353.136380\nL1.nlAnnualSalaryIncrease 0.143857\nL2.nlAnnualSalaryIncrease 0.057078\nL3.nlAnnualSalaryIncrease -0.026694\nL4.nlAnnualSalaryIncrease -0.200692\nL5.nlAnnualSalaryIncrease -0.057046\nL6.nlAnnualSalaryIncrease 0.051390\nL7.nlAnnualSalaryIncrease -0.336088\nL8.nlAnnualSalaryIncrease 0.169833\nL9.nlAnnualSalaryIncrease 0.363891\ndtype: float64\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 39, "cell_type": "code", "source": "predict_al_increases = al_ar_model_res.predict(start='1995-04-01', end='2025-01-01', dynamic=True)\npredict_nl_increases = nl_ar_model_res.predict(start='1995-04-01', end='2025-01-01', dynamic=True)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### Explore Results\n\n#### Step A: Plot comparison between annual league growth predictions.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 40, "cell_type": "code", "source": "plt.plot(predict_al_increases.index,predict_al_increases, label=\"American League\")\nplt.plot(predict_nl_increases.index,predict_nl_increases, label=\"National League\")\nplt.rcParams['xtick.major.pad']='15'\nplt.title('MLB Predictive Annual Averages Salary Increase')\nplt.xlabel('Year')\nplt.ylabel('Average Salary Increases')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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OYRiGYTiSWHaeBVZAQmcscB3wl2oOyqgAn7ZkstGKMQ3ogU/r6g7KMAzDMOpH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\nYWB9EjbIHTPZkSRNislOTX29TgJcQsLS8kPpc7EUxEKijio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"text/plain": "<matplotlib.figure.Figure at 0x7f717d933f50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#### Step B: Plot comparison between annual league salary increases per year and the predicted growth for each league.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 41, "cell_type": "code", "source": "data_to_compare = {'AL Prediction': predict_al_increases,\n 'AL Actual': df_al_annual_salary_inc.alAnnualSalaryIncrease,\n 'NL Prediction': predict_nl_increases,\n 'NL Actual': df_nl_annual_salary_inc.nlAnnualSalaryIncrease\n }\ndf_annual_growth_predictive_analysis = pd.DataFrame(data_to_compare)\nax = df_annual_growth_predictive_analysis.plot()\nax.set_ylabel('Average Salary Increases')\nplt.rcParams['xtick.major.pad']='15'\nplt.title('MLB Annual Salary Growth Predictive Analysis')\nplt.xlabel('Year')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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16SgpPYCFrv0WKlm866tI+vp6Iv45gG1I7nZZhrYaM0aGbeWISbFLw4iSFSNs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"text/plain": "<matplotlib.figure.Figure at 0x7f717dcd32d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## Summary\n\n* Over a 28 year period the actual average increases are a bit sporadic.\n* Given the degree of historic fluctuation and the small amount of data, it is difficult to predict future growth.\n* The prediction models seem to imply (given the small sample set) a linear regression.\n\n## References\n\n* [Lahman\u2019s Baseball Database](http://seanlahman.com/baseball-archive/statistics/) \n* [Data Analysis Workflow Navigation repository](https://github.com/vinomaster/dawn): This notebook outline was derived from the Research Analysis Navigation Template.\n", "cell_type": "markdown", "metadata": {}}, {"source": "<div class=\"alert\" style=\"border: 1px solid #aaa; background: radial-gradient(ellipse at center, #ffffff 50%, #eee 100%);\">\n<div class=\"row\">\n <div class=\"col-sm-1\"><img src=\"https://knowledgeanyhow.org/static/images/favicon_32x32.png\" style=\"margin-top: -6px\"/></div>\n <div class=\"col-sm-11\">This notebook was created using <a href=\"https://knowledgeanyhow.org\">IBM Knowledge Anyhow Workbench</a>. To learn more, visit us at <a href=\"https://knowledgeanyhow.org\">https://knowledgeanyhow.org</a>.</div>\n </div>\n</div>", "cell_type": "markdown", "metadata": {}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.6", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}	mit
coursemdetw/reveal2	content/notebook/exercise.ipynb	2	39863	{ "cells": [ { "cell_type": "markdown", "metadata": { "run_control": { "read_only": false } }, "source": [ "CME 193 Introduction to Python Exercises\n", "1 Basics\n", "Exercise 1.1: The interpreter\n", "Open the Python interpeter. What happens when you input the following statements:\n", "(a) 3 + 1\n", "(b) 3 * 3\n", "(c) 2 ** 3\n", "(d) \"Hello, world!\"\n", "Exercise 1.2: Scripts\n", "Now copy the above to a script, and save it as script1.py. What happens if you run the script? (try:\n", "python script1.py). Can you fix this (hint: use the print function)\n", "Exercise 1.3: More interpreter\n", "Explain the output of the following statements if executed subsequently:\n", "(a) ’py’ + ’thon’\n", "(b) ’py’ * 3 + ’thon’\n", "(c) ’py’ - ’py’\n", "(d) ’3’ + 3\n", "(e) 3 * ’3’\n", "(f) a\n", "(g) a = 3\n", "(h) a\n", "Exercise 1.4: Booleans\n", "Explain the output of the following statements:\n", "(a) 1 == 1\n", "(b) 1 == True\n", "(c) 0 == True\n", "(d) 0 == False\n", "(e) 3 == 1 * 3\n", "(f) (3 == 1) * 3\n", "(g) (3 == 3) * 4 + 3 == 1\n", "(h) 35 >= 44\n", "Exercise 1.5: Integers\n", "Explain the output of the following statements:\n", "(a) 5 / 3\n", "(b) 5 % 3\n", "(c) 5.0 / 3\n", "(d) 5 / 3.0\n", "(e) 5.2 % 3\n", "CME 193 Introduction to Python Exercises\n", "(f) 2001 200\n", "Exercise 1.6: Floats\n", "Explain the output of the following statements:\n", "(a) 2000.3 200 (compare with above)\n", "(b) 1.0 + 1.0 - 1.0\n", "(c) 1.0 + 1.0e20 - 1.0e20\n", "Exercise 1.7: Variables\n", "Write a script where the variable name holds a string with your name. Then, assuming for now your\n", "name is John Doe, have the script output: Hello, John Doe! (and obviously, do not use print \"Hello,\n", "John Doe!\".\n", "Exercise 1.8: Type casting\n", "Very often, one wants to “cast” variables of a certain type into another type. Suppose we have variable\n", "x = ’123’, but really we would like x to be an integer.\n", "This is easy to do in Python, just use desiredtype(x), e.g. int(x) to obtain an integer.\n", "Try the following and explain the output\n", "(a) float(123)\n", "(b) float(’123’)\n", "(c) float(’123.23’)\n", "(d) int(123.23)\n", "(e) int(’123.23’)\n", "(f) int(float(’123.23’))\n", "(g) str(12)\n", "(h) str(12.2)\n", "(i) bool(’a’)\n", "(j) bool(0)\n", "(k) bool(0.1)\n", "2 Control flow\n", "Disclaimer: Some of the following problems are inspired by problems from www.projecteuler.net. Have a\n", "look if you are interested, there are some great challenges and Python is an excellent tool for solving them.\n", "Exercise 2.1: Range\n", "Type range(5) in the interpreter, what does the interpreter return? So what does for i in range(5)\n", "mean?\n", "Let’s also find out whether the interpreter can help us understand the object ‘range(5)’ better. Type\n", "type(range(5)) in the interpreter. More on this soon!\n", "Exercise 2.2: For loops\n", "Use a for loop to:\n", "(a) Print the numbers 0 to 100\n", "Page 2\n", "CME 193 Introduction to Python Exercises\n", "(b) Print the numbers 0 to 100 that are divisible by 7\n", "(c) Print the numbers 1 to 100 that are divisible by 5 but not by 3\n", "(d) Print for each of the numbers x = 2, . . . 20, all numbers that divide x, excluding 1 and x. Hence,\n", "for 18, it should print 2 3 6 9.\n", "Hint: see https://docs.python.org/2.7/library/functions.html#range.\n", "Exercise 2.3: Simple while loops\n", "Instead of using a for loop, use a while loop to:\n", "(a) Print the numbers 0 to 100\n", "(b) Print the numbers 0 to 100 that are divisible by 7\n", "Exercise 2.4: Hangman update 1\n", "Let’s reconsider the hangman code we saw in class.1 We noted that the computer agent is not very good\n", "at guessing. Update the code such that the computer guesses ‘e’ first, and ’a’ second.\n", "Use the simulate.py script to see if this improves performance.\n", "Feel free to play around and see if you can do better!\n", "Exercise 2.5: While loops\n", "Use a while loop to find the first 20 numbers that are divisible by 5, 7 and 11, and print them Hint:\n", "store the number found so far in a variable.\n", "Pseudo-code:\n", "number found = 0\n", "x = 11\n", "while number found is less than 20:\n", "if x is divisible by 5, 7 and 11:\n", "print x\n", "increase number found by 1\n", "increase x by 1\n", "Exercise 2.6: More while loops\n", "The smallest number that is divisible by 2, 3 and 4 is 12. Find the smallest number that is divisible by\n", "all integers between 1 and 10.\n", "Exercise 2.7: Collatz sequence\n", "A Collatz sequence is formed as follows: We start with some number x0, and we find the next number\n", "in the sequence by\n", "xi+1 =\n", "(\n", "xi/2 if xi\n", "is even\n", "3xi + 1 if xi\n", "is odd\n", "If xi = 1, we stop iterating and have found the full sequence.\n", "For example, if we start with x0 = 5, we obtain the sequence:\n", "5 16 8 4 2 1\n", "It is conjectured, though not proven, that every chain eventually ends at 1.\n", "Print the Collatz sequence starting at x0 = 103.\n", "1To obtain the Hangman code, either use $ git clone https://github.com/schmit/hangman.git (if you have git / use\n", "Cloud9) or dowload the code directly: https://github.com/schmit/hangman/archive/master.zip\n", "Page 3\n", "CME 193 Introduction to Python Exercises\n", "3 Functions\n", "Exercise 3.1: Hello\n", "(a) Write a function hello_world that prints ’Hello, world!’\n", "(b) Write a function hello_name(name) that prints ’Hello, name!’ where name is a string.\n", "(c) Explain the difference between the print and return keywords. What would change if instead of\n", "print you would use return?\n", "Exercise 3.2: Polynomial\n", "Write a function that evaluates the polynomial 3x\n", "2 − x + 2.\n", "Exercise 3.3: Maximum\n", "Write a function my_max(x,y) that returns the maximum of x and y. Do not use the max function, but\n", "use if instead in following two ways:\n", "(a) Use both if and else.\n", "(b) Use if but not else (nor elif).\n", "Exercise 3.4: Primes\n", "(a) Write a function is_prime(n) that returns True only if n is prime.\n", "(b) Note that apart from 2 and 3, all primes are of the form 6k ± 1 (though not all numbers of the\n", "form 6k ± 1 are prime of course). Using this, we can improve the computation time by a factor 3.\n", "Update your function to use this.\n", "(c) Write a function that returns all primes up to n.\n", "(d) Write a function that returns the first n primes.\n", "Exercise 3.5: Root finding\n", "Suppose f is a continuous function and f(a) < 0 and f(b) > 0 for some known a and b. For simplicity,\n", "assume a < b. Then, there must exist some c such that f(c) = 0.\n", "(a) Write a function root(f, a, b) that takes a function f and two floats a and b and returns the\n", "root c. Hint: check the sign at the midpoint of the interval.\n", "(b) Remove the assumption that a < b, and that f(a) < 0 and f(b) > 0, if your current code relies on\n", "them.\n", "(c) Add a check that prints\n", "’function evals have same sign’\n", "if f(a) > 0 and f(b) > 0 or if f(a) < 0 and f(b) < 0.\n", "4 Lists\n", "Exercise 4.1: Short questions\n", "(a) Write a function that prints the elements of a list\n", "(b) Write a function that prints the elements of a list in reverse\n", "(c) Write your own implementation of the len function that returns the number of elements in a list.\n", "Exercise 4.2: Copying lists\n", "(a) Create a list a with some entries.\n", "Page 4\n", "CME 193 Introduction to Python Exercises\n", "(b) Now set b = a\n", "(c) Change b[1]\n", "(d) What happened to a?\n", "(e) Now set c = a[:]\n", "(f) Change c[2]\n", "(g) What happened to a?\n", "Now create a function set_first_elem_to_zero(l) that takes a list, sets its first entry to zero, and\n", "returns the list.\n", "What happens to the original list?\n", "Exercise 4.3: Lists of lists\n", "What is the difference between a and b:\n", "a = [[]] * 3\n", "b = [[] for _ in xrange(3)]\n", "Exercise 4.4: Lists and functions\n", "Write a function that takes a list and an index, and sets the value of the list at the given index to 0.\n", "Exercise 4.5: Primes\n", "In Section 3 you wrote a function that prints all primes up to n, and a function that prints the first n\n", "primes. Update these functions such that they return lists instead.\n", "Exercise 4.6: List comprehensions\n", "Let i, j = 1, . . . , n\n", "(a) Generate a list with elements [i,j].\n", "(b) Generate a list with elements [i,j] with i < j\n", "(c) Generate a list with elements i + j with both i and j prime and i > j.\n", "(d) Write a function that evaluates an arbitrary polynomial a0 + a1x + a2x\n", "2 + . . . + anx\n", "n using a list\n", "comprehension, where you are given x and a list with coefficients coefs (hint: use enumerate)\n", "Exercise 4.7: Filter\n", "In lecture we have seen how to implement map using list comprehensions. Implement filter using list\n", "comprehensions. Name your functions myfilter so you can compare with Python’s standard filter.\n", "Exercise 4.8: Flatten a list of lists\n", "Consider having a list with lists as elements, e.g. [[1,3], [3,6]].\n", "Write a function that takes such a list, and returns a list with as elements the elements of the sublists,\n", "e.g. [1, 3, 3, 6].\n", "Exercise 4.9: Finding the longest word\n", "Write a function that returns the longest word in a variable text that contains a sentence. While text\n", "may contain punctuation, these should not be taken into account. What happens with ties?\n", "As an example, consider: “Hello, how was the football match earlier today???”\n", "Exercise 4.10: Collatz sequence, part 2\n", "Recall the Collatz sequence problem from Section 1. Our goal is to find the number n < 1, 000, 000 that\n", "leads to the longest Collatz sequence.\n", "(a) Write a function that for any n, returns its Collatz sequence as a list\n", "Page 5\n", "CME 193 Introduction to Python Exercises\n", "(b) Write a function that finds the integer x that leads to the longest Collatz sequence with x < n.\n", "Exercise 4.11: Pivots\n", "Write a function that takes a value x and a list ys, and returns a list that contains the value x and all\n", "elements of ys such that all values y in ys that are smaller than x come first, then we element x and\n", "then the rest of the values in ys\n", "For example, the output of f(3, [6, 4, 1, 7]) should be [1, 3, 6, 4, 7]\n", "Exercise 4.12: Prime challenge\n", "Write the function primes(n) that return a list with all prime numbers up to n using three (or less)\n", "lines of code.\n", "Hint 1: Use lambda functions and list comprehensions.\n", "Hint 2: Use the first two lines to define two helper (lambda) functions.\n", "5 Tuples\n", "Exercise 5.1: Swapping two values\n", "Suppose you have two variables: a and b. Now you want to set a equal to the value of b and at the\n", "same time set b equal to the value of a.\n", "The following obviously does not work\n", "a = b\n", "b = a\n", "so in some languages, you need to define a third variable like this\n", "t = a\n", "a = b\n", "b = t\n", "However, in Python you don’t need to do this. How can you swap a and b in one line?\n", "Exercise 5.2: Zip\n", "Suppose we have two lists, x and y that give the x and y coordinates of a set of points. Create a list\n", "with the coordinates (x,y) as a tuple. Hint: Find out about the zip function.\n", "You have decided that actually, you need the two seperate lists, but unfortunately, you have thrown\n", "them away. How can we use zip to unzip the list of tuples to get two lists again?\n", "Exercise 5.3: Distances\n", "Suppose we have two vectors, x and y, stored as tuples with n elements. Implement functions that\n", "compute the l1 and l2 distances between x and y. Note that n is not explicitly given.\n", "6 Dictionaries\n", "Exercise 6.1: Printing a dictionary\n", "Write a function that prints key-value pairs of a dictionary.\n", "Exercise 6.2: Histogram\n", "Write a function that takes a list, and returns a dictionary with keys the elements of the list and as\n", "value the number of occurances of that element in the list.\n", "After you are done, look up ‘python collections counter’ in Google. Could you use a counter instead?\n", "Page 6\n", "CME 193 Introduction to Python Exercises\n", "Exercise 6.3: Get method\n", "Dictionaries have a get method, which takes a key and a default value. If the key is in the dictionary,\n", "it returns the value, otherwise, it returns the default value.\n", "Rewrite your code from the previous problem to make use of this get method.\n", "Exercise 6.4: Random text generator\n", "In this question we will start implementing a random text generator. The generated phrases somewhat\n", "resemble English, but are usually nonsense. Next week, after we learn about file I/O, we are ready to\n", "complete the code.\n", "To generate sentences, we first construct a so-called Markov chain based on actual text data. This is a\n", "very basic language model. We can then sample paths from this Markov chain to create new phrases.\n", "This is actually easier than it sounds:\n", "A Markov chain consists of states, and transition probabilities between states: i.e. when I am in state\n", "A, what is the probability that I’ll go to state B?\n", "We focus on the simple case where the state will be the current word. Consider the example sentence\n", "‘the fire and the wind.’ Then, the states that we move through are\n", "BEGIN → the → fire → and → the → wind. → END\n", "where ‘BEGIN’ and ‘END’ are special states for the beginning and the end of the sentence. To find the\n", "transition probabilities, we go over a large body of text and record current word and the next word. In\n", "the above example, ‘BEGIN’ is followed by ‘the’, and ‘the’ is followed by ‘fire’ and ‘wind’.\n", "We won’t be computing actual probabilities. Instead, we create a dictionary that, for every word,\n", "contains all the words that follow it. To generate a phrase, we start at the ‘BEGIN’ state, and pick\n", "randomly one word from the list of words that follows the ‘BEGIN’ state. Then we look up which words\n", "follow that word, and again pick one word at random, until we hit the ‘END’ state, which signals that\n", "we are done.\n", "Before you get started, download the starter code by using\n", "$ git clone https://github.com/schmit/Markov-chain-startercode.git (if you have git / use\n", "Cloud9) or dowload the code directly:\n", "https://github.com/schmit/Markov-chain-startercode/archive/master.zip\n", "Then\n", "• Implement the process line function, which takes a line as input, and returns a list with tuples\n", "with the current state, and the next state.\n", "• Implement the process textfile function, which loops over text, calls process line to extract\n", "the transitions, and adds these to a dictionary. For now, do not worry about reading data from a\n", "file, the lines are given as elements of the list f.\n", "• Implement the generate line function, which generates random phrases based on a dictionary\n", "with transitions.\n", "See markov.py for a more detailed description of each function.\n", "To run the code, use python markov.py <filename>. Since we are not using any files yet, replace\n", "<filename> with a random word.\n", "Exercise 6.5: Vector functions\n", "Let’s implement some vector functions. There are two types of vectors, normal or dense vectors, which\n", "we can represent using lists. For sparse vectors, where many of the elements are zero, this is inefficient.\n", "Instead, we use a dictionary with keys the indices of non-zero values, and then the value corresponding\n", "to the key is the value of the vector at that index. Hence, the vector [1, 2, 4] can be stored as a list: [1,\n", "2, 4] or as a dictionary {0:1, 1: 2, 2: 4}.\n", "Page 7\n", "CME 193 Introduction to Python Exercises\n", "(a) Write a function that adds two (dense) vectors\n", "(b) Write a function that multiplies (i.e. inner product) two (dense) vectors\n", "(c) Write a function that adds two sparse vectors\n", "(d) Write a function that multiplies two sparse vectors\n", "(e) Write a function that adds a sparse vector and a dense vector\n", "(f) Write a function that multiplies a sparse vector and a dense vector\n", "Exercise 6.6: Reverse look-up\n", "Dictionaries are made to look up values by keys. Suppose however, we want to find the key that is\n", "associated with some value. Write a function that takes a dictionary and a value, and returns the key\n", "associated with this value.\n", "What challenges do you face? How would you deal with those challenges?\n", "7 File I/O\n", "Exercise 7.1: Open a file\n", "Write a function that opens a file (input: filename), and prints the file line by line.\n", "Exercise 7.2: Wordcount\n", "On the course website you can find a text file containing the complete works of William Shapespeare.\n", "(a) Find the 20 most common words\n", "(b) How many unique words are used?\n", "(c) How many words are used at least 5 times?\n", "(d) Write the 200 most common words, and their counts, to a file.\n", "Exercise 7.3: Random text generator II\n", "In this exercise, we will finish the implementation of our random text generator from Question 6.4.\n", "Change the process textfile function such that it reads a file line by line and processes it, instead of\n", "using the hardcoded sample. This should require only a few changes to the code.\n", "Generate some random sentences based on the books provided in the data/ folder.\n", "As you will notice, most phrases are very random. To make the phrases seem a little more realistic, we\n", "should use not just the last word, but the two last words. Going back to our previous example: ‘the fire\n", "and the wind.’ we would have the (state, new word) pairs: (‘the fire’, ‘and’), (‘fire and’, ‘the’), (‘and\n", "the’, ‘wind’) etc. Update your code to use states consisting of two words instead of one.\n", "Bonus: Using more than 2 words is infeasible unless we have massive amounts of text data because the\n", "number of states gets increasingly large. Change your code such that you can specify k, the number of\n", "words per state.\n", "Exercise 7.4: Sum of lists\n", "Before you start coding, please read the entire problem.\n", "(a) Data generation\n", "Write a function that takes three integers, n, a and b and a filename and writes to the file a list\n", "with n random integers between a and b.\n", "Page 8\n", "CME 193 Introduction to Python Exercises\n", "(b) Reading the data\n", "Write a function that can read the files as generated above and return the values.\n", "(c) Sum problem\n", "Write a function that given two filenames (pointing to files as generated by the above function) and\n", "an integer k, finds all u and v such that u + v = k, and u is an element of the first list and v is a\n", "member of the second list.\n", "(d) Testing\n", "Test your functions by generating 2 files with n = 2000, a = 1, b = 10000 and k = 5000 and\n", "k = 12000.\n", "(e) Bonus: Efficiency\n", "If you are up to a challenge, write a function that solves the sum problem with the restriction that\n", "you can only go over every number in the both lists once.\n", "8 Classes\n", "Exercise 8.1: Rational numbers\n", "In this problem, we will write a class that can represent rational numbers, i.e. fractions p\n", "q\n", ".\n", "(a) Create a class Rational which is initialized by two integers, p and q, the nominator and denominator\n", "(b) Add a method to print the rational number as p/q (the __str__ or __repr__ method is useful).\n", "(c) We would like to represent 10\n", "20 by 1\n", "2\n", "instead, hence write a function that computes the greatest\n", "common divisor, and ensure that every rational number is simplified\n", "(d) Add a method so that we can add two rational numbers with r1 + r2, here the __add__() method\n", "is useful.\n", "(e) Add a method to subtract two rational numbers. (__sub__)\n", "(f) Add a method to multiply two rational numbers. (__mul__)\n", "(g) Add a method to divide two rational numbers. (__div__)\n", "(h) Add a method that compares whether two rational numbers are equal.\n", "(i) Add a method to convert the rational number to a floating point (the __float__() method may\n", "be handy).\n", "(j) Add any more functionality that you think is useful but I failed to mention.\n", "Exercise 8.2: Rock Paper Scissors\n", "In this problem, we will finish an implementation for Rock-Paper-Scissors. We have written some code\n", "to get you started, now it’s up to you to finish the implementation.\n", "The code consists of 2 files: game.py and agent.py. The code that implements the actual game is coded\n", "in game.py. agent.py defines several agents that can play the game. Download the starter code here\n", "using\n", "$ git clone https://github.com/schmit/Rock-paper-scissors-startercode.git (if you have git\n", "/ use Cloud9) or dowload the code directly:\n", "https://github.com/schmit/Rock-paper-scissors-startercode/archive/master.zip\n", "Page 9\n", "CME 193 Introduction to Python Exercises\n", "(a) Finish the implementation of game.py by implementing the compare function, updating the scores\n", "(where a win is 1 point, and a tie or loss 0 points), and finally the summary function that gives\n", "some information after the game.\n", "(b) Implement the HumanAgent in agent.py, this agent should query the user for the next move, and\n", "ensure that the user gives valid input.\n", "(c) Implement MyAgent, where you can implement your own strategy, try to beat the InstructorAgent\n", "consistently over 100 rounds.\n", "Hint: have a look at the Hangman code.\n", "Exercise 8.3: Hangman agent\n", "Implement your own Hangman computer agent (see exercise 2.4) that is much more effective than the\n", "Agent that guesses random characters.\n", "Make sure you create a new class rather than overwriting the existing Agent class. You can of course\n", "inherit from the Agent class\n", "You can update the simulate.py script to test your implementation.\n", "Exercise 8.4: Sparse and dense vectors\n", "In exercise 6.5 you implemented functions for sparse and dense vector multiplications using lists and\n", "dictionaries. However, this is a bit clumsy to use in practice. Really, we would like to represent sparse\n", "and dense vectors as classes, this way we can overload operators such as + ( add ) and get sensible\n", "output. For example, using + on two dense vectors implemented as lists would append the second vector\n", "to the first, instead of adding the two together.\n", "Implement sparse and dense vectors. Both classes should have the following capabilities:\n", "(a) Print vector\n", "(b) Add two vectors (both if other is dense and sparse)\n", "(c) Multiply two vectors (both if other is dense and sparse)\n", "Do re-use your code from the previous exercise.\n", "Hint: isinstance() might be useful.\n", "Exercise 8.5: Implementing the set class\n", "Write a class mySet that has the same basic functionality as the Python set data structure. Base your\n", "implementation on a dictionary.\n", "Exercise 8.6: Binary search tree\n", "In this exercise, we will implement a binary search tree. See http://en.wikipedia.org/wiki/Binary_\n", "search_tree for an explanation.\n", "(a) Define a class Node, and write the constructor, which takes one argument, value, and initializes\n", "the left and right children to None.\n", "(b) Write a function to print the tree.\n", "(c) Write a function that inserts a new value in the tree at the right location.\n", "(d) Write a function that looks up a value in the tree.\n", "(e) Write a function that removes a value from the tree.\n", "Exercise 8.7: Ordinary least squares\n", "Our goal in this exercise is to write our own least-squares solver to solve regression problems:\n", "arg min\n", "β\n", "ky − Xβk2\n", "Page 10\n", "CME 193 Introduction to Python Exercises\n", "See for example statsmodels ols or LinearRegression. While one can, and should, use written solvers,\n", "it’s a good practice exercise.\n", "(a) Setup an OLS class with fit and predict methods, to be coded later\n", "(b) Write the fit method using numpy’s or scipy’s linear algebra module.\n", "(c) Now write the predict function, that predicts yn given new Xn.\n", "(d) Add a function that summarizes the model\n", "(e) (Optional) Use Patsy and Pandas to support DataFrames and formulas, similar to R.\n", "9 Numpy\n", "Generate matrices A, with random Gaussian entries, B, a Toeplitz matrix, where A ∈ R\n", "n×m and B ∈ R\n", "m×m,\n", "for n = 200, m = 500.\n", "Exercise 9.1: Matrix operations\n", "Calculate A + A, AA>, A>A and AB. Write a function that computes A(B − λI) for any λ.\n", "Exercise 9.2: Solving a linear system\n", "Generate a vector b with m entries and solve Bx = b.\n", "Exercise 9.3: Norms\n", "Compute the Frobenius norm of A: kAkF and the infinity norm of B: kBk∞. Also find the largest and\n", "smallest singular values of B.\n", "Exercise 9.4: Power iteration\n", "Generate a matrix Z, n × n, with Gaussian entries, and use the power iteration to find the largest\n", "eigenvalue and corresponding eigenvector of Z. How many iterations are needed till convergence?\n", "Optional: use the time.clock() method to compare computation time when varying n.\n", "Exercise 9.5: Singular values\n", "Generate an n × n matrix, denoted by C, where each entry is 1 with probability p and 0 otherwise. Use\n", "the linear algebra library of Scipy to compute the singular values of C. What can you say about the\n", "relationship between n, p and the largest singular value?\n", "Exercise 9.6: Nearest neighbor\n", "Write a function that takes a value z and an array A and finds the element in A that is closest to z. The\n", "function should return the closest value, not index.\n", "Hint: Use the built-in functionality of Numpy rather than writing code to find this value manually. In\n", "particular, use brackets and argmin.\n", "10 Scipy\n", "Exercise 10.1: Least squares\n", "Generate matrix A ∈ Rm×n with m > n. Also generate some vector b ∈ Rm.\n", "Now find x = arg minx kAx − bk2.\n", "Print the norm of the residual.\n", "Exercise 10.2: Optimization\n", "Find the maximum of the function\n", "f(x) = sin2\n", "(x − 2)e\n", "−x\n", "2\n", "Page 11\n", "CME 193 Introduction to Python Exercises\n", "0 1 2 3 4 5 6 7 8 9\n", "index\n", "1.5\n", "1.0\n", "0.5\n", "0.0\n", "0.5\n", "1.0\n", "1.5\n", "value\n", "True coefficients\n", "Estimated coefficients\n", "Figure 1: Parameter plot\n", "Exercise 10.3: Pairwise distances\n", "Let X be a matrix with n rows and m columns. How can you compute the pairwise distances between\n", "every two rows?\n", "As an example application, consider n cities, and we are given their coordinates in two columns. Now\n", "we want a nice table that tells us for each two cities, how far they are apart.\n", "Again, make sure you make use of Scipy’s functionality instead of writing your own routine.\n", "11 Matplotlib\n", "Exercise 11.1: Plotting a function\n", "Plot the function\n", "f(x) = sin2\n", "(x − 2)e\n", "−x\n", "2\n", "over the interval [0, 2]. Add proper axis labels, a title, etc.\n", "Exercise 11.2: Data\n", "Create a data matrix X with 20 observations of 10 variables. Generate a vector b with parameters Then\n", "generate the response vector y = Xb+z where z is a vector with standard normally distributed variables.\n", "Now (by only using y and X), find an estimator for b, by solving\n", "ˆb = arg min\n", "b\n", "kXb − yk2\n", "Plot the true parameters b and estimated parameters ˆb. See Figure 1 for an example plot.\n", "Exercise 11.3: Histogram and density estimation\n", "Generate a vector z of 10000 observations from your favorite exotic distribution. Then make a plot that\n", "shows a histogram of z (with 25 bins), along with an estimate for the density, using a Gaussian kernel\n", "density estimator (see scipy.stats). See Figure 2 for an example plot.\n", "12 Recursion\n", "Exercise 12.1: Power\n", "Write a recursive function that computes a\n", "b\n", "for given a and b, where b is an integer. Do not use **.\n", "Page 12\n", "CME 193 Introduction to Python Exercises\n", "0.0 0.2 0.4 0.6 0.8 1.0\n", "0.0\n", "0.5\n", "1.0\n", "1.5\n", "2.0\n", "2.5\n", "Figure 2: Histogram\n", "Exercise 12.2: Recursive map and filter\n", "Write a recursive functions myrecmap and myrecfilter that implement the map and filter functions\n", "using recursion.\n", "Exercise 12.3: Purify\n", "Write two functions, one that uses iteration (say a for loop), and the other using recursion, that achieve\n", "the following: The input of the function is a list with integers. The functions return a (new) list with\n", "only the even integers in the list.\n", "Exercise 12.4: Product\n", "Write two functions, one that uses iteration, and the other using recursion, that achieve the following:\n", "The input of the function is a list with numbers. The functions return the product of the numbers in\n", "the list.\n", "Exercise 12.5: Factorial\n", "Write a recursive function to compute n! = n × (n − 1) × . . . × 1. Note that 0! is defined to equal 1.\n", "Exercise 12.6: Recursive root finding\n", "In Exercise 3.5 you wrote a function to find a root of a function f. Now write a recursive function that\n", "finds the root of a function.\n", "Exercise 12.7: Collatz sequence\n", "Write a recursive implementation of a function that returns a list with the Collatz sequence started at\n", "an arbitrary starting value.\n", "Recall: A Collatz sequence is formed as follows: We start with some number x0, and we find the next\n", "number in the sequence by\n", "xi+1 =\n", "(\n", "xi/2 if xi\n", "is even\n", "3xi + 1 if xi\n", "is odd\n", "If xi = 1, we stop iterating and have found the full sequence.\n", "Exercise 12.8: Fibonacci sequence\n", "The Fibonacci sequence {Fi}i = 0∞ starts with F0 = 0, F1 = 1. Every subsequent value in the sequence\n", "is the sum of the last elements in the sequence:\n", "Fn = Fn−1 + Fn−2\n", "(a) Implement a non-recursive function that computes the Fn\n", "(b) Implement a recursive function that computes Fn\n", "Page 13\n", "CME 193 Introduction to Python Exercises\n", "(c) Compare the runtime for computing F35 recursively versus non-recursively, and explain the difference.\n", "(d) This does not mean a recursion is not feasible for this problem, only that the naive implementation\n", "isn’t the best. We can get a better version using either of the following\n", "(a) Store values already calculated, say in a dictionary, so you can look them up instead of redoing\n", "the calculation.\n", "(b) Generalizing the Fibonacci sequence to an additive sequence with arbitrary starting points t0\n", "and t1, and finding a recursive algorithm to find the nth term in such a sequence. Note that\n", "finding the nth term in a sequence started from t0 and t1 is the same as finding the n − 1th\n", "term in a sequence started from t1 and t0 + t1.\n", "Implement one of the above (or both).\n", "Exercise 12.9: Palindromes\n", "Given a string t, we are interested in finding the largest palindrome in t, where we are allowed to\n", "remove characters from t. For example, consider the string abcdba, then the function should return\n", "abcba. Before you start coding, figure out the recursion on paper first.\n", "Extra: you will notice that if you are not careful, this will take a very long time to compute for longer\n", "inputs. However, a simple modification can speed up your code so that it runs in O(n\n", "2\n", "), where n is the\n", "length of the input string. Find and implement this modification.2\n", "Exercise 12.10: Quicksort\n", "There are many sorting algorithms, see for example http://www.sorting-algorithms.com/. Quicksort\n", "is a well known (and quick) sorting algorithm that works as follows:\n", "(a) Test whether the list is sorted, if not:\n", "(b) Select one part of the list as pivot, any element works.\n", "(c) Create a new list, left and right, and put all elements smaller than the pivot in the left list,\n", "and all elements larger than the pivot in the right list.\n", "(d) Recursively sort the left and right list, and return sorted left + pivot + sorted right.\n", "Implement the quicksort algorithm. However, first think about the following caveats and how to get\n", "around them:\n", "(a) Testing whether a list is sorted takes quite a bit of work, so we do not want to do this. Instead, we\n", "want to perform a much simpler check: what list is trivially sorted?\n", "(b) What happens when the pivot element occurs multiple times in the list? How can we get around\n", "this?\n", "13 Iterators\n", "Exercise 13.1: Collatz sequence\n", "Write a generator that generates the Collatz sequence with initial value n. Use this to print out the\n", "Collatz sequence started at 103\n", "Recall the Collatz sequence problem from last week. A Collatz sequence is formed as follows: We start\n", "with some number x0, and we find the next number in the sequence by\n", "xi+1 =\n", "(\n", "xi/2 if xi\n", "is even\n", "3xi + 1 if xi\n", "is odd\n", "2This exercise is inspired by an exercise in cs221.\n", "Page 14\n", "CME 193 Introduction to Python Exercises\n", "If xi = 1, we stop iterating and have found the full sequence.\n", "Exercise 13.2: Collatz array using Numpy\n", "Use the Collatz generator you wrote in the previous exercise to generate a vector with as ellements the\n", "Collatz sequence started at 61.\n", "Hint: use the np.fromiter function.\n", "Exercise 13.3: Prime numbers\n", "Write an iterator that iterates over the first n prime numbers. Use this to print out the first 10,000\n", "primes.\n", "14 Exception handling\n", "Exercise 14.1: Rational numbers\n", "Edit your code that implements the Rational class such that it raises an exception when the denominator\n", "is 0.\n", "Exercise 14.2: Wordcount\n", "Recall the exercise of finding the 20 most common words in the Complete works of Shakespeare.\n", "Write a script, reusing as much of your code as you can, such that you can specify the filename and k\n", "for the k most common words at the command line and that the script will print the k most common\n", "words, and their counts, from the file.\n", "Make sure you handle errors gracefully, such as a misspecified filename.\n", "15 Unit testing\n", "Exercise 15.1: Factorial\n", "Write unit tests for the factorial function.\n", "Exercise 15.2: Prime numbers\n", "Write a program primes.py that takes two command line arguments, a < b, and returns all prime\n", "numbers between a and b (inclusive). Write a seperate test script primestest.py that contains unittests\n", "for all functions in your script.\n", "Exercise 15.3: Sorting\n", "Write unit tests for a sorting algorithm. Test whether your implementation of quicksort passes the\n", "tests.\n", "16 More modules\n", "Exercise 16.1: Regular expressions to find email addresses\n", "At http://stanford.edu/~schmit/cme193/ex/data/emailchallenge.txt you find some text data\n", "with some email addresses. Your task is to write a script that finds these email addresses (and ignores\n", "the fake ones).\n", "First, use the requests library to download the data directly into Python (so don’t save the file locally).\n", "Then use re to match the email addresses. Make sure to extract the local part and domain part (before\n", "and after @) separately.\n", "Page 15\n", "CME 193 Introduction to Python Exercises\n", "Hint: Although written for the Ruby regex, http://rubular.com/ can be very useful to play around\n", "with your pattern. The Python regular expression should be identical or almost identical.\n", "Exercise 16.2: Flask\n", "Write a Flask app that displays the current date and time.\n", "Hint: Use the datetime module to get the current time.\n", "Page 16" ] } ], "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
fjaviersanchez/McQ_Workshop	McQ_workshop.ipynb	1	12144	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline \n", "#This is an ipython magic to import numpy and matplotlib (to produce graphs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import astropy.io.fits as fits #This library handles FITS files (There are other libraries to do so in python as well)\n", "from skimage.morphology import disk\n", "from skimage.filters import rank\n", "from astropy.visualization import make_lupton_rgb\n", "from reproject import reproject_interp, reproject_exact\n", "from photutils import CircularAperture\n", "from photutils import aperture_photometry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the original images that you took" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "science_image_path_g = 'data/seo_m66_g-band_180s_apagul_1.fits' #Type the path to your image\n", "sci_g = fits.open(science_image_path_g)\n", "sci_im_g = fits.open(science_image_path_g)[0].data\n", "plt.imshow(sci_im_g,cmap='gray', vmax=1800, norm=matplotlib.colors.LogNorm())\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This image is not science-ready yet...\n", "\n", "Dark image: If you take a shot with the shutter closed (i.e., no light/photons incoming in the camera) you still have a non-zero image." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dark_image_path='data/dark.fits' #Type the path to your dark image\n", "drk_im = fits.open(dark_image_path)[0].data\n", "plt.imshow(drk_im,cmap='gray', vmax=2000)\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bias_image_path = 'data/bias.fits' #Type the path to your bias image\n", "bias_image = fits.open(bias_image_path)[0].data\n", "plt.imshow(bias_image, cmap='gray')\n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(drk_im.flatten());\n", "plt.yscale('log')\n", "plt.xlabel('Output counts')\n", "plt.ylabel('Number of pixels')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why is this?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another interesting feature of CCD cameras is that the chips do not respond equally to the same light intensity. For example if you illuminate the camera with uniform light (this is called flat image). " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "flat_image_path = 'data/FLAT_g-band_2016-10-06_bin1_id5908.fits' #Type the path to your flat image here\n", "flat_image = fits.open(flat_image_path)[0].data\n", "#You can try cmap='hot' or cmap='jet' to see how it changes\n", "plt.imshow(flat_image, cmap='gray') \n", "plt.colorbar()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.hist(flat_image.flatten())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def reduce_image(sci_im,drk_im,flat_im, bias_im, filter_dark=True):\n", " from scipy.stats import mode\n", " dkr_im = drk_im - bias_im\n", " #First part: We take \"zero\" the image\n", " #The next part is optional and averages the dark image in a 10 pixel radius\n", " #to get rid of salt/pepper noise\n", " if(filter_dark):\n", " selem = disk(10) #We are going to perform averages in 10 pixel radius disks\n", " selem2 = disk(4)\n", " drk_im = rank.mean(drk_im, selem=selem) #We perform an average to remove salt-pepper noise\n", " flat_im = rank.mean(flat_im, selem=selem2)\n", " #Second part: Make every part have the same sensitivity\n", " #flat_im = (flat_im - drk_im)/mode(flat_im-drk_im,axis=None)[0] #most common pixel value will equal 1\n", " flat_im = (flat_im - drk_im)/np.median(flat_im-drk_im)\n", " #Lower than 1 where the CCD is less sensitive and more than 1 where it's more sensitive\n", " sci_im = (sci_im -drk_im)/flat_im\n", " #Error image\n", " return sci_im" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a better image!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_sci_image_g = reduce_image(sci_im_g,drk_im,flat_image,bias_image, filter_dark=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.imshow(new_sci_image_g, cmap='gray', vmax=4000, vmin=50, norm=matplotlib.colors.LogNorm())\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare to the original!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax = plt.subplots(nrows=1,ncols=3,figsize=(10,8))\n", "ax[0].imshow(sci_im_g,cmap='gray',vmax=1800, norm=matplotlib.colors.LogNorm())\n", "ax[0].set_title('Before reduction')\n", "ax[1].imshow(new_sci_image_g,cmap='gray',vmax=2000, vmin=50, norm=matplotlib.colors.LogNorm())\n", "ax[1].set_title('After reduction')\n", "ax[2].imshow(sci_im_g-new_sci_image_g,cmap='gray', vmax=1050, vmin=1000)\n", "ax[2].set_title('Difference')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "science_image_path_r = 'data/seo_m66_r_180s_apagul_1.fits' \n", "sci_im_r = fits.open(science_image_path_r)[0].data\n", "science_image_path_i = 'data/seo_m66_i-band_180s_apagul_1.fits'\n", "sci_im_i = fits.open(science_image_path_i)[0].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flat_r = fits.open('data/FLAT_r-band_2016-10-06_bin1_id5906.fits')[0].data\n", "flat_i = fits.open('data/FLAT_i-band_2016-10-06_bin1_id5907.fits')[0].data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reduce the rest of images (in principle we should take a different bias image for each filter) because the CCD has different sensitivity at different wavelengths" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_sci_image_r = reduce_image(sci_im_r,drk_im,flat_r,bias_image)\n", "new_sci_image_i = reduce_image(sci_im_i,drk_im,flat_i,bias_image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example from SDSS:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read in the three images downloaded from here:\n", "# g: http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-g-001737-5-0039.fits.bz2\n", "# r: http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-r-001737-5-0039.fits.bz2\n", "# i: http://dr13.sdss.org/sas/dr13/eboss/photoObj/frames/301/1737/5/frame-i-001737-5-0039.fits.bz2\n", "g = fits.open('data/frame-g-001737-5-0039.fits.bz2')[0]\n", "r = fits.open('data/frame-r-001737-5-0039.fits.bz2')[0]\n", "i = fits.open('data/frame-i-001737-5-0039.fits.bz2')[0]\n", "\n", "# remap r and i onto g\n", "r_new, r_mask = reproject_interp(r, g.header)\n", "i_new, i_mask = reproject_interp(i, g.header)\n", "\n", "# zero out the unmapped values\n", "i_new[np.logical_not(i_mask)] = 0\n", "r_new[np.logical_not(r_mask)] = 0\n", "\n", "# red=i, green=r, blue=g\n", "# make a file with the default scaling\n", "rgb_default = make_lupton_rgb(i_new, r_new, g.data, filename=\"ngc6976-default.jpeg\")\n", "# this scaling is very similar to the one used in Lupton et al. (2004)\n", "rgb = make_lupton_rgb(i_new, r_new, g.data, Q=10, stretch=0.5, filename=\"ngc6976.jpeg\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.imshow(rgb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to know more about Jupyter:\n", "https://github.com/fjaviersanchez/JupyterTutorial/blob/master/TutorialJupyter.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aperture photometry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Astronomers use the magnitude scale to characterize the bright of an object. With the magnitude scale you quantify the brightness of an object by comparing it with other objects. Astronomers have agreed to use \"Vega\" as the zero magnitude point (like the freezing point for water is the zero-point for the Celsius temperature scale). The magnitude scale goes \"backwards\" in the sense that brighter objects have smaller magnitude. For example the Sun has magnitude -27, the full Moon -13, and Venus -5. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How can we measure magnitudes from an image?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A first approach is to use an object which magnitude we know, called \"standard\" and refer the rest of the objects in an image to it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what do you use to count the total brightness of an object?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Use the brightest pixel?\n", "* Add the brightness in a certain radius?\n", "* Count only the pixels which belong to each object?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "positions = [(550., 600.), (450., 500.)] #Change it and include the position of an object in your image\n", "apertures = CircularAperture(positions, r=20.)\n", "phot_table = aperture_photometry(new_sci_image_g, apertures)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print phot_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Astronomers use both disks and complicated shapes to measure the brightness and they then refer to a known object!" ] }, { "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
lvrzhn/AstroHackWeek2015	day3-machine-learning/04 - API Summary.ipynb	12	2625	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A recap on Scikit-learn's estimator interface\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``X`` : data, 2d numpy array or scipy sparse matrix of shape (n_samples, n_features)\n", "\n", "``y`` : targets, 1d numpy array of shape (n_samples,)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<table>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><th colspan=2>``model.fit(X_train, [y_train])``</td></tr>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><th>``model.predict(X_test)``</th><th>``model.transform(X_test)``</th></tr>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Classification</td><td>Preprocessing</td></tr>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Regression</td><td>Dimensionality Reduction</td></tr>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Clustering</td><td>Feature Extraction</td></tr>\n", "<tr style=\"border:None; font-size:20px; padding:10px;\"><td> </td><td>Feature selection</td></tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Efficient alternatives, methods for models that don't generalize\n", "``model.fit_predict(X)`` (clustering)\n", "\n", "``model.fit_transform(X)`` (manifold learning)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional methods\n", "__Model evaluation__ : ``score(X, [y])``\n", "\n", "__Uncertainties from Classifiers__: ``decision_function(X)`` and ``predict_proba(X)``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attributes\n", "__Classifiers__: ``classes_``\n", "\n", "__Clustering__: ``labels_``\n", "\n", "__Manifold Learning__: ``embedding_``\n", "\n", "__Linear models__: ``coef_``\n", "\n", "__Linear Decompositions__: ``components_``" ] } ], "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 }	gpl-2.0
aschaffn/phys202-2015-work	assignments/assignment09/IntegrationEx01.ipynb	1	5106	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import integrate" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Trapezoidal rule" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The [trapezoidal](http://en.wikipedia.org/wiki/Trapezoidal_rule) rule generates a numerical approximation to the 1d integral:\n", "\n", "$$ I(a,b) = \\int_a^b f(x) dx $$\n", "\n", "by dividing the interval $[a,b]$ into $N$ subdivisions of length $h$:\n", "\n", "$$ h = (b-a)/N $$\n", "\n", "Note that this means the function will be evaluated at $N+1$ points on $[a,b]$. The main idea of the trapezoidal rule is that the function is approximated by a straight line between each of these points.\n", "\n", "Write a function `trapz(f, a, b, N)` that performs trapezoidal rule on the function `f` over the interval $[a,b]$ with `N` subdivisions (`N+1` points)." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true, "nbgrader": { "checksum": "0502d257f547b022ec1fbe354a75bbc2", "solution": true } }, "outputs": [], "source": [ "def trapz(f, a, b, N):\n", " \"\"\"Integrate the function f(x) over the range [a,b] with N points.\"\"\"\n", " x = np.linspace(a,b,N+1)\n", " h = np.diff(x)[1]\n", " y = f(x)\n", " m = .5 * (y[1:(len(y)-1)] + y[2:])\n", " a = sum(h * m)\n", " return(a)\n", " " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 1.])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0,1,2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.33333349983233068" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trapz(f, 0, 1, 1000)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "f = lambda x: x**2\n", "g = lambda x: np.sin(x)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3ee11e4e20322adf86beac9605ef3b1a", "grade": true, "grade_id": "integrationex01a", "points": 5 } }, "outputs": [], "source": [ "I = trapz(f, 0, 1, 1000)\n", "assert np.allclose(I, 0.33333349999999995)\n", "J = trapz(g, 0, np.pi, 1000)\n", "assert np.allclose(J, 1.9999983550656628)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now use `scipy.integrate.quad` to integrate the `f` and `g` functions and see how the result compares with your `trapz` function. Print the results and errors." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "6.5665886923582661e-06" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrate.quad(f,0,1)[0] - trapz(f, 0, 1, 1000)\n", "integrate.quad(g,0, np.pi)[0] - trapz(g,0,np.pi, 1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "071dda1b7a2edcac2945239a2f53139d", "grade": true, "grade_id": "integrationex01b", "points": 5 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the previous one" ] } ], "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
jeffsilverm/presentation	SeaGL-2018/Experiments_with_multiIndex.ipynb	1	46862	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# High dimensional data notebook: pandas and CSV files\n", "\n", "As part of my demonstration of the effect of packet loss rates on TCP performance, I build a comma separated variable (CSV) file which has data rate as a function of several variables:\n", "1 packet loss rate %\n", "1 Network delay\n", "1 File size\n", "1protocol (HTTP, HTTPS, FTP)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "d=dict()\n", "d={ 'gas': { 'a': { 7: {'IPv4': 20.7, 'IPv6': -20.7},\n", " 8: {'IPv4': 20.8, 'IPv6': -20.8},\n", " 9: {'IPv4': 20.9, 'IPv6': -20.9}},\n", " 'b': { 7: {'IPv4': 21.7, 'IPv6': -21.7},\n", " 8: {'IPv4': 21.8, 'IPv6': -21.8},\n", " 9: {'IPv4': 21.9, 'IPv6': -21.9}},\n", " 'c': { 7: {'IPv4': 22.7, 'IPv6': -22.7},\n", " 8: {'IPv4': 22.8, 'IPv6': -22.8},\n", " 9: {'IPv4': 22.9, 'IPv6': -22.9}}},\n", " 'liquid': { 'a': { 7: {'IPv4': 10.7, 'IPv6': -10.7},\n", " 8: {'IPv4': 10.8, 'IPv6': -10.8},\n", " 9: {'IPv4': 10.9, 'IPv6': -10.9}},\n", " 'b': { 7: {'IPv4': 11.7, 'IPv6': -11.7},\n", " 8: {'IPv4': 11.8, 'IPv6': -11.8},\n", " 9: {'IPv4': 11.8, 'IPv6': -11.8}},\n", " 'c': { 7: {'IPv4': 12.7, 'IPv6': -12.7},\n", " 8: {'IPv4': 12.8, 'IPv6': -12.8},\n", " 9: {'IPv4': 12.9, 'IPv6': -12.9}}},\n", " 'solid': { 'a': { 7: {'IPv4': 0.7, 'IPv6': -0.7},\n", " 8: {'IPv4': 0.8, 'IPv6': -0.8},\n", " 9: {'IPv4': 0.9, 'IPv6': -0.9}},\n", " 'b': { 7: {'IPv4': 1.7, 'IPv6': -1.7},\n", " 8: {'IPv4': 1.8, 'IPv6': -1.8},\n", " 9: {'IPv4': 1.8, 'IPv6': -1.8}},\n", " 'c': { 7: {'IPv4': 2.7, 'IPv6': -2.7},\n", " 8: {'IPv4': 2.8, 'IPv6': -2.8},\n", " 9: {'IPv4': 2.9, 'IPv6': -2.9}}}}\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pprint\n", "pp=pprint.PrettyPrinter(indent=2, width=80)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{ 'gas': { 'a': { 7: {'IPv4': 20.7, 'IPv6': -20.7},\n", " 8: {'IPv4': 20.8, 'IPv6': -20.8},\n", " 9: {'IPv4': 20.9, 'IPv6': -20.9}},\n", " 'b': { 7: {'IPv4': 21.7, 'IPv6': -21.7},\n", " 8: {'IPv4': 21.8, 'IPv6': -21.8},\n", " 9: {'IPv4': 21.9, 'IPv6': -21.9}},\n", " 'c': { 7: {'IPv4': 22.7, 'IPv6': -22.7},\n", " 8: {'IPv4': 22.8, 'IPv6': -22.8},\n", " 9: {'IPv4': 22.9, 'IPv6': -22.9}}},\n", " 'liquid': { 'a': { 7: {'IPv4': 10.7, 'IPv6': -10.7},\n", " 8: {'IPv4': 10.8, 'IPv6': -10.8},\n", " 9: {'IPv4': 10.9, 'IPv6': -10.9}},\n", " 'b': { 7: {'IPv4': 11.7, 'IPv6': -11.7},\n", " 8: {'IPv4': 11.8, 'IPv6': -11.8},\n", " 9: {'IPv4': 11.8, 'IPv6': -11.8}},\n", " 'c': { 7: {'IPv4': 12.7, 'IPv6': -12.7},\n", " 8: {'IPv4': 12.8, 'IPv6': -12.8},\n", " 9: {'IPv4': 12.9, 'IPv6': -12.9}}},\n", " 'solid': { 'a': { 7: {'IPv4': 0.7, 'IPv6': -0.7},\n", " 8: {'IPv4': 0.8, 'IPv6': -0.8},\n", " 9: {'IPv4': 0.9, 'IPv6': -0.9}},\n", " 'b': { 7: {'IPv4': 1.7, 'IPv6': -1.7},\n", " 8: {'IPv4': 1.8, 'IPv6': -1.8},\n", " 9: {'IPv4': 1.8, 'IPv6': -1.8}},\n", " 'c': { 7: {'IPv4': 2.7, 'IPv6': -2.7},\n", " 8: {'IPv4': 2.8, 'IPv6': -2.8},\n", " 9: {'IPv4': 2.9, 'IPv6': -2.9}}}}\n" ] } ], "source": [ "pp.pprint(d)\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " a \\\n", "phase \n", "gas {7: {'IPv4': 20.7, 'IPv6': -20.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 10.7, 'IPv6': -10.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 0.7, 'IPv6': -0.7}, 8: {'IPv4': 0... \n", "\n", " b \\\n", "phase \n", "gas {7: {'IPv4': 21.7, 'IPv6': -21.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 11.7, 'IPv6': -11.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 1.7, 'IPv6': -1.7}, 8: {'IPv4': 1... \n", "\n", " c \n", "phase \n", "gas {7: {'IPv4': 22.7, 'IPv6': -22.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 12.7, 'IPv6': -12.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 2.7, 'IPv6': -2.7}, 8: {'IPv4': 2... \n" ] }, { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'names'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-23-72d962d65fe7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'phase'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 3612\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3613\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3614\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3616\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'names'" ] } ], "source": [ "df=pd.DataFrame.from_dict(d, orient=\"index\").rename_axis('phase')\n", "print(df)\n", "print(df.names)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " a \\\n", "phase \n", "gas {7: {'IPv4': 20.7, 'IPv6': -20.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 10.7, 'IPv6': -10.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 0.7, 'IPv6': -0.7}, 8: {'IPv4': 0... \n", "\n", " b \\\n", "phase \n", "gas {7: {'IPv4': 21.7, 'IPv6': -21.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 11.7, 'IPv6': -11.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 1.7, 'IPv6': -1.7}, 8: {'IPv4': 1... \n", "\n", " c \n", "phase \n", "gas {7: {'IPv4': 22.7, 'IPv6': -22.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 12.7, 'IPv6': -12.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 2.7, 'IPv6': -2.7}, 8: {'IPv4': 2... \n", "========================================\n", "alpha a \\\n", "phase \n", "gas {7: {'IPv4': 20.7, 'IPv6': -20.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 10.7, 'IPv6': -10.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 0.7, 'IPv6': -0.7}, 8: {'IPv4': 0... \n", "\n", "alpha b \\\n", "phase \n", "gas {7: {'IPv4': 21.7, 'IPv6': -21.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 11.7, 'IPv6': -11.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 1.7, 'IPv6': -1.7}, 8: {'IPv4': 1... \n", "\n", "alpha c \n", "phase \n", "gas {7: {'IPv4': 22.7, 'IPv6': -22.7}, 8: {'IPv4':... \n", "liquid {7: {'IPv4': 12.7, 'IPv6': -12.7}, 8: {'IPv4':... \n", "solid {7: {'IPv4': 2.7, 'IPv6': -2.7}, 8: {'IPv4': 2... \n" ] } ], "source": [ "df=pd.DataFrame.from_dict(d, orient=\"index\").rename_axis('phase')\n", "print(df)\n", "print(40*'=')\n", "df.rename_axis('alpha', axis=1, inplace=True)\n", "print(df)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# This is adapted from https://stackoverflow.com/questions/47416113/how-to-build-a-multiindex-pandas-dataframe-from-a-nested-dictionary-with-lists\n", "d2 = {(i,j,k,l): d[i][j][k][l] \n", " for i in d.keys()\n", " for j in d[i].keys()\n", " for k in d[i][j].keys()\n", " for l in d[i][j][k].keys()\n", " \n", " }" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{('gas', 'a', 7, 'IPv4'): 20.7,\n", " ('gas', 'a', 7, 'IPv6'): -20.7,\n", " ('gas', 'a', 8, 'IPv4'): 20.8,\n", " ('gas', 'a', 8, 'IPv6'): -20.8,\n", " ('gas', 'a', 9, 'IPv4'): 20.9,\n", " ('gas', 'a', 9, 'IPv6'): -20.9,\n", " ('gas', 'b', 7, 'IPv4'): 21.7,\n", " ('gas', 'b', 7, 'IPv6'): -21.7,\n", " ('gas', 'b', 8, 'IPv4'): 21.8,\n", " ('gas', 'b', 8, 'IPv6'): -21.8,\n", " ('gas', 'b', 9, 'IPv4'): 21.9,\n", " ('gas', 'b', 9, 'IPv6'): -21.9,\n", " ('gas', 'c', 7, 'IPv4'): 22.7,\n", " ('gas', 'c', 7, 'IPv6'): -22.7,\n", " ('gas', 'c', 8, 'IPv4'): 22.8,\n", " ('gas', 'c', 8, 'IPv6'): -22.8,\n", " ('gas', 'c', 9, 'IPv4'): 22.9,\n", " ('gas', 'c', 9, 'IPv6'): -22.9,\n", " ('liquid', 'a', 7, 'IPv4'): 10.7,\n", " ('liquid', 'a', 7, 'IPv6'): -10.7,\n", " ('liquid', 'a', 8, 'IPv4'): 10.8,\n", " ('liquid', 'a', 8, 'IPv6'): -10.8,\n", " ('liquid', 'a', 9, 'IPv4'): 10.9,\n", " ('liquid', 'a', 9, 'IPv6'): -10.9,\n", " ('liquid', 'b', 7, 'IPv4'): 11.7,\n", " ('liquid', 'b', 7, 'IPv6'): -11.7,\n", " ('liquid', 'b', 8, 'IPv4'): 11.8,\n", " ('liquid', 'b', 8, 'IPv6'): -11.8,\n", " ('liquid', 'b', 9, 'IPv4'): 11.8,\n", " ('liquid', 'b', 9, 'IPv6'): -11.8,\n", " ('liquid', 'c', 7, 'IPv4'): 12.7,\n", " ('liquid', 'c', 7, 'IPv6'): -12.7,\n", " ('liquid', 'c', 8, 'IPv4'): 12.8,\n", " ('liquid', 'c', 8, 'IPv6'): -12.8,\n", " ('liquid', 'c', 9, 'IPv4'): 12.9,\n", " ('liquid', 'c', 9, 'IPv6'): -12.9,\n", " ('solid', 'a', 7, 'IPv4'): 0.7,\n", " ('solid', 'a', 7, 'IPv6'): -0.7,\n", " ('solid', 'a', 8, 'IPv4'): 0.8,\n", " ('solid', 'a', 8, 'IPv6'): -0.8,\n", " ('solid', 'a', 9, 'IPv4'): 0.9,\n", " ('solid', 'a', 9, 'IPv6'): -0.9,\n", " ('solid', 'b', 7, 'IPv4'): 1.7,\n", " ('solid', 'b', 7, 'IPv6'): -1.7,\n", " ('solid', 'b', 8, 'IPv4'): 1.8,\n", " ('solid', 'b', 8, 'IPv6'): -1.8,\n", " ('solid', 'b', 9, 'IPv4'): 1.8,\n", " ('solid', 'b', 9, 'IPv6'): -1.8,\n", " ('solid', 'c', 7, 'IPv4'): 2.7,\n", " ('solid', 'c', 7, 'IPv6'): -2.7,\n", " ('solid', 'c', 8, 'IPv4'): 2.8,\n", " ('solid', 'c', 8, 'IPv6'): -2.8,\n", " ('solid', 'c', 9, 'IPv4'): 2.9,\n", " ('solid', 'c', 9, 'IPv6'): -2.9}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MultiIndex(levels=[['gas', 'liquid', 'solid'], ['a', 'b', 'c'], [7, 8, 9], ['IPv4', 'IPv6']],\n", " labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], [0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]])\n" ] } ], "source": [ "mux = pd.MultiIndex.from_tuples(d2.keys())\n", "df = pd.DataFrame(list(d2.values()), index=mux)\n", "print(mux)" ] }, { "cell_type": "code", "execution_count": 38, "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></th>\n", " <th></th>\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">gas</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>20.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-20.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>20.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-20.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>20.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-20.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>21.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-21.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>21.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-21.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>21.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-21.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>22.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-22.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>22.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-22.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>22.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-22.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">liquid</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>10.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-10.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>10.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-10.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>10.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-10.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>11.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-11.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>11.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-11.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>11.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-11.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>12.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-12.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>12.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-12.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>12.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-12.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">solid</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-0.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-0.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>0.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-0.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-1.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>1.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-1.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>1.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-1.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">7</th>\n", " <th>IPv4</th>\n", " <td>2.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-2.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">8</th>\n", " <th>IPv4</th>\n", " <td>2.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-2.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">9</th>\n", " <th>IPv4</th>\n", " <td>2.9</td>\n", " </tr>\n", " <tr>\n", " <th>IPv6</th>\n", " <td>-2.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "gas a 7 IPv4 20.7\n", " IPv6 -20.7\n", " 8 IPv4 20.8\n", " IPv6 -20.8\n", " 9 IPv4 20.9\n", " IPv6 -20.9\n", " b 7 IPv4 21.7\n", " IPv6 -21.7\n", " 8 IPv4 21.8\n", " IPv6 -21.8\n", " 9 IPv4 21.9\n", " IPv6 -21.9\n", " c 7 IPv4 22.7\n", " IPv6 -22.7\n", " 8 IPv4 22.8\n", " IPv6 -22.8\n", " 9 IPv4 22.9\n", " IPv6 -22.9\n", "liquid a 7 IPv4 10.7\n", " IPv6 -10.7\n", " 8 IPv4 10.8\n", " IPv6 -10.8\n", " 9 IPv4 10.9\n", " IPv6 -10.9\n", " b 7 IPv4 11.7\n", " IPv6 -11.7\n", " 8 IPv4 11.8\n", " IPv6 -11.8\n", " 9 IPv4 11.8\n", " IPv6 -11.8\n", " c 7 IPv4 12.7\n", " IPv6 -12.7\n", " 8 IPv4 12.8\n", " IPv6 -12.8\n", " 9 IPv4 12.9\n", " IPv6 -12.9\n", "solid a 7 IPv4 0.7\n", " IPv6 -0.7\n", " 8 IPv4 0.8\n", " IPv6 -0.8\n", " 9 IPv4 0.9\n", " IPv6 -0.9\n", " b 7 IPv4 1.7\n", " IPv6 -1.7\n", " 8 IPv4 1.8\n", " IPv6 -1.8\n", " 9 IPv4 1.8\n", " IPv6 -1.8\n", " c 7 IPv4 2.7\n", " IPv6 -2.7\n", " 8 IPv4 2.8\n", " IPv6 -2.8\n", " 9 IPv4 2.9\n", " IPv6 -2.9" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df\n" ] }, { "cell_type": "code", "execution_count": 46, "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></th>\n", " <th></th>\n", " <th>IPv4</th>\n", " <th>IPv6</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"9\" valign=\"top\">gas</th>\n", " <th rowspan=\"3\" valign=\"top\">a</th>\n", " <th>7</th>\n", " <td>20.7</td>\n", " <td>-20.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>20.8</td>\n", " <td>-20.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>20.9</td>\n", " <td>-20.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">b</th>\n", " <th>7</th>\n", " <td>21.7</td>\n", " <td>-21.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>21.8</td>\n", " <td>-21.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>21.9</td>\n", " <td>-21.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">c</th>\n", " <th>7</th>\n", " <td>22.7</td>\n", " <td>-22.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>22.8</td>\n", " <td>-22.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>22.9</td>\n", " <td>-22.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"9\" valign=\"top\">liquid</th>\n", " <th rowspan=\"3\" valign=\"top\">a</th>\n", " <th>7</th>\n", " <td>10.7</td>\n", " <td>-10.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>10.8</td>\n", " <td>-10.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>10.9</td>\n", " <td>-10.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">b</th>\n", " <th>7</th>\n", " <td>11.7</td>\n", " <td>-11.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>11.8</td>\n", " <td>-11.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>11.8</td>\n", " <td>-11.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">c</th>\n", " <th>7</th>\n", " <td>12.7</td>\n", " <td>-12.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>12.8</td>\n", " <td>-12.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>12.9</td>\n", " <td>-12.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"9\" valign=\"top\">solid</th>\n", " <th rowspan=\"3\" valign=\"top\">a</th>\n", " <th>7</th>\n", " <td>0.7</td>\n", " <td>-0.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.8</td>\n", " <td>-0.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.9</td>\n", " <td>-0.9</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">b</th>\n", " <th>7</th>\n", " <td>1.7</td>\n", " <td>-1.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1.8</td>\n", " <td>-1.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1.8</td>\n", " <td>-1.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">c</th>\n", " <th>7</th>\n", " <td>2.7</td>\n", " <td>-2.7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2.8</td>\n", " <td>-2.8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2.9</td>\n", " <td>-2.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " IPv4 IPv6\n", "gas a 7 20.7 -20.7\n", " 8 20.8 -20.8\n", " 9 20.9 -20.9\n", " b 7 21.7 -21.7\n", " 8 21.8 -21.8\n", " 9 21.9 -21.9\n", " c 7 22.7 -22.7\n", " 8 22.8 -22.8\n", " 9 22.9 -22.9\n", "liquid a 7 10.7 -10.7\n", " 8 10.8 -10.8\n", " 9 10.9 -10.9\n", " b 7 11.7 -11.7\n", " 8 11.8 -11.8\n", " 9 11.8 -11.8\n", " c 7 12.7 -12.7\n", " 8 12.8 -12.8\n", " 9 12.9 -12.9\n", "solid a 7 0.7 -0.7\n", " 8 0.8 -0.8\n", " 9 0.9 -0.9\n", " b 7 1.7 -1.7\n", " 8 1.8 -1.8\n", " 9 1.8 -1.8\n", " c 7 2.7 -2.7\n", " 8 2.8 -2.8\n", " 9 2.9 -2.9" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is adapted from https://stackoverflow.com/questions/47416113/how-to-build-a-multiindex-pandas-dataframe-from-a-nested-dictionary-with-lists\n", "d3 = {(i,j,k): d[i][j][k] \n", " for i in d.keys()\n", " for j in d[i].keys() \n", " for k in d[i][j].keys()\n", " }\n", "mux = pd.MultiIndex.from_tuples(d3.keys())\n", "mux\n", "df = pd.DataFrame(list(d3.values()), index=mux)\n", "df" ] }, { "cell_type": "code", "execution_count": 60, "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></th>\n", " <th></th>\n", " <th></th>\n", " <th>value</th>\n", " </tr>\n", " <tr>\n", " <th>phase</th>\n", " <th>alpha</th>\n", " <th>idx</th>\n", " <th>protocol</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">solid</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>1.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>1.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>1.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>1.6</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.6</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>1.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>1.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-1.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>2.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-2.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>2.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-2.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>2.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-2.3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">liquid</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>11.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>11.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>11.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>11.6</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.6</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>11.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>11.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-11.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>12.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-12.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>12.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-12.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>12.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-12.3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"18\" valign=\"top\">gas</th>\n", " <th rowspan=\"6\" valign=\"top\">a</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>21.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>21.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>21.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.3</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">b</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>21.6</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.6</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>21.7</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.7</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>21.8</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-21.8</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"6\" valign=\"top\">c</th>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>ipv4</th>\n", " <td>22.1</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-22.1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>ipv4</th>\n", " <td>22.2</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-22.2</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>ipv4</th>\n", " <td>22.3</td>\n", " </tr>\n", " <tr>\n", " <th>IPV6</th>\n", " <td>-22.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " value\n", "phase alpha idx protocol \n", "solid a 1 ipv4 1.1\n", " IPV6 -1.1\n", " 2 ipv4 1.2\n", " IPV6 -1.2\n", " 3 ipv4 1.3\n", " IPV6 -1.3\n", " b 1 ipv4 1.6\n", " IPV6 -1.6\n", " 2 ipv4 1.7\n", " IPV6 -1.7\n", " 3 ipv4 1.8\n", " IPV6 -1.8\n", " c 1 ipv4 2.1\n", " IPV6 -2.1\n", " 2 ipv4 2.2\n", " IPV6 -2.2\n", " 3 ipv4 2.3\n", " IPV6 -2.3\n", "liquid a 1 ipv4 11.1\n", " IPV6 -11.1\n", " 2 ipv4 11.2\n", " IPV6 -11.2\n", " 3 ipv4 11.3\n", " IPV6 -11.3\n", " b 1 ipv4 11.6\n", " IPV6 -11.6\n", " 2 ipv4 11.7\n", " IPV6 -11.7\n", " 3 ipv4 11.8\n", " IPV6 -11.8\n", " c 1 ipv4 12.1\n", " IPV6 -12.1\n", " 2 ipv4 12.2\n", " IPV6 -12.2\n", " 3 ipv4 12.3\n", " IPV6 -12.3\n", "gas a 1 ipv4 21.1\n", " IPV6 -21.1\n", " 2 ipv4 21.2\n", " IPV6 -21.2\n", " 3 ipv4 21.3\n", " IPV6 -21.3\n", " b 1 ipv4 21.6\n", " IPV6 -21.6\n", " 2 ipv4 21.7\n", " IPV6 -21.7\n", " 3 ipv4 21.8\n", " IPV6 -21.8\n", " c 1 ipv4 22.1\n", " IPV6 -22.1\n", " 2 ipv4 22.2\n", " IPV6 -22.2\n", " 3 ipv4 22.3\n", " IPV6 -22.3" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Adapted from https://stackoverflow.com/questions/50665996/pandas-read-in-multiindex-data-from-csv-file\n", "# See also https://stackoverflow.com/questions/24519304/reading-csv-files-w-multiindex for another way to do it.\n", "dfcsv = pd.read_csv(\"high_dimension_data.csv\", index_col=[0,1,2,3])\n", "dfcsv" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('solid', 'b', 17, 'IPv2')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lk=[\"solid\",\"b\",17,\"IPv2\"]\n", "lk\n", "tk=tuple(lk)\n", "tk" ] } ], "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-2.0
ES-DOC/esdoc-jupyterhub	notebooks/cnrm-cerfacs/cmip6/models/cnrm-esm2-1/ocean.ipynb	1	164429	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: CNRM-CERFACS \n", "Source ID: CNRM-ESM2-1 \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 16:53:52" ], "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', 'cnrm-cerfacs', 'cnrm-esm2-1', '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
pysg/pyther	Diagrama_de_fases_para_sustancias_puras_0.ipynb	1	28154	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 Diagrama de fases para sustancias puras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta sección se presentan los diagramas de fases comunes para sustancias puras. Como son:\n", "\n", "\n", "1. Envolvente de fases liquido-vapor\n", "2. Isoterma\n", "3. Isobara\n", "4. Sólido-líquido" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'fortranmagic # activating magic'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-430be5188958>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext fortranmagic # activating magic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/home/andres-python/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m 2156\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\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 2157\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2160\u001b[0m \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/andres-python/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m 2077\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2078\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2080\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 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name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/andres-python/anaconda3/lib/python3.5/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m 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de estado para modelar el comportamiento de fases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ P^l(T, V^l) - P^v(T, V^v) = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ ln \\phi(T, V^l) - ln \\phi(T, V^v) = 0 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ g(T, V^l, V^v) = X_S - S = 0 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ F = \n", "\\begin{bmatrix}\n", "P^l(T, V^l) - P^v(T, V^v)\\\\\n", "ln \\phi(T, V^l) - ln \\phi(T, V^v)\\\\\n", "X_S - S\n", "\\end{bmatrix}\n", "= 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ F = \n", "\\begin{bmatrix}\n", "ln \\left( \\frac{P^l(T, V^l)} {P^v(T, V^v)} \\right)\\\\\n", "ln f_l(T, V^l) - ln f_v(T, V^v)\\\\\n", "0\n", "\\end{bmatrix}\n", "$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ J \n", "\\begin{bmatrix}\n", "\\Delta ln T\\\\\n", "\\Delta ln V^l\\\\\n", "\\Delta ln V^v\\\\\n", "\\end{bmatrix}\n", "+ F = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se inicia con el cálculo de la envolvente de fases de uns sustancia pura.\n", "\n", "$$ n \\left(\\frac{\\partial ln \\hat\\phi_i}{\\partial n_j}\\right)_{T,P} = n \\left(\\frac{\\partial ln \\hat\\phi_j}{\\partial n_j}\\right)_{T,P} = n \\left(\\frac{\\partial^2 F} {\\partial n_j \\partial n_i} \\right)_{T,V} + 1 + \\frac{n}{RT} + \\frac{ \\left(\\frac{\\partial P} {\\partial n_j}\\right)_{T,V} \\left(\\frac{\\partial P} {\\partial n_i}\\right)_{T,V} } {\\left(\\frac{\\partial P} {\\partial V} \\right)_{T,n}}$$\n", "\n", "\n", "Relación entre derivadas de la fugacidad y el coeficiente de fugacidad\n", "\n", "$$ \\left(\\frac{\\partial ln\\hat f_i }{\\partial n_i}\\right)_{T,P} = \\left(\\frac{\\partial \\hat \\phi_i}{\\partial n_i}\\right)_{T,P} + \\left(\\frac{\\delta_{ij}}{n} - \\frac{1}{n} \\right)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "$$RJAC(1,1)=DPDTx/Pl-DPDTy/Pv$$\n", "\n", "$$ RJAC(1,1)=TRJAC(1,1) $$\n", "\n", "$$ RJAC(1,1) = T \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial T}\\right)} {P_l} - \\frac {\\left(\\frac{\\partial P_{y} }{\\partial T}\\right)} {P_v} \\right) $$\n", "\n", "\n", "$$RJAC(1,2)=VlDPDVx/Pl$$\n", "\n", "$$ RJAC(1,2) = -V_l \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial V}\\right)} {P_l} \\right) $$\n", "\n", "$$RJAC(1,3)=-VvDPDVy/Pv$$\n", "\n", "$$ RJAC(1,2) = -V_v \\left( \\frac {\\left(\\frac{\\partial P_{y} }{\\partial V}\\right)} {P_y} \\right) $$\n", "\n", "\n", "\n", "$$RJAC(2,1)=FUGTx(icomp)-FUGTy(icomp)$$\n", "\n", "$$ RJAC(2,1)= \\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right)$$ \n", "\n", "$$RJAC(2,1)=TRJAC(2,1)$$\n", "\n", "$$RJAC(2,1)=T \\left(\\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right) \\right) $$\n", "\n", "$$RJAC(2,2)=VlFUGVx(icomp)$$\n", "\n", "$$ RJAC(2,1)= V_l \\left(\\frac{\\partial f_{ix} } {\\partial V} \\right) $$\n", "\n", "\n", "\n", "$$RJAC(2,3)=-VvFUGVy(icomp)$$\n", "\n", "$$ RJAC(2,3)= V_v \\left(\\frac{\\partial f_{iy} } {\\partial V_{y}} \\right) $$\n", "\n", "\n", "\n", "$$DLFUGT(I)=(ArTn(I)-Arn(I)/T)/RT+1.D0/T ! term DPDT/P is cancelled out $$\n", "\n", "$$DLFUGT(I)=(ArTn(I)-Arn(I)/T)/RT+1.D0/T $$\n", "\n", "$$DLFUGT(I) = \\frac{ ArTn(I) - Arn(I)} {R_GT^2} + \\frac{1} {T} $$\n", "\n", "$$DLFUGT(I) = \\frac{ \\frac{\\partial Ar}{\\partial T \\partial n}\n", " - \\frac{\\partial Ar}{\\partial n }} {R_GT^2} + \\frac{1} {T} $$\n", "\n", "\n", "\n", "$$ DPDT = -ArTV+TOTNRGAS/V $$\n", "\n", "$$ \\frac{\\partial P}{\\partial T} = -\\frac{\\partial Ar}{\\partial T \\partial V} + N_T \\frac{R_{G}} {V} $$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "\n", "\n", "$$RJAC(1,1)=DPDTx/Pl-DPDTy/Pv$$\n", "\n", "$$ RJAC(1,1)=TRJAC(1,1) $$\n", "\n", "$$ RJAC(1,1) = T \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial T}\\right)} {P_l} - \\frac {\\left(\\frac{\\partial P_{y} }{\\partial T}\\right)} {P_v} \\right) $$\n", "\n", "\n", "$$RJAC(1,2)=VlDPDVx/Pl$$\n", "\n", "$$ RJAC(1,2) = -V_l \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial V}\\right)} {P_l} \\right) $$\n", "\n", "$$RJAC(1,3)=-VvDPDVy/Pv$$\n", "\n", "$$ RJAC(1,3) = -V_v \\left( \\frac {\\left(\\frac{\\partial P_{y} }{\\partial V}\\right)} {P_y} \\right) $$\n", "\n", "\n", "\n", "$$RJAC(2,1)=FUGTx(icomp)-FUGTy(icomp)$$\n", "\n", "$$ RJAC(2,1)= \\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right)$$ \n", "\n", "$$RJAC(2,1)=TRJAC(2,1)$$\n", "\n", "$$RJAC(2,1)=T \\left(\\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right) \\right) $$\n", "\n", "$$RJAC(2,2)=VlFUGVx(icomp)$$\n", "\n", "$$ RJAC(2,1)= V_l \\left(\\frac{\\partial f_{ix} } {\\partial V} \\right) $$\n", "\n", "\n", "\n", "$$RJAC(2,3)=-VvFUGVy(icomp)$$\n", "\n", "$$ RJAC(2,3)= V_v \\left(\\frac{\\partial f_{iy} } {\\partial V_{y}} \\right) $$\n", "\n", "\n", "\n", "$$DLFUGT(I)=(ArTn(I)-Arn(I)/T)/RT+1.D0/T ! term DPDT/P is cancelled out $$\n", "\n", "$$DLFUGT(I)=(ArTn(I)-Arn(I)/T)/RT+1.D0/T $$\n", "\n", "$$DLFUGT(I) = \\frac{ \\frac{ArTn(I) - Arn(I)} {T}} {R_GT} + \\frac{1} {T} $$\n", "\n", "\n", "\n", "\n", "$$ DPDT = -ArTV+TOTN*RGAS/V $$\n", "\n", "$$ \\frac{\\partial P}{\\partial T} = -\\frac{\\partial Ar}{\\partial T \\partial V} + N_T \\frac{R_{G}} {V} $$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "_Note: \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "$$\\begin{bmatrix}\n", " T \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial T}\\right)} {P_l} - \\frac {\\left(\\frac{\\partial P_{y} }{\\partial T}\\right)} {P_v} \\right) & x_{12} & x_{13} & \\dots & x_{1n} \\\\\n", " x_{21} & x_{22} & x_{23} & \\dots & x_{2n} \\\\\n", " \\hdotsfor {5} \\\\\n", " x_{d1} & x_{d2} & x_{d3} & \\dots & x_{dn}\n", "\\end{bmatrix}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$J_x = \\begin{bmatrix}\n", "T \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial T}\\right)} {P_l} - \\frac {\\left(\\frac{\\partial P_{y} }{\\partial T}\\right)} {P_v} \\right) & \n", "-V_l \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial V}\\right)} {P_l} \\right) & \n", "-V_v \\left( \\frac {\\left(\\frac{\\partial P_{y} }{\\partial V}\\right)} {P_y} \\right) \\\\\n", " \\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right) & V_l \\left(\\frac{\\partial f_{ix} } {\\partial V} \\right) & V_v \\left(\\frac{\\partial f_{iy} } {\\partial V_{y}} \\right) & \\\\\n", " 0 & 0 & 0 & \n", "\\end{bmatrix}$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected character after line continuation character (<ipython-input-1-72792e2ef634>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-1-72792e2ef634>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m J_x = \\begin{bmatrix}\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected character after line continuation character\n" ] } ], "source": [ "J_x = \\begin{bmatrix}\n", "T \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial T}\\right)} {P_l} - \\frac {\\left(\\frac{\\partial P_{y} }{\\partial T}\\right)} {P_v} \\right) & \n", "-V_l \\left( \\frac {\\left(\\frac{\\partial P_{x} }{\\partial V}\\right)} {P_l} \\right) & \n", "-V_v \\left( \\frac {\\left(\\frac{\\partial P_{y} }{\\partial V}\\right)} {P_y} \\right) \\\\\n", " \\left(\\frac{\\partial f_{ix} } {\\partial T} \\right) - \\left(\\frac{\\partial f_{iy} } {\\partial T} \\right) & V_l \\left(\\frac{\\partial f_{ix} } {\\partial V} \\right) & V_v \\left(\\frac{\\partial f_{iy} } {\\partial V_{y}} \\right) & \\\\\n", " 0 & 0 & 0 & \n", "\\end{bmatrix}$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Envolvente para mezclas" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "$$ f_i = ln K_i + ln \\hat\\phi_i^v(T,P,y) - ln \\hat\\phi_i^l(T,P,x) = 0 $$\n", "\n", "$$ i = 1,2,... C $$\n", "\n", "$$ f_{C+1} = \\sum_{i=1}^C(y_i - x_i) = 0 $$\n", "\n", "$$ f_{C+2} = X -X_{spec} = 0 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ x_i = \\frac{z_i} {1-\\beta+ \\beta K_i}$$\n", "\n", "$$ y_i = \\frac{K_iz_i} {1-\\beta+ \\beta K_i}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ J_{ij} = \\frac{\\partial f_i}{\\partial ln K_j} = \\frac{\\partial ln K_i}{\\partial ln K_j} + \\frac{\\partial \\hat \\phi_i^v}{\\partial ln K_j} - \\frac{\\partial \\hat \\phi_i^l}{\\partial ln K_j} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial ln K_i}{\\partial ln K_j} = \\left\\{ \\begin{array}{lcc}\n", " 1 & i = j \\\\\n", " \\\\ 0 & i \\neq j \\\\\n", " \\end{array}\n", " \\right.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial ln \\hat \\phi_i^v}{\\partial ln K_j} = \\sum_{k=1}^C\\frac{\\partial ln \\hat \\phi_i^v}{\\partial y_k} \\frac{\\partial y_k}{\\partial ln K_j} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial y_k}{\\partial ln K_j} = 0 $$\n", "\n", "$$ k \\neq j $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial ln \\hat \\phi_i^v}{\\partial ln K_j} = \\frac{\\partial ln \\hat \\phi_i^v}{\\partial y_k} \\frac{\\partial y_k}{\\partial ln K_j} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial x_i}{\\partial ln K_j} = K_j \\frac{\\partial x_i}{\\partial K_j} = \\frac{ \\beta K_j z_j}{(1 - \\beta + \\beta K_j)^2} = -\\beta \\frac{x_i y_i}{z_i} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{\\partial y_i}{\\partial ln K_j} = (1 - \\beta) \\frac{x_i y_i}{z_i} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finalmente, el termino $ \\frac{\\partial ln \\hat \\phi_i^v}{\\partial y_k} $" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Descripción del algoritmo\n", "----------------------------" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Donde $J_F$ es la matriz jacobiana de la función vectorial $F$, $Λ$ es el vector de variables del sistema $F=0$, $S_{Spec}$ es el valor asignado a una de las variables del vector $Λ$, $\\frac{dΛ}{ dS_{Spec}}$ es la derivada, manteniendo la condición $F=0$, del vector de variables con respecto al parámetro $S_{spec}$. Observe que si $S_{spec}=Λ_i$, entonces $\\frac{dΛi} {dS_{Spec}} =1$. El vector $\\frac{dΛ}{ dS_{Spec}}$ es llamado “vector de sensitividades”.\n", "\n", "$\\frac{\\partial F} {\\partial S_{Spec}}$ es la derivada parcial del vector de funciones $F$ con respecto la variable $S_{spec}$.\n", "\n", "En la ecuación A.3-1 la matriz jacobiana $J_F$ debe ser valuada en un punto ya convergido que es solución del sistema de ecuaciones $F=0$. Observe en los distintos sistemas de ecuaciones presentados en el capítulo 3, que sólo una componente del vector $F$ depende explícitamente de $S_{spec}$. Por tanto, las componentes del vector $\\frac{\\partial F} {\\partial S_{Spec}}$ son todas iguales a cero, excepto la que depende de $S_{spec}$, en esta tesis el valor de dicha componente es siempre $“-1”$.\n", "\n", "Conocidos $J_F$ y $\\frac{\\partial F} {\\partial S_{Spec}}$ es posible calcular todas las componentes del vector $\\frac{dΛ}{ dS_{Spec}}$ ." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Con dΛ dSSpec conocido es posible predecir los valores de todas las variables del vector\n", "Λ para el siguiente punto de la “hiper-línea que se está calculando, aplicando la\n", "siguiente ecuación:\n", "\n", "\n", "$$ A_{next point}^0 = A _{conve. pont} + \\left(\\frac{dA}{dS_{Spec}}\\right) \\Delta S_{Spec} $$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Aquí Λ0 next point corresponde al valor inicial del vector Λ para el próximo punto a ser\n", "calculado.\n", "Λconv. point es el valor del vector Λ en el punto ya convergido.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Por otra parte, el vector de sensitividades dΛ dSSpec provee información sobre la\n", "próxima variable que debe ser especificada en el próximo punto a ser calculado. La\n", "variable a especificar corresponderá a la componente del vector dΛ dSSpec de mayor\n", "valor absoluto. Supongamos que la variable especificada para el punto convergido fue la\n", "presión P, es decir en el punto convergido Sspec = P. Luego al calcular el vector de\n", "sensitividades para el punto convergido usando A.3-1, supongamos que se determina\n", "que la componente de mayor valor absoluto de dicho vector es la correspondiente a\n", "dT dP , esto implica que en el próximo punto a ser calculado la variable que se debe\n", "especificar ya no es P sino T. Esto es Sspec = T. Cuando existe un cambio de variables\n", "especificadas como el caso del ejemplo anterior, el vector de sensitividades se\n", "normaliza dividiendo todas las componentes del vector por la de mayor valor absoluto,\n", "en el caso anterior se deberían dividir todas las componentes por dT dP . Finalmente se\n", "aplica A.3-2 para encontrar los valores de Λ0 next point " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "La variable\n", "∆SSpec se computa en esta tesis de la siguiente manera:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Donde ∆SSpec _Old es el paso que se dio en la variable que se especifico para obtener el punto convergido. “N” es una constante impuesta por el usuario, e ITER es el número de iteraciones requeridas para el punto convergido. Note que para calcular ∆SSpec se utiliza la componente de dΛ dSSpec de mayor valor absoluto,\n", "\n", "\n", "max\n", "\n", "\n", "Λ\n", "dSSpec\n", "d\n", ", antes de que\n", "el vector\n", "dΛ dSSpec haya sido normalizado." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Referencias\n", "--------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[1] key" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[#] E.L. Allgower, K. Georg, Introduction to Numerical Continuation Methods, SIAM. Classics in Applied Mathematics, Philadelphia, 2003.\n", "\n", "[#] M. Cismondi, M.L. Michelsen, Global phase equilibrium calculations: Critical lines, critical end points and liquid-liquid-vapour equilibrium in binary mixtures, Journal of Supercritical Fluids, 39 (2007) 287-295.\n", "\n", "[#] M. Cismondi, M.L. Michelsen, M.S. Zabaloy, Automated generation of phase diagrams for binary systems with azeotropic behavior, Industrial and Engineering Chemistry Research, 47 (2008) 9728-9743." ] } ], "metadata": { "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 }	mit
aswolf/xmeos	notebooks/sympy-debye-integral.ipynb	1	33651	{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import sympy as sym\n", "import mpmath\n", "\n", "from sympy import \n", "sym.init_printing()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x, t = symbols('x t')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABYAAAAqBAMAAABFIrbeAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIom7VJlmdt1E7xDN\nqzIhoty3AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVQYGWNgAAIhJRMQBQKMAQz+CWAWAwP7\nFwb+BiibcyXD/gNQNpCCq2Fg4G6CCzNqRMPZDAxaExAcrtVQNqMAA/MXKJv/N4LNZcDA8REqzpbA\n4F8A0zs11BLGpCn9HwEI2aMeuwGmhDWB1QDOVmD7AGMzMCDEGRh2PYCLsx+HM1HVMIJCZXutZFHB\nrgeM34DBqsDWznGAQ4AdGHJMG5h+swjwqesKAMUZeOA2MjDwFyCMe/8AxmbZUM/AvgHCm+9gzyAG\nldhbvj0sAcIGAHw/Mg+gDGoPAAAAAElFTkSuQmCC\n", "text/latex": [ "$$\\frac{3}{x^{3}}$$" ], "text/plain": [ "3 \n", "──\n", " 3\n", "x " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3/x3" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAADUAAAAwBAMAAABDBS/wAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEN0iVJnNiUSru3Yy\nZu9l18v4AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJUlEQVQ4EWNgQAbsWycic1HYsQxRKHxkTjjD\nfWQuGhu3PgbGN2hqkbjbNiBxkJiMykAODjNZPgLl7B2QVCOYrAlA2+wvIASQWEwFDMoM+wSQRODM\nzvUrNxzfPhnOR2H4o/BQOWGoXBSeOgoPhcP4A4WLwuH4wIDVjSBF7AksB1AUI3FYJ0gj8VCZzGux\nBwmqqmHF+48TfKChP9kO4DZcHme0MjA44dZmqhaDJsnyEC6QAmdBGJKRoMwBAZ9hDBjNDZfjeAAT\ng9EQOcbqrQbcE4RhglAaItdkwFzAuqEAmxzzEwbRAxyzD2CT43y7+yJUnDFJCQjUwMrAZvJPQNMB\n4ULk0G1CkuMDym3A1ArWx53AIGqAQ45h694LmFKs877NxRRFEgEAQPFUyrHxXgoAAAAASUVORK5C\nYII=\n", "text/latex": [ "$$\\frac{t^{3}}{e^{t} - 1}$$" ], "text/plain": [ " 3 \n", " t \n", "──────\n", " t \n", "ℯ - 1" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t3/(exp(t)-1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#sym.integrate?" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "debye_fun = 3/x3Integral(t*3/(exp(t)-1),(t,0,x))" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\frac{3}{x^{3}} \\int_{0}^{x} \\frac{t^{3}}{e^{t} - 1}\\, dt$$" ], "text/plain": [ " x \n", " ⌠ \n", " ⎮ 3 \n", " ⎮ t \n", "3⋅⎮ ────── dt\n", " ⎮ t \n", " ⎮ ℯ - 1 \n", " ⌡ \n", " 0 \n", "─────────────\n", " 3 \n", " x " ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "debye_fun" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAALQAAAAPBAMAAAC/7vi3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrZAAAAAASUVORK5CYII=\n", "text/latex": [ "$$0.0192957656903455$$" ], "text/plain": [ "0.0192957656903455" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "debye_fun.subs({x:10}).evalf()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "def integral_as_quad(expr, lims):\n", " var, a, b = lims\n", " return scipy.integrate.quad(lambdify(var, expr), a, b)\n", "\n", "f = lambdify(x, debye_fun, modules={\"Integral\": integral_as_quad})\n" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'exp' 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-76-f9e43a4a1965>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(_Dummy_96)\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'exp' is not defined" ] } ], "source": [ "f(.1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "numexpr cannot be used with tuple", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-70-7d4510b09c98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlambdify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdebye_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'numexpr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/aswolf/anaconda/lib/python3.6/site-packages/sympy/utilities/lambdify.py\u001b[0m in \u001b[0;36mlambdify\u001b[0;34m(args, expr, modules, printer, use_imps, dummify)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 377\u001b[0m \u001b[0;31m# Create lambda function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m \u001b[0mlstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlambdastr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlambdarepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"lambda %s: (%s)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/aswolf/anaconda/lib/python3.6/site-packages/sympy/utilities/lambdify.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprinter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m \u001b[0mlambdarepr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdoprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 453\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0mlambdarepr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m:\u001b[0m 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string)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\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 234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_print\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/aswolf/anaconda/lib/python3.6/site-packages/sympy/printing/printer.py\u001b[0m in \u001b[0;36m_print\u001b[0;34m(self, expr, args, 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'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_xab_tostr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimits\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'Integral(%s, %s)'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/aswolf/anaconda/lib/python3.6/site-packages/sympy/printing/printer.py\u001b[0m in \u001b[0;36m_print\u001b[0;34m(self, expr, args, *kwargs)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mprintmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'_print_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprintmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprintmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;31m# Unknown object, fall back to the emptyPrinter.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/aswolf/anaconda/lib/python3.6/site-packages/sympy/printing/lambdarepr.py\u001b[0m in \u001b[0;36mblacklisted\u001b[0;34m(self, expr)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mblacklisted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m raise TypeError(\"numexpr cannot be used with %s\" %\n\u001b[0;32m--> 225\u001b[0;31m expr.__class__.__name__)\n\u001b[0m\u001b[1;32m 226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;31m# blacklist all Matrix printing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: numexpr cannot be used with tuple" ] } ], "source": [ "f=lambdify(x,debye_fun, modules='numexpr')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$3000.0 \\int_{0}^{0.1} \\frac{t^{3}}{e^{t} - 1}\\, dt$$" ], "text/plain": [ " 0.1 \n", " ⌠ \n", " ⎮ 3 \n", " ⎮ t \n", "3000.0⋅ ⎮ ────── dt\n", " ⎮ t \n", " ⎮ ℯ - 1 \n", " ⌡ \n", " 0 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(.1)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "lambdify?" ] }, { "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
mdastro/UV_ETGs	GAMAII/UtilitiesCodes/FetchSpecs.ipynb	1	2110	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import urllib.request as r\n", "import pandas as pd\n", "import numpy as np\n", "import time\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "gama_spectra_url = \"http://www.gama-survey.org/dr3/data/spectra/gama/reduced_27/1d/\"\n", "database_download_path = '../../../GAMADR3_SPECTRA/DATABASE/'\n", "gama_all_list = '../../../GAMADR3_SPECTRA/GAMADR3_ALL.txt'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "gama_to_download = pd.read_csv(gama_all_list, header=None)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "gama_to_download_squeeze = np.squeeze(gama_to_download)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cd ../../../GAMADR3_SPECTRA/DATABASE/" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for i in range(gama_to_download_squeeze.values.size):\n", " item = str(gama_to_download_squeeze.values[i])\n", " address = os.path.join(gama_spectra_url, item)\n", " r.urlretrieve(address, filename=os.path.join(database_download_path, item))\n", "print (time.process_time())" ] }, { "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 }	mit
giotta/EUR8217	notes/.ipynb_checkpoints/seance2-checkpoint.ipynb	1	2683	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Séance 2 - Analyse univariée" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tendance centrale\n", "\n", "* mode\n", "* moyenne\n", "* médiane\n", "\n", "## Position\n", "\n", "* quantiles\n", "\n", "## Dispersion\n", "\n", "* intervalle interquartile\n", "* variance\n", "* écart-type\n", "* coefficient de variation\n", " * standardise l'écart type (on peut comparer la variance de variable indépendamment des valeurs)\n", "\n", "## Forme\n", "\n", "* distribution normale\n", " * skweness=0, kurtosis=0\n", " * approche pour tester si distribution normale\n", " * histogramme\n", " * check skewness + kurtosis\n", " * test Kolmogorov-Smirnov (K-Smirnov)\n", " * on peut normaliser en transformant la variable pour appliquer des méthodes qui exigent distribution normale\n", "* coeff dissymétrie : skewness\n", " * asymétrie négative (à droite)\n", " * asymétrie positive (à gauche)\n", "* coeff aplatissement : kurtosis\n", "* test K-Smirnov\n", " * donne 2 outputs : K-S, P\n", " * P est un % (d'erreur) disant si la valeur K-S est significative ou pas\n", " * P parfois exprimer avec des \\* ou .\n", " * \\\\\\* P = 0,001 = 0,1%\n", " * \\\\ P = 0,01 = 1%\n", " * \\* P = 0,05 = 5%\n", " * . = P = 0,1 = 10%" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transformation de variable\n", "\n", "### Variables anormalement distribuées\n", "\n", "* normaliser, remède à\n", " * outliers\n", " * probl normalité\n", " * probl linéarité\n", " * probl homoscedasticity\n", "* vérifier normalité après transformation\n", "* méthode pour normaliser\n", " * log ou sqrt\n", " * 2 = asymétrie modérée\n", " * 5 = asymétrie importante\n", "\n", "### Variables centrées et réduites (z-score)\n", "\n", "* centrage\n", "* réduction" ] } ], "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
mgahsan/QuantEcon.py	solutions/finite_mc_solutions.ipynb	3	47239	{ "metadata": { "name": "", "signature": "sha256:ef11aa46317b28348e59461a81a9d8dc6f5fd73d218983f81faa0af0fbcb9f6a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "quant-econ Solutions: Finite Markov Chains" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solutions for http://quant-econ.net/py/finite_markov.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function, division # Omit for Python 3.x\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from quantecon import mc_compute_stationary, mc_sample_path\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Compute the fraction of time that the worker spends unemployed,\n", "and compare it to the stationary probability.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "alpha = beta = 0.1\n", "N = 10000\n", "p = beta / (alpha + beta)\n", "\n", "P = ((1 - alpha, alpha), # Careful: P and p are distinct\n", " (beta, 1 - beta))\n", "P = np.array(P)\n", "\n", "fig, ax = plt.subplots(figsize=(9, 6))\n", "ax.set_ylim(-0.25, 0.25)\n", "ax.grid()\n", "ax.hlines(0, 0, N, lw=2, alpha=0.6) # Horizonal line at zero\n", "\n", "for x0, col in ((0, 'blue'), (1, 'green')):\n", " # == Generate time series for worker that starts at x0 == #\n", " X = mc_sample_path(P, x0, N)\n", " # == Compute fraction of time spent unemployed, for each n == #\n", " X_bar = (X == 0).cumsum() / (1 + np.arange(N, dtype=float)) \n", " # == Plot == #\n", " ax.fill_between(range(N), np.zeros(N), X_bar - p, color=col, alpha=0.1)\n", " ax.plot(X_bar - p, color=col, label=r'$X_0 = \\, {} $'.format(x0))\n", " ax.plot(X_bar - p, 'k-', alpha=0.6) # Overlay in black--make lines clearer\n", "\n", "ax.legend(loc='upper right')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.legend.Legend at 0x7f385495ebd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VaP5cfj6ZoamkBR2gqhneMBGgTMEKtG/TnRXrFgKQv1BBQqPvpSqTkGhuhQm7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\nSMaHzSJ5mEcyMYvkERrMK1psgRYtbiIiwhugRUIIIYQwgYFFS2A74ub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"text": [ "<matplotlib.figure.Figure at 0x7f3854a4d890>" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First save the data into a file called `web_graph_data.txt` by executing the next cell" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%file web_graph_data.txt\n", "a -> d;\n", "a -> f;\n", "b -> j;\n", "b -> k;\n", "b -> m;\n", "c -> c;\n", "c -> g;\n", "c -> j;\n", "c -> m;\n", "d -> f;\n", "d -> h;\n", "d -> k;\n", "e -> d;\n", "e -> h;\n", "e -> l;\n", "f -> a;\n", "f -> b;\n", "f -> j;\n", "f -> l;\n", "g -> b;\n", "g -> j;\n", "h -> d;\n", "h -> g;\n", "h -> l;\n", "h -> m;\n", "i -> g;\n", "i -> h;\n", "i -> n;\n", "j -> e;\n", "j -> i;\n", "j -> k;\n", "k -> n;\n", "l -> m;\n", "m -> g;\n", "n -> c;\n", "n -> j;\n", "n -> m;\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting web_graph_data.txt\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Return list of pages, ordered by rank\n", "\"\"\"\n", "import numpy as np\n", "from operator import itemgetter\n", "import re\n", "\n", "infile = 'web_graph_data.txt'\n", "alphabet = 'abcdefghijklmnopqrstuvwxyz'\n", "\n", "n = 14 # Total number of web pages (nodes)\n", "\n", "# == Create a matrix Q indicating existence of links == #\n", "# * Q[i, j] = 1 if there is a link from i to j\n", "# * Q[i, j] = 0 otherwise\n", "Q = np.zeros((n, n), dtype=int)\n", "f = open(infile, 'r')\n", "edges = f.readlines()\n", "f.close()\n", "for edge in edges:\n", " from_node, to_node = re.findall('\\w', edge)\n", " i, j = alphabet.index(from_node), alphabet.index(to_node)\n", " Q[i, j] = 1\n", "# == Create the corresponding Markov matrix P == #\n", "P = np.empty((n, n))\n", "for i in range(n):\n", " P[i,:] = Q[i,:] / Q[i,:].sum()\n", "# == Compute the stationary distribution r == #\n", "r = mc_compute_stationary(P)[0]\n", "ranked_pages = {alphabet[i] : r[i] for i in range(n)}\n", "# == Print solution, sorted from highest to lowest rank == #\n", "print('Rankings\\n *')\n", "for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1):\n", " print('{0}: {1:.4}'.format(name, rank))\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Rankings\n", " *\n", "g: 0.1607\n", "j: 0.1594\n", "m: 0.1195\n", "n: 0.1088\n", "k: 0.09106\n", "b: 0.08326\n", "e: 0.05312\n", "i: 0.05312\n", "c: 0.04834\n", "h: 0.0456\n", "l: 0.03202\n", "d: 0.03056\n", "f: 0.01164\n", "a: 0.002911\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A solution from the [quantecon library](https://github.com/jstac/quant-econ/tree/master/quantecon) can be found [here](https://github.com/jstac/quant-econ/blob/master/quantecon/tauchen.py)\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
statsmodels/statsmodels.github.io	v0.13.0/examples/notebooks/generated/markov_autoregression.ipynb	2	483447	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov switching autoregression models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides an example of the use of Markov switching models in statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.\n", "\n", "This is tested against the Markov-switching models from E-views 8, which can be found at http://www.eviews.com/EViews8/ev8ecswitch_n.html#MarkovAR or the Markov-switching models of Stata 14 which can be found at http://www.stata.com/manuals14/tsmswitch.pdf." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:11.572063Z", "iopub.status.busy": "2021-10-06T09:59:11.571625Z", "iopub.status.idle": "2021-10-06T09:59:16.297953Z", "shell.execute_reply": "2021-10-06T09:59:16.298482Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from datetime import datetime\n", "from io import BytesIO\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import requests\n", "import statsmodels.api as sm\n", "\n", "# NBER recessions\n", "from pandas_datareader.data import DataReader\n", "\n", "usrec = DataReader(\n", " \"USREC\", \"fred\", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hamilton (1989) switching model of GNP\n", "\n", "This replicates Hamilton's (1989) seminal paper introducing Markov-switching models. The model is an autoregressive model of order 4 in which the mean of the process switches between two regimes. It can be written:\n", "\n", "$$\n", "y_t = \\mu_{S_t} + \\phi_1 (y_{t-1} - \\mu_{S_{t-1}}) + \\phi_2 (y_{t-2} - \\mu_{S_{t-2}}) + \\phi_3 (y_{t-3} - \\mu_{S_{t-3}}) + \\phi_4 (y_{t-4} - \\mu_{S_{t-4}}) + \\varepsilon_t\n", "$$\n", "\n", "Each period, the regime transitions according to the following matrix of transition probabilities:\n", "\n", "$$ P(S_t = s_t \| S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00} & p_{10} \\\\\n", "p_{01} & p_{11}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij}$ is the probability of transitioning from regime $i$, to regime $j$.\n", "\n", "The model class is `MarkovAutoregression` in the time-series part of `statsmodels`. In order to create the model, we must specify the number of regimes with `k_regimes=2`, and the order of the autoregression with `order=4`. The default model also includes switching autoregressive coefficients, so here we also need to specify `switching_ar=False` to avoid that.\n", "\n", "After creation, the model is `fit` via maximum likelihood estimation. Under the hood, good starting parameters are found using a number of steps of the expectation maximization (EM) algorithm, and a quasi-Newton (BFGS) algorithm is applied to quickly find the maximum." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:16.301115Z", "iopub.status.busy": "2021-10-06T09:59:16.300468Z", "iopub.status.idle": "2021-10-06T09:59:28.524736Z", "shell.execute_reply": "2021-10-06T09:59:28.525279Z" } }, "outputs": [ { "data": { "image/png": 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D1Atj7mBR59/ApBd9TitMeqn4VBQ7qtk5ruZBMAB0NpgwKWf4iEX+5hcn8O1nLtV6N+qKyQUp89KWFgR3N5ph1mtXhS74oBwET3pCmPYuj/NG9HwfUmmyG40n8Nc/O47//ENxmWVvKIojI27csNGJt121Bls7Hfinh08jEInlfzJRNXzhGLqbzAAAtwoSp2lPGJFYAr86Oo7vl7HqODDlxcEhF956VS9abUbMUia47piTj4kIgi0GHUx6Deb9dKxqyYUZH274f3/AH85OF/ycgWlvUgoBSJlgYPF6ogZlB8GMsV7G2OOMsTOMsVOMsQ8Xu412hwmzvnBFG2cEIrFl1abZHYjghy8O41dHyUc5lSlPCDajDjbjUnc/jYahv92GwemVHwQfuuyCWZ4ZnxxbHtlgMXMfnQ8inihffjDtDSPBgWcvzBaVWXjh4jziCY7rN7ZAq2H41N3bMb4QwlcOXCh7nwj18Idj6HCYwRiwoIJX8JwcALU7jPj0g6dxfNRd0nZ+8MIwDFoN3nhFL1rskv9sQoXvM6EeIuPbYluUzDmtRsoE15iTYwvgHHhqcLagx4eicVye9SeL4gCgp0nOBKuoHFAjExwD8FHO+VYA1wD4AGNsWzEb6GgwgXPpwlYp3vWtF/En331p2ej/nj4/iwQHzk16VQkaVgrT3hDa0/TAgv42+4qXQ0RiCRwbdeN1e7vAGHBi1FPrXSoIUcwQiSdUWfURBZKzvgjOFZH9f/r8LEx6Da5Y2wQAuGpdM167pwtfe/IihudIklUv+MIxOEw6NJj1qmSCRdvcz75xN1psBrzve4eLbsQRjEgFcXft6ECz1YAWmxHxBIeLGnrUFaIArsm66AlMXeNqz6B8bT44NF/Q4y/O+JHgUtG7oNVmhEGnUbU4ruwgmHM+wTk/LP/tBXAGQHcx2xD6TnFhU5t4guP42AKeOT+HX59cHvq/J85Jlf/BaJz00ilMLoTQniaFEGxqt2HGG17RXaZOji8gHEvgpv5WbGi14cSYu9a7VBCpg9bQnL/s7aWOFc+cL9xZ5unzs7hqnRNG3WKDg0+8Yit0GoZ/ePh02ftFqIMvHIPVqEOjWa+KJlhkATe22fClt+/DtDeEj/74WFFZ3IeOj8MbiuFtV0sNeUSmkXTBlYNzjqcGZxCMFL6KO+ePwG7ULTnHKQiuPeenpSD49LgH3lD+c1qs6m5KCYI1GoYelb2CVdUEM8b6AOwF8ILCffcxxg4yxg7OzCy1dhJBzVSFdMEj8wFEYgloNQz/9PCZupdFcM7x5OAM+pxS6v/sxPLI9lWDKU84RxAsnSyD0ys3G3zosqQHvqKvCTu7G3BimcghxtzBZLW2GhnXiQVpEGyxGfDs+cKW1yYXQjg/7cMNG51Lbu9oMOGDL9uI352ewlODy8t2bqXil4PgBotBFdvDVJ3o3jVN+D+v2obHzk7jq08WLoP5wYvD2NBqxdXrmgGkBsGkNa0Uz5yfwzu/9SIePFa4LHDOH0GzXAwncFoNydUAojYMTnvRZNEjwYEjw+68jz836YVOw7Cuxbrk9p5mi6qtk1ULghljNgA/A/CXnPOMqI1z/nXO+X7O+f7W1tYl93U2VDYTLGYgH71jE8bcQXzjyYsVeR21ODflxZQnjD++YR00DDhDXqYApMmBJIdQDoI3tkkC+pVcHHdwaB5rmi1os5uws7sBU55w0jaunhl1BbC/rwk6DVOlOG7KE4JJr8Ed2zvw/MW5guoJnpGD5es3tmTc9yc3rIPVoMVjZwov2iAqQzgWRzTOYTfJmWAVguBZn5QdFFXm77p2LV69qxP/+ui5pNtKLs5MeHBk2I23XrUGjDEAQKvdIG+bguBKISwVi0mQzfvDyQm3YKVlgjnnePj4RN0n9ATReAJDcwG8dk83NAx46XJ+ScTAlA/rWqwZLdN7muowE8wY00MKgL/POf95sc9vtOhh0Gkq5hAhMoPvuGYtXrGjA18+cCGZSapHhBTijm0d6GuxUiZYZt4fQTTOs2qCuxvNsBi0Se2REv/93GW8eKkwTVK9wTnHoSEX9st61p09DQBQ99lgzjnGXEGsdVrR02RWKRMcQofDhBs2tsAfiRdU6PTM+Vk4rQZs7XBk3GfUadHVaK7YRJwoHF9IcuqwGrRotOhVKoyLJK2yAIAxhn95wy4YdBo8dDx/lvEHLwzDoNPgjVf0JG8TmeAZaphREQ4NufDcRUnqVExR25wvkmyVLGi2GRCMxouSVdQzL16axwd+cBiPnloe8s4hufPb7t4GbO9qKCgIHpz2YlOHPeP2niYz5v2RJa3Vy0ENdwgG4FsAznDOP1fiNtDhMFU0E9zuMMJh0uNvXrkVcc7x/+q4ZeoTAzPY0mFHR4MJWzsdOEuZYACSFALI9AgWaDQM/W22rJnghWAUn3rwNH7wwvJpMpHK0FwAs74IruiTguBtnQ5oGHC8zv2CZ3xhhGMJ9DSZscZpxdC8OprgjgYTrl3vBGP5dcGcczx9fhbXbWyBRsMUH9PRYMLEMsiqr3REo4ykJlglOYTTtjQwshl12NHVkPf8CUXj+OWRMbxqZycaLYuBdINZD72WrQpNsDsQwV2ffxLPXihMeqQGX378PJoserQ7jEVlcef8kYxM8GLr5JUxYTkgdwud9iyP9yMSU/1tduzva8LRETciseyrd8GIVAu1qS0zCO4VDhEqZYPVyARfD+CdAF7GGDsq/7yy2I10OCrnFXx+xpdcKu9ttuC+G9fjl0fHcajAKsVq4g/H8NLledy8SZKMbO2wY3g+AJ9Ks57lzFSyW5xyEAxIlaTZHCKeHpyVq7mXZ2tl4Q+8f62kSbQaddjQaqt7m7QxebDqaTJjbbMFQ3OBsl1aJj0hdDaY0WQ1YFunIyl1yMbgtA/T3nCGHjiVzgYTJut4hWi1IMY6u+wOsRCMlm1DNu+PJH1jU9nV04hT4ws55TSnxhfgDcdw146OJbczxuC0rg6v4F8eGcPZSS+ev1ida+bpcQ8eOzuNP75+HboazQUHwZxzuNKy/gCSmeGVIok4IK8WLxfbNyFJXd9qxVV9zQhFEzg5nv26dX7aB86BzR22jPuEV7BaDTPUcId4mnPOOOe7OOd75J9Hit1OR4OpIoVxnHNcmPZhY+vih/n+Wzegw2HCpx48XXcej5K+keMmOQjeIi/dnqNscPL7kU0OAUgOEbO+MFwKg8MTA5Lec7kOhIeG5uEw6dDftvhd3tndgON1HgSLGXt3owVrnRZ4Q7GyKv4TCY4pz6I2/PqNLTg87MrZ8OLpwex6YEFHgxnT3sr6lRP5EUGwKIzjHPCGyksCzPoiaLFlBsG7exsQiiZy1hEIzfC+NU0Z97XYDSs+COac44cvjQBQ1581F1954gJsRh3edW2fVNRW4JjtCcYQS/CMCU+zdeV0jZvyhHBGlkgulwYgg9M+9DSZYTHokiuZB3NIIoTtZao9mkB0jRtR6btYFx3jACkInlwIqe7jO+kJwReOYWPKh2kx6PDXr9iC46ML+OnhUVVfr1yeGJiBWa/FfvmLsqVT2u8zpAtOyiHa7LkzwUBmcRznHE/IS0jLNQg+eNmFfWublizn7+xpwIw3XDFnlUL47anJnANSMghuMmONPICVUxw3J2vDRUHt9RtbEI1zvHQ5e4HTM+dnsa7FmjRbV6JT9isnjWdt8acEwY1muXVysPRzNpHgcrFU5uR5d08jgNySokNDLqx1WtBqz3x+yyroGndibAFnJ71gDFWx67w068fDx8fxjmvWosGil4vaCvuMhdwhPRMs5BDzK0C6Iq5jFoN22VzLzk8vrsa32U3oc1rw4qXs4/XglBcGrQZrmzPHa6fVAJNePa/gugmC2x0mhGMJVTwhUxFalNRMMAC8dk8XtnTY8WN5hlsvPDEwg+s2LPqYdjeaYTfqcHaSguBJTwhOqyGjWjQVYZM2kGaTdmZCctxwWg0lm9v/93OXc85eK4k7EMHgtC9ZFCfY2S0Vx9VKF3xmwoP7/ucQvpijffGoK4Amix42ow5rnZLdTTlewSLg75CD4Cv7mqDXsqxWadF4As9fnMP1OaQQqdubWEbFcZdn/cumQrxQknIIow6NFjkILuO64A5GkeCZgREArHVa0GDWZy2s5Jzj8LBbMQsMyEGwd3kEItlwByI5k08/emlEcmLZ1q6qNVU2vvbEBei1GvzJDesASFKGeX/ufRSITG96YZw49sslaMzFEwMzaLMbsae3cVlktuMJjgszviUrmFf2NePQ0HzWlfiBKS82tNmg02Ze6xlj6GlSzyatboLgZMMMlTNaQouysW1pEMwYw439LTg+ulA3F5HLs34MzQVw8+ZFCznGGLZ02nF2guQQ055QTj0wAHQ1mGAz6jCYlgkWs+fX7O5CIBIv+piHonF8+sHT+N7ztSmqOzws+wPLemDBti6pOK5WDhH/9tsBAMCZHJO0MXcwmYEVmeByHCJEkCrGDItBh71rmvBMlqKdoyNu+CNx3JBDCgFU3qpRbeZ8Ydzx+SfxtSfq2/KxWFLlECIILscrWHgEpxfGAdL4uqunAUdHlM+fUVcQM94w9q3NHgTP+cPLphNpOtPeEK7+v4/hy1nahgcjcTxwdByv3NGJ7V2SJWMlr5cTC0H87PAo7r2yN5l5b7EZEI1zeAqQxAgv4PTCOJtRB4NWsyyCxlzE4gk8NTCDmze1osVWXMFgrRhzBRGOJZbEYFf2NcMViOLirHL9zsCUD5vaM/XAgl4VbdLqJwhukL7wqgfBMz40WvSKerD9fc2IxBN1U1j0pGzUf1P/Uh9l4RCxXAdatZjyhtCRQw8MSBe1jW22DJu0A+emsbXTgS2y5Uqxg8fJsQXEErxmleAHL7ug0zDs6W1ccrvFoMPGNhtOFGARpjZHhl34/ZkpNJj1ODfpRSyLlnbUFUR3o1TMYDZo0WY3liWHEMVrImgFgBs2tuDUuEdRC/704Cw0DLh2fe4gWATV9WyfmMojJycRiSVwaDi/z+1yQsghbCYdGszSuF2OQ4Q4Z1sUCuMASRIxMOVVtM8Sk899axoVnysCNDUaetSCJ87NIBxL4AuPDeLybObqzCMnJuANx/DmK3uTE1g1PVrT+caTl8A5cN9N65O3CT1vIWO2eEx61p8xVpSsol45NuqGJxTDLZvbpPezDOQdovPbxhSnhyvlhjNKkghvKIoxd3BJp7h0eposKzEIli6SUypnYc5PSUVxwuA8FbG0nEtLWE2eODeDtU4L+tI6pGzpcMAXjlV08FkOTC5k7xaXyqZ2W/LEA6ST6tCQC7dsbkVTEQNqKqLDTa30ogeHXNje5YDZoM24b2d3I06Meao+Sfq33w6g2WrAR+/YhHAsgcsKEgfOOUZdgWRFLyAtQZeTCZ70hKDVsCWZves3OsE5kr6iqTx9fhY7exrRIGcVs9Fg1sOk1yybTPCDRyV/2xOj7hU1QU5mgg0pmeAyvILFuZ7eRUywq6cB8QTH6YnMZMihIResBi02Z7kgi2zlctUFPzU4iyaLHgatBn/7q5MZ36MfHRxBn9OCq9c1o7dZOofVKkhKZ2Q+gPtfHMZr93Qv0e4vBsH5P2PxGCUnkJXQMOPAuRlomDTpd1oN8IZjCMfqYyU7G0qr8X1OC1psBkV54S+OjAEAdshSPyV6m81YCEbhKaD9cj7qJghusxvBmPp6vFR7tHScNiPWt1prpvNMJRyL49kLc0lrtFREcdxq9guOxhOY8xcaBNsx64skB7xnzs8hluC4eVNrcnAsVhd8ZESaKM3U4GIXiSVwbMSdIYUQ7Ox2YNYXrpjFoBLPXZjD0+dn8f5bNuAKeTJ5RkGyM+ePIBRNLAmC1zSX5xU8sRBCu90IbUqB4K6eRlgN2iVWaaFoHJ/4+XEcGnLh9i1tebfLGENng3lZeAWPu4N48fI8epvNcAWiK2qC7AvFYNZrodUwNJjL1wQni6UUCuMAYLe8unJMQRJxeNiF3b2NitpEILVhxvILrhIJyTv71s1t+Ngdm/DU4CweOj6RvP/ijA8vXprHm6/sBWMsWZVfieK4UDSOP/+fQ9BrGf7y9v4l94njVkjbY9EZUNTULNmOzbDsPZ2fGJjBvjVNUsGgPKlz+et7FWJw2oc2uzF5LgPSWLt/bTNeTIu9Ls368c+PnMXNm1pxU3/2lTsxSRqdL3/cq5sgWK/VwGk1qlrlPucLY94fyRoEA1I2+NCwq+ZWaQcvuxCMxhWDYJGFWM2d42Z9YXCOgoLgdIeIJwamYTfqcMXaJjRZyssES13rqmuhdWp8AeFYIukYks5OucL9RJWK4zjn+NffnkO7w4h3XLMWG9ts0GmYooPJaNIjeDGzs9ZpKUtbOOUJJYvYBHqtBlevd+LZC1ImeGjOj3u+/Czuf3EE779lA953y4aCtl3Jpj1qIrqcffzOLQDqv2tgMfgjMdhMOgDScbUatGXLIRgDmrKsBLQ7TOhwmHAsTVLkD8dwZsKbnOQpIYLg5ZgJPjm+gHl/BDdtasU7r+3Dzu4GfPqh08ns2o8PjkKrYXjjPqlLXqvNCJNeo3ommHOOT/7iJE5PePCFt+xNBtuC5iKK2ub9kawZ/+WeCZ71hXF8dCEZIyyXBiCpzhCpXLmuGaOuYFJ+Fk9wfOwnx6DXMvy/N+xSXL0XqOkVXDdBMCDpgtXMZmUriktlf18z3IEoLsxkb7VbDZ4cmIFey3DN+swKdqtRh7VOy6rOBIvAJJdHsEBUoQ5OSTrqA+dmcP3GFnmiVXwQPLkQwsRCKPk9qvZAeijZJEP5Yiw6x1UrEDpwbgaHhlz40Mv6YdJrYdRpsbHNphgEj6XYownWOsvLKE0sZAbBgGSVdmnWj+8+exmv/s+nMeYO4lvv3o+P37UlayYvnc6G5REEP3BsHLt6GnDH9nYYtJq67xpYDL5wHDajLvl/o8VQXibYF0aTxZDzO7CrJ7Nz3LFRN+IJnrUoDkCy1mRuGQbBT8rFwjf0t0CrYfi/r9+JOV8Y//boOcTiCfzs8Chu3dyWLEZmjKG3yaJ6Jvh7LwzjZ4dH8eHb+nGrwoqNswiP32xNUYDlHwQ/JdcM3bJZ+oyWQwOQZJ8GpSA46RcsXd++/uRFHBpy4R9et0NxfE9FdI0bUWEFrL6CYIdZ1QvQ+Zn8QfCVfdIScy11wcFIHA+fmMCVfc2wpgz+qWzpsK9qr2DhEVxIJrizwQS7UYeBKR8Gp32YWAglHTccZj00DIoFVNk4KkshXr6tHUD1dcEHL7vQ22zO6oxhNmixqd1elSA4kZCywL3NZrx5f2/y9i0ddsVJmpipdy+RQ8hewSXogjnnUstkhznjPmGB9ncPnML6Fise+tANuG1re1HbF017qrkydGzEXVRh1cUZH06OeXD37i4YdVps6bTjxJi7cjtYZXyhKKzGxeVsqWtc6Rf6OV9mG910dvc24tKsHwspwbZY/dnXmz0IbrIYoNUsz9bJTw7MYnuXI5nN3tnTgHdd24f/fn4I//HYIGa8Ydx7Ze+S5/Q2W1QJPASHh1349IOncOvmVnz4tn7Fx5j0WlgM2oLkEEotkwVOqwG+ZaChzcaBczNosRmwvUtqoJVsAFLH370pTxjecGyJPZpgW6cDFoMWL12ex9lJD/79dwN45c4O3L27K+92Gy16WA3alZkJVlMOcX7aB4tBi66GzAumoM9pgdNqwMEatlD+51+fwagriPffsjHrY7Z0OHBpzq9YwbwamPaKTHD+IJgxho3tNgxMeXHgnNQl7hY5CNZqGBotBswXoQk+MuyGQatJLkNVMwgemPLimfOzuDKLHliwo7sBJ0YXKl4g9ZtTkzg17sGHb9u0xK95a6cDEwshuNM+11FXEA1mPRymxaXocryCveEYApH4EmcIweZ2O27a1Io/ur4PP37vtRnLqoXQ2WCSXECqtMToDUXxxq8+iy8fyO6znM4Dx8bBGPDqXdLFYme3lMUs9tg/e362qMlgtfBnZIL1ZWuClTyCU9nVI/ttp0wmDg25sLHNlrOgUqORXAeWmxzCG4ri8LAr2ZlU8JE7NqHVZsR//OE82uxG3Lp56f1rmi0YmS+/7TkgjaPv/95hdDSY8O/37lnSBCgdp60wZ4c5n3JTFGB5ZE6zEU9wPDkwg5v6W5OfUzEZ8lohVuM3KATBOq0G+9Y04bkLc/jIj47BYdbhH167I6cMQiA06iMrSRMMSHo8VyCqmg/h+WkfNrTacp5cjDHs72tKpuSrzePnpvHfzw3hj69fhxtyCMG3djrAeWYntNXC5EIIOg3Lm9ERbGqzY3DahwPnZrC53Y7OlIlQk0VfVDHBkRE3tnc7kjZf1QqCz0568NavPw+zQYsPviz7BAmQLuJz/oiqhaWRWALnJr146Pg4/v13A/jA9w/jk784gQ2tVrx+b/eSx27tlLITp9NWK9KdIQDp87cbdSUtqyZlMQpBMGMM//3HV+HvXrNdsTCmEIRLTbUkEcdGFhCNcxweKmz84ZzjgWPjuKqvOblkuKunAd5QrKjMuj8cwzu+9QL+65lLJe13JfGGYxlBcHk+wRFFj+BUdnU3AlhsOiM1yXBltUZLZTl2jXvuglQsnG7H6TDp8f+9ZhsA4A1X9GRISHqazPCFy2t7DkhB3YfuPwxXIIKvvP0KNFpyj+vNVmPeYI9zDlcgtyYYqO/MaTZOjC3AFYgu6SHQYNZDq2F1bfsmXJr625TdVfb3NWFw2ofTEx788z278p6nqfQ0mVdeJlhk+dTKBp+f9imm4dO5sq8Zw/MBTFe5KnzOF8bHf3ocm9pt+Phdm3M+dmvSISJTEvHs+dnkjGulMuUJo81uzDmhSaW/3YZ5fwQvXJpPZoEFTmvhJuOxeALHR93Y09uYtEOqhkPE6XEP3vaNF6DTMvzwvmuwvjX393hHBTrHveo/nsKdn38SH/zBEfzHHwZxanwBV6xtxr/fu2eJMwOQ2t576SRtzL3oESxgjGGN01KSHEIE+UqZYDXorHLXOOFDe2JsIavPciqnxj24OOPH3XsWlwx3igCuCDnMmDuIBEfNayGU8IdjS2RhDWZDWYVxuZbIk69h0WNdixXHRtwAgIuzfrgD0ZxFcYIWmwEzyyywenJwBlaDVvH9vWpnJ77xrv14v0Ix6RqVHCIeOTGB5y/O41N3b89phSVwFqDn9YRiiMZ5djnEMu4a98S5GTAG3JgyadFoGJos+rp+P+enfWgwK/dpAICrZDnqG6/oScoNC6WnyYIxV7DsVYm6CoI7VOzY5A1FMbEQUkzDp7NfPhAHC8zGqAHnHJ/4+QksBKL4/L17YdLnzlz1NllgMWgzgoynB2fxjm+9gL+4/8iK8gpNZ9qbv1tcKsJoOy5bo6XSZC184Dg76UUomsDeNU0w6bWwG3UVzwSfGl/A2775PIw6DX5037V5A2BA0ldpNQzPK/jkloI/HMPgtA9vvKIHD//FDTjz6btw4H/fim++ez92yW4UqbTZTWixGZY4mEgewcElzhCCtc7SCmym0rrFqY2aY1AhiCA4FE3gXAGrPA8eG4dOw/DKHZ3J2/rbbTDqNEU1TBEZlIszpVvVVQq/UiY4EC1pfIvEElgIRrMukaeyq6ch6RAhMvPZ2iWn0mozYrZG/uGl8uTALK7d4FRsQc8Yw8u3tcNuypSBCIlROS1r4wmO/3hsEP1ttiV1BbkopKgtW6OM1G2kPm45cWBgGrt6GjOK/pqthrrObA/KichsEodr1jvxuTfvxqfu3l70tnuazPCGY2U3qqmrIDjZtlSFjOwFeXDPVRQn2N7lgEmvwUtV9Av+ycFR/Pb0FD525yZsk4XuudBoGDZ32JdkgkfmA/jQ/Ydh1GlxesKDw3Ihx0pEKoYqPgi2GrTJSY6g2Vq4JviInBnaK3uJttqNFQ2CT44t4G3feAEWvRY/vO+ajMYp2TDptXjtni78z/NDyWxWOYy5Ja3VTZtasb2rIe8kDZAkEantk12BKAKReIYcApC8gkddAcSLLECbWChcG14KzRYDDFpNVTLBnHMcGXbjWtkR5mie45ZIcDx4bBw39rckm74Ako3Yti5HUasAwrru8py/5vaQ6aTLIRrMekTiCQRLkMkJP/B8mmBA8pqe8oQx5Qnh8LALDpMOGwqYgLbYjbKFY319jtm4POvH8HwgQw9cCGp4BT9yYgKD0z58+Pb+glf2nFYD5vyRnJ+xcOhozjLhyaWhredj5w5EcGzErWifWu+OF9mcIQQaDcM9+3qyGgLkIukVXGahZl0FweLCpkYWRsgDCpFD6LUa7OltrJoueGjOj79/8BSuXe/En96wPv8TZLZ0LLZPDkXjeO/3DiGW4PjJe6+F1aDF958fquBe15YpT6ggezRBu8OIRose121sych2NFkMcOUZUAVHh91osRmSgVyL3VgxOcTAlBdv/+YLsBl1+NGfX5ssICuUv3vNdrTZjfhfPzpadgGlyBQqBbDZ2NrpwMCUL7msn2sba50WROMc4+7iBrBJTxAtNoNiBksNNBqGNocx2Zq5klyc9WMhGMXr9nah2WrIO3k5NOzC+EJoiRRCsKu7ASfHFgoOaMWFIxRNYMpbXRnYsxdml7gwpBKNJxCJJZZcFBvLaJghtLrZlmNT2dMrLcsfG3Hj0JAL+9Y2FRSktdgMCMcSyU539c6TstVWuh64EGxGHZqthpILklKzwKmrGflothoQiSXgzzGuieA2mxzCYVLW0A7PBbD3H36HpwdnFZ9Xa85MeJHgyhaZTlvh0r5qM++PYC5Pn4Zy6GuRguDU7rClUFdBsN0k2V6okQkenPbCoNUkNUz5uLKvGacnPMm+9ZXk//vVKWg1DP/25t0Fz4QBSRfsDkQx6Qnhb35xAqfGPfj8vXuwo7sB9+zrwUMnJuqy2rtcgpE4PKFYUXIIxhi+9e4r8fcKyyzNVgNiCQ5vAcf6yIgLe3qbkss5rfbKLH2OugJ417dehFGnwQ/vu6YkZ4MGsx7/9qbduDjrx7/8+kxZ+yP8fXsaCw+Ct3TYEYklcGnWv2Qb3UpBcIkZpcksHsFq0tlgqkomOHXJfXdPQ95M8ANHx2HSa/DybR0Z9+3saYQ/EsfF2cLkDWMp2ZNLVZRELASieMc3X8D3XlCesIvxN10OAZQWBIul4kIKbrZ1NkCrYXj6/CwGp30FSSGA1IYZy2PsfXJgBmuaLQWvMqXTKztElEIpWWAgtagt+9ibbI+dJQiWNLSZmdMvHzgPdyBa1ZXgYriQw+pVZMjrkUL6NJRDf5sdDpMOL1ws77jVVRAMSFXfahTGXZj2oa/FUrBJ/v6+ZsQTPO+FSA2Ojbpx9+4udBURYACLFfj/369O4eeHx/Dh2/qTPqjvuGYtIrEEfnJoRPX9rTXi+1CsDvSKtU0ZRVlAijYsz0VrIRDFxRk/9qZUiLfa1JdDzPnCeNe3XkQgEsN//8lVJQXAgus2tuBPbliH7z43hCdkM/xSGHUHYdBqkhf4Qkh3iFDqFidY4yzNK3iiSFlMKXQ0mKvSgvrwsBt2ecl9T69UJe0NKQd6sXgCj5yYwG1b25cEiIKdcnFRoX7Bo65A8uJUaOCsBpfm/Ejw7MXP3lBmENxgls5XdwlewYstk/NngoXf9s8OjYJzFFQUByyvrnGRWALPXZjDjTmciPLR22QuSRNcahYYWPyMcwV8+YJgQA4aU8b9MXcQPzs8CqC650ExXJjxwazXKo57zVYDFoLRqncxLQSRoa1UEKzVMFy1zll2HYwqQTBj7L8YY9OMsZPlbkutjk2SM4SyLYcSe9c0gjFUfDYYiEj2MkrZsXxs7pDez+9OT+G2LW1LzMU3d9hxZV8Tvv/CcEU0fr87PYXjRRTeqIm4YKqlAxV6yny64KPy+10SBNuN8IZjqvk1+8Ix/NF3XpK6m73nSmzpyK8Pz8f/vnMz+tts+N8/OZbh21soo64gupvMRWVrNrTaoNeyZPHmqCsAu0m3pGe8oLPBDL2WYWi+uAvPpELLZLURmeBK6wSPDLuwp7cRGg3D7t4GcJ6969+hIRfm/JGswcOGVivMem3BuuBRVxD71zbBpNfgchUv/sIbOlsxjz8iB8GmzEywp4QCmGIywYAkifBH4tAwqYFGISSD4GVQHHd42AV/JF6SHliwplmqyi9Wz19qFhgoLHEx6wvDZtTlrF9I19B+9cAFANIq1qXZ+nNKAaTi1fWtVsXPTEzuXCWO85WkkD4N5XLtBicuzwWSrZdLQa1M8HcA3KXGhtod5QfBoWgcw/OBgpwhBA6THls6HBXXBY+7pfemlKHMh8OkR5/Tgj6nBZ9TMBd/xzVrMTQXwFPn1dU2cS719P6T7x4sOagqh8lkEFx4VjIXzbInZT7pyJFhFxjDEjcEYZOmRtYnHIvjz//nIE6Ne/Dlt+9Ldi8sF5Nei3+/dw9cgQg++cuTJQVzY65Ma7N8GHQabGhdbJ+czRkCkGbxvU0WDBeRCQ5F43AHoks8nytBh8OESCwBV5leqLnwhqI4N+VNLrnvkQOubCtRj56agkGnybD7E+i0GmzvcuBEAUFwIBLDnD+C3mYL+pzWpHylGojXynb+CDmEVS05hD8CvZbBYSqs8Eac65s7HIoZdyVa7NJ4shwywU8OzECnYbhug7PkbfQ2WxBL8KICj3KywEBhzg65WiYnt2NbDIKnPCH86OAI3nhFD65Z78SlGX9dFshdmPFlLdCs5wYghfRpKJdr1kvXzHKywaoEwZzzJwGokkLtcJgw7Q2Xlc28NCstuRWbhr+yrwlHhl0F+XWWiigEKlYKIfj2H12Fn7z3OsXs2l07OuC0GvA9lQvkJhZCWAhGMeMN49MPnVZ124UwLVomq5QBLNQq58iwG5vb7UsuhiIInlYh6/M3Pz+JZ87P4TNv2FV0e9987OhuwF/evgkPH5/Alw9cKPo7LQWwxX9Ht3U6kg4mo3kC6WK9gicr7AwhWPQKVr7IX5otv3Oj1OEN2CcvuTdaDFjXYsVRBYcXzjl+e3oSN25syVlFvbOnAafGPXmPdVLv3WTGupbqBsHieGcLGBflEIvZvMakHKKUTHAYzVZDQV2ogMXOcYU0yRA0WwxgDMvCK/jJwRnsW9OkaH9WKKV4BZeTBQYW3T3yySHyBcGpGtqvPXER8QTH+27eiPWtVvgjcVXGdTUJRuIYcwdzBMGFSftqQaF9Gspha4cDDWY9nrtQ4yBYTTpUaFtajDNEKvv7muGPxHF2snJd2coNgte1WJOBWDpGnRZvvrIXj52ZKrrqPhciqLlhYwt+fngMj5+dVm3bhTDlCcEse/SqQSFBMOeSPnxv2sWwVV76LFcXzDnHg8fH8ZYre/GGK3rK2lY23nvzBtyxrR2fffQcXv2fTxc8Ww5F45j1hUtardja6cCUJ4x5fwRj7tyB9NpmySs4NfsyuRDC+79/CI+dmcp4fKUbZQhyeQUHI3G88gtP4TOPni3rNURR3J6UJffdKT61qZyZ8GLUFcQd23NPlHb1NCAYjSftIbMx6l4aBA/PByo68U9FBNzZghl/WJpc2IyLQZpJr4FBqym5MK4Qj2DB5nY77tnbjTcV6F8LSFn4Zkv9t072hKI4OebJ2Zm0EHqFNVWBDhGJMrPAAGAx6GDSa3J2R5vzRfK6gAgN7eRCCD94cQiv29ONNU4L1rdIsUKlmsdEYgl86sFTRdc7XZr1g3NgfatyEWMhk4NaUEyfhnLQaBiuXteM58sojqtaEMwYu48xdpAxdnBmJnvBTrJr3ELml/0TPz+BLz1+Pu9rDU77oGFSwFgMwoKkkrrgcXcQGga0Zwlky+VtV60BB/DDF4dV26bQeH7hLXvQ32bD3/ziBDxZCngqwaRsj1ZoNicfFoMWBp0mpyb4kmxftSdNF6iWHMITjCESS1SsaACQJAdfe+cV+Oo7roA3FMNbvv48PnT/kbzLmMIjuBTduugc99yFOfjCsZxB8BqnFb5wLDkZOTQ0j9d88Wk8cmISX33iQsbj1daGZ0PILZSK4w4PuxCMxvHA0fGyAsfDwy70t9mWrOjs6ZV8atOPz29PT4Ix5F0tSHaOy6PdTy1YXNdiRSzBy/baLBShCXYHlIt5FuUQi5lgxhgaLHoslFAYN+uPFOQRLNBpNfjcvXsyzvt8tCyDhhlCH93bXJ6cqLPRBK2GFZwJfunyPAanfXjfLRvKWhp35mmdPOcPF5QJBoDPPnoOkVgCH7hV6oq3Tg4yK9U85sTYAr79zGU8ca64YuWLsk45XyY4l2tGLRiQG/8Iv/5Kcs16J4bnA8nrVrFULQjmnH+dc76fc76/tTW7KD9bw4xxdxA/fGkYj5yYyPtaF6Z96G22FGTwn0pXoxldDaaKNp0Yc0vV7YW6VhRLb7MFt25uw/0vjahWMXp20oueJjOcNiM+88ZdmPKE8M+PlJcJK4ZpT1jVwIcxhmbZKzgbR+TvwN40myRpabX8TPC07M2aLauvFowx3LWjA7//yM348G39ePTUJG77tydw4Fz2bP5YDleHfAiHiN/Lmdx8mWAAGJoP4P4Xh/GWrz8Pi0GLe/Z24+CQK/kZCUQmuNKFca12I7QappgJfuGSNEGe80fwTIlLcJxzHBlxZ1hw7ZH/T5dEPHpqCvvXNuV16ljfYoXVoM1aXCcYdQVg0GrQajMmEwXVkEQsBKJwBaLok51BlFZivAoWaYDkFVxaJjhclMNJqbTYl0EmWJaTOMqQQgCSr35ng6lgh4jfnJqEQafBHdszrf2KIVd3NM65LIfIfazF/T87PIpX7+pKduPsdJhg0msqdh6IDHOxXtIXpv1gORJ6TbIUp940wYeH3ABQ9GSyFK6V9e3Plzge158cItkwY2lU/8ujY+BcLA/k1guXo0XZ2G5PZisqwcRCEJ0lSiEK5R3XrMGMN4zfnspcUi6FsxOepGvB3jVN+NMb1+P+F4fxjMoFeNmY8oZUz/41WQ2Y92e/qB4ZccFu1GFj2gxcLy99ltswQ2jP2uyVDegEZoMW/+vlm/DYR26G1ajDTw6NZn3saA5/33y02IxotRvxB1kykyuQXisHQ5/8xUl84ucncO2GFjzwgRvw3ls2gHNkfH+nPCHYTbqCC5ZKRathaLMbFb2CX7g4h83tdthNOjxwdLyk7V+c9cMdiGZIbbZ22mHQapKuJIDUFfLMhAd3KHgDp6PRMOzobsjrEDHqCqKr0QSNhiUvrtWwh7okj6tXrJWKWZSCRqXCOEAqjislCJ73RwqyRyuXFpuxYJ/gaDyBD/7gcNXddsTqnUOhnqRY1jQX1vacc45HT07ipv6Wss9bpy17dzRvOIZonBckhxB88GUbk39L54INF4uUQ8z5wtj/j7/PKzUrOQie8aGrwQyzQTmhp9UwNJr1dSeHODzsQm+zueJJHkCSMDVZ9HiuxOI4tSzS7gfwHIDNjLFRxtiflLotp03OwqRkgjnn+MXhMQBAIBLP6eEZiydwcdZXshZlTbO5rJaQ+Rh3B0vWAxfKzZva0OEw4eETpV2kUwlFJQP+rZ2LyxofefkmrGux4q9+drzizUU45xVpkOC0GnLayhwZdmNXb4Pi8p0arZNFlrNNJceLQulttmB3TwMGp7Lr3sfcAeg0rGTJzpYOe7Kfe65MsPBDPjPhwXtv3oBvv+dKNFj06G+zYX2rFb85Obnk8RMLwYp7BAs6FKwaw7E4joy4cUN/C16xowOPnppEqIRWvskmGWk+tEadFlu7HEsywb89LU0E8umBBbt6GnB6wpNzFWgsxbWj2WqAw6Srik3aUDIIlt63UtDoD8dg1GmgT1spazAbii6MC0RiCETiaC5CDlEqUhBc2JgwMOXFQ8cn8PvT6iQpCsUTlMbqcjPBgKQLLqRhxomxBYwvhHBnmVlgIHeLYHGu5gu6hDTmru0dGUv160soEj0y7MasL4xn82QhL0xL2y02CC4klqm31smccxwedhXcbKZcJF1w6X7BarlDvJVz3sk513POezjn3yp1WyILM5miCT417sHgtA+v2iWJ6nPpdobnA4jGeUYGr1DWNFvgDkQronlNJDjGF0LoaqzshVwr+46qUeB3ftqHeIIv8a816bX4zBt3YcwdxJcP5Ndol4MnGEM4lkCbyjPKphwDRzzBMTjlw/auBsX7VQmCPSITXN0gGJB0Whdn/IjElAOlUVcQHQ2lS3a2yZIIm1HZI1hg0mvxN6/cgq++Yx/++hVboJUnHIwx3LW9A89dnFsiWalGtzhBh8OUoc09PrqASCyBq9Y14+7d3fCFYyUViR4ZkZpkKI1Re3sbcWJsIenB+ttTk9jSYS+4hfbOnkZEYomkJk+JVOcPxljVHCIuzUpLu/vWNgJQ1jF6wzHFjGGjRV+0T7BYOm8pojCuVFpsRgQicQQi+YOc0+NSofFIlXTYAm8yE1z+SsoapwWzvkje9/ubk5PQahhuV8H9RnJ2UB53C+1Otr7Fivdc14dPvHJL5n2tVoy4glnHRSWEHeRAnmutyDAL95NCSCQ4Lkz7sSFLUZwgn1a62owvhDDlCVctCAYkq7RRV7CkToZ1J4cApCxMahXlzw+PwaDV4C9eJjWHyLVkMSicIUoUZIvK11LbQuZizh9BJJYoqeq+WDZ3OHB51l9SpioVEUhv6Vz6eV7Z14yr1zVXvN/6lLcyxVDNFn32rIInhEg8gb4sgYcaXeOmvWGY9dqKL+0rsandjliC43IW2c9YifZoAqEL7m405y1mvO+mDbhLoWL8FTs6EU/wpLYYkI5LpZ0hBB0KDTNekDMNV/U149oNTrTYjPhVCZKIw0OLTTLS2d3bgEAkjsFpL+Z8Ybx0eR53bCs8gNgld47LJokQzh+px7daQfDQXACdDlNy/Msmh7ApePpKmuDiLvQiMCimMK5UxDL8rDf/PoqOipW4xuRCJHbKsUcTiO/PSA6HCM45fnNyEtesb042KCqHZqsRoWhCMfAenPKBsewFZAKdVoO/v3u74qRyXYsV8QTHcBENfMSxzDXpjMQSGJKPdTGZ4ElPCMFoPO97qrdMcGo7+Gpx7QbJ8aSUbHB9BsEpWZhYPIEHjo3j1i2t2NRug9WgzWkBJGaE+WZP2RBLtJUYoJL2aBU2+wcknUyCL34epXJ2wgOjTqMYEO7pbcLpCQ/CMXW6pykxWaFiqCbZKkepwn9IDgj6WpT1rK12I2Z84bKM1ae9YbSp6HhRDGIZ8FyW7MWYO4juxtJbN4sguJxAeke3A92N5qQkIhpPYNobrpocorPBhEAknizUAqSiuM3tdjRZDdBqGF6zuxN/ODdd1KqRLxzDQEqTjHT29C4Wxz12dhoJjqIKitY6LXCYdFn1pkqtrNe12DC+ECx7wpyPS7N+9LVYYTPqYNBpFIuc/OEYrIbMILjBrIc/Ei8qSycyzYV2iyuHFnlFp5BagcVMcJWD4GAMGgZYs+hLi2FNAdfJ89M+XJz1K05yS8GZdELI/N4MTnvR21R8MXwqokiuGIcIEQRfnsuecBqe9ydXdnxFjBVCR5zNHk2QSytdCw4Pu2DSazISZ5Wkv82GZquhJKu0ugyC2x0mTMnLxU+fn8WsL4zX7+2Rlu5arTm9/C5M+9DhMJU8213jLN4IvFDK9Qguhs0d0gmda4ZaCGcnvdjcYU8uVaeyp7cB0ThPWqhVgqQtlsoFZGJAVdIZigKebJngFpsRkVhiSYBULNOeUE2kEIA0qGoYFHXBkVgCk55QSUVxqds367UFL+ErIVwtnhqchS8cw4w3DM6BjipMIIHF1xGTsGg8gUNDLly9frGr3927uxCJJfBomnY5F8dG3EjwTD2woM9pQYNZj6Mjbvz21BS6G83Y3lV4K23GGHb3NuLoiHImeFQOvFKPb1+LBZyjqMYlpTA0JwXBjDG0Zikk84ayyyEAJLXmhZBsmVyFwjjhH55PF8w5x+kJDxgDpjzhiiYQ0vGEonCY9apMvHsLaJjx65OStd+dRaxk5CKXv7sajRmKLRL1hqIYmgtgS4eUcMoWl5yX9cB2k66oTLAIxvNJO0V9S7FtrCvF4WE3dvU0Zuj6K4lGw3DN+mY8f3Gu6ORUXQbBHQ0m+MIx+MIx/OLIGBrMety6RbJV29BqyzlTG5z2ob+99JPBYdKj0aKvSBA8lgyCK5/N6nNaYdBqsmb7CuXspAdbOpRndLtl+5NjWVq9qkHSRUHlArKmHAPq0FwABp0ma9ZRFF+UI4mY8Yar5gyRjkmvRV+LFecUguDJhRA4Ly+Lq9dq8KM/vybpwVkqd+3oQCSewB/OTieLYaslh1jsGie97qlxDwKROK5atxgE7+ltxJpmCx44VrgkQqlJRioiiH3+4hyeGpzBy7e1Fx207OltxLlJj+Ky8VhKowyBaBRwabYyjQKATHs0p03ZUswfUZZDNMitzovxChYNl6ojhygsCB51BeENxXCFvBIwVkVdsCcYVaUoDpACL4tBmzOb/ZuTk9i3pgltKq3eNCcbQyz9jGPxBC7O+LGxjOs+IK02tNgMuFRgJlhIBV+3txuAJMlQQgTHu3sai9IEX5jxwW7U5S32a7YawDmKlgtVglA0jtPjC1WVQgiuWe/EmDtYtOd5fQbB8klzYdqHR09N4tW7OmHUScsc63Ms3SUSPGef7UKR7F/UH5wmFkKwGLQ5i4XUQqfVYEObTTHQKZQZbxizvsiSorhUOhwmtNmNFQ2CJxdCaLToy1rmUqLZkj0Ivjzrx9pmS1ZjdzWC4GlvuCr2MdnY1GZXHLRFprCnzNWKXT2NZS9DX7GmCa12I35zcqJqLZMF6VaNST1wShDMGMNr93ThmfOzGZ7G2Tg87MLGtCYZ6ezpbcTluQDCsUTBrhDpz09w4OSYJ+O+UVcQei1bMgETsp9Ls5XLBKevrmQrcvKH44qtoRvlz6sYm7R5XwQWgxYWBXmF2jgL1ASL5XNxXKtZHOcJxVQpigOk734uh4jhuQBOT3hwlwquEAJR4JguhxieDyAST6C/rfzl9/UttmSDinyIorhX7eyEXsuyXmsvzPjQ2WBCm8NYVCb4wowP61uteSfBzfI4Ww+SiJNjC4jGeVFtx9XimvWSX3CxLZTrMwiWszDfffYyQtEE7tnXnbxvfas16ReczoQnhEAkXlYmGJCWeiqlCe4qoFhILTa32/JWreZCtEvOpu1JLr3m8bv8zclJnBrP7V2ajSlPSHUpBLCYCVZqmHFZXrbNRrlBcCAirXJU2x4tlU0ddkUd22JL3dI1wWqh0TDcsa0dj5+dSZ7v1coEi2BbZIJfvDSP9S3WjOz9a/d0IcGBR45nNvGJJzguzfrxm5OT+MLvB/GB7x/Gsxfm8l4g9vRKxW0NZj2u6mvO+VglxArN0RFXxn2SR7B5ibzJbtKjxWasaCZY2KOJ80rqsJZNDpE54RVyiGKC4Dl/JG8HMbXQazVotOjzZoJPj3ugYUi6JVSzOE7NTDAgrpPKQfyjpySJ0F071AuCRSY4Pdgr1BmiEIopEj097kGTRY+eJjPWt9iy2k5emPFjQ6sNdmPxcohCEnpJrXQdBMGHh5XtH6tBf5sNTquh6OK4+gyC5QvQr46NY63TsiS1LkTiSvob8SUs1R5N0NtkwZgrqLrGphoewals6rBjfCFUlI4ulbOy1jdbJhiQsk4XZ/xZXyMUjePDPzyCrz5xsaR9mPKG0V6BwCepL0tbQkokOIbmAsllWyWE/q/UIHjRHq02cggA2NRuU9SxjbqCYKzyXdkK5a4dHQhG4/jZoVEYdZpkMFRpDDoNWmxGTC6EEE9wvHh5fokeWLCxzY6tnQ78KkUSMTDlxacePIUr/vF3uPVfD+C93zuEzz82gJPjC7h5UyvefV1fztcWxXG3bW0ryaauxWZET5MZxxR0waOugKLUJZtHaiLB8S+/PouTebrQ5UPYo4mCKqfNiDl/ZnGpP5tFmjm7hj8bs75wVYriBIV4BZ+e8GBdizUpV6tWu2pA1gSrGgRLnvpKGszfnJrE9i5HUjusBlbR7j4t2BtUMQhe32rFrC9S0DXz9IQH27ocYIyhv1151ZVzjovTPmxotcJm0sEXihWkWfWFY5hYCBXU7yCXVrraHB5yY02zpSpdGtNhjOGa9U48V6QuuPr+TAUgLsDxBMfr9nQvyZwmxesKuh21ZoRrmi2IxBOY8oRUDVrH3CFsK6LIpVyElndwyov9JWSUzkx60O4w5sym7O5pBACcGF3ADf0tGfc/d3EO4Vii5N7mM55Q2QUPSjQJOUTa0tqUN4RwLJGzqKvBrIdey0ruGid0zu01zARvlh0iBqa8S/yQx1xBtNtNMOjqY358zXonGsx6XJz1Y63TUlU3jU7ZJu3spAfeUGyJFCKV1+7pwr/8+iy++dRF/ObkJA4OuaDXMtyxvQM397dic4cd/e22gpflm60GfP7ePcmmEqWwp7cx2fo7lVFXELduzmxbv67FiscUPI+fGJjBV5+4AH84hh3dyr7ZhTA0F0BXgzkpa2qxGRCNc3hCsaQ0JJ7gCEaV5RANWQrjOOf4r2cu4+Vb25NFzYI5X6RqKweA9J4KyQTvW9sEjYahu8lcVYcIr4pyCEC6Tgajccz5I0uCnmlPCIeGXPjoyzep9lqAFORIMprMTHBXg0kVu8lFhwgf9ubQtcbiCZyd9OLd164FII2nDx2fkNxNUvZjxhuGNxzD+lYbApE4YgmOcCyRV94ndMmFuFzVSyZYNMm4Tm5jXAuu2eDEwycmpERWjtXcVOrjSpeGSb+om3393u4l91kMOnQ3mhW9gi/M+NBsNZQ9+19TQOVrsQh/zs4qVbcDKVZYOXTBnPOss6azE96cWWAA2NkjXRiPZZFEHJAvrNl6vudjPlCZJU2DTgO7UZeRCb4s6yKzOUMA0jJ9SxlewclucTXMBPe1WKHXMgxMpWeClTOFtUKv1SSXjqtljyYQXeNekG13rl6nPLi/ZncXAOAfHz6DeX8En3zlVjz/idvwpbftw5uv7MXu3saidamv29tdVhZtT28jxtzBJVrlUDSOGW9YUerS12LFrC+cYff2zaelFZyTJcqZBJfkSYxAqZBMLBUrBTN2ow4aBiykna/HRhfwDw+dxhceG8x4zpw/XJWiOEG+1skLgSjG3MFkM5meJjNGl7Mcokn5Ovmo3AlPTSmEQMkTd3Dai40l9gVIRyTZ8kkiLs1KzYZEUkv0JUi3JD0/IyxbbcmCz0KK4xbt0fIngJqS1nHledeXizTehHNOHirNzf2tYAz40cGRgp9Tl0EwIPld7l/bpBjNr2+1KtqYDE75ypZCAJUJgkVhTzXlEN2NZtiMupwOEW/66nP421+dzLg9Gk/g/LQvr9dfg1mP9a1WHFUojuOc4/FzMwAyK3oLIRSNIxRNVKyQsMlqyNAEX05qF3MHIK32wtukplPLbnECvVaD9S2ZmvExd7Ase7RK8Ar5YlptiUZngwmTnhBevDSPniZz1nO3u9GMb7xrP3543zV47KM3489uWl/VZXgl9iSdWxaD13EFZwiBuPintk8+Pe7BM+fn0GDW48yEpyx52FCazl4Ep6mTY3+OIFijYXCY9RlyiF8eGQMA/ObkBIKRRX075xzz/kj15RA5JsaiKE4ETj1NlqoVxsXiCfgjcVUaZQhE5v1ffn0Wn/vtOTxwbBxnJjx45PgE1rdaVZEnpNOclglOJLgq9miCNc0WaDUsr1dw8lh2SkmgzR3KCSfR02BDmxV2+XtdiC744owPGoYlE8ds6LUaOEw6RTlEMBLHJ35+IqMFfCU4LK881cIZQrDGacHdu7vwnWcuF5ykqtsg+Itv3YcvvX2f4n3rW6y4OONfksHknOP8TP4+24XQ2WiCVsNULVoYr6I9moAxhk3ttqxB8KgrgINDLtz/4giG0zxCL836EYknsDVPJhgA9vQ04uiIOyOjfHHWj+H5AFpsRsz7i/cxFEufldKBNlkNmE8rtLk854dBq8mbsS8vExyGQVs9fWs2+tttGJhe/G7E4glMLITqKhMMADf0t6DFZkiubFSLdocJC8EonrkwmzULLHj5tnZcs95Zk+YnSmzvaoBWw5Y4tyg1yhCIWovUDNi3nr4Ei0GLv7y9H6FoImenzlyk26MByplgEQQrySEA0TVu8XyNxRN46Pg4epvN8Efi+F1Kd0FPKIZonFfFI1jQajfCG45lbZqwGDhJY2pvsxnz/kjyfVcSkX1UUw6xvsWKe/Z2Y8YbxhcfP4+/uP8IXvGFp/DcxTnctb2jIueC02pYkvEccwcRiiZUC4INOg16m8x5M8Gnxz0waDXJ82ZNswVGnSajOO7ijA8WgxYdjkW5hq+gTLBf3mZhrkiSxj4zCH7m/Czuf3EYB84V3969WI7UoEmGEh++rR+ReAJfOXChoMfXbRC8xmnJaoe0vtUGXziW1FYCkh7GHYiqcjLotRp0NphUzQSPyzOxarRMTmVzhx0DU15FycMBOUsLAF95YukXRti/FPKF3t3biBlvOOnlmr79e/Z1I1GCj6FLfrzQ76qNUyETPDQbQG+zWbE5SCrltE6e9obQaq9Nt7hUNrfbMTIfTF6Ep7xhxBO8rG5xlcCk1+IPH7sFf37T+qq+rtCTekMxXJ1FD1yvmA1abOmwL1mhWQyCM8egNc0WMLYYBE95Qnjg2BjevL8X12+UtP6lSiKUms8sZoIXzyHRfEbJJxiQvIJTM8FSIyVJftLZYEpmhVO3W80CnWTr5CwrRKfHPWi1G5PuMmIyUo3iOCFzUVMOodNq8Ll79+Dxj92C05++C7/5yxvxn2/di7+6awv++IZ1qr1OKk45oSJQ0xlCsL7VlrMhFyBNaDZ12JINIbQaho1tNpxLk5cJZwjG2KIcIpy/6E6yRyv8PTVbDRn1LQDw0mVJyjVdhp1nodSiSYYS61tteMO+bnzvhaGCMuB1GwTnQtiGpH5RheepWifDGpVt0kQmuNpLupva7XAFoopFXAfOzaC70Yy3XNmLnx4aSe4jIBmB67UsaaSfi2xNMw6cm0Z/my1ZPJdLL6eEyPo0VkoOYcnUl12e8+fUAwta7dLMu5Ql4pkaewQLNnUs1bGN5QiSao3DpC/JKaEcUs9VJWeIemd3byOOjbqRkL+jY+4AdBqmmFww6bXoaljMgP33c5cRS3D80fV9WN9ihVGnUfQdLgRhj7YuRQ7RbDGAsaVjQi45BCCNA6ma4F8eGYPDpMOtW9rw2j3deGJgJhmAiqxYtSzSgNTstvI4d3rCk8wCA0CvfJ5VwybNExSZ4MqMpSa9Fls6HHjN7i6875YNFZt8NFsNCETiyWz7oLySpWoQ3GLF5Tl/8rxJh3OO0+NLjyUgJRXS5WUXZGcIAAVngoW1YiFFcQIlrTQAvCgHwVOeysohatkkQ4kPvawfnHN88fHMWoF0lmUQLJYgUnU7QoCuZhCsZsOMcXcQrXZjwcsbapHUKqWdnOFYHM9emMUtm1vx3ps3gHPg608u2pidnfBgQ6utIJeArZ126LVsSatWfziGFy7O49YtbYpZn0IQQXBDhWQDzVb9koGDc15wVWmr3Yh4giez1cUw7QnXVA8sSC+cVGqpu5oRkph2hzFZJ7Cc2NMrdagS9ROjrmBS6qXE+lYrLs/6EYjE8P0XhnHHtnasdVqh02qwtdNRsk2asEdLLfTTaTVosix1UxDBgTVLEWGjZVET7A/H8OipKbxqVxeMOi1ev7cb8QTHQ7JVnRhrql0YB0BRFxyJJXB+2rvEHUh8HrkcIh47M5VRrFgKi5ngujSEKph0J4TBKR9a7UY0qrhauK7VilA0gYksgeOMN4w5fyQjCO5vt2PSs2hJGozEMeYOJpN2dlNhmuBxdxDhWKKopl8ttkzXjGAkjhOj0jk75Sk+E+wJRXHv157LmxUHatskQ4neZgvuvbIXP3ppJO8kc1kGwR0OE8x67dIgeMoLq0GrmiVOb7MFs76wYuvRUhhzB9FVA+9VYYWVHgQfuuxCIBLHLZvb0Ntswev3duP+F4eT1eRnJ73Y2lmYnZtRp8W2TseSTPCzF+YQiSdwy+bWxWXCIi1c3BWWQzRZDQhG48mCmmlvGMFoPKdHsKCchhnT3lBNG2UI0nVsIhNcbclOvSLcKK5eVz9a32LYk2ya4QYgBcE9OaQufU6p4Phnh0bhDkTxpzcuyk92dDtwetyTNTuWi3R7NIGk71wcE0RwYM8SqKVqgn93egrBaByv2yM5c2zusGNbpwO/OCoFwSIbW1U5hDwmKC09D057EY3zJYGT02qAWa/NKocYmvPjT757ED9+qfBK92x45MCsUpngapH0xJWP76CKRXECsfqZTQN/KlnguNQycHOH9DwxnorOc6JOyVZgYVzSUaKI99VsNcAViCw5P4+MuBBLcJj0GswU2NEylTPjHrxwaR6PK1gnplPLJhnZ+OCt/WCM4T8UnGNSWZZBsEbDsK7FuqS94fkZHza22VS7WInMT7aOOMVS7UYZAqfNiBabAQNpgv0DAzMwaDVJT7/337oR0XgC33rqEtyBCCYWQthahMB9d28jTowtJOUBj5+bhs2ow/61zXAm210WmQmucGGcyCqIbK6ojM/lESwoNQiOxBJwBaI1tUcTpOvYRl1BtNiMqreoXq6YDVr87zs3409vrIy+sdJsaLXBZtQlJ6f57O/WtVjhDcXwxcfPY3dvI/anXNB2dDXAG46VVCeRbo8mSG8uka8wrsFigCcURSLB8cujY+huNOPKFP/z1+/txrERNy7O+JLBdaUm0Eq0241odxjxs8OjGTUYp8eXOkMAUuFyT5M5a6ZKBBZqaIaTmeBlHgQnVxXlRitqOkMIlIpEUxHHMr1eRrRtFraTSWcIOaNbqEWaSO6tL9DnFgCardLKZOqqwUuXXGAMuHlTa0mZ4Cn52pYeOyhRyyYZ2ehoMOGd16zFz4+M5SzqXZZBMCB9UVPT9Oen1XGGEPSqaJPGOce4W93GG8Wwqd2ekQk+cG4aV65rSl5w1rVY8ZrdXfif54eSvbfzeQSnsrunEb5wDBdnfOCc48DZaVy/0QmDToMGsx5aDSvaK9gdiMKg08BcoaAs2TBDzlBfVijgyUarQnV7IQhtdj3IIQBppSCZCa5De7Ra84FbN2KXrGlfbmg1DDu7G3B0xI1wLI4pj7JHsGCdfPGf8oTxpzesW5JQEI0yTo0XrwtOt0cTONOWcH3JIFj5fG8w68G5VGj31OAs7t7TBU2KtOPuPV1gDPjl0XHM+8NoMOur2vRFp9XgL27rx6EhFx5Pq8Y/PeGBWa/NGFt6m7PbpB0ecgOQzstySbpDLHM5RHMyoRLBpCcEXzimuhVbm90Iq0Gb1Sbt9IQHvc3mjCLD7kYzrAZtMmi8ML3U5syo08Kg1eQNgi/M+NBo0RelZ1dqmPHS5Xls6XBgY5sNM75w0fUr07IcJN1LPh3RJKNepBCpvO+WDTBoNYo+4oJlGwRvaLVh1BVEKBqHJxTFlCecnImpgZpewQvBKILReM2CYMkhwpdcKhl3BzEw5cPNm5Z2jvrArRsRiMTx6YdOAyjMGUKwO2XpdWDKh/GFEG7d3AZAytxL/o7FaoIjaDTrK7YUnd5u8vJcAHotK8jGrtRMsBhY6kEOAUg6tgm5tXa9NcogymfPmkacmfAkm8DkOr4i89TdaE56Mwv6223Qa1nRDhHCHm2dwsQy3VfXF47DoNVkrZsQBbLfe34o2U00lXaHCddvaMEvj4xh1hepqh5Y8Ob9vVjTbMFnHx1YsjR9atyDLZ32DD12r9wwQ8m958iIlAmeWFAhExyMQsOy662XC6lj9qIzhLqWXIyxnA4RZyYyi+IA6TrXn5JwujDjQ2+zZcnKms2kgy+PO4RUTFfcqnb6tSwWT+DwsAtX9TWh3WFCPMGLbqssiunOT/tytiEeXwhh2huuKymEoMVmxHuu78MDKW3t01ElCGaM3cUYO8cYO88Y+2s1tpmP9a1WcC7pzSphk9Jk0cNm1KlSuStm8t1V9AhOZXO7HcFoPLmsJqzLbpGDVMGmdjvu2t6BiYUQnFZDMttZCOtbJDPwoyPuZBYkdftOq6Ekd4hKeuk2pckhhub86G2yFORCYDXqYDFoFYPgYyPuZLCbjtAL1oMcAljUsQ1MeTHuDqGH9MArit09jYglOB49NQkgd9Fjd6MZG1qt+NDLNmacA0adFpva7UUXxwl7NGU5hGGJr67Ucjb7qo8YC356cBRbOx3Jot9UXre3G8PzATx9fraqHsECvVaDj7x8E85MePDwiQkAUqbsjIKbACDZpHnDsaR7gyAYiePMhBRMjbvLr+z3hGKwGXVLMufLEYdJB72WYc4fSTpC9ber35RjXYtVUQ4RiMRwadafbJKRzqZ2W9KxQtijpWIz6vK6Q1yY8RclhQAWg2Cx2npq3INAJI4r1zUnVx2LdYgQEgpfOJa0eFXihNwttl5XzP78pvW4cm12d5+yg2DGmBbAlwC8AsA2AG9ljG0rd7v52JDS47sSQTBjTFqqUiEIFoNYzeQQ8sXi7KS0lPnEwDS6GkyKWqoPvmwjACkLXMxMVKNh2NXbgGOjbjx+dhpbOx1LLKZabMaiNcGuQETVqt90mtPlELOBgjr0CFpsxgzruSlPCG/62nP4zKPnFJ+zGATXSSZYzqI8c34WkXiC5BArjL3yEuXDx6WALFcmWKfV4LGP3oK3XLVG8f4dXQ04Ne7JmRVKR8keTSC6uYnzzxeOZdUDA4tBsDccSxbEpXPn9naY9BosBKPJWoRq85rdXdjcbse//24AsXgCo64gvOHYEj2woLdZtklLc4g4PupGPCFV28/7I0u64ZWCJxhd9npgQLouS3ZgYQxO+9Bk0VdksrO+1So34lj6uZ+d9IJzKB5LQEokzfoimPGGcXHGl2FzZjPqchbGLQSjmPWFi5Z2ilUPcS4Jf+Cr+prRJhf4ThdZHDflCcEoy4nSrd9SOTnmgVbDsEVhUloPNFoM+PF7r816vxqZ4KsAnOecX+ScRwD8EMBrVdhuTsSgekEOgkWnFzVZ02xWRQ4h/HfzdSGrFMIKa2DKi0gsgWfOz+HmzW2KQe6O7gb8r9s34Z3X9BX9Ort7GnF2wotDQy7cunmp1CJd/1cIC8FoxTyCAUljqGGAyx8B51zyCC5iBt5qz2yY8Y0nLyISSyStadKZ9oSgYah5W12B0LGJCmCSQ6ws2h0mdDhMODflhVbDko4XpbC924F5v1Q0WyhK9miC9K5xvnAsq0cwADSYpQs9Y5L+Vwm7SY+Xb5OkHLWQQwCSFvujd2zCxVk/fn54LKmjzpYJBjK9go/IxYyv3NkJABgvUxLhCUVVbZRRS5qtRlkO4UV/W3HJmkJZ17K40pyKKIrLVjQurrUHzk0jHEtkNLywmXQ5NcEiVujNod1XYlEOIZ1LL1yax1qnBW0OU9IXfLrI4rhpbxhXyU2CchXHnRpfQH+bbdkWVKsRBHcDSPVwGZVvqyhWow4dDhMuzvhxftqH9S1W1c30Ja9gZb1WMYy7gzDoNDVZngOk2WdPkxnnpnw4NOSCLxzDLWlBaiofvr0fd6VpAgthd6+09BpLcNy6ZanUIl3/VwiVlkNoNAxNFik4n/GFEYjECyqKE6R3jZv3R/D9F4Zh0Gpwfsan2D512hOG02bM25GuWmg0DBvb7TgmB+311i2OKB9hldbZYCprjNwuW0IVI4nIZo8GpHaNkybH/jxBsBgLrlnnzJlQuGdvt7z92k00X76tHbt7G/H53w/g6IgbGqZcaJzNK/jIsAt9TkuyIHGiTEmEJxhTtWVyLXFapTF7YErdYvhUxErzfz19aUmn0zMTHjhMuqw2kkKi8+uTk0u2I7DnyQS7SmzyYtRpYTPqMOuTbNIOXp5POqcIWWMxDhGcc0x5QtjUbker3ZizOO7kuCc5NixH1Igala7mGVEjY+w+xthBxtjBmZkZhacUz4Y2Ky7M+lV3hhD0NlsQjiVKbo8rEB7BtdRjbW6349ykBwcGpqHTsGQrVDURF1uHSYe98t8Cp80AfyRe1LKeKxCpuMVRk+yvKGb8xcghWu1L5RD/9fQlhGJxfPj2fsQTHGcVlpCmvaG6kUIINqdo6kgOsfLYI0siys3yb+20Q8Oki16hXJr1o69F+ZwSF+eZ1ExwDveCJosB16534r487bNv6G/B6/d2Z6xGVRPGGD5+52aML4Tw7WcuYV2LFWZD5kSgwayH3aRbYoMmVdu7sXdNUzLYGi/TIWJlZYINOD/tw0Iwqro9mmBbpwP37u/Fjw+N4MbPPI4v/mEQ/nBM6vrX5ciafW6zG9Fg1uPpwVkAyJRDmPIEwbIPdpO1+GMlusZdmPHBFYjiKjkINug0aLYaMFWEHMIXjiEQiaPdYVyic05n2hPCjDeM7VnkIcsBNYLgUQC9Kf/3AMgoxeOcf51zvp9zvr+1VZ3BaX2LDeenvBhxBSpyMhTS0acQauURnMrmDjsuzvjx+9NT2N/XlDPjUirtDhPWOi24bWt7RsapRVjbFOgQEYrGEY4lKtYtTtAst04WHsFFZYLtRrgDUURiCSwEo/jus5fxyh2duHu3tFSrlDGb9tZHt7hUxBJeo1wMSqwsRNvycrP8FoMOG1ptOF2EQ8TQnD+r73Z6JjifJlirYbj/vmsyVpnS0Ws1+Pd792BvjVu4Xr+xBddtcCIcS2Q0Vkilt2lp7cmYO4gZbxh71zSi3WECY+XbpK0UTTAgBXtCUlCJojhAWiH7f2/chUf+4kZcvc6Jf/3tAG7+7OM4Ne7JWhQHSJOfTe02ROIJRZuzfIVxrjIaRIkgWLRKvnLdYjFYm92YtVhbCZE1bneY0N9mx2CKu1Qqwi1GrFgsR9QIgl8C0M8YW8cYMwB4C4AHVNhuXta3WuGPxMG5ukVxArVs0iYWaucRLNjcYUcswXFhxp/hCqEmP3nvtfiH1+3IuD39gpcP0Rmq0VzZTHCz1QCXP4rLc37oNKyobJmwSZvzh/Hfz16GNxzD+2/dgJ4mMxrMekVPVSkIrg9nCIEIgkkPvDLZ1dMAk16jyhi5o7sBJ8cKywRflDNSomtlOhaDDma9Nlkw6w/HYFvmFl7pfOzOzQCAXTmChN5m8xKv4CPDbgDAvjVNMOg0aLUZVcgEx1ZMJjhVVqimLaoSWzsd+Oa79+Nn77sOG9tsiMQSuCKPFZgYT5VszmwmHbwFyCFKkQEKmchLl+bRajcu6Xza7jApdjLMRtLK027CJtldSmkidmosswnMcqPsEYdzHmOMfRDAowC0AP6Lc36q7D0rgFTReSVOhu5GMxgDhudKH4Ci8QSmPLUPgjelXIhy6YHLJVuAJ/R5hWaCF2fElR24m6wGHBxy4fKc5JFbjGZSLOdeng3gv565hNu2tCW1Udu7HDiVljGLJzjmfOG68QgWiO8GtUtemViNOvzmwzctcWsple1dDvziyBhmvOHkJDAbDxwbB2PIWV/QYjcsFsaFcsshliP71jThlx+4PutEAJAywU8MzIBzDsYYjgy7YdJrkvrSrkZzUcWI6cQTHL7wCtIEy+Ou3ahDe5XG0ivWNuH+P7sGI/PBpKNHNhaD4MwVELtRh0gsgXAsruiH7QpEYTVos3pl58JpM+Dk+AJeuixJIVID8Da7MekOVQiTchDc7jBCr5W2MzDlzShwPTm+gPUt1mW9gqhKJRnn/BHO+SbO+QbO+T+psc1CEF56GoasurNyMOm16HCYysoET3lCSHCgS4ULUDlsaLVBJ1eH5xqQK4WYvRfqFSwywRWXQ1j1cAUkOUQh7ZJTEUHAFx4bgCsQxQdkezlAypidnfAiGk8kb5vzhZHg9WOPJmh3GNHbbF7WxQ1EbvparKpUby92jsstieCc44Gj47hmnTNZna6E02rEnF8q5vFH4jnlEMuVPb2NinpgQU+TGaFoIjk2Hh52YVd3I/TyhLy70VxWJtiX7Ba3MjLBQmKwsb24hhLlwhjDGqcl72umZoLTEcGiP6xcG+Muwxa02WrElCeMMXcQV/YtzVa3O0yY8RbeNW6JHCLpLpVZHHdyzIPty1gKASzjjnGANDgYdRqsdVpLmjkVQrpeq1hq7REsMOg0uLG/BW+4oruqA4egWDnEQlBeFqqwHKLJYkA8wTEw5V2yfFQILXIw+/zFeVy/0Yl9KRrE7V0OROKJpIc1sOgR3FpncgjGGB79y5vw/ls21HpXiDpHLHvma598atyDi7N+vDaLlZmgRXZYCchOKrYczTJWKqm1J+FYHKfHPdi7tjF5f2eDCWPuYMkuRZ6QlFCwr5Asu7iWbFQIMuuB3b0NuH1rO27b2p5xn02eiGTTBc8HIkU7QwhSZSKpemBASnQkeOErsVOeEOxGHaxGHRrMenQ4TBhMs0lz+SMYcweXdVEcoIIcopZoNAy7ehqSXouVoLfZgmfOz5b8fDGDr3UQDADf/qOravbaFoPUYa3QhhnlVMkWgxhQo3FelEcwIHW8Enzw1v4l96XaSW2V/UGFWXm9ySEA6fgQRD4cJj3WOi15bdJ+dXQMei3DK3Z05nxci82AY6PuZFBgM66MbGUxJINgOdkSiSewt3dxQt3VaEY4lsC8P1KS7dtCUBpLV0phnAj2KlEHpAYWgw7ffPd+xftEJtibpXWyqwxbUBE82426DDs+kXiZ9hRWkzLtDS25TvW32zCQ5hAhJsI7lvkK4rLOBANSYPd/X7+zYttf02zBlDek6PlaCGPJILi+sn+1wGlb1P/lo1qFcalVuMU4QwCSN2Oz1YD9a5twzfqlM+91LVZYDNolGTNhVl5vcgiCKAbROS4biQTHg8cmcPOmtrxyJqdNqmj3yUFBrrbJKxWhxR91BXF4yAUA2Cfb2gGLCZRSdcEiE7xS5BDrWqz4+F2bcc++nlrvStGIbHy2TLC7DFvQZjkpc0VfU4YPvdBOF9o6ecoTXiJj2tRux/lp3xI5hZBELfdM8LIPgm1GXU69VbmscZrBeekWNePuIJosesq0QW6dXGDXOHcwAoNOA5O+sl/R1KWnYjyCBV99xxX4/Fv2ZEhMtBqGbZ1Li+MW5RAUBBPLl+3dDgzPB7AQUM5mvXh5HpOeUF4pBCCNCfEET7ojLOcCm1KxGnVwWg0YdQVwZMSN7kZzstUtsBgkl3oN8gRlTfAKKYxjjOH9t2xcluOo+H5n8wp2+SMlF4OLDLlokpFKsmtcgQ4RU55QWhBsQyiaWCINPTnuQXejGU01agKmFss+CK405dqk1YM9Wr3gtBoLL4zzR9Fk0Vdcvyxm3RqGkmQ1V61rzvq87V0OnB73JP0Vp70hNFn0FdOvE0Q1EMufpyaUJRG/OjoOi0GL2xU0kemI5f0h2ad7NQbBANDTbMHIfBBHh93Ym5IFBoBOeRWx1OK4lZYJXs4I9xOlIDgWT8ATipUcVG7usOPtV6/BPfsyG/a22ArPBHPOJdnEEjmEKI5blEScGltY9llggILgvKTrtYpl1BXI2eJzNdFiMxSsCXYHIxWXQgCLmeCeJgsMOnVPh+3dDfBH4rg8J13gC9VjEUQ9Iy58vz4xmXFfJJbAIycmcMe29oJW6ISufkgeX1eiO0Qh9DaZcXzUjTF3MKPJh9NqgEGnKT0IXmGa4OWMXWiCFeQQbvk4lSqHMOq0+KfX71SMNww6DZxWQ0Gtk92BKCLxBNpTrlWiGdmgXOjtC8dwcda/rJtkCCgIzkOrzQiTXoPhueKD4HAsjosz/op1tVluCP2fUueZdFyBaMXt0QDAYtDKDiPqF1eKYEG0mZ321p9HMEEUi9NmxLuvXYv/eX4I9784vOS+pwZnsBCM4u4CpBDAYoZKtC1ftZngJgs8cmC0Ly0TzBiTbNJK1AR7QzEwthiAEbUjVybYHSi9UUYhtDlMmCmgdbJor5wqh7Cb9OhqMCUzwWcm5KK4bsoEr3gYY1jXYsPzl+aKtqg5P+1DLMGxrXP5f1HUwGk1IpbgyeW5XCwEohVvlAFIx3dnd0Oyz7qa9LfZYdBqcEqupC+kwQBBLAf+9tXbcMvmVvztL0/i6cFF95xfHR1Hk0WPG/sLa8gjdIxitWSlNcsoFNGAwaDVKHbf6mo0lSWHsBl10Giqb41JLMWs10LDlAvj5v3SdbFUi7R8tNmNBWWCFz2Cl16r+tvtSa9g4Q6z3J0hAAqCC+KPr+/DyTEPfn0yc/kvF6flDOBWCoIBLNqRFaILrpYcAgB++r7r8KHb+vM/sEgMOqnr06lxDzjnmKnDlskEUQo6rQb/+da92Nhmw/u+fwiDU14EIjH87vQUXrmzM9noIR9NFgM0DBidX72FcYDkRw9IRYdKNQOdDaU3zPAEV07L5OUOYww2o04xE7zYJbUy1712h7EgTfCUJzMTDEia4wszkkPEyTEPWmzGJQWcyxUKggvgnn092NRuw2cfPbekA1g+zkx4YdJrsK5I/9mVilj6zKcL5pyX5ZdYT2zvcuDk+EJSZ0X2aMRKwW7S41vvuRJGnRZ/9J2X8MMXRxCMxnH37sKkEIDk9d5sNSIST0CnYTCqrMtfLvQ0SZngfWl6YEFXoxnT3jAiscKvPwJPKLpiGmWsBOwmvbImuMJyiHaHCbO+/F3jpuUgOH3Vsr/NhkgsgaE5P06NL6wIKQRAQXBBaDUMH79zCy7N+vHjgyMFP+/0xAI2dzgyPPtWK8mucXls0kLRBCKxRMntI+uJ7d0NcAeiODriBlCfjTIIolS6G8341rv3Y9YXxqcfOo3OBpOiRVMuRHGc1airSTfLemCt04q3XtWLN+1X9r7tbjSB88J9XlPxBKNUFFdHSJngTElgNeQQCZ4/CTXlCaPRos9osy7aQZ8YW8DgtG9FSCEACoIL5ratbbiyrwmf//0gAhFlj79UOOc4M+ElPXAKTqsUAOZrmOEOVnZGXE12yPq+P5ydBgCSQxArjt29jfj8vXsAAK/b21209lSsEK1WKQQgJVr++Z5dGZ2+BMJmsxRJhCdEcoh6wmZSlkO4A5I3vllfGQtNIV3Ipwue8oSWOEMIRIe+B49NIJ7glAlebTDG8Nev2IIZbxjffuZy3sePL4SwEIxiW6e98ju3TGi2GsBYfk2wyy+sYpb/wL2lwwENSw2CKRNMrDzu2tGJ33/kJnzk5ZuKfq5YIVrNQXA+hO3V+EIJQXAwumIaZawEbEadYmGcKxCpqDf+YsOM3KsJU1lcjKxGHXqazDhwTrqWbadM8OrjirXNePm2dnz1wAXM51nSPyMXxSlV+q5WtBqGZkt+r2CRCW6oUmFcJTEbtNjYZkt2eyI5BLFS2dhmL7ggLhWRCV6NLZMLpSvZMKMEOUQoSpngOsJm0sGrkAme90crVhQHLCZg8maCF0IZRXGCTe12xBIcDpMuqWNf7lAQXCQfv3Mz/JEYvvT4+ZyPOy376G3Osry1WnHaDJjLkwkW7VhXghwCWJwx24w6ap9NEGk4UzTBhDIWgw5NFn3RcohEgsMXjpEmuI6wZ8kEuwORigbBrfb8XePiCY4ZXzjDHk0geh7s6G5YMfp9CoKLpL/djjde0YP/eW4Io67sDTTOTHjQ57TQEl8aTqsRc/7cM1FXoLzOOfWGaJpBUgiCyERkgsnBIDddjcXbpPkiMXAOOOizrRtyWaQ1WSs3WdFrNWixGTDtzX79nfNL7hEd2TLBbZK8cyV0ihNQEFwCf3n7JoABX3r8QtbHnJ7wkBRCgUIywSupMA5YHDCoUQZBZJJ0h6BVkpxIQXBxcghqmVx/2Ew6BCLxDKsyV6CycghAKsyezpEJnpalEtn8f8W1bG9vo+r7VisoCC6BrkYzXrWzE785OYGYgm+wNxTF0FwAW0kKkUGLzZjfHSIQhVGnybBoWa6IydBKMBYnCLURrjEkh8hNV4Op6MI4T1DKOJImuH4Qq8Op2eBEgldcDgFINSlTOQrjsjXKEGzusOPhv7gBd27vqMj+1YKygmDG2JsYY6cYYwnG2H61dmo5cOf2drgCUbx4eT7jvnOTUn9tygRn4rQa4AnFcpq+V2MwqCYOkx737OvGy7YU1kqWIFYTLXaSQxRCV6MZ3lAso+38nC+M6//lD3jszFTGc8RjSQ5RP4jveWoQ7A3FkOCVX/1st5uS2V4lsrVMTmV7V8OKasFdbib4JIB7ADypwr4sK27a1AqjToPfnsoceERRHLVLzsQp6/9yuWu4V0i3uFQ+9+Y9eP1eZSN8gljNtNgMaLLosdZJnTVzIbyCJ9IkET94YRhj7iCeOT+X8RySQ9QfNqN0LFKL4+blbnGVapQhaHdIK7FKK9iAlAlmbFGnvxooKwjmnJ/hnJ9Ta2eWExaDDjf2t+K3pybB+VJtz5kJDxotenQ20PJ3OqISPJckwh2IooEGbYJYFRh1Wjz3idtwz97uWu9KXaPUMCMSS+B/nh8CAAxMeTOe4wmRHKLesCUzwYsZfZccBFd6BbTVYZK6xmVJQk17Q3BajSVZHS5XVs87rQB3bm/H+EIIJ8c8S24/Pe7B1g7HirEQURMxw8wZBAdXlhyCIIjcmPTaFbXEWgmSXsEpuuBfn5zAtDeM7kazchCczASTHKJeEJpgb0om2B2oTjF4ex6btClPdnu0lUreIJgx9nvG2EmFn9cW80KMsfsYYwcZYwdnZmZK3+M64vat7dAw4NFTk8nbYvEEzk56SQ+cBVEJnsshYiXKIQiCIMqhzW6CVsOWZIK//cxlrGux4p3XrsW0N5wMpgRCE0xWnfWDkiZ4sUtqpeUQcte4LLrgKU/2RhkrlbxBMOf8ds75DoWfXxXzQpzzr3PO93PO97e2rowCoSarAVeta8ZvTy8GwZfn/AjHEqQHzoLQBGfzCuacS3IICoIJgiCSaDUMHQ5T0ibtyLALR0fcePe1a7GlQ/JvHZjyLXmONxSDzaiDbhUtb9c7SXeIlExwUg5RYU2w6FiazSGCMsFE0dy5vQMDUz5cmvUDAE5PyM4QFAQrYjVoYdRpsmaCg9E4IvEEySEIgiDS6G40J1uwf+fZy7AbdXjj/l5sapeC4HNpkghPMErOEHWGTSkTHIhAq2EVP1YtNiMYU26dHI0nMOcPo81OmeCCYYy9njE2CuBaAA8zxh5VZ7eWD3fIfnlCEnF63AO9lmFjm62Wu1W3MMZkr2DlINgtWiZTYRxBEMQSOhtNmFgIYsoTwsPHJ/Cm/b2wGXXobDDBbtRhMD0IDkXJGaLOEE1hvEsywVE0mvUVryPSazVwWo2YUcgEz/rC4Dy7R/BKpVx3iF9wzns450bOeTvn/E61dmy50N1oxs7uBvxWDoLPTHiwsc0Og46S7Nlw2gxZ5RCuKhUIEARBLDe6Gs2YXAjhv5+7jDjnePd1awFIyYX+dlvSo17gCcbIGaLO0GoYrAZtmiY4UnEphKDNblTMBBfiEbwSoUhNBe7Y1o7Dw25Me0I4PeHB1k57rXeprnFas7dOXhCZYJJDEARBLKGr0YxonOPbz1zGbVvalngrb2q3Y2DKu8Sy0xOKUhOSOsRm0mVogpuqlPhpdxgV3SHydYtbqVAQrAJ37pAkEfe/OIIZb5j0wHlw5mid7A6KIJiyFwRBEKl0yzZpgUgcf3T9uiX3bWq3wxWILpGakRyiPrEZdUsywZIjUnUSP+0OE6a9SplgKQhuo0wwUSz9bTasa7HiW09fBEBFcflw2qRMcHqTESBFDmGmTDBBEEQqnQ1Sw4zN7XZct8G55D5RHJeqC5bkEJQJrjdsJj28KUHwvD+C5ioFwW125a5xU54QtBqGFisFwUSRMMZwx/b2ZHceskfLTYvViEg8sWQQECQL4ygTTBAEsYS1TgvaHUZ84GUbM4qoNnVIxdjCISKR4PBSJrgusRt18MkezsIWtNFanePU5jCBc2QUp095wmizG1dd0xoKglXijm2SJKKzwVQ1gftypcWevWGGOxCBSa+BSa+t9m4RBEHUNRaDDs9/4jbcvbsr475WmxGNFn3SK9gfiSHBqWVyPZIqhwhEqmsLmmyYkeYQMeUJoW2V6YEBCoJVY29vIzocJuzsbqj1rtQ9Tnm5ZU5BF+wORMkjmCAIIgvZbLQYY8niOGDRgotaJtcfqYVx834pGVQtOYRwf3jg6DiiKZKIaU842VZ5NUFBsEpoNAzf/7Or8Q+v21HrXal7nHLrZCWvYHcwigZaviMIgiiaTe22pEOEaJlMmeD6w2bUJeWA1ZYAbu9qwKt2duKbT1/Cq/7jKbx0eR6A1EVutTlDAABNEVVkQys1yCiElhytk92BCOmBCYIgSmBTux3eUAyTnhA8QZEJpvG03rCbJDkE57xqLZMFWg3Dl96+D68/PYW/e+AU3vTV5/DGK3rgDkRXnUcwQEEwUQOE3EFZExylbnsEQRAlIBwiBqZ8iMakpW7KBNcfNqMOnEt64GQQXGUZ4O3b2nHdRie+8NggvvXUJQBYlZpgCoKJqmPQadBg1mNGwavQHYxSJpggCKIEUm3SmuXMIjXLqD9s8jHxhWNw+UUQXP3rnsWgwydesRWv39uNH7wwjJdtaav6PtQa0gQTNWFLhx3PnJ9d4hUsWcVE0EAewQRBEEXTbDWgxWbEuUkvPHLjIZJD1B82oxQEe0MxuGRNcC1rYbZ0OPDp1+5IShVXExQEEzXhDVf04OKsH4eGXMnbApE4onFekxkxQRDESmBTuw0D076kbz1lgusPe2omOBBBg1kPnZbCsVpAnzpRE161sxMWgxY/OTiavI1aJhMEQZTHpnY7Bqe8cAeisBi00FNwVXfYjNI1zidnginxUzvo7CBqgtWow6t2duKh4+MIRKSMhdBGkRyCIAiiNDa12xGIxHF20kNFcXWKkEP4wlHZEYmuebWCgmCiZrxpfy/8kTh+fWISALAgZ4JpVkwQBFEam9old52jI25qlFGnCDmEpAmOJIsYiepDQTBRM67sa0Kf04KfHBoBgKRVDM2KCYIgSqNfdogIROKUCa5TFjPBMbj85IhUSygIJmoGYwxvvKIHz1+cx/BcoOqdcwiCIFYaDWY9OmS/V3KGqE+sIgiWM8HV9ggmFqEgmKgp9+zrAWPATw+NJOUQ1DaZIAiidDZ1SNlgBzlD1CUGnQZGnQZz/ggCkThJAGtIWUEwY+yzjLGzjLHjjLFfMMYaVdovYpXQ1WjGDRtb8LPDY5jzRWDWa2HSa2u9WwRBEMuWTXLXTTvJIeoWu0mHkfkAgOq1TCYyKTcT/DsAOzjnuwAMAPhE+btErDbetL8XY+4gHj01SVIIgiCIMklmgqkwrm6xGXUYcclBMMkhakZZQTDn/Lec85j87/MAesrfJWK1cce2djhMOoy5g1QURxAEUSaifTIVxtUvNpMOI/NBAFQHU0vU1AT/MYBfq7g9YpVg0mvx2j3dAIBG0gMTBEGUxZYOO65e14z9fU213hUiCzajDsFoHADIIq2G5A2CGWO/Z4ydVPh5bcpjPgkgBuD7ObZzH2PsIGPs4MzMjDp7T6wY3rRfWkSgGTFBEER5mPRa/OjPr8UVa5trvStEFkTXOIDkELUkr2CIc357rvsZY+8G8GoAt3HOeY7tfB3A1wFg//79WR9HrE52djfg9q3tuHodDdoEQRDEysae4txByZ/aUZZqnjF2F4C/AnAz5zygzi4RqxHGGL757v213g2CIAiCqDiiYYbVoIVRR45ItaJcTfAXAdgB/I4xdpQx9lUV9okgCIIgCGLFYpMzwVQMXlvKygRzzjeqtSMEQRAEQRCrAZEJbrKSFKKWUMc4giAIgiCIKiI0wVQUV1soCCYIgiAIgqgiyUwwBcE1hYJggiAIgiCIKrIYBJMcopZQEEwQBEEQBFFFRGFcEzXKqCkUBBMEQRAEQVQRu9wsg+QQtYWCYIIgCIIgiCrS2WiCQadBf5ut1ruyqinLIo0gCIIgCIIojhabEcf/7g6Y9NQoo5ZQJpggCIIgCKLKUABceygIJgiCIAiCIFYdFAQTBEEQBEEQqw4KggmCIAiCIIhVBwXBBEEQBEEQxKqDgmCCIAiCIAhi1cE459V/UcZmAAyV+PQGAAsq7o7a22sBMKvi9ur9/db79uh41Nf26HjU1/boeNTX9uh41Nf26HjU1/bKOR5rOeetGbdyzpfVD4Cv1/n2Dtb5/q227dHxqK/t0fGor+3R8aiv7dHxqK/t0fGor+2pejw458tSDvFgnW9Pber9/db79tSm3t9vvW9Pber9/db79tSm3t9vvW9Pber9/db79tSm3t9vvW9PdWoih1jJMMYOcs7313o/CAk6HvUFHY/6go5HfUHHo76g41FfVOJ4LMdMcL3z9VrvALEEOh71BR2P+oKOR31Bx6O+oONRX6h+PCgTTBAEQRAEQaw6KBNMEARBEARBrDooCM4DY+y/GGPTjLGTKbftZow9xxg7wRh7kDHmkG/vY4wFGWNH5Z+vKmzvgdRtEcWh1vFgjN3LGDvOGDvFGPtMLd7LSqCY4yHft0u+75R8vylte3R+lIFax4POD3Uocrx6e8pYdZQxlmCM7UnbHp0fZaDW8aDzQx2KPB56xth35dvPMMY+obC94s8Pte0mVtoPgJsA7ANwMuW2lwDcLP/9xwD+Qf67L/VxCtu6B8APcj2Gfip/PAA4AQwDaJX//y6A22r93pbjT5HHQwfgOIDdKcdBm/I8Oj/q4HjQ+VGb45H2vJ0ALqbdRudHHRwPOj9qczwAvA3AD+W/LQAuA+hLeV5J5wdlgvPAOX8SwHzazZsBPCn//TsAb8i3HcaYDcBHAPyjqju4ylDpeKwHMMA5n5H//30BzyEUKPJ43AHgOOf8mPzcOc55HKDzQy1UOh50fqhEGePVWwHcL/6h80MdVDoedH6oRJHHgwOwMsZ0AMwAIgA8QHnnBwXBpXESwN3y328C0Jty3zrG2BHG2BOMsRtTbv8HAP8GIFClfVxNFHs8zgPYIssldABel/YcojyyHY9NADhj7FHG2GHG2MdTnkPnR+Uo9njQ+VFZco1XgnuREgSDzo9KUuzxoPOjsmQ7Hj8F4AcwASkT/6+ccxFAl3x+UBBcGn8M4AOMsUMA7JBmJIB0cNZwzvdCmpX8gDHmkHVEGznnv6jJ3q58ijoenHMXgPcB+BGApyAtq8Sqvtcrl2zHQwfgBgBvl3+/njF2G50fFaeo40HnR8XJdjwAAIyxqwEEOOcn5f/3gM6PSlLU8aDzo+JkOx5XAYgD6AKwDsBHGWPryz0/dOXv7+qDc34W0lIiGGObALxKvj0MICz/fYgxdgFStuVKAFcwxi5D+szbGGMHOOe3VH/vVx4lHI+DnPMHIXezYYzdB+nkIlQg2/EAMArgCc75rHzfI5D0YD7Q+VExSjgej9H5UTlyHA/BW7A0C3wt6PyoGCUcD9D5UTlyHI+3AfgN5zwKYJox9gyA/ZA02iWfH5QJLgHGWJv8WwPg/wD4qvx/K2NMK/+9HkA/JDH9VzjnXZzzPkgZlwEawNSj2OOR9pwmAO8H8M3q7/nKJNvxAPAogF2MMYu8jHgzgNN0flSWYo9H2nPo/FCZHMdD3PYmAD8Ut9H5UVmKPR5pz6HzQ2VyHI9hAC9jElYA1wA4W+75QZngPDDG7gdwC4AWxtgogL8DYGOMfUB+yM8BfFv++yYAn2aMxSDNDN+bolkhVEDF4/EFxthu+e9Pc84HqvIGVhjFHA/OuYsx9jlI1b8cwCOc84erv9crFxWPB50fKlDkeAVIY9Yo5/xiVXd0laDi8aDzQwWKPB5fkv8+CYAB+Dbn/HjZ+yBbSxAEQRAEQRDEqoHkEARBEARBEMSqg4JggiAIgiAIYtVBQTBBEARBEASx6qAgmCAIgiAIglh1UBBMEARBEARBrDooCCYIgqgjGGN/zxj7WI77X8cY21bNfSIIgliJUBBMEASxvHgdAAqCCYIgyoR8ggmCIGoMY+yTAN4FYATADIBDABYA3AfAAOA8gHcC2APgIfm+BQBvkDfxJQCtAAIA/kxuPUoQBEHkgIJggiCIGsIYuwLAdwBcDamL52FIrUK/zTmfkx/zjwCmOOf/yRj7DoCHOOc/le97DFI3xEHG2NUA/plz/rLqvxOCIIjlBbVNJgiCqC03AvgF5zwAAIyxB+Tbd8jBbyMAG4BH05/IGLMBuA7ATxhj4mZjpXeYIAhiJUBBMEEQRO1RWpL7DoDXcc6PMcbeA+AWhcdoALg553sqtmcEQRArFCqMIwiCqC1PAng9Y8zMGLMDeI18ux3ABGNMD+DtKY/3yveBc+4BcIkx9iYAYBK7q7frBEEQyxfSBBMEQdSYlMK4IQCjAE4D8AP4uHzbCQB2zvl7GGPXA/gGgDCANwJIAPgKgE4AegA/5Jx/uupvgiAIYplBQTBBEARBEASx6iA5BEEQBEEQBLHqoCCYIAiCIAiCWHVQEEwQBEEQBEGsOigIJgiCIAiCIFYdFAQTBEEQBEEQqw4KggmCIAiCIIhVBwXBBEEQBEEQxKqDgmCCIAiCIAhi1fH/A0AfQt837tAjAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the RGNP data to replicate Hamilton\n", "dta = pd.read_stata(\"https://www.stata-press.com/data/r14/rgnp.dta\").iloc[1:]\n", "dta.index = pd.DatetimeIndex(dta.date, freq=\"QS\")\n", "dta_hamilton = dta.rgnp\n", "\n", "# Plot the data\n", "dta_hamilton.plot(title=\"Growth rate of Real GNP\", figsize=(12, 3))\n", "\n", "# Fit the model\n", "mod_hamilton = sm.tsa.MarkovAutoregression(\n", " dta_hamilton, k_regimes=2, order=4, switching_ar=False\n", ")\n", "res_hamilton = mod_hamilton.fit()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:28.529810Z", "iopub.status.busy": "2021-10-06T09:59:28.527549Z", "iopub.status.idle": "2021-10-06T09:59:29.004204Z", "shell.execute_reply": "2021-10-06T09:59:29.004790Z" } }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>rgnp</td> <th> No. Observations: </th> <td>131</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-181.263</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Wed, 06 Oct 2021</td> <th> AIC </th> <td>380.527</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>10:59:28</td> <th> BIC </th> <td>406.404</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>04-01-1951</td> <th> HQIC </th> <td>391.042</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-1984</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.3588</td> <td> 0.265</td> <td> -1.356</td> <td> 0.175</td> <td> -0.877</td> <td> 0.160</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 1.1635</td> <td> 0.075</td> <td> 15.614</td> <td> 0.000</td> <td> 1.017</td> <td> 1.310</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.5914</td> <td> 0.103</td> <td> 5.761</td> <td> 0.000</td> <td> 0.390</td> <td> 0.793</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L1</th> <td> 0.0135</td> <td> 0.120</td> <td> 0.112</td> <td> 0.911</td> <td> -0.222</td> <td> 0.249</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L2</th> <td> -0.0575</td> <td> 0.138</td> <td> -0.418</td> <td> 0.676</td> <td> -0.327</td> <td> 0.212</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L3</th> <td> -0.2470</td> <td> 0.107</td> <td> -2.310</td> <td> 0.021</td> <td> -0.457</td> <td> -0.037</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L4</th> <td> -0.2129</td> <td> 0.111</td> <td> -1.926</td> <td> 0.054</td> <td> -0.430</td> <td> 0.004</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7547</td> <td> 0.097</td> <td> 7.819</td> <td> 0.000</td> <td> 0.565</td> <td> 0.944</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0959</td> <td> 0.038</td> <td> 2.542</td> <td> 0.011</td> <td> 0.022</td> <td> 0.170</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "================================================================================\n", "Dep. Variable: rgnp No. Observations: 131\n", "Model: MarkovAutoregression Log Likelihood -181.263\n", "Date: Wed, 06 Oct 2021 AIC 380.527\n", "Time: 10:59:28 BIC 406.404\n", "Sample: 04-01-1951 HQIC 391.042\n", " - 10-01-1984 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.3588 0.265 -1.356 0.175 -0.877 0.160\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 1.1635 0.075 15.614 0.000 1.017 1.310\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.5914 0.103 5.761 0.000 0.390 0.793\n", "ar.L1 0.0135 0.120 0.112 0.911 -0.222 0.249\n", "ar.L2 -0.0575 0.138 -0.418 0.676 -0.327 0.212\n", "ar.L3 -0.2470 0.107 -2.310 0.021 -0.457 -0.037\n", "ar.L4 -0.2129 0.111 -1.926 0.054 -0.430 0.004\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7547 0.097 7.819 0.000 0.565 0.944\n", "p[1->0] 0.0959 0.038 2.542 0.011 0.022 0.170\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_hamilton.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of the probability at time $t$ based on data up to and including time $t$ (but excluding time $t+1, ..., T$). Smoothed refers to an estimate of the probability at time $t$ using all the data in the sample.\n", "\n", "For reference, the shaded periods represent the NBER recessions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:29.037429Z", "iopub.status.busy": "2021-10-06T09:59:29.006666Z", "iopub.status.idle": "2021-10-06T09:59:30.697709Z", "shell.execute_reply": "2021-10-06T09:59:30.697339Z" } }, "outputs": [ { "data": { "image/png": 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Dc57sh5esOON9bM5ITttmGm+A7aMJFjKFQGdB54qKWLj0dfVFQmQLyncJFRMLhuetZfPVzad/cpRPPXDWd9dnp7GMzVgD460Xt7XJOu4dXiWtWY5Owmm8+8LLPG+lVFnC4WuvWk9/NMSPjyx4sh9esvKMt5lZvm2sZLxjZoZnkH8w2bwiFi41s7PkHz9539l80b555QJ8rjXtc3wmRSqvODKT7vWudBXtebdPuWzuTbzZ8rz7HN3UhhORZTHvVK5ItqDs7y8aDjEQC/vy+1p5xtu8WWwdKRnvlbDazRWUnewC0Gcmrvkp7j25VPqh+fFi13SHQlFxcjYFwGMT/q6V9Zq5hsZbx7wb4Tw3nnneVWXz8LImLdUWX/FIyJff18oz3rMZNgxFy74kqzFCoI13scLzjvjP8z6zUMro15736uXUXIq8KZevNuM9k8rTHw2V3X+caM+7MWWet0fGO1NNNk8YCWvOcOqs6YmPJkrGOxEVX91nLVak8XbGu6G02s0GuDwjW6iIeZvyj59qvc84Siq08V69HJs2vO5YWNi3yoz3bCrPSA2vG7B/w7pUrDZZh8TtdcJaokI2zxaU/RzU9rzTPlxsrVDjXd5o3l7tBjiJKleoHvP2U5e1CUctfTbA51rTHsdmDIN93bYBDkylyrKHq+FVXNMPOJOdqqFl88ZYnveGwZhnpWKpKp633ajFIZ1XC3sktGzeeeZSeRYyhWWed2wFyObZZTFv/xnvycWcXYce5HOtaY/jMylE4Pqdg+SLigNTqZrbnprP8vL/fJSHTvprYlOrNDbeWjZvhLXYWz8U9SzmnakS8x5OmMNJHJ8xU8N4a9m8wxydXV4mBitHNo86PG/LePtJzplYyLF52Dj32vNevRyfTrJpOMEl6w0FrF7ceyaZo6hgyqddrJplNpWvWSYGRihBgIxe3NbEWvivH4x6KJsb7+nMNh8x49rOcrHZVJ5ISBiIlbZLREO+DHOsKON93BxIsszzXjGyud897yybh432gzrmvXo5NpNk63g/6wYijPdHeOxMbeNt3ajzK6AeXCnFTAPPW0SIRaRhKGE1kykUiYWFsT7D8/aiP0fVOu/E8rGgc6kCo31huxkW6Jh3Vzg6myEs2N6fxUooFcuaF7RFImJceH6ScyYWc2wYihEJiTbeq5hj0ym2jfUjIly0vp99p2sbb6uZT24FGO/KGuFa+LX0yC9k84p4JMRYf4R8UbGYbT8pt2qpmD2cpFw2r/z+tPHuAsdnM2wajhNxGDko9RMOsmxu1Hkvl8394nln8kVmU3k2DMWIhUXfnFYpmXyBiYU0W8eMkYsXbejnyEyapUz1G/BK8rwbNWixiIf9KcP6BctRsc6jF0lrmXyRSEjKbMNw3EpYK5fNRxLl359OWOsC1crEwCmbB/cHU1kqlvBZzNuavLN+MEosHNKe9yrl5GwapWDbeD9gGG8FPF5DOreuk/wKuF5mG7RGtYhHQoEO4XWabN5IzrXOoxdJa6lcsUwyB4iEjdh2mWxeZZxrwpzg6Lf22ivGeCulDOM9ttx4rwTZvLJULBISoiHxTZ23NZBk/WCUaFjL5quVY9OGkd5me94DADXj3pZcviI877Q7zzsW0cpUPZZ53h4Y73S+SCIqyx4fqWiRWi3hMB4RFP7L41kxxvtsMk8qV2TbSBXP25LNA7zazRbLS8XAWBH6RTa3RoGuH4oRDUugz7Wmdawab8vzHu2LsHk4VjPj3I55++zG2AqWh1gpu1aiY971yeQNlXG83zvjnckX7TwhJyN9pbGg+YJiIVNY1mTH7mbps2t0xRhvu6d5Fdnc9rwDHGfKVZSKgRH39ovxPuPwvOPmxDPN6uPYdIpoWNgwXGqUdNGGfvZNLFXdfiXGvBvK5jrmXZdsoUgsIvYiyIuYdzpXy/Mu9Tefq6GcJHxqP1aO8TZrvLdXkc1Lnre/Tr5b8oUiRUVZzBtKsRg/cGYhx1A8TF80bMrm/tgvTXc5PpNk82gf4VDpRnnRhn4mFnJMV+mkZnncK2F06EwqTzQk9Mfq31bjWjavSzaviIdDxCIhBmNhT2Le6Xyxar/54UTEzja3Ew4rE9ai/myss2KM96mFLGGB9YOxZc+JCLGwBNZ4l7oDla8c+6Mh30wVO7OYZf1gFMCUzYN5rjXtcWzGKBNzcsE64+8nJ5d3WltpsvloX6SsRrgaWjavj+V5g+EFz6Tab+CTrpKwBtZwEsPzrlUtYBn9tPa8O8NStkh/LFy24ncS5AxP64cerfS8I2H/eN6LOdYPGQsnnW2+ejk+nWTbeF/ZY0NmM4xqIZ7cSpLNk/UbtFjosFJ9jIQ141431h/xRjbP1zLeYRazBfIFVTLe/dWNt9/sx4ox3qlcgf5o7cOJR8R3MQu3WN2YYn6OeS/kbM87FtYdpFYjS5k8Z5eybK3wvEuzBWob75XQpKVRX3MLLZvXxygVK3neXsnmiSr2wW6RmsnXls3tfvT+ukZXjvHOFumLLs8mtIiFg7vazeQNWafSePsl5p3JF5lN5+2QRTTA51rTOidmDVncatBiYV231dQYy6CvlJj3WL87z1sb79pk8g7Puy/iTbZ5rrrxHnZ0WbNK/ZY1afFZTw2LFWO8k7kCfXUSRQzZPJg3iJLnXX58fdEQqXzv67ydDVrAuFlr2Xz1Ydd4j5d73tE6U/1sz9tnkmQruPW8YzrbvC6ZgrKl6tE+I6Gs2GaDlHqyORj9zWdTeYbi4WUdOrXn3WFSuWLZxJhK4pHgZkBbq/Rldd4+GVVnlYltGLI8b13nvRopNWiplM1r91lYKTHvbL5IMldsWOMNJdncbx27/EI2X5rjMNYfoaBgMdPe/aS28S61SK21+PJrk68VY7yT2SL9DWRzv62c3GLJ5n6t855YMDzvdZbnrRNyViXHZlIkoiHWVlR8WPHLamrMSjHeVo1woxpvMIyBYmXE+TuBsxW03SI13brCWFSKTF5VLRWzGrLMm553NeOdMK9fLZt3iFSuWLe+MsgZnnapWJWYd7ageh4vdLZGBUs299eFruk8x2eSbDWniTmxZPN6Me+g9zafcTmUBFZG06hOUSgq8sVSwpoXxtty2qonrFmyeb7qUBJwZJv77PtyZbxF5MUisl9EDojIu6s8PyIiXxGRh0Rkr4i80ftdrU8qV2gomwfdeFeWilnH22vpfGKx1KAF0L3NVynGKNC+ZY9HQkJIVrZs7naiGDhLj4J9zJ3AUiMsz9s6n87+481i3T+ryeaJSIhYWJhL55lLL+9rDk7j7S+HpKHxFpEw8AHgJuBi4GYRubhis7cB+5RSVwA3AP8gIsu7pXSQZIOYtyGb++vkuyWTs2LelbK5WT/b4+OaXMyyYShq/x3kzH5N6xybSS5LVrOIhquXambthLVgXy/NGe9gd3zsJNY5sT3vfuO+Mptqw/Mu1Pa8RcTusjabKlT9/sIhMccc++v7cuN5XwccUEodVEplgc8Ar6zYRgFDYuhlg8A00H5+v0uKShmyeZ2Yd5Blc8tjWdYeNeITz3shxzpHnDMWFvJF1XaGqCY4zCVzLKTzy5LVLIzGPcuv03zRlM0D7nnPuOxrDkZvc0D3QqiCtYizzpFVc+2JbF7F8wZDOj85nyVfVIz0Vbch8UiItM9CgW6M9xbgmOPv4+ZjTt4PXAScBB4BflsptexIReQtIrJHRPZMTk62uMvLsb6cup53kGXzXPU6b+t4ez0WdGopx7qBkuddL8apWZlYNd5bqsjmQM32xCtlMMlsKk9YSt3k6hHTsnlNLHXG8rwjYWEoHi6bud0s6TqyORgZ50fNwVa1Fl+JiP8Snt0Y72r9RiuP4kXAg8Bm4Erg/SIyvOxFSn1YKXWNUuqadevWNbmrtbEyrvtjK9zzrjISFHqbBamUYj5TnqW5EkawappjKWt4nkM1SqWiNVrm2jHvgP42LWZTeYYTEUIN+pqDQzb3mTHwA9VUxrG+SFuyebpOwhoYw0mmlswGLbWMdzSYxvs4sM3x91YMD9vJG4EvKIMDwCHgQm92sTFWzLd+zDu47VHtmHdNz7t3RjKZK1IowrDD44jW6ailWZmkssbNNVEjdBWrUftvSekrwfN2E+8G//bK9gN2zNtxrxvrj7TleZcGO9XwvB1Sea3vMOHDrnhujPe9wG4R2WUmob0GuK1im6PAjQAisgG4ADjo5Y7WI51rLJvHIyFyxd6XVbVCpkaHNUsG6qXxnjebJww7PK6YT0srNJ0jbYZuarUorhW2Wim9zVsx3kFVAjtJKWGtwvP2JOZdXRVxlofVks392KGzofFWSuWBtwPfBB4DPqeU2isit4jILeZmfwY8S0QeAb4DvEspNdWpna4kacnmDYw3BDNJJGuXivnP817IGD+q4Xjppl3qZR28c61pDSvvopbnHa2RsLaSSsVcG2/z96EXt8vJ2glrpXvdaJvGO20rs9WvTafxrtUhz4/DZFxdbUqp24HbKx77kOPfJ4EXertr7rGMV/3BJGaXnFyBvjqxcT+SyRcIC8vGnfqhznveNN7OWJFOWFt9pG3jXX0B3TBhLeDXiva8vSFXxfMe7TNmbheLilCNkc/1sBZJjWTzWFhqqreJSIi5lL++rxXRYc1aWTXqsAb+K7R3g7PXrxO7zruXnnfa+GznirWUsOavi13TOdINFtC16rxXQsy7UFTMpwuuysTAabyDdy/qNNVi3gmznWyr58vONo/Wl81H+yLLugPa+xANZszb97jzvC0vtfdTuJolky8uk8zBH9nmtmyeWC6b65vT6sGSzWupWrXqvFdCk5a5dB6FuwYt4N92m36gWmVNrE3Hq1Gd97DDeNciHtBSMd+TMk9q/Zi31Vw+iMa7sCxZDYy2k9GQ9LTOe96OeWvZfDVjZ5tH6mWbL78eLI87yJ63NRJ3jaPXQT20MlWbkuddut9Zjkur+UqZfJGwGPfLalj9zesZ70QQE9aCQMnzdiGb+2AKV7PUks3B8L57GfNeyBTpj4bKZuBqz3v1kc4XiEVCNWOSteq8rWskiFUgFqfmjcE8m4fddYSOhgVBe97VsBPWHJnhVre1TIuOV9qcD15LEh9x4Xlr2bxDpHJFwqHl2dhOLOO9kmRzMMeC9vCims8UljU20HXeq490tlA3bFVtxntRKayHgnytnJzPALDJpfEWEbNplL+MgR/olOddz7EbjIcJiQvPO698NYPdXZDG56TyRl/zWisrKK3k/LZ6ckO2jvHuveddKCsTg/ZjVJrgkc4Va2aaQ/UOh5bBDocM2dxPN8ZmOD2fZSAWYijuvorFKD0K5vF2klyVhLV27yfpGrO8LcIh4d03bufSjQM1t4lFBGXuQ61yyG6zIox3Oqfqxrsh+AlrtWTzvkjIrnPvBfOZIsOJ8lhfTHveqw5jJG99z7syYc3yPPujYRYyBYKqnJ+cz7J5OF7XeagkyO2aO4l1TqJVwnCtJ6zVX1gCvOziNXWfTziUW78Y7xUjm9e7cUCwS8Uy+YK/Pe+KYQw6YW31kWpwU6s2493621p4BzVp7dR81rVkbhH3YbtNP5AtKGJhKVsIxdqWzVXNTHO3lLpZ+sf5WxHGO5kr0lenxhvKm7QEjXoJa33RUI/boxaWdSXSCWurj0YeiTXj3SmNW8bbWngH0XgrpVo03uK77GU/kC0Ul0ncJdm8xYS1fLF94211s8z6x36sCOOdbpCQAI6EtQCudjP5ItEaWbx90XDPPO9iUbGYKS7zvK0QhZYFVw/pBrJ5tVCKbbxjlufdwR3sEDOpPOl8kc0j8aZeF6uRfb/ayebVMkfFup+0WimUyauGsnkj4trz7gwpNzFvK2HNRyffLQ097x7Vrs+ncyiW9wPW2earj1SDtsPVrglLmekLsGx+cq65MjELPw668AO5glo2+rgZJe+tn9jDN/eeLnvME8/bjnn7Z4W5Mox3vlh3ljc4awX9c/LdkskX7cVHJYlI72Les0mjOUWl5x0OCWHRsvlqolG2eUmNKV0TpZh3cGXzZsvELOIRCeSQpE5jxbyd2I5XAydFKcU3907wowPlM7G88LztbpY+cv5WhPFO5xrL5nZjBB+dfLdk8oU6snnvYt4zScPrGK4yiadWUw7NyiSVbZywBuWhFNt4x4LreVsNWlpKWNO/j2UYxrv8Xm4lwDZa7FjX09nFbNnjmUL9UjE32LK5jnl7S8qF8RYRYmEJbMy7WntUMFaE2UJv5pTPpizPe7nxjmnPYlXROOa9vAIhVymbB9CYnZzPMtYXaVjtUkk8Eqo6qGW1ky2oZSqj21IxS9WZWsyUPe6lbK5j3h5SLCpSeWVLb/WIRcRXsodbGnVYg94k4s2anvdIYvm5r9XLWrMyaZxtvjxumbWzzYObsHZqPtN0vBuMMF5Gh5WWUS3bPO4yYc1yFqaXSp63UkonrPkVe5qRiy8nFpbA9TZXStXvbW5n0ffA807W9ry1bL56UEq5atIC1bPNrYV3IYAd1k7OZdk00oLxjlQfkbrayRWUbawtoi4T1izjfdZhvC1vvX3P23+lxoE33kvZPEDDhDUwVnBBmypmXbB+9LxnTONdrS1ktV7WmpVJtlCkqGqPA4Xq5YO5YnnMO2iLvUJRMbFodFdrFh3zrk412dy69zXKV7KM90wya4cRk9a0O48S1nTM20OSmSY870jwPG9r5Vi7w5pxw0znun8jmEtmGYyFCFdJpouHdfvH1YJV7VAvKci6ITvzIEoxb9PzDljC2pnFHIVi88lqYDQeqWxao6mesGblKzWOeRu2QKlSMq2lzLbrecd1qZj3WJ63W9k8cJ63ecHWq/OG3nnelUNJLKr1stasTCwpsX6d93LvujLmnQuY8T5llom1FvMO7qCkTlLN8wbjftLoXDmftzLOLU85XqPU1i0hcwGhY94esmR63o2atEAwY96NPO+eJqylcgzFq5/3qE5YWzVYN0g3Hdaq1Xlb13DQ1non7TKx1mRzaL1r2EqlmucNuPO8y4y3sbBKe+R5g7Hg0jFvDyl53i6yzQMY87biPP5MWMvWHIOo2z+uHqzflJs672qlYgOmxx48zztLSGDDULTxxhWU2jUH637UaXJVmrQAREONS0+d19bUUoVs3mbMG4zvTMe8PcSOeTcYTAKGdOKnmIUbLE/Fj7L5bDJXx3jrOu/VgjvPu3aHtZLnHTTjnWHdQNQOCTRD3IfZy36gWqkYWJ63u4Q1gGnT805m3edENSIe0bK5p9jZ5q4878YXgN+wZLV6HdbA6O/ebWaSWYYTtWRznbC2Wih5N25k89ox76B1WDOmiTUvmYM/E6B6TaGoyBerOyrNJKxBqVysFPPWxtt3JDOW8V6ZMe+S5127wxp03/POF4ospPMM1UhSikWWz2/WrEys31Q9abKebG6FfoIm1Jycy7K5hRpvcBpv/xiDXmMn51YxtFEXSp7z+SkzYc3LmLcxR8I/31fgjfdSE7JIPBIKruddSzaPmKViXb7zzaWqDyWx0Nnmq4eUi2zzWrJ5LCz2tR0kzztbKDK1lGupTAy08QaYmE+XGVzr3lw15u1CNXUm904vGbK5V6VioBPWPGcpkycstY2bk2g4eDHvehc0QCQsREJCpsuyudXXvFa2eSysm1CsFlzFvCPLZfNcURENi90nIEi9zc8s5lG0ViYG7vt1r1QKRcUL/vH7fOonR+zHMg08b7fZ5htHEstKxTxLWNPG2zuS2QJ90RAijY2331ZObmhU5w2G6tBtz9vqa16rzjumPe9Vg5ts80ioenvUaDhU8rwD1LDk9IKxeG0/5h2s+5FXpHIFFtJ5jpxN2o9ZKmO8asw71Fg2N+83m4b7SjHvnMcxb51t7h1LmbzrTMJYWMgXFfkAGZVGdd5gGe/u3vhmlmq3RgVLNg/OzVjTOiXvprbxtrpklQ8mKRINiW3Yg+R5n140rv9WPe9SqVhw7kVeYi1apsv6kFsqY2t13jnz+U2jCbvOO5UtEJL6zo9b/Fat5MrqiciLRWS/iBwQkXfX2OYGEXlQRPaKyPe93c3aWJ63Gyw5JkhSlRvPO9ELz9uKedeRzXX7x9WB3WGtQcVH5YLO8Lwdxjs4P0tOL+SJhoS1g83XeIMuFbMWfFYbU2gsm7v2vEf6mE/nyeaLpHIF4mFxpcw2Ih72l2y+fBxUBSISBj4A/CxwHLhXRG5TSu1zbDMK/CvwYqXUURFZ36H9XcZSNk+fy9Z3zjjTQGtqV9exVqN1Pe+IP2VzMJOS2mxNqPE36VyRkIu8k0rpM5s3ummVjHdwFnoLmQIjfWFCLRqFUoc1/xiDbmLd16zYtPOx2qVi7uq8N40kAMOrT+UKnkjmEEzZ/DrggFLqoFIqC3wGeGXFNq8FvqCUOgqglDrj7W7WZimTd52MYMVSgrTazbj1vLudsJbMEZLSRKhKqvWy1qxMrHGgjbybyhnv+aIxp15ECIeCZbxrtfF0izX20k8ybDexjrvM887V9rzdtkcVgQ1mHsLZpQzpbKHtvuYWCTNhzS9qopurbwtwzPH3cfMxJ+cDYyJyp4jcJyK/Wu2NROQtIrJHRPZMTk62tscVLGUK9LlcWVkeYJBkc7cx71SXj2kmmWW0P1bT86jWy1qzMknlCnXLxCwqZ7xnHa0wo6FQoIx3pqDa8uhWe8Kaddxnl7K2MbTuddUS1ty0R80UikTDIdYMmsZ70fC8vSgTg1Kowy/2w81RVbs7V/7KIsDVwEuBFwF/IiLnL3uRUh9WSl2jlLpm3bp1Te9sNZLZPH3R5mTzIP1gbONdo8MaQCIS7nrC2mwqx2hf7XhftaYcmpVJOlcgHnFjvMsrEHIFRcS8TiIhCVTMO1cotuXRhUMQktXb29zyvLP5ot3C1JbN2/C84+EQawaMJMKzSxmSHnrefltwuTHex4Ftjr+3AierbPMNpdSSUmoK+AFwhTe7WJ+lJhLWSlKVP06+G7L5IrFI/VK4vmio6/LbbDLLaH9t4239AHWL1JVP2qXnHY9IWe1/tlC0peeIWQkSFDL59mRzK/t+9crmpXuwlXFeL0QYDYvRPrWOkmfdKzvmeZv75ZekNTdHdS+wW0R2iUgMeA1wW8U2XwauF5GIiPQDTwce83ZXq5PM5F3L5tGwv2QPN2TyBXvRUYueZJsnc4z21y6T0bL56iGVLbia6mfI5qXrIW9mm4PleQfHeGfblM0hmB0fvcKpONjGu0HMG+rfTyzjPZyIEA0LZ5eypirkledtGm+fJK01vPqUUnng7cA3MQzy55RSe0XkFhG5xdzmMeAbwMPAT4H/UEo92rndNigWFclcE553AMszMvki8QbH19+DOm/DeNeTzXXC2mohnSu6ShqtLBVzxryDaLzbrR1ezZ630wCWPO967VEbzz/PFUoq5fhAjLOLGVJZL7PNrSFQ/rAfDUvFAJRStwO3Vzz2oYq//w74O+92rTFG5p/71ndBbEmYzRcbxhMTUaOmulBUdqvJTjObzDLaV9vzjtor5eDckDWtkcoVGEo0vpXEwsJcrnrM25JFg4InnncAOz56hVMprJTNqymNUTeetyMMs2YgXpLNw67MXEP85vwFusOaNQ7UfZ138GLeGVMKqkdpLGh3jiubL7KULTBWx/O24kN6pvfKJ51zJ5tX1nnnnDHvkBAkJ9T4XbbpefusY1c3ydSLeVc5r7bjVed8ZR33yjWDMc9l84R5raay/vjOAm28k5nmBq3bqf4B+sFk841ln24n4s2mjB+bls01YFx39VqjWlSTzZ0x7yB53jlPPO9VHPM271UhgemkFfMuIJT64Dspqaa1z1cmX7TvO2sGYo5sc29Lxfzi/AXaeNued9OyuT9OvhvceN6xLpcwzCaN1qj1EtbcyFyalUGqGc+7rEmLsm+2kYD1ws+02aQFLM87OPciL7G68q0ZjDO9WPK8YzVambpJNi73vONMLXhd5+2vmHegjbdVH+i6SYtd5x0cg5LJFRt73pHuHlfJeNcpFdN13quGVNZtk5byOm8jRmk1aQmW523EvHXCWqtYas2agVjJ884XayYBuslXyhZK98o1gzE7J8rzbHNtvNtnMWN53s3FvIPkeRsXZP0bo90nuUvHZbU0HKtXKmbXea/Om9NqIu2iIgKMayJbMZjEkkiNmHcwjHexqIye/W163qs7Yc1Qa8YHYmXZ5rU6SUZd5NBk886EtdK9KeGV8fZZk69AG28r5u022zxidTUK0Go3ky80lM273Sd5zvS8R+p0WIvpbPNVQaGoyOaLLmXz0kjQfFFRVKXrJBwSgrLOs46h3VhqLBIKVOWLl6SyRRLRMGNO451z43nXNpxWqRgY2eYWnpeKBaXO289YMe9+l8ZbRIhHwoHyvJuRzbs1oShpnfc6UqlOWFsduB0HCuUJa9bsbus6iQaow5p1zO1mm692zzseNVqZOrPNazkqrmTzimxzCy2b+5CkJZs3sbJK9KCVqFu++/gEr3j/Xcvjgm4T1rq0KLFqNOtlGOuEtdWBZXzcZJvHwiGKyvC6resiiE1a7Hrktj3v1Wu8M7kCiUiYsf4Yc6kcuULRUBlryeahZmVzh+fdZnjDIiRCPOKfmd6BNt5LWevG4X5llYiGffuDeejYHA8fn2NyIWM/5s7zbtx9yEvc3LCtH4yWzVc2qSY871ISY9H2wK1FXhA972rTr5ohHu5+W2O/YHXlszzk2WSuQcKalddTP2EtWsXzbsY+NKIvFvZNqXGwjXcmTyQkdSduVRL3cZzJSsBzGm93CWtmIkW3PO+cMYe5Xje3cMgYR6dl85WNvZBzmW0OkM0re1FnyebhAE0V88zzDhtjLosBWbR4iZVtbiW9Ti9lyZj3lWpEXcS8Mw7Puz8WtnOhEh553mAsUnXM2wOS2QL9sXDdiVuV+NnzXkwvN96ZnP8S1tKm5FUPESHqSFDSrEysa85NLW3MocZYoaEgDiaxY97tZpv7bD50N7Gyza2s8OmlbF3ZPOYy29xaUImILZ17FfMGw35o2dwDljJ5BuLN9a31tedtJoJNLjqMd76xbN7tJi2ZfIG4yxin9rxXNrZs3oTnnSsU7bKwWABl85Ln3X6dN/in9KibpLIFO9scLONdWzZv1KRFKbUsP8iSzr3KNgdtvD0jmS00b7wD5HkXiop8UTX2vLvcpCWdK9IXczdFSvc2X9lYEqKrmHfE6XmXy+aB9Lzbls27m2jqJ9K5op1tDkaLVDdNWmrdT/JFhVLlaoj13l7VeYPRzdMv9iPQxnspm2fAxYrfSSIa9m2SiBXzPrOQBkoXqt+atLiRzcHIptUJayub5rLNSxUItmzuaNISlAiLlbDU/jzv4M1a8IpM3vC8rRbL04vGEJFaoYhwyMixqXWPs66ncs/bkM29ao8KhsKkY94ekMwU6I+1IJv7ZOVUSaXnnXWZGBMSI2mvqzFvLZtrKMnmbgeTgJHEmM2XZ5tHwsHpsJauM3e6GWzZfJV63olImFgkxFAiwozpeddKWAPr3l39HmfdK8uM94Alm3vpeftHNvdm0GmPWMzk2TyaaOo1iWjYvzHvimxzezi9m2SgLtaMWmUejajsZa1ZeaSbiHmXJaxZMe9I8Hqbe+d5dzfR1E8YDoBx/OMDxvjOTK52whpY7XXrG++ow3N/0aUbSWbrv2ez+CnhOdiedzYfCM/7bZ+6n2/vm2i4nW28Fy3j7f4mEQvXlpS8Jp1363kLGe15r2iayzavVuddinkXFYEw4CXPu/1SMVh9CWv5QpF8Udn3kPGBGDMNEtagvuedqeJ5X7V9jD971aVNVSM1oi8a9s1iK9DGe6mFhLVEtLuNEdK5Al975BR3HZiqu51SqszzVkqVjLdbQ9nFhLVGcXiwZHN/XOiaztBctrnpeeerl4oBgbheSp53m01afDYfultY918ryXG8P8bkQsZIzm3V8/ao33wjdLa5RyQzzSesxSPhrnre1vjM+VSu7nbpXJFCUTE+ECOdK7KYyZdkcxcr/Hgk1MUmLYUmZHP/e1Ka1rGSd1wlMDoS1uwmLaFSzBsIRMa59TvzokkLrD7Z3L5mHLL5qbkUUD+PoN5cCjvm7WFDlmrohDUPKBYVyVyBfp973tb4zLkGxtvyunetHQAM79tOWHMzbrGLs4GbSVjL5P1/M9a0Ttqcehdy0eXQmbBWTTYHyPk0H8WJ5Xm3n7AWvBHFXmC3l3XI5vNmsm60jvFtNmGtE1iet1K9v68F1nhbg9abLhWLhCkUVdfkuVaN95mFTEk2d+N5d3FCUXOet/9vxprWSWcLrmq8oXRjzTk6rJWatJjPFf1/vaTNudPtxlKt+mO/eHLdwlqsOGPeFq3K5tVKxTqBda37Iek5sMbbHgfadJOWxg3uvWRmyZTN0w2Md3q5551p2vPuYra5yzpvLZuvbFK5Joy31SWrymCSUszb/9dLJldseygJlGTj5Coz3pVJjmMujbcrz7vTsnnUPzO9A2u8kxnj5LXSpAW6lyTi1vNeyBjPn1NNNndlKLvT9lUp1US2eUg3aVnhuC0bhIo67xrGOx8ApaZeD+5msEYZL5mq22qhsrHPGtfGu3bMO9Mtz9u0N35IWgus8bY871Z6m0P3PO9Zl8Z7yVyMbBnrIxoWJhczTdV5d0s2zxaKKIVr2VwPJlnZpFzmP4CzztvRYc2KeYeDlW3uhfGOhoVYOGSPNl4tWJ63ZQjLPO86Gfz1HJRqdd6doM8sTfaDWhJY422dvIEm67y773nnzM8r1k1MWTQ97+FElLWD8QrP222Tls7f+GzJy1VHLd1hbaWTzhVclYnBcs87JCWPO0iyuZWk5wUD8fCq87ztrnyR5Z53ow5rtXqbN3OvbId+876nZfM2sC74/nizpWLdncBlyeYA86naP1Ir5j0Qj7BuKF4W83bVYa1LTVoyFZmi9Yhrz3vF47bPPRhtfCMhIZs3Bu44b9RRWzb3v/H2yvMG6I9FbBVxtVCSzavFvGvf69x43t2SzZM++M4CbLxb87zjXc4WtOq8ob50vmAuRoYSEdaZnrdtKF3cHOPh2skcXtJMRy2rztsPZRWazpBqwvMGY5Fp1Xk7b9SW5x2ExV7ao5g3wGA8suo878qY91A8Yi/kGse86zdp6Zrx1jHv1rGzzVsoFYPuet5WRUk9472UyRMJCfFIiHVDcc4sZJrqGhSLSFeatKQryjzqEQ2HzJaXnd4rTa9INVEqBqUFXa5QtA02OJq0BOBi8SrbHAzl0A/x026SrqiiERHGzOliDbPNe9ykxbI3gZHNReTFIrJfRA6IyLvrbHetiBRE5NXe7WJ1kuZqddDnpWKzyRybR/qA+uVii+k8A/EIIsK6oTjTSxn7R+02YS1XUB3vDW0PomhyBKRmZWLNZXaLNWkuVyhvhRm8mLd3nvfiKvO8M7nlDoBV693IeNeKeXerzrs/atibQBhvEQkDHwBuAi4GbhaRi2ts9zfAN73eyWpYGZrNxrwtz7tbLVJnkll2ru0H6rdIXcjk7YXIuqE4RQWnZtNlST31iHUplt9MwpqzKYdmZZJuos4bShUIuYIqywy2k9kC0KTFiHl7YyT6Y2G77HW1kK5IWAP3xjuTL1YNwzWTH9QOQZPNrwMOKKUOKqWywGeAV1bZ7h3A/wBnPNy/miSzhszc7I+om553oaiYS+XYPm7UbteTzRfTeYYShvFeP2QMkT8xmyIWCbnq5NStCUWVySb1iNqetzbeK5VmjbdV+58rlM9uDlJ7VC9j3gOr0PNO54qEQ1L2/VvGu162uWWYqyl5dqlYqFuyee+/MzdHugU45vj7uPmYjYhsAX4O+FC9NxKRt4jIHhHZMzk52ey+lrGUKdAfCzfdorCbpWLzqRxKwY41huc9l6wT886We94Ax2eSrpLVADsG1+lFSWWyST20bL6yUUo1VecNpZa52YKqaryDMJjEy2zzgVWYbZ7KFUhUOCXuPG/jOqsmnWfNxaCbHvvtYF3rfshTcGO8q52Nyl/Ye4F3KaXqHpFS6sNKqWuUUtesW7fO5S5WZymTb7pBCzhLxTpvUKwysQ3DcRLRkKuYN8C6wQQAJ2fTrusWY10aL2glm7jzvLVsvpLJFooUlbtxoBaxiJied4XxDlCTlnTOu5j3QDyyKmXzygXf5tE+YpFQ3ftdPdU0m/culFGPsJlUHIiYN4anvc3x91bgZMU21wCfEZHDwKuBfxWRV3mxg7VItjDLG7rreVsNWkb7Y4z0RRuWig2asvnaIWMVmi0UXcdwrOElnV6UpJsoX9Oed/BI5wr88ZcesTsD1t/Wff6DhZWwli0Uq5aKBWGhl8kXXQ0LcsNALGyUzgUgXOAVRkvd8mvmV56xgy/+5rPq5vfYHfpqGe8Ox7st+mP+qBBwc7T3ArtFZJeIxIDXALc5N1BK7VJK7VRK7QQ+D/ymUupLXu+sk6Vs87O8weiQI9KdfsLWDXCsP8Zwor7xXkznGTIXI/2xiC2hu/a8rZh3h8vFWpHNg3BD1hg8cmKOT95zlHsOnm24bTP5DxbRsJDNF8kXajVp8bcRU0qRyXsom8etdpurRzpP5wvLKhQG4hEu2TxS93UNPe+uGe9IMHqbK6XywNsxssgfAz6nlNorIreIyC2d3sFaJDMF+pts0AIQCglD8UjDXuNeML1kGO9x0/Ou12FtyZFtDqW4d8xlzNuS8TrdqKW5hLXaK2WNP1kwQzuLLqRcSzpsLmHNkM2zlaViAZHN7axmz4y3ce5WU9JapomufE4sta9arXczKmW79MXCvpDNXVk/pdTtwO0Vj1VNTlNKvaH93WrMYibP5tFES68d6Y/aw987idVdbXQgykhflFNz6arbFYqKpYowwLrBOIemllx73vGued5NlIqVZZt3NpFE4w0L5u9iscEIWyhda81mm1tKTCQUPNncWhx7GfMGfyRAdYtmJtE5qSubF7oT8wZLNu/9YiuwHdaS2XxLnjfQUML2iplklojp6deLeVvZplapGMC6YcPzdi+bm5JSF0rFjDIPN13fdMJa0LCNtwtP0PI+ms02N9qjFssMYMl4+93zNhsneRbzNn7zq8nzbmaYjZNGsnmnJ4pZJKLBiXn7EsNTbf4CAEwJuxvGO8dofxQRYbjOZ1pDSQYrPG9w33Qgbmebd1o2L7rqaw7OOm9/35A1JSzjveDGeDeR/2Bh1XlXDiYJSqmY9fvyPObtg4zzTL7QlTkEqRZl80YJa52eKGbRHwsHI+btV5KZfNNDSSy65XnPJrOMmj17h/uiLGTyVduXWslzg4nlMW+3dd5da9KSb2Z+s27SEjSs0bRuEjozFXOZ3eCs8y7LNg9MzNustvBsqpg/Yt65QpFn/tV3+cL9Jzr+WdVKxdxQGipVJeats82DQbGoSOYK9LdQKgam5+0iptcuM8ksY/1R+zOhlBDkxPJyBqokrDWbbd6NJi1uf3i6zjt4lGLezXjeTfQ2j1QfTBISIST+N95pj2Pegz7JNp9P5ZheynJgcrHjn9VsP3wL615YLSm3qwlr0YgvEtYCabzT+QJK0VKpGMBwX3eyzWeTOdvztox3tc+1bpRDbRjvbs0pzzTxw9N13sGjlZh3a+1R1TLpORoS1/O884Ui7//uk11ZhDspxby9myoG3SldrYf1fc/W6QLpFZkm1DsnjdqjdjNhTcvmLWJdaO143ulcseZ4Oa9wet7DpiRerVxssZpsbsa8mzWU3WjS4jZeFdOed+CwY94uPO9Wss2jYSFfVMvao4LRvcrtQu+h47P8/R1P8L3HuzJKwaYU8/bm1ml53ks99uSs73su1bg5T7sYeTOtlIrV8by7Lpv3PsEwkMbbSu5o3fM2DGq9uut2UUoxk8zZc2rret5Vxptaw0nc3iTCISES6vxMbyPm3WTCmq7zDgylOm/3nne8SeNd+nf5dRQJ4drzPj6TArofK7Zj3h7J5n1dbBpVj2563ka2eSuyee2Yd65QdN0To10S0TDpXJFij5MrA2m8rdKqVtqjQsmQdlJyS+UKZPPFkmze31g2dxrv8YEY4ZA0JS8ZF1Xn67zdJijpqWLBw/LA3BiTZma7WzjbilZKz5GQuI55n5g1jLcbhcBLLM+73vSrZhARYzhJj7PNF23Pu7PGO1coki+q1rLNLc+7ijOQyRc9+04aYU8W67F03pr16zFWpl872ebQ2QvV6mtembBWz/N2LkYi4RD/8pqncemWYdefmYiGupKwNmoeSyNCIkRDomXzAGFdi2482mqjHRtRbRiJ/XcT18oJy/PusvH2OtscjC5rq8Xzbqa9ciXxOsY7W+huqRi0Pl/DKwJpvJfsmHe7snkHjbfZGtUuFUvU9vYXM3kS0dAyGfGll29q6jPjkW543s2PgNQJa8HBks3deLTVRjs2IlbmeVfI5mEhX2zW8+5uwprXMW/wx1hQq+Kl0553qUOjxx3Wupiw1mc6jb3OOA+mbJ5pz/Me6TNe18kLdbbC8+6PhYmEpKbnPRh3583WIxENdaG3eXNlHlZ2scb/KKVYSOcRMbybRrkKqRY6ZZXHvNuQzU3P200zGS+xs809inmDobj13PN2VBl0slzPnkrYgucdCgmxcHV1sdsJa9B72TyYxttcpfa3m7DWQclt2pooZg6ZF5GaLVIX03kGW1QRnHTD8262zMNqyqHxP5m8EY+0Kh0aGZRWmm04b7DLY97uKhOUUj2PeXtVKgbGfazX2eZWcx7orCJpLX5akc3BkM79MJgEel+bH0jjnawSI24GW8LuqOddGgdqUdN4O2Z5t0N3Yt7NlXnEIqI974BghXQ2jfYBjePeLRnvKi1RnX+7WejNJnN23kuvYt5eGu9BH3neALMdvC/asnmLhjYWCS1ThApFRaGoiIW7k21uJWhq2bwFrFVqq73NE9Ew8UiowzFvc6JYf0kOH6rR39zwvL0w3p33vFM596ViYPxIO117rvEGy4vdNGxM62tkvFPZQlOZ5lAulS+Lebts0mJ53SGBhUz3Y96xcIhQE3H+RvTHIz1vt+kMP3Qyac2uUGhRNTU87/L7iWXMuy2b9/o7C6TxTmbzRMz4R6sM15ny5QUzySxD8UhZElqtgSiLGQ+NdwfrvHOFIoWiasrbGk5EmE/3vhuRpjGW97Vp1J3xPjKdtPsRuMX5e6gW83aT3GjVeO9cO9ATz7uV1p71GIyHe97b3HkeO9mopZVhNk5i1Yx3oUfGW8e8m2cpU6A/Fm4qy7WSTvc3n01mGR0oT0KrK5t7Yrw76+WmW+hlPZKIMNflG6ymNWzPe8Q03nW+t7OLGQ5OLnHNzvGmPiNe5nlXdlgz2p42wvK8L9w41JOYt9thQW7pj/lANs/kWWvmOnTW87Zk81Y97zDZCgfF9ry7VOddyjbXMe+mWcrk266vG050tr+5s7uaxUhfpGqSnFcx704nrJXKPNz/8Eb7wsx2sJOdxjuspKWNI0bMu14m972HZwC4btdYU59Rr8NaNCSuRoKemEnRFw2zbby/o8b78NQS9x2ZKXss00SHQbcMmLJ5Lzt2LWbybB0zvvdO3hdbcQCcxKvk9XTd89Yx79ZJZgstZ5pbGBJ25374znGgFtYo0sqZuUbM26NSsQ4mrNk/vCZWzdaCpRtzgjXtYS0sN5uedz1v8N7D08QiIS7dMtLUZ9STzcMhcdVK98Rski1jfQwnomQLnZtR8Hd37Of3Pvdg2WOZnPfNQAZ8IMMupvNsMRMVuxHzblk2Dy9PWOt2zLvPB98XBNR4L2Xbl5k7H/PO2TXeFiN9UQpFVVYWkskXyBaKgSgVs7tLNSmbFxQsZXXSmt+xvNiNLmTzew9Pc+W20aYl5FiDOm9Xnvdsii2jffY9oFPe98nZFJMLmbLHWp2IVQ9LRUz2UDpfyOQZ7osy1GFFMm0a2lbzBqp63rZs3p1s83gkhIj2vFsimSnQ32KDFotOx7yNiWKVsvnyFqlWwxmvEtY62aSlFdl8xAwHzPW4d7OmMZax3jCcQKS2bL6UybP35DzXNRnvhso67+XZ5m5KxU7MpNgy1seQeW11KmltYi5NMlsoWxCnO+F5mwv3XiatLabzDCUijPZH7TLXTpBp0/OOR8LLlJZue94iQn80rLPNW2Epm2+5TMzCyvzuRJwpXyiykM6XlYlZnwnl9eX2UJJE+7J5PBIia2aEd4JWJK/RPmNbnXHufxbSOfpjYaLhEAOxSE2jeP/RGQpFxbW7WjDedT3vxlPFktk8M8lcxz3vYlFxxvS6p5dKxqwjnrfpiPTKGOQLRVK5AoPxCKN9sY563q3MgHdSVTYvWF3vumfO+mK9L+8LpvHO5Nv2vIcTUYoKz3oKf+B7B/jpoWmg1OSg0vMeruJ5W3WqXnne0LkRnK00WLBb0Wrj7XsWHP0G6jUOuffwDCGBq7aPNv0ZjdqjNioVs9qibh3rY8hc8Hai1ntqKWNL+E7j3RnP21QQeuR5O9W/0f5oZ5u05AvmMBsvZXPje+pWb3MwysV0tnkLLGULnnje4E1mZaGo+Ps79vOWT+zhxGzKlp1qed6dk82Nr7NTce9WGixYsvl8Rse8/c5iJm9L0YOJSE1jcu+haS7aNGwbz2ZoVOfdqFTsuFkmtmW0JJt3wvM+M1+KdXfc87Zi3j0yBrYDkYgYuUAdLhVrtbsa1PK8Ldm8O6ViYBpvnbDWPEkvPG8Ph5PMJLMoZWRpvuPT99tym5uY96Ljh9Mu1k2lU41a0i30JR61jLf2vH3PfDpnG+TBeKRqzDubL/LAsRmubSHeDYaBtmx21aliDWRzy/PudMz79Fza/nfHPe+YFfPuzW/EWqQNxSOM9nXY826hpa4TPySsgeHAaNm8SYpFRTLX/hzV0ljQ9n/41o/7pks3cv/RWf78q48BtWVzZ8zb8hq89bw7LZu7/5H0x0LGNDWdsOZ7FtIOzzseYbFKQuejJ+dI54pc10K828LyvluSzWdTRELC+qGEI+btvbE5PV/deK/EbPNS3o0hm1crZ/WKdK7YnvGOhO2kN4tuJ6yBEbPX2eZNks4XUKq0Wm0VaziJF5732UXjx/36Z+zg9c/Yzr5T88By2XwoHkGkImHNWvV61KQFOi+bN9NgQUQY7QtrzzsAlMnm8eqy+b1mXsc1O5trzuIkGhbCIZb1B48IDUvFTsyk2DSaIBwSWyXoRKz4zHyakBi15533vHsb87YUFithrVBUHduXdJvtZWNmUq6TXiSs9a9mz/vx0wstvc66qPrb9FTtzG8PVu3Wj3t8IMYfv/RiLt40bP/tJBQShuKRiph3exPSnFhGtVONWlqdxTuciOiEtQCwkM4xZDYLGkxE7HwMJ/cenmHnmn7WDyVa/pxYWIiGlt96ImGhUFR1K0CsGm8wbtbxSKgjMe/T82nWDsYZ64/a432hfdm3Gv3x3g66sDzvoUSEEdPh8KJRS75Q5C0f38M9B8/aj2VyzQ+zcRKPhMgVVFlFTU8871hk9ca8c4ViSx5i0ryhtO15V5GwW2V6yYhxrxmIkYiG+egbr+UDr72qqkEe6Y+WtUhdTOcRKbXca4dEhz1va1HQbGvD0USEeS2b+56FdKlN72A8skyOLhYVe45MtxzvtohFQsskcyiNCM0VS4vPY9PJMgn3xEyKLaP99t9Dieqx+XY5PZ9h40iCsf4Y06ayppQik/fe846GQ8QioZ71N190OBBeJvIePpvkjn0T3LF3wn4s1W7MO7K8oqYU8+6i5x0NB2Oet4i8WET2i8gBEXl3ledfJyIPm//dLSJXuHnfCUdcyS1WaVe7CWvVJOxWOWt63mOmp71hOMFLL99UdVurRarFQibPYCxCKNR+pqTlEXdSNhdp/kcy0hfR2eY+p1BUJLMFWzYfMrPNnYbz4NQis8lcS/XdTqJhqTpEwjLeVtLagTOLXP+33+OTPzkKGAv+iYU0W8we3MZ+RjvieU/MpdkwnGB8IGZ73hm7O5j3iVGD8YhnZavNsph2yubeed5PTBjq6qGpRfsxI+bdnmwO5cY70xPPOwAxbxEJAx8AbgIuBm4WkYsrNjsEPE8pdTnwZ8CH3Xz4qbnmjbclLbWb4FVNwm6V6aUsw4mIq9rFyslii2lvhpIAtkfQuYS1AolI89PcRvs6J5sfm07yjlsf6PkPKeiUpFPj5j0Qj1BUlEmDR6eTAOxeP9jWZ8XC1et8rXuv1WXt6PQSAH/3jceZWsxwei6NUrB1tGS8qykEXjCxkGbDcNww3ksVxrsDRmIgHq4apugGlnIxEIvY8xi8uC/uP20Z7yX7Mese0irWuXd2WcsVelTnHQDZ/DrggFLqoFIqC3wGeKVzA6XU3Uopa/zOPcBWNx9+ugXjvWTHvNtf/VZK2K1ydinLmkF3c40rjbfRLc4b423JUa0Oath3cr5ua0RD8mr+BzKcCLOQ6czUpB88OclXHjrJY6fnPX/v1YSV+zHkaNIC5WVYp+eM8NCG4dbj3WDKxPVkc/NmbPUVn0/n+ZuvP27P8S73vGt3gmuVdK7AbDLHRtPznrGMd4s5H24Y6OFY0EWzOU8oJHaS7awHM70tz/vYTMr2lNvNGYhFluf19CrbPFdQrtr5dgo3R7sFOOb4+7j5WC3eBHy92hMi8hYR2SMie6A9z3ugTdkclkvYrTK9mF2WnFYLqy2rhbOrVbvYCWsteN5KKX75wz/m/d89UHObVss8RhOGF9eJXvITZjONiRauJU2JyqoHu4baYVCsMNe6IXcL1VrEwkKkrvE2rt8pM9b8xmfv5L/vO85tD50EsBPWwPK8vTV61nFasvlMMkuxqEo5Hx3xvHsom2dy9j1oxEPZfP/EApGQkYR4bMZQbdK5YlvZ5vFqxrtgdG0LexB6dIs9WayHip+bs1jtjFR1oUTkZzCM97uqPa+U+rBS6hql1DVhEU7NpdzvqYmdbd5mwhosN6StMr3k3nhXTjNzlue0SztNWhYyeRbSeQ6fXaq5TaurZqtF6kwHOjdNLhg32tMt5E9oSixUyOa25+0w3mcW0qwdjLXc2tIiVsPzDlfEvCcXMgwlIvx/L7yATSMJbv2pEfveNFry/IcSUc/LmqwF4cYRw3gXldHyuNVqCzf0x3onmy9mSqG7RDRMPBJq26lJ5wocnlriWeetBeDQpHFfabdO3kpYc6qL2Xyxq5I5lHKuehmuc3PEx4Ftjr+3AicrNxKRy4H/AF6plDpb+Xwl0XCoNc/bw9Iqrzzvs0tZ1jTheWfyRf7hjv08NbloS1ZekGgjYc1qB2lJk9VotcbV6rLmrJf1CutGq413e1R2+huoIptPzGfalswBfunKdbzuqg3LHo+axtuq451czLBuMM5APMKfvMxIs1k/FC8bQzqUiHiu6Jyu8LzBuHY76XnX6yXfaSrVPy8miz01uUhRwYsuMb7ng2bSWjpXbLtUDJZnm3dTMoeS89jLjHM3VuNeYLeI7AJOAK8BXuvcQES2A18AfkUp9YSbD46GpbWYtyWbexHz9mAsaLGomEm697xfctkm7n5qivd/7wDvMyXqK7aNtrUPFu0krFnxxZOztY13q6vmYXOy2EwHjPcZy/PWsnlbLKTLZXO7e1mFbO6F8X72rpGqj1vOU94sFZtcyLDWlOhvunQjL7hoA5W5ks6s+GYTKWthhWA2DCdsCX16KYulynbG8+7dlKpK9c+LyWJWvPu6neOsGYjZSWut5s1YVJfNu2+8Ldm8l0lrDY23UiovIm8HvgmEgY8opfaKyC3m8x8C/g+wBvhX8weUV0pdU+99o5EWPe9snkhIPJFJhvvazzafT+coFJVr471r7QCfevMzmJhP85WHTvLNvae5fvfatvbBIhoOEQ5JSwlrlhGcT+eNZh1Vhk6kW/zh2Z53B+YEW4rBajTeJ2dT/O8vPsI//tKVdpliq8yna8S8Kzzvy7dWN7xeYNV+W7L51GKGizYaDY9EhA//ytXLjPdgPIJSxqLeKwXr9HyavmiY4USkzPO2zkkiEgKPHa7BeLhnHdYW03k2OhZlI/3RtmPe+08vEg0LO9cOsGvtAAcnl8iZ44rbyTavlrCW6YFsbqkHvZTNXV3tSqnbgdsrHvuQ499vBt7czAdHQyGmFjNNSx5LmQL9sebLlaox0hclnSuSyRfKpLhmsGq81ww2d/PcMJzgzdefw5uvP6elz61FIhJqy/MGo4vVhRurGe9i08cJpZh3u1JcJYWiYmrRTFhbhbL5N/ee5nv7J7nvyAwvuHi5DN0MdqlYvDzmbSVR5QpFzi5l2uqs1oiwVMjmCxmuP690vVXrhWC3SPUw/GQoDHFEpMx4W4uLeDTsufHuN2VzLxUEtyxlKmTzvqhdFtgqT0wscO66QaLhELvWDvD9JyYd7ZW9bdKSK6geyub+jnl3BOuH0OxNdynjXWmVF8NJSq1R28vA9YpENNxSzLvMeNeIe7dao9kfDRENCdNL3sYmzy5mKJp97k/NpTs2TMGv3H90FqDtGy0YrVEjIbGVFSv2bcnpkwsZlGq/TKweziYt6VyBhXS+YWZ7aSyod9eWMzxgDReaSWZb7jDohsF4hHxRNRzM0gkWMuW9Jkb6vPC8Fzh/wxAAu9YNcGYhY8+AaEs2t1tAOxPWCt33vFe38TY+utlEo2S24EmmOXjT39y6IN0mrHUaw3g3fwM4s5CxpaBace90vtDULG8LEWEoHvI85m0lq12yZYRMvuhJ8mGQuP+I0VqhmvHOFYp88p4jrutQrYliltcXj4SJhsWWckvlU51bpFrlY4aXb1wraxv0T7AXGR5Kzqfn02wcMYx3IhpmIBbm7GK2lG3ehuxbC+ue1u2Mc6WMISRDFQlr7fyWFtI5TsymuGCjYbzPWTsAwONmL4Z2cgYsI937hDUz2zzXu4S1nhvvZuPeXjY18WKymHMoiR+IR0Itx7zP3zhELBzieC3j3UZrw5FE2POYtxWnv8KMw66mjPOJ+TQnzO/pWBXjfef+Sf74S4/y3cfPuHq/xczyTn/GWFDLeHvToKUezg5rlhLUyPMerlAI2kUpxcR8piwGPD4Y67jnbd3Tup1xnswaUxqd3/1of4xUrtBym+UnzxiZ5ReYnvc564yOfPtOGUlsbWWbVxm+1IuEtdUtm0eMVfbpJmu9k5mCJw1awJvhJNZQEt8Y7xY978mFDBuH42waTdSVzVv1OoYTYc897zPmDf6yraPA6kpas7zuDcPxqp73U5PGDXTviTlX7+ecKGYxmCiNBbUWSt2QzXMFxZT53Tb0vOOlmLcXzCZzZPNF1juNd3+Ms0ud9byte1q3G7VY3++g47sfafO++ITZFtXyvLeP9yNidHAEb2LezpnevajztrPNV6PxDonRW/zkbCuet1eyufGDacfzPruUZSAW9nxMYKskoq163kYy0pbRvpqyeabFDmsAI/EwMx573paUe9kW0/NeTcb76AyxSIgXXbKRYzPJZfH+g6bxfvSku7ax8+nlzYIG49Ey2Twcko6Gh0ox7yKTi+48b69j3pZ6U+Z5my1SO+t590Y2txSLypg3GI1pWmH/xAL9sbDdCS8RNf792CnLeHswmKTQW9ncD9nmPTPeYHQwavaGa8S8Pfa821i1Ty9lGW8hA7tTJCLNJ6xl8kYv53VDcTaP9tlyrJOCmUzT6g9vOBH2vMPamYUMawZi9k1iNcnm9x+d5bItI5y3fpB0rmTsLA6aHa0ecel5L1Yx3kMO2fz0XIb1Q3FPpt/VojQStOR5N6puqEysaxfbeI+UFg1j5nCSjnrePZLN7ba4FTFvaL1F6hMTC+zeMFR2rexaO2DfV9rzvJe3gM70wHhHw8ZY22QP67x7brxPNXnDXcx453lbMe/2ZPOsbzLNwYgJNSubWz2k1w/F2TLax5mFTFlCCJSyO1v94Q3Hw8wmsxQ8HE5yZj7NuqE4sUiItYOxVVMuls0XeeTEHFdtH2XbuDHbujLufXBqiVg4xORChjMuzstCZnlt/4Cj9vjMQrpMSu4Edsw7byxGRvqiDQ3lYMzbhDXrXDlL4taYxjuTLxINd6aHtiWbd7tj12IVz3u0r73JYvtPL3LBhvLJc1bSGtBWnXckJIhUiXl3WTYHw/tetZ73ppFECzHvvGeet9XHtx3jfXbRfWvUbpCIhJuWzc84Bk5sGetDKZb1nbcWBK22hhxJhIzhJB5mhJ9ZyNgGZcNw8ypOUNl7co5svshV28fYbhpvZ9x7NplleinLDResA+DRk42974VqsnmiXDbf0OZAkkbYsnmxyNRihrUuFK1QSDwdC1ptctrYgJHANZvMdsTrhpJsvthl2dxui1vV824+zHV2McPUYsYuE7PY5TTebcjmIsJwIloWgsv1IGENrK54qzDbHGDjiOHluS1nKRYVyVzBs2xzWD4opFmaGUrSDRIteN5WZu/6oYQ9K7lSOm+3wcKQeXPyMu59Zj5jG5RNI4mWOvYFkfvMZLWrdozZIYOjZ0vf11OmZP6yKzYjAo8crx/3VkpVbXIyGI84jHfGLp/qFBG7t7liciHjenqZl2NBT8+nWTMQKzMG1uL85Gy6I/FuKMnm3TYGdszb8d2P9LdehfPEhJlpvrHCeK8reeLt5gedt36QA2ZGO/QmYQ2MjPNVmW0Oxg1XqfIGIfVI542yhgGP6ryhvf7mSimmmxhK0g1aadJiZW2vHzZi3rC8UUuqTeM9kvDWeBeKisnFDOvNumNnH+qVzgNHZ9ky2seG4QSJaJiNw4kyz9tKVrtsywjnrB1o6Hmnc0XyRbVMNreMYjpXYC6V62imOZQnrE0tZhtmmlt4ORa0Wv92q1HLqblU5zzv2PIpbt2gchQsGKGIkLQW895v1nJfUOF5l8nmbRrv3dWMdw88777YKpbNrZW8W4/JysTs99LzTrTe33wxkydbKPrM827NeIsYHoY1brG25916nTfgWZe16SUjfm7daDcOJ5hJ5lquTQ0S9x+d4WnbR+2/t4/3l8W8n5pcIhoWto31cemWkYblYpbkXCmbD8QipHIFu/pgfadlc0eTlqY9b4+MntUa1YmVNHdqNt3WLOp6JKIhQmKUwnYTS7FwqpmhkBhd1lLNL7QfOj7HuqH4su9u82ifbWDbVS/OWz/I2aUsZ80kzZ4Z72i4p4NJemq8N4+YWcKujbd5oXntebfYHtVvDVrAyMZM55uXzdcMxIiEQ8QjYdYPxZeVi1lSfKvdkYYt2dyjWu8JO7HI9LzNhaA1qGSlcmouxam5NFfvGLMf2zbev8zz3j7eTyQc4tLNI5ycS9s3umosVPG+oJTEZGWud9zzNvPAFtN5FjN59553IupZzHvC0V3NwvK8FzL5jnneIsJAzLtFiFsWM3nikdCyGe2j/THmWrgv7jkyzTU7xpb1Zw+HhJ1rjPyMtj1v06u3vO9eNGkBw/NetbJ5yfN2l7RmNTDwKmENDOPdqufd6lCSThKPhsnmi031+Z5cSJfdKKuVi1lNEVrNFB22PG+PZPNSBy7jGtrU5LUUVO4/MgvAVdtLxnv7eD+n59O26nBwasnuanWpWQNfr967chyohVU+ZDV86bTxtrK4LSWuGc/bi2zzbN6Q6yuPc42jmqRTMW8wvN+ux7wzyxMVwepv3txvdWI+zbHpVNnC0smutQOEQ7JsodAsu9cb1/aTZxYpFpUxmKRHMe9VK5sPJyL0m0Ml3JD0cJa3vQ9txLynF/01lARKN5dME973pCNrG2DLWN+ymHc6355s3hcxxrh6FfOu7LVtNdVY6bXe9x2ZIR4JcdGmYfux7WtKSYb5QpEjZ5c4Z50RY7x4s7Hdo3Wkc3uiWGJ5hzVwGu/OXufhkBASh/F26XkPJ7yJeZeuqXLjPdwXsRcW8Q56eP3xcNebtNSaxtZKf/M9h41Eymt2jld9/tqd4+wwve922DSSYDAe4cCZRbtZS8+yzVdjb3MwpKJmGrXYsrmHMe/R/hjzqdZipZZs7quENdMzbuZ4jO5qpRvl1tE+Ts6mKTpqsu1SsRYlLxFhbCDqmWx+pqL3tSWbr/SktbufmuKKbaNlNytnudjxmRS5guLctYZ3MtIXZcea/rrG25Kcq2WbgxFDj0dCduetThIJhzhpqiduPW9nD/Z2eNxs62l5dhYiYkvnneykOBiP9KQ9amVPe2htstieI9MkoiEu2Txc9fk3PWcX3/7d57W0n05EhPPWD/LkmYWS8e5Fnfdq9rzBKvFxJ3XanreHsvmlm4cpKqN2tlnO+jHmbXrGbsvFisXlZTlbxvrIFopMLZXipF7M4h3rj3mWsHZmIc1Yf6mJx1DcUHGsOt1GHJpa4jc+vsfTUZKtMjGf5m2fvp+5BjfLA2cWefz0Ai++ZGPZ49vGSo1aDk4ZXvK560vZvZduGambcV5LNh9wyOYbhhNdmTMdC4c4ZbZMdhvzHkpESeUKrktOa/HIiTlCUlIrnFgL9E563gOxSEsd1r768En+9c4DLX1mTc+7hXDifUdmuGLraE1ZXEQ869C3e/0gT0wsksv30POOruKYN8DG4T7XnreVzOHVSFAwamWhVDvbDNNLGeKRkKf70y6W5+22UctsKke+qMo8byuR0Cmdlzzv1i+Zsf6Yh7J5pqwLlq3izLtbCH7h/uN8a98EPzow1fRne53R/rWHT/G1h0/xzX2n6253+yOnEIGXXLap7PF1Q3HikRBHzybt5LJz1pa8x0s3j3BsOlVzcWAnrMWXl4qBUTLUacncIhIWO4PXbS7JoEetRR85Pst56wer5tSMDRjnppOe90CTsrlSig987wBv//QD/O039nN8pvm57guZfNlQEos1g3HmUjnX5zSZzbP35DzX1pDMvWb3hkEmFzJ2W+CelYrlCk3lF7nh0NSSq+16brw3jSSYWMi4apuZ7IBsvnYwzs41/S0Z77NmjXc3PBK3WDcXt563NS3KaQi3jC1v1GIZrHbG+Y0PeGe8je5q5QZlYxNd1u4yjfZPDzX3vd/91BSXv+cOnphYaOp19bCuvR8+WX8h8bWHT3HtjvFl2dAiwnYz4/ypySXG+qOMOdSgS7eYce8a3rctm1cZCWrR6daoFpbXNtYfdZ3YNORBf3OlFI+cmOeyLaNVn7eS1jrqeccjTC5mXC28C0XF//nyXv7um/vtTnrfeLT+4q8ai5lc1YS1y80xuw8cnXX1Pg8em6VQVFy9s3qymtfsXm9knO89YSRi9ko2V6q5/KJGzKdzvO7f73G1be+N92iCQlExVaeUxWLJlCi89nSv2jHGfUdmm15B+W0oCZQ8Y7feoVVaVSmbA2XlYuk2e5sD3sa859NlCw4wjPeEi1Kx+XSOh47NAnDv4emmPvdTPzlKtlDkW/smmnpdLZRS9j7c9eRkWZ6BkycnFtg/scBLL99U9XnLeB+cXLQzzS0u3WxmnNeIey+k8wzEwst6djuN+YahLhlvcx/cSuZQMt6tJp6Ckeg4tZjhsi3V47Xd8LxfccVmJhcy/MXXHqu7XbGoeMet9/OJe47w1ueew0d+7Vou3jTM11sx3jVk86t3jBES+KnL38d9h2cQKa+C6CTnmXkJe80qil7J5uDtTO+/+OpjrpNue2+8m2jUspjJEw6J56vfq3eMMbWY4dh0c2VGfhtKAk7P290FVWqNWjqO4USUoUSkqmzezrkf748xm8q1PZzEitNXet4bRowua7UMoMU9T52lqOC6XePsPTnnurZ2LpnjW3sNo31XAy/ZLcemU5xZyHDtzjFmkrma3vFXHzYk85su3Vj1+W1mo5aDU0tl3azA6M29ZbSv5oSxxXT1pCVnbkm3ZPOoeX25TVaDUpZ8O0lrjxw3zo01G76S8S543jdetIG3PPccPv7jI3z5wRM1t3vg2Ay3P3Ka333B+fzhSy4iFBJectlG7jsy01R/f6VUzYS1oUSUizYNc+8hd8b73iMznL9+qCtJjQBbRvvoi4btXKVeZZuDdy1tv/f4GT675xhvfd65rrbvufHeOGw1amlsOH/45CQXbRryXKa26hLvO9qcF+a3oSRQurm4bdRSmbVtsaWi1juTKxCPhNo696P9MZRqbeCBk5lklnxRLRuUsWkkQb6oyhLtqvGjA1P0RcO89bnnUFRwv8uQyW0PnyRbKPLs89Zw35EZTzJN9xwxrrnfecH5QHXpXCnF1x45xdN3jdeUr7eP97OULTC5kFnmeYNxjd/91NmqSV3VJoqBUbplqVydrvG2iLTgeVueYy3ZfC6Va1iFYCerbarueY/3d97zBvj9F13AtTvH+MMvPMKTNUIzX3v4NLFIiF9/zk77sZvMPIhvPHrK9Wdl8kVyBVXV8wajtOuBYzPLJgwqpcquo0JR8cCRma5J5mB0gTtv/SD7TvXO8+6LeTfTey6Z491feJjzNwzyOy/Y7eo1PTfem812nE9OLNbd7vHT8zx6Yp5fuGqr5/uwe/0QQ/FI03Fvvw0lgdLNJeNWNl9IMxALL8sj2DLax/GZ8ph3uzcuqzb5G3ubl/ecWNJ4pSGzDMxEg4zzuw5M8fRzxnn6OWsIiXvp/PP3HefCjUO85bnnki0U+cmhsy3sfTn3Hp5hKBHhmees4ZLNw/zgicll2zwxsciBM4u89PLNNd/HKhcD7BpvJ6+8cjPTS1m+v3/5+08tZKvGPaFkGLtlvK04d3Oed/2+4G//9P383Ad+RL5ONvojJ+Y4f8OQfUOuZHyw8543GMf/vpuvoi8a5n996v5lCWPFouL2R07x3N3ryhZc564b5IINQ9zehHRera+5k+t2jZPOFZepQe/99pM886++wz5Tsn5iYoGFTJ5rajRn6RS71w/aC7ZeNWkBb2Tz//eVvUwtZvmHX7zSdRe/nhvv0f4Y1+0c53/uP15X7vyf+44TDQuvvHKL5/sQDglXbh/lPrN7lRtS2QKpXMGHxrs5z7uyQYvFlrG+8ph3rth2d6lnnDPOFdtG+eCdT7VV1mMl2VVKuW4atZyaS/HU5BLPOW8tg/EIl2we4acupMEnJxZ46Ngsr756K9ftHCcWDnkind93ZNqIL4aE63ev474jM8uM0NcePklIWFYi5mS7o/nFuVWM93PPX8eagRhfeOB42eOHp5a498g015+3tur7WpJq12Rz8ybclOdtJ6wtj3k/ObHAD5+c4uRcmjurLFzATFY7PsdlZje6aox3oc7bYuNIgn+5+Wk8NbnIB+98quy5B47NcHo+zcuq5D7cdNlG7j08bf8+GrFYZaKYEytz3Cmd5wpFPnnPEaYWs7z+P3/CExML7DGdnmt2dCfT3OI8x8zwXvU2B9rub/69/Wf4wgMneNsN53LZ1trXYCU9N94Ar3vGdg6fTXL3U9U9mVyhyBcfOMnzL1zfMWN59Y4x9p+ed133e9aUZv0nm9ePeVcm5Z1ZyFTtZLVltI/5dN6e9Z3Ot+95iwi/9fzzOD6T4ksP1I7pNcJKsluWsGbmT9QLwfzogHGNPds0VtfuHOfBY7MNM3w/f/9xIiHhVU/bQl8szDU7x+yM9VaZTWZ5YmLRvkk+d/da8kXFPY7fgVKKrz58imecs6auN7rVTDIMh4Tt48uNdzQc4uVXbObbj50pKxn72N2HiYSE1z9zR9X3tVqkdivb3BpO0oznPWx6oNVapP7Xjw8Ti4RYMxDj1p8erfr6U3Npzi5l6944x7tQ5+3k2eet5SWXbuJjdx8u+74syfzGi9Yve81LLtuEUvDNve6SKa1FYi3jvW4ozq61A2XK1J37Jzm7lOWPX3oRkZDw2n+/h688eJL1Q3G2jfc1c4htc/760uSyXmWbQ3uyuVKKv/vGfnas6eftz3cnl1v4wni/+NKNjA/E+NRPjlR9/gdPTDK1mOHVV2/r2D5cvWOMooKHjrlr1uLHoSRQatJSTTZPZQu88gM/4q+//rj92NRChnVVvKoXXLyBcEj4V3Pln84VWu5r7uT5F67n4k3D/OudT7WcuGZ5FpU3+LWDccIhqet5/+jAFGsHY/bIwut2jZHJF+t2IMsXinzx/hPccMF62yO8fvc6Hj+94NrLqYYVprFyLq7eOUZfNMwPnyx5iHc/dZaDU0u8rI5kDkbyzNrBONvG+mp6IT9/1Ray+SJfe8SIi86nc/z3nmO8/PLNyxZCFgPxCIPmf92g5Hm7/10ZgzVkWcx7LpXjC/ef4BVXbOaXr93G9/afqdoQ6mErWa2O571tvI9NIwnOcxiMTvP255/HYibPR+8+BNSWzC12rx/k3HUDfP0Rd3Fve5Z3Ddkc4NqdY9x7eMZWRT9/3zHWDsb5tWft5Na3PAMQfnp4mmt2Lh9G0ml299jzLiWstW68v7l3gn2n5nnH83c3fQy+MN7xSJhfvHord+ybqJpY8j/3H2fNQMyuZ+wEV24bRcR9sxY/DiUBR8y7imz+d9/cz8PH5/i3HzxlZ2lWtka1OHfdIL90zTY+9ZMjHDm75IlsDob3/Y7nn8ehqSW++vDJlt5jYj7DSF90mRIQDgnrh+I8fmqh6sJAKcVdB6Z41rlr7U5PVh/mevXePzwwxZmFDK++uhSyuX634bm30uTFYs+RGaJh4QozwzkeCfOMc8btpLXHTs1zyyfv45y1A7zsiuolYk6etn20bpOMy7aMcN76Qb5oSuefu/cYS9kCb3z2rpqvWTMYt736bhBtwfMWkaotUv97zzGS2QJveNZOfvnabRQV/Pee48te/8iJWcIhKesXX8lQIsqP//BGnnnuGtf71S4XbRrmhRdv4CN3HWI+nasrmYNxHl5y2SbuOXjWVentYo3mPE6u3TnOXCrHk2cWmV7K8t3Hz/BzT9tMNBzi3HWD3PobT+fcdQO8vMHishNsHeu3lZDeGG8r5t1atnmxqHjvt59g19oBXnVl8+fPF8Yb4ObrtlMoKj5777Gyx2eWsnx73xleeeWWtqfR1GMoEeWCDUPcd7R0E3/0xBwfuetQVUnVj0NJoHZv8z2Hp/no3Yf4+adtYbQvyp9+ZR/JrDF6sdaN8ndfsJtIKMTf3/EEqVyh5XGglbzoko3sXj/IB753oGFZVzXOLKRrzpa+8aL1fOfxM7zi/XctW4g9MbHI5EKG5+wuxXfXDsY5d125NHjXk1O84B+/z8/+4/f5+X/9EX/8xUcZ64/y/As32NtcvGmY8YFYw8Yq9dhzeJpLNo+UJUldv3sdB6eWuPvAFL/6kZ8yEIvw8TddZ0vD9fi311/N3/zC5TWfFxF+7mlbuPfwDIemlvjY3Ye5bud4Xbn4f7/kIv71dVc1d2BtEAmZCWtNxLzB+P06Q17FouIT9xzhmh1jXLplhB1rBnjOeWv57L3Hli3sHjkxz/kbhroSz26W37pxN/PpPB+/+3Bdydzipks3UVTwJ196tGHZ2GKmenMeJ9ftMhe3h6e57cET5AqKX7i6lDS8e8MQ33nnDXa2ezcJh4RzzcqKnsrmLca8v7n3NI+fXuC3bjyPSAv77xvjvXPtANfvXsutPz1alhX6FbM859VXe59lXslVO8Z44IghEX39kVO8+kN386df3ccr3vejMlk1nSvYRt5vsnk0bExmcvYlTucK/MHnH2bLaB9/9qpLeecLL+Anh6b5r7uNMEUtyXT9cII3PWcXX3noJE9MLHh2cwuFhLc//zyemFjkK3W8b6UUh6aW+MSPD/NHX3yE9377Cf57zzGemlyqmf38Z6+8lPe/9mmcXczyCx+8m9/97IP88MlJMvmCHaN+dkVy1nW7xtlzeJpi0Uhceusn9lAoKrtV5trBGL/zgvPLVvehkPCsc9dw15NTLbVHzOQLPHR8jmsrymuee76xb7/20Z+SKxT5xJuuY+uYu0lMoVDj3tGvetoWROB3P/sgx2dSvPHZO+tuv3EkUbX0rFNEwyFEmv9dDcbLZ2Hf+cQZjpxN8mvP2mk/9prrtnFiNlUWljCS1Wa5vI5k3ksu3TLCjReu5z/uOsTXHjlZUzK3uGjTEL/1/PP4zmNnuOHvv8c/3LGf+XSOVLbAzFKWMwtp+3ptlLAGRhXD+qE49x6a5vP3H+eyLSNcuLG2QtFtLOm8W7kITtrJNje87ic5Z90Ar7iitSRsV4EsEXkx8M9AGPgPpdRfVzwv5vMvAZLAG5RS9ze7M697+nZu+eT93Ll/khsvWs/Dx+f45D1HuHjTcNVhAV5z9fYxPv2To/zvLz3KrT89ytO2j/KGZ+3kL772GK/6wI/4Xzecy1KmwOfvO8Z8Os8V20YZrrNq7QUiwgUbh/n3Hx7i8Nkkv33jbm576CQHp5b41JufzkA8ws3XbeeT9xzhn771BFBfonzr887hUz85wkwyR8LDH8hLL9vEB+98it/97IM8fnqB33nBbjvZ7shZwyu8Y++EXWs+bM5stuxkpdFzHv/LLt/Mz1ywnvd/7wAfuesQX3zgBP2xMP2xMOesHWDLaLkMfO3OcW796TG+9dgE//uLjzDaH+Mzb3lGw/Ko63ev5asPn+LJM4vsXDPAZ/cc47YHT7BuKM6ONQPsWjPAtbvG2bV2eQLZoyfmyOaLXF2RoXvuukE2jySYSeb4yBuuZfcGb2OsW0b7eOY5a7j7qbNsGe3jZy/e0PhFXSQaFsb7Y017IkOJCPMO2fxjdx9hw3CcFzua2rzw4o2sGYjxmZ8e44YLDO/1xGyKmWSOS5vI8u0277hxN6/6wI8A+MOb6nu4IsLvvfACfvGabfztN/fzvu8e4H3fLR9actmWEX7nBbvt81WrVMx6v2t3jfPtxyZIZgu85+UXt3k03nK++fvwShVsBkvl/N7jZ3j0xByPnJhjPpXjZZcbORaX1lkQfv3R0+yfWOCfX3Plss6GbmloeUQkDHwA+FngOHCviNymlNrn2OwmYLf539OBD5r/b4obL9rA+qE4f/rVffzJlx/l1FyacEj4p1++stm3agkrcejWnx7lZZdv4u9/8QoS0TDPO38d77ltL+/77gGiYeFFl2zkdU/fwTPOGfdVX3OLz771GXzsR4f5jx8e5GX7JhCB1z59u+1xhkPC/335Jdxs9tCtJUGDIUe+4/m7+dOv7vNUVoyEQ3zulmfy51/dxwfvfIrvPDbBO56/m68/eopvPHqacEj4mQvWc8sN53L9eWvZsaafbKHI6bk0J2fTXLSpvlEbiEd414sv5Leev5sfH5ziO4+d4YdPTpVJfhZWnPhtn7qfwUSEz77pOld1zc/ZbeRg/MMd+9l7cp7jMyku3DjE2cUsd+ydIG/Ks884Z5ybr9vOiy/daC9Q7rVnH5cvQkSED//qNUTC0jEP5+eetoW7nzrLG561syW5rpNcvWOsrjGpxVAiwgNHZ/mNj+9h74k5Ts6l+b2fPb8s1BaLhPiFq7fykbsOcfdTU1y0cdjurOZXzxuMfJznnr+Oew6erSuZO9k23s/7bn4ab3rOLr6/f5JYJEQiGiKbL/LJnxzhTf+1h0Q0RMRFx8rrdo7ztYdPEQ0Lr+hAqW47vP4ZOzh/Q/c6uzkJmTk2Pzk0zZbRPi7fOmLc1/Yc4xP3HOHSLcO86OKNXL1zjCu3jdIXDfPQ8Tm+8ehpPn/fMc5bP9gwEbUebn4l1wEHlFIHAUTkM8ArAafxfiXwcWXoMfeIyKiIbFJKuW/3gyGZvek5u/inbz/B9bvX8c4XXsCNF64vG7LQSXas6eemSzdy4cZh3vH882wJcrQ/xntf8zTe8txzWTcUbyqZphcMJ6L81o27ecOzd/KxHx3m4eNz/OFNF5Zt88xz1/CSyzZy+yOnGxqq1z1jO5+592hVD7Ld/fzbV1/BTZdu4t1feJh33PoAw4kItzzvXH7tWTuX7Vc8EmbHmgF2rHG/H32xMM+/cENZvLqSrWNGJvFMMst//tq1dhytEVtG+zhn7QDf3DvBpVuG+fNXXcrzzl+HiJAvFDk2k+Lrj57iMz89xm9/5kH6Y2H6omEKSpHMFNi1dqBqPXO9FbsXvPLKLWTy3QlFNcubrz+npdeds26Q7zx+hoOTi1y7a5wrt43yuqcvL3+7+brtfPzHh3ntv/8EMDz9SEi4YGP3sshb4e9/8XKOz6TqSubVuHLbKFduGy177Nefs4sv3n+C933vSWLhxl0TrcXtjRdu8F2YcKQv2lP16Pbfvp6QSNl5mUvm+PJDJ/jvPcf5B1PdjISEkb4oZ5eyRELCM89dw++/6IKWvW4AaRSvE5FXAy9WSr3Z/PtXgKcrpd7u2OarwF8rpe4y//4O8C6l1J6K93oL8BaA7du3X33kSPXSMKVUUx7tkSNHyGQaZ1fG43F27Khez+p3Gh1jK8c2l8zx08PTri7+QlFVvdC8OvdzyRz3Hp7mmeeu8XRqnFvufmqKeCRsqy9ueejYLFOLGZ5/4fqa12yxqLj7qbN8a99p8uZ5DIlwwwXrbPl2JeL22rBo5/eplCKTL7pSh07Ppdl3ao6Dk0scmlpi+3i/637SFs0eWzV6fT/KF4oUlGrY0atYVPzZ1/bxi1dv63j4stZ5bedcefFdtboPc8kc9x+dYc+RaU7MpLh+9zpuvGg9o/31F0Eicp9S6pq627gw3r8IvKjCeF+nlHqHY5uvAX9VYbz/QCl1X633veaaa9SePXtqPa3RaDQazarEjfF2E/A6Dji7o2wFKlOE3Wyj0Wg0Go3GA9wY73uB3SKyS0RiwGuA2yq2uQ34VTF4BjDXbLxbo9FoNBqNOxoGF5VSeRF5O/BNjFKxjyil9orILebzHwJuxygTO4BRKvbGzu2yRqPRaDSrG1eZQUqp2zEMtPOxDzn+rYC3ebtrGo1Go9FoquGvIk+NRqPRaDQN0cZbo9FoNJqAoY23RqPRaDQBo2Gdd8c+WGQSqN6lJbisBVofM+V/9PEFG318wUYfX7Bp5vh2KKXqzsDumfFeiYjInkaF9UFGH1+w0ccXbPTxBRuvj0/L5hqNRqPRBAxtvDUajUajCRjaeHvLh3u9Ax1GH1+w0ccXbPTxBRtPj0/HvDUajUajCRja89ZoNBqNJmBo463RaDQaTcDQxrsOIvIRETkjIo86HrtCRH4sIo+IyFdEZNh8fKeIpETkQfO/DzleExORD4vIEyLyuIj8Qi+Op5Jmjs987nLzub3m8wnz8avNvw+IyL+IiPTieCrx6vgcz9/mfK9e4+H3d7P598Mi8g0RWduL46mkyd/f6xy/vQdFpCgiV4pIv4h8zfzd7RWRv+7dES3Hi2M0nwv8PUZEoiLyX+bjj4nIHzpeE/h7TL3jc7zW/T1GKaX/q/Ef8FzgKuBRx2P3As8z//3rwJ+Z/97p3K7iff4f8Ofmv0PA2l4fWwvHFwEeBq4w/14DhM1//xR4JiDA14Gben1sXh6f+ffPA5+u9R0H9fjMx89Y1yTwt8B7en1szR5fxesuAw6a/+4Hfsb8dwz4oV+uT6+O0fx7JdxjXgt8xvG9HQZ2mn+vhHtMzeMzH2vqHtPzg/f7f1QYZWCeUqLfNmBfte0q3uMYMNDrY2nz+F4CfLLK6zcBjzv+vhn4t14fl1fHZz43CNwFXOz2hxWU4wOiwCSww7wxfgh4S6+Pq9njq3jNXwJ/UeP9/hn4jV4fl9fHuELuMTcDX8FYUK4BngDGV9A9purxmc81fY/RsnnzPAq8wvz3L2J8ORa7ROQBEfm+iFwPICKj5nN/JiL3i8h/i8iG7u1u09Q6vvMBJSLfNI/jD8zHtwDHHa8/bj7mV5o9PoA/A/4BY1a932nq+JRSOeB/AY8AJzFuHv/Z3V1uinq/P4tfBm6tfND8Lb4c+E6nds4jmjrGFXSP+TywBJwCjgJ/r5SaZuXcY2odH7Rwj9HGu3l+HXibiNwHDAFZ8/FTwHal1NOA3wM+bcY6IsBW4EdKqauAHwN/3/3ddk2t44sAzwFeZ/7/50TkRgxvrRI/1x82dXxmTPE8pdQXe7GzLdDs8UUxjPfTgM0Y0vqyWJyPqHV8AIjI04GkUurRiscjGMbuX5RSB7u1sy3S7DGulHvMdUAB4zrcBbxTRM5h5dxjqh5fq/eYiHf7uzpQSj0OvBBARM4HXmo+ngEy5r/vE5GnMLyd+zBWU9YX89/Am7q8266pdXwYq93vK6WmzOdux4j1fBLjxmGxFcOD8yUtHN8icLWIHMb4vawXkTuVUjd0eddd0cLxzZuve8p8/HPAu7u8266pc3wWr6GK143RIONJpdR7O7qDHtDCMZ5lZdxjXgt8w1SDzojIj4BrMPIUVsI9ptbxraGFe4z2vJtERNab/w8Bf4wRI0RE1olI2Pz3OcBujIQShRHnuMF8ixuBfV3ebdfUOj7gm8DlYmTvRoDnYcRyTgELIvIMMwP0V4Ev92DXXdHC8X1QKbVZKbUTw2N9wq+GG5o/PuAEcLGIWBOMfhZ4rLt77Z46x2c99ovAZype8+fACPA7XdvRNmj2GFfQPeYo8HwxGACegRHrXin3mFrH19o9ptfBfj//h7G6PQXkMDyXNwG/jZFo8ATw15QSE34B2As8BNwPvNzxPjuAH2BIkt/BkNcDdXzm9q83j/FR4G8dj19jPvYU8H7na1bC8Tme34mPEtY8/P5uwTDYD2MYgTW9PrYWj+8G4J6K99iKIbE+Bjxo/vfmXh+bl8doPh74ewxG0tZ/m9foPuD3He8T+HtMveNzvJ/re4xuj6rRaDQaTcDQsrlGo9FoNAFDG2+NRqPRaAKGNt4ajUaj0QQMbbw1Go1GowkY2nhrNBqNRhMwtPHWaDQajSZgaOOt0Wg0Gk3A0MZbo9FoNJqAoY23RqPRaDQBQxtvjUaj0WgChjbeGo1Go9EEDG28NRqNRqMJGNp4azSAiLxBRO7y6L12iogyR292FBG5QUSOt/jauvspIn8kIv9RbVsR+bqI/Frre97Ufv65iEyJyOlufJ5XOM+fRuM1Hb+5aDROROQ5wN8ClwAFjFGNv6OUureL+7ATOARElVL5bn1u0FBK/WWd526y/i0ib8AYs/kcr/dBRLYB7wR2KKXOeP3+naTe+dNo2kUbb03XEJFh4KvA/wI+B8SA64FML/fLz4hIZJUvMHYAZ90abn2+NKsFLZtrusn5AEqpW5VSBaVUSil1h1LqYbCl6x+JyD+JyKyIHBSRZ5mPHxORM06pVkRGROTjIjIpIkdE5I9FJGQ+FzL/PmK+7uMiMmK+9Afm/2dFZFFEnul4z78XkRkROSQiN1V81n+KyCkROWFKuWHzubD5uikROQi8tN5JEJHDIvKHIrLP/KyPikjCfO4GETkuIu8yZeKPikhcRN4rIifN/94rIvGK9/wj8/MPi8jrHI+/VEQeEJF58xy+p8ou/br5vqdE5J2O175HRD5Z4xjuFJE3i8hFwIeAZ5rnclZErhWRCaccLyK/ICIP1nivqt+jiLwA+Baw2Xzvj1V5bbXzFRKRd4vIUyJyVkQ+JyLjjtc8R0TuNvf1mKkcYJ7nvxeRo+b+f0hE+szn1orIV83XTIvIDx3X2rvMa2JBRPaLyI3Vzp+IvEJE9prvcad57qznDovI/yciD4vInIh81romNJpqaOOt6SZPAAUR+S8RuUlExqps83TgYWAN8GngM8C1wHnA64H3i8igue37gBHgHOB5wK8CbzSfe4P538+Yzw8C7zefe675/1Gl1KBS6seOz94PrMWQ9v9TRMR87r+AvLkfTwNeCLzZfO43gJeZj18DvNrFuXgd8CLgXIxFzR87ntsIjGN4nW8B/jfwDOBK4ArguirbrwW2AL8GfFhELjCfWzLPyyjGouJ/icirKvblZ4Dd5jG92zSarlBKPQbcAvzYPJejZgjkLPCzjk1fD3yixttU/R6VUt8GbgJOmu/9hhqvrzxfvwW8ynyvzcAM8AEAEdkOfN38zHUY5/RB833+BuO7uBLje94C/B/zuXcCx83XbAD+CFDmeX47cK1SagjjOz1cuYMicj5wK/A75nvcDnxFRGKOzX4JeDGwC7gc4/rVaKqjlNL/6f+69h9wEfAxjBthHrgN2GA+9wbgSce2lwHKet587CzGzTWMIbdf7HjurcCd5r+/A/ym47kLgBxGqGin+b4Rx/NvAA44/u43t9mIcbPOAH2O528Gvmf++7vALY7nXlj5/hXn4HDF9i8BnjL/fQOQBRKO558CXuL4+0XAYcf2eWDA8fzngD+p8dnvBf7J/Ld1Hi50PP+3wH+a/34P8MmKbSPm33dixLmtc3dXxee8C/iU+e9xIAlsqrI/jb7HG4Djda6naufrMeBGx9+bHN/9HwJfrPI+grHQOdfx2DOBQ+a//xT4MnBexevOA84AL8DIoXA+5zx/fwJ8zvFcCDgB3OC4Jl5f8T18qNe/V/2ff//TnremqyilHlNKvUEptRW4FMMzeq9jkwnHv1PmayofG8TwNGPAEcdzRzC8Jcz3rXwugmGIa2FnMyulkuY/BzE8uihwypQ8Z4F/A9Y7PutYxWc1onL7zY6/J5VSacff1Y7Fuf2MUmqp2vMi8nQR+Z4pSc9heMlrm9iXVvkk8HJTJfkl4IdKqVNVtmv0Pbqh8nztAL7o+K4ew0iO3ABsw1gMVbIOY8F2n+N13zAfB/g74ABwhxjhnHcDKKUOYHjT7wHOiMhnRKTa+Sv7DpVSRYzz7jxOZzZ9EuPa02iqoo23pmcopR7H8MIvbeHlUxje1A7HY9sxvBmAk1Wey2MsDlSTn3UMwztcqwxZeFQpNayUusR8/hSGUXB+ViMqtz/p+Lty/6odi3P7MREZqPH8pzHUjW1KqRGM+LRQTr19ccOy86mUOgH8GPg54FeoLZk3+h5b+fxjwE2O72pUKZUw9+kYRqii2n6kgEscrxlRSg2ax7OglHqnUuoc4OXA71mxbaXUp5WRab/D3Je/qfL+Zd+hGY7Z1uRxajQ22nhruoaIXCgi7xSRrebf2zDk53uafS+lVAFDHv4LERkSkR3A72F4fGDEF39XRHaZ3t9fAp9VRibyJFDEiLG6+axTwB3AP4jIsJkQda6IPM/c5HPAb4nIVjOO/24Xb/s2c/txjPjpZ+tseyvwxyKyTkTWYsRhKxPJ/p+IxETkeoz4+3+bjw8B00qptIhcB7y2yvv/iYj0i8glGDkD9falGhPA1or4LcDHgT/ACH98sdoLXXyPrfAh8/12AJjn7ZXmc58CXiAivyQiERFZIyJXmp7wvwP/JCLrzddtEZEXmf9+mYicZxrdeQxPviAiF4jI88VIIExjLAAKVfbpc8BLReRGEYlixNAzwN1tHKdmFaONt6abLGAkhf1ERJYwjPajGDeyVngHRpzyIHAXhpf5EfO5j2B4ez/AqOlOm9tbkvhfAD8yJdJnuPisX8WQd/dhJEB9HiOWCsZN/5vAQ8D9wBdcvN+nMRYEB83//rzOtn8O7MFI5HvE/Azn9qfNfTqJYZxuMVUNgN8E/lREFjCM/ueqvP/3MSTh7wB/r5S6w8X+O/kusBc4LSJTjse/iClhV8j6ldT7HlvhnzHUhjvM474H47pDKXUUI8fgncA0RrLaFebr3oVxHu4RkXng2xi5EmAk9H0bWMRQFP5VKXUnEAf+GsNzP40RSvmjyh1SSu3HSNp7n7nty4GXK6WybRynZhUjSjWrIGo0mnYQkcMYyV7f7vW+dBoReQp462o4Vo2mm2jPW6PRdAQR+QWMGPB3e70vGs1KQ3dY02g0niMidwIXA79ixpM1Go2HaNlco9FoNJqAoWVzjUaj0WgCRs9k87Vr16qdO3f26uM1Go1Go/El991335RSal29bXpmvHfu3MmePXt69fEajUaj0fgSEWnYpVHL5hqNRqPRBAxtvDUajUajCRjaeGs0Go1GEzC08dZoNBqNJmA0NN4i8hEROSMij9Z4XkTkX0TkgIg8LCJXeb+bGo1Go9FoLNx43h8DXlzn+ZswmvbvBt4CfLD93dJoNBqNRlOLhqViSqkfiMjOOpu8Evi4Mlq13SMioyKyyRyjqNFoNKuGYlFx7+FpDk4tMZSIMJyIMpSIEI+EiYaFSDjE+qE4A3HdmbrbHJ9JcuRskmS2QCpXYLQvynPPr1tK7Wu8uIK2YAy4tzhuPrbMeIvIWzC8c7Zv3+7BRxscOXKETCZj/302mWf/ZJpLN/QxnAjbj8fjcXbs2OHZ53aTw4cPs+/UPCfnc5xezDO5lOPGc4e5cF0C6N2xVZ77WgT53Gtaw+21YRGka6Ty2I7PZfn2gQW+89Q8E4v5uq/dOhLlI7+wM1DH2y2s8/r5R2e4eks/u8biQHvXxpEjR1hMpvnlWw+ymC1vs/+RX9jB1pHKMfTV8dv35YXxliqPVW2YrpT6MPBhgGuuucazpuqZTIYUcT7601Pcf2KRozPGj+pXr9nALc/abG+3uLjo1Ud2nS8/Msk//ehM2WNFiXDNrrVA744tk8kwODjYcLsgn3tNa7i9NiyCdI2k0mmOLoa469AcPzw4x6HpNCGBa7YN8dZnjXPF5gGSuSJLmQILmQK5giJfVHz9sWkeOrnI4OBgoI63W2QyGWKJfj780yf5las3cNm2NUB710Ymk+HYkrCYLfK2Z2/mqq1DpPNF3vY/T3L/RI4Lt4y7eh+/fV9eGO/jwDbH31uBkx68b1N88O4TfOeJWa7dPsQrLlnDZx6Y5NT8yplz/8RUmqF4mPf9/HlsHo7za7c+Tr6gh8poNL3g0w9N8/H7pwkLXLllkFdcuoXnnzfGusFo3dc9MZnkvuMLXdrLYDKXLgCQzns3jO6+44sI8PJL1jCcMMze7rV9/PDgHK+9aoNnn9NNvDDetwFvF5HPAE8H5rod7y4UFXcfnucF54/xJy80ZI0fHZrnzOLKMd7H5nLsHE9w/rp+ACIhIVfUkxY1ml4wsZBnrC/Crb9ykW0M3BANh8gVFHqaY23m00bYwVPjfWyB89f1lX1X158zwsfuPc1MMsdYf2nRdWQmzd2H5rn5qvWefX4ncFMqdivwY+ACETkuIm8SkVtE5BZzk9uBg8AB4N+B3+zY3tbg0YkU8+kC158zYj+2fjDKxEKu27vSMY7OZtlhxn/AMN7a89b4lWPTSZ466z7eHTSyhSIDsXBThhuM360C9E+3NnOm8c54ZLwz+SKPnFri6m1DZY9ff84IRQV3H54ve/wf7zzO++46wUKmfu5Cr3GTbX5zg+cV8DbP9qgF7j6yRCwsPH1H6cvZMBRjcmmGQlERDlULyweH2WSW2XSBneMJ+7FoWMgX9R1A4z/yhSJv+OhPWUxl+OKvr+n17nSETF4RjzR/X4mGjdfohXdtZlOm553zxnjvO5MmV1RcvbU8/+L8dX2sH4xy16E5XnqxcZ3uP5Pk3mNGWOPsUp4hH1cFBL7DmlKKu48ucu22IfqipczydYNRCkWYTvp79eTkaw+f4nl/9z3SuULZ409NGokSO8Ycxjsk5PQNQOND/uf+4zw1ucTEYp65VHB+f82QLSjikeZvn1HTkdAhr9rMmzFvrzzvB08mCYfg8s3lxltEeM6uEX5yZMH+rE/eN2E/f3bJ38pt4I3346cXmFjMl0nmABsGjfT/IMW9f3xwiiNnkzx2qlzGOXBmufGOhIWc9rw1PiOVLfBP33qSkT4jhvjEZLLHe9QZsgVFLNz87TNiet564V2bOY9j3g+eSnHR+gEGYuFlzz3nnBHS+SJ7ji1wfDbD9w7M8pxdwwCcTWrj3VHu2DuBYHwJTtYPGTePiYXgGO8jZ40b3cPH58oeP3BmkWhY2DRcqkeMhnXMW+M/Pnb3YU7Pp/nbV18OwP7JVI/3qDNk8sXWZPOQls0b4WXMeyGdY/9Umqu3VS9ZvGrLIP1Ro+Tv1gfOEBbhN5+9BYAp7Xl3lm89dpqL1icY7y8v0bA878lFf38BTg5NLQHLjfdTk0tsG46Wxe6joZD2vDW+YjaZ5V/vPMDzL1zPiy7ZyIbByIr2vFuSzS3PW/92a2LJ5ulc8+fo/3z5Ue7Ye9r++97D0xQVXL11qOr2sUiIZ+wY5vsH5vjavrO8+MJxdozFiYXF9yHXQBvvE7MpHj0xz7O2Dyx7bjgRJh4RJgIim2fyBU7OGl7KIydmy547cGaRbaPlXYAi2vPW+IwP3vkUi5k8f/DiCwA4dzzOE2dWpuedLShiLXjeEVNq17J5baw8iWZlc6UUn7jnCO+49QEeOjYLwN0HzhINC5dtWm4jLK4/Z4TZdJ5cQfHaq9cjIqwdiGrPu5N8e5+RXPDMHcslERFhw2CMMwEpFzs2naSoYMeafg6cWWQpY2VcFjg2k2RbRQs/PyWsvf+uE3zniZle74amh0wtZvjo3Yf5+adt5cKNRszwvLVxjs5mWMoWGrw6eGTaTFjTlSK1mW0x5l0oKpQy5Pa3fGIPE/Np7n7qLBevT9T9rp65c5hwCJ577oidV7RmIKoT1jrJt/ZNcO66gWWGzWL9UDQwnvehKUNefMUVmykq2HvSSFo7OLmEUrC9iufth4zVyaUcn77/DP/241O68cQq5vDUEtl8kVdeWWpHvHuNcSM8MFXb+07nivzFt48w7fPkoEqy+SLxFhLWojphrSGtZptbC6JXXbmZhXSeN370XvadmufKTf11XzeciPD+n9/N7/9MqVHomv4IZ5e0bN4R0rkC9xw8ywsuqt3abn2APO/DZrz7FVcYN7+Hj88CpTKxSuPtl4S1Hx819vv4XIaHTy31eG80vWLB9JaG+0q5J+etMZoK7T9TO+69fzLJ1/ZN8+AJf/WNbkSrMe+I7Xn3fuHtV+xs81yxKYcgWzDO6aVbRvjHX7qSfWbVzpWb+hq+9orNg2V5U2sGojrbvFMsZvLki4otY7W/mA1DMc4mc4GQqA6dXWK0P8ruDUNsGknwyAkjae3AmUVEYMtweUKe0R6198f146NLbB6O0R8Ncfu+6V7vjqZHzKeNG92go6nFeF+Y8f4IT9TJOJ8xk4KC8Bu1UEq1HPPWnnd9CkXFQrpgd6LLNnGecqanHouEePGlG3n3TRdy/oZBLliXaPDK5awZiLKQKXhWa94JAmu8U2YcLRFdXrtnsX4wSlH5P+Uf4MjZJXauMZIqLtsyYmecH5hcZNtY/7JVvh9i3vPpHA+dSvIz543yM7tH+c6TM6RyKy++qWmM7Xk72oWKCBes66+bcW7J5YUAGe9cQVFUtJdtro13VZayRRTGvRuak86tcxo1wxm3PO9c7vjd59lqRzOsNb1wP4dzgmu8TSPRX6Xw3sJu1BKAWu/DU0l2rjFiM5dvHeHQ1BJzqRxPnVnkvPXLE/IiPmiPeuf+SfJFI9HjJRetIZkrcueBucYv1Kw4LOM9lChXiHav6+PQdLrmTXgmZXnend0/L0nnjXtPKzHvSMh4Ta9/u35lLmOc2/XmvbuZFqk5UzaPtvC9VDI+YCxCp3wc9w6u8TY97756nrfVqMXntd7pXIGTcyl2rjU9762jgBH3Pji1xLnrlpc5REOhnse879h7mtFEmIs3DHDl5gE2D8e4/bGzPd0nTW9YzOSIhIREtPyWcsH6fgpFOHg2XfV1Vi2tdeMNApmcJc9q2dxrrGQ1696daeK6yNrGu/1ZFmu05905LM+7nvEOiud9dDqJUrDLNN6XbzG6xd3+yGmy+WJVzztqtkftVYZ3Jl/gzv2TPHP7AOGQICK89OI13Hd8kVPzK3ealKY6C+k8Q4kIIuU3zvPXGTkp+2tI5zPmzTFInqg1e6CdhDU/5Kv4kXnT89441FvPe+2AYbz9HHINrvG2PO86svlAPEx/NOR7z9vqrGbFvMcGYmwb7+NrD58EqCmbA/TKYbnn4DSLmTzPdDTIefGF4wjw9cd04tpqYyGdZ7DKeMzNwzEGY2GerJG0VpLNg2PMMvnWjXdpqlhwlIZuspAxzosV807n3V8X+YqYdzuM9kUIib+HkwTXeOcaG28wR4P6vNbbKhOzZHOAy7eM2kPpz11XxfPu8XSib+07TX8szNM2l2ooNw3HuHrbILc/Nk1R13yvKhbSOYbi0WWPiwjnr++rWS5myea9DgE1g+UNxluQZ+3fbYCOt1McnFxcNkHRks03mJ53MwlrXsrm4ZAw1ufvWu/AGu+ki5g3GCu4CZ/Xeh8+u8T4QMyexARG0hrA2sEYo/3Lm9D0cjpRsaj41r4Jnrt73TLv42fOG+PkfJbT8/5eMGm8Zd6Uzatx/ro+DkylqnrX0wGUzS3PO9aKbK57mwNG6OEl//JDPvqjw2WPz2cKhEPYNddNyeZWqZgHnjf4v9Y7sMbbree9fijm+7Ggzkxzi8tM413N64betll8+MQcE/MZXnjJ8gY5g3Hj+2imPlMTfBbT+WWZ5hYXrOsnW1AcmSlPWsvkiyxljRtukIy37Xm3JZsH53g7wcnZFOlckScnFsoen0sXGI5H6DMTH5tpkWqXirXwvVTD7y1SA2u80y497w2DUaaTebI+rkU5fHapTDIHo0sQVI93Qymu0wvPe5/ZuvW6XePLntO9m1cnC5lcWY23k3PXGklrR6bLjbcV74ZgXS+lhLVWZHPzdxug4+0EJ2eNa+HIdHk4ZSFTYLQvYi+MmjPe3iWsgVHr7WfPu/qvLQC4ls3N2MnkUo6R+pv2hFS2wKm5tJ2sZjGciPLeX76SK7aNVn1dpIexM+tH0hcNUxnJ1KUwq5NaCWtQyhw+XVH14SzDCdL1YsVhW6rz1r8PAE7MGneOoxXGez5TtCdCQqkszw1WzLuVpizVGB+IMJPMUyiqsnHMfiGwnncqVyAWDtkj9mqxwcxa9Gu52JHp5clqFq962ha7fKwS6ybQC4/FXuFWkad07+bVh1LKLhWrxmA8zEAstCz3ZMYxLzlIHdbaKRULCwjBUho6wQnT855cyJDMlq6D+UyBkUSERAuetxWKaCUXoRpr+6MUVKnXut8IrPFO5wrLGkJUw/K8/VouZmWa71pT3UjXopS12n0jaa1wqyWGaM979ZHKFSgUVc2YNxjZwxMVC2in8Q6SMSvFvJv3xkTE6NGwykvFTsyUSgePTZf+PZ82jLcfZPM1Pq/1DqzxTmbz9Mcaq/5+b9RijQLdubb+2LpKoj3MWrVWuNXkKW28Vx+LdmvU2r/HasZ7OmXcFIcT4UDFgNup8wZzqNAq/32cmE3aIU9LOldKMZ8pMNwXJhwSYmFpynh7WSoGJePt16S1wBrvVK7YMNMcIBENMZwI+9bzPnJ2ibWDsbpeSzVsebpHMW8RqsaBwjphbdUxX6OvuZONQ7Gqsnl/NMRgLByo7GvL8261JCnqg7kEvebkbJprdo4Bxj0QYClbIF+EUXMRGI+Emop55+oogq2wpt/Yj7NJLZt7SipbqDtRzMmGwZiPPe+lZclqboj2MOadLRSJhkPLWmGCzjZfjSyY40CH4nU878Eos+l8Wd3udDLPWH+ESChYxsyKebfS2xzM1sYBWqx4TbGoODWX4pLNIwzFIxwzPe+ZJeMePWIa70QkRKaJDmtWnbfXsrn2vD0mlcvXnSjmZP1glDM+9byPTifZ0Ybx7km2eV7ZRrqSXpawaXrDgkvZHGDC0XNhJpVjrC/qiwl5zZDOF4iGhVCVxasbIqFQoI7Xa84sZMgVFFvG+ti+pt8uF5tNlsIoYKimvazzjkdCDMXD2nh7TSpbaFgmZuHnRi1zqRxj/c1J5tDb0YL5YrHmD6SXJWya3lBrHKgT23g7FLDpZJ5xy/MO0PWSyRWJtRFXXe2e94lZI0Ft62gf28f77Zj3TNL0vPtKnncrMW+vSsXAmC7m17GggTXeySZk86F4mMVMofGGXaZYVCSzBfrryI216Knnbcrm1eilnK/pDbZs7sbzdsS9gyqbZ/KFlvqaW6z2hDXLeG8eNTzv49MpikVVMt6OmHcz7VG9HExiMT4Q8e1Y0MAa73Su4Fo2j4WFgvJfLWnazFp1exxOejlaMJtXNZNCSouK1V0Ks5pYzDSWzdcNRAlJyfMuFBVzqTzj/dHAJXClPfC8V3MfhJO28U6wfbyfbKHI6fm0HfMuyebS1GCSXKFIOCSeNlRZOxANdqmYiLxYRPaLyAEReXeV50dE5Csi8pCI7BWRN3q/q+Wkcu5lc2sl5rcbhNUlrhXj3cvRgvli0W4SU4meV7z6mE/nEYGBOqWbkbCwdiBqd1mbS+dRwHhfhHAQPe824qrR1e55z6QYTkQYSkTZMW7k+xydTtqz3YcTrcnmhiLobSe0NWaLVOXDKYkNr0ARCQMfAG4CLgZuFpGLKzZ7G7BPKXUFcAPwDyKyfBSWhySzBVelYoC9SvbbDyaZsYx3C7J5j9uj1pKmLOPtN5VD0zkW0jkGYxFCDTweZ6231aBlLIAx73SuPSMRCcuqXtyemE2xZczoa7F93Pj/0bNJZpNZBmMh+x4SbyHm7aVkDrBmIEImr+wBOn7CzZFeBxxQSh1USmWBzwCvrNhGAUNi1A4NAtNAR6P86Zx74+3XMXzJnHGKWpLNe3hM2bxqGPP220JJ0znqtUZ1smGoNJ7XiiOO9UeJBszzTufai3lHw8FarHjNydkUW0aNYTWbRhOEQ2J73sPx0r2wlTpvr2q8LdaYycR+HFDi5ki3AMccfx83H3PyfuAi4CTwCPDbSqllZ11E3iIie0Rkz+TkZIu7bHxJuYJyLZvHfFq+1I5s3usmLbVifjrbfPWxkM65ajK0waz6KCrFtOl5j/dHAueJpnOFlmu8wZgsFqTj9ZoTMym2jCYAI6S5ZbSPI9NJZpJZhuIlk9S0bF7HqWgVP9d6uznSaldp5ZX3IuBBYDNwJfB+ERle9iKlPqyUukYpdc26deua3NUS1ixvt0avl61E69GWbN7L9qjFYs2BMCISuOxhTXssZtx53hsHY2QLitlU3h4HOtYXxGzz9jy8yCr2vOfTORYyebaM9dmPWeViM8msnawGrdR5187FaZW1Pu5v7uYKPA5sc/y9FcPDdvJG4AvK4ABwCLjQm11cjjXL222pWC/jw/Wwpum0lLAW6p2aYKxwa/9IVnsd62rDvWxeGg06ncwRDQlDcaOPdZCMWduy+SpOWLMGkmwedRjvNf0cm04ys5RjxCGbWx3W3CaL5Yq1q2BaZdxqkerDWm83R3ovsFtEdplJaK8BbqvY5ihwI4CIbAAuAA56uaNOLM/bfba5P2uP25HNrWu0F4lhjRJDIqFgyaCa9jBmebuRzY1tJhZydo23NWUrSAmO6VyxLdncCBP4LwGqG1jGe8touec9vZTlzEKaoUR5zBsg63Khk8t7n7A2FA8TC4sva70bLpeVUnkReTvwTSAMfEQptVdEbjGf/xDwZ8DHROQRDJn9XUqpqU7tdLNGz/8x7+ZlcxHp2Qq+XrY5GMY7SDdjTXsYMW/3nvfEQpaZVJ4xs5NW0BZ7mXxRJ6y1yMk503hXyOZg3J+dCWvWyOd0roibO32uUCTaxqKqGiLStHzfLVxZDaXU7cDtFY99yPHvk8ALvd212lied6LZbHOf/WAs2dxt1nwl0R6t4PMFN7K5/y52TWeYdymbD8XD9EdDhvFO5hg3M3kDF/POFdqSZ6MBW6x4yYmZFLFwiLUDcfsxy3gDDFckrIEx03vAhU3uRKkYGKXGfrMdENAOa6lsc7J5zK8Ja23I5tC7xBc3nrcfL3aN92TyBbL5IsMuZHMRsWu9LdkcCF6dd769bPNIOLRqfx/HZ1NsHk2U9QTYvsZpvJ2yubGNW6+30X2pVaI+/b4CbbzdZ5v7VzaPhUMtX3C9WsFnG9RTBq3dpaZ1Fl1MFHOyYcjosjbjNN4Bul4KRUWuoNpOWAvK8XrNydlUmWQOMJyI2sOZyrLNIyXZ3A25Bopgq0RDYg898RPBNN4tJqz5zfNOZfP0x1vzusFMfOnBgiRfUHVLMnS2+erBmig26HK4zoahGIem0+SKivG+4MnmGXMegZ4q1honZlJsHulb9rglnVeLebvtb945z9uf12cwjbclmzdZ55332WCxpWyBfpcLkGpEezQXuLFsvrqbUKwm3IwDdbJhKEYmb1wbTtm8qKDow/7RlVheYKyN3ubWYsWP/bI7SSZf4MxCZpnnDbB9jdHjfDhROq9xR8zbDYbn3RnZ3G3GezcJpvFu0vP2a8w71UR/9mr0agXvplQsSDFMTeu4GQfqZMNgaeSBVUNr9WHwo3dTSdq897SbbQ6w2n4ip+fSQHmNt8WuNf2EQ1LueTctm3vfHhX8Wx3QfI2SD7CNt9ts81Bz9YLdYimbZ6CFWd4WvUoMyxWKdT0Pv8pMGu+ZbzLmvXGo5KHb2eZ2HwaPd64DWBJuO7HV1dr/36rx3lrFeL/h2bu4dtc4cTVjP9a85+39VDEwvi8d8/YIK0s7EWnO8/abQUlm3Y81rUakR3OB8wVl9zCvhs42Xz1Ys7zdZJtDqdYbsOu8w0H0vNvJNg/Q8XrJidnlNd4W4wMxrt9d3jK7FPPuXZMW0KVinpLOFUhEQw1HEFr4daWbyhZaLhOD3rRZLBYV+WL92FK0R4sKTfexZHO3CWvrBqMIRienkb5y2TwIjX1Ksnkbdd7ma1er8d44knC1faJJzztbUETbyEWoRSSkS8U8I9Wkx+pX472UzdPfjmzeA3naagrTSDb327nWdAY729ylbB4Nh1gzEGXEHEgCwZpEZ8nm7U0VC87xesnRs0k2DieIu1RMLePdVLa5S4euGfzqeQcy5p3MFppqKepXmSrVdra5NDXv1gusxI2GsrnPzrWmMyykc/RFw03JlRuGoiSzpevWinn7MKy4DMvzbqdULOLTMF6nOXR2iXPWDbje3logNZOw1qlSMT/GvANpvC3Z3C0i4svVUzJbaCthLdqDOchW21Odba4B9xPFnLzmyvVlUqi9uA5A6ZRlSNqSzUPBSdDzkkNTS7z0sk2utw+JEI+Ia8873yHZ3K8JuIE03slsvulhHpGQkPXZF5DM5tsqFetFLMZagdb7kfj1Ytd4z4LLWd5Objx/rOxv23gHYMFnN2lpc6oYrC7ZfGYpy2wyx6617j1vMKRzNzFvpVTHepvrOm8PSeWaz9KOhUO+ujlk80VyBdWebN6LmLd5DuvJhn7tBazxHrfjQOvh17BWNawwVbsd1iAYx+sVB6eWADpmvK1z2c73UouYTwctBdR4F11PFLPohcRcD7s/e7uyeZeNZN68iK3a+WoEqd2lpj0W0jmGm/S8KwlSnXfai/aoAVqseMWhFo13PBJyFfN2E85rlV61oW5EMI13Nt+0x+q3DOhkzsjSbadUzDCS3b3j5VzK5n4615rO0UrMu5JAloq10x7VGpQUgOP1ikNTi4RDwjbH+E83xCMhVzHvnFkL3pk6b0NJ9Fs722Aa71zzbUX9Fodtdxwo9MZIZvON5Skj2zwAbpSmbRbSOYbi3sjmQTBmGTthzQPPexUtcA9PJdk+3t+0cU1E3cnmdi5OhzqsKfzXzjaYxjtbJNFCzNtPSQfJjGW8g9Ue1W22eaGI71aqGu9Z9MDzLpWK+f96SecLhENid4VrBTthLQDH6xUHp5bYuaY5rxvMmHeu8XmyFMiOJKz5tC4/oMY737TH6re4RTLrgWzeAzXB+pFEGnRYM7b1z/nWeE+hqFjKFlw3aKmFlT8RhOslnSvazUNaZbXFvItFxeGpJXatHWz6tX6Qzf06Ujpwxlsp1WK2+QqUzU3Pu5sebtb+kdSf5w3+W6lqvGWxyXGgtbBsYSAS1nKFplW/SuwEPZ+NKO4UEwtpUrkCu5po0GKRiEpzsnlH6rzNHAWf3c8CZ7yzhSJF5X6imIXfkqhKxrudbPNQ12Mxlmxeb/RekEp/NK0z3+Q40FoE6XrJ5IttJauBQ4YNwPF6waFJI9P8nCYzzcGSzd1nm3eiVMyvSmLgjLdVYtWs5x0NhXz1Y/FENu9B4oub2JJfV6oab7H6mntXKub/68ULz9uvxqBTtFrjDYbxzriose5kqVjMp0pi8Ix3k7O8Lfw2UN2rbHPo7k3Aks0j9WTzVeZZrFYWbM+7XdncuA0FImEtVyTermy+yrLND08tEY+E2DjsbpqYk3jUB3XeOmHNG1o1etGwv9qjeiKb2xdV94KFrmTz8Oq6Oa1WrFneXsnmXZ6x0xKZfHNzFaoRXWV13oemlti1dsD1CGcniYhRJVRskNfjxqlolZhPv6/AGW9LNm9WuvJbzDuVzSNCWzeCXsiNbkvFnNtqVib2ONA2ugRCKWEtCJ53JudBzHuVyeaW8W6F0ljQ+ufKCufVcypaxa8JuIEz3laHo6Zj3j7rt71kjgMVab9HcjePyx4J6iLbfLXcnFYrXsnm1kIwCNdLOu9BtvkqCivlCkWOTidbNt5xlzO9Oymb61Ixj2hVNvdjqVhfG5I59OYmkG0i29xvF7vGW+bT3srmfvp91iKdK5CItGe8wyEhJKujVOz4TIp8UbXveTdwULIdrfM2F5c+cv4ggMbbSlgLumyezOYZiLebtdr9i8rNCjeqY96rgoV0nlg45JknGoQ670y+SLzNmDesnuE9h9vINAfsc91INrdzcdoY1VqLmPa8vSHdara570rFmm80U0kvsrpdDSbxaXamxlsWM7m2vW6AcIBi3l543uC/KYedop0yMSh53o0atXR6qpjxGf76vlwdqYi8WET2i8gBEXl3jW1uEJEHRWSviHzf290s0U62ed5H/bZT2UJbZWLQmxIG67MidTJHV+PUpNWIMcu7feMtIuYwG/9fL+lcse1sczCcidWgTB2aWmQ4EWF8INbS6xOmJ+3W867XtrlV/Op5N/zliUgY+ADws8Bx4F4RuU0ptc+xzSjwr8CLlVJHRWR9h/a35SYt1heQLRSJe7BybpelbL79LF1bni4C3stF1Wgm23w13JxWM16MA7Uwhtn4/3rJeJCwBr2ZS9ALDk0tsWvdYMuJuYmou4Q1a+hUZ6aK+bPplJtlynXAAaXUQaVUFvgM8MqKbV4LfEEpdRRAKXXG290s0WrM2zJ0WZ8E1lJeyOY9WBHmCsWGU5V0tvnqYGI+zdrBuCfvZcSAPXmrjqGUMpq0eNA/e7XI5ocml1pqi2oRt2XzBqViLhJpW8WvYUA3R7oFOOb4+7j5mJPzgTERuVNE7hORX632RiLyFhHZIyJ7JicnW9rhVLZASGj6BxTz2eop6YFs3ouLKl9QDVe3Ott8dXBsOsm2sebHPFYjCJ6o5f2122ENjN/uSlemMvkCJ+fS7GhhFKiFFfPONugZ0Y1SMb9dn26OtNqduvIoIsDVwEuBFwF/IiLnL3uRUh9WSl2jlLpm3bp1Te8sYE8Ua1aGifrM805m8/R7JZt3uVQsGqp/2UTL5HzNSmQulWM+nWfbeJ8n7xeE7OuM2QJOy+busKbOjfW3Fu8G901acrZs3omYt78cPws31uM4sM3x91bgZJVtppRSS8CSiPwAuAJ4wpO9dNBqfbT/jLfRpKUdoj2ILecKxYZj9/wqM2m849h0EsA7zzsIxtsszPZENg9JINrBtsNSxjhfA204KVbMu5Fsbt3XOxHztkOuPrs+3VyF9wK7RWSXiMSA1wC3VWzzZeB6EYmISD/wdOAxb3fVIJ0r0Bdr/sdjrcgayS/doFhU3sjmPcjqzuVdyOY+zc7UeMfxGdN4j3tnvP2esJbWnndTLJmTEwfauM8102EtGpa2OlbWIuLTpjoNl0RKqbyIvB34JhAGPqKU2isit5jPf0gp9ZiIfAN4GCgC/6GUerQTO9xqolfMR5532rwKPJPNu+l5F4v2FKha6Gzzlc+x6RTgnedtJHB58lYdw/rdelcq5jNr4DFL5uCadjzvWFgQGndYyxUa35fawY8Jhq7OqlLqduD2isc+VPH33wF/592uVSeZa002twyKHzxvL8aBQq+atChijWRznyZ4aLzj2EySoUSEkf72+ppbBMHztmPeHjVpWWogBQedpawlm7d+vkSEeCTkwvNurAi2QzTkr9kYEMQOa9kCfS2sfEsD1X1gvDPtjwMF52CSLo4EzRcb/kishDa/rVQ13uFlpjkY/b79vtizPG9P2qOuBtncA88bjKS1RjHvXKHY0Kloh6gPv6/AGW8r27xZ7Ji3D2TzZM64qL3qsNbtkaCNMjqtp7VsvnI5NpPyLNMc/HlzrCTdYo+JaqyGUjHbeLfppMSj4jLm3TlzFvPZbAwIoPFOZvMteax+yja3ZPNm+7NX0pP2qEXVsAWhiBjZtD672DXeoJTi+Iy3nncQmrSkPZbNV7oy5aXn7aZUrJPGOxIWu4ubXwic8TZ6C7fiefso5m2VULQ7ErQHDfNz+aIdgqiHcXPq/bnWeM/UYpZ0ruhZpjkEJObtpWwegMVKu3gR8wbTeDe4b2cLjcN57RALh3ynDAXOeKdaLBWL+Uk2z3ojm4dECIf8J5uDGdPz2UpV4w3H7DIx72TzINR5a8+7OZYyeSIhabtlaSLqIuad76xs7reR0hBA492qbG5nm/vCeHsjm0P3syBzRXfyVFCmRGmax+sGLRCMxV4p5u1Fb/OVP1VsKZNnIB5pu/Y67ko277Dx9uH9LFDGu1hULcvmvso2z3ojm0P3V/Buss3BTMjx2cWu8YbjM0aN91avY94+v1y87G0eBKWhXZayhbYatFgkIqGGTlfHS8V8uNgKlPG2m5u0cEH4KuZtyuZeeN7djhU2I5v7TWbSeMOx6SRrB+OeXL8WQTBmluetp4q5w/K82yURDZF20aSl47K5z76vQBnvVmd5g89KxTxq0gJWLKaLdd4ufyRGByl/Xewabzg2k/Q03g3Gdez7hLVcAWlhomE1IquhVCxbaLuLJOCySUvn67x1tnkbWLO8WzPefvK8C8TCIU9WipEul2TlCsrOcq+HH1eqGm84Np3yNN4NRpMWvys16bwxy9uL/tnRsFBQRihwpbKUyTPYZqY5uI15d7ZULObDPgTBMt5tJHr5qc47lc17Jjl2u7lFrlB0lT0ahAQkTfMUioqTs942aAEz/OPzyyWTKxD3INMcnK2Ne38/6hRLmdaSiytJRMRlwlpnY95+W1wGy3i34XmHRAiLP4y3V4kc0P0SBtcx75Cu816JnJpLkS8qzz3vYHRYK3qSaQ696dHQbZayeQY9kM37omFyRUW+jmqaLRQbNo9qBz9en8Ey3m2WWHU7PlyLVLbgmecdCYW6PpjEXcxbfDdCT9M+9jQxDxu0QDBiwOl8wZPWqOAY5+sDZ6JTJDOFthu0gJFtDsZQqlq4VQRbRdd5t4n15bVjvP3heXsjJ0H3b3pu5amI7rC2IrEbtHjseQch2zyTK3qSrAYO2dwHzkSnWMzkPSmHtZpyWZ0pq5HvQqmYNt5tkG4j2xyMH4xfEta8yDQH/8rmq2Hwwmrk+HSSkMCm0YSn72vFvJXy7zXjpecd8VEC7f/f3pmHyVHWif/zVh9z30kmM7kTriSQBAm3Cp4ILMcqisrK7oqLqOhP19XFXXfVPb11XU8WL1RAUYGgIChyB3JB7juZzEwyRyZzd/f0/f7+qKqeTk/PpI+6ulOf58mT6erq6nq7qt7v+73NIJ5IEoknDUkVq9ZcFUEtxTYbbpEWh1OMzxt0zdv+CzBhoPC20recSEqSktyEtxttXpZ0D0/Q1lBl+ERZCj7gcCxhSGlUSNe8nTveYggamA6rz/czad5Rtzyqsyk2P9opmncwGjck/xG0QAqLbirdxOfz5mY2dzXv8qN7KMT8JmMjzSG9va39z+d0ROJJQ5qSwGT2S7mazfWOYsYErOWieUtT87z9DkztKynhnaotXKDw9noEUQdEUU1EE1QbFvhinYabEt6KW9v8dEUt0GKsvxvsaW+bL+FY0rBUMSf1WjCDVPMlA83moVOazc3zeTvRzVFSwjtkhM/bAQ+LkT5vKwPW9Ik1t9rmboW1ciMcS9A/FjE8WA0mzcgzpQPZTSSWMCxVrNw174Bm4jaiSEtK857GbJ5MSuJJiTcHpaJQUl0pHXS9Skp4T8QSeBVRsG/DKX6LkNFmc6s17xzMU17X5112HBvR08SMN5t7SsXnbXSqmIPHWwyhiN722Ija5upvrqcKZ6LH/JhdHhWcZSkpLeFdpMbqBM07Gk8SS0jjzOYWtgTNx2xupS/exRoGA1EAWuuNjTSHdLO5cybHTCJx41LFSmG8xRAw0Od9qmjzeB4WwUJxYmpfSQnvYsvt+TyCiM0/frGFZjKxUsNNmc1zCFjzuRXWyg49CMmI9J9MJgPWnLvgM1bzdp4P1UiMbL5UlfJ5T6N560qFqdHmzmlspVNSwjsUS1BdhA/F5xG2VzQKxYydAG2JNndrm5+W6JqPUaV903GiZpNJOG5geVR9vA4SBkZipObt86ilrfXFYyZRC4S33zWbF0coEi/ebG7z5GDkihSs1XDzEt5a0Q2nt3l0yR09z9aoeI10vA4P4IonkiSS0rg87xLw8ReDkVYaIQRVPmUGzVv9Dc0sj+pGmxeJGqVd+M3gdUB51NQEaFB5VI9jo82dbwZ1yY+UNmXQvZtOymzuUGEW1uYNN887N4JFZgZlUulTptW8detFLu68QnE17yIpNsXKp9jfmCSV/2hgeVSrNNx8zebgCu9yQr93jYrXSMfpRVpSNSaMKo+qOC/1yEiCkTg1fg+KYoxArfKKU/q8zUwVc33eRRKKFlfo3gmNSUIGB6zpGq4lwjueu/DW93GqJuWSP8FoAr9HMSUlZ7JoiTPvly2dwwA01/gNOV65a95GpsMCmtncPp+3E90cJSa8i2ul6YRUMV14G9FtB9J8hVYI76RuNs+tMUn6Z1xKn2AkbkiLx2xMWmqcJ8wmogn+7dHdnDmnlqtWzjXkmL6yD1hLGBKsplPpVVKm+Ex0BcFvotl8MjvA/gqdOjkJbyHE24QQ+4QQB4UQd82w34VCiIQQ4ibjTnGSUDRRVKSrz+OEgDWjzeaahmvBsCY179xqm4OzVqouxRGMFBdzMhNO9nl/++kDHBuZ4D9uPNcw7a7cn49ig4szqfKJaTVvK1LF/KVoNhdCeIDvAFcDK4D3CCFWTLPfl4AnjD5JnYlogqpizeaJpK1tB80ym1vhW87nIfE53Ifpkj+hqImat+K8aF6Ag8cD3P3cYd7+mnlcvLTFsOP6HDpeowhE4obWA6j0KtN2FbPCbD55fzpnsZXLaC8CDkopD0spo8ADwA1Z9vso8BvguIHnlyKWSBJNJIvSvL2KQEp7g6i6hkL4vQr1lT5DjmflCj6fh6QUGk245IfRE3I6KbO5g+4XKSX/+shOqnwePnP1ckOP7fTUuGIp1kqaSZVPmbbC2mQWjJvnnck8oDvt9VFtWwohxDzgL4Hvz3QgIcTtQojNQojNAwMDeZ2oERqrE4JENnYMcf6CRsOCfqwMWMunDKEbbV5+qBOyyWZzB1lqHt3ey/pDg3zqbecwu67C0GMrQi08Uq7CO2jwQq9qBs1bd+eZmeddqtHm2WbqzBn5m8A/Siln9OZLKe+WUq6VUq6dPXt2jqeoovs7irkhUqYqmy5AIBJnV88oFy9pNuyYPisD1vIxmztQk3IpjqDBfsx0fA601Dy+o5d5jVW896KFphzf65BGSWYQjMaNDVjzCYLReFaXZypVzMza5g5Q/DLJ5dc9CixIez0f6MnYZy3wgBACYBZwjRAiLqV82IiTBGMqk+kpmnYJ7y2dwyQlXLTEON+ZlTWhC/F5u9Hm5YPRE3I6TmzUMRSM0t5YicegXOVMnJD9YhZGBzdWeRWSUm0Ok5lrb2WqmJOuVy6/7ibgTCHEEuAY8G7gvek7SCmX6H8LIX4C/M5IwQ3GVCazO0hkY8cgXkXwmkWNhh1zckVo2CGnJZ8yhN4yb3l4OhKKFNdbYCYmfd7OmRxHQjEWthjfu1zH64CiUWYgpdQWesbdK5VpzUkyhXfcgvKoKZ+3g67XKUcrpYwDd6JGke8BfiWl3CWEuEMIcYfZJ6hjRIqV3aunjR1DrJzXYOiKdDJVzDrNOxfzlM/BqT8uhREsskjSTDgxwHE4FKWp2pjA0myoZnPnCAOjmIglkNLYGvhVWg53thKpKYugiXneXptdrtnI6deVUj4GPJaxLWtwmpTyb4o/rakYYTa3Mx0lHEuwrXuUv7l8saHH9TjUbG5l8RgX84knkoRjSdPzvJ0izKSUjEzEaKo2pqJaNtRyzeX3fARMaB07U1tQK/K8hRCOaGyVTslUWJsU3sXleQPEbCjBuK17hGgiyUWLjQtWA2sDfaJuY5LTlpDmlzE7z9sp98tELEE0nqTRROHtVZxlhjUK3cVpZKpYpZadky1dLGpBqph6fGfFKJSM8A4aaTa3ocTdxo4hhIALjRbeFqZkxRNJfB6BFpg4I+Wex3q6kZqQTc7zdsr9MhyKAZhqNvcpoizLo5qqeWdJF5vUvM0zm4O62HLK/QklJLwnjIg211b3ERsemI1Hhji7tY4GgycDK/OpY4lkzp17XM27vNAnZLNSxRQhUByU9zwcjAKYq3mXqc/b6P4NoJZHheyadz4Nk4rB1bwLJGhEnrdN9YTjiSRbOocNze/WsTIlK5aQOa9undiFx6Vw9IBRs1LFQDWdOyXAccQCzdtbpj7vYErzNrDCmmY2n5jB5+01KaVPx2mpfSUjvCeiCYSAiiIqk9lVpGVXzxihaIILzRDeFhZDiSaSOVeGc3KjCZf8CRqQqnkqnCTMhkOq5t1kUAvQbDgtAMoogiYs9PRUsayad1Li9yg5ufOKweuAxlbplIzwDkbU0ozFXCCvTaliGzuGAAwPVoPJBvRWDCmeh9ncjTYvL8zQpjLxKs4pjzoS0s3mbqpYvuj3ipGpYpVaGlhWn3c8abq/G8Dvat6FMRGLF92Jy29TOsqGjiGWzKphTn2l4ce2MmAtlpA551L6FLdISzlhhNvqVDipaIkesNZYZWa0uXPGayQBTcDWGmilmSnaPJZI4jOoV8RMOKGldDolI7xVzbs44W1HkRYpJZs7h7hwcZMpx7fS5x1NJHMOCnEbk5QXZgQhZeJxmNm8tsJrWAOhbPgUe9JWzSaU0ryNs9J4FEGlT8ma5x1NSNOD1cB5i62SEd6hInt5w6QfNmLhBRgLxxkJxTirtc6U41vZSjGeSOZcgtAj1I42rvAuD4ImTMiZqAFrzpgcR0IxU03m4DxhYBSBaBy/VzFcoNb4vdNWWPOZHKwGbrR5wYSiccM0bytzK/tGwwDMbTDeZA5WNyaROXfuEULgK1Of3ulIMGK+5q3m0TpjsaeWRjXPZA7OC4AyipABVtJsVFd4pq2wZpnZ3BXe+aNq3gaZzS18YHpGJwBoM0l4exQtP9aiPO98VtNOih52KY5QNE6lTzGtwxY4SxMdtkDz9jlovEZidC9vnek077iFZvOog+azEhLexfcStiNVTNe82xqqTPsOryIsiTaPxvMT3j6PcM3mZYKZTUl0PIpz7pcRKzTvMl3cmnWvVPk9TGRpn5hPLE4x+D2CaNz66pzTUULCO1H0DaFrqVYK797RMIqA2XUVpn2Hz2NNcYu4lk+ZK+U6OZ2OBCMJUyPNwVma6HDQ3I5ioD0fDjLDGoV6rxhvNp/J5+23IFXMaXn5JSW8izWbA/i9iqUTRN/oBLPrKsxtFK8olpnNc/V5g+rTc4om5VIcwUjxlq9T4XFI7eh4IslYOG5qaVQoX593MGqO2bzaP4PP2wqzuUc4KjughIS3MTeEz6NYWtu8dzTMXBNN5mCdkMzbbK4orvAuE0JR8zVvp5RHHZ0wvzQqOK/RhVEEI+aYzWsqvNPUNrfG5+1q3gWQSErCsSRVvuJX/hVexdIL0Dsaps2E4izpWGU2j+WRKgZo0eb2T8YuxROwQPP2KsIRFflSHcVMLI0KqjBISnV+KyfMcrFU+z1ZK6xF87QIFoobbV4AEwb2EvZ7FMtTxdoaTRbeFk168WTuqWLg+rzLiVA0bmpTEnCOD3iyNKr5AWtQftq3ajY3wec9neadp1JRKK7mXQB6xZ5ii7QA+CzUvMfDMQKRuGlpYjpej7Bk9R4rKNrcOTe7S+EEIwlTm5KAc2qbW9HLG9J6LThIIBiBWali1X4P4VhyylxnVaqYrnlL6QyFpDSEd6o0ozGat1Wmj95UgRZzfd6q5m3qVwD5lyF0Ne/ywSxtKh2PQ3zeekcxM+uaQ1ppYwdYG4wiGk8SS0hzirRox8xMF7OySAs4p2pkSQhv3VRihM/Nymjz3lSOt/matxU3VDyZX0qGG21ePoQsShVzghaaMpvXmJ8qBs6pKmcEk93nzNC81WOGMtLF1Dxv833eXhvqhMxESQhvvQG7EWY7v9e6aPM+rbraXLMD1hTFmoC1eBJvXtHmztCkXIojGk8STSRN0abScY7mHcOrCOrMXqx4ys/nneo+Z0q0uUf7jqmatyU+b5taSk9HSQjvYEp4Fz95+Cw2mwsBrSYLb6/HqvKo+ZnNfR5nRA+7FEcoZfk6PXzeI6EojdU+hDBXm9MtvU6wNhhFqga+iZp3ZqGWfHouFIPTAgxLQnhPGDh5VFhpNh8JM6u2wtS2gmBNPrWUkmielYy8ilJWJsHTFX3xbEm0uQPul+FgzPRIc3CeMDCClIvTpAprwJRCLfkG0haKPvdZWSdkJkpCeIcM1Lz9HuuizXvHwqb7u0HzLZs86ekRnm5t89MPM/ozZ8MpjUnUjmLm+rshvcth+TwjulZsxkKvOmU2n+rztipVTP8+J1ASwjtlNjdg8rDSbN43OmG6vxusyfPWNaJ8fN5O0aRciiMQMc+PmY6TKqxZqXk7RRgYQdDEeyWleWcUaoknLeoqpokfJywwoUSEt5FmczXa3JoJonfUQs3b5PtJn2Dyier0unneJcfR4RDnff4JdvWMpralUjVNNpt7FEEsaX8erVWad1mazSPGFdTKRLe8htI070RSkrBIeNvRlXImSkJ46zeEEeVR/V5rNO9AJM54OE5bo7k53mBNYJg+weTjv1ejzc06Ixcz2HF0lPFwnG3dk8Jb16ZML4/qAWlzuVApJcOhmOntQCEtz7uchHfUzFQxXXhPTir6b+fzWlMeFVzhnRcTsQSVPgWPUvwFsqoxiZ4mZoXmbUVKln58r5JvtLkzbnSX3OgcCgFwbCSU2mbmhJyOV9hfBGMiliAaT1pjNi/HVDFd8zapMQmc7PNOKRWuzzs7Qoi3CSH2CSEOCiHuyvL+LUKI7dq/9UKI1UaepJFdaqyKNk9VV7PA5+31mB9tHivQbO76vEuLLk1494yEU9vMNIWm4wQzslWlUSHdDFs+z8jIRBS/R6HSZ7wwrfAqKOJkn3cqFscAxe5UlJzmLYTwAN8BrgZWAO8RQqzI2K0DuEJKuQr4d+BuI09ywqBe3mCd2XyyupoFZnPF/KjuaKFmczfavKToGtQ07+GJ1LaQiYU30tE1UTuD1oaD1jQlgck873LSvI+PRZhTX2FKjrwQghq/N6vmbWV51JIR3sBFwEEp5WEpZRR4ALghfQcp5Xop5bD28mVgvpEnGYwap3n7PNaUYOzVNJfWhgrTv8uKlCx9Qs0vVUwpy5aH5UznUBCAYyOTwjtgYMzJTDhBmI1YqHmXo9m8fyxsalGq6oqT24LqgtSSaHOHlbPNZcTzgO6010e1bdNxG/B4tjeEELcLITYLITYPDAzkfJIhIzVvjycVoWgmfWMTzKr1U+E1d8IDLcUmialRuvoEk495Sn+eXO27NIglkvSMhPEqgr6xMHHtmoe0Xt6KyaZJPabFzqp8elMSs3t5A1Rqq5XMcp+lTN9YmNZ68xSWTM1bn1ssLY+acMb1ymXE2Z7YrE+XEOINqML7H7O9L6W8W0q5Vkq5dvbs2TmfZCiaMMzfppt9zV7t9o6GmWtBsBqk10g2b9KLFmCe8mnBbU7I3XU5NT0jEySSktULGkkkJX1jqvUoGDW/KQmkBazZqnnrZnPzNe/GSg+KgP7R8Kl3LhGOj0VM1byr/J5UrwtIj8VxU8WycRRYkPZ6PtCTuZMQYhVwD3CDlHLQmNNTCUUTVPmMM5uD+SXu+kbDzK03398Nk2MKm5iXpbctzGeFm1pUuJp3SaAHq122rAWY9HurAaMWWJAcYEbWA9bMbgcKqqWhtb6SntGJU+9cAgQicQKRuKnCO1PznjSbWxiw5hBlJJeZeBNwphBiiRDCD7wbWJe+gxBiIfBb4H1Syv1Gn2TIwF7CFRZp3j0jE5akiQE0VakLm8FA1LTviBdQHtVrgUXAxTg6tWC1S5dqwlvze4eicdObkkC6z9tes3lthdf0fgQ6bQ2VqfiYUue4Zqkx02xeXeGZJs/b1bynIKWMA3cCTwB7gF9JKXcJIe4QQtyh7favQAvwXSHEViHEZiNPMhRNGFYgQn8ozbwAwUicsXDcMrP57FpVS+gz0fymm83z6d6j3+xulbXSoGsohN+rcP7CJiBd806Y3pQEJuMp7HSzjIRilpjMddoaq+gtE81bd7O01pmseUfSo801pSKP+hOF4rRo85yeSCnlY8BjGdu+n/b3B4APGHtqk6gBM0aZzc0X3vpN3N5ojfCeVaNONsfHzRPeRZnNE7JEygGd3nQNhljQVEWV38OsWv9JmrcVAVypaF4bF3tqaVTzx6rT3lDJn3b3I6U0vQWp2RwfiwDQaqLSUu2fRvN2W4I6DykloZjxmreZF6AvVaDFGp/3bE14m6l5F2Q2d0D0sEvudA6FWNhcDcC8xqqU8A4YWCRpJlL3i42azbDFmvfchioi8WTK117K9KfM5iZq3hUna96FBNIWikcReBThGM3b8cI7HEsipTFNSWBSczQzYG2yQIs1mndNhYcqr0hp/GZQUIU1B5hBXXJDSknXYJBFLTUAzGuqSpnNjcz2mAlPys1ip9nces0b1BiZUqd/LEKN32Oqi0XXvPW0WH1usSJVTP+ekiqPaiehqLFNEfQVmhkXIJmUPLi5my8+vpfaCq9lPm+AlmpvymxlBoUUQ9AFvZvn7XyGglGC0cQUzVtKSdBAt9VMOKFIy3DQmo5iOnrjot4ySBfrHze3QAuomnc8KVPzt5WpYur3OEfzNv+JLBLdv2GU8K7QLrLRprkdR0f57CM72dY9wvkLG/nC9SupNLkiVTotNV6TNe/CKqylf9bFuegNSdKFdySe5EQgquV5W1NsCOy7X+KJJGPhuCWlUXV0zbuvDILW+kfDzDEx0hwmq/xNRBNUeD2W+rwB/F6PYzTvEhLeBpnNTdC8E0nJX/1wA36vwtfftZob18wzvRpVJi3VXvYPmenzLsJsnpRkr/Xj4hS6NeG9qEUT3k3q/x0ngiSS0poiLSk3iz2T4+iEdaVRdVpqK/Aqgp4y0bwv0DIVzEJfRAajCRqrrS2PCmqqsVM0b8ebzfWE/GqDVv5mRJsfHQ4xOhHjH956Fm9/zXzLBTfALM1sblaJVP338hYUbe6Mm91levQc7wVpmjfA/v5xwPymJGB/gOOg1pSkudb8fgQ6eqGW3hL3eUsp6Te5uhpMKnEhLWitEItgMfg8wjHzmeOFt14Kr9ogE7QZ0eYH+gMAnDGnzrBj5ktLtZdoIslQ0JxCLbECAkPsNoO65E7nYIjW+oqUq2dekyq8D2jC2yi31UykfN42aTb6AkZ3HVhFe2Ol7Zp3JJ7gtV/6M49sPVbQ50cnYkTjSeaY7vOe1LzB2lQxsK4rZS44XnjraQFGme104W1ktPnBAV141xp2zHxpqVZv6n6TgtYK7ecNbsBaKdA9FGJRc03qdUOVj7oKL/t0zdtKs7lNed6dg2pHtUUWC++2BvsLtXQNhjg6PMHzB04U9Pk+C6qrwVTN++l9x6mr9FJXaY2rwxXeeTAR09oRGtZVzHiz+YH+AHPqKmioss5XlsmsGvWm7jcpaC2eSCLEZDpPLvgckPrjkhudQ8GUyVxnXlNVyqpkhfD22Gyp6RoKUVfptTTPG6CtsZL+0QhJG5+TwyfUhcvevrGCPq8rDXPN1rw14R2MJnhu/wDP7BvgY28807Jytj43VSx3glrvVqN8bmYErB0cCNiqdYNqNgfzhHc0IfEpSl5VoKzoduZSPOFYgv6xSCpYTWdeY1XKD2xJYxKbA9Y6B0Msaqm2vNJZW30l0UQy9VvbQYcmvPf3Bwr6/a0o0AKTsU/j4Rj/9dgeFjZXc+tli0z9znT8Hlfzzhk9z9tozdsov5qUkkPHA5xps/Bu1pqTmJUuFksk8/YrebV6w26FNWfTPZTd16v7vcG4bI+ZsLsxSVeG68AqJnO97TOddwyowjsaT6YEeT7oTUlm15ltNlflwL0vdbK3b5y7rj6HCq91Kbl+r6t554zRed4+g33efWNhApG47Zq3zyOYVes31WyebwnCVJEWV/N2NKlArQzNu71xUnhb2ZjEjtrm8USS7qHQlN/ACtob1N+5x8buYh0ngil3wZ6+8bw/3z8WobHaZ3ptC30RubV7hLWLmrj63Lmmfl8mfo/iRpvnSiiawO9RDEsFqPF7aKr2cXgg/9VlNg4etz/SXGdOXaVpAWvRhMz7GrjR5vYRiSf48YsdRHLo8a738c4M1JqXJryNStWcCTvL6faOhoknpeXBaqD6vMHeQi2HTwR449lz8HkEe3rz93v3jYVN93fDyUrcP1+73HIXhxuwlgehaNwwkzmAEILVCxrZdnTEkONNponZq3kDzG2oNK05SSyRTAWg5UrK5+22BLWcP+zs4wuP7ub323tPuW/XUIgav4fmjM5h6WZzK/K8FQFC2FMXYDrrgxU0V/vxexTbSqSOTsQ4EYhy1tw6ls2uZW8Bwvv4WNj0NDFQA8aaa/zcsKY91brWSlzhnQehaMLwYJnV8xvZ3z9+UneaQjk4EKChysesWutKKk5Ha32FaW1BYwWYzXVNyiFWptOKzUeGAXhqz/FT7ntkMMjClpopWsx8TfNWBFT6zJ8qhBD4FMUWS03nkGqJW9xivc9bUQRzG+zL9T6i+biXzKpheVs9ews0m7ea7O/WWXfn5Xz5plWWfFcmPo8992c2SkB4G6t5A6xZ0EhSwo5jo0Uf62C/GqzmhF68rfWVnAhEC14ZfuOP+1l/KHueZ7wAs7lHESg2aVKnO5uODAHw7P6BGe8HKSXbukdY2V4/5b1ZtRX4PQo1fq9l97fXI2yJNu8aDOH3KpaYfrPR1mBflTU9QG3prBrOmVtH72iYkVDuke+JpGQgYH51NZ35TdWWBqml4/cqpnakzIcSEN4Jw3NMV81vAGBb90jRx3JCmpiOPvEUon2fCET4n6cO8M0/Hsj6fjSRTGnS+eBVhBttbjGjoRj7+sdZNb+BQCSeEuTZODQQZDgU48LFU02QiiJob6y0xN+t4/MottQF6BwMsaCpypbSxqAGB9plNj98IogQqstgeZu6iNvTm7v2PRiIkEhK0wu0OAE1VezUcSTFcM/zh3ParySEd5XBEYwttRUsaK4q2u89GIgwFIw6RnjrK99CgtY2dagT/KbOoax+81giWVAhBJ9HmBqAFE8k2dI5bNrxS5FXuoaREj7+ZrV4xZ/29E+772ZNsK9d3Jz1/XlNVZb4u3V8HmFLKk7nUCjVy9wO2hoq6R8Lk7Bh4dJxIsj8pioqvB7OaVMDb/MJWtPnG6s0bzvxe801m/ePhfnSH/bmtG8JCO+4KdWd1ixoYlt3cWbzyUhzpwnv/FfwGzqG8CoCKeH3O6YGOal53gUIb5N9mL/c3M07vrfeECtKKfDMvuP87OXOVP2DbGw8ol7LS5fO4vJlLTy15/i0DWs2dw7TXONn6azsgusDr1vKh99whiHnngteRbHcbC6lpGswaHlN83TaGiqJJyUnAuZki8xEx4kAS2apc9js2gpaavx5VVqzqkCLE/CbXGHtRy925LyAKwHhnTDc5w2wen4Dx0Ymigrw0muan9lqf5oYTNYVLkR4b+wY4uKlzaxoq+fRbT1T3o8lZEHF/70ec4u0PL6jD4AndvWZ9h1OYTQU46P3v8q/PLyTy774Z77+5D4Gs0z2m48Mce68Bqr8Ht60vJWuoRCHtHs1275rFzVN69N+w9lzuOmC+YaOYya8OVpqpJTs7hkzpIveYFDtWZ5ZYc5K2lK53tb6vaWUdAwEU4s3IQTntNXlFbTWP34aCW+vQiIpTbGQjIVj3PdyF9euas9pf+cL74jx0eagBq0BRWnfB/oDVPs9tDc446ZtrlFTTvKtsjY6EWNP3xgXLW7hL1a3sbV7JFV1S6dQzdurKKaZAkdDMV4+PAjAH3dPbxouF+554TDj4ThfuWkVFy1u5lt/Psjrvvx0qvMXqKVOt3WPpnzYbzxnDgB/yhJ1fnw8zJHBEBdOYzK3g1w0m0RS8s8P7+Sabz3Pr7ccLfo79TQxW4W3lutttd97YDxCMJpg6exJy8vyufXs6xvP2QLSPxpGETgi48Zs9DnQjCDc+zZ0MR6J88HXL81pf+cL72jclNKMK9sb8CiiKHPrIS1YzQmR5qCumufUV3A8T5/3ls4hpISLljRznbbq+11GfnDBZnOPMM1s/tTefuJJyQ1r2jlwPJBKeSk1uodC3PaTTakSk9kYDET40QsdXLuqjXeuXcDdt67lj594PQL49tMHU/vtODZKNJFMCeT2xipWtNXz5yzCe4uWTrY2S7CaXZxK8w7HEtx53yvct6GLSp/CQ68W1sIynVQ3MRt93nqVNauF9+G0NDGdc9rqicSTHBkMTfexk+gfizCrtgKvRT217cSMrpTq8RL86IUOXnvGLM6d15DTZxz9a8cTSdPM5lV+D2e31hUVtHagP8AZs53h79Zprc+/UMuGjiF8HsH5CxtZ0FzN6gWN/G77yabzeIFm89k1Pnb1BU3Rvp/c1c+cugr+4a1nA6Wrfd/z/GGe2nucHzw3fZTp9589xEQswSfefGZq25mtddxyySIe3dZDlzbRbuyYGoD25uVz2Nw5xHBG44tNR4ap9CmsbM9tsrACr6KkWoJKKXn+wADPHxhgx9FRDh4P8Lc/3sTjO/v47LXLuf31y3jp8GDRJYE7B0MIAfPTitJYTWO1jwqvYnm6WEc24T1XdQPm6vfuHw+fFiZzINU5Ul/wGcUjr/ZwfDzCB6/ITesGG4V3LrnIv9/RSzwpUyZuo1mzsJFt3SMFteIbD8foGwuzzCHBajpz6yvznsw2dgyxen5jqi7xdava2NUzxuE0P2m0QM37ptWz6RmL8sKR7D7XQgnHEjy7f4C3rmxlQbOa4lKKwjsUjfPbV47hUQT3behiKEtnqf6xMPe+1MmN58+bUob3ttcuwaso/OC5Q4Dqwz5jTu1J1dLeuLyVpIRn9p+sfW/uHGLNgkbL2inmgi8tmnfdth7e98ONvO+HG7nu2y/w5q8/y6YjQ3zz5jV84HVLuX51O1JOtRLlS9dQiPaGKttyh0G1mtmRLtZxIojfq6Q0f4AzW2vxKLmXSe0fi5wWaWIAb1nRSpXPw89f7jTsmMmk5AfPHWJFWz2vPWNWzp+z7ak9NBCYsYtOMin57tOHOGNOLW9Z3mrKOayZ38hYOM6RAlZReqS53d3EMmnNU3iHonF2HB3loiWTmtq1q9qAyUkxGIkTiiQKEt6vW9rAwsYKfrlj2JDgIp3n9g8wEUtw1Uq1McFbVrSyuXMoq/DLxkQ0wZ/39tvaQxng0W09jEfi/OeN5xLWTGeZfOfpgySSko+/6awp77XWV/KOC+bx4Jaj9I2G2dw5PCVne9W8BmbXVaSC+0C9prt6xhzl7wa1B3wskSQaT/K1J/dzztw6HrzjUu65dS1fuWkVD3/kcm48fx6gZnmsbK9n3dbiTOedNkea67Q1VNJjcX3zwwMBlrTUnJTfXuH1sGx2DXtzzPW2qjSqE2io8vH218zjka09UyxZhfL4zj4ODQT54BVL83LB2ia8kxJuv3cLE9HsCe9/2tPPvv5xPnzlMtMKJ6zWNPqtBfi9DzgsTUyntb6CYDTBeDiW0/6vdo0QT8qThHdbQxUXLW7m/o1dvPf/XmbNvz1J31iYOQWUP/QoglsuaOXgYIQXDmav3lYIT+zqp77SyyVLWwB46wpVu3xqhpxmHSkln/r1Nt7/k8386MWpwtJKfrGhi7Naa7n5wgVcfe5cfvrSEcbSrt2e3jHu39jFuy5cMG3d7Q++fhnxRJK7frud8XB8ikBWFMG7L1zAk7v7eVxLA9zaPUIiKafN77YL3ed9/8YuuoZC3HX1OVy4uJk3r2jlnWsXTPEHXr+6nW1HR4uKd+gaCtkarKZzVmsdW7tH+NwjOwkYULo5Fw6fCJ5kMtc5Z249u3tnjubXe2oPBqO2uhys5tZLFxOJJ/nl5u6ijzUYiPC5dTtZ3lbPtee15fVZ24T3guYqdvaM8unfbJ9yg0gp+c4zh5jfVMX1q3MLmy+EM+bUUu335B20trV7hC//YS+zaiscsWJPZ25DfoVaNnQMoYipRTpuWjuf3tEwQ8Eo7798Cfd94GI+ddXZBZ3TVWc30VLt4XvPHCro85nEE0me2tvPm5a3pqwBK9vraW+ozMl0/osNXfxuey9z6ir4yhP7pk2jMpsdR0fZfnSUWy5ehBCCD195BuPhOD97STXJbToyxM0/eImmaj8fe+OZ0x5n8awarjmvjWf2DQBk1aY/9qYzWbOgkU//ZjvdQyE2HVGv+2sWNpoytkLxeRRGJqJ866kDXLq0hSvOmj3j/tdp88O6LOmNoM4lX31iH5/45VbCsamKQiAS50QgaktDkkw+ddXZ/PWli7n35U6u+sZzPLd/wNTviyeSdA2GWDJ7qvC+bFkLvaNhfvzikSnvJZOS32w5yhu/9iz/9/xhbl67gPddssjUc3USZ8+t49KlLfzspc6iYnmklHz24Z2MTcT5+rtW5x3wl9PeQoi3CSH2CSEOCiHuyvK+EEJ8S3t/uxDiNac6Zn2lj09ddTaPbuvhO2nRsgAvHhxkW/cId1yxzNQIRo8iOG9eA1uPTk0Xk1Ly6LYePr9uFy8ePJG6SI/v6OXmH7xEld/DA7df7LgIyzl1+RVq2dgxyLnzGqb0a37nBfPZ8fm38oePv57PXLOcy86YVfBY/V6Ft69sYv2hQUOKqWzsGGIkFOOqlZPuFCEEb17RynMHBqa15gDsPDbKv/1uN1ecNZtHP/paqvwePvmrbbbU075vYyeVPiVlBj53XgNXnj2bH77QwaPbevirezYwq7aC33zostSibDo+dOUyQI15yKYF+TwK//ue8wG48/5XeenQIGfPraeu0mfwqIrD51HY3x9gMBjlH68+55RmxPZG1Uq0bltPViXgC4/u5ttPH+ShV4/xd/duniLA9UC/Rc32RZrr1FR4+fz1K3nwg5dS6VO49Ucb+cxvd0wpyLP+4Amu+98XuOl76/mnh3bw0/VHeKVrOG9BcnR4gnhSZtW837V2AW9Z0cp/PrYnlY4Jai7ybT/dxCcf3Ma8xioe/vDlfOmmVY67j8zmry9bxLGRiRmrF56KR7b28PjOPj7xlrNSZWnz4ZSzsRDCA3wHuBpYAbxHCLEiY7ergTO1f7cD38vlyz90xTJuXNPOV5/cz+fX7WK/lq/6nacPMqeuwpLiEBcubmZb9wgf/sUWdvWoQrx7KMTf/HgTH73/VX72cie33LOBy774FHfe9wof+sUrrGyv5+EPX+6IHt6Z6JN8tojz/rEwj27r4eXDgySSkkg8watdI1yURVMTQhj6QF5zdj11lV6++8zBglerUkrGwzHWbeuhwqvw+gyt7C0rWgnHktOa58fDMT5y3ys0V/v5xs1raK2v5N9uOJet3SPcnUM94ROBCM/uH+C+DV1T8uDzZSwc45GtPVy/uj0VwQpw5xvOYCgY5aP3v5ry9y7Iwbqzsr2B9168kHdduGBagbeguZovv2MV27pH2NAxlLWeud3o9fOvOW9uzoGq161p5+DxwEn1uHXB/ZP1R/jAa5fwlZtW8cLBE9z2000nLe66hvQ0Mfs1b521i5v5/cdexwevWMoDm7r4i2+9wPajI4SicT73yE7ee88GxsMxFCH43bYePrduF2//7nou/q8/cddvtvOn3f3sPDbKzmOj7OoZpWswlNX8rUeaL8uieSuK4OvvWs2i5mruvO8VekcnOHIiyNu/u57nD5zgC9ev5LcfuizlejzdePPyVtobKrn3pSMFfb53dIJ/eWQnFyxq4vYc87ozySWB+iLgoJTyMIAQ4gHgBmB32j43APdK9Q55WQjRKIRok1LOGAYqhOCL71iFIgS/2NDJT9YfYWV7Pbt6xvjstctT0c9m8qErlyGR3Lu+k8d29HHp0ha2do8gBHzuuhXcfOECnt47wEOvHuPJXf3csKadL71jlSXnVgh61OdvXz3K4ROqOXgoGGPD4cFUTidAS42f8xc2EYknT/J3m0WN38Otly7iO08f4uzPPs7chkraG6uY11hFe6P6d12lj4P94+zuHWdv3xjj4Tg+j8DnUZAShkKTHdPeuqJ1Sv7/xUtaqKvw8k8P7eDel44wt76S5lo/oUiCsXCMfX3jHB2e4IHbL0lFY1+3qo0ndvbxjT/up7WukmA0ztHhCXpHw0xEE0TiCSKxJJ1DwSmuiLWLmrjx/Hksb6tjOBhjKBRlNBQjEk8QTUii8SRVPg9z6iuYXVtBc60fKSXRuOTZ/QOEogluufhkc+Paxc1cu6qNWDzJN25ek1dp4P/6y/NOuc/V57XxV5cs5OcvdznO3w2qlcajiFT6Xy5ce14bX1i3i5+s7+D61fMIRuM8vfc4D2zq5u9et4R/umY5QggUIfiHX2/j/T/ZxF9ftphqv4eXDqlapRPM5ulU+jx85urlXHnWHP7+V1t5+3fXM6eugp7RMO+/fAmfuupsqvwepJT0j0XY0DHIH3f387vtvTywaaovdlatn7WLmrlgURPNNX68HsH6g+rY9dKomdRV+rj71gu44dsv8rc/3pQq/vSz2y7m0mUt5g2+BPB6FG65ZBFfeWIfv95ylGg8Se/oBMOhKH6Ph0qfQoXXQ0OVl5baClpq/VR4FQ4NBDl4PMDTe48TT0i+9s7VeAqM6RKnigAWQtwEvE1K+QHt9fuAi6WUd6bt8zvgi1LKF7TXTwH/KKXcPN1x165dKzdvnnx7MBDhoVePcf/GLkLRBH/6+ytynrj2799Pbe2pA8cCgQBnnTU1YhfUKmP3rj/CfRu7WNnewBduWMm8xpPNj8mktK3r0KnGmD6267/9Art61DQPgZrTvnZRE5cua+HiJS0cG5ng8Z19/HlPPwkpeemuN9FUU1h1pHx++8VLz+DhV4/RORTk2PAEPSNhjo1M0JfWkEERsHR2Lcvb6mmu9hFLSuKJJEmpLjhaav201FTwurNmpVwE6Tz06lGe3NVP72hYfZiCMWoqPNRX+air9HLrpYt519oFJ31mKBjlrd94lhMBNXpUTZ2ppNrvpcKn4PcotDdWsbK9npXtauT2k7v7eOiVY6nAxWycqlrY6vkNPPyRyy0v8hOOJVi3tYcbz59nappYrveGTiAQIFA5m2PDEylfdq7c9pNNPLX35FS421+/lM9kmN4ffvUYn3xw20nWn9b6Cjb805vz+r58x5aNmeajdEZDMf513U52HhvlP248b0bBGYkn2NI5TCAcRx/hiUCELUeG2dQ5RPfQydHsc+sreekzb5zxHvzDzl7u+PkrnDmnlnv+eq2pxWym+11z/a3yOWa+ZJ7DUDDKpf/9VKpgiyLUaPRYQhKOJabtjuf3KiydVcPH3nQm10wTpCaE2CKlXDvT+eQivN8JXJUhvC+SUn40bZ/fA/+dIbw/LaXcknGs21HN6ixcuPCCzs6puXJSSpKSvFYjnZ2dRCKnDtCqqKhg0aLSDKw41RgLGVs4lmBsIlZUmocRv30iKTk+HmZ0IsbilhpbrBpHh0P0j4VZ0FTNrNqKnBZpUkr29I7TPx6mpcZPU7WfxmoflT4PXkUghCAaT3IiEOH4eIThYBSPoloS/F7Bstm1NFaXb0nJXO8NnWKez8FAhO3HRqnxe6n2e2iq8U9ZfOscHw9zfCxCOJYgFE0wr6mKZXkWW8p3bNmwYz46EYgQjMSJJyXxhGR2XcVJNQGmY+exUZbMqjGlSVQ60/2uxfxWRlyr6c5hX9844+EY7Y1VzKk7ucpcPJFkdCLGYDDKYCBKOJZgyawaFjRXn1K+GSW8LwU+L6W8Snv9GQAp5X+n7fMD4Bkp5f3a633AlTOZzTM1bxcXFxcXF5fchHcuNrNNwJlCiCVCCD/wbmBdxj7rgFu1qPNLgNFT+btdXFxcXFxcCuOUNhApZVwIcSfwBOABfiSl3CWEuEN7//vAY8A1wEEgBPyteafs4uLi4uJyepOTA0NK+RiqgE7f9v20vyXwEWNPzcXFxcXFxSUbzqow4uLi4uLi4nJKXOHt4uLi4uJSYrjC28XFxcXFpcRwhbeLi4uLi0uJcco8b9O+WIgBwLiO5s5gFmBc30vn4Y6vtHHHV9q44ytt8hnfIinljC31bBPe5YgQYvOpEutLGXd8pY07vtLGHV9pY/T4XLO5i4uLi4tLieEKbxcXFxcXlxLDFd7GcrfdJ2Ay7vhKG3d8pY07vtLG0PG5Pm8XFxcXF5cSw9W8XVxcXFxcSgxXeLu4uLi4uJQYrvCeASHEj4QQx4UQO9O2rRZCvCSE2CGEeFQIUa9tXyyEmBBCbNX+fT/tM34hxN1CiP1CiL1CiHfYMZ5M8hmf9t4q7b1d2vuV2vYLtNcHhRDfEkLM3GneIowaX9r769KPZTcGXr/3aK+3CyH+IISYZcd4Msnz+bsl7dnbKoRICiHWCCGqhRC/1567XUKIL9o3oqkYMUbtvZKfY4QQPiHET7Xte4QQn0n7TMnPMTONL+2zuc8xUkr33zT/gNcDrwF2pm3bBFyh/f1+4N+1vxen75dxnC8A/6H9rQCz7B5bAePzAtuB1drrFsCj/b0RuBQQwOPA1XaPzcjxaa/fDtw33TUu1fFp24/r9yTwZeDzdo8t3/FlfO484LD2dzXwBu1vP/C8U+5Po8aovS6HOea9wANp1+0IsFh7XQ5zzLTj07blNcfYPnin/yNDKANjTAb6LQB2Z9sv4xjdQI3dYylyfNcAP8/y+TZgb9rr9wA/sHtcRo1Pe68WeAFYkeuDVSrjA3zAALBImxi/D9xu97jyHV/GZ/4L+M9pjvc/wN/ZPS6jx1gmc8x7gEdRF5QtwH6guYzmmKzj097Le45xzeb5sxO4Xvv7nagXR2eJEOJVIcSzQojXAQghGrX3/l0I8YoQ4kEhRKt1p5s3043vLEAKIZ7QxvFpbfs84Gja549q25xKvuMD+Hfga0DIutMsmLzGJ6WMAR8CdgA9qJPHD6095byY6fnTuRm4P3Oj9ixeBzxl1skZRF5jLKM55tdAEOgFuoCvSimHKJ85ZrrxQQFzjCu88+f9wEeEEFuAOiCqbe8FFkopzwf+HrhP83V4gfnAi1LK1wAvAV+1/rRzZrrxeYHXArdo//+lEOJNqNpaJk7OP8xrfJpP8Qwp5UN2nGwB5Ds+H6rwPh9oRzWtT/HFOYjpxgeAEOJiICSl3Jmx3Ysq7L4lpTxs1ckWSL5jLJc55iIggXofLgE+KYRYSvnMMVnHV+gc4zXufE8PpJR7gbcCCCHOAq7VtkeAiPb3FiHEIVRtZwvqakq/MA8Ct1l82jkz3fhQV7vPSilPaO89hurr+TnqxKEzH1WDcyQFjC8AXCCEOIL6vMwRQjwjpbzS4lPPiQLGN6Z97pC2/VfAXRafds7MMD6dd5NF60YtkHFASvlNU0/QAAoY4yDlMce8F/iDZg06LoR4EViLGqdQDnPMdONroYA5xtW880QIMUf7XwE+i+ojRAgxWwjh0f5eCpyJGlAiUf0cV2qHeBOw2+LTzpnpxgc8AawSavSuF7gC1ZfTC4wLIS7RIkBvBR6x4dRzooDxfU9K2S6lXIyqse53quCG/McHHANWCCH0DkZvAfZYe9a5M8P49G3vBB7I+Mx/AA3Axy070SLId4xlNMd0AW8UKjXAJai+7nKZY6YbX2FzjN3Ofif/Q13d9gIxVM3lNuD/oQYa7Ae+yGRgwjuAXcA24BXgurTjLAKeQzVJPoVqXi+p8Wn7/5U2xp3Al9O2r9W2HQK+nf6Zchhf2vuLcVDAmoHX7w5Ugb0dVQi02D22Asd3JfByxjHmo5pY9wBbtX8fsHtsRo5R217ycwxq0NaD2j26G/hU2nFKfo6ZaXxpx8t5jnHLo7q4uLi4uJQYrtncxcXFxcWlxHCFt4uLi4uLS4nhCm8XFxcXF5cSwxXeLi4uLi4uJYYrvF1cXFxcXEoMV3i7uLi4uLiUGK7wdnFxcXFxKTH+Py7nnN3ZWIA+AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 504x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, figsize=(7, 7))\n", "ax = axes[0]\n", "ax.plot(res_hamilton.filtered_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec[\"USREC\"].values, color=\"k\", alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title=\"Filtered probability of recession\")\n", "\n", "ax = axes[1]\n", "ax.plot(res_hamilton.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec[\"USREC\"].values, color=\"k\", alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title=\"Smoothed probability of recession\")\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the estimated transition matrix we can calculate the expected duration of a recession versus an expansion." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:30.703192Z", "iopub.status.busy": "2021-10-06T09:59:30.702011Z", "iopub.status.idle": "2021-10-06T09:59:30.708875Z", "shell.execute_reply": "2021-10-06T09:59:30.709291Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.07604743 10.42589391]\n" ] } ], "source": [ "print(res_hamilton.expected_durations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, it is expected that a recession will last about one year (4 quarters) and an expansion about two and a half years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kim, Nelson, and Startz (1998) Three-state Variance Switching\n", "\n", "This model demonstrates estimation with regime heteroskedasticity (switching of variances) and no mean effect. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn.\n", "\n", "The model in question is:\n", "\n", "$$\n", "\\begin{align}\n", "y_t & = \\varepsilon_t \\\\\n", "\\varepsilon_t & \\sim N(0, \\sigma_{S_t}^2)\n", "\\end{align}\n", "$$\n", "\n", "Since there is no autoregressive component, this model can be fit using the `MarkovRegression` class. Since there is no mean effect, we specify `trend='n'`. There are hypothesized to be three regimes for the switching variances, so we specify `k_regimes=3` and `switching_variance=True` (by default, the variance is assumed to be the same across regimes)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:30.715023Z", "iopub.status.busy": "2021-10-06T09:59:30.714629Z", "iopub.status.idle": "2021-10-06T09:59:43.317494Z", "shell.execute_reply": "2021-10-06T09:59:43.318551Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the dataset\n", "ew_excs = requests.get(\"http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn\").content\n", "raw = pd.read_table(BytesIO(ew_excs), header=None, skipfooter=1, engine=\"python\")\n", "raw.index = pd.date_range(\"1926-01-01\", \"1995-12-01\", freq=\"MS\")\n", "\n", "dta_kns = raw.loc[:\"1986\"] - raw.loc[:\"1986\"].mean()\n", "\n", "# Plot the dataset\n", "dta_kns[0].plot(title=\"Excess returns\", figsize=(12, 3))\n", "\n", "# Fit the model\n", "mod_kns = sm.tsa.MarkovRegression(\n", " dta_kns, k_regimes=3, trend=\"n\", switching_variance=True\n", ")\n", "res_kns = mod_kns.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:43.324514Z", "iopub.status.busy": "2021-10-06T09:59:43.321903Z", "iopub.status.idle": "2021-10-06T09:59:43.401947Z", "shell.execute_reply": "2021-10-06T09:59:43.402963Z" } }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>0</td> <th> No. Observations: </th> <td>732</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>1001.895</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Wed, 06 Oct 2021</td> <th> AIC </th> <td>-1985.790</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>10:59:43</td> <th> BIC </th> <td>-1944.428</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>01-01-1926</td> <th> HQIC </th> <td>-1969.834</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 12-01-1986</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0012</td> <td> 0.000</td> <td> 7.136</td> <td> 0.000</td> <td> 0.001</td> <td> 0.002</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0040</td> <td> 0.000</td> <td> 8.489</td> <td> 0.000</td> <td> 0.003</td> <td> 0.005</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 2 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0311</td> <td> 0.006</td> <td> 5.461</td> <td> 0.000</td> <td> 0.020</td> <td> 0.042</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.9747</td> <td> 0.000</td> <td> 7857.416</td> <td> 0.000</td> <td> 0.974</td> <td> 0.975</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0195</td> <td> 0.010</td> <td> 1.949</td> <td> 0.051</td> <td> -0.000</td> <td> 0.039</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->0]</th> <td> 2.354e-08</td> <td> nan</td> <td> nan</td> <td> nan</td> <td> nan</td> <td> nan</td>\n", "</tr>\n", "<tr>\n", " <th>p[0->1]</th> <td> 0.0253</td> <td> 3.97e-05</td> <td> 637.835</td> <td> 0.000</td> <td> 0.025</td> <td> 0.025</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->1]</th> <td> 0.9688</td> <td> 0.013</td> <td> 75.528</td> <td> 0.000</td> <td> 0.944</td> <td> 0.994</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->1]</th> <td> 0.0493</td> <td> 0.032</td> <td> 1.551</td> <td> 0.121</td> <td> -0.013</td> <td> 0.112</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: 0 No. Observations: 732\n", "Model: MarkovRegression Log Likelihood 1001.895\n", "Date: Wed, 06 Oct 2021 AIC -1985.790\n", "Time: 10:59:43 BIC -1944.428\n", "Sample: 01-01-1926 HQIC -1969.834\n", " - 12-01-1986 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0012 0.000 7.136 0.000 0.001 0.002\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0040 0.000 8.489 0.000 0.003 0.005\n", " Regime 2 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0311 0.006 5.461 0.000 0.020 0.042\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.9747 0.000 7857.416 0.000 0.974 0.975\n", "p[1->0] 0.0195 0.010 1.949 0.051 -0.000 0.039\n", "p[2->0] 2.354e-08 nan nan nan nan nan\n", "p[0->1] 0.0253 3.97e-05 637.835 0.000 0.025 0.025\n", "p[1->1] 0.9688 0.013 75.528 0.000 0.944 0.994\n", "p[2->1] 0.0493 0.032 1.551 0.121 -0.013 0.112\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_kns.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the probabilities of being in each of the regimes; only in a few periods is a high-variance regime probable." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:43.408479Z", "iopub.status.busy": "2021-10-06T09:59:43.406090Z", "iopub.status.idle": "2021-10-06T09:59:45.288804Z", "shell.execute_reply": "2021-10-06T09:59:45.289211Z" } }, "outputs": [ { "data": { "image/png": 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ITwUOhi3XmOvqEnBsjUajSTjvVrfx1v5W2lxeutx+XB4/3R4/LT1eejz+fmUFASDTYWVyvpPyPCeT8p3MLMkmP9NOdasLrz9IRVEWWQ4r7S4fJTkOlkzNx2YdXNcQDCpe3NnIy7uaqG510dHrA8ButeCwWciwGX8d5rLDZjm8zWrB4w/i9gWYVpjFWYvKmVmSPaJ7opRiy6FOXt3TzO6GLvY2ddPrM7IYWy0W8pw28jPt5GXacdgs5DntTM53kp1hY1phJjNLsinPc46oDokgGFS8vreFF3c2squhi5ZuL25/gGBQkZ9pp6Ioi2UVBZy1sJwZI7xnGo1m7JMIQVgirIsYnFhErsUwn6CysjIBp9ZoNJr4aeh08/2Ht/D01gYAHFYLuU4b2RnGrzjbQXmuEzFHtfAw610eH7saunh5VxM93kDMc03Jd/Lxk2bwyZNnYbH0Hya73D4+duda3qluJ9thZXpxNkXZDgB8gaAhlHcH8QWCeANBvP7DP4+5HBKUu9x+fvbUDj56wnS+cvY8cp32Id+Xl3Y28sPHtrGvqQeASXlO5pbnMKUgs69Onb1+DrQYArs/GKSj14cv0H+oXzG9kJvet4S55blDrkMi2HKogy/e/y77mnpw2i3MK89lSoGTDLsViwgdvT7erW7nsU11/OSJ7Xzy5Jl86/yFR7SPRqOZOCRCEK4BKsKWpwG1kQoqpe4A7gBYsWKFzuSh0WhGjfoONxf/4VU6en1847z5XLmykvxMOyJDF4K63D521nfh8gaYXpxFhs1KdauLHo+fkpwM9rf0cP/aan76xA5e29PCZ1fPZtWsYgD8gSCf++e7bKrp4BfvX8p7j5mKPYrmOBJKqb5613X08ocX9nD361VsOdTBvZ9YhdNujfs4//fIVu554wBzynL42fuWcM5Rk/qE8mh4/UG63D463X4OtfWypbaDO17ex3v/+Dp/+dgKjjevd7TY19TNFXe8SV6mnZs/tIzzl0wiwxb5PtR19PL7F/bw51f247Rb+eo580e1rhqNJn2IK7OciMwAHlNKLY6w7ULg88AFwCrgFqXUyljHXLFihdLh0zQazWgQDCouv+NNttR28J/rTmTRlLxROe9fXtnHrS/uoc3l4/Onz+Gr58zjt8/u4pYX9vCz9y3h8pWJmxl7bFMtn//nu3ztnHl8/oy5ce3zm2d2cssLe7jmpJnccP78QQXHeKnvcHPVX96k3eXjrquPY/GU/FHRtgaDikv/+BrVrS4e+8LJTCvMirmPUoqv/2cTD75TwxPXn8KCSaPTJzQazegjIuuVUisibYsnfNp9wBvAfBGpEZFPiMh1InKdWeQJYB+wB/gz8NkE1Vuj0WgSwl9e3cfaqlZ+eMniUROCAT55yize+NaZfGhFBX94cQ+3rdnH3944wHlHTUqoEAzwnqVTOHNBGX95df8Rds6ReHxTHbe8sIcPrajge+9ZOGIhGGBSvpM/XnUsPV4/F//hNU76+Qv86+1qAA619/LUljoaOt0jPs9AHttcx6aaDm686Ki4hGAAEeG7Fy4k12nnhv9ujuueaTSa8UdcGuFkoDXCGo0m2Xj9Qf78yj5+/cxOzl5Uzm0fPnZYphAjRSnFx+56m5d3NQHw7+tO4LgZRQk/z+t7m7nyz2/xp6uO4fwlgwfv8fgDnPGrNRRm2/nfZ08asmlGLGrbe3ljbwv3ra1m3YE2Fk3OY3t9J0qB3Sp88pRZfHBFxYgd/EK85/ev4PMrnrz+lCFroJ/aUsdn//EO7z9mGr+87OiE1Eej0aQXI9IIazQazVik0+3jstvf4JdP7+TMheX85oPLUiIEg6F9/PEli1k9v5Tvv2cRK6YXJuU8x80oIjfDxks7m6KWe2RDLYfae/nGuQsSLgQDTCnI5P3HTuOBT5/Ap0+dhd1m4YtnzOXf153AOUdN4k8v7eWi37/KO9VtIz7XofZethzq5H3HTB2WGcZ5iydz9UkzefDdQ1S3uEZcH41GM7ZIWYrldOX1Pc38c201M4qz+czq2WRn6Fuk0Yw1AkHFF+97l62HOvjjVcdwQRTt6GhRWZzF3VfHdJ8YEXarhZPnlrBmV1M/h7qBPLyhlunFWZwytySp9bFYhG9dsLDfuuNmFPHN81xc8ec3+faDm3ny+lNG9IHy/HYjAsjZi8qHfYxrT53Fna/t53/vHuL6s+Kzr9ZoNOMDrREO4819LXzsrrW8sbeFW1/awzf+s4lUmY5oNJrh87vnd/PSziZ+eMnitBCCR5PjZxVT3+mmodMTcXtzt4fX9zZz0dIpKdOQVxRl8aWz5rGjvouXdkXXXsfi9T0tVBZlMas0Z9jHKM9zsnRaAS/tahxRXTQazdhDC8ImB1tdfObv66koyuKFr63mG+cu4PHNdfzjrepUV02j0ZhsPNjOLc/vZltt56Bluj1+7nx1PxcuncyVqyZevPJ5ZgzfnQ1dEbevq2olqOCMhWWjWa0juPjoKUzJd/KnF/eO6DibD3VwdEXBiOuzel4pGw6209bjHfGxNJrxzIGWHp7aUse9bx7gijve5Mo/v8ldr+0nGBybikMtCJv87Mkd+AKKv37sOPIz7Xz61FmcNq+UHz22jfqOxHs5azSaofHq7mYuu/0NfvPsLt77x9d4aedh7d2O+k6u/PObfPD2N/i/h7fS7fHzyZNnprC2qWNeuaEZ3T2IIPzuwXYcVgtHjWL0jEg4bBY+ecos1la1svFg+7CO0dzt4VB7L0un5o+4PifPLUEpWH9g5HbLGs145fU9zZzx6zVc9/d3+N5DW2ju9tDa4+UHj27jT2tG9lGbKrQgDFS3uHhySx1XHV/Z58VssQg/vnQxQaW45YXdKa6hRjOx6XL7+Pp/NjK9KIvnvnIas0tz+MI/3+Vgq4snN9fx3ltfZ1dDN4faevnvOzVcdPQUliVASzgWKc7JoCTHwc76QQTh6nYWTslLSLi0kfK+Y6YCsGaY5hGbazoAWDpt5ILwosl5iMC2usFnGzSaiYwvEOTLD2xgZkk2933qeH7xgaU8ef0pPHn9KZy9qJw/vbSXxiSER0w2WhAGntxSR1DBx06Y0W99RVEWFy2dwhOb68asyl+jGeu09Xi59NbXaOh08/MPLGVOWQ63f+RY3P4Ap/7yRT7zj3dYMDmXJ64/mce/eDK3XnkMN38odREi0oF55bnsauw+Yn0gqNhc08HyNPlIKMhysGBSLm/tbxnW/tvrDaE1EbGhszNszCzOZmttx4iPpUk/ntlazw8e3arbdwS8vKuJhk4PN5y3gBNmF/PBFRXYrBZEhG+evwB/MMin7l1Pbxwp6NMJLQhjOMnNLs1mSkHmEdtOnltCu8vXN+BqNJrR5faX97GvuYe7r17JMZVG2LGKoizOPWoSSsFZC8u571PHU5brpCDLwYVLJ2MdhWxm6UxFYRa17b1HrG/odNPrC/TZEacDx88qZv2BNrz+4JD3PdTWS0GWnVynPSF1WTglj61R7M81Y5MnNtdx7b3r+dvrVVx22xvsb+5JdZXGJA++c4jibAer55cesW12aQ6/u3w5m2raueG/m1JQu+Ez4QVhfyDI21VtHD+rOOL2E2Yb69/YOzyNhUajGT7dHj9/e72Ki5ZO4dR5/Qff71y4kOvPnMvvr1iO0576af50oiwvg5ZuD4EBM1kHW404udMKj/zoTxUrZhTi9gUHNeWIxqH2XqZGUGAMl0WT86hp66VbZ5kbNwSDit88u4t55Tms+frpOGwWPnXPOlq1U+SQUErx1v4WTptfOmjs8XOPmsRnV8/mkY21VI2hj40JLwjvbOii2+Nn1SCC8OT8TGaWZGtBWKNJAWt2NtHrC0SM/jA5P5Mvnz2PTIcWggdSluckqKClu38ItZo2Q0ucToLwosmGWcNwZt0OtSVWEK4oyuo7rmZ88HZVK3sau/nM6tlUFGVx+4ePpbrVxU1PbE911cYUtR1umru9Mc2qPnrCDCwC/1p3cHQqlgAmvCB8wMwkNLt08FSfx88qZu3+VvyBoU/daTSa4fP01nqKsh1JSUc8ninLzQA4IpZwSBCemkaC8PTibJx2CzvqhqYRVkoZGuEEXktIqI5kVqIZmzyxuY4Mm4VzFk0CYNWsYj60ooKHN9TS3B051rbmSEKRXZZOK4harjzPyUlzSvoS3YwFJrwgXG1OFYY0AZE4YXYxXR6/th3TaEaRYFDxyu4mVs8vnfA2v0MlJAg3dvX34D7Y5qI8LyMtIkaEsFqE+ZPy2D7EaA3tLh8ubyChGuHQsWq0IDxueGZbA6vnl/bLEvuxE2fgDQR56N1DKazZ2GJjTTt2q7Bgcmz/guNnFbOroXvMmJ9MeEH4YKuLwiw7eVGcLY6fZWij3tinzSM0mtFiX3M3bS4fx8+MbLakGZyyPCcAjV0DNcIuphUO/tGfKhZNzmXHEE0jDrUn3syjLDcDu1W0Rnic0NTloa7DzcoBY8icshzmluXw4k6dSTBedjd0M7s0J66P6JDMtHZ/a7KrlRDiEoRF5DwR2Skie0TkmxG254vIoyKyUUS2isjVia9qcqhudUXVBgOU5TqZW5bD69pOWKMZNd6uMhIbrJhRmOKajD1Kc0yN8ADTiMZOD5PynamoUlQqirJoc/noGYKTWpMp5JfnJe56LBZhUr5T2wiPE0KzDAsjaDHPWFDG2v2tdLl9o12tMcm+JkMQjoclUwvIsFlYf2CcCMIiYgVuBc4HFgFXiMiiAcU+B2xTSh0NrAZ+LSKOBNc1KRyMQxAGwzxiXVUrPm0nrNGMCuuq2ijOdvQludHEj8NmoTDLfoRpRFO3p09ITiem5Bta3fohBONvcxnTroVZiX3VTMnP5JGNtVSb/iOasUsoOUrIITOcU+aW4gsoNgwzq+FEwuMPUN3qiupLFY7DZmFGcTZVY+QZikcjvBLYo5Tap5TyAvcDlwwoo4BcMSLY5wCtQNrHnwkEDWeLijimCk+YVYzLG2BTTXvyK6bRaNjd2MWiKXkTOjHGSCjPc/ZLD+/2Behy+ynJST8dRUhLPZR09m0uQ5NXkJWYGMIhZpkv+/N+9/KYmdrVHMna/a387MkdTM434osPZK6Zinxf09gJ85UqqltcBBXMLotPIwzGLE8oXGO6E48gPBUIj4NRY64L5w/AQqAW2Axcr5RKe9VpU5cHX0DF5XV83EzD5kXnoddoko9Siv3NPcwo1trg4TK7LIfdYdnlQh7yJWmoEZ5sCsJ1QxCEO1xeRIjq3zEcvnbOfG65YjnFOQ6+9eAmHS1oDLK5poOP3vkWYCTciURZbgbZDiv7mo7MwKjpz17zHs0qiV8QrizKorrVhVLpn5U3HkE4kjpm4JWdC2wApgDLgD+IyBFzESJyrYisE5F1TU3Dyy2fSOo6DDuwKXHYzJXkZFBZlMU7B9qTXCuNRtPu8tHl9jO9OP0cu8YK88tzqW514fIak3PN3YYpQToKwiE73/qO+G1z21w+8jPtWBIcUaQ4J4OLj57Cdy9cxN6mHh7fXJfQ42uSS683wBfvf5eiLAdvf+csfnTp4ojlRIRZpTnsG0OJH1JFKMzsjJL4x+PKokxc3gAtYyByRDyCcA1QEbY8DUPzG87VwIPKYA+wH1gw8EBKqTuUUiuUUitKS49M0TfahLQPk/Pj8zpeXlnAO9VtY+ILR6MZy1S1GC8nrREePqE0yrsbDG1Os+lcVpKbfoKw026lKNsxJI1we68v4fbB4Zy9sJzibAdrdqZeaaOJn8c317G/uYeff2AppTH6+qzSbG0aEQcNnR6yHNYhpTKfbo7dB8aAnXA8gvDbwFwRmWk6wF0OPDKgTDVwJoCIlAPzgX2JrGgyCIXImVIQn9fx8ooCGrs8RwSp12g0iWU4GghNf+ZPMgThnQ1GoorDphHpZyMMhnnEkARhl5f8zMSaRYRjsQgnzC7mtb3NWvkxhnh2Wz2T8pycPKckZtnZpTkcau+l1xsYhZqNXRq73H2xyeOl0pzN+/S96/npE9t53x9f44E0zTYXUxBWSvmBzwNPA9uBB5RSW0XkOhG5ziz2I+BEEdkMPA/coJRqTlalE0Vdhxun3RL3YDrLDB1yoEV/QWo0yWR/cw8ipGXM27FCZVEWWQ4r71a3A+ltIwwwKc9JwxCjRhQm2FFuICfOLqGh06Onz8cIbl+Al3c1c9aisricbEOOkft1+0alsctDWe7QwhTOKsnm2xcswGqBO17eR01bL99+cDPb0jAxmS12EVBKPQE8MWDdbWH/1wLnJLZqyae+w82U/My4vdJDTnW1Q7Bj02g0Q+dASw9T8jNx2tMnA9pYw2oRzlhQxn1rq5mS72R3Yze5Tlva3tP8LDs76uNPs9zu8jG3LHaWq5Fw0hwjEcPre1vijqGqSR1bDnXQ6wtw6tz4TC9Dzl97m7pZNOXIEGsag6YuD0cN8f6ICNeeOptLl0/l7f1tnDi7mLN/u4Z73qjiZ+9fmqSaDo8JnVmutqOXyXGaRcDh9Js62LpGk1yqWlzaLCIBXLLMCPDz62d38cjGWo6eVpDaCkWhINNBR2/8yQ3aXb6Eh04bSGVRFlMLMnl9T9pPcGqgb/ZjeWV8SXhCMcq1nXB0GjvdQ9YIhyjLdXLh0skUZjt44NMn8JP3Lklw7UZOXBrh8Upjp4dVZli0eHDarZTkOPpSe2o0muRwoKWH85dMTnU1xjynzy/l++9ZxLTCTKpbXVy5qjLVVRqU/Ew73R4/vkAQuzW6jsYXCNLt8VOQmVx7ZxHDTvi57Q0EgyrhESo0ieXdg21MK8yM6SQXItNhZWpBJvuadQi1wej2+OnxBijLG7lJ1aw0nVWZ0IJwp9tH3hCdLaYUZFKjNcIaTdLocPloc/mYoUOnjRib1cI1J89MdTXiIj/TeB119voojmHHHNIcJ1sjDLByZhH/WV9DVUtP2r7INQYbqts5dkb8yi3QkSNi0Wja7Q/VWW4sMWFNI4JBRbfHP2RBeGpBZl+0CY1Gk3gOtBovpek6dNqEIpT9Kx7ziB6PERs5OyP5upxQet7tdfHbL2tGH7cvQG2Hm/nlQ/tYWTQ5jx31nXS54zfLmUiEUpkXZadntJlEMGEF4W6vH6Ugzzm0gdQQhOP3bNZoNEMjlJ9eJ9OYWISi97THIQi7fUa2N6c9+a+wOWU5WC3C9rr083bXHCY0UxtPpthwzlhQhi+geGW3tgOPRGev8dGZzFCFqWbCCsKd5mA71PScxTkZ9PoCOu6gRpMkQlNxk/OG9kLTjG3yTTOHeDTCbp8x/maOQgQMp93K7NJsdtRrQTidCfnuTC0Y2gf0sdMLKciy89z2hmRUa8zTaWrKhzp7PpaYwIKw8ZWTlzk0jXDIJq29N/3TBmo0Y5Gmbg8Oq2XIz6ZmbBPSOHUOQRAerVBwCybladOINOfQMDXCNquFYyoL0zK+bTowXKXhWGLiCsLu4TVuQWj6zqXtiTSaZNDU5aE0NyPu+N6a8UH+EMbW3j5BeHReYTNLsqnt6MXj1zOB6cqhdhc2i1A+DKeuOWU57GvuIRDUGQQH0uk2lIa5QzQjHUtMWEG4q69xhygImw4dWhDWaJJDU5eHknHsoayJTEgQjs80wrARzrCNjkZ4enEWSukY8unMobZeJuU7scUIvReJOaU5eP1BDra6klCzsU1nr48MmyVtE/EkggkrCPep+4dpGtGhTSM0mqTQ1OWhNE3TAGuSh91qIdthjUvJENLMZjpG5+VcWWTYnR7QglLaUtvuZkrB8PwKZpcZkSb2NOp4wgPpdPuGrDAca0xcQXi4phGmINymNcIaTVJo7vbEHRBfM77Iz7TH5X8x2jbClWYEE60xTF+ae4Y/bswJCcJNWhAeSGevf9z7a0xcQdh0lssZot1LKJORNo3QaBKPPxCkpcerBeEJSnaGLa6IPH3h02yj8worzckg027lQIsWhNOVth4vRVnDi3Wbn2mnJMdBVbNOrDGQTrdvXDvKwQQWhLvcPrIc1pipPAfitFtw2Cw6aoRGkwRae7wohRaEJyhOu7VP2xuN3lHWCIsIlUVZVGuNcFoSCCrae30UjiDpw7TCLA626fYdSGfv0DPwjjXikgJF5DwR2Skie0Tkm4OUWS0iG0Rkq4isSWw1E89wv3JEhMIsOx1aI6zRJJzGLg8ApTnjN4uRZnAybJY+bW80Rts0AqCiKItqrRFOSzp6fSgFRSNIuV1RlMXBVu0MOZBOt3/IicfGGjEFYRGxArcC5wOLgCtEZNGAMgXAH4GLlVJHAZclvqqJpbPXP+xwIAWZDm0aodEkgboOM5lGvk6mMRFx2q1xhShz+4I4rBasltELsRfSCCulQ2ylG609xgztSDTCFYWZ1Lb36hBqA+hya40wwEpgj1Jqn1LKC9wPXDKgzJXAg0qpagClVGNiq5l4en0BsobpcZyfFZ9Dh0ajGRq1Znao4Xp/a8Y2Tnv8GuGMUYohHGJ6cRa9vgDN3XrsTzfaXEabFI1EEC7Kwh9U1JuZLTWglDKc5bSNMFOBg2HLNea6cOYBhSLykoisF5GPJqqCyaLXFxj2tFphlr3vC1Sj0SSO2vZeHFYLxSN4oWnGLhk2K+44NMIe//DH7+ESCqFW3aodqtKNPo3wMJ3lAKaZGel0ZJDDvLSzCW8gSOEITE7GAvEIwpHmngbOHdiAY4ELgXOB74nIvCMOJHKtiKwTkXVNTU1DrmwicfsCw45BWZyToQVhjSYJ1Ha4mVzgxDKKU96a9CHDbsETh0a41xsYtaxyIUIh1LTDXPoReh+PSCNcaLRvjU6aAhgy0hfvf5eFk/N4/7HTUl2dpBLPSFIDVIQtTwNqI5R5SinVo5RqBl4Gjh54IKXUHUqpFUqpFaWlpcOtc0Lo9QbIHKZGoSTbQWuPV9sSaTQJpra9lynaPnjCMhQb4eGO38NlWmEmIugQamlIIjTCoWyWbVrJBcCaXU10uf186/wFlIzzBEfxCMJvA3NFZKaIOIDLgUcGlHkYOEVEbCKSBawCtie2qoml1zd8Qbg4J4OggnaXfmA0mkRS296r7YMnMHFHjUiBaUSGzUppTgZ17dqGNN1o6/GSabeOKNNgtsOKRQ4n25roPLqxlqJsByfOLk51VZJOzLAJSim/iHweeBqwAncqpbaKyHXm9tuUUttF5ClgExAE/qKU2pLMio8Uty+Ac9imEcZXZ0uPl+Jx/qWk0YwW/kCQhk43Uwqcqa6KJkXErxEO4LSNriAMhsaxTStA0o5Wl3fEdqwiQq7TTmevFoRdXj/Pb2/kfcdMxTbEXAtjkbjihymlngCeGLDutgHLvwR+mbiqJZeRmEYUZxvCb3O3h3nluYmslkYzYWns8hBUOnTaRMZps+ILKAJBFTU0Wq8vmJLYpgVZdh06Mw0x0gCP3KErL9NGp9ufgBqNbZ7f3kivL8B7lk5JdVVGhfEv6kdAKYXbP3wbs5KQRliH0dFoEsbhGMJaIzxRCYVEi5VdzjMC07aRoDXC6UlngmLd5mmNMGBEiyjOdrByZlGqqzIqTEhBOKRxGK7XccgcoqXbk8hqaTQTmnpTEJ6kBeEJi9NmjMkef3Q7YfcIwl+OhMJsO21aI5x2dPb6yE+UIKxthHn3YBvLKwtHNWFNKpmQgvBI89QXZNqxiGEjrNFoEkNdhxG2SGuEJy6hMTmmRtgfJMM2+q+vgiwH7S6vzi6XZnT2+hKS9CEv00Zn78Q2jWh3ednX1MPyyoJUV2XUmJCCcGiQHa6HqcUiFGVn0Kw1whpNwqjvcOO0WxKi2dGMTeIVhL3+II5UCMKZdvxBRbdnYgtL6UaH1ggnjA0H2wG0IDze6fWagvAIptbmT8rh5V3N+AOxQ/1oNJrY1HW6mZyficjEmI7THElGnKYRqRKEQ3FqtcNc+uAPBOnxBsjLHLnzZF7mxLAR3nKogzte3ts3CxfOnsZuABZNzhvtaqWMiSkI+0YuCH/0hBkcau/lue0NiaqWRjOhaehwMylPm0VMZOI2jQikSCNshujSgnD6EIrykCiNcI83MK4VXFtrO3jfn17np0/s4ISbXuCDt7/BofbDAnFNWy+5GbYJNTM3oQXh4cYRBjhrYTn5mXbW7EptqmiNZrxQ1+HW9sETnMNRIwYXRJRSeP1BMlIQ37TQTOGrI0ekDyENbqJshAG6xnEIte8+tIWCTDvfuWAhAGv3t3LNXW/3ZcqtaetlauHEmpmbkIKwOwEaYatFWFZRwDsH2hNUK41m4hIMKho63TpixAQnw0ySES2phi9gvLBTYxphCFtaEE4fQja9idIIg2FzPB5Zf6CNd6vb+dzpc/jUqbNY8/XV/O7yZexs6GLNrkYAatpcTCucWLHcJ7QgPNLwO8dUFrKrsWvCG9drNCOluceDP6i0RniC44xDI+w1p61TIQjnZBiCknaWSx9CQmsi4ggXjOMPnfUHWvnIX9+iIMvO+4+dBsD04mwuWDKZ8rwMrrl7HX95ZR+H2nqZVpiV4tqOLhNSEO71GgPpSAOyL68sQCnYXNORiGppNBOWwzGEJ5YmQtOfkHIimkbYazrSOVJgGpGdYdSvRwvCaUMo3FkinOVKckJZY8efIPynl/aS5bDx4GdOJCfj8L2yWy38/opjmF2azY8f306Xx681whOBRDjLAX3plfc194y4ThrNREZnldNAWNSIaBrhkCBsG/2EGtkOQ4Do9kR35tOMHiGNcCJMI0pyQ4Lw+AqNWtveyws7Grn8uApmleYcsX3lzCLu+OiKvuWJJgiPfrL2NOCws9zIvgPKcjNwWC3UtLoSUS2NZsIS0giX66gRE5q+qBHxaIRTYBphsQjZDivd49iZaqzRZZom5ibAWa4kx3CGbO4aX4LwU1vqCSr4gGkSEYnZpTk8/sWTeWF7I6vnl41i7VLPhBKEXV4/f3llP64ExBEGY1CcWphJTduRsfg0Gk381HW4sVuFYtMrXzMxsZvmDt4ocYS9AWP8ToUgDJCdYdOmEWlEqC2yEpByO8NmJc9pG3ca4We21TO/PJcZJdlRyx01JZ+jpuSPUq3Sh7hGEhE5T0R2isgeEflmlHLHiUhARD6QuComjtp2N797fje3rdkLjNxZDowphINtWiOs0YyEuo5eyvOcWCZIbntNZEJ2v/7g4CmMvX4zaoQ1NX0lJ8NGt1cLwulCtydAtsOasLGjJDdjXNkId/T6eLuqjbMXlae6KmlLTEFYRKzArcD5wCLgChFZNEi5nwNPJ7qSiWJOWQ5XraoEDG2CPQHOFhVFWVojrNGMkKoWF9OLJ5ansuZI7KZw64uqEU6daQRAjlNrhNOJHo+f7IzETW6X5GTQNI40wusPtBIIKk6aU5LqqqQt8fSelcAepdQ+ABG5H7gE2Dag3BeA/wLHJbSGCear58ynKNvBsdMLE3K8aYWZtPZ4E/4wajQTiarmHt6zdHKqq6FJMVaLIAK+KJm9DkeNGH1nOTAc5rSNcPrQ7fX3i4IwUkpzMthe35mw46WatfvbsFuNvAeayMTzST0VOBi2XGOu60NEpgLvBW5LXNWSQ36mnS+dNY9T5pYm5HiheHvhKQo1Gk38tPV46ej1MTOG/Zpm/CMi2K0WvIFophGp1QhnZ9h0HOE0IvEaYce4cpZbV9XK4qn5ZI4gk+54J56RJJLhzcBR6mbgBqVU1JgyInKtiKwTkXVNTeMjNXGZGW6laRw9OBrNaLK/xQg/OKNYC8IasFskukY4xc5yuU4bPdpGOG0wBOHECXlleU463X5c46CNlVJsq+vk6GkFqa5KWhPPSFIDVIQtTwNqB5RZAdwvIlXAB4A/isilAw+klLpDKbVCKbWitDQxGtlUU6oFYY1mRFSZcbhjeTRrJgZ2myVO04hUaYR1+LR0otsTSKhpRGhmal/T2M8P0NjlweUNMLtUj63RiGckeRuYKyIzRcQBXA48El5AKTVTKTVDKTUD+A/wWaXUQ4mubDqiBWGNZmQcbDXMiiqKJlYQd01k7NbogrAnDUwjenRCjbQh0aYRc8qMhBN7m7oTdsxUsV8rGeIiZu9RSvlF5PMY0SCswJ1Kqa0icp25Pe3tgpNJboaNDJtl3MUd1GhGi9YeD/mZdjJSkClMk344rBZ8cdgIZ6QqaoTDhjcQxOsPpkwY1xwm0YLw9OIsLAJ7x4FGuE8Q1mZnUYmr9yilngCeGLAuogCslPr4yKs1dhARI9yK1ghrNMOi1eWjMGvkWaE04wO7NZaNcOrDp4EhgDlsOgFMqun2JDZqRIbNSmVRFnsbx75GuKq5B4fVwpQCPdsWDf05mwBKc8dX3EGNZjRp6/FSqDPKaUximUak3kbYELp05IjU4w8E8fiDZDsSG7p0dmnOuDGNqCzOwqoTFUVFC8IJoDRXa4Q1muHS2uOlKEsLwhoDu9XSlz0uEqkOn5ajBeG0IWSrncioEQCzy3LY19xDIEqGw7HAjvou5pXnpLoaaY8WhBOAFoQ1muHT5tIaYc1hYppGpFgQzjLjsbq82mEu1YRSXSfSNAJgTmkOXn+QQ2M4a2xrj5fqVhdLdei0mGhBOAFMyXfS0uOlVw+MGs2Qae3xUqQFYY1JTNOIQBARsKVoutdpNwRhj0+P96kmlOo60VldZ5cZzmVj2TxiU007AEun5ae2ImMALQgngOmmR2Z1qyvFNdFoxha93gAef5BCbRqhMYnHRthhtSCSGkE40xSEe7UgnHJC5imJ1gjPKhn7IdQ2HuxABJZM1YJwLLQgnABCoUmqWsZ+uBWNZjRpdXkBKMrWUSM0BkZCjcFtMz0pDlsW0gi7fYML65rRIVka4cJsB8XZDvaMQuSI+g437iR8VL2xr5n55bnkOvXYGgstCCeAyuIsAA5oQVijGRJtPYYgXKA1whoTRwwbYV8gmLIYwqA1wulE6GMk1CaJZFpRFofak2sj/O91BznlFy9w6i9eZFdDV8KO2+Pxs/5AG6fNGx8ZfJONFoQTQH6mncIsO1Ut2jRCoxkKrT0hjbAWhDUG8ZpGpAqn3Th3MrR4mqGRTMfJ4mxH3/iUDO58dT9f/88mjp1eSK83wG1r9ibs2G/tb8EXUJwyVwvC8aAF4QQxvTibqmatEdZohkJ7rw+Agkw9facxsMXKLBdIrWlERp9phBaEU403YLRBMvpDURIF4cYuN798eidnLCjj3k+s4uJlU3h8Ux17GhOjFd5wsAOLwLHTCxNyvPGOFoQTxLKKAtYdaKPD5Ut1VTSaMUOvGf4oK8E2fpqxi90qfZq+SKQ6tXGmFoTThmRrhFt6vCiV+FjC/3izGo8/wPffswi71cK1p84iL9POFX9+K2rfj5fdDV1ML84m06HT1seDFoQTxAeOnYbXH+ShDYdSXRWNZswQisWaDBs/zdjEEY9pRAoFYbtVsIi2EU4HkpllsCjbgdcfpCcJYVHfrmrlqCn5zCgxHO2nF2fz8/cvoanLwyu7m0Z8/F0NXcwt04k04kULwgli8dR85pTl8PyOxlRXRaMZM4SEiSytudCYxLIRdnkDKf1wEhEy7VYdNSIN8CRRIxzyW2jtTqx5hD8QZMPB9iPMFk6eU0p+pp3HNtWN6Pgef4CqFhfzJ+WO6DgTCS0IJ5DjZhTybnUbwTGellGjGS16vQFESGkUAE16Ybda8EexEW5zeVMed9ppt2rTiDTAa34wJWP8KM4x+lhLT2Kzxu6o78LlDXDMAEHYYbNw6rxS1u5vHdHx95upoedojXDcxNV7ROQ8EdkpIntE5JsRtl8lIpvM3+sicnTiq5r+HFNZSJfbP6aDcGs0o0mvqd1LVXIETfpht0mfgBOJdBGEtWlE6kmuaUQGYPS3RLK9rhOInOhiwaRcDrX30uUevq9RVbMRvWp2qRaE4yVm7xERK3ArcD6wCLhCRBYNKLYfOE0ptRT4EXBHois6Fgh94X32H+/Q3J3Yr0iNZjzi8qV2mluTfkSzEVZK0dbjozDF4facdgsebRqRcrz+IDaLYElCuu0i82OrJcGmETVtvYjA1ILMI7aF7Hp3jyCRRyifQSi/gSY28XxGrQT2KKX2KaW8wP3AJeEFlFKvK6XazMU3gWmJrebYYFZJNh8+vpIDLS5++sT2VFdHM0q0u5LjWTwRcHsD2rNZ0w+bxUJQQSCCiVmPN4A3EEx5JsJMh9YIpwPJdJwsMk0jEh1C7WCbi0l5zoj1Dtn17h5Bco2qFhfF2Q7ydEa5uImnB00FDoYt15jrBuMTwJMjqdRYRUT48aVLuObkmTz4ziG21XamukqaJBIMKq6//12W/fBZLv7Dazp03jBweQPaUU7TD7vN0O5F0gqnSyZCp03bCKcDyYwpne2w4rBZEi4I17T1UlEYWVtbUZiF025hZ/3INMLTtTZ4SMTTgyLNOURUf4nI6RiC8A2DbL9WRNaJyLqmppGHCElXPnPabHIybNz60p5UV0WTRJ7Z1sDDG2p57/KpbKnt4I+6ven2+Nle1xm3w2ivNo3QDCBk7xnJTjhkr1mUakFY2winBcnMMigifbGEE0lNq4tphUeaRQBYLMKM4myqW4efnOtAi4sZxdnD3n8iEk8PqgEqwpanAbUDC4nIUuAvwCVKqZZIB1JK3aGUWqGUWlFaOn5T/+Vn2bnq+Eqe3FxHU5e2FR6v3PHyXqYXZ/HLDyzlvcuncvfrVfR4/KmuVsrocvs47+aXOf93r/D5+96JSxju1aYRmgHYTcHGFyGxQEg7l3obYR0+LR1IdkzpRGeX8/qD1He6mVY0uMa2PM9JQ+fw5Aa3L0BtR6+2Dx4i8fSgt4G5IjJTRBzA5cAj4QVEpBJ4EPiIUmpX4qs59rh02VSCCp7ZVp/qqmiSwL6mbt6pbufDq6Zjs1q47NgKPP4gr+xuTnXVUsZvnt3FofZeTptXyhOb6/nbG1Ux93H5/FojrOlHSBD2R/iQajfNjwqzUmv/6LRbtGlEGuBJcrrtogRrhOs6egkqBtUIA0zKc1Lf6R7W8Xc3dKMUzC/XMYSHQswepJTyA58Hnga2Aw8opbaKyHUicp1Z7PtAMfBHEdkgIuuSVuMxwoJJucwozuLprQ2proomCTyysRYRuOjoKQCsmFFIntPGc9snZnv3ePw88PZB3rtsKndffRynzC3hN8/simk33esNkOXQ6ZU1h7FbDWu8SKlmQ9q5ohRrhDMnaBxhXyCYcJvZkeDxJc80Aow0y60JjCNc32EIuFPyBxeEy/OdNHd7oiaVGYzt9YZfkk6mMTTi6kFKqSeUUvOUUrOVUj8x192mlLrN/P+TSqlCpdQy87cimZUeC4gIJ8wuZlNNu44oMA55cUcjx1QWMinfCRharFWzinm3ui3GnuOTxzfX0eMNcMWqSkSEL501jy6Pn5d2Rc+02OsN4NQaYU0YIQ1fRGc5lxeLkHKP+ImWUGNXQxdfuO9dlv3gGY750bP85tn0mPj1BoJkJHH8KMrOSGhmuZCmtzwvY9Ay5XkZKMWwzCp31nfhtFuYrm2Eh4RO55RE5pbl0u7y0aRjCo8rXF4/W2s7WTWzqN/6+eW5VLW48PgnzgsyxP1rq5ldms0KM5b2sooCCrLsvLwruqlIr09HjdD0x2YJCcKRTSPyM+1JiRs7FCZS+LS6jl6u/PNbrNnZyMXLpnBMZQF/eGE36w+MLANaIvD6A2QkUyOc46DHG0jYR0+jaftblucctMwkc1vDMMwjdtR3Mr88F2uKn4+xhhaEk8i88lBMQJ1pbjyx4WA7/qDiuBn9BeG55TkEgor9zcP3+B2L7Gro4p3qdi4/rrIvQ5zVIpw0p4RXdjdFnRFxaWc5zQBCphGRNMKdbkMQTjVOmwW3LzghZvvufr2KdpeX/37mRG5631L+ds1KphRk8pUHNqZcK55sZ7lQBsNEmYM0dLrJtFvJcw5uDlY+AkF4b2MPs3Vq5SGjBeEkMq/c6JDf+M8m7nptf8oHDU1ieOeAYf5wTGX/XPGhD5+d9cMPhj4W+dfbB7Fbhfcd0z+8+KqZRTR2eQZ1/AgGFR5/UDvLafphtw0ePq2z10duGiQKmGTaeG4d57Hig0HFw+/Wctq8Uuaa41uu086PLlnMgRYXz25LrU9EMuMIw2Fb9IQJwl0eyvMyoqaUD5nbheyJ4yUYVDR3e/o0ypr40YJwEinNNeyADrX38oNHt/Ht/21OcY00iWBvUw9TCzLJH+C5Pqs0G6tF2DWCrECJwu0LUNfRm/TzePwBHnynhnMWTaI4p7/d24JJeQDsGOTDIDS1rE0jNOE4ooRP63T7yctMvXPlhUsmk2Gz8M+11amuSlLZUNNOfaebi5dN6bf+1HmlTMl38t93alJUM4NkxhEGwzQCEigId7ijmkWAESPbbhXqhxhCrb3Xhz+oKMkZ3P5YExktCCcREeG3HzqaP111DF88Yw4PvnOIjQfbU10tzQgZLHNPhs3Kosl5vLYnYhjtUeNQey9n/noNJ9z0Aj94dGvCj//CjgZOvOl5zrv5Zf788j7aXD4+dFzFEeVCIXx21EUXhLVphCackIbPE0kQ7vWl3FEOjFjx5y+exJOb68a1eUQoO+qKAWZgVotwyfKpvLyriY7e1GXUTLZpRKkpVA43nNlAGrrcMTW2FotQluukcYjnbDZ9kUIKOE38aEE4ybx3+TTOXzKZa06eidUiOq7wOKC6tZfKQQKin7OonA0H24dl35Uo/u/hLXT0+jh1Xil3vVbFqwmMbVzT5uL6+zaQ47TR2OXhV8/sIs9p4+Q5JUeUzc+yMyXfyc76/tPHSikaO93cb2rTtGmEJpyQDXAkAavTnR6CMMDKmcW0uXwcaHGluipJY0d9J7lOG1PyjxTeVs8rJajgjb2p+/BPtiBcURRKeTzyWT6lFA2d7qgRI0KU52UMWfgORZnQGuGhowXhUaIgy8FxMwpTblOlGRkur5/mbg8VgwnCR00CjPBqqeBgq4vndzRyzUkz+MtHV1CYZec/6w8m7Ph/eGEPnkCQv37sOO65ZiVZDivXnzVvUC/+BZPz2HSoo09r5g8E+dQ961j50+f51TO7sAjMKNGhfjSHCTkotbuOnI7u7E0P0wgwIqOA4Tw7XtlR18XCSXkRbVqXVxaS7bDy6p6mFNTMINk2wlaLMH9SHtvrRm4L/sa+Fty+IEdNyY9ZdlL+0JNqaI3w8NGC8Chy9qJJ7Gro5kDLxIoqMJ442GrY3Q6mEZ5XnkO2wzqoXWyyeejdQwBcuWo6DpuFMxeW8/yOxmEFZx9Ic7eH/75Tw+XHVVBRlMXiqfls+P45fOLkmYPuc8aCMvY19fDPtdU8vbWe0375Es9tb+S602bz4GdPZNsPzzsi+oZmYlNg2t639vTXCHv9QXp9gbTRCM8rzyHTbh23grBSih31XSyYHDk5g8NmYeXMIt7cl7owap4k2wgDLJqcy7a6zhGbwPzr7YPkOW2ct3hSzLJluU4ahugsF9IIl2qN8JDRgvAocs6icgCtFR7DVLca06CDCcIiwpyynJQ5zK2tamV+eW6f5/F5R02iy+3nuQT0udf2NOMLKD5w7LS+dbG0MZcun4rTbuE7/9vCp+9dj9Nu4fdXLOeb5y/gmMpCnUxDcwR2q4Vcp422ARrhLrchGOelQfg0AJvVwqIpeX12tOONpm4P3R4/s0sHD8e1rKKQvU3ddHv8o1izw3j9QTKSqBEGWDQ5j3aXj7ohCqbhBIKKF7Y3cv7iyXGNeZPynfR4A333dWttB/97t4ZHN9Zy0xPbWbPrSC18U7cHh9WSNjMmYwl9x0aRiqIsFkzK5cWdjXzylFmpro5mGMQShAHmlufy0s7Rmy5USuH2BbFZhfUH2rgsTFA9fUEZM4qzuPWlPZy3eFLUsD2xeGNvC3lOW1xTeyFyMmzcc80qDrW72NvYw0dOmN4XJ1OjGYyibMcRnvqdbkMoSKcX/ZzSHJ7fkXjFRjCoaOzy9H3QpoLQ7FdF0eDpgJdW5KMUbK7p4ITZxaNVNcAY95JtGgH0fQhUtfQwpWDwexGN7XWddHn8nDgnvnsUcqir73Bz7xtV/O2NA33bROD2l/fx0/cu4cpVlX3rm7o8lOZGD82miUz6jCgThOWVBTy9VWuExyoHW13kZtj6pm8jMa88h/+sr6Gtx0uhGYcyEkopttd1UZLjiBlSZzD2N/fwhfveYcuhw1qp48Iy3lktwqdOncV3/reFLYc6WTItfiF2IG/sa2HVrOIhZy1aObMI0OYPmvgpzHIMrhFOE9MIMEIm/mudlw6X74hwisPB4w9w0xM7eHprPXUdbo6dXsgHV0zjQ8dVxt45wRyM46P/6GkFAGyqaR91QdgfVChF0k0jhhvXN5w39xkOhatmxnePQsqCu17bzz/equaqVZVcfdJMWnu8LJicy/X3vct3HtqM027hfccYio+a1t6UfjiNZbRpxCgzpyyX1h4vLTrt8pikutVFRVFW1K/uUOD5aOYRXn+QT92zngtueYXjb3qenzy+bcg2aEopvnT/u9S09fKVs+dxxcpKPnx8JafPL+tX7oLFk7FahCe31A3p+OF0ug3v+IFJRDSaZFCUfaQg3Nkb0ginjyAc0hbubY6dPfT1Pc389tldfPehzRFNlZRSXH/fBu5+vYrFU/P54hlz6Oj1ccN/N/PAusQ5vMZLSBCeVji4IFyU7aCiKJN3q9tHqVaH8Zrh9ZKtEZ5sJk8ZiWnEq3uamVGcFbegumhyHgVZdv7xVjWLp+bxfxcdxZyyHFbOLCLPaedPHz6WE2YV87V/b2TNriaCQcXW2g6OmpI37DpOZLRGeJSZa6Y/3NXQzQnaqH3MUd3qYk4UmzmAJVMNreummg5WzYqsAbj79f08t72BL581j5o2F39+ZT8LJ+f1fd3Hw9NbG9hY08EvP7CUy1YcGcc3RGG2g+NnFfHwhlquWz2b3Q1diAjLKwrinkbb12Q4eM4u1REeNMmnIMt+RMiqzjTUCIfS2X7tgY1cuHQyly6f2icc+wJBHt9Ux6H2Xjz+ILc8vxsRyHbY+Pub1fzwkqP48KrpfRFX7n3zAE9tree7Fy7sM5374plzufLPb/GLp3ZyybIpZNhGz6b+YJuL0tyMmDatK2cU8+LORoJBNWj0mGQQikOebBvhTIeVgiz7ERrhhk43P31iO9tqO1leWcBXz5kf0eyrw+Xj1d3NXBPFqXgg+Vl2fn3Z0dz05A5++8FlRwj7TruVv3xsBRf9/lVufGQrv7psKT3eAIuHYLamOUxcgrCInAf8DrACf1FK/WzAdjG3XwC4gI8rpd5JcF3HBXPNtMu7G7tGfSpprOD2BdhU08GkPCeVERJXpIpgUHGw1cUZC8qilivJyWBaYeag3uR7Gru4+bndnLGgjOvPmksgqNjb1M1NT+7ggiWDO1Psb+7hqS31lOQ4uOjoKfz9zQNMLciMS3j+3Olz+Ohf13LCT5+nx2u8QI6fVcSPLlncp8GOxr4mQ+M1K8ZHgEaTCIqy+tsIN3V5+PMr+7BZJK3CQ1UUGtrCfc093PriHm5fs4+7rz6OpRUFfPJvb/eLqHD2onJ+d/ky7FYL19z9Nt9/eCuv7Wnmtg8fC8BfXtnPyhlF/aKw2KwWPn/GHD5651rmf/cplkzN53eXL0vIc+gPBNnT1I3bF8QfCNLa46U0N4Nl5gfywdbevuuLxvGzivjvOzXsbuxm/qTYY0miCIU0G40xaVKes59GOBBUXPf39Wyv6+Sk2SU8srGWHfVd/Pu6E474WHl6Wz3+oOLCJZOHdM4zF5Zz5sLyQbdnOWx89z2LuPqut3n/n94AYPFULQgPh5iCsIhYgVuBs4Ea4G0ReUQptS2s2PnAXPO3CviT+VczgEl5TnKdNr7/8Fae3dbAdafN5qQIyQgmInUdvfzxxb28sKORQ+29iMCZC8rJddooyXFQmO3ggysqUhYwvKnbg8cfHDSGcDjLKgp4bFMdZ6yv4cQ5xX3Ta3ubuvnYnW+T5bBy0/uWAIYd79fPXcAVf36Tj9+1lk+fOpvTw4TtquYebluzl0c31vYJsT97cgctPV6+dNbcuGx2T5xdwr2fWMV/36lhqWkn/OtndnH+717hEyfP5ItnziU7Y/DhYF9TD1aLRLUX1GgSRWG2g15fgMc31XHPG1Vsre3EHwzy+yuWUxTF7n60sVktPP7Fk8lz2smwW7jqz2/xxfvfZeHkPN6uauOXH1jK6vll9HoDVBRl9s3A3Pnx47jtpb38+tldfP0/m7j46ClUt7r44plzj5ilOWVuCZcsm4JVhDW7mvjwX97i+a+uHlFGxrX7W/n6fzZGTAZy/uJJXLJsKlsOdfTFRY/G8eas17/XHeQ7Fy5MirPWwVYX2+o6+cdb1by5t4WFk3OZVpiFCCyrLEj4+QYyOd9JfWcvh9p72V7bya0v7eHd6nZ+d/kyLlk2lae31vPpe9fzkb+u5e6rjyPLcXgsfWJzHRVFmX3jbiI5fX4Zj33hZC76w6sodVjRphkaEssuUUROAG5USp1rLn8LQCl1U1iZ24GXlFL3mcs7gdVKqUGNElesWKHWrVs38isYg6zZ1cTDGw7x1r5WDrX38tWz53F8BO1wpKYZrL0irY24f8SSkQ8Q7zHBmLas63CT57Sxt6kHXyDI9OIsirMz2FrbgccfZP6kXHrMcDAzirMJBJVhL93jRaG4fc0+3L4AyyoK+NiJM9hR18m9bx7AZrXQ4fLhDQQpy83gG+ctIM9pI9dpZzAZcLDBONLqYFARVMa9DSoIKkWX289jm2rZaoZGWlZRQKbdyr/WHeRf1x4/qMlDiHvfqOJ7DxvpjR02C2cuKOPkuSXc+ep+2l0+7r56ZT/HNaUUP39qJ49sOERth5svnzWPxVPz+PubB3htTwt2q3DC7GJuvPgo3q5q5asPbGR6cTb/uvb4YTvatXR7+PlTO3hgXQ1luRnMKcthydR8ajvcbK/rxCKGvfPSqfnc/vI+8jPtvPi11cM6l0YzFN7a18KVf3mLQFAxtSCTU+eVcOXK6SNy9hwNXtvTzFV/eQuAH1x8FB87ccagZZVS/OypHfzllf0IRobFN759JjlRPkjf2NvCFX9+k8uOncbSigI6e33kOm3kOm209vjY39xNQaaDFTMKcVgttLq8uLwBCrMcNHd76Oz1YbUIP39qB1MLMvns6XPIz7Rjswj5mXbe2NvC71/YgzcQJNth5bEvnsLMOBLefOVfG3jw3UN8cMU0zlpYTlCBPxgkEFRYRCjLzSCgFJtrOuhy+5lVmk2GzYrVItitgt1qwWYVrCKICEGl2FHXyfM7GjnQ4uqL1lOWm8H5iyfx+OZ6mrs9FGc7WP+9s4fURsPh6//eyL/X12ARCCojacW3zl/QbzbuoXcP8aV/beC8oyaxcHIedptQnuvkhv9u4pOnzOKb5y9IWv06en0cautlkbYRHhQRWa+UWhFxWxyC8AeA85RSnzSXPwKsUkp9PqzMY8DPlFKvmsvPAzcopQaVdCeyIBzC7QvwhfveHXdxhe1WwWax9Nlw2SyCzSq4fdGTOkwvzuLuq1f2G3iVUn1C7dbaDj5259t9GXSSTUlOBsfPKkJhvIBae7xcsmwKN39oWUyth9cfZEd9Jw6bhb+9XsXLu5o51N6L1SLce81KThxkFsDrD/LF+97lqa1GKu78TDuXHTuNa06e2S90T2Onm+KcjCFHcIjE+gNt3PriHho63eyo76I428GyigIUxvRjTZsRRunCJZO59apjRnw+jSYedtR3sr+phzMXlifdISpRKKW46A+vYrNYePAzJ8ZlM/vIxlp+8dQOfvGBpZw4O/bs4BV3vMkb+yKnNc512uj1BvAHo7/XF0zK5V/XnhAx0kWPx89b+1soyclgqRkVIhbBoOLnT+3g9pf3xSwbEibjYV55DvPKczl6WgFHVxSwrKIAh83CjvpOzrv5FT5w7DR+ddnR8R1sBPz11f386LFtfOT46VywZDJLp+VHnEH7waNbueu1qiPWP/aFk7XZQooZqSB8GXDuAEF4pVLqC2FlHgduGiAIf0MptX7Asa4FrgWorKw89sCBA0x0lFLsbOiiqcuDcOSgGUneGnRojVg2vmMOdtxIAl+k/TPtVqYVZtLc7WVSvpNsh5WGTg/tvV6mFmSSabdyoNVFQaadoILq1h5sFgtF2Q6Kcxx09PoozHLEdMzo8fip63DT6w3Q5fHFrcmGwbXZFgtYRMyfcc02i7Bgcm6fvZc/EGR7XRfzJ+UO66WslKKqxUWWwxozjq4/EGTDwXZEjCgj+Sn2km/p9lDb7mZGSRa5aeSopNGkI90ePxah3/R4Iul0+9hR18XkfCdleRl09vrp9vjJddooznbQ4w2wuaYDixgRNnIybLT0eCnJcZCfaaepy8O0wqykfFw0dXmo6+g1Nb0WrBbBFwjS3OU1x7McCrMcHGxz4QsE8QcU/qDCHwjiCyiCYYP0pHxn1GQeB1tdFGY7omrQE4XXH6Sh0x3TLC4YVDT3eCjMcuDxB6lr78UbiC+tsia5jFQQ1qYRGo1Go9FoNJoxSTRBOJ5PwreBuSIyU0QcwOXAIwPKPAJ8VAyOBzqiCcEajUaj0Wg0Gk2qiTmnoJTyi8jngacxwqfdqZTaKiLXmdtvA57ACJ22ByN82tXJq7JGo9FoNBqNRjNy4jKuUUo9gSHshq+7Lex/BXwusVXTaDQajUaj0WiSR0wb4aSdWKQJ0N5ykSkBmlNdCQ2g2yLd0O2RXuj2SC90e6QXuj3Sh+lKqdJIG1ImCGsGR0TWDWbUrRlddFukF7o90gvdHumFbo/0QrfH2GBsBGfUaDQajUaj0WgSjBaENRqNRqPRaDQTEi0Ipyd3pLoCmj50W6QXuj3SC90e6YVuj/RCt8cYQNsIazQajUaj0WgmJFojrNFoNBqNRqOZkGhBWKPRaDQajUYzIdGC8CggIneKSKOIbAlbd7SIvCEim0XkURHJM9evFJEN5m+jiLw3bJ9jzfJ7ROQWEZFUXM9YZyjtEba9UkS6ReRrYet0eySAIT4fM0SkN+wZuS1sH90eI2Soz4aILDW3bTW3O831ui0SwBCfjavCnosNIhIUkWXmNt0eCWCI7WEXkb+Z67eLyLfC9tHtkU4opfQvyT/gVOAYYEvYureB08z/rwF+ZP6fBdjM/ycDjWHLa4ETAAGeBM5P9bWNxd9Q2iNs+3+BfwNfC1un22OU2wOYEV5uwHF0e4xuW9iATcDR5nIxYNVtkZr2GLDfEmBf2LJuj1FuD+BK4H7z/yygCpih2yP9flojPAoopV4GWgesng+8bP7/LPB+s6xLKeU31zsBBSAik4E8pdQbyniS7gEuTXLVxyVDaQ8AEbkU2AdsDVun2yNBDLU9IqHbIzEMsS3OATYppTaa+7YopQK6LRLHCJ6NK4D7QD8biWSI7aGAbBGxAZmAF+jU7ZF+aEE4dWwBLjb/vwyoCG0QkVUishXYDFxnCsZTgZqw/WvMdZrEELE9RCQbuAH4wYDyuj2Sy6DPBzBTRN4VkTUicoq5TrdH8hisLeYBSkSeFpF3ROQb5nrdFskl2rMR4kOYgjC6PZLNYO3xH6AHqAOqgV8ppVrR7ZF2aEE4dVwDfE5E1gO5GF+LACil3lJKHQUcB3zLtLuLZEOkY98ljsHa4wfAb5VS3QPK6/ZILoO1Rx1QqZRaDnwF+Kdpk6fbI3kM1hY24GTgKvPve0XkTHRbJJtB3x1gKFIAl1IqZMeq2yO5DNYeK4EAMAWYCXxVRGah2yPtsKW6AhMVpdQOjKlFRGQecGGEMttFpAdYjPHVOC1s8zSgdhSqOiGI0h6rgA+IyC+AAiAoIm4Mm2HdHklisPZQSnkAj/n/ehHZi6GZ1M9HkojybNQAa5RSzea2JzDsJ/+OboukEce743IOa4NBPxtJJUp7XAk8pZTyAY0i8hqwAngF3R5phdYIpwgRKTP/WoDvAreZyzNNmyJEZDqG/VGVUqoO6BKR400P048CD6ek8uOQwdpDKXWKUmqGUmoGcDPwU6XUH3R7JJcoz0epiFjN/2cBczGcgnR7JInB2gJ4GlgqIlnmmHUasE23RXKJ0h6hdZcB94fW6fZILlHaoxo4QwyygeOBHbo90g8tCI8CInIf8AYwX0RqROQTwBUisgvYgfE1eJdZ/GRgo4hsAP4HfDakcQE+A/wF2APsxfA21QyRIbZHNHR7JIAhtsepwCYR2Yhhg3edaXcHuj1GzFDaQinVBvwGw2t+A/COUupx81C6LRLAMMaqU4EapdS+AYfS7ZEAhtgetwI5GDbEbwN3KaU2mdt0e6QROsWyRqPRaDQajWZCojXCGo1Go9FoNJoJiRaENRqNRqPRaDQTEi0IazQajUaj0WgmJFoQ1mg0Go1Go9FMSLQgrNFoNBqNRqOZkGhBWKPRaDQajUYzIdGCsEaj0Wg0Go1mQqIFYY1Go9FoNBrNhEQLwhqNRqPRaDSaCYkWhDUajUaj0Wg0ExItCGs0Go1Go9FoJiRaENZoNBqNRqPRTEi0IKzRhCEiHxeRVxN0rBkiokTElojjxTjXahGpGea+UespIt8Wkb9EKisiT4rIx4Zf8yHV88ci0iwi9aNxvuEiIjeKyN/N/ytFpFtErKmu11AZzbZNBbrvDs5ojl0aTarRnVyTEkTkZOAXwFFAANgOfEkp9fYo1mEGsB+wK6X8o3XesYZS6qdRtp0f+l9EPg58Uil1cqLrICIVwFeB6UqpxkQfP1kopaqBnFTXYziEt+14ZLSubzT7rojcDdQopb6bzPOMBD3uatINrRHWjDoikgc8BvweKAKmAj8APKmsVzqjNTNMB1rGkhA8VhGDtH83jKFnYth9dwxdYx/JrvNY6Z+asYPuTJpUMA9AKXWfUiqglOpVSj2jlNoEfeYJr4nIb0WkXUT2iciJ5vqDItIYPqUpIvkico+INInIARH5bmigFBGLuXzA3O8eEck3d33Z/NtuTl+fEHbMX4lIm4jsF5HzB5zrryJSJyKHzClPq7nNau7XLCL7gAuj3QQRqRKRb4nINvNcd4mI09y2WkRqROQGczr1LhHJEJGbRaTW/N0sIhkDjvlt8/xVInJV2PoLReRdEek07+GNEap0jXncOhH5ati+fVP9Ea7hJRH5pIgsBG4DTjDvZbuIHCciDeEvRhF5v4hsGORYEdtRRM4CngWmmMe+O8K+hSLymLlvm/n/tBj3/usisklEesw2LRdjurxLRJ4TkcKw8seLyOvmdW0UkdVh22aKyBpzv2eBkrBtA01JqszrOeLehpW92myjNhG5zryPm8xz/2GQ68kwty8OW1cqIr0iUhbr/pjt+BMReQ1wAbNCbWtuny0iL4hIi9m//iEiBQPu59fMenaIyL9CfdncfomIbDD7314ROS+szSM+TxGu8UYR+Y+I/F1EOoGPR9tfjOfx12Z994vI5we0Rfj1DXXMyRDjWa8Wo4/fJiKZEeocse+KyMUistU810tiPD/h9/IGEdkE9MgAwVIMfmvWqcO854tF5FrgKuAb5rkeNcsvNM/Rbp7z4rBjZZr36IB5rFcHuY73m/VaHGFbpLHKIiLfNNu6RUQeEJEic5cjxl0ZMMbIkc9NpP6pxHg+dovRp28VETHLzxHjmeww2/9fkfqURgOAUkr/9G9Uf0Ae0AL8DTgfKByw/eOAH7gasAI/BqqBW4EM4BygC8gxy98DPAzkAjOAXcAnzG3XAHuAWRhT1A8C95rbZgAKsA04tw/4lHnuzwC1gJjbHwJuB7KBMmAt8Glz23XADqACQ9P94sDjD7jOKmBLWPnXgB+b21ab9+Dn5jVnAj8E3jTPWwq8DvxoQPnfmOVPA3qA+WHbl2B8/C4FGoBLB9yH+8zrWgI0AWeZ228E/h7pngEvYZhDhO7dqwOucRtwftjy/4CvDnI/orXjaowp38H6VDHwfiDL3P/fwENRyleZ97IcY0aiEXgHWG7evxeA/zPLTsXorxeY9+9sc7nU3P5G2H0/FaNvDna/qkL3Ncq9vQ1wYvRzN0afKwur52mDXNOdwE/Clj8HPBXP/THbsRrDVMkG2Ae07RzzujMw+t7LwM0D7udaYApGX94OXGduWwl0mPtbzOtYEOt5inB9N2I8m5eax8mMtj/G87gNmAYUAs8Rve8OZcy5GXjEvNZc4FHgpkHqvZqwvouhCOgx74cd+AbGGOUIu5cbMMaFzAjHOxdYDxQAAiwEJpvb7sYcQ8xlu3nsbwMO4AzzOkLjwq3mfZhqXveJ5vXOCN0r857sAeZEub6BY9WXMJ6vaea624H7ooy7N2I+B1HGmYH9U2HMLBYAlRhj1nlm+fuA72D0EydwcjLeZfo3Pn4pr4D+TcyfOXjfDdSYg+gjQLm57ePA7rCyS8xBrzxsXQuwzBy8PcCisG2fBl4y/38e+GzYtvkYL1PbIAPyx4E9YctZZplJGEKTh7CXE3AF8KL5/wuYL39z+ZyBxx9wD6oGlL8A2Gv+vxrwAs6w7XuBC8KWzwWqwsr7geyw7Q8A3xvk3DcDvzX/D92HBWHbfwH81fy/7yU1yAsqmiB8A/AP8/8iDG3O5Aj1idWOq4kiCEc43jKgLcr2KuCqsOX/An8KW/4CpqBoXsO9A/Z/GvgYxgt44H3/Z5T7VUVsQXjqgH7+oQH1/NIg13QWsC9s+TXgo/HcH7MdfzigTF/bRtj/UuDdAffzwwP6z23m/7eH+tqAY0R9niKUvxF4Od79MZ7HT4dtO4vofTfeMUcwBNnZYdtOAPYPUu/V9BeEvwc8ELZsAQ4Bq8Pu5TVR+u4ZGB+JxwOWAdvupr8gfApQH14OQ0i80TxvL3B0hHOE+uLXMD8motRnNUeOVduBM8OWJxN93L2R2ILwwP6pCBNwMca7b5r/3wPcEa3e+qd/od+Ysz/SjA+UUtsxXj6IyALg7xjC2RVmkYaw4r3mPgPX5WBMQzuAA2HbDmBoOMDQUA3cZsN4iQ5Gn2e3UsplzrblYAhydqDOXAfGy+Rg2LkOHj5Mv/MOxsDyU8KWm5RS7rDlSNcSXr5NKdUTabuIrAJ+BizGuF8ZGFrBaHVZEkf9Y/F3YLuI5AAfBF5RStVFKBerHaMiIlnAb4HzMLR/ALkiYlVKBQbZbWB/itS/wLDxvExELgrbbsfQ+E8h8n2viKfeI6mXiGw16wbGzMoLQKbZ1vUYQtv/zLLx3J/w9u+HiJQBt2AIVrkY/b5tQLHwiAguDvfNCuCJCIedTvTnKRLh22LtP/B5jHZciH/MKcX4QF4fdl7B+JiLh37PsVIqKCIH6d/XB62rUuoFMUxkbgUqReR/wNeUUp2DnOugUioYti70XJVgaEv3Rqnr1zEE0FgRaQaOVdOB/4lI+HkDRB93YxHpngzsc6Fn9hvAj4C1ItIG/FopdecIzq0Zx2gbYU3KUUrtwNBkHGF/FgfNGJqG6WHrKjE0LGCYNQzc5sd46akhnusghgaqRClVYP7ylFJHmdvr6C8AVcZxzIHla8OWB9Yv0rWEly8UkexBtv8TQ+teoZTKx5h+F/oTrS7xcMT9VEodwjAdeC/wEeDeQfaN1Y6x+CqGtn+VUioPw0QBjrzG4XAQQyNcEPbLVkr9DKPNI933wejBEKJCTBpupZRSRymlcszfK6aw8wDGx+SVwGNKqS6zeDz3J9rzcJO5fam5/4eJ/94eBGYPsj7a8xSJ8DrG8zyG24mP5OMknGYMofiosPPmK6XijQ7S7zk27Vor6N/Xo45NSqlblFLHYpgKzMMQWCPtVwtUSH/nstBz1YxhehOpbUKcA3xXRN4frT4RznsQwyQq/JlxmuNBpGuL57mIe7xWStUrpT6llJqCMbP0RxGZE+/+momFFoQ1o46ILBCRr4rprCNGeKErMGzKhoSpzXoA+ImI5IrIdOArGJpIMKYBvyyGQ1MO8FPgX8oI29MEBDHsh+M5Vx3wDPBrEckzHUJmi8hpZpEHgC+KyDQxHK2+GcdhP2eWL8Kw44vm1HEfxkupVERKgO+HXWeIH4iIQ0ROAd7DYa1vLtCqlHKLyEoMQWkg3xORLBE5CsMucKgOJg3ANBFxDFh/D4aGZgmmhnIgcbRjLHIxhJN2817+3xDrHo2/AxeJyLliOGA5TQehaUqpA8A6Dt/3k4GLohxrA3C5iNhFZAXwgQTWE4wPng9hOE39M2z9SO9PLtBt7j+Vw4JXPPwVuFpEzjSfmakisiCO5ykqcT6P15vnK8AwcRkx5gfHn4HfmppyzHOcG+chHgAuNO+HHeMjxYNh8x8TMZwnV5n79mAIsyGtfgP9x7O3zDLfMPvcaoz+eb95HXcCvxGRKWbfPkH6O+BuxZhFuFXCnOzi4DaMZ3m6WedSEbnE3BZp3N0AnCpG3O184FtDONcRiMhlctgZtA1DiB5sZkgzwdGCsCYVdAGrgLdEpAdDAN6C8UIYDl/AGOz3Aa9iCAChabA7MbSQL2PErnSb5VFKuYCfAK+J4VF9fBzn+ijGFP42jAH2Pxj2b2C8HJ8GNmI4Xj0Yx/H+ifEy32f+fhyl7I8xhK5NwGbzHOHl68061QL/wLA/3mFu+yzwQxHpwhCgH4hw/DUYTjHPA79SSj0TR/3DeQHjxVkvIs1h6/+HOVU6wIRgINHaMRY3YzjpNGP0p6eGVPMoKKUOApdgfKg0YWi7vs7h8fNKjP7ciiFg3hPlcN/D0MC1YYQM/GeUssOpa0jwmQI8GbbpZkZ2f34AHIPh9PY48fXtUJ3WYnxY/dbcfw2HNaLRnqd4iPU8PoPxvLyLYZ7hJzEC0Q0Yz8qbYkSweA5D4x4TpdRODI367zHa4yLgIqWUN85z52FcWxuGmUML8Ctz21+BReZ49pB5zIsxTGeagT9i2I2HxoWvYYwlb2P0358zQC5QSm3E+Kj+s4RF0InB7zBmoJ4xx5w3MZ6RiOOuUupZjA/vTRiOgI/FeZ7BOA7j/dJt1uN6pdT+ER5TM04JecJrNJpRRkSqMJx1nkt1XZKNiOzFcFwa99eqSU9MIe42pdT0mIU1Gs2EQWuENRpNUjHtCxWGxlijGRXEiJF7gYjYTHOO/2MQ0xyNRjNx0VEjNBpN0hCRl4BFwEcGeK5rNMlGMEw6/oVhH/04hlmQRqPR9KFNIzQajUaj0Wg0ExJtGqHRaDQajUajmZCkzDSipKREzZgxI1Wn12g0Go1Go9FMANavX9+slCqNtC1lgvCMGTNYt25dqk6v0Wg0Go1Go5kAiMigmV5jmkaIyJ0i0igiWwbZLiJyi4jsEZFNInLMSCqr0Wg0Go1Go9GMBvHYCN+NkVlmMM4H5pq/a4E/jbxaGo1Go9FoNBpNcolpGqGUellEZkQpcglwjzLCT7wpIgUiMtlMf6mZ4HR7/NR3uHF5/XR7/DisFhZPzcdpt/YrFwgqLAIikqKaJo/mbg/b6zqp63AztSCTE2cXp9V1KqXYfKiDug43+5t7yM6wMa8shxynDaVAKSjOcTClIDPVVdVoxh1KKZq7vZTmZsQubJbv7PXT6fbR7THGVQEWTckjyzH4K72tx8u+5h7aXV6WTMunLNeZoCvQhPAHguyo76Io20F5nhOrJX3Gec3gJMJGeCpGytEQNea6IwRhEbkWQ2tMZWVlAk6tSVf2NHZx0xM7eGlXE4Fg/xB9DquF85dM4uhpBXR7/Dy2qZb9zT1Myndy5crpLKsoYNXMIly+ABsPtjOrNJvJ+WNLCDvQ0sNta/bS7vLx3PYGfIHD9+ArZ8/jC2fMiUsY3niwnRd2NDJ/Ui7zynOZU5ZDj8fPrS/u4eiKAs49atKI6rmroYsfPLqV1/a0xCx77amz+PYFC0d0Po0mHvyBILe/vI+ttR1cuGQKx80sHBOCm1KKrzywEYBffGApdmv0SdfGLjc/eXw7D2+o5YtnzOEr50TP0qyU4vr7N/DIxtojtmXarVyxspKvnzufTIehaPD6g/zr7Woe21THW/tb+8rmZtj4zoULuWxFRT9hraHTzT/ePMC2uk7mlefy9XPnx/3RfrDVRUevj8VT8+MqP1YJBhUSQWnjCwT5xN/W8fKuJgCKsh189ITpfHb1HBw2HaArnUmEIBzpKYkYnFgpdQdwB8CKFSt0AONxyuaaDj50xxs4bBY+dcosFk7OJdthIyvDissT4NU9zfzzrWoe3mAM5qtmFrH6pDI2VLfz86d2AFBRlElzl5deXwCrRTjvqEn89L1LyM+yp/LS4uZfbx/kvrUHmVqQyeXHVXLBkslMKXBy83O7+c2zu6jvdPOTSxf3Dab+QJDr/r6epm4vp84t4XOnz+Ghdw/x7f9tJvw74sIlk9nV0MXuxm4A7r76OFbPL4urTkop9jYZHxyBgOK3z+3i3jcPkO2w8r33LGJ5ZQHzynPpcvvY1dCN2xfAIoIA3394C2t2NmlBWDMqPLyhll8+vZPCLDtPbK4H4H3Lp/Kz9y9Na6HioQ2H+N+7hwDocvv4zYeWkeeMPGZVt7i45NZX6ej1cXRFAbe8sIeLl01hTlnuEWV3NXThtFl5eMMhHtlYyxUrK1leUUCO00au04bXH+TJLfXc+dp+Grvc/OHKYwgEFR/+y1usrWpldmk2Xz5rHkum5ZGTYec3z+7kmw9u5qdPbCc7w4bVIuQ67dS0uejx+Jmcn8lz2xuZWZLNZSsqol6zUoov3Pcuj20ydF/vWTqZH1+6mIIsxwjv5mEau9z8861qLCK875ipTCvMStixh8ITm+v47kNbEOD42cX89FLjndTu8vLxu95mw8F2vnzWPEpyHby0s4mbn9tNu8vHjRcfldR6vbWvhX3NPVyxUisYh0MiBOEaIPxJmQYc+bmqmRD4A0G+8d9N5DptPPy5k5mUf6QW56xF5Xz5rHn4gkF6vQEqig4Paofae3l9TzPPbW+gZG4GZy0s5839Ldz1ahVNXR7uv/Z4LGNguumd6jaWTsvnkc+f3G/9ry87mrLcDG5/eR8LJ+fxkeOnA/DPtdU8t72RSXlOfv/CHh7eUEtNm4tT55Xy2w8uo6qlh0c21vL4pjpKcjK47cPH8t2HtvD3Nw/EJQi7vH4+ftfbrN3fSn6mnaBS9Hj8XLmqkq+cPZ+i7MMvrZwM2xEa+PXVbfzllX34AsGYWi6NZqRUt7oAeOvbZ7GuqpWXdjVxx8v7aOhyc+81q9JqDPjBo1vxmzM+962tZnllARcfPYUfP76di37/KkunFdDrDfDRE6Zz6jwjetNjm2q58ZFtBBU8ef2pFGU7OOGm5/nX2wf5zoWL+h2/qcvDe255FW/ASMx40dFT+Mmli4+4B2cuLKeyKIvfPLuLj53Yyva6TtZWtfLT9y7hylX9BaR/fvJ4ntxSzyu7m/D4g/iDinaXlwWTcvnc6XOYWZLNWb9Zw1Nb6mMKwjsbunhsUx0fPr6S0hwnv39hN1UtPTz8uZMTYhrQ1OXhgt+9SkuPB4Dfv7Cbj584g29fsHBUTcz2NHbxpfs3sHByLnPLc3no3UN09vr4xMkz+dNLe9lW28kfrlzOe5ZOAeCqVdP5waNbueu1Kk6bV8rpC+JTWAyFdpeXb/9vc9/H4oVLJw/64aUZnEQIwo8AnxeR+4FVQIe2D45Op9tHpt06LgWKTYc62F7Xya8vOzqiEBxiMM3u1IJMLltR0W/wPX1BGRWFWXz3oS2sr27juBlFCa93IvEHgmw82MGHjjvyBWKxCDect4BtdZ38/MkdnD6/lI5eHzc9sYOT55Rw7ydW8tvndnPL87tZMb2QW688huwMG4XZDpZXFvJ/Fx3WLGw+1M6fXtrLnsauiFqkcH719C7WVbXy9XPns7uhC5vVwjUnzWTRlLy4rmleeQ6+gKKquYe55dHPpdGMlDaXl/xMOw6bhRPnlHDinBJyM2z8+tldVLe6mFGSneoqAsZYftdrVQDYrcLFR0/hB5ccRa7TzqLJefzkie28ta8Fm0W45u63uXjZFPKcdu598wCLp+bz40sWM3+S8Tydvaicf75VzRUrK5lVmtN3jnveqMIbCPLh4ytZPa+MMxeWDSoAfvKUmfzhhT38480DPL+jkZPmFHPFysjj0IVLJ3Ph0smDXtvcshz2N/fEvAev7m4G4LOr5zClIJOZpdl88b53+efa6r4P/ZHw48e30en28ejnT6Yo28Fvnt3Fn1/ZT3mek0+eMmvEx4+XW57fg9Nu4a8fP46SnAyOnV7I9x/ewiu7m8mwWbjpfUv6hOAQN5y3gDf2tvD1/2xkzddPJzsjfpHL7QvwdlUrJ80uGfTD71fP7OTprQ3MKM6iqsXFlkMdnDi7ZETXORGJ2Soich+wGigRkRrg/wA7gFLqNuAJ4AJgD+ACrk5WZccDSilO+8WLVBZn89BnT0wrp6lE8M6BNgBOmZfYh/G9y6fy48e38ejG2rQXhHc2dNHrC7C8siDidotF+NEli7nwllc46zdr8PqDFGVn8OsPHo2I8OWz5nLNSTNiTi1efdJM7nn9AN9/eCt/uPIY2lxeMu3WI5zaAkHFo5tqOfeoSXzu9DnDuqZ5pvC7s6FLC8KapNPa4+03SwGwcLLx0dbp9qWiShHZa5oo3f6RYzlrYXk/DeiqWcV9M0Jdbh/ffHAzr+1ppqXby+p5pdx8+TJyw7R333vPIi685RU+dtdafmQKyI9vquO2NXu5YMkkfnzpkpj1yXLYWDWriIc21GK3Cj+4ePGw3zGVRVm8vLsJpVTUY7yyu5nZpdl9485FSyfzwNsH+enj2zlpdnE/oX6odLp9PLm5nquOr+yzPf7lB5ZS297L3988MGqCsFKKN/a1cPqCMkpyDKfGK1ZWcvr8MqpaelgwKTfieO20W/np+5bwvj++zj/eOsC1p86O+5zf+d8W/vtODd++YEHE/R58p4Z/vFXNx06YwRfPnMsxP3qWzTVaEB4O8USNuCLGdgV8LmE1GufsbuymzeWjzdXO01sbOG/xyJyd0o31B9qoLMpKuGNLdoaNk+eU8uqe5oQeNxnsqOsC4KgpgzuNzCjJ5pEvnMzdr1VRnOPgqlXT+7zGRSQu+7qSnAy+dcFCvvPQZo750bN96x/7wsn9HFbWH2gzpheXDK79icXs0hysFmFbbecRWg+NJtG0ubwUDpg1yss0ljt7/amoUkT2mILw3LKcqGYAuU47t15phNgPBlVEDd+UgkzuvnolH7trLR+/6+2+9UdPy+em9y2Nu04nzynhld3NfPnsecwpG74QWlGUhdsXpKnbM+h4Hggq1h9o49Llh8cEEeHXHzya03/1Eret2csvPnD0sOvw3LYGvIFgvzFHRDhrYTk/fGwbh9p7mToK0Wz2N/fQ1OVh1czifusn5TujznwCHFNZyImzi/n7m9VxC8K7Grr47zs1lOQ4+MVTOzluRhHLKwv7tj+84RBf+/dGTpxdzDfPX4DTbmVaYSabajqGfnGa1GWWm6i8HibIPb65blwJwkop1h1o46TZxbELD4NZpdm8vLtp0BdJurCroQuH1cKM4ugOHbNLc/jRpYtHdK4rV1WycHIub1cZHuE/fWIHr+xu7icI/+2NKnIybJwxAhs1p93K0dPyeX1v7OgSg9Hh8o0ZZ0dNamnt8TG1oL+AkZdpvK7SSiPc1IPdKlQWxe+8FW3sOrqigFdvOIO1+1vYcLCDC5ZMYn557pC0uh87cQbzynNZPT9iNtm4CV3TwVbXoILw3qZuuj1+llcU9ltfnufkkmVTeOjdWr77nkXDtlt9eVcTpbkZLK8o6Lf+BPMd8/2HtvC7K5aTMwSTg+Gw1oy4sXLm8GYjz188ie89vJUDLT1ML45t1lPdYtjI//qDy/j2g5u54s9vsqyigLYeHwsm5/LYpjqOm1HEXz56XF8o0oWT89jV0DWs+k10xp+RapqzZlcTFUWZvG/5VF7dfWRosbHMO9XtNHV5+hxCEk1FURZev6GhSGd2NXQxqzQb2yjZgC+vLOTaU2dz7amzmVOWw9r9h4XV/c09PLG5jo+eMH1I9mmROGVuKZtq2ml3eePeRynF1XetZdVPn+PoHz7Dzc/twuMPjKgemvFPW4+XwgGzIiFhqrM3fQThPY3dzChO7LNufLSW85Wz57FgUt6QTRucdiunLxjcjjheQk7MIcfFSGyobgdgWQQzsA8cW0GvL8BLO5uGXYc9Td0snJx3xMfD/PJcirIdPL+jkYc3HBr28ePltb0tlOZmMLt0eLbpp8w13omh0GqxqO90A4Zvxn2fOp73Lp+GP6AozLbzwvZGzllUzp0fP64vTB5AeV4GzWn+bkxXtCA8ihxsdbFmVxMXHz2FU+eV0ubysa22M9XVShiPbarFYbNw9qLypBy/otCYAos2MKcDuxq6+2xqR5uVM4tYV9WGz/Qwf8QMUfexE2eM+NinzC0hqOCFHY1x77PhYDsv7mzC6w8yvTiLm5/bzepfvkRNW3q3oSZ1KKVodXkpyhkgCIdMI9JII7yzoZO55cM3P0hnphVmIgL7m6MIwjXt5DptzIyg5Tx6Wj5ZDivrq1oj7BkbpRT7m3qYFcEx0mIRnrr+FAC63ck1lQkGFa/vaebkOSXD/riYXpxFZVEWz2xriKt8Q6cbi0BpTgaVxVnc9L4l/OczJ3L/tSew+Qfn8qcPH3uEYqM0x0mby4fXHxxWHScyWhAeRe5/uxowwqqEHD+qWmJ75Y4VXtvTzAmzivs5gCSSirCpunSlo9fHofZe5qXo5Xj2wnK6PH6e3mqE03lqaz3HVhZSnjdym+1jKguZW5bDH1/aSyCo8AWC3PriHvY2dQ+6z7/X15DtsPLKDWfwwldX86erjqHb4+e0X77E+//0Or95ZmdaCTaa1OPyBgwH0gEa4WyHFYukj41wc7eHg629HD2tINVVSQpOu5WZxdnsrB9cWfPmvhaOnV4Y0dzDZrWwrKKAdaYD9VBp6PTQ4w0MqoUNOVMmW/DbUd9FS4+Xk+cM3wlNzPjHr+xu5kAc7/z6DjclORlDmmkoyTXuRyjMnCZ+tCA8SiileHxTHSfNKWFKQSaTTMGkwZwCGesopTjY2jsi54xYhJwiDrb2Ju0cI2WNOfW1alZy7KRjcdq8UqYXZ3Hbmr08v72B7XWdCbNDt1iEL589jz2N3fzm2Z187d8b+eXTO/nO/zYPus+mmnaOmV5Ijhm0//wlk/nXtSdw7amzONDi4pYX9vDiEDTMmvFPa49helM4IGqEiJCXaU+bD6eQWUC4E9N4Y8HkXHbUR7Y7rWlzsa+pp2/aPxIrpheyva6THs/QP172mR/Yg0WdsFktWC2CJ8mC8GumX89JIxCEgb5kF6HEI9Go73THdMIbSKkZzaKpSwvCQ0ULwknG7QsQDCo2H+qgqsXF+YsNz/28TBsZNguN46TTNncbWeBC5gvJwGm3Up6XkdamEU9vrackx8ExKXo5WizC18+dz5ZDnXzib+uYU5bDVatGHsszxPmLJ/He5VO59cW9PLyhFosYjiT1HUd+0PkDQXY1dPfNfoRYNCWPG85bwKNfOAkwNIAaTYg+QThC5JQ8pz1tbIQ3HGzHahGWjOOUwgsn5XGgxUV3BEH2FTN+8KlzBxcQZ5flEFRQF2F8iMU+M4bxzCgxox1WS9J9Dl7d08ycspwhC6YDKc9zUpTt4FB7bEVOQ6d7yLN4oahD2k546OioEUnmyj+/yf7mHgqzHBRm2Tnf1M6JCOV5znGjEQ4Jp5UxIiWMlJkl2exvHnwqPpUopXh1dzPnLCpPSEal4fKepVPItFvZfKiDy1ZU9HOoGCkiwi8/sJSLjp5MToad8rwMTvvlS/x73UG+cObcfmWrWnrw+oMsmBTZXjrLbgw/WhDWhBPqD5EiAeRl2uhMsk1oPCileGJLHcdUFiT0+Uo3FpgfsdvrOo+I3/74pjoqijKjzgKGYu62dHuGPFsY+iAKCXiRyLBbkqoR9vgDvLW/hcuPS0zq4vI8Jw1xfBQ0dB4Zqi0WofukNcJDR2uEk4jbF+Cd6nbaXD5c3gB/+vCx/ab7yvMyxo0gHHJ+qkhyDvjZpTnsaezGCF+dXjR0eujo9fULXZYqzlxYzpfOmpeUGJs2q4UzFpSzcmYR04uzOWlOMfe/fZDggAgo2814ygsmRc5e53QYw0+vN/WCjSZ9CDl62q1Hfkymi0Z47f5W9jX18MEY6YfHOsfNKMRuFZ7eUt9v/aH2Xl7b28z7j5kW1YGsOCdkt2oItesPtPHDR7fR1hM78ky3x4/TbomagTXDZkmqjfD+5h7cviDHTE/MDF95XgYNXdHf+W5fgI5e35A10CXaNGLYaEE4iexrMqZ2brliOW9++0yOH2A3WpbnpLFzfHTakAPbtCQLwnPKcuh0+2nujj+E12ix04zhmKqIEani8uMqOdTeyysDkp1sqmnHYbUwuyzy1KbDtPHr9WmNsOYwhwXhI19Pec70sBF+y4wrO5IkNWOBgiwHp88v4+GNtf0Ezl88tQOLCO8/ZlrU/YuzD2uEm7o8fPD2N7jztf187+EtMc/d5faTkxHd8TrDZk2qRrjFfM+UR9FKD4VJeU7qO6K/80NmZkM1jXDareQ5bVoQHgZaEE4ioeDW8wcRjMpzx49pRE1bLyU5jqRPE4am10IZndKJ3X2C8PgMpzQY5xxVTmGWnfvXVvdb//KuZlbMKCTDFrlPiAiZdqs2jdD0I6ognGlLi6gRvb4AdquMODb3WOCq46fT1OXh+vvfZXNNB49urOXhDbV84Yw5fZF8BqMwy46I4UPyzLZ6AkHF+Ysn8dimupjJH7rcPvKc0e+vw5ZcG+GQvW1xTmIE4fI8Jy09nr4+HolQDOFJw4j0M6UgkwNp7EOTrmhBOInsbOjCZpFBjf3L8zLo8QYiOiKMNTrdPvIzk581bLbpQbwnSsiuVLGzvouSHEfCBs2xQobNygeOncaz2xr6tBH1HW52NnTFTK6S6bDi1hphTRjegGFi47AdOeWen2mnvTf1s0FuXwDnIB94443T5pXy9XPn8+y2Bi76w6t84b53OXpaPp87fU7MfW1WC4VZDpq7PTy8oZZZJdn84JKjsEjs6AndHj85MQThDJsFjy/5GuGSnNgp7+OhPM+JUtHNF0LKsUn5Q3+PLJycx85BonxoBkcLwklky6EOZpVm47BFvs2hOIjx2EulO92eQNLTXILxlWwRaEyhJr2h083z2xuOsFPeVNNxRISEicKHjqvEH1T8950aAP697iBAzDSvWQ6tEdb0xx9FI1yY7cDtC9Kb4j7j9gVwjmMnuYF87vQ5vP2ds/jlB5bytXPmcftHVkS13Q2nONvBP96qZu3+Vj58/HTKcp2smlnM45tqo/p6dLv9Md8pGTYL3ija1ZHS3O3BZpFhp4geSEi4rY/y/hquaQTAgkm51HW4h5T9U6MF4aTR4fLx5r4WVs8vG7RMKEf4eNCIuTz+UZkmtFiEXKedjhQ6zHzvoS184m/r+MGj2/rWHWx1sbOhi9OSlF463ZlTlsPKmUX87fUq3q1u49aX9nD+4kmDOsqF0KYRmoFEM40IJdloS/GL3u0L4rRPrNdnYbaDy1ZU8Pkz5g7JkSvkIH76/FKuPmkGABcunczepp4+v4pIdLn95MbUCFuTrhEuynZETBgyHMpyjfsWKdxkiPpON9kO67ASU4WifAwW+1kTmbieZBE5T0R2isgeEflmhO35IvKoiGwUka0icnXiqzq2eHZ7A76A6guXFonDgvDYT4nYPUqCMBjTo6kShD3+QF+A9btfr+pLoBHK5HbmwuSklx4LfPP8BdR3unnvH18nJ8PO9y9aFHMfbRqhGUjINCKSIFxgCsKtKZ5F6/UGyLRPHI3wSKg14+ZevGxKX4SJ8xZPwiJGCLbB6PbEdpZLto1wS48noaZu08w4+4faBo8l3NDppnyYMYtDoSp31A2eDVBzJDEFYRGxArcC5wOLgCtEZOAb7nPANqXU0cBq4NcikhijmjGIUoq7X9/PzJLsqOk3QxoFd5IDgo8GPV4/2aM0VZifmboQSm/ua6XHG+C2Dx/D7NJsvv3gZpq7Pdzx8j6OnV4YNfj7eOeYykJ+f8VyPnrCdP7+yZVMzo8duk1rhDUD8fkHD58WMidrd6U2coTbH+hTZGiiEzIrWD3v8OxoSU4Gx04v5NUBkWbC6XL74tAIJzeOcHO3N2H2wWC8u3IzbH3hRiNR3+EelqMcQFluBrkZNvY3x07jrDlMPBrhlcAepdQ+pZQXuB+4ZEAZBeSK8bmXA7QCY98DbJi8sKORLYc6+czq2VGnVMaTaUSPJzBqGuG8TFvKNMJV5gCzYkYRP3v/Ug6197Lqp8/T2OXhhvMWpKRO6cR7lk7hh5csjmkSESLLYU25vacmvYhqGpFtCFWtKTaN6PVOHGe5kXLHR4/lrquPOyJl9uzSnL6wmwNRStHticM0wm5Nahzhlh5PX3zeRCAiTCvK4mAUjXBNW29cSoTBjj+9JIuqFh05YijEI7lMBQ6GLdcAqwaU+QPwCFAL5AIfUkod0TtF5FrgWoDKysRkakk3djd08dMntjOrJJv3Lp8atWxoIB0vphGj4SwHxld1NBurZNJjJn/Idtg4bkYR935iJc9ua+DcoyaxcmZRjL01A3HarTqOsKYf0QThUNrlVDsYu/3BUYmSMx6YVpgVMb58RVEWzd1eeiKY1bm8AYIqcnbBcJKtEW7p9lKcndjJ7YrCzEE1th0uH41dnhGF4JxenM3WQx3D3n8iEo9GOJJKc6Cr57nABmAKsAz4g4gcoRJSSt2hlFqhlFpRWjr+nIo63T4uvOVV9jb18L2LFsX0qu0zjRjjgoAvEMTrD46qjXCq0qz2egOIHG67U+aW8sNLFnPSnJKU1GesozXCmoEcthGOHD4N0sBZzhvAOUg0IE18VJoxiA9GMBPoMsf3WOHTkmkj7PL6cXkDCQ+HWVGURU1bb8SIGbsaR56UaUaxcfxosYo1/YnnSa4BwvNITsPQ/IZzNfCgMtgD7Acm3Dzxhup2vIEgv/3Q0ZweJVpEiPFiGuHyGPXPGiUb4bwUOsv1eAJkO2xR04pq4ifLYcOlUyxrwvAHgtitEvEZs1kt5Gfa00AjHEh68qDxTkgQro4wjd/tMcb3WJETkhlHOBRDuDiBNsJgaIR7fYG+tNPhhGIAz5s0EkE4G39Q9TkpamITjyD8NjBXRGaaDnCXY5hBhFMNnAkgIuXAfGBfIis6Flh/oA2LwFlxRg7I6HOWG9tfbt2mIDNaphF5TjtefzAlHxAur1+/ABOINo3QDMQXCEadTSvKdtCaYmc5bSM8cg5rhI8U2EIa4dyYphHJS7EcElQT6SwHMMN0qN7dcGRSqN0NXeRk2JgyzKgR4cfXDnPxE1MQVkr5gc8DTwPbgQeUUltF5DoRuc4s9iPgRBHZDDwP3KCUGtwddJzyTnUb88pz447/F9IIe8a4INBjZsYbTdMIICWRI1zewKhFx5gIZDms+AJKT+Np+vAFVFRBuDArDTTCPq0RHikFWXZyBomgEK9pRCihRrTEHMOlJZReOTuxphHLKgoAQ14YyNtVbSyanDeiGcdZpiC8pzH9sq+mK3FJLkqpJ4AnBqy7Lez/WuCcxFZtbNHp9rF2fytXrIzfCfCws9z4EIRH01kOoKPXR9kww8wMF5fXT5ZjdK5zIhCKxdrrC8SdqUozvvHG0AjnpYVpRLBvRk8zPESEomxHxLYMzRLFitUcytrq8QcTHs4uWaYRBVkO5pTlsP5Af0F4X1M32+o6+d57Ysdfj0ZxTgYlOY6IGmdNZPSTnCAe3ViLxx+MGSkiHLtVsMjYjxrRY9oIj7ZGOBV2wi5vYNRsoScCIa2aWzvMaUx8/mBER7kQDmtyIwXEIhBUeP1BnVAjARRk2WmLYOYSUg7FEm4zwgThRNOUJI0wwIrphaw/0Nbn6OcPBLn5ud0AXLhk8oiPP7csN2rWPk1/tCCcIJ7d1sCs0myWTsuPex8RwWkf+5m1uk2N8Gg6y0FqBOEeb4CsURL4JwKhPqOTamhCxLIRdpjT4akiJLzohBojpyDLQXuECCAhB7hYaawzQuaFSYgc0dLtJdthTYoJzIVLJ9PR6+Mfb1YDcN/aah7ZWMuXz5o3pPTVgzGvPIc9jd1JMRkZj2hBOEFUNfcMy7bHabeO+cxyo20aEQqyHhLARxOXZ/Qy6E0EQn2m051a5ydN+mDYCEfRCNssSU2iEItQuD+tER45hVl22iMoNEKCbUYMh8SQRjgZ/aGlx0NJbuK1wQAnzynhlLkl/OypHTy84RD/eKuaJVPzuf6suQk5/oLJeXR7/OzTDnNxoQXhBOALBKlp62VG8dDT6zptljFvGhEKfzVaphEh4SklgrBXO8kkklDK3EjTo5qJSSyNcEaKBeFQlJ9Y2kpNbAoGsfd2x6sRTqJpRDKSaYQQEX53+XKWTSvg+vs3sKO+i48cPz1hxz9xdjEAr+6ecDELhoV+khNAbXsv/qBievGR2XNiMT5MI0I2wqMjIIYE7p6UCMJ+srWzXMIIpV2NND2qmZj4AsE+J6hI2K2pNY0IaYS1acTIKchy0On24x/QnkO2EU6CMqmxy53wZBrhFGU7uPeTK/nSWXP53eXLuGzFtIQde3pxNpVFWbyyuylhxxzP6Dd6Agjl9Q7F7xsKGXbrmNcIx+vhmyiyzPOEBPDRxLAR1i/ARFFkpsxtTXEUAE36ECt8msOaYo1wnEKaJjaFWWYoTLe/b3YIDA2vRcBmiW5qGDKdSLR5YSCoqGpxxZUYayRk2Kx86ax5STn2yXNLeGRDLYGgwhrjPk50tEY4ARxoMexwhqcRTl6KyNGi1+sn024dtWxrFouQ7bCOukbYb6aSzrLr78dEkZdpxyKkPByWJn3wBmJEjUi1aYQWhBNGQVbINKr/8+/2BXDG8U4JzQ66EqwUOdjqwusPMrssJ6HHHU2WVxQYdsJNOoxaLLQgnACqW1w47RZKhzGN4rSNfdOI3hQEl8/OsI26IOzyja4JyETAahHyM+20atMIjUk8USP8QUUwmBqP+NAMnnaWGzkFpkZ4oGmU2x+I60MjNBaHUjInir2m8Di7dOwKwqHEHRtrOlJbkTGAFoQTQGOXh/I857A0ohn2se8s1+sd/Zia2Rm2UXeWC2kddEKNxFKY7aCtRzvLaQziEYSBlNkJ9/ZphPXrc6QUhjTCA55/jy/YZ/8bjdwMQ5BOtJlcKCvbnDEsCM8qzSHbYWVTTXuqq5L26Cc5ATR3e4alDYbxoRE2prFGtytlZ4y+aUQoOoZOqJFYirIc2kZY04fPHyN8mjV5kQLiYbTDRY5n+jTCA0KouePMFBdKwdyd4PCLexq7KcnJIN+s31jEahGOmprP1trOVFcl7dGCcAJo6vJQMlxB2G4Z83GEU2Ia4bD1ZbQbLUJJH7QgnFgKsx1H2AhqJi6+YOzwaZCc2LHx0K0F4YQRcpBr7fH0W+/2BeLSCIdMI3oSnJBnb1M3c8qG7vyebswuzWa/jiUcEy0IJ4Cmbg+lwwy87RwPUSO8gVE3jcjJsNHjHV2NcEgDFS20k2boaI2wJhxfINin9Y1Eqk0jQoLwaMVNH8/kZNhw2Cw0d/d//j3+YF/WuGhk2Kw4rBa63Il7Fyil2NPYzZwx7CgXYkZxNq093pRkYR1L6Df6CPH4A7S7fCMUhMeDRnh0XwqpcJYLxbqMpq3SDJ2QRlinA9VAyDQiDkE4haYRInpmKBGICKU5GTR3HakRdsapcMjOsCbUWa6520un2z+mHeVChEK6VmmtcFTi6mkicp6I7BSRPSLyzUHKrBaRDSKyVUTWJLaa6UuL+SU7XEE4w2ZJma1bojA0wqNtI2wb9TjCAdNLXcdkTCyT8jLwBRRN3Z7YhTXjHl8giN0WzUbYEEBTaRqR47CNWrjI8U5JjoPmATNCHl98USPAsBNOpJlcn6PcONAIzwwJwi1aEI5GTDWeiFiBW4GzgRrgbRF5RCm1LaxMAfBH4DylVLWIJDcKdRrRbL68h2sjbLNKn4A1Vun1pcI0IgVxhM12ihXkXTM0DmstXJTlOlNcG02q8QaC2CzpqxHudvu1WUQCKcnJoK7D3W+dxx9f1Agw/EUSaRqxZxyETgtRWZSFCNpOOAbx9LSVwB6l1D6llBe4H7hkQJkrgQeVUtUASqnGxFYzfWkyp3SGqxG2WiwEgmpMTwunwlkuy2Gj1xcY1Y8IrRFODlproQknVorlwzbCqTEp6/H6dSzxBFKc4+hTKIVwD0EjnOtMrJncobZe7FZhcv7Y/yh32q1UFGaxvU5HjohGPILwVOBg2HKNuS6ceUChiLwkIutF5KORDiQi14rIOhFZ19Q0PnJg15pfspPyhvfQ2E2haixrhd3e+AetRBHy2B5Nh7nDGmFtI5xIphZkYrOItmPTAKEUy+kbPq3bE9ARIxJISU4GLT3efglS3L5g3CE5Ex1TvrnbiAI1XkxfVkwvZP2BtjGtbEs28fS0SL1h4B21AccCFwLnAt8TkSMSaCul7lBKrVBKrSgtLR1yZRNBVXMPH7z9Db7/8JaEHK+m1YXDZqFsuBphc8D3j2FBOBWmEaGpydE0jwgEjRev1ggnFpvVQkVRltYIawgGFYFg+jvLheLXakZOSU4GgaDqF0vY4w+QYYvTRjjBjtMhQXi8cNzMIpq7vVS1uFJdlbQlHkG4BqgIW54G1EYo85RSqkcp1Qy8DBydmComlt8+t4u1+1u5540DCflCOtjmYlpBJpZhCke2Ma4R9gWC+INq1D2o++JHjqLDXJ9GOIq2SjM8ZhRnsb9ZD9QTHV8wdmSWkEbYF0jNmNnt9pOts0smjBJTidQSZh4xFI1wToaNroQLwo6EHS/VHDejEIC39rWkuCbpSzw97W1grojMFBEHcDnwyIAyDwOniIhNRLKAVcD2xFZ15Oxt6ubRjYdl+ERMrdW09TK1MHPY+1vNafaxqhE+nG50dAXhkAa6N8GB1KOhbYSTR0VRFrXtvamuhibFhITbuOIIpzJqhDaNSBghoTMUNUYphds/hKgRidYId3nHlUZ4dmkOUwsyeX7HhHHdGjIxBWGllB/4PPA0hnD7gFJqq4hcJyLXmWW2A08Bm4C1wF+UUomxPUgg5XlOvnbufD67ejZAQuL3Hmx1UVGUNez9Qxphf4qCw4+UkCCaCmc5OJz2eDTwB3TUiGQxKd9JR69vVNtTk374TOE22qxLejjLaUE4UYSEzlBSDV9AoRTxR43IsOHyJsZxOhhUhkZ4mKaO6YiIcNbCMl7Z3RSX4miszk6PhLh6mlLqCaXUPKXUbKXUT8x1tymlbgsr80ul1CKl1GKl1M1Jqu+IyMmw8dnVc/oE194RCsLdHj9tLh8VhSMQhK1j2zSiTxAebY2wKXiPtA2HgtYIJ4+Qs2n9gDBKmolFaJYumn1oKjXCSim63dpGOJH0CcJmBCaP3xjT47URzjXbojsBIdQ6en34g2pcaYQBzjlqEm5fkOe2NwxaxusP8tE71zL720+wrXZiRZmYkO7viZpWP2A690wbgWlEn0Z4rArCvtQIwiGb5NE0jdBRI5LHpHwtCGvChaDYNsKpEIQ9fsMnQptGJI6CTDtWi/SFUAsM0Rcjz2kHoNM98uxyoToMNxxqunL8rGKmFWZy39rqiNuVUtz12n5e3mVE89pyqGM0q5dyJuQbPWR75PaNbCDdVGN0lsVT84d9jJCN8JjVCIdshEfZNCIkeLuSKAg3dLr53kNbuPXFPcDhqBHaWS7xTM43PiYHBtbXTCxCY3I0+9CQRjgV4dNCtqjZOr1ywrBYhOJsR1+WVv8QZ97yMg1BuKN35IJwU1+CrPHjLAfGvbxyVSWv723h6//eeESggM/98x1uenIHR0/LxyJQ0zaxHJcnpCCcqGn1dw60UZTtYEZxAmyEx6Ag3NHr45N/WwekTiPsSqJpxD/fqubeNw/wy6d3UtfRqzPLJZE+04hOLQhPZNx9zreDv5oy+myEUyEIG/XTNsKJpSQn4wiNcLyCcL4pCHcmQBAOCePjzTQC4JMnz+Ijx0/n3+treKe6vW/9vqZunthcz4mzi/nrx49jcn4mNW0Ty3F5YgrCfRrhEQrC1W0srygYUeBt6xh2lrvn9SpazRzxqbIRdidRI/zSrqY+gfuV3c3aRjiJZDqsFGTZqeuYWAOwpj9x2Qin0DSivdcY70LClyYxlOQeKQjHq3DIyzQ+ShJhGhE6xnhsX4fNwjfPX0Cu08Zta/ZS3+HmU/es4z2/fxW7Vbj58mWU5GQwtVALwhOCRNgIH2x1sbeph2OmF46oLmNVI/zq7mbueHkfk/OdnLWwnNllo5uX/XDUiOQIwq09XjbVtHPtqbMoycng1d3N2kY4yUzKc1Lf4YldUDNuiUcjbLEINoukRBBucxmCUmH2+Jo6TzUl2Y6+qBEhQdgSp4IpP4GmEZ29hulLyO54vJGdYeO602bz7LYGTvr5Czy7rQGXN8D33rOIslxjVm5aYeaEM42YkPM7oUF2JKYRf3u9CqtFeN8xA7NNDw2bdWzaCH/3oc2U5Gbwt6tXUjkC05DhYrUIDpsFly854bZ21HeiFBw3o4jNNR3sbuxm/qTcvnNrEs/kfCf1nRNLE6HpjzvOuOQOmyU1GmGXIawVZo1PQSlVlORm0NTtQSk15MRFh00jRv4u6HT7sFsl7mQeY5HrTpvNwVYXz21v5ItnzmF5RSFLph32c5pWmEV95yG8/mCfPf54Z4IKwiO3EX52ewOnzy/rc/IZLmNRI9zh8lHV4uL/27vvMLfKK/Hj36M6vXhm3Ds2xjbYBgyGJLQAoSWBBJKQsMmmLUs2ZHdTfim7m03d9CXZVEJ6BZKQEJIQSiD0asA2uOLucZteNaP6/v6492pkWTOjmblqM+fzPH48kq6kK73S1dHRec/7/y5ZVpAg2FHu9+asa4RT8tFYFaSpOsimg93aRzjHZtaW8+IUm62sjjVUGjHyB3DA5ylIjXCXnRGuq9CMsJsaqwJEYgl6w7GUErQs+wgHfHjErYxwlJoy/4TKHYud1yN86epVw14+f1oFCQP7O/pZMr06j3tWOFMj3E+TrC+dQCDc2hue0CQ5hze5xHLp1Ag7wcqquePvluGGikDuAmFn0kRDVYCGqgAd/RGi8QQijHs5bTWyWbVltPVFki201NSTdUbYW5iMcKedEa6bhDWkhVRvf7HoDkWHAuEsg1GPR6gp97sSCPcOxpJdKKaq1fbn+oYDUycpMTUD4QnWCIciMUKROA0uzCwdWlmudDLCmw52AXDKBNrGuaE84M1Z14j2/ggi1gG6sSpIPGFo7w9rNjiHnM4RLT1aJzxVDToZ4VF+mg74PAVpn9YVilJd5kuWtCl3OBn2rtRAeAzH2tpyv2uT5Wqm+GIpJzRVUR308cL+zkLvSt5MyXfzREsjUrOFE+UtwdKI7Ud6mVNXXvCfB3OZEe7oDycbvTtfeI50D2p9cA45i2poL+GpKxzNblWxMr+3IL8cdIYi1Gl9sOucOt+ugciYu0aANbnNtdKIKZ4R9niENfPrjmmxNtlNyUDYmWg17kA4WT868UDQmRBQSoFwVyhaFA3Hy/1eQpHcTJbr6I8wzZ4Z7jzWIz1h7RiRQ7OSgbBOmJuqnCzvaJOVyvyevK4q6egKRZM/4yv3OF8urCWOrdfAWDPCrgTCg7FJ2zFiLM5YOI1tR3o4OkX6uk/ZT/Vyv3fcPWjb7X6HDZVulEY4XSNKp0a4dzBKdREcLMoDPgYmuDrgcNr6IslMcJP9f0uPZoRzSTPCajAaR2SoV/Bwyv3eCa8MOh5doUjBfwmbjJya665QlIQZZ2mEaxnhqV0aAXD5KbMwBu5+8XChdyUvpnQgXFSlESVUI9w7GKO6COqoKvxeBnKYEW6wM8JOQNzeH9Ea4RyqLvNTHfRxuEszwlPVYDRO0OcZddZ+md/LYEFKI6LaOi0HUpdJHk93ntoKf7Kjx0RYNcI6vkumV3HSzGr+tPFQoXclL7IKhEXkUhHZLiI7ReTjI2x3hojEReQa93YxN8oD3nFnE9v6XcwIe52uERoIj1VFwJvTBTWc0ginVhi0h3Cuza4r55BmhKescCwxascIsGqI850RNsbQ2huelMvvFlqZ30uZ30NXaKhGeCzdeaZVBOgaiJKYwOdoOBZnMJqY8jXCjtetns3z+7s4OAUSE6MGwiLiBb4DXAasAN4qIiuG2e7LwL1u72QulE2gB217X4TKgDfZhm0inG+90RIKhIvlW3NZjibLxROGztBQRtjjkWRQrBnh3JpdV8ahKXDgVZkNRuOUjTJRDqwa4XCOOsYMp60vwkA0zvxpheudPpnVlQfoHogSN2PPCNdXBognzIQ6Rzg1xlO9a4TjdatmA3DbM/sLvCe5l01G+ExgpzFmtzEmAtwGXJlhuw8AdwAtLu5fzpT7PeOeddzeF3aldRoMNQ0vlRrhWDxBKBIvihrhigmUt4ykeyCKMccuo+q03PNmudqRGp/ZdeUaCE9hg9HEqK3TwE5k5DkQ3t/RD6CBcI7UllvlDbFxtE9zkhbOQkjjcbjL+iVq5gQXyZos5jdUcMnKGXzrwZ3ct/lIoXcnp7IJhOcAB1JON9vnJYnIHOANwM0j3ZCIXC8i60VkfWtr61j31VXlE8gmdoSirq01X2p9hPvCVk1uMZRGWOUtcYxx97kbWkZ1aIydla60a0Ruza4rpzMULUhHAFV44Vh2GWFrsly+A+EQQEFX05zMaiv8dA1EicfHHgg7n8fOgifjcaDTGt950zQQdnzrracB8NKhngLvSW5l86me6dWYHnl8A/iYMWbEI5Mx5hZjzFpjzNqmpqYsdzE3ynzjry/tCkVcmzBRajXCvYPFEwgHvB6Mcf+567J/IqtNGWNnzXWtEc6t2XVW54hD2kJtShqMJkZtnQZWaUS+a4T3tYcQgTl1GijlQl2531pZbhxdI6ZVOBnh8ZdGHOiwjjlz6/WLjiPg81Ad9LnSkaOYZRMINwPzUk7PBdKnEq4FbhORvcA1wHdF5Co3djBXqsp89I+z40BXKOraEpultqCGU4NVDKURzupOUZez6V0ZllEdyghrIJxLs+2fJbU8YmqyukZkUyNsdY1w+9egkezvCDGzpiyryXxq7OoqrF7A41lZrr7SOlZ39I9/VcoDnSHqK/xUBQuf5CkmNeX+ZAJssspmxJ8FlorIIuAgcC3wttQNjDGLnL9F5KfAn40xd7q3m+6rKRt/30E3e0kO9REujUDYeUMUQ69Fv51Nj8QTlOPeh5PThie1NEIzwvkxQ5dZntLCsURWvzaV+b0Yk32XCTcc6AhpfXAOVQX99A5Gx7WynNPBaSIZ4ebOAebp+B6nusznyvLVxWzUjLAxJgbciNUNYivwG2PMZhG5QURuyPUO5oo1uLExZxSsmakx15bZdAKraLw0Jsv1JGfWFj4j7ASnMZefOycQrjumNML6sNWMcG41VVsfaK19GghPRYPReJbt06z3fjiP5RH72jUQzqWKgJdQNJ6yslz28zHKA1b7tYnUCDd3hJhbr2Uv6WpcWqykmGWV1jPG3A3cnXZexolxxph3Tny3cq+m3E88YRiIxqkIZJ/ddF4QbpVGOIFVqWWEi6FG2J/D0giRY4P9oGaE86Iy6KMi4KW1VwPhqchZUGM0TuvKwVicWnL/pXwgEqelN8wCnSiXM+UBK8vvzN3xjrKoSrppFYFxd42IxRM0dw5w8coZ47r+ZFZT5p/0vYSn7BR4J8jpGRhb7YvzjdO10ghvadUIO3XVlUVQR+XLUTa9ayBKbbn/mIbuAe0akTeNVUHaNCM8JfUMxqjNIsngdJbIV+eIoY4CGgjnitOiss9Otoy1VWV95fgD4QOdA0TiCZY0VY3r+pNZTblOlpu0nBrXsda+ZOooMBGlViMciVlBZzZZm1xzgtNIDkoj0jP+mhHOn6bqoGaEpyBjDN32l9DROOUT+eocsb/dbp2mgXDOVNhZfudXx7GWoc2oKePwOFel3NnSB1hLC6tj1ZT5tUZ4snIywr1jHODukLulEc57vVQywk4ZglOWUEjOPrjdg7kzw2TIZNcIXVAj55qqNBCeivojceIJk9X8C6fFWr4W1XB6CC9oqMzL/U1FTrmL06veM8bSiDl15Ry0M/dj5QTCJ2ggfJyaMh994diElq8udoWPZgrEqXEda2lE18Dxiy1MhIjg80jJrCznlCEUUyDsdmlE90D0uA/jgFczwvnSVB3UyXJTkNO2cGwZ4fwFwlVBn2v949XxnNKI8WaE59aX0zMYG3NyC6xAeEZNsCgmgRebmnI/xkBvePK2UCt8NFMgNfbBdsylERk6CkyU1yMls7JcNJ7AI8UREKa2T3NTxtIIv3aNyJem6iBdoei4l0BXpanbKTsrHz3JkO9AePuRXk6YXoWMMUupsudMWncC2bHWCM+xOz6MZ2LX1sM9WhYxjPH+el5Kpm4g7EyWG2Oj6M5QFBF3F5Twez0lVRpRDNlgSMkIx9wNhAeiccrTOok4GeGx/lynxs5podbWN/5WSKr0OGVn2WWErfdjPmqEjTFsPdLDilnVOb+vqSy9NGKsXSOcFf+aO8YWCB/pHmTL4R5ecULjmK43VSTnU43x1/NSUhwRTQEMlUaM7VvOke4BmqqCrmZEvR4pmcly0Xii6AJht79EROOJ4yYDFsPkwKnC+UA70DG+ej9VmpyMcHY1wlbQlI9fDY72hOkKRTlpZk3O72sqq0gPhMf4GTvejPAD244CcPEKbZ2WyVDSUDPCk06Z30vA5xnz4B7sGki+4dzi80iyiXixswLh4siK5qo0IhI7/jE6HSoSeVzSdapaaE9I2tfeX+A9Ufk0VBoxeiDs1JM6PWdzaevhHgCWz9JAOJfS26eNtQytqSpImd/DvvaxfYF+encHs2rLWKqlERk5ZaRdE1ispNhN2UAYnGWWx5buP9g5wOw6dwNhzQiPT65KIzI9RicQLpVxKmWz68rweYS9Y/xAU5k5s72L/bXbNYZAODnHIw/9TTc2dyECJ2lpRE45GWGnXHGsGWERYcWsGl482DWm6+1q7WPpjGqt/x7GdGe1z0ncyac4IpoCqa/w09Gf/eAmEoZD3YPMdTkQ9nnE9dXRcqUoa4RdfO6MMUTjJhn4OoJ2A/9SqeUuZT6vh/nTKjQj7IIDHSHWfPY+Lvn6I6z5zH38ccPBQu/SsLoHovi9kgyIRlIZ8OLzSDJ4zqUndrVz8uxa7SiQY0M1wlE8wrgC01Pn17OpuTvrTkLGGPa09bO4UdviDaehKohHoEUD4clpZm0ZR8bQgLutP0wklnC9NMLrHcoIH+0Z5IeP7k4uXFFsirE0ws2yksgw7eG0NCK/FjRUsLdNM8IT9ccNB+kZjLH9aC+1FX6+ePe2Qu/SsJzFNLIJgESEuopAzn+uHYjEeWF/J684oSGn96OGSiMGo4lxr+C5Zl4d4ViCbYd7s9r+SM8goUicE5o0EB6O1yM0VAVp6Zl4IPzrp/ezu7XPhb1y15QOhGfXlnNoDIHwwU6rCH+Oyxlhv8fqGjEYjXPNzU/w+b9s5fZn97t6H24pxtIIN780ONnlwDCBcKm0uSt1Cxoq2dfej9EvHhNy18ZDnLGwnt1fuJzXrZ5NW1+4aBvjd4eyW1XOUV/hT7azzJVn93YQjRtesUQ7CuSaz+sZ6s4zzo+Y0xbUA/D0nvastt/dav3qtFiXVh7RjJogLb1jX7VvMBrn2w++zCVff4RfPLWP/7zzRX78+J4c7OHEFEdEUyCz6spo6wtnHUg124FwbmqEE3zrwZc5YLd++fbfd7q+UIQbIrHiKY1wglM3SyOc18LxpRGaEc6nhQ0V9Efi2kJtArpDUXYc7eOCk6bj8QiNVUFiCZOclFZsWvvCNFQGs96+rsJPZ44zwk/sasfnEc5YWJ/T+1EWpzxivBnhOXXlLJ1exQNbW7La3slOnqCB8IimV5eNuTSieyDK2V98gK/dt4O97f188s6XWDCtgk9ctjxHezl+Wb3aRORSEdkuIjtF5OMZLr9ORDbZ/54QkdXu76r7ZteWY4xVjpCNlw514/cKi1yuJ/J6hK5QlFse2c0bT53Dj/5xLUd7wty/5air9+OGaDyBv0haiTmzit38wjDcynk6WS6/FjRq54iJ2tFi/Ty83G77NdSfuThr/Y50DzKrrizr7a3SiNwG9U/uauPU+XXJxR5UbjnlERPpTnrxihk8s7cjq7KZXa39VAa8zKjJ/gvYVDS9OsjRMZZG7GzpozMU5X3nn8CjH72AW95+One+/5VUBovvvTRqRCMiXuA7wGXACuCtIrIibbM9wHnGmFXA54Bb3N7RXJhZax10D2XZd/CF/V2snF2b7GHpFp9X2Hyoh2jccMWqWZy/bDpz6sr57foDrt6PG2KJBIFiqRFOZoRdrBGOOYHwsY8xqIFwXjkt1LRzxPjtOGoFwktnWNmuxiprxbZiXL7aGMOR7sHkMTkbdeW5LY1o6RnkxYPdnK0LLeSNM1HSN4FfHV+7ajYJY/jKvdtH3XZ3Wz+Lmiq1Y8QoplcHae8PExvDZ63TB/7q0+YyvaaM16ycSV3F6KtGFkI2r7YzgZ3GmN3GmAhwG3Bl6gbGmCeMMZ32yaeAue7uZm7MtrMPh7OoE47GE2xq7uLU+XWu74fX40n+XLlsZjVej7Bu8TS22P0ri0k0Zsb9s5XbAjnoGuFMlhuuNCKupRF5Mbe+HK9HNCM8ATuO9FIZ8CbnNDRVFe+KfR39ESLxBLNqsg+E6ysDdA3k7rH83wMv4xHhjafOydl9qGM5pRETWbBqxewarj9nMb9+ej8fvH0DGw50DbvtrpY+FjdqWcRoZtVZv55nEys59rWHELGO5cUum4hmDpCammy2zxvOe4C/ZrpARK4XkfUisr61tTX7vcyRWbXZr0Sz9XAPg9EEp853v1bMb7/pq4O+5IfWkulVHO0JF91qLpEiKo0Yap/mfmnEcZPlvNYBugjLticlv9fD3Ppy9rRpIDxe24/2cuLMof6ojVXF2w/U+YCdWZv9h2ZtuZ/BaILBqLuLatz5wkHO/+rf+dXT+/mHsxawUFtr5Y1TGjHW5ZXT/b9LlnHR8hn84YWDfOqPL2XcZjAa51D3AIu1Y8SonPZyu8dwPN7fEWJmTZnrv6DnQjYRTaZXZMa0mIhcgBUIfyzT5caYW4wxa40xa5uamrLfyxypDPqor/AnJ8GN5Knd1izUdYumub4fzrffZSkfWkvs4v1dLcXVaiQaL57SCK9H8IjLgXDMemmn1wj7fdZjjpfICoCTwYKGSna1aiA8Xgc6BlgwrSJ5urbcj88jRVkj7LSxnDWG0oh6+2dWt8sj/vLiYfa2h3jH2Qv4j8uLb2LPZOZGRhis0oofvON03nT6XLYd6c34k/6etn6M0Y4R2TjBXnVvLK3PDnSEmJdy/Clm2QTCzcC8lNNzgUPpG4nIKuCHwJXGmOx6lxSB+dMqaO4cvQ7xyV3tLG6qZMYYfrrLllMCcWZKkL3EfuHtLMJAuFi6RoB1wBvLEsvxhOHRl1uHDZ4jcSu7lJ71djIUWiOcP6vn1rL9SA994bGt/qismtvW3vAxxyuPR2ioCtBWlBlhKxkxlkC4rsJqteZ254hDXQNcsKyJz1558nElUiq3hmqEJ55sERHOWtxAOJZgb4YSq412ycTymbpi4GgaKgPUlPnYNYZAeF9HP/MnUSD8LLBURBaJSAC4FrgrdQMRmQ/8Hni7MWaH+7uZO/OmVbC/Y+RAOBZP8OzeTs5enJum6jPtD6sbzj8hed78aRUEvJ6iy4jF4mZCExncFvB6su7ta4zhX297gbf/6Bm+9eDOjNtEYpn7CDsZCg2E82ftwmkkDGzY31XoXSk53QNRIvFEslOEo6k6WJST5Q51D+KzG/dna1qllRFud7nm+WDXgOuLJqnsuFUa4Vg+y+qYsiXDAhsPbmthTl15MumkhicinDC9il0t2cUjR7oHOdoT5qQS+ZIxakRjjIkBNwL3AluB3xhjNovIDSJyg73ZfwMNwHdFZIOIrM/ZHrts3rQKDnYOjBjgvHiwm75wjLNztLrQL9+7jsc+dsExS3j6vB6aqsfXxDqXIkW0shxY3R2yLY3Y2x7iL5sOA/D9h3dlfG6TNcK+Yx9jMhDWyXJ5c9r8OjxiLWqgxsbp+Tk97Res2bXlyYWBisnLR3tZ1Fg5pp/EZ9v1xNl2/clGfzhGVyjqeq94lZ1yu03dREsjHEumV+H3CpsPdh9z/mA0zmM727jgpCbtGJGlE5qq2JllRthZ0OSsHCUP3ZZVas8Yc7cx5kRjzAnGmP+xz7vZGHOz/fd7jTH1xpg19r+1udxpN82fVkEsYZI/zWXy5O7cDuqMmjLm1h//E0JdHlZOGiurRrh4MsJ+ryfrQPiRHdYEzZ+86wzCsQS/f/7gcdsMtU879jHOra/gqjWz+d51p09wj1W2qsv8rJxdy6MvF35ibalxlkOdnpYRnltfQXPnQNGt2Lf1cG8ye5etmbVliGQ32TlbTlDt9uqhKjsVLtUIOwI+D2sXTONvW48e85q/+8XDhCJxLl05y5X7mQpWzKqhtTecrOcfydN7OqgO+sb8ni6U4oloCsSpYRmpPOLJXe0sm1GdnHWdL/UVgZyvnDRW0XjxrCwHVsDqlDOM5u/bW1jQUMEFy6azdkF9xj7N0WHap3k9wjeuPZWT59ROfKdV1i5aPoMXDnTRkuWiN8ri/NqRHgjPm1bOQDROR3/xHFe6B6Ic7BrgpFlj+xk14PMwo7rM1UD4oAbCBeV2IAxw+apZ7GrtZ7vdVzsSS/CDR/ewZHoVr1xSGhnLYrB6Xh0AG5u7RtwunjA8tK2FdYunuTqOuVQ8EU2BLLNrWDY1d2e8PBJLsH5vZ87KIkZSlBnhWMKViQxu8XuFWBadHH702B4e2t7Ka1dZGYALl89gV2v/ce3pIsOsLKcK45KTZ2AM/PWlIxkvd7t11mQxXGmE88vTgSIqj9h+5NgV8MZidl2Zq6UeyUBYa4QLwmm15XMxgLrs5Jn4vcKvntqPMYb/97uNbD3cwwcvOlHLIsZg5ewafB5h0yiB8KMvt3Koe5A3nFoSy0kAGgjTWBVk6fQqntyVudHFxuYuBqLxgtS6FGVGOFGapRG3PbOfMxbW86GLlwFDTb4Pdx2baXRKI4rpMU5ly2ZUs3puLbc8sjs5No7fP9/MKZ++N1nyooa09ISpCHipSlvO1HndZ9MpJ1+cD9aVs8ceCM+pr3A1I7yzpY9yv5fp1e53B1Kjy0VGuLEqyDWnz+P2Zw/w8Tte5I8bDvHhi0/kilVaFjEWZX4vy2ZW88ye4eds9A5G+dJft9FQGeCiFdPzuHcTo5/2WLW/6/d2ZAyontzVjgictdj9/sGjqa/w0z0QLapOBaVYGtHRH+Hllj4uOGl68gDrTIZJn2jjrFKnbZOKg4jwodcs42DXAO/52bPJDHAiYbjp/h1E44Ybfvkc9285WuA9LS5HewaPK4sAkn099xXR0tVP7e5gYUPFcdnrbMypK+dw9wAJl46Rmw/1sHxWdcn8pDvZ5CIQBvjgRUtZOqOK29cf4Ko1s3n/BUtcvf2p4tKVM3l2b2dy+eRUsXiCG3/9Ai+39PH1t6wh6Cv+hTQc+mkPvHJJI/2ROA9tPzazFE8Y/rzpECfPri3IGtl1FQGMgZ6B4iiPiCcM8USRBcK+0TPCTteBMxcOfZlxltdOzyZFtTSi6Jx3YhP/84aTefTlNr714MsA/OzJvTR3DvCfly9n6fQqrv/Fer75wMv86LE9XPqNR3hhf+cotzq57Wzpy7hQQFXQx/xpFbw4TClYviUShmf3drBu0fh+cVvYUEE0btg3SgvMbBhj2HqohxXjyEwrdzilEW4HwtNryrjrxlfxwicv5hvXnopHv+iMyxtOsxYV/uVT+4677Pb1B3h4RyufvXIl555Y+AXTxsI3+iaT34XLpzOnrpzvP7yLi5ZPT9YN3fFcMzuO9vHtt51akP2qrxxqGF9fmf9APF0ySPQVz0HE7xm9Rvj5/Z0EvB5OmTs00W16dRlejxzXLWSoa0TxPEYF161bwPP7uvjeQ7vYfqSXB7e1cPGKGbznVYt4+9kL+MTvX+Sm+60W5kGfh3f8+Bme+PirqU5pSThVROMJdrdZv4BksmZe3Yg/b+bTxuYuugeixywmNBbOe/rFg90smuBSyM2dA/SGY6ycrRNiC6XCbp/m87ifiPB6pCg+R0vZ3PoKrj5tLrc8uptNzd00d4X4+KXL8XuFm+7bwRkL63nbmfMLvZtjpmkvrOzf+84/gfX7OvnuQ7sA2NvWz2f/vIW1C+q5/OTC1BLVlVtv2s4imTAXs39+9OfgIDVefq8nuSzycHa39rOgoeKYn2q8HqG+ws93/r6LDfYKQzA0WU5LI4rP5686mYuWz+BvW1u47JRZ/N+1a/B4hDK/l5vevJpb/+ksfvXeddx6/Vn0Dsa447nmQu9yQexr7ycaN5w4I/NCAafOr+NIz2BWbZBy7YeP7aE66OPilTPGdf0TZ1QT8Hl4cZQJPNl43v4V4RTtDFMwTmmEZmyL1+euWsnVp82lrS9MIgHv//XzXP+L5wj4PHzuqpNLcgKiZoRt162bz/q9HXz13u14RPjTxkN2y6w1BXtTOkuIdg8Ux4S5aBFmS/0+DwOjlI7sa+9nYYZs0dz6Ctr6Irz7p8/y94+cT225fyjrXUTBvrKUB7zc8o61GGOOO9iKyDGdXVbPq+N7D+/i8lWzptzEp+1HrKb3J87I3I7s1Pn1gNX0/so1c/K2X+n2tPXz1xcP88/nnXDMYkJj4fd6WDGrZtiuP2Px0PZWGioDrCiR3qeTUS66Rih3VQR8fO1NqwFrAZpNzd2IwMlzao+bnFsq9NPeJiJ86epVnLV4Gl++Zxu7Wvu46c2rMy50kS/1dl3yu3+6vihW1xoqjSiel01glJXlEgnDvvYQCxuOH8eb3ryaz165ko7+CD97Yi9glUb4PKIZiSKWTcbhC284mZ6BGP/8i+emXIu1lw514/XIsEvHnjKnlunVweQqi4Vy80O78Hk9vOuVCyd0O2vm1bGxuWtC45xIGB7e0cq5Jzbpe7+AkhnhEswqTkWVQR9nn9DAWYsbSjYIBg2Ej1Hm93LrP53Fvf9+Ls/850VcuHx8P9e5ZUZNGY1VVjB8/c/X0xeOFXR/irHHrs/joS8cG3alrMM9g4RjiYwZ4cVNVbzj7IWsWzSNO184iDHGWjmviAJ9NT4rZ9dy05tX88L+Lq695SleOlj4yWHGGF462J3zwPzh7a2cvqA+mV1L5/UIV6yaxUPbW+nKQ3vGI92DPPpyK/328Wv7kV4+fscmbl9/gLedOX/CGftXnzSdwWiCx3e2jfs2HtzWQkd/hAuXl07Lp8nICYQ1I6zyST/x04gIy2ZWU1te+Ek25QEv6//rYn75nnV0hqI8W+AJLk5rsWIqjXjlkgb2tYf4xO9fZGdLX3JFLce+tn4AFjUMP5HmDafOYXdbPy8e7C669nBq/C47ZRbfve409rb3c+V3Hi94v+FbnznAa7/1GOd99e9056juv6VnkC2Hezh/2ciztq89Yz7RRIJbHtmdk/1wPLGrjcv+7xHe/qNnOOsLD/COHz/Da7/1KHduOMi1Z8zjP69YPuH7WLd4GlVBH/duzrzoymj6wzG+dt925k0r59KVMye8P2r8yp2uEUX0GaMmP/3ELwGnL6gn4PXw1O7Mi37kS6wIM8L/cNYC3nf+Cdz27AEuuulhzv7igzy4bain7C47EM6UEXZcevJMvB7h3s1HCMcSRfX41MRcfsosHv7IBSydXsWNv36+YEs172zp5cv3bEMEjvaE+e1zxy/v7Ya7Nh4C4IJlI2c2l82s5vWrZ/Ojx/awMWWyqBviCcPn/ryFC772EG/7wdNUlfn43nWn8ZqVMznUNcCb187j8Y+9mi9dvcqV91rQ5+W1q2Zx54ZDx3WBycYHb9/Ayy19fOq1K/Hpe7+gyp0+wloaofJI3/UloDzgZc28Oh7Y1sJ+lxvhD0TifOZPm/nY7zaNGiQUY2mEiPCxS0/i3n8/l69es4oZ1UE+dseLybrhrYd7qCnzMat2+J9f6yoCnLGwnvu3HCUaTxDU0ohJpbbCz3evO41wLMHH7tjk2uIL2egKRfjRY3t443efIODz8PcPn88ZC+v54aN7aO8Lu3pfvYNRvv/Ibl5xQgPLs5jw9cnXrmB6TZD3/Gx9xgb54/W9h3byo8f2MLe+nM9fdTL3f/A8LjtlFv/75tX87UPn8T9vOIWGquMX+5iIG1+9BGMMn//L1mHLpDJ5ZEcr9205yodfcyIXrShsKZwayghraYTKp6w+8UXkUhHZLiI7ReTjGS4XEfmmffkmETnN/V2d2q5ZO5ddrX2c+9W/880HXnblNo0xfPi3G/jpE3v5w4aD/MuvRs6YJVddK6JA2LFsZjVvWjuP/3rtClp7w8ma0G2HezhpVs2oE6wuWTmTHUf7eHJXe1GVfih3LG6q4j+vWM7ft7fy0Ts20TuY25aExhie39/Jld95nM/9eQsnzazhjhtewcLGSv7rihV0hiJc7+JEvkNdA7z3Z+vp7I/w4dcsy+o6jVVBfvLOM4jE4lz5nce5a+OhMQWR6aLxBF+/fwdfu28Hr1s9m5+/+0z+4awFw9Yqu2lufQX/ftGJ/GXTYb770K6sHseBjhAf+e1GFjVW8p5XLcr5PqrR+bweAl6Pruyn8mrUaX4i4gW+A1wMNAPPishdxpgtKZtdBiy1/60Dvmf/r1zy5rXzeOWSRj77p81884GXWT6rhjXz6qgIeKkM+ojFE2yyg79T59WRMBCOxfn98wdp74vwtnXzaUpZcrVnMMrPHt/L3S8e4aOXLmNufQUfun0Dr//249z3oXOpKfMTiSUIRWJsOdTDdx7ayeM7rdIMXxEHik5j/qf3dLB6bh3bjvTy5rXzRr3eW86Yxy2P7OZg1wDzppXnejdVAbz9rAW09IT57kM7efTlVl6/ejZN1UGqy/xUBn1UB33UVwaoKbMOi04o5cRUfeEYR7oHONI9yOGeQXYc6SUUiSd7VO/rCBEKx2ioCrC3LcT2o73UVfi5/fqzWLf42NZuX3/LGv7lV8/z+m8/xpvXzuOEpiqmVQYwWCVIkViCcCxBxP47kuHvoM9D0Odhx9E+fvdcMwlj+NqbVnP6gvqsn5Ml06u5432v4CO/3ci/3voCX7lnG5esnMnyWTUsnV5FVZkPweoh3j0QpSsUpWcgSjSeoL0/QltfmJ6BGAe7Qmw/0ktnKMrVp83li288Je/9RG847wS2H+nlq/du5w8vHOTq0+aycnYN02uClPm8eD1CVyjKrtY+1u/r4M4XDiHAL96zrqSWg53sygNeDYRVXslo35xF5Gzg08aYS+zTnwAwxnwxZZvvAw8ZY261T28HzjfGDNufZ+3atWb9+vUTfwRTTFtfmNd96zEO283wPQINVUEGI3F67VnZ1UEfveEYPo8kF8HweYS59eUc7h5kek2Qzv4ofeEYy2ZU8+d/fRV+r4fn93dyzfeeYHp1GUG/h/0doWQQMKMmyNEe66fc264/i7MWj29J1Hy48H8forU3TGN1kN2t/Xz56lN4yxmjr3bzwNajvOdn1mty75euyPVuqgLZcKCLr9yzjWf2dCTfH2Pl9woLGiqpLfezvyNEJJZg/rQKyv1eugYiNFUHuXTlTN5w2txh2wrdt/kIN92/g21HeifycCj3e7l4xYzkF9rxiCcMf9xwkD9uOMSTu9qTZVCjqQr6qAr6mFtfzsLGSq5YNWvU+uRcSiQMf3jhID97cu+IvYXL/V4uWjGDj16yjHnTCtciUx3v6/fvYM28umFXRlRqPETkOWPM2oyXZREIXwNcaox5r3367cA6Y8yNKdv8GfiSMeYx+/QDwMeMMevTbut64HqA+fPnn75v3/HrVavRDUbjPLGrjf3tITpCUVp7w3g9sG5RA10DUbYc6mZGTRm9gzEuWTmThqoAv1l/gIOdAzRWBWnvj1AV9HLJypmsXTjtmA/qO55r5sFtLYjA4sZKaisCNFQGuPTkmfQOxvj5k3v5l/OXJCc1FKO7XzzMnzcdQhCCfg+fuGz5Mdnwkdz5wkHiCcPVp8/N8V6qQjPG0BeO0TsYIxSx/u/oj9A7GCM9mSkiVPi9zKwtY2ZtGdMqAq71m23pGeRAZ4iuUBQRqyVgwGf/81pZ39TTAZ8Hv9dDJJ5gMBpnWkXA1UlesXiCfR0hdrb0JUs3vB6httxPbbmfmjI/AZ+HqjLfuBfCyIeO/oidpY4wGI0TTxhqy/3Mm1bB0ulVOjFOqSlkooHwm4BL0gLhM40xH0jZ5i/AF9MC4Y8aY54b7nY1I6yUUkoppXJtpEA4m6/EzUBqkeVc4NA4tlFKKaWUUqpoZBMIPwssFZFFIhIArgXuStvmLuAddveIs4DukeqDlVJKKaWUKrRRu0YYY2IiciNwL+AFfmyM2SwiN9iX3wzcDVwO7ARCwLtyt8tKKaWUUkpN3Kg1wjm7Y5FWQGfLZdYItBV6JxSgY1FsdDyKi45HcdHxKC46HsVjgTEm49rzBQuE1fBEZP1wRd0qv3QsiouOR3HR8SguOh7FRcejNGj/GKWUUkopNSVpIKyUUkoppaYkDYSL0y2F3gGVpGNRXHQ8iouOR3HR8SguOh4lQGuElVJKKaXUlKQZYaWUUkopNSVpIKyUUkoppaYkDYTzQER+LCItIvJSynmrReRJEXlRRP4kIjX2+WeKyAb730YReUPKdU63t98pIt8UESnE4yl1YxmPlMvni0ifiHwk5TwdDxeM8f2xUEQGUt4jN6dcR8djgsb63hCRVfZlm+3Ly+zzdSxcMMb3xnUp74sNIpIQkTX2ZToeLhjjePhF5Gf2+VtF5BMp19HxKCbGGP2X43/AucBpwEsp5z0LnGf//W7gc/bfFYDP/nsW0JJy+hngbECAvwKXFfqxleK/sYxHyuV3AL8FPpJyno5HnscDWJi6Xdrt6Hjkdyx8wCZgtX26AfDqWBRmPNKudwqwO+W0jkeexwN4G3Cb/XcFsBdYqONRfP80I5wHxphHgI60s5cBj9h/3w9cbW8bMsbE7PPLAAMgIrOAGmPMk8Z6J/0cuCrHuz4pjWU8AETkKmA3sDnlPB0Pl4x1PDLR8XDHGMfiNcAmY8xG+7rtxpi4joV7JvDeeCtwK+h7w01jHA8DVIqIDygHIkCPjkfx0UC4cF4CXm///SZgnnOBiKwTkc3Ai8ANdmA8B2hOuX6zfZ5yR8bxEJFK4GPAZ9K21/HIrWHfH8AiEXlBRB4WkXPs83Q8cme4sTgRMCJyr4g8LyIftc/Xscitkd4bjrdgB8LoeOTacOPxO6AfOAzsB75mjOlAx6PoaCBcOO8G3i8izwHVWN8WATDGPG2MWQmcAXzCrrvLVEOkve/cM9x4fAb4ujGmL217HY/cGm48DgPzjTGnAh8Cfm3X5Ol45M5wY+EDXgVcZ///BhG5EB2LXBv2swOsRAoQMsY4daw6Hrk13HicCcSB2cAi4MMishgdj6LjK/QOTFXGmG1YPy0iIicCV2TYZquI9AMnY31rnJty8VzgUB52dUoYYTzWAdeIyFeAOiAhIoNYNcM6Hjky3HgYY8JA2P77ORHZhZWZ1PdHjozw3mgGHjbGtNmX3Y1VP/lLdCxyJovPjmsZygaDvjdyaoTxeBtwjzEmCrSIyOPAWuBRdDyKimaEC0REptv/e4D/Am62Ty+ya4oQkQVY9Ud7jTGHgV4ROcueYfoO4I8F2flJaLjxMMacY4xZaIxZCHwD+IIx5ts6Hrk1wvujSUS89t+LgaVYk4J0PHJkuLEA7gVWiUiFfcw6D9iiY5FbI4yHc96bgNuc83Q8cmuE8dgPvFoslcBZwDYdj+KjgXAeiMitwJPAMhFpFpH3AG8VkR3ANqxvgz+xN38VsFFENgB/AP7FybgA7wN+COwEdmHNNlVjNMbxGImOhwvGOB7nAptEZCNWDd4Ndt0d6HhM2FjGwhjTCdyENWt+A/C8MeYv9k3pWLhgHMeqc4FmY8zutJvS8XDBGMfjO0AVVg3xs8BPjDGb7Mt0PIqILrGslFJKKaWmJM0IK6WUUkqpKUkDYaWUUkopNSVpIKyUUkoppaYkDYSVUkoppdSUpIGwUkoppZSakjQQVkoppZRSU5IGwkoppZRSakrSQFgppZRSSk1JGggrpZRSSqkpSQNhpZRSSik1JWkgrJRSSimlpiQNhJVSSiml1JSkgbBSEyAi7xSRx1y6rYUiYkTE58btjXJf54tI8zivO+J+ish/iMgPM20rIn8VkX8c/56PaT8/LyJtInLEhdvaKyIXDXPZOSKyPcvbGffz7gYR6RORxYW6/1zL1+MTkXIR+ZOIdIvIb3N9f25w81il1GSigbAqCSLyKhF5wv7g6RCRx0XkjDzvQ94C1VJmjPmCMea9w1x2mTHmZ5DbD2YRmQd8GFhhjJmZi/twGGMeNcYsy+V9uMUYU2WM2V3o/ciVPD6+a4AZQIMx5k25vKORvoQVCw2yVSnTQFgVPRGpAf4MfAuYBswBPgOEC7lfxUyDdRYA7caYlkLvSDEohdeDWErlM2kBsMMYExvrFUthLNLlep9L8TlRk0epHHTU1HYigDHmVmNM3BgzYIy5zxizCZLZiMdF5Osi0iUiu0XkFfb5B0SkJfXneBGpFZGfi0iriOwTkf9yPoBFxGOf3mdf7+ciUmtf9RH7/y77J9izU27zayLSKSJ7ROSytPv6kYgcFpGD9s/1Xvsyr329NhHZDVwx0pNgZ4Y+ISJb7Pv6iYiU2ZedLyLNIvIxuxTgJyISFJFviMgh+983RCSYdpv/Yd//XhG5LuX8K0TkBRHpsZ/DT2fYpXfbt3tYRD6cct1Pi8gvh3kMD4nIe0VkOXAzcLb9XHaJyBkicjT1Q1FErhaRDcPcVsZxtLNn9wOz7dv+aYbr1ovIn+3rdtp/zx32ybesEZFN9q8St6c/9ym3fZr93PWKyG/tbT+fdv8ftl9fh0XkXcM8vrNE5IjzerHPe4OIOK/7M0XkSfu5Oywi3xaRQMq2RkTeLyIvAy+nnLfE/nvYMZahXz/+UUT226+R/0y53Gu/dnbZj/M5sbLwiMhJInK/WL/cbBeRNw/3hNqvh/8RkceBELB4pOuLSINYJQk9IvKsWO+nx1IuT318PxWR74pVjtMn1jFipv0+6BSRbSJyasp1Z4vIHfZrYo+I/Osw+/wZ4L+Bt9i3+x4Z4biR8ly+R0T2Aw9muM1G+zXYZT/uR+3b/AUwH/iTfV8ftbd/vYhstrd/SKz3k3Nb80Tk9/bjaBeRbw/zOL4qIo/J0PEt9bJPi8jvROSXItIDvFOGOZZJhvdyyti+N+U2j8kap78+ZegYlvG9ISKXi3Xs67Xv/yOZHpdSY2aM0X/6r6j/ATVAO/Az4DKgPu3ydwIx4F2AF/g8sB/4DhAEXgP0AlX29j8H/ghUAwuBHcB77MveDewEFgNVwO+BX9iXLQQM4Eu77yjwT/Z9vw84BIh9+Z3A94FKYDrwDPDP9mU3ANuAeViZ7r+n337a49wLvJSy/ePA5+3Lzrefgy/bj7kc+CzwlH2/TcATwOfStr/J3v48oB9YlnL5KVhfllcBR4Gr0p6HW+3HdQrQClxkX/5p4JeZnjPgIeC9Kc/dY2mPcQtwWcrpPwAfHub5GGkczweaR3hNNQBXAxX29X8L3DnC9nvtsZttP/dbgRvS7wsIAPuAfwP8wBuBSIZx+qx9+eVYAWD9MPe7C7g45fRvgY/bf58OnAX47Me/Ffj3lG0N1heCaUB5ynlLxjDGP8B6La3G+gVmuX35/wNeBJYBYl/eYL8eDmC9F33AaUAbsHKYx/cQ1nt1pb197UjXB26z/1UAK+xtH0t7zM7j+6l93dOBMqwAdA/wDoaOE3+3t/UAz2EFuAGs9/9u4JJh9vvT2K/xMRw3fm4/P+UZbu+LWMGk3/53DkPHkL3Y7y379IlY79WL7W0/at93wH5cG4Gv2/dVBrwq9f1mP9YfAPcCFSM8vihwlb19OSMfy97J8e/lh7Df65m2Ie31ySjvDeAwcI79dz1wWi4/d/Tf1PlX8B3Qf/ovm3/AcvuDrdk+WN4FzLAveyfwcsq2p9gH2Rkp57UDa+wPijBW7ahz2T8DD9l/PwD8S8ply+wPBCfYyBQI70w5XWFvMxOrhjCc+sEHvJWhD98HsYMp+/Rr0m8/7TnYm7b95cAu++/zsQKuspTLdwGXp5y+BNibsn0MqEy5/DfAJ4e5728AX7f/dp6Hk1Iu/wrwI/vvTzP+QPhjwK/sv6fZH4SzMuzPaON4PiMEwhlubw3QOcLle4F/SHu8N6ffF3AucBA7iLHPe4xjA+GBtNdQC3DWMPf7eeDH9t/VWAHQgmG2/XfgDymnDfDqtG2SgWKWYzw35fJngGvtv7cDV2a4jbcAj6ad933gU8Pc50PAZ7O5vj3mUewvaynPz0iB8A9SLvsAsDXl9ClAl/33OmB/2v1+AvjJMPv9aY4NhLM5biwe4fX1WawvdceNDccHwp8EfpNy2mO/5s4Hzsb6UnrcMQTr/fY0cDtwBxAYYX8+DTyScnq0Y9k7GV8g/OqU0+czwnsD6wvTPwM1w+23/tN/4/mndTmqJBhjtmIdSBGRk4BfYn1wv9Xe5GjK5gP2ddLPqwIaGcraOfZh1R2DlfFLv8yH9UEwnGRXAmNMSESw72saVmbjsH0eWB9aB1Lu68DQzRxzv8NJ3352yulWY8xgyulMjyV1+05jTH+my0VkHfAl4GSs5yuIlY0caV9OyWL/R/NLYKuIVAFvxgqKDmfYbrRxHJGIVGBlzS7Fyi4BVIuI1xgTH+Zqqd0nQhz7XDpmAweNMSblvANp27SbY2tLQ0CViMzHyogD1sQv4NfAEyLyPqzs8vPGmH32YzgRK6O/FusLmA8rq5kq/b6Tshzj9MdcZf89D+uLVroFwDrn53GbD/jFcPuRto8jXb/J/jt1+2Efny39GJDpmODc7+y0+/UCj45y+45sjhsj7etXsYLP++xjxS3GmC9lc1/GmISIHMB67UeBfWb42uUlWNn7M40xkRH2J31/FzDysWy8snpv2H9fDfwX8CWxyoM+box5coL3r5TWCKvSY4zZhpXtOXkcV2/D+rBYkHLefKyMClhlDemXxbA+QFODm2wcwMqiNBpj6ux/NcaYlfblh7ECitT7Gk369odSTqfvX6bHkrp9vYhUDnP5r7Gy7vOMMbVYP9sKxxppX7Jx3PNpjDkIPAm8AXg7wwdQo43jaD6MlbVbZ4ypwcrkwvGPcawOA3MkJVrg2OdpWMaY/cbqelBlB8EYY7ZgBT2XAW/DGhfH97BKa5baj+E/Muz/SK/ZbMZ4OAeAE4Y5/+GU13ud/XjeN8JtpX9pGO76rVjvxdRa7qye2ywcAPak3W+1MebyLK8/0nHDMexYGGN6jTEfNsYsBl4HfEhELhzmesfcl/1am4f12j8AzJfhJ59txSo7+auIjNbpJH1cRjqWZXps/Vhf0ByZurdkfUw1xjxrjLkSqyzjTqxfsJSaMA2EVdGzJ898WOzJTGJNynkrVv3rmNjZvt8A/yMi1SKyAPgQViYSrLrXD4rIIjsr+QXgdjtL0QoksOoAs7mvw8B9wP+KSI09+eUEETnP3uQ3wL+KyFwRqQc+nsXNvt/efhpW4HP7CNveCvyXiDSJSCNW/WP6JLbPiEhARM4BXstQRrAa6DDGDIrImVhBWLpPikiFiKzE+nAdaV8yOQrMlZQJXrafY9U9noJVI3ycLMZxNNVYGcEu+7n81Bj3fThPAnHgRhHxiciVwJkTvM1fA/+KFaynZmyrgR6gz/6VZKRgM5Nsxng4PwQ+JyJLxbJKRBqwurucKCJvFxG//e+M1Mlcoxj2+vaY/x74tP26Owmr3tcNzwA9Yk02LbcngZ0s2bdoHOm4MSoRea2ILLGD2h6s15Dzy8RRjj3m/Aa4QkQuFBE/1pe6MNYcgGewvox9SUQqRaRMRF6Zel/GmFuxjh1/E5FMX2aOk8WxLNN7eQPwRnuslgDvyea+MrGPUdeJSK0xJsrQc6TUhGkgrEpBL1YN39Mi0o8VAL+E9QEwHh/Aylbsxqrf/DXwY/uyH2NlIR/BmlgzaG+PMSYE/A/wuFiztc/K4r7egfWz8xagE/gdMMu+zJmwshF4HutDfjS/xvpA2m3/+/wI234eWA9swprY9Hza9kfsfToE/Aqr/nibfdm/AJ8VkV6sADpT9uVhrEk6DwBfM8bcl8X+p3oQ2AwcEZG2lPP/gJXx+kNa6Ua6kcZxNN/AmqDThvV6umdMez4M++fmN2J96HcB/4AV3E2k1d+tWPWTDxpjUp+nj2AFr71Yr6WxfhHJZoyHc5O9/X1YQcmPsOpHe7Fq3a/Fel0dYWgC56iyuP6NWBPqjmC9T2/FhTaKdpD9Oqxa8T1Yr4sf2veVjWGPG1laCvwN6MP6MvVdY8xD9mVfxPpC2yUiHzHGbMd6XX3L3s/XAa8zxkRSHscSrJraZqy66/TH+zOsuuQHRWRhlvs40rEs03v561jzFo5iTXT+VZb3M5y3A3vF6mJxA9ZzoNSEObNSlVJFTkT2Yk0++Vuh9yXXRGQX1oz0kn+sIvI01sS6nxR6XyYbEfkyMNMY84+F3helVGnSjLBSqqiIyNVYtYPH9VstBSJynlj9an1i9a9ehUsZ56nOLpNaZZdjnImVec9YPqOUUtnQrhFKqaIhIg9h9Yd9uzEmUeDdGa9lWGUDVVidFa4ZpvOFGrtqrHKI2Vittf4Xq+2YUkqNi5ZGKKWUUkqpKUlLI5RSSiml1JRUsNKIxsZGs3DhwkLdvVJKKaWUmgKee+65NmNMU6bLChYIL1y4kPXr1xfq7pVSSiml1BQgIsOu3DpqaYSI/FhEWkTkpWEuFxH5pojsFJFNInLaRHZWKaWUUkqpfMimRvinwKUjXH4ZVjPwpcD1WMt+KqWUUkopVdRGDYSNMY8AHSNsciXwc2N5CqgTkVkjbK9cZIwhtfOHMYZo/PiuU/GEoWcwijGGvnCMwWjm1SkTCUN3KJrcNtP9FTvnMXSHooQiWa1wesx1E4nif4xKKaWUmjg3aoTnAAdSTjfb52nfzBy556XD/O65ZvZ3hOjoj9LWF8YjsG5RA10DUXYc7aWhMkDvYIxLVs6gsSrI755vpisUJejzEI4lCHg9nL+siatPn8slK2cCEIsnuO6HT/P0Hut7T2NVgNpyPw2VQf7xFQvpHojyxbu38pd/PYf5DRWFfApG9JHfbuT3LxwEwCPw2xtewekL6ke93mA0zmu+/ggnzazmlneszfVuKqWUStMXjhHwegj4tKmVyg83AmHJcF7GlJqIXI9VPsH8+fNduOup57GX27jhl88zt76cFbNqOHmOj3n1FfSHYzy2sw0R4T2vWkTPQJRYwnDXxkNEYgmuWDWLNXPrONQ9QFN1kAMdIe547iAP72jlbx86j4aqAP/zl608vaeDfzpnEQ1VQXa39tEXjrHtSC/v//XzyX1o7gwVbSCcSBj+tvUo6xZN46LlM/ifu7fy7N6OrALhL969lf0dIfZ3hPKwp0oppdJd870nuHjFDD78mmWF3hU1RbgRCDcD81JOzwUOZdrQGHMLcAvA2rVr9ffnMegORfn+I7v4zfoDzJ9WwX0fPJcyv3fU633+qpPpGYwyvbrsuMs+8OqlXPi/D/Oh32xgWmWAezcf5e1nLeA/Ll+OyND3m3jCcM9LR3h4Rwu/Wd9MtIhLB7Yd6aVnMMZbzpjHG0+by08e38PWwz2jXm/r4R5+8ZQ1qfSEpspc76ZSSqkMmjsHaOkJF3o31BTixm8PdwHvsLtHnAV063Ki7vvSPdu4+eFdnDynlu9ed1pWQTBAmd+bMQgGmF1XzleuWcWzezu5d/NRPnHZSXzuqpOPCYIBvB7hilWz+IezFgAQjRXvyrd/23oUgHWLGwBYPqsmq0D49mcP4Pd6uPCk6UQy1FgrpZTKLWMMoUiMWBEnW9TkM2pGWERuBc4HGkWkGfgU4AcwxtwM3A1cDuwEQsC7crWzU9njO9u4aPkM12tXX7d6Nstn1TAYjbNyds2I2/q91vemTJPxCqm9L8xN9+9gU3M3Lx7s5qLlM5hTVw5YgfBDO1oZjMaH/fJgjOH+LUc5Z2kjDZVBNh8aPXBWSinlrnAsQcJAogQmZavJY9RA2Bjz1lEuN8D7XdsjdZyDXQPs7wjxzlcszMntL5leldV2yUC4iL6tt/QM8sbvPcHRnkHOXDSNK1bN4jOvX5m8/MSZ1cQThr3t/Zw0M3Ogv/lQDwe7BvjXC5fw4sHuogv0lVJqKnC6GWlGWOVTwVaWU9n586ZD/H1bKwBn2T/3F0rACYSLqDTipvt30NYX5jf/fDanzj9+QtyiBqved2/b8IHwXRsP4fMIr1kxk21HeokU0eNTSqmpIhSxAuF4Qo/BKn80EC5irb1hbvz1CwBcvGIGy2dVF3R//D6rdriYMqYd/REWNlRmDIIBFjZa3S32tGXuBBGNJ/jjhoOcv6yJ+soAAZ9Ha4SVUqoAhgJhzQir/NFGfUWsoz8CwNmLG/i/a9ccN4kt33ye4qsRjsYTI/abrC7z01gVYG9b/3GX/eCR3Sz9z79ytCfM29ZZ7fwCXk9RPT6llJoqBjQQVgWggXAR6wxZgfCNr15CRaDwyftkaUS8eA5S0bhJ1i4PZ2FDJXvajw+Ef/L4HgCuWDWLV580A7DqoBPGWlxEKaVU/gxojbAqAA2Ei1iXHQjXVfgLvCeWYiyNiMYT+DwjZ8oXNlZmzAj3R+Jcc/pc/u8ta5LnOdnlYgr2lVJqKghFYoBmhFV+aSBcxDpDUQDqKwIF3hNLMbZPG600AuCkmdW09IZp6RlMntcfjtE9EGVxUyW+lIyy8xi1TlgppfJLSyNUIWggXMSc0ohiCYSdzGukiLKl2ZRGnLloGgDP7O1Inne4ewAg2W/Y4QTV2jlCKaXyy5ksp6URKp80EC5iXaEoQZ+H8kB2q8jlmogU3WSyaDyB3ztyacSKWTVUBrw8s2coED7YZWWHZ9WmBcLe4iv/UEqpqcCpEU5oIKzySAPhItbZHymabLDD55WimkgWiSdGzQj7vB7OWDSNuzYeYou9atzhLisjPLvu2OWnk6URmhFWSqm8GtCMsCoADYSLWGcoWjQT5Rx+r6eoJpLFsiiNAPjv167A5/HwlXu3AbDtSC8Br4cZNccGwkOT5TQQVkqpfNI+wqoQNBAuYl2h4ssI+73FteBENqURAIubqnjtqlk8tbud3sEof9xwkItXzDguiB5pstyBjpAGyEoplSOhqHaNUPmngXAR6wxFqK8sroxwwCtFtcRyNIvSCMe5JzYyGE3wmT9toTMU5Zq1c4/bJjBMaURnf4RzvvJ3PnXX5onvtFJKqeMMakZYFYAGwkXgl0/t43/v237c+R39EeqKLSPsK67JcpFY9oHwukUNlPu9/O65ZlbMquHcpU3HbTNcH+GuAauV3eM72ya4x0oppTIZ6hpRPJ8xavLLKoIQkUtFZLuI7BSRj2e4vFZE/iQiG0Vks4i8y/1dnbz+686X+NaDO3kiJcjqDkXpDEVZMK2igHt2PJ9HiBbRt/Vo3IzaR9hRGfTxo3eu5aSZ1Xz+DSfjzbAQx3C9kuP2gdlb4GWulVJqsgo5XSOK5yNGTQGjRhAi4gW+A1wGrADeKiIr0jZ7P7DFGLMaOB/4XxEprlRmETt1fh0Av3hqX/K8na29ACyZXlWIXRqW3+spqtKIWGL0leVSveKERu7593M5bX59xsudeuP00ggnLs4UPCullJq4Qc0IqwLIJpV2JrDTGLPbGBMBbgOuTNvGANUiIkAV0AHEXN3TSczvsYZh25He5Hk7W/qA4guEA0VUGmGMyWpBjbFILqiR9hidA7MGwkoplRvJrhFF1JlITX7ZRBBzgAMpp5vt81J9G1gOHAJeBP7NGFMc0VIJiNpB1t72/uRa6ztb+gj4PMytL67SiGJqn+bsR7alEdkIDFMa4dyXBsJKKZUbTmlE3BTHZ4yaGrKJIDJ98qe/Si8BNgCzgTXAt0Wk5rgbErleRNaLyPrW1tYx7urk5cyQNQZePmplgrcd6WVxY2XRBV5+rxRN+zQnWM2mfVq2hlti2TldbOOhlFKTxUBE26ep/MsmEG4G5qWcnouV+U31LuD3xrIT2AOclH5DxphbjDFrjTFrm5qOn7E/VcXiJlkCselgN0e6B3liVzvnLSu+58jv9RTNynJOIOzzuJcRHm6yXDhmZSo0EFZKqdxwlljWleVUPvmy2OZZYKmILAIOAtcCb0vbZj9wIfCoiMwAlgG73dzRySyeMCxurMTnEb794MvsaukjYQzXnbmg0Lt2nGIsjfC7WBoxtKDGsY8xmRHWrhFKKZUTA9pHWBXAqBGEMSYG3AjcC2wFfmOM2SwiN4jIDfZmnwNeISIvAg8AHzPGaMPVLMUSCfw+D1++ehVHe8L89Im9vGbFDOY3FFd9MFhlCMUyWc7Zj0AeSyM8mhFWSqmc0CWWVSFkkxHGGHM3cHfaeTen/H0IeI27uzZ1xBMGn0dYPa+Oz7x+Jf9733Y+/Jplhd6tjIppieWhGuHcT5YLx5wyDA2ElVLKbcYYLY1QBZFVIKxyKxo3ydrTf3zFQq5bNx+fi8GdmwLe4mmflotA2Jl4l94rWSfLKaVU7oRjCYwBj0BCA2GVR8UZbU0x8YRJ9hIGijYIBmeyXHEcpCIxu0bYxdIIn9eDR47vI+xMlvNojbBSSrnOKYuoCvqIJQxGW6ipPCneiGsKiSUMXheDuVzyFVGNsLPIhZsZYef2jg+EtTRCKaVyxemhX13mB3SZZZU/GggXgfgYlwkuJL/Xc9xEskLJRWkE2KvnxdK6RsR1spxSSuWK0zGiusyq2NQJcypfNBAuArGEKZnaU2uJ5eI4QA2VRrgcCHs9ROLxY84LR7V9mlJK5YozUU4DYZVvGggXgVjclFBGWJIlCYWWi5XlwAr2ncDX4WSEE1q3ppRSrkutEQaK5nNGTX4aCBeBeMLgdXF1tFxyFtQohokMuSqNqC330z0QPeY8pxxEsxRKKeU+pzSiyqkR1jhY5UlpRF+TXCyRcD2rmStDSxAXPiBMriznciBcV+GnK3RsIOx0jdD+lkop5b700gjNCKt80UC4wBIJQ8KUTn/aZJ/dIugckVxZzufuc1dfEaAzFDnmPM0IK6VU7jilEdVBrRFW+aWBcIHF7RKD0qkRzrzyWiHkqjSivnL4QFizFEop5b4Bu32aUyMcL4LyOzU1aCBcYM633lKpES73e4Ghb++F5ATCbi9AUl/hpzMUPaYOOqwZYaWUyplQWvu0Ylm4SU1+pRF9TWLJYK5EMsJOs/PewViB9wQicfdXlgOrNCKeMPSkPMahjLAenJVSym1OjbAzWU6TDipfNBAuMOfN7iuRyXLOt/XewegoW+ZezKkRdj0jHACgK6U8QjPCSimVOwOROAGfJ5nY0NIIlS9ZRRAicqmIbBeRnSLy8WG2OV9ENojIZhF52N3dnLycDGPpZISdQLjwGeHc1QhbGYnOlM4RyYyw/lynlFKu64/EqAx48dllgpp0UPniG20DEfEC3wEuBpqBZ0XkLmPMlpRt6oDvApcaY/aLyPQc7e+kU2o1wk5pRE8RZIRz1z7Nygh39qdkhOOaEVZKqVzpCkWpqwjgHM416aDyJZsI4kxgpzFmtzEmAtwGXJm2zduA3xtj9gMYY1rc3c3Jq9QywjVFlBF2srRu1whPcwLh1NIIu35Nf65TSin3dQ9EqS33J5NCuoqnypdsAuE5wIGU0832ealOBOpF5CEReU5E3uHWDk52Tp1rqfQRLqbJcoOxOAGvBxH3J8sBdKRkhCOaEVZKqZzpHohSV+FPJoV0YrLKl1FLI4BMUUb6K9QHnA5cCJQDT4rIU8aYHcfckMj1wPUA8+fPH/veTkKxEpssV+a3JjMUw2S5wUic8oDX9dutLvPhEY5ZXU77CCulVO50haIsaqzEYwfCcT3WqjzJJiPcDMxLOT0XOJRhm3uMMf3GmDbgEWB1+g0ZY24xxqw1xqxtamoa7z5PKsmuESVSIywiVJf5i6JGOBSJU5GDQNjjkeNWl3My4HGtW1NKKdd1hSLUlQ9lhItgzSY1RWQTfT0LLBWRRSISAK4F7krb5o/AOSLiE5EKYB2w1d1dnZycCQGlUhoBVsa0GEojQtHcZIQB6ir8yUA4EkvQPWAF/vpznVJKucvp215bEUh+FuqvbypfRi2NMMbERORG4F7AC/zYGLNZRG6wL7/ZGLNVRO4BNgEJ4IfGmJdyueOTRbzEJstB8QTCA5F4cqU7t9VXBOjst4Jfp1bYI1ojrJRSbnNK7erK/clAWI+1Kl+yqRHGGHM3cHfaeTennf4q8FX3dm1qiNrfer0lUiMMUB30F0WNcCgSy0lpBEB9ZYADHSEA2vrCAMyoKSuKpaWVUmoyceZj1FVoIKzyrzQKUyexUs0I9wwUQUY4mqA8kNV3uTGrTymNaLczwtNryvTgrJRSLuuyS89qj6kR1mOtyg8NhAvMqREulclyAHPqy9nfEUq2fiuUgUiMilyWRoSiGGNodzLC1UGtW1NKKZc5y9nXVfjxiLZPU/lVOtHXJBUvsfZpAKvm1jIQjbOrtb+g+5GrrhFglUZEYglCkTjtfdZBeoZmhJVSynXdyYxwIPlZmNBjrcoTDYQLzMkwllLXiFPm1ALw4sHugu7HQCROWa4C4Qpr4ZDOUIS2/jABr4e6Cr9mKZRSymWpNcK6oIbKNw2EC6wUa4QXNVZRGfDyyI7WgmZIQ5F4TksjADr7o7T3RWioCuDzeDBGMxVKKeWmtr4wHrGOu05phP76pvIlNzONVNaiJdhH2OsRrlg1i9+sb2Z6dZBV8+p43apZri91PBJjDAPR3JVGTKu0AuGW3kFae8NMqxz6yS6WMARKaLyUUqqYtfVZx1ivR5LzZTQQVvmigXCBldrKco4vX72KnoEYP3xsDwBLp1exfFZN3u5/MGqVlOSqa8TyWTX4vcLTezrY2NzFRctnaFsfpZTKgba+CI1VQQCcj0I9zqp8Ka3oaxJyaoRLabIcWEstv/+CJcnT/eH8tlMLRaz7y1VGuDLo49T59fzg0d10haK8cklDSu2ado5QSim3tPWFk4GwkxTSGmGVLxoIF1gp1gg7Tplby/fffjoAA9H8LjThLGyRq5XlAM5Z0oixj8VnL27UjLBSSuWAFQhb5WjJ46zR46zKDy2NKDDnW28p1QinmlNXDlgdHPLJCbzLc5QRBrj2zPl0hCJMqwgws7ZMZzMrpVQOtPUOlUYkA+EC96lXU4cGwgVWigtqpHIC0XxnhJ3AO1elEQBN1UE+9bqVydNencShlFKu6g/HGIjGaUgLhDXhoPKlNKOvSSRegn2EUzmlCfnOCCdLI3IYCKfz2u8WPUArpZQ72uyVO53SiKDPOtBGNCOs8kQD4QJzgip/iU2WcyQD4XxnhKPOZLn8/aiRzAjHNRBWSik3JAPhaisj7ATCTmcgpXItq0BYRC4Vke0islNEPj7CdmeISFxErnFvFye3eInXCBeqNCIfk+XSadcIpZRyV2uvtYR9k10aISIEfB7Csfx+pqipa9RAWES8wHeAy4AVwFtFZMUw230ZuNftnZzMYiXaR9gR9HkQgcF8l0aEc18jnE67RiillLuGSiOCyfPKfB7CmhFWeZJN9HUmsNMYs9sYEwFuA67MsN0HgDuAFhf3b9Ir9YywiFDu9yYztPnSZ/ctrgrmrzTCp219lFLKVU4g3GDXCAOU+b0M5vlXRjV1ZRMIzwEOpJxuts9LEpE5wBuAm0e6IRG5XkTWi8j61tbWse7rpBS1JwSUYh9hR7nfm/fSCGcBj8o8BsLJ2cxaI6yUUq5o6wtTV+HH7x0KR4J+D+GYZoRVfmQTCGeK0NIjgW8AHzPGjBgNGWNuMcasNcasbWpqynIXJ7d4wuAR8JRwIFxWiEA4Eifg9RDw5a+kxFn9T0sjlFLKHe0pyys7ynyaEVb5k006rRmYl3J6LnAobZu1wG0iAtAIXC4iMWPMnW7s5GQWS5iSrQ92lAfyf9DqD8eoDOavPhiGukZo+zSllHJH6qpyDi2NUPmUTSD8LLBURBYBB4FrgbelbmCMWeT8LSI/Bf6sQXB2orFEMtNYqsr93rz3EbYC4fyuB+PTyXJKKeWqtr4IK2fXHHNe0KelESp/Ro0kjDExEbkRqxuEF/ixMWaziNxgXz5iXbAa2WAsTlkeW4DlQnkg/6URfeFYXifKQeqKR3qAVkopN7T1ho8vjfB7CUViBdojNdVkFUkYY+4G7k47L2MAbIx558R3a+oYjCYoy2Oday6U+710hSJ5vc/+iGaElVKqlA1G4/SGYxlKIzx09GvCQeVHaUdgk0A4liBY6hnhAkyW6wvH8x8I27Oao7r0p1JKTVhHv5VAaUjLCAd9Xl1QQ+WNBsIFNhiNJ5eULFWFKI3oD8eozONiGjC09Kc2eldKqYnrtH9JrK/wH3N+0O/RJZZV3pR2BDYJDEZLv0a4zO9lIJLfg1YhJsslA2GdxKGUUhPWFYoCUFdxbGmEZoRVPmkgXGDhaIIyf2kPQ3kBWt0UYrKc84VF2/oopdTEDQXCx2aEy/y6xLLKn9KOwCaBcCxO0FfaGeEKuzTC5GnpYWNMQfoIa0ZYKaXcM1QakaGPsGaEVZ5oIFxgg5MhIxzwEk8YInmaRDYYTZAw+V1eGTQjrJRSbnK6DaVnhIM+D9G40Q49Ki9KOwKbBCZDH+EKe9JaKJyfALHf7i+Z79IIJyOskziUUmriukJRKgLe434VdT4TtU5Y5YMGwgU2GI1TVuKlEU5mti+cnwbo/fb9VAby3z7N5xE9OCullAs6Q1Hqyv3HnV+mSQeVRxoIF5jVR7i0h8EJSPvztBJQ76CdES7LbyAMdu2aHpyVUmrCukKR4zpGAMne+lqGpvKhtCOwSWAytE9zJq3156k0omfAmmlcmyGTkGtBn0czwkop5YLOUIT6ygwZYb9OTFb5o4FwARljJsUSy06tbn+eSiO67UC4piz/gbBmhJVSyh1dA9GMGWGnXFAzwiofSjsCK3FOl4VSX2K5IpDfQLhn0M4IVxQmI6xtfZRSauK6hqsRtj8T871iqZqaNBAuICezWOpLLFflebJcdyFLI/xebfSulFITNBCJ09EfYXZd+XGXOfM/8pVcUVNbVhGYiFwqIttFZKeIfDzD5deJyCb73xMistr9XZ18wva33clSIxyK5Ofbe/dAFK9HqAzk/3nTGmGllJq4A50hAOZNqzjusmRyZVADYZV7owbCIuIFvgNcBqwA3ioiK9I22wOcZ4xZBXwOuMXtHZ2MnIxw6QfC+c0I9wzEqCnzISJ5ub9UuvSnUkpN3P52KxCeP0Ig3KsZYZUH2WSEzwR2GmN2G2MiwG3AlakbGGOeMMZ02iefAua6u5uTk5NZLPWV5YI+D16P5HWyXCHKIkCX/lRKKTfs7xg+EK4u04ywyp9sIrA5wIGU0832ecN5D/DXTBeIyPUisl5E1re2tma/l5PUUI1waWeERawyhXyWRtQUKBAO+jQjrJRS2eoPx5LZ31T7O0JUBrzUZ5j0nO9fGdXUlk0gnOn354wLgIvIBViB8McyXW6MucUYs9YYs7apqSn7vZykBidJRhisn7LyVhoxqBlhpZQqBR+8fQPnfvXvDKQlSva19zNvWkXGEje/10OZ36OBsMqLbCKwZmBeyum5wKH0jURkFfBD4EpjTLs7uze5DU6SyXIAFUFfXksjCtFDGOz2adrSRymlsvLcPqtq8sndbcnzBqNxntnTwZp5dcNeryroT64iqlQuZRMIPwssFZFFIhIArgXuSt1AROYDvwfebozZ4f5uTk7OT+xlJV4aAdZPWf15Ko3oKWBpRJnfq6sdKaVUlk6YXgXAfZuPJs97eEcr/ZE4V6yaNez1asry9yujmtpGDYSNMTHgRuBeYCvwG2PMZhG5QURusDf7b6AB+K6IbBCR9Tnb40nE+Yk9OClKI7x5yQgbYwo6WU4zwkoplT0nq3v7+gPcv8UKhu9+8TD1FX7OXtww7PWqynz02osnKZVLvmw2MsbcDdyddt7NKX+/F3ivu7s2+Q1OooxwRcBHe9/xEyLc1jMQIxo3TMuwPn0+OBlhY0xB2rcppVQpae8L8/rVs9l8qJvvP7yLc5Y28rctR3n9mtn4vMMngaqCPu0aofKi9FORJcxZIc1pFVPKqoO+vNRz7WztBWBxY1XO7yuTMr8XY4aWx1ZKKZWZMYaO/ghz68t57arZPL+/k98+12yVRZwye8Tr5nMCtpraNBAuoJbeQQJeD3UZ2seUmuk1ZbT0DhJPZGwo4pqdLX0ALJlemEDYWQ5b64SVUmpkPQMxYglDQ1WQC5dPJ2Hgk3e+xOLGSs5aPG3E61qlERoIq9zTQLiAWnvDNFUHJ8VP7POnVRCNG470DOb0fna19hPweTIuy5kPQbvDx2CeJgYqpVSpausPA9BYFeDk2bVcuWY2Xo/w6devHLEsAqxfGTUjrPJBA+ECcgLhyWDetHIADnTktk54Z0sfixsr8XoK8+Whzp6k1xnSSRxKKTWS9r4IAA2VQTwe4f+uPZUXP/0azj1x9HUEquyuEcbk9ldGpTQQLqCWnskTCDvLZO7PcSC8u7WPE5oKUxYBMN0er5be3Ga+lVKq1LX3WRnhhqpA8ryKQHZzYqqCfuIJw4B26VE5poFwAbX2hZOBVambXVeOR6A5h4GwMYZD3YPMqS/P2X2MZnpNGWB9iVFKKTW85s4BgHF9zs2us461B+3bUCpXNBAukEgsQUd/ZNJkhP1eD7Nqy9mXw0C4vT9CJJZgdm1Zzu5jNEMZYQ2ElVJqJM/t62TetHIaqsb+ObeosRKw5oUolUsaCBdIm/2T0fTqwgV1bls9r5bHd7YRy1FrsUNdVmZgdl3hMsKVQR+VAa+WRiil1AiMMazf18EZC0buDjEcJxDe06aBsMotDYQLxOmuMFkywgBXrplDW1+ER3e2jb7xOBzqsp6zQgbC4LSK04ywUkoNZ3dbP219EdYuHF8gXF3mp6k6yO7WvoyXv/WWp/jJ43smsotZO9AR4tdP78/Lfan800C4QJ7f1wnAytk1Bd4T95y/rInqoC+5jKbbiiEjDFZ5RKvWCCulilA8YdjZ0lvo3eD3zzfjEThv2egdIoazuLEyY0a4rS/Mk7vbufvFwxPZxaz95PG9/McfXuRIt/4SOBlpIFwgj77cxglNlQUP6twU9HlZPa+OTc1dObn9w90DlPk91Bd4ARJn8RCllCo2v3vuABfd9Ai/e665YPsQisS4/dlmLlg2nTkT+IxbNrOaLYd7GEzrHLH5UA8Am5q7ieRhcaMth7sB2JijzzZVWBoIF0DvYJRn9nRwztLxf1MuVqvn1bLtcO9xBy43HOgYYHZtecEXIJlVW8bh7kHCMW3ro5QqLn/f1grAp/74Ei05XuBoOJ+8czPt/WH++bwTJnQ7Fy2fQSgS57GXjy2323zICkzDsQRbD/dM6D5GY4xhSzLw7srpfanC0EC4AL730C4GonGuPm1uoXfFdavm1hFLmOSByi2xeIKn9rSzZn6dq7c7HmcunEY4lmD93s6c3P49Lx3mlE/dywdv35CziYdKqcknkTA8s7eD0+bXEYkn+Oq92/O+D8/s6eCO55t5//lLOHPR+OqDHWctbqCmzMefNx065vzNB3uoKfMl7y+XmjsH6LGXet7UPLbPNWOMllOUgKwCYRG5VES2i8hOEfl4hstFRL5pX75JRE5zf1cnh87+CD9+fA9XrpnNKXNrC707rjt9QT0Bn4cfP753xO2MMRhjsg70XjjQRVcoyoUnzXBhLyfmFUsaCHg9/H1bS05u/3sP7aI3HOMPLxzkuw/tysl9KKUmn6f3dNDRH+G6dQt49ysX8dvnmtl4oCtv9//C/k7+7bYXmFlTxvsvWDLh2wv4PLzxtLn8adNhdhy16p5begd5YNtRLlk5kxNnVPH37bk5Djvus+e8nLloGk/v7kjOVcnGzQ/v5uwvPcATu3IzgVy5Y9RAWES8wHeAy4AVwFtFZEXaZpcBS+1/1wPfc3k/XdNd4KVxf/TYHgajCf7l/IkfJIpRY1WQD1ywhL9sOsw//Xw9924+ckyZRCJhuOelI6z7wgOc/Kl7WfGpe/neQ7tYv7eDo8P8jNfSM8in/riZioCXc05szNdDGVZFwMfZJzTwhxcODrvP42GM4ffPN7OxuZtPv24FV66Zzf898DIP72h17T7GIxpP8OSudm1jpFQWth3pYWdL5k4HbjjUNcCD244mjz3GGFp7wzz6ciuf/ONLzKot47JTZnLjq5fQVB3kH374NO//1fP8fXsLiYTJyZLF0XiCnz6+hzd//0m8HuGH/7iW8oDXldv+wKuXUBnw8qabn+S///gSN/ziOaJxw/svWMKrT5rBM3s66OyPuHJf6fa19/PNB17mnKWN3PTm1RgMH/3dJg6M0C/fGEMkluC2Z/bzjb/twBj47J+20JGjfVQTJ6O9KUTkbODTxphL7NOfADDGfDFlm+8DDxljbrVPbwfON8YMO6Vz7dq1Zv369RN/BGNwuHuAs7/4IIsaK1kzr44FDRUEfB4CXuv7gDFgMPb/x562Ljcp26VtbwyxhCGeSP0/QTxhEBE8Ajtb+nhqdweXnzKT7153el4fez7F4gm+99AufvjYHroHolQEvEyvDnKoezA5seGkmdWsXVjPvvYQj9r1XwGfh1ee0EB9ZYCgz0soEmNvWz/bjvRigB+8Yy3nZbFGfT68fLSX13/7cSqDPl65pIGZtWUIgjGGaNxwtGeQnsEo0yoDROMJegdj7O8I0T0QZc28OpqqgtRV+BERYnFD10CEp3d3cLBrgLUL6vn5e84kGjdc9Z3H2dPWz4pZNSxsrKC+IkC530vCfu0BeEXweiRZOy0CHgGPCALE7NdkJJYgGk/g93oo83tJGEMsbognEsnXbDTtdH8kzqZmKxvv9woXr5hBY1WQcr8Xv9dDwjh7McSp4Bax3iPO7SaM856AuP2/wVBXHqC23I9Hhq6XXgfunBSGHqOMcNnQ9UavJ3cjMBjtJo5/lsZzGxO7fjb7MdGnIpvncrRN4sZ6/xzoGCAUiTGrrpyg10PE/vWoqTpIbbkfv9eD3yt4Pc7x+9gbTh63Ofa4PdxlmR7H0HbHXi9hDOFYgkgsQSQeJxozRBPWIkmP7GhFRDh3aSMza8upCnrxeASPSPKzImEMCQP94RgHuwbY2WItHb+osZKAz4PfO/SYEsbQPRAlGjeEIjEe2t5KOJbA6xFm1pTR3h9mMGo9N3UVfr7xljWcv2w6AHvb+vnafdt5Zk8HLb1h/F4hGjcsbKhg9bw66iuspY+j8UTysytmH68GonEqgz6qgj4qg14qgz4CXk/KscK6TktPmCd2tdEzGOPCk6Zz05vXUOvyhOadLX18+Z5tPPZyG5VBH/9x+Um88bS5bD7UzZXffpx50yq48KTp1FX4k8+dI9MhQBg602Adn2Jx6/jkPAddoSj3b7WywXe87xWc0FTFz5/cyxfu3krCwGnz61jcVEXA68EjQlcowvajvcma5YSBMxbWc83pc/nknZsJ+jycuqCehsoADZUBqsv81vHTGOLGWMdDYz23cWPoGYjRH44xrSpAdZkPknHHUIySMIb2vghHugeJxBOc0FRFQ1UAjwg+j/W54PMIHk9h59UADETitPaGOfuEBq46dU7e719EnjPGrM14WRaB8DXApcaY99qn3w6sM8bcmLLNn4EvGWMes08/AHzMGLM+7baux8oYM3/+/NP37ds3/kc1Dm19YW5/9gAbDnSx8UCX671grYOy4PN4jnkBWsFAghk1ZVx16hze+6pF+LyTvzw7Gk/w1O52/rblKG39EWbXllER8LGosZLLT5lFwOfBGMPG5m7aesM8vKOVZ/d20BeOMRhNEPR5WNRYyYkzqnn72QuSDdaLxcYDXXzrwZfZdqTXWnI5JQCdXh2kptxPVyhKwOehMuhjbn05ZT4vWw730BWK0BmyMgQ+j4eqoI+T59RyycoZvG71bMr8VjZlIBLn1mf2c9+WI7T1RejojxCOxhEZOpTH7YPn0Ie1daB0Pmx9HkkGDX47oAhHE8nXqNebetAceu36vELQ52XpjCouWj6DR19u4/GdbfQMRhmIxIklDIITlFp7kymw8HkFrwg+r3XbXo8kg3eArlCE/ohOPFRDplUGmFdfTkXAx5Ee6wt00OfBYP1CVAyvl4DXYyVSfNZ7y+fxUF3m45yljRhjdQZq7w/TH47b78WhpIhHrMC4IuClsSrIkulV7Grt41DXANG49aXVOZ4IQnWZjzK/F59XOGPBNN5w2hye2NXO/vZ+GquCzK0vZ1FTFacvqKcq6DtuX8OxOPe8dIQth3oI+Dxsau5mT1s/nf0RPCnvd+f9XxHwUhX00ReO0R+J0R+O0xeOEYsn8Hk9xwRZdRUBTptfz6Unz+TCk6bnPeh6YmcbX75nG9uO9BJ2oYOEM5ZVZT5Om1/Hh1+zjBNnVCcvP9Q1wA8e3c0L+7vY3xEiZn8hqKsIMKeunNMW1OPzCGcsmsY5SxrxeIQth3r48eN7ePloL+391nE8ZL+GxX49eEXweIb+ri7zURH00dkfoTccO+ZY6yQCPCJMqwowo6YMn0fY1dpHz0AsmYhLuJ/8HzePWL8YX7duAf920dK83/9EA+E3AZekBcJnGmM+kLLNX4AvpgXCHzXGPDfc7RYiI5zO+VbrZBqsF5oc94Ij0+n0bQvcyUCpUubUiju/tFh/Hx9UD3fZMV8C0rbFAKO8PbN5+462yWjHgGyOEKPth4xyK24chia6D9ndxshGSxQ4x+5YwhCPm+QNph6frdOSdjrzLwfHXlfSTme+rWLIsqljhWNx4inRX6bwJlPE4yQLvHkc03jCWF90chg7OL9U56AaZswKnZkeKRA+/qvj8ZqBeSmn5wKHxrFN0bGyU95k9k0pVRhT4RcS5R7n2K1UqqCvdF4T+Qi6RQS/V7+wjSabT59ngaUiskhEAsC1wF1p29wFvMPuHnEW0D1SfbBSSimllFKFNmpG2BgTE5EbgXsBL/BjY8xmEbnBvvxm4G7gcmAnEALelbtdVkoppZRSauKyKY3AGHM3VrCbet7NKX8b4P3u7ppSSimllFK5M+pkuZzdsUgrkN+2EaWjEdAO3MVBx6K46HgUFx2P4qLjUVx0PIrHAmNMxv6rBQuE1fBEZP1wsxtVfulYFBcdj+Ki41FcdDyKi45HadCp2koppZRSakrSQFgppZRSSk1JGggXp1sKvQMqSceiuOh4FBcdj+Ki41FcdDxKgNYIK6WUUkqpKUkzwkoppZRSakrSQFgppZRSSk1JGgjngYj8WERaROSllPNWi8iTIvKiiPxJRGrs888UkQ32v40i8oaU65xub79TRL4pIrqI+DiMZTxSLp8vIn0i8pGU83Q8XDDG98dCERlIeY/cnHIdHY8JGut7Q0RW2Zdtti8vs8/XsXDBGN8b16W8LzaISEJE1tiX6Xi4YIzj4ReRn9nnbxWRT6RcR8ejmBhj9F+O/wHnAqcBL6Wc9yxwnv33u4HP2X9XAD7771lAS8rpZ4CzAQH+ClxW6MdWiv/GMh4pl98B/Bb4SMp5Oh55Hg9gYep2abej45HfsfABm4DV9ukGwKtjUZjxSLveKcDulNM6HnkeD+BtwG323xXAXmChjkfx/dOMcB4YYx4BOtLOXgY8Yv99P3C1vW3IGBOzzy8DDICIzAJqjDFPGuud9HPgqhzv+qQ0lvEAEJGrgN3A5pTzdDxcMtbxyETHwx1jHIvXAJuMMRvt67YbY+I6Fu6ZwHvjrcCtoO8NN41xPAxQKSI+oByIAD06HsVHA+HCeQl4vf33m4B5zgUisk5ENgMvAjfYgfEcoDnl+s32ecodGcdDRCqBjwGfSdtexyO3hn1/AItE5AUReVhEzrHP0/HIneHG4kTAiMi9IvK8iHzUPl/HIrdGem843oIdCKPjkWvDjcfvgH7gMLAf+JoxpgMdj6KjgXDhvBt4v4g8B1RjfVsEwBjztDFmJXAG8Am77i5TDZH2vnPPcOPxGeDrxpi+tO11PHJruPE4DMw3xpwKfAj4tV2Tp+ORO8ONhQ94FXCd/f8bRORCdCxybdjPDrASKUDIGOPUsep45NZw43EmEAdmA4uAD4vIYnQ8io6v0DswVRljtmH9tIiInAhckWGbrSLSD5yM9a1xbsrFc4FDedjVKWGE8VgHXCMiXwHqgISIDGLVDOt45Mhw42GMCQNh++/nRGQXVmZS3x85MsJ7oxl42BjTZl92N1b95C/RsciZLD47rmUoGwz63sipEcbjbcA9xpgo0CIijwNrgUfR8SgqmhEuEBGZbv/vAf4LuNk+vciuKUJEFmDVH+01xhwGekXkLHuG6TuAPxZk5yeh4cbDGHOOMWahMWYh8A3gC8aYb+t45NYI748mEfHafy8GlmJNCtLxyJHhxgK4F1glIhX2Mes8YIuORW6NMB7OeW8CbnPO0/HIrRHGYz/warFUAmcB23Q8io8GwnkgIrcCTwLLRKRZRN4DvFVEdgDbsL4N/sTe/FXARhHZAPwB+Bcn4wK8D/ghsBPYhTXbVI3RGMdjJDoeLhjjeJwLbBKRjVg1eDfYdXeg4zFhYxkLY0wncBPWrPkNwPPGmL/YN6Vj4YJxHKvOBZqNMbvTbkrHwwVjHI/vAFVYNcTPAj8xxmyyL9PxKCK6xLJSSimllJqSNCOslFJKKaWmJA2ElVJKKaXUlKSBsFJKKaWUmpI0EFZKKaWUUlOSBsJKKaWUUmpK0kBYKaWUUkpNSRoIK6WUUkqpKen/AyZKZT3e1Gf3AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 720x504 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, figsize=(10, 7))\n", "\n", "ax = axes[0]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[0])\n", "ax.set(title=\"Smoothed probability of a low-variance regime for stock returns\")\n", "\n", "ax = axes[1]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[1])\n", "ax.set(title=\"Smoothed probability of a medium-variance regime for stock returns\")\n", "\n", "ax = axes[2]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[2])\n", "ax.set(title=\"Smoothed probability of a high-variance regime for stock returns\")\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filardo (1994) Time-Varying Transition Probabilities\n", "\n", "This model demonstrates estimation with time-varying transition probabilities. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn.\n", "\n", "In the above models we have assumed that the transition probabilities are constant across time. Here we allow the probabilities to change with the state of the economy. Otherwise, the model is the same Markov autoregression of Hamilton (1989).\n", "\n", "Each period, the regime now transitions according to the following matrix of time-varying transition probabilities:\n", "\n", "$$ P(S_t = s_t \| S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00,t} & p_{10,t} \\\\\n", "p_{01,t} & p_{11,t}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij,t}$ is the probability of transitioning from regime $i$, to regime $j$ in period $t$, and is defined to be:\n", "\n", "$$\n", "p_{ij,t} = \\frac{\\exp\\{ x_{t-1}' \\beta_{ij} \\}}{1 + \\exp\\{ x_{t-1}' \\beta_{ij} \\}}\n", "$$\n", "\n", "Instead of estimating the transition probabilities as part of maximum likelihood, the regression coefficients $\\beta_{ij}$ are estimated. These coefficients relate the transition probabilities to a vector of pre-determined or exogenous regressors $x_{t-1}$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:45.294641Z", "iopub.status.busy": "2021-10-06T09:59:45.291787Z", "iopub.status.idle": "2021-10-06T09:59:49.164946Z", "shell.execute_reply": "2021-10-06T09:59:49.165426Z" } }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:title={'center':'Leading indicator'}>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the dataset\n", "filardo = requests.get(\"http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn\").content\n", "dta_filardo = pd.read_table(\n", " BytesIO(filardo), sep=\" +\", header=None, skipfooter=1, engine=\"python\"\n", ")\n", "dta_filardo.columns = [\"month\", \"ip\", \"leading\"]\n", "dta_filardo.index = pd.date_range(\"1948-01-01\", \"1991-04-01\", freq=\"MS\")\n", "\n", "dta_filardo[\"dlip\"] = np.log(dta_filardo[\"ip\"]).diff() * 100\n", "# Deflated pre-1960 observations by ratio of std. devs.\n", "# See hmt_tvp.opt or Filardo (1994) p. 302\n", "std_ratio = (\n", " dta_filardo[\"dlip\"][\"1960-01-01\":].std() / dta_filardo[\"dlip\"][:\"1959-12-01\"].std()\n", ")\n", "dta_filardo[\"dlip\"][:\"1959-12-01\"] = dta_filardo[\"dlip\"][:\"1959-12-01\"] * std_ratio\n", "\n", "dta_filardo[\"dlleading\"] = np.log(dta_filardo[\"leading\"]).diff() * 100\n", "dta_filardo[\"dmdlleading\"] = dta_filardo[\"dlleading\"] - dta_filardo[\"dlleading\"].mean()\n", "\n", "# Plot the data\n", "dta_filardo[\"dlip\"].plot(\n", " title=\"Standardized growth rate of industrial production\", figsize=(13, 3)\n", ")\n", "plt.figure()\n", "dta_filardo[\"dmdlleading\"].plot(title=\"Leading indicator\", figsize=(13, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The time-varying transition probabilities are specified by the `exog_tvtp` parameter.\n", "\n", "Here we demonstrate another feature of model fitting - the use of a random search for MLE starting parameters. Because Markov switching models are often characterized by many local maxima of the likelihood function, performing an initial optimization step can be helpful to find the best parameters.\n", "\n", "Below, we specify that 20 random perturbations from the starting parameter vector are examined and the best one used as the actual starting parameters. Because of the random nature of the search, we seed the random number generator beforehand to allow replication of the result." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T09:59:49.171046Z", "iopub.status.busy": "2021-10-06T09:59:49.170335Z", "iopub.status.idle": "2021-10-06T10:00:24.865527Z", "shell.execute_reply": "2021-10-06T10:00:24.866447Z" } }, "outputs": [], "source": [ "mod_filardo = sm.tsa.MarkovAutoregression(\n", " dta_filardo.iloc[2:][\"dlip\"],\n", " k_regimes=2,\n", " order=4,\n", " switching_ar=False,\n", " exog_tvtp=sm.add_constant(dta_filardo.iloc[1:-1][\"dmdlleading\"]),\n", ")\n", "\n", "np.random.seed(12345)\n", "res_filardo = mod_filardo.fit(search_reps=20)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:00:24.872662Z", "iopub.status.busy": "2021-10-06T10:00:24.869855Z", "iopub.status.idle": "2021-10-06T10:00:24.973515Z", "shell.execute_reply": "2021-10-06T10:00:24.974499Z" } }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>dlip</td> <th> No. Observations: </th> <td>514</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-586.572</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Wed, 06 Oct 2021</td> <th> AIC </th> <td>1195.144</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>11:00:24</td> <th> BIC </th> <td>1241.808</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>03-01-1948</td> <th> HQIC </th> <td>1213.433</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 04-01-1991</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.8659</td> <td> 0.153</td> <td> -5.658</td> <td> 0.000</td> <td> -1.166</td> <td> -0.566</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.5173</td> <td> 0.077</td> <td> 6.706</td> <td> 0.000</td> <td> 0.366</td> <td> 0.668</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.4844</td> <td> 0.037</td> <td> 13.172</td> <td> 0.000</td> <td> 0.412</td> <td> 0.556</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L1</th> <td> 0.1895</td> <td> 0.050</td> <td> 3.761</td> <td> 0.000</td> <td> 0.091</td> <td> 0.288</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L2</th> <td> 0.0793</td> <td> 0.051</td> <td> 1.552</td> <td> 0.121</td> <td> -0.021</td> <td> 0.180</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L3</th> <td> 0.1109</td> <td> 0.052</td> <td> 2.136</td> <td> 0.033</td> <td> 0.009</td> <td> 0.213</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L4</th> <td> 0.1223</td> <td> 0.051</td> <td> 2.418</td> <td> 0.016</td> <td> 0.023</td> <td> 0.221</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0].tvtp0</th> <td> 1.6494</td> <td> 0.446</td> <td> 3.702</td> <td> 0.000</td> <td> 0.776</td> <td> 2.523</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0].tvtp0</th> <td> -4.3595</td> <td> 0.747</td> <td> -5.833</td> <td> 0.000</td> <td> -5.824</td> <td> -2.895</td>\n", "</tr>\n", "<tr>\n", " <th>p[0->0].tvtp1</th> <td> -0.9945</td> <td> 0.566</td> <td> -1.758</td> <td> 0.079</td> <td> -2.103</td> <td> 0.114</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0].tvtp1</th> <td> -1.7702</td> <td> 0.508</td> <td> -3.484</td> <td> 0.000</td> <td> -2.766</td> <td> -0.775</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "================================================================================\n", "Dep. Variable: dlip No. Observations: 514\n", "Model: MarkovAutoregression Log Likelihood -586.572\n", "Date: Wed, 06 Oct 2021 AIC 1195.144\n", "Time: 11:00:24 BIC 1241.808\n", "Sample: 03-01-1948 HQIC 1213.433\n", " - 04-01-1991 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.8659 0.153 -5.658 0.000 -1.166 -0.566\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.5173 0.077 6.706 0.000 0.366 0.668\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.4844 0.037 13.172 0.000 0.412 0.556\n", "ar.L1 0.1895 0.050 3.761 0.000 0.091 0.288\n", "ar.L2 0.0793 0.051 1.552 0.121 -0.021 0.180\n", "ar.L3 0.1109 0.052 2.136 0.033 0.009 0.213\n", "ar.L4 0.1223 0.051 2.418 0.016 0.023 0.221\n", " Regime transition parameters \n", "=================================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", "p[0->0].tvtp0 1.6494 0.446 3.702 0.000 0.776 2.523\n", "p[1->0].tvtp0 -4.3595 0.747 -5.833 0.000 -5.824 -2.895\n", "p[0->0].tvtp1 -0.9945 0.566 -1.758 0.079 -2.103 0.114\n", "p[1->0].tvtp1 -1.7702 0.508 -3.484 0.000 -2.766 -0.775\n", "=================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_filardo.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the smoothed probability of the economy operating in a low-production state, and again include the NBER recessions for comparison." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:00:24.979648Z", "iopub.status.busy": "2021-10-06T10:00:24.977495Z", "iopub.status.idle": "2021-10-06T10:00:25.734569Z", "shell.execute_reply": "2021-10-06T10:00:25.735390Z" } }, "outputs": [ { "data": { "text/plain": [ "[Text(0.5, 1.0, 'Smoothed probability of a low-production state')]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 3))\n", "\n", "ax.plot(res_filardo.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec[\"USREC\"].values, color=\"gray\", alpha=0.2)\n", "ax.set_xlim(dta_filardo.index[6], dta_filardo.index[-1])\n", "ax.set(title=\"Smoothed probability of a low-production state\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the time-varying transition probabilities, we can see how the expected duration of a low-production state changes over time:\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-10-06T10:00:25.740326Z", "iopub.status.busy": "2021-10-06T10:00:25.738258Z", "iopub.status.idle": "2021-10-06T10:00:26.942895Z", "shell.execute_reply": "2021-10-06T10:00:26.943800Z" } }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:title={'center':'Expected duration of a low-production state'}>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0S9vKd1JKIcYGCYyFEEII0S9SSiHGGgmMhRBCCNEv1nRtMo+xGCskMBZCCCFEv1gL3kmNsRgrJDAWQgghRL9EpMZYjDESGAshhBCiX6TGWIw1EhgLIYQQol+klEKMNRIYCyGEEKJf7FKKsJRSiLFBAmMhhBBC9ItdSiEZYzFGSGAshBBCiH6xpmmT6drEWCGBsRBCCCH6xV4SWgbfiTFCAmMhhBBC9ItM1ybGGgmMhRBCCNEvZlxMRENEyinEGCCBsRBCCCH6JazbgmEZgCfGAgmMhRBCCNEvkajAWAbgibFAAmMhhBBC9EtUXExQ6ozFGCCBsRBCCCH6JTpLHJaZKcQYIIGxEEIIIfolupRCMsZiLJDAWAghhBD9El1WLDXGYiyQwFgIIYQQ/RI9RZss8iHGgh4DY6XUw0qpCqXU9qjbblZKFSulNpv/zo+67walVI5Sao9S6nOHasOFEEIIMbwiMl2bGGN6kzF+FDi3k9v/obVebP57E0AptQD4BnCE+Zx7lFLOwdpYIYQQQowc7UsppMZYjH49BsZa65VATS9f70LgGa21X2u9H8gBjh/A9gkhhBBihNLRg++klEKMAQOpMf6hUmqrWWoxzrxtIlAY9Zgi8zYhhBBCjDHtpmuTUgoxBvQ3ML4XmAksBkqBv5m3q04e2+k3RSl1jVJqvVJqfWVlZT83QwghhBDDJToWDoallEKMfv0KjLXW5VrrsNY6AjxIW7lEETA56qGTgJIuXuMBrfUSrfWSjIyM/myGEEIIIYaRLAktxpp+BcZKqeyoXy8CrBkrXgW+oZTyKqWmA7OBtQPbRCGEEEKMRBGpMRZjjKunByilngZOB9KVUkXA74HTlVKLMcok8oHvAWitdyilngV2AiHgOq11+JBsuRBCCCGGlWSMxVjTY2Cstb6kk5v/083jbwNuG8hGCSGEEGLki46FQzJdmxgDZOU7IYQQQvSLrHwnxhoJjIUQQgjRL7LynRhrJDAWQgghRL9IKYUYayQwFkIIIUS/yOA7MdZIYCyEEEKIfolENMpc2kumaxNjgQTGQgghhOiXiAa30wglwlJKIcYACYwHoCUQYsOB2uHeDCGEEGJYRLTGawbGMvhOjAUSGA/AS5uK+fr9q2nyh4Z7U4QQQoghF9Eaj8sMjKWUQowBEhgPQJMvRDiiaQ3I4n5CCCEOP5EIbYGxZIzFGCCB8QBYB4FAWOqqhBBCHH7aZ4zlXChGPwmMB8DqNgqE5GAghBDi8BPR2h58JxljMRZIYDwA1mTmQblKFkIIcRiKaPDYs1JIYCxGPwmMB8AupZCMsRBCiMOQkTFWKCWlFGJskMB4AKyDgNQYCyGEOByFIxqlFG6HQ0opxJgggfEASMZYCCHE4UxrcChwOpQExmJMkMB4AKzBd1JjLIQQ4nAU0RqHUrgcSuYxFmOCBMYDIBljIYQQh7OI1jgcCpdT2QPShRjNJDAeALvGWAJjIYQQh6FIxCqlkBpjMTZIYDwAYVngQwghxGHMKqVwOxVhKaUQY4AExgMQlFIKIYQQh7GI1jgdCqdDEZRSCjEGSGA8AGF7gQ+5ShZCCHH4CWuM6dqcDlngQ4wJEhgPQNBeEjo8zFsihBCit+paApTUtQ73ZowJWuu26dokSSTGAAmMB8C6OpaMsRBCjB5/enMX3/3v+uHejDEhojVOc7o2mbpUjAUSGA9AUFa+E0KIUaGuJcAvnttCkz9EZaOfsnrfcG/SmBCOGKUUHpdDAmMxJkhgPABWxtgvg++EEGJEW51bzXMbithaVEdLIExdaxCtpbdvoKxSCq/LIedCMSb0GBgrpR5WSlUopbZH3ZaqlHpPKbXP/H9c1H03KKVylFJ7lFKfO1QbPhLIyndCCDE6lJoZ4tZAmNZgmHBE0+QPDfNWjX7WrBQel0NmaBJjQm8yxo8C53a47dfAMq31bGCZ+TtKqQXAN4AjzOfco5RyDtrWjjDWKj9yMBBCiJGtrMEIjJsDYVoCxoDpupbgcG7SmBDR4FAKj9MhZYViTOgxMNZarwRqOtx8IfCY+fNjwJeibn9Ga+3XWu8HcoDjB2dTR55QRDLGQggxGlg1xS3+EK1mYFzfKoHxQEUiGqXA43LgD8q5UIx+/a0xztJalwKY/2eat08ECqMeV2TeNiaFwrLAhxBCjAZ2YGyWUoAExoPBWvnO63JKxliMCYM9+E51clunoxuUUtcopdYrpdZXVlYO8mYMDSmlEEKI0aG0wZi3uCUQoiVg1BZLKcXARTRSYyzGlP4GxuVKqWwA8/8K8/YiYHLU4yYBJZ29gNb6Aa31Eq31koyMjH5uxvCySinkKlkIIUYurTXl9X4AmvxhfGaXf11rYDg3a0wIR5dSSGAsxoD+BsavAleaP18JvBJ1+zeUUl6l1HRgNrB2YJs48uwoqac1EJZSCiGEGAVqmgN2AqO6yW/fLhnjgdN2KYUDv6wCK8aA3kzX9jSwGpirlCpSSl0N/Bk4Wym1Dzjb/B2t9Q7gWWAn8DZwndZ6TH1TWgNhLrp7Fc9vKIxa+U4CYyGEGKlKoxbzqG5uyxI3SI3xgEU0OJWUUoixw9XTA7TWl3Rx15ldPP424LaBbNRI5guGCYQj1LcGZeU7IYQYBaJXuauSjPGgCmuNwwFec7o2rTVKdTbcSIjRQVa+6yMrCA6EIm3TtYVk9SQhhBiprDmMMxO9VDe1ZYylxnjgrEDY63aiNQTDcj4Uo5sExn1kdRX5wxFC4bafhRBCjExl9T6cDsWU1DgqJWM8qOxSCqcRTkgPqhjtJDDuo2AnGWOpqxJCiJGrrjVAcqybxBiXfbxOjnXLPMaDIBzROMxZKUDOh2L0k8C4j4JRM1HIyndCCDHy+YMRYlwO4rxtw2qyk2MkMB4EEbOUwgqMZWYKMdpJYNxHVhAcjCqlkCtkIYQYuQLhCB6Xgzi3075tQkqslFIMAm0u8OGVjLEYIyQw7iOrfsofimAmjCVjLIQQI5g/GMHrchLfIWPcGgzjC0qGcyCMJaGllEKMHRIY91HQ/NK3BNoOpnIgEEKIkcvKGMd62jLG2ckxgMxlPFBGjXHb4DtZ/U6MdhIY95GVMW4JhA66TQghxMjjD4XxuhzERwXGWUlmYOyTwHggtMaerg0kMBajnwTGfRQMt88YuxxKMsZCCDGC+YNWxtgopXA6FEmxbgB8QTl+D0REa5wO2qZrk/OhGOUkMO6jgLmYR6sZGMd6nPZqP0IIIUaeQDjSLmMc53YSa2Y4pcZ4YMLaLKWQWSnEGCGBcR91zBjHeYzVfsIRCYyFEGIkassYG8FwjMdJjB0YS4azv7TWbaUUMvhOjBESGPdRsEONcZzZNSd1xkIIMTIZGWMn8ebxOs7jJMZtnP5aJWPcb1ZHqTM6MJZzoRjlJDDuo44ZY6s7LhiSjLEQQoxE/qAx+C7OzBjHSinFoAibkXH0dG1+ycCLUU4C4z4KmCvfWVkG60DrD8vBVQghRiJ7gQ9vdMZYAuOBiliBsUPhdRntKRljMdpJYNxHVv2U1YVk1axZS0ULIYQYWawFPqxERpzHhdcspfBJTWy/WefB6MF3UmMsRjsJjPuo4yp31oF2OA8GZfU+3t5eNmzvL4QQI5nfyhhbpRTRGeOAZIz7K9JZKYXMSiFGOQmM+yjYIQC2BnMMZ2D89NoCvv/EBuplBSchhGhHa00gZE3X1lZKITXGA2fNxhS98p1kjMVoJ4FxH3XMGLeVUgzfwcCaIWNfeeOwbYMQQoxE1kps0dO1xXmcuJ0OnA6FTzKc/WbNUupwKNxOhVISGIvRTwLjPgp0qCW2B98N48HAeu89EhgLIUQ71mAwr8uB12UEw7FuI3Mc43LQGpBArr90VCmFMrPGsiS0GO1cw70Bo83BGWNXp7cPJWt6nL1lEhgLIUQ06/jodTlQSvGzs+dw8qx0wOjxk4xx/0WXUoCRlZfAWIx2Ehj30UgcfGcd2CVjLIQQ7bVljI1j9XWfnWXf53U5pcZ4AOxSCiMuxutyynRtYtSTUoo+6hgAj4TA2MqI7ClrtLu2hBBCGIt7APb0bNFi3A5ZkGIAdNQ8xmBk5aU9xWgngXEfdbwatle+G8arZCtjXNsSpKopMGzbIYQQI411zLZmTYgW63HKktAD0LbyXVsphWSMxWgngXEfdVzII86arm2Ya4ytriyZmUIIIdrYNcadZYyllGJADi6lcBCQmm0xyklg3Ecd5zFOS/AAUNnoH47NAYwJ1dMTvICRNRZCCGFoyxg7D7ovxi2B8UBEZPCdGIMGFBgrpfKVUtuUUpuVUuvN21KVUu8ppfaZ/48bnE0dGTqWTGQlxZCV5GVHScMwbRH4ghHGxRkBujWnsRBCiB4yxm4HPqmJ7bfoJaHBKFcZ7PE2936Yy1WPrhvU1xSiO4ORMf6s1nqx1nqJ+fuvgWVa69nAMvP3MaNjyYTLoVg4MZntxfXDtEVGxjglzg3IKk5CCBEtEDaOiZ3VGEvGeGDsGmOzaT2uwQ+Mt5fUs25/zaC+phDdORSlFBcCj5k/PwZ86RC8x7DpmDF2ORVHTEgmt7Jp2LK17TPGcpAXQghL9xljCYwHItJh8J33EJRStAbCNPpD+KV2WQyRgQbGGnhXKbVBKXWNeVuW1roUwPw/s7MnKqWuUUqtV0qtr6ysHOBmDJ1AKILTGmkAOM2McUTDrtLhKafwhyJ2xlhGWAshRBt7SehOM8YOfFIT22+6s1kpDkFgDFDTLDMuiaEx0MD4ZK31McB5wHVKqdN6+0St9QNa6yVa6yUZGRkD3IyhEwxr4j1tgzjcDgdHTkwGYHvxMAXGwTCxHidel8M+iAghhGibY97rPnjwXazbKcfMAbA6UNsC48Ff4KPFTPZUy1SkYogMKDDWWpeY/1cALwHHA+VKqWwA8/+KgW7kSBIMR0jwti0Y6HQqspK8pCd4h63O2B+K4HU5ZU5OIcRh7/WtJSzf03basbrgva4uSilCYVkYqZ+sUgorGW8s8DG456BWs0Sxqmn4Zn4Sh5d+B8ZKqXilVKL1M3AOsB14FbjSfNiVwCsD3ciRJBCOEB8VGLsdDpRSZCfHDMsXNxLRBMIRYtwO4txOqTEWQhzW7vogh4c+yrN/t0spugiMtR7eeehHMyswVodwgQ8r2SOlFGKouHp+SJeygJfML4QLeEpr/bZSah3wrFLqaqAA+NrAN3PkCIYjJJtzBgN2vXG810mzf+iDUuug73U5iZGMsRDiMNcSCOONOha3HSM7D4zBGMDsdR1caiG6F+lYSuE8NIPvQEopxNDpd2Cstc4DFnVyezVw5kA2aiQLhnS7UgqXFRh7XJQ1+IZ8e6xuwhi3gziP1MsJIQ5vLYEQbmfbAOlAD4PvwJjmMjnWPTQbOIYcVErhPnSBcVWzlFKIoTGQjPFhKRiOEGcOvnMocJiBcZzXNSxlDNbk9F6XUwaSCCEOe83+sJ3BBCNj7HE57O7+aDEuK2Msx83+6FhK4XU5CYQiRCLaPjcOhNbaHnxXIxljMUQkMO6jQKitxtgVlYFI8Dpp9g/9PMbRGeNYj4v6VlkSWghxeApHNK3BMEpBoy/Ij57ehNvpwNtJthgg1tNWSiH6ruM8xtaMTS3BcLue1f7yhyL26nrVUmMshogExn0UiJqVwhV1RRzncQ1LYNw+Y+ygvF4yHyONPxSW+kUhhoA1xqIlEGZXaSMf7qnE43SQFNv5qc4qpZCxGf0TsZeENv63kkbN/tCgBMbRPaDVMiuFGCKHYuW7QVXR6ONn/9vMcbe9T2l963BvDsGoWSmiA+N4j5OWYJhIZGin/WlfY+yiJTg8q++Jzr2/s5zFf3iPuhbJdghxqLVEJSeK61oAI5nRWX0xSCnFQFnnO6eZMbaC4aZBShK1RP1dJGMshsqIC4zzq5p5dn2h/fu/lu3jxU3FVDb62VveNIxbZnTTRbRxMHWo9qUU8V4XWoNviJetbDcrhdtJa0C6BEeStfk1tAbDlNYP/cBMIQ43zVEZxsKatkRKZ4t7RN8ugXH/WHkgq8bYShq1DNIMTVbGOCvJK7NSiCEz4gLjR1fl88vntxI050IsrGkl0fyy1QzzqFRrm9wuhcflaF9KMchXyr1lHdC95qwUI/0A/97Ocpbc+t5hM0hwT1kjgNR+CzEEosvZimpb7J+7yhjHuqXGeCDaaoyN3+O9RnsO1nnQOk9MGhdHazBMS0B6RMWhN+IC4wPVzQA0mIFERaOf+dlJwPDOY7i/qpm1+2sA4yDrcToOKqWAwbtS7i2/eUCPMWelaAmERvQqTrtKG6hqClBcN/xlMUNhX7kExuLQq2sJ8NiqfPvi/XAVPTNQUW10xriLUoqo6doOd5sL67jx5W19On+0TdfWNm0pMGjjbaza70njYgGZy1gMjZEXGNcYV/l1VmDc4GNmZgIuhxrWGqO/vruH657cCBir+3hczoNKKQCah/iK1ird8LodxHqcREb4Kk61Zq1tZePgZf8rG/2sz68ZtNcbLA2+ICVmCUV9iwTG4tB5e3sZv391Bw9Grfh2OFidW92ufj/6+NsuMO5kcQ+IXuBDAuN3d5TxxJoCGnxGGzb6gnaiqitdlVIM1nnQyhBPTY0DoHwY1goQh58RFRiHI5pCKzBuCRIIRahuDjA+KYZx8R57HsONBbWc848VQ1q2UNMUoNF8P7fTgcepOmSMrSvl4c0YAyO6TMEKECsaB+8A98DKXC596FNCI+yCYF9UTbxkjMWhVN5gXGje+f4+8iqHdyzGUPEFw1z2n0/57+oD9m3RPXYlUb1SnS0HDdGlFCP3mDlUrGSUdYy+64McvnLvqm6fYw2+s06Fgz34zvq7zMxMALATDWLoRCKaG17cyubCuuHelCEzogLj0vpWgmHji9bQGqTKnJ4lK8lLWrzHzhivyatmb3kT+VXdX80OpujAxu10GDXGzugaY+MAO9Ar5cKaFt7fWd7j4/yhMD/932Z2lDQAbTXGMLKnHjoUGeOSeh/+UKRdhmgk2GuWUYAExuLQqmzyEe9xEgpHeHlzyXBvzpCobg4Qjuh2AXD08TcU0XbA1tV0idZxu9EntatW5t06RudVNVPVFKDRZxy7Hv1kP79+YWu75xxUSmGdBwdrVgozyTMzwwyMD5MSvJGktMHH02sLeXt72XBvypAZUYHxgeq2wRJ1rQG72yQzyUtqvMcefFdsBkA1Q1ha0T4wNgbfOR3RC3wMzmjc/3y8n2uf2tjjtG97yhp5aVMxb20vBcyMsVXnPIIzxlZWYlBLKcxs2f4hvFDqjT1ljcS6naTEuSUwFodUZaOfieNimZ4ez67ShuHenCFh9SBGd6+3dAjIrICqq8F3XpeTpBiXnYQ5nNU2G8coKzAuM7OzVvsu31PJi5uKCUedm9rmMW5fY9zUy/NgeYOPim7KI6xzWUail6QYlwTGw+CAeV4djW2vteadHWXU9jFWHLmBcUvQ7h7MTIwhLcFrB8LWwK3qIZyloiEqsPGYGWN3dMbYMzgZ46omP4FQxD44dcUKAq1pwLxux6gopahraRtUOVgqzZNa3ggLjHMrm5iVmUBKrNu+IBDiUKho9JOZGMP87KTDJjC2jv/Rx5LmDse+ueMTcTlUl4PvADKTYgb1eDRa2aUU5v/WugFl9UbbWOem6LpjK0i2Vtt2OBRxHudBFyjRVuVWcfQt71LXEuAnz2zmlx2y0NGsUopYj5MJKbGjMjgb7axxX6Ol7W9+dQcf7atEa82f39rN9x7fwNPrCvr0GiMsMG62r+zrWoJ2HWpWUky7UgorYzxUI1TDEW3XF4NRSuF2OuzuIxi80bhW4GhdFHQlt7J9EOh1OeyM8Ugupag7BKUUVsZhpNVWFtS0MDUtjuQ4j2SMxSFV2egnI9HL/OwkimpbWbG3kp88s6lddm+sqbYzxn4qGn089FEezf4QLoeyp/hMi/eweHIK09Liu3ydjATvoB6PhkokovnNS9vYUVI/KK9nl1I0B/CHwlR1yMhbbRS9noDuUEoBxgC87hJE24vrqW0JklvZzP6qZgqiEmIdWRnjWLeTiSmxFNdJjfFQyzcvhEbDTFKtgTCPrsrn0U/yeWdHOfevNAYj97XMcoQFxi1MTo0lKcZFfWuQigY/TociLd5DaryHRl+IQChi/4GqBjkwDoQi3P7WroPS7g0dghqPy0FSjLvdkpdWrdpAyxisrHh5D4PTossGPC4HSqm2GuMRmjGORLQdIA7W4Ltmf8jOEu2vaqa8wTciBtKEwhGKa1uZkhpHcqyUUgynUDgy4gZmDiattZkx9rLAnNryF89t4eXNJZSN4VH81rGyutnPM2sLufWNXewuayTO4yQxxjg2J8d5eO77S/np2XO6fJ2MRK/d6zSalDb4eOrTAt7d0fOYlN6wkjK1Lca511LW4CMS0XZial/U2ImOpRRglBV2V0phXdAU1rRQ0ejrNlvfGgzjdircTodkjIeJdeFS3uAb8dNBlpi9HGv31/DmtlLGxbmZNz6xz/vNiAqMcyqbmJYWT7JZk1ne4CMjwYvDoUiN9wBG8GMFn4O9dvrWojruX5HHex0Gv3UMatxOB7d+6Uj+dNFC+zavy4nbqQYhY2wGxj2Mvo3OjlpTEVlTD43UGuNGX4iINkYwD1aGxnodj8vBrtIGzvr7Cq5/bsugvPZAlNb7CEW0kTGOdVM/zEtClzf4+Ms7u7np5e3d1vSNRVc/tp6fPTv8+8Rg0lqzu8womWgwEwYZiV7mZScCbeUFpWM4kLACNa2NadsAciqaiPe6SDAD45RYtz2VWFcyE71UNPhH9PzvnbFqgAcjyeALhu2exvrWYLtAorzBR21LwO592FvRdu7puMAHGGWF3Z0HrYuQTQW1RLQxg0X0wh2RiLaTO62BsH1em5ASS31rcNAG9h0KLYHQQZnV1kCYZ9cVjrr9y5JvBsYRPfKny7P220Z/iDe2lXL63Ewmp8ZR2seehhETGNc0B8ipaOKYqeNIifVQ1xKgvNFPVpIXMLrEwAheLYM9r7F1MulYq2oFxtbx1eNSTE6NY7I5t6IlzuMa8Je2pqWte7ArWut2GWPrwBFnlnP85Z3dnP/Pjwa0HYeCVTc9JTWOWnM6voGy/mZHT06htiVIoy/EG1tL2VhQO+DX7vG9G3ys2Ftp/761qI5XNhcDbfXyk1PjSI51DXvG+KGP8rh7eS6PrznAOzsOn9HF4Yjm0/3VvL2jzJ5C6s739/LWtlJyK5s4/58fHbIuwkZf8JBNKflJTjXn3vkRb24rtS8OMxK9jE+KISXObT9uLC9FHr0S6gbz+15Y20Kcx2n35iXHujt9brSMRC+twfBB9ckjnRWk9FR21xt1UfOs17YE7J4Gr8tBWb3P7p11OhR7y6IzxlaNcftSiu72eytjvP5A2zE6OkN974pcTvvLcpr9IVoDYbsndEJKDNBW+zwS3fn+Ps67c2W7c9tLm4r55Qtb2Vo0OCUvQ0lrTUF1MzMzjFKkkkNQytLkD/Vp4HxxXWuXyZ3iqJKJcETz2XmZTOxHT8OICYzX7jeu+E+YnkpKnDFYqaLBR0ai8WWwMsbbio2dKy3eM+gZY6uxO04DZwU11iTj7i5GOMd7nAM6uLYGwvbSpN2VUpQ3+GkJhO0VAa2MsTX4LreymZ2lDTT4Rkb3vTUvpjW4Y3aWkdXqqfuyoLqlXVnE8j0VB30mKyg4YUYaAOcsyCI9wctf3t5DMBzhons+OWTTzPz9vb1c9eg6O7tx0ys7uPGl7ca2mwMWpqbFkxLrocE3vCsSfrSviqUz0vC4HBSOsGntDqX9Vc34ghECoQgf7qmgvjXIv5bt4y/v7uHZ9YXsLG1g+e6KQ/Le1z65keuf3XxIXtsaYPf39/bamcOMRC9KKU6YnsqxU8cBbVnF0S4QivDwx/vxh9qOBzXNAbu21QpEtDYCs8QYIyCOvkjoSkaikXwZbXXGHWeN6C1/KMztb+7i7uU59m11rW1JprqWoB0AHTkxmfIGn902R01KJq+qye5St6dr61BK0V2CyJoBJHqQaHQ5xaaCWiob/Ty/oYiWYNg+r01MMVa/G8l1xp/ur6HBF2JbcZ19m/WzNYhtNKlqCtAcCHPSzHTg0AzAu+uDHL7w7497nSj73uPrueHFbZ3eV1LXikPB9PR4nA7FZ2ZnkJ0cQ6M/1Kd4aMQExmvyaohxOzhqUgpJsW7qW4IU17baV4lpCUZgvMW86jpqUvIhyxh3vHqxGnTueCOg6zIw9rrsLqFn1xWypY8TYkfPRNFdd7dVRnHyTCMYtDLG1gHEklsx/IPRXttSwrG3vkdhTYv9+eZagXE3J6Ka5gDn3LmCP7+1G4BXt5Tw7UfW8dgn+e0eZ3UjfuGobM6Yl8lNFyzg0hOmsGZ/NR/tq2RTQV27rO5g0VqzYm8l4YhmX0UjORVNbCmso9Efor41yIGaZtxOxfikGJJj3YQj+pBkD6ub/D3WlFc0+thd1sipc9KZNC7WXkTncLDTPPm6HIp3dpSzOreKiIa8ymaeWmOMVN54YPB7F7TWbCmsY9sgZIn2VzXz4Mq8dkFhXpXx3c6paOLhT/YDRkkAwF3fPIanv3sicR7nmMkYv7uzjFte38n7O9suYqqbA3YmK1qcx9lWStGLwDjTTL6MthKjtoxx77dba83l/1nL/SvzuPfDXLs8wpqqzeNyUNcSoKy+lcQYFzPS4ylr8NnB7Cmz0gmGtT1He8SMZRwdMsbdBcZWxjh6XGj0Z8gxz1sPf7KfZn+IWLMndIIZGI/UOuNAKMIuc10Bq7QHsDPFBT2sIjgSFdQY27zUjDV66l17bFU+Vz+6rk/vsau0gSZ/qFeDSH3BMLtKG9nXRWxTVNfK+KQYrj5lOlcunUZynNveb/pSTjFiAuO1+2s4Zso4PC4HKbFu8qubafSHOGKCkRVNjTcO+jtL6onzOJmZkTDos1JYXVL51c3t5hG2Msbzxhvb0tUqSnHmoIMGX5AbXtrGPR/mdPq4rliDSRyq++4xa1GPk2cZV3F2xtjTITCuHN4votaaBz/KIxTRbCqsszPHs7OMuUW7OxG9uLEIXzDCCxuKyKts4qaXjUzsWnPpZ38ozJ3v72VnSQMuh2JmRgIPf+s4JqfGccrsdLSGf7y3D4D9VYN/gZBT0WQHHbvLGnlhY5F9X3FtK4U1LUweF4fToezu3LpDsCz0V+5dxe1v7er2MZ/kVAFw6qwMpqTG2dnsw8HOkgbcTsUXF0/gg13lvLK5hDiPE5dD0egPEet2tuvSBQYls1/dHKDBF6Kk3teufrI/7nhrN7e9uYuv37faHoOQW9HMkqnjmJuVyAdmxtvqXbMWIMpOjhnR3c598Wme8b3fU9aWZaxpDjA7K9Gub7Vis3iPy56VIjnW0+Nr2xnjUTYAzyp3qGoK9HpQVFmDj7X7a1g4MZkmf8iuU683M8bT0owyt9J6HxOSYxmfHENlo99+rwsXTwDgwz1GsiGs20/XBpDgdXY5+E5r3W6aVWv1WCsp5QuGKahpYW5WIgeqW1iVW2WXUmQlxRDjdrC7tIGSulbuXp5DJKJZubeS9eZ5AWDZrnJ2lzXgC4Z5bFX+kA0Y213WQMB8rzXm/uoLhtljlp6MxuPubnPbj5iQRGq8p8eLkg92V7Bsd0WvSkqtDLGViNxYUNfjc/aUNRKOaIpqWzrNMJfUtTIhJZbLTpzK776wAGi7oCqoaSGnovGg53RmRATG4YhmV1kDJ0w3rkpS4tz21eQRE5KN22LdxLqdBMOaaWnxpJt1YQM96USzso/+UITcyiZ7NKYVGF96whRu/Px8ZqR3PvVPvDl/45rcasIRzfbivs0nagVO09LiuxxNrrXm2fWFHDUpmWOmGN2lVsbY7VR216JSbVfew2VzYZ19tbyjpN7OGFsXOwe6mKZHa81TawtIT/DS6A9x4d2fEApHOHV2OpsK6ghHNC9sKObO9/fx3IYi0s0BmpZFk1KI8zjtsptDsfCHlYV2ORS7Sxt5eVMx2clGYFJc12rOsGKU3iSbWavBrjOubwmSX91i96J05aO9VYyLc3PEhCQmj4s77DLGszMT+f5nZhIIR3hrexknz0rn1NnpuByKq06ZRoE5Oj4S0bQEQiy9/QN+9PQme8Wv/siLuiiN3v9W5VTxf2/v7vXr1LUE+GB3BcdPT2VLUT2Prso3Xr+qiZkZCVx96nTAminH1e652cmxYyZjvCbPyMDtiqpvrW4KkJHgJT3BCGyPmmicK+K8LrvGuC+lFBWDUKs7lKLLZHpbBmIFad86aRoA6/ONi8LaqHNPXUuA0nof45NjyEqKIaJhd2kDHqeDmRkJLJyYbJcfdTpdm8fV5Xm5oTVEMKztMUPT0+PxOB32uXd/VTMRDVefMh2P04EvGLF7Qp0OxdIZaazcV8X9K3L5yzt72FfRxE2vbOePr+8EjITJD5/axB1v7eadHWX83pzTdihYx+GzF2Sx/kANgVCE3WWNhCIapUZnYPxJThXZyTFMSY1jQkoMuT1MiWod67rK6Fp2lNSz8OZ3eG9nOUW1Rrv0ZlyQlRSMaOznRSup8zFxXGy726yqg5tf3cG5d37UqxLcEREYtwbDaA1HT0kB2gZMuJ2KOWa3u8OheP4HS3noiiXcf/mx9hdrV2nDoE39Vdnot08uVzy8li/e/TGhcIT61iAep4OMRC/fOXVGl6Ocjfkbw3y0z8jQFde19ml1Pmvg3bzsRKqa/J1OMbUuv5Z9FU1cdsJUkmJdxHmcdsZYKUWc20lWkpdZGQk97sSDoaLBx+l/Wd5p2chjq/JJ8LqYmRHPzpKGdoH/xJRYtpgDKXMrm1h6+zJ7YOX6A7XkVTbzy3PnMiszgZZAmLsvPYavHDOJJn+InSUNPLAy136fTHOApsXjcnD89FTAyKaXN/gHfSTzB7srmJkRz4IJSby6pZjSeh9Xn2IEKcW1LRRUG3MYQ9v+3HHaP0txXStXP7qOtftrOr2/KzmVxkkup7yxyyxnfWuQt3eUceb8LBwOxeTUWBp8oWEfDDhUdpY0sGBCEnOyErnus7MAOG12Or//whE8cMWxnDEvC4Dz//kR33hgDRsP1FHW4OO1LSX88KlN/X7f6FljooPk+1fmcc+HuXbmtyOtNYFQxO7ifn1rKYFwhN9dsIDT5mTwzNpCqpv8VDUFmJkZz4WLJ5CR6CUjwXvQcWl8cowdPD2ztoCL7vnELrvRWo+IaQ17o6rJz76KJpTCznD6Q2Ga/CHSEzxkmeVKR5qBcbzHyfjkGOI9zl4NvkuJdeN2qmHJGOdXNfc6i9VReYPPvgDobTnFPnMO4s/OyyQ7OYZ1ZqbVSlpMT4+nwRcip6KJ6enx9sX+psI6u4b9s/My2VhQS01zoNPp2oySwnCnq7dabbxwkvG3mpASa0yXZ16UWMmcIycms2SakfiJ7gn9zJwM9lc188JGY5DzlsI6DlS3sKu0kUAowqaCOlqDYTYV1tlB/+6y/rVvX20prCMt3sPXjp2ELxjh0ofW8PDHRpnT8dNSu5yvuaC6hec3FHV632ALhCL85JlNvSpbCEc0q3KrOXlWOkopzpiXxZq8Gu5bkdvp4/2hsB2s7i1rpLiutdOsrtaaP7y2E785biCijTLQjiVt4Yjm38v22RdzQLvt7phYi0Q0pfWtdobYkpkYg9OhKK5rJRTRbC3u+bOPjMDYPFhbB7YUs/tr7vjEdmULR0xI5qwFWUxOjbNrji996FN+NIATWLSKRr8dUJXW+6hrCbKtuJ6G1iBJvZj2J96cpubjHCNDB22DBXvDOlnOG5+E1u3naa5vDfLlez7hioc/JSnGxRcWTUApRXZyTLva4oQYF0dPHseszIQhqTH+YHcF+dUtvLK5hGW7yrn51R1orals9PPGtlK+euwklkxNZXtxPXUtAZJiXLicDhZPTrED44c+2k9pvTEnJ8CLG4uJ8zj5/MJs7rn0GJ76zgmcPjfTPlD+/tXt5Fe38Ktz5+FQxgT9HZ1sDha44Cij668vWeM73t5tZ6g68/b2MlblVvPlYyYxNyuRqqYAbqfia8dOxuty8On+Ghr9IWZnGiUj1sm5q2D0nuU5LNtdwaUPreGJNQd63ZVvnUSaA+Eua7+eXVdISyBsZ4immFns/mSN86uaue2Nnfz93T19XmKzvzabdbqRiOblTcU0+Y3pkHozYK60vpWqJr89t++1p8/itouO5CvHTmJaejxnzMviyIlJJHpd1LYEWZtfwyubi3EouOrk6azcV9mnutP8qmb7wJ1XZSxWpBT2BaovGOZTc5Dx5i7GH/zi+a3MufEtltz6nj0AaU5WAkdMSOKyE6ZQ1uDjgY+MSetnpCfgdTn585cX8uOzZh/0WtnJMVQ0+giEIty1PIdNBXXcvTwHrTXXPrmRC/798Yib31lrzdNrC7jq0XXkVzWzp6yRJ81a8DPmZlJYY4xItzJHqfFejpmSwimz0+0TYpzHxWUnTuWtH5/W5XiQaA6HIn2IFvl4Ys0Bbn51h/37D5/eyNWPre/yO3/r6zvtmW7AyBIHwxG01pQ1+FhonjN7OzPF3vJG0hO8pMZ7WDItlXX5NWitqW8J4nE5GG8Gwq3BMCfPSjfKG50ODlS3kG6ec8+cl0lEw/LdFfYFXPR0bfHerleBtbJ1VnZ/QkoMGYleu5Qix7wAmpERz6mzM4D2Y2c+MzcTwB6v8drWEgAC4Qi7yxrssrG6liCvm/ftPYSBcdgMxopqW1ixt5JFk1M4a34Wvzl/HhWNfl7dUkJ6gocTZ6RR2uBrN07A8qc3d/Hz57Z0u9DJYNlcWMfLm0t4zOx56s6OknrqWoKcOts4l/7kzNl8/qhs7nh7d6clFYU1LfaF0oYDtZzz9xX89qWDB8m9s6OctftriPM4WW2eZ89bOJ7SemOWJ+uC6sGP8vjbe3v5+XNb7Nt2lDTY59W8qmZe3lRsx4+VTX6CYX1QYOx0GGN9LNt7Me5jZATGwTATU2LtmSesrucjzTKKzqSZNce+YISNBbUDXtQiEIpQ0xzgyInJxLqddhfc6rxq6luDJMe6engFowuvtL6V/VXNXGkGIts7BMavbSnhqkfX2SNy1+6v4SfPbCIUjtjZZStLHh3M/fmtXWwurOOioyfxl68tsq+ib7nwSH50ZttJ8W9fX8Svz5vHzIwEDtS08MOnNrJ8z6EZdQ+wyhxk8OHeCv7yzh4eXZXP+gO1PL22gGBYc8XSqSyYkERtS5BdpY2kxBl/40WTkymsaSW3somXNhXhUPDmtlKa/CHe3FbKOQuyiPe6mJOVaM84MTEllvFJMWwsqOOMeZlcc9oM/vDFI7jCbOtoXz12Ej86YxZXnjQV6H1gXFDdwr0f5vKAuWKO5T8f7+c3L23jzW2l/PalbRwxIYlrTpthD8g8aWY6yXFuJqbE2jWfR5ulLlZgvDqvmvtX5HLtkxvseuuKRh/PbSjii4smcOKMNG58eTs/f67rJVLBCB5C4Yid/QHa/fzEmgP85Z3dhMIRHl2Vz/HTU+2Lzknj2gLjvk6X95d39vCfj/fz7+U5/OqFrYdslo3txfX86OlNFNW2cNWj6/j+Ext4f1c5P/nfZm57YyfXPrmRqx9b1+NAkBVmHeRJs4z9x+NycOkJU+1pDcGYf/zNH5/Kc99fCsALG4uYn53EN0+YjNbwxrbSXm/3j5/ZxJUPryUQipBXaWTcJiTH2hnjdfk19qwzWwoPPjg3+0O8tqWE46aNo7YlyK9e2Mrmwjq+efwUM2OTyaRxsTxo7pszzIFnZ87P4utLJh/0euOTjW7w5zcUUVTbyrS0OO5fmcv1z23hre1l5FQ02ftqV7TWvL+znB88sYFfPb/1oOPZYPvj67u44cVtrNhbyVl/X8Hn7lzJP97fS3Ksm6+Zn/Hz//6Yr9y7CjBmK/rDhUdy9zePsbOb8V4nMW4nU9LiunyfjjISvYMyg8f7O8u5/tkt7epatdasyqkip6KRW17fyaOr8tlSWEdhTQvbixvsjGdHGw7U8NDH+/ndKzuobw2yt7yR0/6ynOuf3UJDawhfMMJRk43vdV5Vkz3gS2tNOKIprGlh2a7ydj0DeyuamGOO8ThxRirlDX7uW5FHbUuAcXFuxpnHZ5dDsXRmGuPiPZy3cDyAXbKycGIyU1LjeHptgX0McHRY+Q6guZM6YyvZs3BSCmCU+2Qlee2Md05lE5PHxRHjdtoBWVxUxnhaWhyTU2OJ8ziZnBprB8JglDJ8nFNll8ZY5SF7yvuXILKOsx1ZwW0oHOG6Jzey9PYPOOOvK/AFw/z4zNk4HIprTpvJ8utP55lrTuTBK5YwNS0OrY3jbvS+UdHg4/1dxroJXU2jGQoby3B31ctkvU70eX53WQP3fJjD46vz232GT81A9P1dFT2uimn1fFszUjgciv93xmy0pl15yvbier73+HpW7jUe73U5eGlTMc2BMM9tKGJVblW7BMNDH+UxJTWOa06bYd92+YlTcTsVVz68lpte2c6u0gb+/u5eJo2LZVtxPW9uLyVkXvycOjuDRK+LRz7Zz0/+t5m7l+fQEgjx5JoDAEzqEBgDTE2LY0pqHNPS4nqVrOw52hsCvmDYrjuFtkDiiIndBMbm1Wucx0lLIMymwlr7D9gf1qjbrKQYrj9nDtPS4vnLO3tYnVtNROtedckleF0Ew8aiDt88YQovbSpmU0EdLQFjkM8fX9/Fw5/sx+lQfJxTxWPfPp6HP9nPezvL+fIxk6hrCZIU4+L46amkxXv47cvbePm6k1mVU83Tawu55rQZ/Ob8+e3e0xqAZ7HaYGZmPOGI5vWtpewoaeC02RmEI7rTgYOtAWN1IVcP2ZVAKMK24jqmpcXzo6c3MT45hlW51XhcjnbdxX95ew+5lU2cNieDGRkJdsC/saDWDtAWmQfG65/dgi8Y4Ybz5nH7W7v55fNbqG8NcuHREw96f6UUvzpvLtVNAb598nScDsXlS6d1uq3j4j1cf85c+6SQU9FEmVk3150V5hd+dW41vqAxuXyzP8Tf3t1DSyDMU58WMCU1jjsvXozb6bCzkecdaZw8Jo6LJa+qmRi3g3lm0Dw+KYbPHZHFf1cfMD+H0SNx0dETeW59EaFwhJ+dPYcpqXHc/tYuHvxoP1csncqiySmdbuO/luXw+JoDTE2LM5dJbWVveSOfnZdJVZOf297YhS8UJjnWTXFdKzdd0LbPWHXPv3t1Bze+vJ3/fe9EthbVc9SkZGZlJnbZLhWNPt7ZUcZVJ08nI9HL7W/t5sWNxXzl2ElUNPho8IVYsbcSl0NxxdKpB/WuaK357+oDZCfHcM4R4+3bl+0qZ0thHT86czZup4NIRPPrF7eyvbiB1bnV9r5zozn48um1hfZzn1lbwPXnzLV/9wXD/O4Vozfh7PlZrMuvYUJyjD0LSlcmp8YxaVwsk8bFUlTbynHTUpmVmcj87CRe21LCt0+eftBnuePtPWw4UENavJfzFo7nyInJdo3h+7vKyatsZu74RJoDYTYW1HLVo+to8ofwOB1kJXvt3hJLJKJZvqcCfyjCz8+Zy/0r8/hgdwWJMS47IHQ5Hdz1zWO4+P7VRLQ+aB71jiYkGyeIfy7bS3qCl2euWcpP/reJFzcWs3RGGvnVzTy+5kC7v0fHbfrjGzt55JN8MhK9NPtDvLSpmCe+c4Lds9aVmuYAcR6nPf6hN17fWsLDnxj7/ndOmcF9K3OZNz6RIyYkk50cY08NVtnoJ8Zt1J9a5wHA/m5HX/j01nHTUnl0Vb4xaLaHdu3Kuvwarn1qI4FQhKUz0/jqsZMAeG5DEb98fitOh8LjdJDodfHAyjy7dFApeHtHGUoZx4+kGDe+YJi7l+eS6DXmQL/ltZ3sKW8gEIrw6pYSu/dsQXYSTofiH+/tJRjWPP/9pfzj/b18ktPW47VwYjL3XX4sE5JjyClvtPenrx07mTV5Ndzx9m7iPU4mp8bZCaFjp46zyzQuOX4Kr2wusQNOh/kdv/WNXXYZW8eV74BOZ+GxBt4tmpTM906bweePyqay0c+aPCNzvaO4nllmRnBBdhJT09qvF6CU4pefm0ejL8QnuVUU1rQS43YQ73Hx4e4KthTWce3ps3h8zQHqW4PMG59IboUxvVxveg+i3fTKdlblVvP6j06x96m/v7eXB1fm8dR3T+Dx1Qd4e0cZl54whVBYc8VJU+0xUVY7nWgmdawg9Gv3rWZKahwPXLGEX72wldZAmFBEk50cw9s7yvjuaTP437oC1uTVcPuXF9IaCHPFw2vZVlxPjNvB898/ibJ6H75Q2O4NBfjxM5tZnVfNPZcegy8Y5oYXt+E3Ex8Oh+LSE4wE0dr8GhzK+H6+stnogVPA8dPTmJOVYB+3fcEwT68tYNHkFPvvDjAnK4GsJC8r91UxIyOB17eU8Oz6IlqDYVaZ+9xn5mTw7s5yxifFEIpE+OaDnwLwr0uOZlZGAusP1HLj5+ezcGIyd7KPrCQvR08Zx7rfnsUdb+/hqbUFrNxXSVKsm5euPZnLHvqUm17ezhtbS/EFI5w0M421+dX2GK5HPtnPir2VbCuu58QZqZ0em/785aPQaP767l425PdcsjgiAmN/KGIHTGCUVHxx0QQ+tyCry+dYwcbXjp3Mdx9fz9r9NXZQ+MzaAl7dUsJDVy7p9iAZiWhe3FTMGfMy7a6czEQvZx4/BYCPc6r437pCu/C8J18+ZiJel4NrTptBYoyboyal8NqWEo78/TscMSGZbcX1fOukafzg9JlcdPcn3PH2bns6qZc3FROKaMaZy1/ffekxXPbQp5zx1xXUtQRYPDmFn57V9bKmHZ05P4vvnDKdtAQvd7y9m28+uIZNhXX8/gsL7C8JGFewX7tvNZNTY3nquyd2efDQWvOrF7by0qZiXA5FKOpq83unzeD+lXl4XA6+euwknvq0gPQED781g/j52UkkxrhIT/Dy6/PmAcbf2KGMrp2vL5nE1adM55FP8nlzWxnjk2I4dVbnFzkXHT2p120AxsDEiSmx3L08h7uW5/DStSdxlBmUd2bFnkocyujFWJdfw6mzM3hjWyktgTB3XrwYr8vBGfMz8bqME/6JM9L45zcWc/7CbKBtrs2jJqbYFxoOh+K+y47l45wqXA4H9a0BfvT0JjYV1DExJZZ/XLyYaeaAzh+fNYdn1xfxt/f2ctHRE1g6I71dMJ9f1czdy3MIhCNUNfm5cPEEArkR9pY3EY5o7vogB38ojEMpbn9rN5PGxXL2grbAJznWTXKsm8pGP16Xg/P++RHBsGZuViKv/egU1h+owRcMMzUtHodS7C5tIBTRfLSvklBE880TpjA1LZ5luyr45QtbeXFTUbuTMBjTEx05MckeKHnUpGR2ljbwyuYSYtwO/nfNUrYV16OBW17bQTCsWbmvCq01Dodie3EDJ0xP5dP9NZw6O51dpY1UNPr51knTeGt7KdPS4onzOHlmXSHXnj7L7j35w2s7eHZ9EbMzE7jtzV14nA6+tmRSjyVQYJxwPzs3k8fXHOC4acaB9cLFE/jzW7t55JP97YLjD/dUct+KXBZOTGZLUR1v7yhjRno8Shk9WfetyKWgpoXzFo6n2R9m5d5KysyVEE+amcaElFiW765Aa81z64u4d0UuZfU+spNjSE8wuriDYc0Huyu45PgpdgYOYPHkFO677Fh2lzX2eKLPNo9Ztc1BbrvoSMYnx/DMNUvZX9VMZqKXhz7azz/e38ve8ka7l8qitebm13bw39UHuOrk6fzmfCMY+cp9q7jm8fX85MzZnDbHmOWk4wX1U58W8NuXt+FUinnZiZw6O4P/d8ZsYj1OIhHNI6vyeXTVfq47fRbnLczm3g9zeXTVfnzBCEdPSeGmCxbgdjrarSxqbVNGopdFk5K56pTp/Pmt3Xa3KrR99xK8vQ/GLd89dQaPrz7APR/mcPuXj+rx8fWtQW59fSdul7GdORVNfOex9UxKicXlVNy3IpcjJiSRlRTDfStymZEeT2q8h4uOmUhRbSv3r8hl/YEa5o1PJCnGzX0rcvnXsn32ctZWacTPzp5j1tQavWp3XryYv7yzh1vfMGaimZASS2ai1x5k+Z3/rqeuJchlJ05hdmYiCV4XN7+6g8se+pR/X3I0zYGwPSuQx+XgnxcvZlycm/+uPkByrNvu0TttTob9WU+YnspFR0/kjHmZ9m1fP24y/3hvL29uKzM/Q9s+Gu+xMsYHB8ZVjX6UMjL9N5jnh/HJMdS3BvnPx/vJr27hh2cYPaAOh+Ldn56Gp8P+9YVFRkBY1eTnDUqZk5VIWryHZbsr8DgdfP6obHaU1LN8TyUXHzeZP7y2kwPVzd1e+He0taiOJ8wSnn9/kMOvzp3HY6vy+deyfbgciksf+pSWQJifnT2H/3fmwWVMHVklbE3+EFuK6jn/nx9R3xokojWnzk7nuGmp/P29vTy2Kp9bXt9JOGKUIxbVtlBS7+PGz8/n7uU5/Py5LeRWNhEMa5btqmDpjDRcTsXqvGoSY1xc++RGAJZMHcfdlx7DdU9u5F/L9rFoUgrJsW42HKjloqMn8dqWkoNWA/3s3Az+cfFiAuEIj3yST1FtK//31fbfBaUUp87O4J3tZby/sxynQ3HijFR8wQir86pJT/By3LRU3t1ZzhcWZXPW/CzWH6jlwz0V/PzZLUxKjSXG7TDKDt0OPE4HM9KN/TElzsMN58/jvZ1lFNa08vC3lpCR6OX+y4/lykfW8tb2Mn5w+kzOWpDFy5uL2V7cwOcXZvPm9lJ2lzVw32XHcO6R2Z23v9l7tHCikez4j1n73ZVDFhgrpc4F/gk4gYe01n/u7vELowLjBK+Lf11ydLev73I6uP/yJQDMH5/UNoigOcBtb+yi0R/iT2/u4rITp5KdHEtVk5+7l+ewOreaY6aM46dnz+HDPRXc+sYuzl6QZV/dW3NaApw0M41HV+Wzp7yR+dk9f6nmjU+yp3QD+MU5c1k8OYWy+lae21DEVSdP56YL5qOU4oqTptlz9M4bn8jbO8pYkJ1kd2WdOCONJ79zAvetyCWs4a5vHn3QdGzdSYpxc+MFCwhHjFksPt1fw6zMBH770nbe3l7G544YT01zgOc2FFLfGqQs38fvXtnBTRfMJ9btZH9VszFlT0os4+LcPPTRfl7aVMzXl0yiORDmG8dN5v/e3sO24nouO3Eqy3ZXsHhyCtefPQeHgqtPmcF0M9iL97r4+FdnEO9x2ifReK+LoyalEIpEuOXCI3E5Hbz541Mpb/CRlRTTY/a6L+abgxlj3E5ueW0nz31/qR0sBcMRmnwhnt9QRHFdK6tzq/jS0RN5fUsp/119gM0Fdby5vYzp6cZAp45BlsOhuHBxW3bbOjlb2SCLdUCxbLgpnRZ/mPQET7vPmuB18a2TpvHPZftYubeS9AQvV58ynfIGH75gmNV51bidinnZyWwtqmd2ZgJVTX7e21nG4lvKaPSF+Oqxk2gNhHljWynfOmlauxHjAOceMZ44r5MLF0/kNy9uY+HEZP63vpBz71x50KqP0T5j9gAAPPzt47jmv+vZcKCWH352FtPT41k0OZkXNhZz74e5vLDR6J52OxRvbCtFKbhy6VRe3FTMhXd/Yr/mrMwELjl+CvetyGV6WjwVjT5On5vBg1cs4YGVeXxx0QSeW1/IvSty+e5pM/jJWUaAtSavhisfXsvxf3qfiSnG97uqKcC1p8/kR2fM5pw7V1BY09ruZN6Trxw7iXX5NZxkztn57ZOnsamglj+8tpOXN5cwMz2eWLMublpaHC9eexIuh+Kn/9vMy5tLOHFGKifPTOdv7+0lJc7NeUdmEwhHKKv3ceMF89le3MCszHhW59Xw/IYibnndyMYePSWFrCQva/Jq+OYJU3A6FCfPSuP+y4/llE4uED87L5PP9uJzzc1K5E8XLeSEGanMzGgLIK3v5eVLp/Kfj/P44+s7uf6cuXySU2UG8BEqGwO8v6uc7546nd+cbxyzxsV7eORbx/H/ntnMza8ZswBMTYvj1i8dyZq8aj7cU0lxXatdl7hwYjKbCuq4b0Uu72wvY+74RLYV11NUa8w1+usXt/Frc6L+C47KZk5WIt84fnKXAb9Sitd/dAopcW68Liev/vCUdvdPSY3j9i8v5HNdZMC7Mz45houPm8wz6wr4xnFTWDQ5hZK6VrYX15OVFGNM4ak1ZfV+lu+pYHdpAw0+I/Cbn53EfR/m4nYqHrvqeDYW1PLjZzZz3j8/wu1UBMOaf19ytB3QNfiCFNa08PrWUq5YOo3kWDfrDtTwvdNm0OAL0RoIMSszgViPi0uOn4zL4eD7n5lJUqyL7ORYZmclcNPL29lR0sC0tHjGJ8fQEghzxdKp/PuDHE6Zlc4fLzzSPlZNS4/jkgc/5Uvm9y66B8XhUPzhi0cwKzOBrKQYFmQn8d1Tp7crzVFK8Y+LF7drr6QYN7//4hEU1bTwg9NntTuGWRdy0QPPg+EI7+8sZ0NBLalx7Y95Xzp6Ig+szOPWN3YxOTXWnhIOsBMQnbHK2OZmJTJ3fCIf51Rx72XHMD87iW+dPJ0jJiTb2cO/v7cXgBiXk88flc3Js9Lb9WZEIpoSswxyT1kjT60tIC3eqA1+cGUea/fXsOFALWfNz+SqU6Zz+X/W8pVjjHK93shI9PLdU6fz2XmZPPlpAW9sLeX2Ly/krPlZxHqcVDX6eWBlHr9/dQcz0uO54Khs/vVBDvPGJ/LYt49n6cw0XA7Fza/tJDs5hvMXZvPYqnxe2mTUn2cnx/Dkd07gr+/u4az5WXxx0QRcTge/+NxcLn5gDRf8+2McypjN4Yx5mWQkeimpa+UnZ83G63by2pYS/vbuHhbf8p69zecdOb7TXvhTZ6fz/IYikmPdvPfT08hMiuHt7WWszqtmRno8S6aNw+1UXHT0JBZMSOKEGWlcesIUfvn8VsobfFx/9ly7XPa6z85iWnpbj0BSjJt7LzuWvMome2D0tPR4Xrr2ZNbl13COmSydmZGAUvDr8+bx2XmZTEiO4aQukmnRrASsNYtJV9ShqBNUSjmBvcDZQBGwDrhEa93p1nizZ+vCvdvaBaV9cfOrO3hizQGOmpRMRBtXe2fMy7Jrd7wuBxrwOh0snZnGJzlVtAbD9hyzVU0BTpmVzsc5VXz6mzPJMgu1Q+EI33t8A8t2V3Dl0qn84cIj+7V9YGQ7ooOqupYAJ96+jMQYN3ddcjQXP7AGMHbah791XL/fpzO7Shsob/Bx6uwMHvlkP/d+mGsvjjJvfCK3XbSQN7eV8p+P9xPjduB1OTsdKPaFRRP458WL7XqywhpjnsmLj5tCsz9kz5/aW42+IG6no0/drf1R1eQnEIqwcm8lv35xGx6Xg6wkL2nxXrYV19vdXB6ng0A4wkNXLOGx1fl2jRXAb86fxzWnzezxvV7cWMTPnt3S7dVrT3zBMG9tLyUjIYYbXtpKYU0riV4XXreTWZnxXH3KDGLcDi7/z1oe+fZx7Ciu554Pczn3yPF8Zk4G5x45nrzKZv727l7+fvEikmK6LwPSWvPle1expbCO35w/n2OmjmNfeSPhiLGQTozbgVKKiSmxB51MWoJhu+vUUtscIKzbpmRq8IVwOxVxHhfv7CjjufVF/OB0oy3njk886PkdBcMRSut8B9WMrsqt4tXNJdQ0B0iOdXPM1HF8fclknA7Fp3nVxgwQlx4zoP0rGDZGTr+9o4yqJj915rLj9156DOeZvQStgTA3vrydry2ZxNFTUliVW83SGWldvm9+VTMX3fMJtS1BTpyRyqPfPh6308GrW4o5dXaGXcs5FB7+eD+3RJ0kUuLceJwOnA7F544Yz+8uWNCuftSyt7yRTQW1/O3dvVQ0+nE6FMdNG8fszETGJ8fw3VNn2MeCj/ZVcsfbu/EFI0xPj+fzC7P54qIJvL6tlPJ6HwsmJB1UEjYcapsDXPDvj4lozeLJKby/q5xg+ODz41GTklmQncRXjp3EL5/fyv6qZpJiXDz5nRNZOCmZcETz3PpCYj1Olu2qoK41yMNXLjnoYr+s3kd6ggenw5gRoy/nP601LYEw8V4XGw7U2N/Vez7M5eLjJtsX6JZVuVW8u6OcmZkJXHr8lE7/poPlQHUzZ/5tBcpcgczjclDV2LbM9KJJybzS4aLmg93lXPPfDdzxlaP4yrG96xUsqG7htL8s53cXLOBbJ02jwRe0M94WXzDMkb9/h1BEMz09nvrWoL1iYmaiF5dT4VCK0npfuzEXMzLi+e3581kyNZU73tnNuv01nL0gi5+dPQeX00FFg++gaUJ7qyUQYnNBHUtnph0UE2wqqGNediLZyUaJ3ITkmHZJnNvf3M2FiyewaHIKwXCEgpoWVuypZNHkZI6d2nl50/MbilAYi+Ssyq1m+c9P7/QYs6mglmW7KshM8pKdHMtpc9I7vTCpawnwpbs/4dfnzbPPcf5QmJP//AGfX5jNHy480i5DPFTqW4PkVTbZ43h6yxcM86sXtnLyzHQuPn7KBq31ks4ed6gC46XAzVrrz5m/3wCgtb69s8fPOWKR3rtjS2d39UpeZRN3L8+luK6F3WWNfGnxRH593jyeWHOAtAQPmwvqCEU0PzlrDhmJXqqb/Nz7YS7rD9Ryz6XH8N3/rmdHSQMpcW7W//asdgewlkCI37y4jS8dPZHT5/Y++9Qbz64vJMbt5IuLJvD8hiL+uWwvXzlmEj/pQ8lEf/hDYWqbg8R7nfbSqVprNhbU8tqWUsIRzZysBGZmJFBa76OswccJ01NZMq37usKRLhzRPL46n9J6HweqW6hq8rNkWiqp8W6WTEtlelo86/KNA2BJvY/ciiaOmTqO+tYg2UkxvToIVjX5+es7e7jpggXtusD7KxCK0OALdnogy6loYmZGPEopIhE9oJNddZOfika/vcy46Fwkoqlq8pOZ1L+L+OjXKalvZUJy7CENUnoSDEe44cVtTE+P5/KlU3u8iOqoosHHq1tKOG9h9kHB2Gi0vbieSx5YQ3KcmzPmZfKFRROobgowNS0Or8tBnMfVrrRpVW4Vf31nD7ddtFC+O1H2VzXz5JoDFNW2EgxHcJmz9mSnxJCR4O30+9PoC9rno976cI8xx3d3JZMf7qkgOdbN0VPGEQxH+GhfJRsP1FHR6CMU1oQimvHJMUxLi2daehyzMxPb1dWOJaFwZFB7Y6NZ0wcOxnlvKCilhjww/ipwrtb6O+bvlwMnaK1/GPWYa4BrAKZMmXLsgQMHBn07eisQilDe4CMlzt3nL6YQQoixo2PvnhBi7OkuMD5U07V1dlRpF4FrrR/QWi/RWi/JyMjo5OFDx+NyMDk1ToJiIYQ4zElQLMTh7VAFxkVA9MSak4CSQ/ReQgghhBBCDNihCozXAbOVUtOVUh7gG8Crh+i9hBBCCCGEGLBDUiWttQ4ppX4IvIMxXdvDWusdPTxNCCGEEEKIYXPIhg9qrd8E3jxUry+EEEIIIcRgOlSlFEIIIYQQQowqh2S6tj5vhFKVQHfztaUDVd3c35NkoH4Ynjvczx/Odhvo84e73QfSdsO97cPZ7vJd7R/5rvaffFf7R76r/SPf1f4bSd/VqVrrzqdE01qP+H/A+gE+/4HheO5wP384220MtHu/224EbPtwtrt8V0dZu42Bdpfv6hC32wjYdvmujrL3HmjbDeW2Hy6lFK8N03NHwvOH871Hc7sP53uP5nYfqNG8zxyu+5y02/A8X76rw/f84Xzv0dzuw/nevX7+iCil6IlSar3uYoUS0TVpt/6Ttusfabf+kXbrP2m7/pF26x9pt/4bLW03WjLGDwz3BoxS0m79J23XP9Ju/SPt1n/Sdv0j7dY/0m79NyrablRkjIUQQgghhDjURkvGWAghhBBCiENq2AJjpdTDSqkKpdT2qNsWKaVWK6W2KaVeU0oldXjOFKVUk1Lq51G3XayU2qqU2qGU+r+h/AzDoS/tppSappRqVUptNv/d18nrvRr9WmPVYLWb7G/df0+VUkeZ9+0w74/p8HqHxf4Gg9d2ss91+129NOp7ulkpFVFKLe7weofFPjdY7Xa47W/Q57ZzK6UeM2/fpZS6oZPXk32uD+024va5gUx/McCpM04DjgG2R922DviM+fNVwB87POcF4Dng5+bvaUABkGH+/hhw5nB9ppHWbsC06Md18lpfBp7q7jFj5d9gtJvsbz22mwvYCiyKai9n1PMOm/1tsNpO9rnu263D8xYCeR1uO2z2ucFot8Nxf+tr2wHfBJ4xf44D8oFpUc+Tfa4P7TYS97lhyxhrrVcCNR1ungusNH9+D/iKdYdS6ktAHrAj6vEzgL1a60rz9/ejnzMW9bXduqKUSgB+Btw6qBs4Qg1Su8n+Zuiq3c4Btmqtt5jPrdZah+Hw299g0NpO9jlDb76rlwBPW78cbvvcILXbYbe/QZ/bTgPxSikXEAsEgAaQfc7U13YbcfvcSKsx3g580fz5a8BkAKVUPPAr4A8dHp8DzDO7vl3Al6znHGY6bTfTdKXUJqXUCqXUqVG3/xH4G9AyRNs4EvW13WR/M3TVbnMArZR6Rym1USn1y6jnyP5m6GvbyT5n6O67armYqMAY2eeg7+0m+1ubrtrueaAZKMXIdP5Va20Fh7LP9b3dRtw+N9IC46uA65RSG4BEjCsKMALif2itm6IfrLWuBX4A/A/4CCM1HxqyrR05umq3UmCK1vpojKvYp5RSSWYt2Syt9UvDsrUjR5/aTfY3W1ft5gJOAS41/79IKXWm7G/t9KntZJ+zddVuACilTgBatNbbzd8XI/sc9LHdZH9rp6u2Ox4IAxOA6cD1SqkZss/Z+tRuI3Gfcw3nm3ektd6N0aWIUmoO8HnzrhOAr5pF2SlARCnl01rfpbV+DXNFE6XUNRgNf1jpqt201n7Ab/68QSmVi5GZOg44VimVj7EPZCqlPtRanz70Wz98+tFu62V/6/Z7WgSs0FpXmfe9iVF/1oTsb0C/2m6Z7HPdtpvlG7TPFi9F9rn+tBuyvxm6abtvAm9rrYNAhVLqE2AJRq2s7HN9b7e8kbbPjaiMsVIq0/zfAdwI3AegtT5Vaz1Naz0NuBP4k9b6rg7PGQdcCzw09Fs+vLpqN6VUhlLKaf48A5iNsRPeq7WeYLbnKRj1PacPx7YPp762W4fnyP7Wod2Ad4CjlFJxZpfYZ4Cdsr+16WvbdXiO7HMHt5t129eAZ6zbZJ8z9LXdOjznsN3foNu2KwDOUIZ44ERgt+xzhr62W4fnjIh9btgyxkqpp4HTgXSlVBHweyBBKXWd+ZAXgUd68VL/VEotMn++RWu9d9A3dgTpY7udBtyilAphXIF9P6oW6rAyiO0m+1sX7aa1rlVK/R1jVLIG3tRavzH0Wz0yDGLbyT7X/bnhNKBIa503pBs6wgxiux1W+xv0ue3uNn/eDijgEa311qHd4pFhENttRO1zsvKdEEIIIYQQjLBSCiGEEEIIIYaLBMZCCCGEEEIggbEQQgghhBCABMZCCCGEEEIAEhgLIYQQQggBSGAshBBCCCEEIIGxEEIIIYQQgATGQgghhBBCAPD/AYGP4Vc/UkT/AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_filardo.expected_durations[0].plot(\n", " title=\"Expected duration of a low-production state\", figsize=(12, 3)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "During recessions, the expected duration of a low-production state is much higher than in an expansion." ] } ], "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": "3.8.11" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-3-clause
hendrycks/error-detection	NLP/Categorization/Reuters8.ipynb	1	28605	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import re\n", "import collections\n", "import sklearn.metrics as sk" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_data(filename='./data/r8-train.txt'):\n", " '''\n", " :param filename: the system location of the data to load\n", " :return: the text (x) and its label (y)\n", " the text is a list of words and is not processed\n", " '''\n", "\n", " # stop words taken from nltk\n", " stop_words = ['i','me','my','myself','we','our','ours','ourselves','you','your','yours',\n", " 'yourself','yourselves','he','him','his','himself','she','her','hers','herself',\n", " 'it','its','itself','they','them','their','theirs','themselves','what','which',\n", " 'who','whom','this','that','these','those','am','is','are','was','were','be',\n", " 'been','being','have','has','had','having','do','does','did','doing','a','an',\n", " 'the','and','but','if','or','because','as','until','while','of','at','by','for',\n", " 'with','about','against','between','into','through','during','before','after',\n", " 'above','below','to','from','up','down','in','out','on','off','over','under',\n", " 'again','further','then','once','here','there','when','where','why','how','all',\n", " 'any','both','each','few','more','most','other','some','such','no','nor','not',\n", " 'only','own','same','so','than','too','very','s','t','can','will','just','don',\n", " 'should','now','d','ll','m','o','re','ve','y','ain','aren','couldn','didn',\n", " 'doesn','hadn','hasn','haven','isn','ma','mightn','mustn','needn','shan',\n", " 'shouldn','wasn','weren','won','wouldn']\n", "\n", " x, y = [], []\n", " with open(filename, \"r\") as f:\n", " for line in f:\n", " line = re.sub(r'\\W+', ' ', line).strip()\n", " x.append(line[1:])\n", " x[-1] = ' '.join(word for word in x[-1].split() if word not in stop_words)\n", " y.append(line[0])\n", " return x, np.array(y, dtype=int)\n", "\n", "def get_vocab(dataset):\n", " '''\n", " :param dataset: the text from load_data\n", "\n", " :return: a _ordered_ dictionary from words to counts\n", " '''\n", " vocab = {}\n", "\n", " # create a counter for each word\n", " for example in dataset:\n", " example_as_list = example.split()\n", " for word in example_as_list:\n", " vocab[word] = 0\n", "\n", " for example in dataset:\n", " example_as_list = example.split()\n", " for word in example_as_list:\n", " vocab[word] += 1\n", " \n", " # sort from greatest to least by count\n", " return collections.OrderedDict(sorted(vocab.items(), key=lambda x: x[1], reverse=True))\n", "\n", "def text_to_rank(dataset, _vocab, desired_vocab_size=1000):\n", " '''\n", " :param dataset: the text from load_data\n", " :vocab: a _ordered_ dictionary of vocab words and counts from get_vocab\n", " :param desired_vocab_size: the desired vocabulary size\n", " words no longer in vocab become UUUNNNKKK\n", " :return: the text corpus with words mapped to their vocab rank,\n", " with all sufficiently infrequent words mapped to UUUNNNKKK; UUUNNNKKK has rank desired_vocab_size\n", " (the infrequent word cutoff is determined by desired_vocab size)\n", " '''\n", " _dataset = dataset[:] # aliasing safeguard\n", " vocab_ordered = list(_vocab)\n", " count_cutoff = _vocab[vocab_ordered[desired_vocab_size-2]] # get word by its rank and map to its count\n", " \n", " word_to_rank = {}\n", " for i in range(len(vocab_ordered)):\n", " # we add one to make room for any future padding symbol with value 0\n", " word_to_rank[vocab_ordered[i]] = i\n", " \n", " for i in range(len(_dataset)):\n", " example = _dataset[i]\n", " example_as_list = example.split()\n", " for j in range(len(example_as_list)):\n", " try:\n", " if _vocab[example_as_list[j]] >= count_cutoff and word_to_rank[example_as_list[j]] < desired_vocab_size:\n", " # we need to ensure that other words below the word on the edge of our desired_vocab size\n", " # are not also on the count cutoff\n", " example_as_list[j] = word_to_rank[example_as_list[j]] \n", " else:\n", " example_as_list[j] = desired_vocab_size-1 # UUUNNNKKK\n", " except:\n", " example_as_list[j] = desired_vocab_size-1 # UUUNNNKKK\n", " _dataset[i] = example_as_list\n", "\n", " return _dataset\n", "\n", "def text_to_matrix(dataset, _vocab, desired_vocab_size=1000):\n", " sequences = text_to_rank(dataset, _vocab, desired_vocab_size)\n", " \n", " mat = np.zeros((len(sequences), desired_vocab_size), dtype=int)\n", " \n", " for i, seq in enumerate(sequences):\n", " for token in seq:\n", " mat[i][token] = 1\n", " \n", " return mat\n", "\n", "def get_vocab(dataset):\n", " '''\n", " :param dataset: the text from load_data\n", "\n", " :return: a _ordered_ dictionary from words to counts\n", " '''\n", " vocab = {}\n", "\n", " # create a counter for each word\n", " for example in dataset:\n", " example_as_list = example.split()\n", " for word in example_as_list:\n", " vocab[word] = 0\n", "\n", " for example in dataset:\n", " example_as_list = example.split()\n", " for word in example_as_list:\n", " vocab[word] += 1\n", "\n", " # sort from greatest to least by count\n", " return collections.OrderedDict(sorted(vocab.items(), key=lambda x: x[1], reverse=True))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def partion_data_in_two(dataset, dataset_labels, in_sample_labels, oos_labels):\n", " '''\n", " :param dataset: the text from text_to_rank\n", " :param dataset_labels: dataset labels\n", " :param in_sample_labels: a list of newsgroups which the network will/did train on\n", " :param oos_labels: the complement of in_sample_labels; these newsgroups the network has never seen\n", " :return: the dataset partitioned into in_sample_examples, in_sample_labels,\n", " oos_examples, and oos_labels in that order\n", " '''\n", " _dataset = dataset[:] # aliasing safeguard\n", " _dataset_labels = dataset_labels\n", " \n", " in_sample_idxs = np.zeros(np.shape(_dataset_labels), dtype=bool)\n", " ones_vec = np.ones(np.shape(_dataset_labels), dtype=int)\n", " for label in in_sample_labels:\n", " in_sample_idxs = np.logical_or(in_sample_idxs, _dataset_labels == label * ones_vec)\n", "\n", " \n", " return _dataset[in_sample_idxs], _dataset_labels[in_sample_idxs],\\\n", " _dataset[np.logical_not(in_sample_idxs)], _dataset_labels[np.logical_not(in_sample_idxs)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# our network trains only on a subset of classes, say 6, but class number 7 might still\n", "# be an in-sample label: we need to squish the labels to be in {0,...,5}\n", "def relabel_in_sample_labels(labels):\n", " labels_as_list = labels.tolist()\n", " \n", " set_of_labels = []\n", " for label in labels_as_list:\n", " set_of_labels.append(label)\n", " labels_ordered = sorted(list(set(set_of_labels)))\n", " \n", " relabeled = np.zeros(labels.shape, dtype=int)\n", " for i in range(len(labels_as_list)):\n", " relabeled[i] = labels_ordered.index(labels_as_list[i])\n", " \n", " return relabeled" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_size = 32\n", "vocab_size = 1000\n", "num_epochs = 5\n", "n_hidden = 512\n", "nclasses_to_exclude = 2 # 0-3" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random_classes = np.arange(8)\n", "np.random.shuffle(random_classes)\n", "to_include = list(random_classes[:8-nclasses_to_exclude])\n", "to_exclude = list(random_classes[8-nclasses_to_exclude:])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading Data\n", "Data loaded\n" ] } ], "source": [ "print('Loading Data')\n", "X_train, Y_train = load_data()\n", "X_test, Y_test = load_data('./data/r8-test.txt')\n", "\n", "vocab = get_vocab(X_train)\n", "X_train = text_to_matrix(X_train, vocab, vocab_size)\n", "X_test = text_to_matrix(X_test, vocab, vocab_size)\n", "\n", "# shuffle\n", "indices = np.arange(X_train.shape[0])\n", "np.random.shuffle(indices)\n", "X_train = X_train[indices]\n", "Y_train = Y_train[indices]\n", "\n", "indices = np.arange(X_test.shape[0])\n", "np.random.shuffle(indices)\n", "X_test = X_test[indices]\n", "Y_test = Y_test[indices]\n", "\n", "# split into train/dev\n", "X_dev = X_train[-500:]\n", "Y_dev = Y_train[-500:]\n", "X_train = X_train[:-500]\n", "Y_train = Y_train[:-500]\n", "\n", "in_sample_examples, in_sample_labels, oos_examples, oos_labels =\\\n", "partion_data_in_two(X_train, Y_train, to_include, to_exclude)\n", "dev_in_sample_examples, dev_in_sample_labels, dev_oos_examples, dev_oos_labels =\\\n", "partion_data_in_two(X_dev, Y_dev, to_include, to_exclude)\n", "test_in_sample_examples, test_in_sample_labels, test_oos_examples, dev_oos_labels =\\\n", "partion_data_in_two(X_test, Y_test, to_include, to_exclude)\n", "\n", "# safely assumes there is an example for each in_sample class in both the training and dev class \n", "in_sample_labels = relabel_in_sample_labels(in_sample_labels)\n", "dev_in_sample_labels = relabel_in_sample_labels(dev_in_sample_labels)\n", "test_in_sample_labels = relabel_in_sample_labels(test_in_sample_labels)\n", "\n", "num_examples = in_sample_labels.shape[0]\n", "num_batches = num_examples//batch_size\n", "\n", "print('Data loaded')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", " x = tf.placeholder(dtype=tf.float32, shape=[None, vocab_size])\n", " y = tf.placeholder(dtype=tf.int64, shape=[None])\n", " is_training = tf.placeholder(tf.bool)\n", " \n", " # add one to vocab size for the padding symbol\n", "\n", " W_h = tf.Variable(tf.nn.l2_normalize(tf.random_normal([vocab_size, n_hidden]), 0)/tf.sqrt(1 + 0.45))\n", " b_h = tf.Variable(tf.zeros([n_hidden]))\n", " \n", " def gelu_fast(_x):\n", " return 0.5 * _x * (1 + tf.tanh(tf.sqrt(2 / np.pi) * (_x + 0.044715 * tf.pow(_x, 3))))\n", " \n", " h = tf.cond(is_training,\n", " lambda: tf.nn.dropout(gelu_fast(tf.matmul(x, W_h) + b_h), 0.5),\n", " lambda: gelu_fast(tf.matmul(x, W_h) + b_h))\n", " \n", " W_out = tf.Variable(tf.nn.l2_normalize(tf.random_normal([n_hidden, 8-nclasses_to_exclude]), 0)/tf.sqrt(0.45 + 1))\n", " b_out = tf.Variable(tf.zeros([8-nclasses_to_exclude]))\n", " \n", " logits = tf.matmul(h, W_out) + b_out\n", " \n", " loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y))\n", "\n", " global_step = tf.Variable(0, trainable=False)\n", " lr = tf.train.exponential_decay(1e-3, global_step, 4num_batches, 0.1, staircase=True)\n", " optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss, global_step=global_step)\n", "\n", " acc = 100tf.reduce_mean(tf.to_float(tf.equal(tf.argmax(logits, 1), y)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n" ] } ], "source": [ "# initialize\n", "sess = tf.InteractiveSession(graph=graph)\n", "tf.initialize_all_variables().run()\n", "# create saver to train model\n", "saver = tf.train.Saver(max_to_keep=1)\n", "\n", "print('Initialized')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1 \| Minibatch loss 0.404 \| Minibatch accuracy 90.625 \| Dev accuracy 97.447\n", "Epoch 2 \| Minibatch loss 0.041 \| Minibatch accuracy 100.000 \| Dev accuracy 97.447\n", "Epoch 3 \| Minibatch loss 0.006 \| Minibatch accuracy 100.000 \| Dev accuracy 97.447\n", "Epoch 4 \| Minibatch loss 0.019 \| Minibatch accuracy 100.000 \| Dev accuracy 97.447\n", "Epoch 5 \| Minibatch loss 0.006 \| Minibatch accuracy 100.000 \| Dev accuracy 97.660\n" ] } ], "source": [ "best_acc = 0\n", "\n", "for epoch in range(num_epochs):\n", " # shuffle data every epoch\n", " indices = np.arange(num_examples)\n", " np.random.shuffle(indices)\n", " in_sample_examples = in_sample_examples[indices]\n", " in_sample_labels = in_sample_labels[indices]\n", "\n", " for i in range(num_batches):\n", " offset = i * batch_size\n", "\n", " x_batch = in_sample_examples[offset:offset + batch_size]\n", " y_batch = in_sample_labels[offset:offset + batch_size]\n", "\n", " _, l, batch_acc = sess.run([optimizer, loss, acc], feed_dict={x: x_batch, y: y_batch, is_training: True})\n", "\n", "\n", " curr_dev_acc = sess.run(\n", " acc, feed_dict={x: dev_in_sample_examples, y: dev_in_sample_labels, is_training: False})\n", " if best_acc < curr_dev_acc:\n", " best_acc = curr_dev_acc\n", " saver.save(sess, './data/best_r8_model.ckpt')\n", "\n", " print('Epoch %d \| Minibatch loss %.3f \| Minibatch accuracy %.3f \| Dev accuracy %.3f' %\n", " (epoch+1, l, batch_acc, curr_dev_acc))\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best model restored!\n", "Dev accuracy: 97.6596\n" ] } ], "source": [ "# restore variables from disk\n", "saver.restore(sess, \"./data/best_r8_model.ckpt\")\n", "print(\"Best model restored!\")\n", "\n", "print('Dev accuracy:', sess.run(acc, feed_dict={x: dev_in_sample_examples, y: dev_in_sample_labels, is_training:False}))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = tf.nn.softmax(logits)\n", "s_prob = tf.reduce_max(s, reduction_indices=[1], keep_dims=True)\n", "kl_all = tf.log(8. - nclasses_to_exclude)\\\n", " + tf.reduce_sum(s * tf.log(tf.abs(s) + 1e-10), reduction_indices=[1], keep_dims=True)\n", "m_all, v_all = tf.nn.moments(kl_all, axes=[0])\n", "\n", "logits_right = tf.boolean_mask(logits, tf.equal(tf.argmax(logits, 1), y))\n", "s_right = tf.nn.softmax(logits_right)\n", "s_right_prob = tf.reduce_max(s_right, reduction_indices=[1], keep_dims=True)\n", "kl_right = tf.log(8. - nclasses_to_exclude)\\\n", " + tf.reduce_sum(s_right * tf.log(tf.abs(s_right) + 1e-10), reduction_indices=[1], keep_dims=True)\n", "m_right, v_right = tf.nn.moments(kl_right, axes=[0])\n", "\n", "logits_wrong = tf.boolean_mask(logits, tf.not_equal(tf.argmax(logits, 1), y))\n", "s_wrong = tf.nn.softmax(logits_wrong)\n", "s_wrong_prob = tf.reduce_max(s_wrong, reduction_indices=[1], keep_dims=True)\n", "kl_wrong = tf.log(8. - nclasses_to_exclude)\\\n", " + tf.reduce_sum(s_wrong * tf.log(tf.abs(s_wrong) + 1e-10), reduction_indices=[1], keep_dims=True)\n", "m_wrong, v_wrong = tf.nn.moments(kl_wrong, axes=[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reuters8 (w/class subset) Error (%)\| Prediction Prob (mean, std) \| PProb Right (mean, std) \| PProb Wrong (mean, std):\n", "2.53346 \| 0.979176 0.0754985 \| 0.984653 0.0592522 \| 0.768477 0.210652\n", "\n", "Success Detection\n", "Success base rate (%): 97.47\n", "KL[p\|\|u]: Right/Wrong classification distinction\n", "AUPR (%): 99.6\n", "AUROC (%): 88.86\n", "Prediction Prob: Right/Wrong classification distinction\n", "AUPR (%): 99.61\n", "AUROC (%): 89.26\n", "\n", "Error Detection\n", "Error base rate (%): 2.53\n", "KL[p\|\|u]: Right/Wrong classification distinction\n", "AUPR (%): 27.47\n", "AUROC (%): 88.86\n", "Prediction Prob: Right/Wrong classification distinction\n", "AUPR (%): 34.62\n", "AUROC (%): 89.26\n" ] } ], "source": [ "err, kl_a, kl_r, kl_w, s_p, s_rp, s_wp = sess.run(\n", " [100 - acc, kl_all, kl_right, kl_wrong, s_prob, s_right_prob, s_wrong_prob],\n", " feed_dict={x: test_in_sample_examples, y: test_in_sample_labels, is_training: False})\n", "\n", "print('Reuters8 (w/class subset) Error (%)\| Prediction Prob (mean, std) \| PProb Right (mean, std) \| PProb Wrong (mean, std):')\n", "print(err, '\|', np.mean(s_p), np.std(s_p), '\|', np.mean(s_rp), np.std(s_rp), '\|', np.mean(s_wp), np.std(s_wp))\n", "\n", "print('\\nSuccess Detection')\n", "print('Success base rate (%):', round(100-err,2))\n", "print('KL[p\|\|u]: Right/Wrong classification distinction')\n", "safe, risky = kl_r, kl_w\n", "labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", "labels[:safe.shape[0]] += 1\n", "examples = np.squeeze(np.vstack((safe, risky)))\n", "print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", "print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", "print('Prediction Prob: Right/Wrong classification distinction')\n", "safe, risky = s_rp, s_wp\n", "labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", "labels[:safe.shape[0]] += 1\n", "examples = np.squeeze(np.vstack((safe, risky)))\n", "print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", "print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", "\n", "print('\\nError Detection')\n", "print('Error base rate (%):', round(err,2))\n", "safe, risky = -kl_r, -kl_w\n", "labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", "labels[safe.shape[0]:] += 1\n", "examples = np.squeeze(np.vstack((safe, risky)))\n", "print('KL[p\|\|u]: Right/Wrong classification distinction')\n", "print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", "print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", "print('Prediction Prob: Right/Wrong classification distinction')\n", "safe, risky = -s_rp, -s_wp\n", "labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", "labels[safe.shape[0]:] += 1\n", "examples = np.squeeze(np.vstack((safe, risky)))\n", "print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", "print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_ood_detection_results(error_rate_for_in, in_examples, out_examples):\n", " kl_oos, s_p_oos = sess.run([kl_all, s_prob], feed_dict={x: out_examples, is_training: False})\n", "\n", " print('OOD Example Prediction Probability (mean, std):')\n", " print(np.mean(s_p_oos), np.std(s_p_oos))\n", "\n", " print('\\nNormality Detection')\n", " print('Normality base rate (%):', round(100in_examples.shape[0]/(\n", " out_examples.shape[0] + in_examples.shape[0]),2))\n", " print('KL[p\|\|u]: Normality Detection')\n", " safe, risky = kl_a, kl_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[:safe.shape[0]] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Prediction Prob: Normality Detection')\n", " safe, risky = s_p, s_p_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[:safe.shape[0]] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Normality base rate (%):', round(100(1 - err/100)in_examples.shape[0]/\n", " (out_examples.shape[0] + (1 - err/100)in_examples.shape[0]),2))\n", " print('KL[p\|\|u]: Normality Detection (relative to correct examples)')\n", " safe, risky = kl_r, kl_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[:safe.shape[0]] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Prediction Prob: Normality Detection (relative to correct examples)')\n", " safe, risky = s_rp, s_p_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[:safe.shape[0]] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", "\n", " print('\\n\\nAbnormality Detection')\n", " print('Abnormality base rate (%):', round(100out_examples.shape[0]/(\n", " out_examples.shape[0] + in_examples.shape[0]),2))\n", " print('KL[p\|\|u]: Abnormality Detection')\n", " safe, risky = -kl_a, -kl_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[safe.shape[0]:] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Prediction Prob: Normality Detection')\n", " safe, risky = -s_p, -s_p_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[safe.shape[0]:] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Abnormality base rate (%):', round(100out_examples.shape[0]/\n", " (out_examples.shape[0] + (1 - err/100)in_examples.shape[0]),2))\n", " print('KL[p\|\|u]: Normality Detection (relative to correct examples)')\n", " safe, risky = -kl_r, -kl_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[safe.shape[0]:] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100sk.roc_auc_score(labels, examples), 2))\n", "\n", " print('Prediction Prob: Normality Detection (relative to correct examples)')\n", " safe, risky = -s_rp, -s_p_oos\n", " labels = np.zeros((safe.shape[0] + risky.shape[0]), dtype=np.int32)\n", " labels[safe.shape[0]:] += 1\n", " examples = np.squeeze(np.vstack((safe, risky)))\n", " print('AUPR (%):', round(100sk.average_precision_score(labels, examples), 2))\n", " print('AUROC (%):', round(100*sk.roc_auc_score(labels, examples), 2))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Held-out subjects\n", "\n", "OOD Example Prediction Probability (mean, std):\n", "0.718123 0.230031\n", "\n", "Normality Detection\n", "Normality base rate (%): 95.57\n", "KL[p\|\|u]: Normality Detection\n", "AUPR (%): 99.53\n", "AUROC (%): 91.7\n", "Prediction Prob: Normality Detection\n", "AUPR (%): 99.52\n", "AUROC (%): 91.52\n", "Normality base rate (%): 95.46\n", "KL[p\|\|u]: Normality Detection (relative to correct examples)\n", "AUPR (%): 99.56\n", "AUROC (%): 92.47\n", "Prediction Prob: Normality Detection (relative to correct examples)\n", "AUPR (%): 99.56\n", "AUROC (%): 92.42\n", "\n", "\n", "Abnormality Detection\n", "Abnormality base rate (%): 4.43\n", "KL[p\|\|u]: Abnormality Detection\n", "AUPR (%): 47.48\n", "AUROC (%): 91.7\n", "Prediction Prob: Normality Detection\n", "AUPR (%): 44.08\n", "AUROC (%): 91.52\n", "Abnormality base rate (%): 4.54\n", "KL[p\|\|u]: Normality Detection (relative to correct examples)\n", "AUPR (%): 57.04\n", "AUROC (%): 92.47\n", "Prediction Prob: Normality Detection (relative to correct examples)\n", "AUPR (%): 56.14\n", "AUROC (%): 92.42\n" ] } ], "source": [ "print('Held-out subjects\\n')\n", "show_ood_detection_results(err, test_in_sample_examples, test_oos_examples)" ] } ], "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
vinomaster/dawn	guides/sdlc/pa_guide.ipynb	1	5168	{ "metadata": { "name": "", "signature": "sha256:dd5cd64bb20ec217a9b3e2d6d73e6c46c6f65d59330e1043e1022cec26f151a1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[ Process Assessment (pa_guide): A template for performing reproducible research within environments that are compatible with IPython Notebook. It is a sample guide within the [Data Analysis Workflow Navigation repository](https://github.com/vinomaster/dawn). ]\n", "\n", "# Process Assessment Title\n", "[ The intent of this system analysis workflow guide is to help streamline the activities associated with tuning (improving) a specific software system process. The objective is to produce an assessment report based on an investigation into a specific performance of a process along with reproducible code for future analysis. The approach of the analysis is to identify characteristics of a process bottleneck and to measure possible adjustments to the process that will remove the bottleneck. ]\n", "\n", "## Problem Statement\n", "[ Provide an overview of the system and the problem.]\n", "\n", "## Process Overview\n", "[ Describe the system process to be analyzed.]\n", "\n", "### Desired Process Behavior\n", "[ Describe the desired or expected characteristics of a successful execution of the process. ]\n", "\n", "### Acceptable Process Metrics\n", "[ Describe the numeric values (metrics) associated with the desired process results. ]\n", "\n", "## Assessment Environment\n", "[ Your analysis may include several external source code libraries, data sets and it may produce several file artifacts. Organizing your workarea is imperative to achieve optimal reproducible results. Use this section to outline the preparation of your analysis environment. ]\n", "\n", "### Environment Setup\n", "[ Outline your project workarea folder structure and provide a description of the purpose of all sub-folders. Provide the inline code necessary for setting up your working environment (i.e: working directory, system options, handling of warning messages, etc). ]\n", "\n", "### Assessment Data\n", "[ Provide the inline code necessary for accessing data that will be used to tune your process. ]\n", "\n", "### External Experiment Dependencies\n", "[ Provide the inline code necessary for loading any required libraries and other source files to be included. These are dependencies for your reproducible research code. As such, you should call out these dependencies textually as well as within the inline code. ]\n", "\n", "## Current Process Baseline\n", "\n", "### Assumptions\n", "[ Describe any assumptions associated with the current state of the system and the process being analyzed. ]\n", "\n", "### Code\n", "[ Provide the inline code necessary to model and measure the current state of the system process. ]\n", "\n", "### Observations\n", "[ Describe the characteristics of the results of these measurements. This is your baseline for comparison with potential proces improvements. ]\n", "\n", "## Bottleneck Identification\n", "[ A process improvement exercise requires the identification and isolation of a specific aspect of the process, the focus area for process improvement. This is known as the process bottleneck. Describe the bottleneck and why it is being considered the focus of this process with respect to the problem being addressed. ]\n", "\n", "## Experiments\n", "[ Enumerate several experiments that can be run to alter the performance of the bottleneck and compare each result to the baseline and our Acceptable Process Metrics. ]\n", "\n", "### Process Assessment #1\n", "[ Describe the specifics about this experiment. What process attributes have been tweaked (altered) and how? What is the expected result of these modifications? ]\n", "\n", "#### Run Assessment\n", "[ Provide the inline code necessary to model and measure the modifications captured by this process experiment. ]\n", "\n", "#### Assessment Observations\n", "[ Describe what you have observed. Make note of deltas in results as compared to baseline, the desired results and other assessments that have been documented herein. ]\n", "\n", "## Assessment Results\n", "\n", "### Findings\n", "[ Summarize your results. ]\n", "\n", "### Outcomes\n", "[ What are the pros/cons associated with the improvements you have identified. ]\n", "\n", "### Actionable Insights\n", "[ Identify system recommendations specific to the resolution of this process problem. ]\n", " \n", "### Lessons Learned\n", "[ Identify general system observations that may have been raised by this assessment. ] \n" ] } ], "metadata": {} } ] }	bsd-3-clause
jcmgray/xyzpy	docs/examples/basic output example.ipynb	1	3156546	null	mit
ProjectsUCSC/NLP	Assignment 1/ipython notebooks/main.ipynb	1	68004	{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from preprocess import \n", "from feature_extraction import \n", "from lsi import \n", "from kmeans import \n", "import CMUTweetTagger\n", "\n", "\n", "def get_feature_sets(filename):\n", "\n", " df = preprocess(filename)\n", " print \"length of data\", len(df)\n", "\n", " data_sample = df['text'].str.lower()\n", "\n", " #TFIDF features without text processing\n", " [data_fs2, vectorizer, no_features] = vectorize(data_sample, TFIDF) #feature set 2\n", " #Unigram features without text processing\n", " [data_fs1, vectorizer, no_features] = vectorize(data_sample, UNI) #Feature set 1\n", "\n", " #Text preprocessing - stopwords, stemming, lowercase\n", " data_fs3 = tokenize_and_stopwords(data_sample)\n", " data_fs3 = stemmer(data_fs3)\n", " #use CMU tagger and remove NNP and NNPS\n", " \n", " print \"CMU tagger\"\n", " all_tags = CMUTweetTagger.runtagger_parse(data_fs3)\n", " for i in range(len(all_tags)):\n", " for tag in all_tags[i]:\n", "# print tag[1]\n", " if tag[1] == 'NNP' or tag[1] == 'NNPS':\n", " data_fs3[i] = data_fs3[i].replace(tag[0], '') \n", "\n", " [data_fs3, vectorizer, no_features] = vectorize(data_fs3, TFIDF) #Feature set 3\n", " data_fs4 = lsa(data_fs3) #feature set 4 \n", " print data_fs1.shape\n", " print data_fs2.shape\n", " print data_fs3.shape\n", " print data_fs4.shape\n", " \n", " return [data_fs1, data_fs2, data_fs3, data_fs4, df]\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def kmeans_analysis(filename): \n", " no_clusters = 5\n", " filename = \"clinton-50k.csv\"\n", " [data_fs1, data_fs2, data_fs3, data_fs4, df1] = get_feature_sets(filename)\n", " df = df1[['tweet_id','text', 'retweets']].copy()\n", " df['cluster_fs1'] = run_kmeans(data_fs1, 7)\n", " df['cluster_fs2'] = run_kmeans(data_fs2, 7)\n", " df['cluster_fs3'] = run_kmeans(data_fs3, 7)\n", " df['cluster_fs4'] = run_kmeans(data_fs4, 7)\n", " result_filename = filename.replace(\".csv\", \"\") +\"-test.csv\"\n", " df.to_csv(result_filename)\n", " return df\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def perform_analysis(df):\n", " print df.groupby(['cluster_fs1']).describe()\n", " print df.groupby(['cluster_fs2']).describe()\n", " print df.groupby(['cluster_fs3']).describe()\n", " print df.groupby(['cluster_fs4']).describe()\n", " df.corr()\n", " result1 = df.sort(['cluster_fs1'])\n", " result2 = df.sort(['cluster_fs2'])\n", " result3 = df.sort(['cluster_fs3'])\n", " result4 = df.sort(['cluster_fs4'])\n", " result4" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def main():\n", " f1 = \"trump-50k.csv\"\n", " f2 = \"clinton-50k.csv\"\n", " df1 = kmeans_analysis(f1)\n", " perform_analysis(df1)\n", " df2 = kmeans_analysis(f2)\n", " perform_analysis(df2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length of data 50000\n", "stemming \n", "CMU tagger\n", "Running LSA\n", "(50000, 100)\n", "(50000, 100)\n", "(50000, 100)\n", "(50000, 10)\n", " cluster_fs2 cluster_fs3 cluster_fs4 retweets \\\n", "cluster_fs1 \n", "0 count 6737.000000 6737.000000 6737.000000 6737.000000 \n", " mean 1.769779 1.696007 1.990055 21.885706 \n", " std 2.071550 2.149799 1.811759 386.541178 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 0.000000 0.000000 1.000000 0.000000 \n", " 50% 1.000000 1.000000 1.000000 0.000000 \n", " 75% 3.000000 3.000000 4.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "1 count 7896.000000 7896.000000 7896.000000 7896.000000 \n", " mean 5.291920 1.499113 2.004813 18.976570 \n", " std 1.562615 2.015671 1.729600 187.945780 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 6.000000 0.000000 1.000000 0.000000 \n", " 50% 6.000000 0.000000 1.000000 0.000000 \n", " 75% 6.000000 2.000000 3.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 9557.000000 \n", "2 count 6732.000000 6732.000000 6732.000000 6732.000000 \n", " mean 0.886958 1.985888 2.346554 9.112894 \n", " std 1.848126 1.190334 1.030649 108.015872 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 0.000000 2.000000 1.000000 0.000000 \n", " 50% 0.000000 2.000000 3.000000 0.000000 \n", " 75% 1.000000 2.000000 3.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 5192.000000 \n", "3 count 6716.000000 6716.000000 6716.000000 6716.000000 \n", " mean 1.995831 1.255360 1.793627 10.963371 \n", " std 1.069986 1.862743 1.512029 115.656744 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 1.000000 0.000000 \n", " 50% 2.000000 0.000000 1.000000 0.000000 \n", " 75% 2.000000 2.000000 3.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 5812.000000 \n", "4 count 12416.000000 12416.000000 12416.000000 12416.000000 \n", " mean 1.165432 1.333682 1.962629 0.861147 \n", " std 1.816148 1.961621 1.668741 7.339446 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 0.000000 0.000000 1.000000 0.000000 \n", " 50% 0.000000 0.000000 1.000000 0.000000 \n", " 75% 3.000000 3.000000 4.000000 0.000000 \n", " max 5.000000 6.000000 6.000000 303.000000 \n", "5 count 1225.000000 1225.000000 1225.000000 1225.000000 \n", " mean 4.000000 2.000000 2.000000 0.043265 \n", " std 0.000000 0.000000 0.000000 0.355520 \n", " min 4.000000 2.000000 2.000000 0.000000 \n", " 25% 4.000000 2.000000 2.000000 0.000000 \n", " 50% 4.000000 2.000000 2.000000 0.000000 \n", " 75% 4.000000 2.000000 2.000000 0.000000 \n", " max 4.000000 2.000000 2.000000 6.000000 \n", "6 count 8278.000000 8278.000000 8278.000000 8278.000000 \n", " mean 1.232786 2.927036 3.494443 47.158734 \n", " std 0.925145 2.318769 2.348681 458.825604 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 1.000000 1.000000 \n", " 50% 1.000000 3.000000 4.000000 2.000000 \n", " 75% 2.000000 5.000000 6.000000 9.000000 \n", " max 3.000000 6.000000 6.000000 31783.000000 \n", "\n", " tweet_id \n", "cluster_fs1 \n", "0 count 6.737000e+03 \n", " mean 1.801443e+06 \n", " std 7.058985e+05 \n", " min 3.163570e+05 \n", " 25% 1.195259e+06 \n", " 50% 2.042541e+06 \n", " 75% 2.377391e+06 \n", " max 2.678732e+06 \n", "1 count 7.896000e+03 \n", " mean 1.705424e+06 \n", " std 7.523128e+05 \n", " min 3.163150e+05 \n", " 25% 9.946095e+05 \n", " 50% 2.007374e+06 \n", " 75% 2.360434e+06 \n", " max 2.678850e+06 \n", "2 count 6.732000e+03 \n", " mean 1.158924e+06 \n", " std 8.316978e+05 \n", " min 3.163160e+05 \n", " 25% 3.434955e+05 \n", " 50% 7.048105e+05 \n", " 75% 2.031512e+06 \n", " max 2.678890e+06 \n", "3 count 6.716000e+03 \n", " mean 1.743311e+06 \n", " std 7.460642e+05 \n", " min 3.163110e+05 \n", " 25% 1.001803e+06 \n", " 50% 2.030056e+06 \n", " 75% 2.376600e+06 \n", " max 2.678886e+06 \n", "4 count 1.241600e+04 \n", " mean 1.842138e+06 \n", " std 6.024604e+05 \n", " min 3.163520e+05 \n", " 25% 1.631508e+06 \n", " 50% 2.002042e+06 \n", " 75% 2.365838e+06 \n", " max 2.678849e+06 \n", "5 count 1.225000e+03 \n", " mean 1.639788e+06 \n", " std 2.078353e+04 \n", " min 1.604422e+06 \n", " 25% 1.632814e+06 \n", " 50% 1.637839e+06 \n", " 75% 1.643534e+06 \n", " max 1.680809e+06 \n", "6 count 8.278000e+03 \n", " mean 1.731183e+06 \n", " std 6.952790e+05 \n", " min 3.163310e+05 \n", " 25% 1.066951e+06 \n", " 50% 1.833227e+06 \n", " 75% 2.357281e+06 \n", " max 2.678810e+06 \n", " cluster_fs1 cluster_fs3 cluster_fs4 retweets \\\n", "cluster_fs2 \n", "0 count 19163.000000 19163.000000 19163.000000 19163.000000 \n", " mean 2.927203 1.394041 1.411418 9.667641 \n", " std 1.707712 1.975340 1.186644 158.210595 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 1.000000 0.000000 \n", " 50% 4.000000 0.000000 1.000000 0.000000 \n", " 75% 4.000000 2.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 14819.000000 \n", "1 count 5565.000000 5565.000000 5565.000000 5565.000000 \n", " mean 5.044744 3.463971 3.857143 40.458041 \n", " std 1.990296 2.138543 2.348095 481.668886 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 6.000000 2.000000 1.000000 1.000000 \n", " 50% 6.000000 5.000000 6.000000 2.000000 \n", " 75% 6.000000 5.000000 6.000000 8.000000 \n", " max 6.000000 6.000000 6.000000 31783.000000 \n", "2 count 7760.000000 7760.000000 7760.000000 7760.000000 \n", " mean 2.845232 1.633634 1.971521 20.356959 \n", " std 1.617415 2.078745 1.748114 339.125413 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 3.000000 0.000000 1.000000 0.000000 \n", " 50% 3.000000 0.000000 1.000000 0.000000 \n", " 75% 3.000000 3.000000 3.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "3 count 6535.000000 6535.000000 6535.000000 6535.000000 \n", " mean 3.109870 1.236725 3.726549 13.790360 \n", " std 2.050760 1.026078 0.931223 155.472062 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 1.000000 4.000000 0.000000 \n", " 50% 4.000000 1.000000 4.000000 0.000000 \n", " 75% 4.000000 1.000000 4.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 7649.000000 \n", "4 count 1227.000000 1227.000000 1227.000000 1227.000000 \n", " mean 4.996740 1.999185 1.999185 0.049715 \n", " std 0.090255 0.028548 0.028548 0.421648 \n", " min 2.000000 1.000000 1.000000 0.000000 \n", " 25% 5.000000 2.000000 2.000000 0.000000 \n", " 50% 5.000000 2.000000 2.000000 0.000000 \n", " 75% 5.000000 2.000000 2.000000 0.000000 \n", " max 5.000000 2.000000 2.000000 8.000000 \n", "5 count 1440.000000 1440.000000 1440.000000 1440.000000 \n", " mean 4.000000 4.000000 5.000000 0.007639 \n", " std 0.000000 0.000000 0.000000 0.108422 \n", " min 4.000000 4.000000 5.000000 0.000000 \n", " 25% 4.000000 4.000000 5.000000 0.000000 \n", " 50% 4.000000 4.000000 5.000000 0.000000 \n", " 75% 4.000000 4.000000 5.000000 0.000000 \n", " max 4.000000 4.000000 5.000000 3.000000 \n", "6 count 8310.000000 8310.000000 8310.000000 8310.000000 \n", " mean 1.023345 1.605174 1.806017 21.036582 \n", " std 0.562310 2.075057 1.643320 214.662668 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 1.000000 0.000000 \n", " 50% 1.000000 0.000000 1.000000 0.000000 \n", " 75% 1.000000 3.000000 3.000000 2.000000 \n", " max 3.000000 6.000000 6.000000 10004.000000 \n", "\n", " tweet_id \n", "cluster_fs2 \n", "0 count 1.916300e+04 \n", " mean 1.717102e+06 \n", " std 7.665425e+05 \n", " min 3.163160e+05 \n", " 25% 9.977970e+05 \n", " 50% 2.017529e+06 \n", " 75% 2.373836e+06 \n", " max 2.678890e+06 \n", "1 count 5.565000e+03 \n", " mean 1.686093e+06 \n", " std 7.070837e+05 \n", " min 3.163310e+05 \n", " 25% 1.007599e+06 \n", " 50% 1.809627e+06 \n", " 75% 2.347455e+06 \n", " max 2.678810e+06 \n", "2 count 7.760000e+03 \n", " mean 1.701865e+06 \n", " std 7.698233e+05 \n", " min 3.164260e+05 \n", " 25% 9.844118e+05 \n", " 50% 2.015080e+06 \n", " 75% 2.371116e+06 \n", " max 2.678763e+06 \n", "3 count 6.535000e+03 \n", " mean 1.582939e+06 \n", " std 7.279300e+05 \n", " min 3.163110e+05 \n", " 25% 9.615650e+05 \n", " 50% 1.766396e+06 \n", " 75% 2.118306e+06 \n", " max 2.678832e+06 \n", "4 count 1.227000e+03 \n", " mean 1.639939e+06 \n", " std 2.121400e+04 \n", " min 1.604422e+06 \n", " 25% 1.632815e+06 \n", " 50% 1.637908e+06 \n", " 75% 1.643588e+06 \n", " max 1.786686e+06 \n", "5 count 1.440000e+03 \n", " mean 1.649164e+06 \n", " std 3.472618e+04 \n", " min 8.675960e+05 \n", " 25% 1.634522e+06 \n", " 50% 1.639980e+06 \n", " 75% 1.644792e+06 \n", " max 1.770109e+06 \n", "6 count 8.310000e+03 \n", " mean 1.696490e+06 \n", " std 7.627692e+05 \n", " min 3.163150e+05 \n", " 25% 9.881280e+05 \n", " 50% 2.010452e+06 \n", " 75% 2.358236e+06 \n", " max 2.678850e+06 \n", " cluster_fs1 cluster_fs2 cluster_fs4 retweets \\\n", "cluster_fs3 \n", "0 count 21101.000000 21101.000000 21101.000000 21101.000000 \n", " mean 2.770106 1.755130 1.156012 14.385148 \n", " std 1.848761 2.340575 0.608218 266.181865 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 1.000000 0.000000 \n", " 50% 3.000000 0.000000 1.000000 0.000000 \n", " 75% 4.000000 2.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "1 count 6136.000000 6136.000000 6136.000000 6136.000000 \n", " mean 3.040906 2.971480 3.991362 15.660202 \n", " std 2.009239 0.786379 0.152432 172.023894 \n", " min 0.000000 0.000000 1.000000 0.000000 \n", " 25% 1.000000 3.000000 4.000000 0.000000 \n", " 50% 4.000000 3.000000 4.000000 0.000000 \n", " 75% 4.000000 3.000000 4.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 7649.000000 \n", "2 count 9364.000000 9364.000000 9364.000000 9364.000000 \n", " mean 2.462196 1.956536 2.436459 7.906130 \n", " std 1.497252 2.242091 0.869262 90.135390 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6.000000 5.000000 6.000000 \n", "5 count 4975.000000 4975.000000 4975.000000 4975.000000 \n", " mean 4.365025 1.846432 5.882211 45.420101 \n", " std 2.435581 1.834691 0.600402 517.945374 \n", " min 0.000000 0.000000 1.000000 0.000000 \n", " 25% 1.000000 1.000000 6.000000 1.000000 \n", " 50% 6.000000 1.000000 6.000000 2.000000 \n", " 75% 6.000000 2.000000 6.000000 8.000000 \n", " max 6.000000 6.000000 6.000000 31783.000000 \n", "6 count 3944.000000 3944.000000 3944.000000 3944.000000 \n", " mean 2.816430 1.676724 0.004310 22.526876 \n", " std 2.039127 2.240929 0.094118 149.352267 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 0.000000 0.000000 \n", " 50% 3.000000 0.000000 0.000000 0.000000 \n", " 75% 4.000000 2.000000 0.000000 3.000000 \n", " max 6.000000 6.000000 3.000000 3723.000000 \n", "\n", " tweet_id \n", "cluster_fs3 \n", "0 count 2.110100e+04 \n", " mean 1.836213e+06 \n", " std 6.824786e+05 \n", " min 3.163810e+05 \n", " 25% 1.362619e+06 \n", " 50% 2.046974e+06 \n", " 75% 2.388019e+06 \n", " max 2.678886e+06 \n", "1 count 6.136000e+03 \n", " mean 1.572405e+06 \n", " std 7.198915e+05 \n", " min 3.163170e+05 \n", " 25% 9.614235e+05 \n", " 50% 1.763256e+06 \n", " 75% 2.115240e+06 \n", " max 2.678809e+06 \n", "2 count 9.364000e+03 \n", " mean 1.179238e+06 \n", " std 7.709769e+05 \n", " min 3.163160e+05 \n", " 25% 5.123998e+05 \n", " 50% 9.583535e+05 \n", " 75% 1.781745e+06 \n", " max 2.678890e+06 \n", "3 count 3.021000e+03 \n", " mean 1.822107e+06 \n", " std 6.996226e+05 \n", " min 3.163680e+05 \n", " 25% 1.362381e+06 \n", " 50% 2.060101e+06 \n", " 75% 2.379874e+06 \n", " max 2.678850e+06 \n", "4 count 1.459000e+03 \n", " mean 1.655039e+06 \n", " std 8.500740e+04 \n", " min 6.169490e+05 \n", " 25% 1.634538e+06 \n", " 50% 1.640051e+06 \n", " 75% 1.644934e+06 \n", " max 2.671737e+06 \n", "5 count 4.975000e+03 \n", " mean 1.741655e+06 \n", " std 6.752916e+05 \n", " min 3.163310e+05 \n", " 25% 1.069214e+06 \n", " 50% 1.831875e+06 \n", " 75% 2.356020e+06 \n", " max 2.678763e+06 \n", "6 count 3.944000e+03 \n", " mean 2.105259e+06 \n", " std 4.979767e+05 \n", " min 3.163110e+05 \n", " 25% 1.998756e+06 \n", " 50% 2.111720e+06 \n", " 75% 2.502467e+06 \n", " max 2.678849e+06 \n", " cluster_fs1 cluster_fs2 cluster_fs3 retweets \\\n", "cluster_fs4 \n", "0 count 4283.000000 4283.000000 4283.000000 4283.000000 \n", " mean 2.799673 1.679897 5.719122 22.739435 \n", " std 2.043440 2.252323 0.988376 147.136238 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 6.000000 0.000000 \n", " 50% 3.000000 0.000000 6.000000 0.000000 \n", " 75% 4.000000 2.000000 6.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 3723.000000 \n", "1 count 23794.000000 23794.000000 23794.000000 23794.000000 \n", " mean 2.706481 1.728083 0.445280 13.427503 \n", " std 1.837638 2.346297 0.986194 250.776197 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 0.000000 0.000000 \n", " 50% 3.000000 0.000000 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1.072958 15.048442 \n", " std 2.007175 0.818812 0.506395 165.790559 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 3.000000 1.000000 0.000000 \n", " 50% 4.000000 3.000000 1.000000 0.000000 \n", " 75% 4.000000 3.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 3.000000 7649.000000 \n", "5 count 1508.000000 1508.000000 1508.000000 1508.000000 \n", " mean 3.937003 4.855438 3.865385 0.035146 \n", " std 0.462971 0.822086 0.720208 0.553170 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 4.000000 5.000000 4.000000 0.000000 \n", " 50% 4.000000 5.000000 4.000000 0.000000 \n", " 75% 4.000000 5.000000 4.000000 0.000000 \n", " max 6.000000 6.000000 4.000000 19.000000 \n", "6 count 4947.000000 4947.000000 4947.000000 4947.000000 \n", " mean 4.372549 1.834647 4.907823 46.664645 \n", " std 2.432520 1.830597 0.542194 525.198659 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 1.000000 5.000000 1.000000 \n", " 50% 6.000000 1.000000 5.000000 2.000000 \n", " 75% 6.000000 2.000000 5.000000 8.000000 \n", " max 6.000000 6.000000 5.000000 31783.000000 \n", "\n", " tweet_id \n", "cluster_fs4 \n", "0 count 4.283000e+03 \n", " mean 2.107246e+06 \n", " std 4.989315e+05 \n", " min 3.163110e+05 \n", " 25% 1.999204e+06 \n", " 50% 2.114811e+06 \n", " 75% 2.501846e+06 \n", " max 2.678849e+06 \n", "1 count 2.379400e+04 \n", " mean 1.853439e+06 \n", " std 6.643012e+05 \n", " min 3.163160e+05 \n", " 25% 1.495676e+06 \n", " 50% 2.051500e+06 \n", " 75% 2.388213e+06 \n", " max 2.678890e+06 \n", "2 count 1.366000e+03 \n", " mean 1.626001e+06 \n", " std 2.223969e+05 \n", " min 3.271520e+05 \n", " 25% 1.628331e+06 \n", " 50% 1.637978e+06 \n", " 75% 1.644521e+06 \n", " max 2.673290e+06 \n", "3 count 7.331000e+03 \n", " mean 9.792313e+05 \n", " std 7.752636e+05 \n", " min 3.163500e+05 \n", " 25% 3.352245e+05 \n", " 50% 6.101320e+05 \n", " 75% 1.765522e+06 \n", " max 2.678810e+06 \n", "4 count 6.771000e+03 \n", " mean 1.563080e+06 \n", " std 7.261663e+05 \n", " min 3.163170e+05 \n", " 25% 9.569880e+05 \n", " 50% 1.684840e+06 \n", " 75% 2.115740e+06 \n", " max 2.678832e+06 \n", "5 count 1.508000e+03 \n", " mean 1.658689e+06 \n", " std 1.449377e+05 \n", " min 3.224040e+05 \n", " 25% 1.634538e+06 \n", " 50% 1.640169e+06 \n", " 75% 1.645240e+06 \n", " max 2.671737e+06 \n", "6 count 4.947000e+03 \n", " mean 1.761253e+06 \n", " std 6.620330e+05 \n", " min 3.163310e+05 \n", " 25% 1.105948e+06 \n", " 50% 1.836332e+06 \n", " 75% 2.358328e+06 \n", " max 2.678763e+06 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/shubhi/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", "/Users/shubhi/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", "/Users/shubhi/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", "/Users/shubhi/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "length of data 50000\n", "stemming \n", "CMU tagger\n", "Running LSA\n", "(50000, 100)\n", "(50000, 100)\n", "(50000, 100)\n", "(50000, 10)\n", " cluster_fs2 cluster_fs3 cluster_fs4 retweets \\\n", "cluster_fs1 \n", "0 count 6737.000000 6737.000000 6737.000000 6737.000000 \n", " mean 1.769779 1.696007 1.990055 21.885706 \n", " std 2.071550 2.149799 1.811759 386.541178 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 0.000000 0.000000 1.000000 0.000000 \n", " 50% 1.000000 1.000000 1.000000 0.000000 \n", " 75% 3.000000 3.000000 4.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "1 count 7896.000000 7896.000000 7896.000000 7896.000000 \n", " mean 5.291920 1.499113 2.004813 18.976570 \n", " std 1.562615 2.015671 1.729600 187.945780 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 6.000000 0.000000 1.000000 0.000000 \n", " 50% 6.000000 0.000000 1.000000 0.000000 \n", " 75% 6.000000 2.000000 3.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 9557.000000 \n", "2 count 6732.000000 6732.000000 6732.000000 6732.000000 \n", " mean 0.886958 1.985888 2.346554 9.112894 \n", " std 1.848126 1.190334 1.030649 108.015872 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 0.000000 2.000000 1.000000 0.000000 \n", " 50% 0.000000 2.000000 3.000000 0.000000 \n", " 75% 1.000000 2.000000 3.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 5192.000000 \n", "3 count 6716.000000 6716.000000 6716.000000 6716.000000 \n", " mean 1.995831 1.255360 1.793627 10.963371 \n", " std 1.069986 1.862743 1.512029 115.656744 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 1.000000 0.000000 \n", " 50% 2.000000 0.000000 1.000000 0.000000 \n", " 75% 2.000000 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" 25% 1.001803e+06 \n", " 50% 2.030056e+06 \n", " 75% 2.376600e+06 \n", " max 2.678886e+06 \n", "4 count 1.241600e+04 \n", " mean 1.842138e+06 \n", " std 6.024604e+05 \n", " min 3.163520e+05 \n", " 25% 1.631508e+06 \n", " 50% 2.002042e+06 \n", " 75% 2.365838e+06 \n", " max 2.678849e+06 \n", "5 count 1.225000e+03 \n", " mean 1.639788e+06 \n", " std 2.078353e+04 \n", " min 1.604422e+06 \n", " 25% 1.632814e+06 \n", " 50% 1.637839e+06 \n", " 75% 1.643534e+06 \n", " max 1.680809e+06 \n", "6 count 8.278000e+03 \n", " mean 1.731183e+06 \n", " std 6.952790e+05 \n", " min 3.163310e+05 \n", " 25% 1.066951e+06 \n", " 50% 1.833227e+06 \n", " 75% 2.357281e+06 \n", " max 2.678810e+06 \n", " cluster_fs1 cluster_fs3 cluster_fs4 retweets \\\n", "cluster_fs2 \n", "0 count 19163.000000 19163.000000 19163.000000 19163.000000 \n", " mean 2.927203 1.394041 1.411418 9.667641 \n", " std 1.707712 1.975340 1.186644 158.210595 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 1.000000 0.000000 \n", " 50% 4.000000 0.000000 1.000000 0.000000 \n", " 75% 4.000000 2.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 14819.000000 \n", "1 count 5565.000000 5565.000000 5565.000000 5565.000000 \n", " mean 5.044744 3.463971 3.857143 40.458041 \n", " std 1.990296 2.138543 2.348095 481.668886 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 6.000000 2.000000 1.000000 1.000000 \n", " 50% 6.000000 5.000000 6.000000 2.000000 \n", " 75% 6.000000 5.000000 6.000000 8.000000 \n", " max 6.000000 6.000000 6.000000 31783.000000 \n", "2 count 7760.000000 7760.000000 7760.000000 7760.000000 \n", " mean 2.845232 1.633634 1.971521 20.356959 \n", " std 1.617415 2.078745 1.748114 339.125413 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 3.000000 0.000000 1.000000 0.000000 \n", " 50% 3.000000 0.000000 1.000000 0.000000 \n", " 75% 3.000000 3.000000 3.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "3 count 6535.000000 6535.000000 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7.760000e+03 \n", " mean 1.701865e+06 \n", " std 7.698233e+05 \n", " min 3.164260e+05 \n", " 25% 9.844118e+05 \n", " 50% 2.015080e+06 \n", " 75% 2.371116e+06 \n", " max 2.678763e+06 \n", "3 count 6.535000e+03 \n", " mean 1.582939e+06 \n", " std 7.279300e+05 \n", " min 3.163110e+05 \n", " 25% 9.615650e+05 \n", " 50% 1.766396e+06 \n", " 75% 2.118306e+06 \n", " max 2.678832e+06 \n", "4 count 1.227000e+03 \n", " mean 1.639939e+06 \n", " std 2.121400e+04 \n", " min 1.604422e+06 \n", " 25% 1.632815e+06 \n", " 50% 1.637908e+06 \n", " 75% 1.643588e+06 \n", " max 1.786686e+06 \n", "5 count 1.440000e+03 \n", " mean 1.649164e+06 \n", " std 3.472618e+04 \n", " min 8.675960e+05 \n", " 25% 1.634522e+06 \n", " 50% 1.639980e+06 \n", " 75% 1.644792e+06 \n", " max 1.770109e+06 \n", "6 count 8.310000e+03 \n", " mean 1.696490e+06 \n", " std 7.627692e+05 \n", " min 3.163150e+05 \n", " 25% 9.881280e+05 \n", " 50% 2.010452e+06 \n", " 75% 2.358236e+06 \n", " max 2.678850e+06 \n", " cluster_fs1 cluster_fs2 cluster_fs4 retweets \\\n", "cluster_fs3 \n", "0 count 21101.000000 21101.000000 21101.000000 21101.000000 \n", " mean 2.770106 1.755130 1.156012 14.385148 \n", " std 1.848761 2.340575 0.608218 266.181865 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 1.000000 0.000000 \n", " 50% 3.000000 0.000000 1.000000 0.000000 \n", " 75% 4.000000 2.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "1 count 6136.000000 6136.000000 6136.000000 6136.000000 \n", " mean 3.040906 2.971480 3.991362 15.660202 \n", " std 2.009239 0.786379 0.152432 172.023894 \n", " min 0.000000 0.000000 1.000000 0.000000 \n", " 25% 1.000000 3.000000 4.000000 0.000000 \n", " 50% 4.000000 3.000000 4.000000 0.000000 \n", " 75% 4.000000 3.000000 4.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 7649.000000 \n", "2 count 9364.000000 9364.000000 9364.000000 9364.000000 \n", " mean 2.462196 1.956536 2.436459 7.906130 \n", " std 1.497252 2.242091 0.869262 90.135390 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 2.000000 0.000000 \n", " 50% 2.000000 1.000000 3.000000 0.000000 \n", " 75% 3.000000 4.000000 3.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 5192.000000 \n", "3 count 3021.000000 3021.000000 3021.000000 3021.000000 \n", " mean 2.701423 1.999338 1.508772 14.857994 \n", " std 1.991939 2.295816 1.353790 96.943382 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 1.000000 0.000000 \n", " 50% 3.000000 1.000000 1.000000 0.000000 \n", " 75% 4.000000 3.000000 1.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 1991.000000 \n", "4 count 1459.000000 1459.000000 1459.000000 1459.000000 \n", " mean 3.984236 4.955449 4.994517 0.016450 \n", " std 0.246565 0.458200 0.148047 0.212124 \n", " min 0.000000 0.000000 1.000000 0.000000 \n", " 25% 4.000000 5.000000 5.000000 0.000000 \n", " 50% 4.000000 5.000000 5.000000 0.000000 \n", " 75% 4.000000 5.000000 5.000000 0.000000 \n", " max 6.000000 6.000000 5.000000 6.000000 \n", "5 count 4975.000000 4975.000000 4975.000000 4975.000000 \n", " mean 4.365025 1.846432 5.882211 45.420101 \n", " std 2.435581 1.834691 0.600402 517.945374 \n", " min 0.000000 0.000000 1.000000 0.000000 \n", " 25% 1.000000 1.000000 6.000000 1.000000 \n", " 50% 6.000000 1.000000 6.000000 2.000000 \n", " 75% 6.000000 2.000000 6.000000 8.000000 \n", " max 6.000000 6.000000 6.000000 31783.000000 \n", "6 count 3944.000000 3944.000000 3944.000000 3944.000000 \n", " mean 2.816430 1.676724 0.004310 22.526876 \n", " std 2.039127 2.240929 0.094118 149.352267 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 0.000000 0.000000 \n", " 50% 3.000000 0.000000 0.000000 0.000000 \n", " 75% 4.000000 2.000000 0.000000 3.000000 \n", " max 6.000000 6.000000 3.000000 3723.000000 \n", "\n", " tweet_id \n", "cluster_fs3 \n", "0 count 2.110100e+04 \n", " mean 1.836213e+06 \n", " std 6.824786e+05 \n", " min 3.163810e+05 \n", " 25% 1.362619e+06 \n", " 50% 2.046974e+06 \n", " 75% 2.388019e+06 \n", " max 2.678886e+06 \n", "1 count 6.136000e+03 \n", " mean 1.572405e+06 \n", " std 7.198915e+05 \n", " min 3.163170e+05 \n", " 25% 9.614235e+05 \n", " 50% 1.763256e+06 \n", " 75% 2.115240e+06 \n", " max 2.678809e+06 \n", "2 count 9.364000e+03 \n", " mean 1.179238e+06 \n", " std 7.709769e+05 \n", " min 3.163160e+05 \n", " 25% 5.123998e+05 \n", " 50% 9.583535e+05 \n", " 75% 1.781745e+06 \n", " max 2.678890e+06 \n", "3 count 3.021000e+03 \n", " mean 1.822107e+06 \n", " std 6.996226e+05 \n", " min 3.163680e+05 \n", " 25% 1.362381e+06 \n", " 50% 2.060101e+06 \n", " 75% 2.379874e+06 \n", " max 2.678850e+06 \n", "4 count 1.459000e+03 \n", " mean 1.655039e+06 \n", " std 8.500740e+04 \n", " min 6.169490e+05 \n", " 25% 1.634538e+06 \n", " 50% 1.640051e+06 \n", " 75% 1.644934e+06 \n", " max 2.671737e+06 \n", "5 count 4.975000e+03 \n", " mean 1.741655e+06 \n", " std 6.752916e+05 \n", " min 3.163310e+05 \n", " 25% 1.069214e+06 \n", " 50% 1.831875e+06 \n", " 75% 2.356020e+06 \n", " max 2.678763e+06 \n", "6 count 3.944000e+03 \n", " mean 2.105259e+06 \n", " std 4.979767e+05 \n", " min 3.163110e+05 \n", " 25% 1.998756e+06 \n", " 50% 2.111720e+06 \n", " 75% 2.502467e+06 \n", " max 2.678849e+06 \n", " cluster_fs1 cluster_fs2 cluster_fs3 retweets \\\n", "cluster_fs4 \n", "0 count 4283.000000 4283.000000 4283.000000 4283.000000 \n", " mean 2.799673 1.679897 5.719122 22.739435 \n", " std 2.043440 2.252323 0.988376 147.136238 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 6.000000 0.000000 \n", " 50% 3.000000 0.000000 6.000000 0.000000 \n", " 75% 4.000000 2.000000 6.000000 2.000000 \n", " max 6.000000 6.000000 6.000000 3723.000000 \n", "1 count 23794.000000 23794.000000 23794.000000 23794.000000 \n", " mean 2.706481 1.728083 0.445280 13.427503 \n", " std 1.837638 2.346297 0.986194 250.776197 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 0.000000 0.000000 0.000000 \n", " 50% 3.000000 0.000000 0.000000 0.000000 \n", " 75% 4.000000 2.000000 0.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 27137.000000 \n", "2 count 1366.000000 1366.000000 1366.000000 1366.000000 \n", " mean 4.737189 3.740849 1.922401 0.614202 \n", " std 0.957968 1.022904 0.462337 9.939365 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 5.000000 4.000000 2.000000 0.000000 \n", " 50% 5.000000 4.000000 2.000000 0.000000 \n", " 75% 5.000000 4.000000 2.000000 0.000000 \n", " max 6.000000 6.000000 5.000000 276.000000 \n", "3 count 7331.000000 7331.000000 7331.000000 7331.000000 \n", " mean 2.197927 1.741918 1.802346 11.303233 \n", " std 1.316775 2.269350 0.885586 93.034429 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 2.000000 0.000000 2.000000 0.000000 \n", " 50% 2.000000 1.000000 2.000000 0.000000 \n", " 75% 2.000000 2.000000 2.000000 1.000000 \n", " max 6.000000 6.000000 6.000000 3680.000000 \n", "4 count 6771.000000 6771.000000 6771.000000 6771.000000 \n", " mean 3.016393 2.963669 1.072958 15.048442 \n", " std 2.007175 0.818812 0.506395 165.790559 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 3.000000 1.000000 0.000000 \n", " 50% 4.000000 3.000000 1.000000 0.000000 \n", " 75% 4.000000 3.000000 1.000000 1.000000 \n", " max 6.000000 6.000000 3.000000 7649.000000 \n", "5 count 1508.000000 1508.000000 1508.000000 1508.000000 \n", " mean 3.937003 4.855438 3.865385 0.035146 \n", " std 0.462971 0.822086 0.720208 0.553170 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 4.000000 5.000000 4.000000 0.000000 \n", " 50% 4.000000 5.000000 4.000000 0.000000 \n", " 75% 4.000000 5.000000 4.000000 0.000000 \n", " max 6.000000 6.000000 4.000000 19.000000 \n", "6 count 4947.000000 4947.000000 4947.000000 4947.000000 \n", " mean 4.372549 1.834647 4.907823 46.664645 \n", " std 2.432520 1.830597 0.542194 525.198659 \n", " min 0.000000 0.000000 0.000000 0.000000 \n", " 25% 1.000000 1.000000 5.000000 1.000000 \n", " 50% 6.000000 1.000000 5.000000 2.000000 \n", " 75% 6.000000 2.000000 5.000000 8.000000 \n", " max 6.000000 6.000000 5.000000 31783.000000 \n", "\n", " tweet_id \n", "cluster_fs4 \n", "0 count 4.283000e+03 \n", " mean 2.107246e+06 \n", " std 4.989315e+05 \n", " min 3.163110e+05 \n", " 25% 1.999204e+06 \n", " 50% 2.114811e+06 \n", " 75% 2.501846e+06 \n", " max 2.678849e+06 \n", "1 count 2.379400e+04 \n", " mean 1.853439e+06 \n", " std 6.643012e+05 \n", " min 3.163160e+05 \n", " 25% 1.495676e+06 \n", " 50% 2.051500e+06 \n", " 75% 2.388213e+06 \n", " max 2.678890e+06 \n", "2 count 1.366000e+03 \n", " mean 1.626001e+06 \n", " std 2.223969e+05 \n", " min 3.271520e+05 \n", " 25% 1.628331e+06 \n", " 50% 1.637978e+06 \n", " 75% 1.644521e+06 \n", " max 2.673290e+06 \n", "3 count 7.331000e+03 \n", " mean 9.792313e+05 \n", " std 7.752636e+05 \n", " min 3.163500e+05 \n", " 25% 3.352245e+05 \n", " 50% 6.101320e+05 \n", " 75% 1.765522e+06 \n", " max 2.678810e+06 \n", "4 count 6.771000e+03 \n", " mean 1.563080e+06 \n", " std 7.261663e+05 \n", " min 3.163170e+05 \n", " 25% 9.569880e+05 \n", " 50% 1.684840e+06 \n", " 75% 2.115740e+06 \n", " max 2.678832e+06 \n", "5 count 1.508000e+03 \n", " mean 1.658689e+06 \n", " std 1.449377e+05 \n", " min 3.224040e+05 \n", " 25% 1.634538e+06 \n", " 50% 1.640169e+06 \n", " 75% 1.645240e+06 \n", " max 2.671737e+06 \n", "6 count 4.947000e+03 \n", " mean 1.761253e+06 \n", " std 6.620330e+05 \n", " min 3.163310e+05 \n", " 25% 1.105948e+06 \n", " 50% 1.836332e+06 \n", " 75% 2.358328e+06 \n", " max 2.678763e+06 \n" ] } ], "source": [ "main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-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": 1 }	mit
nick-youngblut/SIPSim	ipynb/bac_genome/fullCyc/.ipynb_checkpoints/Day1_rep10_noPCR-checkpoint.ipynb	1	394693	{ "cells": [ { "cell_type": "markdown", "metadata": { "code_folding": [] }, "source": [ "# Goal\n", "\n", "* Extension of `Day1_rep10` simulations: subsampling OTU table without performing PCR simulation first\n", "* Seeing how this affects the abundance distribution of the overlapping taxa in the dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Init" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import glob\n", "import re\n", "import nestly" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext rpy2.ipython\n", "%load_ext pushnote" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: \n", "Attaching package: ‘dplyr’\n", "\n", "\n", " res = super(Function, self).__call__(new_args, new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", " res = super(Function, self).__call__(new_args, *new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n", " res = super(Function, self).__call__(new_args, *new_kwargs)\n" ] } ], "source": [ "%%R\n", "library(ggplot2)\n", "library(dplyr)\n", "library(tidyr)\n", "library(gridExtra)\n", "library(phyloseq)\n", "\n", "## BD for G+C of 0 or 100\n", "BD.GCp0 = 0 0.098 + 1.66\n", "BD.GCp100 = 1 * 0.098 + 1.66" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Nestly\n", "\n", "* assuming fragments already simulated\n", "* assuming `Day1_rep10` notebook already ran" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "workDir = '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/'\n", "buildDir = os.path.join(workDir, 'Day1_rep10')\n", "R_dir = '/home/nick/notebook/SIPSim/lib/R/'\n", "\n", "fragFile= '/home/nick/notebook/SIPSim/dev/bac_genome1147/validation/ampFrags.pkl'\n", "targetFile = '/home/nick/notebook/SIPSim/dev/fullCyc/CD-HIT/target_taxa.txt'\n", "\n", "physeqDir = '/var/seq_data/fullCyc/MiSeq_16SrRNA/515f-806r/lib1-7/phyloseq/'\n", "physeq_bulkCore = 'bulk-core'\n", "physeq_SIP_core = 'SIP-core_unk'\n", "\n", "nreps = 10\n", "prefrac_comm_abundance = '1e9'\n", "\n", "seq_per_fraction = ['lognormal', 9.432, 0.5, 10000, 30000] # dist, mean, scale, min, max\n", "bulk_days = [1]\n", "nprocs = 12" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# building tree structure\n", "nest = nestly.Nest()\n", "\n", "## varying params\n", "nest.add('rep', [x + 1 for x in xrange(nreps)])\n", "\n", "## set params\n", "nest.add('bulk_day', bulk_days, create_dir=False)\n", "nest.add('abs', [prefrac_comm_abundance], create_dir=False)\n", "nest.add('percIncorp', [0], create_dir=False)\n", "nest.add('percTaxa', [0], create_dir=False)\n", "nest.add('np', [nprocs], create_dir=False)\n", "nest.add('subsample_dist', [seq_per_fraction[0]], create_dir=False)\n", "nest.add('subsample_mean', [seq_per_fraction[1]], create_dir=False)\n", "nest.add('subsample_scale', [seq_per_fraction[2]], create_dir=False)\n", "nest.add('subsample_min', [seq_per_fraction[3]], create_dir=False)\n", "nest.add('subsample_max', [seq_per_fraction[4]], create_dir=False)\n", "\n", "### input/output files\n", "nest.add('buildDir', [buildDir], create_dir=False)\n", "nest.add('R_dir', [R_dir], create_dir=False)\n", "nest.add('fragFile', [fragFile], create_dir=False)\n", "nest.add('targetFile', [targetFile], create_dir=False)\n", "nest.add('physeqDir', [physeqDir], create_dir=False)\n", "nest.add('physeq_bulkCore', [physeq_bulkCore], create_dir=False)\n", "\n", "\n", "# building directory tree\n", "nest.build(buildDir)\n", "\n", "# bash file to run\n", "bashFile = os.path.join(buildDir, 'SIPSimRun.sh')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting /home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/SIPSimRun.sh\n" ] } ], "source": [ "%%writefile $bashFile\n", "#!/bin/bash\n", "\n", "export PATH={R_dir}:$PATH\n", " \n", " \n", "echo '# subsampling from the OTU table (simulating sequencing of the DNA pool)'\n", "SIPSim OTU_subsample \\\n", " --dist {subsample_dist} \\\n", " --dist_params mean:{subsample_mean},sigma:{subsample_scale} \\\n", " --min_size {subsample_min} \\\n", " --max_size {subsample_max} \\\n", " OTU_abs{abs}.txt \\\n", " > OTU_abs{abs}_sub.txt\n", " \n", "echo '# making a wide-formatted table'\n", "SIPSim OTU_wideLong -w \\\n", " OTU_abs{abs}_sub.txt \\\n", " > OTU_abs{abs}_sub_w.txt\n", " \n", "echo '# making metadata (phyloseq: sample_data)'\n", "SIPSim OTU_sampleData \\\n", " OTU_abs{abs}_sub.txt \\\n", " > OTU_abs{abs}_sub_meta.txt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-02-01 18:28:08,846 * INFO * Template: ./SIPSimRun.sh\n", "2016-02-01 18:28:08,928 * INFO * [167134] Started ./SIPSimRun.sh in Day1_rep10/7\n", "2016-02-01 18:28:14,583 * INFO * [167134] Day1_rep10/7 Finished with 0\n", "2016-02-01 18:28:14,611 * INFO * [167180] Started ./SIPSimRun.sh in Day1_rep10/9\n", "2016-02-01 18:28:18,302 * INFO * [167180] Day1_rep10/9 Finished with 0\n", "2016-02-01 18:28:18,339 * INFO * [167226] Started ./SIPSimRun.sh in Day1_rep10/8\n", "2016-02-01 18:28:21,941 * INFO * [167226] Day1_rep10/8 Finished with 0\n", "2016-02-01 18:28:21,961 * INFO * [167272] Started ./SIPSimRun.sh in Day1_rep10/3\n", "2016-02-01 18:28:25,572 * INFO * [167272] Day1_rep10/3 Finished with 0\n", "2016-02-01 18:28:25,613 * INFO * [167318] Started ./SIPSimRun.sh in Day1_rep10/10\n", "2016-02-01 18:28:29,048 * INFO * [167318] Day1_rep10/10 Finished with 0\n", "2016-02-01 18:28:29,063 * INFO * [167365] Started ./SIPSimRun.sh in Day1_rep10/2\n", "2016-02-01 18:28:32,939 * INFO * [167365] Day1_rep10/2 Finished with 0\n", "2016-02-01 18:28:32,953 * INFO * [167411] Started ./SIPSimRun.sh in Day1_rep10/1\n", "2016-02-01 18:28:36,539 * INFO * [167411] Day1_rep10/1 Finished with 0\n", "2016-02-01 18:28:36,582 * INFO * [167457] Started ./SIPSimRun.sh in Day1_rep10/4\n", "2016-02-01 18:28:40,155 * INFO * [167457] Day1_rep10/4 Finished with 0\n", "2016-02-01 18:28:40,182 * INFO * [167503] Started ./SIPSimRun.sh in Day1_rep10/5\n", "2016-02-01 18:28:43,776 * INFO * [167503] Day1_rep10/5 Finished with 0\n", "2016-02-01 18:28:43,788 * INFO * [167549] Started ./SIPSimRun.sh in Day1_rep10/6\n", "2016-02-01 18:28:47,408 * INFO * [167549] Day1_rep10/6 Finished with 0\n" ] } ], "source": [ "!chmod 777 $bashFile\n", "!cd $workDir; \\\n", " nestrun --template-file $bashFile -d Day1_rep10 --log-file log.txt -j 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# BD min/max\n", "\n", "* what is the min/max BD that we care about?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Min BD: 1.67323 \n", "Max BD: 1.7744 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "## min G+C cutoff\n", "min_GC = 13.5\n", "## max G+C cutoff\n", "max_GC = 80\n", "## max G+C shift\n", "max_13C_shift_in_BD = 0.036\n", "\n", "\n", "min_BD = min_GC/100.0 * 0.098 + 1.66 \n", "max_BD = max_GC/100.0 * 0.098 + 1.66 \n", "\n", "max_BD = max_BD + max_13C_shift_in_BD\n", "\n", "cat('Min BD:', min_BD, '\\n')\n", "cat('Max BD:', max_BD, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Emperical" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SIP data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R \n", "# simulated OTU table file\n", "OTU.table.dir = '/home/nick/notebook/SIPSim/dev/fullCyc/frag_norm_9_2.5_n5/Day1_default_run/1e9/'\n", "OTU.table.file = 'OTU_abs1e9_sub.txt'" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "phyloseq-class experiment-level object\n", "otu_table() OTU Table: [ 7025 taxa and 25 samples ]\n", "sample_data() Sample Data: [ 25 samples by 17 sample variables ]\n", "tax_table() Taxonomy Table: [ 7025 taxa by 8 taxonomic ranks ]\n", "phy_tree() Phylogenetic Tree: [ 7025 tips and 7024 internal nodes ]\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i physeqDir -i physeq_SIP_core -i bulk_days\n", "\n", "# bulk core samples\n", "F = file.path(physeqDir, physeq_SIP_core)\n", "physeq.SIP.core = readRDS(F) \n", "physeq.SIP.core.m = physeq.SIP.core %>% sample_data\n", "\n", "physeq.SIP.core = prune_samples(physeq.SIP.core.m$Substrate == '12C-Con' & \n", " physeq.SIP.core.m$Day %in% bulk_days, \n", " physeq.SIP.core) %>%\n", " filter_taxa(function(x) sum(x) > 0, TRUE)\n", "physeq.SIP.core.m = physeq.SIP.core %>% sample_data \n", "\n", "physeq.SIP.core" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [3 x 20]\n", "\n", " OTU sample abundance primer_number fwd_barcode rev_barcode\n", " (chr) (chr) (dbl) (int) (fctr) (fctr)\n", "1 OTU.1101 12C-Con.D1.R2_F23 2 134 TCGACGAG TGAGTACG\n", "2 OTU.1101 12C-Con.D1.R2_F18 0 129 CTACTATA TGAGTACG\n", "3 OTU.1101 12C-Con.D1.R2_F20 1 131 AGAGTCAC TGAGTACG\n", "Variables not shown: Substrate (fctr), Day (int), Microcosm_replicate (int),\n", " Fraction (int), Buoyant_density (dbl), Sample_type (fctr), library (fctr),\n", " Exp_type (fctr), Sample_location (lgl), Sample_date (lgl), Sample_treatment\n", " (lgl), Sample_subtreatment (lgl), core_dataset (lgl), n_taxa (int)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R \n", "\n", "## dataframe\n", "df.EMP = physeq.SIP.core %>% otu_table %>%\n", " as.matrix %>% as.data.frame\n", "df.EMP$OTU = rownames(df.EMP)\n", "df.EMP = df.EMP %>% \n", " gather(sample, abundance, 1:(ncol(df.EMP)-1)) \n", "\n", "df.EMP = inner_join(df.EMP, physeq.SIP.core.m, c('sample' = 'X.Sample')) \n", "\n", "df.EMP.nt = df.EMP %>%\n", " group_by(sample) %>%\n", " mutate(n_taxa = sum(abundance > 0)) %>%\n", " ungroup() %>%\n", " distinct(sample) %>%\n", " filter(Buoyant_density >= min_BD, \n", " Buoyant_density <= max_BD)\n", " \n", "df.EMP.nt %>% head(n=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### bulk soil samples" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "phyloseq-class experiment-level object\n", "otu_table() OTU Table: [ 4950 taxa and 1 samples ]\n", "sample_data() Sample Data: [ 1 samples by 17 sample variables ]\n", "tax_table() Taxonomy Table: [ 4950 taxa by 8 taxonomic ranks ]\n", "phy_tree() Phylogenetic Tree: [ 4950 tips and 4949 internal nodes ]\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "physeq.dir = '/var/seq_data/fullCyc/MiSeq_16SrRNA/515f-806r/lib1-7/phyloseq/'\n", "physeq.bulk = 'bulk-core'\n", "physeq.file = file.path(physeq.dir, physeq.bulk)\n", "physeq.bulk = readRDS(physeq.file)\n", "physeq.bulk.m = physeq.bulk %>% sample_data\n", "physeq.bulk = prune_samples(physeq.bulk.m$Exp_type == 'microcosm_bulk' &\n", " physeq.bulk.m$Day %in% bulk_days, physeq.bulk)\n", "\n", "physeq.bulk.m = physeq.bulk %>% sample_data\n", "physeq.bulk" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "phyloseq-class experiment-level object\n", "otu_table() OTU Table: [ 4950 taxa and 1 samples ]\n", "sample_data() Sample Data: [ 1 samples by 17 sample variables ]\n", "tax_table() Taxonomy Table: [ 4950 taxa by 8 taxonomic ranks ]\n", "phy_tree() Phylogenetic Tree: [ 4950 tips and 4949 internal nodes ]\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "physeq.bulk.n = transform_sample_counts(physeq.bulk, function(x) x/sum(x))\n", "physeq.bulk.n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " OTU bulk_abund\n", "1 OTU.1101 1.234263e-04\n", "2 OTU.1130 6.171316e-05\n", "3 OTU.9833 0.000000e+00\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# making long format of each bulk table\n", "bulk.otu = physeq.bulk.n %>% otu_table %>% as.data.frame\n", "ncol = ncol(bulk.otu)\n", "bulk.otu$OTU = rownames(bulk.otu)\n", "bulk.otu = bulk.otu %>%\n", " gather(sample, abundance, 1:ncol) \n", "\n", "bulk.otu = inner_join(physeq.bulk.m, bulk.otu, c('X.Sample' = 'sample')) %>%\n", " dplyr::select(OTU, abundance) %>%\n", " rename('bulk_abund' = abundance)\n", "bulk.otu %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " OTU sample abundance primer_number fwd_barcode rev_barcode\n", "1 OTU.1101 12C-Con.D1.R2_F23 2 134 TCGACGAG TGAGTACG\n", "2 OTU.1130 12C-Con.D1.R2_F23 0 134 TCGACGAG TGAGTACG\n", "3 OTU.9833 12C-Con.D1.R2_F23 0 134 TCGACGAG TGAGTACG\n", " Substrate Day Microcosm_replicate Fraction Buoyant_density Sample_type\n", "1 12C-Con 1 2 23 1.69362 unknown\n", "2 12C-Con 1 2 23 1.69362 unknown\n", "3 12C-Con 1 2 23 1.69362 unknown\n", " library Exp_type Sample_location Sample_date Sample_treatment\n", "1 150721_V4_Lib4 SIP NA NA NA\n", "2 150721_V4_Lib4 SIP NA NA NA\n", "3 150721_V4_Lib4 SIP NA NA NA\n", " Sample_subtreatment core_dataset bulk_abund\n", "1 NA TRUE 1.234263e-04\n", "2 NA TRUE 6.171316e-05\n", "3 NA TRUE 0.000000e+00\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# joining tables\n", "df.EMP.j = inner_join(df.EMP, bulk.otu, c('OTU' = 'OTU')) %>%\n", " filter(Buoyant_density >= min_BD, \n", " Buoyant_density <= max_BD) \n", " \n", "df.EMP.j %>% head(n=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulated" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/7/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/9/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/8/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/3/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/10/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/2/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/1/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/4/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/5/OTU_abs1e9.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/6/OTU_abs1e9.txt']" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#OTU_files = !find $buildDir -name \"OTU_abs1e9_sub.txt\"\n", "OTU_files = !find $buildDir -name \"OTU_abs1e9.txt\"\n", "OTU_files" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon fraction BD_min BD_mid BD_max count rel_abund SIM_rep\n", "1 1 OTU.1 -inf-1.660 -Inf 1.659 1.659 8384 0.02576038 7\n", "2 1 OTU.1 1.660-1.663 1.660 1.661 1.663 2207 0.02087215 7\n", "3 1 OTU.1 1.663-1.666 1.663 1.664 1.666 2130 0.02214114 7\n", "4 1 OTU.1 1.666-1.668 1.666 1.667 1.668 2272 0.03469762 7\n", "5 1 OTU.1 1.668-1.673 1.668 1.671 1.673 5888 0.03673250 7\n", "6 1 OTU.1 1.673-1.678 1.673 1.675 1.678 3972 0.02432825 7\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i OTU_files\n", "# loading files\n", "\n", "df.SIM = list()\n", "for (x in OTU_files){\n", " SIM_rep = gsub('/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/', '', x)\n", " #SIM_rep = gsub('/OTU_abs1e9_sub.txt', '', SIM_rep)\n", " SIM_rep = gsub('/OTU_abs1e9.txt', '', SIM_rep)\n", " df.SIM[[SIM_rep]] = read.delim(x, sep='\\t') \n", " }\n", "df.SIM = do.call('rbind', df.SIM)\n", "df.SIM$SIM_rep = gsub('\\\\.[0-9]+$', '', rownames(df.SIM))\n", "rownames(df.SIM) = 1:nrow(df.SIM)\n", "df.SIM %>% head" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [6 x 4]\n", "Groups: SIM_rep, library [1]\n", "\n", " SIM_rep library BD_mid n_taxa\n", " (chr) (int) (dbl) (int)\n", "1 1 1 1.677 1928\n", "2 1 1 1.681 1925\n", "3 1 1 1.684 1925\n", "4 1 1 1.687 1898\n", "5 1 1 1.690 1928\n", "6 1 1 1.693 1930\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "## edit table\n", "df.SIM.nt = df.SIM %>%\n", " filter(count > 0) %>%\n", " group_by(SIM_rep, library, BD_mid) %>%\n", " summarize(n_taxa = n()) %>%\n", " filter(BD_mid >= min_BD, \n", " BD_mid <= max_BD)\n", "df.SIM.nt %>% head " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 'bulk soil' community files" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/7/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/9/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/8/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/3/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/10/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/2/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/1/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/4/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/5/bulk-core_comm_target.txt',\n", " '/home/nick/notebook/SIPSim/dev/fullCyc/n1147_frag_norm_9_2.5_n5/Day1_rep10/6/bulk-core_comm_target.txt']" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# loading comm files\n", "comm_files = !find $buildDir -name \"bulk-core_comm_target.txt\"\n", "comm_files" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon_name bulk_abund SIM_rep\n", "1 1 OTU.14142 0.05918292 7\n", "2 1 OTU.2 0.05856579 7\n", "3 1 OTU.12920 0.04424833 7\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i comm_files\n", "\n", "df.comm = list()\n", "for (f in comm_files){\n", " rep = gsub('.+/Day1_rep10/([0-9]+)/.+', '\\\\1', f)\n", " df.comm[[rep]] = read.delim(f, sep='\\t') %>%\n", " dplyr::select(library, taxon_name, rel_abund_perc) %>%\n", " rename('bulk_abund' = rel_abund_perc) %>%\n", " mutate(bulk_abund = bulk_abund / 100)\n", "}\n", "\n", "df.comm = do.call('rbind', df.comm)\n", "df.comm$SIM_rep = gsub('\\\\.[0-9]+$', '', rownames(df.comm))\n", "rownames(df.comm) = 1:nrow(df.comm)\n", "df.comm %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon fraction BD_min BD_mid BD_max count rel_abund SIM_rep\n", "1 1 OTU.1 1.673-1.678 1.673 1.675 1.678 3972 0.024328248 7\n", "2 1 OTU.1 1.678-1.682 1.678 1.680 1.682 3680 0.025616038 7\n", "3 1 OTU.1 1.682-1.687 1.682 1.684 1.687 4915 0.006947979 7\n", " bulk_abund\n", "1 0.02801777\n", "2 0.02801777\n", "3 0.02801777\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "## joining tables\n", "df.SIM.j = inner_join(df.SIM, df.comm, c('SIM_rep' = 'SIM_rep',\n", " 'library' = 'library',\n", " 'taxon' = 'taxon_name')) %>%\n", " filter(BD_mid >= min_BD, \n", " BD_mid <= max_BD)\n", " \n", "df.SIM.j %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [3 x 8]\n", "\n", " OTU mean_rel_abund min_BD max_BD BD_range BD_range_perc dataset\n", " (chr) (dbl) (dbl) (dbl) (dbl) (dbl) (chr)\n", "1 OTU.1 0.028017773 1.676135 1.773391 0.09725564 100 emperical\n", "2 OTU.10 0.001172550 1.676135 1.773391 0.09725564 100 emperical\n", "3 OTU.100 0.001049124 1.676135 1.773391 0.09725564 100 emperical\n", "Variables not shown: SIM_rep (fctr)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R \n", "# filtering & combining emperical w/ simulated data\n", "\n", "## emperical \n", "max_BD_range = max(df.EMP.j$Buoyant_density) - min(df.EMP.j$Buoyant_density)\n", "df.EMP.j.f = df.EMP.j %>%\n", " filter(abundance > 0) %>%\n", " group_by(OTU) %>%\n", " summarize(mean_rel_abund = mean(bulk_abund),\n", " min_BD = min(Buoyant_density),\n", " max_BD = max(Buoyant_density),\n", " BD_range = max_BD - min_BD,\n", " BD_range_perc = BD_range / max_BD_range * 100) %>%\n", " ungroup() %>%\n", " mutate(dataset = 'emperical',\n", " SIM_rep = NA)\n", "\n", "## simulated\n", "max_BD_range = max(df.SIM.j$BD_mid) - min(df.SIM.j$BD_mid)\n", "df.SIM.j.f = df.SIM.j %>%\n", " filter(count > 0) %>%\n", " group_by(SIM_rep, taxon) %>%\n", " summarize(mean_rel_abund = mean(bulk_abund),\n", " min_BD = min(BD_mid),\n", " max_BD = max(BD_mid),\n", " BD_range = max_BD - min_BD,\n", " BD_range_perc = BD_range / max_BD_range * 100) %>%\n", " ungroup() %>%\n", " rename('OTU' = taxon) %>%\n", " mutate(dataset = 'simulated')\n", "\n", "## join\n", "df.j = rbind(df.EMP.j.f, df.SIM.j.f) %>%\n", " filter(BD_range_perc > 0,\n", " mean_rel_abund > 0)\n", "\n", "df.j$SIM_rep = reorder(df.j$SIM_rep, df.j$SIM_rep %>% as.numeric)\n", "\n", "df.j %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dKU6nEzoxxKqbikAgUprUVtAwIRsqUJlMZjQaRRELI6BjsRjHcQqFQojnNxKJ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\n+bfJ7Xabzeaenp60tDR4JT8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4EPc1q/OuwwhZfyNPnTp17bXXPvbYY/v37x//2hkMtav3\nMPw1SbdNGxoa8vPzDx48CMcM+ynDZDJhGMayLFTTYmUSwupLwWAQ/mu1WlEONwIxjsHkabOlxkIM\nIwbQzgCA559/vqKiYsWKFR0d/+4ta7VaBW4yjQlCFbTX6+3bSUxIQTt+2xQA8Otf//qNN97o7Oxs\nbGwsKioaeTVYm82mVCrhJqFKpRKrEDjHcW1tbfDJoKenp6ysTBSxCARi9OjP75zI8ePHP//8882b\nN/NXYCem0VzXiBCkoP/0pz/df//9fYPYhMSfJd02bWhoaGlpmTBhgtvtXrJkyRtvvJGXlzfElQMA\ngFwuLywsbGho0Ov1GRkZYm0SYhimUChOnz4tlUonTpwoVng1AoEYWy5evDjWSxgagnzQv/rVrzZv\n3gwL2CciZG56erpGo6FpesuWLWvXroXbpgzDlJSUHDp0qL6+Xq1WV1RU8ON37ty5YsWKFStWwDjr\nQQmHwzNnzrTZbB6PR6xgkmg02tHRkZGRYTKZmpubURQHAoEYEwRZ0PF4/MEHHxy2nuq7bconJQIA\nKisrMzMzHQ4HDMCYMWPG2rVrAQACKwrm5uZSFCWVSidMmMCyrCg6GqYOdnV1AQB0Ol08Hh/PXioE\nAnG5IkhBz5kzp7a2durUqcO4Adw2ffHFF3/84x/zKn7Lli0rV66cOHEiAAAGsfH7hAUFBQUFBQCA\nw4cPDypcqVR6PB6v18tx3IwZM8SKh2NZ1ufzabVaGGSdooUKEQhEqiNIoz311FNr1qx57LHHZsyY\nIZP97yapEOd60m3T6urq7du3v/3220aj8amnniovL9doNMNYfSAQcDqdTqcTulyKiopEsaAJgsjN\nzWVZFsdxiqJQpB0CMU6gKCocDldXVw91Yoo2VRGkoJctWwYAuOeee3pdF/Kek26bVlZWPvjgg6Wl\npSRJlpeXb926dShr/l+6urrcbjdMOu/u7vb7/aI0E7BYLLFYTK1Wsyw7derUsUpkRyAQvZBKpQqF\nAj5kC+f06dMpupMkSEGP5Menv23Tbdu2DVsmj1wuJ0kSWrhSqVQsTzFJkrfddpvdbscwzGq1iiIT\ngUCIAoZhV07bz2F6V6PR6IULF8RdyjDIzc0tKiqCZefmzp0roqkLC3FIJBJU2RmBQIwVQnfVAoEA\n70QGAOzfv3/Dhg1er3d0ViUUgiCWLVtWVFQkkUhErCRH03R1dbVOp2NZNhqNZmVlpejzEQKBSGkE\nWdA7duwwGAxFCTz66KPr1q0b7cUJJCMjQ9w6n5FIRKFQkCQplUo7OzvRJiECcdmwffv2KVOmqFSq\nefPmff3112O9nEEQpKCfe+65++67z+/3z50798yZM62trTNmzLjxxhtHe3FjBUmS8Xgc/Kfn4ZXj\n8EIgUheOYzhuEIdkY2Pj/fff//bbb7tcroqKiptvvllIx9ExRJCCbmpqgpFwK1euPHHiRG5u7rPP\nPrthw4bRXpwQPB7P0aNH29vbQ6GQWDIVCkV+fr7L5XK5XNOnTxcxDhqu9uLFi8FgUCyZCATC4z1z\nvv7/O1/3ejDYPMCwQ4cOlZaWLl26VC6XP/TQQ7A25yVb5DAQpHpUKpXdbgcAwPxsAEBOTs4wQhFF\nJxqNnjx5sru7u6qqqqqqSsRQR5PJtGDBgoULFw4vQDsp0Wi0vr7eZDKFQqHTp0+j7UcEQhSiUYfD\neVSjnqTRTLnYvotlqf5G3nvvvfv37+c4zu/3f/jhhxMnThznDeoEbRLOnTv39ddfnzlz5qxZsx57\n7LGurq59+/aNh/ZOfr+/ra3N5/NhGObz+WKx2HjujxCPx2HRJQzDJBIJqsGEQIgCy8Vx/N8JdDgu\nZdk4jg/0zTpy5MjixYsxDPvXv/41zvf/BSnol1566frrr//ss89efPHFO++8MysrSyKRfPDBB6O9\nuEEJBAI9PT3QFKUoapy7kxQKRTgchs1tMzIykHZGIERBLrPEok4cIziOMZnmkaRq4PGLFi3yer3v\nvPPOzTff3N3dPZ5rOQhS0DNnzmxvbw8EAgCAV199dcOGDTKZjC8iOoYEAgGGYeDnS9N0LBYbD6vq\nD5Ik58yZ4/P5cBwXq3Q1AoHAcUlhwWORaCcGCJVqoCrzb7/9NoZhP/3pT3U63QMPPLB+/fqurq6s\nrKxLttShIuino7S0tKWlBWZUAwCMRuM40YOw8bZUKoVphOPfJpXL5Var1WKxoPJ4CISIEIRMrZqg\nUuUMrNNMJtPzzz9/+vTpcDi8efPm3Nzcy8EHrVQqjxw5kp+fP9qrGSoWi0Wj0ZAkieO40Wgczw5o\nBAIx5tx8883nzp0rLy93u92zZs36/PPPx7N/AwhU0Bs3bnziiSfi8fisWbOUyv/tKzPmrWJ0Ot31\n119fW1vLsuysWbNQ+20EAjEAGIZtGpx8WgAAIABJREFU3Lhx48aNY70QoQjSaNdccw0A4P777+91\nfcwr+LEs29nZabVa+SYv43xPFoFAIIQjyLzn+mG0FzcokUhEKpXG43GGYdrb21FONgKBuJxIbZ8A\nx3EtLS1ms5llWYfDMW/evLFeEQKBGEVomo5EIqdPnx7SrHFiUA6D1FbQAACJRFJfXw8AsNlsYnX1\nHj18Ph+MVjQajYnefAQCIQSCIKRS6VAD41K39XNqK+hgMNjc3GwwGFiWra2tXb58+VivaCBisVht\nba1er+c4rq2tbf78+eN8BxmBGG/Aav1DbZyUotoZDLtg/zghFAopFIpgMBiJRORyucBG4GMFRVEy\nmQzDMBzH+UYwCAQC0R+pbUGr1epgMAjdGgzDiJs+E4vFCIIYUuheLBYjSbI/N4tMJoM/JCzLxuNx\nlKuCQCAGRmhPwp6eHgzD0tLSxtXDAo7jcrk8EAhgGAYdHWJJ7ujogN0UJ02aJKQsFMdxnZ2d7e3t\nLMtOmTLFZDL1HSOVSmfOnAlTvSdPnjyuPkkE4oriwoULM2bMOHbs2JgncwzMQC4OhmH+/Oc/z5s3\nT6VSZWRkpKenq9Xq0tLSHTt2jJOyRN3d3eFwWCKRkCTp9/tjsZgoYkOhUFdXl0KhUCqVjY2NQnwR\nwWCwp6dHr9cbjca6urr+Ph+lUpmRkWG1Wsd/VjoCcbnCMMzdd9/t9/vHeiGD06+CZhjmpptuWrdu\n3Y033nj06FG32+3xeI4cOXLDDTc88MADq1atGifljBOjZ8SKpKFp2ufztbe3t7W1BQIBIWITc2Rw\nHE/RmB4EIkVxxDpqfcfP+771x92DDn7xxRcnT558CVY1cvp1cbz88ssHDx48fvz41KlT+YszZ86c\nOXPmzTffPHfu3FdfffWZZ565JIvsFxgRwbIsjuNqtVrEWhwul0uj0UDhQsar1epwOAwAYFk2NzdX\nxKTzeDweDAZxHNdoNCjqA4HoS4j2H+j6NEM5gePYaveBO/KexLF+w22rqqo+/PDD48ePv/fee5dy\nkcOjXz3y17/+9ac//WmiduaZNm3a/fff//nnn4+5glYqlQqFAsZFqNVqsfQXQRATJ06ENZiCwaAQ\nZzGO42VlZaFQCMdxEfcqaZo+duyYVqtlGMZisWRnD1RKEYG4MgnTARWpwwCGYYScUMXYiIJQJx0Z\nDAbXrFnzpz/9ScROSaNKvxrt1KlTJSUl/b06a9askydPjs6ShgBFUenp6QaDQafTEQRBUf22uhkS\n0OdOUVQ4HJ40aZJAcxgaueJGkoRCIbVaLZPJlEplR0cHbGWLQCASUUv0ftrNcSzNUlEmJMP7TQF7\n6qmnbr311rKysku5vJHQr+qJxWIDRIMbDAaxduRGglwul0qlZrMZx3GHwyHWzhuGYVlZWVDs2AbD\nJYZL0zSNyvUhEH1REKrrMu/uDLcQOFlquRbH+rU7W1pa/v73v/PdoK655pqnn376iSeeuFQrHTKp\n/YU3mUzFxcUnT57kOG7JkiUihkYEAgHY6tBsNstkMrHEDhWVSmWz2ZqamjiOKy4uRpF5CERS9FKL\nXmoZdNiePXv4YwzD9u3bN87D7AZS0O3t7efPn+/vpdFZz5ApLCycNGkSjuMibqDFYrHjx49DvdzQ\n0HDVVVeNoWaEHVgwDEPaGYG40hhIQT/66KOXbB3DpqmpqaqqimXZZcuWidW9xu/3ezwe6OHp6emB\nKdqiSB4eKHgDgRCdlIiF7feb318N6HFVD9rj8VRXV1utVqvVun//frGqWxAEAQPsWJZFdisCgRgr\nUtsHHYlEoG0Ld/MoihJlG02j0ZjNZpIkOY6bN2/e2JrPCATiimXI6szpdMrlcrU6eZihcLxe7513\n3nnkyJGFCxdu27ZNr9cPQ4jRaAyFQnK5nGGYSZMmiZWoIpFISktLvV4vjuNJq2ogEIgxgWXZWCzW\n0NAwpFnj4XF/ePTr4qBp+qWXXpo0aRKsv/Hhhx92dnaWlJRYLBatVnvrrbf6fL6R3Hj9+vUajaah\noUGj0axfv354QuRy+U033ZSbmzt58uSpU6eK6KtVKBSwaAaKbEMgxg/Q6ygdIiBldfRAqd6bNm16\n+eWXy8rKmpqann766fXr1y9fvnz//v1Op/OWW275+c9/vmXLluHdlWXZnTt37tmzx2KxPPnkk9de\ne+0f//jH4Xl71Wr1OA+UQSAQYkGSpFQqzc3NHdIsl8uVoptJ/Zqc77333qOPPvrII4/MmTOnoqKi\nsrLSbre//PLLJpOpoKDgiSee+OKLL4Z9V6/X6/f7oWKdMmWK1+sdoT2OQCAQlx/9KuimpqaZM2fy\np7NnzwYAZGZmwlOTydTV1TXsu3o8HgAAzIqG7myXywVf2rlz54oVK1asWPHll18OWz4CgUBcBgzk\nYFUoFL2OxXpMgFuC4XBYq9UGg0EAAJ9WPmPGjLVr1wIAxnn/KgQCkXK4XK7E/hs/+MEPdu3aNYbr\nGZSx2QEzGAxarbaxsXH27NmNjY1arZZX0AUFBQUFBQCAw4cP19XVob5QCESqMP434hoaGvLz8w8e\nPAhPRSxQPEoITfV2OBwAAP50hKneOI7feuutb7755ubNm996662Kioq+trlerz9x4sRI7oJAIC4l\nmZmZDMP015NzUAYonzkoETZ+MeojMDxXrif7L5bU2NhYVFSUQmV7h5bqXVRUJNaNX3311dWrV2dm\nZi5YsOCjjz7qO6C4uLi4uFis2yEQiMsVimM2X/wmS65jONZBhUq12f05YxsaGlpaWiZMmOB2u5cs\nWfLGG2/k5eVd2sUOjTFL9dbr9bt373a73V9++eXwslQQCAQCAOCkwmapUkNI9aS8OtAZZvst+cAw\nTElJyaFDh+rr69VqdUVFxaVc5zBAWRgIBCK1keFEhKGBBAAAomxc2n/C2qZNm/jjysrKzMxMh8Nh\nsQxep3SsQGXSEAhEamOSKJcaJtSHnXVh561p0yX9NyTcsmVLc3MzPIZJwuN8nxBZ0AgEIuWZqkqb\nqkobdFh1dfX27dvffvtto9H41FNPlZeXj/PmhONXQZ86daqnp2fYO8IIBOISc/78+ZkzZw47W8Lj\n8dxwww3iLqkXlZWVDz74YGlpKUmS5eXlW7duHdXbjZzxq6CDweCSJUsSk2UQCMR45ujRoyP5wkaj\nUREXkxSNRrNt27bRvouI9Kug+2t2lQiqUoRAIBCjR78KWkjI8/hPHEIgEIjURfw4aI7jvve97yUa\n4F6vt7y8XK/Xl5eXe73eAS4iEAjEwDAM4x4iqWtKDjPMLhqNXrhwoddFjuM++eSTO+64o7q6OvF6\n0tr8ohTsh6zapb+xM3MkEsQSu+rU4FMGEPtI61WrTiXJ2VlzqnB9542JV/5+9vW7/158Epzkr/zi\nwI1/6Pwv/vQPzU98Vvc2PN57cetrDQ/A40Ndn21p+fen/d6F//6y50MAQJ3/xD/s2wAAnYG6/3th\nawAEmoI1h52fAwAavdUXovV2YD/g+swesn/j+r/n7SeaI+dOdf+zO9p61vu1O9JxrOfvbd7Gen+1\nl3Y6ox31/urWWH1L+FxXvK093OCmepzhjo5gc5gNMhzNAS7GRjxUT3ekrYu60BFt8sU8zlhnkPLR\nXNwd6emJtvnizggdjNLhMB1wUz0AAB/j9jFuFrB+2h3jol7K6ae9bqrbTdlpjo6xEYZjYmyE5ugw\nE4iyYQ5wMSYS5SIOqpMBDMdxYToQYYIUG6XYqJdyhOlArw+ZYqM0G+/74TMcTbFR/hvOcmyEDUXY\nEAe4xLkMlzw5oj+x/cFybJD2xhhB3lj4Yfopd5QNC78FT5yjKDY28Bj4uQ1D+ChBURRFUZ1DBKTs\n4z4mcN2BQKCjo4M/3b9//4YNG3pZvgzDPPzwwwCA//N//k9tbS30ULMsazAY9uzZU1paWlVVde21\n18IftL4Xe23+Hj58eM6cOQPvOTwHntu79wU2zmEYkJD4P1eI0zR2GGKXHlwaix7CAAcAwDj8yHVJ\npgwsdulBCRVmAA4AwGYXLtqc908AwEeHP/pDcE0vsUt3SyicAQBwGJavv/qXpW/eu7eIF7t9xTe3\n7SllWA4AIMElhBSLhikoVoZLY//+QmIcAP8Wi+McywIACAJnGBYAgOMYzXI4ADiBsXGA4YAFHIYD\nwAIAAAcAxgGOAxgGcAxjAYfBiwADAMdInKXjAAAcwxiOkwAJgRE0R8ml6hxFwVzjsin6Of9z8YNT\n/sPReIjlaCmpYNi4mtTJcY1FntkaPE+xYQWhtMiy0hS2xsBZjUSnl5pdsW6WY/M1xVEm3Bg4LcFl\nNBcPxX0SQj5FUzLTeJUj2mGWZRx37VcQSgzg3zMt64lc/KrzI9P/396dBkZRpA8Df6q6e+4zN7kI\nRI6IAoLACnJrPPBAhaznqiC6q/u6rKKI4i7mj4KCsLu4iHgtrIAgKl7o4goRUBckCLoIJOHKfU0y\nmUnm6u6q98NAzOYOZJIJPL9Pk57q6md6Js/UVFdXaWOuiL423tjvP+Vba1VnlBSf78n1KrUR2pjp\nKY+OjblJICIHdqQm+5Bzj8LkcXE3x+v71r8pZb6C7aWbJKIdaL18oGW4zP3rT7yc6zoABG5J+u1g\n+5Vaqjviyv7ZuVfmgQmxt/bSp9Tv27DasbE3JRhS2/wU1co1b+QtqPAVyjxwb9+nB9lGtVLYq9bl\nug98UvDG4ZrsGG3ir/s8Oirq2jYPUe+4+7/7HF8BgWEREy8yD25aQGHy7vJP/lXyDudwY9KMK6Ku\noy2PLw7KzMycMmVK+2No5MSJE9OmTWu9jMvlKioq6uicE/v37x88eHBPXB2pXRFv2rTpzjvvVFW1\nfguldO7cuY2KCYIQXGPltddeq9/Y7Nz8jLGmG4M3fO/du3fHjh0AYDQahw8f3npgX33+wsp/VUUR\nhVGt25w/YUhMVkx5e15R67KyXlj52f4oksiodvul74/aE7lnlKP1XQT12zf/85PeEWciUkVEwY1K\n/Cc3Frde7d17It85U+3atWtJzJUffv6RCoqsc9/BUiEFAODv/hmv/ucbq6O/yNTPo1fPgRuXXvfJ\noIOD51dv515RNZTdfVX/B7OGzN3x1nDvDYxqV1z6u7t2XvmHvStHl93mJ/6nJ99Wyorf+zybAP05\nMuuNX7245LNPBKD7kneXaw5NyJtFmPzGiMeuz/2d1R/5VfyHwyuvtAXi/5uwNdF1qc5j3hf/aYQ/\nURECGtWU7OpXbaywuCI9Um2tqYbIqiALnFKTapWBUcprxPIaY7VCPfraaGPAWGOutHitLsnh1lWb\nmMVPPV5DbV2se7tpg1JOJtb8OtKfoPOZRS6UWE5yqnh1rv+assdXT7O4o5x6R8BQW2DIHV03xcBN\n+03bR9SmA8AP9u0xkJDiuyTGm+wPBIzMJomkKPpEndsZCTGMKJGeuORAPyLBUdd+EqBXld9uViyl\nVQX7jd+Mr7uhOlCZQw/EKSmRnl46vXabsq6feWiCoW+Fr/hExX+TnH25yLLYh7f3nV2fif5dtKFv\n3cU0QIsCxyK0MbVyjaHOPKx0HCdwnPw3QhtnEe2nyg4nufqChu9Q37u97+P0zEw9lf6S+mp3so9+\n3fcPbSa4I65s6qDDK8cF9L4s3XsXWQZraYsNlMK6vLyCH9gxNtEzTTWpW+CNQbbRJtHS+iGCPIr7\nQPnXvWsuAoAf5W/idMkmqfFPtxLfqV0nPh7umAicfSm/e5FpSKw+qT2Vo07UrgS9YMGCGTNmvPzy\ny5MnT37zzTctFsvUqVNvvPHGtvdsdW7+RhuDCbq0tDTYQ9KeL8mX95TGKgRAEoDZ/QkTVz4CC9oT\nVBteyiqNVUQAEICl75t68uRJaK0pAwCw7JMKu0IIgMqZ3RFvjWnml+NLWbtjlT7NVnsy+eQ/P/iA\ncspB1HrtG7eegOuD1f4Uq/QiAECEGx2PLI54HwDml+4gKhCuSnUxb20pOXrxp8NctwAABfWP+17Z\nd+i6Ed4bAMDItX/797/9RBa4AACXVF79t63XM64CsJGnJnGYTEABont032rCKRC44/gfAQTg6lW5\nDwHhhPDk3MGyIIuMES5SDswJpxvQDg4ECBBgClCREoFwroBCgHAgBDhwgArgRCBcJQRkIgMBBmrx\nyaN+sS7eNYAT0aAYJCYxDoSQGrHco6mdAtygmg0BMyHES2tcuhqzbHXpS6+u/Z3LUKwSNpiml1gP\n962+XKvqIuriA4JfFvzkMJxM2Rdb0ccRUXhx5QRVChAgQwLpVYaS5KrBNVLZrxRzufmYXjAZ3ZGj\nxJv1AWtA9ElcLDuRx1JlMACrC8R9kSRYKVGprZed9WHBTMqBW47bjAUmrmGxNb3UWwLcqaR8lSZ4\ntMBBNfhBL6uRSty2RMFGiEwtvSKg7y+/R1ldIO5fSYKlcbWtoCfZ6G1TFa1HVLU6x8+sv9paH2Rx\nIDYr5aKCMYoQ8OtqTaUWZZAP2pegVUUx59h0FQYgYI2yKvEyNJnWl1X7R399g5nZgBOL2Eu9KAA4\n5LXLtStBHzt2bNGiRWazOT09ff/+/ffee+9TTz319NNPB5u6rWt2bv5gv0qzE/bfdNNNN910EwDs\n3r27zcp7O0QOEOxJJwCPl9/TnpfTpmiHTEHDAM5U23YXeYQcIFR/ultSZe+8e7xpTo929KZAm632\nTzC+lIkqAAAFDhKcXjT9IhavAAMQAIBwyHz7HRgFRKXBKlQAvaAdlncbAABQAiAADPNeEzwhDAAY\nE4lw+koDB8ZkAApABWAKp0A0wUiABA8MDDgnPLgugwoEQJVkMbg3IaCCKgQjAUaDh6ACBco4AyAE\nKAcgnAABIBSAUQBGCAAQriEcFIlo/f0MPiILksUnUQAOlBPOgWt4FJEjjLIoCxwIBc4DghDtiarU\nKqpilZikqn28Iov2UoH1NwTMnAMFjcTMPlH2iz5WNea/xgqrL1XDI5w0wIiq06h67yVVklWr2h26\nusi6y/KsHovWlOROkimhCpTqPQaPGOWIhjiwVUXu0Bx0yUYO6hV5lwnjKegAAAgjqUdG7TQWEb+o\n13pucwzWODX7aLVHp2NE1Wlr+lfGGMGyXX+wzm/kRBmTN5xMJvWZzuqwf6ep9gU0nKpD8y4VJgig\nbeNTFFPUe6++ysf7yLry/oXDtW4N2FssbKuM2c+csVq7l4h+UntRQYzFZQNDG4cIMrgNxvzU7yQn\nEHLxyT6moaamO0ZVxOYGPHkaAAIJXoO93Aa4wH2Xa9dFQqPRWF5eDgDBiaAAIDk5udGVwJbUz80P\nAPVz8ze78Syi94H0y0vgkBXTOV/xFCzszGMBICvG1eYuRLTUXzQilP5sLWpaxqxp2Lrh7obVThgP\nQCDYwCJMPvPF6Sca4L80uz6OO/3gdO81U2UQvFQCdvokqMApNGimEQpwegMlAPzMIeBMj3ID7HRX\nssCAn+nMIoQG28OqypkA4ulS5PTXjHAmYiDBHA0UCAAFIABU5gEAApwQQikQohJKjDpu0qhaSihw\nAqASIIRTykUCGgqUcIFwQrmgZRoKko7rBC4JqkCZVqvqjbKBcIte1UlcywihQESuMQbMCtFYAkmy\nEm1SojSyXcOiTIEkYJF6RScLokWOICxSVG1GxSJyvZZrtUxnVC1E7fVzbTUAHPfUeGrTIGChStRP\ndRaVnjkzFEoUiSjRRLUG6vofclfvd9XKchJldq0S7ZFTDgbK8zxOuTaNqDYqxxz0mIjwy3UUh99F\nXX21skUvR59wazlt+2LP916P3teXgGKQo8v8SXUk0ErhH1wuv5xsCkTomF6r2mWW7BTbe6NHDfPm\nuaOoGkmViBOeqCrezOpFh33Vkj9Fq0ZoFDtVkirlunZWHua8Xu8999xjt9tHjBiRk5PT3eG0oV0J\nesSIEcuXL//+++8vu+yyzz77rKSk5Kuvvmq4ckxrBzgzN7/P56ufm7/ZjWcR/QcXSwyE4LUuQac9\nkmI9i0par9Yv8vZU+3F/AUhwFwhQ9l5zM4K/myIpZ6pVRFjXoNqJWV+reg1XAYBTqvmwf3Z9tTJl\n9dVWmJMBwKuVBABgXBC0RRH6z1IkUfyl2o1RZhBP3yDvJWp9tTJwt6QGH3Mq+mkgWC2jjAMAcCWY\nQRjnXOGgAHDCRSYAAQpUoIQCcCHY2AYOjAGAClwFDsA5D151FAKno+UUQCQaxrlKgXDGABTCA5Qf\njBSrtJRRoIRyQoGDTDlwRoCX6ykFoJyrBHwUSo1UUrhBFsqtkkEllgDJtdJqreijoFGJKhDOgHNe\nYhTcEpQaXAFKqrSKLaBVQT1pIrKgtyhg8Yv5xsAPMcQWkCSVlBmpnxLKuE6B7Gh2QlcDAAV6V3mk\nw1ZHbW7+Uy+HSzmd5hiwf0V77S5urSVOW3WBobbAVrvfXptcq9OqUKzTn4hy5uvc5fYqqxtsteyn\nXtUe5ZeU6tDV1kRXmtxgrJGPJ5Z62zEw47DFfczq7uWRJKbui60pU1obPpFvrT1lcTslFu0VbH7d\n7l61ef7qNg8RVEY9x2MrbTXM6mKnYhwlpJkDHdfW/BTtivFqYj3afdFKqSasx8JygCpZcTW4TtaS\n+fPnezyeI0eOjBgx4ve//30XxHYu2tXF8eKLL15//fXvv//+4sWL77rrroSEBEmS1q5d285jNDs3\nf5sT9reHHvwf9TOwagDb10DGf5by7RwYf3ZVtVatuLPNajmX3++rgxoA+9eMjOOF/wfw50ZlHKL/\nw74GcIFq+1og4z9L+aXa+/NPfZQ4lldLEwF22EExjK6v9pO+ZqgBsO8GuNJVPR/g+a2JEtRoTPZT\ntdAbBCVf8G1MMYELVNsRgQx0gvt9uzmhGorMAFpOVODVUiJAoQW4wEk1iQcotgMwLdQA2EsB4sAF\nFoBaM3A3GC2uOrBSAF0t1JlA9YNOAzIBG4ATwFAHHh1w6gO/jqigESDAQS95vapeC6BoQFUBGIAU\nMBBNnR9ECoyADKARuI9Dua4WBPmURc9loheoRgVRAB+DOkHxUZURlXCNxS8CEK/G76MqZRqP6I33\nm4oMAQIQ5dOdMpT19sR4qGxmGpETlxRQOBRYqgTVrIhVBr82QGUOilEV/bQmxhftlXyUSXUa964Y\nn90fQUHWq5KPyiqFGm31SLMKAIkmeF/HS1I9DHic8ZRdf/rSNAVqjzm4y9RHoOAJSKOtMoO6Cqt+\n2dBig0o9xqLx9tQoUD7Wys5+1QpQm75EL/1yWdtm4sd0nuI0P1NZnLHAoJ3U5gcvwlLxo613dpRH\n4AKI/nhza78IIy21/7EZvZzWEdkr1um42L/V8g3FGsViSXUMkAHAVyfG6ZvJAwlG5auIqsMRes5B\nIlVTrWfzG7drMM73uWv3u+tUzsfbrZcYW+zo4ZyvXbt227ZtsbGxL7zwwp49e7oyzrPQrgQ9dOjQ\nwsJCt9sNAEuXLn366ae1Wm3wEl+zGg3dC87N36hMsxs76plo/9OVkmQXCRnnt3jHZ2XBhE5I0I2r\nzcuCthL0M0mB+UWiwS6oZJwkeS+67m9NE/TfIg49XT1UEkRCxnn/N9rk5JMHLXUabtwuAACbby57\nDJIA4PmkgqdO9hHtgkrGSJLnuuu+AHj+pKY2yW5y02RVo861OMfSLbEF90p2kZABRdT3lWHrNNdt\nxXaBcDgQ4fFW89E2Y4EACrDvk+QrqLYIQAbmA261CyqJLZc8VpteBiiiLMZCZWp2aVRRppIFXIxx\nA5U4UOA1jEiEe41E5aAKWlXHJRVULWgZ1BKdArxK5FQFQcONMhWoVEIZk4hXYAonIhBZYHWUn9DW\nBKgQK0kximBUiVEFBcAr8Roqu8Rah0ZO8ETZRVYr8UpRqdV4Iv1GIHyvQbZxFTjJNlQbiL9A46Uq\nN6hUAuIT1FJtIEJf55L1Zo3nmOjRMwqC6hJcNuooc1kFkJ2aWh89aeEJRWLAR/0aZjIpxCuoxFze\nOyIBAGJMFhJTG/AJQCDepuGckdNX83iiXTjJ9cCAmElve4RJpzVVBLw+2S3xKQm6SYlDPLWnrul7\n8JjbrJP4ZbYaaDA4Wm8018TEBvwy19AkW12Dals0KM6+odRjCUgy5ZfHHpKEi1op3M8eFbDU7la8\nNkXyS6y39UuN2N4hbnpRM7hP6X+r4zkHKU41aTVNyyRH20utpdRrBgCi9xmN4TvrW4Ws/FhXl6DV\nAMBX1c6L9DpdC1NC19TUVFZWbty4cfLkyampqW+++WbXRtph7R3FMWfOHKv19E/yiIiI4uLiv//9\n708++WQoY2ubyyy+W+cfyAKMwH+0pGzwPQs6pVpBfCJeBgjeXyAY+s9ps9o6FlhhF2P8nAMcNxKm\nddzdtFrNwCdiz1RLwJD+5/pqs+BPmdwNvYNdgQLVnb4cUyH2mp185lczgSTn9lMAL8cLAKc7DQnR\nZml+/VXqL9VS8cbv7fU/tAno+bpgYRGAkQ2Jza6V3mAjZ8FagKsgAFVlFrzsJQCop6uEht+/HIDC\nmcHQHIAABQAOXKBc5cC5IIhqQKXUwqqN4POBNldbWiNaGaMMBAFUAszIqwzUV8V7iXqHSgQG1MYq\n3SQiVvezjiunSN9EOEWAVZJekczjFCook50QKUHAyhx6qkZQXy/9LsZi8vWKUa0QRaIBDVVpmeVf\nHhYZQwtd4tBoeV8JNR4ng0ReogNfLBSLmignSYgHcJHYPsIeuwk4qKXSKAUkzelXRgq0Ey7Tf0OI\n5Aa1hlxaSz1DpM9Ft1vl/kuEqyknen2v/vTkJSYjg0Bs7DUNU7ALYmPpHrsROFcLxF/VV9uKak1a\nL/0+PZQxiVSJl3u4oZXONbemd5y064g+rwYqjAIUaUfWilHtu0YIXrD4pYGXGf4LQOpMg+qIvWlr\nqxqSFKkkmuwFgDJN32qIbXs2z25yZhg+QJOPZyPBIWSKopw4cWLJkiV33nnnTz/9dNbT73WB1hJ0\n/e3azz333OTJkxuuO/D111/pYXS/AAAgAElEQVRnZmZ2e4JO8KkPn5KOGUDgcF0ZPA16uLUTqqVc\nVkE6cxlNtXzpgtEt/lwIsnnFq6ugUAMU+K9qICv6EMCgxtUGdKpODfbeAgdLVQlAr+BTCyZA5ofC\n6Y8WUYnTA2AAAFLX4P+aQLqxCMBKOPAznyjCVYNbdWvO/LbloAHFB1KDT+mZahUG9emD1KdUAEog\n2At9OgVTIAQ4A+CgEkIoAc45OV3fLxcXCVACjIKoADtdFRFErjJg/PS/CVNAEKnKBQYqUMJ0NsYd\nxMA4AyYEB40Er4IYuBQTkKskUVA1XKCcg54bPSDpmd3GPfmC1qoaOZBKarCA6pKNeuoPAFdBMjIz\nZSRaAkkZKkqQ7zFFCAZJIUzRRxIfl6MTRY8oX+xm1iixd61iEKlBx0QtSBaWSFSDSVYAwKAoNvni\nCH0AQKgpC4jA6scFiX5DlHAJp6rolQwBRfZwm7+/RnQyoteV1PDYCsFg6O+f4DcwAUTp5xpokMMM\nimJTBkXo/AQEV6ksDuYAbSQCa62q8yVFSDYOEnNQbWupBkxeFsNSRUWyEJ2Vg7NKMDGlnU0uLVNJ\nJYkwDQAK3kqi5ypA49a9RVH1nrhIjZYDdwfiTErjq8rhI1oSBxkMB2vrVIBxVqu+5RVVLBYLADzz\nzDNWq/Wxxx57/vnny8rK4uLiWirf7Vq7SJh2BgCMGzcurYFHHnlk5syZXRVki/oEdsmEJci0l0w5\nUSf3X9kp1X5c+CBRT9+eK3D+Dcxvc5eyQ6+4RQIAjJIA5//N2tK0jH//s6cHCFAQCC8+vOyX57Ky\nRHa62UuAzfvh9MI8dT8uJOrp0XeCny378E0AEOVgg5cRwl/88d+V+5fQ4LAOCgLhB3f/hZDTu5iY\n3y6fvv5DCNiD9+xSIDIjwWtWFAR6urAUvMOCMIEy4AwokYTgdUGiYTIwRjiczl3BNjJjAAowCA76\nIBQERZa4nwSr5SqhogBEx2VZFA1MtnAeI9c9VLk/VvVaZD9QUccZVVmc7NXLglkl11bnCEA4iCZV\nscvq1TV5HjA5IfLamrxaYnMT282uPHvA3092EZlaFJmCKDFIrzmRFPBfpmF2GaYGiiqZxUh0N3sK\nEqvK9cRQI0dNYK4nqvYW8zgfsUxynYxUAwoz1lHbo76jCaoHAGKZ71cmls+jToL9Tn01YWcyEef3\n1B05DhH5LGqIiSYxb3/qS3U58mlcsggDRJn7fNznE/QmgyZGq4kgggjslywWy3y/Mqr5POoE2G83\nVFHW9vWry3nNNUJtDkkKEPsDQqlWbW0URz/qGSN4Bsi+ahpdoZrnKkcMgfbe8G1k8g1G70kecUqN\nvNbkM7NmDpSk1M5k+TkkMYckPezN6dXcSI8wIRAyymqeHhN1V2zUUHNrbamIiAij0SjLMgAwxqBH\nr6hS35VMCCkpKQnD75k4f0mSIp8Sa0WAGEU7steATql2oDt+InnhIneZTOkxfWzS+Nvb3IVI4hs5\nn1JSDSAQcIO5ubvD9frab54SqKqAQDnAlFt+eWrCBO/cxxhXg81YyXD6fZFEs2/vs8HHjFPtLbcB\ngCf7eQUYBVAYF6jOHxdX+82fBKoCCIyDNj6y7tv5HABA1RAtAGecgcBBAU65yokIoAKHM81pxgkV\nOLAz7cbTrwdUDgIACBRUzjinAgVVUQglnHGglKicCSBQYIwSQoAzDpxy4ETgHAhhnADlnIMARKWC\nIBCgFKJjCVefObqTqQoPqIQpnBLCGDdZqSQCpaRU4b4AMBn0JqrR8ApOIiP4sTySnMwZg5JSmjaQ\n5ReCxcIqyqkgcK2WqIwkDgNnNYmM4jX5f5CKQKYgO7nNyPP3U5uN19YRu+VGns0rirjCiMNLDAZO\nBRoVS01GAKBG4yV+x+BI4IpMbfGk/oZgQuJSk+c4ykGUeE21YErWicJIK4zwHgMGIOqJwUAMBlZb\nSzRakGXauzc0aLtRo+ESv+PSCA6qQm3x0I77jA1263TXdxlRVcznJZKGaFobOK01GkcKdZc7dhOj\nnhvN3FMHJlObhzizs7av7Hw8UuScQ7WTa3VN2/bUbL5aV5tO9gMAGBg1tPEjsnsRgAip7TMsCMKt\nt96amZm5cOHCF1544corrwzzFavb9YOooKAgPNdVvGH0r3a7aw1+gRH+n7iSOcM6MBdBK3o/s2Lp\nsrt2G70ig/87bhSfubrNXciCedKcPxMwEFADRg4LGl8hBABYsEC34HGfhwCliipoJ0xo+KTbz8wi\nlylQ0MGCl+p3YXPmiFQBzv1Gq/bKKwFAGJXEs08QRkQC0osvaQBgweM+D+GUMoNN+8c/0cy5Qq0f\niEYYNtKpKOYf9sgUqMasmTBY3fG9zAJUsnJBoB4ncO7v00dfWSHLXp09UvHWkoDMTFaqqGrAw8xm\nzkWJc1lRRMZUv0dDNQGmcI0uQIgWVKaRxFoPkVRQtWqknWpNgkB5ZRkE/KLVyhWFGyQqGAgBIIRZ\nzOKAQSBJrChfKC4lfg+v9RBJ4HodiYwFs5lb7ZB/iricTG+kMVFgsdGKSiYCvXQ4+NxEYWTUaABC\nk/uCQUdqXOByUUmChEQaGc39PqLR8qRk8PtBFInFCqCokbkEgERF0/hEVloEifFEbwCPF+pcxGAk\n/QYSowkAQKcTh13Oa5xEEEjk/wweJb0SQKPlikyTk8FoIkYTHXY5LysDABIbRyIigRA6dBh31RBR\narQv6PTCsOFQUwNNqm1RXC868WqorBC0WtKnL7T8Ux0AiD2CXjqYRkepFZXEbpP69mu9/P8QBPGK\nMdzhIASg3wDS3JcHMZmEK8ezwnwClCQlw/mydMby5cvvvPPO5OTkyy+/vP1D0bpLeydL+uijj5Yu\nXXr48GFVVS+++OK5c+cG7/cLnfZMlnT4xP7X9zpqZDNwGiU4n73xMpM5HL9IELoQ4GRJna5dX7kb\nN26cNm3auHHjtmzZ8tlnn02cOPG222577733Qh1cm2It9pHaMqrRmAwwUlOk0XfeSCDOuccD/pCv\nwYMQQi1p11fK4sWL586du3Dh6StXo0ePZowtWrRo+vTpoYytHXTJLyiWOI+JAPlPdMptYif193PO\nC/NZYSEwRlMvInG9OqdahBDqiHYl6JycnEWLFjXcMnbs2L/97W+hCakD5v3g+FljPBbhBwBvwLg9\n3zkpuRO6/HmtmxUXE6sVAFhejhAVBWKTyb4QQl0uEAh4PJ52TgTUUA+dsL9dCTopKennn3++9tpf\nLsEdOnQoOTk5ZFG1l/K/155pc3esno1GI1Z75DuL0HlIo9Ho9frU1LZXP2jo0KFD4Xw3SivaldFm\nzZr13HPPxcbGXnfddQDw+eefZ2ZmLliwILShtUPmEPu/vnKWgoFzPkDjnRDdvmvlbSEmE4ntxUuL\ngXGaehFI2HxGKFwQQrTatmZuPV+0K0H/8Y9/lGX50UcfraqqAoCIiIinn3569uzZIY6tbQkm6cBV\n+hf/Wxdv1P3xks7JzgAAlNLeKTwyigjCeTO6CCHU47QrQVNKn3rqqblz51ZUVABAdHR0+PxeiDKa\nloxq9/j8jiDtH/aPEEIh0K5hdgsWLKitrSWExMTExMTEEEKKi4tfeumltvdECKGwsWrVKvK/br+9\n7fuEu1HPniwJIYTa7+67777hhhuCjznnN99884MPPti9IbWutQTd8HadcePGNXxKEIRHHnkkVEEh\nhFBHHK1lH5UywvmdiWKCvsUOWJPJZDrTdfmPf/wjPT190qS2F1LoRj17siSEEKqW4asKlmYinJN3\ni9Q/pIpiW9fIqqqqli9f/u2333ZJgGevXX3QYTtZEkIIeRSuFwAACAEdBZ/a9p0L8+bN+93vftfK\nslBhol0JOjExURDaWK0HIYS6RaSGOALgVcGt8OF2amyr/VxSUrJ58+Z77rmna8I7Fz1veieEEGpI\nJ8Dv+oj5HiZS2lvf9hDgN99889Zbbw3/5jO0swWNEELhzChAmpn2MxJNO1La5s2bgzdFh78WX01m\nZmZZWVnw8fz584NLeiOEUI9WUlJy8ODBMWPGdHcg7dJiF8eyZcvcbvd9990nCMLzzz9/zTXXNL1O\nOHDgwBCHhxBCnalXr149aGa7FhP0ihUr5s2bt3Tp0uCfjcZBB/Wg14kQQj1Oi10c99xzT2FhIec8\nmIVLSkp4E10YJ0IIXXDaNYqjpKQEx0EjhLqdoiher/fQoUMd2qvnNijblaDj4uK6ftFYhBBqhBAi\nimJkZGSH9ioqKgqfCTg7pF0JeuPGjXffffeTTz65aNEiSunWrVtvu+229evXd/+ahAihC4kgCJIk\ndXTaieLi4hDFE2o9fNFYhBA6f7XrRpWcnJwrr7yy4ZaxY8fm5OSEJiSEEEIA7UzQwUVjG24Jk0Vj\nEUKoQ7744ovBgwcbjcbRo0efxergXaxdCTq4aOy6deuqqqqqqqrWrVuXmZn5wAMPtGffZpcwcDgc\nDbdMnTr13F4FQgi1rby8fNq0abNnzy4qKrrlllumTp3KGOvuoFoT8kVjm13CIDc3NzU1NSsrK7hd\np9OdTewIIQQAAD4vuN1ACVhsIEktFjty5IjZbJ4xYwYA/Pa3v33yySdLS0vj4+O7LtAOCvmisc0u\nYfDOO++kpaUlJiaeXdAIIVRPluHIz2CxAmOQfwqGDoeW8tPgwYMVRXnrrbeuv/761atXp6Wl9erV\nq2uD7ZgOTDcaXDT2rI/UcAmD3NzcEydO9OnTp6qqaty4cStWrEhJSQkW27t3744dOwDAaDQOHz78\nrA+HELpABPyg0wOlQClIGlCUFhvRNpvthRdemDlzJgAQQvbt2xfm46O7brrRhksYqKo6ZMiQXbt2\n5eTkmEymjIyM+mJOp/P48ePHjx93uVxdFhtCqOeSNBDwA+fAOSgyiC03O7/88suFCxd+8cUXpaWl\nf/7znzMyMsL8DkPSNfGVlJRccskl+fn5TSfJLikpiY+PLy8vb3Q3+e7du4cPH67X67sgPITQucvM\nzJwyZcpZ737ixIlp06a1XsblchUVFTVczzqo1g11tQAErDZoeklr//79gwcPFkVx9uzZgUBg5cqV\nAOB2uy0WS1FRUTj3QXdRC7rREgarVq06fvx48LEoioDXCRFC58BkhtheEBvXTHZuaPTo0R9//PGX\nX35ZXl6+bNmyhISEMF8Lu8MJWlXVoqKijra7Gy1hkJ2dff/99x89erSiouLxxx+fMmWK2WzuaCQI\nIdQh06dPnzNnzm9/+9uUlJQvvvhiy5YtlIb1qlIdC+7rr79OSEhITEwcMGBA+yeUarqEwbJlyxIT\nE0eNGpWWlkYIWbNmTYfCQAihs0AImT179rFjxzwez3fffXf55Zd3d0Rt6ECC5pz/5je/mTFjRmFh\n4ZgxYx566KF27hhcwiA2NrZ+i9lsXrdundPprKysXLNmTUfnpkIIoQtBawk6Nze34Z91dXX5+fkP\nP/xwQkLCzJkzDx48GOLYEELogtbaOOjf/OY3Q4cOffbZZ4NXOU0m09ChQ+fOnTtjxozFixePHz++\nq4JECCEAAM65LMuFhYUd3StE8YRaay3ob775ZtKkSVOmTJk3b151dTUAbNy4saCg4Oabb5YkafXq\n1V0VJEIIAQCoqsoY83YQAIT5nBstaa0FTSmdPn36Lbfc8vbbb48bN+7uu+/+f//v/+3cubPLgkMI\noYZEUdRqtf369evQXvv37w/z0RotaTtoURRnzZq1d+9eSumoUaNee+01WZa7IDKEELrAtZagCwoK\nrr32WrPZPH78+MLCwieeeGLXrl35+fkjRozYuHFjD/3JgBDqYj6fL9iqc7lcwQ4H1E6tJehZs2YJ\ngrBly5b4+Pg77rgDAGw22/PPP//FF1/s3Lnziiuu6KogEUI91b///e+77rorPz8/Ozv7N7/5zZ13\n3hmcMa27nDx5cvLkyRaL5eqrry4rK+vGSNqjtQS9e/fu5557bvLkyS+99FJ2dnZdXV1we1xc3N//\n/vcNGzZ0SYQIoR7s3Xff/dOf/pSamvr+++/PmjXrD3/4w/r167sxnrvvvnv48OFFRUUDBw589NFH\nuzGS9mgtQV966aWrV68uLi7++9//3qdPH4PB0PDZvn37hjg2hFCPV11dPWDAAKf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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -h 400\n", "## plotting\n", "ggplot(df.j, aes(mean_rel_abund, BD_range_perc, color=SIM_rep)) +\n", " geom_point(alpha=0.3) +\n", " scale_x_log10() +\n", " scale_y_continuous() +\n", " labs(x='Pre-fractionation abundance', y='% of total BD range') +\n", " facet_grid(dataset ~ .) +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16),\n", " panel.grid = element_blank()#,\n", " #legend.position = 'none'\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## BD span of just overlapping taxa\n", "\n", "* Taxa overlapping between emperical data and genomes in dataset\n", "* These taxa should have the same relative abundances in both datasets.\n", " * The comm file was created from the emperical dataset phyloseq file." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1] 194\n", " cluster\n", "1 212\n", "2 762\n", "3 663\n", " ssu_ID\n", "1 rRNA_NC_014259_Acinetobacter_oleivorans_DR1__Acinetobacter_oleivorans_DR1_3479770-3481295_DIR-\n", "2 rRNA_NC_019973_Mesorhizobium_australicum_WSM2073__Mesorhizobium_australicum_WSM207_1746046-1747518_DIR+\n", "3 rRNA_NC_013037_Dyadobacter_fermentans_DSM_18053__Dyadobacter_fermentans_DSM_18053_4003859-4005353_DIR-\n", " genome_fileID\n", "1 /var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/Acinetobacter_oleivorans_DR1.fna\n", "2 /var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/Mesorhizobium_australicum_WSM2073.fna\n", "3 /var/seq_data/ncbi_db/genome/Jan2016/bac_complete_spec-rep1_rn/Dyadobacter_fermentans_DSM_18053.fna\n", " genomeID\n", "1 Acinetobacter_oleivorans_DR1\n", "2 Mesorhizobium_australicum_WSM2073\n", "3 Dyadobacter_fermentans_DSM_18053\n", " genome_seqID\n", "1 NC_014259_Acinetobacter_oleivorans_DR1__Acinetobacter_oleivorans_DR1\n", "2 NC_019973_Mesorhizobium_australicum_WSM2073__Mesorhizobium_australicum_WSM207\n", "3 NC_013037_Dyadobacter_fermentans_DSM_18053__Dyadobacter_fermentans_DSM_18053\n", " OTU\n", "1 OTU.188\n", "2 OTU.226\n", "3 OTU.121\n", " OTU_taxonomy\n", "1 Bacteria:Proteobacteria:Gammaproteobacteria:Pseudomonadales:Moraxellaceae:Acinetobacter:Unclassified:Unclassified\n", "2 Bacteria:Proteobacteria:Alphaproteobacteria:Rhizobiales:Phyllobacteriaceae:Mesorhizobium:Unclassified:Unclassified\n", "3 Bacteria:Bacteroidetes:Cytophagia:Cytophagales:Cytophagaceae:Dyadobacter:Unclassified:Unclassified\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i targetFile\n", "\n", "df.target = read.delim(targetFile, sep='\\t')\n", "df.target %>% nrow %>% print\n", "df.target %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1] 21202\n", "[1] 1014\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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I3QOcRzZc4aveHvJ6gu7bt++cOXOeeeaZ1NTUTZs2xcfHx8TEkCQ5evTojRs3btiwYdOm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\nCgENAMBTCGgAAJ5CQAMA8BQCGgCApxDQAAA8hYAGAOApBDQ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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# filtering to just target taxa\n", "df.j.t = df.j %>% \n", " filter(OTU %in% df.target$OTU) \n", "df.j %>% nrow %>% print\n", "df.j.t %>% nrow %>% print\n", "\n", "## plotting\n", "ggplot(df.j.t, aes(mean_rel_abund, BD_range_perc, color=SIM_rep)) +\n", " geom_point(alpha=0.5, shape='O') +\n", " scale_x_log10() +\n", " scale_y_continuous() +\n", " #scale_color_manual(values=c('blue', 'red')) +\n", " labs(x='Pre-fractionation abundance', y='% of total BD range') +\n", " facet_grid(dataset ~ .) +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16),\n", " panel.grid = element_blank()#,\n", " #legend.position = 'none'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation between relative abundance and BD_range diff\n", "\n", "* Are low abundant taxa more variable in their BD span" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%R\n", "# formatting data\n", "df.1 = df.j.t %>% \n", " filter(dataset == 'simulated') %>%\n", " select(SIM_rep, OTU, mean_rel_abund, BD_range, BD_range_perc)\n", "\n", "df.2 = df.j.t %>%\n", " filter(dataset == 'emperical') %>%\n", " select(SIM_rep, OTU, mean_rel_abund, BD_range, BD_range_perc)\n", "\n", "df.12 = inner_join(df.1, df.2, c('OTU' = 'OTU')) %>%\n", " mutate(BD_diff_perc = BD_range_perc.y - BD_range_perc.x)\n", "\n", "\n", "df.12$SIM_rep.x = reorder(df.12$SIM_rep.x, df.12$SIM_rep.x %>% as.numeric)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BtFuQikI1d9hiEWSqoG4uULKaDEeQwQUqi13OyBbUfPVAEK0W5GF0/3Wqs5rE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\nsuJdmaZwSt/Lf1aVViqskVUHqCBIkUKxlx/+H++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BnkenDN/d4E9pRGgK8vuwlJgC9qOh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\nBADQABAEANAAEAQA0MD/A0lO07IsyYE2AAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 500\n", "\n", "ggplot(df.12, aes(mean_rel_abund.x, BD_diff_perc)) +\n", " geom_point(alpha=0.5) +\n", " scale_x_log10() +\n", " labs(x='Pre-fractionation relative abundance', \n", " y='Difference in % of gradient spanned\\n(emperical - simulated)',\n", " title='Overlapping taxa') +\n", " facet_wrap(~ SIM_rep.x) +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16),\n", " panel.grid = element_blank(),\n", " legend.position = 'none'\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notes\n", "\n", "* between Day1_rep10, Day1_richFromTarget_rep10, and Day1_add_Rich_rep10:\n", " * Day1_rep10 has the most accurate representation of BD span (% of gradient spanned by taxa).\n", " * Accuracy drops at ~1e-3 to ~5e-4, but this is caused by detection limits (veil-line effect)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing abundance distributions of overlapping taxa" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of overlapping OTUs between emperical & simulated: 92 \n", "\n", " OTU sample abundance Buoyant_density bulk_abund dataset\n", "1 OTU.170 12C-Con.D1.R2_F23 2 1.69362 0.0005554184 emperical\n", "2 OTU.24 12C-Con.D1.R2_F23 25 1.69362 0.0020982473 emperical\n", "3 OTU.11 12C-Con.D1.R2_F23 4 1.69362 0.0004319921 emperical\n", " SIM_rep rel_abund\n", "1 <NA> 0.0006006006\n", "2 <NA> 0.0075075075\n", "3 <NA> 0.0012012012\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "\n", "join_abund_dists = function(df.EMP.j, df.SIM.j, df.target){\n", " \n", " ## emperical \n", " df.EMP.j.f = df.EMP.j %>%\n", " filter(abundance > 0) %>%\n", " #filter(!OTU %in% c('OTU.32', 'OTU.2', 'OTU.4')) %>% # TEST\n", " dplyr::select(OTU, sample, abundance, Buoyant_density, bulk_abund) %>%\n", " mutate(dataset = 'emperical', SIM_rep = NA) %>%\n", " filter(OTU %in% df.target$OTU) \n", " \n", " ## simulated\n", " df.SIM.j.f = df.SIM.j %>%\n", " filter(count > 0) %>%\n", " #filter(!taxon %in% c('OTU.32', 'OTU.2', 'OTU.4')) %>% # TEST\n", " dplyr::select(taxon, fraction, count, BD_mid, bulk_abund, SIM_rep) %>%\n", " rename('OTU' = taxon,\n", " 'sample' = fraction,\n", " 'Buoyant_density' = BD_mid,\n", " 'abundance' = count) %>%\n", " mutate(dataset = 'simulated') %>%\n", " filter(OTU %in% df.target$OTU) \n", " \n", " ## getting just intersecting OTUs\n", " OTUs.int = intersect(df.EMP.j.f$OTU, df.SIM.j.f$OTU)\n", " \n", " df.j = rbind(df.EMP.j.f, df.SIM.j.f) %>%\n", " filter(OTU %in% OTUs.int) %>%\n", " group_by(sample) %>%\n", " mutate(rel_abund = abundance / sum(abundance))\n", " \n", " cat('Number of overlapping OTUs between emperical & simulated:', \n", " df.j$OTU %>% unique %>% length, '\\n\\n')\n", " return(df.j)\n", " }\n", "\n", "\n", "df.j = join_abund_dists(df.EMP.j, df.SIM.j, df.target)\n", "df.j %>% head(n=3) %>% as.data.frame " ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " OTU sample abundance Buoyant_density bulk_abund dataset\n", "1 OTU.170 12C-Con.D1.R2_F23 2 1.69362 0.0005554184 emperical\n", "2 OTU.24 12C-Con.D1.R2_F23 25 1.69362 0.0020982473 emperical\n", "3 OTU.11 12C-Con.D1.R2_F23 4 1.69362 0.0004319921 emperical\n", " SIM_rep rel_abund rel_abund_c\n", "1 NA 0.0006006006 0.0006006006\n", "2 NA 0.0075075075 0.0075075075\n", "3 NA 0.0012012012 0.0012012012\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# closure operation\n", "df.j = df.j %>%\n", " ungroup() %>%\n", " mutate(SIM_rep = SIM_rep %>% as.numeric) %>%\n", " group_by(dataset, SIM_rep, sample) %>%\n", " mutate(rel_abund_c = rel_abund / sum(rel_abund)) %>%\n", " ungroup()\n", "\n", "df.j %>% head(n=3) %>% as.data.frame" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of overlapping taxa: 92 \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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/PPXUUwCQn58/fPjwpKQkpYMKlF11MRPDrysdRaB0PDGUUjoG9UA7lm7UKhhA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\n0NI04lqFdt1Lu3YFcgrW7Nm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1Pd8MAMcSqqFFrQAAwO4AU5sL9nfbjwCQEJns9kN/b3Ta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4BPNf8vtwwXqlD5orSrrhHzg1j6PpH6q21bPBjzKp2lpMGB/BVHucc0h0IyanvvBdTf1VVW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\nrkJggg==\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -h 1500 -w 800\n", "# plotting \n", "plot_abunds = function(df){\n", " p = ggplot(df, aes(Buoyant_density, rel_abund_c, fill=OTU)) +\n", " geom_area(stat='identity', position='dodge', alpha=0.5) +\n", " labs(x='Buoyant density', \n", " y='Subsampled community\\n(relative abundance for subset taxa)') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none',\n", " axis.title.y = element_text(vjust=1), \n", " axis.title.x = element_blank(),\n", " plot.margin=unit(c(0.1,1,0.1,1), \"cm\")\n", " )\n", " return(p)\n", " }\n", "\n", "\n", "# simulations\n", "df.j.f = df.j %>%\n", " filter(dataset == 'simulated')\n", "p.SIM = plot_abunds(df.j.f)\n", "p.SIM = p.SIM + facet_grid(SIM_rep ~ .)\n", "\n", "# emperical\n", "df.j.f = df.j %>%\n", " filter(dataset == 'emperical')\n", "p.EMP = plot_abunds(df.j.f)\n", "\n", "# status\n", "cat('Number of overlapping taxa:', df.j$OTU %>% unique %>% length, '\\n')\n", "\n", "# make figure\n", "grid.arrange(p.EMP, p.SIM, ncol=1, heights=c(1,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating center of mass for overlapping taxa\n", "\n", "* weighted mean BD, where weights are relative abundances" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "\n", "center_mass = function(df){\n", " df = df %>%\n", " group_by(dataset, SIM_rep, OTU) %>%\n", " summarize(center_mass = weighted.mean(Buoyant_density, rel_abund_c, na.rm=T),\n", " median_rel_abund_c = median(rel_abund_c)) %>%\n", " ungroup()\n", " return(df)\n", "}\n", "\n", "df.j.cm = center_mass(df.j) " ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of OTUs: 92 \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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27duJqiZPngwABw4cCAoKEgqF3bp1u3HjhtFDQk2FXK6kKVJvtjtz8oTmasES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\n8OFly5YdOnSob9++KSkpsbGxly9fFrJpvZpGWxeWDHnrrbc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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 650\n", "# getting mean cm for all SIM_reps\n", "df.j.cm.s = df.j.cm %>%\n", " group_by(dataset, OTU) %>%\n", " summarize(mean_cm = mean(center_mass, na.rm=T),\n", " stdev_cm = sd(center_mass),\n", " median_rel_abund_c = first(median_rel_abund_c)) %>%\n", " ungroup() %>%\n", " spread(dataset, mean_cm) %>%\n", " group_by(OTU) %>%\n", " summarize(stdev_cm = mean(stdev_cm, na.rm=T),\n", " emperical = mean(emperical, na.rm=T),\n", " simulated = mean(simulated, na.rm=T),\n", " median_rel_abund_c = first(median_rel_abund_c)) %>%\n", " ungroup()\n", "\n", "# check\n", "cat('Number of OTUs:', df.j.cm.s$OTU %>% unique %>% length, '\\n')\n", "\n", "# plotting\n", "ggplot(df.j.cm.s, aes(emperical, simulated, color=median_rel_abund_c,\n", " ymin = simulated - stdev_cm,\n", " ymax = simulated + stdev_cm)) +\n", " geom_pointrange() +\n", " stat_function(fun = function(x) x, linetype='dashed', alpha=0.5, color='red') +\n", " scale_x_continuous(limits=c(1.69, 1.74)) +\n", " scale_y_continuous(limits=c(1.705, 1.74)) +\n", " scale_color_gradient(trans='log') +\n", " labs(title='Center of mass') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notes\n", "\n", "* Leaving out the PCR simulation does not help with simulation accuracy for center of mass on overlapping taxa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### plotting taxon abundance vs diff between emperical & simulated" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "\n", "df.j.cm.s.f = df.j.cm.s %>%\n", " mutate(CM_diff = emperical - simulated)\n", "\n", "ggplot(df.j.cm.s.f, aes(median_rel_abund_c, CM_diff)) +\n", " geom_point() +\n", " scale_x_log10() +\n", " labs(x='Relative abundance', y='Center of mass (Emperical - Simulated)', title='Center of mass') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notes\n", "\n", "* No clear pattern between OTU relative abundance (relative for just the overlapping taxa) and the difference in center between simulated and emperical data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What is causing the inter-SIM_rep varition in center of mass?\n", "\n", "possibilities:\n", "\n", "1. subsampling simulation\n", " * definately contributes to a lot of the variation (re-ran the above cells with the OTU table files)\n", " * the samples then become more centered around a BD of 1.725 (approx. center of gradient)\n", " * This suggests that the DBL is causing too much smearing. \n", " * Need to try a run w/out DBL \n", "2. DBL 'smearing' simultion\n", "3. diffusion simulation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": true, "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
magic2du/contact_matrix	Contact_maps/DeepLearning/DeepLearningTool/.ipynb_checkpoints/DL_contact_matrix_load2-new10fold_12_15_2014-checkpoint.ipynb	1	55316	{ "metadata": { "name": "", "signature": "sha256:1239aaf06f1e3f2c0c469a35e5de554b69b0e65a03155e85c790f84292d23390" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys, os\n", "sys.path.append('../../../libs/')\n", "import os.path\n", "import IO_class\n", "from IO_class import FileOperator\n", "from sklearn import cross_validation\n", "import sklearn\n", "import numpy as np\n", "import csv\n", "from dateutil import parser\n", "from datetime import timedelta\n", "from sklearn import svm\n", "import numpy as np\n", "import pandas as pd\n", "import pdb\n", "import pickle\n", "import numpy as np\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.cross_validation import KFold\n", "from sklearn import preprocessing\n", "import sklearn\n", "import scipy.stats as ss\n", "from sklearn.svm import LinearSVC\n", "import random\n", "from DL_libs import *\n", "from itertools import izip #new\n", "import math\n", "from sklearn.svm import SVC" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#filename = 'SUCCESS_log_CrossValidation_load_DL_remoteFisherM1_DL_RE_US_DL_RE_US_1_1_19MAY2014.txt'\n", "#filename = 'listOfDDIsHaveOver2InterfacesHave40-75_Examples_2010_real_selected.txt' #for testing\n", "\n", "# set settings for this script\n", "settings = {}\n", "settings['filename'] = 'ddi_examples_40_60_over2top_diff_name_2014.txt'\n", "settings['fisher_mode'] = 'FisherM1'\n", "settings['with_auc_score'] = False\n", "settings['reduce_ratio'] = 1\n", "settings['SVM'] = 1\n", "settings['DL'] = 0\n", "settings['SAE_SVM'] = 1\n", "settings['SVM_RBF'] = 1\n", "settings['SAE_SVM_RBF'] = 1\n", "settings['SVM_POLY'] = 0\n", "settings['DL_S'] = 0\n", "settings['DL_U'] = 0\n", "\n", "settings['finetune_lr'] = 1\n", "settings['batch_size'] = 100\n", "settings['pretraining_interations'] = 5001\n", "settings['pretrain_lr'] = 0.001\n", "settings['training_epochs'] = 1502\n", "settings['hidden_layers_sizes'] = [100, 100]\n", "settings['corruption_levels'] = [0,0]\n", "\n", "\n", "filename = settings['filename']\n", "file_obj = FileOperator(filename)\n", "ddis = file_obj.readStripLines()\n", "import logging\n", "import time\n", "current_date = time.strftime(\"%m_%d_%Y\")\n", "\n", "logger = logging.getLogger(__name__)\n", "logger.setLevel(logging.DEBUG)\n", "\n", "logname = 'log_DL_contact_matrix_load' + current_date + '.log'\n", "handler = logging.FileHandler(logname)\n", "handler.setLevel(logging.DEBUG)\n", "\n", "# create a logging format\n", "\n", "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", "handler.setFormatter(formatter)\n", "\n", "# add the handlers to the logger\n", "\n", "logger.addHandler(handler)\n", "\n", "logger.info('Input DDI file: ' + filename)\n", "#logger.debug('This message should go to the log file')\n", "for key, value in settings.items():\n", " logger.info(key +': '+ str(value))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:Input DDI file: listOfDDIsHaveOver2InterfacesHave40-75_Examples_2010_real_selected.txt\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "number of lines in listOfDDIsHaveOver2InterfacesHave40-75_Examples_2010_real_selected.txt:1\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "['6PGD_int_NAD_binding_2']" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "class DDI_family_base(object):\n", " #def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/home/du/Documents/Vectors_Fishers_aaIndex_raw_2014/'):\n", " #def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/home/sun/Downloads/contactmatrix/contactmatrixanddeeplearningcode/data_test/'):\n", " def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/'):\n", " \"\"\" get total number of sequences in a ddi familgy\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", " LOO_data['FisherM1'][1]\n", "\n", " \"\"\"\n", " self.ddi = ddi\n", " self.Vectors_Fishers_aaIndex_raw_folder = Vectors_Fishers_aaIndex_raw_folder\n", " self.ddi_folder = self.Vectors_Fishers_aaIndex_raw_folder + ddi + '/'\n", " self.total_number_of_sequences = self.get_total_number_of_sequences()\n", " self.raw_data = {}\n", " self.positve_negative_number = {}\n", " self.equal_size_data = {}\n", " for seq_no in range(1, self.total_number_of_sequences+1):\n", " self.raw_data[seq_no] = self.get_raw_data_for_selected_seq(seq_no)\n", " try:\n", " #positive_file = self.ddi_folder + 'numPos_'+ str(seq_no) + '.txt'\n", " #file_obj = FileOperator(positive_file)\n", " #lines = file_obj.readStripLines()\n", " #import pdb; pdb.set_trace()\n", " count_pos = int(np.sum(self.raw_data[seq_no][:, -1]))\n", " count_neg = self.raw_data[seq_no].shape[0] - count_pos\n", " #self.positve_negative_number[seq_no] = {'numPos': int(float(lines[0]))}\n", " #assert int(float(lines[0])) == count_pos\n", " self.positve_negative_number[seq_no] = {'numPos': count_pos}\n", " #negative_file = self.ddi_folder + 'numNeg_'+ str(seq_no) + '.txt'\n", " #file_obj = FileOperator(negative_file)\n", " #lines = file_obj.readStripLines()\n", " #self.positve_negative_number[seq_no]['numNeg'] = int(float(lines[0]))\n", " self.positve_negative_number[seq_no]['numNeg'] = count_neg\n", " except Exception,e:\n", " print ddi, seq_no\n", " print str(e)\n", " logger.info(ddi + str(seq_no))\n", " logger.info(str(e)) \n", " # get data for equal positive and negative\n", " n_pos = self.positve_negative_number[seq_no]['numPos']\n", " n_neg = self.positve_negative_number[seq_no]['numNeg']\n", " index_neg = range(n_pos, n_pos + n_neg)\n", " random.shuffle(index_neg)\n", " index_neg = index_neg[: n_pos]\n", " positive_examples = self.raw_data[seq_no][ : n_pos, :]\n", " negative_examples = self.raw_data[seq_no][index_neg, :]\n", " self.equal_size_data[seq_no] = np.vstack((positive_examples, negative_examples))\n", " def get_LOO_training_and_reduced_traing(self, seq_no, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " \"\"\" get the leave one out traing data, reduced traing\n", " Parameters:\n", " seq_no: \n", " fisher_mode: default 'FisherM1ONLY'\n", " Returns:\n", " (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \"\"\"\n", " train_X_LOO = np.array([])\n", " train_y_LOO = np.array([])\n", " train_X_reduced = np.array([])\n", " train_y_reduced = np.array([])\n", "\n", " total_number_of_sequences = self.total_number_of_sequences\n", " equal_size_data_selected_sequence = self.equal_size_data[seq_no]\n", " \n", " #get test data for selected sequence\n", " test_X, test_y = self.select_X_y(equal_size_data_selected_sequence, fisher_mode = fisher_mode)\n", " total_sequences = range(1, total_number_of_sequences+1)\n", " loo_sequences = [i for i in total_sequences if i != seq_no]\n", " number_of_reduced = len(loo_sequences)/reduce_ratio if len(loo_sequences)/reduce_ratio !=0 else 1\n", " random.shuffle(loo_sequences)\n", " reduced_sequences = loo_sequences[:number_of_reduced]\n", "\n", " #for loo data\n", " for current_no in loo_sequences:\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_LOO.ndim ==1:\n", " train_X_LOO = current_X\n", " else:\n", " train_X_LOO = np.vstack((train_X_LOO, current_X))\n", " train_y_LOO = np.concatenate((train_y_LOO, current_y))\n", "\n", " #for reduced data\n", " for current_no in reduced_sequences:\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_reduced.ndim ==1:\n", " train_X_reduced = current_X\n", " else:\n", " train_X_reduced = np.vstack((train_X_reduced, current_X))\n", " train_y_reduced = np.concatenate((train_y_reduced, current_y)) \n", "\n", " return (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \n", " #def get_ten_fold_crossvalid_one_subset(self, start_subset, end_subset, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " def get_ten_fold_crossvalid_one_subset(self, train_index, test_index, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " \"\"\" get traing data, reduced traing data for 10-fold crossvalidation\n", " Parameters:\n", " start_subset: index of start of the testing data\n", " end_subset: index of end of the testing data\n", " fisher_mode: default 'FisherM1ONLY'\n", " Returns:\n", " (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \"\"\"\n", " train_X_10fold = np.array([])\n", " train_y_10fold = np.array([])\n", " train_X_reduced = np.array([])\n", " train_y_reduced = np.array([])\n", " test_X = np.array([])\n", " test_y = np.array([])\n", "\n", " total_number_of_sequences = self.total_number_of_sequences\n", " \n", " #get test data for selected sequence\n", " #for current_no in range(start_subset, end_subset):\n", " for num in test_index:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if test_X.ndim ==1:\n", " test_X = current_X\n", " else:\n", " test_X = np.vstack((test_X, current_X))\n", " test_y = np.concatenate((test_y, current_y))\n", " \n", " #total_sequences = range(1, total_number_of_sequences+1)\n", " #ten_fold_sequences = [i for i in total_sequences if not(i in range(start_subset, end_subset))]\n", " #number_of_reduced = len(ten_fold_sequences)/reduce_ratio if len(ten_fold_sequences)/reduce_ratio !=0 else 1\n", " #random.shuffle(ten_fold_sequences)\n", " #reduced_sequences = ten_fold_sequences[:number_of_reduced]\n", " \n", " number_of_reduced = len(train_index)/reduce_ratio if len(train_index)/reduce_ratio !=0 else 1\n", " random.shuffle(train_index)\n", " reduced_sequences = train_index[:number_of_reduced]\n", "\n", " #for 10-fold cross-validation data\n", " #for current_no in ten_fold_sequences:\n", " for num in train_index:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_10fold.ndim ==1:\n", " train_X_10fold = current_X\n", " else:\n", " train_X_10fold = np.vstack((train_X_10fold, current_X))\n", " train_y_10fold = np.concatenate((train_y_10fold, current_y))\n", "\n", " #for reduced data\n", " for num in reduced_sequences:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_reduced.ndim ==1:\n", " train_X_reduced = current_X\n", " else:\n", " train_X_reduced = np.vstack((train_X_reduced, current_X))\n", " train_y_reduced = np.concatenate((train_y_reduced, current_y)) \n", "\n", " return (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \n", " def get_total_number_of_sequences(self):\n", " \"\"\" get total number of sequences in a ddi familgy\n", " Parameters:\n", " ddi: string\n", " Vectors_Fishers_aaIndex_raw_folder: string\n", " Returns:\n", " n: int\n", " \"\"\"\n", " folder_path = self.Vectors_Fishers_aaIndex_raw_folder + self.ddi + '/' \n", " filename = folder_path +'allPairs.txt'\n", " all_pairs = np.loadtxt(filename)\n", " return len(all_pairs)\n", "\n", " def get_raw_data_for_selected_seq(self, seq_no):\n", " \"\"\" get raw data for selected seq no in a family\n", " Parameters:\n", " ddi: \n", " seq_no: \n", " Returns:\n", " data: raw data in the sequence file\n", " \"\"\"\n", " folder_path = self.Vectors_Fishers_aaIndex_raw_folder + self.ddi + '/' \n", " filename = folder_path + 'F0_20_F1_20_Sliding_17_11_F0_20_F1_20_Sliding_17_11_ouput_'+ str(seq_no) + '.txt'\n", " data = np.loadtxt(filename)\n", " return data\n", " def select_X_y(self, data, fisher_mode = ''):\n", " \"\"\" select subset from the raw input data set\n", " Parameters:\n", " data: data from matlab txt file\n", " fisher_mode: subset base on this Fisher of AAONLY...\n", " Returns:\n", " selected X, y\n", " \"\"\"\n", " y = data[:,-1] # get lable\n", " if fisher_mode == 'FisherM1': # fisher m1 plus AA index\n", " a = data[:, 20:227]\n", " b = data[:, 247:454]\n", " X = np.hstack((a,b))\n", " elif fisher_mode == 'FisherM1ONLY': \n", " a = data[:, 20:40]\n", " b = data[:, 247:267]\n", " X = np.hstack((a,b))\n", " elif fisher_mode == 'AAONLY':\n", " a = data[:, 40:227]\n", " b = data[:, 267:454]\n", " X = np.hstack((a,b))\n", " else:\n", " raise('there is an error in mode')\n", " return X, y\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": true, "input": [ "import sklearn.preprocessing\n", "\n", "def performance_score(target_label, predicted_label, with_auc_score = False, print_report = True): \n", " \"\"\" get performance matrix for prediction\n", " Attributes:\n", " target_label: int 0, 1\n", " predicted_label: 0, 1 or ranking\n", " with_auc_score: bool if False, predicted_label is from 0, 1. If Ture, predicted_label is ranked, need to get AUC score.\n", " print_report: if True, print the perfromannce on screen\n", " \"\"\"\n", " import sklearn\n", " from sklearn.metrics import roc_auc_score\n", " score = {}\n", " if with_auc_score == False:\n", " score['accuracy'] = sklearn.metrics.accuracy_score(target_label, predicted_label)\n", " score['precision'] = sklearn.metrics.precision_score(target_label, predicted_label, pos_label=1)\n", " score['recall'] = sklearn.metrics.recall_score(target_label, predicted_label, pos_label=1)\n", " if with_auc_score == True:\n", " auc_score = roc_auc_score(target_label, predicted_label)\n", " score['auc_score'] = auc_score\n", " target_label = [x >= 0.5 for x in target_label]\n", " score['accuracy'] = sklearn.metrics.accuracy_score(target_label, predicted_label)\n", " score['precision'] = sklearn.metrics.precision_score(target_label, predicted_label, pos_label=1)\n", " score['recall'] = sklearn.metrics.recall_score(target_label, predicted_label, pos_label=1)\n", " if print_report == True:\n", " for key, value in score.iteritems():\n", " print key, '{percent:.1%}'.format(percent=value)\n", " return score\n", "\n", "def saveAsCsv(with_auc_score, fname, score_dict, arguments): #new\n", " newfile = False\n", " if os.path.isfile('report_' + fname + '.csv'):\n", " pass\n", " else:\n", " newfile = True\n", " csvfile = open('report_' + fname + '.csv', 'a+')\n", " writer = csv.writer(csvfile)\n", " if newfile == True:\n", " if with_auc_score == False:\n", " writer.writerow(['no.', 'method', 'isTest']+ score_dict.keys()) #, 'AUC'])\n", " else:\n", " writer.writerow(['no.', 'method', 'isTest'] + score_dict.keys())\n", " for arg in arguments: \n", " writer.writerow([i for i in arg])\n", " csvfile.close()\n", "\n", "def LOO_out_performance_for_all(ddis):\n", " for ddi in ddis:\n", " try:\n", " one_ddi_family = LOO_out_performance_for_one_ddi(ddi)\n", " one_ddi_family.get_LOO_perfermance(settings = settings)\n", " except Exception,e:\n", " print str(e)\n", " logger.info(\"There is a error in this ddi: %s\" % ddi)\n", " logger.info(str(e))\n", "\n", " \n", "class LOO_out_performance_for_one_ddi(object):\n", " \"\"\" get the performance of ddi families\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", "\n", " \"\"\"\n", " def __init__(self, ddi):\n", " self.ddi_obj = DDI_family_base(ddi)\n", " self.ddi = ddi\n", "\n", " def get_LOO_perfermance(self, settings = None):\n", " fisher_mode = settings['fisher_mode']\n", " analysis_scr = []\n", " with_auc_score = settings['with_auc_score'] \n", " reduce_ratio = settings['reduce_ratio'] \n", " for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):\n", " print seq_no\n", " logger.info('sequence number: ' + str(seq_no))\n", " if settings['SVM']:\n", " print \"SVM\"\n", " (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y) = self.ddi_obj.get_LOO_training_and_reduced_traing(seq_no,fisher_mode = fisher_mode, reduce_ratio = reduce_ratio)\n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(scaled_train_X, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = Linear_SVC.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " # Deep learning part\n", " min_max_scaler = Preprocessing_Scaler_with_mean_point5()\n", " X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)\n", " X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)\n", " x_test_minmax = min_max_scaler.transform(test_X)\n", " pretraining_X_minmax = min_max_scaler.transform(train_X_LOO)\n", " x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax, \n", " train_y_reduced\n", " , test_size=0.4, random_state=42)\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = settings['training_epochs']\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " if settings['DL']:\n", " print \"direct deep learning\"\n", " # direct deep learning \n", " sda = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))\n", "\n", " test_predicted = sda.predict(x_test_minmax)\n", " y_test = test_y\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))\n", "\n", " if 0:\n", " # deep learning using unlabeled data for pretraining\n", " print 'deep learning with unlabel data'\n", " pretraining_epochs_for_reduced = cal_epochs(1500, pretraining_X_minmax, batch_size = batch_size)\n", " sda_unlabel = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y, \n", " pretraining_X_minmax = pretraining_X_minmax,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs_for_reduced, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_unlabel.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_unlabel.predict(x_test_minmax)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " if settings['DL_S']:\n", " # deep learning using split network\n", " print 'deep learning using split network'\n", " # get the new representation for A set. first 784-D\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " \n", " x = x_train_minmax[:, :x_train_minmax.shape[1]/2]\n", " print \"original shape for A\", x.shape\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])\n", " x = x_train_minmax[:, x_train_minmax.shape[1]/2:]\n", " \n", " print \"original shape for B\", x.shape\n", " a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])\n", " \n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])\n", " new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])\n", " new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])\n", " new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])\n", " new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))\n", " new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))\n", " new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))\n", "\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = settings['training_epochs']\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " \n", " sda_transformed = trainSda(new_x_train_minmax_whole, y_train_minmax,\n", " new_x_validationt_minmax_whole, y_validation_minmax , \n", " new_x_test_minmax_whole, y_test,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " \n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_transformed.predict(new_x_train_minmax_whole)\n", " y_train = y_train_minmax\n", " \n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_transformed.predict(new_x_test_minmax_whole)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " \n", " \n", " \n", " report_name = filename + '_' + '_'.join(map(str, hidden_layers_sizes)) + \\\n", " '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(reduce_ratio)+ \\\n", " '_' +str(training_epochs) + '_' + current_date\n", " saveAsCsv(with_auc_score, report_name, performance_score(y_test, test_predicted, with_auc_score), analysis_scr)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": true, "input": [ "#for 10-fold cross validation\n", "\n", "def ten_fold_crossvalid_performance_for_all(ddis):\n", " for ddi in ddis:\n", " try:\n", " process_one_ddi_tenfold(ddi)\n", " except Exception,e:\n", " print str(e)\n", " logger.debug(\"There is a error in this ddi: %s\" % ddi)\n", " logger.info(str(e))\n", "def process_one_ddi_tenfold(ddi):\n", " \"\"\"A function to waste CPU cycles\"\"\"\n", " logger.info('DDI: %s' % ddi)\n", " one_ddi_family = {}\n", " one_ddi_family[ddi] = Ten_fold_crossvalid_performance_for_one_ddi(ddi)\n", " one_ddi_family[ddi].get_ten_fold_crossvalid_perfermance(settings=settings)\n", " return None\n", "class Ten_fold_crossvalid_performance_for_one_ddi(object):\n", " \"\"\" get the performance of ddi families\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", "\n", " \"\"\"\n", " def __init__(self, ddi):\n", " self.ddi_obj = DDI_family_base(ddi)\n", " self.ddi = ddi\n", " def get_ten_fold_crossvalid_perfermance(self, settings = None):\n", " fisher_mode = settings['fisher_mode']\n", " analysis_scr = []\n", " with_auc_score = settings['with_auc_score']\n", " reduce_ratio = settings['reduce_ratio']\n", " #for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):\n", " #subset_size = math.floor(self.ddi_obj.total_number_of_sequences / 10.0)\n", " kf = KFold(self.ddi_obj.total_number_of_sequences, n_folds = 10, shuffle = True)\n", " #for subset_no in range(1, 11):\n", " for ((train_index, test_index),subset_no) in izip(kf,range(1,11)):\n", " #for train_index, test_index in kf;\n", " print(\"Subset:\", subset_no)\n", " print(\"Train index: \", train_index)\n", " print(\"Test index: \", test_index)\n", " #logger.info('subset number: ' + str(subset_no))\n", " (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = self.ddi_obj.get_ten_fold_crossvalid_one_subset(train_index, test_index, fisher_mode = fisher_mode, reduce_ratio = reduce_ratio)\n", " if settings['SVM']:\n", " print \"SVM\" \n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(scaled_train_X, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = Linear_SVC.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", "\n", " \n", " if settings['SVM_RBF']:\n", " print \"SVM_RBF\"\n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, train_y_reduced)\n", "\n", " predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " if settings['SVM_POLY']:\n", " print \"SVM_POLY\"\n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)\n", "\n", " predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " # direct deep learning \n", " min_max_scaler = Preprocessing_Scaler_with_mean_point5()\n", " X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)\n", " X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)\n", " x_test_minmax = min_max_scaler.transform(test_X)\n", " pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)\n", " x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax, \n", " train_y_reduced\n", " , test_size=0.4, random_state=42)\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = settings['training_epochs']\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " if settings['SAE_SVM']: \n", " # SAE_SVM\n", " print 'SAE followed by SVM'\n", " x = X_train_pre_validation_minmax\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)\n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(new_x_train_minmax_A, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(new_x_test_minmax_A)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = Linear_SVC.predict(new_x_train_minmax_A)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " if settings['SAE_SVM_RBF']: \n", " print 'SAE followed by SVM RBF'\n", " x = X_train_pre_validation_minmax\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)\n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)\n", " L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_minmax_A, train_y_reduced)\n", " \n", " \n", " predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_minmax_A)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_minmax_A)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " \n", " \n", " \n", " if settings['DL']:\n", " print \"direct deep learning\"\n", " sda = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))\n", "\n", " test_predicted = sda.predict(x_test_minmax)\n", " y_test = test_y\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))\n", "\n", " if settings['DL_U']:\n", " # deep learning using unlabeled data for pretraining\n", " print 'deep learning with unlabel data'\n", " \n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " sda_unlabel = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y, \n", " pretraining_X_minmax = pretraining_X_minmax,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_unlabel.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_unlabel.predict(x_test_minmax)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " if settings['DL_S']:\n", " # deep learning using split network\n", " y_test = test_y\n", " print 'deep learning using split network'\n", " # get the new representation for A set. first 784-D\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " \n", " x = x_train_minmax[:, :x_train_minmax.shape[1]/2]\n", " print \"original shape for A\", x.shape\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])\n", " x = x_train_minmax[:, x_train_minmax.shape[1]/2:]\n", " \n", " print \"original shape for B\", x.shape\n", " a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])\n", " \n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])\n", " new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])\n", " new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])\n", " new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])\n", " new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))\n", " new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))\n", " new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))\n", "\n", " \n", " sda_transformed = trainSda(new_x_train_minmax_whole, y_train_minmax,\n", " new_x_validationt_minmax_whole, y_validation_minmax , \n", " new_x_test_minmax_whole, y_test,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " \n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_transformed.predict(new_x_train_minmax_whole)\n", " y_train = y_train_minmax\n", " \n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_transformed.predict(new_x_test_minmax_whole)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " \n", " \n", " report_name = filename + '_' + '_test10fold_'.join(map(str, hidden_layers_sizes)) + \\\n", " '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(reduce_ratio)+ \\\n", " '_' + str(training_epochs) + '_' + current_date\n", " saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "ten_fold_crossvalid_performance_for_all(ddis[:])" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'ten_fold_crossvalid_performance_for_all' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-1-d7148173e208>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mten_fold_crossvalid_performance_for_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mddis\u001b[0m\u001b[1;33m[\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[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'ten_fold_crossvalid_performance_for_all' is not defined" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#LOO_out_performance_for_all(ddis)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "x = logging._handlers.copy()\n", "for i in x:\n", " log.removeHandler(i)\n", " i.flush()\n", " i.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0
turbomanage/training-data-analyst	courses/machine_learning/deepdive/02_tensorflow/labs/f_ai_platform.ipynb	2	20493	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing AI Platform Training Service\n", "Learning Objectives:\n", " - Learn how to make code compatible with AI Platform Training Service\n", " - Train your model using cloud infrastructure via AI Platform Training Service\n", " - Deploy your model behind a production grade REST API using AI Platform Training Service\n", "\n", "## Introduction\n", "\n", "In this notebook we'll make the jump from training and predicting locally, to do doing both in the cloud. We'll take advantage of Google Cloud's [AI Platform Training Service](https://cloud.google.com/ai-platform/). \n", "\n", "AI Platform Training Service is a managed service that allows the training and deployment of ML models without having to provision or maintain servers. The infrastructure is handled seamlessly by the managed service for us." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Uncomment and run if you need to update your Google SDK\n", "# !sudo apt-get update && sudo apt-get --only-upgrade install google-cloud-sdk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make code compatible with AI Platform Training Service\n", "In order to make our code compatible with AI Platform Training Service we need to make the following changes:\n", "\n", "1. Upload data to Google Cloud Storage \n", "2. Move code into a Python package\n", "3. Modify code to read data from and write checkpoint files to GCS " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Upload data to Google Cloud Storage (GCS)\n", "\n", "Cloud services don't have access to our local files, so we need to upload them to a location the Cloud servers can read from. In this case we'll use GCS.\n", "\n", "Specify your project name and bucket name in the cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PROJECT = \"cloud-training-demos\" # Replace with your PROJECT\n", "BUCKET = \"cloud-training-bucket\" # Replace with your BUCKET\n", "REGION = \"us-central1\" # Choose an available region for AI Platform Training Service\n", "TFVERSION = \"1.14\" # TF version for AI Platform Training Service to use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jupyter allows the subsitution of python variables into bash commands when using the `!<cmd>` format.\n", "It is also possible using the `%%bash` magic but requires an [additional parameter](https://stackoverflow.com/questions/19579546/can-i-access-python-variables-within-a-bash-or-script-ipython-notebook-c). " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gcloud config set project {PROJECT}\n", "!gsutil mb -l {REGION} gs://{BUCKET}\n", "!gsutil -m cp .csv gs://{BUCKET}/taxifare/smallinput/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Move code into a python package\n", "\n", "When you execute a AI Platform Training Service training job, the service zips up your code and ships it to the Cloud so it can be run on Cloud infrastructure. In order to do this AI Platform Training Service requires your code to be a Python package.\n", "\n", "A Python package is simply a collection of one or more `.py` files along with an `__init__.py` file to identify the containing directory as a package. The `__init__.py` sometimes contains initialization code but for our purposes an empty file suffices." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Package Directory and \\_\\_init\\_\\_.py\n", "\n", "The bash command `touch` creates an empty file in the specified location." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "mkdir taxifaremodel\n", "touch taxifaremodel/__init__.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Paste existing code into model.py\n", "\n", "A Python package requires our code to be in a .py file, as opposed to notebook cells. So we simply copy and paste our existing code for the previous notebook into a single file.\n", "\n", "The %%writefile magic writes the contents of its cell to disk with the specified name." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 1\n", "\n", "In the cell below, write the content of the `model.py` to the file `taxifaremodel/model.py`. This will allow us to package the model we \n", "developed in the previous labs so that we can deploy it to AI Platform Training Service. You'll also need to reuse the input functions and the `EvalSpec`, `TrainSpec`, `RunConfig`, etc. that we implemented in the previous labs.\n", "\n", "Complete all the TODOs in the cell below by copy/pasting the code we developed in the previous labs. This will write all the necessary components we developed in our notebook to a single `model.py` file. \n", "\n", "Once we have the code running well locally, we will execute the next cells to train and deploy your packaged model to AI Platform Training Service." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile taxifaremodel/model.py\n", "# TODO: Your code goes here. Import the necessary libraries (e.g. tensorflow, etc)\n", "\n", "CSV_COLUMN_NAMES = # TODO: Your code goes here\n", "CSV_DEFAULTS = # TODO: Your code goes here\n", "FEATURE_NAMES = # TODO: Your code goes here\n", "\n", "def parse_row(row):\n", " # TODO: Your code goes here\n", " return features, label\n", "\n", "def read_dataset(csv_path):\n", " # TODO: Your code goes here\n", " return dataset\n", "\n", "def train_input_fn(csv_path, batch_size = 128):\n", " # TODO: Your code goes here\n", " return dataset\n", "\n", "def eval_input_fn(csv_path, batch_size = 128):\n", " # TODO: Your code goes here\n", " return dataset\n", " \n", "def serving_input_receiver_fn():\n", " # TODO: Your code goes here\n", " return tf.estimator.export.ServingInputReceiver(features = features, receiver_tensors = receiver_tensors) \n", " \n", "def my_rmse(labels, predictions):\n", " # TODO: Your code goes here\n", " return {\"rmse\": tf.metrics.root_mean_squared_error(labels = labels, predictions = pred_values)}\n", "\n", "def create_model(model_dir, train_steps):\n", " # TODO: Your code goes here\n", " return model\n", "\n", "def train_and_evaluate(params):\n", " OUTDIR = params[\"output_dir\"]\n", " TRAIN_DATA_PATH = params[\"train_data_path\"]\n", " EVAL_DATA_PATH = params[\"eval_data_path\"]\n", " TRAIN_STEPS = params[\"train_steps\"]\n", "\n", " model = # TODO: Your code goes here. \n", "\n", " train_spec = # TODO: Your code goes here\n", " \n", " exporter = # TODO: Your code goes here\n", "\n", " eval_spec = # TODO: Your code goes here\n", "\n", " tf.logging.set_verbosity(tf.logging.INFO) \n", " shutil.rmtree(path = OUTDIR, ignore_errors = True)\n", "\n", " tf.estimator.train_and_evaluate(estimator = model, train_spec = train_spec, eval_spec = eval_spec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modify code to read data from and write checkpoint files to GCS \n", "\n", "If you look closely above, you'll notice two changes to the code\n", "\n", "1. The input function now supports reading a list of files matching a file name pattern instead of just a single CSV\n", " - This is useful because large datasets tend to exist in shards.\n", "2. The train and evaluate portion is wrapped in a function that takes a parameter dictionary as an argument.\n", " - This is useful because the output directory, data paths and number of train steps will be different depending on whether we're training locally or in the cloud. Parametrizing allows us to use the same code for both.\n", "\n", "We specify these parameters at run time via the command line. Which means we need to add code to parse command line parameters and invoke `train_and_evaluate()` with those params. This is the job of the `task.py` file. \n", "\n", "Exposing parameters to the command line also allows us to use AI Platform Training Service's automatic hyperparameter tuning feature which we'll cover in a future lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 2\n", "\n", "Add two additional command line parameter parsers to the list we've started below. You should add code to parse command line parameters for the `output_dir` and the `job-dir`. Look at the examples below to make sure you have the correct format, including a `help` description and `required` specification." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile taxifaremodel/task.py\n", "import argparse\n", "import json\n", "import os\n", "\n", "from . import model\n", "\n", "if __name__ == \"__main__\":\n", " \n", " parser = argparse.ArgumentParser()\n", " \n", " parser.add_argument(\n", " \"--train_data_path\",\n", " help = \"GCS or local path to training data\",\n", " required = True\n", " )\n", " parser.add_argument(\n", " \"--train_steps\",\n", " help = \"Steps to run the training job for (default: 1000)\",\n", " type = int,\n", " default = 1000\n", " )\n", " parser.add_argument(\n", " \"--eval_data_path\",\n", " help = \"GCS or local path to evaluation data\",\n", " required = True\n", " )\n", " parser.add_argument(\n", " # TODO: Your code goes here\n", " )\n", " parser.add_argument(\n", " # TODO: Your code goes here\n", " )\n", " args = parser.parse_args().__dict__\n", "\n", " model.train_and_evaluate(args)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train using AI Platform Training Service (local)\n", "\n", "AI Platform Training Service comes with a local test tool ([`gcloud ai-platform local train`](https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train)) to ensure we've packaged our code directly. It's best to first run that for a few steps before trying a Cloud job. \n", "\n", "The arguments before `-- \\` are for AI Platform Training Service\n", "- package-path: speficies the location of the Python package\n", "- module-name: specifies which `.py` file should be run within the package. `task.py` is our entry point so we specify that\n", "\n", "The arguments after `-- \\` are sent to our `task.py`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "!gcloud ai-platform local train \\\n", " --package-path=taxifaremodel \\\n", " --module-name=taxifaremodel.task \\\n", " -- \\\n", " --train_data_path=taxi-train.csv \\\n", " --eval_data_path=taxi-valid.csv \\\n", " --train_steps=1 \\\n", " --output_dir=taxi_trained " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train using AI Platform Training Service (Cloud)\n", "\n", "To submit to the Cloud we use [`gcloud ai-platform jobs submit training [jobname]`](https://cloud.google.com/sdk/gcloud/reference/ml-engine/jobs/submit/training) and simply specify some additional parameters for AI Platform Training Service:\n", "- jobname: A unique identifier for the Cloud job. We usually append system time to ensure uniqueness\n", "- job-dir: A GCS location to upload the Python package to\n", "- runtime-version: Version of TF to use. Defaults to 1.0 if not specified\n", "- python-version: Version of Python to use. Defaults to 2.7 if not specified\n", "- region: Cloud region to train in. See [here](https://cloud.google.com/ml-engine/docs/tensorflow/regions) for supported AI Platform Training Service regions\n", "\n", "Below the `-- \\` note how we've changed our `task.py` args to be GCS locations" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "OUTDIR = \"gs://{}/taxifare/trained_small\".format(BUCKET)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gsutil -m rm -rf {OUTDIR} # start fresh each time\n", "!gcloud ai-platform jobs submit training taxifare_$(date -u +%y%m%d_%H%M%S) \\\n", " --package-path=taxifaremodel \\\n", " --module-name=taxifaremodel.task \\\n", " --job-dir=gs://{BUCKET}/taxifare \\\n", " --python-version=3.5 \\\n", " --runtime-version={TFVERSION} \\\n", " --region={REGION} \\\n", " -- \\\n", " --train_data_path=gs://{BUCKET}/taxifare/smallinput/taxi-train.csv \\\n", " --eval_data_path=gs://{BUCKET}/taxifare/smallinput/taxi-valid.csv \\\n", " --train_steps=1000 \\\n", " --output_dir={OUTDIR}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can track your job and view logs using [cloud console](https://console.cloud.google.com/mlengine/jobs). It will take 5-10 minutes to complete. Wait until the job finishes before moving on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deploy model\n", "\n", "Now let's take our exported SavedModel and deploy it behind a REST API. To do so we'll use AI Platform Training Service's managed TF Serving feature which auto-scales based on load." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gsutil ls gs://{BUCKET}/taxifare/trained_small/export/exporter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "AI Platform Training Service uses a model versioning system. First you create a model folder, and within the folder you create versions of the model. \n", "\n", "Note: You will see an error below if the model folder already exists, it is safe to ignore" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "VERSION='v1'\n", "!gcloud ai-platform models create taxifare --regions us-central1\n", "!gcloud ai-platform versions delete {VERSION} --model taxifare --quiet\n", "!gcloud ai-platform versions create {VERSION} --model taxifare \\\n", " --origin $(gsutil ls gs://{BUCKET}/taxifare/trained_small/export/exporter \| tail -1) \\\n", " --python-version=3.5 \\\n", " --runtime-version {TFVERSION}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Online prediction\n", "\n", "Now that we have deployed our model behind a production grade REST API, we can invoke it remotely. \n", "\n", "We could invoke it directly calling the REST API with an HTTP POST request [reference docs](https://cloud.google.com/ml-engine/reference/rest/v1/projects/predict), however AI Platform Training Service provides an easy way to invoke it via command line." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Invoke prediction REST API via command line\n", "First we write our prediction requests to file in json format" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile ./test.json\n", "{\"dayofweek\": 1, \"hourofday\": 0, \"pickuplon\": -73.885262, \"pickuplat\": 40.773008, \"dropofflon\": -73.987232, \"dropofflat\": 40.732403}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we use [`gcloud ai-platform predict`](https://cloud.google.com/sdk/gcloud/reference/ml-engine/predict) and specify the model name and location of the json file. Since we don't explicitly specify `--version`, the default model version will be used. \n", "\n", "Since we only have one version it is already the default, but if we had multiple model versions we can designate the default using [`gcloud ai-platform versions set-default`](https://cloud.google.com/sdk/gcloud/reference/ml-engine/versions/set-default) or using [cloud console](https://pantheon.corp.google.com/mlengine/models)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gcloud ai-platform predict --model=taxifare --json-instances=./test.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Invoke prediction REST API via python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise 3*\n", "\n", "In the cell below, use the Google Python client library to query the model you just deployed on AI Platform Training Service. Find the estimated taxi fare for a ride with the following properties\n", "- ride occurs on Monday\n", "- at 8:00 am\n", "- pick up at (40.773, -73.885)\n", "- drop off at (40.732, -73.987)\n", "\n", "Have a look at this post and examples on [\"Using the Python Client Library\"](https://cloud.google.com/ml-engine/docs/tensorflow/python-client-library) and [\"Getting Online Predictions\"](https://cloud.google.com/ml-engine/docs/tensorflow/online-predict) from Google Cloud." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from googleapiclient import discovery\n", "from oauth2client.client import GoogleCredentials\n", "import json\n", "\n", "credentials = # TODO: Your code goes here\n", "api = # TODO: Your code goes here\n", "\n", "request_data = {\"instances\":\n", " [\n", " {\n", " # TODO: Your code goes here\n", " }\n", " ]\n", "}\n", "\n", "parent = # TODO: Your code goes here\n", "\n", "response = # TODO: Your code goes here\n", "\n", "print(\"response = {0}\".format(response))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenge exercise\n", "\n", "Modify your solution to the challenge exercise in e_traineval.ipynb appropriately. Make sure that you implement training and deployment. Increase the size of your dataset by 10x since you are running on the cloud. Does your accuracy improve?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
turbomanage/training-data-analyst	quests/serverlessml/05_feateng/solution/feateng_bqml.ipynb	2	35433	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BigQuery ML models with feature engineering\n", "\n", "In this notebook, we will use BigQuery ML to build more sophisticated models for taxifare prediction.\n", "\n", "This is a continuation of our [first models](../../02_bqml/solution/first_model.ipynb) we created earlier with BigQuery ML but now with more feature engineering.\n", "\n", "## Learning Objectives\n", "1. Apply transformations using SQL to prune the taxi cab dataset\n", "2. Create and train a new Linear Regression model with BigQuery ML\n", "3. Evaluate and predict with the linear model\n", "4. Create a feature cross for day-hour combination using SQL\n", "5. Examine ways to reduce model overfitting with regularization\n", "6. Create and train a DNN model with BigQuery ML\n", "\n", "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/feateng_bqml.ipynb) -- try to complete that notebook first before reviewing this solution notebook. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "export PROJECT=$(gcloud config list project --format \"value(core.project)\")\n", "echo \"Your current GCP Project Name is: \"$PROJECT" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "PROJECT = \"your-gcp-project-here\" # REPLACE WITH YOUR PROJECT NAME\n", "REGION = \"us-central1\" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n", "\n", "# Do not change these\n", "os.environ[\"PROJECT\"] = PROJECT\n", "os.environ[\"REGION\"] = REGION\n", "os.environ[\"BUCKET\"] = PROJECT # DEFAULT BUCKET WILL BE PROJECT ID\n", "\n", "if PROJECT == \"your-gcp-project-here\":\n", " print(\"Don't forget to update your PROJECT name! Currently:\", PROJECT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a BigQuery Dataset and Google Cloud Storage Bucket\n", "\n", "A BigQuery dataset is a container for tables, views, and models built with BigQuery ML. Let's create one called __serverlessml__ if we have not already done so in an earlier lab. We'll do the same for a GCS bucket for our project too." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BigQuery dataset already exists, let's not recreate it.\n", "Bucket exists, let's not recreate it.\n" ] } ], "source": [ "%%bash\n", "\n", "## Create a BigQuery dataset for serverlessml if it doesn't exist\n", "datasetexists=$(bq ls -d \| grep -w serverlessml)\n", "\n", "if [ -n \"$datasetexists\" ]; then\n", " echo -e \"BigQuery dataset already exists, let's not recreate it.\"\n", "\n", "else\n", " echo \"Creating BigQuery dataset titled: serverlessml\"\n", " \n", " bq --location=US mk --dataset \\\n", " --description 'Taxi Fare' \\\n", " $PROJECT:serverlessml\n", " echo \"\\nHere are your current datasets:\"\n", " bq ls\n", "fi \n", " \n", "## Create GCS bucket if it doesn't exist already...\n", "exists=$(gsutil ls -d \| grep -w gs://${PROJECT}/)\n", "\n", "if [ -n \"$exists\" ]; then\n", " echo -e \"Bucket exists, let's not recreate it.\"\n", " \n", "else\n", " echo \"Creating a new GCS bucket.\"\n", " gsutil mb -l ${REGION} gs://${PROJECT}\n", " echo \"\\nHere are your current buckets:\"\n", " gsutil ls\n", "fi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 4: With some transformations\n", "\n", "BigQuery ML automatically scales the inputs. so we don't need to do scaling, but human insight can help.\n", "\n", "Since we we'll repeat this quite a bit, let's make a dataset with 1 million rows. " ] }, { "cell_type": "code", "execution_count": 33, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "CREATE OR REPLACE TABLE serverlessml.feateng_training_data AS\n", "\n", "SELECT\n", " (tolls_amount + fare_amount) AS fare_amount,\n", " pickup_datetime,\n", " pickup_longitude AS pickuplon,\n", " pickup_latitude AS pickuplat,\n", " dropoff_longitude AS dropofflon,\n", " dropoff_latitude AS dropofflat,\n", " passenger_count1.0 AS passengers\n", "FROM `nyc-tlc.yellow.trips`\n", "# The full dataset has 1+ Billion rows, let's take only 1 out of 1,000 (or 1 Million total)\n", "WHERE ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 1000)) = 1\n", "AND\n", " trip_distance > 0\n", " AND fare_amount >= 2.5\n", " AND pickup_longitude > -78\n", " AND pickup_longitude < -70\n", " AND dropoff_longitude > -78\n", " AND dropoff_longitude < -70\n", " AND pickup_latitude > 37\n", " AND pickup_latitude < 45\n", " AND dropoff_latitude > 37\n", " AND dropoff_latitude < 45\n", " AND passenger_count > 0" ] }, { "cell_type": "code", "execution_count": 34, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "# Tip: You can CREATE MODEL IF NOT EXISTS as well\n", "CREATE OR REPLACE MODEL serverlessml.model4_feateng\n", "TRANSFORM(\n", " EXCEPT(pickup_datetime)\n", " , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean\n", " , CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek\n", " , CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday\n", ")\n", "OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg') \n", "AS\n", "\n", "SELECT * FROM serverlessml.feateng_training_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the training is done, visit the [BigQuery Cloud Console](https://console.cloud.google.com/bigquery) and look at the model that has been trained. Then, come back to this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that BigQuery automatically split the data we gave it, and trained on only a part of the data and used the rest for evaluation. We can look at eval statistics on that held-out data:" ] }, { "cell_type": "code", "execution_count": 35, "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>training_run</th>\n", " <th>iteration</th>\n", " <th>loss</th>\n", " <th>eval_loss</th>\n", " <th>learning_rate</th>\n", " <th>duration_ms</th>\n", " <th>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>26.025462</td>\n", " <td>26.404982</td>\n", " <td>None</td>\n", " <td>20076</td>\n", " <td>5.101516</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " training_run iteration loss eval_loss learning_rate duration_ms \\\n", "0 0 0 26.025462 26.404982 None 20076 \n", "\n", " rmse \n", "0 5.101516 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT , SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL serverlessml.model4_feateng)" ] }, { "cell_type": "code", "execution_count": 36, "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>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>5.138578</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rmse\n", "0 5.138578" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model4_feateng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yippee! We're now below our target of 6 dollars in RMSE.\n", "We are now beating our goals, and with just a linear model. \n", "\n", "## Making predictions with BigQuery ML\n", "\n", "This is how the prediction query would look that we saw earlier [heading 1.3 miles uptown](https://www.google.com/maps/dir/'40.742104,-73.982683'/'40.755174,-73.983766'/@40.7481394,-73.993579,15z/data=!3m1!4b1!4m9!4m8!1m3!2m2!1d-73.982683!2d40.742104!1m3!2m2!1d-73.983766!2d40.755174) in New York City." ] }, { "cell_type": "code", "execution_count": 37, "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>predicted_fare_amount</th>\n", " <th>pickuplon</th>\n", " <th>pickuplat</th>\n", " <th>dropofflon</th>\n", " <th>dropofflat</th>\n", " <th>passengers</th>\n", " <th>pickup_datetime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>6.855489</td>\n", " <td>-73.982683</td>\n", " <td>40.742104</td>\n", " <td>-73.983766</td>\n", " <td>40.755174</td>\n", " <td>3.0</td>\n", " <td>2019-06-03 04:21:29.769443+00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " predicted_fare_amount pickuplon pickuplat dropofflon dropofflat \\\n", "0 6.855489 -73.982683 40.742104 -73.983766 40.755174 \n", "\n", " passengers pickup_datetime \n", "0 3.0 2019-06-03 04:21:29.769443+00:00 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT FROM ML.PREDICT(MODEL serverlessml.model4_feateng, (\n", " SELECT \n", " -73.982683 AS pickuplon,\n", " 40.742104 AS pickuplat,\n", " -73.983766 AS dropofflon,\n", " 40.755174 AS dropofflat,\n", " 3.0 AS passengers,\n", " TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime\n", "))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improving the model with feature crosses\n", "\n", "Let's do a [feature cross](https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture) of the day-hour combination instead of using them raw" ] }, { "cell_type": "code", "execution_count": 38, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "CREATE OR REPLACE MODEL serverlessml.model5_featcross\n", "TRANSFORM(\n", " * EXCEPT(pickup_datetime)\n", " , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean\n", " , ML.FEATURE_CROSS(\n", " STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,\n", " CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)\n", " ) AS day_hr\n", ")\n", "OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg') \n", "AS\n", "\n", "SELECT * FROM serverlessml.feateng_training_data" ] }, { "cell_type": "code", "execution_count": 39, "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>training_run</th>\n", " <th>iteration</th>\n", " <th>loss</th>\n", " <th>eval_loss</th>\n", " <th>learning_rate</th>\n", " <th>duration_ms</th>\n", " <th>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>25.965221</td>\n", " <td>26.327813</td>\n", " <td>None</td>\n", " <td>19431</td>\n", " <td>5.095608</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " training_run iteration loss eval_loss learning_rate duration_ms \\\n", "0 0 0 25.965221 26.327813 None 19431 \n", "\n", " rmse \n", "0 5.095608 " ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT , SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL serverlessml.model5_featcross)" ] }, { "cell_type": "code", "execution_count": 40, "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>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>5.131063</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rmse\n", "0 5.131063" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model5_featcross)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes (not the case above), the training RMSE is quite reasonable, but the evaluation RMSE is terrible. This is an indication of overfitting.\n", "When we do feature crosses, we run into the risk of overfitting (for example, when a particular day-hour combo doesn't have enough taxirides).\n", "\n", "## Reducing overfitting\n", "\n", "Let's add [L2 regularization](https://developers.google.com/machine-learning/glossary/#L2_regularization) to help reduce overfitting. Let's set it to 0.1" ] }, { "cell_type": "code", "execution_count": 41, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "CREATE OR REPLACE MODEL serverlessml.model6_featcross_l2\n", "TRANSFORM(\n", " EXCEPT(pickup_datetime)\n", " , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean\n", " , ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,\n", " CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr\n", ")\n", "OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg', l2_reg=0.1) \n", "AS\n", "\n", "SELECT * FROM serverlessml.feateng_training_data" ] }, { "cell_type": "code", "execution_count": 42, "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>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>5.131063</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rmse\n", "0 5.131063" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model6_featcross_l2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These sorts of experiment would have taken days to do otherwise. We did it in minutes, thanks to BigQuery ML! The advantage of doing all this in the TRANSFORM is the client code doing the PREDICT doesn't change. Our model improvement is transparent to client code." ] }, { "cell_type": "code", "execution_count": 43, "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>predicted_fare_amount</th>\n", " <th>pickuplon</th>\n", " <th>pickuplat</th>\n", " <th>dropofflon</th>\n", " <th>dropofflat</th>\n", " <th>passengers</th>\n", " <th>pickup_datetime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>7.122171</td>\n", " <td>-73.982683</td>\n", " <td>40.742104</td>\n", " <td>-73.983766</td>\n", " <td>40.755174</td>\n", " <td>3.0</td>\n", " <td>2019-06-03 04:21:29.769443+00:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " predicted_fare_amount pickuplon pickuplat dropofflon dropofflat \\\n", "0 7.122171 -73.982683 40.742104 -73.983766 40.755174 \n", "\n", " passengers pickup_datetime \n", "0 3.0 2019-06-03 04:21:29.769443+00:00 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT * FROM ML.PREDICT(MODEL serverlessml.model6_featcross_l2, (\n", " SELECT \n", " -73.982683 AS pickuplon,\n", " 40.742104 AS pickuplat,\n", " -73.983766 AS dropofflon,\n", " 40.755174 AS dropofflat,\n", " 3.0 AS passengers,\n", " TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime\n", "))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's try feature crossing the locations too\n", "\n", "Because the lat and lon by themselves don't have meaning, but only in conjunction, it may be useful to treat the fields as a pair instead of just using them as numeric values. However, lat and lon are continuous numbers, so we have to discretize them first. That's what ML.BUCKETIZE does.\n", "\n", "Here are some of the preprocessing functions in BigQuery ML:\n", "* ML.FEATURE_CROSS(STRUCT(features)) does a feature cross of all the combinations\n", "* ML.POLYNOMIAL_EXPAND(STRUCT(features), degree) creates x, x^2, x^3, etc.\n", "* ML.BUCKETIZE(f, split_points) where split_points is an array " ] }, { "cell_type": "code", "execution_count": 44, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "\n", "-- BQML chooses the wrong gradient descent strategy here. It will get fixed in (b/141429990)\n", "-- But for now, as a workaround, explicitly specify optimize_strategy='BATCH_GRADIENT_DESCENT'\n", "\n", "CREATE OR REPLACE MODEL serverlessml.model7_geo\n", "TRANSFORM(\n", " fare_amount\n", " , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean\n", " , ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,\n", " CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday), 2) AS day_hr\n", " , CONCAT(\n", " ML.BUCKETIZE(pickuplon, GENERATE_ARRAY(-78, -70, 0.01)),\n", " ML.BUCKETIZE(pickuplat, GENERATE_ARRAY(37, 45, 0.01)),\n", " ML.BUCKETIZE(dropofflon, GENERATE_ARRAY(-78, -70, 0.01)),\n", " ML.BUCKETIZE(dropofflat, GENERATE_ARRAY(37, 45, 0.01))\n", " ) AS pickup_and_dropoff\n", ")\n", "OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg', l2_reg=0.1, optimize_strategy='BATCH_GRADIENT_DESCENT') \n", "AS\n", "\n", "SELECT * FROM serverlessml.feateng_training_data" ] }, { "cell_type": "code", "execution_count": 45, "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>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>4.28441</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rmse\n", "0 4.28441" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model7_geo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yippee! We're now below our target of 6 dollars in RMSE." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DNN\n", "\n", "You could, of course, train a more sophisticated model. Change \"linear_reg\" above to \"dnn_regressor\" and see if it improves things.\n", "\n", "__Note: This takes 20 - 25 minutes to run.__" ] }, { "cell_type": "code", "execution_count": 48, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "-- This is alpha and may not work for you.\n", "CREATE OR REPLACE MODEL serverlessml.model8_dnn\n", "TRANSFORM(\n", " fare_amount\n", " , ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat)) AS euclidean\n", " , CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),\n", " CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS day_hr\n", " , CONCAT(\n", " ST_AsText(ST_SnapToGrid(ST_GeogPoint(pickuplon, pickuplat), 0.01)),\n", " ST_AsText(ST_SnapToGrid(ST_GeogPoint(dropofflon, dropofflat), 0.01))\n", " ) AS pickup_and_dropoff\n", ")\n", "-- at the time of writing, l2_reg wasn't supported yet.\n", "OPTIONS(input_label_cols=['fare_amount'], model_type='dnn_regressor', hidden_units=[32, 8]) \n", "AS\n", "\n", "SELECT * FROM serverlessml.feateng_training_data" ] }, { "cell_type": "code", "execution_count": 49, "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>rmse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>6.296184</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rmse\n", "0 6.296184" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL serverlessml.model8_dnn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We really need the L2 reg (recall that we got 4.77 without the feateng). It's time to do [Feature Engineering in Keras](../../06_feateng_keras/labs/taxifare_fc.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "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": 4 }	apache-2.0
JuliaOpt/Mosek.jl	notebooks/Examining problems with Julia and Mosek.jl.ipynb	1	22336	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Empty Task\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Mosek,Mosek.Ext\n", "\n", "t = maketask()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load the file `25fv47.task` which is a liner optimization problem. The file includes solutions.\n", "\n", "The `show()` functions can be used to format the whole problem or parts of it. `25fv47.task` is too large to be shown fullly, so `show()` will cut off some parts. To show the entire task use `showall()` instead." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Task ''\n", " Minimize\n", " R0000: - 0.1 MSEXP + 0.101 MS1MP - 0.075 LDEXP + 0.076 LD1MP - 0.1 PKEXP + 0.101 PK1MP - 0.0875 RKEXP + 0.0885 RK1MP + 0.2 5C0ST + 0.25 6C0ST + 0.001 CCRFT + 2.275 CRUDE - 1.5303 AABDH + 0.9632 HABDH + 0.3687 JABDH - 1.4254 1AAGJA + 1.007 1BAGJA - 1.1387 EAGJA + 0.9839 1HAGJA + 1.0109 11AGJA ... (707 terms omitted)\n", " Subject to\n", " F1X.0: = 0.0\n", " 2SF145: + BNF64 + HNF64 + 1NF64 + JNF64 + YNF64 = 29.0\n", " 2SF089: + HNF89 + 1NF89 = 60.0\n", " 2SF129: + BNF80 + HN129 + JN129 + MN129 + YN129 - 90A930 = 73.0\n", " 30M00: + HNM00 = 77.0\n", " 30M94: + 1HNM94 + 2HNM94 + JNM94 = 27.0\n", " 30M91: + HNM90 + 1NM90 + JNM90 = 44.0\n", " 30ATK: + BNATK + HNATK + 1NATK + JNATK = 4.0\n", " 30PGK: + BNK19 + HNK19 + 1NK19 + JNK19 = 23.0\n", " 30G30: + ANG30 + BNG30 + HNG30 + 1NG30 = 44.0\n", " 30G17: + ANG17 + BNG17 + HNG17 + 1NG17 + JNG17 + MNG17 - NNG17 = 164.0\n", " 30G44: + 1HNG44 + 11NG44 = 31.0\n", " 30G22: + ANG22 + BNG22 + HNG22 + 1NG22 + JNG22 + 1MNG22 + YNG22 - 90AS30 = 79.0\n", " 30D22: + BND22 + HND22 + 1ND22 = 19.0\n", " 30D30: + BND30 + HND30 + 1ND30 = 1.0\n", " 30121: + 0.5 HNF21 + 0.9 1NF21 = 4.0\n", " 30F52: + 0.5 HNF21 + BNF52 + HNF52 + 1NF52 = 86.0\n", " 30F48: + BNF48 + HNF48 + 1NF48 + JNF48 + ZNF48 - 128FNN = 88.0\n", " 30128: + 128FNN = 20.0\n", " RB017: + 0.511 2CB002 - CB096 - CB095 - CB084 = 0.0\n", " ... (801 constraints omitted)\n", " Variables\n", " MSEXP ∈ [0.0;+Inf[\n", " MS1MP ∈ [0.0;+Inf[\n", " LDEXP ∈ [0.0;+Inf[\n", " LD1MP ∈ [0.0;+Inf[\n", " PKEXP ∈ [0.0;+Inf[\n", " PK1MP ∈ [0.0;+Inf[\n", " RKEXP ∈ [0.0;+Inf[\n", " RK1MP ∈ [0.0;+Inf[\n", " 1G0EXP ∈ [0.0;+Inf[\n", " 1G01MP ∈ [0.0;+Inf[\n", " 1F0EXP ∈ [0.0;+Inf[\n", " 1F01MP ∈ [0.0;+Inf[\n", " 5C0ST ∈ [0.0;+Inf[\n", " 6C0ST ∈ [0.0;+Inf[\n", " CCRFT ∈ [0.0;+Inf[\n", " CRUDE ∈ [0.0;+Inf[\n", " AABDH ∈ [0.0;+Inf[\n", " HABDH ∈ [0.0;+Inf[\n", " JABDH ∈ [0.0;+Inf[\n", " 1AAGJA ∈ [0.0;+Inf[\n", " ... (1551 variable bounds omitted)\n" ] } ], "source": [ "readdata(t,joinpath(Pkg.dir(\"Mosek\"),\"test\",\"25fv47.task\"))\n", "\n", "show(t)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Objective('R0000': - 0.1 MSEXP + 0.101 MS1MP - 0.075 LDEXP + 0.076 LD1MP - 0.1 PKEXP + 0.101 PK1MP - 0.0875 RKEXP + 0.0885 RK1MP + 0.2 5C0ST + 0.25 6C0ST + 0.001 CCRFT + 2.275 CRUDE - 1.5303 AABDH + 0.9632 HABDH + 0.3687 JABDH - 1.4254 1AAGJA + 1.007 1BAGJA - 1.1387 EAGJA + 0.9839 1HAGJA + 1.0109 11AGJA ...(707 terms omitted))" ] } ], "source": [ "show(t[Obj()])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Constraint('2SF145': 29.0 = + BNF64 + HNF64 + 1NF64 + JNF64 + YNF64 )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show(t[Con(2)])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Variable('MSEXP' ∈ [0.0;+Inf[)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "show(t[Var(1)])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interior Solution, status = Unknown\n", " Objective: 5501.846030254401 \| 5501.8460280611325\n", " Variable solution\n", " name level dual lower dual upper dual conic\n", " MSEXP : 5.3141e+01 -5.3777e-09 0.0000e+00 0.0000e+00\n", " MS1MP : 9.0695e-05 1.0000e-03 0.0000e+00 0.0000e+00\n", " LDEXP : 8.4537e-05 1.0000e-03 0.0000e+00 0.0000e+00\n", " LD1MP : 3.4227e+01 2.8868e-09 0.0000e+00 0.0000e+00\n", " PKEXP : 8.5445e-05 1.0000e-03 0.0000e+00 0.0000e+00\n", " PK1MP : 7.3501e+00 1.1459e-08 0.0000e+00 0.0000e+00\n", " RKEXP : 8.6610e-05 1.0000e-03 0.0000e+00 0.0000e+00\n", " RK1MP : 1.7316e+01 3.7289e-09 0.0000e+00 0.0000e+00\n", " 1G0EXP : 2.9831e+02 -2.8936e-08 0.0000e+00 0.0000e+00\n", " 1G01MP : 3.0640e+00 2.8936e-08 0.0000e+00 0.0000e+00\n", " 1F0EXP : 3.5337e+02 -2.6446e-08 0.0000e+00 0.0000e+00\n", " 1F01MP : 2.6324e+00 2.6446e-08 0.0000e+00 0.0000e+00\n", " 5C0ST : 2.0818e+03 -3.9886e-08 0.0000e+00 0.0000e+00\n", " 6C0ST : 0.0000e+00 5.0000e-02 0.0000e+00 0.0000e+00\n", " CCRFT : 6.2500e+01 0.0000e+00 0.0000e+00 0.0000e+00\n", " CRUDE : 1.2942e+03 0.0000e+00 0.0000e+00 0.0000e+00\n", " AABDH : 2.8170e-06 0.0000e+00 0.0000e+00 0.0000e+00\n", " HABDH : 3.7382e+02 0.0000e+00 0.0000e+00 0.0000e+00\n", " JABDH : 1.3218e+02 0.0000e+00 0.0000e+00 0.0000e+00\n", " 1AAGJA : 1.4654e+02 0.0000e+00 0.0000e+00 0.0000e+00\n", " ... (1551 variables omitted)\n", " Constraint solution\n", " name level dual lower dual upper y\n", " F1X.0 : 0.0000e+00 0.0000e+00 0.0000e+00 -0.0000e+00\n", " 2SF145 : 2.9000e+01 2.8297e+00 0.0000e+00 2.8297e+00\n", " 2SF089 : 6.0000e+01 2.9002e+00 0.0000e+00 2.9002e+00\n", " 2SF129 : 7.3000e+01 2.7619e+00 0.0000e+00 2.7619e+00\n", " 30M00 : 7.7000e+01 9.5324e+00 0.0000e+00 9.5324e+00\n", " 30M94 : 2.7000e+01 8.8149e+00 0.0000e+00 8.8149e+00\n", " 30M91 : 4.4000e+01 8.3345e+00 0.0000e+00 8.3345e+00\n", " 30ATK : 4.0000e+00 7.3382e+00 0.0000e+00 7.3382e+00\n", " 30PGK : 2.3000e+01 7.0821e+00 0.0000e+00 7.0821e+00\n", " 30G30 : 4.4000e+01 6.2145e+00 0.0000e+00 6.2145e+00\n", " 30G17 : 1.6400e+02 6.2145e+00 0.0000e+00 6.2145e+00\n", " 30G44 : 3.1000e+01 6.1717e+00 0.0000e+00 6.1717e+00\n", " 30G22 : 7.9000e+01 6.0318e+00 0.0000e+00 6.0318e+00\n", " 30D22 : 1.9000e+01 5.8042e+00 0.0000e+00 5.8042e+00\n", " 30D30 : 1.0000e+00 6.0273e+00 0.0000e+00 6.0273e+00\n", " 30121 : 4.0000e+00 4.0584e+00 0.0000e+00 4.0584e+00\n", " 30F52 : 8.6000e+01 4.0584e+00 0.0000e+00 4.0584e+00\n", " 30F48 : 8.8000e+01 2.9224e+00 0.0000e+00 2.9224e+00\n", " 30128 : 2.0000e+01 2.9224e+00 0.0000e+00 2.9224e+00\n", " RB017 : 0.0000e+00 2.2373e+00 0.0000e+00 2.2373e+00\n", " ... (801 constraints omitted)\n" ] } ], "source": [ "sol = t[Sol(MSK_SOL_ITR)]\n", "show(sol)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSEXP: 53.14086064879275, dual lower: -5.37768665709315e-9, dual upper: 0.0, dual conic: 0.0\n" ] } ], "source": [ "show(sol[Var(1)])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F1X.0: 0.0, dual lower: 0.0, dual upper: 0.0, dual conic: -0.0\n" ] } ], "source": [ "show(sol[Con(1)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we load another file, `lj-inner.task`, which is a problem with one semidefinite variable and some second order cones. The file includes solutions." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Task 'lownerjohn_inner'\n", " Maximize\n", " #obj: + t[0] \n", " Subject to\n", " qc0[0]: + 2.0 d[0] + 3.0 d[1] - qc0[0].coneslack = 0.0\n", " qc0[1]: - 2.0 C[0,0] - 3.0 C[0,1] - qc0[1].coneslack = -0.0\n", " qc0[2]: - 2.0 C[1,0] - 3.0 C[1,1] - qc0[2].coneslack = -0.0\n", " qc1[0]: + 3.0 d[0] - d[1] - qc1[0].coneslack = -0.0\n", " qc1[1]: - 3.0 C[0,0] + C[0,1] - qc1[1].coneslack = -0.0\n", " qc1[2]: - 3.0 C[1,0] + C[1,1] - qc1[2].coneslack = -0.0\n", " qc2[0]: + d[0] - 4.0 d[1] - qc2[0].coneslack = -11.0\n", " qc2[1]: - C[0,0] + 4.0 C[0,1] - qc2[1].coneslack = -0.0\n", " qc2[2]: - C[1,0] + 4.0 C[1,1] - qc2[2].coneslack = -0.0\n", " qc3[0]: - 3.0 d[0] - 2.0 d[1] - qc3[0].coneslack = -23.0\n", " qc3[1]: + 3.0 C[0,0] + 2.0 C[0,1] - qc3[1].coneslack = -0.0\n", " qc3[2]: + 3.0 C[1,0] + 2.0 C[1,1] - qc3[2].coneslack = -0.0\n", " qc4[0]: - 3.0 d[0] + 4.0 d[1] - qc4[0].coneslack = -17.0\n", " qc4[1]: + 3.0 C[0,0] - 4.0 C[0,1] - qc4[1].coneslack = -0.0\n", " qc4[2]: + 3.0 C[1,0] - 4.0 C[1,1] - qc4[2].coneslack = -0.0\n", " #c16: + #MX1 ⋅ #X̄1 = 0.0\n", " #c17: + #MX2 ⋅ #X̄1 = 0.0\n", " #c18: + #MX3 ⋅ #X̄1 = 0.0\n", " #c19: + #MX4 ⋅ #X̄1 = 0.0\n", " #c20: + C[0,0] + #MX5 ⋅ #X̄1 = 0.0\n", " ... (7 constraints omitted)\n", " qc0[0]: (qc0[0].coneslack,qc0[1].coneslack,qc0[2].coneslack) ∈ 𝒞_q(3)\n", " qc1[0]: (qc1[0].coneslack,qc1[1].coneslack,qc1[2].coneslack) ∈ 𝒞_q(3)\n", " qc2[0]: (qc2[0].coneslack,qc2[1].coneslack,qc2[2].coneslack) ∈ 𝒞_q(3)\n", " qc3[0]: (qc3[0].coneslack,qc3[1].coneslack,qc3[2].coneslack) ∈ 𝒞_q(3)\n", " qc4[0]: (qc4[0].coneslack,qc4[1].coneslack,qc4[2].coneslack) ∈ 𝒞_q(3)\n", " #k6: (#x23,#x24,#x25) ∈ 𝒞_qr(3)\n", " Variables\n", " d[0] ∈ ]-Inf;+Inf[\n", " d[1] ∈ ]-Inf;+Inf[\n", " C[0,0] ∈ ]-Inf;+Inf[\n", " C[0,1] ∈ ]-Inf;+Inf[\n", " C[1,0] ∈ ]-Inf;+Inf[\n", " C[1,1] ∈ ]-Inf;+Inf[\n", " qc0[0].coneslack ∈ ]-Inf;+Inf[\n", " qc0[1].coneslack ∈ ]-Inf;+Inf[\n", " qc0[2].coneslack ∈ ]-Inf;+Inf[\n", " qc1[0].coneslack ∈ ]-Inf;+Inf[\n", " qc1[1].coneslack ∈ ]-Inf;+Inf[\n", " qc1[2].coneslack ∈ ]-Inf;+Inf[\n", " qc2[0].coneslack ∈ ]-Inf;+Inf[\n", " qc2[1].coneslack ∈ ]-Inf;+Inf[\n", " qc2[2].coneslack ∈ ]-Inf;+Inf[\n", " qc3[0].coneslack ∈ ]-Inf;+Inf[\n", " qc3[1].coneslack ∈ ]-Inf;+Inf[\n", " qc3[2].coneslack ∈ ]-Inf;+Inf[\n", " qc4[0].coneslack ∈ ]-Inf;+Inf[\n", " qc4[1].coneslack ∈ ]-Inf;+Inf[\n", " ... (6 variable bounds omitted)\n", " #X̄1 ∈ S(4)\n", " Symmetric matrixes\n", " #MX1 (4): [ (3,1,0.5),(3,3,-1.0) ]\n", " #MX2 (4): [ (4,3,-0.5) ]\n", " #MX3 (4): [ (4,3,-0.5) ]\n", " #MX4 (4): [ (4,2,0.5),(4,4,-1.0) ]\n", " #MX5 (4): [ (1,1,-1.0) ]\n", " #MX6 (4): [ (2,1,-0.5) ]\n", " #MX7 (4): [ (2,1,-0.5) ]\n", " #MX8 (4): [ (2,2,-1.0) ]\n", " #MX9 (4): [ (3,3,1.0) ]\n", " #MX10 (4): [ (4,4,1.0) ]\n" ] } ], "source": [ "readdata(t,joinpath(Pkg.dir(\"Mosek\"),\"test\",\"lj-inner.task\"))\n", "show(t)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SemidefiniteVariable('#X̄1' ∈ 𝒞_S(4))" ] } ], "source": [ "show(t[Barvar(1)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In constraint `#c16` ... `#c20` we have some terms of the form `#MX1 ⋅ #X̄1`; these are semidefinite terms, i.e. the `#MX1` is a symmetric matrix and `#X̄1` is a symmetric positive semidefinite matrix variable. We can check what the matrixes look like:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SparseMatrixCSC{Float64,Int64}\n", "[0.0 0.0 0.5 0.0; 0.0 0.0 0.0 0.0; 0.5 0.0 -1.0 0.0; 0.0 0.0 0.0 0.0]\n" ] } ], "source": [ "m = t[Symmat(1)]\n", "println(typeof(m))\n", "println(full(m))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Interior Solution, status = Optimal\n", " Objective: 2.6454571279494123 \| 2.645457125709574\n", " Variable solution\n", " name level dual lower dual upper dual conic\n", " d[0] : 3.2903e+00 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " d[1] : 1.1774e+00 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " C[0,0] : 2.9228e+00 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " C[0,1] : 2.3978e-01 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " C[1,0] : 2.3978e-01 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " C[1,1] : 2.4043e+00 -0.0000e+00 -0.0000e+00 -0.0000e+00\n", " qc0[0].coneslack : 1.0113e+01 -0.0000e+00 -0.0000e+00 -5.6478e-02\n", " qc0[1].coneslack : -6.5650e+00 -0.0000e+00 -0.0000e+00 -3.6663e-02\n", " qc0[2].coneslack : -7.6923e+00 -0.0000e+00 -0.0000e+00 -4.2960e-02\n", " qc1[0].coneslack : 8.6935e+00 -0.0000e+00 -0.0000e+00 -4.6425e-02\n", " qc1[1].coneslack : -8.5287e+00 -0.0000e+00 -0.0000e+00 -4.5545e-02\n", " qc1[2].coneslack : 1.6849e+00 -0.0000e+00 -0.0000e+00 8.9985e-03\n", " qc2[0].coneslack : 9.5806e+00 -0.0000e+00 -0.0000e+00 -5.4059e-02\n", " qc2[1].coneslack : -1.9637e+00 -0.0000e+00 -0.0000e+00 -1.1081e-02\n", " qc2[2].coneslack : 9.3772e+00 -0.0000e+00 -0.0000e+00 5.2911e-02\n", " qc3[0].coneslack : 1.0774e+01 -0.0000e+00 -0.0000e+00 -5.2526e-02\n", " qc3[1].coneslack : 9.2480e+00 -0.0000e+00 -0.0000e+00 4.5086e-02\n", " qc3[2].coneslack : 5.5278e+00 -0.0000e+00 -0.0000e+00 2.6949e-02\n", " qc4[0].coneslack : 1.1839e+01 -0.0000e+00 -0.0000e+00 -4.9570e-02\n", " qc4[1].coneslack : 7.8094e+00 -0.0000e+00 -0.0000e+00 3.2699e-02\n", " ... (6 variables omitted)\n", " PSD Variable solution\n", " #X̄1: Symmetric 4 × 4\n", " \| 2.92e+00 2.40e-01 2.92e+00 1.20e-01 \| \| -4.54e-01 2.27e-02 4.53e-01 2.97e-13 \|\n", " X̄ = \| 2.40e+00 1.20e-01 2.40e+00 \| S̄ = \| -5.52e-01 3.52e-13 5.51e-01 \|\n", " \| 2.92e+00 -2.82e-15 \| \| -4.53e-01 -2.27e-02 \|\n", " \| 2.40e+00 \| \| -5.51e-01 \|\n", " Constraint solution\n", " name level dual lower dual upper y\n", " qc0[0] : 0.0000e+00 -5.6478e-02 -0.0000e+00 -5.6478e-02\n", " qc0[1] : 0.0000e+00 -3.6663e-02 -0.0000e+00 -3.6663e-02\n", " qc0[2] : 0.0000e+00 -4.2960e-02 -0.0000e+00 -4.2960e-02\n", " qc1[0] : -0.0000e+00 -4.6425e-02 -0.0000e+00 -4.6425e-02\n", " qc1[1] : -0.0000e+00 -4.5545e-02 -0.0000e+00 -4.5545e-02\n", " qc1[2] : -0.0000e+00 -0.0000e+00 -8.9985e-03 8.9985e-03\n", " qc2[0] : -1.1000e+01 -5.4059e-02 -0.0000e+00 -5.4059e-02\n", " qc2[1] : 0.0000e+00 -1.1081e-02 -0.0000e+00 -1.1081e-02\n", " qc2[2] : 0.0000e+00 -0.0000e+00 -5.2911e-02 5.2911e-02\n", " qc3[0] : -2.3000e+01 -5.2526e-02 -0.0000e+00 -5.2526e-02\n", " qc3[1] : 0.0000e+00 -0.0000e+00 -4.5086e-02 4.5086e-02\n", " qc3[2] : 0.0000e+00 -0.0000e+00 -2.6949e-02 2.6949e-02\n", " qc4[0] : -1.7000e+01 -4.9570e-02 -0.0000e+00 -4.9570e-02\n", " qc4[1] : 0.0000e+00 -0.0000e+00 -3.2699e-02 3.2699e-02\n", " qc4[2] : 0.0000e+00 -3.7256e-02 -0.0000e+00 -3.7256e-02\n", " #c16 : 0.0000e+00 -9.0693e-01 -0.0000e+00 -9.0693e-01\n", " #c17 : 0.0000e+00 -4.1990e-02 -0.0000e+00 -4.1990e-02\n", " #c18 : 0.0000e+00 -3.3285e-03 -0.0000e+00 -3.3285e-03\n", " #c19 : 0.0000e+00 -1.1026e+00 -0.0000e+00 -1.1026e+00\n", " #c20 : 0.0000e+00 -4.5440e-01 -0.0000e+00 -4.5440e-01\n", " ... (7 constraints omitted)\n" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optimize(t)\n", "sol = t[Sol(MSK_SOL_ITR)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can also check the solution to a single semidefinite variable:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d[0]: Symmetric 4 × 4\n", " \| 2.92e+00 2.40e-01 2.92e+00 1.20e-01 \| \| -4.54e-01 2.27e-02 4.53e-01 2.97e-13 \|\n", " X̄ = \| 2.40e+00 1.20e-01 2.40e+00 \| S̄ = \| -5.52e-01 3.52e-13 5.51e-01 \|\n", " \| 2.92e+00 -2.82e-15 \| \| -4.53e-01 -2.27e-02 \|\n", " \| 2.40e+00 \| \| -5.51e-01 \|\n" ] } ], "source": [ "show(sol[Barvar(1)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.6.0", "language": "julia", "name": "julia-0.6" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
minhpqn/sklearn_pydata2015	notebooks/Index.ipynb	5	2249	{ "metadata": { "name": "", "signature": "sha256:bc2860f990730fd6dabc20dd32ddeeb48f989228997eca69c76980b706d56946" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyData Seattle 2015 Scikit-Learn Tutorial\n", "\n", "This is the main index of the PyData Seattle 2015 Introduction to Scikit-Learn tutorial, presented by [Jake VanderPlas](http://www.vanderplas.com).\n", "Please refer to the [github repository](http://github.com/jakevdp/sklearn_pydata2015) for this tutorial for any updates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial Notebooks\n", "\n", "The following links are to notebooks containing the tutorial materials.\n", "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_pydata2015) in which they are contained.\n", "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n", "\n", "### 1. Preliminaries\n", "\n", " + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n", " \n", "### 2. Introduction to Machine Learning with Scikit-Learn\n", "\n", " + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n", " + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n", " \n", "### 3. Supervised Learning In-Depth\n", "\n", " + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n", " + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n", "\n", "### 4. Unsupervised Learning In-Depth\n", "\n", " + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n", " + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n", " + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n", " \n", "### 5. Model Validation In-Depth\n", "\n", " + [05-Validation.ipynb](05-Validation.ipynb)" ] } ], "metadata": {} } ] }	bsd-3-clause
jeanpat/DeepFISH	dataset/Download_from_github_2164-dataset.ipynb	1	26623	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2164 Full resolution pairs of grayscaled+groundtruth label images\n", "The pairs of images were generated from a [previous dataset](https://github.com/jeanpat/DeepFISH/blob/master/dataset/overlapping_chromosomes_examples.h5?raw=true) which suffered from several defects both in the grayscale and in the ground truth components:\n", "\n", " * The grayscaled images could suffer from two problems:\n", " * Some images had black dots: those images were removed (with the corresponding groundtruth)\n", " * The images dtype is now np.uint8\n", " * The overlapping domain is now more realistic compared with real overlapping chromosomes\n", " * The labels of the groundtruth don't have no more spurious pixels\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy import ndimage as nd\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2020-01-30 16:48:29-- https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true\n", "Résolution de github.com (github.com)… 140.82.118.4\n", "Connexion à github.com (github.com)\|140.82.118.4\|:443… connecté.\n", "requête HTTP transmise, en attente de la réponse… 302 Found\n", "Emplacement : https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz [suivant]\n", "--2020-01-30 16:48:29-- https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz\n", "Réutilisation de la connexion existante à github.com:443.\n", "requête HTTP transmise, en attente de la réponse… 302 Found\n", "Emplacement : https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz [suivant]\n", "--2020-01-30 16:48:30-- https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz\n", "Résolution de raw.githubusercontent.com (raw.githubusercontent.com)… 151.101.120.133\n", "Connexion à raw.githubusercontent.com (raw.githubusercontent.com)\|151.101.120.133\|:443… connecté.\n", "requête HTTP transmise, en attente de la réponse… 200 OK\n", "Taille : 9166198 (8,7M) [application/octet-stream]\n", "Enregistre : «Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true»\n", "\n", "Cleaned_FullRes_216 100%[===================>] 8,74M 1,22MB/s ds 6,9s \n", "\n", "2020-01-30 16:48:38 (1,26 MB/s) - «Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true» enregistré [9166198/9166198]\n", "\n" ] } ], "source": [ "!wget https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true\n", "!mv Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true Clean2164.npz" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2164, 190, 189, 2)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# There's a trick to load and uncompress a numpy .npz array\n", "# https://stackoverflow.com/questions/18231135/load-compressed-data-npz-from-file-using-numpy-load/44693995\n", "#\n", "dataset = np.load('Clean2164.npz')\n", "data = dataset.f.arr_0\n", "data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Have a look to the downloaded images" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fd7164d2e10>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "N=203\n", "plt.figure(figsize=(10,8))\n", "plt.subplot(121)\n", "plt.imshow(data[N,:,:,0], cmap=plt.cm.gray)\n", "plt.subplot(122)\n", "plt.imshow(data[N,:,:,1], cmap=plt.cm.flag_r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The same dataset saved as a hdf5 file can be downloaded" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2020-01-30 22:49:04-- https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true\n", "Résolution de github.com (github.com)… 140.82.118.4\n", "Connexion à github.com (github.com)\|140.82.118.4\|:443… connecté.\n", "requête HTTP transmise, en attente de la réponse… 302 Found\n", "Emplacement : https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5 [suivant]\n", "--2020-01-30 22:49:05-- https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5\n", "Réutilisation de la connexion existante à github.com:443.\n", "requête HTTP transmise, en attente de la réponse… 302 Found\n", "Emplacement : https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5 [suivant]\n", "--2020-01-30 22:49:05-- https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5\n", "Résolution de raw.githubusercontent.com (raw.githubusercontent.com)… 151.101.120.133\n", "Connexion à raw.githubusercontent.com (raw.githubusercontent.com)\|151.101.120.133\|:443… connecté.\n", "requête HTTP transmise, en attente de la réponse… 200 OK\n", "Taille : 4487537 (4,3M) [application/octet-stream]\n", "Enregistre : «Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true.1»\n", "\n", "Cleaned_FullRes_216 100%[===================>] 4,28M 1,55MB/s ds 2,8s \n", "\n", "2020-01-30 22:49:08 (1,55 MB/s) - «Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true.1» enregistré [4487537/4487537]\n", "\n" ] } ], "source": [ "!wget https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true\n", "!mv Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true Clean2164.h5" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dataset is a numpy array of shape: (2164, 190, 189, 2)\n" ] } ], "source": [ "import h5py\n", "filename = './Clean2164.h5'\n", "h5f = h5py.File(filename,'r')\n", "pairs = h5f['chroms_data'][:]\n", "h5f.close()\n", "print('dataset is a numpy array of shape:', pairs.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
experimentalDataAesthetics/ipython_notes	__Heatmaps.ipynb	2	260034	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "http://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "%pylab inline\n", "\n", "data = pd.read_csv(\"iris.csv\", index_col=4)\n", "data_scaled = (data - np.min(data, axis=0))/(np.max(data, axis=0) - np.min(data, axis=0))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def heatmapper ( filename, width, height ):\n", "\n", " # Plot it out\n", " fig, ax = plt.subplots()\n", " heatmap = ax.pcolor(data_scaled, cmap=plt.cm.Reds, alpha=0.8)\n", "\n", " # Format\n", " fig = plt.gcf()\n", " fig.set_size_inches(width, height)\n", "\n", " # turn off the frame\n", " ax.set_frame_on(False)\n", "\n", " # put the major ticks at the middle of each cell\n", " ax.set_yticks(np.arange(data_scaled.shape[0]) + 0.5, minor=False)\n", " ax.set_xticks(np.arange(data_scaled.shape[1]) + 0.5, minor=False)\n", "\n", " # want a more natural, table-like display\n", " ax.invert_yaxis()\n", " ax.xaxis.tick_top()\n", "\n", " #Labels\n", " ax.set_xticklabels(data_scaled.columns, minor=False)\n", " ax.set_yticklabels(data_scaled.index, minor=False)\n", "\n", " # rotate the\n", " plt.xticks(rotation=90)\n", "\n", " ax.grid(False)\n", "\n", " # Turn off all the ticks\n", " ax = plt.gca()\n", " fig.savefig(filename)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "heatmapper ('heatmap_iris.pdf', 20, 60)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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jxpG5Abq7u/nGN77B22+/TTKZJBqN0tzcTG9vL3/5l3/Jn/zJnwDw+PFj3nrrLX76058y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\ndlBeXs6tW7fGw6p58+YxMjJCV1cXW7du/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"text": [ "<matplotlib.figure.Figure at 0x107c10290>" ] } ], "prompt_number": 39 } ], "metadata": {} } ] }	gpl-2.0
taesiri/noteobooks	old:misc/universal approximation/feedforward_1d.ipynb	1	270507	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Traditional Feedforward neural network to approximate a black box function\n", "\n", "This is just a toy example to test the basic functionality of Bokeh interactive plot!" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"417cfff3-9675-490d-99a4-77cb98d5de0b\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" \|\| force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /*\n", " Render data to the DOM node\n", " /\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /\n", " Handle when an output is cleared or removed\n", " /\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id !== undefined) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var element_id = msg.content.text.trim();\n", " Bokeh.index[element_id].model.document.clear();\n", " delete Bokeh.index[element_id];\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /\n", " Handle when a new output is added\n", " /\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") \|\| (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[0]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " / Handle when an output is cleared or removed /\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " / Handle when a new output is added /\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /\n", " Register the mime type and append_mime function with output_area\n", " /\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " / Is output safe? /\n", " safe: true,\n", " / Index of renderer in `output_area.display_order` /\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " }\n", " finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '417cfff3-9675-490d-99a4-77cb98d5de0b' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((root.Bokeh !== undefined) \|\| (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" \|\| force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" \|\| force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null \|\| js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '417cfff3-9675-490d-99a4-77cb98d5de0b' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) \|\| (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"417cfff3-9675-490d-99a4-77cb98d5de0b\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torchvision.datasets as dsets\n", "import torchvision.transforms as transforms\n", "from torch.autograd import Variable\n", "import torch.optim as optim\n", "\n", "import numpy as np\n", "import matplotlib\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "\n", "from bokeh.layouts import gridplot\n", "from bokeh.plotting import figure, show, output_notebook, ColumnDataSource\n", "from bokeh.layouts import column, row, widgetbox\n", "from bokeh.models import CustomJS, Slider, Select\n", "\n", "\n", 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function!\"},\"id\":\"82af51d9-c744-472b-aaeb-c8d4c997889b\",\"type\":\"Title\"},{\"attributes\":{\"overlay\":{\"id\":\"b346ccbb-0811-4cf3-8bc2-065b9b846946\",\"type\":\"BoxAnnotation\"}},\"id\":\"064c6b7e-72cf-4c5e-9799-8426956faeea\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"24551bd8-f613-4d69-bd89-71684390a74c\",\"type\":\"BasicTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"92c06702-61e0-428b-899c-d0ef511551b4\",\"type\":\"PanTool\"},{\"id\":\"d2d1e0dd-489d-4533-8224-9a0ff137446f\",\"type\":\"WheelZoomTool\"},{\"id\":\"064c6b7e-72cf-4c5e-9799-8426956faeea\",\"type\":\"BoxZoomTool\"},{\"id\":\"6a32fe90-f88e-4bd5-8a16-8b61519af094\",\"type\":\"ResetTool\"},{\"id\":\"6e4d7825-9a06-4a31-a22e-a55b938b79fa\",\"type\":\"SaveTool\"},{\"id\":\"888f7784-9b5d-4421-ac5d-4eff5a83cfd4\",\"type\":\"BoxSelectTool\"}]},\"id\":\"9adfae4e-d528-4a99-b5f3-e95754b340b4\",\"type\":\"Toolbar\"},{\"attributes\":{\"label\":{\"value\":\"random graph\"},\"renderers\":[{\"id\":\"fdadeac4-2eda-479b-ba3a-58ff8a893d9f\",\"type\":\"GlyphRenderer\"}]},\"id\":\"9b272ac6-54d3-4350-b8cf-74285ea0a1be\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"6a32fe90-f88e-4bd5-8a16-8b61519af094\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"6e4d7825-9a06-4a31-a22e-a55b938b79fa\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"overlay\":{\"id\":\"eddcc0a6-08ea-48e2-a6c8-528b4caf79f3\",\"type\":\"BoxAnnotation\"},\"renderers\":[{\"id\":\"fdadeac4-2eda-479b-ba3a-58ff8a893d9f\",\"type\":\"GlyphRenderer\"}]},\"id\":\"888f7784-9b5d-4421-ac5d-4eff5a83cfd4\",\"type\":\"BoxSelectTool\"}],\"root_ids\":[\"6c58bdc8-b8bd-44ec-a1e7-18668ee100f9\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n", " var render_items = [{\"docid\":\"0ff22d86-f9dc-4c39-a867-468f6c732b8c\",\"elementid\":\"f1a9d3f5-8854-4e6d-8675-f609dba4e388\",\"modelid\":\"6c58bdc8-b8bd-44ec-a1e7-18668ee100f9\"}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " clearInterval(timer);\n", " }\n", " attempts++;\n", " if (attempts > 100) {\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", " clearInterval(timer);\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "6c58bdc8-b8bd-44ec-a1e7-18668ee100f9" } }, "output_type": "display_data" } ], "source": [ "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save,box_select\"\n", "\n", "p1 = figure(title=\"my ultimate function!\", tools=TOOLS)\n", "p1.line(data__x, data__y, legend=\"random graph\")\n", "\n", "p1.width = 1200\n", "show(p1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# try to estimate using regular Feed forward network" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "gpu_dtype = torch.cuda.FloatTensor \n", "print_every = 2500\n", "\n", "\n", "# class ffNet(nn.Module):\n", "# def __init__(self):\n", "# super(ffNet, self).__init__()\n", "# self.fc1 = nn.Linear(1, 64)\n", "# self.relu = nn.ReLU()\n", "# self.fc2 = nn.Linear(64, 1)\n", " \n", "# def forward(self, x):\n", "# out = self.relu(self.fc1(x))\n", "# out = self.fc2(out)\n", "# return out\n", " \n", "# faster way to define network \n", "\n", "ffNet = nn.Sequential( \n", " nn.Linear(1, 1024),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(1024, 1024),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(1024, 256),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(256, 1)\n", ")" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "def train(data, mode, loss_fn, optimizer, save_every_epoch=1000, num_epochs=2):\n", " model.train()\n", " \n", " history = {}\n", " \n", " xs = torch.from_numpy(data['xs']).unsqueeze(1)\n", " ys = torch.from_numpy(data['ys']).unsqueeze(1)\n", " N = len(ys)\n", " for epoch in range(num_epochs):\n", " x_var = Variable(xs.type(gpu_dtype))\n", " y_var = Variable(ys.type(gpu_dtype))\n", " \n", " scores = model(x_var)\n", " \n", " if (epoch + 1) % save_every_epoch == 0:\n", " history[str(epoch+1)] = scores\n", " \n", " loss = loss_fn(scores, y_var)\n", " if (epoch + 1) % print_every == 0:\n", " print('epoch = %d, loss = %.4f' % (epoch + 1, loss.data[0]))\n", " \n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", " \n", " return history" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch = 500, loss = 4.2065\n", "epoch = 1000, loss = 4.0514\n", "epoch = 1500, loss = 3.9321\n", "epoch = 2000, loss = 3.8473\n", "epoch = 2500, loss = 3.7203\n", "epoch = 3000, loss = 3.6676\n", "epoch = 3500, loss = 3.5577\n", "epoch = 4000, loss = 3.4725\n", "epoch = 4500, loss = 3.5264\n", "epoch = 5000, loss = 3.3610\n", "epoch = 5500, loss = 3.1831\n", "epoch = 6000, loss = 3.1063\n", "epoch = 6500, loss = 3.2245\n", "epoch = 7000, loss = 3.1172\n", "epoch = 7500, loss = 2.8339\n", "epoch = 8000, loss = 3.0797\n", "epoch = 8500, loss = 2.6550\n", "epoch = 9000, loss = 3.0002\n", "epoch = 9500, loss = 2.4694\n", "epoch = 10000, loss = 2.3889\n", "epoch = 10500, loss = 2.2978\n", "epoch = 11000, loss = 2.9131\n", "epoch = 11500, loss = 2.3735\n", "epoch = 12000, loss = 2.0840\n", "epoch = 12500, loss = 2.2256\n", "epoch = 13000, loss = 1.9658\n", "epoch = 13500, loss = 1.8407\n", "epoch = 14000, loss = 1.7629\n", "epoch = 14500, loss = 1.7925\n", "epoch = 15000, loss = 1.6149\n", "epoch = 15500, loss = 1.4879\n", "epoch = 16000, loss = 1.7192\n", "epoch = 16500, loss = 1.4376\n", "epoch = 17000, loss = 1.2416\n", "epoch = 17500, loss = 1.1595\n", "epoch = 18000, loss = 1.2963\n", "epoch = 18500, loss = 1.1328\n", "epoch = 19000, loss = 1.0801\n", "epoch = 19500, loss = 0.9001\n", "epoch = 20000, loss = 1.3833\n", "epoch = 20500, loss = 0.9198\n", "epoch = 21000, loss = 0.7751\n", "epoch = 21500, loss = 1.2914\n", "epoch = 22000, loss = 0.8480\n", "epoch = 22500, loss = 0.7552\n", "epoch = 23000, loss = 0.7460\n", "epoch = 23500, loss = 0.5753\n", "epoch = 24000, loss = 0.5789\n", "epoch = 24500, loss = 0.6598\n", "epoch = 25000, loss = 0.3960\n", "epoch = 25500, loss = 0.3523\n", "epoch = 26000, loss = 0.4780\n", "epoch = 26500, loss = 0.5133\n", "epoch = 27000, loss = 0.5758\n", "epoch = 27500, loss = 0.5305\n", "epoch = 28000, loss = 0.7296\n", "epoch = 28500, loss = 0.6026\n", "epoch = 29000, loss = 0.2554\n", "epoch = 29500, loss = 0.7642\n", "epoch = 30000, loss = 0.3067\n", "epoch = 30500, loss = 0.1999\n", "epoch = 31000, loss = 0.7462\n", "epoch = 31500, loss = 0.4343\n", "epoch = 32000, loss = 0.3805\n", "epoch = 32500, loss = 0.2791\n", "epoch = 33000, loss = 0.1793\n", "epoch = 33500, loss = 0.3254\n", "epoch = 34000, loss = 0.2528\n", "epoch = 34500, loss = 0.1505\n", "epoch = 35000, loss = 0.1767\n", "epoch = 35500, loss = 0.1595\n", "epoch = 36000, loss = 0.2076\n", "epoch = 36500, loss = 0.3516\n", "epoch = 37000, loss = 0.2329\n", "epoch = 37500, loss = 0.2339\n", "epoch = 38000, loss = 0.2779\n", "epoch = 38500, loss = 0.2093\n", "epoch = 39000, loss = 0.2267\n", "epoch = 39500, loss = 0.2981\n", "epoch = 40000, loss = 0.1264\n", "epoch = 40500, loss = 0.2213\n", "epoch = 41000, loss = 0.2837\n", "epoch = 41500, loss = 0.2608\n", "epoch = 42000, loss = 0.4422\n", "epoch = 42500, loss = 0.1218\n", "epoch = 43000, loss = 0.1225\n", "epoch = 43500, loss = 0.1817\n", "epoch = 44000, loss = 0.2644\n", "epoch = 44500, loss = 0.1466\n", "epoch = 45000, loss = 0.1974\n", "epoch = 45500, loss = 0.1618\n", "epoch = 46000, loss = 0.2443\n", "epoch = 46500, loss = 0.2388\n", "epoch = 47000, loss = 0.1580\n", "epoch = 47500, loss = 0.2404\n", "epoch = 48000, loss = 0.1322\n", "epoch = 48500, loss = 0.2841\n", "epoch = 49000, loss = 0.3962\n", "epoch = 49500, loss = 0.2063\n", "epoch = 50000, loss = 0.1152\n", "epoch = 50500, loss = 0.0949\n", "epoch = 51000, loss = 0.2826\n", "epoch = 51500, loss = 0.2664\n", "epoch = 52000, loss = 0.1016\n", "epoch = 52500, loss = 0.0978\n", "epoch = 53000, loss = 0.4301\n", "epoch = 53500, loss = 0.1612\n", "epoch = 54000, loss = 0.2415\n", "epoch = 54500, loss = 0.2549\n", "epoch = 55000, loss = 0.1973\n", "epoch = 55500, loss = 0.2148\n", "epoch = 56000, loss = 0.1397\n", "epoch = 56500, loss = 0.1080\n", "epoch = 57000, loss = 0.0895\n", "epoch = 57500, loss = 0.2470\n", "epoch = 58000, loss = 0.2112\n", "epoch = 58500, loss = 0.0869\n", "epoch = 59000, loss = 0.1487\n", "epoch = 59500, loss = 0.1894\n", "epoch = 60000, loss = 0.0865\n", "epoch = 60500, loss = 0.1104\n", "epoch = 61000, loss = 0.1124\n", "epoch = 61500, loss = 0.0855\n", "epoch = 62000, loss = 0.3597\n", "epoch = 62500, loss = 0.1168\n", "epoch = 63000, loss = 0.1742\n", "epoch = 63500, loss = 0.2496\n", "epoch = 64000, loss = 0.3107\n", "epoch = 64500, loss = 0.1657\n", "epoch = 65000, loss = 0.1014\n", "epoch = 65500, loss = 0.1110\n", "epoch = 66000, loss = 0.0860\n", "epoch = 66500, loss = 0.0923\n", "epoch = 67000, loss = 0.2389\n", "epoch = 67500, loss = 0.1266\n", "epoch = 68000, loss = 0.0731\n", "epoch = 68500, loss = 0.1525\n", "epoch = 69000, loss = 0.3041\n", "epoch = 69500, loss = 0.4641\n", "epoch = 70000, loss = 0.2295\n", "epoch = 70500, loss = 0.0913\n", "epoch = 71000, loss = 0.1009\n", "epoch = 71500, loss = 0.2389\n", "epoch = 72000, loss = 0.1117\n", "epoch = 72500, loss = 0.2231\n", "epoch = 73000, loss = 0.0944\n", "epoch = 73500, loss = 0.0738\n", "epoch = 74000, loss = 0.1754\n", "epoch = 74500, loss = 0.1437\n", "epoch = 75000, loss = 0.1293\n", "epoch = 75500, loss = 0.1677\n", "epoch = 76000, loss = 0.1198\n", "epoch = 76500, loss = 0.2099\n", "epoch = 77000, loss = 0.1137\n", "epoch = 77500, loss = 0.1848\n", "epoch = 78000, loss = 0.0956\n", "epoch = 78500, loss = 0.4917\n", "epoch = 79000, loss = 0.1573\n", "epoch = 79500, loss = 0.1053\n", "epoch = 80000, loss = 0.1048\n", "epoch = 80500, loss = 0.0708\n", "epoch = 81000, loss = 0.0779\n", "epoch = 81500, loss = 0.0601\n", "epoch = 82000, loss = 0.0723\n", "epoch = 82500, loss = 0.2759\n", "epoch = 83000, loss = 0.0831\n", "epoch = 83500, loss = 0.0623\n", "epoch = 84000, loss = 0.1234\n", "epoch = 84500, loss = 0.0623\n", "epoch = 85000, loss = 0.0994\n", "epoch = 85500, loss = 0.0811\n", "epoch = 86000, loss = 0.0704\n", "epoch = 86500, loss = 0.1816\n", "epoch = 87000, loss = 0.0665\n", "epoch = 87500, loss = 0.1104\n", "epoch = 88000, loss = 0.1998\n", "epoch = 88500, loss = 0.1202\n", "epoch = 89000, loss = 0.0572\n", "epoch = 89500, loss = 0.1253\n", "epoch = 90000, loss = 0.1028\n", "epoch = 90500, loss = 0.0678\n", "epoch = 91000, loss = 0.1384\n", "epoch = 91500, loss = 0.0739\n", "epoch = 92000, loss = 0.1431\n", "epoch = 92500, loss = 0.0589\n", "epoch = 93000, loss = 0.0756\n", "epoch = 93500, loss = 0.0549\n", "epoch = 94000, loss = 0.1323\n", "epoch = 94500, loss = 0.0570\n", "epoch = 95000, loss = 0.0823\n", "epoch = 95500, loss = 0.0528\n", "epoch = 96000, loss = 0.0519\n", "epoch = 96500, loss = 0.0524\n", "epoch = 97000, loss = 0.0589\n", "epoch = 97500, loss = 0.5726\n", "epoch = 98000, loss = 0.2076\n", "epoch = 98500, loss = 0.1182\n", "epoch = 99000, loss = 0.1096\n", "epoch = 99500, loss = 0.0867\n", "epoch = 100000, loss = 0.0695\n", "epoch = 100500, loss = 0.0510\n", "epoch = 101000, loss = 0.1228\n", "epoch = 101500, loss = 0.0510\n", "epoch = 102000, loss = 0.0498\n", "epoch = 102500, loss = 0.2786\n", "epoch = 103000, loss = 0.0933\n", "epoch = 103500, loss = 0.0718\n", "epoch = 104000, loss = 0.0579\n", "epoch = 104500, loss = 0.0686\n", "epoch = 105000, loss = 0.1026\n", "epoch = 105500, loss = 0.1076\n", "epoch = 106000, loss = 0.1288\n", "epoch = 106500, loss = 0.0674\n", "epoch = 107000, loss = 0.1445\n", "epoch = 107500, loss = 0.1318\n", "epoch = 108000, loss = 0.0550\n", "epoch = 108500, loss = 0.0648\n", "epoch = 109000, loss = 0.2004\n", "epoch = 109500, loss = 0.0697\n", "epoch = 110000, loss = 0.0459\n", "epoch = 110500, loss = 0.1082\n", "epoch = 111000, loss = 0.0784\n", "epoch = 111500, loss = 0.0932\n", "epoch = 112000, loss = 0.0774\n", "epoch = 112500, loss = 0.0484\n", "epoch = 113000, loss = 0.0894\n", "epoch = 113500, loss = 0.0818\n", "epoch = 114000, loss = 0.1826\n", "epoch = 114500, loss = 0.1722\n", "epoch = 115000, loss = 0.0713\n", "epoch = 115500, loss = 0.1204\n", "epoch = 116000, loss = 0.0536\n", "epoch = 116500, loss = 0.0763\n", "epoch = 117000, loss = 0.1320\n", "epoch = 117500, loss = 0.0989\n", "epoch = 118000, loss = 0.0478\n", "epoch = 118500, loss = 0.1117\n", "epoch = 119000, loss = 0.1045\n", "epoch = 119500, loss = 0.0608\n", "epoch = 120000, loss = 0.2088\n", "epoch = 120500, loss = 0.1179\n", "epoch = 121000, loss = 0.1637\n", "epoch = 121500, loss = 0.0418\n", "epoch = 122000, loss = 0.0534\n", "epoch = 122500, loss = 0.0959\n", "epoch = 123000, loss = 0.0839\n", "epoch = 123500, loss = 0.1120\n", "epoch = 124000, loss = 0.0473\n", "epoch = 124500, loss = 0.0473\n", "epoch = 125000, loss = 0.0731\n", "epoch = 125500, loss = 0.0582\n", "epoch = 126000, loss = 0.0504\n", "epoch = 126500, loss = 0.0477\n", "epoch = 127000, loss = 0.0398\n", "epoch = 127500, loss = 0.0398\n", "epoch = 128000, loss = 0.0480\n", "epoch = 128500, loss = 0.0394\n", "epoch = 129000, loss = 0.0531\n", "epoch = 129500, loss = 0.1273\n", "epoch = 130000, loss = 0.0388\n", "epoch = 130500, loss = 0.0408\n", "epoch = 131000, loss = 0.0390\n", "epoch = 131500, loss = 0.1399\n", "epoch = 132000, loss = 0.1961\n", "epoch = 132500, loss = 0.0463\n", "epoch = 133000, loss = 0.0455\n", "epoch = 133500, loss = 0.0424\n", "epoch = 134000, loss = 0.0438\n", "epoch = 134500, loss = 0.0399\n", "epoch = 135000, loss = 0.0469\n", "epoch = 135500, loss = 0.1087\n", "epoch = 136000, loss = 0.0378\n", "epoch = 136500, loss = 0.0378\n", "epoch = 137000, loss = 0.0901\n", "epoch = 137500, loss = 0.0487\n", "epoch = 138000, loss = 0.0523\n", "epoch = 138500, loss = 0.0861\n", "epoch = 139000, loss = 0.2757\n", "epoch = 139500, loss = 0.0374\n", "epoch = 140000, loss = 0.2074\n", "epoch = 140500, loss = 0.0850\n", "epoch = 141000, loss = 0.0442\n", "epoch = 141500, loss = 0.0979\n", "epoch = 142000, loss = 0.1008\n", "epoch = 142500, loss = 0.1287\n", "epoch = 143000, loss = 0.0427\n", "epoch = 143500, loss = 0.0523\n", "epoch = 144000, loss = 0.2427\n", "epoch = 144500, loss = 0.0967\n", "epoch = 145000, loss = 0.0637\n", "epoch = 145500, loss = 0.0806\n", "epoch = 146000, loss = 0.0702\n", "epoch = 146500, loss = 0.0641\n", "epoch = 147000, loss = 0.0437\n", "epoch = 147500, loss = 0.2105\n", "epoch = 148000, loss = 0.0370\n", "epoch = 148500, loss = 0.1143\n", "epoch = 149000, loss = 0.0785\n", "epoch = 149500, loss = 0.0411\n", "epoch = 150000, loss = 0.0987\n" ] } ], "source": [ "model = ffNet.type(gpu_dtype)\n", "\n", "loss_fn = nn.MSELoss().type(gpu_dtype)\n", "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n", "\n", "xs = np.arange(1,100,0.1)\n", "ys = fx(xs)\n", "\n", "data = {}\n", "data['xs'] = xs\n", "data['ys'] = ys\n", "\n", "history_of_training = train(data, model, loss_fn, optimizer, num_epochs=150000)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "model.eval()\n", "\n", "xs = torch.from_numpy(data['xs']).unsqueeze(1)\n", "x_var = Variable(xs.type(gpu_dtype))\n", "y_pred = model(x_var).data.cpu().numpy().squeeze()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<div class=\"bk-root\">\n", " <div 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\",\"dtype\":\"float64\",\"shape\":[990]},\"y\":{\"__ndarray__\":\"sAMwCI3KFUCarjtekP0VQElPgdb3MxZAeiy2ESRtFkCCrzm5g6gWQGvtPpWQ5RZAiSSBfs0jF0Dq5pXlxGIXQNAt1MIHohdAZELo0CzhF0AQF679zx8YQI9ZEgOSXRhADujPHRiaGEC8WaXaC9UYQFqDivQaDhlAynnJP/dEGUDXOtieVnkZQDk/hv7yqhlAKP2XV4rZGUALek+03gQaQNLYrTi2LBpAJOd0LNtQGkAlqB0GHHEaQCsSHXZLjRpAbA3tcUClGkBN/2Y+1rgaQJ4eEHnsxxpAxmkHIGfSGkAsHFKYLtgaQAR7TrIv2RpABC8erFvVGkBUdeExqMwaQPl9o1sPvxpAhYTeqI+sGkDKt4P5K5UaQNX9d4TreBpAXCN7y9lXGkCeI3OMBjIaQJf/GLCFBxpAwxwINm/YGUCFZjQe36QZQBx5zk/1bBlA/feffdUwGUDm3+wHp/AYQFAr6duUrBhAeIDUUM1kGECU3c8CghkYQI5Tg6vnyhdAWtur+DV5F0AZLqtgpyQXQEZGNfV4zRZAr884NOpzFkApTiHXPBgWQEQnk6C0uhVAeQLDKJdbFUDZHIuoK/sUQI05YsO6mRRAUsdYUI43FEB1pEEi8dQTQJieLM8uchNAumFZd5MPE0DBAsqLa60SQL+snJQDTBJA0zVU96frEUCQgDe9pIwRQOKR71lFLxFAIimMctTTEEAFcRalm3oQQJQM2VDjIxBALKcIv+SfD0AcDaoeHP4OQPg2xnzzYg5AlgZ7bu3ODUCk2O39h0INQDUijUk8vgxAGCq+Jn5CDECHlTjIu88LQPwFT2hdZgtA/V9h98QGC0CSgbLOTbEKQLY12GdMZgpAUxL5GA4mCkCzqQjW2PAJQCMSL/fqxglAY0CFBHuoCUADDkyHt5UJQNwGwODGjglAUEKoJseTCUAZoboFzqQJQP7J66nowQlAcjC9rBvrCUBYVJcJYyAKQCRDOhiyYQpAUTRKjfOuCkC75/mACQgLQEM30HvNbAtA5RiCiRDdC0CgHdZRm1gMQNxPgjcu3wxAaDPxfIFwDUACldZuRQwOQHDSeJQisg5AmlCP5blhD0BCe8mCUg0QQFjeLEI7bhBA8FjpEV3TEECkIlqRejwRQICZ9UVTqRFAxo5KxaMZEkCPWCfhJY0SQOURzdWQAxNARgIQepl8E0DEyURx8vcTQFSo2F5MdRRAlwdyG1b0FEAaVnXrvHQVQNFCybYs9hVAzIC0QVB4FkD6bK5m0foWQIw8+1BZfRdAeMHrt5D/F0A8VJgaIIEYQKAP7vuvARlA2EPlHumAGUAY27fCdP4ZQJpa7t78eRpARzcaXyzzGkBTXhRer2kbQKYWl2Az3RtABcAKj2dNHEBQcV3u/LkcQAQOvpemIh1ARh0U7xmHHUCAeQ7YDucdQGzVo+k/Qh5ApRPhn2qYHkDnheKLT+keQH5Z1oGyNB9ACL7nxFp6H0BmsfMwE7ofQA7P6WGq8x9AXoRebHkTIEDY5OSO4SkgQKpZTPD6PCBAnnBzCrVMIECsOG+GAVkgQLL1NUjUYSBAOIfPeCNnIECvwQOP52ggQGT7f1YbZyBAxRRu9bthIEB4TnjwyFggQP5NNi1ETCBAm8IA8zE8IECKLyrqmCggQOZ0nBmCESBA+Yu3xfHtH0CsoeH4FbIfQPjdedWQbx9A95R47oYmH0DF5HVMIdceQNzPBVaNgR5ARm4UtfwlHkCyZlI5pcQdQFHhw7fAXR1AlxuG54zxHEAzreE7S4AcQIRowbtAChxAk4yn1rWPG0A7uTw39hAbQLi+lZNQjhpARvhOexYIGkDQY50jnH4ZQAQVdzE48hhAKPr1gUNjGEAUKRnxGNIXQMgSCh8VPxdABw0MNJaqFkAZlz2j+xQWQCmcU+ylfhVAGbh3XPbnFECwKXPOTlEUQAeqUGoRuxNAJ8+fZKAlE0Dq84S9XZESQNrVwP+q/hFAETPb/+htEUDUr5ubd98QQOor/Hm1UxBAGuV+l/+VD0AOrq0ZZIsOQOK0gY1LiA1AisgUqGSNDEBG1TZKWZsLQKc57gzOsgpApX0t0WHUCUAGzApTrQAJQMtbw8BCOAhAEpbSVa17B0AcL2L6cMsGQAWnVecJKAZAx7YwT+yRBUBFERUMhAkFQGucEFM0jwRAntnxbFcjBEA8o9R0PsYDQGuxlRwxeANALIRXd205A0ATXj/KJwoDQIvujGOK6gJACyQreLXaAkBRY9QHv9oCQOsR4Mey6gJAJAPLFJIKA0A76ofqUzoDQO99oOPkeQNA9norPyfJA0AJMJft8icEQOa5Q6QVlgRAbJLj91ITBUAal5R9ZJ8FQH00n/L5OQZAo/bEarniBkD+XgOFP5kHQFaRq6YfXQhA3CurPOQtCUAqed4CDwsKQFUcQFEZ9ApAimTGbnToC0CEorrpiecMQD4kU/W78A1AjuxUzGUDD0DXZz8Mbg8QQAktPq82oRBATucNN7E2EUBJzT9rf88RQCZvAK9AaxJArw27PpIJE0AGXUdvD6oTQLP2eO5RTBRAtMTmBPLvFEBJzr/YhpQVQJYMgrGmORZAeURnPOfeFkAsWFvR3YMXQP4VT7gfKBhA1Te6bkLLGECmFR/t22wZQDR3YuyCDBpApPrJKs+pGkDmrHOwWUQbQFG1GRO92xtAZFj1uJVvHECFFpUagv8cQFtIegMjix1A2FRT0RsSHkB2dKixEpQeQOjm0d2wEB9AqI8P1aKHH0D6FZuUmPgfQN5zx+aiMSBAge0+DLFjIEDkTkyTVJIgQN3XH3htvSBAsCFx8N3kIEDoDQaAigghQPcjwwtaKCFAVD046zVEIUBrWJ34CVwhQFRrNJ/EbyFAqhcG6FZ/IUBNMPCEtIohQLYX/tnTkSFAsRgEBa6UIUDn+3fjPpMhQNZAghaFjSFA9ohEBYKDIUBI7lLdOXUhQB4rX5GzYiFAlaYV1vhLIUA7py0dFjEhQDMbr44aEiFAepFxABjvIECZKtnrIsggQBJv12FSnSBACB43/b9uIEAKJzvThzwgQCwdmmLIBiBA9Q3BAUWbH0DS/IqMciIfQO02/O9jox5A60k64mceHkBM9RoU0ZMdQKA0bf31Ax1AMQ63pjBvHEADaYpw3tUbQFDNlNhfOBtAt3uPPBiXGkButzabbfIZQKN4cFPIShlAgfrL4ZKgGEBGu4ScOfQXQPCNNG4qRhdAG0lhj9SWFkBgbRM/qOYVQPDQo3oWNhVALfHutJCFFEABAh2NiNUTQE8qLoVvJhNAp5R6uLZ4EkCrFlaSzswRQLkqB4UmIxFA0NNAwSx8EECggZ3cm7APQLY9Rcbpbw5A6RviwBQ3DUBeDRiY6QYMQI2KT9sv4ApAYuNsWqnDCUDXhwumEbIIQFGfkZMdrAdAiuBtxXqyBkAuB883z8UFQDF2IdK45gRALJec/cwVBEAoWCZBmFMDQFrIz+KdoAJA10oqj1f9AUD2HrEGNWoBQBQgf9Gb5wBAUZuE+eZ1AEB38WzLZhUAQOrqvjzBjP8/fiOFRR0S/z86f0dwP7v+PyKGxJRuiP4/7ONrbNp5/j9+Gk1qm4/+P+jMBqKyyf4/zGLMvQko/z/6AI4Dc6r/P8yBorRUKABA6qkuXCiNAED3DxXgegMBQIpZpUsHiwFAnDftdX0jAkCkoh4xgswCQHg9KIGvhQNAdhNf2ZROBEC0QxRhtyYFQKWh7D6SDQZAm+PL6pYCB0AxoiCGLQUIQFI6Wjq1FAlAMo9NnYQwCkBovkkb6lcLQEsTmmYsigxAFeou7IrGDUDjxyNNPgwPQADVam48LRBAAsGckTPYEEBR/I8xmIYRQCV0CJL7NxJAIkqz3+zrEkDRMid4+aETQLx9KTOtWRRAaswIrZISFUBrut2RM8wVQG4TgekYhhZA6rAGZMs/F0BHsYum0/gXQAF2Jpi6sBhAPbDGrglnGUDgvNM7SxsaQNCtV7gKzRpAA5eFENV7G0BDEmruOCccQBBnlQPHzhxA/F6PURJyHUC4h+VwsBAeQIx+pdY5qh5ABNYVGEo+H0CdPIEsgMwfQHBedFY/KiBASJ65CPZqIEDr5Th2OaggQADsaBHh4SBAEPrrkMYXIUBA0P8IxkkhQIIjbwO+dyFAUsT0lY+hIUApeAF3HschQCqX1hBR6CFAeo/okhAFIkB2iH0BSR0iQAiBfUPpMCJA0mtqLuM/IkDA+neQK0oiQIH/uzi6TyJAhX9x/YlQIkBY10nAmEwiQO2JyHDnQyJAUZunDHo2IkBkm0OeVyQiQM/KDzmKDSJAawoU9B7yIUBoinfiJdIhQCFyHAqyrSFAtvpRWNmEIUCYtKOUtFchQCbqzVFfJiFA2Ubg3PfwIEDXGZkqn7cgQFi0BMN4eiBAQoZtq6o5IEBYa1WduuofQLz/t8d2Wx9A/XRFp+PFHkCxlNE3XyoeQACyjmRLiR1AxPU/yg3jHECIqfd2DzgcQDQ+iqe8iBtA1FPggoTVGkDTclPT2B4aQPZ2Qr4tZRlA5eALevmoGEC3Vp0CtOoXQJSVyMzWKhdAq+2OeNxpFkBrG5WCQKgVQEbp8fR+5hRASn+KFxQlFEB6my8gfGQTQNAmr+IypRJAKa8NgbPnEUDAQhsceCwRQB3+loT5cxBAPpoo2l19D0Bx5ag5GxoOQGizjEcRvwxA4wG8HyFtC0BJCehHJiUKQFeuuSD25whAylbMWl+2B0CVzc9vKZEGQJ1LKSAUeQVArupo9dZuBEDZwuXJIHMDQLKyz1WXhgJAsmQCwtapAUAXgOFAcd0AQK8hgq3uIQBAxHbDYpjv/j/Em8zh9779P0pCv33Jsvw/wFF5rsPL+z+OVEdChQr7PzYDLvGUb/o/SCKX/2D7+T9276HwPq75PxpTTUhriPk/HuGnXQmK+T8gaic9I7P5P7h7P5upA/o/hLpD13N7+j9KgpgOQBr7Pwy1KUCz3/s/WiIVgFnL/D/Od2s7ptz9PypH4Iv0Ev8/Pa6bzcM2AED9thmLxfUAQEIQYNcJxgFAv7yD1g+nAkDykIzHS5gDQDo0YFsnmQRAdleOEgKpBUABfMOhMccGQNhNo1wC8wdAymHHprcrCUB9JZtqjHAKQBLzyZWzwAtAQJLwmlgbDUBW+T/4n38OQNzau8Kn7A9AkEHeGsSwEEA1WCwjqm4RQCy8LB2NLxJA8vYrIfLyEkBTDFGLXLgTQMN3QUtOfxRAprHDNEhHFUCokyxRyg8WQMFjYzFU2BZAVuBHQGWgF0D5WkUVfWcYQL7H3cYbLRlAc533PMLwGUAhf7mC8rEaQC3kvhcwcBtAR1JxQAArHECBP1JV6uEcQDNTAhF4lB1AdIHT3DVCHkDWYLQbs+oeQDIfRHOCjR9Av85vCR0VIEC6vzz8OWAgQKAtiBnnpyBATNpLkfXrIEDABJXaOCwhQNL1rdCGaCFAavnEzregIUBznv7JptQhQFcY4mkxBCJAn6gPHzgvIkBUDTI4nlUiQGgRHfVJdyJAaIUMmCSUIkDcGPh0GqwiQD7G8f4avyJAxsOF0xjNIkAcMhTECdYiQK8NHt3m2SJA2DeAa6zYIkDhwJj/WdIiQLLyU27yxiJAsPQf0Hu2IkCyOsd9/6AiQBNLMwuKhiJA1sAaQCtnIkBYxp4O9kIiQGCa3YcAGiJAzwKBzmPsIUAs3lAHPLohQMhN00eogyFAGDUFg8pIIUDlBjd0xwkhQDAOG4jGxiBAZowTxPF/IECzKtCrdTUgQJarlkoCzx9AIFVytoosH0DkPfU764MeQG4TUYaO1R1AjjGWB+MhHUD4R6ayWmkcQGA317JqrBtAFix0IYvrGkARY0y5NicaQPtSgIjqXxlAiR2/oCWWGEDiOCfGaMoXQHst/hw2/RZAiwx01hAvFkCl7afcfGAVQOpGI37+kRRALlcCGRrEE0DaGQDGU/cSQCpbnAMvLBJABHySYS5jEUAuTtcs05wQQBdKqDg6sw9A2ZAp/RM0DkB0zjrRKr0MQKbkOcxwTwtAg+leVtLrCUAeDcaPNZMIQGXBYbt5RgdAJsE1rnYGBkD75zhD/NMEQK/eONTRrwNAXX0XuLWaAkDzY7bGXJUBQJ2z4eJxoABAfhUOFSt5/z88ygPbutT9P5SvhBaoVPw/Gokdr/D5+j+SODnNesX5Pzo5b0EUuPg/EkVU+3HS9z88dyiQLxX3P/gatNHOgPY/EACadbcV9j9Up1vNNtT1P+DoQI9/vPU/8ORJsKnO9T9AI0dPsgr2P2jDKLF7cPY/oJOKTs3/9j+SznfyU7j3P24vVuqhmfg/YADdRi+j+T9QzfEtWtT6PwqBOT1nLPw/WOoh/YGq/T9ACB1kvU3/PwGB3jSKigBAxLaqVLV/AUARPd6JxoUCQD1l3lcZnANAHZ9ftP7BBECuW5V1vfYFQL9798aSOQdAHZJTpLKJCECYJtxaSOYJQF885A93TgtAvqzxTFrBDEAYZc2QBj4OQJ5SNeWJww9Ael1oPHaoEEDYd4KemHIRQDjfokOrPxJAlFW9WisPE0AA+4dqlOATQLVRJKZgsxRAn5myQgmHFUBru5jNBlsWQC97RYPRLhdA4FE4puEBGEDKFRbWr9MYQCmIkma1oxlA3O32tWxxGkDCEA6DUTwbQPNdPkLhAxxADVObcZvHHECmDLjrAYcdQLKJBTmZQR5AFR+I3+j2HkDMm7Cwe6YfQCVkkwrwJyBAdhkqK1R5IEDM079yNccgQLeZJVBhESFAdyA0iadXIUAWiyta2pkhQDJwhJPO1yFAcrgetlsRIkBf77sNXEYiQKy3s8msdiJALjLSEy6iIkBmU1Elw8giQPZX31lS6iJAoMWlQMUGI0AarkWrCB4jQEo1v7oMMCNAJrI76sQ8I0DoGrMXKEQjQKrMZoowRiNA1iEt99tCI0D0sIqCKzojQDx2l8AjLCNALpSvsswYI0AJwvDCMQAjQMXohr1h4iJAJ9jLx26/IkC3Yz9VbpciQEyaXhp5aiJA3ipi/ao4IkAyYu0EIwIiQOiCuEQDxyFAToVCyHCHIUBHlJd7k0MhQF7ZOhKW+yBAW15D7KWvIEBM97z58l8gQElFX5yvDCBA5vJbDyFsH0BIDS0/mbgeQGzXQKs5/x1ANGGMCXhAHUBAGxyYzXwcQPIR9M+2tBtAPS7QFbPoGkDC+vZoRBkaQM+1UhDvRhlARJYFRjlyGEDoMq/hqpsXQH3XmQHNwxZAxU8HsynrFUD8TtWZSxIVQGEIspe9ORRAKeYZcwpiE0A2dFd+vIsSQB+cvj5dtxFAKDhcFHXlEEA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= figure(title=\"my prediction of my ultimate function!\", tools=TOOLS)\n", "p1.line(data['xs'], y_pred, line_color=\"red\")\n", "p1.line(data['xs'], data['ys'], line_color=\"blue\")\n", "p1.width = 1200\n", "show(p1)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "history_of_training_numpy = {}\n", "history_of_training_numpy['x'] = x=xs.numpy()\n", "\n", "# for kee in history_of_training.keys():\n", "# history_of_training_numpy[kee] = history_of_training[kee].data.cpu().numpy().squeeze()\n", "\n", "for i in range(1,16):\n", " history_of_training_numpy[str(i)] = history_of_training[str(i*10000)].data.cpu().numpy().squeeze() \n", " \n", "master = ColumnDataSource(data=history_of_training_numpy)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<div class=\"bk-root\">\n", " <div class=\"bk-plotdiv\" id=\"2b1fbf59-9d5c-480a-b23f-9bd5b02000bc\"></div>\n", "</div>" ] }, 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var data1 = source1.data;\\n var data2 = source2.data;\\n for (var e in data1) delete data1[e]; //clears the dummy datasource\\n data1['x'] = data2['x'];\\n data1['y'] = data2['y'];\\n\\n source1.change.emit()\\n\"},\"id\":\"53df95e6-62f1-4f48-9bc8-63d1653f3ede\",\"type\":\"CustomJS\"},{\"attributes\":{},\"id\":\"e651fbd3-c5f7-4df4-b36d-2b9691ad9ca5\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x\",\"y\"],\"data\":{\"x\":{\"__ndarray__\":\"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var data = source.data;\\n var master = master.data;\\n var epoch = epoch_number.value;\\n\\n for (var e in data) delete data[e];\\n \\n data['x'] = master['x'];\\n data['y'] = master[epoch.toString()];\\n\\n 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5kD1SONAzGDfQIMo20Af8NZAwa7SQDJqzkBuJcpA8s7FQCFawUDMr7xAmAW4QFZbs0Dfsa5AKiyqQDimpUBRIKFAd5qcQJoUmEDCjpNA8giPQCCDikA4/YVAZJ2BQD0be0Cy+HNA3dlsQPG6ZUD/m15AIX1XQGWGUECbl0lAiahCQJK5O0CbyjRAu9stQK3sJkApVSNABcYiQEI3IkBFqCFAnBkhQOSKIED4+x9ADW0fQOffHkCD5h5A9REgQOA9IUCHaSJANZUjQNbAJEBS7CVA6fsoQP6bL0AhPDZAw+c8QDeaQ0DPTEpAFf9QQImxV0A3aF5AbVNlQCvqbEBcKHVAVvh9QCV9g0CaEYhAXrKMQBJTkUBE/ZVAh/6aQBUDoEAYCaVADCqqQPBKr0Dfa7RA0oy5QKStvkCZzsNAeO/IQFYQzkA6MdNAM1LYQP9y3UD2IuJAFV3mQDCX6kAk0e5APAvzQBs890DdBftAi87+QPgBAUEjbAJBS9YDQYJABUGnqgZByRQIQf9+CUEs6QpBVFMMQXvCDUHHRA9BtkEPQRvJDkGdUA5BEdgNQY1fDUH35gxBc24MQdn1C0FJfQtBzgQLQfBrCkEWwQlBhcEIQQFoB0H3+wVBwn0EQfnvAkEUKQFBZ8T+QFYI+0DjG/dAUy/zQMzX7kDiVepAf8HlQOv64EAc2dtAKLfWQBx20UAwLcxAG+TGQPmawUACUrxADAm3QPS/sUDidqxA5S2nQAnloUDzm5xAzFKXQMQJkkDTwIxA0neHQNQugkCPy3lAZTlvQI6nZED3dFlAG11QQPGASUCPpkJAzM87QH/4NEBnIS5AcUonQJ8YIUCdVBtAjY8VQEPLD0BaBgpA0EEEQHs7A0B6nwJAiQMCQLBnAUCIywBAry8AQEgn/z+M5f4/L38AQE16AkDR8QRAzVAIQL38C0AJ7BBAOFcWQLrCG0BsLiFAaSgnQNQhLkCOgTVADmU9QK9MRUBtgk5A2dVXQKeuYUDiwmtA/NZ1QNQAgEDPV4VA4q6KQDMakECio5VA+CybQFG2oEDEP6ZAIcmrQKJSsUD+27ZAb2W8QNDuwUAueMdAqwHNQAmL0kB0FNhA2p3dQDIn40C9sOhA2tvtQJzl8kAFb/dAbor7QI2g/0BM2wFBbeYDQXjxBUF+/AdBlwcKQaISDEG6HQ5BxigQQdczEkFIXhRBJr4UQettFEGfHRRBZs0TQSB9E0HiLBNBltwSQVmMEkEZPBJBy+sRQSySEUFKOBFBYKgQQZHBD0Eb1A5B5LYNQWFtDEG46wpB+04JQYV0B0H3mQVB5pcDQQp1AUGmpP5ArV76QPWZ9UDouPBA6EnrQBmu5UAIEuBAJ3baQCrR1EA3/c5A4SPJQE5Kw0AGcb1Aope3QBS+sUC95KtALgumQNMxoEBoQJpAdT6UQHA8jkBkOohAHDiCQFwZeEAocGtAbeBfQJFfV0ANzU5A1TpGQICoPUApFjVAuYMsQHXxI0A1XxtAvMwSQIE6CkCPvQRAfZYAQKze+D/1kPA/nULoP5T03z/q7tc/TgXUP7Z81D+k9NQ/R2zVPzdO1j8eL9g/sB/bP4o+3j85YuU/vQftP7+t9D8oD/8/5C4FQCLWCkBZNxFAGz0ZQNVCIUBwSClA04sxQHNCO0DBDEVADERPQL96WUB8smNAb+ltQH0geEC6SIFA0oaHQLjLjUCoEJRAclWaQDOaoEAb36ZAACStQIBTs0DHYrlA+3G/QO2AxUBYkMtAk5/RQKiu10DVvd1A+8zjQDjc6UBY6+9AQIv1QBJy+kAu9P5AI7sBQanlA0FI2gVBb/sHQdUaCkE8OQxBm1cOQQ12EEHmOxJB1AgTQb6UE0F2LRRBPcYUQfNeFUGp9xVBXJAWQQopF0HIwRdBlloYQTL0GEHqjRlBCRoZQV1yF0GtyhVB6iIUQUh7EkGQ0xBBzisPQTOEDUHi2QtBt+MJQYrtB0FZ9wVBhf4DQcjYAUHc8f5A+rH5QJQe9EB4i+5A3MDoQJq44kASsNxAqKfWQHaf0EAkl8pAjo7EQESGvkDbfbhAkHWyQBRtrEDSZKZARlygQMpTmkB0S5RAJkOOQN06iECCMoJAzHh4QD8cbkCav2NA4YVZQG9ST0AIH0VAJuw6QPC4MECBhSZAv1IcQOQeEkDD6wdAx3H7P58K5z8dpNI/JT6+Py/Isz+xKLY/d4m4P8Hpuj/NSb0/saq/P5MKwj+NasQ/ucrGP8EqyT9ri8s/gevNP61L0D9Z/9I/YdLYP0Va4j85pe4/oR/7Pwf+A0CtYAtAkpATQKRIHEAZDSVAgXYvQCdSOkCayUVAp6hRQKWHXUCtZmlAykV1QImSgEDdgYZAuXGMQCJhkkCoUJhAFECeQKcvpEBMH6pA3A6wQED+tUDA7btAct3BQCbNx0BuvM1AKqzTQJKb2UAQi99AnHrlQApq60ArWPFARjf3QAUW/UAJcwFB7FIEQekyB0HpCgpBrdsMQcmoD0HepRFB4RsTQbaRFEGgBxZB4XoXQbTgGEExRRpBkakbQegNHUEEWx5BavcdQcWTHUHrLx1BKcwcQXloHEHIBBxB+KAbQUM9G0Fc2RpBX+AZQROCGEHQIxdBj8UVQQrkE0EXYxFBu+EOQddgDEGk3wlBc14HQYHdBEFkXAJBQIX/QOPo+UBgy/NAIa7tQACQ50DecuFAmVXbQMo31UCtGs9A9vzIQHrfwkD8wbxAb6S2QPCGsEDYaapAT0ykQCcvnkAmEZhA4vORQHjWi0DUuIVAgzZ/QJb7ckDCwGZAuoRaQB/bTkBILkZAnMs9QHtoNUDCBS1AWaMkQD1BHEBf3hNAVXsLQFkZA0BfbPU/J6fkP4/h0z//HMM/A1eyP3WSoT8tzJA/jQiAPyKEXj/C+Tw/9m8bPw6KRD8e4m0/2Z2LP4NKoD9n97Q/uaPJP1FQ3j+3/PI/gNQDQPYqDkBJgRhAodciQAUuLUAH4jdA9a5CQIV7TUDlSFhAoxVjQBHjbUCqr3hAZr6BQNQkh0BQi4xAAvKRQIZYl0CvvpxATCWiQKmLp0Be8qxA5FiyQDC/t0DSJb1AHYzCQNzyx0CAWc1AvL/SQDom2ECJjN1AYPPiQKNZ6EBHwO1AoCbzQPuM+ECm8/1AF60BQUxgBEGHEwdBw8YJQRh6DEE5LQ9BgOARQdeTFEEzFBdBMkEZQYnOGkFvyhtBrZAcQQ9XHUFTHR5B1HweQbppHkE2Ix5BVNwdQcmVHUHEMh1BZEscQQpkG0HPfBpB+VoZQYizF0EVDBZBiGQUQfm8EkFpFRFB+/UOQd5/DEECCgpB7X4HQSCoBEF/0QFBpkr9QM7C9kB6OvBAOrPpQHoq40DtodxAgxrWQNiSz0CHCclA8oHCQCj6u0CNcbVAWumuQGxhqEA32aFAhVCbQHLIlEDGQI5ArLiHQBAwgUA1T3VANUBoQHkwW0CWH05AKw5BQDz+M0BW7iZAq90ZQPMyDkDnZgdATgwCQFqX+T9sE+8/UJHkP6EP2j96js8/jAvFP4uJuj9LB7A/MoWlPwsDmz9HgZA//f6FP7r6dj8k9mE/gPBMP27sNz9e6SI/PuUNPx4jOj80W2k/bEmMP93koz8ngbs/Ix3TPya56j+OKgFAxfgMQOXGGECblCRAP2IwQKg2PEBNDUhA6+RTQPvAX0A7nWtAL3l3QNiqgUAGmYdAy4aNQGR1k0CXY5lAilGfQO8/pUDzLatA8xuxQC8Kt0A9+LxAcebCQL7UyECgws5AzLDUQPCe2kAsjeBAc3vmQHZp7ECKV/JAp0X4QAI0/kAIEQJBIQgFQSf/B0ES6wpBp2gNQTZXD0G2FBFBMtISQbWPFEFHTRZBtgoYQT3IGUG3hRtBT0MdQbUAH0FEviBBx3siQTQ5JEG+9iVBDeMkQQY9IkHjlh9B2fAcQflKGkHIpBdBwv4UQcFYEkF8sg9BpwwNQXlmCkGIwAdBjBoFQWN0AkHInP9Ay1D6QGIE9UCcuO9AbGzqQE8g5UBe1N9Ac4jaQEg81UBY8M9AmqPKQJ5XxUC+C8BAgL+6QHJztUAgJ7BAxtuqQC6PpUD2QqBAFPeaQPiqlUD1XpBA6hKLQN/GhUDzeoBARV12QIPFa0CALGFAs5RWQAX9S0AiZUFA08s2QKE0LEA5NCJA9hMZQMcDFEClCRJAow8QQPITDkBXGgxAch8KQEEkCEBQKgZAOTAEQPs1AkCAOwBAgoH8PxeO+D/amPQ/V6TwP6qt7D88uug/SMTkP0jQ4D/f2tw/y+XYP5Tw1D/8+tA/xwbNPz0SyT90HMU/eCjBPzwzvT9DP7k/SEu1P31WsT9/X60/yWupP7N2pT8DgaE/HYydP9aXmT+Vo5U/\",\"dtype\":\"float32\",\"shape\":[990]},\"7\":{\"__ndarray__\":\"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Application\",\"version\":\"0.12.13\"}};\n", " var render_items = [{\"docid\":\"0763782d-f4c2-4b1c-8dc0-675acd866c75\",\"elementid\":\"2b1fbf59-9d5c-480a-b23f-9bd5b02000bc\",\"modelid\":\"d8a31f66-8791-4cf7-afc8-ade91a1872ba\"}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " clearInterval(timer);\n", " }\n", " attempts++;\n", " if (attempts > 100) {\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", " clearInterval(timer);\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "d8a31f66-8791-4cf7-afc8-ade91a1872ba" } }, "output_type": "display_data" } ], "source": [ "plot = figure(title=\"my prediction of my ultimate function over time!!\", tools=TOOLS)\n", "plot.line('x', 'y', source=source_final, line_width=3, line_alpha=0.6)\n", "plot.width = 1200\n", "\n", "plot.line(data['xs'], data['ys'], line_color=\"gray\")\n", "\n", "callback = CustomJS(args={\n", " 'source': source_final, \n", " 'master' : master}, code=\"\"\"\n", " var data = source.data;\n", " var master = master.data;\n", " var epoch = epoch_number.value;\n", "\n", " for (var e in data) delete data[e];\n", " \n", " data['x'] = master['x'];\n", " data['y'] = master[epoch.toString()];\n", "\n", " source.change.emit()\n", "\"\"\")\n", "\n", "epoch_number = Slider(start=1, end=15, value=5, step=1,\n", " title=\"epoch_number\", callback=callback)\n", "callback.args[\"epoch_number\"] = epoch_number\n", "\n", "layout = column(\n", " plot,\n", " widgetbox(select, epoch_number),\n", ")\n", "show(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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
neilzim/SCDA	old_notebooks/scda_LDZ_test.ipynb	1	34980	{ "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": [ "import scda\n", "import os\n", "import shutil\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "%pylab inline --no-import-all\n", "matplotlib.rcParams['image.origin'] = 'lower'\n", "matplotlib.rcParams['image.interpolation'] = 'nearest'\n", "matplotlib.rcParams['image.cmap'] = 'gray'\n", "\n", "import logging\n", "scda.configure_log()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare two SCDA designs, with and without LS alignment tolerance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set the design parameters" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pupil_params = {'N': 125, 'prim':'hex3', 'centobs':True, 'secobs':'X', 'thick':'025'}\n", "fpm_params = {'rad': 4.}\n", "ls_params_aligntol = {'obscure':0, 'id':25, 'od':80, 'aligntol':5, 'aligntolcon':2.9}\n", "ls_params_noaligntol = {'obscure':0, 'id':25, 'od':80, 'aligntol':None}\n", "image_params = {'c': 10., 'ida':3.5, 'oda':8., 'bw':0.10, 'Nlam':3}" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "work_dir = \"/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test\" # where to write the AMPL source code\n", "if not os.path.exists(work_dir):\n", " os.mkdir(work_dir)\n", "input_dir = \"/astro/opticslab1/SCDA/Apertures/InputMasks\" # location of input TelAp, FPM, and LS arrays\n", "TelAp_dir = os.path.join(input_dir, \"TelAp\")\n", "FPM_dir = os.path.join(input_dir, \"FPM\")\n", "LS_dir = os.path.join(input_dir, \"LS\")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "design_params_aligntol = {'Pupil': pupil_params, 'FPM': fpm_params,\n", " 'LS': ls_params_aligntol, 'Image': image_params}\n", "design_params_noaligntol = {'Pupil': pupil_params, 'FPM': fpm_params,\n", " 'LS': ls_params_noaligntol, 'Image': image_params}\n", "fileorg = {'work dir': work_dir, 'TelAp dir': TelAp_dir,\n", " 'FPM dir': FPM_dir, 'LS dir': LS_dir}\n", "bar_solver = {'method': 'bar'}\n", "barhom_solver = {'method': 'barhom'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initiate the coronagraph objects" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hexap_coron_aligntol = scda.QuarterplaneAPLC(design=design_params_aligntol, fileorg=fileorg, solver=barhom_solver)\n", "hexap_coron_noaligntol = scda.QuarterplaneAPLC(design=design_params_noaligntol, fileorg=fileorg, solver=bar_solver)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show the file organization for both coronagraphs" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'FPM dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/FPM',\n", " 'FPM fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/FPM/FPM_quart_occspot_M050.dat',\n", " 'LDZ fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/LS/LDZ_quart_ann25D80_clear_Tol05_N0125.dat',\n", " 'LS dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/LS',\n", " 'LS fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/LS/LS_quart_ann25D80_clear_N0125.dat',\n", " 'TelAp dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/TelAp',\n", " 'TelAp fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/TelAp/TelAp_quart_hex3X025cobs1_N0125.dat',\n", " 'ampl src dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'ampl src fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.mod',\n", " 'eval dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'exec script dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'exec script fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.sh',\n", " 'job name': 'APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1',\n", " 'log dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'log fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.log',\n", " 'sol dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'sol fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/ApodSol_APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.dat',\n", " 'work dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test'}" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hexap_coron_aligntol.fileorg" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'FPM dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/FPM',\n", " 'FPM fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/FPM/FPM_quart_occspot_M050.dat',\n", " 'LS dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/LS',\n", " 'LS fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/LS/LS_quart_ann25D80_clear_N0125.dat',\n", " 'TelAp dir': '/astro/opticslab1/SCDA/Apertures/InputMasks/TelAp',\n", " 'TelAp fname': '/astro/opticslab1/SCDA/Apertures/InputMasks/TelAp/TelAp_quart_hex3X025cobs1_N0125.dat',\n", " 'ampl src dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'ampl src fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1.mod',\n", " 'eval dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'exec script dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'exec script fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1.sh',\n", " 'job name': 'APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1',\n", " 'log dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'log fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1.log',\n", " 'sol dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test',\n", " 'sol fname': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/ApodSol_APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1.dat',\n", " 'work dir': '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test'}" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hexap_coron_noaligntol.fileorg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the status of input files needed to run the AMPL program" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All the input files for AMPL are in place? False\n" ] } ], "source": [ "hexap_coron_aligntol.check_ampl_input_files()\n", "print(\"All the input files for AMPL are in place? {0:}\".format(hexap_coron_aligntol.ampl_infile_status))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All the input files for AMPL are in place? True\n" ] } ], "source": [ "hexap_coron_noaligntol.check_ampl_input_files()\n", "print(\"All the input files for AMPL are in place? {0:}\".format(hexap_coron_noaligntol.ampl_infile_status))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write the AMPL source file" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "hexap_coron_aligntol.write_ampl(override_infile_status=True, overwrite=True)\n", "hexap_coron_noaligntol.write_ampl(override_infile_status=True, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write serial bash execution script" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bash_fname = os.path.join(hexap_coron_aligntol.fileorg['ampl src dir'], 'run_LDZ_test.sh')\n", "bash_fobj = open(bash_fname, \"w\")\n", "bash_fobj.write(\"#! /bin/bash -x\\n\")\n", "bash_fobj.write(\"ampl {0:s}\\n\".format(hexap_coron_noaligntol.fileorg['ampl src fname']))\n", "bash_fobj.write(\"ampl {0:s}\\n\".format(hexap_coron_aligntol.fileorg['ampl src fname']))\n", "bash_fobj.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a bundled source + input file subdirectory for both designs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bundled_dir = \"/astro/opticslab1/SCDA/Scripts/AMPL/hex3_LStol25\"\n", "bundled_coron_list = scda.make_ampl_bundle([hexap_coron_noaligntol, hexap_coron_aligntol], bundled_dir)\n", "os.listdir(bundled_dir)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Evaluate basic coronagraph metrics" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/ApodSol_APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.dat'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-39-69cefdc1baa2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhexap_coron_noaligntol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_metrics\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/Users/ntz/SCDA/scda_pytools/scda.pyc\u001b[0m in \u001b[0;36mget_metrics\u001b[0;34m(self, fp2res, verbose)\u001b[0m\n\u001b[1;32m 1688\u001b[0m \u001b[0mLS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileorg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LS fname'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1689\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTelAp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[0;32m-> 1690\u001b[0;31m \u001b[0mA_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileorg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sol fname'\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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1691\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTelAp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1692\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_metrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'apod nb res ratio'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTelAp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/stsci/ssbdev/python/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 736\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'U'\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 738\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/ApodSol_APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clear_Img100C_35DA080_BW10Nlam03fpres2_linbarhompre1.dat'" ] } ], "source": [ "hexap_coron_noaligntol.get_metrics()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: '/astro/opticslab1/SCDA/Scripts/AMPL/LDZ_test/ApodSol_APLC_quart_hex3X025cobs1_N0125_FPM400M050_LSann25D80clearTol05s29_Img100C_35DA080_BW10Nlam03fpres2_linbarpre1.dat'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-35-d20c7db56fc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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np.loadtxt(hexap_coron_noaligntol.fileorg['FPM fname'])\n", "FPM = np.concatenate((np.concatenate((FPM_qp[::-1,::-1], FPM_qp[:,::-1]),axis=0),\n", " np.concatenate((FPM_qp[::-1,:], FPM_qp),axis=0)), axis=1)\n", "\n", "LS_qp = np.loadtxt(hexap_coron_noaligntol.fileorg['LS fname'])\n", "LS = np.concatenate((np.concatenate((LS_qp[::-1,::-1], LS_qp[:,::-1]),axis=0),\n", " np.concatenate((LS_qp[::-1,:], LS_qp),axis=0)), axis=1)\n", "\n", "LDZ_qp = np.loadtxt(hexap_coron_aligntol.fileorg['LDZ fname'])\n", "LDZ = np.concatenate((np.concatenate((LDZ_qp[::-1,::-1], LDZ_qp[:,::-1]),axis=0),\n", " np.concatenate((LDZ_qp[::-1,:], LDZ_qp),axis=0)), axis=1)\n", "\n", "An_col = np.loadtxt(hexap_coron_noaligntol.fileorg['sol fname'])[:,-1]\n", "An_qp = An_col.reshape(TelAp_qp.shape)\n", "An = np.concatenate((np.concatenate((An_qp[::-1,::-1], An_qp[:,::-1]),axis=0),\n", " np.concatenate((An_qp[::-1,:], An_qp),axis=0)), axis=1)\n", "#At_col = np.loadtxt(hexap_coron_aligntol.fileorg['sol fname'])[:,-1]\n", "At_qp = At_col.reshape(TelAp_qp.shape)\n", "#At = np.concatenate((np.concatenate((At_qp[::-1,::-1], At_qp[:,::-1]),axis=0),\n", "# np.concatenate((At_qp[::-1,:], At_qp),axis=0)), axis=1)\n", "\n", "plt.figure(figsize=(16,8))\n", "plt.subplot(121)\n", "plt.imshow(AnTelAp)\n", "plt.title('Apodizer, no tol.')\n", "plt.subplot(122)\n", "plt.imshow(AtTelAp)\n", "plt.title('Apodizer, with tol.')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Create translated Lyot stop, check against design tolerance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_shift = (0,1)\n", "max_shift_tol = LS.shape[0]float(hexap_coron_aligntol.design['LS']['aligntol'])/1000\n", "max_shift_tol_int = int(np.floor(max_shift_tol))\n", "print(\"The LDZ accomomdates a translation {0:.1f}% of D={1:d} pixels = {2:.2f} pixels, up to {3:d} whole pixels\".format(\n", " float(hexap_coron_aligntol.design['LS']['aligntol'])/10, LS.shape[0], max_shift_tol, max_shift_tol_int))\n", "print(\"Testing an (x,y) translation of {0:} pixels. Beyond the design tolerance? {1:}\".format(\n", " test_shift, test_shift[0]2 + test_shift[1]2 > max_shift_tol))\n", "LSe = np.roll(np.roll(LS, test_shift[0], axis=1), test_shift[1], axis=0)\n", "LS_err_mask = np.ceil(np.abs(LSe - LS)).astype(bool)\n", "\n", "print(\"LDZ encompasses the LS transmission error region? {0:}\".format(\n", " ~np.any(np.logical_and(LS_err_mask, ~LDZ.astype(bool)))))\n", "\n", "print(\"Total unconstrained \\\"leak\\\" area after translation = {0:d} pixels\".format(\n", " int(np.sum(np.logical_and(LS_err_mask, ~LDZ.astype(bool))))))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(16,6))\n", "plt.subplot(131)\n", "plt.imshow(LSe - LS)\n", "lims = plt.axis('off')\n", "t=plt.title('Change in Lyot stop transmission profile')\n", "plt.subplot(132)\n", "plt.imshow(~LDZ.astype(bool))\n", "lims = plt.axis('off')\n", "t=plt.title('Inverse of LDZ mask')\n", "plt.subplot(133)\n", "plt.imshow(np.logical_and(LS_err_mask, ~LDZ.astype(bool)))\n", "lims = plt.axis('off')\n", "t=plt.title('Lyot leak region (black is good)')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Define coordinates and dimensions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "D = 1.\n", "N = hexap_coron_aligntol.design['Pupil']['N']\n", "bw = hexap_coron_aligntol.design['Image']['bw']\n", "Nlambda = hexap_coron_aligntol.design['Image']['Nlam']\n", "M_fp1 = hexap_coron_aligntol.design['FPM']['M']\n", "fpm_rad = hexap_coron_aligntol.design['FPM']['rad']\n", "rho2 = hexap_coron_aligntol.design['Image']['oda'] + 1.\n", "fp2res = 8.\n", "M_fp2 = int(np.ceil(rho2fp2res))\n", "\n", "# pupil plane\n", "dx = (D/2)/N\n", "dy = dx\n", "xs = np.matrix(np.linspace(-N+0.5,N-0.5,2N)dx)\n", "ys = xs\n", "\n", "# FPM\n", "dmx = fpm_rad/M_fp1\n", "dmy = dmx\n", "mxs = np.matrix(np.linspace(-M_fp1+0.5,M_fp1-0.5,2M_fp1)dmx)\n", "mys = mxs\n", "\n", "# FP2\n", "dxi = 1/fp2res\n", "xis = np.matrix(np.linspace(-M_fp2+0.5,M_fp2-0.5,2M_fp2)dxi)\n", "etas = xis\n", "\n", "# wavelength ratios\n", "wrs = np.linspace(1.-bw/2, 1.+bw/2, Nlambda)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#wrs = [0.95, 1., 1.02]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Fourier propagation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_onax_aplc_psf(TelAp, A, FPM, LS, xs, dx, mxs, dmx, xis, dxi, wrs):\n", " intens_D_polychrom = []\n", " for wr in wrs:\n", " Psi_B = dxdx/wrnp.dot(np.dot(np.exp(-1j2np.pi/wrnp.dot(mxs.T, xs)), TelApA ),\n", " np.exp(-1j2np.pi/wrnp.dot(xs.T, mxs)))\n", " Psi_B_stop = np.multiply(Psi_B, FPM)\n", " Psi_C = ATelAp - dmxdmx/wrnp.dot(np.dot(np.exp(-1j2np.pi/wrnp.dot(xs.T, mxs)), Psi_B_stop),\n", " np.exp(-1j2np.pi/wrnp.dot(mxs.T, xs)))\n", " Psi_C_stop = np.multiply(Psi_C, LS)\n", " Psi_D = dxdx/wrnp.dot(np.dot(np.exp(-1j2np.pi/wrnp.dot(xis.T, xs)), Psi_C_stop),\n", " np.exp(-1j2np.pi/wrnp.dot(xs.T, xis)))\n", " Psi_D_0_peak = np.sum(ATelApLS)dxdx/wr\n", "\n", "# Psi_D_0 = dxdx/wrnp.dot(np.dot(np.exp(-1j2np.pi/wrnp.dot(xis.T, xs)), ATelApLS),\n", "# np.exp(-1j2np.pi/wrnp.dot(xs.T, xis)))\n", "# intens_D_0 = np.power(np.absolute(Psi_D_0), 2)\n", "# intens_D_0_peak = Psi_D_0_peak2\n", " intens_D_polychrom.append(np.power(np.absolute(Psi_D)/Psi_D_0_peak, 2))\n", " return intens_D_polychrom" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "intens_n_polychrom = get_onax_aplc_psf(TelAp, An, FPM, LS, xs, dx, mxs, dmx, xis, dxi, wrs)\n", "intens_ne_polychrom = get_onax_aplc_psf(TelAp, An, FPM, LSe, xs, dx, mxs, dmx, xis, dxi, wrs)\n", "intens_t_polychrom = get_onax_aplc_psf(TelAp, At, FPM, LS, xs, dx, mxs, dmx, xis, dxi, wrs)\n", "intens_te_polychrom = get_onax_aplc_psf(TelAp, At, FPM, LSe, xs, dx, mxs, dmx, xis, dxi, wrs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "plt.figure(figsize=(16,12))\n", "plt.subplot(221)\n", "plt.imshow(np.log10(intens_n_polychrom[2]), vmin=-11, vmax=-7, cmap='CMRmap')\n", "plt.colorbar()\n", "plt.title('On-axis PSF, design without LS alignment tolerance, perfect alignment')\n", "\n", "plt.subplot(222)\n", "plt.imshow(np.log10(intens_ne_polychrom[1]), cmap='CMRmap')\n", "plt.colorbar()\n", "plt.title('On-axis PSF, design without LS alignment tolerance, translated LS')\n", "\n", "plt.subplot(223)\n", "plt.imshow(np.log10(intens_t_polychrom[1]), vmin=-11, vmax=-7, cmap='CMRmap')\n", "plt.colorbar()\n", "plt.title('On-axis PSF, design with LS alignment tolerance, perfect alignment')\n", "\n", "plt.subplot(224)\n", "plt.imshow(np.log10(intens_te_polychrom[1]), vmin=-11, vmax=-7, cmap='CMRmap')\n", "plt.colorbar()\n", "plt.title('On-axis PSF, design with LS alignment tolerance, translated LS')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intensity curve" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_radial_intens(intens_polychrom, xis, seps, wrs):\n", " radial_intens_polychrom = np.zeros((len(wrs), len(seps)))\n", " XXs = np.asarray(np.dot(np.matrix(np.ones(xis.shape)).T, xis))\n", " YYs = np.asarray(np.dot(etas.T, np.matrix(np.ones(etas.shape))))\n", " RRs = np.sqrt(XXs2 + YYs**2)\n", "\n", " for si, sep in enumerate(seps):\n", " r_in = np.max([seps[0], sep-0.5])\n", " r_out = np.min([seps[-1], sep+0.5])\n", " meas_ann_mask = np.logical_and(np.greater_equal(RRs, r_in),\n", " np.less_equal(RRs, r_out))\n", " #meas_ann_ind = np.nonzero(meas_ann_mask)\n", " meas_ann_ind = np.nonzero(np.logical_and(np.greater_equal(RRs, r_in).ravel(),\n", " np.less_equal(RRs, r_out).ravel()))[0]\n", " for wi, wr in enumerate(wrs):\n", " radial_intens_polychrom[wi, si] = np.mean(np.ravel(intens_polychrom[wi])[meas_ann_ind])\n", " return radial_intens_polychrom" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rho0 = hexap_coron_aligntol.design['Image']['ida']\n", "rho1 = hexap_coron_aligntol.design['Image']['oda'] + 1\n", "seps = np.arange(rho0, rho1, 0.25)\n", "\n", "radial_intens_n_polychrom = get_radial_intens(intens_n_polychrom, xis, seps, wrs)\n", "radial_intens_ne_polychrom = get_radial_intens(intens_ne_polychrom, xis, seps, wrs)\n", "radial_intens_t_polychrom = get_radial_intens(intens_t_polychrom, xis, seps, wrs)\n", "radial_intens_te_polychrom = get_radial_intens(intens_te_polychrom, xis, seps, wrs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize=(16,12))\n", "\n", "plt.subplot(221)\n", "plt.plot(seps, np.log10(radial_intens_n_polychrom[0]))\n", "plt.plot(seps, np.log10(radial_intens_n_polychrom[1]))\n", "plt.plot(seps, np.log10(radial_intens_n_polychrom[2]))\n", "plt.legend(['blue','center','red'], loc='upper left')\n", "plt.xlabel('angular sep. (lambda_0/D)')\n", "plt.ylabel('log10(I/I0)')\n", "plt.title('On-axis PSF, design without LS alignment tolerance, perfect alignment')\n", "\n", "plt.subplot(222)\n", "plt.plot(seps, np.log10(radial_intens_ne_polychrom[0]))\n", "plt.plot(seps, np.log10(radial_intens_ne_polychrom[1]))\n", "plt.plot(seps, np.log10(radial_intens_ne_polychrom[2]))\n", "plt.legend(['blue','center','red'], loc='upper left')\n", "plt.xlabel('angular sep. (lambda_0/D)')\n", "plt.ylabel('log10(I/I0)')\n", "plt.title('On-axis PSF, design without LS alignment tolerance, translated LS')\n", "\n", "plt.subplot(223)\n", "plt.plot(seps, np.log10(radial_intens_t_polychrom[0]))\n", "plt.plot(seps, np.log10(radial_intens_t_polychrom[1]))\n", "plt.plot(seps, np.log10(radial_intens_t_polychrom[2]))\n", "plt.legend(['blue','center','red'], loc='upper left')\n", "plt.xlabel('angular sep. (lambda_0/D)')\n", "plt.ylabel('log10(I/I0)')\n", "plt.title('On-axis PSF, design with LS alignment tolerance, perfect alignment')\n", "\n", "plt.subplot(224)\n", "plt.plot(seps, np.log10(radial_intens_te_polychrom[0]))\n", "plt.plot(seps, np.log10(radial_intens_te_polychrom[1]))\n", "plt.plot(seps, np.log10(radial_intens_te_polychrom[2]))\n", "plt.legend(['blue','center','red'], loc='upper left')\n", "plt.xlabel('angular sep. (lambda_0/D)')\n", "plt.ylabel('log10(I/I0)')\n", "plt.title('On-axis PSF, design with LS alignment tolerance, translated LS')" ] }, { "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": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
BinRoot/TensorFlow-Book	ch12_rank/Concept01_ranknet.ipynb	1	23262	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ch `12`: Concept `01`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ranking by neural network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the relevant libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import random\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's fabricate some data. We'll call `get_data()` to generate two datasets: `data_a` and `data_b`.\n", "\n", "We'll use the convention that points in `data_a` are ranked lower than those in `data_b`. So we need to learn a ranking function (i.e. utility function) that scores points in `data_a` lower. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f12eb2e8860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_features = 2\n", "\n", "def get_data():\n", " data_a = np.random.rand(10, n_features) + 1\n", " data_b = np.random.rand(10, n_features)\n", " \n", " plt.scatter(data_a[:, 0], data_a[:, 1], c='r', marker='x')\n", " plt.scatter(data_b[:, 0], data_b[:, 1], c='g', marker='o')\n", " plt.show()\n", " \n", " return data_a, data_b\n", "\n", "def get_data2():\n", " data_a = np.asarray([[0.1, 0.9], [0.1, 0.8]])\n", " data_b = np.asarray([[0.4,0.05], [0.45, 0.1]])\n", " \n", " plt.scatter(data_a[:, 0], data_a[:, 1], c='r', marker='x')\n", " plt.scatter(data_b[:, 0], data_b[:, 1], c='g', marker='o')\n", " plt.xlim([0, 0.5])\n", " plt.ylim([0, 1])\n", " plt.axes().set_aspect('equal')\n", " plt.show()\n", " \n", " \n", " return data_a, data_b\n", "\n", "data_a, data_b = get_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's define our ranking model. It'll take in two items (`x1` and `x2`), and return a score (`s1` and `s2`) for each item. \n", "\n", "Our model introduces a hyper-parameter called `n_hidden` to tweak the number of neurons in the hidden layer of the network." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_hidden = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When defining the model, let's organize it into separate scopes. That way, the TensorBoard visualization will look very clean." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.name_scope(\"input\"):\n", " x1 = tf.placeholder(tf.float32, [None, n_features], name=\"x1\")\n", " x2 = tf.placeholder(tf.float32, [None, n_features], name=\"x2\")\n", " dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_prob')\n", "\n", "\n", "with tf.name_scope(\"hidden_layer\"):\n", " with tf.name_scope(\"weights\"):\n", " w1 = tf.Variable(tf.random_normal([n_features, n_hidden]), name=\"w1\")\n", " tf.summary.histogram(\"w1\", w1)\n", " b1 = tf.Variable(tf.random_normal([n_hidden]), name=\"b1\")\n", " tf.summary.histogram(\"b1\", b1)\n", " \n", " with tf.name_scope(\"output\"):\n", " h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x1,w1) + b1), keep_prob=dropout_keep_prob)\n", " tf.summary.histogram(\"h1\", h1)\n", " h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(x2, w1) + b1), keep_prob=dropout_keep_prob)\n", " tf.summary.histogram(\"h2\", h2)\n", " \n", "\n", "with tf.name_scope(\"output_layer\"):\n", " with tf.name_scope(\"weights\"):\n", " w2 = tf.Variable(tf.random_normal([n_hidden, 1]), name=\"w2\")\n", " tf.summary.histogram(\"w2\", w2)\n", " b2 = tf.Variable(tf.random_normal([1]), name=\"b2\")\n", " tf.summary.histogram(\"b2\", b2)\n", " \n", " with tf.name_scope(\"output\"):\n", " s1 = tf.matmul(h1, w2) + b2\n", " s2 = tf.matmul(h2, w2) + b2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The loss function will involve comparing `s1` and `s2`. \n", "\n", "Since we're trying to acheive the inequality `Score(x1) < Score(x2)`, we need the loss function to insinuate `s1 < s2`. \n", "\n", "In other words, the loss function tries to guarantee that `s1 - s2 < 0`. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.name_scope(\"loss\"):\n", " s12 = s1 - s2\n", " s12_flat = tf.reshape(s12, [-1])\n", " \n", " pred = tf.sigmoid(s12)\n", " lable_p = tf.sigmoid(-tf.ones_like(s12))\n", " \n", " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=tf.zeros_like(s12_flat), logits=s12_flat + 1)\n", " \n", " loss = tf.reduce_mean(cross_entropy)\n", " tf.summary.scalar(\"loss\", loss)\n", " \n", "with tf.name_scope(\"train_op\"):\n", " train_op = tf.train.AdamOptimizer(0.001).minimize(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start the session and prepare peripheral ops." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.InteractiveSession()\n", "summary_op = tf.summary.merge_all()\n", "writer = tf.summary.FileWriter(\"tb_files\", sess.graph)\n", "init = tf.global_variables_initializer()\n", "sess.run(init)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the model with the training data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "for epoch in range(0, 10000):\n", " loss_val, _ = sess.run([loss, train_op], feed_dict={x1:data_a, x2:data_b, dropout_keep_prob:0.5})\n", " if epoch % 100 == 0 :\n", " summary_result = sess.run(summary_op, feed_dict={x1:data_a, x2:data_b, dropout_keep_prob:1})\n", " writer.add_summary(summary_result, epoch)\n", "# print(\"Epoch {}: Loss {}\".format(epoch, loss_val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the results on a grid by accumulating a list of points to test." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grid_size = 10\n", "data_test = []\n", "for y in np.linspace(0., 1., num=grid_size):\n", " for x in np.linspace(0., 1., num=grid_size):\n", " data_test.append([x, y])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the model on all the test points and visualize the utility scores of each point by a color." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "def visualize_results(data_test):\n", " plt.figure()\n", " scores_test = sess.run(s1, feed_dict={x1:data_test, dropout_keep_prob:1})\n", " scores_img = np.reshape(scores_test, [grid_size, grid_size])\n", " plt.imshow(scores_img, origin='lower')\n", " plt.colorbar()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f12e8deadd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualize_results(data_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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
google/eng-edu	ml/cc/exercises/validation_and_test_sets.ipynb	1	23851	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Validation and Test Sets.ipynb", "provenance": [], "private_outputs": true, "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "hMqWDc_m6rUC", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Copyright 2020 Google LLC. Double-click here for license information.\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", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "4f3CKqFUqL2-", "colab_type": "text" }, "source": [ "# Validation Sets and Test Sets\n", "\n", "The previous Colab exercises evaluated the trained model against the training set, which does not provide a strong signal about the quality of your model. In this Colab, you'll experiment with validation sets and test sets.\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3spZH_kNkWWX", "colab_type": "text" }, "source": [ "## Learning objectives\n", "\n", "After doing this Colab, you'll know how to do the following:\n", "\n", " * Split a [training set](https://developers.google.com/machine-learning/glossary/#training_set) into a smaller training set and a [validation set](https://developers.google.com/machine-learning/glossary/#validation_set).\n", " * Analyze deltas between training set and validation set results.\n", " * Test the trained model with a [test set](https://developers.google.com/machine-learning/glossary/#test_set) to determine whether your trained model is [overfitting](https://developers.google.com/machine-learning/glossary/#overfitting).\n", " * Detect and fix a common training problem." ] }, { "cell_type": "markdown", "metadata": { "id": "gV82DJO3kWpk", "colab_type": "text" }, "source": [ "## The dataset\n", "\n", "As in the previous exercise, this exercise uses the [California Housing dataset](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) to predict the `median_house_value` at the city block level. Like many \"famous\" datasets, the California Housing Dataset actually consists of two separate datasets, each living in separate .csv files:\n", "\n", "* The training set is in `california_housing_train.csv`.\n", "* The test set is in `california_housing_test.csv`.\n", "\n", "You'll create the validation set by dividing the downloaded training set into two parts:\n", "\n", "* a smaller training set \n", "* a validation set" ] }, { "cell_type": "markdown", "metadata": { "id": "u84mXopntPFZ", "colab_type": "text" }, "source": [ "## Use the right version of TensorFlow\n", "\n", "The following hidden code cell ensures that the Colab will run on TensorFlow 2.X." ] }, { "cell_type": "code", "metadata": { "id": "FBhNIdUatOU6", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Run on TensorFlow 2.x\n", "%tensorflow_version 2.x" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "S8gm6BpqRRuh", "colab_type": "text" }, "source": [ "## Import relevant modules\n", "\n", "As before, this first code cell imports the necessary modules and sets a few display options." ] }, { "cell_type": "code", "metadata": { "id": "9D8GgUovHbG0", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Import modules\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "from matplotlib import pyplot as plt\n", "\n", "pd.options.display.max_rows = 10\n", "pd.options.display.float_format = \"{:.1f}\".format" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xjvrrClQeAJu", "colab_type": "text" }, "source": [ "## Load the datasets from the internet\n", "\n", "The following code cell loads the separate .csv files and creates the following two pandas DataFrames:\n", "\n", "* `train_df`, which contains the training set.\n", "* `test_df`, which contains the test set.\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "zUnTc_wfd_o3", "colab_type": "code", "colab": {} }, "source": [ "train_df = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\")\n", "test_df = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "P_KBdj2M_yjM", "colab_type": "text" }, "source": [ "## Scale the label values\n", "\n", "The following code cell scales the `median_house_value`. \n", "See the previous Colab exercise for details." ] }, { "cell_type": "code", "metadata": { "id": "3hc7QQhaAFXD", "colab_type": "code", "colab": {} }, "source": [ "scale_factor = 1000.0\n", "\n", "# Scale the training set's label.\n", "train_df[\"median_house_value\"] /= scale_factor \n", "\n", "# Scale the test set's label\n", "test_df[\"median_house_value\"] /= scale_factor" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FhessIIV8VPc", "colab_type": "text" }, "source": [ "## Load the functions that build and train a model\n", "\n", "The following code cell defines two functions:\n", "\n", " * `build_model`, which defines the model's topography.\n", " * `train_model`, which will ultimately train the model, outputting not only the loss value for the training set but also the loss value for the validation set. \n", "\n", "Since you don't need to understand model building code right now, we've hidden this code cell. As always, you must run hidden code cells." ] }, { "cell_type": "code", "metadata": { "id": "bvonhK857msj", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Define the functions that build and train a model\n", "def build_model(my_learning_rate):\n", " \"\"\"Create and compile a simple linear regression model.\"\"\"\n", " # Most simple tf.keras models are sequential.\n", " model = tf.keras.models.Sequential()\n", "\n", " # Add one linear layer to the model to yield a simple linear regressor.\n", " model.add(tf.keras.layers.Dense(units=1, input_shape=(1,)))\n", "\n", " # Compile the model topography into code that TensorFlow can efficiently\n", " # execute. Configure training to minimize the model's mean squared error. \n", " model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),\n", " loss=\"mean_squared_error\",\n", " metrics=[tf.keras.metrics.RootMeanSquaredError()])\n", "\n", " return model \n", "\n", "\n", "def train_model(model, df, feature, label, my_epochs, \n", " my_batch_size=None, my_validation_split=0.1):\n", " \"\"\"Feed a dataset into the model in order to train it.\"\"\"\n", "\n", " history = model.fit(x=df[feature],\n", " y=df[label],\n", " batch_size=my_batch_size,\n", " epochs=my_epochs,\n", " validation_split=my_validation_split)\n", "\n", " # Gather the model's trained weight and bias.\n", " trained_weight = model.get_weights()[0]\n", " trained_bias = model.get_weights()[1]\n", "\n", " # The list of epochs is stored separately from the \n", " # rest of history.\n", " epochs = history.epoch\n", " \n", " # Isolate the root mean squared error for each epoch.\n", " hist = pd.DataFrame(history.history)\n", " rmse = hist[\"root_mean_squared_error\"]\n", "\n", " return epochs, rmse, history.history \n", "\n", "print(\"Defined the build_model and train_model functions.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8gRu4Ri0D8tH", "colab_type": "text" }, "source": [ "## Define plotting functions\n", "\n", "The `plot_the_loss_curve` function plots loss vs. epochs for both the training set and the validation set." ] }, { "cell_type": "code", "metadata": { "id": "QA7hsqPZDvVM", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Define the plotting function\n", "\n", "def plot_the_loss_curve(epochs, mae_training, mae_validation):\n", " \"\"\"Plot a curve of loss vs. epoch.\"\"\"\n", "\n", " plt.figure()\n", " plt.xlabel(\"Epoch\")\n", " plt.ylabel(\"Root Mean Squared Error\")\n", "\n", " plt.plot(epochs[1:], mae_training[1:], label=\"Training Loss\")\n", " plt.plot(epochs[1:], mae_validation[1:], label=\"Validation Loss\")\n", " plt.legend()\n", " \n", " # We're not going to plot the first epoch, since the loss on the first epoch\n", " # is often substantially greater than the loss for other epochs.\n", " merged_mae_lists = mae_training[1:] + mae_validation[1:]\n", " highest_loss = max(merged_mae_lists)\n", " lowest_loss = min(merged_mae_lists)\n", " delta = highest_loss - lowest_loss\n", " print(delta)\n", "\n", " top_of_y_axis = highest_loss + (delta * 0.05)\n", " bottom_of_y_axis = lowest_loss - (delta * 0.05)\n", " \n", " plt.ylim([bottom_of_y_axis, top_of_y_axis])\n", " plt.show() \n", "\n", "print(\"Defined the plot_the_loss_curve function.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "jipBqEQXlsN8", "colab_type": "text" }, "source": [ "## Task 1: Experiment with the validation split\n", "\n", "In the following code cell, you'll see a variable named `validation_split`, which we've initialized at 0.2. The `validation_split` variable specifies the proportion of the original training set that will serve as the validation set. The original training set contains 17,000 examples. Therefore, a `validation_split` of 0.2 means that:\n", "\n", "* 17,000 * 0.2 ~= 3,400 examples will become the validation set.\n", "* 17,000 * 0.8 ~= 13,600 examples will become the new training set.\n", "\n", "The following code builds a model, trains it on the training set, and evaluates the built model on both:\n", "\n", "* The training set.\n", "* And the validation set.\n", "\n", "If the data in the training set is similar to the data in the validation set, then the two loss curves and the final loss values should be almost identical. However, the loss curves and final loss values are not almost identical. Hmm, that's odd. \n", "\n", "Experiment with two or three different values of `validation_split`. Do different values of `validation_split` fix the problem? \n" ] }, { "cell_type": "code", "metadata": { "id": "knP23Taoa00a", "colab_type": "code", "colab": {} }, "source": [ "# The following variables are the hyperparameters.\n", "learning_rate = 0.08\n", "epochs = 30\n", "batch_size = 100\n", "\n", "# Split the original training set into a reduced training set and a\n", "# validation set. \n", "validation_split = 0.2\n", "\n", "# Identify the feature and the label.\n", "my_feature = \"median_income\" # the median income on a specific city block.\n", "my_label = \"median_house_value\" # the median house value on a specific city block.\n", "# That is, you're going to create a model that predicts house value based \n", "# solely on the neighborhood's median income. \n", "\n", "# Invoke the functions to build and train the model.\n", "my_model = build_model(learning_rate)\n", "epochs, rmse, history = train_model(my_model, train_df, my_feature, \n", " my_label, epochs, batch_size, \n", " validation_split)\n", "\n", "plot_the_loss_curve(epochs, history[\"root_mean_squared_error\"], \n", " history[\"val_root_mean_squared_error\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TKa11JK4Pm3f", "colab_type": "text" }, "source": [ "## Task 2: Determine why the loss curves differ\n", "\n", "No matter how you split the training set and the validation set, the loss curves differ significantly. Evidently, the data in the training set isn't similar enough to the data in the validation set. Counterintuitive? Yes, but this problem is actually pretty common in machine learning. \n", "\n", "Your task is to determine why the loss curves aren't highly similar. As with most issues in machine learning, the problem is rooted in the data itself. To solve this mystery of why the training set and validation set aren't almost identical, write a line or two of [pandas code](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb?utm_source=validation-colab&utm_medium=colab&utm_campaign=colab-external&utm_content=pandas_tf2-colab&hl=en) in the following code cell. Here are a couple of hints:\n", "\n", " * The previous code cell split the original training set into:\n", " * a reduced training set (the original training set - the validation set)\n", " * the validation set \n", " * By default, the pandas [`head`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) method outputs the first 5 rows of the DataFrame. To see more of the training set, specify the `n` argument to `head` and assign a large positive integer to `n`." ] }, { "cell_type": "code", "metadata": { "id": "VJQcAZkwJt_p", "colab_type": "code", "colab": {} }, "source": [ "# Write some code in this code cell." ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "EnNvkFwwK8WY", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click for a possible solution to Task 2.\n", "\n", "# Examine examples 0 through 4 and examples 25 through 29\n", "# of the training set\n", "train_df.head(n=1000)\n", "\n", "# The original training set is sorted by longitude. \n", "# Apparently, longitude influences the relationship of\n", "# total_rooms to median_house_value." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "rw4xI1ZEckI8", "colab_type": "text" }, "source": [ "## Task 3. Fix the problem\n", "\n", "To fix the problem, shuffle the examples in the training set before splitting the examples into a training set and validation set. To do so, take the following steps:\n", "\n", "1. Shuffle the data in the training set by adding the following line anywhere before you call `train_model` (in the code cell associated with Task 1):\n", "\n", "```\n", " shuffled_train_df = train_df.reindex(np.random.permutation(train_df.index))\n", "``` \n", "\n", "2. Pass `shuffled_train_df` (instead of `train_df`) as the second argument to `train_model` (in the code call associated with Task 1) so that the call becomes as follows:\n", "\n", "```\n", " epochs, rmse, history = train_model(my_model, shuffled_train_df, my_feature, \n", " my_label, epochs, batch_size, \n", " validation_split)\n", "```" ] }, { "cell_type": "code", "metadata": { "id": "ncODhpv0h-LG", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click to view the complete implementation.\n", "\n", "# The following variables are the hyperparameters.\n", "learning_rate = 0.08\n", "epochs = 70\n", "batch_size = 100\n", "\n", "# Split the original training set into a reduced training set and a\n", "# validation set. \n", "validation_split = 0.2\n", "\n", "# Identify the feature and the label.\n", "my_feature = \"median_income\" # the median income on a specific city block.\n", "my_label = \"median_house_value\" # the median house value on a specific city block.\n", "# That is, you're going to create a model that predicts house value based \n", "# solely on the neighborhood's median income. \n", "\n", "# Shuffle the examples.\n", "shuffled_train_df = train_df.reindex(np.random.permutation(train_df.index)) \n", "\n", "# Invoke the functions to build and train the model. Train on the shuffled\n", "# training set.\n", "my_model = build_model(learning_rate)\n", "epochs, rmse, history = train_model(my_model, shuffled_train_df, my_feature, \n", " my_label, epochs, batch_size, \n", " validation_split)\n", "\n", "plot_the_loss_curve(epochs, history[\"root_mean_squared_error\"], \n", " history[\"val_root_mean_squared_error\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tKN239_miW8C", "colab_type": "text" }, "source": [ "Experiment with `validation_split` to answer the following questions:\n", "\n", "* With the training set shuffled, is the final loss for the training set closer to the final loss for the validation set? \n", "* At what range of values of `validation_split` do the final loss values for the training set and validation set diverge meaningfully? Why?" ] }, { "cell_type": "code", "metadata": { "id": "-UAJ3Q86iz31", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click for the answers to the questions\n", "\n", "# Yes, after shuffling the original training set, \n", "# the final loss for the training set and the \n", "# validation set become much closer.\n", "\n", "# If validation_split < 0.15,\n", "# the final loss values for the training set and\n", "# validation set diverge meaningfully. Apparently,\n", "# the validation set no longer contains enough examples. " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1PP-O8TOZOeo", "colab_type": "text" }, "source": [ "## Task 4: Use the Test Dataset to Evaluate Your Model's Performance\n", "\n", "The test set usually acts as the ultimate judge of a model's quality. The test set can serve as an impartial judge because its examples haven't been used in training the model. Run the following code cell to evaluate the model with the test set:" ] }, { "cell_type": "code", "metadata": { "id": "nd_Sw2cygOip", "colab_type": "code", "colab": {} }, "source": [ "x_test = test_df[my_feature]\n", "y_test = test_df[my_label]\n", "\n", "results = my_model.evaluate(x_test, y_test, batch_size=batch_size)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qoyQKvsjmV_A", "colab_type": "text" }, "source": [ "Compare the root mean squared error of the model when evaluated on each of the three datasets:\n", "\n", "* training set: look for `root_mean_squared_error` in the final training epoch.\n", "* validation set: look for `val_root_mean_squared_error` in the final training epoch.\n", "* test set: run the preceding code cell and examine the `root_mean_squared_error`.\n", "\n", "Ideally, the root mean squared error of all three sets should be similar. Are they?" ] }, { "cell_type": "code", "metadata": { "id": "FxXtp-aVdIgJ", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click for an answer\n", "\n", "# In our experiments, yes, the rmse values \n", "# were similar enough. " ], "execution_count": 0, "outputs": [] } ] }	apache-2.0
anipchile/conicyt	fondecyt/Analisis Fondecyt.ipynb	1	229567	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "params = {'legend.fontsize': 'x-large',\n", " 'figure.figsize': (15, 10),\n", " 'axes.labelsize': 'x-large',\n", " 'axes.titlesize':'x-large',\n", " 'xtick.labelsize':'x-large',\n", " 'ytick.labelsize':'x-large',\n", " 'axes.titlepad': 20,\n", " 'axes.titlesize': 24,\n", " 'axes.labelpad': 20,\n", " 'axes.labelsize': 20,\n", " 'lines.linewidth': 3,\n", " 'lines.markersize': 10,\n", " 'xtick.labelsize': 16,\n", " 'ytick.labelsize': 16}\n", "plt.rcParams.update(params)\n", "sns.color_palette(\"bright\")\n", "\n", "pd.set_option('display.max_rows', 500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fondecyt Postdoctorado" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>año</th>\n", " <th>n_concursados</th>\n", " <th>n_aprobados</th>\n", " <th>monto_solicitado</th>\n", " <th>monto_aprobado</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2007</td>\n", " <td>58</td>\n", " <td>37</td>\n", " <td>NaN</td>\n", " <td>922052</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2008</td>\n", " <td>110</td>\n", " <td>74</td>\n", " <td>1684744.0</td>\n", " <td>1132227</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2009</td>\n", " <td>76</td>\n", " <td>66</td>\n", " <td>1245880.0</td>\n", " <td>1084471</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2010</td>\n", " <td>140</td>\n", " <td>80</td>\n", " <td>6856883.0</td>\n", " <td>3959662</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011</td>\n", " <td>172</td>\n", " <td>90</td>\n", " <td>8686381.0</td>\n", " <td>4662685</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " año n_concursados n_aprobados monto_solicitado monto_aprobado\n", "0 2007 58 37 NaN 922052\n", "1 2008 110 74 1684744.0 1132227\n", "2 2009 76 66 1245880.0 1084471\n", "3 2010 140 80 6856883.0 3959662\n", "4 2011 172 90 8686381.0 4662685" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fondecyt = pd.read_csv('data/tabular/fondecyt postdoc 2007-2017.csv')\n", "fondecyt.head()" ] }, { "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>Año</th>\n", " <th>Concursados</th>\n", " <th>Aprobados</th>\n", " <th>Recursos solicitados</th>\n", " <th>Recursos aprobados</th>\n", " <th>Tasa de aprobación</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2007</td>\n", " <td>58</td>\n", " <td>37</td>\n", " <td>NaN</td>\n", " <td>922052</td>\n", " <td>63.8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2008</td>\n", " <td>110</td>\n", " <td>74</td>\n", " <td>1684744.0</td>\n", " <td>1132227</td>\n", " <td>67.3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2009</td>\n", " <td>76</td>\n", " <td>66</td>\n", " <td>1245880.0</td>\n", " <td>1084471</td>\n", " <td>86.8</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2010</td>\n", " <td>140</td>\n", " <td>80</td>\n", " <td>6856883.0</td>\n", " <td>3959662</td>\n", " <td>57.1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011</td>\n", " <td>172</td>\n", " <td>90</td>\n", " <td>8686381.0</td>\n", " <td>4662685</td>\n", " <td>52.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Año Concursados Aprobados Recursos solicitados Recursos aprobados \\\n", "0 2007 58 37 NaN 922052 \n", "1 2008 110 74 1684744.0 1132227 \n", "2 2009 76 66 1245880.0 1084471 \n", "3 2010 140 80 6856883.0 3959662 \n", "4 2011 172 90 8686381.0 4662685 \n", "\n", " Tasa de aprobación \n", "0 63.8 \n", "1 67.3 \n", "2 86.8 \n", "3 57.1 \n", "4 52.3 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fondecyt=fondecyt.rename(columns = {'año':'Año', 'n_concursados':'Concursados', 'n_aprobados':'Aprobados', \n", " 'monto_solicitado':'Recursos solicitados', 'monto_aprobado':'Recursos aprobados'})\n", "fondecyt['Tasa de aprobación']=np.round(fondecyt['Aprobados']/fondecyt['Concursados']100,decimals=1)\n", "fondecyt_postdoc=fondecyt.copy()\n", "fondecyt.head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hoUAs7YIgCEKKo5QqivEYCMYlUJ0gpAJ1MAr7T6KwC4IgCA8baSVKsSAIgvCA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mTW3X84r5G2dsAi4iIiIiUmxK2meJweEE9z52KO/y09ma3jfSz6EzR8ZNFHd6JL8WyLpg7VjL\neaYlfU646YITw9ncfTnoDxL0B6mtqj1v2aFFQ3z80c+Ou7kxUSgQ4rXLXz0rYiciIiIiUixK2meJ\n7TY2rkv8ZK40EV5/3TLPWtMHRged1vMzZxP0U/HevJ5bHag+p4v7vOrmotdb3ZfPryZYw+b2mzQb\nuoiIiIhIkSlpnyVO9+c3Gdvi1gbaovXnL3gBhhJDHOk7Oi5BPzF8Mq/nhgIhFtcvoj0rSW+pmYf/\nfLOfiWcqYTiBiIiIiEilUdI+SzTV5zcJ2py6UFFebzgRp7P/GIczM7n3dRIbPJHXc6v8QdrqF41r\nRW+tjShBrwCzeTiBiIiIiEgpKGmfJa40Ue64b/+UXeTDVQGuXB0t+NgjyVE3QT87UVzXQIw06fM+\nN+gLsKh+4Vj39vbGNubXRgn4AwXXQ8qDhhOIiIiIiBSPkvZZorY6yK3XtDuzxwdGCTR34QvFSY+E\nSZ5qhWQVt17Tft4J50ZTCY71vzzWvf1QXycvD3TltTyY3+dnUd38cRPFLaybT9Cvy1BERERERCQX\nZUuzyG3XLuXFxHb2xZ/CFzjb4p5O7mFV+Apuu/bmceWTqSTHBro43HdkrBX9aP9xkunzT2jnw8eC\nutZxXdwX1S2gKlBV9PMSERERERGZqZS0zyI/PXg/+xNP4pvQ89wXSLI/8ST/vq+KxQ2LxhL0zv5j\nJFKJ8x7Xh49obWSse3t7YxuL6hcSDhRnfLyIiIiIiMhspaR9lhhKDLH10INTlnmo89G8jhWpmTdu\nHfS2hkWaaExERERERKQElLTPEjtiu8Ytw5WvedXNYwn6koY2ljQsoraqtgQ1FBERERERkYmUtM8S\np+P5zeI9vzbKVfM3ugl6G/WhuhLXTERERERERCajpH2WaAo35FXuliU3cO3Cq0pcGxEREREREcmH\nf7orIN7YGF1H6DwTw4UCITZG13lUIxERERERETkfJe2zRE2whs3tN01ZZnP7TZpQTkREREREpIyo\ne/wssmXpLQBsPfTguEnpQoEQm9tvGtsvIiIiIiIiGZT64wAAEdlJREFU5UFJ+yyzZekt3NB2HTti\nu0hWxQmMhtkYXacWdhERERERkTKkpH0WqglWc+3Cq4hEGujuzm9WeREREREREfGexrSLiIiIiIiI\nlCkl7SIiIiIiIiJlSkm7iIiIiIiISJnSmPYCGGPeDfwZ0AY8A3zQWvvY9NZKRERERESk8infyk0t\n7XkyxrwD+DrwHeBNQC+w1RizbForJiIiIiIiUuGUb01OSXsejDE+4NPAN6y1n7bW3gu8HjgB/Om0\nVk5ERERERKSCKd+ampL2/FwCtAN3ZjZYa0eBe4At01UpERERERGRGUD51hSUtOdnlfv4woTtB4AV\nxpiAx/URERERERGZKZRvTUFJe34a3ce+Cdv7cGJY5211REREREREZgzlW1PQ7PH58bmP6Un2p6Z6\nciTS4Jtq/3SKRBqmuwoVRfEqjOJVGMWrMIpXYRSvwihehVG8CqN4FU4xK0wFxuui8q2ZTi3t+Tnt\nPk68+huApLW23+P6iIiIiIiIzBTKt6agpD0/+93H5RO2Lwf2eVwXERERERGRmUT51hSUtOdnP3AE\neGNmgzGmCngtcP90VUpERERERGQGUL41BV86PdmwAclmjPkfwP8BPg88CrwX+BXgMmvtgemsm4iI\niIiISCVTvjU5Je0FMMbcDnwAaAGeAW631j42vbUSERERERGpfMq3clPSLiIiIiIiIlKmtORbBTPG\nBHDuRL0bWAIcAv4e+DtrbdoY4wM+DrwH527Vo8D7rLV7s44RBv4KeCvO+odbgfdba4+5+78NvGOS\nKvzCWntTCU6tJLyIl1tmMfC/gZtwlqf4Gc5dwljJT7KIPIzXFcCXgGuAU8AdwCestUMlP8kiKka8\nJhzvK8BKa+3rJmxvBr4C3IYzL8l/AB+01p4pyYmViFfxmlDmTuCAtfZPinoyHvDw+lqM8569CagB\nngL+zFr7dElOrEQ8jJfB+bz/FSAOfB/480qb5Xia3o83Ag8AN1trf1G8syk9D6+v24G/zvGU26y1\ndxfrfErNw3gFgf8JvNM9znPAx6y1FTUe2ot4zaTv97OFJqKrbP8T+BzwHeD1wL8DfwN82N3/SeAT\nOB/4vwk0AfcbY5qyjvF14HeAj+J8yG0A7nU/MAA+g5NMZf98zN33rZKcVemUPF7uhBn3AJcDfwj8\nMXAdcGdWTCuFF/FaBfwCaAbeBvwJ8DrgxyU8r1IpRrwAMMa8FycWufwHcCPO9fUn7mt9r1gn4SGv\n4oUxxmeM+TLOjY5KVfJ4GWNqcG4ybnT3vx1nvdyHjTHLinw+peZFvJqB+3C+NL8NuB14C/DdIp+L\nFzx7P7planC+Q/imKlfGvIrXBuARzv0e9t/FOhGPeBWvrwIfxBkP/UbgKHC3MWZ10c7EG17EayZ9\nv58V1NJeodyk54PAl6y1n3U332+MiQAfMsb8A/Ah4FPW2q+6z3kE527du4AvG2NW4CRUb7PW/ptb\n5lnAAm8AfmStfRF4Met1G3E+PP7FWlsxX0y8ihdOsr4OuMVa+4Bb5gxOC/NGYLsX53uxPIzX+3B6\nI/yqtbY7q8xeY8yt1tp7vTnji1OMeLnbosAXgd/m7Hql2a9zE04L6CZr7RPutk7gPmPM5ZXSGupV\nvNwyK3AmtbkBGC7ZSZWQh/F6HbAap0XmBfc5v3CP80fAn5XkBIvMw3i9FZgPXJHpSeW+9reNMW3W\n2s5SnWMxefl+zPKXQHWxz8ULHsdrPfBTa+3jpTqfUvPw7+NKnJvZb7HW/tDd9gvgWeAWIGcrdLnx\nKl4z5fv9bKKW9srVCPwLTuKTzQIR4GagHrhzbIe1p4CHgC3uppvdx7uzyuwHns8qM9HH3Nf+8CT7\ny5VX8Qq7j9ldlXvcx7kXdQbe8ipeq4AdmYTdLWOBE0x+DZajYsQLnO5u1wGbcSZfmehVQCyTsLse\nxLneFK/c/hanJfRazr4XK41X8eoF/jaTsLvHGcRZgqeSWtq9itcdwLUThj6NuI/hHOXLlZfvR4wx\nV+MkV7cXoe7TwZN4uV291wA7i1j36eDV9fUG4CROb7TMcUastWustX938afhGU/fj1kq9fv9rKGW\n9grlvkHfm2PXbUAn0Ob+/uKE/QdwPtjASZiOW2sHcpRZNfHAxpj5OF1s/qLSxmd7GK9twA7gc8aY\n33e3fQHnS2/FdGfzMF5HgBuNMT5rbRrAGDMHp7v80os5By8VKV4A/wB8yFqbMMZ8IsfxVgEvZG+w\n1qaMMQfJ8Z4tVx7GC+Aj1trnAYwxF17paeRVvKy1Pwd+nr3N7RZ/Kc6wn4rgYbxOAb+Ese7eV+O0\nIN/ntmJVBC/fj8aYEPBPOF1/7cXUe7p4GK/VQAjYYoz5PLAQp7fen0y4cVvWPIzXepzW9DcZYz4D\nrMQZ0/4Ba+1DF3EKnvL47yNQ2d/vZxMl7TOImyS+Cng/zt2yuLV2ZEKxPncf7mNfjkP1AYtzbP9D\nIIHzQVDxShEv98PxD4Cf4HRVAufO741ui1XFKtH19V2c7lxfM8b8JU7r1NdwrrO6op6Axy4gXple\nBlOZKqaNObZXjBLFi0zCPtOUKl4TXiOTYA3jzE9RsTyI126cG40nmQEtVSWM1yeAJE633bXFqe30\nK1G81ruP84Hfx5kY8iPAA8aYK+wkk45VghLFK4KTqP8NTitzF0438p8YYzqstQeLU3vvefD5NaO+\n389U6h4/Qxhj3o7zpeqHOOM3fTgTCOWSch/zKZM5vg9nFst/ttb2XnSFp1mp4mWMuQxnYrU9OOND\nX4sznmqrMeaSYtR9OpQqXtbaB3HGyv4u8DKwD+fL71NAxd7kuMB45aNYxykrJYzXjORFvIyz8sO/\nA68Efsdae/RCjlMOPLq+3g28Buez6xFjzIYLPM60K1W8jDHrcW5ovNtaO3qx9SwXJby+HsBpXX2t\ntfbn1to7cbo/91PBN4ZKGK8qIIozj86/WGu34kxGd4YKmY8jl1J/fs207/czmZL2GcAY80HgX3HG\nDr/d7WZ8GggbZzbzbA2cnZDitPv7RNllMq7C6Zr1/WLVe7qUOF7vBQaAW62191hnIrVbcZYC+mRR\nT8Qjpb6+rLVfx+kO3wG0Wms/inOtnSzmeXjlIuKVj0LesxWhxPGacbyIl3FmIN6Kc9PxHdba/7y4\nWk8fr64va+191tqf4sz0fBpnuaaKU6p4uZNr/RPwTeBpd7x2ZkWVgKm81VWA0l5f1trj1tq7s29w\nWGv7cIbhVeRNoRK/H/txbvY/ktngDs97DGeC4Irj0efXjPl+P9Mpaa9wxpjP4awR+6/Ar2d1l9mP\nczdu4uRByzk7jmw/MN8dizdZmYwtOF2NHi1S1aeFB/FaDDxns9botdYO47S+dBTrPLxS6ngZYzqM\nMb9hrR211u6x1va6Y9oXk9/EKWXlIuOVj/3uc7Jf04/TLbfixod6EK8ZxYt4GWNagIdxxme/yVbw\nLMKljpcxZpMx5vXZ29zP+704X4IrSonjtRi4EmfFkFH3J7Oayn1ARa2jDZ5cX680xvxmjl01OJO1\nVhQPPr9ewLkRNPEGUBWTt0yXLQ//Ps6I7/ezgZL2CmaM+QDObI9/C/yutTaRtXsbzjjEN2aVb8ZZ\n9ijzx/F+nA+327LKrMQZZzbxD+grgCczk4VVIo/itQ9Yb4ypzyoTwlnu7aUin1JJeRSvy4DvGmdp\nkow/xPmDVBHLvWUUIV75uB9YYIx5Rda2m3DGsVXUl16P4jVjeBEvt+XmHpwvf5vd7rgVyaPr643A\nv5qstZGNMfNwlv7cdeG1954H8TqG06KX/fN2d98fAu+5mPp7zaPr6xbgn91JwjLHmY8zI3jFTKwG\nnsXrZzjz4mR/55iDs3LItguu/DTw+O9jxX+/ny00EV2FMsYswJmVfBdOl5arzfiZkbfjTOj1GWNM\nCieZ/HOcsT3fAmeNRmPMD4Bvul86TgGfx1leZGJ3yEuBH5TshErMw3j9Dc7a5PcaY/4aZ3zR+4BF\nwG+U8hyLycN43QUcB75njPkCzsQ7fwn8Y6GTZk2nYsQrTw8ATwA/MsZ8GKcF4a+Be6y1T13seXjF\nw3jNCB7G6704X+C+AIwYYzZl7TtVKe9JD+P198AfAHe5n181OBOtjeCulVwJvIiX20q4PXubMSZx\ndndlXFvg6fX1j8AfA/cYYz6Nk5D+L5ylK792kafhGQ/j9XOcpPWf3Jtnx3ASX3CS34owDX8fK/r7\n/WyipL1ybcb5AF+HM15nogjO7JkpnNkz63Huzr3DWps95uWdwFdwPiD8ON3U3m+tTU44XhRnDd9K\n5Um83ET1enf/93Duhm4HNllrK6m7t1fx6jPGbMH5A/QjnC5/n8ZZDqiSFCteU7LWpt3uuF8DvoEz\nV8J/AX96UbX3nifxmkG8ildmuaCPuD/Z7sGZXLMSePV+PGyMeSXOTOjfwekhdB/wa9baly/qDLyl\n92NhvLq+jmVdX9/G6bn2M+D27CF4FcDLv49vxGkc+Kx7nMeAV+r9OKVK/34/a/jSafWGEBERERER\nESlHGtMuIiIiIiIiUqaUtIuIiIiIiIiUKSXtIiIiIiIiImVKSbuIiIiIiIhImVLSLiIiIiIiIlKm\nlLSLiIiIiIiIlCkl7SIiIiIiIiJlSkm7iIiIiIiISJlS0i4iIiIiIiJSppS0i4iIiIiIiJQpJe0i\nIiIiIiIiZUpJu4iIiIiIiEiZUtIuIiIiIiIiUqaUtIuIiIiIiIiUKSXtIiIiIiIiImVKSbuIiIiI\niIhImVLSLiIiIiIiIlKmlLSLiIiIiIiIlCkl7SIiIiIiIiJlSkm7iIiIiIiISJlS0i4iIiIiIiJS\nppS0i4iIiIiIiJSp4HRXQERERApjjPkJsAX4ibX21inK3Qz8M5AGvgHcBfwM+Atr7d95UVcRERG5\nOL50Oj3ddRAREZE8GWPmA51AHKgG2q21nZOU3Q0MATuBNwN1QC+w3lp7xJsai4iIyMVQ93gREZHK\n8nYgAHwR5+/4701R9nbgDdbadwILgJsAo4RdRESkcqilXUREpIIYY54FFgOLgJeBU8Bya63+oIuI\niMxAGtMuIiJSIYwxG4D1wA+stUPGmP8E3gG8GmeseqbcjcCDwDtxWuP/FFgJnAC+D3zSWjs44di/\nAbwfuAxnDPxO4KvW2u+X+LRERERkCuoeLyIiUjl+x338N/cxk1D//iTl3wt8HXgO+CowjNNl/pvZ\nhYwxf+0eaznwPeAOYBlwhzHmC8WqvIiIiBRO3eNFREQqgDEmgDMBXR0QtdYOG2OCwFFgDrDIWnvC\nLXsjTkt7ErjeWvuYu70J2A80A83W2n5jzPXAw8AOYLO1ttstGwEeAC4FbrDWPuzZyYqIiMgYtbSL\niIhUhlcD84EfW2uHAay1CeAHQIizrfDZHsok7G7508A2nOFxbe7m33UfP5RJ2N2y3cBH3V+nmuxO\nRERESkhJu4iISGXIJOV3TNj+XffxXTmesy/HttPuY9h9vAxIAf+do2xm24Y86ygiIiJFponoRERE\nypwxpgF4o/vrT4wxuYp1GGOutdZuy9oWz1EuMy7O5z42AsPW2pGJBa21p40xg0DthdVcRERELpaS\ndhERkfL3ZqAG+CXwdI79BrgRZ0K6bTn2T6UPqDXGzLHW9o47qDHV7uv2FFphERERKQ4l7SIiIuUv\n0zX+g9bac7qxG2OWAC8BbzHGfKDAYz8DbAR+Bbh7wr5fwWmRf77AY4qIiEiRaEy7iIhIGTPGtAOv\nBA4Cj+YqY609jDPTex3w1gJf4tvu4+fdGeMzrxsBvuT++q8FHlNERESKREm7iIhIefttnNbu71lr\np1qn9f+5j5Ot2Z6Tu5Tbl3GWdttpjPmGMeYbwLM4k9R9Qcu9iYiITB8l7SIiIuXtt93H75yn3I9x\nZoa/ClhXyAtYa28HfgunNf/twFtwZp5/k7X2o1M8VURERErMl05PddNeRERERERERKaLWtpFRERE\nREREypSSdhEREREREZEypaRdREREREREpEwpaRcREREREREpU0raRURERERERMqUknYRERERERGR\nMqWkXURERERERKRMKWkXERERERERKVNK2kVERERERETKlJJ2ERERERERkTL1/wG4ZX3zcik2mgAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1109462e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(15, 10))\n", "ax2 = ax.twinx()\n", "\n", "fondecyt.plot(kind='line', x='Año', y='Concursados', marker='o', markersize=10, ax=ax, label='Postdoc Concursados')\n", "fondecyt.plot(kind='line', x='Año', y='Aprobados', marker='o', markersize=10, ax=ax, label='Postdoc Aprobados')\n", "ax.set_xlim([2006.5,2017.5])\n", "ax.set_ylim([0,1300])\n", "ax.yaxis.set_ticks(np.arange(0, 1400, 100))\n", "ax.legend(loc=2)\n", "\n", "fondecyt.plot(kind='line', x='Año', y='Tasa de aprobación', color='red', grid=True, ax=ax2)\n", "ax2.set_ylim([0,100])\n", "ax2.yaxis.set_ticks(np.arange(0,110,10))\n", "ax2.set_xlim([2006.5,2017.5])\n", "ax2.xaxis.set_ticks(np.arange(2007, 2018, 1))\n", "ax2.grid(linestyle='--')\n", "ax2.legend(loc=1)\n", "\n", "ax.set_title('Estadística Proyectos Fondecyt de Postdoctorado')\n", "ax.set_ylabel('Número de proyectos / año')\n", "ax2.set_ylabel('Tasa (%)')\n", "\n", "fig.savefig('figures/estadistica_proyectos_fondecyt_postdoc.pdf')\n", "fig.savefig('figures/estadistica_proyectos_fondecyt_postdoc.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fondecyt Iniciación" ] }, { "cell_type": "code", "execution_count": 30, "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>año</th>\n", " <th>n_concursados</th>\n", " <th>n_aprobados</th>\n", " <th>monto_solicitado</th>\n", " <th>monto_aprobado</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2007</td>\n", " <td>357</td>\n", " <td>128</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2008</td>\n", " <td>350</td>\n", " <td>181</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2009</td>\n", " <td>439</td>\n", " <td>134</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2010</td>\n", " <td>489</td>\n", " <td>168</td>\n", " <td>25623402.0</td>\n", " <td>8487191.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011</td>\n", " <td>525</td>\n", " <td>262</td>\n", " <td>28836874.0</td>\n", " <td>14019657.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " año n_concursados n_aprobados monto_solicitado monto_aprobado\n", "0 2007 357 128 NaN NaN\n", "1 2008 350 181 NaN NaN\n", "2 2009 439 134 NaN NaN\n", "3 2010 489 168 25623402.0 8487191.0\n", "4 2011 525 262 28836874.0 14019657.0" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fondecyt = pd.read_csv('data/tabular/fondecyt iniciacion 2007-2017.csv')\n", "fondecyt.head()" ] }, { "cell_type": "code", "execution_count": 31, "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>Año</th>\n", " <th>Concursados</th>\n", " <th>Aprobados</th>\n", " <th>Recursos solicitados</th>\n", " <th>Recursos aprobados</th>\n", " <th>Tasa de aprobación</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2007</td>\n", " <td>357</td>\n", " <td>128</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>35.9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2008</td>\n", " <td>350</td>\n", " <td>181</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>51.7</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2009</td>\n", " <td>439</td>\n", " <td>134</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.5</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2010</td>\n", " <td>489</td>\n", " <td>168</td>\n", " <td>25623402.0</td>\n", " <td>8487191.0</td>\n", " <td>34.4</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011</td>\n", " <td>525</td>\n", " <td>262</td>\n", " <td>28836874.0</td>\n", " <td>14019657.0</td>\n", " <td>49.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Año Concursados Aprobados Recursos solicitados Recursos aprobados \\\n", "0 2007 357 128 NaN NaN \n", "1 2008 350 181 NaN NaN \n", "2 2009 439 134 NaN NaN \n", "3 2010 489 168 25623402.0 8487191.0 \n", "4 2011 525 262 28836874.0 14019657.0 \n", "\n", " Tasa de aprobación \n", "0 35.9 \n", "1 51.7 \n", "2 30.5 \n", "3 34.4 \n", "4 49.9 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fondecyt=fondecyt.rename(columns = {'año':'Año', 'n_concursados':'Concursados', 'n_aprobados':'Aprobados', \n", " 'monto_solicitado':'Recursos solicitados', 'monto_aprobado':'Recursos aprobados'})\n", "fondecyt['Tasa de aprobación']=np.round(fondecyt['Aprobados']/fondecyt['Concursados']100,decimals=1)\n", "fondecyt_iniciacion=fondecyt.copy()\n", "fondecyt.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1TuvPTUqp3JjpAr4GrgDpXKySzvoM6zVHLGl0fPv+91/nFhrJg59fOs6fd/Uw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ByzZ5A45unQdn6nLuQMNxJ1hg3De/a5AFjy83EOHmDK8E8hxBF4vcQuX0pSr67UanomzlFPHJSw\ne888i+yRz7LvF03mm9PwtLxWEvZjFEipQcasBeT27Rdqs69fR43rWhDz7ZqIve5H32w5IGE/9Xgn\nQ3ucLwm7EEJUPk2CX1thdBYX/hkWbH8CeAx4AeiKUeTtU6VUla8yGk5P+9PATcDjSqleGNXjM4CT\ngP8Fv2rgmfIOUoiKEPPZJ7geGBjaLri8BVkvvQKW4MqGMTFkTZxMynVXYfF6if12DXFvvU7+XfeY\nFLEQojjb338SN/M9HHNmYtu966D9fqcLd/tO5Pfoife8ZkX/tkX5sdvJGTUG31ln43xoMBaPB+u+\nfSR3uJHs58aT3/P2cnupQCDAwi828dE3RUV465+UxP23NCEhLqbcXkcIIUSFaQzs0lp/XHKHUsoF\nDAFGaK0nBtu+xFjrvDcwviIDrWhl7mnXWmdiDH9/B6gN3Ar0B9oBtYC3gcu01hnlH6YQkWX/dS1J\nvXth8Rrr+XobnUPm29MhNvaA47znNiG32JJvzqdHYC1lHWchRAXJycExewYpba+jZvNmJEx68aCE\nvaD5pWROmmIUlRs3Ae/5F0jCHmH53XuSvvAj/McZZW4sXi+uBwfhHDYEPJ5jvr4/EGDWJ38ekLCf\ndVoNHuzSVBJ2IYSovBpjrGFemosBJ0bhcwC01mnAaqB15EMzVzjrtDcHftJa91ZK3QMoIAnIArTW\nuiBCMQoRUdZ/tpLU/RasOUbBJN9JJ5MxewGBpNJH2uTe/zCOpR9i3/AHltxcXA8MJGP+ErCGM9tE\nCHHUAgH49lucr0zBsWhB6N9ucb7adXB36U5+91ul9oRJvBddTNrKz0nq1Y2YdcbvYPFvvo5toybz\njXcI1Kx1VNf1+wO8s3wDX/26I9TWuH4t+rc/hxi7THEQQohKrDGQr5Rag7HO+V5gAsZw+ML1lv8u\ncc4m4OYKi7AMlFI2jKH7NqCwl8ACxGB0dl+vtR4dzjXDGR6/APgRaBtcQH5dOC8kRDSy7N9HctcO\noZ45f3IKGbMX4q9zwqFPcjjImjCZlDZXY/H7if3qC+KmvU3+7b0rKGohqi/rzh0k3dEDfvyBknXd\nA3Y7Bde2Jr9HTwpaXgv2cD7iRCT4Tz6F9A9W4LqvH3FLFgEQ++VqarRuScb0OfjUmWFdz+vzM/XD\n9Xz/x+5Q2wVnHk+fto2w2+TBqRBCVFbBRLcRkIMxDH4rcAMwBogHPIC7lI7iLIyOZNMppeKA14Fb\ngNgjHB6xpD0F+D2ciwtDQkIsiYkHF8TZty8bvz9g2v5ojy/i+7ftIaln16ICVbGxWJe8T83L/nfI\n81NTg2v9tmpB3oD7iJ/4IgCukY/j6tweTiuqkGz6zyfvr0q3P/T+itL4TN+fkQG33gK/lhg5pxT0\n7o2lVy8ctWvjiNb4Td5v2vsr1QWLF8DTT8MTTwBg27KZlDZXU/D2u8R1ah/W9R+/qzm3PbWC/Zn5\nDO15AZc1Panc4y8UTX9/0b5f/v8q+/5C0RpftO4vfI9Fa3zRtB+i9/11BDcC/2it/wpuf66UcgJD\nMeqmBQ5xXrRUhn4cYwr5fmANxtTyf4D/MEapnwDsAu4L98KWQOBQP/uBlFKzgGbApVrrPeG+UHW2\nZ09W2W5yBUtNdbFnT5bZYZjD5yOpdy8cSz8INWW+8Q7umzsc8pSD7ld+PjWuvgz7n0bV4oIrryJj\n7mKZKxtUrd9fR0Hu1xHk55PctQOxa74ytm028rp0J797L7z/u1D+3R1BtLy/Yj9cQtKAPlhycwEI\nWCzkDB9B3sDBh/07zC/wMmnBb/yxNS3UdnWzk+l2TQOsEfi7j5b7VVnI/QqP3K/wyT0LT7Ter9RU\nV9j/YSulbgYWA4OBFwFHcNR34f4JwI1a6/rlFuhRUkppjHn3jbTWGUqpj4AcrXXn4HJ1TwOPAB21\n1ovDuXY4Pe2rgRbAJqXU18BmIK+U4wJa6wfDCUKIChUIkPj4Iwck7Nkjnz1swl6quDiyXnqFlBtb\nYQkEiF29iriZ08nv0aucAxaimvP5SOp3d1HCDvDWW2S3ObiHVkS3ghtvIq3u6STf1g3bv/9gCQRw\nPv0k9vXryHrxZYgvOekBcvM9vDTvV/76r6jO7Q3NT6PDFfWwyMMaIYSoEpRSJ2L0tC8q0UFc+MGQ\nhjEv/HRgY7H99TBWMIsGpwJvFyvM/iNwF4DWOgAMV0pdDwzAeBBRZuFMAJuMUTU+EWPtvL4YTzxK\n+yNE1Ip/ZSIJU18Lbef27U/ePQOO6lre/11EXt/+oe3EJx7Fuv2/Y45RCBEUCOAcNgTHh++HmrIf\nHwm95OFYZeU751zSVnxOQfNLQ21xC+eRcnNrrDu2H3BsZm4Bz8/6+YCEvcMV9eh4ZX1J2IUQompx\nAK9hDC8vriNGkr4QyMdYuQwApVQN4Erg0wqK8Ui8QHqx7T+B2kqp44q1fYYxVD4s4fS0XxXuxYWI\nNo4Fc3GOfDy0nX9zB3KeeuaYrpnzyGPErliKffMmrFmZOIfcR+aMeTJcV4hykDD+eeLfeTO0ndu3\nH3kD7sNpYkzi2AWOO46Mee/jHPYQ8dPfBiBm7c+ktGpB5rsz8Z5/AWlZbl6Y/TM79uWGzut2TQOu\nveAUs8IWQggRIVrrzcHp2KOUUn7gD4yCbh2BdlrrbKXUpGL7NwLDgUxgqllxl7AJOKfY9kaM0QGN\nMZJ1MCrK1wz3wmVO2rXWq8O9uBDRJObL1bgG3RvaLmh+KVmTphz7Um0JCWRPmEzyzW2wBAI4PlmJ\nY+4s3F26H2PEQlRvcdPfIfG5oodq+R06kfPUs/JArKqIjSX7hZfwNjob52NDsfh82HbtJOXmNmx/\nejxj8uqzJz0fMP7Kb299Jpc3OdHkoIUQQkRQb4xiboMxirb9gTH/u3Bt9kcxis4NwZg7vga4rdhw\ndLMtxhgCPwx4BfgFyAAeCk4vPw7oBGwJ98Jhr4ejlKoPdAaaYKw/txf4Fpintd59uHOFMItt/e8k\n3d4Di8eoW+FVZ5L57kyIiyuX63suvoS83n1Cw+6djz2Cp0VL/LXrlMv1hahuYpd9hPOhotlWBVde\nRdbEcnjIJqKLxUJ+7z74GjQk6a5eWNPTsbjdnPRQf264oB3TLuuJxW7n7raNuPCs2mZHK4QQIoK0\n1nkYifmjh9jvxSjk9khFxhWGF4C2GAXndmut3wwWynsCY9i8HWN6+thwLxzWbz9KqSeA9Rgl9zsD\n1wE9gEnAX0qpO8INQIhIs/63jeRuHbFmZQLgq3MCGbMWEEipUa6vkzN8BL5T6xqvmZGO86H7oYyr\nMwghiti//Yakvndg8RsruHianEfm2+9B7JGWPBWVleeKFqSt+Jy8+g1DbR1/WMzjS0Zz37WnScIu\nhBAi6mmts4CLgDuBr4PNI4EnMXrXfwMe0lpPCPfaZU7alVK3ASOAfzGq4DUGjgcaArcDO4E3lFKt\nwg1CiEixZKST3K0jtmBxI7/TRcbM+fhPjsCcyMREsl56ObTpWP4RjkXzy/91hKjCbH+sJ7lnFyz5\nxrBoX93TyZg5n4DTdYQzRWX3l+M4+rV7mu/rXRBqu2DTD1w+oDO2TX8d5kwhhBAiOmitPVrrd7XW\nG4Lbfq31KK31WVrr87XW44/muuH0tN8PbAMu1lq/pbVep7Xeq7X+S2s9DbgC2I1REEAI87ndJN3W\nHfuGPwAIxMSQ+c4MfOecG7GX9Fx2BXm39Q5tOx99CMtumTUiRFlYt/1LctcOWDOMwqv+41JJn7OI\nQGqqyZGJSNP/pDF29s/sDcTyzE3DWNS8U2if/c+NpLRuSczqVSZGKIQQQpSNUiquxHYLpdRjSqlb\nlVJHNWwwnKS9AbBYa723tJ1a653AIuD8owlEiHLl9+Ma2PeAdZ2zJkzGc0WLiL90zpMj8QV78q37\n9+MaNiTirylEZWdJ209y1w5Fo2ISnWTMXoD/9HomRyYi7de/9zF+7i+4C3wAJCTGccprE8h8dSqB\nYN0Ra3o6yV07EP/GqzLtSAghRFRSStmUUi8DaUopZ7CtD8aSdCOBd4FvlVJJ4V47nKR9B0cuT58E\n7As3CCHKW+LIJ4hbvDC0nf3YU7g7damQ1w44XWSNmxjadnywmNgPFlfIawtRKeXmktyjM/aNGgiO\ninl3Jt7GTU0OTETaDxt2M2nBr3i8Rv2CZGcsQ3ucz2l1XLg7dib9/WX46pwAgMXnwzl8KM4HBkJB\ngZlhCyGEEKV5AOgH/A04lVJ2jKJ0uUAfYAxGMfewR6aHk7SPBzorpdqXtlMpdSnGWnovl7ZfiIoS\n/8arJEwuSprz7riLvIGDD3NG+fNcdTV5PXqFtl1DH8CyT55nCXEQr5ekPrcT88P3AAQsFrJeeb1C\nRsUIc3392w5efX8dPr/Rc14rKY5hPc7npOMSQ8d4z2tG+srP8ZzfLNQWP2MaKR3bYtmzp8JjFkII\nIQ6jB/Ar0DQ4Cr0FxjJv72qtp2qthwNLMdaeD0s4S77lB4OYr5T6CvgC+A+IB/4HdMB4inCSUqr4\nBPuA1vrBcAMT4mjEfvA+iY8VrQLhbnMj2c+ONWVd55ynniH2s0+w7diOde9enMMfImvKWxUehxBR\nKxDAOeQ+HCuXh5pynh6Du13Yn2Wikln10zamr9wY2q5dM4GHujalZtLBy3D665xA+uJluB4cRNy8\n2QDEfPcNNa5rQca7s/Cd27jC4hZCCCEOowHwcnBpOoDrgQDwUbFj1gHXhHvhcJL2qcW+vzz4p6RY\n4L4SbQFAknYRcTHfriGp311YgvMdPRdcSOaUN8FmMyWeQFIy2eMmkNz9FgDiFs7HfXNHCtrcYEo8\nQkSbhNGjiJ85PbSdO+gB8u6+18SIREVY9t1W5q36O7R9cqqTB7s2JTnxMLV54uLIevk1vGedTeKo\nJ7AEAti2/UuNtq3InPQaBW1vroDIhRBCiMPKARzFttsABcDqYm11gFJrxB1OOEn7VeFePJoopa4G\nnsVYqm438A4wUmvtU0pZgEeBvhhDGL4GBhaW6g+e78CYh9ANSARWAIO01tsr8ucQpbNt1CT16orF\n7QbAW/8MMt6bA/HxpsZVcM115HfuRtzcWQA4HxpM2sXNCdQ4UnkIIaq2uKlTSHzphdB2ftce5Ax/\n0sSIRKQFAgHe/2ozS77eEmo7/YQk7u/cBGd8zJEvYLGQN+A+fGeeiatvb6xZmVhyc0nu3ZOcIY+Q\nO+QRsIYz608IIYQoV78D7ZRSozHWa1fAMq11LoBSqinGdPLPwr1wmZN2rfXqIx8VnYLz7ZcBM4Fh\nQDNgFOAHngKeAB4BhmIsfP8Y8KlSqpHWOiN4mSnATRijBrKB0cBSpVQzrbWv4n4aUZJ15w5jmaj0\nomWiMmYvJFCzlsmRGbJHjSbm88+w7d6FbfcunI8PI+vl18wOSwjTON5fiHP40NC2+9rrjOKNJkxj\nERUjEAgw57O/WPl//4ba1CkpDOrUmHhHOP0HxsPQ9GWfktSzC/bNmwBIfGEM9j/WkzlpCjid5Rq7\nEEIIUUZjgcVAYaduAHgBQCn1BEaO6QeeCffC1eWR9Bhgpdb6dq31Z1rrscBLwFVKKRcwBBihtZ6o\ntV4CXAe4gN4ASqn6QC+gn9b6Ha31fIw5Co0BGZNnIktWJsndOmHbZvwiGEhIJGPWfPyn1TU3sGIC\nNWqSPfal0Hbc3FnEfrz8MGcIUXXFfLkaV/8+RdNYmv2PzDfehZgy9LSKSsnvD/Ducn1Awn5OvZoM\n7twk7IS9kK+hIn35ZxRcUTQI0PHREmrc2Arrv/8cc8xCCCFEuLTWSzGGxC8DlgMdtNargruzgS+B\nllrr78K9dpVP2pVSqcClwOvF27XWj2itWwAXA05gSbF9aRhzD1oHm1oGv35Y7Jg/MYZAFB4jKlpB\nAUl39sT++28ABGw2Mt98F2+T80wO7GAFbW4gv0On0LZzyGAsmRmHOUOIqsf+2y8k3dYdS3C5Lm+D\nhmTMmAsJCSZHJiLF6/Mz9cP1fPFL0UyyZg1TGdihMY6YY6s3EqhRk4zZC8jtU5R9iokAACAASURB\nVFQHwb5+HTWua0HMt2uO6dpCCCHE0dBaf6q1vlFrfYPW+v1i7eO11ldrrY/qA6rKJ+3AuYAFyFFK\nfaCUyldK7VZKjVBKWYGGweP+LnHepmL7GgI7tdY5hzlGVKRAANcDA4ldvSrUlDV+EgVXtzIxqMPL\nfmYs/uNSAbDt2E7ik2Ev0ShEpWXdspnkrh2xZmcB4KtzAhlzFkXNNBZR/jxeP68uXse363eF2pqf\nXYd72p1NjL2cfv2w28l5+jmyXnyZQHC0hnXvXpI7tiXuvXfL5zWEEEKIcqKUSlZKdQv3vKMbl1a5\npAa/TsOY0z4euBJjTkEexoMLt9a6oMR5WUBS8Puk4HZJWcAp5R2wOLKE0aNCxd0Ach5+FHe3W02M\n6MgCtWqR9dw4knsb67fHz5iG+6b2eK662uTIhIgsy549JHdpj3XPbgD8SclkzF6I/2T577Oqchf4\neHnhr/y+JS3UdtV5J9GjVUOsEahdkN+jF976DUi+swfWvXuxeDy4HhiIbf06ckaOBnt1+HVHCCGE\nmYLFzZ8CugPHAzaMzmOCX2OKbc866AKHUR0+xQonSq7QWj8U/H6VUuo4jMR9DEaRgNL4g18tZTjm\nkGrUSMBuN2fZsSNJTXWZHUL4pkyBYlWnuesuEsc8TWIFFLE65vt1Z09YtgTmzwcgZcggWLcOkpKO\ncGLlVCnfXyaqkvcrOxtu6wLBgmE4HFg/WELNKy4+5ktXyfsVQRV1v3LyPLzw5resL5awd2hxBrff\n2AhLJP+fbtsKfvwRbroJfvkFgISpr5Gw+S+YOxdqhrdqh7y/wiP3Kzxyv8In9yw8cr9MMQQjv/QC\n/wKnAvsxloI7CaOzeA8wLtwLH1PSrpRKLGXIeLTJDn4tWfnrY6A/kA44lFIxWmtPsf0uoHDScUZw\nu6TixxxSWlpuWAFXlNRUF3v2lDaAIHrFLl9KUv/+oUdU7mtakTnyedibfdjzykN53S/LU89R87PP\nsO7fD//+S97A+8l+4aUjn1jJVMb3l5mq5P0qKCD51s7E/vADAAGrlczX3qbgrPPgGH/WKnm/Iqii\n7ld2nodxc9aydWfRa7W7/HRuuOgU9lbA/9PE14DFy0kaeA+OD4NTCT/9FF+zC8iYPgefOrNMl5H3\nV3jkfoVH7lf45J6FJ1rvVzV4kNATI7c8X2u9RSn1KbBNa31bsPj5y0APILKF6JRSFqXUPUqp75RS\n+QQTVqXUAKXUW0qp2uEGUAH+Cn6NLdFe2APvwehJP73E/nqADn7/J1BHKVVy0e/ix4gIs//wPUl9\n78DiNwY3eJqeR+br71S6YY+B1FSynx0b2o6f9hYxX1baFRWFKJ3fj2vQvcR+XrQUafbzL1Jw/Y3m\nxSQiKj3bzXMzfjogYe/a8gxuuvT0yPawl5SYSObUd8l5+NFQk23LZlLaXE3symUVF4cQQojqpj6w\nQGu9Jbj9Pca0bLTWWcCdGDXRHgz3wmVO2pVSdozq6a8ATTDmcxd+Cp8O3A58FazWHk3WA/9hLGRf\n3A0Ya+jNBvKBdoU7lFI1MG7wp8GmTzHmJLQtdkwD4Oxix4gIsm36i+SeXbDk5QHgO60uGTPmV9r1\neN3tO+FufUNo23X/AGMYsRBVROKIx4hbOC+0nfPQMPJ73WFiRCKS9mbkMWbGT/y31xh8ZwFua61o\ndeGp5gRktZI75BEy3pxOILg6gTU7i6SeXYmf+CIEDjXjTQghhDgmu4t9vxE4RSmVDKC19mEsB3du\nuBcNp6d9CMa6cy8CNTGS90JDgScxni4MCzeISNJa+4FHgZuUUq8qpa5WSo0GbgNGaq0zgUnAKKXU\nEKXUTRhD6TOBqcFr/A3MA95QSt2tlOoELAV+BRZX/E9VvVh27ya5Swes+/YB4K9Vi4w5CwmkRtvz\noTBYLGSPfRF/SgoAtn+24nxmhLkxCVFO4l+ZSMKUl0Pbeb3uJHfIIyZGJCJp1/5cxsz4id1pxkNV\nq8XC3W0bcWXTk0yODAra3kzahx/jCxY9tAQCOJ9+Ele/uyH4EFgIIYQoJ/8AZxTbLhzxXTxJ9wBh\nj04PJ2nvBXyttR6itc6lWGE2rbVXaz0K+AyIurGPWutpGFX8LgM+AjoB92itXwse8ijGw4ghGBXm\nM4BrtNbF56vfAcwBnsNI5n8Brg8+MRGRkp1N8q23YNu6BYBAfDwZ783FV++Mw59XCfhr1yF71JjQ\ndvybrxPzzdcmRiTEsXPMnYXzqcdC2+7r25L93DioyOHRosJs25PN6Bk/sT/TDYDdZqFf+3O4+Ow6\nJkdWxHfOuaSt+JyCiy8JtcUtmEtKuzZYd2w/zJlCCCFEWJYBNyulugUrya8F3EBfAKWUE2Pk9n/h\nXjicycD1OHKv8g/AJUc4xhRa61kcorS+1toLPBL8c6jzc4A+wT+iIni9JPW5nZi1PwNFRay8zf5n\ncmDlx925G+73F+L4ZCUArvv6sf/zbyA4nFOIyiTms49xDe4f2i64+BIyp7wJtuhcPUMcm807Mhk/\nZy05+V4AYu1WBnZszNmnh1elvSIEUlPJmL8E5yMPEh9cvz3m559IadWCzHdn4j3/ApMjFEIIUQWM\nAToC7wGJWuupSqmpwACl1BVAPFALeDrcC4fT054OnHaEY+pThmrqQhxRIIDz4ftDySxA9phxFLS+\n3sSgIsBiIfuFCfhdxpJvti2bSRw9yuSghAif/acfSL6zJxavkcB5zzqbzOmzIS7O5MhEJGz8N52x\ns34OJexxsTYe6NI0KhP2kNhYssdNJGv0WALBB0m2XTtJubkNjnmzTQ5OCCFEZae13g2cB4wEfgw2\nDwXeBo7DqJH2CvBMuNcOp6f9E6CjUqqp1nptyZ1KqYuBm4G54QYhREkJ454L9YYA5AweQv7tvU2M\nKHL8J55EzqjRoR7K+Ncn427bDu+FF5kcmRBlY/v7T5J73IIl11je0nfyKUbdieQUkyMTkbBu8z5e\nXvAbBV5jJY/EODsPdGnK6SckmRxZGVgs5Pfui6+BIumuXljT07G43ST170PuH+vJGf6kjAwRQghx\n1LTW+4Gnim3nAb2Df45aOD3tT2KMyf9aKTURuAhAKXWbUuplYBVGFfawu/uFKC5u5nQSn382tJ3f\nuRu5wx43MaLIy+92KwVXXQ0YhZJcg/tJkSRRKVh37TywUGTNmmTMWYS/zgkmRyYi4aeNe5g4/9dQ\nwp6UGMvQHudXjoS9GM8VLUhbvgpvQxVqS3j5JZJ6dcWSlWliZEIIISoLpdQmpdSginitMiftwQrq\nLTHWlhsAtMZY1eUtoB/G8mlttNYbIhCnqCZiP12J88Gi937BlVeRNX5S1S9iZbGQNW4ifqcLAPtf\nf5I4drTJQQlxeJbMDJK7dsT2z1agWKHIBg1NjkxEwje/72TyonV4fUYd2ppJDob1OJ+TUyvn0pv+\nevVJX/Yp7muvC7U5Pl5BSpur4a+/DnOmEEIIAUBdoEKGFYbT047W+iet9blAc4zE/THgfoxkvoHW\nWkpfi6NmX/sTSb1vw+IzCvJ7zmlM5lvTITbW5Mgqhv/kU8h5smg+e/zkidh/+sHEiIQ4jPx8km7r\njv333wAI2GxkTn0X7wUXmhyYiITP1/7H1A/W4w+ub358jXiG9WhG7ZqVu2hmwJVE5rTZ5A68P9Rm\n36jhwguJXbnMxMiEEEKIIuHMaQ/RWn8HfFfOsYhqzLplM8ndb8GSmwMYc2IzZ84j4KpcQy6PVX6v\nO3AsWUTsl6ux+P247utH2idfgsNhdmhCFPH5SOrfh9ivvww1Zb34MgXXtjYxKBEpK7//h9mfFfU8\nn3RcIg92bUqKs4r8v2SzkfP4U3jPaoTr/gFY3G5ISyP51i7kde9JzqjR1e6zSAghRHQ5ZNIeLEt/\nVLTWXxztuaL6sezbR3K3jlj37gHAn5JCxuyF1XNOrMVC1vhJ1LyyOZbcHOx6AwnjnyN32BNmRyaE\nIRDA+ehDOD4oWgE0+7GncHftYWJQIhICgQAffL2FxV9tDrXVrePigS5NccbHmBhZZLg7dcFX/wyS\nbuuObecOAOJnTif2i8/JeukVPFe0MDdAIYQQ0ShFKXVquCdprf8J5/jD9bR/DgTCDSBISq+KssnN\nJblnF+x/G704AYeDjGlz8BUrDlTd+E+rS/bjI3ANewiAhIkvUnDDTXgbNzU5MiEg4cWxxL89NbSd\n2+de8gYONjEiEQmBQIB5q/5m+fdFv1M0ODmZwbc0Id5xVIP0KgXvec1IW7WG454cCnONxXBs2/4l\npdNN5N15N9mPj4TERJOjFEIIEUXuC/4JR4AwR7wf7uCJHJy0dwFqAyuANcB+wAn8D7gJ2Iqx9pwQ\nR+bzkXTvXcT88D0AAYuFzMlT8V7c3OTAzJd/x904liwm9puvsfh8uAb1I23l59Vmfr+ITnHvvUvi\nmKIFQvLbdyRn5OiqXyiymvEHAry3ciOf//xfqO3sujUY0KExjtiq/0w+UKsWzJlD5jVtcA59AGta\nGgDxb71B7GefkDnpNbwXXWxylEIIIaLEP8CWSL/IIZN2rfUBXSdKqT5AKtBWa7205PFKqcuBj4Gq\nN2ZOlL/CIbbLPgw15Tw9hoK2N5sYVBSxWsl68WVqXnUJlrw87OvXkTBhHLkPDTM7MlFNxS5finNI\n0YPkgstbkDVxCljDqmcqopzP7+etjzbwze87Q23nNTiOe24+hxh79fq7drfrSEHzy3ANGYRjhVGU\nzrZlMyk3XUdev0HkDB0OcXEmRymEEMJkb2utR0b6RcL5BB4CLCwtYQfQWn8JzMeoKi/EYcVPeunA\nIbb3DiTv7ntNjCj6+OvVJ6fY+vQJL47F9vs6EyMS1ZX9u29J6nM7Fr+xNrfn3CZkvvOeFEisYjxe\nP1MW/35Awn5xo9rc2676JeyFArVrkzltNpkTX8UfLEZnCQRIeGUCNa69Avvan0yOUAghRHUQzqfw\nScDOIxyTARx39OGI6sAxfw7Op58Mbee373jAUmeiSN7d9+IJLqFl8Xpx3dcPPB6ToxLViW3DHyTf\n2hlLfj4AvrqnkzFrgVTTrmLcHh+TFv7Kjxv3hNquaHIid93YCLuteibsIRYL7q49SPviWwquuCrU\nbNcbSGlzNQnPPQMFBSYGKIQQoqoL55P4T6CtUspV2k6lVG2gPfBbeQQmqqaYLz43Es+ggksvlyG2\nh2OzkTVhMoFgj2bMr2tJeGWCyUGJ6sL63zaSu3bAmpEOgP+4VNLnLCJw/PEmRybKU57by0tzf2Hd\npv2htlb/O4XbWiusVqlXUMh/0slkzFtM1nPjCSQY69NbfD4Sxz1HSpursa3/3eQIhRBCVFXhZEoT\ngbrAKqVUe6XUqUqpGkqpukqpHsBqjCJ1oyMQp6gCbOt+I+n2HliCPcXesxqR+c4MGWJ7BL4GDcl5\neHhoO+GFMdg2/GFiRKI6sKTtJ7lLe2zbjWJk/kQnGbMX4D+9nsmRifKUnefhhdlr0f+mh9puurQu\nXVqegUUKDB7MYiH/jrvYv2oNBRdfEmqO+e0Xalx7BfETx4PXa2KAQgghKtBTGCuuRVyZk3at9VvA\nM0BjjLnrm4G9wN/ANOBUYKDWekkE4hSVnHXbvyR374Q1OwsA3wknkjFzPoHkFJMjqxzy7h2A57zz\nAbAUFOAa3E9+MRSRk5tL8q1dsG/UAARiYsh8Z4YsO1jFZOQU8PzMn9i8IzPU1vmqM2h3eT1J2I/A\nf3o9MhZ9RPZTz4ZGQlk8HpxPjyCl7XXY/v7T3ACFEEJEnNb6Ka31FxXxWmGNSdZaPw40Ah4H5gGf\nBL8+ApyptZ5c7hGKSs+SnkZyt47Ydu4AwO9KImPWAvwnnWxyZJWI3U7WhFcJBJd8i/npR+KnyOqK\nIgK8XpL63kHM/30Xasp6+TU8V151mJNEZbM/M58xM35i256cUFvPVg1pfdGpJkZVydhs5N07gLRP\nvwo9VAWI+fH/qNHyMuJfnwzB4o1CCCHEsQhrUXcArfVfwLMRiEVURfn5JN3WHbveAAR77N6dia/R\n2SYHVvn4zjyL3AeHkjjaKNqX+NzTFLS+Ht8ZDUyOTFQZgQDOIfeFlrcCyB41Gnf7TiYGJcrb7rRc\nxs5ay75Mo7igxQJ3Xn8Wl557gsmRVU6+hor0jz4hYdKLJLwwBovHgyUvD+djjxC77COyXnoF/2l1\nzQ5TCCFEJSbVv0Tk+P24Bt5D7Ddfh5qyJk3Bc9kVJgZVueUOGIzn3CYAWNxuo6ifz2dyVKKqSBgz\niviZ00PbuQPvJ69vfxMjEscqN9/LF79sZ87Hmi9+2c7f2zMYPeOnUMJus1q49+ZzJGE/VnY7ufc/\nRNryVXgbnRNqjv36S2q0uIS4aW9DIGBigEIIISqzsHvahSirxCeHE/f+wtB29hOjcHe4xcSIqoCY\nGLImTKZGqyuxeL3E/N93xE+dIomVOGZxb75G4osvhLbzu3Qn57ER5gUkjtkHa7aw9JutuD2lP9iL\nsVsZ0OFczq1Xq4Ijq7p85zYmbcUqEsY9R8LE8Vj8fqw52biG3IfjoyVGr/sJJ5odphBCiEpGetpF\nRMRPeZmE14rmXOfe1Ze8/oNMjKjq8J1zLrmDh4S2E58diXXT3yZGJCq72CWLcD76cGjbfU0rssZP\nMsZNR5mSPce5+VKQsTQfrNnCoi82HTJht9ksPNC5iSTskeBwkPvoE6R/9DHeYtOXYld9So0rLsYx\nb7b0ugshhAiL9LSLcud4fyHOJx4NbbtvuImcUWOiMgGorHIHD8Gx9EPs69dhycvDdf8AMhZ9JOvd\ni7DFfPUFSf3uxhJMIjzNLiDzjXchJsbkyA5WWs/xrE/+5Prmp9H2krrmBWYCfyCAz+fH4w3g9fnx\n+vx4fH68Xj/ZeR4+XLPlsOdbLRZOre2qmGCrKW+z/5H26VckPjuS+NcnYwkEsGakk9S/D+6PPiBr\n7EsEUlPNDlMIIaKSUsoBrAW+01rfHmyzAI8CfYHjgK8xVi/bYFacFUWSdlGuYr75Glf/PqFtz4UX\nkzn5DbDZTIyqCoqNJWviZFKuuwqLz0fsN18T9/ZU8nv3OfK5QgTZfvuVpF7dsBQUAOA9owEZ782D\nxESTIztYYc9xSW6PL9QeycTd5/fj9QaMxDiYHBvfG0mzx+svSp5LSaS9vkDo+6K2YtcIXa/wmGLX\nCF276Bo+/7H11Hq8fn7YsJvLm8hQ7YiKjydn1GgKrr8R18B7sf2zBQDH0g+I+W4NWc+/REHbm82N\nUQghotOTwJnAd8XansBYtWwosAV4DPhUKdVIa51R4RFWoKNK2pVSpwJNgARgH7Bea729PAMTlY9t\nwx8HJwDTZkF8vMmRVU3exk3JHXg/iS8Z85Cdo56k4JpWUqVYlIl16xaSu3XEmp0FgK/OCWTMWUSg\nVvQNl87N97L0m62HPeaDrzeTEGvDarUUJbwHJMJFCbfHe4iE+oBkOnBAYl0VRzOn5xSYHUK14Wl+\nKfs/X4PzqceJf/dNAKz79pHcuyf5HW4he/RYAjVqmhylEEJEB6XUecAgYG+xNhcwBBihtZ4YbPsS\n2Ar0BsabEGqFCStpV0rVBd4AWpbYFVBKfQb01VpvLqfYRCVi3bHdSAAy0gHwHV+bjNkLCdSMvgSg\nKsl9cCiOZR9i1xuw5ObgemAQGfPfl6kI4rAse/eS3KU9tt27APAnJZMxeyH+U6JrjW5/IMCu/bms\n+P6fQ87NLuT1BZjxyZ8VFFl0sNss2G1W7DYrMXZraNtd4GN/lvuI56ckxlZAlCLE6SR77Iu4r78R\n1/0DsG3/D4C4hfOI+fpLssdPpODa1iYHKYQQ5lJK2YG3gLFA+2K7LgacwJLCBq11mlJqNdAaSdoN\nSqk6GPMGTgD+L/j9dqAGcCVwDbBaKXW+1nrvIS8kqhxLZgbJ3Tph+28bAP5EJ5mz5uM/9TSTI6sG\nHA6yJkwm5fprsPj9xH75OXHT3yG/1x1mRyaiVXY2yT06YQ8WLww4HGROn42v0dmmhuX3B9ixP5et\nOzPZsjOLf3ZmsXV3Nu6C6FjS0ALY7UUJckxhwlzYZgsmzfbC7wuPLZlYG8fFlDzXXqw9uH24a9ps\nVqyHeDiXm+/lwVe+PuyDDkeMjQvOPD5Cd0scjueqq0lb/Q3Oxx4hbs5MAGy7dpLcozN5PXqRM/JZ\nAq4kk6MUQgjTDAVigdEcmLQ3DH4tWX15E1Dl5xmF09P+JEbCfq/W+rWSO5VSdwGvYxQHeKB8whNR\nr6CApDt6Yl+/DoCA3U7mW9PxBtcSF5HnPf8C8u4dSMIrEwBIHPEYBS2vwX/yKSZHJqJOQQHJd95K\nzM8/ARCwWsl89U08zS+t0DB8fj879uayZWcWW3dlsXVnFv/szqLA4z+m655xUhInpzpLJNOWEom1\nFbu9tOQ4mDTbraX2XtusFiyVZARLQpyd65ufVmoNgELXNz+NeIeUtTFLIDmFrElTcF/fFteDg7Du\n3QNA/IxpxK5eRdaEyXguv9LkKIUQomIppc4ChgNXa60LlFLFdycBbq11ybldWcF9VVo4n9g3AB+X\nlrADaK2nKqVuwXjSIUl7MQkJsSQmOg5q37cvG78/YNr+Y45vbxaJ9/Uj9svPQ22WN94gpXO7qPj5\nIrE/NdV1TOdHan/C2NHw8TLYuBFrdha1hj3Avvfm4Q+Y9/4rFA33p7Lsj+j7y++HXvfC55+Fjske\nMw57504kR/Dni4uPweWMO2j/0Cnfsj8zn26tFIO7Nzto/21PrWB/Zj6339CIji0bHHJ/t1aK7ted\nGbH4K+v+my87HQBnooPO1zQ8aL/Z8UX7/kKRfn17x/ZYr78G+vWDefMAsG37l5SObcnr3Qf/s6NJ\nPP7gue5m35/K9PkYjfsLRWt80bq/8D0WrfFF036I3vfXoSilrMBU4E2t9TelHGIBDlVh5tie+lcC\nlkAZq+sopdzARK31Q4c55gWgv9ZaKo8Vs2dPVlSWMEpNdbFnT9ZRn5/49AgSJhZNH8l55DFyH3j4\n0CdUcsd6vyLN/v13pLRtFVq6K3PCZNzdbjUtnmi/X9Em0vcr8cnhJLw6KbSd8+BQcocOL9fX8Hj9\n/Lc3m607jd7zLTuz2LYnG6+vbP8FJjtjqVvbxWl1jD916ySR4ozFYrEcsnp8ofZX1Kt2y76VRZ7b\nyw8bduMBYoALzjxeetjLwIz/vxyLF+Ac+gDWtLRQm/f0emRNeg3vhRdVaCzhkv/vwyP3K3xyz8IT\nrfcrNdV1yCFrSqn7MArNnQtkB5t/AH7BKDTXF5gEOLTWnmLnTQBu1FrXj1Tc0SCcT+5dQOMjHNOY\nYlX+RNUV99YbByTseT3vIPf+Qz7PERXAe+FF5PW5l4TXJgPgfHwYnhYt8Z8gSzpVd/GTJx2QsOf1\nvIPchx89pmt6vD627ckxhrjvzGTrzmy27cku81JkNVwO6gaT89OCiXqK89BP5AsT8pLrtDtibNVy\nnfayinfYubzJiVH7C5wo4m7XEU/zS3E+OAjHyuUA2DdvIqVtK/L6DSJn6HCIO3jUihBCVBHtgZOB\ntBLtTYBeGEm7BTgd2Fhsfz1AV0SAZgonaV8K3K2UukNr/XbJnUqpe4CrMarLRxWlVC1Kf5iwQGvd\nSSllwZiL3xc4DqPI3kCt9YZi13AAY4BuQCKwAhhUHZe6i136Ic5hQ0Lb7uvakP3cOKlYHgVyhj2B\nY8UybFs2Y83MwPnQYDKnz5G/m2rMMW82zhFFPeruNjeS/fz4sN4TBR4f/+7OPmAO+va9OWVO0Gsl\nxVG3jotT67iMRL22i6SjqFze9pK6XNPsZOk5FlWWv3YdMqfPwTFnJs7hQ7FmZWIJBEh4ZQKxn6wg\n6+XX8DY5z+wwhRAiEvoCrhJtMzAS9KeCXycA7YDnAZRShQXRn6q4MM0Rzm86IzBu0lSlVC/gSyAD\nOAm4FLgAozd+ZDnHWB4Kq6K1wihWUGhf8OsTwCMY1Qq3AI8BnyqlGmmtM4LHTAFuAh7EGLIxGliq\nlGqmtY6O8sYVwP79dyTdc2doCLbn/GZkTnkL7PJLc1RISCDrpVdIaXc9AI6Vy3HMn4P7lq4mBybM\nEPPZJ7ju6xfaLrj4EjKnvAk22yHPcRf4+Gd3VtEQ911Z7Nibi7+MU6lSU+JCPed16yRxam0nroTy\nW1pMeo5FlWex4O7aA89lV+AaPIDYL1YBYNcbSGndktz7HzJGtsXEmByoEEKUH631Qb3lSqk8YJ/W\n+ofg9iRglFLKj5HEDwcyMebCV2llzrS01juVUpdiVIi/CuOpRnGrMNZpj8ae58bALq31xyV3KKVc\nGPMnRmitJwbbvgS2YsyfGK+Uqo8xLKO71npO8JhfMIZi3AwsrJCfwmS2v/4kuWdnLPn5gDHXLuO9\neZCYaHJkojjPJZeRd+fdxL9lDHpxDn+YgiuuIlC7tsmRiYpk//lHku/sicXrBcB7ViMyp82C+KKS\nI3luL//symLrrmxjiPuubHbsy6GM+TnH14g/aIh7YpwkEkKUB//Jp5AxdxFx77yJc+TjWHJzsfh8\nJL4whtgVy8h6+TV8ZzUyO0whhKhIj2IUnRuCsWb7GuC2Yp2sVVY467TX1Vr/DVytlPp/9u47Tqrq\nbOD4707daVuAXWBpC6hXRY1dwEaLYMFEjcZo1MSGDVQ0atQYjbHGjhp9Y2zRFDXG2BAFwQKoUbHj\nkbZLXerW6eW+f9zZYXfZNrC7Mzv7fBM+O3PPnbvPHodlnnvOec5gYH/M8vp1wBdKqTVdFGNn2A/4\nqpW20Zj/0V9tOKCUqtJ1/T1gCnAfMCHZ9Hqjc5bpuv5t8pycT9q1jRspOP2UVIGcRL9+1Pzj3xj9\n+mU4MtGS+htvwTH3bayrK7BUV+O75kpqn35epsn3EtYVyyg442doAT8A8UGDqXz6X6yqgXK1OjXF\nfeO2QKtlWBvTgP593OYU9/6+1Fd3nsywEaJLWSyEzr2AyLgJ5F9+CfaPAz4+OQAAIABJREFUzYLK\n9q+/pOjHR+G/5gaCl85oc/aMEEL0VEqp/Zs9j2HOjr4uMxFlTjqfuObruv4/pdRpSqm1wNquCqoL\n7AeEdF1fBByIub79QeAeoGEvnBXNXrMScxSd5DmVSil/C+fsuJdOrqmvp+CXp2FdXQ6A4XZT89wL\nJEbkdJHGns3rpe6+WRT+7EQAnLNfx/nKvwmf9LMMBya6mmVjJb7TTsKy1Vz9E/Dkc/vJv+fLfzX/\nFdcyTYOBfT2Nprj7GFLilXXjQmRQYsRIql95E9fjj+K54w9o4TBaJIL3j7/HOft16h5+jPjIHbdG\nFEIIkRvS+RQ2ADNJ7VF0XbcCewN+zKkUFZh7zt8JuIAoEFZKRZq9tA5zJgFsn1HQXB0wpAvCzh7R\nKAXnn439yyUAGBYLtf/3FLEDD85wYKI90aPGETzr17j+ZtaN9P72aiJHHI1RXJzhyERjgVCMT9Um\nYgbYNDhYL0lrBLs+GKW8spaKyjoqV23gjDsvpm/lagDCNgc3Tb0eZW/5v7lF0yjt526yBn1IiRen\nQ0bthMg6VivBS6YTmXQMvssuxP6F+e+y/bP/UTThCPw33kzwvGlgsWQ4UCGEEJ0tnaT9fWCSrutO\npVS4qwLqIicAq5VSy5PPF+i67sUsPHcbtDpDNJH8qnXgnFYVFbmx2bLzQ3BxcfMijY0YBpx3Hrw7\nN3VIe+wxCs48rRsiy05t9lc2evgBWDAX1qzBsm0b/W6+Dl54odu+fY/rr272r7mKl+YtIxTZXsvy\nn/OW8bOJu/PzSfoO51fXhVmxrprla6tZsbaGFWur2VQVBMAei3Dzy39gaKV5bzWuWbjzhGtQpXsC\nYLVoDB3gY7fBhYwcXMjIwQWUDcwnz9FzR9Dl/ZUe6a/0ZG1/FR8M//sE7rwTbrkFYjG0YBDvDdfi\nfWc2PPUUlJV1f1jZ2l9ZSvorfdJn6ZH+yi3pfFr7C+aG9j/ouj4bWAUEWzqxoaBbNkhWdn+3haa3\ngIswR+Cduq7blVLRRu0+zOr4JL+29M5vfE6rqqoCacXcXdqrvuy++3Y8T23f3c8/8xoCPz0demnF\n5p5ZrVrD/qcHKTz9ZPPpiy9S8+RzRKb+pO2XdYKe2V/d57VF5fzn/R0nL4UicZ6b/T1bqwLsMbjQ\nrOCe3Gqtqq7l+6WWRJyZsx9gv7XfpI7945SrcE89kbOTheIGF3uwN7t5WFcTbHEKUU8g76/0SH+l\np0f017TLsY4dT/5l07At/dY8tmABiX32xX/rHYTOPLvb6pj0iP7KItJf6ZM+S0+29pfcSNh56STt\njYfnLmzjPAPImqRd1/VSzJH2/yilNjdqaiihXIU5kj4cc+uABiMwq8MDLAMG6LruUkoFm53zQZcE\nnmF5zz2D5547U89Dp59J4Nob2niFyFbRCZMI/uKXuP7xHAC+a2eybewRGH37Zjiy3isQivHm4oo2\nz5n90Wpms7rda9ksGld+9CxHLFuUOlb725uYfOXVuxynECK7xffdj6q3F+C+9y7cD92Hlkhg8dfj\nmzkdxxuvUn/fLBIDSzMdphBCiF2UTtL+6y6Loms5gccBD3B/o+OnYCbpLyfbfwrcDaDrehHmlna3\nJM+dB1iBqSRvXui6vjswCnP/+pzimDsH72+uSD2PjJ9I3b0PSeXxHsz/h9txzJ+HtXIDli2b8d5w\nDXWP/TXTYfVaHy+tJByNt39iM3abhaElXoYO8FGWXIe+x3N/xrfov6lzAudPI3zFVZ0ZrhAimzmd\nBK6/icjkY/FNvwjb8mXm4XnvYD9qNPW33034Zz+Xf8OFEKIH04yObsjbg+m6/nfgROAGYClwKuYe\n7D9VSr2q6/rdwOXJ9h+SXwcBoxr2/dN1/QVgMmYxuyrgDsyp9Qclp+C3avPmuqzs5JamztiWfEbh\nScejBcwp/dH99qfmlTcwvDKdJVunGnWU4523mtQjqHn2n0SmHNdl36+n91dnMgyD9Vv8fLNqG9+u\n2sZ3FVUkEu3/WuiT7+SA3YvNvdD7+xjYz421UZGpvOefxXflZannoZ+cTN3jT/aKQlTy/kqP9Fd6\nemx/BYN4bv8Drv97FK3R57vwcVOp+9MDXVaItMf2V4ZIf6VP+iw92dpfxcU+uXu4k9KuQKTrug34\nMeY+7UVKqWt0Xd8XqFNKlXdyfJ3lPOB3wBXAQMzE/RSlVMPe7NdjFpS7GnPP9kXAOQ0Je9KvMUfq\n7wIswFxgRnsJe09iWbWSgjNPTSXs8aHDqHn+RUnYc0Tkx1MInXo6eS/+EwDvb66gavQYjMKiDEeW\nm+oCEb4rr+KbVVv5dtU2quubb1DRvp8cPpwjf9Ty1FbHnNl4r5qReh45chx1Dz/eKxJ2IUQrXC78\nt95B5Njj8c24JLVVq/PN17B/vIi6Pz1I5IQTMxujEEKItKU10q7r+jjgWcxRaA0wlFJWXddvxdzk\n/rdKqXu6ItCerCeMtGtbtlB4/CRsq8zCWImiIqrfmEt8N9n3tUG23rVMh1a1jaIjD8O6aSMAoZ+f\nQd2sx7rke+VCf6UjFk+wYl0N36zaxjertrG6sq7VLSeg7S0pAJx2K/dddniL+6PbPvmYwp9NRQuF\nAIju+yNzRowvf4dzc1Vve3/tKumv9ORCf2n1dXhu/h2uZ59scjx0ymnU3/GnTr1hmwv91Z2kv9In\nfZaebO0vGWnfeR0eadd1fX/gTSAA3A7sCSRLUvMRUAncpeu6Ukq91tmBii4UCFBw1mmphN3Iy6Pm\nby9Iwp6DjKI+1N99PwW/OgOAvH/9nfBPTyYy8ZgMR9bzGIbBxqog3yanvC9dXUU40vrEG7fTxt5l\nRYwa3odRZX1Y/N3GFqvHNzhuzLAWE3br90sp+OWpqYQ9PrSMmr+/1KsSdiFE+wyvj/p7HiB83An4\nrrwM64b1AOT9+wXsH75P/f2ziEyanOEohRBCdEQ60+NvAUKYa7grdF3/PcmkXSn1hq7rhwJfATMB\nSdp7iliM/IvOxf7ZpwAYmkbtn/9K7NDDMhyY6CqR404gdNIp5P3n3wB4Z86g6oOPMfILMhxZ9guE\nonxXXsW35WaivqUm1Oq5Fk1jRGk++wzvw6jhfSgb6GuyHn3q2DIA3lxc0aQondNu5bgxw1LtTa65\nbi0Fp5+MpboagES/flS/8B+M/v075wcUQuSc6IRJVL3/Ed4briXvhX8AYN1YScEZpxL85Tn4b7lN\nbvoJIUSWSydpPxJ4QSnV4j5FSqkNyWJtp7XULrKQYeD97W9wvvVm6lD97XcTOX5qBoMS3aH+9ntw\nfPAeli1bsG5Yj+fmG6m/b1amw8o68USCVRvq+HbVNr5ZtZWV62tpa0VRv4K8ZJLel72GFeLOs7d5\n/aljy5h00GA+/X4TUcAOHLxnSYsj7FrVNgpOPxnr+nUAGG4PNX9/icSIkbvwEwohegOjoJC6hx8n\nfPyJ+K6agWWLuQOu67lncCx4l7oHHyV65NGZDVIIIUSr0kna8zCrpbclxvb9z0W2u+MOXM9s3/Yr\ncNkVhM6blsGARHcx+val7s57KTj/HMD84BY+8SSi4yZkOLLM21Id5JvkSPrS8ioC4Vir5zodVvYa\nak5532dEH0oKXWhpbqvkcto48kelba8/CwYpOOt0bOp7AAy7nZqnnye2/4FpfS8hRO8WOfZ4th06\nGt81V+J87RUArGvXUHjKVALnT8N/4y3gdmc4SiGEEM2lk7QvBX6s67pFKZVo3qjruh1zSzTVWcGJ\nruP819/hhhtSz0OnnIb/xpszF5DodpETTyJ8wss4Xzf3+PbNnE7V+x/1ut0CQpEY31dUm6Pp5dvY\nuC3Q6rkaUDbQl1qXPnJQATZrF1drj8XIn/Zr7J98lDpUN+sxucEihNgpRt++1D7xDM5X/o332pmp\n5TbuJx7H8e5c6h56TJbICSFElkknaf8L8CjwtK7rVzZu0HW9BHgY2B1zv3ORxewfLWqyt3PkyKOp\ne/BR2SqqF6q7817siz7Asm0b1rVr8PzhJurvvj/TYXWphGGwemMd36w0R9OXr6sh3sae6UU+pzmS\nPrwPew0rwud2dF+whoH3N1c0XcLyh9sJn3xq98UghMg9mkb4pJ8RHXsE3pnTcb4zBwDbyhUUnjiZ\n4CUz8F9zPeTlZThQIYQQkEbSrpR6TNf1scAvgTMxi9Kh63o5MBhz7/JXgEc6PUrRqfKe/itazJzy\nG9trFLVPPQeObkxERNYwSkqov+1u8i8+HwDX0381p8kfcVSGI+tcVXXh1Lr078qrqA9GWz3XYbOw\nx9BC9hnel1HD+1Da1532lPfO4r7rj7iefzb1PHDp5QQvuqyNVwghRMcl+g+g9rkXcP7zebw3XIul\nvg4tkcD98AM45s6hbtZjxH50QKbDFEKIXi+dkXaUUmfruv4acB5wIGbdpHzgQ+BppdTTnR6h6HSR\nY6bgfP2/aHvtRc0z/5Sq4b1c+ORTCf/35dRoru/Ky9i2YDF4PBmObOdFonF+WFPNN8nt2NZtabsc\nx5ASrznlfXgf9hhcgN1m7aZIW5f35F/w3Pen1PPQqafj/90tGYxICJGTNI3wL35J9Mij8V1+KY4P\nFgBg+34phcdOJHDF1QSu/A3Y2y6sKYQQoutoRlulkEWn2Ly5Lus6Wauvo9+wAWze2l5tQdGgzUJh\nPZxlYyVFRxyKpcZc2xi44CL8t929S9fszv4yDIO1m/3JPdO3otbUEIvvUHojJd9tTyXpo8r6UOB1\ndkucbWncX47XXiH//HPQkr+fwxN/TO2z/5QPzY3k8t/HriD9lZ5e21+JBHlP/xXvH36HFthe3yO6\n3/7UzXqM+F57t/iyXttfO0n6K33SZ+nJ1v4qLvZlZupiDkhrpF3kDsPrkzXsIiXRfwD1t95B/oyL\nAXA98TjhqScRGz0mw5G1rtYf4bvybanR9Bp/pNVzbVaN3QcXpvZMH1zixZKhKe/tsS/8gPyLz08l\n7NEDD6L2iWclYRdCdD2LhdC5FxAZN4H8GRenCmDav/qCoh8fhf/aGwleMh2smZ+NJIQQvUmHk3Zd\n1y3ApcAZQBnQ2tCUoZTqu+uhCSG6U/jnZ5jT5Oe9g2YY+K64hKp3F2bN9j/RWILl62r4ZtVWvl21\njdUb69s8f2Bfd7KAXF/0IYU4Hdn/IdP6zdfkn/0LtIh5AyI2cjdqnn+pRy9VEEL0PIkRI6n+72xc\njz2C585b0cJhtEgE76034Zz9OnWz/kx85O6ZDlMIIXqNdEbafwfchLnr0UagpksiEkJkhqZRf+9D\n2I88DEtdLbaVK/DcdRv+W27LSDiGYVC5LZAaSf9+dRWRaOtT3j15NvYu65Oq9N4nv4dVPV61ioLT\nT8ZSVwtAvP8Aav71H4y+cg9UCJEBVivBS2cQmXQMvunTsH+xBAD7p59QNOEI6n93C6FzL5RZe0II\n0Q3SSdrPAVYD45RSFV0UjxAigxKlg/Dfchu+mdMBcD3+COGpPyF28KHd8v3rg1GWVlTxbXI0fWtt\nuNVzrRaNkaX5ybXpfSkb4MNiyc4p7+3RtmyBn0zGumkjAAlfPjX/fJnE0GEZjkwI0dvF9T2pfmMu\n7ofuw33vXWixGFowiO/6a3C++Tp1DzwCxftkOkwhhMhp6STtJcAjkrALkdtCZ56N878v43hvPloi\nge/yS6ia92GH9+sNhGJ8qjYRM8CmwcF6Ce68ln/VxOIJVm2oNfdML9/Gqg21tFUbs6TQxagRfdin\nrA97DivC5ezZZTm0qm043l+A66H7YdkyAAyHg9pn/0F8lHwIFkJkCbudwFXXEjlmCr7LLsK29FsA\nHB++T9HRY+CB++HE0yBLa4UIIURPl84n3s+B3boqECFEltA06u6bRdFRo7H467Et+wHPPXfiv/Hm\ndl/62qJy3lxcQTgaTx37x9xlHDdmGFPHlgGwqTpo7pm+civfr64iGI63cjVwOa3sObSIfUb0ZVRZ\nESVF2bG+fqfFYtiWfIZj/jwc8+diW/I5WmL7lH9D06j981+JHn5kBoMUQoiWxfb9EVVvL8Bzz524\nZt2Plkhg8dfDBRdQ+MijBKZfSeS4qVKoTgghOlmHt3zTdf1wYC5wJfC4UirrtjHLVtm45Rtk73YQ\n2aq39VfeU0/gu3YmAIbVSvXsecT2P7DV819bVM5/3l/Zavvwgfn4g1E2VQdbPUfTzPNGlfVhnxF9\nGD4wH5u1Z6+XtKxfh2P+POzz5+F4b35qW70daBp1d95L6Nfnd2+APVRv+/u4q6S/0iP91T7bp5/g\nm34RthXLmxyPjRhJ8JIZhE77RYdnaPU28v5Kn/RZerK1v2TLt52X1j7tuq4/AEwH/MAaoKUFp4ZS\n6qDOCS83SNKeG3pdfyUSFJwyFcfCDwCI7bU3VW+/B84dN44IhGJc9cjCJiPsHdUn35nciq0vew0r\nwuvq4VubhULYFy80R9MXzMP2/dJWTzU0jdiBBxEZNxHP2WeweeDw7ouzh+t1fx93kfRXeqS/OigQ\nwHPXbbif/D8IN/1ImCguIXDhxYR+dR5GQWGGAsxO8v5Kn/RZerK1vyRp33npbPl2JWbCrgFeYK9W\nTs3KBFUIkSaLhbr7H6bPuDFogQC2pd/hvv9PBK67cYdTP1WbOpywO+wW9hxalKryPqCPG60nr4M0\nDKzLl+F49x1zRH3xQrRg67MJ4gMGEhk/keiESUSOGodR1AcAT7EPsvAfWCGEaJXbjf+W23D//gb8\nd96D66knUrOJLJs34b3tFtwP3kfo7F8TnHYJiYGlGQ5YCCF6pnTWtM8AtgJnAguVUoGuCUkIkS0S\nZcPx3/B7vDdcC4D7ofsIH38i8X33S51jGAY/rGllynczR+w3kLOO0bHbevaUd622Bvv77+GYPxfH\n/HlY165p9VzD4SA6+nAi4ycSmTCJ+J57SbEmIURu6d+fwPU3EZxxJXnPPo3r8UewblgPgKW+Dvej\nD+H6y58JnXo6wUsvJ777HhkOWAghepZ0kvb+wGNKqXe6KhghRPYJnjcN56uvYP94MVoshu/yS6ie\nMx/DZuPL5Vt5bdEqVm3o2Ajx7oMKembCnkhg+3KJOeX93bnYPvsfWrz1mQWx3XY3R9PHTyQy5gjw\neLoxWCGEyAzD6yN4yXSC50/D+e8XcD/8ALZlPwCgRaO4/v43XH//G+EpxxOYfgWxQw7LcMRCCNEz\npJO0LwX6dVUgQogsZbFQ9+AjFI0bixYKYf/mK6puupUHdz+B1ZvqO3wZp93KwXuWdGGgncuysdIs\nHrdgHo4F72LZtq3VcxNeH9Gjxpmj6eMnyv7qQojezeEg/ItfEv75GTjefgv3rPux/+/jVLPzrTdw\nvvUGkdFjCU6/gsikyTIDSQgh2pBO0v5H4O+6rr+olHqtqwISQmSf+IjdqL/2Rny3mOvZhz/1MNqZ\nZVBcBoDNamFoiZeVG2pbvcZxY4Zl977q4TD2Tz5Kbsc2D9u3X7d5enT/A5Kj6ZOIHnQI2Ht4AT0h\nhOhsFguRKccRmXIcto8W4374fpxvv5Vqdny0CMdHi4jttTeBSy8nfNLP5HepEEK0IJ1P0Hthjra/\nout6ObAcs4p8c4ZS6pRdD00IkQ3iiQQff7eRN6wHMXOgzp4bFPZEjMvfnsX1Z93DUQcNZfKhQyny\nOVvcp91ptzbZpz2bWFauSK1Ld3z4AVqgpV9ppkRxSWokPXL0BIx+MvFICCE6KjZ6DLWjx2Bd+h3u\nRx7E+fKLaLEYALal35F/2TTid9xK8KJLCZ55Dni9GY5YCCGyRzr7tCc6eE1DKWXd+ZByj2z5lht6\nW3/F4gkWfVPJm4srUnurD966hgefm4kjHgVg6zW/I3H1b5q8LhiO8en3m4gCduDgPUuyZoRdq6/D\n/uEHqUrv1oryVs817Haih44mMn4SkfETiY/aByxdtx6/t72/dpX0V3qkv9Ij/ZWenekvy9o1uB5/\nBNffntnhhmmiqIjgry8geP5FOXmDVN5f6ZM+S0+29pds+bbz0knaO7xIUylVsdMR5SBJ2nNDb+mv\naCzBh1+t582PKtha23TfXZfTylWr3+Gw5x8CzMroVe8uJL6HvsN1sqK/Egls335trk1/dy72/32M\nFo22enq8bHiyyvuPiR5+BIbX122hZkV/9SDSX+mR/kqP9Fd6dqW/tKptuJ56AtcTj2HZsqVJm+Fy\nEfrFLwlcPJ3EsLJOiDQ7yPsrfdJn6cnW/pKkfed1ePhLEnEhclskGue9L9fz1serqaprmqx78mz8\n+JAhTDpoMG7b4US//QD7F0vQIhGzmvzrb4M1OybYaFu24Fhgrkt3LHgXy+ZNrZ5ruD1EjjyKyLhk\nAbkRI7stTiGEEGAU9SEw8xoCF08n7x/P4X50FtbV5QBowSCuJ/9C3jNPEv7JSQQuvaLJlqNCCNFb\ndDhp13U9v6PnKqVar0YlhMgqoUiMBUvW89Ynq6n1R5q0eV12Jh86hAkHDm4yxb3uwT9TNOlItGgU\n+2f/w/X4owQvmd7doZuiUeyffmKOps+fh/3LJW2fvs9+5lZs4ycSPXQ0OBzdFKgQQohWuVyEzr2A\n0Nm/xvnaK7hmPYD9m68A0OJx8l5+ibyXXyIyfiKBy64gesRRUnFeCNFrpLPQtBro6DTv7BhyE0K0\nKhiO8e7na5nzyRrqg02njBd4HEw5bCjj9h+E07HjX+f4XnsTmHkNnrtuA8Bz561EJk8hPnL3bond\nsroitWe6/YP3sNS3PgUs0bcvkaMnmNPex03E6N+/W2IUQgixE2w2wif9jPBPT8G+4F3cDz+A44P3\nUs0NO3xEDziQwGVXEDluatbM9BJCiK7SatKu63o/pVTjxUXv03LS7gZGAH2Bj4CPWzhHCJEl/KEo\ncz9dy9xP1+APxZq0FfmcHDd6GEfuNxCHve0PQYEZM3G88Rr2b75CC4XwXXEZ1f+d3TXF2vx+HIs/\nTK1Nt61Y3uqphtVK7OBDiUwwC8jF9tu/SwvICSGE6AKaRnT8RGrGT8T2xee4Hn4Q5+v/RUuYdZHt\nSz6n4LyziY0YSfCSGYRO+wXk5WU4aCGE6BptjbR/oev66UqpDwGUUuPaupCu65cA9wAzOy+8zqXr\nuhP4AvhYKfWr5DENuB6YBvQDFgLTlVLfN3vdncAvAA8wB5ihlFrfrT+AELugLhDh7f+t4d3P1xIM\nx5u09c3P4/gxwzh834HYbR1McO126h58lKLJ49BiMewfL8b118cJXnDxrgdrGFiXfpcaUbF/tBAt\nEmn19PiQoakq79Ejj8LIL9j1GIQQQmSF2P4HUvfEM/hXrsD96Czy/vU8WtisvWJbuQLf1Zfjues2\nAtMuIXTOuRgFhRmOWAghOler1eOTW7zFgN8qpe7tyMV0XX8DcCmlJnReiJ1H1/Xbgd8CzzRK2n8P\nXAdcC5QDNwKDgL2VUjXJc54CTgSuAuqBOzD3qD9IKdU0+2mBVI/PDT21v2r8EeZ8spr5n69rsn86\nQEmhi+PHDmPMqAHYrDs3Gu2+84947rsbMCv9bluwmMTwEWn3l1a1Dcd781Nr062VG1o913C5iIw9\nguiESUTGTyI+crcev7axp76/MkX6Kz3SX+mR/kpPd/eXtnEj7iceI++pJ7DU1jRpS3h9hM45l+C0\nS0gMGNhtMaVD3l/pkz5LT7b2l1SP33ltjbTviZmkngF0KGkHvgIu29WguoKu6wcAM4AtjY75gKuB\nm5VSDyWPfQBUAOcB9+m6PhI4GzhDKfWv5DlfAgr4CfByd/4cQnRUVV2Y2R9X8P4X64nEEk3aBvZ1\nc8KYMg7duwTrLk4dD8y8Bufs17Et/Q4tGMR35WXUvPx6+y+MxbAt+QzHu3NxLJiHbcnnqWmPLZ6+\n195mlfcJk4geNkamQQohRC9l9O+P/4bfE5hxJXl/ewbX449g3WBOfrTU1+F+5EFc//cooVNPJ3jp\n5cR33yPDEQshxK5pNWlXSv0ATNN13d2RC+m6bgGOBoKdFFun0XXdBjwJ/Ak4qVHTaMALvNpwQClV\npev6e8AU4D6gYdbA643OWabr+rfJcyRpF1lla02INz+u4IMv1xOLN53kMbjYwwljyzhYL8Fi6aSb\nnQ4HdQ8+SuGxE9HicRyLPiTv6b/CtTuulLGsX7e9gNz7C7DUVLd62URhIZGjJ5iV3sdNIFE6qHPi\nFUIIkRMMXz7BS6YTPH8azn+/gPvhB7At+wEALRrF9fe/kfeP54hMOZ7A9CuIHXxohiMWQoid0271\neKVUAEDX9RmtnGLBXOd9LHAY8EynRdd5rgUcmNPaGyftDbdeVzQ7fyXmKHrDOZVKKX8L58itW5E1\nNlUHeXNxOQu/riSeaJqsD+3vZerY4RywRz8sXTCNPLb/gQQvvRz3Q/cB4P3DTfDzk8Hqwb54YXJt\n+lxs20tF7MCwWIgdeLBZ5X38RGIHHCQVgYUQQrTP4SD8i18S/vkZOObMxj3rfuyffgKAZhg4Z7+O\nc/brRMYcTnD6FUQmHtPjl1QJkYt0XXcANwFnYdYa+xi4Win1ebK93VpkuSqdLd8ewKwe39Zvuc8w\n14dnDV3X9wJuACYqpSK6rjduzgfCSqnmFa7qkm0N57S0KKQOGNLJ4QqRtsptAd5YVM7ibzeSaFaj\nYkRpPlPHlrHfyL5oXfwBxX/1dTjeegPbDwot4IcjjqDf1q1ooVCrr4kPLE1VeY8eNQ6jsKhLYxRC\nCJHDLBYixx5PZMpxZnHUhx/A+fZbqWbH4oU4Fi8kttfeBC69nPBJPwO7PYMBCyGauR8zYb8WWA5c\nDszXdX0/pVQFZkLfvBbZPF3XU7XIclU6SfuvWzluABHge6XUF7seUudJTtl/AvirUmpxC6dotL73\nfCKNc9pUVOTGZsvOEcPiYl+mQ+hRsqm/KipreWHuD3z4xTqaDawzakRffj5pD/bfo7jLk/XtfPDM\n03D44ZBIwLp1O97hczrhqKNgyhSYPBnr3nvj0jRc3RRhtsum91eVJDpFAAAgAElEQVRPIP2VHumv\n9Eh/pSfr+mvqZPPPN9/An/4Ef/87xMxtTm1LvyP/smlw920wcyacfz54PN0aXtb1Vw8gfZaentZf\nuq4XABcA1yml/pw89iGwFThL1/UHaacWWUYC7yYdTtqVUtk47b0904GhwPHJde0NtOTzGsCp67pd\nKRVt1O5LtpH82tK7vvE5baqqCqQdeHfI1sqS2Spb+mv1xjpeW1TOZ2rzDm17DSvixMPL0IeaI9Zb\nttR3b3AjR+G5ZAbuhx9IHYrtvoc55X3CJKKjDwd3ozIZ3R1fFsuW91dPIf2VHumv9Eh/pSer+6v/\nMLjnYSxXXIvr8Udw/e0ZczYYwOrVcMUVJG65heC5FxI8/yKMvn27PKSs7q8sJX2Wnmztr3ZuJPgx\nl1qXNzoWxRw8ddKxWmQ5K52R9p7oJGAwUNXs+I8wK8JPwxxJHw780Kh9BGZ1eIBlwABd111KqWCz\ncz7oiqCFaMmqDbW8trCcL5Zv2aFtnxF9OHHscHYbnPn9yf033kxs1D7kOzS27n8YiSFDMx2SEEKI\nXi4xeAj+W+8kMPMaXE/+BdcTj2HZuhUAS1UVnnvvwv3oQ4TOOIvARZeRGFaW2YCF6GWUUjFgCaRm\nS5cBN2Mm7c8Bk5KntlWLLGe1mrTruv7uTl7TUEpN3MnXdrZp7DhK/jxmgn5L8uuDwE+BuwF0XS/C\nrIJ/S/L8eYAVmAq8kDxnd2AU5htJiC61fG0Nry5axTcrt+3Qtv9u/Zh6eBnDB+a38MoMsVgIn3Ia\nFPtIZOFdXiGEEL2XUdSHwFXXErh4Onn/fB73o7Owri4HQAsGcf31/8h7+q+Ef3ISgcuuJL7PvpkN\nWIje6Xdsz7NuUkopXddPpv1aZDmrrZH2cWleq6FIXWvrv7udUko1P6brehDYqpT6NPl8FnCrrusJ\nzCT+BqAWcy08SqkVuq6/CPwludaiCrMK/VfAKx2Jw+124PE4dzi+dWs9iYSRsfZsjy8b2xtP6+nq\n7//JV+t4acEK9tdLuOPSI3do/+r7Sgb29WRV/7QkW+PLxvbufH/19PYG2RpfNrbL+6vj7Q2yNb5s\nbO9x7y+3G8tll2K9aga89BLcdRd8YZZm0uJx8l5+ibyXX4LJk+Haa2HcuFTFeXl/Zaa94T2WrfFl\nUztk7/urg/4DLADGAzclq8oH2cU6Yz2ZZhgt/+zJBLUj9gAeAw7ALEh3p1Lq5k6Jrgvouv4F8IVS\n6lfJ5zbgj8CvMNdJLAJmNN46QNd1D2Y1w59hbnE3N3nO+o58z82b67LmRkZj2breJVt1R38ZhsF3\n5VW8tnAVP6xtWjJBAw7Zq4QTxpYxuNjbpXF0Bnl/pUf6Kz3SX+mR/kqP9Fd6cqK/DAP7gndxP/wA\njg/e26E5esCBBC67kshxJ+zydqQ50V/dTPosPdnaX8XFvrSrI+u6fi9wKWbF+PsBZ+NaZMkCdSco\npUZ2WqBZqNWR9vbK5uu6bgWuwSy178JMdi9QSi3t1Ag7mVJq/2bPY5hbB7S6VV1yj/YLk3+E6HSG\nYfD1yq28urCcletrm7RZNI3Ro/pz/JhhDOzbvdVthRBCiF5B04iOn0jN+InYvvgc18MP4nztFbTk\n4JZ9yecUnHcWsREjCV56OaFTT4e8vAwHLUTu0HV9AHAs8JJSqvEdhyWYheiqaL8WWc7aqUJ0uq4f\nAvwF2BdzHcHVDaX5hRAdlzAMvli2hdcWlVNR2fSOqNWicfi+Azhu9DBKitytXEEIIYQQnSm2/4HU\nPfEM/pUrcD86i7x/PY8WDgNgW7kC31UzcN91G8ELLyb0q/Mw8jNfBFaIHFAIPJl8/FSj48cAmzCX\nJYdouxZZzkoradd13Q3cjjlFwQr8F7i0o9PEhRCmRMLgU7WJ1xeVs3azv0mbzapx5H6lHDt6KP0K\nZAdzIYQQIhMSI0ZSf88D+H/zW9xPPEbeU09gqTUnolo3bcT7x5txP3AvoXPOJTjtEhIDBmY2YNG7\nhcNY1q8z36NHj8l0NGlTSn2v6/q/gXuTa9hXAicDZwHnKqVq26tFlss6nLTrun4c8CjmvucbgOlK\nqZe7KjAhclE8keCTpWayvmFroEmb3Wbh6P1LOfawYRT5drpwhxBCCCE6kdG/P/4bfk9gxpXkPfs0\nrscfwVq5AQBLfR3uRx7E9Zc/Ezr1dIKXXk58t90zHLHIOfE4lo2VWNatxbp+HZa1a7GsX4t13Trz\n69q1WLZs3n7+/vvD7Pm7XH8hA84Gfg/8FhgIfAecqpR6Kdl+PWbRuavZXovsnPaWdeeCVgvRNdB1\nvRh4CDgteegvwLW9oXM6ixSiyw270l+xeILF31byxuIKNlUFm7Q57VbGHziIyYcOpcDj6IxQs4K8\nv9Ij/ZUe6a/0SH+lR/orPb2uv8Jh8v79Aq5HHsS27IcmTYamEZlyPIHpVxA7+NAWX97r+qsT5HSf\nGQbatm1Y163B0pCEr1uHZd2aZFK+DsuG9WjxeMevabezWVWAN7sKF+9MITphanOkXdf1XwN/Avpg\nLvC/UCn1QXcEJkQuiMYSLPxmA28urmBLTahJW57DysSDBnPMIUPwuXMnWRdCCCFymtNJ6IyzCJ1+\nJo45s3HPuh/7p58AoBkGztmv45z9OpExhxOcfgWRicektosTvY9WX2cm4w1JeMNo+bq1qcdaKNT+\nhdphWCwkBgwkMXgI9plXZF3CLnZNq0m7ruvz2L5X++fAXUCRrusntndRpdSrnRKdED1UNBbn/S83\n8OZHFVTVhZu0uZ02fnzIECYdPBhPnj1DEQohhBBil1gsRI49nsiU47B/vBjXrPtxvjMn1exYvBDH\n4oXE9hpF4LLLCf/0FLDLv/s5JbmO3JyyviaZjDeMlq81H9d2zuTkRL9+xEsHkxg0mPigQSRKB5MY\nNIj4oCEkBg0i0X8A2MzUrrjYB7k6M6GXamukfXyjxwcC/+zA9TTMTe973AKK3iQQivGp2kTMAJsG\nB+sluPN2aiMB0Uw4Gue9JeuY/fFqavyRJm1el51jDhnCxIMG43JKfwshhBA5QdOIjh5LdPRYrEu/\nw/3wAzj/8xJaLAaAbem35F96IfE7biV40aVwxWUZDlh0SPN15E2mrLewjnwXJHz5ZuJduj0Jj5cO\nSibog0mUDpItBnu5tjKHnC+d3xu9tqicNxdXEI5uXxfzj7nLOG7MMKaOLctcYD1cMBxj/pJ1zPlk\nNXWBaJO2fI+DKYcOZdwBpeQ5JFkXQgghclV8r72pe+T/8P/2d7geexjXc8+gBczCs9a1a/DeeB3c\nexeFw0dguD0Ybnfyj/mYFo7t+NUNnu3HemCxsczrinXkrX0rpzOVgCdKBxEfPHj7KHnyq2wbKNrT\nagahlJKkPce8tqic/7y/cofj4Wg8dVwS9x21NTMhEIoy97O1vPO/NfhDsSavK/Q6OHb0MI7+USkO\nu/yDKoQQQvQWicFD8P/xLgIzr8H11BO4nngMy9atZmNVFfaqzzrtexlOZ+vJfZs3AZKPPY3Pa9qG\no2fW3Nm+jrxhmnqzdeQb1qMFg+1fqB2pdeSNpqw3T8qNfv2kpoHYZTLs10sEQjHeXFzR5jmvfLCS\nL5dvweWw4rAn/9gsyccWnDYrdrsFhy353G5NPU59bfQap92CzWpB68G/qFqbmTDp4MFYNI25n60l\nGG6arPfNd3Lc6GEcsd9A7DZJ1oUQQojeyujTl8BV1xK4eDp5/3gO959nYV3d9uexdGnhMFo4DFVV\nnXpdAMNm257Iu1xt3wTwpDdjAJdr55LZxuvI1zVaO54aLV+bkXXkQnQleZf1Ep+qTU0Sz5YYBqxc\nX9up31eDVKLvTCX1Oyb4zW8OOOxW7DbLjjcHWnhNV90caGtmwhst3AApLszj+DFljN1nADarpVNj\nEUIIIUQP5nYTOu9CQr86j+Kt66gq34AW8KMFAi18DUDzY8Hg9sf+pq/pSloshlZbA52UBDdmaBq4\nmo74tzjab7dDzTYKV5WbSfnmTZ3y/WUduehJJGnvJWrqw+2f1AUMIBJNEIkmqN/1WUit0iCV9G9P\n6jt2c8Bht2C3Nb05kDDg9UXlHfre/fu4OWHMMEaP6o/VIsm6EEIIIVphtcKoUcRKhnbO9QwDgsFW\nkv9WbgL4A62fFwykHmsBf6qYXlfQDCMZlx9ov6BbOnX3ZR25yDWStPcSBV5nh8479rCh7DmsyEy0\nY3Ei0XjqcTiaIBKNE42ZX8PJr5FonEgs0eJrYnGji38yk4E5At7ebILOdvT+pZx1jI7F0nOXAAgh\nhBCih9I0aBiVpl/nXz8SaZLUNzwmlfzv2NbyzYLk8cYzBnZyb3LDYiExsDQ5Qi7ryEXvIEl7L3Gw\nXsI/5i5rM6l12q2cMLasU7cjiyfMZD6dRD8cTRCNJZ9n2c2B5vrk50nCLoQQQojc5HBgOBwYhUWd\nf+14HC0YAH/LCX/qBkE4hG/EUKq8fWUduei15B3fS7jzbBw3ZliLa7QbHDdmWKfvH261WHA5Lbg6\nNtC/0xIJI5nId87Nga01ITbXtH8HuNDTM6uqCiGEEEJklNWK4fWB10d7Qy++Yh+xzXXdEpYQ2UiS\n9l6kYTu35tXQnXZrj9+n3WLRyHPYyOukHDoQinHVIwvbnZlw8J4lnfMNhRBCCCGEEKIFkrT3MlPH\nljHpoMF8+v0mophFPQ7es6TTR9h7ukzNTBBCCCGEEGJnBGNBlmz6mtimMLaYkwNK9sVlc2U6LNEJ\nJOPohVxOG0f+qJTiYh+bZapRq3J5ZoIQQgghhMgdb5XPY07FfCLxSOrYi8teZfKw8Uwpm5jByERn\nkKRdiDbIzAQhhBBCCJHN3iqfx2sr5+xwPBKPpI5L4t6zSeYhRDtkZoIQQgghhMhGwViQORXz2zxn\nTsV8jh58OC5bXjdFJTqbJdMBCCGEEEIIIYRIT8JIMH/Nh02mxLckEo+wZNPX3RSV6Aoy0i6EEEII\nIYQQWS6aiLGmbi3Lq1exorqclTXlBGLBDr22NlLbxdGJriRJuxBCCCGEEEJkmWAsyMqaClZUl7Oi\nZhUVtWuIJmI7da18R34nRye6kyTtQgghhBBCCJFh1eEaVlSvYkVNOcurV7G+vhIDo83XeGxugrEQ\nCRKtnuOwOjigZN/ODld0I0nahRBCCCGEEKIbGYbBxsDmJkn61tC2dl/Xz9WX3QqGM7KwjJGFwylx\n9WNOxbstVo9vMHnYeClC18NJ0i6EEEIIIYQQXSieiLOmfh3Lq1exsrqcFTXl1Ef9bb5GQ2OwdyAj\nC4ebfwrKKHDuOM29YTu35vu0O6wO2ac9R0jSLoQQQgiBuX50yaaviW0KY4s5OaBkX1w2V6bDylrS\nX0K0LhQLU1672iwaV1NOeU0FkUS0zdfYLTbK8oemEvThBcM6PEI+pWwiRw8+nCWbviZuD2ONNvyd\nlBH2XCBJuxBCCCF6vbfK5+0wSvXisldllKoV0l9CNFUXqWdF9SqW15iV3dfWrydhtL7OHMBtc5nT\n3AvMkfShvkHYLDufnrlseYwtPYTiYh+bN9ft9HVE9ukVSbuu6w7gJuAsoB/wMXC1UurzZLsGXA9M\nS7YvBKYrpb5vdA0ncCfwC8ADzAFmKKXWd+OPIoQQQohO9lb5vBbXg0bikdRxSUS3k/4SvZ1hGGwO\nbmVFTXlyTfoqNgW2tPu6PnlFjCwoS42kD/CUYNEs3RCx6Ol6RdIO3I+ZsF8LLAcuB+brur6fUqoC\nM6G/LtleDtwIzNN1fW+lVE3yGo8BJwJXAfXAHcCbuq4fpJSKd+cPI4QQQohdF0vE2BLcylvl77Z5\n3pur5hI34jgsDgA0TUNDS31tcgwNTQPQsKBh/t9inqWlHjW7hnl+S8csWqoVTdOaXXf79zNf0fga\n24+1eI1mx5KvhtRjS4vXCMfDzGmnv+ZUzOfowYfLtFyRMxJGgrX1682t15LT3WsjbY9ka2gM9PRn\nZOFwdksm6kV5hd0Uscg1OZ+067peAFwAXKeU+nPy2IfAVuAsXdcfBK4GblZKPZRs/wCoAM4D7tN1\nfSRwNnCGUupfyXO+BBTwE+Dl7v2phBBCCNFcJB6lPlpPfcRPXdRPfaSe+qjf/BOpp65Jm59QPNSh\n68aNOG+umtvF0eeOSDzCvNXvccywCTis9kyHI0TaIvEo5bWrU/ujr6qpIBQPt/kam2ZlaP4QRhaU\nsVvhcEYUDMNtd3dTxCLX5XzSDviBwzBH0BtEAQNwAqMBL/BqQ6NSqkrX9feAKcB9wIRk0+uNzlmm\n6/q3yXMkaRdCCCE6WTgeaSHZTibiET/10fpUAl4XrW+yvlpk1uzyebxV/i7F7r6UegZS6h3AIM8A\nSr0D6OfqK1OCRVapj/pTFd1XVK9idd064kbbE2nzrHmMKBzGyILh7FY4nGG+wdjlJpXoIjmftCul\nYsASAF3XLUAZcDNm0v4cMCl56opmL12JOYoOsAdQqZRqvi/DymSbEEIIkXWyqbq3YRiE4uFUsl0f\n9VMXaTQC3jgRj5ij49F2Ki3vKotmwW6xE25nBA1g7z46pd4BGIZBw/8wIIEBGBgGqeOG0doxUo9b\nPkbyukYb16VRDGAkC101uYaRulKzeBuu2/waDee3cw3DIBIPE+xAfzXEtCmwhU2BLXyx+evUcbvF\nzkBP/0aJvJnU5zt8HbquELvCMAy2hapZUbMqWTiunEr/xnZfV+DIN0fQC8vYrWA4pd4BcvNJdJuc\nT9qb+R1mwg5wk1JK6bp+MhBWSjW/PV8HNGyEmJ983lwdMKQrAhVCCCF2RVdX9zYMg2AsmBrpbjIt\nveFxs+npsXZGrnaVVbPitXvwOjz47F68Do/5PPnYZ/fgdXjx2j34HF5ctjzC8TDXL7ytzVF6h9XB\nufucKWu0MW8EtddfFiz0yStka6gqmfY3FU1EWV23ltV1a5sc99o9lHoHpkbkS70DGOgZgNPq6PSf\nQ/QeCSPBBv/G1Fr05dWrqA7XtPu6/u4SdmtU2b1vXlGyJoQQ3a+3Je3/ARYA44GbklXlg9DCvyim\nhn0atA6c06qiIjc2mzW9SLtJcbHc1U6H9Fd6pL/SI/2VHumv1r383ew2q3t7PE5O3vvYJm0JI4E/\nEqAmXEdtqJ7acB214frkH/NxXarNPBZvZzujXWW32Mh3+sh3esnPS35teN7CMbfdtVMfqk/eewr/\n/PrVNtuHDizelR8lh/ja7a/T9j2Bk/c+lnAswtraDayuXsfqmvWsrjG/1oRqW3xdfdTPD1XL+aFq\neeqYhkaJtx9DC0oZWjCIoYXm1wHeYqyW7Pxs1RHy+6tjApEgH639nKpNNRS5Chg9+EDcjrZnC0Xj\nUVZsW833W5bz/eblqC0r8EeDbb7GqlkYXjSUPYt3Y89+I9mz30jy83r2fyN5j+WWXpW0K6W+Sj58\nT9d1H/AbzIrxTl3X7UqpxvPwfEDDbbia5PPmGp/TqqqqwM4H3YVkD8f0SH+lR/orPdJf6ZH+al0w\nFuTl795q85wXvnmd7zasIBgLpqal+2OBdvcU3lUOiz010t14NNxn337Ma/fiS46QO63O9pNwAwhB\nIBQnQP1OxXVk8RH4R4R3mJngsDqYPGw8RxYfIe+3RtLpr3z6sI+vD/v49oXB5nl1kXrW11ey3l/J\n+voNrPNXsqG+kkgLyyEMDDbWb2Zj/Wb+t+7L1HGbxcZAd0lqan1pcnS+wJGf9aOh8vurY1qaLfTk\n5y/sMFsoEA2yqraC5dXm/ugVdWuIJWJtXtthdTAif1hqj/SygqFNZnSE62BzXc/9b5St7zG5kbDz\ncj5p13V9AHAs8JJSqvG7dwlmIboqzJH04cAPjdpHYFaHB1gGDNB13aWUCjY754Ouil0IIYToCMMw\nqInUssG/kUXrP2m3IFvCSPDVlm93+fvmWZ3JZHvHRLxhCnrD9HSfw4Mji6c5TymbyNGDD2fJpq+J\n28NYow01AGRKfEt2pb98Di96n93Q++yWOpYwEmwNVrHev4H19ZWs81eyvr6STYHNLU6xjyVirKlf\nz5r69U2Oe2zu1NT60ob18p7+5Ml/xx7lrfJ5bc4WWle/Aa/dy4qaVayvr2zxPdKYz+41E/Tk/uiD\nvaU9eqaG6H1yPmkHCoEnk4+fanT8GGAT8AoQAn4K3A2g63oRcDRwS/LceYAVmAq8kDxnd2AU29fI\nCyGEEF2uPuJnvb+SDf6N5td683Eg1vb0z45w2VzJdd/JdeCpRLyFxNzuyblKyS5bHmNLD8naUaps\n05n9ZdEsFLv7Uuzuy4+K90kdj8ajVAY2JRN5M6FfX19JTaTlKfb+WIBl1StZVr2yyfG+eUXJRH77\nyHx/d8+eYp+rgrEgcyrmt3nO55u+arO92NU3tRZ9ZGEZJa5+WT8DQ4i25HzSrpT6Xtf1fwP3Jtew\nrwROBs4CzlVK1eq6Pgu4Vdf1BOZo+w1ALfBE8hordF1/EfhLct/3KuAO4CvMpF8IIYToVMFY0EzM\n6xsS9I1sqK+kLrpzU8CbO3zgoRzY/0eNpqV7sFly/mOB6GHsVjtDfIMY4hvU5Hh91M+GRiPy5t+T\nylb30t4aqmJrqIqvtyxNHbNpVvp7SlJT60s9AxjkHUihs0ASvE5k7hwRwh8N4I8GCESD+GMNjwNN\nH0eDbA1tTWv7Rg2Nwb5SdiswK7uPLCijwJnf/guF6EF6y7/OZwO/B34LDAS+A05VSr2UbL8es6Dc\n1Zh7ti8CzlFKNV6v/mvgfuAuwALMBWYopbq2FK4QQoicFo5HqGyUlDeMoHekunFjedY8Sr39KXb1\n5dONX7a5x7DD6uCk3U+Qqd+ix/LaPexeNJLdi0amjplbeVWx3l/Junpzvfx6fyUbA5tbrNcQM+Ks\nq9/AuvoN0GjHL5fNRamnf3JqvZnQD/IOyNh2idki3eQ70PA8FuySehm7F45g8rAJDC8YKssfRM7T\nGvb8FF1n8+a6rOxkmf6XHumv9Eh/pUf6Kz09sb+iiRgb/Zu2T2v3V7KhfmOr22K1JrXHtWcAA739\nGegZQKmnf5PRwdbWgzaYOmJyp2z7lqt64vsrk7K9v6KJGJsCm1lXv6FRAbxKqsLVaV2nyFnIIO+A\nJsl8f3dxh2eoBGNBlmz6mpgtjC3WUAOg+28EZFvyvbPO3PNUxpYekukwslK2/p0sLvbJFJad1FtG\n2oUQQohuEU/E2Rzckho5X+/fyAZ/JZuDW9P6wNswdXegZ3tiXuodQJ+8Iiyapc3XNiTkrVX3loRd\n9CZ2i41B3oEM8g5scjwQDaYS+IZK9uv9lQRjoRavUxWupipczTdbv08ds2gWBrhLmlSwL/UMpE9e\nYZMp9i1VQn9x2au79PexJyXfTqsDt82Nx27+cdvdeGwuPHYPbrsLjy15zO7Gqll5aMnjLe4m0MBh\ndXBAyb7d+BMIkVmStAshhBA7IWEk2BLcxoZkUt6w/nxjYHObU9Obs2gWSlz9zOTcOyA1il7s6rtL\nRbKkGroQbXPbXexWOJzdCoenjhmGQVW4OrVOvqH4XWt/rxNGwkz4/ZVNjjcsVyn1DKA6XMs3W5fu\n8NqGSuiGAeOGjMUfDeKP+ltMvgPJtmxNvhsS7sbJt8fuTp7rSrtexuSyCW3OFpo8bLz8LhO9iiTt\nQgghRBsaPsQ3LgpnJumbiLYxEtSchkZfVx9zWrunP6XJJL3EXYy9iwrASTV0IdKjaRp98orok1fE\nPv32Sh2PJ+JsDGxO7SvfMDq/LVTV4nVC8RAraypYWVPR7vd8fdUcXl/VeoLaFXYm+XbbXV32u6o5\nmS0kRFOStAshhBCYyXltpL7JqHnD49YqUremyFmYXG9ujrSVegYwwFOS1fuUCyFaZ7VYU/u/H9zo\neDAWYkOq8F1lap/5ztiCsSMaJ9/bE+7sSb53hcwWEmK77P8bK4QQQiSlCjlt2rVCTuZ2UY2mtSeL\nwvljgbSu43N4U0n59untJb2+yrQQvYXLlseIgjJGFJSljhmGQU2klnX1lcxf8wFLt/3Q7nWsmpV8\nhy8nk+9dIbOFehdd163A5cAFwFCgAngUeEQpZei6rmHu+jUN6AcsBKYrpb5v5ZI5I7f/pgshhMgZ\nO1PIyRwF25iq1N6QoNdG0vvw57a5zGJwXrMgXENxOK/Ds0s/kxAi92iaRqGzgEJnATXhmg4l7afr\nJ0sldCHgd8B1wK3AR8CRwAOAG7gbuCnZfi1QDtwIzNN1fe9mW3XnHEnahRBCZL3WtjBrKOQUT8TZ\nt9/eya3Uto+cp7ulk9PqaDZqbk5vz3f4mlSCFkKIjjigZF9eXPZqk5uNzUkldCFSo+wzgT8ppW5L\nHp6n63oxcLWu638GrgZuVko9lHzNB5ij8ecB92Ug7G4jSbsQQoisFowFmVMxv81z3iyfy5vlczt8\nTbvFxoCGvc4bjZw336ZJCCF2hcvmYvKw8VIJXYj25QPPAi83O66AYmAC4AVeTTUoVaXr+nvAFCRp\nF0IIITJnyaav2xylaotVs9LfXbx9r3Ov+bWfq0+7e50LIURnkEroQrRPKVUFXNZC01RgLTA4+XxF\ns/aVwE+6MLSsIEm7EEKIrLYxsLlD53nsbnYvHMHAhi3VvAMocfXbpb3OhRCiM0gldCHSp+v6+cAk\nYAbmSHxYKdX8Ln5dsi2nSdLeDdxuBx6Pc4fjW7fWk0gYGWvP9viysb242JfV8WVTe4NsjS8b2+X9\n1bS9JlTHtsQWLhxzOheOOb1J+7T/XkdVqIZTRx3PqfuckJXxZ1u7vL863t4gW+PLxnZ5f3Wk3cfQ\ngRMA2Ly5Lgvjy+72hvdYtsaXTe2Qvb+/OkrX9TOBx4CXgIeB3wJGK6cndvob9RCaYbT2s4vOsnlz\nXVZ2smyfkR7pr/RIf6VH+mu7QDTA3NXvM3/thx2eFu+wOrj98Btl1KoV8v5Kj/RXeqS/0iP9lT7p\ns/Rka38VF/s6VDRG1/WZwD2Y69dPU0pFdF2/FJgFOJVS0bxwkGUAACAASURBVEbnPgicoJQa2RUx\nZwsZaRdCCJEVQrEQ89csZN6a9wjGQk3aChz51ERqW32tFHISQgghej5d12/HHFV/FjhPKRVLNi0D\nNGA40HgfxRGYxepymiTtQgghMioSj/L+ukW8XTEffzTQpK3UM4ATRhzDfv1GMafiXSnkJIQQQuQo\nXdcvx0zYHwSuVEo1nq28CAgBP8Xcsx1d14uAo4FbujnUbidJuxBCiIyIJmIsWv8Jc8rnURNpOo2v\nxN2P44cfw4El+6WqvEshJyGEECI36bo+ELgL+Br4J3CYruuNT/kUc3r8rbquJzBH228AaoEnujfa\n7idJuxBCiG4VT8T5uPJz3lz1DlXh6iZtffOKOHb4jzm0/wEtVn132fIYW3pI1q7XE0IIIcROmQw4\ngX2BxS20FwPXYxaduxpzz/ZFwDlKqZruCjJTJGkXQgjRLRJGgs82fsmbq95hU3BLk7YCRz5TyiYy\ntvQQbBb5p0kIIYToTZRSTwNPd+DU65J/ehX5ZCSEEKJLGYbBl5u/4fVVb7PBv7FJm9fuYfKw8Rwx\naAwOqz1DEQohhBBCZC9J2oUQQnQJwzD4bpvitZVzWFO3rkmby+Zi0tCjGTf4cPJsO7+PqxBCCCFE\nrpOkXQghRKf7oWo5r62cw8qaiibHnVYH44ccycQhR+G2uzIUnRBCCCFEzyFJuxBCiE6zqqaC11bO\nQVUtb3LcbrFx1OCx/HjoOHwOb4aiE0IIIYToeSRpF0II8f/t3Xl0XWd96P2vJFu2bMu2bMvxGE+x\nH2eEQCFpAmQi2EACuZdOwAXKpZTeC4SWhBKG8oabFkrLC4Xctlyg66WUqU0vbTOQOIkzkMahJZCE\nxIkfz47nQZZtyZYtSzrvH3tLOZIlRcfW2ecc6ftZS+ss7efR1rN/a+vo/PYznbHtLTu5e/Mqnmta\n1+t4TVUNr5t7CSsWXM2UcZNL1DpJkqTKZdIuSTptu4/u5Z7N9/PU/md7Ha+uqubSWa9m5cI3Mr2u\noUStkyRJqnwm7ZKkgu0/1sQ9Wx7gyb1PkSPXc7yKKn7trFfylkVvZOaExhK2UJIkaWQwaZckDdnB\n483ct3U1T+x+kq5cV6+yVzZewFsXvYk5k2aVqHWSJEkjj0m7JOllHT7RwqptD/H4zp/RkevsVXb+\n9OVct+hNnD15XolaJ0mSNHKZtEuSBtR68igPbnuUR3Y8zsmuk73Klk5dzPWLV7Jk6sLSNE6SJGkU\nMGmXJJ2iraON1S8+xsPbH+N454leZYsmn831i1cSpp1TotZJkiSNHibtkqQeJzrbeXT74zzw4iMc\n62jrVTZ/0hyuW7yC86cvp6qqqkQtlCRJGl1GRdIeQqgBPgZ8EDgb2Ab8DfDXMcZcCKEK+DTwIWAG\n8Djw0RjjurxzjAP+HHgnMBFYBdwYY9yV5bVIUjGc7DzJY7t+xv1bH6blZGuvslkTz+K6RW/iFY3n\nU11VXaIWSpIkjU6jImkH/gS4BbgN+BnweuCvgAnAXwCfS8s/CWwFPgusDiGcF2M8nJ7jG8DbgJuA\nVuCLwE9CCK+OMfZelUmSKkRHVwdP7H6S+7au5tCJw73KZtRN562LruXXznqlybokSVKJjPikPe1l\n/zjwlzHGP0sPrw4hNAI3hxD+FrgZuDXG+PX0Zx4j6Y3/APCVEMIS4L3Au2KM/5jWeQaIwNuBH2d5\nTZJ0prpyXfznnl/yky0P0nT8YK+yhnFTefOia7h01q9RU11TohZKkiQJRkHSDkwGvsupiXUEGoGr\ngUnAnT0FMTaHEB4FVgJfSesA3J1XZ0MIYW1ax6RdUkXoynXx1L5nuWfLA+w9tq9X2eTaelYsvJrL\n51zC2OrR8O9BkiSp/I34T2UxxmbgI/0UXQ/sALo3Ft7Up3wzSS86wDJgT4zxaD91lg1TUyWpaHK5\nHM8eeJ67t9zPztbdvcomjpnAtQuu5Ip5l1FbU1uiFkqSJKk/Iz5p708I4feANwI3kvTEn4gxtvep\n1pKWkb629HOqFmB+sdopSWcql8uxrnkDd21exbYj23uVja8ZzzVnv56r5r+eujHjS9RCSZIkDWbU\nJe0hhHeTLCr3z8D/Bj4F5Aao3pW+Vg2hzoAaGiYwZkx5zgttbKwvdRMqivEqjPEqzHDH64X9G/jR\nc3fxwv4NvY6Pq6nlzcuu4m3hWiaNmzisvzNL3l+FMV6FMV6FMV6FMV6FM2aFMV4jy6hK2kMIHwe+\nTDJ//d3pdm+HgXEhhLExxpN51euB7qWUD6ff95VfZ0DNzcfOrOFF0thYz/79/Q0gUH+MV2GMV2GG\nM17bjmznrs2reOHg+l7Hx1SP4Q1zf503LbiK+tpJtB3poq3fQUTlz/urMMarMMarMMarMMarcMas\nMOUaLx8knL5Rk7SHEL5A0qv+XeADMcaOtGgDSU/6IiD/E+5iksXquuvMCiHUxRjb+tR5rKgNl6Qh\n2tm6m7s338+vDqztdby6qprL5ryWlQuupmH81BK1TpIkSadjVCTtIYSPkSTsXwP+KMaYP9R9DXAc\nuIFkz3ZCCA3AFcDn0zqrgRqSxev+Ka2zFDgfuLX4V6BSauto46l9z9Kx7wRjOsZx8cwLqRtTV+pm\nST32Ht3HPVse4Jf7fkUubyZPFVW8dtareMuia5lRN62ELZQkSdLpGvFJewhhNvAl4FngR8AlIYT8\nKk8CtwO3hRC6SHrbPwMcAb4NEGPcFEK4A/hWCGEK0Ax8EfgV8K8ZXYpK4L6tq1m17WHaO19ap/CO\nDXeyYsFVrFx4TQlbJsGBtoPcu+VB/mPPL3ol6wCvnvkK3rLoWmZNnFmi1kmSJGk4jPikHVgBjAMu\nBJ7op7wR+DTJgnI3k+zZvgZ4X4wxf776+4GvkjwAqAYeBG6MMXYWr+kqpfu2ruauzatOOd7e2d5z\n3MRdpXDoxGHu3bqaNbv+k65c77UwL5xxHtcvXsHcSbNL1DpJkiQNpxGftMcYvwN8ZwhVb0m/BjrP\nUeD30y+NcG0dbaza9vCgdVZte5gr5l3uVlnKTEt7K/dve5if7nyCjq6OXmXnTlvGdYvfxMLJZ5eo\ndZIkSSqGEZ+0S4XK5XKsfvGxXkPi+9Pe2c4v9j7N6+ZemlHLNFodO3mMB158lEd2PH7KfblkyiKu\nX7yCpQ2LS9Q6SZIkFZNJuwQc7zhObN7IcwfWsbZpHYfbjwzp5/5p/b/xzP61LGtYQmg4h3n1c6iu\nqi5yazVatHUc55Ht/87q7T+lreN4r7IF9fO5fvEKlk9bSlVVVYlaKEmSpGIzadeotffYftYeeIG1\nTZENhzbTmSt8eYLOXCfPH4w8fzDZHbBuTB3Lpi5mWcM5LGtYwuyJZ5lQaVD97U5QU1XDozvW8MCL\nj3D05LFe9edOms11i97EhTPO896SJEkaBUzaNWqc7DzJxkNbeK7pBdY2rWN/W9OAdSfU1HG868Qp\ni3y9nLaONp45sJZn0n2y62snsWxq0gsfpp3D9PHTTLTUo7/dCX4U/4Ux1TWc6DMM/qwJjbx10bVc\nPPMiR3NIkiSNIibtGtGajx/iuaZkyHs8uIH2rpMD1p03aQ7nT1/OBTOWs3Dy2dy/7eF+V4/vdv3i\nFbzmrIuJzZtY37yR9c0bOdze0qtOS3srv9j3DL/Y9wwA08Y39AylX9awhKnjpgzPhariDLQ7QWeu\nk87Ol0Z9TB/fwFsWXctrzrqYmuqaLJsoSZKkMmDSrhGls6uTLUdeZG3TOp478AK7ju4ZsG5tTS3n\nNizl/OnLOX/G8lMS6O7t3Pr2hNbW1Pbap/2yumlcNuc15HI59h7bz/rmjcTmTWxo3sTRjt5Dmw8e\nb+Znu5/kZ7ufBJLe02UN5xAazmFpw2ImjZ04LHFQ+crlcuxs3c29W1e/bN13nHMdb5h3GWOqfauW\nJEkarfwkqIrX0t7K802RtU3reOHgeo51tA1Yd+aEGUlv+vRzWTJ1EWNfJhlaufAarph3OU/te5bO\nsSeoOZnMOe5vm7eqqipmTZzJrIkzecO8y+jKdbGzdTexeSPrmzex8dDmU4Y87z22n73H9vPYzieA\npLe/uyd+ydRFbidX4U52nmT30b3saN2VfLXsZmfrbo53Hn/5HwbGj6kzYZckSRrl/DSoitOV62JH\ny66kN71pHduObCdHrt+6Y6pqWNqwJOlNn76cmRNmFPz76saM57I5r6GxsZ79+1te/gdS1VXVzK+f\ny/z6ubzx7Cvo7OpkW8uOnp74zYe3nrLXdndy99D2x6iuqmZB/byenvhFUxZQWzO24PYrG63tR/OS\n813sbN3NnmP7Cl4XId+RIe5iIEmSpJHLpF0Voa3jOOsObmBtOj/9SPvAyfPUcVPS3vTlLGs4h/Fj\nxmXY0oHVVNeweMoCFk9ZwMqF19DeeZIth7f1JPHbWrb3SvC6cl1sOfIiW468yKptDzGmegyLJy9I\nkvhpS1hQP985ziXQleviQFsTO1p3p8n5Lna07ubQicNDPse4mtpTRl30Z3Lt5DNpqiRJkkYAk3aV\npWR++L5kEbkD69h4eMuAPZZVVLF4ygIumH4u589YzpyJsypihfbamrGEacmq8teT7BW/8dAW1qcL\n2+1o3d1rBEFHVwfrD21i/aFN3L0lmVt/ztRFPYvazZvkHvHDrb2znV1H9/T0nO9IE/T2ISTc3WbW\nzWBu/RzmTZrDvEmzmVc/h9rqsXxmzRcGPU9tTS0Xz7xwOC5DkiRJFcykXWWjvfMkGw5t4rkDSW96\n0/GDA9adNHYi504LXDBjOedOW8bEsRMybGlxjB8zngtmnMsFM84FoPXkUTY0b+7pid97bF+v+u2d\n7TzfFHm+KdkjfsKYOpY1LEmH0y/hrAkzK+LhRbk40t7SOzlv2cXeY/sHnHrR19jqMcyZNLtXcj5n\n4izGD7AuwYoFVw26O8GKBVe5poEkSZJM2lVaTW3N6ZD3F4jNmzg5yJZs8+vnckE6N33B5Pkjvld5\n0tiJXDzzwp7e1kMnDqe98JuIzRs5eLy5V/1jHW08vf85nt7/HACTa+vztpc7hxl10zK/hnLUleti\n37EDPcPad7Qk89AHm3LRV/3YSczr03veWDejoOkKQ92dQJIkSaObSbsy1dnVyebDW3v2Tt99dO+A\ndcfXjGP5tGXpInKBKeNG9/zeqeOm8NpZr+K1s14FwIG2g8TmDT1JfEt7a6/6R9pbeHLv0zy592kA\npo+fRkh74pc1LBkV8TzecYJdR/ckCXpLkqTvbN096MOhfFVUMXNCY5KYT5rTM8x9yrj6YWlfIbsT\nSJIkaXQyaVfRHWlv4fmmyHNN61h3cD1tHQNvd3XWhJk9velLpi50u6tBzKibxoy6S7h8ziXkcjn2\nHNvXs73c+uZNtPXZ+q7p+EHW7D7Imt0/B2DWhJk9Q+mXNiyp6CkGuVyOw+1HkqHtac/5jtZd7D/W\nNOTh7bXVY5k7aXbe/PM5zJk0i3E1tUVt++nuTiBJkqTRwYxIw64r18X2lp08d+AF1jZFtrVsH7Du\nmOoxLEu3ZLtg+nJm1E3PsKUjR1VVFbMnnsXsiWdx5bzLk23xWncRD6Z7xB/ecsqiZ3uO7WPPsX38\ndOcaqqhiXn3eHvFTFpXNqvt9dXZ1sq/twEvJefraevLokM8xpba+V3KeDG+fPuKnXEiSJKnymLRr\nWBw72cYLB9eztmkdzzdFWk62Dli3YdxULphxLudPD4SGc6gtck/maFRdVc3Z9fM4u34e1y64ko6u\nDrYd6d4jfiNbDm+jI9fZUz9Hju0tO9nespPVL/6U6qpqFk6e39MTv2jyAsaWYI/44x3H2dm6p1dy\nvvvoHk722d9+IFVUcdbEmT3D27vnodfXTipyyyVJkqThYdKu05LL5dh9dG/PvumbDm8dcEu26qpq\nlkxZmM5NX87siWe5qnnGxlSPYcnUhSyZupA3L3oj7Z0n2Xx4a89w+m1HtvcaRt6V62Lz4W1sPryN\n+7auTvaIn7KQkCbxZ9fPG3DRtbaONp7a9ywd+04wpqN7jnbdoO3L5XIcOnE4Tc539wxvP9DWNORr\nHFdTy9yenvMkSZ89cRa1JXjYIEmSpNMXQngb8P0YY33esSrg08CHgBnA48BHY4zrStPK7Ji0a8ja\nO9uJzRtZ2xRZ27TulNXL89WPncR50wMXzDiX5Q1LmTB28KRN2aqtGcvyaUtZPm0pkCTa3XvEx+aN\n7Gzd3at+R1cH65s3sr55I3eRLBJ4ztRF6aJ25zB30iyqq6q5b+vqU1ZDv2PDnb1WQ+/s6mTPsX15\nc893s7NlF0c7jg25/VPHTenVcz530mxm1E1zeLskSVKFCyFcBnwP6NvL9zngFuCTwFbgs8DqEMJ5\nMcbDmTYyYybto1AhPaEH2pqSld4PrGP9oU10DDIseUH9fM5PE/X59XNNoCpI3Zg6LpxxHhfOOA+A\nlvZWNhzanPbEb2TfsQO96h/vPMFzTet4ril5sDlx7ATqx9az59ipuwG0d7Zz1+ZVPL3vWQB2H93b\na2j+YKqrqpk1YSbz6pPEvHsO+qTaiWdyuZIkSSozIYRxwMeA24CjQG1eWT1wM3BrjPHr6bHHgG3A\nB4CvZN7gDJm0jzIv1xPa0dXBpkNbea4pWURu77F9A55rfM14zp3+0pZsk2uHZxsslV597SReNfMi\nXjXzIgCajx/qtUd884lDveofPXmMoycH7ynf3rpr0PLxNeOZVz+71xD32RPOKslcekmSJGXuzcCn\ngE8A04Gb8souBSYBd3YfiDE2hxAeBVZi0q6R4r6tq7lr86pTjnf3hP58z1McOnGY450nBjzH7Iln\n9az0vnjKwgHnNWtkaRg/lUtmv5pLZr+aXC7HgbaDPYvaxeaNBa3cDjBtfMNLPefpEPfp4xtc60CS\nJGn0+jmwKMZ4KIRwa5+yZenrpj7HNwNvL3bDSs2kfZRo62hj1baHB62zp59e9bHVYwkNSzh/erLa\n+/S6acVqoipEVVUVjROm0zhhOpfPTfaIv2P9v/HozjUv+7O/Pvs1/Ndz3sqECt4TXpIkScMvxrhz\nkOLJwIkYY3uf4y1p2Yhm0p6BCRNqmTjx1D2vm5pa6erKZVLeOLGe7/3G13qVf+jfbqH5+GF+8/y3\n8psXXHfKz6/Z+DQLJp3N1PqJJW9/uZQ3Ntaf0c+PxPKJE8fx4de9hw/znl7lL3d/lUv7y6nc+2vo\n5d3KtX3lWO79NfTybuXavnIs9/4aenm3cm1fuZZ332Pl2r5yKofyvb/OQBXkbXXUW/9bWI0gVbnc\nQNeu4bJ/f0vJg3zvltXcveXUofF9XTnvcn5j6dscptyPxsZ69u9vKXUzylJbRxuffvzPeq2V0Fdt\nTS1fuPyz1I0Zn2HLKof3V2GMV2GMV2GMV2GMV2GMV+GMWWHKNV6NjfVDTjDS4fE3xxgnpd9/GLgd\nGBdjPJlX72vAdTHGJcPc3LLi8t6jxJRxQ1skbu6kOSbsKljdmDpWLLhq0DorFlxlwi5JkqTTsYGk\nt31Rn+OLgZh9c7Jl0j5KXDzzQmpragetU1tTy8UzL8yoRRppVi68husXrzjlPqutqeX6xSt69mmX\nJEmSCrQGOA7c0H0ghNAAXAGsLlWjsuKc9lGiuye0v9Xju9kTqjO1cuE1XDHvcp7a9yydY09Qc3Ic\nF8+80PtKkiRJpy3G2BpCuB24LYTQBawHPgMcAb5d0sZlwKR9FOnu6ey7T3ttTW3PPu3SmaobM57L\n5rymbOdTSZIkqSJ9mmTRuZtJ9mxfA7wvxni4pK3KgEn7KGNPqCRJkqRyFmO8Fbi1z7EO4Jb0a1QZ\ndUl7COFtwPdjjPV5x6pIntx8CJgBPA58NMa4Lq/OOODPgXcCE4FVwI0xxl0ZNn9Y2BMqSZIkSZVh\nVC1EF0K4DPgeycqD+T4HfBb4MvA7wBRgdQhhSl6dbwDvJXmy837gFcBPQgg1xW63JEmSJGl0GhU9\n7Wkv+ceA24CjQG1eWT3JvIhbY4xfT489BmwDPgB8JYSwhCRhf1eM8R/TOs+QbC/wduDH2V2NJEmS\nJGm0GC097W8GPgV8Ari9T9mlJAsZ3Nl9IMbYDDwKrEwPXZ2+3p1XZwOwNq+OJEmSJEnDarQk7T8H\nFqU96bk+ZcvS1019jm/OK1sG7IkxHh2kjiRJkiRJw2pUDI+PMe4cpHgycCLG2N7neEta1l2nvxXb\nWoD5Z95CSZIkSZJONSqS9pdRxam97926CqgzoIaGCYwZU57r1TU21r98JfUwXoUxXoUxXoUxXoUx\nXoUxXoUxXoUxXoUzZoUxXiOLSTscBsaFEMbGGE/mHa9Py7rr9Hfn59cZUHPzsTNuZDG45VthjFdh\njFdhjFdhjFdhjFdhjFdhjFdhjFfhjFlhyjVePkg4faNlTvtgNpD0pC/qc3wxyerw3XVmhRDqBqkj\nSZIkSdKwMmmHNcBx4IbuAyGEBuAKYHV6aDVQA1yfV2cpcH5eHUmSJEmShtWoHx4fY2wNIdwO3BZC\n6ALWA58BjgDfTutsCiHcAXwrhDAFaAa+CPwK+NfStFySJEmSNNKN+qQ99WmSBeVuJtmzfQ3wvhhj\n/nz19wNfBb5EMkLhQeDGGGNnxm2VJEmSJI0Soy5pjzHeCtza51gHcEv6NdDPHQV+P/2SJEmSJKno\nnNMuSZIkSVKZMmmXJEmSJKlMmbRLkiRJklSmTNolSZIkSSpTJu2SJEmSJJUpk3ZJkiRJksqUSbsk\nSZIkSWXKpF2SJEmSpDJl0i5JkiRJUpkyaZckSZIkqUyZtEuSJEmSVKZM2iVJkiRJKlMm7ZIkSZIk\nlSmTdkmSJEmSypRJuyRJkiRJZcqkXZIkSZKkMmXSLkmSJElSmTJplyRJkiSpTJm0S5IkSZJUpkza\nJUmSJEkqUybtkiRJkiSVKZN2SZIkSZLKlEm7JEmSJEllyqRdkiRJkqQyZdIuSZIkSVKZMmmXJEmS\nJKlMmbRLkiRJklSmTNolSZIkSSpTJu2SJEmSJJWpMaVuQCUJIXwQ+GNgHvA08PEY4xOlbZUkSZIk\nVT7zrf7Z0z5EIYT3Ad8Avge8AzgErAohLCppwyRJkiSpwplvDcykfQhCCFXA54Fvxhg/H2P8CfA2\n4ADwRyVtnCRJkiRVMPOtwZm0D805wALgzu4DMcaTwD3AylI1SpIkSZJGAPOtQZi0D82y9HVjn+Ob\ngSUhhJqM2yNJkiRJI4X51iBM2odmcvra0ud4C0kMJ2bbHEmSJEkaMcy3BuHq8UNTlb7mBijvGuyH\nGxvrqwYrL6XGxvpSN6GiGK/CGK/CGK/CGK/CGK/CGK/CGK/CGK/CGbPCVGC8zijfGunsaR+aw+lr\n37u/HuiMMbZm3B5JkiRJGinMtwZh0j40G9LXxX2OLwbWZ9wWSZIkSRpJzLcGYdI+NBuA7cAN3QdC\nCGOBtwKrS9UoSZIkSRoBzLcGUZXLDTRtQPlCCP8T+N/AF4HHgY8ArwNeGWPcXMq2SZIkSVIlM98a\nmEl7AUIINwEfA2YATwM3xRifKG2rJEmSJKnymW/1z6RdkiRJkqQy5ZZvFSyEUEPyJOqDwNnANuBv\ngL+OMeZCCFXAp4EPkTytehz4aIxxXd45xgF/DryTZP/DVcCNMcZdafl3gPcN0IRHYoxXFeHSiiKL\neKV15gP/L3AVyfYU95M8JdxX9IscRhnG69XAXwK/DjQDPwQ+G2NsK/pFDqPhiFef830VWBpjvK7P\n8Qbgq8D1JOuS/F/g4zHGI0W5sCLJKl596twJbI4x/uGwXkwGMry/5pP8zV4F1AG/AP44xvjLolxY\nkWQYr0Dyfv864ATwI+AzlbbKcYn+Hq8EHgKujjE+MnxXU3wZ3l83AV/u50eujzHePVzXU2wZxmsM\n8CfA+9PzPAd8KsZYUfOhs4jXSPp8P1q4EF1l+xPgC8D3gLcB/wT8FfCJtPxzwGdJ3vB/B5gCrA4h\nTMk7xzeA9wK3kLzJvQL4SfqGAXAbSTKV//WptOzbRbmq4il6vNIFM+4BXgX8AfBh4HLgzryYVoos\n4rUMeARoAN4F/CFwHfAvRbyuYhmOeAEQQvgISSz683+BK0nurz9Mf9cPhusiMpRVvAghVIUQvkLy\noKNSFT1eIYQ6koeMF6fl7ybZL/enIYRFw3w9xZZFvBqAB0k+NL8LuAn4LeD7w3wtWcjs7zGtU0fy\nGaJqsHplLKt4vQJ4jFM/h/37cF1IRrKK19eBj5PMh74B2AncHUJYPmxXko0s4jWSPt+PCva0V6g0\n6fk48Jcxxj9LD68OITQCN4cQ/ha4Gbg1xvj19GceI3la9wHgKyGEJSQJ1btijP+Y1nkGiMDbgR/H\nGDcBm/J+72SSN4/vxhgr5oNJVvEiSdYvBK6JMT6U1jlC0sN8MfBkFtd7pjKM10dJRiO8Kca4P6/O\nuhDCW2KMP8nmis/McMQrPTYT+AvgPby0X2n+77mKpAf00hjjf6THdgAPhhBeVSm9oVnFK62zhGRR\nmyuA40W7qCLKMF7XActJemQ2pj/zSHqe/wH8cVEucJhlGK93ArOAV3ePpEp/93dCCPNijDuKdY3D\nKcu/xzx/Cowf7mvJQsbxugi4L8b4s2JdT7Fl+P9xKcnD7N+KMf5zeuwR4BngGqDfXuhyk1W8Rsrn\n+9HEnvbKNRn4Lkniky8CjcDVwCTgzp6CGJuBR4GV6aGr09e78+psANbm1enrU+nv/sQA5eUqq3iN\nS1/zhyo3pa/TzugKspVVvJYBT3Un7GmdCBxg4HuwHA1HvCAZ7nY5sIJk8ZW+3gjs607YUw+T3G/G\nq39fI+kJvYyX/hYrTVbxOgR8rTthT89zjGQLnkrqac8qXj8ELusz9ak9fR3XT/1yleXfIyGES0iS\nq5uGoe2lkEm80qHe5wK/Gsa2l0JW99fbgYMko9G6z9MeYzw3xvjXZ34Zmcn07zFPpX6+HzXsaa9Q\n6R/oR/opuh7YAcxLv9/Up3wzyRsbJAnTnhjj0X7qWXFFpAAAC2RJREFULOt74hDCLJIhNv+r0uZn\nZxivNcBTwBdCCL+XHvsSyYfeihnOlmG8tgNXhhCqYow5gBDCVJLh8gvP5BqyNEzxAvhb4OYYY0cI\n4bP9nG8ZsDH/QIyxK4SwlX7+ZstVhvEC+GSMcS1ACOH0G11CWcUrxvgA8ED+sXRY/AUk034qQobx\nagZ+Dj3DvS8h6UF+MO3FqghZ/j2GEGqBvyMZ+hvPpN2lkmG8lgO1wMoQwheBOSSj9f6wz4PbspZh\nvC4i6U1/RwjhNmApyZz2j8UYHz2DS8hUxv8fgcr+fD+amLSPIGmS+EbgRpKnZSdijO19qrWkZaSv\nLf2cqgWY38/xPwA6SN4IKl4x4pW+Of4+cC/JUCVInvxemfZYVawi3V/fJxnOdXsI4U9JeqduJ7nP\nJg7rBWTsNOLVPcpgMIPFdHI/xytGkeJFd8I+0hQrXn1+R3eCdZxkfYqKlUG8nid50HiQEdBTVcR4\nfRboJBm2e/7wtLb0ihSvi9LXWcDvkSwM+UngoRDCq+MAi45VgiLFq5EkUf8rkl7mvSTDyO8NIZwX\nY9w6PK3PXgbvXyPq8/1I5fD4ESKE8G6SD1X/TDJ/s4pkAaH+dKWvQ6nTff4qklUs/z7GeOiMG1xi\nxYpXCOGVJAurvUAyP/StJPOpVoUQzhmOtpdCseIVY3yYZK7s7wK7gfUkH35/AVTsQ47TjNdQDNd5\nykoR4zUiZRGvkOz88E/AG4D3xhh3ns55ykFG99cHgTeTvHc9FkJ4xWmep+SKFa8QwkUkDzQ+GGM8\neabtLBdFvL8eIuldfWuM8YEY450kw59bqeAHQ0WM11hgJsk6Ot+NMa4iWYzuCBWyHkd/iv3+NdI+\n349kJu0jQAjh48A/kMwdfnc6zPgwMC4kq5nnq+elBSkOp9/3lV+n22tIhmb9aLjaXSpFjtdHgKPA\nW2KM98RkIbW3kGwF9LlhvZCMFPv+ijF+g2Q4/HnAWTHGW0jutYPDeR1ZOYN4DUUhf7MVocjxGnGy\niFdIViBeRfLQ8X0xxn89s1aXTlb3V4zxwRjjfSQrPR8m2a6p4hQrXuniWn8HfAv4ZTpfu3tHlZpQ\neburAMW9v2KMe2KMd+c/4IgxtpBMw6vIh0JF/ntsJXnY/1j3gXR63hMkCwRXnIzev0bM5/uRzqS9\nwoUQvkCyR+w/AL+RN1xmA8nTuL6LBy3mpXlkG4BZ6Vy8gep0W0ky1OjxYWp6SWQQr/nAczFvj94Y\n43GS3pfzhus6slLseIUQzgsh/HaM8WSM8YUY46F0Tvt8hrZwSlk5w3gNxYb0Z/J/ZzXJsNyKmx+a\nQbxGlCziFUKYAfyUZH72O2IFryJc7HiFEC4NIbwt/1j6fr+O5ENwRSlyvOYDv0ayY8jJ9Kt7N5UH\ngYraRxsyub/eEEL4nX6K6kgWa60oGbx/bSR5ENT3AdBYBu6ZLlsZ/n8cEZ/vRwOT9goWQvgYyWqP\nXwN+N8bYkVe8hmQe4g159RtItj3q/ue4muTN7fq8OktJ5pn1/Qf6WuA/uxcLq0QZxWs9cFEIYVJe\nnVqS7d62DPMlFVVG8Xol8P2QbE3S7Q9I/iFVxHZv3YYhXkOxGpgdQnht3rGrSOaxVdSH3oziNWJk\nEa+05+Yekg9/K9LhuBUpo/vrBuAfQt7eyCGE6SRbfz57+q3PXgbx2kXSo5f/9e607A+AD51J+7OW\n0f11DfD36SJh3eeZRbIieMUsrAaZxet+knVx8j9zTCXZOWTNaTe+BDL+/1jxn+9HCxeiq1AhhNkk\nq5I/SzKk5ZLQe2XkJ0kW9LothNBFkkx+hmRuz7ch2aMxhHAH8K30Q0cz8EWS7UX6Doe8ALijaBdU\nZBnG669I9ib/SQjhyyTziz4KzAV+u5jXOJwyjNddwB7gByGEL5EsvPOnwP8pdNGsUhqOeA3RQ8B/\nAD8OIXyCpAfhy8A9McZfnOl1ZCXDeI0IGcbrIyQf4L4EtIcQLs0ra66Uv8kM4/U3wO8Dd6XvX3Uk\nC621k+6VXAmyiFfaS/hk/rEQQsdLxZVxb0Gm99f/AT4M3BNC+DxJQvr/kGxdefsZXkZmMozXAyRJ\n69+lD892kSS+kCS/FaEE/x8r+vP9aGLSXrlWkLyBX0gyX6evRpLVM7tIVs+cRPJ07n0xxvw5L+8H\nvkryBlFNMkztxhhjZ5/zzSTZw7dSZRKvNFF9fVr+A5KnoU8Cl8YYK2m4d1bxagkhrCT5B/RjkiF/\nnyfZDqiSDFe8BhVjzKXDcW8HvkmyVsK/AX90Rq3PXibxGkGyilf3dkGfTL/y3UOyuGYlyOrv8cUQ\nwhtIVkL/HskIoQeB/xJj3H1GV5At/x4Lk9X9tSvv/voOyci1+4Gb8qfgVYAs/z/eQNI58GfpeZ4A\n3uDf46Aq/fP9qFGVyzkaQpIkSZKkcuScdkmSJEmSypRJuyRJkiRJZcqkXZIkSZKkMmXSLkmSJElS\nmTJplyRJkiSpTJm0S5IkSZJUpkzaJUmSJEkqUybtkiRJkiSVKZN2SZIkSZLKlEm7JEmSJEllyqRd\nkiRJkqQyZdIuSZIkSVKZMmmXJEmSJKlMmbRLkiRJklSmTNolSZIkSSpTJu2SJEmSJJUpk3ZJkiRJ\nksqUSbskSZIkSWXKpF2SJEmSpDJl0i5JkiRJUpkyaZckSZIkqUyZtEuSJEmSVKbGlLoBkiSpMCGE\ne4GVwL0xxrcMUu9q4O+BHPBN4C7gfuB/xRj/Oou2SpKkM1OVy+VK3QZJkjREIYRZwA7gBDAeWBBj\n3DFA3eeBNuBXwG8CE4FDwEUxxu3ZtFiSJJ0Jh8dLklRZ3g3UAH9B8n/8vw9S9ybg7THG9wOzgauA\nYMIuSVLlsKddkqQKEkJ4BpgPzAV2A83A4hij/9AlSRqBnNMuSVKFCCG8ArgIuCPG2BZC+FfgfcC1\nJHPVu+tdCTwMvJ+kN/6PgKXAAeBHwOdijMf6nPu3gRuBV5LMgf8V8PUY44+KfFmSJGkQDo+XJKly\nvDd9/cf0tTuh/r0B6n8E+AbwHPB14DjJkPlv5VcKIXw5Pddi4AfAD4FFwA9DCF8arsZLkqTCOTxe\nkqQKEEKoIVmAbiIwM8Z4PIQwBtgJTAXmxhgPpHWvJOlp7wReH2N8Ij0+BdgANAANMcbWEMLrgZ8C\nTwErYoz707qNwEPABcAVMcafZnaxkiSphz3tkiRVhmuBWcC/xBiPA8QYO4A7gFpe6oXP92h3wp7W\nPwysIZkeNy89/Lvp683dCXtadz9wS/rtYIvdSZKkIjJplySpMnQn5T/sc/z76esH+vmZ9f0cO5y+\njktfXwl0Af/eT93uY68YYhslSdIwcyE6SZLKXAihHrgh/fbeEEJ/1c4LIVwWY1yTd+xEP/W658VV\npa+TgeMxxva+FWOMh0MIx4AJp9dySZJ0pkzaJUkqf78J1AE/B37ZT3kAriRZkG5NP+WDaQEmhBCm\nxhgP9TppCOPT39tUaIMlSdLwMGmXJKn8dQ+N/3iM8ZRh7CGEs4EtwG+FED5W4LmfBi4GXgfc3afs\ndSQ98msLPKckSRomzmmXJKmMhRAWAG8AtgKP91cnxvgiyUrvE4F3FvgrvpO+fjFdMb779zYCf5l+\n+w8FnlOSJA0Tk3ZJksrbe0h6u38QYxxsn9b/L30daM/2fqVbuX2FZGu3X4UQvhlC+CbwDMkidV9y\nuzdJkkrHpF2SpPL2nvT1ey9T719IVoZ/DXBhIb8gxngT8N9IevPfDfwWycrz74gx3jLIj0qSpCKr\nyuUGe2gvSZIkSZJKxZ52SZIkSZLKlEm7JEmSJEllyqRdkiRJkqQyZdIuSZIkSVKZMmmXJEmSJKlM\nmbRLkiRJklSmTNolSZIkSSpTJu2SJEmSJJUpk3ZJkiRJksqUSbskSZIkSWXq/wcWy5+BxuePmQAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11103f400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(15, 10))\n", "ax2 = ax.twinx()\n", "\n", "fondecyt.plot(kind='line', x='Año', y='Concursados', marker='o', markersize=10, ax=ax, label='Proyectos Concursados')\n", "fondecyt.plot(kind='line', x='Año', y='Aprobados', marker='o', markersize=10, ax=ax, label='Proyectos Aprobados')\n", "ax.set_xlim([2006.5,2017.5])\n", "ax.set_ylim([0,1300])\n", "ax.yaxis.set_ticks(np.arange(0, 1400, 100))\n", "ax.legend(loc=2)\n", "\n", "fondecyt.plot(kind='line', x='Año', y='Tasa de aprobación', color='red', grid=False, ax=ax2)\n", "ax2.set_ylim([0,100])\n", "ax2.yaxis.set_ticks(np.arange(0,110,10))\n", "ax2.set_xlim([2006.5,2017.5])\n", "ax2.xaxis.set_ticks(np.arange(2007, 2018, 1))\n", "ax2.grid(linestyle='--')\n", "ax2.legend(loc=1)\n", "\n", "ax.set_title('Estadística Proyectos Fondecyt de Iniciación')\n", "ax.set_ylabel('Número de proyectos / año')\n", "ax2.set_ylabel('Tasa (%)')\n", "\n", "fig.savefig('figures/estadistica_proyectos_fondecyt_iniciacion.pdf')\n", "fig.savefig('figures/estadistica_proyectos_fondecyt_iniciacion.png')" ] }, { "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-2-clause
rishuatgithub/MLPy	ai-hackathon/hate-speech/hate-speech-prediction-notebook.ipynb	1	39234	{ "cells": [ { "cell_type": "code", "execution_count": 24, "id": "43ac70e0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package words to\n", "[nltk_data] /Users/rishushrivastava/nltk_data...\n", "[nltk_data] Package words is already up-to-date!\n" ] } ], "source": [ "import pandas as pd\n", "import re\n", "import emoji\n", "import nltk\n", "nltk.download('words')\n", "words = set(nltk.corpus.words.words())" ] }, { "cell_type": "code", "execution_count": 25, "id": "5f151dd5", "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>Unnamed: 0</th>\n", " <th>count</th>\n", " <th>hate_speech</th>\n", " <th>offensive_language</th>\n", " <th>neither</th>\n", " <th>class</th>\n", " <th>tweet</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 count hate_speech offensive_language neither class \\\n", "0 0 3 0 0 3 2 \n", "1 1 3 0 3 0 1 \n", "2 2 3 0 3 0 1 \n", "3 3 3 0 2 1 1 \n", "4 4 6 0 6 0 1 \n", "\n", " tweet \n", "0 !!! RT @mayasolovely: As a woman you shouldn't... \n", "1 !!!!! RT @mleew17: boy dats cold...tyga dwn ba... \n", "2 !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby... \n", "3 !!!!!!!!! RT @C_G_Anderson: @viva_based she lo... \n", "4 !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you... " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('data/labeled_data.csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "8ec2cc71", "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>count</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>25%</th>\n", " <th>50%</th>\n", " <th>75%</th>\n", " <th>max</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Unnamed: 0</th>\n", " <td>24783.0</td>\n", " <td>12681.192027</td>\n", " <td>7299.553863</td>\n", " <td>0.0</td>\n", " <td>6372.5</td>\n", " <td>12703.0</td>\n", " <td>18995.5</td>\n", " <td>25296.0</td>\n", " </tr>\n", " <tr>\n", " <th>count</th>\n", " <td>24783.0</td>\n", " <td>3.243473</td>\n", " <td>0.883060</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>hate_speech</th>\n", " <td>24783.0</td>\n", " <td>0.280515</td>\n", " <td>0.631851</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " </tr>\n", " <tr>\n", " <th>offensive_language</th>\n", " <td>24783.0</td>\n", " <td>2.413711</td>\n", " <td>1.399459</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>neither</th>\n", " <td>24783.0</td>\n", " <td>0.549247</td>\n", " <td>1.113299</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>class</th>\n", " <td>24783.0</td>\n", " <td>1.110277</td>\n", " <td>0.462089</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count mean std min 25% 50% \\\n", "Unnamed: 0 24783.0 12681.192027 7299.553863 0.0 6372.5 12703.0 \n", "count 24783.0 3.243473 0.883060 3.0 3.0 3.0 \n", "hate_speech 24783.0 0.280515 0.631851 0.0 0.0 0.0 \n", "offensive_language 24783.0 2.413711 1.399459 0.0 2.0 3.0 \n", "neither 24783.0 0.549247 1.113299 0.0 0.0 0.0 \n", "class 24783.0 1.110277 0.462089 0.0 1.0 1.0 \n", "\n", " 75% max \n", "Unnamed: 0 18995.5 25296.0 \n", "count 3.0 9.0 \n", "hate_speech 0.0 7.0 \n", "offensive_language 3.0 9.0 \n", "neither 0.0 9.0 \n", "class 1.0 2.0 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe().T" ] }, { "cell_type": "code", "execution_count": 27, "id": "512209ae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "class\n", "0 1430\n", "1 19190\n", "2 4163\n", "Name: tweet, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby(['class']).count()['tweet']" ] }, { "cell_type": "code", "execution_count": 28, "id": "2c89dde9", "metadata": {}, "outputs": [], "source": [ "class_label = {0:'hate_speech',1:'offensive_language',2:'neither'}" ] }, { "cell_type": "code", "execution_count": 29, "id": "2796288e", "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>category</th>\n", " <th>tweet</th>\n", " <th>label</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2</td>\n", " <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n", " <td>neither</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n", " <td>offensive_language</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n", " <td>offensive_language</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n", " <td>offensive_language</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n", " <td>offensive_language</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category tweet \\\n", "0 2 !!! RT @mayasolovely: As a woman you shouldn't... \n", "1 1 !!!!! RT @mleew17: boy dats cold...tyga dwn ba... \n", "2 1 !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby... \n", "3 1 !!!!!!!!! RT @C_G_Anderson: @viva_based she lo... \n", "4 1 !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you... \n", "\n", " label \n", "0 neither \n", "1 offensive_language \n", "2 offensive_language \n", "3 offensive_language \n", "4 offensive_language " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_refactor = data.copy()\n", "\n", "data_refactor['label'] = data_refactor['class'].map(class_label)\n", "data_refactor.drop(['Unnamed: 0','count','hate_speech','offensive_language','neither'],inplace=True, axis=1)\n", "data_refactor.rename(columns={'class':'category'}, inplace=True)\n", "\n", "data = data_refactor.copy()\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 30, "id": "534f8bd8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "85 \"@Blackman38Tide: @WhaleLookyHere @HowdyDowdy1...\n", "89 \"@CB_Baby24: @white_thunduh alsarabsss\" hes a ...\n", "110 \"@DevilGrimz: @VigxRArts you're fucking gay, b...\n", "Name: tweet, dtype: object" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['category'] == 0]['tweet'][:3]" ] }, { "cell_type": "code", "execution_count": 31, "id": "994fd3e8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 32, "id": "2c035be8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, ..., 1, 2, 1])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train, X_val, y_train, y_val = train_test_split(\n", " data.index.values,\n", " data.category.values,\n", " test_size=0.15,\n", " random_state=17,\n", " stratify=data.category.values, \n", ")\n", "\n", "y_train" ] }, { "cell_type": "code", "execution_count": 33, "id": "381c92bf", "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>category</th>\n", " <th>tweet</th>\n", " <th>label</th>\n", " <th>data_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2</td>\n", " <td>!!! RT @mayasolovely: As a woman you shouldn't...</td>\n", " <td>neither</td>\n", " <td>train</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>!!!!! RT @mleew17: boy dats cold...tyga dwn ba...</td>\n", " <td>offensive_language</td>\n", " <td>train</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...</td>\n", " <td>offensive_language</td>\n", " <td>train</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...</td>\n", " <td>offensive_language</td>\n", " <td>val</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...</td>\n", " <td>offensive_language</td>\n", " <td>train</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category tweet \\\n", "0 2 !!! RT @mayasolovely: As a woman you shouldn't... \n", "1 1 !!!!! RT @mleew17: boy dats cold...tyga dwn ba... \n", "2 1 !!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby... \n", "3 1 !!!!!!!!! RT @C_G_Anderson: @viva_based she lo... \n", "4 1 !!!!!!!!!!!!! RT @ShenikaRoberts: The shit you... \n", "\n", " label data_type \n", "0 neither train \n", "1 offensive_language train \n", "2 offensive_language train \n", "3 offensive_language val \n", "4 offensive_language train " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['data_type'] = ['not_set']data.shape[0]\n", "data.loc[X_train, 'data_type'] = 'train'\n", "data.loc[X_val, 'data_type'] = 'val'\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 34, "id": "a6be7866", "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></th>\n", " <th></th>\n", " <th>tweet</th>\n", " </tr>\n", " <tr>\n", " <th>category</th>\n", " <th>label</th>\n", " <th>data_type</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">0</th>\n", " <th rowspan=\"2\" valign=\"top\">hate_speech</th>\n", " <th>train</th>\n", " <td>1216</td>\n", " </tr>\n", " <tr>\n", " <th>val</th>\n", " <td>214</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th rowspan=\"2\" valign=\"top\">offensive_language</th>\n", " <th>train</th>\n", " <td>16311</td>\n", " </tr>\n", " <tr>\n", " <th>val</th>\n", " <td>2879</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th rowspan=\"2\" valign=\"top\">neither</th>\n", " <th>train</th>\n", " <td>3538</td>\n", " </tr>\n", " <tr>\n", " <th>val</th>\n", " <td>625</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " tweet\n", "category label data_type \n", "0 hate_speech train 1216\n", " val 214\n", "1 offensive_language train 16311\n", " val 2879\n", "2 neither train 3538\n", " val 625" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby(['category', 'label', 'data_type']).count()" ] }, { "cell_type": "code", "execution_count": 35, "id": "0cd483be", "metadata": {}, "outputs": [], "source": [ "from transformers import BertTokenizer\n", "from torch.utils.data import TensorDataset\n", "import torch" ] }, { "cell_type": "code", "execution_count": 36, "id": "fa4c29c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PreTrainedTokenizer(name_or_path='bert-base-uncased', vocab_size=30522, model_max_len=512, is_fast=False, padding_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})\n" ] } ], "source": [ "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',do_lower_case=True)\n", "print(tokenizer)" ] }, { "cell_type": "code", "execution_count": 37, "id": "1da77710", "metadata": { "scrolled": true }, "outputs": [], "source": [ "encoded_data_train = tokenizer.batch_encode_plus(\n", " data[data.data_type=='train'].tweet.values,\n", " add_special_tokens=True,\n", " return_attention_mask=True,\n", " padding=True,\n", " max_length=256,\n", " return_tensors='pt'\n", ")" ] }, { "cell_type": "code", "execution_count": 38, "id": "92c295db", "metadata": {}, "outputs": [], "source": [ "encoded_data_val = tokenizer.batch_encode_plus(\n", " data[data.data_type=='val'].tweet.values,\n", " add_special_tokens=True,\n", " return_attention_mask=True,\n", " padding=True,\n", " max_length=256,\n", " return_tensors='pt'\n", ")" ] }, { "cell_type": "code", "execution_count": 39, "id": "0e2ce44f", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([ 101, 999, 999, 999, 19387, 1030, 9815, 19454, 21818, 2135,\n", " 1024, 2004, 1037, 2450, 2017, 5807, 1005, 1056, 17612, 2055,\n", " 9344, 2039, 2115, 2160, 1012, 1004, 23713, 1025, 2004, 1037,\n", " 2158, 2017, 2323, 2467, 2202, 1996, 11669, 2041, 1012, 1012,\n", " 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0])\n", "tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0])\n" ] } ], "source": [ "print(encoded_data_train['input_ids'][0])\n", "print(encoded_data_train['attention_mask'][0])" ] }, { "cell_type": "code", "execution_count": 42, "id": "c7f059e3", "metadata": {}, "outputs": [], "source": [ "input_ids_train = encoded_data_train['input_ids']\n", "attention_masks_train = encoded_data_train['attention_mask']\n", "labels_train = torch.tensor(data[data.data_type=='train'].category.values)\n", "\n", "input_ids_val = encoded_data_val['input_ids']\n", "attention_masks_val = encoded_data_val['attention_mask']\n", "labels_val = torch.tensor(data[data.data_type=='val'].category.values)" ] }, { "cell_type": "code", "execution_count": 45, "id": "23ecd19c", "metadata": {}, "outputs": [], "source": [ "dataset_train = TensorDataset(\n", " torch.LongTensor(input_ids_train),\n", " torch.LongTensor(attention_masks_train),\n", " labels_train\n", ")\n", "\n", "dataset_val = TensorDataset(\n", " torch.LongTensor(input_ids_val),\n", " torch.LongTensor(attention_masks_val),\n", " labels_val\n", ")" ] }, { "cell_type": "code", "execution_count": 46, "id": "aff74e46", "metadata": {}, "outputs": [], "source": [ "from transformers import BertForSequenceClassification" ] }, { "cell_type": "code", "execution_count": 48, "id": "98ba77a4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n", "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ], "source": [ "model = BertForSequenceClassification.from_pretrained('bert-base-uncased',\n", " num_labels = len(class_label),\n", " output_attentions=False,\n", " output_hidden_states=False)" ] }, { "cell_type": "code", "execution_count": 49, "id": "b73b0046", "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler" ] }, { "cell_type": "code", "execution_count": 51, "id": "07fac730", "metadata": {}, "outputs": [], "source": [ "batch_size = 32\n", "\n", "dataloader_train = DataLoader(dataset_train,sampler=RandomSampler(dataset_train),batch_size=batch_size)\n", "dataloader_val = DataLoader(dataset_val,sampler=RandomSampler(dataset_val),batch_size=batch_size)" ] }, { "cell_type": "code", "execution_count": 52, "id": "d3e63a39", "metadata": {}, "outputs": [], "source": [ "from transformers import AdamW, get_linear_schedule_with_warmup" ] }, { "cell_type": "code", "execution_count": 53, "id": "3127eb70", "metadata": {}, "outputs": [], "source": [ "optimizer = AdamW(model.parameters(),lr=1e-5,eps=1e-8)\n", "epochs = 5\n", "scheduler = get_linear_schedule_with_warmup(optimizer,\n", " num_warmup_steps=0,\n", " num_training_steps=len(dataloader_train)epochs)" ] }, { "cell_type": "code", "execution_count": 54, "id": "89851d5a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "device(type='cpu')" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model.to(device)\n", "\n", "device" ] }, { "cell_type": "code", "execution_count": 58, "id": "a2e6df6e", "metadata": {}, "outputs": [], "source": [ "import random\n", "from tqdm.notebook import tqdm\n", "\n", "seed_val = 17\n", "random.seed(seed_val)\n", "np.random.seed(seed_val)\n", "torch.manual_seed(seed_val)\n", "torch.cuda.manual_seed_all(seed_val)" ] }, { "cell_type": "code", "execution_count": 59, "id": "4e20f862", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import f1_score\n", "\n", "def f1_score_func(preds, labels):\n", " preds_flat = np.argmax(preds, axis=1).flatten()\n", " labels_flat = labels.flatten()\n", " return f1_score(labels_flat, preds_flat, average='weighted')\n", "\n", "def accuracy_per_class(preds, labels):\n", " label_dict_inverse = {v: k for k, v in label_dict.items()}\n", " \n", " preds_flat = np.argmax(preds, axis=1).flatten()\n", " labels_flat = labels.flatten()\n", "\n", " for label in np.unique(labels_flat):\n", " y_preds = preds_flat[labels_flat==label]\n", " y_true = labels_flat[labels_flat==label]\n", " print(f'Class: {label_dict_inverse[label]}')\n", " print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\\n')" ] }, { "cell_type": "code", "execution_count": 60, "id": "151d9821", "metadata": {}, "outputs": [], "source": [ "def evaluate(dataloader_val):\n", "\n", " model.eval()\n", " \n", " loss_val_total = 0\n", " predictions, true_vals = [], []\n", " \n", " for batch in dataloader_val:\n", " \n", " batch = tuple(b.to(device) for b in batch)\n", " \n", " inputs = {'input_ids': batch[0],\n", " 'attention_mask': batch[1],\n", " 'labels': batch[2],\n", " }\n", "\n", " with torch.no_grad(): \n", " outputs = model(inputs)\n", " \n", " loss = outputs[0]\n", " logits = outputs[1]\n", " loss_val_total += loss.item()\n", "\n", " logits = logits.detach().cpu().numpy()\n", " label_ids = inputs['labels'].cpu().numpy()\n", " predictions.append(logits)\n", " true_vals.append(label_ids)\n", " \n", " loss_val_avg = loss_val_total/len(dataloader_val) \n", " \n", " predictions = np.concatenate(predictions, axis=0)\n", " true_vals = np.concatenate(true_vals, axis=0)\n", " \n", " return loss_val_avg, predictions, true_vals" ] }, { "cell_type": "code", "execution_count": null, "id": "2c2d0eb9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3bd52b70f6e48a28eff85fb6012c23e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%\| \| 0/5 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf266eb528e345268d996d06c0d24844", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Epoch 0: 0%\| \| 0/659 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for epoch in tqdm(range(epochs)):\n", " \n", " model.train()\n", " \n", " loss_train_total = 0\n", " \n", " progress_bar = tqdm(dataloader_train, desc='Epoch {:1d}'.format(epoch), leave=False, disable=False)\n", " \n", " for batch in progress_bar:\n", " model.zero_grad()\n", " batch = tuple(b.to(device) for b in batch)\n", " \n", " inputs = {'input_ids': batch[0],\n", " 'attention_mask': batch[1],\n", " 'labels': batch[2],\n", " }\n", " \n", " outputs = model(inputs)\n", " \n", " loss = outputs[0]\n", " loss_train_total += loss.item()\n", " loss.backward()\n", " \n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", "\n", " optimizer.step()\n", " scheduler.step()\n", " \n", " progress_bar.set_postfix({'training_loss': '{:.3f}'.format(loss.item()/len(batch))})\n", " \n", " tqdm.write(f'\\nEpoch {epoch}')\n", " \n", " loss_train_avg = loss_train_total/len(dataloader_train) \n", " tqdm.write(f'Training loss: {loss_train_avg}')\n", " \n", " val_loss, predictions, true_vals = evaluate(dataloader_val)\n", " val_f1 = f1_score_func(predictions, true_vals)\n", " \n", " tqdm.write(f'Validation loss: {val_loss}')\n", " tqdm.write(f'F1 Score (Weighted): {val_f1}')\n", " \n", " torch.save(model.state_dict(), 'model/finetuned_BERT.model')" ] }, { "cell_type": "code", "execution_count": null, "id": "b962795e", "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": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 5 }	apache-2.0
linan7788626/tutmom	mystic.ipynb	1	192172	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimiztion with `mystic`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`mystic`: approximates that `scipy.optimize` interface" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qzyobHN5/347dDXaM7pEj/SuX3LnTViI3ahSYBClVQv7OBDcfmC8i/wZeAz4G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\naWvo2aRn/rYzGpzBz3/9zMT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"text/plain": [ "<matplotlib.figure.Figure at 0x1123dc090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Example:\n", " - Minimize Rosenbrock's Function with Nelder-Mead.\n", " - Plot of parameter convergence to function minimum.\n", "\n", "Demonstrates:\n", " - standard models\n", " - minimal solver interface\n", " - parameter trajectories using retall\n", "\"\"\"\n", "\n", "# Nelder-Mead solver\n", "from mystic.solvers import fmin\n", "\n", "# Rosenbrock function\n", "from mystic.models import rosen\n", "\n", "# tools\n", "import pylab\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " # initial guess\n", " x0 = [0.8,1.2,0.7]\n", "\n", " # use Nelder-Mead to minimize the Rosenbrock function\n", " solution = fmin(rosen, x0, disp=0, retall=1)\n", " allvecs = solution[-1]\n", "\n", " # plot the parameter trajectories\n", " pylab.plot([i[0] for i in allvecs])\n", " pylab.plot([i[1] for i in allvecs])\n", " pylab.plot([i[2] for i in allvecs])\n", "\n", " # draw the plot\n", " pylab.title(\"Rosenbrock parameter convergence\")\n", " pylab.xlabel(\"Nelder-Mead solver iterations\")\n", " pylab.ylabel(\"parameter value\")\n", " pylab.legend([\"x\", \"y\", \"z\"])\n", " pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Diagnostic tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Callbacks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "Example:\n", " - Minimize Rosenbrock's Function with Nelder-Mead.\n", " - Dynamic plot of parameter convergence to function minimum.\n", "\n", "Demonstrates:\n", " - standard models\n", " - minimal solver interface\n", " - parameter trajectories using callback\n", " - solver interactivity\n", "\"\"\"\n", "\n", "# Nelder-Mead solver\n", "from mystic.solvers import fmin\n", "\n", "# Rosenbrock function\n", "from mystic.models import rosen\n", "\n", "# tools\n", "from mystic.tools import getch\n", "import pylab\n", "pylab.ion()\n", " \n", "# draw the plot\n", "def plot_frame():\n", " pylab.title(\"Rosenbrock parameter convergence\")\n", " pylab.xlabel(\"Nelder-Mead solver iterations\")\n", " pylab.ylabel(\"parameter value\")\n", " pylab.draw()\n", " return\n", "\n", "iter = 0\n", "step, xval, yval, zval = [], [], [], []\n", "# plot the parameter trajectories\n", "def plot_params(params):\n", " global iter, step, xval, yval, zval\n", " step.append(iter)\n", " xval.append(params[0])\n", " yval.append(params[1])\n", " zval.append(params[2])\n", " pylab.plot(step,xval,'b-')\n", " pylab.plot(step,yval,'g-')\n", " pylab.plot(step,zval,'r-')\n", " pylab.legend([\"x\", \"y\", \"z\"])\n", " pylab.draw()\n", " iter += 1\n", " return\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " # initial guess\n", " x0 = [0.8,1.2,0.7]\n", "\n", " # suggest that the user interacts with the solver\n", " print \"NOTE: while solver is running, press 'Ctrl-C' in console window\"\n", " getch()\n", " plot_frame()\n", "\n", " # use Nelder-Mead to minimize the Rosenbrock function\n", " solution = fmin(rosen, x0, disp=1, callback=plot_params, handler=True)\n", " print solution\n", "\n", " # don't exit until user is ready\n", " getch()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE IPython does not handle shell prompt interactive programs well, so the above should be run from a command prompt. An IPython-safe version is below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0 has best fit parameters: [ 0.8 1.2 0.7]\n", "Generation 1 has best fit parameters: [ 0.8 1.2 0.7]\n", "Generation 2 has best fit parameters: [ 1.096641 0.92316246 0.85222892]\n", "Generation 3 has best fit parameters: [ 0.96098383 0.92341029 0.85268657]\n", "Generation 4 has best fit parameters: [ 0.96116068 0.92362873 0.85268597]\n", "Generation 5 has best fit parameters: [ 0.96139941 0.92394456 0.85319715]\n", "Generation 6 has best fit parameters: [ 0.96490397 0.9293998 0.86287626]\n", "Generation 7 has best fit parameters: [ 0.97283782 0.9438172 0.8900223 ]\n", "Generation 8 has best fit parameters: [ 0.99282304 0.98392465 0.9676975 ]\n", "Generation 9 has best fit parameters: [ 0.99599362 0.99123752 0.98220233]\n", "Generation 10 has best fit parameters: [ 0.99933371 0.99875944 0.9973022 ]\n", "Generation 11 has best fit parameters: [ 0.99959358 0.99924252 0.99835369]\n", "Generation 12 has best fit parameters: [ 1.00000002 1.00000006 1.00000011]\n", "Generation 13 has best fit parameters: [ 1. 1. 1.]\n", "Generation 14 has best fit parameters: [ 1. 1. 1.]\n", "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 13\n", " Function evaluations: 524\n", "[ 1. 1. 1.]\n" ] } ], "source": [ "\"\"\" \n", "Example:\n", " - Minimize Rosenbrock's Function with Powell's method.\n", " - Dynamic print of parameter convergence to function minimum.\n", "\n", "Demonstrates:\n", " - standard models\n", " - minimal solver interface\n", " - parameter trajectories using callback\n", "\"\"\" \n", " \n", "# Powell's Directonal solver\n", "from mystic.solvers import fmin_powell\n", " \n", "# Rosenbrock function\n", "from mystic.models import rosen\n", " \n", "iter = 0\n", "# plot the parameter trajectories\n", "def print_params(params):\n", " global iter\n", " from numpy import asarray\n", " print \"Generation %d has best fit parameters: %s\" % (iter,asarray(params))\n", " iter += 1\n", " return\n", " \n", "\n", "if __name__ == '__main__':\n", " \n", " # initial guess\n", " x0 = [0.8,1.2,0.7]\n", " print_params(x0)\n", " \n", " # use Powell's method to minimize the Rosenbrock function\n", " solution = fmin_powell(rosen, x0, disp=1, callback=print_params, handler=False)\n", " print solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Monitors" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Powell's Method\n", "===============\n", "Generation 0 has Chi-Squared: 297.000000\n", "Generation 1 has Chi-Squared: 26.522040\n", "Generation 2 has Chi-Squared: 0.002383\n", "Generation 3 has Chi-Squared: 0.002378\n", "Generation 4 has Chi-Squared: 0.001940\n", "Generation 5 has Chi-Squared: 0.001141\n", "Generation 6 has Chi-Squared: 0.000769\n", "Generation 7 has Chi-Squared: 0.000125\n", "Generation 8 has Chi-Squared: 0.000042\n", "Generation 9 has Chi-Squared: 0.000000\n", "Generation 10 has Chi-Squared: 0.000000\n", "Generation 11 has Chi-Squared: 0.000000\n", "Generation 12 has Chi-Squared: 0.000000\n", "Generation 13 has Chi-Squared: 0.000000\n", "Generation 14 has Chi-Squared: 0.000000\n", "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 14\n", " Function evaluations: 529\n", "STOP(\"NormalizedChangeOverGeneration with {'tolerance': 0.0001, 'generations': 2}\")\n", "[ 1. 1. 1.]\n" ] } ], "source": [ "\"\"\"\n", "Example:\n", " - Minimize Rosenbrock's Function with Powell's method.\n", "\n", "Demonstrates:\n", " - standard models\n", " - minimal solver interface\n", " - customized monitors\n", "\"\"\"\n", "\n", "# Powell's Directonal solver\n", "from mystic.solvers import fmin_powell\n", "\n", "# Rosenbrock function\n", "from mystic.models import rosen\n", "\n", "# tools\n", "from mystic.monitors import VerboseLoggingMonitor\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " print \"Powell's Method\"\n", " print \"===============\"\n", "\n", " # initial guess\n", " x0 = [1.5, 1.5, 0.7]\n", "\n", " # configure monitor\n", " stepmon = VerboseLoggingMonitor(1,1)\n", "\n", " # use Powell's method to minimize the Rosenbrock function\n", " solution = fmin_powell(rosen, x0, itermon=stepmon)\n", " print solution" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bmGdms6N9VwJnmtnBhOaXxcAXIbsJP1pVV8PYsWFU6znn5P4PERGR7ZVsJGvy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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10cdc63d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import mystic\n", "mystic.log_reader('log.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Solution trajectory and model plotting" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sSpQ9io5UUe/wM6ORyTVYHa9htb+C1fYyiBBu7tGo3Fm4eceC3aOjzWnG3vY8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\nli3jtddeo7m5maFDh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"text/plain": [ "<matplotlib.figure.Figure at 0x10f778390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import mystic\n", "mystic.model_plotter(mystic.models.rosen, 'log.txt', depth=True, scale=1, bounds=\"-2:2:.1, -2:2:.1, 1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solver \"tuning\" and extension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Solver class interface" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differential Evolution\n", "======================\n", "Generation 0 has Chi-Squared: 76214.552493\n", "Generation 50 has Chi-Squared: 4588.159704\n", "Generation 100 has Chi-Squared: 303.150875\n", "Generation 150 has Chi-Squared: 19.256126\n", "Generation 200 has Chi-Squared: 6.966188\n", "Generation 250 has Chi-Squared: 1.781009\n", "Generation 300 has Chi-Squared: 0.021399\n", "STOP(\"VTR with {'tolerance': 0.01, 'target': 0.0}\")\n", "Generation 332 has best Chi-Squared: 0.007202\n", " 8 7 6 5 4 3 2\n", "128 x - 0.7717 x - 255.1 x + 1.564 x + 158.4 x - 0.9914 x - 31.29 x + 0.1968 x + 0.9804\n", "\n", "Actual Coefficients:\n", " 8 6 4 2\n", "128 x - 256 x + 160 x - 32 x + 1\n", "\n" ] }, { "data": { "image/png": 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VDXUPVyaESCGKEnKEw84yu3M0lRT8GOi6ojkcESn4QrQQihJy6Lq72O4cTSUFPwbhsBIi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\nKXfffWU3j6fiDL+fNyZimSt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"text/plain": [ "<matplotlib.figure.Figure at 0x10893d790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"\"\"\n", "Example:\n", " - Solve 8th-order Chebyshev polynomial coefficients with DE.\n", " - Callable plot of fitting to Chebyshev polynomial.\n", " - Monitor Chi-Squared for Chebyshev polynomial.\n", "\n", "Demonstrates:\n", " - standard models\n", " - expanded solver interface\n", " - built-in random initial guess\n", " - customized monitors and termination conditions\n", " - customized DE mutation strategies\n", " - use of monitor to retrieve results information\n", "\"\"\"\n", "\n", "# Differential Evolution solver\n", "from mystic.solvers import DifferentialEvolutionSolver2\n", "\n", "# Chebyshev polynomial and cost function\n", "from mystic.models.poly import chebyshev8, chebyshev8cost\n", "from mystic.models.poly import chebyshev8coeffs\n", "\n", "# tools\n", "from mystic.termination import VTR\n", "from mystic.strategy import Best1Exp\n", "from mystic.monitors import VerboseMonitor\n", "from mystic.tools import getch, random_seed\n", "from mystic.math import poly1d\n", "import pylab\n", "pylab.ion()\n", "\n", "# draw the plot\n", "def plot_exact():\n", " pylab.title(\"fitting 8th-order Chebyshev polynomial coefficients\")\n", " pylab.xlabel(\"x\")\n", " pylab.ylabel(\"f(x)\")\n", " import numpy\n", " x = numpy.arange(-1.2, 1.2001, 0.01)\n", " exact = chebyshev8(x)\n", " pylab.plot(x,exact,'b-')\n", " pylab.legend([\"Exact\"])\n", " pylab.axis([-1.4,1.4,-2,8],'k-')\n", " pylab.draw()\n", " return\n", "\n", "# plot the polynomial\n", "def plot_solution(params,style='y-'):\n", " import numpy\n", " x = numpy.arange(-1.2, 1.2001, 0.01)\n", " f = poly1d(params)\n", " y = f(x)\n", " pylab.plot(x,y,style)\n", " pylab.legend([\"Exact\",\"Fitted\"])\n", " pylab.axis([-1.4,1.4,-2,8],'k-')\n", " pylab.draw()\n", " return\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " print \"Differential Evolution\"\n", " print \"======================\"\n", "\n", " # set range for random initial guess\n", " ndim = 9\n", " x0 = [(-100,100)]ndim\n", " random_seed(123)\n", "\n", " # draw frame and exact coefficients\n", " plot_exact()\n", "\n", " # configure monitor\n", " stepmon = VerboseMonitor(50)\n", "\n", " # use DE to solve 8th-order Chebyshev coefficients\n", " npop = 10ndim\n", " solver = DifferentialEvolutionSolver2(ndim,npop)\n", " solver.SetRandomInitialPoints(min=[-100]ndim, max=[100]ndim)\n", " solver.SetGenerationMonitor(stepmon)\n", " solver.enable_signal_handler()\n", " solver.Solve(chebyshev8cost, termination=VTR(0.01), strategy=Best1Exp, \\\n", " CrossProbability=1.0, ScalingFactor=0.9, \\\n", " sigint_callback=plot_solution)\n", " solution = solver.Solution()\n", "\n", " # use monitor to retrieve results information\n", " iterations = len(stepmon)\n", " cost = stepmon.y[-1]\n", " print \"Generation %d has best Chi-Squared: %f\" % (iterations, cost)\n", "\n", " # use pretty print for polynomials\n", " print poly1d(solution)\n", "\n", " # compare solution with actual 8th-order Chebyshev coefficients\n", " print \"\\nActual Coefficients:\\n %s\\n\" % poly1d(chebyshev8coeffs)\n", "\n", " # plot solution versus exact coefficients\n", " plot_solution(solution)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finalize\n", "SaveSolver\n", "SetConstraints\n", "SetEvaluationLimits\n", "SetEvaluationMonitor\n", "SetGenerationMonitor\n", "SetInitialPoints\n", "SetMultinormalInitialPoints\n", "SetObjective\n", "SetPenalty\n", "SetRandomInitialPoints\n", "SetReducer\n", "SetSaveFrequency\n", "SetStrictRanges\n", "SetTermination\n", "Solution\n", "Solve\n", "Step\n", "Terminated\n", "UpdateGenealogyRecords\n", "bestEnergy\n", "bestSolution\n", "disable_signal_handler\n", "enable_signal_handler\n", "energy_history\n", "evaluations\n", "generations\n", "solution_history\n" ] } ], "source": [ "from mystic.solvers import DifferentialEvolutionSolver\n", "print \"\\n\".join([i for i in dir(DifferentialEvolutionSolver) if not i.startswith('_')])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Algorithm configurability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Termination conditions" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0 has Chi-Squared: 786.135110\n", "Generation 10 has Chi-Squared: 17.242570\n", "Generation 20 has Chi-Squared: 2.244977\n", "Generation 30 has Chi-Squared: 0.110609\n", "Generation 40 has Chi-Squared: 0.005102\n", "Generation 50 has Chi-Squared: 0.000234\n", "Generation 60 has Chi-Squared: 0.000016\n", "Generation 70 has Chi-Squared: 0.000005\n", "Generation 80 has Chi-Squared: 0.000000\n", "STOP(\"VTR with {'tolerance': 1e-08, 'target': 0.0}\")\n", "[ 1.00001067 1.00001328 1.00002572]\n" ] } ], "source": [ "from mystic.termination import VTR, ChangeOverGeneration, And, Or\n", "stop = Or(And(VTR(), ChangeOverGeneration()), VTR(1e-8))\n", "\n", "from mystic.models import rosen\n", "from mystic.monitors import VerboseMonitor\n", "from mystic.solvers import DifferentialEvolutionSolver\n", "\n", "solver = DifferentialEvolutionSolver(3,40)\n", "solver.SetRandomInitialPoints([-10,-10,-10],[10,10,10])\n", "solver.SetGenerationMonitor(VerboseMonitor(10))\n", "solver.SetTermination(stop)\n", "solver.SetObjective(rosen)\n", "solver.SetStrictRanges([-10,-10,-10],[10,10,10])\n", "solver.SetEvaluationLimits(generations=600)\n", "solver.Solve()\n", "\n", "print solver.bestSolution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Solver population" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Use `mystic` to find the minimun for the `peaks` test function, with the bound specified by the `mystic.models.peaks` documentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Use `mystic` to do a fit to the noisy data in the `scipy.optimize.curve_fit` example (the least squares fit)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functional constraints\n", "\n", "PENALTY: $\\psi(x) = f(x) + kp(x)$\n", "\n", "CONSTRAINT: $\\psi(x) = f(c(x)) = f(x')$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mystic.constraints import \n", "from mystic.penalty import quadratic_equality\n", "from mystic.coupler import inner\n", "from mystic.math import almostEqual\n", "from mystic.tools import random_seed\n", "random_seed(213)\n", "\n", "def test_penalize():\n", "\n", " from mystic.math.measures import mean, spread\n", " def mean_constraint(x, target):\n", " return mean(x) - target\n", "\n", " def range_constraint(x, target):\n", " return spread(x) - target\n", "\n", " @quadratic_equality(condition=range_constraint, kwds={'target':5.0})\n", " @quadratic_equality(condition=mean_constraint, kwds={'target':5.0})\n", " def penalty(x):\n", " return 0.0\n", "\n", " def cost(x):\n", " return abs(sum(x) - 5.0)\n", "\n", " from mystic.solvers import fmin\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", " y = fmin(cost, x, penalty=penalty, disp=False)\n", "\n", " assert round(mean(y)) == 5.0\n", " assert round(spread(y)) == 5.0\n", " assert round(cost(y)) == 4(5.0)\n", "\n", "\n", "def test_solve():\n", "\n", " from mystic.math.measures import mean\n", " def mean_constraint(x, target):\n", " return mean(x) - target\n", "\n", " def parameter_constraint(x):\n", " return x[-1] - x[0]\n", "\n", " @quadratic_equality(condition=mean_constraint, kwds={'target':5.0})\n", " @quadratic_equality(condition=parameter_constraint)\n", " def penalty(x):\n", " return 0.0\n", "\n", " x = solve(penalty, guess=[2,3,1])\n", "\n", " assert round(mean_constraint(x, 5.0)) == 0.0\n", " assert round(parameter_constraint(x)) == 0.0\n", " assert issolution(penalty, x)\n", "\n", "\n", "def test_solve_constraint():\n", "\n", " from mystic.math.measures import mean\n", " @with_mean(1.0)\n", " def constraint(x):\n", " x[-1] = x[0]\n", " return x\n", "\n", " x = solve(constraint, guess=[2,3,1])\n", "\n", " assert almostEqual(mean(x), 1.0, tol=1e-15)\n", " assert x[-1] == x[0]\n", " assert issolution(constraint, x)\n", "\n", "\n", "def test_as_constraint():\n", "\n", " from mystic.math.measures import mean, spread\n", " def mean_constraint(x, target):\n", " return mean(x) - target\n", "\n", " def range_constraint(x, target):\n", " return spread(x) - target\n", "\n", " @quadratic_equality(condition=range_constraint, kwds={'target':5.0})\n", " @quadratic_equality(condition=mean_constraint, kwds={'target':5.0})\n", " def penalty(x):\n", " return 0.0\n", "\n", " ndim = 3\n", " constraints = as_constraint(penalty, solver='fmin')\n", " #XXX: this is expensive to evaluate, as there are nested optimizations\n", "\n", " from numpy import arange\n", " x = arange(ndim)\n", " _x = constraints(x)\n", "\n", " assert round(mean(_x)) == 5.0\n", " assert round(spread(_x)) == 5.0\n", " assert round(penalty(_x)) == 0.0\n", "\n", " def cost(x):\n", " return abs(sum(x) - 5.0)\n", "\n", " npop = ndim3\n", " from mystic.solvers import diffev\n", " y = diffev(cost, x, npop, constraints=constraints, disp=False, gtol=10)\n", "\n", " assert round(mean(y)) == 5.0\n", " assert round(spread(y)) == 5.0\n", " assert round(cost(y)) == 5.0(ndim-1)\n", "\n", "\n", "def test_as_penalty():\n", "\n", " from mystic.math.measures import mean, spread\n", " @with_spread(5.0)\n", " @with_mean(5.0)\n", " def constraint(x):\n", " return x\n", "\n", " penalty = as_penalty(constraint)\n", "\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", "\n", " def cost(x):\n", " return abs(sum(x) - 5.0)\n", "\n", " from mystic.solvers import fmin\n", " y = fmin(cost, x, penalty=penalty, disp=False)\n", "\n", " assert round(mean(y)) == 5.0\n", " assert round(spread(y)) == 5.0\n", " assert round(cost(y)) == 4(5.0)\n", "\n", "\n", "def test_with_penalty():\n", "\n", " from mystic.math.measures import mean, spread\n", " @with_penalty(quadratic_equality, kwds={'target':5.0})\n", " def penalty(x, target):\n", " return mean(x) - target\n", "\n", " def cost(x):\n", " return abs(sum(x) - 5.0)\n", "\n", " from mystic.solvers import fmin\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", " y = fmin(cost, x, penalty=penalty, disp=False)\n", "\n", " assert round(mean(y)) == 5.0\n", " assert round(cost(y)) == 4(5.0)\n", "\n", "\n", "def test_with_mean():\n", "\n", " from mystic.math.measures import mean, impose_mean\n", "\n", " @with_mean(5.0)\n", " def mean_of_squared(x):\n", " return [i2 for i in x]\n", "\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", " y = impose_mean(5, [i2 for i in x])\n", " assert mean(y) == 5.0\n", " assert mean_of_squared(x) == y\n", "\n", "\n", "def test_with_mean_spread():\n", "\n", " from mystic.math.measures import mean, spread, impose_mean, impose_spread\n", "\n", " @with_spread(50.0)\n", " @with_mean(5.0)\n", " def constrained_squared(x):\n", " return [i2 for i in x]\n", "\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", " y = impose_spread(50.0, impose_mean(5.0,[i2 for i in x]))\n", " assert almostEqual(mean(y), 5.0, tol=1e-15)\n", " assert almostEqual(spread(y), 50.0, tol=1e-15)\n", " assert constrained_squared(x) == y\n", "\n", "\n", "def test_constrained_solve():\n", "\n", " from mystic.math.measures import mean, spread\n", " @with_spread(5.0)\n", " @with_mean(5.0)\n", " def constraints(x):\n", " return x\n", "\n", " def cost(x):\n", " return abs(sum(x) - 5.0)\n", "\n", " from mystic.solvers import fmin_powell\n", " from numpy import array\n", " x = array([1,2,3,4,5])\n", " y = fmin_powell(cost, x, constraints=constraints, disp=False)\n", "\n", " assert almostEqual(mean(y), 5.0, tol=1e-15)\n", " assert almostEqual(spread(y), 5.0, tol=1e-15)\n", " assert almostEqual(cost(y), 4(5.0), tol=1e-6)\n", "\n", "\n", "if __name__ == '__main__':\n", " test_penalize()\n", " test_solve()\n", " test_solve_constraint()\n", " test_as_constraint()\n", " test_as_penalty()\n", " test_with_penalty()\n", " test_with_mean()\n", " test_with_mean_spread()\n", " test_constrained_solve()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Powell's Method\n", "===============\n", "Generation 0 has Chi-Squared: 81.100247\n", "Generation 1 has Chi-Squared: 0.000000\n", "Generation 2 has Chi-Squared: 0.000000\n", "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 2\n", " Function evaluations: 81\n", "STOP(\"NormalizedChangeOverGeneration with {'tolerance': 0.0001, 'generations': 2}\")\n", "[ 1. 1. 1.]\n" ] } ], "source": [ "\"\"\"\n", "Example:\n", " - Minimize Rosenbrock's Function with Powell's method.\n", "\n", "Demonstrates:\n", " - standard models\n", " - minimal solver interface\n", " - parameter constraints solver and constraints factory decorator\n", " - statistical parameter constraints\n", " - customized monitors\n", "\"\"\"\n", "\n", "# Powell's Directonal solver\n", "from mystic.solvers import fmin_powell\n", "\n", "# Rosenbrock function\n", "from mystic.models import rosen\n", "\n", "# tools\n", "from mystic.monitors import VerboseMonitor\n", "from mystic.math.measures import mean, impose_mean\n", "from mystic.math import almostEqual\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " print \"Powell's Method\"\n", " print \"===============\"\n", "\n", " # initial guess\n", " x0 = [0.8,1.2,0.7]\n", "\n", " # use the mean constraints factory decorator\n", " from mystic.constraints import with_mean\n", "\n", " # define constraints function\n", " @with_mean(1.0)\n", " def constraints(x):\n", " # constrain the last x_i to be the same value as the first x_i\n", " x[-1] = x[0]\n", " return x\n", "\n", " # configure monitor\n", " stepmon = VerboseMonitor(1)\n", "\n", " # use Powell's method to minimize the Rosenbrock function\n", " solution = fmin_powell(rosen, x0, constraints=constraints, itermon=stepmon)\n", " print solution\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Range (i.e. 'box') constraints" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Use `solver.SetStrictRange`, or the `bounds` keyword on the solver function interface." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Symbolic constraints interface" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing spring.py\n" ] } ], "source": [ "%%file spring.py\n", "\"a Tension-Compression String\"\n", "\n", "def objective(x):\n", " x0,x1,x2 = x\n", " return x0*2 x1 * (x2 + 2)\n", "\n", "bounds = [(0,100)]3\n", "# with penalty='penalty' applied, solution is:\n", "xs = [0.05168906, 0.35671773, 11.28896619]\n", "ys = 0.01266523\n", "\n", "from mystic.symbolic import generate_constraint, generate_solvers, solve\n", "from mystic.symbolic import generate_penalty, generate_conditions\n", "\n", "equations = \"\"\"\n", "1.0 - (x13 x2)/(71785x04) <= 0.0\n", "(4x1*2 - x0x1)/(12566x03 (x1 - x0)) + 1./(5108x02) - 1.0 <= 0.0\n", "1.0 - 140.45x0/(x2 * x12) <= 0.0\n", "(x0 + x1)/1.5 - 1.0 <= 0.0\n", "\"\"\"\n", "\n", "pf = generate_penalty(generate_conditions(equations), k=1e12)\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=pf, npop=40,\n", " gtol=500, disp=True, full_output=True)\n", "\n", " print result[0]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CONVERTED SYMBOLIC TO SINGLE CONSTRAINTS FUNCTIONS\n", "(<function inequality at 0x10fd13230>, <function inequality at 0x10fd131b8>, <function inequality at 0x10fd132a8>, <function inequality at 0x10fd13320>)\n", "()\n", "\n", "THE INDIVIDUAL INEQUALITIES\n", "1.0 - (x[1]3 * x[2])/(71785x[0]4) - (0.0)\n", "(4x[1]*2 - x[0]x[1])/(12566x[0]3 (x[1] - x[0])) + 1./(5108x[0]2) - 1.0 - (0.0)\n", "1.0 - 140.45x[0]/(x[2] * x[1]2) - (0.0)\n", "(x[0] + x[1])/1.5 - 1.0 - (0.0)\n", "\n", "GENERATED THE PENALTY FUNCTION FOR ALL CONSTRAINTS\n", "quadratic_inequality: 1.0 - (x[1]3 * x[2])/(71785x[0]4) - (0.0)\n", "quadratic_inequality: (4x[1]*2 - x[0]x[1])/(12566x[0]3 (x[1] - x[0])) + 1./(5108x[0]2) - 1.0 - (0.0)\n", "quadratic_inequality: 1.0 - 140.45x[0]/(x[2] * x[1]2) - (0.0)\n", "quadratic_inequality: (x[0] + x[1])/1.5 - 1.0 - (0.0)\n", "\n", "PENALTY FOR [-0.1, 0.5, 11.0]: 7590.47619096\n" ] } ], "source": [ "equations = \"\"\"\n", "1.0 - (x13 * x2)/(71785x04) <= 0.0\n", "(4x1*2 - x0x1)/(12566x03 (x1 - x0)) + 1./(5108x02) - 1.0 <= 0.0\n", "1.0 - 140.45x0/(x2 * x1*2) <= 0.0\n", "(x0 + x1)/1.5 - 1.0 <= 0.0\n", "\"\"\"\n", "\n", "from mystic.symbolic import generate_constraint, generate_solvers, solve\n", "from mystic.symbolic import generate_penalty, generate_conditions\n", "\n", "ineql, eql = generate_conditions(equations)\n", "\n", "print \"CONVERTED SYMBOLIC TO SINGLE CONSTRAINTS FUNCTIONS\"\n", "print ineql\n", "print eql\n", "\n", "print \"\\nTHE INDIVIDUAL INEQUALITIES\"\n", "for f in ineql:\n", " print f.__doc__\n", "\n", "print \"\\nGENERATED THE PENALTY FUNCTION FOR ALL CONSTRAINTS\"\n", "pf = generate_penalty((ineql, eql))\n", "print pf.__doc__\n", "\n", "x = [-0.1, 0.5, 11.0]\n", "print \"\\nPENALTY FOR {}: {}\".format(x, pf(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Penatly functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "equations = \"\"\"\n", "1.0 - (x1*3 x2)/(71785x04) <= 0.0\n", "(4x1*2 - x0x1)/(12566x03 (x1 - x0)) + 1./(5108x02) - 1.0 <= 0.0\n", "1.0 - 140.45x0/(x2 * x12) <= 0.0\n", "(x0 + x1)/1.5 - 1.0 <= 0.0\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.012665\n", " Iterations: 553\n", " Function evaluations: 22160\n", "[ 0.05168906 0.35671774 11.28896583]\n" ] } ], "source": [ "\"a Tension-Compression String\"\n", "\n", "from spring import objective, bounds, xs, ys\n", "\n", "from mystic.constraints import as_constraint\n", "from mystic.penalty import quadratic_inequality\n", "\n", "def penalty1(x): # <= 0.0\n", " return 1.0 - (x[1]3 * x[2])/(71785x[0]4)\n", "\n", "def penalty2(x): # <= 0.0\n", " return (4x[1]*2 - x[0]x[1])/(12566x[0]3 (x[1] - x[0])) + 1./(5108x[0]2) - 1.0\n", "\n", "def penalty3(x): # <= 0.0\n", " return 1.0 - 140.45x[0]/(x[2] * x[1]*2)\n", "\n", "def penalty4(x): # <= 0.0\n", " return (x[0] + x[1])/1.5 - 1.0\n", "\n", "@quadratic_inequality(penalty1, k=1e12)\n", "@quadratic_inequality(penalty2, k=1e12)\n", "@quadratic_inequality(penalty3, k=1e12)\n", "@quadratic_inequality(penalty4, k=1e12)\n", "def penalty(x):\n", " return 0.0\n", "\n", "solver = as_constraint(penalty)\n", "\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=penalty, npop=40,\n", " gtol=500, disp=True, full_output=True)\n", " print result[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \"Operators\" that directly constrain search space" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0 has Chi-Squared: 4891.000000\n", "Generation 10 has Chi-Squared: 2316.000000\n", "Generation 20 has Chi-Squared: 1112.000000\n", "Generation 30 has Chi-Squared: 705.000000\n", "Generation 40 has Chi-Squared: 705.000000\n", "Generation 50 has Chi-Squared: 461.000000\n", "Generation 60 has Chi-Squared: 449.000000\n", "Generation 70 has Chi-Squared: 365.000000\n", "Generation 80 has Chi-Squared: 357.000000\n", "Generation 90 has Chi-Squared: 289.000000\n", "Generation 100 has Chi-Squared: 283.000000\n", "Generation 110 has Chi-Squared: 146.000000\n", "Generation 120 has Chi-Squared: 134.000000\n", "Generation 130 has Chi-Squared: 134.000000\n", "Generation 140 has Chi-Squared: 134.000000\n", "Generation 150 has Chi-Squared: 134.000000\n", "Generation 160 has Chi-Squared: 134.000000\n", "Generation 170 has Chi-Squared: 120.000000\n", "Generation 180 has Chi-Squared: 107.000000\n", "Generation 190 has Chi-Squared: 107.000000\n", "Generation 200 has Chi-Squared: 107.000000\n", "Generation 210 has Chi-Squared: 100.000000\n", "Generation 220 has Chi-Squared: 100.000000\n", "Generation 230 has Chi-Squared: 100.000000\n", "Generation 240 has Chi-Squared: 100.000000\n", "Generation 250 has Chi-Squared: 100.000000\n", "Generation 260 has Chi-Squared: 100.000000\n", "Generation 270 has Chi-Squared: 100.000000\n", "Generation 280 has Chi-Squared: 100.000000\n", "Generation 290 has Chi-Squared: 100.000000\n", "Generation 300 has Chi-Squared: 81.000000\n", "Generation 310 has Chi-Squared: 68.000000\n", "Generation 320 has Chi-Squared: 53.000000\n", "Generation 330 has Chi-Squared: 53.000000\n", "Generation 340 has Chi-Squared: 53.000000\n", "Generation 350 has Chi-Squared: 53.000000\n", "Generation 360 has Chi-Squared: 43.000000\n", "Generation 370 has Chi-Squared: 43.000000\n", "Generation 380 has Chi-Squared: 43.000000\n", "Generation 390 has Chi-Squared: 43.000000\n", "Generation 400 has Chi-Squared: 41.000000\n", "Generation 410 has Chi-Squared: 33.000000\n", "Generation 420 has Chi-Squared: 33.000000\n", "Generation 430 has Chi-Squared: 33.000000\n", "Generation 440 has Chi-Squared: 33.000000\n", "Generation 450 has Chi-Squared: 33.000000\n", "Generation 460 has Chi-Squared: 30.000000\n", "Generation 470 has Chi-Squared: 30.000000\n", "Generation 480 has Chi-Squared: 28.000000\n", "Generation 490 has Chi-Squared: 28.000000\n", "Generation 500 has Chi-Squared: 28.000000\n", "Generation 510 has Chi-Squared: 21.000000\n", "Generation 520 has Chi-Squared: 14.000000\n", "Generation 530 has Chi-Squared: 14.000000\n", "Generation 540 has Chi-Squared: 11.000000\n", "Generation 550 has Chi-Squared: 11.000000\n", "Generation 560 has Chi-Squared: 11.000000\n", "Generation 570 has Chi-Squared: 11.000000\n", "Generation 580 has Chi-Squared: 11.000000\n", "Generation 590 has Chi-Squared: 11.000000\n", "Generation 600 has Chi-Squared: 11.000000\n", "Generation 610 has Chi-Squared: 11.000000\n", "Generation 620 has Chi-Squared: 11.000000\n", "Generation 630 has Chi-Squared: 11.000000\n", "Generation 640 has Chi-Squared: 11.000000\n", "Generation 650 has Chi-Squared: 11.000000\n", "Generation 660 has Chi-Squared: 11.000000\n", "Generation 670 has Chi-Squared: 11.000000\n", "Generation 680 has Chi-Squared: 11.000000\n", "Generation 690 has Chi-Squared: 11.000000\n", "Generation 700 has Chi-Squared: 11.000000\n", "Generation 710 has Chi-Squared: 11.000000\n", "Generation 720 has Chi-Squared: 11.000000\n", "Generation 730 has Chi-Squared: 11.000000\n", "Generation 740 has Chi-Squared: 11.000000\n", "Generation 750 has Chi-Squared: 11.000000\n", "Generation 760 has Chi-Squared: 11.000000\n", "Generation 770 has Chi-Squared: 11.000000\n", "Generation 780 has Chi-Squared: 11.000000\n", "Generation 790 has Chi-Squared: 11.000000\n", "Generation 800 has Chi-Squared: 11.000000\n", "Generation 810 has Chi-Squared: 11.000000\n", "Generation 820 has Chi-Squared: 11.000000\n", "Generation 830 has Chi-Squared: 11.000000\n", "Generation 840 has Chi-Squared: 11.000000\n", "Generation 850 has Chi-Squared: 11.000000\n", "Generation 860 has Chi-Squared: 11.000000\n", "Generation 870 has Chi-Squared: 11.000000\n", "Generation 880 has Chi-Squared: 11.000000\n", "Generation 890 has Chi-Squared: 11.000000\n", "Generation 900 has Chi-Squared: 11.000000\n", "Generation 910 has Chi-Squared: 11.000000\n", "Generation 920 has Chi-Squared: 11.000000\n", "Generation 930 has Chi-Squared: 11.000000\n", "Generation 940 has Chi-Squared: 11.000000\n", "Generation 950 has Chi-Squared: 11.000000\n", "Generation 960 has Chi-Squared: 11.000000\n", "Generation 970 has Chi-Squared: 11.000000\n", "Generation 980 has Chi-Squared: 11.000000\n", "Generation 990 has Chi-Squared: 11.000000\n", "Generation 1000 has Chi-Squared: 11.000000\n", "Generation 1010 has Chi-Squared: 11.000000\n", "Generation 1020 has Chi-Squared: 11.000000\n", "Generation 1030 has Chi-Squared: 11.000000\n", "Generation 1040 has Chi-Squared: 11.000000\n", "Generation 1050 has Chi-Squared: 11.000000\n", "Generation 1060 has Chi-Squared: 11.000000\n", "Generation 1070 has Chi-Squared: 11.000000\n", "Generation 1080 has Chi-Squared: 11.000000\n", "Generation 1090 has Chi-Squared: 11.000000\n", "Generation 1100 has Chi-Squared: 11.000000\n", "Generation 1110 has Chi-Squared: 11.000000\n", "Generation 1120 has Chi-Squared: 11.000000\n", "Generation 1130 has Chi-Squared: 11.000000\n", "Generation 1140 has Chi-Squared: 11.000000\n", "Generation 1150 has Chi-Squared: 7.000000\n", "Generation 1160 has Chi-Squared: 7.000000\n", "Generation 1170 has Chi-Squared: 7.000000\n", "Generation 1180 has Chi-Squared: 7.000000\n", "Generation 1190 has Chi-Squared: 7.000000\n", "Generation 1200 has Chi-Squared: 7.000000\n", "Generation 1210 has Chi-Squared: 4.000000\n", "Generation 1220 has Chi-Squared: 4.000000\n", "Generation 1230 has Chi-Squared: 4.000000\n", "Generation 1240 has Chi-Squared: 3.000000\n", "Generation 1250 has Chi-Squared: 3.000000\n", "Generation 1260 has Chi-Squared: 3.000000\n", "Generation 1270 has Chi-Squared: 3.000000\n", "Generation 1280 has Chi-Squared: 3.000000\n", "Generation 1290 has Chi-Squared: 3.000000\n", "Generation 1300 has Chi-Squared: 3.000000\n", "Generation 1310 has Chi-Squared: 3.000000\n", "Generation 1320 has Chi-Squared: 3.000000\n", "Generation 1330 has Chi-Squared: 3.000000\n", "Generation 1340 has Chi-Squared: 3.000000\n", "Generation 1350 has Chi-Squared: 3.000000\n", "Generation 1360 has Chi-Squared: 3.000000\n", "Generation 1370 has Chi-Squared: 3.000000\n", "Generation 1380 has Chi-Squared: 3.000000\n", "Generation 1390 has Chi-Squared: 3.000000\n", "Generation 1400 has Chi-Squared: 3.000000\n", "Generation 1410 has Chi-Squared: 3.000000\n", "Generation 1420 has Chi-Squared: 3.000000\n", "Generation 1430 has Chi-Squared: 3.000000\n", "Generation 1440 has Chi-Squared: 3.000000\n", "Generation 1450 has Chi-Squared: 3.000000\n", "Generation 1460 has Chi-Squared: 3.000000\n", "Generation 1470 has Chi-Squared: 3.000000\n", "Generation 1480 has Chi-Squared: 3.000000\n", "Generation 1490 has Chi-Squared: 3.000000\n", "Generation 1500 has Chi-Squared: 3.000000\n", "Generation 1510 has Chi-Squared: 3.000000\n", "Generation 1520 has Chi-Squared: 3.000000\n", "Generation 1530 has Chi-Squared: 3.000000\n", "Generation 1540 has Chi-Squared: 3.000000\n", "Generation 1550 has Chi-Squared: 3.000000\n", "Generation 1560 has Chi-Squared: 3.000000\n", "Generation 1570 has Chi-Squared: 3.000000\n", "Generation 1580 has Chi-Squared: 3.000000\n", "Generation 1590 has Chi-Squared: 3.000000\n", "Generation 1600 has Chi-Squared: 3.000000\n", "Generation 1610 has Chi-Squared: 3.000000\n", "Generation 1620 has Chi-Squared: 3.000000\n", "Generation 1630 has Chi-Squared: 3.000000\n", "Generation 1640 has Chi-Squared: 3.000000\n", "Generation 1650 has Chi-Squared: 3.000000\n", "Generation 1660 has Chi-Squared: 3.000000\n", "Generation 1670 has Chi-Squared: 3.000000\n", "Generation 1680 has Chi-Squared: 3.000000\n", "Generation 1690 has Chi-Squared: 3.000000\n", "Generation 1700 has Chi-Squared: 3.000000\n", "Generation 1710 has Chi-Squared: 3.000000\n", "Generation 1720 has Chi-Squared: 3.000000\n", "Generation 1730 has Chi-Squared: 3.000000\n", "Generation 1740 has Chi-Squared: 3.000000\n", "Generation 1750 has Chi-Squared: 3.000000\n", "Generation 1760 has Chi-Squared: 3.000000\n", "Generation 1770 has Chi-Squared: 3.000000\n", "Generation 1780 has Chi-Squared: 3.000000\n", "Generation 1790 has Chi-Squared: 3.000000\n", "Generation 1800 has Chi-Squared: 3.000000\n", "Generation 1810 has Chi-Squared: 3.000000\n", "Generation 1820 has Chi-Squared: 3.000000\n", "Generation 1830 has Chi-Squared: 3.000000\n", "Generation 1840 has Chi-Squared: 3.000000\n", "Generation 1850 has Chi-Squared: 3.000000\n", "Generation 1860 has Chi-Squared: 3.000000\n", "Generation 1870 has Chi-Squared: 3.000000\n", "Generation 1880 has Chi-Squared: 3.000000\n", "Generation 1890 has Chi-Squared: 3.000000\n", "Generation 1900 has Chi-Squared: 3.000000\n", "Generation 1910 has Chi-Squared: 3.000000\n", "Generation 1920 has Chi-Squared: 3.000000\n", "Generation 1930 has Chi-Squared: 3.000000\n", "Generation 1940 has Chi-Squared: 3.000000\n", "Generation 1950 has Chi-Squared: 3.000000\n", "Generation 1960 has Chi-Squared: 3.000000\n", "Generation 1970 has Chi-Squared: 3.000000\n", "Generation 1980 has Chi-Squared: 3.000000\n", "Generation 1990 has Chi-Squared: 3.000000\n", "Generation 2000 has Chi-Squared: 3.000000\n", "Generation 2010 has Chi-Squared: 3.000000\n", "Generation 2020 has Chi-Squared: 3.000000\n", "Generation 2030 has Chi-Squared: 3.000000\n", "Generation 2040 has Chi-Squared: 3.000000\n", "Generation 2050 has Chi-Squared: 3.000000\n", "Generation 2060 has Chi-Squared: 3.000000\n", "Generation 2070 has Chi-Squared: 3.000000\n", "Generation 2080 has Chi-Squared: 3.000000\n", "Generation 2090 has Chi-Squared: 3.000000\n", "Generation 2100 has Chi-Squared: 3.000000\n", "Generation 2110 has Chi-Squared: 2.000000\n", "Generation 2120 has Chi-Squared: 2.000000\n", "Generation 2130 has Chi-Squared: 2.000000\n", "Generation 2140 has Chi-Squared: 2.000000\n", "Generation 2150 has Chi-Squared: 2.000000\n", "Generation 2160 has Chi-Squared: 2.000000\n", "Generation 2170 has Chi-Squared: 2.000000\n", "Generation 2180 has Chi-Squared: 2.000000\n", "Generation 2190 has Chi-Squared: 2.000000\n", "Generation 2200 has Chi-Squared: 2.000000\n", "Generation 2210 has Chi-Squared: 2.000000\n", "Generation 2220 has Chi-Squared: 2.000000\n", "Generation 2230 has Chi-Squared: 2.000000\n", "Generation 2240 has Chi-Squared: 2.000000\n", "Generation 2250 has Chi-Squared: 2.000000\n", "Generation 2260 has Chi-Squared: 2.000000\n", "Generation 2270 has Chi-Squared: 2.000000\n", "Generation 2280 has Chi-Squared: 2.000000\n", "Generation 2290 has Chi-Squared: 2.000000\n", "Generation 2300 has Chi-Squared: 2.000000\n", "Generation 2310 has Chi-Squared: 2.000000\n", "Generation 2320 has Chi-Squared: 0.000000\n", "Generation 2330 has Chi-Squared: 0.000000\n", "Generation 2340 has Chi-Squared: 0.000000\n", "Generation 2350 has Chi-Squared: 0.000000\n", "Generation 2360 has Chi-Squared: 0.000000\n", "Generation 2370 has Chi-Squared: 0.000000\n", "Generation 2380 has Chi-Squared: 0.000000\n", "Generation 2390 has Chi-Squared: 0.000000\n", "Generation 2400 has Chi-Squared: 0.000000\n", "Generation 2410 has Chi-Squared: 0.000000\n", "Generation 2420 has Chi-Squared: 0.000000\n", "Generation 2430 has Chi-Squared: 0.000000\n", "Generation 2440 has Chi-Squared: 0.000000\n", "Generation 2450 has Chi-Squared: 0.000000\n", "Generation 2460 has Chi-Squared: 0.000000\n", "Generation 2470 has Chi-Squared: 0.000000\n", "Generation 2480 has Chi-Squared: 0.000000\n", "Generation 2490 has Chi-Squared: 0.000000\n", "Generation 2500 has Chi-Squared: 0.000000\n", "Generation 2510 has Chi-Squared: 0.000000\n", "Generation 2520 has Chi-Squared: 0.000000\n", "Generation 2530 has Chi-Squared: 0.000000\n", "Generation 2540 has Chi-Squared: 0.000000\n", "Generation 2550 has Chi-Squared: 0.000000\n", "Generation 2560 has Chi-Squared: 0.000000\n", "Generation 2570 has Chi-Squared: 0.000000\n", "Generation 2580 has Chi-Squared: 0.000000\n", "Generation 2590 has Chi-Squared: 0.000000\n", "Generation 2600 has Chi-Squared: 0.000000\n", "Generation 2610 has Chi-Squared: 0.000000\n", "Generation 2620 has Chi-Squared: 0.000000\n", "Generation 2630 has Chi-Squared: 0.000000\n", "Generation 2640 has Chi-Squared: 0.000000\n", "Generation 2650 has Chi-Squared: 0.000000\n", "Generation 2660 has Chi-Squared: 0.000000\n", "Generation 2670 has Chi-Squared: 0.000000\n", "Generation 2680 has Chi-Squared: 0.000000\n", "Generation 2690 has Chi-Squared: 0.000000\n", "Generation 2700 has Chi-Squared: 0.000000\n", "Generation 2710 has Chi-Squared: 0.000000\n", "Generation 2720 has Chi-Squared: 0.000000\n", "Generation 2730 has Chi-Squared: 0.000000\n", "Generation 2740 has Chi-Squared: 0.000000\n", "Generation 2750 has Chi-Squared: 0.000000\n", "Generation 2760 has Chi-Squared: 0.000000\n", "Generation 2770 has Chi-Squared: 0.000000\n", "Generation 2780 has Chi-Squared: 0.000000\n", "Generation 2790 has Chi-Squared: 0.000000\n", "Generation 2800 has Chi-Squared: 0.000000\n", "Generation 2810 has Chi-Squared: 0.000000\n", "Generation 2820 has Chi-Squared: 0.000000\n", "Generation 2830 has Chi-Squared: 0.000000\n", "Generation 2840 has Chi-Squared: 0.000000\n", "Generation 2850 has Chi-Squared: 0.000000\n", "Generation 2860 has Chi-Squared: 0.000000\n", "Generation 2870 has Chi-Squared: 0.000000\n", "Generation 2880 has Chi-Squared: 0.000000\n", "Generation 2890 has Chi-Squared: 0.000000\n", "Generation 2900 has Chi-Squared: 0.000000\n", "Generation 2910 has Chi-Squared: 0.000000\n", "Generation 2920 has Chi-Squared: 0.000000\n", "Generation 2930 has Chi-Squared: 0.000000\n", "Generation 2940 has Chi-Squared: 0.000000\n", "Generation 2950 has Chi-Squared: 0.000000\n", "Generation 2960 has Chi-Squared: 0.000000\n", "Generation 2970 has Chi-Squared: 0.000000\n", "Generation 2980 has Chi-Squared: 0.000000\n", "Generation 2990 has Chi-Squared: 0.000000\n", "Generation 3000 has Chi-Squared: 0.000000\n", "Generation 3010 has Chi-Squared: 0.000000\n", "Generation 3020 has Chi-Squared: 0.000000\n", "Generation 3030 has Chi-Squared: 0.000000\n", "Generation 3040 has Chi-Squared: 0.000000\n", "Generation 3050 has Chi-Squared: 0.000000\n", "Generation 3060 has Chi-Squared: 0.000000\n", "Generation 3070 has Chi-Squared: 0.000000\n", "Generation 3080 has Chi-Squared: 0.000000\n", "Generation 3090 has Chi-Squared: 0.000000\n", "Generation 3100 has Chi-Squared: 0.000000\n", "Generation 3110 has Chi-Squared: 0.000000\n", "Generation 3120 has Chi-Squared: 0.000000\n", "Generation 3130 has Chi-Squared: 0.000000\n", "Generation 3140 has Chi-Squared: 0.000000\n", "Generation 3150 has Chi-Squared: 0.000000\n", "Generation 3160 has Chi-Squared: 0.000000\n", "Generation 3170 has Chi-Squared: 0.000000\n", "Generation 3180 has Chi-Squared: 0.000000\n", "Generation 3190 has Chi-Squared: 0.000000\n", "Generation 3200 has Chi-Squared: 0.000000\n", "Generation 3210 has Chi-Squared: 0.000000\n", "Generation 3220 has Chi-Squared: 0.000000\n", "Generation 3230 has Chi-Squared: 0.000000\n", "Generation 3240 has Chi-Squared: 0.000000\n", "Generation 3250 has Chi-Squared: 0.000000\n", "Generation 3260 has Chi-Squared: 0.000000\n", "Generation 3270 has Chi-Squared: 0.000000\n", "Generation 3280 has Chi-Squared: 0.000000\n", "Generation 3290 has Chi-Squared: 0.000000\n", "Generation 3300 has Chi-Squared: 0.000000\n", "Generation 3310 has Chi-Squared: 0.000000\n", "STOP(\"ChangeOverGeneration with {'tolerance': 1e-08, 'generations': 1000}\")\n", "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 3311\n", " Function evaluations: 172224\n", "[ 5. 13. 9. 16. 20. 4. 24. 21. 25. 17. 23. 2. 8. 12. 10.\n", " 19. 7. 11. 15. 3. 1. 26. 6. 22. 14. 18.]\n" ] } ], "source": [ "\"\"\"\n", "\n", " Crypto problem in Google CP Solver.\n", "\n", " Prolog benchmark problem\n", " '''\n", " Name : crypto.pl\n", " Original Source: P. Van Hentenryck's book\n", " Adapted by : Daniel Diaz - INRIA France\n", " Date : September 1992\n", " '''\n", "\"\"\"\n", "\n", "def objective(x):\n", " return 0.0\n", "\n", "nletters = 26\n", "\n", "bounds = [(1,nletters)]nletters\n", "# with penalty='penalty' applied, solution is:\n", "# A B C D E F G H I J K L M N O P Q\n", "xs = [ 5, 13, 9, 16, 20, 4, 24, 21, 25, 17, 23, 2, 8, 12, 10, 19, 7, \\\n", "# R S T U V W X Y Z\n", " 11, 15, 3, 1, 26, 6, 22, 14, 18]\n", "ys = 0.0\n", "\n", "# constraints\n", "equations = \"\"\"\n", "B + A + L + L + E + T - 45 == 0\n", "C + E + L + L + O - 43 == 0\n", "C + O + N + C + E + R + T - 74 == 0\n", "F + L + U + T + E - 30 == 0\n", "F + U + G + U + E - 50 == 0\n", "G + L + E + E - 66 == 0\n", "J + A + Z + Z - 58 == 0\n", "L + Y + R + E - 47 == 0\n", "O + B + O + E - 53 == 0\n", "O + P + E + R + A - 65 == 0\n", "P + O + L + K + A - 59 == 0\n", "Q + U + A + R + T + E + T - 50 == 0\n", "S + A + X + O + P + H + O + N + E - 134 == 0\n", "S + C + A + L + E - 51 == 0\n", "S + O + L + O - 37 == 0\n", "S + O + N + G - 61 == 0\n", "S + O + P + R + A + N + O - 82 == 0\n", "T + H + E + M + E - 72 == 0\n", "V + I + O + L + I + N - 100 == 0\n", "W + A + L + T + Z - 34 == 0\n", "\"\"\"\n", "var = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')\n", "\n", "# Let's say we know the vowels.\n", "bounds[0] = (5,5) # A\n", "bounds[4] = (20,20) # E\n", "bounds[8] = (25,25) # I\n", "bounds[14] = (10,10) # O\n", "bounds[20] = (1,1) # U\n", "\n", "from mystic.constraints import unique, near_integers, has_unique\n", "from mystic.symbolic import generate_penalty, generate_conditions\n", "pf = generate_penalty(generate_conditions(equations,var),k=1)\n", "from mystic.constraints import as_constraint\n", "cf = as_constraint(pf)\n", "from mystic.penalty import quadratic_equality\n", "\n", "@quadratic_equality(near_integers)\n", "@quadratic_equality(has_unique)\n", "def penalty(x):\n", " return pf(x)\n", "\n", "from numpy import round, hstack, clip\n", "def constraint(x):\n", " x = round(x).astype(int) # force round and convert type to int\n", " x = clip(x, 1,nletters) #XXX: hack to impose bounds\n", " x = unique(x, range(1,nletters+1))\n", " return x\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", " from mystic.monitors import Monitor, VerboseMonitor\n", " mon = VerboseMonitor(10)\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=pf,\n", " constraints=constraint, npop=52, ftol=1e-8, gtol=1000,\n", " disp=True, full_output=True, cross=0.1, scale=0.9, itermon=mon)\n", " print result[0]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Special cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Integer and mixed integer programming" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.000000\n", " Iterations: 91\n", " Function evaluations: 3680\n", "[ 6. 0. 8. 4. 9. 3. 9.]\n" ] } ], "source": [ "\"\"\"\n", " Eq 10 in Google CP Solver.\n", "\n", " Standard benchmark problem.\n", "\"\"\"\n", "\n", "def objective(x):\n", " return 0.0\n", "\n", "bounds = [(0,10)]7\n", "# with penalty='penalty' applied, solution is:\n", "xs = [6., 0., 8., 4., 9., 3., 9.]\n", "ys = 0.0\n", "\n", "# constraints\n", "equations = \"\"\"\n", "98527x0 + 34588x1 + 5872x2 + 59422x4 + 65159x6 - 1547604 - 30704x3 - 29649x5 == 0.0\n", "98957x1 + 83634x2 + 69966x3 + 62038x4 + 37164x5 + 85413x6 - 1823553 - 93989x0 == 0.0\n", "900032 + 10949x0 + 77761x1 + 67052x4 - 80197x2 - 61944x3 - 92964x5 - 44550x6 == 0.0\n", "73947x0 + 84391x2 + 81310x4 - 1164380 - 96253x1 - 44247x3 - 70582x5 - 33054x6 == 0.0\n", "13057x2 + 42253x3 + 77527x4 + 96552x6 - 1185471 - 60152x0 - 21103x1 - 97932x5 == 0.0\n", "1394152 + 66920x0 + 55679x3 - 64234x1 - 65337x2 - 45581x4 - 67707x5 - 98038x6 == 0.0\n", "68550x0 + 27886x1 + 31716x2 + 73597x3 + 38835x6 - 279091 - 88963x4 - 76391x5 == 0.0\n", "76132x1 + 71860x2 + 22770x3 + 68211x4 + 78587x5 - 480923 - 48224x0 - 82817x6 == 0.0\n", "519878 + 94198x1 + 87234x2 + 37498x3 - 71583x0 - 25728x4 - 25495x5 - 70023x6 == 0.0\n", "361921 + 78693x0 + 38592x4 + 38478x5 - 94129x1 - 43188x2 - 82528x3 - 69025x6 == 0.0\n", "\"\"\"\n", "\n", "from mystic.symbolic import generate_penalty, generate_conditions\n", "pf = generate_penalty(generate_conditions(equations))\n", "\n", "from numpy import round as npround\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=pf,\n", " constraints=npround, npop=40, gtol=50, disp=True, full_output=True)\n", "\n", " print result[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE:* Convert the following \"Pressure Vessel Design\" code to use explicit penalty functions and not symbolic constraints." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 5804.376208\n", " Iterations: 835\n", " Function evaluations: 33440\n", "[ 0.72759093 0.35964857 37.69901188 240. ]\n" ] } ], "source": [ "\"Pressure Vessel Design\"\n", "\n", "def objective(x):\n", " x0,x1,x2,x3 = x\n", " return 0.6224x0x2x3 + 1.7781x1x22 + 3.1661x0*2x3 + 19.84x02x2\n", "\n", "bounds = [(0,1e6)]4\n", "# with penalty='penalty' applied, solution is:\n", "xs = [0.72759093, 0.35964857, 37.69901188, 240.0]\n", "ys = 5804.3762083\n", "\n", "from mystic.symbolic import generate_constraint, generate_solvers, solve\n", "from mystic.symbolic import generate_penalty, generate_conditions\n", "\n", "equations = \"\"\"\n", "-x0 + 0.0193x2 <= 0.0\n", "-x1 + 0.00954x2 <= 0.0\n", "-pix2*2x3 - (4/3.)pix2*3 + 1296000.0 <= 0.0\n", "x3 - 240.0 <= 0.0\n", "\"\"\"\n", "pf = generate_penalty(generate_conditions(equations), k=1e12)\n", "\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=pf, npop=40, gtol=500,\n", " disp=True, full_output=True)\n", " print result[0]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Linear and quadratic constraints" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 2.499688\n", " Iterations: 6\n", " Function evaluations: 277\n", "[ 0.49974959 1.49987526]\n" ] } ], "source": [ "\"\"\"\n", " Minimize: f = 2x[0] + 1x[1]\n", "\n", " Subject to: -1x[0] + 1x[1] <= 1\n", " 1x[0] + 1x[1] >= 2\n", " 1x[1] >= 0\n", " 1x[0] - 2x[1] <= 4\n", "\n", " where: -inf <= x[0] <= inf\n", "\"\"\"\n", "\n", "def objective(x):\n", " x0,x1 = x\n", " return 2x0 + x1\n", "\n", "equations = \"\"\"\n", "-x0 + x1 - 1.0 <= 0.0\n", "-x0 - x1 + 2.0 <= 0.0\n", "x0 - 2x1 - 4.0 <= 0.0\n", "\"\"\"\n", "bounds = [(None, None),(0.0, None)]\n", "\n", "# with penalty='penalty' applied, solution is:\n", "xs = [0.5, 1.5]\n", "ys = 2.5\n", "\n", "from mystic.symbolic import generate_conditions, generate_penalty\n", "pf = generate_penalty(generate_conditions(equations), k=1e3)\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import fmin_powell\n", " from mystic.math import almostEqual\n", "\n", " result = fmin_powell(objective, x0=[0.0,0.0], bounds=bounds,\n", " penalty=pf, disp=True, full_output=True, gtol=3)\n", " print result[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE:* Solve the `cvxopt` \"qp\" example with `mystic`. Use symbolic constaints, penalty functions, or constraints operators. If you get it quickly, do all three methods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at how `mystic` gives improved [solver workflow](workflow.ipynb)" ] } ], "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
abezuglov/ANN	Storm-Surge/notebooks/Density Network Hurricane Test.ipynb	1	13000	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0, '../code')\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import os\n", "import sys\n", "import time\n", "import tensorflow as tf\n", "import load_datasets as ld\n", "import datetime as dt\n", "from sklearn.metrics import mean_squared_error\n", "\n", "flags = tf.app.flags\n", "FLAGS = flags.FLAGS\n", "\n", "flags.DEFINE_boolean('train', True, 'When True, run training & save model. When False, load a previously saved model and evaluate it')\n", "\n", "# Split the training data into batches. Each hurricane is 193 records. Batch sizes are usually 2^k\n", "# When batch size equals to 0, or exceeds available data, use the whole dataset\n", "# Large batch sizes produce more accurate update gradients, but the training is slower\n", "flags.DEFINE_integer('batch_size', 64193, 'Batch size. Divides evenly into the dataset size of 193')\n", "\n", "# Not currently used. The data is loaded in load_datasets (ld) and put in Dataset objects:\n", "# train_dataset, valid_dataset, and test_dataset\n", "#flags.DEFINE_string('train_dir', './data/', 'Directory to put the training data')\n", "\n", "# Save models in this directory\n", "flags.DEFINE_string('checkpoints_dir', './checkpoints', 'Directory to store checkpoints')\n", "\n", "# Statistics\n", "flags.DEFINE_string('summaries_dir','./logs','Summaries directory')\n", "\n", "# Evaluation\n", "# Output dataset\n", "flags.DEFINE_string('output','./test_track_out2.dat','When model evaluation, output the data here')\n", "# Input dataset\n", "flags.DEFINE_string('input','./test_track.dat','Dataset for input')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 0/324 \n", "\n", "Processed 100/324 \n", "\n", "Processed 200/324 \n", "\n", "Processed 300/324 \n", "\n", "calculate new means, stds for dataset with 43811 samples\n", "using provided means, stds for dataset with 9457 samples\n", "using provided means, stds for dataset with 9264 samples\n", "Num hurricanes in train 227, validation 49, test 48\n", "Step 0 (16.92 op/sec): Training loss: 0.10945, Validation loss: 0.08301\n", "Step 500 (21.52 op/sec): Training loss: 0.01127, Validation loss: 0.01373\n", "Step 1000 (19.72 op/sec): Training loss: 0.00698, Validation loss: 0.01084\n", "Step 1500 (19.55 op/sec): Training loss: 0.00672, Validation loss: 0.00931\n", "Step 2000 (19.55 op/sec): Training loss: 0.00589, Validation loss: 0.00817\n" ] } ], "source": [ "import ilt_three_layers as ilt\n", "#import ilt_density as ilt\n", "\n", "def fill_feed_dict(data_set, inputs_pl, outputs_pl, train):\n", " \"\"\"\n", " Returns feed dictionary for TF. \n", " data_set -- dataset\n", " inputs_pl -- TF placeholder for inputs\n", " outputs_pl -- TF placeholder for outputs\n", " train -- if TRUE, then return DS in batches for training. Otherwise, return complete DS for validation/testing\n", " \"\"\"\n", " if train:\n", " batch_size = FLAGS.batch_size\n", " else:\n", " batch_size = 0\n", "\n", " # Read next batch of data from the dataset\n", " inputs, outputs = data_set.next_batch(batch_size = batch_size)\n", "\n", " # Create dictionary for return\n", " feed_dict = {\n", " inputs_pl: inputs,\n", " outputs_pl: outputs\n", " }\n", " return feed_dict\n", "\n", "def train():\n", " \"\"\"\n", " Finish building the graph and run training on a single CPU's\n", " \"\"\"\n", " # Read datasets \n", " train_dataset, valid_dataset, test_dataset = ld.read_data_sets()\n", "\n", " with tf.Graph().as_default(), tf.device('/cpu:0'):\n", " # Prepare placeholders for inputs and expected outputs\n", " x = tf.placeholder(tf.float32, [None, FLAGS.input_vars], name='x-input')\n", " y_ = tf.placeholder(tf.float32, [None, FLAGS.output_vars], name = 'y-input')\n", "\n", " # Create variables for input data moments and initialize them with train datasets' moments\n", " means = tf.get_variable('means', trainable = False, \n", " initializer = tf.convert_to_tensor(train_dataset.means))\n", " stds = tf.get_variable('stds', trainable = False, \n", " initializer = tf.convert_to_tensor(train_dataset.stds))\n", "\n", " # Normalize input data\n", " x_normalized = tf.div(tf.sub(x,means),stds)\n", "\n", " # Prepare global step and learning rate for optimization\n", " global_step = tf.get_variable(\n", " 'global_step', [], \n", " initializer=tf.constant_initializer(0), trainable=False)\n", " learning_rate = tf.train.exponential_decay(\n", " FLAGS.learning_rate, global_step, FLAGS.max_steps,\n", " FLAGS.learning_rate_decay, staircase=False) \n", "\n", " # Create ADAM optimizer\n", " optimizer = tf.train.AdamOptimizer(learning_rate)\n", " outputs = ilt.inference(x_normalized)\n", " loss = ilt.loss(outputs, y_)\n", " tf.scalar_summary('MSE', loss)\n", " #tf.scalar_summary('CC',tf.get_collection('cc')[0])\n", "\n", "\n", " # Calculate gradients and apply them\n", " grads = optimizer.compute_gradients(loss)\n", " apply_gradient_op = optimizer.apply_gradients(grads, global_step = global_step)\n", "\n", " # Smoothen variables after gradient applications\n", " variable_averages = tf.train.ExponentialMovingAverage(\n", " FLAGS.moving_avg_decay, global_step)\n", " variables_averages_op = variable_averages.apply(tf.trainable_variables())\n", " train_op = tf.group(apply_gradient_op, variables_averages_op)\n", " #train_op = apply_gradient_op\n", "\n", " merged = tf.merge_all_summaries()\n", " \n", " init = tf.initialize_all_variables()\n", " sess = tf.Session(config = tf.ConfigProto(\n", " allow_soft_placement = False, # allows to utilize GPU's & CPU's\n", " log_device_placement = False)) # shows GPU/CPU allocation\n", " # Prepare folders for saving models and its stats\n", " date_time_stamp = dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", " train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir+'/train/'+date_time_stamp) #, sess.graph)\n", " test_writer = tf.train.SummaryWriter(FLAGS.summaries_dir+'/validation/'+date_time_stamp) #, sess.graph)\n", " saver = tf.train.Saver()\n", "\n", " # Finish graph creation. Below is the code for running graph\n", " sess.run(init)\n", " tf.train.start_queue_runners(sess=sess)\n", "\n", " valid_loss = 1.0\n", " train_loss = 1.0\n", " step = 1\n", " # Main training loop\n", " for step in xrange(FLAGS.max_steps):\n", " start_time = time.time()\n", " # regular training\n", " \n", " _, train_loss, summary, lr = sess.run(\n", " [train_op, loss, merged, learning_rate], feed_dict=fill_feed_dict(train_dataset, x, y_, train = True))\n", "\n", " duration = time.time()-start_time\n", " train_writer.add_summary(summary,step)\n", " if step%(FLAGS.max_steps//20) == 0:\n", " # check model fit\n", " feed_dict = fill_feed_dict(valid_dataset, x, y_, train = False)\n", " valid_loss, summary = sess.run([loss, merged], feed_dict = feed_dict)\n", " test_writer.add_summary(summary,step)\n", " print('Step %d (%.2f op/sec): Training loss: %.5f, Validation loss: %.5f' % (\n", " step, 1.0/duration, np.float32(train_loss).item(), np.float32(valid_loss).item()))\n", "\n", " checkpoint_path = os.path.join(FLAGS.checkpoints_dir,'model.ckpt')\n", " saver.save(sess, checkpoint_path, global_step=step)\n", " \n", " feed_dict = fill_feed_dict(test_dataset, x, y_, train = False)\n", " test_loss = sess.run([loss], feed_dict = feed_dict)\n", " print('Test: %.5f' % (np.float32(test_loss).item()))\n", "\n", " outs = outputs.eval(session=sess, feed_dict = feed_dict)\n", "\n", " for out_no in range(0,FLAGS.output_vars):\n", " print(\"Location %d: CC: %.4f, MSE: %.6f\"%(\n", " out_no,\n", " np.corrcoef(outs[:,out_no], test_dataset.outputs[:,out_no])[0,1],\n", " mean_squared_error(outs[:,out_no], test_dataset.outputs[:,out_no])))\n", " sess.close()\n", " return outs, test_dataset.outputs\n", "\n", "\n", "def run():\n", " \"\"\"\n", " Finish building the graph and run it on the default device\n", " \"\"\"\n", " # Assign datasets \n", " test_ds = np.loadtxt(FLAGS.input)[:,1:7].reshape((-1, 6)).astype(np.float32)\n", "\n", " with tf.Graph().as_default(), tf.device('/cpu:0'):\n", " # Prepare placeholders for inputs and expected outputs\n", " x = tf.placeholder(tf.float32, [None, FLAGS.input_vars], name='x-input')\n", "\n", " means = tf.get_variable('means', shape=[FLAGS.input_vars], trainable = False)\n", " stds = tf.get_variable('stds', shape=[FLAGS.input_vars], trainable = False)\n", "\n", " # Normalize input data\n", " x_normalized = tf.div(tf.sub(x,means),stds)\n", "\n", " outputs = ilt.inference(x_normalized)\n", "\n", " init = tf.initialize_all_variables()\n", " sess = tf.Session(config = tf.ConfigProto(\n", " allow_soft_placement = False, # allows to utilize GPU's & CPU's\n", " log_device_placement = False)) # shows GPU/CPU allocation\n", " \n", " start_time = time.time()\n", " # Below is the code for running graph\n", " sess.run(init)\n", "\n", " saver = tf.train.Saver()\n", " ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoints_dir)\n", " if ckpt.model_checkpoint_path:\n", " saver.restore(sess, ckpt.model_checkpoint_path)\n", " print(\"Model %s restored\"%ckpt.model_checkpoint_path)\n", " else:\n", " print(\"Could not find any checkpoints at %s\"%FLAGS.checkpoints_dir)\n", " return\n", "\n", " tf.train.start_queue_runners(sess=sess)\n", "\n", " out = sess.run(outputs, feed_dict = {x:test_ds})\n", " duration = time.time()-start_time\n", " print('Elapsed time: %.2f sec.' % (duration))\n", " np.savetxt(FLAGS.output,out)\n", " print('Outputs saved as %s'%FLAGS.output)\n", " sess.close()\n", " \n", "nn_outs, true_outs = train()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "fig = plt.figure()#figsize=(8, 8))\n", "#point_num = 8\n", "#plt.plot(nn_outs[:1930,point_num],'b-',nn_outs[:1930,point_num+10],'bo',true_outs[:1930,point_num],'r-')\n", "hurricanes = range(0,19310)\n", "for point_num in range(0,10):\n", " ax = fig.add_subplot(10,1,point_num+1)\n", " ax.plot(nn_outs[hurricanes,point_num],'b-',true_outs[hurricanes,point_num],'r-')\n", "#plt.plot(x_data,y_data[:,0],'ro',x_data, y_data[:,1],'bo',alpha=0.3)\n", "plt.show()" ] } ], "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
apryor6/apryor6.github.io	visualizations/bokeh/notebooks/glyphs/triangle.ipynb	1	1877	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bokeh Triangle Glyph" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.plotting import figure, output_file, show\n", "from bokeh.models import Range1d\n", "from bokeh.io import export_png\n", "\n", "fill_color = '#ef8a62'\n", "line_color = '#999999'\n", "output_file(\"../../figures/triangle.html\")\n", "\n", "p = figure(plot_width=400, plot_height=400)\n", "p.triangle(x=0,y=0,size=100, fill_alpha=1,fill_color=fill_color,\n", " line_alpha=1, line_color=line_color, line_dash='dashed', line_width=5)\n", "p.triangle(x=0,y=1,size=100, fill_alpha=0.8, fill_color=fill_color,\n", " line_alpha=1, line_color=line_color, line_dash='dotdash', line_width=8)\n", "p.triangle(x=1,y=0,size=100, fill_alpha=0.6, fill_color = fill_color,\n", " line_alpha=1, line_color=line_color, line_dash='dotted', line_width=13)\n", "p.triangle(x=1,y=1,size=100, fill_alpha=0.4, fill_color = fill_color,\n", " line_alpha=1, line_color=line_color, line_dash='solid', line_width=17)\n", "p.x_range = Range1d(-0.5,1.5, bounds=(-1,2))\n", "p.y_range = Range1d(-0.5,1.5, bounds=(-1,2))\n", "show(p)\n", "export_png(p, filename=\"../../figures/triangle.png\");" ] } ], "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
WenmuZhou/cifar-10-cnn	2_Network_in_Network/Network_in_Network_bn_keras.ipynb	1	39673	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# use Network_in_Network with bn layer to train the cifar-10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# import pakages" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os\n", "import keras\n", "import numpy as np\n", "import tensorflow as tf\n", "from keras.datasets import cifar10\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout, Activation, Flatten\n", "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, AveragePooling2D\n", "from keras.initializers import RandomNormal \n", "from keras.layers.normalization import BatchNormalization\n", "from keras import optimizers\n", "from keras.callbacks import LearningRateScheduler, TensorBoard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# force to use gpu and limit the use of gpu memory" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n", "from keras.backend.tensorflow_backend import set_session\n", "config = tf.ConfigProto()\n", "config.gpu_options.per_process_gpu_memory_fraction = 0.3\n", "set_session(tf.Session(config=config))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# init some parameters" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_size = 128\n", "epochs = 164\n", "iterations = 391\n", "num_classes = 10\n", "dropout = 0.5\n", "log_filepath = r'./nin_bn'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# do some precessing with images" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def color_preprocessing(x_train,x_test):\n", " x_train = x_train.astype('float32')\n", " x_test = x_test.astype('float32')\n", " x_train[:,:,:,0] = (x_train[:,:,:,0] - np.mean(x_train[:,:,:,0])) / np.std(x_train[:,:,:,0])\n", " x_train[:,:,:,1] = (x_train[:,:,:,1] - np.mean(x_train[:,:,:,1])) / np.std(x_train[:,:,:,1])\n", " x_train[:,:,:,2] = (x_train[:,:,:,2] - np.mean(x_train[:,:,:,2])) / np.std(x_train[:,:,:,2])\n", " x_test[:,:,:,0] = (x_test[:,:,:,0] - np.mean(x_test[:,:,:,0])) / np.std(x_test[:,:,:,0])\n", " x_test[:,:,:,1] = (x_test[:,:,:,1] - np.mean(x_test[:,:,:,1])) / np.std(x_test[:,:,:,1])\n", " x_test[:,:,:,2] = (x_test[:,:,:,2] - np.mean(x_test[:,:,:,2])) / np.std(x_test[:,:,:,2])\n", "\n", " return x_train, x_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# set the learning rate changes strategy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def scheduler(epoch):\n", " learning_rate_init = 0.08\n", " if epoch >= 81:\n", " learning_rate_init = 0.01\n", " if epoch >= 122:\n", " learning_rate_init = 0.001\n", " return learning_rate_init" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# define network" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "def build_model():\n", " model = Sequential()\n", " # Weight initialization\n", " model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.01), input_shape=x_train.shape[1:]))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(160, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))\n", " \n", " model.add(Dropout(dropout))\n", " \n", " model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))\n", " \n", " model.add(Dropout(dropout))\n", " \n", " model.add(Conv2D(192, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(192, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " model.add(Conv2D(10, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(0.0001), kernel_initializer=RandomNormal(stddev = 0.05)))\n", " model.add(BatchNormalization())\n", " model.add(Activation('relu'))\n", " \n", " model.add(GlobalAveragePooling2D())\n", " model.add(Activation('softmax'))\n", " \n", " \n", " sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)\n", " model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# load data and build model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 32, 32, 192) 14592 \n", "_________________________________________________________________\n", "batch_normalization_1 (Batch (None, 32, 32, 192) 768 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 32, 32, 192) 0 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 32, 32, 160) 30880 \n", "_________________________________________________________________\n", "batch_normalization_2 (Batch (None, 32, 32, 160) 640 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 32, 32, 160) 0 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 32, 32, 96) 15456 \n", "_________________________________________________________________\n", "batch_normalization_3 (Batch (None, 32, 32, 96) 384 \n", "_________________________________________________________________\n", "activation_3 (Activation) (None, 32, 32, 96) 0 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 96) 0 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 16, 16, 96) 0 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 16, 16, 192) 460992 \n", "_________________________________________________________________\n", "batch_normalization_4 (Batch (None, 16, 16, 192) 768 \n", "_________________________________________________________________\n", "activation_4 (Activation) (None, 16, 16, 192) 0 \n", "_________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 16, 16, 192) 37056 \n", "_________________________________________________________________\n", "batch_normalization_5 (Batch (None, 16, 16, 192) 768 \n", "_________________________________________________________________\n", "activation_5 (Activation) (None, 16, 16, 192) 0 \n", "_________________________________________________________________\n", "conv2d_6 (Conv2D) (None, 16, 16, 192) 37056 \n", "_________________________________________________________________\n", "batch_normalization_6 (Batch (None, 16, 16, 192) 768 \n", "_________________________________________________________________\n", "activation_6 (Activation) (None, 16, 16, 192) 0 \n", "_________________________________________________________________\n", "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 192) 0 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 8, 8, 192) 0 \n", "_________________________________________________________________\n", "conv2d_7 (Conv2D) (None, 8, 8, 192) 331968 \n", "_________________________________________________________________\n", "batch_normalization_7 (Batch (None, 8, 8, 192) 768 \n", "_________________________________________________________________\n", "activation_7 (Activation) (None, 8, 8, 192) 0 \n", "_________________________________________________________________\n", "conv2d_8 (Conv2D) (None, 8, 8, 192) 37056 \n", "_________________________________________________________________\n", "batch_normalization_8 (Batch (None, 8, 8, 192) 768 \n", "_________________________________________________________________\n", "activation_8 (Activation) (None, 8, 8, 192) 0 \n", "_________________________________________________________________\n", "conv2d_9 (Conv2D) (None, 8, 8, 10) 1930 \n", "_________________________________________________________________\n", "batch_normalization_9 (Batch (None, 8, 8, 10) 40 \n", "_________________________________________________________________\n", "activation_9 (Activation) (None, 8, 8, 10) 0 \n", "_________________________________________________________________\n", "global_average_pooling2d_1 ( (None, 10) 0 \n", "_________________________________________________________________\n", "activation_10 (Activation) (None, 10) 0 \n", "=================================================================\n", "Total params: 972,658\n", "Trainable params: 969,822\n", "Non-trainable params: 2,836\n", "_________________________________________________________________\n", "None\n" ] } ], "source": [ "# load data\n", "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", "y_train = keras.utils.to_categorical(y_train, num_classes)\n", "y_test = keras.utils.to_categorical(y_test, num_classes)\n", "\n", "# color preprocessing\n", "x_train, x_test = color_preprocessing(x_train, x_test)\n", "\n", "# build network\n", "model = build_model()\n", "print(model.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# set tensorboard" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# set callback\n", "tb_cb = TensorBoard(log_dir=log_filepath, histogram_freq=0)\n", "change_lr = LearningRateScheduler(scheduler)\n", "cbks = [change_lr,tb_cb]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# processing images" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using real-time data augmentation.\n" ] } ], "source": [ "# set data augmentation\n", "print('Using real-time data augmentation.')\n", "datagen = ImageDataGenerator(horizontal_flip=True,\n", " width_shift_range=0.125,height_shift_range=0.125,fill_mode='constant',cval=0.)\n", "\n", "datagen.fit(x_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# train" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/164\n", "391/391 [==============================] - 35s - loss: 1.7550 - acc: 0.4548 - val_loss: 1.8317 - val_acc: 0.4868\n", "Epoch 2/164\n", "391/391 [==============================] - 33s - loss: 1.3448 - acc: 0.6022 - val_loss: 1.3350 - val_acc: 0.6211\n", "Epoch 3/164\n", "391/391 [==============================] - 33s - loss: 1.1768 - acc: 0.6633 - val_loss: 1.1633 - val_acc: 0.6631\n", "Epoch 4/164\n", "391/391 [==============================] - 33s - loss: 1.0581 - acc: 0.7023 - val_loss: 1.2466 - val_acc: 0.6536\n", "Epoch 5/164\n", "391/391 [==============================] - 33s - loss: 0.9788 - acc: 0.7327 - val_loss: 1.0447 - val_acc: 0.7169\n", "Epoch 6/164\n", "391/391 [==============================] - 33s - loss: 0.9235 - acc: 0.7491 - val_loss: 1.0081 - val_acc: 0.7286\n", "Epoch 7/164\n", "391/391 [==============================] - 33s - loss: 0.8852 - acc: 0.7632 - val_loss: 0.8350 - val_acc: 0.7815\n", "Epoch 8/164\n", "391/391 [==============================] - 34s - loss: 0.8441 - acc: 0.7756 - val_loss: 0.9805 - val_acc: 0.7417\n", "Epoch 9/164\n", "391/391 [==============================] - 33s - loss: 0.8191 - acc: 0.7832 - val_loss: 0.8476 - val_acc: 0.7785\n", "Epoch 10/164\n", "391/391 [==============================] - 34s - loss: 0.7906 - acc: 0.7960 - val_loss: 0.8157 - val_acc: 0.7852\n", "Epoch 11/164\n", "391/391 [==============================] - 33s - loss: 0.7717 - acc: 0.8008 - val_loss: 0.9349 - val_acc: 0.7577\n", "Epoch 12/164\n", "391/391 [==============================] - 34s - loss: 0.7533 - acc: 0.8073 - val_loss: 0.8919 - val_acc: 0.7779\n", "Epoch 13/164\n", "391/391 [==============================] - 33s - loss: 0.7369 - acc: 0.8149 - val_loss: 0.9335 - val_acc: 0.7573\n", "Epoch 14/164\n", "391/391 [==============================] - 33s - loss: 0.7287 - acc: 0.8181 - val_loss: 1.0472 - val_acc: 0.7494\n", "Epoch 15/164\n", "391/391 [==============================] - 33s - loss: 0.7154 - acc: 0.8226 - val_loss: 0.9369 - val_acc: 0.7531\n", "Epoch 16/164\n", "391/391 [==============================] - 33s - loss: 0.7077 - acc: 0.8244 - val_loss: 1.0243 - val_acc: 0.7488\n", "Epoch 17/164\n", "391/391 [==============================] - 33s - loss: 0.6996 - acc: 0.8302 - val_loss: 0.7708 - val_acc: 0.8034\n", "Epoch 18/164\n", "391/391 [==============================] - 33s - loss: 0.6868 - acc: 0.8345 - val_loss: 0.8522 - val_acc: 0.7928\n", "Epoch 19/164\n", "391/391 [==============================] - 33s - loss: 0.6839 - acc: 0.8373 - val_loss: 0.8095 - val_acc: 0.8052\n", "Epoch 20/164\n", "391/391 [==============================] - 33s - loss: 0.6753 - acc: 0.8394 - val_loss: 0.8703 - val_acc: 0.7872\n", "Epoch 21/164\n", "391/391 [==============================] - 33s - loss: 0.6744 - acc: 0.8406 - val_loss: 0.9216 - val_acc: 0.7707\n", "Epoch 22/164\n", "391/391 [==============================] - 33s - loss: 0.6670 - acc: 0.8455 - val_loss: 0.8281 - val_acc: 0.8014\n", "Epoch 23/164\n", "391/391 [==============================] - 33s - loss: 0.6649 - acc: 0.8467 - val_loss: 0.8801 - val_acc: 0.7934\n", "Epoch 24/164\n", "391/391 [==============================] - 33s - loss: 0.6648 - acc: 0.8479 - val_loss: 0.8050 - val_acc: 0.8042\n", "Epoch 25/164\n", "391/391 [==============================] - 34s - loss: 0.6608 - acc: 0.8486 - val_loss: 0.7050 - val_acc: 0.8415\n", "Epoch 26/164\n", "391/391 [==============================] - 34s - loss: 0.6598 - acc: 0.8507 - val_loss: 0.8102 - val_acc: 0.8108\n", "Epoch 27/164\n", "391/391 [==============================] - 33s - loss: 0.6608 - acc: 0.8524 - val_loss: 0.9171 - val_acc: 0.7888\n", "Epoch 28/164\n", "391/391 [==============================] - 33s - loss: 0.6559 - acc: 0.8562 - val_loss: 0.7875 - val_acc: 0.8149\n", "Epoch 29/164\n", "391/391 [==============================] - 34s - loss: 0.6544 - acc: 0.8544 - val_loss: 0.8615 - val_acc: 0.8011\n", "Epoch 30/164\n", "391/391 [==============================] - 33s - loss: 0.6521 - acc: 0.8566 - val_loss: 0.9198 - val_acc: 0.7938\n", "Epoch 31/164\n", "391/391 [==============================] - 34s - loss: 0.6497 - acc: 0.8583 - val_loss: 0.8426 - val_acc: 0.8170\n", "Epoch 32/164\n", "391/391 [==============================] - 33s - loss: 0.6499 - acc: 0.8596 - val_loss: 0.8959 - val_acc: 0.7900\n", "Epoch 33/164\n", "391/391 [==============================] - 33s - loss: 0.6500 - acc: 0.8591 - val_loss: 0.7494 - val_acc: 0.8387\n", "Epoch 34/164\n", "391/391 [==============================] - 33s - loss: 0.6488 - acc: 0.8619 - val_loss: 0.7632 - val_acc: 0.8330\n", "Epoch 35/164\n", "391/391 [==============================] - 33s - loss: 0.6470 - acc: 0.8627 - val_loss: 0.7489 - val_acc: 0.8413\n", "Epoch 36/164\n", "391/391 [==============================] - 33s - loss: 0.6492 - acc: 0.8616 - val_loss: 0.7917 - val_acc: 0.8298\n", "Epoch 37/164\n", "391/391 [==============================] - 33s - loss: 0.6448 - acc: 0.8650 - val_loss: 0.8667 - val_acc: 0.8073\n", "Epoch 38/164\n", "391/391 [==============================] - 33s - loss: 0.6514 - acc: 0.8635 - val_loss: 0.8202 - val_acc: 0.8201\n", "Epoch 39/164\n", "391/391 [==============================] - 33s - loss: 0.6384 - acc: 0.8665 - val_loss: 0.7139 - val_acc: 0.8474\n", "Epoch 40/164\n", "391/391 [==============================] - 33s - loss: 0.6462 - acc: 0.8651 - val_loss: 0.8444 - val_acc: 0.8146\n", "Epoch 41/164\n", "391/391 [==============================] - 33s - loss: 0.6455 - acc: 0.8655 - val_loss: 0.7440 - val_acc: 0.8460\n", "Epoch 42/164\n", "391/391 [==============================] - 33s - loss: 0.6505 - acc: 0.8643 - val_loss: 0.7874 - val_acc: 0.8308\n", "Epoch 43/164\n", "391/391 [==============================] - 34s - loss: 0.6416 - acc: 0.8688 - val_loss: 0.7829 - val_acc: 0.8281\n", "Epoch 44/164\n", "391/391 [==============================] - 33s - loss: 0.6439 - acc: 0.8681 - val_loss: 0.7384 - val_acc: 0.8489\n", "Epoch 45/164\n", "391/391 [==============================] - 33s - loss: 0.6417 - acc: 0.8695 - val_loss: 0.7224 - val_acc: 0.8526\n", "Epoch 46/164\n", "391/391 [==============================] - 33s - loss: 0.6456 - acc: 0.8700 - val_loss: 0.8498 - val_acc: 0.8247\n", "Epoch 47/164\n", "391/391 [==============================] - 33s - loss: 0.6454 - acc: 0.8695 - val_loss: 0.7816 - val_acc: 0.8379\n", "Epoch 48/164\n", "391/391 [==============================] - 33s - loss: 0.6372 - acc: 0.8731 - val_loss: 0.8384 - val_acc: 0.8113\n", "Epoch 49/164\n", "391/391 [==============================] - 33s - loss: 0.6409 - acc: 0.8709 - val_loss: 0.8391 - val_acc: 0.8283\n", "Epoch 50/164\n", "391/391 [==============================] - 34s - loss: 0.6378 - acc: 0.8736 - val_loss: 0.8091 - val_acc: 0.8307\n", "Epoch 51/164\n", "391/391 [==============================] - 33s - loss: 0.6438 - acc: 0.8730 - val_loss: 0.8081 - val_acc: 0.8317\n", "Epoch 52/164\n", "391/391 [==============================] - 33s - loss: 0.6399 - acc: 0.8727 - val_loss: 1.0503 - val_acc: 0.7785\n", "Epoch 53/164\n", "391/391 [==============================] - 33s - loss: 0.6324 - acc: 0.8765 - val_loss: 0.8985 - val_acc: 0.8190\n", "Epoch 54/164\n", "391/391 [==============================] - 34s - loss: 0.6370 - acc: 0.8747 - val_loss: 0.8573 - val_acc: 0.8210\n", "Epoch 55/164\n", "391/391 [==============================] - 34s - loss: 0.6416 - acc: 0.8739 - val_loss: 0.7317 - val_acc: 0.8515\n", "Epoch 56/164\n", "391/391 [==============================] - 33s - loss: 0.6365 - acc: 0.8757 - val_loss: 0.8073 - val_acc: 0.8349\n", "Epoch 57/164\n", "391/391 [==============================] - 33s - loss: 0.6317 - acc: 0.8761 - val_loss: 0.7753 - val_acc: 0.8393\n", "Epoch 58/164\n", "391/391 [==============================] - 33s - loss: 0.6405 - acc: 0.8745 - val_loss: 0.8153 - val_acc: 0.8380\n", "Epoch 59/164\n", "391/391 [==============================] - 34s - loss: 0.6367 - acc: 0.8763 - val_loss: 0.7643 - val_acc: 0.8451\n", "Epoch 60/164\n", "391/391 [==============================] - 33s - loss: 0.6299 - acc: 0.8776 - val_loss: 0.8776 - val_acc: 0.8210\n", "Epoch 61/164\n", "391/391 [==============================] - 34s - loss: 0.6337 - acc: 0.8761 - val_loss: 0.7614 - val_acc: 0.8447\n", "Epoch 62/164\n", "391/391 [==============================] - 34s - loss: 0.6344 - acc: 0.8776 - val_loss: 0.7893 - val_acc: 0.8427\n", "Epoch 63/164\n", "391/391 [==============================] - 33s - loss: 0.6362 - acc: 0.8767 - val_loss: 0.7144 - val_acc: 0.8585\n", "Epoch 64/164\n", "391/391 [==============================] - 34s - loss: 0.6293 - acc: 0.8810 - val_loss: 0.7301 - val_acc: 0.8603\n", "Epoch 65/164\n", "391/391 [==============================] - 34s - loss: 0.6338 - acc: 0.8771 - val_loss: 0.7551 - val_acc: 0.8505\n", "Epoch 66/164\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "391/391 [==============================] - 34s - loss: 0.6331 - acc: 0.8784 - val_loss: 0.7398 - val_acc: 0.8501\n", "Epoch 67/164\n", "391/391 [==============================] - 34s - loss: 0.6331 - acc: 0.8786 - val_loss: 0.7743 - val_acc: 0.8454\n", "Epoch 68/164\n", "391/391 [==============================] - 33s - loss: 0.6337 - acc: 0.8792 - val_loss: 1.0806 - val_acc: 0.7723\n", "Epoch 69/164\n", "391/391 [==============================] - 34s - loss: 0.6347 - acc: 0.8778 - val_loss: 0.7842 - val_acc: 0.8412\n", "Epoch 70/164\n", "391/391 [==============================] - 34s - loss: 0.6315 - acc: 0.8812 - val_loss: 0.7150 - val_acc: 0.8649\n", "Epoch 71/164\n", "391/391 [==============================] - 34s - loss: 0.6334 - acc: 0.8794 - val_loss: 0.7577 - val_acc: 0.8527\n", "Epoch 72/164\n", "391/391 [==============================] - 34s - loss: 0.6327 - acc: 0.8805 - val_loss: 0.7169 - val_acc: 0.8604\n", "Epoch 73/164\n", "391/391 [==============================] - 34s - loss: 0.6308 - acc: 0.8820 - val_loss: 0.7512 - val_acc: 0.8520\n", "Epoch 74/164\n", "391/391 [==============================] - 34s - loss: 0.6343 - acc: 0.8804 - val_loss: 0.7011 - val_acc: 0.8656\n", "Epoch 75/164\n", "391/391 [==============================] - 34s - loss: 0.6268 - acc: 0.8835 - val_loss: 0.7940 - val_acc: 0.8441\n", "Epoch 76/164\n", "391/391 [==============================] - 34s - loss: 0.6282 - acc: 0.8828 - val_loss: 0.8005 - val_acc: 0.8430\n", "Epoch 77/164\n", "391/391 [==============================] - 34s - loss: 0.6300 - acc: 0.8810 - val_loss: 0.7837 - val_acc: 0.8434\n", "Epoch 78/164\n", "391/391 [==============================] - 34s - loss: 0.6324 - acc: 0.8813 - val_loss: 0.7560 - val_acc: 0.8531\n", "Epoch 79/164\n", "391/391 [==============================] - 34s - loss: 0.6317 - acc: 0.8816 - val_loss: 0.8528 - val_acc: 0.8266\n", "Epoch 80/164\n", "391/391 [==============================] - 34s - loss: 0.6299 - acc: 0.8816 - val_loss: 0.7603 - val_acc: 0.8482\n", "Epoch 81/164\n", "391/391 [==============================] - 34s - loss: 0.6299 - acc: 0.8831 - val_loss: 0.8069 - val_acc: 0.8408\n", "Epoch 82/164\n", "391/391 [==============================] - 34s - loss: 0.5511 - acc: 0.9081 - val_loss: 0.5962 - val_acc: 0.8996\n", "Epoch 83/164\n", "391/391 [==============================] - 34s - loss: 0.5092 - acc: 0.9229 - val_loss: 0.5985 - val_acc: 0.8997\n", "Epoch 84/164\n", "391/391 [==============================] - 34s - loss: 0.4876 - acc: 0.9295 - val_loss: 0.5978 - val_acc: 0.9001\n", "Epoch 85/164\n", "391/391 [==============================] - 34s - loss: 0.4758 - acc: 0.9322 - val_loss: 0.5772 - val_acc: 0.9062\n", "Epoch 86/164\n", "391/391 [==============================] - 34s - loss: 0.4654 - acc: 0.9332 - val_loss: 0.5695 - val_acc: 0.9046\n", "Epoch 87/164\n", "391/391 [==============================] - 34s - loss: 0.4548 - acc: 0.9362 - val_loss: 0.5825 - val_acc: 0.9043\n", "Epoch 88/164\n", "391/391 [==============================] - 34s - loss: 0.4496 - acc: 0.9372 - val_loss: 0.5690 - val_acc: 0.9068\n", "Epoch 89/164\n", "391/391 [==============================] - 34s - loss: 0.4373 - acc: 0.9401 - val_loss: 0.5670 - val_acc: 0.9085\n", "Epoch 90/164\n", "391/391 [==============================] - 34s - loss: 0.4359 - acc: 0.9391 - val_loss: 0.5634 - val_acc: 0.9079\n", "Epoch 91/164\n", "391/391 [==============================] - 34s - loss: 0.4316 - acc: 0.9396 - val_loss: 0.5645 - val_acc: 0.9041\n", "Epoch 92/164\n", "391/391 [==============================] - 34s - loss: 0.4202 - acc: 0.9423 - val_loss: 0.5619 - val_acc: 0.9062\n", "Epoch 93/164\n", "391/391 [==============================] - 34s - loss: 0.4131 - acc: 0.9440 - val_loss: 0.5591 - val_acc: 0.9089\n", "Epoch 94/164\n", "391/391 [==============================] - 34s - loss: 0.4077 - acc: 0.9440 - val_loss: 0.5492 - val_acc: 0.9084\n", "Epoch 95/164\n", "391/391 [==============================] - 34s - loss: 0.4053 - acc: 0.9436 - val_loss: 0.5505 - val_acc: 0.9086\n", "Epoch 96/164\n", "391/391 [==============================] - 34s - loss: 0.3996 - acc: 0.9448 - val_loss: 0.5602 - val_acc: 0.9025\n", "Epoch 97/164\n", "391/391 [==============================] - 34s - loss: 0.3914 - acc: 0.9470 - val_loss: 0.5623 - val_acc: 0.9042\n", "Epoch 98/164\n", "391/391 [==============================] - 34s - loss: 0.3864 - acc: 0.9473 - val_loss: 0.5405 - val_acc: 0.9095\n", "Epoch 99/164\n", "391/391 [==============================] - 34s - loss: 0.3799 - acc: 0.9495 - val_loss: 0.5329 - val_acc: 0.9087\n", "Epoch 100/164\n", "391/391 [==============================] - 34s - loss: 0.3787 - acc: 0.9471 - val_loss: 0.5294 - val_acc: 0.9096\n", "Epoch 101/164\n", "391/391 [==============================] - 34s - loss: 0.3707 - acc: 0.9503 - val_loss: 0.5431 - val_acc: 0.9069\n", "Epoch 102/164\n", "391/391 [==============================] - 34s - loss: 0.3678 - acc: 0.9504 - val_loss: 0.5407 - val_acc: 0.9062\n", "Epoch 103/164\n", "391/391 [==============================] - 34s - loss: 0.3591 - acc: 0.9528 - val_loss: 0.5327 - val_acc: 0.9097\n", "Epoch 104/164\n", "391/391 [==============================] - 34s - loss: 0.3568 - acc: 0.9520 - val_loss: 0.5375 - val_acc: 0.9075\n", "Epoch 105/164\n", "391/391 [==============================] - 34s - loss: 0.3531 - acc: 0.9517 - val_loss: 0.5366 - val_acc: 0.9065\n", "Epoch 106/164\n", "391/391 [==============================] - 34s - loss: 0.3528 - acc: 0.9508 - val_loss: 0.5222 - val_acc: 0.9081\n", "Epoch 107/164\n", "391/391 [==============================] - 34s - loss: 0.3473 - acc: 0.9517 - val_loss: 0.5332 - val_acc: 0.9072\n", "Epoch 108/164\n", "391/391 [==============================] - 34s - loss: 0.3435 - acc: 0.9522 - val_loss: 0.5414 - val_acc: 0.9037\n", "Epoch 109/164\n", "391/391 [==============================] - 34s - loss: 0.3410 - acc: 0.9527 - val_loss: 0.5144 - val_acc: 0.9088\n", "Epoch 110/164\n", "391/391 [==============================] - 34s - loss: 0.3370 - acc: 0.9535 - val_loss: 0.5212 - val_acc: 0.9061\n", "Epoch 111/164\n", "391/391 [==============================] - 34s - loss: 0.3327 - acc: 0.9541 - val_loss: 0.5319 - val_acc: 0.9053\n", "Epoch 112/164\n", "391/391 [==============================] - 34s - loss: 0.3304 - acc: 0.9541 - val_loss: 0.5263 - val_acc: 0.9045\n", "Epoch 113/164\n", "391/391 [==============================] - 34s - loss: 0.3235 - acc: 0.9553 - val_loss: 0.5127 - val_acc: 0.9090\n", "Epoch 114/164\n", "391/391 [==============================] - 34s - loss: 0.3203 - acc: 0.9565 - val_loss: 0.5340 - val_acc: 0.9054\n", "Epoch 115/164\n", "391/391 [==============================] - 34s - loss: 0.3215 - acc: 0.9553 - val_loss: 0.5297 - val_acc: 0.9059\n", "Epoch 116/164\n", "391/391 [==============================] - 34s - loss: 0.3191 - acc: 0.9549 - val_loss: 0.5156 - val_acc: 0.9070\n", "Epoch 117/164\n", "391/391 [==============================] - 34s - loss: 0.3126 - acc: 0.9564 - val_loss: 0.5101 - val_acc: 0.9088\n", "Epoch 118/164\n", "391/391 [==============================] - 34s - loss: 0.3135 - acc: 0.9559 - val_loss: 0.5133 - val_acc: 0.9043\n", "Epoch 119/164\n", "391/391 [==============================] - 34s - loss: 0.3086 - acc: 0.9566 - val_loss: 0.5062 - val_acc: 0.9079\n", "Epoch 120/164\n", "391/391 [==============================] - 34s - loss: 0.3081 - acc: 0.9562 - val_loss: 0.5225 - val_acc: 0.9037\n", "Epoch 121/164\n", "391/391 [==============================] - 34s - loss: 0.3025 - acc: 0.9568 - val_loss: 0.5271 - val_acc: 0.9025\n", "Epoch 122/164\n", "391/391 [==============================] - 34s - loss: 0.3003 - acc: 0.9579 - val_loss: 0.5003 - val_acc: 0.9059\n", "Epoch 123/164\n", "391/391 [==============================] - 34s - loss: 0.2863 - acc: 0.9631 - val_loss: 0.4900 - val_acc: 0.9118\n", "Epoch 124/164\n", "391/391 [==============================] - 34s - loss: 0.2794 - acc: 0.9657 - val_loss: 0.4897 - val_acc: 0.9132\n", "Epoch 125/164\n", "391/391 [==============================] - 34s - loss: 0.2758 - acc: 0.9667 - val_loss: 0.4821 - val_acc: 0.9140\n", "Epoch 126/164\n", "391/391 [==============================] - 34s - loss: 0.2767 - acc: 0.9668 - val_loss: 0.4798 - val_acc: 0.9150\n", "Epoch 127/164\n", "391/391 [==============================] - 34s - loss: 0.2721 - acc: 0.9675 - val_loss: 0.4833 - val_acc: 0.9142\n", "Epoch 128/164\n", "391/391 [==============================] - 34s - loss: 0.2743 - acc: 0.9663 - val_loss: 0.4786 - val_acc: 0.9162\n", "Epoch 129/164\n", "391/391 [==============================] - 34s - loss: 0.2720 - acc: 0.9674 - val_loss: 0.4777 - val_acc: 0.9170\n", "Epoch 130/164\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "391/391 [==============================] - 34s - loss: 0.2701 - acc: 0.9697 - val_loss: 0.4792 - val_acc: 0.9161\n", "Epoch 131/164\n", "391/391 [==============================] - 34s - loss: 0.2672 - acc: 0.9692 - val_loss: 0.4806 - val_acc: 0.9159\n", "Epoch 132/164\n", "391/391 [==============================] - 34s - loss: 0.2686 - acc: 0.9684 - val_loss: 0.4814 - val_acc: 0.9153\n", "Epoch 133/164\n", "391/391 [==============================] - 34s - loss: 0.2665 - acc: 0.9696 - val_loss: 0.4808 - val_acc: 0.9160\n", "Epoch 134/164\n", "391/391 [==============================] - 34s - loss: 0.2678 - acc: 0.9686 - val_loss: 0.4797 - val_acc: 0.9160\n", "Epoch 135/164\n", "391/391 [==============================] - 34s - loss: 0.2642 - acc: 0.9688 - val_loss: 0.4848 - val_acc: 0.9150\n", "Epoch 136/164\n", "391/391 [==============================] - 34s - loss: 0.2644 - acc: 0.9695 - val_loss: 0.4821 - val_acc: 0.9152\n", "Epoch 137/164\n", "391/391 [==============================] - 34s - loss: 0.2633 - acc: 0.9701 - val_loss: 0.4778 - val_acc: 0.9165\n", "Epoch 138/164\n", "391/391 [==============================] - 34s - loss: 0.2622 - acc: 0.9705 - val_loss: 0.4793 - val_acc: 0.9149\n", "Epoch 139/164\n", "391/391 [==============================] - 34s - loss: 0.2621 - acc: 0.9702 - val_loss: 0.4803 - val_acc: 0.9148\n", "Epoch 140/164\n", "391/391 [==============================] - 34s - loss: 0.2627 - acc: 0.9700 - val_loss: 0.4793 - val_acc: 0.9167\n", "Epoch 141/164\n", "391/391 [==============================] - 34s - loss: 0.2608 - acc: 0.9705 - val_loss: 0.4774 - val_acc: 0.9151\n", "Epoch 142/164\n", "391/391 [==============================] - 34s - loss: 0.2589 - acc: 0.9718 - val_loss: 0.4807 - val_acc: 0.9150\n", "Epoch 143/164\n", "391/391 [==============================] - 34s - loss: 0.2604 - acc: 0.9700 - val_loss: 0.4768 - val_acc: 0.9167\n", "Epoch 144/164\n", "391/391 [==============================] - 34s - loss: 0.2612 - acc: 0.9703 - val_loss: 0.4777 - val_acc: 0.9150\n", "Epoch 145/164\n", "391/391 [==============================] - 34s - loss: 0.2560 - acc: 0.9724 - val_loss: 0.4798 - val_acc: 0.9154\n", "Epoch 146/164\n", "391/391 [==============================] - 34s - loss: 0.2568 - acc: 0.9721 - val_loss: 0.4790 - val_acc: 0.9155\n", "Epoch 147/164\n", "391/391 [==============================] - 34s - loss: 0.2568 - acc: 0.9709 - val_loss: 0.4755 - val_acc: 0.9162\n", "Epoch 148/164\n", "391/391 [==============================] - 34s - loss: 0.2571 - acc: 0.9718 - val_loss: 0.4767 - val_acc: 0.9152\n", "Epoch 149/164\n", "391/391 [==============================] - 34s - loss: 0.2574 - acc: 0.9706 - val_loss: 0.4824 - val_acc: 0.9146\n", "Epoch 150/164\n", "391/391 [==============================] - 34s - loss: 0.2549 - acc: 0.9724 - val_loss: 0.4809 - val_acc: 0.9154\n", "Epoch 151/164\n", "391/391 [==============================] - 34s - loss: 0.2540 - acc: 0.9726 - val_loss: 0.4845 - val_acc: 0.9142\n", "Epoch 152/164\n", "391/391 [==============================] - 34s - loss: 0.2548 - acc: 0.9714 - val_loss: 0.4780 - val_acc: 0.9163\n", "Epoch 153/164\n", "391/391 [==============================] - 34s - loss: 0.2534 - acc: 0.9718 - val_loss: 0.4788 - val_acc: 0.9154\n", "Epoch 154/164\n", "391/391 [==============================] - 34s - loss: 0.2547 - acc: 0.9717 - val_loss: 0.4798 - val_acc: 0.9174\n", "Epoch 155/164\n", "391/391 [==============================] - 34s - loss: 0.2522 - acc: 0.9722 - val_loss: 0.4812 - val_acc: 0.9150\n", "Epoch 156/164\n", "391/391 [==============================] - 34s - loss: 0.2499 - acc: 0.9731 - val_loss: 0.4789 - val_acc: 0.9165\n", "Epoch 157/164\n", "391/391 [==============================] - 34s - loss: 0.2517 - acc: 0.9724 - val_loss: 0.4824 - val_acc: 0.9164\n", "Epoch 158/164\n", "391/391 [==============================] - 34s - loss: 0.2509 - acc: 0.9724 - val_loss: 0.4805 - val_acc: 0.9152\n", "Epoch 159/164\n", "391/391 [==============================] - 33s - loss: 0.2511 - acc: 0.9719 - val_loss: 0.4795 - val_acc: 0.9158\n", "Epoch 160/164\n", "391/391 [==============================] - 34s - loss: 0.2518 - acc: 0.9712 - val_loss: 0.4815 - val_acc: 0.9159\n", "Epoch 161/164\n", "391/391 [==============================] - 33s - loss: 0.2503 - acc: 0.9727 - val_loss: 0.4777 - val_acc: 0.9161\n", "Epoch 162/164\n", "391/391 [==============================] - 34s - loss: 0.2502 - acc: 0.9723 - val_loss: 0.4757 - val_acc: 0.9176\n", "Epoch 163/164\n", "391/391 [==============================] - 33s - loss: 0.2492 - acc: 0.9726 - val_loss: 0.4766 - val_acc: 0.9174\n", "Epoch 164/164\n", "391/391 [==============================] - 34s - loss: 0.2478 - acc: 0.9737 - val_loss: 0.4785 - val_acc: 0.9150\n" ] } ], "source": [ "# start training\n", "model.fit_generator(datagen.flow(x_train, y_train,batch_size=batch_size),\n", " steps_per_epoch=iterations,\n", " epochs=epochs,\n", " callbacks=cbks,\n", " validation_data=(x_test, y_test))\n", "model.save('nin_with_bn.h5')" ] }, { "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": 2 }	mit
fastai/fastai	nbs/12_optimizer.ipynb	1	87938	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|hide\n", "#\|skip\n", "! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|default_exp optimizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "from __future__ import annotations\n", "from fastai.torch_basics import \n", "from packaging import version" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|hide\n", "from nbdev.showdoc import " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimizers\n", "\n", "> Define the general fastai optimizer and the variants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `_BaseOptimizer` -" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "class _BaseOptimizer():\n", " \"Common functionality between `Optimizer` and `OptimWrapper`\"\n", " def all_params(self,\n", " n:(slice, int)=slice(None), # Extended slicing over the optimizer `param_lists`\n", " with_grad:bool=False # Get all param tuples. If `True` select only those with a gradient\n", " ):\n", " res = L((p,pg,self.state[p],hyper) for pg,hyper in zip(self.param_lists[n],self.hypers[n]) for p in pg)\n", " return L(o for o in res if hasattr(o[0], 'grad') and o[0].grad is not None) if with_grad else res\n", "\n", " def _set_require_grad(self,\n", " rg:bool, # Requires grad: if `True` sets gradient for parameters, else uses state `state[\"force_train\"]`\n", " p:Tensor, # Parameters to set gradient\n", " pg, # Param groups (unused but needed because unpack o)\n", " state: dict,\n", " h # Hyperparameter (unused but needed because unpack o)\n", " ):\n", " p.requires_grad_(rg or state.get('force_train', False))\n", " def freeze_to(self,\n", " n:int # Freeze up to `n` layers\n", " ):\n", " self.frozen_idx = n if n >= 0 else len(self.param_lists) + n\n", " if self.frozen_idx >= len(self.param_lists):\n", " warn(f\"Freezing {self.frozen_idx} groups; model has {len(self.param_lists)}; whole model is frozen.\")\n", " for o in self.all_params(slice(n, None)): self._set_require_grad(True, o)\n", " for o in self.all_params(slice(None, n)): self._set_require_grad(False, o)\n", "\n", " def freeze(self):\n", " assert(len(self.param_lists)>1)\n", " self.freeze_to(-1)\n", "\n", " def set_freeze(self,\n", " n:int,\n", " rg:bool, # Whether grad is required\n", " ignore_force_train=False # Overwrites \"force_train\" or batch norm always trains even if frozen\n", " ):\n", " for p in self.param_lists[n]: p.requires_grad_(rg or (state.get('force_train', False) and not ignore_force_train))\n", "\n", " def set_hypers(self, *kwargs): L(kwargs.items()).starmap(self.set_hyper)\n", " def _set_hyper(self,\n", " k, # Hyperparameter key\n", " v # Hyperparameter value\n", " ):\n", " for v_,h in zip(v, self.hypers): h[k] = v_\n", " def set_hyper(self,\n", " k, # Hyperparameter key or slice of keys\n", " v # Hyperparameter value or slice of values\n", " ):\n", " if isinstance(v, slice):\n", " if v.start: v = even_mults(v.start, v.stop, len(self.param_lists))\n", " else: v = [v.stop/10](len(self.param_lists)-1) + [v.stop]\n", " v = L(v, use_list=None)\n", " if len(v)==1: v = vlen(self.param_lists)\n", " assert len(v) == len(self.hypers), f\"Trying to set {len(v)} values for {k} but there are {len(self.param_lists)} parameter groups.\"\n", " self._set_hyper(k, v)\n", "\n", " def unfreeze(self): self.freeze_to(0)\n", " @property\n", " def param_groups(self): return [{{'params': pg}, hp} for pg,hp in zip(self.param_lists, self.hypers)]\n", " @param_groups.setter\n", " def param_groups(self,\n", " v:dict # List of dicts to set `params` and other hyper parameters\n", " ):\n", " for pg,v_ in zip(self.param_lists,v): pg = v_['params']\n", " for hyper,v_ in zip(self.hypers,v):\n", " for k,t in v_.items():\n", " if k != 'params': hyper[k] = t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "add_docs(_BaseOptimizer, \n", " all_params=\"List of param_groups, parameters, and hypers\",\n", " freeze_to=\"Freeze parameter groups up to `n`\",\n", " freeze=\"Freeze up to last parameter group\",\n", " set_freeze=\"Set `rg` for parameter group `n` only\",\n", " unfreeze=\"Unfreeze the entire model\",\n", " set_hypers=\"`set_hyper` for all `kwargs`\",\n", " set_hyper=\"Set the value(s) in `v` for hyper-parameter `k`\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def _update(\n", " state:dict,\n", " new=None # New values to update `state` dict\n", "):\n", " if new is None: return state\n", " if isinstance(new, dict): state.update(new)\n", " return state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## `Optimizer` -" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "class Optimizer(_BaseOptimizer):\n", " \"Base optimizer class for the fastai library, updating `params` with `cbs`\"\n", " _keep_on_clear = ['force_train', 'do_wd']\n", " def __init__(self,\n", " params:Tensor, # Parameters and hyper parameters\n", " cbs:list, # `Optimizer` callbacks\n", " train_bn:bool=True, # Batch normalization is always trained\n", " defaults # Default values to set on hyper parameters\n", " ):\n", " params = L(params)\n", " self.cbs,self.state,self.train_bn = L(cbs),defaultdict(dict),train_bn\n", " defaults = merge(self.cbs.attrgot('defaults'), defaults)\n", " self.param_lists = L(L(p) for p in params) if isinstance(params[0], (L,list)) else L([params])\n", " self.hypers = L({} for _ in range_of(self.param_lists))\n", " self.set_hypers(*defaults)\n", " self.frozen_idx = 0\n", "\n", " def zero_grad(self):\n", " for p,_ in self.all_params(with_grad=True):\n", " p.grad.detach_()\n", " p.grad.zero_()\n", "\n", " def step(self, closure=None):\n", " if closure is not None: raise NotImplementedError(\"fastai optimizers currently do not support closure\")\n", " for p,pg,state,hyper in self.all_params(with_grad=True):\n", " for cb in self.cbs: state = _update(state, cb(p, {state, *hyper}))\n", " self.state[p] = state\n", "\n", " def clear_state(self):\n", " for p,pg,state,hyper in self.all_params():\n", " self.state[p] = {k: state[k] for k in self._keep_on_clear if k in state}\n", "\n", " def state_dict(self):\n", " state = [self.state[p] for p,_ in self.all_params()]\n", " return {'state': state, 'hypers': self.hypers}\n", "\n", " def load_state_dict(self,\n", " sd:dict # State dict with `hypers` and `state` to load on the optimizer\n", " ):\n", " assert len(sd[\"hypers\"]) == len(self.param_lists)\n", " assert len(sd[\"state\"]) == sum([len(pg) for pg in self.param_lists])\n", " self.hypers = sd['hypers']\n", " self.state = {p: s for p,s in zip(self.all_params().itemgot(0), sd['state'])}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "add_docs(Optimizer, \n", " zero_grad=\"Standard PyTorch API: Zero all the grad attributes of the parameters\",\n", " step=\"Standard PyTorch API: Update the stats and execute the steppers in on all parameters that have a grad\",\n", " state_dict=\"Return the state of the optimizer in a dictionary\",\n", " load_state_dict=\"Load the content of `sd`\",\n", " clear_state=\"Reset the state of the optimizer\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initializing an Optimizer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`params` will be used to create the `param_groups` of the optimizer. If it's a collection (or a generator) of parameters, it will be a `L` containing one `L` with all the parameters. To define multiple parameter groups `params` should be passed as a collection (or a generator) of `L`s.\n", "\n", "> Note: In PyTorch, <code>model.parameters()</code> returns a generator with all the parameters, that you can directly pass to <code>Optimizer</code>." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt = Optimizer([1,2,3], noop)\n", "test_eq(opt.param_lists, [[1,2,3]])\n", "opt = Optimizer(range(3), noop)\n", "test_eq(opt.param_lists, [[0,1,2]])\n", "opt = Optimizer([[1,2],[3]], noop)\n", "test_eq(opt.param_lists, [[1,2],[3]])\n", "opt = Optimizer(([o,o+1] for o in range(0,4,2)), noop)\n", "test_eq(opt.param_lists, [[0,1],[2,3]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`cbs` is a list of functions that will be composed when applying the step. For instance, you can compose a function making the SGD step, with another one applying weight decay. Additionally, each `cb` can have a `defaults` attribute that contains hyper-parameters and their default value. Those are all gathered at initialization, and new values can be passed to override those defaults with the `defaults` kwargs. The steppers will be called by `Optimizer.step` (which is the standard PyTorch name), and gradients can be cleared with `Optimizer.zero_grad` (also a standard PyTorch name).\n", "\n", "Once the defaults have all been pulled off, they are copied as many times as there are `param_groups` and stored in `hypers`. To apply different hyper-parameters to different groups (differential learning rates, or no weight decay for certain layers for instance), you will need to adjust those values after the init. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tst_arg(p, lr=0, kwargs): return p\n", "tst_arg.defaults = dict(lr=1e-2)\n", "\n", "def tst_arg2(p, lr2=0, kwargs): return p\n", "tst_arg2.defaults = dict(lr2=1e-3)\n", "\n", "def tst_arg3(p, mom=0, kwargs): return p\n", "tst_arg3.defaults = dict(mom=0.9)\n", "\n", "def tst_arg4(p, kwargs): return p\n", "\n", "opt = Optimizer([1,2,3], [tst_arg,tst_arg2, tst_arg3])\n", "test_eq(opt.hypers, [{'lr2': 1e-3, 'mom': 0.9, 'lr': 1e-2}])\n", "opt = Optimizer([1,2,3], tst_arg, lr=0.1)\n", "test_eq(opt.hypers, [{'lr': 0.1}])\n", "opt = Optimizer([[1,2],[3]], tst_arg)\n", "test_eq(opt.hypers, [{'lr': 1e-2}, {'lr': 1e-2}])\n", "opt = Optimizer([[1,2],[3]], tst_arg, lr=0.1)\n", "test_eq(opt.hypers, [{'lr': 0.1}, {'lr': 0.1}])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each hyper-parameter, you can pass a slice or a collection to set them, if there are multiple parameter groups. A slice will be converted to a log-uniform collection from its beginning to its end, or if it only has an end `e`, to a collection of as many values as there are parameter groups that are `...,e/10,e/10,e`.\n", "\n", "Setting an hyper-parameter with a collection that has a different number of elements than the optimizer has parameter groups will raise an error." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt = Optimizer([[1,2],[3]], tst_arg, lr=[0.1,0.2])\n", "test_eq(opt.hypers, [{'lr': 0.1}, {'lr': 0.2}])\n", "opt = Optimizer([[1,2],[3],[4]], tst_arg, lr=slice(1e-2))\n", "test_eq(opt.hypers, [{'lr': 1e-3}, {'lr': 1e-3}, {'lr': 1e-2}])\n", "opt = Optimizer([[1,2],[3],[4]], tst_arg, lr=slice(1e-4,1e-2))\n", "test_eq(opt.hypers, [{'lr': 1e-4}, {'lr': 1e-3}, {'lr': 1e-2}])\n", "test_eq(opt.param_groups, [{'params': [1,2], 'lr': 1e-4}, {'params': [3], 'lr': 1e-3}, {'params': [4], 'lr': 1e-2}])\n", "test_fail(lambda: Optimizer([[1,2],[3],[4]], tst_arg, lr=np.array([0.1,0.2])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic steppers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be able to give examples of optimizer steps, we will need some steppers, like the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def sgd_step(p, lr, kwargs):\n", " p.data.add_(p.grad.data, alpha=-lr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tst_param(val, grad=None):\n", " \"Create a tensor with `val` and a gradient of `grad` for testing\"\n", " res = tensor([val]).float()\n", " res.grad = tensor([val/10 if grad is None else grad]).float()\n", " return res" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param(1., 0.1)\n", "sgd_step(p, 1.)\n", "test_eq(p, tensor([0.9]))\n", "test_eq(p.grad, tensor([0.1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def weight_decay(p, lr, wd, do_wd=True, kwargs):\n", " \"Weight decay as decaying `p` with `lrwd`\"\n", " if do_wd and wd!=0: p.data.mul_(1 - lrwd)\n", "\n", "weight_decay.defaults = dict(wd=0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param(1., 0.1)\n", "weight_decay(p, 1., 0.1)\n", "test_eq(p, tensor([0.9]))\n", "test_eq(p.grad, tensor([0.1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def l2_reg(p, lr, wd, do_wd=True, *kwargs):\n", " \"L2 regularization as adding `wdp` to `p.grad`\"\n", " if do_wd and wd!=0: p.grad.data.add_(p.data, alpha=wd)\n", "\n", "l2_reg.defaults = dict(wd=0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param(1., 0.1)\n", "l2_reg(p, 1., 0.1)\n", "test_eq(p, tensor([1.]))\n", "test_eq(p.grad, tensor([0.2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Warning: Weight decay and L2 regularization is the same thing for basic SGD, but for more complex optimizers, they are very different." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making the step" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.step\" class=\"doc_header\"><code>Optimizer.step</code><a href=\"__main__.py#L24\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.step</code>(`closure`=`None`)\n", "\n", "Standard PyTorch API: Update the stats and execute the steppers in on all parameters that have a grad" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This method will loop over all param groups, then all parameters for which `grad` is not None and call each function in `stepper`, passing it the parameter `p` with the hyper-parameters in the corresponding dict in `hypers`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#test basic step\n", "r = L.range(4)\n", "def tst_params(): return r.map(tst_param)\n", "\n", "params = tst_params()\n", "opt = Optimizer(params, sgd_step, lr=0.1)\n", "opt.step()\n", "test_close([p.item() for p in params], r.map(mul(0.99)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#test two steps\n", "params = tst_params()\n", "opt = Optimizer(params, [weight_decay, sgd_step], lr=0.1, wd=0.1)\n", "opt.step()\n", "test_close([p.item() for p in params], r.map(mul(0.98)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#test None gradients are ignored\n", "params = tst_params()\n", "opt = Optimizer(params, sgd_step, lr=0.1)\n", "params[-1].grad = None\n", "opt.step()\n", "test_close([p.item() for p in params], [0., 0.99, 1.98, 3.])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#test discriminative lrs\n", "params = tst_params()\n", "opt = Optimizer([params[:2], params[2:]], sgd_step, lr=0.1)\n", "opt.hypers[0]['lr'] = 0.01\n", "opt.step()\n", "test_close([p.item() for p in params], [0., 0.999, 1.98, 2.97])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.zero_grad\" class=\"doc_header\"><code>Optimizer.zero_grad</code><a href=\"__main__.py#L19\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.zero_grad</code>()\n", "\n", "Standard PyTorch API: Zero all the grad attributes of the parameters" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.zero_grad)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_params()\n", "opt = Optimizer(params, [weight_decay, sgd_step], lr=0.1, wd=0.1)\n", "opt.zero_grad()\n", "[test_eq(p.grad, tensor([0.])) for p in params];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the `Optimizer` `cbs` can be functions updating the state associated with a parameter. That state can then be used by any stepper. The best example is a momentum calculation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tst_stat(p, kwargs): \n", " s = kwargs.get('sum', torch.zeros_like(p)) + p.data\n", " return {'sum': s}\n", "tst_stat.defaults = {'mom': 0.9}\n", "\n", "#Test Optimizer init\n", "opt = Optimizer([1,2,3], tst_stat)\n", "test_eq(opt.hypers, [{'mom': 0.9}])\n", "opt = Optimizer([1,2,3], tst_stat, mom=0.99)\n", "test_eq(opt.hypers, [{'mom': 0.99}])\n", "\n", "#Test stat\n", "x = torch.randn(4,5)\n", "state = tst_stat(x)\n", "assert 'sum' in state\n", "test_eq(x, state['sum'])\n", "state = tst_stat(x, state)\n", "test_eq(state['sum'], 2x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistics" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def average_grad(p, mom, dampening=False, grad_avg=None, kwargs):\n", " \"Keeps track of the avg grads of `p` in `state` with `mom`.\"\n", " if grad_avg is None: grad_avg = torch.zeros_like(p.grad.data)\n", " damp = 1-mom if dampening else 1.\n", " grad_avg.mul_(mom).add_(p.grad.data, alpha=damp)\n", " return {'grad_avg': grad_avg}\n", "\n", "average_grad.defaults = dict(mom=0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`dampening=False` gives the classical formula for momentum in SGD: \n", "```\n", "new_val = old_val mom + grad\n", "```\n", "whereas `dampening=True` makes it an exponential moving average:\n", "```\n", "new_val = old_val * mom + grad * (1-mom)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param([1,2,3], [4,5,6])\n", "state = {}\n", "state = average_grad(p, mom=0.9, state)\n", "test_eq(state['grad_avg'], p.grad)\n", "state = average_grad(p, mom=0.9, state)\n", "test_eq(state['grad_avg'], p.grad * 1.9)\n", "\n", "#Test dampening\n", "state = {}\n", "state = average_grad(p, mom=0.9, dampening=True, *state)\n", "test_eq(state['grad_avg'], 0.1p.grad)\n", "state = average_grad(p, mom=0.9, dampening=True, *state)\n", "test_close(state['grad_avg'], (0.10.9+0.1)p.grad)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def average_sqr_grad(p, sqr_mom, dampening=True, sqr_avg=None, kwargs):\n", " if sqr_avg is None: sqr_avg = torch.zeros_like(p.grad.data)\n", " damp = 1-sqr_mom if dampening else 1.\n", " sqr_avg.mul_(sqr_mom).addcmul_(p.grad.data, p.grad.data, value=damp)\n", " return {'sqr_avg': sqr_avg}\n", "\n", "average_sqr_grad.defaults = dict(sqr_mom=0.99)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`dampening=False` gives the classical formula for momentum in SGD: \n", "```\n", "new_val = old_val mom + grad*2\n", "```\n", "whereas `dampening=True` makes it an exponential moving average:\n", "```\n", "new_val = old_val mom + (grad*2) (1-mom)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param([1,2,3], [4,5,6])\n", "state = {}\n", "state = average_sqr_grad(p, sqr_mom=0.99, dampening=False, state)\n", "test_eq(state['sqr_avg'], p.grad.pow(2))\n", "state = average_sqr_grad(p, sqr_mom=0.99, dampening=False, state)\n", "test_eq(state['sqr_avg'], p.grad.pow(2) * 1.99)\n", "\n", "#Test dampening\n", "state = {}\n", "state = average_sqr_grad(p, sqr_mom=0.99, *state)\n", "test_close(state['sqr_avg'], 0.01p.grad.pow(2))\n", "state = average_sqr_grad(p, sqr_mom=0.99, *state)\n", "test_close(state['sqr_avg'], (0.010.99+0.01)p.grad.pow(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Freezing part of the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.freeze\" class=\"doc_header\"><code>Optimizer.freeze</code><a href=\"__main__.py#L28\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.freeze</code>()\n", "\n", "Freeze up to last parameter group" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.freeze, name=\"Optimizer.freeze\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.freeze_to\" class=\"doc_header\"><code>Optimizer.freeze_to</code><a href=\"__main__.py#L19\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.freeze_to</code>(`n`:`int`)\n", "\n", "Freeze parameter groups up to `n`\n", "\n", "\|\|Type\|Default\|Details\|\n", "\|---\|---\|---\|---\|\n", "\|`n`\|`int`\|\|Freeze up to `n` layers\|\n" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.freeze_to, name=\"Optimizer.freeze_to\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.unfreeze\" class=\"doc_header\"><code>Optimizer.unfreeze</code><a href=\"__main__.py#L57\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.unfreeze</code>()\n", "\n", "Unfreeze the entire model" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.unfreeze, name=\"Optimizer.unfreeze\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Freezing the first layer\n", "params = [tst_params(), tst_params(), tst_params()]\n", "opt = Optimizer(params, sgd_step, lr=0.1)\n", "opt.freeze_to(1)\n", "req_grad = Self.requires_grad()\n", "test_eq(L(params[0]).map(req_grad), [False]4)\n", "for i in {1,2}: test_eq(L(params[i]).map(req_grad), [True]4)\n", " \n", "#Unfreezing\n", "opt.unfreeze()\n", "for i in range(2): test_eq(L(params[i]).map(req_grad), [True]4)\n", "\n", "#TODO: test warning\n", "# opt.freeze_to(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameters such as batchnorm weights/bias can be marked to always be in training mode, just put `force_train=true` in their state." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [tst_params(), tst_params(), tst_params()]\n", "opt = Optimizer(params, sgd_step, lr=0.1)\n", "for p in L(params[1])[[1,3]]: opt.state[p] = {'force_train': True}\n", "opt.freeze()\n", "test_eq(L(params[0]).map(req_grad), [False]4)\n", "test_eq(L(params[1]).map(req_grad), [False, True, False, True])\n", "test_eq(L(params[2]).map(req_grad), [True]4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Serializing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.state_dict\" class=\"doc_header\"><code>Optimizer.state_dict</code><a href=\"__main__.py#L34\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.state_dict</code>()\n", "\n", "Return the state of the optimizer in a dictionary" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.state_dict)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.load_state_dict\" class=\"doc_header\"><code>Optimizer.load_state_dict</code><a href=\"__main__.py#L38\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.load_state_dict</code>(`sd`:`dict`)\n", "\n", "Load the content of `sd`\n", "\n", "\|\|Type\|Default\|Details\|\n", "\|---\|---\|---\|---\|\n", "\|`sd`\|`dict`\|\|State dict with `hypers` and `state` to load on the optimizer\|\n" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.load_state_dict)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param([1,2,3], [4,5,6])\n", "opt = Optimizer(p, average_grad)\n", "opt.step()\n", "test_eq(opt.state[p]['grad_avg'], tensor([[4., 5., 6.]]))\n", "\n", "sd = opt.state_dict()\n", "p1 = tst_param([10,20,30], [40,50,60])\n", "opt = Optimizer(p1, average_grad, mom=0.99)\n", "test_eq(opt.hypers[0]['mom'], 0.99)\n", "test_eq(opt.state, {})\n", "\n", "opt.load_state_dict(sd)\n", "test_eq(opt.hypers[0]['mom'], 0.9)\n", "test_eq(opt.state[p1]['grad_avg'], tensor([[4., 5., 6.]]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "<h4 id=\"Optimizer.clear_state\" class=\"doc_header\"><code>Optimizer.clear_state</code><a href=\"__main__.py#L30\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n", "\n", "> <code>Optimizer.clear_state</code>()\n", "\n", "Reset the state of the optimizer" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_doc(Optimizer.clear_state)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param([1,2,3], [4,5,6])\n", "opt = Optimizer(p, average_grad)\n", "opt.state[p] = {'force_train': True}\n", "opt.step()\n", "test_eq(opt.state[p]['grad_avg'], tensor([[4., 5., 6.]]))\n", "\n", "opt.clear_state()\n", "test_eq(opt.state[p], {'force_train': True})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SGD with momentum" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def momentum_step(p, lr, grad_avg, *kwargs):\n", " \"Step for SGD with momentum with `lr`\"\n", " p.data.add_(grad_avg, alpha=-lr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def SGD(params, lr, mom=0., wd=0., decouple_wd=True):\n", " \"A `Optimizer` for SGD with `lr` and `mom` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " if mom != 0: cbs.append(average_grad)\n", " cbs.append(sgd_step if mom==0 else momentum_step)\n", " return Optimizer(params, cbs, lr=lr, mom=mom, wd=wd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Vanilla SGD\n", "params = tst_params()\n", "opt = SGD(params, lr=0.1)\n", "opt.step()\n", "test_close([p.item() for p in params], [i0.99 for i in range(4)])\n", "opt.step()\n", "[p.item() for p in params]\n", "test_close([p.item() for p in params], [i0.98 for i in range(4)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#SGD with momentum\n", "params = tst_params()\n", "opt = SGD(params, lr=0.1, mom=0.9)\n", "assert isinstance(opt, Optimizer)\n", "opt.step()\n", "test_close([p.item() for p in params], [i0.99 for i in range(4)])\n", "opt.step()\n", "[p.item() for p in params]\n", "test_close([p.item() for p in params], [i(1 - 0.1 (0.1 + 0.11.9)) for i in range(4)])\n", "for i,p in enumerate(params): test_close(opt.state[p]['grad_avg'].item(), i0.19)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test weight decay, notice how we can see that L2 regularization is different from weight decay even for simple SGD with momentum." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_params()\n", "#Weight decay\n", "opt = SGD(params, lr=0.1, mom=0.9, wd=0.1)\n", "opt.step()\n", "test_close([p.item() for p in params], [i0.98 for i in range(4)])\n", "#L2 reg\n", "opt = SGD(params, lr=0.1, mom=0.9, wd=0.1, decouple_wd=False)\n", "opt.step()\n", "#TODO: fix cause this formula was wrong\n", "#test_close([p.item() for p in params], [i0.97 for i in range(4)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RMSProp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def rms_prop_step(p, lr, sqr_avg, eps, grad_avg=None, *kwargs):\n", " \"Step for SGD with momentum with `lr`\"\n", " denom = sqr_avg.sqrt().add_(eps)\n", " p.data.addcdiv_((grad_avg if grad_avg is not None else p.grad), denom, value=-lr)\n", "\n", "rms_prop_step.defaults = dict(eps=1e-8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def RMSProp(params, lr, sqr_mom=0.99, mom=0., wd=0., decouple_wd=True):\n", " \"A `Optimizer` for RMSProp with `lr`, `sqr_mom`, `mom` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " cbs += ([average_sqr_grad] if mom==0. else [average_grad, average_sqr_grad])\n", " cbs.append(rms_prop_step)\n", " return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, wd=wd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RMSProp was introduced by Geoffrey Hinton in his [course](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf). What is named `sqr_mom` here is the `alpha` in the course. Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Without momentum\n", "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = RMSProp(params, lr=0.1)\n", "opt.step()\n", "test_close(params[0], tensor([0.,1.,2.]))\n", "opt.step()\n", "step = - 0.1 0.1 / (math.sqrt((0.010.99+0.01) 0.1*2) + 1e-8)\n", "test_close(params[0], tensor([step, 1+step, 2+step]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#With momentum\n", "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = RMSProp(params, lr=0.1, mom=0.9)\n", "opt.step()\n", "test_close(params[0], tensor([0.,1.,2.]))\n", "opt.step()\n", "step = - 0.1 (0.1 + 0.90.1) / (math.sqrt((0.010.99+0.01) * 0.12) + 1e-8)\n", "test_close(params[0], tensor([step, 1+step, 2+step]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adam" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def step_stat(p, step=0, kwargs):\n", " \"Register the number of steps done in `state` for `p`\"\n", " step += 1\n", " return {'step' : step}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p = tst_param(1,0.1)\n", "state = {}\n", "state = step_stat(p, state)\n", "test_eq(state['step'], 1)\n", "for _ in range(5): state = step_stat(p, state)\n", "test_eq(state['step'], 6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def debias(mom, damp, step): return damp * (1 - momstep) / (1-mom)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def adam_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, kwargs):\n", " \"Step for Adam with `lr` on `p`\"\n", " debias1 = debias(mom, 1-mom, step)\n", " debias2 = debias(sqr_mom, 1-sqr_mom, step)\n", " p.data.addcdiv_(grad_avg, (sqr_avg/debias2).sqrt() + eps, value = -lr / debias1)\n", " return p\n", "\n", "adam_step._defaults = dict(eps=1e-5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def Adam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0.01, decouple_wd=True):\n", " \"A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, adam_step]\n", " return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adam was introduced by Diederik P. Kingma and Jimmy Ba in [Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980). For consistency across optimizers, we renamed `beta1` and `beta2` in the paper to `mom` and `sqr_mom`. Note that our defaults also differ from the paper (0.99 for `sqr_mom` or `beta2`, 1e-5 for `eps`). Those values seem to be better from our experiments in a wide range of situations.\n", "\n", "Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients).\n", "\n", "> Note: Don't forget that `eps` is an hyper-parameter you can change. Some models won't train without a very high `eps` like 0.1 (intuitively, the higher `eps` is, the closer we are to normal SGD). The usual default of 1e-8 is often too extreme in the sense we don't manage to get as good results as with SGD. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = Adam(params, lr=0.1, wd=0)\n", "opt.step()\n", "step = -0.1 * 0.1 / (math.sqrt(0.1*2) + 1e-8)\n", "test_close(params[0], tensor([1+step, 2+step, 3+step]))\n", "opt.step()\n", "test_close(params[0], tensor([1+2step, 2+2step, 3+2step]), eps=1e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RAdam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RAdam (for rectified Adam) was introduced by Zhang et al. in [On the Variance of the Adaptive Learning Rate and Beyond](https://arxiv.org/abs/1907.08610) to slightly modify the Adam optimizer to be more stable at the beginning of training (and thus not require a long warmup). They use an estimate of the variance of the moving average of the squared gradients (the term in the denominator of traditional Adam) and rescale this moving average by this term before performing the update.\n", "\n", "This version also incorporates [SAdam](https://arxiv.org/abs/1908.00700); set `beta` to enable this (definition same as in the paper)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def radam_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, beta, *kwargs):\n", " \"Step for RAdam with `lr` on `p`\"\n", " debias1 = debias(mom, 1-mom, step)\n", " debias2 = debias(sqr_mom, 1-sqr_mom, step)\n", " r_inf = 2/(1-sqr_mom) - 1\n", " r = r_inf - 2stepsqr_momstep/(1-sqr_momstep)\n", " if r > 5:\n", " v = math.sqrt(((r-4) (r-2) * r_inf)/((r_inf-4)(r_inf-2)r))\n", " denom = (sqr_avg/debias2).sqrt()\n", " if eps: denom += eps\n", " if beta: denom = F.softplus(denom, beta)\n", " p.data.addcdiv_(grad_avg, denom, value = -lrv / debias1)\n", " else: p.data.add_(grad_avg, alpha=-lr / debias1)\n", " return p\n", "\n", "radam_step._defaults = dict(eps=1e-5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def RAdam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0., beta=0., decouple_wd=True):\n", " \"A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, radam_step]\n", " return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd, beta=beta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the effective correction reported to the adam step for 500 iterations in RAdam. We can see how it goes from 0 to 1, mimicking the effect of a warm-up." ] }, { "cell_type": "code", "execution_count": null, "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": [ "beta = 0.99\n", "r_inf = 2/(1-beta) - 1\n", "rs = np.array([r_inf - 2sbetas/(1-betas) for s in range(5,500)])\n", "v = np.sqrt(((rs-4) (rs-2) * r_inf)/((r_inf-4)(r_inf-2)rs))\n", "plt.plot(v);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = RAdam(params, lr=0.1)\n", "#The r factor is lower than 5 during the first 5 steps so updates use the average of gradients (all the same)\n", "r_inf = 2/(1-0.99) - 1\n", "for i in range(5): \n", " r = r_inf - 2(i+1)0.99(i+1)/(1-0.99(i+1))\n", " assert r <= 5\n", " opt.step()\n", "p = tensor([0.95, 1.9, 2.85])\n", "test_close(params[0], p)\n", "\n", "#The r factor is greater than 5 for the sixth step so we update with RAdam\n", "r = r_inf - 260.996/(1-0.996)\n", "assert r > 5\n", "opt.step()\n", "v = math.sqrt(((r-4) * (r-2) * r_inf)/((r_inf-4)(r_inf-2)r))\n", "step = -0.10.1v/(math.sqrt(0.1*2) + 1e-8)\n", "test_close(params[0], p+step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### QHAdam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "QHAdam (for Quasi-Hyperbolic Adam) was introduced by Ma & Yarats in [Quasi-Hyperbolic Momentum and Adam for Deep Learning](https://arxiv.org/pdf/1810.06801.pdf) as a \"computationally cheap, intuitive to interpret, and simple to implement\"* optimizer. Additional code can be found in their [qhoptim repo](https://github.com/facebookresearch/qhoptim). QHAdam is based on QH-Momentum, which introduces the immediate discount factor `nu`, encapsulating plain SGD (`nu = 0`) and momentum (`nu = 1`). QH-Momentum is defined below, where g_t+1 is the update of the moment. An interpretation of QHM is as a nu-weighted average of the momentum update step and the plain SGD update step.\n", "\n", "> θ_t+1 ← θ_t − lr * [(1 − nu) · ∇L_t(θ_t) + nu · g_t+1]\n", "\n", "QHAdam takes the concept behind QHM above and applies it to Adam, replacing both of Adam’s moment estimators with quasi-hyperbolic terms. \n", "\n", "The paper's suggested default parameters are `mom = 0.999`, `sqr_mom = 0.999`, `nu_1 = 0.7` and `and nu_2 = 1.0`. When training is not stable, it is possible that setting `nu_2 < 1` can improve stability by imposing a tighter step size bound. Note that QHAdam recovers Adam when `nu_1 = nu_2 = 1.0`. QHAdam recovers RMSProp (Hinton et al., 2012) when `nu_1 = 0` and `nu_2 = 1`, and NAdam (Dozat, 2016) when `nu_1 = mom` and `nu_2 = 1`.\n", "\n", "Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def qhadam_step(p, lr, mom, sqr_mom, sqr_avg, nu_1, nu_2, step, grad_avg, eps, *kwargs):\n", " debias1 = debias(mom, 1-mom, step)\n", " debias2 = debias(sqr_mom, 1-sqr_mom, step)\n", " p.data.addcdiv_(((1-nu_1) p.grad.data) + (nu_1 * (grad_avg / debias1)),\n", " (((1 - nu_2) * (p.grad.data)*2) + (nu_2 (sqr_avg / debias2))).sqrt() + eps,\n", " value = -lr)\n", " return p\n", "\n", "qhadam_step._defaults = dict(eps=1e-8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def QHAdam(params, lr, mom=0.999, sqr_mom=0.999, nu_1=0.7, nu_2 = 1.0, eps=1e-8, wd=0., decouple_wd=True):\n", " \"An `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `nus`, eps` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " cbs += [partial(average_grad, dampening=True), partial(average_sqr_grad, dampening=True), step_stat, qhadam_step]\n", " return Optimizer(params, cbs, lr=lr, nu_1=nu_1, nu_2=nu_2 ,\n", " mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = QHAdam(params, lr=0.1)\n", "opt.step()\n", "step = -0.1 * (((1-0.7) * 0.1) + (0.7 * 0.1)) / (\n", " math.sqrt(((1-1.0) * 0.1*2) + (1.0 0.1*2)) + 1e-8) \n", "test_close(params[0], tensor([1+step, 2+step, 3+step]))\n", "opt.step()\n", "test_close(params[0], tensor([1+2step, 2+2step, 3+2step]), eps=1e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LARS/LARC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def larc_layer_lr(p, lr, trust_coeff, wd, eps, clip=True, *kwargs):\n", " \"Computes the local lr before weight decay is applied\"\n", " p_norm,g_norm = torch.norm(p.data),torch.norm(p.grad.data)\n", " local_lr = lrtrust_coeff * (p_norm) / (g_norm + p_norm * wd + eps)\n", " return {'local_lr': min(lr, local_lr) if clip else local_lr}\n", "\n", "larc_layer_lr.defaults = dict(trust_coeff=0.02, wd=0., eps=1e-8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def larc_step(p, local_lr, grad_avg=None, kwargs):\n", " \"Step for LARC `local_lr` on `p`\"\n", " p.data.add_(p.grad.data if grad_avg is None else grad_avg, alpha = -local_lr)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def Larc(params, lr, mom=0.9, clip=True, trust_coeff=0.02, eps=1e-8, wd=0., decouple_wd=True):\n", " \"A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " if mom!=0.: cbs.append(average_grad)\n", " cbs += [partial(larc_layer_lr, clip=clip), larc_step]\n", " return Optimizer(params, cbs, lr=lr, mom=mom, trust_coeff=trust_coeff, eps=eps, wd=wd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The LARS optimizer was first introduced in [Large Batch Training of Convolutional Networks](https://arxiv.org/abs/1708.03888) then refined in its LARC variant (original LARS is with `clip=False`). A learning rate is computed for each individual layer with a certain `trust_coefficient`, then clipped to be always less than `lr`.\n", "\n", "Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [tst_param([1,2,3], [0.1,0.2,0.3]), tst_param([1,2,3], [0.01,0.02,0.03])]\n", "opt = Larc(params, lr=0.1)\n", "opt.step()\n", "#First param local lr is 0.02 < lr so it's not clipped\n", "test_close(opt.state[params[0]]['local_lr'], 0.02)\n", "#Second param local lr is 0.2 > lr so it's clipped\n", "test_eq(opt.state[params[1]]['local_lr'], 0.1)\n", "test_close(params[0], tensor([0.998,1.996,2.994]))\n", "test_close(params[1], tensor([0.999,1.998,2.997]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = [tst_param([1,2,3], [0.1,0.2,0.3]), tst_param([1,2,3], [0.01,0.02,0.03])]\n", "opt = Larc(params, lr=0.1, clip=False)\n", "opt.step()\n", "#No clipping\n", "test_close(opt.state[params[0]]['local_lr'], 0.02)\n", "test_close(opt.state[params[1]]['local_lr'], 0.2)\n", "test_close(params[0], tensor([0.998,1.996,2.994]))\n", "test_close(params[1], tensor([0.998,1.996,2.994]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LAMB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def lamb_step(p, lr, mom, step, sqr_mom, grad_avg, sqr_avg, eps, kwargs):\n", " \"Step for LAMB with `lr` on `p`\"\n", " debias1 = debias(mom, 1-mom, step)\n", " debias2 = debias(sqr_mom, 1-sqr_mom, step)\n", " r1 = p.data.pow(2).mean().sqrt()\n", " step = (grad_avg/debias1) / ((sqr_avg/debias2).sqrt()+eps)\n", " r2 = step.pow(2).mean().sqrt()\n", " q = 1 if r1 == 0 or r2 == 0 else min(r1/r2,10)\n", " p.data.add_(step, alpha = -lr * q)\n", "\n", "lamb_step._defaults = dict(eps=1e-6, wd=0.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def Lamb(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-5, wd=0., decouple_wd=True):\n", " \"A `Optimizer` for Adam with `lr`, `mom`, `sqr_mom`, `eps` and `params`\"\n", " cbs = [weight_decay] if decouple_wd else [l2_reg]\n", " cbs += [partial(average_grad, dampening=True), average_sqr_grad, step_stat, lamb_step]\n", " return Optimizer(params, cbs, lr=lr, mom=mom, sqr_mom=sqr_mom, eps=eps, wd=wd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LAMB was introduced in [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962). Intuitively, it's LARC applied to Adam. As in `Adam`, we renamed `beta1` and `beta2` in the paper to `mom` and `sqr_mom`. Note that our defaults also differ from the paper (0.99 for `sqr_mom` or `beta2`, 1e-5 for `eps`). Those values seem to be better from our experiments in a wide range of situations.\n", "\n", "Optional weight decay of `wd` is applied, as true weight decay (decay the weights directly) if `decouple_wd=True` else as L2 regularization (add the decay to the gradients)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "opt = Lamb(params, lr=0.1)\n", "opt.step()\n", "test_close(params[0], tensor([0.7840,1.7840,2.7840]), eps=1e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lookahead -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lookahead was introduced by Zhang et al. in [Lookahead Optimizer: k steps forward, 1 step back](https://arxiv.org/abs/1907.08610). It can be run on top of any optimizer and consists in having the final weights of the model be a moving average. In practice, we update our model using the internal optimizer but keep a copy of old weights that and every `k` steps, we change the weights by a moving average of the fast weights (the ones updated by the inner optimizer) with the slow weights (the copy of old weights). Those slow weights act like a stability mechanism." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "class Lookahead(Optimizer, GetAttr):\n", " \"Wrap `opt` in a lookahead optimizer\"\n", " _default='opt'\n", " def __init__(self, opt, k=6, alpha=0.5):\n", " store_attr('opt,k,alpha')\n", " self._init_state()\n", "\n", " def step(self, closure=None):\n", " if closure is not None: raise NotImplementedError(\"fastai optimizers currently do not support closure\")\n", " if self.slow_weights is None: self._copy_weights()\n", " self.opt.step()\n", " self.count += 1\n", " if self.count%self.k != 0: return\n", " for slow_pg,fast_pg in zip(self.slow_weights,self.param_lists):\n", " for slow_p,fast_p in zip(slow_pg,fast_pg):\n", " slow_p.data.add_(fast_p.data-slow_p.data, alpha=self.alpha)\n", " fast_p.data.copy_(slow_p.data)\n", "\n", " def clear_state(self):\n", " self.opt.clear_state()\n", " self._init_state()\n", "\n", " def state_dict(self):\n", " state = self.opt.state_dict()\n", " state.update({'count': self.count, 'slow_weights': self.slow_weights})\n", " return state\n", "\n", " def load_state_dict(self, sd):\n", " self.count = sd.pop('count')\n", " self.slow_weights = sd.pop('slow_weights')\n", " self.opt.load_state_dict(sd)\n", "\n", " def _init_state(self): self.count,self.slow_weights = 0,None\n", " def _copy_weights(self): self.slow_weights = L(L(p.clone().detach() for p in pg) for pg in self.param_lists)\n", "\n", " @property\n", " def param_lists(self): return self.opt.param_lists\n", " @param_lists.setter\n", " def param_lists(self, v): self.opt.param_lists = v" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params = tst_param([1,2,3], [0.1,0.2,0.3])\n", "p,g = params[0].data.clone(),tensor([0.1,0.2,0.3])\n", "opt = Lookahead(SGD(params, lr=0.1))\n", "for k in range(5): opt.step()\n", "#first 5 steps are normal SGD steps\n", "test_close(params[0], p - 0.5g)\n", "#Since k=6, sixth step is a moving average of the 6 SGD steps with the initial weight\n", "opt.step()\n", "test_close(params[0], p 0.5 + (p-0.6g) 0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "@delegates(RAdam)\n", "def ranger(p, lr, mom=0.95, wd=0.01, eps=1e-6, kwargs):\n", " \"Convenience method for `Lookahead` with `RAdam`\"\n", " return Lookahead(RAdam(p, lr=lr, mom=mom, wd=wd, eps=eps, kwargs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OptimWrapper -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`OptimWrapper` provides simple functionality to use existing optimizers constructed with [`torch.optim.Optimizer`](https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def detuplify_pg(d):\n", " res = {}\n", " for k,v in d.items():\n", " if k == 'params': continue\n", " if is_listy(v): res.update(*{f'{k}__{i}': v_ for i,v_ in enumerate(v)})\n", " else: res[k] = v\n", " return res" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tst = {'lr': 1e-2, 'mom': 0.9, 'params':[0,1,2]}\n", "test_eq(detuplify_pg(tst), {'lr': 1e-2, 'mom': 0.9})\n", "tst = {'lr': 1e-2, 'betas': (0.9,0.999), 'params':[0,1,2]}\n", "test_eq(detuplify_pg(tst), {'lr': 1e-2, 'betas__0': 0.9, 'betas__1': 0.999})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def set_item_pg(pg, k, v):\n", " if '__' not in k: pg[k] = v\n", " else:\n", " name,idx = k.split('__')\n", " pg[name] = tuple(v if i==int(idx) else pg[name][i] for i in range_of(pg[name]))\n", " return pg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tst = {'lr': 1e-2, 'mom': 0.9, 'params':[0,1,2]}\n", "test_eq(set_item_pg(tst, 'lr', 1e-3), {'lr': 1e-3, 'mom': 0.9, 'params':[0,1,2]})\n", "tst = {'lr': 1e-2, 'betas': (0.9,0.999), 'params':[0,1,2]}\n", "test_eq(set_item_pg(tst, 'betas__0', 0.95), {'lr': 1e-2, 'betas': (0.95,0.999), 'params':[0,1,2]})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "pytorch_hp_map = {'momentum': 'mom', 'weight_decay': 'wd', 'alpha': 'sqr_mom', 'betas__0': 'mom',\n", " 'betas__1': 'sqr_mom'}\n", "if version.parse(torch.version.__version__)>version.parse('1.12.0'):\n", " # Torch>=1.12 has a foreach param\n", " pytorch_hp_map = merge((pytorch_hp_map,{'foreach': 'foreach'}))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "def _convert_params(o:list) -> list:\n", " splitter = []\n", " for group in o:\n", " if isinstance(group, dict): splitter.append(group)\n", " else: splitter.append({'params':group})\n", " return splitter" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|export\n", "class OptimWrapper(_BaseOptimizer, GetAttr):\n", " \"A wrapper class for existing PyTorch optimizers\"\n", " _xtra=['zero_grad', 'step', 'state_dict', 'load_state_dict']\n", " _default='opt'\n", " def __init__(self, \n", " params:list\|dict=None, # Model parameters to pass to `opt`. If using an already built `opt`\n", " opt:callable\|torch.optim.Optimizer=None, # A torch optimizer constructor, or an already built optimizer \n", " hp_map:dict=None, # A dictionary converting the keys of a built `opt` to the keys of fastai's Optimizer\n", " convert_groups=True, # Whether to convert parameter groups\n", " kwargs\n", " ):\n", " if params is None and opt is None: raise ValueError(\"Both `params` and `opt` cannot be None.\")\n", " if callable(opt):\n", " self.opt = opt(_convert_params(params), kwargs) if convert_groups else opt(params, *kwargs)\n", " else:\n", " if params is not None: raise ValueError(\"Tried using both `params` and a built optimizer. Just pass in `opt`.\")\n", " self.opt = opt\n", " if hp_map is None: hp_map = pytorch_hp_map\n", " self.fwd_map = {k: hp_map[k] if k in hp_map else k for k in detuplify_pg(self.opt.param_groups[0]).keys()}\n", " self.bwd_map = {v:k for k,v in self.fwd_map.items()}\n", " self.state = defaultdict(dict, {})\n", " self.frozen_idx = 0\n", "\n", " @property\n", " def hypers(self):\n", " return [{self.fwd_map[k]:v for k,v in detuplify_pg(pg).items() if k != 'params'} for pg in self.opt.param_groups]\n", "\n", " def _set_hyper(self, k, v):\n", " for pg,v_ in zip(self.opt.param_groups,v): pg = set_item_pg(pg, self.bwd_map[k], v_)\n", "\n", " def clear_state(self): self.opt.state = defaultdict(dict, {})\n", "\n", " @property\n", " def param_lists(self): return [pg['params'] for pg in self.opt.param_groups]\n", " @param_lists.setter\n", " def param_lists(self, v):\n", " for pg,v_ in zip(self.opt.param_groups,v): pg['params'] = v_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sgd = SGD([tensor([1,2,3])], lr=1e-3, mom=0.9, wd=1e-2)\n", "tst_sgd = OptimWrapper([tensor([1,2,3])], torch.optim.SGD, lr=1e-3, momentum=0.9, weight_decay=1e-2)\n", "#Access to param_groups\n", "test_eq(tst_sgd.param_lists, sgd.param_lists)\n", "#Set param_groups\n", "tst_sgd.param_lists = [[tensor([4,5,6])]]\n", "test_eq(tst_sgd.opt.param_groups[0]['params'], [tensor(4,5,6)])\n", "#Access to hypers\n", "_xtra_hypers = dict(dampening=0., nesterov=False, maximize=False)\n", "\n", "if version.parse(torch.version.__version__)>version.parse('1.12.0'):\n", " _xtra_hypers = merge((_xtra_hypers,dict(foreach=None)))\n", " \n", "test_eq(tst_sgd.hypers, [{sgd.hypers[0], _xtra_hypers}])\n", "#Set hypers\n", "tst_sgd.set_hyper('mom', 0.95)\n", "test_eq(tst_sgd.opt.param_groups[0]['momentum'], 0.95)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tst_sgd = OptimWrapper([{'params': [tensor([1,2,3])], 'lr': 1e-3}, \n", " {'params': [tensor([4,5,6])], 'lr': 1e-2}], torch.optim.SGD, momentum=0.9, weight_decay=1e-2)\n", "sgd = SGD([[tensor([1,2,3])], [tensor([4,5,6])]], lr=[1e-3, 1e-2], mom=0.9, wd=1e-2)\n", "#Access to param_groups\n", "test_eq(tst_sgd.param_lists, sgd.param_lists)\n", "#Set param_groups\n", "tst_sgd.param_lists = [[tensor([4,5,6])], [tensor([1,2,3])]]\n", "test_eq(tst_sgd.opt.param_groups[0]['params'], [tensor(4,5,6)])\n", "test_eq(tst_sgd.opt.param_groups[1]['params'], [tensor(1,2,3)])\n", "#Access to hypers\n", "test_eq(tst_sgd.hypers, [{sgd.hypers[i], _xtra_hypers} for i in range(2)])\n", "#Set hypers\n", "tst_sgd.set_hyper('mom', 0.95)\n", "test_eq([pg['momentum'] for pg in tst_sgd.opt.param_groups], [0.95,0.95])\n", "tst_sgd.set_hyper('lr', [1e-4,1e-3])\n", "test_eq([pg['lr'] for pg in tst_sgd.opt.param_groups], [1e-4,1e-3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Ensure we can use an already made optimizer\n", "tst_sgd = torch.optim.SGD([{'params': [tensor([1,2,3])], 'lr': 1e-3}, \n", " {'params': [tensor([4,5,6])], 'lr': 1e-2}])\n", "tst_sgd = OptimWrapper(opt = tst_sgd)\n", "sgd = SGD([[tensor([1,2,3])], [tensor([4,5,6])]], lr=[1e-3, 1e-2])\n", "#Access to param_groups\n", "test_eq(tst_sgd.param_lists, sgd.param_lists)\n", "#Set param_groups\n", "tst_sgd.param_lists = [[tensor([4,5,6])], [tensor([1,2,3])]]\n", "test_eq(tst_sgd.opt.param_groups[0]['params'], [tensor(4,5,6)])\n", "test_eq(tst_sgd.opt.param_groups[1]['params'], [tensor(1,2,3)])\n", "#Access to hypers\n", "test_eq(tst_sgd.hypers, [{sgd.hypers[i], _xtra_hypers} for i in range(2)])\n", "#Set hypers\n", "tst_sgd.set_hyper('mom', 0.95)\n", "test_eq([pg['momentum'] for pg in tst_sgd.opt.param_groups], [0.95,0.95])\n", "tst_sgd.set_hyper('lr', [1e-4,1e-3])\n", "test_eq([pg['lr'] for pg in tst_sgd.opt.param_groups], [1e-4,1e-3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#\|hide\n", "#check it works with tuply hp names like in Adam\n", "tst_adam = OptimWrapper([tensor([1,2,3])], torch.optim.Adam, lr=1e-2, betas=(0.9, 0.99))\n", "\n", "tst_hypers = {'lr': 0.01, 'mom': 0.9, 'sqr_mom': 0.99, 'eps': 1e-08, 'wd': 0, \n", " 'amsgrad': False, 'maximize':False}\n", "if version.parse(torch.version.__version__)>version.parse('1.12.0'):\n", " tst_hypers = merge((tst_hypers,dict(foreach=None)))\n", "\n", "test_eq(tst_adam.hypers, [tst_hypers])\n", "tst_adam.set_hyper('mom', 0.95)\n", "test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.99))\n", "tst_adam.set_hyper('sqr_mom', 0.9)\n", "test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.9))\n", "\n", "tst_adam = torch.optim.Adam([tensor([1,2,3])], lr=1e-2, betas=(0.9, 0.99))\n", "tst_adam = OptimWrapper(opt=tst_adam)\n", "\n", "tst_hypers = {'lr': 0.01, 'mom': 0.9, 'sqr_mom': 0.99, 'eps': 1e-08, 'wd': 0, 'amsgrad': False, \n", " 'maximize':False}\n", "\n", "if version.parse(torch.version.__version__)>version.parse('1.12.0'):\n", " tst_hypers = merge((tst_hypers,dict(foreach=None)))\n", "\n", "test_eq(tst_adam.hypers, [tst_hypers])\n", "tst_adam.set_hyper('mom', 0.95)\n", "test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.99))\n", "tst_adam.set_hyper('sqr_mom', 0.9)\n", "test_eq(tst_adam.opt.param_groups[0]['betas'], (0.95, 0.9))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def _mock_train(m, x, y, opt):\n", " m.train()\n", " for i in range(0, 100, 25):\n", " z = m(x[i:i+25])\n", " loss = F.mse_loss(z, y[i:i+25])\n", " loss.backward()\n", " opt.step()\n", " opt.zero_grad()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m = nn.Linear(4,5)\n", "x = torch.randn(100, 3, 4)\n", "y = torch.randn(100, 3, 5)\n", "try:\n", " torch.save(m.state_dict(), 'tmp.pth')\n", " wgt,bias = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt1 = OptimWrapper(m.parameters(), torch.optim.AdamW, betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2)\n", " _mock_train(m, x.clone(), y.clone(), opt1)\n", " wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt2 = Adam(m.parameters(), 1e-3, wd=1e-2)\n", " _mock_train(m, x.clone(), y.clone(), opt2)\n", " wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()\n", " \n", " test_close(wgt1,wgt2,eps=1e-3)\n", " test_close(bias1,bias2,eps=1e-3)\n", "finally: os.remove('tmp.pth')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#hide\n", "m = nn.Linear(4,5)\n", "x = torch.randn(100, 3, 4)\n", "y = torch.randn(100, 3, 5)\n", "try:\n", " torch.save(m.state_dict(), 'tmp.pth')\n", " wgt,bias = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt1 = torch.optim.AdamW(m.parameters(), betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2)\n", " opt1 = OptimWrapper(opt=opt1)\n", " _mock_train(m, x.clone(), y.clone(), opt1)\n", " wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt2 = Adam(m.parameters(), 1e-3, wd=1e-2)\n", " _mock_train(m, x.clone(), y.clone(), opt2)\n", " wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()\n", " \n", " test_close(wgt1,wgt2,eps=1e-3)\n", " test_close(bias1,bias2,eps=1e-3)\n", "finally: os.remove('tmp.pth')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m = nn.Linear(4,5)\n", "x = torch.randn(100, 3, 4)\n", "y = torch.randn(100, 3, 5)\n", "try:\n", " torch.save(m.state_dict(), 'tmp.pth')\n", " wgt,bias = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt1 = OptimWrapper(m.parameters(), torch.optim.Adam, betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2)\n", " _mock_train(m, x.clone(), y.clone(), opt1)\n", " wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt2 = Adam(m.parameters(), 1e-3, wd=1e-2, decouple_wd=False)\n", " _mock_train(m, x.clone(), y.clone(), opt2)\n", " wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()\n", " \n", " test_close(wgt1,wgt2,eps=1e-3)\n", " test_close(bias1,bias2,eps=1e-3)\n", "finally: os.remove('tmp.pth')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#hide\n", "m = nn.Linear(4,5)\n", "x = torch.randn(100, 3, 4)\n", "y = torch.randn(100, 3, 5)\n", "try:\n", " torch.save(m.state_dict(), 'tmp.pth')\n", " wgt,bias = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt1 = torch.optim.Adam(m.parameters(), betas=(0.9, 0.99), eps=1e-5, weight_decay=1e-2)\n", " opt1 = OptimWrapper(opt=opt1)\n", " _mock_train(m, x.clone(), y.clone(), opt1)\n", " wgt1,bias1 = m.weight.data.clone(),m.bias.data.clone()\n", "\n", " m.load_state_dict(torch.load('tmp.pth'))\n", " opt2 = Adam(m.parameters(), 1e-3, wd=1e-2, decouple_wd=False)\n", " _mock_train(m, x.clone(), y.clone(), opt2)\n", " wgt2,bias2 = m.weight.data.clone(),m.bias.data.clone()\n", " \n", " test_close(wgt1,wgt2,eps=1e-3)\n", " test_close(bias1,bias2,eps=1e-3)\n", "finally: os.remove('tmp.pth')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use an existing PyTorch optimizer, you can define an optimizer function like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt_func = partial(OptimWrapper, opt=torch.optim.SGD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or if you already have an existing one, pass in only `opt`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "opt = torch.optim.SGD([tensor([1,2,3])], lr=1e-2)\n", "opt_func = OptimWrapper(opt=opt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Export -" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converted 00_torch_core.ipynb.\n", "Converted 01_layers.ipynb.\n", "Converted 01a_losses.ipynb.\n", "Converted 02_data.load.ipynb.\n", "Converted 03_data.core.ipynb.\n", "Converted 04_data.external.ipynb.\n", "Converted 05_data.transforms.ipynb.\n", "Converted 06_data.block.ipynb.\n", "Converted 07_vision.core.ipynb.\n", "Converted 08_vision.data.ipynb.\n", "Converted 09_vision.augment.ipynb.\n", "Converted 09b_vision.utils.ipynb.\n", "Converted 09c_vision.widgets.ipynb.\n", "Converted 10_tutorial.pets.ipynb.\n", "Converted 10b_tutorial.albumentations.ipynb.\n", "Converted 11_vision.models.xresnet.ipynb.\n", "Converted 12_optimizer.ipynb.\n", "Converted 13_callback.core.ipynb.\n", "Converted 13a_learner.ipynb.\n", "Converted 13b_metrics.ipynb.\n", "Converted 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migrating_ignite.ipynb.\n", "Converted migrating_lightning.ipynb.\n", "Converted migrating_pytorch.ipynb.\n", "Converted migrating_pytorch_verbose.ipynb.\n", "Converted ulmfit.ipynb.\n", "Converted index.ipynb.\n", "Converted quick_start.ipynb.\n", "Converted tutorial.ipynb.\n" ] } ], "source": [ "#\|hide\n", "from nbdev.export import *\n", "notebook2script()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "split_at_heading": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
ContextLab/quail	docs/tutorial/naturalistic-analyses.ipynb	2	228065	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing naturalistic stimuli\n", "\n", "In traditional list-learning free recall experiments, remembering is often cast as a binary operation: either an item is recalled or it isn't. This allows for a straight forward matching between the presented and recalled stimuli. However, characterizing and evaluating memory in more realistic contexts (e.g., telling a story to a friend about a recent vacation) is much more nuanced. Real-world recall is continuous, rather than binary. Further, the specific words used to describe an experience may vary considerably across participants. To handle this new data regime, we extended classic methods developed for free-recall list-learning experiments to accomodate naturalistic stimuli. Specifically, we provide a more flexible 'matching function', which quantifies the similarity between stimuli and verbal responses in a continuous manner.\n", "\n", "In the tutorial below, we will describe our new analysis approach and demonstrate how to perform the analyses using `quail`. To get started, let's load in the example data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import quail\n", "import numpy as np\n", "import seaborn as sns\n", "from scipy.spatial.distance import cdist\n", "\n", "%matplotlib inline\n", "egg = quail.load_example_data(dataset='naturalistic')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example data used in this tutorial is based on an open dataset from Chen et al., 2017, in which 17 participants viewed and then verbally recounted an episode of the BBC series _Sherlock_. We fit a topic model to hand-annotated text descriptions of the episode and used the model to transform the video annotations and the recall transcriptions for each subject. We then used a Hidden Markov Model to segment the video and recall models into an (optimal) number of events. The result was a matrix of topic vectors representing the \"events\" in the video and list of matrices of topic vectors representing participant's recall \"events\". We created an `egg` from these vector representations of the stimulus and verbal recall, where the topic vectors were passed to quail as a stimulus features. Let's take a closer look at the egg:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of subjects: 17\n", "Number of lists per subject: 1\n", "Number of words per list: 34\n", "Date created: Wed Aug 15 11:35:35 2018\n", "Meta data: {}\n" ] } ], "source": [ "egg.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the egg's `pres` field consists of 34 stimulus events (the number of video segments determined by our HMM). Each stimulus event is represented by a dictionary containing the label of the video segment (`item`) and a topic vector representing that event (`topics`)." ] }, { "cell_type": "code", "execution_count": 3, "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></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>24</th>\n", " <th>25</th>\n", " <th>26</th>\n", " <th>27</th>\n", " <th>28</th>\n", " <th>29</th>\n", " <th>30</th>\n", " <th>31</th>\n", " 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24 25 26 27 28 \n", "\n", " 29 30 31 32 33 \n", "Subject List \n", "0 0 29 30 31 32 33 \n", "1 0 29 30 31 32 33 \n", "2 0 29 30 31 32 33 \n", "3 0 29 30 31 32 33 \n", "4 0 29 30 31 32 33 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The label of each stimulus event...\n", "egg.get_pres_items().head()" ] }, { "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></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " 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1.88632477...</td>\n", " <td>{'topics': [0.0001067916639972405, 0.000106791...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <th>0</th>\n", " <td>{'topics': [1.3095040225638681e-05, 1.30950402...</td>\n", " <td>{'topics': [1.1307998068474283e-05, 1.13079980...</td>\n", " <td>{'topics': [9.693039650300355e-06, 9.693039650...</td>\n", " <td>{'topics': [9.913957255604304e-06, 9.913957255...</td>\n", " <td>{'topics': [1.0626906175571998e-05, 1.06269061...</td>\n", " <td>{'topics': [1.0663216195539594e-05, 1.06632161...</td>\n", " <td>{'topics': [1.0532922580253435e-05, 1.05329225...</td>\n", " <td>{'topics': [1.022682519393662e-05, 1.022682519...</td>\n", " <td>{'topics': [9.815446687603108e-06, 9.815446687...</td>\n", " <td>{'topics': [9.707331800972813e-06, 9.707331800...</td>\n", " <td>...</td>\n", " <td>{'topics': [1.1240052636101565e-05, 1.12400526...</td>\n", " <td>{'topics': [1.2320787734091317e-05, 1.23207877...</td>\n", " <td>{'topics': [1.224700269751644e-05, 1.224700269...</td>\n", " <td>{'topics': [1.1916331700340415e-05, 1.19163317...</td>\n", " <td>{'topics': [1.1204077693770254e-05, 1.12040776...</td>\n", " <td>{'topics': [1.1367721441462295e-05, 1.13677214...</td>\n", " <td>{'topics': [1.0987763886778575e-05, 1.09877638...</td>\n", " <td>{'topics': [1.0659164036611576e-05, 1.06591640...</td>\n", " <td>{'topics': [1.8863247709037007e-05, 1.88632477...</td>\n", " <td>{'topics': [0.0001067916639972405, 0.000106791...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <th>0</th>\n", " <td>{'topics': [1.3095040225638681e-05, 1.30950402...</td>\n", " <td>{'topics': [1.1307998068474283e-05, 1.13079980...</td>\n", " <td>{'topics': [9.693039650300355e-06, 9.693039650...</td>\n", " <td>{'topics': [9.913957255604304e-06, 9.913957255...</td>\n", " <td>{'topics': [1.0626906175571998e-05, 1.06269061...</td>\n", " <td>{'topics': [1.0663216195539594e-05, 1.06632161...</td>\n", " <td>{'topics': [1.0532922580253435e-05, 1.05329225...</td>\n", " <td>{'topics': [1.022682519393662e-05, 1.022682519...</td>\n", " <td>{'topics': [9.815446687603108e-06, 9.815446687...</td>\n", " <td>{'topics': [9.707331800972813e-06, 9.707331800...</td>\n", " <td>...</td>\n", " <td>{'topics': [1.1240052636101565e-05, 1.12400526...</td>\n", " <td>{'topics': [1.2320787734091317e-05, 1.23207877...</td>\n", " <td>{'topics': [1.224700269751644e-05, 1.224700269...</td>\n", " <td>{'topics': [1.1916331700340415e-05, 1.19163317...</td>\n", " <td>{'topics': [1.1204077693770254e-05, 1.12040776...</td>\n", " <td>{'topics': [1.1367721441462295e-05, 1.13677214...</td>\n", " <td>{'topics': [1.0987763886778575e-05, 1.09877638...</td>\n", " <td>{'topics': [1.0659164036611576e-05, 1.06591640...</td>\n", " <td>{'topics': [1.8863247709037007e-05, 1.88632477...</td>\n", " <td>{'topics': [0.0001067916639972405, 0.000106791...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <th>0</th>\n", " <td>{'topics': [1.3095040225638681e-05, 1.30950402...</td>\n", " <td>{'topics': [1.1307998068474283e-05, 1.13079980...</td>\n", " <td>{'topics': [9.693039650300355e-06, 9.693039650...</td>\n", " <td>{'topics': [9.913957255604304e-06, 9.913957255...</td>\n", " <td>{'topics': [1.0626906175571998e-05, 1.06269061...</td>\n", " <td>{'topics': [1.0663216195539594e-05, 1.06632161...</td>\n", " <td>{'topics': [1.0532922580253435e-05, 1.05329225...</td>\n", " <td>{'topics': [1.022682519393662e-05, 1.022682519...</td>\n", " <td>{'topics': [9.815446687603108e-06, 9.815446687...</td>\n", " <td>{'topics': [9.707331800972813e-06, 9.707331800...</td>\n", " <td>...</td>\n", " <td>{'topics': [1.1240052636101565e-05, 1.12400526...</td>\n", " <td>{'topics': [1.2320787734091317e-05, 1.23207877...</td>\n", " <td>{'topics': [1.224700269751644e-05, 1.224700269...</td>\n", " <td>{'topics': [1.1916331700340415e-05, 1.19163317...</td>\n", " <td>{'topics': [1.1204077693770254e-05, 1.12040776...</td>\n", " <td>{'topics': [1.1367721441462295e-05, 1.13677214...</td>\n", " <td>{'topics': [1.0987763886778575e-05, 1.09877638...</td>\n", " <td>{'topics': [1.0659164036611576e-05, 1.06591640...</td>\n", " <td>{'topics': [1.8863247709037007e-05, 1.88632477...</td>\n", " <td>{'topics': [0.0001067916639972405, 0.000106791...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>0</th>\n", " <td>{'topics': [1.3095040225638681e-05, 1.30950402...</td>\n", " <td>{'topics': [1.1307998068474283e-05, 1.13079980...</td>\n", " <td>{'topics': [9.693039650300355e-06, 9.693039650...</td>\n", " <td>{'topics': [9.913957255604304e-06, 9.913957255...</td>\n", " <td>{'topics': [1.0626906175571998e-05, 1.06269061...</td>\n", " <td>{'topics': [1.0663216195539594e-05, 1.06632161...</td>\n", " <td>{'topics': [1.0532922580253435e-05, 1.05329225...</td>\n", " <td>{'topics': [1.022682519393662e-05, 1.022682519...</td>\n", " <td>{'topics': [9.815446687603108e-06, 9.815446687...</td>\n", " <td>{'topics': [9.707331800972813e-06, 9.707331800...</td>\n", " <td>...</td>\n", " <td>{'topics': [1.1240052636101565e-05, 1.12400526...</td>\n", " <td>{'topics': [1.2320787734091317e-05, 1.23207877...</td>\n", " <td>{'topics': [1.224700269751644e-05, 1.224700269...</td>\n", " <td>{'topics': [1.1916331700340415e-05, 1.19163317...</td>\n", " <td>{'topics': [1.1204077693770254e-05, 1.12040776...</td>\n", " <td>{'topics': [1.1367721441462295e-05, 1.13677214...</td>\n", " <td>{'topics': [1.0987763886778575e-05, 1.09877638...</td>\n", " <td>{'topics': [1.0659164036611576e-05, 1.06591640...</td>\n", " <td>{'topics': [1.8863247709037007e-05, 1.88632477...</td>\n", " <td>{'topics': [0.0001067916639972405, 0.000106791...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " 0 \\\n", "Subject List \n", "0 0 {'topics': [1.3095040225638681e-05, 1.30950402... \n", "1 0 {'topics': [1.3095040225638681e-05, 1.30950402... \n", "2 0 {'topics': [1.3095040225638681e-05, 1.30950402... \n", "3 0 {'topics': [1.3095040225638681e-05, 1.30950402... \n", "4 0 {'topics': [1.3095040225638681e-05, 1.30950402... \n", "\n", " 1 \\\n", "Subject List \n", "0 0 {'topics': [1.1307998068474283e-05, 1.13079980... \n", "1 0 {'topics': [1.1307998068474283e-05, 1.13079980... \n", "2 0 {'topics': [1.1307998068474283e-05, 1.13079980... \n", "3 0 {'topics': [1.1307998068474283e-05, 1.13079980... \n", "4 0 {'topics': [1.1307998068474283e-05, 1.13079980... \n", "\n", " 2 \\\n", "Subject List \n", "0 0 {'topics': [9.693039650300355e-06, 9.693039650... \n", "1 0 {'topics': [9.693039650300355e-06, 9.693039650... \n", "2 0 {'topics': [9.693039650300355e-06, 9.693039650... \n", "3 0 {'topics': [9.693039650300355e-06, 9.693039650... \n", "4 0 {'topics': [9.693039650300355e-06, 9.693039650... \n", "\n", " 3 \\\n", "Subject List \n", "0 0 {'topics': [9.913957255604304e-06, 9.913957255... \n", "1 0 {'topics': [9.913957255604304e-06, 9.913957255... \n", "2 0 {'topics': [9.913957255604304e-06, 9.913957255... \n", "3 0 {'topics': [9.913957255604304e-06, 9.913957255... \n", "4 0 {'topics': [9.913957255604304e-06, 9.913957255... \n", "\n", " 4 \\\n", "Subject List \n", "0 0 {'topics': [1.0626906175571998e-05, 1.06269061... \n", "1 0 {'topics': [1.0626906175571998e-05, 1.06269061... \n", "2 0 {'topics': [1.0626906175571998e-05, 1.06269061... \n", "3 0 {'topics': [1.0626906175571998e-05, 1.06269061... \n", "4 0 {'topics': [1.0626906175571998e-05, 1.06269061... \n", "\n", " 5 \\\n", "Subject List \n", "0 0 {'topics': [1.0663216195539594e-05, 1.06632161... \n", "1 0 {'topics': [1.0663216195539594e-05, 1.06632161... \n", "2 0 {'topics': [1.0663216195539594e-05, 1.06632161... \n", "3 0 {'topics': [1.0663216195539594e-05, 1.06632161... \n", "4 0 {'topics': [1.0663216195539594e-05, 1.06632161... \n", "\n", " 6 \\\n", "Subject List \n", "0 0 {'topics': [1.0532922580253435e-05, 1.05329225... \n", "1 0 {'topics': [1.0532922580253435e-05, 1.05329225... \n", "2 0 {'topics': [1.0532922580253435e-05, 1.05329225... \n", "3 0 {'topics': [1.0532922580253435e-05, 1.05329225... \n", "4 0 {'topics': [1.0532922580253435e-05, 1.05329225... \n", "\n", " 7 \\\n", "Subject List \n", "0 0 {'topics': [1.022682519393662e-05, 1.022682519... \n", "1 0 {'topics': [1.022682519393662e-05, 1.022682519... \n", "2 0 {'topics': [1.022682519393662e-05, 1.022682519... \n", "3 0 {'topics': [1.022682519393662e-05, 1.022682519... \n", "4 0 {'topics': [1.022682519393662e-05, 1.022682519... \n", "\n", " 8 \\\n", "Subject List \n", "0 0 {'topics': [9.815446687603108e-06, 9.815446687... \n", "1 0 {'topics': [9.815446687603108e-06, 9.815446687... \n", "2 0 {'topics': [9.815446687603108e-06, 9.815446687... \n", "3 0 {'topics': [9.815446687603108e-06, 9.815446687... \n", "4 0 {'topics': [9.815446687603108e-06, 9.815446687... \n", "\n", " 9 \\\n", "Subject List \n", "0 0 {'topics': [9.707331800972813e-06, 9.707331800... \n", "1 0 {'topics': [9.707331800972813e-06, 9.707331800... \n", "2 0 {'topics': [9.707331800972813e-06, 9.707331800... \n", "3 0 {'topics': [9.707331800972813e-06, 9.707331800... \n", "4 0 {'topics': [9.707331800972813e-06, 9.707331800... \n", "\n", " ... \\\n", "Subject List ... \n", "0 0 ... \n", "1 0 ... \n", "2 0 ... \n", "3 0 ... \n", "4 0 ... \n", "\n", " 24 \\\n", "Subject List \n", "0 0 {'topics': [1.1240052636101565e-05, 1.12400526... \n", "1 0 {'topics': [1.1240052636101565e-05, 1.12400526... \n", "2 0 {'topics': [1.1240052636101565e-05, 1.12400526... \n", "3 0 {'topics': [1.1240052636101565e-05, 1.12400526... \n", "4 0 {'topics': [1.1240052636101565e-05, 1.12400526... \n", "\n", " 25 \\\n", "Subject List \n", "0 0 {'topics': [1.2320787734091317e-05, 1.23207877... \n", "1 0 {'topics': [1.2320787734091317e-05, 1.23207877... \n", "2 0 {'topics': [1.2320787734091317e-05, 1.23207877... \n", "3 0 {'topics': [1.2320787734091317e-05, 1.23207877... \n", "4 0 {'topics': [1.2320787734091317e-05, 1.23207877... \n", "\n", " 26 \\\n", "Subject List \n", "0 0 {'topics': [1.224700269751644e-05, 1.224700269... \n", "1 0 {'topics': [1.224700269751644e-05, 1.224700269... \n", "2 0 {'topics': [1.224700269751644e-05, 1.224700269... \n", "3 0 {'topics': [1.224700269751644e-05, 1.224700269... \n", "4 0 {'topics': [1.224700269751644e-05, 1.224700269... \n", "\n", " 27 \\\n", "Subject List \n", "0 0 {'topics': [1.1916331700340415e-05, 1.19163317... \n", "1 0 {'topics': [1.1916331700340415e-05, 1.19163317... \n", "2 0 {'topics': [1.1916331700340415e-05, 1.19163317... \n", "3 0 {'topics': [1.1916331700340415e-05, 1.19163317... \n", "4 0 {'topics': [1.1916331700340415e-05, 1.19163317... \n", "\n", " 28 \\\n", "Subject List \n", "0 0 {'topics': [1.1204077693770254e-05, 1.12040776... \n", "1 0 {'topics': [1.1204077693770254e-05, 1.12040776... \n", "2 0 {'topics': [1.1204077693770254e-05, 1.12040776... \n", "3 0 {'topics': [1.1204077693770254e-05, 1.12040776... \n", "4 0 {'topics': [1.1204077693770254e-05, 1.12040776... \n", "\n", " 29 \\\n", "Subject List \n", "0 0 {'topics': [1.1367721441462295e-05, 1.13677214... \n", "1 0 {'topics': [1.1367721441462295e-05, 1.13677214... \n", "2 0 {'topics': [1.1367721441462295e-05, 1.13677214... \n", "3 0 {'topics': [1.1367721441462295e-05, 1.13677214... \n", "4 0 {'topics': [1.1367721441462295e-05, 1.13677214... \n", "\n", " 30 \\\n", "Subject List \n", "0 0 {'topics': [1.0987763886778575e-05, 1.09877638... \n", "1 0 {'topics': [1.0987763886778575e-05, 1.09877638... \n", "2 0 {'topics': [1.0987763886778575e-05, 1.09877638... \n", "3 0 {'topics': [1.0987763886778575e-05, 1.09877638... \n", "4 0 {'topics': [1.0987763886778575e-05, 1.09877638... \n", "\n", " 31 \\\n", "Subject List \n", "0 0 {'topics': [1.0659164036611576e-05, 1.06591640... \n", "1 0 {'topics': [1.0659164036611576e-05, 1.06591640... \n", "2 0 {'topics': [1.0659164036611576e-05, 1.06591640... \n", "3 0 {'topics': [1.0659164036611576e-05, 1.06591640... \n", "4 0 {'topics': [1.0659164036611576e-05, 1.06591640... \n", "\n", " 32 \\\n", "Subject List \n", "0 0 {'topics': [1.8863247709037007e-05, 1.88632477... \n", "1 0 {'topics': [1.8863247709037007e-05, 1.88632477... \n", "2 0 {'topics': [1.8863247709037007e-05, 1.88632477... \n", "3 0 {'topics': [1.8863247709037007e-05, 1.88632477... \n", "4 0 {'topics': [1.8863247709037007e-05, 1.88632477... \n", "\n", " 33 \n", "Subject List \n", "0 0 {'topics': [0.0001067916639972405, 0.000106791... \n", "1 0 {'topics': [0.0001067916639972405, 0.000106791... \n", "2 0 {'topics': [0.0001067916639972405, 0.000106791... \n", "3 0 {'topics': [0.0001067916639972405, 0.000106791... \n", "4 0 {'topics': [0.0001067916639972405, 0.000106791... \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ...and their corresponding topic vectors\n", "egg.get_pres_features().head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'temporal': 0,\n", " 'topics': array([1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 6.98128585e-02,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 5.55919207e-02, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 8.73325002e-01,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05,\n", " 1.30950402e-05, 1.30950402e-05, 1.30950402e-05, 1.30950402e-05])}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a closer look at one of the dictionaries\n", "egg.get_pres_features()[0][0][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see above, the dictionary contains a `features` key, which holds a 100D topic vector representing a stimulus event and also a `temporal` key, which describes the serial position of the stimulus. The `rec` field contains the recall events for each subject, similarly represented by a label (`'item'`) and topic vectors it comprises (`'topics'`)." ] }, { "cell_type": "code", "execution_count": 6, "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></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>17</th>\n", " <th>18</th>\n", " <th>19</th>\n", " <th>20</th>\n", " <th>21</th>\n", " <th>22</th>\n", " <th>23</th>\n", " <th>24</th>\n", " <th>25</th>\n", " <th>26</th>\n", " </tr>\n", " <tr>\n", " <th>Subject</th>\n", " <th>List</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <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>0</th>\n", " <th>0</th>\n", " <td>8</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>19</td>\n", " <td>23</td>\n", " <td>26</td>\n", " <td>31</td>\n", " <td>8</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>1</th>\n", " <th>0</th>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>10</td>\n", " <td>8</td>\n", " <td>13</td>\n", " <td>17</td>\n", " <td>21</td>\n", " <td>23</td>\n", " <td>23.0</td>\n", " <td>24.0</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>2</th>\n", " <th>0</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>21</td>\n", " <td>26</td>\n", " <td>29.0</td>\n", " <td>29.0</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>3</th>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>10</td>\n", " <td>15</td>\n", " <td>26</td>\n", " <td>26</td>\n", " <td>31.0</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>4</th>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>21</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>17</td>\n", " <td>21.0</td>\n", " <td>29.0</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", " </tbody>\n", "</table>\n", "<p>5 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 ... 17 18 19 20 \\\n", "Subject List ... \n", "0 0 8 7 10 19 23 26 31 8 NaN NaN ... NaN NaN NaN NaN \n", "1 0 8 13 10 8 13 17 21 23 23.0 24.0 ... NaN NaN NaN NaN \n", "2 0 2 2 4 8 17 17 21 26 29.0 29.0 ... NaN NaN NaN NaN \n", "3 0 0 5 2 5 10 15 26 26 31.0 NaN ... NaN NaN NaN NaN \n", "4 0 1 21 2 4 4 8 17 17 21.0 29.0 ... NaN NaN NaN NaN \n", "\n", " 21 22 23 24 25 26 \n", "Subject List \n", "0 0 NaN NaN NaN NaN NaN NaN \n", "1 0 NaN NaN NaN NaN NaN NaN \n", "2 0 NaN NaN NaN NaN NaN NaN \n", "3 0 NaN NaN NaN NaN NaN NaN \n", "4 0 NaN NaN NaN NaN NaN NaN \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The temporal position of each recall event...\n", "egg.get_rec_items().head()" ] }, { "cell_type": "code", "execution_count": 7, "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></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>17</th>\n", " <th>18</th>\n", " <th>19</th>\n", " <th>20</th>\n", " <th>21</th>\n", " <th>22</th>\n", " <th>23</th>\n", " <th>24</th>\n", " <th>25</th>\n", " <th>26</th>\n", " </tr>\n", " <tr>\n", " <th>Subject</th>\n", " <th>List</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <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>0</th>\n", " <th>0</th>\n", " <td>{'topics': [0.00019430992385381985, 0.00019430...</td>\n", " <td>{'topics': [0.00017027929367231326, 0.00017027...</td>\n", " <td>{'topics': [0.00017369078885231097, 0.00017369...</td>\n", " <td>{'topics': [0.00019384831708918682, 0.00019384...</td>\n", " <td>{'topics': [0.00021172689406944874, 0.00021172...</td>\n", " <td>{'topics': [0.0001967417938833908, 0.000196741...</td>\n", " <td>{'topics': [0.00018739098716436903, 0.00018739...</td>\n", " <td>{'topics': [0.0007029745321813258, 0.000702974...</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>...</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <th>0</th>\n", " <td>{'topics': [0.0001893882261801681, 0.000189388...</td>\n", " <td>{'topics': [0.000147870701282558, 0.0001478707...</td>\n", " <td>{'topics': [0.00019023353033654396, 0.00019023...</td>\n", " <td>{'topics': [0.00018166938444643692, 0.00018166...</td>\n", " <td>{'topics': [0.0001625481095703973, 0.000162548...</td>\n", " <td>{'topics': [0.00017324045166665215, 0.00017324...</td>\n", " <td>{'topics': [0.00020984959154624308, 0.00020984...</td>\n", " <td>{'topics': [0.00021871502948704968, 0.00021871...</td>\n", " <td>{'topics': [0.00023880652209349367, 0.00023880...</td>\n", " <td>{'topics': [0.00020811361546785266, 0.00020811...</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>2</th>\n", " <th>0</th>\n", " <td>{'topics': [0.00018590937074552605, 0.00018590...</td>\n", " <td>{'topics': [0.0002086223458447991, 0.000208622...</td>\n", " <td>{'topics': [0.00014376082951536893, 0.00014376...</td>\n", " <td>{'topics': [0.00017953275349176222, 0.00017953...</td>\n", " <td>{'topics': [0.0001266373016964342, 0.000126637...</td>\n", " <td>{'topics': [0.00012300799059941801, 0.00012300...</td>\n", " <td>{'topics': [0.00012981062271781097, 0.00012981...</td>\n", " <td>{'topics': [0.00013602953297943621, 0.00013602...</td>\n", " <td>{'topics': [0.00014111281486418062, 0.00014111...</td>\n", " <td>{'topics': [0.00012592147339385905, 0.00012592...</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>3</th>\n", " <th>0</th>\n", " <td>{'topics': [0.00018228403991448364, 0.00018228...</td>\n", " <td>{'topics': [0.00018629081950116504, 0.00018629...</td>\n", " <td>{'topics': [0.0001622345358076629, 0.000162234...</td>\n", " <td>{'topics': [0.00023810735183527187, 0.00023810...</td>\n", " <td>{'topics': [0.0001552297878347715, 0.000155229...</td>\n", " <td>{'topics': [0.00016179442394432278, 0.00016179...</td>\n", " <td>{'topics': [0.00016889623308560304, 0.00016889...</td>\n", " <td>{'topics': [0.00019056297121315633, 0.00019056...</td>\n", " <td>{'topics': [0.0005565476190479086, 0.000556547...</td>\n", " <td>{}</td>\n", " <td>...</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " <td>{}</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>0</th>\n", " <td>{'topics': [0.00025446487845416085, 0.00025446...</td>\n", " <td>{'topics': [0.00027871701312132265, 0.00027871...</td>\n", " <td>{'topics': [0.00023918073796144946, 0.00023918...</td>\n", " <td>{'topics': [0.00017315740792590048, 0.00017315...</td>\n", " <td>{'topics': [0.0001356024754279286, 0.000135602...</td>\n", " <td>{'topics': [9.160956058842264e-05, 9.160956058...</td>\n", " <td>{'topics': [9.360224876174755e-05, 9.360224876...</td>\n", " <td>{'topics': [0.00013752072250850062, 0.00013752...</td>\n", " <td>{'topics': [0.0001320162835250347, 0.000132016...</td>\n", " <td>{'topics': [0.00014302384767721986, 0.00014302...</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", " </tbody>\n", "</table>\n", "<p>5 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " 0 \\\n", "Subject List \n", "0 0 {'topics': [0.00019430992385381985, 0.00019430... \n", "1 0 {'topics': [0.0001893882261801681, 0.000189388... \n", "2 0 {'topics': [0.00018590937074552605, 0.00018590... \n", "3 0 {'topics': [0.00018228403991448364, 0.00018228... \n", "4 0 {'topics': [0.00025446487845416085, 0.00025446... \n", "\n", " 1 \\\n", "Subject List \n", "0 0 {'topics': [0.00017027929367231326, 0.00017027... \n", "1 0 {'topics': [0.000147870701282558, 0.0001478707... \n", "2 0 {'topics': [0.0002086223458447991, 0.000208622... \n", "3 0 {'topics': [0.00018629081950116504, 0.00018629... \n", "4 0 {'topics': [0.00027871701312132265, 0.00027871... \n", "\n", " 2 \\\n", "Subject List \n", "0 0 {'topics': [0.00017369078885231097, 0.00017369... \n", "1 0 {'topics': [0.00019023353033654396, 0.00019023... \n", "2 0 {'topics': [0.00014376082951536893, 0.00014376... \n", "3 0 {'topics': [0.0001622345358076629, 0.000162234... \n", "4 0 {'topics': [0.00023918073796144946, 0.00023918... \n", "\n", " 3 \\\n", "Subject List \n", "0 0 {'topics': [0.00019384831708918682, 0.00019384... \n", "1 0 {'topics': [0.00018166938444643692, 0.00018166... \n", "2 0 {'topics': [0.00017953275349176222, 0.00017953... \n", "3 0 {'topics': [0.00023810735183527187, 0.00023810... \n", "4 0 {'topics': [0.00017315740792590048, 0.00017315... \n", "\n", " 4 \\\n", "Subject List \n", "0 0 {'topics': [0.00021172689406944874, 0.00021172... \n", "1 0 {'topics': [0.0001625481095703973, 0.000162548... \n", "2 0 {'topics': [0.0001266373016964342, 0.000126637... \n", "3 0 {'topics': [0.0001552297878347715, 0.000155229... \n", "4 0 {'topics': [0.0001356024754279286, 0.000135602... \n", "\n", " 5 \\\n", "Subject List \n", "0 0 {'topics': [0.0001967417938833908, 0.000196741... \n", "1 0 {'topics': [0.00017324045166665215, 0.00017324... \n", "2 0 {'topics': [0.00012300799059941801, 0.00012300... \n", "3 0 {'topics': [0.00016179442394432278, 0.00016179... \n", "4 0 {'topics': [9.160956058842264e-05, 9.160956058... \n", "\n", " 6 \\\n", "Subject List \n", "0 0 {'topics': [0.00018739098716436903, 0.00018739... \n", "1 0 {'topics': [0.00020984959154624308, 0.00020984... \n", "2 0 {'topics': [0.00012981062271781097, 0.00012981... \n", "3 0 {'topics': [0.00016889623308560304, 0.00016889... \n", "4 0 {'topics': [9.360224876174755e-05, 9.360224876... \n", "\n", " 7 \\\n", "Subject List \n", "0 0 {'topics': [0.0007029745321813258, 0.000702974... \n", "1 0 {'topics': [0.00021871502948704968, 0.00021871... \n", "2 0 {'topics': [0.00013602953297943621, 0.00013602... \n", "3 0 {'topics': [0.00019056297121315633, 0.00019056... \n", "4 0 {'topics': [0.00013752072250850062, 0.00013752... \n", "\n", " 8 \\\n", "Subject List \n", "0 0 {} \n", "1 0 {'topics': [0.00023880652209349367, 0.00023880... \n", "2 0 {'topics': [0.00014111281486418062, 0.00014111... \n", "3 0 {'topics': [0.0005565476190479086, 0.000556547... \n", "4 0 {'topics': [0.0001320162835250347, 0.000132016... \n", "\n", " 9 ... 17 18 \\\n", "Subject List ... \n", "0 0 {} ... {} {} \n", "1 0 {'topics': [0.00020811361546785266, 0.00020811... ... {} {} \n", "2 0 {'topics': [0.00012592147339385905, 0.00012592... ... {} {} \n", "3 0 {} ... {} {} \n", "4 0 {'topics': [0.00014302384767721986, 0.00014302... ... {} {} \n", "\n", " 19 20 21 22 23 24 25 26 \n", "Subject List \n", "0 0 {} {} {} {} {} {} {} {} \n", "1 0 {} {} {} {} {} {} {} {} \n", "2 0 {} {} {} {} {} {} {} {} \n", "3 0 {} {} {} {} {} {} {} {} \n", "4 0 {} {} {} {} {} {} {} {} \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ...and their corresponding topic vectors\n", "egg.get_rec_features().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Defining a matching function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As summarized above, `quail` supports the analysis of naturalistic stimuli by providing a more flexible way to match presented stimuli and recall responses. The matching function can be set using the `match` keyword argument in `egg.analyze`. There are three options: `'exact'`, `'best'`, and `'smooth'`. If `match='exact'`, the recall item must be identical to the stimulus to constitute a recall. This is the traditional approach for free recall experiments (either a subject accurately recalled the stimulus item, or did not) but it is not particularly useful with naturalistic data. For the naturalistic options, quail computes a similarity matrix comparing every recall event to every stimulus event. If `match='best'`, the recall response that is most similar to a given presented stimulus is labeled as recalled. If `match='smooth'`, a weighted-average over recall responses is computed for each presented stimulus, where the weights are derived from the similarity between the stimulus and the recall event. To illustrate this further, let's step through the analysis. First, let's create a matrix representing the presented stimulus where each row is an 'event' and each column is a topic dimension:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11681ef28>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0C8mdGpPTopXAnIhY1MC+34+Io6uU9189fDQifi3pKOC9wCJgVkS0N0nnEEna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\nfQ4AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pres_mtx = np.array([x['topics'] for x in egg.get_pres_features('topics').iloc[0, :].values])\n", "sns.heatmap(pres_mtx, vmin=0, vmax=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll also create a matrix representing the recalled events for a single subject:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1169aef28>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8rDXAnemT0X8j6RGSTrhuAHnDM92I2BQRF0TEcemY7vyIeH5EXEBycc3MrKe0cHhhGXCM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\nqI0DUdAAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rec_mtx = np.array([x['topics'] for x in egg.get_rec_features('topics').iloc[12, :].values if len(x)])\n", "sns.heatmap(rec_mtx, vmin=0, vmax=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To measure similarity between the `pres_mtx` and `rec_mtx` along this feature dimension, we can use the `cdist` function from `scipy`. In this example, we will use correlational distance to measure similarity between each presented event and each recalled event:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x116aefa58>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Zc7r7mtl709u3SvoScJekd3tf4NSWvVxxZ/csccVt6NySGePNSmhp8i2FskeTbymhJ5t8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\nRK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "match_mtx = 1 - cdist(pres_mtx, rec_mtx, 'correlation')\n", "sns.heatmap(match_mtx, vmin=0, vmax=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This matrix quantifies the match between each presented stimulus and each recalled stimulus. The light stripe along the diagonal suggests that this particular subject remembered most of the events in order, since the highest correlation values are roughly along the diagonal. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matching with `'best'`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If `match='best'`, each recall event is mapped to the single stimulus event with the most similar feature vector:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 16, 18, 19,\n", " 20, 22, 24, 26, 27, 28, 30, 32, 32, 12])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.argmax(match_mtx, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that once the data is distilled into this form, many of the classic list-learning analyses (such as probability of first recall, serial position curve, and lag-conditional response probability curve) can be performed. To do this using `quail`, simply set `match='best'`, choose a distance function (euclidean by default) and select the features that you would like to use (e.g. `features=['topics']`)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/paxtonfitzpatrick/Documents/Dartmouth/CDL/quail/quail/analysis/recmat.py:103: RuntimeWarning: Mean of empty slice\n", " return np.nanmean(res, 1)\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></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " 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<td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 ... 24 25 \\\n", "Subject List ... \n", "0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 ... 0.0 0.0 \n", "1 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 ... 1.0 0.0 \n", "2 0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 \n", "3 0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 0.0 0.0 \n", "4 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 \n", "\n", " 26 27 28 29 30 31 32 33 \n", "Subject List \n", "0 0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "1 0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n", "2 0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 \n", "3 0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", "4 0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spc = egg.analyze(analysis='spc', match='best', distance='correlation', features=['topics'])\n", "spc.get_data().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each stimulus event is assigned a binary value for each recall event – it either was matched or it was not. To plot it:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1170d5a90>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pWTuiJMfYcnUbuvkxq9USiJuh0vBiYbGh+xXk4iQWf6tYJTlOwlXWqIhlM5oi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\nyrOuGCwoxCJ6bwbwawD+HMDPMPOfEdFbiOh18cu+C8ARgP9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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spc.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matching with `'smooth'`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "if `match='smooth'`, quail computes a weighted average across all stimulus events for each recall event, where the weights are derived from similarity between the stimulus and recall." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/paxtonfitzpatrick/Documents/Dartmouth/CDL/quail/quail/analysis/recmat.py:103: RuntimeWarning: Mean of empty slice\n", " return np.nanmean(res, 1)\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 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<td>0.121584</td>\n", " <td>0.113003</td>\n", " <td>0.135094</td>\n", " <td>0.105807</td>\n", " <td>0.070675</td>\n", " <td>0.117423</td>\n", " <td>0.115852</td>\n", " <td>0.141819</td>\n", " <td>0.097215</td>\n", " <td>0.097059</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>0</th>\n", " <td>-0.022484</td>\n", " <td>0.071523</td>\n", " <td>0.022116</td>\n", " <td>0.055897</td>\n", " <td>0.094487</td>\n", " <td>0.031469</td>\n", " <td>0.026740</td>\n", " <td>0.073415</td>\n", " <td>0.085413</td>\n", " <td>0.059973</td>\n", " <td>...</td>\n", " <td>0.004742</td>\n", " <td>0.066363</td>\n", " <td>0.116368</td>\n", " <td>0.116870</td>\n", " <td>0.115584</td>\n", " <td>0.120936</td>\n", " <td>0.084752</td>\n", " <td>0.180413</td>\n", " <td>0.163628</td>\n", " <td>0.163519</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 \\\n", "Subject List \n", "0 0 0.007071 0.041853 0.009203 0.016614 0.033991 0.154249 \n", "1 0 0.001898 0.063795 -0.026356 0.029550 0.054097 0.018881 \n", "2 0 -0.027050 -0.020670 0.089779 0.038386 0.175139 0.026303 \n", "3 0 0.145299 0.046259 0.041661 0.058850 0.042346 0.165161 \n", "4 0 -0.022484 0.071523 0.022116 0.055897 0.094487 0.031469 \n", "\n", " 6 7 8 9 ... 24 \\\n", "Subject List ... \n", "0 0 0.017540 0.174531 0.292158 0.267374 ... 0.104524 \n", "1 0 0.053997 0.070017 0.227520 0.263889 ... 0.087016 \n", "2 0 0.047882 0.079458 0.154467 0.155683 ... 0.236841 \n", "3 0 0.127778 0.050394 0.093302 0.110626 ... 0.121584 \n", "4 0 0.026740 0.073415 0.085413 0.059973 ... 0.004742 \n", "\n", " 25 26 27 28 29 30 \\\n", "Subject List \n", "0 0 0.026187 0.097516 0.126987 0.103108 0.128083 0.107675 \n", "1 0 0.008271 0.043307 0.075759 0.167584 0.182543 0.118617 \n", "2 0 0.113010 0.111786 0.086030 0.103987 0.142899 0.118404 \n", "3 0 0.113003 0.135094 0.105807 0.070675 0.117423 0.115852 \n", "4 0 0.066363 0.116368 0.116870 0.115584 0.120936 0.084752 \n", "\n", " 31 32 33 \n", "Subject List \n", "0 0 0.210723 0.185127 0.184987 \n", "1 0 0.164981 0.124798 0.124643 \n", "2 0 0.111640 0.060170 0.060011 \n", "3 0 0.141819 0.097215 0.097059 \n", "4 0 0.180413 0.163628 0.163519 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spc = egg.analyze(analysis='spc', match='smooth', distance='correlation', features=['topics'])\n", "spc.data.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x116c314a8>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/Vao6DuD6fsrNML+xoETF91AV9xp2zDOieHF3vFG12Yy/I5Gu8kavq2MfcwS9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\nO3rBwbgAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spc.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Changing the distance metric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `distance` argument assigns the distance function quail will use to compute similarity between stimulus and recall events. We support any distance metric included in [scipy.spatial.distance.cdist](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html):" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x117227320>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ArxDRJ4joA/q1twH8CJTBeRrAu/V9luMCj9NC1c6CkjhEWaaruI+Gf7oM6QlQBrqvR0Y+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\nSadM9gGSLpa0RtKaV159ZZ/BRLORNyF12OxURJIH8jOzyhv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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spc = egg.analyze(analysis='spc', match='smooth', distance='cosine', features=['topics'])\n", "spc.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Selecting a subset of features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `features` argument tells quail which features to consider when computing distance. This can be a single feature passed as a string, multiple features passed as a list, or all available features (`features=None`; default). " ] } ], "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
Wahlque/Wahlque-Complete	zh-cn/introduction/05-epilogue.ipynb	1	1302	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 结语：宇宙里的自省意识" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "根据截止到2014年6月的统计数据，2009年3月升空的开普勒卫星已经帮助人们发现并确认了近千颗系外行星。这些发现让人们确认了行星在宇宙中的普遍存在。我们有理由进一步相信，宇宙如此广袤，地球上智慧生命的存在应该不是一个孤⽴而特别的事情。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然而，我们的文化对于接受生命在宇宙中普遍存在似乎还没有做好充分的准备。瓦克星计划，某种程度上讲，试图在通过一种不同，来使我们人类保持一种在宇宙里的自我反省的意识。" ] } ], "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 }	cc0-1.0
marsbroshok/tensorflow-rnn-events-prediction	GDC Meetup Paris GDELT-skflow-RNN.ipynb	1	731600	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup\n", "Import all modules. Setup pyplot color style and pandas float format.\n", "\n", "Re: If there is a error with tensorflow's skflow (aka tensorflow learn), check this link: http://stackoverflow.com/questions/37464668/tensorflow-upgrade-failed-on-google-datalab" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set up\n", "from __future__ import division\n", "import StringIO\n", "import datalab.bigquery as bq\n", "import tensorflow as tf\n", "from tensorflow.contrib import skflow, learn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pandas.tools.plotting import autocorrelation_plot\n", "from pandas.tools.plotting import scatter_matrix\n", "from sklearn import cross_validation, metrics\n", "from sklearn import preprocessing\n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.options.display.float_format = '{:.2f}'.format\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load data from bigquery\n", "Prepare and run SQL select query to the GDELT BigQuery table (for more info about GDELT dataset check here: http://gdeltproject.org/data.html#googlebigquery). \n", "\n", "GDELT daaset is quite big, we don't need all of the records and columns. So our select keep only :\n", "* SQLDATE - timestame for event's record\n", "* Actor1Geo_CountryCode - country code where event took place\n", "* QuadClass - describing event \"positivness\" (1 - material coop, 2 - verbal coop, 3 - verbal cnoflict, 4 - material conflict)\n", "* NumArticles - total sum of number of articles about every event per country per quad class per day\n", "\n", "Store query results as pandas DataFrame.\n", "\n", "Show descriptive statistics about GDELT dataframe" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Let`s make a connection to the BigQuery" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%sql --module gdelt_query\n", "SELECT Actor1Geo_CountryCode, QuadClass, SQLDATE, SUM(NumArticles) AS NumArticles\n", "FROM [gdelt_tableau.events_protests_country_sample] \n", "WHERE (Actor1Geo_CountryCode==ActionGeo_CountryCode) AND (DATE(TIMESTAMP(STRING([SQLDATE]))) >= '2000-01-01')\n", "GROUP BY Actor1Geo_CountryCode, QuadClass, SQLDATE \n", "ORDER BY Actor1Geo_CountryCode, QuadClass, SQLDATE" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Perform query and run timer\n", "start = datetime.now()\n", "gdelt_df = bq.Query(gdelt_query).to_dataframe()\n", "print('Query done in {}s\\n'.format(datetime.now()-start))" ] }, { "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>QuadClass</th>\n", " <th>SQLDATE</th>\n", " <th>NumArticles</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>111155.00</td>\n", " <td>111155.00</td>\n", " <td>111155.00</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>2.48</td>\n", " <td>20079496.01</td>\n", " <td>9797.70</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.13</td>\n", " <td>47311.33</td>\n", " <td>40398.93</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.00</td>\n", " <td>20000101.00</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.00</td>\n", " <td>20040405.00</td>\n", " <td>117.00</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2.00</td>\n", " <td>20080714.00</td>\n", " <td>595.00</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3.00</td>\n", " <td>20120701.00</td>\n", " <td>3096.00</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>4.00</td>\n", " <td>20160424.00</td>\n", " <td>896032.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " QuadClass SQLDATE NumArticles\n", "count 111155.00 111155.00 111155.00\n", "mean 2.48 20079496.01 9797.70\n", "std 1.13 47311.33 40398.93\n", "min 1.00 20000101.00 1.00\n", "25% 1.00 20040405.00 117.00\n", "50% 2.00 20080714.00 595.00\n", "75% 3.00 20120701.00 3096.00\n", "max 4.00 20160424.00 896032.00" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show descriptive stats\n", "gdelt_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# (optional) Save/load data from local pkl-file\n", "Sometime it is convients to have a saved copy of query result.\n", "\n", "Here is two helpers to save/load GDELT dataframe to local pkl-file" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Save dataframe to the file to avoid often queries\n", "gdelt_df.to_pickle('./tf_gdelt_quad_countries_2000_now.pkl')" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 111155 entries, 0 to 111154\n", "Data columns (total 4 columns):\n", "Actor1Geo_CountryCode 111155 non-null object\n", "QuadClass 111155 non-null int64\n", "SQLDATE 111155 non-null int64\n", "NumArticles 111155 non-null int64\n", "dtypes: int64(3), object(1)\n", "memory usage: 3.4+ MB\n", "None\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>QuadClass</th>\n", " <th>SQLDATE</th>\n", " <th>NumArticles</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>111155.00</td>\n", " <td>111155.00</td>\n", " <td>111155.00</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>2.48</td>\n", " <td>20079496.01</td>\n", " <td>9797.70</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.13</td>\n", " <td>47311.33</td>\n", " <td>40398.93</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.00</td>\n", " <td>20000101.00</td>\n", " <td>1.00</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.00</td>\n", " <td>20040405.00</td>\n", " <td>117.00</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2.00</td>\n", " <td>20080714.00</td>\n", " <td>595.00</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3.00</td>\n", " <td>20120701.00</td>\n", " <td>3096.00</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>4.00</td>\n", " <td>20160424.00</td>\n", " <td>896032.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " QuadClass SQLDATE NumArticles\n", "count 111155.00 111155.00 111155.00\n", "mean 2.48 20079496.01 9797.70\n", "std 1.13 47311.33 40398.93\n", "min 1.00 20000101.00 1.00\n", "25% 1.00 20040405.00 117.00\n", "50% 2.00 20080714.00 595.00\n", "75% 3.00 20120701.00 3096.00\n", "max 4.00 20160424.00 896032.00" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load dataframe from the file\n", "gdelt_df = pd.read_pickle('./tf_gdelt_quad_countries_2000_now.pkl')\n", "print gdelt_df.info()\n", "gdelt_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare dataset for analysis\n", "Here we are converting SQLDATE string with datetime stamp to datetime index.\n", "\n", "Then we are extracting only records for Egypt (country code 'EG'). This will be out dataset for further analysis and machine learning applications " ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We will create new column for datetime series with helper function\n", "def date_from_int(date_int):\n", " \"\"\"\n", " Helper function to convert date from int to datetime object.\n", " E.g. 20160125 -> 2016-01-25 datetime obj\n", " \"\"\"\n", " date_str = str(date_int)\n", " date = pd.to_datetime(date_str, format='%Y%m%d')\n", " return date\n", "\n", "\n", "# Make a new index with date\n", "gdelt_df['Date'] = gdelt_df['SQLDATE'].apply(date_from_int)\n", "gdelt_df.index = gdelt_df['Date']\n", "gdelt_df.drop(['SQLDATE'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Prepare dataset - take events for country EG\n", "def split_events(gdelt_df, measure_name = 'NumArticles', country = 'EG'):\n", " event_root_codes = gdelt_df.QuadClass.unique()\n", " event_series = [gdelt_df[(gdelt_df.QuadClass==event_code) & (gdelt_df.Actor1Geo_CountryCode==country)][[measure_name]]\n", " for event_code in event_root_codes]\n", " event_by_codes = pd.concat(event_series, axis=1).sort_index()\n", " event_by_codes.columns = map(str, event_root_codes)\n", " event_by_codes = event_by_codes.fillna(method='ffill')\n", " event_by_codes = event_by_codes.fillna(method='bfill')\n", " return event_by_codes\n", "\n", "\n", "event_by_codes = split_events(gdelt_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Analysis\n", "Before summon Machine Learning power, we need to understand our dataset.\n", "\n", "We already had a look at descriptive statistics, and now it seems interesting to do some basic visualization. \n", "\n", "Let's plot articles number per month per quad class over the time: " ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ERERkY3Fao6lgoElE/cdAk4iIiIhsHBlNBWRiRpN7aRJRPzDQJCIiIiIbS49m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\nY489xsMPP3x9E4sQRslnpKNHjwL4P/mOV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1c940ddc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot NumArticle to have a look at the raw data\n", "_ = event_by_codes.resample(rule='1M').mean().plot(figsize=(15, 5), fontsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several very visible charactersrics of our dataset:\n", "1. There are much more data after 2010, then before. In order of magnitude.\n", "2. Number of articels for each quad behave in a similar way: in 2011 there is a peak for \"Quad Class 1\" and there are peaks fol all other classes. Seems like we can try to see if there is a pattern in our dataset.\n", "\n", "Really big difference in the numbers over the time can make further analysis difficult. \n", "\n", "We need \"normalized\" data.\n", "\n", "One way to do data normalization is to apply a logarithme function.\n", "\n", "Log function has 2 advantage:\n", "1. It smooths peak values, and highlights small values\n", "2. It \"centralizes\" data around zero\n", "\n", "Then it is importnat to remeber, that we have events with vector in time. We should exploit date-time information.\n", "\n", "Let's use time vector to transform our data from raw Number of Articles to the relative Growth Rate. And then normalize Growth Rate with Log function.\n", "\n", "For example, in pseudocode our new measure Growth Rate will be: \n", "\n", "`Growth_Rate = log( Num_Articles(Day-1) / Number_Arcticles(Day) )`" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Let's use time component and transform data with log\n", "def log_scale_dataframe(df):\n", " new_df = pd.DataFrame()\n", " for col in df.columns:\n", " new_df[col] = np.log(df[col] / df[col].shift() )\n", " return new_df\n", "\n", "event_by_codes_log = log_scale_dataframe(event_by_codes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a plot of our new dataset.\n", "\n", "It is clear that data is centered around zero and have a comparable values.\n", "\n", "Machine learning algorithms will work much better on this dataset." ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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plPS3L13Af/7lcI32qLYUE5cXz5sYPDkbeB1+57K64tK5RkYTDAyRuJwfZgMC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zbXr8b2Xt+ZsJeUtGUKFuhdfT7i+WfWOJOaNTS3aoqZ+rPRkNJK7f1WWPV+na9Ry0qFb9lHT4Qz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1c96b24b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Growth Rate to have a look at the LOG-normalized data\n", "_ = event_by_codes_log.resample(rule='1M').mean().plot(figsize=(15, 5), fontsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is quite clear now that we have some signal in our data.\n", "\n", "But let's try one more useful and fast visual test to be sure that we are on the right way.\n", "\n", "Scatter Matrix allows us to see if there is a correlation between Quad Classes. See more details here: http://www.originlab.com/doc/Tutorials/ScatterMatrix\n", "\n", "Scatter plot of perfectly correlated variables will be close to the line, and for the opposite - shapeless cloud.\n", "\n", "Our scatter plot matrix shows that all quad classes are correlated and it's a good sign." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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l8BD7JQWPpBrKmkQ3kXnU7kBuYTKCpLn544vIGVWdcbnJgMWywcPi61AGfcqs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\nnQSD4f0gnc7UR76oduEvgva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ASEAYwpuBZSs2fLIVUwsM/TSnl+W86WUksYEchmnOx20f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XVty4EjwShKWxyA7ytHbRh+2Ek8EOe5FtPN4Wu5VY3l0kvD2PiTcjahX3+fethMNeQprCm62Q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gjcHGbUNjDIFfO2x3kJRB6vLDeioQmBUYS699ysDEOjECLN/aIgU4J5l4wlA9D2gWGuUAoG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\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nHHdiOoklqmj8ICCsBuzUAgZnXUyQ8sVek1YtYpAavj28oNGsorXim/3mzHGV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gtpje8KF2LZ7xnzSEgJN+7AF38g9D0iWdv8rPHTwdua8lxuYf+29ukvYilv5pJDD0r4yJpNJYvO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Tl16B/vyfAGfvg/i+H4H8vh8xf/7U72TcMpplXLZGNGeqtgRgdX4lIppeVVvCXijCaTGeEc06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mG/p8m05NYosgx3cs2kdgwM8ZBMxP//bAJsZGKuPciGBnCxWfdtDLBndZQBv2u2z3dBCbDB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\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aPWIkAWzZDhw+CK0UhGScGyT8a/YpZ1DRvbtS7p3z6und1Y+ZXbZxJmOVH+deNKWtxkvG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\nLfb0uJ14TQ6gqFs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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1ca8a7f3d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " _ = scatter_matrix(event_by_codes_log, figsize=(20, 20), diagonal='kde', alpha=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Recurrent Neural Network in TensorFlow\n", "From our everyday life's experience we know that if there is a buzz in news, it has a tail at leat for several days. \n", "\n", "So let's see if information about the past Growth Rate can help predict the future Growth Rate.\n", "\n", "Or, more precisely, let's try to\n", "\n", " predict Material Conflict Growth Rate using information about all 4 quads' Growth Rate from the past.\n", "\n", "\n", "\n", "Reccurent Neural Network shows good perfomance in the tasks where prediction of new value depends in the previous values (more http://colah.github.io/posts/2015-08-Understanding-LSTMs/).\n", "\n", "So let's use TensorFlow build-in Datalab to train RNN regression model to predict Material Conflict Growth Rate using information from the last 7 days. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression Model" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (5957, 4), y shape: (5957,)\n" ] } ], "source": [ "# RNN regression\n", "# Prepare X (all quads classes' Growth Rate) and y (Quad Class '4' - Material Conflict real Growth Rate)\n", "X = event_by_codes_log.values\n", "y = event_by_codes_log['4'].shift(periods=-1).values\n", "\n", "# There no info from the past for the very first day.\n", "# Let's drop out first day.\n", "X = X[1:-1]\n", "y = y[1:-1]\n", "\n", "# Verify that shape of both sets are compatible\n", "print(\"X shape: {}, y shape: {}\".format(X.shape, y.shape)) \n", "\n", "# Split dataset to Train and Test partion in a ratio 80/20. \n", "# All model training will be done on Train part, and only evalution will be done on Test part. \n", "test_split = int(0.2 * len(X))\n", "X_train = X[:test_split]\n", "y_train = y[:test_split]\n", "X_test = X[test_split:]\n", "y_test = y[test_split:]" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #100, epoch #2, avg. train loss: 0.53959, avg. val loss: 0.50400\n", "Step #200, epoch #5, avg. train loss: 0.51154, avg. val loss: 0.49408\n", "Step #300, epoch #7, avg. train loss: 0.49490, avg. val loss: 0.48978\n", "Step #400, epoch #10, avg. train loss: 0.48722, avg. val loss: 0.49210\n", "Step #500, epoch #13, avg. train loss: 0.47957, avg. val loss: 0.49185\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stopping. Best step:\n", " step 308 with loss 0.484341025352\n" ] } ], "source": [ "# Helper function to prepare input data for RNN\n", "def rnn_input_fn(x):\n", " return tf.split(1, 4, x) # 4 - number of features in X vector\n", "\n", "# Validation monitor to do early stopping (https://en.wikipedia.org/wiki/Early_stopping) \n", "val_monitor = skflow.monitors.ValidationMonitor(X_test, y_test,\n", " early_stopping_rounds=200,\n", " n_classes=0,\n", " print_steps=100)\n", "\n", "# RNN Tensorflow regression model\n", "regressor = tf.contrib.learn.TensorFlowRNNRegressor(rnn_size=20, # How many nodes in a layer (aka cells)\n", " cell_type=\"lstm\", # Cell type (lstm has better 'memory', https://en.wikipedia.org/wiki/Long_short-term_memory)\n", " input_op_fn=rnn_input_fn, # Input data transform function\n", " num_layers=7, # How many layers RNN will have (think '# of days in the past RNN will look into')\n", " steps=1000) # Max learning steps\n", "# Run model training\n", "regressor.fit(X_train, y_train, monitor=val_monitor)\n", "\n", "# Predict Growth Rate values on Test part\n", "y_pred = regressor.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE: 0.980281257094\n" ] }, { "data": { "image/png": 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0Ujo0IcdTap468NZbNdEllVqV3KjEsMSUBX10MC+clSbEZHURM7G6iSkLunRG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\n+ieliPU4ZnodrESxtrUlv6dmWdP6Ng9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vjBFO6HzwtBX84/F2PIlI0eTCk7ob8XvsPN8X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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1c6eaa42d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot prediction results and MSE between real Growth Rate of Test part and predictied values\n", "_ = plt.plot(y_test[0:50], label='True values')\n", "_ = plt.plot(y_pred[0:50], label='Predicted values')\n", "plt.legend()\n", "print(\"MSE: {}\".format(metrics.mean_squared_error(y_test, y_pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification Model\n", "Let's try to make a more general prediction. instead of trying to predict actual Growth Rate value, let's predict just Growth direction: \n", "\n", "### Will Growth Rate increase or decrease? \n", "\n", "To do that, we convert Growth Rate to 2 classes: 1 - INCREASE, 0 - DECREASE or not changing.\n", "\n", "All other steps will be the same as for Regression Model\n" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (5957, 4), y shape: (5957,)\n" ] } ], "source": [ "# RNN Classification\n", "# Helper function to convert Growth Rate value into class\n", "def value_to_class(x):\n", " if x<=0:\n", " return 0.0\n", " else:\n", " return 1.0\n", "\n", "# Prepare X (all quads classes' Growth Rate) and y (Quad Class '4' - Material Conflict Increase/Decrease)\n", "X = event_by_codes_log.values\n", "y = event_by_codes_log['4'].shift(periods=-1)\n", "y = y.apply(value_to_class).values\n", "\n", "# There no info from the past for the very first day.\n", "# Let's drop out first day.\n", "X = X[1:-1]\n", "y = y[1:-1]\n", "\n", "# Verify that shape of both sets are compatible\n", "print(\"X shape: {}, y shape: {}\".format(X.shape, y.shape)) \n", "\n", "# Split dataset to Train and Test partion in a ratio 80/20. \n", "# All model training will be done on Train part, and only evalution will be done on Test part.\n", "test_split = int(0.2 * len(X))\n", "X_train = X[:test_split]\n", "y_train = y[:test_split]\n", "X_test = X[test_split:]\n", "y_test = y[test_split:]" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #100, epoch #2, avg. train loss: 0.66180, avg. val loss: 0.65977\n", "Step #200, epoch #5, avg. train loss: 0.63816, avg. val loss: 0.65059\n", "Step #300, epoch #7, avg. train loss: 0.63640, avg. val loss: 0.64793\n", "Step #400, epoch #10, avg. train loss: 0.63458, avg. val loss: 0.64939\n", "Step #500, epoch #13, avg. train loss: 0.63656, avg. val loss: 0.64875\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stopping. Best step:\n", " step 313 with loss 0.64524102211\n" ] } ], "source": [ "# Helper function to prepare input data for RNN\n", "def rnn_input_fn(x):\n", " return tf.split(1, 4, x) # 4 - number of features in X vector\n", "\n", "# Validation monitor to do early stopping (https://en.wikipedia.org/wiki/Early_stopping)\n", "val_monitor = skflow.monitors.ValidationMonitor(X_test, y_test,\n", " early_stopping_rounds=200,\n", " n_classes=2,\n", " print_steps=100)\n", "# RNN Tensorflow classifier model\n", "classifier = tf.contrib.learn.TensorFlowRNNClassifier(\n", " rnn_size=20,\n", " cell_type=\"lstm\",\n", " n_classes=2,\n", " input_op_fn=rnn_input_fn,\n", " num_layers=7,\n", " steps=1000)\n", "\n", "# Run model training\n", "classifier.fit(X_train, y_train, monitor=val_monitor, logdir='./rnn_clf/')\n", "\n", "# Predict Growth Rate class on Test part\n", "y_pred = classifier.predict(X_test)\n", "accuracy_score = metrics.accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Classification report \n", " precision recall f1-score support\n", "\n", " 0.0 0.63 0.66 0.65 2497\n", " 1.0 0.61 0.57 0.59 2269\n", "\n", "avg / total 0.62 0.62 0.62 4766\n", "\n", "Accuracy on test set: 62.17%\n" ] } ], "source": [ "# Measure RNN Classifier performance on Test part (see more: https://en.wikipedia.org/wiki/F1_score#Diagnostic_Testing)\n", "print(\" Classification report \\n{}\".format(\n", " metrics.classification_report(y_test, \n", " y_pred)))\n", "print(\"Accuracy on test set: {:.2f}%\".format(\n", " accuracy_score 100.0))" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f1c9c3f4f50>" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1c6e8d7a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot prediction results and real Growth Rate Class for Test part\n", "plt.ylim(ymin=-0.5, ymax=1.5)\n", "_ = plt.plot(y_test[0:50], 'o', markersize=10, label='True class')\n", "_ = plt.plot(y_pred[0:50], '', markersize=10, label='Predicted class')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Conclusions\n", "\n", " We can tell if Material Conflict will go up or down correctly 6 times out of 10. Not that bad!\n", "* Classification seems to perform better than regression - it's ok, no one can see the future;)\n", "* Modern state-of-the-art Machine Learning tools are really easy to use with Google Datalab\n", "* Datalab is really integrated into Google Cloud Platfrom ecosystem and especially BigQuery\n", "\n", "If you would like to contact me, I'm available on alexander.usoltsev(at)cirruseo.com\n", "\n", "\n", "_______________\n", "(c) 2016, Alexander Usoltsev, Cirruseo (Paris)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
crosscompute/crosscompute-examples	tools/ask-question/run.ipynb	1	2408	{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "87031ba0-dee1-4084-9635-48c4d996c918", "metadata": {}, "outputs": [], "source": [ "from os import getenv, makedirs\n", "input_folder = getenv('CROSSCOMPUTE_INPUT_FOLDER', 'tests/standard/input')\n", "output_folder = getenv('CROSSCOMPUTE_OUTPUT_FOLDER', 'tests/standard/output')\n", "try:\n", " makedirs(output_folder)\n", "except OSError:\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "id": "d64521e3-2e83-4db7-9a22-a58d7eaaa0e8", "metadata": {}, "outputs": [], "source": [ "from os.path import join\n", "question_path = join(input_folder, 'question.txt')\n", "question_text = open(question_path, 'rt').read().strip()\n", "question_text" ] }, { "cell_type": "code", "execution_count": null, "id": "7040de79-33e8-4094-b937-3355775d1280", "metadata": {}, "outputs": [], "source": [ "import re\n", "normalized_question_text = re.sub(r'\\W', ' ', question_text).lower()\n", "normalized_question_terms = normalized_question_text.split()\n", "normalized_question_terms" ] }, { "cell_type": "code", "execution_count": null, "id": "e160b35d-5d55-43a4-a577-82bdf002e0a7", "metadata": {}, "outputs": [], "source": [ "if 'who' in normalized_question_terms:\n", " response_text = 'you'\n", "elif 'what' in normalized_question_terms:\n", " response_text = 'that'\n", "elif 'where' in normalized_question_terms:\n", " response_text = 'anywhere'\n", "else:\n", " response_text = '?'" ] }, { "cell_type": "code", "execution_count": null, "id": "29682d24-77ba-4735-b65d-e967bef066e0", "metadata": {}, "outputs": [], "source": [ "with open(join(output_folder, 'response.md'), 'wt') as response_file:\n", " response_file.write(response_text)" ] } ], "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 }	mit
Gluttton/PslRK	Tools/Modeling/bpsk.ipynb	1	61233	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xMYwAojzlR6F9e9eImyoUd+92jcODBhVuu9wik733dtcj1WtuwwZXNZWpyiiK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\n0KsXvP46nHYaVFXB/PmF+/yNb8Bjj8G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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa09026e400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%run bpsk_convolutinal_code_trivial.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RXGX1nwX99tvOffN59C4/eLBz621iMoFrml/8z8fkRa6y1NQAL70E7LWX89jb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\nlefx10j/oPrnWWqZJ+qC7Ie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"text/plain": [ "<matplotlib.figure.Figure at 0x7f43bc5ab550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%run bpsk_convolutinal_code_animate.py" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f33a2cea2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%run bpsk_convolutinal_code.py" ] } ], "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": 0 }	mit
maqnius/compscie-mc	jupyter_notebooks/presentation_100_particles.ipynb	1	778364	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import particlesim.api\n", "import particlesim.helpers_for_tests\n", "import particlesim.utils.xyz\n", "import particlesim.utils.config_parser\n", "import particlesim.utils.conversion\n", "\n", "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_nacl(traj,left,right,num_na,traj_sample = -1):\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111, projection='3d')\n", "\n", " last_na_pos = traj[traj_sample,:num_na,:]\n", " last_cl_pos = traj[traj_sample,num_na:,:]\n", " \n", " a =(last_na_pos <= right)(last_na_pos >=left)\n", " a = a[:,0]a[:,1]a[:,2]\n", " small_box_na = last_na_pos[a]\n", " b = (last_cl_pos <= right)(last_cl_pos >=left)\n", " b = b[:,0]b[:,1]b[:,2]\n", " \n", " small_box_cl = last_cl_pos[b]\n", " ax.scatter(small_box_na[:,0],small_box_na[:,1],small_box_na[:,2],c='r')\n", " ax.scatter(small_box_cl[:,0],small_box_cl[:,1],small_box_cl[:,2],c='b')\n", " ax.set_xlim([left,right])\n", " ax.set_ylim([left,right])\n", " ax.set_zlim([left,right])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_systemconfig(system_config):\n", "\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111, projection='3d')\n", " num_na = len(system_config.xyz)//2\n", " ax.scatter(system_config.xyz[:num_na,0],system_config.xyz[:num_na,1],system_config.xyz[:num_na,2],c='r')\n", " ax.scatter(system_config.xyz[num_na:,0],system_config.xyz[num_na:,1],system_config.xyz[num_na:,2],c='b')\n", " ax.set_xlim([0,system_config.box_size])\n", " ax.set_ylim([0,system_config.box_size])\n", " ax.set_zlim([0,system_config.box_size])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def beta_fct(beta_start, beta_end, iteration_number):\n", " x = np.linspace(0,np.pi,iteration_number)\n", " beta = (beta_end - beta_start)np.exp(-x)(0.8+0.2np.cos(15x)) + beta_start\n", " return beta[::-1]\n", "\n", "\n", "def multiple_beta(beta_list,iteration_list, linspace=False):\n", " beta=np.asarray([])\n", " if linspace:\n", " for i in range(len(beta_list)):\n", " beta = np.append(beta, 1.0 / np.linspace(1.0 / beta_list[i][1], 1.0 / beta_list[i][0], iteration_list[i])[::-1])\n", " else:\n", " for i in range(len(beta_list)):\n", " beta = np.append(beta,beta_fct(beta_list[i][0], beta_list[i][1], iteration_list[i]))\n", " return beta\n", "\n", "def plot_beta(beta, T=False):\n", " fig = plt.figure()\n", " ax1 = fig.add_subplot(111)\n", " if T:\n", " temp = particlesim.utils.conversion.beta_to_kelvin(beta)\n", " ax1.plot(temp,linestyle = 'dashed',c='r',label=\"T\")\n", " ax1.set_ylabel(r\"$T$ [K]\", fontsize = 20)\n", " ax1.legend(loc=2)\n", " ax = ax1.twinx()\n", " ax.plot(beta,'+',label=\"beta\")\n", " ax.set_ylabel(r\"$\\beta$ [mol/kcal]\", fontsize = 20)\n", " ax.set_xlabel(r\"Iteration\", fontsize = 20)\n", " ax.legend()\n", " else:\n", " ax1.plot(beta,'+',label=\"beta\")\n", " ax1.set_ylabel(r\"$\\beta$ [mol/kcal]\", fontsize = 20)\n", " ax1.set_xlabel(r\"Iteration\", fontsize = 20)\n", " ax1.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Config File:\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "\n", "[general]\n", "box-size = 12\n", "\n", "[particle_class_1]\n", "label = Natrium\n", "type = Na\n", "charge = 1\n", "distribution = uniform\n", "number = 100\n", "\n", "[particle_class_2]\n", "label = Chlor\n", "type = Cl\n", "charge = -1\n", "distribution = uniform\n", "number = 100\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate system configuration from cfg-file with \"problem creator\" and a related sampler" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "creator = particlesim.utils.config_parser.ProblemCreator(\"config/100_particle_nacl_rand.cfg\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "system_config = creator.generate_problem()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_systemconfig(system_config)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sampler = particlesim.api.Sampler(system_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Global parameter for simulated annealing" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "iteration_number = 20000\n", "step = 0.002\n", "beta = [0.09,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Call metropolis simulated annealing function from sampler" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 57s, sys: 49.5 ms, total: 10min 57s\n", "Wall time: 10min 56s\n" ] } ], "source": [ "%%time\n", "traj,pot = sampler.metropolis_sa(iteration_number=iteration_number,step=step,beta=beta)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_nacl(traj=traj,left=0,right=12,num_na=50,traj_sample=-1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "beta_values = 1.0 / np.linspace(1.0 / beta[1], 1.0 / beta[0], iteration_number)[::-1]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-17238.4885042\n" ] } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(pot)\n", "ax.set_xlabel(r\"Iteration\", fontsize=15)\n", "ax.set_ylabel(r\"Potential [kcal/mol]\", fontsize=15)\n", "ax.set_ylim([pot[-1]-1000,-14000])\n", "ax2 = ax.twinx()\n", "ax2.plot(beta_values,c='r')\n", "ax2.set_ylabel(r\"$\\beta$ [mol/kcal]\",fontsize=15)\n", "ax.set_title('Potential curve over iterationsteps')\n", "print(pot[-1])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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g2OOgcUWG1j/EFfjRAh8ySVL/wLp1ySLfu152y2B/ibgd193+l8BrM1dAazNqLYAPvooK664Itx/P4wfD+97H1x6qQJYQc4vwbSBZm4JSKCYBPzu606/DRbAyunfqJFTwG4CSR2Z79wEEvvMn3sOll1WAVQAU8eX+SUggYITUAC704EKYG3uRgBrM6o/AjjoST/4bwGRRdpAM7cEJFBMAn73daffFMDa3BXA2owUwDRGA7n9EmwBRIuQgAQKR8Dvvu50WZn74ovP4uWXl3zbDuA4CPpjH3uG3/524HBqdwF3p4sK/9a+Pgewkd7zS7ARWj4rAQn0CgG/+7rTk2XuEFe+vX0T5pgxcMstj7HSSispgN3pnp54qwJYZzf6JVgnKB+TgAR6ioDffd3pzkoBXGCBt0cAV10Vfv1rBdAp4LSxWZcAPvroox4D88ILA//a8kictAFnbglIoFgEyiLiz4HO9ltwL0X4ZqEAVmevAKaNyWEFcM6cOayyyioDV9iYYPnll+fhhx9mkUE3pshGAhKQQK8S8OdA93p2kUWWZ86ch4HSLV1xB3AkI4AlDgpg2tgcVgCj6Pjwvxa3hFRLTz8NN98M226bVouC5B41apTyV5C+spoSkEDrCAz7c6B1r7GkQQTWWmsUTz759ita4xHXACqArfiw1BTAVrzEMiQgAQlIQAISaIzA6qvDQw+9/Q5gI4BvMTQC2Nh4Gvy0ApjGz9wSkIAEJCCBlhMoy18UXHkLSEz/PvggPPaYm0AUwLRh1z4BvPhi2HRTWGWVtBqaWwISkIAEJNBnBFZYAZ544p2NjunfmTMVwCCjAKZ9KNojgPHPlX32gQsvhOOOg0mTYMEF02pqbglIQAISkECfEDACWLujFcDajIZ7oj0CWH7jr34FEyZA/FPmggtgzTXTamtuCUhAAhKQQB8QMAJYu5MVwNqMuieA8eaXXoJDD4Xzz4cjjoDJk2GhhdJqbW4JSEACEpBADxMYbgOIawBLHa8Apn0A2hsBrKzbTTfB6afDJZfAyJFptTa3BCQgAQlIoIcJGAGs3bkKYG1G3Y0AptXP3BKQgAQkIIG+IrDVVnHVG8yd+/YdwKNHw0EHlSbV3AVsBDD1Q9G5CGBqTc0vAQlIQAIS6AMCtaJ/gUABVABTPwr5EMBHH4X3vhcWXji1PeaXgAQkIAEJFJqAAlhf9zkFXB+noZ7KhwCOGwd/+ANMmwYf/Whai8wtAQlIQAISKDCBwRtAoilxD3D5EGgjgKXOVQDTBnk+BPCNN+Dkk+Hoo+GrX4WjjoLFFktrmbklIAEJSEACBSRgBLC+TlMA6+OU7whguXb33gvjx8PTT5fODYybREwSkIAEJCCBPiKgANbX2QpgfZyKIYBRy9j2dOaZpTMD774bVlwxrYXmloAEJCABCRSEwIYbwh//+Nbu33K1F120tPs3fkVyE4hTwKlDOh9TwNVaMWsWLLVUavvMLwEJSEACEigMgXqifwpgqTuNAKYN6/wKYFq7zC0BCUhAAhIoHAEFsP4uUwDrZ1XtyeIJ4Pz5pe1QJglIQAISkECPEVAA6+9QTaB+Vr0hgCedBHfdBd/6FiyzTFrrzS0BCUhAAhLIEQEFsP7OUADrZ9UbAjhzJuy+O9x8M5xzDnz+82kEzC0BCUhAAhLICQEFsP6OUADrZ9UbAhitiGngyy6DvfaCzTeHs86C5ZZLI2FuCUhAAhKQQBcJ1Ct/UUV3AbsJJHWoFm8NYGWL//Y32HNP+MUv4Oc/h402SuVhfglIQAISkEBXCCiAjWEvWgTwcOAbwMvZDub5wFXAl7NmrwecCYTJPA+cDxw5CEn8eQKwJHAbMAm4p+KZesooP15sASy34qc/hS22gDgoySQBCUhAAhIoIAEFsLFOK6IAbgFUu+JiNPAAMA04ClgT+BlwMnB6huVAYE9gG2AGEEK5Y/ZsSGU9ZVQS7g0BbGzM+LQEJCABCUggdwQUwMa6pJcEcCfgBGAFYF6GYW9gL2CN7M8PAacCZ2V/HgHMBPYDLgHqKUMBbGyM+bQEJCABCUig7QTiYItnn33na5ZeGp555u3/3zWAxVsDGBG7A7Ip4IjY/Q6Ii10eycRunSy6V+7pjYHfAnElxoLZtHD8v5srhsJ04E9ZuSGHw5Xx4qCh1dsRwAcegOuvL+0aXjDwmSQgAQlIQAL5I3DssXDMMTBnztvrFsfefvCDcPvtCuDgXitaBPAfgdnAo8AY4CTgo8D6wBnA4sCXKhq5dra+b6VMAP+aCd79Fc9cBrwA7ApMrVFGRAv7JwL4v/8LX/oSrLwyXHABrL56/j711kgCEpCABPqeQCPTvwHLCGDxIoCDB/koYBbwaeBfa0Tv2hYBnDhxIqNHx/JBGDt27MCvnkkvvgiHHALTpsFRR8E++8CImDk3SUACEpCABPJBoJHpXwWw1GdFiwAOJYD/L4sInjhoDeA+2aaP4dYAPgHsC1yabQipVUb/RAArW3rjjbDLLhCLKS68ENaJmXKTBCQgAQlIoPsEjAA23gdFE8AvAr8EYjlnnFwcU8AfB/4pk9mY2o1dwMdmGz+uBk6p2AUc6wdjF/C2QGwImQLsAKyVrSuMMF6tMvpTAKPVL78Mhx8On/40bFptI3bjA9AcEpCABCQggVQCRgAbJ1g0Afwx8LFsnd5zwA3ZuYAhc5HWBc7JzgGMqeFzgaMHYTkC2A1YAri1yjmA9ZRRLrK3N4E0Pp7MIQEJSEACEug4ASOAjSMvmgA23sL25lAA28vX0iUgAQlIQALDEhhK/hZfHH7wg1ib/87sbgIp/hrAbn8sFMByD8ybB3/+M6wbAVSTBCQgAQlIoDMEGp3+jVopgApg6uhUAMsEb7sNPvEJ2GsvOOIIr5VLHVnml4AEJCCBugg0Ov2rAJawOgVc1/Aa8iEFsBLNPffA+PHw/POlY2P++Z/T6JpbAhKQgAQkUINAmyKARwITgCWB26rsF6is1YeAOEHkg8AbwI3AZCDOHs5tUgDTukYBHMzvjTfgW98q7RaeMAGOOw5iIYZJAhKQgAQk0AYCbRDAA7MTQ7YBZgBxC9mOwJrZiSGVrQiPejK7TvZrwMLZaSQrArmOgiiAaYNRARyKX1wjF+cGxrRwSKBJAhKQgAQk0GICcUHVjFC0QWnUKJgyBQ6Ny2KrpBprAONkkbga9qwsa9x+EDeB7ZeJXmWJ78qOpovoX1wrGymOmrsiO7GkxS1uXXEKYBpLBXA4frEx5LXXYJFF0iibWwISkIAEJFCFQDPRvyhmGAGMKd/ngY2BmyteOT0TvDhPeHCKq2gjHZRFAM8HXsmihrntNwUwrWsUwDR+5paABCQgAQk0TaANAhhTt7F2L667ioshyuky4AVg1yqVjZsRzssuoAiv+iMQ08d/b7phHcioAKZBVgCb5Td3rncKN8vOfBKQgAQkMECgDQLYaARwdeAeYO9s7d9CwMHAl7NbyiISmMukAKZ1iwLYDL+nniqtDTzhBPjsZ5spwTwSkIAEJCCBhgRw+vTpxK9Is2fPZurUqfHbiPg9PghltTWAT2Q7ey8Z9OzngAuAd1f8/7hpLG4j+yjwh7x2kwKY1jMKYDP85s+HSy6BvfeGrbaCM8+E9763mZLMIwEJSEACfUpgww3hjjve2fgRI2CHHeCii4YGU2MTSKzz2zPbzBEyOAXYAViryi7glYF7gX2AC4GR2VrA2DASfxcimMukAKZ1iwKYwu/JJ2HSJLjhhpIEbrcdLOCQTEFqXglIQAL9QqDZ6d/gU8dNIEcAuwERzbu14hzAlYA/A1sDN2WstwSOAtYG5mebRQ4DfpvnvvCnbVrvKIBp/Eq5v/992GMP+Pzn4dxzW1GiZUhAAhKQQI8TaLMA9jg9bwJJ7WAFMJVgOf/TT8Mjj8CH4kB1kwQkIAEJSGB4Agpg2ggxApjGTwFM42duCUhAAhKQQFMEllgCXnzxnVlHj45NHsMXWccUcFN1KlImBTCttxTANH7mloAEJCABCTRMoNkbQMovUgCdAm540A3KoACmEqyVP3YMx73C48bBqqvWetq/l4AEJCCBPiCQEv0LPAqgApj6MVEAUwnWyh9XyU2eDBdfDMceC3vu6QHStZj59xKQgAR6nEDK+j8FsDQ4nAJO+5AogGn86s/9m9/ALrvAcsvBBRfA2rHb3iQBCUhAAv1IwAhgeq8rgGkMFcA0fo3lfvll+MY34LzzYMoU+NrXPDewMYI+LQEJSKAnCBgBTO9GBTCNoQKYxq+53L//PVx9NRxzTHP5zSUBCUhAAoUlkBr9i4a7BtAp4NQPgAKYStD8EpCABCQggQYIKIANwBrmUSOAaRwVwDR+5paABCQgAQk0RCB1+tcIYAm3AtjQsHvHwwpgGr/W546bwddZBxZZpPVlW6IEJCABCXSdgBHA1nSBApjGUQFM49f63NtsAw8/DNOmwSabtL58S5SABCQgga4RiCNhv/tdmDfvnVVYbTV48MH6quYaQCOA9Y2UoZ9SAFMJtjr/G2/AqafCkUfCbruVNoostlir32J5EpCABCTQBQKtiP5FtRVABTB1+CqAqQTblf/++2H8eHjqKZg6FTbbrF1vslwJSEACEugQAQWwdaCdAk5jqQCm8Wtv7rlz4eyz4VvfgrvvNhLYXtqWLgEJSKDtBBTA1iFWANNYKoBp/DqTO66TGzWqM+/yLRKQgAQk0DYCCmDr0CqAaSwVwDR+5paABCQgAQnURWAo+Rs5Eq66CsaOrauYgYdcA+gawPpHS/UnFcBUgt3M//rrEN8cJglIQAISyD2BVkX/FMBSVxsBTBvyCmAav+7mnjABXnkFTj8dll22u3Xx7RKQgAQkMCwBBbC1A0QBTOOpAKbx627uJ56APfaAm24qbRb5whdgAT8S3e0U3y4BCUigOoFFF4U5c975d3Huf/xbvpHkFLARwEbGS7VnFcBUgt3OP38+XHEF7LknfOITcM45sPzy3a6V75eABCQggQoCq68OM2a8E8mCC8JXvgIXXdQYLgVQAWxsxLzzaQUwlWBe8v/977D33jB9Otx+O7z//XmpmfWQgAQk0PcEWhn9C5gKoAKY+qFSAFMJ5i1/TAfHFXJOBeetZ6yPBCTQxwRauf5PASwNJBc8pX2gFMA0fuaWgAQkIAEJ1CRgBLAmooYfUAAbRva2DApgGj9zS0ACEpCABIYlsOGGcMcd1R/ZYIPSqp1Gk1PARgAbHTODn1cAUwkWJf/VV8NDD8GkSRCrjk0SkIAEJNARAq2O/kWlFUAFMHXwKoCpBIuS/4YbYNw4+Id/gAsugDXXLErNracEJCCBQhNo9fo/BbA0HJwCTvtYKIBp/IqV+6WX4NBD4fzz4cgjYfJkGDGiWG2wthKQgAQKRsAIYHs6TAFM46oApvErZu7YKbzLLrDUUvDf/w1rrFHMdlhrCUhAAjknsNVWcP311Ss5ZgzMnNlcA5wCNgLY3Mh5K5cCmEqwqPnjOPpjj4XddoMVVyxqK6y3BCQggVwTaEf0LxqsACqAqQNfAUwlaH4JSEACEpDAEAQUwPYNDaeA09gqgGn8zC0BCUhAAhIYkoAC2L7BoQCmsVUA0/j1Zu5Zs+C+++CjH+3N9tkqCUhAAh0gMNz6v9VWgwcfbL4STgE7Bdz86CnlVABTCfZi/muvhc99DvbYA446ChZbrBdbaZskIAEJtJXAqFHw+uvvfMXIkfDaa2mvVgAVwLQRpACm8uvd/PfeC+PHw9NPl84N3HTT3m2rLZOABCTQBgLtmv6NqiqACmDqkDUCmEqwl/PPnQtnnAHf+EbpEOnjj4fRo3u5xbZNAhKQQMsIGAFsGcqqBbkGMI2vApjGrz9yx0KVCRNKEhi/TBKQgAQkMCyBFVaAJ56o/siWW8J116UBNAJoBDBtBDkFnMqvf/LPm1dqq/cI90+f21IJSKBpAu2c/o1KKYAKYNODM8toBDCVoPklIAEJSEACgwi0c/pXASzBdgo47WOnAKbxM3cQeOUViH/umsYsqAMAACAASURBVCQgAQlIYICAAtj+gaAApjFWANP4mfvOO2HrreGss+Dzn5eHBCQggb4nsMQS8OKL78QQK2jiZK1DD01H5BSwEcDUUaQAphLs9/zz58Nll8Fee8Hmm5dEcLnl+p2K7ZeABPqYQLujf4FWASy2AP4Q+HdgS+CX2WdlPeBMYCPgeeB84MhBn6P48wRgSeA2YBJwT8Uz9ZRRflwB7OMvqZY2/W9/K0ng9deXjo7ZfntYwAB9SxlbmAQkUAgCCmBnuqmoP2F2BLYHtsp+hQDGAWsPANOAo4A1gZ8BJwOnZzgPBPYEtgFmAIcDUVY8+3KdZVT2jALYmXHaP2+58srSDSK77lqa6zBJQAIS6CMCxx5bOjo1JkcGpzhGdfbs1sAwAljMCOCKwG+BjwN/rYgA7gScAKwAZGdusDewF7BGNmQeAk4Fzsr+PAKYCewHXALUU4YC2JrPn6UMReDZZ+Gll2CllWQkAQlIoK8ILLQQxBn6g9OIEfDGG61DoQAWUwCnA1cAF2SiV54CDrFbJ4vulUfJxpksLhUnsGXTwvH/bq4YRlHen4ADMjkcrozBy1KNALbu82hJEpCABCTQ5wQ6Mf0biBXA4gngHtm6v7HZZyQifWUBnAosDnyp4vOzdra+L0IpIYARMQzBu7/imcuAF4BdgVplRLTQCGCff0HZfAlIQAISaA8BI4Dt4Vqt1CKtAVw1i+Z9FHi0igB2LQI4ceJERmd3vI4dO5b4ZZJAywm8/DLsvjtMmQKrr97y4i1QAhKQQDcJLLMMxAqYwSn2w+24I1x0Uetql5MIYHkDa2rDYsXkFo0WUiQBjPV5386ideV6LwPMAi4HfgecBIypWAO4T7bpY7g1gHHb4L7ApdmGkBMHrSMcXIYRwEZHmc+3hkCsCzz4YJg2DY4+GvbZB2JhjEkCEpBADxDoVPQvUOVEAMv7FVJ7LwSw4R8GRRLARYClB1F6DNgOiGuhY3loTO3GLuBjs40fVwOnVOwCjnV+sQt4WyA2hEwBdgDWqtgFXKsMBTB1qJo/jcCNN8L48bDssiUZXCdWNZgkIAEJFJtAnwrgtdkG1mY772DgX3pdAKvBib1CcRRMOYy6LnBOdg5gRAbPBY4elPEIYDdgCeDWKucA1lNGuUg3gTQ7ZM2XRiCmgw8/HM4+uxQN3H//tPLMLQEJSKCLBMaNg4svrl6BVh7/Un5DjiKAMbE9PgH9hdnsZU9HABP4tC2rAtg2tBZcF4Gbb4b77oOdYoWESQISkEAxCXQy+heEciKA9wE/AFIut/sm8Jlsg2tDnV+kKeCGGtahhxXADoH2NRKQgAQk0LsE+lQAu9qhCmAafgUwjZ+5JSABCUhAAiiAnR8ECmAacwUwjZ+520ngmmtg881h0UXb+RbLloAEJJBEYKjDnxdcEOJrrB0nq+VkCjiJW2pmBTCNoAKYxs/c7SLw+uuw2Wbw97+Xdgp/PG5ONElAAhLIH4FOR/+CQE4EcOXE3ojLLZpOCmDT6AYyKoBp/MzdTgJxoeZpp5V2C++yC3zzm7B4XJZjkoAEJJAfAnGc6bwqJ+JFBLDavcCtqHlOBDBaHWf4NZMi30LNZCznUQBT6CmAafTM3RkCDzxQEsDHH4epU+FTn+rMe32LBCQggRoEhrr9I7JtuSVcF6f8tiHlRAAfSRDAoLJKChoFMIWeAphGz9ydIxD/vD7nHPjBD+AXv4D4p7VJAhKQQJcJdCP6F03OiQB2lb4CmIbfKeA0fubuNIH58yEu1jRJQAISyAEBBbB7neBPgjT2CmAaP3NLQAISkECfElh9dZgxo3rjx4yBmTPbB8YIICiAaeNLAUzjZ+68EHjhBVhyybzUxnpIQAJ9QKBb0b9AqwAqgKkfMQUwlaD580Egzgtcbjk480x4z3vyUSdrIQEJ9DQBBXDI7h0D/DuwFhD/Mq8WrItdwLukDBAjgCn03ASSRs/c+SHw5JMwaRLccENJArfbzrWC+ekdayKBniOw4YZwxx3Vm7X00vDMM+1tco4jgHsBJwEjKwiUXa18ZEz8OX4/IoWSAphCTwFMo2fu/BH4/vdhjz1gk01Ku4ZXWCF/dbRGEpBA4Ql0M/oX8HIqgFsAcfDNC8DZwCeBjYHdgTWBzwHvB04H7gQuThkICmAKPQUwjZ6580ng6adh333h5z8vrdBeYol81tNaSUAChSUQJ1HFoQSDUxxSUO1Q6FY3NKcC+BNgW2AT4GbgQmDHikjfwsC5mQhuCDyUwkUBTKGnAKbRM3e+Cfzf/8Eaa+S7jtZOAhIoHIFuT//mOAL4JPAo8OGsUwcLYPzvkMC/ANcC41I6XwFMoacAptEztwQkIAEJ9B2Bbkf/6hTAI4EJ2SaM24BJwD3DdFbI2H7AqsCLwGXAvg127qvAD4H/zPJ9J9voMRp4paKs7wEfA1ZqsPy3Pa4AptBTANPomVsCEpCABPqOQLfX/9UhgAcCewLbAHFS4eHZVGysw3u5SoftD+wBfAX43yxKFzt4/9hg5z4B3Ap8Ost3AnAA8I/A/RVlXQlsDSzWYPkKYAqwQXk9BqaFMC2qIAROOglGjoS99oL4JjdJQAISqJPAcNO/o0fD7Nl1FpT4WI01gLG27lTgrOw18UUXx1JHhO+SQa+ORdLxd9sB1yRWK9b9xRTvB7NyQihjo0cI6SnZ/4to4MPAc9nGkKZfaQSwaXQDGRXANH7mLiKB3/wGdtmldG7gBRfA2msXsRXWWQIS6AKBPEz/RrOHEcA4d+/5bPdtCFk5TQf+lEXkKsmNzcQvJC1260b+OODmIOCuBhGfCEwGVgSeApbO1vuNAs4A/grsBGwAnAPEkTFNJwWwaXQKYBo6cxeawMsvwze+AeedB1OmwP77w0ILFbpJVl4CEmg/gQIIYMhXiNY6g6ZdY01fHM+y6yBKXwa+C9yYRQEjMhfrB2P3bkwDNxLTjM0fxwEnAyGckSYC51W8M7ztEeBDwLMpPaYAptAzAphGz9zFJ/D738P48bD44vCjH8GK8d1pkoAEJPBOAsssA88OoSztvvt3cG1aGAGM9Xo/ztbk/Tx7z4KZ+MW5fWWRSxkSceTLF7OI4L3Z8TCzUgqMvApgGkGngNP4mbsXCMyZA2edBXvuCYss0gstsg0SkEAbCHQ7+jd9+nTiV6TZs2czderU+G38q/XxQc2ttgYwNmjE9OzgNYDliGFsyigLYKwZjGhhqwSwDb2hAKZCVQBTCZpfAhKQgAT6gkAc8jxUqnYodDuh1NgEEjtvYxdwHMocMjgF2CGb0q22C/gHwDLAfwARmYtdw7GBI6aR40iYXCYjgGndogCm8TO3BCQgAQn0AYFRo+D116s3dKed4KKLOguhjptAjgB2A2KXbxzNUj4HMM7e+3M25XtTVuvYmfutLOI3F/hDtlkknmskfQk4NjtSJg56rpYi0hgbQL4GfL+Rwgc/qwCm0HMNYBo9c/c+gfvug+eeg43jOkuTBCTQrwTyFP2LPqhDALvRVXEV3MeB5YHXhqhAHBMTN4b8GvhsSiUVwBR6CmAaPXP3PoFLL4WJE2G33eCYY2CxpHNLe5+XLZRAjxJQAOvq2NjdG782q/F0yN/K2a0jdRVc7SEFsGl0AxmdAk7jZ+5+IHD//aWdwk89BbHoerNa3239AMU2SqB/CORN/nIcAZwDxHrCOFpmuBQbUSL6l/QvagUw7TOoAKbxM3e/EJg7F84+Gw45BHbcEU44AZaIpTUmCUig1wkogHX3cBySc0u2vnC4TLE+MO4CflfdJVd5UAFMoWcEMI2eufuPwEMPlaaDjzrKdYH91/u2uA8JDHf1W5wa9cor3YGS0zWAcZh0XAP3vmEOeY7bQeKg6rszCWwaoALYNLqBjEYA0/iZWwISkIAEephAHqN/gTunArhvdgdxRPi+AAw+cmbRbOdv7ASOq+bixpCmkwLYNDoFMA2duSUgAQlIoNcJKIAN9XAIXhw5Exesx8HTlwL3ZSXE/4tjYlbIrqiLq+CqnUlY9wsVwLpRVX3QCGAaP3NL4C0CsU7w+ech7osySUAChScQ14PHx7pa2nJLuO667jUxpxHAABI3i/wQ2AiYP4hQONsd2QaQmAZOSgpgEj6ngNPwmVsCFQR+/OPSkTFxrdwXvwjDhQ4EJwEJ5J5AXqN/AS7HAhjVCzeLO4ZjqjfWA0YK4Yup4TgrcLAYNjUWFMCmsL2ZyQhgGj9zS+AtAnEX1BVXlO4U3nTT0q7h5eM8VJMEJFA0AuPGwcUXV6913Ak8VGSwU+3MuQB2BIMCmIZZAUzjZ24JvJPA3/8Oe+8NcWn76afDDjsYDXScSKBgBPIc/ctxBDCc4vE6u/pfgWvqfLbqYykC+MuUF1fkjVDmFi0qq9PFKICdJu77+ofAj34EX/0qHH447L57/7TblkqgBwgogE114p3AJsBLNXJvDvwUWLypt2SZUgRwXsqLBwngiBaV1eliFMBOE/d9/UUg7hEeORJGx13rJglIoAgERo2C11+vXtMNNoDbb+9+K3I6BRxeNR34N2CI7TMDB0DH9plRQNwL3HRKFcBYkHhC02+Hg4F/ARTABIhmlYAEJCABCeSFQN6jf8EppwJ4LrAbcAEwsUp/rg/8ClgS+ArwPyl9niqAFwHjEypwIbCjAphA0KwSkIAEJCCBnBAY7uaPESPgjTfyUdGcCuCC2dTuWOAbwHEVtNYCbgCWBb4KfCeVZIoAxuGEcWnxoQmV+CbwGWCdhDK6mdUp4G7S9939S+Dhh0vrA884A9Zcs3852HIJ5IxAEaJ/OY4ARtViXd9vgfWAHbIo3/uBuCYuDoH+GnBKK7o9RQBb8f6il6EAFr0HrX8xCbz0Ehx2GHznO3DEETB5MsSpsyYJSKCrBBTAluAPt/jfLNoXs6xHA6tm/z28JW/IDhtsVVn9WI4C2I+9bpvzQ+Cmm2D8eFhqKZg2DdZdNz91syYS6DMCw8nfMcfAoSnzhS1mmdMp4MpWRgQwIoEREYxg3enA5FZiSIkAjsnuqmumPicBBzaTMWd5FMCcdYjV6UMCr7wCRx5Zmg4++WTYY48+hGCTJdB9AkWJ/gWpAghgVDM2ycZxL7HfYtdW93CKAP4J+ATwfIOVil3DBxR440dlcxXABjvfxyXQNgJ/+APMmQOfiK8lkwQk0EkCiy5a+vhVS0svDc8808na1H5XTgRwqKNeajegdB1c0rqXFAGM82pijnpL4OV6agscC3wdiH1AcYZN0ZMCWPQetP4SkIAEJJBMoEjRv2hsTgQw9Tzl2DXcdEoRwFuBDbIDCePQwlqbu48CDsuei/NrLm+61vnJqADmpy+siQQkIAEJdIHA6qvDjBnVX5yno18qa5gTAexCb731yhQBjLNoYoHiGsD3ge2GaUnsWolfEe7cCbi0q61u3csVwNaxtCQJtIfA/Pml3cI77ggxT2WSgARaSqBo0b8cRQBb2g+NFpYigPGulYCbgBChOJQwDiccnOIwwyMz+YvtzN9ttJI5fl4BzHHnWDUJDBCI6+T+9V9Li5AuuMA1gg4LCbSQwPTpsPXWQxcY//7KY8pJBPALwDUNLKNrKcpUAYzKxOnUcUDhMsDxgw6Gjk3fcX5NzHPvAlzc0tp3vzAFsPt9YA0kUJvA3Llw5pmlswPHjYPjj/d+4drUfEICNQkUMfoXjcqJAIYbvQLEtbpXAlcBL9SE3qIHWiGAUZUNs/vp4sb22OF7WrbZIzZ9RANj+/K0FtU5T8UogHnqDesigVoEHnwQJkyARx6BqVNhy9jDZpKABJoloAA2S24gX8yOfg74AKVdva8Dv8huWfsJ8HRS6TUyt0oA4zWbZha7MPDDrFHRoJbcWddOCAllK4AJ8Mwqga4QmDevtCbwnntKUUGTBCTQFIEiHfw8uIE5iQCWqxX3WcZ0cMhgBNTCnSJ4Fnf/xpW74VRPNNVJw2RqpQDGa2I3cIQxR2TvnASc1+pK56g8BTBHnWFVJCABCUigcwSKGv0LQjkTwMpOex/w+ezXx7JbQEIGb85kMBzrkVb0cooA7jhEBb4IbAv8BrhwmEr+Vysa0OUyFMAud4Cvl4AEJCCBzhMYTv5iZcV113W+To28MccCWNmM5bOoYAhhzLJGcC2ig3dkwbaQwfsaaXflsykCGEba7P6e5BOsm21wi/MpgC0GanES6DqBRx+FleKAA5MEJDAUgSJH/3IeARwK+dLAZ7LI4BbZZRrhUgcBJzczUlMEMEKQzQpg1HWVJio8BYjIY5xB+BpwG3AwcGdFWXGBcizs2Si7pu78bKFl5eti4eUEYMmsjJiqvqfBMuJxBbCJTjSLBHJLIHYLr7MOrL8+nHUWLLdcbqtqxSTQLQJxuHMspa2WxoyBmTO7VbP631uQCOBQDQp3+TTwWeD3wCn1t/ytJ1MEsJn3peaJQ6f/BszK7sDbG/gaMCaT0diF/EC24zhuHomFlT/L7Pj07OUHAnsC2wBxdnkcUB1SGc/GlXb1lFFuhwKY2qPml0DeCPztb7DnnvDLX8IZZ8CXvgTDhTvyVn/rI4E2Eyh69C/wFFwAW9LDRRPAykbHbuPYYRzm+14grpqOW0ZOAFbIdtDE8yGJe2U3lsSfHwJOBc7KCos59fj3yn7AJXWWoQC2ZPhZiARyTOAHP4BJk+AjH4Fzz4V/iH/vmSTQ3wR6IfqXIwFcOXE0/TUlfxEF8F8zUVsqk7w4czCiepFC7NbJontlLhtnV9bF83Fx8vNA/L/YUVNO04E/ZWcY1irjxYp8RgBTRp95JZB3AnF7yOTJcMst8Oc/w4JJd6/nvbXWTwI1CfRC9C9HAtjVvRQpAnhItvbu6pojZugHYrfw+sBxTZTxrixa91i2NTqKmAosDnypory1s/V9sao7vr3DmEMS76945rLs9O04sLpWGZWrGxTAJjrOLBIoHIG//x3e857CVdsKS6CVBHol+pcjAezGXoo3h0SKAIa5XgTE/b7NpjgmJtbflc8NbLScqP9zwCeyCF6t6F1bIoATJ05k9OhYOghjx44d+GWSgAQkIAEJ9BKBXon+5UgAuzo8ii6AC2UbQr6SnYkTMnnioDWA+2SbPmIDSaRqawDjhO19gUszIa1VRrnTjAB2dfj6cglIQAIS6ASB4eRvtdUgblksUnITCKQKYKyHS7mrLo5ziSnbeiOAsaEjpmtjJ3DMx8Rdw3F9SkzpPpXt4I2p3bh3OP4upC+mqGOjSHkXcNxVHLuAY/o5ZDCOltkBWKtiF3CtMhTAIn3SrasE2kVgp51gvfVg330h5sdMEuhRAr0U/TMCWBqkqQLYiqEeZwnW+815FfChTPReAP4AxHEvt1dUZF3gnOwcwDgu5lzg6EEVPQLYDVgCuBUYfA5gPWVEkUYAWzECLEMCRSVw442wyy6w9NIwbRr84z8WtSXWWwJDEhhO/jbYAG6v/AlcEI4FiACOyjymfPzA49m5xXEGcktSigDGfXWtSn9pVUEdLkcB7DBwXyeB3BF45RU4/PDSwdGHHQYHHggjR+aumlZIAs0S6LXoX3DIsQDG0rY4nziOr4sgVWWaDZyRBb7eaLY/y/lSBDD13b2QXwHshV60DRJoBYE4Kmb8eBg1Cn71K1gqTp4ySaDYBIaTv1gBcVFsBS1gyqkAxkbVn8Z+0myGNja5PpzhjdvT3p1denFtdhPIEPex1NchCmB9nIZ6SgFM42duCfQWgVdfhThA2ttDeqtf+7Q106fD1lsP3fj5KZfBdplpTgUwjqI7D4jjYWK/wpWDMMXVb7GnIWZgdwfiqtumkwLYNLqBjApgGj9zS0ACEpBATgn04tRvGXVOBfC3wAbABzIJrDYyIhJ4T7b34eMpQ0cBTKGnAKbRM7cEJCABCeSSwIYbwh139Gb0L1qVUwGMm8puyk4pGW5cxDRxyF9ciNF0UgCbRmcEMA2duSXQZwRiPi0OjP/nf+6zhtvcIhLo5ehfjgXwFeCHwPY1xsz/AJ8BFk0ZW6kCGDd5xPGP92a//g9I3pmS0qAO53UKuMPAfZ0ECkvgO98p3Ss8YQIcdxwsHkegmiSQPwKLLgpz5lSvV1yHPXdu/urcaI1yGgF8AIgjBFYHhqIcx+aFd4VrlS+4aLT5A8+nCmDsQLkN+HlmohGO3CXbpdJUhQqWSQEsWIdZXQl0lcADD5QE8LHHYOpU+NSnulodXy6BagR6PfqX4wjgmcAe2fnFk4HXB/VPnA14WrYB5GwgLsdoOqUKYBzGvBwQYct+TApgP/a6bZZACoF58+Dcc+Hgg2H77eHEEz0yJoWneVtKYDj5GzMGZs5s6eu6VlhOI4DhFHdla/uCdNx8Vj4GZlVgu+yq22eBDwJxOHTTKVUA7wTWb/rtxc+oABa/D22BBLpD4JFH4IAD4IwzYIUVulMH3yqBQQT6IfoXTc6pAEbVPgx8D1i5ymxqONtfsytw4xazpJQqgLcAH0mqQbEzK4DF7j9rLwEJSEACGYF+kb+cC2BUL6Z6vwhslh03F/8von2/zuSwJdfBpQpg3AC4YR9/ehTAPu58my4BCUigVwgssww8GxOLQ6QiH/pcrUk5jgB2bEilCmBYaCxavA64EXipzpp/Griqzmfz/JgCmOfesW4SKCqB55+H116D9763qC2w3gUj0E/RvwJEADsyelIFMHYBly+DiS3JsSM4QpS/AuJE66E2h/whm+fuSCPb+BIFsI1wLVoCfUvg7LPhiCPgzDNhu+1guJ/OfQvJhreKwHDDa+ml4ZlnWvWm/JST4wjgQtn07xbZho9FhgrKAvFM0ylVAB8FjgE2Bz6Z7QiOyoQUxvblWKQYMhi/fgeUTxa6G1i36VrnJ6MCmJ++sCYS6C0C3/seTJoEm2xS2jUcWzBNEmgxgV6+73c4VDkVwPdkx+qtV8cxfeFZcSZg0ylVAAdH8tbJZLAshMtmNYuKxnTxzZkI7gP0wimoCmDTQ8+MEpBATQJPPw377APXXAOnnQY77WQ0sCY0H2iEQL9N/ZbZ5FQA43KNnbKDns8F4nKN2cP0528a6evBz6YKYK1NIBHlCxmM0043Bd5dIYRJ5prS6BbmVQBbCNOiJCCBIQj85Cew++5w/vmw7bZikkBLCCy00NC3evTKjR9DgcqpAP49u+HjA8AwW3Ja0v3JN4HEtuSQoHpSyGacGfg54NDU0GU9L+zAMwpgByD7CglIAHjhBVhiCSOADoaWEejX6F8AzKkAvghcm53z17J+Hqqg1AhgbAKJQwtj80cj6SEgTrUuelIAi96D1l8CEpBAHxIYTv6OOQYOjTBND6ecCuAdQNwA0pEwf6oAxlVwcSp1HOtSvq6kniETwrhRPQ/m/BkFMOcdZPUk0BcE4pA2dwr3RVe3opGLLgpzylsyqxTYa2f+VWOWUwHcEzgBWCuClK3o6+HKSBXA2Mjx2SxcGQsVLwaur6PSv0jdvlzHOzrxiALYCcq+QwISGJrAr34Fhx0GF1wAa68tKQnUJNDPU79lODkVwKje5cAGwF7ZGcsx09qWlCqAlZVaBvh3IHaxlM8GbEulc1SoApijzrAqEuhLAi+/DN/4RumomClTSvcLx+p+kwSqEBhO/mKT+UUX9Qe2HAvgu4DY3RubaON85SeAahIYnrVaSm+1UgBT6lHUvApgUXvOekug1wj87//CzjvD4ovDtGmwXhwlZpLAWwSG2/UbT/XD1G+ZRk4FcKXsVrX4by0/6/o5gP3+2VIA+30E2H4J5IlALOw6+ujSmYFxZMyXv5yn2lmXLhNw6vetDsipAF4K/Gd2k9qp2TmAsTN4qPSXlCFVyzBTyu6HvApgP/SybZRA0Qjcfju8+92wyipFq7n1bROB4eTv2mth7Ng2vTinxeZUAP8GvATEYt5X241OAUwjrACm8TO3BCQgAQm0mcBw8tfrBz4PhTanAhibaa8BtmvzkBgoXgFMo6wApvEztwQkIAEJtJHAhhvCHXG63BCpn9b9VSLIqQDGdblxvN5WbRwSbxatAKZRVgDT+JlbAhLoJIEXX4RTToEDD4TFFuvkm31Xlwi47q86+JwK4A7ANGBD4O52DxkFMI2wApjGz9wSkEAnCTz2GGy3HTz1FEydCptt1sm3+64OExhO/saMgZlx50SfppwKYPTGccAE4BvAz7LLNtrSSwpgGlYFMI2fuSUggU4TmDsXzj4bDjkEdtwRTjihdMewqacIjBgB84Y5Qrhfp37LnVyHAB6ZidiS2XW3k4B7agyS+CD9CYhjXEYOcX7fcEXMbWAQxjEwSQd+KoAN0K7yqAKYxs/cEpBAtwg89BBMmAAzZpSOjPmXf+lWTXxviwmMGwcXx71cQ6R+l7/AUkMADwTiWrZtgBnA4cCOwJrAy8N01wXACkB8mJoRwEZv/VgwZegogCn0QAFM42duCUigmwTCBEL+IlS0++7drInvbiEB1/3VhllDAB8C4hy+s7KSRgAxYb4fcMkQpX8aOAz4enaFWzMCWLviLXxCAUyDqQCm8TO3BCQgAQm0kIDr/uqDOYwAxpTv88DGQOzKLafp2fTuAVXeEFfh3pZFDN8L/LLJCGB9lW/RUwpgGkgFMI2fuSUgAQlIoEUEhpO/eIVTv2+BHkYAV8w2XqwD3F/RNZdlR7TsWqW7Lgf+CHwT+KQC2KIBnfNiFMCcd5DVk4AEEgjceWfpTuFaZpHwCrO2hsAyy8Czzw5dlvIH06dPH/gVafbs2UyNnfAQwvd4BblGI4Bxddv+wEezTR+xtf4XwCigkU0drRkIDZRiBLABWFUeVQDT+JlbAhLIK4FZs2DNNeHjHy/tGl5++bzW1HrFrQ7D/DRX/t45RJpYA/gEMLnKGsALgS8Ac7K3xNq/kMinMzH87jAD9BDgTuDqhEG8LbB+dnxMQ8UogA3hesfDCmAaP3NLQAJ5JvD3v8Pee0foBE4/HXbYwWhgDvvLe34b75QaAhjr/GIXVGdnSQAAIABJREFUcMhVbAiZAsQhzWtV2QW8FLB4RQ02AWJK+H3AM8Arw9Qudv1eBIxvvAVv5ggBjR3KsVGloaQANoRLAUzDZW4JSKCQBH70I/jqVyHuFfv2t2HFmDUz5YHAcPK39NLwTCiI6R0E6jgH8AhgNyDO9rsVKJ8DGGf8/RnYGripCtpG1gAqgAUem0YAC9x5Vl0CEmiAwHPPweTJpesjfv7zBjL6aLsI1Fqa6dTv0OTrEMB2dVtluSGAL2bTxc2+b9ksAmkEsFmCTeZTAJsEZzYJSKCgBF5+2XuEc9B1Cy0EcanLUEn5G76TciSArRhNcSuIAtgKkg2UoQA2AMtHJSABCUggncAKK8ATsSVhiKT81WacEwGMdYKtSn9ptCDXADZK7O3PK4Bp/MwtAQn0CoEIR8UFtKa2Ejj2WDgs7ptQ/pI450QAk9qQmlkBTCOoAKbxM7cEJNALBOIquY99DL74xdI6wZifNLWFgMe9tAarAggKYNpYUgDT+JlbAhLoFQK/+x2MHw9LLgnTpsG66/ZKy3LTjuHk75hj4NBDc1PV3FdEAVQAUwepAphK0PwSkEDvEJgzB448snRm4Ne/DgcfDCPjXFxTKgHv+E0l+Pb8CqACmDqiFMBUguaXgAR6j8Ctt8LOO8Oii8Lvf+/awMQeHk7+wq9fey3xBX2YXQFUAFOHvQKYStD8EpBAbxIIKwn5+2Sci2tqloBn/TVLbvh8CqACmDqyFMBUguaXgAQkIIGqBJS/9g2MnAngYkCslZjVvha/s2Q3gaTRVgDT+JlbAhKQgASqEFD+2jssciSA3wBi+04I4HRgAjAza/1oYBXgOeCxVhNRANOIKoBp/MwtAQn0I4FzzintEt50035sfc02K381ESU/kBMB/AhwDnABsALwuexGj82Ak4D/AMpnKv0fcCpwPhA3fyQnBTANoQKYxs/cEpBAPxI488zSLuFx4+D442F0BDpMQUD568w4yIkAHgj8DLi7otXjgOOB3wAPAu8G1gM+nMngrzJRfCGVlAKYRlABTONnbglIoF8JPPggTJgAjzwCU6fCllv2K4k32638dW4I5EQA9wB+C9w1qOUHAScM+n9LAF8BDgEeAD6VSksBTCOoAKbxM7cEJNDPBOIGkW9/Gw46CLbbDk47rW+jgcpfZz8IORHAfwT+BfjWoNbPzaaCq0FZEvgBcDkwNYWaAphCDxTANH7mloAEJAB/+QuceGJJAEeN6jsiyl/nuzwnAhgNPxeI252fqaAQEcGY9h0qvQv4b+DfUsgpgCn0FMA0euaWgAQk0OcElL/uDIAcCeCywCnA3hXHwKwKPFSDzHezKeGmARZNAL8JbAu8D3gxWyT5tUHbo8OazwQ2Ap7PdswcOYhQ/Dm2Wkco9TZgEnBPxTP1lBGPGwFseuiZUQISkEB/E1D+utf/ORLAgLA8cHK2Gzg2edST+k4AjwW+D/wJiIMTI3Qac+gbZLRiK1ksjpwGHAWsme2wCbCnZ8/Erps9gW2AGcDhwI7Zsy8D9ZRR7hwFsJ5h6jMSkIAEmiUQm0WWWAKWW67ZEnKX79hj4bCY9BsmzW/JQR+5a3puKpQzAQwuCwK7Ah8FbgGuy3YBV2MW3vPl7PzAppkWLQI4uKHrA7cDS2eh052ynTNxns687OEIq+4FrJH9OcKqcZbOWdmfR2SHLu4HXALUU4YC2PSQM6MEJCCBBghMmQJnnw1nnAHbb1/7nJQGiu7Go+GyL8b8lfLXDfxvvjOHAljJI4JasS0+vOVV4Obs10vAPwNbZ4GsV1IgFl0AY/p3dyDmyyOF2K2TRffKXDbOtlkvlRl2TAvH/wug5RSnb0dU8YA6yqj86BoBTBl95pWABCRQD4Ef/AAmTYIPfxjOOw/+Ib56i5dqTflGi4z8daZfcy6AlRDiIOg4MHrz7OiXRYFYDndjtsytaWBFFsCw4x9mByJGqDRSbIleHPhSBZG1s/V9K2UC+NdMEu+veOYyIA5VjPBrrTLKV7REdgWw6aFnRglIQAINEHjmGZg8GX7yEzj1VNh550JFA5W/Bvq6A48WSAAH04ht8psAcVvIe4BYuvZ74Abg6UbQFVUAY+tzLICM6dqfVDS4KxHAiRMnMjo7yX7s2LHEL5MEJCABCbSBwNVXwx57wDXXwAc+0IYXtL7IWvI3YgS88Ubr32uJQxMosAAOblTsh/h4NrM5eMPrsEOgiAIYCx9j/d4XgesHtS42c5yY3alXXgO4TzZXPtwawCeAfYFLsw0htcoov9YIoN8wEpCABDpN4PXXYeTITr+1qffVkr8xY2Bm5bxSU28xU6MEekgAG236m88XTQBj927s7v00cFOVVscO3pjajV3AsWM4pO/q7Iyd8i7gWOcX5cRxMrEhZAqwA7BWFkqtpwwFsOkhZ0YJSEACvU9g0UVhzpzh27nTTnDRRb3PIo8tVAChaAIYUb3Xs10xMaai/rFZPo50KQvhusA52TmAs7KjYo4eNACPAHYD4m69W6ucA1hPGVGkEcA8frKtkwQk0J8E5sYNWnGJVhzu0L1UK+oXNXOzR/f6J96sABZPALs7Yt75dgUwbz1ifSQggf4lEDuEI6Q2bRr8YxyV1vmk/HWeeTNvVAAVwGbGTWUeBTCVoPklIAEJtIrAK6/A4YfDWWeVTlo+8MCOrRUcNw4uvrh2Q4z81WbUiScUQAUwdZwpgKkEzS8BCUig1QRuuQXGj4dRo0rRwA9+sNVveFt59UT9IoPy19ZuaKhwBVABbGjAVHlYAUwlaH4JSEAC7SDw6qtwzDFwyilw5ZWwdVye0PpUj/wtsghEcNKUHwIKoAKYOhoVwFSC5peABCTQTgJ33glrrAGLxXFprUurrw4z4jb5GsmoXy1C3fl7BVABTB15CmAqQfNLQAISKBiBeqJ+0STlL78dqwAqgKmjUwFMJWh+CUhAAgUioPwVqLOGqaoCqACmjmQFMJWg+SUgAQl0g8CDD8I558DRR8PicYX88Kle8Vt6aYhri035JqAAKoCpI1QBTCVofglIQALdIPDQQxBntzz2GEydCp/61JC1qFf+nPLtRkc2904FUAFsbuS8lUsBTCVofglIQALdIjBvHpx7Lhx8MGy/PZx4Iiy11Ju1qVf8IoPy161ObO69CqAC2NzIUQBTuZlfAhKQQH4IPPIITJwI991XOs35U5+iXvlzyjc/3dhITRRABbCR8VLtWSOAqQTNLwEJSCAPBCKEd8EFLDBx57hMuK4aGfWrC1MuH1IAFcDUgakAphI0vwQkIIEcEKj3KrdyVZW/HHRaQhUUQAUwYfgMZFUAUwmaXwISkECXCdQ73RvVvPZaGDu2yxX29ckEFEAFMHUQKYCpBM0vAQlIoEsERoyA2AdSO80H5jF/3oLUvTiwdqE+0UUCCqACmDr8FMBUguaXgAQk0GEC06c3djXwlv/8Etc9sApssknp7MAVVuhwjX1dqwkogApg6phSAFMJml8CEpBABwk0Mt0b1Xpzrd/TT8O++8LVV8Npp8FOOxkN7GC/tfpVCqACmDqmFMBUguaXgAQk0AECjYpf+N1FF1Wp2FVXwe67wz/9E3znO7Dyyh2ova9oNQEFUAFMHVMKYCpB80tAAhJoI4FFF4U5cxp7Qc0dvs8/DwccAIssAmed1VjhPp0LAgqgApg6EBXAVILml4AEJNAGAsceC4cd1ljBNcVvcHFz50LsJDEVjoACqACmDloFMJWg+SUgAQm0mECj073hcG+80eJKWFyuCSiACmDqAFUAUwmaXwISkECLCDQqfvHahqN+9dT15ZdhscXqedJnukRAAVQAU4eeAphK0PwSkIAEEgnkRvyiHS++CGuuCfvsA/vvDwstlNg6s7eDgAKoAKaOKwUwlaD5JSABCTRJoBnxGzkSXnutyRfWm+33v4fx42HxxeHCC0s7hk25IqAAKoCpA1IBTCVofglIQAINEmhG/OIVbZnuHarusfX46KNLZwYedBB8/eswalSDLfXxdhFQABXA1LGlAKYSNL8EJCCBOgkUQvwGt+X222HnnUu3h/zsZ3W21MfaTUABVABTx5gCmErQ/BKQgARqECik+FW2KeacH3oI1l7bvs4JAQVQAUwdigpgKkHzS0ACEhiCQLPit+WWcN11YpXA0AQUQAUw9fOhAKYSNL8EJCCBCgIbbgh33NEcktGjYfbs5vKaq78IKIAKYOqIVwBTCZpfAhKQAKULNebNaw5FYQ9yjl0pe+8Nn/scbL55c403V1MEFEAFsKmBU5FJAUwlaH4JSKCvCTQ7zRvQFlwQ4ja2wqYQwDPPhEMOga98BU48EZZYorDNKVLFFUAFMHW8KoCpBM0vAQn0JYEU8Yu8zUYLcwk7NohMmAAzZsB3vgNjx+aymr1UKQVQAUwdzwpgKkHzS0ACfUMgZZo3IHXkEOdu9UYY7fnnw4EHwhe+AN/+dqnBprYQUAAVwNSBpQCmEjS/BCTQ8wRSon0BZ+ml4Zlneh5TqYF//StcfnlJBE1tI6AAKoCpg0sBTCVofglIoCcJxFK2uBY3Je20E1x0UUoJ5pVAdQIKoAKY+tlQAFMJml8CEugpAqnRvoDR0Svbeoq+jamXgAKoANY7VoZ6TgFMJWh+CUig8ARaIX2KXx3D4MYbYY01YPnl63jYR4YjoAAqgKmfEAUwlaD5JSCBQhIYNw4uvrg1VTfiVyfHvfaCSy6B00+HHXaAVpl3na/vpccUQAUwdTwrgKkEzS8BCRSKQKucw/V9TXb7j38MX/0qbLABnHcerLRSkwX1dzYFUAFM/QQogKkEzS8BCeSeQKukz2neFnX1c8/BfvvBlVfCSSfBxIlGAxtEqwAqgA0OmXc8rgCmEjS/BCSQSwKtlL5FFoFXXsllM4tdqWuvha9/Ha67DpZdttht6XDtFUAFMHXIKYCpBM0vAQnkhkArpc9oX4e6NRZQtrrjOlT1br5GAVQAU8efAphK0PwSkEBXCbTaHUaPhtmzu9okXy6BmgTqEMAjgQnAksBtwCTgnioFvwc4CdgUiDDs08BlwBHAazUr0sUHFujiu3vh1QpgL/SibZBAnxFotfQZ7cvhAIpTuGPufaGFcli57lephgDGNSx7AtsAM4DDgR2BNYGXB9V+FWA74HLgYWBV4IfAL4D9ut/SoWugAKb1jgKYxs/cEpBAhwgofR0CnZfXxFVyv/kNTJsG666bl1rlph41BPAh4FTgrKzCI4CZmdBdUkcj9gHGARvU8WzXHlEA09ArgGn8zC0BCbSRQDukr6/u5W1j37S96Nh1c+SRcMYZcMghcNBBMHJk219blBcMI4Ax5fs8sDFwc0V7pgN/Ag6oo41XA08Cu9TxbNceUQDT0CuAafzMLQEJtJDAiBEwb14LC6woysOa28O17aX+4Q8wfnxpKjiigXF+oIlhBHBF4K/AOsD9FahiXd8LwK418H0D2B34cBY1zC1tBTCtaxTANH7mloAEEgm0I8pXrpLSl9g5ecn+2mtw3HFw4onwu9/BBz+Yl5p1tB7Tp08nfkWaPXs2U6dOjd+G8D1eUZGUCODR2VrBLYAHO9q4Jl6mADYBrSKLApjGz9wSkECDBCKQM3dug5kaeFzpawBW0R6dMQNWXdVjY2C4CGD0arU1gE8Ak4Gh1gCeDWwFhPw9WoShoQCm9ZICmMbP3BKQQB0E2hnli9crfXV0go/0FIEam0BinV/sAt42k8EpwA7AWlV2AccGkf8C1gO2BJ4qCigFMK2nFMA0fuaWgASqEGi38Cl9Drt+J1DHOYBxjt9uwBLArRXnAMbly38GtgZuys7/+xXwKvBGxjXcan52hmBuUSuAaV2jAKbxM7cEJEBnZuRig8gb5R9PUpdAJYFrroGrr4bjj4clwnd6P9UhgD0PQQFM62IFMI2fuSXQlwQ6EeELsMccA4ce2peIbXQjBGJt4C67wCOPwPnnw1axlK23kwLoVXCpI1wBTCVofgn0AYFOCV+gdD1fHwyodjQxzg/69rdL5wVutx2cfDIstVQ73pSLMhVABTB1ICqAqQTNL4EeJNBJ4YvbvuLMX5MEWkLgL3+BXXeFe+6BK66ATTZpSbF5K0QBLJ4Axn17cSHz+sBoII41rzz2NHbhnAlslJ3kfT4QFzpXploXPNdTRrk8BTBvn2rrI4EOEzj2WDjssM6+1ChfZ3n33dtigF10EWyxBay8ck82XwEsngDGwoSlgcWAOMGxUgBDCB8ApgFHZZc2/ww4GTg9G8G1Lniup4zKD4MC2JNfDTZKAkMT6GR0r1yLLbeE666zVyQggVYRUACLJ4Dlvv8k8MtBArgTcAKwQkVUcG9gL2CNLGOtC57rKUMBbNUn0HIkkHMCG24Id9zR+UqGZLbrSrfOt8Y3SiB/BBTA3hLAU7O7+7apGGpxmfNvgVjJumAdFzzXKuPFQcPYCGD+PtfWSAJNE+hGdK9cWad1m+42M3aSwAsvwLXXwhe/2Jnzi9rUNgWwtwQwpoQXB75UMV7WBu4B4uDGEMBaFzzXKmOmAtimT6PFSqDDBLope9FUha/DHe7rWkPgtttg223hwx+G886Df4g4SPGSAthbAlgrete2CODEiRMZPTqWD8LYsWMHfpkkIIH8EOi27Cl8+RkL1qQFBJ59FiZPhh//GE45BcaPL1w0UAHsLQHcEThx0BrAfbL7/IZbAxgXPO8LXArUU0blp8cp4BZ8l1iEBFpFoBs7cqvVfeRIeO21VrXKciSQUwJxg8huu8Haa5cOkH7/+3Na0XdWSwEsngBGFC92/sYmkNjhG3fWzAXiqzamf+/PdgEfm238uBo4pWIXcK0LniOMV6sMBbAwH3Er2ssE8hDVK/N1OreXR5ptG5bArFnwta/B+uvDHnsUBpYCWDwBjF26F2aXLMdAK1+4vDlwA7AucE52DuAs4Fzg6EEjcqgLnsuP1VNG+VkjgIX5uFvRohIYNw4uvjhftVf48tUf1kYCjRJQAIsngI32cbufVwDbTdjy+4pAnqJ6Rvf6aujZ2D4joAAqgKlDXgFMJWj+viSQR9GLjlhtNXjwwb7sEhstgfYQ+Nvf4L3vbU/ZCaUqgApgwvAZyKoAphI0f88S2GoruP76/DbPO3Tz2zfWrEcIPPIIrLsuHHIIHHggxO6onCQFUAFMHYoKYCpB8xeeQF6jeZVgx4yBmYNP8Sw8eRsggQIQuPnm0jExCy8MF15Y2iySg6QAKoCpw1ABTCVo/kIQGDUKXn+9EFX1gOVidJO17CcCr74KxxxTOjMwIoGHHgrxpdLFpAAqgKnDTwFMJWj+3BBYYgl4cfBlh7mpXfWKuBs35x1k9SRQSSAu1o5o4AYbwLRpXWWjACqAqQNQAUwlaP6OEijCdG01IIpeR4eJL5NA+wjEVELcJLLccu17Rx0lK4AKYB3DZNhHFMBUguZvOYGiSl6AcBduy4eDBUpAAlUIKIAKYOoHQwFMJWj+hgkUWfDKjY0ZoNtvb7jpZpCABCTQEgIKoAKYOpAUwFSC5n8bgenTYeuteweKU7e905e2RAJtJTBnDnzmM6VNIlts0dZXReEKoAKYOsgUwFSCfZa/F6J31bpM0euzgWxzJdBqAvPmwbnnwsEHw/bbw4knwlJLtfotb5anACqAqYNLAUwl2EP5e1Xuyl2k5PXQYLUpEsgrgTg8euJEuO8++M53YJtt2lJTBVABTB1YCmAqwYLk73W5U/IKMhCtpgT6gUD8a/OCC2D//eE//xO+/e2Wt1oBVABTB5UCmEqwy/n7RewqMRvJ6/Kg8/USkEB9BB57DOImkc9/vr7nG3hKAVQAGxguVR9VAFMJtil/P4qdUbw2DSaLlYAEeo6AAqgApg5qBTCVYAP5F1wQr/nyrLwGRoyPSkACEqhOQAFUAFM/GwpgIsF+jtQNhc4p2sRBZXYJSKD3CVx6KWy2GaywQlNtVQAVwKYGTkUmBXAQQaN0tYeUglebkU9IQAISGJJAfInuvDP8+Mdw2mmw007QYDRBAVQAUz9hPS+ADX6mUnkWPr9yV/gutAESkEBRCFx1Fey+O/zTP5WOjFl55bprrgAqgHUPliEeLJQAKnPNd/cii8ArrzSf35wSkIAEJNAGAs8/DwccAFdcUTo8etddIaaiaiQFUAGsNUZq/X1XBFCRq9Utjf29UbvGePm0BCQggdwRuO46OPts+N73YOTImtVTABXAmoOkxgNNC+C4cXDxxamvN/9QBOIfgHPnykcCEpCABCTwTgIKoAKY+rkYEEB4Fnh3alnmr4OA0bo6IPmIBCQgAQkMS0ABVABTPyKZAD4PtO/S6tRK5j2/Upf3HrJ+EpCABApMYOZMeO97YaGF3myEAqgApo5oBbAKQYUudViZXwISkIAEWkZgu+1gxgy48MLSjuGYunvsMVZaaaX47YrA4y17V4EKWqBAdc1jVTMBnAUsmcf6JddpxAh4443kYixAAhKQgAQk0B0Cc+bA0UeXzgw86CD4+td57G9/UwC70xs989ZCRQBHj4bZs3uGvQ2RgAQkIAEJ1E/g9ttLB0jPn89jxx/PSttuawSwfno+OYhA1wTQaVbHogQkIAEJSKBBAq+9BiecwGMnnshKL76oADaIz8ffIpC0C1iJcyhJQAISkIAEOk/gsT/9iZXWW08B7Dz6nnlj0+cA9gwBGyIBCUhAAhIoGAE3gbgLOHXIKoCpBM0vAQlIQAIS6DABBVABTB1yCmAqQfNLQAISkIAEOkxAAVQAU4ecAphK0PwSkIAEJCCBDhNQABXA1CGnAKYSNL8EJCABCUigwwQUQAUwdcgpgKkEzS8BCUhAAhLoMAEFUAFMHXIKYCpB80tAAhKQgAQ6TEABVABTh5wCmErQ/BKQgAQkIIEOE1AAFcDUIacAphI0vwQkIAEJSKDDBBRABTB1yCmAqQTNLwEJSEACEugwAQVQAUwdcgpgKkHzS0ACEpCABDpMQAFUAFOHnAKYStD8EpCABCQggQ4TUAAVwNQhpwCmEjS/BCQgAQlIoMMEFEAFMHXIKYCpBM0vAQlIQAIS6DABBVABTB1yCmAqQfNLQAISkIAEOkxAAVQAU4ecAphK0PwSkIAEJCCBDhNQABXA1CGnAKYSNL8EJCABCUigwwQUQAUwdcgpgKkEzS8BCUhAAhLoMAEFUAFMHXIKYCpB80tAAhKQgAQ6TEABVABTh5wCmErQ/BKQgAQkIIEOE1AAFcDUIacAphI0vwQkIAEJSKDDBBRABTB1yCmAqQTNLwEJSEACEugwAQVQAUwdcgpgKkHzS0ACEpCABDpMQAFUAFOHnAKYStD8EpCABCQggQ4TUAAVwNQhpwCmEjS/BCQgAQlIoMMEFEAFMHXIKYCpBM0vAQlIQAIS6DABBVABHG7IHQlMAJYEbgMmAfcMyqAAdvhD6+skIAEJSEACqQTqEMB6HKBcjXcBZwPbAvOAq4E9gVmp9Wxn/gXaWXiByz4w67xtgBnA4cCOwJrAyxXtUgAL3MnNVH369OmMHTu2mazmKSAB+7uAnZZQZfs7AV7BstYQwHodoNzqEL6RwHaUAmuXAy8Bn8kzFgWweu88BJwKnJX99QhgJrAfcIkCmOch3d667bfffpx6agwNUz8QsL/7oZffaqP93T/9XUMA63WAALYy8AiwHnB3RjB+/8fs7x7LK1UF8J09E1O+zwMbAzdX/PV04E/AAQpgXodz++vlD4j2M87TG+zvPPVG++tif7efcV7eMIwANuIA0Zz/B1wGLDaobXOALwA/zUubB9dDAXxnz6wI/BVYB7i/4q+jg18Adh0sgLfccgtjxozJax9brxYSOOqoo5gyZUoLS7SoPBOwv/PcO62vm/3deqZ5LfGJJ57gIx/5SFQvfuY/XlHPRhwgsu0AnAQMloAns1nDS/PKQAFMiwBumG0QyWv/Wi8JSEACEpCABIYmsBFwe8VfGwHs89FSbf7/CWDyoDWAIdAbAE/1OS+bLwEJSEACEigageWAO4D5gyperwNEtlgD+DCwfsUawPh9SOX7ANcAFmxUxDq/2MIdW7pjIMScX4R51xq0C7hgzbK6EpCABCQgAQnUINCoA1yV7QL+crYL+H+AF4HP5pm0U8BD984RwG7AEsCtQ5wDmOe+tW4SkIAEJCABCTRHYCgHWAn4M7A1cFNWdJwDGKeG/FsWTQwhjCBS7BvIbVIAc9s1VkwCEpCABCQggf/f3t28WjfGYRy/MjChlKGhUhJlRj0ieStlogxMmJp4GCnJhCRigLxFDEyVKBkQkfwDRsxk/PQMjLyVrrpXrbPts++11r7WPr/T/u56Zmf91r0+117n+Z31ct8IrCNAA7if65yZwvfbE1unBDyp9/PtVr6//372w3+t+dK9P56/6S1JfjDY0wF9IMk5jz+93KfUSB0PdU4KeCJWr9rjZ3CubrdlPDP/8JmSTSLfXg1yywj08nb2no7jn3Zrzue7p/gar+rUyyrxnckcLVVebo9m+dk632L9XtIzG8/ZJfJK1CifFg3g8ojmzhS+fE9smRRwA3iPpDu3FHXD8KukjyS90FZ++UrSa5LeaD/fy31KjeTxUOukwH2Srm1zcn240QBOySaRb68GmeUEduXtvbgB9Pn+3Sm77GWV+M7kjpZKL0n6tM3J63n33pV0U3sZ0zqJvBI1zkVSNIDLY5ozU/jyvbBlWmBXA/i4pFckXdf+4/C+L0p6UtINbSC93KfUSB8T9f4vcJekbzcawCnZJPLt1SCvvMC2vIcG8N72Xdi2115Wie9M/mipOAgMb9v6jz6vu5vIK1HjXCREA7gsprnzBC3bC1utIeAG0G94eU1n//tJ0nNtKR+v8eYJwL0G9PDx7aIfJV0j6YoJq8T0avi2BZ/1BbY1BL1sEvlOqbH+0R/fHnY1gJ6Q1+u0/ibpPUm+MuzPlN/jie/M8aVxuCP27d8nJF3fdpnIK1HjcAIXoIHSAAAEbklEQVR77IkGcBne3JnCl+2FrdYQ8O2CPyT93mZu9wzut7Vnxt6UdJWkR0c7vrE9L+Q3v/yfe2+VGP/nsquG15Tms77Atoagl00i3yk11j/649vDaQ3g3e2PvH8l+Xax13J/VtL7bQWIfc9n8j6775qv7H4m6WFJX7dhcI7PyIMGcAbW6Een/OW4rDJbHVrgynbr4CFJD3IF8ND8q+2PK4Cr0ZYsfFoDuDlYz+l6v6Q7uAJYMsepg/J0K5+0W75fjDZKXL1L1Jh6HGf6czSAy/nnzBS+fC9subbA0AB6QW+v5fjqxjOAT7X5nHY9A+hVYp6W5DUfH5tQY+1jor60rSGYks1p5/WcfPndcPhv4JwG8AFJF9oQyfvwWe27R8/Y4Dn3HpH0zUYxzvEZujSAM7A2fnTuTOHL98SWSQH/0vDLAZckeRkg3wL21YBb2jQRv7S3gP22mZu+LyW9PnoLuJe73yDr1UgeD7VOCviWnJ/3ckPgN7g9kbtv//3Vbs33sknk26tBZjmBXXnf2s7pn0dvA3uFBl8FfLsNoZfVlPO5VyN3tFTy5MqeocF3bIZJmMcqibwSNc5FUjSA+8XEaiH7+Z3F1p9Lur01A5cl/dDmBfSVAH9ulvROmwfQb5V5moEXNwbay31KjbM49mPYp9/g+3i0tucw16OfBXPWU7JJ5NurcQxZHOIYd+XtR3V8Rd/PbHseQL8E4nPbc3uOP72sEt+ZQ1gcwz48rc/fkv5sBzuc335xb2gIE3klapTPgwawfEQMEAEEEEAAAQQQyArQAGY9qYYAAggggAACCJQXoAEsHxEDRAABBBBAAAEEsgI0gFlPqiGAAAIIIIAAAuUFaADLR8QAEUAAAQQQQACBrAANYNaTaggggAACCCCAQHkBGsDyETFABBBAAAEEEEAgK0ADmPWkGgIIIIAAAgggUF6ABrB8RAwQAQQQQAABBBDICtAAZj2phgACCCCAAAIIlBegASwfEQNEAAEEEEAAAQSyAjSAWU+qIYAAAggggAAC5QVoAMtHxAARQAABBBBAAIGsAA1g1pNqCCCAAAIIIIBAeQEawPIRMUAEEEAAAQQQQCArQAOY9aQaAggggAACCCBQXoAGsHxEDBABBBBAAAEEEMgK0ABmPamGAAIIIIAAAgiUF6ABLB8RA0QAAQQQQAABBLICNIBZT6ohgAACCCCAAALlBWgAy0fEABFAAAEEEEAAgawADWDWk2oIIIAAAggggEB5ARrA8hExQAQQQAABBBBAICtAA5j1pBoCCCCAAAIIIFBegAawfEQMEAEEEEAAAQQQyArQAGY9qYYAAggggAACCJQXoAEsHxEDRAABBBBAAAEEsgI0gFlPqiGAAAIIIIAAAuUFaADLR8QAEUAAAQQQQACBrAANYNaTaggggAACCCCAQHkBGsDyETFABBBAAAEEEEAgK0ADmPWkGgIIIIAAAgggUF6ABrB8RAwQAQQQQAABBBDICtAAZj2phgACCCCAAAIIlBf4D+cW3dDE8bA8AAAAAElFTkSuQmCC\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_beta(beta_values,T=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### create beta values for double simulated annealing of system configuration" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "beta_list=[[0.1,0.9]]+[[0.3,1.]]\n", "iter_list=[iteration_number//2,iteration_number//2]\n", "#beta_list=[beta]\n", "#iter_list= [iteration_number]\n", "beta_many = multiple_beta(beta_list,iter_list, linspace=True)\n", "plot_beta(beta_many,T=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 41s, sys: 32 ms, total: 10min 41s\n", "Wall time: 10min 41s\n" ] } ], "source": [ "%%time\n", "traj_many, pot_many = sampler.metropolis_sa(iteration_number=iteration_number,step=step,beta=beta_many)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-17109.6595125\n" ] } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "ax.plot(pot_many)\n", "ax.set_xlabel(r\"Iteration\", fontsize=15)\n", "ax.set_ylabel(r\"Potential [$kcal$/$mol$]\", fontsize=15)\n", "ax.set_ylim([pot_many[-1]-1000,20000])\n", "ax2 = ax.twinx()\n", "ax2.plot(beta_many,c='r')\n", "ax2.set_ylabel(r\"$\\beta$ [mol/kcal]\",fontsize=15)\n", "print(pot_many[-1])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_nacl(traj=traj_many,left=0,right=12,num_na=50,traj_sample=-1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "system_config_mc = creator.generate_problem()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "system_config_mc.xyz = traj[-1]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sampler_mc = particlesim.api.Sampler(system_config_mc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Do plain metropolis monte carlo with last configuration from simulated annealing with three different temperatures\n", "* T_1 = 200 Kelvin\n", "* T_2 = 800 Kelvin\n", "* T_3 = 1200 Kelvin" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 57s, sys: 15.9 ms, total: 3min 57s\n", "Wall time: 3min 57s\n" ] } ], "source": [ "%%time\n", "beta_mc = particlesim.utils.conversion.kelvin_to_beta(200)\n", "iteration_mc = 2500\n", "step_mc = 0.001\n", "traj_mc_200, pot_mc_200 = sampler_mc.metropolis(iteration_number=iteration_mc,step = step_mc, beta= beta_mc) \n", "\n", "beta_mc = particlesim.utils.conversion.kelvin_to_beta(800)\n", "traj_mc_800, pot_mc_800 = sampler_mc.metropolis(iteration_number=iteration_mc,step = step_mc, beta= beta_mc) \n", "\n", "beta_mc = particlesim.utils.conversion.kelvin_to_beta(1200)\n", "traj_mc_1200, pot_mc_1200 = sampler_mc.metropolis(iteration_number=iteration_mc,step = step_mc, beta= beta_mc)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "#fig = plt.figure()\n", "#ax = fig.add_subplot(111)\n", "axes[0].plot(pot_mc_200, c='r', label = r\"200 $K$\")\n", "axes[0].plot(pot_mc_800, c='b', label = r\"800 $K$\")\n", "axes[0].plot(pot_mc_1200,c='g', label = r\"1200 $K$\")\n", "axes[0].set_xlabel(r\"Iteration\", fontsize=15)\n", "axes[0].set_ylabel(r\"Potential [$kcal$/$mol$]\", fontsize=15)\n", "axes[0].legend()\n", "\n", "#ax.set_ylim([pot_mc[-1]-200,0])\n", "\n", "#ax2 = fig.add_subplot(222)\n", "def create_hist(pot_mc):\n", " hist, edges = np.histogram(pot_mc, bins=50)\n", " rho = hist.astype(np.float64) / np.sum(hist)\n", " return edges,rho\n", "edges200, rho200 = create_hist(pot_mc_200)\n", "edges800, rho800 = create_hist(pot_mc_800)\n", "edges1200, rho1200 = create_hist(pot_mc_1200)\n", "\n", "axes[1].plot(edges200[1:], rho200, c='r', label = r\"200 $K$\")\n", "axes[1].plot(edges800[1:], rho800, c='b', label = r\"800 $K$\")\n", "axes[1].plot(edges1200[1:], rho1200, c='g', label = r\"1200 $K$\")\n", "\n", "axes[1].set_xlabel(r\"$E$ [$kcal$/$mol$]\", fontsize=15)\n", "axes[1].set_ylabel(r\"$\\rho(E)$\", fontsize=15)\n", "axes[1].legend()\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "particlesim.utils.xyz.export_trajectory(labels=system_config.labels, trajectory=traj, \n", " path='../../vmd_traj/final_100_part.xyz')\n", "particlesim.utils.xyz.export_trajectory(labels=system_config_mc.labels, trajectory=traj_mc_200, \n", " path='../../vmd_traj/final_100_part_mc_200.xyz')\n", "particlesim.utils.xyz.export_trajectory(labels=system_config_mc.labels, trajectory=traj_mc_1200, \n", " path='../../vmd_traj/final_100_part_mc_1200.xyz')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
crm416/altcoin-analysis	src/Hypothesis 2.ipynb	1	425607	{ "metadata": { "name": "", "signature": "sha256:0539a710596b211fc6f13c8b2d9a37eb5f46c6682e184540f641d20afb0c9a5f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import config\n", "from coin import Coin\n", "import plot" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "fields = ['block_chain_work', 'block_num_tx', 'price']\n", "coins = [Coin(ticker) for ticker in config.TICKERS]\n", "frames = [coin.get_frame(*fields, normalize='z_score') for coin in coins]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "corrs = {}\n", "for (coin, df) in zip(coins, frames):\n", " plot.plot_timeseries(coin.ticker, df, window=100)\n", " corrs[coin.ticker] = df.corr()\n", " print corrs[coin.ticker]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Y/xIUTTExcPasmvZUqy4oIO3gQc/HatLEOp2QYJ3u2NE+r6yfqLD3zAWjR7te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\nVwOHgPXAHVLKbTbbSF91LP57Mbd/cjt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"text": [ "<matplotlib.figure.Figure at 0x108b372d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.023273 -0.020047\n", "block_num_tx -0.023273 1.000000 0.227554\n", "price -0.020047 0.227554 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Yma5MVu5dGWkZuT5tnJq2KwCrnZ1tJgz2pnlz4+MePRo6dSp297/C3CUWS6wQ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\n+ckvpBZ4J4+2+PoDnwaecH43xHSXe9QJvwQMLiE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"text": [ "<matplotlib.figure.Figure at 0x107720110>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.006589 -0.052038\n", "block_num_tx -0.006589 1.000000 0.134769\n", "price -0.052038 0.134769 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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D8tYjKCy0vG//fuvHqk4REFEKEcUWL2cBOAugjq3j1GJXBIQsSliVo2ZNoGFD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\n/rnn4OyzE+sCt2sH06bBWT4L8dNPw5FH1n5myRKN1y1aSE4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"text": [ "<matplotlib.figure.Figure at 0x15107df50>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.066344 -0.16987\n", "block_num_tx -0.066344 1.000000 0.10410\n", "price -0.169870 0.104100 1.00000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TLrqGXJYvh5Ej/c/H9dtgcMFbyKUguFZwjh2rv7du1Ubb1Rexe8JK+Tbohc22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\nUqj1ZGXB+PH6gmrcGGrW1G32X39djw0wfz5cdZU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"text": [ "<matplotlib.figure.Figure at 0x13d09f4d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 0.044167 -0.260599\n", "block_num_tx 0.044167 1.000000 0.047648\n", "price -0.260599 0.047648 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jSqmGuZflP261RxBOXjjpmHfFSjEpT4y2wLCUNl8x7aJFoXhxOHIEateG6tX1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\n7PQemzVzel1Z8vff0KULJa5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1VKjg3nDiBGV1AVkAIqtW6XnX5sV9mC77qCojeElKc1JA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\nkbtE5A3ga2BUPmmrin7lW43ug6UJ6AvCdiOtFpF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"text": [ "<matplotlib.figure.Figure at 0x17398a150>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.073549 -0.031882\n", "block_num_tx -0.073549 1.000000 0.079505\n", "price -0.031882 0.079505 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xBR5PNvD//vFfNhzZEDG5wxYOY9y6cchh5rws/JV7ttT5Bo0lP/BwyQ3HCzkq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\n/JHWrw8g6dTJmGnvoDh8GBo1gq1boa0fi6fk5MC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"text": [ "<matplotlib.figure.Figure at 0x1702c8b90>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_num_tx price\n", "block_num_tx 1.000000 0.019668\n", "price 0.019668 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Gec899ziXJ0+eLAcOHBiSBpvN5rF+2bJlMjc3V7Zs2VLOnDkz6LnEing9S4sWLYrLcUIh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\nPl/Y5/I5A0Gfy/n8sT/wY7XO0OobuOo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"text": [ "<matplotlib.figure.Figure at 0x16825f850>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 0.091292 0.082153\n", "block_num_tx 0.091292 1.000000 0.105890\n", "price 0.082153 0.105890 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jZFpamvT29pZ///23Vd3nzp2TQgg5depU2aRJExkVFZWtHsM+5mWDgoLkihUrpJRS9u3b\nV3700UfGbaGhoUadhu+zdOlS43L37t3lG2+8YVyeNWuW7Natm0Ma0tLSLNbv3r1b+vn5yTp16sjl\ny5dn+13yE/Pr6cIFNdnc6tXG9VY2tdi4RzTW6E6pkocrTTdmIPNUYObTimVH5inJzJdLly6do7rM\nCQoKMj4lPPvss7mqIz8wtLCz6jctNkbblfyVWos1rqDDFTTkN6403Vh2lClThlu3bhmXL1++nO0+\n5rodwV7DZfbs2SQnJ1OjRg2mTJmSozrzkxLVEanRlHSki003lh0tW7Zk/fr1xMTEcPnyZT7//PM8\n1WeLypUr4+bmZjFX5alTpxg5ciRLlixh4cKFTJkypUj5tIuN0XalGFytxRpX0OEKGvITV5tuzHwf\nW/Tq1YsWLVoQEBBASEiI8Y/D0fociUv38fFhxIgRdOjQgYoVK/LXX3/Rq1cvhg8fTrNmzahfvz4T\nJ06kV69epBiyNRUijrS0i810Y66Q28KAK2gRYwUVvSuyKmhVoWsB1zgnOveIxhUxv57OnYO6dWH5\ncujRw3bukWJjtDWWGIz29feuF7aUYoc22hpnYn49nTkD9evDsmXwwgt6jkiNRuNkXGG6MVfQ4CxK\nVEekK/krtRZrXEGHK2gobvTs2ZP4+Hir15EjR7LfuRhpcBYlymhrrNFZ/jSaokWJ6ojUWCLGCip5\nV+Lae9cKW0qxQ/u0Nc7E/Ho6cQLuvBMWLoTevbVPW6PRaFyaEuUecSV/pdZijSvocAUNGk1WlCij\nrdFoHGfbtm00bmw1paumkHFkRKT2aRdTxFiBv48/V9+9WthSih3ap61xJubX05Ej0Lw5zJsH/fpp\nn3aJQxsWjS1SU1MLW4LGDiXKPeJK/kqtxRpX0OEKGvKTgIAAJk+ebDVzTVhYGDVr1mTKlClUr16d\n/v37W80SExkZydNPP02VKlXw9/dn8ODBxm3z5s2jSZMmVKxYkZCQEItMghrnUiBGWwjxgRDiqBDi\niBBiqRCiVF7r1DgHnU+75GGYuebMmTOcOnWKCRMmIITgypUrxMTEEBERwddff22xT1paGl27diUw\nMJDw8HCioqKM2QF//vlnJk2axJo1a7h27RodO3bkhRdeKIyvViIw+LSznIfY1swIjr6AAOAsUCpj\neQXQJ1MZJ8ztoMkpjEH6T/EvbBnFkmyvadVQyvsrh2Q1c42Xl5dMSkoybjOfJWbHjh2ycuXKVrO7\nSCllSEiI/O6774zLaWlp0sfHR0ZERORYn8Y25tfT/v3qp//66/ybueYGkAL4CCE8AB8gKo91ajRF\nG2eZ7Vxgb+aaypUr4+XlZXOfyMhI6tSpY5xD0Zzw8HCGDBmCn58ffn5+VKpUCYCoKH2b5wf57h6R\nUkYDnwIRwEUgVkr5e17qzC2u5K/UWqxxBR2uoCG/sTdzTVauslq1ahEREUFaWprVttq1azN37lxi\nYmKMr5s3b9K2bVvni9c45B7J02zsQoh6wFsoN0kcsFII0VNKucS8XN++fQkICADUVPYtW7Y05jU2\n3Eh5XTbgrPrysnzo0KFCPb45hw4dKpTju/Lv48zv40pIKZkzZw5du3bF29vb5sw1tggKCqJ69eoM\nHz6csWPH4ubmxoEDB2jfvj2vvfYaI0eOpEWLFjRp0oS4uDg2bdrkUvMqFhfCwsKYOnUBAOvWBdgv\naMtn4ugLeB741my5FzA7U5l89QdpbMMYZOUplQtbRrHEVa/pgIAAOXnyZNmkSRPp6+sr+/btK2/f\nvi1DQ0NlrVq1LMpmXhcRESG7desmK1WqJP39/eWQIUOM2xYtWiSbNWsmy5cvL2vVqiX79+9fYN+p\nJGB+Pe3apXxjX3xh36edp8E1QogWwBLgXiARWADskVLONisj83IMTe7Qg2vyD1cdXBMYGMh3333H\ngw8+WNhSNDnA/HrauRPat4eZM+HNN/NhcI2U8jCwENgH/J2xem5e6swtrvTYqrVY4wo6XEGDRpMV\njnRE5smnrSqXUwDXmYNeY0Tn09ZoihYGY51vHZGuRGFPGmuO1mKNK+hwBQ35yblz5wpbgiaPlKhh\n7BqNRlPUcSTLX7Ex2q7kr9RarHEFHa6gQaPJCkfcI8XGaGs0Gk1Rp0A6Il0FV/JXuooWIYTLaHEF\nHc7UoJNxafIDR9wjxcZoazQFhSvGaGuKByXKPeJK/kqtxRpX0OEKGkDrsIXWotDRIxqNRlOEcKSl\nreeILKaIsYIqZapw5Z0rhS1Fo9E4yMaNEBIC48fDyJF6jkiNRqNxaUqUe0T7xGzjKlpcQYcraACt\nwxZai6JEdURqNBpNUceRkD/t0y6miLGCqmWqcvmdy4UtRaPROMi6dfD44zByJIwfr33aGo1G49Jo\nn3YhobVY4wo6XEEDaB220FoUJcpoazQaTVHHkYl9tU+7mKJ92hpN0WPNGnj6aRg+HCZP1j5tjUaj\ncWlKVMif9olZI4RwGS2uoMMVNIDWYQutRXHwoHrXPu0SinZLaTRFiwkT1Hu+xmkLIXyBb4GmgAT6\nSSl3mW3XPu1CQPu0NZqihyFN+9Ch8Nlntn3azsinPQNYL6V8RgjhAZRxQp0aJ6AT9Ws0RZN8c48I\nISoAHaWU89SBZKqUMi4vdeYW7ROzjatocQUdrqABtA5baC2W5GdHZCBwVQgxXwhxQAjxjRDCJ491\najQaTYkmP6cb8wDuBgZJKfcKIT4HhgOjzAv17duXgIAAAHx9fWnZsqVxvj7Dv1pxWzZQ2Mc3rCvs\n8+EKy8HBwS6jx4A+H665bKAgjx8aGgYsAGDXrgDskaeOSCFENWCnlDIwY/k+YLiUsqtZGd0RWQiI\nsYJqZatxadilwpai0WgcIDkZSpVSnwcOhNmz82FwjZTyMhAphGiYsaozcDQvdeYWWy3MwsJVtAh0\nnLaraQCtwxZaC6SkmD7n92zsg4ElQggv4AzwshPq1DgBiX7C0WiKCqmpps8690gJJF/dI0eOQFAQ\n3LgBnp7Or1+jKYFcvw7+/urzq6/C11/r3CMlDkE+xWlHREBiIsQVSnSnRlMsMW9pl4hh7NonZpt8\n0XLrlnq/fbtwdeQQV9AAWocttBbH3SPFxmhrrMm3EZEGo23ec6LRaPKEoy1t7dMupoixguplq3Nx\n2EXnV/7ll/DGG3DiBDRq5Pz6NZoSyJkzUL+++vzyyzB/vvZpa5zFzZvq3bxpoNFo8oSjIX/Fxmhr\nn5g1+ZZP22C0c+AecYVz4goaQOuwhdbiuHvEGXHampLEsmUwZoz6rH3aGk2euX5ddRPpOO0Sjhgr\nqFGuBlFDo5xb8bhxKtRvxw5ISIBWrWDhQuceQ6MpIZjHCuzdC/feqz6/9BIsXpx/+bQ1JYn0dChX\nDmJi4ORJNcBGo9HkmRIX8qd9YrZxupb0dNU8OHVKLRu6uwtaRy5wBQ2gddhCaymBRluTD9y4AX5+\n8OuvpnVSgpsbNGsGFSpAWlrh6dNoihElzmgb8tO6AsVGS5ky0KmTCiA1kJ6ujPaePfDLLw6H/bnC\nOXEFDaB12EJrsezXz6otVGyMtiYfcHeHwEDLK8jgHilVSiWL0rHaGo1TML+VSoTR1j4xa5yST9vD\nwzqA1M3NtM1B94grnBNX0ABahy20lhJotDX5hLu7tbPN3GjrlrZG4xRKnNHWPjHb5FlL5tZ0Lo22\nK5wTV9AAWocttJYSaLQ1+YStlrZhRIBuaWs0uSZzhEiJM9raJ2Ybp/i0za8gc592ZoOenzqcgCto\nAK3DFiVRS+ZbR0ePaJyDuzv88QeEh6tl7dPWaByiTRv1UHrsmO3tmW+dAo3TFkK4CyEOCiHWOqO+\n3KB9YtYIIZzj096xA8aOVcuZ3SMORo+4wjlxBQ2gddiiOGrZs0e9X79ue3tmo52UZPpcEC3tIcAx\n0NN/Fzs8MtLTGP76M4f86Za2RpMlPj621xea0RZC1AQeBb6F/JpJNntKok/MEfKsxd1dvZsb71wY\nbVc4J66gAbQOWxRnLR520vIVZkt7OvAukIUXRlNkcZLR1mhKKpkN8KFDKn925nT0ycn29zEnT6lZ\nhRBdgf+klAeFEMH2yvXt25eAgAAAfH19admypdFvZPhXK27LBgr7+IZ1ua7v7Fm1bKjrwgXw8VHL\n7u6EJSZCXuovwOXg4GCX0WNAnw/XXDaQ1/ogjD174O67TdsfeABGjQqmXz+1XRHMqVNhwAIAoqIC\nsEeeJkEQQkwEegGpQGmgPLBKStnbrIyeBKEQEGMFNcvXJPLtyLxVlJAAo0erjH/ffAMDBkBQkHq/\neRMqVzbNzq7RaAC4+244eFB93rED2rUzbRMChg+HV14xZTYuVQoGD4Zp09Ryy5Zw6FA+TOwrpfxQ\nSllLShkI9AD+MDfYBYmtFmZhUay0lC0LTZqYntcyu0d07pEco3VYU9y0GAw22L9FLl40fS5VytKn\nXZCpWXWTujji7m5ptPWISI3GYWzdIl99Bfffb1r29LQ02gUyG7uUcquU8gln1ZdTTD6kwsdVtAic\nEKcNlkbbPOTPzU0Z8ayaBRm4wjlxBQ2gddiiOGtJS4MDB2DJEtO62FjT5wkT1C1W4EZbU4wxd4OY\nu0eEyJGLRKMpiaSmwquvqsl6M9OqFYwYoW4p8+iREmG0i5tPzFk4RYt5jhFz90jmbfmtI4+4ggbQ\nOmxRnLTMnm25HBICiYnq86VLlts8PdW7u3sJNNqafCSzT9vN7LJJSoIHHywcXRqNiyElDBpkvd5w\ny9SoYbneYLRLZEu7OPvE8oLTfdpbt1q2tAF27SoYHXnEFTSA1mGL4qLF3C9tTuZbxoBhzJpuaWsA\nlTDKKRiMdnq6ilPq2tU59Wo0RZxFi2DVKtPy7du2y2VntDO3tEvEbOzFySfmLKSUzvNpp6WpK9Lb\nG8qXz3EVrnBOXEEDaB22KKpaevdWnYwrVkB0tMl3nRk3O5ZWt7Q1+YMhQuTWLfspyzSaEoqvL/To\nAd99Z99o57SlXSKMdnHxiTmTPOXTjoqC11+HI0dMESKGlrY5X30Ffn7ZVucK58QVNIDWYYuirKVS\nJfXu4ZFzn/bNm+rd3d2UQKpWrRJitDVOpnRpaN5cuUIM7pHERGuj/fjjagyuRlNECAuzzrCXGwx9\n83fdpd49PGDLFssyBw+q28ieETZ4Ygwt7Vmz4JdfSojRLqo+sfwm11oqVVIt7Tp1LH3apUtbljOP\nLMkPHU7EFTSA1mGLgtTywAPw5Zf2O/sc0RIaChUqqM9lyqh3T0/rcD/D7ZFdzjyDT7tKFShXDjKS\na9qk2BhtTT5iuPJWr7Z+znPQaGs0rsSQIfDbb7nff+NGk2vDMLbM1mQH7u5w9KhKlpkVhpa2h4f9\nTktj2ZzLdU2Ksk8sP3FqnPbt2/Dii7a32XPmOVNHHnEFDaB12KKgtJhfpmXL5l6LwWCDyWgbBsqY\nY5hD5NSprOsztLTd3e37vw0UG6OtsUY4a/a3cuWUcy4+3vrKdHODuDhrt4lG44Jcu2b6bMvIOop5\ny9me0d682WS0s6NEGu2S6p/LDqdoadJEdUhevGh9FTp4VbrCOXEFDaB12KKgtBw+bPocHp47Lb/8\nAgsWmJYNebEzu0c6d3Zcl5ubegrw9Mze/11sjLbGmvA4O1dlbqhUST0TZr4yzY22AylaNZrC5LHH\nTJ9feCF3dTz5pOXyxo3q/fnnrcvai9vOzM6d6vby9Mx+nzxNN+YIerqxwkGMVc9YcrSTzn2zZsoJ\n2LevGgJmIDnZFPKXlAReXs45nkbjZK5fB39/y3W5MU2OZoeQEo4fVw+q2ZUz1Ll1q3qobdUKIB+m\nG9OUIEqVUiMiM7tDzLu6U1KU31sIdfVpNC6EwWAX5LCCO+90rJwh+5+np5o3sncWkzYWG6NdEv1z\njuA0LaVKZe8eSUkx9dBEROSPjjzgChpA67BFQWoxv4T//rtwtRh4/HH17uGhHmi//95+2WJjtDX5\nTKlSyiBnNtrmz4opKaaudEedeRpNATBkiOmz+SXcokXBa7GFQZMjES3FxmiXxJhTR3CaFh8f5fqw\nNYLAQGqqaaBNYiKsWWNsyrjCOXEFDaB12CK/tJw8qTr5Zs40rfPwgDFjcq5FCMf92Xff7Vg5w4Oq\n4T2r28vjsTuOAAAgAElEQVRAnoy2EKKWECJUCHFUCPGPEOLNnOyfLnW0QZGhTBnbPm1zMre0n34a\nOnaEzz6DJwptzmdNCaZTJ2jf3nKdh4fKzJcTHJhRz+oY2VG5svJfg8mrWBAt7RTgbSllU6AtMFAI\n4ZDrfU/UHtzH2TcAt1Nuk5Sa9Sg7c0qqfy47nKalXDn17qjRvnBBvbu5wbBhhK1d6xwdecBVfhet\nw5r80hITY73OwwOeegruucdyvWHQ79ChYVbRq+ZpUx3BcLtkxfHj6ikA4N9/1Xu+G20p5WUp5aGM\nzwnAcaBG1nsp2nzbJsvtPhN9KP2xHmXnMtx3n3q3dVW98YZ6P33a5B6ZOVO1srdvV6nMHEjfqtE4\nG1vG1sMDatdWBtPLy1Rm2DDlBZw+XYUHgnq4XL/eup5M/ewW7NmjZrTJimeeUUMfDLfFrVsmbdnh\nNJ+2ECIAaAXszmtduYnrLgn+udzgNC3Vq6t3W0Z79mwYOFBl4ElNVbkqK1VSow2aNoWWLQm+eRNi\nY1Wm+EKK23eV30XrsKYgtZh3+lWvbnooNLR6IdgYyTpvnhqQY55rBFTOa3vce6/pdjFnwwbT55Ur\nLbflxD3igF3PHiFEWeBHYEhGi9uCvn37EhAQAICvry8tW7Y0bpu1YhbNqjYz/mhhYWF8s/8bAHw8\nfYyPTebb9XL2ywacVn9GbpGwo0ehdGnr7XffDdu3E7ZrFyQmEnz1KgihtktJcEgI+PkRBlCxIsFP\nPVWo50cvl4xl1BUHmJZVq1kth4eH0bMn7NplWT4tLZjISFiyRC03b26/vszLYWGWeu66C/75JzjD\nc2hZ3qA3Pj4YCOPttxdQujRGe2kTKWWeXoAnsBF4y852aQvGYHxlZvae2ZIxyJDFITb3tUVoaKjD\nZfMbV9BiOLdO07J7t5QgZViY7e2rVknZrZsqd++9VptDt2xR+4OUUVHO0ZRDXOF3kVLrsEV+aTFc\ncuav557LejuEyqgoKTt1srdd7bthg5Q9etjeZk7btmr9+fP2y5Qvr9ZHR5trQ0obNjWv0SMC+A44\nJqX8PC91mXMl4QodanUgMVXH+roMVaqod3tON19f5f44f972UHa3TCMnNZpC4NYtWLzYtDxxIthq\n1KalZX+ZhoRAo0bqc0ICzJ+vEl5mxtA3X6eOZaIpcyZMUO8F4dPuALwEPCCEOJjxCslJBb1bWI/X\njLwRScNKDXNktE2PQ4VPsdRSp46aBMFeAKqvr+qq37gRunfPWkchGW1X+V1Kio61a9X/eEFqOXbM\nNOtL5q6TihXVbHnmfuMbN1Q7I5MaZs6EHTuyP54hyqRMGZWWp3x56zJTp8LXX9vWZOB//1PvjiTN\nzGv0yHYppZuUsqWUslXGK0fzQVTxqWK1LjdGW5PPCKHipDLPEWmgVi01kObQIahbN+u6cho/pSmS\nPPGEmve5IGna1DQkID7etN7DwzKXtoHt223XM22aY8dzZNKm4GCTUbZntA0PogUaPZJbJNbfIjJO\nGe3bKbcdridzB1xhUiK1VK6srswDB2yOXAgLC1NZ4X18sp3lJqfsv7if51Y+l205V/ldSpIOe7PD\nZMaZWgzuiO++M60bMsT2aEbbyaOy1nLHHabPzz8Pr7ziuLYHHoCuXa3XG4x2vre0ncGnOz+1WJZS\nEnkjkgYVG3A71XGjrXEySUk5z4/9xReqaZV5CJqBzp1VOGBWRrtzZ5VlPgf8eOxHVh5bmX1BTYFh\naFE6arQh+3kUs2PXLvXu46Pehw5V7wEB9lvOs2bl7ZjNm8M33zhePiBAuY0yYzDabg5Y5EI32gBn\nY86y8PBCpJSEx4Xj7eFN1bJVuZVyy+E6XMVPCMVAy5EjavqwEyeMq/ZE7cl+Pw8PlWvbRrCpUUfp\n0mqI+6hR0Ls38UnxxtzfAGzZonKWAImpiZyNyWJa6gzSpGMTC7vK71ISdBgGizgSdywEfPFFsEOj\nCLPi84xQiMwevHPn7O9jO3VqcJbHyY9hBkJY5tXOikI12s80eQaAejPr0eenPrz/+/skJCdQrWw1\nfDx9sjTan2z/JFeDcDQOEBurRkBmZG9PTkumzbdtSEu3NI5SSraez2He7FKlYPduGD8eFi0iNd1G\nUoeMLvhRoaOoN7NetlXqHDaux40b6j2rZI/JyWq0IcCqVXk/5tWr6t26Y9Fxfv457zrym0Ix2lJK\nqpetzuCgwRbr/7v5H8lpyXi6e+Lj6cPtlNtWhgLUTTp8y3ALf7ir+AmhGGhJTbXoEem8UE12l7n/\n4WzMWYK/D86ZjshIGD5cffbwwCNVGVwL450x6V707WiH6nbUaLvK71ISdGQ22snJ1l6vJUvMp//K\nXsvy5bbzjl27Bn/+aRrZePGiyqjgKEFBps9t28L06fa1DBgAr7/ueN35QaEYbbdxblxKuET5Uqb4\nmJkhMynrVZaUtBS83L1wE254uHmQkm4dHnb9lkoMMGTDEKttGieQyWhvi9gGWKcXcNQtYYG5A9DP\nDxGtMvpYJAfbvRtu3HD4SerQ5UM516HJETdvqtlUHO3mMBjtQYPU+4oV1nMr2ormyIrly5U/eMMG\nU6v63ntVH3inTnD5sqlsgwaO17t7t+l7eXmpDAy2OH8e5s6Fjz7KmW5nU6jukQqlKgAwrN0wKnpX\n5Prt6/x38z+jUfZy9yIlTRntz3d9TsztGNJlOlWmqTDBL/Z+Aagb3lX8hOCYr3DtybV8uOVDl9Bi\nRYbRPhN9hoWHF9otlhP3lFHHffepzkhQRjsjM09qeqrJWXjHHbZHKdgh9HxozjQUMkVRx7p1KgnS\n8eP2y6SlmXzZBqNtIPN+oaHw3nsWagDl061SBV56ybp+w+Xx6KPw6afqGPv2mbZnlQ8kOwy+ZA8P\neOmlYJtl6tTJff3OpFCNtp+3HzHvxzC1y1R8S/uy/J/lPLH8Cc7EnAHA093T2NJ+e+PbLPtnGTU+\ntUwieDvldpHMBvjJX58wafukwpZhm5QU8PCg/qz69Pmpj3F1ZvdIrlraoAx3gwYQEID71j8B+O7g\nd1yKiVR3TZkycPOmzXBQTeGwZIl6/+sv+2XefBMqqHaYhdH+8Uf47z/LsllFXFy9qo6X2fCbx12n\npKgcZeYcPQojRliuM0SSOEKDBqoTUwg4fNh1pzktVKPt4eaBb2lfhBAkp1kPuIhNjCXqRpRx+Yej\nP3Dl5hWLMob9Nm/ZnCctiamJXL15NU91GHDEV1hQnWe59Wmne2QfMJqT79Dz054kJGfEdM2YoeK5\n27VDXFMt7WGbhvHNni+V0fb3p9fcR0zlnURJ8CXnBEd1/PsvbNsGU6aod3v8+KN6SDt3znKo+IAB\nsH+/ZVnrQSnWWnr3Ns0489tvpnSpoEIJbblXDJMKGPjnH/t6M3PqlIqTDgsLo3lzuP9+07bPnZak\nI+8UutE28EQjUw/D223ftlmmfS3r+F+D0f7v5n9W23LC0yueNrpdCgKXbUWGhkL37oSd/cNqk5VP\n20YnsT2WHlnK/osZd66Xl7rrype3CM4tFXdT9VzVqsU9O85z4YbqWbIICdQUONeuqRwbTzxhabSl\nVCH3MTHqZzO0puvWVRkPDMTGqoGyoIzq2LHwww/ZH/fnn01zOz7yiOUkvO7utiNTSmd66A4MzP44\nWSElvPwy9OyZt3qcSaEabXdhas25u7lT2kOd8XEPjAOgedXmJKWZOqi2hls/rxi239/pfqttjnDk\nyhESkhPYcHpD9oUdxBFfYV5a2qejT1N1WlWnabHA0AV/Iz7rcsCX+74Esv4uYqzgZvJNsHXzlCuH\nMPNdN9h7RvUoPfMMgbEgUtNYvRzuPw8bT2/MybewSVH0JecnjuqIjVWDXCtXtuxqmDdPGcmKFe2P\np8rMmTPW8zOqED37Wmylu0lOtj2E3Hy0Ym7JfF7mzQN//7zX6ywK12i7WT6CH33jKBFvRVDWSw2j\n8vbwZuHhhSw6rKaB2BFpncHF0NK2FWXiCM2/as6YsDE8GPhgrvbPLXmJMT98+bDDTxYX4y/mrPKM\nMcDV3StYbWr1dSvKTypv7Cj+er/KgpNdi/uznZ/Z3nDnnXh9v5ipG8ErFXYd3QitWkGVKlS6Bfcf\njOapE9AxHN77/T3bdWjyHYPRLlXKcjDrZ2Y/68GDuav72WctO/jGj7cuY6vuDRtsu2rq1jVFrBRX\nCsxoxyfFWw3EcBOWh6/rV5daFUxdwN6e3kzfNZ3eP1lnAjQw7+A8ALZtzcLZlg1JqUmcjs5BYGc2\nuJJPe/Evi7MvZE5GBj6vFGtDfPzaceKT4zl69aiFy8LQIWkea3386nFjnPWosFFwDuKTM7XeM6Yw\ne2cn3B8OPrfToHx5ZKNGtLgCb66MIMUNJoTCziH/qADZnA6tN6Oo+ZLzG3s6kpOhSxdYulS5BaKj\nTUY7MRHGjYPGjVVGvbxiiCwdNCiML79UmfIc4cAB2+srV1ZD02NiYO/e3Glyld/HHgVmtKf8NYXg\n74Px/thOljgbGNwlWfHxto+BnLe0fzv9G63ntgYg4kYEEXEROdq/MBGOjHXNLampSG9vXupivxPw\nz/A/LZbTZTqRcZF4jjeNWW4ypwmVpqiA15bV1ExFho5eKaVFBzPAwD1QPgkoX55U/4qEvATVo5P5\nUQ3KpOdT6WoERcaMN5r849w5+P13ZbCXLlUeM19fZWDd3FS43cmT2dcTGJj1rOd+fqYIkO7d4bXX\noGZN9cewx4GsCeYYfN+GVO6+vtC6dc7qKCoUiNG+fus6Cw4vAMhRutXMLfGsiKoYZbXu3U3vGkfz\nZWb18dXsv6Q6xrzcbSTtzwOO+AoLqiOyeZvmOSof+u9mFt7rya4sYl5Hho60WE5LTyPyRqRxueVX\nLS22SykhEAJ8AwCYvH0yNafXJDktmTGdYHpbuOcSPH4KqFyZ1PRUdtSGF9+uTe+noOo78NOdqHiz\nX3/NdWu7qPmS8xt7Oq5mCqKKiDAZ31KlrEPx7NG5s8olbY+ff4YOHay1lCqVfXZfA3fdBfXqqcl4\nN+a928NKiytSIEbbf6q/MRIgJ6z/d73DZcdsHcP+i/u5cOMCqemppKWnMW3nNLac20K6TLdKePTN\nAVOgaPMqJsOW1yiUgsBi9GA25NR3/u+V41xPdvCuzGD8n+PxK62mlb5w4wKHrxwG4K4qahBNzfI1\nlZaMP6oRf6hg2mrTqjH2ARj5AFRIhAbRcDokyOhmWVYhglR3+M+QKc7XVzXfli41HvuNX9/IkVZN\n1ty+bcr9bGDpUlP8tSOp0MuVU162uXNVfWDZgfnCC+rddlpUhcFtYh6FYos2bdSQdSHgoYey11Yc\ncIksf07hHLT+pjW1ptfCc7wnHuNNoYKbz2ymzbdtLIqbz5hj3iHqaFRGVjjiE8tLR+TSf5ZmXyiD\nI3uO5Khuj3RJigM5fc2ZumMqc/fPBeD1X02JGQyjWZ9q/BSYZVr74VkV7xWTqIaw3ywFd78KzV6H\nzeGhLPp7kdUxjOGer70GkyapZ/cLF4wRLI7gKr5KV9ORnq781l9+qQaj2Br1aBikkmojv5eBKVPU\noJQbN0xGNyREjW4sX161rLdtU38CAwaoCQsyazFg2L9bN9PM5gcOqEgVcx5/3LHvmhNc5fexh1Nm\nY8+OZ5s869R8x/dUv4fHGjzGuD/HOVTeMPLw+q3rVPSuiBDCYnj2F3u+cJq2rEhLT8NNuOXZJ709\nYrvN+v6K+IsmlZvg5+1ntU9qeip7o/bSrla7LOtOSrpFai7+yj/frUYfrDu1zrju5HXl+Mzc32Bo\nlZtzJiPfwxd7v+DYVeseLkHGOXv1VZWze80aUo8eoVRXSHIg/afGNuvXmydtsk/m+GdQ+avr11cR\nJVeuqMCfzDRooIa/g2Wyp7lzsz5emTIqakUIFXJ36JCqX7inAu6A4Nw52/M7FncKpKVt7u/MCY39\nG1ssG1K5DmkzhKfvfNqycBZB9Ib4bv+p/qw9tZbjVy2bEpcSLlks20wXmgPs+cQ8xnvw0GL1DJdb\nn3a6TCc2MdZY35DfTEmz7pt/H8M2DbMof1eQclH8cPQH2s/LPpg2NTF3RjsrElMTIdD0dJFVX4Ut\ngw1mQ+bLlqVUnwhaf1KP8Igj9D8IFRycK8NVfJWupMOewc4s0ZCMadQo9T5pksqOV6kS1Khh22Dn\nVEtmDC6ZkBBTYsjrAz35v5n9iIrKP4PtKr+PPQrEaO+6sCtX+x343wF29Tfta0gw9VLzl6haNns3\nRvOq1p1wc/fP5Xzs+Sz3O3IlZy6FnPD72d9t5gk3GOK9UXuz/NMwlDMwa4+aeqPTgk4AzD8030L/\nR3+olGSO+sFbRiSR4Nx+Wd7e+LbFcm5yliSnJfPtgW9549c3SE5LZn/0P2yoD7PXQ+wncGkqBT8h\nIZbXikUn++LFKg0t1r+Zq5CVh27DBjUIxhDdYXBXVK6s3l97K9ahhP35wYMtG1CjRvbliit5NtpC\niBAhxAkhxL9CiPedIcqAt6c3bWq24YGAB1jxzApji1gIYR3xYWN2ii51u1it+/XfX3l06aNZHvfu\nuXZmHM+GpNQkklKTbPrEDC4NgOd/fN7Kp+33iR9/X/mboG+D8BzvadfnXXt6bat1yWnJFmF4zb8y\n/VlFHVFRNY7GhTeKSmJNxgPOoVezTnk6/eHpXBp2KcsyRsx+ny6LrH+X7Dhw6QAD1g6w8GEPexi8\nR0CSO/zUGOR777G7ZzCJcddt1mHPVxkeG57tqM5xW61dcfFJ8TT/qrnxt/L+2JtXfnmFtLeGQK9e\npE+dyslrJ/H7xA8xVjB041BupdzKH5/p3r2WuUnN2L/fNEO5Ob17W+v44AP1Xro0jB6tZpAD0ww0\ng68Lvty0Gb9PrF1ceSEn56SyT2WnHjszru7TzpPRFkK4A18AIUAT4AUhhM0JfPLCH33+4Lmmz9E5\n0BS+Zz4E/tvHv7W5X+b5J53JiWsnrNZ1mNeB+xdYD6fff3G/hdvC3O9rzuUE001nr7V9M+Wm1bp3\nNr1jV2fmyA0D03ZM4+cTP9Pv536M35oxDO3oUarEpXI+I7zL1pNK+FvhbH95O/sG7OOttm9RrWw1\n5Ghp7a6ygUQ6Nfd1sgckekLpkfD649DuuXg8/9hKQu8XHK5j6l9TCZgRQPvv2jPv4DziEuMsWsaG\nJ5TRYaPVcHyAmTOJ/XU15SerfPB/hv/JocuH8EiDF4d8h/uMmaxuDD/8MYvGs00uvum7plNmYhkn\nfHMbBAWpwUqZ/uxjY1W8sq1RgoszjbvauxcmTgT5+xblSDbD3BXx+g7l4jPmknEQMVY43H8kpUSM\nFaw6toq09DTOxZwznjsPNw82ndnEXXPustrH0PmdFUeuHCFwRmC2I3mv3cphwu8CIq8t7SDgtJTy\nvJQyBVgOPJnNPrnm7XZvI0eri9K8pX3hxoUsfdp5Jfp2tIVLY8iGIdw5+04i4yx99fsv7WdP1B6a\nBTXjj3N/8PDih0lKTaL1N62tXCK2fNqnrp/KlT6Di8QWg55Vd+uaE2rexfc2v8f8g/N5d/O7dFvR\njfmH5jMqbBRirCBmrjI2aRn/h+YdpokjEpGjJbUr1KZD7Q7cU+Mei+Osem6V8bcxUKFUBaqWyXBj\nBaoWdquv8+j8zILdteC1rlBuvymz0NiwsQz8dSCRcZF0+eMBAt8SlJ+kjO3lhMvG4fG7o3bT/5f+\n+H7ii98nfvx84mcOXT5kEapq7PsYMoRDA7tTJsPjFPx9MK3ntGLxanjwPCxoAZ+3hR5HYeNCaJjp\n3v/ymnpaEGMFy44sw/tjb1YfX+1QRJGU0uq6A0j38VGJPQYNgsGDITER2a8/p/43jZYtYedONWgl\nKkq5PdRPGwyogS3PPaeMuxgriBs0APr3N9Z986YKp8tsEFt/09pumoRrt64hxgp+PqHm7zp5TXVK\nGybUMHyX38/+TmxiLB3v70hqeipirOCXk7/gNk6ZpmdWPoPHeA/qzqxrvIfOxZ7j4cUPc/TqUR5d\n8ihirCBdpjN1x1S8Jii7cPS/o6bGSCZ+OfkL52PP82/0vza3BwcHs+vCLipPzd8WfW7Jq9G+AzC/\ngi5krLNJ3HDHE9tnh7enNzHvq5Cx4IBg7ql+TzZ75AzzYdqVplTi4cUPszNyJ1JKZu5R+SJrf17b\namQfqA7P/1v4f2w6s8mY6/uf/yxzRNp6HB+8YbDVuuz2yQ7DPoaY96k7ptLvl342yy7YM5ft1t4X\nwDpPjD2OvXGMBhXVtCEx78dwbGDuxjobnp6C7gjKpqQl/1aC5KtXQEp+njmQDYvH8OWeOdT+vDbv\n/gXnZsCdZ+NJSk2i9xr76RG6rehGq69bUX+WKddng1kNjP0hweGQMAk6nYPyifDzcmh1Caq8Ay8/\nBZEZnWg+KfD9Gsu6y3iWMbbytkVsIzE1ke4/dKfGZzX46I+PWHrEOqTzYvxFpJT8FfkXtT/P9CMl\nJuJ26xarGwNz5sAXX5BSvRbn5/9Bk5Vj+O5QK7p3FNSqHUrNN3sydv1s8LhNVS6z1Ls/Py5JYum8\nBCJiwymTBN7n1R9Vp5cFR45vxcdb/ZkYUuX63YJ2GQOIm85pymNLH+P6resWkzAbDF63Fd24kXTD\n+MRhyFsD6k+wy6Iu+H3ih8d4DypMViftyeVZt/vG/2kyxoZEbxfjL/L+78o7u/TIUoZtGmZsjCz+\ne7FqlNyOQYwVfBT6kcX3AeUiMyQli7kdw5UElQL6+R+fB1SkVnJaMmeiz2SpzVGm/DUFMVbgNV79\nyaSkpRgbE9mR15A/x0Ig1kD7Zu35bNJnsAuohrFlbPAfGXpsc7LsW9qX0E6hyPOS/Tv2qzoNvtOM\n+rt7d2fVsVWmlrjZ9tODT7N3x15e+PEFm9uP/neUOSvnwDnYznYVfZGp/pXrV5Kalsq7p9817X8Z\naGddn/nyPygj/t437zH1r6lW26fvms77v79Pf7/+fHfgO8b3G8+wdsPs1mdv+bfFv1HvhtnkuFmU\n90qDc7EZ6zK2l4kqw83km0Z3lPn5v34dpk0L4+GHTb/PlaNXmN1kNi3atkAIwd+7/+abZt8w4JcB\nNn8f8+VTg07RcFhD2tZqy321VV6ST+p/woKEBXwf971d/e92eJepF9XQu9jLsLQsPFy7PA9fSqBC\nGtx2h+EDoPUO+LwqfPQ9vPFfaTbfDW5noEISxDQx1fe/fdC4HAwNUcu14iDmTkgoBW1fCWQ5ppx0\ng1bAhgZQIx4e7pUxmvAqRNaGtcHdeL76T/yyHN75C2YFQdIFmL9zPr6llQ/qy5VfGr/P5YTLfLxQ\npWXoGdiT6Q9PZ8efO1S4bKbzFZsYy0/H1/DGnIE8EOXJuOowoy1UPAGbAyHq+mC2VGxPVLTgYb+f\neOvYIabdeJDhJ2FD/aUME4OovsefyrevEdZgM8GRkfh5wQI3GHlHCnGNYesCCFsQTBhwX+9eHHm0\nGQ33wmv74O0rsK86/OYRy++B62l42J9obwgNDrV8EjgHFV6tYNRfM7omoq+gUetGTO0y1aKv41bg\nrRxf34blWtNrGZd7ftrTYnuvz3pBIFScUtFi/78i/mL95vW0q9mOTy99ysYzG9l430Yenvqw8f79\nYd0PtE1py9BTQ431vdDsBZ577DkaVGzA4d2HORtzlsCWgfRs3pPQ0FAuJVzixcdfBNT9kpqWSqPW\njahatirb/9yOm3Dj/a3qDyblTAphYWFsTttM/Ml42nVtRyP/RgRkERoj8jLIQwjRFhgjpQzJWP4A\nSJdSfmJWRjIG0kelI4TgpxM/8dSKp1jWfRk97uqR62NnZtz34xh9fjQXh14kNjGWAN8AfCb6cH7I\neQJmBFiVX9htIb1a9FIaxwoeqvcQ52LO2X1kyhFmRs+ZRLwVQe3Pa/PaPa8RdEeQ3RazgUcbPEr9\nuPrMeH2GQzmpv/kZdteEbzMeWuRoSXhsOBJpHIJuzsCBqmFnyKuc1Qi3OSvnMPDYQJ5v+jxzH5+L\nh5sHZSaWIdA3kHOx5xgcNJiZj8y0u/+NpBv8b+3/GN1pNE3mNKF/q/5M7TLVGJNu/v1EOqSPg3Rg\nfCcomwzDdqo0+08MhxuTVbkUN0hzg9KpkPDBMCq5f0qyB8gxGfVkvMsx8OOd8Ozz8N1P4CZhcz24\n7g2vH3LnyaNp9AmuQ/uPJ1LBx4cvfv8JKdLY8c4iGCNoHQUT/oDmfo2o8dhJOA8EqFb4rWwidbof\nhboxquw5Pzjvq447MjKQ59edo21/6BQOlW/C+11gzq9qhOnVstZ1NbgGp76AG14QFgDbHnmTP2r/\nxv6ASdTZ1p0EL6h4G05XBIR6ijhSFSZugTbR3iQn3cYzHcIrQL8nVZKvB87DkN0Zv5GXOte3PeGc\nLzQbaK3BLvl0zzjC8A7D2XR2EwcuHciTlrA+YcaJrpM+SuKb/d/Q/+7+zNg1g+FbVMxiZZ/K/NTj\nJzrMU+P329dqz/aXtxvdQZP/bzLv36cMuhACKaXVjZtXo+0BnAT+D7gI7AFekFIeNysjGYOFv/PZ\nlc+y6KlFDiWEcgYX4y+y6tgq3vztTeM6cz0nrp2gYaWGxCfFI5FO7xk3Z3n35fRYlbc/K4P2Y1eP\n0XROU4ttv/T4hSeWP2FVPlujLSFhokqVuaGh5XEyk54OH34I33+vAhbuuUdFKGzdqiZf/fRTNW1T\nZsJjI6hVoaYxTvu307/xYOCDTsn98tLql1hyZIlxeeFqWNcQfrgL4t68hMf5CJrPb8OZSvDQaXj0\nX1jTGBp37YM8coSvVyWpEL333jPO3HrTE2SlSpS9fJ3zFaDNALgyDQKHwHk/eOToJa6vDKcNu5nF\nmzZ1lfmwHiHps3jyrg70esmXHwMqMuD5aCZugdf3wfudITQA2lS7hy88LDv2yiWa/mCmtod3M2Um\nXt4UnjwJaQKeeQ42OjCZbUCMMvyYXQ5zHp3DG+vzlg7AO1k9sfxXRv0Jxk4G//fgRha3ePUbUOUm\nLA7xlIQAACAASURBVBu+lw92TeDnkz8jR0vik+KJiIugatmqFn7lYe2GGYML6vnV48OOH/Jyy5eN\nBs8eATHw1TqIKwVxpaFqAsR4w9sPQ0wOpiPLDe1qtmPnhZ1Zltn40kYeXvwwAAueXECfln1ITU/F\n093T+UYbQAjxCPA5apjSd1LKSZm2Wxnt+Hh1oyclqRko3NzUu63P3bo5Z/bjuMQ4FhxawJC2Wc/g\nHhMDFWfmXwCqQwbUgToMGOpa+vRSOtftTOUyla22vdLqFb49+C0LnlzAi81eZPPZzaw7tc4YPvf3\na38z4pchrH5jK4fD9/D57s/ZFr6N82+dB9Q0Tx06qCHKpUrBmjXwdBbBIr//rnI79eunEtXv3atm\nHRk/Xo2Eq1RJDU3281MT2NSsaeM7StNkq46QLtMJjw2n7kzLTENBdwSx+xXVFNwRuYMO8zpw7I1j\nHLh0AG9Pb1PUy4YN3ProY+YfbEXIoPo8nvYWqW5wvMKHuH88kXgvKJcM6xrA44ZZTMakY2H9suEx\n1vGumMzNOsd49HyM1fY3Q+DoUx0IjfoL71S4OVGtdx8F6W5wTxQ0vA6jt0KTgWpdYDQExEJoXSh3\nthfxWwZS7+GNnKk92vGTZ4ePOn7EhG0TjMvfPv4t/Vr1IyU9hZm7Z/LK3a/w9b6vja1Ic9YtgdZe\nAdR+6jzJHtCreS/++W0RVW6a/lyipoGf9ML7ZkZO/IDaeMbFqxS8Y8YgPTz4dt9cXrn7FZJlKqU8\nSvHziZ/ptqKbxT2QlJpE6PlQggOCeeu3t5gRMoOdF3YSHnWMbh//SIU9fxNW9jrzWkG9GGW830hp\nyV/Rh3j5SZC56Nl7+s6nWX3cdmIUjzR4czfsqwF/BuS87va12qu5A8aQP0Y7O2wZ7X//Vfl6w8JU\nqy0tTb3MP6elqSQzb72l8g18840aEmuPsLCwPI9k+u8/qFoVGKPO05A2Q5ixe4ZD+45q/Q5vPfgh\nK4+tpP6N+vzfn//H6E6jaVezHSFLQgBIeXQXHnc1J04kU+fzOsQlmTpmJz44kQ//+NChlrgto530\nUZJVi1WMFRaPerc+vIW3p6kJLKVEpKerf8ejR9WcTpHWkQnTpsG776o8D0ePqpjfDh3UbB7duqnJ\nVC9mO9dCGPZmJ3n7bWjSRI16++QTFd1w4YJqyTdvrvzETZuqP4M9e2DkSFi1Sk2D1b27GpHn56cM\nd+9V/XhSLuCZZ2BfxFFa1mjK3LnQqxfs3h1Gly7BJCerOqtWhcmTVTqTI0fglVdM8cwzZkCzzodJ\n2unPAzsncvv5vqTMX0zC6+9SN7gaeNyG5HKAmlnloYfUH9BHH6ncGo0bq3wb5nMWTpkCDfyuUXZA\nVfxpyuu1X6JdhBvhBFKeG8ynH6m4k4Y7pUhmYz0YHNSAfxuZuew+i4QnX4Z6v1P60oP0C3oerwqx\n/HJ2GWfePUh0tLpfRv4xkprla3Ir5RZXbl7hk78+wcfTh6vvXmXStknKGNtwA2x7eRsd53fkv3f+\no6J3RYscPvaevEB1SH752JdExkWy4ugKXr/7VR4ZPIPUq//x6F2H2Tj+HKJdO7h8mXRPD+LcU4kr\nBat+HMewgYsJe+opghs1UgnBpk1T06x37apCD729VUreSZMcn3I9PR369FExj8uWqR8HKDepHAnJ\nCchXLnDr7ubs8Y7mUDXVAn+rcV/eaxTBvbWe5+4OrZnw5wQ2n91MekICg/bAqjvh8fZ9OXpoE8tH\nHzGmHjbgnQy3veDTIzUYuuoi5yvA3HvgiyCIz8apUKdCHcLjwi1XjrFttJFS5usLkIxBHj4s5enT\nUiYnS7l9u5R33y0dIilJyk8/lVK1vaTs0EHKRx6RcvBgKYcOlXLrVlVnaGioYxWakZws5W+/Sblk\niZTPPGM6RosWpjLp6eny2s1rxuUbNzLK1dgjafepfPejOFmj80oZ4dNQ3ixdUR7ZHitDQ0NlRGyk\ncZ8TV0/IX0+ukxLk7Q/GyMhIKa9elXLvXimvXZMyPiFVSillaqqUhw9L+evuY7Lz/BDZ76f+stT4\nUjL88g15JeGKfG7lc3Lmrpkmcbdvy9NR4ZIxyG3b0mV6uuX3O3LliKSPOv+MwWJbWlrGh4YNZfqL\nL0pZqZKUr71mUebwYSkXL5aycWMpH3vMdH5AythY2+d00SLLco0aSTlunJR33RUqExKkbNbMcrsz\nXm3aSFmnjvrs62u5zc/PfDk0y3oqVJAyPt6xYx46JGVkpO1zkJk335TywAHTcmnWS1+i5cGDUs6f\nL2WVKlK++qqUXVtGSl+iZXMOySdYLd8ZdksePpIqT565Lb9adEVei0mUUkqZmCjlobMOHtwOaelp\nctqSafJW8i05Z88cefr6afnQoodslk1PT5cpaSk5P0hiopQrVsj0O+5QJ+2jj6S8dUvK8+dlypVL\n8mr8FWNRq/v3p5+kHD1ayu7dpXz/fSnfflv9QF26qAtKSik/+0xds6tXS7lsmZQTJki5aZPaNmeO\nlHXrSnn9un19R45IuXSpTB8xQqYHB0vZqZOUVavK0Jo1pQwKkrJBAymXLjUZoMqVpRRCyqpVpaxU\nSabWryuTHwuRlzq3U7oMF5G/v5T79kk5a5ZMv+ceebyqu7zx1GNSPvSQlK+9JuN++kFO2z5VRsRG\nyP0X98snlz0p09LT5Onrp2XzL5tLxiBfW/uaVObZ2qYWTEu77CXurFWN+HjVgvL2Vr7T7793vJ7t\n29Vj9v79anqiHTtUa/zqVdXK69xZDbW9eVOljwwIUNnGrl5Vj/TlyqkG5ZEjKlbVy0u1rO+4Q71/\n8AG0bavqnTBBpafs2FHVvWePcgkMHaoaAbaRbKUT97ONtXSlNwuJxQ8PUkjDnUac5DhNuE1pdtCe\n3bRhMsOJxxTmU4Ur+HONYzQhq8fuJ55QmdjWnb2Ta2l+dPDaBMmqJfHQQ7Bpkyo3aBD0GXyB47EH\n2Tu7Kgm/XqZ2tWTanllCpcQLXKQGT/ILAKd872VAs93c30nQpo1qnJhlQCUlRblJYmPVOStro6PL\nEQyXm5SmBv7OnWq5d2/lQhkzRrnOvv5azR8opTrmjRsqX9S4ccqn7u+vNL6RyR07Y4bKJhcZCbt3\nq/Se5u6cZctgyRKVue6DD+DBB2HFCjU9pZeXOs4HH6iW+H33KTd3QoK6Lm7fVk8cWSX3dwTDA449\nEhNtJ2kqkiQnq4vHkK4vt+zYoVIVBAaq2YH//BMWLlS+N3d3dZGsX6/8bwkJanuzZjk7hpRqcuul\nS1U9R4+qi/+FF9S0Oikpym937RqcOKE6dTw9laHo0UNt8/NTekAZqYUL1YXj76/KffKJ2q91a/Uj\nlyql6r1wARo1QjZuDLVq4TZ0aOG5R9avlzzyiOk7uDsW8uswJ0+qmzMmRvlHk5OVIa5QQZ3PsmXV\nOUtOVn8YjRop10tgoDq/Pj4m/6mU6ia+dEldY+XLK6NSrZp6DK5QQflqW7RQ5ZOT4dYtVUfC+j+p\n+FQnbpStTlKFKnwr/scHF1QX+u1Svhxs1Y9mvVpQbmAfUuvURSTEk+RehuQ7Aghv9jiV182nRvQ/\nxLTuzIlHhnK7TiOq3VOTfX97ceyYOncXL0L5a2d5oNLfPLdMzeJyKygYL5L4o+MYEpq355ctZUhJ\nFSQkqD87NzeYnPYu/WOmEdH0ES40fJDURk3wTLnNBfc6BBxaw3bvh4iq34mkJDVGQ0qYPVul4oyL\ng3vvde5v5kyyi1zRlCB27FAtuz591M3riuzbp8KuXntNXbyJicpo16qlkoPv2gVXryLmzy88o53f\nxwDn+LSdQmoqYZs3EzxunPpnNYwf7tTJsumUnPz/7Z15nBTVtce/hwGGAdlkU1lEI6hRwSiKTyOg\nGBSXaEQDLjGuL9vTaCJGIVEUNai4ZjMxEjEuMXFJXBNXosYlSkRNRMVnXJ9JXIIxihHhvD9+t+ia\nnganu6una8b7+3z603Wrquv++tY9p+4959xTGiE895w62vLlMGwYTJ2qEIx582RgbWrS1GLECD35\nFy7U70BG4Dlz9D14sAyq772na02cCAMHsqCxkQlDh+rVHjffLB51QB7uTx44RB6lEbm0xOpC/tok\nn3ZZOOcchZestZamH7ffDiNHShlNnaoh8bbbwsCBUnJdu370NdsSnTtL0T645jAfunbV9GjMGE2r\n0pgxo5C551//0jz/iSeUqf7II/W6joYGTR+ee07LltdbT3YCM00TFi5snsB4yZLmXrGIiIh2ifyN\ntOfNk5J65x3ZLqZMkWFz4UIZpJctU8b1116TraB7d+WLHD5c04w77+xAxsAqsWSJ7EZDh9ZthB0R\nEVEZarK4ppUV18484i7P2Isv6u2jffvC9ttnbzSPiIiIaGOsTmm373dEJp7aLbdkQa9eCvfIgcLO\nUz7evHDJA488cIDIoxQil9ajfSvtiIiIiI8Z2rd5JCIiIqKDomOaRyIiIiI+ZugwSjtPdqjIpSXy\nwCMPHCDyKIXIpfXoMEo7IiIi4uOAaNOOiIiIyCGiTTsiIiKiA6DDKO082aEil5bIA488cIDIoxQi\nl9ajwyjtiIiIiI8Dok07IiIiIoeINu2IiIiIDoAOo7TzZIeKXFoiDzzywAEij1KIXFqPipW2mZ1j\nZovN7HEzu97MemdJrFwsWrSontU3Q+TSEnngkQcOEHmUQuTSelQz0r4d2MzdRwPPAidlQ6kyLF26\ntJ7VN0Pk0hJ54JEHDhB5lELk0npUrLTd/Q53XxmKDwNDsqEUEREREbE6ZGXTPhy4NaNrVYQXXnih\nntU3Q+TSEnngkQcOEHmUQuTSeqwx5M/M7gDWKXFohrvfFM6ZCWzl7lNWc40Y7xcRERFRATJ/3ZiZ\nHQocBUx09/crpxYRERER0RpU/DZ2M9sNmA6Mjwo7IiIiom1Q8UjbzJYAXYG3wq4H3f2rWRGLiIiI\niGiJmi9jj4iIiIjIDh1mRWRbw8yazKwpBzymmNmMYK6qJ499zWyimdWtT5nZZDM7KGzXtW+b2TAz\n61ZPDoFHl/DdwqFVBy69zWydevMxs3Fmdp2ZbVwvDtWgXShtM+sRVmDOMrPP5IDPSWhx0Q/NbGid\nOAwxs9uAo4E3gJ+Z2c514tIAfA84CKiLIJjZAOAC4AwzG5haQ1APLocDfwSm1YtD4DEb+EU9OSQw\ns02AJcA3AeqcRe5TwObAWDPrVUceFSH3StvM9gceAroh5XSsmW1eJy79zewPwGjg80AP4Dv14AKM\nBH7l7hPc/SfAJUC9Ri8DgL8DK4FtzaxnW1YeRm3LgF8BdwNnt2X9KR6JPC0HFgDbmNnIFMe25NIE\nbA2MN7Pt3N3rPNpeiR5k3c1s78CxXnz6AouBbYBRdeJQMXKvtJGyPsjdjwauAF4EnqkTl6XAV919\nmru/BtwAvGBmPdqicjNbN1W8393nhf3fBE4EVpkH2oCLpb5XAJcjoRxP6dj+rOtvDN+dwqitH7At\neohuYWab1ppDMVKj+yHAP4AX0MO9TUeWoU2WAXeh+3KO1SlHckoxDwE+BB4FJplZt7bgk8iMCQ2B\nz5vAmcAHwCgz62tm3WvNJSvkTmmb2fpmNiy16wp3f8LM1gOuBKYAp5vZtHB+zf6DmfU0s8PNbP2w\na4W7Px46wImBz1bAr8xssxry2M7M/o5MMgC4+wfh2EZAIzAOuBeYndgNa8BjdzNbYmb/lYzcguCt\nB0wKI/63gJlmdpqZ9asBhz3N7C7gSyBFGfrAe8Bj7v4q8BPgKjO7rMb9Y7SZTUum2GaWhND+DbgF\neAwYaGZTzWyrGvJYJTNm1jm0ydrATignkAF7Jw+6WiIlM8OKDi1Fs4+HgX8DR5jZ+BryaCYzLqwI\n/XU0UtgXAVOR3Hy6VlyyRm6UdlCEp6LkUz9L9qeexpsAvwS2ABYixdCnVrZLM9sa+AtwFrCjmTUV\nKarbgO7uvh+y1e1eIx7dgR2BGcA7ZnZY2J/cu+fd/Ux3f8jdbwSeBA6pAY9tgEORGWQGNLs3fwPu\nDKOa7YH9gPfd/c2MOWwIzAReATY2s9GBx0pgENDPzDYA9gI2BN4OCqzi9Qhr4PIFpJSPQQ9u3P3D\ncHgrpKSeQu3x/cAvaw4tZMbdPww+hqXAs+7+H+BcYD7w51o8SFN80jIzLpGZcHhDJC9PAcOQCWvn\n8LtM9dAaZKZzGGm/hAYaFwAbAf8L5Du1Xwq5UdpAT6AXGh18EIRilffb3e929/nu/g/geuDPwCdr\nyGc58AXkOBmLHhqrFJW7P55aVPR7YLusKg6da6SZdXf394Dr3P1S4HRgupn1TB5W6YdWaKs30Igm\nCx5mhQiIvwKz3P3TwDAzOzB16nBgFnA/8GvgfKBbidFWJRxW9VF3fx44ONT1BrBv6tS3kHPpYeAP\nyCm6p5l1TSnTTGBmXYGXkU30t0hBDQ7HDHgOOAG1x8uoTWohayVlxt1XAP3R1P8YYDYy19zq7m+m\nTBZZo6TMBLwLbGZmTwKD0Sz1ncC36oFXK2XmwyC/nwCuBu5DcrscmFCLh3tN4O51+6AbOwJYK5TX\nDd9TkO2rcyh3KvrdrsBNQK8MuYxEo7idkIAlMeyNyMl3NNC3xO8+gRxgX8uIx75IwH6DHk59i47/\nGpiTtAua+g5G6QT+BPwIaMyAx9eBB4BLgZFFx6YAjwNNqXb6IjAwbI8A/hvoWiWHo9Bodg6wb9Gx\nycCPgV1DuR/wOaBP6pwvIZ+IZdAeuyK/wYhQ7hq+RyFfy96p/vo/wGWpc49GI/LObSUzYd8NyIm/\nKdAH+BewYRvLzNph3zjgWmDPUN4D+C7Qry1lJpR7Ab1T5Z2Bpqzapdaf+lQqYf8BctRcCtxYdLwB\nhSrNLtFhrwQeAT4X9mUhkJ9BU/y5aOQ0A+ifOj45COHE1L51gW+hqekJGbVLDzSNHRvK84BTUd7y\ntKD8NSWsjUGI5wJjMuIxBrgTTR1PBn4O7F50zu/QyLv4t10y4rBNUEJjg0J6CNgtdXwAcDxwUa04\npK53CnJ+nwdch5zR6ePT0exidHJPio63eNhXwKEsmUEP8wFF53wqwzZprczssprf986IRyUy0zdp\n0yz7SVt96mUeGQxs4u7D3f0IoLeZfSOZirumd+cD+4SpaDIlbQQWufs27n5DODcLD/TmwLfc/XgU\nfdAb+EZy0N1vA15H083eZjbGFT3yALCdu58dOJbdnpaKE3X3d9GoqH/YNReNFCcmUzd3fxYJ7WVm\ndgVworsvcffj3f3RcutP8Uhz3wjNbp5DU+tFyASQjsg4Btj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"text": [ "<matplotlib.figure.Figure at 0x185340ad0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.065853 -0.142190\n", "block_num_tx -0.065853 1.000000 0.103507\n", "price -0.142190 0.103507 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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s+dUSGjdWFTBvibT5DNoADwIdhRBrjE+XUIvSaCx0/bIrD8560GX9xmMbrTGD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\n5WOkSwFJdulOQDF/3S8O5w7EQb28OMVR1q+lkZ6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"text": [ "<matplotlib.figure.Figure at 0x1702c8490>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 0.083396 0.260938\n", "block_num_tx 0.083396 1.000000 0.239360\n", "price 0.260938 0.239360 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AAW07kFi3BrG7gDiI/CiSOQ/NIXXAKAAaWoJ2TD/+6JgMxu77aT3+f//8R8ue\nLR3Ob2XUjFHsOb/Htjx/yXwiSkTk7f+Ly5oIlpv9z5yB0NAYDh+G2rVN2EsO5P1iMpmYMWMGZ86c\nyXEdsyPcxbX6GqFd8e+Ai1LKl520kZ7qEqO0D1COKFqTOhTeI0YJbqx4I3sH7g20FMPS+pvWbDix\ngZ2dFnLTrV2pPgROPb4FmjUDKdkyfAAb//ya57to7X8Pepyu7/zoeJDJkyE2lppBn3DSkirA+v3c\ndmYbTac2ZePTG2lZwzH1sRglGNN+DN9t/47Y+Fjb+q+7fs1TzXSSz3iBGCXY8NQGbq15a672378f\nunaFw4e1tP7Ll+dJTr4hhEBKqesrC4Qb5zbgcaC9EGKr5dUxADoUCoUThKXvlD2mftP5rRSzi5q0\nGvpf+7XKWjl7NnzyCQ/twSnOOnP52fnUS6zmKamp2kzZlBTjGnp3BCIaZ62UMkhKGS2lbGp5/enL\nc7j7OeNPjKQFjKVHadEnkFpsxtbyV+Dosz9wJY5QnRD5c9Xssn2t04qUxxzJowYd8nJtTiWeytV+\nvXtDjx5QqpRjWcGCds+oGbQKhYckpSWx/cz2QMvwC1dStKriQRItwsZCRkiQNqs2G+bgnJ6D7vuz\n3g9ZOsTrQia+JrJspNf7SAm//AKzZmnlBgsyhdLYx9gPDgUYI2kBY+kpaFrG/D2G6KnRhtCS3ySl\nagFyQsLo3a86bNMrOB6SZnGRnDsHHbO8ssLyYPhow0fc9f1dNlfKrF2zcqXL39fm8mXNfdO8uZaf\nPpBaXOGJlkJp7BUKZ+Qlzvta+jUfKjE2NUpp8xwFEH8twbb+QLVQcgz/de3K/sbVebQHUKkSNNAm\nxKcGQ1gGhFjcPsnpyYxfOx7QJm/poTepKns6ZX/SqRPUqxew0/uUQmnsC5ovzZ8YSY/Sok8gtVgN\na+liJQHNjWPv6j557SxpduX3LpQA+c03XCldjNmNLSv/9z/45hvSgmHMKjhsZ9d/3fury/O7G0TN\ny7XJTZnD3bthxQrfa/E1ymevUOQD3WZ3C7SEfEdKzf8iJBwpB489qP2ySZNmhwid4mZ4b9OHfLP1\nm6yVN9zAxV7dSAuGzgeh1hXPz5spcw4IpGSkcCH5glf6/zvlm9w7165pA7OFgUJp7AuaL82fGElP\nILQ4cwl4o+X3/b/7SI0+RviMZKZmdIMkZNaDWTdD+LhwMkVWOGa/LVA6Df6L35Vj/61ntpJUDBp4\nZ6PJlJm6KrEHAAAgAElEQVQ5euADFw+k0gQtp76n16b5V805e/WsdyfPRnq6NkBbrJj+diN8TlaU\nz16h8CGB9B37C2vYo61nn227vbH/1vLM+z0uZ+S0lJJfLblsPUuR5nh+Pf6K+4vOMzs7rOvyUxf2\nX9iv217vV4I3XLumJTwzyse+5/wejl0+luv9C6WxL2i+NH9iJD1Kiz6G0GKNs5dAXNZqe2MPcOcT\n0CbyNtuyGCX45/g/XEq5xEFL9IoU8NUCePZfx1OIUYLFBxc7rHNloH/Z8wtLli9BjBK2XvsfB/9g\nxWF9p3peH85vv63lw3GGvz+nRp83osP3HXKtpVAae4XCGSqfvYdYjH32GbT2xj5DwN+1ISElwaHN\ngYsH+G3/b7aZttdC4Omt0HtHztPsPe+YusLVIKp9r3/HWZ2DucHb2bnz5mkvI3Ew/mCu9y2Uxr6g\n+dL8iZH0KC36GEGLgxunTtZ6m7GXECLBHKRveDMyMyhuMfaltGzJlEnV9rPnj4N/WM4nbX+dplJA\n2rTM3TPXbWRPXrl2DerXd77dCJ+TFeWzVygUuSPTec8+OFN7mQU5nfoWDiccZv6N8Mo92vLuShCe\nDp2ydUxXHVnFK0tf4XDCYctpM4m7FIce9i6er7Z8RY+fe3j0r2w7s82jdvbs3q2l5S9RwutdDUuh\nNPaG8HlaMJIWMJYepUUfI2ixD72099mbLT37YAkZTqyHlJLNpzZzsCJ82Ab+1wVe6wCr6uiHYX64\n4UP6/94fgPiUeBeapIMWd1hddk2nNtX29zDOPjUVnnwSBgzQZs86wwifkxXls1coslEUImp8QYpl\ntrCzaJwQa8/eCRXDK9reT2sOf9wAx8vA1EVQ43LO9n8f/RuAGdtmeK3V159pv36awR8/3jiROFYq\nlKjgvpETCqWxL2i+NH9iJD1Kiz5G0PLZxskAtDqBrs8+JNN5z/6Pg3/kyFUPcLSc9rd6omca5Ei4\nyS5U/uutXztocUduHwKnTmkp+cuUcd2uXtN63Pndnbk6hzMiP4pk97ndTreXL1Fed73y2SsUilxx\nJeUyW6tqvXJ7MgWUvwahZufGft7eeTlCKiHL/9/yJJRK9UxHFQ8KliZcS9Bdnz3y6nDCYV5c8qLL\nY6Wna7ncyuvbVAfWn1jPqiOr3Df0guNXjrP51Gan2/PyK6ZQGvuC5kvzJ0bSEwgtzkIvrVoOJxzm\n263fsv/CflsVNHf7+hojfEZBQmR5uLPH2QNj/9Iicbzhaqj297Ml2v6ekCPpmo7P/u1VbxMyOoSv\n/vvK5bF+2fMLkzdN5scdPzpNv/Dii5CcDDVrute2a5M2c3jiPxPdN/aC3Bh0w/rshRDfCiHOCiF2\nBuL8CtfM2DaDAxcPBFpGvuDui/Te2vd46venOJvkONU+4VoC2856H9VRUBFSx9CiGXuA1sed9+yd\n8duNsNfiyi+V5qaxdPjjFrM0899p1/lwFh1YBECf+X2Yunlqju0//KDF1f/wg2c9eyu/7dMto51r\n8qtTEaie/XQg30oRGsHnacVIWsAzPf0W9GPM32MMocVfWLV8tUXrHWb/wg1ZNgTTEZNtefHBxXy8\n4eN81RIIrCGQQdj17LP57EHLeeOtsc8IhliLEXVnxItbkl+GZJ9Q68Jn785ImqVOiS0LP/4IL7wA\n48ZBmzZuxFlo2KIhkLtsmq5w1SFx9j96cs+EuG2RD0gp1wghogJxboXCGZ9syMrFm5KRAsC6Y+s4\ncukIqRmOTubXVrzGrnO7eKnVS37VmN+cTz4PaMY+00XPvpibaBxnnC3pWTvrhCy9EojOyG4kvXGH\nrF4Nb74JTz/t+fnsJ4L5ElcPLeWzz4YRfJ5WjKQFPNfjD/+0ka6NyWTipaVZhvueH7XZQLdPv53H\n5z+e40u261zOTI++1BJoDpzfl+XGsY+zt7MYbl0xOlgjcvRcRPaUtBy7bvaxVxdx9qevnvZeENqg\n7LFjcN113u23+18tauZc0rlcndcZhcpn7yvysxK9onDi7CHmrhi1tadfVEg3p+s6J9JC4EWLA7Zi\nLgp3ba+i/dU7dgmLgX9/GWz4Wnsf4cVlz+47d9dh+eoruP126N4d9u6Fm27y/FynEk+RmKrFkB5K\nOOT5jgEkIG4cT+jbty9RUVEAlCtXjujoaJtfyvoUa9eundY4Tltn77eyX7a2D8RyTExMQM+fGz3E\nwdngs/AAAdfry2Ur2bcvXbGU3vN6Z/mDrb1Hu+Vf4351ut3Xeq3rAnW9iIMr57L1vuOy/t8/Q8AE\nxNhvA5fXz7p8vqS27/5EuP0orK2tbS9mhuQfoen/4IbNcDgNIrG4ceyPV8f9+VatWgVxdr50nfZv\nx73NQ0lvkZgI69aZGDEC6td3f32upV+j8WuNORR/yOF4vvy89v67F9PFnMcD7QFm395kMjFjxowc\n7fQQgeodW3z2C6WUjXW2SU90SSkJGq39OJEjVC/fV4hRgieaPMF33b8LtBSfIkYJmlRpwrZnHaNq\nrqZdpfR4F7lsXVDY7jtruGmbYzBhGdym48OuGw+HPoWR7WBUe++O3/wk/PsVLLgBuu2HykPhfCko\new0uvQ/jbocnt0ONRLgSCtNugW+awYVwuOCBv79tZFtMfU0Ejw6mQokKbHt2G7U+qqXbtsoXkgUL\noGpViIx0P1v2atpV7p91v25sfbNqzVjQawE1y3gQs+kCMUowq8cset3US3dbg4oN2DNwj/P9hUBK\nfSdZoEIvZwH/APWFEMeFEP1ycxxno+BG8HlaMZIWcK8nzZwLR2wuCcS1ceoP9SLnSn5jhHvGIfQy\n27U5XB5Chntm6CuFV3JYtg7qdrPUG7G6aYItX+U318Kp0vB1Uxh+p9az3zsFvlikryU7a46tsbl3\nL167yM6zzqO7Bw+GW2+F2rU9S4tQenxpR0Nvp2XL6S1sPb3V/UE8IDfjZSaTyW393oAYeynlo1LK\n6lLK4lLKWlLK6YHQocjJ+2vfB4pW3nc19pMTgevwSHOwi412nBvmOHiZPVyzrNXYZ2rFy9OCoHQq\nfN4CkotBpCWPzlkv6sB6GgrZ7HYvaya6wR95l1ydo9gYJ/UTLRToAVpnGDF+2yi40/OO6R3/CCEw\n1+ZKqpPq117kXLFn6LKhxF9znqkxN/jzumw4sYE95zW3gP2vOoeefS6vjR5HysHSelnL5ex69hlB\nEJoJN16EcyVheV3obvkFYPZCi6cP76DqeeyJZ9PiaQdp97ndnLl6xun23Dw0PLlnCrSxVz0yhbdY\nJw3Zk5dJMZPWT2L1kdV5kRRQWn/Tmk4zOwEwevVo23p3PfvckhgGHftkLUekwDe/wX0HssI6J7WG\nk2XhaERWu+JexNs74pvedvZ5FnqsObaGEatGOKw7nXia04mOIaE3fXETD/38kNPjuIyzz8P/U6CN\nvTOM4PO0YiQt4EWcvR9+khrl2nibJz3H/j42i/6+LlJKjl46yvmk87Z1rnz2vqRiMvTfBn12ZLl4\nVkXlbGedUeuJlnmmWNv75cudt/PmHtf99ZZNy4R/JjD679EO62764iYaf5EjBoVUs/OHh72uu3+4\nO8fDQg9P7pkCbex9/SVTFE2K+n10/Mpxoj6JcrgOxTKx1ZD1Nc80e4bN1bT31rq0LU9qrhoxQst9\nb6XBQC2u35ue/aNrG9reT5vmvJ03vWRPHgx6xdLjr8Vz8drFXJ97xeEVLrNgekOBNvbOMJKf3Eha\nwHM9/higNdS18aFf2hXx1+LZfma7yzZGuC5NzmgOkBIhJXx+bUa1H8VtT8FHraDNCW1diQyLGyfb\nbbevEhwvCxHWCVxeaunbz7MH+ZbTW3SNtZUvN3+Zc2U+3TNBwrlZdvbQKfQ+e4XCF/hz7OfZRc8S\nPTXab+fzhq1nsgYs04Jhc3U4/UpOF8KX9+kYPi+oWqoqaSHwVTPH9Xq5eADiykFNJ+Pq7rinY7pH\n7W6ZdgsL9y+0LWfvTY9aPcrjcx65dMTjtnoUtqyXPsH+S2ofY2oUXzAYSwsYS08gtczZNYe95/dm\nrciLz97Nw2LCugnajEtc+2qtBOq62Bs4gWZ8y4aVzXFtHm38qE/Ot7ey9veipaj3CSeVoS6Ew03n\nocduvP6cXPXWn1n0jMOyNRopLiGOFl+1sK0fs9pJBlgnWt766y2HZaPkmSrQxt4e+w9HkXcKe5x9\nr3m9eHXFq0Deffbu9n91xatcN1nLsmX/YHBWYckIBMmsnrZ1Nmf10tUBKFO8DPUr1PfZuV6y5NpJ\nd2KNLoZrf3MkRfOAB+Y84HTb4YTDmDNzDgZkn5zkbTiyXh6lLae3OA/7zUZugiM8yd1UoI29/Zds\n25msKfBG8HlaMZIWMJYeo2hZeXhlnvyvPef2pMS7JWzL55LOsfyQfhjIwgNZroLyH5Rn/4X9OdrY\nX5eMzAx+3v1zrrV5amCyY2/sa0fXBhwfVINaDMq1puxYB4LDnXhcUorBN00tPv0on50WgJAxWenB\n9NwvLmelOrlnsv/SE0Jwy7RbGP7XcNu6Lae3OLSZsmmK7Vy56WjViXZ/AxdoY6/IP/wRehkIKoZX\ntL1ff3w9kFWwJC+kZKTQfXZ3APr+1teWItkd7ozxP8f/4ZFfHtHddvLKSbfHL/teWZLTk126M/QI\nkllx9u+v02ZVN6rcyKtjzHxwpkftzlty3rjquZsFTFoGMUey1v0wD6b97pUkl1h/6dmTmwlz2X/p\nWY13mjmNBlMaADkLqQxaMsjm6nOFirPPhvJLO8dIegKtpWaZmpgzzSw/vNwnseQr41YipWRJ7BK3\nbe1roCanJzvMXrW/Ls7GA/Zd2EfNj5wn3Tpx5QTTt2pZSGp9VIv+C/rnaOPKjSTsevbWa/Niyxd5\nu+3bmi4XrquIZM2l+k7f23jHzgMiR0iHxHHVS1enxFuwyBJqWcJFJzrZkgkg+UTWusd3woAt+Hz2\nV7Np2sjxPT/c47o8p5N75uzVs7rrN57cyL4L+5weztrBclmpysm2Tes2Od3HSoE29moGbf5xNe1q\noCXkO7XK1mLDiQ0+O97VtKu2LKwAfx/922G7vX94lEmL7hBCUHJcSYqPLa57zOw98ozMDHae3eny\n89l2Rsv02P93zcDHX4tn08mcxqDb7G5Oj2Hvxln42EKGtRlG5+s7M+bOMWRmQvJlxxSU7at1o8n+\nuVSO78aUaO1cg16QLF4M8x6ex+TGm9i8Ge65B3r2hHVPbGFX/2OkFIN3bvyZjr3hAZ0fMC1rtATg\nWKViNl1WUiz5eeQoCPHhnADrtV1+eLlDKUpPWX9C+8X44foPgaxevH20kytc9d53nt1pO663FGhj\nb09ocKjtvVF8wWAsLeC5Hmt5Ok8Qo4RjZIuPteQXBy4eyOqhunF55sZH/dPOnxyW7f3DK+JWAPpf\nbPvrkr0HPX3rdG7+8maXHR1PH9SuPmN7Y9/lni58cPcHXDgfzHPPQZky8EanJ2m4eoet/c6pQ4kO\nfYjtb/zGo5ZgndatJTt3wqC7HmRwzxa0aAE7d8KGDVA+tSltWmvWevRowdLrYU2Uo4YOdTvY3n+4\nMI15DaBGBUeNVsrlobaMfeEaa0ESK+uOr3O+o5t75pVlr7jc/vqK11kau9S27G7+BWj3g/W4Ukpb\nZ0BGue/4Fhpjr3r5viVYeJjW0IKrxE5Gwv4+OXDxAJ9t+syj/Z5q9hRd6nfx6lxT/5tK0Cj9r5j1\n5/zeCzkfkmevnuWNFW8gRokcP/uT05MB124UX0RSBdmnS0Cr5FS7Nly4AGvXwpVLIdzf6ibqX34O\ngPnzYcYMLTc8wK01bqVx3SqcPw+nT0NYGDz7LAwbBtdfDw0bwjXLRKkXnqpE5juOLp4dz+5g/iPz\nebHlizzf/HkAmke1oepVeG851E7QkqatsBjc8xPc/EMSvv8VbtEZ5qjxYQ3b+5u+cCxX9Wfsn26v\nlR6VJlRy2+b9de/TcWZH2/LDvzwMeD5eNmf3HIJHB7P73G6e/O1Jt+0LtLFX+ey9x1M9QSJI96e/\nM/KrbmZ+cDE5a/r6nN1ztDdufPZ1I+rmyoi6C8vsM7+Pw/LKwyupOqgq7617D4CBiwcCWu/zzNUz\ntjq5I0yOCbfcnVNvneuEW9C8huZ7HznSRPPmMGgQzJ0L0dFQujSMHyfY/+HnuvtveHoD4cXCKVMG\nZs2CX36BL76AIUNg6VJIS4MjRyDhtQQ+HdLOlk++XJhWpLZxlcaUCi1F75t7M+W+KQDUbn4XDy2B\n19bBmFVa8fK73ds4AG4/puXf6RgLwS5cPscuH/PsgODynrEfk/EWT+6zyI8ibR2BQUsGeTTmZNiy\nhN5S1POb+Jqd53Zy69e3Mv+R+dxV5y5KF3ddyamgxOULITgYf9CrfaZ1mUaZ4mXoWr+rQ+ikr1l9\nZLXTB2zkR5EOERxrjq7J9XkyZabb6JwgCSmXq9C6teZ2+fFH6N07d+frla3oUjG7tOtW425l/VPr\nnWeYvO22rGPuAnOwY27OMilajp0V9XLu2u6I9rfLARi7Cg6W1wqj1HvR89z8+UWH7zu4b5SN41eO\n275zno4rBKpSVUchxD4hxEEhxGu5PY79T3L794H2BdtjJC3guR5rdMgDcx5gxrYZbtvb/xz1Vsuw\nZcPcVtnxhFOJp3IUDh/w+wAW7FtgW5ZS6rv87PyvsS/EOmxqVbOVdqxbBuRZoytivovhzb/e1PUF\nZw/VS0pPAuDzzyUTJ8Jvv8Gnn8LHH8OXOtkMkq7CX39p7pbg0cEUG1OMc+dytrMSJCH+XBnat4fk\n5Bi3ht5XD/sbK95Ik6pN9Dfeey8pt1djfU0tUVtYumRql6m2zV8sguU/oBud8+xmbaZuK4sb5/p4\nqH0ZPlyas63H+Cg3zsq4lQ7Lnv5K/ufEP15p8XvPXggRDHwGdABOAv8KIX6XUno9wudt7LDCc+y/\nvPYT1rKTbtZmwngyg88ZE9dPZEjrIVQrXS3XxwBo9HkjgkUwF17N+gn99dav+e/0f3S7MSvyxN19\nU698Vtewd+Pe3FS5MenpkJKHQcD84oNFv9IsrAcLF2q+8BIlAJ0fYcdT9vHQbx1pe/RPsOak0S9V\nyrA2r/FaipniYaUQzr1FDtQuVztX+r1lztB7Cfp2Bq0tIZjP3PIM8D8AHtulrbvvAMz7GYbdDRtr\nwidLoGYitH8SHt0JI9rDmdJwTyx8thgG+0W55wgEO87ucJvaeNmhZV4dNxBunJZArJTyCIAQYjbQ\nDXAw9qmpkJntO5m9Q7Y4NuuJKJEkaZ0d/v7bRNu2MS73dbXel+vWrjVx++0x+X4eZ+uyr//nHxNt\n2rjXY87MWjgef4ETJ/TbpWRk9TiP6bg7XZ1nwwYTrVrF2PZNCfd83y92jWfJ0bn8ft8W2/pLKZcQ\nCGJjHdvuOreLgxbPjdkMhw7rHDgOW++oWze4Meg39kV3549JD1LmSW0wMSwMGJZzV59jp8UdR299\niCN2A5vp5nT+OW5mpk6t+IQKS1nwKQhLTq+LwfqFq19p/TJhpk8hWPNvmEwml78I/Vl0/dFSj3Lz\nvQ3gd+cOgUWztL+f2o2tPtQTTHW0l5WD5SEkL/1FLz4nb7iSeoXOX3a2Ld8/+37e7/B+nrUEwtjX\nAI7bLZ8Abs3e6P774e+/s691LAxsrp8OltQXmWaobEmsZDZDiM5/5uzXkd56X61LT8/yUebneVyt\ns1+fmgrFi7toq3WSSEi8Bhbdy4/9TvRDSwk7cW+O48lgwFIu/vbbXZ87+7qUFIsB7Qc9HoJiOj1n\nZ/ueuncxKVW2co/9RNUntId+x44gg1I50+5BqAnpGZl07gw8DleuwIiREnSKZbdOG8760DH07g3V\nqnXj47h+jPj+diIrQNmy2nmthtJI7D2/lwaVtJmZoWNDKR3qenzFHVWSgH374JZbfKDOt4QGh1K1\njJajhwVZ7rmkYlAyHR5/AIb+A9HZ5jX9eV3OYyUXgzqXoNFZSA2Bk6XhWmjOdv7GGnFljy2QIA8E\nwth71A1Y6oEvbd4eeGiuZSEo09azh5jc6MonYgItIBsxLrdajVmpkkEkZk3qpMoTQ9j9/O4c7VMy\noMS72nu9nr0nWsQo+HcTVPPCRt0xXbDmGBw6ZNWRYtOxccdFktOTifx4sbYiyMzBg9p5MsMucqT9\nHTkPWAe63BnG+r/gYS0CjrZtv83RrG90X/ac30O/6H4898dzngv2Bi97iw0/14p1rO6rlUdMTEt0\n2valP19yf8C33oJff4UWWjSOkcadYmJiYLo2M5j777etn9lYM+jzG8LMJiBHQqunYF9FrQJWks6c\ntfMlITYCdn2hLV8qDhVe08YrMuwHbSX61Q3zKZ999qyZkDOXTm60BMLYnwRq2S3XQuvdO9C3b1+i\noqIAKFeuHNHR0babzhqyJytbnhvZwo6s27O3L+rLbdq2ITQ41G174iCRxKwbKA4S47MMiH17vZJ+\n3uojDtavXc+DnR70eP9L+y5BeNb54hKyRFR8viJzHrLrCcVlC/O0Nq3juDyszTBa1Wzl8vzTu03X\nlq9Cylsp3Pvjvaw2rdY9nr+X281o57b9Jxs/cbn93nr3YlqvfR1jgoJy/P+GWN65U1u2yDYB1Svf\nxfyGK23/T6XnK3Kh8gXbcsnQkiTVSHL4fzPrwCM9YZKlmlVMKphHw7ia8NZd2vUINsOKMdDpMUix\nJvrM58/z9M7TnrePA6xDao6BTTkQ/p6MJIQIAfYDdwGngE3Ao/YDtEII6YmuX/b8Qs+5PW3LVt+h\nOx+jPzGSFjFKML7eeF585EXCi4U7baPHdeWv4+ALOUMWk9KSKDW+FOC979ZkMnFHuzsIHh3M6VdO\nU7VUVY/3bTejHX8f/dt2zn0X9tmSTAEce+kYkR9H2pblCOn0fwMgDuSM3H0XXB43N+STL9gTBjQb\nwLQ/gmDqVHj/fXj1VUPdwyaTiZjYWBgwIGtQRgh47jmOjnuN4KBgKoVXIjgomJk7ZtJ3QV8Apneb\nTr8F/XIeUEKLk3CuJBz5JGt1pWFwoSSUvQaX3oe6L0Jc+Wz7BvBzyoFVy0iQUn/k3e+hl1LKDGAQ\nsBTYA8zJTSSOwjusUTNvrHgjx0SevGA/v6HqRM+NtRVrZIy3kVV6ecjt2X7Wcep5fkZuze4x2/Y+\nbrDW7Xqo4UP5dr78YE2/NZwbeo5PO32aNcjUx3f3iU956CFdP2/tcrWpWaYmxUOKExIUQp8mWfob\nVNQ6AvfWu5c/e9uN3Ar4tyYcjYArdv76ox9p+XZKWVyZfXbA0Q81419QCUicvZRyiZTyBinldVLK\n8d7uf+LKCcQo4dCrt8covRAwjhZbJsY6cPzycdeNPSQlI8UhPv5s0lmGLhvqcpJHmfFlbPvExMSw\nKm4V4DrdxcGLOX9RZM9Z8tH6jxyWu87q6rAcPNr1zJmgurn/Kjxy0yPMf2Q+m57eRPkSWvfvlz2/\nkDE869p83fVr2/t2tTV3S+2yWrhi48qNHQ/oorf42yO/5VqnK26ocAOVSlYiLCRMi3D47DOopoXC\nGuUeBouWcuVwHJnXJ0gEsenpTazrv45ba96KHCH58/E/ufe6rECDW6plDUJbI3P6dYPwDIg+ow36\nAowyQeQVrZdfyZp6yCi9evBIS4FJl5CRmcH5JC1x09TNU920Vthz/eTruZae1SUxSzOXUy47pGKd\n9t80tp52npVPzxiXeLcEEe9HOKybtH4S7b9rz/fbv2fu7rm29WKU4P5Z95OYlugQk3/08lEADsYf\nJCktiYzMDIcHSKbMpP5n9Tl++bitd66XIXLalmlOtXvCM82ecd/IBd1v7E6LGi0oU1yrrdelfheC\ng4J5udXLgJZbZ9njy5j54EwW9FrAwkcXcuSlI8gRkrX91/JgA228YuLdE7kwzPlU+7vr3Z0nndkZ\n2W4k99S7x3Emq9lsC7ss6LSo0YI2tdrkWD+09VAAVj6xkkWPLuLoS0dtxj7J8sOmbgIs/x6Sq1YA\nk4n07Vu5UqY4L0pt4DqybKTDMSuGV3RIyGg0DG/sE64lEH8tnsFLBlN5YmW2nt7K2DVjddtajUug\n89E8s/AZvvj3C0NoAYiNj81ya8RpI/tNvmxC1UlVOZ14mv0X9vO/Rf+z5fHW41DCIa+SzT3525P0\nW9CP1IxU20Ml7pLjSK7JZLIZ8PbfteelP1+i2JhiFBuTNZ/e6q6J/DiS4NHB9F/Q3yH3uy/85d91\n/477Qu/L83Hs6XK9ljTtw3s/5MTL2oDn3fXu5rHGj1E2rKxDUrUyxcsw7+F5yBGSV9q8ws5NO5Ej\nJIlvJBL/ajzmd8z82ftPZvWYRXixcE68fIIDgw4wpv2YPM9cHd5uOEsfX0qxYMs1//FHmDYNgrJM\ngxHuYSu+0jLhngnIEZKyYWW5r/59VAqvRKjF2P92I9C6NT/N03rzYWGloV07it0cTZk27Xl70r90\nOARPRTzlcMwTL59wuDf9ShxsecZ1xI6hjf2Xm7+k/AflqfBBBT7frCVccmWQHv7lYcQoYfsF4An5\nUQf0qy1f8cXmL3x+XG+4mHyRObvm2Ax09lmw1h519Q+rc+OUGz06ZseZHfkr7i8Anv79abftk9KT\nCHs3jKqTNF9+06pNAVh7bC0AS2OXsuZYVo6Xr7dmuTqsX5oTVxwDtaZvm+6RVndYi3AAPNHkCUqF\nlvLJcQHODT3nkFqhRpkaLlo7p1RoKSJKRBAkgrj3untttWBrlKnB9RWu5+073iZzhP5YRJ1yzn/X\nD78jqzxekMhmAqyz5wpaFtk8VlYrUayE7ThpYyRMmkSw5RIEPWKXaP/LL+GHH1i69xbu6DuCc3ct\n5dl/YWPv1RQPKc7vvbTSWfUi6tE+SmcyB9r9kVdebZNVVWvVk6tY1XcVTas1dbmPoROh5TaO+eHN\nD/NllS/pdmM3hwgPay8w7e00igUX43zSeSpPrMwLLV/QBqZ0WHJwCSFBIW5/PkspWX54OffU03yJ\nIfHjP70AABtbSURBVEHapc2rv3P+3vncHnk7lUq6T5l6Ne0qu8/t5sDFAzzx2xOAVoABHH32uWXZ\noWUsO7SMaV2m8c3Wb7zef9GBRQB0mtmJng17MvfkXC0QV4cGUxqw67lddP6ps34DO45eOuq1ljF3\njiEsJIwD8VolIl/6pT35rFzhrZbTr5xm86nNdJ3VlWldpjHglgGkm9OJvxbP4YTD7Dm/hxNXTjBy\n9Uhb1NOYv8fkSEIG2M2WK6K5pqyzMcto7jhmzwZ7Y1+7NtSuTdDs2VroZ9t7+QJghPaw6FK/C388\n9gedr8+6b+1/fTap0kT3/tjx7A46/NCBc0nnGHzrYC1E1sKafmtoO72tbfnSa5coG1aWRpW19CAx\nUTEe1eb1e+ilJ1hDL30Z0vbm7W8ybu042/LRl45S++OsfB5VS1Xl2/u/pVLJStxS7RZi42Op/1l9\nvUPRP7o/X3b5kpLjSvLrI7/y7KJn+azzZzww5wEODDpA/c/q07x6c/4d8G+utG48sZHDCYeZ8M8E\nW3UbvbBGKSVXUq9QNqwsa4+tdbghCgNDWg3hww25q8rjipNDTlK9dHWfH7dQ8O678Pbb8Pnn8Fw+\nTRrzNULA88/DlCl5P054OCQlQVwc1K2rzSS+4Qb99lJmubtWrQI3D6KLyRcpF1aO4KBgjl8+TtdZ\nXXm40cM8etOj1Ilw7IVtOLGB1t+05rHGjzHzwZk2W5j5TqbbsoXOQi8LrLF/vvnzNtfOzVVuZsfZ\nrKo5hox/zcb8R+bzwJwHbMtTu0zlf4v+R92IuhxOOOx3PQHBz1pW9FlB29ptdQfRDBdLHigto0fD\niBEOxt7w18aXxr5UKUhMhLNntUosZ85AlSrOtbS3uGqmToVn8jbI74pD8Ye4knrFqavGel1cGXtD\n++zteaX1Kxx9Sfu5/uE9HzLlvim2Asbbn93Of8/85xBGZXTsDT3A/xZpSWny1dAXcgSCpDeTeO22\n17j46kXiX40nY3gGd9a5k9Exo7mr7l2GjpYwBBmWSCgDdgJdkkeffQ7Kl4cHHoAKFdy3hayxjnPn\nsq6hPVevQs2a8MknObd5QL3y9dz65N1h2J597MVYrpt8HdVKVePUK1k5yv+K+4uYqJicA0sWVh5e\nSYcfOvB99+9tfmtXzO4xm17zerltV1RZ/Nhip35z6+fwacdP+ePgH7SPak+lkpWYs2sOc3bPYVib\nYTza+FH2nN9Do88bETc4jkrhlUjPTGfr6a00qtyIdjPacXOVm3mu+XO0/6697ReOKzY8tYHjV45z\nOeUyN1S8gbbT27K8z3IaVGyQ68FQhYU334Tx42HyZK00VUFACBg4UJsbkNfjlCypGWZPCQ7W0vM+\n+yxERmrXb+xYrSTX5MnwqmUgNTZWy0EN+fogddWztxVzMNILkIzUXqNNo2VeSctIk2kZaTIlPUVu\nOrFJho4JlccvH5fmTLOUUspmU5vJAxcOyMspl23nZSQyKS1JxifHSymlvJZ+TaZmpEoppfzz4J+y\n26xucsnBJZKRyGmbp8nPNn5m22/u7rly+5ntsuaHNWWXn7rI77Z9JxmJnL93vhy/Zryt3d3f3y2n\nbZ4me/7cUzISecPkGyQjkauPrJYJ1xLk1tNbZWJqovz7yN9y8JLB8ukFT8vHf31cbj+zXU7ZNEXu\nOrtLXv/p9XLo0qGSkcgH5zwoG3zWQJ5JPGP7385dPScvXbskk9KSHK7H7nO75YELB+TJKyfl6cTT\n8oHZD8j9F/bLzMxMmZiaKBfuX2g7xtXUq3n+DHLD+aTz8mLyxYCcu8gybJiUIOWnnwZaieeAlAMH\n+uY44eHe7ZORIeWUKdq+9q+5c7W/Zu07JPfuzdqWj2gm3YlddbYhkC9A3vbNbfKVpa/k6h9etWpV\nrvbLD4ykRUpj6VFa9AmoliFDNLPwySfG0JMNXS2+NPYlSnivxWTKaeytryRLJ+vtt7PWpaXlXasT\nLa6MvWF99mv7r2XiPRMDLUOhKFokWrKbOhmUNCyhPhiLCQ3NCrn0hnbttAFtPaZMgcaNNdeOlZOW\neOMZM+DSJe/Pl0sM67M3oi6FotDToQM89ZRWJdzXg575xc8/Q+vWUKuW+7auWLlSSwJ3h069A08Q\nQntZ+/CPPQazZuVsd/Ag1KsH7dtrg8CDfVcYscCGXioUCj/ToIGWMsGAVaoMjxBw001gybfP6dOw\naJFWUeuPP7R1depAo0bw2mtamubz52H1am2dHlY76OGDt1CEXnpDYczl4SuMpEdp0cfvWlav1kIM\nv/lGKzdWt25g9bjA8FrsC2dXq6YZ9EWLoHt3bd369bBihfZrpHNnuHhRe0DMmaPVYV23Dv79V6tn\n+ttv2qSt226DAwe0Op7Tp8P//gfbt2sJ68xmkNKj62LodAkKhcIPdO+u+Y6fflqbHBQR4X4fRU6e\ney7Hg9KGNYtolSpa9XorkyZpvfZeLsK/16/POYt32jQt3HPaNHjnHbj1Vv34fjuUG0ehKOqEhWmV\n6EHr2efV963ISa9eWu9dz661bg0bNmjvjxyBU6egXz/Yvx+io2GbYxJDJk2C+HgttUU2BM4rVSlj\nr1D4kitXoHRpRx+r2az9HLcO3BkpV3xKCpQokbWckKAVB1H4ljVrYMkSGDdOf3tsLHTtCnvtivYl\nJmr3UkgItGypjad8+62WyqFSJfjrLy2HT5MmULw4xMYievQwxqQqoCewGzADzVy080nMqREwkhYp\njaWnUGm5ckWL0QYpY2Ict7VrJ2XbtlLee6+UQUFuJyx5pCUxUcojRxzXrVgh5cqVUp4/73rfzEzt\nr9ksZXS0Y1y4Tgx4ofqcfIjftOzdK+XJk1LGxkr5xRcuteAizt7fPvudwAOAKjWlKFyUKKFN2V+4\nEEwm7af50qVaL3716qx2K1ZoA2654fRpaN5c23+upQrYzTdD5cpaGt7Bg7Xt+/ZpA3ply+ofp2FD\nrc2zz8KuXVC/vubz/eGHrPqzCuNwo129iXr1cn2YgLhxhBCrgFeklLqlVZQbR1GgMZm0Qc+rVzUX\nzl13aVEZrVtr+VNyy5w5WlbKPXu05dGjoUsXLbJjwQJ48EFt8k6PHvDrr5pb4NZbIS1N+5n/0kua\nJvsJU8ePawm6FIUCw8XZK2OvKPRcuaL5Wvft03rOpXxXCcst8fHQqRNs2qQZ9rNnnbdV37NChStj\n73M3jhBiOVBVZ9ObUsqFnh6nb9++REVFAVCuXDmio6NteaytMaXOlj/++GOv2ufnsn38q9LjuJxd\nUyD1bNu2jZdeesn3x2/WzOv983z/7tgBY8cSc/31UKECpm+/haAgYl58UdveogW88QYxFveA+j7l\nbjm7pkBdjxkzZnDmzBlatWqFS5w58/PzBaxCDdAGBCPpUVr0yVctFy9qLy8oMtfGS4yoBRcDtIF0\n4wyVUv7nZLsMhC6FQqEoyBgmXYIQ4gEhxHGgFfCHEGKJP8+vUCgURRW/Gnsp5XwpZS0pZQkpZVUp\nZaf8OI+9Ty3QGEkLGEuP0qKPkbSAsfQoLfp4oqVQJkJTKBQKhSMqXYJCoVAUEgzjs1coFApFYCiU\nxr6g+dL8iZH0KC36GEkLGEuP0qKP8tkrFAqFAlA+e4VCoSg0KJ+9QqFQFHEKpbEvaL40f2IkPUqL\nPkbSAsbSo7Too3z2CoVCoQCUz16hUCgKDcpnr1AoFEWcQmnsC5ovzZ8YSY/Soo+RtICx9Cgt+iif\nvUKhUCgA5bNXKBSKQoPy2SsUCkURp1Aa+4LmS/MnRtKjtOhjJC1gLD1Kiz6G89kLISYIIfYKIbYL\nIX4VQpTNj/Ns27YtPw6bK4ykBYylR2nRx0hawFh6lBZ9PNHi7579MqCRlLIJcAB4Iz9OcunSpfw4\nbK4wkhYwlh6lRR8jaQFj6VFa9PFEi7/LEi6XUmZaFjcCNf15foVCoSiqBNJn3x9YnB8HPnLkSH4c\nNlcYSQsYS4/Soo+RtICx9Cgt+niixeehl0KI5UBVnU1vSikXWtq8BTSTUvZwcgwVd6lQKBS5wFno\npd/j7IUQfYEBwF1SyhS/nlyhUCiKKCH+PJkQoiMwDGinDL1CoVD4D7/27IUQB4FQIN6yar2U8nm/\nCVAoFIoiiiHTJSgUCoXCtxTIGbRCiNZCiOst73UHI/ysp5UQIjTQOgCEEJWFEN2FEA0MoKWbEOJ5\nIUSLQGsBEELcJYSoYAAdlYUQ/YUQrQKtBUAI0UMI8abFzRpwhBD98mvCpRcahBCirBDiXSFEe+u6\nAOq5QwgxTwhxQ26PUaCMvRCighBiKbAUeFgIES6llIH6ECxfkn+AccDXQogu/2/vzIOtKK4w/jvv\n8XgIYXUDBAsNPDdAKVTcEAWMKwZwjRqjQS1JVOISF0wgYnAhGqMxSQmCiqDlkogoGgoXgmvUmIqJ\nWyyjETckUUHLCrKc/HHOyDC8C3eZe2ee9Km6dad7tq/76+10nz6TBY4YnouAPwGHAwtEZJ+McPQQ\nkYeA84HNgdkiMiwLLI7nKBF5ErgQmC4iozPEcimwANgTuENEBmeIpYeIPAycDfwHuEVEhmaFxzEN\nB6YDh2U5gHJPjAOAM4CjRKRjxt4ZBwB9gUEi0qGcB7Soxh5oC8wDxgHtgcHwFTE1Fe/tx2ANyMHA\nIszKKBMRkX5YYRitqqcDN2KL4VnI7sDjqrq/ql4O/BoYmwUQERkCHA9MVNWDgYVA2aOjCrH0ArYD\njlXVM4E7gf2zwOLSBNyjqgeo6lRgGpC1ptwFeAUbsGybMZYewBxgKXBCxlg6A68CewD9y3lA7ht7\nEekWC34E/Ba4B1gB7CUiXf26qhfSxDteByap6pOquhJ4A/hQRBpqpWm4mhlZVH2I7WV41cM3A1vU\nSh1O8PQccGssvBQrqFnw9CJwkqo+KiJtgdHAMhHp79dWtQ4kOFqsqqep6usishtwJNBJRIbWsMzE\neXpSVWd4/PnAxcChInJijbCI/7dK8DAOWAUcXQscjqGjiNT7cb1HvwcsB94C+tVqCjDiyKeS6j2f\n/ovNIHwJ9BeRzl6ei5bcNvY+D74E86cDgKquUNVVqvo58Bg2Chjm56o6uheR8cDjMSzvYy4fImkL\nNKnqyhpgaSMis4EHgF0dz1JVXRy7bB9guaouqzKWiKcFUZyqvq+qS2MNWA+gk5+rKU/A56q6QkS6\nAzcASzCtcIGIbBtz35E2juY4Wu3nWgMnYYOW14CLgKrOlxeoT1/6ud5AI6ZlLAIujwZRVcTzFU+q\nuip2qgnTvH4EHCQi14nIwVXEEedpN8ez2k8PwiwGZ2KDy/t8qrRaWNbhSE1We53ZFWvobwCOw3ja\nr5Tn57Kx9x5rMDAe+ExETvV4iTUgi4DFwI4i0l5E2lUJS52InItlbG8RucTjWyXWC/oCT1QDQwJP\nAzACM2FdDOwpIp39nMRGJb2x+fvovtT3VCR4Wh7jKVmuhmMNG9UaHRXiCYjy4wPgIlU9RlWvBu4F\nflYlLBvkyBvZC1V1kqpOx7TEqq2vbKA+RTz9S1WvUNVnVXUu8Hfg5CphKcRTVD7fwgZRTcBOwHeB\n96uEpSBPLv8EOrvWcxywJfC835uqJrYBjlr5u94BugO/wur2m0BpbjdVNRc/jOwmoK2Ht/f/w7A5\nvPaxa+v8vxPW072AVeauKeJpjL1nAPANYEfg0whLdN6Pr8BGAr2xuc8+KefPtrHj7lhHfRAwE9uk\nFp2r9//rgG8BQzAfRLtkwJP49bcAPYGrgL8CHWrMU30z940FTs+Co2buuwA4N2UsRfOUuK8BWyDd\nM2U8G+XJz10JrPY6fSq2xjI8XtdqxRNwHtaozgMO9PD1QEOtOQJuA5Y5hp7Y4OlYoFXR70uT0AoS\nPRqbj78f+APQOXF+DnCVH9exdn/AicBK4CZgi5Sw1GON9d3AZbH46J13ArP8uCF2/iWsUX0euCDF\nvOmJqXWLgClA/8T5KcAEoGeE0wvRG8BfgEeAozLgKep0ugBrsFHS9UCXrHjCRnCdgEnYqGhwRhzV\nY9N+fYG7sDWOnVMsMyXXJ2AbzMDgReB3QGOtefLw5sQ6YaxTPrLGPPXycC9g79j5fZL31IIjD3cA\nOsbCQ4HNSnpnWgWsgkS3w3qtQR6eAVxGbCSK9X5vAd083NH/jwH2TRFLHfATx7MtNg3y0+i9sUxf\nBgyMCq1XlLeBa0ipMYu97zzgF944/BwbJQ+Mnd8VmB2vENgoaj42VZAlT+0c36y0Kkm5PHlcT69E\nU9PkqUyOtgB+A1yScnkph6dGoI+X392z5qlav3J48vjWOeCos/+X1MCv895qZ3CBxHZIhJ8DDvfj\nnYGrgXOIqSheaOYDt2NWMNXCNgsY48c7+fu+4xUiGo2cj6mX/YGzPK5X7Bn10bUp4HkAGOnH3TCV\n/9bENadj6u81wE0e1xjHkwFPs4AJOeLpbI/bOm2eyuBoqsfFNcOi1fEq8DQxJzz1w2z+15smyYCn\nKcD0FPOhEo5mEtOKyv3VfIFWRCYAj4nI1SJyvEffB/QVkTpVfQWbEumJzX9H0glTXRar6oSUsGwj\nIteIyJjIFA9TY9uJSDs1M8YngL2BHuoMYCOC/bFpm3cBVPVtX3yq17Ur6KXiGSwi80XkChEZ4dGP\nAaf5Oz7A5g8bReSw2K2fYRVmD9zkUc0Cpc4XBFdToqTA0zuqOqnU9xbAkgZPiwFUdYkvZNeVw1NK\nHM3wa1fGOIpbpJSCJw2eLivn3c1gqZSnhx3PyuSzM+BpEDb9VLGkwNG7qjqxUhw1a+xFpKuI3IUl\n5lQsceNEpD22+t8VW0wEU/cGYnO90cYYBbZT1fEp4RmLjSZWYT3rRBHZCmsUtmftxpu7MBU3sn3d\nzeOmqGoPVZ0TPVNV15TZsLZyU7QbsV78NWCmW9DcDqwWkZF++UdYfm3t926Nzf+NVdUhqvpMZCng\neEqtJJsCT6olmlymzNGzlXDkz/y68nR/hTiqwlOZWHLFUVVUtwJqTHvghFh4K4yMJiyzx2Oq0+Z+\n/vesVXNSW4n35zVgZnd9PbwNNn+6H9DRj3+IjT4ArsXVKGzxM75QUrb6HXtGW2xlPT7F8BBwqh+f\njFWkVh7+JVYgITFFUymewFP+OQo8tQye8sSRao2mcVxN/QybM4tEsTm6T1V1iSd0M+BOEZmJjQZe\nAhv9pIilTk1NnIrZN6Oq72Hziaq2CWmOv/9KERkA7EVsA4iqLnP1u07LVL/joqpfAAvVphga3P73\nY9yOVm1Tx4eY/52xmGq3xM9FG3XqInzl4gg8FZa8cOTPCTw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"text": [ "<matplotlib.figure.Figure at 0x15c4b6d10>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 0.050167 -0.345186\n", "block_num_tx 0.050167 1.000000 0.326527\n", "price -0.345186 0.326527 1.000000\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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NNdC+PSxYINcdOQLJybJskv1E6VYPuqAg+gSds2fDpZdGVzYWdOoEb74pf6vt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"text": [ "<matplotlib.figure.Figure at 0x15e197b90>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " block_chain_work block_num_tx price\n", "block_chain_work 1.000000 -0.009246 0.598207\n", "block_num_tx -0.009246 1.000000 0.051958\n", "price 0.598207 0.051958 1.000000\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create histograms of correlation coefficient distribution\n", "def plot_histogram(m1, m2, num_bins=10, color='red'):\n", " data = [corrs[ticker][m1][m2] for ticker in corrs if ticker != 'nmc']\n", "\n", " # the histogram of the data\n", " plt.hist(data, num_bins, facecolor=color, alpha=0.75)\n", " plt.xlabel('Correlation Coefficient')\n", " plt.ylabel('Count')\n", " plt.title(m1 + ' vs. ' + m2)\n", " plt.grid(True)\n", " plt.show()\n", "\n", "plot_histogram('price', 'block_chain_work', num_bins=8)\n", "plot_histogram('price', 'block_num_tx', num_bins=8, color='green')\n", "plot_histogram('block_chain_work', 'block_num_tx', num_bins=8, color='blue')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x168c513d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x16f0ddd50>" ] } ], "prompt_number": 47 } ], "metadata": {} } ] }	mit
VVard0g/ThreatHunter-Playbook	docs/notebooks/windows/05_defense_evasion/WIN-190407183310.ipynb	1	7360	{ "cells": [ { "cell_type": "markdown", "id": "81a309eb", "metadata": {}, "source": [ "# Enable Remote Desktop Conections Registry" ] }, { "cell_type": "markdown", "id": "a5851541", "metadata": {}, "source": [ "## Metadata" ] }, { "cell_type": "markdown", "id": "95e64f19", "metadata": {}, "source": [ "\n", "\| Metadata \| Value \|\n", "\|:------------------\|:---\|\n", "\| collaborators \| ['@Cyb3rWard0g', '@Cyb3rPandaH'] \|\n", "\| creation date \| 2019/04/07 \|\n", "\| modification date \| 2020/09/20 \|\n", "\| playbook related \| [] \|" ] }, { "cell_type": "markdown", "id": "d25f9d7e", "metadata": {}, "source": [ "## Hypothesis\n", "Adversaries might be modifying registry key values to enable remote desktop connections in my environment" ] }, { "cell_type": "markdown", "id": "2d51e879", "metadata": {}, "source": [ "## Technical Context\n", "Remote desktop is a common feature in operating systems. It allows a user to log into an interactive session with a system desktop graphical user interface on a remote system.\n", "Microsoft refers to its implementation of the Remote Desktop Protocol (RDP) as Remote Desktop Services (RDS)." ] }, { "cell_type": "markdown", "id": "38aece1a", "metadata": {}, "source": [ "## Offensive Tradecraft\n", "Adversaries may connect to a remote system over RDP/RDS to expand access if the service is enabled and allows access to accounts with known credentials.\n", "There are several settings that must be configured to enable Remote Desktop connections.\n", "First, you must enable Remote Desktop connections by using the fDenyTSConnections setting.\n", "Setting fDenyTSConnections=False in the Microsoft-Windows-TerminalServices-LocalSessionManager component (HKLM:\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server) specifies whether Remote Desktop connections are enabled.\n", "\n", "An adversary can also specify how users are authenticated.\n", "Setting UserAuthentication=0 in the Microsoft-Windows-TerminalServices-RDP-WinStationExtensions component (HKLM:\\System\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp) helps make sure that users can connect remotely from computers that don't run Remote Desktop by using network-level authentication.\n", "This is the equivalent of Allow connections from computers running any version of Remote Desktop (less secure) security setting." ] }, { "cell_type": "markdown", "id": "8097acbc", "metadata": {}, "source": [ "## Security Datasets" ] }, { "cell_type": "markdown", "id": "158975f8", "metadata": {}, "source": [ "\n", "\| Metadata \| Value \|\n", "\|:----------\|:----------\|\n", "\| docs \| https://securitydatasets.com/notebooks/atomic/windows/defense_evasion/SDWIN-190518203650.html \|\n", "\| link \| [https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/defense_evasion/host/empire_enable_rdp.tar.gz](https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/defense_evasion/host/empire_enable_rdp.tar.gz) \|" ] }, { "cell_type": "markdown", "id": "e52bed46", "metadata": {}, "source": [ "## Analytics" ] }, { "cell_type": "markdown", "id": "5bdb62b1", "metadata": {}, "source": [ "### Initialize Analytics Engine" ] }, { "cell_type": "code", "execution_count": null, "id": "ea23ac77", "metadata": {}, "outputs": [], "source": [ "from openhunt.mordorutils import \n", "spark = get_spark()" ] }, { "cell_type": "markdown", "id": "91d8673e", "metadata": {}, "source": [ "### Download & Process Security Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "d7737f9f", "metadata": {}, "outputs": [], "source": [ "sd_file = \"https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/defense_evasion/host/empire_enable_rdp.tar.gz\"\n", "registerMordorSQLTable(spark, sd_file, \"sdTable\")" ] }, { "cell_type": "markdown", "id": "f20bed5b", "metadata": {}, "source": [ "### Analytic I\n", "Look for any process updating fDenyTSConnections or UserAuthentication registry key values" ] }, { "cell_type": "markdown", "id": "e608f7c5", "metadata": {}, "source": [ "\n", "\| Data source \| Event Provider \| Relationship \| Event \|\n", "\|:------------\|:---------------\|--------------\|-------\|\n", "\| Windows registry \| Microsoft-Windows-Sysmon/Operational \| Process modified Windows registry key value \| 13 \|" ] }, { "cell_type": "code", "execution_count": null, "id": "914eeb57", "metadata": {}, "outputs": [], "source": [ "df = spark.sql(\n", "'''\n", "SELECT `@timestamp`, Hostname, Image, TargetObject\n", "FROM sdTable\n", "WHERE Channel = \"Microsoft-Windows-Sysmon/Operational\"\n", " AND EventID = 13\n", " AND (TargetObject LIKE \"%fDenyTSConnections\"\n", " OR TargetObject LIKE \"%UserAuthentication\")\n", " AND Details = \"DWORD (0x00000000)\"\n", "'''\n", ")\n", "df.show(10,False)" ] }, { "cell_type": "markdown", "id": "e003b2b7", "metadata": {}, "source": [ "## Known Bypasses" ] }, { "cell_type": "markdown", "id": "7186c136", "metadata": {}, "source": [ "## False Positives\n", "None" ] }, { "cell_type": "markdown", "id": "e66d8fb2", "metadata": {}, "source": [ "## Hunter Notes\n", " if the activity defined above happens frequently in your environment, you cshould Stack the processeses modifying the registry key values." ] }, { "cell_type": "markdown", "id": "7e6424ab", "metadata": {}, "source": [ "## Hunt Output\n", "\n", "\| Type \| Link \|\n", "\| :----\| :----\|\n", "\| Sigma Rule \| [https://github.com/SigmaHQ/sigma/blob/master/rules/windows/registry_event/sysmon_rdp_registry_modification.yml](https://github.com/SigmaHQ/sigma/blob/master/rules/windows/registry_event/sysmon_rdp_registry_modification.yml) \|" ] }, { "cell_type": "markdown", "id": "bd2d121b", "metadata": {}, "source": [ "## References\n", "* https://attack.mitre.org/techniques/T1076/\n", "* https://github.com/EmpireProject/Empire/blob/master/lib/modules/powershell/management/enable_rdp.py\n", "* https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-terminalservices-localsessionmanager-fdenytsconnections\n", "* https://docs.microsoft.com/en-us/windows-hardware/manufacture/desktop/enable-remote-desktop-by-using-an-answer-file" ] } ], "metadata": { "kernelspec": { "display_name": "PySpark_Python3", "language": "python", "name": "pyspark3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 5 }	mit
ahuang11/ahh	sketches/references/xarray.ipynb	1	5187	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import xarray as xr\n", "from ahh import exp, sci, ext, era" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = exp.arr_ds() # import experimental dataset; NCEP tmp 1948" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lats = ds['lat'].values\n", "lons = ds['lon'].values\n", "time = ds['time'].values\n", "air = ds['air'].values\n", "da = xr.DataArray(air, coords={'time': time, 'lat': lats, 'lon': lons},\n", " dims=('time', 'lat', 'lon')) # create a data array\n", "da.attrs = ds.attrs # copy over attributes (although a bit different)\n", "dv = da.variable # leave out coordinate data\n", "\n", "da_2 = ds['air'] # also can do this, but assuming you don't start out with a xr.dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.datetime64('1948-01-01T00:00:00.000000000')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "numpy.datetime64('1949-12-31T00:00:00.000000000')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "(numpy.datetime64('1948-01-01T00:00:00.000000000'),\n", " numpy.datetime64('1949-12-31T00:00:00.000000000'))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_t2 = exp.arr_ds(timestep=2) # import another ds; NCEP tmp 1949\n", "ds_list = [ds, ds_t2] # insert the datasets to concatenate across time axis\n", "ds_comb = xr.concat(ds_list, 'time') # combined dataset\n", "ds.time[0].values # first time step\n", "ds_t2.time[-1].values # last time step\n", "ds_comb.time[0].values, ds_comb.time[-1].values # contains both time steps" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (lat: 73, lon: 144, time: 366)\n", "Coordinates:\n", " * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 ...\n", " * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n", " * time (time) datetime64[ns] 1948-01-01 1948-01-02 1948-01-03 ...\n", "Data variables:\n", " t2m (time, lat, lon) float64 238.1 238.1 238.1 238.1 238.1 238.1 ...\n", " rh (time, lat, lon) float64 89.25 89.25 89.25 89.25 89.25 89.25 ..." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_v2 = exp.arr_ds(var='rh') # import another ds; NCEP rh 1948\n", "da = ds.air # get data array out\n", "da2 = ds_v2.rhum # get data array out\n", "ds_comb_v = xr.Dataset({'t2m': da, 'rh': da2}) # merge two different variables with same dimensions into one dataset\n", "ds_comb_v" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (month: 12)\n", "Coordinates:\n", " * month (month) <U3 'NDJ' 'DJF' 'JFM' 'FMA' 'MAM' 'AMJ' 'MJJ' 'JJA' ...\n", "Data variables:\n", " air (month) float64 276.5 275.3 274.7 275.7 277.9 279.1 279.2 ...\n", " terc_air (month) float64 nan nan 275.5 275.2 276.1 277.6 278.7 279.2 ..." ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = exp.arr_ds() # import experimental dataset; NCEP tmp 1948\n", "ds_mth = ds.groupby('time.month').mean()\n", "da_seas = era.mth2terc(ds_mth['month'].values) # get seasonal values\n", "ds_seas = ds_mth.copy() # copy over dataset to prevent contamination\n", "ds_seas['month'] = da_seas\n", "ds_seas.assign(terc_air=(('month'), sci.get_terc_avg(ds_seas.air.values)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
aasensio/SolarnetGranada	notebooks/Stray-light.ipynb	2	404773	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as pl\n", "import wavefront as wf\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us consider the point spread function of a telescope with a diameter of 0.5 m at 630 nm, both with and without seeing. First, let us build the aperture:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "npix = 64\n", "aperture = wf.aperture(npix = npix, cent_obs = 0.2, spider=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we compute the wavefront in both cases. The case with seeing is obtained by a linear combination of Zernike polynomials with normal random coefficients." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "D = 0.5 # m\n", "r0 = 10 # cm\n", "pixSize = 0.03 # arcsec\n", "lambda0 = 6301.0 # A\n", "wavefront = wf.seeing(D * 100.0 / r0, npix = npix, nterms = 0, quiet=True)\n", "wavefrontSeeing = wf.seeing(D * 100.0 / r0, npix = npix, nterms = 40, quiet=True)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x843a150>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GJ+YPbNe6WQExMbZr6zifheBYty5TJ0zzzNl1E1Lnka889TBLS8wmcs4qJKrZ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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x78326d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = pl.subplots(ncols=3, nrows=1, figsize=(15,8))\n", "ax[0].imshow(aperture)\n", "ax[1].imshow(wavefront)\n", "ax[2].imshow(wavefrontSeeing)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PSF is computed from the aperture and wavefront as the absolute value of the Fourier transform of the following function\n", "\n", "$$\n", "h(x,y) = A(x,y) e^{i\\phi(x,y)}\n", "$$" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "psf = wf.psf(aperture, wavefront, overfill = wf.psfScale(D, lambda0, pixSize))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "psfSeeing = wf.psf(aperture, wavefrontSeeing, overfill = wf.psfScale(D, lambda0, pixSize))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x8ebcc50>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3Og1W9sRMc+nyDt/o98ezCa/fD8PZCG0RQtWmma0d3Y1T7vnANVVqZGEQj/ol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\no7AV6sqQryo27x3YfeuWnXDpxXZiPwS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fSh5kbR6vJLvW2v9dCPH+5Ol/Dfir/f5/A/yI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uxUL+t7rM/tSS8Bu5e8P3X+8LqmAj2eOlb4HX/+N6e1N/txTQUyhjAB7pSWFUKgGyoEx58emN1T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Nuj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/adc6brlmPDXKYx3+f45iA8FwPceD2eUtcB6HIh1xV56Rnd0pxYmCPv6te0TUJdZzw1jxzr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rq3d4sX6Ha/suDhZxM5oiutgFLuV6qKUVk+d+ymutQeYcD+f0Wi8D1dw29UuGhF0LD8NK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\nxELU4R6U7bFeC2nTn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dO4qqWEAlt6oBryh4Rn6bZd41IfsPjtnwDcMOgE0IERYkKg1jTE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"text/plain": [ "<matplotlib.figure.Figure at 0x8f22490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = pl.subplots(ncols=2, nrows=2, figsize=(12,12))\n", "ax[0,0].imshow(psf)\n", "ax[0,1].imshow(np.log(psf))\n", "ax[1,0].imshow(psfSeeing)\n", "ax[1,1].imshow(np.log(psfSeeing))" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
omoju/Fundamentals	Data/data_ML_1_LinearRegression.ipynb	1	401654	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "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": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import libraries\n", "from __future__ import absolute_import, division, print_function\n", "\n", "# Ignore warnings\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import sys\n", "sys.path.append('/Users/omojumiller/mycode/tools')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as st\n", "from tools import plot_features_by_target\n", "\n", "\n", "\n", "# Graphing Libraries\n", "import matplotlib.pyplot as pyplt\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\") \n", "\n", "# Configure for presentation\n", "np.set_printoptions(threshold=50, linewidth=50)\n", "import matplotlib as mpl\n", "mpl.rc('font', size=16)\n", "\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(200, 5)" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/Advertising.csv')\n", "df.shape" ] }, { "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>Unnamed: 0</th>\n", " <th>TV</th>\n", " <th>Radio</th>\n", " <th>Newspaper</th>\n", " <th>Sales</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>230.1</td>\n", " <td>37.8</td>\n", " <td>69.2</td>\n", " <td>22.1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>44.5</td>\n", " <td>39.3</td>\n", " <td>45.1</td>\n", " <td>10.4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>17.2</td>\n", " <td>45.9</td>\n", " <td>69.3</td>\n", " <td>9.3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>151.5</td>\n", " <td>41.3</td>\n", " <td>58.5</td>\n", " <td>18.5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>180.8</td>\n", " <td>10.8</td>\n", " <td>58.4</td>\n", " <td>12.9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 TV Radio Newspaper Sales\n", "0 1 230.1 37.8 69.2 22.1\n", "1 2 44.5 39.3 45.1 10.4\n", "2 3 17.2 45.9 69.3 9.3\n", "3 4 151.5 41.3 58.5 18.5\n", "4 5 180.8 10.8 58.4 12.9" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_vars = ['TV', 'Radio', 'Newspaper']\n", "y_vars=['Sales']" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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P+vr6CewRZapBsgZbbW4cPNrCO/bigXN4dttSzK8Iv31Gso4JiSYBfbgcm6Q+7zvSioXV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Lycs3VDMr755hsEBwdj6dKlnIwPP/wQaWlpOHjwIMaPHw8AEIvFGDVqlBWuePuHHGjCKkyaNAlb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\nB/yehXrYYJAKAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d73cbd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_features_by_target(df, x_vars, y_vars)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Hjx7FLbfcgqVLl2LLli3QaDSRuo2owG534A9/PoWhYW5Wz5/dPOYVDpaUJDnu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+9i+fLl7YqNyFQ0Gg127tyJL7/80riMwtnZGa+++ipiY2Nb1cen675F5o26vWf/7/E56NO7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\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wpnTBcfsC2N1eNAqoKC5gf20j/UNmhi1WsjOlntrS0lIKCwvp7e1l+/btbN68+XztugTO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\nQm6Bmux8tcm2xt7O5GXceuAMENLSs1LgrNfrb6tMYTnqROA8YcIEXF1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YtkTc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vR6lQcN7pU/jTa6sJBCQ0SgWj8nJ4f79wlE/PjWND0GkeGSfjQGsvIEOnkMjWOiizigxYmtqN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\nB58AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d2e8090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.PairGrid(df.ix[:, [1,2,3,4]], diag_sharey=False)\n", "g.map_lower(sns.kdeplot, cmap=\"Blues_d\")\n", "g.map_upper(pyplt.scatter)\n", "g.map_diag(sns.kdeplot, lw=3);" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = df.ix[:, [1,2,3]]\n", "y = df.ix[:, [4]]" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn import linear_model\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n", "\n", "\n", "\n", "reg = linear_model.LinearRegression()\n", "reg.fit(X_train, y_train)\n", "y_preds = reg.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "slope of regression is [[ 0.04359498 0.19927632 0.0146631 ]]\n", "intercepts of regression is 2.58\n", "\n", " ******stats on dataset****\n", "\n", "r-squared score on testing data: 0.872100481605\n", "r-squared score on training data: 0.904261364891\n" ] } ], "source": [ "print( \"slope of regression is\", reg.coef_)\n", "print (\"intercepts of regression is %.2f\" % reg.intercept_)\n", "\n", "print (\"\\n ****stats on dataset*****\\n\")\n", "print (\"r-squared score on testing data: \", reg.score(X_test, y_test))\n", "print (\"r-squared score on training data: \", reg.score(X_train, y_train))\n", "\n" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import glob\n", "glob.glob('_[0-9].*')\n" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[34mdata\u001b[m\u001b[m/ data_ML_LinearRegression.ipynb\r\n", "dataSampling_2_ABTesting.ipynb data_ML_LogisticRegression.ipynb\r\n", "dataSampling_NormalApproximation.ipynb \u001b[1m\u001b[34mimages\u001b[m\u001b[m/\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
automactic/GISInfoProcess	PolygonBorderProcessing.ipynb	1	1618	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Polygon Border Processing\n", "\n", "## Common borders\n", "Given poly1 and poly2, they are neiboring polygons, but they may not share the same border, i.e., the space between these two polygons are vacant, and we want to extend the area of these two polygons till they share the same border\n", "### Strategy 1: \n", "for each pair of points in poly1 and poly2, if dist(pair of points) are closer than $\\epsilon$, move both of them to central point\n", "\n", "### Strategy 2: \n", "1. find a LineString in poly2\n", "2. for each Point in ploy1, if Point is closer than $\\epsilon$ to that LineString, then move point onto LineString \n", "\n", "## Simplify\n", "* for any 2 consecutive line segment, if their slope diff is less than $\\epsilon$, remove center point / common point\n", "* for any adjacent 2 points in polygon, if dist(p1, p2) is less than $\\epsilon$, replace points with mid-point" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
CalebBell/thermo	docs/Examples/Chemical Thermodynamics for Process Simulation/Example 14.2 Joule-Thomson Effect.ipynb	1	4217	{ "cells": [ { "cell_type": "markdown", "id": "senior-herald", "metadata": {}, "source": [ "# Example 14.2 Joule-Thomson Effect" ] }, { "cell_type": "markdown", "id": "narrative-examination", "metadata": {}, "source": [ "A stream of nitrogen is expanded from T1 = 300 K, P1 = 200 bar, to 1 bar by a throttling valve. An ideal throttling valve has the conditions of being adiabatic (no heat loss, energy is conserved); and is either solved using a valve Cv to solve for pressure or solved with the outlet pressure directly specified. \n", "\n", "Calculate the outlet temperature using:\n", "\n", "(1) A high precision (helmholtz fundamental) equation of state\n", "\n", "(2) The Peng-Robinson equation of state" ] }, { "cell_type": "code", "execution_count": 1, "id": "wireless-aaron", "metadata": {}, "outputs": [], "source": [ "# Set the conditions and imports\n", "from scipy.constants import bar\n", "from thermo import ChemicalConstantsPackage, PRMIX, CEOSLiquid, CoolPropLiquid, CEOSGas, CoolPropGas, FlashPureVLS\n", "fluid = 'nitrogen'\n", "constants, correlations = ChemicalConstantsPackage.from_IDs([fluid])\n", "\n", "T1 = 300.0\n", "P1 = 200bar\n", "P2 = 1bar\n", "zs = [1]" ] }, { "cell_type": "code", "execution_count": 2, "id": "lightweight-migration", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "269.1866854380218" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Thermo can use CoolProp to provide properties of one or all phases\n", "# For pure species this is quite reliable within the temperature,\n", "# pressure, etc. limits of the EOSs implemented by CoolProp \n", "\n", "backend = 'HEOS'\n", "gas = CoolPropGas(backend, fluid, T=T1, P=P1, zs=zs)\n", "liquid = CoolPropLiquid(backend, fluid, T=T1, P=P1, zs=zs)\n", "\n", "flasher = FlashPureVLS(constants, correlations, gas=gas, liquids=[liquid], solids=[])\n", "\n", "state_1 = flasher.flash(T=T1, P=P1)\n", "state_2 = flasher.flash(H=state_1.H(), P=P2)\n", "T2_precise = state_2.T\n", "T2_precise" ] }, { "cell_type": "code", "execution_count": 3, "id": "trained-burke", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "265.50610736019723" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use the default originally published Peng-Robinson models\n", "eos_kwargs = dict(Tcs=constants.Tcs, Pcs=constants.Pcs, omegas=constants.omegas)\n", "liquid = CEOSLiquid(PRMIX, HeatCapacityGases=correlations.HeatCapacityGases, eos_kwargs=eos_kwargs)\n", "gas = CEOSGas(PRMIX, HeatCapacityGases=correlations.HeatCapacityGases, eos_kwargs=eos_kwargs)\n", "flasher = FlashPureVLS(constants, correlations, gas=gas, liquids=[liquid], solids=[])\n", "\n", "state_1 = flasher.flash(T=T1, P=P1)\n", "state_2 = flasher.flash(H=state_1.H(), P=P2)\n", "T2_PR = state_2.T\n", "T2_PR" ] }, { "cell_type": "markdown", "id": "voluntary-elite", "metadata": {}, "source": [ "The outlet temperature anwsers given in the book are 269.19 K for the high-precision EOS, and for the PR EOS they used a very low precision Cp of 1 J/(g*K) and obtained an outlet temperature of 283.05 K.\n", "\n", "The book textbook cites this 14 K difference as coming from the cubic EOS's lack of precision but the above calculation shows that if an accurate heat capacity is used the difference is only ~ 4K." ] } ], "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": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 5 }	mit
ecabreragranado/OpticaFisicaII	Trabajo Filtro Interferencial/.ipynb_checkpoints/Filtro_Interferencial-checkpoint.ipynb	1	18073	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Selección de un filtro interferencial para evitar daño ocular" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Consultar el manual de uso de los cuadernos interactivos (notebooks) que se encuentra disponible en el Campus Virtual" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grupo de trabajo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta celda los integrantes del grupo: modificar el texto\n", "\n", "* Juan Antonio Fernández \n", "* Alberto Pérez\n", "* Juan \n", "\n", "Incluir las direcciones de correo electrónico" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introducción" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El trabajo consiste en encontrar un filtro interferencial comercial que sirva para \n", "proteger el ojo de la radiación visible de un puntero láser de alta potencia. El trabajo\n", "se divide en las siguientes tareas:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <div class='alert'> Tarea 1. Exposición máxima permisible (MPE). </div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La exposici\u0013óm m\u0013áxima permisible (MPE maximum permissible exposure) es la m\u0013áxima densidad de potencia \n", "o de energí\u0013\u0010a (W/cm$^2$ o J/cm$^2$) de un haz de luz que puede alcanzar el ojo humano sin producir daño. \n", "La MPE se mide en la c\u0013órnea, y depende de la longitud de onda de la radiaci\u0013ón y del tiempo de exposici\u0013ón. \n", "En la siguiente fi\f", "gura se muestra la MPE en la córnea (en unidades de irradiancia (W/cm$^2$)) en función \n", "del tiempo de exposición para distintos rangos del espectro electromagnético.\n", "Figura de http://en.wikipedia.org/wiki/Laser_safety " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"http://upload.wikimedia.org/wikipedia/commons/thumb/2/28/IEC60825_MPE_W_s.png/640px-IEC60825_MPE_W_s.png\"/>" ], "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image, display\n", "Image(url=\"http://upload.wikimedia.org/wikipedia/commons/thumb/2/28/IEC60825_MPE_W_s.png/640px-IEC60825_MPE_W_s.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tarea 1 (a). Irradiancia máxima." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como estamos considerando el haz láser de un puntero que emite\n", "en el visible, como tiempo de exposición emplearemos el tiempo que\n", "se tarda en cerrar el párpado. Así con este tiempo de exposición\n", "estimar de la gráfica la irradiancia máxima que puede alcanzar el\n", "ojo. \n", "\n", "Escribir el tiempo de exposición empleado y el correspondiente valor de la irradiancia.\n", "\n", "* Tiempo de exposición (parpadeo) = s\n", "\n", "* Irradiancia máxima permisible = W/cm$^2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tarea 1 (b). Potencia máxima." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a considerar que el haz que alcanza nuestro ojo está colimado\n", "con un tamaño equivalente al de nuestra pupila. Empleando dicho\n", "tamaño calcular la potencia máxima que puede alcanzar nuestro ojo\n", "sin provocar daño.\n", "\n", "Escribir el tamaño de la pupila considerado, las operaciones y el resultado final de la potencia (en mW)\n", "\n", "* Diámetro o radio de la pupila = mm\n", "\n", "* Cálculos intermedios\n", "\n", "* Potencia máxima permisible = mW" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <div class='alert'> Tarea 2. Elección del puntero láser. </div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Buscar en internet información sobre un\n", "puntero láser visible que sea de alta potencia.\n", "Verifi\f", "car que dicho puntero l\u0013áser puede provocar daño ocular (teniendo en cuenta el resultado de la Tarea 1 (b))\n", "\n", "Escribir aquí las características técnicas de dicho láser \n", "\n", "* potencia\n", "* longitud de onda \n", "* precio\n", "* otras características\n", "* página web http://www.ucm.es\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### <div class='alert'> Tarea 3. Elección del filtro interferencial. </div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a buscar en internet un filtro interferencial\n", "comercial que permita evitar el riesgo de daño ocular para el\n", "puntero láser seleccionado. Se tratará de un filtro que bloquee \n", "la longitud de onda del puntero láser." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tarea 3 (a). Busqueda e información del filtro interferencial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a emplear la información accesible en la casa Semrock ( http://www.semrock.com/filters.aspx )\n", "\n", "Seleccionar en esta página web un filtro adecuado. Pinchar sobre cada filtro (sobre la curva de transmitancia, \n", "sobre el Part Number, o sobre Show Product Detail) para obtener más información. Escribir aquí \n", "las características más relevantes del filtro seleccionado: \n", "\n", "* transmitancia T o densidad óptica OD \n", "* rango de longitudes de onda\n", "* precio\n", "* página web del filtro seleccionado (cambiar la siguiente dirección) http://www.semrock.com/FilterDetails.aspx?id=LP02-224R-25\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tarea 3 (b). Verificación del filtro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Empleando el dato de la transmitancia (T) a la longitud de onda del\n", "puntero láser comprobar que dicho filtro evitará el riesgo de\n", "lesión.\n", "\n", "Para ello vamos a usar los datos de la transmitancia del filtro seleccionado\n", "que aparecen en la página web de Semrock. Para cargar dichos datos en nuestro notebook seguimos los siguientes pasos:\n", "\n", "* Pinchar con el ratón en la página web del filtro seleccionado sobre ASCII Data, que se encuentra en la leyenda de la figura (derecha).\n", "\n", "![ejemplo_filtro](files/ejemplofiltro.png)\n", "\n", "* Copiar la dirección de la página web que se abre (esta página muestra los datos experimentales de la transmitancia)\n", "\n", "* Pegar esa dirección en la siguiente celda de código, detrás de `filename = ` \n", " \n", " (Nota: asegurarse de que la dirección queda entre las comillas)\n", " \n", "\n", "En la siguiente celda de código se representa la transmitancia del filtro en escala logarítmica \n", "en función de la longitud de onda (en nm). " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "ename": "IOError", "evalue": "http://www.semrock.com/_ProductData/Spectra/NF01-229_244_DesignSpectrum.txt not found.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-20-38e9e6a000f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'pylab inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#,dtype=float,skip_header=4) # Carga los datos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[0mlongitud_de_onda\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\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[0;32m 13\u001b[0m \u001b[0mtransmitancia\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\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;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/sage/sage-6.5/local/lib/python2.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mgenfromtxt\u001b[1;34m(fname, dtype, comments, delimiter, skiprows, skip_header, skip_footer, converters, missing, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise)\u001b[0m\n\u001b[0;32m 1342\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1343\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1344\u001b[1;33m \u001b[0mfhd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rbU'\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 1345\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1346\u001b[0m \u001b[0mfhd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/sage/sage-6.5/local/lib/python2.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(path, mode, destpath)\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[0mds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/local/sage/sage-6.5/local/lib/python2.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, path, mode)\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_file_openers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mext\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfound\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%s not found.\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIOError\u001b[0m: http://www.semrock.com/_ProductData/Spectra/NF01-229_244_DesignSpectrum.txt not found." ] } ], "source": [ "# MODIFICAR LA DIRECCIÓN DE LA PÁGINA WEB. LUEGO EJECUTAR\n", "########################################################\n", "\n", "filename = \"http://www.semrock.com/_ProductData/Spectra/NF01-229_244_DesignSpectrum.txt\"\n", " # asegurarse de que la dirección web queda entre comillas\n", "\n", "# DESDE AQUÍ NO TOCAR \n", "##############################################################################################################################\n", "\n", "%pylab inline\n", "data=genfromtxt(filename,dtype=float,skip_header=4) # Carga los datos \n", "longitud_de_onda=data[:,0]\n", "transmitancia=data[:,1]\n", "\n", "print \"Datos cargados OK\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'module' object is not callable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-5-e39f7e3add97>\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[0mplotly\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ofii'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'i6jc6xsecb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlongitud_de_onda\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'y'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtransmitancia\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'title'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'Transmitancia Filtro Escogido'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'yaxis'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'type'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'log'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable" ] } ], "source": [ "import plotly\n", "\n", "py = plotly.plotly('ofii','i6jc6xsecb')\n", "data = [{'x': longitud_de_onda, 'y':transmitancia}]\n", "layout={'title': 'Transmitancia Filtro Escogido','yaxis':{'type':'log'}}\n", "py.iplot(data,layout=layout,fileopt='overwrite')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esta gráfica nos permite obtener el valor de la transmitancia a la longitud de onda de nuestro \n", "puntero láser. Explora la curva moviendo el ratón sobre ella y haciendo zoom utilizando los\n", "controles que aparecen en la parte superior derecha de la figura. Localiza la longitud de onda\n", "del puntero láser seleccionado y apunta el valor de la transmitancia en esta celda.\n", "\n", "* $\\lambda$ = \n", "\n", "* T = \n", "\n", "\n", "Empleando el valor de la transmitancia del filtro a la longitud de onda del puntero láser verificar \n", "que el filtro evitará el riesgo de daño ocular. Escribir a continuación la estimación realizada.\n" ] }, { "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": "2.7.8" }, "name": "Filtro_Interferencial.ipynb" }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
CartoDB/cartoframes	docs/examples/data_management/upload_to_carto.ipynb	1	4742	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Upload data to CARTO\n", "\n", "This example illustrates how to upload local data to a CARTO account.\n", "\n", "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", "\n", "set_default_credentials('creds.json')" ] }, { "cell_type": "code", "execution_count": 2, "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>cartodb_id</th>\n", " <th>the_geom</th>\n", " <th>field_1</th>\n", " <th>name</th>\n", " <th>address</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>POINT (-73.95901 40.67109)</td>\n", " <td>0</td>\n", " <td>Franklin Ave & Eastern Pkwy</td>\n", " <td>341 Eastern Pkwy,Brooklyn, NY 11238</td>\n", " <td>1321040.772</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>POINT (-73.96122 40.57796)</td>\n", " <td>1</td>\n", " <td>607 Brighton Beach Ave</td>\n", " <td>607 Brighton Beach Avenue,Brooklyn, NY 11235</td>\n", " <td>1268080.418</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>POINT (-73.98976 40.61912)</td>\n", " <td>2</td>\n", " <td>65th St & 18th Ave</td>\n", " <td>6423 18th Avenue,Brooklyn, NY 11204</td>\n", " <td>1248133.699</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cartodb_id the_geom field_1 \\\n", "0 1 POINT (-73.95901 40.67109) 0 \n", "1 2 POINT (-73.96122 40.57796) 1 \n", "2 3 POINT (-73.98976 40.61912) 2 \n", "\n", " name address \\\n", "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", "\n", " revenue \n", "0 1321040.772 \n", "1 1268080.418 \n", "2 1248133.699 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes import read_carto\n", "\n", "gdf = read_carto(\"SELECT * FROM starbucks_brooklyn WHERE revenue > 1200000\")\n", "gdf.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Success! Data uploaded to table \"starbucks_brooklyn_filtered\" correctly\n" ] }, { "data": { "text/plain": [ "'starbucks_brooklyn_filtered'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cartoframes import to_carto\n", "\n", "to_carto(gdf, 'starbucks_brooklyn_filtered', if_exists='replace')" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
SXBK/kaggle	zillow/rf.ipynb	1	111128	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from scipy import stats\n", "import numpy as np # linear algebra\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "color = sns.color_palette()\n", "%matplotlib inline" ] }, { "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>parcelid</th>\n", " <th>airconditioningtypeid</th>\n", " <th>architecturalstyletypeid</th>\n", " <th>basementsqft</th>\n", " <th>bathroomcnt</th>\n", " <th>bedroomcnt</th>\n", " <th>buildingclasstypeid</th>\n", " <th>buildingqualitytypeid</th>\n", " <th>calculatedbathnbr</th>\n", " <th>decktypeid</th>\n", " <th>...</th>\n", " <th>numberofstories</th>\n", " <th>fireplaceflag</th>\n", " <th>structuretaxvaluedollarcnt</th>\n", " <th>taxvaluedollarcnt</th>\n", " <th>assessmentyear</th>\n", " <th>landtaxvaluedollarcnt</th>\n", " <th>taxamount</th>\n", " <th>taxdelinquencyflag</th>\n", " <th>taxdelinquencyyear</th>\n", " <th>censustractandblock</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10754147</td>\n", " <td>NaN</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>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>9.0</td>\n", " <td>2015.0</td>\n", " <td>9.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10759547</td>\n", " <td>NaN</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>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>27516.0</td>\n", " <td>2015.0</td>\n", " <td>27516.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10843547</td>\n", " <td>NaN</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>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>650756.0</td>\n", " <td>1413387.0</td>\n", " <td>2015.0</td>\n", " <td>762631.0</td>\n", " <td>20800.37</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10859147</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>7.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>571346.0</td>\n", " <td>1156834.0</td>\n", " <td>2015.0</td>\n", " <td>585488.0</td>\n", " <td>14557.57</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10879947</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</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>193796.0</td>\n", " <td>433491.0</td>\n", " <td>2015.0</td>\n", " <td>239695.0</td>\n", " <td>5725.17</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 58 columns</p>\n", "</div>" ], "text/plain": [ " parcelid airconditioningtypeid architecturalstyletypeid basementsqft \\\n", "0 10754147 NaN NaN NaN \n", "1 10759547 NaN NaN NaN \n", "2 10843547 NaN NaN NaN \n", "3 10859147 NaN NaN NaN \n", "4 10879947 NaN NaN NaN \n", "\n", " bathroomcnt bedroomcnt buildingclasstypeid buildingqualitytypeid \\\n", "0 0.0 0.0 NaN NaN \n", "1 0.0 0.0 NaN NaN \n", "2 0.0 0.0 NaN NaN \n", "3 0.0 0.0 3.0 7.0 \n", "4 0.0 0.0 4.0 NaN \n", "\n", " calculatedbathnbr decktypeid ... numberofstories \\\n", "0 NaN NaN ... NaN \n", "1 NaN NaN ... NaN \n", "2 NaN NaN ... NaN \n", "3 NaN NaN ... 1.0 \n", "4 NaN NaN ... NaN \n", "\n", " fireplaceflag structuretaxvaluedollarcnt taxvaluedollarcnt \\\n", "0 NaN NaN 9.0 \n", "1 NaN NaN 27516.0 \n", "2 NaN 650756.0 1413387.0 \n", "3 NaN 571346.0 1156834.0 \n", "4 NaN 193796.0 433491.0 \n", "\n", " assessmentyear landtaxvaluedollarcnt taxamount taxdelinquencyflag \\\n", "0 2015.0 9.0 NaN NaN \n", "1 2015.0 27516.0 NaN NaN \n", "2 2015.0 762631.0 20800.37 NaN \n", "3 2015.0 585488.0 14557.57 NaN \n", "4 2015.0 239695.0 5725.17 NaN \n", "\n", " taxdelinquencyyear censustractandblock \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", "[5 rows x 58 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p=pd.read_csv('D:\\\\data\\\\properties_2016.csv', dtype={'hashottuborspa':'bool','propertycountylandusecode':'object','propertyzoningdesc':'object', 'fireplaceflag':'bool', 'taxdelinquencyflag':'object'})\n", "p.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n", " RuntimeWarning)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcelid</th>\n", " <th>airconditioningtypeid</th>\n", " <th>architecturalstyletypeid</th>\n", " <th>basementsqft</th>\n", " <th>bathroomcnt</th>\n", " <th>bedroomcnt</th>\n", " <th>buildingclasstypeid</th>\n", " <th>buildingqualitytypeid</th>\n", " <th>calculatedbathnbr</th>\n", " <th>decktypeid</th>\n", " <th>...</th>\n", " <th>yardbuildingsqft26</th>\n", " <th>yearbuilt</th>\n", " <th>numberofstories</th>\n", " <th>structuretaxvaluedollarcnt</th>\n", " <th>taxvaluedollarcnt</th>\n", " <th>assessmentyear</th>\n", " <th>landtaxvaluedollarcnt</th>\n", " <th>taxamount</th>\n", " <th>taxdelinquencyyear</th>\n", " <th>censustractandblock</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2.985217e+06</td>\n", " <td>811519.000000</td>\n", " <td>6061.000000</td>\n", " <td>1628.000000</td>\n", " <td>2.973755e+06</td>\n", " <td>2.973767e+06</td>\n", " <td>12629.000000</td>\n", " <td>1.938488e+06</td>\n", " <td>2.856305e+06</td>\n", " <td>17096.0</td>\n", " <td>...</td>\n", " <td>2647.000000</td>\n", " <td>2.925289e+06</td>\n", " <td>682069.000000</td>\n", " <td>2.930235e+06</td>\n", " <td>2.942667e+06</td>\n", " <td>2.973778e+06</td>\n", " <td>2.917484e+06</td>\n", " <td>2.953967e+06</td>\n", " <td>56464.000000</td>\n", " <td>2.910091e+06</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.332586e+07</td>\n", " <td>1.931166</td>\n", " <td>7.202607</td>\n", " <td>646.883292</td>\n", " <td>2.209143e+00</td>\n", " <td>3.088949e+00</td>\n", " <td>3.725948</td>\n", " <td>5.784787e+00</td>\n", " <td>2.299263e+00</td>\n", " <td>66.0</td>\n", " <td>...</td>\n", " <td>278.296562</td>\n", " <td>1.964262e+03</td>\n", " <td>1.401464</td>\n", " <td>1.708836e+05</td>\n", " <td>4.204790e+05</td>\n", " <td>2.014999e+03</td>\n", " <td>2.524780e+05</td>\n", " <td>5.377607e+03</td>\n", " <td>13.892409</td>\n", " <td>6.048431e+13</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>7.909966e+06</td>\n", " <td>3.148587</td>\n", " <td>2.436290</td>\n", " <td>538.793473</td>\n", " <td>1.077754e+00</td>\n", " <td>1.275859e+00</td>\n", " <td>0.501700</td>\n", " <td>1.805352e+00</td>\n", " <td>1.000736e+00</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>369.731508</td>\n", " <td>2.344132e+01</td>\n", " <td>0.539076</td>\n", " <td>4.020683e+05</td>\n", " <td>7.263467e+05</td>\n", " <td>3.683161e-02</td>\n", " <td>4.450132e+05</td>\n", " <td>9.183107e+03</td>\n", " <td>2.581006</td>\n", " <td>3.249035e+11</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.071172e+07</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>20.000000</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>1.000000</td>\n", " <td>1.000000e+00</td>\n", " <td>1.000000e+00</td>\n", " <td>66.0</td>\n", " <td>...</td>\n", " <td>10.000000</td>\n", " <td>1.801000e+03</td>\n", " <td>1.000000</td>\n", " <td>1.000000e+00</td>\n", " <td>1.000000e+00</td>\n", " <td>2.000000e+03</td>\n", " <td>1.000000e+00</td>\n", " <td>1.340000e+00</td>\n", " <td>0.000000</td>\n", " <td>-1.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.164371e+07</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>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>50%</th>\n", " <td>1.254509e+07</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>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>75%</th>\n", " <td>1.409712e+07</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>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>max</th>\n", " <td>1.696019e+08</td>\n", " <td>13.000000</td>\n", " <td>27.000000</td>\n", " <td>8516.000000</td>\n", " <td>2.000000e+01</td>\n", " <td>2.000000e+01</td>\n", " <td>5.000000</td>\n", " <td>1.200000e+01</td>\n", " <td>2.000000e+01</td>\n", " <td>66.0</td>\n", " <td>...</td>\n", " <td>6141.000000</td>\n", " <td>2.015000e+03</td>\n", " <td>41.000000</td>\n", " <td>2.514860e+08</td>\n", " <td>2.827860e+08</td>\n", " <td>2.016000e+03</td>\n", " <td>9.024622e+07</td>\n", " <td>3.458861e+06</td>\n", " <td>99.000000</td>\n", " <td>4.830301e+14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " parcelid airconditioningtypeid architecturalstyletypeid \\\n", "count 2.985217e+06 811519.000000 6061.000000 \n", "mean 1.332586e+07 1.931166 7.202607 \n", "std 7.909966e+06 3.148587 2.436290 \n", "min 1.071172e+07 1.000000 2.000000 \n", "25% 1.164371e+07 NaN NaN \n", "50% 1.254509e+07 NaN NaN \n", "75% 1.409712e+07 NaN NaN \n", "max 1.696019e+08 13.000000 27.000000 \n", "\n", " basementsqft bathroomcnt bedroomcnt buildingclasstypeid \\\n", "count 1628.000000 2.973755e+06 2.973767e+06 12629.000000 \n", "mean 646.883292 2.209143e+00 3.088949e+00 3.725948 \n", "std 538.793473 1.077754e+00 1.275859e+00 0.501700 \n", "min 20.000000 0.000000e+00 0.000000e+00 1.000000 \n", "25% NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN \n", "max 8516.000000 2.000000e+01 2.000000e+01 5.000000 \n", "\n", " buildingqualitytypeid calculatedbathnbr decktypeid \\\n", "count 1.938488e+06 2.856305e+06 17096.0 \n", "mean 5.784787e+00 2.299263e+00 66.0 \n", "std 1.805352e+00 1.000736e+00 0.0 \n", "min 1.000000e+00 1.000000e+00 66.0 \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max 1.200000e+01 2.000000e+01 66.0 \n", "\n", " ... yardbuildingsqft26 yearbuilt numberofstories \\\n", "count ... 2647.000000 2.925289e+06 682069.000000 \n", "mean ... 278.296562 1.964262e+03 1.401464 \n", "std ... 369.731508 2.344132e+01 0.539076 \n", "min ... 10.000000 1.801000e+03 1.000000 \n", "25% ... NaN NaN NaN \n", "50% ... NaN NaN NaN \n", "75% ... NaN NaN NaN \n", "max ... 6141.000000 2.015000e+03 41.000000 \n", "\n", " structuretaxvaluedollarcnt taxvaluedollarcnt assessmentyear \\\n", "count 2.930235e+06 2.942667e+06 2.973778e+06 \n", "mean 1.708836e+05 4.204790e+05 2.014999e+03 \n", "std 4.020683e+05 7.263467e+05 3.683161e-02 \n", "min 1.000000e+00 1.000000e+00 2.000000e+03 \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max 2.514860e+08 2.827860e+08 2.016000e+03 \n", "\n", " landtaxvaluedollarcnt taxamount taxdelinquencyyear \\\n", "count 2.917484e+06 2.953967e+06 56464.000000 \n", "mean 2.524780e+05 5.377607e+03 13.892409 \n", "std 4.450132e+05 9.183107e+03 2.581006 \n", "min 1.000000e+00 1.340000e+00 0.000000 \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max 9.024622e+07 3.458861e+06 99.000000 \n", "\n", " censustractandblock \n", "count 2.910091e+06 \n", "mean 6.048431e+13 \n", "std 3.249035e+11 \n", "min -1.000000e+00 \n", "25% NaN \n", "50% NaN \n", "75% NaN \n", "max 4.830301e+14 \n", "\n", "[8 rows x 53 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2985217, 58)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parcelid</th>\n", " <th>logerror</th>\n", " <th>transactiondate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>11016594</td>\n", " <td>0.0276</td>\n", " <td>2016-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>14366692</td>\n", " <td>-0.1684</td>\n", " <td>2016-01-01</td>\n", " </tr>\n", " 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<td>NaN</td>\n", " <td>NaN</td>\n", " <td>6.059042e+13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 61 columns</p>\n", "</div>" ], "text/plain": [ " parcelid logerror transactiondate transaction_month \\\n", "0 11016594 0.0276 2016-01-01 1 \n", "1 14366692 -0.1684 2016-01-01 1 \n", "2 12098116 -0.0040 2016-01-01 1 \n", "3 12643413 0.0218 2016-01-02 1 \n", "4 14432541 -0.0050 2016-01-02 1 \n", "\n", " airconditioningtypeid architecturalstyletypeid basementsqft bathroomcnt \\\n", "0 1.0 NaN NaN 2.0 \n", "1 NaN NaN NaN 3.5 \n", "2 1.0 NaN NaN 3.0 \n", "3 1.0 NaN NaN 2.0 \n", "4 NaN NaN NaN 2.5 \n", "\n", " bedroomcnt buildingclasstypeid buildingqualitytypeid calculatedbathnbr \\\n", "0 3.0 NaN 4.0 2.0 \n", "1 4.0 NaN NaN 3.5 \n", "2 2.0 NaN 4.0 3.0 \n", "3 2.0 NaN 4.0 2.0 \n", "4 4.0 NaN NaN 2.5 \n", "\n", " decktypeid finishedfloor1squarefeet calculatedfinishedsquarefeet \\\n", "0 NaN NaN 1684.0 \n", "1 NaN NaN 2263.0 \n", "2 NaN NaN 2217.0 \n", "3 NaN NaN 839.0 \n", "4 NaN NaN 2283.0 \n", "\n", " finishedsquarefeet12 finishedsquarefeet13 finishedsquarefeet15 \\\n", "0 1684.0 NaN NaN \n", "1 2263.0 NaN NaN \n", "2 2217.0 NaN NaN \n", "3 839.0 NaN NaN \n", "4 2283.0 NaN NaN \n", "\n", " finishedsquarefeet50 finishedsquarefeet6 fips fireplacecnt \\\n", "0 NaN NaN 6037.0 NaN \n", "1 NaN NaN 6059.0 NaN \n", "2 NaN NaN 6037.0 NaN \n", "3 NaN NaN 6037.0 NaN \n", "4 NaN NaN 6059.0 NaN \n", "\n", " fullbathcnt garagecarcnt garagetotalsqft hashottuborspa \\\n", "0 2.0 NaN NaN NaN \n", "1 3.0 2.0 468.0 NaN \n", "2 3.0 NaN NaN NaN \n", "3 2.0 NaN NaN NaN \n", "4 2.0 2.0 598.0 NaN \n", "\n", " heatingorsystemtypeid latitude longitude lotsizesquarefeet \\\n", "0 2.0 34280990.0 -118488536.0 7528.0 \n", "1 NaN 33668120.0 -117677556.0 3643.0 \n", "2 2.0 34136312.0 -118175032.0 11423.0 \n", "3 2.0 33755800.0 -118309000.0 70859.0 \n", "4 NaN 33485643.0 -117700234.0 6000.0 \n", "\n", " ... poolsizesum pooltypeid10 pooltypeid2 pooltypeid7 \\\n", "0 ... NaN NaN NaN NaN \n", "1 ... NaN NaN NaN NaN \n", "2 ... NaN NaN NaN NaN \n", "3 ... NaN NaN NaN NaN \n", "4 ... NaN NaN NaN 1.0 \n", "\n", " propertycountylandusecode propertylandusetypeid propertyzoningdesc \\\n", "0 0100 261.0 LARS \n", "1 1 261.0 NaN \n", "2 0100 261.0 PSR6 \n", "3 010C 266.0 LAR3 \n", "4 122 261.0 NaN \n", "\n", " rawcensustractandblock regionidcity regionidcounty regionidneighborhood \\\n", "0 6.037107e+07 12447.0 3101.0 31817.0 \n", "1 6.059052e+07 32380.0 1286.0 NaN \n", "2 6.037464e+07 47019.0 3101.0 275411.0 \n", "3 6.037296e+07 12447.0 3101.0 54300.0 \n", "4 6.059042e+07 17686.0 1286.0 NaN \n", "\n", " regionidzip roomcnt storytypeid threequarterbathnbr \\\n", "0 96370.0 0.0 NaN NaN \n", "1 96962.0 0.0 NaN 1.0 \n", "2 96293.0 0.0 NaN NaN \n", "3 96222.0 0.0 NaN NaN \n", "4 96961.0 8.0 NaN 1.0 \n", "\n", " typeconstructiontypeid unitcnt yardbuildingsqft17 yardbuildingsqft26 \\\n", "0 NaN 1.0 NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN 1.0 NaN NaN \n", "3 NaN 1.0 NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", " yearbuilt numberofstories fireplaceflag structuretaxvaluedollarcnt \\\n", "0 1959.0 NaN NaN 122754.0 \n", "1 2014.0 NaN NaN 346458.0 \n", "2 1940.0 NaN NaN 61994.0 \n", "3 1987.0 NaN NaN 171518.0 \n", "4 1981.0 2.0 NaN 169574.0 \n", "\n", " taxvaluedollarcnt assessmentyear landtaxvaluedollarcnt taxamount \\\n", "0 360170.0 2015.0 237416.0 6735.88 \n", "1 585529.0 2015.0 239071.0 10153.02 \n", "2 119906.0 2015.0 57912.0 11484.48 \n", "3 244880.0 2015.0 73362.0 3048.74 \n", "4 434551.0 2015.0 264977.0 5488.96 \n", "\n", " taxdelinquencyflag taxdelinquencyyear censustractandblock \n", "0 NaN NaN 6.037107e+13 \n", "1 NaN NaN NaN \n", "2 NaN NaN 6.037464e+13 \n", "3 NaN NaN 6.037296e+13 \n", "4 NaN NaN 6.059042e+13 \n", "\n", "[5 rows x 61 columns]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df=pd.merge(train, p, on='parcelid', how='left')\n", "train_df.head()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "size = int(train_df.shape[0]1)\n", "y_test_df = train_df[size:]['logerror'].values\n", "y_train_df = train_df[0:size]['logerror'].values" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:132: 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", " self._setitem_with_indexer(indexer, value)\n" ] } ], "source": [ "train_df=train_df.drop(['parcelid', 'logerror', 'transactiondate', \"propertycountylandusecode\", \"propertyzoningdesc\"], axis=1)\n", "cat_cols = [\"hashottuborspa\", \"fireplaceflag\", \"taxdelinquencyflag\"]\n", "train_df.hashottuborspa.ix[train_df.hashottuborspa==True]=1\n", "train_df.hashottuborspa.fillna(0)\n", "train_df.fireplaceflag.ix[train_df.fireplaceflag==True]=1\n", "train_df.fireplaceflag.fillna(0)\n", "train_df.taxdelinquencyflag.ix[train_df.taxdelinquencyflag=='Y']=1\n", "train_df.taxdelinquencyflag.fillna(0)\n", "for col in cat_cols:\n", " train_df[col]=train_df[col].astype('category')\n", "\n", "mean_values=train_df.mean(axis=0)\n", "train_df_new = train_df.fillna(mean_values, inplace=True)\n", "\n", "x_test_df = train_df[size:]\n", "x_train_df = train_df[0:size]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cat_cols = [\"hashottuborspa\", \"fireplaceflag\", \"taxdelinquencyflag\"] #drop object value\n", "train_df = train_df.drop(cat_cols, axis=1)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_test_df = train_df[size:]\n", "x_train_df = train_df[0:size]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=100, n_jobs=17, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import ensemble\n", "model = ensemble.RandomForestRegressor(n_estimators=100,n_jobs=17)\n", "model.fit(x_train_df, y_train_df)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "re = model.predict(x_test_df)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "target: -0.0161 predict: 0.009848\n", "target: 0.0218 predict: 0.00431\n", "target: 0.0218 predict: 0.0015\n", "target: 0.0315 predict: 0.019917\n", "target: 0.0257 predict: 0.008933\n", "target: 0.1345 predict: 0.025632\n", "target: 0.008 predict: -0.006804\n", "target: -0.5447 predict: 0.019211\n", "target: 0.0825 predict: 0.017207\n", "target: 0.004 predict: 0.014104\n" ] } ], "source": [ "for i in range(10):\n", " print('target:', y_test_df[i], 'predict:',re[i])" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rmsle(y, y_, convertExp=True):\n", " if convertExp:\n", " y = np.exp(y),\n", " y_ = np.exp(y_)\n", " log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))\n", " log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))\n", " calc = (log1 - log2) * 2\n", " return np.sqrt(np.mean(calc))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.091112651498839056" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmsle(y_test_df, re)" ] }, { "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>parcelid</th>\n", " <th>airconditioningtypeid</th>\n", " <th>architecturalstyletypeid</th>\n", " <th>basementsqft</th>\n", " <th>bathroomcnt</th>\n", " <th>bedroomcnt</th>\n", " <th>buildingclasstypeid</th>\n", " <th>buildingqualitytypeid</th>\n", " <th>calculatedbathnbr</th>\n", " <th>decktypeid</th>\n", " <th>finishedfloor1squarefeet</th>\n", " <th>calculatedfinishedsquarefeet</th>\n", " <th>finishedsquarefeet12</th>\n", " <th>finishedsquarefeet13</th>\n", " <th>finishedsquarefeet15</th>\n", " <th>finishedsquarefeet50</th>\n", " <th>finishedsquarefeet6</th>\n", " <th>fips</th>\n", " <th>fireplacecnt</th>\n", " <th>fullbathcnt</th>\n", " <th>garagecarcnt</th>\n", " <th>garagetotalsqft</th>\n", " <th>hashottuborspa</th>\n", " <th>heatingorsystemtypeid</th>\n", " <th>latitude</th>\n", " <th>longitude</th>\n", " <th>lotsizesquarefeet</th>\n", " <th>poolcnt</th>\n", " <th>poolsizesum</th>\n", " <th>pooltypeid10</th>\n", " <th>pooltypeid2</th>\n", " <th>pooltypeid7</th>\n", " <th>propertycountylandusecode</th>\n", " <th>propertylandusetypeid</th>\n", " <th>propertyzoningdesc</th>\n", " <th>rawcensustractandblock</th>\n", " <th>regionidcity</th>\n", " <th>regionidcounty</th>\n", " <th>regionidneighborhood</th>\n", " <th>regionidzip</th>\n", " <th>roomcnt</th>\n", " <th>storytypeid</th>\n", " <th>threequarterbathnbr</th>\n", " <th>typeconstructiontypeid</th>\n", " <th>unitcnt</th>\n", " <th>yardbuildingsqft17</th>\n", " <th>yardbuildingsqft26</th>\n", " <th>yearbuilt</th>\n", " <th>numberofstories</th>\n", " <th>fireplaceflag</th>\n", " <th>structuretaxvaluedollarcnt</th>\n", " <th>taxvaluedollarcnt</th>\n", " <th>assessmentyear</th>\n", " <th>landtaxvaluedollarcnt</th>\n", " <th>taxamount</th>\n", " <th>taxdelinquencyflag</th>\n", " <th>taxdelinquencyyear</th>\n", " <th>censustractandblock</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10754147</td>\n", " <td>NaN</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>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6037.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>34144442.0</td>\n", " <td>-118654084.0</td>\n", " <td>85768.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>010D</td>\n", " <td>269.0</td>\n", " <td>NaN</td>\n", " <td>6.037800e+07</td>\n", " <td>37688.0</td>\n", " <td>3101.0</td>\n", 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fullbathcnt garagecarcnt garagetotalsqft hashottuborspa \\\n", "0 NaN NaN NaN NaN NaN \n", "\n", " heatingorsystemtypeid latitude longitude lotsizesquarefeet poolcnt \\\n", "0 NaN 34144442.0 -118654084.0 85768.0 NaN \n", "\n", " poolsizesum pooltypeid10 pooltypeid2 pooltypeid7 \\\n", "0 NaN NaN NaN NaN \n", "\n", " propertycountylandusecode propertylandusetypeid propertyzoningdesc \\\n", "0 010D 269.0 NaN \n", "\n", " rawcensustractandblock regionidcity regionidcounty regionidneighborhood \\\n", "0 6.037800e+07 37688.0 3101.0 NaN \n", "\n", " regionidzip roomcnt storytypeid threequarterbathnbr \\\n", "0 96337.0 0.0 NaN NaN \n", "\n", " typeconstructiontypeid unitcnt yardbuildingsqft17 yardbuildingsqft26 \\\n", "0 NaN NaN NaN NaN \n", "\n", " yearbuilt numberofstories fireplaceflag structuretaxvaluedollarcnt \\\n", "0 NaN NaN NaN NaN \n", "\n", " taxvaluedollarcnt assessmentyear landtaxvaluedollarcnt taxamount \\\n", "0 9.0 2015.0 9.0 NaN \n", "\n", " taxdelinquencyflag taxdelinquencyyear censustractandblock \n", "0 NaN NaN NaN " ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.ix[p.parcelid==10754147]" ] }, { "cell_type": "code", "execution_count": 134, "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>ParcelId</th>\n", " <th>201610</th>\n", " <th>201611</th>\n", " <th>201612</th>\n", " <th>201710</th>\n", " <th>201711</th>\n", " <th>201712</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>10754147</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>1</th>\n", " <td>10759547</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>2</th>\n", " 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<td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>7.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5068.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5068.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6037.0</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>1200</td>\n", " <td>47.0</td>\n", " <td>LAC2</td>\n", " <td>6.037141e+07</td>\n", " <td>12447.0</td>\n", " <td>3101.0</td>\n", " <td>27080.0</td>\n", " <td>96424.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1948.0</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>571346.0</td>\n", " <td>1156834.0</td>\n", " <td>2015.0</td>\n", " <td>585488.0</td>\n", " <td>14557.57</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10879947</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>10879947</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1776.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1776.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6037.0</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>1210</td>\n", " <td>31.0</td>\n", " <td>LAM1</td>\n", " <td>6.037123e+07</td>\n", " <td>12447.0</td>\n", " <td>3101.0</td>\n", " <td>46795.0</td>\n", " <td>96450.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1947.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>193796.0</td>\n", " <td>433491.0</td>\n", " <td>2015.0</td>\n", " <td>239695.0</td>\n", " <td>5725.17</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 65 columns</p>\n", "</div>" ], "text/plain": [ " ParcelId 201610 201611 201612 201710 201711 201712 parcelid \\\n", "0 10754147 0 0 0 0 0 0 10754147 \n", "1 10759547 0 0 0 0 0 0 10759547 \n", "2 10843547 0 0 0 0 0 0 10843547 \n", "3 10859147 0 0 0 0 0 0 10859147 \n", "4 10879947 0 0 0 0 0 0 10879947 \n", "\n", " airconditioningtypeid architecturalstyletypeid basementsqft bathroomcnt \\\n", "0 NaN NaN NaN 0.0 \n", "1 NaN NaN NaN 0.0 \n", "2 NaN NaN NaN 0.0 \n", "3 NaN NaN NaN 0.0 \n", "4 NaN NaN NaN 0.0 \n", "\n", " bedroomcnt buildingclasstypeid buildingqualitytypeid calculatedbathnbr \\\n", "0 0.0 NaN NaN NaN \n", "1 0.0 NaN NaN NaN \n", "2 0.0 NaN NaN NaN \n", "3 0.0 3.0 7.0 NaN \n", "4 0.0 4.0 NaN NaN \n", "\n", " decktypeid finishedfloor1squarefeet calculatedfinishedsquarefeet \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN 73026.0 \n", "3 NaN NaN 5068.0 \n", "4 NaN NaN 1776.0 \n", "\n", " finishedsquarefeet12 finishedsquarefeet13 finishedsquarefeet15 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN 73026.0 \n", "3 NaN NaN 5068.0 \n", "4 NaN NaN 1776.0 \n", "\n", " finishedsquarefeet50 finishedsquarefeet6 fips fireplacecnt \\\n", "0 NaN NaN 6037.0 NaN \n", "1 NaN NaN 6037.0 NaN \n", "2 NaN NaN 6037.0 NaN \n", "3 NaN NaN 6037.0 NaN \n", "4 NaN NaN 6037.0 NaN \n", "\n", " fullbathcnt garagecarcnt garagetotalsqft hashottuborspa \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", " ... poolsizesum pooltypeid10 pooltypeid2 pooltypeid7 \\\n", "0 ... NaN NaN NaN NaN \n", "1 ... NaN NaN NaN NaN \n", "2 ... NaN NaN NaN NaN \n", "3 ... NaN NaN NaN NaN \n", "4 ... NaN NaN NaN NaN \n", "\n", " propertycountylandusecode propertylandusetypeid propertyzoningdesc \\\n", "0 010D 269.0 NaN \n", "1 0109 261.0 LCA11 \n", "2 1200 47.0 LAC2 \n", "3 1200 47.0 LAC2 \n", "4 1210 31.0 LAM1 \n", "\n", " rawcensustractandblock regionidcity regionidcounty regionidneighborhood \\\n", "0 6.037800e+07 37688.0 3101.0 NaN \n", "1 6.037800e+07 37688.0 3101.0 NaN \n", "2 6.037703e+07 51617.0 3101.0 NaN \n", "3 6.037141e+07 12447.0 3101.0 27080.0 \n", "4 6.037123e+07 12447.0 3101.0 46795.0 \n", "\n", " regionidzip roomcnt storytypeid threequarterbathnbr \\\n", "0 96337.0 0.0 NaN NaN \n", "1 96337.0 0.0 NaN NaN \n", "2 96095.0 0.0 NaN NaN \n", "3 96424.0 0.0 NaN NaN \n", "4 96450.0 0.0 NaN NaN \n", "\n", " typeconstructiontypeid unitcnt yardbuildingsqft17 yardbuildingsqft26 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN 2.0 NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN 1.0 NaN NaN \n", "\n", " yearbuilt numberofstories fireplaceflag structuretaxvaluedollarcnt \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN 650756.0 \n", "3 1948.0 1.0 NaN 571346.0 \n", "4 1947.0 NaN NaN 193796.0 \n", "\n", " taxvaluedollarcnt assessmentyear landtaxvaluedollarcnt taxamount \\\n", "0 9.0 2015.0 9.0 NaN \n", "1 27516.0 2015.0 27516.0 NaN \n", "2 1413387.0 2015.0 762631.0 20800.37 \n", "3 1156834.0 2015.0 585488.0 14557.57 \n", "4 433491.0 2015.0 239695.0 5725.17 \n", "\n", " taxdelinquencyflag taxdelinquencyyear censustractandblock \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", "[5 rows x 65 columns]" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sub_df['parcelid']=sub_df['ParcelId']\n", "test=pd.merge(sub_df, p, on='parcelid', how='left')\n", "\n", "test.head()" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2985217, 7)" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sub_df.shape" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Program Files\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:132: 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", " self._setitem_with_indexer(indexer, value)\n" ] } ], "source": [ "drop_month=['201610','201611','201612','201710','201711','201712']\n", "test=test.drop(['parcelid', \"propertycountylandusecode\", \"propertyzoningdesc\"]+drop_month, axis=1)\n", "cat_cols = [\"hashottuborspa\", \"fireplaceflag\", \"taxdelinquencyflag\"]\n", "test.hashottuborspa.ix[test.hashottuborspa==True]=1\n", "test.hashottuborspa.fillna(0)\n", "test.fireplaceflag.ix[test.fireplaceflag==True]=1\n", "test.fireplaceflag.fillna(0)\n", "test.taxdelinquencyflag.ix[test.taxdelinquencyflag=='Y']=1\n", "test.taxdelinquencyflag.fillna(0)\n", "for col in cat_cols:\n", " test[col]=test[col].astype('category')\n" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2985217, 56)" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean=test.mean(axis=0)\n", "test.fillna(mean, inplace=True)\n", "test.shape" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test = test.drop(cat_cols, axis=1)" ] }, { "cell_type": "code", "execution_count": 147, "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>airconditioningtypeid</th>\n", " <th>architecturalstyletypeid</th>\n", " <th>basementsqft</th>\n", " <th>bathroomcnt</th>\n", " <th>bedroomcnt</th>\n", " <th>buildingclasstypeid</th>\n", " <th>buildingqualitytypeid</th>\n", " <th>calculatedbathnbr</th>\n", " <th>decktypeid</th>\n", " <th>finishedfloor1squarefeet</th>\n", " <th>calculatedfinishedsquarefeet</th>\n", " <th>finishedsquarefeet12</th>\n", " <th>finishedsquarefeet13</th>\n", " <th>finishedsquarefeet15</th>\n", " <th>finishedsquarefeet50</th>\n", " <th>finishedsquarefeet6</th>\n", " <th>fips</th>\n", " <th>fireplacecnt</th>\n", " <th>fullbathcnt</th>\n", " <th>garagecarcnt</th>\n", " <th>garagetotalsqft</th>\n", " <th>heatingorsystemtypeid</th>\n", " <th>latitude</th>\n", " <th>longitude</th>\n", " <th>lotsizesquarefeet</th>\n", " <th>poolcnt</th>\n", " <th>poolsizesum</th>\n", " <th>pooltypeid10</th>\n", " <th>pooltypeid2</th>\n", " <th>pooltypeid7</th>\n", " <th>propertylandusetypeid</th>\n", " <th>rawcensustractandblock</th>\n", " <th>regionidcity</th>\n", " <th>regionidcounty</th>\n", " <th>regionidneighborhood</th>\n", " <th>regionidzip</th>\n", " <th>roomcnt</th>\n", " <th>storytypeid</th>\n", " <th>threequarterbathnbr</th>\n", " <th>typeconstructiontypeid</th>\n", " <th>unitcnt</th>\n", " <th>yardbuildingsqft17</th>\n", " <th>yardbuildingsqft26</th>\n", " <th>yearbuilt</th>\n", " <th>numberofstories</th>\n", " <th>structuretaxvaluedollarcnt</th>\n", " <th>taxvaluedollarcnt</th>\n", " <th>assessmentyear</th>\n", " <th>landtaxvaluedollarcnt</th>\n", " <th>taxamount</th>\n", " <th>taxdelinquencyyear</th>\n", " <th>censustractandblock</th>\n", " <th>transaction_month</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.931166</td>\n", " <td>7.202607</td>\n", " <td>646.883292</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.725948</td>\n", " <td>5.784787</td>\n", " <td>2.299263</td>\n", " <td>66.0</td>\n", " <td>1380.630396</td>\n", " 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<td>1.010009</td>\n", " <td>5.999555</td>\n", " <td>1.181171</td>\n", " <td>319.803397</td>\n", " <td>278.296562</td>\n", " <td>1964.261641</td>\n", " <td>1.401464</td>\n", " <td>170883.577166</td>\n", " <td>27516.0</td>\n", " <td>2015.0</td>\n", " <td>27516.0</td>\n", " <td>5377.607139</td>\n", " <td>13.892409</td>\n", " <td>6.048431e+13</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.931166</td>\n", " <td>7.202607</td>\n", " <td>646.883292</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.725948</td>\n", " <td>5.784787</td>\n", " <td>2.299263</td>\n", " <td>66.0</td>\n", " <td>1380.630396</td>\n", " <td>73026.000000</td>\n", " <td>1760.000608</td>\n", " <td>1178.900678</td>\n", " <td>73026.000000</td>\n", " <td>1388.944578</td>\n", " <td>2414.339439</td>\n", " <td>6037.0</td>\n", " <td>1.16871</td>\n", " <td>2.244165</td>\n", " <td>1.823517</td>\n", " <td>383.769357</td>\n", " <td>4.012053</td>\n", " <td>33989359.0</td>\n", " <td>-118394633.0</td>\n", " <td>63085.0</td>\n", " <td>1.0</td>\n", " <td>519.71098</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>47.0</td>\n", " <td>6.037703e+07</td>\n", " <td>51617.0</td>\n", " <td>3101.0</td>\n", " <td>193476.407415</td>\n", " <td>96095.0</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " <td>1.010009</td>\n", " <td>5.999555</td>\n", " <td>2.000000</td>\n", " <td>319.803397</td>\n", " <td>278.296562</td>\n", " <td>1964.261641</td>\n", " <td>1.401464</td>\n", " <td>650756.000000</td>\n", " <td>1413387.0</td>\n", " <td>2015.0</td>\n", " <td>762631.0</td>\n", " <td>20800.370000</td>\n", " <td>13.892409</td>\n", " <td>6.048431e+13</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.931166</td>\n", " <td>7.202607</td>\n", " <td>646.883292</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.000000</td>\n", " <td>7.000000</td>\n", " <td>2.299263</td>\n", " <td>66.0</td>\n", " <td>1380.630396</td>\n", " <td>5068.000000</td>\n", " <td>1760.000608</td>\n", " <td>1178.900678</td>\n", " <td>5068.000000</td>\n", " <td>1388.944578</td>\n", " <td>2414.339439</td>\n", " <td>6037.0</td>\n", " <td>1.16871</td>\n", " <td>2.244165</td>\n", " <td>1.823517</td>\n", " <td>383.769357</td>\n", " <td>4.012053</td>\n", " <td>34148863.0</td>\n", " <td>-118437206.0</td>\n", " <td>7521.0</td>\n", " <td>1.0</td>\n", " <td>519.71098</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>47.0</td>\n", " <td>6.037141e+07</td>\n", " <td>12447.0</td>\n", " <td>3101.0</td>\n", " <td>27080.000000</td>\n", " <td>96424.0</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " <td>1.010009</td>\n", " <td>5.999555</td>\n", " <td>1.181171</td>\n", " <td>319.803397</td>\n", " <td>278.296562</td>\n", " <td>1948.000000</td>\n", " <td>1.000000</td>\n", " <td>571346.000000</td>\n", " <td>1156834.0</td>\n", " <td>2015.0</td>\n", " <td>585488.0</td>\n", " <td>14557.570000</td>\n", " <td>13.892409</td>\n", " <td>6.048431e+13</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.931166</td>\n", " <td>7.202607</td>\n", " <td>646.883292</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.000000</td>\n", " <td>5.784787</td>\n", " <td>2.299263</td>\n", " <td>66.0</td>\n", " <td>1380.630396</td>\n", " <td>1776.000000</td>\n", " <td>1760.000608</td>\n", " <td>1178.900678</td>\n", " <td>1776.000000</td>\n", " <td>1388.944578</td>\n", " <td>2414.339439</td>\n", " <td>6037.0</td>\n", " <td>1.16871</td>\n", " <td>2.244165</td>\n", " <td>1.823517</td>\n", " <td>383.769357</td>\n", " <td>4.012053</td>\n", " <td>34194168.0</td>\n", " <td>-118385816.0</td>\n", " <td>8512.0</td>\n", " <td>1.0</td>\n", " <td>519.71098</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>31.0</td>\n", " <td>6.037123e+07</td>\n", " <td>12447.0</td>\n", " <td>3101.0</td>\n", " <td>46795.000000</td>\n", " <td>96450.0</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " <td>1.010009</td>\n", " <td>5.999555</td>\n", " <td>1.000000</td>\n", " <td>319.803397</td>\n", " <td>278.296562</td>\n", " <td>1947.000000</td>\n", " <td>1.401464</td>\n", " <td>193796.000000</td>\n", " <td>433491.0</td>\n", " <td>2015.0</td>\n", " <td>239695.0</td>\n", " <td>5725.170000</td>\n", " <td>13.892409</td>\n", " <td>6.048431e+13</td>\n", " <td>10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " airconditioningtypeid architecturalstyletypeid basementsqft bathroomcnt \\\n", "0 1.931166 7.202607 646.883292 0.0 \n", "1 1.931166 7.202607 646.883292 0.0 \n", "2 1.931166 7.202607 646.883292 0.0 \n", "3 1.931166 7.202607 646.883292 0.0 \n", "4 1.931166 7.202607 646.883292 0.0 \n", "\n", " bedroomcnt buildingclasstypeid buildingqualitytypeid calculatedbathnbr \\\n", "0 0.0 3.725948 5.784787 2.299263 \n", "1 0.0 3.725948 5.784787 2.299263 \n", "2 0.0 3.725948 5.784787 2.299263 \n", "3 0.0 3.000000 7.000000 2.299263 \n", "4 0.0 4.000000 5.784787 2.299263 \n", "\n", " decktypeid finishedfloor1squarefeet calculatedfinishedsquarefeet \\\n", "0 66.0 1380.630396 1827.162124 \n", "1 66.0 1380.630396 1827.162124 \n", "2 66.0 1380.630396 73026.000000 \n", "3 66.0 1380.630396 5068.000000 \n", "4 66.0 1380.630396 1776.000000 \n", "\n", " finishedsquarefeet12 finishedsquarefeet13 finishedsquarefeet15 \\\n", "0 1760.000608 1178.900678 2739.187235 \n", "1 1760.000608 1178.900678 2739.187235 \n", "2 1760.000608 1178.900678 73026.000000 \n", "3 1760.000608 1178.900678 5068.000000 \n", "4 1760.000608 1178.900678 1776.000000 \n", "\n", " finishedsquarefeet50 finishedsquarefeet6 fips fireplacecnt \\\n", "0 1388.944578 2414.339439 6037.0 1.16871 \n", "1 1388.944578 2414.339439 6037.0 1.16871 \n", "2 1388.944578 2414.339439 6037.0 1.16871 \n", "3 1388.944578 2414.339439 6037.0 1.16871 \n", "4 1388.944578 2414.339439 6037.0 1.16871 \n", "\n", " fullbathcnt garagecarcnt garagetotalsqft heatingorsystemtypeid \\\n", "0 2.244165 1.823517 383.769357 4.012053 \n", "1 2.244165 1.823517 383.769357 4.012053 \n", "2 2.244165 1.823517 383.769357 4.012053 \n", "3 2.244165 1.823517 383.769357 4.012053 \n", "4 2.244165 1.823517 383.769357 4.012053 \n", "\n", " latitude longitude lotsizesquarefeet poolcnt poolsizesum \\\n", "0 34144442.0 -118654084.0 85768.0 1.0 519.71098 \n", "1 34140430.0 -118625364.0 4083.0 1.0 519.71098 \n", "2 33989359.0 -118394633.0 63085.0 1.0 519.71098 \n", "3 34148863.0 -118437206.0 7521.0 1.0 519.71098 \n", "4 34194168.0 -118385816.0 8512.0 1.0 519.71098 \n", "\n", " pooltypeid10 pooltypeid2 pooltypeid7 propertylandusetypeid \\\n", "0 1.0 1.0 1.0 269.0 \n", "1 1.0 1.0 1.0 261.0 \n", "2 1.0 1.0 1.0 47.0 \n", "3 1.0 1.0 1.0 47.0 \n", "4 1.0 1.0 1.0 31.0 \n", "\n", " rawcensustractandblock regionidcity regionidcounty regionidneighborhood \\\n", "0 6.037800e+07 37688.0 3101.0 193476.407415 \n", "1 6.037800e+07 37688.0 3101.0 193476.407415 \n", "2 6.037703e+07 51617.0 3101.0 193476.407415 \n", "3 6.037141e+07 12447.0 3101.0 27080.000000 \n", "4 6.037123e+07 12447.0 3101.0 46795.000000 \n", "\n", " regionidzip roomcnt storytypeid threequarterbathnbr \\\n", "0 96337.0 0.0 7.0 1.010009 \n", "1 96337.0 0.0 7.0 1.010009 \n", "2 96095.0 0.0 7.0 1.010009 \n", "3 96424.0 0.0 7.0 1.010009 \n", "4 96450.0 0.0 7.0 1.010009 \n", "\n", " typeconstructiontypeid unitcnt yardbuildingsqft17 yardbuildingsqft26 \\\n", "0 5.999555 1.181171 319.803397 278.296562 \n", "1 5.999555 1.181171 319.803397 278.296562 \n", "2 5.999555 2.000000 319.803397 278.296562 \n", "3 5.999555 1.181171 319.803397 278.296562 \n", "4 5.999555 1.000000 319.803397 278.296562 \n", "\n", " yearbuilt numberofstories structuretaxvaluedollarcnt \\\n", "0 1964.261641 1.401464 170883.577166 \n", "1 1964.261641 1.401464 170883.577166 \n", "2 1964.261641 1.401464 650756.000000 \n", "3 1948.000000 1.000000 571346.000000 \n", "4 1947.000000 1.401464 193796.000000 \n", "\n", " taxvaluedollarcnt assessmentyear landtaxvaluedollarcnt taxamount \\\n", "0 9.0 2015.0 9.0 5377.607139 \n", "1 27516.0 2015.0 27516.0 5377.607139 \n", "2 1413387.0 2015.0 762631.0 20800.370000 \n", "3 1156834.0 2015.0 585488.0 14557.570000 \n", "4 433491.0 2015.0 239695.0 5725.170000 \n", "\n", " taxdelinquencyyear censustractandblock transaction_month \n", "0 13.892409 6.048431e+13 10 \n", "1 13.892409 6.048431e+13 10 \n", "2 13.892409 6.048431e+13 10 \n", "3 13.892409 6.048431e+13 10 \n", "4 13.892409 6.048431e+13 10 " ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test=test.drop(['ParcelId'],axis=1)\n", "test.head()" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [], "source": [ "month_pridict=[10,11,12,22,23,24]\n", "result=[]\n", "for m in month_pridict:\n", " test['transaction_month']=m\n", " re = model.predict(test)\n", " result.append(re)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sub = pd.read_csv('D:\\\\data\\\\sample_submission.csv')\n", "for i in range(len(drop_month)):\n", " sub[drop_month[i]]=result[i]" ] }, { "cell_type": "code", "execution_count": 153, "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>201610</th>\n", " <th>201611</th>\n", " <th>201612</th>\n", " <th>201710</th>\n", " <th>201711</th>\n", " <th>201712</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10754147</th>\n", " <td>-0.123942</td>\n", " <td>0.542828</td>\n", " <td>0.437484</td>\n", " <td>0.455293</td>\n", " <td>0.488694</td>\n", " <td>0.488966</td>\n", " </tr>\n", " <tr>\n", " <th>10759547</th>\n", " <td>0.479547</td>\n", " <td>0.479818</td>\n", " <td>-0.123942</td>\n", " <td>0.487762</td>\n", " <td>0.488694</td>\n", " <td>-0.123942</td>\n", " </tr>\n", " <tr>\n", " <th>10843547</th>\n", " <td>0.491397</td>\n", " <td>0.396038</td>\n", " <td>-0.123942</td>\n", " <td>-0.123942</td>\n", " <td>0.035097</td>\n", " <td>0.533572</td>\n", " </tr>\n", " <tr>\n", " <th>10859147</th>\n", " <td>0.519695</td>\n", " <td>0.489749</td>\n", " <td>0.485059</td>\n", " <td>0.540942</td>\n", " <td>0.491397</td>\n", " <td>0.491397</td>\n", " </tr>\n", " <tr>\n", " <th>10879947</th>\n", " <td>0.508219</td>\n", " <td>-0.123942</td>\n", " <td>0.435985</td>\n", " <td>0.457975</td>\n", " <td>0.484786</td>\n", " <td>0.429386</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 201610 201611 201612 201710 201711 201712\n", "10754147 -0.123942 0.542828 0.437484 0.455293 0.488694 0.488966\n", "10759547 0.479547 0.479818 -0.123942 0.487762 0.488694 -0.123942\n", "10843547 0.491397 0.396038 -0.123942 -0.123942 0.035097 0.533572\n", "10859147 0.519695 0.489749 0.485059 0.540942 0.491397 0.491397\n", "10879947 0.508219 -0.123942 0.435985 0.457975 0.484786 0.429386" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#out=pd.DataFrame(X,columns=drop_month, index=sub_df['parcelid'].values)\n", "#out.head()\n", "#out.to_csv(\"D:\\\\data\\\\1.csv\")" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sub.to_csv(\"D:\\\\data\\\\1.csv\", index=False, float_format='%.4f')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": 1 }	gpl-3.0
eyaltrabelsi/my-notebooks	Lectures/Generators.ipynb	1	5356	{ "cells": [ { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The memory_profiler extension is already loaded. To reload it, use:\n", " %reload_ext memory_profiler\n" ] } ], "source": [ "%load_ext memory_profiler\n", "\n", "import itertools" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "def generators(numbers):\n", " for num in numbers:\n", " if num % 7 or \"7\" in str(num):\n", " yield num" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "def iterators(numbers):\n", " even = []\n", " for num in list(numbers):\n", " if num % 7 or \"7\" in str(num):\n", " even.append(num) \n", " return even" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Memory of Genrators Vs Iterators 😇" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- For the generator's work, you need to keep in memory the variables of the generator function.\n", "- But you don't have to keep the entire collection in memory, so usually this is EXACTLY the trade-off you want to make." ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4711503365894179\n", "peak memory: 6958.47 MiB, increment: 0.00 MiB\n" ] } ], "source": [ "%%memit\n", "g = generators(range(108))\n", "print(sum(g))" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4711503365894179\n", "peak memory: 8475.12 MiB, increment: 1644.42 MiB\n" ] } ], "source": [ "%%memit\n", "i = iterators(range(108))\n", "print(sum(i))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performance of Genrators Vs Iterators 😃" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4711503365894179\n", "CPU times: user 599 ms, sys: 2.03 ms, total: 601 ms\n", "Wall time: 599 ms\n" ] } ], "source": [ "%%time\n", "g = generators(range(108))\n", "print(sum(i))" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4711503365894179\n", "CPU times: user 13.4 s, sys: 482 ms, total: 13.9 s\n", "Wall time: 13.9 s\n" ] } ], "source": [ "%%time\n", "i = iterators(range(108))\n", "print(sum(i))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Consumed once 😱" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every time you want to reuse the elements in a collection it must be regenerated.\n" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First consumption: 4711503365894179\n", "Second consumption: 0\n" ] } ], "source": [ "g = generators(range(108))\n", "print(f\"First consumption: {sum(g)}\")\n", "print(f\"Second consumption: {sum(g)}\")" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First consumption: 4711503365894179\n", "Second consumption: 4711503365894179\n" ] } ], "source": [ "g = generators(range(108))\n", "g1, g2 = itertools.tee(g, 2)\n", "print(f\"First consumption: {sum(g1)}\")\n", "print(f\"Second consumption: {sum(g2)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In High Level" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Essentially it boils down to a discussion about Lazy vs Eager evaluation.\n", "- You trade-off CPU overhead for the capability of streaming processing (as opposed to bulk-processing with eager evaluation).\n", "- The code can become a bit more tricky to read if using a lazy approach, so there could be a trade-off between performance and simplicity there as well." ] }, { "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
mrminy/ProjectAmazonTextAnalysis	johannes/Amazon_notebook.ipynb	1	20476	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.18.1\n" ] } ], "source": [ "import os\n", "path = 'C:\\\\users\\johannes\\ProjectAmazonTextAnalysis\\johannes'\n", "os.chdir(path)\n", "import pickle\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import sklearn\n", "print(sklearn.__version__)\n", "from sklearn.model_selection import train_test_split\n", "\n", "from collections import Counter\n", "import gzip\n", "\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem import SnowballStemmer\n", "from nltk import ngrams\n", "from nltk.corpus import stopwords\n", "\n", "\n", "\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# import spacy\n", "# nlp = spacy.load('en')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step: 1000\nStep: 2000\nStep: 3000\nStep: 4000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step: 5000\nStep: 6000\nStep: 7000\nStep: 8000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step: 9000\nStep: 10000\nStep: 11000\nStep: 12000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step: 13000\nStep: 14000\nStep: 15000\nStep: 16000\nStep: 17000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step: 18000\nStep: 19000\nStep: 20000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Time : 1.4663589000701904\n" ] } ], "source": [ "def parse(path):\n", " g = gzip.open(path, 'rb')\n", " for l in g:\n", " yield eval(l)\n", "\n", "sample_size = 20000\n", "\n", "def get_training_data(path):\n", " \"\"\"\n", " Get all usable data\n", " :param path: path to compressed data\n", " :return: panda data frame\n", " \"\"\"\n", " i = 0\n", " df = {}\n", " for d in parse(path):\n", " i += 1\n", " if i <= sample_size:\n", " df[i] = d\n", " else:\n", " break\n", " if (i + 1) % 1000 == 0:\n", " print(\"Step:\", i + 1)\n", " return pd.DataFrame.from_dict(df, orient='index')\n", "\n", "\n", "# def get_test_data(path):\n", "# \"\"\"\n", "# Do not call this before the real test!!!!\n", "# \"\"\"\n", "# pass\n", "# i = 0\n", "# df = {}\n", "# for d in parse(path):\n", "# i += 1\n", "# if i > 1400000:\n", "# df[i] = d\n", "# return pd.DataFrame.from_dict(df, orient='index')\n", "\n", "\n", "start_time = time.time()\n", "df = get_training_data('reviews_Electronics_5.json.gz')\n", "\n", "print(\"Time :\", time.time() - start_time)\n", "\n", "df_1 = df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 0 ns\n" ] } ], "source": [ "%time\n", "def fix_dataframe(df = df_1):\n", " y = df['overall'].values\n", " X = df['reviewText']\n", " df = pd.DataFrame(np.column_stack((X,y)), columns = ['text', 'review_labels'])\n", " return df\n", "df = fix_dataframe(df_1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 0 ns\n(15000, 2)\n" ] } ], "source": [ "%time\n", "def split_data(df = df):\n", " train_df, test_df = train_test_split(df)\n", " # print(train_df.head())\n", " # return pd.DataFrame(train_df, columns=['text', 'labels']), pd.DataFrame(test_df, columns=['text', 'labels'])\n", " return train_df, test_df\n", "train_df, test_df = split_data(df)\n", "train_df.head()\n", "print(train_df.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first_time: 12.586816787719727\nTotal time: 12.59432053565979\n" ] } ], "source": [ "# Using the standard stopwords given by nltk. Can also do feature relevance according to word frequency limits.\n", "# Could also test a limit for word appearance in a given percentage of texts.\n", "start_time = time.time()\n", "def find_words(df = train_df, \n", " stopword = False, \n", " word_frequency = [sample_size, np.log(sample_size)], \n", " number_of_words = 3000):\n", " # stemmer = SnowballStemmer('english')\n", " start_time = time.time()\n", " texts = df['text'].values\n", " # dictionary = np.unique([word.lower() for text in texts for word in word_tokenize(text)])\n", " # word_count = Counter([word.lower() for text in texts for word in word_tokenize(text)])\n", " \n", " if stopword == False:\n", " word_count = Counter([word.lower() for text in texts \n", " for word in word_tokenize(text)])\n", " if word_frequency != None:\n", " word_count = {word: count for word, count in word_count.items() \n", " if count < word_frequency[0] and count > word_frequency[1]} \n", " elif stopword == True:\n", " word_count = Counter([word.lower() \n", " for text in texts \n", " for word in word_tokenize(text) \n", " if word not in stopwords.words('english')])\n", " else:\n", " raise ValueError('stopword argument needs to be True/False')\n", " print('first_time:', time.time() - start_time)\n", " dictionary = [word for word, count in word_count.items()]\n", " word_count = sorted([(word, count) for word, count in word_count.items()], key = lambda x: -x[1])\n", " return word_count, dictionary[:number_of_words]\n", "word_freq, dictionary = find_words()\n", "# print(word_freq[:100])\n", "print('Total time:', time.time() - start_time)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def find_bigrams(words):\n", " return zip(words, words[1:])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total time: 13.651100873947144\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total time: 13.588640213012695\n" ] } ], "source": [ "start_time = time.time()\n", "\n", "def get_bigrams(df=train_df, \n", " lower_limit=np.log(sample_size), \n", " upper_limit = sample_size, \n", " number_of_bigrams = 1000):\n", " \n", " texts = df['text'].values\n", " # lower case text\n", " texts_lower = [[word.lower() for word in word_tokenize(text)] for text in texts]\n", " # bigrams from the lower case text\n", " bigrams = [gram for text in texts_lower for gram in find_bigrams(text)]\n", " # Count of bigrams sorted\n", " bigram_count = Counter(bigrams)\n", " \n", " # start_time = time.time()\n", " # bigrams = [bigram for bigram, count in bigram_count.items() if count > lower_limit]\n", " \n", " sorted_bigrams = sorted([(bigram, count)\n", " for bigram, count in bigram_count.items()\n", " if count > lower_limit and count < upper_limit],\n", " key=lambda x: -x[1])\n", " bigrams = [bigram for bigram, count in sorted_bigrams]\n", " ######### sorted_bigrams = np.sort(np.array(bigram_count.items())\n", " # sorted_bigrams = sorted(bigram_count.items(), key = lambda x: -x[1])\n", " # sorted_bigrams = [i for i in sorted_bigrams if i[0] in bigrams]\n", " # print('sort bigram time:', time.time() - start_time)\n", "\n", " return bigrams[:number_of_bigrams], sorted_bigrams, texts_lower\n", " # (',', 'but) , ('do', \"n't\"), ('the', 'price'), ('.', 'if'), ('but', 'it'), ('did', \"n't\"), \n", "bigrams, bigram_count, texts_lower = get_bigrams()\n", "# print(bigrams)\n", "# print(bigram_count[:100])\n", "print('Total time:', time.time() - start_time)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# start_time = time.time()\n", "# \n", "# \n", "# def word_dataframe(texts = train_df.text.values, \n", "# words = dictionary):\n", "# word_occurances = []\n", "# start_time = time.time()\n", "# # texts_lower = [[word.lower() for word in text] for text in texts]\n", "# for text in texts:\n", "# text_occurences = np.zeros(len(words))\n", "# for word in word_tokenize(text):\n", "# word = word.lower()\n", "# if word in words:\n", "# index = words.index(word)\n", "# text_occurences[index] += 1\n", "# word_occurances.append(text_occurences)\n", "# print('loop time:', time.time() - start_time)\n", "# X_words = pd.DataFrame(np.array(word_occurances), columns = words)\n", "# return X_words\n", "# \n", "# \n", "# _ = word_dataframe()\n", "# \n", "# print('Total time:', time.time() - start_time)\n", "# _.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# start_time = time.time()\n", "def word_dataframe(texts = train_df.text.values, \n", " words = dictionary):\n", " texts_lower = [[word.lower() for word in word_tokenize(text)] for text in texts]\n", " counts = [Counter(text) for text in texts_lower]\n", " word_occurances = np.array([[counts[i][word] for word in words] for i in range(len(counts))])\n", " X_words = pd.DataFrame(word_occurances, columns = words)\n", " return X_words\n", "# _ = word_dataframe()\n", "# print('total time:', time.time() - start_time)\n", "# _.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# start_time = time.time()\n", "# \n", "# def bigram_dataframe(texts = train_df.text.values, \n", "# bigrams = bigrams):\n", "# bigram_occurances = []\n", "# for text in texts:\n", "# text_occurences = np.zeros(len(bigrams))\n", "# text_words = [word.lower() for word in word_tokenize(text)]\n", "# bigrams_in_text = [gram for gram in find_bigrams(text_words)]\n", "# for gram in bigrams_in_text:\n", "# if gram in bigrams:\n", "# index = bigrams.index(gram)\n", "# text_occurences[index] += 1\n", "# bigram_occurances.append(text_occurences)\n", "# \n", "# cols = [str(gram) for gram in bigrams]\n", "# # print(cols)\n", "# X_bigrams = pd.DataFrame(np.array(bigram_occurances), columns = cols)\n", "# return X_bigrams\n", "# _ = bigram_dataframe()\n", "# print('Total time:', time.time() - start_time)\n", "# _.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# start_time = time.time()\n", "def bigram_dataframe(texts = train_df.text.values, \n", " bigrams = bigrams):\n", " texts_lower = [[word.lower() for word in word_tokenize(text)] for text in texts]\n", " # print(texts_lower[0])\n", " bigrams_in_text = [[gram for gram in find_bigrams(text)] for text in texts_lower]\n", " bigram_counts = [Counter(gram) for gram in bigrams_in_text]\n", " bigram_occurances = np.array([[bigram_counts[i][gram] for gram in bigrams] for i in range(len(bigram_counts))])\n", "\n", " cols = [str(gram) for gram in bigrams]\n", " X_bigrams = pd.DataFrame(np.array(bigram_occurances), columns = cols)\n", " return X_bigrams\n", "# _ = bigram_dataframe()\n", "# print('total time:', time.time() - start_time)\n", "# _.head()\n", "# bigram_dataframe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total time: 78.97760486602783\n" ] } ], "source": [ "start_time = time.time()\n", "\n", "# Dataframes of bigrams/unigrams in the train and test datasets.\n", "train_bigrams = bigram_dataframe(texts = train_df.text.values)\n", "test_bigrams = bigram_dataframe(texts = test_df.text.values)\n", "\n", "train_unigrams = word_dataframe(texts = train_df.text.values)\n", "test_unigrams = word_dataframe(texts = test_df.text.values)\n", "\n", "print('Total time:', time.time() - start_time)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " considerably communicate fixed ideal remembered invested distort \\\n0 0 0 0 0 0 0 0 \n1 0 0 0 0 0 0 0 \n2 0 0 0 0 0 0 0 \n3 0 0 0 0 0 0 0 \n4 0 0 0 0 0 0 0 \n\n perspective rom v2 ... conversion tripplite was regarding \\\n0 0 0 0 ... 0 0 0 0 \n1 0 0 0 ... 0 0 0 0 \n2 0 0 0 ... 0 0 0 0 \n3 0 0 0 ... 0 0 0 0 \n4 0 0 0 ... 0 0 0 0 \n\n proprietary lens.i recharges pal reception lifespan \n0 0 0 0 0 0 0 \n1 0 0 0 0 0 0 \n2 0 0 0 0 0 0 \n3 0 0 0 0 0 0 \n4 0 0 0 0 0 0 \n\n[5 rows x 3000 columns]\n ('.', 'i') (',', 'and') ('.', 'the') (',', 'but') ('of', 'the') \\\n0 0 1 0 0 0 \n1 0 1 0 0 0 \n2 0 1 0 1 0 \n3 0 0 0 0 0 \n4 0 0 0 0 0 \n\n ('.', 'it') (',', 'i') ('i', 'have') ('it', \"'s\") ('on', 'the') \\\n0 0 0 0 0 0 \n1 0 0 0 0 0 \n2 0 0 0 0 0 \n3 0 0 0 0 0 \n4 0 0 0 0 0 \n\n ... ('my', 'head') ('a', 'more') ('for', 'over') ('to', '.') \\\n0 ... 0 0 0 0 \n1 ... 0 0 0 0 \n2 ... 0 0 0 0 \n3 ... 0 0 0 0 \n4 ... 0 0 0 0 \n\n ('light', ',') ('and', 'all') ('love', 'this') ('a', 'must') \\\n0 0 0 0 0 \n1 0 0 0 0 \n2 0 0 0 0 \n3 0 0 0 0 \n4 0 0 0 0 \n\n ('my', 'lens') ('got', 'it') \n0 0 0 \n1 0 0 \n2 0 0 \n3 0 0 \n4 0 0 \n\n[5 rows x 1000 columns]\n####################\n(15000, 3000)\n(5000, 3000)\n" ] } ], "source": [ "print(train_unigrams.head())\n", "print(train_bigrams.head())\n", "print(20*'#')\n", "print(train_unigrams.shape)\n", "print(test_unigrams.shape)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Combining the bigrams and unigrams dataframes to one dataframe. \n", "def merge_grams(unigrams, bigrams):\n", " combined = pd.concat([bigrams, unigrams], axis = 1)\n", " return combined" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15000,)\n(5000,)\n" ] } ], "source": [ "# Get the length of each review\n", "train_lengths = [len([word for word in word_tokenize(text)]) for text in train_df.text.values]\n", "test_lengths = [len([word for word in word_tokenize(text)]) for text in test_df.text.values]\n", "print(np.array(train_lengths).shape)\n", "print(np.array(test_lengths).shape)\n", "# lengths_test = [len(text) for text in word_tokenize(test_df.text.values)]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "X_train = merge_grams(train_bigrams, train_unigrams)\n", "X_test = merge_grams(test_bigrams, test_unigrams)\n", "y_train = train_df['review_labels']\n", "y_test = test_df['review_labels']" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "X_train['review_lenght'] = train_lengths\n", "X_test['review_lenght'] = test_lengths" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "X_train.to_pickle('X_train.pkl')\n", "X_test.to_pickle('X_test.pkl')\n", "y_train.to_pickle('y_train.pkl')\n", "y_test.to_pickle('y_test.pkl')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jpilgram/phys202-2015-work	assignments/assignment09/IntegrationEx01.ipynb	1	4478	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import integrate" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Trapezoidal rule" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The [trapezoidal](http://en.wikipedia.org/wiki/Trapezoidal_rule) rule generates a numerical approximation to the 1d integral:\n", "\n", "$$ I(a,b) = \\int_a^b f(x) dx $$\n", "\n", "by dividing the interval $[a,b]$ into $N$ subdivisions of length $h$:\n", "\n", "$$ h = (b-a)/N $$\n", "\n", "Note that this means the function will be evaluated at $N+1$ points on $[a,b]$. The main idea of the trapezoidal rule is that the function is approximated by a straight line between each of these points.\n", "\n", "Write a function `trapz(f, a, b, N)` that performs trapezoidal rule on the function `f` over the interval $[a,b]$ with `N` subdivisions (`N+1` points)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "nbgrader": { "checksum": "0502d257f547b022ec1fbe354a75bbc2", "solution": true } }, "outputs": [], "source": [ "def trapz(f, a, b, N):\n", " \"\"\"Integrate the function f(x) over the range [a,b] with N points.\"\"\"\n", " #I worked with James A\n", " # YOUR CODE HERE\n", " #raise NotImplementedError()\n", " h = (b-a)/N\n", " k = np.arange(1,N)\n", " return h(0.5f(a)+0.5f(b)+ (f(a+kh)).sum())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "f = lambda x: x**2\n", "g = lambda x: np.sin(x)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "3ee11e4e20322adf86beac9605ef3b1a", "grade": true, "grade_id": "integrationex01a", "points": 5 } }, "outputs": [], "source": [ "I = trapz(f, 0, 1, 1000)\n", "assert np.allclose(I, 0.33333349999999995)\n", "J = trapz(g, 0, np.pi, 1000)\n", "assert np.allclose(J, 1.9999983550656628)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now use `scipy.integrate.quad` to integrate the `f` and `g` functions and see how the result compares with your `trapz` function. Print the results and errors." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "((0.33333333333333337, 3.700743415417189e-15), (2.0, 2.220446049250313e-14))" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# YOUR CODE HERE\n", "#raise NotImplementedError()\n", "integrate.quad(f,0,1), integrate.quad(g,0,np.pi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "071dda1b7a2edcac2945239a2f53139d", "grade": true, "grade_id": "integrationex01b", "points": 5 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the previous one" ] } ], "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
mlamoureux/PIMS_YRC	2D_Plots.ipynb	1	184469	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 2D Plots, in Python\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many tools for plotting in Python. We will use here pyplot which is part of matplotlib. It is very standard, and a lot like Matlab. (But see also Bokeh and D3 plotting.)\n", "\n", "We need a special command, inline to tell matplotlib to draw on our browser screen. Then we can load in the toolbox for pyplot. \n", "\n", "I am going to be lazy and just import everything in pyplot, without and namespace encodings. This is considered sloppy coding, but it is okay for short, interactive notebooks. \n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib.pyplot import \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is about the simplest plot command you can get." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x118f3db70>]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x118b14b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot([1,2,3,4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also plot y versus x values, as follows:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x119032748>]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BnkGclwqOJSjzYkCT8sd1gZnA4IDOS0XGEoh5Ka/1V8DLh6q3uuckrq7Q3f1z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\n5eYxyr9RAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118f68240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot([1,2,3,4],[1,4,9,16])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use various modifiers to get different styles of plots. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x119125898>]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7q+pwVS0C1wGfr6obNnXb0zmZqe8UTXILvbvZh5KcAW6id4OEqno/8Gl6d4m/DWwAb5xO\npbsbYCyvBd6c5FHgf4Drqn8bfMZcDbwO+Eb/GifAu4AjMHfzMshY5mVeLgU+lOQieuH2saq6Pcnv\nw9zNyyBjmZd5+Rn7OSe+KSpJjZi3Sy6SpG0Y6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrok\nNeL/AdGa6K1bQ8T4AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1190494a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot([1,2,3,4],[1,4,9,16],'or') # 'o' for dots, 'r' for red" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A scatter plot is also available:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x11921a860>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dV5JNnA3B26vq4Hm6jMW6rDSPcVqTc6rqFPAl4OpFh8ZiTc5Zah5jsiZXAW9I8h3g48Cr\nknxsUZ9VX49xDPTPAH/UuWJ8JfDDqnp01EX1I8kLk6Tz+qWcXY//GW1Vv6hT44eBh6vqfUt0W/fr\n0s08xmhNppJMdl5PAK8FHlnUbRzWZMV5jMOaVNXeqtpWVduB64G7qurGRd1WfT3W3XeKJrmDs1e1\nNyc5DtzM2QslVNUHgc9x9mrxt4GfAG8dTaUr62IubwTenuQJYAG4vjqXw9eZq4A3AUc7e50A7wUu\nhbFal27mMS5rcjHwkSQbOBtwn6iqzyb5ExirNelmHuOyJr9grdfDJ0UlqRHjuOUiSToPA12SGmGg\nS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEb8P+qBNUX6EhUBAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11913b6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scatter([1,2,3,4],[1,4,9,16])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numerical plotting.\n", "\n", "To do something more complex, we should load in the numpy package.\n", "\n", "Then, we can plot sines and cosines, and other nice functions.\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x119312710>]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1191e55f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(-2,2)\n", "y = x3-x\n", "plot(x,y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get fancy and draw a figure with subplots.\n", "\n", "The trick is to create a figure then use the add_subplot** command to include each subplot. Each subplot can have its own labels, colours, etc. \n", "\n", "The number 131 (in subplot) tells us that there will be 1 row and 3 columns in the figure, and we are inserting the 1st subplot." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1194990f0>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+/AWA7MYYpUuOpF7VVKFClo4ByCmGDePxp8A1QwgOjXUSOT79lA2FEhI4A5Os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\nfTsncgcOsOtemTKuIxKRMJo0ibus99wDvPJKFq4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"text/plain": [ "<matplotlib.figure.Figure at 0x1193211d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(-3,3)\n", "\n", "fig = figure(figsize=figaspect(0.2))\n", "\n", "ax = fig.add_subplot(131)\n", "ax.plot(x,cos(x),color='b')\n", "ax.set_title('Cosine')\n", "\n", "ax = fig.add_subplot(132)\n", "ax.plot(x,sin(x),color='r')\n", "ax.set_title('Sine')\n", "\n", "ax = fig.add_subplot(133)\n", "ax.plot(x,cos(x),'b',x,sin(x),'r')\n", "ax.set_title('Both')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatter plots\n", "\n", "Scatter plots are a whole lot more useful when we are comparing sets of real data. \n", "\n", "Here we have 3 random data sets $(x,y)$, showing positive, negative, and zero correlation. \n", "\n", "Using the subplot command, we can plot all three together in one figure." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x119685f60>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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olQpyWFzkeNu1iyuFWIzGvFSisd++nde7dIlzVgqyqYnjd2WFz1MosA2JBNt8zz3UCefP\n8/u2Nl53aIjP09m5NlRzPfxwdwUyM0OFvrTEtkYifDYp94YG9kVnJ/vq8GE6QQcOsF8mJrj6f/jh\nSk/+oYfYXy++eJso/GqoRctwpcRXc56XlyvZFMKUfZ+DOJNhZweDPK5QoOLTi5yd5QvJ5WzAZDJU\nIoUC/w0O0kDEYlTmpZJBKX/yJxzMd9/NwGa5TIU9MsKJ0NzM67a3cwIUClSGmzebQRHeuLzM85eW\nODgWFvgc2axFzstlfpbPcwJFIvQE5KlHozwuleIzTk7y74EBKsr2drbH83j9YtHuB5hBUj8ODXGC\nNDSwjVLWoRDbEI/z/NOnOYmkmH/8Y7ZvZMSwWWGqkQivWyzyftu3895DQ5y8qRQ9wXic7yeTYR8n\nk+yLd0huK8Xu+1SSk5NUkgpaKkiowPTZsxzXnmfe55kzfEfhsOHyYo8JEvQ8eu3FIvnrLn5cCyf2\nPMINu3dzRZdMGsYvWGhgwFafq6sWtPU83n9x0Z6vWOR4Uwwqk6GSHx0FfuEXKiGXcpkO2MqKscnc\n77SyuFZxVyCDgxzXs7NsSyJB45HL8RnCYf5TbCMWozHr6eG1xsepJwSZNjTwvN//fV53yxYq+NXV\nSujmllLuV0ov/9a3qLT6+oxFkUgYTlwuUzEEg/x9ZYWKRdfVoC4WaTnjcQtmhkKVA8fzDGscGuLv\nLS1cUo2Pmwe8fTvv393N859+mj8XFniNlRUq9oYGHr93LydSczNf7OHDZoBWVqi0AgH+vbzM+wp7\nVLsUQPR9Pl8sxoFdKNiKIBKxyVAu2xKxWOTf6TS/z+ftflL2pRLPCwR4XDjMvuvstL4aHeVAk7Kf\nneVkbG/nMcEg+y0aZRtlNIJBHt/czHOamvjZ1q2839ycGejeXn4upfKrv/qO0x1vG8UuOt7p0xyL\noRDfld5fNMp+Fwsqm7X09YEBju9czt5XQwO/b27mOa2tPFaerqu012qPq/hfeYVea1sb59APfkAD\nolVnYyN/+j7HdzBoyTqay9FoZfwonebqrr29No6+fbvx75NJXtflh+/cSW//Wnjp7grk936PfRSJ\nUKcocLy8zLlTLPJ7OWYKEiuWNj3NuTQ1xXueOkXjOjfHv4tF66/9+98ZTv47LrWglt27+aJPnmSg\nEjBM3fP4M5ulAhC8AJjH7fvmYUsx5vPGUvE8DhQt+UIh/l0o8MUr+LeyQuXV0MCBvbLCl1Iq0Ytf\nWOC/aJQvrLmZ15SlXlmh9zoxwXbqmsLSJydN0ZZKVJ6CXsTe8Tzz2MtlHptKWV9IcWtCLC4afhiJ\n8PvZWQ4kdxUgpoMGooxHPs82LC9zEk5NsX+XlmzVk8lwUqVS9NLvvpsTtFQyL0aUOID9K6w/nebg\nFctAtMxEgsG5TZtuWOAUuI0Uu+h4999P+mBzs63mfJ9joVxmny8t0TEZHOS5iu9cvMjx0NpqzsL0\nND8LBAjvZLPAv/k3vOZaHmStkgTFIpPt7rzTxsfSEseEyAHFIu8lYx6JcNzGYmyPS08U5n7pEvBz\nP8dxOjjIcZtI0BlsauIq+TOfefPKYudO4Pvff3PZhGvxkJuaOM/a2my8uw6YiAzFIp3RxkZbqSvg\n3NjIe8upURxMqyp5+aEQdd/v/R7w0z99dcbNTQHbXCm93PfNC9y6lThvZycnfFMTj81kzAvVclGD\nolSygeAqwNVVUyYu/CB8emWFx0gpZjIc6IuLnACDg2zHyAiNkJSwDEk4bKuDhgYeI6ZLJsMXKoMj\nA6KAzPIyf5fXLWXupmkDPE4etqvYNYByOVPg4bBh88WieTa6fzTKY0MhPjdgz6PlfChkk69UMmqn\nIK6pKX4GWFB4ddU8fMU7SiUzVgMDnGR9fTyvoYHtHBvj+75BSUq3jWKXoWxro1Kfm+N7ELa+vEwl\nUi4z0NnQwPfpebYK6+jguN6xg+9ufNwS7TRONm6kMc/n11Z+tTJCDxyg53nyJM/Zu5dj6+WXOWZE\nexXmD5gDJimXjSUmLzke56p5fp6/ayV9+DCf86Mfrb2yuHDh2qmQSnL6ylfY/kiEc7ylhc7YxISt\nJBQXK5cJLWaz5hS1tXE+Kg5QLtNB3LCB/al3oWdfXCTzLRYz5+taGTfvqnKv9tYFtTz9NDuqtZUv\n8plnOOBSKT5cLMaBm06zkwVpSHFLEcnDleKTQs3nzWPXOcGg4WKuZ1AqUSmtrrI98jAUCHShCMEw\nChYChhmKDrhxozF21M5olOdIAgHzpF3FDpgHXyjYM+j6+j2T4cAX/qf2a2Ui7Htlxe4PVBqTUIie\ngAymjMjUFCeijs/n+d4U4HLbqKSW1VUzFoGAJVuJZqqJ9S547bdNgpIC7p5Hr23zZmN9iZHR3U2j\neeAA+1hw3/KyORUPPcRzu7s5VpNJGl9lmGYyHANPPEFlV0tqcb67uuglz8xYoFW5GoIu5c3m8/wX\nCvGzQoHXkKPV0UFlmEpxDJ0/z3FdKtGDX1jgXD1/3nDta2kj8OZSuVp9fOlLhFIUJM3l+BzbtvG+\nGvuCMffts2xSwCCue+7hfX3f7i0GXTZrTm46zf5ZXDQ2X6FAWPff/3vgD/6ARrdY5LnvSv32teqh\n10ovL5e5BBHX2/O4hAMsKAcYDru6yg7KZs26a2mkY6szzOTFSNEUi6aM5Hnn85XXLhQM3lhcNA9U\ng85dKUjJCX7RNbUElfWVggU4sbSEk2Kv9l4AtlGxA9fb0oCKRLiiSSbNq2lq4kAUpNTSwnPkpZfL\n/N7tp3S6MmgrQ6RUcHf5qWC2+kLPqkC2+kriBozVR4BhwmNj/PsGJCndNh67S8fbsYOT/o47LIAe\ni3Hiz80Re15ZAX7xFy2LeOtWwp5dXTYX/v7vCe2srlpWczhMxTk/D/yH/wB8+MNvrvRYKyPU82hQ\nzp3jKnxw0OiAY2O20vQ8o2gKYy+XeawolePjfI7lZY5n3+fzNjfTmI2Ncbzt2UNvuhaOPj19dSqk\nW51ycpJ/q0zCnj301ru66Bh5HvD+93MVcOkS+0vEgFKJ70KxgJ07qePk/ExPc65GIrw3wGvncjzf\n9y0ZMJWiYRXRwmXcvBV+/tuSWnVFannripIrANfQwA6WAhdk0d1NaxaJGBdcylnepwZnJmNeKMCB\nqXRgQTnirMszlYIHzNN3jUEuZ16rsGhBMr5vuJm8ZOHlCghFIoY/i6miaLr46vJAqmEYXUu/JxLG\nQPF99plKHLS2cnAJ8hHGClgmoettqA89z9qjlYTwTxkk1/BogLr9rvupTwEzoqUS760l7oYNbLOu\n+9prFmR9h+mOt43H7tYgUVEpwY6plMVUurqoNPJ5Bu16esgo6eqqTPUfGjIIxq0LEw5bSYinnyY0\nUZ2q39Hx5pIEAMdGOMx3f8899Gq3bOEKIZHg2JWyVU2nQoHKTsyTSITPI/gjFGI7N23iM2WznMud\nneTkP/dc7ZIC4pzXEq0KVJ2yUOC4LRTYF2Nj7M+ODs6nn/s5wh+PPEKHSLBuocC+zOf5uxy+/fv5\nHK+/zuPzeT5LJELDp2RK6YyuLrZrZsZ03eQkDUIkUlnH/YbVb69VK7266qK8dXl9wgAbGw3u8H0O\nAHmOXV22zFR0WhYrmTTsyfV45SGLgqeMumSyEipw8Sl3GQgYdqxj5b0KygFsgMkblnKUYlPGqUSK\nV8ZEbblaLQsdLyXpBp/UBjFnymXr41zO0rG1SnAhH91bkI+Mm5buwgOVuQtULi2l8HXtUIjvKhq1\nd6CAXiplCVOxmL2LlRX+fscddbrjtYhLx0sm+bfqBgmWaWvjO89kOH9UemNhgXCL6qwrwHjkCJWb\nWxcG4EpLHu9LL1nhum3bmLiXTJKtsnUrDYwU0+Qk4bWVFf7s7LQAZ1OTccJl/MtlW4G2tVkgMRo1\n50BzWaUJolHet1ym8zc9TU+6Gke/GhXynntouBob+WxLS8beuXzZYkpjY7xGV5fVnxFVGDAm2+XL\n7K8tW/iOzpzhcfE4rzk5yfYHg5yf+Tx12L59VoVVkKpWy9ksDU1TE392d9/A+u3VtdKPH2dyT6lU\nSWOUty5PtrmZRkBeorBxgDiTOO8uLTAQ4EuIxYxupZcVCFTi7dksrWWhYMEQF0qQuB5zNTVQym11\nlYNT34unLgUqY6RryeuVZ+zeq1o5VgdBqvH2QsEyWwHzyOUdqM/kZfm+0cZSKTMIgPWbePdaHYip\no/fg+2a0XBF0Jq/djW0IFtI1pMQLBbaloYGZhIpV1OmO1yfVCUEzM8yQ/tf/2pgSFy7QW927982K\nbmiI1QSrl/Df+IZRVyXT0xzj0SjH0MaN/HniBMfb3r1U6OfP87q7dnG8CAsPh2lwTp827zoeZ3tn\nZ4F/+S8J3fb3m5MyNmaUSyVNZTLUIWfPmucbDFIHbNhA79d9Vrevmpp4//5+Pt/u3WzD4iI978lJ\ng12UbHTxIsem7tXSwvsdPsx+n5rieJ+Y4LW0ykgkOBeGh2n8jh83EsWGDVTEi4v8vr2dhumuu6gv\nm5rY3nTaDIuQAK1+FhasYOD18vPfktSqlT47S09AN5e3rgi+ouNicySTlhSTTlMhpFJWjCiVMkXU\n0GB0v7k5WlkXFtA9lH4vqqKWStWKSiKFq+WYi3MLXllYMDbP3ByvK49USlyKTwpbXrHr9bpYv8uD\nrdU21ekQL1bGT9ebmbHEEhX7Sqc5eLq6jF3U3s5na23ldcQyWlri8yl2IUPmYv+CjNz2y4iWSla0\nTDz7xkbDUXVPsZGUyVqnO741uRq3XMXBXnyxkhbY1VUbm921i9ituOKRiJWQDgY5fpQbInhS8NvH\nP04FdfEiIR9h/6dOVToO8/PmkI2NUcnpbwXa5bxp5eni44uLlfg8QIXuebzW1q2VfaCErHPn+Dzv\nfz8NzpEjNCy/8Rvsgz/8Q95DnHsl9Yl9I0g2mSSF8/Bhft/UxH/ptGW2a8XzvvcZZKu8FtVLSiaJ\nvcfjvP/+/VwxHDtmBker/1iMBmFlxVACwWc3pH67662PjLBzJibYIa+8wgF17BgfdnjYuOSAeaBK\nYRfcoKXI7CwfShBBNGrW3bVgrofr+xYMlLJS+V9582spUCkwea/CCwGeo80DZKRUM0ZGoPraaoNq\nZLi8eyl9WWUdXyuI6HlWq10evDwEUcPkiTc0sN+mp60cQjrNvhMDR8pW0XZF85UkomQjUR7lgauf\nADN6MlrFIvtHzAcZSaWZizuvAmBKjqrTHddPfJ8r5ZERS9N3g28KerriecBv/iYZIYJwkkmONdUR\n6ujgGDp71iDF2Vn+7O42eECB8q4uzk15tCISBIO8hu9TyTY18Z4ujx7g/TIZji3Rn1tbLYciHObY\n3raNzzUyQh0iJ29piZ/F49RLW7YYdDM0ZPOvs5PwibJwVXW1sdEU6t693BkMMMgyFKIRlCO6tETd\ntXMnSzicO8c+VNDU980rF+X39GkqdcAMiuZyY6PRmn2ffZrLsZ9/7dduQKnfam9d+1oCVgDnuecq\nz2lstEJF8sqVOafgqbyCfN6KHUnhNzby2KkpMwBAZYqzvM1YzHZ0kWfpBllrPY8LLQgjdtklWl20\ntXGSyON1MWeX3eIGfN0ApwyQ6s6o/EEtkTKPRIyn3Ndn9XJCIdsgQbBPKmWUQ0E3kYgVMuvqMk/e\nDRzJs5eXocCzICg3RuF55qVHIjy/pcXiKqpP4w7oYPCGe+23TfD0aqLiYImElbOIx/lOL17keKnO\n/gaoKD79aXqX99zDdyEKbXs75+vAgLHSCgWOrZMnbf4VCry3skubmmxciSmm/XmHh4nbf+c7ZJeI\nOKFM6dlZjiMpx95eczz27wcee4zB4EKBWz4+/jh59+fOWeGuXI7tnpmxdlbTHA8dYlsGBqjA77iD\nz9fYSMemr49KvaeHBmHDBospATyuq8vieJs3876nT1s/CSMfGzMW2eXLJA+EwzRMfX2mzMtlc2bH\nxqjnikWrnbMW/XRdxd0YY2zMXkpnpxV7On6cAycSYcd1dFjAThmiUrh6OFGLALOisRjv09zMh5QC\nEyQAmLJpbOTxoRCXRqL5iWGjY8W2kTKWsgXsdxkZQSX9/Rzw4p66z6G2SNHJa1VNG2Wj1kpKUnCz\nlujY6WnP9poDAAAgAElEQVTLSp2b44Dxfd5fNFKtJvJ5BmM6OuhJ7NzJQRSNmsFraaEXIGw9ErHl\nY6lkqwLAPCr1GcCfsZhR7WTEduzgv2DQBqRWWvLa5QDcSnRHz/OCAF4BMOb7/qPrdd31EhUHO3PG\nitTNzNhceeUV4AtfePN5tbD73bvpJc/O0qNVMb5s1vjuzz7LMbhhgxUhk3Mhh2F52bJPQyGOu+lp\njgU5OomEwYNykB56iGPiBz/g+GpupiJ+6CGe88QTVq21tdUKzwn2W1rimBdU1NPDse4ySnbt4t8i\nHIhJl04T4kkmbavA5WUeu327OU1i562s8Lt77+Xx99/PxCKVUlZtHhXW8zwaKyVRipmkUiJKoFQs\nQcmVkQjwR39EB/pnf7YyA1XxxredpepuuqDt3hR0VB1mVXFTvfT2dnprkQiVjnZrkTUWdBEI8EUq\ngJHN8ljR/nQfWWEpSv2uOg4qF5BOW6YpYMrd9a6r8W79DARoUJSunU5TWUrp61oupq6/1S5BJuFw\nJc0SMG/FTV6qJVqxqCzviROWaKXVhPB3JTZNTlrRMsA24QZo+RMJPo+7c9PiIgeydlTXKkKrGddz\nF/SjrFPBZxMTlhsg3F/9roSLCxcssn8LVXf8XwG8DqB5Ha+5bjIzQ0WWzRLqUEISYNTC8+drL+Wr\nsfsLF+gNi5kB2GbXSp1XfEkbzuj9a5xFowZXRCIWiPQ8Gy+i4spJ2LyZn/3jP/Ke0SjPW10l4+7l\nl3nMuXOG03d2GlU5nTYoSfeKxahvqhklnmc0TFEjt22zsgTK3wD4HFNTrAsP0KiMjvL3TZu4d4HI\nAm1tdGy0QpmfN+P6wANU7jIKS0u839iYOYty1kolS/TKZtnWeJx9MD9vGajAm8s7vOUsVXnrihir\nqJUG0uqqWbmLF20DCS1PlN0pRRgIVGaEukpQgb75eXba8LApRzfoKGzchV0GByvrpEjRut5nrcCg\ngojxOBXevfey/cIIRXVSJp0UvNoiOhdgS1jXeEiUOOQq+7XE962ImTa8kMHRKkh4v9owM4N/qrI4\nMWHB06Ulo2C5sFCxyH5WgFht1LtSf+t37bwuFlGhwPcjmqor8vjCYU6KfftseXyz0x09z9sM4FMA\n/i8Av70e11xvUQJTT4+xNTR3enq4ejp8mLDDlZJbNB5WV6mcEgm+IwULs1leS4o9k+Ex4+NWTE/Z\n3FJW4bDtF6B7aNwq5qMy1tIF0SiV2fi4rQamp41IoLLYbtKT2CTi6wO2rV8tRklXF6/94IPWrpMn\nqbMAzrW5Oba/s9Poip/8JL8Xbv8v/gWTvRTwleOnZMVIxDzvtjbbIEUJkZmMOYqKBbqsunSa8JVg\nzZ4eJnUq63VddpFyvfXnn7cXGInY8kIKQ5ZKwQy9HHE5NfndinRSVi67xFWIUpT6zsXVBa3on5tU\nI2voetZApcKt9v4VNB0ftx2Q5ufNw5bXoSCp2lN9L60kavHWr0WpS8plCzSHQvQMVNNaFMuGBvNc\nduxg+dVSiQGyhx8mzSwUonIfGjLeuZKHFDfI5czAuswed/mn2jlKLFNpZaWp1xJhle9//zvOiAHW\nz2P/vwH8LoCmtQ7wPO8LAL4AAFurqRo3QFRPXLXwNRflSe/cyfcsZkyt5fsDD9Crf+EFKr19+6jo\nRkepKNxAqYLl27ZxDExMWB2khgaLuehzF2KQAhKZQJt6+L4lvLW0mHJ2qcgqYyGlNzNj2LRKeAif\nX17mvIhGazNK3BrsUqQHDvDZX3qJsaBNm5igdP48FWitKpK7dplhXVggK0mMGjk05TIZQ3fdRX04\nN8fv0mn+k6gESCRizy/HR6WYv/999vsdd1hfrVU64cgR/n1VmObECf7bvp0HagmkIjnqUAUaGhos\nRdYtLSq+qxgRq6uGxYlpIU83m7VKb65nLYXjsjRkGcUHFa/bzUITDq5BU82u0XeAFSpTYFeBRtWT\ncLF1UQBdo+Mai7Vw9GsRwS7ag1UJHFp+DQ/zRYonrhf9P/4HJ5eMq3alGhy0+tLyaoSru1CSG4+Q\nyMDEYnwvsRjP7+mhYrn7bk6QtWRl5YYwYoB1UOye5z0KYNr3/WOe531oreN83/8qgK8CwH333VfD\nhL+zogQm0fi0AUomYzsg5XKWSl9r+f6lL1HhaCu6HTu4Su3rs+JXyaQ5OMrmVmp8KGTwiILnWpWq\nJIbmihwdjSsVvsvneV3RnkWAmJ8350XJirlcZa0kQTotLTRSi4vUGf/sn9GrrlZm1UlfUtiZDGmR\nLoyxezdXO2vVp5eREPyrnduETuzezb8HB9kXqZQ5Ri68KYOlLHX1kQgKGzdS9wwO0gDv2bN26YRo\nlLGQ11+/CkxTLLKg/qVLVBaqBhgIWNKOvHXAPFXVM5didDMi5R3Lc9ZyDDDlqNK04nBXp/Hr2Ool\nno5zYQsFO6VkBSWovW7Z31DIyvl2dFin62VXY+xAZSDQ/dxV6tWrhmuVYNBiDqEQB5HqmwcC5vko\nISuVovcwMsLJ9pOfcJJrizEpaJe3L9hLA0nB0WqcLhy2OhcqfKagkhJN1pJbjMf+MIDPeJ73MwAa\nATR7nvcXvu//z+tw7XUTBUHPnmWgtFymsrrnHqsPI4y5VnXGxkZi1zMznGsqTeB5xsASzXdhwcpV\nLCxwRSuYoaODillwxfAwYYqZGY4Z7W/qOmaC+zQvlKyjipSiBcsjn5+3eS9nTvBHRwc97WDQoMi1\nCoXVChzLQwe4X0S1l7sWrCEj8Xd/Z3sv6HmiUfahaMHq82jU5qzmoXSh6KZqp5xId45fuMAVg2oI\nVcvo6NqZuRUwzYkTVjtiaMgsnJSA6ojL2sjjVoIKYPCMllZu9UIFAF3Frpcuj9tVlhINOil14YuC\nZBSkdRW4WyPF5aCrTbqGmD/ZLCfC/Lzx0lUqVwpRv7vtq4aSdP+3Iuq7lRUqVVW9VDBLQTLx1Gdm\nbG9Tl5ki3FJ88tVVWvRolMvtkyf5zDMzfMdbtqzdpuVl28X+/HkuEa8WDAZuHR677/v/DsC/A4A3\nPPYv3mxKXeJ59ExTqUqlDVRizGtVPsxk+PnAAI3/mTNWKmJx0SADMZ2U+SzvGaAyyWSIW3sej/3I\nR6jclf0t+FIBes19rQxVoC+fZzs2baKxEfau2JuOFyPrgQeYOHXhAv/t3UvHZ3x87WBideB4rdXM\n1YKRMhJf+hKNqwqqJRJW2nd1lfNLK2/BmM3NtpJSvEr6So6pIFhRS+X0VsNJ7vteKzM3EACSTSUc\n/do57P4/d9Bb9zwOEClqKRnxy4XXurVWXG9W1EaX6ijMTKR84c4KTlYrQjdNX8sY/e77tgRysTxl\nh7kBTnel4F5PRkcGSvz52Vm+hKUlWzKKiaIsWaAyKKsVicu5f6uK3X1+GTN53qqcKJhImbfZrC3D\nDxzggNLEUsZvW5sNoGyWk0H7rra0cEm+VmRdxmLrVqPG9fZe/RlukNf+nklQkqwFL7h4sBvoc0VK\nSFsvKlFJK0SNMQVH3dLS8rTb23mv6Wka78VFJkFdusS8FkGlmhPyVJWPobkh52B6mtdVNnkgYNVM\nRRpobOSzfuITLAy2sEAl73rG1xpM1Gpm2zbO99deMzTiiSfMCKxFL+zu5rN2dHCezs9bDLJcZlsU\ntFXNd/H+NZfdgoNS6EAlIUHPfeQI++jUKSrxLVtsr2GVcUapZDscvbHUiaUuY+YfXwTuPMbUY88z\nBSaMXdmT2nhBjXOLXklJKiNNospngO1v6iplBfQkrmKsrrui++bzlUZBeLA6Rrxx7VJULfK6xUdf\nWjJeqwKVgnsUN1BsQN6nq8hl6NZLBHF1drJtzc2Vma2iGmoXKk2Knh4OVGGZgQAHrwaxAlfaTETX\nm5oy2mQtmZvjpNWu80pCuprcAK99Xa/q+/4zAJ5Zz2uut6wFL7h4sFsC2JW+Ptspq7OT419lgeUw\nKKHu8uVKnFvU5Olp3u/ECToEjzxi99QG2YBhy9PTNj+Vb7JlC3WMAqNKTlTSE2A6RJDHzp3Mcn/+\neY7D6uC9m6B0JcV+9Cjn0+nTfHYFgs+eZXuefZarkUiEMI/okvLot20jpq19DeTYKt7n+1S4Cws2\n5+Lxygxt6SyhBp5nO2UJkslm+Q7GxpivMzLCFXMqxcQqFXkbHweis+O2w9HGjcD5c8i8eBGbsqPA\nt543PqdrqbW9lTx0RXWBSqaEkhcU2JBClccL0JINDdneiKJAytMWrqZ7aZApMKOBHQjwRSvivm+f\ntalYZMQ7meSzuBiXri0OrJ4xlbJlpe+bNY7FTNGn05UJEno+xQzcbevWQ7T8DASsdo67cYHyCcQ7\n1mQRNCMFro0VFGhTtqo8NeGCyrCrJUtLHGDd3eyDVOrKhsCVW4jHfstINbzgihTLt79NxdHUZPVk\nOjr4r7+fY0aKZHGR1EPlcvg+FfXCgu1fCnD8ib6azbLo2K5dxKq3b+d4e+4523dBG8MAHC+FAq+r\nlbTK9uZytj1lsUg9JNabxnFnJ9umvXqXl+m9K74AXFvJW8GX2id2YsKIBsEgDdFTT3E+hMNUqi5u\n3d7O/nzlFcvEVSxB7CBl1m7YwOvNzhoXf/NmetnpNOdlqWT6RNcRzTiX47w7cICGfNcu6s8HH7R3\n//ifldDafwaB5mZap3IZ5Z8cxsJYBJ/dN86HO3SIN0kkrLiPG+UW/qW/xbsUpu37lrklLz0SsZRb\nlZzVSxOWJu/X9XqFvctrUDKCaqAMD1sywp49Vizo5ZcNc+/qYgdKIartKimsZaUG9L59fNEqeu+K\nkoFaWrgUi0b5PAr6KhFrvZS773NQdHSwT8VukXGdnbUViqhmQ0OWDKbvhLm79WQ0kFRXXll3a3nh\nAwN8vv37r81Td+VW4LHfLiL8WF7t+Djf9egoHbmeHo7x2VlzFJqbK7OMFxdJsxP918WF77iDykqr\nBCmX6WlLGNReuYJHFSjUFpWNjVb6Ip+3zxQ8VTa6mCMqVjYyYjVegkHOwUzGNqlwg8dr9U1/P3Hp\n554zJo9q0KgwmOJ2S0vMjB0c5DP39XHui+68Z4/l3CjuJz14+jS/HxiwMuYLC5Z8KaPW28s5OTJi\n+SGiQopW7GbWVq9Kdu0CHt50Cc//JIpkdyNii1PIPH0CCxONeMT7CXY1XOYNjh6lgtQWc4IjtIRQ\nJqK8YBfmkBcpUr+KWkmRiE4lay24wU1kqhYpKBdjV52GUokDTEk5asORI5Zc4WawulCGOliF72Wo\nLl9mxxYK7MDJSYu46x7BoGWnKolLz7AezBhBX+qn5mbbVEM1XsQcUG2QhQUrNiRj6DISXDaSJoBb\nu2JpiYNRBbyqZccO9sMNKsV7PXLztOQmEOHH27dTEU1PUzGl01TkH/wgodiPfITzpL3dxsjcHD1B\n1aGZmbF9giMRepo9PTZv29p4T9/nvDl82Dz8ctlW44IRtfKUopc+EX5+7pydq7r/vs+x2tnJOXDn\nnfy8v5/fqU5OT4+tMGqVvHUN3syMMXhESXSTFlXpUnBvKmUbhQv/T6fprKZS/FtxCaEQp06Znmlt\ntYxs1W8fHWX5hD17SGkMhzn3duxg20ZG2A8bN9o+zd3dfBZ3VeKVing0+zfY86EuHJ3sxUzKx6bx\nY/hs6WnsapmGNwS+vPl5WgJFxbVUF4tEdV5Un0WYHGAd1NTEFzc1xZc6NMTjhdurzrgUTq2kIjdJ\nRp0uSSYN6y+V+FKFJR4/XrlzSzpt13aDnS4Tp7mZnb2ywr/vuYfnbNvGl7d9O89XpuZ999FrHR0l\n5HPqlGXm1ooPVHPoryZ6ZuF1qVRlNUvA3snCAtvp+xwMWkW43oe7ktD7VB8rbpDPc3K6pQBqyTsM\nq7wVqSt2R6rZMKrOCHD8vvgix7PS4i9epKJQ8O6llwhvTE/b/qjaXrJYNJbWpk30/H2fsMXx41Z2\nREl0bqbpvn2VEKE8ctGcFZScmLAqqIIZu7vpeHR3G/zX0sK2yIM/eZKG7OGHee1vfrMy6On7NDxi\n6XR2Gvyqe2nXJe3Xqg1jCgUaOMUHDxwwaFT11AEL9mrTkGPH+H1fHxX42bO8juiR58/z+/Z2GjWA\nLCUVOAuHuSoRy0aiEg3f/CYw/doUyscPwG9tRRAldC0P4dDSD7ArfwZetM0ojaEQrQNgywIVpxLW\nJkK9gnNS5noRLS3GlQ6FqDDEh5ZX79YmcSEPNyApZS68CeDAicXMCjY38zqnT/NFvfCCVZSUV+By\n4nVt929VKBSvdHSU1z1yxIIsgNWyOXuWxuTMGaubIdZAtQJ/K0FVF/8X5cyNV4TDho8uLRnHfW7O\nDJx2jVdpAoDPW70TlGiqnZ387tIlKwVQS95hWOWtSF2xO1Jrr1JJLMY5s38/3/uBAxzHg4McTx0d\nPLdYZImNrVutnIi4793d9DQzGSrM732P+4lGIhaQd4v+KW7V1ESseudOZlXKUVEBsIkJwhXd3fTG\nFdz1PJ4nQyPHqbmZ3O3Llwk3lct0uMbGLHvUpTHmcrx+fz/btXs3jUEmY6iB8HE390NGaGaGz6EE\nS/3TfHf1oqtztmyxxL4DB0hsWFw0Z2thwarfCgVpaCD0I2bi0JDtYFUq0WC0twOlfAnDz0/j/Px+\n4HIAuxNjyM824PH0Z/FwaDMenX8KXjhkwUK5+aqnLAWgCm/6J8Wil6hsyWKRD7S0xIEUi9k+gvJs\nq7M3q71adap+F8WyWORLbm+3HYAmJ2mATpzgfbT3poIVrmhgSOGKduSyYEZHObDn5rgUGhriub29\nvFc6zRe0sGCYtvrDFXkvVxKXnukas2LRWD5a3roBWjeHQO9Ly0A32K3jJEpIEq3M8/iee3p43dbW\nG1YKYL2krtgdWYsNA9hWksLAPe/NHv3QkGWednZynp04we+l+FZWmOkJUIlq7jc0cCy5LBcxb7Qy\nFAd/2zaW9VWQNRq1Ojj//J9zRbBpk5U1EUavTXZ27uQcEUzT2Um98N//O1ccfX0WzGxtBf76r+nN\nq1zB1JTNO+kCbTSiPWEzGdvIGuBzd3fz59yc1cVSLEAQlXD45mZLiFQNKDmyKuA2Oko9KZy/t9fI\nEIKhlJ8wN2dO98GDwOypaYzMxbGpIwvP9zEyEsLW7DJ6vRE8X3wQe8rnsNsfNq9ciQiKYrtMFRen\nlqjWuPA61SJJJum1t7SYlZI3LSgEqO3pVn8uTqc6GDBIJxBg587OmhFyjYGMhiibCo4oCBuPG9as\nQO3KihXvlyVNpRgMkVFSRFxKvVbQVMqz1vO5ilztlIeguIaWdyq/oKxePZ/a5RoovR/FBKo30nBL\nOWhf0okJw2SffPKGlAJYL7mK6Xxvibshtitu8tKVvhcG7PuEGOfm6D3G47ai6+sDPvUpwjriaAsa\nDQSo5FtazIsX/NfWZlBRMEiFu2tX5f6nYnCdOUNK4ac+RebN/ffzu+Zm0iu10lAG9J491DX6XBtD\nA2xTRwehD5FCFJsTlu/OQ8UQIxGjVCYShK6KRdsxzmXFpdPmjGnuKpFP1VlnZmxvVSUdAjRSAI8d\nHuZcFFFCOuzSJTqZu3bR6QyihMEXZ9AY9xDwAC+bQayQxmCmG4FQAEmkcLR4nzFTlpZMObjJMGK+\nKDouvD0a5XdbtjAj7LHH+CIeeoiBmMces+25tmyx7Eel9bvKrVrcz0WFFBe2WLRtqVpaLOPN87g8\nE5alNmsgSwGLcyrcUM+t+jDCv1SxUvs9ZjLsK6XkyyjUCjgCleUJqp9JuH+1V+/CLq5CF+zlGlt5\nG9UZsdV96n7nro6EdYqjXCwSdxX8dAvIrWF+bpBcLXnp4x/nu17r+/l5Kq7FRTox2ihanqpiZhcv\nGuzT10fPvbnZWG9ytlSfaONGKtInn7RaRnKm+vps0wkl5qk88Pe/T87+v/23hAiff57K7vRpjt35\neauTc/KkOX9usBEg9PLCC9QZr79uq17xyRMJC/YqeJtIAB/+MJ9XQdapKTo9wsJdEW0xEOB9RI0e\nG7O+UVC5udkydwGLFxaLtqWgyi3IYXvwQSaeNTYCGB/n6sNfAhpjwOIiIrlFLCMB+EDMz2DGc3Zd\nETDv+7YccJN+5GUrmCrFMj5OXmciwfNPn2anHzzIF6SiQfLmlTorxVWduSkM3E3+EUVIJWk3bODA\nfPVV69BIxH5X5cJqhad7yUuWAtVyUQFLUcKUiSp6oY5RRq6Cwi6WL29X15UXrfZU8/XVNgU09TIj\nESuS1N5usItb4EwriHDYMPS1KJduopeMku5zi3rtN38Lb6BcS/LSlb7v77cKkprfgMEC99xjFSQF\n+3R10Zs9ccI22xBLa+tWGoyTJ4kNDw1ZuW+xRjZutHT71VW2oVBgDXQ3k9Rt95EjvP+ePQbDTE4a\nbClyh8tvb2picFjOXjZbWS4hkbDVg5IWT5zg9V9/3fZE1mbYmsNuYpXncV5pcxJt4gFYDRklW7nO\nVyJhez1HIrb5uHTRjh3W55cvlbB4dAiTKxuRSnloXiyjMx+Al/fQGsgA5RIygWZs8iYrlau7R6dq\nlivaK6UmHEqWJpcjXqZEmclJviyB/io1K458KGQVIqVs9KBusNHl0ZdKthVYPm/7ED73HK+nAK5g\nn85Oqz0uzrZ2I3KZK1KGeg79/tJLtLq9vQb/xOPs/HzeOMKRiOGWLo9dbXcTotzACmB9rZ96mcqG\nEzym6opK3tCE08AUPKN3WAv6cQuBKU4CWLU7wDj9Z87cdOyXtaSu2KvkSslL+l6lbaXctZ3czp3X\nXkHy05+2GiYHDtBDfvZZrpjl7W7dSu95asrqvrv5K9o7wN2PdWGBbXzqKSq4tjbgd3+3st3PP8/7\nDAyQUSLiQKHA62UyNCYHDnC8Dw+zfffeS2765cuVq/ZEwmKESupaXiYEMj/PdojEce6cQTGiHysY\n6u6s1tlJuCoSISzT1MSVjosYAFbTXtBoqcR+UB8lEjSoMzM0bt/9RhrLg91oWF1CyWvHysIKFktd\naCg34Z7g91EuAQteEj8XeAIXin04mrsPM+hEZ2YOhwIvYVfpHDx3qe9WW9MDKCAwPW01Ex59FPjL\nvySt6vJly1TVUmfLFh5fLhuvVbVaBI24ylHKX4HC3l5+PzXF6wuHLpUsEUrbdrkFx1RmQFi2vFVB\nOFK84uLLQquglpI3VA1Su6tLRAsThcqNIWhC6Z8bTVd7XC9egwUwSqksvpab8qJkQFyM/moTX4kU\nqgGkINT8PL2DXK6yzMNNLHXFfp0iTvdaRbA+9anKCpItLVTQi4vAD3/Ia9x/vxkBwTotLcBHP0qv\nPB4n772ry9hxKo/h+1wdCt71fSrKlhbLtpTyS6WA//bfCMM8+iihmcOHOW5F0R0cpE7ZvJmfjYxw\n5fnqq1T8Cm7u30/lvm0b5/SmTZZgpHkk7F95LPk8Vw5DQ+aYujE2MeWk1JV8FYvxfgoiT0+bsp+a\n4n212p+aYrs8z+Co+Xm2o72d9d1VKhnFIjA1Cb/sIeFl0B4OYTobhV8uI+QFseC3YMWL4mHvCM6X\nd+GF0gNo8ecR97IYK2/A4+VfwsP+83h09Ul4WrIA5lXLwmQyFlgpFPiST55kJ8XjtG49PdyxWy91\nYMA84EymUnG7Bb+AyrR6wOh9jY1U8tPTtnRSJTqAA0PJQ27gUDuw6Flc5Ssl624EItjH89jR2nFd\nRfJlnV1ao7xioJIz78Isur/7mSAvZeXKMy8WaSTlVd91F8+bn+egloei7flcYyMvXsq/uoBbT49x\n+Lu7eezevfxe3shNLnXFfp2yVklft4jWz/6sKcX+fiq1nh4qu+Fhzr1z54APfIDJTpOTVGibNwM/\n//OVddH/1b/iWBob4z/VRnFzWe68k+P/5Emj9CqBbnIS+P3fJ748MUGj0tvL+ffss3yGCxfMWNx5\np+XRFIvAT/0UYaCTJ/nZ7CwNgXY3Eva9vMzVgauAQyHCPo2NNG4XLljcUStlxcBUT2fnTuBXfoXP\nePfdfGZlkCvh6PXXuSpuaeF8bm6mvhKXPpfjMx48yL+Hh9mvR/96DPe2DaNUSmHQ60YotYIuLwug\nhFwggWWvCV+MfwXlMvCHud9E3g9gAL1IYAV93jC2lYfxPB7BnvIF7M4OG+dUy3fAMi/1eyjEBuRy\nBPoXFvjwgmUEQwhSAEyBKnhbDSMIQ3bL9i4tGYVIJYTdbFiJSiAok1P30Gdqv6v4tWoA+PIEF6kC\nW0OD7SSuAIzLvKnmrLtYt4uDuxsStLQYTSoatQEpYzM1xYEgKEwFu6ToRQETJVWGUH0rTN0tsSzj\nMztrkNrkJD2ds2f5/m7QRhlvV27u1t2EslZJX6WrHzlCJT83RyhC++NeukRHormZ54+MkDlz7hw9\n91/+5cq5dOEC73XunNWj0RxTCQHRBcWlF/tN81xIwcAAvex4nNfdsoWUy3DY9vQtFumNixt+771s\ny9atliV68iQdmC1bzCNXvRZl2G7aZA6X71sAGeB3g4OWaCXmYCzGfgmH6Rgp0KmaNGIMAvinrSzP\nnTMDMjjI73fssDieig+++uoblSf/roRj31nGjmgIWwKz6E7MAsuTABaAIJANJlAMRrDLv4DfXvl9\nnPd3oNWfRQNWkfJbcNg/hB24iE24jKN4ALvzFwwmkPcnDqk8Z7FJlOk5OmoJTsPDxg6p3jWkmgro\nbmIhkTJSJTQVIHKhFRf6cEUeuusNu3z6aoYIYLVsBGssLloiVCJhEfJqhovb3rXETeqS5+x5htsL\n5tGGBa2t/DyV4rJWNW/icf6LRi3+IQhKXnn1qkGiQQtYtT7tHqXKfGNjPO4WwNnriv065WpJTK+/\nzt8/9jHi0RpHKjOweTM9cK0aH3igMshZDfVs3szvcznOI409MbyUAS6nUXNE2dXxOO8l7nhzM43M\n4qKV1RCPvLubinRpiQp+82ZeU8wdMf/EqNu3j/ffto3X0ubSmzfz3u3tbzaAYgkqThWL8bhIhHP0\nMw7SslAAACAASURBVJ+xrfSOHjVHErD7i4E2MMDjWlvN2Cpwmk5bdd2uLiAyO4FiZhU/Ge3G/qYc\nDuSPwcvnAfiAF0Cm1IBNwWn0F3pxorwfXf4kVhHBCLZgFQ2IYBUncBdakMIMnOisqizqb7c+iqg+\n8sonJ9lAN0KtbFVXkbrZW664Cl+dIuWay/EFANaeauqRxM3UlOddnb3pigr6CKbR82n/1urEHzFd\nqtt8JWlpqcwQjcW4HFtc5PW7uviyZ2aIWba00Gp/7nNvplUWCsBXvsKBOzLC6wq7lIfuBk0FryiJ\nQoYxmeTniQTv39hI7+EmyzKtJXXFfp1ytSSm+Xl6lcr32LmT82VwkONmbs6yy1V90S1MVQ313H8/\nd/ZSgp2uK70QjdpKU3PJXbEC5rCoXnwySWenWCQEo7LAi4uWDX76NMf71BSfeccOzhFRmFU6YO9e\n6qulJeqs7m4+Y7nMFa2Yc4JrtD1lRwfnilY1nZ3sq1SKffDAA8DXv17JcBEso2xTMQRjMV7L93k9\nEUz6+9l/7e2At7iKA5tTeH6+BRdXNqLHG0S3nwYiDSgjgIVCEp+N/hBHMu9HBAW8ioNYRgINWEUz\n0igjgBUk8Cw+hC/g/7VGaQkvTFgBSkW63d2LVlYqa8IIyhF8oOPcXX9cceENWUx5qaOjVHpa0oln\nfiWRJdTGAo2NlqghhayMTZdi6QYa9b0Gn3Dqa1HmLiSilY6YOgsLtkw9fpwDS0vSf/gHVtRTYLh6\nD+Xjxy1CLx4+YOnXriwv05OIx5mmrZow4+NcRt/knvlaUk9Quk65WhKTkpQAKhjNrcVFY4qINTU0\nROhmedmSgmrVq+noMIhBzoLmvFabLjNPc0UVWctlc+xCISt5K8LG/Dw/e+klzhN3J6anngL+5m9s\nE3CtBpJJUjEPHTLdomJid9/N9oyOEvpZXqaST6V4nWSSSvynfornb97Mvtq0yXZzOn+erJzJSZ6f\nShkbR32q1cnUFNs/Ps7fh4bYVypqePIk4G/rRVdrATv3BZGNtuBk6CCy8U7Mte/CUOxOPBJ7DTsT\nk3i24ROYDG9CCm2IeEXk0YDL2IwxbEIBQQxiO7rxxk4rUl6CShSIXF21UgMuK0OMDXfHIZUScMXF\n2t3v3IqPspiKGIsjq3Nd3F/egKvUtMNQPA78+q8Dv/VbPEZsEwVDNdBdTLl6Sz6XKqm2Sa6m4JV5\nGwhwkG7YwAGhSpGiToqBEwhwwA4Pc6moZevrr9uq4cgRDqilJdsFx41LuP/icQ6stjarh5NM0nDc\nAp75WlL32K9TrpbEpCSlxkaDMFIpfi9FNDbG8bdjB78bHCQlz/ffDPV4Hlekr73Gv7UZj9Lo5eBo\nj1XAnEHBNdqVKJu12JqboyJoRE6OEiClVOWYeZ45R3Jynn+e9/ngB9nOU6fYVhXTA2i08nmDVUIh\nPvvUFJXu6ioNQCLBPtu2jf32yU9aUuOpU6Yz1c5YjH2tGIawexlCESguXgR6/Gl0Z7M40LuEZKKE\nwVPARNNezBea0Za/hPnGrfjBQg5ToY0IRMJoLi1jsZRAyQsj4BeRRRQFhBHHCv4BP42fCvwYnm8v\nwG9oRP/KRhz192PG70Tn0jwOeS9iV+A8vGDAAopSgtpxqRo/FyZ8NQjDNRQqG+AWypJIgbnnaJAU\nClyWqdTowoJFtDWI1BaXD+7ia3qetZJ/riS6luq0FIu2HZ2CRxMTDOooESuft53c77vPaktoo5TJ\nSbZJQZZYjPeSUVQRJVeUSt3ZecvVhFlL6h77dYqSlD7/eXqYxSJ/fv7z/PzBB217RcWYTp0ySFLz\nu6PDytUKlunv59gS601y8CDHneakSmqL6hyNcn5Go1Y2GKikQqdSNAZqA2Dsu7a2SlICwOMuXDAm\n3fHjVO4PPsjjRZQIhcjuOXCA8+PiRePaK27oxq2WlmyLwCef5D0uXeLnly/TUJw+zVXL1BT5/l1d\nnOcqgSJoe3nZ6I1DQ7a71O7dVqohEACi0TIGX5wBmprgAYivzqM7MAX4wHZvCBv9cYyvtuNPVn4d\nq5kixnJtyJYaUEIIAb+AAHxEUEADCrgLp3ASd6M/sOefFJ4fjuB7Kx/G46v/E8awGWGviLFiFx4v\n/CK+5/8M/GLpzewWeZdryVpKXbxSQSPBoG0OoCxRYXwuD7yW8p2cpEL92teAr37VOOFuwlC1MdC1\npByF+71V0SDSjkUTE7aKGRnhwBUXdnDQ6Jq5HCGZVIq0rUiES07tL5lKVdJFAWPKVEtzsy2Vn3qq\ndrD6FpO6x/4W5EpJTLt2sSTIN75BR0qYtvJQVGNcZXmHh6kYN23iCrKnh7s3KdYj0X4BmrtyopQV\nqsJYUsyrq1SeKjymRB7AVtkiTahYoRR7Q8MbWZqXLXs8maRDdPSoVZL8lV9h+VuRBQYGbOtHwPjq\nMmbKlM3lTKm7O8iNj9PgXbzI+83MEKpR4pao0SJvSKe4+S/z8+xPr1zC4dMpxHa2oSGzgKUVD1Or\nLbg42oRzw1FEc/P4cOtFtKUm4UWBhvQMUl4bxlZ7EAnksIBmAD7KCCCEAkIoIY4VNHoFNPkrOFq+\nH7uD/UC5jP6VjThcOIje8kUEPB/wgUasoBXzpEf657Hbu1CpWGspj2sNNLrsFBXAEr1RFt/lueu4\naikWaREbG9lxgh7EW3fxcjdoW+21vxVv3T1XQaBymYNg40YOPGWcaeepVMpq8YjydOAAr9PdTQ8q\nnTYMVFikREavFkMnnTba1y3Aerma1BX7OovnkeHS3s7x9eqrVthL22aKXCBcfGrKHI+dO+kRq9qq\nsqdTKYsfxePmrYqFpv10V1d5/64uYvlaFWheC54RVRioTEQUG09zTXTF1lY+hxKyxP45dMgyaCcm\nbB9WxQfjceoNzTMZl2PHrMBZMknjAdAYbtxoexv8+Z/b5tfz80bEcNlqKkmgjX8AoKs4jh25QVwc\nfh/82RRWyy344dk2+AB8r4iZ1Sb85cgj2BTcgQ+2ngZCi1gOtyLuZxHxV5HxooBfRhkBFBFGS3AJ\nXeVZZIMJ3Okdwwx6/umBjq4eREtwEQFI+fG/gOchGUjjKB7G7sBQZUJTLQV+LUq9OktUAQe33K4U\ncS0Iplqmpohpy2tQ5TXFB9wKidUR+vUQ9UVbm1n5zZutyH40avVo9MwbNvC70VFOlE98onIDA225\ndT2iqP4tjq1L6or9HRB3Q47Ll7mijMfNS15YoOIXhTEY5DGqq75zJx2pWIzzdnqa2HZvL52Sri7i\n0BcvWp1z4ehbt3LVUC7Tg1axPaXYuyW2AfN+m5uNHdfWRqMC8LzFRd5H9MJg0Jh1ijk89xzn4uys\nQaS6NmCedk+PwTHd3baiUba2yiNolbB3r3HZlTejelZaZRNuscStv/qrMu4ILiMRasHuleM4md2M\n1XAEezoXUCwBr0wk0BAsoTE3i5FgN34w6SPiFdAVnMNlvw1euYhOzGAeSURQQA4NmC51IBgooS8w\nghgy6AyngVAYfr6As+XdGC1vQd57HxL+Mvr8QXR5M/DgI1ZcwkxjD1CoqvVS7UleD5zhFsmS8nWv\nJRG+LuVf6z5uzXTBOQrqKiFA6c3ySNxEH3fnoet5BrVT9xSmuLTE5Zwi4Gr/5CSXpoJT5IG8+CLw\n0z/Na33841wC/8Iv3PIe99uVOsb+DogCoAoaCvpobrY5MTFhzkkoRGXf20sY8cQJeqg7dtCBUO0T\nZTtPThq1FuCc2LmT9+zoYPLR2BjnRGen1Xt3KYJi8WgDd2VeA1SiojTPzlqW/MICjcnkpJX89TyW\nUVAhLsC46mLkaCcp3+fKWXugbt9Or3xiwsrtKut9cdFonImEMdVEcJA+EVFCtXlmZoBLQ2VMpBow\nnmvFy8OdaIlk8em7LmFn9xLm5gNoCy0iWMoj5BcRxwoKJQ9jhW5E/FUky/MoIIRNGEEAPpYRQx4R\nNCKHrd5lzORb8GrhAB7wj6KcXcWfFj+PJ/FJvIx7MeJvxmV/Iw7jIZz072JsE1F0Ficrk3eqlbqw\npGuVWpg3ULkiqK6TciXFOzrK41TKQKUQdM1wmEpVLBLRNLUq0MDSM1zLs7h8/UCAg004o3ZHl5cu\nTF2RfdHS5Kk884xdt7PztsHJ347UFfs7IAqADg5SSXZ12U5e4pILL/Y8Ks+WFstdGRkx6q3G/dgY\ns5oVjLx0ycptr6zw74MHgS9+kUFcwFbVcrjcoKp+ShdI4e/dS2WrsuNSrIUCYc90mueJSAFQ2b/8\nMpV1dzfb3tJiRAfF4u6+m9nYgQCTm1ZXzeiosm04zH7YvZvKfnqa15VOUdKkdJfm7+ysYKoywn4e\nF1LduDwdQVMphf6pJlyeCmHgcgOiyzPoCs4jv0olF1pdRt5vQNzLYDLThBak8FH8GC1YQggFNGMZ\nGzCJ7RhCAkvwQFjDX83jq4XH8HX8EjoxgyDKWEUDxrERWTTgIvowiW4soAWHij+xl1ktCmper7cr\nj1lK1YVdpGx934oHXUm09JHFdL14DRJh93qh+lswjxtEfSueu0oTaMeWYJDWXANDMJFeujJdm5pY\nU1rLzXicHsgtVDv9nZA6FPMOiHDndJpjdfNmKjpRDbu7OY5VNOveewk3LCyYQhfrbGzMFHggwN/l\nATc08Jjdu8kG+ehH+bvnUcm/+qo5XqI4V2eCKjNzxw5T8opNRaO2I5TquWh17srRo9QdPT1sl4K5\niuuJtbN9O1fKn/40jV5DA5OvlGRULNIZu/9+fjY+zn7p67O8H8CQDN+3VYYg4eJqCc2RLFb9RlKf\nUUSwXMDTF7eiLZpBKO9jfLkFqXITSvkAmpFGd2ABiVAOA343HsBR3IGzSKMZ9+EVFBHCMHrRFlhE\nq7+AewODCPglfK38GH7gfwRFhOChjCaksYwmRJDHFHrQgRm8hPvxG/h/sAv9Vx4w16MI5Q2ojoN2\nYxGTpFDgi1KnqELblVL6AWOniAurdrnWXxibcHd9pmzO6xG1SYXDymUmL2jgqJiQVhpqm8oMyxsK\nBKjkn3mG/FjAvPZboKbLOyXvzad+h0W486lTFhwMBKjcDh6khz0xwSDhgw/aeYcP8+eWLebVT09z\n/jY32+5Dcs5aW8nAUXndw4fpcWtznnCYq2x3lzLNVWWiimCxdy8V86lTpDY2NXG1oXpSbvVYOVES\n7W60vGxZ301NbF86TYO1YQPb9OCDtnfrT35izB2ttB96iFDSzAxXLhcuGPTU3m4ZrG4pb98vk40C\nD6V8ESuBKFpiecQLGaQiXdgaXkB/KYn++Xbki52IlZcQC60iVwxhBp0IAdhaOoVt3jk0+UuYRxtS\naEUJQRS8BnzAP4xufxon/LvwhPco5vxWLCGBOJaRRApZxFBAGE1YRgAlFL0ISn4Id3ln8CiehPc2\n2IA1RV5yqWTMD1WHE1YdiXCJBZhXXM1wqRZh5i5N0t2VyI1YCxaRdXWTtNx21rqHriujoCQn7fbu\nZrYqhqC2uXXiVRY0HqfX/qEP8fnjcXoQtwG75a1KXbG/AyKueyQCfPnLViP8nnvoHU9OMtD40EN2\njmqevPSSFdI6f57jOxYzZ0XKWU6OqpV6nhUhO3+efPCDB6nUL10y0oRojfKiNW8GBqjYd+8mtCL2\nWXc3A6Wzs2ynMlg7Oyvb3t3NYzZtstWJvPcNG0iT3rPHztHGHwsLDDB3d9Mz7+qyVURTE+dnV5cl\nHMmxTKfL8MtkrQQ8H+ViGaEgEEQZ+XIQTaUFhFDEqteICFJIFNIYzycQKBUQCRaQ9yMoIIIoMkgV\nmtCPrfhk8DzaS/Pw4KMRWZQQxv14FROBDfhu+VEsoQme52HRT2IVIZQQhAegEzOIII8lJLATA9ga\nmMSSl8AdXj+8EoBgqDLYqeWGmx0meqHghuoCVYApXJdaJY9XFRJVy12ptzoeuLaVgTxcbVgbjdrO\nUIDtS9jUxHuurNhy0jUaV+Lha8Bq/8J4nEs1l7olj14vXH2nZZui6FpGZjJ1r92Rt/3EnudtAfBN\nAN0AfABf9X3/j9/udW918TyysKq30puf51z42Mc4FufmOHdU//yuuzjOlV8hXFm1VUZHzcmZmyPt\nMJ3meI9ESCZobSXs0ddH7/0v/oIrBO2dKlaZKkQ2NHDePvuszaNEggpXCr211cqajI1ZwqHnEXo6\nc4b3HBzkc27ZwnZPTLD0cDXnX7kAX/wiYaveXn42Pc1rpNN8lve9j8ZkZYVMoJERftfaVEB5OYtc\nIA6/WEQpGECx4CMfCiEaLKBxeQ6rjc2IhovIejEE8yvo8mawggYslhMo+wE0IosiQsggihFsRKrU\njCTmkfaa8SH/OQx5ffADAZwu34kcGtGKBcyiE0EU0YplrCCOBbSiCcuIIosI8phBB7r9WfaLf8Tq\nOLi1VABT3vk8O98tTFUNayjQ4i67hLMvL1fWq1GwU7v+VF/nSiIDo+OUgqyMNMCCHJs3W6aqPnPZ\nOrUqPUqhx2JmdFpaOLBFdZIB0fNq8xKJWyRJq4twmAN0aMg8mPe4174epqwI4Hd833/V87wmAMc8\nz/uB7/tn1+Hat7TIc6+1ld7OnfSMjx5lUHR+nuySnh6bDy+8QA9861bbg7Sxkco1l6Py//a3Cen0\n9NDpOX2ayrCvj9fp6SEs9PrrvF8kYh51qcQ5pvK5o6NUsHffzRVFMkklK08/nzev/IknqMg/8Qka\nj0ceoQHbv58rklSK93/sMeALX3jzHO/v57NPT/NZXnmF+mF8vDKrdXGRHr8gq29/G/C8MkoreYTD\nQFs5hclCEwKBMoIoo1gKoVzKY7LcjpVME1r9VfQ1X8Z4sQfxUhrJQBHLaEYJHnJeFPlyEK1IIYgS\nRrAZWwNj6MUlDKEP2zGEv/c/g5TfgiBKKHgRrJRjiCKHMoA8IvDgYxJd2IBJBOBjCc0oI4LHGv4K\nu7LnK8vwup63imeFQuz06WnDmIRzCydTDRct1cTtVInM1lYrJpTJ0GKOj/Mz8VLdF6D2rBXMDYdt\nwwwdr8puKk4k/EzBDbf0gHutanHLCav9o6NmCLT7lCCZ3t7K642N8fdNm4zTv2+ftXF8nF4F8J72\n2t/20/q+PwFg4o3flzzPex3AJgDvecUOXDlLVZ9/85tUru3tld/v2EF++OKiKfaODtZikaMmWuHw\nMH/v7ubYnp62c/r6LKFPG8m3tlrOSTJJT9jzqKT37eN3zz7L43I5KutAgPNp92626ctfprJ/9NFK\nA7ZxI+fUoUOVBmxmhu3PZulcJZN0rLq62L6hIRqx5ma2eXCQBuLiRRqo7m62Z+/mZZw7BwQCHsK5\nZbR4PkrlIAKBMhZLCawgiiKC6C5PY1skjbnlBpT8ADJoRCvSiGEZfjCIpVICPnzk0IgWLCKKLAb9\nbegOTCOJNKLlLO4KnMYEOlFGCFFkEcfyG1Ufc4giiwyiKCCCLGLwAcSQw2PBb+ILhT8jgyYYMqUl\nCEYQgxTs+DiVfK1Nl8V4kddeLlv0WhxS0RpTKS61RketloRbjdG9pjDrauVbKvGFKHVfHnwoZNQu\ncWWrYZ5qmmM1pCS8XtCOVgTuJrvKjBONK52251WA2Pf5uTDKhQUOjKYmekkbN77nvfZ1NWOe5/UC\nOAjgxfW87u0ua9V47+qi8zE6yrGaz1uZ23LZ5q47bxMJKvWnnqIyTCToWe/cSaWuErfT05wP2mu4\nWAQ+8hEGOD2PPwcGeC9RHO+/3zJEk0me88QTXA2oFG9PD9syM8PVxo9/XKnET55kkPfgQVtVKN4X\njXL+ySABPDYapZLv7gaa4iUUshPY2JpEsJjH4jKwERPI+A2Y9jvRiFV0YRorgQS2BcbRvprGqteI\nst8ODz7K5TJWvCaslOPI+xEEUMQqSEWaRQdCPhVhDFnMBLrRVppFECXk0YAsGlFCAAEUEUQJjcgh\nhAJiyMCDjw2Ywv8R/SN8wn+Ktd6luGopVxXy0a4otQKb+lveuwr6SARt5PPm7Wvj2mSSL64WlbJW\neyTlMs9T8FJK1lXA2g9UGwu4lR4ltehXomdKecdiVgda9SfEhJEBWVw0xS6uL8Bn1IbWmYwdo5oW\n73Gvfd2e1PO8BID/D8D/5vt+usb3XwDwBQDYWl0/+T0uV6rx3tfHrO/Tp23ubtxIhasVezRKT9fz\nLNlHZXAXFgjpbN9OZT08TEXc1ERlD3C+7N5N2NTNMdmwwcoLaJcjyeqqJTCpsuWPfsTA7YYNVg31\n8mUWzNu+3Squ9vRQUW/caEo8k+GqRQpc/dLezhWK9EwkncLQbBPak6uYGcuj4DcgVw4j6adQQhBb\ncQk9oTlMBTci5zegvFpAa2QFP+OdwQW/D6/gPqT9ZoT9AgoIoowQ2pBCN6YwjW50gRusLqMB/d5e\nDGMLJtHDajF+6A0j4COLGIIoogWL2I5hLCCJBuRwqnwnpgpJHCq/gF3+iDmx1Qkzgi+kFGvRBaV4\nFWT0fVpPt8g9YAV/mprsWPHbw2FTuvLsJWqcq+ClwGMxoze6eLmuEQzSg1fAp9pQrIXvK5agc93S\nxqrEGI3a7kWxGAcKwAGvY1ZWODi0GYaktZWTQ/UyAC4332Ne+7oods/zwqBS/6++7/9trWN83/8q\ngK8CwH333bfeBLBbWh54APjSl6gURSfcvp0K+uxZKtz+fsawZmetjG4iwXHe2ck5rV2MursNAhU0\n++qrwM/8DPXCBz5ABa/Sv75vNeJ37OC1pG9GRmh0WlupdJXJKibdhg22Mp6aYrtOnKDR0P2PHuX1\nPvABGgPBwa4STySMcKH6OSr+pZozc7NlzJ7OIxgOoJgpolQoI44MCn4Yq2hEJ6bQDjaszZtHGQF8\nPPhjIF9GJpxAqtyBzuIcVvw4PHiIII8yAohhBRk0YhZJvIT3YwRbsIgWBP0yNuIyAihhDt0oIowA\nioiggCCK8BFGBDmU4ZEvHwkiEgbGSlvwuPe/4OHwsf+fvTcNsuu47jx/mffet9dehR3EQgIgKImr\nRIKibIVtydO2ZVsTanscPdFWuBXjnoloO+ju6PEHfZkP44j5YLc5YU+4w+5Wj+SOjg7L02ZYm90W\nw5BEiosIkuIGgAAKxFKFKtT61rtm5nzIl/fdVygAXEBSJHEYYFXd9969972XefLk//zP//C5ke8j\nwt5Ao6HYLON6TSlctO4KAlwFZpYNdFE8b6BYGEUDXujq6kC21rXscxRFt8i4pK5zws5JO3aOW9WL\nvHYnU+r7g3u6XlLWOf1i4ti1/nLXKyZL09QOLFcsFQQWIyxWxbkF5zOfubKD0kYbH/9A6L+8GbsR\nrBgB/EfguDHm3739W/pwmTHWoa+sWPhidHTQwajZtEVHbifslBrD0Ebyrdagf3GjYTnfa2t2Trtq\nV1cEdeutFtIZG7PQyKVL9u+iMFkcW0darQ5YKufP2/t0BIzlZXstB2m6lpSzswMhvrExe+0ss36l\nVLIQzO23D9pjOqliZw5Tn5qyz33xxYE8SLvd59V76+igA9UyhzvP8Zzcw1pWZYo2PhkzLJJQIjAx\ncSaZEK385i/o3SyZGQ7IWcomZV5bEleKzxw7MWh8IKNMhzohVSpELDOFRhKQAoIMjxRo0OUjvMwc\nO3md/YyzRlVdphw3qUjBhGzzuP4kh+otDnYfHzgflxkuJlSvVeDjkowOAonjQdLRKbrBYMFwBUnd\nrv1ynO67u1axWcdm7Bt3zGXLi7K3RSmD4uJUfGyjOajGPe7er6tWdTsRd2/OmTtsPUnsoNy7d6Dk\n6Gx+3kZFH6JI/I3ajZAUeAj458DPCiFe6P/7xRtw3g+FnTploZLPfMZqGM3M2PHsyvK3b7dzdHLS\nOr+77rLHjbHPdY2un3vOOuoss4uDMRYjjyI79nfvts7yhRcs4+XUqYEsQRQNHLGDQGdn7YLx4IPk\nDaR93zpep/nuRL7277dOut0eFDO5ytji3J2dtc+Nor6cQE3nnW+mpweJ1ZdfHoiIxbGFlHbv1Pz4\nWEYnlCyvSn64dIAZucSEWWOaJXpUOM0BznAr66pOqMrsV6chy9BGcCLdz0HxGi1GaZkGYzQpk6Dx\nyAhIqZDgI9E06OKT0aFOm1EMkjIJDbqUSfDJiKhwkd30qJMQ0GaEJTXJi+lhjFJIoxlPl3jq4q7h\nrZErJIJB9Hutqk0Hc7hko/swXWLTrcoOYun1BhrNzmEW9SucOfzevdY53yJUU3TaRcfsHnsjLfDc\n40XhsFJp+DnF8mFXcOSYQ/X6ILnklOHcvw+IEuM7YTeCFfM4IK77xJu2qTl9f8+zsISDJv7hHwbs\nERflOi2mLVts5OwKfubnB8Jc4+MDeLHRsFDObbcNnO3oqPUzTuqjON9g4IwdxdH37aLgVF0dfXhx\n0d7fxz5m76fRsIuDo2RXq3bhOX3anrdWs+dwvu3VV+HwzjbPtS+z/NI2VtQEd901QANOnbLndI2z\nWV2n1RGsJGOMyA6RmqKnAy4zhUYwxTIlEgIUz3Efh8wJGmaNFSZZlxNs1YtUCYkpE5iUBh0UEo2P\nRKEICFCUSIkpI7FRj0ISU6ZChLE8F1JKZPiEVMiQaDxu4ww7meM0+9mmF9mqV62yYzYy+LKLUfrG\nROlmyUwXyTsuvNNSAev0XBNs18kE7JfjnuOok86xFyP3IovFOdqicJBbTIpSoC756e7pWvdefMxB\nRi4J7Jg2Tvd9bGwA/dx++0BhztnKij32Aelu9G7YTRGw99iuxohxch+dziDKdQ54aso625WVAQX4\nwAHLWnHzJ4oGjT6+/W3LE7/nHuuQlbKvLWo7OSfvnLorgFxasv5kYsImSCcm7NzeutXOwY9+1N7n\n6KhNorqG09WqfQ/T0zaAdJDRY4/Zazxwv2bpXI+vn/k4Pzpmo3cp7c5jchLuuUuzw19kpK5pNzWv\nvBCzFlZpZVVWUusshbARhcInokaFmLv4MV/gr6kSssg2dsp5vjTydT5deorXxCGmGhEz3iqr3oyu\nOgAAIABJREFUTNJkDND9ClKNh6JGl5QADZh+vJLhk1AioozAINBIDMvM0GWEgJTRvkBYjZBZ7Ban\nJxvM0G8w7RphuMh7swh6oxU7/yTJQNPYwSlFLZXieYo0RgfZOAjE/V5UUnMRsJMAHR0d6E3kH3Th\nn6NgSjms0LjR3Pt01ype3+H9Ds8PgkEhw8bPwlG9vvOdD71q4xu1Dw//5yfUrsaIKWLOTqTr9Gn7\nPNf0plKxFMUf/MBGw+22daRO9VRKG7C12/bnzIydQ/Pzg+Bso08oynu7KD+OrUPfutXey2uvWQgn\ny+Ab37BBV7tt/cHFizYAc02xR0ctn91RtQ8dsu/l5JPrrPdK7JrskUSGaLnDq2oUISxUdM+uZS5d\nWGY1rrG85tNuBvhS0VUVMIZR0yE0FcrEBES0aXCIE3Sx7e8Oc4LDHOc39V9CXIGS4qu9f8Z0tM4u\nE9LqQyhpfwqUifHJEECViIRRPBQZHhpBSBWJQiCQfZfvkZER0GGEJ7mfEdpMs0Jo6qjsR6wzwhd4\najh5WOx89GY6DyXJIEJ3BTzFL61YnQrDTThg4MTdouAiACkHtEZXxekieJeULRYeOX2aIpRztWIn\nF4m4BKzrtOKSQw4CcvBUmtqt6eLilWyXbneQ4b8ZtV/Xbjr299iKHYiKQY/DnEslm5A8cMA68mef\ntXPo8GELsWzZYh2hS7462dwkGcz9qSnr1B9/fLjrWZEx5/yO2/07hUeHo997r4V7vvc9C5PccYdV\npTTGFlFt3Wp3DLfeahk3jg59xx0DLZktW+x7WpxXvPhcRrkkaScBUeaRnE6444imk0jiSLN+coHp\ncXjyTMZopYPOPFLjUSbCR9HVVWS/v1GHERICjnMHO5ljhDbn2MUa4/wmf4mIIg40TnGXeIlj6V1M\nyiZaC8ZoEpD0E6IWyGkyikDjkxIT4PYEEjB4CBQKD4PBQxKQkPYXiJgyBkGLMb5rfoYv8lWr7Ki9\n4aKgfgRrgFMc4CmOsMQ0MyxzhKc4wKlhbNMlSX0fg+CUOcBT6r7Ba8zTHDCvDb9mI/WwWAHrHndf\neq83KN0vNuZwFaLuHtwC4hK2RQxvM3MsF7ezcLo4TirUURedlcuDAb9t2/CEcIygdvvq17tpud10\n7O+xOSXIop6Ma2rxxS/aCPfpp63Tvusu6xyVsg7S2b332sh5ZMRCMy4Ympiwgc7Bg4OWdg4Hn5y0\n88c592Ig6XlWl8Xt/Ldsge9+1+4Uul2bTHXRehgOcgS33mqFzZzey+Ki9WFf+pItZHKB4Oxz67RC\nj4QKvqfxPEMY+8yeiBmZLlNbn+d8UmN6BvaUL9HqBSgzQdkkbJML+CjW9ShdqmgC0n4jDICYMh0a\njNBmgW2c4gAHOYXQiv91+q/5o/UxkuoYQVujleYwxzEc4hz7+mCM/QcCH40ktUJjgBSa2AT9xzIa\nMqSrKzlk06VORIVbeJ1lpiiRkKefinrMtRqm0+Wb2S/wBA8xxhp1usyxg6/wWzzEE3yObyGc0+zD\nFEYbvik+xxPqfsZo9l+zi69wh32N/ua1k11Fvnnxd5dodX0QXV/DYuWqS7q48zh4ZSOHvVgIAcO0\nSFeF5lpyuWq4jba2ZqOBnTuHj8/P9xMuN+16dtOxv8cmxNX1ZA4csI8XVRH/6I+u1EM/fNjCNC+/\nPFCBVMrOz+3bbfADgwQm2LnsOO7FCvd63TrusTG7kIyPW9G8uTnrXyYn7T0uLQ1keYWwuYDvfU/z\n63eeZOvBg2zd6uV5u4MH7Tx98UVYXdY8+aShmdapBBpfGjIt8HxDuBaxtCaZ6Kb88sfOc0HvQiYR\nIswY8cospiOsmnFmWGaaJUqMssA2fDIqRJRJKJP0NdG3cgfHeYojHPRmIU05OLnML8sneHz1MFOV\nBX7cvZWQGooS0yzRZByDRGLwSTEItrFIhxEiKnhoFNZVl0RKR1dJKAEagcnR94gaTUb5E/4Vqazx\nOf/vrcMtbIFOiYM84f0Ue/VZpLEOs0LMBE0eFz/FIXGGg/L0UPL0VLafJ7xPspfTeX/VisyY0Gu2\naTYnOcipzZOZRejE/e1+uuRqEaJx5cgbpQiK0XpR3Kw4oIvyB8UOL+5cvm+3mFE0HKE4830boRw+\nPBy1fwj56G/Vbjr2nwC7lp7MRtsMk5fSNq/YutVSJ9fWbGCzd++wqJjTfXGiem7H7Zy7EPCRj8Cv\n/ZqlORpjm0nv2WNfe+6cjfC73cG1HVFjyxa4eCbhcucMWxsNzK7dnD9vF5I//EO76Dz/POydaJGk\n4ElDJ/ZRGpSW+J4GlSDSHi3doLmU0fMTFsNRYi2omA4NBCtM06PKOGv2s0MjUWg8qvT6br3EGE2O\nc4hlpkHBEf0MBy5e5HP1b3Ao+wFPqk+yRsAx7mGKVTrUmaBJizEqxGR92KXJGHdWT7GYTlH3I86k\ne8iEjzIBSntoYxkIGr8f66dEVJhjNyF1/r35bUq+x89nf4dwMprr6zzlPcSY6SG0ZDHdxiz76NCg\nIUMm5RpPik9xkDMDB1ku85R6kDG11nfqA8qk9DzGVdMuYuL05towDt928r5u4LgBUKsN9FWcFIGD\nYYpRvjMHy2wmg1Bsx+d0YVy1qZMJTZJB1L6Zzc/fZMG8Dbvp2N9n5jD58XHLepmdtdFyvW7nyyOP\nwJ/+6UB615kxg56oxtj5VJzXS0vWgf/bfztYYJw42ZkzA/jT9SZVys69Ws0FXxrdUfxV+Anu+/Yi\n8YGdnJmV/NRPWVhpdtZCGedPhXh+CU9pyj604hIlqWiUFSYzpAnsHW2x01/kB0t30Ml8dgSXEXFI\njTYBKUvMsMYkMyyyg4uE1HPZ3AoxJRLWGe2zXuC/8uv8lfl1fqn9bX4m/D7PqHtZFuN8Wnwf36S8\nyEcJKDFKm4OcZsTrMav3ct7sRJIRpgEzXOZyMkNJxES6hDKCzEi8PgnSoPoRe0CXOj4ZGT6ZkfxJ\n9L+QCMPnwn9ABDbKXVKj1OjwYnoHZ9hPhR5lEtb0KOfNTtbMKL/p/b/WfSsFUcSSmaaOw5iHMfQa\nHZaY3jyRacwgm+6icFcQBINkrEuwOBaOi+CLEbgzN3jcguGOOZ66uwcHwzip3rGxQYPclZVBI+CN\ndjM6f1t207G/z+zAAYtjf/Wr1rE7yHFx0UbzcWzlf7/6VRutl8v22PLycF3Ia6/ZCF8IG6DNzNio\n/8CBwXMcFdPx6J0mk8uzdbuD6tHWuqEqDZdaVZ55fYa18xl3f7zEgQM2RzA5Cbvq65xfiZA1SawD\nlDIYwGiBMgKTZNziX+bO+hzn1E78LKIsS3RNlZKx6Pc4TegnLadYZZw1OtQZp8kKUzQZZYlpUgJi\nStToMsk6ntD8sfldHs1+hU+Lx2mYNvNyJ/NmJyllPsKr+GS0GeGM2kuLUVaZwCPjeLafMdrURMgY\nTVqmgRQSYUCiMH3WsOy7doNE4VEhYpx1MhXwuHyQQ+FxDqazYAwz2QI/5k7OsJ9JlnNoJSADA4vM\n9PMDr9kPWGtmuMwcO6gQYyN2B6doetTYyfzgyytCLkW+uhPgctG0S6w6J16kExa10F2EXmyLdzUY\nptjow1W8OcGwKLKRw5YtdlDdey98/ONvZgrctDdgNx37+8wc5j41ZR1ur2d/PvCAjcZ/+EOrgS6E\nTW66BKjjpR85YufY889bh75zp21F9/nPD/qlOnOwz/79ViDPNc5xLLhB4KeZLnfwUZDGtOIK+yYv\no9V2lpY8qw9T0shz88yMlxF+h5lGRClq8cLaHrJEsce/zJR3gTUmaHY8XtWHuBxW8ckYFStWmRGo\n0WMHlwhIOcxxNIKX+Civ8FGq9FhimjYjpJSYYBWfjNfZS8N00Ujm2IESHhViKibmE/IYX9P/M8tM\noXCt8lJa1IkoY6j2OS8ZHdPAM4rbS7Oc07vRuoTEkCIR0KdDGjQSD80oTWJKTLDOuGjxlDnCQXUK\nymWO8Cx/lf5TKkR0abDEDDFlyiR4KD7BMxZaKffxryzjCE/xFX6LCdaQQgACjEYjWGeCL3h/C7qA\ncTtIxNGdnON2lZ4w6JNYqdiBFEUDtbnx8UH07TquuKj8as69yM8vRvpKDdpvJYld6dPUUhhvOvYb\nbjcd+/vQnn7aJjg36reDnYvPPAP/8l9ajvujj8Lf/d0wPdJh+mfPWsbK1bB9B/vs2TMI9iYnB/pN\njvbcKGdUdEJF99jWWOdkdwcX12vMXIh4/vm6LV463yJIU4JyhWYYkPUSzl4eRauIMdrMtE5znlvA\n80hjj7ay7lTh0TSjVEXMjFlkF/N0qeGjOCxOEIo639efZj+nmWM3LcbQSMpE/ZhWUqfLRXZQo0NM\niUf1L/VjbMMu5tnLWV7jIBViRmhymS0ssYUA1QdYIKJKnS4NelR0j2mxQg2fmAoS3deADxAoPDJK\nxEyxSkiN+3iemhezVLoFdBl27+ZAt8WWi0sc4x5CapbVI6BlRhmhQ5sxLuvpIdndA5ziIZ7gcT7F\nuFmnRo8eNdYZ51P8gAPmtcGX59rmueozY4b7iRadrnPEq6uDxKkrVtq2zW7NlpcH6m3V6kBq2OlH\nu6jf8c+dJrQQdsGoVCwf1tGwHnzQLiKLi8Pt727aDbGblafvQ7tatSpYKHNpaeC8t22zGjQPPTSA\nXmBQcPjUU1e/jqNiuiYe+/bZ85fLdm7v2gUjIxo/i+ikJYRRLGSTCGNIlSRZ6/LKy5o0VkQXV1Gl\nCudX68wveozES+wvzxMkXZppjcV0kpruMup1uKi2Y7IMjcQntU7TeJziAM9xF6e4DYHmSO0lqmXF\nJ3kSgaTJGDV6lIkp9ZOYi2wBLG9llUmWmeYce4koE1HhZfMRlpihRIxA0WaUDiMEJJRIadCjREpA\nggBE4LNc2s4t8gKfbjzHTm+Rhuj0uzBlmH60Pk6ThDK3cYYtLNHLSkwnl3hNHOJr85/h33V/mw4N\nK4cgVgnIaMged4iTHOY4J8VBlOcPcckF8Dm+xZf4CjuZJ8NnJ/N8ia9YeqQUA746DIqOXMWZg12c\n8w4CO5BKJfvPCX+5Fdv1ZoyiQYKm0bDbNkfNcpWnrqK2XB4kZFxfVKf9srg4wPPm5uzx9XWbWb9p\nN9RuLpPvQ9uMGWOM5Y+/+KKda1/7mo24L1++/iJwNStSMf/wD21V6Z13wq/+qhUTO3cOknZCajRT\nXgsjfBpejA6glUq8qMNELWPpbI2ZoMm55lbmVip8lFcQJkMnHr/o/3d0pvl7fp6KiVmLplBGItA0\n6BBSIaRClwZVIttTlCUUkpP6AEvZBLW+aNd25kkocZFdfa6MoMk4l0jReHQYoU6XUVrUiMCTVFXE\nGpOA5OdKP+BldZi2GiWhZBOyUhGbgNiU8CSYRoPdOxLuWTkHSnFr6SgvNm/hgtpJmYQWI1SIuJOX\nuIWLNBnlv/NZQl3mgfQYr3ofZZw29S3jtFrjNNUEk+WQW8tzedWYFmXw6ojaBKZb4lS2n6d4YKiI\n6Z/zl1dy1rUeyPS6KtdGw26vHAWq2HgjTQcdwout6qrVgfOfmxt0K5LSOu21tUETbYfXO66slDaa\nr1QGPUy7XTtoXeFRvT5IuH5IG2G803bzk3wf2sZqVWOsQz992u62f/qn7Xz8ylfsfJ6ZeWOLgEuc\nun6kS0uDFnf/5t9Y6uPevfaa99wDva5me/c8r/QmKekY4QUYDFLCDm+RS50JxsIuzTVYq28FT7LL\nm6euOjSSNrfoVZq6SocqBljWk/go6iJkVLSomB5rjHGBWxAoqoSM0+Sf8nWmxSrfiD9Ly4xynIME\nJBgEHhllYlaZxEMBmnXGiKj061RLlEgtYVCI3DnWgoTl6m620SVJlliIxtDG4tSeMYzpFpNei1hN\nMnHpFHdMzzEZzvH4yu18Qhzj094P6Koyx7gHg6BOj5e5HR9Nhs8Ibb5j/gfuiX/M/urriPk1GmYH\nt3Ce88kuSiWYjOeJqRFS51BwBoHmm/FneYIHCgVJG4qYigPDyekWW8m5JGaR4VL8vVSyz19fH0T3\nTgCsXB449WJHFNcVxUX2MzODcuj1dav1XIwmul3r0LW22z7X3Qg+1O3r3km76djfh7axWrXTsfOi\nXLZqi7t327k5MQHHjg1kf6+3CHzyk/b8P/yhnbeuWtU99slP2lZ14+N92DRu8tKlCWSWMJ9O0TAJ\ngfAZL/VIIhgzTSoqJgk1cXmcWtpjl54jzeCJ8G7a1JlilT283i/r95kUy5RNSEXYiM45xVFafIxX\nGGedrSzyormL18x+qvQIqfZ7l9boUEOgGaFFhwYS23TaOnnQwCoTZHg0si4pVbayQBnNcjrOuNdm\nJR1hxUyS4VHNInwyJv11MnxMGDLuX+JB/QQHgpMcMk/wlPwES2qCXVzkC/w1Z9nHn/I7TNBkhDb7\nxTlmzR7AMGv2ssOssrV3nhE/JJUdquYCkWmghceE1+G+sfN4KExQ5wk+yV7OIkW/IMmsMsEaP+BT\n/UKsbcNyBPFZxOTkoODAcdLhyjZ5xgxaz0XRQE/GaS63WtaJj44OIuyVFevMFxct1OKokWAj8sXF\nQZWts0rF8mT37x/uSersZtR+w+3mp/g+tI3Vqt/5jk2K3nnnIDkKdo7u22ejc9d39FqLwDe+YX+/\n994By61SsY898QT8i39hFR2ffBK+94+KdGGFn969QrLc4dneIVpJjUopIY0NTT3CVi6xxVtFCMHE\nlmnOzQX8Y+c+yiIhxcMn7rNYGih8ysRkxkPRoGxWEUBGABhKxPSoci/PcZltnGE/W7mMRnAL5zjD\nbSgE64wDhhoRUyzTZgzRx8oVEh9NhZA2I4zQ5jbvLMIofCFIagHPtw+zltX7GjA12owQELNqxgHJ\nYfMKv1z6ew6krw4Sj54HTtkWwSJbeYjHmWINS0mUvMjHqJAAktlkF1vFRfbXLnFJ72VctCFc5bPl\nH4C0INTZZAeTqxcY06l16gWHLDAssI0/4Xd5iMf7kfxOG8mrJ/hc+DRC9znq1xMaK9IfYZjj7iRF\nHUzjHodBQwwnJlQuW9EiF+1vNJcoBcu13SgX8CFsX/dO2k3H/j61YrXq0pINojabT/W6TXL+yq9c\nfxFYXx/8XjSXaH36afjN37THjh9d5v49LyKXLsNOwaF0lb+5cB/rSQOymLppE8o6p9UYZVJuiS7Q\n5HYSUyLUZTQCjyo+CQpBQokGXdYZQ2JQSCqEpAR4KCSG2zjNFi7zFEeoEpIQMM46JWJe41BfvCvL\nf2YE1OjwMV7tQyN30KXODCuM0qJCSJ0uy94WPJOxEo0hlKZGj5SASZrESLr9Jhp3lE7ze/6f8fO1\nZ3itci//vveb/Ni7hSl1mUPiJWIR8B/lnZxWt1AhIRFVGrTZL85S92OaZowyMR1Vg0aVLZU2t+qL\nvNTex9bsPGGpTI8R1rsjfKr8JCeTcaqkYDTFlgeX2cIcOxinSUbA89xDhwY1uvwtv8LB1jkO1S8O\n67a4zk3FAVSrDRKsjcagOrReH1AkXWLV9+2Aqdcthu70KLZvH1wnimwUv1Hcy1mjYc9RqVyp3niz\nIOmG2k3H/gGwazXD7vVscPRGFgEXlG1mxUTrU08oxi4dR+q+RGS9Dv1GPp6OWUlGqRCRyRLGGMZF\nhwvnDGsEZMYjJiAgRSFJqfV53ykKjxmWaNPXWwduZZZLbOdnOMpHeRmBoEODgIQmY+zhPCc4xH7O\nsMwMBkGGzxYWucAt7GeW7VwCYAuXWWAHq4z1G2qMc0FpFBUS4xH1YEQ0GSOhS4OQCgEB06wxKjvc\nKl7nqPwZnlh/kMXsVl6r3sXWnR3Ss4JnuY/9chYjBC9wL9tZYKe3yBqTPKG2M1VN6JlJTNJkghXw\nfUSlzEf1LEkz4jb/FFkGO8uX+YL4Bgeaz7LG/1QoSBpE7LPsx0PRZJwn+CRVQsrEtBhjlQn+LP0t\n/rfmX/C0+YSFacwyR8wxDphX7fJQ1HNxVaeuMrReH+Dyjs6YJBaW2bFj0AEdLCzz8Y8PMPNu177u\nX//rm5DKe2w3P/0PgF1N+ldrG4V/4QuDY9daBIp1KxvNLRAASy8tUM9asLqS46lnOzPcUlvhYrfM\nErsxGOrEVLyYTEueyz5CmzoShY3XrVkZXKueWCZGEbCVy9zKGRbEdg6K03yMVzijb+UMtzLBGm0a\n9KjyMV6mySg1QmZYZpwmp7mVFSapEbKNS3lVqEEwRov7eZaL7OA0tzJCxBH5I9aDLayqMc6ktxCQ\n5tWka4wzzQoxZZqmwetqJ9uCZZ5Jj7Cju8KEXKAUdSipFjWteFF8DKEy9jHLZbaxR10gEDE102G5\nt4XJ2hqzyQy3+qcIU49ep8S6GuVX+Aafq/wDwmgo9avO0mS4IKng2Fv9rIOPYiuL/b5O4Peb/D3G\nz9IzdfZxljo9qwCZfYSH+MGwaqQr91dq0GO0UrERuZPa9f0BXn/u3IDL7gbMc8/ZSNxh5jchlZ8I\nu+nYPwB2LenfT31qWCbgWovA+PiwEquT33V9UX/3d8GkGTMXn2cu8qi4aB3oZFWSWNNMq+zjLCll\n6qZHTI2mrrPIFsuYwTBKm5gqHikJZXwUBiiRsIVLbGEJg2SrWbQJVbPCYU6wwFbWmCCiTIWQj/Ay\n/8jPUibGQL8NhmYHl2jToEGbVabz5tS3cYaP8DJ38AoHOM2X+ApP8ilelHVOJPtZZgqJokbIKpN0\nqdGjarnsJkZr+F50BE+kXIhnuDVYgKgLxhYxhboMlNjOAiF1ls0ENRNSEhlkilZU4rf8/0zVi1lm\nOzvVBb7AUxwIXkV4PtDXXe9LcA4VJDEoSIqoINBMsZw7dWuCNSb6QmgBU56V3qx4HSbiNR4XP80h\n7ywHvTODL991OEpTy3xxDXXDcNBj1dEho2hQNerMtelyUftNSOUnwoS5XjPad8A+/vGPm2efffZd\nv+47ZY889QhL3SsJ4TP1GR4+8vANPf8zc8/QS21Nfy2ocf/O++21ajP84tTDV9AUnfRvfq4nH+GH\nLy5x4Twsl59ByZ6V9shqfGLn/ZzqPsP6OpSW7me1rw8jBWwbneGXph7me6M/T9I6yWwWIryB3rhO\nGlRf+Seo9gxHnn6QFiPMsr9fmalo90v0PTQVYnS/TYb9B/qBP0Uc+BaVYBUzsoCUMQZBGGjGYxjr\n1CklPvvm7QLx4+kSW5eniCiztOMscaBJCSinkh3zOwmpcnl6BVUK8YIOJSIadPHQtGoxe5qGaVZ4\nenKKsBKCNGjtYZIR0AFi9TY49QvIp3+nLw/Q4oj4Ec8f+R69Wpdkx/OUgnUwkBJAWkPM30elO8KD\nTx/h7AN/y8lP/X+kgcW3bA9VhScTGqngCyd2Mae3E6oyVS/mY+kqf3AyQzTXYX09b8LxfzwwxvH6\nNKtMIjBMsMYO5jjZ/RT3Pv3TnHngm8R1uxAs7zhDK/AJyKikkvvmfc7uXAIM++ZmeG3HOqLUZQtL\n1DLB/XOAFMz0BA8fC3jkCCztGLfReRxBYLH1V0dj7lj3eWa7oVcvw8Q4CKiJEvezkxlT4+H//b9d\nF355I/PEPac4zotj/dWlV7lj5o78uHtecS5sPOdbtWvdL/COzvmrmRDimDHmuhoMNyP2TezNOOpH\nnnqEr7/ydcYqY/mxs+tnwcBkdXLoPG/mSy/ew3956b8ghY2w1qN1to9sB2A1XOXXPvJrPDb7GEdf\nP8rS3iXYCc9gB/sf/P0yybcTUm2dSyADUp0yUhph760fo7scEKT7KNchGF3lM/fu4eWn/htNPyFW\nAXIcfA+kD5dkSqv1MBdYY8LXGNkh01WioI2WGaa6TuvIfwIj+Luf8UAFBJfupDR/F1l3Gz4KWe+i\nkHT6jSoMgqy7HfH070B9BTE6jzKKtJRgPIURBu3HtD1JKQ1o1Xt40QijZ++ns+cYka5DaZnWxBqq\ntgIyIxGa09tP4EUjJPUWMg0YbY2Q+IZLYysIkaH9lJcnQEmAleEPvmSdiam0YHIWtef7sHw7TTTf\n2/EDujteQfsZyIRujicZMAL2HqOtJd96YAZPxiTVDk6syzLqDamEyDP85aFVdi1WqKU+exfv5Ghl\nkm9yG5/r/jkA3+RzPM4n+ccD/5lULwNWqyUlpklMW/6Qx/a8RHfHq+ggIQtijLRq8aERtI3k6HaP\nuBQhjeD07gWU1AgDc9oyay42AAxRAF8/pHhlGoRcxNOGsVhQUhn7wjJPT8fE0jBbU1SNhsSDks+q\n7vFrjfs4tzLL//gffp7vN39MopOhjzOQAZ/e82n+5jf+hm+99i0C70q8L1UpDx95eGguza7NUg0s\nH7/slxmrjLFnfA9HXz/KLxz4hfy1zy88z77GPlbDVfaM78mPn1s/94bmVtE2zs+l7tLQOTee+1qP\nvdf2gXHs1/uy3oyzLn6h3539Lp3EShgeXzrO11/5OvNtq6K3Y2QH55rniLKIbY1tVPwKt0/fzvnm\neRDkg9HZm/nSi/cghWS6bhsStOIW9ZKFP86snuHRE4/yzNwzKK1Y6CwA0IybbGtsYyVcYVtjG/VS\nnYXOQv4+4iwmUcegDBPjE+yfvp3Vfi3LQvsyrSzG39JCi5zFRyI7/FXrsySlS7RMGyoKSYjRCmFs\nmboxEoxESYPxQ/SOl8gmzqOrq4BAqRoyqQGCFIEotUEFmAPfQUyeQo/OEaJBmn6uUIAwJCXN2lgH\nsjLr++bwtn+XpNrCazTxshKq3AE/QSR1hMzQqga9bVBfRZdjWmMK7aXoUpR/vmqTxj2AddLCQNAF\nP4T9y7D1xxhVogdI7WOUxMjEtrwTBoSTv81AClR9BS0zkMNUw3xvLCCq9Li4bY6wFPPy7otoL+ML\nh45RpWQVZ9IneeDZhDAwTKyX8tcnjBBNH6dXv2xVbPwU7aXWqQsDRoKnMEYRCYWRBrcrN8LSMRGQ\nSJgbgbKCTEI9gV4AWhi0gFbJKm+eG42IPHhuyjrsTECatsCrEJLwaPQCzVKHE4uXCX0ozu7FAAAg\nAElEQVQoeaWh99xNuvxo/kd8+bEvc3z5OEorMp0Rq5iyV6YVt0h1SuX/rKCNRhtNySuR6YztI9vZ\n1thGN+lyI+16Dvta9tjsY1xsXQQYCuZGSiP83P6fu3E3+TbtA+PYr/dlXevxjVDH8eXjlL0yJa9E\nohKqQZWyX0YKyb6JfTTjJiu9FZpxkziL6aU9VsNVUnUNWskmdi2IpZf2GKuMMd+eZzVcZTlcRmtN\nlEW8evlVhBAkKuGlxZdoxk0EgkxbNxxlEalK80h9obPAXGuOIuy2Hllu48X2RaIsIkxD/vjJP6aZ\nNDHSkIkMz1RQIsaIDIOi1XgWTYjxEsCAtkrkCN1HZDQYm6jECHSpiy53BhEtPXTFA+MhutP2dVLD\n9ucxQQekGnw4AopMkNTTaBRGgOptx1Q7iKwKSQVGL4PIMEGIERohFHpsDvwMA6R+hgkGTv2aJkzh\npwJPoUYv2XvFDN6LNGDUhuf3f8poCPnezLQw9EopeAqtaghlkO1thI3L9LyMbKzFPz74HKrSpbPt\nDFpqu5AYCUIjRYaWetByQ7rr54R6jGc/TyOGrxv24fV2GbraPh4GfWFI93zTX/yk/XupYtPQggwP\nRVkZQqk4nywTmpS2jkFJosx+zp7wUEZhMCx0FviL5/6CVKVkOqPkW7ZUo9ygnbStBDICKSTa2MVQ\nacVqbxWwTrNoj80+Rjux8NPJ5ZNUgyphGvLY7GPvuHNtJ+3coU9WB7mG1XD1Hb3um7UPjGO/lj3y\n1CMcff3o0AoLdsDcNnnbkNN/fuF5xivj1Et1ukmXqqzmvxct0xn1Up3AC/Ayj7Jv2SFuYG+0785+\nl7nWHF9+7Mv5saOvH2Xn6E4+s/8z+ZYS7CAZ88aYrE5ycvkkvvRJdUrZLxNmIcootNYorVgJV9Ba\nI6XM7yHVKUIItNGs9FboJB200f3Uop04bgKh4MTyCbTReMLrgwagRYwWblttnUbmrQ9mvR4cHzLn\nkMWGx9zfXgZkmJFLgAQV2NNoH0x65ev6pjwFnk3KmZ3H7PustPphqHNq9n6NUBgvyxcKE4RFGvgb\nM6Hs/RW7FbkFbKMzf0sm7Hv2Uui31VNIlIc9JhWZn6GF7u8KLPNfaA/tK5QYvG/zpt/c8G0403Lw\njWb93zNReMzJv/eT4BpD3a+BiZGZdfsCgRD9Bd+d12iWe8v5a0VqHw+bYT4OU51S8kq5gxdC4Elv\n02Dpxcsv4gnLwmnFLcIsJFUpL15+8V2Jms+unc0DPmdhGvLd2e9yYPLANV757tmHwrEvdZcYq1hH\neWL5RO58wzTkYusi8+15Kn6FfRP7OLl8klbcIvACjDFM1aw27kq4QpRGvLDwAoudRbpplwvNC3TT\na28THZRzcvkkYBcOgPn2PO24nUMkLvIo+2W21rde85zOsWcmQ2cajUZrTTNuEsggj9zBLkCetJNA\nCLGpL1bGOsDMqL7DdlZwavYMgGcdpr4BSXcXHgo9HFa+GSs61yGHW3gjb+XURYjlnTBhILALla4t\nQb/JH6W2dexCYfzEftZ9eMh4GQiDcA7diP7v5ro7hKuZKQyJ4jk2O5/JnbwgJMNgOC6WKftlMmXv\nQ6Px8PrnMPlPUfgS8uPG5L8ro0hVijKKRCUoo+gkHZS2x8M05NETj3J8+TjGmHzMxypGConBkOns\nTUXtRZj17NpZ2kmbo68fZbm3zHRtmnPNc4yURtg3YQOuRsm2JHNO3UGizty5fhLsA+3YXZQMFo6o\nBlUWO4vUSjW2NWyH57HKGAudBTzpMVmdtNu6LKTsl+nEgy8qUxm+9Iei9FjFhGmIQLDcXUYZRZiG\neNJjrDxGo9Sgk3Ty88Jg++YcuTvWS3uEWUgrajG7OguAL326aRcpJKlOqfpWEyVVaT4hdMGBZTrL\nk6zOelkPpe2WOI/Si3aFI99oG6a70DfGqUsNStq2cn6/DdvbioDfxyatU6RxaWgtNX5fkdFF7WiM\np/uHAuv83+al8wj9TSx+pnCTxhhqpRor4SAJvek4w8bz11yChD2f6sshKKPopl3CLKSXWZiyl/aQ\nQuIJD096eNqz5+yf9pm5Z7jYukgtqA3tjjfLpbm5CXC+eZ7xyjj7Jvax0Flg38Q+zjXPsdBZIFF2\nJxgr+32kKmWiOsH55nkyZYOoTGesR+ukKuWRpx55R5kxb8Q+0I69k3Ry+KUZN3OnfD0sPPAC4iym\nm3aZa82R6pQoixBC8OrlV/OIIsxsxFV0pp70GCmNsH9iPwcmD3D09aOcXTvLueY5hBAsdhZpRk2y\nfmd6X/osdhZZj9btgiE8qkE1v0+daMbKY6xFawQyoOJVSHWaJ5qK8AqA1hpjDIlKEAiUVvlEuWJS\nXUdGZDMrySqJvkHJLKkwLmJ/O3DCB8mGNkgasgp4CTZ/IXPoya6L5oZ9bOoNdGboQ+74WqI9ie6P\np2KgAVeOMxete9JD97VrNnPy7rHicV/6VPwKZa/MRHUCgEudS/njDn5U2kb6qU7zOe92xy7SdjZT\nn+Hc+jmaUTM/FqYh49VhmYPxyjjjlXHu3maLrRyOfnL5JLdP306URXnU3k263DJ2C5+//fM/EcyY\nG+LYhRD/BPi/AQ/4D8aY/+tGnPfNmPuyitaMmuwa3UU7aVPxK3STbu7Uu0mXMA3zL30lXOGx2cdo\nxs0cv0tUgic9jDGUvBK+9AfbwL5T9aRHxa/QKDUIvABf+PzGR3+DP/i5PwDgs1/7bM5WcXRDjc4H\nb6Yz2kmbzGRkWYZAkOiEqaqFgLTWTNWmiLKIrY2tdmGI7YB0CaqiadPH271yjrVLIcmKOiHwBp16\nH08vWKaHcwgiK1HtjiGA3sgKMm6gyj1ADRJ6Vz29AT8d/H4jrIi5v99NGPCjAsTU/64NqGDwvUst\n0Nf7rG+AuStoYzBG5UOom3RzbHzjeLSvG0AvG49v5uCLx7XRJCqxpIGlV/O/R8ujpDrNz6mNJtMZ\n3aTL5e5lbp++PT/fxsSmi6Y/+7XPDh2Ps5gXFl7I59dybxmlFS8svABY51/yS6yEK6yGq4RpyEq4\nQqYs3OkICO2kzTNzz9xwXv2bsbft2IUQHvD/AJ8FLgI/EkL8rTHm1bd77jdjm31oX37sy+wZ38Oj\nJx7Nv2j3Jd297W5Ww1U+s/8zvHT5JVbDVXzp5847UQna2ASlNhpPernzHimN5KyYelDH93zLQulH\nLkdfP8qXH/syM/UZemmPalDFEx6+tB+32BBmFSP+4rYy/xvygZPpzGKTxuB7PhKZL1YCgZSDbWqY\nhTnTwF0zn0SStxSxa9SQvzdSEdVaGKkwQqP8Pg3Qy655nnfMPihO3dlm72dDlC6NyKPnd9o0lllj\nNCChkw7gyqJT38xhFx930XstqJHqFKUV45VxuqkNvoQQpDrFk3beOBqki8yVVsRZbNlgZjDWmnGT\nHy/8mHPr56j4FT6999OcXT9LM2oOQTMAp1dP89AtD3F8+XiehAW7039h4QU6SYfR8ugQln73trs5\nGh/l87d/nkdPPMr55vmhqB1spB94wVumOt8IuxER+/3AaWPMLIAQ4r8Cvwq8q479WjZSGslX7TC1\nX95quJrTqPaN7yPJhhMiF5oXaMdtRsojpCol8ALKfpk4szjbVG2KTGdsbdhEp3vduXWLyz2/8DzN\nqMm55jnWo3VLQezTD4cGuPDs4HbOWQimalPsHtud3+/d2+7m2KVj7Bvfx9m1s7YASvfZLxuSUpnO\nyMhQkcoHvCiwFTaLqK5umzgLx4rpZ9K8rEw9rtCtdLHlMQYjs5vIyrtoSmxYoTfmvDeae/x6z7vK\nS4smkUNQ4OB55ornFI+5BL9jefnCz3NXmbgyVzR0TSGp+BViFeNLH5WpoURtZjLWojW00Tx6/FGM\nMOwb33cF3XktWsvJEFEW0Uk7GGNzWKvhKnEW06bNa8uvUQ2qpCrNHf5XX/gq8+35ofus+JWrsuLe\nbbsRjn0ncKHw90XggRtw3htmxSz5n/3oz1jqLdGMmjSjZv4FteLW0GtSlQ59aYEXsNJbIdNZvjiE\nWYgf2gjfOXalbcLnfPM8YRoSZRFxFudJIcdQcaaMYj1aRxudR+vtuM2F5oX8GieWT5BkCZPVSS53\nL9vBrK0ySpFZULQiAyZPtG5Mam0WtfeLboSRBNojlQqZVfoOW6O8BPouHAzaS+nUI4zQSC1BV2yx\nzFtwGu97e7fecwFad9+8cBs9Qc51v6rlL+7TOd/qLkdzfbitb8UFQGLpjMYYO5aVQhnF5e7lKwKP\nRCUWEhVeHqG7RSLTWU4M2Gie9BBa4Hs+URZxdu0sj554dOg5URrlSV8hBEqpfM43o6Zl6GgLAzli\nQjtpM1Wd4ot3f5GvvvDVnG3nzCEC77W9a8lTIcRvA78NcMstt7wr15ypz/D1V74+pDmx1FtipDSS\n88edFb+kE8snAOsI23EbZRSBF5CohEAGOaTiCY9MZazH67STNsYYoizCYFjqLmEw+MJuIzU2qRmI\n4IpEUzHqdjili+ClkLy+/jpAHl24xcHRvDZz7m53ULzOkGnRd80DjyBUGXQAXoIEStpHyQzl24XM\nSr66Kst+okta2hsCjNRQ6jLkeT4s9m4uZBtYq1de+g3eiNSbE6GuZRsuVpUVejoaGscbx9rGGo9q\nUM2T/570EEr0aygMHp7V+ywUK7kdp8HCj4lK2NbYRjNqUvJLOTOtuHNwTJZ20kYbzbl1u3N29OWK\nX6EaVPNcVr1U50LzQs6G29rYytm1s4xVxliP1hkp2919L7GsnEdPPMp8e55XLr+Sq2V60kNpRSWo\nvMmd8Y23G+HY54BCE0N29Y8NmTHmz4E/BysCdgOue117+MjDV1ScPnriUSark9esFIuyiMAL0GiS\nLLF0w37VnBQS3/PBwGhllKnqFM24Sc2v5VF9qtN8q1vcgl6NclicCJ7wqAf1HOJxiVeB4PTqaRKV\nDJ6/4VOU9B39JqwENzFsn09BIC2sJITGCGOrSGUKnm2R5mclQCCNBFXCpA0CMuL6Bm2VIR75Jjf1\nTtt7nSzVclCNeq0o+Z20jeUJ8g06FSPe/H1vWDOcU4ers+nDLBzC3MM0HIzT/q0WcXIY3l0Wx60U\nsi/ybC3O4quOebDzSQqZM17aSTvPh4VZyGLHdnWqla5sDlILauwe202q0hwadaSLyeokh2cOc3L5\nZC734TD2eqnOcnd508/i3bIb4dh/BBwQQuzDOvTfAP7ZDTjvu2KuSGG+Pc98e55UpzTjJkor6kGd\nRrnBzpGd3D59O9967VvsHN2Z05+OLx/PMXdn+SAz9veNK7cxZiih6f4rPu52BGCj8jALqXiVoQjG\nXatom2GdxecZDMLY7alUKYGAUlpFez5eWCcLumgZUzFVJjpjrNXX7RZagK6sEr9Xjuta9l4nS50e\njPbfX1x80//fW8TanfkIMq5fIuWgF/sHCGMdvWN2bUy2bvzb7Urd7nexs5hLFlztenkVrDa043b+\nmCc9tNH4YjDPeslgV5/vyKVHN+nmbBsYMGOclbzS8GNeieXucr5LcOYUId8te9uO3RiTCSH+FfD3\nWLrjV4wxr7ztO3uXzBUpHJ45zOdv/zxgo/qz62fZN74vf95quEpmMir+oENFnMXUS/UhR1y0jUlL\n97swA2cuhD0msc4+8IJ8ki33lq9a7OEgm+IEuG4BSN+0VsQ6QwooeRGZZzDVLqFn9VBSndAb66KF\n2294YARClzGyd93zfyjNe3M6Qe+55Y68v9swbw0+S7WmEEBvOgYFgrJXHtR9IPNipFyg7BrjVjJI\nllb8Ss5Xd3j4phTLfgDlIJle2ssDH3ePNd9G6WW/TNmzcFEv6xFlEXOtuRxCAnKBv407/X0T+3KM\nfTVczX3IufVzOeX5vbAbgrEbY74NfPtGnOu9smfnn83L/ufb8xgMC+0FSl6J+3bcB4CHN8SP3cwc\n3rbZQN0IxRTxcXdcG50XO6yGq7gio5h4CDd3g/lahSEbr51fUxVZORJ0xtZQcLEmUULjq4BKViUW\nkHgJeDEIjTHvM+d1096gvfVdhi8l09RYkXEOVV6xS8UMsUXyOdJ36i6ZWgxUiguERudFeQ6KTHWK\nL30rmSG8nPZYZOAUAx4pJMZYSMeXlp7se7ayO9MZlGydSZiGbG9sH9KCaZQarIVrrIarNKNmzoYp\n+aWcTQfkhYA/Cfa+rzx9s00uHPWxGTWHqs+00TlWdrl7GU96jFfH6cSDsmPEcLGDY8dgyOmQVd/S\notzAlELmKpHa2ARqzoCBvNipaG7rl6qUTtKxinh9gaRrOW9XWAV20gwgHoPrtqGNsooAwpIaIqHI\ngEvVLK8+FBi0Bu0Ji8EDGK9Pcbzp3D8wpvusGFUGf7iv6hu1DE1TR6irwIDOik7WRceZzhBCUA/q\nxCpGKTU0L5wVoUoHYTr5X7cYeMJDInNevBSS/RP7Obl8krJfznVojDFkyib8tbY7ZF/6lL0yH9/x\ncS62LvLFu784pCDJmGXJ3LPtnpyQ4fRjXlh4IWfEvde4etHe9479enK9GytSb5u8LT/+8JGH8yIm\nF60DSCnzAohMZ7kz31rfyj3b7smf14yajFXGWOouoY2mE3dy7Qg3QCUyL05SRhHIAKMG9MNixZ4U\nku0j2+nEHe7edjcvLLxA1squYLgUrejsi/RMKSWZzijLAJlmts2Z9MjikLIytD2NNOBrg5JDu2kq\nQhEYEMYQKg8trTzBW9Xp+tCYFTzvY9ZvA2tPa7bi1NUMyOvkNq5Hb9z0NdAHu5HaR5v0TYqeWeli\nAZS8gNhEuSqjhe77UGMffnTWKDVyaYC4FeeOPtMZZc9KY8dZzHh1HKUVvbRnJbT70fFIeYSl7hKB\nDOy80Qrfs5TjTGeMlEdoRs286MnRIl0FtrsnY0weyMVZTDWoDhw5XCEkVoRWvvXat3KfoIzKHXoR\nV3+3MfWN9r537Nezt1LGWw/sCrx7bDfL3eWr4mZuUYBhbeZjl46RZINOMm6r5kqUXWVolEV5VZ1R\nJh/UbjFxgmIVv4IQgk7cQbH55JPYRcHRJHeP7ea15dfYGZdYlaFlH/hlelmYB2YCG7UbBiq8RthO\nP4nUaDI85aM9v7+AgJbJZpcfWF78soFpbQr8/VzUyj1lmNkitMAzgkw6nfcrn4OBcuqTecruNN6p\npKVzrm90UXu7Tt0Iqw/jR4NdkoyHP0+BjbYdXdH0CxKuc4+eFgjtobyMUlJGRhOEI0t4acVKHZdC\nW8eQs2quYMvntQ9CihzsUL4HqRh66sZEqDY6x7yduJ5LqDZKDVKdEmdx/jyJJNZxLuFR9spIJJPV\nSVpxKy8cdOqOziark1R9S6f8vQd/j9//h9/PGTFr4Rqx6teUKJUTH1KVDuXOrmf377z/qsHke4mr\nF+0D79ivZy6ij1WcZ7dTlW5Kf7raa4tiQgB3brkTg2GuNZc3zQDb3MJJAjger/snhaTqV4cWk0dP\nPJqrQLrt3mq4msM7BkM9qLPUW7K7AuHTSlu5Lo6nDCgNgbQNidsd3Mxzgk6lfgedQNtGC0LD4VaZ\nZdWgIzJAsCptNanx0/72ndx5Ce1Ram0lLXeoZAG+F9Gu9DB98qXut2vLI0rt9a880D2R2rPPk3Zl\nESpAaA8RRJaKifVhWg6crDC2EYTOQ+QB6zC3wmJgWRj0nSBvnBLoXmO4fuScVZDxKGAwQQ8T9PrO\nt08rvG7k3Y9yvQGFEO2BKiGMTbAbkSJNQDkapVtdBTlorHE1czVEWoDsQ2uZZxBBhMjK+O0d6JFF\nlB/1F5UYhEKYAKsD7wg/HlKCF0h8Kft0WcFIeYRYxTkX3egBDCiEYPvIdpa6S4xXxpmsTuasMrBy\nuU986YmhIKkop/vi4ouUvTLVoJonMJtR00KUJsMXfk5eGCmN5FIhbk560ssduNK2whVho+sihXGz\nxOj72T70jt1F9EdfP7opbvZGXlsclEXbLMI/tXqKY/PHqAZVXl9/PZ8AJa+EFHKIKpUqmzByWL77\n2xUo1YN6zrMdLY/y+5/6/ZynjzY8uXSJSlDBmITIZEiVEAUDx+5r69ArmaUzqf72fLmsSU0TlTQQ\nMsYzhqC5lbTWQaqAxARIL8T7/9l77yC7rvvO83Nuevn165yARgONboAkCIJJJAhSwQojS7RHVfR4\nxusd78jySpu8NeuZtWtqaqfW88/Olu1Z13hV47EtKng9rpGDEi1ZWaIAEqQpBhAgcjdS5/RyuuHs\nH793+r6GGGRTpoZi/6pevXTfvffde873fM/3F47TItfIEm2MEWRXaSda2FGI51royCWKPJzAQ7cK\n4FUI3SZeK4UKPRq9CxBZ2BqyLYu6Z+N70gE1Cr9Te9zMAJS2UDoUwqjBCV0SgcKLIkpJiFR3wpWK\n0Wwbg5RrbYWOLGABMeBGFltzGMO4tUJFDlpbqM6yf9qrxzvdWogDTM30yGlCkBA5xW1iVpXaOpdt\nA8/29yoQ1uiWxvB7r2M1e9AotFtF2z6RClBKroHvVsBPolq92KvThP2X0dkVVGTLCldWsDUz0oAX\nAaGNHbq0vDaJ4jAUJwkH5ojcuizlF6Zw1g4SZJYhvYEdeGg7wrZ9fKuNbQWozmpHQRRsERITBGDb\ndhzG25E+EnaCkezIy/rBXsm6Ewe7C/mBEJvbh2+n1CyRdiV3pHu2fLPtyu/aCnVsh+2t0ruu5W4R\nuW62nnbTL1vX5cctrfxt7S0P7MaGMkPMbc4BMoKbpeMybuY1dbOXqyz5Stu/Z997tkIsTW2Jht/g\nwMABSs0S75x85zbHrxkMzKIDtXZtK8PVsRzWamto9NZak1kvK8yjXCaKQhKuxz49QHp9g7fNOXxn\nV8B4mKFKm76yDx3H7UYK8DywLD60PohutTlVn+KbAxWCkTl0okLTbeDbLkppHNVZRSfMYFuaqTMf\noBV5pNtFlm//CqmWS9gY5Aa7aY+8hFftA69KrlTAPf9Blu/7U0JbZgRhYGE1HdzUOoEN1vzdqESR\nKLdM5FVlQQo/heuW8LQmAg6u+QzX4UMXFJ86mOKyN4rbSrPSt4kTeASZNRzfpZmuggqxOlKSRglg\nWQF2K0u21E9p8Dp2swBobKuJXRsgSFSwifDWp2g7AbYKafcsETjNziyEWKdGg7Zx1qZgYz+RV8fF\nRxeuQnYeu51FRTbtZJXQa23FJ9mhQ2hLyWK37WFXRgmcNplyP+XsEqFXQwUuSltYfgaFpkf73Fu1\nOXppjN9T76ASZsiUBmgfaNG0G4Sp8jZQB3lyIrCiCLeWI9Nskzj/Tho6hW+3abkBeFKhMUxtQORi\nlXaRrIziJxp4JBkanqXHC1jWZZTbg+9DSJEMPbyt/92cdp7k6O6jnF07y9XiVWHHnaiVtdraluzS\n8BvbmHHaffWZcdpNb/nFbu5bq7VVLm5c/AGmXWqWGMoMcbV4lQ9Of/AVf9e9MIZh+f/otn/0mvLt\n36a//7jsTQ/sP6qL/Ll/8rm/8zn8sDq+OVc/9JnbnCNhS/zsaHZ0y+N+8766/8edI3durY2adtNb\nZUGfnn8akBnCdN80RBHMneR9qXfyz/s+AFeuwFf+HCoVfrdVZ3XQ4+lUjblsXKwr7QNRAMrmargB\nUZueAZ93Rj1Uzt3DXTd+gWfH/oS5XA/ZkQyODXOJs6wSQEJRG6wQtgNajSb5Woqm59MqLBIRYrtl\nwkQbFTlEVo6g5waUx1A4KCJwS1jYhM1e8HOopTtwa70EOFiZNfTYM3huCTt3nYRVJRe1SIRQtNN8\nqTBDePEWPDVKkN7AUefxXZ/ADQgsH40Ca7tnIvBk7VRUCKGH3U5jRxEOIYkAvKai0RzGrvVx5M/+\nT1JugI9H7dijNJIVNoMM9ShFXScJcHFqBalTnlkApbC1T4DDxFKK6dowmaf+ezYpcP2+v6SVqRCi\nWB+bJZdYZSUT0MQjV82DgrDWT//SHtzL7wJLke+dpzLwAk03Iuc2iCyHklOmNVrm9sUys40Ufs85\nfMsnWRrELfXSzq0RYJOgRZ8qMlYTh2bK1/TNF1hr3Er45K+wqEdIqwaLapT2fR/Hz5RRClqRK1Kd\n3aKvkcdNO4yPrAJ1Vpsp6r7Gcm1cUqgoyfcvz6HTDrObcyTt5FaAQXc77W633QEIpn2/Uj/+4MwH\nX7F//e7J333FfvZqffLv+jtjP+5FNH4YUzfXSH4j7J577tHPPPPMG37ct4w9/zx89rMwOQlhCH/2\nZzA7C82miKXpNJTL4PvgdMb2oBM5k83Kd0rB8DCkUtBoQH8/XLvGZ/r/N+Yf/Mf03z4GwJNPQrEI\nmQxUnz5N79UXONp+HI3mAgc4zgOsMcAwKwyyRo4yi4zwAkdQhOxiHgtNC4/r7MLHo591KuSwiFAi\nRnCE5xlnnhGW+WUe5QIHOKEexLcSvBDdTkZXuMh+lhihQYoEza0aZwnaFNhEozjESxxRL4JSnI/2\nc4ZbmOYyh60zDCVKqGYDjWJFD3LKOsz1aJzd7jKH05fQkeaJ8H76Wef51gFWwj6yVBhSm2xY/eSi\nEmhNkySDrPAP+CoDrLPMECc4Rh8bWGhqZCiwyX3Oc8zqveyz5piL9tCjSszpvTweHqWtUozbiwwU\nIhpWmv2NFzn8tiQb2T2M99b5paMX+fRjfXzzcYflRg9p1cBrV2njUdcphlnmp6zv8kDqOU4G97DK\nAIP2JvfxFDgOv934n3g2PEybJJFSlFSBapSmTQJFRMIKyLlNVBQyNtjmY++d4+i+FR49cYDJQ1ms\n6amt5hZFMDcHH/kIzMy84a39LWVKqe9rre95re3e9Ix9x26yIICvfhUGO0z/+nWYnxeAT6XkeXNT\nvnMcsKz4dRgKSoehgH+tBvm8AP7yMuRy3F/+Go8+/zZ6Dw5juTb79sGJE5D0IhpNi7u9eXQbnucw\nz3EPt/ISz3InKwywxiDDLFEjhUuTCKcD4nkWGSHEIUWDJklyVHEICLAJsDnPAQ1LRI4AACAASURB\nVM5zgF42OMUhdjHPXfpZno6OkqWCRtHGAzR9bFAji4+Fj0OdNHWSzHCRCMUT6gGm1GVus84RRYqj\n1tMkVRvaQEerH2aFB6PjLDICocWgVeSp1mFCy2LO2stSOEALj342wFJkqZG0ffbpy6zqfnp0ibLO\n08cGQ6wyxWUuMUWKFhUy7OMyc8E4D9nf44PBl7jEfk6q+/GiOosMYaHxlCbX8rl7uMaQ/xLqxhDp\nw6OsVpMQhhytf4uX3LuZSi5ypVigaicpWDWOWGeptT2aqR4+oX6ZwEuwxDCbqo/P6l/g4fzj/IuH\nTvPJuQHSdosXlof5dmk3jchDo4i0Q4hLEHmSnaxCXhrcg5qAvAWrIcw+CdWq8IC9e4Uj/PZvw4ED\n0vTuvx+mp7fSJ3bsDbYdYH+zWhDAd78L73hHzLoBTp+GUilm6ydPQqsF7TYUCsLYKxX5TXb7kmFU\nq8LqEwl55HIwMBB/F0VMh+c41v42x5/ZT2FqgNziJYYGpzn/XJ0DlUvk6stcYJrnuJs7eYY7OE2K\nOi9ymBoZlhjGJmKSa+xjDo82C4wyy15cRMLwcXCRCpNVsoTYhNj0sUGIzfe5h+vswSWkQo6EE3E9\nGqMVJtE41MiS9EKakUsrkGvjEOESkrVbpDMulzhCO8xxpP0SNXIk3c5/tyy5bkCdJLdwjj69yfHG\nuzkVHqTSzuI4GlOItkSeKLTIqyot5Uq0YmSxl1mOcYLjPEiBItNcIEmTcxxgD9c4zCmOcpLp6DIK\nzQwXmNEXQMGQ2mCecfq9OmBBOSVTos1N6qt1xic9WFhguvx9HkxEHK/fzYw+R9ppUidDMcqzj4vM\nNvZRcdOcVoeo6zRhIoWtNP9x858QBWc49g6HE5fHuXu8xvHHI9otRxZqceSwzcjF86DYFEY+OCiq\n3rVrkExKE9nYgBdekEs2NgaHDgmPePRROHYMHn54B9x/HLYD7G9WO30aPv95kUiOHBGg/9a34Pvf\n387Wr12LAWu9U5kxDGX+3FVaAK3jAUApoWBRBAsL8p3WsLGBKhR4uPQnHOjp42TwflZfOMO7/0GS\nXxw6x9InvslaZFMhxzFOMMMFFHAHpxhliVn2scwQddLczbNMc4EVhrnGbgIcWnhbyeRlerAJOw5P\ncAnoYwOFJkWdAJtT9hGG9ApBMkvT76ERZWjoJBYaVwWQSJCx2jhBg2qUYoM+fBZoNRTrKoMOR+hj\niTPhQe71/4YRGigdgo6IsChS4BE+x7S6jOf7fFX9FB4+uaCER4USeZqkKdIDWuPqkBfUYUBzknuB\niF42JIoFh2FWGGIZu1MvSK57FM+aogiU4n71FI/qD9PbKmF5DrpcYaVnmsv1Ia4+28/7kxUutGym\n63U+WHgCr7LOX4TvZykaZcRa4RH78ywG/QTa5qnwbpo6gedoHBUSKI965PHJ5+/ij48+wcHREp9/\nbg8t38axNY4rp2NZ8SSuVoNTp+T58mVh4lEkzatUkkme48DUlAB+Mgm9vXD8uDD4HXnmjbcdYH8z\nWrMJH/847NoFX/ua0KTTp4UmZTJwX2edk4UFebZtiXipVmUAMEAShvI5CKB3FhPG9+U3Sonebr53\nXWg0ULbNzOxfMzN5Dn56CLxLMNoL9v8HapPf4V/g6uZWJJ9SFsOsMaxXaJBikVE0mhc4zGluZ5kR\nxphnkTHqpLEIUOJ27Tw7DLBCghZVsvSxSZsE9TABNjTaNrUoQU2nJdzc0viBpu5Dxg5J0EYRgrII\nlU3FT+E6DWxCxlhgkSG+wvuZ4QJ36e/TIE2RAg9ygmkuoCyH5WiIdzvf5Yw6SH+wRIUsl9iPh88q\nA1xnF3kq9KoKde1S6MwsxlhkhHk82iwzQpFefO3h4nOcB3iYL/Ez0Zc7MTti01wUth8dI9+u8kJ0\nO6fWj9BWHrvVAmfn8zx+8RcYatxPj1On3q4ymVngkHOdWpji2413sGIPsBr2UtE5et0KyvPAArsn\niR1ZbNQzfOGFPfzG+08x0tMglwpQrTbaS2BZFpYlzSEIpJlUq9Ikmk1x4Sgl4F0qyXfttoD/kSPi\nmrEsmSCePLkD7D8O+yHWJt+x/+rsC1+A556TXlcsSk/78pdlXryyIj0xDKXXpVIwOios3vPk854e\nedg2DA3Jd0rJ+0QiZvOZjLD4SkXeu64c0/PgxRdlDt7fLzOBv/5r6eGuy6Bao2blwWQE2nHWaZ0M\nt9gX2ctVnuVuamRI0MQlJEONAVbpZ41+1gmxaOPRzxo9FKmQJYvEJK92tqxZOfrZoKZTSM6sSCRo\nRUI3CQKLFh45q86+xDx3JM/jKp/hcIkRe42UE3CME/w0X2GdfpYYZZwFPsKjPGx9BdUZ4FadUSai\nOaasOdb0gETzoFllmBAbF58sNRajIfJUmeA6Q6yyzBAN0nyWn+cst+DjkqCFj8tV9vBp/jsu0BXO\n19EtZrhAHxt8LniYb0bvxFIRB/rXaaV6eHxhmrVWhnP2IZ7YPMhVtZf55BSJXIL+dINJb4Fle5QL\nzJCkKbv02xD40GwS+Zqc2+D52R4ol1ldg55kGwefXMLH6VQgBgFo2xYQ9zxR5up18advbsYumUxG\nXn/96wLwxke/+sOHr+/Yj9B2gP3NZs0mfOpTAsYvvQR9ffDHfwwXL0oPDAJh6vPz8mwklXJZemMQ\nbH/e2JBHrSbbtTslA8pl6ZW+L9uZnm7b4kgtFmFtTebjly7BN78pPdyyuN96mhI9UmwM5Fg6IkJR\npIej0QlS1HmQEx31PCRNnTt5jrt4nkHWGWCNSa6QpImPR5kMDj5VsmgUaeo0SHM1nGBejTPhLZJW\nTWwCImw5toaWdmhHDgP2BiPWCrPNMZK6SUMn2MfclhxlAWlqXKWTaGZ3HMuJBFpZhF6a4/oBloN+\nMlaDupUlwGOCq2Spk6eKpWCUJSIsqlYBy4IUDZ7kAXxcAhyyVgPXjshSI0GLMxzi1/gdPsM/5YJ7\nG5GyeUx/gE/yz1hRwzTIMGxvoIAN+ignBum3N6g2HMp1h7AVMpCscKk5zkrQC1pjJVwOerNUyLGt\nsJeyaNRhpZ6m6qeYK/dxoTnBwIBi10ADL2ERRiKl5HLy97UWQJ+els+0Fi7gujFvyGblOZORW33p\nkvCLej1WBXfsjbUdKebNZl/4gvSoffuEKW9uCmPXWubASoksY9vCtqOoUwCsM4YnEtL7MhkRRpud\ncqpay2864IzWsv9sNpZlQPZZrUrvX1+XkJhz54TVd7T5aXWRY/oExzlKgRJpatS35I3vMa3P8yU+\nwB6ucog+NimQpbb1F5M0mGccBfSxQYI2oLnOHjyaJPAJcRhRq0zri7xo3cut1nnunlzgyyv3sNrK\n4YceFj5JmkTAelRgPchzKdiDIuROnmfQn0cDp7iDy+yjhUeJHCV6+Gz4j3k48TV+JfNn/FHzF/my\n/x6u6VEGolXyyRZL4RB9usyUfY1nghxT1hWWw0GsKMBXDqv2MHmqJKI2ywyTpyRRO537cIMxVhkE\ndEesGePR8BCT0RxzTLKXWZ5SR1EqIhVVsGyLG+sF8j0RVrOGm3LYKDu42ChLkdItnq/NkHfGqUYp\nPNun362yGfRh6xJO0majnaUU5kj1uJSiBLbbw6+d/HnuvRd2WbDyrNxGwwXCUJrI2JhILEpJeGsm\nI2O9UjJhW1wUXmBZwgfGx+HZZ0WCeeSRN6hf7Ng22wH2N5NVqxJTNjoq73M5ETHNvLi/X1h0oyE9\n1AB1LicAbebM9bro84WCMO56XQC/UhHqZVnC3KtVAfJUKtbbq1UB/UZDev3p0/I6iuR4UYRyLB7W\nX+IA5zjJfawywDgLPMJfMs1FFDDIGvOMsY9ZTnCMNHWsDrvcpI8yPYyywAwXeIlbmGMvbVx8XHxa\npGjhWT6jLNNI3GCxOcSl8gALrX6IAlK6gUOLABuNohJlKUVpCmxgo1lngBf1bQyzzGU1RUN7LDJK\njgp5KjTx+GTrv+G7m8eYU1OMRIsEWrOsByn6Nm3t0IgcXCtil72EZ4Uk/DoNncQhoBV5oCNapHBo\nE3YShogiKlGGVQbJUKNJApuIPjZoRwn+Ix+lQJEVBlnUw6Sp0VYJEmiIIpoNi7xt4fhNIj+N7UYQ\nRqy3e1gNerklNUdC+ayFPfTZRRzl0w4sas0EjcAhlfBpVBRej8vUlIXjwFe+AnfeKYFUN24IsLda\n8pzLSeDVsKzUyPi4+OMbDWky5bI8DB9QShyss7Oxj17rnciYN9p2gP3NZB//uPQ8M791HHmfywkL\nX1sTmcRIL5YlPdJEw+Rycez6xgaMjIh4qpQ8Go3YmWqSlFot0ePbbRkATA82nrV6fSuaYxu4K5ix\nLjMTXojPv1PSFeB+TvKo+gh79NWuGO8GHj7XOo7IaS5xO6foZ50b7O6s6KrIU+UO9SLZqMpJ6350\nCa7bu2huJrDxQWt8bHySWEQkaXFInWNCX2GfusIJfZRe1rnElMTPa4s1BnEJ2MU8Dj5ZArKU+Ebw\nDu5wz5KlQpYK/YkKK2EfV9hD1q0xmKpwJHORJyqH6W9vMhvuwVUhKdUg0hYN0uxnlnMcYJpLEEWs\nMoCLj0LTIMUUlzqzhikaJHFJs0kf84wT4FDSPTh+SMv28H1Fo2eAzbWQlnao6CwL/gBLfh8DbpGM\n1ehIYJp3ZZ/hhcY066qHs60BaqTRDYtCssH0aERfnyzQPDIiYP2rvyr6+HPPyT0aH5fJ4eHDMTC/\n4x3irzeMfXNTmoPnSVPRMv6QSEjT/Df/RvYzPi4Szk58+xtjO8D+ZrFqVaJeCgWZ+xYKonNblnyn\nlGjqu3ZJsLHryu9M7wwC+c7Y5qYMEEeOCDX7/OeFtpkeWSzKa5CeePmygLbWwuC7QyOjKI7/Nj3b\ngH+36ZsiP/QJjlsPMa4XKehNLjDDaidL9SEeZ4IbANxgNxlq+Li4+GSok6dMxe6lorNEKJqBQ1sr\nHCI82gRYpGkRYJGjSjVKUSXDkFpmiktcQjInrzBJhKKNyzgL5DA1uTUV8gBUghQjnYUfcpTJqU0y\nqs6iHsNCM+xsMmVf4ZIaJkuFJcbIRlWW1TDjziKe8qn5GZqkcAiokybC7jiDq+xigee5kwCLJmnq\nZBlTK6R0g/NMk6eKj0sUKspBkvqaTRSE9CaqNFSGpxqHJQw0rVjybCwVMZ1fYbgnQfHqCFcqw9gO\neEiFx6ZOsVkM2N25He223PJPfxp++qfhN39TbjnAY49J2GJPj8gtFy5IcyoU4mZl27FrxvAC245d\nNEtLMlH0/Z349jfKdoD9zWIf/7j0rPFxYeXr6+KhSqelV2odhynW67HXa2FBWHmjEcssIMz9+nW4\n5RaJfV9aEqA3wO44At7ZrGSnLC1JLzZM3ffl2Vg3a4c49v0VTAEf5DE8K+Qvgn/IEsOMsMSv8AkW\nGWERKVlwitt5iVtJ0OoIMBY+Ljf0GK0gSRuHNQZwlU+Iot2V4OTSxMGmjUONNFmqKB1xmFMMs8wz\n3MMa/fi4DLFKnrj8coUcC4wRYDOvRxlmmZyqosIQFPRFa2zqHhqBy0aQYzo6RzJZ5Vx7H7uCZ+mz\nNuiP1rklfY37E89xrjLOY/77KEZZLEICHPrYYDfX+CbvZpVB0tSxCWiT5iV9kDYevWzQJkGPKrIW\n9WGRpNFW9KVCEmmXUiNBqBxsK6Lh5Vm2Mxwe3+D2/QnOLt/DnD9Bf1+TlNPm6oaH05sFLBbLMLgo\nt2t+Xm73+fMC1p/9rADvRz8qzzMz8Pu/L0FQfX0CzHNz4iRtt+PxHsRl47ryWbEoTaqvT45x9OhO\nfPsbZTvA/mawbrZerwvAnz8voG7bArK1mgD58rL0Ut+X95WKeL+SScn93r1bvvvWt8QBe8cd8LnP\nwa23Cqin0/Js2/LI56VXtttx7200fvAcjZBqnm9m6zdvDvwVH+BE+BB7meUQL1Ijw7d5F5PMUaSH\nNh6XmaaPTWqkiCixTj8FipxnmgYpGqTQKAKdRKGxiTrA6VInQ54SDTL0UJYoGGWBjlhmiBJ5buUs\n19mNj8tl9jOIxOetMIiPS5YqPgkuM8Wg2mBcz6Nsi3boMmwt878mP8MyE6yGHnd4L/Ex75NMl78v\ncelKA2moh8xYz3FL9BwnOcpL3MKL3EqeMjfYzQqDpKgTYVEjQy8blOihTpo0dVmqUFsC/JYio2qE\ndo5aK0FPoknataj7Dlm3xS1D66yV0qyuWzw724tr+aScNoOZBlfX0kRNHyedwHUFmHM5AWPblluf\nz8v7T35SmtLkJJw9K0FXb3+7cInZWeEIhYKwcnOrjaLn+zHgb2wI0JsyRTvx7W+M7QD7f81mygY8\n+aQw9L17hTl7nujpu3ZtD2NoSa1pEok4sqXZFNa+f7+ER46NSRTNU09JT/4P/0Go1dSU9Nrnn4+T\nlRIJ6bl1WShkq2d2Z6zebD/k/Poi05zgGJN6dstpmqRFL5vMso99zPElfgaNpsAmq/Rj49PPGvVO\nmYEmCSIkRt4mQAoJK8JO1cgmKbJUaONxiNMMsQIaVhjmEtMkafEAT3CegzzBUXKUucYEEYo0DSa4\nyiqDjHCNGlkWGCVNHS9os8gwH3b+lPepb6CcFPRGcbkGBfid2UyzKT6HKGKGi8zYs2gU/yn8MJ/i\nw7RxSdIkQFap6mWDJkl8PGxCWiTopcSwvcYiI/SwSiKfYM2H0Z4GCTei2bZplVw2mmmsRIUUMFsd\npNROo2yLwUJALgm7Bttc3syQ6gTnlErxBC6dlubUXWniM5+BD31IAN73JaLVTBpNOKNpEmbMN2O7\ncZqaS1KvC+MfHoZnnpFJ4Ve+Ikrghz4kIL8jzfzobAfYf5z2SvVejJ0+LfPiL35ReorJBgHpMSb7\no9WS3uX78txsSu9sNoValcsiuxQKcPWqDBTZrEgwzSZMTMh+TOx6EMRhjuXydvb9aqDezdpfw06q\nB+jRlS1QN2ah6aVIiga38yLXGadFkgmuUaSHEj2MsESVLGV6OpJLRJMEDn5nDzayIFtIkzTH+B4l\nevgCP0svG2wwQIMUt3O6k+a/Amhe4A7qpPBoM84CioBBVglxKKgKK3qQy3qSpG6y354lGVa52NjF\ntL2IGh2RmYwZBCHOyTcIpxRYFiqKSNHkGE/wOA+Ro0KZPP2soYAyBVyCTu0cm8PeObJUuBhMYdng\n5ApAH84AYEMSSHV07tpoHkvBchPUAGQsyN0ma4zunwIuCai2WnJ6SaknxtCQsHcQIDYx6hsbsq3J\nMl1clO8zma2/s61JGECHWHv3POENTzwR++BTKZkJPP20FB/9yEfgYx+LB5ode322A+w/Tru53ku3\nmSqNly/HoYxGL6/VZH5rQg9NmQBDlczvjfhZrQpAHzkiEozJHJmbk+9NvNr6eqybB4F892pAfrOZ\nXvlDAPuqM0qGFvjEGaqdRT/S1FljgFs5Sw9l+tlAo/kq7+UqewHVqay4ToMkIU4nMcnCJcRHYRFR\noMQe5hhhiXkmWGGQG4xTI8vdPMvt1kuyMhPwdv09DqpLfE7/Q9p47FLz7NOXGWSVVXuUy+EeKipP\nWyV5T/pJJrwlFoO9PNq+jWOc4eHhedTSYpxla9I3uwZFreFitJ+T4T38BY+Qo8xurhFiUaKXFYao\nkMUiIMShQYohVshZNZSy6I+KrKpBnJamdyS+xSDgeeCAjN3Ly8K+H3kE/vAP5RbadlwWIJ+XWz05\nKWPR8LAA7tmzMkkzES+ZTFzBsViMJ4dhKNs5jjTD7r/ZnbFqmqZJg9jcjCWaQEr50Nsr+/7935dt\nPvaxHeb+o7AdYP9xmQHu4eG43svNVRpXVyXTQykB43Y7zhBVKg4UNq+72XKjIT3SZJnatuyz3Zbe\n1GrJvsy+b9yIwxVNbZlS6eXP/eVMqVigNUlPr8TeHYdBr8S8P0jSaXY5WkX/rpNmnEXut5/h0eg2\nelUZFYXUyeAQUCHHJn3kKXXA3O84HRNb/D/EIkOVa0ywxgB5aoyyjEubS0gd9kPWOUbUSkcqgWGW\nmbKvgFIc5W+2ru1wqgyt68xF+7k3eprCkMfz6p1U19pknBJfbI4zs/5FDqSKcTioZW3zRWjgS3yQ\nx8KfYZNebjBOm70kaRJicZCL5K0aL+jbQStyVo2Ep+mzG9TTw3gJxUCtyUrTIzGcpr9fXB8gtzSX\ngwcfFOZtaqPv3y+vv/71OC3BbH/bbcKiv/Md2U82K82vXhdW7jjSTExEy/x8HEFrxnpT2dkAt2k6\nxo9vJpHVqgRqtVryyOXiKFuQ90rBl74EP/VTO9r7j8J2Jj4/LjPldYeGhLKcPh1/Z0D/zBnpacYJ\nqpQ8g/TO7mk+bAdRkzrYHYJo5tW+H+/PHG92dvu+6/XY4/VqZjxm5pimR78a7dKa+4PjlIIMkVbb\npAqpqtjL/dbTTNuzHFNPMqsnOcEDXGeCa+ximWFsApqksAmpkqFFgqgj7FiEnXj4PazTT4hNgMUV\n9lCkl73MUaaH54JD26pcRlpTiDbojdaJws45ReKnuBxOEoUBobY4sbiXzYqLFbYp+mkuNHbz+2ff\nTnT1Ohdq43wm/EV+p/2/SJkAptHABab5NL/EVSbwcRllhRYJamQoUeA6u7GsiGF7jYTlM+au8a7B\nl3jfvksUsgGRchh0S3xg9Dnu2bdBoSAhiM2mAO/ddwsYz80JwE9Py234t/9WoltMBKpSorzt2SNy\nSBDE4zBI82g2pam89FLMF+p14RmlUpzAZFi7qSdjqj2DfN7TE2+ztCS/yWQkSqa7eXRSH9BanKo7\n9vpth7H/OOzmxTAGB7ez9tOnZa586pTMdbvDDrQW4O0ONXw58DXUynHiDFTTe4x4WijIPtfXY6Zp\nhNHu3v5y1h0B4ziyb8NQDW27Wcbp6sHTwVmOqSc5rh6iwCbpqEJdSwncB60nmHbmUJ7Lw/w1nt/i\n9/if6WWTZYYYYLUDiD1ASJoaJXpxaZOhteU81Wg0FhV6yFInQ5VVBsmrCiN6kXMc4N7wGaqkeVbf\nyVUmGdCr7NNXeIYj7GOODA3qQZqr7GKURdbpo6+9jNVIQuDjonGjFs/XZ/iD+s9yRU/QoypkopKU\nCeDDHOMEL3ErawwwzvwWmxonyQpDRChcWrgEqChkwNrg3c53GY2aqOQoI6kykVbMlfv45ZGvoPbe\nxclDv8LKur11S009t5/7ue0JQKb87h13iMPT1FEvFsXFUijIZ9WqNAnfFybebMb5bzduCMPu6xOf\nfaUi20xPS1PZ3IwjbY0Clc9LM2s25djLyxIDb1h9oyFs3/flPHO5OMpmx16/vS5gV0r9FvAzyNoz\nl4EPa62LP4oT+4m27sUwQGjMlSvy+aFDAvrz80KTcjnpcQY8XTfuDfDycodh0UZchRhkjZZuPFhG\ng+8UvNqaU5uMk5c7hjkXs2+zn0olrktjNHrY7hHrII5C83Di6xzIrnGyfpjVRobxaIFHUl9h2r2C\nQhBIRVWW24Mc4wnOM80qA6wzgINPghY10jRJdWrItHEIsImw8WlRIOro8VWypGji0maNAYZYYZBV\nVhjgm7y7w6KXSFNljklStMhSZ5daYNxa4v3hX/MMd5GkiaXDToygJGEFnciWx6Kf5mHry1gKINiK\n8jnOg5zphDd2O4t3MU8PFa6ymyK9fJj/zH3e9znv3saJ4D42qiHpzSb1RIFiO82DAy8xoy6ilmvM\n/NP7ftAv8wp29qy4Z4aG4lvhurGbZnhYKj1/+9tya43kYti7qTCRTourZ2gonghOTEiUzMLC9qJh\nRoJJpSQaxrhxuuUgw/ZNRYp6PV7XZcden71exv514F9prQOl1P8N/CvgN17/af0E281s3Zhh7Wbp\nunPn4oJdWsfMvRt0DTO+GdiNxtsdnmDKBYShDBSmdowZJLoX3ugGdfjB/Xfr+UpJ7zfvTVnf7hlF\n9+9MpAgC7jPOLDP5WWiugKMg3w/JgqBK5ziraoiMqrMUjWEBYyxSI4OPQ5IW5U74Y4oGE9zgCnuw\n0VvA3yZBhRwDrGETUNR51ijQQ4k59tIgxShL5KmQo0yBMqsMUrPz/Kz1R8xYl7gQ7uGrvI8B1kBr\nKmGGVT1EU6XwtcMgKyRVhJXt1OJBQScAs0CJ1U6s+hoDtEiQoNlZA7bCOPMo5Po9pj/IQLDBT+We\nYUmNslb3GB+o8MjwKaZzS6j2oFxvg5A/hG1sSBO4OeIkmYxDEctl0dzTaYl4bTS2l+iPIuEcrZaw\n9slJAfnbbpM6cB/4gJQw+tKX4oTka9fiCJqREeEyq6vyWX9/XHIokZBI3sVF2W7HXr+9LmDXWn+t\n6+1J4Ode3+m8Bexmtm4skxGd+zOficsEjI6KaGoSkYzc0Z3G/2qJQEa2MdubQcAAsqkFYxi4AfDX\nioTpHky6Swl0H7PbzLG6f2NZQucGBmQG4XWCq/v75VqUyzJ/15pBe5N5xqhFaRQRylIQ0UlICimw\nSZMkNpoq2a3yAxnqnVh3a0vPrpCnTpI0zU5af44IiyWGWWUAh6CTFKSwInjSOsqMf5ZpLnCYUzzL\nXbRxWdFDNMMkgfLIUeGi3sc+ZtH1Birwt/39FA0iFLNM0ccmDj4N0lxmPwOsscYAg2qNeXs3GTdg\nwdvH2dQujo1d4ZcSj6GO3CG0mCHZ4cLCDy5r+CrW1ydN6+ZiXMYBm0pJc/M8eb+5KeDbPSkLgq1l\nb7nttjiK9vbb4eBB4SFrawLMrZbcUteVHLjuyB2zkJfxDYyMyAzAtqVLLC3FzejiRdHcV1d31lH9\n29qPUmP/ZeC//Aj395Nnr8TWjfm+zFsNEFarcWwZxM7JbgB+OWA3TlMjiRjrpmCVSrw8jvGqdX//\nWvHo3fTv5hi3m8/JHKP7dRSJyDo/LwOdkYHW1mKkaTYhCLjfeYZH/f+WFHXK7CGKnI47NKRBiiQN\nAlwibAY6cswKg1hEMhB0VO1lhgixGWURsKmS7gwIAev0YRMS4pCmjkvAi8UWMgAAIABJREFUuu7n\nO+GD/FL0Ryjgf+Q/8X/wmzzF22iSJEmLPquCHbaYZ4x5xjgTTFGhh2qnDsw+ZtmkQIYG/azRJEkL\njwZJ2iQ6SU81fk79JX1WkZVohNloP+XGAC9eO4g3FfG+a8+hDh6MEbJQQNcbXLywHfjMwllPPbUd\nDA8elLFgeVk4gufJrW63BXiNLj43J2kOxr9u1lUxE7EgEOC97z6Rb1ZXRaufmZHwyvPn4zQIkIHD\nnLIZ7/N5OYdcTgYFE065b598tiYTIh57TGYCPT1xVM5OnZkf3l4T2JVS3wBeboL0r7XWX+hs86+B\nAPiTV9nPR4GPAkyYhJi3mr0SWwcBvCtXpKUvL0uLNsKlWQDDALax10jb3wbM3Wn+Zp/1elzYI4pi\n1m6A3oC00d27KZ8JdDYLdmgtPbNW287gzfZmv911XINA6Jtx7jqO/H5jIw7TjCKmbSkY9iT30sbr\nrF5k4+OQpYaHOG3beFTJkaRGgEWZPhx80tQI8CjTwzg3yFNhnQGwbKwooo1HkxQW4VYWqAW0UbwY\n3coFpjnARaa5gN8p3pWkhSIkCKFBhixlFhjlv/Dz3M4Z+lhnk16O8yDr9PIQx2mR5Du8nXX6sYhw\n6NxLZbPi7mI1McVsOEEy6ZAY7yeoJ/m9jV+kPbSbh992GHXnka1L+dhjcOKLMfDduCGl+kEiZLLZ\nGAwnJ4UZT03FTaxQEIm+VpMQwz/9U0mZSCbjOPNO0uxWOOPcnLD/q1fj+usHDsRj9sSE3MZvfEOy\nS3M5eR8E0nyHhmTQKJdl4Dh6dHtzXVuTY/3Wb8mCXLt3y39LJHbWUf3b2msCu9b6Pa/2vVLqnwEP\nA+/W+pUpntb6D4A/ALjnnnteO4PlJ81eja2HobRYE1duolKiznI2pmd0A/lryTA3f28kk26QNpEr\nLyendN/KmzNNzCBhHKUm9i2ZlMHCMHcD5hBHzxg6Z2rcmHMzA4BtixZgImyUQkUhD6u/4nEeZJlh\nLEJ8PDxa9FBmgDU26SFLjQjVqd2eIEmDCAuHAI8AjWa9sz7Tbm6Q9jRNP00j9DqLZltY6K2wyZzV\noBl5/HP+H3o6wH2Wg1vFupp4bNKLAjx8Iiwq5LnKHpokGWAdjaLVkX56KdHHJh5tivQDkKJFzqpz\nKjqE1bbY7S5iBS60UhTSFmGkOL5+Cwf+898wc7tETV28KGx2cjK+vGbtUXNLuxeVnpsTRjw3J4CY\nTsttKhbhoYdE3x4ZEcDc2JDLr1ScrGRWRTSleZ96Cu69V5j7zbq9bcvAYsr8R5FMwCYmBOgrFWH9\nY2PCX2Zn5bzTadHY9+yR8zJq3IkTMiAdPizH6umRnL6RkR2J5tXs9UbFvB/4deAdWuv6a23/lrZX\nY+vXr0ua/+SkAF+hID2st1fAsFSSXmVi0G7O4+5aU3QLUF9ODoE4WsYAt5F3urd/NQnG9B7HkV6a\nSEivT6WkR5ptbtbVzXzfOHDNsc3xjBSTSMh/NwNOp6CZcl2G9Spvjx7nKhOkaJCg3ZE1UhzkPL1s\ncJkpPHwGWaNKFgefOlkCHHKqTlWL/HKVPaRaLSwHwlBqzNApTSClgWskojprDLLMCIc4wwJjNEjR\nJsEQywBU6YGtYSEiwqbYkWLSNHiQE7zIIS4wQ4YqAQ4+HgU2cQhZZJRNXaCNRyGqYlkK2iJSt3tT\nFNJtCokGJ0+lmTl9Go4c4eRJAbhuUDWFuZSS12ZhDFN0K5WSpCUj3YyPS2bq9LSsrFgoCLDfuCHA\nDnKrjN/eNLFEQlw/lcrL14IDAeUjRyRad+/e7c0znY7z8Iyj1UTfaC3yzdycdAPjgrl4Mdbi5+Zk\nZvHe98q+XnhBqm4MDUlljqNHd0AeXr/G/v8CCeDrSq7kSa31//C6z+onzV6LrT/1lLR+I72Uy8Le\nk0npPab8nmHAJqXPmAlk7q6F3g38N+vlN4O+kVu6t3m5GYEB2iCQ3m7m4729Mavvjrs3pX8NIzfn\nZrT97utjtjOU0wxAZiBzXYb0Ju32NXbreWaZpEqWXorczbOdEMeAy53a6i0SOESEuLTw8PDJJVps\nNHvQWAS4tICMbmIRoQGHEI1iN/OMsMg1JgixSdOgRYIsVXpJsEY/awzQxkOj0Fg0SQKQoY5NRIYa\ni4yyzDDTXOYkb6NElhIFcpRRAEr2rWlRDntIqEBWewozrJYH2Gj1cdtEharls5KZ2kLS1VUBz26r\nVmNXzM0BM+m0yBwzMy8vYZj97dsnrLm/P1bKzKQskRCQbbelGedyoqlPTMgtW1kRwF1aEnVtcFAe\nZ84IKGez0lyKRZF+5ubkWCbm3az98q1vxdp8oyGDVC4n+waJhZ+cFEno1Cn53PdlwLp8WerOmJLD\nb+W6M683Kmb/a2+1Y6/J1s0qSCZEwZTdC0OZzxoPlgHbm0G4u/hWd5hj9/evZd0OUCPXGGANAnk2\nyJHNbpdWHnxQepbnbdfguxn5zSsy3Bx5Y3LNTRGRMIxXbRgagnSa+0tneXTzIJPhJYZVnMkShTDH\nJH1scLf1AjqyeZp7UR1gz1PFsTX1toMUB9NoZdHSHkGgsAmJUKSoM8Qqd6nnmdWTtPDQWPSpTVo6\ngUNIH5uUyVMlQ4SDR5sIRYiNTYTT4eQhNi4+l5jiNs5ymFN8m3cS4HTUdZu2SjJuLYFtcTrqpax6\nuOpMseLn0MDwuEu0Zx/HN+GuGdD3SxDl4KA0lWQyvnzZbCyhFArbL2293gmqeQUz+xsaEtnjxo14\n3DacwRQLKxSEsY+NySQzDKV5X7okzbZYjCeW+/fHzdn3RWb5tV+TFZrOnpXusL4ut75YjCeOxaI8\np9NyHlevyn+7dEm2NYVIL18W8F9bi6Nue3ul5DC8tevO7GSe/n2Zqdx47Nhrs3XjyAxDabGmNTab\ncRSMAdmb67CYEnuGKZsW/kr2w+jynrd94QwTDml+a8TXyUmhXQMDQqHSaTl/k31iYueNBy6R2O4r\nMIOH+Q/9/fFarWbBzXJ5S2CdPn2GY/VnOF6/k4IukqZOPUpRVD08qL/HeQ6Q0i3uVC9wUe/HJmCD\nPmHHkaKiMzj4+CqBo0Js7RNg4dEGIjSKXczja4cyOVokSdBgt77OGv3USZGkSYEiNcZQRFvpUDYa\nB78D3DZtLBK0SFktzusD/I7zGywE45zQx2ioNCmaTLjLjGQr4HqUwhqrfg/z0ShWSpOMmtRaLtUr\nArAgaQ7vfa/ILJ/9rIBtLidMe+9emfBpDXfdtf12F4vxotIvF0Y4NATf+54w8GpVQNskN9fr8v3g\noNyedluOVSzKLf70pwV0h4YEpB1HXoNUbrz1VmH1//JfitQD8KlPSdMwIZGmWZv/GYYiNS0ubi90\n5vsygJ0/L2C/vBwnNZkInmw2jqd/K9ed2QH2vy8zlRvNOqRjYwJS3TY/Lz0kn5ceZRbIMPNfU/Dr\nZvmim+0aTd3INd0A3G3dUg28fDijYcjdUk539mqrtT3f3IQ6/Pmfy0pMAwPyP0y0zM2Dghk0KpWY\nAiYS8p+Vip3H5bJcr1pNBoxWC44cQS0s8LB+ggPRWU6272I16mfcWuSR6C+Y5oKsE6omGGKVw/oU\nT3C0U5/dItI2bYR1O1YbzwpwdAtP1+mhxCKjpKlSoEgLjxR10lQZYYU8ZRSaS0zh0UajcDvOUomm\nj2ShaqwOy4cMDTJOi4o3xEQ4y4X+B6lVRhhql2h4eWztMc8EtbxDT9LnUK7Gi/Mey5U0Ck2xnac5\nZ6G8iFzOYmEBfv3X4YEHBLz6+gTIKxWZ9I2OxqHt3Sn7c3Py+Re/KODcaMhnhcL2aBpT+Cufjx2l\nnifHufXWGOSnpmTb556Tc7l4UW75pUvCzruLjyYSkqS0sSHVG//9v4/LHW1ubq8ZY5qukYAaje1r\nqTabwvi1lvK/pvlFkQxQyWSc3GT4w1t5MY8dYP/7sO7Kjd/9rniSbhb8wlBa/f790hKjSIp3mLVE\nCwUZFEz8mam02M1yb67hbgaAlwPtmyNkusMRDfO3bemZ6+vS642Eks3K63JZPk8m5fy0jj1a9bpQ\nunxetllc3O7hq1bj5fSUip3BZqahdbyIpuPIOZgwiPl5oYlDQyitmQlWmAn/Snq/yYHH4f7ob3jU\nu5ter81D1SdBWzyt72GTXkk8spqEyqU/3yAZVFmrp8hELTI0mdYXGWORn1FfZlX3EeLwDHdRpECE\nrHU6yCqrDFIhh01ACh+NRbuzKIZDu1PYQBbHKIY5BoIKfWqdE5u38LbMCzzpH8Zz1lnT/TTbFrVy\nnnvHVmi0Xeotm6Tt0wps2pGF0prQj6jXra3Fsz7/eXjnO8VRuLoqGnS5LOD5q78ql+mpp2TiZCZ/\nAwPCH77xDSnstX+/7CORiKNpslkZm8tleX/33bEKePq03KY9e+SSnz0ry+MeOCCDyqFDon0Xi3Ie\nBrCNj35oSJycFy/GUTkvlwNnJmqGfzhO3FT6+uS75WVpZqbOjFH/ymX57xDXj38r153ZAfa/D+vW\n1Ot1eNvbfrCux/PPy3zS6O7XrgntqdXilHGlBERN9mh3MhEIgBpHqgHicjkGbZNV8kqhj7A9Nt2s\ni2ook+fFkorR74tFAXXblnM1pQ+uX4/DHmu1WCs35vtxWUDbjmcYphhJd3kDw957euKCI9/5jlyr\nYjF2thrZqSMET0cXOeZ/h+PBQxTUJveqZxmOlnlcP0RLJWmrBC07TapVIwhtZpw5doXX0GHEEoP0\nqtLWtRtmiXfxLb7Oe7nBLvKUyVChRroD6nXyVPAR3X6dAaLOwnwePv1s4CuPK9FuNuhhVC/R67eo\n2D2U6mlGCi125cuULMVsaZDBbBNlWfQkQ8pNC01IPbBJOBF+Z8zLZASwTp8WFj08LI8oEtnj3/07\nGVdHRiQUcWVFwPf0adGjV1flkl6/LqV8Dx+Wy2yiacpliSrRWgD0q18VoN6zJ5Y6nntObsMdd8hv\nTK32ZDK+paY5hWHcnPr7Ywa9d28s+5gmZm67qfFuommNuyWZjIOqKhU5b5OEnUjI/zY1ZxoNmVm8\nUh7gW8F2gP1Hba9VubF7m74+oT9790o4wPq6iKbptGSSmB5nPFhGzjDyhlLSU00GaqslvaHdjsH8\nlUD95ZyvprSvbcfAafYHMaVqtWKfwPq6oE2lIjOM8XFBENeN999qxamHRm83/+Pl5CUjrNbrcdhk\npSL/1ay92l03xxQWsy0eDr7IAes8J+1jrOoBjqjTfFR/giuJGf6v8H/ncjDJephlQt0g5bY56x+g\nolPYRATaZVJdIavLLDBGkR7eyzcpk+d57gAUb+d7XGI/s+xlmFVqZFllAFBUyNHCwyGk5WSIUPSk\nWlxv7+GK3sdh7wq7ptKk1xVLzn4awyn27BEN+upVGEsKGPtAOwLLBmWDFQiQ9fbGMoMJaYwi0ZPP\nnBHQ6++XfX3zm7LfTEZAvbc3ji1vtWI9PpOJna0mDv7UKXksL8cTtGZTwDKVksnY174WTx7X1uS4\nly5td1a225KRWq/DPffEDPrWW+X4s7Mxy/f9uFkbOajVip2o+XwcF2+aouEWhkOUy9IcTRmD++//\nO/bhnwDbAfYfpQUBfOITIiDu2yefdVduNKzdMHrbjpn7hQvxwtMGNA3jNqy2O/LFZGlOTMQFOFIp\n6RFKyT66VzswIH8z0HfLMGbg6K4ImUrFy94YvT2K5PwNUJsVFIJA/q+RWMz+TWkEk5xkzqs7Qubm\nczIyEEiP9by41HClIudlhNiuY8naoheYya3IR2HEY833cMJ9F/cMLuBsyqB1uT3BVZIMq0VG1Cpz\nTNKvisxbExyOniepyvSGJeacaT4S/iG/oX9LjuF5fNr/BVo6xTJDpFST3foGQ9Y6z3OEZpQk6zTI\new0G1Ro6meGGP0IQWaw3M4y0awwPJBgMZ1nPHGR01ObWW6UJZDLCA7rL4JrLlkrJuGaYuwlpPHtW\nHiYDNZWSx8qKNKkVuQzMzQmImqbTbApwu64At4l3N9EmBiwzGWHl6XQc+XLjhoB7oRCvxGjcJmay\naSJo2m2Rfsx/A5kVnDkjxyiXBbQLBdm3qSyZTss16O+X7YaG5PiNhpxzJhNLTMVizCVuu03+30MP\nSTz7W9XewpGefw/2wgsSJtAdYw4xazdAbdj6mTPSgo8fj1cGXl2N1zAz2Rs3L6hhdGrjOYJ4RSQD\nhvV6jAzderoBcTNnNmBvwhOM/p7LCZi6rvS6fD6eN5uyfEauMWumBUFc9Ntx4m1hu8zSHZ5pvGUv\nZ1rH0k1/fywG5/PS000tWZNmaQa1rn1eZJoTwduYDC4x45/hoHWJtm/h6jaJqE5LJ7iopvFUQMGt\ncklPsZLYDckkVipBgSInnQflfDwPslmO5l9i1F7hAftpeq0ykZOgVxXJ2nXydpV+t0xbe6zpAW7U\n+8g7dWwVseHnt5KvrGaDlF/h3DlhlnfeGY/rt9wS+7pNMNGuXXIJTCVn4yh99tk4OblbejDLry4s\nSDPZ3IzHYK3lN0a+WF+XprNvn7DoZDJmy/9/e28aHOl1nQc/t9HdQKOxNvbB7CtnhhpyKIocitRQ\npkhKtEhRluklySfLjhLGqsR0YsWKK66Kq+JKlfOVXEo+Vblk5QstiinL5TiJJFIiLVGLSY4GM9xm\n4+yYwWAGg72xo7F095sfDx6e243GALNvfapQAHp533vfe++5557znOfomjrodXZaMS8FaVULtaSE\n07qmhu6azZsJCmtoYAbp4cPkuAsCHlJPn+YpYOtW3juRsOCr3C/JJPsrD58OqrW13MCam3mv6mq2\ns6kJ+Gf/rMgnU7TYr5Sk05y14TAt9PXrzfL1rXbArPVUiqtZgUX5nBUxklIHLGAKWDqglOq2bVxp\n/f0WZZLyzs84LVScUtmnfmKQLOb+fppG0gg+DYBgFN3d5gydnqapJDjC4cPsZ2enuXWUoy7J3wjV\nXwVVEwk+l0yGmmflSmoqUQTrc37Fp6kpoLwcbam7UJ1OIuSmgbFRbEu/he5sDOddM0azcWTgUBMa\nQQkyOJ1egYrMKN6b2Yrq0BjGs3FEMxMYcZXIAjiZXY+2zCfQl6nDNKJIhuqxwZ1CPJbFxEwUP57+\nBNKRMgTRaTiXxWRQhu6pWlSWpFBeMo2ZbAQTsxFEZwJMl9Whtz+EcK3VKi8ro1ujspIKr73dDmpT\nU5w+s7PAwYO0to8d42uCGGrvl8Us6v6JCQ6PYvATE7yHQjNTU1SIYnNIp/ledbUVuAYMySq3yJkz\nueShy5bZQfXDH+ae++67bOfGjbz+Sy8R7hgOk5xMXHe1tWxLZ6dRCMmvPjjI38uXzy/sIfqkDRvY\nrpaW2xcJ40tRsV8p2b+fjsnmZirbri4qIElDA/DKK/w7kSBmKxajK8YvSiHl6tPoAmaqSLnLRBse\nNrzb9LRFlGpqDBjsc7cUSlbyrWYfQ6/Pj40ZFFOmm0hA/OxQ+eU7OoBHH+XK6+/n/0pqGhqya8Ri\nc9zlC4h86bqv6IyHhsyvoI1K2kgonLnTUX+6BnFMfBCcdek0JsOViGbTWBV0InAlKA1nkArKEElP\noQut6M82YlvofZS6KQygAcNBDf4D/hSz2ShqZqYRj0yjMTKMjmwV+sMtiJaNIRyPYNlAP5LlrYhW\nx3kwAhAfC2F0qgbl0TQ+1DSCmtpGjEXrMF5Sj/QIsLzZ2BblYolEqCQzGQ6tFHsmw8/NznJ6nTpl\nsfHxcW4EDQ1Wgk42Qipl6NJIhENRU8PHuHEjLet/+k+Jpjl4kNd85BHGxf3YejJp1wmHrT6LvHPj\n48AnPsEAq8rhJZPAE09wU3j7bftcezuv39rKvoqrfcUK3lN12xUnX72anzt2zFww09PGQyNOutsZ\nCeNLUbFfCZG1LuBtRQUdn62tuVa7LPbmZkvvGx/PBfBK8vlbgNxVJr92JMIVLl6Znp7caBMwn4LA\nl/zzqs7/frbr1BRXtPzv0jCRCFeuThey8kdGmPEi5S+zTsFQ9U/K+EIi/4E2p0jEaH5lTgYBtZTc\nP3JVOYcGDKCrpBVlGGTaZshhorQOoSmHjIsi5lJoqM6gfbIeWVeCqUwcZW4EFZUhBKkQkHVYm23H\na6HH8UT0J6hzSSASQ1lkConZwzgd3oDPJN5EW/Un8ZA7jp9PJzBethzR0tAHybpDPUA2Djz0m1Vo\nbqa1/eab3N+3bTNP0mOP0cLdsMGG4J13+IiOHLESdrOzVJRVVfx7ctK673vdUila4l1dRrQl3piq\nKiYytbbSEt64kd/p7qansLycClR0A8KNa4oLhSs8gIplK3jZ3Myf6mr2o72d02hgwA5ryaR58aTw\nZ2b4elMTTy2AtV1ImJoaOxloigwO5iJhCiVi3U5kYUUfeyFJpwkrWMj3my+y1uvI2IfSUq42lZEH\nqAx7e6l4d+/mDD92zKxcfQawTNNC4pe0GRkxX7VggQ0NXG0yy8rKzK+eP6NVD9UXnxxM//sFPny/\nuDjdFThVm9NpPpPjx2mtC+KQTtv5GliYRapQn/3zfjxOrfTww/y5807ruyAVVVXkco++i5FwHbKh\nMDA9A8AhPj2EzGwWM0EUDW4QlZN9aAh6MZBJEKyTmULvRAX2T2/EeLYc72E7wtlpdKRXsA1zhTRC\n4RBq0oNoG9mM/uEwVtWPY0fkbSRKxz9QzLEY3STV1YYcPXCAv9evtyxNgEMhpfjlL/MQtn07h/GO\nOyzFXpxw2ayhVoKAU2p6mtOss9O8Y3V1NgQ9PdwENm2ib3tkhNDIl18mxW8mQwV5+jRtDyFbBwep\n/GMxbkg6KUh04vjRj2j5v/IKvXDl5bQ7slm2T6hchXeU8jA9zSE7e5aKvqHBUhtOnSIGf88eTjnV\nXZ+ZYX8OHGDQ9cgRQwqpP11d7LsojF9+eWkMGze7FC32QqKs0bq6xetK5lvrEt9qB2iiKdLU28vZ\nNzpaGJK4FHHOIlgyrbZuJZbswAGCjx9/nGfrd94xN4gyQuWjF8mIRIHUfN4ZWcsy0WZnef/SUrOo\nJTK/APbX961ns/PZHS8kCtTqO3JCj4wAO3ey/2+9ZZQGKvMzd+LYUNWLB4ODeHN0G2rSfSgPZ1GZ\nHkM4lEWkJINQSQnSGYcaN4zS0Cyi2UmkXQQ9Mwm0oBcJ9ON9bCXhV6YEO6Lv4oPt0YVQjnH0p2vQ\nMNmJrtKtuKuqAy2xN3Fq46cwPhlCRQUPU01NVNT9/XyEO3fS7ZC/15aXmztB1rIIvpSBqWHS1Kmq\n4vsDAxZ3FzmXhlcbjXztu3dT2X3hC7yeTwNcV8fvnzxJRfupTwGf/Szv92u/ZvuxlLufVDQ0ZFb6\noUN0qaj0nmLbSrLW4SqbtX7NzLDNFRXcWE6fpqWupKaxMW42585ZbF0w0Pp6ooZPn+bPmjU2LW83\nPveiYs8XP2v0lVd45n3kkflZnhJZ6/nZEKWlNHO6ujibjxzhyj571gpKamX4hFlLFcEmBBZub6ey\nq6/ndY8dA37pl+jYrK3lChgby6UIUDuFlwdMgTY320rSyaW01LTR6CgdnZGIJQ1pU9AqFVbdp+71\n/fQXIzMz3IhSKQvmit0qk2HGzMSEmY4nTgCxGFyiFk+m38SmiffQFvkw+l0z7o3sR01sGq01kzgT\nWo3xQYfa0ATurujA2dEqxEIzWDHTDpdJY8xVIRWUYRyVmAzKcdhtwRZ3Em5uvCaCSpSkZtGdrcSP\nRjaipmwlXP80QrFhVK5KYPVqNutXfsWUybe/bdMiX9QlHdra2njoERJVICEl6IqlQTQDqZTxqqvm\nqOqO+sk8paX8/KZNvMfsLC1iv6LRRz/K6zQ3s+1BQB/6rl28t1L5S0vZBqVgzM7yFJDNAj/8obFD\niLRM3reGBjvwKUzT0mIH0K4uyy5NJGwTq6w0nEFdnfHlKID80ks84eQnewvSeTtQDRQVe774WaN7\n9lBxNzYWttwLWeuZDKNCd9zBFfL++5xtK1ZQGSrpR7wo+rmY86HP8Dg2RnhCKGRJTTKBfvYzmjbx\nuFHsiodGPC2hEDcG4ecGBvjdu+/mZ7q6rCYpwFVaU8N+CdGTzeaezf3+zM7mwhGHhtgecbcvVbSq\nKyqo+X7jN1hbLRZjv1esYDrlli0W7J0rsul6zmFjNo2NFWeAVApBOIKX3WfwZnIHNla/j/LoECaD\nMpxKTeMstqIu2w8XZNGFZegPGhFB+gMOmL+dfBJbIifwcOke1IcG8c7sNtRhEKsifYikJ/D2yB0I\nYxbNnTOoDHM/f/RRul0kO3bQLVBbO/+gMzwMfO5zdBmcPEm7or6efweBJen4HrzaWu5ndXWcYps2\nWULw8ePmSctkDPofj9OibWsj60VnJx9taSmHSAUuNmywE4RzwJe+ZElOJ0/mHjonJzlEvotp0yZO\nTylwhWiEt5+Y4H1aW9mmvXt5zZ4eS+eQR3F83NI2zp3jEn3ssdxpoqnX22ukY774J6JbWYqK3Rc/\na1Q+8dlZWu5+5qikkLV+9izPgfE4Z97583x91SoiYJwzN4aUupAwF6vcpaCV/Tk9zXurIPRbb9Gc\nmZmxU4IUrCJhinbJVJqctOIZn/oUN4dNmyzlMZPh6jp1ihuAIJk+WkfFNNQ3reRw2LJo8hX7Uvov\n30ImQx/73XcDf/7nfP7nz7OPtbVsm3wV/f25nPAAXJDFk+nvYVNpO3aPfBhHwncima1BItOL8tA6\n9M7WIR5E0ItmRDEzV2IvgwAhBEEGHViF0XQNEuExVEQnsD1+BgOZGjiU4u66AQykazE0U4WWCuK4\nJyepBGUlbtjA1998k0rLr2j00Bxkftcu+tcjER4+6uo4tYSOUbxagcPWVu5n999vNUXl5hCSVY+5\ns9P45+rrTSFrb49ELCGprIyHIcnGjcBTT5lLY2KCFnIqxWnx8MNU6n19HIaRETtNvP02h2r9eg7X\n+Dj/D4Wo3MX3PjPD6wPsd2mpfVYwzVRqftxdvPBDQ1wGKqbd2GjNt1neAAAgAElEQVTTczEK41tF\niordF99a7+w0bpYTJ3IzRwGz1nVGniu8jBMnOKPef98ItXwrHbCMS1nPcnUsxMxYSGR2yQRTauL4\nOE8LySRnd1UV+yQTT+deIXIEWWhutiwW57hC9u83ci+A2qS/nw5agZrzC3745feEs5vDlGNqiitT\nm50viyl1BYuVBvnii7xPby/dZu+8wz51d7Pv2ohEUi63ldw50VIEoRLsm92C/dN3oi46isZYEo3Z\nEXQGjTifrUJJKEDIOZSVZNFc0ovxTDnS2RBKowFKyyKYCtVga2IEJdWtOHVuOcprKxBfVYfqhkaM\nj3M4Wlo4Bfzjv3NMoJEbJL+i0be/zce6dy+VtTjYolEqKR2OZGdo3/xH/4jkXt/6FqewqIe01wr7\nrWGR/7usjI9282ZTgJpSR4+S11xSqO07dnBY1T8VwBA0srzcgsJnzrAvra3G975yJdvV0QF85jPA\npz/Nuqd/93cGpIpGacm3t/N5KMnanz66b0kJ2++fPLZt42d8CuNbWYqKXZJvrR85Ytjr/v75Vvuh\nQ5w5K1aYcjlzhgpIJsaJE+Zj7ugw8K+s9XxldrE4rETColUCPk9NUbGdOmWuDx9WqPqpAk+XlND9\nMjDAfoq4a3SUWPvNm3Pvmc0aLMHH3/six6h8AMK4K4noUkVQyupqbjr799P1cvYs36+qsmcu+IjK\nAMkHEAohmE3j5eDTeGnmMziWXocmdGMW5XjbbUNdyTBqSkYxEmnFmrLzGJqJIQinEYqUIZSOYEu8\nB2WhaWQbmzExE0FffB3uWB/HeKoZpcjQ7IRNAaDw8d+5whWNgiDXNTI7S7fE+fO8jtAxd99t9UIB\ncrFIAes04FvreuxTU1aFUAUw5CXcu9cOlNoH6+o4DQCDCua3/fhxupayWW4Er7/OIRAk8r77jAT0\n93+fy6WvL5eKv6GBh7D9+4G//mtjuJbNpKBvKmVVnvr6iMKprLShV8intJQbW3OzQUXDYZ6Ibgeq\ngaJil+Rb676lOjaWa7VrE9i+nUq0q4urY98+mmhStufO0Qk4NsYZq5xw5XRLlAh0MYo9k+GKF758\nZoYbS0MDV5r4VUZH7USglaRC2QraptM0dxIJO48rA0TmHWCujdFRvn4hKgBxrOsEMjubq+0uRQTO\n1srdvx/4T/8J+Df/hgHuZJLup2XL2Pbu7lxYxFyS0onwZuxK34+ZkjBqg0FEgylEs2mUI42BVBUa\nQr04k23BmVQDwsggm5nBeDaGhtIkKt0YxlMlqJ0aRLS0AsmRU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A0OWvk6Wa1B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"text/plain": [ "<matplotlib.figure.Figure at 0x1194e3dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots()\n", "num = 1000\n", "s = 121\n", "x1 = linspace(-0.5,1,num) + (0.5 - random.rand(num))\n", "y1 = linspace(-5,5,num) + (0.5 - random.rand(num))\n", "x2 = linspace(-0.5,1,num) + (0.5 - random.rand(num))\n", "y2 = linspace(5,-5,num) + (0.5 - random.rand(num))\n", "x3 = linspace(-0.5,1,num) + (0.5 - random.rand(num))\n", "y3 = (0.5 - random.rand(num))\n", "ax.scatter(x1, y1, color='r', s=2*s, marker='^', alpha=.4)\n", "ax.scatter(x2, y2, color='b', s=s/2, marker='o', alpha=.4)\n", "ax.scatter(x3, y3, color='g', s=s/3, marker='s', alpha=.4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
acnz/summer-prj	graphs/Inv100sN.ipynb	1	199041	{ "metadata": { "name": "" }, "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": [ "f2 = open('invdev100sN.dat', 'r');\n", "lines = f2.readlines();\n", "f2.close();\n", "k = []\n", "for line in lines:\n", " p = line.split()\n", " if len(p) > 1:\n", " k.append([float(p[0]),float(p[1])]) \n", "d = np.array(k)\n", "f2 = open('invpots100sN.dat', 'r');\n", "lines = f2.readlines();\n", "f2.close();\n", "p = []\n", "t = lines[0].split()\n", "l1 = int(t[0])\n", "lines.pop(0)\n", "for i in xrange(l1):\n", " graph = []\n", " t = lines[0].split()\n", " l2 = int(t[0])\n", " lines.pop(0)\n", " for w in xrange(l2):\n", " t = lines[0].split()\n", " graph.append([float(t[0]),float(t[1])])\n", " lines.pop(0)\n", " p.append(graph) \n", "f2 = open('invrdfs100sN.dat', 'r');\n", "lines = f2.readlines();\n", "f2.close();\n", "r = []\n", "t = lines[0].split()\n", "l1 = int(t[0])\n", "lines.pop(0)\n", "for i in xrange(l1):\n", " graph = []\n", " t = lines[0].split()\n", " l2 = int(t[0])\n", " lines.pop(0)\n", " for w in xrange(l2):\n", " t = lines[0].split()\n", " graph.append([float(t[0]),float(t[1])])\n", " lines.pop(0)\n", " r.append(graph) \n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.cm as cm\n", "plt.figure(figsize=(20,12), dpi=300)\n", "\n", "plt.subplot2grid((12,2), (0,0) , rowspan=8)\n", "plt.xlabel(r'$r,\\ (\\AA)$',fontsize=20)\n", "plt.ylabel(r'$\\mathcal{U}(r),\\ (\\frac{kJ}{mol})$',fontsize=20)\n", "plt.axis([3, 20, 0,7])\n", "catch = 32\n", "for i in xrange(len(p)):\n", " if(i % 2 == 0 and i != catch):\n", " c = cm.Greens(0.1+(((20(i))/float(len(p)))/10),1)\n", " g = np.array(p[i])\n", " plt.plot(g[:,0],g[:,1],label=i+1,color=c)\n", "g = np.array(p[catch])\n", "plt.plot(g[:,0],g[:,1],'r-',label=i+1)\n", "plt.subplot2grid((12,2), (0,1) , rowspan=8)\n", "plt.xlabel(r'$r,\\ (\\AA)$',fontsize=20)\n", "plt.ylabel(r'$g(r),\\ RDF$',fontsize=20)\n", "plt.axis([0, 20, 0,4])\n", "for i in xrange(len(r)):\n", " if(i % 2 == 0 and i != catch):\n", " c = cm.Greens(0.1+(((20(i))/float(len(p)))/10),1)\n", " g = np.array(r[i])\n", " plt.plot(g[:,0],g[:,1],label=i+1,color=c)\n", "g = np.array(r[catch])\n", "plt.plot(g[:,0],g[:,1],'r-',label=catch+1)\n", "plt.legend( title='Step',loc='upper left')\n", "plt.subplot2grid((12,2), (9,0) , rowspan=3, colspan=2)\n", "plt.yscale('log')\n", "plt.plot(d[:,0],d[:,1],'go')\n", "plt.plot(d[catch,0],d[catch,1],'ro')\n", "for i,j in zip(d[:,0],d[:,1]):\n", " plt.annotate('%0.2f'%(j),rotation=90,xy=(i-0.40,j))\n", "plt.xlabel(r'$Inversion \\ steps$',fontsize=20)\n", "plt.ylabel(r'$RDF \\ Deviation$',fontsize=20)\n", "plt.xlim([0, 40])\n", "plt.xticks(xrange(41))\n", "savefig(\"Conv100sN.png\",bbox_inches='tight', dpi=300)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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JssPb/V6Kw0dLqF0/eIqIJDglofqZjOQ0vlxFXuqykFHZxICUbDoDQTrDIVpD7YRiQUKx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\nT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JvxejUKTDqGV2kcqyxuk34EQRCE9tUmSaj4+HgOHjzIqFGj2qJ5QWjCZDQRFx5NSkYa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m2Pwm7YqTarCVjVOA0XUY5o3e8bIfVJ4xc0g0zuk8zoIh+X7DtInCs+0iGJiEgT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"text": [ "<matplotlib.figure.Figure at 0x7fb4d09c2d50>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0
DickJC123/mxnet	example/bi-lstm-sort/bi-lstm-sort.ipynb	5	20037	{ "cells": [ { "cell_type": "markdown", "source": [ "# Licensed to the Apache Software Foundation (ASF) under one\n", "# or more contributor license agreements. See the NOTICE file\n", "# distributed with this work for additional information\n", "# regarding copyright ownership. The ASF licenses this file\n", "# to you under the Apache License, Version 2.0 (the\n", "# \"License\"); you may not use this file except in compliance\n", "# with the License. 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,\n", "# software distributed under the License is distributed on an\n", "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n", "# KIND, either express or implied. See the License for the\n", "# specific language governing permissions and limitations\n", "# under the License." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "# Using a bi-lstm to sort a sequence of integers" ], "metadata": {} }, { "cell_type": "code", "execution_count": 1, "source": [ "import random\n", "import string\n", "\n", "import mxnet as mx\n", "from mxnet import gluon, np\n", "import numpy as onp" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Data Preparation" ], "metadata": {} }, { "cell_type": "code", "execution_count": 2, "source": [ "max_num = 999\n", "dataset_size = 60000\n", "seq_len = 5\n", "split = 0.8\n", "batch_size = 512\n", "ctx = mx.gpu() if mx.device.num_gpus() > 0 else mx.cpu()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "We are getting a dataset of dataset_size sequences of integers of length seq_len between 0 and max_num. We use *split100% of them for training and the rest for testing.\n", "\n", "\n", "For example:\n", "\n", "50 10 200 999 30\n", "\n", "Should return\n", "\n", "10 30 50 200 999" ], "metadata": {} }, { "cell_type": "code", "execution_count": 3, "source": [ "X = mx.np.random.uniform(low=0, high=max_num, size=(dataset_size, seq_len)).astype('int32').asnumpy()\n", "Y = X.copy()\n", "Y.sort() #Let's sort X to get the target" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 4, "source": [ "print(\"Input {}\\nTarget {}\".format(X[0].tolist(), Y[0].tolist()))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Input [548, 592, 714, 843, 602]\n", "Target [548, 592, 602, 714, 843]\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "For the purpose of training, we encode the input as characters rather than numbers" ], "metadata": {} }, { "cell_type": "code", "execution_count": 5, "source": [ "vocab = string.digits + \" \"\n", "print(vocab)\n", "vocab_idx = { c:i for i,c in enumerate(vocab)}\n", "print(vocab_idx)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0123456789 \n", "{'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, ' ': 10}\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We write a transform that will convert our numbers into text of maximum length max_len*, and one-hot encode the characters.\n", "For example:\n", "\n", "\"30 10\" corresponding indices are [3, 0, 10, 1, 0]\n", "\n", "We then one hot encode that and get a matrix representation of our input. We don't need to encode our target as the loss we are going to use support sparse labels" ], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "max_len = len(str(max_num))seq_len+(seq_len-1)\n", "print(\"Maximum length of the string: %s\" % max_len)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Maximum length of the string: 19\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": 7, "source": [ "def transform(x, y):\n", " x_string = ' '.join(map(str, x.tolist()))\n", " x_string_padded = x_string + ' '(max_len-len(x_string))\n", " x = [vocab_idx[c] for c in x_string_padded]\n", " y_string = ' '.join(map(str, y.tolist()))\n", " y_string_padded = y_string + ' '(max_len-len(y_string))\n", " y = [vocab_idx[c] for c in y_string_padded]\n", " return mx.npx.one_hot(mx.nd.array(x), len(vocab)), mx.np.array(y)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 8, "source": [ "split_idx = int(splitlen(X))\n", "train_dataset = gluon.data.ArrayDataset(X[:split_idx], Y[:split_idx]).transform(transform)\n", "test_dataset = gluon.data.ArrayDataset(X[split_idx:], Y[split_idx:]).transform(transform)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 9, "source": [ "print(\"Input {}\".format(X[0]))\n", "print(\"Transformed data Input {}\".format(train_dataset[0][0]))\n", "print(\"Target {}\".format(Y[0]))\n", "print(\"Transformed data Target {}\".format(train_dataset[0][1]))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Input [548 592 714 843 602]\n", "Transformed data Input \n", "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n", "<NDArray 19x11 @cpu(0)>\n", "Target [548 592 602 714 843]\n", "Transformed data Target \n", "[ 5. 4. 8. 10. 5. 9. 2. 10. 6. 0. 2. 10. 7. 1. 4. 10. 8. 4.\n", " 3.]\n", "<NDArray 19 @cpu(0)>\n" ] } ], "metadata": {} }, { "cell_type": "code", "execution_count": 10, "source": [ "train_data = gluon.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=20, last_batch='rollover')\n", "test_data = gluon.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=5, last_batch='rollover')" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Creating the network" ], "metadata": {} }, { "cell_type": "code", "execution_count": 11, "source": [ "net = gluon.nn.HybridSequential()\n", "net.add(\n", " gluon.rnn.LSTM(hidden_size=128, num_layers=2, layout='NTC', bidirectional=True),\n", " gluon.nn.Dense(len(vocab), flatten=False)\n", ")" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 12, "source": [ "net.initialize(mx.init.Xavier(), ctx=ctx)" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 13, "source": [ "loss = gluon.loss.SoftmaxCELoss()" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "We use a learning rate schedule to improve the convergence of the model" ], "metadata": {} }, { "cell_type": "code", "execution_count": 14, "source": [ "schedule = mx.lr_scheduler.FactorScheduler(step=len(train_data)10, factor=0.75)\n", "schedule.base_lr = 0.01" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 15, "source": [ "trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate':0.01, 'lr_scheduler':schedule})" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Training loop" ], "metadata": {} }, { "cell_type": "code", "execution_count": 16, "source": [ "epochs = 100\n", "for e in range(epochs):\n", " epoch_loss = 0.\n", " for i, (data, label) in enumerate(train_data):\n", " data = data.as_in_context(ctx)\n", " label = label.as_in_context(ctx)\n", "\n", " with mx.autograd.record():\n", " output = net(data)\n", " l = loss(output, label)\n", "\n", " l.backward()\n", " trainer.step(data.shape[0])\n", " \n", " epoch_loss += l.mean()\n", " \n", " print(\"Epoch [{}] Loss: {}, LR {}\".format(e, epoch_loss.item()/(i+1), trainer.learning_rate))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch [0] Loss: 1.6627886372227823, LR 0.01\n", "Epoch [1] Loss: 1.210370733382854, LR 0.01\n", "Epoch [2] Loss: 0.9692377131035987, LR 0.01\n", "Epoch [3] Loss: 0.7976046623067653, LR 0.01\n", "Epoch [4] Loss: 0.5714595343476983, LR 0.01\n", "Epoch [5] Loss: 0.4458411196444897, LR 0.01\n", "Epoch [6] Loss: 0.36039798817736035, LR 0.01\n", "Epoch [7] Loss: 0.32665719377233626, LR 0.01\n", "Epoch [8] Loss: 0.262064205702915, LR 0.01\n", "Epoch [9] Loss: 0.22285924059279422, LR 0.0075\n", "Epoch [10] Loss: 0.19018426854559717, LR 0.0075\n", "Epoch [11] Loss: 0.1718730723604243, LR 0.0075\n", "Epoch [12] Loss: 0.15736752171670237, LR 0.0075\n", "Epoch [13] Loss: 0.14579375246737866, LR 0.0075\n", "Epoch [14] Loss: 0.13546599733068587, LR 0.0075\n", "Epoch [15] Loss: 0.12490207590955368, LR 0.0075\n", "Epoch [16] Loss: 0.11803316300915133, LR 0.0075\n", "Epoch [17] Loss: 0.10653189395336395, LR 0.0075\n", "Epoch [18] Loss: 0.10514750379197141, LR 0.0075\n", "Epoch [19] Loss: 0.09590611559279422, LR 0.005625\n", "Epoch [20] Loss: 0.08146028108494256, LR 0.005625\n", "Epoch [21] Loss: 0.07707348782965477, LR 0.005625\n", "Epoch [22] Loss: 0.07206193436967566, LR 0.005625\n", "Epoch [23] Loss: 0.07001185417175293, LR 0.005625\n", "Epoch [24] Loss: 0.06797058351578252, LR 0.005625\n", "Epoch [25] Loss: 0.0649358110224947, LR 0.005625\n", "Epoch [26] Loss: 0.06219124286732775, LR 0.005625\n", "Epoch [27] Loss: 0.06075144828634059, LR 0.005625\n", "Epoch [28] Loss: 0.05711334495134251, LR 0.005625\n", "Epoch [29] Loss: 0.054747099572039666, LR 0.00421875\n", "Epoch [30] Loss: 0.0441775271233092, LR 0.00421875\n", "Epoch [31] Loss: 0.041551097910454936, LR 0.00421875\n", "Epoch [32] Loss: 0.04095017269093503, LR 0.00421875\n", "Epoch [33] Loss: 0.04045371045457556, LR 0.00421875\n", "Epoch [34] Loss: 0.038867686657195394, LR 0.00421875\n", "Epoch [35] Loss: 0.038131744303601854, LR 0.00421875\n", "Epoch [36] Loss: 0.039834817250569664, LR 0.00421875\n", "Epoch [37] Loss: 0.03669035941996473, LR 0.00421875\n", "Epoch [38] Loss: 0.03373505967728635, LR 0.00421875\n", "Epoch [39] Loss: 0.03164981273894615, LR 0.0031640625\n", "Epoch [40] Loss: 0.025532766055035336, LR 0.0031640625\n", "Epoch [41] Loss: 0.022659448867148543, LR 0.0031640625\n", "Epoch [42] Loss: 0.02307056112492338, LR 0.0031640625\n", "Epoch [43] Loss: 0.02236944056571798, LR 0.0031640625\n", "Epoch [44] Loss: 0.022204211963120328, LR 0.0031640625\n", "Epoch [45] Loss: 0.02262336903430046, LR 0.0031640625\n", "Epoch [46] Loss: 0.02253308448385685, LR 0.0031640625\n", "Epoch [47] Loss: 0.025286573044797207, LR 0.0031640625\n", "Epoch [48] Loss: 0.02439300988310127, LR 0.0031640625\n", "Epoch [49] Loss: 0.017976388018181983, LR 0.002373046875\n", "Epoch [50] Loss: 0.014343131095805067, LR 0.002373046875\n", "Epoch [51] Loss: 0.013039355582379281, LR 0.002373046875\n", "Epoch [52] Loss: 0.011884741885687715, LR 0.002373046875\n", "Epoch [53] Loss: 0.011438189668858305, LR 0.002373046875\n", "Epoch [54] Loss: 0.011447292693117832, LR 0.002373046875\n", "Epoch [55] Loss: 0.014212571560068334, LR 0.002373046875\n", "Epoch [56] Loss: 0.019900493724371797, LR 0.002373046875\n", "Epoch [57] Loss: 0.02102568301748722, LR 0.002373046875\n", "Epoch [58] Loss: 0.01346214400961044, LR 0.002373046875\n", "Epoch [59] Loss: 0.010107964911359422, LR 0.0017797851562500002\n", "Epoch [60] Loss: 0.008353193600972494, LR 0.0017797851562500002\n", "Epoch [61] Loss: 0.007678258292218472, LR 0.0017797851562500002\n", "Epoch [62] Loss: 0.007262124660167288, LR 0.0017797851562500002\n", "Epoch [63] Loss: 0.00705223578087827, LR 0.0017797851562500002\n", "Epoch [64] Loss: 0.006788556293774677, LR 0.0017797851562500002\n", "Epoch [65] Loss: 0.006473606571238091, LR 0.0017797851562500002\n", "Epoch [66] Loss: 0.006206096486842378, LR 0.0017797851562500002\n", "Epoch [67] Loss: 0.00584477313021396, LR 0.0017797851562500002\n", "Epoch [68] Loss: 0.005648705267137097, LR 0.0017797851562500002\n", "Epoch [69] Loss: 0.006481769871204458, LR 0.0013348388671875003\n", "Epoch [70] Loss: 0.008430448618341, LR 0.0013348388671875003\n", "Epoch [71] Loss: 0.006877245421105242, LR 0.0013348388671875003\n", "Epoch [72] Loss: 0.005671108281740578, LR 0.0013348388671875003\n", "Epoch [73] Loss: 0.004832422162624116, LR 0.0013348388671875003\n", "Epoch [74] Loss: 0.004441103402604448, LR 0.0013348388671875003\n", "Epoch [75] Loss: 0.004216198591475791, LR 0.0013348388671875003\n", "Epoch [76] Loss: 0.004041922989711967, LR 0.0013348388671875003\n", "Epoch [77] Loss: 0.003937713643337818, LR 0.0013348388671875003\n", "Epoch [78] Loss: 0.010251983049068046, LR 0.0013348388671875003\n", "Epoch [79] Loss: 0.01829354052848004, LR 0.0010011291503906252\n", "Epoch [80] Loss: 0.006723233448561802, LR 0.0010011291503906252\n", "Epoch [81] Loss: 0.004397524798170049, LR 0.0010011291503906252\n", "Epoch [82] Loss: 0.0038475305476087206, LR 0.0010011291503906252\n", "Epoch [83] Loss: 0.003591177945441388, LR 0.0010011291503906252\n", "Epoch [84] Loss: 0.003425112014175743, LR 0.0010011291503906252\n", "Epoch [85] Loss: 0.0032633850549129728, LR 0.0010011291503906252\n", "Epoch [86] Loss: 0.0031762316505959693, LR 0.0010011291503906252\n", "Epoch [87] Loss: 0.0030452777096565734, LR 0.0010011291503906252\n", "Epoch [88] Loss: 0.002950224184220837, LR 0.0010011291503906252\n", "Epoch [89] Loss: 0.002821172171450676, LR 0.0007508468627929689\n", "Epoch [90] Loss: 0.002725780961361337, LR 0.0007508468627929689\n", "Epoch [91] Loss: 0.002660556359493986, LR 0.0007508468627929689\n", "Epoch [92] Loss: 0.0026011724946319414, LR 0.0007508468627929689\n", "Epoch [93] Loss: 0.0025355776256703317, LR 0.0007508468627929689\n", "Epoch [94] Loss: 0.0024825221997626283, LR 0.0007508468627929689\n", "Epoch [95] Loss: 0.0024245587435174497, LR 0.0007508468627929689\n", "Epoch [96] Loss: 0.002365282145879602, LR 0.0007508468627929689\n", "Epoch [97] Loss: 0.0023112583984719946, LR 0.0007508468627929689\n", "Epoch [98] Loss: 0.002257173682780976, LR 0.0007508468627929689\n", "Epoch [99] Loss: 0.002162747085094452, LR 0.0005631351470947267\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Testing" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We get a random element from the testing set" ], "metadata": {} }, { "cell_type": "code", "execution_count": 17, "source": [ "n = random.randint(0, len(test_data)-1)\n", "\n", "x_orig = X[split_idx+n]\n", "y_orig = Y[split_idx+n]" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 41, "source": [ "def get_pred(x):\n", " x, _ = transform(x, x)\n", " output = net(mx.np.expand_dims(x.to_device(ctx), axis=0))\n", "\n", " # Convert output back to string\n", " pred = ''.join([vocab[int(o)] for o in output[0].argmax(axis=1).asnumpy().tolist()])\n", " return pred" ], "outputs": [], "metadata": {} }, { "cell_type": "markdown", "source": [ "Printing the result" ], "metadata": {} }, { "cell_type": "code", "execution_count": 43, "source": [ "x_ = ' '.join(map(str,x_orig))\n", "label = ' '.join(map(str,y_orig))\n", "print(\"X {}\\nPredicted {}\\nLabel {}\".format(x_, get_pred(x_orig), label))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "X 611 671 275 871 944\n", "Predicted 275 611 671 871 944\n", "Label 275 611 671 871 944\n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "We can also pick our own example, and the network manages to sort it without problem:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 66, "source": [ "print(get_pred(onp.array([500, 30, 999, 10, 130])))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "10 30 130 500 999 \n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "The model has even learned to generalize to examples not on the training set" ], "metadata": {} }, { "cell_type": "code", "execution_count": 64, "source": [ "print(\"Only four numbers:\", get_pred(onp.array([105, 302, 501, 202])))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Only four numbers: 105 202 302 501 \n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "However we can see it has trouble with other edge cases:" ], "metadata": {} }, { "cell_type": "code", "execution_count": 63, "source": [ "print(\"Small digits:\", get_pred(onp.array([10, 3, 5, 2, 8])))\n", "print(\"Small digits, 6 numbers:\", get_pred(onp.array([10, 33, 52, 21, 82, 10])))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Small digits: 8 0 42 28 \n", "Small digits, 6 numbers: 10 0 20 82 71 115 \n" ] } ], "metadata": {} }, { "cell_type": "markdown", "source": [ "This could be improved by adjusting the training dataset accordingly" ], "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
MLWave/kepler-mapper	docs/generated/gallery/digits.ipynb	1	3649	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nDigits Dataset\n================\n\nThis digits example shows two ways of customizing the tooltips options in the HTML visualization. It generates the visualization with tooltips set as the y-label, or number of the image. The second generated result uses the actual image in the tooltips. \n\n`Visualization with y-label tooltip <../../_static/digits_ylabel_tooltips.html>`_\n\n`Visualization with custom tooltips <../../_static/digits_custom_tooltips.html>`_\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import io\nimport sys\nimport base64\n\nimport numpy as np\nimport sklearn\nfrom sklearn import datasets\nimport kmapper as km\n\ntry:\n from scipy.misc import imsave, toimage\nexcept ImportError as e:\n print(\"imsave requires you to install pillow. Run `pip install pillow` and then try again.\")\n sys.exit()\n\n\n# Load digits dat\ndata, labels = datasets.load_digits().data, datasets.load_digits().target\n\n# Create images for a custom tooltip array\ntooltip_s = []\nfor image_data in data:\n output = io.BytesIO()\n img = toimage(image_data.reshape((8, 8))) # Data was a flat row of 64 \"pixels\".\n img.save(output, format=\"PNG\")\n contents = output.getvalue()\n img_encoded = base64.b64encode(contents)\n img_tag = \"\"\"<img src=\"data:image/png;base64,{}\">\"\"\".format(img_encoded.decode('utf-8'))\n tooltip_s.append(img_tag)\n output.close()\n\ntooltip_s = np.array(tooltip_s) # need to make sure to feed it as a NumPy array, not a list\n\n# Initialize to use t-SNE with 2 components (reduces data to 2 dimensions). Also note high overlap_percentage.\nmapper = km.KeplerMapper(verbose=2)\n\n# Fit and transform data\nprojected_data = mapper.fit_transform(data,\n projection=sklearn.manifold.TSNE())\n\n# Create the graph (we cluster on the projected data and suffer projection loss)\ngraph = mapper.map(projected_data,\n clusterer=sklearn.cluster.DBSCAN(eps=0.3, min_samples=15),\n cover=km.Cover(35, 0.4))\n\n# Create the visualizations (increased the graph_gravity for a tighter graph-look.)\nprint(\"Output graph examples to html\" )\n# Tooltips with image data for every cluster member\nmapper.visualize(graph,\n title=\"Handwritten digits Mapper\",\n path_html=\"output/digits_custom_tooltips.html\",\n color_function=labels,\n custom_tooltips=tooltip_s)\n# Tooltips with the target y-labels for every cluster member\nmapper.visualize(graph,\n title=\"Handwritten digits Mapper\",\n path_html=\"output/digits_ylabel_tooltips.html\",\n custom_tooltips=labels)" ] } ], "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
StartupsPoleEmploi/labonneboite	ROME_NAF/preparation/clean_offres.ipynb	1	5003	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import collections\n", "\n", "import pandas as pd\n", "pd.set_option('display.max_columns', 500)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "filename_input = 'LBB_EOF_OFFRES_20160307_20170407_20170407_191410_sep.csv'\n", "filename_output = 'LBB_EOF_OFFRES_20160307_20170407_20170407_191410_clean.csv'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "f_input = open(filename_input, 'r')\n", "f_output = open(filename_output, 'w')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "header_intput = f_input.readline()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "column_names = header_intput[:-1].split('\|')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "147" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_kept = [\n", " 'dn_nbrpostesoffertscreation',\n", " 'dc_rome_id',\n", " 'dc_appelationrome_id',\n", " 'dc_naturecontrat_id',\n", " 'dc_typecontrat_id',\n", " 'dc_unitedureecontrat',\n", " 'dc_duree_contrat_id',\n", " 'dc_naf2',\n", "]\n", "header_output = '\|'.join(columns_kept) + '\\n'\n", "f_output.write(header_output)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[0, 6, 7, 8, 9, 10, 11, 13]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_kept = [\n", " column_names.index(column_name)\n", " for column_name in columns_kept\n", "]\n", "index_kept" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "index_dc_rome_id = column_names.index('dc_rome_id')\n", "index_dc_naf2 = column_names.index('dc_naf2')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1000000\n", "2000000\n", "3000000\n", "4000000\n", "5000000\n", "6000000\n", "7000000\n" ] } ], "source": [ "for i, line_input in enumerate(f_input):\n", " cells = line_input[:-1].split('\|')\n", " \n", " # Remove lines with no ROME\n", " set_null = {'NULL', 'null', ''}\n", " dc_rome_id = cells[index_dc_rome_id]\n", " if dc_rome_id in set_null:\n", " continue\n", " \n", " # Remove lines with no NAF\n", " dc_naf2 = cells[index_dc_naf2]\n", " if dc_naf2 in set_null:\n", " continue\n", " \n", "\n", " cells_kept = [\n", " cells[i]\n", " for i in index_kept\n", " ]\n", " line_output = '\|'.join(cells_kept) + '\\n'\n", " f_output.write(line_output)\n", " \n", " if i % 1000000 == 0:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "f_input.close\n", "f_output.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }	agpl-3.0
pair-code/what-if-tool	WIT_COMPAS_with_SHAP.ipynb	1	14066	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "WIT COMPAS with SHAP", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "f1Id_0GukgNv", "colab_type": "text" }, "source": [ "## What-If Tool and SHAP on COMPAS keras model\n", "\n", "This notebook shows:\n", "- Training of a keras model on the [COMPAS](https://www.kaggle.com/danofer/compass) dataset.\n", "- Explanation of inference results using [SHAP](https://github.com/slundberg/shap).\n", "- Use of What-If Tool on the trained model, including SHAP values.\n", "\n", "For ML fairness background on COMPAS see:\n", "\n", "- https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing\n", "- https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm\n", "- http://www.crj.org/assets/2017/07/9_Machine_bias_rejoinder.pdf\n", "\n", "This notebook trains a model to mimic the behavior of the COMPAS recidivism classifier and uses the SHAP library to provide feature importance for each prediction by the model. We can then analyze our COMPAS proxy model for fairness using the What-If Tool, and explore how important each feature was to each prediction through the SHAP values.\n", "\n", "The specific binary classification task for this model is to determine if a person belongs in the \"Low\" risk class according to COMPAS (negative class), or the \"Medium\" or \"High\" risk class (positive class). We then analyze it with the What-If Tool for its ability to predict recidivism within two years of arrest.\n", "\n", "A simpler version of this notebook that doesn't make use of the SHAP explainer can be found [here](https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/WIT_COMPAS.ipynb).\n", "\n", "Copyright 2019 Google LLC.\n", "SPDX-License-Identifier: Apache-2.0" ] }, { "cell_type": "code", "metadata": { "id": "x1HvYDrvor2i", "colab_type": "code", "colab": {} }, "source": [ "#@title Install What-If Tool Widget and SHAP library\n", "!pip install --upgrade --quiet witwidget shap" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wzEwkr3SoyLh", "colab_type": "code", "colab": {} }, "source": [ "#@title Read training dataset from CSV {display-mode: \"form\"}\n", "import pandas as pd\n", "import numpy as np\n", "import tensorflow as tf\n", "import witwidget\n", "import os\n", "import pickle\n", "\n", "from tensorflow.keras.layers import Dense\n", "from tensorflow.keras.models import Sequential\n", "\n", "from sklearn.utils import shuffle\n", "\n", "df = pd.read_csv('https://storage.googleapis.com/what-if-tool-resources/computefest2019/cox-violent-parsed_filt.csv')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "DBx6BTumpb1-", "colab_type": "code", "colab": {} }, "source": [ "# Preprocess the data\n", "\n", "# Filter out entries with no indication of recidivism or no compass score\n", "df = df[df['is_recid'] != -1]\n", "df = df[df['decile_score'] != -1]\n", "\n", "# Rename recidivism column\n", "df['recidivism_within_2_years'] = df['is_recid']\n", "\n", "# Make the COMPASS label column numeric (0 and 1), for use in our model\n", "df['COMPASS_determination'] = np.where(df['score_text'] == 'Low', 0, 1)\n", "\n", "df = pd.get_dummies(df, columns=['sex', 'race'])\n", "\n", "# Get list of all columns from the dataset we will use for model input or output.\n", "input_features = ['sex_Female', 'sex_Male', 'age', 'race_African-American', 'race_Caucasian', 'race_Hispanic', 'race_Native American', 'race_Other', 'priors_count', 'juv_fel_count', 'juv_misd_count', 'juv_other_count']\n", "\n", "to_keep = input_features + ['recidivism_within_2_years', 'COMPASS_determination']\n", "\n", "to_remove = [col for col in df.columns if col not in to_keep]\n", "df = df.drop(columns=to_remove)\n", "\n", "input_columns = df.columns.tolist()\n", "labels = df['COMPASS_determination']\n", "df.head()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "U0ZfePT1rTmZ", "colab_type": "code", "colab": {} }, "source": [ "# Create data structures needing for training and testing.\n", "# The training data doesn't contain the column we are predicting,\n", "# 'COMPASS_determination', or the column we are using for evaluation of our\n", "# trained model, 'recidivism_within_2_years'.\n", "df_for_training = df.drop(columns=['COMPASS_determination', 'recidivism_within_2_years'])\n", "train_size = int(len(df_for_training) * 0.8)\n", "\n", "train_data = df_for_training[:train_size]\n", "train_labels = labels[:train_size]\n", "\n", "test_data_with_labels = df[train_size:]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "5T2XThgosWX-", "colab_type": "code", "colab": {} }, "source": [ "# Create the model\n", "\n", "# This is the size of the array we'll be feeding into our model for each example\n", "input_size = len(train_data.iloc[0])\n", "\n", "model = Sequential()\n", "model.add(Dense(200, input_shape=(input_size,), activation='relu'))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dense(25, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "\n", "model.compile(loss='mean_squared_error', optimizer='adam')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fjgNhSCDsayt", "colab_type": "code", "colab": {} }, "source": [ "# Train the model\n", "model.fit(train_data.values, train_labels.values, epochs=4, batch_size=32, validation_split=0.1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lI18CwYiQotq", "colab_type": "code", "colab": {} }, "source": [ "# Create a SHAP explainer by passing a subset of our training data\n", "import shap\n", "explainer = shap.DeepExplainer(model, train_data.values[:200])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iywHwbJJkeYG", "colab_type": "code", "colab": {} }, "source": [ "# Explain predictions of the model on the first 5 examples from our training set\n", "# to test the SHAP explainer.\n", "shap_values = explainer.shap_values(train_data.values[:5])\n", "shap_values" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KF00pJvkeicT", "colab_type": "code", "colab": {} }, "source": [ "#@title Show model results and SHAP values in WIT\n", "from witwidget.notebook.visualization import WitWidget, WitConfigBuilder\n", "num_datapoints = 1000 #@param {type: \"number\"}\n", "\n", "# Column indices to strip out from data from WIT before passing it to the model.\n", "columns_not_for_model_input = [\n", " test_data_with_labels.columns.get_loc(\"recidivism_within_2_years\"),\n", " test_data_with_labels.columns.get_loc(\"COMPASS_determination\")\n", "]\n", "\n", "# Return model predictions and SHAP values for each inference.\n", "def custom_predict_with_shap(examples_to_infer):\n", " # Delete columns not used by model\n", " model_inputs = np.delete(\n", " np.array(examples_to_infer), columns_not_for_model_input, axis=1).tolist()\n", "\n", " # Get the class predictions from the model.\n", " preds = model.predict(model_inputs)\n", " preds = [[1 - pred[0], pred[0]] for pred in preds]\n", "\n", " # Get the SHAP values from the explainer and create a map of feature name\n", " # to SHAP value for each example passed to the model.\n", " shap_output = explainer.shap_values(np.array(model_inputs))[0]\n", " attributions = []\n", " for shap in shap_output:\n", " attrs = {}\n", " for i, col in enumerate(df_for_training.columns):\n", " attrs[col] = shap[i]\n", " attributions.append(attrs)\n", " ret = {'predictions': preds, 'attributions': attributions}\n", " return ret\n", "\n", "examples_for_shap_wit = test_data_with_labels.values.tolist()\n", "column_names = test_data_with_labels.columns.tolist()\n", "\n", "config_builder = WitConfigBuilder(\n", " examples_for_shap_wit[:num_datapoints],\n", " feature_names=column_names).set_custom_predict_fn(\n", " custom_predict_with_shap).set_target_feature('recidivism_within_2_years')\n", "\n", "ww = WitWidget(config_builder, height=800)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "AsFCKdt2HJHO", "colab_type": "text" }, "source": [ "#### What-If Tool exploration ideas\n", "\n", "- Organize datapoints by \"inference score\" (can do this through binning or use of scatter plot) to see points ordered by how likely they were determined to re-offend.\n", " - Select a point near the boundary line (where red points turn to blue points)\n", " - Find the nearest counterfactual to see a similar person with a different decision. What is different?\n", " - Look at the partial dependence plots for the selected person. What changes in what features would change the decision on this person?\n", "- Explore the attribution values provided by SHAP.\n", " - For a variety of selected datapoints, look at which features have the highest positive attribution values. These are making the model predict higher risk for this person.\n", " - Look at which features have the lowest negative attribution values as well. These are making the model predict lower risk for this person.\n", " - How well do these attribution scores line up with the partial dependence plots for those datapoints?\n", " - Use the attribution scores in the datapoints visualizations to look for interesting patterns. As one example, you could set the scatter X-axis to \"attributions__age\" and the scatter Y-axis to \"attributions__priors_count\" with the points colored by \"Inference score\" to investigate the relationship between feature importance of those two features and how those relate to the score the model gives for each datapoint being \"High risk\".\n", "- In \"Performance and Fairness\" tab, slice the dataset by different features (such as race or sex)\n", " - Look at the confusion matrices for each slice - How does performance compare in those slices? What from the training data may have caused the difference in performance between the slices? What root causes could exist?\n", " - Use the threshold optimization buttons to optimize positive classification thresholds for each slice based on any of the possible fairness constraints - How different do the thresholds have to be to achieve that constraint? How varied are the thresholds depending on the fairness constraint chosen?\n", "\n", "- In the \"Performance + Fairness\" tab, change the cost ratio so that you can optimize the threshold based off of a non-symmetric cost of false positives vs false negatives. Then click the \"optimize threshold\" button and see the effect on the confusion matrix. \n", " - Slice the dataset by a feature, such as sex or race. How has the new cost ratio affected the disparity in performance between slices? Click the different threshold optimization buttons to see how the changed cost ratio affects the disparity given different fairness constraints.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "W9qxcBj72Q_E", "colab_type": "text" }, "source": [ "#### Further exploration ideas\n", "\n", "- Edit the training data so that race fields are not included as a feature and train a new model with this data as input (make sure to create a new explainer and a new custom prediction function that filters race out of model input and uses the right explainer and model).\n", "- Load the new model with set_compare_custom_predict_fn and compare it with the original model.\n", " - HINT: You'll need to make edits in 3 separate code cells.\n", " - Is there still a racial disparity in model results? If so, what could be the causes?\n", " - How did the SHAP attributions change?" ] } ] }	apache-2.0
letsgoexploring/teaching	winter2017/econ129/python/Econ129_Class_05.ipynb	1	9703	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Class 5: Pandas \n", "\n", "Pandas is a Python package for data analysis. Documentation and examples: http://pandas.pydata.org/\n", "\n", "\n", "## Pandas basics\n", "\n", "To learn how Pandas works, we'll make use of a dataset containing long-run averages of inflation, money growth, and real GDP. The dataset is available here: http://www.briancjenkins.com/data/quantitytheory/csv/qtyTheoryData.csv. Recall that the quantity theory of money implies the following linear relationship between the long-run rate of money growth, the long-run rate of inflation, and the long-run rate of real GDP growth in a country:\n", "\n", "\\begin{align}\n", "\\text{inflation} & = \\text{money growth} - \\text{real GDP growth},\n", "\\end{align}\n", "\n", "Generally, we treat real GDP growth and money supply growth as exogenous so this is a theory about the determination of inflation.\n", "\n", "Now, we could download the data manually, but we might as well use Python to do it. The `requests` module is good for this." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Use the requests module to download money growth and inflation data\n", "url = 'http://www.briancjenkins.com/data/quantitytheory/csv/qtyTheoryData.csv'\n", "r = requests.get(url,verify=True)\n", "\n", "with open('qtyTheoryData.csv','wb') as newFile:\n", " \n", " newFile.write(r.content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Pandas" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import data from a csv file\n", "\n", "Pandas has a function called `read_csv()` for reading data from a csv file into a Pandas `DataFrame` object. Let's import the quantity thery data into a variable called `df`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import quantity theory data into a Pandas DataFrame called df with country names as the index.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the first 5 rows\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the last 5 rows\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the type of df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Properties of `DataFrame` objects\n", "\n", "Like entries in a spreadsheet file, elements in a `DataFrame` object have row and column coordinates. Column names are always strings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the columns of df\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a new variable called money equal to the 'money growth' column and print\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the type of the variable money\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the first 5 rows of just the inflation, money growth, and gdp growth columns\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The set of row coordinates is the index. Index values can be strings, numbers, or dates." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the index of df\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a new variable called usa equal to the 'United States' row and print\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the inflation rate of the United States\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the inflation rate of the United States in a different way\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a new variable called first equal to the first row in the DataFrame and print\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create new columns by name." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a new column called 'difference' equal to the money growth column minus the inflation column and print the column\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methods\n", "\n", "A Pandas `DataFrame` has a bunch of useful methods defined for it. `describe()` returns some summary statistics." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the summary statistics for df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While Pandas' `describe` function provides some good summary information, NumPy also has some useful functions for computing statistics. For example, the NumPy function `corrcoef()` computes the coefficient of correlation for two series." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print the correlation coefficient for inflation and money growth\n", "\n", "\n", "# Print the correlation coefficient for inflation and real GDP growth\n", "\n", "\n", "# Print the correlation coefficient for money growth and real GDP growth\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sort_values()` returns a copy of the original `DataFrame` sorted along the given column. The optional argument `ascending` is set to `True` by default, but can be changed to `False` if you want to print the lowest first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# Print rows for the countries with the 10 lowest inflation rates\n", "\n", "\n", "# Print rows for the countries with the 10 lowest money growth rates\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print rows for the countries with the 10 highest inflation rates\n", "\n", "\n", "# Print rows for the countries with the 10 highest money growth rates\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sort_index()` returns a copy of the original `DataFrame` sorted along the index. The optional argument `ascending` is set to `True` by default, but can be changed to `False` if you want to print the lowest first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# Print df with the index descending alphabetical order\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quick plotting example\n", "\n", "Construct a graph that visually confirms the quantity theory of money by making a scatter plot with average money growth on the horizontal axis and average inflation on the vertical axis. Add a 45 degree line and labels and a title." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Construct a well-labeled scatter plot of inflation against money growth\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
CalPolyPat/phys202-2015-work	assignments/midterm/AlgorithmsEx03.ipynb	1	7428	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Algorithms Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "source": [ "from IPython.html.widgets import interact" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Character counting and entropy" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a function `char_probs` that takes a string and computes the probabilities of each character in the string:\n", "\n", "* First do a character count and store the result in a dictionary.\n", "* Then divide each character counts by the total number of character to compute the normalized probabilties.\n", "* Return the dictionary of characters (keys) and probabilities (values)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "nbgrader": { "checksum": "f11bac096ada913538c9a47721fb98a1", "solution": true } }, "outputs": [], "source": [ "def char_probs(s):\n", " \"\"\"Find the probabilities of the unique characters in the string s.\n", " \n", " Parameters\n", " ----------\n", " s : str\n", " A string of characters.\n", " \n", " Returns\n", " -------\n", " probs : dict\n", " A dictionary whose keys are the unique characters in s and whose values\n", " are the probabilities of those characters.\n", " \"\"\"\n", " chars = list(s)\n", " chardic = {}\n", " probs = {}\n", " for ch in chars:\n", " if ch in chardic:\n", " chardic[ch]+=1\n", " else:\n", " chardic[ch]=1\n", " for ch in chardic:\n", " probs[ch]=chardic[ch]/len(chars)\n", " return probs" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "97f4091c66f9a555c766706bcf4a7681", "grade": true, "grade_id": "algorithmsex03a", "points": 4 } }, "outputs": [], "source": [ "test1 = char_probs('aaaa')\n", "assert np.allclose(test1['a'], 1.0)\n", "test2 = char_probs('aabb')\n", "assert np.allclose(test2['a'], 0.5)\n", "assert np.allclose(test2['b'], 0.5)\n", "test3 = char_probs('abcd')\n", "assert np.allclose(test3['a'], 0.25)\n", "assert np.allclose(test3['b'], 0.25)\n", "assert np.allclose(test3['c'], 0.25)\n", "assert np.allclose(test3['d'], 0.25)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The [entropy](http://en.wikipedia.org/wiki/Entropy_%28information_theory%29) is a quantiative measure of the disorder of a probability distribution. It is used extensively in Physics, Statistics, Machine Learning, Computer Science and Information Science. Given a set of probabilities $P_i$, the entropy is defined as:\n", "\n", "$$H = - \\Sigma_i P_i \\log_2(P_i)$$ \n", "\n", "In this expression $\\log_2$ is the base 2 log (`np.log2`), which is commonly used in information science. In Physics the natural log is often used in the definition of entropy.\n", "\n", "Write a funtion `entropy` that computes the entropy of a probability distribution. The probability distribution will be passed as a Python `dict`: the values in the `dict` will be the probabilities.\n", "\n", "To compute the entropy, you should:\n", "\n", "* First convert the values (probabilities) of the `dict` to a Numpy array of probabilities.\n", "* Then use other Numpy functions (`np.log2`, etc.) to compute the entropy.\n", "* Don't use any `for` or `while` loops in your code." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true, "nbgrader": { "checksum": "93e205f7727df5161387fa73af53718b", "solution": true } }, "outputs": [], "source": [ "def entropy(d):\n", " \"\"\"Compute the entropy of a dict d whose values are probabilities.\"\"\"\n", " probs = np.array(list(d.values()))\n", " probdist = probs*np.log2(probs)\n", " H=-np.cumsum(probdist)[-1]\n", " return H" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "0499a53c730bb4fbb2cd81d7c34486da", "grade": true, "grade_id": "algorithmsex03b", "points": 4 } }, "outputs": [], "source": [ "assert np.allclose(entropy({'a': 0.5, 'b': 0.5}), 1.0)\n", "assert np.allclose(entropy({'a': 1.0}), 0.0)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use IPython's `interact` function to create a user interface that allows you to type a string into a text box and see the entropy of the character probabilities of the string." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.58496250072\n" ] } ], "source": [ "def show_entropy(s):\n", " d=char_probs(s)\n", " H=entropy(d)\n", " print(H)\n", "interact(show_entropy, s=\"Hello World\");" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "2eeb2ebb1993a6f046deec7ff81c4930", "grade": true, "grade_id": "algorithmsex03c", "points": 2 } }, "outputs": [], "source": [ "assert True # use this for grading the pi digits histogram" ] }, { "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
oseledets/fastpde2017	lectures/Lecture-3.ipynb	1	21359	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 3: FFT" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Previous lecture\n", "- Basic discretization schemes (Galerkin, collocation, Nystrom)\n", "- Some approaches to compute singular integrals" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Todays lecture\n", "- Fast methods: computation of the convolution via FFT, idea of precorrected FFT.\n", "- More integral equation kernels" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Integral equations and non-local interactions\n", "\n", "The main problem with integral equation methods (we talked about boundary integral equations, but there are also volume integral equations is that we have to deal with non-local interactions.\n", "\n", "After discretization, we have to compute the sum \n", "\n", "$$v_i = \\sum_{j=1}^N A_{ij} q_j,$$\n", "\n", "and all $A_{ij}$ are not small (i.e., they can not be approximated by sparse matrices)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Storage and complexity \n", "\n", "- The naive approach is to compute all matrix elements $A_{ij}$ and store them in a matrix.\n", "- The storage complexity is $N^2$ elements\n", "- The LU-complexity is $\\mathcal{O}(N^3)$, but we can also go with iterative methods, and have $\\mathcal{O}(N_{iter} N^2)$ complexity." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Do we need fast methods?\n", "\n", "If we have two-dimensional PDE, the integral equation will be defined on a curve. \n", "\n", "For a second-order discretization, 1000 elements are needed, i.e. the matrix will be\n", "\n", "$10^{3} \\times 10^{3}$, and storage is just 8 megabytes, i.e. fast methods are not very needed.\n", "\n", "\n", "For 3D problems and surface integral equations, the storage problem becomes much more complicated:\n", "\n", "For $h = 10^{-3}$ we have $10^6$ unknowns and the matrix is $10^6 \\times 10^6$, more than 8 Terabytes." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Solving the storage problem\n", "One of the ways to deal with the storage problem is to solve the system using matrix-by-vector product.\n", "\n", "Apply iterative method, and compute\n", "\n", "$$v_i = \\sum_{j=1}^N A_{ij} y_j $$\n", "\n", "on the fly.\n", "\n", "The complexity, however, stays $\\mathcal{O}(N^2)$ as well.\n", "\n", "Moreover, the evaluation of matrix elements can be expensive, especially if Galerkin/collocation methods are used.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Translation-invariant case\n", "One of the way to solve this problem is to use translation-invariance of many popular integral equation kernels.\n", "\n", "This, however, requires the special properties of the geometry and basis functions." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Boundary integral equations with translation-invariant kernels\n", "\n", "The first kind integral equation reads\n", "\n", "$$\\int_{\\partial \\Omega} \\frac{q(y)}{\\Vert x - y \\Vert} dy = f(x), \\quad x \\in \\partial \\Omega,$$\n", "where $\\Omega$ is a certain domain in 3D.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Model problem\n", "\n", "Consider the integral equation on a square:\n", "\n", "$$\\int_{\\Omega} \\frac{q(y)}{\\Vert x - y \\Vert} dy = f(x), $$ \n", "\n", "Let us discretize using shifts of the same basis functions, \n", "\n", "$$q(y) = \\sum_{ij} \\phi_{ij} q_{ij}, $$\n", "\n", "where $$\\phi_{ij}(y_1, y_2) = \\phi( y_1 - h i, y_2 - h j).$$\n", "\n", "What would be the structure of the matrix?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Block Toeplitz with Toeplitz blocks\n", "\n", "The matrix will have two-level block Toeplitz with Toeplitz blocks (BTTB) structure,\n", "\n", "$$A_{ij} = A(i_1 i_2, j_1 j_2) = A(i_1 - j_1, i_2 - j_2),$$\n", "\n", "i.e. it is defined by $(2 n - 1) \\times (2n - 1) \\approx 4 n^2 = 4 N$ parameters." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## BTTB matrix by vector multiplication\n", "\n", "Recall from numerical linear algebra, that BTTB matrix can be multiplied by a vector in $\\mathcal{O}(N \\log N)$ operations.\n", "\n", "The idea is a generalization of the idea for one level Toeplitz matrix: any $N \\times N$ matrix can be embedded into a $2N \\times 2N$ circulant matrix." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Circulant matrices and spectral theorem\n", "\n", "A matrix is called circulant, if \n", "\n", "$$A_{ij} = A( (i -j)\\mod N),$$\n", "\n", "i.e. it wraps periodically" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Spectral theorem\n", "\n", "Any circulant matrix can be represented as\n", "\n", "$$C = F D F^,$$\n", "\n", "where \n", "$$ D = \\mathrm{diag}(d), \\quad d = F c, $$\n", "\n", "where $c$ is the the first column* of $C$ and $F$ is a DFT matrix,\n", "\n", "$$\n", "F_{kl} = w^{kl}, \\quad w = \\exp\\left( \\frac{2 \\pi i}{n}\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Fast Toeplitz matrix-by-vector product\n", "\n", "- Embed a Toeplitz matrix $T$ into a circulant matrix $C$\n", "- Pad the vector $q$ by zeros for matching dimensions\n", "- Use the fact that\n", " $$C \\begin{bmatrix} q \\\\ 0 \\end{bmatrix} = \\begin{bmatrix} Tq \\\\ * \\end{bmatrix}.$$\n", "- Compute circulant matrix-by-vector product using two FFTs and multiplication by diagonal matrix." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Two-level (BTTB) case\n", "\n", "For two-level model problem on a square, the matrix is not Toeplitz, but block Toeplitz with Toeplitz blocks.\n", "\n", "It can be embedded into block circulant with circulant blocks, which can be diagonalized in $\\mathcal{O}(N)$ operations using two-dimensional FFT:\n", "\n", "$$F_2 = F \\otimes F.$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Putting it all together\n", "\n", "For a translation-invariant case with a square uniform grid we have a matrix with BTTB structure, which can be:\n", "- Stored with $\\mathcal{O}(N)$ memory\n", "- Multiplied with $\\mathcal{O}(N \\log N)$ complexity\n", "\n", "But if the domain is not a square (i.e. a sphere or a complicated domain) using Toeplitz structure becomes very difficult.\n", "\n", "What to do? The idea of precorrected FFT comes to mind, where 2D structure is embedded into a regular 3D grid." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Precorrected FFT\n", "\n", "Suppose we are given a set of conducting surfaces, each surface is discretized into panels, and piecewise-constant basis functions are used.\n", "\n", "We solve our favourite first kind integral equation \n", "\n", "$$\\psi(x) = \\int_{surfaces} \\frac{q(y)}{\\Vert x - y \\Vert} dy.$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Embedding the panels into a parallelepiped\n", "\n", "Consider a 3D parallelepiped splitted into $k \\times l \\times m$ array of small cubes in such a way that each cube contains only a small number of panels.\n", "\n", "The key idea is that for all evaluation points, distant for the particular cell, it can be represented as a weighted sum of point charges located on a uniform grid." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## pFFT approach\n", "\n", "1. Project the panel charges onto a uniform grid of point charges \n", "2. Compute the grid potentials due to grid charges using an FFT\n", "3. Interpolate the grid potentials onto the panels\n", "4. Directly compute nearby interactions\n", "\n", "The name precorrected comes from the exact evaluation of close interactions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Step 1: projection\n", "\n", "In a cell, we want to match the potential, created by $m$ panels, by a uniform grid of $p \\times p \\times p$\n", "point charges. \n", "Then, we just select a test surface of radius $r_c$, select test points $z_i$, $i=1, \\ldots, N_{test}$ and require that\n", "\n", "$$\\psi_{uniform}(z_i) \\approx \\psi_{panels}(z_i), \\quad i=1,\\ldots,N_{test}$$ on the test points.\n", "\n", "We have $p^3$ unknowns and $N_{test}$ equations, and it is solved as a least squares.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Computing grid potentials\n", "\n", "Once the uniform grid charges have been evaluation, we compute \n", "\n", "$$V(i, j, k) = \\sum_{i'j'k'} H(i-i', j-j',k-k') \\widehat{q}(i', j', k'),$$\n", "\n", "using 3D FFT.\n", "\n", "The main benefit is that highly optimized FFT code (including parallel versions) has been developed." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Interpolation grid potentials\n", "\n", "To get the value at evaluation point, it is sufficient to use the transpose matrix to the matrix that interpolates the charges from a non-uniform grid to a uniform grid.\n", "\n", "The projection/interpolation have similar accuracy.\n", "\n", "For collocation, the result can be written as\n", "\n", "$$\\psi_{c} = V^{\\top} H W q, $$\n", "\n", "and for Galerkin method:\n", "\n", "$$\\psi_{G} = W^{\\top} H W q,$$\n", "\n", "i.e. pFFT preserves the symmetry of the Galerkin method." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Final step: precorrecting\n", "\n", "The main diffuculty is that the projection-FFT-interpolation step does not accurately represent the nearby interactions.\n", "\n", "Let $(k, l)$ be a pair of nearby cells.\n", "\n", "Then, \n", "\n", "$$\\psi_{c (k, l)} = V^{\\top}(k) H(k, l) W(l) q_l,$$\n", "\n", "adding and subtracting the wrong contribution we have\n", "\n", "$$\\psi_{(k, l)} = \\psi_{c (k, l)} - V^{\\top}(k) H(k, l) W(l) q_l + P_{k, l} q_l.$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Precorrected grid operator\n", "\n", "This can be effectively achieved by introduction of corrected operator\n", "\n", "$$\\widehat{P}_{k,l} = P_{k, l} - V^{\\top}(k) H(k, l) W(l).$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Final algorithm\n", "\n", "- Precomputation: for all close cells $(k, l)$ compute sparse precorrected matrix $\\widehat{P}$.\n", "- $\\psi = \\widehat{P} q + V^{\\top} H W q,$\n", "as a product of 3 matrices: sparse $V$ and $W$, 3D Toeplitz matrix $H$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Reading\n", "\n", "[A Precorrected-FFT Method for Electrostatic Analysis of Complicated 3-D Structures, Phillips, White](https://pdfs.semanticscholar.org/b176/ed57e1e5e1997a0722199d114363550e9e00.pdf)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## More integral equations kernels\n", "\n", "The simplicity of pFFT is that it can be applied very easily to other integral equation kernels with a similar efficiency, and few additional programming.\n", "\n", "A disadvantage is that for simple sufraces many of the panel charges will be zero, i.e. the representation can be redundant. \n", "\n", "Finally, let us present some other integral equation kernels that are often used." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Stokes problem\n", "Stokes equation define the laminar flow in low Reynolds number regime:\n", "\n", "\\begin{equation}\n", "\\begin{split}\n", "- \\nabla p + \\mu \\Delta v = -F \\delta(r) \\\\\n", " \\nabla \\cdot v = 0\n", "\\end{split}\n", "\\end{equation}\n", "\n", "Here $p$ is pressure, $v$ is velocity. \n", "\n", "The solution is so-called Stokeslet, and it is a matrix:\n", "\n", "$$\\mathbb{J}(r) = \\frac{1}{8 \\pi \\mu} \\left( \\frac{I}{r} + \\frac{r_i r_j}{r^3} \\right), \\quad u = F J , \\quad p = \\frac{(F, \\mathbf{r})}{r^3}.$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Instationary Maxwell equations\n", "\n", "$$\n", "\\begin{split}\n", "\\nabla \\times H = \\frac{\\partial D}{\\partial t} + J, \\\\\\quad \\nabla \\times E = -\\frac{\\partial B}{\\partial t}, \\quad \\nabla \\cdot D = \\rho, \\quad \\nabla \\cdot B = 0. \\\\\n", "D = \\varepsilon E, \\quad H = \\mu B.\n", "\\end{split}\n", "$$\n", "\n", "If we consider time-harmonic case, i.e. the dependence from time is $e^{i w t}$, we get time-harmonic Maxwell equations.\n", "\n", "However, for Maxwell equations the most natural setting is scattering from inhomogenious media, \n", "and volume integral equations (or volume integro-differential) equations are used instead." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Demo\n", "\n", "We will illustrate how the [BEM++](www.bempp.org) package works, which is quite general package for solving boundary integral equations (unfortunately, installation can be painful).\n", "\n", "\n", "[Interior Laplace](laplace_interior_dirichlet.ipynb)\n", "\n", "[Maxwell scattering](maxwell_screen.ipynb)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Summary\n", "\n", "- Precorrected FFT\n", "- Demo of BEM++" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Next lecture\n", "- Elaborate on the idea of close/fast once more in Barnes-Hut method" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " /width:80%;/\n", " /margin-left:auto !important;\n", " margin-right:auto;/\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " }\n", " h3{\n", "\t\tfont-family: 'Fenix', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", "\th4{\n", "\t\tfont-family: 'Fenix', serif;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\t \n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 1.2;\n", " font-size: 120%;\n", " /width:70%;/\n", " /margin-left:auto;/\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", "\t\tline-height: 110%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\t\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #CD2305;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " li {\n", " line-height: 110%;\n", " }\n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\$\",\"\\\$\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"./styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
WilliamLubega/PGD-Computer-Science-_Project	william/python/CNN training and evaluation_intestinalparasites.ipynb	1	877567	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import nolearn\n", "from nolearn.lasagne import NeuralNet\n", "from progress_bar import ProgressBar\n", "import createdata \n", "import lasagne\n", "from lasagne import layers\n", "from sklearn import metrics\n", "import detectobjects as det" ], "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": [ "opts = {'img_dir': '../data/intestinalparasites_Images/',\n", " 'models_dir': '../models/',\n", " 'annotation_dir': '../data/intestinalparasites_annotation/',\n", " 'train-dir': 'train_dir/',\n", " 'test-dir': 'test_dir/',\n", " 'val-dir': 'val_dir/',\n", " 'patches_dir': 'patches_dir/',\n", " 'augment-training-data': False,\n", " 'model': '2C-1FC-O',\n", " 'threshold': 0.9, \n", " 'overlapThreshold': 0.3, \n", " 'lim': 0, \n", " 'gauss': 1,\n", " 'prob': det.non_maximum_suppression, \n", " 'pos': det.non_maximum_suppression, \n", " 'probs_area': 90,\n", " 'input_scale': None,\n", " 'raw_scale': 255,\n", " 'image_dims': (600,600),\n", " 'image_downsample' : 10,\n", " 'channel_swap': None,\n", " 'probs_area': 40,\n", " 'detection-step': 10,\n", " 'patch-creation-step': 40,\n", " 'object-class': 'hookworm',\n", " 'negative-training-discard-rate': .9\n", " }\n", "opts['patch_stride_training'] = int(opts['image_dims'][0].5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "reload(createdata)\n", "trainfiles, valfiles, testfiles = createdata.create_sets(opts['img_dir'], train_set_proportion=.6, \n", " test_set_proportion=.39,\n", " val_set_proportion=.01)\n", "\n", "train_y, train_X = createdata.create_patches(trainfiles, opts['annotation_dir'], opts['img_dir'],\n", "opts['image_dims'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], \n", "objectclass=opts['object-class'], negative_discard_rate=opts['negative-training-discard-rate'])\n", "\n", "test_y, test_X = createdata.create_patches(testfiles, opts['annotation_dir'], opts['img_dir'], \n", "opts['image_dims'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], \n", "objectclass=opts['object-class'], negative_discard_rate=opts['negative-training-discard-rate'])\n", "\n", "val_y, val_X = createdata.create_patches(valfiles, opts['annotation_dir'], opts['img_dir'], \n", "opts['image_dims'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], \n", "objectclass=opts['object-class'], negative_discard_rate=opts['negative-training-discard-rate'])\n", "\n", "# For training/validation, cut down on disproportionately large numbers of negative patches\n", "train_X, train_y = createdata.balance(train_X, train_y, mult_neg=100)\n", "val_X, val_y = createdata.balance(val_X, val_y, mult_neg=100)\n", "\n", "# Create rotated and flipped versions of the positive patches\n", "train_X, train_y = createdata.augment_positives(train_X, train_y)\n", "val_X, val_y = createdata.augment_positives(val_X, val_y)\n", "test_X, test_y = createdata.augment_positives(test_X, test_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", "[ 0% ]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[ 1% ] 2 of 168 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[ 2% ] 3 of 168 complete" ] }, 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}, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[*************98%************* ] 108 of 110 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[*************99%**************] 109 of 110 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[************100%**************] 110 of 110 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[ 0% ]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[*************67%* ] 2 of 3 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r", "[************100%***************] 3 of 3 complete" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "'%d positive training examples, %d negative training examples' % (sum(train_y), len(train_y)-sum(train_y))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "'128 positive training examples, 267 negative training examples'" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "print '%d positive testing examples, %d negative testing examples' % (sum(test_y), len(test_y)-sum(test_y))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "128 positive testing examples, 158 negative testing examples\n" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "print '%d patches (%.1f%% positive)' % (len(train_y)+len(test_y), 100.((sum(train_y)+sum(test_y))/(len(train_y)+len(test_y))))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "681 patches (0.0% positive)\n" ] } ], "prompt_number": 56 }, { "cell_type": "raw", "metadata": {}, "source": [ "View a random selection of positive and negative patches to see if they look right" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "N_samples_to_display = 10\n", "pos_indices = np.where(train_y)[0]\n", "pos_indices = pos_indices[np.random.permutation(len(pos_indices))]\n", "for i in range(N_samples_to_display):\n", " plt.subplot(2,N_samples_to_display,i+1)\n", " example_pos = train_X[pos_indices[i],:,:,:]\n", " example_pos = np.swapaxes(example_pos,0,2)\n", " plt.imshow(example_pos[:,:,[2,1,0]])\n", " \n", "neg_indices = np.where(train_y==0)[0]\n", "neg_indices = neg_indices[np.random.permutation(len(neg_indices))]\n", "for i in range(N_samples_to_display,2N_samples_to_display):\n", " plt.subplot(2,N_samples_to_display,i+1)\n", " example_neg = train_X[neg_indices[i],:,:,:]\n", " example_neg = np.swapaxes(example_neg,0,2)\n", " plt.imshow(example_neg[:,:,[2,1,0]])\n", " \n", "plt.gcf().set_size_inches(1.5N_samples_to_display,3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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bs1cjZ1qJUXyp5FPBBKzLZ9TGYayXYiFHSNKIcM5iorsln0rRbsQXtfjfrP6CAEPX5ZdT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4AAAg\nAElEQVQMPQ/e/Cr3/9rfIk1TltcSAjr0nXBb+4HEZkKrwBO8POA7dFkZhTaWNNsdbkP8T9AA54WP\nqbVmsdmidUJQA23Xxm5VNsXd6NB7R73t8Hi63jGeTkX7ZMSiHuXQBJRyKOfxvQKbRktwA7GpUErh\nes2AQllN0EIfA4f3A97JA3v50ds8e/+7MmWImoYXXdN//ru/Rdds8a7j9M6b3Hv4pdioDhiUiDVH\nY0bzA9bLLRdPH1MUOer+A8mr6iUroSzLSK+Q9V0ulgx9z3Q6Zbvd4p0jn8/xznF+dkH2sKQcjyWz\nqKk5mB9yeHLCk0eP0F4cYz784AfkRRY5s4CCvpdQ6L7v+Stf/Sm6vt0bG7gdX7lr6Xopor13DE6s\n9g8PD8W9sq7phnofMDmZjnn2+Pt89IM/uaEtvMSavvWN/y6OvRMevvlXmJ++gVKwrbfkRUYxLjGJ\nxWvNpt7w/OyM9fIanSQkWcZ2dc308IhhGGiamqAkNDmflFSffMyd01PmBwJCVEYzdBUfPXnC6e17\nwpUmMD85IS0Kvv/9dzF64M7de0xmc6q24fnFGYezGa7t6IYtidFiRBNuqDLGaAguhk8LlddoRZpm\n9G3PZr0mANPpGB1kEiS28EFycpzj/PE7fPDuW3J/fHipNf29f/I/CcXNO155/fP82Jf/6p6O2fdC\nXVHxgBWReaT1qTjBitTDfTNhDGGXL6KSvdZBR12B0RaVCWVO6RjwbDI6Neaj77/D6d2BcTZGZ1Oc\n0izrFYlrcKHDlgckOpfMr8Tu0eGd0DyEIGZBfS+gAoGgdwVcLNDr+lO2+Yo8SQg24eMP3uP9d/8F\nXdfvw01fdE2/+bv/4x5NfviZL3H7/puEINq+XUBzHnn2z87PJZ8xmsQEFKkVXa2gdzKpLccWm6R8\n660/lglMOYo5iAKWdX1PU7cMg6PMC0blhLv37+PdQF3XZHlBluVU6zUX5xc8fFiitabebuXnz+es\nVivccsn0YBanoQmjUUmapmzqCu89m+U1Tz95TF03nNx5wPTwkK1SBC9unp6w1+ZcPHuf86ffR5uU\nNB+91Jr+/j/+r/f//8EbP8mrn/uqUGODBPNKAxtdXoMmtVIYBDxohcnsPvgd7/EB+k4atSEGAZfj\nMUHBersmaEVTVdg05Qufv8OoHHF2doYPHuUCwuRRe63aboLdqRuq0a7R08mIJB/TD3L+pYlFB8vr\nr32RdrMmpJZyNGYyHjMpRjx6/DFt39O2jjsHD7h77w4/+Bdv4xVcf/wnLM/e/SEd6Yuu6fd+/38Q\nJ0OtuPPwS9x+5YuYRKzq0UL/l+e2jg6oCudCdCQDrQzt4DiYHIiG03eA53pxxdHhCYmxVO2Wi4sz\nVssrynzEuBxzeXZOkqaMJxOh6mrFnXv3ePL4KdvNGld4ytEIGiXW9FETos3NJHKI69u2LX3fopDG\nIULGe8bdrnkzxoibbZBiLgSh5/ZKcfnkXc4f/VlE3F9uTeX9/0cQqU6n9z/H7buf5eDwGK0VNi8o\nJ1PSJKHeVrz99jtMZhNSm5EYaQ5G0zFXVwuZknjR4WsjuZXPz885Pj4myzOCDvTdQLXeMHSOg6MT\nFtfXXC9XjCczstQyGRfURUnE0shsBr7nj//ojzgoR4xG5Z5OGz5Vr+90UwLAWslz3BW3NgFl4qRX\nzlBU1JhHZoIxmg++/x0+/sH34lkiRg4vuqb/xz/5h/tcubuv/zhZeYs0s9L3Kcm3kny7hHw8oWvr\nqD1WokdUAReBlL7v5WxI5HNnNqWtKqEoJ4ZmveLP3v4WxfiIyfiMvBix3jRcL5YcHBxwfHgLrRKu\nry5p2o5yNmWzWpMrxytv3ONPv/cW6iLwxme+wJMnH/HqZ16P56BIR7SyDG6gH3p6J1NG0TCl2CSl\nH7o9yG2MQelEJmHDwCcf/xmPP/weeu+0/BJ76j/9b6jrNYnRvPmln+bBaz8BKtnb86e5mI9NJpMI\n2huGvqOJurKT2Yyu7eO4U8Cctm25urri7t27HB0d4X2g7wdCcPuGMMSHTQW1pysmiWU8npClGWhF\n7zwGRZ5lQmMcRDKhjEzHjVIEpehdoBjPODjsePTxh9x/8EBYKE7MzuT5lAHCeDyW8Pe6Am5cI99/\n79t89P5344P/o10+/8JGLITA3/t7f48vfvGL/P2///f3f/6Lv/iL/OZv/ia/8iu/wm/+5m/ub9S/\nfH3tb/37ezH7bhQ59C3D0ImDogp7vniWi129axp5SVNx/+ujU5ik2LPnxO40YhK22O8RqqEfCMjX\n7BK8+34QRAfFMLSSGq/kMLM2I8vy/YQAPKGXyczBdEpd11RVhU46irIkaMfgAs53sekSG4rZ0TE6\n0WjlGbqaZlvjtFAxbr36RaZHt+mqGnTGe3/0v73wmv7U3/x3CUNH39Z0XUuS5nsROEqhEaT6YH5C\n2wdm8znz+YzD4yOMSVgsFgxdz3a7RlcbJrOp6K9iAVzXtYhAo0OTDYGm63jv/R9wenrCqCii9kSh\nnOfWyS384XFEBcWJyibmUzQtmB+I0LptGy4vL3HOUZQFeZ4R8Cg0mRLa166YHfqezWYj98lq+l5o\niUEpxuMxr7z+Y9x/9fP7ydrv/eN/9MJr+rmv/puCXFihQWWZIdElAcislZG+gnq1YHHxnCQvmdhs\n30SaxFJvthiVEtqWYjJmNJsxtIHj+U3a+3Q6wRjNxx9fUGQTMpMSrMQ4XF8vee/d72NSRZpOeOft\nt2mbmtN797n/yuuslguCTjBJRlAKm6ckRtM2GzQZ3aD2+qH1phLqopFE+slsxng6jZS2HQ9fmtq+\n7+mdOBfeffVL3Lr/OdlUmpbv/p//8wuv6d/4hX+bpmlIjGE8GYnNuU0ZBsdmvSB4z3QyFvBlT5dk\nL/C1Rhof2TwH8JGfrcWJcHe2V1WN947ZbIZSAZUd8PzyjOC2zI9mpKbBacOTR2c03/+AW7fv8sZn\n3mTdJoxPPiPuVm6grjZ0XcdBWu73AtGoycQkT1NBQf3N9MLo6GblB8bjMUNEmnWSoBNL09Rk5ZSv\n/vWfIzGGtuv45u/94xde05/9hX8nagxAa8mQ2mw2Yg5hlEQAKKELFVnGZDzeo4zs7nki5gVN01JX\nFSZJOJof8ZnXPhMpcbvMtYDREiyeHAp1TiFB8mqHPnpP19RcX19xfn5BcJ68lPWbzeeSw1bXe2YA\nqGhatBLr78HR9B1Hx8ccnhwzuIHFYklWFhwcnuA7L8YjkRXhnCfJJ9x97cu8+rmvYbMCjOV7//zF\nn9O//rd+WdDLOH3vuh3t2cQccRVz6BQ4CWRNyxJjtOR4RVMniFRPo8Xdd7Pm8PCQIi8xAQyafDRB\naUVvM4bBcfHBIy60YgiezjuhEgbRVlmbYozda9TyskR9iu6bZpm4EnYtSZJCsKSjMV/4ya/yjf/r\n95keHJBqHQudQJKnPPzMa1w/v2a73RCWS67anpPpmCF47nz+r2B+7GsyzRwGvvPN337hNf2Zf+Pv\nwo7GF2mlzU6sHoOS/SBT79556q4FJbRVq+X5S2zC4voKbRLyvGCUjgVqR3G9WNL0nWT+hUCaaK6u\nn2OSjMENVE2NV7CoG373d/8pWTpmNjtAJwnVdktRlhLT4YTSpY049iljcENLVVV0dYXyErPQtU3U\nLIUbjUgIewAGQH0qn9Abg7GWW698kTuv/hggjJ4//vp//8JrCvBTP/tLUrtEJkDXddiiJM8yyfkb\nxDHRZhnWSLhvUzekNiUj5erjj2nrRlx8Adc7OloSbzk6OkLrROziUSzXa777ve8wDD33HzxgOpmS\nZ46mrYGB5ScLRoUY3LjexemfIVGaspT9fohTj77vKcsSj2iobZIIK0kFWduoW5M1FUv3ncZc8A0n\nmrdorHT7/me5ff9NvHf8r7/9XwK88Jr+3L/1HxCQaX7Td0zmB5w/eULoOqbTicg4vGM2O+Gdt/+E\n115/nawo6PsOouvz0PcYa/csEMln1ayWS5lwJ9JgzqZH/PRP/yxplnB+sZBpSmFIk4z7D9+g6zsu\nr59TbddC0a9rsqJAabg8WzCZnJKicG3L6a0D8tGI7XpNkuT0rqFtG7Is3wdKq8hq2GfnIf/bdT1J\nEql70zH1asF0espP/vQvUkwnKA2/+7/8Vy/8rP7Vv/FLrJbPUQyUoxHPz5+CSsjzkUxnnTAyfN/h\ntcLaDJWm5EWxv+8mESt77RUYTVlkmJOjSG+U+jOLESA+ghM6TiojpyECYSKR0drs89tCfHcUAl5q\nI5KIrm8i3T8htZbZwYFMdq8vefL4Y45PbwuoGRu/ruuw1rJYLAjhxsm4d45Eax6+9mM8ePWL+z//\nxu/9t3/uev2Fjdg3v/lNfuu3fouf+Imf4Ctf+QoA/+Af/AN+9Vd/lV/6pV/iN37jN3j11Vf57d/+\n7T/3+90woCMdbSekWC5XBOf3B3NejoSvbS1ZUdA1rSC5MRixbuJG96mfoQLgIrXJR01S5IQ7N2CS\nNFIhICg5MJNoIgFK7IQVhEjPIbB3dbRGR6F6S1VL9kvQki9Co5geHKKD6G5c78WEYTolyRII4hzm\nBi+CXRPoWvn8xlhs5rg6ewTA17/+9RdaU6MVJGmcBCVYmxGCPFA2TSmKgtFohNaaWycn2CAISLWt\nsdaRp5mEEyYJWdyQm6omhECaFxRFyZMnn9D3HXfu3KEoSl5/4zOMcuERX19dSqbVwRw/BJI8I8tS\nuqaRTfvh/SgqRlx5hl4CMZWEBo/HBUpJEekGcaTcbcI2TfdudjsdE8aQqgyNpvHd3hnnxnkMHr3/\n9kutKd4x9FFQGQLb9QoCnJ7eEqrJ0EtwY9VEKpJGhwBtQwDK8g5pnpNo0bgRoNqsWS1XGGA0PUAp\nzdmzM66urzg6vcWTx49RSRIRrEE0XqMR1fUVddOxqbY02xXqmSYrpxRpSjoagxLXUT8MtE5AhmFw\nUhgmliSx0XEqwRhFoncAg/y9c92eMgXsufg6WvL3kft+9vGfvdSadhEd1PrTYclC/yuLAhX/7SRJ\nGIKYejjn94Hio7IUXQAq6jpjI4FiW1V7+9lyXLKtB95975pMPeX09kMOxwVOTeidZbnegLtAa0NW\nzvCkXC5qVustH3z4hwQ8fdtjkozp0TE2C9igxXXVe3BetJ8h2uIj+U4Et+eCDztaTaTRBAUqMaTe\nMh6VIgY2CU8+/uCl1lQFouA9ThODaIy8c2Q2JUktIdKQdu/PzgGy7zppoLxHYchTcf4kSKMmxicS\nDGoSKUqVkoydxx8+JkktaZbTbmqGtkVbxfr6mqIomB8ecfqFY7ZNgzEJdV3x7NkzrE25e/cedV0x\nDD1NVe31ZF3UeeZpRlPX9L0EOs/jpF0rzWR+gEmtPJedPLd5VorBTCqNyrNPXu45PT4+pmlblqsV\nfSd5L0op0X8qCRcn0hRRUd8YianD4CE4tBGKtCFh6DxGGSaZ5Cqmty0XTz9hdbliOj3m8OiY59fP\ncUPH6ekdDmaHdE3HYnnFqJywc8Nstx2bdom2noP5AYtVRWqz6DwpTZ9TXoJcncP3HXXf8/gH7/La\n3btsl0uh96qdw5/GYJhMCp49/4Sq6bh3+wG35jOGCOTtgMvrJ++81JruGtuwoyInCaXNhHru4xRG\nG4ITu/j54ZyApo2Outpo0qzAhSpqoaSZT9NM9CDWUqQJzre0VYXzAWskrqNpa/q2wKhjAoqh60h0\nzXrlCG7EeDxhUddkabrXpu+mLt5Bnue0VU3wEiWCEg1xCCp+bnE/EvZDg9sFwHLj+GrTdD/Nd060\n62cfv+QZBfh+2JvhCNWtpxhNYqizuOZprdBG07Yt42zKeFxQb2suzi+YTsdk08keHN9h9EPfxwgL\nT16UwvRIU46ODnnt9c/QdRI5kyiPTVLaYUAhhhWJMdhSDCyqaoO2ogmXrLKbKUDf92LOothHatzk\nLYZYjxmhR6tdfl40VIgUeu+chCtrmfB98tE7vP3dbwC88Jquovum1oZxUVBow2g25erpU+qmQZuE\nNMto2oq2bXn65CkHh4fCMtJCtzNK7Wuw4D1tVaNUjOEBFBK9tN0uuXh2QZaLy6cycQLoNnzy+GPS\nzIpmNJV8xizKRBwBx0CSZGgf6Nqek8MTxuMjls+vounZTjMs915pjQmyT3nlqevtXlCX7gzFlCYx\nmuRgivdLuqGjqtZ88uGfvNSzGoLIjLq2Z7W4FkpsWuADtG1NU+dMJ1OSNCPNMzyivyrLMgJ0N2ZS\nwXlcjKPJbLr/Pb0IO0UKYDQKAXD1ztFskHM6hCDT7kglDS3R9VpqVk/AaCWNX7Dx62UoQfSvGI0n\nvPfuO+R5yWRnQx+Bzf0Q5F/6/V2UUoRIff+Xv+bT11/YiP3Mz/zMDxkhfPr6nd/5nb/o24Eofo7F\nabPdRsv0Hb/aSlGupBitLp7jhl4sLRVst1uhq0WNmOdm5Bh/eKQbqh2FmGHopAGLQkkfpCtu6m3k\nPoeYlSQiS5tYVNfhg8dohQ5G9B5a03QdSZpis0ycz7qOvmvR1gii5H103crFSCIG8+441jK1G+ib\nOnb6genJAwC+/e1vv9CaaqUwqcViCWkOIeBcv59oJXET3Y2f06wQl5zoQqm0IAhZmhF8oGs6jBUu\n+KNHj7h3/z6j8QQfrcWVhrpaY5DNtG5b1pv13sji/PkzDk9PRVvWt5yfP8Now3Zb0bUNCmmwpEks\nybOcxfU1zjtG4wLX9RTZCJNZuTfe3XCbtYoHmtB1bCo6gqIsaZqtbHzB752TXnRN00wQ6qapcW4g\nywrJS+oGQtOijSYvHGDIRiXeeXo30LZBAmedJ9iMrEgxStF1Ne1mi04zQtthEk1qE1w05tAxv+Ly\n6orxaIIOms1qxfTwAB8849GU1CZcX56z3W559vQxP/kTX6GqthIYnqX0bSNmBVmOUvFzdIMUz0l0\nFYq6S0K89/H3FQqoFAzWWpmqRIpfYgzBaO698rmXWlPJgUkwSkuTFYiCWRWdEsXtD60JcaPdCXLT\nVCZnWps9BVmcooTmIpNsS93C4FrUUGH6S7QZUElG0w2sl88ZlKGcHxFCSyAhTQu6tuHxox/Q9R3V\naolODOVoik4Uvm85OnqN93/wfSZ5vjeE2U0JiEWyFDFK7NxDLNQJexrb0PW0XUueZdhU7SmJb3z+\nx15qTdsYaKsjAJEXJcE5BicuUErJJMf5iARGoCmzGZnNcW1P3W5xScpqdYXRhoP5nKbtqOuKvhNg\nJCC5geWoFNp3kINyuV5wdX7BbDqh20hAJ9pQ9sJIaKotaj4nsSnT6RxttDivPX3C/fuv4L2jazrw\nRIMOv5+YBA+JsajE7C2AbZqStG3UTalIwRWgQcVi7u4rX3ipNd2sN5GSolGRUq0+hXoC+8Nf9lg5\nj4iOaPt8Iy/2+u16y9B2lAdTlDFs1x1Np9BWLM3X24rGD2iruFxf0A0dic64uLwkKDlTyqIgS1M0\noI3HuITQ9zy7eEpZTrl155VoMS6NuS0ysjyj63raqmY0G5PZlJ5+//lDbCZtmjObzEFthYtgElFb\nqmjwAtx57cdfak2dcxLC/ikDi10QLk7OIKVlUjA/PEIZTdcNEPUrSmsx7TBWgJbtlsX1NbdOblFX\nDeP5AZvtmq5zUsj6gElSuq5BKzFS2C6vhS3ipWHxw4A1miKXmABB3RN82DXeCTsVR2oT+tTGpkam\nwc7Js6qiA2lwPjrTstcY+liP9F0n7IRoAIyC0/svt58CeyvwEFTMhxQqslZm36h457BpIpPHtiXN\nctKioIgNj40h7D4EdAixVnIYE/bPgAKyNGUWWRRaJzjf42Ko7uADxiqGocENMmVPUiuUfK1p6hrS\nlCy1e4MIpSQQVyKIoow3uleKPkngDeLf7eiCIcgEOPkhAFYmPfcefJb/6D/5h/wX/+nf5a233nqh\nNd05mwobJmEYHKMsxx3MxYk0RvWsNlumsxnbzQbzqYwz70VzKwCmAIbWCohlbUrXtgLooXF9oG1r\niT3SGheg7WKEkgoSNaQUaVoISBvEkMuFHqOkou376CQbFE8/eipumGlKG3ZGMjJICPIBRDsejaQS\nKzRqD8Ik0yJL0CYjH41w25re9dx99Usv9az20Up/cJJLquLnUkr+LjiHCpAV5R68VGkmERNK7Z/j\nXe+R55L1W9c1SWpp246nZ89o2obJZEKWWrabLcfHx4ynU1wroN/R8eme0VJvt9TVlq5pBXzsh/0e\nkCU5WZ6JlCI2VkYpnNaYJGE8njI/PKJuZEKZpRmOmzNBjDyim7v/YVCLeG69VCP2sldZlmI4EKkx\nu5BVr8QVaoeIeO/ABfquxaYWm6V7+qGOCK/zkmj9Q4nqsQjycUqmiBzO0KOU2PjiHZ3rMU7f5AxE\nnKJra1SQDTYIOR3nRcBn8jT+EyEO0WQx+6GnsAl5lhMyQdBSaxiciMyDczs/C9mAAD9IoPTghr1G\n7EUvHbt3rbUIvbset+1As3eXw3tQ4hCZZWlsakSsP8QpVWIMg3N0TYNNU+bzA4ZhYLFckGfyYDrn\nuL5esl4tcIOnqmtWmzUgxgHj6YTHTx5TjEdoY1isllSVJgxSiLZNTWI048mUJCsoyjEEEZhiBLmL\nY0uyNKNp633RPXiZKirnaaqGwfUom7AOmtVyQa4gNT/sePbCVywWosv+3sHRA4nNsFasin0QatbQ\nS+Cn0ZCnVqyRbUJeFuK6tZFm8uDkFNMHUAN1vabvB9LUsl4tODyYc3FxTmYt1mYMg2QQBaWjbX1J\n3w80nSDEg49hr/GZSmO4sE4sXStW3s71dEriC9LMohK9R3wSrXGRzrMTmH/aOcs5QVuViu/QjwBg\n/rJXEhuGmyvsw2qJxe6OPiCfI35fNJLoul6AgrZDaaSpM4bEStC79xXegRvADQHoqFrYrmvWqyuW\nyytMPkJnNu4zHUkmAezbag2DIFpFWTI5iCiXb+i2KzrvQRsSowk6uon5Xbi7F5vwuOl652SKoiVX\nTykxlNHKiOsT7LVv/yrR+F/m2lv/E2i7nkW7QClLRqBtWlzvKcoCozSD70kTwxCnInmWUTVDPHg1\nxhALH3GL22y3DG3FZr1mcJ48L0mXKSqxdIPHWLGw32xW3Lt/l/Vqg2fDcrMiaEWZF9T1ikVZkuXj\n2Bi0LJaLKMC2XF+Lu11qLTYRlNiomywnabYkfysxMl2Wgi2umw/YwpKkNp4H/l8pgv7LXJcXF6RZ\nKjTsqO24efI/NQ2Tmyh6ItHr7zUwgOi06gZCICkyqqElT0e0dUDrMVkmzr3VuiIdTRh8z7bakqYD\n86NThuDYbCXypO1a5gdzyqnkPSplKbOCrbGAR2lHlqVU64Esz2QKHjXL1bYWp1Cj0U7fTG3j1DME\nzXx+TFFM9sYTKp4lAX4Y6HzRKwITPurdiI3sDrjcsxqMIS9KqkomX9YYAmLE1US9hbOWrhWKHSjc\nIMHvTSMZmiZJoyNcEq3bR1GP5RjamgEITnKF2sFRNy1lNHjZVhU2S8mzNP7snl0UiFIaYy1ZXu4b\ncRc15sGLrkTzqbgBH6JVtce7htD3QuONWlde8t0HCXTWNkZUDG5PEa6325gjJfVREnaZVT4yhCSc\nHedJs0KAm3gviA3Pbu+Xc1Ce977ruLi4IEvHBDS9E6fGoDRZLpmqQ9dFB2yZuhhVcrlaMZ/PyfI5\nO4c5HSeJwe/AC2k+pLyLrpNReuKDiq6pih16qI0hizE9PvjIPHnJ5xQxMpJmW++b9ixLmYwOZH8H\nyBRjFKlWNNuKdltRZznleCzZodqg1E1G4B50AFCK9XbNdl3RbLcSxZNZBtdTNR1979DKkBhhe63W\nK8bTGUnIaPst42Ic96SAZ5AmJwScC1w/v+LW7Skmk/dNRwqenEk3tvBaxyljpJP7OCUiiPtqGATI\nSJKerm+kQXuJyw3D/j6GXW3FznBPcjzD1ssk3ojzMWhUeqMPZ9fkOE+WZdg05ZOnTyjKgqbpOD9/\nBloxO5iy2Wy4Xiwwiaaqa2FQtKJrznIJk9+uVmy3G6HKDj2T0Xg/ufZeWC3O+/17umOdJLHmePDw\nVZbL1V7mpKPOcUer3QEEgVhD7f8sAnb86DX9/70Ry4piH968n9hYKzcp8jqVEn2DtQnWTqJmSGGt\n2qPkygji75yLVKH4D+w2tyBZVUELXU/HojkEcT4M3ssYGL0XgRqjqfs6HuRCNfJDwBGnZV42474T\nAaE2hjwvyIs82rtLmJ84sYFSVqya6aPhhyZNLX2SYBMrGWoo0jR/qTUVyn90ejFGQukayUGw0UFP\nUHKZxhkFXgvFJsTC20Vr0a4faLuOfhgwieb45JjrxRWL60uKUkT4280a5zx1XbFer2iamlE5Ji1K\nRpMpZTnBdR2Lq0sWiwWH8wPqqmY+n5MkKa6vUcqT2ITVckkyP+T45IigxJ3HGMO6XqNSHcWOgfF4\nRCAwnR9Qbbb024qqaeg7TVtY1s+f88p8hs1TESonL/coiyDck6biYubDQL1ZMz06pRyNxFHSDQTn\n5PCVkgWbJKTjEXlZUIymTCYS0mwI+LykTFOm8ynL1SXn52eCvsebOMpKFHKw+RAwNmFwHlREfmIx\nMBpPOLl9l6vFNXki43STSEbGDlnu6i1GiaGNd5663pClCWm0z85SS2oMvWvwQy/UXMANPW0cnbfR\nYXMYHF0/iBblJS7vHEFHe/AIEOwoMLtNa4cmffo83TU9iTEEJ8WAtdFyXhlMWtD1a/q2wuoMbxKW\nW9iGlPVqzWiyFEp0PsFkJdcX5+ANQfUxjiIn6Qdc6FEhkOclfefwOBQN7/zpdzi69xqj8QgVC77B\nOeyOKqvU3hmx39kXR1ttuzPwceLQJsVvsncte9kCd/ecex9o2p5HiyXTo1ukdYX1ntEowaQp5XjE\n9dUVWhu6rpGmNgTW9ZosEbv0k9MTVNBU9SDvpU1wnTAV8qIgzQsWy2uKQnN9vU74gNoAACAASURB\nVJBw2hCiRfaMtveYZMW22si9JDB4z/n5OaNxQ9PIgZjYlKPjY1brFVW13eupQET7JiR790xtZALp\nuhaMPH8mrqtzDhcgSS3KqFiIuZsm7QWvrm8wiSJ4S4hTxZspJ/sDVO+fY5mK3GjvPIlO6ZwjoChn\nE7Lc8PSjj1GpBl0QPGw3FX1XoWzB0aTAD5pidMj04JDpbMyoyHn3gw+hbVlXMq0aewHN7pxOKM2M\nB+UY51rC0FBORvR1RzEes1mv6bqOyXgMwWGNod5FMETWwC6A1HlPmuVk2e4cksydvc2+38kGXvza\n/SzvPBqFievlnNuH+IK8D03TsDcfUTttpoSgOudEIlDV7O5GVhSsl0uq9Qa86Bg73xIGhwtB6LWp\nReuoP9JiPDMETd0N+PWatqm4des226pimiSYxMpEI9pQayOREN77GA0hAEzT3GjTZcoT96uhj3WG\nI/heagnvxRjHppDYPWPnpdY1kTxUQd9jHppzbNdLnBPg0hgJvh+cY1qWaKVotlt8N3B0cky3o34B\n+9B6LZpTdhOy2IyHEKL5kyZNc5m2eAF4tTYMLhom+Er2pGZNZg3X11cU5Q8HlEPMQYva8bglypRP\nIsKkcRx24GDMdYyPolLsi94dwCfN98s9q4MLGCUmMX0nmlZ7fIRzAdeJM7exhlFRyKQ7y/ZZsn5U\nRoq4/JfYFCJFdddEJIml65Y8f/6MZltzeucUrQ1938k9UFoA8bYjTQWc6poa78QCnjRl6AM6S9GJ\n0DpNkKywyXQa6bKKIi9xSSqTpBDIPpVtp7TCYES+o4iArpjiSO6sRLMkicUmAy8HwYKP7K99bqeP\nzRkSOC36xhZQ2DSHCH4YHaOOlEJpQ9u0dE0DiHv5k2ePyfOCNC3QWjOdzTg8OuHs7Jy8GLNab6nP\nL+O7pzg7P+Pw+ERYSq2Y8zjvWW+2jF4b0zZN1CM6qmHAexdrqp0pj5L3XxvKcsy2avbDDhXre601\nTdPL82kTEq1+2LF4X2v9f5Qj9iJXtd1G9EM4vg7J1RGh7Q29MI0jwvV6SVVvKXLROV1drZjOD8Q8\nww+oXmh4xE1oJ5T0IUgGkBI7ea0NRDtRH8Q5xcWpkElMpPM5jE1JrCCKwjUc8H6g7yvYeHQSm0YC\niU1xScrBfMZ2W4kmKzGiCxscWVmQpVM215dUXYMyOWlmcaMROsjUTxy0Dl56XUPUYHktYdPz46M9\nD1WyFpxobdoWlBQRolmTBHmxCVesNxuyNKMsR7z9Z29z995dNpuVGJTUBdPpjCTPuLq6YLVccXR8\nwvHxKXVVcfb0E7QOTGZTVstrlEk4ODxitbxmPJlwfHqPqhLb5MEHbs2n/P7X/4Cf/tpfI7UjcW3y\n0jzUmxVpmtANQxzrQlrkzF65i79eUZY5148fc3lxyYODI47v3scOLbs8KfeSTYNOLGFwZHnGaDSi\n6xqqzRqC6BO6tgIGZvNDtLJk2SFNU7NerVivVyyWS77whUOKNKFtBoK1DKrn7OkT+sMGhSZLCxZ+\nzXK7ZTY74JOnj2P+jEyjPIrttiYET1VVFKlozoqiJM1K/NBIg2VTJrOZZGEMLX1XE8IgUzmTRnTS\nsbi6JMsrxtMZRZaijY40kV2WniCsiZUipO+7PbW1mEz2blQveu3cSn0EYEKIBV/cpJxze5OOPmad\nwM3kyCQG5wfRF2oJ3R7qHsJAd/0J0+IWwYxYLx+zWnzA/PgES8bhw4cMrqTabhiGBevrQRDHYElt\nhleKwTvaoZe8td7TrVd412J0T1c33H3lTYpyJIHviyWbzYZbt0739ERi8LjxFhsPDaUUVTS/MMbQ\ndb0UvdYi1tY7IfGLXzs9SgiB3Ka8duc+vQo8OltzdHLM8d3b5LMZs/mUbVUzdAO9F/RY1YG62VLM\njvBe0bU9Rls22y3f+fZ3+Nm/+dd5vF6g05zD4zvk5Yi6bVmtlswPj2iaLcE5prM5y8UVZ0+foLXh\n+PiUarvhww/fZzqbcXJ3ynqzYbVa0rY1RVGSZRlPfvCEz735edbrDevNlsP5QdS7SRHk46R+l8WV\nZBlZlpEkErCZ57lEafhBvm54+aktiF5g1xwIXUgazsELPVq0KjEnKH62nYZMBwhBSdZgAq0LYBVp\nkXHn1j2SXHH27JrNakm9XRFcSx5g+eQJ46MTyjzn+vkZ77/9FkPvOb33eazJsTZQtQ3nH1xQlhl3\n7t5D+x3PLSMER71YMy6nGGtZrlc8Pz9nMppw9/ZtFtfX+yJ3R5/c0fS1iW6fCFDpIyrt3U3m34+S\nI/ylLyUNbIjdh0mE7p9YiW/YvUdyLgu44YYBH6cJO4OOrmtJbEY3BNpe9qxiVNJuenzf0lQbMQFK\nM7qui3uOoe9a2RPtiMl8jh96NtstbdMytA3PqyXz+RGH8yNsloLWaJtgBnmPk9Qymx/um7MQAq7r\ncE0t+aCRneMRBzUAgo+xOz0oMfsyg8ENPS5JCWn25y7V/5tr1+DuMv6sFXOyfDym78ReP0tF962B\nNM1p2456W4lhR1Hwp9/5FkfzI/JMoky0FqqaNUY085FxMRqNeO211/jw40e0zQbXdyTGkmcFm82a\nupbGIbF5zE/ywCDGaaHfM5eERmhomoauE1t0GzMvUTd5o4EYKB8nNqmV/Ey3o7cptT+TjDGY1EKw\ne8fFF712YJnznvV6xXqxZDQaUW23aALleIw2huvrSw4PTzDKS8ZdkUfzqX4PykgW5+7eSESDMZZb\nJ7co0pzF1bU0sq1nu16TFSNskYtxRJZS11sOT++QRtMYbXKOb53y7Oljbt2+y9B3dDajzErSLOH4\ndETfrSMIKxIAbRTGyzBjJ91RUffqXCCxhqauGJynLEsSazA6wXuweY7JUnE1fomr7RqpMRQoJX4M\nAhTE6VwkGLgQ6IaeNLLfBjfsz//gFVeXV1TVBn/u6IbouhoU+WjKya1bKDTXVyuMziiLhO32iixN\nSaPGbhk0AUPb9aRpSZ6nUmeYDKzl7JPH2DRjMj2ga1uMMnRDtz931NCjouRgGAYODg6k54h1qjEJ\n3js2mw15nu+d1CX/0uwbcoi5wz/i+tGkRaBpGr72ta/x5S9/mS9+8Yv82q/9GgBXV1f8/M//PJ/9\n7Gf5hV/4BRaLxY/8GdvNhqauqbdbNts1/dBzdnbGdr3BqYDJUrGyRQ630XjMZDLF+8DiWpxIOsCX\nBUk5Io+HupIkVhQSyJYkkg/m+0HyPwYntqix+QJQ8g0R6fHS0eYjbF6yD4JTij6GwxIRmKIcMZke\nMJlOsWnC5cWFCGMjNUYFTxgcvu9puw31diU5XZnc9PnhAZPjY07u3ePW/buMZ2K3/KLratNMEu5N\nQm4yEmUw0XFm14QNQ0e9WbNaLG46cXnyGdoBguLi+XPKouTo8Ii2rrh89oQPf/Au5+dnDA5u3b7P\na2+8ifeBw+M7PHzj82R5Sds0uOBpmi11vaWqKrJ8xHg8FfRaQ7dd8f67b3NxcY5TmqZzvP/BR9x9\n+ArpKGO1XnL+9JzF5QpjDQ/uv0Ke5pyenHL3/n3GownWJ3znm3/Ae3/8Lfrn15zYnPvjCTMD+VAR\nfLdHyXZheS+6pkonkSPsWG9WdF1HmuekiWU0GpOlBfW65smHj/jggw+4unpOkiTMj064//qbfPVf\n+9c5PDm5Qf+8ww8dx4eH+EF0gyfHt3nw8HXGkwNAM55OKcsxDqFhuqHj/PyCrmtJU4NSgePjYz73\n5hdoqw4XPFmekuc5q+WSDz94H1Bs1xVn5xe0fcd4OuLo6ID57IDp9IjDoxPKshTheXwf2raljcYH\nfd+xXC1o21aabptibEo5GpMX2UutqUls5L6rfeOltSbNc4y9OYR3f36DMqsbKqBRHB0fMpmOCUlO\n1Qea548Zj26xaR1X2yW1A/QBvlako1t89P4nXF48Ybl+zsePnnJ5cUFV1bRdoBsc/eDAiSZyfniX\nqtpSbRd07YBnzPzWq6Q258NHj7heLCiKgtlsFqd2OyqlBpVIAacl/Hvw0LtA1zu6wUXgwzJ4cV/s\nh4EqOqy96JruDwgF+I58qJgZuD+ecGpz+udXvPfH3+I73/wDbEgYjybcu3+fk5NT8jTnwf1XMNaw\nuFxx/vSc1XpJOsq588oDfvDBRzSdw6E5vzjjg3ffptuusFpcA8fjGVk+itSPLU2zlWDcpiErSh6+\n8XkOj2/jfeD1Nz7L6e379AOcnz/jw/ff4fLZJzR1zfHhEWWe8/z5cwhK9iMX9jQjNwysFwuazTo+\ns7If50WBSVMSZchNRqoT0ZSpl3v3i6IgsSk7y3odhGkgW6ZMCDzcCMhVYHCigTAmochHaJuwXq5Y\nXF6wvL6kaVumh1PybBxDyRW3Tk+5d/s+B6M5Ni24Xq746Ad/xtMP36PebOncQDadMJodorXQLw9P\njrC24J/93j9lu12SpDK9SYzFeYUxG5pmw9HBnM88fI2ToyPYNZNx6uRi46CNwaZpNGrw8Qy8EZGr\nqCV1w0AbaYEvuqa9GyjyMWU+wePxiIW/IMyJZHu2g2SGBdEVmSQR7VKkrSVpxmgyoxyNhTIaFGjN\npqrYbrb0bYNWMiXuu5a2bSiykVDwvBdDMJ1QbTY4F01YgjBmjo9OOH9+Qdd0dI3QHr0P+4n2bg/S\nSpOZhASo1ms2myXVdknfVgTXgxNzLonh6fd6db8rNF1P122pqxXr1dVLrSlAH5uqarsVy/y+p20a\nmm1NvRWNpw/ictwNN3ur0Qnr1YrvfOtb6CA5b2kiBiPDIJOXIQxRVhFAa2yaUY4nLK5XeA9t39AO\nFdoEUJ7ZdEKepgQ/kBjNfDYnS3M++uBDFotLvB8iACcTmMX1NdXOIyB4tJZnM9ESoi3NnExsQxCG\nCsj5nqViBrIDueTc8HRdwz/8z/9jgBde0/V6y/XVNZvVSmKUEs0nH74vETXrDc+ePePx0yfMTu9w\ncXGBSi2mLBhQErkjvNW9LGWILt9JImYmt26dcjA5xGAxacrxvbuc3L1LmqZoFaLJhOFgeoS1I0bl\nVBy+g2a12PDuO+/g0Tx9dsFq3aCNxShFU3uSXKbzXT/sm1xxf04JTujIu4l+0zRiLjOI4UoeNVkE\naSxClIh4B3X1cucUSqzxd1RnY2ycuieyn/oooIxU7/3kToHzQ3Ta9Tx4+ArFaAzGMjs6ZT6/zYMH\nn2FxdU3bNCRa01ZShybWkKdjgoe6kXfBJglFkTOZzZgeHjKezRlND5hMZ3gPWTni4PQYUs2Ti2cs\nFwvKtJCJct8LjT+6Le7o83VdC43fOclt9Z75fC6W9cShhzF7FuAeWDY/WpL0r2zE8jzn61//Ot/+\n9rf57ne/y9e//nW+8Y1v8Ou//uv8/M//PO+++y4/93M/x6//+q//yJ+xe4HGkwlHJyegoMxzpqMR\niQ+4umYYOtEsRdqNtRKqNxpJrpfqOnzXglaoJBHt1y6AUUVULyKVJtpT7izQTWJjeKQRJ8UQosg5\nYDMxKuhil71LjM/SgtF4RprlMuq3lizPKWLuTV4IOmuthLT1MeCzH3p826NDQmIycX3MMtpa7Fmz\nUUk5HTM7konYi66rJpAXOeV4RJanEByuqVkvr6k2G55+/CHf/cM/4OMP3uPw+IjRaIJNMhKTYKwm\nSROK0YjDgxmjoiArMiZHM27dvccrr3+Wk1sPODw8Zr1c8Pb3vod3Mj5u2x6PhaTAB6ELXV+vcT6w\nWi5ZXl3JSDpo0mKMNhblA4ns22wWa2aHU/7FW2+xulpzfHyL6UzMBrZbMd7ouoariwsuz57R1iuG\nqmVUlIzKkrLIKbMUmg6b5tG5TBr4PAqwX3RN+64WtAV5hrq+px/kc7VNLY5aSuOUYT6dUaQ5TVXR\n1Gtcu+XR++/wvT/6JuvlJcPQYdOU2cGRUEkSw2x2wAcfvsc77/wpWgX6pmK9rlE2Q1uLiWnv4yJF\nOc9qseTs7CnPnj1lvdkym40YFSVKJ7R9R5Fn3L13myxNmB3Oefjqa0ymE2mOt2vyPGd+dIBJTETD\nYGhbNqsVGEVRShB0koj2ZOhF0+cI2DwnSVOSSF160TXdoeo7Ro6ABKLBI2jcILS/3RRX3ikb0fpI\nD1Pi5EeSoZWnUB3j0YxqO3B2veDq4gnri8dslguaIeXjJ49YLC9ZL54wbJ5Tak+R5ty5+5BEw/Mn\nT7h88hTle4osl2I0QJHl5FYxVNcszp/w1h/+3xTliHI8kWlNCDgPg5PAZt/3dG0HAXRQhEF+jusH\ncX1tOrq2w/cDKtKuZG/KXmpNd6HMgnhroXU0LUWWUhS5vCtFyVC1dPWKy7NnXF1c0HUSCbLd7qZa\nI46Pb7G8WvEnb73F7HDK5nqN8sT3NaATS1qM8UHjh4Hl1RWr5QrnPNfXa6y1+KBlP8DStj02zfGD\n4k+/9102yyVHh8ec3n7Ag9c/x+nd+0yOZmRFxqgsmR/MKEYlSZqQWHG2tUnGaDTh4OiQDz/4Pt/9\nw2/y9NGHbNdr1strXFNDcGR5SjkZRVTavtx+aqw0ATuaLH4/LRKKojy0O6e6oe8hSCi5VlJEfuXL\nn2M0LXBekfqcfLA8evqUt/74O5w//YR6u6HterZ9z6pZM55MyfMRk/ER5XiOKUtGR3d4fvaYIs/J\nRyOG3lFvarJUMTs64eLpBUNbMz0YkY3GQqTdJvyz//13+OiD90kyCdY1RuhcomNEtExKkSTp/l3b\naXZ2waMQg0rjNCRJXm5NjRVdxaeLrCRJmE5mZFmBRjN0HYvnz6mrDduq2mcM7vaK3efr+14yJY8l\np/Hs2TlZJjVCkiT0zrFarWRi4lratsLjSdKMvu9YLxcMbUcSTYCMNrghsFwu2axXbJYrLi8uuDg/\ni7lEQSZbfYfrGgkq3m4Jrie4juA7/CARPLsMI6Er3QBIuJ22XIBim9g9FfRl6qkdc8DFgn8XIL7T\nqmgtlL+jozmTySQWgJrZfMbD11/jiz/+JWaTCW1d09VS95ioE/ODox86+pjtut2sePThRxirycuS\nohjhPTR1x3RyAGGgbVsOj0+YHMzZdh3Ht+5x97XP8eZnv8zB7ATvPG1TsVxcoZXm4OAght9GRtJO\nNxuEVbBrtBJrmU4m0Vkz5l1GhtPOXMNoTZYX/Hv/4X/2/9D2Zs2WZul9128N77ynM2TmyarMyuqu\nVrV6Qt2SAtmCICyEJMBYoSvBBYqGL8CtI7jxPcEHIILwRUNgIoQjEAYMpmWbwQoJG8myJrda1VXV\nVZXzGff0Tmvg4lnvPtlG3VZkBjuiejiV5+TZa79rref5P/8B4LXXdLu+ZBw7MWKxihhkslVWBcVi\nTnF6l/rkbXLTUOeWMHiIGqstJiqch08++YSLiwuAg8nOBBD99m//Dt//9GOUQeQGwXB+cUlWzjBZ\nuoMIjH7g6GTJdn3N86fP2Ld7yqqkzAtms4ZqVlHNGvK8SJPDXnSUcqGSZRlFWbBr91zeXPP46pJN\n17PfD+z2PavjFWM/sNluD03ufr9nvV4ncG86/yB/w2dVp6Y5y0ryQmoNbTKUydFZRVY2ZHmVtIDh\n4NgZgkxHq7Lk8uKSoetTzV1RlxWnxyegLc1siQ+RfuixRjN2ey7OnzKO3UE61HUdRmva/Z7ddsN+\nt6Pre3wIZFnBfrtltVpxfn7Bn/3Z99hud/Rh5OL8BX27l7NhuyEmmcQEZFVNLXFPCUjohz7JnYRt\n5MdBAI6hS1oxn9xMf3i79SMbMRCzDeCg8zo6OuLv/J2/wze/+U0AvvnNb/Ibv/EbP/T7ldXifrjf\nc3V1lS4uc0BzghenwbywB+GmTjx9YwxlWTB2Lf3mhm69JowuoXoqcV0diJdiEp3eptlPF44ULwHS\n6HsyCHGDaLlUlMtXxKPStRtbYLIck2Ui5C5ybGYpKhENFlWOsSIq7/s0LleCjhdNRVZlBD/g/UiM\nyVrciCOdeYXT/DrrKq5dyeCESGatHJz7Ld//6Ls8+fQjjNYcnZz+4FSCxNMNMi1smoaqruTrXtz3\ndrsON7Rsby4Zu46mmpFludiOdj3b7RbnAk2zpJkd4bw4RI3jyOAkc8mWFWjLMHaHYN0sz3nr4Tvk\nWcmsqamTrirLC2IQO9Uss/Rdy836ms1uTT/0vPeF95kvlpJJozRZntPu92RVQZ6LQH0aAb/Jmvpx\nctERGmvdzMjznNXRESF41slwYHl0wth3eNejwkihFbk19N2Azku8T3EK3Bph5EVB1IGyKinynHEY\n0Ary3NC1O/a7HePgIBqMKTk+OsFkonNoUrHZ7neEwTP0LbvdNoVgBj779FO6fcusqTFouu2O/XZL\n17X0fYfNBMHfrDdcnF8QQ6SqG0KUgMQhIag6GWtUVUVVlWR54mu/wZoGF/AevIfgIgopXEO4pW4U\neYnQ9hCOvclSwWiEv56mUN1+Q7/booKYDVxdb5it5uTNDGMrFs2cWVPQWAujQ+sKZUpCDDR1xXa3\nE6pQavjq2YysyInec3R8l2ZxQjk7Yba6k4KRPUWeoXUyALCFGF8l+2U3Dox9J9a63qeg5yBxDePI\nOCQHLSW6FqHVOLab3ZutaRDKQ55l5EVGVhW0+31CV6UQXCxXvPeF9+mGns12LRPPriPPLHlCafM8\np6yrBHjV5FnJW++8I+LldCkPQw/aYssKHyKDE6H4OAw4H2hmRzTNEucC2+2WvusJEbI8p6lmjF0r\nRU7fst9Jvo13ctFPERtjOo8EuU3FS2oajk/uoLXmyScf8f2PvstuL/Qo0clFEdJH/wN0z9dZ0ygJ\n3en/JMYEAgjmeZ7E5BzMD6ZmZYokGEfH9z74hHt3z7j/9hkK2F/tqfM5P/2zP8vp2Sm2MFTNjMXi\nhL7dsb66AD8wW6w4OfscJ/feYzY/ghg4P39OcIGyqIku0O866npO1IbHj5/wyYffo92tsUXGZnPD\n1776JR48fIjWqRDwQTSMCAVxynCTpjJ9Td/+A3JX2kTNVAmlfpM1HUfHxdVLrq/PccMoU5ztlnEY\n8IPQfYpCzIZO79xFJ12FBMA7hpSRCQIILxcLmhRz44Pj+dPHXF9eSERGFOOiyXRCKaG2j84TlaKs\nZ3TDQNv1h4l723WHWBqd7mbRqBi8k5Bg7+Rua3c7yToNqVGNog+OQXTWQvm2B0v2mNzrphw4rS3K\nmoPL5+uuKXBwNR2Hgb7r2O92EKUGqOoaYy3tvmV9swbEhtuniIDgPQ/ffojNLPcevIUjcHl1iR9F\n9zw5kx40ujbjzp27hHFkv9smWpj8HlPEiNaKdi8a1DzL6DqhL56e3qMqqnReZeR5xWyxSBIQKfjd\nK5oZHwT07Lo+hc0bxnQnT34CWukDyP4qoFDWzRutqdI5Vb0kr+dEnRG1wYWAyit0VqFVhh8cVxcv\nWS1WLBfHHK1OmTdzlBLTi9lqxWw2J8vyQ+MQvNz/RydzNrtrLq5eEoLj0+9/xPZmw/pmw/X1NV3X\noQKsr2/Q0bxSg+agNP0w4IZA1/b4scdaJZKXMB4aBJtZfAxs2z1OwfLkGD+23NxcMQZHWRZs1xsx\ncpq+J88pylLM3ODwde/cAZR+3XUVWnRyZLUZKI0y2eG9qeREqlEHd8QQAkM/0HUdeZbx9oOHRBRD\nyqLrdq249KIpihJrM0KEwYs2dBgG9u0+6ZADeZ4navGAzeyBGbRZrzFJ/7ndbmj3LQrxrhi9xzsn\n+wrRfHnvGEYBKELwWKPFvC1dPNPQRysx6Xry+FM+/N4H9P1wqHluvUj//Ne/tBELIfD1r3+de/fu\n8XM/93N85Stf4fnz59y7dw+QpO3nz5//8L8gHYyTFZVk7XDoyJMdUSpQu4OtpU6NVJ5nFGWeGhgJ\nhC3LUrQuwd0uTNqcNsvESiH4V7z75fQQLnE80NhCGoHnuUWlJk0bGckXKU8rSxex0irpy6RhsNak\nsDw5hLPk+GWznLwqycoMYxRKJ+2aF2Rci30UwGuv62TIMWkaQvCsry958ewJ3o2sjk+5c/aAxfIY\n76TIkO9RCVGygqjAwRL04vylTE3GjtxaMXRQUM8XODey3ayTWYqTrJa2I2Co60ooCEajjWFMfHr5\nbD3eJ4QtTSHdICJhVAAdEhon6+G8UOeil6BXkije5JbRSxaZLQqyppbD5vSEsqkPouI3WVOA2XxB\nM1+C0sToGPs9g3MENNoW2FyEz8v5nPlszmp1zHy2oMgymtmc+Uq0NyFIk+zGnhglNsCFnoAjywxF\nZhn6njB62u2Ovt3jhx43jAQfybNKkKS8JITIzfqK8xfP2W937Ns+iWwV0UnAdWYtRiuKIqMsSzlM\nNdhSKKCXVxcMzlFUlTR4yVVpcnfSxkhRn2eUpThEimj7zZ5TNdnWDy7R+W4/J4VMlqyRTKRxcOni\n9YcGdhxcMuoRapyK4J2ImiNKHFGVIq/nFM2SfbuVae6dO9jMMowBsMwXkkdW1wvq2YK8btA6Y3ez\nod3eiM1uAGxOOV9R1EuyspKm0XuCC0TkUinrKomNBd3MMkHYq7LGGMuUlyjvM6bjN4m5UeksfP01\nnZ71sqlZnpzgFGRNgy0KnBtxfpRoDS0BxM6LhiUGj/Pj9EPkDNSBqAWBdEPan84xugEfBoju8HeO\nzqXphiYER12XBAxd2zEOnVxWQ892u8a5kXqxACC4kdxa3Cjo4MX5S6GdHJqbIJbRyXkyyzLEOS2w\nWB5z9+wBq+NTvBt5+ewJ6+tLEfsnlz+5E7I3WtODTXKEKXPLe4dL05GpWXHjeNg3U4jzmKhh3//4\nE26uLuW8zzXjsGd//YL5csn86AhblAzjyNB3NEUFzlPkOVlZkdVzynKGb1u67TX77U0Ca4R1gJHm\neAgDuszxxnK9vmEcZZp89+5D6kqCuydjKjEM8MmlLN1tKdg5Ts9luh8PTRrc6oxfoXu+zppqZdK6\nJlOI5MQ6DsOhsNPGCO0wyxmTmYxQpaI4IzqfGCzJ+CI1FfP5nL7v6HrJeA0q8wAAIABJREFUrYzB\nkedWaK5OpAjiEDsSki4jBJKRRZbOITHhMEkfZozQ3yZ0+6DD0pPuWh2moDZJIayxKaaGpNniBxpc\nrQzG5FibY22RjBze7I4K6cwPzh2cbadstslgRiFARt00yZ4/0u5bNusNbhjYbDYoralnM4wx7Ha7\nZN4l9YBSomfV2rBYrqhTnuMEqE7yB9Iz5L3D9T2ul0laaS1VdgsMyf1SHIA9o2/dqyeAODkjMMXV\naK3TM/Gqf6nwPW/NP9TBsn9au9dZU5Rl3w2pIDfk9YyymbM8OsEoTXtzTXtziQuOzXZH13eHzFQf\nIpvdDptn1E1DngmQWOQlMYoj48nxMUVRyER83zL0rWRWBU/XD3gP2uSMo0RooMBmhsViwemdu1T1\njMzk6OgZ2j3ejShjuF5v6NougWaO9c2G7c1GXFZD5Gi5oKklIHm7XrPfbCnKEmPFov9gmpUy8UIC\nDF+N5HiTZ1XuO3UA17Q2iaoYDpM3lSjJIO6NUwTAOI635iGZaL60FkfEfmjJc3lGhtExjA5tpW53\nIQhlW9vDlE+n/uM2biAQ3Ejf7dlvd7cZmsk9MRhNuZzjiazXG3Z7MZCbDI8mYymVzMYmXdhUT+dF\nRV6UwtJK731yiP5hr3+pWYfWmt///d/n5uaGX/qlX+If/sN/+INLPS30D/t+JTbJVVXJpnUOtIhM\nSd0wSaDZd12aYInZhkqOOTbPkmBW3mgIAZsZdK8O4YbS3UVMljF6CRWFpDsJghDq3KZJhT9csJMp\nAIlzrK2hqApB6qOIRsMrQXHG6FvKB9L4aUuaOslFHqMhfUnGlSHStq1w1DESIAmvva6ZtaAUbhzY\nXF+y263p2g6N4vT+2yyWR2mML9MYYkSbVBYqcXwj2Wu6IOhf17Y4N/LOo3fph5Grly/IipJoNbvt\nmr4T16osy3CjhFkv7RHWpvVX4izjnfDlKXImZzw0hNFzc3VOUVTkWUk/DCJWzTI5bLXYcVtjmc/m\nB677ixfPQcGw2xJDIK8bTFXhkTw55/zBcfBN1pToqMuCoq5Yr68Z9lu6ds/N1QXVYkW1OsKNAUNk\nMctRNkdnGSp6cIFZ07Dbbtm7Hq1qIjIpMTbDO8d6s6Hve8lVMRlu9IeiocxzjIbgB7JM41NYlhuF\ncrPbye9S1xVeGaqqwqQQ19VyTlmVBD+mzJY5/TDQe8nBu7pZE6PiaHnEbDYTOkknDXWW0MVDmHZm\nD3bMMaHLb7KmShuCE+OQqaAJaXJ9GEAkBD9GKdSGyYwC6NqOsiowVU1VakzsGPqRrvWYXDG0HWEc\nAYWLBq0iiztvsbm5pN1tUFGTZTXK5MxWc4btjsFviEHjHbjBYQz0+z1BybRIXoa8LtjvtuQmx40j\nPkaKyqJNAcqJG1pC0IMVF7q+70A5lBJbaDGAkA0QozSeRXFLTXydNTWZPTiiKRvxUaGrkr5rGfY7\nlNHkbsZus4EojIYsBdf2w0CWy36L3rHt9uLUlRXsN9eSb5OmKNMlM3R7un7AZjKltFmGTUYlMgm7\nEeF9ZvF+pN1tid7RzBeUdYW1iqM7d8nyjE++/7E0buNIlgpEYwxECMolBBW01+RFzugcR6f3OL57\nxvrmisvzlzIVe/YZTbM4FPGTtuy19/6hWEgGFkSckwLXWjEMUKkJIBWWIJoxl8Lq3ThyPg40syWz\n5RzvXnLx4glPPvsEY0uyYqQfnTRTecO8qeiB7XZNUQwYIq7d4/s9uc1xfZdCjQWhz4ucqCoWJ6eE\nEFmfn9PUpeQp7h3t0BFNpChyAdTcVDAkM4SUETgV6yR6q9CBhOomYfBG3OzecE0zm6HL1LwYS57n\nqcGdQo9ThEJm2Ww2OOcoy/JQoGvtElUq0nX94e8JMXJ8fMRmcymmDsk50xiN8yTwKE2tvCPEwKhI\nkykxhokEsrLA2JKiaoijaL2MNgxupK5qvDOHPKAsz9BEos9QMRJVSCySWzv7ad8wudRHibAxKSt1\notu90XOanrnJVGWi8TnnDuHZSknodTObCaU7To2oZ7fd8Nnjz1ABLp6/5PjOHe6cnXF9cc4w9Cjn\nJX4DDhNghWa+WLBvh0RXkwY/yytiMq2xRozRnBvJi4w8z8RQLWl+pgmhH9zBkU5jDgW5SgBPVf6g\nm/QUqzKFScQQcD7liyad2ORcCfDZZ5+91poCbDc3jEMhdaq25EVNbi390BOGTuJqmHO92VB5h0JY\nBSbP2W03aCLV6R1hcmiJLfGhl32nM7SyjKPHB8ksNZmhmjXQGUxeiMunUYxjixtb3Dii9ZLl6lgY\nGcZg+sg4iDGdshatLaNz2MzS9aOETxPJgmXsRPMdAuw3W7rtjjyzuCAAiPcD3gkgMwwjWouOM0va\n7Ql8et1nVa6928iGqdb2MSbN2u0ENisKVNJUmfTnunaPySzGaBbNnN1+y26/Y4yezfaSpq7TEIdE\nJQ8H4xkx6lAC3CeKNSi6lOemYmR9dUFwg+SYpX2zmC8oyoJ+6FBapzpXnM6n6es0uRNvCpXMSNTh\neS3KijtleQCxJ1BHwY909/wLuyYul0v+6l/9q/zu7/4u9+7d49mzZ5ydnfH06VPu3r37Q7/vt/7u\nf3nYsO+8/1M8+LGvp08KcRFLtKSuux2FKq3EBtJmQlXohScqOo3k8qQ1eZmLNXvSbygjb9SHQFWV\nyclmRCuweYHNK9w4JCFyuM0UC8lFMI6gDFkhFrFaGYgeq7P0IQti492Izm4NIuSgiCgtY0yCTzle\niqKUDKPv/dEf8vSjP0lI6i3K8zrr+n/8vW8Ro9jJl9UJx3fe4f7Dz/Po7pkUu2kKFrJIM2/o9juG\nYUjFoKbv9mmcH3FjT57lnBzd4fmLp7z3/vs8ffqcOIqrlR97hv1ONEMI3UUFj1GG3Fqu15eUZUlk\nSA5jwv3fDR1lXZFXDVmWMXZb9u2WzXrHj3/xa0KfuF6zWswlRNNA2w/UVcVqdcQ4DlxeXmJVgeta\nNucvGd1AsVhQH51yWp7wW9/+H3ny2XcpqloOkDdY0z/8nb8tJihZzt233+f4+CFWa+LQEsaCqBU2\nzzlarLh+8THaZmA0mc0o8wIdI+vLK3kuC0uIhmEYWKwKYoxcnt+wWh2RZ5bNdsvDR5+TfKVBCidx\nDIosVkuC9sQovGIAo3PRWuYaY3NMVuBchDBSVKXYzKeoAmsts7rGrdfcXFxgspJZVZNpS9d2nN49\n4umnTyQ013tpxBOiWuQl3/vn/4SPv/tPJZz0FTeq11nT//3v/W1p8kLg0Xtf4otf+Ul5LtNUAS0U\nlWEQlyJBO8OhUIsxoqLCdbJP8ywKLfFmwz5c09QrNIq+azG25K2HbzMqzfZ6j8JSVyUBz65TLBYl\n22cvGfsOm5eYvObu2w/EharfEwliO9t1+Paa+u4Dri8vWDRLKYyHXvbROEKUw3z0jjjIRbDZbAFw\noycSydNUPQbPn33nD/n4gz9hCnt9kzX9B3/3v5UpTLvnrQfv81N/5d/l/Oqa/fU5/fqGLCuYn4At\nK9zQcXx8Qpbl7HY79l1LVmV4RPS/vtlgbcH9txZ857t/RJbn1NWMrJwJONP1dJs1EaFZTdMrm+Ws\n11tWi2OKhExaY1Pgqqff7/BjL1qQumF1esrZvbs8+fQxJ0en4jQ2DnJmoui6nQA87rYhKOtaMmYS\ng+LOvfuc3LnHyxfPePbJh1ye/zZde0FV1QfA4HXX9He+/bcOiObD936Cd774k+RWE7RMx6L0aBhr\nDy68MUrRL1oNjwKauuHo+ATvA7vNDbNmRfQDoLF5ST3LCAGefPIZd+7d5+XjTxjHlsVixmy+oKhK\nrDnGR3BDR17kZEVG2/VkdsZq9Rbb9YbN9SVlnjGbLfCDI7rA2LVgNc2sPrBBXAgpw0r/QKF+KKBS\nNMzkRPf4w9/n6cd/KDOzidXw2mv6XyOIOLzzha/z6P2fZHr0p0m8Sk56fhzFajsZdUz/PkTRBk33\n9DiORGC+mFNXM9rtDq+cgASpuBQAbBQ7eUa0EiaIUrDfrdEG6qZOk6qCoqrAWvwwYkxGZVPTGDx7\nN7xyBiqqqmIwkhc6jrcmMtZKSC6kptZJw2mM4erZh1w8/1AmZK/QPV+3nvrdf/Trh8/x7OGXefvd\nrx0KSSm0BP1v25ahE6ttoxVFWTL0Axfnl9y7e8bF5SXdrOX07h3quuTls+ecX5wT/VLOYq3FmTnI\nND16n4pJg80KZrMZXd+Lhh8lDSrgo8MZTe9GysTCmSKKpv1zKN5jCv62cnZMDp4HEzR1qwGfAITg\nA8EFPv3+P+OTD/+YxCV+ozX943/y36fnTHP/0Vc5uvsexmiur65o5nMWqxXXV5fcXF6wXMwwtsAH\nR+8jdhS9ar++lsJcyzliU0Mx9AOlKen2kg1YNXPGrmcYPFXdoIxN+9HjfcfF1UAM0nDtNlu6RZek\nBCO5qsjzQkAOo3nwzjt0fcdut8UTJMsVyBMd0A0ebaSRbqqSzXpNu99RVg1FUaLS2k2N/Yd/9H/z\n5OM/Ril1cFV+3XX9k//nf5IpkFKcnH2BswdflOfEaJi0/eketTZRd+NtuLYELYtjZplnbLeRq/Wa\n0Uu8gRsdRV5RVyVuHNm3A/3QY2wprqVpwhpDBCW6vXa3F1aX69ncXHK0WjJbLBlHR1WWvP3gLfKy\n4KMPPuD5x5+wXK44PTnBx3Cg4So4PG/ys4X+a/SUv3C7BiEEPvnwD/nkwz9IrecPf/3IRuz8XJzh\nVqsVbdvy7W9/m7/xN/4Gv/zLv8y3vvUt/vpf/+t861vf4ld+5Vd+6M/4V3/h16SpSfqo0b0iWlPC\nwTTGpEM20ZaUXL7D2NP3fRLBCZKiEgqilcIoTdQSCByU0AaKvKQfNjK+zESD1A8dyhrc0Kdp1Cur\nlex1vRsg0RTLqkDFHHCM/ZD4nWmShkeFCCbRLYkQfUKQJoMBldA8z36/xznHO+//BJ//8k9RliXd\nbs1v/y//DcBrreu//gv/IddX14whsDhasZrNkTyWPmlxJOfKKM3L58+ErpboaGMKrAwhstttsMpS\n5CUnqxXvfv5dvv/RR6zXG1T07HdbLq+vIUJp5pg8Ew0UgeAiu/0OrSLD0IEX6ouxmuXxMVoFBh8Y\nXKDvd7TbG4ievuv44M/+mM+/9x6L2TH77R5bFDz97AXnFxfcf/AWJrOiiYqKej5nfv+MJyHy8tkL\nQpAst7YfmC9XfC7/CvXyFFvU/IP/+fXX9Gf+rf9I3Ju0Zuh7hr5F60jb9ii1IaoN3miWTcZ+cCzq\nUuiHweHJsQYePXokwaQEcSXkFln+yz/7s7TblseffZZCvQOnJyd8+tF3pSgxBZFIZnLOzy9YHR2x\nXC7YTYJRBdvtDm09IQ+oXEb2SheUVU232wDJ3neEWVVTZrnsDSbbWM315TVZkVNV1cGWua5rQYCc\n49GPfYPPf+mncd5zefGC3/pf/6vXXtN/89/+VaHNJKvq4BP4oW61KOAP+3+ifAn6JOLz3Fo++fgp\n83nB6qhEIcYx508uCb2j3+2Yr5Ycnx3xp3/6zzk9e5sH75zy9PGnvDy/Yr5acvbW24ROMww7irqg\nma0oioLFquK7f/o9ovPEcY0JA03d8Oidt3h6ccHn3v1xmmqGIlJVJfv9jq7dst2smc1nLJYLQeJd\noOvaQ8Fgs5w8r4hR4SO8/6Wf4PM/9hXGcWS3XfN//eb/8Npr+tWf/iU2V8/Y31yQVzPafqCsKraX\nhhAsi6NT3vrc59hsdly3PdvdlqppuN5sePr4CafbE07v3sW5kXtnJ6y3az744E/o9h0xwNDfUM8U\naIvOC1ZVTYhaEO7BE7xjiAGtZP9bI9QSk+eyFsPI4PZ89vEHHK9W1PWM8+fPGdo9P/OX/zU2l1e0\n+z390OGjo5ktUVqnKVmOtpKvs3m+piobJmvr4ESL8eDhIx6+8y7X2w3rq2tyY8gy+D+//frP6b/x\n7/3HhGQEEJNRyYGiA7w67bh19VQHpLQohK7lXeD65Uv6ENl5mOVHPLz3Nn/8p9+mKBua4j5ttyez\nIxc3l1R1w8zOxHBJazrXE8k4vnvKzcUFvfOUi2OyUXHx5FM+HYXFsWjmzBfHRGXQBkIRKaoCZeR3\n6oeBsiwPWhzSNG9oW5pGirXok2twyj3y3vP2577Og/e+ToyR/e6Gf/z3X39Nf/rn/oPESiHp1LSA\nr1ODl+74iWo2fX2a5Flr0fEVbRsIRams0doe9K1ZWVItjonOM3RXrLebZPpi0crifaDMc5wbMEbY\nIGFwuNjRqp7j/g5VlqOy6e/IGFKupkgjCgiBEWlas7IgDtKcmyxLTpMqvSVDJNGTR4VRitMHX+Te\noy+T5TnRj/zR77z+3gf40jf+HaaQ3hihb1uUMUl6IBIKkgShbVtym1Et5pw9eEAzn7O5vuY7v//P\nWK5WhFGyP1enJ3zpy1/m29/+exR5kbRusie73Y6by0vqZkFd1+J46T2r1QqlFI8fP6bve/lskm7n\n5GhFFjUh6XNQoqPJMn24c2TibqX+CsIgGIcxRQrdumdqZaWaiT7p0Xuh497/PG89fJ92v0EpxW/9\n/f/utdf0p/7Kr4nl/hSfgvgUNE1DUQq1TyfQ5er6muVqxWKxBCV2/w8fvkNtDUPXJe1ey267JrM5\n0Tlurq/53Ltf4PjuGR9/9qnkr9YFYzcIo0PBtm/Z7gcWq4bFosEqy2p1xHw+4+mLC3TmKLSizAra\ntuf6Zs3bDx9yc3nF4yefsVituHP3HtYYtteXDP2WZtHI/lEKWxQy2dzvaXe7NKGGwY3YTGqJR+/9\nBA/e/SraGrbrC/7xb/6t135Wf/wnfxFtc1TKpgNRoGR5TkQTnZMGP8UDLRYLAZ6S7EejyLOcfT/S\ndntmyyXvrVZ850/+mPnyJJ0VHh+kwVcoxq4lZjBEz+romOXRCY8//ZT5bCZOuIk6nBUV1b37XF48\nJytn8v5zQ9e3PHn6hO9/9DH3jk/QRjMmSY1E6+jE4PGHs386Y6fYBZFc6EN26rtf+AkevfevpHMQ\n/tHf/1t/7nr9yEbs6dOnfPOb3zyM437t136Nn//5n+cb3/gGv/qrv8rf/Jt/k3fffZdf//Vf/6E/\nIyoQIw0FmIP16OQ8RXKimkToMRlpjCmZuy7LwxvVSKJ6DImKEEUfpvMMHyPOB4zVVE1DGB3VrMFk\nOd1+R9u2qDgKLcFq+Tk4rErJ48qiKyhmFlNoTEh5IplJeqpXRNqZYXA9mTUH3npUCvUKl106+yk8\n+tahqu1azp99Bgj/9nXWdddu6F3HvFmwrBu2NzdkeQ4+4qcAbKWwVr8ivvUoLfbXPgkZ+25El0LX\nkGI/sGiWlGXDZrdl241ATp5btutLlssli/mczgQ26z3z+ZztTtDH0fVkdcPx8bEI2Ls9/fUV0WQU\neUY2WxJDpJiNvH3vHrmt2Gz2+DCyWMxYHi85Oj0my6RJmM/nXF1fM3Qtl11L0Jr56QlKG65ennPn\n7C6zxSkXbcflxTP2/f6N1nQxF5QohMjN1RVuF3jw7ufZ73YoIs2sQduMqxdPuXPnDt1ujR96yqYh\nzzPJulIak2X4ZOerlEr844wXz5/jg8Mz4oeBl48fkxm59AuTodHEMHC9vmAcd+xaTW4MqMBiNefu\n2Vs8e/oZZ/fu4N3AsO/wNqMsNa4Tq+Ys0TOnPB03inujVbfbvCxTyLDVtP0g9EhrsEYuS7QCJVTW\nfn/9Rms67VOtM9BysI6JLpQnB7fpJZMQeyh+ffBEH+hj5OTeEdZMOhdoqoy/9DM/ycsnV7T1EeVs\nSQglTbbg04++x3J1QlOtODmuxHJ5u2N78xmZjRhdc315w3b9nNlsBdrgtmuO7pxQrxYUWc7Du/e5\n3v0BVtuD3k/cxyxDt+bo6IiiLNE6I3mlCaUiFQ/EyNj3eGtwfiTmOSJB0mw2N2+0pk+efsi421Da\nktnyFELg6uVLirIS1oA2XJ6fy+RgueBotcI5x9ERzGYNIXqquuLm8oqw2ZFnFQ8ffY7Hz5+Tx0zo\ngVlGP4xE52hmM7KyxnvH5csXjG1LlhdomzFrSjbrlqqaUZYFu82a3XpNUTYMoyPogmqxYtbMyI1l\nfXXBbrsTVkEMdP3I0XFB8A6TZYdzagLaIkLfnWjkWqtDsbOcz3Fdx3a35vrm/I3WdApTBcjznCnX\nbEhh88baA51mcvYlKqJO9Kh0P3bDSFFoFIFMQ3a85E//6e9RpwynXDuyQvGkveHt+w9AmWQGFDl/\n+RJjNPOTUxZHJxwfndLvNqzXN+Lcut/xU3/pZ6nKgudPn3J9c0HdNGTaUs1zTGnYbfdst2vJg/oX\n7p1XnRJlmhMOem0xqbAHPY7Smn7/Zs/panVC1/UMfUck0nUDWfqMjRHZQUgiezG6iWS5uBgHFw5n\ngwCbpGIuYpNJ11e/+hX+9Dvw4sUL+u0atCbgyLP0fm1OXstUWKUpuzV5CpZ2EDXlrMGNHdfba1SE\nZr5gHCOXlxdURSGgkVGgodDFQf9srJwLU/7Z1LwnuBzvM/JCctskE1Moduv1yzda0+mcnO4VqZks\nZZ6L+6wxVJM+zRiyomDsenb7Pfl6Q9+1PP70E8r5jMXJMfWspmv3fPq9D/nObss7Dx8RUFgjk6u2\n6wijO5hBmEQr7LqODz74gKZpcOOARnBprRA9+XZLHjXWpGywBHJMhXCe50kf1d0Gfb8appuocZML\nrTZTo6swWfYDVMybq3P+t9/4LwD4mZ/5mderUYOYUxRZQV3XjGPPfrthcCNut2UYWqp5xeLoCKNy\nQNF1HZv1JdvNmu//2Xf40pe/zGIugDjaoG3O+eUlx0fHKKt5ef6MF+cv2W63LJcrtps1fvRk1gqT\nSmvu3j0lEBgGj8kzXp6/5MmLp9y5/4DgRLqDFzOmejbDuYFmXnPv7h1CUGzXO3zw7Lc34vY57Jkv\nlsyXC5EgpMlzTBNJm2VUdcM4jlxfX6OVSIW6zvPy6ZvVqAJekUYCIVEGS5S1Qg5WBgxYpZC2H6xN\nxnpxUrIKpToEePH4MTe7DavVETH6gw60yAuquqaoKmxW4r2jrgRYXt9suHf3jKvLc/KsILOSGWyM\nGPSYYkEYB9r9Dms1ZVMRQuSnvv6T5HnG6MfD5LvruuRLESSvL/lF5NrilUiuRLWYdNxKkSfzDwHD\n+JEjsR/ZiH3ta1/j937v9/4/Xz8+PuY3f/M3f9S3Hl4xCjdTK0VRyiE/dZLTxEA+BHvgBE/FzDSu\njkmUL06Kijwz+Oh5ub5hsTgiT8UlypFllqJa8fL5M3GWygr6vmMcegprxGZaTU16TJoNTbe75uTo\nAYvlAkWHsSLIF41uJPiJPubJioKyyJncFydOrfc+ccgVIdEhItIcenfLGT2+/w4g/NvXWdfMFnzl\nxx7hQ+DF1QVlUYi4UsUUkiyNWN+Hw6FdlmWamnWEEDi5c4/V0YoXz57TFz1nZ2cEF2mHjRzoxnB2\n5w737txDacs//86fEFG0+w3eOcqiYBx7sfn1IuzNjCG4gZvLC85fPqOu5xgUwTjqWU1VnlJWCh0d\n7X4n62YNL54/5/jkiOvrG4a+x4+DjPXHkX4YGMYBP0oie55bVtWMdiPap+O7Z+y7HVlyrnzdNTVW\n0DljJN/LZhlFVaKVwQ8O78ArTzNfsNtu6Xc7+T21Ia8kU66cYhO4NYgR4atnjIGXF+c8ffyEfrfn\nzv27mKKgYU4/Orp2J3oNJyGZVy+eUlcNNhO+/eXzFyif8eL5OdpGXNex22yYLRpurnbM5gZlbtEZ\noXh4LPGw55RWqTmSvCRBzYVeGRGKYqYzxr6j3bfMV/ffaE2VSUg4iLuRGw8mFy7pb/I8F9psoiMR\nby2aM2tRStPMBYUNY9KXxI5RNeRFQecGLm+uMUFxdLQCJYf3OI5EFckyzX7bYcuG3cVj8FYyq8KA\n79dkRYaZVTRHJ2gUu5s9/VnGO++8kxydpBEIyAShrGqqujkg0iFGgohXyYuCWdPgvWe720GiVUlh\nIYDN6d0Hb7SmoFgen1KXDTFCt12zWq7Yty1DN8p/u5EsEzrr1eWl7Pu+J8TIcrXk+fNn1GXN6IRi\nVZUFn3/n83RtpO1est+1+H5k7Fo+/vAlJ3fOmC/mST9kCD4CEttRFgVu7GiDI6LB1nz+x35czIkI\nZMYSh4E2dBhtaZqap0+f0Q89d8/uUTcN6+tLtBbHTpC8G4C+6xDNbSSi0Uaej7wo2Oy2PDh7C6Me\n8OT82RuuqTRgMOke9O2FGmMC7cAow363oywK1NSEiTsCUYk+awITiqIgz0refustwj8NPH/8MU8v\nnlOWFU0Ut9RmcQRaitxmteTq6pLaZqyfP6FtdxitMGjGrsVmNTfrC3Y7MTZZzBbYGFF4Pv3gu+TN\ngrJaYCdw46BLuAU7DoYcaRo9UcQEcBS66PSeT84+90Zr6tJ0wU7Uq6TlPmTrpD/nvT/kggYf6Fx/\nADKnvDznk95KCyLdd3s+/fRTlNIcHx2xXq/xIabCD7Sx5GVFXc/xY0nXbvFDC3rAITqVvKwJMfLs\n6VPmdcHZvfssV8dcX18x9B11itIQcX5E5xlFXhzywqQpT2H0zmGUGBz4pMGRhl2akOn5Ob3/6I3W\nFG7pmTazaG5dL62VzKbRjcnoQJ7nsqrY7/cMw3MWi7mEMe92DM7RIDrb66tr5vMFWluZHGhNls6t\nzXjNervFBUVQhqKsMdoyXyyIzrO5uZRiuCpRGsa24/KypbEF81lNltvDFJlXKK96oswmoFilqdlk\n+qUj9H54hVIr71n5WzdsBbz97o/xzf/kP+M//0//ff7gD/7gtdY0uBEVYeh6/DASYmJtKS226U1N\n8E6clRnp9jsxE4uBeVNT1jXdMJINDg8M40hwgbtn91nOltysL+iHFqNgNZ+hCNw7e5uPP/pQzIbS\nGtV1yXa3p64qrLF0G8lrHPo9WkFVzRi6jvX65qD7VNqilGUcdiIkRlowAAAgAElEQVSjCIGh7zg5\nOSGvKjGgiDFZxHu8D+QJjHfOpby7SJblaAW7vqfdbqmbkzd6VpVSBOdROiZDO0PEE5KTqVbJXyH9\nO2WlZhLHc00MgedPniaXTMO8qQEPKnB+dUFdlhiTyTOlAv0osRXe9yg1Jg1xQd+1FGUu2nQlwMno\nncTKaE3RlMyWNUZBt9tTWYM2CudHLi8viCGwWq7EBT0EBteLPjzlFqMVJmpBIl6RHEXvRbAUxGVZ\nqQP57s99/YU1Yq/7mvRQ04YCDlkQMU2YxH5ehJ2jd8mxJzmlKclDQU+OOjKq7t10IIk4VGmhCxRF\nLuYQMRD8SFCK4B1Wy2ROxXArIg2yqaewyxAy3Ag2GzEaoo8S2Oa9/CzvcP2QBKoFzkvTc3BNiT/I\nrT/8r4NAUV7qR7XGf6GXiP/d6BjGEZOEjyHKuknXb9i3e0JypJwyIqactXa/RSuYzWc0TUNe5rx4\n/oKmrglOghjrssDYjKA0j955SG4kN01Q2j3NfEFRziQXq+/o+pbzF+fEEPna177Btr0iek1VNTTz\nGqsyrIkUeYHVmq7vIEbmTcN+uxfqgxZqUt+2onMYhkO+hLEpHT54UKDzjKZZkvcN/vz8jVY0M5M7\nlhRGWW7RGuF9F2KH7LynHQZyBfuuExeeokzFok8bPeKGEaU4XIZaawmIzgtOTu7RVTuJbQjC4Y6j\nCJqtMdi8RsWANRl5UWGLQmiOJsdYJBx1Qhijx40D2/VGio4gRawbkhlCCIk2krL0kLU77L2pQVPy\nVGZWzGzcOAo1x+Zv9pQetDuCjk07ICQkOUSJfCiLirZvcaM7oKGZypKeMtnBO3+wDh/HkbAd6TrP\nzeUV2/2eup6zXC5QxhKiJ7MZ1jR47/BuR7fvMXYBxpDnmlV+ihs6oS47z/bimtlswfGdU4osEvJC\nDGMS2BKTOHs2n0tOE6QiS/RCRVEKuhtC2oca7xxZnhF8+nPcCntf91VkJavjU/KiYr/bg5Lpobj9\naSkYXEDFUWi245gyEGcE79lv98ybebLyh7KoKKuSfvCYWlFUJ2S2Jc+3LE8amvILfPThx/RdT17k\nrI6OJSssCqq4u1nTVDWLxRKlDEfe09QlOga8GwXpTnqD9eaau/fusjxest9JcdG1O8pS9LzT+WSt\nlSYsNWcCgLkDzS54z5AylHSW8xcw//2RrylI9tWzWyZF5uAgGOAAEILs1xBvwRalbpue21wuQVOz\nZk61OsHtt/jBU2vL/vyS3c2azeUL8rJkcJ7txSUvupbjkxMUJaAweckKy2JRcXl5yXK2oi5mB2qP\nbWT6Y5S4oeoklRWJxCvvKbFO0mY/AARToPqrLnjAD2gZX+c1DL24ouYZk0b81fWdAlNDqgVQkUNu\nm7Lp95MzNyaWCWn6OPY9ZVHhtEGbjKUR6mGZZ4zOM7rAMHq2mxusycBkzJbHBC926CrcfnbOOYZB\ntCxlUaAQZ9ZJqyJjmXhYy8k9UCU9ljKWLK1ZDB4V1OEZsFnO0HXgpIaxr+iYX/c1Ucrc0OOdJ88L\n8kQNjtxaaY/jyMnJCV3bst1t6bqOsiyS47Sl2++5HEdi8CxXK4qiZHSjACfBy1QngeInR8eYIrnt\nHUKrod3eYLUi+IG+bymzHKUkELwbHLNamtkD2yl9vs4lGUq8BdeTldhBu9j3PVfX1yyWS7I8Bdgr\nhLqemjYAjeFVZ8XXeRkjLpFtt5eJSlNTzeaiPdpuhfZpxIU2xAhBEVHyrPUtg4vs+kSTM5r9divZ\ntHlG//IlKMdquaKpZ7S9ZHXmWUFEU1QNeVbQbzdYoyGGg7FdXAd22w3nz57w41/6Cv2+JS9LUNDu\nd3J/2wy0wcdI8GJOMZsvqedLdGJE6VQzaWPlpJzOAGvJ84J922Ize8jLyjP7xhWqDAZ0onmaW0At\nSriz3M9Cqa2qUur8VJSHGA9umFop0JGyyNHMGP3IV74q7o1DN4AO+Ch3WZ4bIGe/2+BDYHF8F++D\naOaVoqgKtJa6uOs6bHKXLvMaEyE6T1XXjE7YEUYrARfT8+adTBQV6gAuxZTcftDfwuEZN0YLW8YY\nbumNf/7r//dGzCfbY5MQG5Uc2mxmBVEOIaHOUoR5L5OPA4KCmijYr1BUND546hQGaqzBx4AJmiw3\nRCKroyPa/Y4YA0VR0O03jKPQnbSySXinGIaWrutYLY8IOLabNVUdqDJ5sFGpMB0GCJI9htaiFUtF\n46HROvDf/4U1mASqUokdrIFff01Hzi/OU9bJiC7KZNLhbtEirQ+H4IRETvQvay277ZphGFgu59RN\nk/KkPDbLef7sKWWVMZ/N0SpispyzOyeSa3rYSBvmqyWgsCbStjc8f7qh3W84ObnPo0ef4+VVjus8\neVZhM4XrO5wPlMWMpqnRGkbnqGcznr94QTObEdMkLMZ4SIonSKZJlsv7Wa/XzOcNWV6iTI7NoKpn\nb7im8vyhk/VpkTMGx2Z3fcjC6PuB7XZLnVt8jMyamqppkrjUJ1pVYMp3004sjGVAFpk1c+bNku3m\niqdPPkWbnLKsqOuMYRBLYpRCRcVydSwaBGupi1LybeKIUQqNQWcFlog2itXpEaPb42Jk6Hv6tsPM\nJFvFh4AOAR08RNEtTnQlRUJpFFLEGUW73x2oH2/aNEz7eyrEJGhWEcIo4voQiONIYXMRYSeqzeSM\n6p003CHIRCdGqLKcIitZX6zJ8obc5lhaDJFh7BjciE7ZXVleYGLN9rql222ZzU/R1gBOAoQTqlXl\nCh0VVVlzcrIi9N3h9z64Himx3z3oXNJel4JOi7FQCHT9cKDgeB8kjFS4FulSerOmoapn2LxCm5ws\nF1e89XpNUZSSm4IATxjROposA5ItvLXsdzuOVispMGxGVVXiPjrI9KkqKrJFQV0VZKXhZPU2L59d\ncHH5lKpacXy8oKqWOC/ns8WwmM+ZzeeSV6UBJZPd4Ec22w19P3Lv7hkheoZxYLlcYK1Ml2LwFEVy\n1J2mKNbiE1o6rVdIRZw2BoKEZl5cXMjUwo1vtKYq0cumQmHSpR1cXxWoKBTUyRToVdBtsnwPPh6e\nF5C9dnN5QdFUnD54xLDfM2y3RD/ity06RFTXS0YSillRsFrN6LuW6IPYnivLW/fuibvnrqWyNXVR\n0YUN/bghCwtWJ2e0uz3dfieWya+AnoffM0axPIdDQ/AD2ix/G+Y80S3f5CUN1RRyqlJx4hJDIDKO\ng0R4pCZruktvkf5EakrFmGQKip7IDYHjlUyB9/tW7gEV2a6vpdjtBoZhLTmeSlHNFigVGboeg8Gm\ns8Jqzag1Pkb6fjjo0X8g8FqId0zOstrYhMSDj4HRezIjuXZRKaK+bdYniqAMzPUb732ALJNGqR8F\nHI5eCujg/WEKbrTGjSN1VaGA5XLBft+yb1t88CxmM8Z+oHcdRVmwWCwZnJhERCVFcEzuhEop7pyc\nst4LA0RbifrZ7Xb03Z7V6ugwNQw+iClIJ+fxZEV/cMKcXOdSpEtMhgpTaSRB2PIMOi8RFRNVEWIy\nd1OvNMWvoHtv8kq0V6WTZC09q0Vu8H1P9CNlVaEQF+CpofEh0vUjzkGhLd2+xSTweBx7Xjx/Dv3A\n8uSIk+Nj2O252e5YnZxweXFFWdfkeZnYXxplLSp6urZlGCSD0Y093gestgx6yuEUIEpAwB6lpOnR\nNk/W6UXSZsneikFM7ozNUwatgIKTM3lMjXKfshy1MSj/Zvs/hCDxToht+6E0VinuKc/JraUsC4qy\nIIxOaKxEiFOodJb2jMguFJCHjFUzZ1dtcEayfQMwdF26P8TEzkXFShlWyyPqPGPvBqqyxChNGAYG\nNHGQGAAThLmBEbfeq+sriiKnKisBqoJHT8ZpaRI7seAUqQZIddT0rKJIAIEATP+yWuovdDJ47/nG\nN77BX/trfw2Ay8tLfuEXfoH333+fX/zFX+T6+vqHfm+MQdLTUYcH3ruBoe/w40D07tAkOOckp0On\n7DGl5KMJogtzKdgtxICPkbqZYTJz4JIaazBWYa3i7N5d6qrCGE1dVweqm4sputlojDWMQ0/X9TLG\n1Y6haxlanQ78TpqHQZA7592BJ+7dLedZLi5/MAI5GHSqySI4oY3pgpmCH193TQtrGYaO7W5DGAbK\nVygxCuFpD0N/mH5Nn2FI/wzjIEG0TorycRjYbjYs5kuIcLO+pm13hDgKLSMdeM6N9ENPWeTcu3eP\npiqZVyW51TR1ztHxjDunK+6cnvLyxTNsLCiyHBUHfN9KAG4I7DYS7ClIfY0bR7Iso21b1tdX7DY3\n9O2edruR4NzhtriVgzwwtD2g6HsJl83L6o3WdHr+xmHEDT3WGnZ9x831Fe1eqHq77RqrNOuba2bN\njPlcCkqSPW9w7kChMFMmRZCGUiuF1RptIiZT5HlJkRfkecmsnlEdXAPBFiVFVTGMLUO3I1MRN3bg\nhB4hVIKMMs/RKnL/0X18FPfBkOyoYwwp804y54SmJIfvAW2KUehPiToX/CCUS+fF7jkdHq+7/yWr\n5HZvi92zHPCHUPVRxLjS7IhTqayZZI9NlKbps+5HR900uL1QOJZHpyyXJ8zqWpzugqcsc4iOsd/g\nhi37mx1WBYwNiXIcCGMvYZY2Y1FU3Dk7Y3lyhGs7Li5vxDBZK7I8SxebTu5JPtHVpEGMSZM50T6k\nbpf/NMYSIoL8avVKsOPrr2le1jgX6PsBUAytFLM+hMN0UwLWB9rthr7ds92s5Tlu2xQ/MVI1NUUp\nU7/dZpsmDR2+b1FxoMhyTCw4f/mMO3dOuXO64uiooalzcquZVyVNVXJ27x5FkTMMPS5RT7USp7QQ\nR9puy83NDSD2wNv1hnEYMFrjnQS1DuNwOJumszLPcykO0s8VFohotMoixw8j292GYegoEjr+ums6\nNXnTdGgKFk43bgobnyg29gcaMBCg7pDrmDq3A/CgFbN5w2q5ZHl8h+X9t5mdvcXp/bf50ld+gnc/\n90Xunr7F2Z23efT5L/Kl97/M5nrN5fk1/a5DdT1LW+B3jrOje5S5ZRj2hBhwsafrlcSBtC1X58/Z\nrq+S6cgrKO1UsKYi81X91VR4AQd99qvI7euuqfp/aXu3WFvX877r9x6+0zjM8zrvw9p7296x41h2\noS1w44pSiQskxAUNFYoipApxQQWtShNULpAAYdQgUYGoEEokLiraoorKiUKjRtSlEBGnjZ24qRPv\ng733Xvbe6zTXnGOO8Z3eExfP+31j7sZO7LXokLZsrbXmYbzj/d73ef7P/4AiuDBrJ0h7RNh7x9B1\nDMOQwVUzW7EbnSdheZo6m6MgdvHee4a+x48DVVmyaOo8STFsWynGxrEnpshisaSuS+qmom+3tLsN\nbpRsR6Nz8a2UuChaQ9u22MKyXq+v5Vzp+XOf9IJFWYnDri3mz9hYiy0KpixB0TiJvKIs87/LZ9+L\n1FPT51kWBXXd5ElNkGwpEFp8lJ/77Ok5ZWE5Pj6iKAwXF8+4utyInixrtYyxOT9KZ6vvqemRHLei\nlAyn5DxaJZpcOCutaJoVtqhpVoeUZUX0I4vFiqqsODw8FL1lbuqFVSBWASntnxHYg/ExUztDCNii\n4PTsVHSF1yiNKjd3E+AxhT3D89/9EyBQ1TWLxRIFAmqUBev1kvV6JWwdEsOulRw8L4YNZSkylcpq\nwjji2g6rDavVmjgOeUIYGUcvMothJIXE1XbDyckxxiiiiqwOD/AxoZVmu73i4fcesDl/wqppuHX7\nHpvLS0AzdD27q62whFKS4PNBps9V2WBMIUDHMMrz53wOxb4WGQOzOVe7k/DjFKWm9SHgQsSF9EJr\nKmepz6wALzl1ueErypKqFsfSoizmxma6S/e/pUyWlFaYwqILK8ZuHzygUoaTw0OODg5ZLRZZxgQ+\nJKpmweHBAYfLJWcnJyyaBcuypkRjYqI2lsNmyaKoWFeN0JDLIsfmZMZNyLEKNn/fHD4eo9wNKtMr\nUwYcJmv6lCflphAKuUzcRzEj+wPs63+oRuyv/tW/ymc+85n5wfnSl77En/pTf4pvfetb/Mk/+Sf5\n0pe+9AO/trBysMqITqOt5nKz4emTJ3RtOy/8lJa+zxGRIkgKWINVwiGVaYOibhYsVku0zk5l1tIs\n8qGcEsE7To6PODk+whjF2dkNmtWaoq4oKrH/DCRsueDk5i0GtyN6T2VXlGaVm63IdrMV1LYsJL8r\nW+svl2LuUGQbe58DKYGM9sSZDuIyesEkpM0X4fOu6a3b92iWK6zRlNayubhg7HtiNjiR9dOzyYpS\ngoirTFUKQaaUTV2jlQQnClVJ4f3IG298gpu3XkKbBS4lNl3H6AJdP7K53HFx0bLrHUkryqaQ7Kdi\nzcuvfJof/+wXWC0roU5k0euubXEhiv4rBIZh4CrnapVFkcM35YLdbp7x5Hsf8PDBd2j7Lefnj4kp\n0Pcd212L0pbj0zO5cN2Ij2J36/P07HnX9OBAXKFSSlx1HV0K7C43lLZg0TSATJvqekFZ1dy5d4eD\ng7WIUeMUQu0Fscqfdb1YUDc1zjmcdyQ8hS04WJ5x7+5L3L59hs9i0b6TA/Gll17l8OhUHuJhwHUd\nQ9diraEuxZLd+4EYJbPlYvOE7fYptiqEMpMbVu9lEmyNQYLFhSq4yFQIgNELsBBTxHnH1cUlu+2I\nomSxWLNcrV/o+Z9QZCDTfFRuyMJ8sJVllSfmBT5G2r6n6/ssRhenouBhUTWolHj0+BG7vuf4+JTN\n1YbHz57gVOTg9JTTO7cJCk5O7nKwPubq/CPe+9Y/YhifUC8qSuuw2tFUmmVTQEi88uqn2LqE9z0E\nx64Vys+kxTg8PpZIgXxAywUTZyQ2eNnPE0g00WinonGmZquPTxifd029G2S/R9n/IUaOz85QyrLd\ntfR9R0yBZ+ePafstDz/4Dk8+fJ/t5oKh7wnOY7WEIA9DPz+HIQTGYcCFwK5thd7hBvw4sFpW/Phn\nv8DLr3waU6xxIVI2BUkrdr3j2cWOzeWOfnCMLrDpOlxKaLPg1q2XeeONN8S1Tmu8d7jRoZXkBont\nuFCspyn+RKOOcW/aEHI8w9j3bC4uqKzFGgmlvXX77gvu0zDTmyZqpDEGU8gzJYZHEU/MSGea8ysn\n3a00A5MbHEx6GKVUdihMVJVl0VSslw237t3h9ifvU986IC4UphaR+vsPHvHaG5/lsz/xx3j55Tdo\nViu2Xc9qdcDiYEkqRi7ap1xsd6wP76ELS0iRg/WS5Ho+eOf38H2fdUx7O2hlNFhzrcFQ13TawgiY\n6KFTkf5i+9QzDiNDN+AGaaYnZsh8J4YgWXvGopgCkEX7FDILZXq++r7DuRHnHd4H2r5nGHqKsmC1\nXDB0La+/9glOzs4w2Wym6wfQmna7wbse73rGfsfoepQx9EPLarng7p17nByfEqPHjaKXnor9Ccyc\n7PUF3UbQb6M4OTlh8k82VgvQg2jDYT9Rj9fokC9ST5VNgykKcWatqj3dS0EKQgO2Vow6vve979Hu\npOHdbq/oWqH/LxYL0R7lCImUxLBl0umpJIYdZVnQNAuqqub11z/J5z73eU5Oz9juOg4Pj2lWa0bv\nCfluS8lRWcNhs2S5FH2zc6NMKLy7lrm2p/TCXkerJsA+v1wYiQgrYvYWyBNTeW5ljbWWPf28a0rO\nR6zqhsVqxY3btzg5PWMMgaquOTk5ZXVwQLOoCWHEDR1Dt8OPcj5ePP2QJ997j+3lOePYAbBYrbl9\n9y6rw4M8lYrcun2LT33yE1w8fczJ4RFH60OWywXr9ZrVaoXbbinLgsPDA5aLJev1ITdu3+HmjRuE\nFOh2W8m8DBFtJYC8KCzNcin7wspkdhwG+rZl6PrsnQAQBShTE0hkcp0gOuGubUVOMckY7AuuKeIm\nKvfgSHAjRE9TF6xXC1brFdWiFjp3lCmoRD/4uSELITCMPX02aJrMXG7cuc3J2SlVXUGKGODo8JA3\nP/1pPvOZz/KFL/wxPvmJN2nqgrHfMbiOspB4iUSgagqOTg956f7LHJ4eU2S/h2HoGYaBmzdvslgu\n52fLZsdpk9lk0zkQcoPJNCRIKRvzZBq2VnlqmVGwP2B8+4c2Yg8ePOCXf/mX+bN/9s/ORdWXv/xl\nfvqnfxqAn/7pn+bv/J2/8wO/fuaiI/bDxpTcvvsyq4ODa/oG9bEmbBwH+q4ljKPoWrJJhrpOncgL\nVJYlZVVQFIJ0B+9ImWYRQqCuKg6XKzaXF9y+fYtm2dB1A5vLLd5H6sWSrtthS8Ptl17ipddeZnVc\nikONVpIXZi2L9ZqzW7c4ODqiqku0kZF6nEWPQk1QWVOgyZoja1msVpRFkSeDiYsnHwI895o+uzpn\nebBmfXjCru8ZnGMcepSC5XJBVZezK+XsQKT2VJtpOiZhqtI0aqNwfiREByTGvpMLrOuw3jMOV8TY\nslxqDg8LauvxfcvV5plQ5FLAjz3BOxHoRsfDDx/w6MljvDEsb5xSLhekFLBGUVXyOz559JjNZiOf\nbYi43RY/7CAFXHcFfshZQpairKnrmm674+TGTQlGNAatoN1evtCaunGQQPG8pz54612xs9WGzW7L\n5eUVbbvj4aMHvPnpHwfEOnii8MSsFcwTalKKODcw+J6Dw7Xor1KCFLjcPOXXf+Mf8o+/9ts8u3jI\n6Lbsuh0Pnzzk7Xe+SXf1jNPjQxbLNbqoMHWDN4ak4fTmEdurR5yff4SpKh49vKTf7fIktCclT1WJ\nec0wDDg/MIxi9SzPGplWC2WZg3i9Z7vZofWSszsvc+vePY5Pjlg0Uow97/M/W4DnZzHFuEdjiyI7\nJ1pCAOcC1kjRK0YD0vhMTawLnuVyyeHBId95/318TCyU5v7pKUudePfdb/Ldh9/lC//SH+f2J17C\npcRuN7A8OODH/8U/ztHNlzCu46Ry3Ll5zPHtVzm79RI3bxzxxmuvs6iXs5FNVTVYIxq18/NzLi83\nWFNC0kJPCUL5nJtMFFWm0sVrqN6Uh6K10BlClEn+i6ypVrLfjTGUVc3J2Q3areisirIGJaHc+EGe\nHwK+3+F2W1TIYZabK548eixZLFUp7mYpUC4XLG+c4bXh0ePHPPzwASk6ylL48X7s0SlgiFxtnuH7\nltp6jg5LFktNCDvG4QrrPb6T82McJAg+RIfzY0bwBaQyxsxTMKX2+2WeLCVx163qkuVyIYL6Qc67\nXd+zPjphsV7z7Or8hdY0xTxtM/pj95BC6Gc+ilmEzrrctpUi1nlHcC7nKDLTefe6qzgbz8j7dITo\nBQCJjo8ePMB1I029RFcV5cGaenXCnVdeYXlywEW/5cOnj/GFIVYKVZdUy1NOb77Czbt3ZG8F0UKX\nixXNwREhep48ejBTZ6a7clrT6b3Nz2emDWmkCZ60N9Pk+nnXtK4bmqZBa0U/tIxuYMrdDEEc87q+\nwxQFdTZ6EMAWSImyqqnrhuADfTcwjA5T19x79RPce/0THJyesjo+osoh0GdnZ/zT3/knnD99RkqK\nxWLN+vAYZSztpDesK0qrGfsd58/OWa/X1EXJ0La0ux1XV1u++c3fo+/7j1E6gZniX5Zy92stWVF9\n2+6nqDn2YGrGxnx+yVk8SmP4AmsKYG2JMSVKGRRir1/nIryua6q6wsdA17WcnZ2xbS/RWnH39j3u\n3blHWZQcHx9jtJ3rrLEf8vuVOsBY0e4sVwvObpyhrKD73373Xd55+y3a7Za+6xjGkTt37vDq/Vd5\n9f59bt25TbvdSrSAExp/3/WZorkHoI2x2agjZvBC5SbWYK2Zi12NTHKN1tiJMqryZDGDnylGLs4f\nAc9/96/Xa9brNQcHhxwcnTKMgbbt6HJt5X2AqGgOjji6cSZ1z5OPGHYXaCU1Z7NaUq8POLh5k9Xp\nCbasaZZrnpxfcnB4yOnZqcgLQuTOnRt0wxVDGOndyOhHjo8P+eIX/wRhTIztFW9++tP8K3/iX+Pe\ny/c5vzzn8OAAFT3Bi5mZ60fc4DHVCl0sKKslRV1jqoL18SHL9YLFSp6rq8tLHj96xG63k3NO6XlI\nYLSmzPeuMSZr4SO7q2cvtKZFVVFUDcYWohnsLlFhhOiAgFKRwog2X6lIWUoMSGGtfK5emvaikBql\nqIoMhihcbpi8l1rVWsOiaSiUotAanRIpeFL0FKXNRiFCifVhP6HyWcojukcvNMk8GYvZ8XnaixOo\nrTVsd1dsNhtijFn+Ixb3moQ4xId5im7LCSDXMz3z+z7XP/iv5PXn//yf56/8lb/CZrOZ/+zhw4fc\nunULgFu3bvHw4cMf+PVuchgqSqwtsIUmBTnERucISqYzPhc2KR9qzjnGYWB1cIDYy6u5S1aZVzw1\nZTGIVuri4oL33nufH//MZ6ibJocNCg3x7su3efpwx93bL3Gw2rLrOtCJq8tn+MHRXrU8Nu+zWq2p\nrEwMbFFiMzLovcf5EaX2FBYR7CkKU2Cj6LAEsZHMlBD33NCJqohS/P2//T8DH9eL/EhrOo5YY1ks\nG24Vd8XJaRBjgWEcSDnlPqWIyRxV76fiu5wDPCfe/kQBmYplVRSYaZKZYBh6Fqslaoi0uy1Ka5aL\npUwVQ0QZTfCOvpfvZTP392C1oh1G8I5he0lpE13vcw66plktaQ7WXF1uSMPIdrNhGAZk8jnS7xzr\n9Qlu2OH7ltF1xCC5JBeXF/iMosc48v/86t94oTVVRtMsKup6gQ+RcRglZd2NbK9akoJmIQ/7sL2a\nU99jLtRCioK6JABpyIxWWZTuZ/pP8IG6XvBjb34BqsiqPiEMHQfLjnt3b2OU5sGDD1iuaw6OT1BX\nW2KMrNdHhP6KD979LrePXma5MmA67t57BT+Os5GNTGMDxlgKW2DKLJKOMsVxfpx1YhOyk6KnLDWm\nMvjY0g0dsYv0veyT533+9wY2IJqZSFPX89+HGBj7QDf0lEbMRbQxWRcWKFWJStkJEnmulqslr5Qv\ncXR4RD+OeOe5YU85WGouLr7N0+9ccuvGTV45tZx+9tP4YO03CVAAACAASURBVKmKhmADq9tvsrl4\nwrgZuXXzBn0/8vTJBSnqa2G9os33vctUzgl93QfLytoJpU1pJXbMYf+sT857s1YoZXrvNUOI511T\nHwbimNC6JGUQyI0ON16w20qkgs3FzfZqQ1kWaFMyDH3OPztAFYaDgxPGYaTfbgmjXGhNbRm2F6Qw\nsmwaFnVFXVf0w4Df7YSWlTNbUohCqwmRXbsjxcRiKU6W7XYnOU7Git5ASYj3TP/zPjcAE8VPZ4e6\nvU2waARlSjP00kTE7J67XK84OjmRs2wccOOL7dPvvv8+y1XN0fGphLVOzaEWKqkxUhROrJlJTB+9\n3AFFWQoSOjdhWU80N5Yi7p8mZE1VoZQiODdTcsTi22MMPPjgHcZB7pqTG6dYG+jbK3wPSSlClCl8\nkYEfkD+rlweUizXf+Npv8C+f3sy0MqGHjeO4pylCPutTpt4BuXmYDEcmItPzrqm2AljEUaho1hiW\nyzXjKJEAy9Va9kSMdG0nLqlZu2as5dbNmzx+9GhG6E1hMdby5NFH1IuadndFDCEHiUsZ86lPfZK+\nHzhYLTk8PCTEyK69Ynt5wf2XX2a5rLm8fMbmasO9sxtcXVxy6/U3KIuSvm1RSvHmm29+HAxgHzIP\nck/IZIZcwMWZPue9mF8YpWmaRQ7M9jg3YosoxgovsKaQge2qJFrRSkrTKtb6gUjXtwzjCFpjtJU9\nFiS2ZrlcEFPko48+ytmR+ZNOUkAaJe62zo0EJbE2IUpAttUWqwyrZiEW6IBRlsPjQ5wbubzMbsq2\noK4qyrrMVPmAtSY7AmdqatYumTx1kalhpopyDWg3BZkIRZg02NmYyLuALaQ5+z9/8Rdkzz3n3R9C\nEsfh0YHzWFswpJZVs4IA3bajahK2LFg1K8KtW6yODrD1gqKs2Tx6RDe0ue5pWK3WqASbZ5fiCrvd\nicsqiX7oWC+XvPbaG7z19lukmKjrRiJ5jk74xCc+hXM92hravsPHyNnZTS6fPMKNHeR6Iw6giwMM\nSM2MmJlVVS1O4DHgxk4kHApW6zVVXQubI8uYdMorrhXKGJF5jOIg+Y///l9/oTWd4kZImqAiQQVG\n1+HGirGXvLlC52zhEMSD4Rrwcf0ZnHRjimvaVwXOSb053csy2TWZoTZI3bpYM8VITc3RVGdMQOn8\nXGmdGULMU+yiKHLcQpy1iev1GoXc71dXV9hi7+JJiqLps3Y+o1XWuu5pl7//9Qc2Yr/0S7/EzZs3\n+cIXvsBXvvKV7/tvrh/u3+9lCgvZrCl4GetOYWhFVV37RhO/EmnYjEVpoTKlOGbDCT9zc01ZSB5C\nSrNQr1kseP311/PlFuVSUUITMNpycLRkt9swdB0xeJQynJ2csCsvuXF6hxANySVUIS5Hk70qJHyQ\nMaQt7Gx+IeJhQAstYQ6lJYeQ5qLDZAtRrRTv/s5XWRwc5Y3z/VvkP2xNq7pk6DtAiwtSUHRain1p\nwvbCQuGwaqyVYihkKgHIe1RaCgkJVBT60PRATEiutjLCLkuZYsUkB5csjWy8qXARC2pBq+rFgqSy\nINQH/OBZHR/POVchBqySS6zrezxJ3JniiO8keDok8App1J1jGEVnllItRiXG8ME7v02zPHyhNe37\nDluW0lz2A7u2R1tNN7SE5IQu0CzYbjY8ePSQ1159RdbIiZGIaBjCTGtKSfbM5tklxyenWfgtY+tC\nWVYHS4rKUpYNatFw+ewJz84fUdYNdbPCDyNPnz7i4vxDrIXQ3ePkxn1uHK6wRYepIsY0KKw0TEkL\n3z8DGovFWgrEKA6YGEMIgc1mM0+SZWIeGfuBy4sLymUzRz0473j/W2IH/LzP/wxC5P0Uk+jnpkym\nFJNczjERkkcXxUyZCEH0oRN9iTzNnYrZ7VYCrLXSFFUNLOifKoaxZucaYlBctlf020tOQ8/ZyRk+\nGuJKLHLd6CAFxjHMwIQgrqJRS0AKCa1ySCqBkDPXFKKBQokz2qzBU3u9w0S/VEoxjp6ylILxd7/5\nGy+0ptpUmfo45oiC/ZTRe0+Q8Q4hZbdZxHnPVDU+Jbqhp1o24gYbA0lBuVxQlgXddkuhrFyQTUNd\ny/lcFIKQu0wXk0m66HKUUTPlXPL0PMrA6Eaxoc5T92m6JXbE2SzCp4xqMjctU/D45O45reP0XCmg\n1hWmLIQSqBLfeesfv9CaCj0v69rcSIoBrW22GxYTgpgA9i6johXMjrmZBQFyHZhrLI45viBTU6d7\nY6Lo5QeE5AMutERb4MceUqQoSoqykOn62LPZbinLkqKqZ43HdOYInbDm9t1XSDHy3lu/y6d+/PNy\nV2VA8Pc5FyaZLOikCFNDkeSZe+ebv/ZCa0qmb1Z1jY3lfJdPd2RVVfkuCgSCnKFZV+KD52p7JSBb\nkqJJawmBFYC2zUJ5AQJclO9Z1/VcbPV9T7frOFytqV57nYP1WpD1oubw4JSyrihVhQaenT8hpMTR\n6SmltTgvjUe4pvue2DUhxVkbKqZjhhinz9KI/GD6vK1otiYL8Xe/+dUXW1ME3DOmFCqkysZfSs32\n9ZNMIylxpizKku32irbdcrA+pMlsCVtYpsw+Y6zoXXTCpJyLl/bUaq0UPsjEQCFB7pDwhWVzecGz\n83OIiZs3bkCmH15eXFCUZQYtIDk3P+Oy7WWfTcHZwmzJwOA8wdUZaBkkF62wuEHAN5Xf4zu/91Xq\n5UHecs9394/jmCMPKmw2ijq7eZN6seRwfYjrey6enUtzTeTo9IzBe1wShsLp7bv020tcjNmAKGKN\nJkbParlCG8NVdl+s6gVtN5C842B1RJmf8cJarq42FKVhs+0xQRgPZVHK51tVlGFJVB1hGAnRM3Qt\nRazwSlEUJdZUaCXa2zHf46CpF0vRA2ZjjpQ/9/yYEmPWQOU/e//t36RevNiaWju5WUaMTiQCKcm9\nUDnJqIzB44L0Aj5rrpVSKKPxzmO1mc9LYwza6o9ljNmc43t96h+jmJ1MdyJKBhcq7zGRCDD/h9Zo\nLCplwDAPLCZK91TLOedQ2fhK3JKlfp2MocUZP98hXt6rtYWwqnII9A9aS/hDGrFf+7Vf48tf/jK/\n/Mu/TN/3bDYbfuqnfopbt27x0Ucfcfv2bT788ENu3rz5A7/Hr//d/yV/cJp7r/8Ed1/7rNhaK5VR\nGZ3FmZa2bVGJuYOdpl8Sopb1RV2P94HlekXdNLOzjy0LyrLi+Lhkt9vKKFxlDjziGNU0Bf6qp8hc\neW0Mq0VDbQ1VBT7Kw69VQlkZQwoaI+4n88hcT3q1fJFmt6H5laQRM2qPhn/n977Og299g/e/9XUe\nPfg2AH/mz/yZ51rTX/nf/ydikE372qf+CJ9684+iySJWJkRJkA/pleK+ufIu81rV3MGT/804uUZl\nG3M3DCituH//dT54732C3wuug5dogMSUUeGzWFksvEMUHnO9UNk0wtK5gLIFOgZ81wrlLGv+dFGK\nPb4WOlBCgx/AFriYu10lD6hzIwrFw++9xYfv/x4fvf8tnj357gut6a/9vb+e37fi5ks/xvGdT7G9\n2uZCUtZRa0s/BI5vHEvh5j0y7S0y0OBn58qU90FRTlkXzAdBIlIUhrpYYArD0A9EBUXToEzF2c0b\nFAWshxbDKUWhxV2xScQ+EpTQ3bQu5vgEoXl5UhIAoqhypEOchNh271qV6cApW+FHJiOMwIP3/ikf\nvPMNgnc8+u63AHjttdee6/n/yt/937LQGl6+/2O8fP/HUCpHUeScPhIU+fLL7Uym98nlb/NkSYpw\nNf+7kK3ulTJQWsp6zfHxPZrFKQkJIE6qQBcFCXGb0ilSl7W4To0O+P1uRlOhnUZxbooxQpw0SoHS\nTJNE+XwFJYOoEJv6ELNle5yL5O+89U/47vu/hzaab3/rGy+0pv/o//oyk53+nVfe5NbdTzL0PUVR\nghJyhIsRbIGtRA9o6wW2XlJWDUaLVm+73THsdqiUKOsKZUuUKSiKkkWjZ7fbkCJWGbHCH/cgj8p6\nLxHjJwKi5UkoXnntPu/87lsEL+/fhSDU2Lwe05mjlDhSkc9IawtpSmLMAm5yYSZNjDYGQiRp+Ke/\n/Wt8+1u/SUqJB995sTV962u/Ml+6917/Ce6++mlMmUOHk2iFU4xoLUWr/O4Rre3eJEnt85AE35Cv\nifOfT6juHv2fL+bpf6JnaEdsYcFOzr55oqzEhdG5UbQKppqb7zghyVrTLNfcffk1fvd3vs7VxTna\n2nldp0bwOso9PZ8xRt5/6zd58O7XiTHy4J2vvdCa/t//xy/Map+X3vg8L3/i86IDJLuy5efOaAlJ\nnZr2KTc05HgPFLTtDqsNzWIh+8k5YWJkRopK+0m0MaL3KVNiESMHBwf0jx4RUsKiqKsFdSWhvWLz\nPs7TrO3lJavVClQi5SiIGMNMl4wp54RN63eNTktCqEsZHA4xYI3m/bd/i/ff/jok+ODtr7/Qmsrz\n/zelmNSaV1//HK+/+UekmaHE+zCzB2KCspZpqBsH1usVt27d4Onjc9kHPjCOAwnREhe2IKhrlOp8\ntqlsOmK0RI3IdFu0xce3Ttk8OaffXHF4eMSybgS4nM7K/DtfnwRMk44Q5B53ozgxLnKGoABZe7CC\nvF+0grFvOX/yCOdgs3nIow/f5oN3v8GjD98Dnv/u/81/+Ddk8lFVvP6pP8rxnU9ig0zBnRvZ7ra0\nXUu9WLDb7VitDzNwEzGlRtUVZVzitle4YRAwu5IpeXCieRJDoojRhr7txNNguYIUcU7ocl3fST5b\nEuMMpxzKJHSM2FIAOO0CjJ4UpNkKUUAMhcKZHPeUGy43OKo6U1eNaLEmLbNs6SQmU0nurQ/e/i0+\n+PY/4bvf/m2ePf7ghdb0G1/9pQyYB27cfIWTs9sYK0Yi07MTU0STOcm5ppZBhkZnQFmcTPX8ntAq\n6y9VdrlUAphmJo0bxjmHsq6bWV+4dz7e08dTkrpMZQd1BaD3zJe9C/aUJ5kgybpNPYF4RMjap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51pCiYbVcZ414zAUQqIQUl/JDsj7PEFWk27WYrA+bkG+UuBdOUwBSwruRvm2pmgZUwocEKa+9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\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vvffiwx/+8Ov+jF/5wL9GDMBqtcFqdYiuo/ZGGTABPCV0/YCD/QMsd3YBQAqtRGe1EJAN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\nU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"text": [ "<matplotlib.figure.Figure at 0xaed0410c>" ] } ], "prompt_number": 12 }, { "cell_type": "raw", "metadata": {}, "source": [ "CNN training" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def CNN(n_epochs):\n", " net1 = NeuralNet(\n", " layers=[\n", " ('input', layers.InputLayer),\n", " ('conv1', layers.Conv2DLayer), #Convolutional layer. Params defined below\n", " ('pool1', layers.MaxPool2DLayer), # Like downsampling, for execution speed\n", " ('conv2', layers.Conv2DLayer),\n", " ('hidden3', layers.DenseLayer),\n", " ('output', layers.DenseLayer),\n", " ],\n", " \n", " input_shape=(None, 3, opts['image_dims'][0]/opts['image_downsample'], \n", " opts['image_dims'][0]/opts['image_downsample']),\n", " conv1_num_filters=7, \n", " conv1_filter_size=(5, 5), \n", " conv1_nonlinearity=lasagne.nonlinearities.rectify,\n", " \n", " pool1_pool_size=(2, 2),\n", " \n", " conv2_num_filters=12, \n", " conv2_filter_size=(2, 2), \n", " conv2_nonlinearity=lasagne.nonlinearities.rectify,\n", " \n", " hidden3_num_units=50,\n", " output_num_units=2, \n", " output_nonlinearity=lasagne.nonlinearities.softmax,\n", "\n", " update_learning_rate=0.0001,\n", " update_momentum=0.9,\n", "\n", " max_epochs=n_epochs,\n", " verbose=1,\n", " )\n", " return net1\n", "\n", "cnn = CNN(50).fit(train_X, train_y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "# Neural Network with 438432 learnable parameters\n", "\n", "## Layer information\n", "\n", " # name size\n", "--- ------- --------\n", " 0 input 3x60x60\n", " 1 conv1 7x56x56\n", " 2 pool1 7x28x28\n", " 3 conv2 12x27x27\n", " 4 hidden3 50\n", " 5 output 2\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " epoch trn loss val loss trn/val valid acc dur\n", "------- ---------- ---------- --------- ----------- -----\n", " 1 \u001b[36m43.89158\u001b[0m \u001b[32m9.55640\u001b[0m 4.59290 0.38235 1.53s\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2 \u001b[36m5.15587\u001b[0m \u001b[32m0.62822\u001b[0m 8.20714 0.57353 1.35s\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 3 \u001b[36m0.73944\u001b[0m \u001b[32m0.58266\u001b[0m 1.26908 0.60294 1.38s\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 4 \u001b[36m0.61494\u001b[0m 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"input": [ "\n", "\n", "y_pred = cnn.predict_proba(test_X)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])\n", "roc_auc = metrics.auc(false_positive_rate, true_positive_rate)\n", "plt.title('Receiver Operating Characteristic: AUC = %0.2f'% roc_auc)\n", "plt.plot(false_positive_rate, true_positive_rate, 'b')\n", "plt.legend(loc='lower right')\n", "plt.plot([0,1],[0,1],'r--')\n", "plt.ylim([-.05, 1.05])\n", "plt.xlim([-.05, 1.0])\n", "plt.ylabel('True Positive Rate')\n", "plt.xlabel('False Positive Rate')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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s7e1VFE7lePBZPRUXA35+gIsLsG6d2NEwxspT2uCzsbEx5syZg8TERLx69Up2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\n9VWV2+/atQtXrlzBhg0b6jwWphu4x8D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"text": [ "<matplotlib.figure.Figure at 0xa7cdfcac>" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])\n", "true_positive_rate.shape, thresholds.shape\n", "plt.plot(true_positive_rate, thresholds,label='False positive rate')\n", "plt.plot(false_positive_rate, thresholds, label='True positive rate')\n", "plt.xlabel('Threshold')\n", "plt.legend(loc='upper left')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<matplotlib.legend.Legend at 0xa97fb7ec>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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L78ZlYPDki48b/vHz3AWNnl+N5Z6dnV3hrjlOTk6qGxQ/JkkSjh49Cj8/Pzg6OuJvf/sb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\nn7J8+XK5I2gNjsUTTX0sruC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"text": [ "<matplotlib.figure.Figure at 0xa7816c0c>" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "precision, recall, thresholds = metrics.precision_recall_curve(test_y, y_pred[:,1])\n", "average_precision = metrics.average_precision_score(test_y, y_pred[:, 1])\n", "\n", "plt.plot(recall, precision)\n", "plt.xlabel('Recall')\n", "plt.ylabel('Precision')\n", "plt.ylim([0.0, 1.05])\n", "plt.xlim([0.0, 1.0])\n", "plt.title('Precision-Recall: AUC={0:0.2f}'.format(average_precision))\n", "plt.legend(loc=\"lower left\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xb73c606c>" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Examine mistakes to understand network performance - false positives" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Find the negative-labelled patches with highest prediction score" ] }, { "cell_type": "code", "collapsed": false, "input": [ "neg_indices = np.where(test_y==0)[0]\n", "neg_scores = y_pred[neg_indices,1]\n", "neg_indices = neg_indices[neg_scores.argsort()]\n", "neg_indices = neg_indices[::-1]\n", "\n", "neg_scores = y_pred[neg_indices,1]\n", "neg_scores\n", "\n", "N_samples_to_display = 10\n", "\n", "for i in range(N_samples_to_display,2N_samples_to_display):\n", " plt.subplot(2,N_samples_to_display,i+1)\n", " example_neg = test_X[neg_indices[i],:,:,:]\n", " example_neg = np.swapaxes(example_neg,0,2)\n", " plt.imshow(example_neg[:,:,[2,1,0]])\n", "\n", "print neg_scores[:N_samples_to_display]\n", "\n", "plt.gcf().set_size_inches(1.5N_samples_to_display,3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.51415054 0.5072187 0.50292046 0.50292046 0.50292046 0.50292046\n", " 0.50292046 0.50292046 0.50292046 0.50292046]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JN4yDU0y6MQ62nVEa0c1x8MGHniI04TVxsACb9fqmOHjx0rtfNw4658hwUxy8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\nURSzHL9aVpvvgkR5Xrh7ge2TMlyaNhBIsMi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NAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0xa796c4ac>" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "See highest-scored test patches" ] }, { "cell_type": "code", "collapsed": false, "input": [ "prob_range = [.9,1.]\n", "\n", "tmp_scores = y_pred.copy()[:,1]\n", "tmp_scores[tmp_scores<prob_range[0]] = -1\n", "tmp_scores[tmp_scores>prob_range[1]] = -1\n", "\n", "pos_indices = tmp_scores.argsort()\n", "pos_indices = pos_indices[::-1]\n", "\n", "N_samples_to_display = 10\n", "\n", "for i in range(N_samples_to_display,2N_samples_to_display):\n", " plt.subplot(2,N_samples_to_display,i+1)\n", " example_neg = test_X[pos_indices[i],:,:,:]\n", " example_neg = np.swapaxes(example_neg,0,2)\n", " plt.imshow(example_neg[:,:,[2,1,0]])\n", "\n", "plt.gcf().set_size_inches(1.5N_samples_to_display,3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Hto9z8esvEcLA1Z1LfP5zX5GYS+XvmOzfDbvlcapUie0rmqZhf2+f5WqFqyq0MZPfxi0q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6qSitYOiniq4YV3YUENlwa3JKoTa0xZNI0o2FCOTb8ETQs7p2SJMjrs9/jg6boW5wNxf+D+gx/i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\nz39JvNFBmoExjlrkVKEbakEhZ4u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"text": [ "<matplotlib.figure.Figure at 0xa7cb2b2c>" ] } ], "prompt_number": 19 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Lowest scored test patches" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Object detection in an entire field of view" ] }, { "cell_type": "code", "collapsed": false, "input": [ "imfile = opts['img_dir'] + testfiles[5]\n", "%timeit found = det.detect(imfile, cnn, opts)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/lubega/env/lib/python2.7/site-packages/skimage/filters/_gaussian.py:13: skimage_deprecation: Call to deprecated function ``gaussian_filter``. Use ``skimage.filters.gaussian`` instead.\n", " multichannel=None):\n", "/home/lubega/env/lib/python2.7/site-packages/skimage/filters/_gaussian.py:13: skimage_deprecation: Call to deprecated function ``gaussian_filter``. Use ``skimage.filters.gaussian`` instead.\n", " multichannel=None):\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/home/lubega/env/lib/python2.7/site-packages/skimage/filters/_gaussian.py:13: skimage_deprecation: Call to deprecated function ``gaussian_filter``. Use ``skimage.filters.gaussian`` instead.\n", " multichannel=None):\n", "/home/lubega/env/lib/python2.7/site-packages/skimage/filters/_gaussian.py:13: skimage_deprecation: Call to deprecated function ``gaussian_filter``. Use ``skimage.filters.gaussian`` instead.\n", " multichannel=None):\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 7.95 s per loop\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "testfiles[5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "'intestinalparasites-phone-0003.jpg'" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "import cv2\n", "im = cv2.imread(imfile)\n", "\n", "plt.box(False)\n", "plt.xticks([])\n", "plt.yticks([])\n", "\n", "for f in det.detect(imfile, cnn, opts):\n", " f = f.astype(int)\n", " cv2.rectangle(im, (f[0], f[1]), (f[2],f[3]), (0,0,255), 8)\n", " \n", "plt.gcf().set_size_inches(10,10)\n", "plt.title('Detected objects in %s' % (imfile))\n", "plt.imshow(im[:,:,[2,1,0]])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/home/lubega/env/lib/python2.7/site-packages/skimage/filters/_gaussian.py:13: skimage_deprecation: Call to deprecated function ``gaussian_filter``. Use ``skimage.filters.gaussian`` instead.\n", " multichannel=None):\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "<matplotlib.image.AxesImage at 0xa97aa72c>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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B9ddf/y9FdqoYehSLRSxevBhBEGDDhg249tprK2rp+gvf9zFv3rx/KbLzr4iq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d/rIMOAvVHBqy9fIYoVj58/w9bmGr1OTJQmVDXMZwuK5tqsVtPqusY0ykTPqRFCtJXEVXdm3+Kr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InYqZtCbote1lElM2MYeofC9pEdIa96XmOXxDsXcJnPOJ2OWJYLiO6bpUCMknNmnRQjanvv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\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tNU3YHoYRr732OkrJklPVgSM8+DPEGNE1LYbthsNjhTfCYIET6/vIRoxDHLFn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\nIO2heb0c5+M6Xc5lOcxVYEn/vgx+rtvmTeqRAU+lbh/ixW9Sl3vT+9p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"text": [ "<matplotlib.figure.Figure at 0xa9833b4c>" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
bw4sz/DeepMeerkat	training/Detection/slim/slim_walkthrough.ipynb	5	46294	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TF-Slim Walkthrough\n", "\n", "This notebook will walk you through the basics of using TF-Slim to define, train and evaluate neural networks on various tasks. It assumes a basic knowledge of neural networks. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "\n", "<a href=\"#Install\">Installation and setup</a><br>\n", "<a href='#MLP'>Creating your first neural network with TF-Slim</a><br>\n", "<a href='#ReadingTFSlimDatasets'>Reading Data with TF-Slim</a><br>\n", "<a href='#CNN'>Training a convolutional neural network (CNN)</a><br>\n", "<a href='#Pretained'>Using pre-trained models</a><br>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation and setup\n", "<a id='Install'></a>\n", "\n", "Since the stable release of TF 1.0, the latest version of slim has been available as `tf.contrib.slim`.\n", "To test that your installation is working, execute the following command; it should run without raising any errors.\n", "\n", "```\n", "python -c \"import tensorflow.contrib.slim as slim; eval = slim.evaluation.evaluate_once\"\n", "```\n", "\n", "Although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from [here](https://github.com/tensorflow/models/tree/master/research/slim). Let's suppose you install this into a directory called TF_MODELS. Then you should change directory to TF_MODELS/research/slim before running this notebook, so that these files are in your python path.\n", "\n", "To check you've got these two steps to work, just execute the cell below. If it complains about unknown modules, restart the notebook after moving to the TF-Slim models directory.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import matplotlib\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import math\n", "import numpy as np\n", "import tensorflow as tf\n", "import time\n", "\n", "from datasets import dataset_utils\n", "\n", "# Main slim library\n", "from tensorflow.contrib import slim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating your first neural network with TF-Slim\n", "<a id='MLP'></a>\n", "\n", "Below we give some code to create a simple multilayer perceptron (MLP) which can be used\n", "for regression problems. The model has 2 hidden layers.\n", "The output is a single node. \n", "When this function is called, it will create various nodes, and silently add them to whichever global TF graph is currently in scope. When a node which corresponds to a layer with adjustable parameters (eg., a fully connected layer) is created, additional parameter variable nodes are silently created, and added to the graph. (We will discuss how to train the parameters later.)\n", "\n", "We use variable scope to put all the nodes under a common name,\n", "so that the graph has some hierarchical structure.\n", "This is useful when we want to visualize the TF graph in tensorboard, or if we want to query related\n", "variables. \n", "The fully connected layers all use the same L2 weight decay and ReLu activations, as specified by arg_scope. (However, the final layer overrides these defaults, and uses an identity activation function.)\n", "\n", "We also illustrate how to add a dropout layer after the first fully connected layer (FC1). Note that at test time, \n", "we do not drop out nodes, but instead use the average activations; hence we need to know whether the model is being\n", "constructed for training or testing, since the computational graph will be different in the two cases\n", "(although the variables, storing the model parameters, will be shared, since they have the same name/scope)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def regression_model(inputs, is_training=True, scope=\"deep_regression\"):\n", " \"\"\"Creates the regression model.\n", "\n", " Args:\n", " inputs: A node that yields a `Tensor` of size [batch_size, dimensions].\n", " is_training: Whether or not we're currently training the model.\n", " scope: An optional variable_op scope for the model.\n", "\n", " Returns:\n", " predictions: 1-D `Tensor` of shape [batch_size] of responses.\n", " end_points: A dict of end points representing the hidden layers.\n", " \"\"\"\n", " with tf.variable_scope(scope, 'deep_regression', [inputs]):\n", " end_points = {}\n", " # Set the default weight _regularizer and acvitation for each fully_connected layer.\n", " with slim.arg_scope([slim.fully_connected],\n", " activation_fn=tf.nn.relu,\n", " weights_regularizer=slim.l2_regularizer(0.01)):\n", "\n", " # Creates a fully connected layer from the inputs with 32 hidden units.\n", " net = slim.fully_connected(inputs, 32, scope='fc1')\n", " end_points['fc1'] = net\n", "\n", " # Adds a dropout layer to prevent over-fitting.\n", " net = slim.dropout(net, 0.8, is_training=is_training)\n", "\n", " # Adds another fully connected layer with 16 hidden units.\n", " net = slim.fully_connected(net, 16, scope='fc2')\n", " end_points['fc2'] = net\n", "\n", " # Creates a fully-connected layer with a single hidden unit. Note that the\n", " # layer is made linear by setting activation_fn=None.\n", " predictions = slim.fully_connected(net, 1, activation_fn=None, scope='prediction')\n", " end_points['out'] = predictions\n", "\n", " return predictions, end_points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's create the model and examine its structure.\n", "\n", "We create a TF graph and call regression_model(), which adds nodes (tensors) to the graph. We then examine their shape, and print the names of all the model variables which have been implicitly created inside of each layer. We see that the names of the variables follow the scopes that we specified." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.Graph().as_default():\n", " # Dummy placeholders for arbitrary number of 1d inputs and outputs\n", " inputs = tf.placeholder(tf.float32, shape=(None, 1))\n", " outputs = tf.placeholder(tf.float32, shape=(None, 1))\n", "\n", " # Build model\n", " predictions, end_points = regression_model(inputs)\n", "\n", " # Print name and shape of each tensor.\n", " print(\"Layers\")\n", " for k, v in end_points.items():\n", " print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n", "\n", " # Print name and shape of parameter nodes (values not yet initialized)\n", " print(\"\\n\")\n", " print(\"Parameters\")\n", " for v in slim.get_model_variables():\n", " print('name = {}, shape = {}'.format(v.name, v.get_shape()))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's create some 1d regression data .\n", "\n", "We will train and test the model on some noisy observations of a nonlinear function.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def produce_batch(batch_size, noise=0.3):\n", " xs = np.random.random(size=[batch_size, 1]) * 10\n", " ys = np.sin(xs) + 5 + np.random.normal(size=[batch_size, 1], scale=noise)\n", " return [xs.astype(np.float32), ys.astype(np.float32)]\n", "\n", "x_train, y_train = produce_batch(200)\n", "x_test, y_test = produce_batch(200)\n", "plt.scatter(x_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's fit the model to the data\n", "\n", "The user has to specify the loss function and the optimizer, and slim does the rest.\n", "In particular, the slim.learning.train function does the following:\n", "\n", "- For each iteration, evaluate the train_op, which updates the parameters using the optimizer applied to the current minibatch. Also, update the global_step.\n", "- Occasionally store the model checkpoint in the specified directory. This is useful in case your machine crashes - then you can simply restart from the specified checkpoint." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def convert_data_to_tensors(x, y):\n", " inputs = tf.constant(x)\n", " inputs.set_shape([None, 1])\n", " \n", " outputs = tf.constant(y)\n", " outputs.set_shape([None, 1])\n", " return inputs, outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The following snippet trains the regression model using a mean_squared_error loss.\n", "ckpt_dir = '/tmp/regression_model/'\n", "\n", "with tf.Graph().as_default():\n", " tf.logging.set_verbosity(tf.logging.INFO)\n", " \n", " inputs, targets = convert_data_to_tensors(x_train, y_train)\n", "\n", " # Make the model.\n", " predictions, nodes = regression_model(inputs, is_training=True)\n", "\n", " # Add the loss function to the graph.\n", " loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n", " \n", " # The total loss is the user's loss plus any regularization losses.\n", " total_loss = slim.losses.get_total_loss()\n", "\n", " # Specify the optimizer and create the train op:\n", " optimizer = tf.train.AdamOptimizer(learning_rate=0.005)\n", " train_op = slim.learning.create_train_op(total_loss, optimizer) \n", "\n", " # Run the training inside a session.\n", " final_loss = slim.learning.train(\n", " train_op,\n", " logdir=ckpt_dir,\n", " number_of_steps=5000,\n", " save_summaries_secs=5,\n", " log_every_n_steps=500)\n", " \n", "print(\"Finished training. Last batch loss:\", final_loss)\n", "print(\"Checkpoint saved in %s\" % ckpt_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training with multiple loss functions.\n", "\n", "Sometimes we have multiple objectives we want to simultaneously optimize.\n", "In slim, it is easy to add more losses, as we show below. (We do not optimize the total loss in this example,\n", "but we show how to compute it.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.Graph().as_default():\n", " inputs, targets = convert_data_to_tensors(x_train, y_train)\n", " predictions, end_points = regression_model(inputs, is_training=True)\n", "\n", " # Add multiple loss nodes.\n", " mean_squared_error_loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n", " absolute_difference_loss = slim.losses.absolute_difference(predictions, targets)\n", "\n", " # The following two ways to compute the total loss are equivalent\n", " regularization_loss = tf.add_n(slim.losses.get_regularization_losses())\n", " total_loss1 = mean_squared_error_loss + absolute_difference_loss + regularization_loss\n", "\n", " # Regularization Loss is included in the total loss by default.\n", " # This is good for training, but not for testing.\n", " total_loss2 = slim.losses.get_total_loss(add_regularization_losses=True)\n", " \n", " init_op = tf.global_variables_initializer()\n", " \n", " with tf.Session() as sess:\n", " sess.run(init_op) # Will initialize the parameters with random weights.\n", " \n", " total_loss1, total_loss2 = sess.run([total_loss1, total_loss2])\n", " \n", " print('Total Loss1: %f' % total_loss1)\n", " print('Total Loss2: %f' % total_loss2)\n", "\n", " print('Regularization Losses:')\n", " for loss in slim.losses.get_regularization_losses():\n", " print(loss)\n", "\n", " print('Loss Functions:')\n", " for loss in slim.losses.get_losses():\n", " print(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's load the saved model and use it for prediction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.Graph().as_default():\n", " inputs, targets = convert_data_to_tensors(x_test, y_test)\n", " \n", " # Create the model structure. (Parameters will be loaded below.)\n", " predictions, end_points = regression_model(inputs, is_training=False)\n", "\n", " # Make a session which restores the old parameters from a checkpoint.\n", " sv = tf.train.Supervisor(logdir=ckpt_dir)\n", " with sv.managed_session() as sess:\n", " inputs, predictions, targets = sess.run([inputs, predictions, targets])\n", "\n", "plt.scatter(inputs, targets, c='r');\n", "plt.scatter(inputs, predictions, c='b');\n", "plt.title('red=true, blue=predicted')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's compute various evaluation metrics on the test set.\n", "\n", "In TF-Slim termiology, losses are optimized, but metrics (which may not be differentiable, e.g., precision and recall) are just measured. As an illustration, the code below computes mean squared error and mean absolute error metrics on the test set.\n", "\n", "Each metric declaration creates several local variables (which must be initialized via tf.initialize_local_variables()) and returns both a value_op and an update_op. When evaluated, the value_op returns the current value of the metric. The update_op loads a new batch of data, runs the model, obtains the predictions and accumulates the metric statistics appropriately before returning the current value of the metric. We store these value nodes and update nodes in 2 dictionaries.\n", "\n", "After creating the metric nodes, we can pass them to slim.evaluation.evaluation, which repeatedly evaluates these nodes the specified number of times. (This allows us to compute the evaluation in a streaming fashion across minibatches, which is usefulf for large datasets.) Finally, we print the final value of each metric.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.Graph().as_default():\n", " inputs, targets = convert_data_to_tensors(x_test, y_test)\n", " predictions, end_points = regression_model(inputs, is_training=False)\n", "\n", " # Specify metrics to evaluate:\n", " names_to_value_nodes, names_to_update_nodes = slim.metrics.aggregate_metric_map({\n", " 'Mean Squared Error': slim.metrics.streaming_mean_squared_error(predictions, targets),\n", " 'Mean Absolute Error': slim.metrics.streaming_mean_absolute_error(predictions, targets)\n", " })\n", "\n", " # Make a session which restores the old graph parameters, and then run eval.\n", " sv = tf.train.Supervisor(logdir=ckpt_dir)\n", " with sv.managed_session() as sess:\n", " metric_values = slim.evaluation.evaluation(\n", " sess,\n", " num_evals=1, # Single pass over data\n", " eval_op=names_to_update_nodes.values(),\n", " final_op=names_to_value_nodes.values())\n", "\n", " names_to_values = dict(zip(names_to_value_nodes.keys(), metric_values))\n", " for key, value in names_to_values.items():\n", " print('%s: %f' % (key, value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading Data with TF-Slim\n", "<a id='ReadingTFSlimDatasets'></a>\n", "\n", "Reading data with TF-Slim has two main components: A\n", "[Dataset](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset.py) and a \n", "[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py). The former is a descriptor of a dataset, while the latter performs the actions necessary for actually reading the data. Lets look at each one in detail:\n", "\n", "\n", "## Dataset\n", "A TF-Slim\n", "[Dataset](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset.py)\n", "contains descriptive information about a dataset necessary for reading it, such as the list of data files and how to decode them. It also contains metadata including class labels, the size of the train/test splits and descriptions of the tensors that the dataset provides. For example, some datasets contain images with labels. Others augment this data with bounding box annotations, etc. The Dataset object allows us to write generic code using the same API, regardless of the data content and encoding type.\n", "\n", "TF-Slim's Dataset works especially well when the data is stored as a (possibly sharded)\n", "[TFRecords file](https://www.tensorflow.org/versions/r0.10/how_tos/reading_data/index.html#file-formats), where each record contains a [tf.train.Example protocol buffer](https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/core/example/example.proto).\n", "TF-Slim uses a consistent convention for naming the keys and values inside each Example record. \n", "\n", "## DatasetDataProvider\n", "\n", "A\n", "[DatasetDataProvider](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py) is a class which actually reads the data from a dataset. It is highly configurable to read the data in various ways that may make a big impact on the efficiency of your training process. For example, it can be single or multi-threaded. If your data is sharded across many files, it can read each files serially, or from every file simultaneously.\n", "\n", "## Demo: The Flowers Dataset\n", "\n", "For convenience, we've include scripts to convert several common image datasets into TFRecord format and have provided\n", "the Dataset descriptor files necessary for reading them. We demonstrate how easy it is to use these dataset via the Flowers dataset below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download the Flowers Dataset\n", "<a id='DownloadFlowers'></a>\n", "\n", "We've made available a tarball of the Flowers dataset which has already been converted to TFRecord format." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "from datasets import dataset_utils\n", "\n", "url = \"http://download.tensorflow.org/data/flowers.tar.gz\"\n", "flowers_data_dir = '/tmp/flowers'\n", "\n", "if not tf.gfile.Exists(flowers_data_dir):\n", " tf.gfile.MakeDirs(flowers_data_dir)\n", "\n", "dataset_utils.download_and_uncompress_tarball(url, flowers_data_dir) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display some of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datasets import flowers\n", "import tensorflow as tf\n", "\n", "from tensorflow.contrib import slim\n", "\n", "with tf.Graph().as_default(): \n", " dataset = flowers.get_split('train', flowers_data_dir)\n", " data_provider = slim.dataset_data_provider.DatasetDataProvider(\n", " dataset, common_queue_capacity=32, common_queue_min=1)\n", " image, label = data_provider.get(['image', 'label'])\n", " \n", " with tf.Session() as sess: \n", " with slim.queues.QueueRunners(sess):\n", " for i in range(4):\n", " np_image, np_label = sess.run([image, label])\n", " height, width, _ = np_image.shape\n", " class_name = name = dataset.labels_to_names[np_label]\n", " \n", " plt.figure()\n", " plt.imshow(np_image)\n", " plt.title('%s, %d x %d' % (name, height, width))\n", " plt.axis('off')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional neural nets (CNNs).\n", "<a id='CNN'></a>\n", "\n", "In this section, we show how to train an image classifier using a simple CNN.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the model.\n", "\n", "Below we define a simple CNN. Note that the output layer is linear function - we will apply softmax transformation externally to the model, either in the loss function (for training), or in the prediction function (during testing)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def my_cnn(images, num_classes, is_training): # is_training is not used...\n", " with slim.arg_scope([slim.max_pool2d], kernel_size=[3, 3], stride=2):\n", " net = slim.conv2d(images, 64, [5, 5])\n", " net = slim.max_pool2d(net)\n", " net = slim.conv2d(net, 64, [5, 5])\n", " net = slim.max_pool2d(net)\n", " net = slim.flatten(net)\n", " net = slim.fully_connected(net, 192)\n", " net = slim.fully_connected(net, num_classes, activation_fn=None) \n", " return net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply the model to some randomly generated images." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "with tf.Graph().as_default():\n", " # The model can handle any input size because the first layer is convolutional.\n", " # The size of the model is determined when image_node is first passed into the my_cnn function.\n", " # Once the variables are initialized, the size of all the weight matrices is fixed.\n", " # Because of the fully connected layers, this means that all subsequent images must have the same\n", " # input size as the first image.\n", " batch_size, height, width, channels = 3, 28, 28, 3\n", " images = tf.random_uniform([batch_size, height, width, channels], maxval=1)\n", " \n", " # Create the model.\n", " num_classes = 10\n", " logits = my_cnn(images, num_classes, is_training=True)\n", " probabilities = tf.nn.softmax(logits)\n", " \n", " # Initialize all the variables (including parameters) randomly.\n", " init_op = tf.global_variables_initializer()\n", " \n", " with tf.Session() as sess:\n", " # Run the init_op, evaluate the model outputs and print the results:\n", " sess.run(init_op)\n", " probabilities = sess.run(probabilities)\n", " \n", "print('Probabilities Shape:')\n", "print(probabilities.shape) # batch_size x num_classes \n", "\n", "print('\\nProbabilities:')\n", "print(probabilities)\n", "\n", "print('\\nSumming across all classes (Should equal 1):')\n", "print(np.sum(probabilities, 1)) # Each row sums to 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the model on the Flowers dataset.\n", "\n", "Before starting, make sure you've run the code to <a href=\"#DownloadFlowers\">Download the Flowers</a> dataset. Now, we'll get a sense of what it looks like to use TF-Slim's training functions found in\n", "[learning.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/learning.py). First, we'll create a function, `load_batch`, that loads batches of dataset from a dataset. Next, we'll train a model for a single step (just to demonstrate the API), and evaluate the results." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from preprocessing import inception_preprocessing\n", "import tensorflow as tf\n", "\n", "from tensorflow.contrib import slim\n", "\n", "\n", "def load_batch(dataset, batch_size=32, height=299, width=299, is_training=False):\n", " \"\"\"Loads a single batch of data.\n", " \n", " Args:\n", " dataset: The dataset to load.\n", " batch_size: The number of images in the batch.\n", " height: The size of each image after preprocessing.\n", " width: The size of each image after preprocessing.\n", " is_training: Whether or not we're currently training or evaluating.\n", " \n", " Returns:\n", " images: A Tensor of size [batch_size, height, width, 3], image samples that have been preprocessed.\n", " images_raw: A Tensor of size [batch_size, height, width, 3], image samples that can be used for visualization.\n", " labels: A Tensor of size [batch_size], whose values range between 0 and dataset.num_classes.\n", " \"\"\"\n", " data_provider = slim.dataset_data_provider.DatasetDataProvider(\n", " dataset, common_queue_capacity=32,\n", " common_queue_min=8)\n", " image_raw, label = data_provider.get(['image', 'label'])\n", " \n", " # Preprocess image for usage by Inception.\n", " image = inception_preprocessing.preprocess_image(image_raw, height, width, is_training=is_training)\n", " \n", " # Preprocess the image for display purposes.\n", " image_raw = tf.expand_dims(image_raw, 0)\n", " image_raw = tf.image.resize_images(image_raw, [height, width])\n", " image_raw = tf.squeeze(image_raw)\n", "\n", " # Batch it up.\n", " images, images_raw, labels = tf.train.batch(\n", " [image, image_raw, label],\n", " batch_size=batch_size,\n", " num_threads=1,\n", " capacity=2 * batch_size)\n", " \n", " return images, images_raw, labels" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datasets import flowers\n", "\n", "# This might take a few minutes.\n", "train_dir = '/tmp/tfslim_model/'\n", "print('Will save model to %s' % train_dir)\n", "\n", "with tf.Graph().as_default():\n", " tf.logging.set_verbosity(tf.logging.INFO)\n", "\n", " dataset = flowers.get_split('train', flowers_data_dir)\n", " images, _, labels = load_batch(dataset)\n", " \n", " # Create the model:\n", " logits = my_cnn(images, num_classes=dataset.num_classes, is_training=True)\n", " \n", " # Specify the loss function:\n", " one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)\n", " slim.losses.softmax_cross_entropy(logits, one_hot_labels)\n", " total_loss = slim.losses.get_total_loss()\n", "\n", " # Create some summaries to visualize the training process:\n", " tf.summary.scalar('losses/Total Loss', total_loss)\n", " \n", " # Specify the optimizer and create the train op:\n", " optimizer = tf.train.AdamOptimizer(learning_rate=0.01)\n", " train_op = slim.learning.create_train_op(total_loss, optimizer)\n", "\n", " # Run the training:\n", " final_loss = slim.learning.train(\n", " train_op,\n", " logdir=train_dir,\n", " number_of_steps=1, # For speed, we just do 1 epoch\n", " save_summaries_secs=1)\n", " \n", " print('Finished training. Final batch loss %d' % final_loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate some metrics.\n", "\n", "As we discussed above, we can compute various metrics besides the loss.\n", "Below we show how to compute prediction accuracy of the trained model, as well as top-5 classification accuracy. (The difference between evaluation and evaluation_loop is that the latter writes the results to a log directory, so they can be viewed in tensorboard.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datasets import flowers\n", "\n", "# This might take a few minutes.\n", "with tf.Graph().as_default():\n", " tf.logging.set_verbosity(tf.logging.DEBUG)\n", " \n", " dataset = flowers.get_split('train', flowers_data_dir)\n", " images, _, labels = load_batch(dataset)\n", " \n", " logits = my_cnn(images, num_classes=dataset.num_classes, is_training=False)\n", " predictions = tf.argmax(logits, 1)\n", " \n", " # Define the metrics:\n", " names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({\n", " 'eval/Accuracy': slim.metrics.streaming_accuracy(predictions, labels),\n", " 'eval/Recall@5': slim.metrics.streaming_recall_at_k(logits, labels, 5),\n", " })\n", "\n", " print('Running evaluation Loop...')\n", " checkpoint_path = tf.train.latest_checkpoint(train_dir)\n", " metric_values = slim.evaluation.evaluate_once(\n", " master='',\n", " checkpoint_path=checkpoint_path,\n", " logdir=train_dir,\n", " eval_op=names_to_updates.values(),\n", " final_op=names_to_values.values())\n", "\n", " names_to_values = dict(zip(names_to_values.keys(), metric_values))\n", " for name in names_to_values:\n", " print('%s: %f' % (name, names_to_values[name]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using pre-trained models\n", "<a id='Pretrained'></a>\n", "\n", "Neural nets work best when they have many parameters, making them very flexible function approximators.\n", "However, this means they must be trained on big datasets. Since this process is slow, we provide various pre-trained models - see the list [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).\n", "\n", "\n", "You can either use these models as-is, or you can perform \"surgery\" on them, to modify them for some other task. For example, it is common to \"chop off\" the final pre-softmax layer, and replace it with a new set of weights corresponding to some new set of labels. You can then quickly fine tune the new model on a small new dataset. We illustrate this below, using inception-v1 as the base model. While models like Inception V3 are more powerful, Inception V1 is used for speed purposes.\n", "\n", "Take into account that VGG and ResNet final layers have only 1000 outputs rather than 1001. The ImageNet dataset provied has an empty background class which can be used to fine-tune the model to other tasks. VGG and ResNet models provided here don't use that class. We provide two examples of using pretrained models: Inception V1 and VGG-19 models to highlight this difference.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download the Inception V1 checkpoint\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datasets import dataset_utils\n", "\n", "url = \"http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz\"\n", "checkpoints_dir = '/tmp/checkpoints'\n", "\n", "if not tf.gfile.Exists(checkpoints_dir):\n", " tf.gfile.MakeDirs(checkpoints_dir)\n", "\n", "dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Apply Pre-trained Inception V1 model to Images.\n", "\n", "We have to convert each image to the size expected by the model checkpoint.\n", "There is no easy way to determine this size from the checkpoint itself.\n", "So we use a preprocessor to enforce this." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import tensorflow as tf\n", "\n", "try:\n", " import urllib2 as urllib\n", "except ImportError:\n", " import urllib.request as urllib\n", "\n", "from datasets import imagenet\n", "from nets import inception\n", "from preprocessing import inception_preprocessing\n", "\n", "from tensorflow.contrib import slim\n", "\n", "image_size = inception.inception_v1.default_image_size\n", "\n", "with tf.Graph().as_default():\n", " url = 'https://upload.wikimedia.org/wikipedia/commons/7/70/EnglishCockerSpaniel_simon.jpg'\n", " image_string = urllib.urlopen(url).read()\n", " image = tf.image.decode_jpeg(image_string, channels=3)\n", " processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n", " processed_images = tf.expand_dims(processed_image, 0)\n", " \n", " # Create the model, use the default arg scope to configure the batch norm parameters.\n", " with slim.arg_scope(inception.inception_v1_arg_scope()):\n", " logits, _ = inception.inception_v1(processed_images, num_classes=1001, is_training=False)\n", " probabilities = tf.nn.softmax(logits)\n", " \n", " init_fn = slim.assign_from_checkpoint_fn(\n", " os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n", " slim.get_model_variables('InceptionV1'))\n", " \n", " with tf.Session() as sess:\n", " init_fn(sess)\n", " np_image, probabilities = sess.run([image, probabilities])\n", " probabilities = probabilities[0, 0:]\n", " sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]\n", " \n", " plt.figure()\n", " plt.imshow(np_image.astype(np.uint8))\n", " plt.axis('off')\n", " plt.show()\n", "\n", " names = imagenet.create_readable_names_for_imagenet_labels()\n", " for i in range(5):\n", " index = sorted_inds[i]\n", " print('Probability %0.2f%% => [%s]' % (probabilities[index] * 100, names[index]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download the VGG-16 checkpoint" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datasets import dataset_utils\n", "import tensorflow as tf\n", "\n", "url = \"http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz\"\n", "checkpoints_dir = '/tmp/checkpoints'\n", "\n", "if not tf.gfile.Exists(checkpoints_dir):\n", " tf.gfile.MakeDirs(checkpoints_dir)\n", "\n", "dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Apply Pre-trained VGG-16 model to Images.\n", "\n", "We have to convert each image to the size expected by the model checkpoint.\n", "There is no easy way to determine this size from the checkpoint itself.\n", "So we use a preprocessor to enforce this. Pay attention to the difference caused by 1000 classes instead of 1001." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import tensorflow as tf\n", "\n", "try:\n", " import urllib2\n", "except ImportError:\n", " import urllib.request as urllib\n", "\n", "from datasets import imagenet\n", "from nets import vgg\n", "from preprocessing import vgg_preprocessing\n", "\n", "from tensorflow.contrib import slim\n", "\n", "image_size = vgg.vgg_16.default_image_size\n", "\n", "with tf.Graph().as_default():\n", " url = 'https://upload.wikimedia.org/wikipedia/commons/d/d9/First_Student_IC_school_bus_202076.jpg'\n", " image_string = urllib.urlopen(url).read()\n", " image = tf.image.decode_jpeg(image_string, channels=3)\n", " processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)\n", " processed_images = tf.expand_dims(processed_image, 0)\n", " \n", " # Create the model, use the default arg scope to configure the batch norm parameters.\n", " with slim.arg_scope(vgg.vgg_arg_scope()):\n", " # 1000 classes instead of 1001.\n", " logits, _ = vgg.vgg_16(processed_images, num_classes=1000, is_training=False)\n", " probabilities = tf.nn.softmax(logits)\n", " \n", " init_fn = slim.assign_from_checkpoint_fn(\n", " os.path.join(checkpoints_dir, 'vgg_16.ckpt'),\n", " slim.get_model_variables('vgg_16'))\n", " \n", " with tf.Session() as sess:\n", " init_fn(sess)\n", " np_image, probabilities = sess.run([image, probabilities])\n", " probabilities = probabilities[0, 0:]\n", " sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]\n", " \n", " plt.figure()\n", " plt.imshow(np_image.astype(np.uint8))\n", " plt.axis('off')\n", " plt.show()\n", " \n", " names = imagenet.create_readable_names_for_imagenet_labels()\n", " for i in range(5):\n", " index = sorted_inds[i]\n", " # Shift the index of a class name by one. \n", " print('Probability %0.2f%% => [%s]' % (probabilities[index] * 100, names[index+1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fine-tune the model on a different set of labels.\n", "\n", "We will fine tune the inception model on the Flowers dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Note that this may take several minutes.\n", "\n", "import os\n", "\n", "from datasets import flowers\n", "from nets import inception\n", "from preprocessing import inception_preprocessing\n", "\n", "from tensorflow.contrib import slim\n", "image_size = inception.inception_v1.default_image_size\n", "\n", "\n", "def get_init_fn():\n", " \"\"\"Returns a function run by the chief worker to warm-start the training.\"\"\"\n", " checkpoint_exclude_scopes=[\"InceptionV1/Logits\", \"InceptionV1/AuxLogits\"]\n", " \n", " exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]\n", "\n", " variables_to_restore = []\n", " for var in slim.get_model_variables():\n", " excluded = False\n", " for exclusion in exclusions:\n", " if var.op.name.startswith(exclusion):\n", " excluded = True\n", " break\n", " if not excluded:\n", " variables_to_restore.append(var)\n", "\n", " return slim.assign_from_checkpoint_fn(\n", " os.path.join(checkpoints_dir, 'inception_v1.ckpt'),\n", " variables_to_restore)\n", "\n", "\n", "train_dir = '/tmp/inception_finetuned/'\n", "\n", "with tf.Graph().as_default():\n", " tf.logging.set_verbosity(tf.logging.INFO)\n", " \n", " dataset = flowers.get_split('train', flowers_data_dir)\n", " images, _, labels = load_batch(dataset, height=image_size, width=image_size)\n", " \n", " # Create the model, use the default arg scope to configure the batch norm parameters.\n", " with slim.arg_scope(inception.inception_v1_arg_scope()):\n", " logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)\n", " \n", " # Specify the loss function:\n", " one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)\n", " slim.losses.softmax_cross_entropy(logits, one_hot_labels)\n", " total_loss = slim.losses.get_total_loss()\n", "\n", " # Create some summaries to visualize the training process:\n", " tf.summary.scalar('losses/Total Loss', total_loss)\n", " \n", " # Specify the optimizer and create the train op:\n", " optimizer = tf.train.AdamOptimizer(learning_rate=0.01)\n", " train_op = slim.learning.create_train_op(total_loss, optimizer)\n", " \n", " # Run the training:\n", " final_loss = slim.learning.train(\n", " train_op,\n", " logdir=train_dir,\n", " init_fn=get_init_fn(),\n", " number_of_steps=2)\n", " \n", " \n", "print('Finished training. Last batch loss %f' % final_loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply fine tuned model to some images." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from datasets import flowers\n", "from nets import inception\n", "\n", "from tensorflow.contrib import slim\n", "\n", "image_size = inception.inception_v1.default_image_size\n", "batch_size = 3\n", "\n", "with tf.Graph().as_default():\n", " tf.logging.set_verbosity(tf.logging.INFO)\n", " \n", " dataset = flowers.get_split('train', flowers_data_dir)\n", " images, images_raw, labels = load_batch(dataset, height=image_size, width=image_size)\n", " \n", " # Create the model, use the default arg scope to configure the batch norm parameters.\n", " with slim.arg_scope(inception.inception_v1_arg_scope()):\n", " logits, _ = inception.inception_v1(images, num_classes=dataset.num_classes, is_training=True)\n", "\n", " probabilities = tf.nn.softmax(logits)\n", " \n", " checkpoint_path = tf.train.latest_checkpoint(train_dir)\n", " init_fn = slim.assign_from_checkpoint_fn(\n", " checkpoint_path,\n", " slim.get_variables_to_restore())\n", " \n", " with tf.Session() as sess:\n", " with slim.queues.QueueRunners(sess):\n", " sess.run(tf.initialize_local_variables())\n", " init_fn(sess)\n", " np_probabilities, np_images_raw, np_labels = sess.run([probabilities, images_raw, labels])\n", " \n", " for i in range(batch_size): \n", " image = np_images_raw[i, :, :, :]\n", " true_label = np_labels[i]\n", " predicted_label = np.argmax(np_probabilities[i, :])\n", " predicted_name = dataset.labels_to_names[predicted_label]\n", " true_name = dataset.labels_to_names[true_label]\n", " \n", " plt.figure()\n", " plt.imshow(image.astype(np.uint8))\n", " plt.title('Ground Truth: [%s], Prediction [%s]' % (true_name, predicted_name))\n", " plt.axis('off')\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": 1 }	gpl-3.0
bernalde/Laboratorio_Metodos_Computacionales	Repositories/2014-1/PCA/PCA_exercise.ipynb	3	62854	{ "metadata": { "name": "PCA_exercise" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Recordemos que la la matriz de covarianza se define como:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\sigma_{ij} = \\dfrac{\\sum_{k=1}^{M}(d^k_i - \\bar{d_{i}})(d^k_j - \\bar{d_{j}})}{M-1} $" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Calcular la matriz de covarianza\n", "2. Encontrar los autovalores y aotovectores de $\\sigma_{ij}$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "Image(filename=\"data.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "png": 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"prompt_number": 5, "text": [ "<IPython.core.display.Image at 0x3617390>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al final se debe obtener la siguiente figura!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(filename=\"PCA.png\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "png": 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CMGTIECgUCnz77bcICQlB+/btHzufHTt2YPr06ejWrRsSEhKQkZGB5ORkAOrd5B07dsSs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\nYAQRAIARRAAAJm3jOffCwjHVAAAAAElFTkSuQmCC\n", "prompt_number": 6, "text": [ "<IPython.core.display.Image at 0x3617590>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
Cesaaar/od_la_grande_fuga	G9_data_analysis.ipynb	2	7852	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## G9 - Istruzione" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import librerie per analisi dati (Pandas) e dati Istat\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "from IPython.core.display import HTML\n", "import istat\n", "import jsonstat\n", "\n", "# cache dir per velocizzare analisi in locale\n", "cache_dir = os.path.abspath(os.path.join(\"..\", \"tmp/od_la_grande_fuga\", \"istat_cached\"))\n", "istat.cache_dir(cache_dir)\n", "istat.lang(0) # lingua italiano\n", "\n", "dir_df = os.path.join(os.path.abspath(''),'stg')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "DCCV_POPTIT(8):Popolazione 15 anni e oltre per titolo di studio</br><table><tr><th>nr</th><th>name</th><th>nr. values</th><th>values (first 3 values)</th></tr><tr><td>0</td><td>Territorio</td><td>28</td><td>1:'Italia', 3:'Nord', 4:'Nord-ovest' ...</td></td></tr><tr><td>1</td><td>Tipo dato</td><td>1</td><td>12:'popolazione 15 anni e oltre'</td></td></tr><tr><td>2</td><td>Misura</td><td>1</td><td>9:'valori assoluti'</td></td></tr><tr><td>3</td><td>Sesso</td><td>3</td><td>1:'maschi', 2:'femmine', 3:'totale' ...</td></td></tr><tr><td>4</td><td>Classe di età</td><td>17</td><td>2:'15-19 anni', 3:'20-24 anni', 4:'15-24 anni' ...</td></td></tr><tr><td>5</td><td>Titolo di studio</td><td>7</td><td>3:'licenza di scuola elementare, nessun titolo di studio', 4:'licenza di scuola media', 5:'diploma 2-3 anni (qualifica professionale)' ...</td></td></tr><tr><td>6</td><td>Cittadinanza</td><td>3</td><td>1:'italiano-a', 2:'straniero-a', 3:'totale' ...</td></td></tr><tr><td>7</td><td>Tempo e frequenza</td><td>63</td><td>1977:'2004', 1979:'T1-2004', 1983:'T2-2004' ...</td></td></tr></table>" ], "text/plain": [ "DCCV_POPTIT(8):Popolazione 15 anni e oltre per titolo di studio" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "area_istruzione = istat.area(12)\n", "ds_istruzione = area_istruzione.dataset('DCCV_POPTIT')\n", "ds_istruzione" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Tempo e frequenza' (1977:'2004', 1979:'T1-2004', 1983:'T2-2004', 1988:'T3-2004', 1992:'T4-2004', 1996:'2005', 1998:'T1-2005', 2002:'T2-2005', 2007:'T3-2005', 2011:'T4-2005', 2015:'2006', 2017:'T1-2006', 2021:'T2-2006', 2026:'T3-2006', 2030:'T4-2006', 2034:'2007', 2036:'T1-2007', 2040:'T2-2007', 2045:'T3-2007', 2049:'T4-2007', 2053:'2008', 2055:'T1-2008', 2059:'T2-2008', 2064:'T3-2008', 2068:'T4-2008', 2072:'2009', 2074:'T1-2009', 2078:'T2-2009', 2083:'T3-2009', 2087:'T4-2009', 2091:'2010', 2093:'T1-2010', 2097:'T2-2010', 2102:'T3-2010', 2106:'T4-2010', 2110:'2011', 2112:'T1-2011', 2116:'T2-2011', 2121:'T3-2011', 2125:'T4-2011', 2129:'2012', 2131:'T1-2012', 2135:'T2-2012', 2140:'T3-2012', 2144:'T4-2012', 2148:'2013', 2150:'T1-2013', 2154:'T2-2013', 2159:'T3-2013', 2163:'T4-2013', 2167:'2014', 2169:'T1-2014', 2173:'T2-2014', 2178:'T3-2014', 2182:'T4-2014', 2186:'2015', 2188:'T1-2015', 2192:'T2-2015', 2197:'T3-2015', 2201:'T4-2015', 2207:'T1-2016', 2211:'T2-2016', 2216:'T3-2016')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_istruzione.dimension('Tempo e frequenza')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NORD\n", "spec_n = {\n", " \"Territorio\":4,\n", " \"Sesso\":3,\n", " \"Classe di età\":28,\n", " \"Cittadinanza\":3,\n", " \"Tempo e frequenza\":2186\n", "}\n", "\n", "#SUD\n", "spec_s = {\n", " \"Territorio\":88,\n", " \"Sesso\":3,\n", " \"Classe di età\":28,\n", " \"Cittadinanza\":3,\n", " \"Tempo e frequenza\":2186\n", "}" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def istr(ds, spec, nome):\n", " c = ds.getvalues(spec)\n", " ds = c.dataset(0)\n", " df = ds.to_data_frame('Titolo di studio')\n", " df.reset_index(level=0, inplace=True)\n", " df.columns = ['Titolo',nome]\n", " return df" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_s = istr(ds_istruzione,spec_s, 'Sud')\n", "df_n = istr(ds_istruzione,spec_n, 'Nord')" ] }, { "cell_type": "code", "execution_count": 37, "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>Titolo</th>\n", " <th>Sud</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>licenza di scuola elementare, nessun titolo di...</td>\n", " <td>4005.88</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>licenza di scuola media</td>\n", " <td>6169.13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>diploma 2-3 anni (qualifica professionale)</td>\n", " <td>401.85</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>diploma 4-5 anni (maturità)</td>\n", " <td>5331.22</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>diploma</td>\n", " <td>5733.07</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>laurea e post-laurea</td>\n", " <td>1960.92</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>totale</td>\n", " <td>17869.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Titolo Sud\n", "0 licenza di scuola elementare, nessun titolo di... 4005.88\n", "1 licenza di scuola media 6169.13\n", "2 diploma 2-3 anni (qualifica professionale) 401.85\n", "3 diploma 4-5 anni (maturità) 5331.22\n", "4 diploma 5733.07\n", "5 laurea e post-laurea 1960.92\n", "6 totale 17869.00" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_s" ] } ], "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
sillyfellow/PES	many.ipynb	1	6154	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "import math\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rotations(n): \n", " nstr = str(n)\n", " return set([ int(nstr[-i:] + nstr[:-i]) for i in range(len(nstr))])" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from math import sqrt, ceil\n", "import numpy as np\n", "\n", "def rwh_primes(n):\n", " # http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188\n", " \"\"\" Returns a list of primes < n \"\"\"\n", " sieve = [True] * n\n", " for i in xrange(3,int(n*0.5)+1,2):\n", " if sieve[i]:\n", " sieve[ii::2i]=[False]((n-ii-1)/(2i)+1)\n", " return [2] + [i for i in xrange(3,n,2) if sieve[i]]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 3, 5, 7]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rwh_primes(10)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def count_circular_primes(n): \n", " primes = set(rwh_primes(n))\n", " return len(filter(lambda x: rotations(x).issubset(primes), primes)) " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "55\n" ] } ], "source": [ "answer_31 = count_circular_primes(1000000)\n", "print answer_31" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "squares = set([x**2 for x in range(1, 501)])" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pairs = filter(lambda x: abs(x[0] - x[1]) in squares, itertools.combinations(squares, 2))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pairs = [(max(x[0], x[1]), min(x[0], x[1])) for x in pairs]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [], "source": [ "triplets = [(x[0], x[1], x[0] - x[1]) for x in pairs]" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "eligible = filter(lambda x: x <= 1000, [sum([int(math.sqrt(x)) for x in y]) for y in triplets])" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'Counter' 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-1-a23cfcbac3cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meligible\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0manswer_39\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmost_common\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0manswer_39\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'Counter' is not defined" ] } ], "source": [ "data = Counter(eligible)\n", "answer_39 = data.most_common(1)[0][0]\n", "print answer_39" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16695334890" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Problem 43\n", "def works(x):\n", " return int(x[3]) % 2 == 0 and int(''.join(x[2:5])) % 3 == 0 and int(x[5]) % 5 == 0 and int(''.join(x[4:7])) % 7 == 0 and int(''.join(x[5:8])) % 11 == 0 and int(''.join(x[6:9])) % 13 == 0 and int(''.join(x[7:10])) % 17 == 0\n", "\n", "sum([int(''.join(x)) for x in itertools.permutations(\"0123456789\", 10) if x[0] != '0' and works(x)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
stefanpede/KSS	KNN/KSS_Tensorflow.ipynb	1	77861	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats\n", "import tensorflow as tf\n", "%matplotlib inline \n", "\n", "base_path = '/home/emp/ownCloud/Private/Master/python/Data/BA_Thiel/'\n", "# base_path = '/media/emp/Thiel/BA_Caro/'\n", "\n", "feature_path = os.path.join(base_path, 'features.hdf5')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "store = pd.HDFStore(feature_path)\n", "features = store['features']\n", "store.close()" ] }, { "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>Header_Pseudonummer</th>\n", " <th>Header_Walzlos</th>\n", " <th>Header_Leitguete</th>\n", " <th>Header_Anzahl_Bloecke_im_Los</th>\n", " <th>Header_Soll_AD</th>\n", " <th>Header_Soll_WD</th>\n", " <th>WAS_Blockmasse</th>\n", " <th>WAS_Blocklaenge</th>\n", " <th>WAS_Oberflaechentemperatur</th>\n", " <th>DHO_Entkohlung</th>\n", " <th>...</th>\n", " <th>SRW_Ha2_peak</th>\n", " <th>SRW_Ha2_Einschwingzeit</th>\n", " <th>SRW_Ha2_Mittel</th>\n", " <th>SRW_Ha2_std</th>\n", " <th>SRW_WD_vdev</th>\n", " <th>SRW_WD_vdeh</th>\n", " <th>SRW_WD_Mittel</th>\n", " <th>SRW_WD_std</th>\n", " <th>SRW_WD_trend</th>\n", " <th>SRW_WD_flaeche</th>\n", " </tr>\n", " <tr>\n", " <th>DPRD_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <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>24124442</th>\n", " <td>1294224751</td>\n", " <td>71616569</td>\n", " <td>10300</td>\n", " <td>192</td>\n", " <td>25</td>\n", " <td>2.8</td>\n", " <td>189.6</td>\n", " <td>690.1</td>\n", " <td>712.0</td>\n", " <td>1.1096</td>\n", " <td>...</td>\n", " <td>2034.12</td>\n", " <td>66.0</td>\n", " <td>983.246293</td>\n", " <td>174.537636</td>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>2.163944</td>\n", " <td>0.078727</td>\n", " <td>-0.000030</td>\n", " <td>1500.891326</td>\n", " </tr>\n", " <tr>\n", " <th>23594161</th>\n", " <td>1287122151</td>\n", " <td>71609783</td>\n", " <td>10300</td>\n", " <td>79</td>\n", " <td>25</td>\n", " <td>2.8</td>\n", " <td>178.8</td>\n", " <td>653.8</td>\n", " <td>734.0</td>\n", " <td>1.1564</td>\n", " <td>...</td>\n", " <td>1790.58</td>\n", " <td>112.0</td>\n", " <td>1081.444222</td>\n", " <td>168.001019</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>2.155106</td>\n", " <td>0.076665</td>\n", " <td>-0.000101</td>\n", " <td>1429.573371</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 86 columns</p>\n", "</div>" ], "text/plain": [ " Header_Pseudonummer Header_Walzlos Header_Leitguete \\\n", "DPRD_ID \n", "24124442 1294224751 71616569 10300 \n", "23594161 1287122151 71609783 10300 \n", "\n", " Header_Anzahl_Bloecke_im_Los Header_Soll_AD Header_Soll_WD \\\n", "DPRD_ID \n", "24124442 192 25 2.8 \n", "23594161 79 25 2.8 \n", "\n", " WAS_Blockmasse WAS_Blocklaenge WAS_Oberflaechentemperatur \\\n", "DPRD_ID \n", "24124442 189.6 690.1 712.0 \n", "23594161 178.8 653.8 734.0 \n", "\n", " DHO_Entkohlung ... SRW_Ha2_peak \\\n", "DPRD_ID ... \n", "24124442 1.1096 ... 2034.12 \n", "23594161 1.1564 ... 1790.58 \n", "\n", " SRW_Ha2_Einschwingzeit SRW_Ha2_Mittel SRW_Ha2_std SRW_WD_vdev \\\n", "DPRD_ID \n", "24124442 66.0 983.246293 174.537636 6.0 \n", "23594161 112.0 1081.444222 168.001019 5.0 \n", "\n", " SRW_WD_vdeh SRW_WD_Mittel SRW_WD_std SRW_WD_trend SRW_WD_flaeche \n", "DPRD_ID \n", "24124442 5.0 2.163944 0.078727 -0.000030 1500.891326 \n", "23594161 5.0 2.155106 0.076665 -0.000101 1429.573371 \n", "\n", "[2 rows x 86 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.head(2)" ] }, { "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></th>\n", " <th></th>\n", " <th>count</th>\n", " </tr>\n", " <tr>\n", " <th>Header_Leitguete</th>\n", " <th>Header_Soll_AD</th>\n", " <th>Header_Soll_WD</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">10300</th>\n", " <th rowspan=\"2\" valign=\"top\">25</th>\n", " <th>2.55</th>\n", " <td>5338</td>\n", " </tr>\n", " <tr>\n", " <th>2.80</th>\n", " <td>6223</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <th>2.60</th>\n", " <td>1434</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">40200</th>\n", " <th>30</th>\n", " <th>2.70</th>\n", " <td>7994</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <th>2.60</th>\n", " <td>6036</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count\n", "Header_Leitguete Header_Soll_AD Header_Soll_WD \n", "10300 25 2.55 5338\n", " 2.80 6223\n", " 38 2.60 1434\n", "40200 30 2.70 7994\n", " 38 2.60 6036" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.groupby([\"Header_Leitguete\",\"Header_Soll_AD\",\"Header_Soll_WD\"])[\"Header_Pseudonummer\"].agg([\"count\"])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.text.Text at 0x7fc51d103fd0>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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duDECAAAAgFxhEQQAAAAgV1gE5c7+pCeQUXT1sH8/XT3Q1QddfdDVB1390DYd\nWATlzmTSE8gounqYnKSrB7r6oKsPuvqgqx/apgOLoNzZl/QEMoquHvbto6sHuvqgqw+6+qCrH9qm\nA4sgAAAAALnCIggAAABArrAIAgAAAJArLIJyZ03SE8gounpYs4auHujqg64+6OqDrn5omw4sgnKH\nbzH2QVcPfOu2D7r6oKsPuvqgqx/apgOLoNxZnfQEMoquHlavpqsHuvqgqw+6+qCrH9qmA4sgAAAA\nALnCIggAAABArrAIyp1DSU8go+jq4dAhunqgqw+6+qCrD7r6oW06sAjKne1JTyCj6Oph+3a6eqCr\nD7r6oKsPuvqhbTqwCMqdvUlPIKPo6mHvXrp6oKsPuvqgqw+6+qFtOrAIyp3BpCeQUXT1MDhIVw90\n9UFXH3T1QVc/tE0HFkEAAAAAcoVFEAAAAIBcYRGUO5uSnkBG0dXDpk109UBXH3T1QVcfdPVD23Rg\nEZQ7w0lPIKPo6mF4mK4e6OqDrj7o6oOufmibDiyCcmdD0hPIKLp62LCBrh7o6oOuPujqg65+aJsO\nLIIAAAAA5AqLIAAAAAC5wiIod6aTnkBG0dXD9DRdPdDVB1190NUHXf3QNh1YBOXO5qQnkFF09bB5\nM1090NUHXX3Q1Qdd/dA2HVgE5c7OpCeQUXT1sHMnXT3Q1QddfdDVB1390DYdWATlDrdt9EFXD9xm\n1AddfdDVB1190NUPbdOBRRAAAACAXGERBAAAACBXuloEmdk6M7vbzB4xsy+Y2fNjPu8XzeyEmRW7\neV/0wkTSE8ioxl1LpZKKxWLLR7lcXuS5psfEBMerB7r6oKsPuvqgqx/apsPSTp9gZqskvU/Sb0r6\noqSNkm42s2eGECotnneGpOsk3SLpzO6mi4WbSXoCGVXf9YikJRofH2/7zIGBQR0+XOIzxA3MzHC8\neqCrD7r6oKsPuvqhbTp0vAhStOi5JoRwvSSZ2WWSXiFpraTtLZ53taQbJJ2U9Kou3hc9sS3pCWRU\nfddjig71PZJGWjyvpNnZcVUqFRZBDWzbxvHqga4+6OqDrj7o6oe26dDRIsjMTpc0JumP57aFEIKZ\n3SLp3BbPWyPpbElvlHR5d1MF0mhE0mjSkwAAAECNTs8EDUk6TdLRuu1HJa1o9AQze4aiRdOLQggn\nzazjSQIAAABAr7jeHc7Mlij6CNyWEMI35jbHf4X1kgp1j3Ml7W8wdmODbesk7a7bVqq+Tv3lS1s0\n/+L2I9Wvi82/AAAcgklEQVSx03Xbd0jaVLdtpjr2UN32SUnXNJjbKs3fj4OKLreq12g/7q7+84G6\n7Y32Y25f79ap+91oP6SoZaP9WNNgbLP9KDQY22g/itWxD9dtb7QfZTXu0+z3o9HYZvshSbfW/brZ\nfkjz9/kzanxcXa3G+zH/uNqxY4c2bTp1P2ZmZlQoFHTo0Km/H5OTk1qzZv5+rFq1Svv3nzq3gwcP\nqlCYvx/r1q3T7t2n/n4Ui0UVCgVVKqfux5YtW+Zd6Fkul1UoFDQ97bcftfNI837U6of9mBub9v3o\nt9+P2u1p3o9a/bAflUolE/sh9dfvR/37pXU/6vXDflxxxRWZ2I+kfj8mJydVKBRUKBR0/vnna/ny\n5Vq/fv288QsWQoj9kHS6pBOSCnXbr5X0iQbjz1B0YcT3q887IenRmm0XNHmfUUlBmgxSaPlYuvTp\nIRo71WbsnpjjOhnb63GL8ZqvTPn+9FPL2u31XeO+5lSQFKampgLme+UrX5n0FDKJrj7o6oOuPujq\nh7a9NzU1Vf17lUZDiL92afXo6ExQCOGEpClJF85ts+jzbRdKuqPBUx6S9HOSzpH0vOrjakX/Cfx5\nkv6hk/dHL2xNegIZtTXpCWTS1q1bk55CJtHVB1190NUHXf3QNh26uTvc+yVda2ZTeuwW2YOKzgbJ\nzK6Q9OQQwptCCEHSv9U+2cy+I2k2hFBayMTRLS7S90FXD6OjdPVAVx909UFXH3T1Q9t06HgRFEK4\n0cyGJL1b0ff93CXpohDC/dUhyyU9pXdTBAAAAIDe6eZMkEIIV0m6qsnPml11PvfzbeLLagAAAAAk\nxPXucOhH9XdnQ2/Q1UP93W7QG3T1QVcfdPVBVz+0TQcWQblTTHoCGUVXD8UiXT3Q1QddfdDVB139\n0DYdWATlzq6kJ5BRdPWwaxddPdDVB1190NUHXf3QNh1YBAEAAADIFRZBAAAAAHKFRRAAAACAXGER\nlDuFpCeQUXT1UCjQ1QNdfdDVB1190NUPbdOhq+8JQpqtT3oCGbU4XcvlsiqVSttxQ0NDGh4eXoQZ\n+Vq/nuPVA1190NUHXX3Q1Q9t04FFUO6sTHoCGeXftVwua8WKEc3OzrQdOzAwqMOHS6lfCK1cyfHq\nga4+6OqDrj7o6oe26cAiCEiJSqVSXQDtkTTSYmRJs7PjqlQqqV8EAQAAeGARBKTOiKTRpCcBAACQ\nWtwYIXf2Jz2BjKKrh/376eqBrj7o6oOuPujqh7bpwCIodyaTnkBG0dXD5CRdPdDVB1190NUHXf3Q\nNh1YBOXOvqQnkFF09bBvH1090NUHXX3Q1Qdd/dA2HVgEAQAAAMgVbowA9IFSqdSTMQAAAGiPRRCQ\nqCOSlmh8fDzpiQAAAOQGH4fLnTVJTyCjuu16TNJJRd/9M9Xm8Z6OXrlUKqlYLLZ9lMvlLufub80a\njlcPdPVBVx909UFXP7RNB84E5Q7fYuxjoV3jfPdP3I/DdXZ2aWBgUIcPl/ryi1X51m0fdPVBVx90\n9UFXP7RNBxZBubM66QlkVD91rT27NNJmbEmzs+OqVCp9uQhavbqfumYHXX3Q1QddfdDVD23TgUUQ\nkFlxzi4BAADkD9cEAQAAAMgVFkG5cyjpCWQUXT0cOkRXD3T1QVcfdPVBVz+0TQcWQbmzPekJZBRd\nPWzfTlcPdPVBVx909UFXP7RNBxZBubM36QlkFF097N1LVw909UFXH3T1QVc/tE0HFkG5M5j0BDKK\nrh4GB+nqga4+6OqDrj7o6oe26cAiCAAAAECusAgCAAAAkCssgnJnU9ITyCi6eti0ia4e6OqDrj7o\n6oOufmibDiyCcmc46QlkFF09DA/T1QNdfdDVB1190NUPbdOBRVDubEh6AhlFVw8bNtDVA1190NUH\nXX3Q1Q9t04FFEAAAAIBcYREEAAAAIFdYBOXOdNITyCi6epiepqsHuvqgqw+6+qCrH9qmA4ug3Nmc\n9AQyiq4eNm+mqwe6+qCrD7r6oKsf2qYDi6Dc2Zn0BDKKrh527qSrB7r6oKsPuvqgqx/apgOLoNzh\nto0+6OqB24z6oKsPuvqgqw+6+qFtOrAIAgAAAJArLIIAAAAA5AqLoNyZSHoCGZX9ruVyWcVise2j\nXC737D0nJrLfNQl09UFXH3T1QVc/tE2HpUlPAIttJukJZFS2u5bLZa1YMaLZ2fb7OTAwqMOHSz35\nTPTMTLa7JoWuPujqg64+6OqHtunAIih3tiU9gYzKdtdKpVJdAO2RNNJiZEmzs+OqVCo9WQRt25bt\nrkmhqw+6+qCrD7r6oW06sAgC0IERSaNJTwIAAGBBuCYIAAAAQK6wCMqdStITyCi6eqhU6OqBrj7o\n6oOuPujqh7bpwMfhcmetpANJTyKD0t21VCot6Ofdjh8aGmp57dDatWt14EB6u/Yruvqgqw+6+qCr\nH9qmA4ug3Nma9AQyamvSE+jSEUlLND4+nsjrtbuT3NatW3s0L9Siqw+6+qCrD7r6oW06sAjKHS5q\n95HWrscknVT7u77dJOnyHr6eFOdOcqOjae3a3+jqg64+6OqDrn5omw4sggCo/V3fOvs4HHeRAwAA\n/YwbIwAAAADIFRZBubM76QlkFF097N5NVw909UFXH3T1QVc/tE2HrhZBZrbOzO42s0fM7Atm9vwW\nY19tZgfN7Dtm9qCZ3WFmK7ufMhammPQEMoquHopFunqgqw+6+qCrD7r6oW06dLwIMrNVkt4naYuk\nn5f0FUk3m9lQk6ecJ+mgpJcpukjgVkl/bWbP62rGWKBdSU8go+jqYdcuunqgqw+6+qCrD7r6oW06\ndHMmaKOka0II14cQpiVdJmlG0RelzBNC2BhC+NMQwlQI4RshhD+Q9DVJr+x61gAAAADQpY4WQWZ2\nuqQxSZ+Z2xZCCJJukXRuzNcwST8q6budvDcAAAAA9EKnZ4KGJJ0m6Wjd9qOSlsd8jU2SHifpxg7f\nGwAAAAAWbFHvDmdmb1D0jYuvCyFU2j9jvaRC3eNcSfsbjN3YYNs6zb9rV6n6OvVvv0XSRN22I9Wx\n03Xbdyhay9WaqY49VLd9UtI1Dea2SvP346Ciy63qNdqPu6v/fKBue6P9mNvXu6tznNNoP6SoZaP9\nWNNgbLP9KDQY22g/itWxD9dtb7QfZTXu0+z3o9HYZvshRZer1Wq2H9L8fT5PjY+rq9V4P3pxXDX7\n/fhy3bZWvx+fq9s29/tRvx8fb/D8ZvshSX9W9+tm+9H4m7VXrVql/fv3q1B4bN4HDx485ddz1q1b\nN+9OPMViUYVCQZXKqfuxZcsWTUyc+vtRLpdVKBQ0PX3qfuzYsUObNp36+zEzM6NCoaBDh07dj8nJ\nSa1ZM//3Y24/avXDfrz85S/PxH702+9H7eukeT9q9cN+FAqFTOyH1F+/H/XzS+t+1OuH/XjOc56T\nif1I6vdjcnJShUJBhUJB559/vpYvX67169fPG79gIYTYD0mnSzohqVC3/VpJn2jz3NdL+i9JvxLj\nfUYlBWkySKHlY+nSp4do7FSbsXtijutkbK/HLcZr3pzy/emnlrXb67umfX8W472ngqQwNTUVmrn5\n5pub/gzdo6sPuvqgqw+6+qF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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc51d1da080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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YtWHr1qTkyKypKHuA6bKLkHpmEyRJKl29XqfRaHRc5sQDkqRBswmSJJWqXq+z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\naOUptDds2Mh73/sHXHXVVUu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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc51d1da550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 2 Gruppen erstellen: Eine für hohe, eine für niedrige Exzentrizität. Das Netz wird später auf die Gruppenlabels (0 oder 1) trainiert\n", "N = 8000\n", "selection = features\n", "g1 = selection.nlargest(N, 'STB_Ex_mitte')\n", "g2 = selection.nsmallest(N, 'STB_Ex_mitte')\n", "\n", "fig1, ax1 = plt.subplots(1, 1, figsize=(10, 4))\n", "fig2, ax2 = plt.subplots(1, 1, figsize=(10, 4))\n", "g1.hist(column='STB_Ex_mitte', bins=70, normed=True, ax=ax1)\n", "g2.hist(column='STB_Ex_mitte', bins=70, normed=True, ax=ax2)\n", "ax1.set(title='large_ex')\n", "ax2.set(title='small_ex')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Header_Pseudonummer</th>\n", " <th>Header_Walzlos</th>\n", " <th>Header_Leitguete</th>\n", " <th>Header_Anzahl_Bloecke_im_Los</th>\n", " <th>Header_Soll_AD</th>\n", " <th>Header_Soll_WD</th>\n", " <th>WAS_Blockmasse</th>\n", " <th>WAS_Blocklaenge</th>\n", " <th>WAS_Oberflaechentemperatur</th>\n", " <th>DHO_Entkohlung</th>\n", " <th>...</th>\n", " <th>SRW_Ha2_Einschwingzeit</th>\n", " <th>SRW_Ha2_Mittel</th>\n", " <th>SRW_Ha2_std</th>\n", " <th>SRW_WD_vdev</th>\n", " <th>SRW_WD_vdeh</th>\n", " <th>SRW_WD_Mittel</th>\n", " <th>SRW_WD_std</th>\n", " <th>SRW_WD_trend</th>\n", " <th>SRW_WD_flaeche</th>\n", " <th>target</th>\n", " </tr>\n", " <tr>\n", " <th>DPRD_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <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>23597266</th>\n", " <td>1287168001</td>\n", " <td>71604975</td>\n", " <td>10300</td>\n", " <td>107</td>\n", " <td>25</td>\n", " <td>2.8</td>\n", " <td>186.4</td>\n", " <td>684.2</td>\n", " <td>738.0</td>\n", " <td>1.1224</td>\n", " <td>...</td>\n", " <td>115.0</td>\n", " <td>1092.952026</td>\n", " <td>147.831179</td>\n", " <td>6.0</td>\n", " <td>4.0</td>\n", " <td>2.168239</td>\n", " <td>0.077982</td>\n", " <td>-0.000024</td>\n", " <td>1499.650152</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23907349</th>\n", " <td>1291342401</td>\n", " <td>71615108</td>\n", " <td>40200</td>\n", " <td>564</td>\n", " <td>38</td>\n", " <td>2.6</td>\n", " <td>150.4</td>\n", " <td>546.7</td>\n", " <td>707.0</td>\n", " <td>1.5032</td>\n", " <td>...</td>\n", " <td>137.0</td>\n", " <td>919.185033</td>\n", " <td>308.272611</td>\n", " <td>3.0</td>\n", " <td>6.0</td>\n", " <td>1.975324</td>\n", " <td>0.070004</td>\n", " <td>-0.000015</td>\n", " <td>1474.291780</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 87 columns</p>\n", "</div>" ], "text/plain": [ " Header_Pseudonummer Header_Walzlos Header_Leitguete \\\n", "DPRD_ID \n", "23597266 1287168001 71604975 10300 \n", "23907349 1291342401 71615108 40200 \n", "\n", " Header_Anzahl_Bloecke_im_Los Header_Soll_AD Header_Soll_WD \\\n", "DPRD_ID \n", "23597266 107 25 2.8 \n", "23907349 564 38 2.6 \n", "\n", " WAS_Blockmasse WAS_Blocklaenge WAS_Oberflaechentemperatur \\\n", "DPRD_ID \n", "23597266 186.4 684.2 738.0 \n", "23907349 150.4 546.7 707.0 \n", "\n", " DHO_Entkohlung ... SRW_Ha2_Einschwingzeit SRW_Ha2_Mittel \\\n", "DPRD_ID ... \n", "23597266 1.1224 ... 115.0 1092.952026 \n", "23907349 1.5032 ... 137.0 919.185033 \n", "\n", " SRW_Ha2_std SRW_WD_vdev SRW_WD_vdeh SRW_WD_Mittel SRW_WD_std \\\n", "DPRD_ID \n", "23597266 147.831179 6.0 4.0 2.168239 0.077982 \n", "23907349 308.272611 3.0 6.0 1.975324 0.070004 \n", "\n", " SRW_WD_trend SRW_WD_flaeche target \n", "DPRD_ID \n", "23597266 -0.000024 1499.650152 0 \n", "23907349 -0.000015 1474.291780 0 \n", "\n", "[2 rows x 87 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1['target'] = 0\n", "g2['target'] = 1\n", "g_total = pd.concat([g1, g2])\n", "g_total.head(2)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Daten nach Vorverarbeitung:\n", "Anzahl der Kennwerte: 81\n", "Anzahl der vermessenen Rohre: 13615\n", "Anzahl der gefahrenen Produkte: 5\n", "Anzahl der Walzlose: 120\n", "\n", "Auszug:\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Header_Pseudonummer</th>\n", " <th>Header_Walzlos</th>\n", " <th>Header_Leitguete</th>\n", " <th>Header_Anzahl_Bloecke_im_Los</th>\n", " <th>Header_Soll_AD</th>\n", " <th>Header_Soll_WD</th>\n", " <th>WAS_Blockmasse</th>\n", " <th>WAS_Blocklaenge</th>\n", " <th>WAS_Oberflaechentemperatur</th>\n", " <th>DHO_Entkohlung</th>\n", " <th>...</th>\n", " <th>SRW_Ha2_Einschwingzeit</th>\n", " <th>SRW_Ha2_Mittel</th>\n", " <th>SRW_Ha2_std</th>\n", " <th>SRW_WD_vdev</th>\n", " <th>SRW_WD_vdeh</th>\n", " <th>SRW_WD_Mittel</th>\n", " <th>SRW_WD_std</th>\n", " <th>SRW_WD_trend</th>\n", " <th>SRW_WD_flaeche</th>\n", " <th>target</th>\n", " </tr>\n", " <tr>\n", " <th>DPRD_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <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>23597266</th>\n", " <td>1287168001</td>\n", " <td>71604975</td>\n", " <td>10300</td>\n", " <td>107</td>\n", " <td>25</td>\n", " <td>2.80</td>\n", " <td>186.4</td>\n", " <td>684.2</td>\n", " <td>738.0</td>\n", " <td>1.1224</td>\n", " <td>...</td>\n", " <td>115.0</td>\n", " <td>1092.952026</td>\n", " <td>147.831179</td>\n", " <td>6.0</td>\n", " <td>4.0</td>\n", " <td>2.168239</td>\n", " <td>0.077982</td>\n", " <td>-0.000024</td>\n", " <td>1499.650152</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23907349</th>\n", " <td>1291342401</td>\n", " <td>71615108</td>\n", " <td>40200</td>\n", " <td>564</td>\n", " <td>38</td>\n", " <td>2.60</td>\n", " <td>150.4</td>\n", " <td>546.7</td>\n", " <td>707.0</td>\n", " <td>1.5032</td>\n", " <td>...</td>\n", " <td>137.0</td>\n", " <td>919.185033</td>\n", " <td>308.272611</td>\n", " <td>3.0</td>\n", " <td>6.0</td>\n", " <td>1.975324</td>\n", " <td>0.070004</td>\n", " <td>-0.000015</td>\n", " <td>1474.291780</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23627716</th>\n", " <td>1287480001</td>\n", " <td>71604946</td>\n", " <td>10300</td>\n", " <td>153</td>\n", " <td>25</td>\n", " <td>2.55</td>\n", " <td>176.0</td>\n", " <td>638.9</td>\n", " <td>725.0</td>\n", " <td>1.1314</td>\n", " <td>...</td>\n", " <td>71.0</td>\n", " <td>1066.746923</td>\n", " <td>192.454045</td>\n", " <td>7.0</td>\n", " <td>6.0</td>\n", " <td>1.981085</td>\n", " <td>0.090813</td>\n", " <td>-0.000062</td>\n", " <td>1375.832727</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>24112087</th>\n", " <td>1294090051</td>\n", " <td>71615050</td>\n", " <td>10300</td>\n", " <td>113</td>\n", " <td>25</td>\n", " <td>2.80</td>\n", " <td>177.2</td>\n", " <td>646.4</td>\n", " <td>726.0</td>\n", " <td>1.1390</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>1076.983140</td>\n", " <td>402.225269</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>2.217356</td>\n", " <td>0.084263</td>\n", " <td>-0.000048</td>\n", " <td>1402.661742</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23595167</th>\n", " <td>1287142251</td>\n", " <td>71598374</td>\n", " <td>10300</td>\n", " <td>230</td>\n", " <td>25</td>\n", " <td>2.80</td>\n", " <td>184.2</td>\n", " <td>670.3</td>\n", " <td>690.0</td>\n", " <td>1.2072</td>\n", " <td>...</td>\n", " <td>82.0</td>\n", " <td>987.118195</td>\n", " <td>398.031941</td>\n", " <td>7.0</td>\n", " <td>7.0</td>\n", " <td>2.179587</td>\n", " <td>0.094261</td>\n", " <td>-0.000060</td>\n", " <td>1462.089432</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 81 columns</p>\n", "</div>" ], "text/plain": [ " Header_Pseudonummer Header_Walzlos Header_Leitguete \\\n", "DPRD_ID \n", "23597266 1287168001 71604975 10300 \n", "23907349 1291342401 71615108 40200 \n", "23627716 1287480001 71604946 10300 \n", "24112087 1294090051 71615050 10300 \n", "23595167 1287142251 71598374 10300 \n", "\n", " Header_Anzahl_Bloecke_im_Los Header_Soll_AD Header_Soll_WD \\\n", "DPRD_ID \n", "23597266 107 25 2.80 \n", "23907349 564 38 2.60 \n", "23627716 153 25 2.55 \n", "24112087 113 25 2.80 \n", "23595167 230 25 2.80 \n", "\n", " WAS_Blockmasse WAS_Blocklaenge WAS_Oberflaechentemperatur \\\n", "DPRD_ID \n", "23597266 186.4 684.2 738.0 \n", "23907349 150.4 546.7 707.0 \n", "23627716 176.0 638.9 725.0 \n", "24112087 177.2 646.4 726.0 \n", "23595167 184.2 670.3 690.0 \n", "\n", " DHO_Entkohlung ... SRW_Ha2_Einschwingzeit SRW_Ha2_Mittel \\\n", "DPRD_ID ... \n", "23597266 1.1224 ... 115.0 1092.952026 \n", "23907349 1.5032 ... 137.0 919.185033 \n", "23627716 1.1314 ... 71.0 1066.746923 \n", "24112087 1.1390 ... 6.0 1076.983140 \n", "23595167 1.2072 ... 82.0 987.118195 \n", "\n", " SRW_Ha2_std SRW_WD_vdev SRW_WD_vdeh SRW_WD_Mittel SRW_WD_std \\\n", "DPRD_ID \n", "23597266 147.831179 6.0 4.0 2.168239 0.077982 \n", "23907349 308.272611 3.0 6.0 1.975324 0.070004 \n", "23627716 192.454045 7.0 6.0 1.981085 0.090813 \n", "24112087 402.225269 6.0 6.0 2.217356 0.084263 \n", "23595167 398.031941 7.0 7.0 2.179587 0.094261 \n", "\n", " SRW_WD_trend SRW_WD_flaeche target \n", "DPRD_ID \n", "23597266 -0.000024 1499.650152 0 \n", "23907349 -0.000015 1474.291780 0 \n", "23627716 -0.000062 1375.832727 0 \n", "24112087 -0.000048 1402.661742 0 \n", "23595167 -0.000060 1462.089432 0 \n", "\n", "[5 rows x 81 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Daten Sauber machen (Von Michi)\n", "df=g_total\n", "relNaNsCol = np.array(np.sum(np.isnan(df))/df.shape[0]100)\n", "# schmeiße zunächst alle Spalten heraus, die mehr als bestimmte Prozent an NaNs haben\n", "spaltenSchranke = 15 # % der NaNs in Spalte\n", "keep = [i for i in np.arange(len(relNaNsCol)) if relNaNsCol[i] <= spaltenSchranke]\n", "dfVV = df[df.columns[keep]] # extrahiere Spalten\n", "\n", "# gleiches auf Zeilen anwenden\n", "zeilenSchranke = 5 # % der NaNs in Zeile\n", "relNaNsRow = np.array(dfVV.isnull().sum(axis=1)/dfVV.shape[1]100)\n", "keep = [i for i in np.arange(len(relNaNsRow)) if relNaNsRow[i] <= zeilenSchranke]\n", "dfVV2 = dfVV.iloc[keep] #extraheire Zeilen\n", "\n", "#übrige NaNs mit Mittelwert aus Spalten auffüllen\n", "dfVV2 = dfVV2.fillna(dfVV2.mean())\n", "g_clean = dfVV2.iloc[:,:]\n", "# Ausgabe\n", "print(\"Daten nach Vorverarbeitung:\")\n", "print(\"Anzahl der Kennwerte: \"+str(dfVV2.shape[1]))\n", "print(\"Anzahl der vermessenen Rohre: \"+str(dfVV2.shape[0]))\n", "print(\"Anzahl der gefahrenen Produkte: \"+str(dfVV2.groupby([\"Header_Leitguete\",\"Header_Soll_AD\",\"Header_Soll_WD\"])[\"Header_Pseudonummer\"].agg([\"count\"]).shape[0]))\n", "print(\"Anzahl der Walzlose: \"+str(len(pd.unique(dfVV2[\"Header_Walzlos\"]))))\n", "print(\"\\nAuszug:\")\n", "dfVV2.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Daten für das Netz vorbereiten\n", "X_total = stats.zscore(np.array(g_clean.iloc[:, 6:-1]),axis=0)\n", "y_total = np.array(g_clean.target, dtype=np.bool)\n", "y_total = np.array([y_total, ~y_total]).T # One Hot encoding der Targets (Ist für das Netz einfacher zu trainieren)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Split nach Walzlosen, nicht nach Rohren, um auswendiglernen des Netztes zu schlechten Ergebnissen führen zu lassen.\n", "walzlose =np.unique(g_clean.Header_Walzlos)\n", "np.random.shuffle(walzlose)\n", "test_anteil = 0.4\n", "w_test, w_train = np.hsplit(walzlose, [np.asarray(np.floor(test_anteil*len(walzlose)), dtype=np.int)])\n", "test_pattern = np.array(g_clean.Header_Walzlos.isin(w_test))\n", "train_pattern = ~test_pattern" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_test, y_test, X_train, y_train = (X_total[test_pattern, :], y_total[test_pattern, :], X_total[train_pattern, :], y_total[train_pattern, :])\n", "idx = np.random.permutation(X_test.shape[0])\n", "teX = X_test[idx, :]\n", "teY = y_test[idx, :]\n", "idx = np.random.permutation(X_train.shape[0])\n", "trX = X_train[idx, :]\n", "trY = y_train[idx, :]\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Modell für Tensorflow (Feed-Forward mit Dropout, geklaut von hier: https://github.com/nlintz/TensorFlow-Tutorials/blob/master/04_modern_net.ipynb)\n", "def model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden):\n", " X = tf.nn.dropout(X, p_keep_input)\n", " h = tf.nn.relu(tf.matmul(X, w_h))\n", "\n", " h = tf.nn.dropout(h, p_keep_hidden)\n", " h2 = tf.nn.relu(tf.matmul(h, w_h2))\n", "\n", " h2 = tf.nn.dropout(h2, p_keep_hidden)\n", "\n", " return tf.matmul(h2, w_o)\n", "\n", "def init_weights(shape):\n", " return tf.Variable(tf.random_normal(shape, stddev=0.01))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = tf.placeholder(\"float\", [None, X_total.shape[1]]) # None-Dimension für Batch-Size freihalten\n", "Y = tf.placeholder(\"float\", [None, 2]) # 2 Ausgänge für 2 Klassen da One-Hot kodierung\n", "neurons = 300\n", "w_h = init_weights([X_total.shape[1], neurons])\n", "w_h2 = init_weights([neurons, neurons])\n", "w_o = init_weights([neurons, 2])\n", "\n", "p_keep_input = tf.placeholder(\"float\")\n", "p_keep_hidden = tf.placeholder(\"float\")\n", "py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)\n", "\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))\n", "train_op = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08).minimize(cost)\n", "# train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)\n", "predict_op = tf.argmax(py_x, 1)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoche: 0 Klassifikationsrate: 0.975960591133\n", "Epoche: 1 Klassifikationsrate: 0.985221674877\n", "Epoche: 2 Klassifikationsrate: 0.988177339901\n", "Epoche: 3 Klassifikationsrate: 0.986009852217\n", "Epoche: 4 Klassifikationsrate: 0.987783251232\n", "Epoche: 5 Klassifikationsrate: 0.987783251232\n", "Epoche: 6 Klassifikationsrate: 0.993891625616\n", "Epoche: 7 Klassifikationsrate: 0.993694581281\n", "Epoche: 8 Klassifikationsrate: 0.994285714286\n", "Epoche: 9 Klassifikationsrate: 0.990738916256\n", "Epoche: 10 Klassifikationsrate: 0.995665024631\n", "Epoche: 11 Klassifikationsrate: 0.991921182266\n", "Epoche: 12 Klassifikationsrate: 0.994679802956\n", "Epoche: 13 Klassifikationsrate: 0.991133004926\n", "Epoche: 14 Klassifikationsrate: 0.996650246305\n", "Epoche: 15 Klassifikationsrate: 0.992906403941\n", "Epoche: 16 Klassifikationsrate: 0.995270935961\n", "Epoche: 17 Klassifikationsrate: 0.99684729064\n", "Epoche: 18 Klassifikationsrate: 0.99645320197\n", "Epoche: 19 Klassifikationsrate: 0.99802955665\n", "Epoche: 20 Klassifikationsrate: 0.996256157635\n", "Epoche: 21 Klassifikationsrate: 0.996650246305\n", "Epoche: 22 Klassifikationsrate: 0.99842364532\n", "Epoche: 23 Klassifikationsrate: 0.99763546798\n", "Epoche: 24 Klassifikationsrate: 0.996650246305\n", "Epoche: 25 Klassifikationsrate: 0.998226600985\n", "Epoche: 26 Klassifikationsrate: 0.99802955665\n", "Epoche: 27 Klassifikationsrate: 0.99724137931\n", "Epoche: 28 Klassifikationsrate: 0.99763546798\n", "Epoche: 29 Klassifikationsrate: 0.99842364532\n", "Epoche: 30 Klassifikationsrate: 0.99802955665\n", "Epoche: 31 Klassifikationsrate: 0.99684729064\n", "Epoche: 32 Klassifikationsrate: 0.99763546798\n", "Epoche: 33 Klassifikationsrate: 0.997044334975\n", "Epoche: 34 Klassifikationsrate: 0.998226600985\n", "Epoche: 35 Klassifikationsrate: 0.99921182266\n", "Epoche: 36 Klassifikationsrate: 0.998226600985\n", "Epoche: 37 Klassifikationsrate: 0.99881773399\n", "Epoche: 38 Klassifikationsrate: 0.998620689655\n", "Epoche: 39 Klassifikationsrate: 0.99842364532\n", "Epoche: 40 Klassifikationsrate: 0.998620689655\n", "Epoche: 41 Klassifikationsrate: 0.998226600985\n", "Epoche: 42 Klassifikationsrate: 0.999014778325\n", "Epoche: 43 Klassifikationsrate: 0.999014778325\n", "Epoche: 44 Klassifikationsrate: 0.99763546798\n", "Epoche: 45 Klassifikationsrate: 0.99802955665\n", "Epoche: 46 Klassifikationsrate: 0.99802955665\n", "Epoche: 47 Klassifikationsrate: 0.99921182266\n", "Epoche: 48 Klassifikationsrate: 0.99881773399\n", "Epoche: 49 Klassifikationsrate: 0.998620689655\n" ] } ], "source": [ "batch_size = 30\n", "with tf.Session() as sess:\n", " # you need to initialize all variables\n", " tf.initialize_all_variables().run()\n", "\n", " for i in range(50):\n", " for start, end in zip(range(0, len(trX), batch_size), range(batch_size, len(trX)+1, batch_size)):\n", " sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],\n", " p_keep_input: 0.6, p_keep_hidden: 0.5})\n", " print('Epoche: ' + str(i) + ' Klassifikationsrate: ' + \n", " str(np.mean(np.argmax(teY, axis=1) ==\n", " sess.run(predict_op, feed_dict={X: teX, Y: teY,\n", " p_keep_input: 1.0,\n", " p_keep_hidden: 1.0}))\n", " )\n", " )\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ZMMzhangmingming/liupengyuan.github.io	chapter2/homework/localization/4-19/201611680949.ipynb	19	3851	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "55\n" ] } ], "source": [ "#将前面几章用while循环的习题，用for循环实现，并尽量写成函数。\n", "def sum_(numbers):\n", " total=0\n", " for number in numbers:\n", " total+=number\n", " return total\n", "\n", "numbers=[1,2,3,4,5,6,7,8,9,10]\n", "print(sum_(numbers))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入想要倒序的字符123\n", "321\n" ] } ], "source": [ "#练习1\n", "\n", "list=input('请输入想要倒序的字符：')\n", "\n", "def reverse():\n", " print(list[len(s)-1:0:-1]+list[0])\n", " \n", "reverse()\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入行数：3\n", "请输入指定符号：s\n", "\n", "s\n", "ss\n", "sss\n" ] } ], "source": [ "#练习2\n", "\n", "def triangle():\n", " n=int(input('请输入行数：'))\n", " a=str(input('请输入指定符号：'))\n", " for i in range(n+1):\n", " print(ai)\n", " \n", "triangle()\n", "\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入单词：shelf\n", "shelves\n" ] } ], "source": [ "#练习3\n", "\n", "def pl():\n", " word=input('请输入单词：')\n", " if word.endswith('s') or word.endswith('ch') or word.endswith('sh'):\n", " print(word,'es',sep='')\n", " elif word.endswith('f'):\n", " print(word[0:len(word)-1]+'ves',sep='')\n", " elif word.endswith('y'):\n", " print(word[0:len(word)-1]+'ies',sep='')\n", " else:\n", " print(word,'s',sep='')\n", " \n", " \n", "pl()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入上底长：2\n", "请输入下底长：5\n", "请输入指定符号：i\n", "ii\n", "iii\n", "iiii\n", "iiiii\n" ] } ], "source": [ "#练习4\n", "\n", "def tixing():\n", " n1=int(input('请输入上底长：'))\n", " n2=int(input('请输入下底长：'))\n", " s=str(input('请输入指定符号：'))\n", " for i in range(n1,n2+1):\n", " print(si)\n", " \n", "tixing()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jvines/Metodos-Numericos	Catedras/Catedra_01-02.ipynb	1	56460	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Catedra 01\n", "===========" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Primera tarea sera anunciada hoy.\n", "- Entrega el segundo miercoles, despues de haber tenido 2 auxiliares." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## La primera parte del curso\n", "- Derivadas e Integrales numericas.\n", "- Manejo de errores debido a precision." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib as mp\n", "import numpy as np\n", "import math\n", "\n", "# Esta linea hace que los graficos aparezcan en el notebook en vez de una ventana nueva.\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nota:\n", "- matplotlib.org -> documentacion de pyplot." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Cambia el tamano de los ticks (los puntos de los ejes)\n", "mp.rcParams['xtick.labelsize'] = 13\n", "mp.rcParams['ytick.labelsize'] = 13" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Manejo de errores:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error de truncacion\n", "- Calculo del numero e a traves de la expansion de Taylor: $\\sum_{k=0}^{\\infty}\\frac{x^{k}}{k!}$\n", "\n", "### Forma basica:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parametros iniciales.\n", "e = 1.\n", "k_factorial = 1.\n", "N_max = 10\n", "e_vs_n = [e] # Lista que va a contener los elementos de la serie.\n", "for i in range(1,N_max): # Ciclo que calcula los elementos de la serie y los suma.\n", " k_factorial = i\n", " e += 1. / k_factorial\n", " e_vs_n.append(e)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 1.0)\n", "(1, 2.0)\n", "(2, 2.5)\n", "(3, 2.6666666666666665)\n", "(4, 2.708333333333333)\n", "(5, 2.7166666666666663)\n", "(6, 2.7180555555555554)\n", "(7, 2.7182539682539684)\n", "(8, 2.71827876984127)\n", "(9, 2.7182815255731922)\n" ] } ], "source": [ "# Instruccion para imprimir nuestra aproximacion a e en cada iteracion.\n", "for i in range(N_max):\n", " print(i, e_vs_n[i])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10c9d4990>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c9a2650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(range(N_max), e_vs_n) # Genera un grafico de e_vs_n v\n", "plt.axhline(math.e, color='0.5') # Genera una linea en 'e'\n", "plt.ylim(0,3) # Cambia los limites del eje y del grafico\n", "# Cambia los labels del grafico.\n", "plt.xlabel('N', fontsize=20)\n", "plt.ylabel('$e_{N}$', fontsize=20)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"\\nNOTA:\\nLo anterior es equivalente a hacer\\nplt.semilogy(range(N_max), diferencia))\\nplt.xlabel('N', fontsize=20)\\nplt.ylabel('$e_N - e_{real}$', fontsize=20)\\n\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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be53NqczsPUISauHu6wofpUh21JIRSYYVwCWEfY1i2+VTJNeUZESS\n40bCismnR3sciRQ9JRmRhHD3tcCFQEvCHkciRU9JRiRB3P2vwLPA0WYW65bEIrmgJCOSPL8gbM72\nh7gDEWkqJRmRhPGw3fX9QH8zOzbueESaQklGJJkuAr4ELo/2oBcpSkoyIgnk7u8CNwHdgbNiDkck\na5qMKRKzaDLmv929S53jmxNKmh1YB3RAkzGlyKglI5JQ7r4E+D2wOdAx5nBEsqIkI5IMDXUpXA+8\nF51Xt4MUHXWXiYhI3qglIyIieaMkIyIieaMkIyIieaMkIyIieaMkIyIieaMkIyIieaMkIyIieaMk\nIyIieaMkIyIieaMkIyIiefP/AYSNr6oR9D13AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11203f410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Se hace un ciclo for dentro de una lista. i.e. Comprehension list\n", "diferencia = [math.fabs(e_i - math.e) for e_i in e_vs_n] # Nota. fabs convierte a float y luego calcular abs\n", "# Se hace un grafico con escala logaritmica en el eje y.\n", "plt.plot(range(N_max), diferencia)\n", "plt.yscale('log')\n", "plt.xlabel('N', fontsize=20)\n", "plt.ylabel('$e_N - e_{real}$', fontsize=20)\n", "\n", "\"\"\"\n", "NOTA:\n", "Lo anterior es equivalente a hacer\n", "plt.semilogy(range(N_max), diferencia))\n", "plt.xlabel('N', fontsize=20)\n", "plt.ylabel('$e_N - e_{real}$', fontsize=20)\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Efectos de precision sobre integrales\n", "### Error de Redondeo\n", "Calculando la derivada de $\\sin(x)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ahora usamos numpy" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.00000000e-01 8.37677640e-03 7.01703829e-04 5.87801607e-05\n", " 4.92388263e-06 4.12462638e-07 3.45510729e-08 2.89426612e-09\n", " 2.42446202e-10 2.03091762e-11 1.70125428e-12 1.42510267e-13\n", " 1.19377664e-14 1.00000000e-15]\n" ] } ], "source": [ "epsilon = np.logspace(-1, -15, 14, base=10.)\n", "print(epsilon)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'list'>\n", "<type 'numpy.ndarray'>\n" ] } ], "source": [ "print(type(e_vs_n))\n", "print(type(epsilon))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.49736375 0.5367716 0.54000703 0.54027757 0.54030023 0.54030213\n", " 0.54030229 0.54030231 0.54030266 0.54029766 0.54034525 0.54065914\n", " 0.53940522 0.55511151]\n" ] } ], "source": [ "dsindx = (np.sin(1.+epsilon) - np.sin(1.)) / epsilon\n", "print(dsindx)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1125a7f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.semilogx(epsilon, dsindx - np.cos(1.), 'o')\n", "plt.axhline(0, color='0.8')\n", "plt.xlabel('epsilon', fontsize=20)\n", "plt.ylabel('$\\\\frac{d}{dx}\\\\sin(x) - \\\\cos(x)$', fontsize=20)\n", "_ = plt.xticks(epsilon[::2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Catedra 02 (Continuacion de Catedra 01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Integrales Numericas\n", "\n", "$$g(x) = \\int_{a}^{x} f(x')dx'$$\n", "\n", "Equivale a resolver una ecuacion diferencial:\n", "\n", "$$g'(x) = f(x)$$ con condiciones de borde: \n", "$$\\int_{a}^{x}g'(x')dx' = \\int_{a}^{x}f(x')dx'$$\n", "\n", "$$g(x)-g(a) = \\int_{a}^{x}f(x')dx'$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Integracion Numerica Directa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regla Trapezoidal\n", "\n", "### Idea: Aproximar el area de la curva como el area bajo un trapecio entre dos puntos de la funcion\n", "\n", "![title](../img/trapezoidal_demo.png \"Idea del metodo. La linea roja intenta aproximar el area bajo la curva azul.\")\n", "\n", "$$\n", "\\int_{x_{0}}^{x_{0}+\\Delta x}f(x')dx'\n", "$$\n", "\n", "Haciendo una expansion de taylor sobre la integral se obtiene:\n", "\n", "$$\n", "\\int_{x_{0}}^{x_{0}+\\Delta x}\\left[\n", " f(x_{0}) + f'(x_{o})(x'-x_{0}) + \\frac{1}{2}f''(x_{0})(x'-x_{0})^{2} + \\ldots\n", "\\right]dx'\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= f(x_{0}\\Delta x) + \\frac{f'(x_{0})(x-x_{0})^{2}}{2}\\biggr\\vert_{x_{0}}^{x} + \\frac{1}{2}f''(x_{0})\\frac{(x-x_{0})^{3}}{3}\\biggr\\vert_{x_{0}}^{x} + \\ldots\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= f(x_{0})\\Delta x + f'(x_{0})\\frac{\\Delta x^{2}}{2} + \\frac{1}{6}f''(x_{0})\\Delta x^{3} + \\ldots\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\Delta x}{2}\\left[\n", " f(x_{0}) + \\left(\n", " f(x_{0}) + f'(x_{0})\\Delta x + f''(x_{0})\\frac{\\Delta x^{2}}{2} + \\ldots\n", " \\right) - f''(x_{0})\\frac{\\Delta x^{2}}{6}\n", "\\right]\n", "$$\n", "\n", "Note que el error de truncacion es del mismo orden en $\\Delta x$ que el ultimo termino previo a truncar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\int_{x_0}^{x_{0}+\\Delta x}f(x')dx' = \\frac{\\Delta x}{2}\\left[\n", " f(x_{0}) + f(x_{0}+\\Delta x) + O(\\Delta x^2)\n", "\\right] = \\left(\n", " f(x_{0}) + f(x_{0}+\\Delta x)\n", "\\right)\\frac{\\Delta x}{2} + O(\\Delta x^3)\n", "$$\n", "\n", "Donde $O(\\Delta x^3) \\sim -f''(x_{0})\\frac{\\Delta x^3}{12}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(graficos van aca)\n", "\n", "dividir [a,b] en $N = \\frac{b-a}{\\Delta x}$ tramos.\n", "\n", "$$\n", "\\int_{a}^{b}f(x)dx \\sim \\sum_{i=0}^{N-1}\\left[\n", " \\frac{f(a+\\Delta xi)+f(a+\\Delta x(i+1))}{2}\\Delta x + O^{}(\\Delta x^3)\n", "\\right]\n", "$$\n", "\n", "Nos interesa encontrar una cota superior para nuestro error. Como estamos sumando N tramos, estamos incluyendo N errores, y dado que el error de cada tramo va como $\\Delta x^3$ y N va como $\\Delta x$, el error finalmente queda como $O(\\Delta x^2)$\n", "\n", "$$\n", "\\sim \\left[\n", " \\sum_{i=0}^{N-1}\\frac{f(a+\\Delta xi)+f(a+\\Delta x(i+1))}{2}\\Delta x\n", "\\right] + NO^{}(\\Delta x^3) \\rightarrow O(\\Delta x^2)\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\int_{a}^{b} f(x)dx \\sim \\frac{f(a)\\Delta x}{2} + \\sum_{i=1}^{N-1}f(a+i\\Delta x)\\Delta x + \\frac{f(b)\\Delta x}{2} + O(\\Delta x^2)\n", "$$\n", "\n", "La regla compuesta asumiendo $\\Delta x$ __constante__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Una implementacion simple de este algoritmo es la siguiente:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def integral_trap(f, intervalo):\n", " '''\n", " Integracion numerica utilizando el metodo o regla trapezoidal.\n", " Note que el intervalo debe estar equiespaciado para este metodo.\n", " \n", " Parameters\n", " ----------\n", " f : numpy.ndarray\n", " Integrando.\n", " intervalo : numpy.ndarray\n", " El intervalo de integracion.\n", " \n", " Returns\n", " -------\n", " res : double\n", " Resultado de la integracion.\n", " '''\n", " res = f[0]+f[len(intervalo-1)] # Se inicializa la variable que contendra el resultado final.\n", " dx = intervalo[1] - intervalo[0] # Se escribe el delta x.\n", " for i in range(len(intervalo)-1): # Se escribe el ciclo que calculara la integral numericamente.\n", " res += 2(f[i]+f[i+1])\n", " \n", " res = dx/2.\n", " \n", " return res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regla de Simpson\n", "\n", "### Idea: Mas evaluaciones (terminos de la expansion) a cambio de mayor precision.\n", "\n", "![title](../img/simpson_demo.png \"Idea del metodo. Aqui P corresponde a la funcion que interpola f para aproximar el area.\")\n", "\n", "Se hace una suerte de \"doble Taylor\"\n", "\n", "$$\n", "\\int_{x_{0}}^{x_{0}+2\\Delta x}f(x)dx = \\int_{x_{0}}^{x_{0}+2\\Delta x}\\left[\n", " f(x_{0} + f'(x_{0})(x-x_{0}) + f''(x_{0})\\frac{(x-x_{0})^{2}}{2} + f'''(x_{0})\\frac{(x-x_{0})^{3}}{6} + f^{\\text{iv}}(x_{0})\\frac{(x-x_{0})^{4}}{24} + \\ldots\n", "\\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= f(x_{0})2\\Delta x + f'(x_{0})\\frac{(2\\Delta x)^{2}}{2} + \\frac{f''(x_{0})}{2}\\frac{(2\\Delta x)^{3}}{3} + \\frac{f'''(x_{0})}{6}\\frac{(2\\Delta x)^{4}}{4} + \\frac{f^{\\text{iv}}(x_{0})}{24}\\frac{(2\\Delta x)^{5}}{5}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\Delta x}{3}\\left[\n", " f(x_{0}) + 4f(x_{0}) + 4f'(x_{0})\\Delta x + 4 f''(x_{0})\\frac{\\Delta x^2}{2} + 4f'''(x_{0})\\frac{\\Delta x^3}{6} \\\\\n", " + f(x_{0}) + f'(x_{0})(2\\Delta x) + f''(x_{0})\\frac{(2\\Delta x)^{2}}{2} + f'''(x_{0})\\frac{(2\\Delta x)^{3}}{6} + O(\\Delta x^4)\n", "\\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "= \\frac{\\Delta x}{3}\\left[\n", " f(x_{0}) + 4f(x_{0}+\\Delta x) + f(x_{0} + 2\\Delta x)\n", "\\right] + O(\\Delta x^5)\n", "$$\n", "\n", "Note que para la regla compuesta el orden del error va como $\\Delta x^4$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regla compuesta\n", "\n", "$$\n", "\\int_{a}^{b}f(x)dx = \\left[\n", " \\sum_{i=1}^{N-1}\\frac{\\Delta x}{3}\\left[\n", " f_{i-1} + 4f_{i} + f_{i+1}\n", " \\right]\n", "\\right] + O(\\Delta x^4)\n", "$$\n", "\n", "Donde $f_{i} = f(a+i\\Delta x)$\n", "\n", "Note que este metodo tambien asume $\\Delta x$ constante.\n", "\n", "(grafico aca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Note que la regla anterior tambien puede ser escrita como:\n", "$$\n", "\\int_{a}^{b}f(x)dx = \\frac{\\Delta x}{3}\\left(\n", " f(a) + 4\\sum_{i=1}^{N/2-1}f_{2i+1}+2\\sum_{i=1}^{N/2-1}f_{2i} + f(b)\n", "\\right)\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Una implementacion simple de este algoritmo es la siguiente:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def simpson(f, a, b, n):\n", " '''\n", " Integracion numerica utilizando el metodo o regla trapezoidal.\n", " Note que el intervalo debe estar equiespaciado para este metodo.\n", " \n", " Parameters\n", " ----------\n", " f : function\n", " Integrando.\n", " a : int or double\n", " Valor inical del intervalo sobre el cual se va a integrar.\n", " b : int or double\n", " Valor final del intervalo sobre el cual se va a integrar.\n", " n : int\n", " Numero de particiones del intervalo. Debe ser par.\n", " \n", " Returns\n", " -------\n", " res : double\n", " Resultado de la integracion.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " Cuando n es impar.\n", " '''\n", " if n % 2: # El algoritmo funciona solo para n par. Aca nos aseguramos de recibir un n par.\n", " raise ValueError(\"n debe ser par (se recibio n=%d).\" % n)\n", " \n", " dx = float(b - a) / n # Se inicializa el dx.\n", " i = a # Se inicializa en a un iterador\n", " res = 0. # Se inicializa res, en donde se guardara el resultado de la integral.\n", " sumaPar = sumaImp = 0. # Se inicializan las sumas parciales.\n", " k = 0\n", " \n", " while k < n/2-1: # El ciclo que calcula la integral.\n", " i += dx\n", " sumaImp += f(i)\n", " i += dx\n", " sumaPar += f(i)\n", " k += 1\n", " \n", " sumaImp += f(i+dx) # Se agrega el ultimo termino de la suma impar.\n", " res += dx/3.(f(a) + 4 * sumaImp + 2 * sumaPar + f(b))\n", " \n", " return res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Limitaciones:\n", "- Malos para funciones de alto contraste (grafico)\n", "- No aplican si hay divergencias (integrables) en el intervalo [a,b] (si se crea una grilla que contiene una divergencia, entonces el algoritmo falla, dado que no sabe como manejarlas) (grafico)\n", "- El intervalo debe ser finito. i.e. en general no se puede calcular $\\int_{a}^{\\infty}f(x)dx$. Aunque en ocasiones con un cambio de variables se cambian los limites de integracion tal que resulte un intervalo finito." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metodo trapezoidal para $\\Delta x$ no constante\n", "\n", "Cuando $\\Delta x$ no es constante, la expresion anterior pierde validez. En este caso se debe implementar un $\\Delta x$ variable y la expresion queda de la siguiente manera:\n", "\n", "$$\n", "\\int_{a}^{b}f(x)dx \\sim \\frac{1}{2}\\sum_{i=0}^{N-1}(x_{i+1}-x_{i})(f(x_{i+1})+f(x_{i}))\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Los acentos no se escribieron a proposito." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
mdastro/Challenge	Simplest_Pysynphot/Photometry Simulation.ipynb	1	3537	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:44: UserWarning: PYSYN_CDBS is undefined; functionality will be SEVERELY crippled.\n", " warnings.warn(\"PYSYN_CDBS is undefined; functionality will be SEVERELY \"\n", "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:119: UserWarning: Extinction files should be moved to $PYSYN_CDBS/extinction for compatibility with future versions of pysynphot.\n", " warnings.warn('Extinction files should be moved to '\n", "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:155: UserWarning: Extinction files not found in grid/extinction\n", " warnings.warn('Extinction files not found in %s' % (extdir,))\n", "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/_tmg.fits file\n", " warnings.warn(\"PYSYN_CDBS is undefined; cannot find %s file\" % template)\n", "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/_tmc.fits file\n", " warnings.warn(\"PYSYN_CDBS is undefined; cannot find %s file\" % template)\n", "/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/*_tmt.fits file\n", " warnings.warn(\"PYSYN_CDBS is undefined; cannot find %s file\" % template)\n" ] } ], "source": [ "import numpy as np\n", "import os\n", "import pysynphot as s" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sdss_spectrum = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Specs/0443.51873.152.7xt'\n", "jplus_filter = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/JPLUS_SDSS_filters/gSDSS_2cols.txt'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setting the T80 M1 effective area in cm^2 ------------------------------------------------------------------------\n", "s.refs.PRIMARY_AREA = 4400" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.10091416843e+19\n", "4887.4329977\n", "-24.7310893127\n" ] } ], "source": [ "jplus_filter_bandpass = s.FileBandpass(jplus_filter) \n", "sdss_spectrum = s.FileSpectrum(sdss_spectrum)\n", "photometry = s.Observation(sdss_spectrum, jplus_filter_bandpass, binset=np.arange(3000, 11000, 1), \n", " force='extrap')\n", "print photometry.countrate()\n", "print photometry.efflam()\n", "print photometry.effstim('abmag')" ] } ], "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
hugues-talbot/optimization_textbook	Scripts/Signaldeblur.ipynb	1	68425	{ "metadata": { "name": "", "signature": "sha256:29ea9d4a97587343227056d09a2ca2fa6e9408be2ff3b547bfe785543ea7724a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Restoring a 1D signal using Tikhonov regularization" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\\label{code:restore1d}" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Code prerequisites" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\"\"\"\n", "Deblurring and denoising a 1D signal.\n", "\"\"\"\n", "import numpy as np\n", "import scipy.ndimage as spnd\n", "import matplotlib.pyplot as plt\n", "\n", "# using common functions for signal/image restoration\n", "from restore_common import " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Artificial signal generation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code produces a signal with both soft and sharp variations" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## produce a signal from a range input\n", "def makesignal(mys):\n", " sig= 1.(np.cos(mys2)>0)+ 2np.sin(mys/4.0) + 0.1np.cos(mys3+1.0)\n", " return sig" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We illustrate how the signal looks like with the following:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## Produce some signal \n", "N1 = 250 # length of the signal \n", "myrange=np.linspace(0,3*np.pi,num=N1)\n", "mysignal1=makesignal(myrange)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,6), dpi=100)\n", "plt.plot(myrange,mysignal1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "[<matplotlib.lines.Line2D at 0x1158115d0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x114e45650>" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This signal is interesting because it shows some soft and hard transitions. We now blur the signal and add some noise." ] }, { "cell_type": "code", "collapsed": false, "input": [ "n = 25 # quite a large kernel\n", "k1 = np.zeros(n)\n", "k1[n/2] = 1\n", "mykernel=spnd.gaussian_filter1d(k1,sigma=n/6.0) ## already normalized\n", "mysignal = np.pad(mysignal1, n,'reflect')\n", "N = mysignal.size\n", "myconv=makespdiag(mykernel,N).toarray() ## degradation matrix\n", "myblurred = np.dot(myconv,mysignal) ## Convolve the signal\n", "observed = myblurred + np.random.normal(scale=0.05,size=N) ## add noise" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we pad the signal left and right with a reflective boundary condition. This ensures our result is as correct as possible near its start and its end. When we display it, we only show the unpadded middle part." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print(\"Condition number of base convolution matrix= %.3g\\n\" % np.linalg.cond(myconv))\n", "fig = plt.figure(figsize=(12,6))\n", "## better deblurring\n", "plt.plot(myrange,mysignal[n:-n],'b')\n", "plt.plot(myrange,observed[n:-n],'m.')\n", "## Adding a \"small vector\" prior\n", "deblurred_id = restore_tikh(observed, myconv,0.01,'Id')\n", "# small gradient prior (mostly flat signal)\n", "deblurred_grad = restore_tikh(observed, myconv,0.3,'Grad1')\n", "# small Laplacian (mostly piecewise linear)\n", "deblurred_lap = restore_tikh(observed, myconv,0.4, 'Lap')\n", "plt.plot(myrange,deblurred_lap[n:-n],'k')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Condition number of base convolution matrix= 3.53e+04\n", "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "[<matplotlib.lines.Line2D at 0x115c36a50>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ZgpcHlwmYJSB2fHl50GxpCpsf30yjE40qnT8urK902oSkHzkWw8XlqFGwcyf8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"text": [ "<matplotlib.figure.Figure at 0x1158904d0>" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The observed signal is represented by the purple dots. The restored signal is in black. The procedure we have used restores some of the sharp edges at the expense of some oscillations in the smooth regions. This is similar to the effect we observed on image deblurring." ] } ], "metadata": {} } ] }	cc0-1.0
drufat/dec	examples/schur.ipynb	1	19770	{ "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" }, "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Schur" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from dec.schur import schur, schur\u03bb, bmat, rand\n", "from dec.spectral import I_diag" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "n, m = 5, 2\n", "B = np.arange(nm).reshape((n,m))\n", "C = np.arange(nm).reshape((m,n))\n", "D = np.arange(mm).reshape((m,m))\n", "print(B)\n", "print(C)\n", "print(D)\n", "print(B.dot(np.eye(B.shape[-1], B.shape[-1])))\n", "print(C.dot(np.eye(C.shape[-1], C.shape[-1])))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[0 1]\n", " [2 3]\n", " [4 5]\n", " [6 7]\n", " [8 9]]\n", "[[0 1 2 3 4]\n", " [5 6 7 8 9]]\n", "[[0 1]\n", " [2 3]]\n", "[[ 0. 1.]\n", " [ 2. 3.]\n", " [ 4. 5.]\n", " [ 6. 7.]\n", " [ 8. 9.]]\n", "[[ 0. 1. 2. 3. 4.]\n", " [ 5. 6. 7. 8. 9.]]\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def fourier_sin(N, a, b):\n", " r'''\n", " Corresponds to :math:`f(x) \\mapsto \\int_{x+a}^{x+b} f(\\xi) \\sin(\\xi) d\\xi`\n", " '''\n", " I = I_diag(N+2, a, b) / 2j\n", " \n", " def K(x):\n", " assert x.shape == (N,)\n", " x = np.array(x, dtype=np.complex)\n", " \n", " x = np.hstack([[0], x, [0]])\n", " x = (np.roll(x,+1) - np.roll(x,-1))\n", " x = I\n", " rslt = x[1:-1]\n", "\n", " rslt[ 0] += x[-1]\n", " rslt[-1] += x[0]\n", " return rslt\n", " \n", " def Kinv(x):\n", " # Make sure type is coerced to complex, otherwise numpy ignores the complex parts\n", " # and reverts to reals.\n", " assert x.shape == (N,)\n", " x = np.array(x.copy(), dtype=np.complex)\n", " x /= I[1:-1]\n", "\n", " if (np.isclose(I[0], I[N]) or \n", " np.isclose(I[1], I[N+1]) or \n", " np.isclose(I[0]I[1], I[N]I[N+1])):\n", " raise ValueError(\"Singular operator.\")\n", "\n", " y = np.zeros(N, dtype=np.complex)\n", " # The computations below are essentially Schur's complement?\n", " E = np.sum(x[::2]); O = np.sum(x[1::2])\n", " if N % 2 == 0:\n", " y[0] = O/(1-I[0]/I[N])\n", " y[-1] = E/(I[N+1]/I[1]-1)\n", " else:\n", " y[0] = (I[1]/I[N+1]E+O)/(1-I[1]I[0]/I[N]/I[N+1])\n", " y[-1] = (I[N]/I[0]E+O)/(I[N]I[N+1]/I[0]/I[1]-1)\n", " \n", " x[0] -= y[-1]I[N+1]/I[1]\n", " x[-1] -= -y[0]I[0]/I[N]\n", "\n", " # This should be the crux of the inverse\n", " x = np.hstack([[-y[0]], x , [y[-1]]])\n", " y[::2] = -np.cumsum(x[::2])[:-1]\n", " y[1::2] = np.cumsum(x[1::2][::-1])[:-1][::-1]\n", "\n", " return y\n", " \n", " return K, Kinv" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 4\n", "K, Kinv = fourier_sin(N, 0, 0.1)\n", "x = np.ones(N)\n", "y = K(x)\n", "assert np.allclose(x, Kinv(y))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import dec.spectral as sp\n", "from dec.helper import to_matrix\n", "\n", "def A_bidiagonal(x):\n", " f = np.hstack([ [-x[1]], x[:-2]-x[2:], [x[-2]] ])\n", " return f\n", "\n", "to_matrix(A_bidiagonal, 6)\n", "#print(np.linalg.inv(to_matrix(Aa, 6)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 5, "text": [ "array([[-0., -1., -0., -0., -0., -0.],\n", " [ 1., 0., -1., 0., 0., 0.],\n", " [ 0., 1., 0., -1., 0., 0.],\n", " [ 0., 0., 1., 0., -1., 0.],\n", " [ 0., 0., 0., 1., 0., -1.],\n", " [ 0., 0., 0., 0., 1., 0.]])" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "def fourier_sin_schur_old(N, a, b):\n", " \n", " I = sp.I_diag(N+2, a, b) / 2j\n", " zeros = lambda n: np.zeros(n, dtype=np.complex)\n", "\n", " n = N - 2\n", " m = 2\n", "\n", " def A(x): \n", " return A_bidiagonal(x)I[2:-2]\n", "\n", " def Ainv(f):\n", " raise NotImplemented\n", "\n", " def B(\u03bb):\n", " f = zeros(n)\n", " f[0] = I[ 2]\u03bb[0]\n", " f[-1] = -I[-3]\u03bb[1]\n", " return f\n", "\n", " def C(x):\n", " g = zeros(m)\n", " g[0] = -I[ 1]x[ 0]\n", " g[1] = I[-2]x[-1]\n", " return g\n", "\n", " def D(\u03bb):\n", " g = zeros(m)\n", " g[0] = I[-1]\u03bb[1]\n", " g[1] = -I[ 0]\u03bb[0]\n", " return g\n", " \n", " def K(x):\n", " assert len(x) == N\n", " x = np.array(x, dtype=np.complex)\n", " \n", " x, \u03bb = x[1:-1], x[[0, -1]]\n", " f = A(x) + B(\u03bb)\n", " g = C(x) + D(\u03bb)\n", " \n", " return np.hstack([ [g[0]], f, [g[1]] ])\n", " \n", " Minv = None\n", " \n", " def Kinv(f):\n", " raise NotImplemented\n", "\n", " return K, Kinv\n", "\n", "for N in range(4, 9):\n", " K_, Kinv_ = fourier_sin(N, 0, 1)\n", " K, Kinv = fourier_sin_schur_old(N, 0, 1)\n", " x = np.random.rand(N)\n", " assert np.allclose(K(x), K_(x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def A_lower_triangular(x):\n", " if len(x)<3: return x\n", " return np.hstack([ [x[0], x[1]], x[2:]-x[:-2] ])\n", "\n", "to_matrix(A_lower_triangular, 5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 7, "text": [ "array([[ 1., 0., 0., 0., 0.],\n", " [ 0., 1., 0., 0., 0.],\n", " [-1., 0., 1., 0., 0.],\n", " [ 0., -1., 0., 1., 0.],\n", " [ 0., 0., -1., 0., 1.]])" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "np.linalg.inv( to_matrix(A_lower_triangular, 5) )" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 8, "text": [ "array([[ 1., 0., 0., 0., 0.],\n", " [ 0., 1., 0., 0., 0.],\n", " [ 1., 0., 1., 0., 0.],\n", " [ 0., 1., 0., 1., 0.],\n", " [ 1., 0., 1., 0., 1.]])" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "def A_lower_triangular_inv(x):\n", " if len(x)<3: return x\n", " y = np.zeros_like(x)\n", " y[::2] = np.cumsum(x[::2])\n", " y[1::2] = np.cumsum(x[1::2])\n", " return y\n", "to_matrix(A_lower_triangular_inv, 5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 9, "text": [ "array([[ 1., 0., 0., 0., 0.],\n", " [ 0., 1., 0., 0., 0.],\n", " [ 1., 0., 1., 0., 0.],\n", " [ 0., 1., 0., 1., 0.],\n", " [ 1., 0., 1., 0., 1.]])" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "for n in range(4, 10):\n", " x = np.random.rand(n)\n", " assert np.allclose(x, A_lower_triangular(A_lower_triangular_inv(x)))\n", " assert np.allclose(x, A_lower_triangular_inv(A_lower_triangular(x)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "def fourier_sin_schur(N, a, b):\n", "\n", " I = sp.I_diag(N+2, a, b) / 2j\n", " zeros = lambda n: np.zeros(n, dtype=np.complex)\n", "\n", " n = N - 2\n", " m = 2\n", "\n", " def A(x): \n", " return A_lower_triangular(-x)I[2:-2]\n", "\n", " def Ainv(f): \n", " return A_lower_triangular_inv(-f/I[2:-2])\n", "\n", " def B(\u03bb):\n", " f = zeros(n)\n", " f[0] = I[2]\u03bb[0]\n", " f[1] = I[3]\u03bb[1]\n", " return f\n", "\n", " def C(x):\n", " g = zeros(m)\n", " g[0] = I[-1]x[-1]\n", " g[1] = I[-2]x[-2]\n", " return g\n", "\n", " def D(\u03bb):\n", " g = zeros(m)\n", " g[0] = -I[1]\u03bb[1]\n", " g[1] = -I[0]\u03bb[0]\n", " return g\n", " \n", " def K(x):\n", " assert len(x) == N\n", " x = np.array(x, dtype=np.complex)\n", " \n", " x, \u03bb = x[2:], x[:2]\n", " f = A(x) + B(\u03bb)\n", " g = C(x) + D(\u03bb)\n", " \n", " return np.hstack([ [g[0]], f, [g[1]] ])\n", " \n", " Minv = schur\u03bb(Ainv, B, C, D, n, m)\n", " \n", " def Kinv(f):\n", " assert len(f) == N\n", " f, g = f[1:-1], f[[0,-1]]\n", " x, \u03bb = Minv(f, g)\n", " return np.hstack([\u03bb, x])\n", "\n", " return K, Kinv\n", "\n", "for N in range(4, 9):\n", " K_, Kinv_ = fourier_sin(N, 0, 1)\n", " K, Kinv = fourier_sin_schur(N, 0, 1)\n", " x = np.random.rand(N)\n", " assert np.allclose(K(x), K_(x))\n", " assert np.allclose(Kinv(x), Kinv_(x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def fourier_sin_schur_simple(N, a, b):\n", "\n", " I = sp.I_diag(N+2, a, b) / 2j\n", " zeros = lambda n: np.zeros(n, dtype=np.complex)\n", "\n", " n = N - 2\n", " m = 2\n", " K, _ = fourier_sin(N, a, b)\n", "\n", " zn = lambda: zeros(n)\n", " zm = lambda: zeros(m)\n", "\n", " def Kb(x=zn(), \u03bb=zm()):\n", " y = K(np.hstack([\u03bb, x]))\n", " f, g = y[1:-1], y[[0, -1]]\n", " return f, g\n", " \n", " def Ainv(f): \n", " return A_lower_triangular_inv(-f/I[2:-2])\n", "\n", " def A(x): return Kb(x=x)[0]\n", " \n", " def B(\u03bb): return Kb(\u03bb=\u03bb)[0]\n", "\n", " def C(x): return Kb(x=x)[1]\n", "\n", " def D(\u03bb): return Kb(\u03bb=\u03bb)[1]\n", " \n", " Minv = schur\u03bb(Ainv, B, C, D, n, m)\n", " \n", " def Kinv(f):\n", " assert len(f) == N\n", " f, g = f[1:-1], f[[0,-1]]\n", " x, \u03bb = Minv(f, g)\n", " return np.hstack([\u03bb, x])\n", "\n", " return K, Kinv\n", "\n", "for N in range(4, 9):\n", " K_, Kinv_ = fourier_sin(N, 0, 1)\n", " K, Kinv = fourier_sin_schur_simple(N, 0, 1)\n", " x = np.random.rand(N)\n", " assert np.allclose(K(x), K_(x))\n", " assert np.allclose(Kinv(x), Kinv_(x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "def I_diag_sy(N, a, b):\n", " from sympy import symbols\n", " l = symbols(['I{}'.format(i) for i in range(N)])\n", " return np.array(l)\n", "\n", "#def I_diag_sy(N, a, b):\n", "# from sympy import I, exp, Integer, symbols\n", "# l = []\n", "# for n in map(int, sp.freq(N)):\n", "# if n == 0:\n", "# l.append(b-a)\n", "# else:\n", "# n = Integer(n)\n", "# l.append( (exp(Inb) - exp(Ina))/(In) )/2/I\n", "# return np.array(l)\n", "\n", "def fourier_sin_schur_sym(a, b):\n", " from sympy import I, exp, Integer\n", " \n", " def K(x):\n", " N = x.shape[0]\n", " x = np.hstack([[0], x, [0]])\n", "\n", " x = (np.roll(x,+1) - np.roll(x,-1))\n", " x = I_diag_sy(N+2, a, b)\n", " rslt = x[1:-1]\n", "\n", " rslt[ 0] += x[-1]\n", " rslt[-1] += x[0]\n", " return rslt\n", "\n", " return K, None\n", "\n", "import sympy as sy\n", "a, b = sy.symbols('a b')\n", "K, Kinv = fourier_sin_schur_sym(a, b)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.array(sy.symbols('\u03bb0 x0 x1 x2 x3 x4 x5 x6 x7 x8 \u03bb1'))\n", "K(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 14, "text": [ "array([-I1x0 + I12\u03bb1, I2(-x1 + \u03bb0), I3(x0 - x2), I4(x1 - x3),\n", " I5(x2 - x4), I6(x3 - x5), I7(x4 - x6), I8(x5 - x7),\n", " I9(x6 - x8), I10(x7 - \u03bb1), -I0\u03bb0 + I11x8], dtype=object)" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# This is better because we have a lower triangular matrix.\n", "X = np.array(sy.symbols('\u03bb0 \u03bb1 x0 x1 x2 x3 x4 x5 x6 x7 x8'))\n", "K(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 15, "text": [ "array([-I1\u03bb1 + I12x8, I2(-x0 + \u03bb0), I3(-x1 + \u03bb1), I4(x0 - x2),\n", " I5(x1 - x3), I6(x2 - x4), I7(x3 - x5), I8(x4 - x6),\n", " I9(x5 - x7), I10(x6 - x8), -I0\u03bb0 + I11x7], dtype=object)" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.array(sy.symbols('\u03bb0 \u03bb1'))\n", "K(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 16, "text": [ "array([-I1\u03bb1 + I3\u03bb1, -I0\u03bb0 + I2\u03bb0], dtype=object)" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.array(sy.symbols('\u03bb0 \u03bb1 x0'))\n", "K(X)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 17, "text": [ "array([-I1\u03bb1 + I4x0, I2(-x0 + \u03bb0), -I0\u03bb0 + I3*\u03bb1], dtype=object)" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "A_lower_triangular(I_diag_sy(5, a, b))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 18, "text": [ "array([I0, I1, -I0 + I2, -I1 + I3, -I2 + I4], dtype=object)" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 1000\n", "K, Kinv = fourier_sin(N, 0, 0.1)\n", "K_, Kinv_ = fourier_sin_schur(N, 0, 0.1)\n", "K__, Kinv__ = fourier_sin_schur_simple(N, 0, 0.1)\n", "\n", "np.random.seed(1)\n", "x = np.random.rand(N)\n", "assert np.allclose(K_(x), K(x))\n", "assert np.allclose(K__(x), K(x))\n", "assert np.allclose(Kinv_(x), Kinv(x))\n", "assert np.allclose(Kinv__(x), Kinv(x))\n", "%timeit K(x)\n", "%timeit Kinv(x)\n", "%timeit K_(x)\n", "%timeit Kinv_(x)\n", "%timeit K__(x)\n", "%timeit Kinv__(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10000 loops, best of 3: 29.9 \u00b5s per loop\n", "10000 loops, best of 3: 118 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 37.9 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 66.7 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 29.9 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "10000 loops, best of 3: 143 \u00b5s per loop" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": true, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 } ], "metadata": {} } ] }	gpl-3.0
tcw176/ThinkStats2	code/chap04ex.ipynb	59	48334	{ "metadata": { "name": "", "signature": "sha256:5516b4b36bbf834796c83314bbf6124f83b1a48673190abc466d6993eebf6e85" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise from Think Stats, 2nd Edition (thinkstats2.com)<br>\n", "Allen Downey\n", "\n", "Read the pregnancy file." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import nsfg\n", "preg = nsfg.ReadFemPreg()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select live births, then make a CDF of <tt>totalwgt_lb</tt>. " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the CDF." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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amDx5svjggw9cnoeLj4iIZCR0p1AQEZHHGOpERDLCUCcikhGGOhGRjDDUiYhk\nhKFORCQjDHUiIhlhqBMRycj/AfQDtuhyDNjJAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x3f52bd0>" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find out how much you weighed at birth, if you can, and compute CDF(x). " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "0.81422881168400085" ] } ], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are a first child, look up your birthweight in the CDF of first children; otherwise use the CDF of other children." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "0.79657754010695192" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the percentile rank of your birthweight" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "81.422881168400082" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the median birth weight by looking up the value associated with p=0.5." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "7.375" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the interquartile range (IQR) by computing percentiles corresponding to 25 and 75. " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "(6.5, 8.125)" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a random selection from <tt>cdf</tt>." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "7.0" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "[6.25, 5.1875, 8.1875, 6.5, 7.9375, 6.6875, 5.75, 6.5625, 7.8125, 5.25]" ] } ], "prompt_number": 49 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a random sample from <tt>cdf</tt>, then compute the percentile rank for each value, and plot the distribution of the percentile ranks." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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fAwcOLPlccVs0dXV1Gh8f18TEhK5fv67+/n41NzdHHPPee+9px44dev3111VRUbHkwVgZ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\nAFgUAQ8AFkXAA4BF/T8RiQJEMgtbWgAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x62826d0>" ] } ], "prompt_number": 50 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate 1000 random values using <tt>random.random()</tt> and plot their PMF." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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2Pz+f8vJyAMrLy1m1ahUAy5Yt49133+XChQsMDg7y+uuvM2/evCv29tgPn+Cv/voHrHrg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\nOXJkHLocG6E8LwD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"text": [ "<matplotlib.figure.Figure at 0x3518bd0>" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming that the PMF doesn't work very well, try plotting the CDF instead." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x5158910>" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "0.5" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
adamsumm/SMBRNN	Torch/Birthday Problem.ipynb	1	7202	{ "cells": [ { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-171-147b9c96255a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrial\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0muniq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mallf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msupport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0muniq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mallf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python2.7/random.pyc\u001b[0m in \u001b[0;36mshuffle\u001b[0;34m(self, x, random)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[1;32m 289\u001b[0m \u001b[0;31m# pick an element in x[:i+1] with which to exchange x[i]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m \u001b[0mj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\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 291\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from os import listdir\n", "from os.path import isfile, join\n", "\n", "mypath = 'TEMP'\n", "\n", "onlyfiles = [join(mypath,f) for f in listdir(mypath) if isfile(join(mypath, f))]\n", "\n", "allf = []\n", "for f in onlyfiles:\n", " with open(f,'rb') as infile:\n", " for ii in range(4):\n", " infile.readline()\n", " \n", " l = infile.readline()\n", " allf.append(l[1: l[1:].find(';')].replace('v','').replace('','-'))\n", "\n", "allf.append(l[1: l[1:].find(';')].replace('v','').replace('','-'))\n", "\n", "import random\n", "trials = 10000\n", "support = 900\n", "#support = len(allf)\n", "bad = 0\n", "for trial in range(trials):\n", " uniq = set()\n", " random.shuffle(allf)\n", " for s in range(support):\n", " uniq.add(allf[s])\n", " if len(uniq) != support:\n", " bad += 1.0\n", "print bad/trials\n", "print len(allf)" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "12814\n" ] } ], "source": [ "from os import listdir\n", "from os.path import isfile, join\n", "\n", "mypath = 'TEMP'\n", "\n", "onlyfiles = [join(mypath,f) for f in listdir(mypath) if isfile(join(mypath, f))]\n", "\n", "allf = []\n", "for f in onlyfiles:\n", " with open(f,'rb') as infile:\n", " for ii in range(4):\n", " infile.readline()\n", " \n", " l = infile.readline()\n", " allf.append(l[1: l[1:].find(';')].replace('v','').replace('','-'))\n", "\n", "\n", "import random\n", "trials = 1\n", "support = len(allf)\n", "bad = 0\n", "for trial in range(trials):\n", " uniq = set()\n", " random.shuffle(allf)\n", " for s in range(support):\n", " uniq.add(allf[s])\n", " if len(uniq) != support:\n", " bad += 1.0\n", "print bad/trials\n", "print len(allf)" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5345\n", "13000\n" ] } ], "source": [ "from os import listdir\n", "from os.path import isfile, join\n", "\n", "\n", "\n", "allf = [0] + list(range(12999))\n", "\n", "import random\n", "trials = 10000\n", "support = 9500\n", "#support = len(allf)\n", "bad = 0\n", "for trial in range(trials):\n", " uniq = set()\n", " random.shuffle(allf)\n", " for s in range(support):\n", " uniq.add(allf[s])\n", " if len(uniq) != support:\n", " bad += 1.0\n", "print bad/trials\n", "print len(allf)" ] }, { "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
vickydasta/musicboxandhumanbrain	plot_matching.ipynb	1	14493	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"document\" id=\"robust-matching-using-ransac\">\n", "<h1 class=\"title\">Robust matching using RANSAC</h1>\n", "<p>In this simplified example we first generate two synthetic images as if they\n", "were taken from different view points.</p>\n", "<p>In the next step we find interest points in both images and find\n", "correspondences based on a weighted sum of squared differences of a small\n", "neighborhood around them. Note, that this measure is only robust towards\n", "linear radiometric and not geometric distortions and is thus only usable with\n", "slight view point changes.</p>\n", "<p>After finding the correspondences we end up having a set of source and\n", "destination coordinates which can be used to estimate the geometric\n", "transformation between both images. However, many of the correspondences are\n", "faulty and simply estimating the parameter set with all coordinates is not\n", "sufficient. Therefore, the RANSAC algorithm is used on top of the normal model\n", "to robustly estimate the parameter set by detecting outliers.</p>\n", "</div>\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "from skimage import data\n", "from skimage.util import img_as_float\n", "from skimage.feature import (corner_harris, corner_subpix, corner_peaks,\n", " plot_matches)\n", "from skimage.transform import warp, AffineTransform\n", "from skimage.exposure import rescale_intensity\n", "from skimage.color import rgb2gray\n", "from skimage.measure import ransac\n", "\n", "\n", "# generate synthetic checkerboard image and add gradient for the later matching\n", "checkerboard = img_as_float(data.checkerboard())\n", "img_orig = np.zeros(list(checkerboard.shape) + [3])\n", "img_orig[..., 0] = checkerboard\n", "gradient_r, gradient_c = np.mgrid[0:img_orig.shape[0],\n", " 0:img_orig.shape[1]] / float(img_orig.shape[0])\n", "img_orig[..., 1] = gradient_r\n", "img_orig[..., 2] = gradient_c\n", "img_orig = rescale_intensity(img_orig)\n", "img_orig_gray = rgb2gray(img_orig)\n", "\n", "# warp synthetic image\n", "tform = AffineTransform(scale=(0.9, 0.9), rotation=0.2, translation=(20, -10))\n", "img_warped = warp(img_orig, tform.inverse, output_shape=(200, 200))\n", "img_warped_gray = rgb2gray(img_warped)\n", "\n", "# extract corners using Harris' corner measure\n", "coords_orig = corner_peaks(corner_harris(img_orig_gray), threshold_rel=0.001,\n", " min_distance=5)\n", "coords_warped = corner_peaks(corner_harris(img_warped_gray),\n", " threshold_rel=0.001, min_distance=5)\n", "\n", "# determine sub-pixel corner position\n", "coords_orig_subpix = corner_subpix(img_orig_gray, coords_orig, window_size=9)\n", "coords_warped_subpix = corner_subpix(img_warped_gray, coords_warped,\n", " window_size=9)\n", "\n", "\n", "def gaussian_weights(window_ext, sigma=1):\n", " y, x = np.mgrid[-window_ext:window_ext+1, -window_ext:window_ext+1]\n", " g = np.zeros(y.shape, dtype=np.double)\n", " g[:] = np.exp(-0.5 * (x2 / sigma2 + y2 / sigma2))\n", " g /= 2 * np.pi * sigma * sigma\n", " return g\n", "\n", "\n", "def match_corner(coord, window_ext=5):\n", " r, c = np.round(coord).astype(np.intp)\n", " window_orig = img_orig[r-window_ext:r+window_ext+1,\n", " c-window_ext:c+window_ext+1, :]\n", "\n", " # weight pixels depending on distance to center pixel\n", " weights = gaussian_weights(window_ext, 3)\n", " weights = np.dstack((weights, weights, weights))\n", "\n", " # compute sum of squared differences to all corners in warped image\n", " SSDs = []\n", " for cr, cc in coords_warped:\n", " window_warped = img_warped[cr-window_ext:cr+window_ext+1,\n", " cc-window_ext:cc+window_ext+1, :]\n", " SSD = np.sum(weights * (window_orig - window_warped)*2)\n", " SSDs.append(SSD)\n", "\n", " # use corner with minimum SSD as correspondence\n", " min_idx = np.argmin(SSDs)\n", " return coords_warped_subpix[min_idx]\n", "\n", "\n", "# find correspondences using simple weighted sum of squared differences\n", "src = []\n", "dst = []\n", "for coord in coords_orig_subpix:\n", " src.append(coord)\n", " dst.append(match_corner(coord))\n", "src = np.array(src)\n", "dst = np.array(dst)\n", "\n", "\n", "# estimate affine transform model using all coordinates\n", "model = AffineTransform()\n", "model.estimate(src, dst)\n", "\n", "# robustly estimate affine transform model with RANSAC\n", "model_robust, inliers = ransac((src, dst), AffineTransform, min_samples=3,\n", " residual_threshold=2, max_trials=100)\n", "outliers = inliers == False\n", "\n", "\n", "# compare \"true\" and estimated transform parameters\n", "print(tform.scale, tform.translation, tform.rotation)\n", "print(model.scale, model.translation, model.rotation)\n", "print(model_robust.scale, model_robust.translation, model_robust.rotation)\n", "\n", "# visualize correspondence\n", "fig, ax = plt.subplots(nrows=2, ncols=1)\n", "\n", "plt.gray()\n", "\n", "inlier_idxs = np.nonzero(inliers)[0]\n", "plot_matches(ax[0], img_orig_gray, img_warped_gray, src, dst,\n", " np.column_stack((inlier_idxs, inlier_idxs)), matches_color='b')\n", "ax[0].axis('off')\n", "ax[0].set_title('Correct correspondences')\n", "\n", "outlier_idxs = np.nonzero(outliers)[0]\n", "plot_matches(ax[1], img_orig_gray, img_warped_gray, src, dst,\n", " np.column_stack((outlier_idxs, outlier_idxs)), matches_color='r')\n", "ax[1].axis('off')\n", "ax[1].set_title('Faulty correspondences')\n", "\n", "plt.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(0.9, 0.9) [ 20. -10.] 0.2\n", "(0.9150655074453423, 0.8816374935151021) [-10.39162063 19.11736021] -0.194718117629\n", "(0.8999194500188994, 0.9000463268800499) [-10.00096544 19.97336708] -0.199907842544\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot as plt\n", "\n", "from skimage import data\n", "from skimage.feature import corner_harris, corner_subpix, corner_peaks\n", "from skimage.transform import warp, AffineTransform\n", "from skimage.draw import ellipse\n", "\n", "\n", "tform = AffineTransform(scale=(1.3, 1.1), rotation=1, shear=0.7,\n", " translation=(210, 50))\n", "image = warp(data.checkerboard(), tform.inverse, output_shape=(350, 350))\n", "rr, cc = ellipse(310, 175, 10, 100)\n", "image[rr, cc] = 1\n", "image[180:230, 10:60] = 1\n", "image[230:280, 60:110] = 1\n", "\n", "coords = corner_peaks(corner_harris(image), min_distance=5)\n", "coords_subpix = corner_subpix(image, coords, window_size=13)\n", "\n", "fig, ax = plt.subplots()\n", "ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray)\n", "ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3)\n", "ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15)\n", "ax.axis((0, 350, 350, 0))\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "from skimage import io, color, morphology\n", "from scipy.signal import convolve2d\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "img = color.rgb2gray(io.imread('samples/xsampel-a.jpg'))\n", "\n", "# Reduce all lines to one pixel thickness\n", "snakes = morphology.skeletonize(img < 1)\n", "\n", "# Find pixels with only one neighbor\n", "corners = convolve2d(snakes, [[1, 1, 1],\n", " [1, 0, 1],\n", " [1, 1, 1]], mode='same') == 1\n", "corners = corners & snakes\n", "\n", "# Those are the start and end positions of the segments\n", "y, x = np.where(corners)\n", "\n", "plt.imshow(img, cmap=plt.cm.gray, interpolation='nearest')\n", "plt.scatter(x, y)\n", "plt.axis('off')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Skeletonize requires a 2D array", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-3e84901b6560>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'samples/xsampel-b.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# Reduce all lines to one pixel thickness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0msnakes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmorphology\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mskeletonize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# Find pixels with only one neighbor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/skimage/morphology/_skeletonize.pyc\u001b[0m in \u001b[0;36mskeletonize\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;31m# - binary image with only 0's and 1's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mskeleton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Skeletonize requires a 2D array'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 108\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mskeleton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Image contains values other than 0 and 1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Skeletonize requires a 2D array" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from skimage.feature import corner_harris,corner_peaks\n", "import skimage.io as io\n", "\n", "# More pyplot!\n", "def show_corners(corners,image,title=None):\n", " \"\"\"Display a list of corners overlapping an image\"\"\"\n", " fig = plt.figure()\n", " plt.imshow(image)\n", " # Convert coordinates to x and y lists\n", " y_corner,x_corner = zip(corners)\n", " plt.plot(x_corner,y_corner,'o') # Plot corners\n", " if title:\n", " plt.title(title)\n", " plt.xlim(0,image.shape[1])\n", " plt.ylim(image.shape[0],0) # Images use weird axes\n", " fig.set_size_inches(np.array(fig.get_size_inches()) * 1.5)\n", " plt.show()\n", "\n", "image = io.imread('ub174.jpg', flatten=True)\n", " \n", "corners = corner_peaks(corner_harris(image),min_distance=2)\n", "show_corners(corners,image,\n", " title=\"Harris Corner Algorithm\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
smenon8/AnimalWildlifeEstimator	Notebooks/ImageShareabilityClassifiers.ipynb	1	1793762	null	bsd-3-clause
albi3ro/M4	Prerequisites/Phase-Transitions.ipynb	1	324506	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phase Transitions in Magnets\n", "### Christina Lee\n", "### Category: Prerequisites\n", "### Physics Prerequisites: Statistical Mechanics\n", "\n", "### Monte Carlo Physics Series\n", "* [Monte Carlo: Calculation of Pi](../Numerics_Prog/Monte-Carlo-Pi.ipynb)\n", "* [Monte Carlo Markov Chain](../Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb)\n", "* [Monte Carlo Ferromagnet](../Prerequisites/Monte-Carlo-Ferromagnet.ipynb)\n", "* [Phase Transitions](../Prerequisites/Phase-Transitions.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Monte Carlo Ferromagnets, we only looked at the state of a magnet at one temperature. While I displayed how it looked at both a high temperature and a low-temperature situation, that had to be done manually. Today we are going to automate our study of temperature's effect. \n", "\n", "First off, what's the theory? \n", "\n", "The standard apparatus for phase transitions is <b>Landau Theory</b>. In Landau theory, we assume that we have an <b>order parameter</b>. \n", "\n", "Order parameters have\n", "* local physical meaning\n", "* global observational consequences\n", "\n", "For example, in magnets we use the local magnetization $m$; in freezing fluids, the difference in density $\\rho$. The value of an order parameter should undergo a jump across a phase transition, usually from a zero to a non-zero value. \n", "\n", "To construct Landau theory, we postulate that we can create a <b>Free Energy</b> for the system that is\n", "* continuous in the order parameter\n", "* respects the symmetries of the system\n", "\n", "Given those constraints, we get\n", "\\begin{equation}\n", "F=f_0(T) + \\alpha_0 (T-T_c) m^2+ \\frac{1}{2} \\beta m^4 - h m + ...\n", "\\end{equation}\n", "From this simple equation, we can derive many of the qualitative properties of phase transitions. I won't repeat that here, but you can look it up in a Statistical Mechanics textbook.\n", "\n", "Non-zero order parameters also represent symmetry breaking. In an unmagnetized magnet, all directions are equivalent, but once it magnetizes, it chooses a preferred direction. That direction is arbitrary up until the magnet spontaneously chooses it. \n", "\n", "Back in the early days of cars, streets didn't have lanes and a specified side for cars to drive on. Driving was symmetric. When two cars approached each other on the same road, the drivers had to figure out how to avoid a collision, either by everyone going to their left or everyone going to their right. Eventually, all the cars in a specific area picked up one convention. In the United States, we choose the right side of the road. In Japan, they chose left. Neither is wrong; it was pure chance. The solution breaks the symmetry of the problem.\n", "\n", "![Symmetry Breaking](../Images/PhaseTransitions/symmetrybreaking.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Specific Heat and Magnetic Susceptibility\n", "\n", "In addition to magnetization and energy, today I introduce the physical observables specific heat and magnetic susceptibility. These numbers describe how the old quantities change with temperature. \n", "\n", "Let's take a look at the first one, <b>Specific Heat</b>:\n", "\\begin{equation}\n", "C_h=\\frac{d \\langle E \\rangle}{dT}\n", "\\end{equation}\n", "\n", "While the above is the formal definition, we can calculate a more convenient expression by plugging in the Maxwell-Boltzmann Distribution:\n", "\n", "\\begin{equation}\n", "\\frac{ d\n", "\\frac{\\sum_{i} E_{i} e^{-\\beta E_i}}{ \\sum_j e^{-\\beta E_j} }\n", "}{dT}=\\beta^2 \\left( \\langle E^2 \\rangle - \\langle E \\rangle ^2 \\right)\n", "\\end{equation}\n", "\n", "This equation we use to calculate the specific heat, as it uses the values calculated at only one temperature, eliminating the need for a numerical derivative. For each temperature, we will need to calculate both the expectation of the Energy and the expectation of the Energy Squared.\n", "\n", "While in general, a material may be difficult to describe, near phase transitions certain properties diverge according to corresponding <b>critical exponents</b>. These fall out of the phenomenology of Landau theory, which you can find more about in a textbook.\n", "\n", "These critical exponents obey <b>universality</b>. Phase transitions in the same class will follow the same critical exponents. Determining the exponent for the 2D square lattice through our simulations also determines the exponent for situations like the mixing of two materials or the percolation problem.\n", "\n", "We define our specific heat critical exponent this way,\n", "\\begin{equation}\n", "C_h \\propto \\left( T- T_c \\right)^{-\\alpha_{\\pm}}.\n", "\\end{equation}\n", "\n", "The divergent scaling of most properties around a critical temperature depends on the divergence of the <b>correlation length</b> $\\xi$, the size of fluctuations in the system. At the critical temperature, fluctuations exist at <i>every length scale</i>. Once we get close enough to the transition temperature for the fluctuations to become greater than our finite simulation lattice, our computed numbers no longer agree with infinite-lattice-assumed theory. The divergence gets modified by some function of the ratio between the correlation length and our system size,\n", "\\begin{equation}\n", "C_h \\propto \\left( T - T_c \\right)^{-\\alpha_{\\pm}} g\\left( \\frac{L}{\\xi (T)} \\right).\n", "\\end{equation}\n", "\n", "Magnetic Susceptibility fulfills the same general role as specific heat, but for magnetization instead of energy.\n", "\\begin{equation}\n", "\\chi =\\frac{d \\langle M \\rangle}{dT}=\\beta^2 \\left( \\langle M^2 \\rangle - \\langle M \\rangle ^2 \\right)\n", "\\end{equation}\n", "\n", "Again, this property also has a corresponding critical exponent.\n", "\\begin{equation}\n", "\\chi \\propto \\left( T - T_c \\right)^{-\\gamma_{\\pm}}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exact Solution\n", "\n", "The 2D Square Ising model possesses an exact solution, first discovered by Lars Onsager in 1944, but since rewritten in a variety of different ways. I won't go over how to achieve the solution, but I will show the results for comparison.\n", "\n", "The critical temperature:\n", "\\begin{equation}\n", "J \\beta_c= \\frac{\\ln \\left( \\sqrt{2}+1 \\right)}{2} \\approx .4406\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "T_c \\approx 2.269 J\n", "\\end{equation}\n", "\n", "The magnetization curve when $T<T_c$ also $\\beta > \\beta_c$\n", "\\begin{equation}\n", "M=\\left(1-\\sinh^{-4} \\left(2 \\beta J \\right) \\right)^{1/8}\n", "\\end{equation}\n", "\n", "And finally, the much more complicated expression for internal energy per site,\n", "\n", "\\begin{equation}\n", "k=\\sinh (2 \\beta J)^{-2}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "U=-J \\coth (2 \\beta J) \\left(\n", "1 + \\frac{2}{\\pi} \\left( 2 \\tanh^2 (2 \\beta J) - 1 \\right)\n", "\\int_0^{\\pi/2} \\frac{1}{\\sqrt{1-4k (1+k)^{-2} \\sin^2 (\\theta)}}\n", "d \\theta\n", "\\right)\n", "\\end{equation}\n", "\n", "If we can achieve these results to perfect accuracy on pen and paper, why do we even bother simulating them in a computer? If you try some other lattices and coupling constants, you might soon find out why. We want to make <i>sure</i> our code is running well on something where we know the results before venturing into unknown territory. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load our packages. \n", "\n", "`Lattices.jl` is the same class I used in the previous post. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "init_cell": true }, "outputs": [ { "data": { "text/plain": [ "Plots.PyPlotBackend()" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "push!(LOAD_PATH,\"../Packages\")\n", "using Lattices;\n", "using Statistics\n", "using QuadGK\n", "using DelimitedFiles\n", "using Plots\n", "pyplot()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Eexact (generic function with 1 method)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The critical inverse temperature\n", "betac=log(1+sqrt(2))/2\n", "\n", "# The exact magnetization\n", "# Note: Does not take arrays\n", "function Mexact(beta::Float64)\n", " if beta>betac\n", " return (1-sinh(2betaJ)^(-4.0))^(1.0 ./8.0)\n", " else \n", " return 0\n", " end\n", "end\n", "\n", "# The exact energy\n", "# Note: Does not take arrays\n", "function Eexact(beta::Float64)\n", " k=sinh(2betaJ)^(-2.0)\n", " \n", " # the inside of the integral. Define a function so we can use quadgk\n", " insides(θ)=(1-4k(1+k)^(-2)sin(θ)^2)^(-0.5)\n", " #we run a numerical integration\n", " integ=quadgk(insides,0,π/2)[1]\n", " \n", " return -Jcoth(2betaJ)(1+2/π(2tanh(2betaJ)^2-1)integ )\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we set up our lattice.\n", "\n", "If looking for a mind bender, look at a `\"Checkerboard\"`, with an anti-ferromagnetic coupling constant, like `J=-1`. Otherwise, just stick with a square. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "init_cell": true }, "outputs": [], "source": [ "## Define l here\n", "l=10;\n", "\n", "lt=MakeLattice(\"Square\",l);\n", "S=ones(Int8,l,l); #Our spins\n", "dt=1/(lt.N);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same as last time. \n", "\n", "These functions calculate properties of our lattice." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "init_cell": true }, "outputs": [ { "data": { "text/plain": [ "\"defined functions\"" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The energy contribution of just one site\n", "function dE(i::Int)\n", " Eii=0;\n", " for j in 1:lt.nnei\n", " Eii+=S[lt.neigh[i,j]];\n", " end\n", " Eii=-JS[i]; # we are computing J sz_i sz_j for one i\n", " return Eii;\n", "end\n", "# The energy of the entire lattice\n", "function E()\n", " Evar=0;\n", " for k in 1:lt.N\n", " Evar+=.5dE(k);\n", " end\n", " return Evar;\n", "end\n", "# The magnetization of the entire lattice\n", "function M()\n", " Mvar=0;\n", " for k in 1:lt.N\n", " Mvar+=S[k];\n", " end\n", " return Mvar;\n", "end\n", "\"defined functions\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adjustable Parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Parameters set\"" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta0=.1;\n", "betaf=1;\n", "dbeta = .1 ; # coarse grained for faster computation\n", "# dbeta=.025; # fine grained if you have a faster computer\n", "\n", "J=1;\n", "t=100000;\n", "nskip=10; # don't measure every sweep= better decorrelation\n", "\"Parameters set\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"done\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nmeas=Int64(t/nskip); # how many times we will measure\n", "betas=collect(beta0:dbeta:betaf)\n", "\"done\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exact Solution Data Points" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Mex=zeros(length(betas))\n", "Eex=zeros(length(betas))\n", "for ii in 1:length(betas)\n", " Mex[ii]=Mexact(betas[ii])\n", " Eex[ii]=Eexact(betas[ii])\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I took what we ran last time and wrapped it into a function, `MCMCMagnet`. We aren't looking at spin configurations this time; we only want the final measureables, which get returned at the end." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MCMCMagnet (generic function with 1 method)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function MCMCMagnet(beta::Float64)\n", " tm=1; #Our measurement time step\n", " \n", " Ma=Array{Int32}(undef,nmeas); # our magnetization measurements\n", " Ea=Array{Int32}(undef,nmeas); # our energy measurements\n", " Ma2=Array{Int32}(undef,nmeas); # magnetization squared\n", " Ea2=Array{Int32}(undef,nmeas); # energy squared\n", " \n", " for ti in 1:t\n", " for j in 1:lt.N\n", " i = rand(1:lt.N); #Choosing a random site\n", " de=dE(i);\n", " if(de>0 \|\| rand()<exp(2betade) ) \n", " S[i]=-S[i]; #Switch the sign\n", " end\n", " end\n", " if isapprox(mod(ti,nskip),0)\n", " Ma[tm]=M();\n", " Ma2[tm]=Ma[tm]^2;\n", " \n", " Ea[tm]=E();\n", " Ea2[tm]=Ea[tm]^2;\n", " \n", " tm+=1;\n", "\n", " end\n", " end\n", " Mave=mean(Ma);\n", " Mstd=std(Ma)/lt.N;\n", " Eave=mean(Ea);\n", " Estd=std(Ea)/lt.N;\n", " \n", " E2ave=mean(Ea2);\n", " M2ave=mean(Ma2);\n", " \n", " Ch=beta^2(E2ave-Eave^2)/lt.N;\n", " χ=beta(M2ave-Mave^2)/lt.N;\n", " \n", " return Mave/lt.N,Mstd,Eave/lt.N,Estd,Ch,χ\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Temperature Loop\n", "\n", "This cell looks pretty simple: initialization and one for loop. But this is where everything ties together. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "beta: 0.1\tM: -0.001804\tE: -0.203736\n", "beta: 0.2\tM: -0.000968\tE: -0.428008\n", "beta: 0.3\tM: 0.0013059999999999999\tE: -0.706624\n", "beta: 0.4\tM: -0.00808\tE: -1.186944\n", "beta: 0.5\tM: 0.32823399999999997\tE: -1.7440280000000001\n", "beta: 0.6\tM: 0.973302\tE: -1.908696\n", "beta: 0.7\tM: 0.9904219999999999\tE: -1.964804\n", "beta: 0.8\tM: 0.9960840000000001\tE: -1.984992\n", "beta: 0.9\tM: 0.9984439999999999\tE: -1.99392\n", "beta: 1.0\tM: 0.999256\tE: -1.9971\n" ] } ], "source": [ "Mm=zeros(length(betas))\n", "Mstd=zeros(length(betas))\n", "Ee=zeros(length(betas))\n", "Estd=zeros(length(betas))\n", "Ch=zeros(length(betas))\n", "χ=zeros(length(betas))\n", "for ii in 1:length(betas)\n", " Mm[ii],Mstd[ii],Ee[ii],Estd[ii],Ch[ii],χ[ii]=MCMCMagnet(betas[ii])\n", " println(\"beta: \",betas[ii],\"\\tM: \",Mm[ii],\"\\tE: \",Ee[ii])\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That was the temperature loop for the MCMC method. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Storing and Restoring Data\n", "\n", "Since running these simulations take slightly longer than some of my previous posts, I've outputed data files of the results of three different sizes lattices (l=10, l=20, l=50), for runs of length 100,000. Feel free to take a look at them, or make your own data :)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# if you want to write a new set of data\n", "writedlm(string(\"PhaseTransitionsL\",l,\"t\",t,\"dbeta\",dbeta,\".dat\"),[betas Mm Mstd Ee Estd Ch χ])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "temp=readdlm(\"PhaseTransitionsL50t100000dbeta0.025.dat\")\n", "betas50=temp[:,1];\n", "Mm50=temp[:,2];\n", "Mstd50=temp[:,3];\n", "Ee50=temp[:,4];\n", "Estd50=temp[:,5];\n", "Ch50=temp[:,6];\n", "χ50=temp[:,7];\n", "\n", "temp=readdlm(\"PhaseTransitionsL10t100000dbeta0.025.dat\")\n", "betas10=temp[:,1];\n", "Mm10=temp[:,2];\n", "Mstd10=temp[:,3];\n", "Ee10=temp[:,4];\n", "Estd10=temp[:,5];\n", "Ch10=temp[:,6];\n", "χ10=temp[:,7];\n", "\n", "temp=readdlm(\"PhaseTransitionsL20t100000dbeta0.025.dat\")\n", "betas20=temp[:,1];\n", "Mm20=temp[:,2];\n", "Mstd20=temp[:,3];\n", "Ee20=temp[:,4];\n", "Estd20=temp[:,5];\n", "Ch20=temp[:,6];\n", "χ20=temp[:,7];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Section" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(betas,Mex,label=\"Exact\",linewidth=20,color=:black)\n", "plot!(betas10,abs.(Mm10),label=\"l=10\",linewidth=15)\n", "plot!(betas20,abs.(Mm20),label=\"l=20\",linewidth=10)\n", "plot!(betas50,abs.(Mm50),label=\"l=50\",linewidth=5)\n", "\n", "vline!([betac],label=\"\")\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Magnetization\",\n", " title=\"2D Square Ferromagnet Phase Transition\",legend=:left)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(betas,Eex,label=\"Exact\",linewidth=20,color=:black)\n", "plot!(betas10,Ee10,label=\"l=10\",linewidth=15)\n", "plot!(betas20,Ee20,label=\"l=20\",linewidth=10)\n", "plot!(betas50,Ee50,label=\"l=50\",linewidth=5)\n", "\n", "vline!([betac],label=\"\")\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Energy\",\n", " title=\"2D Square Ferromagnet Phase Transition\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(betas50,Ch50,label=\"l=50\",linewidth=5)\n", "plot!(betas20,Ch20,label=\"l=20\",linewidth=5)\n", "plot!(betas10,Ch10,label=\"l=10\",linewidth=5)\n", "\n", "\n", "vline!([betac],label=\"\")\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Specific Heat\",\n", " title=\"Specific Heat Divergence with Finite Size Scaling\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I presented two different equivalent formulas for the specific heat. Let's check to make sure they're equivalent. \n", "\n", "For the formal defination, we'll take a numeric derivative with respect to $\\beta$,\n", "\\begin{equation}\n", "\\frac{d \\langle E \\rangle}{dT} \\approx \\frac{ \\Delta \\langle E \\rangle}{\\Delta \\beta}\\frac{d \\beta}{d T}\n", "\\end{equation}\n", "\n", "Note: I corrected the scaling by hand." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ch2=(Ee50[1:end-1]-Ee50[2:end]).betas50[2:end].^2\n", "plot(betas50[2:end],Ch235,label=\"dE/dT\")\n", "plot!(betas50,Ch50,label=\"beta2(<E2>-<E>2)\")\n", "\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Specific Heat\",\n", " title=\"Comparing Specific Heat Calculations\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(betas10,χ10,label=\"l=10\")\n", "plot!(betas20,χ20,label=\"l=20\")\n", "plot!(betas50,χ50,label=\"l=50\")\n", "\n", "vline!([betac],label=\"\",linecolor=:black)\n", "\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Susceptibility\",\n", " title=\"Magnetic Susceptibility with Finite Size Scaling\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M2=(Mm50[2:end]-Mm50[1:end-1]).(betas50[1:end-1]).^2\n", "plot(betas50[1:end-1],M2*750,label=\"dM/dT\")\n", "plot!(betas50,χ50,label=\"(<M2>-<M>2)beta2\")\n", "plot!(xlabel=\"Inverse Temperature β\",ylabel=\"Susceptibility\",\n", " title=\"Comparing Methods of Computing Susceptibility\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's all for now. Plenty more to talk about on this subject, but hopefully that can keep you occupied. \n", "My plots are saved in M4/Images/PhaseTransitions ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "anaconda-cloud": {}, "kernelspec": { "display_name": "Julia 1.0.5", "language": "julia", "name": "julia-1.0" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.0.5" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
ProfessorKazarinoff/staticsite	content/code/matplotlib_plots/bar_plot_with_statistics_module_and_matplotlib.ipynb	1	100562	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Engineers collect data and make conclusions based on the results. An important way to view results is with statistical charts. In this post we will build a bar chart to compare the tensile strength of 3D-printed ABS plastic compared to the tensile strength of 3D-printed HIPS plastic. We will add error bars to the chart to show the amount of uncertainty in the data. In the bar plot we construct, the height of the bars will represent the mean or average tensile stength. One bar will represent the average strength of ABS and the other bar will show the average strength of HIPS. We will then add error bars to the plot which will represent +1/-1 standard deviation about the mean.\n", "\n", "We will use Python, the statistics module (part of the Python standard library), and matplotlib to build the bar plot. I recommend that undergraduate engineers use the [Anaconda](https://www.anaconda.com/download/) distribution of Python, which comes with matplotlib already installed. For help installing Anaconda, see a previous blog post: [Installing Anaconda on Windows 10](http://pythonforundergradengineers.com/installing-anaconda-on-windows.html). If matplotlib is not available in your version of Python, open a terminal or the Anaconda Prompt and type:\n", "\n", "\n", "```\n", "$ pip install matplotlib\n", "```\n", "\n", "or \n", "\n", "```\n", "> conda install matplotlib\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data we are going to plot is from the tensile testing of two different kinds of 3D-printed plastic, ABS and HIPS (HIPS stands for High Impact Polystyrene). You can download the data using the link below:\n", "\n", "[3D-printed-tensile-bar-data.xlsx](https://github.com/ProfessorKazarinoff/staticsite/raw/master/content/code/matplotlib_plots/3D-printed_tensile_test_data.xlsx)\n", "\n", "I'm constructing the plot in a jupyter notebook. You could also build the code in a __.py__ file and run the code to produce the plot. \n", "\n", "A note about using matplotlib on MacOSX: if you recieve an error message that matplotlib is not installed as a framework, consider using the Anaconda distribution of Python and running the code in a jupyter notebook. \n", "\n", "To open a new jupyter notebook go to the Anaconda Prompt or a terminal and type:\n", "\n", "```\n", "> jupyter notebook\n", "\n", "```\n", "\n", "Alternativly, you can start a new jupyter notebook by cliking the Windows start button and searching for [Anaconda3] --> [Jupyter Notebook]\n", "\n", "If jupyter is not installed on your system, you can install it using the Anaconda Prompt or use a terminal and ```pip```:\n", "\n", "```\n", "> conda install jupyter\n", "```\n", "\n", "or\n", "\n", "```\n", "$ pip install jupyter\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " At the top of the jupyter notebook (or .py file), we need to import the required packages:\n", "\n", " * statistics (part of the Python standard library, but still needs to be imported) and\n", " * matplotlib\n", "\n", "From the statistics module we will import two functions: ```mean``` (average) and ```stdev```(standard deviation). If we use this import line:\n", "\n", "```\n", "from statistics import mean, stdev\n", "```\n", "\n", "We can use the names ```mean()``` and ```stdev()``` in our code. However, if we use a more general import line:\n", "\n", "```\n", "import statistics\n", "```\n", "\n", "Then we will need to call ```statistics.mean()``` and ```statistics.stdev()``` in our code.\n", "\n", "matplotlib also needs to be imported. The typical way to do this is with the line:\n", "\n", "```\n", "import matplotlib.pyplot as plt\n", "```\n", "\n", "Then thoughout our code, we can use ```plt()``` instead of writing out ```matplotlib.pyplot()``` each time we want to use a matplotlib method.\n", "\n", "\n", "The ```%matplotlib inline``` magic command is added so that we can see our plot right in the jupyter notebook. If you build the plot in a __.py__ file, the ```%matplotlib inline``` command should be left out as it will return an error. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import packages\n", "from statistics import mean, stdev\n", "import matplotlib.pyplot as plt\n", "#include if using a jupyter notebook, remove if using a .py file\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create two variables which contains the data for ABS and HIPS as a list of individual tensile strength values.\n", "\n", "After the import lines, we need to create two variables: one variable for the ABS data and one variable for HIPS data. We will assign the data points as a list of numbers saved in two variables. The general format to create a list in Python is to use ```list_name = [item1, item2, item3]``` with square brackets on the outside and commas between the items. The items for the two lists came from the .xlsx file that contains the data ([3D-printed-tensile-bar-data.xlsx](https://github.com/ProfessorKazarinoff/staticsite/raw/master/content/code/matplotlib_plots/3D-printed_tensile_test_data.xlsx)). The tensile strength is in column [F] labeled [Tensile Strength (Mpa)]. Rows 2-17 contain data for ABS and rows 18-37 contain data for HIPS." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data\n", "ABS = [18.6, 21.6 ,22, 21, 18, 20.9, 21, 19.3, 18.8, 20, 19.4, 16, 23.8, 19.3, 19.7, 19.5]\n", "HIPS = [10.4, 4.9, 10.2, 10.5, 10.9, 12.9, 11.8, 8.4, 10, 10.6, 8.6, 9.7, 10.8, 10.7, 11, 12.4, 13.3, 11.4, 14.8, 13.5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the mean and standard deviation for each set of data\n", "\n", "We'll use the ```mean()``` and ```stdev()``` functions from the statistics module to find the mean (or average) and standard deviation of the two data sets. A summary of these two functions is below:\n", "\n", "\|statistics module function\| description \|\n", "\| --- \| --- \|\n", "\| ```mean()``` \| calculate the mean or average of a list of numubers \|\n", "\| ```stdev()``` \| calculate the standard deviation of a list of numbers \|\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find the mean using the mean() function from the statistics library\n", "ABS_mean = mean(ABS)\n", "HIPS_mean = mean(HIPS)\n", "\n", "# find the standard deviation using the stdev() function from the statistics library\n", "ABS_stdev = stdev(ABS)\n", "HIPS_stdev = stdev(HIPS)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build a simple bar plot\n", "\n", "Matplotlib's bar plot fuction can be accessed using ```plt.bar()```. We need to include at least two arguments as shown below:\n", "\n", "```\n", "plt.bar (['list', 'of' ,'bar', 'labels'], [list, of, bar, heights])\n", "```\n", "\n", "We will pass in ```['ABS', 'HIPS']``` for our list of bar labels, and ```[ABS_mean, HIPS_mean]``` for our list of bar heights. The command ```plt.show()``` will show the plot in a jupyter notebook or show the plot in a new window if running a .py file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fc6c8080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Build a bar plot\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add axis labels and title\n", "\n", "The plot looks pretty good, but we should add axis labels (with units) and a title to our plot. We can add the axis labels and titles with ```plt.xlabel()```, ```plt.ylabel()``` and ```plt.title()```. We need to pass in __strings__ enclosed in quotes ``` ' ' ``` with these methods. A summary of the matplotlib functions is below:\n", "\n", "\|matplotlib function\| description \|\n", "\| --- \| --- \|\n", "\| ```plt.bar()``` \| build a bar plot \|\n", "\| ```plt.xlabel()``` \| x-axis label \|\n", "\| ```plt.ylabel()``` \| y-axis label \|\n", "\| ```plt.title()``` \| plot title \|\n", "\| ```plt.show()``` \| show the plot \|\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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IsBPp5vQbRsQzkBIM79yWs2gTFr9F50xq3M4039xosqTJc+fO7cmwzcyshqb/wjzfJe1K4MSIeDndUbPr1arMqzqCY0SMJ91Uh46Ojm6P8jjklOu7u6ot42aM/Whvh2DWdpra8si35LwSuDgiOm9CP1vSRrl8I9IduirNZPH7O29KuiObmZm1gWZebSXgAuChiPhhoeg63rnv8mjSLUcr3QSMzPcEXw8YSf3bdJqZWQs1s+WxO3AEsKekqflvX9K9dT8s6THS/YHHAkjqkHQ+QETMI93z96789608z8zM2kDTznlExB1UP3cB8KEqy08Gji48ngBMaE50Zma2NPwLczMzK83Jw8zMSnPyMDOz0pw8zMysNCcPMzMrzcnDzMxKc/IwM7PSnDzMzKw0Jw8zMyvNycPMzEpz8jAzs9KcPMzMrDQnDzMzK83Jw8zMSnPyMDOz0pw8zMysNCcPMzMrrWl3EpQ0AdgPmBMR2+d5lwPb5EXWBV6MiKFV1p0BzAfeAhZFREez4jQzs/KaljyAXwPjgIs6Z0TEIZ3Tkn4AvFRn/RER8VzTojMzs25r5j3Mb5M0pFqZJAGfAvZsVv1mZtY8vXXO4wPA7Ih4rEZ5ADdLulvSmHobkjRG0mRJk+fOndvjgZqZ2ZJ6K3mMAi6tU757ROwM7AMcL2mPWgtGxPiI6IiIjoEDB/Z0nGZmVkXLk4ekFYEDgctrLRMRs/L/OcDVwLDWRGdmZo3ojZbHXsDDETGzWqGkNSSt1TkNjAQeaGF8ZmbWhaYlD0mXAn8DtpE0U9JRuehQKrqsJG0s6Yb8cEPgDkn3AncC10fEjc2K08zMymvm1Vajasw/ssq8WcC+efoJYMdmxWVmZkvPvzA3M7PSnDzMzKw0Jw8zMyvNycPMzEpz8jAzs9KcPMzMrDQnDzMzK83Jw8zMSnPyMDOz0pw8zMysNCcPMzMrzcnDzMxKc/IwM7PS6o6qK+l9wOGk28ZuBCwk3VvjeuCSiJjf9AjNzKzt1Ewekn4PPA9cC/wAmAOsCmwNjACul/TdiPh9KwI1M7P2Ua/lcVREzK6Y9xrpBk13AmdL2qBpkZmZWduqec6jSuKotsycng3HzMz6gi5PmEt6n6RJkl6S9Jqk1yW93MB6EyTNkfRAYd4Zkp6WNDX/7Vtj3b0lPSJpuqRTyu2SmZk1WyNXW/0MGA08AawFnAD8qIH1fg3sXWX+ORExNP/dUFkoqR9wLrAPsB0wStJ2DdRnZmYt0kjyWCEiHgFWjIg3I+KXwF5drRQRtwHzuhHTMGB6RDwREW8AlwH7d2M7ZmbWJI0kj1ckrQzcK+nbkr4ArLkUdZ4g6b7crbVelfJNgKcKj2fmeVVJGiNpsqTJc+fOXYqwzMysUY0kjyPzcicAbwFbAQd1s76fA1sAQ4FnSJcAV1KVeVFrgxExPiI6IqJj4MCB3QzLzMzK6OpHgu8lfdn3i4jHgG8uTWXFK7gk/RKo9huRmcCgwuNNgVlLU6+ZmfWsmi0PSacC1wCHARMlfXZpK5O0UeHhAaRfq1e6C9hK0ua5u+xQ4LqlrdvMzHpOvZbHYcAOEfGKpIHADcCERjcs6VJgODBA0kzgdGC4pKGkbqgZwDF52Y2B8yNi34hYJOkE4CagHzAhIqaV3jMzM2uaesnj9Yh4BSAi5koqNYhiRIyqMvuCGsvOAvYtPL6BlKzMzKwN1Use75Z0VZ4WsEXhMRFxYFMjMzOztlUveXyy4vG4ZgZiZmZ9R83kERG3tDIQMzPrO+oNyT6l3ooRsXPPh2NmZn1BvW6rlYE3gUtIN396vSURmZlZ26s3JPv2wBHAusDFpB8IbgE8GRGPtyY8MzNrR3Uvv42IByLi6xGxE/AHUivkyy2JzMzM2lZXw5O8CziENJbVAuBk4MoWxGVmZm2s3gnzW0hdVleQBkecWyhbOyK6vCGUmZktm+q1PLYhDSNyPPD5wnzl+YObGJeZmbWxer/z2LSVgZiZWd9Rb1TdQbXKcrnygIZmZracqddt9WNJbwLXAneTznmsCmwJjABGAt/C99owM1vu1Ou2OlDSDqSh2T8PbAS8CjxEGvF2r4hY2JIozcysrdS9VDci7gPua1EsZmbWR5S6R4eZmRk4eZiZWTc0LXlImiBpjqQHCvO+J+lhSfdJulrSujXWnSHpfklTJU1uVoxmZtY9DSUPSe+SNEzSbp1/Daz2a2DvinkTge0jYgfgUeC/6qw/IiKGRkRHIzGamVnr1D1hDiDp28DhwMPAW3l2ULjneDURcZukIRXzbi48nEQaM8vMzPqYLpMH6Xa0W0fEaz1c92eBy2uUBXCzpAB+ERHja21E0hhgDMDgwR4xxcysFRrptnqyweUaJunrwCLSfUKq2T3fqXAf4HhJe9TaVkSMj4iOiOgYOHBgT4ZpZmY11BtV9xxSC2A+cI+kP1K4m2BEnNSdCiWNBvYDPhQRUW2ZiJiV/8+RdDUwDLitO/WZmVnPq9dt1XmV1DTgxoqyql/6XZG0N/A14IMR8WqNZdYAVoiI+Xm6cxgUMzNrE/WGJ7kAQNIJETGuWCbphK42LOlSYDgwQNJM4HTS1VWrABMlAUyKiGPzAIvnR8S+wIbA1bl8ReCSiKhMXmZm1osaOWH+WWBcxbyjqsxbTESMqjL7ghrLziJfvRURTwA7NhCXmZn1knrnPA4BDgU2l3RVoWgt4MVmB2ZmZu2rXsvjTuB5YFPg3ML8+cA9zQzKzMzaW71zHk+SLtP9Y+vCMTOzvqCRX5i/wJJXV70ETAZOjogZTYjLzMzaWCMnzH8KzAYuAUQ6DzIQmA78inRXQTMzW440kjxGRsSuhcc/kzQpInaV9NVmBWZmZu2r0VF1D6yYVn74djOCMjOz9tZI8jgc+JykeZKeBz4HHCFpdeDEpkZnZmZtqctuq4iYThqgsJr/69lwzMysL2jkaqsBpF+ZDykuHxFjmheWmZm1s0ZOmF9LunHTHbxzMygzM1uONZI81oiILzc9EjOra8gp1/d2CNamZoz9aMvrbOSE+R8kjWx6JGZm1mc0kjyOBW6UtCBfcfWCpHnNDszMzNpXI91WA5oehZmZ9Sldtjwi4i3gYOBreXojYGizAzMzs/bVZfKQNI40ftURedarwHnNDMrMzNpbI+c8douIY4DXACJiHrByIxuXNEHSHEkPFOatL2mipMfy//VqrDs6L/OYpNGN1GdmZq3RSPJ4U9IK5GHZJfWn8TGtfg3sXTHvFOCWiNgKuCU/Xoyk9Un3PN8FGAacXivJmJlZ6zWSPM4FrgQGSjqT9GPBsxvZeETcBlRembU/cGGevhD4RJVVPwJMjIh5EfECMJElk5CZmfWSRsa2ukjS3cBepNF0D46IB7pYrZ4NI+KZvO1nJG1QZZlNgKcKj2fmeUuQNAYYAzB48OClCMvMzBpVN3lI6gdMiYgdgWmtCSlVXWVe5d0M08yI8cB4gI6OjqrLmJlZz6rbbZUvzX1QUtWj/m6aLWkjgPx/TpVlZgKDCo83BWb1YAxmZrYUGjnnMQB4SNJNkq7q/FuKOq8DOq+eGk0aeLHSTcBISevlE+Uj8zwzM2sDjfzCfGx3Ny7pUmA4MEDSTNIVVGOB30g6Cvgn6QeISOoAjo2IoyNinqSzgLvypr6VLxE2M7M20Ejy+FBEnFqcIenbpMts64qIUbW2WWXZycDRhccTgAkNxGdmZi3WSLdVtUtkWz/+r5mZtY2aLQ9Jx5BG1N1a0pRC0VrA5GYHZmZm7atet9VvSF1T32HxX4HPj4hqV0iZmdlyol631ULgyYg4OCIeJ/32Yl9g15ZEZmZmbate8rgJ2AJA0hbAncB2wEn5hLmZmS2n6iWP9SPi0Tw9GrgsIo4jjTv1saZHZmZmbate8igO9bEnaXBCIuJ1Gh9V18zMlkH1TphPkzQWeBrYGrgZQNI6VB97yszMlhP1Wh5HAwuAbYG9I+KVPH974IfNDszMzNpXzZZHThb/XWX+X4C/NDMoMzNrb438wtzMzGwxTh5mZlZaw8lD0irNDMTMzPqOLpOHpGGS7gcey493lPTTpkdmZmZtq5GWx0+A/YDnASLiXmBEM4MyM7P21kjyWCEi/lEx761mBGNmZn1DIzeDekrSMCAk9QO+ADzaxTpmZrYMa6TlcRxwEjAYmE0aVfe47lYoaRtJUwt/L0s6sWKZ4ZJeKixzWnfrMzOzntdlyyPfu+PQnqowIh4BhgLklszTwNVVFr09IvbrqXrNzKzn1LuT4DksPjjiYiLipB6o/0PA41XOqZiZWRur1/J4oAX1HwpcWqPs/ZLuBWYBX4mIadUWkjQGGAMwePDgpgRpZmaLqze21QXNrFjSysDHgf+qUjwF2CwiFkjaF7gG2KradiJiPDAeoKOjo2ZLyczMek69bqsfRMSXJV1Nle6riDhwKeveB5gSEbOrbPvlwvQNkn4maUBEPLeUdZqZWQ+o1211ef4/rkl1j6JGl5WkdwGzIyLyZcIrkH+kaGZmva9et9Wd+f8tnfPyjaA2iYgHl6ZSSasDHwaOKcw7Ntd3HnAQcJykRcBC4NCIcJeUmVmb6PJSXUm3AAcA/YB7gXmSJkbEyd2tNCJeBfpXzDuvMD2O5rV4zMxsKTXyI8H18zmIA4ELI2Io8JHmhmVmZu2skeSxoqSBwMHA75ocj5mZ9QGNJI//Af4P+GdE3Cnp3cCTzQ3LzMzaWSPDk1wGXFZ4/ASwfzODMjOz9tbICfMBwGeBIcXlI2JM88IyM7N21siQ7NcCk4A78H08zMyMxpLHGhHx5aZHYmZmfUYjJ8z/IGlk0yMxM7M+o5HkcSxwo6QFkuZJekHSvGYHZmZm7auRbqsBTY/CzMz6lC5bHhHxFukHgl/L0xuR7wRoZmbLpy6Th6RxwAjgiDzrVeC82muYmdmyrpFuq90iYmdJ9wBExLx8IyczM1tONXLC/E1JK5BvCCWpP/B2U6MyM7O2VjN5SOpslZwLXAkMlHQm6ceCZ7cgNjMza1P1uq3uBHaOiIsk3Q3sBQg4OCIeaEl0ZmbWluolD3VORMQ0YFrzwzEzs76gXvIYKOmkWoUR8cOlqVjSDGA+abysRRHRUVEu4MfAvqQrvI6MiClLU6eZmfWMesmjH7AmhRZIE4yIiOdqlO0DbJX/dgF+nv+bmVkvq5c8nomIb7UskiXtD1wUEQFMkrSupI0i4plejMnMzKh/qW4zWxyQLv29WdLdkqrdG2QT4KnC45l53mIkjZE0WdLkuXPnNilUMzMrqpc8PtTkunePiJ1J3VPHS9qjorxa8oolZkSMj4iOiOgYOHBgM+I0M7MKNZNHRDR15NyImJX/zwGuBoZVLDITGFR4vCkwq5kxmZlZYxr5hXmPk7SGpLU6p4GRQOVvR64D/kPJrsBLPt9hZtYeGhnbqhk2BK5OV+OyInBJRNwo6ViAiDgPuIF0me500qW6n+mlWM3MrEKvJI+IeALYscr88wrTARzfyrjMzKwxvdJtZWZmfZuTh5mZlebkYWZmpTl5mJlZaU4eZmZWmpOHmZmV5uRhZmalOXmYmVlpTh5mZlaak4eZmZXm5GFmZqU5eZiZWWlOHmZmVpqTh5mZlebkYWZmpTl5mJlZaU4eZmZWWsuTh6RBkm6V9JCkaZK+WGWZ4ZJekjQ1/53W6jjNzKy23rgN7SLgyxExRdJawN2SJkbEgxXL3R4R+/VCfGZm1oWWtzwi4pmImJKn5wMPAZu0Og4zM+u+Xj3nIWkIsBPw9yrF75d0r6Q/SHpPnW2MkTRZ0uS5c+c2KVIzMyvqteQhaU3gSuDEiHi5ongKsFlE7Aj8FLim1nYiYnxEdEREx8CBA5sXsJmZ/UuvJA9JK5ESx8URcVVleUS8HBEL8vQNwEqSBrQ4TDMzq6E3rrYScAHwUET8sMYy78rLIWkYKc7nWxelmZnV0xtXW+0OHAHcL2lqnncqMBggIs4DDgKOk7QIWAgcGhHRC7GamVkVLU8eEXEHoC6WGQeMa01EZmZWln9hbmZmpTl5mJlZaU4eZmZWmpOHmZmV5uRhZmalOXmYmVlpTh5mZlaak4eZmZXm5GFmZqU5eZiZWWlOHmZmVpqTh5mZlebkYWZmpTl5mJlZaU6OwewwAAAIZklEQVQeZmZWmpOHmZmV5uRhZmal9UrykLS3pEckTZd0SpXyVSRdnsv/LmlI66M0M7NaWp48JPUDzgX2AbYDRknarmKxo4AXImJL4Bzg7NZGaWZm9fRGy2MYMD0inoiIN4DLgP0rltkfuDBP/xb4kKS69z03M7PWWbEX6twEeKrweCawS61lImKRpJeA/sBzlRuTNAYYkx8ukPRIj0e8/BlAled6eSW3e9uV36fZUr5HN+vOSr2RPKq1IKIby6SZEeOB8UsblL1D0uSI6OjtOMzq8fu0d/VGt9VMYFDh8abArFrLSFoRWAeY15LozMysS72RPO4CtpK0uaSVgUOB6yqWuQ4YnacPAv4UEVVbHmZm1not77bK5zBOAG4C+gETImKapG8BkyPiOuAC4H8lTSe1OA5tdZzLOXcDWl/g92kvkg/ozcysLP/C3MzMSnPyMDOz0pw8llOSDpAUkrbNj4dIWihpqqR7Jf1V0ja5bHVJF0u6X9IDku6QtGbv7oEtiyQtqHh8pKRxefoMSV/J07+W9GR+v06R9P48f9c8pNFUSQ9JOqPlO7GccPJYfo0C7mDxixEej4ihEbEj6Rf+p+b5XwRmR8R7I2J70vAxb7Y0WrMlnRwRQ4FTgF/keRcCY/L87YHf9FZwyzonj+VQbjXsTkoCta5kWxt4IU9vBDzdWRARj0TE600N0qxxtwFb5ukNgGcAIuKtiHiw16JaxvXGL8yt930CuDEiHpU0T9LOpEuit5A0FVgLWJ13ho2ZANws6SDgFuDCiHisNwK3Zd5q+T3YaX2W/B1YpY8B9+fpc4BHJP0ZuJH0Xn2tx6M0tzyWU6NIA1KS/4/K053dVlsAJ5Kvo4+IqcC7ge+RPsx3Sfq31oZsy4mF+T04NHc9nVZn2e/lRDOG1IomIr4FdAA3A58mJRBrArc8ljOS+gN7AttLCtIPNQP4WcWi1wG/6nwQEQuAq4CrJL0N7As81JKgzao7OSJ+WzkzIh4Hfi7pl8BcSf0j4vnWh7dsc8tj+XMQcFFEbBYRQyJiEPAkaYyxon8HHgeQtLuk9fL0yqT7sPyjhTGbNUTSRwu3b9gKeAt4sRdDWma55bH8GQWMrZh3JenKqs5zHgLeAI7O5VuQjuREOuC4Pq9j1m6OAM6R9CqwCDgsIt7q5ZiWSR6exMzMSnO3lZmZlebkYWZmpTl5mJlZaU4eZmZWmpOHmZmV5uRhPULSqpLuzCPyTpN0ZqHsz5IekXSfpIcljZO0bg/U+dcGljlR0upLWc8Zkp7OI7VOlTQ2zz9f0nZ5eoakAXl6Qb3tNZOkU+uUzZB0e8W8qZIe6GKbQyR9upvxNPIa9drzZd3n5GE95XVgzzwi71Bgb0m7FsoPi4gdgB3ystd2tyJJ/QAiYrcGFj+RNE5X6e1XOKcwbMYpuf6j23DgvZrJI1tL0iCAEkPMDCEN9dGwkq+R9UFOHtYjIuk8glwp/y3xI6KIeAP4KjBY0o6V5fko/38l/UnSY5I+l+cPl3SrpEvIg+B1HrHmsj9L+m1u2Vys5D+BjYFbJd2alx0p6W/5HhBXdN6XJB+VnybpDuDgRvY519lRp3xNSbfkuu6XtH+ePyTHeX6+P8rFkvaS9Je8z8PycmtImiDpLkn3FNY/UtJVkm7My383zx9LHlhQ0sU1wvoNcEieHgVcWoh3iKTbc7xTJHV+8Y8FPpC3+yVJ/SR9L8d1n6RjGniNqj4X1odFhP/81yN/pHGypgILgLML8/8MdFQsew1wSJVtnAHcC6wGDACeIiWA4cArwOaFZRfk/8OBl0hDrKwA/A3491w2AxiQpweQhu9eIz/+GnBaYbmv1tivM0hD0k/Nfx+p3K+KejrjWhFYu1D3dNKv94eQfv383hzv3aSRiwXsD1yT1/k2cHieXhd4FFgDOBJ4AlgHWJU0VMygYt019mMGsDXw1/z4HtJQMw/kx6sDq+bprYDJhef394XtjAG+kadXASYDm3fxGlV9LrqK2X/t++fhSazHRBoGYmg+n3G1pO0jolZ/umrMB7g2IhYCC3OLYRhpfKI7I+LJGuvcGREzIfXjk76g76hYZlfSl+Vf8vBHK5MSTafL68R0TkR8v055NQK+LWkP4G1gE2DDXPZkRHQenU8DbomIkHR/jh1gJPBx5bvnkRLF4Dx9S0S8lNd/ENiMlGi7Mg94QdKhpIEtXy2UrQSMkzSUNCbU1jW2MRLYQWmIfkhJbCvSkDa1XqNaz8WzDcRsbcjJw3pcRLyodD+FvYElkkfuD38v8JCk44HP5aJ9OzdRucn8/5U61RZvTvUW1d/bAiZGxKgqZV1tvzsOAwYC/19EvClpBikBwOLxvl14/DbvxC7gkxHxSHGjknahsf2t5XLgXFILpuhLwGxgR1KLqNZ9MAR8ISJuqohrOLWfw3rPhfVBPudhPULSwNziQNJqwF7Aw1WWWwn4DvBURNwXEefGOyeiZ+XF9le6eqs/qSvkrqUIbT7p5lYAk4DdJW2ZY1ldUq2j656wDjAnf1mOILUOyrgJ+IJyM0nSTg2s82Z+juu5Gvhu3n5lvM9ExNukAQY7LxwoPoedcR3XWY+krSWt0UWdS/tcWJtx8rCeshHpxPR9pC/7iRHx+0L5xbnsAVK/fb0TpneSRu6dBJxVSCrdMR74g6RbI2Iu6Wj70hzLJGDbpdh2Vy4GOiRNJh15L5FMu3AWqSvpPqXLac9qYJ3xeflaJ8yJiPkRcXakixeKfgaMljSJ1GXV2Yq4D1ikdBn2l4DzgQeBKTmuX9B1y2dpnwtrMx5V19qKpDNIJ1DLnl8wsxZyy8PMzEpzy8PMzEpzy8PMzEpz8jAzs9KcPMzMrDQnDzMzK83Jw8zMSvt/qYLKAow3DyUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fc6c8160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build a bar plot\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean])\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add error bars to the plot\n", "\n", "We have a nice looking bar plot with two bars, x-axis label, y-axis label and a title. Next we will add error bars to the plot. We will add the error bars by passing a __keyword argument__ in the ```plt.bar()``` function. The __keyword argument__ is ```yerr = [list, of, error, bar, lengths]```. A __keyword argument__ is a specific type of argument passed to a function or method that must have a name associated with it. Regular function arguments just need to be in the proper order. __Keyword arguments__ need to be pass with the form ```keyword_argument_name = value```. The general form of the entire ```plt.bar()``` line will be:\n", "\n", "```\n", "plt.bar (['list', 'of' ,'bar', 'labels'], [list, of, bar, hights], yerr=[list, of, error, bar, lengths])\n", "```\n", "\n", "The first two arguments, ```['list', 'of' ,'bar', 'labels']``` and ```[list, of, bar, hights]``` just need to be in the correct order. The third argument, a __keyword argument__ needs to include ```yerr = ```. \n", "\n", "Our list of error bar lengths will contain the standard deviation for each set of data, ```ABS_stdev``` and ```HIPS_stdev```.\n", "\n", "```\n", "yerr=[ABS_stdev, HIPS_stdev]\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fca6e550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build a bar plot\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean],yerr=[ABS_stdev, HIPS_stdev])\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add \"caps\" to the error bars\n", "\n", "The error bars are on the plot, but they are just vertical lines. Typically, error bars have a horizontal lines at the top and bottom and look sort of like the capital letter I.\n", "\n", "We can add these horizontal lines or \"caps\" to the top and bottom of the error bars by passing an additional __keyword argument__ to the ```plt.bar()``` function called ```capsize=```. We will set the ```capsize=10```, which is a good size for this plot. You can change the ```capsize=``` number to make the horizontal lines longer or shorter.\n", "\n", "Now our ```plt.bar()``` function call contains 4 different arguemnts:\n", "\n", "```\n", "plt.bar (['list of bar labels'], [list of bar hights], yerr = [list of error bar lengths], capsize = width)\n", "```\n", "\n", "A summary of the arugments passed to the plt.bar() function is below:\n", "\n", "\|plt.bar() Arguments \| description \|\n", "\|:------:\|:------\|\n", "\| ```[list of bar labels]``` \| 1st argument, a list of strings which provide the labels below the bars \|\n", "\| ```[list of bar heights]``` \| 2nd argument, a list of numbers which determines the height of each bar \|\n", "\| ```yerr = [list of error bar lengths]``` \| a keyword argument, must include ```yerr = ```. Denotes the height of the error bars. Needs to be a list of numbers \|\n", "\| ```capsize = width``` \| a keyword argument, must include ```capsize = ```. Denotes the width of the error bar horizontal \"caps\". Needs to be a number, not a string \|" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fcad1ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build the bar plot\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean],yerr=[ABS_stdev, HIPS_stdev], capsize=10)\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the plot\n", "\n", "The plot looks complete: two bars, x and y axis labels, title and error bars with caps. Now let's save the plot as an image file so we can import the plot into a Word document or PowerPoint presentation. If you are using a jupyter notebook, you can just right-click on the plot and select [copy image] or [Save Image As...]. To save a plot as an image programmatically, we use the line:\n", "\n", "```\n", "plt.savefig('filename.extension')\n", "```\n", "\n", "Matplotlib will save the plot as an image file using the file type we specify in the filename extension. For example, if we call ```plt.savefig('plot.png')```, the plot will be saved as a .png image. If we call ```plt.savefig('plot.jpg')``` the plot will be saved as a .jpeg image." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fcb238d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build a bar plot and save it as a .png image\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean],yerr=[ABS_stdev, HIPS_stdev], capsize=10)\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.savefig('plot.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Increase the .png file image resolution\n", "\n", "Depending on how the .png image file is viewed: in a jupyter notebook, on the web, in a Word document or in a PowerPoint presentation, the image may look a little blurry. This is because the .png image we created has a fairly low resolution. We can change the resolution by coding:\n", "\n", "```\n", "plt.savefig('filename.png', dpi = 300)\n", "```\n", "\n", "Where ```dpi=300``` specifies a resolution of 300 dots per square inch. We can specify a higher resolution or lower resoltution then 300 dpi. A higher resolution will increase the image file size, but will look better when magnified.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fcb4f668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# build a bar plot and save it as a .png image\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean],yerr=[ABS_stdev, HIPS_stdev], capsize=10)\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.savefig('plot.png', dpi = 300)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The full script\n", "\n", "A summary of the full script is below:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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FpKsl3Zv/b96uGMzMrOfa2WJ4AfhcRLwK2Av4hKRXAycCUyJiR2BKfm1mZn1E2xJDRMyNiFvy8GLgTmAr4CDgnDzZOcB72hWDmZn1XEeuMUgaDuwB/AN4WUTMhZQ8gC3rzDNe0jRJ0xYsWNCJMM3MDFi73RVI2hi4EDguIp6S1NJ8EXEWcBak32Pobf3DT7yit7P2O48+sBDwNqmYM/Gd3Q7BrE9qa4tB0jqkpDA5Ii7Ko+dJGprLhwLz2xmDmZn1TDvvShLwC+DOiPhuoegy4Kg8fBRwabtiMDOznmtnV9I+wJHAbZJm5HEnAROB30o6GvgXcGgbYzAzsx5qW2KIiOuBehcU3tKues3MbOX4m89mZlbixGBmZiVODGZmVuLEYGZmJU4MZmZW4sRgZmYlTgxmZlbixGBmZiVODGZmVuLEYGZmJU4MZmZW4sRgZmYlbf+hHuuMJ66fzJM3nNfStA9OOrDpNJvtM5aB+45b2bDMbDXkxNBPDNx3nA/kZrZKuCvJzMxKnBjMzKzEicHMzEqcGMzMrMSJwczMSpwYzMysxInBzMxKnBjMzKyk4RfcJL0OOAJ4AzAUWArMAq4Azo2IxW2P0MzMOqpuYpB0ObAQuBT4DjAfWB/YCRgDXCHptIi4vBOBmplZZzRqMRwdEfOqxj0L3JT/Jknasm2RmZlZV9S9xlAjKdSaZv6qDcfMzLqt6cVnSa+TNFXSk5KelfScpKc6EZyZmXVeK09X/RHpAvT5wJ7AB4FhbYzJzMy6qJXbVdeKiLuBtSNiWUT8DNivzXGZmVmXtNJieFrSusCtkk4F5gIbtzcsMzPrllZaDB/M030SWA7sCLyvjTGZmVkXNfuC22uB7YEBEXEv8OWORGVmZl1Tt8Ug6STgEmAccLWkD3csKjMz65pGLYZxwG4R8bSkIcAfgLM7E5aZmXVLo2sMz0XE0wARsaDJtGZm1k80ajG8UtJFeVjA9oXXRMQhbY3MzMy6olFieG/V6zPaGYiZmfUNdRNDREzpZCBmZtY3NHrs9i2NZoyIkas+HDMz67ZGXUnrAsuAc0k/zPNcTxYs6WzgQGB+ROyax00AjgEW5MlOiog/9DBmMzNro0aP3d4VOBIYCEwmfblte2B2RNzfwrJ/BRxQY/zpETEi/zkpmJn1MQ1vQY2IWRHxxYjYA/gjqfXwuVYWHBF/AxatfIhmZtZJDRODpJdL+oyk60jPTDoB+OlK1vlJSTMlnS1p8wZ1j5c0TdK0BQsW1JvMzMxWsUaPxJhCurawASkpHAZcmMs27WV9PyZ1R40gPaX1O/UmjIizImJURIwaMmRIL6szM7OeanTxeWcggE8AHy+MVx6/TU8rK/5cqKSfAZf3dBlmZtZejb7HsPWqrkzS0IiYm18eDMxa1XWYmdnKafQ9hmER8VCDcgFDI+KROuXnAaOBwZIeBr4KjJY0gtTimAN8tPehm5lZOzTqSvq+pGXApcDNpO8erA/sAIwB9ge+BtRMDBExtsboX6xUtGZm1naNupIOkbQb6fHbHweGAs8Ad5Iewb1fRCztSJRmZtYxDX/BLSJmAjM7FIuZmfUB/o0FMzMrcWIwM7MSJwYzMytpeI2hQtLLSV9o+8/0EXFju4IyM7PuaZoYJJ0KHAHcBSzPowN4RxvjMjOzLmmlxfBeYKeIeLbdwZiZWfe1co1hdovTmZlZP9DokRink7qMFgPTJf2Zwq+4RcTx7Q/PzMw6rVFXUuUBd7cDV1aVRXvCMTOzbmv0SIxfAEj6ZEScUSyT9Ml2B2ZmZt3RyrWDD9cYd/SqDsTMzPqGRr/gdpiki4HtJF1U+LsaeKJzIZpZfzBhwgQkrbK/CRMmdHuV+q1G1xhuAhYCWwNnFsYvBqa3Mygz638mTJjQ9GA+evRoAK699tq2x2P1NbrGMJt0q+qfOxeOmZl1WyvffH6cFe9CehKYBpwQEXPaEJeZmXVJK998/iEwDzgXEHA4MAS4D/gl6dfczMysn2glMewfEXsVXv9I0tSI2EvS59sVmJmZdUdLj7qQdEjVsPLLF9sRlJmZdU8rieEI4BhJiyQtBI4BjpS0IXBcW6MzM7OOa9qVFBH3AW+vU/zXVRuOmZl1Wyt3JQ0mfft5OOUf6hnfvrDMzKxbWrn4fCkwFbiel36ox8zM+qlWEsNGEfG5tkdiZmZ9QisXn/8oaf+2R2JmZn1CK4nhWOBKSUvynUmPS1rU7sDMzKw7WulKGtz2KMzMrM9o2mKIiOXAocAX8vBQYES7AzMzs+5omhgknUF6HtKRedQzwE/aGZSZmXVPK11Je0fESEnTASJikaR12xyXmZl1SSsXn5dJWov86G1Jg/AzkszM+q1WEsOZwIXAEEknk77oNqmtUZmZWde08qykX0u6GdiP9FTVQyNiVtsjMzOzrmiYGCQNAG6JiN2B2zsTktmaZfiJV3Q7hD7j0QcWAt4mRXMmvrPjdTbsSsq3p94haasOxWNmZl3W6hfc7pT0d+DpysiIOKT+LGZmtrpqJTFMbHsUZmbWZ7RyV9JbImJK8Q94S7OZJJ0tab6kWYVxW0i6WtK9+f/mKxO8mZmteq0khgNqjGvlasivasx7IjAlInYEpuTXZmbWh9RNDJI+mr/tvLOkWwp/9wJ3NFtwRPwNqH4K60HAOXn4HOA9vYzbzMzapNE1ht+Szuq/SfnMfnFEzO9lfS+LiLkAETFX0pa9XI6ZmbVJo66kpcDsiDg0Iu4nfbntHcBenQhM0nhJ0yRNW7BgQSeqNDMzGieGq4DtASRtD9wEvBo4XtKpvaxvnqSheZlDgbotj4g4KyJGRcSoIUOG9LI6MzPrqUaJYYuIuCcPHwWcHxEfA94GvKuX9V2Wl1VZ5qW9XI6ZmbVJo8QQheE3A1cDRMRztPB0VUnnAX8nXbx+WNLRpO9EvDVfwH4r/o6EmVmf0+ji8+2SJgL/BnYC/gQgaTPS9YaGImJsnaKm34EwM7PuadRi+AiwBNgFOCAiKo/D2BX4brsDMzOz7qjbYsiJ4Os1xt8A3NDOoMzMrHta+eazmZmtQZwYzMyspOXEIGm9dgZiZmZ9Q9PEIGlPSbcB9+bXu0v6YdsjMzOzrmilxfAD4EBgIUBE3AqMaWdQZmbWPa38UM9aEfGgVPrqwvI2xWNm/dQT10/myRvOa2naBycd2HSazfYZy8B9x61sWFZDK4nhIUl7AiFpAPAp4J4m85iZlQzcd5wP5KuJVrqSPgYcD2wDzCM9XfVj7QzKzMy6p2mLIf/2wuEdiMXMzPqAuolB0umUH6RXEhHHtyUiMzPrqkYthlkdi8LMzPqMRs9K+kUnAzEzs76hUVfSdyLic5IupkaXUkQc0tbIzMysKxp1Jf0m/z+jE4GYmVnf0Kgr6ab8f0plXP6Rnq0i4o4OxGZmZl3QyrOSpkjaVNLmwG3AuZK+1f7QzMysG1r5gtsWEfEUcAhwTkSMAN7W3rDMzKxbWkkMa0saAhwK/L7N8ZiZWZe1khi+AfwV+FdE3CTplcDs9oZlZmbd0sojMc4Hzi+8fgA4qJ1BmZlZ9zRNDJIGAx8Ghhenj4jx7QvLzMy6pZXHbl8KTAWux7/DYGbW77WSGDaKiM+1PRIzM+sTWrn4/EdJ+7c9EjMz6xNaSQzHAldKWiJpkaTHJS1qd2BmZtYdrXQlDW57FGZm1mc0bTFExHLSl9u+kIeHAiPaHZiZmXVHK89KOgMYAxyZRz0D/KSdQZmZWfe00pW0d0SMlDQdICIWSVq3zXGZmVmXtHLxeZmktcg/1iNpEPBiW6MyM7OuqZsYJFVaE2cCFwJDJJ1M+qLbpA7EZmZmXdCoK+kmYGRE/FrSzcB+gIBDI2JWR6IzM7OOa5QYVBmIiNuB29sfjpmZdVujxDBE0vH1CiPiu22Ix8zMuqxRYhgAbEyh5WBmZv1fo8QwNyK+1rFIzMysT2h0u6pbCmZma6BGLYa3tKtSSXOAxaTfd3ghIka1qy4zM+uZuokhItr9BNUxEfFYm+swM7MeauWbz2ZmtgbpVmII4E+SbpZU87ejJY2XNE3StAULFnQ4PDOzNVe3EsM+ETESeDvwCUlvrJ4gIs6KiFERMWrIkCGdj9DMbA3VlcQQEY/k//OBi4E9uxGHmZmtqOOJQdJGkjapDAP7A372kplZH9HK7zGsai8DLpZUqf/ciLiyC3GYmVkNHU8MEfEAsHun6zUzs9b4dlUzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK+lKYpB0gKS7Jd0n6cRuxGBmZrV1PDFIGgCcCbwdeDUwVtKrOx2HmZnV1o0Ww57AfRHxQEQ8D5wPHNSFOMzMrIa1u1DnVsBDhdcPA6+vnkjSeGB8frlE0t0diG1NMBh4rNtB9AWa1O0IrA7vowUruZ9u25uZupEYVGNcrDAi4izgrPaHs2aRNC0iRnU7DrN6vI92Xze6kh4GhhVebw080oU4zMyshm4khn8CO0raTtK6wOHAZV2Iw8zMauh4V1JEvCDpk8BVwADg7Ii4vdNxrMHcPWd9nffRLlPECt37Zma2BvM3n83MrMSJwczMSpwY+hlJB0sKSbvk18MlLZU0Q9Ktkm6UtHMu21DSZEm3SZol6XpJG3d3Day/krSk6vUHJZ2RhydI+p88/CtJs/M+e4uk/8rj95L0jzz+TkkTOr4Sawgnhv5nLHA96W6vivsjYkRE7A6cA5yUx38GmBcRr42IXYGjgWUdjdasthMiYgRwIvDTPO4cYHwevyvw224F1985MfQj+Wx/H9IB/vA6k20KPJ6HhwL/rhRExN0R8VxbgzTrmb8BO+ThLYG5ABGxPCLu6FpU/Vw3vvls7fMe4MqIuEfSIkkjgUXA9pJmAJsAG/LSI0jOBv4k6X3AFOCciLi3G4HbGmGDvB9WbEHz7zC9C7gtD58O3C3pWuBK0v767CqP0txi6GfGkh5KSP4/Ng9XupK2B44j3yceETOAVwLfIn1I/ynpVZ0N2dYgS/N+OCJ3B32lwbTfyklkPKkFTER8DRgF/An4ACk5WBu4xdBPSBoEvBnYVVKQvjwYwI+qJr0M+GXlRUQsAS4CLpL0IvAO4M6OBG1W3wkR8bvqkRFxP/BjST8DFkgaFBELOx9e/+YWQ//xPuDXEbFtRAyPiGHAbNKzqIr2Be4HkLSPpM3z8Lqk38d4sIMxm7VM0jslVR7CuSOwHHiiiyH1W24x9B9jgYlV4y4k3YFUucYg4HngI7l8e9LZl0gnCVfkecz6oiOB0yU9A7wAjIuI5V2OqV/yIzHMzKzEXUlmZlbixGBmZiVODGZmVuLEYGZmJU4MZmZW4sRgDUlaX9JN+cmst0s6uVB2raS7Jc2UdJekMyQNXAV13tjCNMdJ2nAl65kg6d/5aZ0zJE3M438u6dV5eI6kwXl4SaPltZOkkxqUzZF0XdW4GZJmNVnmcEkf6GU8rbxHXdtetnKcGKyZ54A35yezjgAOkLRXoXxcROwG7JanvbS3FUkaABARe7cw+XGk5z71ePlVTi88puHEXP9H+uAD2uomhmwTScMAevBYk+GkR0u0rIfvka2mnBisoUgqZ37r5L8VvvwSEc8Dnwe2kbR7dXk+O/9fSX+RdK+kY/L40ZKukXQu+WFplTPNXHatpN/lFslkJZ8GXgFcI+maPO3+kv6en99/QeV3JfLZ9FckXQ8c2so65zpHNSjfWNKUXNdtkg7K44fnOH+ef99isqT9JN2Q13nPPN1Gks6W9E9J0wvzf1DSRZKuzNOflsdPJD+ATtLkOmH9FjgsD48FzivEO1zSdTneWyRVDuoTgTfk5X5W0gBJ38pxzZT00Rbeo5rbwlZzEeE//zX8Iz13aQawBJhUGH8tMKpq2kuAw2osYwIZsDw8AAADOElEQVRwK7ABMBh4iHRwHw08DWxXmHZJ/j8aeJL0WI+1gL8D++ayOcDgPDyY9HjmjfLrLwBfKUz3+TrrNYH02PEZ+e9t1etVVU8lrrWBTQt130f6Vvlw0jdyX5vjvZn0BFsBBwGX5HlOBY7IwwOBe4CNgA8CDwCbAeuTHk8yrFh3nfWYA+wE3JhfTyc93mRWfr0hsH4e3hGYVti+lxeWMx74Uh5eD5gGbNfkPaq5LZrF7L++/edHYlhTkR47MCJfP7hY0q4RUa//WnXGA1waEUuBpflMf0/Ss25uiojZdea5KSIehtRvTjr4Xl81zV6kA+EN+VE665KSSMVvGsR0ekR8u0F5LQJOlfRG4EVgK+BluWx2RFTOqm8HpkRESLotxw6wP/Bu5V8sIyWBbfLwlIh4Ms9/B7AtKYk2swh4XNLhpIcgPlMoWwc4Q9II0vOFdqqzjP2B3ZQeww4pQe1IeoxKvfeo3rZ4tIWYrY9yYrCWRcQTSs/CPwBYITHk/ufXAndK+gRwTC56R2UR1YvM/59uUG3xh4OWU3ufFXB1RIytUdZs+b0xDhgC/J+IWCZpDungDuV4Xyy8fpGXYhfw3oi4u7hQSa+ntfWt5zfAmaSWR9FngXnA7qSWTL3fMBDwqYi4qiqu0dTfho22ha2mfI3BGpI0JLcUkLQBsB9wV43p1gG+CTwUETMj4sx46aLuI3myg5TuchpE6p7450qEtpj0w0MAU4F9JO2QY9lQUr2z4lVhM2B+PhCOIZ3V98RVwKeUmzeS9mhhnmV5GzdyMXBaXn51vHMj4kXSg+gqF+GL27AS18cq9UjaSdJGTepc2W1hfZATgzUzlHSRdybpQH51RFxeKJ+cy2aR+skbXXy8ifQE16nAKYWE0RtnAX+UdE1ELCCdJZ+XY5kK7LISy25mMjBK0jTSGfMKibKJU0jdOzOVbik9pYV5zsrT17v4TEQsjohJkW4EKPoRcJSkqaRupMrZ/0zgBaVbkT8L/By4A7glx/VTmrdYVnZbWB/kp6taR0iaQLoY2dP+fDPrMLcYzMysxC0GMzMrcYvBzMxKnBjMzKzEicHMzEqcGMzMrMSJwczMSv4/ORSF34RgD1AAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1e7fcbba5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# import packages\n", "from statistics import mean, stdev\n", "import matplotlib.pyplot as plt\n", "#include if using a jupyter notebook, remove if using a .py file\n", "%matplotlib inline \n", "\n", "# data\n", "ABS = [18.6, 21.6 ,22, 21, 18, 20.9, 21, 19.3, 18.8, 20, 19.4, 16, 23.8, 19.3, 19.7, 19.5]\n", "HIPS = [10.4, 4.9, 10.2, 10.5, 10.9, 12.9, 11.8, 8.4, 10, 10.6, 8.6, 9.7, 10.8, 10.7, 11, 12.4, 13.3, 11.4, 14.8, 13.5]\n", "\n", "# find the mean using the mean() function from the statistics library\n", "ABS_mean = mean(ABS)\n", "HIPS_mean = mean(HIPS)\n", "\n", "# find the standard deviation using the stdev() function from the statistics library\n", "ABS_stdev = stdev(ABS)\n", "HIPS_stdev = stdev(HIPS)\n", "\n", "# build a bar plot and save it as a .png image\n", "plt.bar(['ABS', 'HIPS'],[ABS_mean, HIPS_mean],yerr=[ABS_stdev, HIPS_stdev], capsize=10)\n", "plt.xlabel('3D-printer Fillament Material')\n", "plt.ylabel('Tensile Strength (MPa)')\n", "plt.title('Tensile Strength of 3-D Printed ABS and HIPS Tensile Bars')\n", "plt.savefig('plot.png', dpi = 300)\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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
martinjrobins/hobo	examples/optimisation/nelder-mead.ipynb	1	323158	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimisation: Nelder-Mead\n", "\n", "This example shows you how to run a local optimisation with the [Nelder-Mead](http://pints.readthedocs.io/en/latest/optimisers/nelder_mead.html) downhill simplex method.\n", "\n", "The [Nelder-Mead method](https://en.wikipedia.org/wiki/Nelder–Mead_method) is a classical (deterministic) derivative-free optimisation method. It can be very fast if started near the true solution, but can easily get stuck on difficult problems. Nelder-Mead is essentially sequential in nature and can not easily be parallelised." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimising error measure\n", "Using Nelder-Mead\n", "Running in sequential mode.\n", "Iter. Eval. Best Time m:s\n", "0 3 865.9531 0:00.0\n", "1 4 832.5452 0:00.0\n", "2 5 832.5452 0:00.0\n", "3 6 628.243 0:00.0\n", "20 23 4.95828 0:00.0\n", "40 43 3.525867 0:00.0\n", "60 63 2.377579 0:00.0\n", "80 83 1.114115 0:00.0\n", "100 103 0.551 0:00.0\n", "120 123 0.237 0:00.0\n", "140 143 0.0666 0:00.0\n", "160 163 0.00181 0:00.0\n", "180 183 6.96e-06 0:00.0\n", "200 203 2.66e-08 0:00.0\n", "220 223 5.06e-11 0:00.0\n", "240 243 2.43e-15 0:00.0\n", "260 263 5.58e-18 0:00.0\n", "280 283 7.74e-20 0:00.0\n", "300 303 6.66e-23 0:00.0\n", "320 323 1.86e-25 0:00.0\n", "340 343 3.16e-28 0:00.0\n", "360 364 3.08e-31 0:00.0\n", "380 390 0 0:00.0\n", "400 416 0 0:00.0\n", "420 443 0 0:00.0\n", "422 444 0 0:00.0\n", "Halting: No significant change for 200 iterations.\n", "Found: [1. 1.]\n" ] } ], "source": [ "import pints\n", "import pints.toy\n", "\n", "# Create Rosenbrock error (optimum is at 1,1)\n", "f = pints.toy.RosenbrockError()\n", "\n", "# Pick starting point\n", "x0 = [-0.75, 3.5]\n", "x, fx = pints.optimise(f, x0, method=pints.NelderMead)\n", "\n", "print('Found: ' + str(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the ask-and-tell interface to visualise its progress through the search-space:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Create figure\n", "fig = plt.figure(figsize=(12, 12))\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.set_xlabel('x')\n", "ax.set_ylabel('y')\n", "\n", "# Show function\n", "x = np.linspace(-1.5, 1.5, 150)\n", "y = np.linspace(-0.5, 4, 225)\n", "X, Y = np.meshgrid(x, y)\n", "Z = [[np.log(f([i, j])) for i in x] for j in y]\n", "levels = np.linspace(np.min(Z), np.max(Z), 20)\n", "ax.contour(X, Y, Z, levels = levels)\n", "\n", "# Show initial position\n", "ax.plot(x0[0], x0[1], 'ks', markersize=10)\n", "ax.text(x0[0] + 0.05, x0[1] - 0.03, 'Initial position', fontsize=18)\n", "\n", "# Run 400 iterations with ask-and-tell, storing all positions\n", "e = pints.SequentialEvaluator(f)\n", "nm = pints.NelderMead(x0)\n", "path = [x0]\n", "for i in range(400):\n", " xs = nm.ask()\n", " fs = e.evaluate(xs)\n", " nm.tell(fs)\n", " path.append(nm.xbest())\n", "\n", "# Plot path\n", "path = np.array(path).T\n", "ax.plot(path[0], path[1], 'x-', color='tab:orange', markersize=15)\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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	bsd-3-clause
shareactorIO/pipeline	source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/Labs/lab2_NN.ipynb	3	200505	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## import tensorflow and other packages" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define initial input x and output y" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-2. -1.87755102 -1.75510204 -1.63265306 -1.51020408 -1.3877551\n", " -1.26530612 -1.14285714 -1.02040816 -0.89795918 -0.7755102 -0.65306122\n", " -0.53061224 -0.40816327 -0.28571429 -0.16326531 -0.04081633 0.08163265\n", " 0.20408163 0.32653061 0.44897959 0.57142857 0.69387755 0.81632653\n", " 0.93877551 1.06122449 1.18367347 1.30612245 1.42857143 1.55102041\n", " 1.67346939 1.79591837 1.91836735 2.04081633 2.16326531 2.28571429\n", " 2.40816327 2.53061224 2.65306122 2.7755102 2.89795918 3.02040816\n", " 3.14285714 3.26530612 3.3877551 3.51020408 3.63265306 3.75510204\n", " 3.87755102 4. ]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f68fe2a8790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_examples = 50\n", "x=np.linspace(-2, 4, num_examples)\n", "y=np.linspace(-6, 6, num_examples)\n", "print x\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(x, y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate random pertubation " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.69468812 0.76433985 0.48907605 0.20173005 0.81662296 0.28544471\n", " 0.22351182 0.48968533 0.83867993 0.72399452 0.12300451 0.70340524\n", " 0.86799523 0.98513867 0.04058264 0.23750989 0.20795213 0.72676881\n", " 0.50832674 0.50872813 0.84726839 0.84757519 0.82978698 0.6304305\n", " 0.0112158 0.78987729 0.73265985 0.28673174 0.66022837 0.33783113\n", " 0.68723565 0.66386675 0.08096569 0.12619504 0.16989061 0.1244708\n", " 0.42372693 0.72509119 0.14952221 0.51519253 0.93420552 0.42191764\n", " 0.16374122 0.16174883 0.4072154 0.94776896 0.99793733 0.64637544\n", " 0.0657503 0.17723147]\n" ] } ], "source": [ "randnum=np.random.random([num_examples])\n", "print randnum" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MKol2wHnsX27lsf/UO0HwTnuLwP7vVw17yKJ34vfBGu84\n7KGq+lz+ec0eyKCu403s3mVJ7GGA1wEv9uyFjGIJwTtk8Q72Z6EU9lTIb4B4gvfze12PY/8jNLl8\nvNYMYEw3MoXL47z4tX4AY0ruaewHQ85h/+URzFOyGnLlf08fMDmAMV3P1T6vPqBLIIO6jon8/Vk4\nhD3j5p6ARpR6wTztLQZ7hsV57MXpZwC3BzQiERERERERERERERERERERERERERERERERERERERER\nERHJkv4f3FH6UruF0c8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f68fdcae490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x += randnum #an 1-d array with random numbers\n", "y += np.random.random([num_examples])\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(x, y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add a constant element to input array" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. -1.30531192]\n", " [ 1. -1.11321115]\n", " [ 1. -1.26602602]\n", " [ 1. -1.43092299]\n", " [ 1. -0.6935811 ]\n", " [ 1. -1.10231042]\n", " [ 1. -1.0417943 ]\n", " [ 1. -0.65317184]\n", " [ 1. -0.18172823]\n", " [ 1. -0.17396466]\n", " [ 1. -0.6525057 ]\n", " [ 1. 0.05034401]\n", " [ 1. 0.33738297]\n", " [ 1. 0.57697541]\n", " [ 1. -0.24513164]\n", " [ 1. 0.07424458]\n", " [ 1. 0.1671358 ]\n", " [ 1. 0.80840147]\n", " [ 1. 0.71240836]\n", " [ 1. 0.83525872]\n", " [ 1. 1.29624796]\n", " [ 1. 1.41900373]\n", " [ 1. 1.52366459]\n", " [ 1. 1.44675708]\n", " [ 1. 0.94999129]\n", " [ 1. 1.85110176]\n", " [ 1. 1.91633332]\n", " [ 1. 1.59285414]\n", " [ 1. 2.08879972]\n", " [ 1. 1.88885152]\n", " [ 1. 2.36070514]\n", " [ 1. 2.45978522]\n", " [ 1. 1.99933302]\n", " [ 1. 2.16701126]\n", " [ 1. 2.33315587]\n", " [ 1. 2.4101851 ]\n", " [ 1. 2.83189011]\n", " [ 1. 3.25570345]\n", " [ 1. 2.80258346]\n", " [ 1. 3.29070282]\n", " [ 1. 3.83216476]\n", " [ 1. 3.44232583]\n", " [ 1. 3.30659842]\n", " [ 1. 3.42705488]\n", " [ 1. 3.79497051]\n", " [ 1. 4.457973 ]\n", " [ 1. 4.63059044]\n", " [ 1. 4.40147734]\n", " [ 1. 3.9433012 ]\n", " [ 1. 4.17723131]]\n" ] } ], "source": [ "x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)\n", "print x_with_bias" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train a neural network with Gradient Descent \n", "The objective is minimizing L2 loss" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "losses = []\n", "training_steps = 50\n", "learning_rate = 0.002\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors, variables, and operations.\n", " input = tf.constant(x_with_bias)\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", " \n", " tf.initialize_all_variables().run()\n", "\n", " # yhat is a vector in this case\n", " yhat = tf.matmul(input, weights)\n", " yerror = tf.sub(yhat, target)\n", " loss = tf.nn.l2_loss(yerror)\n", " \n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", " \n", " for _ in range(training_steps):\n", " update_weights.run()\n", " losses.append(loss.eval())\n", " #print _ #It takes on value from 0 to 49\n", " #print losses #this shows losses array keep increasing in size: [18] , [18, 13],...\n", "\n", " # Training is done, get the final values for the graphs\n", " betas = weights.eval()\n", " yhat = yhat.eval()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show the actual and predicted data points" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Yi9//6/6voZTC55tOmzat5B7Z4k+rCQcij9qIESNITEw8aF1mZiaZmZkWVSSi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\noBilu1BrbHz/5kw6OSbx5Zd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"text/plain": [ "<matplotlib.figure.Figure at 0x7f68fdbcdc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(4,4))\n", "\n", "plt.scatter(x, y, alpha=.9) #plot original x and y\n", "plt.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6) #plot x and yhat\n", "\n", "x_range = (-4, 6)\n", "plt.plot(x_range, [betas[0] + a * betas[1] for a in x_range], \"g\", alpha=0.6)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the prediction error over time" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2CkvL++22s29dG2d4SyXQ0i++zz5t1zc3hztDX3657TJ/Ptx/PywpeEhgz54h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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f68fdd35650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show the loss over time.\n", "plt.figure(figsize=(4,4))\n", "\n", "plt.plot(range(0, training_steps), losses)\n", "#plt.set_ylabel(\"Loss\")\n", "#plt.set_xlabel(\"Training steps\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise: Build a neural network to predict room occupancy" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import csv\n", "\n", "# Split data into inputs (5 cols) and output (1 col)\n", "def load_data(filename):\n", " x=[]\n", " target=[]\n", " a=[]\n", " with open(filename) as csv_file:\n", " data_file = csv.reader(csv_file)\n", " for row in data_file:\n", " a.append(row)\n", " print a[0]\n", " np_a=np.array(a)\n", " x=np_a[1:,:-1].astype(np.float32) #read after first row\n", " target=np_a[1:,-1].astype(np.float32)\n", " return x, target" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Temperature', 'Humidity', 'Light', 'CO2', 'HumidityRatio', 'Occupancy']\n" ] } ], "source": [ "room_X, room_y=load_data('data/room/train.csv')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8143\n", "8143\n" ] } ], "source": [ "print len(room_X)\n", "print len(room_y)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8143\n", "5\n" ] } ], "source": [ "#*** Inspect data\n", "n_samples=len(room_X)\n", "n_features = len(room_X[0])\n", "print n_samples\n", "print n_features" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.31800003e+01 2.72719994e+01 4.26000000e+02 7.21250000e+02\n", " 4.79298783e-03] 1.0\n" ] } ], "source": [ "print room_X[0], room_y[0]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "losses = []\n", "training_steps = n_samples\n", "learning_rate = 0.01\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors, variables, and operations.\n", " input = tf.constant(room_X)\n", " target = tf.constant(np.transpose([room_y]).astype(np.float32))\n", " weights = tf.Variable(tf.random_normal([5, 1], 0, 0.1))\n", " \n", " tf.initialize_all_variables().run()\n", "\n", " # yhat is a matrix \n", " yhat = tf.matmul(input, weights)\n", " \n", " # loss is a matrix\n", " lossDistribution = tf.nn.sigmoid_cross_entropy_with_logits(yhat, target, name=\"loss\")\n", " \n", " # take the mean of the lossDistribution vector\n", " lossAvg = tf.reduce_mean(lossDistribution)\n", "\n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(lossAvg)\n", " \n", " for _ in range(training_steps):\n", " update_weights.run()\n", " losses.append(lossAvg.eval()) \n", " \n", " #print _ #It takes on value from 0 to 49\n", " #print losses #this shows losses array keep increasing in size: [18] , [18, 13],...\n", "\n", " # Training is done, get the final values for the graphs\n", " finalWeightsBetas = weights.eval()\n", " yhat = yhat.eval()\n", " finalLossDistribution = lossDistribution.eval()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ -9.32804585]\n", " [ -6.64313936]\n", " [-10.12156296]\n", " ..., \n", " [-13.14401722]\n", " [-10.09729576]\n", " [ 4.59316492]]\n" ] } ], "source": [ "print yhat" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-16.84068871]\n", " [ -6.88164139]\n", " [ 1.0107038 ]\n", " [ 0.19155027]\n", " [ -0.04718706]]\n" ] } ], "source": [ "print finalWeightsBetas" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 9.32813454e+00]\n", " [ 6.64444160e+00]\n", " [ 1.01216030e+01]\n", " ..., \n", " [ 1.31440191e+01]\n", " [ 1.00973368e+01]\n", " [ 1.00698778e-02]]\n" ] } ], "source": [ "print finalLossDistribution" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[789.47266, 334.03677, 160.24374, 627.5849, 72.658607, 61.194012, 31.808559, 30.265984, 29.010735, 27.743101, 26.483156, 25.223295, 23.963549, 22.704079, 21.445812, 20.194349, 18.930952, 17.723143, 16.658493, 18.824482, 30.051092, 181.03429, 377.53107, 474.97122, 33.004196, 31.329283, 30.069895, 28.810574, 27.551325, 26.292164, 25.033403, 23.77598, 22.528273, 21.265467, 20.070555, 18.928513, 18.654415, 18.22019, 28.255669, 208.44014, 611.05084, 59.11417, 60.572044, 88.997482, 128.33411, 530.06171, 55.657677, 43.60556, 42.348003, 41.081902, 39.822655, 38.563583, 37.304638, 36.045841, 34.787247, 33.528885, 32.270676, 31.012621, 29.754728, 28.497103, 27.23995, 25.983778, 24.733503, 23.507786, 22.3475, 21.264435, 21.747021, 23.006727, 23.611254, 31.039991, 21.343451, 30.974005, 46.28936, 373.97656, 485.28079, 29.938646, 28.17234, 26.955231, 25.702318, 24.543949, 23.359936, 22.906746, 20.787708, 19.791063, 18.68371, 23.066298, 30.651405, 20.393946, 28.913679, 23.395157, 157.08276, 639.29657, 74.215729, 130.48499, 545.15027, 53.41238, 39.90855, 38.732216, 37.046619, 35.797993, 34.567192, 33.285934, 32.055374, 30.788738, 29.556143, 28.285269, 27.055674, 25.792442, 24.580627, 23.357342, 22.28178, 21.16156, 22.325499, 24.463057, 21.253048, 26.031832, 18.08239, 23.523394, 17.577148, 28.520018, 72.546547, 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F3AV80l8/JWU/549BOz1Y4XMJL7tseH3oIdhjD/jTn7LnESfKRVn8YZKGOm6n+0IQXJHn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\n8ryBCkIctRB5lzd/3giOtLR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"text/plain": [ "<matplotlib.figure.Figure at 0x7f68fdb2f490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(4,4))\n", "\n", "plt.plot(range(0, 1999), losses[0:1999])" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
rsterbentz/phys202-2015-work	assignments/assignment04/TheoryAndPracticeEx01.ipynb	1	39062	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Theory and Practice of Visualization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Graphical excellence and integrity" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Find a data-focused visualization on one of the following websites that is a positive example of the principles that Tufte describes in The Visual Display of Quantitative Information.\n", "\n", "* [Vox](http://www.vox.com/)\n", "* [Upshot](http://www.nytimes.com/upshot/)\n", "* [538](http://fivethirtyeight.com/)\n", "* [BuzzFeed](http://www.buzzfeed.com/)\n", "\n", "Upload the image for the visualization to this directory and display the image inline in this notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "9c86bcce96065a2133bab497403e3291", "grade": true, "grade_id": "theorypracticeex01a", "points": 2 } }, "outputs": [ { "data": { "image/png": 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OJq1zz+ZTmCl2+I8/x1JBkdqN7tg8Ur/6qcvZgwcyffCnbmbF5P3x2Zj+AghC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\n8SAeMInTUTpOB5OIB/GASZyO0nE6mEQ8iAdM4nSUjtPBJOJ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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='infograph1.png')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Describe in detail the ways in which the visualization exhibits graphical integrity and excellence:" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "80145499593a6a8f756ab550388d18ea", "grade": true, "grade_id": "theorypracticeex01b", "points": 8, "solution": true } }, "source": [ "The information is clear and labeled well. Different colored lines make the two data types distinct. Its data-ink ratio is pretty good, although there may be a bit too many vertical grid lines. The x and y scales are linear, making understanding the data easier. Anybody who reads this would agree with \"Custodial Mothers Are More Likely To Be Poor.\"" ] } ], "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
McIntyre-Lab/papers	fear_ase_2016/scripts/wiggles/ago1.ipynb	2	63390	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "GENENAME = 'AGO1'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Importing commonly used libraries: \n", " os, sys \n", " numpy as np \n", " scipy as sp \n", " pandas as pd \n", " matplotlib as mp \n", " matplotlib.pyplot as plt\n", " datetime as dt \n", " mclib_Python/flagging as fg\n", "\n", "Creating project level variables: \n", " MCLAB = /home/jfear/mclab \n", " PROJ = /home/jfear/mclab/cegs_ase_paper \n", " TODAY = 20151115\n", "\n", "Adding ['scripts/mclib_Python', 'scripts/ase_Python'] to PYTHONPATH\n", "\n" ] } ], "source": [ "%run '../ipython_startup.py'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import gff as mcgff\n", "import bam as mcbam\n", "import numpy as np\n", "from glob import glob\n", "import pandas as pd\n", "import seaborn\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import GFF File and Get gene location\n", "db = mcgff.FlyGff('/home/jfear/mclab/useful_dmel_data/flybase551/flybase_files/dmel-all-no-analysis-r5.51.gff')\n", "gene = mcgff.FlyGene(GENENAME, db)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_pileup(fname, chrom, start, end):\n", " \"\"\" Function to pull out reads from BAM files. \"\"\"\n", " bam = mcbam.Bam(fname)\n", " pileup = bam.get_pileup(chrom, start, end)\n", " return pd.Series(pileup)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get list of lines\n", "with open('/home/jfear/lines.txt', 'r') as FH:\n", " lines = FH.read().rstrip('\\n').split('\\n')\n", "\n", "# Iterate over lines\n", "matrix = list()\n", "for LINE in lines: \n", " pileups = list()\n", " for FILE in glob('/home/jfear/cegs_oe/combined/{}_.sorted.bam'.format(LINE)):\n", " pileups.append(get_pileup(FILE, gene.chrom, gene.start, gene.end))\n", "\n", " # Sum coverage\n", " matrix.append(pd.concat(pileups, axis=1).fillna(0).sum(axis=1))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.concat(matrix, axis=1).fillna(0)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df.columns = lines" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-31-dac86c793b49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mseaborn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/jfear/opt/miniconda2/envs/ase/lib/python2.7/site-packages/seaborn/matrix.pyc\u001b[0m in \u001b[0;36mheatmap\u001b[1;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, ax, xticklabels, yticklabels, mask, *kwargs)\u001b[0m\n\u001b[0;32m 444\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msquare\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 445\u001b[0m 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\u001b[0msh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": 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HPH8hAOtG2P67dcgmW8jbOaIhuw5GG8jUFgeJGhGCxCq9ZSsHEwxaagS10Bbk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc91e19cb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "seaborn.heatmap(df.T)" ] }, { "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 }	lgpl-3.0
hightower8083/chimera	doc/space-charge-demo(vs_ocelot).ipynb	1	470065	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Space charge fields of a relativistic electron beam \n", "\n", "Here is a test of the account for the space-charge fields of a relativistic electron beam. We compare \n", "- the particle tracker code OCELOT (<https://github.com/iagapov/ocelot/>), where the Poisson equation is solverd in a beam rest frame via FFT technique\n", "\n", "with CHIMERA, where two modes of space-charge modeling are avaliable \n", "\n", "- full PIC modeling with quasi-cylindrical PSATD solver\n", "- space-charge kicker, where relativistic Maxwell equations are solved in static approximation via FFT+DHT technique\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "initializing ocelot...\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from scipy.constants import e\n", "import matplotlib.pyplot as plt\n", "import sys\n", "\n", "import ocelot as oclt\n", "\n", "from chimera.moduls.species import Specie\n", "from chimera.moduls.solvers import Solver\n", "from chimera.moduls.chimera_main import ChimeraRun\n", "from chimera.moduls.diagnostics import Diagnostics\n", "from chimera.moduls import fimera as chimera" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a simplest test, we consider a 20 $\\mu$m drift of a 30 pC electron beam with the gaussian density profiles $\\sigma_z=\\sigma_r=3$ $\\mu$m, with the longitudinal momentum $p_z=50\\, m_e c $. Initially beam has no energy spread and zero divergence." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SimLgth = 20.\n", "pz0 = 50.\n", "Chrg = 30e-12\n", "Size = 3.\n", "\n", "nmax = Chrg/e/((Size1.e-4)3(2np.pi)1.5)/(1.1e21)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## OCELOT simulation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length of the cell: 1.9999999999999998e-05 m\n", "2.0000000000000005e-05/1.9999999999999998e-05" ] } ], "source": [ "Np = int(1e6)\n", "BeamCharge = Chrg\n", "parts0 = np.zeros((6,Np))\n", "\n", "parts0[0] = Size1e-6np.random.randn(Np)\n", "parts0[2] = Size1e-6np.random.randn(Np)\n", "parts0[4] = Size1e-6np.random.randn(Np)\n", "p_array_init = oclt.ParticleArray(n=Np)\n", "p_array_init.rparticles[:] = parts0 #.T.flatten()\n", "p_array_init.E = (1+pz02).50.511e-3\n", "p_array_init.s = 0.0\n", "p_array_init.q_array = (BeamCharge/Np)np.ones(Np)\n", "\n", "D1 = oclt.Drift(l = SimLgth1e-6 )\n", "D2 = oclt.Drift(l=0)\n", "cell = (D1,D2)\n", "\n", "sc1 = oclt.SpaceCharge()\n", "sc1.nmesh_xyz = [63, 63, 63]\n", "sc1.low_order_kick = False\n", "sc1.step = 1\n", "\n", "method = oclt.MethodTM()\n", "lat = oclt.MagneticLattice(cell,method=method)\n", "print(\"length of the cell: \", lat.totalLen, \"m\")\n", "\n", "navi = oclt.Navigator(lat)\n", "navi.add_physics_proc(sc1,lat.sequence[0],lat.sequence[-1])\n", "p_array = oclt.deepcopy(p_array_init)\n", "\n", "dz = 1e-6\n", "LL = p_array.s\n", "SS = []\n", "Sx = []\n", "Sy = []\n", "Sz = []\n", "Ex = []\n", "Ey = []\n", "\n", "while LL<lat.totalLen:\n", " oclt.tracking_step(lat, p_array, dz,navi)\n", " proc_list = navi.get_proc_list()\n", " for p in proc_list:\n", " p.z0 = navi.z0\n", " p.apply(p_array, dz)\n", " LL += dz\n", " sys.stdout.write('\\r'+str(LL)+'/'+str(lat.totalLen))\n", " sys.stdout.flush()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CHIMERA with solver in \"StaticKick\" mode" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constructing solver with cylindric boundaries:\n", " left=-21.4, right=21, radius=90.1\n", "Spatial and temporal resolutions:\n", " dx=0.14, dr=0.3, dt=1\n", "Solver is active from t=0 to t=inf\n", "Grid sizes are:\n", "** Nx=304, Nr=300, Mo=2\n", "Charge density will be considered\n", "20/20" ] } ], "source": [ "xmin, xmax = -7.Size,7.Size\n", "lrg = 30Size\n", "Nx = 300\n", "Nr = 300\n", "\n", "dx = (xmax-xmin)/Nx\n", "dr = lrg/Nr\n", "dt = 1.0\n", "\n", "solver_in = {\n", " 'Grid':(xmin, xmax,lrg,dx,dr),'TimeStep':dt,'MaxAzimuthMode':1,\n", " 'Features':('StaticKick',),'Xchunked':(4,6)\n", "}\n", "\n", "beam_in = {\n", " 'Grid':(xmin, xmax,lrg,dx,dr),'TimeStep':dt,'Charge':-1.,'Mass':1.,\n", " 'Density':nmax, 'FixedCell':(4,8,8),'MomentaMeans':(pz0,0.,0.),\n", " 'Xchunked':(4,6)\n", "}\n", "\n", "solver = Solver(solver_in)\n", "beam = Specie(beam_in)\n", "\n", "MovingFrame = {'TimeStep':dt,'Steps':1,'Features':('NoSorting','Staged')}\n", "\n", "chimera_in = {\n", " 'Solvers':(solver,),'Particles':(beam,),'MovingFrames':(MovingFrame,)\n", "}\n", "\n", "fu = lambda x,y,z: np.exp(-0.5(x2+y2+z2)/Size2)\n", "beam.add_particles(beam.gen_parts(Domain=(-3.5Size,3.5Size, 0.0, 3.5Size),ProfileFunc=fu))\n", "\n", "Chimera = ChimeraRun(chimera_in)\n", "Diags = Diagnostics(Chimera,(),out_folder=None)\n", "\n", "for i in range(int(SimLgth/dt)+1):\n", " Chimera.make_step(i)\n", " sys.stdout.write('\\r'+str(i)+'/'+str(int(SimLgth/dt)))\n", " sys.stdout.flush() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CHIMERA with solver in full PIC mode" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Constructing solver with cylindric boundaries:\n", " left=-21.4, right=21, radius=90.1\n", "Spatial and temporal resolutions:\n", " dx=0.14, dr=0.3, dt=0.06\n", "Solver is active from t=0 to t=inf\n", "Grid sizes are:\n", "** Nx=304, Nr=300, Mo=2\n", "Charge density will be considered\n", "333/333" ] } ], "source": [ "xmin, xmax = -7.Size,7.Size\n", "lrg = 30Size\n", "Nx = 300\n", "Nr = 300\n", "\n", "dx = (xmax-xmin)/Nx\n", "dr = lrg/Nr\n", "dt = 0.06\n", "\n", "solver_in1 = {\n", " 'Grid':(xmin, xmax,lrg,dx,dr),'TimeStep':dt,'MaxAzimuthMode':1,\n", " 'Features':('SpaceCharge',),'Xchunked':(4,6)\n", "}\n", "\n", "beam_in1 = {\n", " 'Grid':(xmin, xmax,lrg,dx,dr),'TimeStep':dt,'Charge':-1.,'Mass':1.,\n", " 'Density':nmax, 'FixedCell':(4,8,8),'MomentaMeans':(pz0,0.,0.),\n", " 'Features':(),'Xchunked':(4,6)\n", "}\n", "\n", "solver1 = Solver(solver_in1)\n", "beam1 = Specie(beam_in1)\n", "\n", "MovingFrame = {'TimeStep':dt,'Steps':1,'Features':('NoSorting','Staged')}\n", "\n", "chimera_in1 = {\n", " 'Solvers':(solver1,),'Particles':(beam1,),'MovingFrames':(MovingFrame,)\n", "}\n", "\n", "fu = lambda x,y,z: np.exp(-0.5(x2+y2+z2)/Size2)\n", "beam1.add_particles(beam1.gen_parts(Domain=(-3.5Size,3.5Size, 0.0, 3.5Size),ProfileFunc=fu))\n", "\n", "Chimera1 = ChimeraRun(chimera_in1)\n", "Diags1 = Diagnostics(Chimera1,(),out_folder=None)\n", "\n", "for i in range(int(SimLgth/dt)+1):\n", " Chimera1.make_step(i)\n", " sys.stdout.write('\\r'+str(i)+'/'+str(int(SimLgth/dt)))\n", " sys.stdout.flush()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f8ea6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3,figsize=(16,12),dpi=200)\n", "\n", "dpmax = (beam.Data['momenta'][0].max()-pz0)1e3\n", "range1 = [[SimLgth-10,SimLgth+10],[-2dpmax,+2dpmax]]\n", "range2=[[-15,15],[-2,2]]\n", "weight2pC = beam.Args['weight2pC']\n", "\n", "dns_max = 1e-3\n", "\n", "ax1.hist2d(beam1.Data['coords'][0],(beam1.Data['momenta'][0]-pz0)1e3,weights=-beam1.Data['weights'],\n", " bins=120,range=range1,vmax=dns_max,cmap=plt.cm.spectral);\n", "ax4.hist2d(beam1.Data['coords'][2],beam1.Data['momenta'][2]1e4,weights=-beam1.Data['weights'],\n", " bins=120,range=range2,vmax=dns_max,cmap=plt.cm.spectral);\n", "\n", "ax2.hist2d(beam.Data['coords'][0],(beam.Data['momenta'][0]-pz0)1e3,weights=-beam.Data['weights'],\n", " bins=120,range=range1,vmax=dns_max,cmap=plt.cm.spectral);\n", "ax5.hist2d(beam.Data['coords'][2],beam.Data['momenta'][2]1e4,weights=-beam.Data['weights'],\n", " bins=120,range=range2,vmax=5e-4,cmap=plt.cm.spectral);\n", "\n", "ax3.hist2d((-p_array.tau()+p_array.s)1e6,(p_array.p())p_array.E1e6/0.511,weights=p_array.q_array,\n", " bins=120,range=range1,vmax=dns_maxweight2pC1e-12,cmap=plt.cm.spectral);\n", "ax6.hist2d(p_array.x()1e6,p_array.px()p_array.E1e7/0.511,weights=p_array.q_array,\n", " bins=120,range=range2,vmax=dns_maxweight2pC*1e-12,cmap=plt.cm.spectral);\n", "\n", "ax1.set_title('CHIMERA full PIC',fontsize=18);\n", "ax2.set_title('CHIMERA kicker',fontsize=18)\n", "ax3.set_title('OCELOT',fontsize=18)\n", "\n", "for ax in (ax4,ax5,ax6): ax.set_xlabel('coordinate ($\\mu$m)',fontsize=18)\n", "ax1.set_ylabel('$(p_\\parallel-p_{z0})\\cdot 10^3$',fontsize=18)\n", "ax4.set_ylabel('$p_\\perp\\cdot 10^4$',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11fe92550>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HAAAg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"text/plain": [ "<matplotlib.figure.Figure at 0x11dd8d358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2,figsize=(16,10),dpi=200)\n", "\n", "extent = (solver1.Args['Xgrid'].min(),solver1.Args['Xgrid'].max(),\n", " -solver1.Args['Rgrid'].max(),solver1.Args['Rgrid'].max())\n", "\n", "ee = Diags1.fld_out( {'Features':('Return',)} )[0]\n", "nn = Diags1.dns_out( {'Features':{'Return':0,'MaxMode':1}} )[0]\n", "\n", "ex = np.real(np.hstack((ee[:,::-1,0,0] + ee[:,::-1,1,0],ee[:,1:,0,0] -ee[:,1:,1,0])))\n", "ey = np.real(np.hstack((ee[:,::-1,0,1] - ee[:,::-1,1,1],ee[:,1:,0,1] +ee[:,1:,1,1])))\n", "bz = np.real(np.hstack((ee[:,::-1,0,5] + ee[:,::-1,1,5],ee[:,1:,0,5] -ee[:,1:,1,5])))\n", "ne = np.real(np.hstack((nn[:,::-1,0] + nn[:,::-1,1],nn[:,1:,0] -nn[:,1:,1])))\n", "\n", "pl1 = ax1.imshow(ex.T ,aspect='auto', extent=extent,origin='lower',cmap=plt.cm.seismic)\n", "pl2 = ax2.imshow((ey+bz).T ,aspect='auto', extent=extent,origin='lower',cmap=plt.cm.seismic)\n", "\n", "extent = (solver.Args['Xgrid'].min(),solver.Args['Xgrid'].max(),\n", " -solver.Args['Rgrid'].max(),solver.Args['Rgrid'].max())\n", "\n", "ee = Diags.fld_out( {'Features':('Return',)} )[0]\n", "nn = Diags.dns_out( {'Features':{'Return':0,'MaxMode':1}} )[0]\n", "\n", "ex = np.real(np.hstack((ee[:,::-1,0,0] + ee[:,::-1,1,0],ee[:,1:,0,0] -ee[:,1:,1,0])))\n", "ey = np.real(np.hstack((ee[:,::-1,0,1] - ee[:,::-1,1,1],ee[:,1:,0,1] +ee[:,1:,1,1])))\n", "bz = np.real(np.hstack((ee[:,::-1,0,5] + ee[:,::-1,1,5],ee[:,1:,0,5] -ee[:,1:,1,5])))\n", "ne = np.real(np.hstack((nn[:,::-1,0] + nn[:,::-1,1],nn[:,1:,0] -nn[:,1:,1])))\n", "\n", "pl3 = ax3.imshow(ex.T ,aspect='auto', extent=extent,origin='lower',cmap=plt.cm.seismic)\n", "pl4 = ax4.imshow((ey+bz).T ,aspect='auto', extent=extent,origin='lower',cmap=plt.cm.seismic)\n", "\n", "#fig.colorbar(pl1,ax=ax1)\n", "#fig.colorbar(pl2,ax=ax2)\n", "#fig.colorbar(pl3,ax=ax3)\n", "#fig.colorbar(pl4,ax=ax4)\n", "\n", "ax1.set_title('$E_x$',fontsize=18);\n", "ax2.set_title('$E_y$',fontsize=18)\n", "for ax in (ax3,ax4,): ax.set_xlabel('x-coordinate ($\\mu$m)',fontsize=18)\n", "ax1.set_ylabel('CHIMERA full PIC \\n y-coordinate ($\\mu$m)',fontsize=18)\n", "ax3.set_ylabel('CHIMERA kicker \\n y-coordinate ($\\mu$m)',fontsize=18)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
usantamaria/iwi131	ipynb/12-Actividad-FuncionesYCondicionales/Actividad2.ipynb	2	7993	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "\"\"\"\n", "IPython Notebook v4.0 para python 2.7\n", "Librerías adicionales: Ninguna.\n", "Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT. (c) Sebastian Flores.\n", "\"\"\"\n", "\n", "# Configuracion para recargar módulos y librerías \n", "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "from IPython.core.display import HTML\n", "\n", "HTML(open(\"style/iwi131.css\", \"r\").read())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<header class=\"w3-container w3-teal\">\n", "<img src=\"images/utfsm.png\" alt=\"\" align=\"left\"/>\n", "<img src=\"images/inf.png\" alt=\"\" align=\"right\"/>\n", "</header>\n", "<br/><br/><br/><br/><br/>\n", "# IWI131\n", "## Programación de Computadores\n", "\n", "### Sebastián Flores\n", "\n", "http://progra.usm.cl/\n", "\n", "https://www.github.com/usantamaria/iwi131" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Grupos Actividad 2\n", "\n", "1. CANTILLANA, CASTRO, ARÉVALO\n", "2. GALLARDO, MORALES, ZEGERS\n", "3. ALMEIDA, ARRATIA, CANALES\n", "4. ALVARADO, BAHAMONDE, CASTILLO\n", "5. CARRIEL, CODDOU, ESTRADA\n", "6. CATALAN, COLLAO, ETEROVIC\n", "7. CHAURA, CURIHUAL, FLORES\n", "8. ESTAY, DURÁN, GRUNERT\n", "9. KANZUA, HERRERA, LÓPEZ C.\n", "10. LOBOS, OGALDE, PÉREZ\n", "11. LOPEZ, PROBOSTE, REQUENA\n", "12. SALAS, SANTELICES, TORREBLANCA\n", "13. MEDIANO, REYES\n", "14. SCHWERTER, SANDOVAL" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Actividad 2\n", "\n", "* Un grupo por fila.\n", "* Designar un líder de grupo.\n", "* Discutir problema (inputs, outputs, requerimientos específicos) antes de comenzar a escribir código.\n", "* Actividad: 3 preguntas completamente independientes." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# (a) Nota Mínima\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "164\n", "64\n", "6\n", "-36\n" ] } ], "source": [ "def nota_minima(nota1, nota2):\n", " nota3 = 164 - nota1 - nota2\n", " return nota3\n", "\n", "print nota_minima(0,0)\n", "print nota_minima(35,65)\n", "print nota_minima(88,70)\n", "print nota_minima(100,100)\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# (a) Nota Mínima\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "164\n", "64\n", "6\n", "-36\n" ] } ], "source": [ "def nota_minima(nota1, nota2):\n", " nota3 = -100\n", " while nota3<200:\n", " if (nota1+nota2+nota3)/3.0>=54.5:\n", " return nota3\n", " nota3 += 1\n", " return nota3\n", "\n", "print nota_minima(0,0)\n", "print nota_minima(35,65)\n", "print nota_minima(88,70)\n", "print nota_minima(100,100)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# (b) Recuperativo\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "164\n", "74\n", "54\n", "-14\n", "-36\n" ] } ], "source": [ "def recuperativo(nota1, nota2, nota3):\n", " peor_nota = min(nota1, nota2, nota3)\n", " nota_recuperativo = 164 - nota1 - nota2 - nota3 + peor_nota\n", " return nota_recuperativo\n", "\n", "print recuperativo(0,0,0)\n", "print recuperativo(50,40,30)\n", "print recuperativo(35,65,45)\n", "print recuperativo(88,70,90)\n", "print recuperativo(100,100,100)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# (b) Recuperativo\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def recuperativo(nota1, nota2, nota3):\n", " rec1 = nota_minima(nota2, nota3)\n", " rec2 = nota_minima(nota1, nota3)\n", " rec3 = nota_minima(nota1, nota2)\n", " nota_recuperativo = min(rec1, min(rec2, rec3))\n", " return nota_recuperativo\n", "\n", "print recuperativo(0,0,0)\n", "print recuperativo(50,40,30)\n", "print recuperativo(35,65,45)\n", "print recuperativo(88,70,90)\n", "print recuperativo(100,100,100)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# (c) Alumnos" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Alumno 1\n", "Certamen 1: 65\n", "Certamen 2: 35\n", "nota minima C3 es: 64\n", "Certamen 3: 14\n", "nota minima CR es: 64\n", "Alumno 2\n", "Certamen 1: 100\n", "Certamen 2: 80\n", "nota minima C3 es: -16\n", "Certamen 3: 10\n", "Alumno 3\n", "Certamen 1: 100\n", "Certamen 2: 55\n", "nota minima C3 es: 9\n", "Certamen 3: 50\n", "Alumno 4\n", "Certamen 1: -1\n", "Alumno mas critico: 1\n" ] } ], "source": [ "def alumno_critico():\n", " alumno_critico = 0\n", " nota_alumno_critico = -1\n", " alumno_actual = 1\n", " while True:\n", " print \"Alumno\", alumno_actual\n", " nota1 = int(raw_input(\"Certamen 1: \"))\n", " if nota1==-1:\n", " break\n", " nota2 = int(raw_input(\"Certamen 2: \"))\n", " nota3_necesaria = nota_minima(nota1, nota2)\n", " print \"nota minima C3 es: \", nota3_necesaria\n", " nota3 = int(raw_input(\"Certamen 3: \"))\n", " if nota3_necesaria > nota3:\n", " CR = recuperativo(nota1, nota2, nota3)\n", " print \"nota minima CR es: \", CR\n", " if CR<=100 and CR>nota_alumno_critico:\n", " alumno_critico = alumno_actual\n", " nota_alumno_critico = CR\n", " alumno_actual += 1\n", " print \"Alumno mas critico:\", alumno_critico\n", "\n", "alumno_critico()" ] } ], "metadata": { "celltoolbar": "Slideshow", "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 }	cc0-1.0
Upward-Spiral-Science/team1	code/Imaging Cortical Layers.ipynb	1	5638	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Imaging Cortical Layers" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import axes3d\n", "import matplotlib.pyplot as plt\n", "#%matplotlib inline \n", "import numpy as np\n", "import urllib2\n", "import scipy.stats as stats\n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'\n", " '/data/master/syn-density/output.csv')\n", "data = urllib2.urlopen(url)\n", "csv = np.genfromtxt(data, delimiter=\",\")[1:] # don't want first row (labels)\n", "\n", "# chopping data based on thresholds on x and y coordinates\n", "x_bounds = (409, 3529)\n", "y_bounds = (1564, 3124)\n", "\n", "def check_in_bounds(row, x_bounds, y_bounds):\n", " if row[0] < x_bounds[0] or row[0] > x_bounds[1]:\n", " return False\n", " if row[1] < y_bounds[0] or row[1] > y_bounds[1]:\n", " return False\n", " if row[3] == 0:\n", " return False\n", " \n", " return True\n", "\n", "indices_in_bound, = np.where(np.apply_along_axis(check_in_bounds, 1, csv,\n", " x_bounds, y_bounds))\n", "data_thresholded = csv[indices_in_bound]\n", "n = data_thresholded.shape[0]\n", "\n", "\n", "def synapses_over_unmasked(row):\n", " s = (row[4]/row[3])(64*3)\n", " return [row[0], row[1], row[2], s]\n", "\n", "syn_unmasked = np.apply_along_axis(synapses_over_unmasked, 1, data_thresholded)\n", "syn_normalized = syn_unmasked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract images from the imaging site of our proposed cortical layers" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'np' 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-1-8e8330cefa5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mimage_builder\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_image\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_thresholded\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\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 6\u001b[0m \u001b[0mys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_thresholded\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;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined" ] } ], "source": [ "# Looking at images across y, and of the layers in the y-direction\n", "#########################################################################################\n", "from image_builder import get_image\n", "\n", "xs = np.unique(data_thresholded[:,0])\n", "ys = np.unique(data_thresholded[:,1])\n", "\n", "# Layer across y\n", "get_image((0,1),(0,len(ys)-1),xs,ys, \"across_y\")\n", "\n", "# Each y-layer defined by bounds of local minima in total syn density at each y\n", "y_bounds = [(1564,1837), (1837,2071), (2071,2305), (2305,2539), (2539,3124)]\n", "\n", "for _, bounds in enumerate(y_bounds):\n", "\ty_lower = np.where(ys==bounds[0])[0][0]\n", "\ty_upper = np.where(ys==bounds[1])[0][0]\n", "\tprint y_lower,y_upper\n", "\ti = get_image((0,1),(y_lower,y_upper),xs,ys,str(bounds[0])+\"_\"+str(bounds[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Held at one x-value, spanning across all Y\n", "<img src=\"across_y.bmp\">\n", "### Y Idcs 1564:1837\n", "<img src=\"1564_1837.bmp\">\n", "### Y Idcs 1837:2071\n", "<img src=\"1837_2071.bmp\">\n", "### Y Idcs 2071:2305\n", "<img src=\"2071_2305.bmp\">\n", "### Y Idcs 2305:2539\n", "<img src=\"2305_2539.bmp\">\n", "### Y Idcs 2539:3124\n", "<img src=\"2539_3124.bmp\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0

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