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GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/recommendation_systems/labs/basic_retrieval.ipynb	1	93685	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Z17OmgavQfp4" }, "source": [ "# Recommending movies: retrieval\n", "\n", "Learning Objectives\n", "\n", "In this notebook, we're going to build and train such a two-tower model using the Movielens dataset.\n", "\n", "We're going to:\n", "\n", "1. Get our data and split it into a training and test set.\n", "2. Implement a retrieval model.\n", "3. Fit and evaluate it.\n", "4. Export it for efficient serving by building an approximate nearest neighbours (ANN) index.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "kCeYA79m1DEX" }, "source": [ "## Introduction\n", "Real-world recommender systems are often composed of two stages:\n", "\n", "1. The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this model is to efficiently weed out all candidates that the user is not interested in. Because the retrieval model may be dealing with millions of candidates, it has to be computationally efficient.\n", "2. The ranking stage takes the outputs of the retrieval model and fine-tunes them to select the best possible handful of recommendations. Its task is to narrow down the set of items the user may be interested in to a shortlist of likely candidates.\n", "\n", "In this notebook, we're going to focus on the first stage, retrieval. If you are interested in the ranking stage, have a look at our [ranking](basic_ranking) tutorial.\n", "\n", "Retrieval models are often composed of two sub-models:\n", "\n", "1. A query model computing the query representation (normally a fixed-dimensionality embedding vector) using query features.\n", "2. A candidate model computing the candidate representation (an equally-sized vector) using the candidate features\n", "\n", "The outputs of the two models are then multiplied together to give a query-candidate affinity score, with higher scores expressing a better match between the candidate and the query.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "H7QZ3kkMQo48" }, "source": [ "### The dataset\n", "\n", "The Movielens dataset is a classic dataset from the [GroupLens](https://grouplens.org/datasets/movielens/) research group at the University of Minnesota. It contains a set of ratings given to movies by a set of users, and is a workhorse of recommender system research.\n", "\n", "The data can be treated in two ways:\n", "\n", "1. It can be interpreted as expressesing which movies the users watched (and rated), and which they did not. This is a form of implicit feedback, where users' watches tell us which things they prefer to see and which they'd rather not see.\n", "2. It can also be seen as expressesing how much the users liked the movies they did watch. This is a form of explicit feedback: given that a user watched a movie, we can tell roughly how much they liked by looking at the rating they have given.\n", "\n", "In this notebook, we are focusing on a retrieval system: a model that predicts a set of movies from the catalogue that the user is likely to watch. Often, implicit data is more useful here, and so we are going to treat Movielens as an implicit system. This means that every movie a user watched is a positive example, and every movie they have not seen is an implicit negative example.\n", "\n", "Each learning objective will correspond to a __#TODO__ in the notebook where you will complete the notebook cell's code before running. Refer to the [solution](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/recommendation_systems/solutions/basic_retrieval.ipynb) for reference.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "sawo1x8kQk9b" }, "source": [ "## Imports\n", "\n", "\n", "Let's first get our imports out of the way." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:54:09.971545Z", "iopub.status.busy": "2020-11-19T23:54:09.968706Z", "iopub.status.idle": "2020-11-19T23:54:15.327905Z", "shell.execute_reply": "2020-11-19T23:54:15.327244Z" }, "id": "0vJOdh9WbTpd" }, "outputs": [], "source": [ "!pip install -q tensorflow-recommenders\n", "!pip install -q --upgrade tensorflow-datasets\n", "!pip install -q scann" ] }, { "cell_type": "markdown", "metadata": { "id": "sawo1x8kQk9b" }, "source": [ "Note: Please ignore the incompatibility errors and re-run the above cell before proceeding for the lab.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:54:15.333605Z", "iopub.status.busy": "2020-11-19T23:54:15.332879Z", "iopub.status.idle": "2020-11-19T23:54:23.005957Z", "shell.execute_reply": "2020-11-19T23:54:23.005309Z" }, "id": "SZGYDaF-m5wZ" }, "outputs": [], "source": [ "# Importing necessary modules\n", "import os\n", "import pprint\n", "import tempfile\n", "\n", "from typing import Dict, Text\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import tensorflow_datasets as tfds" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:54:23.010486Z", "iopub.status.busy": "2020-11-19T23:54:23.009830Z", "iopub.status.idle": "2020-11-19T23:54:23.373194Z", "shell.execute_reply": "2020-11-19T23:54:23.372587Z" }, "id": "BxQ_hy7xPH3N" }, "outputs": [], "source": [ "import tensorflow_recommenders as tfrs" ] }, { "cell_type": "markdown", "metadata": { "id": "5PAqjR4a1RR4" }, "source": [ "## Preparing the dataset\n", "\n", "Let's first have a look at the data.\n", "\n", "We use the MovieLens dataset from [Tensorflow Datasets](https://www.tensorflow.org/datasets). Loading `movielens/100k_ratings` yields a `tf.data.Dataset` object containing the ratings data and loading `movielens/100k_movies` yields a `tf.data.Dataset` object containing only the movies data.\n", "\n", "Note that since the MovieLens dataset does not have predefined splits, all data are under `train` split." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:54:23.378511Z", "iopub.status.busy": "2020-11-19T23:54:23.377856Z", "iopub.status.idle": "2020-11-19T23:55:34.157674Z", "shell.execute_reply": "2020-11-19T23:55:34.157128Z" }, "id": "aaQhqcLGP0jL" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mDownloading and preparing dataset movielens/100k-ratings/0.1.0 (download: 4.70 MiB, generated: 32.41 MiB, total: 37.10 MiB) to /home/kbuilder/tensorflow_datasets/movielens/100k-ratings/0.1.0...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/movielens/100k-ratings/0.1.0.incompleteN2R8B5/movielens-train.tfrecord\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mDataset movielens downloaded and prepared to /home/kbuilder/tensorflow_datasets/movielens/100k-ratings/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mDownloading and preparing dataset movielens/100k-movies/0.1.0 (download: 4.70 MiB, generated: 150.35 KiB, total: 4.84 MiB) to /home/kbuilder/tensorflow_datasets/movielens/100k-movies/0.1.0...\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/movielens/100k-movies/0.1.0.incompleteH88HCK/movielens-train.tfrecord\n", "\u001b[1mDataset movielens downloaded and prepared to /home/kbuilder/tensorflow_datasets/movielens/100k-movies/0.1.0. Subsequent calls will reuse this data.\u001b[0m\n" ] } ], "source": [ "# TODO 1 - Your code goes below here.\n", "# Ratings data.\n", "# Features of all the available movies.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JRHorm8W1yf3" }, "source": [ "The ratings dataset returns a dictionary of movie id, user id, the assigned rating, timestamp, movie information, and user information:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:34.162932Z", "iopub.status.busy": "2020-11-19T23:55:34.162262Z", "iopub.status.idle": "2020-11-19T23:55:34.213467Z", "shell.execute_reply": "2020-11-19T23:55:34.213898Z" }, "id": "_1-KQV2ynMdh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'bucketized_user_age': 45.0,\n", " 'movie_genres': array([7]),\n", " 'movie_id': b'357',\n", " 'movie_title': b\"One Flew Over the Cuckoo's Nest (1975)\",\n", " 'raw_user_age': 46.0,\n", " 'timestamp': 879024327,\n", " 'user_gender': True,\n", " 'user_id': b'138',\n", " 'user_occupation_label': 4,\n", " 'user_occupation_text': b'doctor',\n", " 'user_rating': 4.0,\n", " 'user_zip_code': b'53211'}\n" ] } ], "source": [ "# Printing the user information and movie information\n", "for x in ratings.take(1).as_numpy_iterator():\n", " pprint.pprint(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "qGLGCjSt_q96" }, "source": [ "The movies dataset contains the movie id, movie title, and data on what genres it belongs to. Note that the genres are encoded with integer labels." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:34.218885Z", "iopub.status.busy": "2020-11-19T23:55:34.218204Z", "iopub.status.idle": "2020-11-19T23:55:34.248818Z", "shell.execute_reply": "2020-11-19T23:55:34.249261Z" }, "id": "kHLsIHhw_x1d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'movie_genres': array([4]),\n", " 'movie_id': b'1681',\n", " 'movie_title': b'You So Crazy (1994)'}\n" ] } ], "source": [ "# Printing the data on what genres it belongs to\n", "for x in movies.take(1).as_numpy_iterator():\n", " pprint.pprint(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "YUdT-f4RxMKs" }, "source": [ "In this example, we're going to focus on the ratings data. Other notebooks explore how to use the movie information data as well to improve the model quality.\n", "\n", "We keep only the `user_id`, and `movie_title` fields in the dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:34.255269Z", "iopub.status.busy": "2020-11-19T23:55:34.254434Z", "iopub.status.idle": "2020-11-19T23:55:34.303953Z", "shell.execute_reply": "2020-11-19T23:55:34.303149Z" }, "id": "uhbEvPJqxLec" }, "outputs": [], "source": [ "# Here, we are focusing on the ratings data\n", "ratings = ratings.map(lambda x: {\n", " \"movie_title\": x[\"movie_title\"],\n", " \"user_id\": x[\"user_id\"],\n", "})\n", "movies = movies.map(lambda x: x[\"movie_title\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "Iu4XSa_G1nyN" }, "source": [ "To fit and evaluate the model, we need to split it into a training and evaluation set. In an industrial recommender system, this would most likely be done by time: the data up to time $T$ would be used to predict interactions after $T$.\n", "\n", "\n", "In this simple example, however, let's use a random split, putting 80% of the ratings in the train set, and 20% in the test set." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:34.309737Z", "iopub.status.busy": "2020-11-19T23:55:34.308925Z", "iopub.status.idle": "2020-11-19T23:55:34.313579Z", "shell.execute_reply": "2020-11-19T23:55:34.313049Z" }, "id": "rS0eDfkjnjJL" }, "outputs": [], "source": [ "# Here, using tf.random module to shuffle randomly a tensor in its first dimension\n", "tf.random.set_seed(42)\n", "shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)\n", "\n", "train = shuffled.take(80_000)\n", "test = shuffled.skip(80_000).take(20_000)" ] }, { "cell_type": "markdown", "metadata": { "id": "gVi1HJfR9D7H" }, "source": [ "Let's also figure out unique user ids and movie titles present in the data. \n", "\n", "This is important because we need to be able to map the raw values of our categorical features to embedding vectors in our models. To do that, we need a vocabulary that maps a raw feature value to an integer in a contiguous range: this allows us to look up the corresponding embeddings in our embedding tables." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:34.319472Z", "iopub.status.busy": "2020-11-19T23:55:34.318762Z", "iopub.status.idle": "2020-11-19T23:55:38.398905Z", "shell.execute_reply": "2020-11-19T23:55:38.399392Z" }, "id": "MKROCiPo_5LJ" }, "outputs": [ { "data": { "text/plain": [ "array([b\"'Til There Was You (1997)\", b'1-900 (1994)',\n", " b'101 Dalmatians (1996)', b'12 Angry Men (1957)', b'187 (1997)',\n", " b'2 Days in the Valley (1996)',\n", " b'20,000 Leagues Under the Sea (1954)',\n", " b'2001: A Space Odyssey (1968)',\n", " b'3 Ninjas: High Noon At Mega Mountain (1998)',\n", " b'39 Steps, The (1935)'], dtype=object)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Displaying the corresponding data according to the embedded tables.\n", "movie_titles = movies.batch(1_000)\n", "user_ids = ratings.batch(1_000_000).map(lambda x: x[\"user_id\"])\n", "\n", "unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))\n", "unique_user_ids = np.unique(np.concatenate(list(user_ids)))\n", "\n", "unique_movie_titles[:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "eCi-seR86qqa" }, "source": [ "## Implementing a model\n", "\n", "Choosing the architecure of our model a key part of modelling.\n", "\n", "Because we are building a two-tower retrieval model, we can build each tower separately and then combine them in the final model." ] }, { "cell_type": "markdown", "metadata": { "id": "z20PyfSXP3Um" }, "source": [ "### The query tower\n", "\n", "Let's start with the query tower.\n", "\n", "The first step is to decide on the dimensionality of the query and candidate representations:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.404100Z", "iopub.status.busy": "2020-11-19T23:55:38.403412Z", "iopub.status.idle": "2020-11-19T23:55:38.405427Z", "shell.execute_reply": "2020-11-19T23:55:38.405826Z" }, "id": "QbIy1FP8aCTq" }, "outputs": [], "source": [ "embedding_dimension = 32" ] }, { "cell_type": "markdown", "metadata": { "id": "IJYwjpLRaEzj" }, "source": [ "Higher values will correspond to models that may be more accurate, but will also be slower to fit and more prone to overfitting.\n", "\n", "The second is to define the model itself. Here, we're going to use Keras preprocessing layers to first convert user ids to integers, and then convert those to user embeddings via an `Embedding` layer. Note that we use the list of unique user ids we computed earlier as a vocabulary:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.411279Z", "iopub.status.busy": "2020-11-19T23:55:38.410595Z", "iopub.status.idle": "2020-11-19T23:55:38.434393Z", "shell.execute_reply": "2020-11-19T23:55:38.434901Z" }, "id": "kHQZJEhXP93N" }, "outputs": [], "source": [ "user_model = tf.keras.Sequential([\n", " tf.keras.layers.experimental.preprocessing.StringLookup(\n", " vocabulary=unique_user_ids, mask_token=None),\n", " # We add an additional embedding to account for unknown tokens.\n", " tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "Qvo2pEcdaiec" }, "source": [ "A simple model like this corresponds exactly to a classic [matrix factorization](https://ieeexplore.ieee.org/abstract/document/4781121) approach. While defining a subclass of `tf.keras.Model` for this simple model might be overkill, we can easily extend it to an arbitrarily complex model using standard Keras components, as long as we return an `embedding_dimension`-wide output at the end." ] }, { "cell_type": "markdown", "metadata": { "id": "dG4YFy9SQ08d" }, "source": [ "### The candidate tower\n", "\n", "We can do the same with the candidate tower." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.442915Z", "iopub.status.busy": "2020-11-19T23:55:38.442232Z", "iopub.status.idle": "2020-11-19T23:55:38.450293Z", "shell.execute_reply": "2020-11-19T23:55:38.450728Z" }, "id": "qNUwfIJTQ332" }, "outputs": [], "source": [ "movie_model = tf.keras.Sequential([\n", " tf.keras.layers.experimental.preprocessing.StringLookup(\n", " vocabulary=unique_movie_titles, mask_token=None),\n", " tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension)\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "r10RiPtqVIAl" }, "source": [ "### Metrics\n", "\n", "In our training data we have positive (user, movie) pairs. To figure out how good our model is, we need to compare the affinity score that the model calculates for this pair to the scores of all the other possible candidates: if the score for the positive pair is higher than for all other candidates, our model is highly accurate.\n", "\n", "To do this, we can use the `tfrs.metrics.FactorizedTopK` metric. The metric has one required argument: the dataset of candidates that are used as implicit negatives for evaluation.\n", "\n", "In our case, that's the `movies` dataset, converted into embeddings via our movie model:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.455869Z", "iopub.status.busy": "2020-11-19T23:55:38.455118Z", "iopub.status.idle": "2020-11-19T23:55:38.526325Z", "shell.execute_reply": "2020-11-19T23:55:38.525724Z" }, "id": "1dLDL6pZVPO8" }, "outputs": [], "source": [ "# Here, tfrs.metrics.FactorizedTopK function computes metrics for across top K candidates surfaced by a retrieval model.\n", "metrics = tfrs.metrics.FactorizedTopK(\n", " candidates=movies.batch(128).map(movie_model)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "nCaCqJsXSkCo" }, "source": [ "### Loss\n", "\n", "The next component is the loss used to train our model. TFRS has several loss layers and tasks to make this easy.\n", "\n", "In this instance, we'll make use of the `Retrieval` task object: a convenience wrapper that bundles together the loss function and metric computation:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.534258Z", "iopub.status.busy": "2020-11-19T23:55:38.533537Z", "iopub.status.idle": "2020-11-19T23:55:38.535402Z", "shell.execute_reply": "2020-11-19T23:55:38.535806Z" }, "id": "tJ61Iz2QTBw3" }, "outputs": [], "source": [ "# TODO 2 - Your code goes below here.\n", "# Here, the function bundles together the loss function and metric computation.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "9-3xFC-1cbz0" }, "source": [ "The task itself is a Keras layer that takes the query and candidate embeddings as arguments, and returns the computed loss: we'll use that to implement the model's training loop." ] }, { "cell_type": "markdown", "metadata": { "id": "FZUFeSlWRHGx" }, "source": [ "### The full model\n", "\n", "We can now put it all together into a model. TFRS exposes a base model class (`tfrs.models.Model`) which streamlines bulding models: all we need to do is to set up the components in the `__init__` method, and implement the `compute_loss` method, taking in the raw features and returning a loss value.\n", "\n", "The base model will then take care of creating the appropriate training loop to fit our model." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.542749Z", "iopub.status.busy": "2020-11-19T23:55:38.542072Z", "iopub.status.idle": "2020-11-19T23:55:38.543933Z", "shell.execute_reply": "2020-11-19T23:55:38.544345Z" }, "id": "8n7c5CHFp0ow" }, "outputs": [], "source": [ "class MovielensModel(tfrs.Model):\n", "\n", " def __init__(self, user_model, movie_model):\n", " super().__init__()\n", " self.movie_model: tf.keras.Model = movie_model\n", " self.user_model: tf.keras.Model = user_model\n", " self.task: tf.keras.layers.Layer = task\n", "\n", " def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n", " # We pick out the user features and pass them into the user model.\n", " user_embeddings = self.user_model(features[\"user_id\"])\n", " # And pick out the movie features and pass them into the movie model,\n", " # getting embeddings back.\n", " positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n", "\n", " # The task computes the loss and the metrics.\n", " return self.task(user_embeddings, positive_movie_embeddings)" ] }, { "cell_type": "markdown", "metadata": { "id": "I7B8PdfNqyuN" }, "source": [ "The `tfrs.Model` base class is a simply convenience class: it allows us to compute both training and test losses using the same method.\n", "\n", "Under the hood, it's still a plain Keras model. You could achieve the same functionality by inheriting from `tf.keras.Model` and overriding the `train_step` and `test_step` functions (see [the guide](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit) for details):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.555145Z", "iopub.status.busy": "2020-11-19T23:55:38.554389Z", "iopub.status.idle": "2020-11-19T23:55:38.556388Z", "shell.execute_reply": "2020-11-19T23:55:38.556792Z" }, "id": "Z3QywMjqsH4F" }, "outputs": [], "source": [ "class NoBaseClassMovielensModel(tf.keras.Model):\n", "\n", " def __init__(self, user_model, movie_model):\n", " super().__init__()\n", " self.movie_model: tf.keras.Model = movie_model\n", " self.user_model: tf.keras.Model = user_model\n", " self.task: tf.keras.layers.Layer = task\n", "\n", " def train_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n", "\n", " # Set up a gradient tape to record gradients.\n", " with tf.GradientTape() as tape:\n", "\n", " # Loss computation.\n", " user_embeddings = self.user_model(features[\"user_id\"])\n", " positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n", " loss = self.task(user_embeddings, positive_movie_embeddings)\n", "\n", " # Handle regularization losses as well.\n", " regularization_loss = sum(self.losses)\n", "\n", " total_loss = loss + regularization_loss\n", "\n", " gradients = tape.gradient(total_loss, self.trainable_variables)\n", " self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n", "\n", " metrics = {metric.name: metric.result() for metric in self.metrics}\n", " metrics[\"loss\"] = loss\n", " metrics[\"regularization_loss\"] = regularization_loss\n", " metrics[\"total_loss\"] = total_loss\n", "\n", " return metrics\n", "\n", " def test_step(self, features: Dict[Text, tf.Tensor]) -> tf.Tensor:\n", "\n", " # Loss computation.\n", " user_embeddings = self.user_model(features[\"user_id\"])\n", " positive_movie_embeddings = self.movie_model(features[\"movie_title\"])\n", " loss = self.task(user_embeddings, positive_movie_embeddings)\n", "\n", " # Handle regularization losses as well.\n", " regularization_loss = sum(self.losses)\n", "\n", " total_loss = loss + regularization_loss\n", "\n", " metrics = {metric.name: metric.result() for metric in self.metrics}\n", " metrics[\"loss\"] = loss\n", " metrics[\"regularization_loss\"] = regularization_loss\n", " metrics[\"total_loss\"] = total_loss\n", "\n", " return metrics" ] }, { "cell_type": "markdown", "metadata": { "id": "uHnl2nHMs_E0" }, "source": [ "In these notebooks, however, we stick to using the `tfrs.Model` base class to keep our focus on modelling and abstract away some of the boilerplate." ] }, { "cell_type": "markdown", "metadata": { "id": "yDN_LJGlnRGo" }, "source": [ "## Fitting and evaluating\n", "\n", "After defining the model, we can use standard Keras fitting and evaluation routines to fit and evaluate the model.\n", "\n", "Let's first instantiate the model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.563618Z", "iopub.status.busy": "2020-11-19T23:55:38.562890Z", "iopub.status.idle": "2020-11-19T23:55:38.580740Z", "shell.execute_reply": "2020-11-19T23:55:38.581184Z" }, "id": "aW63YaqP2wCf" }, "outputs": [], "source": [ "# Compiling the model.\n", "model = MovielensModel(user_model, movie_model)\n", "model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))" ] }, { "cell_type": "markdown", "metadata": { "id": "Nma0vc2XdN5g" }, "source": [ "Then shuffle, batch, and cache the training and evaluation data." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.585846Z", "iopub.status.busy": "2020-11-19T23:55:38.585173Z", "iopub.status.idle": "2020-11-19T23:55:38.589474Z", "shell.execute_reply": "2020-11-19T23:55:38.588968Z" }, "id": "53QJwY1gUnfv" }, "outputs": [], "source": [ "cached_train = train.shuffle(100_000).batch(8192).cache()\n", "cached_test = test.batch(4096).cache()" ] }, { "cell_type": "markdown", "metadata": { "id": "u8mHTxKAdTJO" }, "source": [ "Then train the model:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:38.595867Z", "iopub.status.busy": "2020-11-19T23:55:38.595164Z", "iopub.status.idle": "2020-11-19T23:55:48.304441Z", "shell.execute_reply": "2020-11-19T23:55:48.303920Z" }, "id": "ZxPntlT8EFOZ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:The dtype of the source tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Gradients do not exist for variables ['counter:0'] when minimizing the loss.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/10 [==>...........................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 4.8828e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0031 - factorized_top_k/top_10_categorical_accuracy: 0.0060 - factorized_top_k/top_50_categorical_accuracy: 0.0294 - factorized_top_k/top_100_categorical_accuracy: 0.0575 - loss: 73817.4219 - regularization_loss: 0.0000e+00 - total_loss: 73817.4219" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/10 [=====>........................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 9.7656e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0053 - factorized_top_k/top_10_categorical_accuracy: 0.0100 - factorized_top_k/top_50_categorical_accuracy: 0.0401 - factorized_top_k/top_100_categorical_accuracy: 0.0722 - loss: 73818.6172 - regularization_loss: 0.0000e+00 - total_loss: 73818.6172" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/10 [========>.....................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 8.1380e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0053 - factorized_top_k/top_10_categorical_accuracy: 0.0103 - factorized_top_k/top_50_categorical_accuracy: 0.0455 - factorized_top_k/top_100_categorical_accuracy: 0.0812 - loss: 73820.3542 - regularization_loss: 0.0000e+00 - total_loss: 73820.3542" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4/10 [===========>..................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 7.6294e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0059 - factorized_top_k/top_10_categorical_accuracy: 0.0111 - factorized_top_k/top_50_categorical_accuracy: 0.0511 - factorized_top_k/top_100_categorical_accuracy: 0.0915 - loss: 73818.2363 - regularization_loss: 0.0000e+00 - total_loss: 73818.2363" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5/10 [==============>...............] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 7.5684e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0062 - factorized_top_k/top_10_categorical_accuracy: 0.0125 - factorized_top_k/top_50_categorical_accuracy: 0.0590 - factorized_top_k/top_100_categorical_accuracy: 0.1058 - loss: 73806.6672 - regularization_loss: 0.0000e+00 - total_loss: 73806.6672" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6/10 [=================>............] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 8.7484e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0069 - factorized_top_k/top_10_categorical_accuracy: 0.0146 - factorized_top_k/top_50_categorical_accuracy: 0.0692 - factorized_top_k/top_100_categorical_accuracy: 0.1244 - loss: 73773.9023 - regularization_loss: 0.0000e+00 - total_loss: 73773.9023" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/10 [====================>.........] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 9.4169e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0077 - factorized_top_k/top_10_categorical_accuracy: 0.0163 - factorized_top_k/top_50_categorical_accuracy: 0.0791 - factorized_top_k/top_100_categorical_accuracy: 0.1420 - loss: 73709.2377 - regularization_loss: 0.0000e+00 - total_loss: 73709.2377" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/10 [=======================>......] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0010 - factorized_top_k/top_5_categorical_accuracy: 0.0084 - factorized_top_k/top_10_categorical_accuracy: 0.0179 - factorized_top_k/top_50_categorical_accuracy: 0.0873 - factorized_top_k/top_100_categorical_accuracy: 0.1569 - loss: 73613.5605 - regularization_loss: 0.0000e+00 - total_loss: 73613.5605 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/10 [==========================>...] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.0094 - factorized_top_k/top_10_categorical_accuracy: 0.0197 - factorized_top_k/top_50_categorical_accuracy: 0.0954 - factorized_top_k/top_100_categorical_accuracy: 0.1695 - loss: 73498.6771 - regularization_loss: 0.0000e+00 - total_loss: 73498.6771" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0011 - factorized_top_k/top_5_categorical_accuracy: 0.0090 - factorized_top_k/top_10_categorical_accuracy: 0.0190 - factorized_top_k/top_50_categorical_accuracy: 0.0961 - factorized_top_k/top_100_categorical_accuracy: 0.1735 - loss: 71511.2137 - regularization_loss: 0.0000e+00 - total_loss: 71511.2137" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - 2s 238ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0011 - factorized_top_k/top_5_categorical_accuracy: 0.0090 - factorized_top_k/top_10_categorical_accuracy: 0.0190 - factorized_top_k/top_50_categorical_accuracy: 0.0961 - factorized_top_k/top_100_categorical_accuracy: 0.1735 - loss: 69885.1072 - regularization_loss: 0.0000e+00 - total_loss: 69885.1072\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/10 [==>...........................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0021 - factorized_top_k/top_5_categorical_accuracy: 0.0171 - factorized_top_k/top_10_categorical_accuracy: 0.0363 - factorized_top_k/top_50_categorical_accuracy: 0.1646 - factorized_top_k/top_100_categorical_accuracy: 0.2823 - loss: 71919.5703 - regularization_loss: 0.0000e+00 - total_loss: 71919.5703" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/10 [=====>........................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 0.0024 - factorized_top_k/top_5_categorical_accuracy: 0.0183 - factorized_top_k/top_10_categorical_accuracy: 0.0370 - factorized_top_k/top_50_categorical_accuracy: 0.1612 - factorized_top_k/top_100_categorical_accuracy: 0.2811 - loss: 71759.9648 - regularization_loss: 0.0000e+00 - total_loss: 71759.9648" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/10 [========>.....................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 0.0024 - factorized_top_k/top_5_categorical_accuracy: 0.0184 - factorized_top_k/top_10_categorical_accuracy: 0.0364 - factorized_top_k/top_50_categorical_accuracy: 0.1639 - factorized_top_k/top_100_categorical_accuracy: 0.2832 - loss: 71605.0417 - regularization_loss: 0.0000e+00 - total_loss: 71605.0417" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4/10 [===========>..................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0187 - factorized_top_k/top_10_categorical_accuracy: 0.0368 - factorized_top_k/top_50_categorical_accuracy: 0.1663 - factorized_top_k/top_100_categorical_accuracy: 0.2876 - loss: 71451.6758 - regularization_loss: 0.0000e+00 - total_loss: 71451.6758" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5/10 [==============>...............] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0027 - factorized_top_k/top_5_categorical_accuracy: 0.0185 - factorized_top_k/top_10_categorical_accuracy: 0.0368 - factorized_top_k/top_50_categorical_accuracy: 0.1666 - factorized_top_k/top_100_categorical_accuracy: 0.2877 - loss: 71345.9578 - regularization_loss: 0.0000e+00 - total_loss: 71345.9578" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6/10 [=================>............] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0027 - factorized_top_k/top_5_categorical_accuracy: 0.0190 - factorized_top_k/top_10_categorical_accuracy: 0.0374 - factorized_top_k/top_50_categorical_accuracy: 0.1685 - factorized_top_k/top_100_categorical_accuracy: 0.2897 - loss: 71229.7331 - regularization_loss: 0.0000e+00 - total_loss: 71229.7331" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/10 [====================>.........] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0191 - factorized_top_k/top_10_categorical_accuracy: 0.0376 - factorized_top_k/top_50_categorical_accuracy: 0.1689 - factorized_top_k/top_100_categorical_accuracy: 0.2906 - loss: 71136.4208 - regularization_loss: 0.0000e+00 - total_loss: 71136.4208" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/10 [=======================>......] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0029 - factorized_top_k/top_5_categorical_accuracy: 0.0192 - factorized_top_k/top_10_categorical_accuracy: 0.0380 - factorized_top_k/top_50_categorical_accuracy: 0.1691 - factorized_top_k/top_100_categorical_accuracy: 0.2921 - loss: 71031.2070 - regularization_loss: 0.0000e+00 - total_loss: 71031.2070" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/10 [==========================>...] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0030 - factorized_top_k/top_5_categorical_accuracy: 0.0190 - factorized_top_k/top_10_categorical_accuracy: 0.0378 - factorized_top_k/top_50_categorical_accuracy: 0.1691 - factorized_top_k/top_100_categorical_accuracy: 0.2925 - loss: 70959.6267 - regularization_loss: 0.0000e+00 - total_loss: 70959.6267" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0187 - factorized_top_k/top_10_categorical_accuracy: 0.0374 - factorized_top_k/top_50_categorical_accuracy: 0.1686 - factorized_top_k/top_100_categorical_accuracy: 0.2919 - loss: 69069.6859 - regularization_loss: 0.0000e+00 - total_loss: 69069.6859" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - 2s 224ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0028 - factorized_top_k/top_5_categorical_accuracy: 0.0187 - factorized_top_k/top_10_categorical_accuracy: 0.0374 - factorized_top_k/top_50_categorical_accuracy: 0.1686 - factorized_top_k/top_100_categorical_accuracy: 0.2919 - loss: 67523.3707 - regularization_loss: 0.0000e+00 - total_loss: 67523.3707\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/10 [==>...........................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0042 - factorized_top_k/top_5_categorical_accuracy: 0.0190 - factorized_top_k/top_10_categorical_accuracy: 0.0422 - factorized_top_k/top_50_categorical_accuracy: 0.1798 - factorized_top_k/top_100_categorical_accuracy: 0.3037 - loss: 70034.4141 - regularization_loss: 0.0000e+00 - total_loss: 70034.4141" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/10 [=====>........................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0033 - factorized_top_k/top_5_categorical_accuracy: 0.0197 - factorized_top_k/top_10_categorical_accuracy: 0.0419 - factorized_top_k/top_50_categorical_accuracy: 0.1766 - factorized_top_k/top_100_categorical_accuracy: 0.3035 - loss: 69989.7812 - regularization_loss: 0.0000e+00 - total_loss: 69989.7812" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/10 [========>.....................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 0.0034 - factorized_top_k/top_5_categorical_accuracy: 0.0208 - factorized_top_k/top_10_categorical_accuracy: 0.0447 - factorized_top_k/top_50_categorical_accuracy: 0.1816 - factorized_top_k/top_100_categorical_accuracy: 0.3083 - loss: 69905.5260 - regularization_loss: 0.0000e+00 - total_loss: 69905.5260" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4/10 [===========>..................] - ETA: 1s - factorized_top_k/top_1_categorical_accuracy: 0.0034 - factorized_top_k/top_5_categorical_accuracy: 0.0218 - factorized_top_k/top_10_categorical_accuracy: 0.0450 - factorized_top_k/top_50_categorical_accuracy: 0.1852 - factorized_top_k/top_100_categorical_accuracy: 0.3118 - loss: 69823.8223 - regularization_loss: 0.0000e+00 - total_loss: 69823.8223" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5/10 [==============>...............] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0033 - factorized_top_k/top_5_categorical_accuracy: 0.0218 - factorized_top_k/top_10_categorical_accuracy: 0.0448 - factorized_top_k/top_50_categorical_accuracy: 0.1863 - factorized_top_k/top_100_categorical_accuracy: 0.3122 - loss: 69787.3266 - regularization_loss: 0.0000e+00 - total_loss: 69787.3266" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6/10 [=================>............] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0035 - factorized_top_k/top_5_categorical_accuracy: 0.0224 - factorized_top_k/top_10_categorical_accuracy: 0.0455 - factorized_top_k/top_50_categorical_accuracy: 0.1873 - factorized_top_k/top_100_categorical_accuracy: 0.3150 - loss: 69728.5768 - regularization_loss: 0.0000e+00 - total_loss: 69728.5768" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/10 [====================>.........] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0035 - factorized_top_k/top_5_categorical_accuracy: 0.0226 - factorized_top_k/top_10_categorical_accuracy: 0.0460 - factorized_top_k/top_50_categorical_accuracy: 0.1874 - factorized_top_k/top_100_categorical_accuracy: 0.3151 - loss: 69698.6596 - regularization_loss: 0.0000e+00 - total_loss: 69698.6596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/10 [=======================>......] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0036 - factorized_top_k/top_5_categorical_accuracy: 0.0226 - factorized_top_k/top_10_categorical_accuracy: 0.0460 - factorized_top_k/top_50_categorical_accuracy: 0.1881 - factorized_top_k/top_100_categorical_accuracy: 0.3162 - loss: 69642.8701 - regularization_loss: 0.0000e+00 - total_loss: 69642.8701" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/10 [==========================>...] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0036 - factorized_top_k/top_5_categorical_accuracy: 0.0225 - factorized_top_k/top_10_categorical_accuracy: 0.0460 - factorized_top_k/top_50_categorical_accuracy: 0.1883 - factorized_top_k/top_100_categorical_accuracy: 0.3159 - loss: 69621.0391 - regularization_loss: 0.0000e+00 - total_loss: 69621.0391" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.0035 - factorized_top_k/top_5_categorical_accuracy: 0.0222 - factorized_top_k/top_10_categorical_accuracy: 0.0457 - factorized_top_k/top_50_categorical_accuracy: 0.1877 - factorized_top_k/top_100_categorical_accuracy: 0.3158 - loss: 67796.0961 - regularization_loss: 0.0000e+00 - total_loss: 67796.0961" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10/10 [==============================] - 2s 219ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0035 - factorized_top_k/top_5_categorical_accuracy: 0.0222 - factorized_top_k/top_10_categorical_accuracy: 0.0457 - factorized_top_k/top_50_categorical_accuracy: 0.1877 - factorized_top_k/top_100_categorical_accuracy: 0.3158 - loss: 66302.9609 - regularization_loss: 0.0000e+00 - total_loss: 66302.9609\n" ] }, { "data": { "text/plain": [ "<tensorflow.python.keras.callbacks.History at 0x7f04c679ca20>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 3a - Your code goes below here.\n", "# Training the model.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "YsluR8audV9W" }, "source": [ "As the model trains, the loss is falling and a set of top-k retrieval metrics is updated. These tell us whether the true positive is in the top-k retrieved items from the entire candidate set. For example, a top-5 categorical accuracy metric of 0.2 would tell us that, on average, the true positive is in the top 5 retrieved items 20% of the time.\n", "\n", "Note that, in this example, we evaluate the metrics during training as well as evaluation. Because this can be quite slow with large candidate sets, it may be prudent to turn metric calculation off in training, and only run it in evaluation." ] }, { "cell_type": "markdown", "metadata": { "id": "7Gxp5RLFcv64" }, "source": [ "Finally, we can evaluate our model on the test set:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:48.310172Z", "iopub.status.busy": "2020-11-19T23:55:48.309127Z", "iopub.status.idle": "2020-11-19T23:55:49.757116Z", "shell.execute_reply": "2020-11-19T23:55:49.757537Z" }, "id": "W-zu6HLODNeI" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 4.8828e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0083 - factorized_top_k/top_10_categorical_accuracy: 0.0215 - factorized_top_k/top_50_categorical_accuracy: 0.1299 - factorized_top_k/top_100_categorical_accuracy: 0.2319 - loss: 32467.8516 - regularization_loss: 0.0000e+00 - total_loss: 32467.8516" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "2/5 [===========>..................] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 9.7656e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0085 - factorized_top_k/top_10_categorical_accuracy: 0.0198 - factorized_top_k/top_50_categorical_accuracy: 0.1234 - factorized_top_k/top_100_categorical_accuracy: 0.2305 - loss: 32508.3057 - regularization_loss: 0.0000e+00 - total_loss: 32508.3057" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "3/5 [=================>............] - 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1s 118ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0010 - factorized_top_k/top_5_categorical_accuracy: 0.0097 - factorized_top_k/top_10_categorical_accuracy: 0.0226 - factorized_top_k/top_50_categorical_accuracy: 0.1244 - factorized_top_k/top_100_categorical_accuracy: 0.2327 - loss: 31079.0628 - regularization_loss: 0.0000e+00 - total_loss: 31079.0628\n" ] }, { "data": { "text/plain": [ "{'factorized_top_k/top_1_categorical_accuracy': 0.0010000000474974513,\n", " 'factorized_top_k/top_5_categorical_accuracy': 0.009650000371038914,\n", " 'factorized_top_k/top_10_categorical_accuracy': 0.022600000724196434,\n", " 'factorized_top_k/top_50_categorical_accuracy': 0.12439999729394913,\n", " 'factorized_top_k/top_100_categorical_accuracy': 0.23270000517368317,\n", " 'loss': 28244.76953125,\n", " 'regularization_loss': 0,\n", " 'total_loss': 28244.76953125}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 3b - Your code goes below here.\n", "# Evaluating the model.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JKZyP9A1dxit" }, "source": [ "Test set performance is much worse than training performance. This is due to two factors:\n", "\n", "1. Our model is likely to perform better on the data that it has seen, simply because it can memorize it. This overfitting phenomenon is especially strong when models have many parameters. It can be mediated by model regularization and use of user and movie features that help the model generalize better to unseen data.\n", "2. The model is re-recommending some of users' already watched movies. These known-positive watches can crowd out test movies out of top K recommendations.\n", "\n", "The second phenomenon can be tackled by excluding previously seen movies from test recommendations. This approach is relatively common in the recommender systems literature, but we don't follow it in these notebooks. If not recommending past watches is important, we should expect appropriately specified models to learn this behaviour automatically from past user history and contextual information. Additionally, it is often appropriate to recommend the same item multiple times (say, an evergreen TV series or a regularly purchased item)." ] }, { "cell_type": "markdown", "metadata": { "id": "NB2v43NJU3Xf" }, "source": [ "## Making predictions\n", "\n", "Now that we have a model, we would like to be able to make predictions. We can use the `tfrs.layers.factorized_top_k.BruteForce` layer to do this." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:49.783831Z", "iopub.status.busy": "2020-11-19T23:55:49.782958Z", "iopub.status.idle": "2020-11-19T23:55:49.986051Z", "shell.execute_reply": "2020-11-19T23:55:49.986495Z" }, "id": "IRD6bEtZW_8j" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Recommendations for user 42: [b'Bridges of Madison County, The (1995)'\n", " b'Father of the Bride Part II (1995)' b'Rudy (1993)']\n" ] } ], "source": [ "# Create a model that takes in raw query features, and\n", "index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)\n", "# recommends movies out of the entire movies dataset.\n", "index.index_from_dataset(\n", " tf.data.Dataset.zip((movies.batch(100), movies.batch(100).map(model.movie_model)))\n", ")\n", "\n", "# Get recommendations.\n", "_, titles = index(tf.constant([\"42\"]))\n", "print(f\"Recommendations for user 42: {titles[0, :3]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "cOoLv6ZMKg0L" }, "source": [ "Of course, the `BruteForce` layer is going to be too slow to serve a model with many possible candidates. The following sections shows how to speed this up by using an approximate retrieval index." ] }, { "cell_type": "markdown", "metadata": { "id": "pFlvp5DK4Ow8" }, "source": [ "## Model serving\n", "\n", "After the model is trained, we need a way to deploy it.\n", "\n", "In a two-tower retrieval model, serving has two components:\n", "\n", "- a serving query model, taking in features of the query and transforming them into a query embedding, and\n", "- a serving candidate model. This most often takes the form of an approximate nearest neighbours (ANN) index which allows fast approximate lookup of candidates in response to a query produced by the query model." ] }, { "cell_type": "markdown", "metadata": { "id": "NmhfltdpWZ06" }, "source": [ "In TFRS, both components can be packaged into a single exportable model, giving us a model that takes the raw user id and returns the titles of top movies for that user. This is done via exporting the model to a `SavedModel` format, which makes it possible to serve using [TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving).\n", "\n", "To deploy a model like this, we simply export the `BruteForce` layer we created above:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:49.993195Z", "iopub.status.busy": "2020-11-19T23:55:49.991304Z", "iopub.status.idle": "2020-11-19T23:55:50.846812Z", "shell.execute_reply": "2020-11-19T23:55:50.847256Z" }, "id": "oJkRNBfCW5_E" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2021-09-29 13:25:05.366590: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n", "WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tmpklbnqnat/model/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tmpklbnqnat/model/assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Recommendations: [b'Bridges of Madison County, The (1995)'\n", " b'Father of the Bride Part II (1995)' b'Rudy (1993)']\n" ] } ], "source": [ "# Export the query model.\n", "# TODO 4 - Your code goes here.\n", "\n", " # Save the index.\n", " tf.saved_model.save(index, path)\n", "\n", " # Load it back; can also be done in TensorFlow Serving.\n", " loaded = tf.saved_model.load(path)\n", "\n", " # Pass a user id in, get top predicted movie titles back.\n", " scores, titles = loaded([\"42\"])\n", "\n", " print(f\"Recommendations: {titles[0][:3]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "nBBGodbE5ENC" }, "source": [ "We can also export an approximate retrieval index to speed up predictions. This will make it possible to efficiently surface recommendations from sets of tens of millions of candidates.\n", "\n", "To do so, we can use the `scann` package. This is an optional dependency of TFRS, and we installed it separately at the beginning of this notebook by calling `!pip install -q scann`." ] }, { "cell_type": "markdown", "metadata": { "id": "15PtZqoO5k_k" }, "source": [ "Once installed we can use the TFRS `ScaNN` layer:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:50.871341Z", "iopub.status.busy": "2020-11-19T23:55:50.870436Z", "iopub.status.idle": "2020-11-19T23:55:51.206268Z", "shell.execute_reply": "2020-11-19T23:55:51.206720Z" }, "id": "rTz8yxyp5uwU" }, "outputs": [ { "data": { "text/plain": [ "<tensorflow_recommenders.layers.factorized_top_k.ScaNN at 0x7f04c6617dd8>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scann_index = tfrs.layers.factorized_top_k.ScaNN(model.user_model)\n", "scann_index.index_from_dataset(\n", " tf.data.Dataset.zip((movies.batch(100), movies.batch(100).map(model.movie_model)))\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "UpLnoUv256bS" }, "source": [ "This layer will perform _approximate_ lookups: this makes retrieval slightly less accurate, but orders of magnitude faster on large candidate sets." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:51.216049Z", "iopub.status.busy": "2020-11-19T23:55:51.215339Z", "iopub.status.idle": "2020-11-19T23:55:51.274544Z", "shell.execute_reply": "2020-11-19T23:55:51.275002Z" }, "id": "Te_MGu1Q6JrU" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Recommendations for user 42: [b'Bridges of Madison County, The (1995)'\n", " b'Father of the Bride Part II (1995)' b'Rudy (1993)']\n" ] } ], "source": [ "# Get recommendations.\n", "_, titles = scann_index(tf.constant([\"42\"]))\n", "print(f\"Recommendations for user 42: {titles[0, :3]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Izo0IUMA6TQm" }, "source": [ "Exporting it for serving is as easy as exporting the `BruteForce` layer:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2020-11-19T23:55:51.281935Z", "iopub.status.busy": "2020-11-19T23:55:51.281218Z", "iopub.status.idle": "2020-11-19T23:55:52.228819Z", "shell.execute_reply": "2020-11-19T23:55:52.229297Z" }, "id": "K7NUqgxU6W_T" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tmpnw8jix65/model/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tmpnw8jix65/model/assets\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Recommendations: [b'Bridges of Madison County, The (1995)'\n", " b'Father of the Bride Part II (1995)' b'Rudy (1993)']\n" ] } ], "source": [ "# Export the query model.\n", "with tempfile.TemporaryDirectory() as tmp:\n", " path = os.path.join(tmp, \"model\")\n", "\n", " # Save the index.\n", " tf.saved_model.save(\n", " index,\n", " path,\n", " options=tf.saved_model.SaveOptions(namespace_whitelist=[\"Scann\"])\n", " )\n", "\n", " # Load it back; can also be done in TensorFlow Serving.\n", " loaded = tf.saved_model.load(path)\n", "\n", " # Pass a user id in, get top predicted movie titles back.\n", " scores, titles = loaded([\"42\"])\n", "\n", " print(f\"Recommendations: {titles[0][:3]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "JAoYm6Rk6xLI" }, "source": [ "To learn more about using and tuning fast approximate retrieval models, have a look at our [efficient serving](https://tensorflow.org/recommenders/examples/efficient_serving) notebook." ] }, { "cell_type": "markdown", "metadata": { "id": "ws4w5jQ3fX87" }, "source": [ "## Next steps\n", "\n", "This concludes the retrieval notebook.\n", "\n", "To expand on what is presented here, have a look at:\n", "\n", "1. Learning multi-task models: jointly optimizing for ratings and clicks.\n", "2. Using movie metadata: building a more complex movie model to alleviate cold-start." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i0UFyyocksOF" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "basic_retrieval.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
MieRobot/Blogs	tensorflow_SimpleANN_Blog.ipynb	1	30726	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A simple Tensorflow Addition to test that all is ok " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 35. 24. 720.]\n" ] } ], "source": [ "import os\n", "import tensorflow as tf\n", "\n", "# Turn off TensorFlow warning messages in program output.This is an optional step\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "\n", "# Define computational graph\n", "X = tf.placeholder(tf.float32, name=\"X\")\n", "Y = tf.placeholder(tf.float32, name=\"Y\")\n", "\n", "addition = tf.add(X, Y, name=\"addition\")\n", "\n", "\n", "# Create the TF session\n", "with tf.Session() as session:\n", "\n", " result = session.run(addition, feed_dict={X: [11, 12, 410], Y: [24, 12, 310]})\n", "\n", " print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Scale input values using this code block" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400, 9)\n", "(400, 1)\n", "Note: Y values were scaled by multiplying by 0.0000036968 and adding -0.1159\n" ] } ], "source": [ "import tensorflow as tf\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "# Load training data set from CSV file\n", "training_data_df = pd.read_csv(\"MieRobot_ANN_training.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_training = training_data_df.drop('total_earnings', axis=1).values\n", "Y_training = training_data_df[['total_earnings']].values\n", "\n", "# Load testing data set from CSV file\n", "test_data_df = pd.read_csv(\"MieRobot_ANN_test.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_testing = test_data_df.drop('total_earnings', axis=1).values\n", "Y_testing = test_data_df[['total_earnings']].values\n", "\n", "# All data needs to be scaled to a small range like 0 to 1 for the neural\n", "# network to work well. Create scalers for the inputs and outputs.\n", "X_scaler = MinMaxScaler(feature_range=(0, 1))\n", "Y_scaler = MinMaxScaler(feature_range=(0, 1))\n", "\n", "# Scale both the training inputs and outputs\n", "X_scaled_training = X_scaler.fit_transform(X_training)\n", "Y_scaled_training = Y_scaler.fit_transform(Y_training)\n", "\n", "# It's very important that the training and test data are scaled with the same scaler.\n", "X_scaled_testing = X_scaler.transform(X_testing)\n", "Y_scaled_testing = Y_scaler.transform(Y_testing)\n", "\n", "print(X_scaled_testing.shape)\n", "print(Y_scaled_testing.shape)\n", "\n", "print(\"Note: Y values were scaled by multiplying by {:.10f} and adding {:.4f}\".format(Y_scaler.scale_[0], Y_scaler.min_[0]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train the model now" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training pass: 0\n", "Training pass: 1\n", "Training pass: 2\n", "Training pass: 3\n", "Training pass: 4\n", "Training pass: 5\n", "Training pass: 6\n", "Training pass: 7\n", "Training pass: 8\n", "Training pass: 9\n", "Training pass: 10\n", "Training pass: 11\n", "Training pass: 12\n", "Training pass: 13\n", "Training pass: 14\n", "Training pass: 15\n", "Training pass: 16\n", "Training pass: 17\n", "Training pass: 18\n", "Training pass: 19\n", "Training pass: 20\n", "Training pass: 21\n", "Training pass: 22\n", "Training pass: 23\n", "Training pass: 24\n", "Training pass: 25\n", "Training pass: 26\n", "Training pass: 27\n", "Training pass: 28\n", "Training pass: 29\n", "Training pass: 30\n", "Training pass: 31\n", "Training pass: 32\n", "Training pass: 33\n", "Training pass: 34\n", "Training pass: 35\n", "Training pass: 36\n", "Training pass: 37\n", "Training pass: 38\n", "Training pass: 39\n", "Training pass: 40\n", "Training pass: 41\n", "Training pass: 42\n", "Training pass: 43\n", "Training pass: 44\n", "Training pass: 45\n", "Training pass: 46\n", "Training pass: 47\n", "Training pass: 48\n", "Training pass: 49\n", "Training pass: 50\n", "Training pass: 51\n", "Training pass: 52\n", "Training pass: 53\n", "Training pass: 54\n", "Training pass: 55\n", "Training pass: 56\n", "Training pass: 57\n", "Training pass: 58\n", "Training pass: 59\n", "Training pass: 60\n", "Training pass: 61\n", "Training pass: 62\n", "Training pass: 63\n", "Training pass: 64\n", "Training pass: 65\n", "Training pass: 66\n", "Training pass: 67\n", "Training pass: 68\n", "Training pass: 69\n", "Training pass: 70\n", "Training pass: 71\n", "Training pass: 72\n", "Training pass: 73\n", "Training pass: 74\n", "Training pass: 75\n", "Training pass: 76\n", "Training pass: 77\n", "Training pass: 78\n", "Training pass: 79\n", "Training pass: 80\n", "Training pass: 81\n", "Training pass: 82\n", "Training pass: 83\n", "Training pass: 84\n", "Training pass: 85\n", "Training pass: 86\n", "Training pass: 87\n", "Training pass: 88\n", "Training pass: 89\n", "Training pass: 90\n", "Training pass: 91\n", "Training pass: 92\n", "Training pass: 93\n", "Training pass: 94\n", "Training pass: 95\n", "Training pass: 96\n", "Training pass: 97\n", "Training pass: 98\n", "Training pass: 99\n", "Training is complete!\n" ] } ], "source": [ "import os\n", "import tensorflow as tf\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler\n", "tf.reset_default_graph()\n", "\n", "# Turn off TensorFlow warning messages in program output\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "\n", "# Load training data set from CSV file\n", "training_data_df = pd.read_csv(\"MieRobot_ANN_training.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_training = training_data_df.drop('total_earnings', axis=1).values\n", "Y_training = training_data_df[['total_earnings']].values\n", "\n", "# Load testing data set from CSV file\n", "test_data_df = pd.read_csv(\"MieRobot_ANN_test.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_testing = test_data_df.drop('total_earnings', axis=1).values\n", "Y_testing = test_data_df[['total_earnings']].values\n", "\n", "# All data needs to be scaled to a small range like 0 to 1 for the neural\n", "# network to work well. Create scalers for the inputs and outputs.\n", "X_scaler = MinMaxScaler(feature_range=(0, 1))\n", "Y_scaler = MinMaxScaler(feature_range=(0, 1))\n", "\n", "# Scale both the training inputs and outputs\n", "X_scaled_training = X_scaler.fit_transform(X_training)\n", "Y_scaled_training = Y_scaler.fit_transform(Y_training)\n", "\n", "# It's very important that the training and test data are scaled with the same scaler.\n", "X_scaled_testing = X_scaler.transform(X_testing)\n", "Y_scaled_testing = Y_scaler.transform(Y_testing)\n", "\n", "# Define model parameters\n", "learning_rate = 0.001\n", "training_epochs = 100\n", "\n", "# Define how many inputs and outputs are in our neural network\n", "number_of_inputs = 9\n", "number_of_outputs = 1\n", "\n", "# Define how many neurons we want in each layer of our neural network\n", "layer_1_nodes = 50\n", "layer_2_nodes = 100\n", "layer_3_nodes = 50\n", "\n", "# Section One: Define the layers of the neural network itself\n", "\n", "# Input Layer\n", "with tf.variable_scope('input'):\n", " X = tf.placeholder(tf.float32, shape=(None, number_of_inputs))\n", "\n", "# Layer 1\n", "with tf.variable_scope('layer_1'):\n", " weights = tf.get_variable(\"weights1\", shape=[number_of_inputs, layer_1_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases1\", shape=[layer_1_nodes], initializer=tf.zeros_initializer())\n", " layer_1_output = tf.nn.relu(tf.matmul(X, weights) + biases)\n", "\n", "# Layer 2\n", "with tf.variable_scope('layer_2'):\n", " weights = tf.get_variable(\"weights2\", shape=[layer_1_nodes, layer_2_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases2\", shape=[layer_2_nodes], initializer=tf.zeros_initializer())\n", " layer_2_output = tf.nn.relu(tf.matmul(layer_1_output, weights) + biases)\n", "\n", "# Layer 3\n", "with tf.variable_scope('layer_3'):\n", " weights = tf.get_variable(\"weights3\", shape=[layer_2_nodes, layer_3_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases3\", shape=[layer_3_nodes], initializer=tf.zeros_initializer())\n", " layer_3_output = tf.nn.relu(tf.matmul(layer_2_output, weights) + biases)\n", "\n", "# Output Layer\n", "with tf.variable_scope('output'):\n", " weights = tf.get_variable(\"weights4\", shape=[layer_3_nodes, number_of_outputs], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases4\", shape=[number_of_outputs], initializer=tf.zeros_initializer())\n", " prediction = tf.matmul(layer_3_output, weights) + biases\n", "\n", "# Section Two: Define the cost function of the neural network that will measure prediction accuracy during training\n", "\n", "with tf.variable_scope('cost'):\n", " Y = tf.placeholder(tf.float32, shape=(None, 1))\n", " cost = tf.reduce_mean(tf.squared_difference(prediction, Y))\n", "\n", "# Section Three: Define the optimizer function that will be run to optimize the neural network\n", "\n", "with tf.variable_scope('train'):\n", " optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initialize a session so that we can run TensorFlow operations\n", "with tf.Session() as session:\n", "\n", " # Run the global variable initializer to initialize all variables and layers of the neural network\n", " session.run(tf.global_variables_initializer())\n", "\n", " # Run the optimizer over and over to train the network.\n", " # One epoch is one full run through the training data set.\n", " for epoch in range(training_epochs):\n", "\n", " # Feed in the training data and do one step of neural network training\n", " session.run(optimizer, feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", "\n", " # Print the current training status to the screen\n", " print(\"Training pass: {}\".format(epoch))\n", "\n", " # Training is now complete!\n", " print(\"Training is complete!\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test the model now" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 0.0667528 0.0737708\n", "5 0.0321336 0.0305474\n", "10 0.0108414 0.0116709\n", "15 0.0121536 0.0135033\n", "20 0.0061399 0.00672782\n", "25 0.00612877 0.00630204\n", "30 0.00320352 0.00334384\n", "35 0.00269075 0.00277665\n", "40 0.00198481 0.00198061\n", "45 0.00134662 0.00140551\n", "50 0.00120571 0.001271\n", "55 0.00103251 0.0010365\n", "60 0.000831294 0.000861058\n", "65 0.00067845 0.00068965\n", "70 0.000577899 0.000599851\n", "75 0.00050239 0.000524183\n", "80 0.0004463 0.000462744\n", "85 0.00039605 0.000422027\n", "90 0.000350747 0.000374055\n", "95 0.000311117 0.000340319\n", "Training is complete!\n", "Final Training cost: 0.0002832856844179332\n", "Final Testing cost: 0.00030901801073923707\n" ] } ], "source": [ "import os\n", "import tensorflow as tf\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler\n", "tf.reset_default_graph()\n", "\n", "# Turn off TensorFlow warning messages in program output\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "\n", "# Load training data set from CSV file\n", "training_data_df = pd.read_csv(\"MieRobot_ANN_training.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_training = training_data_df.drop('total_earnings', axis=1).values\n", "Y_training = training_data_df[['total_earnings']].values\n", "\n", "# Load testing data set from CSV file\n", "test_data_df = pd.read_csv(\"MieRobot_ANN_test.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_testing = test_data_df.drop('total_earnings', axis=1).values\n", "Y_testing = test_data_df[['total_earnings']].values\n", "\n", "# All data needs to be scaled to a small range like 0 to 1 for the neural\n", "# network to work well. Create scalers for the inputs and outputs.\n", "X_scaler = MinMaxScaler(feature_range=(0, 1))\n", "Y_scaler = MinMaxScaler(feature_range=(0, 1))\n", "\n", "# Scale both the training inputs and outputs\n", "X_scaled_training = X_scaler.fit_transform(X_training)\n", "Y_scaled_training = Y_scaler.fit_transform(Y_training)\n", "\n", "# It's very important that the training and test data are scaled with the same scaler.\n", "X_scaled_testing = X_scaler.transform(X_testing)\n", "Y_scaled_testing = Y_scaler.transform(Y_testing)\n", "\n", "# Define model parameters\n", "learning_rate = 0.001\n", "training_epochs = 100\n", "\n", "# Define how many inputs and outputs are in our neural network\n", "number_of_inputs = 9\n", "number_of_outputs = 1\n", "\n", "# Define how many neurons we want in each layer of our neural network\n", "layer_1_nodes = 50\n", "layer_2_nodes = 100\n", "layer_3_nodes = 50\n", "\n", "# Section One: Define the layers of the neural network itself\n", "\n", "# Input Layer\n", "with tf.variable_scope('input'):\n", " X = tf.placeholder(tf.float32, shape=(None, number_of_inputs))\n", "\n", "# Layer 1\n", "with tf.variable_scope('layer_1'):\n", " weights = tf.get_variable(\"weights1\", shape=[number_of_inputs, layer_1_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases1\", shape=[layer_1_nodes], initializer=tf.zeros_initializer())\n", " layer_1_output = tf.nn.relu(tf.matmul(X, weights) + biases)\n", "\n", "# Layer 2\n", "with tf.variable_scope('layer_2'):\n", " weights = tf.get_variable(\"weights2\", shape=[layer_1_nodes, layer_2_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases2\", shape=[layer_2_nodes], initializer=tf.zeros_initializer())\n", " layer_2_output = tf.nn.relu(tf.matmul(layer_1_output, weights) + biases)\n", "\n", "# Layer 3\n", "with tf.variable_scope('layer_3'):\n", " weights = tf.get_variable(\"weights3\", shape=[layer_2_nodes, layer_3_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases3\", shape=[layer_3_nodes], initializer=tf.zeros_initializer())\n", " layer_3_output = tf.nn.relu(tf.matmul(layer_2_output, weights) + biases)\n", "\n", "# Output Layer\n", "with tf.variable_scope('output'):\n", " weights = tf.get_variable(\"weights4\", shape=[layer_3_nodes, number_of_outputs], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases4\", shape=[number_of_outputs], initializer=tf.zeros_initializer())\n", " prediction = tf.matmul(layer_3_output, weights) + biases\n", "\n", "# Section Two: Define the cost function of the neural network that will measure prediction accuracy during training\n", "\n", "with tf.variable_scope('cost'):\n", " Y = tf.placeholder(tf.float32, shape=(None, 1))\n", " cost = tf.reduce_mean(tf.squared_difference(prediction, Y))\n", "\n", "# Section Three: Define the optimizer function that will be run to optimize the neural network\n", "\n", "with tf.variable_scope('train'):\n", " optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initialize a session so that we can run TensorFlow operations\n", "with tf.Session() as session:\n", "\n", " # Run the global variable initializer to initialize all variables and layers of the neural network\n", " session.run(tf.global_variables_initializer())\n", "\n", " # Run the optimizer over and over to train the network.\n", " # One epoch is one full run through the training data set.\n", " for epoch in range(training_epochs):\n", "\n", " # Feed in the training data and do one step of neural network training\n", " session.run(optimizer, feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", "\n", " # Every 5 training steps, log our progress\n", " if epoch % 5 == 0:\n", " training_cost = session.run(cost, feed_dict={X: X_scaled_training, Y:Y_scaled_training})\n", " testing_cost = session.run(cost, feed_dict={X: X_scaled_testing, Y:Y_scaled_testing})\n", "\n", " print(epoch, training_cost, testing_cost)\n", "\n", " # Training is now complete!\n", " print(\"Training is complete!\")\n", "\n", " final_training_cost = session.run(cost, feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", " final_testing_cost = session.run(cost, feed_dict={X: X_scaled_testing, Y: Y_scaled_testing})\n", "\n", " print(\"Final Training cost: {}\".format(final_training_cost))\n", " print(\"Final Testing cost: {}\".format(final_testing_cost))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# This is the final Neural network code below# " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 - Training Cost: 0.024694375693798065 Testing Cost: 0.02702219784259796\n", "Epoch: 5 - Training Cost: 0.008877929300069809 Testing Cost: 0.009789608418941498\n", "Epoch: 10 - Training Cost: 0.005550766829401255 Testing Cost: 0.00584864616394043\n", "Epoch: 15 - Training Cost: 0.004309948533773422 Testing Cost: 0.004305743146687746\n", "Epoch: 20 - Training Cost: 0.0032398439943790436 Testing Cost: 0.0031237872317433357\n", "Epoch: 25 - Training Cost: 0.0018691291334107518 Testing Cost: 0.0019262742716819048\n", "Epoch: 30 - Training Cost: 0.001225202577188611 Testing Cost: 0.0011948688188567758\n", "Epoch: 35 - Training Cost: 0.0008175538969226182 Testing Cost: 0.0009072692482732236\n", "Epoch: 40 - Training Cost: 0.0006240037619136274 Testing Cost: 0.0006439540302380919\n", "Epoch: 45 - Training Cost: 0.000448241044068709 Testing Cost: 0.0004834662831854075\n", "Epoch: 50 - Training Cost: 0.00035360027686692774 Testing Cost: 0.0003569948603399098\n", "Epoch: 55 - Training Cost: 0.0002707366365939379 Testing Cost: 0.00031056092120707035\n", "Epoch: 60 - Training Cost: 0.00021171879780013114 Testing Cost: 0.00023380108177661896\n", "Epoch: 65 - Training Cost: 0.00016061236965470016 Testing Cost: 0.00020543806022033095\n", "Epoch: 70 - Training Cost: 0.0001227235043188557 Testing Cost: 0.00015429411723744124\n", "Epoch: 75 - Training Cost: 0.00010051235585706308 Testing Cost: 0.00012369819160085171\n", "Epoch: 80 - Training Cost: 8.735605661058798e-05 Testing Cost: 0.00011311905109323561\n", "Epoch: 85 - Training Cost: 7.444804941769689e-05 Testing Cost: 9.646387479733676e-05\n", "Epoch: 90 - Training Cost: 6.438292621169239e-05 Testing Cost: 8.881351095624268e-05\n", "Epoch: 95 - Training Cost: 5.7762692449614406e-05 Testing Cost: 8.162772428477183e-05\n", "Final Training cost: 5.315389353199862e-05\n", "Final Testing cost: 7.359918527072296e-05\n", "The actual earnings of Game #1 were $247537.0\n", "Our neural network predicted earnings of $248473.1875\n" ] } ], "source": [ "import os\n", "import tensorflow as tf\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler\n", "tf.reset_default_graph()\n", "\n", "# Turn off TensorFlow warning messages in program output\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", "\n", "# Load training data set from CSV file\n", "training_data_df = pd.read_csv(\"MieRobot_ANN_training.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_training = training_data_df.drop('total_earnings', axis=1).values\n", "Y_training = training_data_df[['total_earnings']].values\n", "\n", "# Load testing data set from CSV file\n", "test_data_df = pd.read_csv(\"MieRobot_ANN_test.csv\", dtype=float)\n", "\n", "# Pull out columns for X (data to train with) and Y (value to predict)\n", "X_testing = test_data_df.drop('total_earnings', axis=1).values\n", "Y_testing = test_data_df[['total_earnings']].values\n", "\n", "# All data needs to be scaled to a small range like 0 to 1 for the neural\n", "# network to work well. Create scalers for the inputs and outputs.\n", "X_scaler = MinMaxScaler(feature_range=(0, 1))\n", "Y_scaler = MinMaxScaler(feature_range=(0, 1))\n", "\n", "# Scale both the training inputs and outputs\n", "X_scaled_training = X_scaler.fit_transform(X_training)\n", "Y_scaled_training = Y_scaler.fit_transform(Y_training)\n", "\n", "# It's very important that the training and test data are scaled with the same scaler.\n", "X_scaled_testing = X_scaler.transform(X_testing)\n", "Y_scaled_testing = Y_scaler.transform(Y_testing)\n", "\n", "# Define model parameters\n", "learning_rate = 0.001\n", "training_epochs = 100\n", "\n", "# Define how many inputs and outputs are in our neural network\n", "number_of_inputs = 9\n", "number_of_outputs = 1\n", "\n", "# Define how many neurons we want in each layer of our neural network\n", "layer_1_nodes = 100\n", "layer_2_nodes = 200\n", "layer_3_nodes = 100\n", "\n", "# Section One: Define the layers of the neural network itself\n", "\n", "# Input Layer\n", "with tf.variable_scope('input'):\n", " X = tf.placeholder(tf.float32, shape=(None, number_of_inputs))\n", "\n", "# Layer 1\n", "with tf.variable_scope('layer_1'):\n", " weights = tf.get_variable(\"weights1\", shape=[number_of_inputs, layer_1_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases1\", shape=[layer_1_nodes], initializer=tf.zeros_initializer())\n", " layer_1_output = tf.nn.relu(tf.matmul(X, weights) + biases)\n", "\n", "# Layer 2\n", "with tf.variable_scope('layer_2'):\n", " weights = tf.get_variable(\"weights2\", shape=[layer_1_nodes, layer_2_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases2\", shape=[layer_2_nodes], initializer=tf.zeros_initializer())\n", " layer_2_output = tf.nn.relu(tf.matmul(layer_1_output, weights) + biases)\n", "\n", "# Layer 3\n", "with tf.variable_scope('layer_3'):\n", " weights = tf.get_variable(\"weights3\", shape=[layer_2_nodes, layer_3_nodes], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases3\", shape=[layer_3_nodes], initializer=tf.zeros_initializer())\n", " layer_3_output = tf.nn.relu(tf.matmul(layer_2_output, weights) + biases)\n", "\n", "# Output Layer\n", "with tf.variable_scope('output'):\n", " weights = tf.get_variable(\"weights4\", shape=[layer_3_nodes, number_of_outputs], initializer=tf.contrib.layers.xavier_initializer())\n", " biases = tf.get_variable(name=\"biases4\", shape=[number_of_outputs], initializer=tf.zeros_initializer())\n", " prediction = tf.matmul(layer_3_output, weights) + biases\n", "\n", "# Section Two: Define the cost function of the neural network that will measure prediction accuracy during training\n", "\n", "with tf.variable_scope('cost'):\n", " Y = tf.placeholder(tf.float32, shape=(None, 1))\n", " cost = tf.reduce_mean(tf.squared_difference(prediction, Y))\n", "\n", "# Section Three: Define the optimizer function that will be run to optimize the neural network\n", "\n", "with tf.variable_scope('train'):\n", " optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n", "\n", "# Create a summary operation to log the progress of the network\n", "with tf.variable_scope('logging'):\n", " tf.summary.scalar('current_cost', cost)\n", " summary = tf.summary.merge_all()\n", "\n", "\n", "# Initialize a session so that we can run TensorFlow operations\n", "with tf.Session() as session:\n", "\n", " # Run the global variable initializer to initialize all variables and layers of the neural network\n", " session.run(tf.global_variables_initializer())\n", "\n", " # Create log file writers to record training progress.\n", " # We'll store training and testing log data separately.\n", " training_writer = tf.summary.FileWriter(\"./logs/training\", session.graph)\n", " testing_writer = tf.summary.FileWriter(\"./logs/testing\", session.graph)\n", "\n", " # Run the optimizer over and over to train the network.\n", " # One epoch is one full run through the training data set.\n", " for epoch in range(training_epochs):\n", "\n", " # Feed in the training data and do one step of neural network training\n", " session.run(optimizer, feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", "\n", " # Every 5 training steps, log our progress\n", " if epoch % 5 == 0:\n", " # Get the current accuracy scores by running the \"cost\" operation on the training and test data sets\n", " training_cost, training_summary = session.run([cost, summary], feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", " testing_cost, testing_summary = session.run([cost, summary], feed_dict={X: X_scaled_testing, Y: Y_scaled_testing})\n", "\n", " # Write the current training status to the log files (Which we can view with TensorBoard)\n", " training_writer.add_summary(training_summary, epoch)\n", " testing_writer.add_summary(testing_summary, epoch)\n", "\n", "\n", " # Print the current training status to the screen\n", " print(\"Epoch: {} - Training Cost: {} Testing Cost: {}\".format(epoch, training_cost, testing_cost))\n", "\n", " # Training is now complete!\n", "\n", " # Get the final accuracy scores by running the \"cost\" operation on the training and test data sets\n", " final_training_cost = session.run(cost, feed_dict={X: X_scaled_training, Y: Y_scaled_training})\n", " final_testing_cost = session.run(cost, feed_dict={X: X_scaled_testing, Y: Y_scaled_testing})\n", "\n", " print(\"Final Training cost: {}\".format(final_training_cost))\n", " print(\"Final Testing cost: {}\".format(final_testing_cost))\n", "\n", " # Now that the neural network is trained, let's use it to make predictions for our test data.\n", " # Pass in the X testing data and run the \"prediciton\" operation\n", " Y_predicted_scaled = session.run(prediction, feed_dict={X: X_scaled_testing})\n", "\n", " # Unscale the data back to it's original units (dollars)\n", " Y_predicted = Y_scaler.inverse_transform(Y_predicted_scaled)\n", "\n", " real_earnings = test_data_df['total_earnings'].values[0]\n", " predicted_earnings = Y_predicted[0][0]\n", "\n", " print(\"The actual earnings of Game #1 were ${}\".format(real_earnings))\n", " print(\"Our neural network predicted earnings of ${}\".format(predicted_earnings))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tensorflow", "language": "python", "name": "tensorflow" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
kohpangwei/influence-release	scripts/fig5_attack_indiv.ipynb	1	9228	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "from __future__ import division\n", "from __future__ import print_function\n", "from __future__ import absolute_import\n", "from __future__ import unicode_literals \n", "\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import linear_model, preprocessing, cluster\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import scipy.linalg as slin\n", "import scipy.sparse.linalg as sparselin\n", "import scipy.sparse as sparse\n", "import IPython\n", "import copy\n", "\n", "import tensorflow as tf\n", "from tensorflow.contrib.learn.python.learn.datasets import base\n", "\n", "from influence.inceptionModel import BinaryInceptionModel\n", "from influence.binaryLogisticRegressionWithLBFGS import BinaryLogisticRegressionWithLBFGS\n", "import influence.experiments as experiments\n", "from influence.image_utils import plot_flat_bwimage, plot_flat_bwgrad, plot_flat_colorimage, plot_flat_colorgrad\n", "from influence.dataset import DataSet\n", "from influence.dataset_poisoning import generate_inception_features\n", "\n", "from load_animals import load_animals, load_dogfish_with_koda\n", "\n", "sns.set(color_codes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Attacking individual test images" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading animals from disk...\n" ] } ], "source": [ "num_classes = 2\n", "num_train_ex_per_class = 900\n", "num_test_ex_per_class = 300\n", "\n", "dataset_name = 'dogfish_%s_%s' % (num_train_ex_per_class, num_test_ex_per_class)\n", "image_data_sets = load_animals(\n", " num_train_ex_per_class=num_train_ex_per_class, \n", " num_test_ex_per_class=num_test_ex_per_class,\n", " classes=['dog', 'fish'])\n", "\n", "train_f = np.load('output/%s_inception_features_new_train.npz' % dataset_name)\n", "train = DataSet(train_f['inception_features_val'], train_f['labels'])\n", "test_f = np.load('output/%s_inception_features_new_test.npz' % dataset_name)\n", "test = DataSet(test_f['inception_features_val'], test_f['labels'])\n", "validation = None\n", "\n", "data_sets = base.Datasets(train=train, validation=validation, test=test)\n", "\n", "Y_train = image_data_sets.train.labels\n", "Y_test = image_data_sets.test.labels" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of parameters: 2048\n", "Using normal model\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/u/nlp/packages/anaconda2/envs/pw/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "LBFGS training took [41] iter.\n", "After training with LBFGS: \n", "Train loss (w reg) on all data: 0.012129\n", "Train loss (w/o reg) on all data: 0.00397613\n", "Test loss (w/o reg) on all data: 0.048454\n", "Train acc on all data: 1.0\n", "Test acc on all data: 0.985\n", "Norm of the mean of gradients: 3.74273e-07\n", "Norm of the params: 4.03805\n" ] } ], "source": [ "input_dim = 2048\n", "weight_decay = 0.001\n", "batch_size = 30\n", "initial_learning_rate = 0.001 \n", "keep_probs = None\n", "decay_epochs = [1000, 10000]\n", "max_lbfgs_iter = 1000\n", "num_classes = 2\n", "\n", "tf.reset_default_graph()\n", "\n", "model = BinaryLogisticRegressionWithLBFGS(\n", " input_dim=input_dim,\n", " weight_decay=weight_decay,\n", " max_lbfgs_iter=max_lbfgs_iter,\n", " num_classes=num_classes, \n", " batch_size=batch_size,\n", " data_sets=data_sets,\n", " initial_learning_rate=initial_learning_rate,\n", " keep_probs=keep_probs,\n", " decay_epochs=decay_epochs,\n", " mini_batch=False,\n", " train_dir='output_ipynb',\n", " log_dir='log',\n", " model_name='%s_inception_onlytop' % dataset_name)\n", "\n", "model.train()\n", "weights = model.sess.run(model.weights)\n", "\n", "orig_Y_train_pred = model.sess.run(model.preds, feed_dict=model.all_train_feed_dict)\n", "orig_Y_pred = model.sess.run(model.preds, feed_dict=model.all_test_feed_dict)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "num_train_attacks_needed = np.empty(len(Y_test))\n", "num_train_attacks_needed[:] = -1\n", "mask_orig_correct = np.zeros(len(Y_test), dtype=bool)\n", "\n", "step_size = 0.02\n", "weight_decay = 0.001\n", "max_deviation = 0.5\n", "\n", "model_name = '%s_inception_wd-%s' % (dataset_name, weight_decay)\n", "\n", "for test_idx in range(len(Y_test)):\n", " if orig_Y_pred[test_idx, int(Y_test[test_idx])] >= 0.5:\n", " mask_orig_correct[test_idx] = True\n", " else:\n", " mask_orig_correct[test_idx] = False\n", " \n", " filenames = [filename for filename in os.listdir('./output') if (\n", " (('%s_attack_normal_loss_testidx-%s_trainidx-' % (model_name, test_idx)) in filename) and \n", " (filename.endswith('stepsize-%s_proj_final.npz' % step_size)))]\n", " \n", " assert len(filenames) <= 1\n", " \n", " if len(filenames) == 1:\n", " attack_f = np.load(os.path.join('output', filenames[0]))\n", " indices_to_poison = attack_f['indices_to_poison']\n", " num_train_attacks_needed[test_idx] = len(indices_to_poison)\n", " poisoned_X_train_image = attack_f['poisoned_X_train_image']\n", " for counter, idx_to_poison in enumerate(indices_to_poison):\n", " image_diff = np.max(np.abs(image_data_sets.train.x[idx_to_poison, :] - poisoned_X_train_image[counter, :]) * 255 / 2) \n", " assert image_diff < max_deviation + 1e-5\n", " assert np.all(poisoned_X_train_image >= -1)\n", " assert np.all(poisoned_X_train_image <= 1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of test predictions flipped as the number of training images attacked increases:\n" ] }, { "ename": "NameError", "evalue": "name 'num_train_attacks_needed' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-a40e6f438b99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of test predictions flipped as the number of training images attacked increases:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_train_attacks_needed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_orig_correct\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'num_train_attacks_needed' is not defined" ] } ], "source": [ "print('Number of test predictions flipped as the number of training images attacked increases:')\n", "pd.Series(num_train_attacks_needed[mask_orig_correct]).value_counts()" ] } ], "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": 1 }	mit
juancarlosqr/datascience	python/software_carpentry_plotting_python/07_reading_tabular_dataframes.ipynb	1	122837	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import glob" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['data/gapminder_gdp_americas.csv', 'data/gapminder_gdp_europe.csv', 'data/gapminder_all.csv', 'data/gapminder_gdp_oceania.csv', 'data/gapminder_gdp_africa.csv', 'data/gapminder_gdp_asia.csv']\n" ] } ], "source": [ "print(glob.glob('data/*.csv'))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "data_americas = pd.read_csv('data/gapminder_gdp_americas.csv')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " continent country gdpPercap_1952 gdpPercap_1957 \\\n", "0 Americas Argentina 5911.315053 6856.856212 \n", "1 Americas Bolivia 2677.326347 2127.686326 \n", "2 Americas Brazil 2108.944355 2487.365989 \n", "3 Americas Canada 11367.161120 12489.950060 \n", "4 Americas Chile 3939.978789 4315.622723 \n", "5 Americas Colombia 2144.115096 2323.805581 \n", "6 Americas Costa Rica 2627.009471 2990.010802 \n", "7 Americas Cuba 5586.538780 6092.174359 \n", "8 Americas Dominican Republic 1397.717137 1544.402995 \n", "9 Americas Ecuador 3522.110717 3780.546651 \n", "10 Americas El Salvador 3048.302900 3421.523218 \n", "11 Americas Guatemala 2428.237769 2617.155967 \n", "12 Americas Haiti 1840.366939 1726.887882 \n", "13 Americas Honduras 2194.926204 2220.487682 \n", "14 Americas Jamaica 2898.530881 4756.525781 \n", "15 Americas Mexico 3478.125529 4131.546641 \n", "16 Americas Nicaragua 3112.363948 3457.415947 \n", "17 Americas Panama 2480.380334 2961.800905 \n", "18 Americas Paraguay 1952.308701 2046.154706 \n", "19 Americas Peru 3758.523437 4245.256698 \n", "20 Americas Puerto Rico 3081.959785 3907.156189 \n", "21 Americas Trinidad and Tobago 3023.271928 4100.393400 \n", "22 Americas United States 13990.482080 14847.127120 \n", "23 Americas Uruguay 5716.766744 6150.772969 \n", "24 Americas Venezuela 7689.799761 9802.466526 \n", "\n", " gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 \\\n", "0 7133.166023 8052.953021 9443.038526 10079.026740 \n", "1 2180.972546 2586.886053 2980.331339 3548.097832 \n", "2 3336.585802 3429.864357 4985.711467 6660.118654 \n", "3 13462.485550 16076.588030 18970.570860 22090.883060 \n", "4 4519.094331 5106.654313 5494.024437 4756.763836 \n", "5 2492.351109 2678.729839 3264.660041 3815.807870 \n", "6 3460.937025 4161.727834 5118.146939 5926.876967 \n", "7 5180.755910 5690.268015 5305.445256 6380.494966 \n", "8 1662.137359 1653.723003 2189.874499 2681.988900 \n", "9 4086.114078 4579.074215 5280.994710 6679.623260 \n", "10 3776.803627 4358.595393 4520.246008 5138.922374 \n", "11 2750.364446 3242.531147 4031.408271 4879.992748 \n", "12 1796.589032 1452.057666 1654.456946 1874.298931 \n", "13 2291.156835 2538.269358 2529.842345 3203.208066 \n", "14 5246.107524 6124.703451 7433.889293 6650.195573 \n", "15 4581.609385 5754.733883 6809.406690 7674.929108 \n", "16 3634.364406 4643.393534 4688.593267 5486.371089 \n", "17 3536.540301 4421.009084 5364.249663 5351.912144 \n", "18 2148.027146 2299.376311 2523.337977 3248.373311 \n", "19 4957.037982 5788.093330 5937.827283 6281.290855 \n", "20 5108.344630 6929.277714 9123.041742 9770.524921 \n", "21 4997.523971 5621.368472 6619.551419 7899.554209 \n", "22 16173.145860 19530.365570 21806.035940 24072.632130 \n", "23 5603.357717 5444.619620 5703.408898 6504.339663 \n", "24 8422.974165 9541.474188 10505.259660 13143.950950 \n", "\n", " gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 \\\n", "0 8997.897412 9139.671389 9308.418710 10967.281950 \n", "1 3156.510452 2753.691490 2961.699694 3326.143191 \n", "2 7030.835878 7807.095818 6950.283021 7957.980824 \n", "3 22898.792140 26626.515030 26342.884260 28954.925890 \n", "4 5095.665738 5547.063754 7596.125964 10118.053180 \n", "5 4397.575659 4903.219100 5444.648617 6117.361746 \n", "6 5262.734751 5629.915318 6160.416317 6677.045314 \n", "7 7316.918107 7532.924763 5592.843963 5431.990415 \n", "8 2861.092386 2899.842175 3044.214214 3614.101285 \n", "9 7213.791267 6481.776993 7103.702595 7429.455877 \n", "10 4098.344175 4140.442097 4444.231700 5154.825496 \n", "11 4820.494790 4246.485974 4439.450840 4684.313807 \n", "12 2011.159549 1823.015995 1456.309517 1341.726931 \n", "13 3121.760794 3023.096699 3081.694603 3160.454906 \n", "14 6068.051350 6351.237495 7404.923685 7121.924704 \n", "15 9611.147541 8688.156003 9472.384295 9767.297530 \n", "16 3470.338156 2955.984375 2170.151724 2253.023004 \n", "17 7009.601598 7034.779161 6618.743050 7113.692252 \n", "18 4258.503604 3998.875695 4196.411078 4247.400261 \n", "19 6434.501797 6360.943444 4446.380924 5838.347657 \n", "20 10330.989150 12281.341910 14641.587110 16999.433300 \n", "21 9119.528607 7388.597823 7370.990932 8792.573126 \n", "22 25009.559140 29884.350410 32003.932240 35767.433030 \n", "23 6920.223051 7452.398969 8137.004775 9230.240708 \n", "24 11152.410110 9883.584648 10733.926310 10165.495180 \n", "\n", " gdpPercap_2002 gdpPercap_2007 \n", "0 8797.640716 12779.379640 \n", "1 3413.262690 3822.137084 \n", "2 8131.212843 9065.800825 \n", "3 33328.965070 36319.235010 \n", "4 10778.783850 13171.638850 \n", "5 5755.259962 7006.580419 \n", "6 7723.447195 9645.061420 \n", "7 6340.646683 8948.102923 \n", "8 4563.808154 6025.374752 \n", "9 5773.044512 6873.262326 \n", "10 5351.568666 5728.353514 \n", "11 4858.347495 5186.050003 \n", "12 1270.364932 1201.637154 \n", "13 3099.728660 3548.330846 \n", "14 6994.774861 7320.880262 \n", "15 10742.440530 11977.574960 \n", "16 2474.548819 2749.320965 \n", "17 7356.031934 9809.185636 \n", "18 3783.674243 4172.838464 \n", "19 5909.020073 7408.905561 \n", "20 18855.606180 19328.709010 \n", "21 11460.600230 18008.509240 \n", "22 39097.099550 42951.653090 \n", "23 7727.002004 10611.462990 \n", "24 8605.047831 11415.805690 \n" ] } ], "source": [ "print(data_americas)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead 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>gdpPercap_1952</th>\n", " <th>gdpPercap_1957</th>\n", " <th>gdpPercap_1962</th>\n", " <th>gdpPercap_1967</th>\n", " <th>gdpPercap_1972</th>\n", " <th>gdpPercap_1977</th>\n", " <th>gdpPercap_1982</th>\n", " <th>gdpPercap_1987</th>\n", " <th>gdpPercap_1992</th>\n", " <th>gdpPercap_1997</th>\n", " <th>gdpPercap_2002</th>\n", " <th>gdpPercap_2007</th>\n", " </tr>\n", " <tr>\n", " <th>country</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>Albania</th>\n", " <td>1601.056136</td>\n", " <td>1942.284244</td>\n", " <td>2312.888958</td>\n", " <td>2760.196931</td>\n", " <td>3313.422188</td>\n", " <td>3533.003910</td>\n", " <td>3630.880722</td>\n", " <td>3738.932735</td>\n", " <td>2497.437901</td>\n", " <td>3193.054604</td>\n", " <td>4604.211737</td>\n", " <td>5937.029526</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>6137.076492</td>\n", " <td>8842.598030</td>\n", " <td>10750.721110</td>\n", " <td>12834.602400</td>\n", " <td>16661.625600</td>\n", " <td>19749.422300</td>\n", " <td>21597.083620</td>\n", " <td>23687.826070</td>\n", " <td>27042.018680</td>\n", " <td>29095.920660</td>\n", " <td>32417.607690</td>\n", " <td>36126.492700</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>8343.105127</td>\n", " <td>9714.960623</td>\n", " <td>10991.206760</td>\n", " <td>13149.041190</td>\n", " <td>16672.143560</td>\n", " <td>19117.974480</td>\n", " <td>20979.845890</td>\n", " <td>22525.563080</td>\n", " <td>25575.570690</td>\n", " <td>27561.196630</td>\n", " <td>30485.883750</td>\n", " <td>33692.605080</td>\n", " </tr>\n", " <tr>\n", " <th>Bosnia and Herzegovina</th>\n", " <td>973.533195</td>\n", " <td>1353.989176</td>\n", " <td>1709.683679</td>\n", " <td>2172.352423</td>\n", " <td>2860.169750</td>\n", " <td>3528.481305</td>\n", " <td>4126.613157</td>\n", " <td>4314.114757</td>\n", " <td>2546.781445</td>\n", " <td>4766.355904</td>\n", " <td>6018.975239</td>\n", " <td>7446.298803</td>\n", " </tr>\n", " <tr>\n", " <th>Bulgaria</th>\n", " <td>2444.286648</td>\n", " <td>3008.670727</td>\n", " <td>4254.337839</td>\n", " <td>5577.002800</td>\n", " <td>6597.494398</td>\n", " <td>7612.240438</td>\n", " <td>8224.191647</td>\n", " <td>8239.854824</td>\n", " <td>6302.623438</td>\n", " <td>5970.388760</td>\n", " <td>7696.777725</td>\n", " <td>10680.792820</td>\n", " </tr>\n", " <tr>\n", " <th>Croatia</th>\n", " <td>3119.236520</td>\n", " <td>4338.231617</td>\n", " <td>5477.890018</td>\n", " <td>6960.297861</td>\n", " <td>9164.090127</td>\n", " <td>11305.385170</td>\n", " <td>13221.821840</td>\n", " <td>13822.583940</td>\n", " <td>8447.794873</td>\n", " <td>9875.604515</td>\n", " <td>11628.388950</td>\n", " <td>14619.222720</td>\n", " </tr>\n", " <tr>\n", " <th>Czech Republic</th>\n", " <td>6876.140250</td>\n", " <td>8256.343918</td>\n", " <td>10136.867130</td>\n", " <td>11399.444890</td>\n", " <td>13108.453600</td>\n", " <td>14800.160620</td>\n", " <td>15377.228550</td>\n", " <td>16310.443400</td>\n", " <td>14297.021220</td>\n", " <td>16048.514240</td>\n", " <td>17596.210220</td>\n", " <td>22833.308510</td>\n", " </tr>\n", " <tr>\n", " <th>Denmark</th>\n", " <td>9692.385245</td>\n", " <td>11099.659350</td>\n", " <td>13583.313510</td>\n", " <td>15937.211230</td>\n", " <td>18866.207210</td>\n", " <td>20422.901500</td>\n", " <td>21688.040480</td>\n", " <td>25116.175810</td>\n", " <td>26406.739850</td>\n", " <td>29804.345670</td>\n", " <td>32166.500060</td>\n", " <td>35278.418740</td>\n", " </tr>\n", " <tr>\n", " <th>Finland</th>\n", " <td>6424.519071</td>\n", " <td>7545.415386</td>\n", " <td>9371.842561</td>\n", " <td>10921.636260</td>\n", " <td>14358.875900</td>\n", " <td>15605.422830</td>\n", " <td>18533.157610</td>\n", " <td>21141.012230</td>\n", " <td>20647.164990</td>\n", " <td>23723.950200</td>\n", " <td>28204.590570</td>\n", " <td>33207.084400</td>\n", " </tr>\n", " <tr>\n", " <th>France</th>\n", " <td>7029.809327</td>\n", " <td>8662.834898</td>\n", " <td>10560.485530</td>\n", " <td>12999.917660</td>\n", " <td>16107.191710</td>\n", " <td>18292.635140</td>\n", " <td>20293.897460</td>\n", " <td>22066.442140</td>\n", " <td>24703.796150</td>\n", " <td>25889.784870</td>\n", " <td>28926.032340</td>\n", " <td>30470.016700</td>\n", " </tr>\n", " <tr>\n", " <th>Germany</th>\n", " <td>7144.114393</td>\n", " <td>10187.826650</td>\n", " <td>12902.462910</td>\n", " <td>14745.625610</td>\n", " <td>18016.180270</td>\n", " <td>20512.921230</td>\n", " <td>22031.532740</td>\n", " <td>24639.185660</td>\n", " <td>26505.303170</td>\n", " <td>27788.884160</td>\n", " <td>30035.801980</td>\n", " <td>32170.374420</td>\n", " </tr>\n", " <tr>\n", " <th>Greece</th>\n", " <td>3530.690067</td>\n", " <td>4916.299889</td>\n", " <td>6017.190733</td>\n", " <td>8513.097016</td>\n", " <td>12724.829570</td>\n", " <td>14195.524280</td>\n", " <td>15268.420890</td>\n", " <td>16120.528390</td>\n", " <td>17541.496340</td>\n", " <td>18747.698140</td>\n", " <td>22514.254800</td>\n", " <td>27538.411880</td>\n", " </tr>\n", " <tr>\n", " <th>Hungary</th>\n", " <td>5263.673816</td>\n", " <td>6040.180011</td>\n", " <td>7550.359877</td>\n", " <td>9326.644670</td>\n", " <td>10168.656110</td>\n", " <td>11674.837370</td>\n", " <td>12545.990660</td>\n", " <td>12986.479980</td>\n", " <td>10535.628550</td>\n", " <td>11712.776800</td>\n", " <td>14843.935560</td>\n", " <td>18008.944440</td>\n", " </tr>\n", " <tr>\n", " <th>Iceland</th>\n", " <td>7267.688428</td>\n", " <td>9244.001412</td>\n", " <td>10350.159060</td>\n", " <td>13319.895680</td>\n", " <td>15798.063620</td>\n", " <td>19654.962470</td>\n", " <td>23269.607500</td>\n", " <td>26923.206280</td>\n", " <td>25144.392010</td>\n", " <td>28061.099660</td>\n", " <td>31163.201960</td>\n", " <td>36180.789190</td>\n", " </tr>\n", " <tr>\n", " <th>Ireland</th>\n", " <td>5210.280328</td>\n", " <td>5599.077872</td>\n", " <td>6631.597314</td>\n", " <td>7655.568963</td>\n", " <td>9530.772896</td>\n", " <td>11150.981130</td>\n", " <td>12618.321410</td>\n", " <td>13872.866520</td>\n", " <td>17558.815550</td>\n", " <td>24521.947130</td>\n", " <td>34077.049390</td>\n", " <td>40675.996350</td>\n", " </tr>\n", " <tr>\n", " <th>Italy</th>\n", " <td>4931.404155</td>\n", " <td>6248.656232</td>\n", " <td>8243.582340</td>\n", " <td>10022.401310</td>\n", " <td>12269.273780</td>\n", " <td>14255.984750</td>\n", " <td>16537.483500</td>\n", " <td>19207.234820</td>\n", " <td>22013.644860</td>\n", " <td>24675.024460</td>\n", " <td>27968.098170</td>\n", " <td>28569.719700</td>\n", " </tr>\n", " <tr>\n", " <th>Montenegro</th>\n", " <td>2647.585601</td>\n", " <td>3682.259903</td>\n", " <td>4649.593785</td>\n", " <td>5907.850937</td>\n", " <td>7778.414017</td>\n", " <td>9595.929905</td>\n", " <td>11222.587620</td>\n", " <td>11732.510170</td>\n", " <td>7003.339037</td>\n", " <td>6465.613349</td>\n", " <td>6557.194282</td>\n", " <td>9253.896111</td>\n", " </tr>\n", " <tr>\n", " <th>Netherlands</th>\n", " <td>8941.571858</td>\n", " <td>11276.193440</td>\n", " <td>12790.849560</td>\n", " <td>15363.251360</td>\n", " <td>18794.745670</td>\n", " <td>21209.059200</td>\n", " <td>21399.460460</td>\n", " <td>23651.323610</td>\n", " <td>26790.949610</td>\n", " <td>30246.130630</td>\n", " <td>33724.757780</td>\n", " <td>36797.933320</td>\n", " </tr>\n", " <tr>\n", " <th>Norway</th>\n", " <td>10095.421720</td>\n", " <td>11653.973040</td>\n", " <td>13450.401510</td>\n", " <td>16361.876470</td>\n", " <td>18965.055510</td>\n", " <td>23311.349390</td>\n", " <td>26298.635310</td>\n", " <td>31540.974800</td>\n", " <td>33965.661150</td>\n", " <td>41283.164330</td>\n", " <td>44683.975250</td>\n", " <td>49357.190170</td>\n", " </tr>\n", " <tr>\n", " <th>Poland</th>\n", " <td>4029.329699</td>\n", " <td>4734.253019</td>\n", " <td>5338.752143</td>\n", " <td>6557.152776</td>\n", " <td>8006.506993</td>\n", " <td>9508.141454</td>\n", " <td>8451.531004</td>\n", " <td>9082.351172</td>\n", " <td>7738.881247</td>\n", " <td>10159.583680</td>\n", " <td>12002.239080</td>\n", " <td>15389.924680</td>\n", " </tr>\n", " <tr>\n", " <th>Portugal</th>\n", " <td>3068.319867</td>\n", " <td>3774.571743</td>\n", " <td>4727.954889</td>\n", " <td>6361.517993</td>\n", " <td>9022.247417</td>\n", " <td>10172.485720</td>\n", " <td>11753.842910</td>\n", " <td>13039.308760</td>\n", " <td>16207.266630</td>\n", " <td>17641.031560</td>\n", " <td>19970.907870</td>\n", " <td>20509.647770</td>\n", " </tr>\n", " <tr>\n", " <th>Romania</th>\n", " <td>3144.613186</td>\n", " <td>3943.370225</td>\n", " <td>4734.997586</td>\n", " <td>6470.866545</td>\n", " <td>8011.414402</td>\n", " <td>9356.397240</td>\n", " <td>9605.314053</td>\n", " <td>9696.273295</td>\n", " <td>6598.409903</td>\n", " <td>7346.547557</td>\n", " <td>7885.360081</td>\n", " <td>10808.475610</td>\n", " </tr>\n", " <tr>\n", " <th>Serbia</th>\n", " <td>3581.459448</td>\n", " <td>4981.090891</td>\n", " <td>6289.629157</td>\n", " <td>7991.707066</td>\n", " <td>10522.067490</td>\n", " <td>12980.669560</td>\n", " <td>15181.092700</td>\n", " <td>15870.878510</td>\n", " <td>9325.068238</td>\n", " <td>7914.320304</td>\n", " <td>7236.075251</td>\n", " <td>9786.534714</td>\n", " </tr>\n", " <tr>\n", " <th>Slovak Republic</th>\n", " <td>5074.659104</td>\n", " <td>6093.262980</td>\n", " <td>7481.107598</td>\n", " <td>8412.902397</td>\n", " <td>9674.167626</td>\n", " <td>10922.664040</td>\n", " <td>11348.545850</td>\n", " <td>12037.267580</td>\n", " <td>9498.467723</td>\n", " <td>12126.230650</td>\n", " <td>13638.778370</td>\n", " <td>18678.314350</td>\n", " </tr>\n", " <tr>\n", " <th>Slovenia</th>\n", " <td>4215.041741</td>\n", " <td>5862.276629</td>\n", " <td>7402.303395</td>\n", " <td>9405.489397</td>\n", " <td>12383.486200</td>\n", " <td>15277.030170</td>\n", " <td>17866.721750</td>\n", " <td>18678.534920</td>\n", " <td>14214.716810</td>\n", " <td>17161.107350</td>\n", " <td>20660.019360</td>\n", " <td>25768.257590</td>\n", " </tr>\n", " <tr>\n", " <th>Spain</th>\n", " <td>3834.034742</td>\n", " <td>4564.802410</td>\n", " <td>5693.843879</td>\n", " <td>7993.512294</td>\n", " <td>10638.751310</td>\n", " <td>13236.921170</td>\n", " <td>13926.169970</td>\n", " <td>15764.983130</td>\n", " <td>18603.064520</td>\n", " <td>20445.298960</td>\n", " <td>24835.471660</td>\n", " <td>28821.063700</td>\n", " </tr>\n", " <tr>\n", " <th>Sweden</th>\n", " <td>8527.844662</td>\n", " <td>9911.878226</td>\n", " <td>12329.441920</td>\n", " <td>15258.296970</td>\n", " <td>17832.024640</td>\n", " <td>18855.725210</td>\n", " <td>20667.381250</td>\n", " <td>23586.929270</td>\n", " <td>23880.016830</td>\n", " <td>25266.594990</td>\n", " <td>29341.630930</td>\n", " <td>33859.748350</td>\n", " </tr>\n", " <tr>\n", " <th>Switzerland</th>\n", " <td>14734.232750</td>\n", " <td>17909.489730</td>\n", " <td>20431.092700</td>\n", " <td>22966.144320</td>\n", " <td>27195.113040</td>\n", " <td>26982.290520</td>\n", " <td>28397.715120</td>\n", " <td>30281.704590</td>\n", " <td>31871.530300</td>\n", " <td>32135.323010</td>\n", " <td>34480.957710</td>\n", " <td>37506.419070</td>\n", " </tr>\n", " <tr>\n", " <th>Turkey</th>\n", " <td>1969.100980</td>\n", " <td>2218.754257</td>\n", " <td>2322.869908</td>\n", " <td>2826.356387</td>\n", " <td>3450.696380</td>\n", " <td>4269.122326</td>\n", " <td>4241.356344</td>\n", " <td>5089.043686</td>\n", " <td>5678.348271</td>\n", " <td>6601.429915</td>\n", " <td>6508.085718</td>\n", " <td>8458.276384</td>\n", " </tr>\n", " <tr>\n", " <th>United Kingdom</th>\n", " <td>9979.508487</td>\n", " <td>11283.177950</td>\n", " <td>12477.177070</td>\n", " <td>14142.850890</td>\n", " <td>15895.116410</td>\n", " <td>17428.748460</td>\n", " <td>18232.424520</td>\n", " <td>21664.787670</td>\n", " <td>22705.092540</td>\n", " <td>26074.531360</td>\n", " <td>29478.999190</td>\n", " <td>33203.261280</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \\\n", "country \n", "Albania 1601.056136 1942.284244 2312.888958 \n", "Austria 6137.076492 8842.598030 10750.721110 \n", "Belgium 8343.105127 9714.960623 10991.206760 \n", "Bosnia and Herzegovina 973.533195 1353.989176 1709.683679 \n", "Bulgaria 2444.286648 3008.670727 4254.337839 \n", "Croatia 3119.236520 4338.231617 5477.890018 \n", "Czech Republic 6876.140250 8256.343918 10136.867130 \n", "Denmark 9692.385245 11099.659350 13583.313510 \n", "Finland 6424.519071 7545.415386 9371.842561 \n", "France 7029.809327 8662.834898 10560.485530 \n", "Germany 7144.114393 10187.826650 12902.462910 \n", "Greece 3530.690067 4916.299889 6017.190733 \n", "Hungary 5263.673816 6040.180011 7550.359877 \n", "Iceland 7267.688428 9244.001412 10350.159060 \n", "Ireland 5210.280328 5599.077872 6631.597314 \n", "Italy 4931.404155 6248.656232 8243.582340 \n", "Montenegro 2647.585601 3682.259903 4649.593785 \n", "Netherlands 8941.571858 11276.193440 12790.849560 \n", "Norway 10095.421720 11653.973040 13450.401510 \n", "Poland 4029.329699 4734.253019 5338.752143 \n", "Portugal 3068.319867 3774.571743 4727.954889 \n", "Romania 3144.613186 3943.370225 4734.997586 \n", "Serbia 3581.459448 4981.090891 6289.629157 \n", "Slovak Republic 5074.659104 6093.262980 7481.107598 \n", "Slovenia 4215.041741 5862.276629 7402.303395 \n", "Spain 3834.034742 4564.802410 5693.843879 \n", "Sweden 8527.844662 9911.878226 12329.441920 \n", "Switzerland 14734.232750 17909.489730 20431.092700 \n", "Turkey 1969.100980 2218.754257 2322.869908 \n", "United Kingdom 9979.508487 11283.177950 12477.177070 \n", "\n", " gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 \\\n", "country \n", "Albania 2760.196931 3313.422188 3533.003910 \n", "Austria 12834.602400 16661.625600 19749.422300 \n", "Belgium 13149.041190 16672.143560 19117.974480 \n", "Bosnia and Herzegovina 2172.352423 2860.169750 3528.481305 \n", "Bulgaria 5577.002800 6597.494398 7612.240438 \n", "Croatia 6960.297861 9164.090127 11305.385170 \n", "Czech Republic 11399.444890 13108.453600 14800.160620 \n", "Denmark 15937.211230 18866.207210 20422.901500 \n", "Finland 10921.636260 14358.875900 15605.422830 \n", "France 12999.917660 16107.191710 18292.635140 \n", "Germany 14745.625610 18016.180270 20512.921230 \n", "Greece 8513.097016 12724.829570 14195.524280 \n", "Hungary 9326.644670 10168.656110 11674.837370 \n", "Iceland 13319.895680 15798.063620 19654.962470 \n", "Ireland 7655.568963 9530.772896 11150.981130 \n", "Italy 10022.401310 12269.273780 14255.984750 \n", "Montenegro 5907.850937 7778.414017 9595.929905 \n", "Netherlands 15363.251360 18794.745670 21209.059200 \n", "Norway 16361.876470 18965.055510 23311.349390 \n", "Poland 6557.152776 8006.506993 9508.141454 \n", "Portugal 6361.517993 9022.247417 10172.485720 \n", "Romania 6470.866545 8011.414402 9356.397240 \n", "Serbia 7991.707066 10522.067490 12980.669560 \n", "Slovak Republic 8412.902397 9674.167626 10922.664040 \n", "Slovenia 9405.489397 12383.486200 15277.030170 \n", "Spain 7993.512294 10638.751310 13236.921170 \n", "Sweden 15258.296970 17832.024640 18855.725210 \n", "Switzerland 22966.144320 27195.113040 26982.290520 \n", "Turkey 2826.356387 3450.696380 4269.122326 \n", "United Kingdom 14142.850890 15895.116410 17428.748460 \n", "\n", " gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 \\\n", "country \n", "Albania 3630.880722 3738.932735 2497.437901 \n", "Austria 21597.083620 23687.826070 27042.018680 \n", "Belgium 20979.845890 22525.563080 25575.570690 \n", "Bosnia and Herzegovina 4126.613157 4314.114757 2546.781445 \n", "Bulgaria 8224.191647 8239.854824 6302.623438 \n", "Croatia 13221.821840 13822.583940 8447.794873 \n", "Czech Republic 15377.228550 16310.443400 14297.021220 \n", "Denmark 21688.040480 25116.175810 26406.739850 \n", "Finland 18533.157610 21141.012230 20647.164990 \n", "France 20293.897460 22066.442140 24703.796150 \n", "Germany 22031.532740 24639.185660 26505.303170 \n", "Greece 15268.420890 16120.528390 17541.496340 \n", "Hungary 12545.990660 12986.479980 10535.628550 \n", "Iceland 23269.607500 26923.206280 25144.392010 \n", "Ireland 12618.321410 13872.866520 17558.815550 \n", "Italy 16537.483500 19207.234820 22013.644860 \n", "Montenegro 11222.587620 11732.510170 7003.339037 \n", "Netherlands 21399.460460 23651.323610 26790.949610 \n", "Norway 26298.635310 31540.974800 33965.661150 \n", "Poland 8451.531004 9082.351172 7738.881247 \n", "Portugal 11753.842910 13039.308760 16207.266630 \n", "Romania 9605.314053 9696.273295 6598.409903 \n", "Serbia 15181.092700 15870.878510 9325.068238 \n", "Slovak Republic 11348.545850 12037.267580 9498.467723 \n", "Slovenia 17866.721750 18678.534920 14214.716810 \n", "Spain 13926.169970 15764.983130 18603.064520 \n", "Sweden 20667.381250 23586.929270 23880.016830 \n", "Switzerland 28397.715120 30281.704590 31871.530300 \n", "Turkey 4241.356344 5089.043686 5678.348271 \n", "United Kingdom 18232.424520 21664.787670 22705.092540 \n", "\n", " gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 \n", "country \n", "Albania 3193.054604 4604.211737 5937.029526 \n", "Austria 29095.920660 32417.607690 36126.492700 \n", "Belgium 27561.196630 30485.883750 33692.605080 \n", "Bosnia and Herzegovina 4766.355904 6018.975239 7446.298803 \n", "Bulgaria 5970.388760 7696.777725 10680.792820 \n", "Croatia 9875.604515 11628.388950 14619.222720 \n", "Czech Republic 16048.514240 17596.210220 22833.308510 \n", "Denmark 29804.345670 32166.500060 35278.418740 \n", "Finland 23723.950200 28204.590570 33207.084400 \n", "France 25889.784870 28926.032340 30470.016700 \n", "Germany 27788.884160 30035.801980 32170.374420 \n", "Greece 18747.698140 22514.254800 27538.411880 \n", "Hungary 11712.776800 14843.935560 18008.944440 \n", "Iceland 28061.099660 31163.201960 36180.789190 \n", "Ireland 24521.947130 34077.049390 40675.996350 \n", "Italy 24675.024460 27968.098170 28569.719700 \n", "Montenegro 6465.613349 6557.194282 9253.896111 \n", "Netherlands 30246.130630 33724.757780 36797.933320 \n", "Norway 41283.164330 44683.975250 49357.190170 \n", "Poland 10159.583680 12002.239080 15389.924680 \n", "Portugal 17641.031560 19970.907870 20509.647770 \n", "Romania 7346.547557 7885.360081 10808.475610 \n", "Serbia 7914.320304 7236.075251 9786.534714 \n", "Slovak Republic 12126.230650 13638.778370 18678.314350 \n", "Slovenia 17161.107350 20660.019360 25768.257590 \n", "Spain 20445.298960 24835.471660 28821.063700 \n", "Sweden 25266.594990 29341.630930 33859.748350 \n", "Switzerland 32135.323010 34480.957710 37506.419070 \n", "Turkey 6601.429915 6508.085718 8458.276384 \n", "United Kingdom 26074.531360 29478.999190 33203.261280 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_europe" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 30 entries, Albania to United Kingdom\n", "Data columns (total 12 columns):\n", "gdpPercap_1952 30 non-null float64\n", "gdpPercap_1957 30 non-null float64\n", "gdpPercap_1962 30 non-null float64\n", "gdpPercap_1967 30 non-null float64\n", "gdpPercap_1972 30 non-null float64\n", "gdpPercap_1977 30 non-null float64\n", "gdpPercap_1982 30 non-null float64\n", "gdpPercap_1987 30 non-null float64\n", "gdpPercap_1992 30 non-null float64\n", "gdpPercap_1997 30 non-null float64\n", "gdpPercap_2002 30 non-null float64\n", "gdpPercap_2007 30 non-null float64\n", "dtypes: float64(12)\n", "memory usage: 3.0+ KB\n" ] } ], "source": [ "data_europe.info()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',\n", " 'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',\n", " 'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],\n", " dtype='object')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_europe.columns" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>country</th>\n", " <th>Albania</th>\n", " <th>Austria</th>\n", " <th>Belgium</th>\n", " <th>Bosnia and Herzegovina</th>\n", " <th>Bulgaria</th>\n", " <th>Croatia</th>\n", " <th>Czech Republic</th>\n", " <th>Denmark</th>\n", " <th>Finland</th>\n", " <th>France</th>\n", " <th>...</th>\n", " <th>Portugal</th>\n", " <th>Romania</th>\n", " <th>Serbia</th>\n", " <th>Slovak Republic</th>\n", " <th>Slovenia</th>\n", " <th>Spain</th>\n", " <th>Sweden</th>\n", " <th>Switzerland</th>\n", " <th>Turkey</th>\n", " <th>United Kingdom</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>gdpPercap_1952</th>\n", " <td>1601.056136</td>\n", " <td>6137.076492</td>\n", " <td>8343.105127</td>\n", " <td>973.533195</td>\n", " <td>2444.286648</td>\n", " <td>3119.236520</td>\n", " <td>6876.140250</td>\n", " <td>9692.385245</td>\n", " <td>6424.519071</td>\n", " <td>7029.809327</td>\n", " <td>...</td>\n", " <td>3068.319867</td>\n", " <td>3144.613186</td>\n", " <td>3581.459448</td>\n", " <td>5074.659104</td>\n", " <td>4215.041741</td>\n", " <td>3834.034742</td>\n", " <td>8527.844662</td>\n", " <td>14734.23275</td>\n", " <td>1969.100980</td>\n", " <td>9979.508487</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1957</th>\n", " <td>1942.284244</td>\n", " <td>8842.598030</td>\n", " <td>9714.960623</td>\n", " <td>1353.989176</td>\n", " <td>3008.670727</td>\n", " <td>4338.231617</td>\n", " <td>8256.343918</td>\n", " <td>11099.659350</td>\n", " <td>7545.415386</td>\n", " <td>8662.834898</td>\n", " <td>...</td>\n", " <td>3774.571743</td>\n", " <td>3943.370225</td>\n", " <td>4981.090891</td>\n", " <td>6093.262980</td>\n", " <td>5862.276629</td>\n", " <td>4564.802410</td>\n", " <td>9911.878226</td>\n", " <td>17909.48973</td>\n", " <td>2218.754257</td>\n", " <td>11283.177950</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1962</th>\n", " <td>2312.888958</td>\n", " <td>10750.721110</td>\n", " <td>10991.206760</td>\n", " <td>1709.683679</td>\n", " <td>4254.337839</td>\n", " <td>5477.890018</td>\n", " <td>10136.867130</td>\n", " <td>13583.313510</td>\n", " <td>9371.842561</td>\n", " <td>10560.485530</td>\n", " <td>...</td>\n", " <td>4727.954889</td>\n", " <td>4734.997586</td>\n", " <td>6289.629157</td>\n", " <td>7481.107598</td>\n", " <td>7402.303395</td>\n", " <td>5693.843879</td>\n", " <td>12329.441920</td>\n", " <td>20431.09270</td>\n", " <td>2322.869908</td>\n", " <td>12477.177070</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1967</th>\n", " <td>2760.196931</td>\n", " <td>12834.602400</td>\n", " <td>13149.041190</td>\n", " <td>2172.352423</td>\n", " <td>5577.002800</td>\n", " <td>6960.297861</td>\n", " <td>11399.444890</td>\n", " <td>15937.211230</td>\n", " <td>10921.636260</td>\n", " <td>12999.917660</td>\n", " <td>...</td>\n", " <td>6361.517993</td>\n", " <td>6470.866545</td>\n", " <td>7991.707066</td>\n", " <td>8412.902397</td>\n", " <td>9405.489397</td>\n", " <td>7993.512294</td>\n", " <td>15258.296970</td>\n", " <td>22966.14432</td>\n", " <td>2826.356387</td>\n", " <td>14142.850890</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1972</th>\n", " <td>3313.422188</td>\n", " <td>16661.625600</td>\n", " <td>16672.143560</td>\n", " <td>2860.169750</td>\n", " <td>6597.494398</td>\n", " <td>9164.090127</td>\n", " <td>13108.453600</td>\n", " <td>18866.207210</td>\n", " <td>14358.875900</td>\n", " <td>16107.191710</td>\n", " <td>...</td>\n", " <td>9022.247417</td>\n", " <td>8011.414402</td>\n", " <td>10522.067490</td>\n", " <td>9674.167626</td>\n", " <td>12383.486200</td>\n", " <td>10638.751310</td>\n", " <td>17832.024640</td>\n", " <td>27195.11304</td>\n", " <td>3450.696380</td>\n", " <td>15895.116410</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1977</th>\n", " <td>3533.003910</td>\n", " <td>19749.422300</td>\n", " <td>19117.974480</td>\n", " <td>3528.481305</td>\n", " <td>7612.240438</td>\n", " <td>11305.385170</td>\n", " <td>14800.160620</td>\n", " <td>20422.901500</td>\n", " <td>15605.422830</td>\n", " <td>18292.635140</td>\n", " <td>...</td>\n", " <td>10172.485720</td>\n", " <td>9356.397240</td>\n", " <td>12980.669560</td>\n", " <td>10922.664040</td>\n", " <td>15277.030170</td>\n", " <td>13236.921170</td>\n", " <td>18855.725210</td>\n", " <td>26982.29052</td>\n", " <td>4269.122326</td>\n", " <td>17428.748460</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1982</th>\n", " <td>3630.880722</td>\n", " <td>21597.083620</td>\n", " <td>20979.845890</td>\n", " <td>4126.613157</td>\n", " <td>8224.191647</td>\n", " <td>13221.821840</td>\n", " <td>15377.228550</td>\n", " <td>21688.040480</td>\n", " <td>18533.157610</td>\n", " <td>20293.897460</td>\n", " <td>...</td>\n", " <td>11753.842910</td>\n", " <td>9605.314053</td>\n", " <td>15181.092700</td>\n", " <td>11348.545850</td>\n", " <td>17866.721750</td>\n", " <td>13926.169970</td>\n", " <td>20667.381250</td>\n", " <td>28397.71512</td>\n", " <td>4241.356344</td>\n", " <td>18232.424520</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1987</th>\n", " <td>3738.932735</td>\n", " <td>23687.826070</td>\n", " <td>22525.563080</td>\n", " <td>4314.114757</td>\n", " <td>8239.854824</td>\n", " <td>13822.583940</td>\n", " <td>16310.443400</td>\n", " <td>25116.175810</td>\n", " <td>21141.012230</td>\n", " <td>22066.442140</td>\n", " <td>...</td>\n", " <td>13039.308760</td>\n", " <td>9696.273295</td>\n", " <td>15870.878510</td>\n", " <td>12037.267580</td>\n", " <td>18678.534920</td>\n", " <td>15764.983130</td>\n", " <td>23586.929270</td>\n", " <td>30281.70459</td>\n", " <td>5089.043686</td>\n", " <td>21664.787670</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1992</th>\n", " <td>2497.437901</td>\n", " <td>27042.018680</td>\n", " <td>25575.570690</td>\n", " <td>2546.781445</td>\n", " <td>6302.623438</td>\n", " <td>8447.794873</td>\n", " <td>14297.021220</td>\n", " <td>26406.739850</td>\n", " <td>20647.164990</td>\n", " <td>24703.796150</td>\n", " <td>...</td>\n", " <td>16207.266630</td>\n", " <td>6598.409903</td>\n", " <td>9325.068238</td>\n", " <td>9498.467723</td>\n", " <td>14214.716810</td>\n", " <td>18603.064520</td>\n", " <td>23880.016830</td>\n", " <td>31871.53030</td>\n", " <td>5678.348271</td>\n", " <td>22705.092540</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_1997</th>\n", " <td>3193.054604</td>\n", " <td>29095.920660</td>\n", " <td>27561.196630</td>\n", " <td>4766.355904</td>\n", " <td>5970.388760</td>\n", " <td>9875.604515</td>\n", " <td>16048.514240</td>\n", " <td>29804.345670</td>\n", " <td>23723.950200</td>\n", " <td>25889.784870</td>\n", " <td>...</td>\n", " <td>17641.031560</td>\n", " <td>7346.547557</td>\n", " <td>7914.320304</td>\n", " <td>12126.230650</td>\n", " <td>17161.107350</td>\n", " <td>20445.298960</td>\n", " <td>25266.594990</td>\n", " <td>32135.32301</td>\n", " <td>6601.429915</td>\n", " <td>26074.531360</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_2002</th>\n", " <td>4604.211737</td>\n", " <td>32417.607690</td>\n", " <td>30485.883750</td>\n", " <td>6018.975239</td>\n", " <td>7696.777725</td>\n", " <td>11628.388950</td>\n", " <td>17596.210220</td>\n", " <td>32166.500060</td>\n", " <td>28204.590570</td>\n", " <td>28926.032340</td>\n", " <td>...</td>\n", " <td>19970.907870</td>\n", " <td>7885.360081</td>\n", " <td>7236.075251</td>\n", " <td>13638.778370</td>\n", " <td>20660.019360</td>\n", " <td>24835.471660</td>\n", " <td>29341.630930</td>\n", " <td>34480.95771</td>\n", " <td>6508.085718</td>\n", " <td>29478.999190</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_2007</th>\n", " <td>5937.029526</td>\n", " <td>36126.492700</td>\n", " <td>33692.605080</td>\n", " <td>7446.298803</td>\n", " <td>10680.792820</td>\n", " <td>14619.222720</td>\n", " <td>22833.308510</td>\n", " <td>35278.418740</td>\n", " <td>33207.084400</td>\n", " <td>30470.016700</td>\n", " <td>...</td>\n", " <td>20509.647770</td>\n", " <td>10808.475610</td>\n", " <td>9786.534714</td>\n", " <td>18678.314350</td>\n", " <td>25768.257590</td>\n", " <td>28821.063700</td>\n", " <td>33859.748350</td>\n", " <td>37506.41907</td>\n", " <td>8458.276384</td>\n", " <td>33203.261280</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>12 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ "country Albania Austria Belgium \\\n", "gdpPercap_1952 1601.056136 6137.076492 8343.105127 \n", "gdpPercap_1957 1942.284244 8842.598030 9714.960623 \n", "gdpPercap_1962 2312.888958 10750.721110 10991.206760 \n", "gdpPercap_1967 2760.196931 12834.602400 13149.041190 \n", "gdpPercap_1972 3313.422188 16661.625600 16672.143560 \n", "gdpPercap_1977 3533.003910 19749.422300 19117.974480 \n", "gdpPercap_1982 3630.880722 21597.083620 20979.845890 \n", "gdpPercap_1987 3738.932735 23687.826070 22525.563080 \n", "gdpPercap_1992 2497.437901 27042.018680 25575.570690 \n", "gdpPercap_1997 3193.054604 29095.920660 27561.196630 \n", "gdpPercap_2002 4604.211737 32417.607690 30485.883750 \n", "gdpPercap_2007 5937.029526 36126.492700 33692.605080 \n", "\n", "country Bosnia and Herzegovina Bulgaria Croatia \\\n", "gdpPercap_1952 973.533195 2444.286648 3119.236520 \n", "gdpPercap_1957 1353.989176 3008.670727 4338.231617 \n", "gdpPercap_1962 1709.683679 4254.337839 5477.890018 \n", "gdpPercap_1967 2172.352423 5577.002800 6960.297861 \n", "gdpPercap_1972 2860.169750 6597.494398 9164.090127 \n", "gdpPercap_1977 3528.481305 7612.240438 11305.385170 \n", "gdpPercap_1982 4126.613157 8224.191647 13221.821840 \n", "gdpPercap_1987 4314.114757 8239.854824 13822.583940 \n", "gdpPercap_1992 2546.781445 6302.623438 8447.794873 \n", "gdpPercap_1997 4766.355904 5970.388760 9875.604515 \n", "gdpPercap_2002 6018.975239 7696.777725 11628.388950 \n", "gdpPercap_2007 7446.298803 10680.792820 14619.222720 \n", "\n", "country Czech Republic Denmark Finland France \\\n", "gdpPercap_1952 6876.140250 9692.385245 6424.519071 7029.809327 \n", "gdpPercap_1957 8256.343918 11099.659350 7545.415386 8662.834898 \n", "gdpPercap_1962 10136.867130 13583.313510 9371.842561 10560.485530 \n", "gdpPercap_1967 11399.444890 15937.211230 10921.636260 12999.917660 \n", "gdpPercap_1972 13108.453600 18866.207210 14358.875900 16107.191710 \n", "gdpPercap_1977 14800.160620 20422.901500 15605.422830 18292.635140 \n", "gdpPercap_1982 15377.228550 21688.040480 18533.157610 20293.897460 \n", "gdpPercap_1987 16310.443400 25116.175810 21141.012230 22066.442140 \n", "gdpPercap_1992 14297.021220 26406.739850 20647.164990 24703.796150 \n", "gdpPercap_1997 16048.514240 29804.345670 23723.950200 25889.784870 \n", "gdpPercap_2002 17596.210220 32166.500060 28204.590570 28926.032340 \n", "gdpPercap_2007 22833.308510 35278.418740 33207.084400 30470.016700 \n", "\n", "country ... Portugal Romania Serbia \\\n", "gdpPercap_1952 ... 3068.319867 3144.613186 3581.459448 \n", "gdpPercap_1957 ... 3774.571743 3943.370225 4981.090891 \n", "gdpPercap_1962 ... 4727.954889 4734.997586 6289.629157 \n", "gdpPercap_1967 ... 6361.517993 6470.866545 7991.707066 \n", "gdpPercap_1972 ... 9022.247417 8011.414402 10522.067490 \n", "gdpPercap_1977 ... 10172.485720 9356.397240 12980.669560 \n", "gdpPercap_1982 ... 11753.842910 9605.314053 15181.092700 \n", "gdpPercap_1987 ... 13039.308760 9696.273295 15870.878510 \n", "gdpPercap_1992 ... 16207.266630 6598.409903 9325.068238 \n", "gdpPercap_1997 ... 17641.031560 7346.547557 7914.320304 \n", "gdpPercap_2002 ... 19970.907870 7885.360081 7236.075251 \n", "gdpPercap_2007 ... 20509.647770 10808.475610 9786.534714 \n", "\n", "country Slovak Republic Slovenia Spain Sweden \\\n", "gdpPercap_1952 5074.659104 4215.041741 3834.034742 8527.844662 \n", "gdpPercap_1957 6093.262980 5862.276629 4564.802410 9911.878226 \n", "gdpPercap_1962 7481.107598 7402.303395 5693.843879 12329.441920 \n", "gdpPercap_1967 8412.902397 9405.489397 7993.512294 15258.296970 \n", "gdpPercap_1972 9674.167626 12383.486200 10638.751310 17832.024640 \n", "gdpPercap_1977 10922.664040 15277.030170 13236.921170 18855.725210 \n", "gdpPercap_1982 11348.545850 17866.721750 13926.169970 20667.381250 \n", "gdpPercap_1987 12037.267580 18678.534920 15764.983130 23586.929270 \n", "gdpPercap_1992 9498.467723 14214.716810 18603.064520 23880.016830 \n", "gdpPercap_1997 12126.230650 17161.107350 20445.298960 25266.594990 \n", "gdpPercap_2002 13638.778370 20660.019360 24835.471660 29341.630930 \n", "gdpPercap_2007 18678.314350 25768.257590 28821.063700 33859.748350 \n", "\n", "country Switzerland Turkey United Kingdom \n", "gdpPercap_1952 14734.23275 1969.100980 9979.508487 \n", "gdpPercap_1957 17909.48973 2218.754257 11283.177950 \n", "gdpPercap_1962 20431.09270 2322.869908 12477.177070 \n", "gdpPercap_1967 22966.14432 2826.356387 14142.850890 \n", "gdpPercap_1972 27195.11304 3450.696380 15895.116410 \n", "gdpPercap_1977 26982.29052 4269.122326 17428.748460 \n", "gdpPercap_1982 28397.71512 4241.356344 18232.424520 \n", "gdpPercap_1987 30281.70459 5089.043686 21664.787670 \n", "gdpPercap_1992 31871.53030 5678.348271 22705.092540 \n", "gdpPercap_1997 32135.32301 6601.429915 26074.531360 \n", "gdpPercap_2002 34480.95771 6508.085718 29478.999190 \n", "gdpPercap_2007 37506.41907 8458.276384 33203.261280 \n", "\n", "[12 rows x 30 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_europe.T" ] }, { "cell_type": "code", "execution_count": 25, "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>gdpPercap_1952</th>\n", " <th>gdpPercap_1957</th>\n", " <th>gdpPercap_1962</th>\n", " <th>gdpPercap_1967</th>\n", " <th>gdpPercap_1972</th>\n", " <th>gdpPercap_1977</th>\n", " <th>gdpPercap_1982</th>\n", " <th>gdpPercap_1987</th>\n", " <th>gdpPercap_1992</th>\n", " <th>gdpPercap_1997</th>\n", " <th>gdpPercap_2002</th>\n", " <th>gdpPercap_2007</th>\n", " </tr>\n", " <tr>\n", " <th>country</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>Albania</th>\n", " <td>1601.056136</td>\n", " <td>1942.284244</td>\n", " <td>2312.888958</td>\n", " <td>2760.196931</td>\n", " <td>3313.422188</td>\n", " <td>3533.003910</td>\n", " <td>3630.880722</td>\n", " <td>3738.932735</td>\n", " <td>2497.437901</td>\n", " <td>3193.054604</td>\n", " <td>4604.211737</td>\n", " <td>5937.029526</td>\n", " </tr>\n", " <tr>\n", " <th>Austria</th>\n", " <td>6137.076492</td>\n", " <td>8842.598030</td>\n", " <td>10750.721110</td>\n", " <td>12834.602400</td>\n", " <td>16661.625600</td>\n", " <td>19749.422300</td>\n", " <td>21597.083620</td>\n", " <td>23687.826070</td>\n", " <td>27042.018680</td>\n", " <td>29095.920660</td>\n", " <td>32417.607690</td>\n", " <td>36126.492700</td>\n", " </tr>\n", " <tr>\n", " <th>Belgium</th>\n", " <td>8343.105127</td>\n", " <td>9714.960623</td>\n", " <td>10991.206760</td>\n", " <td>13149.041190</td>\n", " <td>16672.143560</td>\n", " <td>19117.974480</td>\n", " <td>20979.845890</td>\n", " <td>22525.563080</td>\n", " <td>25575.570690</td>\n", " <td>27561.196630</td>\n", " <td>30485.883750</td>\n", " <td>33692.605080</td>\n", " </tr>\n", " <tr>\n", " <th>Bosnia and Herzegovina</th>\n", " <td>973.533195</td>\n", " <td>1353.989176</td>\n", " <td>1709.683679</td>\n", " <td>2172.352423</td>\n", " <td>2860.169750</td>\n", " <td>3528.481305</td>\n", " <td>4126.613157</td>\n", " <td>4314.114757</td>\n", " <td>2546.781445</td>\n", " <td>4766.355904</td>\n", " <td>6018.975239</td>\n", " <td>7446.298803</td>\n", " </tr>\n", " <tr>\n", " <th>Bulgaria</th>\n", " <td>2444.286648</td>\n", " <td>3008.670727</td>\n", " <td>4254.337839</td>\n", " <td>5577.002800</td>\n", " <td>6597.494398</td>\n", " <td>7612.240438</td>\n", " <td>8224.191647</td>\n", " <td>8239.854824</td>\n", " <td>6302.623438</td>\n", " <td>5970.388760</td>\n", " <td>7696.777725</td>\n", " <td>10680.792820</td>\n", " </tr>\n", " <tr>\n", " <th>Croatia</th>\n", " <td>3119.236520</td>\n", " <td>4338.231617</td>\n", " <td>5477.890018</td>\n", " <td>6960.297861</td>\n", " <td>9164.090127</td>\n", " <td>11305.385170</td>\n", " <td>13221.821840</td>\n", " <td>13822.583940</td>\n", " <td>8447.794873</td>\n", " <td>9875.604515</td>\n", " <td>11628.388950</td>\n", " <td>14619.222720</td>\n", " </tr>\n", " <tr>\n", " <th>Czech Republic</th>\n", " <td>6876.140250</td>\n", " <td>8256.343918</td>\n", " <td>10136.867130</td>\n", " <td>11399.444890</td>\n", " <td>13108.453600</td>\n", " <td>14800.160620</td>\n", " <td>15377.228550</td>\n", " <td>16310.443400</td>\n", " <td>14297.021220</td>\n", " <td>16048.514240</td>\n", " <td>17596.210220</td>\n", " <td>22833.308510</td>\n", " </tr>\n", " <tr>\n", " <th>Denmark</th>\n", " <td>9692.385245</td>\n", " <td>11099.659350</td>\n", " <td>13583.313510</td>\n", " <td>15937.211230</td>\n", " <td>18866.207210</td>\n", " <td>20422.901500</td>\n", " <td>21688.040480</td>\n", " <td>25116.175810</td>\n", " <td>26406.739850</td>\n", " <td>29804.345670</td>\n", " <td>32166.500060</td>\n", " <td>35278.418740</td>\n", " </tr>\n", " <tr>\n", " <th>Finland</th>\n", " <td>6424.519071</td>\n", " <td>7545.415386</td>\n", " <td>9371.842561</td>\n", " <td>10921.636260</td>\n", " <td>14358.875900</td>\n", " <td>15605.422830</td>\n", " <td>18533.157610</td>\n", " <td>21141.012230</td>\n", " <td>20647.164990</td>\n", " <td>23723.950200</td>\n", " <td>28204.590570</td>\n", " <td>33207.084400</td>\n", " </tr>\n", " <tr>\n", " <th>France</th>\n", " <td>7029.809327</td>\n", " <td>8662.834898</td>\n", " <td>10560.485530</td>\n", " <td>12999.917660</td>\n", " <td>16107.191710</td>\n", " <td>18292.635140</td>\n", " <td>20293.897460</td>\n", " <td>22066.442140</td>\n", " <td>24703.796150</td>\n", " <td>25889.784870</td>\n", " <td>28926.032340</td>\n", " <td>30470.016700</td>\n", " </tr>\n", " <tr>\n", " <th>Germany</th>\n", " <td>7144.114393</td>\n", " <td>10187.826650</td>\n", " <td>12902.462910</td>\n", " <td>14745.625610</td>\n", " <td>18016.180270</td>\n", " <td>20512.921230</td>\n", " <td>22031.532740</td>\n", " <td>24639.185660</td>\n", " <td>26505.303170</td>\n", " <td>27788.884160</td>\n", " <td>30035.801980</td>\n", " <td>32170.374420</td>\n", " </tr>\n", " <tr>\n", " <th>Greece</th>\n", " <td>3530.690067</td>\n", " <td>4916.299889</td>\n", " <td>6017.190733</td>\n", " <td>8513.097016</td>\n", " <td>12724.829570</td>\n", " <td>14195.524280</td>\n", " <td>15268.420890</td>\n", " <td>16120.528390</td>\n", " <td>17541.496340</td>\n", " <td>18747.698140</td>\n", " <td>22514.254800</td>\n", " <td>27538.411880</td>\n", " </tr>\n", " <tr>\n", " <th>Hungary</th>\n", " <td>5263.673816</td>\n", " <td>6040.180011</td>\n", " <td>7550.359877</td>\n", " <td>9326.644670</td>\n", " <td>10168.656110</td>\n", " <td>11674.837370</td>\n", " <td>12545.990660</td>\n", " <td>12986.479980</td>\n", " <td>10535.628550</td>\n", " <td>11712.776800</td>\n", " <td>14843.935560</td>\n", " <td>18008.944440</td>\n", " </tr>\n", " <tr>\n", " <th>Iceland</th>\n", " <td>7267.688428</td>\n", " <td>9244.001412</td>\n", " <td>10350.159060</td>\n", " <td>13319.895680</td>\n", " <td>15798.063620</td>\n", " <td>19654.962470</td>\n", " <td>23269.607500</td>\n", " <td>26923.206280</td>\n", " <td>25144.392010</td>\n", " <td>28061.099660</td>\n", " <td>31163.201960</td>\n", " <td>36180.789190</td>\n", " </tr>\n", " <tr>\n", " <th>Ireland</th>\n", " <td>5210.280328</td>\n", " <td>5599.077872</td>\n", " <td>6631.597314</td>\n", " <td>7655.568963</td>\n", " <td>9530.772896</td>\n", " <td>11150.981130</td>\n", " <td>12618.321410</td>\n", " <td>13872.866520</td>\n", " <td>17558.815550</td>\n", " <td>24521.947130</td>\n", " <td>34077.049390</td>\n", " <td>40675.996350</td>\n", " </tr>\n", " <tr>\n", " <th>Italy</th>\n", " <td>4931.404155</td>\n", " <td>6248.656232</td>\n", " <td>8243.582340</td>\n", " <td>10022.401310</td>\n", " <td>12269.273780</td>\n", " <td>14255.984750</td>\n", " <td>16537.483500</td>\n", " <td>19207.234820</td>\n", " <td>22013.644860</td>\n", " <td>24675.024460</td>\n", " <td>27968.098170</td>\n", " <td>28569.719700</td>\n", " </tr>\n", " <tr>\n", " <th>Montenegro</th>\n", " <td>2647.585601</td>\n", " <td>3682.259903</td>\n", " <td>4649.593785</td>\n", " <td>5907.850937</td>\n", " <td>7778.414017</td>\n", " <td>9595.929905</td>\n", " <td>11222.587620</td>\n", " <td>11732.510170</td>\n", " <td>7003.339037</td>\n", " <td>6465.613349</td>\n", " <td>6557.194282</td>\n", " <td>9253.896111</td>\n", " </tr>\n", " <tr>\n", " <th>Netherlands</th>\n", " <td>8941.571858</td>\n", " <td>11276.193440</td>\n", " <td>12790.849560</td>\n", " <td>15363.251360</td>\n", " <td>18794.745670</td>\n", " <td>21209.059200</td>\n", " <td>21399.460460</td>\n", " <td>23651.323610</td>\n", " <td>26790.949610</td>\n", " <td>30246.130630</td>\n", " <td>33724.757780</td>\n", " <td>36797.933320</td>\n", " </tr>\n", " <tr>\n", " <th>Norway</th>\n", " <td>10095.421720</td>\n", " <td>11653.973040</td>\n", " <td>13450.401510</td>\n", " <td>16361.876470</td>\n", " <td>18965.055510</td>\n", " <td>23311.349390</td>\n", " <td>26298.635310</td>\n", " <td>31540.974800</td>\n", " <td>33965.661150</td>\n", " <td>41283.164330</td>\n", " <td>44683.975250</td>\n", " <td>49357.190170</td>\n", " </tr>\n", " <tr>\n", " <th>Poland</th>\n", " <td>4029.329699</td>\n", " <td>4734.253019</td>\n", " <td>5338.752143</td>\n", " <td>6557.152776</td>\n", " <td>8006.506993</td>\n", " <td>9508.141454</td>\n", " <td>8451.531004</td>\n", " <td>9082.351172</td>\n", " <td>7738.881247</td>\n", " <td>10159.583680</td>\n", " <td>12002.239080</td>\n", " <td>15389.924680</td>\n", " </tr>\n", " <tr>\n", " <th>Portugal</th>\n", " <td>3068.319867</td>\n", " <td>3774.571743</td>\n", " <td>4727.954889</td>\n", " <td>6361.517993</td>\n", " <td>9022.247417</td>\n", " <td>10172.485720</td>\n", " <td>11753.842910</td>\n", " <td>13039.308760</td>\n", " <td>16207.266630</td>\n", " <td>17641.031560</td>\n", " <td>19970.907870</td>\n", " <td>20509.647770</td>\n", " </tr>\n", " <tr>\n", " <th>Romania</th>\n", " <td>3144.613186</td>\n", " <td>3943.370225</td>\n", " <td>4734.997586</td>\n", " <td>6470.866545</td>\n", " <td>8011.414402</td>\n", " <td>9356.397240</td>\n", " <td>9605.314053</td>\n", " <td>9696.273295</td>\n", " <td>6598.409903</td>\n", " <td>7346.547557</td>\n", " <td>7885.360081</td>\n", " <td>10808.475610</td>\n", " </tr>\n", " <tr>\n", " <th>Serbia</th>\n", " <td>3581.459448</td>\n", " <td>4981.090891</td>\n", " <td>6289.629157</td>\n", " <td>7991.707066</td>\n", " <td>10522.067490</td>\n", " <td>12980.669560</td>\n", " <td>15181.092700</td>\n", " <td>15870.878510</td>\n", " <td>9325.068238</td>\n", " <td>7914.320304</td>\n", " <td>7236.075251</td>\n", " <td>9786.534714</td>\n", " </tr>\n", " <tr>\n", " <th>Slovak Republic</th>\n", " <td>5074.659104</td>\n", " <td>6093.262980</td>\n", " <td>7481.107598</td>\n", " <td>8412.902397</td>\n", " <td>9674.167626</td>\n", " <td>10922.664040</td>\n", " <td>11348.545850</td>\n", " <td>12037.267580</td>\n", " <td>9498.467723</td>\n", " <td>12126.230650</td>\n", " <td>13638.778370</td>\n", " <td>18678.314350</td>\n", " </tr>\n", " <tr>\n", " <th>Slovenia</th>\n", " <td>4215.041741</td>\n", " <td>5862.276629</td>\n", " <td>7402.303395</td>\n", " <td>9405.489397</td>\n", " <td>12383.486200</td>\n", " <td>15277.030170</td>\n", " <td>17866.721750</td>\n", " <td>18678.534920</td>\n", " <td>14214.716810</td>\n", " <td>17161.107350</td>\n", " <td>20660.019360</td>\n", " <td>25768.257590</td>\n", " </tr>\n", " <tr>\n", " <th>Spain</th>\n", " <td>3834.034742</td>\n", " <td>4564.802410</td>\n", " <td>5693.843879</td>\n", " <td>7993.512294</td>\n", " <td>10638.751310</td>\n", " <td>13236.921170</td>\n", " <td>13926.169970</td>\n", " <td>15764.983130</td>\n", " <td>18603.064520</td>\n", " <td>20445.298960</td>\n", " <td>24835.471660</td>\n", " <td>28821.063700</td>\n", " </tr>\n", " <tr>\n", " <th>Sweden</th>\n", " <td>8527.844662</td>\n", " <td>9911.878226</td>\n", " <td>12329.441920</td>\n", " <td>15258.296970</td>\n", " <td>17832.024640</td>\n", " <td>18855.725210</td>\n", " <td>20667.381250</td>\n", " <td>23586.929270</td>\n", " <td>23880.016830</td>\n", " <td>25266.594990</td>\n", " <td>29341.630930</td>\n", " <td>33859.748350</td>\n", " </tr>\n", " <tr>\n", " <th>Switzerland</th>\n", " <td>14734.232750</td>\n", " <td>17909.489730</td>\n", " <td>20431.092700</td>\n", " <td>22966.144320</td>\n", " <td>27195.113040</td>\n", " <td>26982.290520</td>\n", " <td>28397.715120</td>\n", " <td>30281.704590</td>\n", " <td>31871.530300</td>\n", " <td>32135.323010</td>\n", " <td>34480.957710</td>\n", " <td>37506.419070</td>\n", " </tr>\n", " <tr>\n", " <th>Turkey</th>\n", " <td>1969.100980</td>\n", " <td>2218.754257</td>\n", " <td>2322.869908</td>\n", " <td>2826.356387</td>\n", " <td>3450.696380</td>\n", " <td>4269.122326</td>\n", " <td>4241.356344</td>\n", " <td>5089.043686</td>\n", " <td>5678.348271</td>\n", " <td>6601.429915</td>\n", " <td>6508.085718</td>\n", " <td>8458.276384</td>\n", " </tr>\n", " <tr>\n", " <th>United Kingdom</th>\n", " <td>9979.508487</td>\n", " <td>11283.177950</td>\n", " <td>12477.177070</td>\n", " <td>14142.850890</td>\n", " <td>15895.116410</td>\n", " <td>17428.748460</td>\n", " <td>18232.424520</td>\n", " <td>21664.787670</td>\n", " <td>22705.092540</td>\n", " <td>26074.531360</td>\n", " <td>29478.999190</td>\n", " <td>33203.261280</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \\\n", "country \n", "Albania 1601.056136 1942.284244 2312.888958 \n", "Austria 6137.076492 8842.598030 10750.721110 \n", "Belgium 8343.105127 9714.960623 10991.206760 \n", "Bosnia and Herzegovina 973.533195 1353.989176 1709.683679 \n", "Bulgaria 2444.286648 3008.670727 4254.337839 \n", "Croatia 3119.236520 4338.231617 5477.890018 \n", "Czech Republic 6876.140250 8256.343918 10136.867130 \n", "Denmark 9692.385245 11099.659350 13583.313510 \n", "Finland 6424.519071 7545.415386 9371.842561 \n", "France 7029.809327 8662.834898 10560.485530 \n", "Germany 7144.114393 10187.826650 12902.462910 \n", "Greece 3530.690067 4916.299889 6017.190733 \n", "Hungary 5263.673816 6040.180011 7550.359877 \n", "Iceland 7267.688428 9244.001412 10350.159060 \n", "Ireland 5210.280328 5599.077872 6631.597314 \n", "Italy 4931.404155 6248.656232 8243.582340 \n", "Montenegro 2647.585601 3682.259903 4649.593785 \n", "Netherlands 8941.571858 11276.193440 12790.849560 \n", "Norway 10095.421720 11653.973040 13450.401510 \n", "Poland 4029.329699 4734.253019 5338.752143 \n", "Portugal 3068.319867 3774.571743 4727.954889 \n", "Romania 3144.613186 3943.370225 4734.997586 \n", "Serbia 3581.459448 4981.090891 6289.629157 \n", "Slovak Republic 5074.659104 6093.262980 7481.107598 \n", "Slovenia 4215.041741 5862.276629 7402.303395 \n", "Spain 3834.034742 4564.802410 5693.843879 \n", "Sweden 8527.844662 9911.878226 12329.441920 \n", "Switzerland 14734.232750 17909.489730 20431.092700 \n", "Turkey 1969.100980 2218.754257 2322.869908 \n", "United Kingdom 9979.508487 11283.177950 12477.177070 \n", "\n", " gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 \\\n", "country \n", "Albania 2760.196931 3313.422188 3533.003910 \n", "Austria 12834.602400 16661.625600 19749.422300 \n", "Belgium 13149.041190 16672.143560 19117.974480 \n", "Bosnia and Herzegovina 2172.352423 2860.169750 3528.481305 \n", "Bulgaria 5577.002800 6597.494398 7612.240438 \n", "Croatia 6960.297861 9164.090127 11305.385170 \n", "Czech Republic 11399.444890 13108.453600 14800.160620 \n", "Denmark 15937.211230 18866.207210 20422.901500 \n", "Finland 10921.636260 14358.875900 15605.422830 \n", "France 12999.917660 16107.191710 18292.635140 \n", "Germany 14745.625610 18016.180270 20512.921230 \n", "Greece 8513.097016 12724.829570 14195.524280 \n", "Hungary 9326.644670 10168.656110 11674.837370 \n", "Iceland 13319.895680 15798.063620 19654.962470 \n", "Ireland 7655.568963 9530.772896 11150.981130 \n", "Italy 10022.401310 12269.273780 14255.984750 \n", "Montenegro 5907.850937 7778.414017 9595.929905 \n", "Netherlands 15363.251360 18794.745670 21209.059200 \n", "Norway 16361.876470 18965.055510 23311.349390 \n", "Poland 6557.152776 8006.506993 9508.141454 \n", "Portugal 6361.517993 9022.247417 10172.485720 \n", "Romania 6470.866545 8011.414402 9356.397240 \n", "Serbia 7991.707066 10522.067490 12980.669560 \n", "Slovak Republic 8412.902397 9674.167626 10922.664040 \n", "Slovenia 9405.489397 12383.486200 15277.030170 \n", "Spain 7993.512294 10638.751310 13236.921170 \n", "Sweden 15258.296970 17832.024640 18855.725210 \n", "Switzerland 22966.144320 27195.113040 26982.290520 \n", "Turkey 2826.356387 3450.696380 4269.122326 \n", "United Kingdom 14142.850890 15895.116410 17428.748460 \n", "\n", " gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 \\\n", "country \n", "Albania 3630.880722 3738.932735 2497.437901 \n", "Austria 21597.083620 23687.826070 27042.018680 \n", "Belgium 20979.845890 22525.563080 25575.570690 \n", "Bosnia and Herzegovina 4126.613157 4314.114757 2546.781445 \n", "Bulgaria 8224.191647 8239.854824 6302.623438 \n", "Croatia 13221.821840 13822.583940 8447.794873 \n", "Czech Republic 15377.228550 16310.443400 14297.021220 \n", "Denmark 21688.040480 25116.175810 26406.739850 \n", "Finland 18533.157610 21141.012230 20647.164990 \n", "France 20293.897460 22066.442140 24703.796150 \n", "Germany 22031.532740 24639.185660 26505.303170 \n", "Greece 15268.420890 16120.528390 17541.496340 \n", "Hungary 12545.990660 12986.479980 10535.628550 \n", "Iceland 23269.607500 26923.206280 25144.392010 \n", "Ireland 12618.321410 13872.866520 17558.815550 \n", "Italy 16537.483500 19207.234820 22013.644860 \n", "Montenegro 11222.587620 11732.510170 7003.339037 \n", "Netherlands 21399.460460 23651.323610 26790.949610 \n", "Norway 26298.635310 31540.974800 33965.661150 \n", "Poland 8451.531004 9082.351172 7738.881247 \n", "Portugal 11753.842910 13039.308760 16207.266630 \n", "Romania 9605.314053 9696.273295 6598.409903 \n", "Serbia 15181.092700 15870.878510 9325.068238 \n", "Slovak Republic 11348.545850 12037.267580 9498.467723 \n", "Slovenia 17866.721750 18678.534920 14214.716810 \n", "Spain 13926.169970 15764.983130 18603.064520 \n", "Sweden 20667.381250 23586.929270 23880.016830 \n", "Switzerland 28397.715120 30281.704590 31871.530300 \n", "Turkey 4241.356344 5089.043686 5678.348271 \n", "United Kingdom 18232.424520 21664.787670 22705.092540 \n", "\n", " gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 \n", "country \n", "Albania 3193.054604 4604.211737 5937.029526 \n", "Austria 29095.920660 32417.607690 36126.492700 \n", "Belgium 27561.196630 30485.883750 33692.605080 \n", "Bosnia and Herzegovina 4766.355904 6018.975239 7446.298803 \n", "Bulgaria 5970.388760 7696.777725 10680.792820 \n", "Croatia 9875.604515 11628.388950 14619.222720 \n", "Czech Republic 16048.514240 17596.210220 22833.308510 \n", "Denmark 29804.345670 32166.500060 35278.418740 \n", "Finland 23723.950200 28204.590570 33207.084400 \n", "France 25889.784870 28926.032340 30470.016700 \n", "Germany 27788.884160 30035.801980 32170.374420 \n", "Greece 18747.698140 22514.254800 27538.411880 \n", "Hungary 11712.776800 14843.935560 18008.944440 \n", "Iceland 28061.099660 31163.201960 36180.789190 \n", "Ireland 24521.947130 34077.049390 40675.996350 \n", "Italy 24675.024460 27968.098170 28569.719700 \n", "Montenegro 6465.613349 6557.194282 9253.896111 \n", "Netherlands 30246.130630 33724.757780 36797.933320 \n", "Norway 41283.164330 44683.975250 49357.190170 \n", "Poland 10159.583680 12002.239080 15389.924680 \n", "Portugal 17641.031560 19970.907870 20509.647770 \n", "Romania 7346.547557 7885.360081 10808.475610 \n", "Serbia 7914.320304 7236.075251 9786.534714 \n", "Slovak Republic 12126.230650 13638.778370 18678.314350 \n", "Slovenia 17161.107350 20660.019360 25768.257590 \n", "Spain 20445.298960 24835.471660 28821.063700 \n", "Sweden 25266.594990 29341.630930 33859.748350 \n", "Switzerland 32135.323010 34480.957710 37506.419070 \n", "Turkey 6601.429915 6508.085718 8458.276384 \n", "United Kingdom 26074.531360 29478.999190 33203.261280 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_europe" ] }, { "cell_type": "code", "execution_count": 27, "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>gdpPercap_1952</th>\n", " <th>gdpPercap_1957</th>\n", " <th>gdpPercap_1962</th>\n", " <th>gdpPercap_1967</th>\n", " <th>gdpPercap_1972</th>\n", " <th>gdpPercap_1977</th>\n", " <th>gdpPercap_1982</th>\n", " <th>gdpPercap_1987</th>\n", " <th>gdpPercap_1992</th>\n", " <th>gdpPercap_1997</th>\n", " <th>gdpPercap_2002</th>\n", " <th>gdpPercap_2007</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>5661.057435</td>\n", " <td>6963.012816</td>\n", " <td>8365.486814</td>\n", " <td>10143.823757</td>\n", " <td>12479.575246</td>\n", " <td>14283.979110</td>\n", " <td>15617.896551</td>\n", " <td>17214.310727</td>\n", " <td>17061.568084</td>\n", " <td>19076.781802</td>\n", " <td>21711.732422</td>\n", " <td>25054.481636</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>3114.060493</td>\n", " <td>3677.950146</td>\n", " <td>4199.193906</td>\n", " <td>4724.983889</td>\n", " <td>5509.691411</td>\n", " <td>5874.464896</td>\n", " <td>6453.234827</td>\n", " <td>7482.957960</td>\n", " <td>9109.804361</td>\n", " <td>10065.457716</td>\n", " <td>11197.355517</td>\n", " <td>11800.339811</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>973.533195</td>\n", " <td>1353.989176</td>\n", " <td>1709.683679</td>\n", " <td>2172.352423</td>\n", " <td>2860.169750</td>\n", " <td>3528.481305</td>\n", " <td>3630.880722</td>\n", " <td>3738.932735</td>\n", " <td>2497.437901</td>\n", " <td>3193.054604</td>\n", " <td>4604.211737</td>\n", " <td>5937.029526</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3241.132406</td>\n", " <td>4394.874315</td>\n", " <td>5373.536612</td>\n", " <td>6657.939047</td>\n", " <td>9057.708095</td>\n", " <td>10360.030300</td>\n", " <td>11449.870115</td>\n", " <td>12274.570680</td>\n", " <td>8667.113214</td>\n", " <td>9946.599306</td>\n", " <td>11721.851483</td>\n", " <td>14811.898210</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5142.469716</td>\n", " <td>6066.721495</td>\n", " <td>7515.733738</td>\n", " <td>9366.067033</td>\n", " <td>12326.379990</td>\n", " <td>14225.754515</td>\n", " <td>15322.824720</td>\n", " <td>16215.485895</td>\n", " <td>17550.155945</td>\n", " <td>19596.498550</td>\n", " <td>23674.863230</td>\n", " <td>28054.065790</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>7236.794919</td>\n", " <td>9597.220820</td>\n", " <td>10931.085347</td>\n", " <td>13277.182057</td>\n", " <td>16523.017127</td>\n", " <td>19052.412163</td>\n", " <td>20901.729730</td>\n", " <td>23321.587723</td>\n", " <td>25034.243045</td>\n", " <td>27189.530312</td>\n", " <td>30373.363307</td>\n", " <td>33817.962533</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>14734.232750</td>\n", " <td>17909.489730</td>\n", " <td>20431.092700</td>\n", " <td>22966.144320</td>\n", " <td>27195.113040</td>\n", " <td>26982.290520</td>\n", " <td>28397.715120</td>\n", " <td>31540.974800</td>\n", " <td>33965.661150</td>\n", " <td>41283.164330</td>\n", " <td>44683.975250</td>\n", " <td>49357.190170</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \\\n", "count 30.000000 30.000000 30.000000 30.000000 \n", "mean 5661.057435 6963.012816 8365.486814 10143.823757 \n", "std 3114.060493 3677.950146 4199.193906 4724.983889 \n", "min 973.533195 1353.989176 1709.683679 2172.352423 \n", "25% 3241.132406 4394.874315 5373.536612 6657.939047 \n", "50% 5142.469716 6066.721495 7515.733738 9366.067033 \n", "75% 7236.794919 9597.220820 10931.085347 13277.182057 \n", "max 14734.232750 17909.489730 20431.092700 22966.144320 \n", "\n", " gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \\\n", "count 30.000000 30.000000 30.000000 30.000000 \n", "mean 12479.575246 14283.979110 15617.896551 17214.310727 \n", "std 5509.691411 5874.464896 6453.234827 7482.957960 \n", "min 2860.169750 3528.481305 3630.880722 3738.932735 \n", "25% 9057.708095 10360.030300 11449.870115 12274.570680 \n", "50% 12326.379990 14225.754515 15322.824720 16215.485895 \n", "75% 16523.017127 19052.412163 20901.729730 23321.587723 \n", "max 27195.113040 26982.290520 28397.715120 31540.974800 \n", "\n", " gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 \n", "count 30.000000 30.000000 30.000000 30.000000 \n", "mean 17061.568084 19076.781802 21711.732422 25054.481636 \n", "std 9109.804361 10065.457716 11197.355517 11800.339811 \n", "min 2497.437901 3193.054604 4604.211737 5937.029526 \n", "25% 8667.113214 9946.599306 11721.851483 14811.898210 \n", "50% 17550.155945 19596.498550 23674.863230 28054.065790 \n", "75% 25034.243045 27189.530312 30373.363307 33817.962533 \n", "max 33965.661150 41283.164330 44683.975250 49357.190170 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_europe.describe()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method head in module pandas.core.generic:\n", "\n", "head(n=5) method of pandas.core.frame.DataFrame instance\n", " Return the first `n` rows.\n", " \n", " This function returns the first `n` rows for the object based\n", " on position. It is useful for quickly testing if your object\n", " has the right type of data in it.\n", " \n", " Parameters\n", " ----------\n", " n : int, default 5\n", " Number of rows to select.\n", " \n", " Returns\n", " -------\n", " obj_head : type of caller\n", " The first `n` rows of the caller object.\n", " \n", " See Also\n", " --------\n", " pandas.DataFrame.tail: Returns the last `n` rows.\n", " \n", " Examples\n", " --------\n", " >>> df = pd.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion',\n", " ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})\n", " >>> df\n", " animal\n", " 0 alligator\n", " 1 bee\n", " 2 falcon\n", " 3 lion\n", " 4 monkey\n", " 5 parrot\n", " 6 shark\n", " 7 whale\n", " 8 zebra\n", " \n", " Viewing the first 5 lines\n", " \n", " >>> df.head()\n", " animal\n", " 0 alligator\n", " 1 bee\n", " 2 falcon\n", " 3 lion\n", " 4 monkey\n", " \n", " Viewing the first `n` lines (three in this case)\n", " \n", " >>> df.head(3)\n", " animal\n", " 0 alligator\n", " 1 bee\n", " 2 falcon\n", "\n" ] } ], "source": [ "help(data_americas.head)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead 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>continent</th>\n", " <th>country</th>\n", " <th>gdpPercap_1952</th>\n", " <th>gdpPercap_1957</th>\n", " <th>gdpPercap_1962</th>\n", " <th>gdpPercap_1967</th>\n", " <th>gdpPercap_1972</th>\n", " <th>gdpPercap_1977</th>\n", " <th>gdpPercap_1982</th>\n", " <th>gdpPercap_1987</th>\n", " <th>gdpPercap_1992</th>\n", " <th>gdpPercap_1997</th>\n", " <th>gdpPercap_2002</th>\n", " <th>gdpPercap_2007</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Americas</td>\n", " <td>Argentina</td>\n", " <td>5911.315053</td>\n", " <td>6856.856212</td>\n", " <td>7133.166023</td>\n", " <td>8052.953021</td>\n", " <td>9443.038526</td>\n", " <td>10079.026740</td>\n", " <td>8997.897412</td>\n", " <td>9139.671389</td>\n", " <td>9308.418710</td>\n", " <td>10967.281950</td>\n", " <td>8797.640716</td>\n", " <td>12779.379640</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Americas</td>\n", " <td>Bolivia</td>\n", " <td>2677.326347</td>\n", " <td>2127.686326</td>\n", " <td>2180.972546</td>\n", " <td>2586.886053</td>\n", " <td>2980.331339</td>\n", " <td>3548.097832</td>\n", " <td>3156.510452</td>\n", " <td>2753.691490</td>\n", " <td>2961.699694</td>\n", " <td>3326.143191</td>\n", " <td>3413.262690</td>\n", " <td>3822.137084</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Americas</td>\n", " <td>Brazil</td>\n", " <td>2108.944355</td>\n", " <td>2487.365989</td>\n", " <td>3336.585802</td>\n", " <td>3429.864357</td>\n", " <td>4985.711467</td>\n", " <td>6660.118654</td>\n", " <td>7030.835878</td>\n", " <td>7807.095818</td>\n", " <td>6950.283021</td>\n", " <td>7957.980824</td>\n", " <td>8131.212843</td>\n", " <td>9065.800825</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Americas</td>\n", " <td>Canada</td>\n", " <td>11367.161120</td>\n", " <td>12489.950060</td>\n", " <td>13462.485550</td>\n", " <td>16076.588030</td>\n", " <td>18970.570860</td>\n", " <td>22090.883060</td>\n", " <td>22898.792140</td>\n", " <td>26626.515030</td>\n", " <td>26342.884260</td>\n", " <td>28954.925890</td>\n", " <td>33328.965070</td>\n", " <td>36319.235010</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Americas</td>\n", " <td>Chile</td>\n", " <td>3939.978789</td>\n", " <td>4315.622723</td>\n", " <td>4519.094331</td>\n", " <td>5106.654313</td>\n", " <td>5494.024437</td>\n", " <td>4756.763836</td>\n", " <td>5095.665738</td>\n", " <td>5547.063754</td>\n", " <td>7596.125964</td>\n", " <td>10118.053180</td>\n", " <td>10778.783850</td>\n", " <td>13171.638850</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " continent country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \\\n", "0 Americas Argentina 5911.315053 6856.856212 7133.166023 \n", "1 Americas Bolivia 2677.326347 2127.686326 2180.972546 \n", "2 Americas Brazil 2108.944355 2487.365989 3336.585802 \n", "3 Americas Canada 11367.161120 12489.950060 13462.485550 \n", "4 Americas Chile 3939.978789 4315.622723 4519.094331 \n", "\n", " gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 \\\n", "0 8052.953021 9443.038526 10079.026740 8997.897412 \n", "1 2586.886053 2980.331339 3548.097832 3156.510452 \n", "2 3429.864357 4985.711467 6660.118654 7030.835878 \n", "3 16076.588030 18970.570860 22090.883060 22898.792140 \n", "4 5106.654313 5494.024437 4756.763836 5095.665738 \n", "\n", " gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 \\\n", "0 9139.671389 9308.418710 10967.281950 8797.640716 \n", "1 2753.691490 2961.699694 3326.143191 3413.262690 \n", "2 7807.095818 6950.283021 7957.980824 8131.212843 \n", "3 26626.515030 26342.884260 28954.925890 33328.965070 \n", "4 5547.063754 7596.125964 10118.053180 10778.783850 \n", "\n", " gdpPercap_2007 \n", "0 12779.379640 \n", "1 3822.137084 \n", "2 9065.800825 \n", "3 36319.235010 \n", "4 13171.638850 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_americas.head()" ] }, { "cell_type": "code", "execution_count": 31, "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>continent</th>\n", " <th>country</th>\n", " <th>gdpPercap_1952</th>\n", " <th>gdpPercap_1957</th>\n", " <th>gdpPercap_1962</th>\n", " <th>gdpPercap_1967</th>\n", " <th>gdpPercap_1972</th>\n", " <th>gdpPercap_1977</th>\n", " <th>gdpPercap_1982</th>\n", " <th>gdpPercap_1987</th>\n", " <th>gdpPercap_1992</th>\n", " <th>gdpPercap_1997</th>\n", " <th>gdpPercap_2002</th>\n", " <th>gdpPercap_2007</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>20</th>\n", " <td>Americas</td>\n", " <td>Puerto Rico</td>\n", " <td>3081.959785</td>\n", " <td>3907.156189</td>\n", " <td>5108.344630</td>\n", " <td>6929.277714</td>\n", " <td>9123.041742</td>\n", " <td>9770.524921</td>\n", " <td>10330.989150</td>\n", " <td>12281.341910</td>\n", " <td>14641.587110</td>\n", " <td>16999.433300</td>\n", " <td>18855.606180</td>\n", " <td>19328.70901</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Americas</td>\n", " <td>Trinidad and Tobago</td>\n", " <td>3023.271928</td>\n", " <td>4100.393400</td>\n", " <td>4997.523971</td>\n", " <td>5621.368472</td>\n", " <td>6619.551419</td>\n", " <td>7899.554209</td>\n", " <td>9119.528607</td>\n", " <td>7388.597823</td>\n", " <td>7370.990932</td>\n", " <td>8792.573126</td>\n", " <td>11460.600230</td>\n", " <td>18008.50924</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Americas</td>\n", " <td>United States</td>\n", " <td>13990.482080</td>\n", " <td>14847.127120</td>\n", " <td>16173.145860</td>\n", " <td>19530.365570</td>\n", " <td>21806.035940</td>\n", " <td>24072.632130</td>\n", " <td>25009.559140</td>\n", " <td>29884.350410</td>\n", " <td>32003.932240</td>\n", " <td>35767.433030</td>\n", " <td>39097.099550</td>\n", " <td>42951.65309</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Americas</td>\n", " <td>Uruguay</td>\n", " <td>5716.766744</td>\n", " <td>6150.772969</td>\n", " <td>5603.357717</td>\n", " <td>5444.619620</td>\n", " <td>5703.408898</td>\n", " <td>6504.339663</td>\n", " <td>6920.223051</td>\n", " <td>7452.398969</td>\n", " <td>8137.004775</td>\n", " <td>9230.240708</td>\n", " <td>7727.002004</td>\n", " <td>10611.46299</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Americas</td>\n", " <td>Venezuela</td>\n", " <td>7689.799761</td>\n", " <td>9802.466526</td>\n", " <td>8422.974165</td>\n", " <td>9541.474188</td>\n", " <td>10505.259660</td>\n", " <td>13143.950950</td>\n", " <td>11152.410110</td>\n", " <td>9883.584648</td>\n", " <td>10733.926310</td>\n", " <td>10165.495180</td>\n", " <td>8605.047831</td>\n", " <td>11415.80569</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " continent country gdpPercap_1952 gdpPercap_1957 \\\n", "20 Americas Puerto Rico 3081.959785 3907.156189 \n", "21 Americas Trinidad and Tobago 3023.271928 4100.393400 \n", "22 Americas United States 13990.482080 14847.127120 \n", "23 Americas Uruguay 5716.766744 6150.772969 \n", "24 Americas Venezuela 7689.799761 9802.466526 \n", "\n", " gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 \\\n", "20 5108.344630 6929.277714 9123.041742 9770.524921 \n", "21 4997.523971 5621.368472 6619.551419 7899.554209 \n", "22 16173.145860 19530.365570 21806.035940 24072.632130 \n", "23 5603.357717 5444.619620 5703.408898 6504.339663 \n", "24 8422.974165 9541.474188 10505.259660 13143.950950 \n", "\n", " gdpPercap_1982 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 \\\n", "20 10330.989150 12281.341910 14641.587110 16999.433300 \n", "21 9119.528607 7388.597823 7370.990932 8792.573126 \n", "22 25009.559140 29884.350410 32003.932240 35767.433030 \n", "23 6920.223051 7452.398969 8137.004775 9230.240708 \n", "24 11152.410110 9883.584648 10733.926310 10165.495180 \n", "\n", " gdpPercap_2002 gdpPercap_2007 \n", "20 18855.606180 19328.70901 \n", "21 11460.600230 18008.50924 \n", "22 39097.099550 42951.65309 \n", "23 7727.002004 10611.46299 \n", "24 8605.047831 11415.80569 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_americas.tail()" ] }, { "cell_type": "code", "execution_count": 32, "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>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>15</th>\n", " <th>16</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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>gdpPercap_1997</th>\n", " <td>10967.3</td>\n", " <td>3326.14</td>\n", " <td>7957.98</td>\n", " <td>28954.9</td>\n", " <td>10118.1</td>\n", " <td>6117.36</td>\n", " <td>6677.05</td>\n", " <td>5431.99</td>\n", " <td>3614.1</td>\n", " <td>7429.46</td>\n", " <td>...</td>\n", " <td>9767.3</td>\n", " <td>2253.02</td>\n", " <td>7113.69</td>\n", " <td>4247.4</td>\n", " <td>5838.35</td>\n", " <td>16999.4</td>\n", " <td>8792.57</td>\n", " <td>35767.4</td>\n", " <td>9230.24</td>\n", " <td>10165.5</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_2002</th>\n", " <td>8797.64</td>\n", " <td>3413.26</td>\n", " <td>8131.21</td>\n", " <td>33329</td>\n", " <td>10778.8</td>\n", " <td>5755.26</td>\n", " <td>7723.45</td>\n", " <td>6340.65</td>\n", " <td>4563.81</td>\n", " <td>5773.04</td>\n", " <td>...</td>\n", " <td>10742.4</td>\n", " <td>2474.55</td>\n", " <td>7356.03</td>\n", " <td>3783.67</td>\n", " <td>5909.02</td>\n", " <td>18855.6</td>\n", " <td>11460.6</td>\n", " <td>39097.1</td>\n", " <td>7727</td>\n", " <td>8605.05</td>\n", " </tr>\n", " <tr>\n", " <th>gdpPercap_2007</th>\n", " <td>12779.4</td>\n", " <td>3822.14</td>\n", " <td>9065.8</td>\n", " <td>36319.2</td>\n", " <td>13171.6</td>\n", " <td>7006.58</td>\n", " <td>9645.06</td>\n", " <td>8948.1</td>\n", " <td>6025.37</td>\n", " <td>6873.26</td>\n", " <td>...</td>\n", " <td>11977.6</td>\n", " <td>2749.32</td>\n", " <td>9809.19</td>\n", " <td>4172.84</td>\n", " <td>7408.91</td>\n", " <td>19328.7</td>\n", " <td>18008.5</td>\n", " <td>42951.7</td>\n", " <td>10611.5</td>\n", " <td>11415.8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows × 25 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 \\\n", "gdpPercap_1997 10967.3 3326.14 7957.98 28954.9 10118.1 6117.36 6677.05 \n", "gdpPercap_2002 8797.64 3413.26 8131.21 33329 10778.8 5755.26 7723.45 \n", "gdpPercap_2007 12779.4 3822.14 9065.8 36319.2 13171.6 7006.58 9645.06 \n", "\n", " 7 8 9 ... 15 16 17 \\\n", "gdpPercap_1997 5431.99 3614.1 7429.46 ... 9767.3 2253.02 7113.69 \n", "gdpPercap_2002 6340.65 4563.81 5773.04 ... 10742.4 2474.55 7356.03 \n", "gdpPercap_2007 8948.1 6025.37 6873.26 ... 11977.6 2749.32 9809.19 \n", "\n", " 18 19 20 21 22 23 24 \n", "gdpPercap_1997 4247.4 5838.35 16999.4 8792.57 35767.4 9230.24 10165.5 \n", "gdpPercap_2002 3783.67 5909.02 18855.6 11460.6 39097.1 7727 8605.05 \n", "gdpPercap_2007 4172.84 7408.91 19328.7 18008.5 42951.7 10611.5 11415.8 \n", "\n", "[3 rows x 25 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_americas_flipped = data_americas.T\n", "data_americas_flipped.tail(3)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method to_csv in module pandas.core.frame:\n", "\n", "to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression=None, quoting=None, quotechar='\"', line_terminator='\\n', chunksize=None, tupleize_cols=None, date_format=None, doublequote=True, escapechar=None, decimal='.') method of pandas.core.frame.DataFrame instance\n", " Write DataFrame to a comma-separated values (csv) file\n", " \n", " Parameters\n", " ----------\n", " path_or_buf : string or file handle, default None\n", " File path or object, if None is provided the result is returned as\n", " a string.\n", " sep : character, default ','\n", " Field delimiter for the output file.\n", " na_rep : string, default ''\n", " Missing data representation\n", " float_format : string, default None\n", " Format string for floating point numbers\n", " columns : sequence, optional\n", " Columns to write\n", " header : boolean or list of string, default True\n", " Write out the column names. If a list of strings is given it is\n", " assumed to be aliases for the column names\n", " index : boolean, default True\n", " Write row names (index)\n", " index_label : string or sequence, or False, default None\n", " Column label for index column(s) if desired. If None is given, and\n", " `header` and `index` are True, then the index names are used. A\n", " sequence should be given if the DataFrame uses MultiIndex. If\n", " False do not print fields for index names. Use index_label=False\n", " for easier importing in R\n", " mode : str\n", " Python write mode, default 'w'\n", " encoding : string, optional\n", " A string representing the encoding to use in the output file,\n", " defaults to 'ascii' on Python 2 and 'utf-8' on Python 3.\n", " compression : string, optional\n", " A string representing the compression to use in the output file.\n", " Allowed values are 'gzip', 'bz2', 'zip', 'xz'. This input is only\n", " used when the first argument is a filename.\n", " line_terminator : string, default ``'\\n'``\n", " The newline character or character sequence to use in the output\n", " file\n", " quoting : optional constant from csv module\n", " defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`\n", " then floats are converted to strings and thus csv.QUOTE_NONNUMERIC\n", " will treat them as non-numeric\n", " quotechar : string (length 1), default '\\\"'\n", " character used to quote fields\n", " doublequote : boolean, default True\n", " Control quoting of `quotechar` inside a field\n", " escapechar : string (length 1), default None\n", " character used to escape `sep` and `quotechar` when appropriate\n", " chunksize : int or None\n", " rows to write at a time\n", " tupleize_cols : boolean, default False\n", " .. deprecated:: 0.21.0\n", " This argument will be removed and will always write each row\n", " of the multi-index as a separate row in the CSV file.\n", " \n", " Write MultiIndex columns as a list of tuples (if True) or in\n", " the new, expanded format, where each MultiIndex column is a row\n", " in the CSV (if False).\n", " date_format : string, default None\n", " Format string for datetime objects\n", " decimal: string, default '.'\n", " Character recognized as decimal separator. E.g. use ',' for\n", " European data\n", "\n" ] } ], "source": [ "help(data_americas_flipped.to_csv)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "data_americas_flipped.to_csv('data/data_americas_flipped.csv')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data_americas_flipped.csv gapminder_gdp_asia.csv\r\n", "gapminder_all.csv gapminder_gdp_europe.csv\r\n", "gapminder_gdp_africa.csv gapminder_gdp_oceania.csv\r\n", "gapminder_gdp_americas.csv\r\n" ] } ], "source": [ "ls 'data'" ] }, { "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
NORCatUofC/rain	sewer-overflows/notebooks/CSO and Rainfall analysis.ipynb	1	177753	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function, unicode_literals\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from datetime import timedelta, datetime\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 2, "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>Outfall Structure</th>\n", " <th>Outfall Location</th>\n", " <th>Tide Gate #</th>\n", " <th>Waterway Reach</th>\n", " <th>Plant</th>\n", " <th>Open date/time</th>\n", " <th>Close date/time</th>\n", " <th>Gate Open Period</th>\n", " <th>Duration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:54:00</td>\n", " <td>2016-04-01 23:07:00</td>\n", " <td>0 days 05:13:48.000000000</td>\n", " <td>05:13:00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:53:00</td>\n", " <td>2016-04-01 19:42:00</td>\n", " <td>0 days 01:48:55.000000000</td>\n", " <td>01:49:00</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>DS-N10B</td>\n", " <td>Imlay St & Milwaukee (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 18:10:00</td>\n", " <td>2016-04-01 18:26:00</td>\n", " <td>0 days 00:16:08.000000000</td>\n", " <td>00:16:00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:53:00</td>\n", " <td>2016-04-01 19:42:00</td>\n", " <td>0 days 01:48:55.000000000</td>\n", " <td>01:49:00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:54:00</td>\n", " <td>2016-04-01 23:07:00</td>\n", " <td>0 days 05:13:48.000000000</td>\n", " <td>05:13:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Outfall Structure Outfall Location Tide Gate # \\\n", "0 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "1 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "2 DS-N10B Imlay St & Milwaukee (W) TG1 \n", "3 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "4 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "\n", " Waterway Reach Plant Open date/time \\\n", "0 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:54:00 \n", "1 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:53:00 \n", "2 NBCR Upper (NSC Confluence) Stickney 2016-04-01 18:10:00 \n", "3 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:53:00 \n", "4 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:54:00 \n", "\n", " Close date/time Gate Open Period Duration \n", "0 2016-04-01 23:07:00 0 days 05:13:48.000000000 05:13:00 \n", "1 2016-04-01 19:42:00 0 days 01:48:55.000000000 01:49:00 \n", "2 2016-04-01 18:26:00 0 days 00:16:08.000000000 00:16:00 \n", "3 2016-04-01 19:42:00 0 days 01:48:55.000000000 01:49:00 \n", "4 2016-04-01 23:07:00 0 days 05:13:48.000000000 05:13:00 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csos = pd.read_csv('data/merged_cso_data.csv')\n", "csos['Open date/time'] = pd.to_datetime(csos['Open date/time'])\n", "csos['Close date/time'] = pd.to_datetime(csos['Close date/time'])\n", "csos['Duration'] = csos['Close date/time'] - csos['Open date/time']\n", "csos.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1970-01-01 04:00:00 0.0\n", "1970-01-01 05:00:00 NaN\n", "1970-01-01 06:00:00 NaN\n", "1970-01-01 07:00:00 0.0\n", "1970-01-01 08:00:00 NaN\n", "Freq: H, Name: HOURLYPrecip, dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rain_df = pd.read_csv('data/ohare_hourly_20160929.csv')\n", "rain_df['datetime'] = pd.to_datetime(rain_df['datetime'])\n", "rain_df = rain_df.set_index(pd.DatetimeIndex(rain_df['datetime']))\n", "rain_df = rain_df['19700101':]\n", "chi_rain_series = rain_df['HOURLYPrecip'].resample('1H', label='right').max()\n", "chi_rain_series.head()" ] }, { "cell_type": "code", "execution_count": 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" <th>Outfall Structure</th>\n", " <th>Outfall Location</th>\n", " <th>Tide Gate #</th>\n", " <th>Waterway Reach</th>\n", " <th>Plant</th>\n", " <th>Open date/time</th>\n", " <th>Close date/time</th>\n", " <th>Gate Open Period</th>\n", " <th>Duration</th>\n", " <th>24hr_rain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:54:00</td>\n", " <td>2016-04-01 23:07:00</td>\n", " <td>0 days 05:13:48.000000000</td>\n", " <td>05:13:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:53:00</td>\n", " <td>2016-04-01 19:42:00</td>\n", " <td>0 days 01:48:55.000000000</td>\n", " <td>01:49:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>DS-N10B</td>\n", " <td>Imlay St & Milwaukee (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 18:10:00</td>\n", " <td>2016-04-01 18:26:00</td>\n", " <td>0 days 00:16:08.000000000</td>\n", " <td>00:16:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:53:00</td>\n", " <td>2016-04-01 19:42:00</td>\n", " <td>0 days 01:48:55.000000000</td>\n", " <td>01:49:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-01 17:54:00</td>\n", " <td>2016-04-01 23:07:00</td>\n", " <td>0 days 05:13:48.000000000</td>\n", " <td>05:13:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-02 10:17:00</td>\n", " <td>2016-04-02 10:20:00</td>\n", " <td>0 days 00:02:48.000000000</td>\n", " <td>00:03:00</td>\n", " <td>0.08</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-02 10:44:00</td>\n", " <td>2016-04-02 11:58:00</td>\n", " <td>0 days 01:13:52.000000000</td>\n", " <td>01:14:00</td>\n", " <td>0.08</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-02 15:40:00</td>\n", " <td>2016-04-02 22:10:00</td>\n", " <td>0 days 06:30:32.000000000</td>\n", " <td>06:30:00</td>\n", " <td>0.08</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 05:14:00</td>\n", " <td>2016-04-06 06:59:00</td>\n", " <td>0 days 01:44:52.000000000</td>\n", " <td>01:45:00</td>\n", " <td>0.12</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>DS-M84</td>\n", " <td>Roscoe St (W)</td>\n", " <td>TG4</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 11:48:00</td>\n", " <td>2016-04-06 13:19:00</td>\n", " <td>0 days 01:31:00.000000000</td>\n", " <td>01:31:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 11:21:00</td>\n", " <td>2016-04-06 13:45:00</td>\n", " <td>0 days 02:23:56.000000000</td>\n", " <td>02:24:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 13:59:00</td>\n", " <td>2016-04-06 14:00:00</td>\n", " <td>0 days 00:00:44.000000000</td>\n", " <td>00:01:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 11:33:00</td>\n", " <td>2016-04-06 14:17:00</td>\n", " <td>0 days 02:43:24.000000000</td>\n", " <td>02:44:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 14:34:00</td>\n", " <td>2016-04-06 14:35:00</td>\n", " <td>0 days 00:00:48.000000000</td>\n", " <td>00:01:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>DS-M84</td>\n", " <td>Roscoe St (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 11:51:00</td>\n", " <td>2016-04-06 14:36:00</td>\n", " <td>0 days 02:44:52.000000000</td>\n", " <td>02:45:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 14:57:00</td>\n", " <td>2016-04-06 14:59:00</td>\n", " <td>0 days 00:01:32.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 16:14:00</td>\n", " <td>2016-04-06 16:15:00</td>\n", " <td>0 days 00:00:44.000000000</td>\n", " <td>00:01:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>DS-N05</td>\n", " <td>Forest Glen Ave (S), West of Cicero</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 09:11:00</td>\n", " <td>2016-04-06 16:29:00</td>\n", " <td>0 days 07:17:24.000000000</td>\n", " <td>07:18:00</td>\n", " <td>0.24</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>DS-M15</td>\n", " <td>Kenton Ave (Kostner Ave Ext.) (N)</td>\n", " <td>TG2</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 11:41:00</td>\n", " <td>2016-04-06 16:30:00</td>\n", " <td>0 days 04:49:28.000000000</td>\n", " <td>04:49:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>DS-M03</td>\n", " <td>67th St ext. (E)</td>\n", " <td>TG1</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 19:15:00</td>\n", " <td>2016-04-06 21:15:00</td>\n", " <td>0 days 02:00:20.000000000</td>\n", " <td>02:00:00</td>\n", " <td>0.30</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>DS-M03</td>\n", " <td>67th St ext. (E)</td>\n", " <td>TG1</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 21:16:00</td>\n", " <td>2016-04-06 21:18:00</td>\n", " <td>0 days 00:02:20.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>DS-M03</td>\n", " <td>67th St ext. (E)</td>\n", " <td>TG1</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 21:19:00</td>\n", " <td>2016-04-06 21:23:00</td>\n", " <td>0 days 00:04:08.000000000</td>\n", " <td>00:04:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>DS-M03</td>\n", " <td>67th St ext. (E)</td>\n", " <td>TG1</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-06 21:24:00</td>\n", " <td>2016-04-06 21:25:00</td>\n", " <td>0 days 00:01:36.000000000</td>\n", " <td>00:01:00</td>\n", " <td>0.31</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-20 22:28:00</td>\n", " <td>2016-04-20 22:31:00</td>\n", " <td>0 days 00:02:38.000000000</td>\n", " <td>00:03:00</td>\n", " <td>0.39</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>DS-M15</td>\n", " <td>Kenton Ave (Kostner Ave Ext.) (N)</td>\n", " <td>TG2</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-20 23:05:00</td>\n", " <td>2016-04-20 23:11:00</td>\n", " <td>0 days 00:05:14.000000000</td>\n", " <td>00:06:00</td>\n", " <td>0.49</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>DS-N08</td>\n", " <td>Mango Ave ext. (Leonard & Miltmore) (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-20 21:51:00</td>\n", " <td>2016-04-20 23:12:00</td>\n", " <td>0 days 01:21:04.000000000</td>\n", " <td>01:21:00</td>\n", " <td>0.36</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>DS-M08</td>\n", " <td>Natchez Ave ext. (S)</td>\n", " <td>TG1</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-20 23:08:00</td>\n", " <td>2016-04-20 23:14:00</td>\n", " <td>0 days 00:05:44.000000000</td>\n", " <td>00:06:00</td>\n", " <td>0.49</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>DS-N10B</td>\n", " <td>Imlay St & Milwaukee (W)</td>\n", " <td>TG1</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-25 21:56:00</td>\n", " <td>2016-04-25 22:22:00</td>\n", " <td>0 days 00:25:28.000000000</td>\n", " <td>00:26:00</td>\n", " <td>0.09</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>DS-M79</td>\n", " <td>Logan Blvd (W)</td>\n", " <td>TG2</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-25 22:53:00</td>\n", " <td>2016-04-25 22:55:00</td>\n", " <td>0 days 00:02:44.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.09</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>DS-M84</td>\n", " <td>Roscoe St (W)</td>\n", " <td>TG4</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>Stickney</td>\n", " <td>2016-04-25 22:54:00</td>\n", " <td>2016-04-25 23:26:00</td>\n", " <td>0 days 00:31:32.000000000</td>\n", " <td>00:32:00</td>\n", " <td>0.09</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>10849</th>\n", " <td>DS-M82</td>\n", " <td>DS-M82</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 02:31:00</td>\n", " <td>2009-08-27 11:02:00</td>\n", " <td>0 days 08:31:00.000000000</td>\n", " <td>08:31:00</td>\n", " <td>1.17</td>\n", " </tr>\n", " <tr>\n", " <th>10850</th>\n", " <td>DS-M82</td>\n", " <td>DS-M82</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 23:48:00</td>\n", " <td>2009-08-27 23:59:00</td>\n", " <td>0 days 00:11:00.000000000</td>\n", " <td>00:11:00</td>\n", " <td>0.95</td>\n", " </tr>\n", " <tr>\n", " <th>10851</th>\n", " <td>DS-M11</td>\n", " <td>DS-M11</td>\n", " <td>NaN</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 05:03:00</td>\n", " <td>2009-08-27 13:16:00</td>\n", " <td>0 days 08:13:00.000000000</td>\n", " <td>08:13:00</td>\n", " <td>0.89</td>\n", " </tr>\n", " <tr>\n", " <th>10852</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 03:03:00</td>\n", " <td>2009-08-27 12:06:00</td>\n", " <td>0 days 09:03:00.000000000</td>\n", " <td>09:03:00</td>\n", " <td>1.01</td>\n", " </tr>\n", " <tr>\n", " <th>10853</th>\n", " <td>DS-N16</td>\n", " <td>DS-N16</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 08:43:00</td>\n", " <td>2009-08-27 08:57:00</td>\n", " <td>0 days 00:14:00.000000000</td>\n", " <td>00:14:00</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>10854</th>\n", " <td>DS-M13</td>\n", " <td>DS-M13</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 07:11:00</td>\n", " <td>2009-08-27 07:16:00</td>\n", " <td>0 days 00:05:00.000000000</td>\n", " <td>00:05:00</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>10855</th>\n", " <td>DS-N18</td>\n", " <td>DS-N18</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 05:32:00</td>\n", " <td>2009-08-27 06:14:00</td>\n", " <td>0 days 00:42:00.000000000</td>\n", " <td>00:42:00</td>\n", " <td>0.89</td>\n", " </tr>\n", " <tr>\n", " <th>10856</th>\n", " <td>DS-N18</td>\n", " <td>DS-N18</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 08:47:00</td>\n", " <td>2009-08-27 09:40:00</td>\n", " <td>0 days 00:53:00.000000000</td>\n", " <td>00:53:00</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>10857</th>\n", " <td>DS-M15</td>\n", " <td>DS-M15</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 05:30:00</td>\n", " <td>2009-08-27 05:57:00</td>\n", " <td>0 days 00:27:00.000000000</td>\n", " <td>00:27:00</td>\n", " <td>0.89</td>\n", " </tr>\n", " <tr>\n", " <th>10858</th>\n", " <td>DS-M15</td>\n", " <td>DS-M15</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 06:39:00</td>\n", " <td>2009-08-27 07:34:00</td>\n", " <td>0 days 00:55:00.000000000</td>\n", " <td>00:55:00</td>\n", " <td>0.87</td>\n", " </tr>\n", " <tr>\n", " <th>10859</th>\n", " <td>DS-N17</td>\n", " <td>DS-N17</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 05:28:00</td>\n", " <td>2009-08-27 06:15:00</td>\n", " <td>0 days 00:47:00.000000000</td>\n", " <td>00:47:00</td>\n", " <td>0.89</td>\n", " </tr>\n", " <tr>\n", " <th>10860</th>\n", " <td>DS-N17</td>\n", " <td>DS-N17</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 08:41:00</td>\n", " <td>2009-08-27 09:35:00</td>\n", " <td>0 days 00:54:00.000000000</td>\n", " <td>00:54:00</td>\n", " <td>0.98</td>\n", " </tr>\n", " <tr>\n", " <th>10861</th>\n", " <td>DS-M80</td>\n", " <td>DS-M80</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-27 04:54:00</td>\n", " <td>2009-08-27 13:05:00</td>\n", " <td>0 days 08:11:00.000000000</td>\n", " <td>08:11:00</td>\n", " <td>0.93</td>\n", " </tr>\n", " <tr>\n", " <th>10862</th>\n", " <td>DS-M76</td>\n", " <td>DS-M76</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-26 05:59:00</td>\n", " <td>2009-08-26 06:36:00</td>\n", " <td>0 days 00:37:00.000000000</td>\n", " <td>00:37:00</td>\n", " <td>0.57</td>\n", " </tr>\n", " <tr>\n", " <th>10863</th>\n", " <td>DS-D16</td>\n", " <td>DS-D16</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2009-08-26 16:13:00</td>\n", " <td>2009-08-26 16:28:00</td>\n", " <td>0 days 00:15:00.000000000</td>\n", " <td>00:15:00</td>\n", " <td>0.83</td>\n", " </tr>\n", " <tr>\n", " <th>10864</th>\n", " <td>DS-N10B</td>\n", " <td>DS-N10B</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-21 17:40:00</td>\n", " <td>2009-08-21 18:13:00</td>\n", " <td>0 days 00:33:00.000000000</td>\n", " <td>00:33:00</td>\n", " <td>0.07</td>\n", " </tr>\n", " <tr>\n", " <th>10865</th>\n", " <td>DS-N03</td>\n", " <td>DS-N03</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-21 17:59:00</td>\n", " <td>2009-08-21 18:37:00</td>\n", " <td>0 days 00:38:00.000000000</td>\n", " <td>00:38:00</td>\n", " <td>0.07</td>\n", " </tr>\n", " <tr>\n", " <th>10866</th>\n", " <td>DS-M85</td>\n", " <td>DS-M85</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-20 10:32:00</td>\n", " <td>2009-08-20 13:07:00</td>\n", " <td>0 days 02:35:00.000000000</td>\n", " <td>02:35:00</td>\n", " <td>0.42</td>\n", " </tr>\n", " <tr>\n", " <th>10867</th>\n", " <td>DS-M85</td>\n", " <td>DS-M85</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-19 18:10:00</td>\n", " <td>2009-08-19 19:08:00</td>\n", " <td>0 days 00:58:00.000000000</td>\n", " <td>00:58:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10868</th>\n", " <td>DS-M76</td>\n", " <td>DS-M76</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-19 17:54:00</td>\n", " <td>2009-08-19 18:48:00</td>\n", " <td>0 days 00:54:00.000000000</td>\n", " <td>00:54:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10869</th>\n", " <td>DS-D16</td>\n", " <td>DS-D16</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2009-08-19 17:53:00</td>\n", " <td>2009-08-19 18:23:00</td>\n", " <td>0 days 00:30:00.000000000</td>\n", " <td>00:30:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10870</th>\n", " <td>DS-M13</td>\n", " <td>DS-M13</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-19 18:18:00</td>\n", " <td>2009-08-19 18:32:00</td>\n", " <td>0 days 00:14:00.000000000</td>\n", " <td>00:14:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10871</th>\n", " <td>DS-M08</td>\n", " <td>DS-M08</td>\n", " <td>NaN</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2009-08-19 17:49:00</td>\n", " <td>2009-08-19 18:30:00</td>\n", " <td>0 days 00:41:00.000000000</td>\n", " <td>00:41:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10872</th>\n", " <td>DS-D07</td>\n", " <td>DS-D07</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Upper</td>\n", " <td>NaN</td>\n", " <td>2009-08-18 10:51:00</td>\n", " <td>2009-08-18 10:53:00</td>\n", " <td>0 days 00:02:00.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>10873</th>\n", " <td>DS-D49</td>\n", " <td>DS-D49</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Lower</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 06:45:00</td>\n", " <td>2009-08-17 07:57:00</td>\n", " <td>0 days 01:12:00.000000000</td>\n", " <td>01:12:00</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>10874</th>\n", " <td>DS-M76</td>\n", " <td>DS-M76</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 06:04:00</td>\n", " <td>2009-08-17 07:11:00</td>\n", " <td>0 days 01:07:00.000000000</td>\n", " <td>01:07:00</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>10875</th>\n", " <td>DS-M76</td>\n", " <td>DS-M76</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 14:55:00</td>\n", " <td>2009-08-17 15:36:00</td>\n", " <td>0 days 00:41:00.000000000</td>\n", " <td>00:41:00</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>10876</th>\n", " <td>DS-D27I</td>\n", " <td>DS-D27I</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 06:37:00</td>\n", " <td>2009-08-17 10:32:00</td>\n", " <td>0 days 03:55:00.000000000</td>\n", " <td>03:55:00</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>10877</th>\n", " <td>DS-D47,50,51</td>\n", " <td>DS-D47,50,51</td>\n", " <td>NaN</td>\n", " <td>Salt Cr</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 06:46:00</td>\n", " <td>2009-08-17 08:29:00</td>\n", " <td>0 days 01:43:00.000000000</td>\n", " <td>01:43:00</td>\n", " <td>0.99</td>\n", " </tr>\n", " <tr>\n", " <th>10878</th>\n", " <td>DS-D26</td>\n", " <td>DS-D26</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2009-08-17 07:24:00</td>\n", " <td>2009-08-17 09:49:00</td>\n", " <td>0 days 02:25:00.000000000</td>\n", " <td>02:25:00</td>\n", " <td>1.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10879 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " Outfall Structure Outfall Location Tide Gate # \\\n", "0 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "1 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "2 DS-N10B Imlay St & Milwaukee (W) TG1 \n", "3 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "4 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "5 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "6 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "7 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "8 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "9 DS-M84 Roscoe St (W) TG4 \n", "10 DS-M79 Logan Blvd (W) TG2 \n", "11 DS-M79 Logan Blvd (W) TG2 \n", "12 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "13 DS-M79 Logan Blvd (W) TG2 \n", "14 DS-M84 Roscoe St (W) TG2 \n", "15 DS-M79 Logan Blvd (W) TG2 \n", "16 DS-M79 Logan Blvd (W) TG2 \n", "17 DS-N05 Forest Glen Ave (S), West of Cicero TG1 \n", "18 DS-M15 Kenton Ave (Kostner Ave Ext.) (N) TG2 \n", "19 DS-M03 67th St ext. (E) TG1 \n", "20 DS-M03 67th St ext. (E) TG1 \n", "21 DS-M03 67th St ext. (E) TG1 \n", "22 DS-M03 67th St ext. (E) TG1 \n", "23 DS-M79 Logan Blvd (W) TG2 \n", "24 DS-M15 Kenton Ave (Kostner Ave Ext.) (N) TG2 \n", "25 DS-N08 Mango Ave ext. (Leonard & Miltmore) (W) TG1 \n", "26 DS-M08 Natchez Ave ext. (S) TG1 \n", "27 DS-N10B Imlay St & Milwaukee (W) TG1 \n", "28 DS-M79 Logan Blvd (W) TG2 \n", "29 DS-M84 Roscoe St (W) TG4 \n", "... ... ... ... \n", "10849 DS-M82 DS-M82 NaN \n", "10850 DS-M82 DS-M82 NaN \n", "10851 DS-M11 DS-M11 NaN \n", "10852 DS-M84 DS-M84 NaN \n", "10853 DS-N16 DS-N16 NaN \n", "10854 DS-M13 DS-M13 NaN \n", "10855 DS-N18 DS-N18 NaN \n", "10856 DS-N18 DS-N18 NaN \n", "10857 DS-M15 DS-M15 NaN \n", "10858 DS-M15 DS-M15 NaN \n", "10859 DS-N17 DS-N17 NaN \n", "10860 DS-N17 DS-N17 NaN \n", "10861 DS-M80 DS-M80 NaN \n", "10862 DS-M76 DS-M76 NaN \n", "10863 DS-D16 DS-D16 NaN \n", "10864 DS-N10B DS-N10B NaN \n", "10865 DS-N03 DS-N03 NaN \n", "10866 DS-M85 DS-M85 NaN \n", "10867 DS-M85 DS-M85 NaN \n", "10868 DS-M76 DS-M76 NaN \n", "10869 DS-D16 DS-D16 NaN \n", "10870 DS-M13 DS-M13 NaN \n", "10871 DS-M08 DS-M08 NaN \n", "10872 DS-D07 DS-D07 NaN \n", "10873 DS-D49 DS-D49 NaN \n", "10874 DS-M76 DS-M76 NaN \n", "10875 DS-M76 DS-M76 NaN \n", "10876 DS-D27I DS-D27I NaN \n", "10877 DS-D47,50,51 DS-D47,50,51 NaN \n", "10878 DS-D26 DS-D26 NaN \n", "\n", " Waterway Reach Plant Open date/time \\\n", "0 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:54:00 \n", "1 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:53:00 \n", "2 NBCR Upper (NSC Confluence) Stickney 2016-04-01 18:10:00 \n", "3 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:53:00 \n", "4 NBCR Upper (NSC Confluence) Stickney 2016-04-01 17:54:00 \n", "5 NBCR Upper (NSC Confluence) Stickney 2016-04-02 10:17:00 \n", "6 NBCR Upper (NSC Confluence) Stickney 2016-04-02 10:44:00 \n", "7 NBCR Upper (NSC Confluence) Stickney 2016-04-02 15:40:00 \n", "8 NBCR Upper (NSC Confluence) Stickney 2016-04-06 05:14:00 \n", "9 NBCR Lower (NSC Confluence) Stickney 2016-04-06 11:48:00 \n", "10 NBCR Lower (NSC Confluence) Stickney 2016-04-06 11:21:00 \n", "11 NBCR Lower (NSC Confluence) Stickney 2016-04-06 13:59:00 \n", "12 NBCR Upper (NSC Confluence) Stickney 2016-04-06 11:33:00 \n", "13 NBCR Lower (NSC Confluence) Stickney 2016-04-06 14:34:00 \n", "14 NBCR Lower (NSC Confluence) Stickney 2016-04-06 11:51:00 \n", "15 NBCR Lower (NSC Confluence) Stickney 2016-04-06 14:57:00 \n", "16 NBCR Lower (NSC Confluence) Stickney 2016-04-06 16:14:00 \n", "17 NBCR Upper (NSC Confluence) Stickney 2016-04-06 09:11:00 \n", "18 CSSC Upper (SWRP) Stickney 2016-04-06 11:41:00 \n", "19 CSSC Lower (SWRP) Stickney 2016-04-06 19:15:00 \n", "20 CSSC Lower (SWRP) Stickney 2016-04-06 21:16:00 \n", "21 CSSC Lower (SWRP) Stickney 2016-04-06 21:19:00 \n", "22 CSSC Lower (SWRP) Stickney 2016-04-06 21:24:00 \n", "23 NBCR Lower (NSC Confluence) Stickney 2016-04-20 22:28:00 \n", "24 CSSC Upper (SWRP) Stickney 2016-04-20 23:05:00 \n", "25 NBCR Upper (NSC Confluence) Stickney 2016-04-20 21:51:00 \n", "26 CSSC Lower (SWRP) Stickney 2016-04-20 23:08:00 \n", "27 NBCR Upper (NSC Confluence) Stickney 2016-04-25 21:56:00 \n", "28 NBCR Lower (NSC Confluence) Stickney 2016-04-25 22:53:00 \n", "29 NBCR Lower (NSC Confluence) Stickney 2016-04-25 22:54:00 \n", "... ... ... ... \n", "10849 NBCR Lower (NSC Confluence) NaN 2009-08-27 02:31:00 \n", "10850 NBCR Lower (NSC Confluence) NaN 2009-08-27 23:48:00 \n", "10851 CSSC Lower (SWRP) NaN 2009-08-27 05:03:00 \n", "10852 NBCR Lower (NSC Confluence) NaN 2009-08-27 03:03:00 \n", "10853 NBCR Upper (NSC Confluence) NaN 2009-08-27 08:43:00 \n", "10854 CSSC Upper (SWRP) NaN 2009-08-27 07:11:00 \n", "10855 NBCR Upper (NSC Confluence) NaN 2009-08-27 05:32:00 \n", "10856 NBCR Upper (NSC Confluence) NaN 2009-08-27 08:47:00 \n", "10857 CSSC Upper (SWRP) NaN 2009-08-27 05:30:00 \n", "10858 CSSC Upper (SWRP) NaN 2009-08-27 06:39:00 \n", "10859 NBCR Upper (NSC Confluence) NaN 2009-08-27 05:28:00 \n", "10860 NBCR Upper (NSC Confluence) NaN 2009-08-27 08:41:00 \n", "10861 NBCR Lower (NSC Confluence) NaN 2009-08-27 04:54:00 \n", "10862 NBCR Lower (NSC Confluence) NaN 2009-08-26 05:59:00 \n", "10863 DesPlaines Middle NaN 2009-08-26 16:13:00 \n", "10864 NBCR Upper (NSC Confluence) NaN 2009-08-21 17:40:00 \n", "10865 NBCR Upper (NSC Confluence) NaN 2009-08-21 17:59:00 \n", "10866 NBCR Lower (NSC Confluence) NaN 2009-08-20 10:32:00 \n", "10867 NBCR Lower (NSC Confluence) NaN 2009-08-19 18:10:00 \n", "10868 NBCR Lower (NSC Confluence) NaN 2009-08-19 17:54:00 \n", "10869 DesPlaines Middle NaN 2009-08-19 17:53:00 \n", "10870 CSSC Upper (SWRP) NaN 2009-08-19 18:18:00 \n", "10871 CSSC Lower (SWRP) NaN 2009-08-19 17:49:00 \n", "10872 DesPlaines Upper NaN 2009-08-18 10:51:00 \n", "10873 DesPlaines Lower NaN 2009-08-17 06:45:00 \n", "10874 NBCR Lower (NSC Confluence) NaN 2009-08-17 06:04:00 \n", "10875 NBCR Lower (NSC Confluence) NaN 2009-08-17 14:55:00 \n", "10876 DesPlaines Middle NaN 2009-08-17 06:37:00 \n", "10877 Salt Cr NaN 2009-08-17 06:46:00 \n", "10878 DesPlaines Middle NaN 2009-08-17 07:24:00 \n", "\n", " Close date/time Gate Open Period Duration 24hr_rain \n", "0 2016-04-01 23:07:00 0 days 05:13:48.000000000 05:13:00 0.12 \n", "1 2016-04-01 19:42:00 0 days 01:48:55.000000000 01:49:00 0.12 \n", "2 2016-04-01 18:26:00 0 days 00:16:08.000000000 00:16:00 0.12 \n", "3 2016-04-01 19:42:00 0 days 01:48:55.000000000 01:49:00 0.12 \n", "4 2016-04-01 23:07:00 0 days 05:13:48.000000000 05:13:00 0.12 \n", "5 2016-04-02 10:20:00 0 days 00:02:48.000000000 00:03:00 0.08 \n", "6 2016-04-02 11:58:00 0 days 01:13:52.000000000 01:14:00 0.08 \n", "7 2016-04-02 22:10:00 0 days 06:30:32.000000000 06:30:00 0.08 \n", "8 2016-04-06 06:59:00 0 days 01:44:52.000000000 01:45:00 0.12 \n", "9 2016-04-06 13:19:00 0 days 01:31:00.000000000 01:31:00 0.30 \n", "10 2016-04-06 13:45:00 0 days 02:23:56.000000000 02:24:00 0.30 \n", "11 2016-04-06 14:00:00 0 days 00:00:44.000000000 00:01:00 0.31 \n", "12 2016-04-06 14:17:00 0 days 02:43:24.000000000 02:44:00 0.30 \n", "13 2016-04-06 14:35:00 0 days 00:00:48.000000000 00:01:00 0.31 \n", "14 2016-04-06 14:36:00 0 days 02:44:52.000000000 02:45:00 0.30 \n", "15 2016-04-06 14:59:00 0 days 00:01:32.000000000 00:02:00 0.31 \n", "16 2016-04-06 16:15:00 0 days 00:00:44.000000000 00:01:00 0.31 \n", "17 2016-04-06 16:29:00 0 days 07:17:24.000000000 07:18:00 0.24 \n", "18 2016-04-06 16:30:00 0 days 04:49:28.000000000 04:49:00 0.30 \n", "19 2016-04-06 21:15:00 0 days 02:00:20.000000000 02:00:00 0.30 \n", "20 2016-04-06 21:18:00 0 days 00:02:20.000000000 00:02:00 0.31 \n", "21 2016-04-06 21:23:00 0 days 00:04:08.000000000 00:04:00 0.31 \n", "22 2016-04-06 21:25:00 0 days 00:01:36.000000000 00:01:00 0.31 \n", "23 2016-04-20 22:31:00 0 days 00:02:38.000000000 00:03:00 0.39 \n", "24 2016-04-20 23:11:00 0 days 00:05:14.000000000 00:06:00 0.49 \n", "25 2016-04-20 23:12:00 0 days 01:21:04.000000000 01:21:00 0.36 \n", "26 2016-04-20 23:14:00 0 days 00:05:44.000000000 00:06:00 0.49 \n", "27 2016-04-25 22:22:00 0 days 00:25:28.000000000 00:26:00 0.09 \n", "28 2016-04-25 22:55:00 0 days 00:02:44.000000000 00:02:00 0.09 \n", "29 2016-04-25 23:26:00 0 days 00:31:32.000000000 00:32:00 0.09 \n", "... ... ... ... ... \n", "10849 2009-08-27 11:02:00 0 days 08:31:00.000000000 08:31:00 1.17 \n", "10850 2009-08-27 23:59:00 0 days 00:11:00.000000000 00:11:00 0.95 \n", "10851 2009-08-27 13:16:00 0 days 08:13:00.000000000 08:13:00 0.89 \n", "10852 2009-08-27 12:06:00 0 days 09:03:00.000000000 09:03:00 1.01 \n", "10853 2009-08-27 08:57:00 0 days 00:14:00.000000000 00:14:00 0.98 \n", "10854 2009-08-27 07:16:00 0 days 00:05:00.000000000 00:05:00 0.98 \n", "10855 2009-08-27 06:14:00 0 days 00:42:00.000000000 00:42:00 0.89 \n", "10856 2009-08-27 09:40:00 0 days 00:53:00.000000000 00:53:00 0.98 \n", "10857 2009-08-27 05:57:00 0 days 00:27:00.000000000 00:27:00 0.89 \n", "10858 2009-08-27 07:34:00 0 days 00:55:00.000000000 00:55:00 0.87 \n", "10859 2009-08-27 06:15:00 0 days 00:47:00.000000000 00:47:00 0.89 \n", "10860 2009-08-27 09:35:00 0 days 00:54:00.000000000 00:54:00 0.98 \n", "10861 2009-08-27 13:05:00 0 days 08:11:00.000000000 08:11:00 0.93 \n", "10862 2009-08-26 06:36:00 0 days 00:37:00.000000000 00:37:00 0.57 \n", "10863 2009-08-26 16:28:00 0 days 00:15:00.000000000 00:15:00 0.83 \n", "10864 2009-08-21 18:13:00 0 days 00:33:00.000000000 00:33:00 0.07 \n", "10865 2009-08-21 18:37:00 0 days 00:38:00.000000000 00:38:00 0.07 \n", "10866 2009-08-20 13:07:00 0 days 02:35:00.000000000 02:35:00 0.42 \n", "10867 2009-08-19 19:08:00 0 days 00:58:00.000000000 00:58:00 0.37 \n", "10868 2009-08-19 18:48:00 0 days 00:54:00.000000000 00:54:00 0.37 \n", "10869 2009-08-19 18:23:00 0 days 00:30:00.000000000 00:30:00 0.37 \n", "10870 2009-08-19 18:32:00 0 days 00:14:00.000000000 00:14:00 0.37 \n", "10871 2009-08-19 18:30:00 0 days 00:41:00.000000000 00:41:00 0.37 \n", "10872 2009-08-18 10:53:00 0 days 00:02:00.000000000 00:02:00 0.02 \n", "10873 2009-08-17 07:57:00 0 days 01:12:00.000000000 01:12:00 0.99 \n", "10874 2009-08-17 07:11:00 0 days 01:07:00.000000000 01:07:00 0.99 \n", "10875 2009-08-17 15:36:00 0 days 00:41:00.000000000 00:41:00 0.37 \n", "10876 2009-08-17 10:32:00 0 days 03:55:00.000000000 03:55:00 0.99 \n", "10877 2009-08-17 08:29:00 0 days 01:43:00.000000000 01:43:00 0.99 \n", "10878 2009-08-17 09:49:00 0 days 02:25:00.000000000 02:25:00 1.00 \n", "\n", "[10879 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csos['24hr_rain'] = cum_rainfall(csos['Open date/time'], 24)\n", "csos" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Outfall Structure</th>\n", " <th>Outfall Location</th>\n", " <th>Tide Gate #</th>\n", " <th>Waterway Reach</th>\n", " <th>Plant</th>\n", " <th>Open date/time</th>\n", " <th>Close date/time</th>\n", " <th>Gate Open Period</th>\n", " <th>Duration</th>\n", " <th>24hr_rain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4959</th>\n", " <td>DS-D43</td>\n", " <td>DS-D43</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Lower</td>\n", " <td>NaN</td>\n", " <td>2013-05-30 10:16:00</td>\n", " <td>2013-05-30 10:28:00</td>\n", " <td>0 days 00:12:00.000000000</td>\n", " <td>00:12:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>282</th>\n", " <td>DS-D43</td>\n", " <td>Near Burling Rd (Library)</td>\n", " <td>TG1</td>\n", " <td>DesPlaines Lower</td>\n", " <td>Stickney</td>\n", " <td>2016-05-13 03:41:00</td>\n", " <td>2016-05-13 03:48:00</td>\n", " <td>0 days 00:07:08.000000000</td>\n", " <td>00:07:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td>DS-D43</td>\n", " <td>Near Burling Rd (Library)</td>\n", " <td>TG1</td>\n", " <td>DesPlaines Lower</td>\n", " <td>Stickney</td>\n", " <td>2016-05-13 03:34:00</td>\n", " <td>2016-05-13 03:40:00</td>\n", " <td>0 days 00:06:44.000000000</td>\n", " <td>00:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>7457</th>\n", " <td>DS-N08</td>\n", " <td>DS-N08</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-05-30 14:11:00</td>\n", " <td>2011-05-30 14:14:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>00:03:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6750</th>\n", " <td>DS-D21,25</td>\n", " <td>DS-D21,25</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 14:58:00</td>\n", " <td>2011-09-03 15:35:00</td>\n", " <td>0 days 00:37:00.000000000</td>\n", " <td>00:37:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>7458</th>\n", " <td>DS-N08</td>\n", " <td>DS-N08</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-05-30 14:30:00</td>\n", " <td>2011-05-30 14:33:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>00:03:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>7459</th>\n", " <td>DS-N08</td>\n", " <td>DS-N08</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-05-30 15:07:00</td>\n", " <td>2011-05-30 15:09:00</td>\n", " <td>0 days 00:02:00.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6749</th>\n", " <td>DS-D11</td>\n", " <td>DS-D11</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:33:00</td>\n", " <td>2011-09-03 15:48:00</td>\n", " <td>0 days 00:15:00.000000000</td>\n", " <td>00:15:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6748</th>\n", " <td>DS-D11</td>\n", " <td>DS-D11</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:33:00</td>\n", " <td>2011-09-03 15:48:00</td>\n", " <td>0 days 00:15:00.000000000</td>\n", " <td>00:15:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8524</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2010-08-07 06:49:00</td>\n", " <td>2010-08-07 06:54:00</td>\n", " <td>0 days 00:05:00.000000000</td>\n", " <td>00:05:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6747</th>\n", " <td>DS-M80</td>\n", " <td>DS-M80</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:11:00</td>\n", " <td>2011-09-03 15:15:00</td>\n", " <td>0 days 00:04:00.000000000</td>\n", " <td>00:04:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6746</th>\n", " <td>DS-M15</td>\n", " <td>DS-M15</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:51:00</td>\n", " <td>2011-09-03 17:57:00</td>\n", " <td>0 days 02:06:00.000000000</td>\n", " <td>02:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6745</th>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>NaN</td>\n", " <td>Addison Cr</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:00:00</td>\n", " <td>2011-09-03 16:30:00</td>\n", " <td>0 days 01:30:00.000000000</td>\n", " <td>01:30:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6744</th>\n", " <td>DS-M79</td>\n", " <td>DS-M79</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:06:00</td>\n", " <td>2011-09-03 16:46:00</td>\n", " <td>0 days 01:40:00.000000000</td>\n", " <td>01:40:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>6743</th>\n", " <td>DS-N08</td>\n", " <td>DS-N08</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-09-03 15:28:00</td>\n", " <td>2011-09-03 15:30:00</td>\n", " <td>0 days 00:02:00.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8523</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2010-08-08 07:16:00</td>\n", " <td>2010-08-08 07:21:00</td>\n", " <td>0 days 00:05:00.000000000</td>\n", " <td>00:05:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8262</th>\n", " <td>CDS-21</td>\n", " <td>CDS-21</td>\n", " <td>NaN</td>\n", " <td>Grand Cal R</td>\n", " <td>NaN</td>\n", " <td>2011-02-22 00:01:00</td>\n", " <td>2011-02-22 05:02:00</td>\n", " <td>0 days 05:01:00.000000000</td>\n", " <td>05:01:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1109</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-09-12 09:20:00</td>\n", " <td>2015-09-12 13:00:00</td>\n", " <td>0 days 03:40:00.000000000</td>\n", " <td>03:40:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1457</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-08-16 22:54:00</td>\n", " <td>2015-08-17 00:00:00</td>\n", " <td>0 days 01:06:00.000000000</td>\n", " <td>01:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1458</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-08-16 22:54:00</td>\n", " <td>2015-08-17 00:00:00</td>\n", " <td>0 days 01:06:00.000000000</td>\n", " <td>01:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1459</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-08-16 22:54:00</td>\n", " <td>2015-08-17 00:00:00</td>\n", " <td>0 days 01:06:00.000000000</td>\n", " <td>01:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1460</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-08-16 22:54:00</td>\n", " <td>2015-08-17 00:00:00</td>\n", " <td>0 days 01:06:00.000000000</td>\n", " <td>01:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>283</th>\n", " <td>DS-D43</td>\n", " <td>Near Burling Rd (Library)</td>\n", " <td>TG1</td>\n", " <td>DesPlaines Lower</td>\n", " <td>Stickney</td>\n", " <td>2016-05-13 03:49:00</td>\n", " <td>2016-05-13 03:55:00</td>\n", " <td>0 days 00:06:08.000000000</td>\n", " <td>00:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1461</th>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>RAPS (DS-M27, DS-M28, DS-M29)</td>\n", " <td>NaN</td>\n", " <td>SF SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2015-08-16 22:54:00</td>\n", " <td>2015-08-17 00:00:00</td>\n", " <td>0 days 01:06:00.000000000</td>\n", " <td>01:06:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>284</th>\n", " <td>DS-D43</td>\n", " <td>Near Burling Rd (Library)</td>\n", " <td>TG1</td>\n", " <td>DesPlaines Lower</td>\n", " <td>Stickney</td>\n", " <td>2016-05-13 04:00:00</td>\n", " <td>2016-05-13 04:03:00</td>\n", " <td>0 days 00:03:04.000000000</td>\n", " <td>00:03:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>286</th>\n", " <td>DS-D43</td>\n", " <td>Near Burling Rd (Library)</td>\n", " <td>TG1</td>\n", " <td>DesPlaines Lower</td>\n", " <td>Stickney</td>\n", " <td>2016-05-13 04:19:00</td>\n", " <td>2016-05-13 04:21:00</td>\n", " <td>0 days 00:02:04.000000000</td>\n", " <td>00:02:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8742</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2010-07-26 00:00:00</td>\n", " <td>2010-07-26 08:58:00</td>\n", " <td>0 days 08:58:00.000000000</td>\n", " <td>08:58:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8741</th>\n", " <td>DS-M19</td>\n", " <td>DS-M19</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2010-07-26 00:00:00</td>\n", " <td>2010-07-26 08:54:00</td>\n", " <td>0 days 08:54:00.000000000</td>\n", " <td>08:54:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8740</th>\n", " <td>DS-D47,50,51</td>\n", " <td>DS-D47,50,51</td>\n", " <td>NaN</td>\n", " <td>Salt Cr</td>\n", " <td>NaN</td>\n", " <td>2010-07-26 00:00:00</td>\n", " <td>2010-07-26 09:24:00</td>\n", " <td>0 days 09:24:00.000000000</td>\n", " <td>09:24:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8739</th>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>NaN</td>\n", " <td>Addison Cr</td>\n", " <td>NaN</td>\n", " <td>2010-07-26 00:00:00</td>\n", " <td>2010-07-26 05:30:00</td>\n", " <td>0 days 05:30:00.000000000</td>\n", " <td>05:30:00</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>7072</th>\n", " <td>TG-M71</td>\n", " <td>TG-M71</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:23:00</td>\n", " <td>2011-07-23 12:18:00</td>\n", " <td>0 days 09:55:00.000000000</td>\n", " <td>09:55:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7133</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:14:00</td>\n", " <td>2011-07-23 03:09:00</td>\n", " <td>0 days 00:55:00.000000000</td>\n", " <td>00:55:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7063</th>\n", " <td>TGNASH</td>\n", " <td>TGNASH</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:18:00</td>\n", " <td>2011-07-23 02:21:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>00:03:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7061</th>\n", " <td>DS-M10</td>\n", " <td>DS-M10</td>\n", " <td>NaN</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:06:00</td>\n", " <td>2011-07-23 11:03:00</td>\n", " <td>0 days 08:57:00.000000000</td>\n", " <td>08:57:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7054</th>\n", " <td>DS-M82</td>\n", " <td>DS-M82</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:39:00</td>\n", " <td>2011-07-23 15:23:00</td>\n", " <td>0 days 12:44:00.000000000</td>\n", " <td>12:44:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7085</th>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>NaN</td>\n", " <td>Addison Cr</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:00:00</td>\n", " <td>2011-07-23 14:00:00</td>\n", " <td>0 days 12:00:00.000000000</td>\n", " <td>12:00:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7075</th>\n", " <td>CRCW</td>\n", " <td>CRCW</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:30:00</td>\n", " <td>2011-07-23 12:50:00</td>\n", " <td>0 days 09:20:00.000000000</td>\n", " <td>09:20:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7171</th>\n", " <td>DS-D19,23</td>\n", " <td>DS-D19,23</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:08:00</td>\n", " <td>2011-07-23 09:09:00</td>\n", " <td>0 days 06:01:00.000000000</td>\n", " <td>06:01:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7134</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:28:00</td>\n", " <td>2011-07-23 03:37:00</td>\n", " <td>0 days 00:09:00.000000000</td>\n", " <td>00:09:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7135</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:45:00</td>\n", " <td>2011-07-23 03:52:00</td>\n", " <td>0 days 00:07:00.000000000</td>\n", " <td>00:07:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7136</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:57:00</td>\n", " <td>2011-07-23 03:59:00</td>\n", " <td>0 days 00:02:00.000000000</td>\n", " <td>00:02:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7115</th>\n", " <td>DS-D15</td>\n", " <td>DS-D15</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:07:00</td>\n", " <td>2011-07-23 07:08:00</td>\n", " <td>0 days 04:01:00.000000000</td>\n", " <td>04:01:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7060</th>\n", " <td>CDS-18</td>\n", " <td>CDS-18</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:17:00</td>\n", " <td>2011-07-23 08:29:00</td>\n", " <td>0 days 03:12:00.000000000</td>\n", " <td>03:12:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7053</th>\n", " <td>CDS-10</td>\n", " <td>CDS-10</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:16:00</td>\n", " <td>2011-07-23 07:22:00</td>\n", " <td>0 days 02:06:00.000000000</td>\n", " <td>02:06:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7051</th>\n", " <td>CDS-11</td>\n", " <td>CDS-11</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:19:00</td>\n", " <td>2011-07-23 07:07:00</td>\n", " <td>0 days 01:48:00.000000000</td>\n", " <td>01:48:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7059</th>\n", " <td>CDS-12</td>\n", " <td>CDS-12</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 06:09:00</td>\n", " <td>0 days 00:54:00.000000000</td>\n", " <td>00:54:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7145</th>\n", " <td>CDS-4</td>\n", " <td>CDS-4</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7097</th>\n", " <td>CDS-20</td>\n", " <td>CDS-20</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:42:00</td>\n", " <td>2011-07-23 07:31:00</td>\n", " <td>0 days 01:49:00.000000000</td>\n", " <td>01:49:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7099</th>\n", " <td>CDS-22</td>\n", " <td>CDS-22</td>\n", " <td>NaN</td>\n", " <td>Grand Cal R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:17:00</td>\n", " <td>2011-07-23 07:45:00</td>\n", " <td>0 days 02:28:00.000000000</td>\n", " <td>02:28:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7125</th>\n", " <td>PULASKI RD PS (18E-PS)</td>\n", " <td>PULASKI RD PS (18E-PS)</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7165</th>\n", " <td>CDS-2</td>\n", " <td>CDS-2</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7096</th>\n", " <td>CDS-20</td>\n", " <td>CDS-20</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:42:00</td>\n", " <td>2011-07-23 07:26:00</td>\n", " <td>0 days 01:44:00.000000000</td>\n", " <td>01:44:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7137</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:14:00</td>\n", " <td>2011-07-23 07:55:00</td>\n", " <td>0 days 03:41:00.000000000</td>\n", " <td>03:41:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7114</th>\n", " <td>DS-M40</td>\n", " <td>DS-M40</td>\n", " <td>NaN</td>\n", " <td>SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:24:00</td>\n", " <td>2011-07-23 04:35:00</td>\n", " <td>0 days 00:11:00.000000000</td>\n", " <td>00:11:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7186</th>\n", " <td>DS-M109S</td>\n", " <td>DS-M109S</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:36:00</td>\n", " <td>2011-07-23 07:47:00</td>\n", " <td>0 days 03:11:00.000000000</td>\n", " <td>03:11:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7143</th>\n", " <td>DS-D27I</td>\n", " <td>DS-D27I</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 06:15:00</td>\n", " <td>2011-07-23 07:43:00</td>\n", " <td>0 days 01:28:00.000000000</td>\n", " <td>01:28:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7049</th>\n", " <td>TG-M81</td>\n", " <td>TG-M81</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 06:08:00</td>\n", " <td>2011-07-23 06:11:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>00:03:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7095</th>\n", " <td>TG-M94</td>\n", " <td>TG-M94</td>\n", " <td>NaN</td>\n", " <td>NSC Lower (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:08:00</td>\n", " <td>2011-07-23 04:15:00</td>\n", " <td>0 days 00:07:00.000000000</td>\n", " <td>00:07:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7100</th>\n", " <td>CDS-21</td>\n", " <td>CDS-21</td>\n", " <td>NaN</td>\n", " <td>Grand Cal R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 07:10:00</td>\n", " <td>2011-07-23 23:59:00</td>\n", " <td>0 days 16:49:00.000000000</td>\n", " <td>16:49:00</td>\n", " <td>7.86</td>\n", " </tr>\n", " <tr>\n", " <th>7050</th>\n", " <td>TG-M81</td>\n", " <td>TG-M81</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 07:56:00</td>\n", " <td>2011-07-23 08:04:00</td>\n", " <td>0 days 00:08:00.000000000</td>\n", " <td>00:08:00</td>\n", " <td>7.86</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10879 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " Outfall Structure Outfall Location \\\n", "4959 DS-D43 DS-D43 \n", "282 DS-D43 Near Burling Rd (Library) \n", "281 DS-D43 Near Burling Rd (Library) \n", "7457 DS-N08 DS-N08 \n", "6750 DS-D21,25 DS-D21,25 \n", "7458 DS-N08 DS-N08 \n", "7459 DS-N08 DS-N08 \n", "6749 DS-D11 DS-D11 \n", "6748 DS-D11 DS-D11 \n", "8524 MWRD DS-M114N MWRD DS-M114N \n", "6747 DS-M80 DS-M80 \n", "6746 DS-M15 DS-M15 \n", "6745 WCPS (DS-D34-AI) WCPS (DS-D34-AI) \n", "6744 DS-M79 DS-M79 \n", "6743 DS-N08 DS-N08 \n", "8523 MWRD DS-M114N MWRD DS-M114N \n", "8262 CDS-21 CDS-21 \n", "1109 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "1457 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "1458 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "1459 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "1460 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "283 DS-D43 Near Burling Rd (Library) \n", "1461 RAPS (DS-M27, DS-M28, DS-M29) RAPS (DS-M27, DS-M28, DS-M29) \n", "284 DS-D43 Near Burling Rd (Library) \n", "286 DS-D43 Near Burling Rd (Library) \n", "8742 DS-M84 DS-M84 \n", "8741 DS-M19 DS-M19 \n", "8740 DS-D47,50,51 DS-D47,50,51 \n", "8739 WCPS (DS-D34-AI) WCPS (DS-D34-AI) \n", "... ... ... \n", "7072 TG-M71 TG-M71 \n", "7133 DS-M54 DS-M54 \n", "7063 TGNASH TGNASH \n", "7061 DS-M10 DS-M10 \n", "7054 DS-M82 DS-M82 \n", "7085 WCPS (DS-D34-AI) WCPS (DS-D34-AI) \n", "7075 CRCW CRCW \n", "7171 DS-D19,23 DS-D19,23 \n", "7134 DS-M54 DS-M54 \n", "7135 DS-M54 DS-M54 \n", "7136 DS-M54 DS-M54 \n", "7115 DS-D15 DS-D15 \n", "7060 CDS-18 CDS-18 \n", "7053 CDS-10 CDS-10 \n", "7051 CDS-11 CDS-11 \n", "7059 CDS-12 CDS-12 \n", "7145 CDS-4 CDS-4 \n", "7097 CDS-20 CDS-20 \n", "7099 CDS-22 CDS-22 \n", "7125 PULASKI RD PS (18E-PS) PULASKI RD PS (18E-PS) \n", "7165 CDS-2 CDS-2 \n", "7096 CDS-20 CDS-20 \n", "7137 DS-M54 DS-M54 \n", "7114 DS-M40 DS-M40 \n", "7186 DS-M109S DS-M109S \n", "7143 DS-D27I DS-D27I \n", "7049 TG-M81 TG-M81 \n", "7095 TG-M94 TG-M94 \n", "7100 CDS-21 CDS-21 \n", "7050 TG-M81 TG-M81 \n", "\n", " Tide Gate # Waterway Reach Plant Open date/time \\\n", "4959 NaN DesPlaines Lower NaN 2013-05-30 10:16:00 \n", "282 TG1 DesPlaines Lower Stickney 2016-05-13 03:41:00 \n", "281 TG1 DesPlaines Lower Stickney 2016-05-13 03:34:00 \n", "7457 NaN NBCR Upper (NSC Confluence) NaN 2011-05-30 14:11:00 \n", "6750 NaN DesPlaines Middle NaN 2011-09-03 14:58:00 \n", "7458 NaN NBCR Upper (NSC Confluence) NaN 2011-05-30 14:30:00 \n", "7459 NaN NBCR Upper (NSC Confluence) NaN 2011-05-30 15:07:00 \n", "6749 NaN DesPlaines Middle NaN 2011-09-03 15:33:00 \n", "6748 NaN DesPlaines Middle NaN 2011-09-03 15:33:00 \n", "8524 NaN NSC Upper (NSWRP) NaN 2010-08-07 06:49:00 \n", "6747 NaN NBCR Lower (NSC Confluence) NaN 2011-09-03 15:11:00 \n", "6746 NaN CSSC Upper (SWRP) NaN 2011-09-03 15:51:00 \n", "6745 NaN Addison Cr NaN 2011-09-03 15:00:00 \n", "6744 NaN NBCR Lower (NSC Confluence) NaN 2011-09-03 15:06:00 \n", "6743 NaN NBCR Upper (NSC Confluence) NaN 2011-09-03 15:28:00 \n", "8523 NaN NSC Upper (NSWRP) NaN 2010-08-08 07:16:00 \n", "8262 NaN Grand Cal R NaN 2011-02-22 00:01:00 \n", "1109 NaN SF SB Chicago R NaN 2015-09-12 09:20:00 \n", "1457 NaN SF SB Chicago R NaN 2015-08-16 22:54:00 \n", "1458 NaN SF SB Chicago R NaN 2015-08-16 22:54:00 \n", "1459 NaN SF SB Chicago R NaN 2015-08-16 22:54:00 \n", "1460 NaN SF SB Chicago R NaN 2015-08-16 22:54:00 \n", "283 TG1 DesPlaines Lower Stickney 2016-05-13 03:49:00 \n", "1461 NaN SF SB Chicago R NaN 2015-08-16 22:54:00 \n", "284 TG1 DesPlaines Lower Stickney 2016-05-13 04:00:00 \n", "286 TG1 DesPlaines Lower Stickney 2016-05-13 04:19:00 \n", "8742 NaN NBCR Lower (NSC Confluence) NaN 2010-07-26 00:00:00 \n", "8741 NaN CSSC Upper (SWRP) NaN 2010-07-26 00:00:00 \n", "8740 NaN Salt Cr NaN 2010-07-26 00:00:00 \n", "8739 NaN Addison Cr NaN 2010-07-26 00:00:00 \n", "... ... ... ... ... \n", "7072 NaN NBCR Lower (NSC Confluence) NaN 2011-07-23 02:23:00 \n", "7133 NaN Chicago R NaN 2011-07-23 02:14:00 \n", "7063 NaN CSSC Upper (SWRP) NaN 2011-07-23 02:18:00 \n", "7061 NaN CSSC Lower (SWRP) NaN 2011-07-23 02:06:00 \n", "7054 NaN NBCR Lower (NSC Confluence) NaN 2011-07-23 02:39:00 \n", "7085 NaN Addison Cr NaN 2011-07-23 02:00:00 \n", "7075 NaN NaN NaN 2011-07-23 03:30:00 \n", "7171 NaN DesPlaines Middle NaN 2011-07-23 03:08:00 \n", "7134 NaN Chicago R NaN 2011-07-23 03:28:00 \n", "7135 NaN Chicago R NaN 2011-07-23 03:45:00 \n", "7136 NaN Chicago R NaN 2011-07-23 03:57:00 \n", "7115 NaN DesPlaines Middle NaN 2011-07-23 03:07:00 \n", "7060 NaN Little Cal R (North) NaN 2011-07-23 05:17:00 \n", "7053 NaN Cal Sag Ch NaN 2011-07-23 05:16:00 \n", "7051 NaN Cal Sag Ch NaN 2011-07-23 05:19:00 \n", "7059 NaN Little Cal R (North) NaN 2011-07-23 05:15:00 \n", "7145 NaN Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7097 NaN Little Cal R (North) NaN 2011-07-23 05:42:00 \n", "7099 NaN Grand Cal R NaN 2011-07-23 05:17:00 \n", "7125 NaN Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7165 NaN Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7096 NaN Little Cal R (North) NaN 2011-07-23 05:42:00 \n", "7137 NaN Chicago R NaN 2011-07-23 04:14:00 \n", "7114 NaN SB Chicago R NaN 2011-07-23 04:24:00 \n", "7186 NaN NSC Upper (NSWRP) NaN 2011-07-23 04:36:00 \n", "7143 NaN DesPlaines Middle NaN 2011-07-23 06:15:00 \n", "7049 NaN NBCR Lower (NSC Confluence) NaN 2011-07-23 06:08:00 \n", "7095 NaN NSC Lower (NSWRP) NaN 2011-07-23 04:08:00 \n", "7100 NaN Grand Cal R NaN 2011-07-23 07:10:00 \n", "7050 NaN NBCR Lower (NSC Confluence) NaN 2011-07-23 07:56:00 \n", "\n", " Close date/time Gate Open Period Duration 24hr_rain \n", "4959 2013-05-30 10:28:00 0 days 00:12:00.000000000 00:12:00 0.00 \n", "282 2016-05-13 03:48:00 0 days 00:07:08.000000000 00:07:00 0.00 \n", "281 2016-05-13 03:40:00 0 days 00:06:44.000000000 00:06:00 0.00 \n", "7457 2011-05-30 14:14:00 0 days 00:03:00.000000000 00:03:00 0.00 \n", "6750 2011-09-03 15:35:00 0 days 00:37:00.000000000 00:37:00 0.00 \n", "7458 2011-05-30 14:33:00 0 days 00:03:00.000000000 00:03:00 0.00 \n", "7459 2011-05-30 15:09:00 0 days 00:02:00.000000000 00:02:00 0.00 \n", "6749 2011-09-03 15:48:00 0 days 00:15:00.000000000 00:15:00 0.00 \n", "6748 2011-09-03 15:48:00 0 days 00:15:00.000000000 00:15:00 0.00 \n", "8524 2010-08-07 06:54:00 0 days 00:05:00.000000000 00:05:00 0.00 \n", "6747 2011-09-03 15:15:00 0 days 00:04:00.000000000 00:04:00 0.00 \n", "6746 2011-09-03 17:57:00 0 days 02:06:00.000000000 02:06:00 0.00 \n", "6745 2011-09-03 16:30:00 0 days 01:30:00.000000000 01:30:00 0.00 \n", "6744 2011-09-03 16:46:00 0 days 01:40:00.000000000 01:40:00 0.00 \n", "6743 2011-09-03 15:30:00 0 days 00:02:00.000000000 00:02:00 0.00 \n", "8523 2010-08-08 07:21:00 0 days 00:05:00.000000000 00:05:00 0.00 \n", "8262 2011-02-22 05:02:00 0 days 05:01:00.000000000 05:01:00 0.00 \n", "1109 2015-09-12 13:00:00 0 days 03:40:00.000000000 03:40:00 0.00 \n", "1457 2015-08-17 00:00:00 0 days 01:06:00.000000000 01:06:00 0.00 \n", "1458 2015-08-17 00:00:00 0 days 01:06:00.000000000 01:06:00 0.00 \n", "1459 2015-08-17 00:00:00 0 days 01:06:00.000000000 01:06:00 0.00 \n", "1460 2015-08-17 00:00:00 0 days 01:06:00.000000000 01:06:00 0.00 \n", "283 2016-05-13 03:55:00 0 days 00:06:08.000000000 00:06:00 0.00 \n", "1461 2015-08-17 00:00:00 0 days 01:06:00.000000000 01:06:00 0.00 \n", "284 2016-05-13 04:03:00 0 days 00:03:04.000000000 00:03:00 0.00 \n", "286 2016-05-13 04:21:00 0 days 00:02:04.000000000 00:02:00 0.00 \n", "8742 2010-07-26 08:58:00 0 days 08:58:00.000000000 08:58:00 0.00 \n", "8741 2010-07-26 08:54:00 0 days 08:54:00.000000000 08:54:00 0.00 \n", "8740 2010-07-26 09:24:00 0 days 09:24:00.000000000 09:24:00 0.00 \n", "8739 2010-07-26 05:30:00 0 days 05:30:00.000000000 05:30:00 0.00 \n", "... ... ... ... ... \n", "7072 2011-07-23 12:18:00 0 days 09:55:00.000000000 09:55:00 7.73 \n", "7133 2011-07-23 03:09:00 0 days 00:55:00.000000000 00:55:00 7.73 \n", "7063 2011-07-23 02:21:00 0 days 00:03:00.000000000 00:03:00 7.73 \n", "7061 2011-07-23 11:03:00 0 days 08:57:00.000000000 08:57:00 7.73 \n", "7054 2011-07-23 15:23:00 0 days 12:44:00.000000000 12:44:00 7.73 \n", "7085 2011-07-23 14:00:00 0 days 12:00:00.000000000 12:00:00 7.73 \n", "7075 2011-07-23 12:50:00 0 days 09:20:00.000000000 09:20:00 7.84 \n", "7171 2011-07-23 09:09:00 0 days 06:01:00.000000000 06:01:00 7.84 \n", "7134 2011-07-23 03:37:00 0 days 00:09:00.000000000 00:09:00 7.84 \n", "7135 2011-07-23 03:52:00 0 days 00:07:00.000000000 00:07:00 7.84 \n", "7136 2011-07-23 03:59:00 0 days 00:02:00.000000000 00:02:00 7.84 \n", "7115 2011-07-23 07:08:00 0 days 04:01:00.000000000 04:01:00 7.84 \n", "7060 2011-07-23 08:29:00 0 days 03:12:00.000000000 03:12:00 7.85 \n", "7053 2011-07-23 07:22:00 0 days 02:06:00.000000000 02:06:00 7.85 \n", "7051 2011-07-23 07:07:00 0 days 01:48:00.000000000 01:48:00 7.85 \n", "7059 2011-07-23 06:09:00 0 days 00:54:00.000000000 00:54:00 7.85 \n", "7145 2011-07-23 15:48:00 0 days 10:33:00.000000000 10:33:00 7.85 \n", "7097 2011-07-23 07:31:00 0 days 01:49:00.000000000 01:49:00 7.85 \n", "7099 2011-07-23 07:45:00 0 days 02:28:00.000000000 02:28:00 7.85 \n", "7125 2011-07-23 15:48:00 0 days 10:33:00.000000000 10:33:00 7.85 \n", "7165 2011-07-23 15:48:00 0 days 10:33:00.000000000 10:33:00 7.85 \n", "7096 2011-07-23 07:26:00 0 days 01:44:00.000000000 01:44:00 7.85 \n", "7137 2011-07-23 07:55:00 0 days 03:41:00.000000000 03:41:00 7.85 \n", "7114 2011-07-23 04:35:00 0 days 00:11:00.000000000 00:11:00 7.85 \n", "7186 2011-07-23 07:47:00 0 days 03:11:00.000000000 03:11:00 7.85 \n", "7143 2011-07-23 07:43:00 0 days 01:28:00.000000000 01:28:00 7.85 \n", "7049 2011-07-23 06:11:00 0 days 00:03:00.000000000 00:03:00 7.85 \n", "7095 2011-07-23 04:15:00 0 days 00:07:00.000000000 00:07:00 7.85 \n", "7100 2011-07-23 23:59:00 0 days 16:49:00.000000000 16:49:00 7.86 \n", "7050 2011-07-23 08:04:00 0 days 00:08:00.000000000 00:08:00 7.86 \n", "\n", "[10879 rows x 10 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What is the least amount of rain that causes a CSO?\n", "csos = csos.sort_values('24hr_rain')\n", "csos" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total rows: 10879\n", "Rows with 0 rain in previous 24 hours: 357\n" ] } ], "source": [ "# How many rows are there? How many have a value of 0?\n", "print('Total rows: %s' % len(csos))\n", "print('Rows with 0 rain in previous 24 hours: %s' % len(csos[csos['24hr_rain'] == 0]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Outfall Structure</th>\n", " <th>Outfall Location</th>\n", " <th>Tide Gate #</th>\n", " <th>Waterway Reach</th>\n", " <th>Plant</th>\n", " <th>Open date/time</th>\n", " <th>Close date/time</th>\n", " <th>Gate Open Period</th>\n", " <th>Duration</th>\n", " <th>24hr_rain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>3295</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:04:00</td>\n", " <td>2014-08-25 15:08:00</td>\n", " <td>0 days 02:04:00.000000000</td>\n", " <td>0 days 02:04:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3294</th>\n", " <td>DS-M82</td>\n", " <td>DS-M82</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:44:00</td>\n", " <td>2014-08-25 15:13:00</td>\n", " <td>0 days 02:29:00.000000000</td>\n", " <td>0 days 02:29:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3293</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 16:28:00</td>\n", " <td>2014-08-25 16:32:00</td>\n", " <td>0 days 00:04:00.000000000</td>\n", " <td>0 days 00:04:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3296</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:20:00</td>\n", " <td>2014-08-25 15:07:00</td>\n", " <td>0 days 01:47:00.000000000</td>\n", " <td>0 days 01:47:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3274</th>\n", " <td>CDS-43</td>\n", " <td>CDS-43</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (South)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:58:00</td>\n", " <td>2014-08-25 14:39:00</td>\n", " <td>0 days 00:41:00.000000000</td>\n", " <td>0 days 00:41:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3297</th>\n", " <td>DS-N08</td>\n", " <td>DS-N08</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:30:00</td>\n", " <td>2014-08-25 12:35:00</td>\n", " <td>0 days 00:05:00.000000000</td>\n", " <td>0 days 00:05:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3299</th>\n", " <td>DS-M79</td>\n", " <td>DS-M79</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 16:27:00</td>\n", " <td>2014-08-25 16:30:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4269</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-01-03 12:50:00</td>\n", " <td>2014-01-03 12:58:00</td>\n", " <td>0 days 00:08:00.000000000</td>\n", " <td>0 days 00:08:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4268</th>\n", " <td>DS-M84</td>\n", " <td>DS-M84</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-01-03 12:50:00</td>\n", " <td>2014-01-03 12:58:00</td>\n", " <td>0 days 00:08:00.000000000</td>\n", " <td>0 days 00:08:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3292</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 16:15:00</td>\n", " <td>2014-08-25 16:23:00</td>\n", " <td>0 days 00:08:00.000000000</td>\n", " <td>0 days 00:08:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3300</th>\n", " <td>DS-M79</td>\n", " <td>DS-M79</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:56:00</td>\n", " <td>2014-08-25 17:20:00</td>\n", " <td>0 days 04:24:00.000000000</td>\n", " <td>0 days 04:24:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3301</th>\n", " <td>DS-N02</td>\n", " <td>DS-N02</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:30:00</td>\n", " <td>2014-08-25 12:33:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4204</th>\n", " <td>DS-N06</td>\n", " <td>DS-N06</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-01-25 07:40:00</td>\n", " <td>2014-01-25 07:51:00</td>\n", " <td>0 days 00:11:00.000000000</td>\n", " <td>0 days 00:11:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3275</th>\n", " <td>CDS-12</td>\n", " <td>CDS-12</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:47:00</td>\n", " <td>2014-08-25 15:17:00</td>\n", " <td>0 days 01:30:00.000000000</td>\n", " <td>0 days 01:30:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3276</th>\n", " <td>CDS-11</td>\n", " <td>CDS-11</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:47:00</td>\n", " <td>2014-08-25 15:51:00</td>\n", " <td>0 days 02:04:00.000000000</td>\n", " <td>0 days 02:04:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3277</th>\n", " <td>CDS-10</td>\n", " <td>CDS-10</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:47:00</td>\n", " <td>2014-08-25 15:48:00</td>\n", " <td>0 days 02:01:00.000000000</td>\n", " <td>0 days 02:01:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3393</th>\n", " <td>DS-N07</td>\n", " <td>DS-N07</td>\n", " <td>NaN</td>\n", " <td>NBCR Upper (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-20 18:05:00</td>\n", " <td>2014-08-20 18:08:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3810</th>\n", " <td>DS-D06</td>\n", " <td>DS-D06</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Upper</td>\n", " <td>NaN</td>\n", " <td>2014-06-23 04:15:00</td>\n", " <td>2014-06-23 04:56:00</td>\n", " <td>0 days 00:41:00.000000000</td>\n", " <td>0 days 00:41:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3813</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-06-23 04:24:00</td>\n", " <td>2014-06-23 06:02:00</td>\n", " <td>0 days 01:38:00.000000000</td>\n", " <td>0 days 01:38:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3298</th>\n", " <td>DS-M79</td>\n", " <td>DS-M79</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:56:00</td>\n", " <td>2014-08-25 16:12:00</td>\n", " <td>0 days 03:16:00.000000000</td>\n", " <td>0 days 03:16:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3272</th>\n", " <td>CDS-14</td>\n", " <td>CDS-14</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:53:00</td>\n", " <td>2014-08-25 14:07:00</td>\n", " <td>0 days 00:14:00.000000000</td>\n", " <td>0 days 00:14:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3273</th>\n", " <td>CDS-14</td>\n", " <td>CDS-14</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:53:00</td>\n", " <td>2014-08-25 14:07:00</td>\n", " <td>0 days 00:14:00.000000000</td>\n", " <td>0 days 00:14:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3291</th>\n", " <td>MWRD DS-M114N</td>\n", " <td>MWRD DS-M114N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:25:00</td>\n", " <td>2014-08-25 16:15:00</td>\n", " <td>0 days 02:50:00.000000000</td>\n", " <td>0 days 02:50:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3494</th>\n", " <td>TG-I28 & I29</td>\n", " <td>TG-I28 & I29</td>\n", " <td>NaN</td>\n", " <td>SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2014-07-22 23:55:00</td>\n", " <td>2014-07-22 23:59:00</td>\n", " <td>0 days 00:04:00.000000000</td>\n", " <td>0 days 00:04:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3493</th>\n", " <td>TG-I28 & I29</td>\n", " <td>TG-I28 & I29</td>\n", " <td>NaN</td>\n", " <td>SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2014-07-23 00:00:00</td>\n", " <td>2014-07-23 00:11:00</td>\n", " <td>0 days 00:11:00.000000000</td>\n", " <td>0 days 00:11:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3287</th>\n", " <td>DS-M104</td>\n", " <td>DS-M104</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 12:46:00</td>\n", " <td>2014-08-25 12:49:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3286</th>\n", " <td>DS-M109 N</td>\n", " <td>DS-M109 N</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:34:00</td>\n", " <td>2014-08-25 15:20:00</td>\n", " <td>0 days 01:46:00.000000000</td>\n", " <td>0 days 01:46:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3285</th>\n", " <td>CDS-4</td>\n", " <td>CDS-4</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:34:00</td>\n", " <td>2014-08-25 23:59:00</td>\n", " <td>0 days 10:25:00.000000000</td>\n", " <td>0 days 10:25:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>4390</th>\n", " <td>DS-M20</td>\n", " <td>DS-M20</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2013-11-16 00:00:00</td>\n", " <td>2013-11-17 00:00:00</td>\n", " <td>1 days 00:00:00.000000000</td>\n", " <td>1 days 00:00:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>3288</th>\n", " <td>DS-M80</td>\n", " <td>DS-M80</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2014-08-25 13:11:00</td>\n", " <td>2014-08-25 14:11:00</td>\n", " <td>0 days 01:00:00.000000000</td>\n", " <td>0 days 01:00:00</td>\n", " <td>0.01</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>7072</th>\n", " <td>TG-M71</td>\n", " <td>TG-M71</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:23:00</td>\n", " <td>2011-07-23 12:18:00</td>\n", " <td>0 days 09:55:00.000000000</td>\n", " <td>0 days 09:55:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7133</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:14:00</td>\n", " <td>2011-07-23 03:09:00</td>\n", " <td>0 days 00:55:00.000000000</td>\n", " <td>0 days 00:55:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7063</th>\n", " <td>TGNASH</td>\n", " <td>TGNASH</td>\n", " <td>NaN</td>\n", " <td>CSSC Upper (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:18:00</td>\n", " <td>2011-07-23 02:21:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7061</th>\n", " <td>DS-M10</td>\n", " <td>DS-M10</td>\n", " <td>NaN</td>\n", " <td>CSSC Lower (SWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:06:00</td>\n", " <td>2011-07-23 11:03:00</td>\n", " <td>0 days 08:57:00.000000000</td>\n", " <td>0 days 08:57:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7054</th>\n", " <td>DS-M82</td>\n", " <td>DS-M82</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:39:00</td>\n", " <td>2011-07-23 15:23:00</td>\n", " <td>0 days 12:44:00.000000000</td>\n", " <td>0 days 12:44:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7085</th>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>WCPS (DS-D34-AI)</td>\n", " <td>NaN</td>\n", " <td>Addison Cr</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 02:00:00</td>\n", " <td>2011-07-23 14:00:00</td>\n", " <td>0 days 12:00:00.000000000</td>\n", " <td>0 days 12:00:00</td>\n", " <td>7.73</td>\n", " </tr>\n", " <tr>\n", " <th>7075</th>\n", " <td>CRCW</td>\n", " <td>CRCW</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:30:00</td>\n", " <td>2011-07-23 12:50:00</td>\n", " <td>0 days 09:20:00.000000000</td>\n", " <td>0 days 09:20:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7171</th>\n", " <td>DS-D19,23</td>\n", " <td>DS-D19,23</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:08:00</td>\n", " <td>2011-07-23 09:09:00</td>\n", " <td>0 days 06:01:00.000000000</td>\n", " <td>0 days 06:01:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7134</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:28:00</td>\n", " <td>2011-07-23 03:37:00</td>\n", " <td>0 days 00:09:00.000000000</td>\n", " <td>0 days 00:09:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7135</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:45:00</td>\n", " <td>2011-07-23 03:52:00</td>\n", " <td>0 days 00:07:00.000000000</td>\n", " <td>0 days 00:07:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7136</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:57:00</td>\n", " <td>2011-07-23 03:59:00</td>\n", " <td>0 days 00:02:00.000000000</td>\n", " <td>0 days 00:02:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7115</th>\n", " <td>DS-D15</td>\n", " <td>DS-D15</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 03:07:00</td>\n", " <td>2011-07-23 07:08:00</td>\n", " <td>0 days 04:01:00.000000000</td>\n", " <td>0 days 04:01:00</td>\n", " <td>7.84</td>\n", " </tr>\n", " <tr>\n", " <th>7060</th>\n", " <td>CDS-18</td>\n", " <td>CDS-18</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:17:00</td>\n", " <td>2011-07-23 08:29:00</td>\n", " <td>0 days 03:12:00.000000000</td>\n", " <td>0 days 03:12:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7053</th>\n", " <td>CDS-10</td>\n", " <td>CDS-10</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:16:00</td>\n", " <td>2011-07-23 07:22:00</td>\n", " <td>0 days 02:06:00.000000000</td>\n", " <td>0 days 02:06:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7051</th>\n", " <td>CDS-11</td>\n", " <td>CDS-11</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:19:00</td>\n", " <td>2011-07-23 07:07:00</td>\n", " <td>0 days 01:48:00.000000000</td>\n", " <td>0 days 01:48:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7059</th>\n", " <td>CDS-12</td>\n", " <td>CDS-12</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 06:09:00</td>\n", " <td>0 days 00:54:00.000000000</td>\n", " <td>0 days 00:54:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7145</th>\n", " <td>CDS-4</td>\n", " <td>CDS-4</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>0 days 10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7097</th>\n", " <td>CDS-20</td>\n", " <td>CDS-20</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:42:00</td>\n", " <td>2011-07-23 07:31:00</td>\n", " <td>0 days 01:49:00.000000000</td>\n", " <td>0 days 01:49:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7099</th>\n", " <td>CDS-22</td>\n", " <td>CDS-22</td>\n", " <td>NaN</td>\n", " <td>Grand Cal R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:17:00</td>\n", " <td>2011-07-23 07:45:00</td>\n", " <td>0 days 02:28:00.000000000</td>\n", " <td>0 days 02:28:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7125</th>\n", " <td>PULASKI RD PS (18E-PS)</td>\n", " <td>PULASKI RD PS (18E-PS)</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>0 days 10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7165</th>\n", " <td>CDS-2</td>\n", " <td>CDS-2</td>\n", " <td>NaN</td>\n", " <td>Cal Sag Ch</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:15:00</td>\n", " <td>2011-07-23 15:48:00</td>\n", " <td>0 days 10:33:00.000000000</td>\n", " <td>0 days 10:33:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7096</th>\n", " <td>CDS-20</td>\n", " <td>CDS-20</td>\n", " <td>NaN</td>\n", " <td>Little Cal R (North)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 05:42:00</td>\n", " <td>2011-07-23 07:26:00</td>\n", " <td>0 days 01:44:00.000000000</td>\n", " <td>0 days 01:44:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7137</th>\n", " <td>DS-M54</td>\n", " <td>DS-M54</td>\n", " <td>NaN</td>\n", " <td>Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:14:00</td>\n", " <td>2011-07-23 07:55:00</td>\n", " <td>0 days 03:41:00.000000000</td>\n", " <td>0 days 03:41:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7114</th>\n", " <td>DS-M40</td>\n", " <td>DS-M40</td>\n", " <td>NaN</td>\n", " <td>SB Chicago R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:24:00</td>\n", " <td>2011-07-23 04:35:00</td>\n", " <td>0 days 00:11:00.000000000</td>\n", " <td>0 days 00:11:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7186</th>\n", " <td>DS-M109S</td>\n", " <td>DS-M109S</td>\n", " <td>NaN</td>\n", " <td>NSC Upper (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:36:00</td>\n", " <td>2011-07-23 07:47:00</td>\n", " <td>0 days 03:11:00.000000000</td>\n", " <td>0 days 03:11:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7143</th>\n", " <td>DS-D27I</td>\n", " <td>DS-D27I</td>\n", " <td>NaN</td>\n", " <td>DesPlaines Middle</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 06:15:00</td>\n", " <td>2011-07-23 07:43:00</td>\n", " <td>0 days 01:28:00.000000000</td>\n", " <td>0 days 01:28:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7049</th>\n", " <td>TG-M81</td>\n", " <td>TG-M81</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 06:08:00</td>\n", " <td>2011-07-23 06:11:00</td>\n", " <td>0 days 00:03:00.000000000</td>\n", " <td>0 days 00:03:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7095</th>\n", " <td>TG-M94</td>\n", " <td>TG-M94</td>\n", " <td>NaN</td>\n", " <td>NSC Lower (NSWRP)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 04:08:00</td>\n", " <td>2011-07-23 04:15:00</td>\n", " <td>0 days 00:07:00.000000000</td>\n", " <td>0 days 00:07:00</td>\n", " <td>7.85</td>\n", " </tr>\n", " <tr>\n", " <th>7100</th>\n", " <td>CDS-21</td>\n", " <td>CDS-21</td>\n", " <td>NaN</td>\n", " <td>Grand Cal R</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 07:10:00</td>\n", " <td>2011-07-23 23:59:00</td>\n", " <td>0 days 16:49:00.000000000</td>\n", " <td>0 days 16:49:00</td>\n", " <td>7.86</td>\n", " </tr>\n", " <tr>\n", " <th>7050</th>\n", " <td>TG-M81</td>\n", " <td>TG-M81</td>\n", " <td>NaN</td>\n", " <td>NBCR Lower (NSC Confluence)</td>\n", " <td>NaN</td>\n", " <td>2011-07-23 07:56:00</td>\n", " <td>2011-07-23 08:04:00</td>\n", " <td>0 days 00:08:00.000000000</td>\n", " <td>0 days 00:08:00</td>\n", " <td>7.86</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10522 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " Outfall Structure Outfall Location Tide Gate # \\\n", "3295 DS-M84 DS-M84 NaN \n", "3294 DS-M82 DS-M82 NaN \n", "3293 MWRD DS-M114N MWRD DS-M114N NaN \n", "3296 DS-M84 DS-M84 NaN \n", "3274 CDS-43 CDS-43 NaN \n", "3297 DS-N08 DS-N08 NaN \n", "3299 DS-M79 DS-M79 NaN \n", "4269 DS-M84 DS-M84 NaN \n", "4268 DS-M84 DS-M84 NaN \n", "3292 MWRD DS-M114N MWRD DS-M114N NaN \n", "3300 DS-M79 DS-M79 NaN \n", "3301 DS-N02 DS-N02 NaN \n", "4204 DS-N06 DS-N06 NaN \n", "3275 CDS-12 CDS-12 NaN \n", "3276 CDS-11 CDS-11 NaN \n", "3277 CDS-10 CDS-10 NaN \n", "3393 DS-N07 DS-N07 NaN \n", "3810 DS-D06 DS-D06 NaN \n", "3813 MWRD DS-M114N MWRD DS-M114N NaN \n", "3298 DS-M79 DS-M79 NaN \n", "3272 CDS-14 CDS-14 NaN \n", "3273 CDS-14 CDS-14 NaN \n", "3291 MWRD DS-M114N MWRD DS-M114N NaN \n", "3494 TG-I28 & I29 TG-I28 & I29 NaN \n", "3493 TG-I28 & I29 TG-I28 & I29 NaN \n", "3287 DS-M104 DS-M104 NaN \n", "3286 DS-M109 N DS-M109 N NaN \n", "3285 CDS-4 CDS-4 NaN \n", "4390 DS-M20 DS-M20 NaN \n", "3288 DS-M80 DS-M80 NaN \n", "... ... ... ... \n", "7072 TG-M71 TG-M71 NaN \n", "7133 DS-M54 DS-M54 NaN \n", "7063 TGNASH TGNASH NaN \n", "7061 DS-M10 DS-M10 NaN \n", "7054 DS-M82 DS-M82 NaN \n", "7085 WCPS (DS-D34-AI) WCPS (DS-D34-AI) NaN \n", "7075 CRCW CRCW NaN \n", "7171 DS-D19,23 DS-D19,23 NaN \n", "7134 DS-M54 DS-M54 NaN \n", "7135 DS-M54 DS-M54 NaN \n", "7136 DS-M54 DS-M54 NaN \n", "7115 DS-D15 DS-D15 NaN \n", "7060 CDS-18 CDS-18 NaN \n", "7053 CDS-10 CDS-10 NaN \n", "7051 CDS-11 CDS-11 NaN \n", "7059 CDS-12 CDS-12 NaN \n", "7145 CDS-4 CDS-4 NaN \n", "7097 CDS-20 CDS-20 NaN \n", "7099 CDS-22 CDS-22 NaN \n", "7125 PULASKI RD PS (18E-PS) PULASKI RD PS (18E-PS) NaN \n", "7165 CDS-2 CDS-2 NaN \n", "7096 CDS-20 CDS-20 NaN \n", "7137 DS-M54 DS-M54 NaN \n", "7114 DS-M40 DS-M40 NaN \n", "7186 DS-M109S DS-M109S NaN \n", "7143 DS-D27I DS-D27I NaN \n", "7049 TG-M81 TG-M81 NaN \n", "7095 TG-M94 TG-M94 NaN \n", "7100 CDS-21 CDS-21 NaN \n", "7050 TG-M81 TG-M81 NaN \n", "\n", " Waterway Reach Plant Open date/time \\\n", "3295 NBCR Lower (NSC Confluence) NaN 2014-08-25 13:04:00 \n", "3294 NBCR Lower (NSC Confluence) NaN 2014-08-25 12:44:00 \n", "3293 NSC Upper (NSWRP) NaN 2014-08-25 16:28:00 \n", "3296 NBCR Lower (NSC Confluence) NaN 2014-08-25 13:20:00 \n", "3274 Little Cal R (South) NaN 2014-08-25 13:58:00 \n", "3297 NBCR Upper (NSC Confluence) NaN 2014-08-25 12:30:00 \n", "3299 NBCR Lower (NSC Confluence) NaN 2014-08-25 16:27:00 \n", "4269 NBCR Lower (NSC Confluence) NaN 2014-01-03 12:50:00 \n", "4268 NBCR Lower (NSC Confluence) NaN 2014-01-03 12:50:00 \n", "3292 NSC Upper (NSWRP) NaN 2014-08-25 16:15:00 \n", "3300 NBCR Lower (NSC Confluence) NaN 2014-08-25 12:56:00 \n", "3301 NBCR Upper (NSC Confluence) NaN 2014-08-25 12:30:00 \n", "4204 NBCR Upper (NSC Confluence) NaN 2014-01-25 07:40:00 \n", "3275 Little Cal R (North) NaN 2014-08-25 13:47:00 \n", "3276 Cal Sag Ch NaN 2014-08-25 13:47:00 \n", "3277 Cal Sag Ch NaN 2014-08-25 13:47:00 \n", "3393 NBCR Upper (NSC Confluence) NaN 2014-08-20 18:05:00 \n", "3810 DesPlaines Upper NaN 2014-06-23 04:15:00 \n", "3813 NSC Upper (NSWRP) NaN 2014-06-23 04:24:00 \n", "3298 NBCR Lower (NSC Confluence) NaN 2014-08-25 12:56:00 \n", "3272 Little Cal R (North) NaN 2014-08-25 13:53:00 \n", "3273 Little Cal R (North) NaN 2014-08-25 13:53:00 \n", "3291 NSC Upper (NSWRP) NaN 2014-08-25 13:25:00 \n", "3494 SB Chicago R NaN 2014-07-22 23:55:00 \n", "3493 SB Chicago R NaN 2014-07-23 00:00:00 \n", "3287 NSC Upper (NSWRP) NaN 2014-08-25 12:46:00 \n", "3286 NSC Upper (NSWRP) NaN 2014-08-25 13:34:00 \n", "3285 Cal Sag Ch NaN 2014-08-25 13:34:00 \n", "4390 CSSC Upper (SWRP) NaN 2013-11-16 00:00:00 \n", "3288 NBCR Lower (NSC Confluence) NaN 2014-08-25 13:11:00 \n", "... ... ... ... \n", "7072 NBCR Lower (NSC Confluence) NaN 2011-07-23 02:23:00 \n", "7133 Chicago R NaN 2011-07-23 02:14:00 \n", "7063 CSSC Upper (SWRP) NaN 2011-07-23 02:18:00 \n", "7061 CSSC Lower (SWRP) NaN 2011-07-23 02:06:00 \n", "7054 NBCR Lower (NSC Confluence) NaN 2011-07-23 02:39:00 \n", "7085 Addison Cr NaN 2011-07-23 02:00:00 \n", "7075 NaN NaN 2011-07-23 03:30:00 \n", "7171 DesPlaines Middle NaN 2011-07-23 03:08:00 \n", "7134 Chicago R NaN 2011-07-23 03:28:00 \n", "7135 Chicago R NaN 2011-07-23 03:45:00 \n", "7136 Chicago R NaN 2011-07-23 03:57:00 \n", "7115 DesPlaines Middle NaN 2011-07-23 03:07:00 \n", "7060 Little Cal R (North) NaN 2011-07-23 05:17:00 \n", "7053 Cal Sag Ch NaN 2011-07-23 05:16:00 \n", "7051 Cal Sag Ch NaN 2011-07-23 05:19:00 \n", "7059 Little Cal R (North) NaN 2011-07-23 05:15:00 \n", "7145 Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7097 Little Cal R (North) NaN 2011-07-23 05:42:00 \n", "7099 Grand Cal R NaN 2011-07-23 05:17:00 \n", "7125 Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7165 Cal Sag Ch NaN 2011-07-23 05:15:00 \n", "7096 Little Cal R (North) NaN 2011-07-23 05:42:00 \n", "7137 Chicago R NaN 2011-07-23 04:14:00 \n", "7114 SB Chicago R NaN 2011-07-23 04:24:00 \n", "7186 NSC Upper (NSWRP) NaN 2011-07-23 04:36:00 \n", "7143 DesPlaines Middle NaN 2011-07-23 06:15:00 \n", "7049 NBCR Lower (NSC Confluence) NaN 2011-07-23 06:08:00 \n", "7095 NSC Lower (NSWRP) NaN 2011-07-23 04:08:00 \n", "7100 Grand Cal R NaN 2011-07-23 07:10:00 \n", "7050 NBCR Lower (NSC Confluence) NaN 2011-07-23 07:56:00 \n", "\n", " Close date/time Gate Open Period Duration 24hr_rain \n", "3295 2014-08-25 15:08:00 0 days 02:04:00.000000000 0 days 02:04:00 0.01 \n", "3294 2014-08-25 15:13:00 0 days 02:29:00.000000000 0 days 02:29:00 0.01 \n", "3293 2014-08-25 16:32:00 0 days 00:04:00.000000000 0 days 00:04:00 0.01 \n", "3296 2014-08-25 15:07:00 0 days 01:47:00.000000000 0 days 01:47:00 0.01 \n", "3274 2014-08-25 14:39:00 0 days 00:41:00.000000000 0 days 00:41:00 0.01 \n", "3297 2014-08-25 12:35:00 0 days 00:05:00.000000000 0 days 00:05:00 0.01 \n", "3299 2014-08-25 16:30:00 0 days 00:03:00.000000000 0 days 00:03:00 0.01 \n", "4269 2014-01-03 12:58:00 0 days 00:08:00.000000000 0 days 00:08:00 0.01 \n", "4268 2014-01-03 12:58:00 0 days 00:08:00.000000000 0 days 00:08:00 0.01 \n", "3292 2014-08-25 16:23:00 0 days 00:08:00.000000000 0 days 00:08:00 0.01 \n", "3300 2014-08-25 17:20:00 0 days 04:24:00.000000000 0 days 04:24:00 0.01 \n", "3301 2014-08-25 12:33:00 0 days 00:03:00.000000000 0 days 00:03:00 0.01 \n", "4204 2014-01-25 07:51:00 0 days 00:11:00.000000000 0 days 00:11:00 0.01 \n", "3275 2014-08-25 15:17:00 0 days 01:30:00.000000000 0 days 01:30:00 0.01 \n", "3276 2014-08-25 15:51:00 0 days 02:04:00.000000000 0 days 02:04:00 0.01 \n", "3277 2014-08-25 15:48:00 0 days 02:01:00.000000000 0 days 02:01:00 0.01 \n", "3393 2014-08-20 18:08:00 0 days 00:03:00.000000000 0 days 00:03:00 0.01 \n", "3810 2014-06-23 04:56:00 0 days 00:41:00.000000000 0 days 00:41:00 0.01 \n", "3813 2014-06-23 06:02:00 0 days 01:38:00.000000000 0 days 01:38:00 0.01 \n", "3298 2014-08-25 16:12:00 0 days 03:16:00.000000000 0 days 03:16:00 0.01 \n", "3272 2014-08-25 14:07:00 0 days 00:14:00.000000000 0 days 00:14:00 0.01 \n", "3273 2014-08-25 14:07:00 0 days 00:14:00.000000000 0 days 00:14:00 0.01 \n", "3291 2014-08-25 16:15:00 0 days 02:50:00.000000000 0 days 02:50:00 0.01 \n", "3494 2014-07-22 23:59:00 0 days 00:04:00.000000000 0 days 00:04:00 0.01 \n", "3493 2014-07-23 00:11:00 0 days 00:11:00.000000000 0 days 00:11:00 0.01 \n", "3287 2014-08-25 12:49:00 0 days 00:03:00.000000000 0 days 00:03:00 0.01 \n", "3286 2014-08-25 15:20:00 0 days 01:46:00.000000000 0 days 01:46:00 0.01 \n", "3285 2014-08-25 23:59:00 0 days 10:25:00.000000000 0 days 10:25:00 0.01 \n", "4390 2013-11-17 00:00:00 1 days 00:00:00.000000000 1 days 00:00:00 0.01 \n", "3288 2014-08-25 14:11:00 0 days 01:00:00.000000000 0 days 01:00:00 0.01 \n", "... ... ... ... ... \n", "7072 2011-07-23 12:18:00 0 days 09:55:00.000000000 0 days 09:55:00 7.73 \n", "7133 2011-07-23 03:09:00 0 days 00:55:00.000000000 0 days 00:55:00 7.73 \n", "7063 2011-07-23 02:21:00 0 days 00:03:00.000000000 0 days 00:03:00 7.73 \n", "7061 2011-07-23 11:03:00 0 days 08:57:00.000000000 0 days 08:57:00 7.73 \n", "7054 2011-07-23 15:23:00 0 days 12:44:00.000000000 0 days 12:44:00 7.73 \n", "7085 2011-07-23 14:00:00 0 days 12:00:00.000000000 0 days 12:00:00 7.73 \n", "7075 2011-07-23 12:50:00 0 days 09:20:00.000000000 0 days 09:20:00 7.84 \n", "7171 2011-07-23 09:09:00 0 days 06:01:00.000000000 0 days 06:01:00 7.84 \n", "7134 2011-07-23 03:37:00 0 days 00:09:00.000000000 0 days 00:09:00 7.84 \n", "7135 2011-07-23 03:52:00 0 days 00:07:00.000000000 0 days 00:07:00 7.84 \n", "7136 2011-07-23 03:59:00 0 days 00:02:00.000000000 0 days 00:02:00 7.84 \n", "7115 2011-07-23 07:08:00 0 days 04:01:00.000000000 0 days 04:01:00 7.84 \n", "7060 2011-07-23 08:29:00 0 days 03:12:00.000000000 0 days 03:12:00 7.85 \n", "7053 2011-07-23 07:22:00 0 days 02:06:00.000000000 0 days 02:06:00 7.85 \n", "7051 2011-07-23 07:07:00 0 days 01:48:00.000000000 0 days 01:48:00 7.85 \n", "7059 2011-07-23 06:09:00 0 days 00:54:00.000000000 0 days 00:54:00 7.85 \n", "7145 2011-07-23 15:48:00 0 days 10:33:00.000000000 0 days 10:33:00 7.85 \n", "7097 2011-07-23 07:31:00 0 days 01:49:00.000000000 0 days 01:49:00 7.85 \n", "7099 2011-07-23 07:45:00 0 days 02:28:00.000000000 0 days 02:28:00 7.85 \n", "7125 2011-07-23 15:48:00 0 days 10:33:00.000000000 0 days 10:33:00 7.85 \n", "7165 2011-07-23 15:48:00 0 days 10:33:00.000000000 0 days 10:33:00 7.85 \n", "7096 2011-07-23 07:26:00 0 days 01:44:00.000000000 0 days 01:44:00 7.85 \n", "7137 2011-07-23 07:55:00 0 days 03:41:00.000000000 0 days 03:41:00 7.85 \n", "7114 2011-07-23 04:35:00 0 days 00:11:00.000000000 0 days 00:11:00 7.85 \n", "7186 2011-07-23 07:47:00 0 days 03:11:00.000000000 0 days 03:11:00 7.85 \n", "7143 2011-07-23 07:43:00 0 days 01:28:00.000000000 0 days 01:28:00 7.85 \n", "7049 2011-07-23 06:11:00 0 days 00:03:00.000000000 0 days 00:03:00 7.85 \n", "7095 2011-07-23 04:15:00 0 days 00:07:00.000000000 0 days 00:07:00 7.85 \n", "7100 2011-07-23 23:59:00 0 days 16:49:00.000000000 0 days 16:49:00 7.86 \n", "7050 2011-07-23 08:04:00 0 days 00:08:00.000000000 0 days 00:08:00 7.86 \n", "\n", "[10522 rows x 10 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "csos_without_zero = csos[csos['24hr_rain'] != 0]\n", "csos_without_zero" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
NeuroStat/Python-scripts	python_scripts_day1/.ipynb_checkpoints/day2_func_conn-checkpoint.ipynb	1	1818294	null	gpl-3.0
liangjg/openmc	examples/jupyter/post-processing.ipynb	1	244101	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigenvalue calculation. The problem we will use is a simple reflected pin-cell." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from IPython.display import Image\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import openmc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Input Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", "fuel.add_nuclide('U235', 3.7503e-4)\n", "fuel.add_nuclide('U238', 2.2625e-2)\n", "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", "water.add_nuclide('H1', 4.9457e-2)\n", "water.add_nuclide('O16', 2.4732e-2)\n", "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our three materials, we can now create a materials file object that can be exported to an actual XML file." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection\n", "materials = openmc.Materials([fuel, water, zircaloy])\n", "\n", "# Export to \"materials.xml\"\n", "materials.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a Universe to encapsulate a fuel pin\n", "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", "\n", "# Create fuel Cell\n", "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", "fuel_cell.fill = fuel\n", "fuel_cell.region = -fuel_outer_radius\n", "pin_cell_universe.add_cell(fuel_cell)\n", "\n", "# Create a clad Cell\n", "clad_cell = openmc.Cell(name='1.6% Clad')\n", "clad_cell.fill = zircaloy\n", "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", "pin_cell_universe.add_cell(clad_cell)\n", "\n", "# Create a moderator Cell\n", "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", "moderator_cell.fill = water\n", "moderator_cell.region = +clad_outer_radius\n", "pin_cell_universe.add_cell(moderator_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Create root Cell\n", "root_cell = openmc.Cell(name='root cell')\n", "root_cell.fill = pin_cell_universe\n", "\n", "# Add boundary planes\n", "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", "\n", "# Create root Universe\n", "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", "root_universe.add_cell(root_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", "geometry = openmc.Geometry(root_universe)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Export to \"geometry.xml\"\n", "geometry.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 90 active batches each with 5000 particles." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", "settings = openmc.Settings()\n", "settings.batches = 100\n", "settings.inactive = 10\n", "settings.particles = 5000\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", "settings.source = openmc.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot = openmc.Plot.from_geometry(geometry)\n", "plot.pixels = (250, 250)\n", "plot.to_ipython_image()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a 2D mesh tally." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", "tallies = openmc.Tallies()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Create mesh which will be used for tally\n", "mesh = openmc.RegularMesh()\n", "mesh.dimension = [100, 100]\n", "mesh.lower_left = [-0.63, -0.63]\n", "mesh.upper_right = [0.63, 0.63]\n", "\n", "# Create mesh filter for tally\n", "mesh_filter = openmc.MeshFilter(mesh)\n", "\n", "# Create mesh tally to score flux and fission rate\n", "tally = openmc.Tally(name='flux')\n", "tally.filters = [mesh_filter]\n", "tally.scores = ['flux', 'fission']\n", "tallies.append(tally)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", "tallies.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we a have a complete set of inputs, so we can go ahead and run our simulation." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%%%%%%\n", " ##################### %%%%%%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%\n", " ################# %%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%\n", " ############ %%%%%%%%%%%%%%%\n", " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", " \| The OpenMC Monte Carlo Code\n", " Copyright \| 2011-2019 MIT and OpenMC contributors\n", " License \| http://openmc.readthedocs.io/en/latest/license.html\n", " Version \| 0.11.0-dev\n", " Git SHA1 \| 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", " Date/Time \| 2019-07-19 06:22:24\n", " OpenMP Threads \| 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", " 1/1 1.04359\n", " 2/1 1.04323\n", " 3/1 1.04711\n", " 4/1 1.03892\n", " 5/1 1.02459\n", " 6/1 1.03936\n", " 7/1 1.03529\n", " 8/1 1.01590\n", " 9/1 1.03060\n", " 10/1 1.02892\n", " 11/1 1.03987\n", " 12/1 1.04395 1.04191 +/- 0.00204\n", " 13/1 1.04971 1.04451 +/- 0.00285\n", " 14/1 1.03880 1.04308 +/- 0.00247\n", " 15/1 1.03091 1.04065 +/- 0.00310\n", " 16/1 1.03618 1.03990 +/- 0.00264\n", " 17/1 1.04109 1.04007 +/- 0.00223\n", " 18/1 1.02978 1.03879 +/- 0.00232\n", " 19/1 1.06363 1.04155 +/- 0.00344\n", " 20/1 1.06549 1.04394 +/- 0.00390\n", " 21/1 1.03469 1.04310 +/- 0.00362\n", " 22/1 1.01925 1.04111 +/- 0.00386\n", " 23/1 1.03268 1.04046 +/- 0.00361\n", " 24/1 1.03906 1.04036 +/- 0.00334\n", " 25/1 1.02632 1.03943 +/- 0.00325\n", " 26/1 1.03906 1.03940 +/- 0.00304\n", " 27/1 1.05058 1.04006 +/- 0.00293\n", " 28/1 1.03248 1.03964 +/- 0.00279\n", " 29/1 1.04076 1.03970 +/- 0.00264\n", " 30/1 1.00994 1.03821 +/- 0.00292\n", " 31/1 1.04785 1.03867 +/- 0.00281\n", " 32/1 1.03080 1.03831 +/- 0.00270\n", " 33/1 1.01862 1.03746 +/- 0.00272\n", " 34/1 1.05370 1.03813 +/- 0.00269\n", " 35/1 1.02226 1.03750 +/- 0.00266\n", " 36/1 1.02862 1.03716 +/- 0.00258\n", " 37/1 1.04790 1.03755 +/- 0.00251\n", " 38/1 1.03762 1.03756 +/- 0.00242\n", " 39/1 1.02255 1.03704 +/- 0.00239\n", " 40/1 1.06094 1.03784 +/- 0.00245\n", " 41/1 1.03842 1.03786 +/- 0.00237\n", " 42/1 1.00628 1.03687 +/- 0.00249\n", " 43/1 1.04916 1.03724 +/- 0.00245\n", " 44/1 1.06237 1.03798 +/- 0.00248\n", " 45/1 1.08153 1.03922 +/- 0.00271\n", " 46/1 1.05649 1.03970 +/- 0.00268\n", " 47/1 1.06265 1.04032 +/- 0.00268\n", " 48/1 1.05728 1.04077 +/- 0.00265\n", " 49/1 1.07343 1.04161 +/- 0.00271\n", " 50/1 1.04640 1.04173 +/- 0.00265\n", " 51/1 1.05143 1.04196 +/- 0.00259\n", " 52/1 1.03639 1.04183 +/- 0.00253\n", " 53/1 1.04846 1.04199 +/- 0.00248\n", " 54/1 1.02435 1.04158 +/- 0.00245\n", " 55/1 1.04806 1.04173 +/- 0.00240\n", " 56/1 1.04798 1.04186 +/- 0.00235\n", " 57/1 1.06621 1.04238 +/- 0.00236\n", " 58/1 1.05734 1.04269 +/- 0.00233\n", " 59/1 1.04581 1.04276 +/- 0.00228\n", " 60/1 1.02682 1.04244 +/- 0.00226\n", " 61/1 1.05971 1.04278 +/- 0.00224\n", " 62/1 1.02357 1.04241 +/- 0.00223\n", " 63/1 1.02645 1.04211 +/- 0.00221\n", " 64/1 1.00711 1.04146 +/- 0.00226\n", " 65/1 1.06171 1.04183 +/- 0.00225\n", " 66/1 1.03444 1.04170 +/- 0.00221\n", " 67/1 1.05875 1.04199 +/- 0.00219\n", " 68/1 1.04640 1.04207 +/- 0.00216\n", " 69/1 1.04376 1.04210 +/- 0.00212\n", " 70/1 1.07078 1.04258 +/- 0.00214\n", " 71/1 1.03916 1.04252 +/- 0.00210\n", " 72/1 1.01843 1.04213 +/- 0.00211\n", " 73/1 1.03666 1.04205 +/- 0.00207\n", " 74/1 1.04625 1.04211 +/- 0.00204\n", " 75/1 1.05277 1.04228 +/- 0.00202\n", " 76/1 1.04944 1.04238 +/- 0.00199\n", " 77/1 1.01898 1.04203 +/- 0.00199\n", " 78/1 1.03283 1.04190 +/- 0.00197\n", " 79/1 1.02304 1.04163 +/- 0.00196\n", " 80/1 1.01539 1.04125 +/- 0.00196\n", " 81/1 1.03988 1.04123 +/- 0.00194\n", " 82/1 1.02138 1.04096 +/- 0.00193\n", " 83/1 1.02473 1.04073 +/- 0.00192\n", " 84/1 1.03810 1.04070 +/- 0.00189\n", " 85/1 1.07438 1.04115 +/- 0.00192\n", " 86/1 1.03048 1.04101 +/- 0.00190\n", " 87/1 1.06778 1.04135 +/- 0.00191\n", " 88/1 1.07341 1.04177 +/- 0.00192\n", " 89/1 1.06729 1.04209 +/- 0.00193\n", " 90/1 1.05069 1.04220 +/- 0.00191\n", " 91/1 1.07675 1.04262 +/- 0.00193\n", " 92/1 1.06470 1.04289 +/- 0.00193\n", " 93/1 1.02609 1.04269 +/- 0.00191\n", " 94/1 1.04761 1.04275 +/- 0.00189\n", " 95/1 1.08802 1.04328 +/- 0.00194\n", " 96/1 1.04162 1.04326 +/- 0.00192\n", " 97/1 1.04573 1.04329 +/- 0.00190\n", " 98/1 1.03232 1.04317 +/- 0.00188\n", " 99/1 1.03473 1.04307 +/- 0.00186\n", " 100/1 1.04505 1.04309 +/- 0.00184\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 6.4445e-01 seconds\n", " Reading cross sections = 6.1129e-01 seconds\n", " Total time in simulation = 2.0000e+02 seconds\n", " Time in transport only = 1.9970e+02 seconds\n", " Time in inactive batches = 2.9966e+00 seconds\n", " Time in active batches = 1.9701e+02 seconds\n", " Time synchronizing fission bank = 4.0040e-02 seconds\n", " Sampling source sites = 3.1522e-02 seconds\n", " SEND/RECV source sites = 8.3459e-03 seconds\n", " Time accumulating tallies = 9.3582e-03 seconds\n", " Total time for finalization = 4.6582e-02 seconds\n", " Total time elapsed = 2.0072e+02 seconds\n", " Calculation Rate (inactive) = 16685.4 particles/second\n", " Calculation Rate (active) = 2284.19 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", " k-effective (Collision) = 1.04342 +/- 0.00159\n", " k-effective (Track-length) = 1.04309 +/- 0.00184\n", " k-effective (Absorption) = 1.04107 +/- 0.00140\n", " Combined k-effective = 1.04195 +/- 0.00117\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } ], "source": [ "# Run OpenMC!\n", "openmc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tally Data Processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Load the statepoint file\n", "sp = openmc.StatePoint('statepoint.100.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we need to get the tally, which can be done with the ``StatePoint.get_tally(...)`` method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tally\n", "\tID =\t1\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['flux', 'fission']\n", "\tEstimator =\ttracklength\n", "\n" ] } ], "source": [ "tally = sp.get_tally(scores=['flux'])\n", "print(tally)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](http://openmc.readthedocs.io/en/latest/methods/tallies.html#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[0.40767451, 0. ]],\n", "\n", " [[0.40933814, 0. ]],\n", "\n", " [[0.4119165 , 0. ]],\n", "\n", " ...,\n", "\n", " [[0.40854327, 0. ]],\n", "\n", " [[0.40970805, 0. ]],\n", "\n", " [[0.40948065, 0. ]]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tally.sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, the mean and standard deviation of the mean are usually what you are more interested in. The Tally class also has properties ``mean`` and ``std_dev`` which automatically calculate these statistics on-the-fly." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10000, 1, 2)\n" ] }, { "data": { "text/plain": [ "(array([[[0.00452972, 0. ]],\n", " \n", " [[0.0045482 , 0. ]],\n", " \n", " [[0.00457685, 0. ]],\n", " \n", " ...,\n", " \n", " [[0.00453937, 0. ]],\n", " \n", " [[0.00455231, 0. ]],\n", " \n", " [[0.00454978, 0. ]]]),\n", " array([[[2.03553236e-05, 0.00000000e+00]],\n", " \n", " [[1.83847389e-05, 0.00000000e+00]],\n", " \n", " [[1.68647098e-05, 0.00000000e+00]],\n", " \n", " ...,\n", " \n", " [[1.71606078e-05, 0.00000000e+00]],\n", " \n", " [[1.87645811e-05, 0.00000000e+00]],\n", " \n", " [[1.94447454e-05, 0.00000000e+00]]]))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(tally.mean.shape)\n", "(tally.mean, tally.std_dev)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tally data has three dimensions: one for filter combinations, one for nuclides, and one for scores. We see that there are 10000 filter combinations (corresponding to the 100 x 100 mesh bins), a single nuclide (since none was specified), and two scores. If we only want to look at a single score, we can use the ``get_slice(...)`` method as follows." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tally\n", "\tID =\t2\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['flux']\n", "\tEstimator =\ttracklength\n", "\n" ] } ], "source": [ "flux = tally.get_slice(scores=['flux'])\n", "fission = tally.get_slice(scores=['fission'])\n", "print(flux)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the bins into a form that we can plot, we can simply change the shape of the array since it is a numpy array." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "flux.std_dev.shape = (100, 100)\n", "flux.mean.shape = (100, 100)\n", "fission.std_dev.shape = (100, 100)\n", "fission.mean.shape = (100, 100)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x14d12e58cb38>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.subplot(121)\n", "fig.imshow(flux.mean)\n", "fig2 = plt.subplot(122)\n", "fig2.imshow(fission.mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's say we want to look at the distribution of relative errors of our tally bins for flux. First we create a new variable called ``relative_error`` and set it to the ratio of the standard deviation and the mean, being careful not to divide by zero in case some bins were never scored to." ] }, { "cell_type": "code", "execution_count": 22, "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": [ "# Determine relative error\n", "relative_error = np.zeros_like(flux.std_dev)\n", "nonzero = flux.mean > 0\n", "relative_error[nonzero] = flux.std_dev[nonzero] / flux.mean[nonzero]\n", "\n", "# distribution of relative errors\n", "ret = plt.hist(relative_error[nonzero], bins=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source Sites" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Source sites can be accessed from the ``source`` property. As shown below, the source sites are represented as a numpy array with a structured datatype." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([((-0.28690552, -0.23731283, 0.51447853), ( 0.02705364, -0.14292142, 0.98936422), 1780128.70101981, 1., 0, 0),\n", " ((-0.28690552, -0.23731283, 0.51447853), (-0.16786951, 0.86432444, -0.47409186), 1553436.10501094, 1., 0, 0),\n", " (( 0.17162994, 0.134092 , 0.42932363), ( 0.25199134, -0.11168216, 0.96126347), 829530.02360943, 1., 0, 0),\n", " ...,\n", " ((-0.24444068, -0.01351615, -0.41772172), ( 0.10437178, -0.86754673, 0.486281 ), 807617.55637656, 1., 0, 0),\n", " ((-0.2146841 , 0.14307096, 0.07419328), ( 0.89645066, -0.35557279, -0.26446968), 6036005.44157462, 1., 0, 0),\n", " ((-0.2146841 , 0.14307096, 0.07419328), (-0.95287644, -0.25857878, 0.15863005), 4923751.04163063, 1., 0, 0)],\n", " dtype=[('r', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('u', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('E', '<f8'), ('wgt', '<f8'), ('delayed_group', '<i4'), ('particle', '<i4')])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp.source" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want, say, only the energies from the source sites, we can simply index the source array with the name of the field:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1780128.70101981, 1553436.10501094, 829530.02360943, ...,\n", " 807617.55637656, 6036005.44157462, 4923751.04163063])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp.source['E']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can look at things like the energy distribution of source sites. Note that we don't directly use the ``matplotlib.pyplot.hist`` method since our binning is logarithmic." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9999999999999999\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Probability/eV')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create log-spaced energy bins from 1 keV to 10 MeV\n", "energy_bins = np.logspace(3,7)\n", "\n", "# Calculate pdf for source energies\n", "probability, bin_edges = np.histogram(sp.source['E'], energy_bins, density=True)\n", "\n", "# Make sure integrating the PDF gives us unity\n", "print(sum(probabilitynp.diff(energy_bins)))\n", "\n", "# Plot source energy PDF\n", "plt.semilogx(energy_bins[:-1], probabilitynp.diff(energy_bins), drawstyle='steps')\n", "plt.xlabel('Energy (eV)')\n", "plt.ylabel('Probability/eV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also look at the spatial distribution of the sites. To make the plot a little more interesting, we can also include the direction of the particle emitted from the source and color each source by the logarithm of its energy." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5, 0.5)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.quiver(sp.source['r']['x'], sp.source['r']['y'],\n", " sp.source['u']['x'], sp.source['u']['y'],\n", " np.log(sp.source['E']), cmap='jet', scale=20.0)\n", "plt.colorbar()\n", "plt.xlim((-0.5,0.5))\n", "plt.ylim((-0.5,0.5))" ] } ], "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": 1 }	mit
google/timesketch	notebooks/Stolen_Szechuan_Sauce_Data_Upload.ipynb	1	31915	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Stolen Szechuan Sauce - Data Upload.ipynb", "provenance": [], "private_outputs": true, "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/google/timesketch/blob/master/notebooks/Stolen_Szechuan_Sauce_Data_Upload.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "myeER0OQAt1r" }, "source": [ "# The Stolen Szechuan Sauce\n", "\n", "This is a simple colab demonstrating one way of uploading data from the Stolen Szechuan Sauce challenge (found [here](https://dfirmadness.com/the-stolen-szechuan-sauce/)).\n", "\n", "This colab will not go into any analysis of the data, only uploading data to a sketch.\n", "\n", "A word of notice, this notebook can be run on the cloud runtimes, but then few changes need to be made. However it is assumed that you are connecting to a local runtime, see [instructions here](https://research.google.com/colaboratory/local-runtimes.html). This makes it easier to import data that is already on your system.\n", "\n", "For a more generic instructions of Colab can be [found here](https://colab.research.google.com/github/google/timesketch/blob/master/notebooks/colab-timesketch-demo.ipynb)" ] }, { "cell_type": "code", "metadata": { "id": "40A429x4Ajfc", "cellView": "form" }, "source": [ "# @title Import libraries\n", "# @markdown This cell loads libraries that we will use througout the notebook.\n", "import io\n", "import os\n", "import codecs\n", "\n", "import altair as alt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from timesketch_api_client import config\n", "from timesketch_import_client import helper\n", "from timesketch_import_client import importer" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ITjKgNRtBBdx" }, "source": [ "## AutoRuns File\n", "\n", "Let's read the file that contains the output of the AutoRuns file." ] }, { "cell_type": "code", "metadata": { "id": "rxtTjR8RA8SB", "cellView": "both" }, "source": [ "# @markdown This needs to be changed to reflect the correct path.\n", "\n", "PATH_TO_FOLDER = '/mnt/chromeos/MyFiles/Downloads' # @param {type: \"string\"}\n", "# @markdown the path to the folder will be used for all subsequent paths\n", "# @markdown as a root folder.\n", "AUTO_RUN_FILENAME = 'autoruns-desktop-sdn1rpt.csv' # @param {type: \"string\"}\n", "\n", "PATH_TO_CSV = os.path.join(PATH_TO_FOLDER, AUTO_RUN_FILENAME)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "LFKpjpo66pve" }, "source": [ "Now we can read the content of the file:" ] }, { "cell_type": "code", "metadata": { "id": "L05NWGvRBC0R" }, "source": [ "df = None\n", "with codecs.open(PATH_TO_CSV, 'r', encoding='utf-8', errors='replace') as fh:\n", " df = pd.read_csv(fh, error_bad_lines=False)\n", "\n", "print(df.shape)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TORG4J8CHI4P" }, "source": [ "Quite a few errors, let's look at the data." ] }, { "cell_type": "code", "metadata": { "id": "lOcjM6ECFVkj" }, "source": [ "df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-_NPKhANJSc-" }, "source": [ "This does not look right, let's look at the content of the file, let's look at the hex code (for that we will use the `!` which allows us to execute shell commands)" ] }, { "cell_type": "code", "metadata": { "id": "t-wWKfrEJW50" }, "source": [ "!dd if=$PATH_TO_CSV bs=128 count=1 \| xxd" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ERWnpjHNJflb" }, "source": [ "This file is not UTF-8, it's encoded as UTF-16, so let's now read the file in again, this time as UTF-16" ] }, { "cell_type": "code", "metadata": { "id": "UPdW8UvlFvn1" }, "source": [ "df = None\n", "with codecs.open(PATH_TO_CSV, 'r', encoding='utf-16', errors='replace') as fh:\n", " df = pd.read_csv(fh, error_bad_lines=False)\n", "\n", "print(df.shape)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8B4ew2-PJpj9" }, "source": [ "No errors, let's look at the content" ] }, { "cell_type": "code", "metadata": { "id": "3rs0U_wZH59M" }, "source": [ "df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8hxAw8QKJslL" }, "source": [ "This looks correct now, let's make the data a bit more Timesketch ready.\n", "\n", "The first thing is to create a datetime field that contains the timestamp. We will use the built-in conversion in pandas:" ] }, { "cell_type": "code", "metadata": { "id": "qtn7JXS-Iob-" }, "source": [ "df['datetime'] = pd.to_datetime(df['Time'])" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7deDQiar682s" }, "source": [ "The next thing is to add few fields that Timesketch expects:" ] }, { "cell_type": "code", "metadata": { "id": "1sCIIv0YJ57-" }, "source": [ "df['data_type'] = 'autoruns:record'\n", "df['timestamp_desc'] = 'Entry Recorded'\n", "df['message'] = 'AutoRun: [' + df['Category'] + ' - ' + df['Profile'] + '] ' + df['Image Path']\n", "\n", "df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "v9Hivygb7Axp" }, "source": [ "We can take a quick look at the data frame we just read in:" ] }, { "cell_type": "code", "metadata": { "id": "hQ7WkDPOM4mk" }, "source": [ "df.info()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "cUUjqZWUKIna" }, "source": [ "### Upload To TS\n", "\n", "Let's upload this data to TS. For that we first need to get a copy of the Timesketch client, then we will need to get a copy of a sketch object." ] }, { "cell_type": "code", "metadata": { "id": "VZWk8dRWKDRr" }, "source": [ "ts_client = config.get_client()\n", "[(x.id, x.name) for x in ts_client.list_sketches()]" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0VEfTaLOBtZs" }, "source": [ "# @markdown This needs to be changed to reflect the correct sketch.\n", "\n", "SKETCH_ID = 6 # @param {type: \"integer\"}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DFKwoCTi7NXm" }, "source": [ "Now we are ready to upload the data. The sketch that we want to use is the one with the ID of 6.\n", "\n", "We will use the importer client to import the data as a data frame, for that we need to setup an import streamer:" ] }, { "cell_type": "code", "metadata": { "id": "riyNlxSXKh4m" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('autoruns_desktop_sdn1rpt')\n", "\n", " streamer.add_data_frame(df)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xdJJbJfL7YVq" }, "source": [ "What we did there was create a copy of the Import client, and then configured it (defining the sketch to use and what the name of the timeline we are going to choose)." ] }, { "cell_type": "markdown", "metadata": { "id": "6AWrR8-8MSs0" }, "source": [ "Now this data has been uploaded to the sketch in TS but there is an error in TS import, that is if we go and visit the sketch we can see that the sketch hasn't been uploaded correctly, so let's copy the error here.\n", "\n", "Then we will delete/remove the timeline from the sketch so that there isn't an error one in TS.\n", "\n", "\n", "```\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.6/dist-packages/timesketch/lib/tasks.py\", line 558, in run_csv_jsonl\n", " for event in read_and_validate(file_handle):\n", " File \"/usr/local/lib/python3.6/dist-packages/timesketch/lib/utils.py\", line 225, in read_and_validate_jsonl\n", " linedict['timestamp'] = parser.parse(linedict['datetime'])\n", " File \"/usr/local/lib/python3.6/dist-packages/dateutil/parser/_parser.py\", line 1374, in parse\n", " return DEFAULTPARSER.parse(timestr, kwargs)\n", " File \"/usr/local/lib/python3.6/dist-packages/dateutil/parser/_parser.py\", line 646, in parse\n", " res, skipped_tokens = self._parse(timestr, kwargs)\n", " File \"/usr/local/lib/python3.6/dist-packages/dateutil/parser/_parser.py\", line 725, in _parse\n", " l = _timelex.split(timestr) # Splits the timestr into tokens\n", " File \"/usr/local/lib/python3.6/dist-packages/dateutil/parser/_parser.py\", line 207, in split\n", " return list(cls(s))\n", " File \"/usr/local/lib/python3.6/dist-packages/dateutil/parser/_parser.py\", line 76, in __init__\n", " '{itype}'.format(itype=instream.__class__.__name__))\n", "TypeError: Parser must be a string or character stream, not NoneType\n", "```\n", "\n", "This does indicate issues with datetime parsing. Let's take a closer look at the data frame in question. The first thing we check is to see whether there are any empty dates in the frame:" ] }, { "cell_type": "code", "metadata": { "id": "srRmvAEKMs0S" }, "source": [ "df[df.datetime.isna()]" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "R-sFv9JmOj9V" }, "source": [ "The check we use is `isna` which checks to see if a field is empty or (Not a number).\n", "\n", "There are quite a few records with an empty date field. Let's exclude those. For that we will need to upload a slice of the data frame that doesn't contain any records with an empty date." ] }, { "cell_type": "code", "metadata": { "id": "s5U6sWVEOPDV" }, "source": [ "df.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "zcm01gnJOoWB" }, "source": [ "df[~df.datetime.isna()].shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "G5Xsyhl1OsHC" }, "source": [ "There seem to be 9 records without a date... let's remove them from the upload (by just uploading a slice of the data)." ] }, { "cell_type": "code", "metadata": { "id": "gdrtzphrOqwM" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('autoruns_desktop_sdn1rpt_w_time')\n", "\n", " streamer.add_data_frame(df[~df.datetime.isna()].copy())" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UKmKE3hbgHaR" }, "source": [ "### Server AutoRun File\n", "\n", "Let's take the server Autoruns next" ] }, { "cell_type": "code", "metadata": { "id": "vDFCbU41O1yx" }, "source": [ "DC_FILENAME = 'autorunsc-citadel-dc01.csv' # @param {type: \"string\"}\n", "\n", "dc_path = os.path.join(PATH_TO_FOLDER, DC_FILENAME)\n", "\n", "auto_server_df = None\n", "with codecs.open(dc_path, 'r', encoding='utf-16', errors='replace') as fh:\n", " auto_server_df = pd.read_csv(fh, error_bad_lines=False)\n", "\n", "print(auto_server_df.shape)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mTYjxb01grgw" }, "source": [ "Let's `groom` it for TS" ] }, { "cell_type": "code", "metadata": { "id": "-sxxS7CZgi3k" }, "source": [ "auto_server_df['datetime'] = pd.to_datetime(df['Time'])\n", "auto_server_df['data_type'] = 'autoruns:record'\n", "auto_server_df['timestamp_desc'] = 'Entry Recorded'\n", "auto_server_df['message'] = 'AutoRun: [' + auto_server_df['Category'] + ' - ' + auto_server_df['Profile'] + '] ' + auto_server_df['Image Path']\n", "\n", "auto_server_df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "R8OhonO8g0ip" }, "source": [ "And upload (using the same method as before)" ] }, { "cell_type": "code", "metadata": { "id": "SUeAQtEqgvfi" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('autoruns_citadel_dc01_w_time')\n", "\n", " streamer.add_data_frame(auto_server_df[~auto_server_df.datetime.isna()].copy())" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mh2SAJOYhBBU" }, "source": [ "Now we've got both autoruns in there\n", "\n", "## Plaso Files\n", "\n", "Let's in the plaso files, using:\n", "\n", "```shell\n", "$ timesketch_importer --sketch_id 6 20200918_0417_DESKTOP-SDN1RPT.plaso \n", "```\n", "\n", "or using the importer client in colab" ] }, { "cell_type": "code", "metadata": { "id": "EiotgxxjxMUE", "cellView": "form" }, "source": [ "DESKTOP_PATH = '20200918_0417_DESKTOP-SDN1RPT.plaso' #@param {type: \"string\"}\n", "SERVER_PATH = '20200918_0347_CDrive_new.plaso' #@param {type: \"string\"}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Q4ePXTVZg97K" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('desktop-sdn1rpt.plaso')\n", " streamer.add_file(os.path.join(PATH_TO_FOLDER, DESKTOP_PATH))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UZYiobwO0-uj" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('dc1_plaso')\n", " streamer.add_file(os.path.join(PATH_TO_FOLDER, SERVER_PATH))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7f_qM5Xd8HCL" }, "source": [ "## PCAP Files\n", "\n", "Another important factor in the challenge are the provided PCAP files. We need to get them checked into TS.\n", "\n", "Let's start parsing them. There are essentially two different methods of doing so:\n", "\n", "1. Using Wireshark to do the parsing and work with a CSV file\n", "2. Parse the PCAP file using python libraries and use that.\n", "\n", "Let's explore both options." ] }, { "cell_type": "markdown", "metadata": { "id": "GfEUJ4z5NI6p" }, "source": [ "### Wireshark Route\n", "\n", "Wireshark has a neat feature to export a set of packages or all packets into various other formats. This also includes CSV. As Timesketch is able to handle CSV data, this is worth an attempt.\n", "\n", "To export packets to csv use:\n", "\n", "```Wireshark → File → Export Packet Dissections```\n", "\n", "And choose CSV.\n", "\n", "The exported CSV will include all displayed columns. One thing to note here is that the time by default is relative to the first packet in the capture. You need to adjust that. \n", "\n", "Go to:\n", "\n", "```Wireshark → View → Time Display Format```\n", "\n", "And select ```UTC Date and Time of the Day```\n", "\n", "To learn more about Time settings in Wireshark, visit wireshark.org\n", "\n", "The now exported CSV looks promising. Some things need to be adjusted like the datetime column name and the format, but we already know how to do that from the autoruns csv file.\n" ] }, { "cell_type": "code", "metadata": { "id": "MHApjgTmFZro", "cellView": "form" }, "source": [ "# @markdown Change the path to what fits on your system.\n", "PCAP_CSV_PATH = 'all_packets.csv' #@param {type: \"string\"}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ruqzafD72LLH" }, "source": [ "pcap_df = pd.read_csv(\n", " os.path.join(PATH_TO_FOLDER, PCAP_CSV_PATH),\n", " encoding='utf-8', parse_dates=False)\n", "\n", "pcap_df.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7xA4laexGYFl" }, "source": [ "#### Modify DataFrame\n", "\n", "Now let's rename fields and add other fields to make it work better for Timesketch." ] }, { "cell_type": "code", "metadata": { "id": "wz3y2J0hOE-y" }, "source": [ "pcap_df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ylJwSl15OGaC" }, "source": [ "Now we've got a general idea about how the data looks like, so we can change it." ] }, { "cell_type": "code", "metadata": { "id": "tXNcE60jEEIx" }, "source": [ "# convert the 'Date' column to datetime format \n", "pcap_df['Time']= pd.to_datetime(pcap_df['Time']) \n", "pcap_df['data_type'] = 'pcap:wireshark:entry'\n", "pcap_df['timestamp_desc'] = 'Time Logged'\n", "pcap_df['source_short'] = 'LOG'\n", "pcap_df['source'] = 'Network'\n", "pcap_df['message'] = '[' + pcap_df['Protocol'] + '] ' + pcap_df['Info'] + ' (' + pcap_df['Source'] + ':' + pcap_df['src port'].astype('str') + ' -> ' + pcap_df['Destination'] + ':' + pcap_df['DST port'].astype('str') + ')'\n", "\n", "pcap_df = pcap_df.rename(columns={'Time': 'datetime'})\n", "\n", "pcap_df.info()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "AShtCJv4PucM" }, "source": [ "Let's look at the data frame now" ] }, { "cell_type": "code", "metadata": { "id": "SsL4agghGcKT" }, "source": [ "pcap_df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hpjRqMdlGfpb" }, "source": [ "Adjust ports" ] }, { "cell_type": "code", "metadata": { "id": "ik9sjjIdGjLD" }, "source": [ "pcap_df['DST port'] = pcap_df['DST port'].astype(pd.Int32Dtype())\n", "pcap_df['src port'] = pcap_df['src port'].astype(pd.Int32Dtype())\n", "\n", "pcap_df.head(3)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tITKx61LGn_I" }, "source": [ "#### Upload CSV\n", "\n", "Now we can upload the data to TS" ] }, { "cell_type": "code", "metadata": { "id": "x2dVlUyLHzzX" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper) \n", "\n", " streamer.set_timeline_name('wireshark_decoded_pcap')\n", "\n", " streamer.add_data_frame(pcap_df[~pcap_df.datetime.isna()].copy())" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "cDCabjJFLOYE" }, "source": [ "### Using Python Libraries\n", "\n", "Now we can use python libraries, such as scapy. This is a much slower method than using Wireshark and a CSV. It is however more flexible, there are more things that can be done here.\n", "\n", "(for this we also have a progress bar since this will take some time to execute)\n", "\n", "Make sure that your environment has scapy installed, if not you can execute:" ] }, { "cell_type": "code", "metadata": { "id": "FzAVgN5PLR5O" }, "source": [ "!pip install -q scapy\n", "!pip install -q tqdm\n", "!pip install -q ipywidgets" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Cz0xDJm5LUxN" }, "source": [ "# @markdown Import needed libraries for using scapy.\n", "import binascii\n", "import datetime\n", "import pytz\n", "\n", "import tqdm\n", "from tqdm import tqdm_notebook, tnrange\n", "\n", "import ipywidgets as widgets\n", "\n", "from scapy import all as scapy_all" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "WCxbpYo5LX8G", "cellView": "form" }, "source": [ "# @markdown Change this to the correct path on your system.\n", "PCAP_PATH = 'case001.pcap' # @param {type: \"string\"}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "G-34CmPKUss8" }, "source": [ "Let's read in the PCAP file, word of warning, this will take a really long time" ] }, { "cell_type": "code", "metadata": { "id": "gpL9tqlALZVe" }, "source": [ "packets = scapy_all.rdpcap(\n", " os.path.join(PATH_TO_FOLDER, PCAP_PATH))" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "TG57s2ZOLbM1" }, "source": [ "# @markdown check how many packets are in there\n", "packets" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "_tDnPoVYWBLK" }, "source": [ "Now we can start going through the packets to generate a data frame, since that's what we want so that we can upload data to TS\n", "\n", "To convert the data to a dataframe we are borrowing code from : https://github.com/secdevopsai/Packet-Analytics/blob/master/Packet-Analytics.ipynb (see the [medium post here](https://medium.com/hackervalleystudio/learning-packet-analysis-with-data-science-5356a3340d4e))" ] }, { "cell_type": "code", "metadata": { "id": "V9B7TMZJLdmS" }, "source": [ "# @markdown Collect field names from IP/TCP/UDP\n", "# @markdown These will be columns in DF\n", "ip_fields = [(field.name) for field in scapy_all.IP().fields_desc]\n", "tcp_fields = [(field.name) for field in scapy_all.TCP().fields_desc]\n", "udp_fields = [(field.name) for field in scapy_all.UDP().fields_desc]\n", "\n", "print(ip_fields)\n", "print(tcp_fields)\n", "print(udp_fields)\n", "\n", "ip_fields_new = [(\"ip_\"+field.name) for field in scapy_all.IP().fields_desc]\n", "tcp_fields_new = [(\"tcp_\"+field.name) for field in scapy_all.TCP().fields_desc]\n", "udp_fields_new = [(\"udp_\"+field.name) for field in scapy_all.UDP().fields_desc]\n", "\n", "dataframe_fields = ip_fields_new + ['time'] + tcp_fields_new + ['payload', 'datetime', 'raw']" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "a06-AzxItGfC" }, "source": [ "#### Upload Data To Timesketch\n", "\n", "Now that we've got the columns sorted out, we can now move on to go through each of the packets, create a dict and upload that directly to Timesketch.\n", "\n", "Let's use the code from our previous example, except this time adding a progress bar. We are going to stream the results from the parsing directly to Timesketch.\n", "\n", "Word of warning: this will also take considerable amount of time to execute and it may even crash your notebook. You have been warned!" ] }, { "cell_type": "code", "metadata": { "id": "fqYR84a7Liks" }, "source": [ "sketch = ts_client.get_sketch(SKETCH_ID)\n", "import_helper = helper.ImportHelper() \n", "\n", "with importer.ImportStreamer() as streamer:\n", " streamer.set_sketch(sketch)\n", " streamer.set_config_helper(import_helper)\n", "\n", " # Lower the threshold, which defines how many entries we go through before we flush the buffer.\n", " streamer.set_entry_threshold(1000)\n", " streamer.set_data_type('scapy:pcap:entry')\n", " streamer.set_timestamp_description('PCAP Entry')\n", " streamer.set_timeline_name('network_pcap_with_scapy')\n", " streamer.set_message_format_string('{raw:s}')\n", "\n", " for packet in tqdm_notebook(packets[scapy_all.IP]):\n", " # Field array for each row of DataFrame\n", " \n", " field_values = []\n", " # Add all IP fields to dataframe\n", " for field in ip_fields:\n", " if field == 'options':\n", " # Retrieving number of options defined in IP Header\n", " field_values.append(len(packet[scapy_all.IP].fields[field]))\n", " else:\n", " field_values.append(packet[scapy_all.IP].fields[field])\n", " \n", " field_values.append(packet.time)\n", " layer_type = type(packet[scapy_all.IP].payload)\n", " for field in tcp_fields:\n", " try:\n", " if field == 'options':\n", " field_values.append(len(packet[layer_type].fields[field]))\n", " else:\n", " field_values.append(packet[layer_type].fields[field])\n", " except:\n", " field_values.append(None)\n", " \n", " # Append payload\n", " field_values.append(len(packet[layer_type].payload))\n", " \n", " date_value = datetime.datetime.fromtimestamp(packet.time, tz=pytz.utc)\n", " field_values.append(date_value.isoformat())\n", " field_values.append(str(packet.show2))\n", "\n", " # Create a dict and upload it to timesketch.\n", " packet_dict = dict(zip(dataframe_fields, field_values))\n", " ip_flags = packet_dict.get('ip_flags')\n", " if not ip_flags is None:\n", " packet_dict['ip_flags'] = ip_flags.names\n", "\n", " tcp_flags = packet_dict.get('tcp_flags')\n", " if not tcp_flags is None:\n", " packet_dict['tcp_flags'] = tcp_flags.names\n", "\n", " del packet_dict['time']\n", "\n", " streamer.add_dict(packet_dict)" ], "execution_count": null, "outputs": [] } ] }	apache-2.0
HrantDavtyan/Data_Scraping	Week 2/Intro_3.ipynb	1	113824	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting and Plotting stock data\n", "#### by Hrant Davtyan\n", "\n", "This Jupyter notebook describes the steps necessary to take to get and plot stock data using Python. There is a standout package in Python called pandas-datareader, which provides simple interface for getting data from [Google finance](https://www.google.com/finance), [Yahoo! finance](https://finance.yahoo.com/), [World Bank](http://data.worldbank.org/) etc.\n", "- to install pandas-datareader open a new command prompt window (black guy) and type the following command: '''pip install pandas-datareader'''\n", "- to learn more about pandas-datareader and the websites it can get data from, check its [official documentation](https://pandas-datareader.readthedocs.io/en/latest/remote_data.html)\n", "\n", "To start using pandas-datareader (assuming it is already installed in our computer), we fist need to import the library. As it has quite long name, we will import it as web (shorter name)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas_datareader.data as web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The DataReader function from above imported library provides the data on stocks available in Google/Yahoo! finance. Hence, the function takes to mandatory arguments: name of the stock (name is a text/string, so it shoulg be in quotes) and name of the website (which is again string, so again we should use quotes).\n", "\n", "Let's get the IBM stock data from Google Finance." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = web.DataReader(\"IBM\",\"google\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now, the IBM stock data is already downloaded and saved in our variable called data. To view the first 5 observations/raws of the data, the function head() can be used:" ] }, { "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>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " </tr>\n", " <tr>\n", " <th>Date</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>2010-01-04</th>\n", " <td>131.18</td>\n", " <td>132.97</td>\n", " <td>130.85</td>\n", " <td>132.45</td>\n", " <td>6155846</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-05</th>\n", " <td>131.68</td>\n", " <td>131.85</td>\n", " <td>130.10</td>\n", " <td>130.85</td>\n", " <td>6842471</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-06</th>\n", " <td>130.68</td>\n", " <td>131.49</td>\n", " <td>129.81</td>\n", " <td>130.00</td>\n", " <td>5605290</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-07</th>\n", " <td>129.87</td>\n", " <td>130.25</td>\n", " <td>128.91</td>\n", " <td>129.55</td>\n", " <td>5840569</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-08</th>\n", " <td>129.07</td>\n", " <td>130.92</td>\n", " <td>129.05</td>\n", " <td>130.85</td>\n", " <td>4197105</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Open High Low Close Volume\n", "Date \n", "2010-01-04 131.18 132.97 130.85 132.45 6155846\n", "2010-01-05 131.68 131.85 130.10 130.85 6842471\n", "2010-01-06 130.68 131.49 129.81 130.00 5605290\n", "2010-01-07 129.87 130.25 128.91 129.55 5840569\n", "2010-01-08 129.07 130.92 129.05 130.85 4197105" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please note, that the function head() above gives the very first 5 observations by default (whenever we do not explicitly mention anything else inside brackets). If one is interested in viewing the very first 10 observations, that's also doable. THe only necessary step to take is to give 10 as an argument to our head() function:" ] }, { "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>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " </tr>\n", " <tr>\n", " <th>Date</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>2010-01-04</th>\n", " <td>131.18</td>\n", " <td>132.97</td>\n", " <td>130.85</td>\n", " <td>132.45</td>\n", " <td>6155846</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-05</th>\n", " <td>131.68</td>\n", " <td>131.85</td>\n", " <td>130.10</td>\n", " <td>130.85</td>\n", " <td>6842471</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-06</th>\n", " <td>130.68</td>\n", " <td>131.49</td>\n", " <td>129.81</td>\n", " <td>130.00</td>\n", " <td>5605290</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-07</th>\n", " <td>129.87</td>\n", " <td>130.25</td>\n", " <td>128.91</td>\n", " <td>129.55</td>\n", " <td>5840569</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-08</th>\n", " <td>129.07</td>\n", " <td>130.92</td>\n", " <td>129.05</td>\n", " <td>130.85</td>\n", " <td>4197105</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-11</th>\n", " <td>131.06</td>\n", " <td>131.06</td>\n", " <td>128.67</td>\n", " <td>129.48</td>\n", " <td>5731177</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-12</th>\n", " <td>129.03</td>\n", " <td>131.33</td>\n", " <td>129.00</td>\n", " <td>130.51</td>\n", " <td>8083354</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-13</th>\n", " <td>130.39</td>\n", " <td>131.12</td>\n", " <td>129.16</td>\n", " <td>130.23</td>\n", " <td>6458302</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-14</th>\n", " <td>130.55</td>\n", " <td>132.71</td>\n", " <td>129.91</td>\n", " <td>132.31</td>\n", " <td>7114544</td>\n", " </tr>\n", " <tr>\n", " <th>2010-01-15</th>\n", " <td>132.03</td>\n", " <td>132.89</td>\n", " <td>131.09</td>\n", " <td>131.78</td>\n", " <td>8502320</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Open High Low Close Volume\n", "Date \n", "2010-01-04 131.18 132.97 130.85 132.45 6155846\n", "2010-01-05 131.68 131.85 130.10 130.85 6842471\n", "2010-01-06 130.68 131.49 129.81 130.00 5605290\n", "2010-01-07 129.87 130.25 128.91 129.55 5840569\n", "2010-01-08 129.07 130.92 129.05 130.85 4197105\n", "2010-01-11 131.06 131.06 128.67 129.48 5731177\n", "2010-01-12 129.03 131.33 129.00 130.51 8083354\n", "2010-01-13 130.39 131.12 129.16 130.23 6458302\n", "2010-01-14 130.55 132.71 129.91 132.31 7114544\n", "2010-01-15 132.03 132.89 131.09 131.78 8502320" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, one can view the very last 5 or 10 observations by just using the tail() function, instead of head():" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " </tr>\n", " <tr>\n", " <th>Date</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>2017-05-24</th>\n", " <td>152.21</td>\n", " <td>152.76</td>\n", " <td>151.23</td>\n", " <td>152.51</td>\n", " <td>3732399</td>\n", " </tr>\n", " <tr>\n", " <th>2017-05-25</th>\n", " <td>153.25</td>\n", " <td>153.73</td>\n", " <td>152.95</td>\n", " <td>153.20</td>\n", " <td>2582815</td>\n", " </tr>\n", " <tr>\n", " <th>2017-05-26</th>\n", " <td>152.85</td>\n", " <td>153.00</td>\n", " <td>152.06</td>\n", " <td>152.49</td>\n", " <td>2443507</td>\n", " </tr>\n", " <tr>\n", " <th>2017-05-30</th>\n", " <td>151.95</td>\n", " <td>152.67</td>\n", " <td>151.59</td>\n", " <td>151.73</td>\n", " <td>3666032</td>\n", " </tr>\n", " <tr>\n", " <th>2017-05-31</th>\n", " <td>152.03</td>\n", " <td>152.80</td>\n", " <td>151.65</td>\n", " <td>152.63</td>\n", " <td>3543404</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Open High Low Close Volume\n", "Date \n", "2017-05-24 152.21 152.76 151.23 152.51 3732399\n", "2017-05-25 153.25 153.73 152.95 153.20 2582815\n", "2017-05-26 152.85 153.00 152.06 152.49 2443507\n", "2017-05-30 151.95 152.67 151.59 151.73 3666032\n", "2017-05-31 152.03 152.80 151.65 152.63 3543404" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The type of resulted datasets (i.e. the type of the variable called data) is knows as *DataFrame* as it represents the data inside a frame. We could also learn about that by using the type() function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The DataFrames are very user friendly types wo work with. Many operatinos on DataFrames are similar to this we did with lists. For example, if one is interested in choosing only one column of the DataFrame, s/he just needs to put square brackets and name of the chosen column side (note, name is a string, so it should be inside quotes):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Date\n", "2010-01-04 131.18\n", "2010-01-05 131.68\n", "2010-01-06 130.68\n", "2010-01-07 129.87\n", "2010-01-08 129.07\n", "2010-01-11 131.06\n", "2010-01-12 129.03\n", "2010-01-13 130.39\n", "2010-01-14 130.55\n", "2010-01-15 132.03\n", "2010-01-19 131.63\n", "2010-01-20 130.46\n", "2010-01-21 130.47\n", "2010-01-22 128.67\n", "2010-01-25 126.33\n", "2010-01-26 125.92\n", "2010-01-27 125.82\n", "2010-01-28 127.03\n", "2010-01-29 124.32\n", "2010-02-01 123.23\n", "2010-02-02 124.79\n", "2010-02-03 125.16\n", "2010-02-04 125.19\n", "2010-02-05 123.04\n", "2010-02-08 123.15\n", "2010-02-09 122.65\n", "2010-02-10 122.94\n", "2010-02-11 122.58\n", "2010-02-12 123.01\n", "2010-02-16 124.91\n", " ... \n", "2017-04-19 161.76\n", "2017-04-20 161.32\n", "2017-04-21 162.05\n", "2017-04-24 161.29\n", "2017-04-25 161.78\n", "2017-04-26 160.53\n", "2017-04-27 160.29\n", "2017-04-28 160.50\n", "2017-05-01 160.05\n", "2017-05-02 159.44\n", "2017-05-03 158.74\n", "2017-05-04 158.89\n", "2017-05-05 153.52\n", "2017-05-08 152.80\n", "2017-05-09 152.60\n", "2017-05-10 151.65\n", "2017-05-11 151.05\n", "2017-05-12 150.30\n", "2017-05-15 150.62\n", "2017-05-16 151.66\n", "2017-05-17 153.30\n", "2017-05-18 150.86\n", "2017-05-19 151.01\n", "2017-05-22 152.10\n", "2017-05-23 152.57\n", "2017-05-24 152.21\n", "2017-05-25 153.25\n", "2017-05-26 152.85\n", "2017-05-30 151.95\n", "2017-05-31 152.03\n", "Name: Open, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"Open\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, one can choose to show only selected rows from the DataFrame (note, this operation work for Year and Month arguments only):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " </tr>\n", " <tr>\n", " <th>Date</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>2015-05-01</th>\n", " <td>173.20</td>\n", " <td>174.00</td>\n", " <td>172.42</td>\n", " <td>173.67</td>\n", " <td>3312052</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-04</th>\n", " <td>174.47</td>\n", " <td>176.30</td>\n", " <td>173.70</td>\n", " <td>173.97</td>\n", " <td>4027978</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-05</th>\n", " <td>173.51</td>\n", " <td>174.23</td>\n", " <td>171.96</td>\n", " <td>173.08</td>\n", " <td>3593620</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-06</th>\n", " <td>172.90</td>\n", " <td>174.05</td>\n", " <td>168.86</td>\n", " <td>170.05</td>\n", " <td>3612606</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-07</th>\n", " <td>169.63</td>\n", " <td>171.98</td>\n", " <td>169.04</td>\n", " <td>170.99</td>\n", " <td>2472687</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-08</th>\n", " <td>172.94</td>\n", " <td>173.33</td>\n", " <td>172.24</td>\n", " <td>172.68</td>\n", " <td>3092602</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-11</th>\n", " <td>172.65</td>\n", " <td>172.99</td>\n", " <td>170.86</td>\n", " <td>171.12</td>\n", " <td>2661030</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12</th>\n", " <td>170.55</td>\n", " <td>171.49</td>\n", " <td>168.84</td>\n", " <td>170.55</td>\n", " <td>2962412</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-13</th>\n", " <td>171.24</td>\n", " <td>172.74</td>\n", " <td>170.75</td>\n", " <td>172.28</td>\n", " <td>2457521</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-14</th>\n", " <td>173.50</td>\n", " <td>174.40</td>\n", " <td>173.22</td>\n", " <td>174.05</td>\n", " <td>2439070</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-15</th>\n", " <td>173.91</td>\n", " <td>174.41</td>\n", " <td>172.60</td>\n", " <td>173.26</td>\n", " <td>2916579</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-18</th>\n", " <td>173.44</td>\n", " <td>173.49</td>\n", " <td>172.30</td>\n", " <td>173.06</td>\n", " <td>1970630</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-19</th>\n", " <td>172.97</td>\n", " <td>173.75</td>\n", " <td>171.93</td>\n", " <td>173.48</td>\n", " <td>2523002</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-20</th>\n", " <td>173.33</td>\n", " <td>174.44</td>\n", " <td>172.46</td>\n", " <td>173.76</td>\n", " <td>2300693</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-21</th>\n", " <td>173.32</td>\n", " <td>174.14</td>\n", " <td>173.04</td>\n", " <td>173.34</td>\n", " <td>2295596</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-22</th>\n", " <td>173.04</td>\n", " <td>173.39</td>\n", " <td>172.19</td>\n", " <td>172.22</td>\n", " <td>2849692</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-26</th>\n", " <td>172.11</td>\n", " <td>172.12</td>\n", " <td>169.13</td>\n", " <td>170.13</td>\n", " <td>3854170</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-27</th>\n", " <td>171.16</td>\n", " <td>172.48</td>\n", " <td>170.49</td>\n", " <td>172.00</td>\n", " <td>2764378</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-28</th>\n", " <td>171.45</td>\n", " <td>171.84</td>\n", " <td>170.66</td>\n", " <td>171.71</td>\n", " <td>1731372</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-29</th>\n", " <td>171.35</td>\n", " <td>171.35</td>\n", " <td>169.65</td>\n", " <td>169.65</td>\n", " <td>4091981</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Open High Low Close Volume\n", "Date \n", "2015-05-01 173.20 174.00 172.42 173.67 3312052\n", "2015-05-04 174.47 176.30 173.70 173.97 4027978\n", "2015-05-05 173.51 174.23 171.96 173.08 3593620\n", "2015-05-06 172.90 174.05 168.86 170.05 3612606\n", "2015-05-07 169.63 171.98 169.04 170.99 2472687\n", "2015-05-08 172.94 173.33 172.24 172.68 3092602\n", "2015-05-11 172.65 172.99 170.86 171.12 2661030\n", "2015-05-12 170.55 171.49 168.84 170.55 2962412\n", "2015-05-13 171.24 172.74 170.75 172.28 2457521\n", "2015-05-14 173.50 174.40 173.22 174.05 2439070\n", "2015-05-15 173.91 174.41 172.60 173.26 2916579\n", "2015-05-18 173.44 173.49 172.30 173.06 1970630\n", "2015-05-19 172.97 173.75 171.93 173.48 2523002\n", "2015-05-20 173.33 174.44 172.46 173.76 2300693\n", "2015-05-21 173.32 174.14 173.04 173.34 2295596\n", "2015-05-22 173.04 173.39 172.19 172.22 2849692\n", "2015-05-26 172.11 172.12 169.13 170.13 3854170\n", "2015-05-27 171.16 172.48 170.49 172.00 2764378\n", "2015-05-28 171.45 171.84 170.66 171.71 1731372\n", "2015-05-29 171.35 171.35 169.65 169.65 4091981" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"2015-05\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A specific date range is also acceptable as an input:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Open</th>\n", " <th>High</th>\n", " <th>Low</th>\n", " <th>Close</th>\n", " <th>Volume</th>\n", " </tr>\n", " <tr>\n", " <th>Date</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>2015-05-01</th>\n", " <td>173.20</td>\n", " <td>174.00</td>\n", " <td>172.42</td>\n", " <td>173.67</td>\n", " <td>3312052</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-04</th>\n", " <td>174.47</td>\n", " <td>176.30</td>\n", " <td>173.70</td>\n", " <td>173.97</td>\n", " <td>4027978</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-05</th>\n", " <td>173.51</td>\n", " <td>174.23</td>\n", " <td>171.96</td>\n", " <td>173.08</td>\n", " <td>3593620</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-06</th>\n", " <td>172.90</td>\n", " <td>174.05</td>\n", " <td>168.86</td>\n", " <td>170.05</td>\n", " <td>3612606</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-07</th>\n", " <td>169.63</td>\n", " <td>171.98</td>\n", " <td>169.04</td>\n", " <td>170.99</td>\n", " <td>2472687</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-08</th>\n", " <td>172.94</td>\n", " <td>173.33</td>\n", " <td>172.24</td>\n", " <td>172.68</td>\n", " <td>3092602</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-11</th>\n", " <td>172.65</td>\n", " <td>172.99</td>\n", " <td>170.86</td>\n", " <td>171.12</td>\n", " <td>2661030</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-12</th>\n", " <td>170.55</td>\n", " <td>171.49</td>\n", " <td>168.84</td>\n", " <td>170.55</td>\n", " <td>2962412</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-13</th>\n", " <td>171.24</td>\n", " <td>172.74</td>\n", " <td>170.75</td>\n", " <td>172.28</td>\n", " <td>2457521</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-14</th>\n", " <td>173.50</td>\n", " <td>174.40</td>\n", " <td>173.22</td>\n", " <td>174.05</td>\n", " <td>2439070</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-15</th>\n", " <td>173.91</td>\n", " <td>174.41</td>\n", " <td>172.60</td>\n", " <td>173.26</td>\n", " <td>2916579</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-18</th>\n", " <td>173.44</td>\n", " <td>173.49</td>\n", " <td>172.30</td>\n", " <td>173.06</td>\n", " <td>1970630</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-19</th>\n", " <td>172.97</td>\n", " <td>173.75</td>\n", " <td>171.93</td>\n", " <td>173.48</td>\n", " <td>2523002</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-20</th>\n", " <td>173.33</td>\n", " <td>174.44</td>\n", " <td>172.46</td>\n", " <td>173.76</td>\n", " <td>2300693</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-21</th>\n", " <td>173.32</td>\n", " <td>174.14</td>\n", " <td>173.04</td>\n", " <td>173.34</td>\n", " <td>2295596</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-22</th>\n", " <td>173.04</td>\n", " <td>173.39</td>\n", " <td>172.19</td>\n", " <td>172.22</td>\n", " <td>2849692</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-26</th>\n", " <td>172.11</td>\n", " <td>172.12</td>\n", " <td>169.13</td>\n", " <td>170.13</td>\n", " <td>3854170</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-27</th>\n", " <td>171.16</td>\n", " <td>172.48</td>\n", " <td>170.49</td>\n", " <td>172.00</td>\n", " <td>2764378</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-28</th>\n", " <td>171.45</td>\n", " <td>171.84</td>\n", " <td>170.66</td>\n", " <td>171.71</td>\n", " <td>1731372</td>\n", " </tr>\n", " <tr>\n", " <th>2015-05-29</th>\n", " <td>171.35</td>\n", " <td>171.35</td>\n", " <td>169.65</td>\n", " <td>169.65</td>\n", " <td>4091981</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-01</th>\n", " <td>170.21</td>\n", " <td>171.04</td>\n", " <td>169.03</td>\n", " <td>170.18</td>\n", " <td>2985479</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-02</th>\n", " <td>169.66</td>\n", " <td>170.45</td>\n", " <td>168.43</td>\n", " <td>169.65</td>\n", " <td>2571862</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-03</th>\n", " <td>170.50</td>\n", " <td>171.56</td>\n", " <td>169.63</td>\n", " <td>169.92</td>\n", " <td>2131031</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-04</th>\n", " <td>169.53</td>\n", " <td>170.60</td>\n", " <td>167.93</td>\n", " <td>168.38</td>\n", " <td>3079334</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-05</th>\n", " <td>168.25</td>\n", " <td>168.91</td>\n", " <td>167.20</td>\n", " <td>167.40</td>\n", " <td>3100505</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-08</th>\n", " <td>167.17</td>\n", " <td>167.28</td>\n", " <td>165.02</td>\n", " <td>165.34</td>\n", " <td>3758726</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-09</th>\n", " <td>165.34</td>\n", " <td>166.02</td>\n", " <td>163.37</td>\n", " <td>165.68</td>\n", " <td>3395901</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-10</th>\n", " <td>166.49</td>\n", " <td>169.39</td>\n", " <td>166.06</td>\n", " <td>168.92</td>\n", " <td>4680545</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-11</th>\n", " <td>169.26</td>\n", " <td>170.44</td>\n", " <td>168.54</td>\n", " <td>168.78</td>\n", " <td>3464013</td>\n", " </tr>\n", " <tr>\n", " <th>2015-06-12</th>\n", " <td>168.23</td>\n", " <td>168.30</td>\n", " <td>166.69</td>\n", " <td>166.99</td>\n", " <td>3065085</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-19</th>\n", " <td>146.47</td>\n", " <td>146.95</td>\n", " <td>142.61</td>\n", " <td>144.00</td>\n", " <td>13149148</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-20</th>\n", " <td>144.24</td>\n", " <td>147.20</td>\n", " <td>144.00</td>\n", " <td>146.11</td>\n", " <td>6721442</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-21</th>\n", " <td>146.58</td>\n", " <td>150.12</td>\n", " <td>146.46</td>\n", " <td>149.30</td>\n", " <td>5992604</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-22</th>\n", " <td>149.44</td>\n", " <td>151.00</td>\n", " <td>147.50</td>\n", " <td>148.50</td>\n", " <td>5190627</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-25</th>\n", " <td>148.16</td>\n", " <td>148.90</td>\n", " <td>147.11</td>\n", " <td>148.81</td>\n", " <td>2845511</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-26</th>\n", " <td>148.65</td>\n", " <td>149.79</td>\n", " <td>147.90</td>\n", " <td>149.08</td>\n", " <td>2978004</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-27</th>\n", " <td>149.35</td>\n", " <td>150.78</td>\n", " <td>148.97</td>\n", " <td>150.47</td>\n", " <td>3086611</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-28</th>\n", " <td>149.75</td>\n", " <td>150.18</td>\n", " <td>146.72</td>\n", " <td>147.07</td>\n", " <td>3771853</td>\n", " </tr>\n", " <tr>\n", " <th>2016-04-29</th>\n", " <td>146.49</td>\n", " <td>147.34</td>\n", " <td>144.19</td>\n", " <td>145.94</td>\n", " <td>4217744</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-02</th>\n", " <td>146.56</td>\n", " <td>147.00</td>\n", " <td>144.43</td>\n", " <td>145.27</td>\n", " <td>3499020</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-03</th>\n", " <td>144.65</td>\n", " <td>144.90</td>\n", " <td>142.90</td>\n", " <td>144.13</td>\n", " <td>3558829</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-04</th>\n", " <td>143.36</td>\n", " <td>145.00</td>\n", " <td>143.31</td>\n", " <td>144.25</td>\n", " <td>2575776</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-05</th>\n", " <td>145.95</td>\n", " <td>147.30</td>\n", " <td>145.45</td>\n", " <td>146.47</td>\n", " <td>6492015</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-06</th>\n", " <td>144.86</td>\n", " <td>147.97</td>\n", " <td>144.47</td>\n", " <td>147.29</td>\n", " <td>4882514</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-09</th>\n", " <td>147.70</td>\n", " <td>148.20</td>\n", " <td>147.01</td>\n", " <td>147.34</td>\n", " <td>4298800</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-10</th>\n", " <td>148.24</td>\n", " <td>150.04</td>\n", " <td>147.74</td>\n", " <td>149.97</td>\n", " <td>3982554</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-11</th>\n", " <td>149.71</td>\n", " <td>151.09</td>\n", " <td>148.74</td>\n", " <td>148.95</td>\n", " <td>3075213</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-12</th>\n", " <td>149.21</td>\n", " <td>149.39</td>\n", " <td>147.11</td>\n", " <td>148.84</td>\n", " <td>3247675</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-13</th>\n", " <td>148.79</td>\n", " <td>149.86</td>\n", " <td>147.42</td>\n", " <td>147.72</td>\n", " <td>2372098</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-16</th>\n", " <td>147.65</td>\n", " <td>149.99</td>\n", " <td>147.44</td>\n", " <td>149.46</td>\n", " <td>3061873</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-17</th>\n", " <td>149.21</td>\n", " <td>149.50</td>\n", " <td>147.29</td>\n", " <td>148.00</td>\n", " <td>3489779</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-18</th>\n", " <td>147.99</td>\n", " <td>148.52</td>\n", " <td>146.36</td>\n", " <td>147.34</td>\n", " <td>2482097</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-19</th>\n", " <td>146.48</td>\n", " <td>146.93</td>\n", " <td>143.96</td>\n", " <td>144.93</td>\n", " <td>3618752</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-20</th>\n", " <td>145.71</td>\n", " <td>147.51</td>\n", " <td>145.55</td>\n", " <td>147.25</td>\n", " <td>3576766</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-23</th>\n", " <td>147.61</td>\n", " <td>147.95</td>\n", " <td>146.66</td>\n", " <td>146.77</td>\n", " <td>2088554</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-24</th>\n", " <td>146.88</td>\n", " <td>148.75</td>\n", " <td>146.88</td>\n", " <td>148.31</td>\n", " <td>2827106</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-25</th>\n", " <td>148.93</td>\n", " <td>152.09</td>\n", " <td>148.50</td>\n", " <td>151.69</td>\n", " <td>4347009</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-26</th>\n", " <td>151.55</td>\n", " <td>152.51</td>\n", " <td>151.05</td>\n", " <td>152.44</td>\n", " <td>3042788</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-27</th>\n", " <td>152.35</td>\n", " <td>152.93</td>\n", " <td>152.15</td>\n", " <td>152.84</td>\n", " <td>2456289</td>\n", " </tr>\n", " <tr>\n", " <th>2016-05-31</th>\n", " <td>152.56</td>\n", " <td>153.81</td>\n", " <td>152.27</td>\n", " <td>153.74</td>\n", " <td>5836645</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>273 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " Open High Low Close Volume\n", "Date \n", "2015-05-01 173.20 174.00 172.42 173.67 3312052\n", "2015-05-04 174.47 176.30 173.70 173.97 4027978\n", "2015-05-05 173.51 174.23 171.96 173.08 3593620\n", "2015-05-06 172.90 174.05 168.86 170.05 3612606\n", "2015-05-07 169.63 171.98 169.04 170.99 2472687\n", "2015-05-08 172.94 173.33 172.24 172.68 3092602\n", "2015-05-11 172.65 172.99 170.86 171.12 2661030\n", "2015-05-12 170.55 171.49 168.84 170.55 2962412\n", "2015-05-13 171.24 172.74 170.75 172.28 2457521\n", "2015-05-14 173.50 174.40 173.22 174.05 2439070\n", "2015-05-15 173.91 174.41 172.60 173.26 2916579\n", "2015-05-18 173.44 173.49 172.30 173.06 1970630\n", "2015-05-19 172.97 173.75 171.93 173.48 2523002\n", "2015-05-20 173.33 174.44 172.46 173.76 2300693\n", "2015-05-21 173.32 174.14 173.04 173.34 2295596\n", "2015-05-22 173.04 173.39 172.19 172.22 2849692\n", "2015-05-26 172.11 172.12 169.13 170.13 3854170\n", "2015-05-27 171.16 172.48 170.49 172.00 2764378\n", "2015-05-28 171.45 171.84 170.66 171.71 1731372\n", "2015-05-29 171.35 171.35 169.65 169.65 4091981\n", "2015-06-01 170.21 171.04 169.03 170.18 2985479\n", "2015-06-02 169.66 170.45 168.43 169.65 2571862\n", "2015-06-03 170.50 171.56 169.63 169.92 2131031\n", "2015-06-04 169.53 170.60 167.93 168.38 3079334\n", "2015-06-05 168.25 168.91 167.20 167.40 3100505\n", "2015-06-08 167.17 167.28 165.02 165.34 3758726\n", "2015-06-09 165.34 166.02 163.37 165.68 3395901\n", "2015-06-10 166.49 169.39 166.06 168.92 4680545\n", "2015-06-11 169.26 170.44 168.54 168.78 3464013\n", "2015-06-12 168.23 168.30 166.69 166.99 3065085\n", "... ... ... ... ... ...\n", "2016-04-19 146.47 146.95 142.61 144.00 13149148\n", "2016-04-20 144.24 147.20 144.00 146.11 6721442\n", "2016-04-21 146.58 150.12 146.46 149.30 5992604\n", "2016-04-22 149.44 151.00 147.50 148.50 5190627\n", "2016-04-25 148.16 148.90 147.11 148.81 2845511\n", "2016-04-26 148.65 149.79 147.90 149.08 2978004\n", "2016-04-27 149.35 150.78 148.97 150.47 3086611\n", "2016-04-28 149.75 150.18 146.72 147.07 3771853\n", "2016-04-29 146.49 147.34 144.19 145.94 4217744\n", "2016-05-02 146.56 147.00 144.43 145.27 3499020\n", "2016-05-03 144.65 144.90 142.90 144.13 3558829\n", "2016-05-04 143.36 145.00 143.31 144.25 2575776\n", "2016-05-05 145.95 147.30 145.45 146.47 6492015\n", "2016-05-06 144.86 147.97 144.47 147.29 4882514\n", "2016-05-09 147.70 148.20 147.01 147.34 4298800\n", "2016-05-10 148.24 150.04 147.74 149.97 3982554\n", "2016-05-11 149.71 151.09 148.74 148.95 3075213\n", "2016-05-12 149.21 149.39 147.11 148.84 3247675\n", "2016-05-13 148.79 149.86 147.42 147.72 2372098\n", "2016-05-16 147.65 149.99 147.44 149.46 3061873\n", "2016-05-17 149.21 149.50 147.29 148.00 3489779\n", "2016-05-18 147.99 148.52 146.36 147.34 2482097\n", "2016-05-19 146.48 146.93 143.96 144.93 3618752\n", "2016-05-20 145.71 147.51 145.55 147.25 3576766\n", "2016-05-23 147.61 147.95 146.66 146.77 2088554\n", "2016-05-24 146.88 148.75 146.88 148.31 2827106\n", "2016-05-25 148.93 152.09 148.50 151.69 4347009\n", "2016-05-26 151.55 152.51 151.05 152.44 3042788\n", "2016-05-27 152.35 152.93 152.15 152.84 2456289\n", "2016-05-31 152.56 153.81 152.27 153.74 5836645\n", "\n", "[273 rows x 5 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"2015-05\":\"2016-05\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now let's move forward and plot the data we received. For ploting purposes, the matplotlib.pyplot library is usually used in python. Let's import it first. As it has quite a long name, we will call it plt inside our Jupyter notebook." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make some sample plot, and then move to our dataset. To make a plot and show it one needs to use two functions from plt: plt.plot() and plt.show()." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa3aa390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# a sample plot of bisector line\n", "plt.plot([1,2,3,4],[1,2,3,4])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please note, that without the plt.show() function the plot would be generated but shown. This is sometimes useful when you want to generate a plot and save it, instead of showing it. However, if one wants to always show the plotted graphs, s/he could just put the following arguments when importing the matplotlib.pyplot library:\n", "\n", "'''\n", "%matplotlib inline\n", "'''\n", "\n", "This arguments tells Jupyter notebook to show inline (inside the notebook) all the generated plots. So if one has that argument, there is no need for typing plt.show() every single time.\n", "\n", "We do not have it, so we have to show all the plots separately. Let's now plot the highest price of IBM stock." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3Cfd6Q4RuOnpTauglj54NfSo50NyFBb/4ELPuXwEAmFRRiJYuKWTzxSfW9ufU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\ntmk6AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x91db550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(data[\"High\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get the Apple stock data also, and plot it's daily highest price together with IBM (to compare)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_apple = web.DataReader(\"AAPL\",'google')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have also Apple stock data. To make two plots on the same graph one just needs to have two plt.plot() functions followed by a single plt.show() function in the end:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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H+cWlHkW8Ja7GeNOhbI7kON1TNREKNDNnS8sl4dUo+O4L9Iy+Abh/Si9b6dr2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\njV+RfdC5X1YKoRbTkO6sksb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"text/plain": [ "<matplotlib.figure.Figure at 0xa4d7be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(data[\"High\"])\n", "plt.plot(data_apple[\"High\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are interested in customization of your plot (colors, apperance etc.) you may check the [official tutorial](https://matplotlib.org/users/pyplot_tutorial.html), which provides some nice features available for plt.plot() function (and not only)." ] } ], "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
ioam/holoviews	examples/reference/elements/plotly/Scatter3D.ipynb	2	2346	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd> Scatter3D Element</dd>\n", " <dt>Dependencies</dt> <dd>Matplotlib</dd>\n", " <dt>Backends</dt> <dd><a href='../matplotlib/Scatter3D.ipynb'>Matplotlib</a></dd> <dd><a href='./Scatter3D.ipynb'>Plotly</a></dd>\n", "</dl>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "from holoviews import dim, opts\n", "\n", "hv.extension('plotly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``Scatter3D`` represents three-dimensional coordinates which may be colormapped or scaled in size according to a value. They are therefore very similar to [``Points``](Points.ipynb) and [``Scatter``](Scatter.ipynb) types but have one additional coordinate dimension. Like other 3D elements the camera angle can be controlled using ``azimuth``, ``elevation`` and ``distance`` plot options:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y,x = np.mgrid[-5:5, -5:5] * 0.1\n", "heights = np.sin(x2+y2)\n", "hv.Scatter3D((x.flat, y.flat, heights.flat)).opts(\n", " cmap='fire', color='z', size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like all regular 2D elements, ``Scatter3D`` types can be overlaid and will follow the default color cycle: \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(hv.Scatter3D(np.random.randn(100,4), vdims='Size') * hv.Scatter3D(np.random.randn(100,4)+2, vdims='Size')).opts(\n", " opts.Scatter3D(size=(5+dim('Size'))*2, marker='diamond')\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For full documentation and the available style and plot options, use ``hv.help(hv.Scatter3D).``" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
anugrah-saxena/pycroscopy	jupyter_notebooks/Data_Format.ipynb	1	7836	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Developing Scientific Workflows in Pycroscopy - Part 0: Data Format\n", "\n", "#### Suhas Somnath\n", "8/8/2017\n", "\n", "This set of notebooks will serve as examples for developing and end-to-end workflows for and using pycroscopy. \n", "\n", "This preliminary document goes over the pycroscopy data format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why should you care?\n", "\n", "The quest for understanding more about samples has necessitated the development of a multitude of microscopes, each capable of numerous measurement modalities. \n", "\n", "Typically, each commercial microscope generates data files formatted in proprietary data formats by the instrument manufacturer. The proprietary natures of these data formats impede scientific progress in the following ways:\n", "1. By making it challenging for researchers to extract data from these files \n", "2. Impeding the correlation of data acquired from different instruments.\n", "3. Inability to store results back into the same file\n", "4. Accomodating files from few kilobytes to several gigabytes of data\n", "5. Requiring different versions of analysis routines for each format\n", "\n", "Future concerns:\n", "1. Several fields are moving towards the open science paradigm which will require journals and researchers to support journal papers with data and analysis software \n", "2. US Federal agencies that support scientific research require curation of datasets in a clear and organized manner\n", "\n", "To solve these and many more problems, we have developed an __instrument agnostic data format__ that can be used to represent data from any instrument, size, dimensionality, or complexity.\n", "\n", "## Pycroscopy data format\n", "\n", "Regardless of origin, modality or complexity, imaging data have one thing in common:\n", "* __The same measurement is performed at multiple spatial locations__\n", "\n", "The data format in pycroscopy is based on this one simple ground truth. The data always has some spatial dimensions (X, Y, Z) and some spectroscopic dimensions (time, frequency, intensity, wavelength, temperature, cycle, voltage, etc.). Pycroscopy, the spatial dimensions are collapsed onto a single dimension and the spectroscopic dimensions are flattened to the other dimensions. Thus, all data are stored as two dimensional grids. Here are some examples of how some familar data can be represented using this paradigm:\n", "* __Grayscale photographs__: A single value (intensity) in is recorded at each pixel in a two dimensional grid. Thus, there are are two spatial dimensions - X, Y and one spectroscopic dimension - \"Intensity\". The data can be represented as a N x 1 matrix where N is the product of the number of rows and columns of pixels. The second axis has size of 1 since we only record one value (intensity) at each location. __The positions will be arranged as row0-col0, row0-col1.... row0-colN, row1-col0....__\n", " * In the case of a color image, the data would be of shape N x 3. Where the red, green, blue intensity values would be stored separately. \n", "* A __single Raman spectra__: In this case, the measurement is recorded at a single location. At this position, data is recorded as a function of a single (spectroscopic) variable such as wavelength. Thus this data is represented as a 1 x P matrix, where P is the number of points in the spectra\n", "* __Scanning Tunelling Spectroscopy or IV spectroscopy__: The current (A 1D array of size P) is recorded as a function of voltage at each position in a two dimensional grid of points (two spatial dimensions). Thus the data would be represente as a N x P matrix, where N is the product of the number of rows and columns in the grid and P is the number of spectroscopic points recorded. \n", " * If the same voltage sweep were performed twice at each location, the data would be represented as N x 2 P. The data is still saved as a long (2P) 1D array at each location. The number of spectroscopic dimensions would change from just ['Voltage'] to ['Voltage', 'Cycle'] where the second spectroscopic dimension would account for repetitions of this bias sweep.\n", " __The spectroscopic data would be stored as it would be recorded as volt_0-cycle_0, volt_1-cycle_0..... volt_P-1-cycle_0, volt_0-cycle_1.....volt_P-1-cycle-1. Just like the positions__\n", " * Now, if the bias was swept thrice from -1 to +1V and then thrice again from -2 to 2V, the data bacomes N x 2 * 3 P. The data now has two position dimensions (X, Y) and three spectrosocpic dimensions ['Voltage', 'Cycle', 'Step']. The data is still saved as a (P * 2 * 3) 1D array at each location. \n", " \n", "#### Making sense of such flattened datasets:\n", "Each main dataset is always accompanied by four ancillary datasets: \n", "* the position value and index of each spatial location (row)\n", "* the spectroscopic value and index of any column in the dataset\n", "In addition to serving as a legend or the key, these ancillary datasets are necessary for explaining:\n", "* the original dimensionality of the dataset\n", "* how to reshape the data back to its N dimensional form\n", "\n", "From the __IV Spectorscopy__ example with [X, Y] x [Voltage, Cycle, Step]:\n", "* The position datasets would be of shape N x 2 - N total position, two spatial dimensions. \n", " * The position indices datasets may start like: \n", " \n", "\| 0 \| 0 \|\n", "\| 0 \| 1 \|\n", "\| a \| t \|\n", "\n", "\n", " * 0, 0\n", " * 0, 1\n", " * ....\n", " * 0, N/2\n", " * 1, 0 ....\n", " would be structured exactly\n", "\n", "#### Channels\n", "The pycroscopy data format also allows multiple channels of information to be recorded as separate datasets in the same file. For example, one channel could be a spectra (1D array) collected at each location on a 2D grid while another could be the temperature (single value) recorded by another sensor at the same spatial positions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My hope is that this notebook will serve as a comprehensive example for:\n", " \n", "1. __Data Access__\n", " 1. Loading, reading, writing, and manipulating HDF5 / H5 files.\n", " \n", "2. __Visualization__\n", " 1. Visualizing results of analyses and processing using pycroscopy functions\n", " 2. Developing simple interactive visualizers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Among the numerous benefits of __HDF5__ files are that these files:\n", "* are readily compatible with high-performance computing facilities\n", "* scale very efficiently from few kilobytes to several terabytes\n", "* can be read and modified using any language including Python, Matlab, C/C++, Java, Fortran, Igor Pro, etc." ] }, { "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": 2 }	mit
ES-DOC/esdoc-jupyterhub	notebooks/mohc/cmip6/models/ukesm1-0-mmh/aerosol.ipynb	1	84296	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: MOHC \n", "Source ID: UKESM1-0-MMH \n", "Topic: Aerosol \n", "Sub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "Properties: 69 (37 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:15" ], "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', 'mohc', 'ukesm1-0-mmh', 'aerosol')" ], "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 --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n", "[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n", "[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n", "[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n", "[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n", "[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n", "[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n", "[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Key properties of the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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 aerosol model code" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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. Scheme Scope\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Atmospheric domains covered by the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \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: STRING    Cardinality: 1.1\n", "### Basic approximations made in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Prognostic variables in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of tracers in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are aerosol calculations generalized into families of species?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.family_approach') \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": [ "# 2. Key Properties --> Software Properties \n", "Software properties of aerosol code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. 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.aerosol.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": [ "### 2.2. 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.aerosol.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": [ "### 2.3. 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.aerosol.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": [ "# 3. Key Properties --> Timestep Framework \n", "Physical properties of seawater in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Mathematical method deployed to solve the time evolution of the prognostic variables" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (integrated)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol advection (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol physics (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \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. Integrated Timestep\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Timestep for the aerosol model (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Integrated Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the type of timestep scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Meteorological Forcings \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "Is Required: FALSE    Type: STRING    Cardinality:** 0.1\n", "### Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \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. Variables 2D\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Two dimensionsal forcing variables, e.g. land-sea mask definition" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Frequency\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Frequency with which meteological forcings are applied (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \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 --> Resolution \n", "Resolution in the aersosol model grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. 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.aerosol.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": [ "### 5.2. Canonical Horizontal Resolution\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\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.aerosol.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": [ "### 5.3. Number Of Horizontal Gridpoints\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\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.aerosol.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": [ "### 5.4. Number Of Vertical Levels\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\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.aerosol.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": [ "### 5.5. Is Adaptive Grid\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\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.aerosol.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. Key Properties --> Tuning Applied \n", "Tuning methodology for aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. 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.aerosol.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": [ "### 6.2. 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.aerosol.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": [ "### 6.3. Regional Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List of regional metrics of 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.aerosol.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": [ "### 6.4. 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.aerosol.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": [ "# 7. Transport \n", "Aerosol transport" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of transport in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.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": [ "### 7.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method for aerosol transport modeling" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to ensure mass conservation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Transport by convention" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "Atmospheric aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of emissions in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to define aerosol species (several methods allowed because the different species may not use the same method)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Sources of the aerosol species are taken into account in the emissions scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### Specify the climatology type for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed via a climatology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed as spatially uniform" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.7. Interactive Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an interactive method" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.8. Other Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an "other method"" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.9. Other Method Characteristics\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Characteristics of the "other method" used for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Concentrations \n", "Atmospheric aerosol concentrations" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of concentrations in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.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": [ "### 9.2. Prescribed Lower Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the lower boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Prescribed Upper Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the upper boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as mass mixing ratios." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as AOD plus CCNs." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \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. Optical Radiative Properties \n", "Aerosol optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.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": [ "# 11. Optical Radiative Properties --> Absorption \n", "Absortion properties in aerosol scheme" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Dust\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Organics\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \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. Optical Radiative Properties --> Mixtures \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Is there external mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there internal mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If there is internal mixing with respect to chemical composition then indicate the mixinrg rule" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Optical Radiative Properties --> Impact Of H2o \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Does H2O impact size?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \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": [ "### 13.2. Internal Mixture\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does H2O impact internal mixture?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Optical Radiative Properties --> Radiative Scheme \n", "Radiative scheme for aerosol" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of radiative scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.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": [ "### 14.2. Shortwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of shortwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \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. Optical Radiative Properties --> Cloud Interactions \n", "Aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.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": [ "### 15.2. Twomey\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the Twomey effect included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \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": [ "### 15.3. Twomey Minimum Ccn\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If the Twomey effect is included, then what is the minimum CCN number?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Drizzle\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect drizzle?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \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": [ "### 15.5. Cloud Lifetime\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect cloud lifetime?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \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": [ "### 15.6. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \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. Model \n", "Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.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": [ "### 16.2. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Processes included in the Aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Other model components coupled to the Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of gas phase aerosol precursors." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of species covered by the bulk scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon / soot\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \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
saga-survey/saga-code	ipython_notebooks/DECALS low-SB_completeness AnaK overlap.ipynb	2	3637979	null	gpl-2.0
pprett/sklearn_pycon2014	notebooks/04_supervised_in_depth.ipynb	1	445496	{ "metadata": { "name": "", "signature": "sha256:892fecdd55d872cfffa1d99ec77a28037deceea3242d9629ea652f0a895e88ad" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was originally put together by [Jake Vanderplas](http://www.vanderplas.com) for PyCon 2014. [Peter Prettenhofer](https://github.com/pprett) adapted it for PyCon Ukraine 2014. Source and license info is on [GitHub](https://github.com/pprett/sklearn_pycon2014/).</i></small>" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Supervised Learning In-Depth: SVMs and Tree Ensembles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many machine learning algorithms available; here we'll go into brief detail on two of the most common and interesting ones: Support Vector Machines (SVM) and Tree Ensembles.\n", "\n", "By the end of this section you should:\n", "\n", "- have an intuitive understanding of Support Vector Machines.\n", "- understand how decision trees work\n", "- understand how multiple decision trees are combined into Random Forests and Boosting\n", "\n", "As before, we'll start by getting our notebook ready for interactive plotting:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Support Vector Machines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for classification or for regression. SVMs are a discriminative classifier: that is, they draw a boundary between clusters of data.\n", "\n", "Let's show a quick example of support vector classification. First we need to create a dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets.samples_generator import make_blobs\n", "X, y = make_blobs(n_samples=50, centers=2,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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5asTyGk/2qdcCWgHbPLhOIUQRmc1m/u+xx/iP2UwrIByoCQyxWIjdsYMlS5ao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\nXbhQLGdUbNy4Mf8ePEjvp5/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"text": [ "<matplotlib.figure.Figure at 0x75a4550>" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal in classification is to create a decision rule (aka hypotheses) that separates the red from the blue instances for new (unseen) data. Lets assume for now that we limit ourselves to a straight line as our decision rule.\n", "\n", "Which inductive principle would you follow to select a decision rule?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inductive Principle\n", "\n", " 1. Minimize error on the training set (aka risk minimization)\n", " 2. Maximize margin between the separating line and the nearest data points.\n", "\n", "The latter principle is called the max-margin classifier and was developed by Vladimir N. Vapnik and colleagues in the 1990s." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.svm import SVC # \"Support Vector Classifier\"\n", "clf = SVC(kernel='linear')\n", "clf.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_svc_decision_function(clf):\n", " \"\"\"Plot the decision function for a 2D SVC\"\"\"\n", " x = np.linspace(plt.xlim()[0], plt.xlim()[1], 30)\n", " y = np.linspace(plt.ylim()[0], plt.ylim()[1], 30)\n", " Y, X = np.meshgrid(y, x)\n", " P = np.zeros_like(X)\n", " for i, xi in enumerate(x):\n", " for j, yj in enumerate(y):\n", " P[i, j] = clf.decision_function([xi, yj])\n", " return plt.contour(X, Y, P, colors='k',\n", " levels=[-1, 0, 1],\n", " linestyles=['--', '-', '--'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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dOXOGTp06ZSTf9JZts2bNWLBggUP5lJQUDh06lFHOEzfchCjK3DW5xgh0BfR3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\n4gphFSqQcPYs2RcstQIJZjP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"text": [ "<matplotlib.figure.Figure at 0x409d650>" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the dashed lines touch a couple of the points: these points are known as the \"support vectors\", and are stored in the ``support_vectors_`` attribute of the classifier:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "<matplotlib.collections.PathCollection at 0x773c510>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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lk08+Q6Xyx2Ix4uvrwezZ0+jSpQsAkydP5tixYyxcuNCpsRVmkrTFA0VHR9O8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\nb9WqVVm9erVN+dOnTzNlyhS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"text": [ "<matplotlib.figure.Figure at 0x75c2bd0>" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "People often refer to the SVM as a stable classifier since you can make pertubations to your training data but unless the support vectors are not changed it won't have an effect on the hypothesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Non-seperable case\n", "\n", "What if the data cannot be separated by a straight line?\n", "\n", " * Kernel-trick\n", " * Soft-margin SVM\n", " \n", "#### Kernel trick\n", "\n", "The idea is to map your (non-seperable) data into a higher dimensional feature space in the hope that you can find a linear separator in that mapped space. This is very likely since there are many more degrees of freedom in high dimensional spaces. In general, any (linear) separator that you found in this mapped space is a non-linear separator in the original feature space.\n", "\n", "The key advantage of the SVM is that certain mappings can be done implicitly trough similarity computations among data points rather than making an explicit mapping into the higher dimensional space.\n", "In scikit-learn you can use the ``kernel`` argument to specify the mapping.\n", "The above example uses a ``'linear'`` kernel; it is also possible to use radial basis function (``'rbf'``) kernels as well as others." ] }, { "cell_type": "code", "collapsed": false, "input": [ "clf = SVC(kernel='rbf')\n", "clf.fit(X, y)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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hVGrlNbimL/AcMBkIAKZRzZI2QFRUFH379mX79u1VduBLWWna1JPo6FZAo2J7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\nIMuWLWP48OEGa1cIgXudOvS5dYuiq1zHAjc8PTn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"text": [ "<matplotlib.figure.Figure at 0x78ecb10>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Soft-margin SVM\n", "\n", "We can also relax the constraint that the line needs to separate our data perfectly and provide a budget for margin violations that we call the complexity parameter. The higher the parameter the less budget we have for violations and the \"thinner\" the separator gets. \n", "\n", "The complexity parameter ``C`` is a nuance parameter (or hyperparameter) that needs to be tuned for each problem. We will cover hyperparameter optimization later in the tutorial." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# add one data point on the opposite side of the hyperplane\n", "X_prime, y_prime = np.r_[X, np.array([[1, 2.75]])], np.r_[y, 1]\n", "\n", "plt.scatter(X_prime[:, 0], X_prime[:, 1], c=y_prime, s=50)\n", "# set complexity budget to 1 (higher C will lead to a smaller margin)\n", "clf = SVC(kernel='linear', C=1.0)\n", "clf.fit(X_prime, y_prime)\n", "plot_svc_decision_function(clf)\n", "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n", " s=200, facecolors='none')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "<matplotlib.collections.PathCollection at 0x7bf1950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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dGkmS+O67mUydOh2ZzBOzWYebmyMLFsymS5cuAHz77becPn2aZcuW5WrdijMR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\no6OjuXTpEmXLli32UydndaVtj9/iKuA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"text": [ "<matplotlib.figure.Figure at 0x78e1b50>" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effect of the complexity parameter on the decision boundary seen interactively by running the ``fig_code/svm_gui.py`` script:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# %run fig_code/svm_gui.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "SVMs can also be applied to [regression problems](http://scikit-learn.org/stable/modules/svm.html#regression) -- and even [unsupervised problems](http://scikit-learn.org/stable/modules/svm.html#density-estimation-novelty-detection) -- but their main usage is in classification. \n", "\n", "With the advent of deep learning, SVMs lost most of their reputation that originated from the field of computer vision but (linear) Support Vector Machines are still one of the major work horses in machine learning.\n", "\n", "We'll leave SVMs for the time being and take a look at another powerful classifier.\n", "\n", "#### Quick Exercise\n", "\n", "Do you think ``SVC`` and ``KNeighborsClassifier`` are related? If so, what are their similarites and what are the differences?\n", "\n", "What does an SVM need to consider when predicting the label of a new instance? What is the runtime complexity at prediction time?" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ensembles techniques" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ensemble, or mixtures of experts, combine multiple (weak) learners to create an even more accurate (strong) learner. In principal, these ensemble techniques can work with any weak learner but decision trees are the most popular choice so we limit ourselves to tree ensembles here. " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Decision Trees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll explore a class of algorithms based on Decision trees. Decision trees at their root (Ha!) are extremely intuitive. They encode a series of binary choices in a process that parallels how a person might classify things themselves, but using an information criterion to decide which question is most fruitful at each step. Below we show an example of a decision tree that was built to predict the median house price for block census groups in California (1999 census) using features such as the median income in that area, the average house size, or the average number of occupants.\n", "\n", "![foobar](images/decision_tree.png)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.tree import DecisionTreeClassifier" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "X, y = make_blobs(n_samples=300, centers=4,\n", " random_state=0, cluster_std=0.60)\n", "plt.scatter(X[:, 0], X[:, 1], c=y, s=50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "<matplotlib.collections.PathCollection at 0x7c0f590>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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FBnLp4EFqWCxUBE6eO0fdJUsoHhrK6VOnKOBwcMXpJAHQeXvTr18/ylesyNAhQ3DbbLgU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"text": [ "<matplotlib.figure.Figure at 0x7735810>" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll start by defining a convenience function which allows us to plot the predicted value in the background:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_estimator(estimator, X, y):\n", " estimator.fit(X, y)\n", " x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n", " y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n", " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),\n", " np.linspace(y_min, y_max, 50))\n", " Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " plt.figure()\n", " plt.pcolormesh(xx, yy, Z, alpha=0.3)\n", "\n", " # Plot also the training points\n", " plt.scatter(X[:, 0], X[:, 1], c=y, s=50)\n", " plt.axis('tight')\n", " plt.axis('off')\n", " plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=10)\n", "plot_estimator(clf, X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Cbb2a31+grEdWodpNHHN6lnGq4VjMpvKYkFHoTB67iUtW0+ooSo4KfVfNIpEW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"text": [ "<matplotlib.figure.Figure at 0x78f0950>" ] } ], "prompt_number": 33 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Tree ensembles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One problem with decision trees is that they can end up over-fitting the data. They are such flexible models that, given a large depth, they can quickly memorize the inputs, which doesn't generalize well to previously unseen data. \n", "\n", "Nowadays, decision trees are hardly used in isolation but rather in so-called tree ensembles. These ensembles come in three flavors: Bagging, RandomForest, and Boosting.\n", "\n", "#### Random Forests\n", "\n", "[Bagging](http://scikit-learn.org/stable/modules/ensemble.html#bagging-meta-estimator) and [RandomForest](http://scikit-learn.org/stable/modules/ensemble.html#random-forests) are similar in the sense that they train a large number of trees in a randomized fashion and then average the prediction of those trees. Boosting, on the other hand, is quite different from the other two and will be discussed later." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "clf = RandomForestClassifier(n_estimators=10, max_features='sqrt', random_state=0)\n", "plot_estimator(clf, X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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v8vdpFHfNKlS7hZPOyTJOlZyMWZSdFDIKncdTt3HlWpqfQNEJoe/ymSTSgtEt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\nzOoDMhl45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"text": [ "<matplotlib.figure.Figure at 0x7be9690>" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's difficult to see in this example, but for more complicated data, random forests can be a very powerful technique." ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Boosting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boosting differes from Bagging and RandomForest in that it learnes the ensemble in a sequential fashion: each member of the ensemble is an expert on the errors of its predecessor. On of the first boosting algorithms was AdaBoost, for adaptive boosting, developed by Y. Freund and R. Schapire. \n", "\n", "The algorithm proceedes in stages, at each stage we train a (shallow) decision tree to a re-weighted version of the original dataset. After we trained a tree we up-weight examples we got wrong and down-weight examples we got right. The next tree is then trained on this re-weighted version of the dataset and takes more emphasize on up-weighted examples and less emphasize on down-weighted examples.\n", "\n", "This adaptive re-weighting scheme is sketched below (read from left to right):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%run fig_code/adaboost.py" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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OJupP20uXbstgTWoO9ZMi0HWNa19ejENV8aDz1QN9T1pMMiXewZjuDf429vRs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\n3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7+o1FR5IfTuqkRKrzFf4uDqSnD1Om/Ut1ee/Oge38KiT0SqcsQU+xiLNo5cbi87WEqR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\nAJBcoE/s0Ylrl69jnSU/0fNm81nnr2j8zD27QmQEKxtbjM98D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"text": [ "<matplotlib.figure.Figure at 0x3f4b710>" ] } ], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn provides two boosting implementations: [AdaBoostClassifier](http://scikit-learn.org/stable/modules/ensemble.html#adaboost) and [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting). The latter is a generalization of boosting to arbitrary loss functions (regression, robust regression, quantile regression, ranking, etc.).\n", "\n", "In general, boosting algorithms require many more trees than RandomForest to get to the same level of accuracy, however, the trees that are used in boosting are usually shallow (``max_depth=3``) whereas the trees in RandomForest are deep." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exercise: Classifying Digits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We previously saw the hand-written digits data. Let's use that here to test the efficacy of the SVM and Random Forest classifiers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "digits.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "['images', 'data', 'target_names', 'DESCR', 'target']" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "X = digits.data\n", "y = digits.target\n", "print(X.shape)\n", "print(y.shape)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1797, 64)\n", "(1797,)\n" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To remind us what we're looking at, we'll visualize the first few data points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set up the figure\n", "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "for i in range(64):\n", " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n", " \n", " # label the image with the target value\n", " ax.text(0, 7, str(digits.target[i]))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Tpk1oaWlBIBDAww8/jDfeeANLly697v8zE79W9NhpmZmZWL16NebOnYtBgwYh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\npPNhNEPXKqmfagWDtYwzrbhuf6D162HDhon7pAx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"text": [ "<matplotlib.figure.Figure at 0x7e64f90>" ] } ], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly classify the digits using a decision tree as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn import metrics\n", "\n", "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)\n", "clf = DecisionTreeClassifier(max_depth=5)\n", "clf.fit(Xtrain, ytrain)\n", "ypred = clf.predict(Xtest)\n", "\n", "plt.imshow(metrics.confusion_matrix(ypred, ytest),\n", " interpolation='nearest', cmap=plt.cm.binary)\n", "plt.colorbar()\n", "plt.xlabel(\"true label\")\n", "plt.ylabel(\"predicted label\")\n", "\n", "print('F1-score: %.4f' % metrics.f1_score(ytest, ypred))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "F1-score: 0.6538\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x78e0250>" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "1. Perform this classification task with ``sklearn.svm.SVC``. How does the choice of kernel affect the results?\n", "2. Perform this classification task with ``sklearn.ensemble.RandomForestClassifier``. How does ``max_features`` and ``n_estimators`` affect the results?\n", "3. Try a few sets of parameters for each model and check the F1 score (``sklearn.metrics.f1_score``) on your results. What's the best F1 score you can reach?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# run this to load the solution\n", "# %load solutions/04_svm_rf.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 } ], "metadata": {} } ] }	bsd-3-clause
mackelab/pyRRHDLDS	dev/generate_LDS_data_cosyne16ws.ipynb	2	445284	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "from __future__ import division\n", "import numpy as np\n", "import numpy.random as npr\n", "import matplotlib.pyplot as plt\n", "\n", "from pybasicbayes.distributions import Regression\n", "from pybasicbayes.util.text import progprint_xrange\n", "from autoregressive.distributions import AutoRegression\n", "import os\n", "\n", "absolute_code_path = '/home/marcel/Desktop/Projects/Stitching/code/pyLDS_dev/'\n", "os.chdir(absolute_code_path +'pylds')\n", "\n", "from pylds.models import LDS, DefaultLDS\n", "from pylds.distributions import Regression_diag, AutoRegression_input\n", "from pylds.obs_scheme import ObservationScheme\n", "from pylds.user_util import gen_pars, rand_rotation_matrix, init_LDS_model, collect_LDS_stats\n", "\n", "from scipy.io import savemat # store results for comparison with Matlab code \n", "\n", "def update(model):\n", " model.EM_step()\n", " return model.log_likelihood() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for illustration #1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "np.random.seed(1 * 42 + 1) \n", "\n", "#########################\n", "# set some parameters #\n", "#########################\n", "\n", "n = 3\n", "p = 9\n", "T = 10000\n", "\n", "num_sets = 10;\n", "\n", "# tentative observation scheme:\n", "obs_scheme = {'sub_pops': (np.arange(0, p//2 + 1), np.arange(p//2, p)),\n", " 'obs_pops': np.array((0,1)),\n", " 'obs_time': np.array((T//2,T))\n", " }\n", "\n", "for idx_d in range(num_sets):\n", " \n", " print('generating set #', idx_d)\n", " pars_true, _ = gen_pars(n, p, u_dim=0, \n", " pars_in=None, \n", " obs_scheme=None,\n", " gen_A='full', lts=np.linspace(0.95, 0.98, n),\n", " gen_B='random', \n", " gen_Q='identity', \n", " gen_mu0='random', \n", " gen_V0='stable', \n", " gen_C='random', \n", " gen_d='scaled', \n", " gen_R='fraction',\n", " diag_R_flag=True,\n", " x=None, y=None, u=None)\n", "\n", " ###################\n", " # generate data #\n", " ###################\n", "\n", " truemodel = LDS(\n", " dynamics_distn=AutoRegression(A=pars_true['A'].copy(),sigma=pars_true['Q'].copy()),\n", " emission_distn=Regression_diag(A=np.hstack((pars_true['C'].copy(), pars_true['d'].copy().reshape(p,1))),\n", " sigma=pars_true['R'].copy(), affine=True),\n", " )\n", " truemodel.mu_init = pars_true['mu0'].copy()\n", " truemodel.sigma_init = pars_true['V0'].copy()\n", "\n", " data, stateseq = truemodel.generate(T)\n", "\n", "\n", " save_file = '../../../results/cosyne_poster/illustration_1/data/LDS_save_idx'+str(idx_d) \n", " save_file_m = {'x': truemodel.states_list[0].stateseq, \n", " 'y': truemodel.states_list[0].data,\n", " 'u' : [], \n", " 'T' : truemodel.states_list[0].T, \n", " 'Trial': len(truemodel.states_list), \n", " 'truePars':pars_true,\n", " 'obsScheme' : obs_scheme}\n", "\n", " savemat(save_file,save_file_m) # does the actual saving\n", " \n", " np.savez(save_file, x=truemodel.states_list[0].stateseq,\n", " y= truemodel.states_list[0].data,\n", " T=truemodel.states_list[0].T, \n", " Trial=len(truemodel.states_list), \n", " truePars=pars_true,\n", " sub_pops=obs_scheme['sub_pops'], \n", " obs_time=obs_scheme['obs_time'], \n", " obs_pops=obs_scheme['obs_pops']) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for illustration #2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "np.random.seed(1 * 42 + 2)\n", "\n", "if False:\n", "\n", " save_file = '../../../results/cosyne_poster/illustration_2/data/LDS_save_idx'+str(idx_d) \n", " from scipy.io import savemat # store results for comparison with Matlab code \n", " from scipy.linalg import solve_discrete_lyapunov as dtlyap # solve discrete-time Lyapunov equation\n", " save_file_m = {'x': truemodel.states_list[0].stateseq, \n", " 'y': truemodel.states_list[0].data,\n", " 'u' : [], \n", " 'T' : truemodel.states_list[0].T, \n", " 'Trial': len(truemodel.states_list), \n", " 'truePars':pars_true,\n", " 'obsScheme' : obs_scheme}\n", "\n", " savemat(save_file,save_file_m) # does the actual saving\n", " \n", " np.savez(save_file, x=truemodel.states_list[0].stateseq,\n", " y= truemodel.states_list[0].data,\n", " T=truemodel.states_list[0].T, \n", " Trial=len(truemodel.states_list), \n", " truePars=pars_true,\n", " sub_pops=obs_scheme['sub_pops'], \n", " obs_time=obs_scheme['obs_time'], \n", " obs_pops=obs_scheme['obs_pops']) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for simulation #1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ " \n", "#########################\n", "# set some parameters #\n", "#########################\n", "\n", "\n", "ns = np.sort(np.hstack([np.arange(1,10)+1,np.arange(1,10)+1]))\n", "num_sets = ns.size;\n", "\n", "p = 30\n", "T = 10000\n", "\n", "for idx_d in range(num_sets):\n", " \n", " n = ns[idx_d]\n", " \n", " if np.mod(idx_d,2)==0:\n", " overlap = ns[idx_d]\n", " else:\n", " overlap = 2\n", " \n", " # tentative observation scheme:\n", " obs_scheme = {'sub_pops': (np.arange(0,p//2+np.ceil(overlap/2.).astype(np.int64)), \n", " np.arange(p//2-np.floor(overlap/2.).astype(np.int64),p)),\n", " 'obs_pops': np.array((0,1)),\n", " 'obs_time': np.array((T//2,T))\n", " }\n", " \n", " print('generating set #', idx_d)\n", " pars_true, _ = gen_pars(n, p, u_dim=0, \n", " pars_in=None, \n", " obs_scheme=None,\n", " gen_A='full', lts=np.linspace(0.95, 0.98, n),\n", " gen_B='random', \n", " gen_Q='identity', \n", " gen_mu0='random', \n", " gen_V0='stable', \n", " gen_C='random', \n", " gen_d='scaled', \n", " gen_R='fraction',\n", " diag_R_flag=True,\n", " x=None, y=None, u=None)\n", "\n", " ###################\n", " # generate data #\n", " ###################\n", "\n", " truemodel = LDS(\n", " dynamics_distn=AutoRegression(A=pars_true['A'].copy(),sigma=pars_true['Q'].copy()),\n", " emission_distn=Regression_diag(A=np.hstack((pars_true['C'].copy(), pars_true['d'].copy().reshape(p,1))),\n", " sigma=pars_true['R'].copy(), affine=True),\n", " )\n", " truemodel.mu_init = pars_true['mu0'].copy()\n", " truemodel.sigma_init = pars_true['V0'].copy()\n", "\n", " data, stateseq = truemodel.generate(T)\n", "\n", "\n", " save_file = '../../../results/cosyne_poster/simulation_1/data/LDS_save_n'+str(n)+'_idx'+str(np.mod(idx_d,2)) \n", " save_file_m = {'x': truemodel.states_list[0].stateseq, \n", " 'y': truemodel.states_list[0].data,\n", " 'u' : [], \n", " 'T' : truemodel.states_list[0].T, \n", " 'Trial': len(truemodel.states_list), \n", " 'truePars':pars_true,\n", " 'obsScheme' : obs_scheme}\n", "\n", " savemat(save_file,save_file_m) # does the actual saving\n", " \n", " np.savez(save_file, x=truemodel.states_list[0].stateseq,\n", " y= truemodel.states_list[0].data,\n", " T=truemodel.states_list[0].T, \n", " Trial=len(truemodel.states_list), \n", " truePars=pars_true,\n", " sub_pops=obs_scheme['sub_pops'], \n", " obs_time=obs_scheme['obs_time'], \n", " obs_pops=obs_scheme['obs_pops']) \n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for simulation #2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 0)\n", "[ 0.08041555 2.41141779 5.39066511 0.51141868 1.76749152 0.36100967\n", " 0.67565817 2.507835 2.19437423 0.39861525 0.45512821 5.5919181\n", " 1.54060512 1.17361731 0.29567936 0.10746876 0.19333431 5.15528153\n", " 0.8387103 0.18606898 0.71316522 0.09461609 0.47192663 1.55599805\n", " 2.69837455 0.23568308 0.91195805 0.38911765 5.77888481 0.25213552\n", " 0.45021681 4.31198694 0.90629184 0.11113422 0.35921161 1.85417014\n", " 0.39723608 0.2121694 0.35691693 4.07717271 0.43799398 5.13237147\n", " 0.59260317 0.53066335 0.13288587 0.29399097 0.20426312 1.60485695\n", " 0.35237996 1.79454918]\n" ] }, { "data": { "image/png": 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AfonGHPgp3vufJdjuNABvAPiS9/6vLd1vnNmOlyX40dkJ2kVEREQOa865DAC/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\nxHd2btg5zGEvhLxR9eT9ZPuI+zuRZN771wC8tvffWxD5Ey0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21da36b850>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 1)\n", "[ 1.1817524 0.69118016 0.05115285 1.68975635 0.23309574 0.30208495\n", " 0.67280853 6.61077173 1.08490742 0.76261863 0.19484888 1.89118961\n", " 0.57233719 0.91705998 0.1022232 1.08804795 0.27897009 1.83097284\n", " 0.56766334 5.18929532 0.66200123 0.54903129 0.39065475 1.16420759\n", " 3.13872622 2.7206222 0.32555172 2.41954168 4.48688011 0.29655658\n", " 0.7358491 2.63606253 6.29638905 0.42854415 0.79939644 0.37599484\n", " 0.19612706 2.46438002 0.43976268 0.21373472 0.85121878 0.23332082\n", " 0.46100377 0.84947213 0.78656519 2.39893767 0.33131968 0.3549975\n", " 0.62029731 1.56280504]\n" ] }, { "data": { "image/png": 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LUd4h+56ItARwWgjhGBGpsgmEmfNFRERE5Ps7gIq5YFVSqoIjX0RERJQe1dgLGf0pMNpm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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d6705d10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 2)\n", "[ 0.2434892 2.5692362 0.12178734 0.06535063 3.4248303 2.17583101\n", " 0.20317261 0.72776813 1.58902347 0.75142953 1.58210113 0.58510663\n", " 1.53305142 1.06701907 0.73543631 0.40517531 0.28547902 0.62442381\n", " 0.29879228 0.83921505 0.32533759 0.65035838 3.15212478 1.79186414\n", " 0.49167099 0.84455722 0.53854854 1.98087174 0.36803341 2.8670013\n", " 0.44643021 4.87533579 0.09542034 0.77401327 0.50972836 0.37655454\n", " 5.89788838 1.35659834 0.38335973 0.46532242 3.18766815 0.33489886\n", " 0.50073556 0.44517037 1.16031387 0.95138925 5.83404222 0.38931887\n", " 1.44883317 1.19491328]\n" ] }, { "data": { "image/png": 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JLtXOl4PNmhwD4AoA9wPoB+CFnXtYIiIisqdLZGm50VRKTTyF2q+0rZxzywHc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d67bf350>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 3)\n", "[ 0.63601422 0.42097544 10.70967577 0.14137123 0.21598698\n", " 0.23886794 1.13070607 0.84337777 0.45513682 1.15566397\n", " 2.08569249 1.45253819 1.25743552 0.70075403 2.02393295\n", " 0.65612947 0.14045964 1.55296046 0.33081416 0.65761784\n", " 1.21973861 1.39563078 0.44715619 0.35331771 1.90038664\n", " 0.35594523 1.74455715 0.65944964 1.76495434 0.58479927\n", " 3.57299071 0.75447062 1.21902388 0.38306042 0.62961473\n", " 0.45412005 0.41802345 1.64408909 1.04294176 0.30994404\n", " 1.38354636 1.98213026 0.26227704 2.55071799 0.63649405\n", " 1.79306701 4.68618658 0.7777049 0.48851258 0.34900214]\n" ] }, { "data": { "image/png": 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IF19mVrJCCH1CCONDCBNDCBcU+3jMSk6ugP9tRHzxZWYlKYRQBuAmAIcA6ALg2BDCjsU9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\nB3JVcK/OaheRU8Xw+c5ae1rk1PnaXeTU7R4Wudq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buVpkh2U6vU16B7bf0FeS9psTP572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\nzb621vlqGbWrfvnGl5Pvj9/6uqTdsl+6/xf1iaT9vnol7XalFchXZKa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d64ac410>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 4)\n", "[ 0.83926185 2.4844601 0.28056951 0.38768115 0.53526919 0.19459178\n", " 6.10117808 0.8256265 1.35390905 0.1746355 0.92213737 0.60224484\n", " 1.06615931 7.30283848 0.24726248 2.79647175 3.7763462 0.17549807\n", " 0.46846439 0.05350613 0.53919014 0.17209406 0.89745481 0.82463789\n", " 1.48944586 1.23641531 1.42073772 1.5543579 3.50893598 0.07151076\n", " 0.08520676 0.52665041 1.38653182 5.60434749 0.56484217 1.38507525\n", " 1.01952406 0.3126516 1.16180406 1.22764178 2.18520643 0.49496695\n", " 0.43246618 0.6045049 0.8724705 1.23278401 0.74230057 0.31631146\n", " 0.2627339 2.38377523]\n" ] }, { "data": { "image/png": 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IFxERERGVNXa+iIiIiGKInS8iSlgi0l1EFojIIhG5K97HQ5RwkmP4XznCzhcRJSQRSQLw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\np4116fbd+qRt+I73Sco/XJZmph7Z+/lcm1MbG/RFzFflrMhETz4TpM/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d6332a10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 5)\n", "[ 1.03079664 0.48525595 0.48667826 3.26141767 0.94284639 0.08978848\n", " 2.2660315 7.88912646 0.03627034 0.23648772 0.33174794 0.35562708\n", " 0.81250358 0.40377003 0.97326256 0.74203952 1.07455644 0.93567619\n", " 5.5752553 0.2758721 0.55257333 3.21742703 0.57499891 0.38157551\n", " 4.05821838 0.8514615 0.2567031 0.34828739 3.54832367 0.33635653\n", " 0.75379485 0.29627285 1.04369165 0.86871284 1.73771868 0.62640597\n", " 0.50993969 0.74167215 0.7315778 2.42267965 0.26208633 2.12129927\n", " 0.2568302 1.46266224 0.12718745 1.04830654 0.51406034 6.08126908\n", " 0.83397612 1.08262986]\n" ] }, { "data": { "image/png": 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RU8XUqut6M/H61HpVEf5LIgfoLu6qaP5hkSv26FLHg3pP1fOp4vpiX/MnRa6jocL1YveN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\n7hf2kr77BZO7D/h9Q8ysvb8ggZVIf3/mzcx1S4k/YtM65/cSCfcA0ES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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d612eb10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 6)\n", "[ 2.33719088 0.72690871 0.25642317 1.80775257 0.45834347 6.04825204\n", " 0.2901746 0.37856848 0.42566785 0.11197798 0.66152728 1.86348603\n", " 0.59476536 1.03683878 0.17483116 0.9359669 0.95947619 4.64552379\n", " 0.48473802 6.83219991 0.66330482 3.17515449 0.55008349 0.2570004\n", " 1.30206768 0.85703152 0.50326923 0.70624317 3.75133708 1.83542434\n", " 1.27216185 0.38810268 0.64228604 1.64848031 0.24462102 0.41489528\n", " 0.51762306 1.35343861 1.5544578 0.34052145 0.59661256 0.68102597\n", " 0.65095548 0.54688589 0.23241169 0.68984792 0.49333305 5.59209771\n", " 0.90597649 0.6006024 ]\n" ] }, { "data": { "image/png": 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cRL4GMBLAjQCeEZFzACwCcGq1G7nQxL2cZQY4Ty69G+mH4qTr4t87zzmudtbt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d5f26c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 7)\n", "[ 0.41353031 0.15468253 0.1084281 7.54122824 0.16214543 1.10201838\n", " 2.59109376 8.49158635 0.16081662 4.94850261 0.13830612 0.88252214\n", " 2.90388278 0.5891539 8.67924363 0.39639123 0.39004942 1.23963615\n", " 1.05013571 0.61936015 0.15457723 4.3178211 0.84137394 0.58773003\n", " 1.07978031 0.94186318 0.10609541 0.19542614 0.70671352 0.67964292\n", " 0.13805459 0.16178713 0.1241608 0.41066361 1.30639081 1.33185109\n", " 0.15563594 1.3394972 0.75798948 3.12792893 3.06407262 3.56410715\n", " 0.41052108 0.86239886 0.16429716 0.37537365 1.783401 0.19000098\n", " 0.30769017 0.94844423]\n" ] }, { "data": { "image/png": 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H+LJjlprc2iWV9sbNTuvzDV081lFOF/eVtsg9p7LR5EpK60zuvbeG2u1G2ULn3fNWmdyC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\nclGZ1DVe7+T8WcWpdt3YuBFRE+Gv6DB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BRJQJIjIYQFsIoU5E+gI4HsDPAdwPYDqAXwA4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d5f98a10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 8)\n", "[ 0.18607451 0.52803386 0.76967362 0.17313301 1.46058071 1.46304979\n", " 2.14442243 0.17408198 0.41641782 0.5135621 0.89280876 0.51128912\n", " 0.30426581 2.91378733 0.44864319 0.20624235 2.50136514 5.17005671\n", " 0.66910048 3.50268046 0.38667246 0.07211982 0.95669082 2.5660284\n", " 0.94503957 3.13266083 0.56640332 3.31580452 0.4750198 0.1680614\n", " 3.83757614 0.59404849 1.57886305 0.24210176 0.77730984 0.48043145\n", " 0.37685383 0.23403548 6.01452091 0.03724274 0.45639226 1.64403128\n", " 2.74505474 1.41939665 1.50562143 1.53976078 1.224153 0.59408716\n", " 0.37695744 0.72801957]\n" ] }, { "data": { "image/png": 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CgJndA2AUgFmfLGBmKU9EEQkhNH8qlyJNuC/Sly0igm4A3mkUL0LDBRkZA2ACgCoAtwYe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\nt0YQT5TUP7dQW7VxO1eko9voi0ZTdL4AoLE9JD3fr7yqXteOprh7j1T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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d60873d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('generating set #', 9)\n", "[ 1.01044422 0.59682749 0.20878676 0.12251406 0.70217139 2.9537514\n", " 0.99536573 0.38070889 0.59925018 0.4092269 1.41632622 1.78031387\n", " 0.61881187 1.07212945 0.47491108 0.26898255 0.61493297 0.47337411\n", " 0.96110053 0.3573508 1.77890909 0.55421505 0.72009487 0.48206232\n", " 1.32320032 2.88426115 3.2890529 0.76545805 1.40428598 0.50442935\n", " 0.08562314 0.8110513 1.16777632 1.48920644 0.51179097 0.61403784\n", " 1.65228795 1.11742276 0.86649399 1.04945531 0.46763787 1.91396703\n", " 0.52992731 0.55593802 4.28584103 4.10787585 0.31466251 1.18228471\n", " 1.30818591 6.00865934]\n" ] }, { "data": { "image/png": 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WnmHiH11ra4td/fj9Ji445xMTv7t5uInPPsde8wHB+pQ37YHZBm+VNeKtsrh5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\nSMq3rkq3v+XadJ1COz4d9Zx22tlJ+Sr9Y1I+bPITSXlPS2OH3h6ajo6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f21d6120550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "def update(model):\n", " model.EM_step()\n", " return model.log_likelihood() \n", "\n", "#########################\n", "# set some parameters #\n", "#########################\n", "\n", "\n", "num_sets = 10;\n", "\n", "n = 5\n", "p = 50\n", "T = 5000\n", "\n", "overlap = 2\n", "\n", "# tentative observation scheme:\n", "sub_pop1 = np.arange( 0,16)\n", "sub_pop2 = np.arange(14,30)\n", "sub_pop3 = np.hstack([sub_pop1[0],np.arange(sub_pop2[-1], p)])\n", "obs_scheme = {'sub_pops': (sub_pop1, sub_pop2, sub_pop3),\n", " 'obs_pops': np.array((0,1,2)),\n", " 'obs_time': np.array((T//3,T//2,T))\n", " }\n", "\n", "for idx_d in range(num_sets):\n", " \n", " \n", " print('generating set #', idx_d)\n", " pars_true, _ = gen_pars(n, p, u_dim=0, \n", " pars_in=None, \n", " obs_scheme=None,\n", " gen_A='full', lts=np.linspace(0.95, 0.98, n),\n", " gen_B='random', \n", " gen_Q='identity', \n", " gen_mu0='random', \n", " gen_V0='stable', \n", " gen_C='random', \n", " gen_d='scaled', \n", " gen_R='fraction',\n", " diag_R_flag=True,\n", " x=None, y=None, u=None)\n", " \n", " means_C = np.sum(pars_true['C'],axis=1)\n", " pars_true['C'] -= means_C.reshape(p,1)\n", " norms_C = np.sum(pars_true['C']pars_true['C'],axis=1)\n", " pars_true['C'] /= np.mean(np.sqrt(norms_C.reshape(p,1)))\n", " #pars_true['R'] = \n", " \n", " print(np.sum(pars_true['C']pars_true['C'],axis=1))\n", "\n", " ###################\n", " # generate data #\n", " ###################\n", "\n", " truemodel = LDS(\n", " dynamics_distn=AutoRegression(A=pars_true['A'].copy(),sigma=pars_true['Q'].copy()),\n", " emission_distn=Regression_diag(A=np.hstack((pars_true['C'].copy(), pars_true['d'].copy().reshape(p,1))),\n", " sigma=pars_true['R'].copy(), affine=True),\n", " )\n", " truemodel.mu_init = pars_true['mu0'].copy()\n", " truemodel.sigma_init = pars_true['V0'].copy()\n", "\n", " data, stateseq = truemodel.generate(T)\n", "\n", " plt.figure(figsize=(10,10))\n", " plt.subplot(1,2,1)\n", " plt.imshow(np.cov(data.T), interpolation='none')\n", " plt.subplot(1,2,2)\n", " plt.imshow(np.corrcoef(data.T), interpolation='none')\n", " plt.colorbar()\n", " plt.show()\n", " \n", " save_file = '../../../results/cosyne_poster/simulation_2/data/LDS_save_idx'+str(idx_d) \n", " save_file_m = {'x': truemodel.states_list[0].stateseq, \n", " 'y': truemodel.states_list[0].data,\n", " 'u' : [], \n", " 'T' : truemodel.states_list[0].T, \n", " 'Trial': len(truemodel.states_list), \n", " 'truePars':pars_true,\n", " 'obsScheme' : obs_scheme}\n", "\n", " savemat(save_file,save_file_m) # does the actual saving\n", " \n", " np.savez(save_file, x=truemodel.states_list[0].stateseq,\n", " y= truemodel.states_list[0].data,\n", " T=truemodel.states_list[0].T, \n", " Trial=len(truemodel.states_list), \n", " truePars=pars_true,\n", " sub_pops=obs_scheme['sub_pops'], \n", " obs_time=obs_scheme['obs_time'], \n", " obs_pops=obs_scheme['obs_pops']) \n", " \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'pars_true' 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-1-095a16d726d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpars_true\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'pars_true' is not defined" ] } ], "source": [ "pars_true['R']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for experiment #1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## generated by Matlab neural network simulation!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data for experiment #2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## generated by Matlab neural network simulation!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
tpin3694/tpin3694.github.io	machine-learning/calculate_the_trace_of_a_matrix.ipynb	2	1973	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Calculate The Trace Of A Matrix \n", "Slug: calculate_the_trace_of_a_matrix \n", "Summary: How to calculate the trace of a matrix in Python. \n", "Date: 2017-09-02 12:00 \n", "Category: Machine Learning \n", "Tags: Vectors Matrices Arrays \n", "Authors: Chris Albon " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load library\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Matrix" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create matrix\n", "matrix = np.array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate The Trace" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the tracre of the matrix\n", "matrix.diagonal().sum()" ] } ], "metadata": { "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
rvuduc/cse6040-ipynbs	hw2.ipynb	1	13377	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CSE 6040, Fall 2015: Homework #2\n", "\n", "This assignment has the following learning goals:\n", "1. Reinforce your data preprocessing skills, on basic files and in SQL, using a real dataset.\n", "2. Allow you to flex your creative muscles on an open-ended data analysis problem.\n", "\n", "In particular, you will work on the data provided for the [2015 Yelp! Dataset Challenge](http://www.yelp.com/dataset_challenge). Start by downloading this data and reviewing the information at that page under the section heading, _Notes on the Dataset_.\n", "\n", "> Note that the official challenge from Yelp! is an open competition to produce the coolest analysis of their dataset. The \"open-ended\" part of this homework assignment might be a first step toward helping your team win the $5,000 prize! (Entries for that competition are due December 31.)\n", "\n", "You may work in teams of up to 2 students each. If you want a partner but can't find one, try using some combination of your basic social skills, the instructor's knowledge of the class, and Piazza to reach out to your peers.\n", "\n", "Upload your completed version of this notebook plus all your output files to T-Square by Friday, October 16, 2015 at 5:00pm Eastern.\n", "\n", "> Actually, we will set up T-Square to accept the assignment on Friday, Oct 16, 2015 \"anywhere on earth\" (AOE). However, there will be no instructor Q&A after 5pm Friday, so Shang can party and Rich can make progress on clearing his email backlog." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 0: List your team members here\n", "\n", "Team members:\n", "1. (name goes here)\n", "2. (name goes here)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: JSON to SQLite [20 points]\n", "\n", "The learning goal in Part 1 is to reinforce the basic lessons on data conversion and SQL, by having you preprocess some \"raw\" data, turning it into a SQLite database, and then running some queries on it.\n", "\n", "> Hint: If you inspect the Yelp! Academic Dataset, you will see that each file is a sequence of JSON records, with one JSON record per line. So, you will most likely want to read the relevant input file line-by-line and process each line as a JSON record." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# As you complete Part 1, place any additional imports you\n", "# need in this code cell.\n", "\n", "import json\n", "import sqlite3 as db\n", "import pandas\n", "from IPython.display import display\n", "import string" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A little helper function you can use to quickly inspect tables:\n", "def peek_table (db, name):\n", " \"\"\"\n", " Given a database connection (`db`), prints both the number of\n", " records in the table as well as its first few entries.\n", " \"\"\"\n", " count = '''SELECT COUNT () FROM {table}'''.format (table=name)\n", " display (pandas.read_sql_query (count, db))\n", " peek = '''SELECT FROM {table} LIMIT 5'''.format (table=name)\n", " display (pandas.read_sql_query (peek, db))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# By way of reminder, here's how you open a connection to a database\n", "# and request a cursor for executing queries.\n", "\n", "db_conn = db.connect ('yelp-rest.db')\n", "db_cursor = db_conn.cursor ()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 1(a). _[5 points]_ From the Yelp! Academic Dataset, create an SQLite database called, `yelp-rest.db`, which contains the subset of the data pertaining to _restaurants_.\n", "\n", "In particular, start by creating a table called `Restaurants`. This table should have the following columns: the business ID (call it `Id`), restaurant name (`Name`), city (`City`), state (`State`), coordinates (two columns, called `Lat` and `Long`), and a semicolon-separated string of the restaurant's categories (`Cats`).\n", "\n", "> Note: This table should _only_ contain businesses that are categorized as restaurants.\n", "\n", "> Hint: When performing large numbers of inserts into the database, it may be helpful to execute `db_conn.commit()` to save the results before proceeding." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Quickly inspect your handiwork\n", "peek_table (db_conn, \"Restaurants\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 1(b). _[5 points]_ Next, create a table called `Reviews`, which contains _only_ reviews of restaurants.\n", "\n", "This table should have the following columns: the restaurant's business ID (call it `BizId`), the reviewer's ID (`RevId`), the numerical rating (`Stars`), the date (`Date`), and the number of up-votes of the review itself (three columns: `Useful`, `Funny`, and `Cool`).\n", "\n", "> Note: This table should _only_ contain the subset of reviews that pertain to _restaurants_. You may find your results from Task 1(a) helpful here!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "peek_table (db_conn, \"Reviews\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Task 1(c). _[5 points]_ Next, create a table called `Users`, which contains _all_ users.\n", "\n", "This table should have the following columns: the user's ID (`Id`), name (`Name`), and number of fans (`NumFans`).\n", "\n", "> Note: This table should contain _all_ users, not just the ones that reviewed restaurants!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "peek_table (db_conn, \"Users\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 1(d). _[5 points]_ Create a table, `UserEdges`, that stores the connectivity graph between users.\n", "\n", "This table should have two columns, one for the source vertex (named `Source`) and one for the target vertex (named `Target`). Treat the graph as _undirected_: that is, if there is a link from a user $u$ to a user $v$, then the table should contain _both_ edges $u \\rightarrow v$ and $v \\rightarrow u$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "peek_table (db_conn, \"UserEdges\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Summary statistics [20 points]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 2(a). _[2 point]_ Compute the average rating (measured in \"stars\"), taken over all reviews." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 2(b). _[5 points]_ For each distinct state, compute the number of reviews and the average restaurant rating (in \"stars\"). You may ignore businesses that have no reviews. Store these in a dataframe variable, `df`, with three columns: one column for the state (named `State`), one column for the number of reviews (`NumRevs`), and one column for the average rating (named `AvgStars`). The rows of the `df` should be sorted in descending order by number of reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ... Your code to compute `df` goes here ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "display (df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 2(c). _[3 points]_ On average, how many reviews does each user write? You may ignore users who write no reviews.\n", "\n", "Write Python code to answer this question in the code cell below, and enter your answer below, rounded to the nearest tenth. For instance, you would enter \"5.2\" if your program computes \"5.24384\". __(type your answer here)__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 2(c). _[5 points]_ On average, how many friends does each user have? In computing the average, _include_ users who have no friends.\n", "\n", "Write Python code to answer this question in the code cell below, and enter your answer here, rounded to the nearest integer: __(your answer)__\n", "\n", "> Hint: There is at least one relatively simple way that combines [left (outer) joins](http://www.techonthenet.com/sqlite/joins.php) and the [`IFNULL(...)`](http://www.sqlite.org/lang_corefunc.html#ifnull) function. Although we haven't covered these in class, you should be comfortable enough that you can read about them independently and apply them." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Task 2(d). _[5 points]_ Use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/tutorial/distributions.html) or [Plotly](https://plot.ly/python/histograms-and-box-plots-tutorial/) to create a histogram of ratings in the state of Nevada." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Your code goes here. Feel free to use additional code cells\n", "# to break up your work into easily testable chunks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Rank the Restaurants [20 points]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Come up with your own scheme, inspired by either the _A-priori_ association mining algorithm or the PageRank algorithm, to compute a global ranking of the restaurants.\n", "\n", "Only consider restaurants with 25 reviews or more.\n", "\n", "Explain your scheme and compare its top rated results against a baseline scheme that simply returns restaurants in descending order based on average rating." ] }, { "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 }	bsd-3-clause
wbinventor/openmc	examples/jupyter/pandas-dataframes.ipynb	1	124131	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import glob\n", "\n", "from IPython.display import Image\n", "import matplotlib.pyplot as plt\n", "import scipy.stats\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import openmc\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Input Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to define materials that will be used in the problem. We will create three materials for the fuel, water, and cladding of the fuel pin." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", "fuel.add_nuclide('U235', 3.7503e-4)\n", "fuel.add_nuclide('U238', 2.2625e-2)\n", "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", "water.add_nuclide('H1', 4.9457e-2)\n", "water.add_nuclide('O16', 2.4732e-2)\n", "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our three materials, we can now create a materials file object that can be exported to an actual XML file." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection\n", "materials_file = openmc.Materials([fuel, water, zircaloy])\n", "\n", "# Export to \"materials.xml\"\n", "materials_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "# Use both reflective and vacuum boundaries to make life interesting\n", "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", "max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n", "min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n", "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n", "min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n", "max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create fuel Cell\n", "fuel_cell = openmc.Cell(name='1.6% Fuel', fill=fuel,\n", " region=-fuel_outer_radius)\n", "\n", "# Create a clad Cell\n", "clad_cell = openmc.Cell(name='1.6% Clad', fill=zircaloy)\n", "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", "\n", "# Create a moderator Cell\n", "moderator_cell = openmc.Cell(name='1.6% Moderator', fill=water,\n", " region=+clad_outer_radius)\n", "\n", "# Create a Universe to encapsulate a fuel pin\n", "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin', cells=[\n", " fuel_cell, clad_cell, moderator_cell\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Create fuel assembly Lattice\n", "assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n", "assembly.pitch = (1.26, 1.26)\n", "assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n", "assembly.universes = [[pin_cell_universe] * 17] * 17" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Create root Cell\n", "root_cell = openmc.Cell(name='root cell', fill=assembly)\n", "\n", "# Add boundary planes\n", "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", "\n", "# Create root Universe\n", "root_universe = openmc.Universe(name='root universe')\n", "root_universe.add_cell(root_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now must create a geometry that is assigned a root universe and export it to XML." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Create Geometry and export to \"geometry.xml\"\n", "geometry = openmc.Geometry(root_universe)\n", "geometry.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 minimum active batches each with 2500 particles. We also tell OpenMC to turn tally triggers on, which means it will keep running until some criterion on the uncertainty of tallies is reached." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", "min_batches = 20\n", "max_batches = 200\n", "inactive = 5\n", "particles = 2500\n", "\n", "# Instantiate a Settings object\n", "settings = openmc.Settings()\n", "settings.batches = min_batches\n", "settings.inactive = inactive\n", "settings.particles = particles\n", "settings.output = {'tallies': False}\n", "settings.trigger_active = True\n", "settings.trigger_max_batches = max_batches\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", "settings.source = openmc.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Instantiate a Plot\n", "plot = openmc.Plot(plot_id=1)\n", "plot.filename = 'materials-xy'\n", "plot.origin = [0, 0, 0]\n", "plot.width = [21.5, 21.5]\n", "plot.pixels = [250, 250]\n", "plot.color_by = 'material'\n", "\n", "# Instantiate a Plots collection and export to \"plots.xml\"\n", "plot_file = openmc.Plots([plot])\n", "plot_file.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Run openmc in plotting mode\n", "openmc.plot_geometry(output=False)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert OpenMC's funky ppm to png\n", "!convert materials-xy.ppm materials-xy.png\n", "\n", "# Display the materials plot inline\n", "Image(filename='materials-xy.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", "tallies = openmc.Tallies()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a fission rate mesh Tally" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Instantiate a tally Mesh\n", "mesh = openmc.Mesh(mesh_id=1)\n", "mesh.type = 'regular'\n", "mesh.dimension = [17, 17]\n", "mesh.lower_left = [-10.71, -10.71]\n", "mesh.width = [1.26, 1.26]\n", "\n", "# Instantiate tally Filter\n", "mesh_filter = openmc.MeshFilter(mesh)\n", "\n", "# Instantiate energy Filter\n", "energy_filter = openmc.EnergyFilter([0, 0.625, 20.0e6])\n", "\n", "# Instantiate the Tally\n", "tally = openmc.Tally(name='mesh tally')\n", "tally.filters = [mesh_filter, energy_filter]\n", "tally.scores = ['fission', 'nu-fission']\n", "\n", "# Add mesh and Tally to Tallies\n", "tallies.append(tally)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a cell Tally with nuclides" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Instantiate tally Filter\n", "cell_filter = openmc.CellFilter(fuel_cell)\n", "\n", "# Instantiate the tally\n", "tally = openmc.Tally(name='cell tally')\n", "tally.filters = [cell_filter]\n", "tally.scores = ['scatter']\n", "tally.nuclides = ['U235', 'U238']\n", "\n", "# Add mesh and tally to Tallies\n", "tallies.append(tally)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a \"distribcell\" Tally. The distribcell filter allows us to tally multiple repeated instances of the same cell throughout the geometry." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Instantiate tally Filter\n", "distribcell_filter = openmc.DistribcellFilter(moderator_cell)\n", "\n", "# Instantiate tally Trigger for kicks\n", "trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n", "trigger.scores = ['absorption']\n", "\n", "# Instantiate the Tally\n", "tally = openmc.Tally(name='distribcell tally')\n", "tally.filters = [distribcell_filter]\n", "tally.scores = ['absorption', 'scatter']\n", "tally.triggers = [trigger]\n", "\n", "# Add mesh and tally to Tallies\n", "tallies.append(tally)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", "tallies.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we a have a complete set of inputs, so we can go ahead and run our simulation." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%%%%%%\n", " ##################### %%%%%%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%%\n", " ####################### %%%%%%%%%%%%%%%%%\n", " ###################### %%%%%%%%%%%%%%%%%\n", " #################### %%%%%%%%%%%%%%%%%\n", " ################# %%%%%%%%%%%%%%%%%\n", " ############### %%%%%%%%%%%%%%%%\n", " ############ %%%%%%%%%%%%%%%\n", " ######## %%%%%%%%%%%%%%\n", " %%%%%%%%%%%\n", "\n", " \| The OpenMC Monte Carlo Code\n", " Copyright \| 2011-2018 MIT and OpenMC contributors\n", " License \| http://openmc.readthedocs.io/en/latest/license.html\n", " Version \| 0.10.0\n", " Git SHA1 \| 199126b2fcc5cb094f2cc820ae13e1a972cacddd\n", " Date/Time \| 2018-10-11 16:41:25\n", " OpenMP Threads \| 8\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", " Building neighboring cells lists for each surface...\n", " Reading U235 from /home/jan/openmc/nndc_hdf5/U235.h5\n", " Reading U238 from /home/jan/openmc/nndc_hdf5/U238.h5\n", " Reading O16 from /home/jan/openmc/nndc_hdf5/O16.h5\n", " Reading H1 from /home/jan/openmc/nndc_hdf5/H1.h5\n", " Reading B10 from /home/jan/openmc/nndc_hdf5/B10.h5\n", " Reading Zr90 from /home/jan/openmc/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", " 1/1 0.55921 \n", " 2/1 0.63816 \n", " 3/1 0.68834 \n", " 4/1 0.71192 \n", " 5/1 0.67935 \n", " 6/1 0.68254 \n", " 7/1 0.65804 0.67029 +/- 0.01225\n", " 8/1 0.66225 0.66761 +/- 0.00756\n", " 9/1 0.66336 0.66655 +/- 0.00545\n", " 10/1 0.68037 0.66931 +/- 0.00505\n", " 11/1 0.71728 0.67731 +/- 0.00899\n", " 12/1 0.66098 0.67498 +/- 0.00795\n", " 13/1 0.69969 0.67806 +/- 0.00755\n", " 14/1 0.70998 0.68161 +/- 0.00754\n", " 15/1 0.70092 0.68354 +/- 0.00702\n", " 16/1 0.71586 0.68648 +/- 0.00699\n", " 17/1 0.65949 0.68423 +/- 0.00677\n", " 18/1 0.67696 0.68367 +/- 0.00625\n", " 19/1 0.65444 0.68158 +/- 0.00615\n", " 20/1 0.69766 0.68266 +/- 0.00583\n", " Triggers unsatisfied, max unc./thresh. is 1.17617 for absorption in tally 3\n", " The estimated number of batches is 26\n", " Creating state point statepoint.020.h5...\n", " 21/1 0.64126 0.68007 +/- 0.00603\n", " 22/1 0.69287 0.68082 +/- 0.00572\n", " 23/1 0.70254 0.68203 +/- 0.00552\n", " 24/1 0.68198 0.68203 +/- 0.00523\n", " 25/1 0.67214 0.68153 +/- 0.00498\n", " 26/1 0.68171 0.68154 +/- 0.00474\n", " Triggers satisfied for batch 26\n", " Creating state point statepoint.026.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", " Total time for initialization = 6.5303E-01 seconds\n", " Reading cross sections = 5.8105E-01 seconds\n", " Total time in simulation = 4.6015E+00 seconds\n", " Time in transport only = 3.5767E+00 seconds\n", " Time in inactive batches = 4.5008E-01 seconds\n", " Time in active batches = 4.1514E+00 seconds\n", " Time synchronizing fission bank = 2.3493E-03 seconds\n", " Sampling source sites = 1.7160E-03 seconds\n", " SEND/RECV source sites = 4.7010E-04 seconds\n", " Time accumulating tallies = 2.3040E-04 seconds\n", " Total time for finalization = 2.6451E-02 seconds\n", " Total time elapsed = 5.3123E+00 seconds\n", " Calculation Rate (inactive) = 27772.9 particles/second\n", " Calculation Rate (active) = 12646.3 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", " k-effective (Collision) = 0.67976 +/- 0.00436\n", " k-effective (Track-length) = 0.68154 +/- 0.00474\n", " k-effective (Absorption) = 0.68320 +/- 0.00518\n", " Combined k-effective = 0.68122 +/- 0.00432\n", " Leakage Fraction = 0.34011 +/- 0.00283\n", "\n" ] } ], "source": [ "# Remove old HDF5 (summary, statepoint) files\n", "!rm statepoint.\n", "\n", "# Run OpenMC!\n", "openmc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tally Data Processing" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# We do not know how many batches were needed to satisfy the \n", "# tally trigger(s), so find the statepoint file(s)\n", "statepoints = glob.glob('statepoint..h5')\n", "\n", "# Load the last statepoint file\n", "sp = openmc.StatePoint(statepoints[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyze the mesh fission rate tally" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tally\n", "\tID =\t1\n", "\tName =\tmesh tally\n", "\tFilters =\tMeshFilter, EnergyFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['fission', 'nu-fission']\n", "\tEstimator =\ttracklength\n", "\n" ] } ], "source": [ "# Find the mesh tally with the StatePoint API\n", "tally = sp.get_tally(name='mesh tally')\n", "\n", "# Print a little info about the mesh tally to the screen\n", "print(tally)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the new Tally data retrieval API with pure NumPy" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[0.17581417]]\n", "\n", " [[0.06842901]]\n", "\n", " [[0.30578219]]\n", "\n", " [[0.12436752]]]\n" ] } ], "source": [ "# Get the relative error for the thermal fission reaction \n", "# rates in the four corner pins \n", "data = tally.get_values(scores=['fission'],\n", " filters=[openmc.MeshFilter, openmc.EnergyFilter], \\\n", " filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n", " ((0., 0.625),)], value='rel_err')\n", "print(data)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"3\" halign=\"left\">mesh 1</th>\n", " <th>energy low [eV]</th>\n", " <th>energy high [eV]</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>z</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>fission</td>\n", " <td>2.24e-04</td>\n", " <td>3.94e-05</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>nu-fission</td>\n", " <td>5.46e-04</td>\n", " <td>9.59e-05</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>fission</td>\n", " <td>8.42e-05</td>\n", " <td>6.79e-06</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>nu-fission</td>\n", " <td>2.22e-04</td>\n", " <td>1.65e-05</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>fission</td>\n", " <td>1.85e-04</td>\n", " <td>2.70e-05</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>nu-fission</td>\n", " <td>4.52e-04</td>\n", " <td>6.58e-05</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>fission</td>\n", " <td>6.82e-05</td>\n", " <td>5.29e-06</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>nu-fission</td>\n", " <td>1.81e-04</td>\n", " <td>1.35e-05</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>fission</td>\n", " <td>2.05e-04</td>\n", " <td>2.25e-05</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>nu-fission</td>\n", " <td>5.00e-04</td>\n", " <td>5.49e-05</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>fission</td>\n", " <td>7.53e-05</td>\n", " <td>7.06e-06</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>nu-fission</td>\n", " <td>1.99e-04</td>\n", " <td>1.81e-05</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>fission</td>\n", " <td>2.06e-04</td>\n", " <td>2.79e-05</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>nu-fission</td>\n", " <td>5.03e-04</td>\n", " <td>6.80e-05</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>fission</td>\n", " <td>6.65e-05</td>\n", " <td>3.91e-06</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>nu-fission</td>\n", " <td>1.75e-04</td>\n", " <td>1.04e-05</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>fission</td>\n", " <td>2.03e-04</td>\n", " <td>2.78e-05</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0.00e+00</td>\n", " <td>6.25e-01</td>\n", " <td>nu-fission</td>\n", " <td>4.94e-04</td>\n", " <td>6.78e-05</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>fission</td>\n", " <td>6.26e-05</td>\n", " <td>5.71e-06</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6.25e-01</td>\n", " <td>2.00e+07</td>\n", " <td>nu-fission</td>\n", " <td>1.64e-04</td>\n", " <td>1.53e-05</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", "0 1 1 1 0.00e+00 6.25e-01 fission 2.24e-04 \n", "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.46e-04 \n", "2 1 1 1 6.25e-01 2.00e+07 fission 8.42e-05 \n", "3 1 1 1 6.25e-01 2.00e+07 nu-fission 2.22e-04 \n", "4 2 1 1 0.00e+00 6.25e-01 fission 1.85e-04 \n", "5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.52e-04 \n", "6 2 1 1 6.25e-01 2.00e+07 fission 6.82e-05 \n", "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", "8 3 1 1 0.00e+00 6.25e-01 fission 2.05e-04 \n", "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.00e-04 \n", "10 3 1 1 6.25e-01 2.00e+07 fission 7.53e-05 \n", "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.99e-04 \n", "12 4 1 1 0.00e+00 6.25e-01 fission 2.06e-04 \n", "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.03e-04 \n", "14 4 1 1 6.25e-01 2.00e+07 fission 6.65e-05 \n", "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", "16 5 1 1 0.00e+00 6.25e-01 fission 2.03e-04 \n", "17 5 1 1 0.00e+00 6.25e-01 nu-fission 4.94e-04 \n", "18 5 1 1 6.25e-01 2.00e+07 fission 6.26e-05 \n", "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.64e-04 \n", "\n", " std. dev. \n", " \n", "0 3.94e-05 \n", "1 9.59e-05 \n", "2 6.79e-06 \n", "3 1.65e-05 \n", "4 2.70e-05 \n", "5 6.58e-05 \n", "6 5.29e-06 \n", "7 1.35e-05 \n", "8 2.25e-05 \n", "9 5.49e-05 \n", "10 7.06e-06 \n", "11 1.81e-05 \n", "12 2.79e-05 \n", "13 6.80e-05 \n", "14 3.91e-06 \n", "15 1.04e-05 \n", "16 2.78e-05 \n", "17 6.78e-05 \n", "18 5.71e-06 \n", "19 1.53e-05 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a pandas dataframe for the mesh tally data\n", "df = tally.get_pandas_dataframe(nuclides=False)\n", "\n", "# Set the Pandas float display settings\n", "pd.options.display.float_format = '{:.2e}'.format\n", "\n", "# Print the first twenty rows in the dataframe\n", "df.head(20)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a boxplot to view the distribution of\n", "# fission and nu-fission rates in the pins\n", "bp = df.boxplot(column='mean', by='score')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f39196c0b00>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Extract thermal nu-fission rates from pandas\n", "fiss = df[df['score'] == 'nu-fission']\n", "fiss = fiss[fiss['energy low [eV]'] == 0.0]\n", "\n", "# Extract mean and reshape as 2D NumPy arrays\n", "mean = fiss['mean'].values.reshape((17,17))\n", "\n", "plt.imshow(mean, interpolation='nearest')\n", "plt.title('fission rate')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyze the cell+nuclides scatter-y2 rate tally" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tally\n", "\tID =\t2\n", "\tName =\tcell tally\n", "\tFilters =\tCellFilter\n", "\tNuclides =\tU235 U238 \n", "\tScores =\t['scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] } ], "source": [ "# Find the cell Tally with the StatePoint API\n", "tally = sp.get_tally(name='cell tally')\n", "\n", "# Print a little info about the cell tally to the screen\n", "print(tally)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cell</th>\n", " <th>nuclide</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>U235</td>\n", " <td>scatter</td>\n", " <td>3.81e-02</td>\n", " <td>1.65e-04</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>U238</td>\n", " <td>scatter</td>\n", " <td>2.33e+00</td>\n", " <td>9.59e-03</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cell nuclide score mean std. dev.\n", "0 1 U235 scatter 3.81e-02 1.65e-04\n", "1 1 U238 scatter 2.33e+00 9.59e-03" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a pandas dataframe for the cell tally data\n", "df = tally.get_pandas_dataframe()\n", "\n", "# Print the first twenty rows in the dataframe\n", "df.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the new Tally data retrieval API with pure NumPy" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[0.00958717]\n", " [0.00016469]]]\n" ] } ], "source": [ "# Get the standard deviations the total scattering rate\n", "data = tally.get_values(scores=['scatter'], \n", " nuclides=['U238', 'U235'], value='std_dev')\n", "print(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyze the distribcell tally" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tally\n", "\tID =\t3\n", "\tName =\tdistribcell tally\n", "\tFilters =\tDistribcellFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['absorption', 'scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] } ], "source": [ "# Find the distribcell Tally with the StatePoint API\n", "tally = sp.get_tally(name='distribcell tally')\n", "\n", "# Print a little info about the distribcell tally to the screen\n", "print(tally)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the new Tally data retrieval API with pure NumPy" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[[0.03500496]]\n", "\n", " [[0.02745568]]\n", "\n", " [[0.02988488]]\n", "\n", " [[0.04474905]]\n", "\n", " [[0.03697764]]\n", "\n", " [[0.0409214 ]]\n", "\n", " [[0.03366461]]\n", "\n", " [[0.03210393]]\n", "\n", " [[0.03216398]]\n", "\n", " [[0.04003553]]]\n" ] } ], "source": [ "# Get the relative error for the scattering reaction rates in\n", "# the first 10 distribcell instances \n", "data = tally.get_values(scores=['scatter'], filters=[openmc.DistribcellFilter],\n", " filter_bins=[tuple(range(10))], value='rel_err')\n", "print(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the distribcell tally dataframe" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"2\" halign=\"left\">level 1</th>\n", " <th colspan=\"3\" halign=\"left\">level 2</th>\n", " <th colspan=\"2\" halign=\"left\">level 3</th>\n", " <th>distribcell</th>\n", " <th>score</th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>univ</th>\n", " <th>cell</th>\n", " <th colspan=\"3\" halign=\"left\">lat</th>\n", " <th>univ</th>\n", " <th>cell</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>id</th>\n", " <th>id</th>\n", " <th>id</th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>id</th>\n", " <th>id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>558</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>279</td>\n", " <td>absorption</td>\n", " <td>6.81e-04</td>\n", " <td>2.84e-05</td>\n", " </tr>\n", " <tr>\n", " <th>559</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>279</td>\n", " <td>scatter</td>\n", " <td>8.82e-02</td>\n", " <td>1.86e-03</td>\n", " </tr>\n", " <tr>\n", " <th>560</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>280</td>\n", " <td>absorption</td>\n", " <td>6.65e-04</td>\n", " <td>3.46e-05</td>\n", " </tr>\n", " <tr>\n", " <th>561</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>280</td>\n", " <td>scatter</td>\n", " <td>8.37e-02</td>\n", " <td>2.02e-03</td>\n", " </tr>\n", " <tr>\n", " <th>562</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>9</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>281</td>\n", " <td>absorption</td>\n", " <td>5.61e-04</td>\n", " <td>2.91e-05</td>\n", " </tr>\n", " <tr>\n", " <th>563</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>9</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>281</td>\n", " <td>scatter</td>\n", " <td>7.52e-02</td>\n", " <td>1.79e-03</td>\n", " </tr>\n", " <tr>\n", " <th>564</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>282</td>\n", " <td>absorption</td>\n", " <td>4.77e-04</td>\n", " <td>2.33e-05</td>\n", " </tr>\n", " <tr>\n", " <th>565</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>10</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>282</td>\n", " <td>scatter</td>\n", " <td>6.68e-02</td>\n", " <td>1.14e-03</td>\n", " </tr>\n", " <tr>\n", " <th>566</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>283</td>\n", " <td>absorption</td>\n", " <td>4.64e-04</td>\n", " <td>2.05e-05</td>\n", " </tr>\n", " <tr>\n", " <th>567</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>11</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>283</td>\n", " <td>scatter</td>\n", " <td>6.20e-02</td>\n", " <td>1.61e-03</td>\n", " </tr>\n", " <tr>\n", " <th>568</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>284</td>\n", " <td>absorption</td>\n", " <td>4.44e-04</td>\n", " <td>2.93e-05</td>\n", " </tr>\n", " <tr>\n", " <th>569</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>12</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>284</td>\n", " <td>scatter</td>\n", " <td>5.47e-02</td>\n", " <td>1.53e-03</td>\n", " </tr>\n", " <tr>\n", " <th>570</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>13</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>285</td>\n", " <td>absorption</td>\n", " <td>3.67e-04</td>\n", " <td>2.63e-05</td>\n", " </tr>\n", " <tr>\n", " <th>571</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>13</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>285</td>\n", " <td>scatter</td>\n", " <td>4.68e-02</td>\n", " <td>1.52e-03</td>\n", " </tr>\n", " <tr>\n", " <th>572</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>286</td>\n", " <td>absorption</td>\n", " <td>2.76e-04</td>\n", " <td>1.75e-05</td>\n", " </tr>\n", " <tr>\n", " <th>573</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>14</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>286</td>\n", " <td>scatter</td>\n", " <td>3.81e-02</td>\n", " <td>1.28e-03</td>\n", " </tr>\n", " <tr>\n", " <th>574</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>287</td>\n", " <td>absorption</td>\n", " <td>2.08e-04</td>\n", " <td>1.69e-05</td>\n", " </tr>\n", " <tr>\n", " <th>575</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>287</td>\n", " <td>scatter</td>\n", " <td>2.85e-02</td>\n", " <td>1.13e-03</td>\n", " </tr>\n", " <tr>\n", " <th>576</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>288</td>\n", " <td>absorption</td>\n", " <td>1.32e-04</td>\n", " <td>1.30e-05</td>\n", " </tr>\n", " <tr>\n", " <th>577</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>16</td>\n", " <td>16</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>288</td>\n", " <td>scatter</td>\n", " <td>1.86e-02</td>\n", " <td>7.12e-04</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " level 1 level 2 level 3 distribcell score \\\n", " univ cell lat univ cell \n", " id id id x y id id \n", "558 3 4 2 7 16 1 3 279 absorption \n", "559 3 4 2 7 16 1 3 279 scatter \n", "560 3 4 2 8 16 1 3 280 absorption \n", "561 3 4 2 8 16 1 3 280 scatter \n", "562 3 4 2 9 16 1 3 281 absorption \n", "563 3 4 2 9 16 1 3 281 scatter \n", "564 3 4 2 10 16 1 3 282 absorption \n", "565 3 4 2 10 16 1 3 282 scatter \n", "566 3 4 2 11 16 1 3 283 absorption \n", "567 3 4 2 11 16 1 3 283 scatter \n", "568 3 4 2 12 16 1 3 284 absorption \n", "569 3 4 2 12 16 1 3 284 scatter \n", "570 3 4 2 13 16 1 3 285 absorption \n", "571 3 4 2 13 16 1 3 285 scatter \n", "572 3 4 2 14 16 1 3 286 absorption \n", "573 3 4 2 14 16 1 3 286 scatter \n", "574 3 4 2 15 16 1 3 287 absorption \n", "575 3 4 2 15 16 1 3 287 scatter \n", "576 3 4 2 16 16 1 3 288 absorption \n", "577 3 4 2 16 16 1 3 288 scatter \n", "\n", " mean std. dev. \n", " \n", " \n", "558 6.81e-04 2.84e-05 \n", "559 8.82e-02 1.86e-03 \n", "560 6.65e-04 3.46e-05 \n", "561 8.37e-02 2.02e-03 \n", "562 5.61e-04 2.91e-05 \n", "563 7.52e-02 1.79e-03 \n", "564 4.77e-04 2.33e-05 \n", "565 6.68e-02 1.14e-03 \n", "566 4.64e-04 2.05e-05 \n", "567 6.20e-02 1.61e-03 \n", "568 4.44e-04 2.93e-05 \n", "569 5.47e-02 1.53e-03 \n", "570 3.67e-04 2.63e-05 \n", "571 4.68e-02 1.52e-03 \n", "572 2.76e-04 1.75e-05 \n", "573 3.81e-02 1.28e-03 \n", "574 2.08e-04 1.69e-05 \n", "575 2.85e-02 1.13e-03 \n", "576 1.32e-04 1.30e-05 \n", "577 1.86e-02 7.12e-04 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a pandas dataframe for the distribcell tally data\n", "df = tally.get_pandas_dataframe(nuclides=False)\n", "\n", "# Print the last twenty rows in the dataframe\n", "df.tail(20)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th>mean</th>\n", " <th>std. dev.</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2.89e+02</td>\n", " <td>2.89e+02</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>4.17e-04</td>\n", " <td>2.05e-05</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2.42e-04</td>\n", " <td>8.32e-06</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>2.27e-05</td>\n", " <td>4.04e-06</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>2.01e-04</td>\n", " <td>1.40e-05</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>4.00e-04</td>\n", " <td>2.05e-05</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6.08e-04</td>\n", " <td>2.60e-05</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>9.38e-04</td>\n", " <td>4.27e-05</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mean std. dev.\n", " \n", " \n", "count 2.89e+02 2.89e+02\n", "mean 4.17e-04 2.05e-05\n", "std 2.42e-04 8.32e-06\n", "min 2.27e-05 4.04e-06\n", "25% 2.01e-04 1.40e-05\n", "50% 4.00e-04 2.05e-05\n", "75% 6.08e-04 2.60e-05\n", "max 9.38e-04 4.27e-05" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show summary statistics for absorption distribcell tally data\n", "absorption = df[df['score'] == 'absorption']\n", "absorption[['mean', 'std. dev.']].dropna().describe()\n", "\n", "# Note that the maximum standard deviation does indeed\n", "# meet the 5e-5 threshold set by the tally trigger" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform a statistical test comparing the tally sample distributions for two categories of fuel pins." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mann-Whitney Test p-value: 0.3933685843661936\n" ] } ], "source": [ "# Extract tally data from pins in the pins divided along y=-x diagonal \n", "multi_index = ('level 2', 'lat',)\n", "lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n", "upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n", "lower = lower[lower['score'] == 'absorption']\n", "upper = upper[upper['score'] == 'absorption']\n", "\n", "# Perform non-parametric Mann-Whitney U Test to see if the \n", "# absorption rates (may) come from same sampling distribution\n", "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n", "print('Mann-Whitney Test p-value: {0}'.format(p))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the symmetry implied by the y=-x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (e.g., $\\alpha$=0.05) one would not reject the null hypothesis that the two sampling distributions are identical.\n", "\n", "Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mann-Whitney Test p-value: 7.927841393301949e-42\n" ] } ], "source": [ "# Extract tally data from pins in the pins divided along y=x diagonal\n", "multi_index = ('level 2', 'lat',)\n", "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n", "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n", "lower = lower[lower['score'] == 'absorption']\n", "upper = upper[upper['score'] == 'absorption']\n", "\n", "# Perform non-parametric Mann-Whitney U Test to see if the \n", "# absorption rates (may) come from same sampling distribution\n", "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n", "print('Mann-Whitney Test p-value: {0}'.format(p))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the asymmetry implied by the y=x diagonal ensures that the two sampling distributions are not identical. Indeed, as illustrated by the test above, for any reasonable significance level (e.g., $\\alpha$=0.05) one would reject the null hypothesis that the two sampling distributions are identical." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jan/.local/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " after removing the cwd from sys.path.\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f39195e74e0>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Extract the scatter tally data from pandas\n", "scatter = df[df['score'] == 'scatter']\n", "\n", "scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n", "\n", "# Show a scatter plot of the mean vs. the std. dev.\n", "scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f39195d7cf8>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot a histogram and kernel density estimate for the scattering rates\n", "scatter['mean'].plot(kind='hist', bins=25)\n", "scatter['mean'].plot(kind='kde')\n", "plt.title('Scattering Rates')\n", "plt.xlabel('Mean')\n", "plt.legend(['KDE', 'Histogram'])" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
opalytics/opalytics-ticdat	examples/expert_section/notebooks/different_slicings.ipynb	1	9433	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Slicing with pandas, gurobipy.tuplelist, and O(n) slicing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run a little python script that sets up the performance comparisons." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "run prep_for_different_slicings.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The slicing will be over small, medium, and large tables." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1200, 31800, 270000]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[len(getattr(td, \"childTable\")) for td in (smallTd, medTd, bigTd)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will run three series of four tests each.\n", "\n", "Each series tests\n", " 1. slicing with `.sloc` and `pandas`\n", " 1. slicing with `gurobipy.tuplelist`\n", " 1. slicing with `ticdat.Slicer` (with the `gurobipy` enhancement disabled)\n", " 1. O(n) slicing \n", "\n", "First, we see that with a small table (1,200) rows, the `pandas` slicing is only somewhat faster than the O(n) slicing, while `Slicer` slicing is quite a bit faster and `tuplelist` faster still." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 1.59 ms per loop\n" ] } ], "source": [ "%timeit checkChildDfLen(smallChildDf, smallChk)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 5.71 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 5.26 µs per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(smallSmartTupleList, smallChk)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 23.3 µs per loop\n" ] } ], "source": [ "%timeit checkSlicerLen(smallSlicer, smallChk)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 5.71 ms per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(smallDumbTupleList, smallChk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we see that with a table of 31,800 rows, `pandas` slicing is now ~100 faster than O(n) slicing (but `tuplelist` and `Slicer` are still the fastest by far)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 1.87 ms per loop\n" ] } ], "source": [ "%timeit checkChildDfLen(medChildDf, medChk)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 4.05 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 5.17 µs per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(medSmartTupleList, medChk)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 25 µs per loop\n" ] } ], "source": [ "%timeit checkSlicerLen(medSlicer, medChk)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 164 ms per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(medDumbTupleList, medChk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we see that with a table of 270,000 rows, `pandas` slicing is ~1000X faster than O(n) slicing. Here, `tuplelist` is blindingly fast - nearly as much an improvement shows over `pandas` as `pandas` shows over O(n). `Slicer` again comes in a respectably close second. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 4.06 ms per loop\n" ] } ], "source": [ "%timeit checkChildDfLen(bigChildDf, bigChk)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 loops, best of 3: 5.27 µs per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(bigSmartTupleList, bigChk)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 69 µs per loop\n" ] } ], "source": [ "%timeit checkSlicerLen(bigSlicer, bigChk)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 1.42 s per loop\n" ] } ], "source": [ "%timeit checkTupleListLen(bigDumbTupleList, bigChk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bottom line? `pandas` isn't really designed with \"iterating over indicies and slicing\" in mind, so it isn't the absolutely fastest way to write this sort of code. However, `pandas` also doesn't implement naive O(n) slicing. \n", "\n", "For most instances, the `.sloc` approach to slicing will be fast enough. In general, so long as you use the optimal big-O subroutines, the time to solve a MIP or LP model will be larger than the time to formulate the model. However, in those instances where the slicing is the bottleneck operation, `gurobipy.tuplelist` or `ticdat.Slicer` can be used, or the model building code can be refactored to be more pandonic. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Addendum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There was a request to check `sum` as well as `len`. Here the results vindicate `pandas`, in as much as all three \"smart\" strategies are roughly equivalent." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 4.22 ms per loop\n" ] } ], "source": [ "%timeit checkChildDfSum(bigChildDf, bigChk)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 2.9 ms per loop\n" ] } ], "source": [ "%timeit checkTupleListSum(bigSmartTupleList, bigTd, bigChk)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 2.94 ms per loop\n" ] } ], "source": [ "%timeit checkSlicerSum(bigSlicer, bigTd, bigChk)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 1.42 s per loop\n" ] } ], "source": [ "%timeit checkTupleListSum(bigDumbTupleList, bigTd, bigChk)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
balarsen/pymc_learning	SolarWind/IMF Bmag EVA.ipynb	1	1011005	{ "cells": [ { "cell_type": "raw", "metadata": { "deletable": true, "editable": true }, "source": [ "Los Alamos National Laboratory\n", "ISR-1 Space Science and Applications\n", "Brian Larsen, Ph.D.\n", "[email protected]\n", "505-665-7691" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "#%matplotlib notebook\n", "%load_ext version_information\n", "%load_ext autoreload\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/json": { "Software versions": [ { "module": "Python", "version": "3.5.2 64bit [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]" }, { "module": "IPython", "version": "5.1.0" }, { "module": "OS", "version": "Darwin 15.6.0 x86_64 i386 64bit" }, { "module": "matplotlib", "version": "1.5.3" }, { "module": "numpy", "version": "1.11.1" } ] }, "text/html": [ "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.5.2 64bit [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]</td></tr><tr><td>IPython</td><td>5.1.0</td></tr><tr><td>OS</td><td>Darwin 15.6.0 x86_64 i386 64bit</td></tr><tr><td>matplotlib</td><td>1.5.3</td></tr><tr><td>numpy</td><td>1.11.1</td></tr><tr><td colspan='2'>Wed Feb 15 18:04:06 2017 MST</td></tr></table>" ], "text/latex": [ "\\begin{tabular}{\|l\|l\|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 3.5.2 64bit [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)] \\\\ \\hline\n", "IPython & 5.1.0 \\\\ \\hline\n", "OS & Darwin 15.6.0 x86\\_64 i386 64bit \\\\ \\hline\n", "matplotlib & 1.5.3 \\\\ \\hline\n", "numpy & 1.11.1 \\\\ \\hline\n", "\\hline \\multicolumn{2}{\|l\|}{Wed Feb 15 18:04:06 2017 MST} \\\\ \\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Software versions\n", "Python 3.5.2 64bit [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]\n", "IPython 5.1.0\n", "OS Darwin 15.6.0 x86_64 i386 64bit\n", "matplotlib 1.5.3\n", "numpy 1.11.1\n", "Wed Feb 15 18:04:06 2017 MST" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import bisect\n", "import datetime\n", "import os\n", "import sys\n", "import warnings\n", "\n", "warnings.simplefilter(\"ignore\")\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import spacepy.datamodel as dm\n", "import spacepy.plot as spp\n", "import spacepy.toolbox as tb\n", "import spacepy.time as spt\n", "import scipy\n", "import tqdm\n", "\n", "%version_information matplotlib, numpy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = [10, 6]\n", "plt.rcParams['savefig.dpi'] = plt.rcParams['figure.dpi'] # 72\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(228648, 7)\n" ] } ], "source": [ "# 1 YEAR I4 \n", "# 2 DOY I4 \n", "# 3 Hour I3 \n", "# 4 Scalar B, nT F6.1 \n", "# 5 BZ, nT (GSM) F6.1 \n", "# 6 SW Plasma Speed, km/s F6.0 \n", "# 7 Dst-index, nT I6 \n", "\n", "fname = 'omni2_21972.lst'\n", "data = np.loadtxt(fname)\n", "print(data.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# make the datetime\n", "tm = data[:,0:3].astype(int)\n", "# year = data[:,0]\n", "# doy = data[:,1]\n", "# hour = data[:,2]\n", "\n", "dt = np.asarray([datetime.datetime.strptime('{0:04}{1:03}{2:02}'.format(y,d,h), '%Y%j%H') for y,d,h in tm])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# grab the data into a numpy record array\n", "dtype = [('DT', object), ('B', float), ('Bz', float), ('Vsw', float), ('Dst', int)]\n", "# dat = np.array(data[:,3:], dtype=[('B', float), ('Bz', float), ('Vsw', float), ('Dst', int)])\n", "dat = np.ma.zeros(data.shape[0], dtype=dtype) \n", "# dat\n", "\n", "dat['DT'][:] = dt\n", "dat['B'][:] = np.ma.masked_greater_equal(data[:,3], 999.9)\n", "dat['Bz'][:] = np.ma.masked_greater_equal(data[:,4], 999.9)\n", "dat['Vsw'][:] = np.ma.masked_greater_equal(data[:,5], 9999.)\n", "dat['Dst'][:] = data[:,6]\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1990-01-01 00:00:00 2016-01-31 23:00:00\n", "[datetime.datetime(1990, 1, 1, 0, 0) datetime.datetime(1990, 1, 1, 1, 0)\n", " datetime.datetime(1990, 1, 1, 2, 0) datetime.datetime(1990, 1, 1, 3, 0)\n", " datetime.datetime(1990, 1, 1, 4, 0) datetime.datetime(1990, 1, 1, 5, 0)\n", " datetime.datetime(1990, 1, 1, 6, 0) datetime.datetime(1990, 1, 1, 7, 0)\n", " datetime.datetime(1990, 1, 1, 8, 0) datetime.datetime(1990, 1, 1, 9, 0)]\n" ] } ], "source": [ "print(np.asarray(dat['DT']).min(), np.asarray(dat['DT']).max())\n", "print(np.asarray(dat['DT'])[0:10])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10e35be48>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PmWvlDgmTlrmRlgbMer91og+HUsS4xW6s2yPhii42DHm5VaIPJ27szsZgh8wYNBERAX67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\nAACgMlVkYLZs2bJccMEF+c53vpPHHnssL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"text/plain": [ "<matplotlib.figure.Figure at 0x10bb667b8>" ] }, "metadata": { "image/png": { "height": 374, "width": 614 } }, "output_type": "display_data" } ], "source": [ "spp.plot(dat['DT'], dat['Dst'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f681780>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mAAAAAEiFYAoAAIohSbsAABj4BFMAAAAApEIwBQAAAEAqKop58B07dsRf/dVf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y3t8TXQqKCWqBbW7I6gsl57kZ1VO5WdlGV6MCrFwQFu5JQiDMWQ8KOCggJMmTIFN9xwA0aNGgUA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\nC9sgWZeW1e1lxWQ9chpKiDEKXApSH8tfi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"text/plain": [ "<matplotlib.figure.Figure at 0x112086550>" ] }, "metadata": { "image/png": { "height": 374, "width": 595 } }, "output_type": "display_data" } ], "source": [ "spp.plot(dat['DT'], dat['B'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107da4a20>" ] }, "metadata": { "image/png": { "height": 374, "width": 615 } }, "output_type": "display_data" } ], "source": [ "b, h, p = spp.plt.hist(dat['B'].compressed(), 100, normed=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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LLyGEwKOPPoqk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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10bb784e0>" ] }, "metadata": { "image/png": { "height": 374, "width": 615 } }, "output_type": "display_data" } ], "source": [ "b, h, p = spp.plt.hist(dat['Bz'].compressed(), 100, normed=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10bd2d278>" ] }, "metadata": { "image/png": { "height": 374, "width": 632 } }, "output_type": "display_data" } ], "source": [ "b, h, p = spp.plt.hist(dat['Vsw'].compressed(), 100, normed=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x110790cf8>" ] }, "metadata": { "image/png": { "height": 374, "width": 628 } }, "output_type": "display_data" } ], "source": [ "b, h, p = spp.plt.hist(dat['Dst'].compressed(), 100, normed=True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Lets now make a block maxima\n", "Take the hourly data and make 1 day blocks of maxima" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "days = spt.tickrange(np.asarray(dat['DT']).min(), np.asarray(dat['DT']).max(), 1)\n", "B_daily = []\n", "for d1, d2 in tqdm.tqdm_notebook(list(zip(days[:-1], days[1:]))): # not the most efficient, but zip objects have not len\n", " ind1 = bisect.bisect_right(np.asarray(dat['DT']), d1)\n", " ind2 = bisect.bisect_right(np.asarray(dat['DT']), d2)\n", " B_daily.append(dat['B'][ind1:ind2].max())\n", "B_daily = np.asarray(B_daily)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x124562c50>]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Q7SZLWa0/0nb98Hq8ccfg87WNYlpTUxM2URXpM469lrk9ba9C52R3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\ntatrkHbVJ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5YskSQVFhaqb9++2rp1qy6//HJJ0qxZsyI2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\nhBBCCCEe8f8Brpx71U7Tbd0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12530a630>" ] }, "metadata": { "image/png": { "height": 374, "width": 595 } }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(2, 1, sharex=True)\n", "ax[0].plot(dat['DT'], dat['B'])\n", "ax[1].plot(days.UTC[0:-1], B_daily)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x12530ada0>" ] }, "metadata": { "image/png": { "height": 384, "width": 628 } }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(2, 1, sharex=True)\n", "bins = np.linspace(0, 70, 100)\n", "_ = ax[0].hist(dat['B'], bins)\n", "ax[0].set_title('All B');\n", "_ = ax[1].hist(B_daily[np.isfinite(B_daily)], bins)\n", "ax[1].set_title('Block maxima B');\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Now fit this to a generalized extreme value distribution" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pymc3 as mc3" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "2.1976202783924776" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scipy.special.gamma(3.1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Applied interval-transform to alpha and added transformed alpha_interval_ to model.\n", "Applied interval-transform to beta and added transformed beta_interval_ to model.\n", "Assigned NUTS to alpha_interval_\n", "Assigned NUTS to beta_interval_\n", " [-----------------100%-----------------] 10000 of 10000 complete in 68.0 sec" ] } ], "source": [ "with mc3.Model() as model:\n", " alpha = mc3.Uniform('alpha', 0, 100, testval=10)\n", " beta = mc3.Uniform('beta', 0, 100, testval=10)\n", " weibull = mc3.Weibull('weibull', alpha=alpha, beta=beta, observed=B_daily[np.isfinite(B_daily)]-B_daily[np.isfinite(B_daily)].min()+0.001)\n", " trace = mc3.sample(10000)\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "alpha:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 1.539 0.246 0.009 [1.507, 1.552]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " \|--------------\|==============\|==============\|--------------\|\n", " \n", " 1.508 1.522 1.530 1.538 1.553\n", "\n", "\n", "beta:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 7.179 0.291 0.009 [7.065, 7.269]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " \|--------------\|==============\|==============\|--------------\|\n", " \n", " 7.068 7.134 7.170 7.204 7.274\n", "\n" ] }, { "data": { "image/png": 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BgQ4cOKD4+HglJSXJy8tLzz77rIKCgur1LGYbAAAAQPNjajLqz3/+c+WHeXlp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Kz60i8iGVUBoaGhOnHihFJSUsosVzAXfHlJq5Lk5eVJksxms3Wx34CAADVr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\n6+oXEkVEOsM0TeqbgoRCOseI9Cc6pkX6hriepu/ss8/m6NGjfPrpp9x00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3UKhazjIxiy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\nX78eZWVlGDF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"text/plain": [ "<matplotlib.figure.Figure at 0x12bedb6d8>" ] }, "metadata": { "image/png": { "height": 273, "width": 849 } }, "output_type": "display_data" } ], "source": [ "mc3.traceplot(trace);\n", "mc3.summary(trace)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "mean = trace['beta']scipy.special.gamma(1 + 1.0/trace['alpha'])\n", "variance = trace['beta']2 scipy.special.gamma(1+ 2.0/trace['alpha'] - mean*2)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING (theano.gof.compilelock): Overriding existing lock by dead process '28684' (I am process '39749')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Applied log-transform to weibullout and added transformed weibullout_log_ to model.\n", "Assigned NUTS to weibullout_log_\n", " [-----------------100%-----------------] 100000 of 100000 complete in 24.7 sec" ] } ], "source": [ "with mc3.Model() as model2:\n", " weibullout = mc3.Weibull('weibullout', alpha=trace['alpha'].mean(), beta=trace['beta'].mean())\n", " traceout = mc3.sample(100000)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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25p8Wax81xpgx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\niIhM+D+/QjGvfgIGMwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12c7b30b8>" ] }, "metadata": { "image/png": { "height": 374, "width": 615 } }, "output_type": "display_data" } ], "source": [ "bins = np.linspace(0, 35, 51)\n", "_ = spp.plt.hist(traceout['weibullout'], bins,histtype='step', color='b', normed=True)\n", "_ = spp.plt.hist(B_daily[np.isfinite(B_daily)]-B_daily[np.isfinite(B_daily)].min(), bins,histtype='step', color='r', normed=True)\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-27-04920f6ef0f8>, line 4)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-27-04920f6ef0f8>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m scipy.in\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "# integrate to the 1 in 5 year, that is 1 in 5365\n", "pct = 1.0/(5*365)\n", "hist = np.histogram(traceout[''])\n", "scipy.in" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "hist = np.histogram(B_daily[np.isfinite(B_daily)]-B_daily[np.isfinite(B_daily)].min(), bins)\n", "np.trapz(hist[0][0:-1])/np.trapz(hist[0])\n", "hist[1][-1]\n", "print(\"So is {0} the 1 in {1} chance? This is 1 in {2} days ({3} years) \".format(hist[1][-1], \n", " np.trapz(hist[0][0:-1])/np.trapz(hist[0]), \n", " 1.0/(1-np.trapz(hist[0][0:-1])/np.trapz(hist[0])), \n", " 1.0/(1-np.trapz(hist[0][0:-1])/np.trapz(hist[0]))/365.))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## GEV dist" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import theano.tensor as T\n", "\n", "with mc3.Model() as gev:\n", " xi = mc3.Uniform('xi', lower=0, upper=60)\n", " \n", " def alpha(value=5):\n", " \"\"\"Scale parameter\"\"\"\n", " return 1./value\n", " \n", " kappa = mc3.Beta('kappa', alpha=5., beta=6.)\n", "\n", " x = [data, kappa, xi, alpha]\n", " D = mc3.DensityDist('D', lambda x: T.log(mc3.distributions.gev_like(x[0], x[1], x[2], x[3])))\n", " \n", "\n", " " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pymc.gev_like?" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pymc\n", "\n", "\n", "xi = pymc.Uniform('xi', rseed=True, value=1, lower=0, upper=60, doc='Location parameter')\n", "\n", "@pymc.deterministic\n", "def alpha(value=5):\n", " \"\"\"Scale parameter\"\"\"\n", " return 1./value\n", "\n", "# kappa = pymc.Beta('kappa', rseed=True, alpha=5., beta=6., doc='Shape parameter')\n", "kappa = pymc.Uniform('kappa', -10, 10)\n", "\n", "\n", "@pymc.data\n", "def D(value=B_daily[np.isfinite(B_daily)], location=xi, scale=alpha, shape=kappa):\n", " return pymc.gev_like(value, shape, location, scale)\n", "\n", "gev_model = pymc.Model((xi, alpha, kappa, D))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 40000 of 40000 complete in 40.3 secPlotting xi\n", "Plotting kappa\n", "Plotting alpha\n" ] }, { "data": { "image/png": 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9yc3NDXqM/SPHPtLH/gmNfaSP/RMa+0hfbm5u0M/6xmLmzJl4//33dbfp2rUrXnvtNdlj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pPP1+B5DG85fhC/++OL0v7+vl4MDiaVufl9VBGGwG/vm8Dnrtpi1e8Pf1OPCS71Odoylp4/jrNO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EQUDXrl21lnv22WdRo0YNJCcno7i42Oz1V2Yffvgh7t27h+nTp5u0XExMDO7e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JsHAPCOV1ex7UIPn3hrzblfprRFIDWTpw35LZ5j2nlNhGjR8zEsSKvUq1/uup3aoOxVJo/zxsns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\nvO47co6XWuKcccNViInw34FJ7rxXyHE8L74nODjY5He9LxB0x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iYiMTHRS60jIiIi8k8M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MMBszbNgwNW3a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5P7l21HEAQUFBS0PGgT1xrnVIcPH4ZMJtN+gWHOpM5n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\nAdhY6+4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x114117a20>" ] }, "metadata": { "image/png": { "height": 369, "width": 615 } }, "output_type": "display_data" } ], "source": [ "M = pymc.MCMC(gev_model)\n", "M.sample(iter=40000, burn=5000, thin=50)\n", "pymc.Matplot.plot(M)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "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": "3.5.3" }, "widgets": { "state": { "4bfa03476152400cbe4f0455e91e5b7c": { "views": [ { "cell_index": 15 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
lionell/university-labs	eco_systems/lab3.ipynb	3	8385	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy.optimize import minimize, curve_fit\n", "from scipy.misc import derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='http://veedif.com/files/thumbs/monopoly-3d.jpg' width='800px' />" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def U(x): return np.log(x[0] - 3) + 3.4np.log(x[1]) + 8.2np.log(x[2] - 11) + 6.7np.log(x[3] - 4)\n", "\n", "p = np.array([1, 2, 3, 4])\n", "I = 1000\n", "bounds = [\n", " (4, np.inf),\n", " (1, np.inf),\n", " (12, np.inf),\n", " (5, np.inf),\n", "]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def partial_derivative(f, i, p):\n", " def wrapper(x):\n", " a = p\n", " a[i] = x\n", " return f(a)\n", " \n", " return derivative(wrapper, p[i], dx = 1e-6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Marshall approach\n", "\n", "$$ U(x) \\to max, $$\n", "$$ px \\leq I $$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution: [ 52.15025268 83.48315766 145.25903298 86.27658577]\n", "Objective: 88.66\n", "Lagrange multipliers: [0.020345780171737715, 0.02036338742072985, 0.020358652837633901, 0.020358161378908335]\n" ] } ], "source": [ "x0 = [15, 15, 15, 15]\n", "\n", "x = minimize(lambda x: -U(x), x0, bounds=bounds, constraints=[{'type': 'ineq', 'fun': lambda x: I - x @ p}]).x\n", "Ls = [partial_derivative(U, i, x) / p[i] for i in range(len(p))]\n", "print('Solution: {}'.format(x))\n", "print('Objective: {:.2f}'.format(U(x)))\n", "print('Lagrange multipliers: {}'.format(Ls))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Higs approach\n", "\n", "$$ px \\to min, $$\n", "$$ U(x) \\geq Q $$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution: [ 5.34364123 3.98424524 17.40581003 7.92557422]\n", "Objective: 97.23\n", "Lagrange multipliers: [2.3436402309049384, 2.3436730828664629, 2.3435886635414058, 2.3436258005304635]\n" ] } ], "source": [ "x0 = [15, 15, 15, 15]\n", "q = [6, 5, 15, 10]\n", "\n", "x = minimize(lambda x: p @ x, x0, bounds=bounds, constraints=[{'type': 'ineq', 'fun': lambda x: U(x) - U(q)}]).x\n", "Ls = [p[i] / partial_derivative(U, i, x) for i in range(len(p))]\n", "print('Solution: {}'.format(x))\n", "print('Objective: {:.2f}'.format(p @ x))\n", "print('Lagrange multipliers: {}'.format(Ls))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "K = [5700, 4740, 4390, 5330, 5200, 4160, 5200, 4690, 5890, 4930]\n", "L = [12245, 13340, 13860, 14400, 14000, 11000, 14145, 13900, 15050, 13060]\n", "F = [140330, 120355, 125000, 137330, 121570, 113100, 133000, 126165, 149000, 120950]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiplicative production function\n", "\n", "$$ F = a \\cdot K^b L^c$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coeffs: [ 139.02507691 0.62259873 0.16045785]\n" ] } ], "source": [ "def cobb_douglas(x, a, b, c):\n", " return a x[0]*b x[1]*c\n", "\n", "coeffs, _ = curve_fit(cobb_douglas, (K, L), F)\n", "print('Coeffs: {}'.format(coeffs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scale effect and replacement elasticity" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Profit is decreasing with the scale.\n", "Replacement elasticity for Cobb-Douglas function is equals to 1\n" ] } ], "source": [ "if np.isclose(coeffs[1] + coeffs[2], 1): print('Profit to scale is const.')\n", "elif coeffs[1] + coeffs[2] > 1: print('Profit is increasing with the scale.')\n", "else: print('Profit is decreasing with the scale.')\n", "\n", "print('Replacement elasticity for Cobb-Douglas function is equals to 1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Full competition" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "price = 70\n", "w = [100, 100]\n", "\n", "def pi(x): return np.dot(w, x) - price cobb_douglas(x, coeffs)\n", "\n", "bounds = [\n", " (0, np.inf),\n", " (0, np.inf),\n", "]\n", "constraint = {\n", " 'type': 'ineq', \n", " 'fun': lambda x: 100000000 - x[0] - x[1]\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Short-term profit" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution: [ 29328607.78334537 21137714.98190209]\n", "Profit: 1449882747.74\n" ] } ], "source": [ "short_x = minimize(pi, [1, 1], bounds=bounds, constraints=constraint).x\n", "print('Solution: {}'.format(short_x))\n", "print('Profit: {:.2f}'.format(-pi(short_x)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Long-term profit" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solution: [ 96659920.15248498 13702650.4375944 ]\n", "Profit: 1697360921.77\n" ] } ], "source": [ "long_x = minimize(pi, [1, 1], bounds=bounds).x\n", "print('Solution: {}'.format(long_x))\n", "print('Profit: {:.2f}'.format(-pi(long_x)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monopoly" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Resources usage: [ 196.37637898 116.51225428]\n", "Resources prices: [3.9094094746108627, 0.91280635702792123]\n", "Price: 100.17411635964717\n", "Production volume: 7986.070691228226\n" ] } ], "source": [ "def price_func(x): return -x / 80 + 200\n", "\n", "def wK(x): return 0.025 x[0] - 1\n", "def wL(x): return 0.025 * x[1] - 2\n", "\n", "def monopoly_pi(x):\n", " q = cobb_douglas(x, coeffs)\n", " return (wK(x), wL(x)) @ x - price_func(q) q\n", "\n", "monopoly_x = minimize(monopoly_pi, [1, 1], bounds=bounds).x\n", "\n", "print('Resources usage: {}'.format(monopoly_x))\n", "print('Resources prices: {}'.format([wK(monopoly_x), wL(monopoly_x)]))\n", "print('Price: {}'.format(price_func(cobb_douglas(monopoly_x, coeffs))))\n", "print('Production volume: {}'.format(cobb_douglas(monopoly_x, coeffs)))" ] } ], "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 }	mit
palindromed/data-science	problem_set1/Day1.ipynb	1	32574	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/Users/wohlfea/401d2/data-science'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "length: 891 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "TEST_DATA = 'train.csv'\n", "titanic_dataframe = pd.read_csv(TEST_DATA, header=0)\n", "print('length: {0} '.format(len(titanic_dataframe)))\n", "titanic_dataframe.head(5)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Describe the Data:\n", "* We have 891 rows in the train.csv data.\n", "* Columns in the data:\n", "* PassengerId - a seemingly random, unique number given to each row in the data.\n", "* Survived - a binary column. 1 means the person survived and 0 means they died.\n", "* Pclass - an indicator of socio-economic status.\n", "* Name - the name of a passenger on the Titanic.\n", "* Sex - the gender of the passenger\n", "* Age - the age of a given passenger\n", "* SibSp - is the number of siblings and spouses aboard.\n", "* Parch - number of parents/children\n", "* Ticket - Ticket number of a given passenger\n", "* Fare - The cost of the ticket\n", "* Cabin - Where they stayed on the ship\n", "* Embarked - Port of Embarkation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of all Titanic passengers:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "29.69911764705882\n" ] } ], "source": [ "avg_all = np.mean(titanic_dataframe['Age'])\n", "print(avg_all)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of Titanic survivors:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "28.343689655172415\n" ] } ], "source": [ "avg_survive = np.mean(titanic_dataframe[titanic_dataframe.Survived==1].Age)\n", "print(avg_survive)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of non-surviving first class passenger:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "43.6953125\n" ] } ], "source": [ "avg_nonsurvivor_firstclass = np.mean(titanic_dataframe[(titanic_dataframe.Survived==0) & (titanic_dataframe.Pclass==1)].Age)\n", "print(avg_nonsurvivor_firstclass)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Average age of male survivors over 30 from anywhere but Queenstown:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41.48780487804878\n" ] } ], "source": [ "avg_male_survived_30_noq = np.mean(titanic_dataframe[(titanic_dataframe.Survived==1) & (titanic_dataframe.Sex=='male') &\n", " (titanic_dataframe.Age > 30) & (titanic_dataframe.Embarked!='Q')].Age)\n", "print(avg_male_survived_30_noq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Difference between mean and median for all passengers, filling in NaN with mean" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.69911764706\n" ] } ], "source": [ "median_forall = np.median(titanic_dataframe[titanic_dataframe.Age > 0].Age)\n", "print(median_forall - avg_all)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Difference between mean and median ages for survivors:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.343689655172\n" ] } ], "source": [ "median_survivor = np.median(titanic_dataframe[(titanic_dataframe.Age >0) & (titanic_dataframe.Survived==1)].Age)\n", "print(median_survivor - avg_survive)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Difference between mean and median ages for non surviving first class passengers:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.5546875\n" ] } ], "source": [ "median_first_dead = np.median(titanic_dataframe[(titanic_dataframe.Age >0) & (titanic_dataframe.Survived==0) &\n", " (titanic_dataframe.Pclass==1) ].Age)\n", "print(median_first_dead - avg_nonsurvivor_firstclass)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Difference between mean and median ages for survivor men over 30 not from Queenstown:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-3.48780487805\n" ] } ], "source": [ "median_men_noq = np.median(titanic_dataframe[(titanic_dataframe.Survived==1) & (titanic_dataframe.Sex=='male') &\n", " (titanic_dataframe.Age > 30) & (titanic_dataframe.Embarked!='Q')].Age)\n", "print(median_men_noq - avg_male_survived_30_noq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most common passenger class:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_dataframe['Pclass'].mode().item()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mode of Port of Embarkation:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'S'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_dataframe['Embarked'].mode().item()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mode of siblings/spouses aboard:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_dataframe['SibSp'].mode().item()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The median ticket price is 0-1 stds from the mean. It is below the mean" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "14.4542" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(titanic_dataframe['Fare'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32.2042079685746" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(titanic_dataframe['Fare'])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8.05" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_dataframe['Fare'].mode().item()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "49.693428597180905" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.std(titanic_dataframe['Fare'], ddof=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cost difference between the 90th and 5th percent tickets and their classes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70.7333\n" ] } ], "source": [ "nintieth_cost = titanic_dataframe['Fare'].quantile(.9)\n", "fifth_cost = titanic_dataframe['Fare'].quantile(.05)\n", "print(nintieth_cost - fifth_cost)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19 3\n", "26 3\n", "203 3\n", "244 3\n", "354 3\n", "522 3\n", "553 3\n", "598 3\n", "661 3\n", "693 3\n", "773 3\n", "875 3\n", "Name: Pclass, dtype: int64\n" ] } ], "source": [ "class_level = titanic_dataframe[(titanic_dataframe.Fare == titanic_dataframe['Fare'].quantile(.05))].Pclass\n", "print(class_level)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "275 1\n", "627 1\n", "765 1\n", "Name: Pclass, dtype: int64\n" ] } ], "source": [ "upper_class = titanic_dataframe[(titanic_dataframe.Fare == titanic_dataframe['Fare'].quantile(.9))].Pclass\n", "print(upper_class)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The port with the most expensive average ticket price is Cherbourg" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27.07981180124218 13.276029870129872 59.95414404761905\n" ] } ], "source": [ "south = np.mean(titanic_dataframe[(titanic_dataframe.Embarked == 'S')].Fare)\n", "queens = np.mean(titanic_dataframe[(titanic_dataframe.Embarked == 'Q')].Fare)\n", "cherb = np.mean(titanic_dataframe[(titanic_dataframe.Embarked == 'C')].Fare)\n", "print(south, queens, cherb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The port with the most similar passenger class is Queenstown because it has the smallest std" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7887893083528223 0.3668687504370267 0.9412858178734583\n" ] } ], "source": [ "southie = np.std(titanic_dataframe[(titanic_dataframe.Embarked=='S')].Pclass)\n", "queenie = np.std(titanic_dataframe[(titanic_dataframe.Embarked=='Q')].Pclass)\n", "cherbie = np.std(titanic_dataframe[(titanic_dataframe.Embarked=='C')].Pclass)\n", "print(southie, queenie, cherbie)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The average surviving passenger with family members was ~6.88 years younger than the average non surviving passenger with no family" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-6.888171076642337\n" ] } ], "source": [ "avg_surv_w_fam = np.mean(titanic_dataframe[(titanic_dataframe.Survived==1) & ((titanic_dataframe.SibSp > 0) \| (titanic_dataframe.Parch > 0))].Age)\n", "avg_non_no_fam = np.mean(titanic_dataframe[(titanic_dataframe.Survived==0) & (titanic_dataframe.SibSp==0) & (titanic_dataframe.Parch==0)].Age)\n", "print(avg_surv_w_fam-avg_non_no_fam)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the relationship (i.e. make a plot) between survival rate and the quantile of the ticket price for 20 integer quantiles. Make sure you label your axes." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/wohlfea/anaconda3/envs/first_conda/lib/python3.5/site-packages/ipykernel/__main__.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x113dc94e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as pl\n", "%matplotlib inline\n", "\n", "fare_quantiles = np.percentile(titanic_dataframe['Fare'], np.arange(5, 105, 5.0))\n", "survival_quantiles = []\n", "latest_quantile = 0\n", "for f_q in fare_quantiles:\n", " folks= titanic_dataframe[(latest_quantile < titanic_dataframe.Fare) & (titanic_dataframe.Fare < f_q)]\n", " survival_quantiles.append(len(folks[titanic_dataframe.Survived == 1]) / float(len(folks)))\n", "graph = pl.plot(np.arange(5, 105, 5.0), survival_quantiles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Surviving men who paid less than median fare price:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "45\n", "14.4542\n" ] } ], "source": [ "median_fare = np.median(titanic_dataframe.Fare)\n", "surviving_first_less_median_fare = len(titanic_dataframe[(titanic_dataframe.Survived==1) \n", " & (titanic_dataframe.Pclass==1)\n", " & (titanic_dataframe.Sex=='male')\n", " & (titanic_dataframe.Fare < median_fare)])\n", "\n", "surviving_first = len(titanic_dataframe[(titanic_dataframe.Survived==1) \n", " & (titanic_dataframe.Pclass==1)\n", " & (titanic_dataframe.Sex=='male')])\n", "print(surviving_first_less_median_fare)\n", "print(surviving_first)\n", "print(median_fare)" ] }, { "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 }	mit
mgalardini/2017_python_course	notebooks/6-Useful_third_party_libraries_for_data_analysis.ipynb	1	51200	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Useful third-party libraries `numpy`, `scipy`, `biopython` and `networkx`\n", "================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How to install third-party libraries\n", "-------\n", "\n", "There are basically two ways to install third party libraries:\n", "\n", "1. using the provided `setup.py` file\n", "2. using python software managers like `pip`\n", "3. using software managers like `anaconda` (binaries) or `linuxbrew/homebrew`\n", "4. using your operating system package managers (e.g. `apt-get` for debian-based linux distributions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# setup.py example\n", "# %%bash\n", "# wget https://github.com/biopython/biopython/archive/biopython-168.tar.gz\n", "# tar -xvf biopython-168.tar.gz\n", "# cd biopython-168.tar.gz\n", "# sudo python setup.py install" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# using pip\n", "# !pip install biopython " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# using anaconda\n", "# !conda install biopython" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# using ap-get\n", "# !sudo apt-get install python-biopython python3-biopython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy: array operations in python\n", "-----------------------------------------------------------\n", "\n", "You already encountered `numpy` as the \"engine\" that powers `pandas`. It is also used by many other third-party library to allow fast computation on arrays (i.e. `scipy` or `scikit-learn`).\n", "\n", "The most important feature of NumPy is the `ndarray` (n-dimensional-array), which is a multidimensional matrix with fixed-type. This allows faster computation and more intuitive array manipulations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# simple array creation\n", "a = np.array([2,3,4])\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# what type is my array?\n", "a.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "b = np.array([1.2, 3.5, 5.1])\n", "b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "b.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the dimensions of the array by using the `shape` method" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "b.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy offers several array constructors that can be quite handy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# like range, but returns an array\n", "np.arange(10, step=0.2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# evenly-spaced numbers\n", "np.linspace(0, 10, num=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# evenly spaced numbers on a logaritmic space (base 2)\n", "np.logspace(0, 10, num=20, base=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.zeros(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# multidimensional\n", "z = np.zeros((10, 5))\n", "z" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "z.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.ones((10, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the operations you applied to dataframes apply to numpy arrays too" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a = np.array((np.linspace(0, 10, num=10),\n", " np.logspace(0, 10, num=10),))\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# equivalent to a.transpose(), but more concise\n", "a.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a.T.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.std(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.median(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also cahnge the dimension of the array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# rows/columns\n", "a.reshape(4, 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# rows/columns\n", "a.reshape(6, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mathematical operations on arrays are quite simple" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "b = np.array((np.linspace(0, 10, num=5),\n", " np.logspace(0, 10, num=5),))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a + b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "b = np.array((np.linspace(0, 10, num=10),\n", " np.logspace(0, 10, num=10),))\n", "a + b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a - b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a*2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a > 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a[a > 5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# element-wise product\n", "a b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# matrix product\n", "a = np.random.random((3, 3))\n", "b = np.random.random((3, 3))\n", "np.dot(a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indexing and splicing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a = np.random.random((3, 5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# get row 1 (2nd row)\n", "a[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# get column 1 (2nd column)\n", "a[:,1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# three-dimensional array\n", "a = np.random.random((3, 5, 2))\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a[1, 1:4, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SciPy: scientific python\n", "-----------------------------------------------------------\n", "\n", "SciPy is a very large library of scientific calculations and statistics to be performed on numpy array.\n", "\n", "The modules contained in this library are the following:\n", "\n", "- Special functions (scipy.special)\n", "- Integration (scipy.integrate)\n", "- Optimization (scipy.optimize)\n", "- Interpolation (scipy.interpolate)\n", "- Fourier Transforms (scipy.fftpack)\n", "- Signal Processing (scipy.signal)\n", "- Linear Algebra (scipy.linalg)\n", "- Sparse Eigenvalue Problems with ARPACK\n", "- Compressed Sparse Graph Routines (scipy.sparse.csgraph)\n", "- Spatial data structures and algorithms (scipy.spatial)\n", "- Statistics (scipy.stats)\n", "- Multidimensional image processing (scipy.ndimage)\n", "- File IO (scipy.io)\n", "- Weave (scipy.weave)\n", "\n", "They are clearly too many to go through them all, but it is worth highlighting the statistical (`stats`) and `spatial` modules." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from scipy import stats" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# get samples from a normal distribution\n", "# loc: mean\n", "# scale: std\n", "n = stats.norm.rvs(loc=0, scale=1, size=100)\n", "n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "stats.normaltest(n)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "n1 = stats.norm.rvs(loc=0, scale=1, size=100)\n", "n2 = stats.norm.rvs(loc=0.5, scale=1, size=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# ttest\n", "stats.ttest_ind(n1, n2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# Kolmogorov-Smirnoff test\n", "stats.ks_2samp(n1, n2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "table = [[1, 15],\n", " [10, 20]]\n", "stats.fisher_exact(table)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "table = [[1, 15],\n", " [10, 20]]\n", "stats.fisher_exact(table, alternative='less')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.spatial import distance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.random.random((3, 5))\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "distance.pdist(a, metric='canberra')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "distance.squareform(distance.pdist(a, metric='canberra'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Biopython: the swiss-army-knife library for bioinformatics\n", "-----------------------------------------------------------\n", "\n", "Biopython (http://biopython.org/wiki/Biopython) is a collection of libraries to manipulate files related to computational biology, from sequence data to pdb files. It allows the conversion between formats and even the interrogation of commonly used databases, such as NCBI and KEGG." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sequence manipulations\n", "\n", "Biopython uses a complex series of objects to respresent biological sequences: `SeqRecord`, `Seq` and so on. In most cases the user is not expected to create a sequence but to read it, so learning how to manipulate sequences is relatively easy.\n", "\n", "When a sequence is read from a file it comes as a `SeqRecord` object, which can handle annotations on top of a sequence.\n", "\n", "As in many biopython modules, parsing can be done either through the `parse` or the `read` method. The first one acts as an `iterator`, which means that it can be used in a `for` loop to access one sequence at a time. The latter is used when the file contains one and only one record." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from Bio import SeqIO\n", "\n", "s = SeqIO.read('../data/proteome.faa', 'fasta')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "sequences = SeqIO.parse('../data/proteome.faa', 'fasta')\n", "sequences" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "for s in sequences:\n", " print(s.id)\n", " break" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "type(s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "dir(s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "s.description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sequence objects can be sliced as strings; the actual sequence can be found under the attribute `seq`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# first 10 aminoacids\n", "s[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "s[:10].seq" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "str(s[10:20].seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sequence formats conversion\n", "\n", "We are going to take a genome in genbank format (https://www.ncbi.nlm.nih.gov/Sitemap/samplerecord.html) and convert it to the much simpler Fasta format." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# a quick look at how a GenBank file looks\n", "!head ../data/ecoli.gbk" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# a quick look at how a GenBank file looks\n", "!tail ../data/ecoli.gbk" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "s = SeqIO.read('../data/ecoli.gbk', 'genbank')\n", "SeqIO.write(s, 'ecoli.fasta', 'fasta')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# a quick check of the result\n", "!head ecoli.fasta" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!tail ecoli.fasta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sequence manipulation example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# forward\n", "str(s[1000:2000].seq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# reverse complement\n", "str(s[1000:2000].reverse_complement().seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "GenBank format features extraction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# first four features of this genbank file\n", "for feat in s.features[:5]:\n", " print(feat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "type(feat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Features (more properly `SeqFeature` objects), contain all the information related to an annotation that belongs to a sequence. The most notable attributes are `position`, `strand` and `qualifiers`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "feat.location.start, feat.location.end, feat.strand" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "feat.qualifiers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# we can also translate the original sequence\n", "s[feat.location.start:feat.location.end].seq.translate()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# we can also translate the original sequence (let's remove the last codon)\n", "s[feat.location.start:feat.location.end-3].seq.translate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SeqRecord as a prime example of OOP in python\n", "\n", "As you might have noticed, parsing sequence data in python involves storing a variety of information, not only the sequence name and the sequence itself. This is especially true for annotation-rich formats such as the `Genbank` format (or its cousing, the `GFF` format).\n", "\n", "In order to flexibly expose those useful annotations whenever a sequence file is parsed, Biopython uses the `SeqRecord` object. As you have seen it has several `attributes` with \"standard\" types (`id`, `description`, ...), a series of methods (`reverse_complement`, ...) and more interestingly, a series of attributes that are `instances` of other BioPython objects. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s.seq, type(s.seq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s.seq.alphabet, type(s.seq.alphabet)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Bio.Alphabet` submodule contains a series of alphabets to build biological sequences. This ensures that no forbidden chars are used in making a sequence. Since the alphabet is an abstract concept, it is prone to be inherited and extended by subclasses." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from Bio import Alphabet\n", "\n", "dir(Alphabet)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help(Alphabet.Alphabet)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help(Alphabet.SingleLetterAlphabet)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help(Alphabet.NucleotideAlphabet)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "help(Alphabet.ThreeLetterProtein)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Bio.Seq.Seq` object is a lower level representation of the biological sequence, meant to represent the sequence itself and its name only. It also features several utility functions, such as `reverse_complement`, `transcribe`, `translate`, and so on..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(s.seq, type(s.seq))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dir(s.seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the `Seq` class seem to be an extension of the string class, with which it shares several `methods` and `attributes`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s.features[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat = s.features[2]\n", "feat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dir(feat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `SeqFeature` class is also a very interesting example: apart from having a series of \"regular\" attributes (`id`, `qualifiers`, ...), it also contains instances of biopython-defined classes. The most interesting one is probably the `location` attribute, which contains a complex representation of the position of the feature inside the parent sequence. This is because locations are not always exactly defined (biology is messier than informatics!)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat.location.start, type(feat.location.start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from Bio import SeqFeature\n", "help(SeqFeature.ExactPosition)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat.location" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we had to draw an extremely simplified version of the `SeqRecord` hierarchy, it will look like this:\n", "\n", "```\n", "SeqRecord\n", " \|\n", " +---- Alphabet\n", " \|\n", " +---- Seq\n", " \|\n", " +---- features\n", " \|\n", " +---- SeqFeature\n", " \|\n", " +---- FeatureLocation\n", " \|\n", " +---- position\n", "[...]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading PDB files\n", "\n", "BioPython also contains a useful module to parse protein 3D structures, again in a variety of formats" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# fetch pdb file\n", "!wget http://www.rcsb.org/pdb/files/1g59.pdb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from Bio.PDB.PDBParser import PDBParser\n", "\n", "parser = PDBParser()\n", "structure = parser.get_structure('1g59', '1g59.pdb')\n", "header = parser.get_header()\n", "# fetch the structural method and the resolution\n", "print('Method: {0}'.format(header['structure_method']))\n", "print('Resolution: {0}'.format(header['resolution']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned object (`structure`) has a complex structure, which follows the structure, model, chain, residue, atom hierarchy (SMCRA).\n", "\n", "```\n", "Structure['1g59']\n", " \|\n", " +---- Model[0]\n", " \|\n", " +---- Chain['A']\n", " \| \|\n", " \| +---- Residue[' ', 1, ' ']\n", " \| \| \|\n", " \| \| +---- Atom['N']\n", " \| \| \|\n", " \| \| +---- [...]\n", " \| \| \|\n", " \| \| +---- Atom['CE']\n", " \| \|\n", " \| +---- [...]\n", " \| \|\n", " \| +---- Residue[' ', 468, ' '] [...]\n", " \|\n", " +---- Chain['B'] [...]\n", " \|\n", " +---- Chain['C'] [...]\n", " \|\n", " +---- Chain['D'] [...]\n", " \|\n", " +---- Chain[' ']\n", " \|\n", " +---- Residue['W', 1, ' ']\n", " \| \|\n", " \| +---- Atom['O']\n", " \|\n", " +---- [...]\n", " \|\n", " +---- Residue['W', 283, ' '] [...]\n", "```\n", "\n", "Q: why do you think that there can be more than one model inside a single PDB file?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "model = structure[0]\n", "chain = model['A']\n", "residue = chain[(' ', 1, ' ')]\n", "atom = residue['CE']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "chain.id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "residue.id[1], residue.resname" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "atom.name, atom.occupancy, atom.bfactor, atom.coord" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read/manipulate phylogenetic trees\n", "\n", "The `Bio.Phylo` module allow to read/write/manipulate phylogenetic treesd, as well as run complex evolutionary analysis software like `codeml`.\n", "\n", "Note: even though `Bio.Phylo` can be used to draw phylogenetic trees, other libraries such as `ete3` are suggested for their great power and versatility." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from Bio import Phylo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "tree = Phylo.read('../data/tree.nwk', 'nexus')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "tree = Phylo.read('../data/tree.nwk', 'newick')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "dir(tree)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# very simple visualization of a tree\n", "Phylo.draw_ascii(tree)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# get a list of terminal nodes\n", "tree.get_terminals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each bifurcation and terminal node in the tree is a `Clade` object, with several network-like properties. Most of the attributes and methods are shared with the `Tree` object." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "node = tree.get_terminals()[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "dir(node)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# distance between root and our node\n", "print('Distance between root and \"{0}\": {1}'.format(node.name,\n", " tree.distance(tree.root, node)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# the root can be changed too\n", "tree.root_at_midpoint()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "Phylo.draw_ascii(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interrogate the NCBI database using Bio.Entrez\n", "\n", "The NCBI has a very useful programmatic interface for data retrieval, for which BioPython has a very complex module. Find more information about Entrez here: https://www.ncbi.nlm.nih.gov/books/NBK3837/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from Bio import Entrez\n", "Entrez.email = '[email protected]'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this minimal example we are going to link a Bioproject ID to a NCBI taxonomy record. The possibility of the interface are numerous and complex, given that also pubmed and its reach metadata can be reached through Entrez." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "r = Entrez.esearch(db='bioproject',\n", " term='PRJNA57779')\n", "h = Entrez.read(r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "h" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "bioproject_id = h['IdList'][0]\n", "bioproject_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "r = Entrez.elink(dbfrom='bioproject', id=bioproject_id, linkname='bioproject_taxonomy')\n", "h = Entrez.read(r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "h" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "taxonomy_id = h[0]['LinkSetDb'][0]['Link'][0]['Id']\n", "taxonomy_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "r = Entrez.efetch(db='taxonomy', id=taxonomy_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "h = Entrez.read(r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have to deal with complex analysis involving taxonomy, a better way to go is to use the [`ete3`](http://etetoolkit.org/) library. Have a look [at this page](http://etetoolkit.org/docs/latest/tutorial/tutorial_ncbitaxonomy.html) for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Entrez module is also useful for doing literature searches through PubMed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "handle = Entrez.esearch(db='pubmed', \n", " sort='relevance', \n", " retmax='20',\n", " retmode='xml', \n", " term='Escherichia coli')\n", "results = Entrez.read(handle)\n", "results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "handle = Entrez.efetch(db='pubmed',\n", " retmode='xml',\n", " id=results['IdList'][0])\n", "results = Entrez.read(handle)\n", "results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NetworkX: Cytoscape-like library\n", "-----------------------------------------------------------\n", "\n", "This library collects many well-known algorithms to inspect graphs and network properties.\n", "\n", "Graphs are encoded in a dictionary-like way, allowing easy and intuitive parsing. Simple plotting functions are available as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import networkx as nx" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# undirected graph\n", "g = nx.Graph()\n", "\n", "# add nodes\n", "g.add_node('eggs', price=2.5)\n", "g.add_node('spam', price=3.1, rating=1)\n", "\n", "# add edges\n", "g.add_edge('eggs', 'spam', rating=3)\n", "g.add_edge('steak', 'spam')\n", "\n", "# add edges (and implicitly new nodes)\n", "g.add_edge('eggs', 'omelette')\n", "g.add_edge('vanilla', 'fudge')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "g.nodes()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "g.edges()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# access nodes and edges with a dictionary-like syntax\n", "g.node['eggs']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "g['eggs']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "g['eggs']['spam']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "g['spam']['eggs']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the obvious properties can be easily computed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nx.degree(g)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nx.betweenness_centrality(g)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nx.edge_betweenness_centrality(g)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "for component in nx.connected_components(g):\n", " print(component)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graph visualization example" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set_style('white')\n", "import random" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nx.draw_networkx_nodes?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Generate a series of random graphs\n", "gs = [nx.random_graphs.powerlaw_cluster_graph(n=random.randint(10, 20),\n", " m=random.randint(1, 3),\n", " p=random.random()0.05)\n", " for x in range(7)]\n", "\n", "# Concatenate then in a single graph\n", "# (there might be a more efficient way)\n", "g = gs[0]\n", "for g1 in gs[1:]:\n", " i = max(g.nodes()) + 1\n", " g.add_edges_from([(x+i, y+i) for (x, y) in g1.edges()])\n", "\n", "# Calculate nodes and edge properties\n", "# to have something to plot\n", "betw_cent = nx.betweenness.betweenness_centrality(g).values()\n", "edge_betw_cent = nx.edge_betweenness_centrality(g).values()\n", "\n", "# Graph layout\n", "graph_pos = nx.layout.fruchterman_reingold_layout(g)\n", "\n", "plt.figure(figsize=(9, 9))\n", "\n", "# Draw nodes\n", "nx.draw_networkx_nodes(g, graph_pos,\n", " # Node size depends on node degree\n", " node_size=[x15 for x in nx.degree(g).values()],\n", " # Node color depends on node centrality\n", " node_color=list(betw_cent),\n", " cmap=plt.get_cmap('Blues'),\n", " vmax=max(betw_cent),\n", " vmin=0)\n", "# Draw edges\n", "nx.draw_networkx_edges(g, graph_pos,\n", " # Width depends on edge centrality\n", " width=[x*250 for x in edge_betw_cent],\n", " color='k')\n", "sns.despine(bottom=True, left=True)\n", "plt.xticks([])\n", "plt.yticks([]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other useful libraries\n", "--------\n", "\n", "- GOAtools: GO terms enrichment analysis in python\n", "- statmodels: advanced statistics\n", "- rpy2: useful interface to R, when your favorite library doesn\\'t have a python alternative\n", "- pysam: read and manipulate sam files\n", "- pyvcf: read and manipulate VCF files\n", "\n", "...and many more" ] } ], "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": 0 }	gpl-2.0
myinxd/agn-ae	utils/cross-match-discarded-redshift.ipynb	1	6193	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate new samplelist" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle\n", "from pandas import read_csv" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load csv\n", "f = read_csv(\"../out.csv\", sep=' ')\n", "ra = f['RAJ2000'] # RA\n", "dec = f['DEJ2000'] # DEC\n", "label = f['A']\n", "redshift = f['z']\n", "snvss = f['SNVSS']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "def gen_rename(ra, dec):\n", " # get params\n", " temp_c = SkyCoord(ra=rau.degree, dec=decu.degree, frame='icrs')\n", " # Coordinate transform\n", " ra_rms = tuple(temp_c.ra.hms)\n", " dec_dms = tuple(temp_c.dec.dms)\n", " # save name\n", " ra_h = \"%02d\" % (int(ra_rms[0]))\n", " ra_m = \"%02d\" % (int(ra_rms[1]))\n", " ra_s_i = np.fix(np.round(ra_rms[2]100)/100)\n", " ra_s_f = np.round(ra_rms[2]100)/100 - ra_s_i\n", " ra_s = \"%02d.%02d\" % (int(ra_s_i),int(ra_s_f100))\n", " if dec_dms[0] > 0:\n", " de_d = \"+%02d\" % (int(dec_dms[0]))\n", " else:\n", " de_d = \"-%02d\" % (abs(int(dec_dms[0])))\n", " de_m = \"%02d\" % (abs(int(dec_dms[1])))\n", " de_s_i = np.fix(np.abs(np.round(dec_dms[2]10)/10))\n", " de_s_f = np.abs(np.round(dec_dms[2]10)/10) - de_s_i\n", " de_s = \"%02d.%01d\" % (int(de_s_i),np.round(de_s_f10))\n", " rename = 'J' + ''.join([ra_h,ra_m,ra_s,de_d,de_m,de_s])\n", " \n", " return rename" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Generate labels\n", "sample_best = {\"name\":[], \"label\": [], \"redshift\":[], \"snvss\":[]}\n", "for i in range(len(ra)):\n", " fname = gen_rename(ra=ra[i],dec=dec[i])\n", " sample_best[\"name\"].append(fname)\n", " sample_best[\"label\"].append(label[i])\n", " sample_best[\"redshift\"].append(float(redshift[i]))\n", " sample_best['snvss'].append(float(snvss[i]))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "fname = \"../sample-list-cross-match.csv\"\n", "samples = read_csv(fname, sep=\" \")\n", "# Count matched samples\n", "sample_match = samples[\"Match\"]\n", "sample_idx = samples[\"Index\"]\n", "match_idx = np.where(np.array(sample_match) != \"nomatching\")[0]\n", "\n", "name = np.array(sample_best[\"name\"])\n", "label = np.array(sample_best[\"label\"])\n", "redshift = np.array(sample_best[\"redshift\"])\n", "snvss = np.array(sample_best[\"snvss\"])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Discard cross matched\n", "name_new = np.delete(name,np.array(sample_idx[match_idx]).astype(int))\n", "label_new = np.delete(label,np.array(sample_idx[match_idx]).astype(int))\n", "redshift_new = np.delete(redshift,np.array(sample_idx[match_idx]).astype(int))\n", "snvss_new = np.delete(snvss,np.array(sample_idx[match_idx]).astype(int))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# add redshift restriction\n", "z_max = 0.3\n", "idx_z = np.where(redshift_new <= z_max)[0]\n", "# remove\n", "name_z = name_new[idx_z]\n", "label_z = label_new[idx_z]\n", "redshift_z = redshift_new[idx_z]\n", "snvss_z = snvss_new[idx_z]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17609,)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name_new.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(11671,)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name_z.shape" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(11671,)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_z.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# save\n", "sample_bkp = {\"name\": list(name_z), \"label\": list(label_z),\n", " \"redshift\":list(redshift_z), \"snvss\": list(snvss_z)}" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"../sample-best-cross-discard-redshift.pkl\", 'wb') as fp:\n", " pickle.dump(sample_bkp, fp)" ] }, { "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
gscottstukey/Data-Science-45min-Intros	decision_trees_101/decision_trees_101.ipynb	2	13337	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Introduction\n", "\n", "Idea behind decision tree: split the space of the attribute variables with recursive, binary split, with the aim of high purity for all regions.\n", "\n", "The collection of paths to all regions makes a tree.\n", "\n", "## Vocabulary\n", "\n", "* _attribute_, or attribute variable: a dimension in which a data point has a value (typically excluding the target variable)\n", "\n", "* _target_ variable: the variable whose value is to be predicted \n", "\n", "* the attributes of the i-th data point are labeled X_i.\n", "\n", "* the value of the target variable for the i-th data point is labeled y_i. \n", "\n", "* Trees that predict a quantitative target variable are called _regression trees_, and trees that predict qualitative targets are called _classification trees_.\n", "\n", "# Play \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get iris data and make a simple prediction" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import datasets\n", "iris = datasets.load_iris()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import random" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create training and test data sets" ] }, { "cell_type": "code", "collapsed": false, "input": [ "iris_X = iris.data\n", "iris_y = iris.target\n", "\n", "r = random.randint(0,100)\n", "np.random.seed(r)\n", "idx = np.random.permutation(len(iris_X))\n", "\n", "subset = 25\n", "\n", "iris_X_train = iris_X[idx[:-subset]] # all but the last 'subset' rows\n", "iris_y_train = iris_y[idx[:-subset]]\n", "iris_X_test = iris_X[idx[-subset:]] # the last 'subset' rows\n", "iris_y_test = iris_y[idx[-subset:]]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train a classification tree" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier()\n", "clf = clf.fit(iris_X_train,iris_y_train)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the predicted class of iris" ] }, { "cell_type": "code", "collapsed": false, "input": [ "clf.predict(iris_X_train)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the true class of iris" ] }, { "cell_type": "code", "collapsed": false, "input": [ "iris_y_train" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "clf.predict(iris_X_test)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "iris_y_test" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.externals.six import StringIO # StringIO streams data as a string to \"output file\"\n", "from IPython.display import Image # need Image to display inline\n", "\n", "# export the tree data as a string to a file\n", "dot_data = StringIO() \n", "tree.export_graphviz(clf, out_file=dot_data) \n", "\n", "#\n", "import pydot \n", "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n", "Image(graph.create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split definition\n", "\n", "Need a metric to minimize: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(filename=\"gini.png\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where p_mk is the proportion of training data in the m-th region that are from the k-th class.\n", "\n", "Values of p_mk close to 0 or 1 represent better purity, so we minimize G.\n", "\n", "\n", "## Cross validation: a side note\n", "\n", "Cross validation is a generalization of the testing/training data set paradigm. A reasonable test for the validity of a tree is re-sample the training and testing data set, re-fitting the tree each time. Small variations in the resulting trees indicate a stable model.\n", "\n", "\n", "# A Problematic Example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "classifier_1 = tree.DecisionTreeClassifier()\n", "X = numpy.array([[0],[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n", "Y = numpy.array([0,1,2,3,4,5,6,7,8,9,10])\n", "classifier_1 = classifier_1.fit(X,Y)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "classifier_1.predict(X)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.externals.six import StringIO # StringIO streams data as a string to \"output file\"\n", "from IPython.display import Image # need Image to display inline\n", "\n", "# export the tree data as a string to a file\n", "dot_data = StringIO() \n", "tree.export_graphviz(classifier_1, out_file=dot_data) \n", "\n", "#\n", "import pydot \n", "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n", "Image(graph.create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "classifier_2 = tree.DecisionTreeClassifier(max_depth=3)\n", "classifier_2 = classifier_2.fit(X,Y)\n", "classifier_2.predict(X)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dot_data = StringIO() \n", "tree.export_graphviz(classifier_2, out_file=dot_data) \n", "\n", "graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n", "Image(graph.create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Notes:\n", "\n", "* trees aren't great at predicting linear relationships between attrtibute and target variables. But standard linear regression _is_.\n", "\n", "* tree size needs to be controlled to avoid over training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression Trees\n", "\n", "## Concepts\n", "\n", "* The predicted target variable is the _mean_ of all the training target variable in the region\n", "\n", "* The split betwee R_1 and R_2 minimizes the following:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(filename=\"rss.png\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where x_i and y_i are the attribute and target variables for the i-th training data point, and y_hat is the mean of the target variables in the region." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a random dataset\n", "rng = np.random.RandomState(1)\n", "# Set the range to [0,5] and sort it numerically\n", "X = np.sort(5 * rng.rand(80, 1), axis=0)\n", "# for target, take the sine of the data, and place it in an array\n", "y = np.sin(X).ravel()\n", "# add some noise to every fifth point\n", "y[::5] += 3 * (0.5 - rng.rand(16))\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# use a regression tree model\n", "from sklearn.tree import DecisionTreeRegressor\n", "\n", "clf_1 = DecisionTreeRegressor(max_depth=2)\n", "clf_2 = DecisionTreeRegressor(max_depth=5)\n", "clf_3 = DecisionTreeRegressor()\n", "clf_1.fit(X, y)\n", "clf_2.fit(X, y)\n", "clf_3.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# generate test data in correct range, and place each pt in its own array \n", "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n", "y_1 = clf_1.predict(X_test)\n", "y_2 = clf_2.predict(X_test)\n", "y_3 = clf_3.predict(X_test)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure()\n", "plt.scatter(X, y, c=\"k\", label=\"data\")\n", "plt.plot(X_test, y_1, c=\"g\", label=\"max_depth=2\", linewidth=2)\n", "plt.plot(X_test, y_2, c=\"r\", label=\"max_depth=5\", linewidth=2)\n", "plt.plot(X_test, y_3, c=\"b\", label=\"max_depth=inf\", linewidth=1)\n", "\n", "plt.xlabel(\"data\")\n", "plt.ylabel(\"target\")\n", "plt.title(\"Decision Tree Regression\")\n", "plt.legend()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "dot_data = StringIO()\n", "tree.export_graphviz(clf_1, out_file=dot_data)\n", "tree.export_graphviz(clf_2, out_file=dot_data)\n", "tree.export_graphviz(clf_3, out_file=dot_data)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "graph = pydot.graph_from_dot_data(dot_data.getvalue()) " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(graph[0].create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(graph[1].create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "Image(graph[2].create_png())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options for overfitting:\n", "\n", "* maximum depth\n", "\n", "* minimum training data points per region\n", "\n", "* pruning\n", "\n", "\n", "# Pruning\n", "\n", "* Not implemented in scikit-learn\n", "\n", "* uses cross validation to remove nodes from the tree in such a way that one makes an optimal tradeoff between the tree's complexity and its fit to the training data.\n", "\n", "# Bagging\n", "\n", "* create an ensemble of trees, based on a subdivision of the training data\n", "\n", "* average the results of the ensemble\n", "\n", "# Random forests\n", "\n", "* deal with the fact that tree production is greedy, and always uses the strongest split first.\n", "\n", "* base each split on a random subset of attributes\n", "\n", "* combine as in bagging\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	unlicense
fdcl-gwu/MAE3134_examples	Second Order Response.ipynb	1	173471	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from scipy import signal\n", "import matplotlib.pylab as plt\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to investigate the relationship between the poles of a second order system, the damping ratio and natural frequency, and the various performance specifications. \n", "\n", "Consider the general second order response for the transfer function:\n", "\n", "\\begin{align}\n", " G(s) = \\frac{b}{s^2 + a s +b} = \\frac{\\omega_n^2}{s^2 + 2 \\zeta \\omega_n s + \\omega_n^2}\n", "\\end{align}\n", "\n", "subject to the the unit step input:\n", "\n", "\\begin{align}\n", " R(s) = \\frac{1}{s}\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overdamped Response\n", "\n", "Two real poles in the left half plane\n", "\n", "$$\n", "G(s) = \\frac{9}{s^2 + 9s + 9}\n", "$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11f160908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G1 = signal.TransferFunction([9], [1, 9, 9])\n", "time, resp1 = signal.step(G1)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(time, resp1)\n", "ax.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Underdamped Response\n", "\n", "Two complex poles in the left half plane\n", "\n", "$$\n", "G(s) = \\frac{9}{s^2 + 2s + 9}\n", "$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1216ec9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G2 = signal.TransferFunction([9], [1, 2, 9])\n", "time, resp2 = signal.step(G2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(time, resp2)\n", "ax.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Undamped Response\n", "\n", "Two imaginary poles on the imaginary axis\n", "\n", "$$\n", "G(s) = \\frac{9}{s^2 + 9}\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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XW7s81BTlmpY2u2nhF5FtwFPAJ5RSl5ctzxeRAuNv4B5gxcwgO7HoD3B1bNZS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\nN+gczfZdpM3VHpNjQuLaNqa9bSJyF0H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"text/plain": [ "<matplotlib.figure.Figure at 0x1216ec7b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G3 = signal.TransferFunction([9], [1, 0, 9])\n", "time, resp3 = signal.step(G3)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(time, resp3)\n", "ax.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Critically Damped Response\n", "\n", "Two repeated poles on the real axis\n", "\n", "$$\n", "G(s) = \\frac{9}{(s +3)^2}\n", "$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1219aac18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G4 = signal.TransferFunction([9], [1, 6, 9])\n", "time, resp4 = signal.step(G4)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(time, resp4)\n", "ax.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All basic second order responses\n", "\n", "Now we'll plot everything on a single plot" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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NNfpS39wa+f4ebHTjvrM40nQkIOcLJTwZPQFy/O62Dsf5/UvY8fsIq8NKhDoC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\nbyTtlZX4RTkvTd+46HGcajmF0Wy0e9vKxu7pTIqdRHVbNY0djXZvW61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H3yLMP4zLsy53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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x121948fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(time, resp1, label='Overdamped')\n", "ax.plot(time, resp2, label='Underdamped')\n", "ax.plot(time, resp3, label='Undamped')\n", "ax.plot(time, resp4, label='Critically Damped')\n", "ax.grid()\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effect of $\\zeta$" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GW01vr9Vcrp02LRue005sXiEwsd1FbFTxCyE+BC4DCqWUgxo4L4A3gOlADXCb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\nV1FMqbmmZfby8mLevHk8/vjjRERE4OjoSHR0NAAjR44kLi6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gSyHEM2gzFD/WKNdWM3cV\nRVFaF3Pp6lEURVH0pAK/oihKK6MCv6IoSiujAr+iKEorowK/oihKK6MCv6IoSiujAr+iKEorowK/\noihKK/P/4BqNAa7CKF0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x121a462b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot many responses for varying zeta\n", "time = np.linspace(0, 10, 100)\n", "u = np.ones_like(time)\n", "wn = 1\n", "zeta = np.linspace(0.1, 1.1, 5)\n", "fig, ax = plt.subplots()\n", "\n", "for z in zeta:\n", " num = [wn*2]\n", " den = [1, 2zwn, wn2]\n", " tout, resp, x = signal.lsim((num, den), u, time)\n", " ax.plot(tout, resp, label='zeta={:.2f}'.format(z))\n", "\n", "ax.grid()\n", "ax.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effect of $\\omega_n$" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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oPUwGHUadwKDXoRP+uWF/yMrrk/ikxOvTJgrdXonT46XZpU0WNru82F1e7C4P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\nMquU165QKM4AQkbcnTZ/ud/6CgzxCa0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"text/plain": [ "<matplotlib.figure.Figure at 0x1216389e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot many responses for varying zeta\n", "time = np.linspace(0, 10, 100)\n", "u = np.ones_like(time)\n", "wn = np.linspace(1, 5, 5)\n", "zeta = 0.5\n", "fig, ax = plt.subplots()\n", "\n", "for w in wn:\n", " num = [w2]\n", " den = [1, 2zetaw, w*2]\n", " tout, resp, x = signal.lsim((num, den), u, time)\n", " ax.plot(tout, resp, label='wn={}'.format(w))\n", "\n", "ax.grid()\n", "ax.legend()\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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
surgebiswas/poker	PokerBots_2017/sample_bot/.ipynb_checkpoints/Johnny-checkpoint.ipynb	1	24702	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import argparse\n", "import socket\n", "import sys\n", "import numpy as np\n", "import random as random\n", "from random import sample\n", "import copy as cp\n", "from HandEvaluator import HandEvaluator\n", "from Brains import RationalBrain\n", "\n", "np.set_printoptions(linewidth=150)\n", "\n", "\n", "class Johnny:\n", " \"\"\"\n", " self.bot_name = bot_name in config file. Corresponds to PLAYER_X_NAME field.\n", " \n", " self.hand = a dictionary storing properties of the current hand. Has the following keys.\n", " hand_id = int. ID of hand.\n", " button = boolean. Are we the dealer?\n", " hole1 = hole card 1\n", " hole2 = hole card 2\n", " board = current known board cards \n", " action_history = a list of the last actions. action_history[i] = another list that describes\n", " the previous actions up until action point i.\n", " result = int. Chips won (can be a negative integer if we lost chips)\n", " \n", " self.state = A dictionary representing the our state in the current match. Has the following keys.\n", " my_bank = our current bankroll \n", " their_bank = their current bankroll\n", " time_bank = cumulative time remaining in the match\n", " \n", " \n", " \"\"\"\n", " \n", " \n", " def __init__(self, bot_name, brain=RationalBrain):\n", " self.bot_name = bot_name\n", " self.brain = brain()\n", " self.state = {}\n", " self.reset_hand()\n", " \n", " \n", " def reset_hand(self):\n", " self.hand = {}\n", " self.hand['action_history'] = []\n", " self.hand['pot_size'] = []\n", " self.temporal_feature_matrix = []\n", " self.possible_actions = []\n", " \n", " \n", " \n", " \n", " \n", " ### ------- PARSING ------- ### \n", " def parse_data(self, data):\n", " splits = data.split()\n", " packet_type = splits[0]\n", " \n", " if packet_type == \"NEWHAND\":\n", " self.parse_new_hand(splits)\n", " elif packet_type == \"GETACTION\":\n", " self.parse_get_action(splits)\n", " elif packet_type == \"HANDOVER\":\n", " self.parse_hand_over(splits)\n", " \n", " def parse_win_result(self, wr):\n", " splits = wr.split(\":\")\n", " amt = int(splits[1])\n", " winner = splits[2]\n", " \n", " if winner == self.bot_name:\n", " return amt\n", " else:\n", " return -amt\n", " \n", " \n", " def parse_hand_over(self, data_splits):\n", " # HANDOVER Stack1 Stack2 numBoardCards [boardCards] numLastActions [lastActions] timeBank\n", " # Can ignore Stack1 and Stack2 because we'll get it on the next NEWHAND packet.\n", " num_board_cards = int(data_splits[3])\n", " counter = 4\n", " self.hand['board'] = data_splits[counter:counter+num_board_cards]\n", " counter += num_board_cards\n", " \n", " num_last_actions = int(data_splits[counter])\n", " counter += 1\n", " self.hand['action_history'].append(data_splits[counter:counter+num_last_actions-1])\n", " self.hand['result'] = self.parse_win_result(data_splits[counter+num_last_actions-1])\n", " counter += num_last_actions\n", " \n", " self.state['time_bank'] = float(data_splits[-1])\n", " \n", " def check_for_hand_update_and_update_hand(self, last_actions):\n", " for i in range(len(last_actions)):\n", " splits = last_actions[i].split(\":\")\n", " if len(splits) == 4 and splits[0] == \"DISCARD\":\n", " # DISCARD:(oldcard):(newcard):PLAYER\n", " if self.hand['hole1'] == splits[1]:\n", " self.hand['hole1'] = splits[2]\n", " else:\n", " self.hand['hole2'] = splits[2]\n", " print(\"Updated hand ... \")\n", " print([self.hand['hole1'], self.hand['hole2']]) \n", " \n", " \n", " \n", " def parse_get_action(self, data_splits):\n", " # GETACTION potSize numBoardCards [boardCards] numLastActions [lastActions] numLegalActions [legalActions] timebank\n", " self.hand['pot_size'].append(int(data_splits[1]))\n", " \n", " num_board_cards = int(data_splits[2])\n", " counter = 3\n", " self.hand['board'] = data_splits[counter:counter+num_board_cards]\n", " counter += num_board_cards\n", " \n", " num_last_actions = int(data_splits[counter])\n", " counter += 1\n", " self.check_for_hand_update_and_update_hand(data_splits[counter:counter+num_last_actions]) # update hand if discard was made.\n", " self.hand['action_history'].append(data_splits[counter:counter+num_last_actions])\n", " counter += num_last_actions\n", " \n", " num_legal_actions = int(data_splits[counter])\n", " counter += 1\n", " self.possible_actions = data_splits[counter:counter+num_legal_actions]\n", " counter += num_legal_actions\n", " \n", " self.state['time_bank'] = float(data_splits[-1])\n", "\n", " def parse_new_hand(self, data_splits):\n", " # NEWHAND handId button holeCard1 holeCard2 myBank otherBank timeBank\n", " self.reset_hand()\n", " self.hand['hand_id'] = int(data_splits[1])\n", " self.hand['button'] = data_splits[2]\n", " self.hand['hole1'] = data_splits[3]\n", " self.hand['hole2'] = data_splits[4]\n", " \n", " self.state['my_bank'] = int(data_splits[5])\n", " self.state['their_bank'] = int(data_splits[6])\n", " self.state['time_bank'] = float(data_splits[7])\n", " ### ----------------------- ### \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " ### ------- FEATURE GENERATION ------- ###\n", " def update_temporal_feature_matrix(self):\n", " # Columns represent time steps in a hand.\n", " # Rows are as follows:\n", " # 0 - hero action\n", " # 1 - villain action\n", " # 2 - street\n", " # 3 - hero discard?\n", " # 4 - villain discard?\n", " al = [item for sublist in self.hand['action_history'] for item in sublist] # linearize action history list\n", " \n", " if len(al) > 0:\n", " if len(self.temporal_feature_matrix) > 0: # check if it's started to fill out.\n", " start_from_idx = self.temporal_feature_matrix.shape[1]\n", " else:\n", " self.temporal_feature_matrix = self.build_temporal_feature_vector(al[0])\n", " start_from_idx = 1\n", "\n", " for i in range(start_from_idx, len(al)):\n", " self.temporal_feature_matrix = np.hstack((self.temporal_feature_matrix, \n", " self.build_temporal_feature_vector(al[i])))\n", " \n", " \n", " \n", " def build_temporal_feature_vector(self, performed_action):\n", " # Performed actions to expect.\n", " # BET:amount[:actor]\n", " # CALL[:actor]\n", " # CHECK[:actor]\n", " # DEAL:STREET\n", " # FOLD[:actor]\n", " # POST:amount:actor\n", " # DISCARD[:actor]\n", " # RAISE:amount[:actor]\n", " # REFUND:amount:actor\n", " # SHOW:card1:card2:actor\n", " # TIE:amount:actor\n", " # WIN:amount:actor\n", " \n", " NFEATURES = 5\n", " STACKSIZE = 200\n", " hero_idx = 0\n", " villain_idx = 1\n", " street_idx = 2\n", " hero_discard_idx = 3\n", " villain_discard_idx = 4\n", " \n", " if len(self.temporal_feature_matrix) == 0: # if has not been initialized\n", " street = 0 # preflop\n", " else:\n", " street = np.max(self.temporal_feature_matrix[2])\n", " \n", " \n", " splits = performed_action.split(\":\")\n", " fv = np.zeros((NFEATURES,1))\n", " if splits[0] == \"BET\":\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " amount = float(splits[1])\n", " fv[actor_idx] = amount/STACKSIZE\n", " fv[street_idx] = street \n", " elif splits[0] == \"CALL\":\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " player_to_call = 1 - actor_idx\n", " call_amt = np.max(self.temporal_feature_matrix[player_to_call])\n", " fv[actor_idx] = call_amt\n", " fv[street_idx] = street\n", " elif splits[0] == \"CHECK\":\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " fv[actor_idx] = 0\n", " fv[street_idx] = street\n", " elif splits[0] == \"DEAL\":\n", " if splits[1] == \"FLOP\":\n", " street = 1\n", " elif splits[1] == \"TURN\":\n", " street = 2\n", " elif splits[1] == \"RIVER\":\n", " street = 3\n", " fv[street_idx] = street\n", " elif splits[0] == \"FOLD\":\n", " # Hand is now over. nothing to do here.\n", " pass\n", " elif splits[0] == \"POST\":\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " amount = float(splits[1])/STACKSIZE\n", " fv[actor_idx] = amount\n", " fv[street_idx] = street\n", " elif splits[0] == \"DISCARD\":\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " fv[actor_idx+3] = 1\n", " fv[street_idx] = street\n", " elif splits[0] == \"RAISE\":\n", " # Raise specifies the amount raised to, not the amount raised.\n", " actor_idx = self.get_actor_idx(splits[-1]) \n", " amount = float(splits[1])/STACKSIZE\n", " fv[actor_idx] = amount\n", " fv[street_idx] = street \n", " elif splits[0] == \"REFUND\":\n", " # Hand is now over. nothing to do here.\n", " pass\n", " elif splits[0] == \"SHOW\":\n", " # Hand is now over. nothing to do here.\n", " pass\n", " elif splits[0] == \"TIE\":\n", " # Hand is now over. nothing to do here.\n", " pass\n", " elif splits[0] == \"WIN\":\n", " # Hand is now over. nothing to do here.\n", " pass\n", " \n", " return fv\n", " \n", " def get_actor_idx(self, actor):\n", " if actor == self.bot_name:\n", " actor_idx = 0\n", " else:\n", " actor_idx = 1\n", " return actor_idx\n", " \n", "\n", " ### ------------------------------- ###\n", " \n", " \n", " def run(self, input_socket):\n", " # Get a file-object for reading packets from the socket.\n", " # Using this ensures that you get exactly one packet per read.\n", " f_in = input_socket.makefile()\n", " while True:\n", " # Block until the engine sends us a packet.\n", " data = f_in.readline().strip()\n", " # If data is None, connection has closed.\n", " if not data:\n", " print \"Gameover, engine disconnected.\"\n", " break\n", "\n", " # Here is where you should implement code to parse the packets from\n", " # the engine and act on it. We are just printing it instead.\n", " print(\"PACKET -> \", data)\n", " self.parse_data(data)\n", " self.update_temporal_feature_matrix()\n", " \n", " \n", " # When appropriate, reply to the engine with a legal action.\n", " # The engine will ignore all spurious responses.\n", " # The engine will also check/fold for you if you return an\n", " # illegal action.\n", " # When sending responses, terminate each response with a newline\n", " # character (\\n) or your bot will hang!\n", " word = data.split()[0]\n", " if word == \"GETACTION\":\n", " \n", " action = self.brain.make_decision(self)\n", " print(action)\n", " s.send(action + \"\\n\")\n", " elif word == \"REQUESTKEYVALUES\":\n", " # At the end, the engine will allow your bot save key/value pairs.\n", " # Send FINISH to indicate you're done.\n", " s.send(\"FINISH\\n\")\n", " # Clean up the socket.\n", " s.close()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# data = []\n", "# data.append(\"NEWHAND 98 false 9c 4s -111 111 9.931115\")\n", "# data.append(\"GETACTION 4 0 3 POST:1:P2 POST:2:P1 CALL:P2 2 CHECK RAISE:4:200 9.931115124999993\")\n", "# data.append(\"GETACTION 4 3 4d 5c Js 2 CHECK:P1 DEAL:FLOP 3 CHECK DISCARD:9c DISCARD:4s 9.930735672999992\")\n", "# data.append(\"GETACTION 4 3 4d 5c Js 2 CHECK:P1 CHECK:P2 2 CHECK BET:2:198 9.930290227999992\")\n", "# data.append(\"GETACTION 4 4 4d 5c Js Jh 3 CHECK:P1 CHECK:P2 DEAL:TURN 3 CHECK DISCARD:9c DISCARD:4s 9.929636328999992\")\n", "# data.append(\"GETACTION 4 4 4d 5c Js Jh 2 CHECK:P1 DISCARD:P2 2 CHECK BET:2:198 9.928944276999992\")\n", "# data.append(\"GETACTION 83 4 4d 5c Js Jh 2 CHECK:P1 BET:79:P2 3 FOLD CALL RAISE:158:198 9.928641209999991\")\n", "# data.append(\"HANDOVER -113 113 4 4d 5c Js Jh 3 FOLD:P1 REFUND:79:P2 WIN:4:P2 9.928236565999992\")\n", "\n", "\n", "data = []\n", "data.append(\"NEWHAND 80 false Kd Qh -77 77 9.942149\")\n", "data.append(\"GETACTION 4 0 3 POST:1:P2 POST:2:P1 CALL:P2 2 CHECK RAISE:4:200 9.942148733999993\")\n", "data.append(\"GETACTION 4 3 9d 4h Ts 2 CHECK:P1 DEAL:FLOP 3 CHECK DISCARD:Kd DISCARD:Qh 9.941793586999992\")\n", "data.append(\"GETACTION 4 3 9d 4h Ts 2 CHECK:P1 CHECK:P2 2 CHECK BET:2:198 9.941348511999992\")\n", "data.append(\"GETACTION 56 3 9d 4h Ts 2 CHECK:P1 BET:52:P2 3 FOLD CALL RAISE:104:198 9.940922597999993\")\n", "data.append(\"HANDOVER -79 79 3 9d 4h Ts 3 FOLD:P1 REFUND:52:P2 WIN:4:P2 9.940476787999993\")\n", "\n", "\n", "#data = []\n", "data.append(\"NEWHAND 91 true 3c Ah 410 -410 9.904622\")\n", "data.append(\"GETACTION 3 0 2 POST:1:P1 POST:2:P2 3 CALL FOLD RAISE:4:200 9.904621749000007\")\n", "data.append(\"GETACTION 400 3 9d 5h 9s 4 RAISE:200:P1 CALL:P2 DEAL:FLOP CHECK:P2 3 CHECK DISCARD:3c DISCARD:Ah 9.904199743000007\")\n", "data.append(\"GETACTION 400 4 9d 5h 9s Tc 3 CHECK:P1 DEAL:TURN DISCARD:P2 3 CHECK DISCARD:3c DISCARD:Ah 9.902036590000007\")\n", "data.append(\"HANDOVER 210 -210 5 9d 5h 9s Tc 2s 5 CHECK:P1 DEAL:RIVER SHOW:3c:Ah:P1 SHOW:Ad:5d:P2 WIN:400:P2 9.901503757000008\")\n", "\n", "\n", "# data = []\n", "# data.append(\"NEWHAND 34 false Ks 2d 7 -7 9.968365\")\n", "# #data.append(\"NEWHAND 34 false 9d 4h 7 -7 9.968365\")\n", "# data.append(\"GETACTION 4 0 3 POST:1:P2 POST:2:P1 CALL:P2 2 CHECK RAISE:4:200 9.968364926000001\")\n", "# data.append(\"GETACTION 4 3 5s Kd Qc 2 CHECK:P1 DEAL:FLOP 3 CHECK DISCARD:9d DISCARD:4h 9.968108437000001\")\n", "\n", "\n", "# #data.append(\"GETACTION 4 3 5s Kd Qc 2 CHECK:P1 DISCARD:P2 2 CHECK BET:2:198 9.967825413000002\")\n", "# data.append(\"GETACTION 4 3 5s Kd Qc 2 DISCARD:2d:Qh:P1 DISCARD:P2 2 CHECK BET:2:198 9.967825413000002\")\n", "\n", "\n", "# data.append(\"GETACTION 4 4 5s Kd Qc Ah 3 CHECK:P1 CHECK:P2 DEAL:TURN 3 CHECK DISCARD:9d DISCARD:4h 9.967515728000002\")\n", "# data.append(\"GETACTION 4 4 5s Kd Qc Ah 2 CHECK:P1 DISCARD:P2 2 CHECK BET:2:198 9.967211695000001\")\n", "# data.append(\"GETACTION 4 5 5s Kd Qc Ah Tc 3 CHECK:P1 CHECK:P2 DEAL:RIVER 2 CHECK BET:2:198 9.966950326000001\")\n", "# data.append(\"HANDOVER 5 -5 5 5s Kd Qc Ah Tc 5 CHECK:P1 CHECK:P2 SHOW:Qh:9c:P2 SHOW:9d:4h:P1 WIN:4:P2 9.966612284000002\")\n", " \n", "\n", "#data = \"GETACTION 4 3 5d Kd 6h 2 CHECK:P1 DEAL:FLOP 3 CHECK DISCARD:Jc DISCARD:9s 9.995735496999998\"" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jp = Johnny(\"P1\")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NEWHAND 80 false Kd Qh -77 77 9.942149\n", "GETACTION 4 0 3 POST:1:P2 POST:2:P1 CALL:P2 2 CHECK RAISE:4:200 9.942148733999993\n", "GETACTION 4 3 9d 4h Ts 2 CHECK:P1 DEAL:FLOP 3 CHECK DISCARD:Kd DISCARD:Qh 9.941793586999992\n", "GETACTION 4 3 9d 4h Ts 2 CHECK:P1 CHECK:P2 2 CHECK BET:2:198 9.941348511999992\n", "GETACTION 56 3 9d 4h Ts 2 CHECK:P1 BET:52:P2 3 FOLD CALL RAISE:104:198 9.940922597999993\n", "HANDOVER -79 79 3 9d 4h Ts 3 FOLD:P1 REFUND:52:P2 WIN:4:P2 9.940476787999993\n", "NEWHAND 91 true 3c Ah 410 -410 9.904622\n" ] } ], "source": [ "for i in range(0,8):\n", " print(data[i])\n", " jp.parse_data(data[i])\n", " jp.update_temporal_feature_matrix()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'action_history': [],\n", " 'button': 'true',\n", " 'hand_id': 91,\n", " 'hole1': '3c',\n", " 'hole2': 'Ah',\n", " 'pot_size': []}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jp.hand" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a Q-bet round\n", "showdown prob:0.63\n", "Current stake: 2.0\n", "Stake worth: 151\n", "Stake diff: 149.0\n", "ACTION_TAKEN -> BET:149.0\n" ] }, { "data": { "text/plain": [ "'BET:149.0'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jp.brain.make_decision(jp)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.005, 0. ],\n", " [ 0. , 0.01 ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ],\n", " [ 0. , 0. ]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jp.update_temporal_feature_matrix()\n", "jp.temporal_feature_matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "startTime = datetime.now()\n", "for i in range(len(data)):\n", " print(data[i])\n", " jp.parse_data(data[i])\n", " if jp.possible_actions:\n", " print(jp.possible_actions)\n", " jp.update_temporal_feature_matrix()\n", " print(jp.temporal_feature_matrix)\n", " \n", "print datetime.now() - startTime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "board = ['Th', 'Js', 'Qh']\n", "hand = ['Kh']\n", "nsim = 100\n", "\n", "print(jp.evaluate_showdown_probabilities(hand, board, nsim))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "card_evaluator.evaluate(board_sim,hand_sim)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "al = [item for sublist in jp.hand['action_history'] for item in sublist]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "al" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.hstack((np.zeros((3,)), np.zeros((3,1))))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "card_evaluator.evaluate(board_sim,villain_hand)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "street = 1 if \"FLOP\" == \"FLOP\" else 0\n", "print street" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yo = ['A','B', \"C\", 'D', 'E']\n", "\n", "bo = cp.copy(yo)\n", "bo.extend(\"F\")\n", "print(bo)\n", "print(yo)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(bo[0:2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(bo[2:2+3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(bo[(2+3):])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "v = 0.0\n", "v += False\n", "v += True\n", "print(v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jp.possible_actions" ] }, { "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": 0 }	mit
yingjun2/project-spring2017	part1/Question1-Xiaoliang-FinalVersion.ipynb	1	1791572	null	bsd-3-clause
lem-usp/corr-param	notebooks/.ipynb_checkpoints/Random Matrices Tests-checkpoint.ipynb	1	579686	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import randomCorr\n", "\n", "c = randomCorr.mkRandCorr(40, 10*-3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Exemplo de matriz de correla\u00e7\u00e3o aleat\u00f3ria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O m\u00e9todo utilizado \u00e9 o descrito no artigo:\n", "\n", "http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048902" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pcolor(c)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "<matplotlib.collections.PolyCollection at 0x2a84650>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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PObPg8XfeVO2bp5Uo8/tKVksPOasFABJ6yMX/Rx6dpo7x6Mj5on4EVUT9SUxX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\no5BrgwDAY0cTRT27hpeoh2O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"text": [ "<matplotlib.figure.Figure at 0x3979890>" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "M\u00e9todo usual para gerar matrizes de correla\u00e7\u00e3o rand\u00f4micas" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def rand_ortho_matrix(eigen_values):\n", " size = len(eigen_values)\n", " A = np.mat(np.random.random((size,size)))\n", " Q, R = np.linalg.qr(A)\n", " \n", " return Q np.diag(eigen_values) * Q.T\n", "\n", "n_traits = 40\n", "rand_eigen = np.array(np.random.random(n_traits))\n", "p = rand_ortho_matrix(rand_eigen)\n", "\n", "pcolor(numpy.array(p))\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.collections.PolyCollection at 0x3df0050>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x397e8d0>" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def CalcR2(Matrix):\n", " tr = Matrix.shape[1]\n", " x, y = np.asarray(np.invert(np.tri(tr, tr, dtype=bool)), dtype=float).nonzero()\n", " R2Tot = np.mean(Matrix[x, y] * Matrix[x, y])\n", " return R2Tot" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def flexibility(matrix1, num_vectors=1000):\n", " traits = matrix1.shape[0]\n", " rand_vec = np.random.multivariate_normal(np.zeros(traits),\n", " np.identity(traits, float),\n", " num_vectors).T\n", " \n", " rand_vec = rand_vec/np.sqrt((rand_vecrand_vec).sum(0))\n", " \n", " delta_z1 = np.dot(matrix1, rand_vec)\n", "\n", " ndelta_z1 = delta_z1/np.sqrt((delta_z1delta_z1).sum(0))\n", "\n", " return np.mean(np.diag(np.dot(ndelta_z1.T, rand_vec)))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "s = 1000\n", "r2 = np.zeros(s)\n", "flex = np.zeros(s)\n", "isoc = np.zeros(s)\n", "\n", "for i in xrange(s):\n", " c = randomCorr.mkRandCorr(40, 10*-3) \n", " r2[i] = CalcR2(c)\n", " flex[i] = flexibility(c)\n", " isoc[i] = np.abs(np.dot(eig(c)[1][:,0], iso))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Histograma correla\u00e7\u00e3o com vetor isom\u00e9trico" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hist(isoc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 77, "text": [ "(array([277, 239, 189, 138, 85, 39, 21, 7, 2, 3]),\n", " array([ 3.80143281e-04, 5.41018694e-02, 1.07823596e-01,\n", " 1.61545322e-01, 2.15267048e-01, 2.68988774e-01,\n", " 3.22710500e-01, 3.76432226e-01, 4.30153953e-01,\n", " 4.83875679e-01, 5.37597405e-01]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x6adf7d0>" ] } ], "prompt_number": 77 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Histograma R2" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hist(r2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 78, "text": [ "(array([ 15, 99, 203, 277, 212, 113, 51, 21, 7, 2]),\n", " array([ 0.15437342, 0.16973832, 0.18510322, 0.20046811, 0.21583301,\n", " 0.23119791, 0.2465628 , 0.2619277 , 0.2772926 , 0.2926575 ,\n", " 0.30802239]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x6cfb850>" ] } ], "prompt_number": 78 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Histograma da flexibilidade" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hist(flex)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 79, "text": [ "(array([ 3, 20, 54, 126, 210, 267, 174, 97, 42, 7]),\n", " array([ 0.28946314, 0.29843751, 0.30741188, 0.31638625, 0.32536062,\n", " 0.33433499, 0.34330937, 0.35228374, 0.36125811, 0.37023248,\n", " 0.37920685]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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B8dY9d2KzTaNz5ztwu/9EvZcAtuD3HyU6OhqAAQOGsmVLVY4fX8nx47+xc2cL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\nTZtW5PjatWsZEhLC0NBQhoWFcevWrSXuqz593J71QiKRVCqys7PZtGk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"text": [ "<matplotlib.figure.Figure at 0x6f437d0>" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "corrcoef(r2, flex)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "array([[ 1. , -0.94217028],\n", " [-0.94217028, 1. ]])" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "p = np.dot(c, eig(c)[1][:,0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "np.sqrt(np.dot(p, p))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ "14.85366928000486" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "eigvals(c)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "array([ 1.48536693e+01, 9.00525414e+00, 6.62282829e+00,\n", " 3.48049664e+00, 1.91936855e+00, 1.55731768e+00,\n", " 1.10373711e+00, 5.31852658e-01, 4.31979031e-01,\n", " 1.63227311e-01, 1.39492590e-01, 7.87821502e-02,\n", " 6.57679478e-02, 2.93080476e-02, 7.58424751e-03,\n", " 4.27840324e-03, 1.65061907e-03, 7.47785040e-04,\n", " 5.04088158e-04, 3.14290933e-04, 3.08745768e-04,\n", " 2.71951107e-04, 2.34327079e-04, 1.89527289e-04,\n", " 1.74323924e-04, 1.61352610e-04, 1.20677024e-04,\n", " 1.11145417e-04, 8.05011263e-05, 7.09764560e-05,\n", " 4.97751631e-05, 2.69545672e-05, 1.72473102e-05,\n", " 1.31837392e-05, 5.46194444e-06, 2.14067989e-06,\n", " 5.49471321e-07, 2.51270615e-07, 4.25199418e-08,\n", " 2.57099037e-10])" ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "ms =[m1,m2,m3,m4]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "params = []\n", "\n", "for m in ms:\n", " mb = mkB(m)\n", " mp = calc_params(mb)\n", " params.append((mb, mp))\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(params[0][1][:,0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 79, "text": [ "[<matplotlib.lines.Line2D at 0x8852710>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x87fff90>" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "diff = (m4-m3)/100\n", "diff\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 102, "text": [ "array([[ 0.00000000e+00, -6.22541420e-04, 8.67440500e-05, ...,\n", " -2.62268036e-03, 1.36925238e-03, -5.29693630e-04],\n", " [ -6.22541420e-04, 0.00000000e+00, -7.83092710e-04, ...,\n", " -3.65399300e-05, 1.39710180e-03, -1.12024273e-03],\n", " [ 8.67440500e-05, -7.83092710e-04, 0.00000000e+00, ...,\n", " 9.54287040e-04, 4.27959982e-03, 6.99147560e-04],\n", " ..., \n", " [ -2.62268036e-03, -3.65399300e-05, 9.54287040e-04, ...,\n", " 0.00000000e+00, -4.75033010e-04, 2.14057603e-03],\n", " [ 1.36925238e-03, 1.39710180e-03, 4.27959982e-03, ...,\n", " -4.75033010e-04, 0.00000000e+00, 2.15962038e-03],\n", " [ -5.29693630e-04, -1.12024273e-03, 6.99147560e-04, ...,\n", " 2.14057603e-03, 2.15962038e-03, 0.00000000e+00]])" ] } ], "prompt_number": 102 }, { "cell_type": "code", "collapsed": false, "input": [ "m_min = m3\n", "for i in xrange(101):\n", " pcolor(m_min)\n", " savefig('{}.png'.format(i))\n", " m_min += diff\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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w6yNmpNHd3D7BD8j77EliIFnPMG5V3UXYb2b2wkfVYgZwMV0+ABDu5lI5ZmAf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\niIjH6SAXEfE4HeQiIh6ng1xExOP+Pzguwf0AWNciAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x8d0cb50>" ] } ], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "import randomCorr as rc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "m1 = np.loadtxt(open(\"../matrices/1-gorilla.csv\",\"rb\"),delimiter=\",\")\n", "m2 = np.loadtxt(open(\"../matrices/2-peramelidae.csv\",\"rb\"),delimiter=\",\")\n", "m3 = np.loadtxt(open(\"../matrices/3-molossidae.csv\",\"rb\"),delimiter=\",\")\n", "m4 = np.loadtxt(open(\"../matrices/4-hyllostomidae.csv\",\"rb\"),delimiter=\",\")\n", "#m5 = np.loadtxt(open(\"../matrices/5.csv\",\"rb\"),delimiter=\",\")\n", "#m6 = np.loadtxt(open(\"../matrices/6.csv\",\"rb\"),delimiter=\",\")\n", "\n", "ms =[m1,m2,m3,m4]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "bp = []\n", "\n", "for i in xrange(4):\n", " b_n = rc.triang_decomp(ms[i])\n", " p_n = rc.calc_params(b_n)\n", " bp.append((b_n, p_n))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in xrange(6):\n", " print np.max(ms[i] - np.dot(bp[i][0], bp[i][0].T))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4.4408920985e-16\n", "5.55111512313e-16\n", "7.77156117238e-16\n", "6.66133814775e-16\n", "3.46944695195e-18\n", "0.238453007\n" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in xrange(4):\n", " bp_i = rc.triang_from_params(bp[i][1])\n", " print np.max(ms[i] - np.dot(bp_i, bp_i.T))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "9.11302650008e-09\n", "5.11961184557e-09\n", "7.61498694657e-09\n", "9.09617700606e-09\n" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "diff = (bp[1][1] - bp[0][1])/100\n", "\n", "p = bp[0][1]\n", "m0 = ms[0]\n", "\n", "for i in xrange(101):\n", " new_b = rc.triang_from_params(p)\n", " m0 = np.dot(new_b, new_b.T)\n", " pcolor(m0)\n", " savefig('{}.png'.format(i))\n", " p += diff" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xc363ed0>" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "diff = (ms[1] - ms[0])/100\n", "\n", "m0 = ms[0]\n", "\n", "for i in xrange(101):\n", " pcolor(m0)\n", " savefig('m-{:03d}.png'.format(i))\n", " m0 += diff" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x510f790>" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "tm = rc.triang_from_params(bp[0][1] + 100diff)\n", "pcolor(np.dot(tm, tm.T))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "<matplotlib.collections.PolyCollection at 0x50f8490>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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PlCr7e1OqVsEUcy4Yn1K1PgZ3PN424gzDxjhJ1UrG742ZkXiNqjXMekfthyIi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\n", "text": [ "<matplotlib.figure.Figure at 0x3cd0ad0>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "m0 = ms[0]\n", "s = 100\n", "r2 = np.zeros(s)\n", "flex = np.zeros(s)\n", "isoc1 = np.zeros(s)\n", "isoc2 = np.zeros(s)\n", "\n", "iso = np.ones(m0.shape[0])/np.sqrt(m0.shape[0])\n", "diff = (bp[3][1] - bp[0][1])/100\n", "p = bp[0][1]\n", "\n", "for i in xrange(100):\n", " r2[i] = CalcR2(m0)\n", " flex[i] = flexibility(m0)\n", " isoc1[i] = np.abs(np.dot(eig(m0)[1][:,0], iso)) \n", " isoc2[i] = np.abs(np.dot(eig(m0)[1][:,1], iso)) \n", "\n", " p += diff\n", " new_b = rc.triang_from_params(p)\n", " m0 = np.dot(new_b, new_b.T)\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(r2[0], flex[0])\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 156, "text": [ "<matplotlib.collections.PathCollection at 0x78ede90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7301190>" ] } ], "prompt_number": 156 }, { "cell_type": "code", "collapsed": false, "input": [ "CalcR2(ms[0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "0.071071941176470629" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "eig(ms[2])[1][:,0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "array([-0.14639117, -0.18927692, -0.17999167, -0.11383435, -0.13866206,\n", " -0.1512532 , -0.03624093, -0.20780105, -0.25858205, -0.1263813 ,\n", " -0.2860151 , -0.23674419, -0.24766999, -0.15076605, -0.26594475,\n", " -0.19303911, -0.21734474, -0.08107354, -0.07217581, -0.16727413,\n", " -0.08215141, -0.16877756, -0.15115711, -0.12454392, -0.13828721,\n", " -0.05198079, -0.22440013, -0.13631722, -0.21921859, -0.16375375,\n", " -0.0965526 , -0.1552577 , -0.1792374 , -0.05408101, -0.05769688])" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "eig(ms[3])[1][:,0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "array([ 0.00548838, -0.12234011, -0.20956893, -0.14645006, -0.23492048,\n", " -0.20782179, -0.10886239, -0.16620609, -0.25482124, -0.03853063,\n", " -0.21770677, -0.06025946, -0.25561981, -0.26166642, -0.27268206,\n", " -0.25697442, -0.24683595, -0.07062001, -0.11089805, -0.19085818,\n", " -0.09255424, -0.08386025, -0.05861632, -0.11048438, -0.12644895,\n", " -0.07465616, -0.20866868, -0.02547831, -0.16566946, -0.15009225,\n", " -0.16838086, -0.05432352, -0.27710192, -0.13242886, -0.0538359 ])" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "def calc_path(i, j, s=100, pcs=[0]):\n", " m0 = ms[i]\n", " r2 = np.zeros(s)\n", " flex = np.zeros(s)\n", " isoc = []\n", " \n", " iso = np.ones(m0.shape[0])/np.sqrt(m0.shape[0])\n", " diff = (bp[j][1] - bp[i][1])/100\n", " p = bp[i][1]\n", " \n", " for i in xrange(100):\n", " r2[i] = CalcR2(m0)\n", " flex[i] = flexibility(m0)\n", " for j in pcs:\n", " isoc.append(np.abs(np.dot(eig(m0)[1][:,j], iso)))\n", " \n", " p += diff\n", " new_b = rc.triang_from_params(p)\n", " m0 = np.dot(new_b, new_b.T)\n", " \n", " return r2, flex, isoc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "rn, fn, ino = calc_path(2,3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(rn, fn, c=range(0,100))\n", "annotate('3', xy=(rn[0], fn[0]), xycoords='data',\n", " xytext=(0.05, 0.06), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='blue', shrink=0.05),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", "\n", "annotate('4', xy=(rn[-1], fn[-1]), xycoords='data',\n", " xytext=(0.05, 0.2), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='red', shrink=0.05),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", "savefig('3-4-r2_vs_flex.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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A8LagUChZ86eGvSFHqFu3bl758+fPc+jQIRwdHenWrdtTDzTv2bOHPgMHkHznLp716rLt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\n/3Z6pw6w2IuMAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x4c9a150>" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "y = np.sqrt(np.diagonal(ms[]))\n", "y = (y[:, np.newaxis] * y)\n", "corrP = ms[5] / y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 118 }, { "cell_type": "code", "collapsed": false, "input": [ "ms[4] = corrP" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.ax" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 125, "text": [ "array([[ 1. , -0.13356862, 0.2657388 , ..., 0.07197488,\n", " -0.05065331, 0.17577846],\n", " [-0.13356862, 1. , 0.45599007, ..., 0.08454455,\n", " 0.32414673, -0.06027531],\n", " [ 0.2657388 , 0.45599007, 1. , ..., 0.07615338,\n", " 0.1457147 , 0.10191853],\n", " ..., \n", " [ 0.07197488, 0.08454455, 0.07615338, ..., 1. ,\n", " 0.28592874, 0.31562355],\n", " [-0.05065331, 0.32414673, 0.1457147 , ..., 0.28592874,\n", " 1. , 0.22368994],\n", " [ 0.17577846, -0.06027531, 0.10191853, ..., 0.31562355,\n", " 0.22368994, 1. ]])" ] } ], "prompt_number": 125 }, { "cell_type": "code", "collapsed": false, "input": [ "t4 = rc.triang_decomp(ms[4])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 109 }, { "cell_type": "code", "collapsed": false, "input": [ "np.max(np.dot(t4, t4.T) - ms[4])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 111, "text": [ "7.7715611723760958e-16" ] } ], "prompt_number": 111 }, { "cell_type": "code", "collapsed": false, "input": [ "np.savetxt('np5.csv', ms[4], delimiter=',')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 154 }, { "cell_type": "code", "collapsed": false, "input": [ "#m7 = np.loadtxt(open(\"../matrices/7.csv\",\"rb\"),delimiter=\";\")\n", "#m8 = np.loadtxt(open(\"../matrices/8.csv\",\"rb\"),delimiter=\";\")\n", "\n", "m7 = rc.rand_corr(35, 10-3)\n", "m8 = rc.rand_corr(35, 10-3)\n", "\n", "ms =[m7, m8]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "bp = []\n", "\n", "for i in xrange(2):\n", " b_n = rc.triang_decomp(ms[i])\n", " p_n = rc.calc_params(b_n)\n", " bp.append((b_n, p_n))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "import randomCorr as rc\n", "reload(rc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 38, "text": [ "<module 'randomCorr' from '/home/walrus/corr-param/randomCorr.pyc'>" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "r,f,i = rc.calc_path(ms, bp, 0,1, pcs=[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(r)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "[<matplotlib.lines.Line2D at 0x52e6990>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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7W+Lj40VEJCcnR0aPHi0tLS1iNpslMDBQcnNzlb4ePXpU4uPj5Z577ulq+HJ9ehITRd55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\nZjCUiIjIZjCUiIjIZjCUiIj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D4cOH46OPPgIAVFRUYNiwYQCAkSNH4sMPP2zXVmBgIPz9/VFaWopz585Bp9MhICAAAJCS\nktKmzogRIwAASUlJKCwsVMoTExOhVqvRvXt3GI1GFBcXtzn3zz//HHv37sW4ceO+e5oTEdEd1+Vn\nSpWVlSgrK0NcXFybcpPJpITNkSNHUFVVhbq6OhgMBhw8eBBXrlzBF198gR07dqC2thYAMGTIECU8\nNm3ahJqaGqWtbdu2obW1FRaLBUePHkVtbS10Oh1Onz6NqqoqOBwObN26VWnLZDIpAbVlyxbYbDY0\nNjbCZDKhuLgYLS0taGhowL59+5Q6N23duhUjR45Ez549O73uBQsWKL9KSkq62l1ERPeEkpKSNvfJ\n2+XblZ2uXbuGzMxM5ObmtruBz5kzB6+++iqioqJgMBgQFRUFHx8fhIeHIycnB2lpaejRoweioqKg\nVt/IwPz8fMyaNQsLFy5Eeno6unXrBgCYPn06zGYzoqOjMXDgQCQkJMDHxwf+/v7Iy8vDhAkToFar\nkZCQgLNnzwIA3n77bcycOROrVq1CYmIigoOD4ePjg9TUVJSWliIhIQGBgYGIj49Xjn/T+vXr8fOf\n/9zltd+JTiYi+qFKSkpCUlKS8vn111+/vQbdze/Z7XZJS0uTP/7xj12aDwwJCRGbzdaufO7cuZKX\nl9eu/PTp0xIbG9thWwkJCWI2m9uVr1y5UnJyctqV22w20Wg0HbY1efJkKSoqUj5fvnxZAgIC5Kuv\nvur0WrrQPUREdIvbvW+6nL4TEWRnZyMiIgKzZ8/ucJ+rV6/CbrcDAP70pz9h+PDhymjq0qVLAIDq\n6mps2bIFkydPBgBcvnwZAOB0OvHGG29gxowZAICWlhY0NzcDAHbv3g0/Pz+Eh4e3aauxsRF5eXl4\n4YUXAABWqxVOpxMAsHjxYmRnZyttW61WAEB5eTnKy8uRlpamnPfmzZvx5JNPKqM0IiK6+1xO3/3t\nb3/DBx98AKPRiKioKADAokWLUF1dDQB48cUXcerUKUydOhUqlQp6vR7vv/++Uj8zMxNWqxV+fn5Y\nsWIFevfuDeDGtNny5csBABkZGZg6dSoAoL6+HqNHj4ZarYZGo8HatWuVtmbPno3jx48DAObPnw+t\nVgvgxnzm3LlzoVKpMHz4cKVdu92OxMREAECfPn2wbt26NtN3BQUFmDt37vfsNiIi+mdQfT3cog6o\nVCqwe4iIuu5275v8RgciIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUi\nIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIa\nDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCUiIvIaLkOppqYGycnJGDJkCPR6Pd55551O9y0tLYWv\nry8+/PBDpSw3NxcGgwF6vR65ublK+fHjxxEfHw+j0Yj09HTYbDYAgN1ux7Rp02A0GhEZGYn9+/cr\ndQoKCmAymaDX6zFnzhylvKqqCikpKTCZTEhOTkZdXZ2yLScnBwaDAQaDARs3blTKs7OzERkZCaPR\niKeffhpXr17tSl8REdE/m7hw4cIFKSsrExERm80mYWFhcurUqXb7ORwOSU5OlrFjx8rmzZtFROTE\niROi1+ulpaVFHA6HjBw5Us6cOSMiItHR0XLgwAEREcnPz5d58+aJiMiyZctk+vTpIiJy6dIlGTp0\nqIiINDQ0yCOPPCINDQ0iIpKVlSV79uwREZHMzExZs2aNiIjs3btXpkyZIiIi27dvl9TUVGltbZXm\n5maJiYmRzz//XERE+a+IyK9+9StZuHBhh9fvpnuIiOhbbve+6XKkFBQUhMjISABAz549MXjwYJw/\nf77dfu+++y4yMzMRGBiolJnNZsTFxeG+++6Dj48Phg8fjo8++ggAUFFRgWHDhgEARo4cqYyuzGYz\nkpOTAQCBgYHw9/dHaWkpzp07B51Oh4CAAABASkpKmzojRowAACQlJaGwsFApT0xMhFqtRvfu3WE0\nGlFcXAwA6NWr181ARktLCx588MHvGOVERPTP0OVnSpWVlSgrK0NcXFyb8rq6OhQWFmLGjBkAAJVK\nBQAwGAw4ePAgrly5gi+++AI7duxAbW0tAGDIkCFKeGzatAk1NTUAAJPJhG3btqG1tRUWiwVHjx5F\nbW0tdDodTp8+jaqqKjgcDmzdulVpy2QyKQG1ZcsW2Gw2NDY2wmQyobi4GC0tLWhoaMC+ffuUOgAw\nbdo0PPTQQygvL8cLL7zwvTqPiIjuLN+u7HTt2jVkZmYiNzcXPXv2bLNt9uzZWLJkCVQqFUQEN0Zv\nQHh4OHJycpCWloYePXogKioKavWNDMzPz8esWbOwcOFCpKeno1u3bgCA6dOnw2w2Izo6GgMHDkRC\nQgJ8fHzg7++PvLw8TJgwAWq1GgkJCTh79iwA4O2338bMmTOxatUqJCYmIjg4GD4+PkhNTUVpaSkS\nEhIQGBiI+Ph45fgA8Oc//xlOpxMzZ87Em2++ifnz53d47QsWLFB+TkpKQlJSUtd6lojoHlBSUoKS\nkpI716C7+T273S5paWnyxz/+scPtjz76qISEhEhISIj07NlT+vXrJ4WFhe32mzt3ruTl5bUrP336\ntMTGxnbYdkJCgpjN5nblK1eulJycnHblNptNNBpNh21NnjxZioqK2pXv379fxo4d22GdLnQPERHd\n4nbvmy6n70QE2dnZiIiIwOzZszvc59y5c7BYLLBYLMjMzEReXh7S09MBAJcuXQIAVFdXY8uWLZg8\neTIA4PLlywAAp9OJN954Q5n6a2lpQXNzMwBg9+7d8PPzQ3h4eJu2GhsbkZeXp0y5Wa1WOJ1OAMDi\nxYuRnZ2ttG21WgEA5eXlKC8vR1paGgDgzJkzyvVt27YNUVFRXQ5xIiL653E5ffe3v/0NH3zwAYxG\no3LjXrRoEaqrqwEAL774osvGMzMzYbVa4efnhxUrVqB3794AgPXr12P58uUAgIyMDEydOhUAUF9f\nj9GjR0OtVkOj0WDt2rVKW7Nnz8bx48cBAPPnz4dWqwVwY+g4d+5cqFQqDB8+XGnXbrcjMTERANCn\nTx+sW7cOarUaTqcTU6dOxeeffw4AiI6OVuoQEdHdpfp6uEUduPmcjIiIuuZ275v8RgciIvIaDCUi\nIvIaDCUiIvIaDCUiIvIaDCUiIvIaDCU3vn4FioiIPICh5IbDcbfPgIjo3sFQcuP69bt9BkRE9w6G\nkhsMJSIiz2EoucFQIiLyHIaSGwwlIiLPYSi5wYUORESew1BygyMlIiLPYSi5wVAiIvIchpIbDCUi\nIs9hKLnBUCIi8hyGkhtc6EBE5DkMJTc4UiIi8hyGkhsMJSIiz2EoucFQIiLyHIaSGwwlIiLPYSi5\nwYUORESew1BygyMlIiLPcRlKNTU1SE5OxpAhQ6DX6/HOO+90uN+sWbOg0+lgMplQVlamlBcXFyM8\nPBw6nQ5Lly5Vyq9cuYLU1FSEhYUhLS0NTU1NyrbFixdDp9MhPDwcu3btUsqPHj0Kg8EAnU6HV199\nVSn/6quvMGHCBOh0OvzoRz9CVVWVsm316tUICwtDWFgY1qxZo5RnZ2cjMjISRqMRTz/9NK5evdpp\nHzCUiIg8SFy4cOGClJWViYiIzWaTsLAwOXXqVJt9duzYIWPGjBERkUOHDklcXJyIiDgcDgkNDRWL\nxSJ2u11MJpNS97e//a0sXbpURESWLFkiOTk5IiJy8uRJMZlMYrfbxWKxSGhoqDidThERiYmJkcOH\nD4uIyJgxY6SoqEhERJYvXy4zZswQEZENGzbIhAkTRETEarXKY489Jo2NjdLY2Kj8LCLy+eefK+f/\nq1/9ShYuXNjh9QOQNWtc9RAREd3KTay45XKkFBQUhMjISABAz549MXjwYJw/f77NPtu2bUNWVhYA\nIC4uDk1NTbh48SKOHDkCrVaLkJAQ+Pn5YeLEiSgsLGxXJysrC1u3bgUAFBYWYtKkSfDz80NISAi0\nWi0OHz6MCxcuwGazITY2FgDw/PPPK3VubSsjIwN79uwBAOzcuRNpaWnw9/eHv78/UlNTUVxcDADo\n1avXzUBGS0sLHnzwwU77gCMlIiLP6fIzpcrKSpSVlSEuLq5NeV1dHQYMGKB81mg0qKurw/nz5zss\nB4D6+nr0798fANC/f3/U19cDAM6fPw+NRtNhW7eWBwcHK23denxfX1/06dMHVqu107ZumjZtGh56\n6CGUl5fjhRde6PS6udCBiMhzfLuy07Vr15CZmYnc3Fz07Nmz3fYbIzbXRAQqlapduUql6rD8n+3P\nf/4znE4nZs6ciTfffBPz58/vcL/CwgW4OThMSkpCUlKS506SiMjLlZSUoKSk5I615zaUrl+/joyM\nDDz33HMYN25cu+3BwcGoqalRPtfW1kKj0eD69evtyoODgwHcGB1dvHgRQUFBuHDhAvr16+eyreDg\nYNTW1rYrv1mnuroaDz/8MBwOB65evYqAgAAEBwe36aiamhqMGDGizbmr1WpMnDgRf/jDHzq9/tTU\nBZg9210vERHdm779l/XXX3/9ttpzOX0nIsjOzkZERARmd3JnTk9PV1a2HTp0CP7+/ujfvz+io6NR\nUVGByspK2O12FBQUID09XamzevVqADdWyN0Mu/T0dGzYsAF2ux0WiwUVFRWIjY1FUFAQevfujcOH\nD0NEsHbtWjz11FPt2tq8eTNSUlIAAGlpadi1axeamprQ2NiI3bt3Y9SoUQCAM2fOKNe3bds2REVF\nddoHfKZERORBrlZBHDx4UFQqlZhMJomMjJTIyEj5y1/+Iu+995689957yn6vvPKKhIaGitFolKNH\njyrlf/nLXyQsLExCQ0Nl0aJFSrnVapWUlBTR6XSSmpqqrIoTEXnzzTclNDRUBg0aJMXFxUr5p59+\nKnq9XkJDQ+UXv/iFUv7ll1/K+PHjRavVSlxcnFgsFmVbfn6+aLVa0Wq1smrVKhERaW1tlR//+Mdi\nMBjEYDDItGnT5Isvvujw+gHIm2+66iEiIrqVm1hxS/V1I9QBlUqF118XvPba3T4TIqL/G1QqVZfW\nGXSG3+jgBqfviIg8h6HkBkOJiMhzGEpuMJSIiDyHoeQGQ4mIyHMYSm7wGx2IiDyHoeQGR0pERJ7D\nUHKDoURE5DkMJTcYSkREnsNQcoOhRETkOQwlN7jQgYjIcxhKbnCkRETkOQwlNxhKRESew1Byg6FE\nROQ5DCU3GEpERJ7DUHKDCx2IiDyHoeQGR0pERJ7DUHKDoURE5DkMJTcYSkREnsNQcoOhRETkOQwl\nN7jQgYjIcxhKbnCkRETkOQwlNxhKRESew1Byg6FEROQ5LkNp+vTp6N+/PwwGg8tGSktL4evriw8/\n/FApy83NhcFggF6vR25urlJ+/PhxxMfHw2g0Ij09HTabDQBgt9sxbdo0GI1GREZGYv/+/UqdgoIC\nmEwm6PV6zJkzRymvqqpCSkoKTCYTkpOTUVdXp2zLycmBwWCAwWDAxo0blfK9e/di6NChMBgMmDp1\nKlpbW11eG0OJiMiDxIUDBw7IsWPHRK/Xd7qPw+GQ5ORkGTt2rGzevFlERE6cOCF6vV5aWlrE4XDI\nyJEj5cyZMyIiEh0dLQcOHBARkfz8fJk3b56IiCxbtkymT58uIiKXLl2SoUOHiohIQ0ODPPLII9LQ\n0CAiIllZWbJnzx4REcnMzJQ1a9aIiMjevXtlypQpIiKyfft2SU1NldbWVmlubpaYmBix2WzS2toq\nAwYMkIqKChERee211+T999/v9NoAyP33u+ohIiK6lZtYccvlSGnYsGF44IEHXIbau+++i8zMTAQG\nBiplZrMZcXFxuO++++Dj44Phw4fjo48+AgBUVFRg2LBhAICRI0cqoyuz2Yzk5GQAQGBgIPz9/VFa\nWopz585Bp9MhICAAAJCSktKmzogRIwAASUlJKCwsVMoTExOhVqvRvXt3GI1GFBUVwWq1olu3btBq\nte2O3xmOlIiIPOe2ninV1dWhsLAQM2bMAACoVCoAgMFgwMGDB3HlyhV88cUX2LFjB2prawEAQ4YM\nUcJj06ZNqKmpAQCYTCZs27YNra2tsFgsOHr0KGpra6HT6XD69GlUVVXB4XBg69atSlsmk0kJlS1b\ntsBms6FBWmkQAAAYJ0lEQVSxsREmkwnFxcVoaWlBQ0MD9u3bh9raWgQGBsLhcODo0aMAgM2bNyvH\n74zDAYjcTi8REVFX+d5O5dmzZ2PJkiVQqVQQEcjXd+/w8HDk5OQgLS0NPXr0QFRUFNTqG/mXn5+P\nWbNmYeHChUhPT0e3bt0A3Hh+ZTabER0djYEDByIhIQE+Pj7w9/dHXl4eJkyYALVajYSEBJw9exYA\n8Pbbb2PmzJlYtWoVEhMTERwcDB8fH6SmpqK0tBQJCQkIDAxEfHy8cvwNGzbgl7/8Jb766iukpaXB\nx8fH5TWqVAswfz6gVt8YjSUlJd1OlxER/aCUlJSgpKTkzjXobn7PYrF0+kzp0UcflZCQEAkJCZGe\nPXtKv379pLCwsN1+c+fOlby8vHblp0+fltjY2A7bTkhIELPZ3K585cqVkpOT067cZrOJRqPpsK3J\nkydLUVFRu/KdO3fKhAkTOqwjcmNu9F/+RaS5udNdiIjoFl2IFZdua/ru3LlzsFgssFgsyMzMRF5e\nHtLT0wEAly5dAgBUV1djy5YtmDx5MgDg8uXLAACn04k33nhDmfpraWlBc3MzAGD37t3w8/NDeHh4\nm7YaGxuRl5eHF154AQBgtVrhdDoBAIsXL0Z2drbSttVqBQCUl5ejvLwcaWlpbY7/1Vdf4Q9/+ANe\neukll9fo58dvdSAi8hSX03eTJk3C/v370dDQgAEDBuD111/H9a+f/L/44osuG87MzITVaoWfnx9W\nrFiB3r17AwDWr1+P5cuXAwAyMjIwdepUAEB9fT1Gjx4NtVoNjUaDtWvXKm3Nnj0bx48fBwDMnz9f\nWahQUlKCuXPnQqVSYfjw4Uq7drsdiYmJAIA+ffpg3bp1yvTdW2+9he3bt8PpdOLll192Ox3n58fF\nDkREnqL6erhFHVCpVOjXT3D8OBAUdLfPhojI+91cY/B98Rsd3OBIiYjIcxhKbvj6MpSIiDyFoeQG\nFzoQEXkOQ8kNTt8REXkOQ8kNhhIRkecwlNxgKBEReQ5DyQ0udCAi8hyGkhtc6EBE5DkMJTc4fUdE\n5DkMJTcYSkREnsNQcoOhRETkOQwlN7jQgYjIcxhKbnChAxGR5zCU3OD0HRGR5zCU3GAoERF5DkPJ\nDYYSEZHnMJTc4EIHIiLPYSi5wYUORESew1Byg9N3RESew1Byg6FEROQ5DCU3GEpERJ7DUHKDCx2I\niDyHoeQGFzoQEXmO21AqLi5GeHg4dDodli5d2m57Y2Mjnn76aZhMJsTFxeHkyZPKttzcXBgMBuj1\neuTm5irlx48fR3x8PIxGI9LT02Gz2QAAdrsd06ZNg9FoRGRkJPbv36/UKSgogMlkgl6vx5w5c5Ty\nqqoqpKSkwGQyITk5GXV1dcq2nJwcGAwGGAwGbNy4sd25z5o1C7169XJ5/Zy+IyLyIHHB4XBIaGio\nWCwWsdvtYjKZ5NSpU232+c1vfiO///3vRUTkH//4h6SkpIiIyIkTJ0Sv10tLS4s4HA4ZOXKknDlz\nRkREoqOj5cCBAyIikp+fL/PmzRMRkWXLlsn06dNFROTSpUsydOhQERFpaGiQRx55RBoaGkREJCsr\nS/bs2SMiIpmZmbJmzRoREdm7d69MmTJFRES2b98uqamp0traKs3NzRITEyOff/65ct6lpaUyZcoU\n6dWrV6fXD0D+3/8TefVVV71EREQ3uYkVt1yOlI4cOQKtVouQkBD4+flh4sSJKCwsbLOP2WxGcnIy\nAGDQoEGorKzEpUuXYDabERcXh/vuuw8+Pj4YPnw4PvroIwBARUUFhg0bBgAYOXIkPvzww3ZtBQYG\nwt/fH6WlpTh37hx0Oh0CAgIAACkpKW3qjBgxAgCQlJSknJ/ZbEZiYiLUajW6d+8Oo9GI4uJiAEBr\nayv+7d/+DX/4wx9wow87x5ESEZHnuAyluro6DBgwQPms0WjaTI8BgMlkUsLmyJEjqKqqQl1dHQwG\nAw4ePIgrV67giy++wI4dO1BbWwsAGDJkiBIemzZtQk1NjdLWtm3b0NraCovFgqNHj6K2thY6nQ6n\nT59GVVUVHA4Htm7dqrRlMpmUgNqyZQtsNhsaGxthMplQXFyMlpYWNDQ0YN++fUqdZcuW4amnnkJQ\nUJDbDuJCByIiz/F1tVGlUrltYM6cOXj11VcRFRUFg8GAqKgo+Pj4IDw8HDk5OUhLS0OPHj0QFRUF\ntfpGBubn52PWrFlYuHAh0tPT0a1bNwDA9OnTYTabER0djYEDByIhIQE+Pj7w9/dHXl4eJkyYALVa\njYSEBJw9exYA8Pbbb2PmzJlYtWoVEhMTERwcDB8fH6SmpqK0tBQJCQkIDAxEfHw8fHx8cP78eWze\nvBklJSVuR0kAUFS0ANXVwIIFN0ZiSUlJbusQEd0rSkpKUFJScucadDW398knn8ioUaOUz4sWLZIl\nS5a4nA8MCQkRm83Wrnzu3LmSl5fXrvz06dMSGxvbYVsJCQliNpvbla9cuVJycnLaldtsNtFoNB22\nNXnyZCkqKpIdO3ZIUFCQhISESEhIiKjVatHpdB3WASCrV4s891yHm4mI6FvcxIpbLkdK0dHRqKio\nQGVlJR5++GEUFBRg/fr1bfa5evUq7r//fnTr1g1/+tOfMHz4cPTs2RMAcOnSJfTr1w/V1dXYsmUL\nDh8+DAC4fPkyAgMD4XQ68cYbb2DGjBkAgJaWFjidTvTo0QO7d++Gn58fwsPD27TV2NiIvLw8bNq0\nCQBgtVrxwAMPQK1WY/HixcjOzgYAOJ1ONDY2IiAgAOXl5SgvL0daWhrUajUuXLignH+vXr3w2Wef\nddoHfKZEROQ5LkPJ19cXy5Ytw6hRo9Da2ors7GwMHjwYK1euBAC8+OKLOHXqFKZOnQqVSgW9Xo/3\n339fqZ+ZmQmr1Qo/Pz+sWLECvXv3BgCsX78ey5cvBwBkZGRg6tSpAID6+nqMHj0aarUaGo0Ga9eu\nVdqaPXs2jh8/DgCYP38+tFotgBtDx7lz50KlUmH48OFKu3a7HYmJiQCAPn36YN26dcr04a3cTVEy\nlIiIPEf19XCLOqBSqbBliyA/H9i27W6fDRGR91OpVF16Xt8ZfqODG/xGByIiz2EoucHpOyIiz2Eo\nucFQIiLyHIaSGwwlIiLPYSi5wW90ICLyHIaSG1zoQETkOQwlNzh9R0TkOQwlNxhKRESew1Byg6FE\nROQ5DCU3uNCBiMhzGEpucKEDEZHnMJTc4PQdEZHnMJTcYCgREXkOQ8kNhhIRkecwlNzgQgciIs9h\nKLnBhQ5ERJ7DUHLDx+fGf1tb7+55EBHdCxhKXcDnSkREnsFQ6gKGEhGRZzCUuoCLHYiIPIOh1AVc\n7EBE5BkMpS7g9B0RkWcwlLqAoURE5BluQ6m4uBjh4eHQ6XRYunRpp/uVlpbC19cXH374oVKWm5sL\ng8EAvV6P3Nxcpfz48eOIj4+H0WhEeno6bDYbAMBut2PatGkwGo2IjIzE/v37lToFBQUwmUzQ6/WY\nM2eOUl5VVYWUlBSYTCYkJyejrq5O2ZaTkwODwQCDwYCNGzcq5RaLBXFxcdDpdJg4cSKuu0kchhIR\nkYeICw6HQ0JDQ8VisYjdbhe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"text": [ "<matplotlib.figure.Figure at 0x4eeabd0>" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(r, f, c=range(0,100))\n", "annotate('7', xy=(r[0], f[0]), xycoords='data',\n", " xytext=(0.05, 0.06), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='blue', shrink=0.05),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", "\n", "annotate('8', xy=(r[-1], f[-1]), xycoords='data',\n", " xytext=(0.05, 0.2), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='red', shrink=0.05),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", "savefig('7-8_r2_vs_flex.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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YE7oZVxcnPvvkwwIfHiqEeDbSCSyeyoQvJ/PeJ18RRieWnSuFb/1GxMfHmzss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\nY6QGHXVoaUTQhMJ5/LQQIkgyRoahIx0tWUA2Cjn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"text": [ "<matplotlib.figure.Figure at 0x312b950>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "def calc_path(mi, mj, s=100, pcs=[0]):\n", " m0 = mi\n", " r2 = np.zeros(s)\n", " flex = np.zeros(s)\n", " isoc = []\n", " \n", " iso = np.ones(m0.shape[0])/np.sqrt(m0.shape[0])\n", " diff = (mj - mi)/100\n", " \n", " for i in xrange(100):\n", " r2[i] = CalcR2(m0)\n", " flex[i] = flexibility(m0)\n", " for j in pcs:\n", " isoc.append(np.abs(np.dot(eig(m0)[1][:,j], iso)))\n", " \n", " m0 += diff\n", " \n", " return r2, flex, isoc" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "r,f,i=calc_path(ms[0], ms[1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(r)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "[<matplotlib.lines.Line2D at 0x5a76950>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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2UOxM4FtZZ33YsGFMmDChYEBCXR2UKMka7WPGjCn4PSYmhpiYmFuuK0RJmM1m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\nXvPBBx+o3r17q06dOpVJjLaIs1+/fmr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"text": [ "<matplotlib.figure.Figure at 0x5ca1250>" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "m = rc.rand_corr(35, 10-3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "import randomCorr as rc\n", "\n", "r2 = []\n", "flex = []\n", "ms = []\n", "for x in xrange(1000):\n", " m = rc.rand_corr(35, 10-3)\n", " r2.append(rc.calc_r2(m))\n", " flex.append(rc.flexibility(m))\n", " ms.append(m)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "lm = map(lambda m: m[3][7], ms)\n", "hist(lm)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "(array([ 48, 79, 101, 112, 134, 149, 140, 111, 86, 40]),\n", " array([-0.98154615, -0.78466887, -0.58779159, -0.39091432, -0.19403704,\n", " 0.00284024, 0.19971751, 0.39659479, 0.59347207, 0.79034934,\n", " 0.98722662]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x68e4cd0>" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "r_f = zip(r2,flex)\n", "rf = filter(lambda r: r[0] > 0.2 and r[0] < 0.23, r_f)\n", "rs = map(lambda i: i[0], rf)\n", "fs = map(lambda i: i[1], rf)\n", "scatter(rs,fs, c=range(0, len(rs)))\n", "print corrcoef(rs,fs)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. -0.71620113]\n", " [-0.71620113 1. ]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AF3KAW/duoZyHHxwdHbH3py1wd3dHYGAgKvoH4FTPwcgbNRIXv56H24/TMeXTTzFgQMkH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\nwqrWYDKVwWj0Qa3VIWtd3qdaS42HC72hVU0rzds79V4yWLBCR+06FZk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79rDo5UU4K5Yl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\nQ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"text": [ "<matplotlib.figure.Figure at 0x3d1bf90>" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(r2, flex, c=range(1000))\n", "print corrcoef(r2, flex)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. -0.94002555]\n", " [-0.94002555 1. ]]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pgq/Qage10Mb2bz0AqhO+B4YAOWhLTrgDLgfodWULzOlgVH8Y/TW0csJ5PZyz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\nbNmSqKgonnzySRITEwFYu3Yts2fPZsWKFURFRREVFUVKSkr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"text": [ "<matplotlib.figure.Figure at 0x3a9a110>" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "np.mean(" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-2-clause
michaelaye/iuvs	notebooks/Pie plotting.ipynb	1	567573	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from iuvs import meta, plotting" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l1bmeta = meta.l1b_summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "filenum\n", "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 False\n", "11 False\n", "12 False\n", "13 False\n", "14 False\n", "...\n", "32999 True\n", "33000 True\n", "33001 True\n", "33002 True\n", "33003 True\n", "33004 True\n", "33005 True\n", "33006 True\n", "33007 True\n", "33008 True\n", "33009 True\n", "33010 True\n", "33011 True\n", "33012 True\n", "33013 True\n", "Name: filename, Length: 33014, dtype: bool" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l1bmeta.filename.str.contains('orbit')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fullframes = meta.get_full_frames(l1bmeta)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 2101 entries, 770 to 17267\n", "Data columns (total 25 columns):\n", "filename 2101 non-null object\n", "OBS_ID 2101 non-null int64\n", "XUV 2101 non-null object\n", "INT_TIME 2101 non-null int64\n", "NX 2101 non-null int64\n", "NY 2101 non-null int64\n", "NZ 2101 non-null int64\n", "X1 2101 non-null int64\n", "X2 2101 non-null int64\n", "Y1 2101 non-null int64\n", "Y2 2101 non-null int64\n", "BINX 2101 non-null int64\n", "BINY 2101 non-null int64\n", "MCP_HV 2101 non-null int64\n", "SHUT_STATE 2101 non-null object\n", "PHASE 2101 non-null int64\n", "MODE 2101 non-null int64\n", "CYCLE 2101 non-null float64\n", "DET_TEMP 2101 non-null float64\n", "CASE_TEMP 2101 non-null float64\n", "FOV_DEG 2101 non-null object\n", "STIM_STATE 2101 non-null object\n", "FILL_BINS 2101 non-null object\n", "TARGET 0 non-null float64\n", "PURPOSE 0 non-null float64\n", "dtypes: float64(5), int64(14), object(6)\n", "memory usage: 426.8+ KB\n" ] } ], "source": [ "fullframes.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols = 'XUV INT_TIME NX NY NZ BINX BINY MCP_HV PHASE'.split()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAsQAAALxCAYAAABb4rCdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XecXFX5x/HPsy27syUFBAJIFVCqNKVKaCIdG4YmXVFK\n", "VBBR6SCKKCIqRVpAkF6kBH8UCR0iNXQiPZCEkLp9dmee3x9nFpZle3b3zJ35vl+vYXfv3Jn9zpKZ\n", "eebcc59j7o6IiIiISLEqiR1ARERERCQmFcQiIiIiUtRUEIuIiIhIUVNBLCIiIiJFTQWxiIiIiBQ1\n", "FcQiIiIiUtRUEIuIiIhIUVNBLCJDzsyyfVwO7LTv1Ny2bXq5v8ldb5fb/nZu+0r9yNTn7+nHfUzo\n", "x2NbqdP+U7u5vt7MnjWzk82suo/f93kzy+Ru95s+9u24/4yZrdbLfg909/8hd93kfjy+K7rcptTM\n", "DjezB81svpmlzWyOmT1vZpeY2e69/1VFROIrix1ARAqWA6f1cN2z3ezbn1WCuttnIKsL9ff39OVt\n", "YHIP1y3qZtvk3G0MWAHYCzgV2NPMNnf3dA/3dVjuNgAHm9nJ7p7pJVc74XX9UODXXa80szWAbTrt\n", "19Pf4jbguR6u+3i7mZUCdwI7AQty388EKoB1gX2BtYA7esksIhKdCmIRGTbufno/d7W+dxkSQ/V7\n", 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"B9t/GcvhCi3wAAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f2eeb6cb438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotting.plot_pie_overview(fullframes, cols, 'FULL FRAMES');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "standard_FFs = fullframes[(fullframes.PHASE == 50) & (fullframes.MCP_HV == 796) & (fullframes.INT_TIME == 6000)]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 485 entries, 776 to 12826\n", "Data columns (total 25 columns):\n", "filename 485 non-null object\n", "OBS_ID 485 non-null int64\n", "XUV 485 non-null object\n", "INT_TIME 485 non-null int64\n", "NX 485 non-null int64\n", "NY 485 non-null int64\n", "NZ 485 non-null int64\n", "X1 485 non-null int64\n", "X2 485 non-null int64\n", "Y1 485 non-null int64\n", "Y2 485 non-null int64\n", "BINX 485 non-null int64\n", "BINY 485 non-null int64\n", "MCP_HV 485 non-null int64\n", "SHUT_STATE 485 non-null object\n", "PHASE 485 non-null int64\n", "MODE 485 non-null int64\n", "CYCLE 485 non-null float64\n", "DET_TEMP 485 non-null float64\n", "CASE_TEMP 485 non-null float64\n", "FOV_DEG 485 non-null object\n", "STIM_STATE 485 non-null object\n", "FILL_BINS 485 non-null object\n", "TARGET 0 non-null float64\n", "PURPOSE 0 non-null float64\n", "dtypes: float64(5), int64(14), object(6)\n", "memory usage: 98.5+ KB\n" ] } ], "source": [ "standard_FFs.info()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['filename', 'OBS_ID', 'XUV', 'INT_TIME', 'NX', 'NY', 'NZ', 'X1', 'X2', 'Y1', 'Y2', 'BINX', 'BINY', 'MCP_HV', 'SHUT_STATE', 'PHASE', 'MODE', 'CYCLE', 'DET_TEMP', 'CASE_TEMP', 'FOV_DEG', 'STIM_STATE', 'FILL_BINS', 'TARGET', 'PURPOSE'], dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "standard_FFs.columns" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['mvn_iuv_l1b_IPH2-cycle00001-mode050-fuv_20140419T033853_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH2-cycle00001-mode050-muv_20140419T033853_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH2-cycle00002-mode050-fuv_20140419T113855_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH2-cycle00002-mode050-muv_20140419T113855_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH2-cycle00003-mode050-fuv_20140419T193856_v01_r01.fits.gz'], dtype=object)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "standard_FFs[standard_FFs.filename.str.contains('IPH2')].head().filename.values" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array(['mvn_iuv_l1b_IPH1-cycle00001-mode050-fuv_20140403T050451_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH1-cycle00001-mode050-muv_20140403T050451_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH1-cycle00002-mode050-fuv_20140403T130453_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH1-cycle00002-mode050-muv_20140403T130453_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_IPH1-cycle00003-mode050-fuv_20140403T210455_v01_r01.fits.gz'], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "standard_FFs.head().filename.values" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "IPH2 = l1b_metadata[l1b_metadata.filename.str.contains('IPH2')]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAArMAAALxCAYAAABPdy58AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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dpi=150);" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "orbits = l1bmeta[l1bmeta.filename.str.contains('orbit')]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 'mvn_iuv_l1b_apoapse-orbit00107-mode2001-fuv_20141018T091335_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_apoapse-orbit00107-mode2001-fuv_20141018T091856_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_apoapse-orbit00108-mode2001-fuv_20141018T135038_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_apoapse-orbit00108-mode2001-fuv_20141018T135558_v01_r01.fits.gz',\n", " 'mvn_iuv_l1b_apoapse-orbit00109-mode2001-fuv_20141018T182740_v01_r01.fits.gz'], dtype=object)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orbits[(orbits.NX==256) & (orbits.NY==10) & (orbits.NZ==21)].head().filename.values" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { 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"output_type": "execute_result" } ], "source": [ "pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	isc
SyrakuShaikh/python	learning/scientific_computation/Cython/working_with_numpy.ipynb	1	7117	{"nbformat_minor": 2, "cells": [{"execution_count": 1, "cell_type": "code", "source": "# It seems that, for now, Azure Notebook does not support '.pyx' file compiling-and-importing.", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": null, "cell_type": "code", "source": "# OK! After reading the documention, I find how to use Cython in notebook: magic command %%cython", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": 1, "cell_type": "code", "source": "%load_ext Cython", "outputs": [], "metadata": {"collapsed": true}}, {"source": "# Now, Try the `convolve` sample in Cython tutorial.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "%%cython\n\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.int\nctypedef np.int_t DTYPE_t\n\n# cimport cython\n# @cython.boundscheck(False)\n# @cython.wraparound(False)\ndef naive_con1(np.ndarray f, np.ndarray g):\n if g.shape[0] % 2 != 1 and f.shape[0] % 2 != 1:\n raise ValueError(\"Only odd dimensions on filter supported!\")\n assert f.dtype == DTYPE and g.dtype == DTYPE\n \n cdef int vmax = f.shape[0]\n cdef int wmax = f.shape[1]\n cdef int smax = g.shape[0]\n cdef int tmax = g.shape[1]\n cdef int smid = smax // 2\n cdef int tmid = tmax // 2\n cdef int xmax = vmax + 2smid\n cdef int ymax = wmax + 2tmid\n \n cdef np.ndarray h = np.zeros([xmax, ymax], dtype=DTYPE)\n\n cdef int x, y, s, t, v, w\n cdef int s_from, s_to, t_from, t_to\n\n cdef DTYPE_t value\n\n # Do convolution\n for x in range(xmax):\n for y in range(ymax):\n # Calculate pixel value for h at (x,y). Sum one component\n # for each pixel (s, t) of the filter g.\n s_from = max(smid - x, -smid)\n s_to = min((xmax - x) - smid, smid + 1)\n t_from = max(tmid - y, -tmid)\n t_to = min((ymax - y) - tmid, tmid + 1)\n value = 0\n for s in range(s_from, s_to):\n for t in range(t_from, t_to):\n v = x - smid + s\n w = y - tmid + t\n value += g[smid - s, tmid - t] * f[v, w]\n h[x, y] = value\n return h", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": 3, "cell_type": "code", "source": "%%cython\n\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.int\nctypedef np.int_t DTYPE_t\n\n# cimport cython\n# @cython.boundscheck(False)\n# @cython.wraparound(False)\ndef naive_con2(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):\n if g.shape[0] % 2 != 1 and f.shape[0] % 2 != 1:\n raise ValueError(\"Only odd dimensions on filter supported!\")\n assert f.dtype == DTYPE and g.dtype == DTYPE\n \n cdef int vmax = f.shape[0]\n cdef int wmax = f.shape[1]\n cdef int smax = g.shape[0]\n cdef int tmax = g.shape[1]\n cdef int smid = smax // 2\n cdef int tmid = tmax // 2\n cdef int xmax = vmax + 2smid\n cdef int ymax = wmax + 2tmid\n \n cdef np.ndarray[DTYPE_t, ndim=2] h = np.zeros([xmax, ymax], dtype=DTYPE)\n\n cdef int x, y, s, t, v, w\n cdef int s_from, s_to, t_from, t_to\n\n cdef DTYPE_t value\n\n # Do convolution\n for x in range(xmax):\n for y in range(ymax):\n # Calculate pixel value for h at (x,y). Sum one component\n # for each pixel (s, t) of the filter g.\n s_from = max(smid - x, -smid)\n s_to = min((xmax - x) - smid, smid + 1)\n t_from = max(tmid - y, -tmid)\n t_to = min((ymax - y) - tmid, tmid + 1)\n value = 0\n for s in range(s_from, s_to):\n for t in range(t_from, t_to):\n v = x - smid + s\n w = y - tmid + t\n value += g[smid - s, tmid - t] * f[v, w]\n h[x, y] = value\n return h", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": 4, "cell_type": "code", "source": "%%cython\n\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.int\nctypedef np.int_t DTYPE_t\n\ncimport cython\[email protected](False)\[email protected](False)\ndef naive_con3(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):\n if g.shape[0] % 2 != 1 and f.shape[0] % 2 != 1:\n raise ValueError(\"Only odd dimensions on filter supported!\")\n assert f.dtype == DTYPE and g.dtype == DTYPE\n \n cdef int vmax = f.shape[0]\n cdef int wmax = f.shape[1]\n cdef int smax = g.shape[0]\n cdef int tmax = g.shape[1]\n cdef int smid = smax // 2\n cdef int tmid = tmax // 2\n cdef int xmax = vmax + 2smid\n cdef int ymax = wmax + 2tmid\n \n cdef np.ndarray[DTYPE_t, ndim=2] h = np.zeros([xmax, ymax], dtype=DTYPE)\n\n cdef int x, y, s, t, v, w\n cdef int s_from, s_to, t_from, t_to\n\n cdef DTYPE_t value\n\n # Do convolution\n for x in range(xmax):\n for y in range(ymax):\n # Calculate pixel value for h at (x,y). Sum one component\n # for each pixel (s, t) of the filter g.\n s_from = max(smid - x, -smid)\n s_to = min((xmax - x) - smid, smid + 1)\n t_from = max(tmid - y, -tmid)\n t_to = min((ymax - y) - tmid, tmid + 1)\n value = 0\n for s in range(s_from, s_to):\n for t in range(t_from, t_to):\n v = x - smid + s\n w = y - tmid + t\n value += g[smid - s, tmid - t] * f[v, w]\n h[x, y] = value\n return h", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": 5, "cell_type": "code", "source": "import numpy as np\nN = 300\nf = np.arange(NN, dtype=np.int).reshape((N, N))\ng = np.arange(99, dtype=np.int).reshape((9, 9))", "outputs": [], "metadata": {"collapsed": true}}, {"execution_count": 6, "cell_type": "code", "source": "%timeit -n3 -r3 naive_con1(f, g)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "3.69 s \u00c2\u00b1 43.2 ms per loop (mean \u00c2\u00b1 std. dev. of 3 runs, 3 loops each)\n"}], "metadata": {"collapsed": false}}, {"execution_count": 7, "cell_type": "code", "source": "%timeit -n3 -r3 naive_con2(f, g)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "15.8 ms \u00c2\u00b1 467 \u00c2\u00b5s per loop (mean \u00c2\u00b1 std. dev. of 3 runs, 3 loops each)\n"}], "metadata": {"collapsed": false}}, {"execution_count": 8, "cell_type": "code", "source": "%timeit -n3 -r3 naive_con3(f, g)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "9.78 ms \u00c2\u00b1 188 \u00c2\u00b5s per loop (mean \u00c2\u00b1 std. dev. of 3 runs, 3 loops each)\n"}], "metadata": {"collapsed": false}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3.6", "name": "python36", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "3.6.0", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython3", "codemirror_mode": {"version": 3, "name": "ipython"}}}}	gpl-3.0
ronak3shah1/ml_lab_ecsc_306	labwork/lab1/Assignment 1.2.ipynb	1	1729	{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "3\n", "216\n" ] } ], "source": [ "import tensorflow as tf\n", "tf.reset_default_graph()\n", "x=int(input())\n", "y=int(input())\n", "with tf.name_scope(\"Split\"):\n", " with tf.name_scope(\"a3\"):\n", " a = tf.multiply(x,x)\n", " b = tf.multiply(x,a)\n", " with tf.name_scope(\"b3\"):\n", " c = tf.multiply(y,y)\n", " d = tf.multiply(y,c)\n", " with tf.name_scope(\"3ab\"):\n", " e = tf.multiply(x,y)\n", " k = 3\n", " f = tf.multiply(k,c)\n", " g = tf.add(x,y)\n", " h = tf.multiply(f,g)\n", " \n", "with tf.name_scope(\"final\"):\n", " i = tf.add(b,d)\n", " j = tf.add(i,h)\n", "\n", "with tf.Session() as sess:\n", " writer = tf.summary.FileWriter(\"/tmp/tboard/output_2\", sess.graph)\n", " print(sess.run(j))\n", " writer.close()" ] }, { "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 }	apache-2.0
probml/pyprobml	notebooks/book2/28/bernoulli_hmm_example.ipynb	1	480916	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "<a href=\"https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/bernoulli_hmm_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "gr9tjnvk2LB3" }, "source": [ "# Bernoulli HMM Example Notebook\n", "\n", "Modified from https://github.com/lindermanlab/ssm-jax-refactor/blob/main/notebooks/bernoulli-hmm-example.ipynb\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nkQzzSK02LB7", "outputId": "9912423a-6920-4c82-db07-be7916a278e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+git://github.com/lindermanlab/ssm-jax-refactor.git\n", " Cloning git://github.com/lindermanlab/ssm-jax-refactor.git to /tmp/pip-req-build-j0n1k4xi\n", " Running command git clone -q git://github.com/lindermanlab/ssm-jax-refactor.git /tmp/pip-req-build-j0n1k4xi\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.19.5)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.4.1)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.2.2)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.2)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (4.62.3)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.11.2)\n", "Collecting jax==0.2.21\n", " Downloading jax-0.2.21.tar.gz (756 kB)\n", "\u001b[K \|████████████████████████████████\| 756 kB 13.7 MB/s \n", "\u001b[?25hRequirement already satisfied: jaxlib in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.1.71+cuda111)\n", "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (3.1.0)\n", "Requirement already satisfied: jupyter in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (1.0.0)\n", "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (7.6.5)\n", "Requirement already satisfied: tensorflow-probability in /usr/local/lib/python3.7/dist-packages (from ssm==0.1) (0.15.0)\n", "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (1.0.0)\n", "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.2.21->ssm==0.1) (3.3.0)\n", "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from 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(2.0.0)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (1.1.0)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->ssm==0.1) (3.0.0)\n", "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn->ssm==0.1) (1.3.5)\n", "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn->ssm==0.1) (2018.9)\n", "Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.1.6)\n", "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (1.3.0)\n", "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability->ssm==0.1) (0.4.0)\n", "Building wheels for collected packages: ssm, jax\n", " Building wheel for ssm (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for ssm: filename=ssm-0.1-py3-none-any.whl size=75282 sha256=07a1ee07356d240c4bf185042fad0cc0216b5f6a00fb7ba950c498cc897b251e\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-1b2n7kks/wheels/78/93/24/866323c03bb6444c9ad2485bc0abe61ad5e6828d66c2c2fda3\n", " Building wheel for jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for jax: filename=jax-0.2.21-py3-none-any.whl size=869303 sha256=ea52c5240af54eab126396cb09cc3730651e7b07fd3cef4ded61073ccd7e50fd\n", " Stored in directory: /root/.cache/pip/wheels/5c/69/0d/3784dd6d281be0837d8cef1db0c8b37d108c8bff727b961178\n", "Successfully built ssm jax\n", "Installing collected packages: jax, ssm\n", " Attempting uninstall: jax\n", " Found existing installation: jax 0.2.25\n", " Uninstalling jax-0.2.25:\n", " Successfully uninstalled jax-0.2.25\n", "Successfully installed jax-0.2.21 ssm-0.1\n" ] } ], "source": [ "\n", "!pip install -qq git+git://github.com/lindermanlab/ssm-jax-refactor.git" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "XCe-qVEF3DM5" }, "outputs": [], "source": [ "try:\n", " import ssm\n", "except ModuleNotFoundError:\n", " %pip install -qq ssm\n", " import ssm" ] }, { "cell_type": "markdown", "metadata": { "id": "UB5m_zGy2LB9" }, "source": [ "#### Imports and Plotting Functions " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "L6NuJ07T2LB9" }, "outputs": [], "source": [ "import jax.random as jr\n", "import jax.numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "try:\n", " from tensorflow_probability.substrates import jax as tfp\n", "except ModuleNotFoundError:\n", " %pip install -qq tensorflow-probability\n", " from tensorflow_probability.substrates import jax as tfp\n", "\n", "try:\n", " from ssm.hmm import BernoulliHMM\n", "except ModuleNotFoundError:\n", " %pip install -qq ssm\n", " from ssm.hmm import BernoulliHMM\n", "from ssm.plots import gradient_cmap\n", "from ssm.utils import find_permutation\n", "import warnings\n", "\n", "import seaborn as sns\n", "\n", "sns.set_style(\"white\")\n", "sns.set_context(\"talk\")\n", "\n", "color_names = [\"windows blue\", \"red\", \"amber\", \"faded green\", \"dusty purple\", \"orange\"]\n", "\n", "colors = sns.xkcd_palette(color_names)\n", "cmap = gradient_cmap(colors)\n", "\n", "\n", "def plot_transition_matrix(transition_matrix):\n", " plt.imshow(transition_matrix, vmin=0, vmax=1, cmap=\"Greys\")\n", " plt.xlabel(\"next state\")\n", " plt.ylabel(\"current state\")\n", " plt.colorbar()\n", " plt.show()\n", "\n", "\n", "def compare_transition_matrix(true_matrix, test_matrix):\n", " fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n", " out = axs[0].imshow(true_matrix, vmin=0, vmax=1, cmap=\"Greys\")\n", " axs[1].imshow(test_matrix, vmin=0, vmax=1, cmap=\"Greys\")\n", " axs[0].set_title(\"True Transition Matrix\")\n", " axs[1].set_title(\"Test Transition Matrix\")\n", " cax = fig.add_axes(\n", " [\n", " axs[1].get_position().x1 + 0.07,\n", " axs[1].get_position().y0,\n", " 0.02,\n", " axs[1].get_position().y1 - axs[1].get_position().y0,\n", " ]\n", " )\n", " plt.colorbar(out, cax=cax)\n", " plt.show()\n", "\n", "\n", "def plot_hmm_data(obs, states):\n", " lim = 1.01 * abs(obs).max()\n", " time_bins, obs_dim = obs.shape\n", " plt.figure(figsize=(8, 3))\n", " plt.imshow(\n", " states[None, :],\n", " aspect=\"auto\",\n", " cmap=cmap,\n", " vmin=0,\n", " vmax=len(colors) - 1,\n", " extent=(0, time_bins, -lim, (obs_dim) * lim),\n", " )\n", "\n", " for d in range(obs_dim):\n", " plt.plot(obs[:, d] + lim * d, \"-k\")\n", "\n", " plt.xlim(0, time_bins)\n", " plt.xlabel(\"time\")\n", " plt.yticks(lim * np.arange(obs_dim), [\"$x_{}$\".format(d + 1) for d in range(obs_dim)])\n", "\n", " plt.title(\"Simulated data from an HMM\")\n", "\n", " plt.tight_layout()\n", "\n", "\n", "def plot_posterior_states(Ez, states, perm):\n", " plt.figure(figsize=(25, 5))\n", " plt.imshow(Ez.T[perm], aspect=\"auto\", interpolation=\"none\", cmap=\"Greys\")\n", " plt.plot(states, label=\"True State\")\n", " plt.plot(Ez.T[perm].argmax(axis=0), \"--\", label=\"Predicted State\")\n", " plt.xlabel(\"time\")\n", " plt.ylabel(\"latent state\")\n", " # plt.legend(bbox_to_anchor=(1,1))\n", " plt.title(\"Predicted vs. Ground Truth Latent State\")\n", " # plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "fp81_RN32LB_" }, "source": [ "# Bernoulli HMM" ] }, { "cell_type": "markdown", "metadata": { "id": "-XWX-gWE2LB_" }, "source": [ "### Let's create a true model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "QvPXlyux2LB_" }, "outputs": [], "source": [ "num_states = 5\n", "num_channels = 10\n", "\n", "transition_matrix = 0.90 * np.eye(num_states) + 0.10 * np.ones((num_states, num_states)) / num_states\n", "\n", "true_hmm = BernoulliHMM(\n", " num_states, num_emission_dims=num_channels, transition_matrix=transition_matrix, seed=jr.PRNGKey(0)\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 307 }, "id": "WQ-11h7l3S2a", "outputId": "abbb676f-2fc8-42ba-c1f4-d8707dfea50e" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_transition_matrix(true_hmm.transition_matrix)" ] }, { "cell_type": "markdown", "metadata": { "id": "9hvaBbCr2LCB" }, "source": [ "### From the true model, we can sample synthetic data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "qlZrdw462LCC" }, "outputs": [], "source": [ "rng = jr.PRNGKey(0)\n", "num_timesteps = 500\n", "\n", "states, data = true_hmm.sample(rng, num_timesteps)" ] }, { "cell_type": "markdown", "metadata": { "id": "XQQj_2642LCC" }, "source": [ "### Let's view the synthetic data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 494 }, "id": "q6zR2Q3d2LCC", "outputId": "f695c056-3202-4a1c-c8ef-4f16fc0e4e5f" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x576 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n", "axs[0].imshow(data.T, aspect=\"auto\", interpolation=\"none\")\n", "# axs[0].set_ylabel(\"neuron\")\n", "axs[0].set_title(\"Observations\")\n", "axs[1].plot(states)\n", "axs[1].set_title(\"Latent State\")\n", "axs[1].set_xlabel(\"time\")\n", "axs[1].set_ylabel(\"state\")\n", "\n", "plt.savefig(\"bernoulli-hmm-data.pdf\")\n", "plt.savefig(\"bernoulli-hmm-data.png\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "22kr-vlF2LCD" }, "source": [ "## Fit HMM using exact EM update" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "3523416789944244af48b853ecce97c6", "37f9a4cc25114b8587b627132fdd0b8d", "ce71d7fb0f39477c976b44659568ce39", "a8d3e3c830ec401db96d6dc194971cc0", "01ecf9c6081e49d3b28e6d780dcaaf51", "9777708593804ea0a79542de9db3dd0c", "e47f6d3e1a064b618faf36c449710a03", "244d58edf45b4770ad2813b12028dee4", "d092533bba704366b4b432b135c1466b", "85ab6de8b5374592a565f14108f2e383", "c7f6720e4e3649909c3bbde21c0107bd" ] }, "id": "xudb-_xh2LCD", "outputId": "13052d07-f613-402b-be06-c5531d564920" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing...\n", "Done.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3523416789944244af48b853ecce97c6", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%\| \| 0/100 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_hmm = BernoulliHMM(num_states, num_channels, seed=jr.PRNGKey(32))\n", "lps, test_hmm, posterior = test_hmm.fit(data, method=\"em\", tol=-1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 320 }, "id": "xyCJKRlf2LCE", "outputId": "3496149c-e02b-45eb-b813-b7eef202b669" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'log likelihood')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the log probabilities\n", "plt.plot(lps)\n", "plt.xlabel(\"iteration\")\n", "plt.ylabel(\"log likelihood\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H06brDCo6MmJ", "outputId": "1fab7d20-1ca9-43a5-e13b-bd9702ad81b3" }, "outputs": [ { "data": { "text/plain": [ "DeviceArray([[0.84915197, 0.04904636, 0.00188023, 0.07304415, 0.02687721],\n", " [0.00975706, 0.9331732 , 0.02004754, 0.02332079, 0.01370143],\n", " [0.00132545, 0.02912631, 0.9396162 , 0.0288132 , 0.00111887],\n", " [0.06562801, 0.03183502, 0.00170273, 0.850693 , 0.0501412 ],\n", " [0.01220625, 0.03129839, 0.00972043, 0.01064176, 0.93613315]], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_hmm.transition_matrix" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 353 }, "id": "zBZLGSHj51pT", "outputId": "12827428-d501-4745-9eba-1e95cc12e2eb" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compare the transition matrices\n", "compare_transition_matrix(true_hmm.transition_matrix, test_hmm.transition_matrix)\n", "plt.savefig(\"bernoulli-hmm-transmat-comparison.pdf\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "PkGp5-B42LCE", "outputId": "afa13acd-5a61-4fb0-c760-7ef6e07b8717" }, "outputs": [ { "data": { "image/png": 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RERE1BQPXRERE1Kp069atwcCKIS0trd7XjEHeIsnMzGx22yJpamZ1fYQQyM3NxUMPPVTvPLVrUR+uU045BbNnz8brr7+OkSNH4ssvv8TOnTtxww03hM03duxYrFmzBh999BH++9//Yu3atVi+fDmGDh2KRYsWwW63H9J63W43OnbsiJ9//hkejycsSz03N9f83o0BFyNp6Htvivq+q4YGUKx9gcRKCHFY7WmuQ/0cmprdW1tD7z1avvnmG1x00UXo2rUrrr/+enTu3BlOpxOSJOHPf/5zkz9jY77Zs2ejbdu2EeepHahuqe/Wbrejf//+6N+/P04++WRMnDgRy5cvb1LgetGiRfj73/+OkSNH4rzzzkPbtm1hs9mwd+9e3HzzzYf0+bT0voaIiIhSGwPXRERERE3UrVs3AIAsy40Gv41M0m3bttV5bcuWLU1aX/fu3fHxxx/jxx9/xIABA+qdr6HAdrdu3bB9+3YMHDjQzEiuT5cuXaDrOrZv314nwGRkmzZFbm4uTjjhBKxatQqVlZV4/fXXIcsyTjvttDrzZmdn4/TTT8fpp58OIQTmzJmDp59+GqtXr8aECROavE7DuHHjsGjRIrzxxhsRB9c7VLm5uXC5XBG/s9LSUuzfvx9HHnmkOS0rKwtAIJvWmskfrazsSNvToXw3hys7Oxvff/99nemR3t/hXHCJprfffhuapmHhwoVhgeWqqiqUlZU1eTlGFnpOTk6TLn4dimh+Vj179kRWVhb27t3bpOW/8cYb6NSpExYuXBh2p8PHH398SO08lH0NERERUVOwxjURERFRE/Xt2xcFBQVYunRpxECd3+83b/9v06YNBg0ahDVr1oSV2PB6vXjuueeatL5JkyYBAB5++GF4vd46rxuZkEb93ki39E+ePBm6ruPhhx+OuA5raYMTTzwRAOqU0Vi1alWTy4QYpkyZgurqarz55pt49913MWLECLRr1858XdO0OkFDSZLQt2/fOu9l9+7d2Lp1K3w+X6Prveiii5CXl4cHHngAX375ZcR5DiXTVZZljB49Gj/88EOdQN5TTz0FXdcxduxYc5oR3Kxd69goU9JcRx11FNq3b4/XXnsNxcXF5vSKigosXbr0sJZ9KLp3747KysqwutC6rkfcphvaLltSfVnPCxYsqFMWAwi0O1IZjwkTJsBut2PevHmoqamp83p5eXnE32lTHOpntX//fmzcuDHia+vXr0dJSUlYeZuGli/LMiRJCvtd+P1+LFy48JDaeSj7GiIiIqKmYMY1ERERURNJkoQHHngAs2bNwmmnnYZp06ahd+/eqKmpwY4dO/DBBx/guuuuMwd1vPnmmzFz5kycffbZ+MMf/oCMjAy88847TS6rMGDAAFxyySVYuHAhpk6digkTJiA/Px+7du3Ce++9h1deeQWZmZno3bs3XC4XXnzxRTidTmRmZiI3NxfHHnssxo8fj6lTp2LJkiX4/vvvMXr0aOTk5GDPnj34+uuvsWPHDqxevRoAcPzxx2P06NFYsWIFSkpKcPzxx2Pnzp1YtmwZCgoKDmkQxVGjRiE7Oxtz5sxBRUUFpkyZEvZ6ZWUlRo4ciTFjxqBv377Izc3Frl278NJLLyErKwujR482573pppvwxRdfYPXq1RFrIlvl5+djwYIFuOKKK3Duuedi1KhRGDZsGLKzs1FaWorNmzfjvffeg8PhQJs2bZr0Xq677jqsXbsWV155Jc455xx07doV69evxzvvvINhw4aFvbdTTz0Vc+fOxe23345t27YhOzsbn3zyCQ4ePNjkzy4SRVFwyy234Nprr8UZZ5yBM888E4qi4NVXX0V2djZ27959WMtvqjPPPBOLFi3ClVdeifPOOw82mw3vvfdexG26oe0ymnbs2IEnn3wy4mvnn38+xo4di+eeew6XXHIJZsyYAZvNhk8//RSbNm1CTk5Onb8ZNGgQ1q1bh6eeegodO3aEJEk45ZRT0L59e9xxxx247bbbMHHiRJx22mno1KkTiouLsXnzZqxatQorV65sdBuNZODAgQCAOXPmYNKkSXA4HOjTpw8KCgoizr9nzx5Mnz4dAwcOxLHHHovOnTubda7feust2Gw2/PnPfzbn79+/P2RZxvz581FaWor09HR07twZAwcOxPjx4/HQQw/hkksuwUknnYSKigq8/fbbUNW6p4rR2tcQERERNQUD10RERESH4Mgjj8SKFSuwYMECrFmzBkuXLoXL5UKnTp0wZcqUsKDc4MGDsWjRIjz00EN46qmnkJGRgXHjxuHss882s6kbc8MNN+CII47AkiVL8PTTT0MIgfbt2+OEE06A0+kEADidTsydOxePPPII7rvvPni9XhxzzDFmW+6//34MHz4cL7/8MhYsWACfz4f8/Hz07dsX119/fdj6HnnkETzyyCN46623sHbtWhQUFGDevHl4++23Dylwbbfbceqpp2LJkiVwu91hWclGm2fNmoV169Zh3bp1qKysRNu2bTFmzBhcdtllYdnZh6p///5YuXIlXnrpJaxZswbz589HVVUV3G43evTogYsuugjTp09H+/btm7S8Tp064eWXX8Zjjz2GN998E+Xl5WjXrh0uu+wy/PGPfwwL8Lndbjz11FO4//77sWDBAqSnp+Pkk0/Ggw8+iGHDhjX7PQHA+PHj8dhjj+GJJ57AvHnzkJeXhylTpmDYsGG48MILD2vZTdWlSxc88cQTePjhh/Hoo4+apV6mTZtWp7RLY9tltPz888949NFHI752xhlnYMiQIZg3bx6efPJJPProo3A4HBgxYgSWLFmCc889t87f/O1vf8Ndd92F+fPno7KyEkCgbjsATJs2Dd27d8ezzz6LZcuWoby8HNnZ2ejRoweuueYa5OfnN+s9DBkyBDfccAOWLl2K//u//4Pf78ef/vSnegPXPXv2xO233461a9di5cqVKCoqgs/nQ9u2bXHiiSfiwgsvNO9eAICOHTvivvvuw8KFC3HnnXfC5/NhypQpGDhwIC666CIIIbB8+XLce++9yM/Px4QJEzBt2jRMnDgxbL3R3NcQERERNUYS8RrxhYiIiIiIiIiIiIgoAta4JiIiIiIiIiIiIqKEwsA1ERERERERERERESUUBq6JiIiIiIiIiIiIKKEwcE1ERERERERERERECUVtfJbk0bdvX+i6DrfbHe+mEBEREREREREREVE9KioqIMsyfvjhh4ivp1TgWtd1CCHi3YyoKykpidu6jYsAqhraVKyfsSRJLd6m5igtLTUfG+3Pzs6OV3OaTdM087GiKHFsCRERJTvj2JgofadoHpf9fr/52Hq8TJZ+S6xUVlZGnO5yuVq4JUT1i9RvjyW73W4+TktLMx+39v1FfQ7l3DQrK8t8zM+TiKKlpWNkyRg7SiZCCOi6Xu/rKRW4NoKs69evj3NLosvamfL5fFFbrrXzUF8w+tNPPwUAHHvssea0qqoq83F6enrU2tMUNTU15mOn09nkv7O2s7q6GkDinKgfii+//NJ8PGTIkDi2pHmMnZE1AG+z2eq8DoRvh6nY0TW2v1R8b0SJJhkvuLYEI0BjPbZaPx/rReto9j/qE43jssfjAQCsXbvWnDZ06FDzcUZGxmGvI5lNnjzZfGw9Fi9fvtx8bByXZZkVBSk+IvXbG2Jsqw2d9DZk1qxZ5uM5c+aYj9u0adOs5bWEQzmuWS/kWffrzWV83k3ZZ1uDS9YgtqE19f2JWotI+4Zo/Kat+zJrDOFwWfs71n2SdX/5008/mY+N95co/aRUOM+x9tUjSYxPmoiIiIiIiIiIiIgoiIFrIiIiIiIiIiIiIkooDFwTERERERERERERUUJh4JqIiIiIiIiIiIiIEgoD10RERERERERERESUUBi4JiIiIiIiIiIiIqKEwsA1ERERERERERERESUUBq6JiIiIiIiIiIiIKKEwcE1ERERERERERERECYWBayIiIiIiIiIiIiJKKAxcExEREREREREREVFCYeCaiIiIiIiIiIiIiBIKA9dERERERERERERElFAYuCYiIiIiIiIiIiKihMLANRERERERERERERElFAauiYiIiIiIiIiIiCihMHBNRERERERERERERAmFgWsiIiIiIiIiIiIiSigMXBMRERERERERERFRQolr4Nrr9eLBBx/EyJEjMWDAAJx55plYt25dPJtERERERERERERERHEW18D1zTffjOeffx6nnXYabr31VsiyjEsuuQRfffVVPJtFRERERERERERERHEUt8D1N998g5UrV+KGG27AjTfeiBkzZuD5559Hhw4dMGfOnHg1i4iIiIiIiIiIiIjiLG6B63fffRc2mw1nnHGGOc3hcGD69On48ssvsW/fvng1jYiIiIiIiIiIiIjiSI3Xijdu3IgePXrA5XKFTR8wYACEENi4cSPatm0bp9YlFqfTjnNGFSDPpUAXOgBA0yXM39HFnGdgZjlG5h2sdxmVfgXP7exkPh8q/4TB8k/mcwEJ9rY9IDnSIQXnf+23dli5pRrZThnDNB2qkrxjeWa6nThrZE+4HTI0XcNvJT74vB7Y7I54N42IiCguunXIxeRhHSFBN6dJltclScanxTnYUJYJTdcAAJd12wlVFvUuc/X+XPxY4QYAKJKOy7vvarANK/fkY3t1GgDg6U+2wWlTcEr/Dshx2Zv5rqhhAsMyi9DBUQVdF/AJGWsPtol3o4jqGNGvK37XK7TvsdpZ7cSbe0LniRPbHUDP9GoIRN43/VSRjvf3h7bzKe33omOax3zeNa8EALDB1wVL1v8GVZYwqlcO2vCnEVGvLvmYekxnFPvsWLa7vTl9dJti9M2oAAB4fAJL/7MtXk0kohSXn5uJaSP74IXfQjGxoVmlGJ5bWu/flPpULNnV0Xx+XO5BDMoqh2Tp/VqPI/u9TrzySzaqN30a5dYfvmqvhre+2Y2yah+EEMhx2XFq/w5w2uMW4o2puL2r/fv3o127dnWm5+fnAwAzri3+OP4IzO77U9i0amHHy73uNJ+fpPwLt9sW17uM3SIXb/b5m/l8ivoyrlJ31p7LfOQXMgqOLMSD31cDAPoesRcT+3c4jHcRXzed2gd/7vpj2LQvVy3BkIkXxalFRERE8XX76b0wPGMfukt7oEiRAz53+/6A7dop5vM7HBfAJXkizgsA1/e6HPv0EwAAdvhwv3NWg23Y22syyvSjAQD3rNwIAPjhtzLcN6X/Ib0XappeaRW4tee3YdP6ues/ySOKh0y3E29POACn9FvE1z/W+uM/fS82n8+yzcUE5Zt6l/eGNgLrfaH5/2i/G7+Tt1jmCDwuE+kY8u+x8EHFms3FeO1PnUB1LT07D0OcW7BNb48PjrjNnD5DXYhz1K/N56O69IpH84ioFXjkrN6Y0OY3fHzkzahEIAFivPI6brS9XO/fbNPb452C283nU9UluEStf3y9L/U+WFV4BWy5naPX8ChZ8tkO3PvOxrBp1V4NM4/tHp8GxVjcAtc1NTWw2Wx1pjscgQxYj6f+k6LWplNG4ApQuXBiuzcHAFAjVHj2hDpce11V+Daz/sDyfs0Fz77A/PZ2PbFPZOPrIgd0bxUAQE7PgprRBkLoqPRJuKZoGnb6c+DqAPh0YG9ZTazeXovo4Aplk9UIG5ySD76Dv8axRURERPF1Y/V5OD6tGLdIz0OKkNUIAHvLa+Ct3AohAoHt79u3RZrkr3eZ+0vK4KkO9Dd0+PFth4Yveh8sLobHE5i/U++jcKDCg53FVc15O9QEebZAf04TQKnfjlybF3l29rkpsXTIzYBTCmRBb/S2g1+E3/W51ZMGT3HoPGhHjh3fOuvf1+yoVuApCc2/NS8dLntofpeqo6eyF5lSFbq6BbZWAAcqfdF6Oymng60SANBT3hN2ProzS8K36R2Qr1ahvVKKbunV8WoiEaW4jmk+5EgVuFhfjgf2HQsA2OP24NuM+o8Fv2pZZkwMAH7N1PCtq/75tyEPACC7c6PU6ujZE4zPZThU6EKg0qthb1nq9ufiFrh2Op3w+ep2CIyAtRHAJkAJ3rnweUUHnPTwBssr15qPngz+a9j/AABdrn0Zi6WT8fB7n6N6yxcAgIxh45Ez+gJ4ftuMvYuvB/AdAOD4e97BjqSaoWAAACAASURBVDINml7/bcHJ4I7dI/CY608o2fQ5XjriQwx07AZE5JN0IiKi1kCSFazTj8KIdzqi8ttVgWlS6HZJVVUBbAOwxOyzDW90qZvCng1oZO4sxyb0yJGhC2DWCz/ioQ82o8JTf2CcDo+RWV+lqbh3S18c4S5DkdeBP8e5XURWe3wuzPJeBheq8ewjD8NTXfti1iYAq8xnV8qBwLau64hsE4CV5rNza73614un4N5OewEAFxzTFretOQi/ltznPrEkB8tLXfh5d+x5N3Q+enPw37VTBmPugFJkyN74NJCIUp4RI/Pv3YI9/1wGAJgb/NewL8xHdwb/yXLo4qj1OJI37kS4BwVK5yUavxZo54jeeajyavjkpwPw13sMTH5xC1zn5+dHLAeyf/9+AGB9awujtLQmpIZnbCKhBS8YKKGMd0lRg6+FnyzKwVXqIrk7byV6OmrQBtU+h/k5ihT+YRMRETVKUgL/a/ELFI/tqWL5mekoqtLxhiPQF6lk4DpmjMC1JiRsrsrE5qpMAGDgmhJKlZyOj/SBAABPTezv+iz2p+GJfcOgQUaOmg7gILQkP/eJJXM/Uk9iU3lNIDnIxcA1EcWIsR/SY5hgeXzGrzhWeQObs8pito7m8gfft6rIsMl62LRUFLfA9RFHHIHFixejsrIybIDGDRs2mK9TwIbKPLyhjcBXlTKArxudvzFGcNoIVgMA1GAQW/NhRBcFg9rJ2F6qozoYuNaSPcYbvEomdB0/eNtC2NNRacuLc6OIiIji53rXO8hQgWezK7A2Tm3wB/sXiizB7TQC17wjKlZ+rnZj0a+94BWJlz1EZJDsgXqlwlcDiNifhFQLG/5bFahhOtbmBJDaAYDDpQQzrusLXJdVB8413bIH3JsTUSwYgWt/DC8ynpz9K/5o+xDv5/aI2Tqay7grSJUlKMFMVy2F7xSKW691/Pjx8Pl8eOWVV8xpXq8Xr732Go4++uiIAze2VsuLeuMa35/wj/2DorPAYMa1FJZxHXgsND/OOFLF4xOduORoG+TgLcNakmcnD0g/gN/LX6O34yBu2DsR07x34vv8Uxr/QyIiohR1jvNTXKT+C31c8atDavSxVRlwBzOuWSokdn71uPDG/q7414HEG2iIyJCVZkNHHIDLV9zi61aDt5sycF0/RWo4u29PmYYNek98rfduoHwLEVHzmZUBYriL8Qd3cUrsVtFsZsa1LEMJfhi+FN7fxi3jeuDAgRg/fjzmzJmD/fv3o2vXrlixYgV2796N+++/P17NSkyykS0cnWvWwgxch77+UKkQn3kSqUhSqExJkv8Grmv7Bcbbl2JB20L8rexIAOAtgERE1KoZWXP+OB7kjVUrEuCylAoRQoTV26boOzFvD/7YdQv2eJzxbgpRmGndy/Cw82psU9ugVwusT4JAmuyDAgFbMEc42cf3iSUz47qeY8fnu3Wc7r0HALDWp8Pl4B0eRBRdavACmi+GWcZmH1VOvGCYkViqyhJsspFsmrrHrbgFrgHggQcewCOPPII33ngDpaWlKCwsxFNPPYUhQ4bEs1mJxygGH6Vb5UKlQiJlXPvMqzeKHLqSlexBXrMGkgBE8HOMZT0kIiKiRBeqUxrHwLUI9TncjkBOi18X8Ph1OG2JmOOSOiQADlmHPQFPyKh1y7AHzn0qdHuLrM8tezGv6zsAgFXV5wMAB2dswIOeaXAqAhtL1kV8XfeG7uKp9GrmRUkiomhprNZ+NCRyxrXPiNkpkplxncrHrbgeRRwOB2666SbcdNNN8WxGwru247cYa1+H9/MzcHsUlhcanDFSxrU/7LZd4/p4spcKMUed1SVclvMFBtrWQdk/DkBhXNtFREQUL0owszCeHV1/hIxrIJB1zcB19I3P+xWzOm7F9moX/rW/AwBAlVL3RIeSU4Yj8H+F1jKnqjpCd3fYpGDGdZIn7cTSc9o4SLBhb9l/I74uLIHrKi+rXBNR9C2uPBYdJeDzok0xW4du9lET73hg1LO2yRJswTIJqVziipc/k0CBswTD5S3Y6uwTleWJCDWuoYQGZ7TetiunyOCM1ozrE9K3Y6yyHZ9VdI1zq4iIiOJHQTBbJRFKhcgSXJZAdYXHjzy3I06tSl02WUeaosEu69BFoJMnI3VPdCg5uYOnJeX+ljlV1UQocB24oCen9C3Xh62Ru4EVfxXuVxfCLVXDf+D/gLz+Ldg4ImoNXqwZAVtaBxQVPRqzdYRK6Cbe8SBUJUE2S5kke7JpQxi4TgJytOtMRywVUjfjWpEBORi51pM860A2Rp3VRahzKpgBQERErZOmC7NOqS+egWtL98JtDwWPOEBjbBg1ITUhwR/sDzHjmhJNhj2wTVb4W+auCw2hGsxqMHCti0BZQeNciAKEEJDkwPdS3/hLvpoanK1+CABYX7YbAAPXRBRdktLwfiga/MFFGwPSJhK/UeNakcxBhWNZ7zveGLhOAsYJhV9Ep+MUeXDGUI3ryBnXyf0jMD5DTViyKmK4kyMiIkpkfl03S4XEM0Nj/W4NvR8rhyaATbeGMqwrPTxGx4KRZa9DghYsj5CImUTUurnVwD6pwtcyg/pZM65VhPaHfl3AzsB1GF3T8L3jAmhQMKmNGx//UnceAYEK4YBb8sBfXd7yjSSi1CcFA9da7PqLxT4VP+vt8JuWFbN1NJcRn1NlCWqwVEiyx+wawsB1EjDqM0drOzQGZ0TEwRn9KPcK7K3QUVJjrXGd3D+CUPH+UOdUYsY1ERG1Un5N4GtRAFVoOFhdFLd21PiBrQcDx2i7TYFdleH166hkxnVMmP0hIZn9oUTMJKLWza0G+ujlvpYJGltrXKvwAwhcRPPrOuxomeB5svD7vXBJHgCAJNLrna9Cd8CteKB5KlqqaUTUiqzMmYOOSglu7JKJ5zbGZh3P7eyEFT1vg/fAVvwam1U0m3G3pCpbB2dM3f4cA9dJQDHLXERneZFqXIdKhfjw6OeBfwBwxu8Cryd94BrWjOvgRAauiYiolfLrAjO8gSGfd++7Ks6tCXE7VBT7vSwVEiORA9eCVa4pobiVwO+/wttSawz8HhRJmOV0gNQe6Kq5dEt2Y0NBkkrdDiiAYOCaiGIgTy5HvlQKG1wxW4cw7kiUEu8CpplxrciwKYF2pvIxi4HrJGAdWDAahL9uqRAz+zr4mkGWAic1yT6y9vQ9F8LZvjtKty/FY9nbAAASS4UQEVErFXZBOo6lQlw2YHhnBaoMwO+Fy6GguBLMuI4R2RK43l7twsM/F0ITEq6Oc7uIrK4rORPt5Axs3bm6xdZpBq4ROj/QUrheaHNpWmjf3FBiU7luB8DANRHFhpmYGMtgbXAAWikBA9ehwRlDGdfJnmzaEAauk4BRKsSvR+l2Ob3hwRmtzBrXSd5x88p2CDjh0SUz45qlQoiIqLWyZsrFcmCbxnTPlrH6vGC2jKccLnugP8KM69hQLYHrYp8DHxa3AwAGrimh/CJ3wq+iPcrKWy5w/fC+EdCFhPFtBgLYAQDwxfGiXqLS/JbAfgNBkgq/CjgAyVvZEs0iolbGGGA8llnGQzNLcKb6Ag5kagD+GLP1NIdfs9S4NgZnZOCa4um9it7Y6eyDz8uiM7iFkXEduca1D0M6yJjYR0VRlUBp8PVkz7g2b+8QOr6tykWeNhgVjl4YFt9WERERxYXfW4W5tiegQ8ZNLi+2FsenHWHXxXU/3A4GrmPpk4Nt8UuNC8U+R+MzE8WJZA/UThbemhZb5481+QCACWnZMALXqZy91ly6Fro7t6FSIeVaYF8u+ZhxTUTRZ4zPocVwcMZ+mWW4VP0K21xtYraO5vJbSoVwcEZKCIvLhsCRPQDl+1+OyvKEmXEd+vqtNa6HdVRw5+8d+PGAhvuD8V49yX8E49I3IlPehbXOCvzjtyOxpO04nJ7TEZPj3TAiIqI40H0eTFE+BQDcb7c1MnfshMU9hAa3M9AfYamQ2NhanYmt1ZkAAAl+2CQ9UJJOCEBqmYHwiBozxLELVZIb32gtn61r3HINhDLaKCSsVEgDgev/luVBduWiWO2Gfi3RMCJqNYQQoYzrGO6njRtMjHUlEr9lcEaVgzNSQrBkC0dDqMZ1pIxrP/zB1amylDI1rv+a+S562/bjhrwCbC0LXp1L8mA8ERFRc/ktY1rEsxxYWP9C1+AyM65ZzivWeqZV4JG+XwEAPN4KwJER5xYRAT6vByvS7wEAjM/oiD0ttN42aiVskg6bv8yclsoDXTWXtbRUQ5/PozsLkdFmIk5Ky8PElmgYEbUafl1AboHAtdFHNcacSyRahBrXqXzMYuA6GcgKAMuopocpUsa1WTZE80FTjR9oqMZ1sv8IjJ2NpgkgWNuagWsiImqtdEudUr8Wv+xmf+2MazszrmNJNW6tFRI0WDKsdX7elBiqykuQFXxcXu1rcN5ouqX9J8hTq/HbzhMBdAMAaKxxXYfP5sYNvssgQ8eeihfqnU/3VgMAKnkRkoiiTNMFVBjJiLHbx2iJnHEdjGXZFAmqLIdNS0UMXCeBf7RbgSPti/Bkhxw8Fo0FRqxxHSoVYpxEKnJoYMhkLxViXJHTBHBu/k84zfYgKooGAhgS34YRERHFgb+Jt3vHWtiqLRnXDFzHxpVdfsTo3L1YVdQer+3pFHpBb7kAIVFDqipCgevSSk+LrVcTgRN/GaEgiI+lQurQlHQs10YBAEprFtc7nwgGrqu8DFwTUXT5NIHZ/pmww4fvi96K2XqMjGs5ATOujbIgiixDlfWwaamIgesk0M12EIXyr8hW06OyPKE1VCrEZ55EqnIo4zrZs5ONjGu/LtAvrRQnKpvxtccZ51YRERHFh+4PBYZ9MRzYpjHhgzNqcDsCd5lxcMbYMPpDupDgDwbqAhMYXKLEUFMZKtVRXuVtsfUadyCownJRL8nPf2Ih7DNpoIzl2Kyd+JNtNryluQCej33DiKjV0HQdS7UxAIDdpbELXBtlSNREz7gOZpum8jGLgeskoCBY5iJKG6LQIg3OGKpxbZxEWkuFJPuPIHQriTCvnEmCJ2lERNQ6aZqlxrU/QTKuhQaXww4AqPQycB0LZuk0SAjr2rFUCCUIT0Wp+bissqbF1quLwEmPNeM6lW+7bi6/psEOHzTIZvnFSNqpFfi98jN2+9u2YOuIqDUIi03F8MK7cQxIxFIh1hrXKmtcUyII1SOMzvIiZlyroYxra6mQVBmc0bi9Q9NDnyMD10RE1FppmrXGdfyOhweqBHJml8GvA+W3F8Ll2AmAdVFjxUyGEBI0YalxrbFUCCUGX1UgcO0TCjzelgtcawiWChHWfWPiBSviTS3ejM3OWQCA9k4Jeysiz1dWE/gc01DdUk0jolbCZwnQihgGros8Mr7Se6Ncs+P4mK2leXzB45MqS1CMGtcpXN6KgeskIFvKXERDKOM6GLiWLZuB5kepR+CH/RpKalIn41oxM651+PW6HVMiIqLWxCs58L42BDJ0VHk+jls7BIASIzYly8hwBvok5TXMAI4FM+NayGGlQiShIbl7epQqfNWBUiGVwgGgBQPXwQs5imDGdUN0y4XOhu7WKa0KXAxziWqGrokoqjSfD9scf4AGGWPbKfj4YGzW8+neNEzx3gbdW40dsVlFsxnxOVWWYVOCNa5TeEBhBq6TQKhUSJQWaGTVBEuFGNnWQCDjetU2Df3+UQUAuPq4wHQ9yTOuvVDhETb49NDnKDVQl42IiCiV1Tjb4lLf9RC6hqLqj+LdHJPLzsEZYykUuJbM0ggAWCqEEoa/phwAUCEcLbpeI3AtCQ2yBOgi+RN3YkG3DOzbUJCkvDpwvmmX/Cj11MDu4NhCRBQdmt8HWRKQoUHTpcb/oJlEcB8nSXIjc7Y8I+tcUSQoMmtcUwJQjFIhUYqz1i4VIlkyroUWfuJiZFwn+20HwyvmQLano+TX2ejbNhCUZ8Y1ERG1Vj4j4BDnQflkCZjeV4UiAag8AJcj0Cep9mnQdGF2xik6rIHrKl3BnT8dBQ0Sbs3sEueWEQX8mHcyrqnJhlzyC4D7Wmy9RqkQ6H4osgRdE+at2BQSlnHdQOC6zDKwZnVlGQPXRBQ1fkvMSovlftpIdJSV2K2jmYwgtU2WQzWukzxm1xAGrhOUtfak06YAAvjDzPPw6IovD3vZH/ywF5f8cz2ycnKxU9ext6wGw+9bDQDY+P236JXvNud9bPVPwKbNYRnXaWlph92G5nI6m9fpcbkyUO3T8OorryD987nA9p8SPuPa7w/skKurQzfY/eMf/zAfP/98aIRu6/YiWjA7/lDXJQfrLxn/A4AkhYISihI6KOiWznBLvqeWYrzvlnhv1s9YT+FbiKh+1n2E9XfWWlh/AxSgBTu37vS0+O5jNR9wdxsAgL5/E9Ltfc2XSiqqkOG0weGom3lp7Q+kp6ebj9u2DQwEdvbZZ5vTRo0aFfVmJ6uB/Y4CdvwHZ8w4G2eMviXezWm1rL+50tJATedevXqZ04qLi1u8TYki89gzkXPCeTh6QC9UVd3WpL8xPs/D2tcXbQX8HrjcbWH7bD18mpbQ2WuH8l5VNXqn/Loeqoe/b+8+ONPdEefb/fOPwPPDAQDnzzwHvxVVYPv27QCAmppQCZjW1Pe3Hres53cG63tOxX6LcW4LhJ8LGny+0LaVkZER8e+M7cU6bzK6+OKLzcfPPPNMg/Nat4Xm/i4OZRnZ2dnm42uvvdZ8PHLkSADhfar69i2xOs811idZlvvu++8jq+ewqK7H8NWXn2HLinsh2wQk6XRzeqTfZ0v+foUQYYMz2tRgjesEPmYdLgauk8AyZRKkmmL0y+7b+MxNYA9u2N5gXTKvpT6ZXZGBA1uA/z4NCB1K2qUAkv+2A2NwSUWWcNBdiOXaCahJ744j4twuIiKieLCXbsXztr9DkhXANwqwxemitGS5kKLrcKWHuqaVHg0ZTluEP6JmG3sHUHUAyO3V2JxEcSHbA/si4+6LFpMX+k0Yd3r4Ujh7rdksF8Jlpf7vKM2dZT5u8e+SiFJa2EWQBvZDh8vuKcYZamAcGF3TICdI8o81QK0qEhTNCFynboIajyJJYKk8AXv9HszNOTIqy7Mpgc6YV9MhhIDXcnuFXZWBot3A54HMXnlUagSuZ2IlNEVCWnVnbG57Au7ztUW/tEycG++GERERxYHsKcVI5ZvAk3hmtskyAAmAAIQGtz10UlDhZd3lqOtSKyupbHegvrUrP34XL4gsBudUo6v8NXpI0TnvaQ6bEggCJPv5TyzouvUOrvpDCemZ2XjFfwIqkIYyHABQ0gKtI6LWQLOUClHV2CU4WAPVmuZPmMC19dikyhJsRqmQFD5mMXCdBIy4shylWw4caujWHJ8m6mZcW7KfVDlYXzvJb9f6P3UxAOC7ipMgS/0ARHGwSyIioiRjrVMa99p9shIInuoa3M7wjGuKsYf7AhDAeW8CPVlSheLv8s5bcKb9XXxRNhHA1JZb8YEtgbsRXPlmxnUqZ681l3VwxkjlHgwORxpuFVfAq+mw+xdCwW8t0TwiagWsgetYlkCU5PDAtQ0tO2hwfcIyrmUZSjBmJwSg6wJyCo4Pw8B1EjDqS6sNdA4OhZFFAASyrq0Dj9hUOewEVg1u88kc5NU1c7gVyLIKNThquJ7CV6SIiIgaEha4luIcuJYUAH5A9yPNpkCWAF0AFR5mXEdd+V4AAnBmBTKsZRXQfYF/RAkgQw3sm3Sbq2VXvPoOYONbwLBLoMoTAKT2QFfNVZ5VgIu818MmCcxv5NzU5VDgrdIhVA7MSETRo/tDfRZFiWXGdShcar1oF29+S3BOkSWoSihQ7dN1OOKdkBIDDFwngRXaVUh3VGHH3gcAnHXYy7NbM679et2Mazm0WagI/ECTOciraf5Q4FpR0f23NXjN/iTKK9sBOCGeTSMiIooLoVkClfHu4MoqoHkAoUGSJKTbVVR4/My4joXnJwEHNgET5wDHXAIotmDgmp81JQa3Gswcs0ce9C9m5GDwQ/ebQQCWCqnLY8/Ban1I4JyxES6HioNVPkBNjCxFIkoNHlsWrvReDUXS8Vhmu5itxxq41vwJFLi2HJtsigSfFtofp+pxi4HrJJAnSuCWqrFLROfHUjvj2lrj2qZIgBR6XZUCG34y3yqn+X0wr8MpCtJ8B3G0vAU7tap4NouIiChujDqlGmQoMR79vFFG4FwE+hpuhxIIXLPGdfQZmdVGkoLxv87PmhKDWwluo46WDlwbvwWfeZdrKtcLbS4jKNKUG4Hv9D+KzvYtWNS+A974JcYNI6JWwys7sVL/XSB25ciI2XokOVEzrkPHJkWWzPJWQOoet6JTe4JiSkbgRE6K0oip1ivkXkvGtV2RIUlSeMZ1MHCdzNu/FlaLTTVrFclgdhEREbVORgdcT4SuoGIL9D2CpdFcjkA/hBnXMWAEqM3AdfCigcZSIZQYjMC1HMNgRERm4FpjjesGCF818nEQ+XJFo/N2wj4UyrvQzsH9CxFFj7FvVmJcy1mpNThjorAem2yKHFYqJFVLXDHjOgkowcC1LEfn67IOzujVLIFrY3pYjetgRlYSR641Sx1PWVHMjqki2BklIqLWyci4TojA9Y3bAv8HO+KhwHXinCSkDKMkiFET0lIegSgRuGUvAEB2tnDgWgndfaAagesUDQAcjrzfPsJ/nVejDC4AZzQ4r1dJB3yAy8ZzLiKKHs3nQyYqkC7bA0kPsbpz0JGJNdogaJAxKN7jwVhYY3OKLIWNhZeqF1wZuE4CSjAzWIrSiKm2WhnXvmCnzGZcqbG7gK7HApICKfgDTebAtW6pRyQrNmZcExFRq1dmy8dS/++R7XJifLwbU4vLHgxce3mcjjqtvlIh/KwpMbhlDwBATcts2RUbvwXNZ2avpWoA4HDowRKTWhMuevrVdACAS+XnSETR4yzdgm+clwaeVG0DXHkxWY/I6owLfTcCANbZs2KyjubwWS6qqrJkXmwFUveCKwPXSUCVgqVCojR4UtjgjJoObzAj2Zye0x248F0AgP6/XQB2JHXgWhM6dotcKNAhqQ5L4JqdKCIiap32pffBg/5LMTgrO+EC1xlOZlzHjFkqJNintNT1JUoE6QgErm1p8SoV4meN6waI4D5EQ+PnpZoaqFPuUnhhjIiix3pHfZMK7jeTdQyYRIqHWduiKjJURY/4Wiph4DrB6VqoWyArtgbnbao6Gdd+UWe6wagbpInk/QFojhyM8DwOAFid1xvStvUAGLgmIqLWy+jYqjGuD9gkG5YBNSVA9+OB/CPgcgR6Psy4jgEjs9ooEXLWksC07G7xaxNRkGRzoLdnMVyowZKuI1p25WbZHM3cL2opmrl2WPSmj4+g2Y3ANS9CElH0CGt5sxiW8FAstaMT6QYcnxZqTN1SIal53GLgOsFpmg9GuFqOUsa1zfID9Go6PFqtGtcWRuBaT+IfgDXorkiSOTqswlIhRETUSvm1lhnYpkk+vBco2QFMeiwQuA6WCqlgxnX06bVKhXQYGL+2ENUi29MhIKMC6XCnOVt25aNuBI67GlCdUP65EQDgS+Lzn1gRhzA+gmDgmohiQPNbM65jF9JUPSVYaJsTGHOuvADI6xOzdR0Ka1a1TZHgDSsVkkAR9ihi4DrBaULCbb4LoEDHtKzoZMNIkgS7IpsDM/qMwRmNjOuqYuBfNwFCQ3rXawAk95Uba50fRZZQndkDC/0T4VfS8cc4touIiCheuhSvxev2x6AXtwXwXnwbU6vOMgdnjKFZbweC120K4t0Sojoke5r52NgPtJi0bPOhcReqlkgpdgnCDFw3IctRCgau3Yo3pm0iotYlLOM6SsmdkSjCh5OU/wEAdtSUx2w9h8pfa3BGa2JqMsftGsLAdYLToGCJdhIAYKqrXdSWa1dDgWtv7YxrzQt8+3JgWqeLACR3xjWqinCDugwaZCi+oajOPgL3+s9FuqwwcE1ERK2S01uMQfI2FPkSoCNunHQET0TcRqkQD++MirrOQ8KfF20FPOVARvvAP6I46pDlwNnKalSKNLhsY+LWDuNOlFQNAByOQ8m4Lskfgsc2T8buGgeAT2LcMiJqLXStpUqFhEr16gk0iLU1q1qVZSgya1xTnOki/GpKtNhVGfAERiStk3Ft+fGrUmD9yVzjGtXF+JP6BgBgv3YHFCUwwnWq/qiJiIgaowcHthFS7Aa1aTKj3yEC/REz49rLjOuYe/ViYPf/gLF3ACP/HO/WUCt3RL6K+23PAAA0x10tu/KircCBzYAjA6rsABB+1yYFGYMzNiFYVN1+KB722wCtFGkMXBNRlIQFrmOYcS0roXCp7k+cPqkRx5KkujWufSwVQvFQ+zaAaDFuJ/BqmplxbQ7OaPnxGwMYJnPGgXUnI8mKOTosA9dERNRaSeYAW7Hr8DdZrVIhbrNUSOJkt6QEXQf2fhf4vHN7ALY0wMgm0hPnhIxar8y0wG+/SjiQrrTwvumHN4DVdwKdhkJ1zgbAc4VItuePwQMeFe1zM/FEI/O6HcH9i+qIebuIqPUw7vzQIJuxnViQLcchPYH6Sf5aA6yrllIhqXrcYuA6wekV+7HN8Qf4IWN36Sqgw9FRWa5RFsTnF/D6a5UKsQSulWDgOplLhVh3MoqiIqvoa6yxXwcJAsDE+DWMiIgoToxbHkUMb7FsMiNTJHi8ZsZ1jPirgQXHBx5fvCZQNsS4aKDxs6b4yzAC11Ia0lt65eYFNJ+ZvZaqmWuHo8KWhy9FIQptGY3O6wqWfYLqhEACDARMRClhT3ohzvbeij5t0hDLe3MUNRQuFYlUKkQPH2BdtSS4+lL0TiEGrhOc0PyQJQE7NMhy9G7nNbKrPZpeN+PachJrCwauk7lUiHE7NADIqg024UVPeY/5mtzSGR1ERETxpidiqRAj4zpU41okcf8j4VizhYzbX81gHQPXFH8ZzsBvv1pq8bB12J0fRvZaqmauHQ69VqZfQ/KqtuFTcxWuhAAAIABJREFUx1XIQDVGO/Nj3TQiaiWqlAys04+CNy0nputRErRUiFHGyhaMD0qSBEWWoOkiZY9bDFwnOM2SASPL0fu6jHrWPr9uZlw7ImRcy1LgNSECHRU5iuVKWoq1BpKiKJAsOyBN8zNwTURErY5IpMB1eh7gbgeoaQBCGdd+XcDjZ8Zj1ITVhKwduPa1fHuIaslyBhNr5LSWX7mlbA4HZ6yfzVOEQukXdNQbD0SnOezoJBUBAFxOhh2IKDqMfXM0S+lGEha4TqAL/Ob7t5QIMQLXRjZ2quERJMFZg67W4vCHyygL4tV08zY4o+61NePaKBUCBLKu5SS8zSs8cK1CsgTmNc0PG1h3jYiIWhkRrHEtJUBX8Nzlgf+DnW2XPdQm1rmOIutJlxwM0tWqL04UTxmOwHmGR4lHxnXw/EDzmdnEfpYKqaNw9wq853gCmyoKAUxvcN40d7b5ON2RAMcaIkoJkrcC3aQ9aB/jYLKiqHhVGwldyOhtz4vpug5FqMZ1KPlElSV4kbqDCvMIkuDCy1xEP+Paa8m4DtW4VoHCUwBZhnBmA9gPIHC7nC0Jk5Ot9YhkVQ0fHZY1HYmIqBXabi/AfP+p6JLbA53i3Zha3JYAB+tcR5E1q9oI0hlZphozrin+MuyB/32Kq+VXbimbowbPk5hxHUHwvEpvwvgIae4s87GLgWsiipKuxevwkeOvqNifAeD0mK1HkmX8xX8FdAG8kNkjZus5VFow0cNasklN8TuFeARJcNagqxKDjGufppsF3EOBaxk4+8XA6ztLYASu9SStM+mXnfhe7wYZOgoUG+SwjGtmGBERUevzo6M/3vO3xax23XBKvBtTizmgF4AKZlxHjx6pVIhS9zWiOPFLdpSJNPiaMPBf1Bl3IeiaGQBI1Vqhh+UQBvZNd2WGHjuSMPuJiBKSESPTEfv9iiJL0LXEqh1txO+spVKMC66J1M5oimvget++ffjnP/+JDRs24LvvvkNVVRX++c9/Yvjw4fFsVkLRLBkw1hIXh8tmybg26keagzNaWH8MyXr1piznSJzjvR8A8LOiQDKyiwDofmYYERFR66OZ9QEToMb1N68ARVuAzscAvUaHZeZVehhQjRprORCjLzThAWDsnYAzK/LfELWgu/Yej4dyb8U5PbpiWEuv3FLv3Tj/8aXoLdeHJTiIrkDjxw5ZUVAhHHBLHrjtyVdukogSkziEOz8OlyxJABIrcG20xVarxjWAlK1xHdezlZ9//hkLFy7E3r17UVhYGM+mJCyhxTbj2qvp8Gq1SoVYBH6oAXoC/VgPhfHblaXAiKvWwRg1lgohIqJWyB+h0xs33ywDPvo7pG0fAghcSDcGjGapkCiylgMxgnQZ7YHcHkB6bnzaRGQh2wO1rd3xKCtx1BTgtv3AdT+ayTxaigYADotujI/QtIBRpXACANIZuCaiaAnuh1pigPFFyr1YYb8dGXu/iPm6msoYf8GaZGozx2ZIzphdY+KacX3UUUfhs88+Q05ODlatWoUrr7wyns1JSDVp7XGl92oo0HC3M3q3zVlrXPuMGtfWjOtl5wLeSrgHXGNOSqSrTIdCE+HF64WrHR7wnQkdMi5UnfFsGhERUVycVPIK7rS/jvJtQwAsjW9jIgwQ6HIo8Ph1Ds4YTbk9gav+F/ic0xiopsQj2dMAhA/Q2mIsCUJKitcKPRzSIZQKAYBK3Q7IgNvW+LxERE0hjAtoLZCH21/aggypGl95SmK+rqYKJZ+E3r+ipHbGdVwD1263O56rTwpeWyZW6r8DANxnj16Q1cy49lsyrq2B663/BrzlsBWcCyCw3mQNXDuLN+Je9Rn4ZDuACZBceXhSmwwAmBWPUcsPkwYZmqRCTstChl3AqQCaCJ3YC0stcq8GlHpCV+Iy7QINJbF4/ECZNzR/tqP+ATlLyqqb/yZqUW025GWlQbGUw9FFaKcrhEBxNaCJQNtsskB2Iz+HA1WAQGB+hyKQ6Wh4/v1Vofedpgq47fXPKwRwoDo0f7pNwNVAh1zTgeKa0PxumzAzT0SE2vE+DSg5hO+t9vec5RCwW743KXjnxP7iivoXQkStSrpWhm7yPuzwHYh3U0J1li3HMpddRXGlD/vKPZDTs+rsZx320E49zemEJoCDHhnCHuhbaroGG/zQOZxLiGoH8nqFTzvwE1DyC+BqA3QYGJ92kUkC0CbbBZtc906DompAD/aD7IpAVqP9GmOJ0e8H6QIosvSDXDaB9Ab6QX4dOFirH5QWYf6/tP0MmrIFXbxTAfRpuMExlOqZa4cl2D9vasb14xVj4HS58T9PMTKzNsKpAB5vaOOy9v1r/LrZnxVC1OnP1lbjB8qbeN4CANU+oMIXmj/XKRChUqap0gdUWeZvkyYgNZA4XuEFqv2hGfLTw7cfh92BiioPqmq89S+ETE6nHVnpTvh1v3nuJcsKdF/0zkHjyS+p0CGhXZvMBucr80rwaoHtSoKOvLSGl3uwBvDrgflVWSDHGToXBOqee1qPLTZZID/bDSH8gLca6aIKPtjgkxLrypMIBmebegHtcGjBOtoigQaxDpX7s2Zcp/agwuzNJzjrgIhKQ0fKQ2TcGhwYnDFY49paKiS44csIBQ+1JB2c0VG+C39QV5u3qlk/x2QLxlcoGfhv1ihosg0drjgF96sLcbb6Yb3zf6QNwCzfzebz+ba5GK/8t975X9dG4Frfn8zny+x3Ybj8Y8R5S/Q87P75R3TscUQz3kmImtcZJ866Fu9m3AWg/sy6Ezxz8YtoBwAYIX+HF+33NbjcATULUYbAqPSnyJ/hCftj9c6rCQm9PC+Yz89VPsA9tkX1zn9AZGKoZ775/CrlNVxvW17v/D/pnXCS90Hz+e3qP3Gh+m6983+hF+JM79/M5w/bnsRU5T/1zv++NgSX+q43ny+yzcZoZYNljsB2vs3fDZ6aKjicyXfBhuj/2TvvMDeqc/9/pkjabq/XvRcwphgbE9NMMWAIxcEEAiGBEFqASyCBEJJrQsKPFOASQkJiCAQIEMIloZdwCS30jgEbGwzYxmB73bc3adrvD82MZnclbZM0M9rzeZ59HpWzM+9qNXPOec/3fF9BbpEsOzEmB2Ao6Gz1tDorrgGu+fenTLjoXi5UHuHHkQc8vxTv9Hi1OZYFievpsF+ZpCfvs5usYbQ0/ZCKqqF5/RNCy1u3wDu3w26L4OS/+R3NIMfi6XPHsHdsQ9p39+/4E5uoAWC+/AF3Ra/LerQZHXfSQTJbfbz8Kn+I3pyxbYcVYUb8bvf5mcpTXBm5J2P7TdYw9o8vcZ//SL2fH6iPZmz/sTmRoxPXus9/pf6V76jPpWn5PABvt+8KHJXxeHmhaROs+Q9YJoqcFA2FbZ5QEPq4Rf/R5t0xS3aCyfCnc+o4WX2pS4vUff8/xmzO0n7iPr8t8juOUJZmPPZDxoFcql3gPn8g+v+YK3+asf0d+tH8Sv+O+/y56I/ZSa7N2P567SSWGF93ny+NnUeN1Jyx/RXamfzdOMJ9vjZ2KrLk/Q7F6bBUvvH8+IzHECTZe8Z4nvt6C+VSsq9vssrYM347AGainY9qm9htbPaEb5DZFJvARxVzKJESbP7+/2Zt+93ET3jJTC4sj2Mbr5X8MGv74+K/YrmVXKTeXfqcJ2M/w5kLpuPA+B/YYI1MPpY/5O/Razzv3oKGwt+tE3v+owpJAT2uHVW3ZQZnB6BTf0H1JK6VIi8qHICKPIJsmHqCKlqooK1LxzcwOnlcp7MKsSeyijdxHdKLwLRvMoY9wPLWoQrbToqmSDWKnFxt9Zuhchu1K7oOPvtOyfjdkXO4m0CQmanqdmrXfOh3GAKBIAi4apUADAUdxZ2ZUpnOnVSdk0OPkerY+GnmxMegor0e1r4I615Nbh2C1MKFqPnhO0OryjImrQcTCUth+C4HFP7E21bBYxfAEz9AdQQ+YZsoFIAXR32Xw+LXc/+Ii3rVXt6xNs8RhYsSSWf+zuV+hxF4Dp4xzE1ad0WOlvLOuroCR5RbdkRGBmP81QsiGEwmWH2TVUCPa1dxHaDEtVN/QfVahRR5UeEAyGwE2Sjb+h7LS84FwJC+3kPr3hO1CxQmdCt9cUZ79Ur2qJ9CO3azb2zOTSfato33Y+eiYNJY/yzU7OFndH3imJJlXFjyO1abY5h5a5yflZlcFxuC6f3neJTxrfrn1Db/l/v8ggqTykhmi56mxIfUtqban1ZpUqZ2b/+9r+/PpsgEDqga+GcnKRHWWGOZv+3HNDx/m/u61eVvWt14JZq97en/VItZldmthj5tuNTd9vTPqMWr5dnb19an/u7bYiZPlGVub1gmtQ2p9r8rMflbaeb2CbOR2sZU+yvLTG50POvT7GRo02s7/d9+VG5yVTTz8Zu1T6ltSbU/p8KkwvN/jkRV3j0p6csVb23KeByBQDB4kOz+XZLzr1bpEdfjOnXfv+KYXTh9v4lohsns2bO5ocTknlLvfS211TwWi6GZFq3N/82wmqQi9YtjDgKSO1uMRAcCYMtK+Nui5ONf1IMkeT57kbj2m4hHWXHSi+P4dGNjp/c/a/yZu/37sYjFrB4sFz+v/6FrmXZv1OKFLOMgi87joD+XmDycZVyjW4lO46D/KTW5oyRz+7hRR21Tqv3Pykyuj6VvX7u9mW1X7ZvxWHlDsbfCWyaqLRYKq2gnnzQrQ1lrjWVGdFSv2kc+eQZl/bugRLi8/kuuiw5DS6SsMiQl1Qc1x9dS22IrqC2L87uMZ7vSmFjWad5yaqVJaZp5i0Nd/FW2tL3uPv/aEIOonLn9tvZ/s73jGff5IUMtFClz+81tD1Iff9h9vld15wT1X06oZt/S9VntTwRJHCXpZ9pIvvFAO6YFtQ0XsOS7e7BLaRNa7WnAef4GOQBMO+E6JL6VWQ9lv5d/2XoLrbZlzTbJZNaQHu79Tb+lw7YWqVMsZlVV0Mnjpsvcc3XjL9w59pMRi0MOPonI1Lnomz7h3sn/YhxbkDEDIJtL8U7FfO5aP4qjZkzkB3k+l+ujHaBxkp7OKqTIiwqLxHXAMY1U4liWc7eiFFGTX/LMims7ce25RYXV6N3sYt4vKxLVUtLrt14Pl8eYbG/zMSwZvW4D6+tgPWAY6T2uu/L5jr6db3WG9tc0HElk2Dh2K8nBVjdFJU6UT1qr2LY6tV3Pm4zv+jc1Asu39P4UO+yf3rLV/uktm+yf3rJhB2yUMntcd+WLPv7f1nZpL0kSj+pH0k4J0+TwbqsTCAQ5pI8FtvKK1N3jWpIkJtUkbY20Heu73WdLSlI7dcrKUvZHcjTZ5ytyBM1QiEgGZsj6+rzh+DNKcmr7mZu4Do5342Cl2YhwTuJSFAyeX3Ub9Vsyjywa7J/est3+6S1b7J/eUmv/9Jb1O5Lj10DhsU1SpeQYVHhcd8exseyLhaXcllTHfrlhExuBjo7UYqLiSVx3Hft3Hc/2xGd9bL+qj+1X9rH98i7t67WRUNo52SRIj+NV3mJG+HD1Gvf1vdVy9lY28kbrFz5FlhsuLXmMmZEN/MecwUWrs989vf7UmmWxfFvvz6MBy7dm97j20gB8Ui9TYo1H1xtcuyk5UGlraKSKj6zJ7FM2Oe/ncnJIZoAU13oWqxDhcS3wBWcbhGFJKDlMXMfsJLWmexLX6RTXHt8xM6Qe19hJXTdx7S0AGKAbUG9QHAWIzy4/TnEC57szEOYOa+Vg5Sm+LOng7p6bC/rJRc3fRSmt4ubYRL9DEQgEAcBRXAfC47pqLIzYFSrSKPgSrbzw3TJ0E/7ryQ5W1/W+30mgEsHA0ITiGnAXKzr9z4XiOjDELYXnzL0BaO641edoBiGe6yIqJa+VsIp28smwts/ZV/qMUboBzPE7nFBx9rqjiEyfR9uGZ/i+38EEHFtj5+4ycUiY9hw45AvS4+Qd7CZ/wTJpnN+hdMcdKyhsZiQSFvUMIUgGN05yVi3AIpAhKcltSQHKG7l/v0d4qhZ5UeF+zVYSiQT19fVUV1cTjWYpOS0YMI6XjoFCLjVRzlaChLc4YyfFdZrijCEdu3k/QwBVSVXF9Sraw4BCUBLXOgoGujbwQcOCEXUsjtzDG1UTROI6j1iJdiitoiUukhMCgQAky+7Ug2AVsuBKWHBlZ4soByPB/MnJ4WppH0etGioQx9SFmhhIJafl1DjItUcI0IRs0OLZ/RAkL81BgzdxLSfH28WqXBsIh2z7Xy6L/Zt3dhwFHOd3OKFCN00iSEhKABaMA46juNa7zHkTlv3cCHfiOuIsjgWx5JzT/0gyz0kHuy8f6FM46ahMbGO2tJpRCRnYLa/n+ndkAWZbPRPLp+X1PH1BtxNz3sS9U5uhWPutPl0pK1eu5PTTT2fOnDnMnz+fpUuTxW527NjBd7/7XV5//fUejiDoK6bh+DPn9qbmFmfMpLje/QSYczpUpy7Q0KoOHKsQ20vK66VmhqwYkexsXbT8TTQ8Ouo21pR8hz3W3TXgY8XsJfW4GcCOu4gwE+0AtIrEtUAgAF5U9uVa7RTWjz3a71Cy41EC93WTj2brM0yhuE7i2IF0Ulzb4wlDJPf9JqLIjKCeapqQhAK+8HiuiwjCKiQTsmUXRQvComfYcO6zHhGVID23bNyZA+N/4PubFnZ63VFcS2FPXNu72jWf5/RpCZKwIQMHtPybR2O/4Ji1v8n7uf5RcjJX66eydcieeT9Xb0mnOFdt4akeVrVpD/R6ue/jjz/m1FNPpbq6mkWLFvHww6nCAzU1NcTjcR555BEOOKBvVaBvvvlmANasSXoXPfbYYyxdupSqqipOO+20Ph2rGHHUR2aOE9eOulozTLfyaETxbLU44qrk+Ts0IFmUIqx569bocF4zdqctWsNYQFFTX3srZBMDxU5c+6241u2ih7lY7Y7YqpaESFznlV9WPsLc6A7qPj8Z5i32OxyBQOAzbzCbWmMGO4+d5Xco2RlA4votc1fKaUeKDMtxUCHF+Sy9aj9HfR2y8VAxstNQg1dKkgYCI2OwrdXngAYbnmRiRLbH20WqXBsQdlIrEPURQsbepbXspTzHuiF9qaQzOGkyS7GskXQkNnR6PWFbh4Q+cW0rrg0rePNfJz8SiOLdmbATU1YB7O4cP/8g9QeGaxXSXXEdpDhzSa//0zfeeCMjR47kkUceIR6P89BDD3V6f7/99uOpp57qcwA33nhjp+fOcceNGycS1yQtGQAMKT+K67hukjDSKK5tVI+vthFSj+sNww/kCm0oU4eUcwQgeyZsoVNcB8QqJJf+YjHFOaYoVJJPpqnbmCWv443WDT03FggERY+eZtDrGx8/AZ+/AiNmwN5ndH7Pk1DV+jgYv1w/lyarhF8P3T0HQRYBzpjHO9Hb9zz4ypmgCOs/v/Fa9mm6sAopOLIKkXJQVFTJsQoJqWonj7j1EXI8Nx0MLKpey3mRp/h3zVS/Qwk8kpOD6HINOjt0JTPkiWtbca0HMHHtfuayzFzrAyaxgXWMJ0hmIc7Oj0Lch5yih0FKCDt9k7cGniqKMyZZunQp5557LuXl5SQS3W8UY8eOZevWvq8efvLJJ33+nUGF1dmfOVdE7cFxW0Lv9poXbz3IIF2sfcGtfm1fzKriVVyHa2LgeFz77YcVz+E2LZG4Lgwtul1wNdHicyQCgSAIOANbpQCFbXrkizfg7VuRph+FlSVx3VfFtbPYmyjSbZN9Jp3HdaQ0+SPwHUUkrv2lZhr8rBYAfVktsFFYhaRBtuemQnHdd+L2ZR2TRZ/UE9PLW5kmL2d9ZAveDFPCKBbFdbI/9ntOnxZP8e6xbGZ3PkVDZYe/UXXG9eHO/33o8tZrGBddzfoN3wMuy/v5eoPTN0U8Y3jFLc5YnPeXXieu4/E4lZWVGd9vaRHJkHywtXoOpyYWU1ESI5f1xR11dWvc6PYaAA+fB9s+Rt3nfGAIEN7EtXNhOxezoka4XDsbHZmTyyf5GVqfeSoxh+eV/bASceA93+LQnG1aOVjtjirJ/09cJK7zSrNmf/81sfdYIBDAlcafODT2Djs+PAX2/L2/wbjKqjS7oIz+J64V26dWK9JBfJ/Z40TY5SgI6Q66Ysdr2afp4doRWGxEirzI1YBw/W9FgcG+krCn3Y5NoiAz54z5jLOiT/LYiJ142fP6m00jkIeORSvZk7m+RTdwgqy4tjzFGR27WpmAjaMK6MM90tzGFHkLmxMNeT9Xb0knPlHtxe9i7bd63eNMnDiRlStXZnz/zTffZKeddspJUIIU7dEaXjNnMlKJ5fS4TpK6xVOorVPievsnsGkZcutWwp64Hr/1BZZE/kFrx0TgYCRZ5j7zcCwLFpUM9zu8PvG5OZIN5jRGGLW+xhHP4Wp3zPG4FuKivNLiJK51kbgWCARQarVTJbXRaMX9DiWlmEmzLd9bpE7rYz/xffURJrAFo/YE4IIBBFgkqNHkj5f6dbDhXVBLYNeFaX9NUBhUr+JaC1iSYJChFHmRq4HgFmcUViF9JuEqrsWkpyectITeJf3w9y1TeHTssXy1YhSnFj6snHGPdjhVSgef6+V+h9IdO3EtyYoncR2wPJAdYyGKxJr2GDVIRZNTdn9prEKKdKdQrxPXCxcu5Oabb+boo49m1113BUCyjcr/+te/8sorr/Czn/0sP1EOYgwrP1t5I2lsQTq9Zq+iS5aBLIFppSw3wkZVy+fsp7zFZ1qd+5oiSeiWFbpkvEXye+D3qmfCPn0uFNeNZgmbrWrqNFFhO580JZLfnYjR5nMkAoEgCMiWCVJAiu84MVjdJ/NW6VCWvJ1AlaG96wy2B2ZLq5kjf8obrXvlIsriZN1r8NgFMGSiSFz7jKPyNS0JyxIJ04KjtcM7t4OhUVp1NFC8yrWBIBVQ6VhsuIprSVzfPeGo0ruuHTn1v7SQJ+fuMr6KZsWYqb3tdyjdcRTXsoLp5h6CtdgiuR7XBUhcO5a9AeqXDVvooXoV17JQXANw1lln8dprr3H22WczdepUJEnimmuuoa6uju3bt3PAAQfw7W9/O5+xDkrkjiYmSluokSpyetx0hRg7eVy76icDRZYwDSu0F4FlT4RNjzJAkcE0TQwjWDfhnqiQOxhBPeVWu69xOIprOQeK659uPoKrKvahce3DwLsDPp4gPc3x5PUbFYlrgUAASPYCqCQHYNHQ2XKeru5E5RgueqqjX4fV7cmGFXIvzJyx7VPYsgLKamDqIcnX3M9e8y8uAeBRSwXR83QwoMfhmSsAKDt6DhDe3ab55E/DfsbStVs4ffx09vc7mJARtxdfoyJx3SNOgVTN7HI/NJJ9VaKv3mEBw1EyS0Gz4IDU7jdJcZO2QVNcF3IBzd1dEqDaaJqRxirELSIZwO9UDuh14joajXLnnXfy97//nccff5xYLMa6deuYNGkSZ555JqeffjqyLAZauWbcpqd5OfZLNsRHAyfn7LheHz2HTslsOZW4liUJsDDDOngzuxcReU85k3K1gw823gK7fsuvyPrMRSVPsTC2lP+T5/CKj3HcvWkKbw5byIQh09l7gMeSnGKZRnC23xQjzfFkJxYzReJaIBjsWJaFYqUUNb7jLpbnth/QnAmXEQA7lCDw6b/h2Z/DmNlw3kvJ15w+OEBbYAcrquOrnOOC7IJeoqQW8VTHf9a0sCzL3WUsgBbKqKMKXc2tqGowELeTTcIqpGeUDFYh3xq5lnOiV1C3YyfggYLHlSucZKgcIBWvg2WlU1wHK86/KN/i+paj+O8992RCns/l/K+sNLsC/cJZVPXm9BT7sRbWnF0P9KmqgqqqnHHGGZxxxhl5CkfQDSfpmmP1RaxHxbX92DJQZYk4IVYd2B2C6ZkIODdhK0ArZ71BlZLxGpa/A+hlTZWsM+cyXx0x4GNJ9kTBMoTaK5+8vz3CX/RjaY2N5BK/gxEIBL6im5Y7CZGVACTJ5NSYI5fozjBXKK6TOKpqb1E1WSSug8K7DUM4MX4lkt4OXOF3OIMPz3XhtXIwTMtdVBCk5oNqjm0sBwOtCYl6q4Imq9TvUAJPREpvFTJSbWO2vJZPQmwxaZkmb5ZchIbCksjBPOZ3QF1xPa5TxRmVgCWuG60yNiNjluW/Xpkp2Ra6AcobpYozpvJ3EUdxHXIbnUz0Ohu6ePFili1blvH95cuXs3jx4pwEJfBgT+KMHPv3pPW4zqS4di6CkHpcO0b63iIizudphWyiptjbdAy/t5Ha6uhcbNM6Y9gHXKI+yNzKbQM+liAzb29WuFo/lTv0o/0ORSAQ+IxhWq6iUJL7pGHID0MmwsQDYPTMbm9JG99l+fnlvPu9vhcwcpSrkrDBSOL6VqZJXItdT77TYMRYau3CO5oodu8LHtskxePnGlarxHyxa/x9viq/zbCOL/0OJXTc/2U1e8X/wnGNP/E7lMCj2h7XCbPzAkncvjQVK7x9lqZpjJQaGCftQAmg4trrcb2FEaxkOl8wzt+YupAucZsvLCk/4oqB4BQO9i4gKsLjOskjjzzCAQccwKxZs9K+v2HDBh599FGuueaanAUnSBUgsHK8bbBHj2uPAkdx/XLCeRE4qmqvx7WzemiFbKKmuIprfxPXMauDGhopTQxcbXFq5QfMVDexuWonXshBbIL0mImkRUhrQhfbXgWCQY5mmCiS43EdAMX1rG/CrG9ipfPlizczc5RCoh8KEkdxLeegkHBR4CzWeywR3GRdyBbyixJnO7L4X/iDLANJe0TV0u3HxZsE6C+nNt/J9OinvFGnAwv8DidUOPNO1yZRkBHVVVx3vv4c0ZRqhbdf1xLtRO3Hus+7qNPi8Y9eIc1gBTMAONDHkLoyzVjDHvI2qpvLgbF5PdcbFUfydNMURlfuz355PVPvcfol724g125ssHtc90RbWxuqKm7COSdN0jUXRNMprr3b4Hb/elL5NGkeylvJl8KauHZWx7we127iOkBbPnpDUBTX541fw1Ul/8Wn9dOBYwZ0rKjt85bQQvr9CglWIlntfEyHAAAgAElEQVTQ07KgLWFQHhP3a4FgsGKYFn/Wv8bD0kGcP+UIv8PJjq2W7s8GH1dxLayokjifg3exQs6Pv7ig7wyJmkyW1tNOG+v9DmawokTASNjb4pPXRrFuu+4vblG0HO8GHhQ492AlvDYXheKc2kVUTtqDhnX/Apa6rzsFLtUwK67jqbobQUyvuPmRAF/jp5r/4rjoy2xcvQYOnJ/Xc71XeSjPGLtxRuXkvJ6nL6RTXKshF5v2RNbMRW1tLRs3bnSfr127lnfeeadbu8bGRu677z4mTZqU+wgHO64/c24TlV2tQqKK3FmBuddp7kNFfi4ZQ0itQraU7sS/jP3oKNmVPe3XDByrkLAlroPhcZ1wt2kNPBngVNbuj5pO0HsmxFp5OvojKqQO2ne8TPlYcb8WCAYrmmHxorkXAOeMSr+TLjDY/bTWj+76U2sSCUOiI7YHc3IcVihxktNdrUIkJZnAtiwQu3F84+jR9dwY+ynr1WFM9DuYwYqsgpGwrZSScwWtSNVr/cWpjxCI3TohY0JpG99RHkWJacA3/A4n0CSkElooo13v3Cc580U1B3NQv9ATHe5jv+f0afF4XAd1WCBbOkiFuQ85SuYgJYTdWgOenJ6TuNa6GsMXCVkT1w8//DBLlixBkiQkSeKWW27hlltu6dbOsixkWebqq6/OW6CDFWe7oJnjFa+uxRnTWYc4OFYhekgTi8uqj+RObToLqke5Q4SU4jpcq7XO1m6/Fdcdeu4GDVEp+T+I58AvW5AZLd7OVLkZgPUt9YBIXAsEgxXv4FtNswOr4Kx9Ed67B6l8JNZXf9P5Pbuf7s92/eetuazQv8Yh5SP4Zg7CDD2ub6VH7Tf1ELiyzp94BJ1wJp26z3Zwg5rqyaC1I0diQHqrgsGO7LEREPSNSaXt/CRyPwCW+VekAvjzhhY5fT2quJb8/kUIb+I6EZLENcAurGZf630aqaIuQGYhklMssgB1WmQpeLZRepoiuc54vlj7rKz/6QULFjBu3Dgsy+Lyyy/n5JNPZq+99urURpIkysrKmDlzJmPGjMlrsIMSs7vNRS7oqriOZKmW7VysYS3OaLrm/Z7XJDk5Hg2Z4lrFUeD7rLjOYeI6Yvt2O1u/BPmhoaUD55Yfb23yNxiBQOArummiomMiowZhzlS3FlY8CMOmQZfEtWQ4ieu+H1a2E0/Fqj7pMyLhFGgiSvL7qvtdgHswc8EbAGg72sCuvBKkZEUQkEkVbhP0DSfpCqBpCaKxEh+jCTaHlq2lRm7k7ZImvPv9nTlohHCJz7wYWngS15W0MJX1bKWGIC1xOwtohVBcn7L19/w69jwrN3wVuDPv5+sNjqBU6VScMXgJ9lySNXE9Y8YMZsxImrHX1tZy5JFHMn369IIEJkjy4chF/Oaziew8fAR/yOFxuyqsuymu/70YPv4X7HECqjIfSCWAw4aTcPde2DfGzqexqYmjh+3rV1j94pr4SWDEiHVsB572LQ5HHR3JheLaHgB7B3OC3NPc0gFUANDR1uhvMAKBwFd0w+KJ6BXsKn9J47JfwmE/9DcgZ3E+XcV228eyP12EYhd3SogdPUmOvhaOuiZpCSIIHG4xdKG49h3FI+jRxcJXJ2TLsREQieu+4p3rJOLtInGdhctqXmav6EauHD69U+L600aFW/VjScilXORbdAOjs1WIj4FkwLJS31PHXtWxCAoChml5LIvyr7iOWXGGSq1Ezba8n6u3OAUY01mFhNUloSd6/Z++8MIL8xmHIAPNkWGstCZTFa3J6XG7K667DJLbdkDjl9C2HSXkiuv9t9zHMZEX2FF/ALA3AO9F92a12cKBJfmtQptr1pjjaLSGMc30d3uUWxgjB6vdjlVIQg+X+j1sWFi0WjHKpTh6W7Pf4QgEAh/RPYN+WQlA8sEtEJimHzD7r7g+SHqPxdHbaGqYCNzf//iKCUnqbFjZVgcrHkp+znufCRGRSPGLiD0U10Ti2nciHrFLsarX+ktqi34A+o6Q4Z3raIl4lpYCZ0du177/43qFa/RTkSVCm7hui43g59oZRNHQfJ7Tp8Vjz+LsAApS4lo3Tbful1SAMazjfCAFaKd+NquQYu2z+rxEsX37dlasWEFjYyNWmkTm8ccfn5PABEkMs7taOBd0tQbppriWUpNIOeQVSke0f84+yke8m0glqZUAehX1Bsu2CJHwN27XXywHiutV5gTKpAT17eHd8hUWWpzEdbuwChEIBjOGaaG4ietID60LgKOYSaO4tkbuzlUvxWmK973fq6GJfeRPWKMHcGIYFJo3w//9OPl45kkice0jqqu4DuDW8cHC63+Clq3EphzjvhTW+U++cCyYCqF0LDbiCU9C0KO6FXTH2TGldbn+LLs/Ny17LJPjHEkh6IgM5R7jSAAOsx7zOZo0mMFXXCuFLBIr2XkyKzifQTqrEHcMUaQFhXvd45imyVVXXcWDDz6ImeXDEInr3FLeXstsaTVjjNyuykqSRFSV3e2z0a6Ka6dYhGl4LoJwDtwk+ybj9QmvkNqooRFJC86Wj94wUd5KQmqhglZf44jbioGB+osZpsUJ2q8A2Lz5pwOOS5CdFjPKKBmMDqG4FggGM5phujZNhVCr9IiUWXFtjd6T//di/8ZAzoRLtRL9Dq2oWP081H8OI3eDSQckX/MuXISsYHWxoQrFtf+8eyfUrSFSORkYDQiP/K6cGP0zmxvb+O3EPdnH72BCRofmUVzHReI6G6q9QNLV9sAyUgvRCd2kNBqAMUwfiXtk5FKAEsIunrGYEUDFtWYUOHFtn0NKZ2fnE05ezitGVYRVSJI77riDf/7znxx33HHMmzePn/70p/z4xz+mvLycu+++m8rKSn70ox/lM9ZByd5bHuDC2H0sa9gHOCGnx44qnsR1JsW1ZaSKM4Y2cd29wOX/NF/OTiVreHPDxcBVPkXWd35fchs7K7XcpC3gCR/jeGeLyjfjPychRXjYspCk/q12eycD3oGIID+0GFFQwYqLxLVAMJjxqlUUJQCqOc9ieS5xJly5KCRcFLx/D6x8JGkJ4iSuvZM+kbj2FVVOjrOF4tpHbBWxYqWuhbDOf/KFYVpYyCjCKqTPJHSP4loTietsZLIKmV7ZwSPRS4hKOom2dyjNsZ1qIdDsxKJkmQTxbm95xmK6pYCEO2YMAoZpcbZ2GYpm8Pg+BzMiz+dzckhygBLXjse1IqdyeE4SO2yOAr2l17OVRx99lIMOOojrrruO+vp6AHbffXf2339/Fi1axHHHHcfKlSvZf//98xbsoMS+QEwp9xPLqCqDLWLqrrhOqZ+U0Cuuu1e/Nu0bkBUgr6LeoNiduOFzN1fXZvKWtStYyZtjV+uZ3uJdcRaJ6/xze91eDB9+ONPK92Y/v4MRCAS+oZsmsr0NVw5C4jpbccYBkFJci4QskEpMe7f4ex+LfthX/rplF54adRZtW9cBK/wOZ3BiXw8yHmVskarX+ouZpui9oHe0x3XWmqPRUFFM8fllQ5GSc8Su159k6kyRdwCwvaMNCF/iuurL51gdu4B6KlnMkX6H0x2PJYYeQKsQ3TDRUNFQkQtgb2blaYw6EJzktLceg5PELtaCwr2eraxfv55vfvObAMjOh2KvGpaVlXHCCSfwwAMPcM455+QhzN7T0NDQb/VnNtL5eecTxd66KzuJVSn32wa9ycZuxRmdiYyppzyu7c8gH59vPkmnuDZxEvPBncyqavJ/UFlZ6b7WpAAW7L//PLTrH/QpMvhgfQPH3/QakFRNd/v+9BK9rYGr1dvQUDnozWeZOmVaLsMU2Dj3r3P+9i53fryVppse5TsnClunMNLfvkjppx1E2O73XjZu3Og+HjNmjPvY+zel+/uqq6vdx85CfdipqKhwHzc1NaEZFqqdmAmE4rpqHMxYiBSrRLLHmNFoNPneW3/B+u10GDsbvv3PHg+VSCRtQSzLYtnTd8F7DxJBc8eugxpnTOm1B5Ej3d8X5B3vvWfo0KEAnH7hYm55aQ0HHzSbN+682q/QQknO+iql/4prw0hdP/3tc8PAscZ/SChxKtpHAuN6bP/xxx/nP6iQ0BLX2ePKpwF4vGJSt/fDPObqDd7rIt3fGovFUo/tPMXZ53yPPz/+X+7rG9ashHuSO4Z0LZwFLi2tA1UyUTG5/fbbuf322/0OqRPbmuPM/c1zABz+zfNh7VBKo+UcfvjhfT5Wpu90X77rzz77rPt4wYIFnRTF/RXP9QnXKiR7QriQ16+RxuNaKK5tSkpK3ERaWVkZkiSxY8cO9/0RI0awefPm3Ec42HGTrrmfcHntQTJbhZiux7UZ1ovAucl4PkP38wyZeb3i/C0+F9OKoTFN2kgUnUQ8Tlm0f4kPra2Rb6svALDR/H85jFCQjgr7/yTHynyORCAQ+Ilhmp7ijAFIXE+YC6fcm/69eCO0bIa2uj4fVlaTk2ChuLZxFNXeLf5exXWAF/MHA45KShVKVv+wrwfJu1U+ZHOFfHOpdRdVkTbea9oLmOt3OKHCu8M50dUDQ9AJAxnNUtzFJAc1mkpu6yH1CTf15AK73vtUXEHx9kFtw3aF3X7vYzTdMUyL+fL7VNNCrH4s1OyV1/N9OuwwHttQzrCROzE7r2fqPZrdL6lpPK7D6pLQE72+WsaOHcv69esBiEQiTJw4kVdeecUtxvj6669TUxO+rRqBJ01hwVzhVcl2U8zucjRUjYFh01Beci6CnIdQEFzFtWdyZgZwy0dvUOxiiH5X8i5rXc/zscsA2N66ECrL+3Ucw1NRW4nmf6vPYKc8Zq8Yi89aIBjUaIbFpdr5VMgaN08MuHFQOpVwL5HVpGo7irDAADJYhXg9rsXn5CdD2r5gH+ljxutxRELQJ9zEtY4qS+imVbSFrvqLYpkggSw8rvuMVx2aCOvEukAcLf+ZhjaNP0+c0+l11TOHMUKquDb1ZNya5K8QLROK53saxPufZphcrD7EbHktzetqYKf8Jq43DZnFfUYFB8WG5/U8fcFJTque3YTOgkOxFhTudfZrv/3249lnn+WnP/0pAIsWLeKPf/wjW7duBeDdd9/lrLPOyk+Ug5h0Nhe5wrvqG+uquJ56SPIHkF95A0gqtMLIhyVzWdlUSmnFnu5rKcV1uNRFir21W1L9TVyr0VL3sRZv7/dx9ERqwBHxrKAL8sNR2+/mF7G7eW2ncSzwOxiBQOAbhmnxsjmLUkWBIeP9Dic7brK17+Mgfcgk7tS/Spwo5w2gkHDR4H6WnsmyEoXyEXbCbpB/Pj6zz9b7uSj2MO81HEyuC7ILeolzbZg6qpJMXBereq2/OF63Uj9tAgczkiSxOPJPYlY7ke1DYNqBfocUWNJZIQBEPIlrLRFOxbVlJ9yNgCquI55kaBBtJ7wFxuUC2DIpUrCUzJZlud7vXnW8k8QOSpy5ptdXy1lnncW8efNIJBJEo1HOO+886urqePzxx5FlmZNPPpmLLroon7EOSpytavlIXMeyWYV4cC+CAvt854rnKxbyUu2+nDd8qvuaq7gOWTLeSVzLPluFqB4PMn0AgwbvgMObDBfkh4gqUyJpVCjhWrARCAS5RTe7D3h9ZfOH8OyVyV1mpz/a+T3X3qLvEzytehpX6d8F4OwBFBIuGtIprmMVcNlqf+IRdEKyFe9mQFV4g4IR00Frg8pR9vzHDGTixk9cEY0svqf94VT5GSqkdt5vXASIxHUm0lkhAEQ9ievQelzbViGaFPU5kvR4Fwui21bAk/+d3P123ivdrFv8QDctFApXYDz5eViYRjDmz94uSfUsICrC4zrJyJEjGTlypPtcURSuuOIKrrjiirwEJkiST8V1Z6uQzJM5tzhjuHK8Lk71a9lzE3Y+TylkViGqZYDk/2AxEvEkrrVEv49jaKnEdSwm7CvyjRSrAhCJa4FgkKMbJiOop0KOgZ4A1efJU3sDrHk++diywKuMTqcS7iXenWUDKSRcNMSqoKwGov2z9xLkGUesIiwY/GNhystVefIZIOU9LkjiKB39ti0MK5qU/NxMXVgzZePbPIWmSJS3jwdGua9HPfNFM6SJawxbcS0F8xryihpMrSMpLkg+CUbi2rCIugto+Y9nvw13cEnJLazasRvwRt7P1xPeugtKJ8W1SFwDsGTJEo488kimT5+e9v3PPvuMp59+mgsvvDBnwQnggZEX8YOtX+PosVPYJ8fHzlqc8dXfw8u/g/FfQZEuB8JrFaKn2Urx8LBz+M3ar3Lg8NkE3N2zE4uMa7EMjSvH+Bt1JJZSRw9EcW14kt4R4bucd+SSSgAq5P4vNggEgvBjmBavxn5IzNLh82rY2WfzoE4+y0bniZHjcd2PyYl3bJPQTcqCKW4qHKfe73cEgixIdhFRK6DJjMFGpMjVa/3BMk0UKfl5CKuQ/qGRXIS19HDaXBQEy+IXyt2gwIrWw4FZ7luKqnKdfgpxS+HI0rH+xTgAHMW1IQdzUCLLErKUVPYaeK7zgFis6qZJqb2AphQgkS65gsdg5MK8vuOZrEKsIrTH63WPs2TJEj755JOM73/22WfcdNNNOQlKkKJFqqSW4XREqnN+7KzFGQ0dEs2gtXkqlOY8hIJwbv31/F90MbO2POK+tik2jbetXamLjvExsr7zuTmKNdY4pFilr3F4/agHUhjDWSnXLRnFZ9/uwYBIXAsEAkgWZ3RUc8gBSD54k9Jdd0I5BQP7oUIta9vIM9HLeDF6CVrzlgEEWOS8/Fv4z29gxxq/IxnUyHZSwBJK1kCguOq1kE6A8oDp+Sxk8T3tF7q9MOUkLwXdMYzUOCCdPeZfpeO5wziW1pLRhQwrZ3xSfQg/SHyfRyu/5XcoGXGSoAnLM/YygrFLIGkVUri6X84uKIlg9AXexVSvlY73cTEuuObsPx2Px1EKYI4+2HDM1bsWJsgFWRXXzkTWNNxzmyH1uB6tb2RX+Qsa9Xr3tVQyPjx/k2VZaSvI+oHXX8xI9D9x3a6U87IxE0WWmJeLwARZiZQmE9flklB5CASDGcMwUCV7AJ4HK7I+443B1AFPsd7dvw41O8OwKX0+bESGyfJGADZ1tA0wyCLm5etB74AJ+0DNNL+jGbQ4iuv+7C4Q5Iild8GGd2DSPFQ5mRTzqtsGO7qh0WhVoGAiRYKpFg06uhQBC0w9pDYXBUDXEzijAlntnriOKjIdmklCD0Yisa9sik3mcdPk4IoRfoeSEVWRSBhg0GVHXADQPeILpQDWWpJ9DjkgFrPe/JU3J+RVXxumRSQAw/tcknVk1NLSQlNTk/u8oaGB2trabu0aGxt54oknGDMmXOrVMDC+YxXz5XWMTADskdNje70fo10V186g2TJCmeT1IjvbOjw3tmqzjmnSRsoSZT5F1XcMw+AAeQWGpRA19gBqfItFVhQSlkJUMpLeV/1kx5CZnK8tpqpEZXkO4xOkJ1KW9LgukXQiqoqmB2PLl0AgKCyGd/IRhCSZdzG268Ro8oHJn36g5qgeQ9Hw1l8g3gg7HwljUluv3e9AQLYBD1YkR3EdhMWkwcrnr8CKB0GSUZUTgPDOf/KBKUWZE/8LAI+MnONzNOHEcK1CRJ+UCV1LuMvXstJ9gcQR3CVCuqik6cm4owEuGO3kfzS6Cgv8xzAt4kTpsCKUqLGef2GgOIlrgpG49tZdUNJYhcAgVFzfddddrv2HJElcffXVXH311WnbWpbFZZddlvsIBzkL6/7G7OibvLn9W8CxOT12J8V118S1M2g2dWQp3B5vzk1G8kwETth+C1fHnuPtTccCh/oUWd/QEx38bzR5/a1q2guY5Gs837D+h+aExM+H7d3vYyTsG29UFZO0QhAtH+I+HlJRyvaGZh+jEQgEfmHo3sR1AO6/3kRdDhUt3toJA6nHUDS8eRPUr4OKUekT1wHZBjxYcdVcQVhMGqy4izgp4Y4W0vlPPjA8u2/zsRt4MKDLETABobjOiO4R1qSzkrzPuJSxsc2s/Pz/wawLChhZjkg0U0EbJUpwFeOOelfzWoWYwRgjaKbJEYnfElVlPp11dP5PKDmK62D8v7w5uYhn8cN7Ty7GosJZR0b77JMsB2hZFjfddBNHHHEEu+yyS7d25eXlzJo1izlzxMprrnEHsVLurSE6Ka67WYU4iWvTvXGZIR24SWkU15b9eQbFZL836J7q04rq//a8L5RJNCY0OqT+r3Q6W7yCvOJcTERrJvFj7TxarFLapD8DInEtEAxGDMOjmgmCurNTccbc9ctqp8S1SBJkLHQpFNeB4KaaxSxdvYlTx08PVeHwokJJXQsRp9BVESYA+othiMT1QFkXmUZdQqExElybCL8xPXNeOc2ct5QE5VIcS2svZFg546gNN/KLkid5d+sC4CG/w0mLaueJNCt4xRmd+1CkQPcgSQmYx3WG+3BkMHtc77PPPm7yura2llNOOYVZs2Zl+xVBjpHsxLWVB0VURPV+0TNYhZg6smMVElKP65SCxZu4Tv59UkBuwL3B8GxzTleootA4ix3aAAb0NZtf4c+RO2g2xgCH5ygyQSbKq2p40DgEAE252+doBAKBX3RKXAehOGPpMNjrO8l+umv/9sCZsOpfMPccOOqaPh02Ek1NeI0B2FoVDY6iOmPiOhjbYAcrLVYpOxiCGfW3APegxrP7IFWcMZzzn3xgJFq5UHkEA4VofDdgSI+/I+jMX6sv4Y36HVxUsxNf9TuYgNJ5zts9XaVLKliAEU67FdmO25T9F6JlwhEudsgVcNCPk/fGkqE+R5VEz2MNuHRIgVNcp+Lw5vCULh7XxUav96Jdc03fJguC3JBSC+d+22DUU0yzm+LaUXhbBooUbo9rZ3VM8n6G7t8XjBtQb+isuPZ/G+ke0ud0yPXIzaOAcf06RlnzOg5V3mGdOTG3wQnSUh5LXfNyrNTHSAQCgZ9YnRLX/vcnVI2BRUvSv2ckkj/96K+9ViGG8LhOqaW6/s+dxYKAbAMerDiTcVXsQvMPj3DH+T+IxHUKs6OZH0ceAOCLjnOBvhfNHey4/swhLSxYCHTTZJM1DBW9U60K930pmfC1Qmq3Itl9rZXGvzsoOEnQuFIOh//c52g6o5sm31GeYYgE1O0Mw6bm9XxbR+7P+e9fTGnlEH6f1zP1Dm9OLpPH9UCEhUGl17OV5cuXs2rVKk4++WT3teeee44bb7yRhoYGvv71r/OjH/0oL0EOZiTHBD4PW3m9iutuHteTD4LjlkDpUJSPwp24VkijuLYfSwGpDtsbTMPr9+W/4voq7QYmRmt5a7MMfKVfx7DsFWdd8v/vGQzEVAXVaKdC0WksLSGcwz2BQDBQ2uUSToxfyW6jy/nVsGl+h5OdTCrhXpCrQsJFQ6bEtax0fl/gCzPa36Na3kZNvASY7nc4gxPZWcQxXMVhWOc/+cDy7MqQlADYTIUQRyGZKMLEUq5IlI7i4HhyMfvV6u41nQx757QVVsW1mYzbkoM7/3W+p0FcuDNMi8vUf1JltcOWI/KeuG4vn8i/zX0YJwVD9KV5rEJUb+JaEYprAJYsWYIsy27iura2lksvvZTS0lKGDRvGbbfdxqRJkzjxxBPzFuxgJJ3NRa6IZfO4HjE9+QMoqz4EwmsV8qh6NFLbdqYP2T31ouQkrsMzaNC1VJoxXYXlQmNIEbAGttrt/K4hEtcF453SH1Att3La5Incu9rvaAQCgR9opsxSaxeUkmEQq/A7nOxkSrb2kputk4jrcGDphBwGFVKcz7KrHcvw6RAph1hV4WMSuJzWchfTo5/yxg4DYZ/mE55FHEe9VozKtf5idNr9Kcbu/WFByxMsjLyNteVAYPce2w9GvFYIaho7M8Ox2NDDmri2r6MAzOczkbJKCt79TzMsFPLnStAVJyEclGSwNw7Vk8/zJrG9ye1iodf/6VWrVnHaaae5z5988kksy+Kxxx5j1KhRnHPOOdx///0icZ1j5DSFBXOF1xOnm8e1ByXkxRkfUI9lvd7O/wyb6b5muV5F4VEXeRXXagCsQlyV9ABWuyV7wGEEeMW52GixolTTSmWJUMoIBIMVR0ETCYolQcs2+N+Tkh7Lp9wLQz32UWb/FdcAdyon0JjQ2bN0fA4CDTmZFgFOfaDwsQi6odhjUimNp6ugQAyfDjstgDGzUNuClawIAqbhUVwHwWYqhEyLr2Su8jrvtATDLziIdE7MdR+nOIInyQjn3lHFcqxCutugBAUnCWpqCbhh9+RY7Fv/gHFzfI4MDNNEJX85sq4olkY1TVSawdi5p3Va2PEqrlP5vGLst3rd4zQ0NDB8+HD3+auvvsrcuXMZNWoUAIcddhg33nhj7iMc5Lj+zHmwCvGqrLsprk0zOcGxTGQp3B5vTuVZxbti69zkQqS47qxy8H+F1hk0DEhxbSe9heK6cLQYUVCgSiSuBYJBi6Un2EX6kglGG+hzwO8+xTKg9v3k466WHs7W9H4mSaJiW3aKmp1B74CYKP4XRFK7LEVC0Df2/m7yB1A+fwsI7/wnH3hFNLKwCukXjj2EZIZTLVwIrObN/Ei9H91SUc2DgM4JXtMRPBnhrMugOAvyfo+9suB6/FsSNG1IvqgHI3Grmxayk7iW8l9gfNzm//B+yY+o16sA/0W6nRZ2PIlrr991EJXyA6XXI6Oqqiq2b98OQCKRYNmyZZx33nnu+5IkEY+Hc9UryFxW+T98uqmeH0/cg/1yfOysieuVD8NDZ0P5CNRdHwfCq7h2LE68ovK3x5zKz7+cw8xx45jtU1x9pb1iMnt2/AUVk2crRvgdTkolrfd/0CC5VZVF4rpQtJjJz7oyGhClpUAgKDglHdt4OvbfsAlongvVk/0NyJuo61p7wpmY9lOF6uwoi4tCWPBfr/odgSALsl2TRepq5SLwBSchoItFLxfDu/tT7AzoF05BPlkkrjMitWzmB+qjALRwfbf3Xxh6Avc0zWanoXNznh8pBI7iOthWIfaivyUBEmAFpg6GXmCrENkWPHzUYisAACAASURBVDp9tN/oRqbijFLaNsVCr//TM2bM4MEHH+SAAw7g2WefJR6Pc+CBB7rvb9iwgZqamrwEOZiJWyrtlORlRa6zVUiXJJbr8Wa4F0RYPa7/lriUqlgTX27+JfAdANpiI/nMama0PDz7LwcIHYkmkl6kasT/SY2buB6IVYijuJaD23EXG8168rZfGROJa4FgsGJ5Jx952NHVZ7yKma4TowF6XN+qXc6E2Ho+XvcT+MrF/QywyHnjZqhbC9OPgp0X+B3NoEV1rEKE4joQqAEuTuYX3r5DWNr0DydxLQUkCRhETI93tZrGS31N1b48YUzgW7Fw1q5YUnIejXVbOXb0gezvdzAZiDj5H8NKjr9MLTAKd90wkCX7vlwAqxBn/CkHZKe+o6ZWZAlJSl+csRj7rV73OBdccAFnn302J510EpZlMW/ePGbOTHkGv/jii8yaNSsvQQ5mnK0A3hWUXOFVWce6Kq6dQbNlIIe8qvZIdlAtNVPr8bMOY6VwPUMFWb8w7WTzQPzF1pfszFZjP/SymfjvmDU4aNGTHXxlNDzffYFAkFss3TNhDkKSzBuD2UXRctgV0LodxuzZr0NX0sZQqRVJaxtAgEXOx4/Dl29A+QiRuPYR1ypEJAT9Y9WT8P69UD0JVf4GEK65Qr4xUFljjkHBZGiAbQ6CjJO4VoTiOiNeSxol0v17Fg35TqoV0k6sNcewoGqS36FkxBEuaqYncd11fOYTpjeBXgDxhbNI56q8fUbPkB/0FjItxn6r1yOjOXPm8PDDD/Pqq69SWVnJMccc475XX1/PvHnzOOKII/IS5GBm/8Tr7C43MaS9Epic02NHsxVnlDyKayl8SV4vzk1G9vyNVdoWviKtYnx8NIRkk5HVtoNj5TfRUVDlI/0OB9MpjGH2f/X1japjeEjbkxNHjOfruQpMkJVmLXkdVEaC0fkKBILCY3qVXoVQq/SEN4auViE7HT6gQ6cKCQ9yOzutA579RXICuv/3Yci41HvOwoFQAPqKYm9DloVViH/UfQ6fPAmjZ6JUnQSAVoRbrvtLe9UUFiZ+B8CysmqfowkpdkE+kbjOjOmxoUxnSRNVIIKO1bUmRkjQbPuhaFAKZKfByQ0ZpglKBPT2VLFsn9ENkw3WcEoUi+GRkryfT7LHqEFJXDv127omrr1Pi9Hiqk9L+lOmTGHKlCndXq+urubyyy/v04mXL1/OI488wltvvUVtbS1Dhw5lr7324uKLL2bSpOCuPhWac+N3Mz66ibfqhgMH5/TYWT2u01mFhDRxLVtm0prJsyI3c+u/ODd2Kx837g6c6ltsfSFa/xk3Rf8IgCFd4XM0cP/Yyzjrw1M5duQ09u3nMZxiWd2+f4K80aIlr+cKtfg6NIFA0Dssr2omEFYhnhhyXFBGz0Eh4aJAa4O3b00+nv0tkbgOIG6xKaG49g9n0cA0OiduBEDmomCCPmB/xxRL3G8z4ShqdUvupCJ1WLT5j1xT8iDv1R4E/KvA0Q2c4VotlqRTSnDHJU7+RzctT14oGN9ZjQgHxv/I3MnVPDB2r7yfzylEKwckce21CvEiSRIRRUIzrMFtFZJrbr/9dt577z2OOuoodtllF7Zt28a9997L8ccfz4MPPsi0adP8Ci1QOBeIlAdFVCSb4to5n5VKXJsh9bh2P0PvRMAx2e+q7AowXr8vOU0nXmj06BAaaKbd6r8ySNODv+JcbNz55TheHn4iX9ZtBZb5HY5AIPAB7zZcAtCfdLYK6TIx6mhKemCrJf1K6Bmu4joYSiHf8C5WdLWHcRPXg/wz8pkF0l9oak9w04T8T8QFGXDmP4aWStwIxbWLt95R16SJoHc0VUzjGWNvmkqnMsPvYAKKM0bRUdImqyy7zlJYVet/1RZTHWti6bYbgJ39Dictqvf+54wRAjKOctTEhboHyVKwFNdOUrpb/o7kZ5JMXAcj1lziW+L6jDPO4PrrrycaTfkWHXPMMXzta1/jtttu49prr/UrtEDhJFbzUQCjk+I6o1WI3nnFLYS4ViHeiZr9WArIDag3WHYnrlkKkQAkGpzvT3wAW1EWbfsz342spK7+OGCPHEUmyMbHDSqbzZnE+dTvUAQCgU9Y2ZKYfiArMO+HyViqxnZ+75Z50PAlLLoJ9jqtz4d2Cwnr4Zzg5oxO9jBdFpw9KlOBfxgmmMgoQnHtH57dB07h+rDOf/KB2vgFP1fvwUBGto4EArBjJ2RsHHMEv1w6jhmxSr7hdzABxbKtQoxM3y/bJ1wOaeI6YmkggazG/A4lI06hP8O04NCfgR6HMbN9jipJtsRtXrDHSLJkYRqGq8D2C2cxNV3iPiLLdGAW5YKrbyOjOXO6l2KbPHkyO++8M2vWrPEhomCST8W1N1ndvTijo7g2ccSwZkgHbo5noOS5yUh24jdUimvP6nMQ3A/HJdZxlPwuk5onQT9LK46Pr2am8hFvJObmNjhBRsxEOwBytNTnSAQCgV8EzypEgiN+mf49Rx3ezwS7W4/BCOcEN2d41dRdx5QelanAPzRbIaWKXWj+IacWccJulZgP1JZNnK0+BYBZqKRRkRGx59yJkBYWLAQJpZSPzYkk5Biz0jVwfcKDYV3RVyIk45YjAU5c27kS3TThK2f6HE1nZK2Vy9R/MKGpElqmQsWIvJ4vUbMbpySuwLBk/tcCv+98WRXXSvH2W4Fa0rcsi+3btzNjxgA2zkgS3zrtm5w76qOMTZrNKOdsXOQ+P6HqY745ZEXG9p80xTB0HUUt/MfleunkI3GtpgbG3b74I3eHU/4XJAVls30BhNAqxLIsVClN8t+eqIdJce1YhWRcfS4we9c/xfej97Ksfi5wVr+O4fq7BXjFudiI6a1MkTbxq1F3o186M22b51qmclv93u7za0c/y5RIQ8ZjPtI0g380po5109gnGa60ZWz/t4ZZPNk8PfV8/CPEpMyDz5vr5vJS62QAIhj8fcLDGdsCXLdtHks7kqrNYUobfx77ZNb2V2w5jM8SNQBMijRw3ehns7b/4aaj2KxXArB7bCu/GPlS1vZnbzyOFjP5Hd+vdD2XDH8za/tvrj/JfXxExRrOqX6PuClz3cutWX8vHyyctws/2LeEiH0f/UIbyk82pwoxn139HkdWZF5sXhEfya+2HuI+v6TmDfYr25Cx/Ztt4/n9jv3d5z8f+RJ7xLZmbP9MyzTuqE8tnF03+lkmRRp4bkPvlvcqZh1F2S4HuM9/P+bfjIu2uM8tc3yn9v9s3IOHm3Z1n98+7jEq5cwJ0dvr5/BsS8r67J8THsgaz++378eb7ROSsclx7hj3eNb2v9x6CCvjIwEYrTZz45h/p20nSRK6JXHr+8l+/At1CofFr+fE2aP5vpr/wjYDwhxY4tqwlVlSSJVZOcM7we9a/M9N1oUzCVAsfMN6Dk2RKOuYCIzyO5zBicc2x0ncaEVY5Kq/mJ7FLb9Vh2ElZs+5E+J7lZGtw/fl5MS11JRHWZquger4hIevX7dMk5iUvI6UACeu822VNGp4FX88ZSdGxlL3lDfaJvCHHfu5z38x8kV2lb5g1oqrAInNIw4GFiBrrXxffRwagfaL8564tkqH8Ka5GwCGhe8CQiODxzWkLF40kbjOL48//jhbtmzhkksu6fcxJFllysQxzI88lbFNnVVB6ZTURHeq8iXzI19kbD+/Ala99wIz9jkiY5t84aqFu27rzAHDK5I3y9KIQlmsy+CjvAZmHAuAvO1zIJweb6Zp8SvtdBRMFg7xFBa1B6ZKiBTXlj2hNCS/1/mSWHYyQLH6r9ByvMmkrpNoQd6YU1LLM7FLk08q0rfZWLITpUNT98i50QfZQ858j1xRMpvSYan2+8XuYry0PWP7V2IHUjo81f7A2J8olzIXKHkiejSlZrJ9jATzS36fsS3A36Inue2rqGN+yc1Z24+ITGaDlfSYGyZ9yfzY7VnbV0+cTqM1BoBR8krmRzN/NgAVk2Zi2B/2WFnrsb23f5qk1Ln9U+mhhS9c/OuDJGbFPneff2RCaWkqvl3VD5ivZv57IqUVlJan2u8ReYX5Sub2LaWjKa1Ktd8r8hTzsrRfX7Jzl+/qA+wmf8H8GfBp4w4qhtRk/F1N15l6xKm0KZVo9nBo3+i9TJM3Zfydd2L7UlqTOt+82C0Mk1oytn82djilRqr9/JLrM7YF+Gf0ePe7W0EL80v+lLX9rdEJrDV3B6Ba2sT82K0Z226yhnHWV0YD0GFFWGuNpaFiSjA8rrPhKIX7mbh+Y8hCHmyYzoTKOf0uJFwUdPI17/JZjpgBkw6EGlFfxjcsi1+ryb5nRcvBQPqFZUGe8RQhU4XiuhvObh3dkoOVRAgRYxqWcnfkd+gd5cBhfocTSJxrLpOHsWQLnsJY4FLTEjhGuWokuMIBNc9WSWccMpWTa1Z3eq2ldEyXOcD/cYCyDRq2AVDV/Bnwa9c+FciLuLMripT6HgahP9DsnFy6ArnOgmsxFhXudZ/z6KOP8pWvfIXx48enfX/Dhg28++67HH/88f0KZM2aNfzyl79k7733ZtGiRT3/QgYsU+eNd5Zzw7jpGdu0GQqNX9zvPn956DaUYenbDxs7ib8YX+Payl19KaDg+jPnYTvWpJpylnx7L6rLosTUzBe9axUSQsW1YcFdxlEAHF05zn3dUV+HSnHtWoUEZKjobtMaQOJaKK4LznPvrObC0bOYWpP5e7S0qZnG7al75N/GVTE6lvme+mr9NhrrU+1vmziKIeqwjO1f3/EljY2p9n+aPJmonPn+8u7WVTS2bAZAlUxumJI5FoAVm9+nsS05GLIUjRsmZW+/euNrNMbfB2BdtIMbxmdvv379czRqye/sqpJWbhibvf22dY/TbiY/72XlTdwwKnv7xrWpz+atqjpemjGJQ8q/YFik8NXHq5UOAJ5pmsKK+gib4zEaN6bie2p4M9uqMv89n7dHaNyUav/oSINPKzK3/6jFoHFrqv0/xkR4qzRz+3e7fFcfmjqE3ez8fntzfdbEdVvjdpaVfx+Agz/8Gh9uV/jrhBpGlQx12+hG58XNl+s20tiQOt/NkyZQpmReAH1r+2oam+rc5zdMzf6/X7blQxpbk4n6hKxzw+Ts7VfVvk1jx0oA1kfi3DAhfXu1fCgvVR7N2y3D+ZKUEkMJUtL6pv0g0QLH/QmmHZp63bE16Wfiem3VPjxsTOCEknE9Ny5msnlcz/9p8kfgG4aR2k8ni8V8/6ieDDNPhpIhoa/xkw8su080MhTNE/RMmVbP3spy6q1Kv0MJLNkScwCSLZ5SQ6i41hIdbuI62FYhjuLahNsOg03L4au/gX3Py8nxh5Umj/+lMYwHNwwH4KMWPTUHkCQemzeP962dOLJmBzs3voZqJOckplnYxLVqJZgq1SJjYhg6fmt/neR5OluxYi4q3OtPffHixVx33XUZE9fLly9n8eLF/Upcb9u2jfPOO48hQ4Zw4403Ig9kImVZvPDsf3ihx4ZvuY/+Zf+kY8LFP0GOlRG3/BlExq0IcSLdJxk5YuGeY9O/YWjQVmcXZ7RfCuHAzZtsl72dn32Tk63wJK5dxXVArEJQB664dgYckkhcFwwLi5ue+KBPv/PbPp4jg1NtRha/3Lf2l2Z35gDedR81AJf2eMT+t//A/ult+zfsn962fx7YTZ7NIXtBuVx4/9kKOZks/9uHBvc+t8x+9TX3/fvsn+yk/sF39PH8S/rY/g8flPPzS5L3SK29OWvbtpZGhtiPN7/zDA3rt3E1UF1d7bapr6/Peoyf9zG+S3v8rufnuzts5qFUHrMLUjRpN1OiN7Kf/BETW1rBl2X5NDRthHgTaF1shpwJSj+TeZEcFBIuCtQYTDwg+Xmq0Z7bCwqKridSiWtVJK59Y/xXkj+A+vQqwPZ4FQCphJHhu8treHHsIaIDmD8VO9Xbl/JL9R4S5gjg8G7vO/NGNYSfoRbvcB+r0SArrh2PayuZFzK1nNbBqIwk76sftQ3j0rveTdvmH3MfQVIijBi6mp0bX0sKDg0dUy9snZbSprX8J/ZjABrbj4cyf/9vjn1VOvFJvpXyftLrxLXVg9pW07R+JZybm5v53ve+R3NzM/fddx8jRuTXo6avOMlCv/zNDjZvpV0zuMMeRBWMrR/DrQcBULLgVQDC+P33Jtu92zy+HLeQC5eOYVR1RcZFi6CxbsyxnNQxgfFDYrzqdzDgVnQeWOI6eX05K+cCgaA7TR3JAVpFFi/lfOFYuDS1h2Ny0NjSjuOBo3VktvAA6GhpTP1ea+HV7IXE0pJFWaVIKZZlMTn+CT+L/hrjIxU42d/gHBwbrK4+y85EqZ+qmmQhagtDC8d3OG/UTIOzMtvoCfxF1xI4S/hCcR0M3OJkRahc6y/OFn1TJK77jWwvHDoF+gTdqWj8lNPVZ/lST79TavuoeZzz3qVEy4eS3QwweOiJ1HgzyB7XnaySXO//3H1nK+zEdYuZub8zE+0opRHaTc/4z0hgeguMF0BxLSuplKlh+H/dGm5xxswe14M6cQ3J4j7paGpq4qWXXupz0jkej3P++eezbt067rrrLqZOndqn3y8Ec6WPqZKjKM2jgDEFP39PHk95w3MTcIobhlFxoLc38mnsOxgobKx/HCbYRb+i5WxjKFGr1N8A+4BzA1Ky2LoUEmfgNZDVbud3heJaIMhMc0dykFSRxQc8H8Tj7W7RzOaQJK4N06TDilAiaeg9KK7jrZ7EdUt7vkPzlZ1Lmzhd/QcmMu3aUa7fsSkpQdnD4/GW7WK94hZn7F8y76jNt/Krkr+zbPM+QPbCq4OWD+6D1c/CmNkw7wd+RzMo0fXUZNiPYvCC7hRzAqDf2LWBglJvJ4zIUVtxLelYpokUJMuuoGCmxijp0Ksm8Jy5N8Ot8Amf2iNDOSp+LVE0bqqe6Hc4GXFyT5phdSpamytWtw/hNWN3Pm4bmrGNlWiH0iraDc/4z4i7wlKg3zZyfUHynMPU/U9c61nyg6kF1/Dl7Xoi6396yZIl3HTTTUAyaX3ZZZdx2WWXZWx/5pln9vrEhmFw8cUX88EHH3DzzTcze/bsXv9uIbmx8m9MUOp5e0sVUGDVM2BYPiWuJW/iOhlDCPPWWLpGVDIAo9OOACWEBVecm1Qmv69Ck4ttWk9I84lpjUweIgpCCQSZaGxLXmOlUgJdS6BGCjNQb03ACfGrKaed5Zv7atrhHy1WjBJJw4xnV1wn2poA0CyFeCIcifn+MqkkWYG93YrSHDfcAltWAbZY9hp3YuRJXFsWfOeR5Guj+1mszk6IO8WABWnYvBxWPASJNpG49glTT92DZGHl4h+blsFT/50szjjtz4BIXHuJq1UsNXcmIZexv9/BhBTVI9ZJaHFisfCIqAqFa48ppU9VRW2laVwPX3IigcIqK5mwjsTKfI4mMxHFU+TP2QXUVVgwAJZs3oO7xy6kdetrQPrdYGYiKSrZoIzh9Tk3Yskq82JVnRPXBRjHKkrqHEYOVef9Rc9WnFEJX46rt2RNXM+YMYPjjz8ey7Lc4owTJkzo1q68vJxZs2axcOHCXp/42muv5T//+Q+HHnooDQ0NPPbYY52Ot2DBgj78GfkjYSqggKUVfhuxZVmcIf0LU5EpbZsIFNBGxbOypGKvrofwAvBu55A9N53y+FaOkN+l3CghnXdWEKlq+JjTlGeJGaOA+X6H4yauB+IT/ifrZJp0nVtGBHPhSiAIApsaNV4wZtFCKQe3dTBkSIES15rFSmsyAE2tHdkbB4gms4SoZKL1oIrQ25OJ6xYruB6DuSJhJ+Yj6LTEdVc1ZwVJNedMPiyvd6HUuVBjf3BsrXKoFAolWz+GV36X/DyO77K5Og/bgAV9w9BSCyvCKsRHEq3w5esARHZOznuMMCp38sSWmn34YeIqhlfESO9KK+gJxeNrrMU7ROI6HbZFmJlhT1hM0hlBPZUhVJV67WejaoDGYF3oVJzW2e2dyzGC08/pmcdmTuK6xYjSVmbX2ZMV4qbKCnMyw8tURhegv/T2yWYgrEKS3yE1ncd1Ee8Uypq4XrBggZtA3rhxIxdccAH775+b9dVVq5IFL1544QVeeKFzKcVx48YFJ3FtJS9USy+8Usc0TX4euReAj9qOBfqpNuoPXnUyjlVI+C4As1PiOvV1H17/PrdFb6DeqAJ+6kNkfWfUjrf4deRO1sSnAov9Doctk7/GlDfGU11ewnv9PEbC7rxjAe64BQK/+XRbgjO15H3qNSviFhTMNy3x1P3TjLdlaRksDtz4A6KjduKiIePYLUs7vSNpJdISwq2mfaUjnlx8VyWT1vZ4eBTXuSAHhYSLgqZa+PCBpOVKxsT1IP+MfEQ3TXZYlUQwUCMice0bHuFOxBbuCI/rFKa7E9jnQEKMGk0prr2F+gQprB6sQobveI93Sr6PYUnA6QWMbODobU3sJX1GggjRAA3BuuImQA0LovZ9MYfFGSU7GWxlOaZlzz06ugwLa9VxLExczVlzp/CLkqqcxZQJr/gxCIlrdxd+Go9rd8EhhIs6PdFrU5h77rknpyfO9fHyhZO4xii84trQ9VSF8QIYz3fC01EojlVIDwU6g0gnxbVnMCrZn6dMjifI+cTIvm2q0ERUFQsZbQDbtDTDKS4gRsACQSYcxQH8f/bOO06uqvz/79tmdrZnN5seEkJLgIQeIIiAQapSRVFBVFBQlK+CDex8FStFVEQE/IqoP5Qm2OgISBGEIDWUAAnpbbNtZm47vz/uvTN3k5nZ2d2ZW3bv+/XKK7Mzd+aeKefccz7neT4P9OeDmzDlejaxQHqdfhp4S4+PB7StOwvBAaPy+C5ybsS1NfaF63xeB5zJ7EB2AMldFEZLuC5RnNG2oHu5Iya1TClG6AwDqQaFhMcE3oZAKT/IOqQBJwwPPTOZA/K/BOCRCduH3JpxjK9/pOT4Bu7UC08LUcrUvUoYGqlpIn80D0FH5YjoVJmIFkOseRXXMk+RhKOXxKgugLrhJW5LfxOAvPTxkFtTHi+a1ynO6M0RarcG+en2D9OiPc9VjW1ljEKK6x/DNGjfshRZmJDfvzAmlypOWA+iFnFd0ePa1VTG4nVr2L08m82ycuVKuru7ESWEzP32268mDYsKedv58oUZvHBt+3eglIAHZJ9QrrnFGeNoFWJbxUWYrBYHHc9kfzQ2F0FT2H2OyCTHS2/Kj3BHzzJN7lIvwEDF2nIVcHANW5eQMHYQPtG4L0DhWln1JHekv44uFNIxshDwJroDeuWx6cX2w7hSVxlYvxJ4NYCWhYduCTzhOpvNItw0w0gJ1+/4POR7YYavnki2G650raQ+9RhMrhRDX4YaFBIeE3jR1KXEf7n20VQJw8NfAD3ZzA8Rv1WiFF+rxHrRsf4JLtWuJ2d1ERerxaihtE7lS+bZAByiNIfcmmgy1Oa6ovl8wvNZMmpLIO2qBabPfjblex9Rw4vmNWwbFn0G5p8CXTvX7PWPaHuLTmUptyiLyh7jrX9sU2ff577h3HnQ4YUxOagacJGLuC54XJe3ChmL162q1dCBgQG+//3vc+utt2JZ20ZkCCGQJImXXnqppg0MG0M4PwgphMl8uWjhQPCdT3b9Ju0YdgB/+ol/0PEirj0blFjgCddB/xbK0JTfwKnK/aSEibCPHHZVbEPPsqO8CoClJIvlhIRyCDPPVerltEkDSCsvhu2CsdIys56VRrw8oN+ZfoV9lDVM7N4F+EDZ49bSyb/s+WR7xv7449/vyGYHij7SUfK43uej297nt64Y4bVPVhLhGihGSpXK4Es8rkPHv8gMvCB7QhHfxk7KXSMYYzDleqQ09b7Ju5RHeNuaGnZTYovf11hPflsl2ZyawmPWrnSndmC3Eo/7fcJ1PU+mKT7Cte0K17pQSQ1z7RwkgwTQ7d9Z89dvlp05Wa9R/nrnBaL0W775n6nTmV/BJerv2PWNCWBfO8jith7ITZ0cmf8+FjJXtcyq67mqwSx4XJe3CjHGoMVV1auASy65hJtvvplDDjmEAw44gPb29nq2KzIUIq5DsAox/dHCSsBRUZkO+Ng/QFYxuicB3bFMOfCL/4oval1yb8dSuI5IhFxLdgXf164FwDB/iJYa3q5xPp/Hm3aow3xuQsJ44yDledqkLM/0rAzsnJZXvNCOV/88rvUVPqK9yhNbDqWScO3ZrtgxskEZKYbvUpfL5bmbg7gttyv/e9RuHBtes4bGL6SONPPMjWjSxrtw7W3kl9oASDyuQ8fuXcu5yu2YKKj2wUC8xt0xg9/jWk4irrfBC2aK0qZnzEj5Mir0UdgtjmWemfhernlxNw7t7OLoEo9rmt8nPF5zOMutm2agEmWjOsXvcV1rTJ20O772VZh2CN3xuO4zfdqHpdNmbOBD6v2wFpCuq337tkJRNJaK7QAwpPBrUFTyuPYytsZiUeGqVwH33HMPxx57LJdeemk92xM5dNv5QYQRcS1Mf7RwwFG2agpmOYU4pYF1AFgx9Lg2MpM4Tz8XFYsLG4qbLXLB4zpGndqKlnAta77d7nx22MK1f6KhavGK6ExICJp+O02bksUY6A3snFbejbi2w5+kDYc+3blua1Z/xePUvlXMlNaywuxmQxANC5E+HZ7Xp2KoTfQZNgNCYROtWJmJYTetMn7heoQR190T9+VT+v8gN7Tw8xo1K5YUPK5L9OfOHWHeeyHxVg4NuW81X9T+CEAfl4TcmnGMLyNBdevgjMXItRHjjiNRsS2MI5rIc3vqa6SwkNddA1MPCLtJkaOSFQIMjrj2W2/EAdtw6rDokkZTyG2pRKE4o207G99m3snSSzWO/sX1vsLNXn3oiOsBW8ZGdnQbK++zT5WRA/DbV3wCcRT0YKvQP5KI65Lous7+++9fz7ZEkrPfPJzMzos4Zeocgr6sDLIKCVq49uF1gDhahRhaM3fYBwHwtXRxoI2jVYjkRkKJiBRnVH273aY+/EmDpRcraavpRLhOSKhEAaVb1gAAIABJREFUn50CBexccMK1yDsTyz4rXsJ1r+5cq1LWQMXjjlt1Od9MP8ovp+7MOUE0LETWDcCRK85Em74r59GBZTm/Iy1KlgS/PhZWPQPv+hoc+GnnPmv0wrXVMoO/2/vTYkXj2hkadoWI67nHOP8SQsN2o/AAVDXKcXhjnIZ22P8ckFWklCMrjcXItZHiF4wSRkZK09hTXgbAy26R6ITBWBWsEABUv3Cdz5U8JqoIV2g3h19qLlAGFfm783Ow5EZYcCqc9MvRv3i+uJbp1csf5gnXOROErIGdd0R0dwNNBJT5oSDYW3oFBRuRXwC0BXLechhu/1BKbOx4BSvHYqZQ1T1m9913580336xjU6LJgCEhkSZvBb+4s4RgpehEwUYKehJr27D+ZRAWmuV0zjhahfjTW/yegd5GgCwJbMsK3oplBOhSmo2ihVxECnn4Jw3GCCYNhk/sTqxCEhIq02tpoIHQgxOuJXdi2Rszwa8n74z7abty+qjmCtue0D3W8Yrc9OdNptmr2V9eRtcGHTgx3IZ5mFkw+sFvzTYo4npkGyjaKAsJjxnaZsDc90BjR9gtSSiBv+CTosZrs3BM0dgBR/8AAPuFNcAbsVz/1A0v4joi2Z9xRNOKa3rTiJfoGhR7rr+dfbWHGehdCOyzzeP+LN/YRVx7ViERsJyoxCCPay8TpVZ1MHzCdU++/NxM5J15es4S2KqKQt6J/BaecB3MOCQLg1vT3wLg5U17wA4zAzlvOTxRWithFeKJ2WPxulX1avSCCy7gnHPO4eijj2b+/Pn1bFOk8Ir7hRFubzZM5KD8TwF4cMKcYE9u5eEXjlVI89G3FO62bYEcpQitIbAtkyayWMj4+3b/1P3ZMXcDFjKvIMUibuCeKZ/gI28ewdE7TSEKuQ/+iGtjBBMvy/ecxCokIaEyfZ54nA9QuDYcq40+I14L1F53EtwgKkdcexHZvfmxN7krhXDH3D7d4iDxFF9N3UDfkl3gsIgI194CxPYVAB8kXI/sd5iWbNrpJWWZCNsediHhMcOcQ51/CZHENou/dTURriOBWk+P17iSCNejRpJlpzCfZGIbFcJNxzEz+l5kP+VxnsqVDtZSmzp4X/4b6Gh8r3FGwK0bHcJ0hHYr4hHXgzyua10HY5BVSPnD/BHXdkoDC7B0hBWscO2vk+bfZA4L75pUqpBz8bo19oI1qu4xN910E1OmTOEDH/gAe+65JzNnzkTeavIvSRKXXDK2fNkWNKxhD/lhZvfsCOwZ6LlDrTDuSyVVfXYalhDIxEe4Tq97lhcazgQga70NOIsBWVYKKTqWLdBiMP8qGvFHY9Gt+ew9TH10EddaYhWSkFARTzyWfZO9eqMYzrl6zWiMOdXSm3MmtBmRpdKSMG07wnXPOBGuvz/1XjLq8yzt/TiScK/rURJxPWFa+Cbb/kXSCK1CJmz+L0sazgbAME8Zdj2GccGyB+GRyx2bhPf/JuzWjEtsyxmtTCGX9XVNCBa1UORqfFwjqsJL0Y9FyE900dFIYQ4K4knwIby6TqU38dLpNE+JuQDkpXhZK708+Vj+5+nJ7DipiRvDbkwFtIJViA2K+z34AwtGQ2YCf8zuR1NKQjfKv6aXKZi3wPasUi0dyf19hCFce6J5mJiuVYhWQhMaFCk/xqh6FXDbbbcVbj/99NM8/fTT2xwzFoXr93e+zjmpv7Nk8yLgrEDPbfuKIQYe5ewbCGSKHTQuIq+H8A2wfjsQ/0aAHZOik97OWTm/r6AZrcd1Nt3Jpcb70CSTTzZEuTxFQkL4eOKxbFQuOFhLftP+WT6//lg2vvEA8J/AzjtaerLOhLZpCOE64wrXntA91vnwxFdQpaX8cOAYTNsChUHX+tApRPT4olkm7gJnP+TclxrZdUL1pWUbei4RrkvRv8ERr5snh92ScYsXxWWiRDwOb4yj98Ov3gWWQeuBTtarkXhcF9jcMIN7rL0ZaNyeXcJuTIwxXBHONmsUwTrGkIRXTLj0HMUv2BlmvPpnjjRr6KRLC9cneShKR1zXKNq4axfO7zsdtWUiwrq07GFexLUAHpr/Q9KayqHzj0H8zbFzCsrjWpJlLCGhSKLg8x8mnihdMuLatRgwxrNw/fLLL9ezHZFF94KSapUaMQzs/g18Qb0JCxnN3B/IBHdyX7SHv4Bh3HZv/Okc/mI3ab2bU5QHUbAx9UMgFQ3f6Ers2v1PPqP8lwn9+xJ09H8ptHQDW0QjOirmCCYNA6kufmqdBMC5qSTiOiGhEg9vnojdPhM9tYD9AjrnBrOBFWIy3cEFedeEjf0mb4uJ9IkMTZaFUqaGQaPIggS9ufAnoUGQt2VUxcLIZ1Fxr+UjtN+oC94CxB/Rk2qEqXuM6mWVresxNEd7sVg3nr8FnvkddM2Fo7YKMqm1f2XCsBGugGURoT45HpFkp8YPkHLrJAgRP6vEevFy5+H8xJjFwrYOjg+7MTHGcDOAhZlEXJfC011EmUwrRZbYRV6JKnSs7DygM8DWjQ7dXTOn1GhnLXj+yabf49qqnR4muVHcosJresI1QK/ciqJKoGhsFK08bO3OnClTmF6zFlXGQkbBioRViGdhXCqY0cvYGotFhZNN/SHIu79NWQQvXMv9G/iM+mcANpj/G/j5kRQQFjI2uClhVkyikz1s3yLMb22TGljDj7RrANiS/xI0R1+4nt/zIPtq9/HvvhxwTtjNQWtsY27+WgBuad952M/XLa8irhS8FU5CQsz4/dtT+OvM93NQupOPBnTOPvcCKPKVixxGjcfetnlH/koA7jIFLSV0IGHbNOIsGL0I7bGOYUuggJ7PkfYyqUZov1EX6iSeKr4aCsYIsoPGDJuWwev3DfKWLOD9DiKwIBuvGHKK1+2p6HKaeWE3ZjzjKwKrSMVNNNMWpJK5aiFLNSrZn3HFlFQQxUJ9CYPxIq7LCdcAt2lfo1HKs2TNRNgt4Fpgo6C593UOlZ9hqrUdsCjs5pRFkX1WSd64WMP5meTab4gK8w7hE65zpoC0M+48xa78zbiInxywZ2DCtY0MWIOy+cPCKtjHlirOOHZrMwx7xTIwMMCSJUvYsGEDixYtYuLEifVoV2TQPfNzO/gLi190DaXCuKyAZbkR17Lbpnh1AuGKo5aQCgMwDLYNsWKyUPM2T4LycxqKlC9NSzeH/7sw3O8mFRHP7oSEKONFHfTlg5swze1/ilY5x2NSNz2BnXX0+CM0BnSblhLOEAP9PTRJzrjVMzA+Fo55200f1HPFTKqIXE+AYluE7zee7YZNrzuLpqkLRvSyg61C4rUJU1PsCpsVdViUJgyPtV2LOEW/lM6mVIyMmcYgviwUVfiFa5tU4utcqLeTBJyMjju1ozEGutm+aaewmxJJilYh5aUqU1KBfKHYYVyYu/ZOPpa6kf/27Qt8JOzmlMXbnDIs22cVUqM1yFO/5tbmn7FE7MRnK0ZcF4ust295gckDPbB2aijjkJMNZVQU2oPC87hWStTDKBRnjJlmVw3DEq5///vfc9lll9HX14ckSVx//fVMnDiRjRs3cuihh/L1r3+d97///fVqayh4GoEigv+RWr4K47ISQlSUrIKlD7IKiVsn8NJPLJRByZey7K8OG/7OWTVIlRadISDLEpoiYViiED09HNqW38eT6S+zRWoDjqp9AxMSxhBe1EF/Prhr0ZdyV9CV2syZE2bxRmBnHT3+ie6AXnp877c03pv/Mc1keX7tD4NqWqgYljuZ1XPIkjNmS1GyCjnw07D7STDJF2/69pPwu/eBkoavrxvRy2qpJOIaKKb4lvrOa+1fmTBsvOioRBAMGUkqZJwqWHiBO3Fb/9SL3df+meu1v9HdswDYP+zmxJbbGt/Hyz29fL0pya8ohexdiypsruuu3YplxOu6LrmFeC052kUlvWheyxaw4P0wYz9onFCT1xablrGv8hr9ViNU8HkXetFKZ681f2T6wIswJYVp7e20McBCxpbkBXGGrxuZlaxClLF7zapaAbvrrru4+OKLWbx4MYcddhhf+9rXCo91dnZy8MEHc++994454drzIVJDiLj2pyKU8+isK+7FQvYJ17GLuHY3HKytoiRktfjTt2ro11RPZK+CrhxC9H0ZzlT+TooBtE0TgK5hPVcY/XRJPcgki7SEhKE4bMJqvpO6CLO3EXgskHN6HtBxs9KQ9CxfUG+iiRyi+1PQuW0JqT5DsExMA6C/f3x4THob8bIwkaUIelzv8K5t7/MWr8rIr3v+QsJxW+DWFO+zLBlx7dm0xGM+NBbxFplakoUWPooGpoXqE66tMZh2PRI6B97gAGUJS/SkNs1o8PyNjREE/owHXk7txtv9EqK5fES66cpYImZ2K55wXckGJQoovshd0TEHqXOHmr22ne9FAfpoQFibKhwpsPMDyOlGDE+2tPIstJ/lJO0edn12Luz+05q1qxInSVfQnbX4zuSDAjlfJcwKViGemD0WPa6rnh1dd9117L///vz85z9n8eLF2zy+++678+qrr9a0cVEg51ogKAS/cPebvythRFx/8kH47NPoMw8u3BU3j+uCVchWxW4GR1zHQ5Qp+n1FR2g4R76V87Wbaeh+ZdjPFa6A4BUoSUhIKE+jyDFffpMdxJuBnM+2LJqkeHpAW0aeTyl38DH1LuQtK0oe0++zXPFHaI9lvODzFAaXmu9n+9yNrH7v78Jt1FAUxNaRX/f8EdfmeI64LnyWJa653saAsGEMLnbiQPvGp/ma+ltOt24LuykJ7hpBoXidMJJ+4eAGVQkp2WAZDZ5Noj6C4vbjgVubT+XTxudYOvnYsseYknPdsmO2IS25G8R21COufdHMVo0DF0WuF4B+kalYnBGK9n96Qbg2mCbWcIzybyauebim7apEt9LBBtowpPB1C88qpFTEtVKweImXZlcNVauhr7zyCl/4whfKPt7V1cXGjRtr0qgooVuCvNAwRPDCsT/i2h8hHBgTd3TOvam4qK/1wFVvNk3Yg4/oX6YhpXKN737ZF9Fix2S324u4LrnoDAlPdB7JpMHbIbekaO84JyREgS1ZZ2LXJLII20aqc3rcQH8PXsnaOHpA99FAG1msfIlCdECuZz0LpZfoI8Nb+viIuH5srcaalnmsFp0ACGQ0LTrXE2zLsbOQZFDdBV3B3mLk1wm1oYkP6l/FEApfbZ49+nbGlUoR1y1TYZ+Puo/Fa543VmjpfoWz1L+zwpwWdlMS3D6i+jJO47b+qReSl/0ZpfoIMeS8nh+zQ3oJr791KnBJ2M2JHJWsEDwMSXMuV1a8hGvZjbiOvHDti+Y1c72oxhZnftY2Y9SvXRCuaRjSM9qzSixEXJt5JBF8nRZZcj4POwJBnMX+se1aUPNbvIwxql4JyLKMXWG3ed26dWQymZo0Kkrc8PoE/rbrd5ncmuaJgM/tjwRWR5EmO1pk30Ujbp0gm+rgIXsPJmwl9iq+zzM+ViHR8riGYpqWPYLCGF4xjSjsXCYkRJ2eAWecUiRBNttPpqmlrufL9m2JtXDdb6dpU7LYZYRrbdWT/DH9v/SLNM3jRKj7/EONTJj15UH3RcpP97az4bk/wV6nw/E/c+4r1HYY+XVC0zQes3cDICeNvXlq1VSKXu/cAd77k2DbkzAYNwrPSgTB8Dn4ArB0rM6dgbeBolAw7nEFo0S4Hh3toofp0kZW6N1hNyWSVLJC8LDc9WPsrELcsV4oEReu/fPDZ/8f3P0lmLgzfObJUb+2yDvCdR/VR1znhft9WzqSJx4H6HF9AP8lJ/eQ6u0Cpgd23lJYFfqHV7DRjElg5nCoWgGbO3cujzzyCB/5yLbVT23b5h//+Afz58+vaeOigHAn+mGE2xtKA8/bs1GwmBtgxyzw9lOg95NqmF24K27CtWdtsvXiXFJVekUGGwkRgZ2zalgvT+R1eyr5dEfYTSng7XYLY/iTBk+4NqVoX7gTEqJAb39xc6i/tzsA4bq4mOqJoQd0n50CBSgjXJvZHuc4kS75+FhEGNnC7X2ll9lJXknTSg0mHBFiq3x4QojwTbYrRQlXiSxLqLKEaY+skPCYYeb+YOZh2p5htyShBN56I8lCiwAHnQeAtCVHQbiO2fqnXhRsCxPhelRY3masFS/RNSjO6bmSJu1tchvOAHYseYxnFULMhGvZ9jyuox245Y/mtT3L1VoVcNaduXm/aChm1pVBbGUVIsw8iidhBhjM9xXzF0xLrePJ9ROAhYGdtxTe9ahU8IkXcT0Wr1lVf9unnXYa559/PldccQUnnHACAEIIli1bxuWXX85rr71W0UokKNrb22sqRN79who++dv/jNiDyt8WSRpeZFPvhPmcpl+CLMHrkiOwDvc1RsWfPgpbVpA+6kpgIlA5PWI077US/kj/3t7ewu1strgIb2xsBKClpSjmSJKEnO9jhrSeFpoGvabcPJn5+esAuL4vTXrNmsJjU6ZMqVnba8llLV/k2e5uvjxjLoeG3RiX/rwFKbjlT3/gwFO/VLi/XB9UXcubdDrNV07cjQN2SBZpQeDvj3HZqEkYzJoVr8F1+wBw8KKFLH1z7bC+y1SquEGk60NP8g+cP5tHT3Juv/TK6zS31qaSeFC88p39wFzHy889zS/uOp+bb7658JhlWZx3wp7suwfoSvM2n2MuVxTqGxrGRgEqy7LIGRa7feseAI5XHuV09V5yz67Fmrd4UAFo/+fhvy3XewPdjQS2DB0jl0NRFCQj50xUh+Fx7f3W/W2fZiwnrUr8z9lX8+yDf6llq2OB/xqwdOlScGvS+O/fb7/9Cre9uZZlFS3rgmhf0NencnNVf7+/8cYbAQprHyjfF0Y1D3YX7zaJIAhgmr6s0wDsEpcsWVK47QVp2almOPyrAOw8dy7mxre3eZ5/7PRfZ/1rlOHg/914vzP/780wjJLHDtV3/Ouj7u7ixvSwx3UvCybxuB4Vnmj50vPPssj3Hfi/00qZ7vXA38+830U180U/3vOmTp1auO+8884r3P7Sl760zXNKsbPxMnOUN3lcX7fNY95v/bRXF9M47Wze+r9/sPxj9ddH/J+P/7sZzvekKAp3fW53aIb+nMFTTz0FwL777lu7htYIfzSv7a7VhW3CVmON//37x21vDPT/hmbNmgXAs2c3sHO7E3G94q03mdJWfq591m+e5N6X1mEqzjErl7+FwmznwQA30GyvUK9pkM/nC2O///3ff//9hdsXXHBB4faLL75Y8bWHmi9sPb5PO/tatPYpnPPJT3D6c/cOeqx57/fScfgneWbJs0jSOyu+btTYYYcdaG9vL/t41TOBY445hqVLl3L11VdzzTWOW/BZZ52FEAIhBJ/5zGc45JBDRt/iiNGcX8vHlL+TtgVwZKDn9ozXQ0vldSclBYsK4rd7M3nV3TyS/iprzInAKYX761lwoF54KR+V/L6CRhfOoJ2ukMpVjrRXYyHiHl8JCVEg01y8kLc01j9KuCVTjARpbGqt+/lqTV5pAhMa5NLRIa1p5xqQl8ePdUTDv3/Kr1J/5T5rLxTXu1WKkPVUQZz2zTkkL7pnlHZpd2S+Tbs8wLmde/DsqF5pbKL2reKBU21UGY6/VcIXI5AQEF7EtZ1s5kcHX/ZHpMbKEEkirmuD52+cluOxBg0a2S2MKinl+92KXCMZMZkeGoNqVk04fdUptG43j+O3a/YpE9HDrz8VLKyG8KOult++0sicvQ/iRXsWKbXyJliTKxjkhYpAwtne9axCghuHhCtc+2vQhYXkve9SbbGH7jtxZVjv6POf/zxHHHEEd955J8uWLUMIwaxZszj++OPHpE0IQOvAW3xT+y0Awv5p3Qti+bHL2FwEhtspZBHj4iRu57W3igzwf41xeUuV/IzCQhfOB5kawdh4z+bp9Ew7gImdM9m1xu1KSBhrNDUXxeO2xvpv9jy8qYOdcj+iK23yqBK/BaqpNkEeMmWE65a0M47qSrwWPKNBXvU075b/wyq7oyhcR+m7lbZNRbV3fx/2DovR1NEJ116KqRahtxslJNtkz8nO7XTyGYWD5QnXyRcQNv/3zjXMaTG47KUubnDvkwIUSKLM65n5vLkxi9S0gP2GPjyhDLbrb5xSYrIIDRilmrpO7lwhbgKdkFPkSBeLUEcUzR/k522o1sgq5IoX2ulccIZzniF0jWZXuP5148fpOuLLvLRsBfL6PzsPBjgu25LiFAONgHDtve9SIrq3CR5kNHpQDLun77bbbuy22271aEskUbVi6oJh6KTSwaUNZza9xHfV6zDkBuCowM5bwP3By5I/HSb4ZowGr0NvnXqpCJNPKneiINAGPgREPw3+lOxNCHUjU3o+CGwfdnMAWK63MiE1jS3W8PvFs9nJvGIdweKWSXVoWULC2ELVUvzSPp4+S+Nt/em6n09OZTBQsdJNQx8cQdZn5vBU7zpWis6Sj7e46wVDief7GwlCSSEBGiaK5EVcR2hiW4i49k00Us3OP22UwrVwprsj2WQdK/zg8DSHzlJof/G3dO96+qDHhO93METwU0KdkJKI68jQqtm0p20ysk8UiNJYGSKPtB7LX8y9+eDE7Xhf2I2JMZ5VSCqJuC6J4kZcyxWyrXZU1jJbfo43mvt5LKiG1QDJ3YgfSrANG8XXPqvgcV3Zj7paJN/3OlTEtSdcD5gSSBK2gNfEdO60DmDRjP0Di7f3tCRRK5/vUSBVEK4LEddh1MerM1W/o8WLF3PfffeVffyBBx5g8eLFNWlUlJC1Ykq2oQdboCrdu4IPq/dxkvRgoOctUCriOmb+uIXUy61+6jI2F2l/4Mva/yM9sKbUUyPHEfr9nKX+nY7+18NuSoFPrzyGw/Ufc/2anYb/ZMWLgBt7A2tCQj34Vep0fmqdxAqjre7nklKOhYaXohc3Hpl5Nu/Tv8Vvc6UtzFo051pmauNHuEZx5jNpyUQuRFxHqDiQVCH1cZR4tlapcXy52bFDYr/pCurA2m0eEz6xNBGuw2GL1sV/7J1YlZoVdlPGPYa71PEyUyBim3whUswGDrkhMUd412M5ZhFhASFXEXF9weQn+F3qe5wz/dWAWlUbjm5ayonyw0w2V4bdlIr4rUlNajw/832v2hACq7cOybr15iwBd9n78VnjPLYcUJ1nei0oZO9HIOK6klWI8KJMY5aJUA1Vv6OVK1cyMDBQ9vFsNsuqVatq0qgooaZ8Edf5HLRUOLjGCDdt0Kp+f6G2uIOKLIo7S1bcQq7LWIWovrRjIcIfgKpBIYIpUW4xoZGIH16K+lA7rQkJCQ7NaZUNfXpBVK4ne080Wag8SFqaDZEpB1s93kTXKlO1vUVzrmWm1hxYm0LHTUvVKF7TZbngFBg+JTyu2bQMafMb0NIF0/cZ8UsXhOtxrD1p3iK0VPpoEnEdOv/pOoFrXlrAoZ1dvDvsxoxzLOH0FVXyjY6JcA0UbQuV4RYfTRjE6x3v5J6VKq9u6Qf+G3ZzIodaxZo3b7l2lTGLWr+w/R521Nbz9+yngJ3Dbk5Z/MJ1wcLKGn3E9eQmePjEN8imvshZ1leQh7DE9ebz0/Jv0LHsUbbbrAOLgGCD3wpBkFHQjSpGXLt9J7EKKc+GDRsGVeEeKyi+iGsz4IhrIUpHCweGV5wRv8d1OE0ZKV6HFltbhfguhMKKwABUBZ7fV5QKxHibKyMRrq/c/l+clP4NT649Frixxi1LSBh7eJM3OQDh+j3TN/FF7RpeyO0BnFv389UaL7XQkkqPTeetPpbvZObxoe3msH+QDQsTz1MTE9ObV0RJjNnrdPTpByCai/ZR8gu3ojz0fdjuQPj4P0b80oY7B4h6am498QRpUeI79xda0xLhOhRMy61jMgbTe+OGt9ZRZYGwLSRZSSKuXU5e/wvOTi1h3Yb3AruH3ZzYsqZjP66zJpDLPhd2UyJJsQ5HeR9ow+2ncduQ1iQvmjxCGW8l8F+Lerr2Zvon7q/sOV4lrWmY2WQCK5GrkCKb3cIb843/MmPJb8k0zMITroOsAycKEdfhi2GVI67d+6JUw6ZGVPy1PPnkkzzxxBOFv++55x7eeuutbY7bsmULf/vb35g3b17tWxgymi/iWtfzwZ7c8vyZw4q4dn7wUpyLM7pir7XVrpMky9hCQpZEjCKuXeF6lEWqasnxrS9xoPo6T07q55fDfG5KtlEle8id1oSEBIcLB37MLumnuXqnaXzzyfqeqzXlFi9U42mlMafvGS7VfsOApPBXthX686lW1tOO3DQxhNaFhFKMuO6jhS2iiZZUhL7fybtit80ZfJ/nJTjKxZJue1YhMZvD1JBCJHUpD2Xf55tYAISD6S6G1WROFDqmG3GtSMIRBmQlWpt8ITLZWM4e8ms8rsfDZjGqpNyBNlJZtBHidg4hbfYzu2W7ssfk3elB3OxW0nERrn0b/YbWCtO3gxpYxnrrCwC9ijozXtBOv62CBLJtcJpyD8fKT9D22BNw9MWjblM1fL3jxzy1fAufnbYTI8//qxFVeVyPvWtWxdHyiSee4Gc/+xkAkiRx9913c/fdd5c8dtasWVx44YW1b2HI+K1CTD0b6Llt94dnEdIP7/TbQAgkrRH5r/dhixgK125EsCgh/pvIpLBKd/oIUihUEaGI60ObV/Bh9VWa23ccgXDtmQhGu6pyQkJUaJGydEk9dDRMqfu5ml0PaEsNquxJbek013C08jCrpA7+WiKmWk4576spPfYmdmUpRFwbfME4h8aUwnOHR9yUoFbCtXDmANo4Fq41z4mlVMS1nHhch80e6+/kx9qjGL37QPjL4nFNMeLaEQYkxqYIMBK8YKZS40hC9Xg2iZGqMxEhfmSfxoBpcd3E8lH9uu0IoHHbkE5JrjYRoUC0Uvijmc0a6j/NvmW/pQydQeoJ1wOWAqrjf769tIYDlRfRN8ysWbuGQsgpLBSs0H9uUnUR1+PNKuSMM87gxBNPRAjB4YcfzkUXXbRNAUZJkmhsbKS9vb2uDQ0LLZ1hjZiPDZQ3AAAgAElEQVSAIdSadtpqKFYYD2kVkZlQuKnIErYlYlec8aXJx/Ldlyax/ZQOrtzqMSeS3YqEyX41qMIEKVqTnNFMGlKyN/mNzvtJSIgyphv93JKq/zjseUBbMfWAVjNOQYomqbTF15Etr6PIm5go6r8JEBXElPk8O+EI/rWuAwg2xbIq8n3Qu8GJJGnqcu6rkXD95Y3vRZu4Hd0blwCPjK6dMcWL5BUlFjNCVrn8STBtWNcfdMsSALbre479lId4Kpds5ofNNhHXUJMU+bFAoWheqcyNhKrZffWtvJD+Ea9P62TPsBsTQTzNpdI8Je/+FFMxi7guWoVEuw8N8rjO9cPq5U5A4LQ9C3ayI6HFvcT1iQZUtRqrELc4o+38rwijWGA8wA00zzkldC3M954rRlyPN6uQlpYWWlqcxd8NN9zAjjvuSEdHRyANiwpaSyd75X8OwB3N2wd6bu/HGJpViA9FljAsgR2ziOs+tZPnxBxSqQnbPOZEshuxEa49vy+5ikE+KHT3o0uPRLj2it6oySItIaEaLM0VrrX6T9KbVff6k4qncK01tgLQRA5Rovzgj9v+SKfcx3/6pjNuoht3PYF/vL0rV69eBviK9UWFx35Ow4OXYM/YH/20O5z7aiRcr9BbEGI6snhtlI2ML4VI6lKfpaxx8aPhzzXHNW5dG5EIgqHzh9dbuHdVIy93pxBdXtp10j8AJDF209CDRJWhScrTJI++2N1YxLQ866Ty/S5vOnO7uAnXqULhyWivf/2bBqnNS+H3JyEB4sKVMAqbuWbXKqSfBtJVpHh5wrXhypayMH0e6MFdL/fUn2Y75Q0m9QwAOwR23q0ZNPZa5jaPF8TsMThGV/1tL1y4sJ7tiCz+Qj5GwJUJ+1NdPGLtxkC6i+mBntnlrcegbw1M3LlQPTpuViHerlip6tdvM4m0nceQon3h8FgidiIldOSG6Gweebvd2ggmDd5zhJIe4siEhAQA241+blbrfy1qUdzJUGyF6zYAVMku6RnrRWKrmdZA2xU23gLgOPlRdmYzvJWBWYtCbpWLXKJiuydcj3ZxMoYn8tXy22cNHnrL4oOHJwXVoognCIqIR+GNB/65pmiRNZZFgJGQWIXUBtkN2ilE3yYUsC2bv2pfwURGdF8JHFLyOC94KiXFS7hOS55wHe2MY0mSUGUJ0xaYfttae1uxdDgUI64zaFUU1fDmrTrO56UIE5XgN9CO6buVBdpTPLp5ADgusPNug28zR4gSv30v4lqSwdlqCKZdATCs2ZFpmtx77708++yz9PT0YG9VVVOSJC655JKaNjBsUr4OlTeDHRiXTzyYbxod7DShmVBcKP/5A1j2ALzj88jygUBtPY6CIJNdy+7SMqba2xbWPIUf0aNbfKt9RizqYn/YuAgh4HeT9g67KQVGE3HtFdOI+oU7ISEypDzhenSTxmpoVpwoICndUvdz1YOGprbC7a1trDVVocFdOKQax5dw7Xl6f0B5gIPsF7CXTUFERrh2p6R2CeF6lGLebHk9E6WX6c708MaoXim+XPWU06dP+Pm+IbckoRSy7Xw/iXAdMQqFrpLvBSik6I/GKiABJNUJ2klT//lc3LAsnXnycgBeLrF+9/jFsmncst2X6Fu3AvhCQK0bHaqq0iMypDFjsf5VPOFa1EG4JlPweq+E53Gdp5RVSHDjsnDHPCnkTP1B77mSxzWAopSMyo4rVX/b3d3dfOQjH+HVV19FCIEkSQg3mtW7PRaFa0mS+Jx2G2mRQ93UCTtMDOzcXnSzHFY6r8/43YtYs8P29RkmC1b/ibPTv+G5LXsBJw16zPtY46DF27YoFPKNUsX50fiLFaIM1CTiOiGhGqS0I1y3KPVPLdUljaxIoTTEX7hObTXTaWsuFoNJNbYxbnj1bo79zw+ZrCkoXoRSlKLmpG2LzUgF4Xp0C7xPdzzOKekXubVzHveO6pXGLo+dZtOSgs/cK3HnC2G3ZvwhFbyDI9QnxzXCiVVLIq4HIYvabCaOdzzhOom43hbTMPCu+JWsIAYMQQ9N6HJ8iohbksqC/HUAfL+tg86Q2zMUmiKTN22MGkZc/3O54FuvzaN/1sFVCtfOuXOkyaltGCgohmufGuC4XKgPIkLus36P61KitH8OLSulj4kpVV91rrjiCpYtW8Z3vvMdFi5cyLvf/W6uu+46pk6dylVXXcVbb73FddddV8+2hsYn5DtpknIs6T4GOCCw89oVbC4CodBB7YLPUdysQrzBxS7hGSjHSIw3fNkNqhIh4brgLzb8Qfy7PccyqbWBxZMOrnWzEhLGJJIrIjcH4In4nr6voWRa+fkOe9X9XPUg01wsGJ1WBo/xrc0NgA4MFrjHOtLARro2Pc0CuZNVwl0uRUkkk7ddFFhH/Qjr3d9B00a3wWlYznVTG0F20HhhWjO0pqEx0aNCoWAVEoMovLHO/+6zgeNn9XPrm0181k48nf08kDqMB3M7MbUtDrmq0UVxr2kpaeyISrXCNItzXEUrb+cpLDczMECf49Hij7KuQrMNHU//MYWvsdbo1iD/WSN4eeVcmmccygFVaBppVUGV4XF7V/6w759Yv349O7zyC+fBQCOunWtA+BHXvmtRCRF9UMS1rALlsxbiRtXf9j//+U9OOOEETj75ZDZv3gyALMvMmTOHH//4x5x++ulceumlfPvb365bY8PCcEVPywz2i5+x9n5+rt1Ef2428I5Azw0MiriWY+px7Q0uokRK28fE7aTULUzoOQ6YEnDLhoelZ7lC+xkWCo39M4Fo+Fy/PZDiSXtnXjFagaXDeu6/zF1R7Ckc2LZjfRqXkDDG6J+8L9965iOsz6nAT+p6LjnlRCU3NcRnQeCnqaWNB6w96CfDgGgY9FhbY1G4zjSPH+EatxBQCqNQ2CZSUYSlolkUzfmnjU7My9vOHCZuRZxqycMfa2Rqs0Rm+X30bbd4m8fdAKZYLKbHIrIXxRalzaRxirfSUSUQhayP5HsB+HP6vbxo9vC1znlhNyXWSJ5wnViFbINl6IXbcoWNvOmpfhbLj6M39nFtEA2rBb73E7kC2SXwar3VMuIaigJ+Sq1uXM2oMr26TdYU2AL+Ze1OjgY+vH1wwW8FLamUr3SQ+COu7VIe18XvR5KVMeRwPQzhev369cyfP995kuqapOvFgWXx4sVcd911Y1O4dhNWhBGscN3Wv4wDlX+z1NgS6HkLFIRrM/YR16LEQuBkcQ/T1PXc3787cGiw7Rompp7jBOVRAF4zekNuTZFb3u7g/nmfwtiyAnhgeE92d8hTVRRmSEhIANE1l/+zjsImT12Fa0UtTCq9oihxQ9VSfDx/AUJW2cW+f9BjrU3FCJ7GlvatnzpmET5PzaJwHaHvtzDnqP2iwBCucD2O07JntUnMaJVZY+ZKPu6VcdGSS3IovKbtwrp+E71pTthNGfdY7kaXKovCeJREXDt4WapyWNnAYwQvkng8X5PKMSjiWi0vXO8/YQs/T13JerUlNsJ1e0bhbOVOdFQaxElAtOusFCOuaytcFzWA6saRjCbRq0PWsLEF3Gq/k79JB/PhPd4z+rZUSSHiOmSrkEHXohI2IIPEbHlsTeiqXrG0t7eTzWYBaGpqQlVVVq9eXXhc0zR6enpq38IIYEgaCBABR1xjlxddA8EX/VQQrmNgq+GnkM5RIuJa4O2cRX/SYBn+i3h0hAbPN2kkBSYmqDlsBgpFGhMSEirT5Jo1y1raGdPqNHa1NDXyWeVW+snQKs0nKhkew0UVBgYq1lb+yJmGFKaQsZFIN2TKPHsM4kZca37hOkoFtnyb5R7SW49A72qYvCtM23PEL60XrELG7/XGi6QuV/zPTCKuQ+WPLafx+PpNnDdlJ44JuzHjHMtd6igSCMu1ClES4RqKAUxRsi2MI1bHznzBOBtdaEjKj8aUD+1osX3CtVxBuM67F6042a1Mala4UPsDAP+0jybqwrXqCp+68PX3UQrXt5ys8o7Zf+IaYbBM/VRVz8loMu10s/eqf6DovfyO9yCkhqGfWEMi43HtuxaJUm0Z5HEdHc2oFlT9bmbPns1rr70GOBYhu+66K7fddhsnnXQSlmVx++23M3PmzLo1NEws92OyTX2II2uMm4pgE9IqwmcV4gnXdkwjru0S4r8tyU4+YNgDUBVYlv8iHp1ihp6/GCPwF3u4/Zu0SVmeXvcz4PTaNiwhYQzS7Np2qJjIqQxWrq8u55ncluYC7WYAVonP1uUcQSDbBigZrK1qHDy0qZMd8z9Cy27k1fEUNVbSKiRCk9pdjiXfvhNoxUJL8hO/QH71Ljjg06MSrg03gjItjV/hupCWXCZyNBGuw8V01dIoFeAer3jjhSKJokiTWLgA8L3+r9OZWsPba84Hzgm7ObFFapnMzdYhzh+KVjJycrwyOOK6vMd13vW3ShH9dbxHOlUcR+QK7y0qeBtUWbUdzluCkBVoHp296oQGaFUNJENUnXWdUSWENMBh628AoI3D6A1YuDbkBnpEhmLp0HCQ/Nciq5TH9WCrkLFE1SuWgw46iOuvv55vfOMbpFIpPvrRj3L++eezcOFCJEkil8tx8cUX17OtoWFKqfEdcY0oFIg0YyZcV6rSbrl+TSX9gSKG6fP7UiJUhGLP1i18TruKfIvgtGE+15toqBUKbyQkJBRpy69mafoM0pLBnM4Mb6ysj3Dd2pTGK+bRGGMP6AtTN7Gzupb7mmbxsO9+KZUBJLJ6fBY7NcEVrhVJsNaeQLtsMLkhQlYpLZMR2la/Ny9yZJSTb929zI/ntOxCxHWJYtWQeFyHjZlEskYGL+JalYuFrsaaADBSpthrmSGvZb01EHZTYk3KN9BKioYgG2JrooWpNnGleQIqNidlys9B86bTN1PUv2B5rUhrxetvlDKoy+EFLupCho5ZUIPM+2Z32d9PBq1a4VqT6RbFz+t72rVMlTfDo2/AomACbG6d/iU+uOZDHDdpCvsFcsYyDPK4rhxxPdZqM1TdY8455xzOPPNMUinn13bMMcegqip33HEHsixz1FFHccwxYzO5zfQm+VbAEdfujokdVirvib+Ek64BSUK57J9OW2JmFUKF4oxeJHvYXkXVYJvF3bNKaVNBMy2d42TlEXrl4e96ehMNSQ12xzQhIa5kGptJS06/ccTl+tDaODY8oOcrb7GPsoyX1QmD7vcKT9r6OFso+qJ7zjE+x/xpk/h/exwYYoOqoFAYbXTXPcO9zGvjWLjWvCzXIaxCEo/rcDiz9yoy2kqsjacBSdHqMLHctHhVKnpck1iFAKB40a0RCqKJI5oskcJAw0RWCjlQCYCeauMy8/0AvK+x/BxUd3dbFUkgyzJ2DALR0r4LrKxEP3DLywCyrNp9ti0p5zX7aBi0gVOJjCph+GTLHeRVzJA2QN+6mrVrKLxN5bC1MMk/9pbQsPwBmWNtw7Xqq44kSQXR2uOII47giCOOqHmjosZKbRZZw6JXCXgBX6GwYCD4DN3jWpzxzkmf4vy1R/CuKbPYd6vHClYhpXarIobfKiRKEcq6u9LVhlkVW5ElJwUTUFLRsT5JSIgyTT4R2S8u15rWjCMS5oVKOh3fjaV+2/mMGuTB49PCrhwHKPfwmhJwFlXYNE/BXvhJfv34KgRS9CwJcluQ1r0GkozomufcZ7vXvlFamty0dha3Tz6HXG83cO7o2hlTCuvDMnPKK/8j0ZaGx1cF16aEIrvpzzNHeZPH84vDbsq4x9vEUaRi2rWUWIUAILs2lsnnMToyA6t4peEMAHaa0MDr9UmgiyX+7G61QnG5vFFcvzekNAZy0Z/TNWjFflOp8GRU8D5/07Lh9QecYILp+0BmwhDPLI8Xcd0nMkwaRsS17rPoyLhZoUFGFHsFaWuo4Y8IydcnRAmrkEEe5ONVuK41zz33HFdffTUvvvgiGzdupKWlhblz53Luueey9957h9Wsklzd8UUe797EZyfuyLuDPHGFaOGgKXbWeAnXvXILb4tJZLXObR6ziYjJfhXYPqsQOUJRDjl3t7thmIUxUikV3PiCSv5lCQkJRdINjZhCRpXs+grXjc7ksF+kifO20oBwPqPMVsL1e6du5n+0X/NA6+wQWhUibTMQR36fq/5zH3ndKGxIR4bX7iV988cRjZ3kz3vRuc/bWB5BAWA/Ayb00gby+PURTbnRQuUirv/wUsR+D+MMGa8IYPTFjLHOP95u4uUtKTbmFOhKIoz9yO7cfaxF8gWNlioGBTRoyW/Lj+f3D5Wtk/zCdTqlxkK49iKMdaEMEiCjivf5m7aA356ABIgz7oTZB4/4NVvdhUU/GdJVRlw3qhK6T7ZswNVFAtxA27H/ac5WHqO9dydgfmDn3Qb/HK5E8KUYVJxxbI3TZUfK22+/fUQveMIJJ1R13IoVK7Asi1NOOYWuri56e3u58847Oe200/jVr37FQQcdNKLz14OU6nzpesBbLKszO3GndQB6w24sCPTMLssfh7UvQPssFNcKIm7CdcGnrsSF7y1lO2cxq4x81zAodCXDXda+KFjsm24c+gkBoZvFwVFVFUyzuk0AZ5LmXHTUVHwjOhMSgkSSZfpFA23SAK2Z+i10Whuca16fnaajbmepP1nbEYAy8mD/w5aUc2HoM8fWhK5amlIKH87dxLweAavTMHXkRQ9rijcZ96f7etlGo518W7Xxyo4zF9ydQ5UlzjlhbBZSjzuKF0QRpYKp45RXe1K82uNsfIqOxOPajydcJ9Ypo0PzZZumtOSz9KOte47H0+dioqBaz0CZYniecJ0XGqlUPDZePasQPbzY0WFRsAoROCKxsEaVqZ5SipvofaJhWB7XfquQJsmLuA7uc5y35WE+pv2JZ3oXAZ8J7Lxb412LHIG6hC43Hj2uv/KVryBJEmIYPi6SJFUtXB9zzDHbeGJ/8IMf5PDDD+eGG26IlnDtdirPFiEonp1wJL8x5nJkx2ROCvTMLs/fAv++Bua+B0X+NABWzDyup2Vf5R3yMiblga3k/ysaz+OF3ixntk9i/1BaVz35pumcbZwPwHOZ1pBbUySvD07T6qtSuNYGCddxjulMSAiWPpGijYGCuFwPWtLONa/PjnfkX9Z2pjiN0uD6FM2acx3rNeKx0Kk1TWmVD6n3MaV3M/b6dyOiIlx7kTPCFxVdI4/rLrWf+fJTkMpx7aheKb5c/rizCXBW05SQW5JQCs87WE6E62jhiQARyH6NAsnvtDZoPhu2hlTyWfoRRpYp0mYA9AoZKC9tsJid+x0gsSH/sYBaNzoMVFbYXQzY8fjOi1YhwhGJLasYUDACWnzJov1kqve41iRsZCxkBjnCBynMuucKvTaa957LeLqPy4jrG264Ich2AJDJZOjo6KCnpyfwc1fikP5/8C71KdLrFwG7BXZeTyT2bDoCx5uUCLuQUmzHLOL6yI03cGHqYR7feDJw7KDHvM81bJP9ajB80f6V/L6CJjfIX0ylr8oi4/5JWhJxnZBQPX12ChRoqaNw3S81ssyewgqjhV3rdpb6k3UrkDduHXGtOuNWrxGdsTQQst3I/3c01/f2FhaFkYrGKEzGfYsCRUMoqVHbJ+zZvImrU5fRo2XGrXA9FH863uagGXDZkxLfvC/s1ow/ZFEbW5yE0aPJgozizLtXC8/CJR5CU70pRFwnwvWo8FuFVCvejRdsq7h5Xamuk+Pv66zl49I/713fycH6d7H6NnFb2I2pAk//MW3buTZZ+cEeysOk34Az7jCZvOhEVjV3DkO4do5bJs2kSbGZZq5wHghyQ9ENrpAIV7geHHFdgkER1/HoF9VS9t0sXLgwkAb09fWh6zrd3d3cfvvtvPLKK5x7brQK58zLPcO+6gP8uycT6Hk9W47QCih5g4Ftokg+j6MYUdgVK7E497JTrBi8Jb9FSyW/r6DRjeLFq23uAQz0WOzammVBe3/J42VJptdUeDi7PafqC0lh8IO2qUE1NyEh9vRZGmjQ3tFJ026HsXNLjr0nlO5vADlL5vaVE1BV53K/e2s/c5vLVwHqMRRushdzp76Q7FtPsbbm7yA4cpYz7mckncZdDwXg6Kmb2b6xF4BePQaDfy2RZKQNS/GPuCJKwnUh4rq4UWt99B8AaNroxDzP6q0BnTOPHpx9defKdvrd38qUBp1DJ/VWfK2bV3RgCuc6PKsxz4ETy/cnW8AfVxRrbFTbXz12bxtg97Zs2eO3GAp/X10s2rrPhH52asmVOFKiz5T5y6p27nm1GBzij4vYrbmdjNrN3nMm8HF1ttt+p4/ctLwD4QoEc5pyLOws/x5MW+Lmt4smQ/Nas+zRXn5Xe8CUuWOVd7xgj/YB5rWWf8+bdZW71rQV/l7Y0cec5vLepmtyGg+uK2aqHdzVy/SMl4VRwkauP8V/eovtf2qDTGe6/mOF6ha5josAM5Z5//a9fHmPzaweUPjftS+RlsGY3U3OHTvuWdPGRt35njrTNkdO6QZA9tlnmGZxfvyXVe0Fa6rJDQaHTaoUoCVxy4oJGEJGliW2a8yzyDfG2IOi7CQEcNPyTryU8Z2ac+zTMbh/mt2rSVk5LAH3vzXMD2MrrrLfh2zlOah9p9G90DhH8wmyR8/v4Mn8LMAZk9/Z1cO0jFE26/2N/jRPbGwu/P3uKVvoTJUXE1/uaWBJd1Ph7/dM66ZZ3Vb0kt11/7NbGlna5xyvmSYnz9iEJpcfA5/c1MTrfQ2F9p+63SYaG11by7717Go+h0DiLWX7sq/hxzad8dkW0qA+tTXCjfx9h/wcjYdtj53rKnncvWta2aA7c4h2zeSoqVsqnv+vq9rpdfvrpLTBuyY7/VX2iaTCZ9Fwy4p2dNt5bGZjnoMqzAm0zgx/97U96gzyuC4EF4xcuM6Z8MeXbDr2OhCtual6qxDVaceJ1g/YqdHim/rX2VNeFqgw682XZVGdA0M6pbBopwlkNIkDZxXnnSsGUvxrQ0vh78Mm9TC5ofxn+lpfmqc2+frvvEamyg8jlByb5m/7/p9bZ7PJtpBkhcycvTl5nlLYiC3Fks2NvNxb1DhPmbkJRSrf35/Y2Mwb/U62vCIJTpm5qeyxAP9c18LqnDPeNSoWx03vLnnc5rzCs8srz79Dnx1ddNFF3HXXXYCzMDn11FM555xzQm7VYGzZ+bBlO9hB5h1rf8dx2kNs2HwQsFeg5wYGRT/FNeK6EMFSwrz/uNwdfEh9Hav7YOD0YBs2TLT1L/Ib7fuO35d0dNjNKbB5wOQea290VJrf+TEm0sr7lD/zJe2mss9ZZk/hXfpHedwdQ9MNzWWPTUhIGMwP1h7AhKnbsbRzBhPfM5X3KHdxsfabssevE+08kv9O4e+TlVv4vHZL2eOX2jM4Unc2ru18lSkUEWWZ3sHvzXexXEyi45jjALg49SV2kR05vqeUvjeWKRXJGWBhmyHxsolG4Z9YjrzhzF1SksW1C98c9Ng78j/hbeEseBfJz3Jt6gcVX+vRBd9gAGeRvlj+F1emfl7+vELl/j0uKfz9XuUuvl2hv67dqr+eqt7M59Rbyx7/sj2TJ/ULCn9/XP01Z6j3lD2+RzRywsNPs0xM2+ax6yfMBpZw9ORNHD158ELkwQUXY7pLhiOVB/mRdk3Zc/SKDA/u+d3C3ycof+Gr2u/LHr/C7uJRvfieT1P/wKfUO8se/197e/7je8+fUH/FB9UHyh7/T2sBzxvF4/9Hu5yjlCfLHn+7tYjPGUX/yt8vc/4/anUP86bWz6rNS3+Wk4jr0NFtZ80ztdHiqu0fBB6EqeDt+h2fv5hnxY4A7Ca9yTXpiyq+3sH5y1khJgNwgPwc16a+V/H4JxZ8lV4c4e9Q+TF+lvpp2WNNIXPfgmJ/O1a5h+9ovy5xpPOeHlw+unXc/4ljyFk2+7XNGdXrjHckWSYrUmQknVO26+aqKd8sPHaB9kPepSwp+9w/mofwunl24e9vpL7BXvKbZY//pXksb5sfLvz9vdTn2V4uH5bwA+NUfmEdV/j7yvRZtEnl54NfMc7i/1nvct4XNr9qOG3wAfodALwtzwC+XfZ1PLyIaxOFSmXIZUvnw8q97Cyt4IwFz5c97qT8t3ha7AzAXGk516a/UvH8h+Uv5Q3hdPb95Re4NvXdisf/e/41bMFZyx4qP8EvUj+pcPSbzM59CmHqFY6JDl7wpGnZvjokoy9y7W3QVhtx3eRGXPfpNs9slLhJOYzntT04bWZwZq/SMK1CPrWojU90bduP/2Ltz1Ljfwp/f1n7Lu9QXi37Ojeai3nTPLPw99dTF7Kb/DCkgBO3Dar97sN5fmkaSCmFtgNO4Yfph5gubSz7+v9rnMYGq2jffFX6o2Sk8r/P8/VzuNV+JwBpdK5t+GjZYwE+qn+RB21Hx5zCRq5t+GzJ4563Z3PS6spZ+KEL1+eeey4f+MAHWLNmDX/+85/RdR3DMEilKg1VwSIUT7gOdpCZlHuT/ZQXeVIPqZBOwSqkKFzHzeNa8nbFSqSS7G08w57qM/wzOzngVg0fObuRQ5T/YgkpUlWIV63ZwIeW7EN65u5AHlhPT8Zkldxe9jnrrCas3g0AGMuX0NF0bNljExISBvPXf71E+2EHupO+9fQ2GBX72wa7GbNnPZIbWtmTsYfon42YPesRRo6+Z+8CKot4UeaNbpm7tOPR1UbMAWfMWdfeSIvSznqzkZseX8HFIbcxUJQS86ooRVzXcFG0NUve2MzDu89hh9S2kSH53s14JUwGtAFWKeX7B4DRswETJ9qkP5WreLyOgtmzvvB3b4Nesf+tt1sGHb8lY7OqscLxVmbQ8d1NEquk8sc/Y8yir3EGXSXmRL/bvB+zm1bSwLZBGmbPBkzcoq3pPKuay5+jX6QHtamnofKcYK3dNPg9NApWZSq8Z3N473mjoQ06flOLyqpU+eM35xWs/g2Fv9WWTgQSb2zor6tw/TcW0WD1M7V5Rt3OkVAdD63J8N9NKSY1WJg2yA3Ng9ITsv29mHFmNakAACAASURBVKbzm8opPaxSK48Zem83phuRmdX6hx5jejdiin5AGnKMsZArjzHummGa5Ix9E0bpzudlgMphZQOPIX765mw+vN06ntBnY/YWv8ONLVrlMUqXMPuKx69va6j4G9ySE5gDxePXtTeRrvCb6smZWDlnDBRCsLqjlX6pvC7TN5DHzDuvL2GzSmt3+ovbZ9qlLI1k6bArR2Z62G408lDCtZnPsv2Gh3n3pPWsssq/n2x/n6+/bhmyv+Z7uzFdD+oBtZ9VbdX0VydLqD+VHbJ/m1vW0vf0X4B9Kx4XBVQ3ItqJuHY3VUcxR3OKM4LkRtKnqswinz85zfxJaVb3mZimyT3yu8jMAGYdOOK2DBvPKqTKiOvZTc5vok800GMXB95NeXVQ/93QmmYVleYkEma/v79nWKW2Y+tZhL5tdlpPHnr+fSvNCw4HJNZNaEaSy4vtvVkDM1d8/TUdraSl8t9xf7bY3xUMp79XYKB/ANNwjtflLWWPX282IOweoHzts9CF61122YVddtkFgOOOO46TTz6ZCy+8kCuvvDLklhURbsS1FHDENe6OjggrIkoqRlzLcrytQkSJVBK7kJYcssl+FYjCRVwlQjIDAJv+djlAIaXtEuC7ZTY4HLuCVaTTfjugy+vbwISEMUT2tSfIvvYE4PS5K4ArhnzWxwqbwZcA39IrbcIuB+JR5GYoUlaW3dc4VhM333wzAIcBlhX9Mb8uyApCUgZHi0TJ/67gHyjAvYZIz9yAZOZg5yNg4shT0w3T4szb+oE0r766dWTL+YVbt7j/KlPMCrze/VeZYn+6nGqueMXjv+X+K8/g/vo599/WSAXRbQVLl+5Y4n7Yb79T8WK7e3uddM1iXzmrcNzV7r/KFNv0Q/dfeZYjSR8HnDHtIqBy/Opy4PHCX592/1U+vhiR/WHfI1LJGjIrOHj7B9ilE5b3wNr338RmHfrytd9Q8fNd+2MMGBbXde1R1/MkDM3arMppDzoRl8899xyweasjvl64tUZR2M697Q+6ymb9gkIx4v829185nN+k84uWZZnfAr/zBawYhrHVsQAfL8zBr3T/ASjNncw418nw6P79+djdq53bP6vQgCHwhGslrPpLY4iLfvcMFwG2vQK4F3C+01Pdx+0yxdecMe2Owl9HDnmm5cD/K/x1QJmjPEs5uAFZvhEAXdeZN+TrF7OOZFlmJjB19o6op1wGwNeb/8aZ5o2kKG/p5Ed4EddV6B+f/NXTznMqBtZ9o3BrJTB9yFf94qDjvU+6+PkM/m5su7im/a37rxyKogCfdP/61pAtCRsv4tqyRU2CC967k8yNx6tssL/Jvvo1w/K4/u7hk+haeiP5dctY1bE/m1qG/mXWEk9LkqhOuG6Snc/pN+t24TNXP+x7ZDnw98JfJ1JuLuKxAiGKmXeH+R4p9zwhfs+WfzmZbnsP2dJfuf8chs6lGZxRMHR/KmYYVe5/y9lhhx0qvlKEViyOVcjixYv5xS9+QS6Xo6EhIkXb3LQ9JWDh2tvRCU249luFuP0itlYhJaLKBM5gWe3OWZh4u88W0Ym2TkhISEiIGWoaDF/Kb5CFbYZi+j7kP/GvQddr5aEfIPWtheZJoxKuExKGw8f3kDljgcwfXrD5iSIAif58fTe8vMAQJYlkTagRtl4c6yVt9HWahG2zJHUWFjJvb7gOdjpq1K+ZMEYxiiL1W8zgjtSJ6FKK9wkxuLhCCURhzRu1UK3xh3c9MiwBn34MIUmgjlyfa3b39gaEE1Vbrce1x4QVd9PQ8wZ9DVMDF651rY3ldhebpQlDHwz00MRa0c5mPVJya6yJ3CeZy+UQQtDf3x8Z4VqoTucK2uNaFu6OVlipvKkmyHRAugXFdAYWK/oa7yAKu2IlxH8hecJ19KPv7GHsPickJCQkJJRE0fCcIKwpe0IFS4bASTUiOnccfJ/nd50UrEsIEK9wa0sKGtxpV3+dI65Nd4KtRsgOLiHeCL1YyEHSRr+mtm2LVtfreHWyv5JQCdtEWCaSorKRNl5Wd3XuryJSf1PLznzbOJ10uoHKbtQJ9cYTli3bhobWQjbcSGlxhet+t05IapjCte3aley+4gZ2XHMnbDcAC94/qjZVy0szP8AZz+/B/K5Wbq7i+E/1fQJBF6nVt+LP+koYOaGtBDZt2kRHR8eg+/r6+rjrrruYOnUqnZ2dZZ4ZAq4vpCICNtIPO+J60Wedf4Dy26eAYoX5uCBViLi2C15F0ReuvbQpK3p7TQkJCQkJcUFxNuKzx/wMc94JNDY2htygIfACBqJkaZIw5ulzp/vNKUgr3n31E65ty+ZW7WtYKGhbLgcOrtu5EsYTAjs/gJxupLUxxQf3diMe8z2QHr5fu2kahRhYOUr1ERKiiZEDpRlDDE+c7Gmcxa+to5mqJcJ12Cg1tor1Iq773OKz1VqFeAi5WLy4wdgMZnBV1j17pKrdB9zAV8msziInYWhCWwl87nOfI51Os9dee9HV1cXq1au59dZbWbNmDZdddllYzSpJf9N2PGTNZ3PDnCp8nmpHQVCNQJRtceCKV8j1N9u+w0tvb+LcmfPY2r7fi7gmBlYhwnJWUYlVSEJCQkLCSBGLzkPP9mF3BZtiWRX5PuS3ngBhYs9cBIpS9FJMhOuEAOl1heuWFDTIziK1nlYhlmWwp7wMgJetbYstJSSMFFvPIqcbacmofO9QV3jpWz8i4dr21YeQEuE6YSjMHNCMLiRSIkda6I6YPUT0f2KbFB08j2vTEvDa/2/vzuOcKO8/gH9mJsfuJrvA4nKJcgkLiIhIudR6gAdUERQvFG21Uqu2oq31qr9qtbVVtFrPllpLqVWqPxWPegD601atFyKKHAWRS45lWfZOJpl5fn9MJpllk90km8xMdj/vvqjZyWTyZHfyzDPf+c73WQ407gX6HglUDM9qe6U+Y3uNZuA6w4xra+AagK0xskznezMrNoCB65xx7ExgxowZWLp0KRYvXoy6ujqUlpZizJgxuPvuuzF+/HinmpXUln7TccfKgRheVIozbXzfeI1rFwwOFLkwS4WE4Ec9SoAktd0ECjHj2vl9gYiICpOYeCXUpqb2V3RCzWb4njoLABCavx7wFQGaGbj2tvFCotwyM65L/VI84zqfpUKiERXmHi6xLA7lkFCNCyGNcjCxUG3IalualvgOKNxPqT0RIxtWCIH5zfcay3aeARw6oc2XRTUjMOhh4NpxHsUyOeOKOyDtXAVx8h1ZB64TGdexUiEdyLgGYGtSQ9+61bjR8wykUE+knuLUoOsabi56Fk3w4wWlFhvsaWKn59hRZ/bs2Zg9e7ZTb58R80ul2hy1/axoPD6vD6KkdLSt7xu3YyWwaQVQ0hOKdBSAwpucsa3Zr7f7B+OthhpUeQei7TlMnVdbfAiejp4AUdQNFzjdGCIiKlzRMIqW3WBkqpx4A9BjoNMtMlgzZ8xMa93huT6oS7LWuPYrZsZ1HgPX0cQcOorXl7f3oa5HjwWum6RAYmG4PqttadHEd4AXWKhdscB1M/zQIEOBnta+N+Trp7Hefze+bh4A4JM8N5LaYs65ENH0RJBYy37Ot3iNa2EkFGY6OWPrwLV9Y8OeDesw3fMStqj9211Xj4Qxz/MKAODfyqh8N63L4FEnDT7FnFHV3sD1ssDp+PfOifhhz8G2vm/c9o+BN+8EKoZDrvgDgNzVOLLL0eEPMUTeh+6hAICWv8fl3WbjtV1TcXKwG77tTPPStrv7kbglOg+DiwIMXBMRUfb0CLxfGlPLaBPnuSdwbT0B0XVACMs8FRyukn2S1rjOZ6mQSCIQICu8u4Byx8y4Ft5iNEYEAl4p68C1bsm4lhVeTKS2iWgYEgBVyFDhRzGaAbX9fU9oKvxSBF7kd0Jcap+Z9a7pwpjcG0gkFGQhGC8Vkl3Gtd6qVIiNJVQlYxyqoP2xQDSSKPmVzzJjXQ3PBNLQp3YVHvE+CD1UAuAk2943ni3s1Azj5kmkHk0UpC+wyRkvbX4CA33b8MG+IIATWjwXux6BQojFa6z3RUREHSQ/fT6CG5cnFrhgDo04a3BaaIDQIMoOBvQopCTlvojypU4FmiICjSrgl4zxV0NeM64Tk78rHgauKXfMjGvJV4IGFQh4kZNSITIvJlJ7YhnXqpChyj4Ui2YgnMa+FwuM6m4an3RRimKp62x+5zsQuL79HQ1/XluMplNPAJB5xnW4bBCq6/agZ8NaY4GN/ZBZ119G+4msImwJXId4ASZXeNRJQ1Ddi+OVD1Gjl9r6vpowg5W2vm1CPHCttaxxVEDinUuSW0nMIvuFEIyPaAxcExFRB4Ub4vNnAHBXCQ5r5oweBWQPoj/6DADg9TKYR/Z5Z6vAQb8zTjZ/GCvNms9SIS0yWRm4phwS8cB1ERpUoHcA2QeuvQHcELkcCnTMC/bOYSupU4plnUaEDBWxGhHp7HsaA9duEZ+cUdcBTyKhMVtr9gqsDRehQhwKAPBnmHG9e+Rl2Bo8GaeuvAxePWTrGFaK3WUipzE3mh4NxR835XHs0NUwcJ0G2WN0tl5h7453Vc09uN33X+zYfRGAn9n63gASV7GEBjmWca0VQJDXSjZP0JMc/I6pfw0zvP9GTd3hAG6xt2EZGrzjRSz1/QV7mocAri9sQkREruQ5oH6unbdZtsdyAlIIkyZT1xCfnFHN7+SMJuXA7yhRB5gZ1/AWo87czdLJek0iqhRjiXYiAOAHJT1y0Drq1CJh4z9CRljyAwJp7XtCN0onaRLDVE4za1xHNZGYJLsDgWugZX38TEuFmNYeMgeKHsHhvUZ2qC2ZyCjj2lIqpDmcfU1waok9QhoUb6wOD+zd8XpHd2C4vBX7o7W2vm+clMi4NjN9Na3AAtexziXZJCL91K/xbeUTfKy6/2tQHNqNI+WvsF5jJg4REWVJ8bf82U23e1vbojNwTe5QZAau81kqxBPAY9HT4YGG04vL8vY+1PXoahMAQPIWYUstEPAKHOYPZrcty123cpJJ74laiJqlQhRLxnUa9dVjx39mXDuvRY3rHJQKOawHECmVoSMCFd6MS4WYtlZMAQAcXj4o67ZkLPb50wlcS7F9v0H421mTMuGiMxb3kr3GTueTohC6DsmmmtPx4u9O3corJwlcF1zGtfE7lJL9DmMHxBa3TbuU4EGciIg66sCJ31xVKsQ6OaMGhOshv/d7o42TrgQCPZ1rG3UpigRcMVZGqQ84SOwF0BeNqgYhBKQ8BOxUXzf8JjoHADCDmayUQ9ZSIRc/byzbf//3stqWpulQoEGDHC8hSZSKiNW4jggZqhQLXKdV49pIFBQ853WcWeM6EpucUSg+ANl/99+7xItSfxUuVz/DMn1cxhnX3bavwFFfvY764kOwse+ZWbcjG2Zd/3QmZ5SixgXDRlGU1zZ1NQxcp8HjTVwtiURU+Pz27ITxgKrTgWuhJSZnLNAa18kC1yJ2i3Q6V84cF5+ogl9ZIiLKjlD8LU853HRiGKhA6MdfGmMPXynQXA3lvd8Zzx01h4Frso0ugHumGN+NZ7XdAPpCCKBJ1RDw534cFrWMrb1OTchOnVIi47rjE9xK+zZiU9FcAMDextVAtwEd3iZ1YpbA9Xve4/CxZwIuOvba9l8XP+flXcZOM49Hmq4D5y0GOpLAKHSU+o0RaAOM/sib4QWwotpN6L3vfVSV1tkeuI76u+MzfTCa5CDGtrNuA4qxSh+MmqgfQBp3GVBaGAVLg+xNBKoj4WbbAtfxbGGngpX+bkDPw4Di8njGdbRAA9fJgv/mldx0iuw7TjOuPjPjmoiIsqYk6udGB50IyVfiYGMOIMtAiSU4bZ1XxE2Z4dTpCQD1qkCpT0IAiUmWGsPR/ASuLWX4FGayUg7FM669HT93FZYSAeZEZUQpRY0a16qQsVfuZSzrfki7L/uix1Qs2VKG/r0HYHQ+20ftisd/clAqVoo0xR83CCNw7cuwVIiI1dmuqF+DaZ98FzjiJWDApA63LR37+0zGPPVOBPwKVraz7qe+cfgf9WjI+7ehGA/a0r6ugIHrNHh8loxrNWzb+8pOlwoZOhUY+onRhNfWAQD0AisVokFGRCiQ5CRXbWNXEQuhVIjEjGsiIuqo2MRvkcozETr9YZSUuChwfSDNMq+Im2pxU5dQHwZKfUAJEpMsNYSj6JWH9/JUrcHbvvmIQoFH+xAAMw0pN/R4qZASnDQAmDIAwId/BMbPy3xbWiLRRzmw7BTRgSJmjevMgpM7iobhBd2HUwK989EqyoCZEZ2LxEVJTZSJaUQRfIqccektYYnnKCIC2Fhr3wzip1N9IGRe44vaFzfsCngmkAappBwvaxMRhgfH6fZ9Qdoqc2G3FsX5C8jJeAx14SgeOzTJTR1mjes0ahU5Lha4Fjx5JyKiLIlRsxEqHwG9h40T2qRLi0Je9xIgNOgDv91ygsZkF5+J8qheNf5bYsm4bgjnabyoNmKAvMd4yIAg5ZBuqXE9vi/wo3ESxJrnIbIKXCcyrmUPz0eoHRFj34tCgVcPoQz1wO41QO/D23xZVDfiH9lO3Ee5o8RLhQhg8zvG3698MDD0lIy3JUUa448bRHHGZUKAJHEQG+9ET8z31v66odhQQWLgOqd41EmDp/vBuDryYwDAvzyltr2vHMsEFi64HcucPbrQAtdmc5PNfh2vcc2MayIi6goGHINIxVFOtyI5PQrfC98HAITnvgwUdUs8x4u2ZLMGVQCQUCQSGdeN4WjqF3SANSDo8fraWJMoMyIcC1wrXtRHZAACUNOYIC8J636quODclNxNRBJBu+Hal5gWeRX4x1vAjz5u83VSNIQihOGVCiCxrJMzExcjmg588QKkT56AOHxWdoFrNRG4bkRRxhMzAoCQDzg+2jgnRLB+E+7yLIRHEgBOanPdY5uW4VTPV/igqARv2NO8LoFnAmmwfrFUzb4g5/96pkFq3ofKspG2vWcLezcCnzwBAFA83wVQeIFr86qteZXMak/xEDynHYsG/6HIvPu113+D4/DprjCKS0e1OyEAERFRKtL+r1H01i+MeR5mPw74Ak43yWC9u0zXAT2S/DkiG5gZ1149BL8sENYlNKp5ClxHjTfThQSZAUHKIXNyRgBo1L0A1OwD15Ya14qHdwZQO6KJu1WaECu7msa+N2P7vbij6FV8tPtUAP/IU+MoHR7Fkrho3g2kZ3cctGZcN8GPQBYZ9eLAu+9sTGooClXhAs9bAID28qhHRL7AZM+H0PyjGLjOIQau02C9VSViY+B6iXw6vomGsKB8lG3v2ULtNuD9hwBJhvLt7wEAtAKrcX0BXkdUEShp7g+gZa2sTd2PwR8jQzHcV+z6wPUXpcfi6egAnNm9Hy5xujFERFSwpNB+eL5aAQDQ3HTHkfWWT6HF7zQCkDhhIrJJQyxw7dFC8CtAWDdqXOeDmckahQLmW1MumZMzAkCD7gOgAuHsAtdoUeOaIQRqR8QSuBaxyUHT2PfMY7+wsQwEJRefnFEXiSBxtoHr2EWL+qgMATmrjGv9wMC1jfuIbAmS65rW5l0nPt3Y9xujLHeTSzzqpMEnwnjKeye8UhRS1cNAn/G2vK8ZJJaTZAvbwvyCCh1mGaJ0CtK7yS3yIngUHZ83nADgyBbPmeVDCmHCyUisoFKyzHEiIqJ0SO/9HoEVtyUWuCmTWZYhIEGCgKRHIRQ/RO8jAD0KiaVCyGbb6gT+u08g0jsAvyKAiITGPNW41mMTkTJwTbmmtwhc+wE0dCDjmoFryoAlcN2IWOBabQCEaHNSPUlwXie38MRKcUQ1PREX0rILXEf6T8K4xyPoNnwCcDTgyyLjOhLog33BYShv2GAssHEMK1v6PE2LwtvG0bpIMHCdD+wR0uD1KJikfAkAWNdca9v7mmU5FBtnTG3B0hl4YhNF5mJWWbsIXYdHMie4bL2rm8H4Qsgi18yJKmys5URERJ3MgYN8t2U0yYqRzSN0oPfhiH7fuC3Ty9vSyWY/WaED0PG3v82Ev8pYlq8a1yIWCNAkjvEot6ylQhpiwUMpGoLQIhnfydJQOgTz1GuhQMejLGlD7YmVQAKABlESeyQAtRHwB1O+TDIvkDBw7TizVEguMq6FL4g1ewWK68tRBmSVcd140Bh8NfjHOHn11cYCO8ewlj5Pi0baWBEojs2N0RhhwmEusUdIg89XFH8ctVw9zLcl0fkI+Buxdc+vAVxg2/vGWToDRTaCu4VU41rXdZifQE6SGVBZ+y884/s79of6AFhoa9syNWvn/bjO9y42Vn0HwANON4eIiAqR4m/5s9tODGWPcVKkc1Imcg9/bDCZr1IhIp5x7bLvIxU+LQqhRSApXjTGg4cwMl+Le2S0qbC/B97Qv8W7PylNAkJthuQrRj0SsRSoDW0Hrplx7Rrm5IxRreM1rk1SLCaTTeAaACJKCVYOugoSdBxV2qdDbcmEtVSIprU9Ri0WzYAENLYd36YM8dJ+GiRZhiqMnVVX2yvHnjsVqEEfqQYekZ+BcrssmVneWOZyIZTVMGmWW1nkJLeSBKM1+Ja8AcP0TXY2Kyul0WocKlchoNU53RQiIipUB2bYuS3D02xPB0+MiHKpSDHGvvkKXNcED8NvIufjz/LsvGyfujazXEgNSvDJLgEx6PisLg7G7wRm4JrSFZugsUEUJ5a1U+daNuMebrsjrAvyxMp5aDnIuFZqt2BCPwkDAkY015tFqRAA0BU/vuk5GTt6HgsUlWW1jWxYkyBFO/1nCYz9vkktnLhZIXDZGYt7RWJZELrltpd8k81Jk5y6HctaKiQWuC6ojGtrDaZktdhiZTdkuGhyqhQkYXSQrWbTJSIiSpcl41pIcpt1Jh0hK0a7hA7sXgP51Z9CXv4/TreKuqBv9ZXw6xNkDN+9NJ5xna8a17WBQXhMm4FnvWfkZfvUtZkTNH4T7Y4T/w6IuS8AgYMy304khJ6oRbnUmOsmUmcVMRL+6q2Ba7W+zZfEz3k5KbPj4hnXug74yyBK+2Z8p4apePVf8X9zvbhzmFF+N5sa1/66zRi/4TeYuP7O+H5ilxY1rtspFVICs1RI4cTNCgHvwUhTWCgISMCCu+/Ck6dcmPbrJMtJocggW9nr9WL/Td1i21Dir5XsPMm0XOm8/tproEy5FuvW/xf9+s2JL9+5c2di9Sw/a3uss7ZOmDAh/jgaTQSm9+zZAwDYsmVLfFkwUIS6n5gHPSm+vsdj7Pbv/ecjnNQHgKbijDMSJwsff/xxztreEcXFxkG+V69eeOLccqAEEHJiX7D+jq2/ezv3EevfwMrW/ZSoC7HensbvWdsef/zx+OOnn3467dcVFRW1v1KBie8rnsRkMpGoDl+SC+NO7lf9fh8FUAQ8diVOGRDFotNU1IWBsml3tVrXevyxPvZ6jeO+deywfv36+OOu+L1JNV6wqqmpsas5rTgyxkXqsaqmaZBWPQnl5R+jZvebaIycAKAXFv19Ce6fMy6tsU9742DZMmfJ4KkXAkedB1kCQiEjU6sz9kOZMMfqdhkzZkz88erVq9N+Xaq/c7b7snV7ycb7ShbJTKfd/w7W7arHb++9H987ZiD8/sQFzFT7cjLnnDASnxy/HfWiCED6dwfout7qsd1/X7ew/r5V1UiGS7Wv2N0fWv9O5j6X6f5mvq6x0bi4cd7jK7FmZz3kst54udvP8dNbbseu+45DVE/0dUDiswaDQbx0+aHGOa+kxMe8ydphba8drO+X7Xl3qm20J51jeC63EQ4bFxzMJEBdAM/u6IUPd88AdgOPXF4aX9fcjwEgEAjEH/fq1QsA0L17dwDAz4/YiTP6A4dUHgnsALwZlAox9yepvga96j4HAKxf9QG+c/qMnCVgpPr9mH8z3RfE29poaJBx+bHHoXpfbYvnAeBHP/qRUZ1AGYsAmjHvultx98ijUV5envZ7W3W18eq4cePafL5rHjWyoAqjw8y2Hk82lFgmsOxUxrU/iK/UntAhQ9Ni9aILaHJAxdLWZDWuzbInMtx/NUyRYm1kxjUREWXLkwhYLN3sxzkONiW5xCDdGzuER91/iKbOyGfUYPVDjZfLk315CibHSuR4WIKB8iDgN86BOjq5qFkiRBOFcy5Izgr4jBhGSFdQ6++H7W0nWwMA7qqZggpPOU7sNR4T2l+d8sh6TOroTfclinEcDUlGYl42Gde6JQ5ynfYw0DgfCPbqWMPS1e0QXBK5EQDQGPlxytVUeHFF5FoAwJKK4bY0ravgkSdNZo1rv8e+QaUZuHZs8qTywbin5lTcW3My6mKzokpuq4fZBlmSsVt0R5Uog+Txt3peF8ZnUgqgVIh50uS6ibSIiKhwKEbGdSgK3Ph+aTsrO8u8YBt1/yGaOiO/JXANI+tP8hW39YqsndNrK1b7v4+HQzflZfvUtZmB6wY1Cun1m/DkLB+O6Z/5+ZyZu6UxfEBpCsTqLIX19OMnn0UOwf/pR6EuODBPraJ05TRw7TEGc81m4DqLmFqrCTttrIMuWy8stxEPiyLRJvPCDeUGo2BpeqZpLHqUeLC+8Rvb3tOsK51sYkHbmbdBuKEtadofUTAh/AgA4J/lw1o9b3bAhZFxXXi/fyIicpk+o3H126UIuzQYPOuwKEp9Au/uUOJBEgauyQkilnGtQEexZNw2LXvzE7guVjSUSU3xuphEuRSMBQ8bwxrw9cs4d6QHb2zS8O72zLZj3smqgXcGUHpKfGbgGugR2oapA4FNNcDm2jZeFAtOchJQ51n/BmVNX+Nk70cICT8eyWJbZuC6qUMZ1weELm2sBKBYy3a0EY+JWALXJQxc5xQD12m6d/+J8PkGYV/dH215P7mdMhd2E7GJIgsp41qy/A6T3X5ZUBnXZhs5UQUREWWrtA9e3dr6DiS3uGl8BIeUClzzlhQPjWiCJ6/kAH/ijoTSWEA5XxnX8UxWyfnxPnU+AZ+lVEjsgkypL/N+1YwzaYLBGEpPMJbtr2oSo0mKEgAAIABJREFUJuz5G6bPVvDzt3Xc/1HqpLG+nnp4UI1ihO1qJqXgsQSXe4S2YpJ3Jar1sqy2FTAD1zAzrjOPKYkDS6baeCe6J7wPf/DeBxk6ri0R2L4/+Xq9pGos8v4GDShCwPM329rXFXCElCYRK04v2RQ4FJDxi8glUKDjjLIBtrxnK037cEnZ+5Ag8KqZZVJANa6tt3Eka/bWcBB/jp6GRt0DYIN97crCX5om4zVZwfAebRetJyIiassxfVXMP7IJ+0LuO55r5jVaKTHfTsT915apM4oF+ABL4Npfkpe3UmL7um7jbc/UdcRLhYSjgM+4IBPM4vqlJ7ajMuOa0pWocS0h4jXmCCj1tfUKYHHFIgz37sL7O38GYESeW0htsSb+RWMlguQsE/5KFKPkVmMscO3NJuNaOiAOZ+Mx0yMiOFX5GAAQ8PZOuV4pmnC8shqq8KDBy4TDXGLgOk1CiwAAJJuyn4UsY5F2KgDgO8E+trxnK5FmTCzeDAAo9RyGeqDNmj5u071IwrWeZ6AJBV51NIBAi+c3hrrjnegZkLUweuIuZxqZpqWhcUBxf9zagwdwIiLKkq5hSJmGsRVRVDW7L/igxZKwPLKIh0ZYKoQcYQlcB6UmAPkrFWKev+tg4JpyL2idnLHICFxnk3HtiU/O6L5jB7mTGbhWdQlR2QhcB9sJXHuk2JwCLrjjvKuzBq612PEp2zvVzYzrBhj7QW4yru2scZ3YH5U2gu4+GDHDRik/44WurHCikA6756CXsNz3U/xw4FZb3k9qkS3s0ADB0hmYnZRUQDWWe5bIuMbzPK7zPgtPtDHJGiL2/wXwNYjtD6z3RUREWaveiF+MN46HZT73ze9gBq5lCdjVJOGd7TJW7i6AYzR1Pv4gRL+jsBmHQI5dRpE83rzcmuyVjR1fZ6kQyoNExrUWn3S0vazXZMwar5yckdJVYk7OqEmIpBu4jp2fSwcGKcl2SpLAtSxlF7g+++0h0H68Giu9xt3jWdW4VnyoRo/EAjsnZ7RcSGkrW9wbC1w3g4HrXOORJ039PLU4TP4GfYoi9rxhO/WZbWHpDBShxZYVzi5jrcuUrE64FKvbDakAgsGxCwbmbXpEREQZUxJnjG48nFhLhbz+tYJzX/Zj/v9lEWEh6ijFB+3S5firdA6qpfL4YjkPda5ZKoTyKViUmxrX/9zdE+eGb8WP952X0/ZR5xWIT84oISoZ9Wnau2iimIFRZlw7LlnGdbalQuqjCtCtPxqEsR9km3H9T/mUxAIbEyo9HkvGdRuxI59kxApDMgPXucYeIU2qbuygdk0OWl4sY4N/LnTI2F7zT6CfA7WNLZ2BHM+4LqDAtaWzVZIc/EYVV2GR76cQAOYg2Op5N3m9/Lfoq9Rizc5bAVzpdHOIiKgQeRJnjFmcM+RdolSIs+0gsvJCiz82Jmisyen2PbGMa8HANeVBMJb12qhG4xnX2dS43hMpxodiBNTwllw2jzoxc2LQsA5EFDPjWoJ513My3lipENmmecUoNWvCXDReKqRjd+upsQyFbDKuAWC3VIG/y7MhQeACG5MPrfPctVUNwR/LuA7L+ZkToytj4DpNqmZ8ufyKPbfWej1y7IqNBtmptChL4NpjXv0soFIh1vpDyepkFUlRHCZ/A11IAIba2LLMBaUwyqTm+O1TREREGVPcnb2sW0qFELmF13JrtJSHjOvX6obgq+4T0LtiAIblfOvU1ZnBw8ZwFKLPaDy/LooPd2SRNWkmLwlOPEDpMS+aqLqEiBQLXLcTj47XUGbGteMUuXXgOpuM64BHw4SDGoGNyyEixjjUm2WGQkgqxlfSoKxe2xGKYomLtRGb80MFAIQVBq5zjT1CmuzOuFYsmc1KHurppUUq7BrX1lIhniQHv0QtTfcHgz28bYqIiDrK5YHrmrCE6mYBVQPOHBLFdwbrWFvNKDY5Q3rnHpwtXsFqVOIpjAEAyL6inL/P6nAffKodh6mBipxvmyg+OaOqQR89B+f976VZbafcp2GItAO1UnUum0edWEkscKIJCeFY4LqsnWx/TyzmILPGteM8lnhUSC7BTr0cmpAB7M5oO32LI7jrqG+Ap86FUvYkACnrjOsLtSUoEc1YIR+f1euzJVsC123NOVYkGYHrKAPXOccoWJrCZuBatifIaf1CSIpDwWJrqRChWZ5o+xYft2jxO/S03tV1y0eQZQm67t7P5IE5wzIP4kRElCWXB67PfyURFPzZOBUzhmjo4WfdEHKG/NWbGIX1qEI5hBaBpHgh+fJwMhpLFHFsThvq1MzJGYUAmiJaO2undvEhu/BL//X4tKIfgF/kqHXUmQX8iVjC58HjMPMXS9DQznRhnlipEMnDc16nWY9JO/yH4Xehc2I/PZLRdgKeRJZ2reYHoGadcd1P7IIfKnqhKqvXZ8vj8eEFbTI0yKhWd6Vc7zN9CJqEDwgcicE2tq8rYOA6TeZx3m9X4NoSrHasxpPsxefhftCEjP1h1bJcBvTsBz52sZYKSZa1bo1TK24PXMcyrpNNMklERJQWS+B6xXYvpjjYlPaY5zQR3pVODhH+Ukgwbv0VajOkYm9eSoUoEiCgt5nFRZQtM3ANxCZozJJ5WqWB+ymlxyxTAwBN8KNObWPlmPOab4JPkTC/4ug8tozSYT0mdSRMUqIYAzmh+NCsGzEuf5YZ12Ypjqn629k3KAuyomB+5GoAQHPTXSnX+6c2ETswHRf06oOpdjWui2AULE3hWJzWroxra+0c2amMa8WDR/afAABYv/8t9DWXSzIA9weuVV3GWv0QeKCjX7sZ1zKQ5Sy5dlDAq89ERNRBsoKobgSFF60rdnXg2msGSdx7TZk6O58xkZ0PEehqCHJxGeQ8BK5/3fdNzC56CB/uOQXA4pxvn7q2oCVwHd36EV453w9ZAqY9Fc5oO+bFxKjgXTCUnoClxmo4zdDBGjEIkvBAKumRp1ZRuqwZ1x0Zi5WYGde+INSo8djrKawLYJIkQeg6JFmOxcKSM2uBB+yqL9yFMHCdpmXVvdDY60isrQsBWJX392uRLeyGLFtrhnUbX1Y3WV1ThGnqbwEA672tC2rplji12z+ROaO97FS9cyIi6hQuWV6GiC5hw373DarPHBLFoDKBj3bLUGKJAsy4JsfEA9cqhNoEAJC8+ci4NvZ1IbnvO0mFL1iUOHcIN9bhlCEKtCzSJ81TU2NSe6L2WUuFSNEmnF0pIegD/vZF6v1PisU9eAeK82RZggQBAQmByD4c4/03FOj4swcIZXDzRjxw7Q8iEjYe+5xKzOwIoQFoO3AdYeA6bxgFS9OKvT3xiXYWQk2f2fJ+1itcbghWCmuUt0AC17BMKJDs2Gc9F1bamB3WDeKTY7rhIgYRERWs/+x2b53r8yujOOEQHQ9+6klkXDNwTU7xG4FrP1ToajMA5CXj2msGrl0w3qfOp8SbCKA0wth/FVlCiReoyyD4FC8VwsA1pcmryFAkAU1IKIrsx6IzjJ3opf9qaGxO8gJJwijpK2hQ4NNG2dtYSkqWjGxrn9aIY7xrAADFGQauA9aM69igzpdljWsn/dV/NwJyBLf1lPDvLcnX+Z33IZRJTdhd/wMAs21tX2fHEVK6tNi306bA4TdNXvxYvQoKdNxSXGbLeyYzr9u/4JOiWFWhYqe5UC6MjkaKTS4pdA2S1HqQVa36cG9kNjTIUPX1ANqZLcJBN0TmQZE0nFc+zOmmEBFRAbu4shkXD2/Gqr3uKz1l3oqqyEbdXwCI6AySkEN8pcZ/YjWuAeSpxnVsx2fGNeWBLEsI+BQ0qhoakJgAt9QH1CULHqbgiZ1LsXwTZcIvCzRpEhpEYt8Lprh+7vcqeNn/cwDA2tqhAPrb0EJqixILXJslMIBE2aB0xQPX/lKosQ7E6/KkwWSOVjYiKIVR7h+acp2R8hZUSLX4t5RB50ppYeA6TSJqBDUlmyZKrIt68KJ+DADgF3kYJKdrpP8b+CUNvYsCicB1gWRcj+gewXzPEwjrCoDprZ6vjXjxoHYWAKBcLIBrTxckGf+rfxsAMDvYt52ViYiIUhvdM4pBZTpC0ewn6cqXaCxI7ZHAjGtynIhnXEdsCVwL2X0Xk6hzCPg9aFQ11OmJ0ollfgk7MtiGEq9xXXgBJ3KOEbgG6kWi7yxNEbj2WEqlcl4ndzDvWu9I4NqcI05YalwXYsa1FisuK7dRxqYEIQCAEhs/UO4U3h7jkIsO3oqP/Fdgad8nbHk/yVrmwsEaT2YdMxmFV+N6YKmGiz3LcKH3zRRrWFIG3PyZZOuBgoNFIiLK3qwhxoRcI8rdN8myWXZVloBVVTJe3KTg0z0uPj5T53bQMKzFYdiMQ6BH8lkqxDiRF7JrUyiowAViEzTWai0zrjPhiV1gYakQyoRZ5rrOErhOlXHtsdQ9dsUcX5S4+60DgetHN1Rg0quVCM/6S3yZT8lubLcdziXxabGJaVN+fiEQkIwxtqeo1KZWdR3sEdLkFRFUSHWoVVpP8pcPiiIjgGZokB3NBBYweiulUIK8FuZVWy3V9RlRIHW7JQk+RBCF0uJKNBERUWdiLRXylzUe/MUop4hfO9ck6sLE0FPxD+lj47Fq3HeY11IhrHFNeWJOkrdfS0QMg77MAtAP7Dgcz1Vcjpoda3BqTltHnZk/lm3bqHuhagI+RUoZuPZaIoKyx73zcXQlZs6cGbQFjLviMqVDQkROxNGyzbheLY9Cf30nqtEDPbPaQvbMmFKqiUOtiZ6eEgauc40jpDSFo0an65PsyVAa3yuC5UWXAQCa8Q2c+lOZX1CPVCBBXguzU9FF8vZWeJvxrP8KKNAxt3gIdofsbF36uhXJ+KzoEgDAxprXgIMnOtwiIiKi3IsHrpnQRy6Tz8kZ42NsBq4pT4I+Y99qUAUaVYGAT0KpP7OOtipagrDoj0Y1xaxkREmYZSLCuoR6FehZnDrb3+uxZFx72B+6gTkeUy2plN4ssypVS+03b5bJeGulYfhG6QMdMuZl14ys6fHAdfLnFUvg2ufgHHWdFXuENNkduLZeyfHYVFc7mUSpkMINXGtIMTATAgdJdca6SSZvdAuPS/YFIiKifDLPaeIZqEQukc8a1/fVnYxFcgCTDhqHsTnfOhEQLIoFrsNRPL4qCgnAttrMJhCIl7HU3VdmitzLryQC1w3xwHXy827FEhFUWOPaFXJR4/qkPnUIenSIXV/Gl2Wbcd0slaAZJVm9tqPMMkmp4kbWwLU/wIzrXHNV4HrhwoVYsGABhg8fjqVLlzrdnBbiheQleyYzUlrUuHYuUBwvFSIVXqkQM3AdTVEqRBfCsq4tTcqKtTyIzIM4ERF1UjsbJWyskVDVLOGWCRGMqdCxdBPr/pJDar7GHPEc/FDxR20QgPwErldGB0LXB2JEcEjOt00EJGpcN4Y1/GpZJKttDCluwABpHbb6anLZNOrkzFIhqiahLgzUqyLlXVVeyxMKS4W4glEuVkJEeLBJ6wsdMpojWzPaxsWD92FEtxBqNq8AcDiA7Gtcn6T9H0aJdVgvHQbg51ltI1tmTCnV/uuxJLgWB5hxnWuuCVxXVVXh0UcfRUmJM1dQ2hOOGIFrr80Z15qQWkzUaDc9WY1rB9uTCfNCXqpSIXqLj+TijOsWV595ECcios7pzg98uPMD4/GT00I4rr+OVVWFMeagTkiPYii+BgAERQXCAGRv7gPXQjIuznACbsqXgM/YxxrD2SdgXXPwGlzofwH/6HNYrppFXYDfUipk0l/bzvL3WiZnlDk5oyuYh6UmqRh/CM8AAGyufSSjbZQoxt894gnEl2WbcX0Q9qEM9ThE7Mjq9R3xTPN4dC+SsL5pb9Ln94kgHojOQhDNmFkcSLoOZc81PcK9996LUaNGQQiBuro6p5vTSjhqBKx9sCfj2qOY2cIKnMyx/SR0KAKyik01X8WXSZKMQriJt71SIbolcu3mUiFKi6vPrvnKEhFRAVI1wKcA2xpkHOJ0Y9rgjZ3TRDO7m50od3zB+MOgaEIY+cm47iE3A6iDX1Jzvm0iAAiaGddq9uex5t23Ud2950zkPrF5QRFO41j+daMPx4YfgAINz5T2ym/DKC1m4NosH5uNEo/xx1eVRIJqthnXw8QmAEAf7Mm6Pdl6oOFkyJ6+CNX/Jenzu1GOZ6LHwIMoZsm8WzDXXJHGsnr1arz44ou46aabnG5KSmbGtV+KAqlqJueQWSpEd/hP9FzDWCyum4h/b9MACOOfLCUe20zE/yX/n1WV6sd/9BFYqQ5Iuq0DM66d+UTts05eoLDGNRERdcCK7cadO29td+sdPMbR2MPANTnNnwhcB9AEQEDy+XNeMu+pno9hZdEVOHzXCzndLpHJLBXSEIriNyd5sWpeEW45NrNkGDOPRnPjyRK5VjzjWms/fhKVFGwXB2GL6AMPy2O6gvm9j4pEDCY10eKRGZ8JeIwE0LCcCFx7s8y4dpIQOgARq4bQOnIUEQoAAS84D0A+OJ6+KYTAHXfcgZkzZ2LEiBFONyelDTUSbo18Fyq8GHjD8RgpbcEr/lvafM2x4fuxXRhXC4+VP8fffHe1uf6o0J/QECs2f4H3EQBbU08saLM/nibh+0UXGj/Mtj5j1O95WxuNSyI3xpc+5v0dTlM+Srm9F7TJmB+5Ov7zEt8vMUFel3L9J6Kn4vboJfgm9vMbvusxTE52i4hRCP++yGz8XjsL56tAZP83SDb/tbDUuJ4+ugduHfkTAMDgKRo2Fc1N2RYAuFy9Dsv0cQCACuzHR0VXtrn+7PD/4GMxHAAwVNqOZf6ftbn+lPA92CQOBgBMUpYB2AwAULxuDTQQEVEh+PUnAfxuVQn2qzIudroxB5g2MIozhmiYdVhi0B9lkISc4g3EqnsC73xnB4AL8b/acfjJz16Mr/Ks7zaMkzek3MSfotNwZzQxplzh+wmGyDsPWGsXAECSHT8to04qEEt7/WTrflw8/ngcrryHw4/34bbjW55X7BI9MDH8cPznaz3P4BrP87GfNgJg4Joy44sFrr8JefCdH92Gk5SV+IHnFQDJSilswWXqSqzQj2bpJJcw/wwvb/Pgfv/nmKm8h3t+as0mTtyFtEwbi8sjRjxFBfBn7904SVkVf/7nr25BR2tcO0rouN/7MGae9B5wkrnQup/ehte1cbg+coUDjev8HN9jXnjhBWzcuBHz5893uilt2rpjNxaFjscS7UQIG35t72ijAQDrwhV5f690CN2eEin5EKlKFrY2Jtzcohm/3y/EIDublJGNsQD2Tr07Srv3dLg1RERUyL5pVLCpzoPqkONDwFYqikWLoDUArK12Xzupi5Ak7IR9t6sH+o+y7b2oazm8b2KisC/0gR3a1po9vA2G0tevOHFMr5S3xoLWbdPr9qDYy1ILbtC3JHGlqlLanvV2wsKLjboR0xh0UEmLiTgz8bJ8KgBgjTQ867ZkS9+X3ufvITXmuSVdkySsaac2a2howGmnnYYLL7wQP/zhDwEAc+fORV1dHZYuXZrx9saNMzJgP/7445y2EwAkSYIS6AFPufGFC3p0HHVQ27XoPqryIawbnW53n4ZRPdpe/73dfmix+kF9gxKGlYXx/pc7sH9/oua3bPPEiObf5c1n/oi+3YvgLe+HsrLE4MesR14TlrFmvz++fHi3MA4qSj2w2dOsYH1t4hag0eUquvmSry9JMr5pUvBVvRcjR44EAIwM7IcPiVmx6+vrAQD79hkzXW9t9GBLvQLoOsI7NyASbo6v64nViT7llFPg98oYUO7B2moJeyNG+9evX4fj+oTb/L18WeNFddj423plgYm92l7/s2of6iLG367Eo+Podvadj6t80D3GFcwBBwUxoqwJa7fV4rM1iax061dXstTolmys152q+7CzDURdSarvPbXW2JgYOAYCXXuSFF1PHF+HDh0af7xp06Y2X2f3/ta3bx+MLBfo5hcQQmBvs4T/7pexa9euVutGo9Gkj71eY2yhWCZ56urfG7d/frN9drct1RhG0xKBlrvvvA19sRuLF/8Vsj+Aam8frK1J7FtHlqsoi49fre03tr2j0Ri/msYdFEaxR0CyrFvRqxeq6lW8+Po78WVFRUXZfzCyTa7HwdbtmY+ty6z9WiZ0XcfmvY3YUxfC1JOnYky5iqBXb3FsAABVk/BBVeJ8bkAwikODif51f0MYH6/dlvJzp3rvAx97uuicPdZjlXlOn2pfsbs/tP6dzL9vpvub+Trr+Gvp0qWoDsuoi0hYcO8CHNE9jDKfjkgk8bswP6rf78faWj/2frMDe3duiz+f7Hju5O8n2/PubLeRi2N4JtsIhxNxjRdffgXbGiREBbDxy1UYKO/Gu//+V/x56/GyQRRjXZ1x7OrRvQeGBRvRt5tx/NvaXILb7n8cAHB4vzJ0K0n0M+15//33449XrFiBHqIG+9ENt9z6P2lvoz2pfj/Wv1mv3n1RObgfDioW0HUt9rrENr797ePQhGKERTGu/+l18eXl5eVpv7eVG8dr+dReLNfRo8ajjz4Kr9eL733ve042I21aYw20RiMwGgawfHP7rzF3uN0AdmVwkN/h9cL+uVJT21CtY0N1E7BpI/r27RtfvnNn4nZH65dr1dbMrod8tC31c9aDlf9go97gptqWB/89e4wC/Vu2JLKr0xlUhSM6NuxWUVdTA/NmufC2NVjeRntabQPA8uRJ3anXT2PfKS42Atd1kV744MC7SomIiDodCV/uM8YSDuZVEMVFJC+2oj/+tVUHUA+gvsX480PLeNE6Dk61/74bW9+aiDJsGDMLKf8GHRTAoIMCULd/iQ9jiYPWfTmZDbF/RB3R06+jpx+IfrMOn8bqfoZCofjzZt8ZDAaTvZwcpEjAwFLjeLYXzdivl+HtVYnAh6omkvGsSSKy1gsbq4G93bvHltRjwqC2A7jpqpF65GQ7GRMa1m/ahvVI9J3WwPaR354BAJAljl/zwbHA9Z49e7Bo0SJcc8012Lt3b3x5OBxGJBLB9u3bUVpaim7dujnVRCIiIiIiIiIiIiJygGOB6+rqakQiESxYsAALFixo9fyUKVNw+eWX46c//akDrSMiIiIiIiIiIiIipzgWuO7fvz8efvjhVsvvv/9+NDU14eabb8bAgQPtbxgREREREREREREROcqxwHVpaSmmTp3aavmiRYugKErS54iIiIiIiIiIiIio85PbX4WIiIiIiIiIiIiIyD6OZVynsnjxYqebQEREREREREREREQOYsY1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbmKJIQQTjciV4YPHw4hBEpLS3O+7f379+d8m+nq3r27Y+/d3NwMAAiHw/Flspy43qHruq3t8Xg88cfWXdd8nKo9yX6HDQ0NSbelaVqH25kLkiS1+C8AlJWVOdUcIqKCYu3Xrf1oV1dXVxd/7LZjirVtVm5rJ3UdoVCoxX/zQVEUAMjL+QvRgXJxTuvkuSm5n3X8FYlE4o/NuMKB65isY7Vu3brlqXWUCevfLxqNAgBUVY0vSzXWtsaLTNke48z3PfBxUVFRVtvLVm1tbZvP+/3+pI95DpKe+vp6SJKEdevWJX2+UwWuR44cCV3XEQwGnW4KEREREREREREREaXQ0NAAWZbx5ZdfJn2+UwWuiYiIiIiIiIiIiKjwscY1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREbkKA9dERERERERERERE5CoMXBMRERERERERERGRqzBwTURERERERERERESuwsA1EREREREREREREblKpwlcq6qKe+65B8ceeyxGjx6Nc889F++//77TzSIil9qzZw8WLFiAuXPn4qijjkJlZSU++OCDpOuuWLECs2bNwhFHHIETTjgBDz30EKLRaKv16urqcOutt2LixIkYM2YMLr74YqxduzbfH4WIXGL16tW4/fbbMX36dIwZMwYnnHACrr32WmzZsqXVuitXrsQFF1yAI488EscccwzuvPNONDc3t1qP4xsi+vzzz3HVVVfhxBNPxOjRo3HMMcfgsssuw8qVK1uty76FiDpi4cKFqKysxJlnntnqOfYvROSEThO4vvHGG7Fo0SLMmDEDt9xyC2RZxuWXX45PP/3U6aYRkQtt3rwZCxcuxO7du1FZWZlyvbfffhtXXXUVunXrhltvvRVTp07Fww8/jLvuuqvFerquY968eXjllVdw0UUX4frrr0d1dTXmzp2LrVu35vvjEJEL/OlPf8KyZcswefJk3HLLLTj33HPx4YcfYubMmdi0aVN8vbVr1+K73/0uwuEwbrzxRsyePRtLlizBtdde22qbHN8Q0bZt26BpGs455xzceuutuOyyy7Bv3z5cdNFFePfdd+PrsW8hoo6oqqrCo48+ipKSklbPsX8hIseITuCzzz4Tw4YNE0888UR8WSgUElOnThVz5sxxrmFE5Fr19fVi3759Qgghli1bJoYNGyb+85//tFpv+vTpYtasWSIajcaX3XfffWL48OFi8+bN8WWvvPKKGDZsmFi2bFl8WXV1tRg3bpy4/vrr8/dBiMg1PvnlZDF3AAALnElEQVTkExEOh1ss27x5sxg1apS44YYb4su+//3vi+OOO040NDTEl/3jH/8Qw4YNE++99158Gcc3RJRKU1OTmDx5spg3b158GfsWIuqIG264QcydO1dcdNFFYsaMGS2eY/9CRE7pFBnXr732GrxeL84555z4Mr/fj9mzZ+OTTz7Bnj17HGwdEblRMBhEjx492lxn48aN2LhxI8477zwoihJfPmfOHOi6jjfeeCO+7PXXX0evXr0wZcqU+LLy8nJMmzYNy5cvRyQSyf2HICJXGTt2LHw+X4tlAwcOxNChQ+MZ1w0NDXjvvfcwc+ZMBAKB+HpnnnkmSkpK8Oqrr8aXcXxDRKkUFxejvLwcdXV1ANi3EFHHrF69Gi+++CJuuummVs+xfyEiJ3WKwPXatWsxaNCgFp0oAIwePRpCCNaYJaKsfPnllwCAUaNGtVjeu3dv9OnTJ/48YPRDhx9+OCRJarHuEUccgcbGRpYLIeqihBDYu3dv/ELZ+vXrEY1GW/UrPp8PI0aMaDFm4fiGiKwaGhqwb98+fPXVV7jvvvuwYcMGTJo0CQD7FiLKnhACd9xxB2bOnIkRI0a0ep79CxE5qVMErquqqtCrV69WyysqKgCAV/WIKCtVVVUAEn2JVUVFRYu+JVU/ZC5jP0TUNb344ovYvXs3pk2bBiA3/QrHN0Rd080334xJkyZh2rRp+POf/4zzzz8fV1xxBQD2LUSUvRdeeAEbN27E/Pnzkz7P/oWInORxugG5EAqF4PV6Wy33+/0AgHA4bHeTiKgTCIVCANDq1n/A6F+ss2iHQqGk65nLzG0RUdexadMm/PKXv8TRRx+NM888E0D7/Yq1r+D4hoisrrrqKpx33nnYtWsXli5dClVVEYlE4PP52LcQUVYaGhpw7733Yt68eUkDzgDHLkTkrE6RcV1UVJS0fqzZKZqdJBFRJoqKigAAqqq2ei4cDsefN9dNtp65zLouEXV+VVVV+MEPfoBu3brhgQcegCwbQ65M+xWOb4jIVFlZiWOOOQZnn302Hn/8caxZsyZej5Z9CxFl49FHH4XX68X3vve9lOuwfyEiJ3WKwPWBt6eYzFtaUl05JCJqi3lLm9mXWB14G1yqfshcxn6IqOuor6/H5Zdfjvr6evzpT39qcWttLvoVjm+IyOv1YsqUKXjjjTcQCoXYtxBRxvbs2YNFixZhzpw52Lt3L7Zv347t27cjHA4jEolg+/btqK2tZf9CRI7qFIHr4cOHY/PmzWhsbGyx/LPPPos/T0SUKXNyki+++KLF8t27d2PXrl0tJi8ZPnw41qxZAyFEi3VXr16NkpISHHrooflvMBE5LhwO44orrsDXX3+NP/zhDxg8eHCL54cNGwaPx9OqX1FVFWvXrm3Vr3B8Q0SphEIhCCHQ2NjIvoWIMlZdXY1IJIIFCxZgypQp8X+fffYZNm3ahClTpmDhwoXsX4jIUZ0icH3aaachEongmWeeiS9TVRXPPfccxo4di969ezvYOiIqVEOHDsXgwYOxZMkSaJoWX/7UU09BlmWccsop8WWnnXYa9uzZgxUrVsSX7du3D6+99hqmTJmStNYbEXUumqZh/vz5WLVqFR544AGMGTOm1TqlpaWYNGkSli5d2uKkbunSpWhqasJpp50WX8bxDREBxnjiQA0NDXj99dfRt29f9OzZk30LEWWsf//+ePjhh1v9Gzp0KA4++GA8/PDDmDlzJvsXInKUJA5MDyxQ11xzDVasWIFLLrkEhx56KJ5//nl88cUXWLRoEY4++minm0dELvTII48AMCZQe/nll3H22Wejf//+KCsrw0UXXQQAeOutt/DDH/4QEydOxPTp07FhwwY8+eSTOO+883DbbbfFt6VpGubMmYP//ve/uPTSS9GjRw889dRT2LlzJ5577jkMGDDAiY9IRDb61a9+hb/+9a848cQTMW3atBbPBQIBTJ06FQCwZs0anH/++Rg6dCjOOecc7Nq1C0888QQmTJiAhQsXtngdxzdEdPHFF8Pv9+Ooo45CRUVFfGyxa9cu3HfffZg+fToA9i1ElBtz585FXV0dli5dGl/G/oWInNJpAtfhcBj3338/XnrpJdTW1qKyshLXXXcdJk+e7HTTiMilKisrky4/+OCD8eabb8Z/Xr58OR566CFs2rQJ5eXlOPvss3HllVfC4/G0eF1tbS3uvvtuLF++HOFwGEcccQRuvPFGHH744Xn9HETkDnPnzsWHH36Y9LkD+5WPP/4YCxYswJdffolgMIjp06fjuuuuQ0lJSYvXcXxDRM8++yyWLl2KjRs3oq6uDqWlpRgzZgwuvfRSjB8/vsW67FuIqKOSBa4B9i9E5IxOE7gmIiIiIiIiIiIios6hU9S4JiIiIiIiIiIiIqLOg4FrIiIiIiIiIiIiInIVBq6JiIiIiIiIiIiIyFUYuCYiIiIiIiIiIiIiV2HgmoiIiIiIiIiIiIhchYFrIiIiIiIiIiIiInIVBq6JiIiIiIiIiIiIyFUYuCYiIiIistkHH3yAyspKPPfcc043hYiIiIjIlRi4JiIiIiLKk7Vr1+LBBx/E9u3bnW4KEREREVFB8TjdACIiIiKizmrt2rV46KGHMH78ePTv3z++/Fvf+hZWr14Nj4fDcSIiIiKiZDhSJiIiIiKymSzL8Pv9TjeDiIiIiMi1WCqEiIiIiCgPHnzwQdx0000AgIsvvhiVlZWorKzEjTfemLTGtXXZk08+iVNPPRVHHHEEzjjjDLz11lsAgPXr1+Oyyy7D2LFjMWHCBNx5552IRCKt3vvrr7/G9ddfj2OPPRajRo3CSSedhN/+9rdoamqy58MTEREREXUQM66JiIiIiPLg5JNPRlVVFZYsWYIrrrgCgwcPBgAceuihUFU15euefPJJ1NXV4ZxzzoHP58PixYtx9dVX44EHHsDPf/5znH766Zg6dSreffddLF68GOXl5bjyyivjr//iiy9wySWXoKysDOeddx569+6NdevWYfHixfj000+xePFieL3evH9+IiIiIqKOYOCaiIiIiCgPhg8fjjFjxmDJkiWYPHkyJkyYEH/ugw8+SPm6PXv24J///CdKS0sBABMnTsSZZ56Jq6++Gr///e9xyimnAAAuuOACnHXWWfj73//eInB98803o6KiAs8++yyCwWB8+aRJk3D11VfjpZdewllnnZXrj0tERERElFMsFUJERERE5CJnnXVWPGgNGAHwYDCIXr16xYPWprFjx6KqqgqNjY0AjFIi69evx+mnnw5VVbFv3774v6OPPholJSV49913bf08RERERETZYMY1EREREZGL9O/fv9Wybt26oU+fPkmXA8D+/fsRCASwadMmAEZ97QcffDDp9vfu3ZvD1hIRERER5QcD10RERERELqIoSkbLAUAI0eLnSy+9FMcdd1zSdcvKyrJvHBERERGRTRi4JiIiIiLKE0mSbH2/AQMGAABkWcbkyZNtfW8iIiIiolxijWsiIiIiojwpKSkBANTW1tryfiNHjsSwYcPw9NNPY9u2ba2ej0aj2L9/vy1tISIiIiLqCGZcExERERHlyRFHHAFZlvHYY4+htrYWJSUlSWtY54okSbj77rtxySWXYMaMGTj77LNx2GGHIRQKYcuWLVi2bBmuu+46nHXWWXlrAxERERFRLjBwTURERESUJ/369cOvf/1rLFy4ELfffjsikQhmzZqFWbNm5e09R4wYgeeffx5/+MMf8Oabb+Lpp59GIBDAwQcfjFmzZmHSpEl5e28iIiIiolyRxIEzuRAREREREREREREROYg1romIiIiIiIiIiIjIVRi4JiIiIiIiIiIiIiJXYeCaiIiIiIiIiIiIiFyFgWsiIiIiIiIiIiIichUGromIiIiIiIiIiIjIVRi4JiIiIiIiIiIiIiJXYeCaiIiIiIiIiIiIiFyFgWsiIiIiIiIiIiIichUGromIiIiIiIiIiIjIVf4fVGRBhoA7XRsAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 1800x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Posterior distribution\n", "Ez = posterior.expected_states.reshape(-1, num_states)\n", "perm = find_permutation(states, np.argmax(Ez, axis=-1))\n", "plot_posterior_states(Ez, states, perm)\n", "\n", "plt.savefig(\"bernoulli-hmm-state-est-comparison.pdf\")\n", "plt.savefig(\"bernoulli-hmm-state-est-comparison.png\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "m6QbapHA2LCE" }, "source": [ "# Fit Bernoulli Over Multiple Trials" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2lj23xF72LCF" }, "outputs": [], "source": [ "rng = jr.PRNGKey(0)\n", "num_timesteps = 500\n", "num_trials = 5\n", "\n", "all_states, all_data = true_hmm.sample(rng, num_timesteps, num_samples=num_trials)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J8qvW3YH2LCF", "outputId": "cd3fc535-a289-4565-d1e8-42beefd14fa7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 500)\n", "(5, 500, 10)\n" ] } ], "source": [ "# Now we have a batch dimension of size `num_trials`\n", "print(all_states.shape)\n", "print(all_data.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ofy3l03J2LCF", "outputId": "0107a6a8-3acf-4568-cd02-55aeff9d6c7a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing...\n", "Done.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "LP: -13234.113: 100%\|██████████\| 100/100 [00:01<00:00, 53.80it/s]\n" ] } ], "source": [ "lps, test_hmm, posterior = test_hmm.fit(all_data, method=\"em\", tol=-1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-bYmeajx2LCG", "outputId": "75cc6486-8e32-4793-d6b9-a546c390fece" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 432x288 with 3 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot marginal log probabilities\n", "plt.title(\"Marginal Log Probability\")\n", "plt.ylabel(\"lp\")\n", "plt.xlabel(\"idx\")\n", "plt.plot(lps / data.size)\n", "\n", "compare_transition_matrix(true_hmm.transition_matrix, test_hmm.transition_matrix)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5rDU6E_p2LCG", "outputId": "74b58ed4-05cb-4022-9f9a-ac0656705d26" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "===== Trial: 0 =====\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1440x144 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "===== Trial: 1 =====\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 1440x144 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "===== Trial: 2 =====\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1440x144 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For the first few trials, let's see how good our predicted states are\n", "for trial_idx in range(3):\n", " print(\"=\" * 5, f\"Trial: {trial_idx}\", \"=\" * 5)\n", " Ez = posterior.expected_states[trial_idx]\n", " states = all_states[trial_idx]\n", " perm = find_permutation(states, np.argmax(Ez, axis=-1))\n", " plot_posterior_states(Ez, states, perm)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "include_colab_link": true, "name": "bernoulli-hmm-example.ipynb", "provenance": [] }, "interpreter": { "hash": "1c9b7abd99f812592e04518a2dddca5f7bc8ca20b74b8ad7e1b8422bf8e8c0a8" }, "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", 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Hyperparticle/deep-learning-foundation	lessons/intro-to-tflearn/TFLearn_Digit_Recognition.ipynb	1	19576	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Handwritten Number Recognition with TFLearn and MNIST\n", "\n", "In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \n", "\n", "This kind of neural network is used in a variety of real-world applications including: recognizing phone numbers and sorting postal mail by address. To build the network, we'll be using the MNIST data set, which consists of images of handwritten numbers and their correct labels 0-9.\n", "\n", "We'll be using [TFLearn](http://tflearn.org/), a high-level library built on top of TensorFlow to build the neural network. We'll start off by importing all the modules we'll need, then load the data, and finally build the network." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import Numpy, TensorFlow, TFLearn, and MNIST data\n", "import numpy as np\n", "import tensorflow as tf\n", "import tflearn\n", "import tflearn.datasets.mnist as mnist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieving training and test data\n", "\n", "The MNIST data set already contains both training and test data. There are 55,000 data points of training data, and 10,000 points of test data.\n", "\n", "Each MNIST data point has:\n", "1. an image of a handwritten digit and \n", "2. a corresponding label (a number 0-9 that identifies the image)\n", "\n", "We'll call the images, which will be the input to our neural network, X and their corresponding labels Y.\n", "\n", "We're going to want our labels as one-hot vectors, which are vectors that holds mostly 0's and one 1. It's easiest to see this in a example. As a one-hot vector, the number 0 is represented as [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], and 4 is represented as [0, 0, 0, 0, 1, 0, 0, 0, 0, 0].\n", "\n", "### Flattened data\n", "\n", "For this example, we'll be using flattened data or a representation of MNIST images in one dimension rather than two. So, each handwritten number image, which is 28x28 pixels, will be represented as a one dimensional array of 784 pixel values. \n", "\n", "Flattening the data throws away information about the 2D structure of the image, but it simplifies our data so that all of the training data can be contained in one array whose shape is [55000, 784]; the first dimension is the number of training images and the second dimension is the number of pixels in each image. This is the kind of data that is easy to analyze using a simple neural network." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading MNIST...\n", "Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting mnist/train-images-idx3-ubyte.gz\n", "Downloading MNIST...\n", "Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting mnist/train-labels-idx1-ubyte.gz\n", "Downloading MNIST...\n", "Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting mnist/t10k-images-idx3-ubyte.gz\n", "Downloading MNIST...\n", "Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting mnist/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Retrieve the training and test data\n", "trainX, trainY, testX, testY = mnist.load_data(one_hot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the training data\n", "\n", "Provided below is a function that will help you visualize the MNIST data. By passing in the index of a training example, the function `show_digit` will display that training image along with it's corresponding label in the title." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6c01010358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualizing the data\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Function for displaying a training image by it's index in the MNIST set\n", "def show_digit(index):\n", " label = trainY[index].argmax(axis=0)\n", " # Reshape 784 array into 28x28 image\n", " image = trainX[index].reshape([28,28])\n", " plt.title('Training data, index: %d, Label: %d' % (index, label))\n", " plt.imshow(image, cmap='gray_r')\n", " plt.show()\n", " \n", "# Display the first (index 0) training image\n", "show_digit(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the network\n", "\n", "TFLearn lets you build the network by defining the layers in that network. \n", "\n", "For this example, you'll define:\n", "\n", "1. The input layer, which tells the network the number of inputs it should expect for each piece of MNIST data. \n", "2. Hidden layers, which recognize patterns in data and connect the input to the output layer, and\n", "3. The output layer, which defines how the network learns and outputs a label for a given image.\n", "\n", "Let's start with the input layer; to define the input layer, you'll define the type of data that the network expects. For example,\n", "\n", "```\n", "net = tflearn.input_data([None, 100])\n", "```\n", "\n", "would create a network with 100 inputs. The number of inputs to your network needs to match the size of your data. For this example, we're using 784 element long vectors to encode our input data, so we need 784 input units.\n", "\n", "\n", "### Adding layers\n", "\n", "To add new hidden layers, you use \n", "\n", "```\n", "net = tflearn.fully_connected(net, n_units, activation='ReLU')\n", "```\n", "\n", "This adds a fully connected layer where every unit (or node) in the previous layer is connected to every unit in this layer. The first argument `net` is the network you created in the `tflearn.input_data` call, it designates the input to the hidden layer. You can set the number of units in the layer with `n_units`, and set the activation function with the `activation` keyword. You can keep adding layers to your network by repeated calling `tflearn.fully_connected(net, n_units)`. \n", "\n", "Then, to set how you train the network, use:\n", "\n", "```\n", "net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')\n", "```\n", "\n", "Again, this is passing in the network you've been building. The keywords: \n", "\n", "* `optimizer` sets the training method, here stochastic gradient descent\n", "* `learning_rate` is the learning rate\n", "* `loss` determines how the network error is calculated. In this example, with categorical cross-entropy.\n", "\n", "Finally, you put all this together to create the model with `tflearn.DNN(net)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Below in the `build_model()` function, you'll put together the network using TFLearn. You get to choose how many layers to use, how many hidden units, etc.\n", "\n", "Hint: The final output layer must have 10 output nodes (one for each digit 0-9). It's also recommended to use a `softmax` activation layer as your final output layer. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define the neural network\n", "def build_model():\n", " # This resets all parameters and variables, leave this here\n", " tf.reset_default_graph()\n", " \n", " #### Your code ####\n", " # Include the input layer, hidden layer(s), and set how you want to train the model\n", " net = tflearn.input_data([None, trainX.shape[1]])\n", " \n", " net = tflearn.fully_connected(net, 196, activation='ReLU')\n", " net = tflearn.fully_connected(net, 64, activation='ReLU')\n", " \n", " net = tflearn.fully_connected(net, trainY.shape[1], activation='softmax')\n", " net = tflearn.regression(net, optimizer='sgd', learning_rate=0.01, loss='categorical_crossentropy')\n", " \n", " # This model assumes that your network is named \"net\" \n", " model = tflearn.DNN(net)\n", " return model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Build the model\n", "model = build_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the network\n", "\n", "Now that we've constructed the network, saved as the variable `model`, we can fit it to the data. Here we use the `model.fit` method. You pass in the training features `trainX` and the training targets `trainY`. Below I set `validation_set=0.1` which reserves 10% of the data set as the validation set. You can also set the batch size and number of epochs with the `batch_size` and `n_epoch` keywords, respectively. \n", "\n", "Too few epochs don't effectively train your network, and too many take a long time to execute. Choose wisely!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Step: 9899 \| total loss: \u001b[1m\u001b[32m0.50359\u001b[0m\u001b[0m \| time: 1.321s\n", "\| SGD \| epoch: 020 \| loss: 0.50359 - acc: 0.9010 -- iter: 49400/49500\n", "Training Step: 9900 \| total loss: \u001b[1m\u001b[32m0.47817\u001b[0m\u001b[0m \| time: 2.331s\n", "\| SGD \| epoch: 020 \| loss: 0.47817 - acc: 0.9059 \| val_loss: 0.30530 - val_acc: 0.9111 -- iter: 49500/49500\n", "--\n" ] } ], "source": [ "# Training\n", "model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=100, n_epoch=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing\n", "After you're satisified with the training output and accuracy, you can then run the network on the test data set to measure it's performance! Remember, only do this after you've done the training and are satisfied with the results.\n", "\n", "A good result will be higher than 95% accuracy. Some simple models have been known to get up to 99.7% accuracy!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 0.9185\n" ] } ], "source": [ "# Compare the labels that our model predicts with the actual labels\n", "\n", "# Find the indices of the most confident prediction for each item. That tells us the predicted digit for that sample.\n", "predictions = np.array(model.predict(testX)).argmax(axis=1)\n", "\n", "# Calculate the accuracy, which is the percentage of times the predicated labels matched the actual labels\n", "actual = testY.argmax(axis=1)\n", "test_accuracy = np.mean(predictions == actual, axis=0)\n", "\n", "# Print out the result\n", "print(\"Test accuracy: \", test_accuracy)" ] } ], "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
tralpha/Computer-Vision-Gatech	ps0_python/.ipynb_checkpoints/Lesson 2A-L3-checkpoint.ipynb	1	311509	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### This Notebook Covers:\n", "### Lesson 2A-L3 Linearity and Convolution" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cv2\n", "import os\n", "import matplotlib.pyplot as plt\n", "from ps0 import *\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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c5ahNg8Yh04SVxXV+9qcvcf2tV7lz6x6T0RTvDY11RFmbMysLtNpt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\nVUS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LRh3AWfaPYa1lY22NP3z/b/PY089wYrSGbuR9a2fRfjUv24inNcD7+/tIF6BThwX2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h1xe/cmS51ltA4bSLuhhFZNHyvirfEMDFbOWpQUeO9m7cFEb7O9Hu89OtLOmahZ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\nburggShFkqUYF5iAZKzKX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8cmxpIOR+idiVtW1Tt3SGab+g7eJvupRu9GHH6v2D9Ytvvv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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d46fdd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "driver_path = 'input/ps0-1-a-1.jpg'\n", "\n", "driver = cv2.imread(driver_path)\n", "show_img(driver)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "driver_blur = cv2.GaussianBlur(driver, (7,7), 3, borderType=2)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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oNamjbg2LBakNtBpSSRV7j1TjUp0irl54T9RA1LXBUsWh0Hjbx/mrqo3Ca1xmiwZKyqQl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\nGRERETEG/g3RRxtW9ZWTIQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1109b4950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_img(driver_blur)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apply a median filter\n", "### Add salt & pepper noise" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid = np.random.normal(0,0.4,driver.shape)\n", "grid = grid.astype('uint8')\n", "driver_salt_pepper = cv2.add(driver, grid)" ] }, { "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
vicente-gonzalez-ruiz/YAPT	02-basics/containers/03-sets.ipynb	1	17158	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Containers\" data-toc-modified-id=\"Containers-1\"><span class=\"toc-item-num\">1  </span>Containers</a></span><ul class=\"toc-item\"><li><span><a href=\"#1.-Tuples\" data-toc-modified-id=\"1.-Tuples-1.1\"><span class=\"toc-item-num\">1.1  </span>1. Tuples</a></span><ul class=\"toc-item\"><li><span><a href=\"#1.1-Tuples-are-(as-the-rest-of-elements-of-Python)-objects\" data-toc-modified-id=\"1.1-Tuples-are-(as-the-rest-of-elements-of-Python)-objects-1.1.1\"><span class=\"toc-item-num\">1.1.1  </span>1.1 Tuples are (as the rest of elements of Python) objects</a></span></li><li><span><a href=\"#1.2.-Tuple-definition\" data-toc-modified-id=\"1.2.-Tuple-definition-1.1.2\"><span class=\"toc-item-num\">1.1.2  </span>1.2. Tuple definition</a></span></li><li><span><a href=\"#1.3.-Counting-ocurrences-in-tuples\" data-toc-modified-id=\"1.3.-Counting-ocurrences-in-tuples-1.1.3\"><span class=\"toc-item-num\">1.1.3  </span>1.3. Counting ocurrences in tuples</a></span></li><li><span><a href=\"#1.4.-Searching-for-an-item-in-a-tuple\" data-toc-modified-id=\"1.4.-Searching-for-an-item-in-a-tuple-1.1.4\"><span class=\"toc-item-num\">1.1.4  </span>1.4. Searching for an item in a tuple</a></span></li><li><span><a href=\"#1.5.-Slicing-in-tuples\" data-toc-modified-id=\"1.5.-Slicing-in-tuples-1.1.5\"><span class=\"toc-item-num\">1.1.5  </span>1.5. Slicing in tuples</a></span></li><li><span><a href=\"#1.6.-Functions-can-return-tuples\" data-toc-modified-id=\"1.6.-Functions-can-return-tuples-1.1.6\"><span class=\"toc-item-num\">1.1.6  </span>1.6. Functions can return tuples</a></span></li><li><span><a href=\"#1.7.-Swapping-pairs-with-tuples-is-fun!\" data-toc-modified-id=\"1.7.-Swapping-pairs-with-tuples-is-fun!-1.1.7\"><span class=\"toc-item-num\">1.1.7  </span>1.7. Swapping pairs with tuples is fun!</a></span></li><li><span><a href=\"#1.8.-Tuples-are-inmutable\" data-toc-modified-id=\"1.8.-Tuples-are-inmutable-1.1.8\"><span class=\"toc-item-num\">1.1.8  </span>1.8. Tuples are inmutable</a></span></li></ul></li><li><span><a href=\"#2.-[0]-Lists\" data-toc-modified-id=\"2.-[0]-Lists-1.2\"><span class=\"toc-item-num\">1.2  </span>2. [0] <a href=\"https://docs.python.org/3.7/library/stdtypes.html#sequence-types-list-tuple-range\" target=\"_blank\">Lists</a></a></span><ul class=\"toc-item\"><li><span><a href=\"#2.1-[1]-(Of-course)-lists-are-objects\" data-toc-modified-id=\"2.1-[1]-(Of-course)-lists-are-objects-1.2.1\"><span class=\"toc-item-num\">1.2.1  </span>2.1 [1] (Of course) lists are objects</a></span></li><li><span><a href=\"#2.2-[0]-Appending-items-to-a-list-(O(1))\" data-toc-modified-id=\"2.2-[0]-Appending-items-to-a-list-(O(1))-1.2.2\"><span class=\"toc-item-num\">1.2.2  </span>2.2 [0] Appending items to a list (O(1))</a></span></li><li><span><a href=\"#2.3-[0]-Inserting-items-(O(n))\" data-toc-modified-id=\"2.3-[0]-Inserting-items-(O(n))-1.2.3\"><span class=\"toc-item-num\">1.2.3  </span>2.3 [0] Inserting items (O(n))</a></span></li><li><span><a href=\"#2.4-[0]-Deleting-items-from-a-list-by-content-(O(n)))\" data-toc-modified-id=\"2.4-[0]-Deleting-items-from-a-list-by-content-(O(n)))-1.2.4\"><span class=\"toc-item-num\">1.2.4  </span>2.4 [0] Deleting items from a list by content (O(n)))</a></span></li><li><span><a href=\"#2.4-[0]-Deleting-items-from-the-begin-of-the-list-(O(1)))\" data-toc-modified-id=\"2.4-[0]-Deleting-items-from-the-begin-of-the-list-(O(1)))-1.2.5\"><span class=\"toc-item-num\">1.2.5  </span>2.4 [0] Deleting items from the begin of the list (O(1)))</a></span></li><li><span><a href=\"#2.4-[0]-Deleting-items-from-the-end-of-the-list-(O(1)))\" data-toc-modified-id=\"2.4-[0]-Deleting-items-from-the-end-of-the-list-(O(1)))-1.2.6\"><span class=\"toc-item-num\">1.2.6  </span>2.4 [0] Deleting items from the end of the list (O(1)))</a></span></li><li><span><a href=\"#2.5-[0]-Sorting-the-elements-of-a-list-(O(n-log-n))\" data-toc-modified-id=\"2.5-[0]-Sorting-the-elements-of-a-list-(O(n-log-n))-1.2.7\"><span class=\"toc-item-num\">1.2.7  </span>2.5 [0] Sorting the elements of a list (O(n log n))</a></span></li><li><span><a href=\"#2.6-[1]-Erasing-all-list-items-(O(1))\" data-toc-modified-id=\"2.6-[1]-Erasing-all-list-items-(O(1))-1.2.8\"><span class=\"toc-item-num\">1.2.8  </span>2.6 [1] Erasing all list items (O(1))</a></span></li><li><span><a href=\"#2.7-[0]-List-slicing-(O(s))\" data-toc-modified-id=\"2.7-[0]-List-slicing-(O(s))-1.2.9\"><span class=\"toc-item-num\">1.2.9  </span>2.7 [0] List slicing (O(s))</a></span></li><li><span><a href=\"#2.8-[1]-Defining-lists-with-list-comprehensions:\" data-toc-modified-id=\"2.8-[1]-Defining-lists-with-list-comprehensions:-1.2.10\"><span class=\"toc-item-num\">1.2.10  </span>2.8 [1] Defining lists with <a href=\"http://www.secnetix.de/olli/Python/list_comprehensions.hawk\" target=\"_blank\"><em>list comprehensions</em></a>:</a></span></li><li><span><a href=\"#2.9-[1]-Lists-are-mutable-objects\" data-toc-modified-id=\"2.9-[1]-Lists-are-mutable-objects-1.2.11\"><span class=\"toc-item-num\">1.2.11  </span>2.9 [1] Lists are mutable objects</a></span></li></ul></li><li><span><a href=\"#3.-[0]--Sets\" data-toc-modified-id=\"3.-[0]--Sets-1.3\"><span class=\"toc-item-num\">1.3  </span>3. [0] <a href=\"https://docs.python.org/3.7/library/stdtypes.html#set-types-set-frozenset\" target=\"_blank\">Sets</a></a></span><ul class=\"toc-item\"><li><span><a href=\"#3.2.-[0]-Sets-can-grow-(O(1))\" data-toc-modified-id=\"3.2.-[0]-Sets-can-grow-(O(1))-1.3.1\"><span class=\"toc-item-num\">1.3.1  </span>3.2. [0] Sets can grow (O(1))</a></span></li><li><span><a href=\"#3.3.-[0]-Sets-can-not-contain-dupplicate-objects\" data-toc-modified-id=\"3.3.-[0]-Sets-can-not-contain-dupplicate-objects-1.3.2\"><span class=\"toc-item-num\">1.3.2  </span>3.3. [0] Sets can not contain dupplicate objects</a></span></li><li><span><a href=\"#3.4.-[1]-Sets-can-not-contain-mutable-objects\" data-toc-modified-id=\"3.4.-[1]-Sets-can-not-contain-mutable-objects-1.3.3\"><span class=\"toc-item-num\">1.3.3  </span>3.4. [1] Sets can not contain mutable objects</a></span></li><li><span><a href=\"#3.5-[0]-Intersection-of-sets-(O(min(len(s),-len(t)))\" data-toc-modified-id=\"3.5-[0]-Intersection-of-sets-(O(min(len(s),-len(t)))-1.3.4\"><span class=\"toc-item-num\">1.3.4  </span>3.5 [0] Intersection of sets (O(min(len(s), len(t)))</a></span></li><li><span><a href=\"#3.6-[0]-Union-of-sets-(O(len(s)+len(t)))\" data-toc-modified-id=\"3.6-[0]-Union-of-sets-(O(len(s)+len(t)))-1.3.5\"><span class=\"toc-item-num\">1.3.5  </span>3.6 [0] Union of sets (O(len(s)+len(t)))</a></span></li><li><span><a href=\"#3.7.-[0]-Sets-are-MUCH-more-efficient-for-searching-by-content-than-lists\" data-toc-modified-id=\"3.7.-[0]-Sets-are-MUCH-more-efficient-for-searching-by-content-than-lists-1.3.6\"><span class=\"toc-item-num\">1.3.6  </span>3.7. [0] Sets are MUCH more <a href=\"https://wiki.python.org/moin/TimeComplexity\" target=\"_blank\">efficient for searching by content</a> than lists</a></span></li></ul></li><li><span><a href=\"#4-[0]-Dictionaries\" data-toc-modified-id=\"4-[0]-Dictionaries-1.4\"><span class=\"toc-item-num\">1.4  </span>4 [0] <a href=\"https://docs.python.org/3.7/library/stdtypes.html#dict\" target=\"_blank\">Dictionaries</a></a></span><ul class=\"toc-item\"><li><span><a href=\"#4.1-[0]-Static-definition-of-a-dictionary\" data-toc-modified-id=\"4.1-[0]-Static-definition-of-a-dictionary-1.4.1\"><span class=\"toc-item-num\">1.4.1  </span>4.1 [0] Static definition of a dictionary</a></span></li><li><span><a href=\"#4.2-[0]-Indexing-of-a-dictionary-by-a-key-(O(1))\" data-toc-modified-id=\"4.2-[0]-Indexing-of-a-dictionary-by-a-key-(O(1))-1.4.2\"><span class=\"toc-item-num\">1.4.2  </span>4.2 [0] Indexing of a dictionary by a key (O(1))</a></span></li><li><span><a href=\"#4.3-[0]-Testing-if-a-key-is-the-dictionary-(O(1))\" data-toc-modified-id=\"4.3-[0]-Testing-if-a-key-is-the-dictionary-(O(1))-1.4.3\"><span class=\"toc-item-num\">1.4.3  </span>4.3 [0] Testing if a key is the dictionary (O(1))</a></span></li><li><span><a href=\"#4.4-[1]-Getting-the-keys-(O(n))\" data-toc-modified-id=\"4.4-[1]-Getting-the-keys-(O(n))-1.4.4\"><span class=\"toc-item-num\">1.4.4  </span>4.4 [1] Getting the keys (O(n))</a></span></li><li><span><a href=\"#4.5-[1]-Getting-the-values-(O(n))\" data-toc-modified-id=\"4.5-[1]-Getting-the-values-(O(n))-1.4.5\"><span class=\"toc-item-num\">1.4.5  </span>4.5 [1] Getting the values (O(n))</a></span></li><li><span><a href=\"#4.4-[1]-Determining-the-position-of-a-key-in-a-dictionary-(O(n))\" data-toc-modified-id=\"4.4-[1]-Determining-the-position-of-a-key-in-a-dictionary-(O(n))-1.4.6\"><span class=\"toc-item-num\">1.4.6  </span>4.4 [1] Determining the position of a key in a dictionary (O(n))</a></span></li><li><span><a href=\"#4.6-[0]-Inserting-a-new-entry-(O(1))\" data-toc-modified-id=\"4.6-[0]-Inserting-a-new-entry-(O(1))-1.4.7\"><span class=\"toc-item-num\">1.4.7  </span>4.6 [0] Inserting a new entry (O(1))</a></span></li><li><span><a href=\"#[0]-4.7-Deleting-an-entry-(O(1))\" data-toc-modified-id=\"[0]-4.7-Deleting-an-entry-(O(1))-1.4.8\"><span class=\"toc-item-num\">1.4.8  </span>[0] 4.7 Deleting an entry (O(1))</a></span></li><li><span><a href=\"#4.8-[1]-Dictionaries-are-mutable\" data-toc-modified-id=\"4.8-[1]-Dictionaries-are-mutable-1.4.9\"><span class=\"toc-item-num\">1.4.9  </span>4.8 [1] Dictionaries are mutable</a></span></li><li><span><a href=\"#4.9-[0]-Looping-a-dictionary-(O(n))\" data-toc-modified-id=\"4.9-[0]-Looping-a-dictionary-(O(n))-1.4.10\"><span class=\"toc-item-num\">1.4.10  </span>4.9 [0] Looping a dictionary (O(n))</a></span></li></ul></li><li><span><a href=\"#5.-Bytes\" data-toc-modified-id=\"5.-Bytes-1.5\"><span class=\"toc-item-num\">1.5  </span>5. <a href=\"http://python-para-impacientes.blogspot.com.es/2014/07/tipos-de-cadenas-unicode-byte-y.html\" target=\"_blank\">Bytes</a></a></span><ul class=\"toc-item\"><li><span><a href=\"#5.1.-Creation-of-bytes-sequence\" data-toc-modified-id=\"5.1.-Creation-of-bytes-sequence-1.5.1\"><span class=\"toc-item-num\">1.5.1  </span>5.1. Creation of bytes sequence</a></span></li><li><span><a href=\"#5.2.-Indexing-in-a-bytes-sequence\" data-toc-modified-id=\"5.2.-Indexing-in-a-bytes-sequence-1.5.2\"><span class=\"toc-item-num\">1.5.2  </span>5.2. Indexing in a bytes sequence</a></span></li><li><span><a href=\"#5.3.-Concatenation-of-bytes-sequences\" data-toc-modified-id=\"5.3.-Concatenation-of-bytes-sequences-1.5.3\"><span class=\"toc-item-num\">1.5.3  </span>5.3. Concatenation of bytes sequences</a></span></li><li><span><a href=\"#5.4.-Bytes-are-inmutable\" data-toc-modified-id=\"5.4.-Bytes-are-inmutable-1.5.4\"><span class=\"toc-item-num\">1.5.4  </span>5.4. Bytes are inmutable</a></span></li></ul></li><li><span><a href=\"#6.-Bytearray\" data-toc-modified-id=\"6.-Bytearray-1.6\"><span class=\"toc-item-num\">1.6  </span>6. <a href=\"http://ze.phyr.us/bytearray/\" target=\"_blank\">Bytearray</a></a></span></li><li><span><a href=\"#7.-Arrays\" data-toc-modified-id=\"7.-Arrays-1.7\"><span class=\"toc-item-num\">1.7  </span>7. <a href=\"https://docs.python.org/3/library/array.html\" target=\"_blank\">Arrays</a></a></span><ul class=\"toc-item\"><li><span><a href=\"#Element-access\" data-toc-modified-id=\"Element-access-1.7.1\"><span class=\"toc-item-num\">1.7.1  </span>Element access</a></span></li><li><span><a href=\"#Slice-access\" data-toc-modified-id=\"Slice-access-1.7.2\"><span class=\"toc-item-num\">1.7.2  </span>Slice access</a></span></li><li><span><a href=\"#Appending-elements\" data-toc-modified-id=\"Appending-elements-1.7.3\"><span class=\"toc-item-num\">1.7.3  </span>Appending elements</a></span></li><li><span><a href=\"#Concatenating-arrays\" data-toc-modified-id=\"Concatenating-arrays-1.7.4\"><span class=\"toc-item-num\">1.7.4  </span>Concatenating arrays</a></span></li><li><span><a href=\"#Deleting-elements\" data-toc-modified-id=\"Deleting-elements-1.7.5\"><span class=\"toc-item-num\">1.7.5  </span>Deleting elements</a></span></li></ul></li></ul></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. [0] [Sets](https://docs.python.org/3.7/library/stdtypes.html#set-types-set-frozenset)\n", " [Sets](https://en.wikipedia.org/wiki/Set_%28abstract_data_type%29) are implemented as [hash table](https://en.wikipedia.org/wiki/Hash_table) of (unordered) objects, therefore sets are good for get/set/delete/searching items and bad for . Sets do not support indexing, slicing, or other sequence-like behavior." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 'a', (1, 2)}\n", "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "print(type(a))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "help(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2. [0] Sets can grow (O(1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.add('a')\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3. [0] Sets can not contain dupplicate objects" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.add('a')\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4. [1] Sets can not contain mutable objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Mutable objects can not be hashed :-(" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "a = set()\n", "a.add([1,2]) # Sets can not contain lists" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = set() # Empty set\n", "a.add({1,2,3}) # Sets can not contain sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.5 [0] Intersection of sets (O(min(len(s), len(t)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = {1,2,3}\n", "b = {2,3,4}\n", "a.intersection(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.6 [0] Union of sets (O(len(s)+len(t)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a.union(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.7. [0] Sets are MUCH more [efficient for searching by content](https://wiki.python.org/moin/TimeComplexity) than lists" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = set(range(1000))\n", "print(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%timeit '0' in a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = list(range(1000))\n", "print(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit '0' in a" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }	cc0-1.0
whitead/numerical_stats	unit_9/hw_2019/homework_9_key.ipynb	1	47260	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 9 Key\n", "#### CHE 116: Numerical Methods and Statistics\n", "\n", "\n", "2/21/2019\n", "\n", "----" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Creating Matrices (12 Points)\n", "\n", "Create the following matrices using the given constraints. 4 Points each.\n", "\n", "1. In 3 lines of python (not including prints/imports), create a 6x12 matrix whose second column (where we count \"first\", \"second\", etc.) is the powers of 2 (e.g., 1, 2, 4, 8, 16). Its third is all 4's. All other elements are zero\n", "2. In 5 lines of python, accomplish the following: create a random 10x10 matrix using `np.random.normal` centered at 10 with standard deviation of 5, replace all negative values with 0 and then modify it so its rows sum to 1 and 0, use `np.round` to round to one decimal place.\n", "3. In at most 3 lines of python, create a 9x9 matrix where all elements are 0 except in the diagonal, which is 1's" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 1. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 2. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 4. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 8. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 16. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [ 0. 32. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "m = np.zeros((6, 12))\n", "m[:,1] = 2np.arange(6)\n", "m[:,2] = 4\n", "print(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.1 0.1 0.1 0.1 0.1 0. 0.1 0.1 0.1 0.1]\n", " [0.1 0.1 0. 0.1 0. 0.2 0. 0.2 0.2 0.1]\n", " [0. 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1]\n", " [0.1 0.1 0. 0.1 0.2 0.1 0.1 0. 0.1 0.1]\n", " [0.1 0.2 0.1 0.1 0.1 0.1 0.1 0. 0.1 0.1]\n", " [0.2 0.2 0.1 0.1 0.1 0. 0. 0.1 0.1 0. ]\n", " [0.1 0.1 0.1 0.1 0. 0.1 0.1 0.1 0.1 0.1]\n", " [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]\n", " [0.1 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0. ]\n", " [0.1 0.1 0.1 0.2 0.1 0.1 0. 0.1 0.1 0.1]]\n" ] } ], "source": [ "data = np.random.normal(size=(10,10), scale=5, loc=10)\n", "\n", "\n", "data[data < 0] = 0\n", "\n", "for i in range(10):\n", " data[i,:] /= np.sum(data[i,:])\n", "print(np.round(data, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", " [0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" ] } ], "source": [ "data = np.zeros((9,9))\n", "for i in range(9):\n", " data[i,i] = 1\n", "print(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Solving Systems of Equations (8 Points)\n", "\n", "Solve the following systems of equations. 4 Points each. Write out your answer in Markdown\n", "\n", "## 1. \n", "$$\\begin{array}{ll}\n", "4x - 2y + z &= 0\\\\\n", "2x - 4y + z &= 1 \\\\\n", "2x + y + 3z &= 3\\\\\n", "\\end{array}$$\n", "\n", "\n", "## 2. \n", "$$\\begin{array}{ll}\n", "e^{x} + y + 4z &= 4\\\\\n", "4e^{x} - 2y - z &= -1 \\\\\n", "3e^{x} + y + z &= 2\\\\\n", "\\end{array}$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.38235294 -0.11764706 1.29411765]\n" ] } ], "source": [ "import numpy.linalg as lin\n", "\n", "A = np.array([[4, -2, 1], [2, -4, 1], [2, 1, 3]])\n", "Ainv= lin.inv(A)\n", "B = np.array([0, 1, 3])\n", "x = Ainv.dot(B)\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "x = -0.38\\,\n", "y = -0.12\\,\n", "z = 1.29\\,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.5198257537444133 0.53125 0.8125\n" ] } ], "source": [ "A = np.array([[1, 1, 4], [4, -2, -1], [3, 1, 1]])\n", "Ainv= lin.inv(A)\n", "B = np.array([4, -1, 2])\n", "x = Ainv.dot(B)\n", "print(np.log(x[0]), x[1], x[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "x = -1.52\\,\n", "y = 0.53\\,\n", "z = 0.81\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Eigenvalue/Eigenvector Problems (8 Points)\n", "\n", "\n", "Calculate the eigenvalues and eigenvectors for the following matrices. Solve in Python and then write out the eigenvalues/eigenvectors in LaTeX*. When writing decimals, only report two significant figures.\n", "\n", "1. [3 Points]\n", "$$A = \\left[\\begin{array}{lcr}\n", "4 & 2 & 2\\\\\n", "4 & -8 & 4\\\\\n", "8 & 6 & -10\\\\\n", "\\end{array}\\right]$$\n", "\n", "\n", "2. [3 Points]\n", "$$A = \\left[\\begin{array}{lcr}\n", "1 & 5 & 1\\\\\n", "2 & -1 & 2\\\\\n", "1 & 2 & -3\\\\\n", "\\end{array}\\right]$$\n", "\n", "\n", "3. [2 Points] Why would you use `eigh` over `eig`?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([ 6.26181582, -6.16107966, -14.10073615]), array([[ 0.77639311, 0.26691702, -0.03470413],\n", " [ 0.36237847, -0.74614174, -0.53196789],\n", " [ 0.51565064, -0.60994083, 0.84605307]]))\n" ] } ], "source": [ "A = np.array([[4, 2, 2], [4, -8, 4], [8, 6, -10]])\n", "print(lin.eig(A))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\lambda_1 = 6.3$$\n", "$$\n", "v_1 = \\left[0.78, 0.36, 0.52\\right]\n", "$$\n", "\n", "$$\\lambda_2 = -6.2$$\n", "$$\n", "v_2 = \\left[0.27, -0.75, -0.61\\right]\n", "$$\n", "\n", "\n", "$$\\lambda_3 = -14.1$$\n", "$$\n", "v_3 = \\left[-0.034, -0.53, 0.85\\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([ 3.92715706, -2.57344229, -4.35371476]), array([[-0.85603357, -0.78017368, 0.4909431 ],\n", " [-0.45042445, 0.47652482, -0.64319425],\n", " [-0.25362244, 0.40528153, 0.58760193]]))\n" ] } ], "source": [ "A = np.array([[1, 5, 1], [2, -1, 2], [1, 2, -3]])\n", "print(lin.eig(A))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\lambda_1 = 3.9$$\n", "$$\n", "v_1 = \\left[-0.85, -0.78, 0.49\\right]\n", "$$\n", "\n", "$$\\lambda_2 = -2.6$$\n", "$$\n", "v_2 = \\left[-0.45, 0.48, -0.64\\right]\n", "$$\n", "\n", "\n", "$$\\lambda_3 = -4.4$$\n", "$$\n", "v_3 = \\left[-0.25, 0.40, 0.59\\right]\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.3\n", "\n", "If you had physical insight about the problem, like your eigenvalues provided solutions to an ODE, that disqualified complex eigenvalues you can use `eigh`. Otherwise, use `eig`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Slicing Practice (6 Points)\n", "\n", "Using numpy, create a sum or difference of array slices that yields the requested quantity. Consider the following example:\n", "\n", "To create this sequence:\n", "$$x_0 , x_2, x_4, \\ldots $$\n", "\n", "Use this slice\n", "```\n", "x[::2]\n", "```\n", "\n", "Use this particular array for this: `x = np.arange(15)`, but use `len(x)` when you need to refer to the length of the array. 2 Points each.\n", "\n", "1. $x_1 \\cdot x_0, x_2 \\cdot x_1, \\ldots x_N\\cdot x_{N - 1}$\n", "2. $ x_0 + x_N, x_1 + x_{N - 1}, \\ldots, x_0 + x_N$\n", "3. $x_N - x_{N - 1}, x_{N - 1} - x_{N - 2},\\ldots ,x_1 - x_0$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 2, 6, 12, 20, 30, 42, 56, 72, 90, 110, 132, 156,\n", " 182])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(15)\n", "x[1:] x[:-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x + x[::-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[:0:-1] - x[-2::-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Numerical Differentation/Integration Methods (5 Points)\n", "\n", "Given the following problems, what is the correct method to use? Do not solve the problems, just state the best method. 1 Point each\n", "\n", "\n", "1. Compute $\\int_0^1 e^{-x^2}\\,dx$\n", "2. You are given $f(x)$ evaluated at the following x values: $[0,0.1, 0.5, 0.9, 1.2]$. Compute the derivative at $f(0.5)$. \n", "3. Using the example from 5.2, compute $\\int_{0.1}^{1.2} f(x)\\,dx$\n", "4. $g(x) = x^3$. What is the derivative $g$ at $g'(x = 0.5)$\n", "5. Compute $\\int_0^1 e^{-x}\\,dx$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1\n", "Gaussian quadrature (quad)\n", "\n", "### 5.2\n", "Central difference\n", "\n", "### 5.3\n", "Trapezoidal\n", "\n", "### 5.4\n", "Analytic\n", "\n", "### 5.5\n", "Analytic or quad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Numerical Differentation/Integration Methods (22 Points)\n", "\n", "1. [4 Points] Compute $\\int_{-\\infty}^{\\infty} x^2 e^{-x^2}\\,dx$. Use `np.inf` to refer to infinity and use a `lambda` function. Make sure it's clear what is the value of the integral in your `print`.\n", "\n", "2. [4 Points] Compute the numerical derivative of the following data:\n", "```\n", "x = [0, 1, 2, 3, 4, 6, 7, 9]\n", "fx = [0.0, 0.84, 0.91, 0.14, -0.76, -0.28, 0.66, 0.41]\n", "```\n", "\n", "3. [2 Points] Compute the numerical derivative of the following data:\n", "```\n", "x2 = [0.0, 0.42, 0.83, 1.25, 1.67, 2.08, 2.5, 2.92, 3.33, 3.75, 4.17, 4.58, 5.0, 5.42, 5.83, 6.25, 6.67, 7.08, 7.5, 7.92, 8.33, 8.75, 9.17, 9.58, 10.0]\n", "fx2 = [0.0, 0.4, 0.74, 0.95, 1.0, 0.87, 0.6, 0.22, -0.19, -0.57, -0.85, -0.99, -0.96, -0.76, -0.43, -0.03, 0.37, 0.72, 0.94, 1.0, 0.89, 0.62, 0.26, -0.16, -0.54]\n", "```\n", "\n", "4. [6 Points] Plot your data from 6.2 and 6.3 against $\\cos(x)$, which is the correct derivative. Does the numerical derivative work even with the non-uniform coarse data in part 2?\n", "\n", "5. [6 Points] Integrate the data from 6.2 and compare against the true integral $\\int_0^{9} \\sin(x)\\,dx$. How accurate is it?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8862269254527599" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.integrate import quad\n", "quad(lambda x: np.exp(-x*2) x*2, -np.inf, np.inf)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.455 -0.35 -0.835 -0.33 0.59 0.4075]\n" ] } ], "source": [ "x = np.array([0, 1, 2, 3, 4, 6, 7, 9])\n", "fx = np.array([0.0, 0.84, 0.91, 0.14, -0.76, -0.28, 0.66, 0.41])\n", "diff = (fx[1:] - fx[:-1]) / (x[1:] - x[:-1])\n", "cdiff = (diff[1:] + diff[:-1]) / 2\n", "print(cdiff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.89082462 0.66463415 0.30952381 -0.09901278 -0.47996516 -0.77380952\n", " -0.95238095 -0.95238095 -0.78571429 -0.50406504 -0.13501742 0.27380952\n", " 0.64053426 0.8786295 0.95238095 0.90301974 0.68873403 0.33333333\n", " -0.06271777 -0.45557491 -0.75 -0.94076655 -0.96457607]\n" ] } ], "source": [ "x2 = np.array([0.0, 0.42, 0.83, 1.25, 1.67, 2.08, 2.5, 2.92, 3.33, 3.75, 4.17, 4.58, 5.0, 5.42, 5.83, 6.25, 6.67, 7.08, 7.5, 7.92, 8.33, 8.75, 9.17, 9.58, 10.0])\n", "fx2 = np.array([0.0, 0.4, 0.74, 0.95, 1.0, 0.87, 0.6, 0.22, -0.19, -0.57, -0.85, -0.99, -0.96, -0.76, -0.43, -0.03, 0.37, 0.72, 0.94, 1.0, 0.89, 0.62, 0.26, -0.16, -0.54])\n", "diff2 = (fx2[1:] - fx2[:-1]) / (x2[1:] - x2[:-1])\n", "cdiff2 = (diff2[1:] + diff2[:-1]) / 2\n", "print(cdiff2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.455 , -0.35 , -0.835 , -0.33 , 0.59 , 0.4075,\n", " 0.41 ])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "np.concatenate((fx[:1],cdiff,fx[-1:]))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(x[1:-1], cdiff, label='6.2 data')\n", "plt.plot(x2[1:-1], cdiff2, label='6.3 data')\n", "plt.plot(x2, np.cos(x2), label='True derivative')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numerical derivative is quite accurate, even though it only exists at 4 points. It may not be accurate though at the end points, which have no central difference derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.5" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7299999999999998 1.9111302618846777\n" ] } ], "source": [ "print(np.sum((fx[1:] + fx[:-1]) / 2 (x[1:] - x[:-1])), quad(np.sin, 0, 9)[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The answer is off by only 0.2, which is not bad since there are only 6 data points." ] } ], "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": "3.6.5rc1" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
pombredanne/gensim	docs/notebooks/Corpora_and_Vector_Spaces.ipynb	1	21548	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 1: Corpora and Vector Spaces\n", "See this gensim tutorial on the web [here](https://radimrehurek.com/gensim/tut1.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don’t forget to set:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "if you want to see logging events.\n", "\n", "## From Strings to Vectors\n", "\n", "This time, let’s start from documents represented as strings:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from gensim import corpora" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "documents = [\"Human machine interface for lab abc computer applications\",\n", " \"A survey of user opinion of computer system response time\",\n", " \"The EPS user interface management system\",\n", " \"System and human system engineering testing of EPS\", \n", " \"Relation of user perceived response time to error measurement\",\n", " \"The generation of random binary unordered trees\",\n", " \"The intersection graph of paths in trees\",\n", " \"Graph minors IV Widths of trees and well quasi ordering\",\n", " \"Graph minors A survey\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a tiny corpus of nine documents, each consisting of only a single sentence.\n", "\n", "First, let’s tokenize the documents, remove common words (using a toy stoplist) as well as words that only appear once in the corpus:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['human', 'interface', 'computer'],\n", " ['survey', 'user', 'computer', 'system', 'response', 'time'],\n", " ['eps', 'user', 'interface', 'system'],\n", " ['system', 'human', 'system', 'eps'],\n", " ['user', 'response', 'time'],\n", " ['trees'],\n", " ['graph', 'trees'],\n", " ['graph', 'minors', 'trees'],\n", " ['graph', 'minors', 'survey']]\n" ] } ], "source": [ "# remove common words and tokenize\n", "stoplist = set('for a of the and to in'.split())\n", "texts = [[word for word in document.lower().split() if word not in stoplist]\n", " for document in documents]\n", "\n", "# remove words that appear only once\n", "from collections import defaultdict\n", "frequency = defaultdict(int)\n", "for text in texts:\n", " for token in text:\n", " frequency[token] += 1\n", "\n", "texts = [[token for token in text if frequency[token] > 1] for text in texts]\n", "\n", "from pprint import pprint # pretty-printer\n", "pprint(texts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your way of processing the documents will likely vary; here, I only split on whitespace to tokenize, followed by lowercasing each word. In fact, I use this particular (simplistic and inefficient) setup to mimic the experiment done in [Deerwester et al.’s original LSA article](http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf) (Table 2).\n", "\n", "The ways to process documents are so varied and application- and language-dependent that I decided to not constrain them by any interface. Instead, a document is represented by the features extracted from it, not by its “surface” string form: how you get to the features is up to you. Below I describe one common, general-purpose approach (called bag-of-words), but keep in mind that different application domains call for different features, and, as always, it’s [garbage in, garbage out](https://en.wikipedia.org/wiki/Garbage_in,_garbage_out)...\n", "\n", "To convert documents to vectors, we’ll use a document representation called [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model). In this representation, each document is represented by one vector where each vector element represents a question-answer pair, in the style of:\n", "\n", "\"How many times does the word system appear in the document? Once\"\n", "\n", "It is advantageous to represent the questions only by their (integer) ids. The mapping between the questions and ids is called a dictionary:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dictionary(12 unique tokens: ['response', 'survey', 'computer', 'user', 'minors']...)\n" ] } ], "source": [ "dictionary = corpora.Dictionary(texts)\n", "dictionary.save('/tmp/deerwester.dict') # store the dictionary, for future reference\n", "print(dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we assigned a unique integer id to all words appearing in the corpus with the [gensim.corpora.dictionary.Dictionary](https://radimrehurek.com/gensim/corpora/dictionary.html#gensim.corpora.dictionary.Dictionary) class. This sweeps across the texts, collecting word counts and relevant statistics. In the end, we see there are twelve distinct words in the processed corpus, which means each document will be represented by twelve numbers (ie., by a 12-D vector). To see the mapping between words and their ids:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'response': 3, 'survey': 4, 'computer': 2, 'user': 5, 'minors': 11, 'time': 6, 'system': 7, 'graph': 10, 'interface': 1, 'human': 0, 'eps': 8, 'trees': 9}\n" ] } ], "source": [ "print(dictionary.token2id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To actually convert tokenized documents to vectors:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0, 1), (2, 1)]\n" ] } ], "source": [ "new_doc = \"Human computer interaction\"\n", "new_vec = dictionary.doc2bow(new_doc.lower().split())\n", "print(new_vec) # the word \"interaction\" does not appear in the dictionary and is ignored" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `doc2bow()` simply counts the number of occurrences of each distinct word, converts the word to its integer word id and returns the result as a sparse vector. The sparse vector `[(0, 1), (1, 1)]` therefore reads: in the document “Human computer interaction”, the words computer (id 0) and human (id 1) appear once; the other ten dictionary words appear (implicitly) zero times." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0, 1), (1, 1), (2, 1)]\n", "[(2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)]\n", "[(1, 1), (5, 1), (7, 1), (8, 1)]\n", "[(0, 1), (7, 2), (8, 1)]\n", "[(3, 1), (5, 1), (6, 1)]\n", "[(9, 1)]\n", "[(9, 1), (10, 1)]\n", "[(9, 1), (10, 1), (11, 1)]\n", "[(4, 1), (10, 1), (11, 1)]\n" ] } ], "source": [ "corpus = [dictionary.doc2bow(text) for text in texts]\n", "corpora.MmCorpus.serialize('/tmp/deerwester.mm', corpus) # store to disk, for later use\n", "for c in corpus:\n", " print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By now it should be clear that the vector feature with `id=10 stands` for the question “How many times does the word graph appear in the document?” and that the answer is “zero” for the first six documents and “one” for the remaining three. As a matter of fact, we have arrived at exactly the same corpus of vectors as in the [Quick Example](https://radimrehurek.com/gensim/tutorial.html#first-example).\n", "\n", "## Corpus Streaming – One Document at a Time\n", "\n", "Note that corpus above resides fully in memory, as a plain Python list. In this simple example, it doesn’t matter much, but just to make things clear, let’s assume there are millions of documents in the corpus. Storing all of them in RAM won’t do. Instead, let’s assume the documents are stored in a file on disk, one document per line. Gensim only requires that a corpus must be able to return one document vector at a time:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class MyCorpus(object):\n", " def __iter__(self):\n", " for line in open('mycorpus.txt'):\n", " # assume there's one document per line, tokens separated by whitespace\n", " yield dictionary.doc2bow(line.lower().split())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download the sample [mycorpus.txt](http://radimrehurek.com/gensim/mycorpus.txt) file (if you have `wget` installed you can simply run the following cell to do this). The assumption that each document occupies one line in a single file is not important; you can mold the `__iter__` function to fit your input format, whatever it is. Walking directories, parsing XML, accessing network... Just parse your input to retrieve a clean list of tokens in each document, then convert the tokens via a dictionary to their ids and yield the resulting sparse vector inside `__iter__`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2016-06-02 20:02:42-- http://radimrehurek.com/gensim/mycorpus.txt\n", "Resolving radimrehurek.com... 104.28.20.65, 104.28.21.65, 2400:cb00:2048:1::681c:1541, ...\n", "Connecting to radimrehurek.com\|104.28.20.65\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 437 [text/plain]\n", "Saving to: 'mycorpus.txt.3'\n", "\n", "mycorpus.txt.3 100%[=====================>] 437 --.-KB/s in 0s \n", "\n", "2016-06-02 20:03:03 (19.8 MB/s) - 'mycorpus.txt.3' saved [437/437]\n", "\n" ] } ], "source": [ "# download the file mycorpus.txt with wget \n", "# alternatively you can download the file manually and skip the following command\n", "!wget http://radimrehurek.com/gensim/mycorpus.txt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.MyCorpus object at 0x10de1bba8>\n" ] } ], "source": [ "corpus_memory_friendly = MyCorpus() # doesn't load the corpus into memory!\n", "print(corpus_memory_friendly)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corpus is now an object. We didn’t define any way to print it, so `print` just outputs address of the object in memory. Not very useful. To see the constituent vectors, let’s iterate over the corpus and print each document vector (one at a time):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0, 1), (1, 1), (2, 1)]\n", "[(2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)]\n", "[(1, 1), (5, 1), (7, 1), (8, 1)]\n", "[(0, 1), (7, 2), (8, 1)]\n", "[(3, 1), (5, 1), (6, 1)]\n", "[(9, 1)]\n", "[(9, 1), (10, 1)]\n", "[(9, 1), (10, 1), (11, 1)]\n", "[(4, 1), (10, 1), (11, 1)]\n" ] } ], "source": [ "for vector in corpus_memory_friendly: # load one vector into memory at a time\n", " print(vector)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the output is the same as for the plain Python list, the corpus is now much more memory friendly, because at most one vector resides in RAM at a time. Your corpus can now be as large as you want.\n", "\n", "Similarly, to construct the dictionary without loading all texts into memory:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dictionary(12 unique tokens: ['response', 'computer', 'survey', 'user', 'minors']...)\n" ] } ], "source": [ "from six import iteritems\n", "\n", "# collect statistics about all tokens\n", "dictionary = corpora.Dictionary(line.lower().split() for line in open('mycorpus.txt'))\n", "\n", "# remove stop words and words that appear only once\n", "stop_ids = [dictionary.token2id[stopword] for stopword in stoplist \n", " if stopword in dictionary.token2id]\n", "once_ids = [tokenid for tokenid, docfreq in iteritems(dictionary.dfs) if docfreq == 1]\n", "\n", "# remove stop words and words that appear only once\n", "dictionary.filter_tokens(stop_ids + once_ids)\n", "\n", "# remove gaps in id sequence after words that were removed\n", "dictionary.compactify()\n", "print(dictionary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And that is all there is to it! At least as far as bag-of-words representation is concerned. Of course, what we do with such corpus is another question; it is not at all clear how counting the frequency of distinct words could be useful. As it turns out, it isn’t, and we will need to apply a transformation on this simple representation first, before we can use it to compute any meaningful document vs. document similarities. Transformations are covered in the [next tutorial](https://radimrehurek.com/gensim/tut2.html), but before that, let’s briefly turn our attention to corpus persistency.\n", "\n", "## Corpus Formats\n", "\n", "There exist several file formats for serializing a Vector Space corpus (~sequence of vectors) to disk. Gensim implements them via the streaming corpus interface mentioned earlier: documents are read from (resp. stored to) disk in a lazy fashion, one document at a time, without the whole corpus being read into main memory at once.\n", "\n", "One of the more notable file formats is the [Matrix Market format](http://math.nist.gov/MatrixMarket/formats.html). To save a corpus in the Matrix Market format:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create a toy corpus of 2 documents, as a plain Python list\n", "corpus = [[(1, 0.5)], []] # make one document empty, for the heck of it\n", "\n", "corpora.MmCorpus.serialize('/tmp/corpus.mm', corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other formats include [Joachim’s SVMlight format](http://svmlight.joachims.org/), [Blei’s LDA-C format](http://www.cs.princeton.edu/~blei/lda-c/) and [GibbsLDA++ format](http://gibbslda.sourceforge.net/)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corpora.SvmLightCorpus.serialize('/tmp/corpus.svmlight', corpus)\n", "corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus)\n", "corpora.LowCorpus.serialize('/tmp/corpus.low', corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conversely, to load a corpus iterator from a Matrix Market file:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corpus = corpora.MmCorpus('/tmp/corpus.mm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corpus objects are streams, so typically you won’t be able to print them directly:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MmCorpus(2 documents, 2 features, 1 non-zero entries)\n" ] } ], "source": [ "print(corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead, to view the contents of a corpus:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[(1, 0.5)], []]\n" ] } ], "source": [ "# one way of printing a corpus: load it entirely into memory\n", "print(list(corpus)) # calling list() will convert any sequence to a plain Python list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 0.5)]\n", "[]\n" ] } ], "source": [ "# another way of doing it: print one document at a time, making use of the streaming interface\n", "for doc in corpus:\n", " print(doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second way is obviously more memory-friendly, but for testing and development purposes, nothing beats the simplicity of calling `list(corpus)`.\n", "\n", "To save the same Matrix Market document stream in Blei’s LDA-C format," ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corpora.BleiCorpus.serialize('/tmp/corpus.lda-c', corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this way, gensim can also be used as a memory-efficient I/O format conversion tool: just load a document stream using one format and immediately save it in another format. Adding new formats is dead easy, check out the [code for the SVMlight corpus](https://github.com/piskvorky/gensim/blob/develop/gensim/corpora/svmlightcorpus.py) for an example.\n", "\n", "## Compatibility with NumPy and SciPy\n", "\n", "Gensim also contains [efficient utility functions](http://radimrehurek.com/gensim/matutils.html) to help converting from/to `numpy` matrices:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import gensim\n", "import numpy as np\n", "numpy_matrix = np.random.randint(10, size=[5,2])\n", "corpus = gensim.matutils.Dense2Corpus(numpy_matrix)\n", "numpy_matrix_dense = gensim.matutils.corpus2dense(corpus, num_terms=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and from/to `scipy.sparse` matrices:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import scipy.sparse\n", "scipy_sparse_matrix = scipy.sparse.random(5,2)\n", "corpus = gensim.matutils.Sparse2Corpus(scipy_sparse_matrix)\n", "scipy_csc_matrix = gensim.matutils.corpus2csc(corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a complete reference (Want to prune the dictionary to a smaller size? Optimize converting between corpora and NumPy/SciPy arrays?), see the [API documentation](https://radimrehurek.com/gensim/apiref.html). Or continue to the next tutorial on Topics and Transformations ([notebook](https://github.com/piskvorky/gensim/tree/develop/docs/notebooks/Topics_and_Transformations.ipynb) \n", "or [website](https://radimrehurek.com/gensim/tut2.html))." ] } ], "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 }	lgpl-2.1
anhaidgroup/py_entitymatching	notebooks/guides/step_wise_em_guides/Reading CSV Files from Disk.ipynb	1	12555	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This IPython notebook illustrates how to read a CSV file from disk as a table and set its metadata." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to import py_entitymatching package and other libraries as follows:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import py_entitymatching as em\n", "import pandas as pd\n", "import os, sys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Different Ways to Read a CSV File and Set Metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to get the path of the CSV file in disk. For the convenience of the user, we have included some sample files in the package. The path of a sample CSV file can be obtained like this:\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get the datasets directory\n", "datasets_dir = em.get_install_path() + os.sep + 'datasets'\n", "\n", "# Get the path of the input table\n", "path_A = datasets_dir + os.sep + 'person_table_A.csv'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID,name,birth_year,hourly_wage,address,zipcode\r\n", "a1,Kevin Smith,1989,30,\"607 From St, San Francisco\",94107\r\n", "a2,Michael Franklin,1988,27.5,\"1652 Stockton St, San Francisco\",94122\r\n" ] } ], "source": [ "# Display the contents of the file in path_A\n", "!cat $path_A \| head -3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we get the CSV file path, we can use it read the contents and set metadata." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Different Ways to Read a CSV File and Set Metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are three different ways to read a CSV file and set metadata:\n", "\n", "1. Read a CSV file first, and then set the metadata\n", "2. Read a CSV file and set the metadata together\n", "3. Read a CSV file and set the metadata from a file in disk " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Read the CSV file First and Then Set the Metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, read the CSV files as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = em.read_csv_metadata(path_A)" ] }, { "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>ID</th>\n", " <th>name</th>\n", " <th>birth_year</th>\n", " <th>hourly_wage</th>\n", " <th>address</th>\n", " <th>zipcode</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>a1</td>\n", " <td>Kevin Smith</td>\n", " <td>1989</td>\n", " <td>30.0</td>\n", " <td>607 From St, San Francisco</td>\n", " <td>94107</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>a2</td>\n", " <td>Michael Franklin</td>\n", " <td>1988</td>\n", " <td>27.5</td>\n", " <td>1652 Stockton St, San Francisco</td>\n", " <td>94122</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>a3</td>\n", " <td>William Bridge</td>\n", " <td>1986</td>\n", " <td>32.0</td>\n", " <td>3131 Webster St, San Francisco</td>\n", " <td>94107</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>a4</td>\n", " <td>Binto George</td>\n", " <td>1987</td>\n", " <td>32.5</td>\n", " <td>423 Powell St, San Francisco</td>\n", " <td>94122</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>a5</td>\n", " <td>Alphonse Kemper</td>\n", " <td>1984</td>\n", " <td>35.0</td>\n", " <td>1702 Post Street, San Francisco</td>\n", " <td>94122</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID name birth_year hourly_wage \\\n", "0 a1 Kevin Smith 1989 30.0 \n", "1 a2 Michael Franklin 1988 27.5 \n", "2 a3 William Bridge 1986 32.0 \n", "3 a4 Binto George 1987 32.5 \n", "4 a5 Alphonse Kemper 1984 35.0 \n", "\n", " address zipcode \n", "0 607 From St, San Francisco 94107 \n", "1 1652 Stockton St, San Francisco 94122 \n", "2 3131 Webster St, San Francisco 94107 \n", "3 423 Powell St, San Francisco 94122 \n", "4 1702 Post Street, San Francisco 94122 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the 'type' of A \n", "type(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then set the metadata for the table. We see `ID` is the key attribute (since it contains unique values and no value is missing) for the table. We can set this metadata as follows:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "em.set_key(A, 'ID')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ID'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the metadata that were set for table A\n", "em.get_key(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the CSV file is read into the memory and the metadata (i.e. key) is set for the table. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read a CSV File and Set Metadata Together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above, we saw that we first read in the CSV file and then set the metadata. These two steps can be combined into a single step like this:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = em.read_csv_metadata(path_A, key='ID')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the 'type' of A\n", "type(A)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ID'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the metadata that were set for the table A \n", "em.get_key(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read a CSV File and Set Metadata from a File in Disk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The user can specify the metadata in a file.\n", "\n", "This file MUST be in the same directory as the CSV file and the file name \n", "should be same, except the extension is set to '.metadata'." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Specify the metadata for table A (stored in person_table_A.csv).\n", "\n", "# Get the file name (with full path) where the metadata file must be stored\n", "metadata_fname = 'person_table_A.metadata'\n", "metadata_file = datasets_dir + os.sep + metadata_fname\n", "\n", "# Specify the metadata for table A . Here we specify that 'ID' is the key attribute for the table. \n", "\n", "# Note that this step requires write permission to the datasets directory.\n", "with open(metadata_file, 'w') as the_file:\n", " the_file.write('#key=ID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: In the above, we used Unix shell command `echo` to write the metadata contents. If you are on Windows, you can use `echo\|set /p` instead of `echo` to acheive the same effect." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If you donot have write permissions to the datasets directory, first copy the file to the local directory and \n", "# then create a metadata file like this (you need to uncomment the following lines and then execute):\n", "\n", "# import shutil\n", "# shutil.copy2('path_A', './person_table_A.metadata')\n", "# metadata_local_file = 'person_table_A.metadata'\n", "# with open(metadata_local_file, 'w') as the_file:\n", "# the_file.write('#key=ID'))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read the CSV file for table A\n", "A = em.read_csv_metadata(path_A)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'ID'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the key for table A\n", "em.get_key(A)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Remove the metadata file\n", "os.remove(metadata_file) if os.path.exists(metadata_file) else None\n", "os.remove('person_table_A.csv') if os.path.exists('person_table_A.csv') else None\n", "os.remove(metadata_fname) if os.path.exists(metadata_fname) else None" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
iutzeler/Introduction-to-Python-for-Data-Sciences	4-1_Scikit_Learn.ipynb	1	56043	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<table>\n", "<tr>\n", "<td width=15%><img src=\"./img/UGA.png\"></img></td>\n", "<td><center><h1>Introduction to Python for Data Sciences</h1></center></td>\n", "<td width=15%><a href=\"http://www.iutzeler.org\" style=\"font-size: 16px; font-weight: bold\">Franck Iutzeler</a> </td>\n", "</tr>\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<br/><br/>\n", "\n", "<center><a style=\"font-size: 40pt; font-weight: bold\">Chap. 4 - Scikit Learn </a></center> \n", "\n", "<br/><br/>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1- Scikit Learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Now that we explored data structures provided by the Pandas library, we will investigate how to learn over it using Scikit-learn.\n", "\n", "Scikit-learn is ont of the most celebrated and used machine learning library. It features a complete set of efficiently implemented machine learning algorithms for classification, regression, and clustering. Scikit-learn is designed to operate over Numpy, Scipy, and Pandas data structures. \n", "\n", "Links: [Scikit-learn webpage](http://scikit-learn.org) [Wikipedia article](https://en.wikipedia.org/wiki/Scikit-learn)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Machine Learning problems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Machine learning is the task of predicting properties out of some data. The dataset consists in several examples or samples and the associated target properties can be available, partially available, or not at all; we respectively call these setting supervised, semi-supervised, unsupervised. The examples are made out of one or several features or attributes that can be of different types (real number, discretes values, strings, booleans, etc.). \n", "\n", "Learning problems can be broadly divided in a few categories:\n", "* supervised learning \n", " * classification: Place incoming data into a finite number or classes by learning over labeled data. Example: Classifying iris into species based on recorded petal and sentil sizes from the 3 species. \n", " * regression: Predict a value from example data. To the difference of classification, the output value is continuous. Example: Predict the carbon monoxide concentration for next years based on previous measures.\n", "* unsupervised learning\n", " * clustering: Place the data (both new and the dataset) into a finite number of classes. To the difference with classification, no labeled data is provided. Example: Create market segments from customer information for targeted advertising.\n", " * dimension reduction: Discard uniformative features for the purpose of visualization or efficient storage. Example: Creation of eigenfaces in visage recognition. \n", " \n", "\n", "The following flowchart can be found on the [Scikit Learn website](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html):\n", "\n", "![Scikit Learn Algorithm cheatsheet](img/ml_map.png \"Scikit Learn Algorithm cheatsheet\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Learning with Scikit Learn\n", "\n", "The process of learning and predicting with Scikit Learn follows three main steps:<br/>\n", "1. Selecting and adjusting a model<br/>\n", "2. Fitting the model to the data<br/>\n", "3. Predicting from this fitted model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will illustrate this process on a simple linear model\n", "$$ y = a x + b + \\nu$$\n", "where \n", "* $(x,y)\\in\\mathbb{R}^m\\times\\mathbb{R}^m$ are the data points. $x$ contains the examples and $y$ the associated outputs \n", "* $a,b$ are the model coefficients to estimate\n", "* $\\nu$ is a standard centered white Gaussian noise" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f0c4fd942b0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = np.random.randn()5 # Drawing randomly the slope\n", "b = np.random.rand()10 # Drawing randomly the initial point\n", "\n", "m = 50 # number of points\n", "\n", "x = np.random.rand(m,1)10 # Drawing randomly abscisses\n", "y = ax + b + np.random.randn(m,1) # y = ax+b + noise\n", "\n", "plt.scatter(x, y)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 1. Selecting and adjusting a model\n", "\n", "As we want to fit a linear model $y=ax+b$ through the data, we will import the `Linear Regression` module from scikit learn with `sklearn.linear_model import LinearRegression`.\n", "\n", "As our model has a non null coefficient at the origin, the model needs an intercept. This can be tuned, along with several other parameters, see Scikit Learn's [linear_model documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LinearRegression()\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model = LinearRegression(fit_intercept=True)\n", "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This terminates our model tuning. Notice that we have described our model, but no learning or fitting has been done." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Fitting the model to the data\n", "\n", "\n", "Applying our model to the data $(x,y)$ is done using the `fit` method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the model is fitted, one can observe the learned coefficients:\n", "* `coef_` for the model coefficients ($a$ here)\n", "* `intercept_` foe the intercept ($b$ here)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learned coefficients: a = 3.637057 \t b = 2.559807\n", "True coefficients: a = 3.674435 \t b = 2.385000\n" ] } ], "source": [ "print(\"Learned coefficients: a = {:.6f} \\t b = {:.6f}\".format(float(model.coef_),float(model.intercept_)))\n", "print(\"True coefficients: a = {:.6f} \\t b = {:.6f}\".format(a,b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Predicting from this fitted model\n", "\n", "From a feature matrix, the method `predict` returns the predicted output from the fitted model. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "xFit = np.linspace(-2,12,21).reshape(-1, 1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "yFit = model.predict(xFit)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f0c276249d0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(x, y , label=\"data\")\n", "plt.plot(xFit, yFit , label=\"model\",color=\"r\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocessing Data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data format\n", "\n", "Scikit Learn can take as an input (i.e. passed to `fit` and `predict`) several format including:\n", "* Numpy arrays. Warning: the data has to be 2D even if there is only one example or one feature.\n", "* Pandas dataframes.\n", "* SciPy sparse matrices.\n", "\n", "The examples/samples of the datasets are stored as rows.<br/>\n", "The features are the columns.\n", "\n", "### Training/Testing sets\n", "\n", "In order to cross-validate our model, it is customary to split the dataset into training and testing subsets. It can be done manually but there is also a dedicated method." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "\n", "xTrain, xTest, yTrain, yTest = train_test_split(x,y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(37, 1) (37, 1)\n", "(13, 1) (13, 1)\n" ] } ], "source": [ "print(xTrain.shape,yTrain.shape)\n", "print(xTest.shape,yTest.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us use cross validation to compare linear model and linear model with intercept." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing Error with intercept: 3.3147332805841767 \t without intercept: 7.139630719389703\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model1 = LinearRegression(fit_intercept=True)\n", "model2 = LinearRegression(fit_intercept=False)\n", "\n", "model1.fit(xTrain,yTrain)\n", "yPre1 = model1.predict(xTest)\n", "error1 = np.linalg.norm(yTest-yPre1)\n", "\n", "model2.fit(xTrain,yTrain)\n", "yPre2 = model2.predict(xTest)\n", "error2 = np.linalg.norm(yTest-yPre2)\n", "\n", "print(\"Testing Error with intercept:\", error1, \"\\t without intercept:\" ,error2)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f0c2757b940>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(xTrain, yTrain , label=\"Train data\")\n", "plt.scatter(xTest, yTest , color= 'k' , label=\"Test data\")\n", "plt.plot(xTest, yPre1 , color='r', label=\"model w/ intercept (err = {:.1f})\".format(error1))\n", "plt.plot(xTest, yPre2 , color='m', label=\"model w/o intercept (err = {:.1f})\".format(error2))\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance metrics\n", "\n", "In order to quantitatively evaluate the models, Scikit Learn provide a wide range of [metrics](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics), we will see some of them in the following examples." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, 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jplourenco/bokeh	examples/plotting/notebook/random_walk.ipynb	43	4206	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import collections\n", "import datetime\n", "import time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bokeh.plotting import cursession, figure, show, output_notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run these examples you must execute the command `python bokeh-server` in the top-level Bokeh source directory first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "output_notebook(url=\"default\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "TS_MULT_us = 1e6\n", "UNIX_EPOCH = datetime.datetime(1970, 1, 1, 0, 0) #offset-naive datetime\n", "\n", "def int2dt(ts, ts_mult=TS_MULT_us):\n", " \"\"\"Convert timestamp (integer) to datetime\"\"\"\n", " return(datetime.datetime.utcfromtimestamp(float(ts)/ts_mult))\n", " \n", "def td2int(td, ts_mult=TS_MULT_us):\n", " \"\"\"Convert timedelta to integer\"\"\"\n", " return(int(td.total_seconds()ts_mult))\n", " \n", "def dt2int(dt, ts_mult=TS_MULT_us):\n", " \"\"\"Convert datetime to integer\"\"\"\n", " delta = dt - UNIX_EPOCH\n", " return(int(delta.total_seconds()ts_mult))\n", " \n", "def int_from_last_sample(dt, td):\n", " return(dt2int(dt) - dt2int(dt) % td2int(td))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "TS_MULT = 1e3\n", "td_delay = datetime.timedelta(seconds=0.5)\n", "delay_s = td_delay.total_seconds()\n", "delay_int = td2int(td_delay, TS_MULT)\n", "\n", "value = 1000 # initial value\n", "N = 100 # number of elements into circular buffer\n", "\n", "buff = collections.deque([value]N, maxlen=N)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "t_now = datetime.datetime.utcnow()\n", "ts_now = dt2int(t_now, TS_MULT)\n", "t = collections.deque(np.arange(ts_now-Ndelay_int, ts_now, delay_int), maxlen=N)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = figure(x_axis_type=\"datetime\")\n", "p.line(list(t), list(buff), color=\"#0000FF\", name=\"line_example\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "renderer = p.select(dict(name=\"line_example\"))[0]\n", "ds = renderer.data_source\n", "show(p)\n", "while True:\n", " ts_now = dt2int(datetime.datetime.utcnow(), 1e3)\n", " t.append(ts_now)\n", " ds.data['x'] = list(t)\n", "\n", " value += np.random.uniform(-1, 1)\n", " buff.append(value)\n", " ds.data['y'] = list(buff)\n", " \n", " cursession().store_objects(ds)\n", " time.sleep(delay_s)" ] }, { "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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
jgomezc1/medios	NOTEBOOKS/.ipynb_checkpoints/Ej2_Estatica-checkpoint.ipynb	1	11643	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejemplo 2. Estática" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image,Latex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por medio de un par de cables se quiere sostener un bloque de peso $W = 200\\; kgf$. Determine la tensión en cada cuerda si las coordenadas de posición de los puntos A, B, C son A(0, -20 cm, 40 cm), B(-40 cm, 50 cm, 0cm) y C(45 cm, 40 cm, 0cm), respectivamente. Suponga que no hay fricción entre la rampa y el contrapeso." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: u'FIGURES/Rampa.png'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-d21f543cb252>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'FIGURES/Rampa.png'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m250\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/adrianamejia/anaconda/lib/python2.7/site-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m 749\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 750\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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"\n", "$\\vec{T_B} = T_B \\; \\hat{n_B} = T_B \\;\\frac{\\vec{R_{AB}}}{MR_{AB}} $" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import array, sqrt, cross, dot, arctan, cos, sin\n", "from scipy.linalg import inv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R_AB = [-40., 70., -40.]\n", "MR_AB = sqrt(dot (R_AB,R_AB))\n", "nB = R_AB / MR_AB\n", "print 'Magnitud =', MR_AB, ',', 'nB =', nB.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y para el cable AC: \n", "\n", "$\\vec{T_C} = T_C \\; \\hat{n_C} = T_C \\;\\frac{\\vec{R_{AC}}}{MR_{AC}} $\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R_AC = [45., 60., -40.]\n", "MR_AC = sqrt(dot (R_AC,R_AC))\n", "nC = R_AC / MR_AC\n", "print 'Magnitud =', MR_AC, ',', 'nC =', nC.round(2) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por analogía, la fuerza normal prodría N podría ser escrita como: \n", "\n", "$\\vec{N} = N \\; \\hat{n_p} $, \n", "\n", "donde las componentes de $\\hat{n_p} $ son determinadas por la inclinación de la rampa. De esta forma. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Np = [0.0, cos(arctan(40./80.)),sin(arctan(40./80.))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo que queda ahora es resolver el sistema de ecuaciones de equilibrio. Para ello lo escribimos de la forma:\n", "\n", "$$AX = B$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "A = array([[nB[0], nC[0], Np[0]], \n", " [nB[1],nC[1], Np[1]],\n", " [nB[2], nC[2], Np[2]]])\n", "\n", "B = [0.0, 200., 0]\n", "print 'A=', A.round(2)\n", "print \n", "print 'B=', B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resolviendo el sistema" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = dot(inv(A),B)\n", "print '[TB, TC, N]=', X.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nota. Problema tomado de Mecánica Vectorial para Ingenieros. Estatica. Sexta Edición. Beer Ferninand; Johnston, Russell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open('./custom_barba.css', 'r').read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "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 }	mit
irfanalidv/Dataiku_US_Census_Analysis	.ipynb_checkpoints/US Census Data Analysis-checkpoint.ipynb	1	21718	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn import model_selection\n", "from sklearn import linear_model\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#define the data path\n", "Learn_Data_Path = './US_Census_Data/census_income_learn.csv'\n", "Test_Data_Path = './US_Census_Data/census_income_test.csv'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Train_Data_Set = pd.read_csv(Learn_Data_Path,sep=',', header=None, index_col=False)\n", "Test_Data_Set = pd.read_csv(Test_Data_Path,sep=',', header=None, index_col=False)" ] }, { "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>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>32</th>\n", " <th>33</th>\n", " <th>34</th>\n", " <th>35</th>\n", " <th>36</th>\n", " <th>37</th>\n", " <th>38</th>\n", " <th>39</th>\n", " <th>40</th>\n", " <th>41</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>73</td>\n", " <td>Not in universe</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>High school graduate</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Widowed</td>\n", " <td>Not in universe or children</td>\n", " <td>Not in universe</td>\n", " <td>...</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>Native- Born in the United States</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>95</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>58</td>\n", " <td>Self-employed-not incorporated</td>\n", " <td>4</td>\n", " <td>34</td>\n", " <td>Some college but no degree</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Divorced</td>\n", " <td>Construction</td>\n", " <td>Precision production craft & repair</td>\n", " <td>...</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>Native- Born in the United States</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>52</td>\n", " <td>94</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 42 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 \\\n", "0 73 Not in universe 0 0 High school graduate \n", "1 58 Self-employed-not incorporated 4 34 Some college but no degree \n", "\n", " 5 6 7 8 \\\n", "0 0 Not in universe Widowed Not in universe or children \n", "1 0 Not in universe Divorced Construction \n", "\n", " 9 ... 32 \\\n", "0 Not in universe ... United-States \n", "1 Precision production craft & repair ... United-States \n", "\n", " 33 34 35 36 \\\n", "0 United-States United-States Native- Born in the United States 0 \n", "1 United-States United-States Native- Born in the United States 0 \n", "\n", " 37 38 39 40 41 \n", "0 Not in universe 2 0 95 - 50000. \n", "1 Not in universe 2 52 94 - 50000. \n", "\n", "[2 rows x 42 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Train_Data_Set.head(2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></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>32</th>\n", " <th>33</th>\n", " <th>34</th>\n", " <th>35</th>\n", " <th>36</th>\n", " <th>37</th>\n", " <th>38</th>\n", " <th>39</th>\n", " <th>40</th>\n", " <th>41</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>38</td>\n", " <td>Private</td>\n", " <td>6</td>\n", " <td>36</td>\n", " <td>1st 2nd 3rd or 4th grade</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Married-civilian spouse present</td>\n", " <td>Manufacturing-durable goods</td>\n", " <td>Machine operators assmblrs & inspctrs</td>\n", " <td>...</td>\n", " <td>Mexico</td>\n", " <td>Mexico</td>\n", " <td>Mexico</td>\n", " <td>Foreign born- Not a citizen of U S</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>12</td>\n", " <td>95</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>44</td>\n", " <td>Self-employed-not incorporated</td>\n", " <td>37</td>\n", " <td>12</td>\n", " <td>Associates degree-occup /vocational</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Married-civilian spouse present</td>\n", " <td>Business and repair services</td>\n", " <td>Professional specialty</td>\n", " <td>...</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>Native- Born in the United States</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>26</td>\n", " <td>95</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 42 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 \\\n", "0 38 Private 6 36 \n", "1 44 Self-employed-not incorporated 37 12 \n", "\n", " 4 5 6 \\\n", "0 1st 2nd 3rd or 4th grade 0 Not in universe \n", "1 Associates degree-occup /vocational 0 Not in universe \n", "\n", " 7 8 \\\n", "0 Married-civilian spouse present Manufacturing-durable goods \n", "1 Married-civilian spouse present Business and repair services \n", "\n", " 9 ... 32 \\\n", "0 Machine operators assmblrs & inspctrs ... Mexico \n", "1 Professional specialty ... United-States \n", "\n", " 33 34 35 36 \\\n", "0 Mexico Mexico Foreign born- Not a citizen of U S 0 \n", "1 United-States United-States Native- Born in the United States 0 \n", "\n", " 37 38 39 40 41 \n", "0 Not in universe 2 12 95 - 50000. \n", "1 Not in universe 2 26 95 - 50000. \n", "\n", "[2 rows x 42 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Test_Data_Set.head(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Naming the columns :\n", "columns = ['AAGE', 'ACLSWKR', 'ADTIND', 'ADTOCC', 'AHGA', 'AHRSPAY', \n", " 'AHSCOL', 'AMARITL', 'AMJIND', 'AMJOCC', 'ARACE', 'AREORGN', \n", " 'ASEX', 'AUNMEM', 'AUNTYPE', 'AWKSTAT', 'CAPGAIN', 'CAPLOSS', \n", " 'DIVVAL', 'FILESTAT', 'GRINREG', 'GRINST', 'HHDFMX', 'HHDREL', \n", " 'MARSUPWT', 'MIGMTR1', 'MIGMTR3', 'MIGMTR4', 'MIGSAME', 'MIGSUN', \n", " 'NOEMP', 'PARENT', 'PEFNTVTY', 'PEMNTVTY', 'PENATVTY', 'PRCITSHP',\n", " 'SEOTR', 'VETQVA', 'VETYN', 'WKSWORK', 'YEAR', 'WAGE']\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Train_Data_Set.columns=columns\n", "\n", "Test_Data_Set.columns=columns" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AAGE</th>\n", " <th>ACLSWKR</th>\n", " <th>ADTIND</th>\n", " <th>ADTOCC</th>\n", " <th>AHGA</th>\n", " <th>AHRSPAY</th>\n", " <th>AHSCOL</th>\n", " <th>AMARITL</th>\n", " <th>AMJIND</th>\n", " <th>AMJOCC</th>\n", " <th>...</th>\n", " <th>PEFNTVTY</th>\n", " <th>PEMNTVTY</th>\n", " <th>PENATVTY</th>\n", " <th>PRCITSHP</th>\n", " <th>SEOTR</th>\n", " <th>VETQVA</th>\n", " <th>VETYN</th>\n", " <th>WKSWORK</th>\n", " <th>YEAR</th>\n", " <th>WAGE</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>99760</th>\n", " <td>30</td>\n", " <td>Private</td>\n", " <td>45</td>\n", " <td>2</td>\n", " <td>Bachelors degree(BA AB BS)</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Married-civilian spouse present</td>\n", " <td>Other professional services</td>\n", " <td>Executive admin and managerial</td>\n", " <td>...</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>Native- Born in the United States</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>52</td>\n", " <td>95</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " <tr>\n", " <th>99761</th>\n", " <td>67</td>\n", " <td>Not in universe</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9th grade</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>Married-civilian spouse present</td>\n", " <td>Not in universe or children</td>\n", " <td>Not in universe</td>\n", " <td>...</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>United-States</td>\n", " <td>Native- Born in the United States</td>\n", " <td>0</td>\n", " <td>Not in universe</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>94</td>\n", " <td>- 50000.</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 42 columns</p>\n", "</div>" ], "text/plain": [ " AAGE ACLSWKR ADTIND ADTOCC AHGA \\\n", "99760 30 Private 45 2 Bachelors degree(BA AB BS) \n", "99761 67 Not in universe 0 0 9th grade \n", "\n", " AHRSPAY AHSCOL AMARITL \\\n", "99760 0 Not in universe Married-civilian spouse present \n", "99761 0 Not in universe Married-civilian spouse present \n", "\n", " AMJIND AMJOCC \\\n", "99760 Other professional services Executive admin and managerial \n", "99761 Not in universe or children Not in universe \n", "\n", " ... PEFNTVTY PEMNTVTY PENATVTY \\\n", "99760 ... United-States United-States United-States \n", "99761 ... United-States United-States United-States \n", "\n", " PRCITSHP SEOTR VETQVA VETYN \\\n", "99760 Native- Born in the United States 0 Not in universe 2 \n", "99761 Native- Born in the United States 0 Not in universe 2 \n", "\n", " WKSWORK YEAR WAGE \n", "99760 52 95 - 50000. \n", "99761 0 94 - 50000. \n", "\n", "[2 rows x 42 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Test_Data_Set.tail(2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#remove the unwanted information for classification\n", "\n", "Train_Data_Set.drop(['MARSUPWT', 'MIGMTR1','GRINREG', 'GRINST','AUNMEM','HHDFMX', 'HHDREL','FILESTAT',\n", " 'PEFNTVTY', 'PEMNTVTY', 'PENATVTY','AREORGN','ADTIND', 'ADTOCC','SEOTR','YEAR',\n", " 'MIGMTR3', 'MIGMTR4', 'MIGSAME', 'MIGSUN','VETQVA', 'VETYN','AUNTYPE'], axis=1, inplace=True)\n", "Test_Data_Set.drop(['MARSUPWT', 'MIGMTR1', 'MIGMTR3', 'GRINREG', 'GRINST','AUNMEM','HHDFMX', 'HHDREL','FILESTAT',\n", " 'PEFNTVTY', 'PEMNTVTY', 'PENATVTY','AREORGN','ADTIND', 'ADTOCC','SEOTR','YEAR',\n", " 'MIGMTR4', 'MIGSAME', 'MIGSUN','VETQVA', 'VETYN','AUNTYPE'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['AAGE',\n", " 'ACLSWKR',\n", " 'AHGA',\n", " 'AHRSPAY',\n", " 'AHSCOL',\n", " 'AMARITL',\n", " 'AMJIND',\n", " 'AMJOCC',\n", " 'ARACE',\n", " 'ASEX',\n", " 'AWKSTAT',\n", " 'CAPGAIN',\n", " 'CAPLOSS',\n", " 'DIVVAL',\n", " 'NOEMP',\n", " 'PARENT',\n", " 'PRCITSHP',\n", " 'WKSWORK',\n", " 'WAGE']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(Test_Data_Set)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Train_Data_Set.dtypes\n", "Train_Data_Set.to_csv('./US_Census_Data/Train_Data_Set.csv')\n", "Test_Data_Set.to_csv('./US_Census_Data/Test_Data_Set.csv')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def annual_wage(df):\n", " wage = []\n", " for w in df['WAGE']:\n", " if w == ' - 50000.':\n", " wage.append(0)\n", " else:\n", " wage.append(1)\n", " return wage\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Test_Data_Set.insert(0, 'WAGE_ANN', int)\n", "Train_Data_Set.insert(0, 'WAGE_ANN', int)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Train_Data_Set['WAGE_ANN'] = annual_wage(Train_Data_Set)\n", "Test_Data_Set['WAGE_ANN'] = annual_wage(Test_Data_Set)\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "Train_Data_Set.drop(['WAGE'], axis=1, inplace=True)\n", "Test_Data_Set.drop(['WAGE'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def dummy_variables(df):\n", " df_type = df.dtypes\n", " for col in df_type.keys():\n", " if df_type[col] == 'object':\n", " df = pd.concat([df, pd.get_dummies(df[col]).rename(columns=lambda x: col + '_' + str(x))], axis=1)\n", " df.drop([col], axis=1, inplace=True)\n", " return df\n", "\n", "trainset_ = dummy_variables(Train_Data_Set)\n", "testset_ = dummy_variables(Test_Data_Set)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = trainset_.iloc[:,1:]\n", "Y = trainset_['WAGE_ANN']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train, X_val, Y_train, Y_val = model_selection.train_test_split(X, Y, \\\n", " test_size=0.20, random_state=100)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LogisticRegressionCV(Cs=10, class_weight=None, cv=None, dual=False,\n", " fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logregCV = linear_model.LogisticRegressionCV()\n", "logregCV.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Y_pred = logregCV.predict(X_val)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score : 0.951\n" ] } ], "source": [ "print ('Accuracy score : %.3f' %(logregCV.score(X_val, Y_val)))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mjbrodzik/ipython_notebooks	charis/Calibration_basin_inputs.ipynb	1	5974200	null	apache-2.0
eyaltrabelsi/my-notebooks	Lectures/beyond_unit_testing_for_data Intensive_apps/Beyond Unit Testing for Data Intensive Apps.ipynb	1	13154	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Beyond Unit Testing for Data Intensive Apps\n", "## Eyal Trabelsi" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "- thanks for watching, I am Eyal I live in Tel Aviv and work for Salesforce.\n", "- and today I am going to talk to you about how to properly test data intensive apps." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What's Data Intensive App 📊" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Amount of data\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Complexity of data\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Data is changing frequently" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## tests a definition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "ac contract between your current self and your future self. what you exect to be right now should hold true in the future. what you expect to be wrong now should still be wrong in the future. unless the requirements have changed" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Why Testing 🤨" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Find bugs" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Validate assumptions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Simplier code" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Data is messy" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Testing Strategies 🎮\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Unit tests \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Property testing\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Component tests\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Integration Testing\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Regression tests\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Data tests" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "https://dev.to/nfrankel/different-kinds-of-testing-j9f" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Base" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- [Preventing, Finding, and Fixing Bugs On a Time Budget](https://www.youtube.com/watch?v=ARKbfWk4Xyw)\n", "- [Example](https://github.com/ericmjl/data-testing-tutorial)\n", "- [Test smarter, not harder](https://lukeplant.me.uk/blog/posts/test-smarter-not-harder/)\n", "- [12 TRAITS OF HIGHLY EFFECTIVE TESTS](https://automationpanda.com/2020/07/09/12-traits-of-highly-effective-tests/)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Doctests" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- https://www.hillelwayne.com/post/python-doctests/\n", "- https://blog.startifact.com/posts/older/i-like-doctests.html\n", "- https://stackoverflow.com/questions/361675/python-doctest-vs-unittest" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Unit tests" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"It's a system for testing your thoughts against the universe, and seeing whether they match\" Isaac Asimov.\n", "Most of the code in a data pipeline consists of a data cleaning process. Each of the functions used to do data cleaning has a clear goal. Let's say, for example, that one of the features that we have chosen for out model is the change of a value between the previous and current day.\n", "For each piece of independent functionality, you would write a unit test, making sure that each part of the data transformation process has the expected effect on the data. For each piece of functionality you should also consider different scenarios (is there an if statement? then all conditionals should be tested). These would then be ran as part of your continuous integration (CI) pipeline on every commit.\n", "In addition to checking that the code does what is intended, unit tests also give us a hand when debugging a problem. By adding a test that reproduces a newly discovered bug, we can ensure that the bug is fixed when we think that is fixed, and we can ensure that the bug does not happen again.\n", "Lastly, these tests not only check that the code does what is intended, but also help us document the expectations that we had when creating the functionality." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- https://towardsdatascience.com/pytest-features-that-you-need-in-your-testing-life-31488dc7d9eb\n", "- https://www.youtube.com/watch?v=fv259R38gqc\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Property tests" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\n", "- https://github.com/HypothesisWorks/hypothesis\n", "- https://engineering.hexacta.com/testing-pandas-b65c0ea8a28es\n", "- https://www.hillelwayne.com/post/contract-examples/\n", "- https://hypothesis.readthedocs.io/en/latest/stateful.html\n", "- https://hypothesis.readthedocs.io/en/latest/examples.html\n", "- https://www.hillelwayne.com/post/property-testing-complex-inputs/\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Regression tests\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- nbval pytests\n", "- tdda\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Mock" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- https://github.com/obspy/vcr\n", "- https://blog.daftcode.pl/the-cleaning-hand-of-pytest-28f434f4b684\n", "- https://medium.com/@light_khan/mock-testing-in-python-using-vcrpy-ff3eb05ae5ec\n", "- https://github.com/spulec/freezegun\n", "- https://github.com/lk-geimfari/mimesis\n", "- https://github.com/joke2k/faker\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Contract tests\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- https://github.com/deadpixi/contract\n", "- https://www.hillelwayne.com/post/pbt-contracts/\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Integration tests\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because \"The unclouded eye was better, no matter what it saw.\" Frank Herbert.\n", "These tests aim to determine whether modules that have been developed separately work as expected when brought together. In terms of a data pipeline, these can check that:\n", "The data cleaning process results in a dataset appropriate for the model\n", "The model training can handle the data provided to it and outputs results (ensurign that code can be refactored in the future)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Data tests" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\n", "- https://github.com/great-expectations/great_expectations\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ml testing\n", "functional \n", "does my model output prediction in the expected range\n", "does my preprocessing parse and engineer the correct features\n", "non-functional\n", "can my model handle 1000 request per minutes\n", "can it respond to a request within 100ms\n", "model performance\n", "how does it perform on different tests datasets\n", "does it perform worse than a previous iteration\n", "\n", "\n", "ML Validation\n", "Why? \"To exhibit the perfect uselessness of knowing the answer to the wrong question.\" Ursula K. Le Guin.\n", "Now that we have tested our code, we need to also test that the ML component is solving the problem that we are trying to solve. When we talk about product development, the raw results of an ML model (however accurate based on statistical methods) are almost never the desired end outputs. These results are usually combined with other business rules before consumed by a user or another application. For this reason, we need to validate that the model solves the user problem, and not only that the accuracy/f1-score/other statistical measure is high enough.\n", "How does this help us?\n", "It ensures that the model actually helps the product solve the problem at hand\n", "It ensures that the values produced by the model make sense in terms of the industry\n", "It provides an extra layer of documentation of the decisions made, helping engineers joining the team later in the process.\n", "It provides visibility of the ML components of the product in a common language understood by clients, product managers and engineers in the same way.\n", "\n", "https://www.jeremyjordan.me/testing-ml/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Others" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "- https://www.youtube.com/watch?v=GEqM9uJi64Q\n", "- https://medium.com/@copyconstruct/testing-in-production-the-hard-parts-3f06cefaf592\n", "- https://github.com/locustio/locust\n", "- https://www.simform.com/functional-testing-types/#sanity\n", "- https://tyrrrz.me/blog/unit-testing-is-overrated" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- immutable data\n", "- canonical formats\n", "- well defined interface\n", "- separation io and computation logic\n", "- explicit arguments for all dependencies\n", "- deterministic behavior\n", "- lots of assertions" ] }, { "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
yausern/stlab	TimeDomain_v2/testing/Testing FSV Acquisition.ipynb	1	136934	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "\n", "import sys\n", "\n", "\n", "from stlab.devices.RS_SGS100A import RS_SGS100A\n", "from stlab.AWG_testing.AWG520_driver_beta import Tektronix_AWG520\n", "# from stlab.devices.rigol_DS1054 import Rigol_DS1054\n", "from stlab.devices.RS_FSV import RS_FSV\n", "\n", "from stlab.AWG_testing import AWG_station\n", "\n", "from stlab.AWG_testing.sequence import Sequence\n", "from stlab.AWG_testing.element import Element\n", "from stlab.AWG_testing import Pulse_lib as pulse\n", "\n", "# import viewer\n", "import imp\n", "\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing directory for AWG file transfering......\n" ] } ], "source": [ "devAWG = Tektronix_AWG520(name='AWG')\n", "AWG = AWG_station.AWG_Station()\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing directory for AWG file transfering......\n", "Instrument State: Idle\n", "Mode: CONT\n", "Trigger impedance (Ohm): 5.0E+01\n", "Trigger level (V): 1.4\n", "Number of points: 1000\n", "Sample rate (Hz): 1.0000000E+09\n", "Reference Oscillator: EXT\n", "Amplitude Channel1 (V): 1.0\n", "Offset Channel1 (V): 0.0\n", "Channel1 Marker1_low (V) 0.0\n", "Channel1 Marker1_high (V) 0.0\n", "Channel1 Marker2_low (V) 0.0\n", "Channel1 Marker2_high (V) 0.0\n", "Channel1 state: off\n", "Amplitude Channel2 (V): 0.5\n", "Offset Channel2 (V): 0.0\n", "Channel2 Marker1_low (V) 0.0\n", "Channel2 Marker1_high (V) 0.0\n", "Channel2 Marker2_low (V) 0.0\n", "Channel2 Marker2_high (V) 0.0\n", "Channel2 state: off\n" ] }, { "ename": "NameError", "evalue": "name 'AWG' 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-29-f45a3599beaa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\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[0mAWG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAWG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdevAWG\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'AWG' is not defined" ] } ], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'devAWG' 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-28-2796a0a0d595>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdevAWG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mid\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 2\u001b[0m \u001b[0mdevAWG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_run_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ENH'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'devAWG' is not defined" ] } ], "source": [ "devAWG.id()\n", "devAWG.set_run_mode('ENH')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "devSGS = RS_SGS100A('TCPIP::192.168.1.37::INSTR')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "devFSV = RS_FSV('TCPIP::192.168.1.105::INSTR')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "devSGS.setCWfrequency(6.0e9)\n", "devSGS.setCWpower(-10)\n", "devSGS.RFon()\n", "devSGS.IQon()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOUR:POW:POW -1.00000000e+01\n" ] } ], "source": [ "devSGS.setCWpower(-10)\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# devOsc = Rigol_DS1054('TCPIP::192.168.1.25::INSTR')\n", "# devOsc.id()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(15, <StatusCode.success: 0>)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "devAWG.stop()\n", "devAWG.set_run_mode('ENH')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "devFSV.se" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(15, <StatusCode.success: 0>)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "devFSV.dev.write(\"TRAC:IQ:BWID?\")" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "ename": "ConnectionResetError", "evalue": "[WinError 10054] An existing connection was forcibly closed by the remote host", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mConnectionResetError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-77-2614fc667e78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdevAWG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\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 2\u001b[0m \u001b[0mdevFSV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"TRAC:IQ ON\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdevFSV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"TRAC:IQ:AVER ON\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdevFSV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"TRAC:IQ:AVER:COUN 1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m 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\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyvisa\\resources\\messagebased.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, message, termination, encoding)\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mterm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m \u001b[0mcount\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_raw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menco\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 208\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyvisa\\resources\\messagebased.py\u001b[0m in \u001b[0;36mwrite_raw\u001b[1;34m(self, message)\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;33m:\u001b[0m\u001b[0mrtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 184\u001b[0m \"\"\"\n\u001b[1;32m--> 185\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtermination\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyvisa-py\\highlevel.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, session, data)\u001b[0m\n\u001b[0;32m 269\u001b[0m \u001b[1;31m# from the session handle, dispatch to the write method of the session object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 270\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msessions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 272\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStatusCode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror_invalid_object\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyvisa-py\\tcpip.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 434\u001b[0m \u001b[1;31m# use select to wait for write ready\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m \u001b[0mselect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterface\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 437\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0moffset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStatusCode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror_io\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mConnectionResetError\u001b[0m: [WinError 10054] An existing connection was forcibly closed by the remote host" ] } ], "source": [ "devAWG.stop()\n", "devFSV.dev.write(\"TRAC:IQ ON\")\n", "devFSV.dev.write(\"TRAC:IQ:AVER ON\")\n", "devFSV.dev.write(\"TRAC:IQ:AVER:COUN 1\")\n", "devFSV.dev.write(\"TRAC:IQ:SET NORM,45 MHz,45 MHz,EXT,POS,0,2048\")\n", "devFSV.dev.write('FORM REAL 32')\n", "devFSV.dev.write('TRAC:IQ:DATA:FORM COMP')\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "devAWG.start()\n", "bla = devFSV.dev.query(\"TRAC:IQ:DATA:MEM?\")\n", "devAWG.stop()\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "times = np.arange(2048)*1./45.0" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "blub = np.fromstring(bla, dtype=float, sep=',')\n", "[I,Q] = np.split(blub, 2)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4096,)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "blub.shape" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0xbd78b70>]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vBgxeMGqvi/66yJPjmhdzcLNfYf+xyN7AhS8sxFlP+BvyQ9U+QHTzOxpSyEyYzVjtbU+m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\nCClJan+a/u3KbszHrkhC4vq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"text/plain": [ "<matplotlib.figure.Figure at 0xc2b37f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(blub)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 8)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jcC5yKYbatTVIILqXdag/2zNGEjcG9/PmiGQVXFtqggpcZ0hFYp/HahGJClxrRTLkJGMw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\nDYmHh4eHx1j4/wG4Y5EiVK4yawAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc2b3e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(times, I)\n", "plt.plot(times, Q)\n", "plt.xlim(0,8)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "integration_indices = np.intersect1d(np.where(times>1.),np.where(times<6.))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.02222222222\n", "5.97777777778\n" ] } ], "source": [ "print(times[integration_indices[0]])\n", "print(times[integration_indices[-1]])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOUR:PHAS 20.00\n" ] } ], "source": [ "devSGS.setCWphase(20)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "360.19999999999999" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.around(360.25, decimals=1)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOUR:PHAS?\n" ] }, { "data": { "text/plain": [ "20.0" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "devSGS.getCWphase()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.7260555715178532e-07" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.average(I[integration_indices])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SOUR:PHAS 0.00\n", "SOUR:PHAS 10.00\n", "SOUR:PHAS 20.00\n", "SOUR:PHAS 30.00\n", "SOUR:PHAS 40.00\n", "SOUR:PHAS 50.00\n", "SOUR:PHAS 60.00\n", "SOUR:PHAS 70.00\n", "SOUR:PHAS 80.00\n", "SOUR:PHAS 90.00\n", "SOUR:PHAS 100.00\n", "SOUR:PHAS 110.00\n", "SOUR:PHAS 120.00\n", "SOUR:PHAS 130.00\n", "SOUR:PHAS 140.00\n", "SOUR:PHAS 150.00\n", "SOUR:PHAS 160.00\n", "SOUR:PHAS 170.00\n", "SOUR:PHAS 180.00\n", "SOUR:PHAS 190.00\n", "SOUR:PHAS 200.00\n", "SOUR:PHAS 210.00\n", "SOUR:PHAS 220.00\n", "SOUR:PHAS 230.00\n", "SOUR:PHAS 240.00\n", "SOUR:PHAS 250.00\n", "SOUR:PHAS 260.00\n", "SOUR:PHAS 270.00\n", "SOUR:PHAS 280.00\n", "SOUR:PHAS 290.00\n", "SOUR:PHAS 300.00\n", "SOUR:PHAS 310.00\n", "SOUR:PHAS 320.00\n", "SOUR:PHAS 330.00\n", "SOUR:PHAS 340.00\n", "SOUR:PHAS 350.00\n", "SOUR:PHAS 360.00\n" ] } ], "source": [ "I_avg=[]\n", "Q_avg=[]\n", "phases =np.linspace(0,360,37)\n", "for phase in phases:\n", " devSGS.setCWphase(phase)\n", " devAWG.start()\n", " devFSV.dev.write(\"TRAC:IQ:SET NORM,45 MHz,45 MHz,EXT,POS,0,2048\")\n", " bla = devFSV.dev.query(\"TRAC:IQ:DATA:MEM?\")\n", " devAWG.stop()\n", " blub = np.fromstring(bla, dtype=float, sep=',')\n", " [I,Q] = np.split(blub, 2)\n", "\n", " I_avg.append(np.average(I[integration_indices]))\n", " Q_avg.append(np.average(Q[integration_indices]))\n" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0xc309198>]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tmZlTfLl45+XhMQ2u/Awl2GHbv8xYtAnv/bjOekK9wK7KSZeuwO23jFX1+gMi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\nOqqvwpR4f91xQmEsgtQrKNhgeUKF50r6n9PTsYb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"text/plain": [ "<matplotlib.figure.Figure at 0xc309470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(times, I)\n", "plt.plot(times, Q)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0xb661710>]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb661860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(phases, I_avg)\n", "plt.plot(phases, Q_avg)" ] }, { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
lyoung13/deep-learning-nanodegree	p4-language-translation/dlnd_language_translation.ipynb	1	488760	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n", "## Get the Data\n", "Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import problem_unittests as tests\n", "\n", "source_path = 'data/small_vocab_en'\n", "target_path = 'data/small_vocab_fr'\n", "source_text = helper.load_data(source_path)\n", "target_text = helper.load_data(target_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "Play around with view_sentence_range to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 227\n", "Number of sentences: 137861\n", "Average number of words in a sentence: 13.225277634719028\n", "\n", "English sentences 0 to 10:\n", "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", "the united states is usually chilly during july , and it is usually freezing in november .\n", "california is usually quiet during march , and it is usually hot in june .\n", "the united states is sometimes mild during june , and it is cold in september .\n", "your least liked fruit is the grape , but my least liked is the apple .\n", "his favorite fruit is the orange , but my favorite is the grape .\n", "paris is relaxing during december , but it is usually chilly in july .\n", "new jersey is busy during spring , and it is never hot in march .\n", "our least liked fruit is the lemon , but my least liked is the grape .\n", "the united states is sometimes busy during january , and it is sometimes warm in november .\n", "\n", "French sentences 0 to 10:\n", "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", "california est généralement calme en mars , et il est généralement chaud en juin .\n", "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n", "\n", "sentences = source_text.split('\\n')\n", "word_counts = [len(sentence.split()) for sentence in sentences]\n", "print('Number of sentences: {}'.format(len(sentences)))\n", "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n", "\n", "print()\n", "print('English sentences {} to {}:'.format(view_sentence_range))\n", "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n", "print()\n", "print('French sentences {} to {}:'.format(view_sentence_range))\n", "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocessing Function\n", "### Text to Word Ids\n", "As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of each sentence from `target_text`. This will help the neural network predict when the sentence should end.\n", "\n", "You can get the `<EOS>` word id by doing:\n", "```python\n", "target_vocab_to_int['<EOS>']\n", "```\n", "You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", " Convert source and target text to proper word ids\n", " :param source_text: String that contains all the source text.\n", " :param target_text: String that contains all the target text.\n", " :param source_vocab_to_int: Dictionary to go from the source words to an id\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " # TODO: Implement Function\n", " source_id_text = [[source_vocab_to_int[word] for word in line.split()] for line in source_text.split('\\n')]\n", " target_id_text = [[target_vocab_to_int[word] for word in line.split()] + [target_vocab_to_int[ '<EOS>']] for line in target_text.split('\\n')]\n", " return source_id_text, target_id_text\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_text_to_ids(text_to_ids)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "import helper\n", "\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Check the Version of TensorFlow and Access to GPU\n", "This will check to make sure you have the correct version of TensorFlow and access to a GPU" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.0.0\n", "Default GPU Device: /gpu:0\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0 You are using {}'.format(tf.__version__)\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n", "- `model_inputs`\n", "- `process_decoding_input`\n", "- `encoding_layer`\n", "- `decoding_layer_train`\n", "- `decoding_layer_infer`\n", "- `decoding_layer`\n", "- `seq2seq_model`\n", "\n", "### Input\n", "Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "\n", "- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n", "- Targets placeholder with rank 2.\n", "- Learning rate placeholder with rank 0.\n", "- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n", "\n", "Return the placeholders in the following the tuple (Input, Targets, Learing Rate, Keep Probability)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate, keep probability)\n", " \"\"\"\n", " # TODO: Implement Function\n", " input = tf.placeholder(tf.int32, [None, None], name=\"input\")\n", " targets = tf.placeholder(tf.int32, [None, None], name=\"targets\")\n", " learning_rate = tf.placeholder(tf.float32, name=\"learning_rate\")\n", " keep_prob = tf.placeholder(tf.float32, name=\"keep_prob\")\n", " return input, targets, learning_rate, keep_prob\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_model_inputs(model_inputs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Process Decoding Input\n", "Implement `process_decoding_input` using TensorFlow to remove the last word id from each batch in `target_data` and concat the GO ID to the beginning of each batch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", " Preprocess target data for dencoding\n", " :param target_data: Target Placehoder\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param batch_size: Batch Size\n", " :return: Preprocessed target data\n", " \"\"\"\n", " # TODO: Implement Function\n", " ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n", " dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n", " return dec_input\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_process_decoding_input(process_decoding_input)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Encoding\n", "Implement `encoding_layer()` to create a Encoder RNN layer using [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n", " \"\"\"\n", " Create encoding layer\n", " :param rnn_inputs: Inputs for the RNN\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param keep_prob: Dropout keep probability\n", " :return: RNN state\n", " \"\"\"\n", " # TODO: Implement Function\n", " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", " cell = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob = keep_prob)\n", " cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)\n", " RNN_output, RNN_state = tf.nn.dynamic_rnn(cell, rnn_inputs, dtype = tf.float32)\n", " return RNN_state\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_encoding_layer(encoding_layer)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Decoding - Training\n", "Create training logits using [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). Apply the `output_fn` to the [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) outputs." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", " output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for training\n", " :param encoder_state: Encoder State\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embed_input: Decoder embedded input\n", " :param sequence_length: Sequence Length\n", " :param decoding_scope: TenorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Train Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", " \n", " # Training Decoder\n", " train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n", " train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(\n", " dec_cell, train_decoder_fn, dec_embed_input, sequence_length, scope=decoding_scope)\n", " \n", " # Apply output function\n", " train_logits = output_fn(train_pred)\n", " \n", " return train_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_train(decoding_layer_train)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Decoding - Inference\n", "Create inference logits using [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n", " maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for inference\n", " :param encoder_state: Encoder state\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embeddings: Decoder embeddings\n", " :param start_of_sequence_id: GO ID\n", " :param end_of_sequence_id: EOS Id\n", " :param maximum_length: The maximum allowed time steps to decode\n", " :param vocab_size: Size of vocabulary\n", " :param decoding_scope: TensorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Inference Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", " \n", " # Inference Decoder\n", " infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(\n", " output_fn, encoder_state, dec_embeddings, start_of_sequence_id, end_of_sequence_id, \n", " maximum_length, vocab_size)\n", " inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, infer_decoder_fn, scope=decoding_scope)\n", " \n", " return inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_infer(decoding_layer_infer)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Decoding Layer\n", "Implement `decoding_layer()` to create a Decoder RNN layer.\n", "\n", "- Create RNN cell for decoding using `rnn_size` and `num_layers`.\n", "- Create the output fuction using [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) to transform it's input, logits, to class logits.\n", "- Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` function to get the training logits.\n", "- Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` function to get the inference logits.\n", "\n", "Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n", " num_layers, target_vocab_to_int, keep_prob):\n", " \"\"\"\n", " Create decoding layer\n", " :param dec_embed_input: Decoder embedded input\n", " :param dec_embeddings: Decoder embeddings\n", " :param encoder_state: The encoded state\n", " :param vocab_size: Size of vocabulary\n", " :param sequence_length: Sequence Length\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param keep_prob: Dropout keep probability\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", " \n", "\n", " start_of_sequence_id = target_vocab_to_int['<GO>']\n", " end_of_sequence_id = target_vocab_to_int['<EOS>']\n", " \n", " # Create RNN cell for decoding using rnn_size and num_layers\n", " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", " cell = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob = keep_prob)\n", " cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)\n", " \n", " # Create the output fuction using lambda to transform it's input, logits, to class logits\n", " output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)\n", " \n", " # Use the your decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n", " # sequence_length, decoding_scope, output_fn, keep_prob) function to get the training logits.\n", " \n", " with tf.variable_scope('decoding') as decoding_scope:\n", " training_logits = decoding_layer_train(encoder_state, cell, dec_embed_input, sequence_length, \n", " decoding_scope, output_fn, keep_prob)\n", " \n", " # Use your decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n", " # maximum_length, vocab_size, decoding_scope, output_fn, keep_prob) function to get the inference logits.\n", " \n", " with tf.variable_scope('decoding', reuse=True) as decoding_scope:\n", " inference_logits = decoding_layer_infer(encoder_state, cell, dec_embeddings, start_of_sequence_id,\n", " end_of_sequence_id, sequence_length, vocab_size, decoding_scope, \n", " output_fn, keep_prob)\n", " \n", " return training_logits, inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer(decoding_layer)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "\n", "- Apply embedding to the input data for the encoder.\n", "- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)`.\n", "- Process target data using your `process_decoding_input(target_data, target_vocab_to_int, batch_size)` function.\n", "- Apply embedding to the target data for the decoder.\n", "- Decode the encoded input using your `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n", " \"\"\"\n", " Build the Sequence-to-Sequence part of the neural network\n", " :param input_data: Input placeholder\n", " :param target_data: Target placeholder\n", " :param keep_prob: Dropout keep probability placeholder\n", " :param batch_size: Batch Size\n", " :param sequence_length: Sequence Length\n", " :param source_vocab_size: Source vocabulary size\n", " :param target_vocab_size: Target vocabulary size\n", " :param enc_embedding_size: Decoder embedding size\n", " :param dec_embedding_size: Encoder embedding size\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", " \n", " # Apply embedding to the input data for the encoder.\n", " enc_embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\n", " \n", " # Encode the input using your encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob).\n", " encoder_state = encoding_layer(enc_embed_input, rnn_size, num_layers, keep_prob)\n", " \n", " # Process target data using your process_decoding_input(target_data, target_vocab_to_int, batch_size) function.\n", " dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n", " \n", " # Apply embedding to the target data for the decoder.\n", " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\n", " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", " \n", " # Decode the encoded input using your decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, \n", " # sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob).\n", " \n", " training_logits, inference_logits = decoding_layer(dec_embed_input, dec_embeddings, encoder_state, target_vocab_size, \n", " sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)\n", " \n", " return training_logits, inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_seq2seq_model(seq2seq_model)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `num_layers` to the number of layers.\n", "- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n", "- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `keep_probability` to the Dropout keep probability" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of Epochs\n", "epochs = 8\n", "# Batch Size\n", "batch_size = 256\n", "# RNN Size\n", "rnn_size = 256\n", "# Number of Layers\n", "num_layers = 2\n", "# Embedding Size\n", "encoding_embedding_size = 256\n", "decoding_embedding_size = 256\n", "# Learning Rate\n", "learning_rate = 0.001\n", "# Dropout Keep Probability\n", "keep_probability = 0.5" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_path = 'checkpoints/dev'\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n", "max_source_sentence_length = max([len(sentence) for sentence in source_int_text])\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " input_data, targets, lr, keep_prob = model_inputs()\n", " sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')\n", " input_shape = tf.shape(input_data)\n", " \n", " train_logits, inference_logits = seq2seq_model(\n", " tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\n", " encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\n", "\n", " tf.identity(inference_logits, 'logits')\n", " with tf.name_scope(\"optimization\"):\n", " # Loss function\n", " cost = tf.contrib.seq2seq.sequence_loss(\n", " train_logits,\n", " targets,\n", " tf.ones([input_shape[0], sequence_length]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 0/538 - Train Accuracy: 0.234, Validation Accuracy: 0.316, Loss: 5.894\n", "Epoch 0 Batch 1/538 - Train Accuracy: 0.231, Validation Accuracy: 0.316, Loss: 5.507\n", "Epoch 0 Batch 2/538 - Train Accuracy: 0.252, Validation Accuracy: 0.316, Loss: 5.066\n", "Epoch 0 Batch 3/538 - Train Accuracy: 0.229, Validation Accuracy: 0.316, Loss: 4.804\n", "Epoch 0 Batch 4/538 - Train Accuracy: 0.242, Validation Accuracy: 0.321, Loss: 4.615\n", "Epoch 0 Batch 5/538 - Train Accuracy: 0.280, Validation Accuracy: 0.332, Loss: 4.371\n", "Epoch 0 Batch 6/538 - Train Accuracy: 0.302, Validation Accuracy: 0.346, Loss: 4.171\n", "Epoch 0 Batch 7/538 - Train Accuracy: 0.282, Validation Accuracy: 0.347, Loss: 4.096\n", "Epoch 0 Batch 8/538 - Train Accuracy: 0.280, Validation Accuracy: 0.347, Loss: 3.988\n", "Epoch 0 Batch 9/538 - Train Accuracy: 0.279, Validation Accuracy: 0.347, Loss: 3.884\n", "Epoch 0 Batch 10/538 - Train Accuracy: 0.260, Validation Accuracy: 0.347, Loss: 3.842\n", "Epoch 0 Batch 11/538 - Train Accuracy: 0.291, Validation Accuracy: 0.364, Loss: 3.725\n", "Epoch 0 Batch 12/538 - Train Accuracy: 0.297, Validation Accuracy: 0.377, Loss: 3.689\n", "Epoch 0 Batch 13/538 - Train Accuracy: 0.364, Validation Accuracy: 0.390, Loss: 3.389\n", "Epoch 0 Batch 14/538 - Train Accuracy: 0.323, Validation Accuracy: 0.392, Loss: 3.477\n", "Epoch 0 Batch 15/538 - Train Accuracy: 0.364, Validation Accuracy: 0.391, Loss: 3.263\n", "Epoch 0 Batch 16/538 - Train Accuracy: 0.353, Validation Accuracy: 0.396, Loss: 3.250\n", "Epoch 0 Batch 17/538 - Train Accuracy: 0.334, Validation Accuracy: 0.395, Loss: 3.298\n", "Epoch 0 Batch 18/538 - Train Accuracy: 0.325, Validation Accuracy: 0.397, Loss: 3.327\n", "Epoch 0 Batch 19/538 - Train Accuracy: 0.329, Validation Accuracy: 0.401, Loss: 3.282\n", "Epoch 0 Batch 20/538 - Train Accuracy: 0.354, Validation Accuracy: 0.403, Loss: 3.099\n", "Epoch 0 Batch 21/538 - Train Accuracy: 0.303, Validation Accuracy: 0.415, Loss: 3.303\n", "Epoch 0 Batch 22/538 - Train Accuracy: 0.365, Validation Accuracy: 0.427, Loss: 3.108\n", "Epoch 0 Batch 23/538 - Train Accuracy: 0.370, Validation Accuracy: 0.426, Loss: 3.072\n", "Epoch 0 Batch 24/538 - Train Accuracy: 0.385, Validation Accuracy: 0.434, Loss: 3.009\n", "Epoch 0 Batch 25/538 - Train Accuracy: 0.360, Validation Accuracy: 0.420, Loss: 3.054\n", "Epoch 0 Batch 26/538 - Train Accuracy: 0.373, Validation Accuracy: 0.436, Loss: 3.048\n", "Epoch 0 Batch 27/538 - Train Accuracy: 0.386, Validation Accuracy: 0.444, Loss: 2.970\n", "Epoch 0 Batch 28/538 - Train Accuracy: 0.425, Validation Accuracy: 0.426, Loss: 2.726\n", "Epoch 0 Batch 29/538 - Train Accuracy: 0.396, Validation Accuracy: 0.440, Loss: 2.899\n", "Epoch 0 Batch 30/538 - Train Accuracy: 0.370, Validation Accuracy: 0.434, Loss: 2.933\n", "Epoch 0 Batch 31/538 - Train Accuracy: 0.406, Validation Accuracy: 0.440, Loss: 2.853\n", "Epoch 0 Batch 32/538 - Train Accuracy: 0.383, Validation Accuracy: 0.440, Loss: 2.864\n", "Epoch 0 Batch 33/538 - Train Accuracy: 0.413, Validation Accuracy: 0.449, Loss: 2.789\n", "Epoch 0 Batch 34/538 - Train Accuracy: 0.401, Validation Accuracy: 0.455, Loss: 2.848\n", "Epoch 0 Batch 35/538 - Train Accuracy: 0.385, Validation Accuracy: 0.454, Loss: 2.858\n", "Epoch 0 Batch 36/538 - Train Accuracy: 0.417, Validation Accuracy: 0.453, Loss: 2.720\n", "Epoch 0 Batch 37/538 - Train Accuracy: 0.407, Validation Accuracy: 0.460, Loss: 2.766\n", "Epoch 0 Batch 38/538 - Train Accuracy: 0.383, Validation Accuracy: 0.455, Loss: 2.817\n", "Epoch 0 Batch 39/538 - Train Accuracy: 0.392, Validation Accuracy: 0.460, Loss: 2.815\n", "Epoch 0 Batch 40/538 - Train Accuracy: 0.462, Validation Accuracy: 0.464, Loss: 2.518\n", "Epoch 0 Batch 41/538 - Train Accuracy: 0.401, Validation Accuracy: 0.456, Loss: 2.745\n", "Epoch 0 Batch 42/538 - Train Accuracy: 0.422, Validation Accuracy: 0.471, Loss: 2.713\n", "Epoch 0 Batch 43/538 - Train Accuracy: 0.409, Validation Accuracy: 0.466, Loss: 2.729\n", "Epoch 0 Batch 44/538 - Train Accuracy: 0.396, Validation Accuracy: 0.463, Loss: 2.757\n", "Epoch 0 Batch 45/538 - Train Accuracy: 0.447, Validation Accuracy: 0.474, Loss: 2.561\n", "Epoch 0 Batch 46/538 - Train Accuracy: 0.409, Validation Accuracy: 0.468, Loss: 2.660\n", "Epoch 0 Batch 47/538 - Train Accuracy: 0.448, Validation Accuracy: 0.477, Loss: 2.550\n", "Epoch 0 Batch 48/538 - Train Accuracy: 0.461, Validation Accuracy: 0.476, Loss: 2.481\n", "Epoch 0 Batch 49/538 - Train Accuracy: 0.406, Validation Accuracy: 0.481, Loss: 2.697\n", "Epoch 0 Batch 50/538 - Train Accuracy: 0.436, Validation Accuracy: 0.480, Loss: 2.579\n", "Epoch 0 Batch 51/538 - Train Accuracy: 0.362, Validation Accuracy: 0.468, Loss: 2.788\n", "Epoch 0 Batch 52/538 - Train Accuracy: 0.433, Validation Accuracy: 0.476, Loss: 2.582\n", "Epoch 0 Batch 53/538 - Train Accuracy: 0.477, Validation Accuracy: 0.491, Loss: 2.381\n", "Epoch 0 Batch 54/538 - Train Accuracy: 0.434, Validation Accuracy: 0.474, Loss: 2.515\n", "Epoch 0 Batch 55/538 - Train Accuracy: 0.434, Validation Accuracy: 0.495, Loss: 2.575\n", "Epoch 0 Batch 56/538 - Train Accuracy: 0.448, Validation Accuracy: 0.489, Loss: 2.455\n", "Epoch 0 Batch 57/538 - Train Accuracy: 0.416, Validation Accuracy: 0.493, Loss: 2.592\n", "Epoch 0 Batch 58/538 - Train Accuracy: 0.408, Validation Accuracy: 0.491, Loss: 2.582\n", "Epoch 0 Batch 59/538 - Train Accuracy: 0.418, Validation Accuracy: 0.485, Loss: 2.526\n", "Epoch 0 Batch 60/538 - Train Accuracy: 0.447, Validation Accuracy: 0.495, Loss: 2.516\n", "Epoch 0 Batch 61/538 - Train Accuracy: 0.440, Validation Accuracy: 0.485, Loss: 2.448\n", "Epoch 0 Batch 62/538 - Train Accuracy: 0.440, Validation Accuracy: 0.493, Loss: 2.429\n", "Epoch 0 Batch 63/538 - Train Accuracy: 0.453, Validation Accuracy: 0.484, Loss: 2.310\n", "Epoch 0 Batch 64/538 - Train Accuracy: 0.458, Validation Accuracy: 0.493, Loss: 2.337\n", "Epoch 0 Batch 65/538 - Train Accuracy: 0.435, Validation Accuracy: 0.502, Loss: 2.495\n", "Epoch 0 Batch 66/538 - Train Accuracy: 0.438, Validation Accuracy: 0.482, Loss: 2.291\n", "Epoch 0 Batch 67/538 - Train Accuracy: 0.462, Validation Accuracy: 0.504, Loss: 2.368\n", "Epoch 0 Batch 68/538 - Train Accuracy: 0.475, Validation Accuracy: 0.496, Loss: 2.237\n", "Epoch 0 Batch 69/538 - Train Accuracy: 0.427, Validation Accuracy: 0.487, Loss: 2.378\n", "Epoch 0 Batch 70/538 - Train Accuracy: 0.466, Validation Accuracy: 0.502, Loss: 2.270\n", "Epoch 0 Batch 71/538 - Train Accuracy: 0.462, Validation Accuracy: 0.511, Loss: 2.314\n", "Epoch 0 Batch 72/538 - Train Accuracy: 0.465, Validation Accuracy: 0.491, Loss: 2.208\n", "Epoch 0 Batch 73/538 - Train Accuracy: 0.431, Validation Accuracy: 0.493, Loss: 2.313\n", "Epoch 0 Batch 74/538 - Train Accuracy: 0.478, Validation Accuracy: 0.508, Loss: 2.204\n", "Epoch 0 Batch 75/538 - Train Accuracy: 0.474, Validation Accuracy: 0.496, Loss: 2.189\n", "Epoch 0 Batch 76/538 - Train Accuracy: 0.421, Validation Accuracy: 0.484, Loss: 2.291\n", "Epoch 0 Batch 77/538 - Train Accuracy: 0.451, Validation Accuracy: 0.507, Loss: 2.253\n", "Epoch 0 Batch 78/538 - Train Accuracy: 0.480, Validation Accuracy: 0.514, Loss: 2.188\n", "Epoch 0 Batch 79/538 - Train Accuracy: 0.434, Validation Accuracy: 0.468, Loss: 2.116\n", "Epoch 0 Batch 80/538 - Train Accuracy: 0.449, Validation Accuracy: 0.510, Loss: 2.257\n", "Epoch 0 Batch 81/538 - Train Accuracy: 0.459, Validation Accuracy: 0.511, Loss: 2.198\n", "Epoch 0 Batch 82/538 - Train Accuracy: 0.414, Validation Accuracy: 0.474, Loss: 2.133\n", "Epoch 0 Batch 83/538 - Train Accuracy: 0.434, Validation Accuracy: 0.492, Loss: 2.197\n", "Epoch 0 Batch 84/538 - Train Accuracy: 0.476, Validation Accuracy: 0.517, Loss: 2.055\n", "Epoch 0 Batch 85/538 - Train Accuracy: 0.470, Validation Accuracy: 0.488, Loss: 1.986\n", "Epoch 0 Batch 86/538 - Train Accuracy: 0.414, Validation Accuracy: 0.471, Loss: 2.112\n", "Epoch 0 Batch 87/538 - Train Accuracy: 0.467, Validation Accuracy: 0.523, Loss: 2.094\n", "Epoch 0 Batch 88/538 - Train Accuracy: 0.467, Validation Accuracy: 0.510, Loss: 2.078\n", "Epoch 0 Batch 89/538 - Train Accuracy: 0.433, Validation Accuracy: 0.481, Loss: 2.040\n", "Epoch 0 Batch 90/538 - Train Accuracy: 0.454, Validation Accuracy: 0.491, Loss: 1.975\n", "Epoch 0 Batch 91/538 - Train Accuracy: 0.459, Validation Accuracy: 0.515, Loss: 2.049\n", "Epoch 0 Batch 92/538 - Train Accuracy: 0.439, Validation Accuracy: 0.493, Loss: 2.008\n", "Epoch 0 Batch 93/538 - Train Accuracy: 0.418, Validation Accuracy: 0.481, Loss: 1.993\n", "Epoch 0 Batch 94/538 - Train Accuracy: 0.446, Validation Accuracy: 0.500, Loss: 2.012\n", "Epoch 0 Batch 95/538 - Train Accuracy: 0.509, Validation Accuracy: 0.510, Loss: 1.794\n", "Epoch 0 Batch 96/538 - Train Accuracy: 0.475, Validation Accuracy: 0.498, Loss: 1.821\n", "Epoch 0 Batch 97/538 - Train Accuracy: 0.428, Validation Accuracy: 0.496, Loss: 1.936\n", "Epoch 0 Batch 98/538 - Train Accuracy: 0.477, Validation Accuracy: 0.499, Loss: 1.803\n", "Epoch 0 Batch 99/538 - Train Accuracy: 0.445, Validation Accuracy: 0.500, Loss: 1.931\n", "Epoch 0 Batch 100/538 - Train Accuracy: 0.450, Validation Accuracy: 0.503, Loss: 1.864\n", "Epoch 0 Batch 101/538 - Train Accuracy: 0.454, Validation Accuracy: 0.512, Loss: 1.874\n", "Epoch 0 Batch 102/538 - Train Accuracy: 0.440, Validation Accuracy: 0.498, Loss: 1.904\n", "Epoch 0 Batch 103/538 - Train Accuracy: 0.458, Validation Accuracy: 0.502, Loss: 1.830\n", "Epoch 0 Batch 104/538 - Train Accuracy: 0.477, Validation Accuracy: 0.509, Loss: 1.810\n", "Epoch 0 Batch 105/538 - Train Accuracy: 0.463, Validation Accuracy: 0.496, Loss: 1.748\n", "Epoch 0 Batch 106/538 - Train Accuracy: 0.451, Validation Accuracy: 0.507, Loss: 1.817\n", "Epoch 0 Batch 107/538 - Train Accuracy: 0.439, Validation Accuracy: 0.503, Loss: 1.842\n", "Epoch 0 Batch 108/538 - Train Accuracy: 0.454, Validation Accuracy: 0.496, Loss: 1.801\n", "Epoch 0 Batch 109/538 - Train Accuracy: 0.465, Validation Accuracy: 0.498, Loss: 1.788\n", "Epoch 0 Batch 110/538 - Train Accuracy: 0.471, Validation Accuracy: 0.524, Loss: 1.816\n", "Epoch 0 Batch 111/538 - Train Accuracy: 0.487, Validation Accuracy: 0.511, Loss: 1.702\n", "Epoch 0 Batch 112/538 - Train Accuracy: 0.440, Validation Accuracy: 0.504, Loss: 1.773\n", "Epoch 0 Batch 113/538 - Train Accuracy: 0.480, Validation Accuracy: 0.528, Loss: 1.780\n", "Epoch 0 Batch 114/538 - Train Accuracy: 0.501, Validation Accuracy: 0.509, Loss: 1.659\n", "Epoch 0 Batch 115/538 - Train Accuracy: 0.464, Validation Accuracy: 0.507, Loss: 1.711\n", "Epoch 0 Batch 116/538 - Train Accuracy: 0.492, Validation Accuracy: 0.529, Loss: 1.698\n", "Epoch 0 Batch 117/538 - Train Accuracy: 0.497, Validation Accuracy: 0.523, Loss: 1.650\n", "Epoch 0 Batch 118/538 - Train Accuracy: 0.479, Validation Accuracy: 0.503, Loss: 1.637\n", "Epoch 0 Batch 119/538 - Train Accuracy: 0.502, Validation Accuracy: 0.526, Loss: 1.605\n", "Epoch 0 Batch 120/538 - Train Accuracy: 0.466, Validation Accuracy: 0.509, Loss: 1.669\n", "Epoch 0 Batch 121/538 - Train Accuracy: 0.482, Validation Accuracy: 0.512, Loss: 1.573\n", "Epoch 0 Batch 122/538 - Train Accuracy: 0.494, Validation Accuracy: 0.520, Loss: 1.600\n", "Epoch 0 Batch 123/538 - Train Accuracy: 0.482, Validation Accuracy: 0.501, Loss: 1.570\n", "Epoch 0 Batch 124/538 - Train Accuracy: 0.484, Validation Accuracy: 0.508, Loss: 1.527\n", "Epoch 0 Batch 125/538 - Train Accuracy: 0.490, Validation Accuracy: 0.522, Loss: 1.586\n", "Epoch 0 Batch 126/538 - Train Accuracy: 0.496, Validation Accuracy: 0.507, Loss: 1.541\n", "Epoch 0 Batch 127/538 - Train Accuracy: 0.437, Validation Accuracy: 0.501, Loss: 1.656\n", "Epoch 0 Batch 128/538 - Train Accuracy: 0.496, Validation Accuracy: 0.513, Loss: 1.547\n", "Epoch 0 Batch 129/538 - Train Accuracy: 0.460, Validation Accuracy: 0.498, Loss: 1.553\n", "Epoch 0 Batch 130/538 - Train Accuracy: 0.482, Validation Accuracy: 0.522, Loss: 1.554\n", "Epoch 0 Batch 131/538 - Train Accuracy: 0.466, Validation Accuracy: 0.509, Loss: 1.606\n", "Epoch 0 Batch 132/538 - Train Accuracy: 0.471, Validation Accuracy: 0.502, Loss: 1.503\n", "Epoch 0 Batch 133/538 - Train Accuracy: 0.499, Validation Accuracy: 0.517, Loss: 1.464\n", "Epoch 0 Batch 134/538 - Train Accuracy: 0.438, Validation Accuracy: 0.507, Loss: 1.635\n", "Epoch 0 Batch 135/538 - Train Accuracy: 0.471, Validation Accuracy: 0.507, Loss: 1.516\n", "Epoch 0 Batch 136/538 - Train Accuracy: 0.484, Validation Accuracy: 0.514, Loss: 1.517\n", "Epoch 0 Batch 137/538 - Train Accuracy: 0.477, Validation Accuracy: 0.521, Loss: 1.511\n", "Epoch 0 Batch 138/538 - Train Accuracy: 0.460, Validation Accuracy: 0.498, Loss: 1.503\n", "Epoch 0 Batch 139/538 - Train Accuracy: 0.458, Validation Accuracy: 0.506, Loss: 1.601\n", "Epoch 0 Batch 140/538 - Train Accuracy: 0.458, Validation Accuracy: 0.517, Loss: 1.588\n", "Epoch 0 Batch 141/538 - Train Accuracy: 0.450, Validation Accuracy: 0.493, Loss: 1.576\n", "Epoch 0 Batch 142/538 - Train Accuracy: 0.492, Validation Accuracy: 0.503, Loss: 1.425\n", "Epoch 0 Batch 143/538 - Train Accuracy: 0.443, Validation Accuracy: 0.506, Loss: 1.532\n", "Epoch 0 Batch 144/538 - Train Accuracy: 0.433, Validation Accuracy: 0.471, Loss: 1.517\n", "Epoch 0 Batch 145/538 - Train Accuracy: 0.463, Validation Accuracy: 0.499, Loss: 1.470\n", "Epoch 0 Batch 146/538 - Train Accuracy: 0.492, Validation Accuracy: 0.502, Loss: 1.384\n", "Epoch 0 Batch 147/538 - Train Accuracy: 0.480, Validation Accuracy: 0.489, Loss: 1.424\n", "Epoch 0 Batch 148/538 - Train Accuracy: 0.432, Validation Accuracy: 0.496, Loss: 1.544\n", "Epoch 0 Batch 149/538 - Train Accuracy: 0.446, Validation Accuracy: 0.496, Loss: 1.459\n", "Epoch 0 Batch 150/538 - Train Accuracy: 0.475, Validation Accuracy: 0.512, Loss: 1.448\n", "Epoch 0 Batch 151/538 - Train Accuracy: 0.459, Validation Accuracy: 0.497, Loss: 1.395\n", "Epoch 0 Batch 152/538 - Train Accuracy: 0.469, Validation Accuracy: 0.497, Loss: 1.396\n", "Epoch 0 Batch 153/538 - Train Accuracy: 0.460, Validation Accuracy: 0.508, Loss: 1.445\n", "Epoch 0 Batch 154/538 - Train Accuracy: 0.458, Validation Accuracy: 0.497, Loss: 1.392\n", "Epoch 0 Batch 155/538 - Train Accuracy: 0.492, Validation Accuracy: 0.506, Loss: 1.375\n", "Epoch 0 Batch 156/538 - Train Accuracy: 0.477, Validation Accuracy: 0.523, Loss: 1.438\n", "Epoch 0 Batch 157/538 - Train Accuracy: 0.445, Validation Accuracy: 0.482, Loss: 1.378\n", "Epoch 0 Batch 158/538 - Train Accuracy: 0.461, Validation Accuracy: 0.509, Loss: 1.449\n", "Epoch 0 Batch 159/538 - Train Accuracy: 0.464, Validation Accuracy: 0.507, Loss: 1.441\n", "Epoch 0 Batch 160/538 - Train Accuracy: 0.430, Validation Accuracy: 0.474, Loss: 1.333\n", "Epoch 0 Batch 161/538 - Train Accuracy: 0.441, Validation Accuracy: 0.493, Loss: 1.387\n", "Epoch 0 Batch 162/538 - Train Accuracy: 0.513, Validation Accuracy: 0.515, Loss: 1.316\n", "Epoch 0 Batch 163/538 - Train Accuracy: 0.463, Validation Accuracy: 0.491, Loss: 1.358\n", "Epoch 0 Batch 164/538 - Train Accuracy: 0.448, Validation Accuracy: 0.489, Loss: 1.416\n", "Epoch 0 Batch 165/538 - Train Accuracy: 0.484, Validation Accuracy: 0.503, Loss: 1.270\n", "Epoch 0 Batch 166/538 - Train Accuracy: 0.462, Validation Accuracy: 0.498, Loss: 1.387\n", "Epoch 0 Batch 167/538 - Train Accuracy: 0.459, Validation Accuracy: 0.488, Loss: 1.272\n", "Epoch 0 Batch 168/538 - Train Accuracy: 0.454, Validation Accuracy: 0.501, Loss: 1.359\n", "Epoch 0 Batch 169/538 - Train Accuracy: 0.476, Validation Accuracy: 0.519, Loss: 1.318\n", "Epoch 0 Batch 170/538 - Train Accuracy: 0.472, Validation Accuracy: 0.496, Loss: 1.292\n", "Epoch 0 Batch 171/538 - Train Accuracy: 0.493, Validation Accuracy: 0.528, Loss: 1.346\n", "Epoch 0 Batch 172/538 - Train Accuracy: 0.480, Validation Accuracy: 0.514, Loss: 1.290\n", "Epoch 0 Batch 173/538 - Train Accuracy: 0.491, Validation Accuracy: 0.526, Loss: 1.269\n", "Epoch 0 Batch 174/538 - Train Accuracy: 0.453, Validation Accuracy: 0.523, Loss: 1.362\n", "Epoch 0 Batch 175/538 - Train Accuracy: 0.427, Validation Accuracy: 0.507, Loss: 1.370\n", "Epoch 0 Batch 176/538 - Train Accuracy: 0.465, Validation Accuracy: 0.525, Loss: 1.344\n", "Epoch 0 Batch 177/538 - Train Accuracy: 0.479, Validation Accuracy: 0.508, Loss: 1.292\n", "Epoch 0 Batch 178/538 - Train Accuracy: 0.483, Validation Accuracy: 0.496, Loss: 1.221\n", "Epoch 0 Batch 179/538 - Train Accuracy: 0.472, Validation Accuracy: 0.510, Loss: 1.281\n", "Epoch 0 Batch 180/538 - Train Accuracy: 0.480, Validation Accuracy: 0.497, Loss: 1.256\n", "Epoch 0 Batch 181/538 - Train Accuracy: 0.463, Validation Accuracy: 0.527, Loss: 1.309\n", "Epoch 0 Batch 182/538 - Train Accuracy: 0.476, Validation Accuracy: 0.538, Loss: 1.286\n", "Epoch 0 Batch 183/538 - Train Accuracy: 0.489, Validation Accuracy: 0.506, Loss: 1.219\n", "Epoch 0 Batch 184/538 - Train Accuracy: 0.503, Validation Accuracy: 0.530, Loss: 1.204\n", "Epoch 0 Batch 185/538 - Train Accuracy: 0.508, Validation Accuracy: 0.532, Loss: 1.228\n", "Epoch 0 Batch 186/538 - Train Accuracy: 0.475, Validation Accuracy: 0.518, Loss: 1.219\n", "Epoch 0 Batch 187/538 - Train Accuracy: 0.541, Validation Accuracy: 0.536, Loss: 1.171\n", "Epoch 0 Batch 188/538 - Train Accuracy: 0.491, Validation Accuracy: 0.534, Loss: 1.237\n", "Epoch 0 Batch 189/538 - Train Accuracy: 0.483, Validation Accuracy: 0.528, Loss: 1.236\n", "Epoch 0 Batch 190/538 - Train Accuracy: 0.498, Validation Accuracy: 0.531, Loss: 1.235\n", "Epoch 0 Batch 191/538 - Train Accuracy: 0.524, Validation Accuracy: 0.531, Loss: 1.168\n", "Epoch 0 Batch 192/538 - Train Accuracy: 0.500, Validation Accuracy: 0.525, Loss: 1.181\n", "Epoch 0 Batch 193/538 - Train Accuracy: 0.544, Validation Accuracy: 0.533, Loss: 1.149\n", "Epoch 0 Batch 194/538 - Train Accuracy: 0.507, Validation Accuracy: 0.542, Loss: 1.220\n", "Epoch 0 Batch 195/538 - Train Accuracy: 0.526, Validation Accuracy: 0.541, Loss: 1.165\n", "Epoch 0 Batch 196/538 - Train Accuracy: 0.507, Validation Accuracy: 0.533, Loss: 1.162\n", "Epoch 0 Batch 197/538 - Train Accuracy: 0.534, Validation Accuracy: 0.538, Loss: 1.147\n", "Epoch 0 Batch 198/538 - Train Accuracy: 0.555, Validation Accuracy: 0.541, Loss: 1.114\n", "Epoch 0 Batch 199/538 - Train Accuracy: 0.505, Validation Accuracy: 0.545, Loss: 1.216\n", "Epoch 0 Batch 200/538 - Train Accuracy: 0.519, Validation Accuracy: 0.541, Loss: 1.175\n", "Epoch 0 Batch 201/538 - Train Accuracy: 0.537, Validation Accuracy: 0.538, Loss: 1.104\n", "Epoch 0 Batch 202/538 - Train Accuracy: 0.514, Validation Accuracy: 0.552, Loss: 1.193\n", "Epoch 0 Batch 203/538 - Train Accuracy: 0.513, Validation Accuracy: 0.546, Loss: 1.199\n", "Epoch 0 Batch 204/538 - Train Accuracy: 0.487, Validation Accuracy: 0.536, Loss: 1.158\n", "Epoch 0 Batch 205/538 - Train Accuracy: 0.527, Validation Accuracy: 0.544, Loss: 1.102\n", "Epoch 0 Batch 206/538 - Train Accuracy: 0.506, Validation Accuracy: 0.553, Loss: 1.165\n", "Epoch 0 Batch 207/538 - Train Accuracy: 0.529, Validation Accuracy: 0.550, Loss: 1.100\n", "Epoch 0 Batch 208/538 - Train Accuracy: 0.505, Validation Accuracy: 0.544, Loss: 1.150\n", "Epoch 0 Batch 209/538 - Train Accuracy: 0.498, Validation Accuracy: 0.552, Loss: 1.149\n", "Epoch 0 Batch 210/538 - Train Accuracy: 0.526, Validation Accuracy: 0.551, Loss: 1.095\n", "Epoch 0 Batch 211/538 - Train Accuracy: 0.510, Validation Accuracy: 0.551, Loss: 1.161\n", "Epoch 0 Batch 212/538 - Train Accuracy: 0.544, Validation Accuracy: 0.549, Loss: 1.096\n", "Epoch 0 Batch 213/538 - Train Accuracy: 0.506, Validation Accuracy: 0.542, Loss: 1.094\n", "Epoch 0 Batch 214/538 - Train Accuracy: 0.524, Validation Accuracy: 0.550, Loss: 1.119\n", "Epoch 0 Batch 215/538 - Train Accuracy: 0.502, Validation Accuracy: 0.545, Loss: 1.124\n", "Epoch 0 Batch 216/538 - Train Accuracy: 0.494, Validation Accuracy: 0.545, Loss: 1.117\n", "Epoch 0 Batch 217/538 - Train Accuracy: 0.530, Validation Accuracy: 0.541, Loss: 1.061\n", "Epoch 0 Batch 218/538 - Train Accuracy: 0.495, Validation Accuracy: 0.542, Loss: 1.088\n", "Epoch 0 Batch 219/538 - Train Accuracy: 0.503, Validation Accuracy: 0.543, Loss: 1.128\n", "Epoch 0 Batch 220/538 - Train Accuracy: 0.507, Validation Accuracy: 0.548, Loss: 1.063\n", "Epoch 0 Batch 221/538 - Train Accuracy: 0.535, Validation Accuracy: 0.547, Loss: 1.054\n", "Epoch 0 Batch 222/538 - Train Accuracy: 0.525, Validation Accuracy: 0.550, Loss: 1.021\n", "Epoch 0 Batch 223/538 - Train Accuracy: 0.509, Validation Accuracy: 0.551, Loss: 1.111\n", "Epoch 0 Batch 224/538 - Train Accuracy: 0.496, Validation Accuracy: 0.550, Loss: 1.113\n", "Epoch 0 Batch 225/538 - Train Accuracy: 0.540, Validation Accuracy: 0.545, Loss: 1.030\n", "Epoch 0 Batch 226/538 - Train Accuracy: 0.535, Validation Accuracy: 0.545, Loss: 1.018\n", "Epoch 0 Batch 227/538 - Train Accuracy: 0.551, Validation Accuracy: 0.538, Loss: 0.989\n", "Epoch 0 Batch 228/538 - Train Accuracy: 0.515, Validation Accuracy: 0.542, Loss: 1.010\n", "Epoch 0 Batch 229/538 - Train Accuracy: 0.517, Validation Accuracy: 0.544, Loss: 1.031\n", "Epoch 0 Batch 230/538 - Train Accuracy: 0.498, Validation Accuracy: 0.547, Loss: 1.056\n", "Epoch 0 Batch 231/538 - Train Accuracy: 0.516, Validation Accuracy: 0.553, Loss: 1.033\n", "Epoch 0 Batch 232/538 - Train Accuracy: 0.527, Validation Accuracy: 0.551, Loss: 1.040\n", "Epoch 0 Batch 233/538 - Train Accuracy: 0.559, Validation Accuracy: 0.550, Loss: 1.022\n", "Epoch 0 Batch 234/538 - Train Accuracy: 0.507, Validation Accuracy: 0.545, Loss: 1.045\n", "Epoch 0 Batch 235/538 - Train Accuracy: 0.518, Validation Accuracy: 0.550, Loss: 0.996\n", "Epoch 0 Batch 236/538 - Train Accuracy: 0.499, Validation Accuracy: 0.551, Loss: 1.035\n", "Epoch 0 Batch 237/538 - Train Accuracy: 0.522, Validation Accuracy: 0.552, Loss: 1.003\n", "Epoch 0 Batch 238/538 - Train Accuracy: 0.556, Validation Accuracy: 0.549, Loss: 0.982\n", "Epoch 0 Batch 239/538 - Train Accuracy: 0.529, Validation Accuracy: 0.550, Loss: 1.049\n", "Epoch 0 Batch 240/538 - Train Accuracy: 0.517, Validation Accuracy: 0.552, Loss: 1.042\n", "Epoch 0 Batch 241/538 - Train Accuracy: 0.496, Validation Accuracy: 0.552, Loss: 1.025\n", "Epoch 0 Batch 242/538 - Train Accuracy: 0.531, Validation Accuracy: 0.550, Loss: 1.004\n", "Epoch 0 Batch 243/538 - Train Accuracy: 0.492, Validation Accuracy: 0.551, Loss: 1.054\n", "Epoch 0 Batch 244/538 - Train Accuracy: 0.519, Validation Accuracy: 0.550, Loss: 0.981\n", "Epoch 0 Batch 245/538 - Train Accuracy: 0.512, Validation Accuracy: 0.552, Loss: 1.017\n", "Epoch 0 Batch 246/538 - Train Accuracy: 0.533, Validation Accuracy: 0.551, Loss: 0.946\n", "Epoch 0 Batch 247/538 - Train Accuracy: 0.497, Validation Accuracy: 0.552, Loss: 1.015\n", "Epoch 0 Batch 248/538 - Train Accuracy: 0.517, Validation Accuracy: 0.554, Loss: 0.994\n", "Epoch 0 Batch 249/538 - Train Accuracy: 0.533, Validation Accuracy: 0.555, Loss: 0.936\n", "Epoch 0 Batch 250/538 - Train Accuracy: 0.504, Validation Accuracy: 0.550, Loss: 0.968\n", "Epoch 0 Batch 251/538 - Train Accuracy: 0.520, Validation Accuracy: 0.554, Loss: 0.980\n", "Epoch 0 Batch 252/538 - Train Accuracy: 0.526, Validation Accuracy: 0.551, Loss: 0.916\n", "Epoch 0 Batch 253/538 - Train Accuracy: 0.520, Validation Accuracy: 0.547, Loss: 0.917\n", "Epoch 0 Batch 254/538 - Train Accuracy: 0.530, Validation Accuracy: 0.544, Loss: 0.981\n", "Epoch 0 Batch 255/538 - Train Accuracy: 0.508, Validation Accuracy: 0.548, Loss: 0.979\n", "Epoch 0 Batch 256/538 - Train Accuracy: 0.505, Validation Accuracy: 0.548, Loss: 0.998\n", "Epoch 0 Batch 257/538 - Train Accuracy: 0.541, Validation Accuracy: 0.549, Loss: 0.933\n", "Epoch 0 Batch 258/538 - Train Accuracy: 0.570, Validation Accuracy: 0.551, Loss: 0.922\n", "Epoch 0 Batch 259/538 - Train Accuracy: 0.533, Validation Accuracy: 0.552, Loss: 0.937\n", "Epoch 0 Batch 260/538 - Train Accuracy: 0.512, Validation Accuracy: 0.554, Loss: 0.960\n", "Epoch 0 Batch 261/538 - Train Accuracy: 0.506, Validation Accuracy: 0.548, Loss: 0.989\n", "Epoch 0 Batch 262/538 - Train Accuracy: 0.492, Validation Accuracy: 0.541, Loss: 0.969\n", "Epoch 0 Batch 263/538 - Train Accuracy: 0.523, Validation Accuracy: 0.553, Loss: 0.959\n", "Epoch 0 Batch 264/538 - Train Accuracy: 0.516, Validation Accuracy: 0.553, Loss: 0.959\n", "Epoch 0 Batch 265/538 - Train Accuracy: 0.485, Validation Accuracy: 0.541, Loss: 0.979\n", "Epoch 0 Batch 266/538 - Train Accuracy: 0.550, Validation Accuracy: 0.538, Loss: 0.941\n", "Epoch 0 Batch 267/538 - Train Accuracy: 0.511, Validation Accuracy: 0.548, Loss: 0.943\n", "Epoch 0 Batch 268/538 - Train Accuracy: 0.521, Validation Accuracy: 0.547, Loss: 0.883\n", "Epoch 0 Batch 269/538 - Train Accuracy: 0.498, Validation Accuracy: 0.548, Loss: 0.937\n", "Epoch 0 Batch 270/538 - Train Accuracy: 0.493, Validation Accuracy: 0.534, Loss: 0.939\n", "Epoch 0 Batch 271/538 - Train Accuracy: 0.509, Validation Accuracy: 0.549, Loss: 0.949\n", "Epoch 0 Batch 272/538 - Train Accuracy: 0.499, Validation Accuracy: 0.553, Loss: 0.993\n", "Epoch 0 Batch 273/538 - Train Accuracy: 0.507, Validation Accuracy: 0.552, Loss: 0.949\n", "Epoch 0 Batch 274/538 - Train Accuracy: 0.483, Validation Accuracy: 0.550, Loss: 0.978\n", "Epoch 0 Batch 275/538 - Train Accuracy: 0.508, Validation Accuracy: 0.547, Loss: 0.958\n", "Epoch 0 Batch 276/538 - Train Accuracy: 0.531, Validation Accuracy: 0.549, Loss: 0.926\n", "Epoch 0 Batch 277/538 - Train Accuracy: 0.532, Validation Accuracy: 0.551, Loss: 0.911\n", "Epoch 0 Batch 278/538 - Train Accuracy: 0.524, Validation Accuracy: 0.554, Loss: 0.918\n", "Epoch 0 Batch 279/538 - Train Accuracy: 0.519, Validation Accuracy: 0.553, Loss: 0.903\n", "Epoch 0 Batch 280/538 - Train Accuracy: 0.553, Validation Accuracy: 0.548, Loss: 0.872\n", "Epoch 0 Batch 281/538 - Train Accuracy: 0.518, Validation Accuracy: 0.547, Loss: 0.944\n", "Epoch 0 Batch 282/538 - Train Accuracy: 0.541, Validation Accuracy: 0.553, Loss: 0.904\n", "Epoch 0 Batch 283/538 - Train Accuracy: 0.530, Validation Accuracy: 0.556, Loss: 0.898\n", "Epoch 0 Batch 284/538 - Train Accuracy: 0.540, Validation Accuracy: 0.556, Loss: 0.898\n", "Epoch 0 Batch 285/538 - Train Accuracy: 0.547, Validation Accuracy: 0.556, Loss: 0.855\n", "Epoch 0 Batch 286/538 - Train Accuracy: 0.512, Validation Accuracy: 0.558, Loss: 0.913\n", "Epoch 0 Batch 287/538 - Train Accuracy: 0.556, Validation Accuracy: 0.557, Loss: 0.875\n", "Epoch 0 Batch 288/538 - Train Accuracy: 0.508, Validation Accuracy: 0.554, Loss: 0.927\n", "Epoch 0 Batch 289/538 - Train Accuracy: 0.547, Validation Accuracy: 0.553, Loss: 0.831\n", "Epoch 0 Batch 290/538 - Train Accuracy: 0.500, Validation Accuracy: 0.555, Loss: 0.904\n", "Epoch 0 Batch 291/538 - Train Accuracy: 0.529, Validation Accuracy: 0.554, Loss: 0.875\n", "Epoch 0 Batch 292/538 - Train Accuracy: 0.544, Validation Accuracy: 0.552, Loss: 0.852\n", "Epoch 0 Batch 293/538 - Train Accuracy: 0.526, Validation Accuracy: 0.553, Loss: 0.863\n", "Epoch 0 Batch 294/538 - Train Accuracy: 0.489, Validation Accuracy: 0.552, Loss: 0.936\n", "Epoch 0 Batch 295/538 - Train Accuracy: 0.561, Validation Accuracy: 0.551, Loss: 0.823\n", "Epoch 0 Batch 296/538 - Train Accuracy: 0.536, Validation Accuracy: 0.557, Loss: 0.855\n", "Epoch 0 Batch 297/538 - Train Accuracy: 0.493, Validation Accuracy: 0.557, Loss: 0.915\n", "Epoch 0 Batch 298/538 - Train Accuracy: 0.513, Validation Accuracy: 0.556, Loss: 0.869\n", "Epoch 0 Batch 299/538 - Train Accuracy: 0.542, Validation Accuracy: 0.554, Loss: 0.873\n", "Epoch 0 Batch 300/538 - Train Accuracy: 0.559, Validation Accuracy: 0.556, Loss: 0.850\n", "Epoch 0 Batch 301/538 - Train Accuracy: 0.522, Validation Accuracy: 0.557, Loss: 0.884\n", "Epoch 0 Batch 302/538 - Train Accuracy: 0.560, Validation Accuracy: 0.555, Loss: 0.841\n", "Epoch 0 Batch 303/538 - Train Accuracy: 0.573, Validation Accuracy: 0.551, Loss: 0.831\n", "Epoch 0 Batch 304/538 - Train Accuracy: 0.519, Validation Accuracy: 0.551, Loss: 0.888\n", "Epoch 0 Batch 305/538 - Train Accuracy: 0.525, Validation Accuracy: 0.559, Loss: 0.841\n", "Epoch 0 Batch 306/538 - Train Accuracy: 0.555, Validation Accuracy: 0.560, Loss: 0.860\n", "Epoch 0 Batch 307/538 - Train Accuracy: 0.535, Validation Accuracy: 0.560, Loss: 0.871\n", "Epoch 0 Batch 308/538 - Train Accuracy: 0.537, Validation Accuracy: 0.559, Loss: 0.836\n", "Epoch 0 Batch 309/538 - Train Accuracy: 0.507, Validation Accuracy: 0.558, Loss: 0.873\n", "Epoch 0 Batch 310/538 - Train Accuracy: 0.523, Validation Accuracy: 0.560, Loss: 0.859\n", "Epoch 0 Batch 311/538 - Train Accuracy: 0.546, Validation Accuracy: 0.556, Loss: 0.835\n", "Epoch 0 Batch 312/538 - Train Accuracy: 0.577, Validation Accuracy: 0.556, Loss: 0.787\n", "Epoch 0 Batch 313/538 - Train Accuracy: 0.533, Validation Accuracy: 0.556, Loss: 0.889\n", "Epoch 0 Batch 314/538 - Train Accuracy: 0.537, Validation Accuracy: 0.560, Loss: 0.866\n", "Epoch 0 Batch 315/538 - Train Accuracy: 0.539, Validation Accuracy: 0.558, Loss: 0.831\n", "Epoch 0 Batch 316/538 - Train Accuracy: 0.544, Validation Accuracy: 0.559, Loss: 0.829\n", "Epoch 0 Batch 317/538 - Train Accuracy: 0.546, Validation Accuracy: 0.560, Loss: 0.850\n", "Epoch 0 Batch 318/538 - Train Accuracy: 0.535, Validation Accuracy: 0.558, Loss: 0.826\n", "Epoch 0 Batch 319/538 - Train Accuracy: 0.536, Validation Accuracy: 0.556, Loss: 0.829\n", "Epoch 0 Batch 320/538 - Train Accuracy: 0.545, Validation Accuracy: 0.553, Loss: 0.828\n", "Epoch 0 Batch 321/538 - Train Accuracy: 0.536, Validation Accuracy: 0.558, Loss: 0.798\n", "Epoch 0 Batch 322/538 - Train Accuracy: 0.541, Validation Accuracy: 0.557, Loss: 0.833\n", "Epoch 0 Batch 323/538 - Train Accuracy: 0.553, Validation Accuracy: 0.557, Loss: 0.805\n", "Epoch 0 Batch 324/538 - Train Accuracy: 0.510, Validation Accuracy: 0.554, Loss: 0.871\n", "Epoch 0 Batch 325/538 - Train Accuracy: 0.528, Validation Accuracy: 0.550, Loss: 0.819\n", "Epoch 0 Batch 326/538 - Train Accuracy: 0.537, Validation Accuracy: 0.561, Loss: 0.830\n", "Epoch 0 Batch 327/538 - Train Accuracy: 0.551, Validation Accuracy: 0.552, Loss: 0.853\n", "Epoch 0 Batch 328/538 - Train Accuracy: 0.545, Validation Accuracy: 0.551, Loss: 0.794\n", "Epoch 0 Batch 329/538 - Train Accuracy: 0.562, Validation Accuracy: 0.547, Loss: 0.806\n", "Epoch 0 Batch 330/538 - Train Accuracy: 0.546, Validation Accuracy: 0.559, Loss: 0.803\n", "Epoch 0 Batch 331/538 - Train Accuracy: 0.532, Validation Accuracy: 0.563, Loss: 0.799\n", "Epoch 0 Batch 332/538 - Train Accuracy: 0.524, Validation Accuracy: 0.559, Loss: 0.831\n", "Epoch 0 Batch 333/538 - Train Accuracy: 0.557, Validation Accuracy: 0.560, Loss: 0.792\n", "Epoch 0 Batch 334/538 - Train Accuracy: 0.568, Validation Accuracy: 0.566, Loss: 0.758\n", "Epoch 0 Batch 335/538 - Train Accuracy: 0.556, Validation Accuracy: 0.564, Loss: 0.795\n", "Epoch 0 Batch 336/538 - Train Accuracy: 0.570, Validation Accuracy: 0.567, Loss: 0.794\n", "Epoch 0 Batch 337/538 - Train Accuracy: 0.560, Validation Accuracy: 0.573, Loss: 0.801\n", "Epoch 0 Batch 338/538 - Train Accuracy: 0.523, Validation Accuracy: 0.566, Loss: 0.813\n", "Epoch 0 Batch 339/538 - Train Accuracy: 0.543, Validation Accuracy: 0.554, Loss: 0.794\n", "Epoch 0 Batch 340/538 - Train Accuracy: 0.531, Validation Accuracy: 0.564, Loss: 0.842\n", "Epoch 0 Batch 341/538 - Train Accuracy: 0.531, Validation Accuracy: 0.564, Loss: 0.807\n", "Epoch 0 Batch 342/538 - Train Accuracy: 0.557, Validation Accuracy: 0.564, Loss: 0.783\n", "Epoch 0 Batch 343/538 - Train Accuracy: 0.556, Validation Accuracy: 0.556, Loss: 0.837\n", "Epoch 0 Batch 344/538 - Train Accuracy: 0.543, Validation Accuracy: 0.567, Loss: 0.805\n", "Epoch 0 Batch 345/538 - Train Accuracy: 0.572, Validation Accuracy: 0.563, Loss: 0.779\n", "Epoch 0 Batch 346/538 - Train Accuracy: 0.565, Validation Accuracy: 0.572, Loss: 0.804\n", "Epoch 0 Batch 347/538 - Train Accuracy: 0.556, Validation Accuracy: 0.583, Loss: 0.814\n", "Epoch 0 Batch 348/538 - Train Accuracy: 0.562, Validation Accuracy: 0.582, Loss: 0.764\n", "Epoch 0 Batch 349/538 - Train Accuracy: 0.533, Validation Accuracy: 0.573, Loss: 0.797\n", "Epoch 0 Batch 350/538 - Train Accuracy: 0.575, Validation Accuracy: 0.585, Loss: 0.807\n", "Epoch 0 Batch 351/538 - Train Accuracy: 0.554, Validation Accuracy: 0.578, Loss: 0.827\n", "Epoch 0 Batch 352/538 - Train Accuracy: 0.596, Validation Accuracy: 0.575, Loss: 0.782\n", "Epoch 0 Batch 353/538 - Train Accuracy: 0.577, Validation Accuracy: 0.576, Loss: 0.815\n", "Epoch 0 Batch 354/538 - Train Accuracy: 0.553, Validation Accuracy: 0.578, Loss: 0.809\n", "Epoch 0 Batch 355/538 - Train Accuracy: 0.548, Validation Accuracy: 0.586, Loss: 0.808\n", "Epoch 0 Batch 356/538 - Train Accuracy: 0.572, Validation Accuracy: 0.589, Loss: 0.741\n", "Epoch 0 Batch 357/538 - Train Accuracy: 0.585, Validation Accuracy: 0.595, Loss: 0.775\n", "Epoch 0 Batch 358/538 - Train Accuracy: 0.570, Validation Accuracy: 0.594, Loss: 0.785\n", "Epoch 0 Batch 359/538 - Train Accuracy: 0.591, Validation Accuracy: 0.591, Loss: 0.765\n", "Epoch 0 Batch 360/538 - Train Accuracy: 0.578, Validation Accuracy: 0.593, Loss: 0.795\n", "Epoch 0 Batch 361/538 - Train Accuracy: 0.577, Validation Accuracy: 0.584, Loss: 0.761\n", "Epoch 0 Batch 362/538 - Train Accuracy: 0.577, Validation Accuracy: 0.588, Loss: 0.745\n", "Epoch 0 Batch 363/538 - Train Accuracy: 0.588, Validation Accuracy: 0.586, Loss: 0.748\n", "Epoch 0 Batch 364/538 - Train Accuracy: 0.550, Validation Accuracy: 0.578, Loss: 0.807\n", "Epoch 0 Batch 365/538 - Train Accuracy: 0.578, Validation Accuracy: 0.583, Loss: 0.771\n", "Epoch 0 Batch 366/538 - Train Accuracy: 0.577, Validation Accuracy: 0.585, Loss: 0.784\n", "Epoch 0 Batch 367/538 - Train Accuracy: 0.575, Validation Accuracy: 0.583, Loss: 0.753\n", "Epoch 0 Batch 368/538 - Train Accuracy: 0.645, Validation Accuracy: 0.589, Loss: 0.679\n", "Epoch 0 Batch 369/538 - Train Accuracy: 0.578, Validation Accuracy: 0.593, Loss: 0.763\n", "Epoch 0 Batch 370/538 - Train Accuracy: 0.579, Validation Accuracy: 0.594, Loss: 0.794\n", "Epoch 0 Batch 371/538 - Train Accuracy: 0.579, Validation Accuracy: 0.592, Loss: 0.748\n", "Epoch 0 Batch 372/538 - Train Accuracy: 0.576, Validation Accuracy: 0.598, Loss: 0.768\n", "Epoch 0 Batch 373/538 - Train Accuracy: 0.572, Validation Accuracy: 0.605, Loss: 0.749\n", "Epoch 0 Batch 374/538 - Train Accuracy: 0.558, Validation Accuracy: 0.596, Loss: 0.770\n", "Epoch 0 Batch 375/538 - Train Accuracy: 0.598, Validation Accuracy: 0.598, Loss: 0.721\n", "Epoch 0 Batch 376/538 - Train Accuracy: 0.578, Validation Accuracy: 0.597, Loss: 0.768\n", "Epoch 0 Batch 377/538 - Train Accuracy: 0.579, Validation Accuracy: 0.598, Loss: 0.756\n", "Epoch 0 Batch 378/538 - Train Accuracy: 0.610, Validation Accuracy: 0.599, Loss: 0.708\n", "Epoch 0 Batch 379/538 - Train Accuracy: 0.602, Validation Accuracy: 0.598, Loss: 0.728\n", "Epoch 0 Batch 380/538 - Train Accuracy: 0.583, Validation Accuracy: 0.596, Loss: 0.747\n", "Epoch 0 Batch 381/538 - Train Accuracy: 0.575, Validation Accuracy: 0.599, Loss: 0.707\n", "Epoch 0 Batch 382/538 - Train Accuracy: 0.586, Validation Accuracy: 0.598, Loss: 0.758\n", "Epoch 0 Batch 383/538 - Train Accuracy: 0.574, Validation Accuracy: 0.592, Loss: 0.752\n", "Epoch 0 Batch 384/538 - Train Accuracy: 0.610, Validation Accuracy: 0.597, Loss: 0.724\n", "Epoch 0 Batch 385/538 - Train Accuracy: 0.605, Validation Accuracy: 0.600, Loss: 0.728\n", "Epoch 0 Batch 386/538 - Train Accuracy: 0.588, Validation Accuracy: 0.600, Loss: 0.762\n", "Epoch 0 Batch 387/538 - Train Accuracy: 0.576, Validation Accuracy: 0.600, Loss: 0.748\n", "Epoch 0 Batch 388/538 - Train Accuracy: 0.591, Validation Accuracy: 0.599, Loss: 0.725\n", "Epoch 0 Batch 389/538 - Train Accuracy: 0.573, Validation Accuracy: 0.599, Loss: 0.782\n", "Epoch 0 Batch 390/538 - Train Accuracy: 0.620, Validation Accuracy: 0.596, Loss: 0.716\n", "Epoch 0 Batch 391/538 - Train Accuracy: 0.596, Validation Accuracy: 0.603, Loss: 0.733\n", "Epoch 0 Batch 392/538 - Train Accuracy: 0.588, Validation Accuracy: 0.605, Loss: 0.720\n", "Epoch 0 Batch 393/538 - Train Accuracy: 0.596, Validation Accuracy: 0.603, Loss: 0.704\n", "Epoch 0 Batch 394/538 - Train Accuracy: 0.538, Validation Accuracy: 0.605, Loss: 0.757\n", "Epoch 0 Batch 395/538 - Train Accuracy: 0.586, Validation Accuracy: 0.607, Loss: 0.757\n", "Epoch 0 Batch 396/538 - Train Accuracy: 0.577, Validation Accuracy: 0.605, Loss: 0.737\n", "Epoch 0 Batch 397/538 - Train Accuracy: 0.571, Validation Accuracy: 0.602, Loss: 0.762\n", "Epoch 0 Batch 398/538 - Train Accuracy: 0.563, Validation Accuracy: 0.587, Loss: 0.744\n", "Epoch 0 Batch 399/538 - Train Accuracy: 0.547, Validation Accuracy: 0.588, Loss: 0.771\n", "Epoch 0 Batch 400/538 - Train Accuracy: 0.579, Validation Accuracy: 0.604, Loss: 0.721\n", "Epoch 0 Batch 401/538 - Train Accuracy: 0.590, Validation Accuracy: 0.603, Loss: 0.752\n", "Epoch 0 Batch 402/538 - Train Accuracy: 0.574, Validation Accuracy: 0.593, Loss: 0.719\n", "Epoch 0 Batch 403/538 - Train Accuracy: 0.572, Validation Accuracy: 0.610, Loss: 0.740\n", "Epoch 0 Batch 404/538 - Train Accuracy: 0.599, Validation Accuracy: 0.612, Loss: 0.701\n", "Epoch 0 Batch 405/538 - Train Accuracy: 0.581, Validation Accuracy: 0.594, Loss: 0.719\n", "Epoch 0 Batch 406/538 - Train Accuracy: 0.596, Validation Accuracy: 0.595, Loss: 0.715\n", "Epoch 0 Batch 407/538 - Train Accuracy: 0.606, Validation Accuracy: 0.601, Loss: 0.738\n", "Epoch 0 Batch 408/538 - Train Accuracy: 0.550, Validation Accuracy: 0.605, Loss: 0.789\n", "Epoch 0 Batch 409/538 - Train Accuracy: 0.576, Validation Accuracy: 0.608, Loss: 0.753\n", "Epoch 0 Batch 410/538 - Train Accuracy: 0.594, Validation Accuracy: 0.610, Loss: 0.718\n", "Epoch 0 Batch 411/538 - Train Accuracy: 0.586, Validation Accuracy: 0.617, Loss: 0.693\n", "Epoch 0 Batch 412/538 - Train Accuracy: 0.612, Validation Accuracy: 0.615, Loss: 0.686\n", "Epoch 0 Batch 413/538 - Train Accuracy: 0.591, Validation Accuracy: 0.604, Loss: 0.721\n", "Epoch 0 Batch 414/538 - Train Accuracy: 0.588, Validation Accuracy: 0.601, Loss: 0.743\n", "Epoch 0 Batch 415/538 - Train Accuracy: 0.576, Validation Accuracy: 0.599, Loss: 0.725\n", "Epoch 0 Batch 416/538 - Train Accuracy: 0.635, Validation Accuracy: 0.601, Loss: 0.684\n", "Epoch 0 Batch 417/538 - Train Accuracy: 0.590, Validation Accuracy: 0.610, Loss: 0.732\n", "Epoch 0 Batch 418/538 - Train Accuracy: 0.561, Validation Accuracy: 0.612, Loss: 0.735\n", "Epoch 0 Batch 419/538 - Train Accuracy: 0.601, Validation Accuracy: 0.606, Loss: 0.697\n", "Epoch 0 Batch 420/538 - Train Accuracy: 0.592, Validation Accuracy: 0.611, Loss: 0.708\n", "Epoch 0 Batch 421/538 - Train Accuracy: 0.606, Validation Accuracy: 0.613, Loss: 0.682\n", "Epoch 0 Batch 422/538 - Train Accuracy: 0.602, Validation Accuracy: 0.610, Loss: 0.711\n", "Epoch 0 Batch 423/538 - Train Accuracy: 0.598, Validation Accuracy: 0.601, Loss: 0.731\n", "Epoch 0 Batch 424/538 - Train Accuracy: 0.598, Validation Accuracy: 0.607, Loss: 0.709\n", "Epoch 0 Batch 425/538 - Train Accuracy: 0.598, Validation Accuracy: 0.610, Loss: 0.699\n", "Epoch 0 Batch 426/538 - Train Accuracy: 0.632, Validation Accuracy: 0.617, Loss: 0.696\n", "Epoch 0 Batch 427/538 - Train Accuracy: 0.590, Validation Accuracy: 0.618, Loss: 0.720\n", "Epoch 0 Batch 428/538 - Train Accuracy: 0.623, Validation Accuracy: 0.617, Loss: 0.679\n", "Epoch 0 Batch 429/538 - Train Accuracy: 0.614, Validation Accuracy: 0.617, Loss: 0.689\n", "Epoch 0 Batch 430/538 - Train Accuracy: 0.605, Validation Accuracy: 0.613, Loss: 0.712\n", "Epoch 0 Batch 431/538 - Train Accuracy: 0.603, Validation Accuracy: 0.609, Loss: 0.689\n", "Epoch 0 Batch 432/538 - Train Accuracy: 0.637, Validation Accuracy: 0.613, Loss: 0.644\n", "Epoch 0 Batch 433/538 - Train Accuracy: 0.572, Validation Accuracy: 0.609, Loss: 0.733\n", "Epoch 0 Batch 434/538 - Train Accuracy: 0.587, Validation Accuracy: 0.613, Loss: 0.728\n", "Epoch 0 Batch 435/538 - Train Accuracy: 0.588, Validation Accuracy: 0.611, Loss: 0.698\n", "Epoch 0 Batch 436/538 - Train Accuracy: 0.572, Validation Accuracy: 0.610, Loss: 0.716\n", "Epoch 0 Batch 437/538 - Train Accuracy: 0.588, Validation Accuracy: 0.613, Loss: 0.717\n", "Epoch 0 Batch 438/538 - Train Accuracy: 0.619, Validation Accuracy: 0.613, Loss: 0.691\n", "Epoch 0 Batch 439/538 - Train Accuracy: 0.612, Validation Accuracy: 0.610, Loss: 0.682\n", "Epoch 0 Batch 440/538 - Train Accuracy: 0.600, Validation Accuracy: 0.613, Loss: 0.728\n", "Epoch 0 Batch 441/538 - Train Accuracy: 0.562, Validation Accuracy: 0.612, Loss: 0.719\n", "Epoch 0 Batch 442/538 - Train Accuracy: 0.615, Validation Accuracy: 0.610, Loss: 0.626\n", "Epoch 0 Batch 443/538 - Train Accuracy: 0.617, Validation Accuracy: 0.608, Loss: 0.686\n", "Epoch 0 Batch 444/538 - Train Accuracy: 0.659, Validation Accuracy: 0.623, Loss: 0.658\n", "Epoch 0 Batch 445/538 - Train Accuracy: 0.622, Validation Accuracy: 0.622, Loss: 0.678\n", "Epoch 0 Batch 446/538 - Train Accuracy: 0.615, Validation Accuracy: 0.613, Loss: 0.659\n", "Epoch 0 Batch 447/538 - Train Accuracy: 0.578, Validation Accuracy: 0.618, Loss: 0.699\n", "Epoch 0 Batch 448/538 - Train Accuracy: 0.625, Validation Accuracy: 0.624, Loss: 0.648\n", "Epoch 0 Batch 449/538 - Train Accuracy: 0.583, Validation Accuracy: 0.616, Loss: 0.712\n", "Epoch 0 Batch 450/538 - Train Accuracy: 0.619, Validation Accuracy: 0.605, Loss: 0.691\n", "Epoch 0 Batch 451/538 - Train Accuracy: 0.593, Validation Accuracy: 0.620, Loss: 0.688\n", "Epoch 0 Batch 452/538 - Train Accuracy: 0.611, Validation Accuracy: 0.621, Loss: 0.664\n", "Epoch 0 Batch 453/538 - Train Accuracy: 0.590, Validation Accuracy: 0.615, Loss: 0.687\n", "Epoch 0 Batch 454/538 - Train Accuracy: 0.595, Validation Accuracy: 0.613, Loss: 0.672\n", "Epoch 0 Batch 455/538 - Train Accuracy: 0.637, Validation Accuracy: 0.615, Loss: 0.609\n", "Epoch 0 Batch 456/538 - Train Accuracy: 0.667, Validation Accuracy: 0.613, Loss: 0.600\n", "Epoch 0 Batch 457/538 - Train Accuracy: 0.606, Validation Accuracy: 0.619, Loss: 0.693\n", "Epoch 0 Batch 458/538 - Train Accuracy: 0.601, Validation Accuracy: 0.614, Loss: 0.658\n", "Epoch 0 Batch 459/538 - Train Accuracy: 0.616, Validation Accuracy: 0.611, Loss: 0.673\n", "Epoch 0 Batch 460/538 - Train Accuracy: 0.591, Validation Accuracy: 0.612, Loss: 0.653\n", "Epoch 0 Batch 461/538 - Train Accuracy: 0.560, Validation Accuracy: 0.610, Loss: 0.720\n", "Epoch 0 Batch 462/538 - Train Accuracy: 0.603, Validation Accuracy: 0.616, Loss: 0.667\n", "Epoch 0 Batch 463/538 - Train Accuracy: 0.577, Validation Accuracy: 0.623, Loss: 0.688\n", "Epoch 0 Batch 464/538 - Train Accuracy: 0.602, Validation Accuracy: 0.621, Loss: 0.692\n", "Epoch 0 Batch 465/538 - Train Accuracy: 0.564, Validation Accuracy: 0.612, Loss: 0.688\n", "Epoch 0 Batch 466/538 - Train Accuracy: 0.590, Validation Accuracy: 0.618, Loss: 0.689\n", "Epoch 0 Batch 467/538 - Train Accuracy: 0.590, Validation Accuracy: 0.618, Loss: 0.663\n", "Epoch 0 Batch 468/538 - Train Accuracy: 0.621, Validation Accuracy: 0.615, Loss: 0.691\n", "Epoch 0 Batch 469/538 - Train Accuracy: 0.603, Validation Accuracy: 0.619, Loss: 0.690\n", "Epoch 0 Batch 470/538 - Train Accuracy: 0.626, Validation Accuracy: 0.622, Loss: 0.652\n", "Epoch 0 Batch 471/538 - Train Accuracy: 0.590, Validation Accuracy: 0.622, Loss: 0.657\n", "Epoch 0 Batch 472/538 - Train Accuracy: 0.621, Validation Accuracy: 0.624, Loss: 0.643\n", "Epoch 0 Batch 473/538 - Train Accuracy: 0.574, Validation Accuracy: 0.623, Loss: 0.679\n", "Epoch 0 Batch 474/538 - Train Accuracy: 0.607, Validation Accuracy: 0.615, Loss: 0.626\n", "Epoch 0 Batch 475/538 - Train Accuracy: 0.595, Validation Accuracy: 0.615, Loss: 0.648\n", "Epoch 0 Batch 476/538 - Train Accuracy: 0.587, Validation Accuracy: 0.621, Loss: 0.665\n", "Epoch 0 Batch 477/538 - Train Accuracy: 0.626, Validation Accuracy: 0.624, Loss: 0.666\n", "Epoch 0 Batch 478/538 - Train Accuracy: 0.623, Validation Accuracy: 0.619, Loss: 0.636\n", "Epoch 0 Batch 479/538 - Train Accuracy: 0.634, Validation Accuracy: 0.623, Loss: 0.628\n", "Epoch 0 Batch 480/538 - Train Accuracy: 0.629, Validation Accuracy: 0.621, Loss: 0.637\n", "Epoch 0 Batch 481/538 - Train Accuracy: 0.613, Validation Accuracy: 0.614, Loss: 0.626\n", "Epoch 0 Batch 482/538 - Train Accuracy: 0.623, Validation Accuracy: 0.615, Loss: 0.581\n", "Epoch 0 Batch 483/538 - Train Accuracy: 0.579, Validation Accuracy: 0.618, Loss: 0.680\n", "Epoch 0 Batch 484/538 - Train Accuracy: 0.637, Validation Accuracy: 0.622, Loss: 0.650\n", "Epoch 0 Batch 485/538 - Train Accuracy: 0.595, Validation Accuracy: 0.608, Loss: 0.647\n", "Epoch 0 Batch 486/538 - Train Accuracy: 0.617, Validation Accuracy: 0.620, Loss: 0.636\n", "Epoch 0 Batch 487/538 - Train Accuracy: 0.621, Validation Accuracy: 0.626, Loss: 0.619\n", "Epoch 0 Batch 488/538 - Train Accuracy: 0.627, Validation Accuracy: 0.618, Loss: 0.643\n", "Epoch 0 Batch 489/538 - Train Accuracy: 0.588, Validation Accuracy: 0.621, Loss: 0.666\n", "Epoch 0 Batch 490/538 - Train Accuracy: 0.598, Validation Accuracy: 0.623, Loss: 0.654\n", "Epoch 0 Batch 491/538 - Train Accuracy: 0.596, Validation Accuracy: 0.622, Loss: 0.679\n", "Epoch 0 Batch 492/538 - Train Accuracy: 0.597, Validation Accuracy: 0.619, Loss: 0.663\n", "Epoch 0 Batch 493/538 - Train Accuracy: 0.574, Validation Accuracy: 0.616, Loss: 0.640\n", "Epoch 0 Batch 494/538 - Train Accuracy: 0.601, Validation Accuracy: 0.618, Loss: 0.660\n", "Epoch 0 Batch 495/538 - Train Accuracy: 0.609, Validation Accuracy: 0.622, Loss: 0.665\n", "Epoch 0 Batch 496/538 - Train Accuracy: 0.610, Validation Accuracy: 0.620, Loss: 0.645\n", "Epoch 0 Batch 497/538 - Train Accuracy: 0.606, Validation Accuracy: 0.613, Loss: 0.631\n", "Epoch 0 Batch 498/538 - Train Accuracy: 0.580, Validation Accuracy: 0.622, Loss: 0.653\n", "Epoch 0 Batch 499/538 - Train Accuracy: 0.650, Validation Accuracy: 0.619, Loss: 0.629\n", "Epoch 0 Batch 500/538 - Train Accuracy: 0.621, Validation Accuracy: 0.602, Loss: 0.599\n", "Epoch 0 Batch 501/538 - Train Accuracy: 0.598, Validation Accuracy: 0.608, Loss: 0.637\n", "Epoch 0 Batch 502/538 - Train Accuracy: 0.590, Validation Accuracy: 0.616, Loss: 0.647\n", "Epoch 0 Batch 503/538 - Train Accuracy: 0.639, Validation Accuracy: 0.623, Loss: 0.619\n", "Epoch 0 Batch 504/538 - Train Accuracy: 0.598, Validation Accuracy: 0.605, Loss: 0.640\n", "Epoch 0 Batch 505/538 - Train Accuracy: 0.617, Validation Accuracy: 0.620, Loss: 0.644\n", "Epoch 0 Batch 506/538 - Train Accuracy: 0.631, Validation Accuracy: 0.629, Loss: 0.629\n", "Epoch 0 Batch 507/538 - Train Accuracy: 0.599, Validation Accuracy: 0.627, Loss: 0.660\n", "Epoch 0 Batch 508/538 - Train Accuracy: 0.592, Validation Accuracy: 0.616, Loss: 0.615\n", "Epoch 0 Batch 509/538 - Train Accuracy: 0.587, Validation Accuracy: 0.620, Loss: 0.651\n", "Epoch 0 Batch 510/538 - Train Accuracy: 0.626, Validation Accuracy: 0.629, Loss: 0.627\n", "Epoch 0 Batch 511/538 - Train Accuracy: 0.626, Validation Accuracy: 0.622, Loss: 0.610\n", "Epoch 0 Batch 512/538 - Train Accuracy: 0.632, Validation Accuracy: 0.621, Loss: 0.619\n", "Epoch 0 Batch 513/538 - Train Accuracy: 0.586, Validation Accuracy: 0.616, Loss: 0.643\n", "Epoch 0 Batch 514/538 - Train Accuracy: 0.596, Validation Accuracy: 0.621, Loss: 0.648\n", "Epoch 0 Batch 515/538 - Train Accuracy: 0.616, Validation Accuracy: 0.624, Loss: 0.625\n", "Epoch 0 Batch 516/538 - Train Accuracy: 0.578, Validation Accuracy: 0.618, Loss: 0.640\n", "Epoch 0 Batch 517/538 - Train Accuracy: 0.608, Validation Accuracy: 0.624, Loss: 0.617\n", "Epoch 0 Batch 518/538 - Train Accuracy: 0.593, Validation Accuracy: 0.619, Loss: 0.660\n", "Epoch 0 Batch 519/538 - Train Accuracy: 0.645, Validation Accuracy: 0.618, Loss: 0.615\n", "Epoch 0 Batch 520/538 - Train Accuracy: 0.607, Validation Accuracy: 0.615, Loss: 0.653\n", "Epoch 0 Batch 521/538 - Train Accuracy: 0.628, Validation Accuracy: 0.624, Loss: 0.655\n", "Epoch 0 Batch 522/538 - Train Accuracy: 0.570, Validation Accuracy: 0.618, Loss: 0.629\n", "Epoch 0 Batch 523/538 - Train Accuracy: 0.594, Validation Accuracy: 0.614, Loss: 0.643\n", "Epoch 0 Batch 524/538 - Train Accuracy: 0.578, Validation Accuracy: 0.622, Loss: 0.670\n", "Epoch 0 Batch 525/538 - Train Accuracy: 0.646, Validation Accuracy: 0.624, Loss: 0.611\n", "Epoch 0 Batch 526/538 - Train Accuracy: 0.602, Validation Accuracy: 0.622, Loss: 0.618\n", "Epoch 0 Batch 527/538 - Train Accuracy: 0.622, Validation Accuracy: 0.624, Loss: 0.633\n", "Epoch 0 Batch 528/538 - Train Accuracy: 0.592, Validation Accuracy: 0.625, Loss: 0.672\n", "Epoch 0 Batch 529/538 - Train Accuracy: 0.601, Validation Accuracy: 0.627, Loss: 0.618\n", "Epoch 0 Batch 530/538 - Train Accuracy: 0.596, Validation Accuracy: 0.626, Loss: 0.651\n", "Epoch 0 Batch 531/538 - Train Accuracy: 0.613, Validation Accuracy: 0.624, Loss: 0.629\n", "Epoch 0 Batch 532/538 - Train Accuracy: 0.579, Validation Accuracy: 0.622, Loss: 0.628\n", "Epoch 0 Batch 533/538 - Train Accuracy: 0.628, Validation Accuracy: 0.628, Loss: 0.629\n", "Epoch 0 Batch 534/538 - Train Accuracy: 0.616, Validation Accuracy: 0.631, Loss: 0.602\n", "Epoch 0 Batch 535/538 - Train Accuracy: 0.619, Validation Accuracy: 0.630, Loss: 0.616\n", "Epoch 0 Batch 536/538 - Train Accuracy: 0.628, Validation Accuracy: 0.625, Loss: 0.620\n", "Epoch 1 Batch 0/538 - Train Accuracy: 0.588, Validation Accuracy: 0.626, Loss: 0.629\n", "Epoch 1 Batch 1/538 - Train Accuracy: 0.604, Validation Accuracy: 0.624, Loss: 0.626\n", "Epoch 1 Batch 2/538 - Train Accuracy: 0.595, Validation Accuracy: 0.617, Loss: 0.650\n", "Epoch 1 Batch 3/538 - Train Accuracy: 0.593, Validation Accuracy: 0.618, Loss: 0.624\n", "Epoch 1 Batch 4/538 - Train Accuracy: 0.635, Validation Accuracy: 0.622, Loss: 0.619\n", "Epoch 1 Batch 5/538 - Train Accuracy: 0.587, Validation Accuracy: 0.622, Loss: 0.633\n", "Epoch 1 Batch 6/538 - Train Accuracy: 0.617, Validation Accuracy: 0.621, Loss: 0.591\n", "Epoch 1 Batch 7/538 - Train Accuracy: 0.621, Validation Accuracy: 0.624, Loss: 0.611\n", "Epoch 1 Batch 8/538 - Train Accuracy: 0.621, Validation Accuracy: 0.621, Loss: 0.614\n", "Epoch 1 Batch 9/538 - Train Accuracy: 0.615, Validation Accuracy: 0.626, Loss: 0.608\n", "Epoch 1 Batch 10/538 - Train Accuracy: 0.566, Validation Accuracy: 0.620, Loss: 0.645\n", "Epoch 1 Batch 11/538 - Train Accuracy: 0.581, Validation Accuracy: 0.624, Loss: 0.625\n", "Epoch 1 Batch 12/538 - Train Accuracy: 0.584, Validation Accuracy: 0.629, Loss: 0.624\n", "Epoch 1 Batch 13/538 - Train Accuracy: 0.638, Validation Accuracy: 0.628, Loss: 0.576\n", "Epoch 1 Batch 14/538 - Train Accuracy: 0.605, Validation Accuracy: 0.633, Loss: 0.617\n", "Epoch 1 Batch 15/538 - Train Accuracy: 0.625, Validation Accuracy: 0.623, Loss: 0.587\n", "Epoch 1 Batch 16/538 - Train Accuracy: 0.601, Validation Accuracy: 0.622, Loss: 0.592\n", "Epoch 1 Batch 17/538 - Train Accuracy: 0.612, Validation Accuracy: 0.623, Loss: 0.626\n", "Epoch 1 Batch 18/538 - Train Accuracy: 0.609, Validation Accuracy: 0.627, Loss: 0.640\n", "Epoch 1 Batch 19/538 - Train Accuracy: 0.593, Validation Accuracy: 0.627, Loss: 0.642\n", "Epoch 1 Batch 20/538 - Train Accuracy: 0.599, Validation Accuracy: 0.624, Loss: 0.614\n", "Epoch 1 Batch 21/538 - Train Accuracy: 0.575, Validation Accuracy: 0.614, Loss: 0.634\n", "Epoch 1 Batch 22/538 - Train Accuracy: 0.597, Validation Accuracy: 0.623, Loss: 0.630\n", "Epoch 1 Batch 23/538 - Train Accuracy: 0.623, Validation Accuracy: 0.626, Loss: 0.632\n", "Epoch 1 Batch 24/538 - Train Accuracy: 0.588, Validation Accuracy: 0.623, Loss: 0.622\n", "Epoch 1 Batch 25/538 - Train Accuracy: 0.598, Validation Accuracy: 0.625, Loss: 0.613\n", "Epoch 1 Batch 26/538 - Train Accuracy: 0.603, Validation Accuracy: 0.624, Loss: 0.636\n", "Epoch 1 Batch 27/538 - Train Accuracy: 0.623, Validation Accuracy: 0.627, Loss: 0.605\n", "Epoch 1 Batch 28/538 - Train Accuracy: 0.632, Validation Accuracy: 0.630, Loss: 0.558\n", "Epoch 1 Batch 29/538 - Train Accuracy: 0.625, Validation Accuracy: 0.640, Loss: 0.580\n", "Epoch 1 Batch 30/538 - Train Accuracy: 0.607, Validation Accuracy: 0.629, Loss: 0.630\n", "Epoch 1 Batch 31/538 - Train Accuracy: 0.604, Validation Accuracy: 0.629, Loss: 0.576\n", "Epoch 1 Batch 32/538 - Train Accuracy: 0.609, Validation Accuracy: 0.631, Loss: 0.584\n", "Epoch 1 Batch 33/538 - Train Accuracy: 0.631, Validation Accuracy: 0.629, Loss: 0.595\n", "Epoch 1 Batch 34/538 - Train Accuracy: 0.618, Validation Accuracy: 0.632, Loss: 0.612\n", "Epoch 1 Batch 35/538 - Train Accuracy: 0.613, Validation Accuracy: 0.629, Loss: 0.603\n", "Epoch 1 Batch 36/538 - Train Accuracy: 0.624, Validation Accuracy: 0.625, Loss: 0.586\n", "Epoch 1 Batch 37/538 - Train Accuracy: 0.606, Validation Accuracy: 0.635, Loss: 0.595\n", "Epoch 1 Batch 38/538 - Train Accuracy: 0.600, Validation Accuracy: 0.640, Loss: 0.604\n", "Epoch 1 Batch 39/538 - Train Accuracy: 0.625, Validation Accuracy: 0.622, Loss: 0.608\n", "Epoch 1 Batch 40/538 - Train Accuracy: 0.660, Validation Accuracy: 0.620, Loss: 0.546\n", "Epoch 1 Batch 41/538 - Train Accuracy: 0.612, Validation Accuracy: 0.626, Loss: 0.610\n", "Epoch 1 Batch 42/538 - Train Accuracy: 0.624, Validation Accuracy: 0.623, Loss: 0.597\n", "Epoch 1 Batch 43/538 - Train Accuracy: 0.633, Validation Accuracy: 0.627, Loss: 0.623\n", "Epoch 1 Batch 44/538 - Train Accuracy: 0.601, Validation Accuracy: 0.622, Loss: 0.619\n", "Epoch 1 Batch 45/538 - Train Accuracy: 0.617, Validation Accuracy: 0.627, Loss: 0.574\n", "Epoch 1 Batch 46/538 - Train Accuracy: 0.628, Validation Accuracy: 0.626, Loss: 0.588\n", "Epoch 1 Batch 47/538 - Train Accuracy: 0.629, Validation Accuracy: 0.628, Loss: 0.597\n", "Epoch 1 Batch 48/538 - Train Accuracy: 0.645, Validation Accuracy: 0.629, Loss: 0.559\n", "Epoch 1 Batch 49/538 - Train Accuracy: 0.610, Validation Accuracy: 0.628, Loss: 0.623\n", "Epoch 1 Batch 50/538 - Train Accuracy: 0.627, Validation Accuracy: 0.628, Loss: 0.599\n", "Epoch 1 Batch 51/538 - Train Accuracy: 0.584, Validation Accuracy: 0.632, Loss: 0.645\n", "Epoch 1 Batch 52/538 - Train Accuracy: 0.637, Validation Accuracy: 0.632, Loss: 0.610\n", "Epoch 1 Batch 53/538 - Train Accuracy: 0.647, Validation Accuracy: 0.632, Loss: 0.548\n", "Epoch 1 Batch 54/538 - Train Accuracy: 0.635, Validation Accuracy: 0.638, Loss: 0.587\n", "Epoch 1 Batch 55/538 - Train Accuracy: 0.602, Validation Accuracy: 0.637, Loss: 0.601\n", "Epoch 1 Batch 56/538 - Train Accuracy: 0.608, Validation Accuracy: 0.631, Loss: 0.580\n", "Epoch 1 Batch 57/538 - Train Accuracy: 0.579, Validation Accuracy: 0.625, Loss: 0.624\n", "Epoch 1 Batch 58/538 - Train Accuracy: 0.602, Validation Accuracy: 0.625, Loss: 0.614\n", "Epoch 1 Batch 59/538 - Train Accuracy: 0.609, Validation Accuracy: 0.628, Loss: 0.601\n", "Epoch 1 Batch 60/538 - Train Accuracy: 0.623, Validation Accuracy: 0.627, Loss: 0.593\n", "Epoch 1 Batch 61/538 - Train Accuracy: 0.611, Validation Accuracy: 0.627, Loss: 0.578\n", "Epoch 1 Batch 62/538 - Train Accuracy: 0.632, Validation Accuracy: 0.622, Loss: 0.576\n", "Epoch 1 Batch 63/538 - Train Accuracy: 0.626, Validation Accuracy: 0.624, Loss: 0.557\n", "Epoch 1 Batch 64/538 - Train Accuracy: 0.618, Validation Accuracy: 0.626, Loss: 0.557\n", "Epoch 1 Batch 65/538 - Train Accuracy: 0.606, Validation Accuracy: 0.628, Loss: 0.607\n", "Epoch 1 Batch 66/538 - Train Accuracy: 0.646, Validation Accuracy: 0.629, Loss: 0.546\n", "Epoch 1 Batch 67/538 - Train Accuracy: 0.636, Validation Accuracy: 0.629, Loss: 0.571\n", "Epoch 1 Batch 68/538 - Train Accuracy: 0.645, Validation Accuracy: 0.638, Loss: 0.547\n", "Epoch 1 Batch 69/538 - Train Accuracy: 0.623, Validation Accuracy: 0.628, Loss: 0.589\n", "Epoch 1 Batch 70/538 - Train Accuracy: 0.628, Validation Accuracy: 0.633, Loss: 0.559\n", "Epoch 1 Batch 71/538 - Train Accuracy: 0.601, Validation Accuracy: 0.634, Loss: 0.597\n", "Epoch 1 Batch 72/538 - Train Accuracy: 0.638, Validation Accuracy: 0.624, Loss: 0.576\n", "Epoch 1 Batch 73/538 - Train Accuracy: 0.602, Validation Accuracy: 0.629, Loss: 0.594\n", "Epoch 1 Batch 74/538 - Train Accuracy: 0.647, Validation Accuracy: 0.629, Loss: 0.558\n", "Epoch 1 Batch 75/538 - Train Accuracy: 0.642, Validation Accuracy: 0.626, Loss: 0.545\n", "Epoch 1 Batch 76/538 - Train Accuracy: 0.625, Validation Accuracy: 0.625, Loss: 0.588\n", "Epoch 1 Batch 77/538 - Train Accuracy: 0.627, Validation Accuracy: 0.634, Loss: 0.582\n", "Epoch 1 Batch 78/538 - Train Accuracy: 0.641, Validation Accuracy: 0.629, Loss: 0.572\n", "Epoch 1 Batch 79/538 - Train Accuracy: 0.628, Validation Accuracy: 0.628, Loss: 0.544\n", "Epoch 1 Batch 80/538 - Train Accuracy: 0.613, Validation Accuracy: 0.636, Loss: 0.601\n", "Epoch 1 Batch 81/538 - Train Accuracy: 0.622, Validation Accuracy: 0.634, Loss: 0.579\n", "Epoch 1 Batch 82/538 - Train Accuracy: 0.614, Validation Accuracy: 0.627, Loss: 0.585\n", "Epoch 1 Batch 83/538 - Train Accuracy: 0.632, Validation Accuracy: 0.636, Loss: 0.581\n", "Epoch 1 Batch 84/538 - Train Accuracy: 0.606, Validation Accuracy: 0.636, Loss: 0.576\n", "Epoch 1 Batch 85/538 - Train Accuracy: 0.647, Validation Accuracy: 0.628, Loss: 0.529\n", "Epoch 1 Batch 86/538 - Train Accuracy: 0.631, Validation Accuracy: 0.631, Loss: 0.585\n", "Epoch 1 Batch 87/538 - Train Accuracy: 0.611, Validation Accuracy: 0.634, Loss: 0.578\n", "Epoch 1 Batch 88/538 - Train Accuracy: 0.652, Validation Accuracy: 0.625, Loss: 0.572\n", "Epoch 1 Batch 89/538 - Train Accuracy: 0.633, Validation Accuracy: 0.623, Loss: 0.566\n", "Epoch 1 Batch 90/538 - Train Accuracy: 0.638, Validation Accuracy: 0.635, Loss: 0.572\n", "Epoch 1 Batch 91/538 - Train Accuracy: 0.626, Validation Accuracy: 0.624, Loss: 0.575\n", "Epoch 1 Batch 92/538 - Train Accuracy: 0.631, Validation Accuracy: 0.615, Loss: 0.587\n", "Epoch 1 Batch 93/538 - Train Accuracy: 0.609, Validation Accuracy: 0.627, Loss: 0.579\n", "Epoch 1 Batch 94/538 - Train Accuracy: 0.638, Validation Accuracy: 0.628, Loss: 0.563\n", "Epoch 1 Batch 95/538 - Train Accuracy: 0.659, Validation Accuracy: 0.627, Loss: 0.526\n", "Epoch 1 Batch 96/538 - Train Accuracy: 0.650, Validation Accuracy: 0.631, Loss: 0.529\n", "Epoch 1 Batch 97/538 - Train Accuracy: 0.624, Validation Accuracy: 0.631, Loss: 0.569\n", "Epoch 1 Batch 98/538 - Train Accuracy: 0.661, Validation Accuracy: 0.625, Loss: 0.528\n", "Epoch 1 Batch 99/538 - Train Accuracy: 0.615, Validation Accuracy: 0.624, Loss: 0.570\n", "Epoch 1 Batch 100/538 - Train Accuracy: 0.627, Validation Accuracy: 0.627, Loss: 0.552\n", "Epoch 1 Batch 101/538 - Train Accuracy: 0.594, Validation Accuracy: 0.630, Loss: 0.580\n", "Epoch 1 Batch 102/538 - Train Accuracy: 0.642, Validation Accuracy: 0.634, Loss: 0.558\n", "Epoch 1 Batch 103/538 - Train Accuracy: 0.617, Validation Accuracy: 0.638, Loss: 0.558\n", "Epoch 1 Batch 104/538 - Train Accuracy: 0.641, Validation Accuracy: 0.641, Loss: 0.545\n", "Epoch 1 Batch 105/538 - Train Accuracy: 0.613, Validation Accuracy: 0.631, Loss: 0.534\n", "Epoch 1 Batch 106/538 - Train Accuracy: 0.635, Validation Accuracy: 0.639, Loss: 0.546\n", "Epoch 1 Batch 107/538 - Train Accuracy: 0.611, Validation Accuracy: 0.630, Loss: 0.575\n", "Epoch 1 Batch 108/538 - Train Accuracy: 0.641, Validation Accuracy: 0.617, Loss: 0.569\n", "Epoch 1 Batch 109/538 - Train Accuracy: 0.655, Validation Accuracy: 0.628, Loss: 0.550\n", "Epoch 1 Batch 110/538 - Train Accuracy: 0.620, Validation Accuracy: 0.635, Loss: 0.566\n", "Epoch 1 Batch 111/538 - Train Accuracy: 0.646, Validation Accuracy: 0.629, Loss: 0.526\n", "Epoch 1 Batch 112/538 - Train Accuracy: 0.651, Validation Accuracy: 0.641, Loss: 0.563\n", "Epoch 1 Batch 113/538 - Train Accuracy: 0.636, Validation Accuracy: 0.646, Loss: 0.574\n", "Epoch 1 Batch 114/538 - Train Accuracy: 0.674, Validation Accuracy: 0.643, Loss: 0.536\n", "Epoch 1 Batch 115/538 - Train Accuracy: 0.629, Validation Accuracy: 0.634, Loss: 0.569\n", "Epoch 1 Batch 116/538 - Train Accuracy: 0.641, Validation Accuracy: 0.650, Loss: 0.558\n", "Epoch 1 Batch 117/538 - Train Accuracy: 0.657, Validation Accuracy: 0.641, Loss: 0.528\n", "Epoch 1 Batch 118/538 - Train Accuracy: 0.665, Validation Accuracy: 0.634, Loss: 0.531\n", "Epoch 1 Batch 119/538 - Train Accuracy: 0.670, Validation Accuracy: 0.642, Loss: 0.512\n", "Epoch 1 Batch 120/538 - Train Accuracy: 0.629, Validation Accuracy: 0.632, Loss: 0.530\n", "Epoch 1 Batch 121/538 - 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Train Accuracy: 0.644, Validation Accuracy: 0.651, Loss: 0.472\n", "Epoch 1 Batch 221/538 - Train Accuracy: 0.680, Validation Accuracy: 0.668, Loss: 0.469\n", "Epoch 1 Batch 222/538 - Train Accuracy: 0.683, Validation Accuracy: 0.653, Loss: 0.464\n", "Epoch 1 Batch 223/538 - Train Accuracy: 0.651, Validation Accuracy: 0.663, Loss: 0.512\n", "Epoch 1 Batch 224/538 - Train Accuracy: 0.649, Validation Accuracy: 0.666, Loss: 0.511\n", "Epoch 1 Batch 225/538 - Train Accuracy: 0.674, Validation Accuracy: 0.647, Loss: 0.481\n", "Epoch 1 Batch 226/538 - Train Accuracy: 0.670, Validation Accuracy: 0.675, Loss: 0.501\n", "Epoch 1 Batch 227/538 - Train Accuracy: 0.678, Validation Accuracy: 0.674, Loss: 0.474\n", "Epoch 1 Batch 228/538 - Train Accuracy: 0.659, Validation Accuracy: 0.647, Loss: 0.474\n", "Epoch 1 Batch 229/538 - Train Accuracy: 0.697, Validation Accuracy: 0.673, Loss: 0.493\n", "Epoch 1 Batch 230/538 - Train Accuracy: 0.649, Validation Accuracy: 0.663, Loss: 0.498\n", "Epoch 1 Batch 231/538 - 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Train Accuracy: 0.687, Validation Accuracy: 0.660, Loss: 0.480\n", "Epoch 1 Batch 243/538 - Train Accuracy: 0.658, Validation Accuracy: 0.661, Loss: 0.506\n", "Epoch 1 Batch 244/538 - Train Accuracy: 0.676, Validation Accuracy: 0.655, Loss: 0.466\n", "Epoch 1 Batch 245/538 - Train Accuracy: 0.682, Validation Accuracy: 0.667, Loss: 0.491\n", "Epoch 1 Batch 246/538 - Train Accuracy: 0.693, Validation Accuracy: 0.664, Loss: 0.447\n", "Epoch 1 Batch 247/538 - Train Accuracy: 0.679, Validation Accuracy: 0.670, Loss: 0.477\n", "Epoch 1 Batch 248/538 - Train Accuracy: 0.688, Validation Accuracy: 0.670, Loss: 0.482\n", "Epoch 1 Batch 249/538 - Train Accuracy: 0.679, Validation Accuracy: 0.671, Loss: 0.450\n", "Epoch 1 Batch 250/538 - Train Accuracy: 0.690, Validation Accuracy: 0.673, Loss: 0.481\n", "Epoch 1 Batch 251/538 - Train Accuracy: 0.678, Validation Accuracy: 0.658, Loss: 0.475\n", "Epoch 1 Batch 252/538 - Train Accuracy: 0.695, Validation Accuracy: 0.666, Loss: 0.449\n", "Epoch 1 Batch 253/538 - Train Accuracy: 0.675, Validation Accuracy: 0.663, Loss: 0.458\n", "Epoch 1 Batch 254/538 - Train Accuracy: 0.681, Validation Accuracy: 0.668, Loss: 0.483\n", "Epoch 1 Batch 255/538 - Train Accuracy: 0.674, Validation Accuracy: 0.666, Loss: 0.475\n", "Epoch 1 Batch 256/538 - Train Accuracy: 0.660, Validation Accuracy: 0.669, Loss: 0.478\n", "Epoch 1 Batch 257/538 - Train Accuracy: 0.712, Validation Accuracy: 0.673, Loss: 0.457\n", "Epoch 1 Batch 258/538 - Train Accuracy: 0.695, Validation Accuracy: 0.675, Loss: 0.456\n", "Epoch 1 Batch 259/538 - Train Accuracy: 0.717, Validation Accuracy: 0.677, Loss: 0.450\n", "Epoch 1 Batch 260/538 - Train Accuracy: 0.668, Validation Accuracy: 0.677, Loss: 0.471\n", "Epoch 1 Batch 261/538 - Train Accuracy: 0.670, Validation Accuracy: 0.667, Loss: 0.479\n", "Epoch 1 Batch 262/538 - Train Accuracy: 0.685, Validation Accuracy: 0.671, Loss: 0.469\n", "Epoch 1 Batch 263/538 - Train Accuracy: 0.687, Validation Accuracy: 0.683, Loss: 0.455\n", "Epoch 1 Batch 264/538 - Train Accuracy: 0.674, Validation Accuracy: 0.672, Loss: 0.470\n", "Epoch 1 Batch 265/538 - Train Accuracy: 0.664, Validation Accuracy: 0.670, Loss: 0.483\n", "Epoch 1 Batch 266/538 - Train Accuracy: 0.680, Validation Accuracy: 0.666, Loss: 0.464\n", "Epoch 1 Batch 267/538 - Train Accuracy: 0.685, Validation Accuracy: 0.678, Loss: 0.472\n", "Epoch 1 Batch 268/538 - Train Accuracy: 0.699, Validation Accuracy: 0.676, Loss: 0.448\n", "Epoch 1 Batch 269/538 - Train Accuracy: 0.689, Validation Accuracy: 0.669, Loss: 0.462\n", "Epoch 1 Batch 270/538 - Train Accuracy: 0.676, Validation Accuracy: 0.674, Loss: 0.470\n", "Epoch 1 Batch 271/538 - Train Accuracy: 0.674, Validation Accuracy: 0.678, Loss: 0.475\n", "Epoch 1 Batch 272/538 - Train Accuracy: 0.673, Validation Accuracy: 0.684, Loss: 0.500\n", "Epoch 1 Batch 273/538 - Train Accuracy: 0.703, Validation Accuracy: 0.680, Loss: 0.468\n", "Epoch 1 Batch 274/538 - Train Accuracy: 0.665, Validation Accuracy: 0.682, Loss: 0.498\n", "Epoch 1 Batch 275/538 - Train Accuracy: 0.668, Validation Accuracy: 0.686, Loss: 0.480\n", "Epoch 1 Batch 276/538 - Train Accuracy: 0.699, Validation Accuracy: 0.674, Loss: 0.471\n", "Epoch 1 Batch 277/538 - Train Accuracy: 0.701, Validation Accuracy: 0.689, Loss: 0.462\n", "Epoch 1 Batch 278/538 - Train Accuracy: 0.710, Validation Accuracy: 0.688, Loss: 0.453\n", "Epoch 1 Batch 279/538 - Train Accuracy: 0.707, Validation Accuracy: 0.678, Loss: 0.451\n", "Epoch 1 Batch 280/538 - Train Accuracy: 0.731, Validation Accuracy: 0.685, Loss: 0.438\n", "Epoch 1 Batch 281/538 - Train Accuracy: 0.671, Validation Accuracy: 0.691, Loss: 0.473\n", "Epoch 1 Batch 282/538 - Train Accuracy: 0.696, Validation Accuracy: 0.695, Loss: 0.464\n", "Epoch 1 Batch 283/538 - Train Accuracy: 0.696, Validation Accuracy: 0.687, Loss: 0.457\n", "Epoch 1 Batch 284/538 - Train Accuracy: 0.674, Validation Accuracy: 0.687, Loss: 0.459\n", "Epoch 1 Batch 285/538 - Train Accuracy: 0.705, Validation Accuracy: 0.690, Loss: 0.433\n", "Epoch 1 Batch 286/538 - 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Train Accuracy: 0.678, Validation Accuracy: 0.681, Loss: 0.472\n", "Epoch 1 Batch 298/538 - Train Accuracy: 0.685, Validation Accuracy: 0.694, Loss: 0.440\n", "Epoch 1 Batch 299/538 - Train Accuracy: 0.705, Validation Accuracy: 0.679, Loss: 0.449\n", "Epoch 1 Batch 300/538 - Train Accuracy: 0.709, Validation Accuracy: 0.673, Loss: 0.437\n", "Epoch 1 Batch 301/538 - Train Accuracy: 0.699, Validation Accuracy: 0.689, Loss: 0.445\n", "Epoch 1 Batch 302/538 - Train Accuracy: 0.710, Validation Accuracy: 0.685, Loss: 0.427\n", "Epoch 1 Batch 303/538 - Train Accuracy: 0.723, Validation Accuracy: 0.696, Loss: 0.433\n", "Epoch 1 Batch 304/538 - Train Accuracy: 0.691, Validation Accuracy: 0.668, Loss: 0.453\n", "Epoch 1 Batch 305/538 - Train Accuracy: 0.723, Validation Accuracy: 0.680, Loss: 0.433\n", "Epoch 1 Batch 306/538 - Train Accuracy: 0.699, Validation Accuracy: 0.679, Loss: 0.432\n", "Epoch 1 Batch 307/538 - Train Accuracy: 0.715, Validation Accuracy: 0.694, Loss: 0.455\n", "Epoch 1 Batch 308/538 - 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Train Accuracy: 0.728, Validation Accuracy: 0.703, Loss: 0.432\n", "Epoch 1 Batch 320/538 - Train Accuracy: 0.692, Validation Accuracy: 0.706, Loss: 0.429\n", "Epoch 1 Batch 321/538 - Train Accuracy: 0.717, Validation Accuracy: 0.698, Loss: 0.410\n", "Epoch 1 Batch 322/538 - Train Accuracy: 0.727, Validation Accuracy: 0.703, Loss: 0.430\n", "Epoch 1 Batch 323/538 - Train Accuracy: 0.715, Validation Accuracy: 0.698, Loss: 0.422\n", "Epoch 1 Batch 324/538 - Train Accuracy: 0.694, Validation Accuracy: 0.695, Loss: 0.453\n", "Epoch 1 Batch 325/538 - Train Accuracy: 0.729, Validation Accuracy: 0.697, Loss: 0.428\n", "Epoch 1 Batch 326/538 - Train Accuracy: 0.689, Validation Accuracy: 0.677, Loss: 0.433\n", "Epoch 1 Batch 327/538 - Train Accuracy: 0.692, Validation Accuracy: 0.689, Loss: 0.436\n", "Epoch 1 Batch 328/538 - Train Accuracy: 0.713, Validation Accuracy: 0.694, Loss: 0.419\n", "Epoch 1 Batch 329/538 - Train Accuracy: 0.727, Validation Accuracy: 0.681, Loss: 0.432\n", "Epoch 1 Batch 330/538 - 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Train Accuracy: 0.722, Validation Accuracy: 0.718, Loss: 0.386\n", "Epoch 1 Batch 430/538 - Train Accuracy: 0.732, Validation Accuracy: 0.715, Loss: 0.377\n", "Epoch 1 Batch 431/538 - Train Accuracy: 0.731, Validation Accuracy: 0.727, Loss: 0.388\n", "Epoch 1 Batch 432/538 - Train Accuracy: 0.760, Validation Accuracy: 0.727, Loss: 0.358\n", "Epoch 1 Batch 433/538 - Train Accuracy: 0.724, Validation Accuracy: 0.720, Loss: 0.414\n", "Epoch 1 Batch 434/538 - Train Accuracy: 0.695, Validation Accuracy: 0.712, Loss: 0.407\n", "Epoch 1 Batch 435/538 - Train Accuracy: 0.729, Validation Accuracy: 0.716, Loss: 0.386\n", "Epoch 1 Batch 436/538 - Train Accuracy: 0.701, Validation Accuracy: 0.714, Loss: 0.391\n", "Epoch 1 Batch 437/538 - Train Accuracy: 0.711, Validation Accuracy: 0.714, Loss: 0.393\n", "Epoch 1 Batch 438/538 - Train Accuracy: 0.731, Validation Accuracy: 0.719, Loss: 0.375\n", "Epoch 1 Batch 439/538 - Train Accuracy: 0.754, Validation Accuracy: 0.734, Loss: 0.371\n", "Epoch 1 Batch 440/538 - 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Train Accuracy: 0.738, Validation Accuracy: 0.735, Loss: 0.368\n", "Epoch 1 Batch 452/538 - Train Accuracy: 0.746, Validation Accuracy: 0.723, Loss: 0.352\n", "Epoch 1 Batch 453/538 - Train Accuracy: 0.732, Validation Accuracy: 0.733, Loss: 0.377\n", "Epoch 1 Batch 454/538 - Train Accuracy: 0.749, Validation Accuracy: 0.728, Loss: 0.363\n", "Epoch 1 Batch 455/538 - Train Accuracy: 0.756, Validation Accuracy: 0.730, Loss: 0.347\n", "Epoch 1 Batch 456/538 - Train Accuracy: 0.789, Validation Accuracy: 0.733, Loss: 0.338\n", "Epoch 1 Batch 457/538 - Train Accuracy: 0.719, Validation Accuracy: 0.734, Loss: 0.388\n", "Epoch 1 Batch 458/538 - Train Accuracy: 0.726, Validation Accuracy: 0.735, Loss: 0.341\n", "Epoch 1 Batch 459/538 - Train Accuracy: 0.748, Validation Accuracy: 0.737, Loss: 0.371\n", "Epoch 1 Batch 460/538 - Train Accuracy: 0.708, Validation Accuracy: 0.733, Loss: 0.363\n", "Epoch 1 Batch 461/538 - Train Accuracy: 0.742, Validation Accuracy: 0.738, Loss: 0.393\n", "Epoch 1 Batch 462/538 - Train Accuracy: 0.743, Validation Accuracy: 0.739, Loss: 0.361\n", "Epoch 1 Batch 463/538 - Train Accuracy: 0.717, Validation Accuracy: 0.735, Loss: 0.375\n", "Epoch 1 Batch 464/538 - Train Accuracy: 0.749, Validation Accuracy: 0.738, Loss: 0.381\n", "Epoch 1 Batch 465/538 - Train Accuracy: 0.732, Validation Accuracy: 0.741, Loss: 0.367\n", "Epoch 1 Batch 466/538 - Train Accuracy: 0.725, Validation Accuracy: 0.727, Loss: 0.375\n", "Epoch 1 Batch 467/538 - Train Accuracy: 0.742, Validation Accuracy: 0.730, Loss: 0.368\n", "Epoch 1 Batch 468/538 - Train Accuracy: 0.753, Validation Accuracy: 0.738, Loss: 0.384\n", "Epoch 1 Batch 469/538 - Train Accuracy: 0.745, Validation Accuracy: 0.730, Loss: 0.379\n", "Epoch 1 Batch 470/538 - Train Accuracy: 0.727, Validation Accuracy: 0.740, Loss: 0.362\n", "Epoch 1 Batch 471/538 - Train Accuracy: 0.735, Validation Accuracy: 0.729, Loss: 0.357\n", "Epoch 1 Batch 472/538 - Train Accuracy: 0.770, Validation Accuracy: 0.736, Loss: 0.359\n", "Epoch 1 Batch 473/538 - Train Accuracy: 0.721, Validation Accuracy: 0.718, Loss: 0.373\n", "Epoch 1 Batch 474/538 - Train Accuracy: 0.760, Validation Accuracy: 0.715, Loss: 0.341\n", "Epoch 1 Batch 475/538 - Train Accuracy: 0.759, Validation Accuracy: 0.718, Loss: 0.360\n", "Epoch 1 Batch 476/538 - Train Accuracy: 0.731, Validation Accuracy: 0.716, Loss: 0.363\n", "Epoch 1 Batch 477/538 - Train Accuracy: 0.742, Validation Accuracy: 0.724, Loss: 0.370\n", "Epoch 1 Batch 478/538 - Train Accuracy: 0.763, Validation Accuracy: 0.712, Loss: 0.345\n", "Epoch 1 Batch 479/538 - Train Accuracy: 0.757, Validation Accuracy: 0.727, Loss: 0.346\n", "Epoch 1 Batch 480/538 - Train Accuracy: 0.736, Validation Accuracy: 0.716, Loss: 0.356\n", "Epoch 1 Batch 481/538 - Train Accuracy: 0.761, Validation Accuracy: 0.725, Loss: 0.361\n", "Epoch 1 Batch 482/538 - Train Accuracy: 0.753, Validation Accuracy: 0.725, Loss: 0.331\n", "Epoch 1 Batch 483/538 - Train Accuracy: 0.724, Validation Accuracy: 0.735, Loss: 0.380\n", "Epoch 1 Batch 484/538 - Train Accuracy: 0.757, Validation Accuracy: 0.726, Loss: 0.371\n", "Epoch 1 Batch 485/538 - Train Accuracy: 0.751, Validation Accuracy: 0.735, Loss: 0.343\n", "Epoch 1 Batch 486/538 - Train Accuracy: 0.754, Validation Accuracy: 0.726, Loss: 0.331\n", "Epoch 1 Batch 487/538 - Train Accuracy: 0.744, Validation Accuracy: 0.739, Loss: 0.336\n", "Epoch 1 Batch 488/538 - Train Accuracy: 0.748, Validation Accuracy: 0.753, Loss: 0.352\n", "Epoch 1 Batch 489/538 - Train Accuracy: 0.741, Validation Accuracy: 0.743, Loss: 0.368\n", "Epoch 1 Batch 490/538 - Train Accuracy: 0.751, Validation Accuracy: 0.748, Loss: 0.341\n", "Epoch 1 Batch 491/538 - Train Accuracy: 0.736, Validation Accuracy: 0.741, Loss: 0.376\n", "Epoch 1 Batch 492/538 - Train Accuracy: 0.756, Validation Accuracy: 0.743, Loss: 0.353\n", "Epoch 1 Batch 493/538 - Train Accuracy: 0.726, Validation Accuracy: 0.741, Loss: 0.339\n", "Epoch 1 Batch 494/538 - Train Accuracy: 0.737, Validation Accuracy: 0.748, Loss: 0.364\n", "Epoch 1 Batch 495/538 - Train Accuracy: 0.749, Validation Accuracy: 0.749, Loss: 0.355\n", "Epoch 1 Batch 496/538 - Train Accuracy: 0.756, Validation Accuracy: 0.744, Loss: 0.344\n", "Epoch 1 Batch 497/538 - Train Accuracy: 0.770, Validation Accuracy: 0.749, Loss: 0.336\n", "Epoch 1 Batch 498/538 - Train Accuracy: 0.736, Validation Accuracy: 0.736, Loss: 0.345\n", "Epoch 1 Batch 499/538 - Train Accuracy: 0.758, Validation Accuracy: 0.743, Loss: 0.328\n", "Epoch 1 Batch 500/538 - Train Accuracy: 0.779, Validation Accuracy: 0.748, Loss: 0.311\n", "Epoch 1 Batch 501/538 - Train Accuracy: 0.780, Validation Accuracy: 0.743, Loss: 0.345\n", "Epoch 1 Batch 502/538 - Train Accuracy: 0.740, Validation Accuracy: 0.747, Loss: 0.339\n", "Epoch 1 Batch 503/538 - Train Accuracy: 0.771, Validation Accuracy: 0.744, Loss: 0.334\n", "Epoch 1 Batch 504/538 - Train Accuracy: 0.756, Validation Accuracy: 0.728, Loss: 0.327\n", "Epoch 1 Batch 505/538 - Train Accuracy: 0.758, Validation Accuracy: 0.742, Loss: 0.333\n", "Epoch 1 Batch 506/538 - Train Accuracy: 0.765, Validation Accuracy: 0.736, Loss: 0.340\n", "Epoch 1 Batch 507/538 - Train Accuracy: 0.725, Validation Accuracy: 0.732, Loss: 0.364\n", "Epoch 1 Batch 508/538 - Train Accuracy: 0.756, Validation Accuracy: 0.737, Loss: 0.324\n", "Epoch 1 Batch 509/538 - Train Accuracy: 0.753, Validation Accuracy: 0.745, Loss: 0.346\n", "Epoch 1 Batch 510/538 - Train Accuracy: 0.737, Validation Accuracy: 0.761, Loss: 0.338\n", "Epoch 1 Batch 511/538 - Train Accuracy: 0.749, Validation Accuracy: 0.748, Loss: 0.330\n", "Epoch 1 Batch 512/538 - Train Accuracy: 0.773, Validation Accuracy: 0.744, Loss: 0.332\n", "Epoch 1 Batch 513/538 - Train Accuracy: 0.742, Validation Accuracy: 0.753, Loss: 0.358\n", "Epoch 1 Batch 514/538 - Train Accuracy: 0.730, Validation Accuracy: 0.727, Loss: 0.347\n", "Epoch 1 Batch 515/538 - Train Accuracy: 0.763, Validation Accuracy: 0.735, Loss: 0.345\n", "Epoch 1 Batch 516/538 - Train Accuracy: 0.735, Validation Accuracy: 0.739, Loss: 0.328\n", "Epoch 1 Batch 517/538 - Train Accuracy: 0.766, Validation Accuracy: 0.735, Loss: 0.333\n", "Epoch 1 Batch 518/538 - Train Accuracy: 0.742, Validation Accuracy: 0.729, Loss: 0.358\n", "Epoch 1 Batch 519/538 - Train Accuracy: 0.763, Validation Accuracy: 0.736, Loss: 0.331\n", "Epoch 1 Batch 520/538 - Train Accuracy: 0.754, Validation Accuracy: 0.743, Loss: 0.345\n", "Epoch 1 Batch 521/538 - Train Accuracy: 0.737, Validation Accuracy: 0.733, Loss: 0.356\n", "Epoch 1 Batch 522/538 - Train Accuracy: 0.729, Validation Accuracy: 0.725, Loss: 0.338\n", "Epoch 1 Batch 523/538 - Train Accuracy: 0.745, Validation Accuracy: 0.733, Loss: 0.349\n", "Epoch 1 Batch 524/538 - Train Accuracy: 0.729, Validation Accuracy: 0.734, Loss: 0.345\n", "Epoch 1 Batch 525/538 - Train Accuracy: 0.775, Validation Accuracy: 0.736, Loss: 0.333\n", "Epoch 1 Batch 526/538 - Train Accuracy: 0.731, Validation Accuracy: 0.723, Loss: 0.327\n", "Epoch 1 Batch 527/538 - Train Accuracy: 0.761, Validation Accuracy: 0.736, Loss: 0.345\n", "Epoch 1 Batch 528/538 - Train Accuracy: 0.724, Validation Accuracy: 0.733, Loss: 0.365\n", "Epoch 1 Batch 529/538 - Train Accuracy: 0.741, Validation Accuracy: 0.736, Loss: 0.337\n", "Epoch 1 Batch 530/538 - Train Accuracy: 0.750, Validation Accuracy: 0.741, Loss: 0.347\n", "Epoch 1 Batch 531/538 - Train Accuracy: 0.760, Validation Accuracy: 0.747, Loss: 0.335\n", "Epoch 1 Batch 532/538 - Train Accuracy: 0.772, Validation Accuracy: 0.749, Loss: 0.342\n", "Epoch 1 Batch 533/538 - Train Accuracy: 0.778, Validation Accuracy: 0.734, Loss: 0.338\n", "Epoch 1 Batch 534/538 - Train Accuracy: 0.766, Validation Accuracy: 0.749, Loss: 0.323\n", "Epoch 1 Batch 535/538 - Train Accuracy: 0.759, Validation Accuracy: 0.744, Loss: 0.324\n", "Epoch 1 Batch 536/538 - Train Accuracy: 0.770, Validation Accuracy: 0.743, Loss: 0.351\n", "Epoch 2 Batch 0/538 - Train Accuracy: 0.760, Validation Accuracy: 0.744, Loss: 0.335\n", "Epoch 2 Batch 1/538 - Train Accuracy: 0.784, Validation Accuracy: 0.743, Loss: 0.335\n", "Epoch 2 Batch 2/538 - Train Accuracy: 0.748, Validation Accuracy: 0.749, Loss: 0.350\n", "Epoch 2 Batch 3/538 - Train Accuracy: 0.753, Validation Accuracy: 0.740, Loss: 0.335\n", "Epoch 2 Batch 4/538 - Train Accuracy: 0.776, Validation Accuracy: 0.737, Loss: 0.332\n", "Epoch 2 Batch 5/538 - Train Accuracy: 0.741, Validation Accuracy: 0.754, Loss: 0.345\n", "Epoch 2 Batch 6/538 - Train Accuracy: 0.775, Validation Accuracy: 0.748, Loss: 0.332\n", "Epoch 2 Batch 7/538 - Train Accuracy: 0.759, Validation Accuracy: 0.746, Loss: 0.335\n", "Epoch 2 Batch 8/538 - Train Accuracy: 0.767, Validation Accuracy: 0.739, Loss: 0.332\n", "Epoch 2 Batch 9/538 - Train Accuracy: 0.755, Validation Accuracy: 0.745, Loss: 0.315\n", "Epoch 2 Batch 10/538 - Train Accuracy: 0.755, Validation Accuracy: 0.739, Loss: 0.346\n", "Epoch 2 Batch 11/538 - Train Accuracy: 0.750, Validation Accuracy: 0.735, Loss: 0.328\n", "Epoch 2 Batch 12/538 - Train Accuracy: 0.732, Validation Accuracy: 0.737, Loss: 0.340\n", "Epoch 2 Batch 13/538 - Train Accuracy: 0.803, Validation Accuracy: 0.732, Loss: 0.310\n", "Epoch 2 Batch 14/538 - 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Train Accuracy: 0.933, Validation Accuracy: 0.902, Loss: 0.086\n", "Epoch 3 Batch 447/538 - Train Accuracy: 0.915, Validation Accuracy: 0.903, Loss: 0.086\n", "Epoch 3 Batch 448/538 - Train Accuracy: 0.911, Validation Accuracy: 0.903, Loss: 0.082\n", "Epoch 3 Batch 449/538 - Train Accuracy: 0.922, Validation Accuracy: 0.903, Loss: 0.091\n", "Epoch 3 Batch 450/538 - Train Accuracy: 0.901, Validation Accuracy: 0.904, Loss: 0.104\n", "Epoch 3 Batch 451/538 - Train Accuracy: 0.919, Validation Accuracy: 0.909, Loss: 0.088\n", "Epoch 3 Batch 452/538 - Train Accuracy: 0.921, Validation Accuracy: 0.908, Loss: 0.083\n", "Epoch 3 Batch 453/538 - Train Accuracy: 0.922, Validation Accuracy: 0.907, Loss: 0.098\n", "Epoch 3 Batch 454/538 - Train Accuracy: 0.914, Validation Accuracy: 0.902, Loss: 0.092\n", "Epoch 3 Batch 455/538 - Train Accuracy: 0.932, Validation Accuracy: 0.907, Loss: 0.090\n", "Epoch 3 Batch 456/538 - Train Accuracy: 0.926, Validation Accuracy: 0.905, Loss: 0.107\n", "Epoch 3 Batch 457/538 - Train Accuracy: 0.915, Validation Accuracy: 0.900, Loss: 0.090\n", "Epoch 3 Batch 458/538 - Train Accuracy: 0.921, Validation Accuracy: 0.896, Loss: 0.083\n", "Epoch 3 Batch 459/538 - Train Accuracy: 0.924, Validation Accuracy: 0.902, Loss: 0.082\n", "Epoch 3 Batch 460/538 - Train Accuracy: 0.894, Validation Accuracy: 0.908, Loss: 0.091\n", "Epoch 3 Batch 461/538 - Train Accuracy: 0.938, Validation Accuracy: 0.912, Loss: 0.095\n", "Epoch 3 Batch 462/538 - Train Accuracy: 0.900, Validation Accuracy: 0.908, Loss: 0.094\n", "Epoch 3 Batch 463/538 - Train Accuracy: 0.896, Validation Accuracy: 0.900, Loss: 0.089\n", "Epoch 3 Batch 464/538 - Train Accuracy: 0.937, Validation Accuracy: 0.896, Loss: 0.084\n", "Epoch 3 Batch 465/538 - Train Accuracy: 0.912, Validation Accuracy: 0.897, Loss: 0.084\n", "Epoch 3 Batch 466/538 - Train Accuracy: 0.896, Validation Accuracy: 0.906, Loss: 0.087\n", "Epoch 3 Batch 467/538 - Train Accuracy: 0.927, Validation Accuracy: 0.900, Loss: 0.089\n", "Epoch 3 Batch 468/538 - Train Accuracy: 0.941, Validation Accuracy: 0.893, Loss: 0.095\n", "Epoch 3 Batch 469/538 - Train Accuracy: 0.943, Validation Accuracy: 0.892, Loss: 0.085\n", "Epoch 3 Batch 470/538 - Train Accuracy: 0.923, Validation Accuracy: 0.895, Loss: 0.081\n", "Epoch 3 Batch 471/538 - Train Accuracy: 0.922, Validation Accuracy: 0.898, Loss: 0.081\n", "Epoch 3 Batch 472/538 - Train Accuracy: 0.961, Validation Accuracy: 0.904, Loss: 0.073\n", "Epoch 3 Batch 473/538 - Train Accuracy: 0.913, Validation Accuracy: 0.902, Loss: 0.093\n", "Epoch 3 Batch 474/538 - Train Accuracy: 0.938, Validation Accuracy: 0.909, Loss: 0.077\n", "Epoch 3 Batch 475/538 - Train Accuracy: 0.937, Validation Accuracy: 0.918, Loss: 0.074\n", "Epoch 3 Batch 476/538 - Train Accuracy: 0.914, Validation Accuracy: 0.923, Loss: 0.078\n", "Epoch 3 Batch 477/538 - Train Accuracy: 0.904, Validation Accuracy: 0.920, Loss: 0.097\n", "Epoch 3 Batch 478/538 - Train Accuracy: 0.924, Validation Accuracy: 0.911, Loss: 0.077\n", "Epoch 3 Batch 479/538 - 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Train Accuracy: 0.940, Validation Accuracy: 0.934, Loss: 0.062\n", "Epoch 4 Batch 494/538 - Train Accuracy: 0.933, Validation Accuracy: 0.921, Loss: 0.065\n", "Epoch 4 Batch 495/538 - Train Accuracy: 0.939, Validation Accuracy: 0.922, Loss: 0.066\n", "Epoch 4 Batch 496/538 - Train Accuracy: 0.939, Validation Accuracy: 0.916, Loss: 0.051\n", "Epoch 4 Batch 497/538 - Train Accuracy: 0.948, Validation Accuracy: 0.918, Loss: 0.055\n", "Epoch 4 Batch 498/538 - Train Accuracy: 0.946, Validation Accuracy: 0.922, Loss: 0.058\n", "Epoch 4 Batch 499/538 - Train Accuracy: 0.928, Validation Accuracy: 0.921, Loss: 0.061\n", "Epoch 4 Batch 500/538 - Train Accuracy: 0.956, Validation Accuracy: 0.924, Loss: 0.048\n", "Epoch 4 Batch 501/538 - Train Accuracy: 0.939, Validation Accuracy: 0.922, Loss: 0.068\n", "Epoch 4 Batch 502/538 - Train Accuracy: 0.925, Validation Accuracy: 0.927, Loss: 0.053\n", "Epoch 4 Batch 503/538 - Train Accuracy: 0.946, Validation Accuracy: 0.933, Loss: 0.067\n", "Epoch 4 Batch 504/538 - Train Accuracy: 0.953, Validation Accuracy: 0.931, Loss: 0.052\n", "Epoch 4 Batch 505/538 - Train Accuracy: 0.937, Validation Accuracy: 0.931, Loss: 0.058\n", "Epoch 4 Batch 506/538 - Train Accuracy: 0.940, Validation Accuracy: 0.938, Loss: 0.049\n", "Epoch 4 Batch 507/538 - Train Accuracy: 0.917, Validation Accuracy: 0.941, Loss: 0.062\n", "Epoch 4 Batch 508/538 - Train Accuracy: 0.930, Validation Accuracy: 0.942, Loss: 0.058\n", "Epoch 4 Batch 509/538 - Train Accuracy: 0.945, Validation Accuracy: 0.939, Loss: 0.060\n", "Epoch 4 Batch 510/538 - Train Accuracy: 0.952, Validation Accuracy: 0.933, Loss: 0.054\n", "Epoch 4 Batch 511/538 - Train Accuracy: 0.934, Validation Accuracy: 0.933, Loss: 0.064\n", "Epoch 4 Batch 512/538 - Train Accuracy: 0.949, Validation Accuracy: 0.933, Loss: 0.067\n", "Epoch 4 Batch 513/538 - Train Accuracy: 0.921, Validation Accuracy: 0.933, Loss: 0.056\n", "Epoch 4 Batch 514/538 - Train Accuracy: 0.938, Validation Accuracy: 0.931, Loss: 0.059\n", "Epoch 4 Batch 515/538 - Train Accuracy: 0.935, Validation Accuracy: 0.931, Loss: 0.067\n", "Epoch 4 Batch 516/538 - Train Accuracy: 0.912, Validation Accuracy: 0.926, Loss: 0.061\n", "Epoch 4 Batch 517/538 - Train Accuracy: 0.935, Validation Accuracy: 0.924, Loss: 0.061\n", "Epoch 4 Batch 518/538 - Train Accuracy: 0.930, Validation Accuracy: 0.927, Loss: 0.064\n", "Epoch 4 Batch 519/538 - Train Accuracy: 0.943, Validation Accuracy: 0.936, Loss: 0.061\n", "Epoch 4 Batch 520/538 - Train Accuracy: 0.939, Validation Accuracy: 0.943, Loss: 0.061\n", "Epoch 4 Batch 521/538 - Train Accuracy: 0.929, Validation Accuracy: 0.942, Loss: 0.068\n", "Epoch 4 Batch 522/538 - Train Accuracy: 0.940, Validation Accuracy: 0.936, Loss: 0.057\n", "Epoch 4 Batch 523/538 - Train Accuracy: 0.942, Validation Accuracy: 0.930, Loss: 0.062\n", "Epoch 4 Batch 524/538 - Train Accuracy: 0.938, Validation Accuracy: 0.930, Loss: 0.056\n", "Epoch 4 Batch 525/538 - Train Accuracy: 0.928, Validation Accuracy: 0.933, Loss: 0.059\n", "Epoch 4 Batch 526/538 - 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Train Accuracy: 0.953, Validation Accuracy: 0.938, Loss: 0.051\n", "Epoch 5 Batch 1/538 - Train Accuracy: 0.954, Validation Accuracy: 0.933, Loss: 0.065\n", "Epoch 5 Batch 2/538 - Train Accuracy: 0.949, Validation Accuracy: 0.925, Loss: 0.067\n", "Epoch 5 Batch 3/538 - Train Accuracy: 0.948, Validation Accuracy: 0.935, Loss: 0.059\n", "Epoch 5 Batch 4/538 - Train Accuracy: 0.930, Validation Accuracy: 0.940, Loss: 0.059\n", "Epoch 5 Batch 5/538 - Train Accuracy: 0.937, Validation Accuracy: 0.944, Loss: 0.068\n", "Epoch 5 Batch 6/538 - Train Accuracy: 0.939, Validation Accuracy: 0.942, Loss: 0.059\n", "Epoch 5 Batch 7/538 - Train Accuracy: 0.948, Validation Accuracy: 0.938, Loss: 0.061\n", "Epoch 5 Batch 8/538 - Train Accuracy: 0.944, Validation Accuracy: 0.943, Loss: 0.065\n", "Epoch 5 Batch 9/538 - Train Accuracy: 0.909, Validation Accuracy: 0.937, Loss: 0.055\n", "Epoch 5 Batch 10/538 - Train Accuracy: 0.928, Validation Accuracy: 0.945, Loss: 0.065\n", "Epoch 5 Batch 11/538 - Train Accuracy: 0.938, Validation Accuracy: 0.932, Loss: 0.061\n", "Epoch 5 Batch 12/538 - 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Train Accuracy: 0.919, Validation Accuracy: 0.945, Loss: 0.057\n", "Epoch 5 Batch 354/538 - Train Accuracy: 0.925, Validation Accuracy: 0.949, Loss: 0.058\n", "Epoch 5 Batch 355/538 - Train Accuracy: 0.949, Validation Accuracy: 0.952, Loss: 0.057\n", "Epoch 5 Batch 356/538 - Train Accuracy: 0.951, Validation Accuracy: 0.957, Loss: 0.046\n", "Epoch 5 Batch 357/538 - Train Accuracy: 0.949, Validation Accuracy: 0.950, Loss: 0.051\n", "Epoch 5 Batch 358/538 - Train Accuracy: 0.952, Validation Accuracy: 0.956, Loss: 0.044\n", "Epoch 5 Batch 359/538 - Train Accuracy: 0.928, Validation Accuracy: 0.955, Loss: 0.053\n", "Epoch 5 Batch 360/538 - Train Accuracy: 0.939, Validation Accuracy: 0.959, Loss: 0.050\n", "Epoch 5 Batch 361/538 - Train Accuracy: 0.957, Validation Accuracy: 0.952, Loss: 0.054\n", "Epoch 5 Batch 362/538 - Train Accuracy: 0.953, Validation Accuracy: 0.947, Loss: 0.045\n", "Epoch 5 Batch 363/538 - Train Accuracy: 0.942, Validation Accuracy: 0.940, Loss: 0.052\n", "Epoch 5 Batch 364/538 - Train Accuracy: 0.938, Validation Accuracy: 0.945, Loss: 0.064\n", "Epoch 5 Batch 365/538 - Train Accuracy: 0.942, Validation Accuracy: 0.949, Loss: 0.051\n", "Epoch 5 Batch 366/538 - Train Accuracy: 0.951, Validation Accuracy: 0.944, Loss: 0.049\n", "Epoch 5 Batch 367/538 - Train Accuracy: 0.951, Validation Accuracy: 0.945, Loss: 0.042\n", "Epoch 5 Batch 368/538 - Train Accuracy: 0.954, Validation Accuracy: 0.948, Loss: 0.046\n", "Epoch 5 Batch 369/538 - Train Accuracy: 0.943, Validation Accuracy: 0.954, Loss: 0.052\n", "Epoch 5 Batch 370/538 - Train Accuracy: 0.943, Validation Accuracy: 0.956, Loss: 0.048\n", "Epoch 5 Batch 371/538 - Train Accuracy: 0.941, Validation Accuracy: 0.953, Loss: 0.053\n", "Epoch 5 Batch 372/538 - Train Accuracy: 0.956, Validation Accuracy: 0.953, Loss: 0.056\n", "Epoch 5 Batch 373/538 - Train Accuracy: 0.946, Validation Accuracy: 0.952, Loss: 0.040\n", "Epoch 5 Batch 374/538 - Train Accuracy: 0.960, Validation Accuracy: 0.948, Loss: 0.047\n", "Epoch 5 Batch 375/538 - 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Train Accuracy: 0.954, Validation Accuracy: 0.948, Loss: 0.054\n", "Epoch 5 Batch 387/538 - Train Accuracy: 0.953, Validation Accuracy: 0.948, Loss: 0.052\n", "Epoch 5 Batch 388/538 - Train Accuracy: 0.935, Validation Accuracy: 0.955, Loss: 0.053\n", "Epoch 5 Batch 389/538 - Train Accuracy: 0.935, Validation Accuracy: 0.948, Loss: 0.060\n", "Epoch 5 Batch 390/538 - Train Accuracy: 0.948, Validation Accuracy: 0.949, Loss: 0.051\n", "Epoch 5 Batch 391/538 - Train Accuracy: 0.947, Validation Accuracy: 0.953, Loss: 0.050\n", "Epoch 5 Batch 392/538 - Train Accuracy: 0.961, Validation Accuracy: 0.955, Loss: 0.045\n", "Epoch 5 Batch 393/538 - Train Accuracy: 0.959, Validation Accuracy: 0.948, Loss: 0.045\n", "Epoch 5 Batch 394/538 - Train Accuracy: 0.939, Validation Accuracy: 0.933, Loss: 0.055\n", "Epoch 5 Batch 395/538 - Train Accuracy: 0.937, Validation Accuracy: 0.935, Loss: 0.057\n", "Epoch 5 Batch 396/538 - Train Accuracy: 0.943, Validation Accuracy: 0.927, Loss: 0.044\n", "Epoch 5 Batch 397/538 - Train Accuracy: 0.941, Validation Accuracy: 0.938, Loss: 0.059\n", "Epoch 5 Batch 398/538 - Train Accuracy: 0.955, Validation Accuracy: 0.943, Loss: 0.051\n", "Epoch 5 Batch 399/538 - Train Accuracy: 0.933, Validation Accuracy: 0.943, Loss: 0.058\n", "Epoch 5 Batch 400/538 - Train Accuracy: 0.959, Validation Accuracy: 0.951, Loss: 0.047\n", "Epoch 5 Batch 401/538 - Train Accuracy: 0.960, Validation Accuracy: 0.960, Loss: 0.042\n", "Epoch 5 Batch 402/538 - Train Accuracy: 0.943, Validation Accuracy: 0.950, Loss: 0.047\n", "Epoch 5 Batch 403/538 - Train Accuracy: 0.947, Validation Accuracy: 0.941, Loss: 0.053\n", "Epoch 5 Batch 404/538 - Train Accuracy: 0.951, Validation Accuracy: 0.955, Loss: 0.057\n", "Epoch 5 Batch 405/538 - Train Accuracy: 0.949, Validation Accuracy: 0.953, Loss: 0.049\n", "Epoch 5 Batch 406/538 - Train Accuracy: 0.951, Validation Accuracy: 0.955, Loss: 0.052\n", "Epoch 5 Batch 407/538 - Train Accuracy: 0.954, Validation Accuracy: 0.947, Loss: 0.051\n", "Epoch 5 Batch 408/538 - Train Accuracy: 0.929, Validation Accuracy: 0.948, Loss: 0.057\n", "Epoch 5 Batch 409/538 - Train Accuracy: 0.961, Validation Accuracy: 0.945, Loss: 0.048\n", "Epoch 5 Batch 410/538 - Train Accuracy: 0.958, Validation Accuracy: 0.943, Loss: 0.048\n", "Epoch 5 Batch 411/538 - Train Accuracy: 0.958, Validation Accuracy: 0.931, Loss: 0.057\n", "Epoch 5 Batch 412/538 - Train Accuracy: 0.942, Validation Accuracy: 0.933, Loss: 0.045\n", "Epoch 5 Batch 413/538 - Train Accuracy: 0.946, Validation Accuracy: 0.929, Loss: 0.048\n", "Epoch 5 Batch 414/538 - Train Accuracy: 0.906, Validation Accuracy: 0.942, Loss: 0.062\n", "Epoch 5 Batch 415/538 - Train Accuracy: 0.936, Validation Accuracy: 0.949, Loss: 0.052\n", "Epoch 5 Batch 416/538 - Train Accuracy: 0.949, Validation Accuracy: 0.947, Loss: 0.050\n", "Epoch 5 Batch 417/538 - Train Accuracy: 0.951, Validation Accuracy: 0.947, Loss: 0.046\n", "Epoch 5 Batch 418/538 - Train Accuracy: 0.952, Validation Accuracy: 0.953, Loss: 0.059\n", "Epoch 5 Batch 419/538 - 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Train Accuracy: 0.934, Validation Accuracy: 0.952, Loss: 0.051\n", "Epoch 6 Batch 27/538 - Train Accuracy: 0.950, Validation Accuracy: 0.950, Loss: 0.043\n", "Epoch 6 Batch 28/538 - Train Accuracy: 0.941, Validation Accuracy: 0.945, Loss: 0.045\n", "Epoch 6 Batch 29/538 - Train Accuracy: 0.944, Validation Accuracy: 0.947, Loss: 0.040\n", "Epoch 6 Batch 30/538 - Train Accuracy: 0.948, Validation Accuracy: 0.947, Loss: 0.053\n", "Epoch 6 Batch 31/538 - Train Accuracy: 0.962, Validation Accuracy: 0.949, Loss: 0.037\n", "Epoch 6 Batch 32/538 - Train Accuracy: 0.952, Validation Accuracy: 0.950, Loss: 0.038\n", "Epoch 6 Batch 33/538 - Train Accuracy: 0.954, Validation Accuracy: 0.947, Loss: 0.052\n", "Epoch 6 Batch 34/538 - Train Accuracy: 0.939, Validation Accuracy: 0.947, Loss: 0.064\n", "Epoch 6 Batch 35/538 - Train Accuracy: 0.952, Validation Accuracy: 0.952, Loss: 0.040\n", "Epoch 6 Batch 36/538 - Train Accuracy: 0.950, Validation Accuracy: 0.954, Loss: 0.043\n", "Epoch 6 Batch 37/538 - 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Train Accuracy: 0.968, Validation Accuracy: 0.958, Loss: 0.029\n", "Epoch 7 Batch 493/538 - Train Accuracy: 0.951, Validation Accuracy: 0.963, Loss: 0.035\n", "Epoch 7 Batch 494/538 - Train Accuracy: 0.956, Validation Accuracy: 0.960, Loss: 0.037\n", "Epoch 7 Batch 495/538 - Train Accuracy: 0.943, Validation Accuracy: 0.961, Loss: 0.037\n", "Epoch 7 Batch 496/538 - Train Accuracy: 0.967, Validation Accuracy: 0.964, Loss: 0.029\n", "Epoch 7 Batch 497/538 - Train Accuracy: 0.975, Validation Accuracy: 0.961, Loss: 0.034\n", "Epoch 7 Batch 498/538 - Train Accuracy: 0.958, Validation Accuracy: 0.951, Loss: 0.036\n", "Epoch 7 Batch 499/538 - Train Accuracy: 0.944, Validation Accuracy: 0.945, Loss: 0.043\n", "Epoch 7 Batch 500/538 - Train Accuracy: 0.974, Validation Accuracy: 0.941, Loss: 0.024\n", "Epoch 7 Batch 501/538 - Train Accuracy: 0.957, Validation Accuracy: 0.942, Loss: 0.037\n", "Epoch 7 Batch 502/538 - Train Accuracy: 0.952, Validation Accuracy: 0.949, Loss: 0.035\n", "Epoch 7 Batch 503/538 - Train Accuracy: 0.968, Validation Accuracy: 0.954, Loss: 0.038\n", "Epoch 7 Batch 504/538 - Train Accuracy: 0.973, Validation Accuracy: 0.955, Loss: 0.027\n", "Epoch 7 Batch 505/538 - Train Accuracy: 0.952, Validation Accuracy: 0.961, Loss: 0.029\n", "Epoch 7 Batch 506/538 - Train Accuracy: 0.960, Validation Accuracy: 0.960, Loss: 0.033\n", "Epoch 7 Batch 507/538 - Train Accuracy: 0.956, Validation Accuracy: 0.960, Loss: 0.040\n", "Epoch 7 Batch 508/538 - Train Accuracy: 0.956, Validation Accuracy: 0.951, Loss: 0.032\n", "Epoch 7 Batch 509/538 - Train Accuracy: 0.955, Validation Accuracy: 0.947, Loss: 0.036\n", "Epoch 7 Batch 510/538 - Train Accuracy: 0.965, Validation Accuracy: 0.949, Loss: 0.033\n", "Epoch 7 Batch 511/538 - Train Accuracy: 0.948, Validation Accuracy: 0.965, Loss: 0.040\n", "Epoch 7 Batch 512/538 - Train Accuracy: 0.961, Validation Accuracy: 0.965, Loss: 0.035\n", "Epoch 7 Batch 513/538 - Train Accuracy: 0.955, Validation Accuracy: 0.961, Loss: 0.035\n", "Epoch 7 Batch 514/538 - Train Accuracy: 0.963, Validation Accuracy: 0.962, Loss: 0.034\n", "Epoch 7 Batch 515/538 - Train Accuracy: 0.951, Validation Accuracy: 0.960, Loss: 0.040\n", "Epoch 7 Batch 516/538 - Train Accuracy: 0.954, Validation Accuracy: 0.955, Loss: 0.034\n", "Epoch 7 Batch 517/538 - Train Accuracy: 0.960, Validation Accuracy: 0.942, Loss: 0.031\n", "Epoch 7 Batch 518/538 - Train Accuracy: 0.956, Validation Accuracy: 0.947, Loss: 0.038\n", "Epoch 7 Batch 519/538 - Train Accuracy: 0.959, Validation Accuracy: 0.949, Loss: 0.036\n", "Epoch 7 Batch 520/538 - Train Accuracy: 0.963, Validation Accuracy: 0.958, Loss: 0.035\n", "Epoch 7 Batch 521/538 - Train Accuracy: 0.967, Validation Accuracy: 0.959, Loss: 0.036\n", "Epoch 7 Batch 522/538 - Train Accuracy: 0.962, Validation Accuracy: 0.963, Loss: 0.032\n", "Epoch 7 Batch 523/538 - Train Accuracy: 0.963, Validation Accuracy: 0.960, Loss: 0.032\n", "Epoch 7 Batch 524/538 - Train Accuracy: 0.966, Validation Accuracy: 0.960, Loss: 0.030\n", "Epoch 7 Batch 525/538 - Train Accuracy: 0.959, Validation Accuracy: 0.960, Loss: 0.035\n", "Epoch 7 Batch 526/538 - Train Accuracy: 0.956, Validation Accuracy: 0.964, Loss: 0.029\n", "Epoch 7 Batch 527/538 - Train Accuracy: 0.965, Validation Accuracy: 0.964, Loss: 0.033\n", "Epoch 7 Batch 528/538 - Train Accuracy: 0.962, Validation Accuracy: 0.968, Loss: 0.034\n", "Epoch 7 Batch 529/538 - Train Accuracy: 0.943, Validation Accuracy: 0.968, Loss: 0.034\n", "Epoch 7 Batch 530/538 - Train Accuracy: 0.943, Validation Accuracy: 0.971, Loss: 0.044\n", "Epoch 7 Batch 531/538 - Train Accuracy: 0.967, Validation Accuracy: 0.971, Loss: 0.039\n", "Epoch 7 Batch 532/538 - Train Accuracy: 0.958, Validation Accuracy: 0.967, Loss: 0.031\n", "Epoch 7 Batch 533/538 - Train Accuracy: 0.968, Validation Accuracy: 0.965, Loss: 0.032\n", "Epoch 7 Batch 534/538 - Train Accuracy: 0.966, Validation Accuracy: 0.966, Loss: 0.027\n", "Epoch 7 Batch 535/538 - Train Accuracy: 0.966, Validation Accuracy: 0.966, Loss: 0.031\n", "Epoch 7 Batch 536/538 - Train Accuracy: 0.964, Validation Accuracy: 0.968, Loss: 0.036\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import time\n", "\n", "def get_accuracy(target, logits):\n", " \"\"\"\n", " Calculate accuracy\n", " \"\"\"\n", " max_seq = max(target.shape[1], logits.shape[1])\n", " if max_seq - target.shape[1]:\n", " target = np.pad(\n", " target,\n", " [(0,0),(0,max_seq - target.shape[1])],\n", " 'constant')\n", " if max_seq - logits.shape[1]:\n", " logits = np.pad(\n", " logits,\n", " [(0,0),(0,max_seq - logits.shape[1]), (0,0)],\n", " 'constant')\n", "\n", " return np.mean(np.equal(target, np.argmax(logits, 2)))\n", "\n", "train_source = source_int_text[batch_size:]\n", "train_target = target_int_text[batch_size:]\n", "\n", "valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])\n", "valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(epochs):\n", " for batch_i, (source_batch, target_batch) in enumerate(\n", " helper.batch_data(train_source, train_target, batch_size)):\n", " start_time = time.time()\n", " \n", " _, loss = sess.run(\n", " [train_op, cost],\n", " {input_data: source_batch,\n", " targets: target_batch,\n", " lr: learning_rate,\n", " sequence_length: target_batch.shape[1],\n", " keep_prob: keep_probability})\n", " \n", " batch_train_logits = sess.run(\n", " inference_logits,\n", " {input_data: source_batch, keep_prob: 1.0})\n", " batch_valid_logits = sess.run(\n", " inference_logits,\n", " {input_data: valid_source, keep_prob: 1.0})\n", " \n", " train_acc = get_accuracy(target_batch, batch_train_logits)\n", " valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)\n", " end_time = time.time()\n", " print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'\n", " .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_path)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Save Parameters\n", "Save the `batch_size` and `save_path` parameters for inference." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params(save_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n", "load_path = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Sentence to Sequence\n", "To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n", "\n", "- Convert the sentence to lowercase\n", "- Convert words into ids using `vocab_to_int`\n", " - Convert words not in the vocabulary, to the `<UNK>` word id." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", " Convert a sentence to a sequence of ids\n", " :param sentence: String\n", " :param vocab_to_int: Dictionary to go from the words to an id\n", " :return: List of word ids\n", " \"\"\"\n", " # TODO: Implement Function\n", " word_list = []\n", " lower_words = sentence.lower().split()\n", " \n", " for word in lower_words:\n", " if(word in vocab_to_int):\n", " word_list.append(vocab_to_int[word])\n", " else:\n", " word_list.append(vocab_to_int['<UNK>'])\n", "\n", " return word_list\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_sentence_to_seq(sentence_to_seq)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Translate\n", "This will translate `translate_sentence` from English to French." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input\n", " Word Ids: [120, 121, 17, 103, 45, 37, 198]\n", " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", "\n", "Prediction\n", " Word Ids: [22, 180, 330, 335, 150, 161, 158, 342, 1]\n", " French Words: ['il', 'a', 'vu', 'un', 'vieux', 'camion', 'jaune', '.', '<EOS>']\n" ] } ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n", "\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_path + '.meta')\n", " loader.restore(sess, load_path)\n", "\n", " input_data = loaded_graph.get_tensor_by_name('input:0')\n", " logits = loaded_graph.get_tensor_by_name('logits:0')\n", " keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", "\n", " translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]\n", "\n", "print('Input')\n", "print(' Word Ids: {}'.format([i for i in translate_sentence]))\n", "print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n", "\n", "print('\\nPrediction')\n", "print(' Word Ids: {}'.format([i for i in np.argmax(translate_logits, 1)]))\n", "print(' French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Imperfect Translation\n", "You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n", "\n", "You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
Joshuaalbert/IonoTomo	src/ionotomo/notebooks/Logger.ipynb	1	1846	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sys import stdout\n", "\n", "class Logger(object):\n", " def __init__(self,logFile=None,standardOut = True):\n", " if standardOut:\n", " self.file = [stdout]\n", " else:\n", " self.file = []\n", " if logFile is not None:\n", " try:\n", " file = open(logFile,\"w\")\n", " self.file.append(file)\n", " except:\n", " print(\"Failed to open log file {0}\".format(logFile))\n", " if len(self.file) == 0:\n", " print(\"No logger files!\")\n", " exit(1)\n", " #print(\"Using log files: {}\".format(self.file))\n", " def __exit__(self):\n", " for file in self.file:\n", " try:\n", " file.close()\n", " except:\n", " pass\n", " def log(self,message,endLine=True):\n", " for f in self.file:\n", " if endLine:\n", " f.write(\"{0}\\n\".format(message))\n", " else:\n", " f.write(\"{0}\".format(message))\n", " f.flush()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:mayavi_env]", "language": "python", "name": "conda-env-mayavi_env-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 }	apache-2.0
Kenji-K/python-para-programadores	python.ipynb	2	66919	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"https://www.python.org/\"><img src=\"./Imagenes/python-logo.png\" alt=\"Python Logo\" style=\"width: 200px; display:inline;\"/></a>\n", "\n", "Python es un lenguaje de programación:\n", "- De [Alto nivel](https://en.wikipedia.org/wiki/High-level_programming_language), es decir, lidiamos con abstracciones y no nos preocupamos de los detalles internos de las computadoras, tal como utilizar direcciones de memoria y registros.\n", "- [Multipropósito](https://en.wikipedia.org/wiki/General-purpose_programming_language), pues es utilizado para construir: websites, programas de escritorio, videojuegos, análisis de datos, etc.\n", "- <a href=\"https://en.wikipedia.org/wiki/Interpreter_(computing)\">Interpretado</a>, ya que Python incluye algo llamado intérprete, lo que va traduciendo el texto escrito por el usuario en instrucciones que la computadora comprende a medida que lo va leyendo.\n", "- [Dinámico](https://en.wikipedia.org/wiki/Dynamic_programming_language)\n", "- [Multiparadigma](https://en.wikipedia.org/wiki/Programming_paradigm)\n", " - [Imperativo](https://en.wikipedia.org/wiki/Imperative_programming)\n", " - [Orientado a objetos](https://en.wikipedia.org/wiki/Object-oriented_programming)\n", " - [Procedural](https://en.wikipedia.org/wiki/Procedural_programming)\n", " - [Funcional](https://en.wikipedia.org/wiki/Functional_programming)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"https://www.continuum.io/\"><img src=\"./Imagenes/anaconda-logo.svg\" alt=\"Anaconda Logo\" style=\"width: 200px; display:inline;\"/></a>\n", "\n", "Plataforma de data science. Incluye:\n", "\n", "- Lenguaje de programación Python\n", "- Entorno de programación tradicional Spyder\n", "- Entorno de programación multipropósito Jupyter Notebook\n", "- Librerías de data science ([sklearn][1], [numpy][2], [pandas][3], [nltk][4], [matplotlib][5], [etc][6].)\n", "\n", "[1]:http://scikit-learn.org/\n", "[2]:http://www.numpy.org/\n", "[3]:http://pandas.pydata.org/\n", "[4]:http://www.nltk.org/\n", "[5]:http://matplotlib.org/\n", "[6]:https://docs.continuum.io/anaconda/pkg-docs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"./Imagenes/spyder-logo.png\" alt=\"Spyder Logo\" style=\"height: 100px;\"/>\n", "\n", "![Spyder](./Imagenes/spyder.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Particularidades de Python\n", "\n", "Python tiene ciertas características que lo definen, y lo diferencian de otros lenguajes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indentación para bloques de código\n", "\n", "En python, las estructuras de control no están delimitadas por caracteres como los tradicionales corchetes:\n", "\n", "```c\n", "if (2 + 3 == 5) {\n", " x = 5 + 3;\n", " y = 2 + 3;\n", "} else {\n", " x = 5 - 3;\n", " y = 2 - 3;\n", "}\n", "```\n", "\n", "En Python los caracteres de espacio importan. En lugar de los corchetes, Python usa una indentación o sangría, por defecto de 4 espacios, para denotar un bloque de código subordinado a una estructura de control:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n", "Verdadero!\n" ] } ], "source": [ "if 2 + 3 == 5:\n", " x = 5 + 3\n", " mensaje = \"Verdadero!\"\n", "else:\n", " x = 5 - 3\n", " mensaje = \"Falso!\"\n", " \n", "print(x)\n", "print(mensaje)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De esta manera, Python estandariza el aspecto del código desde la definición del lenguaje.\n", "\n", "Nota:\n", " - Como se puede observar en el código anterior, en Python, las estructuras de bloque no requieren paréntesis, y se delimitan por el caracter de dos puntos \"`:`\"\n", " - No es necesario el caracter punto y coma \"`;`\" al final de cada expresión si es la única en la linea. Pero puede ser usada para colocar múltiples en una sola línea.\n", " ```python\n", " x = 1 + 1; print(x);\n", " ```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Palabras Reservadas\n", "\n", "Adicionalmente Python cuenta con un conjunto de palabras, llamadas palabras clave o reservadas que tienen un significado especial dentro del lenguaje, las cuales no pueden ser utilizadas como nombre de variables:\n", "\n", "<pre>False class finally is return\n", "None continue for lambda try\n", "True def from nonlocal while\n", "and del global not with\n", "as elif if or yield\n", "assert else import pass\n", "break except in raise</pre>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tipos de dato\n", "\n", "Python maneja varios tipos de dato predefinidos. Mencionaremos algunos tipos de dato comúnmente utilizados:\n", "\n", "## Booleanos\n", "Tipos de datos que expresan valor de verdad." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bool" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los operadores para variables booleanas son sólo 3\n", "\n", "<table border=\"1\" class=\"docutils\">\n", "<colgroup>\n", "<col width=\"25%\">\n", "<col width=\"62%\">\n", "</colgroup>\n", "<thead valign=\"bottom\">\n", "<tr class=\"row-odd\"><th class=\"head\">Operation</th>\n", "<th class=\"head\">Result</th>\n", "\n", "</tr>\n", "</thead>\n", "<tbody valign=\"top\">\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">or</span> <span class=\"pre\">y</span></code></td>\n", "<td>si <em>x</em> es falso, entonces <em>y</em>, en caso contrario <em>x</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">and</span> <span class=\"pre\">y</span></code></td>\n", "<td>si <em>x</em> es falso, entonces <em>x</em>, en caso contrario <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">not</span> <span class=\"pre\">x</span></code></td>\n", "<td>si <em>x</em> es falso, entonces <code class=\"docutils literal\"><span class=\"pre\">True</span></code>,\n", "en caso contrario <code class=\"docutils literal\"><span class=\"pre\">False</span></code></td>\n", "\n", "</tr>\n", "</tbody>\n", "</table>\n", "\n", "Fuente: [Python docs](https://docs.python.org/3.5/library/stdtypes.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numéricos" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "float" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(3.1416)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "La siguiente tabla muestra las operaciones posibles entre variables numéricas:\n", "\n", "<table border=\"1\" class=\"docutils\">\n", "<colgroup>\n", "<col width=\"33%\">\n", "<col width=\"67%\">\n", "</colgroup>\n", "<thead valign=\"bottom\">\n", "<tr class=\"row-odd\"><th class=\"head\">Operación</th>\n", "<th class=\"head\">Resultado</th>\n", "</tr>\n", "</thead>\n", "<tbody valign=\"top\">\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">+</span> <span class=\"pre\">y</span></code></td>\n", "<td>suma de <em>x</em> e <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">-</span> <span class=\"pre\">y</span></code></td>\n", "<td>diferencia de <em>x</em> e <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\"></span> <span class=\"pre\">y</span></code></td>\n", "<td>producto de <em>x</em> e <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">/</span> <span class=\"pre\">y</span></code></td>\n", "<td>cociente de <em>x</em> e <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">//</span> <span class=\"pre\">y</span></code></td>\n", "<td>cociente of <em>x</em> e <em>y</em> con redondeo hacia abajo</td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">%</span> <span class=\"pre\">y</span></code></td>\n", "<td>resto de <code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">/</span> <span class=\"pre\">y</span></code></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">-x</span></code></td>\n", "<td>cambio de signo de <em>x</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">+x</span></code></td>\n", "<td><em>x</em> sin modificación</td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">abs(x)</span></code></td>\n", "<td>valor absoluto o magnitud de <em>x</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">int(x)</span></code></td>\n", "<td><em>x</em> covnertido a entero (integer)</td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">long(x)</span></code></td>\n", "<td><em>x</em> convertido a entero largo (long integer)</td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">float(x)</span></code></td>\n", "<td><em>x</em> convertido a punto flotante (floating point)</td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">complex(re,im)</span></code></td>\n", "<td>un número complejo con parte real\n", "<em>re</em>, parte imaginaria <em>im</em>.\n", "<em>im</em> es por defecto cero.</td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">c.conjugate()</span></code></td>\n", "<td>la conjugación del número complejo <em>c</em>.</td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">divmod(x,</span> <span class=\"pre\">y)</span></code></td>\n", "<td>el par <code class=\"docutils literal\"><span class=\"pre\">(x</span> <span class=\"pre\">//</span> <span class=\"pre\">y,</span> <span class=\"pre\">x</span> <span class=\"pre\">%</span> <span class=\"pre\">y)</span></code></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">pow(x,</span> <span class=\"pre\">y)</span></code></td>\n", "<td><em>x</em> elevado a la <em>y</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\"></span> <span class=\"pre\">y</span></code></td>\n", "<td><em>x</em> elevado a la <em>y</em></td>\n", "\n", "</tr>\n", "</tbody>\n", "</table>\n", "\n", "Fuente: [Python docs](https://docs.python.org/3.5/library/stdtypes.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Secuencias\n", "\n", "Python maneja distintos dipos de secuencias: listas, tuplas y rangos. Cada una tiene sus características, sin embargo la gran mayoría de las siguientes operaciones puede ser utilizada con ellas.\n", "\n", "<table class=\"docutils\" id=\"index-20\">\n", "<colgroup>\n", "<col width=\"38%\">\n", "<col width=\"62%\">\n", "</colgroup>\n", "<thead valign=\"bottom\">\n", "<tr class=\"row-odd\"><th class=\"head\">Operación</th>\n", "<th class=\"head\">Resultado</th>\n", "\n", "</tr>\n", "</thead>\n", "<tbody valign=\"top\">\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">in</span> <span class=\"pre\">s</span></code></td>\n", "<td><code class=\"docutils literal\"><span class=\"pre\">True</span></code> si un elemento de <em>s</em> es\n", "igual a <em>x</em>, en caso contrario <code class=\"docutils literal\"><span class=\"pre\">False</span></code></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">x</span> <span class=\"pre\">not</span> <span class=\"pre\">in</span> <span class=\"pre\">s</span></code></td>\n", "<td><code class=\"docutils literal\"><span class=\"pre\">False</span></code> si un elemento de <em>s</em> es\n", "igual a <em>x</em>, en caso contrario <code class=\"docutils literal\"><span class=\"pre\">True</span></code></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">s</span> <span class=\"pre\">+</span> <span class=\"pre\">t</span></code></td>\n", "<td>la concatenación de <em>s</em> y\n", "<em>t</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td>\n", "<code class=\"docutils literal\"><span class=\"pre\">s</span> <span class=\"pre\"></span> <span class=\"pre\">n</span></code> ó\n", "<code class=\"docutils literal\"><span class=\"pre\">n</span> <span class=\"pre\"></span> <span class=\"pre\">s</span></code></td>\n", "<td>equivalente a añadir <em>s</em> a sí mismo <em>n</em> veces</td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">s[i]</span></code></td>\n", "<td><em>i</em>-ésimo elemento de <em>s</em>, con origen 0</td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">s[i:j]</span></code></td>\n", "<td>subsecuencia (slice) de <em>s</em> desde posición <em>i</em> hasta posición <em>j</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">s[i:j:k]</span></code></td>\n", "<td>subsecuencia (slice) de <em>s</em> desde posición <em>i</em> hasta posición <em>j</em> con pasos de longitud <em>k</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">len(s)</span></code></td>\n", "<td>longitud de <em>s</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">min(s)</span></code></td>\n", "<td>elemento de menor valor en <em>s</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">max(s)</span></code></td>\n", "<td>elemento de mayor valor en <em>s</em></td>\n", "\n", "</tr>\n", "<tr class=\"row-even\"><td><code class=\"docutils literal\"><span class=\"pre\">s.index(x[,</span> <span class=\"pre\">i[,</span> <span class=\"pre\">j]])</span></code></td>\n", "<td>índice de la primera ocurrencia de <em>x</em> en <em>s</em> (en o después del índice \n", "<em>i</em> y antes del índice <em>j</em>)</td>\n", "\n", "</tr>\n", "<tr class=\"row-odd\"><td><code class=\"docutils literal\"><span class=\"pre\">s.count(x)</span></code></td>\n", "<td>cantidad total de ocurrencias de\n", "<em>x</em> en <em>s</em></td>\n", "\n", "</tr>\n", "</tbody>\n", "</table>\n", "\n", "Fuente: [Python docs](https://docs.python.org/3.5/library/stdtypes.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adicionalmente las secuencias pueden ser mutables o inmutables. Las secuencias mutables permiten las siguientes operaciones:\n", "\n", "\| Operación \| Resultado \|\n", "\| --- \| --- \|\n", "\| `s[i] = x` \| elemento i de s es reemplazado por `x ` \|\n", "\| `s[i:j] = t` \| slice (subsección) de `s` desde `i` hasta `j` es reemplazada por los componentes del iterable `t` \|\n", "\| `del s[i:j]` \| igual que `s[i:j] = []` \|\n", "\| `s[i:j:k] = t` \| los elementos de `s[i:j:k]` son reemplazados por los de `t` \|\n", "\| `del s[i:j:k]` \| remueve los elementos de `s[i:j:k]` de la lista \|\n", "\| `s.append(x)` \| añade x al final de la secuencia (igual que `s[len(s):len(s)] = [x]`) \|\n", "\| `s.clear()` \| remueve todos los elementos de `s` (igual que `del s[:]`) \|\n", "\| `s.copy()` \| crea una copia superficial de `s` (igual que `s[:]`) \|\n", "\| `s.extend(t)` ó `s += t` \| extiende `s` con los contenidos de `t` (por lo general igual que `s[len(s):len(s)] = t`) \|\n", "\| `s = n` \| actualiza `s` con sus contenidos repetidos n veces \|\n", "\| `s.insert(i, x)` \| inserta `x` en `s` en la posición `i` inserts (igual que `s[i:i] = [x]`) \|\n", "\| `s.pop([i])` \| obtiene y remueve el elemento en la posición `i` de `s` \|\n", "\| `s.remove(x)` \| remueve el primer elemento de `s` donde `s[i] == x` \|\n", "\| `s.reverse()` \| invierte los lugares de los elementos de `s` \|\n", "\n", "Fuente: [Python docs](https://docs.python.org/3.5/library/stdtypes.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### a) Listas (Secuencias)\n", "\n", "Las listas son la estructura de datos básica de Python. Se pueden definir de la siguiente manera:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "[]\n" ] } ], "source": [ "lista_vacia = []\n", "print(lista_vacia)\n", "\n", "#O equivalentemente\n", "lista_vacia = list()\n", "print(lista_vacia)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También pueden crearse directamente con valores adentro:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo']\n" ] } ], "source": [ "semana = [\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\", \"Domingo\"]\n", "print(semana)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las listas no están restringidas a tener elementos del mismo tipo:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 'Donald Trump', None, 3.5]\n" ] } ], "source": [ "cosas_aleatorias = [1+2, \"Donald Trump\", None, 3.5]\n", "print(cosas_aleatorias)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para acceder a algun valor, solo hace falta indizarlo haciendo uso de la siguiente sintaxis con corchetes:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "Donald Trump\n" ] } ], "source": [ "#En Python, los índices inician en 0\n", "print (cosas_aleatorias[0])\n", "print (cosas_aleatorias[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adicionalmente se pueden seleccionar subsecciones de las listas haciendo slicing.\n", "\n", "Para el slicing, el primer índice indica el inicio de la sublista (inclusivo), el segundo indica el fin (exclusivo), y el tercer número opcional indica cada cuantos elementos coger.\n", "\n", "La selección de sublistas adicionalmente tienen reglas adicionales ilustradas a continuación:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Lunes', 'Martes', 'Miércoles']\n", "['Lunes', 'Miércoles', 'Viernes', 'Domingo']\n", "['Lunes', 'Martes', 'Miércoles']\n", "['Sábado', 'Domingo']\n", "['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes']\n", "['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo']\n" ] } ], "source": [ "print (semana[0:3]) #Desde el primero hasta el tercer elemento\n", "print (semana[0:7:2]) #Toda la lista, pero cada dos elementos\n", "print (semana[:3]) #Desde el inicio de la lista hasta el tercer elemento\n", "print (semana[5:]) #Desde el quinto elemento hasta el final de la lista\n", "print (semana[:-2]) #Desde el primer elemento hasta 2 espacios antes del final de la lista\n", "print (semana[:]) #Toda la lista" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nota: Hay que tener cuidado con un detalle. Las sublistas seleccionadas de esta manera son un reflejo de la lista original. En otras palabras, si se modifica esta sublista, se modifica la lista original." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 3.5]\n" ] } ], "source": [ "cosas_aleatorias[:3] = [1, 2, 3]\n", "print(cosas_aleatorias)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una forma rápida de definir una lista conteniendo una secuencia de enteros es haciendo uso de los rangos." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]\n" ] } ], "source": [ "dias_en_diciembre = list(range(1, 32))\n", "print (dias_en_diciembre)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero si las reglas son un poco más complejas, es necesario hacer uso de los llamados list comprehensions. Las list comprehensions son expresiones que son usadas para describir un conjunto de valores, de forma similar a como se describen los conjuntos por comprensión en matemática:\n", "\n", "$S = \\{x^2 : x > 0 \\wedge x \\le 5\\}$\n", "\n", "Lo cual, expresado por extensión, resulta: \n", "\n", "$S = \\{1, 2, 4, 8, 16\\}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "De la misma manera podemos definir una lista haciendo uso de list comprehensions:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 4, 8, 16]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[2x for x in range(5)]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x for x in dias_en_diciembre if x % 2 == 0] #Días pares en diciembre" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los list comprehensions tienen tres partes:\n", "\n", "1. La función\n", "2. El dominio\n", "3. La condición\n", "\n", "Expresado de la siguiente manera:\n", "\n", "```python\n", "[{Funcion(x)} for x in {dominio} if {condicion}]\n", "```\n", "\n", "Y se puede entender como \"aplica la función a todo elemento del dominio que cumpla con la condición y devuelve los resultados en una nueva lista\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### b) Tuplas (Secuencias)\n", "\n", "Las tuplas son muy similares a las listas, con la crucial distinción de que son inmutables. En otras palabras, no se pueden modificar los valores contenidos en las tuplas:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-53-77a183cb740b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Las tuplas se crean haciendo uso de paréntesis en lugar de corchetes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msemana\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Lunes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Martes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Miércoles\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Jueves\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Viernes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Sábado\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Domingo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msemana\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Enero\"\u001b[0m \u001b[0;31m#Intentar modificar un valor de una tupla genera un error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "#Las tuplas se crean haciendo uso de paréntesis en lugar de corchetes\n", "semana = (\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\", \"Domingo\")\n", "semana[0] = \"Enero\" #Intentar modificar un valor de una tupla genera un error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cadenas de caracteres (Secuencias)\n", "\n", "Las cadenas de caracteres en Python son secuencias inmutables, y se pueden aplicar las mismas operaciones comunes a todos los tipos de dato secuencia inmutables, a la vez que no es permitido modificarlos." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "veloz\n" ] }, { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-0bf33a9cb414>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpangrama\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpangrama\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"lento\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "pangrama = \"El veloz murciélago hindú comía feliz cardillo y kiwi. La cigüeña tocaba el saxofón detrás del palenque de paja\"\n", "print(pangrama[3:8])\n", "\n", "pangrama[3:8] = \"lento\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adicionalmente tienen muchos métodos adicionales (que pueden ver en la [referencia oficial](https://docs.python.org/3/library/stdtypes.html#string-methods)), como por ejemplo upper()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EL VELOZ MURCIÉLAGO HINDÚ COMÍA FELIZ CARDILLO Y KIWI. LA CIGÜEÑA TOCABA EL SAXOFÓN DETRÁS DEL PALENQUE DE PAJA\n" ] } ], "source": [ "print(pangrama.upper())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sets o Conjuntos\n", "\n", "Los sets o conjuntos son colecciones de elementos sin orden particular. Los usos comunes de los sets es realizar operaciones de verificación de pertenencia, intersecciones, uniones y diferencias." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numeros_pares: {0, 2, 4, 6, 8, 10, 12, 14, 16, 18}\n", "multiplos_de_tres: {0, 3, 6, 9, 12, 15, 18} \n", "\n", "Intersección: {0, 18, 12, 6}\n", "Union: {0, 2, 3, 4, 6, 8, 9, 10, 12, 14, 15, 16, 18}\n", "Diferencia: {2, 4, 8, 10, 14, 16}\n" ] } ], "source": [ "numeros_pares = {0, 2, 4, 6, 8, 10, 12, 14, 16, 18} #La creación de conjuntos es con llaves o con la funcion set()\n", "print (\"numeros_pares:\", numeros_pares)\n", "multiplos_de_tres = set(range(0, 20, 3)) #La función set puede recibir otros iterables o secuencias\n", "print (\"multiplos_de_tres:\", multiplos_de_tres, \"\\n\")\n", "\n", "print(\"Intersección:\", numeros_pares & multiplos_de_tres)\n", "print(\"Union:\", numeros_pares \| multiplos_de_tres)\n", "print(\"Diferencia:\", numeros_pares - multiplos_de_tres)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adicionalmente, dentro de un set no pueden existir elementos duplicados*. Transformar una lista o tupla en set remueve los duplicados, lo cual a menudo resulta muy útil." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lista semana: ['Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo', 'Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo', 'Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes', 'Sábado', 'Domingo'] \n", "\n", "Set semana: {'Viernes', 'Jueves', 'Martes', 'Domingo', 'Miércoles', 'Lunes', 'Sábado'}\n" ] } ], "source": [ "semana = [\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\", \"Domingo\"]\n", "semana = 3\n", "print(\"Lista semana: \", semana, \"\\n\")\n", "\n", "semana = set(semana)\n", "print(\"Set semana:\", semana)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Diccionarios\n", "\n", "Los diccionarios son estructuras de datos que guardan colecciones de pares de elementos. Cada par es conocido como llave/valor.\n", "\n", "![Pares llave/valor](./Imagenes/dict.png)\n", "\n", "Los diccionarios pueden crearse de múltiples maneras equivalentes, cada una útil en su propio contexto:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Todas estas maneras de crear diccionarios son equivalentes, definiendo la capital de tres países\n", "capitales_1 = dict(Peru = 'Lima', Ecuador = 'Quito', Argentina = 'Buenos Aires')\n", "capitales_2 = {'Peru': 'Lima', 'Ecuador': 'Quito', 'Argentina': 'Buenos Aires'}\n", "capitales_3 = dict(zip(['Peru', 'Ecuador', 'Argentina'], ['Lima', 'Quito', 'Buenos Aires']))\n", "capitales_4 = dict([('Ecuador', 'Quito'), ('Peru', 'Lima'), ('Argentina', 'Buenos Aires')])\n", "capitales_5 = dict({'Argentina': 'Buenos Aires', 'Peru': 'Lima', 'Ecuador': 'Quito'})\n", "capitales_1 == capitales_2 == capitales_3 == capitales_4 == capitales_5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los valores de los diccionarios se acceden a través de su llave usando la sintaxis\n", "```python\n", "diccionario[llave]\n", "```" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'Peru' -> Lima\n", "'Ecuador' -> Quito\n", "'Argentina' -> Buenos Aires\n" ] } ], "source": [ "\n", "print(\"'Peru' ->\", capitales_1['Peru'])\n", "print(\"'Ecuador' ->\", capitales_1['Ecuador'])\n", "print(\"'Argentina' ->\", capitales_1['Argentina'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es posible recorrer un diccionario, valor por valor, pero cabe mencionar que no se garantiza ninguna clase de orden, a diferencia de las secuencias:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ecuador\n", "Peru\n", "Argentina\n" ] } ], "source": [ "for capital in capitales_1: #En breve veremos que significa 'for'\n", " print(capital)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Estructuras de control\n", "\n", "El verdadero poder de los lenguajes de programación imperativo se encuentra en la posibilidad de utilizar las llamadas estructuras de control, lo que nos permite escribir programas más complejos y más útiles de los que podríamos crear sin ellas.\n", "\n", "![Estructuras de Control](./Imagenes/Estructuras de Control.png)\n", "\n", "Hasta ahora sólo hemos podido escribir programas secuenciales, donde cada estructura se ejecuta una tras de otra hasta el final del programa (salvo que se encuentre un error)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flujo Condicional: If-Else\n", "\n", "Mediante las palabras clave `if` y `else` podemos escribir instrucciones condicionales. Es decir, partes del programa que se ejecutarán únicamente si se cumple con cierta condición. De esta manera podemos responder a situaciones no conocidas antes de ejecutar un programa (por ejemplo: el input recibido, los valores que se lean de un archivo o base de datos, etc.)\n", "\n", "La estructura de un condicional puede ser de tres maneras:\n", "\n", "### 1. if\n", "\n", "```python\n", "if ({expresion condicional}):\n", " {codigo a ejecutar si la condicion evalua a True}\n", " {mas codigo...}\n", "```\n", "\n", "### 2. if-Else\n", "\n", "```python\n", "if ({expresion condicional}):\n", " {codigo a ejecutar si la expresion condicional evalua a True}\n", " {mas codigo...}\n", "else:\n", " {codigo a ejecutar en caso contrario (si la expresion condicional evalua a False)}\n", " {mas codigo...}\n", "```\n", "\n", "### 3. Encadenar if con if-elif\n", "\n", "La palabra reservada `elif` es una abreviación de else if, lo cual traducido significa en caso contrario, si, y sirve para encadenar condiciones adicionales en caso las anteriores no se hayan cumplido.\n", "\n", "```python\n", "if ({expresion condicional 1}):\n", " {codigo a ejecutar si la expresion condicional 1 evalua a True}\n", " {mas codigo...}\n", "elif ({expresion condicional 2}):\n", " {codigo a ejecutar si la expresion condicional 1 evalua a False\n", " y la expresion condicional 2 evalua a True}\n", " {mas codigo...}\n", "elif ({expresion condicional 3}):\n", " {codigo a ejecutar si la expresion condicional 1 evalua a False\n", " y la expresion condicional 2 evalua a False\n", " y la expresion condicional 3 evalua a True}\n", "else:\n", " {codigo a ejecutar si ninguna de las expresiones condicionales anteriores evalua a True}\n", " {mas codigo...}\n", "```\n", "\n", "Notar el uso de indentación para delimitar el código que se ejecutará en cada caso." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Por favor ingrese un entero: 1\n", "El numero ingresado es positivo\n" ] } ], "source": [ "x = int(input(\"Por favor ingrese un entero: \"))\n", "if x < 0:\n", " print(\"El numero ingresado es negativo\")\n", "elif x == 0:\n", " print(\"El numero ingresado es cero\")\n", "else:\n", " print(\"El numero ingresado es positivo\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flujo Iterativo: While\n", "\n", "Adicionalmente, podemos contar con código que repita un grupo de instrucciones una cantidad de veces, dependiendo de una condición, utilizando la palabra clave `while`. De esta manera, al igual que con `if` y `else`, podemos crear programas que funcionen de forma distinta dependiendo de las condiciones en las que se ejecuta el programa.\n", "\n", "La sintaxis es similar a la de `if`.\n", "\n", "```python\n", "while({expresion condicional}):\n", " {(a) codigo a ejecutar mientras la expresion condicional evalua a True}\n", " {(b) mas codigo}\n", "{(c) codigo exterior}\n", "```\n", "\n", "El flujo del programa seguiría de la siguiente manera:\n", "1. Se evalúa la expresión condicional.\n", " 1. Si la expresión condicional evalua a `False`, el código interno no se ejecuta. En otras palabras, la próxima instrucción a ejecutar en el caso anterior será {(c) código exterior} y se habrá salido del bucle.\n", " 2. Si la expresión condicional evalúa a `True`, se ejecuta el código interno (a) y (b).\n", "2. Si se ejecutó el código interno (en el paso B.), se regresa al paso 1: evaluar la expresión condicional." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987," ] } ], "source": [ "#Fibonacci\n", "a, b = 0, 1\n", "while b < 1000:\n", " print(b, end=',')\n", " a, b = b, a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También podemos salir de un bucle en cualquier momento utilzando la palabra reservada `break`. Ejecutar esta instrucción implica terminar la ejecución del bucle más cercano." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Por favor ingrese la contraseña: prueba\n", "Lo sentimos, la contraseña es incorrecta - inténtelo de nuevo.\n", "Por favor ingrese la contraseña: secret\n", "Gracias. Ha ingresado la contraseña correcta.\n" ] } ], "source": [ "password = \"\"\n", "while True: # <- Con True como condición, el bucle se ejecutaría permanentemente\n", " password = input(\"Por favor ingrese la contraseña: \")\n", " if password == \"secret\":\n", " print(\"Gracias. Ha ingresado la contraseña correcta.\")\n", " break # <- Pero con break podemos salir del bucle\n", " else:\n", " print(\"Lo sentimos, la contraseña es incorrecta - inténtelo de nuevo.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Asimismo podemos utilizar la palabra reservada `continue` para terminar temparanamente una iteración y continuar con la siguiente" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 tareas ingresadas. Ingrese una tarea o 'exit' para terminar: Comprar pan\n", "1 tareas ingresadas. Ingrese una tarea o 'exit' para terminar: \n", "Por favor ingrese una tarea\n", "1 tareas ingresadas. Ingrese una tarea o 'exit' para terminar: Pagar recibo de internet\n", "2 tareas ingresadas. Ingrese una tarea o 'exit' para terminar: Llevar ropa a la lavandería\n", "3 tareas ingresadas. Ingrese una tarea o 'exit' para terminar: exit\n", "Su lista de tareas:\n", "Comprar pan\n", "Pagar recibo de internet\n", "Llevar ropa a la lavandería\n" ] } ], "source": [ "tareas = []\n", "while True:\n", " #Formateo de strings https://docs.python.org/3/library/string.html#format-string-syntax\n", " tarea = input(\"%d tareas ingresadas. Ingrese una tarea o 'exit' para terminar: \" % len(tareas))\n", " \n", " if len(tarea) == 0:\n", " print (\"Por favor ingrese una tarea\")\n", " continue # <- Esta palabra reservada termina inmediatamente la actual iteración y continúa con la siguiente\n", " \n", " if tarea == \"exit\": #\n", " break # Todo este código es ignorado durante la actual iteración si\n", " # anteriormente se ejecuta una instrucción continue\n", " tareas.append(tarea) #\n", "\n", "print(\"Su lista de tareas:\")\n", "print(\"\\r\\n\".join(tareas))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Documentación de formateo de strings](https://pyformat.info/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flujo Iterativo: For\n", "\n", "La palabra reservada `for` es similar a `while` en cuanto nos permite ejecutar código de forma iterativa. La diferencia está en que for nos permite realizar fácilmente operaciones por cada elemento de una colección iterable (listas, tuplas, sets, etc).\n", "\n", "La sintaxis es como sigue:\n", "\n", "```python\n", "for ({elemento} in {coleccion}):\n", " {(a) codigo a ejecutar por cada elemento en la coleccion}\n", " #Aquí se puede acceder al elemento actual bajo el nombre dado en {elemento}\n", "```\n", "\n", "Nota: Todo lo que se puede hacer con `for` se puede hacer con `while`, sin embargo, for es más apropiado para las tareas para las que se ha creado, por ser más comprensible y elegante." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Comprar pan\n", "2. Pagar recibo de internet\n", "3. Llevar ropa a la lavandería\n" ] } ], "source": [ "#Añdadir número de tarea a la lista definida anteriormente\n", "i = 1\n", "for tarea in tareas:\n", " print (\"%d. %s\" % (i, tarea))\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El bucle anterior puede ser resumido con el uso de la función predefinida `enumerate`" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Comprar pan\n", "2. Pagar recibo de internet\n", "3. Llevar ropa a la lavandería\n" ] } ], "source": [ "for (i, tarea) in enumerate(tareas):\n", " print (\"%d. %s\" % (i+1, tarea))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Asimismo podemos utilizar `for` para realizar una operación una cantidad determinada de veces haciendo uso de la función predefinida range()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1,1,2,3,5,8,13,21,34,55," ] } ], "source": [ "#Diez primeros dígitos de la secuencia Fibonacci\n", "\n", "a, b = 0, 1\n", "for _ in range(10):\n", " print(b, end=',')\n", " a, b = b, a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También es factible usar `break` y `continue` dentro de los bucles `for`." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1,1,2,3,5,8,13,21," ] } ], "source": [ "#Diez primeros dígitos de la secuencia Fibonacci, o hasta encontrar un múltiplo de 7\n", "\n", "a, b = 0, 1\n", "for _ in range(10):\n", " print(b, end=',')\n", " if b % 7 == 0:\n", " break\n", " a, b = b, a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Funciones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python permite definir funciones, las cuales son bloques de código reutilizable (tales como las ya utilizadas `print()`, `input()`, `list()`, `dict()`, `set()`, `range()` y `enumerate()`).\n", "\n", "En algunos lenguajes de programación las funciones están obligadas a devolver valores, pero en python pueden no hacerlo. Adicionalmente las funciones en Python no están obligadas a devolver un solo valor, sino que pueden devolver más de uno.\n", "\n", "La sintaxis básica para definir una función es como sigue:\n", "\n", "```python\n", "def nombre_de_funcion( {parametros} ):\n", " \"\"\"Documentación del código\"\"\"\n", " {Codigo ejecutable}\n", " return {expresion}\n", "```\n", "\n", "Por ejemplo, definimos una función que devuelva el número n de la secuencia de Fibonacci." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Función que devuelve el elemento n de la serie Fibonacci. Tiene un solo parámetro, el número de elemento deseado.\n", "def fib(n):\n", " \"\"\"Escribe la serie Fibonacci hasta el número n.\"\"\"\n", " a, b = 0, 1\n", " for _ in range(n):\n", " a, b = b, a+b\n", " return a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez definida la función podemos llamarla como sigue:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nnúmero Fibonacci #20: 6765\n" ] } ], "source": [ "x = fib(20)\n", "print(\"Nnúmero Fibonacci #20:\", x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Es importante notar que todas las variables declaradas dentro de una función son accesibles únicamente dentro de la misma función. Por ejemplo si intentamos obtener el valor de `b`, obtendremos un error que indica que la variable no está definida:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'b' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-33-4851c8fca996>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined" ] } ], "source": [ "print(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esto nos permite mantener los contextos de declaración y llamada de la función separados, de tal forma de que no haya que preocuparse por el conflicto de nombres de variables en dichas situaciones. Esto se ilustra en el siguiente ejemplo, donde además se declara una función con más de una variable:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Magnitud del vector [4, 3]: 5.0\n", "Las variables declaradas y manipuladas fuera de la función siguen teniendo sus valores originales: mag = 0\n" ] } ], "source": [ "import math\n", "\n", "def magnitud(x, y):\n", " x = x2\n", " y = y2\n", " mag = x + y #Declarando variable interna mag\n", " mag = math.sqrt(mag)\n", " return mag\n", "\n", "mag = 0 #Declarando variable externa mag para demostrar que es una variable completamente diferente a la interna\n", "print (\"Magnitud del vector [4, 3]:\", magnitud(4, 3))\n", "\n", "#Aquí podemos notar que las variables x e y son independientes de aquellas declaradas en la función\n", "print (\"Las variables declaradas y manipuladas fuera de la función siguen teniendo sus valores originales: mag =\", mag)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos también definir funciones con valores por defecto. De esta manera volvemos dichos parámetros opcionales, y cuando no se ingrese un valor específico durante una llamada, la función se ejecute con el valor indicado en lugar de echar un error." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hola Kenyi!\n", "Buenos días Kenyi!\n" ] } ], "source": [ "def saludar(nombre, saludo=\"Hola\"):\n", " print(\"{0} {1}!\".format(saludo, nombre))\n", "\n", "saludar(\"Kenyi\") #Llamando a la función sin el parámetro saludo hace que la función se ejecute con el valor por defecto \"Hola\"\n", "saludar(\"Kenyi\", \"Buenos días\") #Pero si especificamos el valor, se utiliza ese en su lugar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cuando la función tiene más de un parámetro, es posible llamar a la función nombrando específicamente a cual parámetro va cada valor, para facilitar la lectura en caso de que hayan muchos." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.0\n", "Buenos días Kenyi!\n" ] } ], "source": [ "print( magnitud(x = 12, y = 9) )\n", "\n", "#Otro beneficio de llamar funciones de esta forma es que no es necesario llamar a los argumentos en orden\n", "saludar(saludo = \"Buenos días\", nombre = \"Kenyi\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "positional argument follows keyword argument (<ipython-input-32-20e8e68ed636>, line 2)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-32-20e8e68ed636>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m saludar(saludo = \"Buenos días\", \"Kenyi\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n" ] } ], "source": [ "#Pero si se especifican parámetros con nombre, todas los parámetros subsecuentes deben ser llamados con nombre también\n", "saludar(saludo = \"Buenos días\", \"Kenyi\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "También podemos definir funciones con una cantidad variable de parámetros, de la misma forma que trabaja `print()`, anteponiendo un asterisco \\* al *último* nombre de variable (es importante que sea el último), el cual será tratado como tupla dentro de la función." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14\n", "3\n" ] } ], "source": [ "#En este caso el parámetro sumandos contendrá todos los parámetros ingresados\n", "def sumatoria(sumandos):\n", " total = 0\n", " #sumandos es una tupla\n", " for elemento in sumandos:\n", " total += elemento\n", " return total\n", "\n", "print (sumatoria(3, 5, 6))\n", "print (sumatoria(1, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La siguiente función devuelve dos valores:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nnúmero Fibonacci 20: 6765\n", "Cuadrado: 16 - Cubo: 64\n" ] } ], "source": [ "def cuadrado_y_cubo(n): # devuelve el cuadrado y el cubo del número ingresado\n", " cuadrado = n2\n", " cubo = n3\n", " return cuadrado, cubo\n", "\n", "x, y = cuadrado_y_cubo(4)\n", "print(\"Cuadrado:\", x, \"- Cubo:\", y)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Podemos también definir funciones que no devuelvan un valor (Nota: Estrictamente hablando, las funciones sin valor de retorno especificado devuelven `None`)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765 \n" ] } ], "source": [ "def fib(n): # escribir los primeros n elementos de la serie Fibonacci\n", " \"\"\"Escribe la serie Fibonacci hasta el número n.\"\"\"\n", " a, b = 0, 1\n", " for _ in range(n):\n", " a, b = b, a+b\n", " print(a, end=' ')\n", " print()\n", "\n", "# Ahora podemos llamar la función que acabamos de definir:\n", "fib(20)\n", "#0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una última nota, si se modifica un parámetro de tipo mutable dentro de una función, se modificará tambien fuera de ella. No así con las variables de tipo inmutable:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "['Hola', 'Mundo']\n" ] } ], "source": [ "def mutabilidad(entero, lista):\n", " entero += 10\n", " lista.append(\"Hola\")\n", " lista.append(\"Mundo\")\n", "\n", "entero = 0\n", "lista = []\n", "\n", "mutabilidad(entero, lista)\n", "\n", "print(entero) #Entero sigue conteniendo el valor 0, y no 10\n", "print(lista) #Lista ahora tiene 2 nuevos elementos, añadidos dentro de la función" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lambdas\n", "\n", "Adicionalmente se pueden definir funciones anónimas cortas haciendo uso de la sintaxis lambda:\n", "\n", "```python\n", "lambda {variables} : {expresion de retorno}\n", "```\n", "\n", "Cuyo equivalente declarativo sería:\n", "\n", "```python\n", "def funcion_anonima ( {variables} ): \n", " return {expresion de retorno}\n", "```\n", "\n", "\n", "Por ejemplo, consideremos esta función que duplica el número ingresado:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16\n", "100\n" ] } ], "source": [ "#En este caso, la variable al_cuadrado contendrá una función, y podrá ser llamada posteriormente como tal\n", "al_cuadrado = lambda x: x2\n", "\n", "print(al_cuadrado(4))\n", "print(al_cuadrado(10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Map, Filter, Reduce\n", "\n", "Python nos ofrece tres funciones muy útiles para manejar secuencias de datos de forma concisa y legible: `map()`, `filter()`* y `reduce()`. Estas funciones forman parte del paradigma de programación funcional que python contiene. Usar estas funciones puede ser complicado al principio, pero funciones de este tipo son utilizadas ampliamente en entornos de alta paralelización, especialmente en manejos de grandes cantidades de datos.\n", "\n", "### Map\n", "\n", "La función ``map()`` aplica una función a todos los elementos de una colección de datos iterables (por ejemplo listas, tuplas, sets, diccionarios), y devuelve una lista con todos los valores modificados.\n", "```python\n", "map({funcion_a_aplicar}, {coleccion_iterable})\n", "```\n", "\n", "Por ejemplo si deseamos volver mayúsculas todas las cadenas de caracteres en una lista, podríamos utilizar un bucle `for`:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['LUNES', 'MARTES', 'MIÉRCOLES', 'JUEVES', 'VIERNES', 'SÁBADO', 'DOMINGO']\n" ] } ], "source": [ "semana_mayusculas = []\n", "for i in semana:\n", " semana_mayusculas.append(i.upper())\n", " \n", "print(semana_mayusculas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O podríamos utilizar map." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['LUNES', 'MARTES', 'MIÉRCOLES', 'JUEVES', 'VIERNES', 'SÁBADO', 'DOMINGO']\n" ] } ], "source": [ "semana_mayusculas = []\n", "semana_mayusculas = map(str.upper, semana)\n", "print (list(semana_mayusculas))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comúnmente también veremos map utilizado con lambdas." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 4, 9, 16, 25]\n" ] } ], "source": [ "enteros = [1, 2, 3, 4, 5]\n", "cuadrados = map(lambda x: x2, enteros)\n", "print(list(cuadrados))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter\n", "\n", "La función `filter()` permite, como indica su nombre, filtrar elementos de una colección iterable que no cumplan con determinada condición.\n", "\n", "```python\n", "filter({funcion_condicional}, {coleccion_iterable})\n", "```\n", "\n", "La funcion_condicional debe ser una función que devuelva `True`, en caso el elemento cumpla con la condición, o `False`, en caso contrario.\n", "\n", "Por ejemplo, si deseamos filtrar los números positivos de una lista." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-5, -6, -9, -2]\n" ] } ], "source": [ "lista = [3, -5, -6, 1, 2, -9, 7, -2]\n", "\n", "lista_filtrada = filter(lambda x: x <= 0, lista) \n", "#La función lambda devuelve True si es que el número en cuestión es menor o igual a 0\n", "\n", "print(list(lista_filtrada))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reduce\n", "\n", "La función `reduce()` es un poco más complicada, pero extremadamente útil. Reduce aplica una función a dos elementos de una colección iterable, y luego aplica la misma función al resultado del cálculo anterior con el siguiente elemento, y así sucesivamente hasta terminar.\n", "\n", "La sintaxis es muy similar a ``map()`` y `filter()`:\n", "\n", "```python\n", "reduce({funcion_a_aplicar(x, y)}, {coleccion_iterable})\n", "```\n", "\n", "Por ejemplo, la función `reduce()`** con una función de suma y una lista de enteros:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "113\n" ] } ], "source": [ "from functools import reduce\n", "lista = [47, 11, 42, 13]\n", "sumatoria = reduce(lambda x, y: x + y, lista)\n", "\n", "print(sumatoria)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Internamente el proceso que está ocurriendo es como lo muestra el gráfico:\n", "\n", "![Ilustración de funcion reduce](./Imagenes/reduce.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Mayores Recursos\n", "\n", "Aún falta mucho por ver en Python, por ejemplo programación orientada a objetos, librerías estándar de python, creación de librerías, manipulación de texto y archivos, reflectividad, etc.\n", "\n", "Para referencia futura, no hay lugar con información más exhaustiva que la misma documentación de Python:\n", "\n", "https://docs.python.org/3/index.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Styling del notebook\n", "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": { "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 }	apache-2.0
BrownDwarf/Starfish	notebooks/Interpolator.ipynb	2	3314	{ "metadata": { "name": "", "signature": "sha256:1761e312cf7e33f5d569b606ae24e3a783cbea35d77cd2bca68ff1ba2b0b6411" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Try out the interpolator and see if the error spectra make sense" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from StellarSpectra.grid_tools import HDF5Interface, Interpolator\n", "import StellarSpectra.constants as C\n", "from StellarSpectra.spectrum import DataSpectrum\n", "from StellarSpectra.utils import saveall" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#interface = HDF5Interface(\"../libraries/PHOENIX_F.hdf5\")\n", "interface = HDF5Interface(\"../libraries/PHOENIX_TRES_F.hdf5\")\n", "dataspec = DataSpectrum.open(\"../data/WASP14/WASP14-2009-06-14.hdf5\", orders=np.array([22]))\n", "interpolator = Interpolator(interface, dataspec, trilinear=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Determine Chunk Log: Wl is 8192\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "params = {\"temp\":6150, \"logg\":4.29, \"Z\":-0.4}\n", "fl, errspec = interpolator(params)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[(6100, 6200), (4.0, 4.5), (-0.5, 0.0)]\n", "[(0.5, 0.5), (0.41999999999999993, 0.58000000000000007), (0.80000000000000004, 0.19999999999999996)]\n", "[ 0.168 0.042 0.232 0.058 0.168 0.042 0.232 0.058]\n", "[ 0.40987803 0.20493902 0.48166378 0.24083189 0.40987803 0.20493902\n", " 0.48166378 0.24083189]\n", "[ 0.168 0.042 0.232 0.058 0.168 0.042 0.232 0.058]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "wl = interpolator.wl\n", "ind = (wl > 5160) & (wl < 5164)\n", "wl_raw = wl[ind]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "for spec in errspec:\n", " plt.plot(wl_raw, spec[ind])\n", "plt.show()\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
scientific-visualization-2016/ClassMaterials	Week-02/02_python_overview.ipynb	1	6112	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<img src='https://www.rc.colorado.edu/sites/all/themes/research/logo.png' style=\"height:75px\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objectives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Review python\n", "- Introduction to functional concepts\n", "- Data analysis and visualization workflow" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scientific Hello World" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "r = float(\"4.2\")\n", "s = math.sin(r)\n", "print('hello world! The sin({0}) = {1:0.2f}'.format(r,s))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a lot happening here! " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Functional Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<blockquote>\n", "Python acquired lambda, reduce, filter and map, courtesy of a Lisp hacker who missed them and submitted working patches. -Guido van Rossum\n", "</blockquote>\n", "\n", "- `map` \n", "- `reduce`\n", "- `filter`\n", "- `lambda`\n", "- And more: [itertools](https://docs.python.org/2/library/itertools.html), [pytoolz](https://github.com/pytoolz/toolz/)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## The `map` abstraction\n", "\n", "\n", "How do we apply a function on a list of numbers?\n", "\n", "Function: $x*2$\n", "\n", "List: [1,2,3,4]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def square(x):\n", " return xx\n", "\n", "numbers = [1,2,3,4]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def map_squares(nums):\n", " res = []\n", " for x in nums:\n", " res.append( square(x) )\n", " return res\n", "\n", "map_squares(numbers)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "or..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "results = map(square, numbers)\n", "results" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Anonymous functions: `lambda`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lambda_square = lambda x: xx\n", "map(lambda_square, range(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "map(lambda x: xx, range(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "res = map(lambda x: xx, range(10))\n", "print(res)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `reduce`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply a function with two* arguments cumulatively to the container." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_num(x1, x2):\n", " return x1+x2\n", "\n", "print(reduce(add_num, res))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "np.sum(res)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(reduce(lambda x,y: x+y, res))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `filter`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Constructs a new list for items where the applied function is `True`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def less_than(x):\n", " return x>10\n", "\n", "filter(less_than, res)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filter(lambda x: x>10, res)" ] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
mahmoodm2/appsensor	TFIDF.ipynb	1	17206	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### To See the Results: \n", " 1-Scroll down to the end of the page in and select (by click) the cell after the \"Test the Results\". \n", " 2-Uncomment the desired statement (removing the # character)\n", " 3- Press Ctrl+Enter and wait for some seconds\n" ] }, { "cell_type": "code", "execution_count": 364, "metadata": { "collapsed": false }, "outputs": [], "source": [ "'''\n", "Created on Feb 21, 2017\n", "\n", "@author: mmoham12\n", "'''\n", "import csv\n", "import collections as c\n", "import math \n", "import operator\n", "import numpy as np\n", "import pandas as pd\n", "import collections as c\n", "from scipy import spatial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading raw data form the CSV files.\n" ] }, { "cell_type": "code", "execution_count": 439, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "264505\n", "94875\n" ] } ], "source": [ "gUserID ='30838'\n", "basePath=\"\" #E:\\\\workspaces\\\\RecommSys_A2\\\\data\\\\\"\n", "tagsFile = basePath + \"movie-tags.csv\"\n", "titlesFile = basePath +\"movie-titles.csv\"\n", "ratingFile = basePath + \"ratings.csv\"\n", "\n", "reader = csv.reader( open(tagsFile,encoding=\"ISO-8859-1\"))\n", "items = list(reader)\n", "\n", "itemspd = pd.read_csv(tagsFile,encoding = 'iso-8859-1', header=None, index_col='movie', names=['movie' , 'tag'])\n", "\n", "reader = csv.reader( open(titlesFile,encoding=\"ISO-8859-1\"))\n", "titles = list(reader)\n", "reader = csv.reader( open(ratingFile,encoding=\"ISO-8859-1\"))\n", "\n", "#userID, movieID, rating\n", "ratings = list(reader)\n", "\n", "print(len(ratings))\n", "print (len(items))\n", "#print(items.l.oc[3916])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating the TF for Movie Tags\n" ] }, { "cell_type": "code", "execution_count": 440, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2495\n", "7.822044008185619\n" ] } ], "source": [ "# item Vectors as a dictionary(like hashMaps in Java) : { movie1 :{ tag1 : count1 , tag2:count2 ,...} , movieID: ...}\n", "itemCounts={}\n", " \n", "# Document Vectro as a dictionary : { tag1:count1 , tag2:count2 ,...}\n", "docCount= {} \n", "\n", "\n", "# Calculating the document frequency\n", "# Stroing the length of the vectors in the last field as vectorLength\n", "\n", "for movie,tag in items:\n", " if not tag in docCount:\n", " docCount[tag]=0.0\n", " \n", " add=0\n", " if not movie in itemCounts:\n", " # tagVector = Tag Vector \n", " tagVector={}\n", " tagVector[tag]=1.0\n", " \n", " # Initial Length is 0. Will be computed later\n", " # tagVector[\"vectorLength\"] =0.0\n", " \n", " # if movie not exists in the dict before add a new one with \n", " itemCounts[movie]={}\n", " \n", " # It is new tag for this movie so its count in the docFreq should be incremented\n", " add=1\n", " \n", " else:\n", " tagVector=itemCounts[movie]\n", " \n", " if not tag in itemCounts[movie]: \n", " tagVector[tag]=1.0\n", " \n", " # It is new tag for this movie so its count in the docFreq should be incremented\n", " add=1\n", " \n", " else: \n", " tagVector[tag] = tagVector[tag]+1\n", " \n", " # List of tag vectors for this movie gets u_profilesdated\n", " itemCounts[movie] =tagVector\n", " \n", " # The document frequency for this tag in the current movie.\n", " docCount[tag] +=add \n", " \n", "logN = math.log(len(itemCounts))\n", "\n", "print (len(itemCounts))\n", "print(logN)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating the IDF " ] }, { "cell_type": "code", "execution_count": 441, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "item 4==========================\n", "1.3987970446520999\n", "5.424148735387249\n", "3.814710822953148\n", "3.6025363030095123\n" ] } ], "source": [ "# Calculating the final IDF values\n", "docFreq ={}\n", "for tag,count in docCount.items():\n", " docFreq[tag]= logN - math.log(count)\n", "\n", "# Testing Item 4\n", "print('item 4==========================') \n", "print( docFreq['CLV'])\n", "print( docFreq['characters'])\n", "print( docFreq['chick flick'])\n", "print( docFreq['revenge'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calcularing the TF-IDF" ] }, { "cell_type": "code", "execution_count": 515, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13103\n" ] } ], "source": [ "# Calculating the sum of the power of tf-idf of tags in tag vector of each movie and store it in vectorLength tag\n", "itemVectors = {}\n", "\n", "for movie,tagVector in itemCounts.items():\n", " vectLen= 0.0\n", " for tag,count in tagVector.items():\n", " # TF-IDF = TF * IDF. count = TF , docFreq[tag] = IDF\n", " tfidf= count * docFreq[tag] \n", " \n", " vectLen += math.pow(tfidf, 2)\n", "\n", " \n", " vectLen = math.sqrt(vectLen) \n", " \n", " iv ={}\n", " for tag,count in tagVector.items(): \n", " # Calculating the normalized tf-idf \n", " iv[tag] = count * docFreq[tag] / vectLen\n", " \n", " itemVectors[movie] = iv\n", "print(len(docFreq))\n", "\n", "def getItemVector(docID):\n", " df=pd.DataFrame.from_dict(itemVectors, orient='index')\n", "\n", " d=df.loc['2231']\n", " d=d[d.notnull()]\n", "\n", " d=pd.DataFrame(d)\n", " d.columns=['value']\n", "\n", " d.sort_values('value', axis=0,ascending=[False], inplace=True)\n", " print(d)\n", "\n", "\n", "#df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating the User User Profiles" ] }, { "cell_type": "code", "execution_count": 503, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Building User Profile as { user1 :{ tag1: count1 , tag2:count2, ...} , userID:...}\n", "def buildUserProfiles(userID):\n", " userProfiles={}\n", "\n", " users = sorted(ratings, key= lambda x:x[0], reverse= False)\n", "\n", " if userID !='':\n", " users = [row for row in users if row[0] == userID]\n", "\n", "# Iterating over all user ratings and then the item vector for each move the user has rated\n", " for user,movie,rate in users:\n", " \n", " if not user in userProfiles:\n", " userTagVector={} \n", " userProfiles[user]={}\n", " \n", " else:\n", " userTagVector=userProfiles[user]\n", " \n", " # Selecting movies with rate over the 3.5\n", " if float(rate) >= 3.5 and movie in itemVectors:\n", " \n", " # Iterating over the list of the item vectors for movies the current user rated. User Value = Sigma( tfidf) \n", " # For all tags in the movie rated by the user\n", " for tname, qt in itemVectors[movie].items():\n", "\n", " if not tname in userTagVector:\n", " userTagVector[tname] = 0.0\n", " \n", " userTagVector[tname] += float(qt)\n", "\n", " # userTagVector = list of tags of movie the current user( user) has rated: { tag1:score1 , tag2:score2, ...}\n", " userProfiles[user]= userTagVector\n", " \n", " u=userProfiles[userID]\n", " sorted_x = sorted(u.items(), key= lambda x:x[1], reverse= False)\n", " #print( sorted_x)\n", " \n", " return userProfiles\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A function to calculate Item Scores " ] }, { "cell_type": "code", "execution_count": 404, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def calculateScores( u_profiles, m_items, docID):\n", " # Generating the Item Scores. Having User Profile and Movie tag vector what would be the score of the user for that movie\n", " \n", " m_itemscores={}\n", " \n", " # m_itemscores : { userID: { movie1:score1 , movie1:score2, ...} , user2: ...}\n", " #Iterating over all user profiles for the current movie\n", " for userID, userTagVector in u_profiles.items():\n", " \n", " if not userID in m_itemscores:\n", " m_itemscores[userID]={}\n", " \n", " #P= list(userTagVector.values())\n", " \n", " # Iterating over Item vectors to calculate the user m_itemscores for each Movie\n", " for movieID,itemTagVector in m_items.items(): \n", " \n", " \n", " if docID != '':\n", " if movieID != docID:\n", " continue\n", " \n", " #Q =list(itemTagVector.values())\n", " \n", " \n", " # List of user scores for the current movie. Each field of this list should be to be claculated using cosin \n", " userScores = m_itemscores[userID] \n", " \n", " p=ps=qs=0 \n", " #result = 1 - spatial.distance.cosine(P , Q)\n", " \n", " qs=0.0\n", " for tag1,count1 in itemTagVector.items():\n", " qs += math.pow( count1,2)\n", " \n", " #print(qs)\n", " for tag2,count2 in userTagVector.items(): \n", "\n", " \n", " # TFIDF of Item if the tag exists in the list of movie's tags\n", " if tag2 in itemTagVector:\n", " qt=itemTagVector[tag2] \n", " else:\n", " qt=0\n", "\n", " # User Value for the current tag\n", " pt= count2 \n", " p += (pt * qt) \n", " ps += math.pow(pt,2) \n", " \n", "\n", " # Calculating cosin score for each user/item pair\n", " if ps <= 0.0 or qs <=0.0 :\n", " score= 0.0\n", " else:\n", " score= p / (math.sqrt(ps) * math.sqrt(qs))\n", " \n", " #print(math.sqrt(qs))\n", " #print(math.sqrt(ps))\n", "\n", " # Updating user scores vector \n", " \n", " \n", " userScores[movieID]= score\n", " \n", " #Updating the user scores vector for the current movie\n", " m_itemscores[userID]= userScores \n", " \n", " return m_itemscores" ] }, { "cell_type": "code", "execution_count": 520, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getUserPredictions(userID):\n", " up= buildUserProfiles(userID)\n", " unweightedScores= calculateScores2(up, itemVectors ,'' )\n", "\n", " u=list(unweightedScores.values())\n", "\n", " sorted_x = sorted(u[0].items(), key= lambda x:x[1], reverse= True)\n", " d=pd.DataFrame.from_records(sorted_x)\n", " print(d)\n", " " ] }, { "cell_type": "code", "execution_count": 430, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def buildWightedProfile(userID): \n", "#Building User Profile as { user1 :{ tag1: count1 , tag2:count2} , userID:...}\n", "\n", " weighted_profiles={}\n", "\n", " users = sorted(ratings, key= lambda x:x[0], reverse= False)\n", "\n", " if userID !='':\n", " users = [row for row in users if row[0] == userID]\n", " \n", " # User rating average as { user1:av}\n", " userAvg = {}\n", "\n", " old=users[0][0]\n", " avg=float(users[0][2])\n", " count=1.0\n", " for row in users:\n", " if old != row[0]:\n", " \n", " userAvg[old] = avg\n", " \n", " avg=float(row[2])\n", " count=1.0 \n", " old= row[0]\n", " \n", " else:\n", " avg = ((count * avg) + float(row[2]) ) / ( count + 1)\n", " count +=1.0\n", " \n", " userAvg[old] = avg\n", " \n", " \n", "# Iterating over all user ratings and then the item vector for each move the user has rated \n", "\n", " for user,movie,rate in users:\n", " \n", " if not user in weightedu_profiles:\n", " userTagVector={} \n", " weightedu_profiles[user]={}\n", " \n", " else:\n", " userTagVector=weightedu_profiles[user]\n", " \n", " if movie in itemVectors:\n", " # Iterating over the list of the item vectors for movies the current user rated. User Value = Sigma( tfidf) \n", " for tname, qt in itemVectors[movie].items():\n", "\n", " if not tname in userTagVector:\n", " userTagVector[tname] = 0\n", "\n", " #print(qt)\n", " \n", " userTagVector[tname] += qt * ( float(rate) - userAvg[user])\n", "\n", " # userTagVector = list of tags of movie the current user( user) has rated: { tag1:score1 , tag2:score2, ...}\n", " weighted_profiles[user]= userTagVector\n", "\n", " #print( weightedu_profiles[gUserID])\n", " return weighted_profiles" ] }, { "cell_type": "code", "execution_count": 523, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getWeighUserPredictions(userID):\n", " up= buildWightedProfile('320')\n", "#print(up)\n", " weightedScores= calculateScores(up, itemVectors,'' )\n", "\n", "#print(weightedScores)\n", " u=list(weightedScores.values())\n", "\n", "#print(u[0])\n", " sorted_x = sorted(u[0].items(), key= lambda x:x[1], reverse= True)\n", " d=pd.DataFrame.from_records(sorted_x)\n", " print(d)\n", "#print(sorted_x) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test the Results" ] }, { "cell_type": "code", "execution_count": 527, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value\n", "poker 0.597000\n", "Edward Norton 0.552893\n", "Matt Damon 0.315939\n", "John Turturro 0.260617\n", "gambling 0.245714\n", "John Malkovich 0.239701\n", "card games 0.125276\n", "John Dahl 0.114175\n", "cards 0.107681\n", "2.5 0.067883\n", "watched 2006 0.061096\n", "library vhs 0.060524\n" ] } ], "source": [ "getItemVector('2231')\n", "#getUserPredictions('320')\n", "#getWeighUserPredictions('320')\n" ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
GoogleCloudPlatform/mlops-on-gcp	workshops/kfp-caip-sklearn/lab-02-kfp-pipeline/exercises/lab-02.ipynb	1	25424	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Continuous training pipeline with Kubeflow Pipeline and AI Platform" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learning Objectives:\n", "1. Learn how to use Kubeflow Pipeline (KFP) pre-build components (BiqQuery, AI Platform training and predictions)\n", "1. Learn how to use KFP lightweight python components\n", "1. Learn how to build a KFP with these components\n", "1. Learn how to compile, upload, and run a KFP with the command line\n", "\n", "\n", "In this lab, you will build, deploy, and run a KFP pipeline that orchestrates BigQuery and AI Platform services to train, tune, and deploy a scikit-learn model.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding the pipeline design\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The workflow implemented by the pipeline is defined using a Python based Domain Specific Language (DSL). The pipeline's DSL is in the `covertype_training_pipeline.py` file that we will generate below.\n", "\n", "The pipeline's DSL has been designed to avoid hardcoding any environment specific settings like file paths or connection strings. These settings are provided to the pipeline code through a set of environment variables.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!grep 'BASE_IMAGE =' -A 5 pipeline/covertype_training_pipeline.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pipeline uses a mix of custom and pre-build components.\n", "\n", "- Pre-build components. The pipeline uses the following pre-build components that are included with the KFP distribution:\n", " - [BigQuery query component](https://github.com/kubeflow/pipelines/tree/0.2.5/components/gcp/bigquery/query)\n", " - [AI Platform Training component](https://github.com/kubeflow/pipelines/tree/0.2.5/components/gcp/ml_engine/train)\n", " - [AI Platform Deploy component](https://github.com/kubeflow/pipelines/tree/0.2.5/components/gcp/ml_engine/deploy)\n", "- Custom components. The pipeline uses two custom helper components that encapsulate functionality not available in any of the pre-build components. The components are implemented using the KFP SDK's [Lightweight Python Components](https://www.kubeflow.org/docs/pipelines/sdk/lightweight-python-components/) mechanism. The code for the components is in the `helper_components.py` file:\n", " - Retrieve Best Run. This component retrieves a tuning metric and hyperparameter values for the best run of a AI Platform Training hyperparameter tuning job.\n", " - Evaluate Model. This component evaluates a sklearn trained model using a provided metric and a testing dataset.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Complete TO DOs the pipeline file below.\n", "\n", "<ql-infobox><b>NOTE:</b> If you need help, you may take a look at the complete solution by navigating to mlops-on-gcp > workshops > kfp-caip-sklearn > lab-02-kfp-pipeline and opening lab-02.ipynb.\n", "</ql-infobox>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile ./pipeline/covertype_training_pipeline.py\n", "# Copyright 2019 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\"\"\"KFP orchestrating BigQuery and Cloud AI Platform services.\"\"\"\n", "\n", "import os\n", "\n", "from helper_components import evaluate_model\n", "from helper_components import retrieve_best_run\n", "from jinja2 import Template\n", "import kfp\n", "from kfp.components import func_to_container_op\n", "from kfp.dsl.types import Dict\n", "from kfp.dsl.types import GCPProjectID\n", "from kfp.dsl.types import GCPRegion\n", "from kfp.dsl.types import GCSPath\n", "from kfp.dsl.types import String\n", "from kfp.gcp import use_gcp_secret\n", "\n", "# Defaults and environment settings\n", "BASE_IMAGE = os.getenv('BASE_IMAGE')\n", "TRAINER_IMAGE = os.getenv('TRAINER_IMAGE')\n", "RUNTIME_VERSION = os.getenv('RUNTIME_VERSION')\n", "PYTHON_VERSION = os.getenv('PYTHON_VERSION')\n", "COMPONENT_URL_SEARCH_PREFIX = os.getenv('COMPONENT_URL_SEARCH_PREFIX')\n", "USE_KFP_SA = os.getenv('USE_KFP_SA')\n", "\n", "TRAINING_FILE_PATH = 'datasets/training/data.csv'\n", "VALIDATION_FILE_PATH = 'datasets/validation/data.csv'\n", "TESTING_FILE_PATH = 'datasets/testing/data.csv'\n", "\n", "# Parameter defaults\n", "SPLITS_DATASET_ID = 'splits'\n", "HYPERTUNE_SETTINGS = \"\"\"\n", "{\n", " \"hyperparameters\": {\n", " \"goal\": \"MAXIMIZE\",\n", " \"maxTrials\": 6,\n", " \"maxParallelTrials\": 3,\n", " \"hyperparameterMetricTag\": \"accuracy\",\n", " \"enableTrialEarlyStopping\": True,\n", " \"params\": [\n", " {\n", " \"parameterName\": \"max_iter\",\n", " \"type\": \"DISCRETE\",\n", " \"discreteValues\": [500, 1000]\n", " },\n", " {\n", " \"parameterName\": \"alpha\",\n", " \"type\": \"DOUBLE\",\n", " \"minValue\": 0.0001,\n", " \"maxValue\": 0.001,\n", " \"scaleType\": \"UNIT_LINEAR_SCALE\"\n", " }\n", " ]\n", " }\n", "}\n", "\"\"\"\n", "\n", "\n", "# Helper functions\n", "def generate_sampling_query(source_table_name, num_lots, lots):\n", " \"\"\"Prepares the data sampling query.\"\"\"\n", "\n", " sampling_query_template = \"\"\"\n", " SELECT \n", " FROM \n", " `{{ source_table }}` AS cover\n", " WHERE \n", " MOD(ABS(FARM_FINGERPRINT(TO_JSON_STRING(cover))), {{ num_lots }}) IN ({{ lots }})\n", " \"\"\"\n", " query = Template(sampling_query_template).render(\n", " source_table=source_table_name, num_lots=num_lots, lots=str(lots)[1:-1])\n", "\n", " return query\n", "\n", "\n", "# Create component factories\n", "component_store = # TO DO: Complete the command\n", "\n", "bigquery_query_op = # TO DO: Use the pre-build bigquery/query component\n", "mlengine_train_op = # TO DO: Use the pre-build ml_engine/train\n", "mlengine_deploy_op = # TO DO: Use the pre-build ml_engine/deploy component\n", "retrieve_best_run_op = # TO DO: Package the retrieve_best_run function into a lightweight component\n", "evaluate_model_op = # TO DO: Package the evaluate_model function into a lightweight component\n", "\n", "\n", "@kfp.dsl.pipeline(\n", " name='Covertype Classifier Training',\n", " description='The pipeline training and deploying the Covertype classifierpipeline_yaml'\n", ")\n", "def covertype_train(project_id,\n", " region,\n", " source_table_name,\n", " gcs_root,\n", " dataset_id,\n", " evaluation_metric_name,\n", " evaluation_metric_threshold,\n", " model_id,\n", " version_id,\n", " replace_existing_version,\n", " hypertune_settings=HYPERTUNE_SETTINGS,\n", " dataset_location='US'):\n", " \"\"\"Orchestrates training and deployment of an sklearn model.\"\"\"\n", "\n", " # Create the training split\n", " query = generate_sampling_query(\n", " source_table_name=source_table_name, num_lots=10, lots=[1, 2, 3, 4])\n", "\n", " training_file_path = '{}/{}'.format(gcs_root, TRAINING_FILE_PATH)\n", "\n", " create_training_split = bigquery_query_op(\n", " query=query,\n", " project_id=project_id,\n", " dataset_id=dataset_id,\n", " table_id='',\n", " output_gcs_path=training_file_path,\n", " dataset_location=dataset_location)\n", "\n", " # Create the validation split\n", " query = generate_sampling_query(\n", " source_table_name=source_table_name, num_lots=10, lots=[8])\n", "\n", " validation_file_path = '{}/{}'.format(gcs_root, VALIDATION_FILE_PATH)\n", "\n", " create_validation_split = # TODO - use the bigquery_query_op\n", "\n", " # Create the testing split\n", " query = generate_sampling_query(\n", " source_table_name=source_table_name, num_lots=10, lots=[9])\n", "\n", " testing_file_path = '{}/{}'.format(gcs_root, TESTING_FILE_PATH)\n", "\n", " create_testing_split = # TO DO: Use the bigquery_query_op\n", " \n", "\n", " # Tune hyperparameters\n", " tune_args = [\n", " '--training_dataset_path',\n", " create_training_split.outputs['output_gcs_path'],\n", " '--validation_dataset_path',\n", " create_validation_split.outputs['output_gcs_path'], '--hptune', 'True'\n", " ]\n", "\n", " job_dir = '{}/{}/{}'.format(gcs_root, 'jobdir/hypertune',\n", " kfp.dsl.RUN_ID_PLACEHOLDER)\n", "\n", " hypertune = # TO DO: Use the mlengine_train_op\n", "\n", " # Retrieve the best trial\n", " get_best_trial = retrieve_best_run_op(\n", " project_id, hypertune.outputs['job_id'])\n", "\n", " # Train the model on a combined training and validation datasets\n", " job_dir = '{}/{}/{}'.format(gcs_root, 'jobdir', kfp.dsl.RUN_ID_PLACEHOLDER)\n", "\n", " train_args = [\n", " '--training_dataset_path',\n", " create_training_split.outputs['output_gcs_path'],\n", " '--validation_dataset_path',\n", " create_validation_split.outputs['output_gcs_path'], '--alpha',\n", " get_best_trial.outputs['alpha'], '--max_iter',\n", " get_best_trial.outputs['max_iter'], '--hptune', 'False'\n", " ]\n", "\n", " train_model = # TO DO: Use the mlengine_train_op\n", "\n", " # Evaluate the model on the testing split\n", " eval_model = evaluate_model_op(\n", " dataset_path=str(create_testing_split.outputs['output_gcs_path']),\n", " model_path=str(train_model.outputs['job_dir']),\n", " metric_name=evaluation_metric_name)\n", "\n", " # Deploy the model if the primary metric is better than threshold\n", " with kfp.dsl.Condition(eval_model.outputs['metric_value'] > evaluation_metric_threshold):\n", " deploy_model = mlengine_deploy_op(\n", " model_uri=train_model.outputs['job_dir'],\n", " project_id=project_id,\n", " model_id=model_id,\n", " version_id=version_id,\n", " runtime_version=RUNTIME_VERSION,\n", " python_version=PYTHON_VERSION,\n", " replace_existing_version=replace_existing_version)\n", "\n", " # Configure the pipeline to run using the service account defined\n", " # in the user-gcp-sa k8s secret\n", " if USE_KFP_SA == 'True':\n", " kfp.dsl.get_pipeline_conf().add_op_transformer(\n", " use_gcp_secret('user-gcp-sa'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The custom components execute in a container image defined in `base_image/Dockerfile`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat base_image/Dockerfile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The training step in the pipeline employes the AI Platform Training component to schedule a AI Platform Training job in a custom training container. The custom training image is defined in `trainer_image/Dockerfile`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat trainer_image/Dockerfile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building and deploying the pipeline\n", "\n", "Before deploying to AI Platform Pipelines, the pipeline DSL has to be compiled into a pipeline runtime format, also refered to as a pipeline package. The runtime format is based on [Argo Workflow](https://github.com/argoproj/argo), which is expressed in YAML. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure environment settings\n", "\n", "Update the below constants with the settings reflecting your lab environment. \n", "\n", "- `REGION` - the compute region for AI Platform Training and Prediction\n", "- `ARTIFACT_STORE` - the GCS bucket created during installation of AI Platform Pipelines. The bucket name will be similar to `qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default`.\n", "- `ENDPOINT` - set the `ENDPOINT` constant to the endpoint to your AI Platform Pipelines instance. Then endpoint to the AI Platform Pipelines instance can be found on the [AI Platform Pipelines](https://console.cloud.google.com/ai-platform/pipelines/clusters) page in the Google Cloud Console.\n", "\n", "1. Open the SETTINGS* for your instance\n", "2. Use the value of the `host` variable in the Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SKD section of the SETTINGS window.\n", "\n", "Run gsutil ls without URLs to list all of the Cloud Storage buckets under your default project ID." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gsutil ls" ] }, { "source": [ "HINT: \n", "\n", "For ENDPOINT, use the value of the `host` variable in the Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SDK section of the SETTINGS window.\n", "\n", "For ARTIFACT_STORE_URI, copy the bucket name which starts with the qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default prefix from the previous cell output. Your copied value should look like 'gs://qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default'\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "REGION = 'us-central1'\n", "ENDPOINT = '337dd39580cbcbd2-dot-us-central2.pipelines.googleusercontent.com' # TO DO: REPLACE WITH YOUR ENDPOINT\n", "ARTIFACT_STORE_URI = 'gs://qwiklabs-gcp-xx-xxxxxxx-kubeflowpipelines-default' # TO DO: REPLACE WITH YOUR ARTIFACT_STORE NAME \n", "PROJECT_ID = !(gcloud config get-value core/project)\n", "PROJECT_ID = PROJECT_ID[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the trainer image" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IMAGE_NAME='trainer_image'\n", "TAG='latest'\n", "TRAINER_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Note: Please ignore any incompatibility ERROR that may appear for the packages visions as it will not affect the lab's functionality." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gcloud builds submit --timeout 15m --tag $TRAINER_IMAGE trainer_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the base image for custom components" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IMAGE_NAME='base_image'\n", "TAG='latest'\n", "BASE_IMAGE='gcr.io/{}/{}:{}'.format(PROJECT_ID, IMAGE_NAME, TAG)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gcloud builds submit --timeout 15m --tag $BASE_IMAGE base_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compile the pipeline\n", "\n", "You can compile the DSL using an API from the KFP SDK or using the KFP compiler.\n", "\n", "To compile the pipeline DSL using the KFP compiler." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set the pipeline's compile time settings\n", "\n", "The pipeline can run using a security context of the GKE default node pool's service account or the service account defined in the `user-gcp-sa` secret of the Kubernetes namespace hosting KFP. If you want to use the `user-gcp-sa` service account you change the value of `USE_KFP_SA` to `True`.\n", "\n", "Note that the default AI Platform Pipelines configuration does not define the `user-gcp-sa` secret." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "USE_KFP_SA = False\n", "\n", "COMPONENT_URL_SEARCH_PREFIX = 'https://raw.githubusercontent.com/kubeflow/pipelines/0.2.5/components/gcp/'\n", "RUNTIME_VERSION = '1.15'\n", "PYTHON_VERSION = '3.7'\n", "\n", "%env USE_KFP_SA={USE_KFP_SA}\n", "%env BASE_IMAGE={BASE_IMAGE}\n", "%env TRAINER_IMAGE={TRAINER_IMAGE}\n", "%env COMPONENT_URL_SEARCH_PREFIX={COMPONENT_URL_SEARCH_PREFIX}\n", "%env RUNTIME_VERSION={RUNTIME_VERSION}\n", "%env PYTHON_VERSION={PYTHON_VERSION}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use the CLI compiler to compile the pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Compile the `covertype_training_pipeline.py` with the `dsl-compile` command line:\n", "\n", "<ql-infobox><b>NOTE:</b> If you need help, you may take a look at the complete solution by navigating to mlops-on-gcp > workshops > kfp-caip-sklearn > lab-02-kfp-pipeline and opening lab-02.ipynb.\n", "</ql-infobox>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# TO DO: Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is the `covertype_training_pipeline.yaml` file. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!head covertype_training_pipeline.yaml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deploy the pipeline package" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Upload the pipeline to the Kubeflow cluster using the `kfp` command line:\n", "\n", "<ql-infobox><b>NOTE:</b> If you need help, you may take a look at the complete solution by navigating to mlops-on-gcp > workshops > kfp-caip-sklearn > lab-02-kfp-pipeline and opening lab-02.ipynb.\n", "</ql-infobox>" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "PIPELINE_NAME='covertype_continuous_training'\n", "\n", "# TO DO: Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Submitting pipeline runs\n", "\n", "You can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run.\n", "\n", "### List the pipelines in AI Platform Pipelines" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!kfp --endpoint $ENDPOINT pipeline list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Submit a run\n", "\n", "Find the ID of the `covertype_continuous_training` pipeline you uploaded in the previous step and update the value of `PIPELINE_ID` .\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PIPELINE_ID='0918568d-758c-46cf-9752-e04a4403cd84' # TO DO: REPLACE WITH YOUR PIPELINE ID " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "EXPERIMENT_NAME = 'Covertype_Classifier_Training'\n", "RUN_ID = 'Run_001'\n", "SOURCE_TABLE = 'covertype_dataset.covertype'\n", "DATASET_ID = 'splits'\n", "EVALUATION_METRIC = 'accuracy'\n", "EVALUATION_METRIC_THRESHOLD = '0.69'\n", "MODEL_ID = 'covertype_classifier'\n", "VERSION_ID = 'v01'\n", "REPLACE_EXISTING_VERSION = 'True'\n", "\n", "GCS_STAGING_PATH = '{}/staging'.format(ARTIFACT_STORE_URI)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Run the pipeline using the `kfp` command line. Here are some of the variable\n", "you will have to use to pass to the pipeline:\n", "\n", "- EXPERIMENT_NAME is set to the experiment used to run the pipeline. You can choose any name you want. If the experiment does not exist it will be created by the command\n", "- RUN_ID is the name of the run. You can use an arbitrary name\n", "- PIPELINE_ID is the id of your pipeline. Use the value retrieved by the `kfp pipeline list` command\n", "- GCS_STAGING_PATH is the URI to the Cloud Storage location used by the pipeline to store intermediate files. By default, it is set to the `staging` folder in your artifact store.\n", "- REGION is a compute region for AI Platform Training and Prediction.\n", "\n", "\n", "<ql-infobox><b>NOTE:</b> If you need help, you may take a look at the complete solution by navigating to mlops-on-gcp > workshops > kfp-caip-sklearn > lab-02-kfp-pipeline and opening lab-02.ipynb.\n", "</ql-infobox>" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# TO DO: Your code goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Monitoring the run\n", "\n", "You can monitor the run using KFP UI. Follow the instructor who will walk you through the KFP UI and monitoring techniques.\n", "\n", "To access the KFP UI in your environment use the following URI:\n", "\n", "https://[ENDPOINT]\n", "\n", "\n", "NOTE that your pipeline run may fail due to the bug in a BigQuery component that does not handle certain race conditions. If you observe the pipeline failure, re-run the last cell of the notebook to submit another pipeline run or retry the run from the KFP UI\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<font size=-1>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 [https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n", "\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.</font>" ] } ], "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": 4 }	apache-2.0
mne-tools/mne-tools.github.io	0.17/_downloads/c0c13bba79416f07138e867fac14148e/plot_configuration.ipynb	1	6250	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Configuring MNE python\n\n\nThis tutorial gives a short introduction to MNE configurations.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path as op\n\nimport mne\nfrom mne.datasets.sample import data_path\n\nfname = op.join(data_path(), 'MEG', 'sample', 'sample_audvis_raw.fif')\nraw = mne.io.read_raw_fif(fname).crop(0, 10)\noriginal_level = mne.get_config('MNE_LOGGING_LEVEL', 'INFO')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MNE-python stores configurations to a folder called `.mne` in the user's\nhome directory, or to AppData directory on Windows. The path to the config\nfile can be found out by calling :func:`mne.get_config_path`.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(mne.get_config_path())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These configurations include information like sample data paths and plotter\nwindow sizes. Files inside this folder should never be modified manually.\nLet's see what the configurations contain.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(mne.get_config())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see fields like \"MNE_DATASETS_SAMPLE_PATH\". As the name suggests, this is\nthe path the sample data is downloaded to. All the fields in the\nconfiguration file can be modified by calling :func:`mne.set_config`.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nLogging\n=======\nConfigurations also include the default logging level for the functions. This\nfield is called \"MNE_LOGGING_LEVEL\".\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.set_config('MNE_LOGGING_LEVEL', 'INFO')\nprint(mne.get_config(key='MNE_LOGGING_LEVEL'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default value is now set to INFO. This level will now be used by default\nevery time we call a function in MNE. We can set the global logging level for\nonly this session by calling :func:`mne.set_log_level` function.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.set_log_level('WARNING')\nprint(mne.get_config(key='MNE_LOGGING_LEVEL'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how the value in the config file was not changed. Logging level of\nWARNING only applies for this session. Let's see what logging level of\nWARNING prints for :func:`mne.compute_raw_covariance`.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cov = mne.compute_raw_covariance(raw)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nothing. This means that no warnings were emitted during the computation. If\nyou look at the documentation of :func:`mne.compute_raw_covariance`, you\nnotice the ``verbose`` keyword. Setting this parameter does not touch the\nconfigurations, but sets the logging level for just this one function call.\nLet's see what happens with logging level of INFO.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cov = mne.compute_raw_covariance(raw, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see there is some info about what the function is doing. The logging\nlevel can be set to 'DEBUG', 'INFO', 'WARNING', 'ERROR' or 'CRITICAL'. It can\nalso be set to an integer or a boolean value. The correspondence to string\nvalues can be seen in the table below. ``verbose=None`` uses the default\nvalue from the configuration file.\n\n+----------+---------+---------+\n\| String \| Integer \| Boolean \|\n+==========+=========+=========+\n\| DEBUG \| 10 \| \|\n+----------+---------+---------+\n\| INFO \| 20 \| True \|\n+----------+---------+---------+\n\| WARNING \| 30 \| False \|\n+----------+---------+---------+\n\| ERROR \| 40 \| \|\n+----------+---------+---------+\n\| CRITICAL \| 50 \| \|\n+----------+---------+---------+\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.set_config('MNE_LOGGING_LEVEL', original_level)\nprint('Config value restored to: %s' % mne.get_config(key='MNE_LOGGING_LEVEL'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
Kaggle/learntools	notebooks/feature_engineering/raw/tut3.ipynb	1	11016	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "Creating new features from the raw data is one of the best ways to improve your model. For example, when working with Kickstarter data, you can calculate the number of total projects in the last week and the duration of the fundraising period. The features you create are different for every dataset, so it takes a bit of creativity and experimentation. We're a bit limited here, since we're working with only one table. Typically you'll have access to multiple tables with relevant data that you can use to create new features.\n", "\n", "But you can still see how to make new features using categorical features, and then a few examples of generated numerical features. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#$HIDE_INPUT$\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from pandas.plotting import register_matplotlib_converters\n", "register_matplotlib_converters()\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv',\n", " parse_dates=['deadline', 'launched'])\n", "\n", "# Drop live projects\n", "ks = ks.query('state != \"live\"')\n", "\n", "# Add outcome column, \"successful\" == 1, others are 0\n", "ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))\n", "\n", "# Timestamp features\n", "ks = ks.assign(hour=ks.launched.dt.hour,\n", " day=ks.launched.dt.day,\n", " month=ks.launched.dt.month,\n", " year=ks.launched.dt.year)\n", "\n", "# Label encoding\n", "cat_features = ['category', 'currency', 'country']\n", "encoder = LabelEncoder()\n", "encoded = ks[cat_features].apply(encoder.fit_transform)\n", "\n", "data_cols = ['goal', 'hour', 'day', 'month', 'year', 'outcome']\n", "baseline_data = ks[data_cols].join(encoded)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactions\n", "\n", "One of the easiest ways to create new features is by combining categorical variables. For example, if one record has the country `\"CA\"` and category `\"Music\"`, you can create a new value `\"CA_Music\"`. This is a new categorical feature that can provide information about correlations between categorical variables. This type of feature is typically called an interaction. \n", "\n", "In general, you would build interaction features from all pairs of categorical features. You can make interactions from three or more features as well, but you'll tend to get diminishing returns.\n", "\n", "Pandas lets us simply add string columns together like normal Python strings." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "interactions = ks['category'] + \"_\" + ks['country']\n", "print(interactions.head(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can label encode the interaction feature and add it to our data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "label_enc = LabelEncoder()\n", "data_interaction = baseline_data.assign(category_country=label_enc.fit_transform(interactions))\n", "data_interaction.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the exercise, you'll build interaction terms for all pairs of categorical features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Number of projects in the last week\n", "\n", "Next, we'll count the number of projects launched in the preceeding week for each record. I'll use the `.rolling` method on a series with the `\"launched\"` column as the index. I'll create the series, using `ks.launched` as the index and `ks.index` as the values, then sort the times. Using a time series as the index allows us to define the rolling window size in terms of hours, days, weeks, etc." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# First, create a Series with a timestamp index\n", "launched = pd.Series(ks.index, index=ks.launched, name=\"count_7_days\").sort_index()\n", "launched.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are seven projects that have obviously wrong launch dates, but we'll just ignore them. Again, this is something you'd handle when cleaning the data, but it's not the focus of this mini-course.\n", "\n", "With a timeseries index, you can use `.rolling` to select time periods as the window. For example `launched.rolling('7d')` creates a rolling window that contains all the data in the previous 7 days. The window contains the current record, so if we want to count all the previous projects but not the current one, we'll need to subtract 1. We'll plot the results to make sure it looks right." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "count_7_days = launched.rolling('7d').count() - 1\n", "print(count_7_days.head(20))\n", "\n", "# Ignore records with broken launch dates\n", "plt.plot(count_7_days[7:]);\n", "plt.title(\"Number of projects launched over periods of 7 days\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the counts, we need to adjust the index so we can join it with the other training data. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "count_7_days.index = launched.values\n", "count_7_days = count_7_days.reindex(ks.index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "count_7_days.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now join the new feature with the other data again using `.join` since we've matched the index." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "baseline_data.join(count_7_days).head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Time since the last project in the same category\n", "\n", "Do projects in the same category compete for donors? If you're trying to fund a video game and another game project was just launched, you might not get as much money. We can capture this by calculating the time since the last launch project in the same category.\n", "\n", "A handy method for performing operations within groups is to use `.groupby` then `.transform`. The `.transform` method takes a function then passes a series or dataframe to that function for each group. This returns a dataframe with the same indices as the original dataframe. In our case, we'll perform a groupby on `\"category\"` and use transform to calculate the time differences for each category." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def time_since_last_project(series):\n", " # Return the time in hours\n", " return series.diff().dt.total_seconds() / 3600.\n", "\n", "df = ks[['category', 'launched']].sort_values('launched')\n", "timedeltas = df.groupby('category').transform(time_since_last_project)\n", "timedeltas.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get `NaN`s here for projects that are the first in their category. We'll need to fill those in with something like the mean or median. We'll also need to reset the index so we can join it with the other data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Final time since last project\n", "timedeltas = timedeltas.fillna(timedeltas.median()).reindex(baseline_data.index)\n", "timedeltas.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Transforming numerical features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The distribution of the values in `\"goal\"` shows that most projects have goals less than 5000 USD. However, there is a long tail of goals going up to $100,000. Some models work better when the features are normally distributed, so it might help to transform the goal values. Common choices for this are the square root and natural logarithm. These transformations can also help constrain outliers.\n", "\n", "Here I'll transform the goal feature using the square root and log functions as examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(ks.goal, range=(0, 100000), bins=50);\n", "plt.title('Goal');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(np.sqrt(ks.goal), range=(0, 400), bins=50);\n", "plt.title('Sqrt(Goal)');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(np.log(ks.goal), range=(0, 25), bins=50);\n", "plt.title('Log(Goal)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The log transformation won't help our model since tree-based models are scale invariant. However, this should help if we had a linear model or neural network.\n", "\n", "Other transformations include squares and other powers, exponentials, etc. These might help the model discriminate, like the kernel trick for SVMs. Again, it takes a bit of experimentation to see what works. One method is to create a bunch of new features and later choose the best ones with feature selection algorithms.\n", "\n", "# Your Turn\n", "[Try your hand at generating features](#$NEXT_NOTEBOOK_URL$) to improve performance on your model from the previous exercise." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
shimanluck/AY250-HW	hw_2/hw_2_sd.ipynb	1	365968	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Shiman Ding, [email protected]\n", "# IEOR, 24104985\n", "# 09/10/2016, AY250-HW2\n", "# Data Visualization" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "0) Critique the most important figure from a seminal paper in your field. Provide the original figure/caption. In your own words, what story is this figure trying to convey? What does it do well? What could have been done better? What elements didn't need to be present to still convey the same story?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"hw_2_data/critic.png\">\n", "<br/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure is trying to show how min cuts can be achieved via clustering. The example is simple enough with insights. But G1 and G2 are not clearly defined in the graph. They may also use dashed line for cuts and clusters. They may also use different color for cuts and clusters. If flow is included in the graph, that would be better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Reproduce one graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"hw_2_data/truckcost.png\">\n", "<br/>" ] }, { "cell_type": "code", "execution_count": 32, "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>maxratiode</th>\n", " <th>maxratio_cen_zio</th>\n", " <th>maxratio_cen_pot</th>\n", " <th>maxratiode2</th>\n", " <th>averatiode</th>\n", " <th>averatio_cen_zio</th>\n", " <th>averatio_cen_pot</th>\n", " <th>averatiode2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1.004589</td>\n", " <td>1.001530</td>\n", " <td>1.434960</td>\n", " <td>1.001530</td>\n", " <td>1.003185</td>\n", " <td>1.001062</td>\n", " <td>1.204998</td>\n", " <td>1.001062</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.016423</td>\n", " <td>1.005474</td>\n", " <td>1.551952</td>\n", " <td>1.005474</td>\n", " <td>1.012674</td>\n", " <td>1.004225</td>\n", " <td>1.227594</td>\n", " <td>1.004225</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.034050</td>\n", " <td>1.011350</td>\n", " <td>1.605934</td>\n", " <td>1.011350</td>\n", " <td>1.024761</td>\n", " <td>1.008254</td>\n", " <td>1.524485</td>\n", " <td>1.008254</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.063651</td>\n", " <td>1.021217</td>\n", " <td>1.391803</td>\n", " <td>1.021217</td>\n", " <td>1.042438</td>\n", " <td>1.014146</td>\n", " <td>1.208293</td>\n", " <td>1.014146</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1.075854</td>\n", " <td>1.025285</td>\n", " <td>1.303562</td>\n", " <td>1.025285</td>\n", " <td>1.057829</td>\n", " <td>1.019276</td>\n", " <td>1.259208</td>\n", " <td>1.019276</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " maxratiode maxratio_cen_zio maxratio_cen_pot maxratiode2 averatiode \\\n", "1 1.004589 1.001530 1.434960 1.001530 1.003185 \n", "2 1.016423 1.005474 1.551952 1.005474 1.012674 \n", "3 1.034050 1.011350 1.605934 1.011350 1.024761 \n", "4 1.063651 1.021217 1.391803 1.021217 1.042438 \n", "5 1.075854 1.025285 1.303562 1.025285 1.057829 \n", "\n", " averatio_cen_zio averatio_cen_pot averatiode2 \n", "1 1.001062 1.204998 1.001062 \n", "2 1.004225 1.227594 1.004225 \n", "3 1.008254 1.524485 1.008254 \n", "4 1.014146 1.208293 1.014146 \n", "5 1.019276 1.259208 1.019276 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_table(\"hw_2_data/ay250.txt\", sep=\"\\t\")\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(50, 8)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(data)" ] }, { "cell_type": "code", "execution_count": 63, "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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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5AHYRnIilG0jds1xrz2fYv8I9vvw3G7YxyXcn8DPRr0LobzTcb0MyrEFez9w6IO9G9L54t52vPRH4Dy1YFnW+3gJ8visBwLyYRHK1tb72FxQ5g7YPwsOluIJl5IGlIW704pAhHS3vIN0Pjhni5gabI94DXHOHFR+ug3won6gPKh/qu2a8AzLOiVcSnV3EnXuwnfBqz52rVONabqh8saPEBZyxVDugdi7Yf8s9o2xqYAyrsTBOBYl2POEkIG4dhDHPPaSQ6plTPgc7cLnbya6jr24/dH2/UzJUvybYl78WUXs7QUKFJjP3fZsHYcAXYprX6aUi63xU8iDvzcPtJeOh/o/g+8g/9Bxw+fnbZwX4s9swufWB/eG8/cU9yqjvu8j3ic6bSr7tbj+Hn/OeWIYfmhMxHkVy7ilSpX6A+dXwX884uUBhyKI9yU2/s7yMAT+4TeXOYM3Ww+jcM7/CyQIAachIALQaZpKCpoZBPCyWoF8+mN7B91rV/Bi45dddwiLZ3HvG34GftXzMjGf4gWzLTw8/DqOm/JLiLuKMM5nE/7RL8T2E14O4djzbNQX+R42tjzwC/6vhBc7Z2cOEAw89ugOXiInMB4sBDdu4UXyojmCqavN4vSBw8nI9zZvSBeK5/KYvnYo7x6OifKfwa4j7o1CnH+x/wHny3BdvbTw3EF4fiiefQz1PoH0IRC1IyEcbqFsLRC3M9JdRbo/cbzbx8fnXexTC5ZWqGXIazNexG8lj4xxWT/i2lrwPch5I97b2BajHNWSx9XnPJkFxyzqykOEXOD6YgvnPepyAtfHoIzf45xfuLOwseWlEjY33QZ4xt+4/yyeYy5nWu2KtBzMcfnEMi2fWwbcG4hnPI1nMLvxuMfCLa2g8j5y5MhNRGLLag+k/Q/7AciLBbgKKONM5Pst2opnWLOweBWfwzYJ9eI4ScqoEln8AeeXceoNbpfBiAXb/yxuJz9MNS98LroisjvEzUlYx3k2eSHkzeI9xWBrfGRifjbKyYJsMOr7WUqZ49l/YuuH+zfApjPK0ZI/s7zhektsrfA95c/WTzheB4ZJrHnJ88QEmamI+wOeuxdW/3O474nvBP8I5KDKxWMKwb0tzh9FvHPgwJ+7y/hh0QN7FoD98KyC4HwOx/y998G9N/meBCEgBISAEBACQkAICIEMEIDoGwvD3rcZyEKSCgEhkAoB91Su58jLMOX74Nfar/il1i6hu0TVk8cV4dcez9jTv0q5m+khDJwvKus65ciPglRKCAgBISAEhECuJpBrBCDEX32IPx7Hwl1FSQLWxbqECzX1RXRlcffAVBF/mojshYAQEAJCQAgIgZxEINcIQIzX6IPtDYz94PEmqQZ0N/DaaG4YasRjxSQIASEgBIRANhHAeD4eXylBCAgBBxDINQIQg/d5UDm7tUpx5lsCW1437YOEgdQOwC1ZCgEhIASEgBAQAkIg+wnILGCLNkDXbzOcXsDyHoctLsuhEBACQkAICAEhIARyFIFcYwG0ptXQRcxrcm2wJq6OA2sh+9fUp7IXAkJACAgBISAEnIAA3t25+uUtAjDph7QhZgPPSXop7TP+/Ny4EQ7vEWnHk7uOJcBf46JFvaUtHIvZqtylLazClGWRpD2yDHW6D5K2SBdRlkXgtsjtITcKQLNUw3hAXsD2G4wPZH+jHMrnzZs3zHRo/V8WfyIAreflyJjSFo6ka1ve0ha28XJ0bGkPRxO2Pn9pC+tZSUzHEch1AhCzysprnDhm11fmgHNv84kcCAEhIASEgBAQAkIghxKQSSA5tGGlWkJACAgBISAEhIAQSI2ACMDUyMh1ISAEhIAQEAJCQAjkUAIiAHNow0q1hIAQEAJCQAgIASGQGgERgKmRketCQAgIASEgBISAEMihBEQA5tCGlWoJASEgBISAEBACQiA1AiIAUyMj14WAEBACQkAICAEhkEMJiADMoQ0r1RICQkAICAEhYEng0qWLNG3aRHXp+PEgmj59suXtdI+feqpOunEkgvMQyHXrADpP00hJhYAQEAJCQAhkHgEWgBcvXlAZVqlSlapUed+mzHO55zSbWDlDZBGAztBKUkYhIASEgBDIMQQOHz5Iq1YtJzc3Nzp//hzVrfsEFS9enPbs+YXgX55mzZpHO3f+RD/8sJXu37+n6j1u3BQqUKAA9ezZlRYsWEL+/mWpf/+e1LlzV7jBLEYLF85T8Xx9/WngwME0depEunMngq5fv0ZNmzanAQMG0Zw5M+jKlSvIfxo980xzWrlyqcorLOwszZw5hW7fvk1eXvlo8OBhEIeP0OXLl2jChDEUFXWXHnmkmpl/VFSUyiskJBjljaXnn+9M7dp1NN+XA+cgIF3AztFOUkohIASEgBDIJALFfItRSpt3v56pPoHvpZSGr9kTgoL+oZEjP6C1az+jH3/cSkWKFKXly9dQxYqVaMeO7bR79y/00UdLafXqT6lRoyb0zTebIBJLKHHHYm3t2lXk51eWmjVrqR5/7lwYzZ27kMaOnUTbt/8I0deMFi9eSWvWfErffrsJ4u4WDR06AsKuKg0b9p5Koy16Eyd+QC+80BnP2khvvjmURo8eQTExMfThhzOodet2EIrrqXr1x8zVXL16BVWoEEArVqylRYtW0qZNX9Dp0yHm+3LgHATEAugc7SSlFAJCQAgIgRxEgAUUCzoOhQoVolq1aqvjkiVLUUREOI0bNxlCcBuxsNu/fy+EYWV1nwXZzp07lCBct+5zdY3/+PuXo/z5C6jzLl260aFDB2jjxnV05swpio6Oprt375rjWh7wdbZCPv10U3U5MLAa+fgUpLNnQ4ktlVwODi1atMaYwUnq+M8/98MqGEXff/+dOo+MjKSQkBAqXz5Ancsf5yAgAtA52klKKQSEgBAQAplE4Pr56zbnFL5kFYXbnCr1BB4eHkluurklvo6vXLlMffv2oBdf7Ez16zdQ1sHg4BMqPou5K1cuEVvvLl26RAEBFdX1vHnzmvNbsGCOGuvXqlUbWA8b04EDf1B8vPl2koP4+Lgk53zC19gC6OrqwmfqPj/P1dXUacjdvmPHTjSL0ps3b5K3t7eKJ3+ch4B0ATtPW0lJhYAQEAJCIBcQ4O5hPz9/eumlV6hq1Udo3769amwgV33ZskXojq1Bw4ePokmTxiqhlhwJC76uXV9TVj0WkzwOkEUbjzmMjY1NEt3LKz/5+vrSrl071PWjR4/QjRs3VBdv7dp1acuWzer67t27MB7xvjquVasOun2/VMcs/nr37kahoWfUufxxHgKJPzmcp8xSUiEgBISAEBACOYgAW9oSQ7169dGlGkzdur1Enp6eagIGj7E7duyo6hZeu/ZzTNbwwiSRLZhMsoxYqFmG7t170MSJY2CV86HChQurCR08+7dSpSroXo6g8eNHU4cOncxJxoyZgEkgU1VeHh6eNHnyDHJ3d6chQ95FPh9ABH4LIRqIZ+ZXaXr16kOzZ0+nV1/trIRpjx6vmy2R5kzlwPAEkn7qDF9c4xUwHuH69fBUzevGK3HOLBF6J6hYMW/80pW2yO4WlrbI7hZI+nxpj6Q8svNM2iI76Sd9NrdF8eI+uVoDSRdw0s+EnAkBISAEhIAQEAJCIMcTEAGY45tYKigEhIAQEAJCQAgIgaQERAAm5SFnQkAICAEhIASEgBDI8QREAOb4JpYKCgEhIASEgBAQAkIgKQERgEl5yJkQEAJCQAgIASEgBHI8ARGAOb6JpYJCQAgIASEgBISAEEhKQARgUh5yJgSEgBAQAkJACAiBHE9ABGCOb2KpoBAQAkJACAgBguu4izRt2kSF4vjxIPj2Nfn5dRY2K1Ysod9+2+MsxTV8OcUTiOGbSAooBISAEBACQiDjBFgAskcQDlWqVMX2fsYzzcIcevful4VPy/mPEgGY89tYaigEhIAQEAIGInD48EG4XVuufPOeP3+O6tZ9Al4pitOePb8o12qzZs2jnTt/gqu3rfC/e0+VfNy4KVSgQAHq2bMrLViwhPz9y1L//j2pc+euVLRoMVq4cJ6K5+vrTwMHDqapUyfSnTsRyg9w06bNacCAQTRnzgy6cuUKzZo1jZ55pjmtXLlU5RUWdhau4KbQ7du34e4tHw0ePEy5j0sNWXR0tMqL6+Hh4UFdunSn1q3bEVsVFyz4kKKiolBWb3rnnRGqnG+91Y8CA6vTX38dhp/h66hDHxU/tfy//PJT5X7OBe462P/wuXNhtGHDV7R69QqqWfNxlZbd03322XpycXGlypWr0NChIyhv3rypZSnXUyAgAjAFKHJJCAgBISAEci4B38XFUqxc2/LtaUmLVSne67etJ205vTnFe+f7X0/xeloXg4L+ofXrvyAfHx9q164FDRo0lJYvXwPhNgH+frfTr7/upo8+Wkp58uQh7vr85ptN9Pbbw5S4Y7FWt2598vMrS82atSQWYiySvvrqO8qfvwBt3LiOmjZtRm3bdqDIyDv03HNt4Vf4NSWS2HfwsGHvqTQssDiwv1++//TTTZW/4dGjR9Cnn/5P+QNOqQ6bNn1O4eHheM4miMZbKFMflZa7l6dN+5BKlSpFR478RRMmjFF14jxYFC5atIKCg0+gHgPSFIAvvPAy8caB82jSpBmVKeOrzvkP+0Ves2YlLVu2RvH78MPpELNLIHIHm+PIQfoERACmz0hiCAEhIASEgBDIVAIVKgTA6ldC5VmoUCGqVau2Oi5ZshRFRITTuHGTIQS3KWG3f/9eqlixsrrPlradO3coQbhu3efmMvn7l1Pijy906dKNDh06oITgmTOniC12d+/eNce1PODrbIVk8cchMLAaRFVBCgsLpfLlAyyjmo857/btO6lzjrt27ecQZafowoVzNGrUOxQfH6/u3bp1i2JiYtTxk082VHuuB4tHawJb/O7ejaTkXb+HDx+ihg0bKfHH+XTo8JwSztbkKXESCYgATGQhR0JACAgBIZALCNhjsUvNMmgvLu46tQxubomv4ytXLlPfvj3oxRc7U/36DahIkaLKcsbxWcxduXIJXZ8umNRxiQICKqpsLLs/FyyYo8b6tWrVhho1akwHDvwBUWb5tMTj+Pi4xJOEI76mhdsDN3GBy6qth3z/woXzquu6TBk/WOLWm5Ncv37NbEX09PQ0X7fm4JdfdioBvGTJJw9E5/Jpkck3+Tit8j6QgVxQBGQWsHwQhIAQEAJCQAgYiAB3D/v5+dNLL71CVas+Qvv27VUCi4u4bNkiql69Bg0fPoomTRqbovBhwde1q6lLl8UkC7G4uFg15jA2NjZJTb288pOvry/t2rVDXT969AjG6d1I1frHkXgc3s8/b1fxedwgj/Hz9vZW3cE8zo/Dtm3f07vvDlHHyf9Yirfk9/g8OPgkzZs3i6ZMmUX58uV7IErNmrVVFzl3P3PYvPl/KFOtB+LJhbQJJP7kSDue3BUCQkAICAEhIAQcQsA0Fk9nXa9efQoJCca4vJeILWePPFJNjXs7duyosopxl6uXlxcmiWzBZJJlVLt2XZ1U7bt374FxfWMgynyocOHCakIHz/6tVKkKupcjaPz40eg2NXXhcoIxYyZgEshUlZeHhydNnjzDbLlLknHCSadOL9DcubPotde64Eq8mmDCXdcTJ05Xwo0nbuTLlxfd2JNUCktrIV9Ifq4iWfxZtGi+suqNG/c+xcbGqPh9+w40x+Du81df7aWEJwta7lYePnyk+b4cWEcg6afOujQSy4IAfsnEX78enqp53SKqHDqQAI9lLlbMG790pS0ciNmqrKUtrMKUZZGkPbIMdboPkrZIF1GWReC2KF7cJ1drILEAZtnHTR4kBISAEBACQsA5CHz++Qb6/vvvkljruOuWZxnz7OSMBp7pvG7dqgfyZ+vgxx8vT7HrN6PPlPRJCeRq9ZsUhX1nYgG0j1tmp5Jf1plN1P78pC3sZ+eIlNIejqBqX57SFvZxc0QqbovcbgGUSSCO+GRJnkJACAgBISAEhIAQMDABEYAGbhwpmhAQAkJACAgBISAEHEFABKAjqEqeQkAICAEhIASEgBAwMAERgAZuHCmaEBACQkAICAEhIAQcQUAEoCOoSp5CQAgIASEgBAxG4NKli/DVO1GV6vjxIJo+fbLBSmh/cdhf8t9//5/9GeTClLIMTC5sdKmyEBACQkAI5D4CLAB5QWgOVapUxfZ+joHwf/93iB5/vE6OqU9WVEQEYFZQlmcIASEgBISAEEggcPjwQXjdWK5cs50/f47q1n0CS5IUpz17flEu32bNmkc7d/4ETx9b6f79eyrVuHFTqECBAtSzZ1dasGAJ+fuXpf79e1Lnzl2paNFitHDhPBXP19efBg4cTFOnTqQ7dyKUG7imTZsrbx1z5syAH+ErNGvWNHrmmebw27tU5RUWdhaeQKbAldtteBjJR4MHD1PeQ1JrMPZHzHlxPdincZcu3al163bEVsUFCz6kqKgolNWb3nlnhConu4oLDKxO7Cbuxo3rqEMfFT+1/Dnf5csXU548eVDey1S5clUaOfID9azfftuDe4uU84WHHiqjPID8/vtv6tnTpk2Ce7zpZv/IqeUv100ERADKJ0EICAEhIARyFQFf3wIp1rdt2xhasiQqxXv9+uWlLVtSfmWePx+RYpq0LrK/3/XrvyAfHx9q164FDRo0FMJmDYTbBLh726583fKCyyyCuHvzm2820dtvD1PijsVa3br14S+4LDVr1lIJsXPnwuirr75TCzVv3LiOmjZtRm3bdqDIyDv03HNt4VbuNRo6dIRy9zZs2HsqjXbJNnHiB+r+0083pWPHjtLo0SPo00//l6o7uE2bPqfw8HDauHGT8v87cGAf4rTcvTxt2odUqlQpOnLkL5owYYyqE3NgUbho0Qr4+T2BegxIUwBy/OPH/0FZNygBOWbMe/Tll59Rq1ZtlVBdvHglnlGaNmxYCyE6E8+Zqhatfv31/iL+GJ6VIeVPs5WJnS1aQECAj6ur668xMTHtTp8+HWZZ/sDAwFL4VbMc18pgi3Bzc+sWFBR01jKOHAsBISAEhIAQyAwC7M+2ePESKqtChQpRrVq11TH71I2ICIcf3cnK7y8Lu/379yp/txyBLW07d+5QgnDdus9VGv7j719OiT8+7tKlGx06dAACbR2dOXOK2GJ39+5dvvVA4OvnYYVkAcchMLAaRGlBCgsLpfLlAx6Izxc47/btTb6EOS77Jj59+hRduHCORo16R/nx5Xi3bt0ivG/5kJ58sqHas99eFo/pherVH1Pij+O1bNmGNm/+H5UtW075RWbxx6Fjx04Q0Z/gyBTYU4kE6wnkGgEI8Vcf4o/911RKCQ++IGtx/fOTJ08uq1SpUl84mJ6B884pxZVrQkAICAEh4LwE7LHYpWYZtJcCd51aBje3xNcxd3v27duDXnyxM9Wv34CKFCmqLGccn8XclSuXlAu1S5cumS1eefPmNWe3YMEcNdavVas21KhRYzpw4I9U/dXHx8eZ0+kDvqaFm75mueeyaushX79w4bzqui5Txg/dyuvNUa9fv2a2Inp6epqvW3NgyYPL4+rqpoSlpciLi4tPs5zWPCc3x8k1s4DxYe2D7Q009sXkDV6lSpWiuPYoiz++hy/mJ3FxcaOSx5NzISAEhIAQEAKOJsDdw35+/vTSS69Q1aqP0L59e5XA4ucuW7aIqlevgbFvozDebWyKAogFX9eurymrHotJFmJxcbFqzCGMG0mK7+WVn3x9fWnXrh3q+tGjRzBO70aq1j+OVLPm4/Tzz9tVfB43yGP8vL29VXcwj/PjsG3b9/Tuu0PUcfI/liIu+T19fvToX3Tt2lVV7x9+2EL16tWH9S+QgoKOEU9m4fDtt5tUWfgYvXaUvG58XULqBBJ/cqQeJ0fcCQ4O7s0VgXXvAf/HEHsVcCsM92Zj/wx++YThAzqQ42dVuHzZBWMaxHydVbzlOUJACAgB4xBI+lpisRMSEoxxeS8RW84eeaQaulhD1Pi8HTu2qS5XLy8vTBLZosb01a5dN0lVunfvQRMnjoEo86HChQurCR08+7dSpSroXo6g8eNHU4cOpi5cTjhmzASMrZuq8vLw8KTJk2eYLXdJMk446dTpBZo7dxa99loXXIlXE0y463rixOk0b94sTFy5T/ny5UU39iSVwtJayBeSn6tIyf5w9/iUKePp6tUrxPXr0OE5WAFdISrfRzfzMCX2SpQoSe+994FKyV3MXIdRoz6gRx+tkSw3OU2JQNJPXUoxctg1iLwzEHhPW44BhAXwSYjAXyH6OkAofle5cuXeOO+G4ybpVR9p4m/cCE/VvJ5eer6P7yPGXRTAlzOOtm6NhAXSmlRZG+foUVd0Q8TTQw8ZU6SyY++iRb3xyzVjbZG1VHPm06QtjNWu0h7GaQ9pC+vagmcBr1y5DDOKF1uXwI5Y3BbFivnkOg1kiSrXWAAtK538GGLvMq5FsPjje3fu3NmAX1emOfXJI6dwzsIjI2HrVsJMLcw+KeNGpUtnLK+MlCOttGPGEGalEe3dS/TEE2nFzN57GW2L7C19znq6tIWx2lPawzjt4Qxt8cknn9DXX3+dxFoHe4daimbdunUZhrkVL76lS5c+kD9bBwcPHgxDiBsEmjHfhxmuvEEyyHXqNyULILcFrh/Fh3sEROAWWABfwPEgjAlslF47IV6GLYAvvJCPfvnFHWM77mJWU4yyJsLSbZjAFsqKFQugS4DQLRGBZQkMUzRzQeSXtRlFth9IW2R7EyQpgLRHEhzZeiJtka34kzyc20IsgEmQ5IoTcx8mRN8y6Ldv2PKHAaSd0DW8tGLFitNw7TYGk75mLQ2eeW7v7POLF11o9243jNWIx6Bad3rzzbwwfd+l5s2TDtS1tiyOiLd/vxsGGrtgGn8MxqPYX1dHlC15nhlpi+R5yXnGCEhbZIxfZqeW9shsovbnJ21hPztJmXkEcl0XMKx65TU+HPfRx1jzLxjHTfR5Vu2//NID4tEFlr/7xLP4791zwaBXNv8ZRwDu2WP6mDRqZJwyZVX7yHOEgBAQAkJACOREAgbqaMyJeNOuE/8K/OILk7h68cUYKlHCZJy8etVYPfO//uqmKtKwoWlBz7RrJXeFgBAQAkJACAgBoxMQAZjNLfTxx1Fwz3MPaxzFQgCaFuS8ds04AvC//4j+/tsVA3/jsaK8K1aWz3VG42z+hMjjhYAQEAJCQAhkPgF5m2c+U6tz5EGojz4ah+2+SlO8uPEsgFhqCj4Y72JVeRcaMiQvxgISvfxyBGZuWV1NiSgEhIAQEAJCQAgYjIBYAA3UIEbsAuZJH089FUudO8dQwYLsdscFy+QYCJoURQgIASEgBKwiwB40pk2bqOIePx5E06dPtiqdjvTUU3X0Yar7jz+eR6++2lltixd/lGo8uZH9BEQAZn8bmEtQunQ8XPfEYNVzY062KFzYZKG8eVPMf+ZGkwMhIASEgJMQYAHIHkE4VKlSlUaMeN+mkqfnwWPnzp/gseQUrV79qfIJ/M8/R2nPnl02PUMiZx0B6QLOOtbpPoktgF98cTfdeNkVoVAhkwD87z8X+Kk0HWdXWeS5QkAICAFnJcCeLlatWq78154/f47q1n2CihcvDrH0i/J9O2vWPGIx9cMPW+FW7Z6q5rhxU9QizD17doWHjCXk71+W+vfvid6ZrvCCVIwWLjT5LvD19aeBAwfT1KkT0VsTofwAN23aXLlrmzNnBl25coVmzZpGzzzTHCJtqcorLOws3KhNgS/f2+TllQ8LMQ9T7uMuX75EEyaMoaiou8odneYdFRVFnBe7q2Mfw88/35natesIn8J+KNNAtbizOxaOffjh8nge+1mQYEQCYgE0YqsYtEyWAtCgRZRiCQEhIATSJeDrWwxi5cGtX7+eqableyml4Wv2hKCgf2jkyA/g1/cz+vHHrXC1WZSWL1+DRfcr0Y4d27E+7C/00UdLlTWtUaMm9M03myASSyhxx2Jt7dpV+CFelpo1a6kef+5cGPzzLqSxYyfR9u0/UtOmzWjx4pW0Zs2n9O23myDubmHC4Qhl+Rs27D2VRlv0Jk78gF54oTOetRFr0Q6l0aNHYLhPDH344Qxq3bqdsuZVr/6YuZqrV6+gChUCaMWKtbRo0UratOkL5au4YsXKKH9lFS8sLBRr2/4Ez1ENzenkwFgExAKYDe3xww9uFBrqil9NMfhCO48lrVAhEyy2AEoQAkJACAgB+wmwgGJBx6EQ/rnWqlVbHZcsWQr+4cNp3LjJEILbiIXd/v17zcKKBdnOnTuUIFy37nOVhv/4+5ej/PkLqPMuXbrRoUMHsGrDOqzecIqio6Pp7t2Ue5f4Olshn366qUobGFiNfHwK0tmzocSWSi4HhxYtWmPM4CR1/Oef+2EVjKLvv/9OnUfCl2lISAiVLx+gzk+ePA5xO4zeemsIXJz6qmvyx3gERABmQ5ssWuRJv//ujl+Td2E2N+7ael9/7U516sTiC2wSqX363KdOnaKpWjXTcjXZgE4eKQSEgBDIMIHz56/bnMeSJatsTpNWAg8PjyS33dwSX8fcbdq3bw968cXOVL9+A2UdDA4+oeKzmLty5ZLqZr106RIFBFRU1/OyJ4GEsGDBHDXWr1WrNtSoUWM6cOCPVL1Vxcc/+P+cr7EF0NWVf+yb/v+ztdA1wUcpd/uOHTvRLEpv3rwJb1Ymv70sDtmiOGzYSPVsXSbZG4+AdAFncZuEhbko8cfdqc2bG1f8cTn79s1Hbdt6mf9xVK8eR02axDqV1TKLm1ceJwSEgBDIMAHuHvbz86eXXnqFqlZ9hPbt26vGBnLGy5YtourVa9Dw4aNo0qSxSqglfyALvq5dX1NWPRaT169fU2P14PKU4OY0SXQvr/wwRvjSrl071PWjR4/QjRs3VBdv7dp1acuWzer67t27MB7RtGRZrVp10O37pbrO4q93727o1TqjtvHj36cpU2aK+EtC2ZgniT85jFm+HFcqdv3GoWPHaMqTx7jVS/T+EStr/hm3maRkQkAI5AgCSYfV1KtXX02w6NbtJfhf91QTME6fDqFjx46qbuG1az/HZA0vTBLZgskky7ByRN0kFLp37wEr3BhY5XyocOHCakIHz/6tVKkKupcjaPz40dShQydzmjFjJmASyFSVl4eHJ02ePIN4EseQIe8qa96WLd9CiAbimflVml69+tDs2dPVUi9xcXHUo8fryhLJE0ZiY+MwdnA6DAfxykrZvn0n9By9YH6WHBiHQNJPnXHK5TQlwYc8/vr1cLOVLK2Cs+u3J57Ij8GyrvhVdQfdqw+a3nftcsOgXQ+Mt4jBgsvZZyF844289NVXHjR//t1sLUdaPC3v8cLUxYp545eudW1hmVaOM5eAtEXm8sxobtIeGSWYeemlLTKPZUZz4rYoXtwnV2sg6QLO6KfIhvSHDrkq8ffww3H4xfag+OOsLl92oe++88DgW5P/XRuyz7SoLFS1BZAXgZYgBISAEBACQkAI5CwCIgCzsD0vXnSFlSoOA3ujU+1WNYI3kJAQVwwydsWMrjjzBJAsxCSPEgJCQAgIASEgBBxMQMYAOhiwZfbt28dQq1YxdM+0rqflLfOxXhbm6tXs0+Z79pisjw0bJu2CZssgZvtjcVEX0kLVXHA5EAJCQAgIASEgBJyGgAjALG4qnvmfbPZ/khJoYXXtWvYNTXjllWhM74/D0gOm6f+6gLduEQYRe8MqGIcuanEIrLnIXggIASEgBISAsxEQAWiwFitalGdOxdPVq9knAHk5qZTG/vn4kCqb+AI22IdGiiMEhIAQEAJCwEYC2dfPaGNBc0t0zLyHX8d4TP03dbcaqd68Bih7A4mMdMF6UEYqmZRFCAgBISAEhIAQsIWACEBbaGVR3L/+ukMnT0ZgzaUseqANjylY0NQtLO7gbIAmUYWAEBAC2UQgKOgY/PUuUE//9dfd8N+7xOqSXL58CZMWO1gdXyI6FwERgFnQXv/9Z9tD0hojaFtOmR+7cGERgJlPVXIUAkJACDiGAHvouHnzX5V5w4aN4LWjn9UP4sWcMfDH6vgS0bkIyBhAB7fXv/jeVatWACuhx9DHH0c5+GmOz55d2HGQcYCOZy1PEAJCIGcSYHdsM2dOw7qwIfTffzcxsc6PHn74YXjtKELduvVQlZ48eRzVqlUb7jeb0Zw5M5RnEPbB+/zzneFDviN9//13tHXrZgoPD6e6dethhYl2Kl5UVJQSfOxGjn0BL1++mKKi7tInnyynkiVLYQLfQRo1aiyxy7f582cr926FMLaHXcuVKeOL3qfjNG3aJOXFo0KFAHMD/AdLxsyZk7FE2BV1rWfPPtSgwVPm+3LgfAREADq4zY4fd4OvRpdsndRhbRVDQlzozz/d4D8ylh56yCT0kqdlDyUVKsSp9QyT35NzISAEhIDRCfj06k6e2753WDHvt2hNt1euTTP/I0f+JvbLu2TJKhVv0KD+ym3bzz9vVwLwHtYK++OPfTR06AhavXqF8ss7cuQHEHJRNGDA63ANF6jSXb16hTZu3ESuGKC9YMGHcM3WEx6m6tOlSxfptde6wJdwF3r99f5K9LG7NhaNHGJiYmjcuFFw8zZNuXjbufMndb5s2Rq4gRtHb745BPnUU6Lx8OFDKs28ebOoZcu2ysfvrVv/Ub9+PSkwsDrGhWNguASnJCAC0MHNdvy4qZe9SpWUPX84+PE2Zf/ttx745ZeHBg26R6NHpzzLo3fvaJvylMhCQAgIASGQlECNGjXJx6cgbdr0BYWFhdL58+eoTZv2KlJY2Fk6dSqYHn+8DuXLlw8/yvcr4afFWyQWYw0JCVFxq1SpqsQfnwwc+Dbt37+X1q79RKVnq19qgZ/hg2Ud2L8vB7Yysi/gy5cvw53mNSX++DqXacuWzXyIcuyj0NDT8Be8VJ2zz9+zZ89AANZU5/LH+Qg4jQAMCAjwxa+cNzEmoTj25kEJJ06c6GVk7FoAVq5sfAGo3b81bCju34z8mZKyCQEhYD+B9Kxz9udsfco9e36hZcsWw896V2rbtgO6gf+DP/l4WNja0I4d25SAa9++k8qQu33Hjp2ItVkrq/ObN29SgQIF6KeffqQ8ebBmV0IYM2YErntjCa/G1KxZC2JrYmqBn2Ua35cYg8+9MPMwLi6x98edl6VICCz45s9fAkult7py48Z11WWt78ve+Qg4zSQQiL5PgbcitjNxcXGn9GZ05CdOmBBXrmy9qOJxg59+6k6bNyd++Rxdz7v4scjdv56e8RhPYn1ZHV0uyV8ICAEhkNMIHDz4J0RaS2rdup0SUX/9dRjCK46aN29Fu3btUOP96tatr6pdq1YdWAq/VMcs/nr37gbLW+gDSDjPvn0HEE/04HF+HFjUcVczjzm0DP7+ZdXYwX/+Oaou79ixHd6dSiirYOnSD8EX/G51nUWmDmyR3LTpc3V67lwYde/emW7fvq1vy94JCWSdwsg4nKInT55smPFssi4HfPcoUQBabwFkP7yDBuVDF0Assfu4rAgHDrjBRZ0LPflkjCGXn8kKBvIMISAEhEBWEOjQ4VmMuRtNPPbO09MTEwUfpYsXL8D7UlEIsZJUrlx5NQmDy9KrVx+aPXs6xvd1ViKRx/IFBFSk4OATSYraq1dfeuON3mosoZ+fP7GQ4zx5nN6qVcto4cL5mGhSXqXx8PDA86fQ3Lmz8H8/SlkOJ0yYpu6NGTOBpkwZTytXLkW5qpuf8fbbw2nWrClqbCGkJSaSfCDj/8x0nPPA3JVq9OJXqlRpC34hdcHYB0P95MAvrPjr18PxS+tBguzzd/jwvBhT4UIbNqQ+HiN5yhs3XDA2owD5+cXRwYNZ43Jt6lRPzCDLQ+++e4+GDUt5/F/ychrp3AWf5GLFvME65bYwUllzelmkLYzVwtIexmkPaQtjtUXx4j5Oo4EcQc6ZLID/wpR9BEKQbdNmNQWrYF9HgMmMPPPkIYyZsH3pF15rz80tntgfMAtL/qfh6LBvn5t6hIz/czRpyV8ICAEhIASEQPYTcCYBGAJjm2nqU/Zzc2gJ2OVa8eLxmJHlinEahHEZDn2cyvyzz+7C2uiGdaeSjhVJ/uSLF12wDEFedC/EY3V528Vt8vzkXAgIASEgBISAEMh6Ak4jAGHpGw88LpUrV66EAa3u6Ar+B+cpdLxmPURHPNEkAElZAX18HF/NvJhM1qBB2uKP64nxxLR3rzuVL2/9mEZH8JE8hYAQEAJCQAgIAfsJOI0ArIAlydEFvBlWwFKYEeyGruB/cdw6ODg4yP7qGzdliRIm0Xf1qisWAU1fmGVVTRJ9AWfVE+U5QkAICAEhIASEQGYTQGejcwSIv/kuLi6zYAksjI07RSfj/CPnKL3tpXzvvXtYBiaSqlc3jvjjWrCl0MsrHutWuWBGmu31khRCQAgIASEgBIRA9hNwGgEIVKWx6PMKjQwicBmOS+rznLZ/7LE4qlcvFtPzjVcztgLGxblQRITxyiYlEgJCQAgIASEgBNIn4EwC0CMwMNAsh+AZxAddwI4fHJc+wxRj7N3rhlXdvWjpUo8U7ye/yDUZOzZPli7+nLwM1p4XKmTCfvNmFkxPtrZQEk8ICAEhIASEgBCwmoAzCcD10dHRuzD2byBvGAf4M7qAN1pd0yyOeOSIK1Zjd8NMXutE0pkzLphV64mFNj2ztKSHDrnS+vUeWFTU+o+Cn188+fvHqYWjs7Sw8jAhIASEgBCwiUBQ0DG8WxaoNOzhY8WKJTalz+7I06dPhkOF49ldjBz5fKeZBIIu36kQfhfRCq2xucL49zEmgKwyaqvY6gP4339NQvHWLesEY2bVe8sWd1qwIA9Wfo+Cr0nrBvWtW2dehjGziiH5CAEhIASEgAMIhIaeoZs34V8Ugd3E8eZMYcSI952puE5VVqcRgEwVInA1drwZPhw/blpYuUoV60SVFn56n1UVvHbNZPnjZWckCAEhIASEgOMJsG/emTOn0enTIZhQd5PKlPGDm7aHlV/gbt16qAJMnjwO67LWpiZNmsFL0wzlHzguLpaef74ztWvXkb7//jvaunWz8ulbt249atWqnYoXFRWlBN9LL72Ca21o+fLFFBV1lz75ZDmVLFlK+QkeNWosHT16BI4KZtP9+/eVS7fhw0ehHL6pVj4Cg76nTZtIZ86cIg8PT+rXbyA98UQD+uOPfbRs2SLlpq548eLwfjWKihYtRi++2AHPb6vu3759i4YOfZfq1DH5N07pIYsXf0T79+9VLvDu3LmDJdCuoY474JlqMPwf96MaNWrRmjUradu2H5R/Y/aVPGDAILPLvJTylGtpEzC8AITV7w8Iv7rYR6MqD6gU3MvaPtO0eaq7PJ7v5ElXfDDjrbaq8axaDnfuuFAM3P/yentsFYTvb/h9fKDaKm5m/GE3dRxEAGYGTclDCAgBoxPo9UN32hb6vcOK2aJca1rZam2a+R858rcSMUuWrFLxBg3qr3z4/vzzdmIBeA9+RFlYDR06glavXoGlwAJo5MgPIOSiIHpep0ceCVTprl69Qhs3biIMiUJPzofwF9xTiaxLly4qn70vvdSFXn+9vxJ97EOYRSOHGLxkxo0bRRMnToPb0UDlk5jPly1bo+6n9IeFJAvISZOm04UL52n06HdV2kWL5kNILkH5vZU4mzNnporDeRTALEau4+7du2jx4o/TFID9+79JvHHZhgwZiPL3pjx5sOxEQvj999+Iu7BXrlxH7u7u9P77w+nrr7+iTp1e0FFkbyMBwwtALP/SkeuEfYCNdcu26OwtIzzchcqWjcOSKdYVQwtAjn0b3o4LFyZ67LH8yhXc+fMRDnMHx+7mOBQr5jiRqR4gf4SAEBACQkARqFGjJjw8FaRNm76gsLBQOn/+HLVp017dCws7S6dOBdPjj9ehfPny0Z9/7lfCT4u3yMhIWANNTrGqVKmqxB8nHDjwbWVBW7v2E5WerX6pBX6GD1xMsfjjwFbGGTOmUGTkHbyz8qeY7PDhA/TBB5PUPbYUrlq1AU4BfsU498s0eHB/vKvi1fuKxagOTzzRUB1WrFgJ70S82KwIs2dPU4JX89BJDh06QM2btyJPT5PNp23bDvTDD1tEAGpAduwNLwCDgoIucb1gMp8Ga18XyzrCKsg/43hMoKECi7lq1WLxIbau+5cLb9n1y8dFisQrq9yFC67oIjAJQkdUUgvA4sWtL6sjyiF5CgEhIASygkB61rmsKMOePb/A2raYXn65K7GQ+Q//5FlAtWzZhnbs2KYEXPv2nVRRuNt37NiJ6E2qrM5voluILWs//fRjEgvZmDEjcN2bnnqqMTVr1oLYmphaMIm1pD/6+VpMTOrrzsIIk8QQwSKSy8ZidurU2epRbL0LZ/+lCcHTM486woRNJQ719dT2n3++QVkX585d+ECUuGQLz5rKi+4yCXYTSJTqdmfh2IRw/bYSQm8bntKG9xbbz7hm+kY4tgg25x4YGIcvXyS+4Nb7yk1qATRZ5Sy9gdhcCCsScFc1dwF7eMRjDIgVCSSKEBACQkAIZJjAwYN/QqS1pNat26lxf3/9dViNoWML165dO9R4Px7jxqFWrTqwFH6pjln89e7djc6eDVXnln84z759B6hJHocPH1S3WCSxcOMxh5bB37+sEmr//HNUXd6xYzu6d0sqq6BlPMvjmjVrQ3Tyq5iIu5gHD36DKleuqsYSnjsXpq6vX78aK1lMUccP/kkqOJPf37//d2UR5W5pSyuijscW0e3bf0D3eJTqJubxjzwuUIL9BAxvAUTVFuJDXA0fiMrYr9dVxS+KGJz/rs+dfZ/cAsj10ePy2EpX2QFSlwXg119HosvZJckvu/RY8tI2mza5q/K9+KL8AkuPl9wXAkJACFgS6NDhWYzBG63G3nGXZrVqj9LFixfQ81OUSpQoSeXKlcf/ZJMhoFevPjR79nSM7+usRCKP5QsIqIilu05YZkm9evWlN97orcYS+vn5U+nSD6k8AwOro7t2GS1cOH/HSfoAAEAASURBVB8TTcqrNB4eHnj+FJo7d5YSVGw5nDBhWpL8kp9wObibuEePVyDQXDAGcDzeASXU2MSxY0dCZMah/EXQTTwxIamp/In5JD9PvMNHc+fOVPUbOvQtZVnk+o8cOdbM4cknG0IYn8SYxldVvDp16mGiSZJOwaQZylm6BNJukXSTZ10EWP7Kowv4dEaeyItHQ0j+CjN1u9OnT5t+siRkiHstcI8F5jm+BHF5GMvM9E64neoO8eKvXw+3yrydaia48dpreTFA17Ro9IoVd6l9+xgMAM5D69Z5YvDsXXruOeMIrb/+csVYjPyYARZD33yT+jiTtOqb2ff4f2WxYt6waGa8LTK7bLktP2kLY7W4tIdx2kPawlhtUby4j9NoIEeQcwYLoKo3dNYKiMAHbMgQhU2tAQOBVx8CbyniVkopPn5t1MU2Ce7m5qV039HXLC2AbJHjoC2AV68a6zOqPYFYdls7mo/kLwSEgBAQAo4lsHDhPDXpRFsf9dP8/MrS+PGpde3qWOnveYwfT2axzB/vdsqfvwB99BG/niVkJQGnEYAQb8s1GAwG9cQHiKdMHdHX0tsjfh9sb+DDltr8/Dq4lw8i8zXkdRbbmxCXF9LLN7PuW4qpW7dMuTZoEAuz+j2MczDWBI3ChU063LLMmcVB8hECQkAICIHsITBgwGCHPpjXJuRNgjEIOI0AhGXOPP6P0TVu3HjtxYsXf8HhWGtQ6u5cCLwUzWkQh/9CAG5AvO0VK1bsj/MNyPdpa/LOjDgpWQAbNYol3owWMAENY0DiMXMtRZRGK66URwgIASEgBISAEEhGwGkEYLJyU2hoqDsGz5ZOft3ec1j7euq0EIGLIRSnYgayN4Rn4px2HSHZnsd16PDVV+70229u1KVLNBa9tN5yZymmWAxa5qnzNsoek8owazgeC1W7Yn0qwlpV2V8yzUvvs79EubcEug30PveSMEbNdTvovTFKlTtLodtA73MnBWPUWtqAyGkEIAQZL2pk6nskYslVGVa6PZnxUcL4wDzoYh4CEZhkGhRWY2fvI+mGokW9zXF++41oLTqZ27TxxKQE8+U0D6LxFHi+MYd79zitp/ncUQcXL5o8jnA5WdTZEooWZU8lnM7b6nrakr+9cS3bwt48JF3mEJC2yByOmZWLtEdmkcx4PtIWGWcoOWScgNMIQFR1nUV1WQheg3XOtCiRxQ17DrGq+j0IzB7o+j0C698W7NkauA9WRqsW8rtxI3Hm6V9/sesPN3rooTuYkWqdBdDkjg39qgnh6tUYpHX87NrXXzfNPP7ii0h0qdvW1bx0qSvWD4QSd4lDWXXJs2/Pv+b4n6plW2RfaXL3k6UtjNX+0h7GaQ9pC2O1hXFKkz0lcRoBCOvc6kxCpK2IBNG3DOP+voHo+w77l2FRXAzxNw37K3jWa9Y+j9fT440XKg8OdoVVLJ7Kl4+zemkY9vTBgRdkjo52gVcQU36mq477e+2aq8qc3cBx+W0JvNi1Dram1ekcsddt4Yi8JU/bCEhb2MbL0bGlPRxN2Pr8pS2sZyUxHUfA8AIQIo27YVOSJ9wNHA9haFNfKeKbVsJEYhz30WghAv8Px6al1/VFG/dnz7rQ3bsucNkTCxc91ifW4//KlInH2EYXtTCz9antj5noBi4lvPbnKymFgBAQAkLAGASCgo7Bu8jPWCT6Lfr119104kQQvIn0M0bhMliKKVPGq7qULFkqgznlzuSGF4BwYxPgLE1z4oTJola5cqJ1zJqy6xnA/v5xEICuSfwC82LQu3e70/r1d+ENxLZ803s2C0AXl3h0nYoATI+V3BcCQkAIOCOB0NAzdPMmBmwjNGzYSG3OWI+Uyswu79iaKsE+AoYXgEFBQWd11TArtwG6atuhi9Yd+x9gwduh7xlhf/Uqj4uLt1moaQugn1+cEmRaEHKdbtxwobAwV2L3a5npDo4nnURGumACR5zNE0CMwFrKIASEgBBwVgLsm3fmzGl0+nQIltO6SWXK+MFN28PKL3C3bj1UtSZPHgc/wLWpSZNmNGfODOUfOC4ulp5/vjO1a9dRLajM/nDDw8Opbt161KpVOxUvCkszsODj9fZatWpDy5cvxmoNd+mTT5bD328pYtE0atRY5cN3/vzZdP/+fazqUIiGDx+FcvimijQiIoKmTZtIZ86cwnvOk/r1GwhvUA3ojz/2we/9IuWerXjx4iqfokWLwU1bBzy/rbp/+/YteLZ6FytjpN7JxgtE79y5A+8lHj9/nZ58sgENGvSOKs+WLd/SZ5+tx/vRFe/BKjRkyLv0xRcbEe8anjeIFixYquqQauHlRooEDC8Adakh/rpC9M3A9gWusaltLbqHx0IELtNxsnv/6qvRavmXe/dsK4kWgLzAsjcmFLMnkBh4fnNH6zjKG4hp4kli/raVWGILASEgBJyTQK9eeWnbNse9+lq0iKGVK9OeP3jkyN/44e1GS5asUhAHDeqvfPj+/PN2YgGIFSiUcBo6dAStXr2CKlQIUD53WdwNGPA6PfJIoEp39eoV2rhxE9ZldYUI+hD+gnsqkXXp0kW4F+0CEdgFvnP7K9HHPoRZZHGAO1T4Ah5FEydOo6pVA5VPYj5ftmyNup/SHxaSLCAnTZpOFy6chy/gd1XaRYvm0/z5S1B+b3D9ASJ0porDeRTAorFcx927d8Gl6cdpCkCOf/z4P6jvpyqvt97qi67rHeTvX5bWrFmpyubj40Mffjhd+TYeMGAQbd78Nc2aNV/EH8OzIzjuW2BHYdJKAuE3DB5A6mHG7nmOV6FChZn40G/FoWEEIJeLZ8byZkvQFj/8CKOCBeOVALx9m+BYm+AY3GTfzmx3cLx235Ah99R6fraUVceNjCT8EzBZPMuVM5VR35O9EBACQkAIpE6gRo2a5ONTkDZt+gI9PKF0/vw5LB3Gzq0I52fp1KlgevzxOlhjNZ9yzcbCT4u3SPzzxXtQxa1SpaoSf3wycODbtH//XixD9olKz1a/1AI/g8UUiz8ObGWcMWOKsr55eeVPMdnhwwfogw8mqXtsKVy1agPt3fsreqcu0+DB/dEVa5pMyGJUhyeeaKgOK1asBEslXmrphAYNnoIVtLCK9cwzLZRwvXHjhuq25vJy6NDhOZo6dYI65j/SBWxGYfOB0whA1MxViz+u5alTp87BApi5g+Jsxpc5CbQFkMWfj49JTLEoLFIk3mwB1DN2M+eJJmE5cuR9u7P79Vc3/FL1omefjaalS9P+tWv3QyShEBACQiCTCaRnncvkx6WY3Z49v8CitZhefrkrtW3bAd3A/ykB1bJlG9qxY5sScO3bd1Jpudt37NiJmFxYWZ3fvHlTWdZ++ulHTDbMa85/zJgRuO5NTz3VmJo1a0FsTUwtmMRa0h/ufC0mJvXlwNhiycvY6MAiksvGYnbq1NnqMlsWuUtaB09P02xIDNuySqjxM3SIi4tXVlKeA8pl08FUTnSRScgwgUSpnuGsHJ5BOATfU/opWK6lEY4j9Lkz77XvX/auwSKQA3cDc3CUBVBlnoE/XFYOWrxmICtJKgSEgBDIVQQOHvwTIq0ltW7dTo37++uvw2oMXfPmrVS3Z0hIMMb1mcbL1apVB5bCLxUfFn+9e3ejs2dDH+DFefbtO0BZy3icHwcWSyyqeMyhZeBuVRZq//xzVF3esWM7undLKqugZTzL45o1a9NPP5mW3uUu5sGD38B4vKpqLOG5c2Eq6vr1q9ElO8UymcVxooizuJjkcP/+3+EUIUJ1gbPA5TGDNWo8rmYv8zhCDps3/49q1nxcHZvqJmJQwbDjj9NYAGFWfhddwP+DCORPGqsjf1x71o46Gy6JFlHJLYBc0DJl4mCmj6VSpYxl7OTuag667KYz+SsEhIAQEALpEejQ4VmMwRutxt7BpSlVq/YoXbx4Ab0+RfGjvySVK1ce1jaTEaBXrz40e/Z0jO/rrEQij+ULCKiINWdPJHlMr159sdRLbzWW0M/Pn0qXfkjlGRhYXY2ZW7hwPiaalFdpPDBOady4KTR37iyIrShlOZwwIYkjrCR58wmXg7uJe/R4Bd3OLhgDOB49VCXU2MSxY0dCZMah/EXQTTwxIa2FuVBdSX6eEM1ix5NH3n13iJrE0qJFazXJhG+/+moveuutfkrIsiV0+PCRKlXDhk/jeDAm1MxLcwKLxSPk0IJA+i1iETm7DwMDA4vAxFwXv2pc8QHed+zYMdPc9mwsGMoSf/16oicQe4rSqVM++A92xwDaO7RihSdmO3lgf5fatzfOLxt2V3fokBtmpcWqMY48JrFatQJUtmwcxqhY+LGzB0AmpOH/lcWKeWNWWMbaIhOKkuuzkLYw1kdA2sM47SFtkXpb8BhHPUM59ViZd4fbonhxH6fSQJlXe1NOTmEBrFq1asXo6Oj/IPiuoev3Mn4Z9cI5vNESvO4aI2zY4K4Wge7YMQZCJH1Tt2WptRWNLYDJu4At42XXMXsq6d07H+3Z445lAKLwSzDaPHlElz27yibPFQJCQAgIgcwhsHDhPDXpRFsfda5+fmVp/PjUunZ1rPT3n3++QU1mscyfu6nz5y+gxkKmn4PEyEwChheA6PJtjfELG/GB6QALoBuE3y4A2Izz3hCDheDBY0FmArE3ryVLPCkoyI2efjoCAtC2XBJnAVtOArEtD0fFPnPGhbp2zYdZZ6bBuWfPmoaNotcCX1qesUwwy8P7ceLYXUcVRfIVAkJACAgBBxIYMGCwA3MntTYhr0+YWuAxkRKyjoDpbZ51z7PnSe9jrN9TEHq7If66IINjWPuvO/btWQTak2Fmp+Hu0ZAQV/L0jCd7lkTRVjSe5Z5VFsDnn89HLVp40b9pdKLv3++GpQm8lPgrUsQ0BlFPWGGGb799H+NA7isBmNlMJT8hIASEgBAQAkLAcQQMbwFE1b2PHz9+JAFBQ5iLv+fjEydOhMMCaAi705kzrhQd7YLFOWPV4s0JZbVqxws+R0S4qOVf2IqmBaC2ClqViR2R/v7bTbmcy5/ykk/01VfumOWVF6vEu9Bzz0VTp07R1L27V5JJH4MH27+MjB1FliRCQAgIASEgBIRAJhFwBgugZVWfgADcoy/AApiKfNExsmZ//LgJY5Uqts/U1UJPL6uSsNZlEn/AmV0L9lTCz2Wxmce0TJP5Ebzc0qxZnphNlk+Jv2HD7tGiRVHm5Wh0ec0J5EAICAEhIASEgBBwOgKGtwBC5J3FOMCXsfeC+PMsWLDgXqYM6x8PJDhmBOInTmREAJpqoC1/eq/XAeS7P//sRtu3u2OQbAzWeEq6npM99Wf/whyKF08qWFkYDhmSl7780kN1Z8+dG0UvvGCaiazLpbur7XmupBECQkAICAEhIASMQcDwFkCM/3sHqEZB/M2ACBxw8ODBaPgF5uP5WATStBhQNrPUFsDKlZMKKmuKdfOmSYwlWgBNM4h5coUOhw+7qeVheBmWzAjXrpmeaTlbmUXhCy/kU+KPx/t9+eVds/jjZ+ryiQUwM1pA8hACQkAIZA2BoKBj6MUxzZX89dfdeJcssfrBly9fohdf7JBmfIzNpylTxsP38MtqrcLPP9+YZny5aRwChrcABgUFBQPXo5bIIAZX5smTZ+qRI0duWl7PruOFC6No6ND75OtruwDUgkpb2PReX+c6aW8gWrhltJ46n+LFTWIzJMSFXnnFi0JDXeFjOY7Wr4+k8uWTLmVTsKDpqWIBzCh9SS8EhIAQyDoCoaFn1MLK/MSGDRupzdqn412LqGkvlffFF58qbyOrV3+KpdDuUp8+r8JTRy2z6zprnyXxsp6A4QVgSkgwC/h4Stez6xqPo3vkEdvFH5dXCyptYdMC0LILWHfV8uLLmRF4sklgYCxWk4+D43EXzPTNr8rRoEEMrVx5F66JHnwKp/H2jof7IBf4iySbJ7s8mKNcEQJCQAjkTgLsmm3mzGl0+nQI/vfehBcLP3jpeFi5hevWrYeCMnnyOCy8X5uaNGlGc+bMwGoMwcr37vPPd6Z27Tqq9fS2bt2sXLrVrVuPWrVqp+JFRUUpwcfLrbRq1YaWL19MUVF36ZNPlsPdWynzYstHjx6h+fNnY6z3ffTwFIJHjVHKmwa/XqdNm6Q8kVSoEGBuIPZXPHPmZLpy5Yq6xp5HnnyyId4l1bH8WRN1LV++fPTQQ74qjvZdbM5ADgxHwCkFoOEoZqBAWgBqC5u3N35vucQnmQSS2RbAJk1i8U8lUpV6xQoPJf6eeSaGVq++i7F/qVeGRSoLQLZOFi0aT1u2uGNx0DxqlvB778mM4NTJyR0hIASMQqBXr+7wuqQWk3BIkdiF2cqVa9PM+8iRv5XVbMmSVSreoEH9lQu3n3/eTiwA72FA9h9/7EPP0gj8X16BnpkA5XKNxd2AAa/D4BCo0l29eoU2btwE12yutGDBh+iC7an857Kv3tde64J197rQ66/3V6KPXcixtw0O8KgFV3CjaOLEaXA1Gqhc0vH5smVriIXnm28OQT71lGg8fPiQSjNv3ixq2bItNWrUGO+A/6hfv54oRzV67LEa6j7/+euv/6Pjx/+xcAdnviUHBiQgAjCbG0V39WoLIL7H+EdAWGA50dKmu2ozywJoWeUrV0xWxcaNY9IUf5yGrZPnzvEMYoIAJPwaJdVtfOGC4YeSWlZZjoWAEBAC2UqgRo2aWPqrIG3a9AV6YULp/Plz6Ilpr8oUFnaWTp0Kpscfr0NsUfvzz/2w4EWZxVtkZCSsgSEqbpUqVZX445OBA9+m/fv30tq1n6j0bPVLLfAzfLDkBIs/DmxlnDlzKl2+fBnuNK8p8cfXuUxbtmzmQ5RjH/7fn4Zf4aXqnH3/nj17BtbDmgn399OkSR9AWE6Gb+EC6pr8MTYBEYDZ3D6JFkAea2EKLLRYAIaHk+qO1QLw2rXMF1pXrpjyLFUq8fm6HMn3WqSayhxvnhii65A8vpwLASEgBIxGID3rXFaUd8+eX2BtW0wvv9xVuUDj7lUeb9eyZRvasWObEnDt23dSRYmLi6WxYyeax9TdvHlTCayffvoRy3jlNRd3zJgRuO5NTz3VmJo1a4HVI7ab7yU/4GeZxvcl3uFzLy8v/LBPfBe4uydKBBZ88+cvgYECFgqEGzeuqy5rPv7hhy1qosnkyTPhIz7JkH2+LcGgBDJfUWRyRbEETDCWfDmZ2pbJj8vy7LRnDS2uuAA+PqYvoLYO5sV3/ODBCPq//4vI9PJdvmyyAJYsmfilT+0henyiFny6zOwrODcGbrtOnfLRO+8kW0wxN8KQOgsBIWA1gYMH/4RIa0ns+qxw4SLoOj0M4RVHzZu3ol27dqjxfnXr1lf51apVB5bCL9Uxi7/evbvB8hb6wLM4z759B6hJHocPH1T3WdRhtQx4a0q6fJi/f1k1dvCff46qeDt2bMdkwxLKKli69EPEs4U5sMjUgS2SmzZ9rk7PnQuDY4DOMFTcpgMH/qAlSz5GF/QSEX8alpPsE+W9QQuML8XrBi2aKtZGzHj39naj2rVj7fKHq8WUFlecqT42TQQxCTM/v/QFmj2cdBdwyZLpT2LRgk9PUClc2FQmXQd7nu+saXjNxJ4982GwtQt16GBaK9FZ6yLlFgJCIGsJdOjwLLpKR6uxd56enko4Xbx4gYoUKQohVhIuRcurSRhcql69+tDs2dPVEissEnksX0BARQoOPpGk0Dwp4403equxhH5+/sRCjvPkSRqrVi2jhQvnY6JJeZXGw8MDz59Cc+fOwnjDKGU5nDBhmro3ZswEtazLypVLUa7q5me8/fZwOAmYosYWEsXTqFEfqMkjvKwMC0weQ8iCE0u04X9jH2WJNCeWA0MSMJl/DFm0BwsVGBhYBGMh8uND5oIPsBs+dBUxFmLbgzGz7oqra3x83rzxmM0VgbEYtj+XLUi//eZOP/54B1PnTSLs1VfzwqTuAXdskfgSJf3lZvsT0k5RtWp+mPJdMbYjHOb/tOOOG5cH/0Q8acaMKPwTioYLO8JyMd74hxVHR4/eSTuxg+/ifw4VK+aN8Svh+Cfk2Idx/gMG5EX7eFDZsnG0dWskFtV28EMdW6VMzT0r2yJTC55DM5P2ME7DSlsYqy2KF/dxKg2U2fQMbwHUFUZX8FgsODmGzdkILvxLAzOf+CfQIzpOdux5IgQvAG2P+OPyauuZtvrxNT0jWHcB87XMCtwTMGpUHkz3j6f+/e8r8cddzumJP36+LqMuV3444uPlZHhGcG4KU6Z4KvHHC2Z/+qmIv9zU9lJXISAEhEBOIeA0AhBm5V6Yuv4wBOAsmMHfgfhrg0ZoaISGsMcDiC63FlO6e5Wva6Fl6Q1Ex8/o/t9/XdAd4En+/nFq+RbOz5ruX46ny6VFK/+a3bnTtJwM388NYfVqD5o3Lw+x1XfNmrtYniF3id/c0MZSRyEgBIRAbiBgR6dl9mCBxe/qqVOnsAgJBUEEVsNi0EshCk1z2LOnSOanVqlifzetFlPa6seZJp8EYn5QJhxYegHR4/+smQHMj9YiVU9cyYTiOFUWWC8VM/c81DqN7P2lbt30x006VQWlsEJACAgBIZBrCDiNBRAtElOhQoUSEH3BEIP1cP4D9j5GaCl7LYDsUSMiwgWDdnmmVmJNtKVNWwf5zr//EmYBuykRVquW/cIjUQDGYc0nk/7XC00nliDlIy0AtWhNOVbOvYqx2vTNN3cxQ84NK/HLxI+c29JSMyEgBIRAzifgNBZANMVCrEn0NVZI34Lj3hgTyPPXjxihiapWtU+QaYGnhZWuixaAerYtXz90yA1rRnmpSRg6nj37RAEYD3c9pvGv1loAdbl0ue15vrOn4fGOHTsmij+eELJ9uxscrHs4e9Wk/EJACAgBIZCLCDiNAESX79o7d+48ExoayqvOPQFL4PsYC9glu9tq3TqC70P7xoHprlQtrHRdsEC7CpZCS88yzag3kOvXTaKP89MC0NoxgFqo5lYLoG4fyz2PgxwyJC/cNOXFhB7LO3IsBISAEBACQsC4BJxGAGIJGM/8+fO3qFy5ci+Iv5ZAWhRjAbtmN9quKAGLAHuCFlJaWOk89BhASwug7qbNqDcQbQEsVixRAFpvATSV0FKY6jLn5n2FCiYL8KlTTvN1ys3NJXUXAkJACAgBEHCaNxaWgPkfxvxNxfaixfaCM7eiFoDJLYD6XFsIuY4s2Dhk1AL47rv3lUeRF16INo8BtMYLCD9bl0uXm6/t3etGo0fnwYrxFoMY+YaTB17eZ906DzhNT78iAQEiANOnJDGEgBAQAkLASAScZhIIRF/F4ODgqoBn/5RbI5FHWbQlLTULoL7PxcbC7VglPg6TQVzpLnx8w0e4XSEPvJbpLmtbu4C5DPnzm/wU83qCPHHln39caelST7UsSrNmOaZpaPJkT7g2ykN//OEG/5dRabIuX94kAE+fdprfU2nWR24KASEgBIRAzifgNG8srPt3HN3AdsoeYzaktqRZLgHDJdWWNssuYL6e2A1sZ58zZ2IREgWg9WMYtVjVaxTqc10Xi+yd9jASSxsuXuwJ0R0Pl0ZY+yWdoLuAQ0Kc5uuUTo3kthAQAkJACOR0As5kAVyMbuC/Mfv3N4wBjNYNc+LEiV762Nn22sKnRZQuv7e36Ujf19ffe+8+fC6yJdB6wabTJt9Hg+D1665qCRr26GFtYHF64YLJesm+gHXZc5IAPHDAjaKjXeBUPcbsni8tPtIFnBYduScEhIAQEAJGJOA0AhDwZkL47cLM31PoDjYiS5vLpEWTtvjpDLhrldcGDA93UWPQ3BNaqU0bKwak6UzS2euxhNbOANbZacFnEqeJAvDmzcyxSurnZOeexzVyePJJ67q0y5aNx3jBSDhoN3UFZ2fZ5dlCQAgIASEgBKwh4EwCkJzZ2pdSY+hJHlpUWcZhUcgCMDycqHBhyzuZc6y7f62dAayfqmcoa/HKVkAO+lzHc+b9b7+ZBGCDBtYJQB4b2aKFdXGdmYuUXQgIASEgBHIOAWcatHQE3b91cg76RNGU3ALIddRCK3k3cEbqz4KTZ+uePOlqngGsxxVam2+hQqaYulzJz63Nx6jx2N3b0aNualJLzZoi6ozaTlIuISAEhIAQyBgBZ7IAlkNVf8c6gBfQDXxPVxszgyvpY2fbJ4qoB7u0tSg0TQR58L49df3nHzd65RUvato0hlq2NHUn22oB1OXSFj+2AB48GAErZeaU0Z56ZWYadvd27FgEBQe7QgRmZs6SlxAQAkJACAgB4xBwGgEI0TfCONgypyRaRKXWBcxP0SIxM56oF4G2xwuIfr4uqy67K2zIfn45Q/zpOnp5ET32mIzn0zxkLwSEgBAQAjmPgOEFILp9i8EN3HV4/QjOafi1uEu+DAzXMyV3cHyd57/cuUNUoACf2RYSBWCc2Q2crRZALQD1+EXbSiCxhYAQEAJCQAgIASMQcIYxgNsYFGb+nsd2LmHPx+rcCBDtLYO2ouluVct89DW93h7fYz++vr4FqEkTG9Ztscg0UQDGm8cAWusFRGejy6XLrq/n9j0L8wsXXGjfPtMEktzOQ+ovBISAEBACxiZgeAsgrH+1GCH2D4jVxo0bu+O61YQDAgJ8sKD0rzExMe1Onz4dllJCxKmJOL8jX4eOAOP1/HiWb4EC8aSXebEsT0qTQHicHafTQs4yvjXHOp1lF3CpUrZ1dSZaAHPOsi/WsLMmToMG+SkKTkPOno0g9rgiQQgIASEgBISAUQk8IKqMWlB0Bf+SvGwXL178I/m11M4h7Oqz+MP9VCeN+Pr65kOcBYiDhT0cG3QXqhZUyZ+mLW26m5jv8/qARYvGU2SkC0VEJE+R/rmlALx82STgbJ0FrMslFsCkvF2Ak13CxcW5UGio03ytklZCzoSAEBACQiDXEDC8BRCzfr/FBJAqaBG/ihUrms19WBSayw6nXdYFxO+D7Q10Ha9NLUW+fPlm4/6HiPdFanEy67oWUFpQJc9XX0/uDo6td9eumayAbD20JfTvH40ZwLHErstu3DBZH20dS5jSsi/nzrnQqVOuKl9nnhAyd64nsUW0Y8cYu3wtM1deQoZZVK5sm2XVlnaUuEJACAgBISAEMkrA8KYKiLFBmADSFxXlMX999Ibr3e/fv9/QWgBYLqY3FpL+DfFT7LuEhbA9rH95EW9TanGsfZY18bRlLzULYGqTQLTF7upV25uOPVv06BFNPHM3Pt6FbB3/x/XSwlQLWL62Zo0HvfSSF23bZvjfE1zcFAP7/501y5OGD89LbM2zJ2ifwCwAJQgBISAEhIAQMDIBw7+xjx8/HgqAoeXKlaseGhoaDaH2KARhLMTcEVy3zQSWSks8/PDDJSH+Rru7uz+TEMWmfO0RDJYCMKX0WhiyBdDyvhaAPCHE8noqVUvxsnYDx9YuW/PQ6/1x+XVaXVYWhfpaig924EX9XL239VGHDrnR/fsu1KCBfdY/fp52BXfqVPZxsLXejoiv20DvHfEMydN6Arod9N76lBIzswnoNtD7zM5f8rOegLQBkeEFoG5ODw+PxzAOkK1znrACuuD4FoRgx6CgoKM6jr17T0/PdsizSHR09G7ky/Yfzv8Qup4bh4SE3E4v36JFvdOL8sD9uIQewpIlPahYsQeHHJYta0oSGemO+4n5t2hBqnuycuV8uP5AtlZdYGsXB3//pHmbrqb/l9fJY2FapIi3sib6+ZnSREXlQZmyd/aDPW3BpT982FSH5s3tY8Kpa9c25REW5gkOWFE6lwd72yKXY3NY9aU9HIbW5oylLWxGJgkcQMBpBCC6fBdBpA1CF+1XzAFjA1+KjY1dhMOnMsoF1sQVyIM3FSD+4vTsY30trf2NG+Fqfb604iS/d+4ci7688DZxH8u7mB2bmKPxZAKs9oexerG4nzjUsWNHwhg1U7Tr183RbToIDjY9u3DhlJ+dXmY+PvkxEcUVkx3C1XqFsJwiST66dCkaZcU02GwI/GuO/6na0xZc3O3b8+GvO9WoEYk6xNpVg+LFiUaO9KRq1bjN7MvDrgcbLFFG28Jg1XH64kh7GKcJpS2M1RbGKU32lMRpBCDwuGrxx6gg2j6HUHvfDmzm7l2kXwZR+Q3y/S5ZPuY4ya6neMprwPFmS9Bj6HhMXUppLZeBSem+Lc9KHvfSJdMgtxIl4lJ8dvL4yc+5y/fyZaKbN13I2zueLLuAM7usyZ+d3jk/39YysEWUu4Dz5ImnWrVibU6vy5QfyzMOGQJnwgi2lkHnkZP29rRFTqq/0eoi7WGcFpG2ME5b5OaSOI0AhAUwBFa/JhB+O7nBeL0+XLPZOwgse+V1g+O4jz623OO6w1fztRSAls/Wx94Jvb56rKC+bu8+Otq0jAxPAEkcA2ijak14uJ4IYipbvHliCAtCZwzs95dD7dqx4v/XGRtQyiwEhIAQEAI2E3AaAQhLXQXUbjusdjzmLwbbY7h2TS8NAytequv72UwlCxKktw4gr/nH1jVeLJoXf+bzjIQNGzzovffy0ODB9+32AqKfr5eC0SLWzy+Opk6NwphC51z6hP3+BgdHqKVxdB1lLwSEgBAQAkIgJxNwGgGICRlv56SG0OJJd5+mVDe2tLEADA8ndLOmFMP6azxrODbWhXgCx5UrJkudrV5A9NO0BVDXgZes6d0bJkYnDvkwBNDX1z6LqBNXW4ouBISAEBACuZSA0whAtM9tdPk+xe0Ey9+vmJ17yJnbTHftajGVUl0sxwGmJBR5HAkPKrYmJHoBiYMF0JTInnUA+Vm6LLoO1jxf4ggBISAEhIAQEALGIeAMK9bykiyrsU7fPgjAV4GuD4734tpaHDtD+VNsbW0902IqpUhaHCb3BrJ7txs1a+b1/+xdB3gU1RY+W1MJXYo0EUSKIlWKSJcOAqKignQRBBT1SVFRARuKCmIBKSJFiiAd6dKki3SkF1F6Sd/6/nOXm2yS2c1sskl2Ye73TWb2zi3nnpnM/HMqjR6tPtSIBID58jnhoaqjiAgn8hArzZp+naTrxo30296NLRiYSxX/3bh+bc0aBzQOaBzQOBD4HAh4AAXHj0FgYzGEfCkO54zqsPV7CECwNOruBQjkc0FZpPQsd27P5EugJdvKliEItbdvn4G2bVNvGCgBIEdsyWgWEDm/BK2p6ZLn7+a9DdapFSpEUOXKkZon8N18I2hr1zigcUDjQIBzIOABIPjXBeCv84kTJy5JXsIT+AKCQD+P3y/KumDbSwmgBHlK9HtKB1e5sp1MJif99Rdnr1DqmbaOpX5cZADqjNr/8RiSZrkGrtOKiwMMsNmDOy5Ol6Rq13ijcUDjgMYBjQMaBwKNAwEPAGHvp3cHf5KByADyL86pF4HJjgGwZ69eVuuyGtaUNglIEoUSaN1KlYskNJTo4YcdlJioowMH1F3Chg3tUBvbKOF2nOaM2v8xcZ4kgKz6lAAzaREBfBAfTzRgQCjNn+9fU1iZE/j4cXXXJoBZdOeRhptUF53qH+rOW6W2Io0DGgc0DqTLgWB4Q3FEPCVXB2iCdeqN4NJlRfY1kIBOAilPM7s7gaRuwzHruOzcqQ4Djx6dSLNmxdO1a65LnhkAKNXW7hLAYcNCqHjxSFq2zL9gKvW6/fl71y4DzZljohkzvKDwDEyYnBM4GP69MrDAYOwC4GdevZLytGxMUT26BOMKNJo1Dmgc0DjgVw4E/BsKUr5tsPXrn3rVqHsNAHBL6vpg+C2Bk5TweaJZnleytatRwwUAGcT4UpI9gDMes0/S5e6cwnEKLRYdybX5QlNOtd2yxcW7OnVcvPQXHaVLu3h74kTA/3v5a8mBOw5E0uYliyhP43qU+/mnybR7FxlOHCfd9WuBS7NGmcYBjQMaB7KBAwEvrkGe2eGwAfwDAZ9rwft3C+IBmgD86oM3tbFVzwYe+X0KCejSkwAqAS1JDEsAOeds2bK+AbnMZgHh+SXd7mAvb17of1Hc60RFAP/ZutUFAOvW9S8AlCpgDQDm7MU3bd5IkUPfIOPRI4IQe6n7KO7VNyjhqWeIzEGpPMhZhmqzaxzQOHBHcSDgASBs/c6ULl26lslkGgpp4MsAf4w0NsMJpBrbAQbj1ZAgSQI8T2vw5ATC7YsWddK6dUhi62P57z9/qIBdYM891EkyKPSRoBxq7p7/t1o1/wLAcuUc1LatlR591L/j5hCrgnZapzlEgD/bA+UE8Et8siMRe+loReOAxgGNAxoHKCiehidPnjyLa/XynXK9kiWA3lckAaK0GfTeWt1ZqQLOjBcwZ80ICXEKaR87fnAw6mQAqGSuqY627Gy1ezd7UOuoTh2b3/P/sn3lDz/c9rbJzkVpc6XggK3mo3TjlyVkrVsPEUM1dXwK5mg/NA5oHLjrOaA9FXPgFlArAZQAUAJGf5Aq08BlxgmE6WDaOLVcbKyLKgkAr18PDgB49qxegFh/2//54xppY/iPA9Z69TXw5z92aiNpHNA4cAdxICgkgHcQv8VSJKCToMnT+rx5AXvqo1Q/aZKJNm400ksvWYgDQoeHZzwLiByfab+EyIwMZiMjnXTvvU5q1MgmwtPINoG8f/55K3XsaPUYR9GK1MYc0oaDbmvlzuSA/r9/Kfyrz8lpMlPsBx/emYvUVqVxICMc4IefJjXPCOeCqo8mAcyByyVTqEkJnycS5Hl3b1tPbb3Vs7rzt9+MdOaMTmQBKVwYsdAyKahLHQqG7d5+/jmeXn1VZWRqbwRn0zmOpyjtLN2n5DiNzZuH02OPRVBMjPsZ7ThQOWBetYL0Z077Rh7sF0KnTaawHyeT7qaW19A35mmt72QO5OrfhyKHvK79X9zJFxlrC3gJILx/H/d2DZAabqO384F4TkoApeesJxo5owQX2d71y/e/Mg2cBH2FCvnmOaw0o5ReZpY2pbFzum7NGgPt3+/yEJ440UyDBwcPqM1p3uXI/IjonatvL9LFxdLVgyfImT+/KjIcRYqSpUVrClm6iELnzKL4Pv1U9bvrG7F43FsE+7ueQcHNAP0/5ylk+RLS4f8qBCGUYt4fTYkdn3YZewf30jTqU3Eg4CWA8PqdfHubif06bN9hG49tLdYyIdV6guKntAGUIMoT0Rxbj9Wr0dE62Nspt1q61EgNG4bT9997DmYs08DJMVgCmNkipZNyLZkdL5D6T5qUHCJkwgQzXb2aSXFpIC3uDqTFvGYV6WOi4ezxuGrwJ9kQ372XOAyd+gMnyZbV2l6BA4a/j1LuTu2o4L35KXe7FmTauEGhlVYVLBww7thOufr1JuIE5m7FcW8xurZxOyU2bUb6y5coCm1yd2xDfP0DuRh379T+h328QAEPAP/++++yvCEEzO8AfU1xXAFbZayzLn4f8nG9AdFcgiYJorwRJdtERyu34nfWwYMG2r7dJbFSaiUlgAkJLiBzzz2Zf9FJ8OoeCkZp7mCrO3pUL+wlWTrbvLlVgO9x45IBodr1cI7m9u3DYBcZrraL1i6DHAhdME/0TOzwlM8jWB97nGxlypIRwaFNm373uf/d0EF36yZFvDOU8jaoTebf15MTqgTzH1vIcPbM3bD8O3KN5qWLKc9TbSh0/hwKmfdzmjU6SpaiWzPm0s0fZ5O9WHEyI6Zm3oZ1yHDkcJq2gVBh2raV8rRqSlHdntdAoA8XJOABoFwLwF6Fo0ePrpe/AQJ34PgB+TuY9lJtKkGUN9rTcwSRGUE4JZySAIOlfteuuRw/pIduZkLASFolMJVgVtYH+37yZJck9YUXLPTee4lkMDhpyhQTnT/vmxSQ4wwzmDxwwIC8z8HOlcCln8GJec1v5ATDE1u18Z1QgJmE21LAMJYCaiUlB/BQyd2+NYV/D2ULeBU3cDBdPXySbn39PSV06JSyrfYrKDgQNulbiurZhXRIDB+LwOiJzwI0KRVcb0uLVnRt0w6KG/AaWRo2Jnu5B5Va5mgdZ/XJ9TJMQOC4Yq1eU1NV+3A1ggkAOpH+rYVcG46fxHFQGmdJ0CQdKeSalPYSaHlyBGF1bvHiDrp4Ua8IUhh85MvnJA774q8QMEynBK8SzCrRHoh1u3bpqX//UFq7Nq3ElKWZc+ea4PzmpO7drVS6tJPYWzgxUUeffea7FFDLCJL1d4B5+VLSJSaSpVFTcubJm6EJE57uTNZqNcjS5IkM9b+jOwEExPftL9SB1zdtp9i33yNnvvyUCJ4hnIDi0g2HDpJ55XLyaLei2EurzHIOACBFjBhOkcPfEiAp+tMvKG7Yu+kDpogIin3nfbo1HZJC3A8BVfCBkuvVV8gAu0VLg0YU339gQJEX6MQEvBOIZCDSwb2CVHDzAfyYZggEdfFIC9dOng+mvQRNEtx5o12CRNlHqS2nhTt3Tk+cF7h48ZT2HHnxTjx0KFaENOnaFRGcUbLKBpDtELdtM9D//meh8uUz72iitNbM1q1da6R580xUsqSDGjdOaVg5e7aJ4uJ01KqVlYoVc6nJ33jDIkDhzz+bqF8/Kz3wgPp1MQDcvp2IU8JVqaK+X2bXeDf1N29gU2CCkXrGpVHO3HnoxgrXOHcT79SuNRGp8xI7Pau2OYWPG0uslrcXvZds5SuQo3gJshcvSY4SJSixbfvAAxGqV+bHhmx3Z8je1y97vId/O56ciOR/a+I0sjRLkqeoW5i/w8IAkIb+OEWYEsSOGKmOhlStQqdMopAVS8lR8B5IpSdqoWtS8Se9n0EjATx+/PgfRYsWLQnQ1xxbM6iDS8MDeG96Cwy087jnhUowIsKpypEuPRUwr48BIBcGgJ4K/+/KLCCZDQLNcygB0z17DLRsme/qUk80Z0X9pk0uHqXO/8uq8smTXVK+3r3h5Xi7MFju3dsCAK2jjz7yTQp4//0uEKnlBJbc9P8++psf6MaiFZBQNff/4GpGhLFn+IcfkHntKjWtA7aN7tpV4k2x+Cj1YVWhrUIlMlz4h0LWrqYwAI/Ike9S5JuvauCPGfzjj5S7RWPP/Fa8CJmvTHjhRUp4sgPdWLjMd/DnZXoT7EIZyCnaIHnopz95AqYFrSjXW4Mp7JtxZDhxzENLz9WGgwco8r3hosGtCRPJec89nhtrZxQ5kL2fIIokqKt8+OGHIy5cuPAxpIAVQkJCnkJ4mPGxsbFvoC5O3QiB0YpVsk6nDipUdRIhKSX0ZkdWs6YdAZjtCMbsfUypAvaHDaCSCljSKm0NA4PjyVTs3aunHTuMUIk7KHX+X1YJnzmjh+TSTrVruwC17DlggIWmTzcLcLtnj4WqVvXOZ9lPUwFLTmThHl821tp1s3ACL0MjoTTbUpnXraFrf+zx0lD9qZBFCyh87KeQnFWk2LeGk+O+0uo7+9BShyju5j82kwnOHLwZDx+CDWVbujV1hg+jKDdNfOY5oSLWnz9HhnNnSQ9nEd57UwmzA44zJJQ4fd8dXVjyN348mfb+SXkAgG7MW5x9wAV2stGQ/Pm14H+AYwYaLl0k0+aNFPP5V+SMyu15Cnxph33/DUV8MkqEmbGXKEnRY8eT/f6ynvt4OGMvWYo4v7ejUGGyNmjkoZVW7Y0DQQMAExISvsBC2Be2ENS/FmyFIyMjv8HvbtiCpiTb/7mkQ+kRrkYCWLmyg9as8Y6DWcLlrywgTLMEe3I9XCfjGrrXcX2glLFjXRK8l1+2psn/K0O/sPQvtcAjTx4iBoGjRoWI7Zdf4tO0UVpj2bIOKlPGLuwvlc5rdcHLAd2N65T7+afJtHM72fECgreQXxbjgDqawRhvIUt+pfhuPSlu8Fs+h7fxRgyHcGEvXvfi4JscDwkdwuk4I28HIHVv4Osx/olY9csb1XnMa2/dlSsU9VJ30l2/TnEAvexwoJqfsAELgbo5sV0HRLUNsNeZ9Mpzf6AwjatXk7VJUzLt2U152jWnm8hX7YC6PCgL7EBjvvyacg3oS6H4eDHt3QP18lSyVamWZjm6q1cp9wudyLR7l/Akj+vdl2KHvsuxztK05QqWCtqLFiPi5PNKBf2ix3/nStmkdF6rS5cDUAwGTakJz9/XQa113759sThmo5+aQUP9bUKlLZ+UoKVHvwRasl967T2d51iArMZk9a/788hT+/TqJf3uYWBkXSBKAPfv19PKlSbhvNKjR0rfob//1tPvvxvFuQ4drIpL79XLAt45aPNmI9qqe9kzANy6NY5GjkxUHFOrDE4O6C/+R3mebCXAH4eQYftBDpuhVEJnTofEYwLpot1cwSE1IXhgKhXOXXx92WqKe6m/OB0+6TvKV7MyhX/5mVcJmtJYnuqcBQqSo0ABSmzzJEV/+CldW7+Vrh45DSP/2f4Bf54m9lDvzJeP4vq+Is5GQJ2e++n2xDxOr7AHOIf9iIIHaMQno1M0N61bTVHdX0gT4y5Fo6z8gWDZuQa+TLyeNAWG2bfmLyLro7VF+KE8bVv4nsUmzaA5V2Fp0oyur9tCFgB9w5nTlKf1ExT23ddpVMJOrJvTLtruLwOzjZUUO/pTj+CPzRFyP9VOhHbRnzrpfXH6YIIx3peS3WeDiXOp38wsQnNkN8MyO5+Ujklgl954sp0nL+D0+svzyfZ//mGZpEuuh+eREsDMglVJsz/3YWFOatnSSn37WkhmWJHju4d+8eDYKBwe2SGEy+jRIcKpRvbX9ncQByC14YCySrHReJV6qDL5BWc8dICsj1ShG4t/IwfipCkWgIDwTz+kSMTQy/dIBYp4+y2KHNSP8lcqS6zqVSyQJNpqPEqxIz+ia1t2IdTKU6QHeBQxCtN70THt+/ZSxMgRFDH8f4rDc2U0JDacMeXW5OmU0Ksv2StWylnjeawrHuFlGBTYITE0b9ogYs4xiPNU2P4rT9P6SQ4AbHeYVCDJjHzvbQpZtpjCpsAxILsLAH5Ut+dEdplQpBlUArPOXFF04+cFZKnXAE4QpymqT7c0gCkzZJuRwYMBcnYVzqrDkszYN4YI0B3+6UfEubZTFFzn6EnTBFi01aqd4lTqH7qYGOHVbzqwj/I2eZw4bqFW/M+BYAKAe+EBPBgsMGFfGdtkHO/yP0uydkQJjljjoqbIXLWyn5o+7m0WLjTSTz+Z6Phxl/u+PzyAeXwGSiaTU6Spk5oO6RiCDEIBV8qUcdK0aQn02msppX9sWzlnTnLoF2+EP/ecle67z0F//WUgzsCilTuPA/p/L1Celk0o8i3kQYU6NHVx4h/SGRFJlscep5sLlpITkjSPBeq+6G8mUWLzlmKs8InfUtjsGSJlnfH4MY/d5AlHqfso+rspdH3VBooZ9YmyAwX++RgMsSNK3lpVxMsyfPwXFPbTNPKUyJrBh1/UAJJQP+3Z/u/62k2U2Lod6YVauKdiLtqQubMpb8vGZIRkyFKrDgDFZrK6q5kBomMQ4oRL+EejiK9pdhWOSZcH2VJCVv9GdoAi8YHAJgJKBeFVbs6cSxyGKHocVJn+UM1gHvPihRTVq6uI35g6y4cSGX6rA9/j/jdM/F9EI04kg8LUhe31PKp03Ro7YBt4ffkain++q/gAyt3jBYp4dxj0f6nlQG6dtEOfORA0bzHY/A1GNpCxWGFBbGuxLbdYLAN9XnEOd5ASMylBS48c2c6bE4i3MTiV2b59HJolQTTzhwcwD8TPKqbtyhU9QqcQ4VlGTZva6MKF6IAzxXHnT+pnrAz9wtLB4sW922WaTERDhyZSnz5h9OGHIdSihU2VJ7f7/Npx5jnAceYiIeFK6NbTZfuV+SGTRmBbLAtSYIWsWkkh8/FyxhzuRYSMmQ/DfbZbCg11P5X2GDebtW49sRlOHicGLs6oPMQZSxyFi6Rt76HG9khVD2egQRvyOrkHsLaXdBnGJ7BNHP9TBlnhWI4smWSvUkf+/MT8di8hC+dT1Csviaq4fgMpdvgIxbzEVgDD+M4vCMAd+fYQMab7OFlxzEAz9zPtyYhsGWwacHPur56lw5IA3EMMlvxV2KGHU7fp8GEg7t0csIvke94vBbZ/MV98LVTl7C0cDrWyDVL3RC0AuV/Yy4MEDQCEx6/t/PnzvUEzb6KUKVOGn4x7bv8Mip0EgNJeLj2iJQDMqAQwdRo4tmPzV+E14ENdSAE5rE0OPGsytRQOyaMU+sXboG3b2uDEZ6f9+w3EsQG7dNG+SL3xKyvOcYw585ZNwtBcGP/7eRLODMIAkIFVwos90khmvEr9PNBiL12G4oa84+FsxqutNWshE8oqAYQT27Un28OPpKE346PnUE8A59TAW1KS2LINWR5vSPG4LpY23sPAxr47kkJWLhPONBymx9L4CTmM6j17trK63xkWLuLn8d5W+RFFYBc55A0B/qxVq0GyN9+vjjtqCDYcPUJRXTuTDqGJYgfjA6lLNzXdAr4NZyrh+5qlmhEfjyK+B9L9+Ar4VQUGgUGjAg4PD7+K0C/4rE0uCAnzQ/Kv4DiSThMS2KVHtRovYDnG7NlGql8/nFjty4VVs+z8YTY7STpm+EsCyONLla8EtVwXTIVDv5w+7Qr9UqeOXRXpMGOh4cNdTh1jxpgpENXdqhYSrI1wU7MUiEtCe99z/6pZtqVhE2JJmvHwQTJu36amS4614TAY13btp9h3PwAwqRL84C89ToaE0M15v6YL/ngYJySIsSNGiREj3gf45gei2oK24QAbeTq0JpYg5np9oJCs5e7+vAh3ojRM9JgvKb5Ld7oxf4lfwB+Hz2GVsprC9na5O3ck/c0bQvLJ3tR3UrFXqEjXN++ka3A20cCf/65s0ABALPkK1MAfwvbvFbflB1heGjfKPBxKsOSrBFCNE0h8vI4OHzYg+4TLS/XGDTaZ0FGBAk66dMl1qf1lA8jLk2vIqHTSA4uyrVqGfunVK23oF29ENGxop7p1bQisrYcEEXphL4XV48OHh9CgQemoC72MoZ1K5oBx5w4RU872QDmyV3oo+YQ/j4Dy47v1EiOGTZvkz5H9PxbsrvxlO+Z/4rJoxNR2HF6mSYD0iMPK3PQxjZnhxHEKR4BiJwBn3Ev9hJdy/Is9hb2eHV6sSoUDEXMcPE9hTZT6eKtjZ578FcsItXLojB+Jw6h4KiFzZpHh/DmkRGxCMZ+BBh945GnMgKvnr28PIWMCjtYgIcglKgoOYq9YrdYWJpNpJUBgMYSBGQKyffikC4xFSrAkwVN6VEmPVdnPW/saNVxSLJkRhO3zuBQs6PRrFhBJg5RiSlAr6wNl/8knZuG00aGDLY16+tgxPW3Y4Ar90rGjb2pcfrayFLBlSyONGxci1MBSGpp67WwmNmOGiaCVoTFjYKBtTt1C++0LB0IXzBXNhR1QFr7kEjo/TxFjPiSd1eaSHGXhXL6sX2vrIwcAGjiPra/FDhu+W99Ohh1iAUrPY9XXsVW354cHDI/N69eKjTOpWOvUE2kPE57rkmIY9qJ25s0Hr3FER2NjZa1oHFDBgWCSANKpU6cu2my2BpAE1gII/BHrw9M5uIoESxI8pUc929VFRjopOlrnLZC+GIbz74aHO+ngQTz0Yl2Bn/kEA0B/ZgGRNMs1SLW2rA+E/enTOvrySzO98UYoXb2aVlAsJXfPP49QHco57b0uo3p1B5xArMTXkx1tPBX+aGXPYbtdR2fPpqXDUz+tXoEDNhuFwMORSwJUn1lZnPny0/WliMc3aPCdKU3JSubdIWNbWrXJOfAHHnKQ4yuHTtJNDtfzJKyfkCmFQ+SYly9Jy2F8oCR07a5JyNJyRqvxwoFgAoDi7Xny5Mmbdru9GdaUG1s1L2sLyFNSkqdWAsiLkEArOm1UihRrZLBYpYpdgI29ew0iPdzKlbHwAE6EClgH73tnmhh4KQbw8Ydcg1wTd9+82UBsi5jToHDcOLPgQ9eu1jSZOFKGfkkZFsYXFgwbZoGmxSkcSYBNPBaZEu748WD6d/O4HP+dQLw2XwoHU+acv5b6DclR+n5fumaoLauYhVNFhnprnTQO+IED8OS2tEHA7onT6MphgMFpsygQPTyBAABAAElEQVS+/yA/DKwNoXEA4T+DhQkOhwOuP65y/PjxRKiA2SHkeVkXLPtkCaB6in1xBKlePVkNzOYSnLe2SBGnX7OASMolMJVr4vpPPzXD3i1M5NWV7bJ7f+6cTnjohoQ46ZVX0gI89t6NjdVRs2Y2KlEi41YE5co5ALIdQjq7b5/nf6UyZVye1ydOeG6T3TzK6fkMhw9R3jrVyLxyuWpSWMUV89U3IryG6k5aQ40D6XDAcOzvdFoEyGmERbG0bJ1zua8DhA0aGf7jQMC/kcqVK9eDlwuP365Q+w5z29gGsLT/WJE9I0lpmZSeqZlVAi01jiC1atmFFDBfvmRgI9W//gwBw3TLNcg1uddJr2M16/N3G5b+2Ww6YvVuaqeXjIR+8UZf3bouwM0p4jyV0qU1AJiCN/CwjBz2pgjka/xzV4pTqn5o9niq2KQ18sABZJkwI88yewWHfzGG8j5WQwRP9tBaq9Y4cMdywPNbK0CWjODPJW+TUjZASMowGQw+WDXKdnq+2OlKBwN3oOWJiMaN7dS4MVxP3YoEgKnBkFuTDB1KutwlgEj3KIoaWjM0aTqd/vlHR7NmmcBfJw0YkFb6t26dAbakrtAvErylM6TX0489ZqNvvjEL1fdAD2HJWQXMquKc4onXBeTASbbj4zh+9nuLUdzA19NQoL/wj6jjoMxa0TjgVw7A7CBv84Zk/PsoJTZ5gkIQQ9GJh7Eu0RXaya9zaYNpHAhwDgQ8AISqdwTzEHtYuAZ3YRs+p1MHyZlLIqR2Nb6ogJXG5HAlXPwZA5DH8yYBVHK84D5ZXdgOklO2McC+995kKaicV4Z+6dnTt9Avsn/qPUtcDQYn7dhhEJ6+Sl6+jzziQLzBGDUZkFIPf+f9hndSxIjhYl0x749Om60CX0m5BvQl4197KeajMZT41DOaE8addxfk3IoQNiehcxeKfB+5ggH+HLnz0K1pM0W2lpwjSptZ40DOcCDgASBUvhO9sQbAsI+384F0TkrKpEpXLW2yfXpOIJ7GkxJAfwNASZdcF89frJgL3J4/nzMer7zGMWMSFWO+cuiX9euNwqnG19AvnnjLdpYM8HbvNtCffxro0UfTOjYwKOVNK5B+f/U5GSDh41y6bNyepkASw2nS9Jt+p6j+fShx+VLiALsZyb6RZmytQuMAOBDf52Uyr1st8g3fmjSN7IgpqRWNA3cjBwLeBhAXhfVB3raguW5SBSglZ2oJz6gE8MgRvYj/999/LjCWdTaAySspUcIFAM+cydlbS8lMbNIkiAVRunSx+DVNKquBuWzZ4grALX5of9JyALZXYdN+ICekMDEfIiii0kWCoXv0hIl0c+pMchQoQCHLFlOBCqUp5Ndf0o6n1WgcyAgHoB64iXzO13//QwN/GeGf1ueO4UDAyyUg4XvfE7fhIJLL07lArJeSMik5U0ujbC8BpNp+zZuHU0ICUdOmLoDibxtACWTlupiuatUcNHVqPJUrl1YSppburGjHWVHmzjUJdW2PHr4Ffk6PHrYl/ArB9xkADkbYOK144ADEpdfXbyUT2/89WN5DI1c1x2C7hjy3uRD8NgRxz6L6dKfrRe4l26O1vPbTTmocUMUBpY8PVR21RhoH7hwOBDwAlKwuU6ZMG3gCj8JvKN0gO9DpDHAQyX/7t2zmdY8xojDGZgSTbo14gmfdGwNMNsJ4n6GOxTin0OZFjjno3iazxxLA5cnj20gSAKrxApYjQ9hCcXGcBs6BINBZYwOIEFUCUMl18dycdq5VKy9B8SSB2byfOdMk+NGmjRVq6rS2gZkhp2ZNu3A62bkT9kUA3BzAXyvKHHDA8SPx6c7KJ1PVOgsWpFtTZ4jgzxyqw1ateqoW2k+NAxoHNA5oHMgoB3JWT+cD1QBuYwH6oDeif7B/FWBtPbYJaocA+KvF4A/tH1Doo8dY03D+WUgcK+P8YaSce1OhXaaqpKRMAjq1g0VFuVq6A630+n74YYhowungpA1g4cK+OZ+kNwd/RLMUMDFRR/Hx6bXOufM24NEpU1zZOnr39q/0j1fFmUSqVrUD/OmELWDOrfQOnBk3WWK7DhT3BqI+aYaUd+AF1pakcUDjQE5xIGgAIBgUd/To0RnYb0VQ6BsAaj0BBJuqZRza9sb2MtpfUOjjwHj3HTly5O+KFSsyUrgXgPC6QrtMVUkAKFWnageTgNGX7BpHjyZfWs4CEhrqJAkk1c6rpp0c0xdwqmZcX9qsX2+gb781IduJcq+VK4107pxeZEZRctJQ7uVbrQwpw5lQPJVt2wzIIRxCf/zhuY2nvlq9xgGNAxoH7loOQLUSsnA+hfw8kyBxuGvZ4O+FB40KGAvn4HaMak4CyD2E/UaANJdVP36kV44dO9aT28Cr2JN7qh3nKlut1lVoxnfYUG7vzyIBXEYBoC8q4N69LbRpk5GaN7fSypUm4Z2bFWYvci0Mbv1tY6iW9999ZxbevaVKEfLzpu0lnT+YJ1nBA56xXj07jR3r3RGEQ8VwGBrOUFK7dmDZSKblmlajcUDjwN3CAd2N62T8cw8Zjx5GrDKs2mggpx4fqix1h9OW8/ZeHjsjc7lC54S4NE1ZxSfDkcMUOmMahc77mfTXXTIZ2/gvKPrz8Tmapzmr1pvd4wYTANwCgPYLpH9vQFW7pmzZshUBBGFx5b8CKeBfGK0Qxu6LsefiuK6a0dWCCiklY9Cktg/PnywB1Knu16KFHaAoFinPSABA9gD2ZU41684obWrHVtOOAz9v2GCgyEgndeyYVhW9f78eEjcjFSzooPbtbVnCA6aTU/AxsONwMKwOZ7Vw6iKlj2wrmBXXIvV8OfVbro33/MXO6aucUblzipy7fl7363GnM8O4awfpEC/L2rBxQC41IK4FpGnGg/vJuGc3QB823p847jO/HAXvoYRuPSi+aw9yFi7sc3+PHeLihN1v6PRpZNq5PakZh47ia2v660/K27YZxb/Yg+LefT/DzxZ5LZImuAsPggYAApy9AUeN6sgDfAJAsD+uVVMAwef9cc2KFi0aHhER0QhSwqU8Xmxs7PTIyMhP1Y6dP786Z2RpJ1eiRBicJdSOzmnwXG1jYgzop24u7tGgAdHy26lWS5Y0+tTXNWP6fwsVkm3Ck9aEDEtCHcvrZalcVpbvvxcZnahzZxfoCg9PyZ/p012z9+unR2DolOf8TVedOgTQzUHLc1GTJmlHb4x3EiJQ0N69Rqjjc5FS0Oi0vYK3Jv9fO4kGwOpiAlykDx5MvpGDd0nBQzlLSxYvJpo/n2jdOkJCcMr/8MMEO4jkrXTpO+OacIolftB9/DGL4F3XqHt3oq+/Vv4Sy8mryNcFAEftO8MvpGI++uUXom3bCBHrif6CnMOayhY6Xz6imjU5qCm+2iDVY8NpZE0Re6Xjo0dJD16Hj/mYwr/8nOjpp4k4FRKPkdHCdE1E2N+ZUPNKddk99xB160bUqxeZy5Z10TN+PNHbb1PYj1MobNUK13Xu0CGjs97V/YIGAAL8LYYNYFu+WgCDy7HjzS+lSJEi1ujo6B/gKNIYAPNgrly5OkO9vFHt4FevRisGHk7d/9KlMFQZIf2JpStX1Dtk8P8fUS66ft2JfjGph/X6++hRIA4KhbOGBX1Zs+3fEhbGKgAznT0bj/EFoSItWvv24QBBNpo9Gygwiwo/93/4Aa7IsAzo2BGiToog92tx+TKnhYsQoKtTJ+a5f71/Uy/r0UdZFR1Cy5Yl4jlqSX1a/H744XAhJVy3LhZSQ/X3gOJgAVrJX9b5o0LJ1v8V3O1EMS/2pIRrfH20kpUc0F29SmYEzg5ZuohMGzeQzvXgcE154gQRbwsXJpHgDI8gW4UK2CqRvUJFspeFfxyAge7WTdLfukU6vITFFu3ayzqyWsj2SFWhArTWrUfsrZ0jBbRyfMiw8V+S8fAhQYIdQcR1ADz6qVPJtm07RU/5iexlABxyuOj/+5fCPxpFIbNnkA7hkBKeepriu3Qn+0MA5FlVYCsXOn0q8h1/RvrLl5JmcSJMAV8/W9VqZMVmq1KNHKXuU47LmdQr7YFh318U9sP3FLJgHukYtGGzVq9B8b37kqV1O7wWXI53aXuiBtfIcPwYtr/JCA9/07o1ZIIkkosTDxBrg0aU0KUbWZq3TB7nClJpcenSi/T1m1Lkm68hoPcawsOfEqFliP34M3IUKepqo+IvP6fu9hI0ABCArESDBg2MGzZscKGMjF+5JBQASeIkjLto9+7dSwH+njEYDNNRx7fFeXgB91I7BUu8eEuvSCcQzqGrpr0cDyYYQsXJmUD4o0xKBOV5b3vpAcwZMnyZ09uY7uekeprXJscvXtwFbM6cSa5z7+OvY3amOH1aD7tOO7xwXXMyDZKOH380IT2bjjp1stI992TN+t3XUqcOLg7K5s1G0KAMAFlVzGpitgfkmIl3bJkwgYyw32FwEQ8AKOyK7tjF5tzCdPB84jiJIUsA+rZuIh0/IFCcCKid2KwlJbZpR9YnmlF+g51ubt4G1d9BMhw6QMZD2OMFbNq1U2y+rsC0908E9Z4sutnKPSjAoKXu42St8xg583N0LoUCQGo4e5oMx/Dix0vfcOyoePnrbt4gG4AQAxFr1epkqwRQBPo9FoCH0FnTKfyb8WQ4f040s91fhuIHvEYJHZ8m/b8XKKp3N6EqzN2kPsV8MZ4Sn+zocTiPJ6DC4LzVbK+R2LINP4Q9NvV4AvG4wr8ZJzYGpk6WrqEudOpksTEAS4AKNQGe7n6LTg9gHAqgGf7FGDL8c16QZqlXH/fCkyKUku3BCi5VRGqiVbzD3LvYHqpM0V99QzHvfEBhM2CnN/WHpPvJXmg41MM9wfcOpL94McX1FsDv3Fn3ocSxvVBhSnjuBWxdyVGyVPJ5BbrsxUvSzdm/CMeQyLffwv/AUnz0/E6xb78n5vXpJZk80113FDQYGMBsFcAa3xU7YJ8XJ68UpIF95HFO7EGT8wq+TCTo8EZDzZoRArAcOBAjAIm3tqnPVakSQf/8o6djx6JhE5j6rOffb74ZQj/+aKbx4+PpmWcyi53TzvP11yb64INQevPNRGwu0IPnPBUvHikkb5wD1xfAmnYGzzW//mqkYcNCqH9/C73yilWouOW1sICUqlUjoIrW0+rVsVS5ctaDLZ7zgQcihZPasWMxiu+LJUuM0GaEUp8+Vho50v8SWc/cyvwZ4769FDHqPZHJw1qnHl64ryoOal7zG+V+rpM4d2PRCrLWVmVKqzjWnVJpOHqEQhYtgCH7NXKaIBmBdMTJ9gBiz79NyfUwuGfpnS4OUlOAEN7r4rCXx2xbEQ8p17WrZAQQ091++LBELxFgj1/0lkZNkwAFSzrYdET+byTxFOMYAcIMAIPGgwfIcPI4gGM4bKqihF0V7x142Dhz4XfuPNhykwPHBNG7acc2Mm/ZJECn/tq1pCH5wFa+IlL91YNUsRLpz53BHAz4MM/JE6Tjf5J0Cjsc8IeDS0IFQAhgyNJJBophkydi+570kHZysVapSnEDBpOlRSvhrJA0NOzcIkcMozCAEi7x3XtRzAcfudSbSY2UD8Q86Bc28Vuki7ssGjki8H8NMJPwfFeAqBrpS8sAxENh/xr+8SgyXPxPSLUSOr9A8UOGUz5Ix+PGTaCQmT+JczwB8zWx0zPCno6lsRkqmDNk/hyK+OxjMpw5LYawIph67NB3XA4bGRrUh04skcXHSNik78T94a0nrxdel5DOPkC2suUA+iuR9fGGGQr1pMP/QeR7bwt+85zW6jUpeuz4dIPN8/9FwYJRQYOBvPEzo+eCZvEAgCMUFukEAPxAoT7bqnwBgOXKIRPCdR2dPx/tVTquRHz9+uF0+LCBdu2KoRIlFD6JlDqhrmvXUOEEMm9eHNWv75IOeGiaoeoZM0zIfsGAxkKjRiUDmho1IojTwe3bF5Ol3sF45ghzlghogt1fcvPnG6lfvzCYpNho6dKsU0OnZtrTT4fRhg1GqL7jqHHjtPzmdzfe7ZQrV+qegf1bFxNNeRvWTXqxJHR4iqK/m6JIdO7nO5F59W+QxnSi6G9dUiLFhtlVCVWY4W9Imw4D6DDYwZ4lMQyUElu0zphkRwXtOkg+QhfOo5B5c8i0n/3L/F/4RWpp1sIF+qA2U5KceQSA/iAHYNBw+BCZIX00bcb2x2bSc9odhcKA1w5JnXjp3375cx5eJyRrxr/2CmcEEzsl4ENDBwDnXhzwOtU57EK9y/UWrDUOEj8rHAO8eVRx6JDIwQNJHxtD1spV6NYPP6aULrlNwmrasO8mCLWpHve7mAfjszTVvHY15nd9RLLEk6VUCZ2eVcxRzerMyPffTlJLWwBsYt4bRfZKDwlSk55TFiuZV62ksOlTyLRhXRKQZwAT37U7WR+tTY57CiUBeTdSUx6CrpAlv1L4px8KiSqftD5ShWKHAPg1bOyVPykH8t8vIxw1GAiyU46jREmAPBfYYyDPgM/Jtn18Y/qxmJA/PNfrA8lw+hSxg8rVA8e8zqEBQDZIC54SA7D3uTu5AIXvuf8O5GN+drBda3i402fwx+tKmQ9YPQDMqiwgktfuKmBZx3vOCcwAkLfChdMCIfe2mTlmIQpv7oUFIhMnQqqCwpK27CyPPWYXAJDVwEoA0JtmKzvp9HWuiLeHCPBnqVWH4oa9C8mQ53Q2bBdmLlGMYl8f5us0mWuPC6+HSlACPcNtwMcqJ6kadZ8gZOVycsJdO7F5K0qETZalPgBU6pvJvYOaY7jdh6xcJsJWiJc6/+OjsHorEepJ24PlScdfLZCE6WBLRwABYo9jHY7Zvo6lZE6WDEIax+7kDECEZE4c3/4NaR9FhJPtgQdVSbXUkJ6hNhDv2ytCzc9bbzj8YL0GSBPNWzZCxXyc7CVLiXy7dhjw20uU8ijhsZcuQ4ntn3KRAP4Yjxwi4+5dwkuVQSFLUBkwsKqUJc+2h+GsoKLwmKyqjOrZxeU92rgeRY//ziUxvN3fcOIYhU0YR6FzZ7t4j3kSYcMWx/NA+siF1cos0Qud9RNCpRwR0sWIUSMAvltS/AtdyYp7h2lk4Gdev1b04WvNwM/asIkyEMG9xikPedNDYhc240cxvgmgiTdZGPw6AJgcuIcYEDrgeSf2DA5Ba/i3X+PD5oBrTkhOY98a7rKdw7mcKjaA7eivv8/W6a1Qc1/7fRuFfwfnH0hD/Q0ws3Ux2TRZzt0hKhcI549BkLLhaUf9sU1w68bgtS9AYVG3umw/VCsBhE01lSmTC854DjhhQbXjY3nhhTBatcoIG+44kkGH1QzxyCMRdOGCHo4z0XAEUdPDtzabNhlggxtOzZrZ6KefkiVtgweH0IwZZjjixcNBzP+q59RU8rNOfllv366n1q0j4PXroJ07Y7M1gcTu3XrEIoyAE4gd1yvJUiE1uUH127x0MeXu8YJQU11fv0V80XtbgPu1UGMa4W0s1efwwI/q9SKFLFucpgsDKJbaCGeH8nB6gIqSjeJDfpkrXtYSHDrgms9ZR9iOTJWaT86EuU2bNwrQZ162REib+JRQyeLlzpIifjlxDLWcKDlyPfy8UF00HqAAyB7tC9ObD/Z3uYa8nqQmjHt5AKSm7YQdoRn3DKvRGXQnIE1hfL+Bnh1HAHBNWzdT6MzpwtlGdzsosR1gjNXFLCVk6VPskLeJVb6pHz7pXguAf/Nvy3EvzREfXPrLFzHulfRWR7YyZSkOwI+l2llmc5MuFcHVgK/F3a4CDgYJIMvi+XOPM6yWdbvFGFUwKAyKIh1AZOBkX4lOKQFU15uFD5wFhOPT+WI3qG50Vyu5ntRaHwaE7HhSvrxLAuLLmJlty8GWuXTvDklCNt/hbGvIMQn37dMTg36ZKSWza8rJ/qY9u8T0MexlB3VOIJbwrz4X4M+Brxy2T7TBjoqBnr0itpL3KYIvlsjpLl+Gof8CCp0/l0y7d962MZtI9lL3UUKHTkLSxDZhbOfG9ns6hPFw7fn3dfy+XZ/gUlk6IRGzNGwsQJ9QL7NtglYyzQG2Q8xUgfQ0ety3ZIE9KgPB8G/Hi43HZAkbx5SLf6kfOeBF7LXg+rLambeYj8bgI2KeS2oHFT9/aMQCWMa/Mghq7QzaeACEWgDieEsqkIgyuNTDllB/CYAQjj/ymAM4s70n38vZ/rBLIlA7CFYOBLwEUDIWksCnEQZmrvwdKHu1EkAOSNy4cQTVqmVDeK5kSZnadQwdGkKTJ5tp3Lh4evZZdRI1DoNSsWKkUMfu2uW71FENbWfP6hDOJJIefNBOGzfmnMRLfln/9VcMvGsjhJp9794YypvXtQpW87D3oQgroGZhmWjz3HNhtGaNERLROEhGs079nQkSfe5qBAgU6jBmdDpFXos0Tgfp9MvoadOWTZS7Yxsh+bixeCXZYEOVkaKHk0LoAtjsQTLoS2BcBn22ig9BjfwMJcI2klV1gVSy+3oE0tqVaGGnl6g+3QSojwPoY29VdnLJTGEHGkfuvOlKKLVrkRku+7cvXwtNAuhfnmblaCs4QweCPxcE6Ep6C+W0E4jaBWdWAiht7WQ2ETXz/vefi00sicuqIumS68uqeeS47EBx9apOSBdlnft+yhQYmtt19NRTFgH+OJVQ+Jf4Uv91gfBuvLYHDgAZ/Tp3n8jLcd26NgEA2Q7wTgGAtqrVvaw4505xGJRcL/UQqreYEaMyDP54BY7S91PcG0Mo7vW34JTwJ0JM/CIkLQ4EyXXmyUtinzcfOfBV4RR71PMxS6cAArUSHBxgL9vrG7e7iPXTdWMbRq1oHAg2DmSzgizj7AHom4PwL/mx349Rsg7RZJxErz0lQMqoKjYjKmAZA7Bw4axTw7I3q07nhINLEib3yofMnly3zkAvvhhGfftaacSIZK9jHhdmPpC6udS/LzfYT1HdP0iyCWN7rITnX3QZB2eWiHT6c15gLps3e7b54nPLlhkBVK13djzAdHiVqdNs99evNxmgFktsDmP8vn6yCIFoQATKfaRqpsjTOgcwB/wE/AJ4hRppGgfS5UDQAECspCykfeWwzzo0ky67Mt5AAkBpM+frSBI43rqlHmhltQcwr4Gfo0wbr4/toTnOaVaWWbNcEr5SpdLeBhyMnsPsNKxwgWr3dEmsOExGfK8+FN+nf7rqmYzQzbY4TgMCP7vl9qtY0SHyNx88aCAOkQYBUpqydatBqPTz5XMCAKYfGy3NAFoFJLufkXnjerIXLyEC0mpef9pNoXFA44DGAfUcCCa9xZlq1ap5FqmoX3OOtJQAUKpMfSUiIxJAqQIuXDhrBaZyTVktBWSbRvaEDg11Uvv2KcO7sLfpV1+5uNoLHshsxB/7v2F0bfd+ihv6rkfwx/HhMlo4kXq+mpUpb4PaIkitHMeAu7R2bZed5tatyt9YNWu6pIQ7dwbeLS08LuViAnTPXrfhYz4iDhp8a+JUoZINUFI1sjQOaBzQOBCQHAgmAHgE+Xo3wxlkJOL/DZNbQHJVgSiOAcgl4xJAF4iT47hG8/5XqoDvuSettMx7T9/OyjV5AoD+CgXCwZ1tNh21amVL413L4WiQ5YpKl3ZQ49ZmurbtT2HPxbZbnkrEyBGU77EaCMa6wlMTz/Xw+ozq0UVkZ2AVZPjYMSnacjxALlu2KAO8atUQ1Baqc04LB01mwBQOcZGvaiWR3zNgiEpFiLD769tT2P3FvvuBK2RLqjbaT40DGgc0Dmgc8M6BYAKAHE/hEGwAi2HP4WDKwiYwaCxvMysBlFI2X1TA2S0BTB0K5vvvTfBCjqDp003e70IVZxlEzp7tGue551JK/7j75Mmuc716WVz2+CpsfDg6PZfIIW+I/Jzih9o/SKjO6YY41AdLocJ++E6k0pLdZaxGTwCQbSc5RE50tI6OHAmMf0MOd5LrlZdIz2FP3JLHyzUFxJ7t/l7uddvurxVCd/jJ7i8gFqcRoXFA44DGgezjQGC8eVSsF/Z/3XmzWCyvyWOEhemhomtANJHSMSkt85WojKiAOQ8ul6z0Aubx5ZrkGrmOC2Owy5c5G4h6u0VXz7R///pLD6BkECFtJLiSrWJiiNauNYokDs88kxYcynap94nPPEeWuvWIE8pHII2Sr4XTLN36cRbF93xJZHeIeP/dpCEY3OXL5xA0cyxGpVKjRtaqgfVIuB751mCKeP8dMi9eKLIZKNEh6xgIMy84dZXI6iBPBNCeE9ybN21w2f2N+0aL9h9A10YjReOAxoHg4kDQAMD777+/DNS+B8xm80GogYvi+GD58uXLBgu7c1YCmLUqYCmdlGuU16RkSde8nA4us6VSJQfNmhUnPH+ThHsQC0a8M5Q2TT4FBxQdNWrkY+BleHvGjPlSZAAIm/iNyEGaETrjXv8f2ZDj1PJ4ffinu1T1TKMEquzwoVTYDlCvdyJTizJAVOqjts64dw/lbd6Iwqb+QOETvqLcyJIR9s14j905Z2oo4t9xIOXo8d8GZFgTzvUp7P6QQuvWpGkiNIvHBWknNA5oHNA4oHHAKwcy/2b2Orz/ThoMhvFQ+XJy0UuQ/P2L/WS73f69/2bI2pGkdCyj6dhkRgk5TnrUumcByeic6c0hz3uSAJYo4QJDZ89m/jbjjB5NmtipTZvkINjm5Usp/PsJtGasy5GjXTtJkfq9nVMoDXpd2JNFvj4oQ2Fi2M7w+pZdlABJoLsnqgSAnsLBsC3j8eMxNGyYf72Azchxm+fJlkKNyyrqmPdGU8KTHcjKAFWpsD0jYulxif7sK3IUydHsikoUku7iRYpiuz8AbGH3F6BxCRWJ1yo1Dmgc0DgQgBzI/Js5mxYF8HcPgN/i29M5oQYei+P82TR9pqeR0jEpLfN1QAZAERFO2IyJfOvpdudgyewwwepfCLqytMgQNXKNcrISJfwnAZRjJu0R9C/ynSFkIwMt1SMLBErbtklnfTqIGziYhD0g7Po41ZdiiU0nk0qSWDK5d7IjiLInMLJTIW1ccnt/Hen/OUc68Ceu7yt0a8pPyG06gKInTiNL0+aKU+gSXWnMOA+qpW17xTY5Wini/fVKArTxffrlKDna5BoHNA5oHLgTOBA0ABDOH84GDRokvUlLlSrFuXuUdWsBeGWk5E5KyzJCIoNHToLCIDC9kuwB7JLCpdc+M+flmlIDQAY4BQs6RIxAX7yX1dASPu5zYa+2oUJfuh4bSo88Yqd771XTU6ENghfe/GUJ3Vi0IkU8P9lSf/oU5atVhUKnT5VVqvZlyzqIPbBPnNCTdMhR1TGTjVgSeX3xbxT7AewaDen/izjNIXRj4TKK/uLrTM7s/+7smBL5+kDY/f1OduQhjv5qQgopq/9n1EbUOKBxQOPA3cGBYAKAcy5cuDAHlyUvbAAHwRbwd2DCn4PhMrE6lgFQWJgzU4GSpfRQgklvaz950nVpszILiJxfAkAluho2tFPz5laKj/efGJJztoZ//RUCMBtoQcXhgozmzZNVw5IuX/YiCbyCFE936ybl7vIMGRDw2bRlY5KNn5qxWfKanhpYzTgZaWOrVVt9NySxt8IZRnjRqO+VtS0h9WPAzcA7bNZP5IR0VsT78xLWJ2sJ0kbXOKBxQOPAncWBoAGAx44dGwM18AKwn5M41sPxONSNCobLwV6qDodOZIfIDL2+eAJzzDwuDMCyuki6UoeB4Xm//joBYWASyJ/BqCNHDCOdxUJxPfrQ8h2FxPJatMgcAFTkERIPR/XpTsajR8harTpFf+mj1ylAjASAnsLBKM57l1eatm2lPE3rU643BpH+6lVKbNaCrm/YSoGaj/guv1za8jUOaBwIUg4kqVQDnX5W/27YsGEm6JzJXsCwB7wQ6DRL+qRqVErKZL2ve2lrl14sQFb/rl5tpPBwJz35pPqwKL7SI9vLdSlJAGWbjO7HjjVDjeqkjh2tkKC6Rol9622oNo20p/27dGaSXoSG4bAr/i4RAJrmdWvIfm8xujltNotwVU2hu3KFIka+S7qEeHrsrWmiz6ZN/K+WqKq/2kaGI4dJf+M6WWvVUdsloNvpEYIm4oN3KPRX/s4jYZcZM/JjsjZqEtB0a8RpHNA4oHEgGDkQ8ACwYsWKkVardRHUv5PAYKHyhep3MsLAmENDQ9vu27cvHev8nL8sEhhJFW5GKZKSNjmep3HmzHHly23b1koccDiri1yXBLr+mg/OqfT552YRT/Dpp5OBrL3SQ3Rr2kxa8YVZTMXSP387upiXLaGwmdPJGR5BN3+aQ85CLkmjqrWBmBD010N9XLb7S1S0aGNiT+hz53RUvHham8wNGwy0YqmOekTNpXLD4cqsYLfH2S9C58yCh24R4aWrg7g11yA4Q2Cu66s2kOO+0qpIC8hGcFjhUDXhX38pMqs4onJT3JtDKB4S3oBSSwck8zSiNA5oHNA4kDEOBLwK2GazjcbSTsTExEgPYMJxR9Sdio+P53MBXyQwymw4Fgm0bt3yvGQOQzdrlkk0UMqY4blnxs9ICWB6kklfZ9i/X09Wq44efthBZhfWSzHEypWu75fM2v+lGPT2DycuFkv+bn03mRhw+lKc+fMjDd1bokuuEUOobh2XetqTGnjTrH9p6vQw2vr1QQqbhBh8CsX49xGKhFQxql9vytO+FeXu/rwAmIlt2pGjWHGFHgFexYax8KzmANWcji8CeX0JiD++S3eRxk9k+EC8P61oHNA4oHFA40DWcCDgJYAOh6NRVFRUVYR9SRIBQRoYV6xYsQHh4eE7s4Yt/h1VAkAJ4DI6uuzvTQK4bZuB2AGkTBk7Pfpo1tv/8VpkjEK5zoyuL3U/zpPLhfPmpi7sVfvnnwbKm9eZJetkKVT0FxPIVvPR1FOr+s3Sq9Bpk8n05x5qUGErzaMGtHmzkZ59NtlWUQf1bcQH71LjX6/TeFpEm3O3pF6PhyqOby96L8W+OZT0//0rMnror8E2rv1TJEKi+Fv8qUiBykoAO/NiqHBXLqWoa9cRjiae4AEEyV6cCE0j9vybxbtuxQI1duzoT8j2UGW3Wu1Q44DGAY0DGgeyigMBDwDh7GHbvXt3EviTjDh//nw81MBp6uX5QNpLwCYlZRmlTY0KeMaMZOlfduEC1lgybSwBtOKK+EtwIwFg9eppAaCU/jVpYiOOkejvYn/o4cwNCZFl7IhRlPvFztRsFUsDtxODc1nMSxZRriGvi9h2tcJKASQR/WF8nGzlY0nJX9pR+n6oRYfK7oG3h+jZvGIZRXwyioyHDwn6FIS2op69tx25osgJm0pnwXsQiHswJbbroIV3CbyrqlGkcUDjwB3MgSx4dfqdW84yZcpEHT9+PIXis3Tp0rkxU4jfZ8uCAaV3rJTgZXSK9JxAWDW8dKkRgMhJTz+dLGnK6Hy+9GNwywCQwW6BAint3MaMMdPOnQYaPz7Bp7zEEgDWKHiSyFKY3PXAEgBmhfrXl3V7a2tp3pIs9epTacSwyxceDzvAMBEOiK+jacc2V2Djps3I+MlYKv2UQ0huT53SUenSKfnnbY4cPwfgZ1q/hiI+HkWmvX8KcmwPlCPj28PpRsGi5AgNF0APHkkuwBeG4JBK+vwcX4hGgMYBjQMaB+4uDgS8DSAuxzxIASc//PDDEfLSwAs4l9FonILfi2VdIO/9LQH0ZGu3YIFJxNtr2tQmPGezkycS3CoFfN6xw0AbNhjp1Cn1t9u1a8j5d0lHhQvZqcLANpS3QW2h+uQ1cVgdTq8WEuJEmJvsBbo+8RQiWPZijf5+ClWq7pLMHjzokgLGvjWcbk7+iW7NQP5d2PDVqOGScjKvgqWYtmyiPG2aUZ5nOwrwZy91H92aMJFubNxG1KUL2arXJHvFSsTSS46z6MyN2O0a+AuWy6vRqXFA48AdzgH1b+QcYkTRokU5/p8xISHhIlS+27DtgBfwRWxG2AeOyCGyfJpW2sZJkORTZ7fGsr8SyOJm0vnj+eezXzMu1dtyrW5kizAt/PvsWSXlpnvL5ON8+eD5cyKGVj45noxnTgE85CZHIUgBUdatM5LFoqN69exZkkotmYrMH9krVBS2epUqucLUsGOLKMgBZ4EDh3RfrlnTDudfJ/3zT8D/S5Jx1w7K3bGtcEZhSSY7y0SPHU/XkA85sdOzil7MmeekuhH2Xd5L/df0oUS7f0PuqJtda6VxQOOAxoHg4UDAq4A3bNjAIp72kPrVBeirCTCInXMLgkAHhQMI3wr+kgBKAKgkATxwQE979xqgYnVQo0Zpbeay+paUtMm1us9XsqRLpXnmjG/gJuzyOXp4+jBysiTt489JxIPBwCtWuG7bQFb/uq+fjx96yHVNDhxgCV9agN6+vZV4y4rcwKlpyehvw/59UPWOpJDVv4kh7PcUorjX3qCEF7rBGCPnrTEcTgcNXNePDl09QJfiLtK0FrMowpSkOMjosrV+Ggc0DmgcuCM5EPAAUHIdgZ+34Ji3oCtSKiZt+DK6AG9OIFL617mzNUucItKj2ZsEsGRJl/SLY+H5UiJHDBdx4eJf7Em2ylVEV3YyWbPGdds2axbA6t9UC00jAUx1PqCB34ljFP7J6KQAzQ6IZ+MGDKb47r2IbfsCpeh1eprafAZ1WtyOfj+/np5e8iTNajWPcodw2nCtaBzQOKBxQOOAOwd8eyO799SOVXNASsUkSFLdMVVDKWVLLQHkiBrz57tszBgA5kTxFgpGAsAzZ9SrgE2/r6eQJb+SI29eih36dtKS2JOW+cmhYQoVCh5niTJlHCIX9N9/6ykxSLST+n/OU+TgAZT3sZoC/LHnbuz/htG1Xfspvv/AgAJ/fINMnjyRDmzcR4vbr6QyecrSzv+2U4dFbehK/JWk+0c70DigcUDjgMYBFwc0AJgNd4KUAGYWAEqQJQGlJJ1VojxH3bo2uu++nAFFcm2paWMaS5RwQHvrJJtNPQA0nDhOTjgMxA4bQc58+eVSKRi8f5OIdTtA5BPidHXMg6NHA/vfTqSye2co5atVhcJm/Cji+sT1H0TXdv6FANdDyBmZDell3Hin5vDy5cv0MTyRzeYQKhJRlBY9uZIqFXiY9l/5S0gEbY7gkRarWa/WRuOAxgGNA5nlQNCogDO70JzsL502pAQvo7RwvLuICA63QsSJFPS3ccTMmS7pX3Zl/lCiX65Ngl33NhDiIQ1ajE/xARN69CZL46YpslxwlpNgBYDMj0qV7LRnj4HYXpOzmwRa0SF1Xdg34yns+29IHxtDTtxwrH6Pe/1/wos30OhlepacWESlokrRymnLIRm+QYuRWaRZsxZUMLwgLWy3lF5Y/gz1efhlMuq1R10gXj+NJo0DGgdyjgNB81TkMDDwBP4SrGqNjeleiTRxr5w8efJmzrEv/ZkZtLBULDTUiS399um1YKAVG6sXoVBYIshq1Y0bjSIQc+vWOSflSJYApl0BfDh8An8MaG1YSps2pSifW1SUgwc5n64eUk4HPfBA4AGotCtPWZNsB8iLyrlrlZIqfEicPUMhixYiF+8XpL9+XTjdJHR8Wqh7AznHcLTlFr2+YQDdiLlBeafAbRzlpZeQH/l2Ydu/RU+uILYNHDfuC1HbvXtP5MfGP45WfOIAnr3022/LaQ7yUe/Zs4sGDnyd+vbtj4/QwJZm+7RIrXHQc+DChX8QcmwdPfdcl6BfS3YsIGgAIB5AY8EQEzyAWxhQEAJmAGIBMiDsnh2MyugcixcbyW7XUdGi/gEsDAAvXHCBSnYKmT3bJf3r2NEKG7OMUpn5ft4kgL6OPmGCiY4fNyDMi43y5UtWabtL/xhUBltJ9gT2/NJctsxIv/9uwAvWQsWKJa/dn2tlwGfaupnMiOPHe8O5s0nDJzZvRbFD3iYOXxPoZfJ+xBxMvEEV/6tEB+H5W7t2XUhWH0lBNoO/mJho+vLLz8R+3Lix1APS5d69X6aCBQumaKv9SMuBG0hXOGrU+7Ro0QIhYZUt3ntvONTtJurVq6+s0vYaB3KEA3a7ndYjGP306VNp1aqV0I45EFf1USpb9oEcoSeYJg0aAAim1kY+4MrYy7diT8QEPBjIzGaHhVdecYn93nzTP5b/7p7ARYs66eefXQAwJ2L/ufNeSgBTO6i4t1FzDAGUAH+c4ze1PaMEgC1aBI70TM2aZBu2AWRbSA4F467Cl+d5v2SJkTig92OP2QEA/bNOb4CP57QjELW1zmMU3w3e1gjeHAwlxhJN3+4dL54G8ZuQRw/lpZf6K5IeCZvFOXMWIBPNFzAhWC7A4PTpU8DrVdpLQpFjyZUREZG0fPliAf4ee+xxZBjqDOerwvT99xMgZema3DADR//Ayeh//3uNDiN14A8//EhVq1bPwCjB1QUCjOAiOAio3bhxA+7FToLS8PAI6tixEz5OPCWiDIIFZSOJwQQAWWzi/t/jwD9Tzri8qrhAx4/r6MUXw+DxqaPBgxPp2Wf98zKXoWQYaLGk6MIFvbAty2mbMgkAlWwAVbArqcmff7p0vuzl6y7l++cfHe3bZ6D8+fnrLvvjHCYRmIkDjpjC3sB//22g06fTpnzTnzxBHxwZSa/QNSowOoJyLUf6NM6ZixgxSRt+OzhmTATi2yUkEtvt6WH7poOhqS76Fo6x503UY3/lMhku/peCag7cbK1bjyzYGPg5SpRMCkidomEA/5hyYBJdT7xO1a01adffO6hkyVLC9s8TySwRmD79Zzpy5DCNHPkurUYswxYdGtHq3zbSfUVLe+qmqp5DE7GTz52oDTUhsfeECZNw35al4sVLJPGjYcPGSccZPYiCDcsuBBW/jq++zp07wn7zNypX7sGMDhew/Q4dOoiPunm0A0HT9+7dg3u1JGJ+dqIOHTpRqVL3BQTdiQhNsHDhfNq+/Q9q27Y9Mixl/vpm18Ief7yB+N9v3PgJevLJTviwy49MWAELDbKLLarmCSYAuBMSv+8h3v0KK9PB9gRxKGiXqlVmc6PLl3V4oIXjwaajTp2s9NZbFr9R4C4BnD/fdflyWvrHi5PAVMkLWM3iQyG+1/9zjnbdTu5StWpKkCelf088wRkz1IwYmG3YDpABIEsBS5e+/VEAqUDoj1MoEmq1/HFxJJSvp0A/b34odwLgc2dDjDWGvtk7TlSNfPYjstayUjTALyxD3JspHj/4YHn6ZtIPVL4R0tbZb1HLhU1oROOR9HS5zsJWULGTl0pOWdi0aQRxKKZu3az46LNmexpGL+SpOnX+/DnYEbMURdluKqvAANtizkAqxClTJtEvv8zFs7IdcpmvQtQAfJAEaImNjSWr1SLsSNXcb7yMI0cOwQaVLZj4O4ujABwVHuvstc6gt1at2uJcTvxhFT+rTidN+o4u3v5QnDlzOj6SPvIoUc8JOv/99wIiXdxAJIUKaabn6/DTT3NE/dy5Rnr99VChSZk716UZSNNBq0jiQNAAQPzjDITEbxyA3zpQz9LA5XACGZS0kgA5wPsbaVDD4JyhhxrPRl98kZBCkpVZMqWt3cmTOuERy/lw2f4vp4uky5MEkPkyd64JKdyI+vRJSa8IOzJyhJBk/Vnlf1hKpIjz576mYMz+4U6/PGZPYFbxckq4tm1xI+OhG/lqfwpZu1o0ufFCb2o6ozcViYym6RMuwxs3GpI9bEiArIu5dXsfQ3r8dsKryAHk7YQkxRmVx5UuLwq/uQ4bx+1z5kF9XjhIuItTJTFBur8cd4lK5y5DVe7JTdUK1yByZQhUvZrc4blpxby1NHrX+7Th33XIHvIyTTvwA42u9ylVK4TxfCgjR4YIxyTuMmZMCH31lZnmzYuHPWLKDxgfhszWpqtWraABA/qKlysDL1bzZqacOnUSmon1AMI9BNjhsRg0RbDEWqGwZPaRR6oK+8zfflshQCCDokKFCim0zvmqWbOm0/DhbwlCGMCyFDMK/3ORkMrPnj1fHKemsi4k7W+//R7VrFkba30E6R5PAnBNQT7zjXjO5ZzaOwbPkBo1KifZdj76aG1q0KAR1jFDSAFTryO7f1vwsuB7gnm+fv1ahDmrRzPnzKNQo8usSome7dtdH4H8MaaV9DkQNAAQkr/WSP+WwuEDEsE+WOLE9JeZPS3seOa//HKoCPXx4IN2mjo1HrYI/p1bSgCnTjXjS1QHNYKV8I7P8cIhaiIjnZDE6OD0kjYdLOOP//0vFJJCZxoAGPHRBwL8xbftQPu3uWL+uUsAOYzO1q0GEUi5fn3/qNJzimHSE5glgObFCynXm68Kz1t74SIU/dU3ZG3YmK5sjqBdp/V0omIMpCHOnCI1y+blwMy/nVpOS08uEkGa36wxhJ4o1UL1fPflLk3LOqymWEgCM1oqF69Cc4otpNVnVtI7W4bSnku7qcUvjemHJ36ktmXaqxqWbXxnzjSL0Ew//BAPFZoJL3UDVakS+ODPCr31hx9+APUuK1RIqCMfeaSKqnV7asT2bf369abdu3ciX/dqGoYYnjNmTIP95WzatGk7FcY9rlRYzTxp0o9CDcxq0oMH9wEANlVqmi11djzAWFIHYUOa+UwmM90LE4qbeCix1Jk3tmXksmvXTqThbJKmD9tMDhw4WNTzc7BWrVpQqVeEHbAzCSSn7sQ8ZMksOzWxmpjp8Xdh0Mohk9jD++WXXwEYdX38DBr0OrJJ+QcanD59CuYupxC1oRwVKVJU1TqOHj2C/6vpSG7wM125ckUsOwz2M/vtf1GVH8vTiqfWUanc9ymy4/PPE4k1YlWqOBTPa5UpOeD/uyrl+Jn+hRzATwH8mfEPMBoPmGEYUNCM33yHfgjHkGKZniQTA4Am55Ur0cS2vcOHh+BBZsbDy4F8tXFZ4sXJHrLvv5/8BfTLL3Hwlg2MF07VqhF4aOmh8oiG925apj70UATUDHo6diw6SWVs/HM35WneiNiF+dqWXZRQsBhUpGzXmPwPvGCBESEnwqh5cyvUFdC1KRR+PhYokAsPDNe1UGji/yp+UUAt4WTjPpUu2Feu6KhChUgqHHqd/k1wMSmhfUeR61hI6kBlr16hxN7As2bFwxYnMK6tL8zzdC02/7ORxu76lLZe2Eyct1cWHf6lt3TeRWXylpVV2bpPtCfSxH3f0s+HZ9BvnTZQpAk2lioKS3H79DmIl9o7sI+zA8C8QI0bt4F60M9ffSpo2bZtq3jBsi1k6pL6ejCw6NOnu7C/C4UU+cMPx+Cl2VXVyzn12Kl/s6TmlVdeosuXLyWdYjDx9dffC5CZVKlwwGDq4MEDAEh1FM5mTxV7jPft2xP/o5UAYN/1OikDRaaZweAt2NyyjWSePHk99rmI/NTtf21JLR9oQe/UGCXeGZ4aP/98J2Gnyufz4Av/oYceocqVXVvr1u2EucO56LM0dOMb1Oy+lvRC+RfTXD8G+ddgo3D9+jUBwJQALd6tikDXE11xUOWE8/NOZfn666/ogw/eEa3ZGQtCG2wPwvmqHD3zzHMwl7gnzUj8ETF/vkuly1JJNk14oO6D1H5FS0qwJ9DjxRrSvDa/pllvmoHSqeD/i4IFowIeA6WzjEyd9g/MzxQJ3jvjBmWrYLZILYCNJX6y2IC9PpE/cnr//ff8FWvGP4dTvLizKoQHNHtJhTNs1K0bOACBpXvnz5PISuIevkUSzNKsixeJOCfwQw8BAODhEzn0DdIBPce89iY58GXNr0538Md9pf1f8+bZJ/3T46vVeGA/6a9ADcvb1StwqLgi9q46/L52lXRYAwdMtlV8iGxQ51jxFW2FJ62j1H2KatciB9fRvfoa9E9CEbqQ6wGK/GwIJbZ/SrJI7EePToTHaoJaTJmibyD/SLDFE4PAcGMENS3ZjFrf35ZuJt6kf2Mv5Bj4Y36FGEJoQJVXqV/lAWTQK9sRfr7rE7qEF3icNY7isQ5+4R9dcIT+O/kvXuROeK6TUFNt3rwTALCc4mUYPDhEhIRq0sRGDRrY0E6xmcdK9mBmEMCSFPfCqrL+/fsQ20mxZ+5rr70hpFTubdyPX3vtFQH+7r+/DLxvp1PFipXcT2fquCEk2Bs2/AEa+iNn9yp66qln6A1kj2EpVnqFVao5Cf5YkvfCC88AhO6Hw9lfkGYO8Aro2PaMAZ830Oe+5i/w8XP8xjEa98dJalC4BT1WrK776RTH7NCQCzfIX3/tpRMnjkOCukFsBQoUSFLPzj06m1ZBgs3bjn+30YPHytOSX3+lq1evCscavkdlOXz4FBzokjMqyXolUCjPpd5zoPV69R6ldu064Jq+hef4WdD0O2jcQ999N0URkJUoUYKeeKI57B6PoP0ZaMd2i43Hbt0aNjCUFgB269ZLSIsZ+LHzkSxz2y6ivqt60Ebk+Z5zdBY9++Dz8pS2zyAHggb9QhL4KoxnOe5fQBWWALKqt2fPUOEFOGNGPKQAWQfKOExIz55hggdDhiTCw9gSMPx48skwqGqNsNuIVRTB9+sXKnIWT54cjyDPNgpZCJuZl3qQ7b7SdH3jdryJQ9KshW0Gy5ePFIGvDxyIxRebskqUv+YyLQEEmDNDdRX2w/fYr0lDS+oKlvw58hdwgUM2cnQrDjxsGQxyWBWxf7AChX/xKYVj7Da0mJbi75xvzlHDpwJAf+9Gtz8OPV0LlrStO7uGGhRvRGFG1z2ckflYasEqMd78WViaM3BgP3r11dcBtFKqQ6v/9BCdjT7jmg6OHzQVWzQwvlFHb70xXEh/2MNz9OhPk0iad/RnvPD/psoFq1Jpc3VqUqsMbGBdNBuNToAdOzEYbNbMRvffr3xf82D84mW7s7lzZwsV4+zZv6RYO9P90UcjhTE/g0EOgdGlSzes4w0RsiX19Th16iQkcl9CkzAaZhs+otCk1aV/4Ku0KP0R1bVgyRwDG1/ujz+hiejS5Vm6dOkiPk4rQ3U9Jw3QVje7q1UMrBPYWV8Wu8NOHRa3pj8ubCE62poMi2ZQs4Zh9Hg9h9DgcHQAT7czA7n9+/cJMGizWZPUyfzh8dbGwfTjwSkIj+GkApsL0pU1l+WUwh4xL9IwMfCbMmUGYtHem3QuIwdsL9q9+wswPbIK/vL/oSy//75N0UFDnud9fHw8PpSOQQN0FFqeo/Tmm0NVOW65j7Hq9AqR3ScPgrxvhtbgnvC0ANK9vbdj5vfdLgH07xPUG7cD4FyZMmWi8GDYDOeR1sggctadJJyrinPjUcfy7Tg8PF4G4Nzn3kbpeOtWp7NRIyfsKHT02WcJ1LVr1hqfcuiXTp3C8Q/oxJdULP6pPb84lOjNyroXXwyF6tsEm584RdXlxx+baezYEBoxIgESC/AJLytOO2arWJGsjZoqkrZ+vQGqgnAYUNvgIejZqyv1S05xMA+VOrxgQ2H4HAZvRAMkf1wcMOy2NHlCpEBjkOdA0GAnHqSOAgUF6OM6EYqFG+OBaDx8kIywATLBdse4awcZ8ZJVKk6oit+qvY7GrKsFw/BEEfBZqV2w1q07u5ryheajphUbZJk6noMSjx//JVR07wAQKd83GeHfhAnjAIrehlqqEO7jtSnCnvx67BdiABtuiqAwPe7hnu8Iw//hcF56vHIDxek6L+1Ia8EPUU4T5Q2rSMXiBpL96BN0cm9J8czgc+zE9e23yqYN6/AhwhI7lu6xs8Ho0Z+IWHxK4IYlWF988ZkwmsczDpmHQjHuZEha2mT+40gswr9/Lsddpg+3vy+kV+/U/oCaQ5WppjDuuHZNB22CDqpmHaSpKT+4Z+N/mWM+1qjRAUDjWWhlKqYBg/36WXDvuPotWbIIaus+AqC0aNGavvlmkkenFU/0sTnAH38YiMNY8cYZmo4fj0l6RHA/BmwHr+6nZ4fuoEuLXksxVJEiDhH7kz/qixf37Zl+6uZJ6r7yBTp08gBFOaPoy1YT6InyLbIkFt7Jk8fx3BpCa+G0xiF76tWrj62B2Hty9Emx0HR+sGkIb95SN760qjstPP4LvVixJ42p/0U6I3o+rQFAV0o1zxy6g84A4NUCwJuIJT2gtCycm46H6ksAfVtgp9AE/6zTx/OqmQAAQABJREFU0S5lWgGFjuzJyeBv0KDELAd/PD1/KZpMTmrZ0hZQ4I9pk84onkLBlCrl+mJkD2lRIKmIH/Cq69jD399+c1kpZIX613D4EIVNnkihMDbW3Zbg2R4sT/E9X6IEqK5SPL090CeqYcRug7E2b5zDmIsOahjTbgBBAEITgKERqhwbVG0xY8dR+cPlidaR8AQWje+QPytOLaPev70IkBROh0ocIhO8uTNTGHSVznM/PVzwEVp0fAHlMkdRw+KNoW6aABXdXqHmysz4qfv27dtfxGpbsWIpbOI6iZAkDLq4PFm2Y4rm1X6pKVR/SkBMNhxU7Q2qXbQu7flvN/02fgVdv3qQrhd6iUKLhgL4vwVA8BCAQmWo1FKqdLl/TEw0vffeO5DqTRHDNWjQCAGsJ3iV4tx7bzF8hH6JsV/Dh9anyN6x0C9eprHWWPoTTjKP3fu4XFqm9jaHTXhdf7xjNN2yuNSUefHR4KksW7YEEqz94MkoAfouXrwBVfpWNG8tupw8GZ1C2rZlyyYhaTp+/BOc5w3/b/T07a0C9gTVtOtDnaWFXyMFIkun+vcfRO+8876QbolGKv6sW2egTz4JEaDPvTnHRWVTFw7+LgvfKw8VfJh+/sJCTcoVpuY0hvL/+wzUqEY4SujhPa6HvZzvCQPYKWp5hzU0GGkRV5xaSvfde3+WgD9eR+nSZSCNXkCPP+6EzaszyZZbrjEz+0vw7n9lbR+qVOBhehcfBJ7KqMc+pUIRRYidx/gjgD1/OQ0qhL5a8ZEDd40EsGzZspMB8qYA2P2Er+MGqSSAOpx/EV7G05h/Dz74YH6It+Ff8ndao4k0DHY6+Qt+woSEbLsB+abnB4yCxjQNddlZ8e67IXg5m+nTTxNIyQ2fHSD+/VdHDATV2D/hgxlf8RHiQbplSywMh5MfpqnXxV9zqlTAkIyYEVogbDLUvJs3imGceHJY8OUf3+slERjZoy4m9aQZ/M1BoGvWjITazwGpQWwGRwmsbouPL6S+a5BJBC/3IY++TR81H5kpCeAJ2Eo1nFOX7E47DXt0BE3Y+xU8hi/T+Ae+owHP9RU2Qrt3H8DHkMmvjGC1Zfv2LfFC3yMkGxzaQ2YVSICQjp0jefOlcBiU0aPfozlQ4UbfupWiayN4jf7884Kkup3/bScj/Nsu/XWJusAejTMbsKq2a9fuaaRYSZ1SHfD/DZdbt27gBZ1H3M78v7FpUywCx+uSJF+uVp7/Xoz9jzjd3o8HJ1OcLY72dDlEBcMLpumw5sxvAIf1vYbnkJ22/fsH/W/123TkFPTnN0rSIyHtqbjjcTLeLANVpRMOKSkB0DXY2Var9hBCyUCnSl2xnca2BZsdtsInEFS5OEBvouiLSlFY0sbq+LlzFyIe3EKodc+J+rCwSPD6NK5nKPpBZVrAxaj//vtXhK5hpwQ8HsRzXC2YWLjQiHh5YVAXO2CbZxMe4OwFXqoUe/jeJshtJ59Tf548SMVylUg6c+6cDipeF5BJqnQ72LlTT1u2GIltRznovxJ9vO4TN45nuT3trNkGenVQOD5GHIhvCKeMx1NKYN3IVn34+7n11B/gj+1si0UWp43PbqNIc/rmCW+9FYJoG2bE/kv0Od4uXwtNBaz6EuV8w9KlS+fGw7gWvtqMsEPYcvr06Ru+UgXp3ikAwPqpAGCKYWBv+B3+mYwAgL1SnFD48dxzTuenn0bjoaJw8i6rGjvWjACnIfhCTIRE1DfbxNWrDQinoIfBcLJkk7Op1KkTiVAoDtq5M1bxgSpZLB+sSl7AnCHDBO/EkFUrybx2FWz2ropuDrgqJ3TpTvGIWeZAOrTsKvyCLlPGZdd44kRMCulFdtHgz3nm/z0HX+4vCdXNu7VH0oCqg9SBcS9EMJD8bOdH9MXuz4R9EzetD++/vEvz0q+/LsA9NgL32OteRgBEwHvpyBE9nByUPxwOHNCL0EKp7e8uXboECXtjYbQ+dOg7UMG+KeZ5770QBEw2CAcdT2N6I4htp9j+jgMDc0YS3qrDRpSdDWTpuKgNbfrnd8Q5vJ9Ms5+g6POvIi7bvSkkSbKt0p7Vo0wnfyBK+2D+37BYcsGz1SnicM6bF4cPEGWe8JgHrxyg7/76mhYcm0dWh0tSxpLXDxEn8f48ZZOmZf5uOHSEOv/6HLzaS1KvCoOoVsFGIjxV4cKONHaNDFCazW9Ae9+eS3S9TNI48oA/DHfsSPlBdCPhOu3ctAMAuDOup8sJjEFx/foNhZdueplDeM49e3YJaagTqsWRIz+W0ynuf/qJnflMkAZa4LVswweGYrOkSrZRXr3aKOw41XwYeHtOJQ2qcDBsWAgcdlwvmXz5HFi/HWY2NrEvUuQ24lfolxVV/ZYOphVDR1LsxcJi+D59LOKZrzIQQgqS2DOanWOmIg4n2zG2uK81fdnwa/ImEZYD8Id0nToRAgzzh7SvqnMNAOJjVjIz0PcAZdXxz7wUkrn/IErXAwgWh6Su1ZEjR1gX4K+iB0D8CvNUxRzseZxumTmT4HXF9h3pNr3jG8h0cCzk4H8uX8qMGSZhP1ikSBw8GF1flGvXum7Pxo3TF+/L+eSevXhZ0mdGyi/T1s2kw8tXFmuVqpTQvTe8b6HWg50UFx/JlUNlaM80ckDoP/4wIg+qXvFljNjPeGm5Amf36JFMe4YmzMJOsxA65dV1/cXDe/Rjn1Cfyi8nXXt5LTIyvclgpKG13oGn5OP08ureQvrXrXgv6rWkK0BbGAINs0Qs7cgs4WK70fXrjQBrRpGNZ//+GEgMU/6Dcs7pbt3CxHn2uG7VygUueMRChe4RQX1btWqKe3Kp8Ko9dMiA/LcmkYUmNFRZupOWmpQ1ZrMJdlPlxNauXfuUJ2//qlGkJv0X9y8du3qc6HIHogsPUJNmifTpl1fphY7eJSIsvXr11VDY4JpENILOna3CTOT/7F0FfBTHF/5O4gYE1+DuboXi7rR40WIt2hb6p1AcWqxQKFq0FHcp7u7u7k5cL7n/e3Nccpe7Sy6ekJn8Nmuzs7Nv9na/ffI9llNWsjJ37hxC5k5binR1JM2YP1k6TEHgwSf78dU2Xd9slbboULgz+pTsj8LuOtOpYadZo9++dnnadBev6P94g509ewZTYIqxNo9NoL/VmIbBxTRQeWmQk3zdmC0he/YwwXfJANBwTNkXrOWWJgQG0mL07LF4ff01aZtqoiJlzrC1szXrJ+YV5EkgsxaCNEHksxlIU7BunjkIbQt8bdS+QXfBGij+6Ni2swEBcxWRYzvQx2wYUcIEi2NKlQpDxYq655LhcWyFYfOjtUV/ffq5tccx3+vb4Kc4fyIdnt1LS5yTSsE7ycfv2WM+6M6wbQbCLP/oCr/HLl1S0v1vQ5pwjQmh+dZ7m7H+ySJ0nKlBkQd/Yvw4JyxYYItDh1TkOxlIwVOm95Slc94gl4gGNFYcVc/32piqE9Cj+LdW9ZPbnDrVjjS2CvTqFRwrvlQrxGGp63J7YkuAgNlhMtPW1Z+XfPrq0baT+nVr53TMQ9Ik5oxcv2jRora0bxM9oHeXKFHCPG195INonX5YsnySwMqV/PjQanv1siASYj7VPnxospM3Z8qkO/bVq4jd9evrtm3ZErHN4pKGWIGOHNFqf/pJqy1cWHegDpdrKWOGVtu4sVY7jxS7T59abCIxdwwcqOvi7Nnmz/r6tW4/AZdkXTbe2Ki1GWujnXeWleYJU7wDvbV339/V/vjjj4zitH369DE50blzWm2NGsbDzsNfqpRWy/sil4AArfbbbyPq820TEmJcizSB2tc0EKGhWm3lyrq6I0ca10mINfrI1e6+t1tbZ0lDLSrMFL8pvpZRo7SiL+bO6e+v1TZrputj2rRaLQenRS78O/vmG10dAl7aJ08i19BqCThpS80rpR11YJT2lY/Bj9G0qvbDB62WtIpaIt3V5ivxTmuX95QWeXZrUWCrtmLvxVqfIB8zR1m/6f6H+9osU0m9NZq+L2gqObekNueMnFr1WLV2w40NZhti2dmOsw0/ho/j+9Nloou22+ZuZo/hjV8sIac2Ps8ohda9a09t2ny3wuUOkn3z5hYPtbjjxJMT2j339mi5T3EtgSGB2nyz8ok+Lju6R7tsmVbL1qcBnB/LijJ873Dtn6f/tFiTu7h2rVZbrFjEb6JTJ+Pqz72fa9P9lk70YeUVethTuXVLqy1fXnfM8OHG9aNbo8hobdn5ZbVdN3fVPvjwILrqRvuvXtVqFcQf5uSkpd+o0a4YrZh5paeqTeoUdLWu5KO3V9/fe/fu7SHANlW/Htc5mWcWUhtBFATCpGymn3pRnOD9+0QkH46iH0m9Syk41Bzx6lUI+X+ZRjXakDbOtXM7BAwaCv/hv4R398kTjuhzJr+cMNKw+NGxFIZNrCqHDjkL03qJEr5iW/gB+gV67qjPnoHdxnWw27YZSjLd6Qtn1ggm/qkQmoIpUo1UIvpdAJFFJ3XJm5d/eg44dSqYopyNNSXcN/bxyZrViXy2lJQ32FSDldT915+/eoY6ON3xInK45hQ+f7ydv6zd3V1IMx5/v4s0yEQ+Vm3Ih5R84zr3CD+Xvh9376rIj8uRzhsmfNzYPMa+SfRhIarwPRW5TJjAmli1yFDz++8KyuKhIW1GYPgxCoW9uJaZMwNJW2sP1lD16qW7PyO3Fd/rZdwqY1WTyrhZ+QZ+mLEQFxf3oAABJblChAhNiyHFCGvcO3VyEBplNr1yKjr2l9Vfs348PnzwIa0cKRVfOAizJdNVbd/ub0LavqfVYZ0Whn7CrykVIZvKLWl2Dh/WX7ktAjV5MP/yX8JsH5y2APy9WiFQEfvfmisy0L11CYuvLsTM89Nw+fVlcTLmbXz27rXJPaDvycUuN0ijZAM7tb3geFQqIqID2EXEXKmRtTbs6Pd4+e0lvPFYBOSi6fEXcLo4HHVz1xE5nt+9s/61QCgE/bb3x6U3F7G04Uo0ztNUnFY/FjH9bUw/NwX3PtyjVIXl0KBgBSgL+ZCbgu5K9ONs7rp423OfZ5hyYorQ0mdUZ0OtnHWMqt6+rcTPP9uJYBTewS437dqFULQ5P8d1vx++nk7bu+BDwAe0pICoelmaCvkTMQLlMwYFK9nQPRiCecfXwCfEB52LdDWroeX2GS7eu6ckS08YtjbfDVsVmbZJtJbGho+JXDirlFZrQ/6XQfSsDMalh08FwTzn9La28Fik9pKSAKAyd+7cmR4+fPiaB61w4cJZyBdQd3fGbBTDjyEAuZBu7C30hXaLmuhE023adu5Tc1ryASxLy+H1P203mfENzVNqL4b5gE3kQQ5DTkSZoaB5aI5cRvI6d05Hvlu2bGj49uPHVQgKUpAjvkYE44a3Rwuq69dgTxyCdps3QPX0SYTYS5eGX10CfPUaQkNcXuLtrd+bzMZHT3Z99aoq/Jr1XdXPS5QIFQDw8mUlgRLrXz764xNrzs7s4eNjcFLeZm67QZUYLRYsWJiiW/8Ux0Rulyk95s4NEKbcT1Z9s/Uin7BdOw35CPqje3cdh2WjRo7kbO8XHmDFNCNjx5Kdj8rkyYHCYyDyuSO3GZ/rhdIVwfZxRXC2WYDo48OHSiFTwz78+KO9AH958oRR4IO/MIcZ7tf3h7exn9rChQEUBetIgS4MKFXkdxv53lKIczCVSf/+9hStriLaDz/yW436R2SnoujmMkPwNRH0egV6kluFrq/688dmbq9yQL9SA9ClaHc89npE0Z+Z4W7vLgCquWvkc2RwMOaGs1TPsD/flx5MZOCDxSYOfrlCQJDBoFOHS+hbqprYbk07+ja3398mwJ+Ha27UzdnA5HfAbenbCyYz9fwrf4nUhsMr/KJvInz+zOcp/iBfWGK+xKTqPI+ZXLM6Z8eYKhPwy/HhFKXfDf+13o/8BND1ZehQO4qkVYugjtGjg0Qwiz7IRN/HvwmEs3tAFqes+K369PC+cxsqenx36xYiTLkjj/9PuGssujIfo6uMR/XsNbHnwV54BDaje1RFH7y66d07JdEV+RMPpq1RW9we+7EyOLME0NjVgVIwk59xGPnPBoM0zai1phpFlHujUNoiFGlNz35ZrJJAigGA5L8wkyL+TpOJdh1fGYG2r2jbWKuu0qASgbo8+lVa7qVfprkOhRhskIsxk4DeB9AcDYwdRUCqiXblY/6yOJWvMxxvKMkpXecvcv58BADUn/HAAd2tWasW/dqpqIh/ym4jgT4Cfuq7d/TVoClQUGTSCGrZCukqlkFAYqaCC+9FzBcKFNDR+XCQArsnmnM252i/XbsgogNNX9IxP+fncAS/kMy9GHhb69a6eyWm18lZafbt8yMuOAfUrUsE5Tq8J5rhDxH2x2zRIsTq6NmYnt+a+uXLh5HWzp8op4wJhrff3wrbBldQP2AMpk/VWCRKNzwHK8OXLvfG8fM+BP7SGu4SyyzjNWvUFGRhT9QrCuTOHUY0KawuiRoA6hvK5JgJPMVn4fR8RdMXi88mLbbFILOuUwPU9WhgsU5UO5j0efKZcaLKsAojYKOyiao6nvk+xW+nx4MY8NAyXxsUTFfIqP4oAlXsJ9elSHeUyljGaJ+lFU4lyb8Jpgvj0qtEX9z6cBP/3FxGRMpfYVfrA+GBFuPGBVHqyVARuGeoWda3zfyXf16YIVZn1ZqLNOSTaa4wufu8un/j1+MjBN9hhx1t4fC8PgKWryHbmrFXVb58oZ/uKdOWliyxoWQCagrYCSJ/Wd07wrAWf8Sw3y7/LnVsEi7kO9gbU89NxmCiwuFri4pH0LCt1L6cYgAgmWYXE/h7SBq7+gT8WKfflbYdTO0DmJyuX5+mztOTXxYGhTi2nCaPFxu2NvwTXZu4EJm1jjqHN165wsMJonuI0EToA0Dqpz2NNHUHw+byRVGH/4XmyImgFq0R2LINQolbj5905kBB+AHJcIGjxtkJn7Urd+9GgGHDrpYsqZMHk8wmZWEeOKZ5uUtZLaLi54ptH1+8eI4HD+6TJiACYOiXOfUZU5lQulURwMDyWrUqIN7Hm+/d5ctNicZbtNCQ5svfKmAV2+u39rjIgSysORpJWp3nvs9QuOF2vFMuJqNpkWib4xf6j2e74erbyyjn8x9yGNCRUOpYytBgT0EiOtDSuXMw0dAEpfhI9WiFEo8V1t1ZjTsfb6NwuqJkLmWPoqgLR3z3KfkdZl2cjv8d+wnrm26h+1v3DD309AC2P9iCtHZp8T8KirKmcHDOwIH2RAOkINO1bvwcHRWY/MU0kYru1MsT6LmnK1Y33iDAKQe4lCplmbWBTe672hzAXqL7qZHjyyi78AVp/Pa1PQJOUzfxNAXuuNFzO5gor8hkXau6nch+w8E0GTNG/NYNG2Tt37JlNiIQp2ZNFWm9Q+h+DArnmDWsa0glNrDsUGy7v1lobuddnoPvSg80rCqXLUgg0pvaQq0k3Ewm2fSkqXtH4I/i2EwLgcAXplsTbwu9qLTmqEcSrwfJ50xMiZA9uwu9sLUEapi3S1ccZk2H8/jRCK5aHUd+3Ym69ZyNMnuwBuwGaQSZNJWB0YMHCnpQOCNrxmA88UsPFfkhhWXIiECKnOS8uZxeLTIC4OelVTyA+k4lgzk/pDnabvbsAPK3MdVeMd9j8eLORG8QhvPnjekxEqP7bAZbcWOZoAPxIfMK+1Kd73QN2VyyR3n6mI7FggV/iewC5hpt23Y/0X9UB2uE+cOCM+Ds3+9vkdrFXBuf8zbmS+yztyeZKy8Kfzc2u3UvZhxJaTgeASGB6E5ZI/Y92UNauszY0GwbCqQrKETE+LtJE0dhFmZfyunTg9Cwoel9+TnL05pr44+TD4Ef4O7gblKdwXXllWWEVm9FozWo79HQqI7hWBh878A3xBdV/y0n8mL/XX8FmuZtLo576fsCo0+MQFUi4e5StJtRW5ZWuN21a9UYPtye+BMVpEULJbJqHVeie843FHn7pbhXNjbfLkzqltqJ63b+cHzk9RA5bIuSydZ6qOFLr46ZM23JpcNWpE5k2puffgoWiRaiotphDs3GG+rCLtQO+zseQwF33X1t6Tp4LCQPoCXpJJPtBAAvEAAsQ3PWBRt+NvAdxX56SWq6lQDQ+Ebx8HAm1T7IWd9XBDKwg4dby8awJSqWj7sP4q1HWXoguRAtRxhp/syDmr//tiGnZHv0SLMeizzbIqDTN/D9fYbOgcn4dOFrlh6s4RWS4cKiRTbCzNa7d7Awd5jrIlMysBkkNhxb5tqzZhtrHcafGi2+prk++x59mbM2OhXuKl5o0Zm0rB2LN28UQhOwm+h6Vq5c9qlrrM1V4NkzBWlH2UDBmmOdVqtRoxC6L4LNmoU+HZwqZ6wJnHxmPOZcnCkc/evmqo+ZZKpL75BeyEM/Hk9evkaXne1x+NlBZHXKho3Nt1GmlXyiDpNVHz16mDKTuJDbgQeBP3cy/ZKtOJUXTsFH7kYUsKD76GHwx1pXznqzqfkO5HTNZSQhTm839PAAvA94h+0t94Rr8phbctmyv0U+ZxcXZyK4r0QUK1XFlClTZtEG8y722dtDECEfa39WZNTRN87n1WsF9duim/OHdJ8+DkTrontFFi4cKj6envjeo3sjA1zt3Iya4Owz169fJ9ecImRaJSe7JC7M88ecljt36rTRQ4a8IL7FO0Ts/UbkbOa8zbopYv35q2fQBJHfeCYn3L/8nN5Blq0n/LuQADCJBzkup69Zs6b60KFDSfqJKgGg8QiWLOlE4E9JOTh9ItT29AC1OXVCZNngr9P8+Z2FeeLxYx+zwKZDBwfyyVJjA1qhafEH8NxBwd+Gnv3GpxRr+pdcStLGskN0s2aOlANUg40bTc2PZi4zUTYdeLIP7ba3EiChfeFOggvO0EwYXScMxyIkREMP8G3EofcXBVFMI41mCfJpVFK2GDvhEH7unC8oX71JuX6dncRthPaTueIKFQqNNgjBpJFUtuHIs0OCkDuEAOGhr0+Ga3d4POxJA9NgeSMcf35UmHxZ+5PL1YO0q6GUHWMlaYgmUPT+SyOJsek9a9aslOXCeNJtyyb26TKN8Ld40hbOGHKTfIxvUk5uOzt74rBrI3I1x7ZXDPx+/30iaej/EQCwQoVKFDjzNRo2aYLvT/YGfyTldMmFTS12GJnQ9efzD/EXAI5TFi5YMJfIyzeQNsu8mTVPnrwCCFaqVAVLfBfiQtB5/FJptAio0bcX2zmf8rffbEWGpvr1NQTsAyOey9SoF+VB37VrJ/kAbiXuzP0UeBcksutUrlyN/EPrk09sA/oQyBPb08fqOB8i7meS9Nu3b9H8Bj0nbtG43iRfaWabjLow4LN3tUf9uo0wf87iKCtLAMgf9ymkkAbwMGn7ahh2V68dNNyW2MsSABpLvEYNR/qxqojR3xecDslcqVXLkaglVCLaMnJ6N3ZyL5jXXgRGvHXJg9D9OxDmkdtcM0bb+Mec0kzA7MScN6/OZH7njm9kq7bR9SXmChPwHnq6nzJv1IJKUPvE7Ow8FjY2YWTGmUPZC+bj6adI7RYtKMdy4J/0wtF90RNfHZYuDTAhm43Z2YxrswN+bPps3ErCrLGWjV9urFFijQ7P9RNnqSAutvDtGTNmJGCcLsYd+RD4Hg8876NcZnKT+FR4PA6/2YM269qAo1IZ/GWjyND9lBVn7NhR4mXLVRnkpKGE3i9evKCPuOdE40MM99EUR4ooYYCYNWs2k3kO8tUtQEFa6qjsdtG0H3k3A5Q7d24LoHfjxvXw+evXxuCAr6N792/Rs2cfei7oNKGR2zK3zoBo9uyZBNr+EvmBmXScpw/sHEmFUw/Wql0HT3M/xQ33a8iZzgNbmu80cougTFPio2fhwnkUXXtSHMfpBBmU9urVm7TeaWn/HoqKPYETZBl5+/aNqKP/55LBBbWr10P5shVIG1eMXGOKEFWPqblZX9+auWGgGY/rrl07iAJoC5GlH6JnLfngUOFr4w+069evCSCob5fHkIFgPaLUKl++YryOJ/8OTpFyYM+eXQLsMeh79uyp/tRG84wZM9HzMh9ZjzKTDDOR9i4TzTOKZV7nyZ14aVQclmxF4d+F1ABaIaikrEK+f1vpIVmI1N856GYJvzNone1D/gQKEyc0zIIQJAA0FkyzZsxtp6aIRT+L3GFdu9oLtf6qVf5gLjLDcmTJE7QZVhQ1cAhblr5EcKMmhrstLqdEAMgXU6GCEzgR/PnzvjFOZWRRGFHs4GwHB0nD9++tf9AiXys0pyk+C2s5Zs2aRlx1f1IEqc4PtHDhUuTbOZA0fx3oVLbCR5TpGzhbhKEjd1z68cT7Mbou7Ihr567ALp093DK5wT1renr5Z4C7YzoR8ciO9JxiKg3NmQajdMayMTarxaSPDFbOnTtDL9mDNB0W+WlZ42ZtSUepCnPnziteevzi44nXWWPk5GQcVRlVm/rfxqyjc1GTQP3bB28osGOkMPnycQwwRo0aS1HOdY3kEUhfY6wVZG0YT3pgyPNXr3TrDLwYxFoqTk7OlO6uvACXFStWRpky5azWzL0jgrurVy/Tx+JVAiVXBDC5d++u0FpGPl8G8hEuUqQoXUtR3L9/l54/u0UVBm8dOnRG377fEz2OsbnWsA0eqyVLFmLGjCmUHeajMB3ycT/++DOBivRCO7Zu3WqKTt0ZDo7UjmpoCmuQqXJm/DdoP5xCnbBixTLRzvPnz0TzDEq6du1B/mvdBVjRj4XeUsEA6AExHOjB4MmTxyklpu5Yw/4xyOZxYkDI18nzfPny0++KnKajKNw+A9L3798JTd/27Vvpw/tIuAztybLy5Zd10LRpcwHwXF3dsPnGBizd9jdyvMqJw/sPGmmGWeNbmwAwA8K6deuTb5+xGTmKrhjt4ucEa0XZMsBjbFjSkkmgUKEiNBUmd4/C4ro55V9cQbDhOXhZAsAUoAGkdG8eNFYe9JBZSFNPHjguhPI19KO9/igW+YB1LcTPfwkAjeXYpYu90PBwvlHOV2muHDigIt8NhSDqzZo1QkvIOXvHl9mNmV7dMabSNvTdWtPc4Wa3RX6wmq2UDDf26KGLuGRNmJ6yISG6+cDrPlZR2rY1t//FKz+dqa92zrpENrwhXk/HAKd+/S/FQ71hw8ZE1NqfktdXIQDgLHgdObUW5w5lHq/4KhyhPHBlf/gtIMCpU2ZENM2fifyOYjNzmk8TL9NUIG9BdC3bA20LtoObHe+MW+Frv3btigB7R48eEtqfAHaI/VQ4hy2bT9lMxROTGbBfl35dqVSIdX5pv3z5ksjRjbVa+nZ4zoCAASG/GIsWLS5AAb80WSMXueh/GxcvXsfEieOwfj3RclBh37Phw38h0t+OVmtNIrfN2iP2w+JIbh1IfP4JKL4gst+7QkNnCBBZO1OsWAlKq1ZJgELWOjJIekSpG69fvyrkd/XqFQH6Ipuk+dwM6BgYMNDTAyJejqzlY+3g7Nl/ULq09QLs8HlbtGhNVD+DSF4ROgPu24YNa8k9YXy4prpBg0aU23a0kG3k62UNIYMoBoOsvdMXB3cHUkdAaA15W6lSpUnb1xfNm7cyAmn6sdADQP3xhnPWmLNWjIEvm7T5WljGkQtr7PgesLW1I/Mypb4jEMvASj/Xb+P7ybDwPVKnTn0B+mrXricAeUhoCJbfWCIIq2utrUpcfu8wt84itMrfVowJa+j27t1FeZXPhzfF4JNzMjdp0hwsM2s01qx9XL58MRYvXhh+fzMw79y5K0qXZv/wwp+AMqnnErjwWEgNYAILOb6a9/DwsCewRwbCiELaQReKAiZDWtIVCQCNZf/997pcpIsWBQhCUeO9xmvsC8Yk0Jy1IU/uMLj2/Aaltk3ELRTGgT1eKFbKsgOvcUu6r7mUZgLma5gxw5ayM9jhhx+CRKRb5OuK6zo//Nmf7yCZdPWlNHGJtS/UWTD6xwfw0bern/MXfbp0zhQRnpdjgEQ5e1YpslOQZS7eCvtZsUP+itNLgQXULD0J6jVsiPRp0+PB4wd48uQRXr98hVCN+Q8R0RFnyrriroRHntyoUbIWvihZg16q+eHhkZvcTu2N+spgztPzo9AQ6ed37nyknM1epGE5RxrPwxR1qTMV8oFs+ixbtrzIX1u9ek3SfpU1AgNGjZtZ8fX1EfQ4TJFz//69T8v3xLKnp6fJEQwm8+TJGw4IWUvEYMfV1YVMmrNJMztLgAMnJ2fKdTtIgHMnJ+s1iSYntGIDm7zPnTtLLiGnxHT+/FnK8kNIyaCwnFnbGLlkpmw+xYoVF4CR53wtHh55YgRWnzx5TP5vsynIaHk4OKtduy4GDBgizjmOyOkZtHMpR+wCo0aNIwaCypG7Ynb92bOnWLX2Hyz8Zy48n3mKfrEmjYEft8XjEbnwptg8p96+ffsJDF4TgJD9Hdk/jsGepcKg187OTtxz9vYOqFKlGoG+FvS8rW3yodBvXy+sv7NGBA4x+GucpxkWUzRy5Gug1IgUkb9X5MjW+wzy+fler1q1umi/YcMmBKwyGHWLzfas7Vu3blX4WDP45w/ERmTl4b4mduGxkAAwsaUey/NR7t+m9KU8ng6nRzbflwoVvdzcyQTM60lWJAA0Fv3IkXb0Q7fFuB630XtSVuOdkdbGj7ell5IdRo4k8GP3B979Mhe5QWz/GTS4ci1mXG+xfbBG6lKir+7bpyITlSN9QdMX+HLTlyB3iKP5Tp5U00sllL6QLZvbLHW+L9GEHHy6D20LtEP7wp1RxL2oparxsj0xxoITyX+7pyvuvLkN5VIlwp6HkVajHpng1hi9TNj8xZokBgKGE2ucbt29Ae+P3mavmV982bPnEKZWNgky4IvqZatvRKksQUC3FpFSVycftMoEvhLm8cRBD5QaU4AA1pyxlognBo2RC18LfwjwS7ZLl24YOnS40LJErpcY66wx5P7qAOFpoSVlHzg2Z+pAXolP8+Lx2sd3797h77/niykyeOZzjyCNHwMRllVMC8v27t075NrgJrSqUR0fn78Nvrf5nmZyDNYC2tjYEuCzFcsM/GICqpjyqekmIm0mwumMROJ9+OtTZmluDK+N77W9ZGpnjSj7k+qBPWu0OcK5SZNm9BvKKUziBw7sE4cyUGzWrIUAfqzxS8rCYyEBYFKOQAzOTQEfd+nHOYZ+bN/SfCrNW9H0mh6Cw2LQTLxXpT5IHkADqe7fo0D7TpTXl4Dc6X+uQFnvS4O9xostWzqQP4oaR347iGoj6mGepif6YS7atw+hAALzYMi4hYi1+HywRrSa8EuvXinIRMrasjAyr5inxZk82Zai9+yIKy+INBcU1hfDwtQUrnaugvsrhoearc6mTtaosAmOncK5sLvfhAl2pFkKJsoMbay0HGZPFmkj/dyw9PrfGHX8ZwRpiCB2Wxp4XvAUfdm+fU+M6SsY2J27fgarTqzE4UsH4P2SACHHPrAiz0C5wi9T9k1KkyYtvWDTkbnQnQI63KlSWtLyuRFoyUdRi5QX+XQWAls6EJElSxhatdJQ9GgIaa9iDtwjXXq0qywbBgQMBPWgkOdsUmxC0avDho0U2s1oG0rECtxnBjJszkyMwn6pK4lyaOHCecJ3ceDAoejYsUu8BjZEdR3J+Tm1+9F/mHZ2Mn4lLsmq2apHdRkm+xj8Mcjbvn0z+UnuIk24zv9XX5GDcjp37oYePb4VAUP67Uk5lwAwBfgA6m8QAoCXSdtXkuaT6aGxk4DfcVo+S9vK6OskxZz6IgGggeDtli9Fgx/K4iSqYPIkyrHaw7z5jZ759NXvDOfAd3ieqTTUFHXYJN8N7LhXmCJHozcfG5xSLCbnB2vkvhqu0/uPwIETJUJXkjbHh0ynhnt1y5zWqVs3BzKvhJAGI2bA2LS1uG05ffoUcRf+KHz8SpQoRdF7h8iEpqYvegcyTSqFJnPFisAEAYAfiXx38MHvsfPhNnER5W9VxNnVp4X/165dB6N08rfmqjmC+PCzA1h2fQl2P9yJMB8CbXSfOro6oWruaqiaqR7ubGqPNUsyk1+ZgkBnqKDVKFs2Aty9fKkgvzM1+dnZkFwizFpct00bDfnqhRLw0OU7DQtThC/zfaDfzvcyu0XEn7lcy5oOusd8wk3y1shD1ol/CaTU51RMJMHm/MOHD4oo48ePHwnfy6+/7hCjwKWYnC+2dSUAJNN9bIWXBMex4wg7hT0gDWBxmh8h7JU4n41JcLEp8ZRK0j44jx6B8SiH2jiAGX/Yo30HP7Ncf5wDN8Bfiz3OnQT4861RHwfPFiKHeC35TNFbN5UUfggVK0a0K4eUpLVRoXp1U8CsTwl35UoEoIgsnn2UpskryAutC3wVeVec15nLjM08bO5hjRKXAgUKCrPZvHl2QvMXEqIQJurx4w3UZnE+c0QDp16cQN99PUXaMyax7RjaBTNXTxP+TYsXr4wz+OMzMX1MLQqM4YkzMKyiSOm9j3fh4psL2Hs4EHu3NSXNYBaqGIyibbagVz8vZM9dg47MHN7RLFm0lKA+REw3byopwID4LDfYkP+Wisi+LY9feAOfFnLlCiM/K38Ct4QM41hiY9aM4ynl4alYAuzTWb9+QzGlYjGkiEtPSQCQNX4bKGrrB/Ix2Jc/f/6i9GBLWnVIihjiROokmQVdvusNJfmFVO6UE9UeaXDsmBpLl9oQBUPk0EwQ7YkKIzABVX33IDRrNuztuhT+hxUUGRifmo9EuvY4nqZ48VACgGrSqinNAkA2qXI6pMePleSLRsGsBoEUnG5p9IlfSGv1NxzVTqiStRqyOFv2vWQn7suXL5DZsoLgzLKm6xzxyA7cXJhWZcCAwWRS7IMhQ1xEvxm0DxkSRIEswWRK07XI2Sk8Az/CjwI1OFjDX+MXPtdt8xP+Rv4afwSS31GgJpDW/WkKRGBoxDpv5zoX35wH8xPWzFELvd37o1u7juJE06bNstppX9czEAk5SBumIDlqhSzJZcmksAyHlPsJPfL/hBGjFFi7SufLZ+dxAUGNOuF6xpsYdIQOo6lQusKonq0GqmWvgcpZqiCNPYcZM71KGJntg0ljGkxRnSqivVATnxxH/dKXLE0M/vXLPFepONsDROYGznn8zTcOBB79o+NAF+eS/6QEpASkBGIqgRQDAMnU+wNF/Za7d+/efQKC/elC65JfToeYXrCsnzAScCCAYEvUBaE5PeA3diKGXQ8WAHDWLFvy/QgxSSb/8O+j+BO/IlSphvfCpdi3Q2f7rFXLVAOWMD1OPq2yBpCLzmRoCpYZFJQoodMSshbwiy90Mrrw+hw4eo8pXhj8ja82GRnsMwoOPuboyp+/gMlFHjy4jwBcX7E9d+48IlqRI1WZSoSpOcyVli3bkPnGWfB+seM2O3nv2aMS4C9btjDMmuMD13yXsfzWOXCfzr8+i/ue98w1FettarpPfqk0Bi0ytkKD+rVEJCFHcrJpyZrCaaV271ZTv9UUUKMivzMSKhWFQgcCmZTacEqXTkvXrCV/MRuiq1BS1KRW+GB265YfL/zX4cjTQzhCKdU4+8atDzfFtPDqPGLWp/zNGUqiGuVurUZ+VJUIEDrbulAEZqiYLPWVne+fej/BE59HKPjqI7wHdKacvPY0VvYUxRoogKKlY+V2KQEpASmB2EhA9xSMzZHJ4BgCgt8SMFyQlF2RPoA66SvJ18NlYD/4/TwKmk9Aon17B4oOU5MGJAiDBgVHDBNxVblWrgi7p/fhPXoigvp9R3xSugwiURFIRzRgusQgKTb0CqYtJf6We/cUBA6cRbqzI0eMKTL0vZkwwZYCY+yIpiKQuMxCMPn0OMy8MB2h2lCUy1QBc+osQOjbUAHuzp07I4IzOCgisvmPKRyWLFkkCIqZk8uwXLt2L9rIS7rfRaJ7BnorD1+At+sZ3PhwibR2xsp4Z1tnuNmmIWDqCAcbRzF3FHMnkSKLtzvaOMFBTZkWuI7aHvZi2QH2Kt5msE7bMztlpiTv9hRB2OATx2ATuo5/BBg1vAb9MimkKahGGQ76bt2KML86OGiJUDlMpCNkjZy/f9SPwVq1NJgyJdAsUTdrJa+/v4Zjz45QmrUjOPnyBHyCScX4qagUKpQi2h0GhOxYb6u0JZD3GI+8H4LJqx97PxLz1/7GnH923oVhs/g8fD0dMHhwkMiBrG8zpvOU/NuI6bUm9/pyLJLPCPFYyCjg5DMeZntC9C/1SOOwlHa+pejDJvfv339auHDhYmQKXkAvo+IEAF3MHphIGyUANBA0e7Lzr+pTYZ6/unWdROYHzvlKLAmiOMyZBecxvyCkdBl4/ncAz1+qiATUmQBcGGnB/GKl7UjJD1YGK3nzMlEycPDSNTzwu4ZnPk9QyL0IvsheU8js4UMF8c9x4EGY8Kn8ck1V3P54Ez+W+xn9Sg7A3wsXEJntOKEZY83ezJlzozSNMpBjKhQGizwxT9fIkWNEpgbdKBn/5/orbizF1HOTw4mk9TVYO1fMvTjKZConpnKZKfND3lLEjecbb0EHTNjbvXtnkWKLtZXbtu02ySjBkchHjqgF6Nu7VyUCa/R9zJQpjDIdaMgvSSPM7MQnHF5Y7p6eCmGeZRnrJwaHnKqwQQON4W0dfpy5Bc60wpQax54fFYDw9MuTwoRtrq7hNldbN5GbN6drLiiJIHrb/c3A00pQLj+CsBAbGs8Aio6PnW9sSv5tGMooKZbZV5nlFxv6JXP9lWNhTipJs43HQgLApJG91WclLd8lqryCNBk56SXEJuszNM2naSfRBwx68ODBE1pOsiIBYNSi79bNnhKN2wgfseHDg6F48wbpKpeBkghiP+7YCw3RiPzzD++3R9u2IZgzx1iTFHXrEXtT+oO1QUM7XDhPaZ16VQCynRUX1qVId0yt+UfERRosrb61EkXdi8HJx4moV/qSufC00PZ9+21f0haNMiF6NTg0xoucX3bIwQHh0bc5XHKi7CewVyZjeTJ5lhCaPH3DCTEWEyeOxR9/TKUHdkYReZwtW3YBLq9fV1KaLjVNKqJgUVFe04gPkKJFOSuJRgA4NqGzn11iF/aF5CASoR18cVyAu5yuHgLs5SKwl4uWc7rkCvcb1Pdvy72NGHTwO/hdbAisX0u+lVqsWRNg1kdUf4yleUKMh6VzfU7bN25Uk7bdXrgLNGwYInxcixfXuWvE9jrlWMRWcvF/HI9FageAKcEH0IG0fNNo+FUEBu/SvBmBwa8oA8jW+L8lZIvxLYGffgomrY1akEP37BkCD9JSMfgLbP2VAH98vv37dea52rVjp+GI7z4ndnv7H+/BbVvmACQ+sjfl0KRGDgIFHqiUtbLFrrQrpAuCOHX/hNDg6bR+f5HWr4rFY2Kz48jTw+i7pzfeBr2gLAEZ8Getuaidq15smrL6GNY2cuaDe/fuCM0kZxZZsWKpyGrw55+rCeh5CNB36JBK+OfpG7az0wr6FL2mL3v2uEfQ6tuO7dxWZYuKWSqJKSZtcI7mwumKojtFyd/5OBya/ZPR+RvyYdylpQhs60CIH91Sf/9tS9pSNfmNglLwKYioOOllEhM5JFXdxYtt6EPKTnA6OjtrKSLbRkzxBQST6rrkeaUEDCUQ8blsuDUZLRPou0gAsDR3iZafkSmoPgWCXE8uXZQawOhHok8fe2zcaIPvvnqGWetyckJPfDhBUZ0U/UuJAcj3zVkQCd+44UeRqbF7QaXkL+vuuzpj+7oMwLaFaNXhHeb9YRe9UA1q7NixzWx6J4MqMV5kzdXkMxMw+wJpIClQopRTPfzT9i/KEpAx2rY4h+2HDy/h5RUgshEw+79ushFzJlXWb2POMAZ6nNXi7t3bAvDxeuRsDXzSXLn+IaLjDuKlrO9E/vyh4MAh5s2rVCmUNJ/6PZ/H3DfEF0MOfI/N0xsAF3vCLdMHHNunJiBn+dHN6YeXLbOhLDu2RmZw9n3s3j2EtFrBsf6dfR5StXwV7MXC6RknT7YjjbEWU6cGCf7NhQttxUesl5dO7rEFgin5OWVZailzj9QA0qM9uQ9dJAB4icBgqeTU59QKAO3WrkJQ42agUMloh+P+fQWqVXOCTVgwHmg94DqsB/yH6hK4cERm8+aOREsSKnjPom3MQoWU/GB97fcKS3ZfxvQ+bQSX3s6dxoEgnILs/v17RCKcsCnc9KJ9QBG8ffb2wKW3F4kImVJLHf4dW0f0IP9AU3DOadYuX75E00UwXyAvMwCMa0mTJjtlhyhMILIQJbgvQs2xZrMEZfrQ8UR++aUO9OXIYdqnuJ47uR3PGtEFFxdiVL9S0D6oDdc8N3FklyOyptFFzuv7S7FVwp3ijz9sKf2dzt5ds6aGctMGY98+RwKFWiKbVojo5t69g4meKTjcL1ffRmqeMxH3r7/qUlna2GhF9HXTphFWCS8vUAaRuAHBlPyc+tzuDQkAUwAAJL6/q6QtqEeaPwVNu/XL+puRTMEv9MtJMU+NANCWSIHdunfSBXFQBgZrPOSHtHqFf47lR3+nJfj1RmOhBeTx0ucD/uEHygdM5uLYlpT+YGWtTZ48zmTmBIE9TqMUihMnjlFWifVkwttCGjMVAaw7CZoyi4EGkx//7+hPgrcPb4rAbvu/WDc7v9CuBRPCOHhwPy5ePC/A3pUrl/HmzWuTIctKmt3ixYtRtgxdmi9OHUf+uqTtDaFtGlrWrfMyawM9PPIRKCkkwN69e0Xx8CEDvogcukyEzRo+1vTxh4JNKqV/P3jnPDq2zgLN64JwKLEDm/5xRpnMZUiewNq1akybZkdp33TAr0oVDdjnlrWi+t/G6dO++P13O5GphNPVuboyaXUwmYaDTWiaTAb1M9/AMmQ/5NWrbQTlz5IlAXTPmaeksgQE2cWlQoVQ8Ru2JC79WMisLJYklHjbeSxSuw9gStAA0ncZZbs2D1a1pBGM4HdIvHsn/EypDQAqiEg4XY2KUH74AO+/FiKozdfhsrC4QOjGu2ILFH51iNlucepMQDilRq1ajoL/7r///OjlzkMdu5KcHqxM7fGKtHoZHNILvzkXW1dBYvzI+wHypslv8QK/+MKR0sGpiNtuLHHszTXSpHFy9TlzFlGEag6Rcq1FiwjNhMUGY7CDSZt/ODwIW+9v0h11ph9sDk3ByqUgih4NRdb+R9qR/xE4e2DUKgdjcEq4kiV1U4kSpYlKJoNVlDzHjqmwYIENjh5VU+7QiEcRk17XrBkK9gnleYYMn7+Wz0ioUaxcuv0RjRs5I8QnHZRVp6FJlVy4sq4FRXTr3LnLlQsl4BckgkX4N8El8m+DM5RMmWJL2V10SJrlzWZhNg/Hhwn98WOF8D3kAJ3WrUMouItz/er6khz/kxcCpTK0Fz5+bm5a/PuvP9EoRf8sMgcE2cxesWKoyGbEfJ3M8WkYfBR5LGIqD3aZefhQSXmnleQuoSSXCKX4KGrVypRrNaZtW1PfK8gTE06Nwf4ne1E5a1V0KNRZzCPTTVnTVlLX4bGQADCpRyGFnz9VAUDSELl2bAu7fXsQ2LwVfBYssUr75zj9dzhNHo++mdZj3uvWlHw9mPxsggjgEGlucWeR5eL6dT/GhrEucX2wxvrEZg78/cxEQZei38Xcb8yBp1ba4Hj7s0hrb2y609fr189e5JAF2tGmNSSbkmAS5pYtW4OBFpuo8ufX+UuyltA5QkmmbyLG80deDwn0bcaSawtFmrU0tungv3oxQq83I549BuqXiXvwfwTSDom2Gew1btxUAD4Ge+nTpzc5Z3Rj8fEjMHq0PVat0qEC9rVi8M98ezxxxG5c7gWTDn1mG86dA5q2sEdocASqypb/NSb96oj6dU1/kpbGgzPP/PabnSDHZhFlyBCGr77SENdiCEqVCrNGsR8uWfad42wn8+fbYNcutTA163dyWjvOFMNAUJ8pRr8vqedMHcQZV/gjJGPGMNKkBpCrRfTgz7DfDASXLbOlVIkqkeFITzLOdRhcV6sWKgA5p7jMnZvzMrtEmZeZZckWgUePdCBPD/YY8HG+bcP29f1gonIGgZ06hRClVszGTt8Gz/m8lMnNZOzZOrD9wRZhHYjMWenhmhsclPZ1wQ7I5pLdsLlkvSwBII1zsh6hFNC51AQA7ZcvgcsPAxGaKTM+HjkFbVrzQMZw2JQvXwjaF36y3Fx1EmW7VhCBH8eP+wnajoEDHcSDi7MdxKVYesnFpc2ojvUN9iHA9BwF0xUyqcYULZvursfzJ8/x9tkbeAd7ITQsFOkDM6B8WAU8vfuUIqP30YOWnrQG5a+/bAQwKl/+IAFkF5Fv12C3WGzZ0gHHj6uxdau/MO9F3m/N+kOvB4JnjoEfc9bpyxfZv8Ts2vPw6Gp2Csh4SynJxlAmjGX0Mg9D5sxZKPfvr/QSb0cajaj5VCyNBb/YOB3aiBF2IjiBoyt//pmBQYhRejt9f+TcsgS2b1eT6dYOtpkewb/aUIqk2ozcafJgeIVfwBHEzCWoL5bGQ7//3DkdEDx8OIIUImfOMAp+iB4MMocij+mCBbZE0K37gmMw0r59CGnRQjF3ri35heq2e3jogGCbNtYDQR8fiIwze/eqKVhISeZVLeV/hpizu0TkZaU6GI6uwSiSzwF8Pp4suSmTEYP66UguDSrKJR2Gdev8BUDTyyU2cwaU7NfMfJRHjqhEDmjDdnLkCEPWrErSeIeSbyuI+1Mh+D/1yzw3pDIyPJaX7e21yJcvTESCMzdh5sxh9CyxEeCT/Tu5FCkSKrIvtWkTEqWPJ/8e795ViucwUyjxxOkmefyyZw+jj07d3CWDJ474LMO1kO2A2xPULFoQP1cejuMvjmHVzRW463lHnJez4HCqxvaFOqFB7sZE7G78fBOVIv3jNJEP3r7GNbp3HDSZEeTnKAjaOdiGJ07b6O2tX1aID4iCBUMFHyoDdeZFjW2+bP5dSA1gpAGRqzGTQGoBgMqHD5DuyypQ+PvDc/VGhNSqY5WgXPr1gv36NQjo2AW+M2YLJ2t+KbBpiP1utmyxwezZAULzYFWDFipF95KzcFisN488NhyLDs1HXe/6+HvCCrO+edNJ8zmZNJ/myq5dB0xIl48eVZFcHEljoKGoafoUN1PYSZ3lN358IAEAsgdZWThd3LZ7m4W27+q7y+FHMbVL4zzN0CxvC5GpIiQ4hBzd5xEAnQIfouthkNq//0AyEQ6iF2n0AT/csLmxePZMgWHD7OlFpQMZDRpQNpPJQfQypLeQLLGSAIMNNjkefXFQmOUuc9AOleLpS2JEpV/xZY7aNBb0WqYXnTVZcm7cUNKHhVr8JlnTpC96MNi8eQhpfnXapbdvFSLSeMkSG6Ls0dVlcNOzZzA6dIgAHgwyOG3glCl25DeqA4KchYU1gq1bmweCDx4oxH3CaftYqxgVINL3Mao5k4AzEGTtm24eJuhwhg2zI1OqikBEqOBYzJw5/u/FN28UlBJTRRpGHSjU+2ha6q+trQ7gMtBjGiOm/OGJQQ/Pc+bUmtWOv3ypEBr1lSttwv1AuY1mzTRo3zEAth5n4W6TFe8e5CSgpybeUCXOnFFRtH7EOHOf2C+U7ys9mDTXT06fyMFXfD906hSM9w5nhO/wprsb4BtCiJ1KGrs0aF3gK9T3aATfYF+89n+Jl74v8Yrm7B7z2u8lXjyzhc+xLpSypychYTdzp7JqG2uuGQgyIGTwy8uFCoVF6YvJDfPvQgJAq0QsK1mSQGoBgJRiAg5LF0FBn83+/xtlSRxG29XnziBtozoIc3bBh1MXoc2YkTQ/Cop0dRKmBs7GwGm4rl3zJfNL3B6+1r7kjDoYy5Wrby+jzuovKCSQnFNfaTFmzESKqPzOpDUO3tiyZaPYTvcJsmTJ8slfrjR9xeenB7mxzZt524oVcxayuXjRj+qbymTDBjWdy0FozaIjzX7p+wJrb6/CFvLru/buSnj/MjhkJNDXFM3ytUTlLFWhUqqIWkVLmoTtpIEcQVqAR6Juq1ZtKf/taHoR5Qg/1poFw7FgkJ/b0+4AADp5SURBVM+cahMn2gk/PzazTZoUhCZNrM+uYc05U3sdHj820U08PTY8D3PVrNUFECyfpYJVAFAvQwZtejC4dauNMDvq9zEYLFYslKKK1aTB0mmcKlXSiI8RzphiycTLbe7erQOCek1hnjw6IMiaxnPnVMIUze0agk8GMdWrh1JGIY3wdeN26FEkzs3aR14+++wS1t/cgjfe5FugIa2TP1EqfciLzMHVEPQ2O/nNGoMc/bXwnIOK2OcvbVrDrQmzzH1//lxBwMSZfgu+Qnup12DynCf+7cSlULwVDh9WiWhwNsWHm4vdHgO+mSi2zFgrV6BAqPBZ5OAV9l3MlYsDt6iNG7cwauc83HtEQvbKifzKusgSWgmvX9gKgGmYPpEjzbt0CUH1Wt7Y/XQL2AJyjNIimi0kAzyuTv4Cg4DbzekBSs9ARRic811AiNMjBKnfkJrT02Ai+/qndTunQBRxLY9WbmPw6mEGcY+yP6s+4t3wfHxdR4/6RylPCQBJ9IZCk8sxl0CqAYAxFU1YGNI0qg2bC5TPdNQ4BHw3MLyFSZNsScOk47rjCM+9e41pT8IrxmDBEHTwgzahCptyG2+sgwvLz9NDjM0txcjn6YCJOTe25x80yI5eSLZkJg3CwIGmUdH6vMGsEeAHXOTC/dv/ZI9I27b38W4RfMJ1MjpmQhOh6WtJpMSVBehj0+6tWzdJy3JCANWTJ4+L5sqWLYdx4yYTUK8QuXmr1vVjceSIn4isPH9eB3RZWzBqVJA091olxdhV4lR0/AKecnYSXvq9EI00yt0Ek+tPRBaVh8ieEpOW+bfEwRxMJs3a+gcPdGCKaVJatmTgFyx8Nq1tk9tjYMJBKNeu6e4LlUpL0eERr6Js2cJQp45GpO6rWtUytyO7L/x6fASZIo+K05fIUApjqkzAU0qj+MOhgQgm2qkmeZpjfNn5ePPcWQRPsF8dB1E8eqRL8zdmTFC8+NJae/3630ZiRAFfePAMnSbsxLvjRNf1IT8F4NHzJAs5kOY8pptynEQJj+yonq2GSDnJzwUt/fG9M//yHJFnPB8FrU2rOUsEeuivkceQU+StWGFDPpM2wkTL+1jT2rGjzg9R4/IAq2+vxMXX50UgXAbb7Hh9pibObKpMLjA61yEnpzCR3pC1xnny6B7anEf7EQXRsW8yB9PxnPNm8/Tc56noUybHzPi38TrKQFRSdIlN+TdvsrldKaYbN1TUXhhZlgIlANQPmoV5xK/OQgW5OWoJSABoXj7ME+j6XW9oKC/txyOn2WknvKInfeCVK+csHhxxTXSvbzSxHqxLr/2NnxYNBv5lfxwH0oQcIdNMQX034jxnP5ymTR3FA+zkST+TBxhhNpE3mJ21ORBEb5V97vMMK28ux7/kk/PC77noh6PaCa3yt0Hbgu1QIXMlsHn30qULZAI6KaZTp06TuYe+sD8Vpm/hfMAceBKdn5/+GHNz1szMn+9CJl7WJijEtUybFgh+mcuSOBII0ARg8dWFmHlhKjwpcpMLm/qHlPuJTMQlYtUJPRi8dk0pKFLiklWE2/rvPx0QZG1juXK6XM0M/NiUx79nS+UF+d6ypnPd7dUCsGR1yob/VRqFNgW+Dvd9PPPyNLru6oB3AW+FSXx5w1XJIkAhsZ5TuutvT9f/DmUylMe4IutRNHcaPAu8g6PPD+HIs8OUnvCo8E/Wy5mD1VztXMUxvDygzBAMLDsUdqqIZ7e+rn5OHkH0YaDG8uW2IgCGt3NQV+3aoaQVDBYuAwwU2VXg3TvdxwNrkfWuAq6u+pain/uF+KHvvp7Y9XAHnGycsaje0jhlJeKxkCbg6OUua0QhAQkAzQiHnEg436+KCIG9lq9GcINGJpU4/y8T1q5d6x/+9WdSKQYbEuPB+sb/DSrNKw3fWeTnQg++qVNn0kOuWwx6GX1VfjFWqeIkzGCWAj127FCTeZj8XYoRL9/z3fjnxlJByxCmJXRIpWSG0uhcpCsaZm+MS2cukFP6CQH4mKyZufwiCr9li9HDuhKZlCtRztzG5ADuGLE7FksMDvr3txdf5Jy/lulFhgwJJrAci8bkIXGWANN2sDZn4dV58ArSgf0G5Jc1tNwwlMxYOs7tW2qAQdrhpwdhoyJePfoQ4Sh4njvZfFqmuVhWOyJUo7SKJoazosy+MANzL88GA1xub0CZwehT8jvRfuS+PCONUeed7XD9/VWiZMqIZQ3/RbnM1mm1GTixJnUNTUGhQZSWsYpwl+A5R73GlvYkMZ5TG+6spTzS/UW/W+ZrjT9q/WWUq1svJ9YWsxb1KIHBI88O4cyrU7przVIFU2vMRIF0Mfuw5ajy5cttBJOBIa2T/nzMTdmrV4jIzR3J+0VfJdo5WzhGHf9Z3M8qhQq/fTEdXYrG7hksAaA0AUd7w0VXQQJAUwk5ThoLpxlTEVzjS3it3Rx3xxbTU5hsSYwH660PN9G8dwN8PPyRfNiaE9fZ8li/CEwuwGADp/AaP95ORFLOnGkcHf2KnKcv00P7zMtTWHdnNTlUvxRH8hdx6/xfoXHGJnh+4Tn5W+0kX6CD5CMVcbwdaWFLly5LU2Xy96tBvn7VyL/KhUzOAYYKWoOeWL/Ivkdz59qIFFrsG1aasMWMGX5Cm2N9K7JmQkiAfxtq51BMPjgF8y7/RUBQpxGsm6u+AIJlMpWLt9N+CHyPmeenY/G1BQJMWNOwg9pBaJnsP8116/YiitROZU/gxZ722+PUyxN44/9aaPk6Fu6CnyqMQCZybYiqMGj8bl9v7Hy4TZyDzZlfFWxv9hB6lotzLLv+N7bf3ypMyOYqZnbKQmCwCoHCqsI0WiBtwXDNo7n6htsS8jnF/f/97ERMO/ebOCWD/J/K/8/qZxSD6pcE3D3c8lh9PYbXpl+m738iG7cRQUIcuc0+nuwqULy47gNVXy8uc/6oGXX8f0IDPLDMUPxccWSM+ywBoASAcbkHxbGfNQBk1lEbmxjJSPn4EdJVK0/O2Bp8PHgCoYUKx+j42FZOyAerYZ/8Avzwx6yp6N97APmypTXcFW/LzI9YqpQTbInyYsb2jbjjfxZX3lzCFYre5RegYSmdoQzqOjRA6O0wHNq3j8ww5OPzqXC+3SpVqhMpbU1y8K4suPuUSnuK0nQQNBVFi4YKOhkXF/0RsZs/farA99/bU+YStTD/fP99sMg44e3tE2Ofs9j1QB4VlQQMfxveQd5YdGU+AcHZ+BhEQRNUauWsI4Bg+cwVo2omyn1snltA4HL2pZlgPy7WzjSnICNnG1eRVYb3+/Ok8ac5TRpe94fYTst67XWUJ6GdTDMymvz8irhbnxaR2/6d8lpPPz9FNP996cEiOEZPlcMk6BwstfzGEtz5eFvUYRNok7zN8U3R7sjklBmnX5zEyZfHcfLFceGbZtjPdMTrWZEAIYPRwNBABGkCEfBprl8PJC1iIAEszrFdPHMxNM7VHA3JN5NJ4uOjMHgbdKAfNt3bAO77H7XmCJN4fLSdXNvYdn8L+u/rJWTOri4za82N0lwd+TokAJQAMPI9EeP1zxYAchBHvZrQlCgJvzEToHWJ/kGleP8eaVo1hvrmDQT0+Ba+k6bGWJ6xPcDwJcdm1JRY2Lyx8OpcYTo7MmUoQm7WB5p1B8osCb+cbM7ZUcipCNzfu0Nzm6InD5+lbACPwve7uaUhB/p6ZGZpRH5alDfW1S18H/sPMtn0xo02gvdsxw5/QYcRXiGGCyzn9evVlHnCnihjFKJNdryuXJk4D9NHTXYbw1PJ6nGQgLnfBvNYLr62CHMvzcJ70tpxYR7IFsQhWCVbNeR2zWOV5ogBzQpyQZh+7ne8DaAITirN87bC8Iojosx6Iyoa/AsJDSGNIQEnAk88D6Q5gycGTWyC5Xk6e3eUzlTW4KiYLW68u45AUn/RLpvBvy3ZT5h5t97bJLZxa3nc8pL7RDdBbOzu4G72BGzeZm3kyRcncIoA4e2Pt8zWi24j+9bVIS0sgxees+YzNoVdU775rz3Ovz5LARfpsaTBvwRIK8WmqRR3zNlXp9GFzPx8D3NmkqUNVlok2o98cRIASgAY+Z6I8frnCgDtNq6Da58e0OQvQCZPCne1xO/wSWKKjx8I/DWF+vpVkSPYa/1Wq0BjjAVu4QBzLzkLVZPlZjbf/Hz0B3opE7cMlxstKcHrRqTJdw5N+8yBgni+Ptz/gFvXbhAh7l1dnU//c+XyIGfmZmjRoiG6datglpOQq754oUD9+o6C5mH7dn8KJok9UuZsHj/9ZC8iQ7ntdu1CMGFCIFibmNLHgq/ncypRjQebSJddW4w5pLnjgAl9yeKUFVWyVhPckOYAIWvVmOx88pnx4RqxGgQgmX+wVMYy+maS3fzC63MEljoQL92r8L6plWo0yt1UaPuqZqseY1Mi+wuySwZz4BmbsyPM10yKbE9mbKZcuuR1GssurMCBJ/sQEkZWFirswsGgtGX+1qTlrA1bFXHCWFFuvr+BTju/EpHPBdMWwj+N1yKXq4cVR34+VZjjtMP2NuA5Ry3/23g9mbFzR3uBEgBKABjtTRJdhc8SAJLpNy2ZcdUPH8CL/NyCm7aIUgwKz49wa9McNlcuIaREKXit3wJtAplHLXUkqpecpWOS0/aZ56dhwukxcPByRFvbdvB54IutG28RPcZ16qZx9Cxr+UqWLE2E0dVF4IafXxE0bOgsLoczMDDViru7eXD35AkByQ9sYo69Pw7zjA0YYI+XL5Ui1dXUqTpeP708U/pY6K/jc5lbMx5sjt3xYKuIDGVaFabgMCyGgNCNSH5Z48fBFVxKE+AbUWm0oBIxPCa5LjM/5rd7u+Etac46FO5M2r5ORJOUMVG6azgWHwM+km/idgGkjz4/HG4GZxJljthmkzKbyHWTrzCf65f12/kamO6GTeOL6i2jKN4IjX+iXFAyOcn7gPfo8l87sEaQye3/abQG0fm28ljIKOBkMoAptRufIwC0X7EULkMHIIRAhueeQzqVjoUBUnh5wq0tgb9LFxFSrAS8NpDmz4oUcRaai/VmwwdrfJmAWSvHf3pfoVh3LpoDOdpwwIG+UFHKrLAtdEa21YYXR8qWUYocqUtTYEUZAm5lKKOBsXmOiVuZbHnSJB3Zctq0Wsq4EkhaOQ355IU3FOcFznk6daodUbzotBOct5eDVCLTgSTEWMS586m4gdiMB3PpnXh+DCco3Ren/HoSCRCyOFnb8nPFUYJfMrZRsaltWCyNBZtxt1Fqxs3kw3f65UmrxcK+lt2K9cTYqpMo17guy47VB39mFdkP8vv9fSjb0SYBAq91vRvls5vHQgLAz+wmSOzL+ewAIBHMpatUGirK4RtdyjcFpQpz+6oFbCjwQFO4KDw3bofW3bzfTEKPi6UHa+TzBgYH4oPPe2R1zxZ5l8n6okPzMOHnsZg1Yy6aVmpusj8+Nuwjsmamqgg9TVq+nboWO3ToLII2XF3Lkkm3LAEsBeUr9YvOCk8aOYXIs7t9uy5wp2JFDdEyBMQpywFj0RMnVBQpbAPOPxsYqCBKFwaYQejePUSYeyPLwdqxiHycXE8YCcTHeBgCQtYOMudeu0IdUz3oiOmIWTMWzOm55/EuhJB/JZuG9dQ5+mX93JEodHiZqXZk0UmAXRP+uvQnaU19MYyixKMqPBYSAEYlIbkvWgl8bgDQfuVyuAz+DsGVq8JrMyES/pWYKQpfH7h93Qo2Z09DU7AQgb8d0GbIYKZm4myy5sG67uBqDOjbF90G9sTEvrqIwMi98w3whbODMz74vUexLwpA8zQEX7aujTVzN0WuGud1dtpuvaUp/A/7A3uZQFVJ3IhzSHPXMbxt9tnjZPUrV/pTOixjU3B4pUgLe/eq8PPP9iKh+6ZNAZaGMNJRxqucsmr1ahuRX/TJE50akXn96tXT4H//CxZ5SY2PiFizZiwiasulhJaAHI+ElrD17cuxsF5WCV2TxyK1A8DUrTNO6DssBbYfSOBDS6y9oWRmtIgcKGmta4e2OvBHQSKeG0jzl4TgzxoxD5jaF6unryR6GsDdzbyW8vGrRyhfqgRs3G2gslcJ8OeYxRGLp/5jzSliVOe+513huOy/l8DfYY6xUROP3iJKsN7KqB0OrmAAuGqVjdUAkIFi1ap+8PJSWBxCo5N8WuEMHpymi7V9hw5xfmAd+Oe0cx06hKBNGw09MM37FpprT26TEpASkBKQEki+EpAAMPmOTdL0jCjag1p/ZfnclPvHrfPXsKX8sZq8+eDFZt+MGS3XT+I93v7eaNizFu7uuyN6Uq5NBXzfdojZXl2+f4nyZQIhb0PAf7w8f/5iSrfmZLZ+bDe+9nuFr7a2xMdtH8m+ykngbYlUegVF6DY0abJVqxAyt9oRsbOaUikpiF7FOgDGCT0cHa2ry+1yVpZ162zw8aMO9Lm4cK7XYAH8SpeOOjWXSaflBikBKQEpASmBZC8BCQCT/RAlow6Sf6Bbl/awPXYEoR65BfgLy5Q5GXXQuCtnb59G207N4f+YtGx2wHdjBmFU97HGlQzWmlVtgRdPPuDivfM4fvkY8ucogPqVTNPYGRwS40VvSsf19dZWeLrmCXAWcHBwJD+9VahBWVPMFTc3oFEjjeDu27BBjd69CZjGY2H/vj597PHqlc7MW7WqRmQgadJEQwAyHk8km5ISkBKQEpASSFYSkAAwWQ1HMu4MpRRz69oBtkcOIjSnBzw37UBYlqzJuMPAhVvn4P/EH7ZZ7LBi6Sp8WbpOtP1Vq9QoX7CimKKtHMMKTGjbdUdH3Fh8DbgMODs7k7l1AypVqhxlS2x+ZfJmNs1++635wIsoGzCzk1O3zZhhS1G9tggLU5B5WYNx4wLjJS+zmdPJTVICUgJSAlICyUwCEgAmswFJrt1xmjgWtgf3IzRHTgJ/2xGWLXty7Wp4v3o37w9P74/o2rQXMqWJOmdo+EEJtMDRaf1398KxWUeI5BlwJdXeurWbRW7e6E5ZrVoocuQIw82bKly+rIwThx+fi1PN9e1rj2PH1OR7qKOM6dMnfoBldNci90sJSAlICUgJJA8JpCoAmC9fPleKtDym0WiaPHjwgGxwpqVAgQJjaGvonTt3xpru/Ty3KJ88RljOXBYvTnX9GhwWzoWWfNW8Vm9EGIHAlFKGdf4l0bvK/IFeQZ6UbeA1Xvm9FNP++3uxddJmgFwR07qnw8b121G0aDGr+kbBwSLTxpQpdkILWKoURWvEshw8qEL//vbkT6gUqdsWLAhAmTKGvIOxbFgeJiUgJSAlICWQoiSQagAggb9KBP4W0OgUMDdCefLkcVOpVNNp39c0/W6uzue4TfXgHmX9qICgRk3hs3CpaeQvEcG5/DgICrIZ+g36AaEU9SuLTgKcv5TTYZ17fAbP3jzDy3cv8Pbdawqk+IgQX/LVC6B65H4o5m9oTlP6jOmxdfNu5MuXX9eIlf+//joEDAA3bbLBmDFB5Dto5YGfqjFZ9G+/2RJxMzlDUmnSJIRMwGTWJx9DWaQEpASkBKQEUp8EUg0AJKb6XjT1Je3MCnPDTOCPkq+Cs3pPM7f/s9xG4M558PdQEDoIy5rNFPzRRdv/uwI2586IoA//gUOTnRiCQ4LRfUxnojtRYNe8HYnSPwZ+Ky4uxeT54+F5iCJ5KUOGNSVrjmzYsvE/cO7emJacObWoXl2Do0fV+O8/NVq1IkRnZWFOv9697XHmjJoijrUYOzaICKalyddK8clqUgJSAlICn6UEUg0AvHv3bg8eQTLx6nguIg0n7V/6af+vkXZ9tqts1rU9eVwEdfj/9LPJdSrev4fTuFFiu89kwsXED5icyonrx9C5F+XNvedNRHrA9WHXkcklZ4J1kYHfwhPzMP2v3+B9nM4ZqDuVc1oXMuumRbq07siYPiMypc8M93TpKQNHOsqVm07Mebl48RKkuYuh6s7gajgYhAEgB4NYCwB37VJh4EAHQe+SJ08YFi4MoH6EGbQqF6UEpASkBKQEUqMEUg0ATMjBZUbxlFZU9+7CacIYaKnzPrP+AlxcEPkynMeOhJLMmUHNWkJTu47J/qS65jDSXH5PxM5rZ64Cggn7uasxYdpvKJq7KN6/94n3bgVTSqY/9/6B2XP+gO9ZX/IQ1Z2idLUyGP3jeFSmrCmJkQu1cWMNXF21BAJVePpUQT585nn+iKoRmzeriV7GFufPE5khlTZt2IQcSJHHur4n5H/970E/T8hzybajl4B+HPTz6I+QNRJKAvox0M8T6jyy3eglIMcgehl9djVIA/iQ/P0sqolo/6806dReVlw9p4JLcSUkRKutWJFCFSjZw+DB5rt/9Khuv7OzVvvsmfk6SbD1zpM72oylMjLyEVPhBoW1L9+/TJCeBGmCtMOWDNM6FHcIP59CpdDWbVVXe/Xq1QQ5Z3SN9iUnBh620aNNa3KXvvtOq3Vz09Xhepkza7WLF2u1YWGm9eUWKQEpASmB1CwBK17xn3UVqQGMh+FlrRO/clNKUXh7wZnMlSoKRPAcPBx4F0lrFhKCNN/2ZqsqfIf9D4F2rqZ1kupiNbb4+NITShclfhw/HD90oP6TRZPHwN3dRczjOhZM2fLr8l+weM5CBD/URdwq7ZVo3LYpxg2dhGyfKHDeRZZbIsikVSslpYxzwuLFYUTl4gdO37Z1qxrLltni7Fmdto+7UbOmBt98E0LZRTSwoVzxZM1PtMJf1vE1FonW6c/4RHI8ks/gyrFIXmORfHqTND1JjQAwHKqRpm8hff1sIf+/7XERv17fEpc2EvNYrYsbvJetguLDB8r7Sz5p4RLR9cJh3l9Q37wBTdHiCOjRx2R/YvY18rnSOKfDsiX/Ine2PMibJZ8J8I7rWBy8uh+9h3aH5yUK7qBi42aDtpT9ZMyA8RQxm0ZsiyvAFI3E8l+JEmEoXDhUcAL26mWPI0fU8PTUGe/Tpw8Tqds6dQqBh0fEoCZVf+M6FrEUkTzMggTkeFgQTBJslmORBEKXpzSRQGS3L5MKckPUEmD1OWuCkuolG3XvYr5X+ewp0lUrDwU5kn3csRea8hVj3kgSHMFf1unTuxC/XezG4qP/R3wzqgNOrT4u/AqVTkp0/b4HRvebQLEvySv4Zd48G4waFdEnjg5mbV+DBhqK8k0C4Uc6ZVzHIlJzcjWOEpDjEUcBxuPhciziUZhxbIrHIkMG11SNgVKjBjCOt83nfbjziGEC/AV07ppiwF9cRoTwO/7YOBVTRk+C5rWOWqV4/RJYMW0NsmYkapxkWDp2DMGlSyqQfx+6dAmW6duS4RjJLkkJSAlICSR3CUgAmNxHKBH7Z7vnP9j9R2ne3N3h98voRDyz6akOXtyHn8YPwa4lB+Dumt60QjxsufzwIr4Z0gEvjj8XrTnlcML0qbPR8svW8dB6wjVBAduYN+8TB03CnUa2LCUgJSAlICXwGUuAkkzJ8tlLIDAQio8for5MMvk6/+8nUcf31/HQEm9dUpUrDy6hQ7u2eHz0EXr++k28dyMwOBDfjOuAujVqCPCnsFOg7cB2uHPqSbIHf/EuDNmglICUgJSAlECqlIAEgKlg2J0mj0faLyrB5sQxi1fr+MdUqCgncHClKgj6uoPFegm94+mbx2jcqi5CP4YiTeG0WDFxTbydks29i3bOQ4EqOfHfnxT3Q0o0j6p5cOToacwZsYCiZSlcVhYpASkBKQEpASmBVCABaQL+zAdZfeokHOb+CY4OCHM3b0pV3bkNxzkzoVWr4fsbpUNm79gkKB99PqBmi6oIekG5bnM64simU3B2iDtzcWhYKH7/dyIWzp0L37u+4spsM9ri1/Hj0KtF3yS4UnlKKQEpASkBKQEpgaSVgASASSv/hD27nx9cB/SBgjRfvsNHIrRgIdPz0T7nYUOgIO4///4DEVq4iGmdRNjCZtkv2lYUad1sMthg7+bDyJwuS5zO7B/kj5Hzh2PN4n8R/IJShlBh/sB67Rpi7i+L4OTgFKf25cFSAlICUgJSAlICKVUCEgCm1JGzot/OlMdX9eghQipUQkCf/maPcJw5DbbHjyKUyI39hg4zWycxNmpCibDYzlYAtA3rtqFA9oKxPu1b7zf4ccYg7P73P2FK5oYYVLbu+hUm9PsdLk4URSGLlICUgJSAlICUQCqWgASAn+ngOyycC4fFC6F1dIT3rLmAKiJLhP6SHf76E04Tx0JLvm8+M2YjURLF6k8eac6m3jMbL+PyvYsoW7B8pL3Wrd55egfdhnfHyc0noPXXESE75nJCjz7fYvg3v8BGLX38rJOkrCUlICUgJSAl8LlLQALAz3SEQ8qUg9bBAT5TZyIsT16Tq3RY8BecR48Qfn/eC5chpGYtkzqJvUGtUscY/L32fI15G2Zjx39b8ejkQyBE1+t0Rd0xeOCP6NWsD5RKGeuU2GMpzyclICUgJSAlkLwlIAFg8h6fWPdOU7Y83l+8AW06d5M27Ekz6PzLcGhJK+g9fzGCGzUxqZOcN5y/fRbz1s/B0f2H8OEm0duEfuotxa5kr5wDI4eORcsvkjeXX3KWr+yblICUgJSAlMDnLwEJAD/jMTYL/pYvgcvwodCSVsxnzgIEN22R6BIICwvD5fsXUTp/WavOzf6B6w+twcoty3H56EUEPjcgQaY72L24O76oXRP/6z0cHukLfjZp+awSjqwkJSAlICUgJSAlEAsJSAAYC6Ell0NsTp2A/bLF8Jk936yPX+R+2q36By4/DISWaF58yC8wqFXbyFUSfP3QpQPoPbg7vJ544vypq8iWIYfFc3r6eqL7qE44seUYwnzCwuspnBXwKJcHTRo2w7ct+yFTmkyCuUafCzi8olyQEpASkBKQEpASkBIwKwEJAM2KJXlvVLx/DyeK8HX4d4XoaDD570VH3my3dhVcBukigX0p4CPoq/aJepEM5r4Z0R4n1x0HKOUu07GcuHYcbb9sZ7Yfk1aMx58Tp0PzXpef1y6LHYpXLYkOzTuhXZ1OYH9BWaQEpASkBKQEpASkBGInAfkWjZ3ckuwoJdG6pG1UB8p3bxHm5Az/4SMQ1PqrKPtjt3EdXAb0FXyAHBQS2KFzlPXje+f01b9j6pjJOjBHfnrFm5TAiilrkNU9m8mpDl85iL5De+Ld5bdin1MeZ0z9bQZa1/japK7cICUgJSAlICUgJSAlEDsJSAAYO7klzVHkO+cy+DsB/oK/rC2oW8KymoIow87Zbt0El/7fQkHH+kyaisAu3Qx3J/jy87dPMfmn8SLtGoO56VP+RMvqpgEa773foeuIjji9/qQI6lA4K9FxQBf8/v10qe1L8FGSJ5ASkBKQEpASSG0SkAAwBY243eYNOtLmHDnh/fdyaJ2jJjS23bkdrn16QBEaCt/xkxHY49tEv1r28es4uAtCKNPIH0PnmIA5DggZt+RXzPttNkI9KZyXGFuKNS6OFb+vjtI/MNEvRJ5QSkBKQEpASkBK4DOSgASAKWgwg5q1hO+zp9CUKhM1+KP0bvb/LIMzRfsqNBr4/joeAd/2S7IrnTGYSKbNlP3n96LfD73w8TpRuVBxye+KmVPnoEnl5mZqy01SAlICUgJSAlICUgLxJQEJAONLkonRjlqNgAFDoj5TQACcf/4hPEDEd+RYBPQfEPUxcdzLNC3jloxGwyqNUKlIlWhbe+v1Fl2Gt8P5zWeFuZcDQrqR39+EPr9L0uZopScrSAlICUgJSAlICcRdAhIAxl2GyaYF5eNHcO3eGTZXLyPM1U3H81e/YYL1zz/QH7/MG441i/5FyJtg7K+zG8f+JVBnobC5d8KyMfhr0qxwc2+pFmWwfPIqZE6XxcJRcrOUgJSAlICUgJSAlEB8S0ACwPiWaBK1Z7t/D1z69oTS0xOaIsXgtXiF2RRw8dE9pnQZNmsIti7fhNAPujQctpltUaP6lxabP371KHoN6Roe3etawBWzpy9AgwqNLB4jd0gJSAlICUgJSAlICSSMBCQATBi5Jl6rpFVznP47HKdMEjQvgW3bwWfKH4CjY4L1oULjEvC86Snad8jpiJ79e+PnLiNNAjy4gre/N7qN7Iijqw4L/j8mce4yqBt++266NPcm2AjJhqUEpASkBKQEpASiloAEgFHLJ0n32m3ZiNBcHiLow1xHFJ4f4dKvF+z27YHWxgY+439DYNceEGkxzB0QT9uatWqJTWs34PuBgzGgzWCLQG7Wuun4bfQEhLwNoT4BRRoWJf6/1ciRMVc89UQ2IyUgJSAlICUgJSAlEBsJ0GtZlrhIQEvl3TufeM8/y/586WpUBoKD8OHkBYQREDQsavLzc+3WGaonjxCaJaughdGUq2BYJcGW2ZdPSbmELZVL9y6i6+AOeHH6uajimMsRU37/w2LWD0vtxGQ7ZbeDPhUcBUHLkoQSkGORhMI3c2o5HmaEkkSb5FgkkeDNnJbHIkMG11SNgaQG0MyNkeSbCGBx2jaFvx8CunQ3AX92q1fC5afBUAQGIrjaF/CevwTaDBnitdtsun314SUKZC9o0q458MeRwMt3LcGyNX/j5oEbBFzpMHsFWvVtg1k/zIWtja1JO3KDlICUgJSAlICUgJRA0khAAsCkkXuUZ7Vf+nc44bPf6HERdUm15ThpHJz+mCq2+X8/GH4/jwSIHiY+y5Kdf2PksGFwzuCCG/vuW9T2sSZw/aHVWLhqPq4euoIwL11ACPcld408WDZtFQrlLByfXZNtSQlICUgJSAlICUgJxIME4hc5xEOHUnsTbPp1HjtKiMFnxuwIwmfKpOEydADsSfuntbeH95wFCG7aIl7FxWnbvh7UGnf23hLtBtj64/GbR8idOY/ReXac3Ia5K//Ehf3ndPl9P+21y2qHSnWrYEDnIaheoobRMXJFSkBKQEpASkBKQEog+UhAAsDkMxaiJ8K0+8n0G/JFTV3v/Pzg2rML7PbvRViaNPBasRaaipXitedjF4/CXxNnIcw7DLAB6nVriEWjlsHe1l6c597zuxg2fQhO7zuF4JdB4edWp1ejbJ3y6NdxABpWbBy+XS5ICUgJSAlICUgJSAkkXwlIAJjMxoYzdzjZ2UFv+lW8ewe3jm1gc/ECQrNlh9dqigwuWChee73u4GrMHk7UMVTSFUuHxX/+gypFq4n1997v0GdcDxxefRD4hPtUaVQoXrMkenfsi5bV21o0EYsG5D8pASkBKQEpASkBKYFkJ4FUHQETH6ORUFHA3Dflo4dw+7ol1A8fQFO4CLxWbUBY1mzx0W2TNmp2rYIK5Spicr9pAtAFBgfix5mDsG7+ap1WkI7IWjEbBvb9AZ3rf2OW88+k0UTcIKPrElHY0ZxKjkU0Akrk3XI8ElngUZxOjkUUwknkXTwWMgo4kYUuT2edBNRXLsGtXWso371FcOWq8F6+Clq3NNYdHItah5aeEEdxYMekFeMxh8ikg19xKC/gWsAN48dMRrvaHcW6/CclICUgJSAlICUgJZCyJWCZzC1lX1eK7r3NoQNwa95IgL8gCvTwWrMpzuBv09ENqNGlEoJDdKDu/+2dC1xVVb7H/8cDBzkKPkgNxOKlYijCkFaaxuSok4pWWjrizVczOXdsLA3vtVIrqylzevu+o2X2uI7NNJPZ3EyHfJRlmKkEKgKWglzNTBB5n/n9jxw4dBiS16bj+a2Py/V+7O9i7/07a++9Vm2AXt+yTrrfeJU8N2exXfz5BPpI0vPz5PD2YxR/tQFjHAmQAAmQAAm4KQEKwJYaOMy0+Wz8XxG4zsbnz29Ju4njpNX5Arkw/TdybtVarKd38UMM53yX6t+w7Q3pPTxC7hk7VdL+8ZUsXP2gS9Hkfduk78hIuT9xpuRnnJNW7cySOO8uOfr5CUmaOI/v+LkQYwQJkAAJkAAJuDcBfgTSEuNXXCx+v58hrf/6thQeSpfzDy20C0Hfl1+Qto/DD1Pw8CNyAev8NWRbt7yzeTLrqd/K3k9SsGfvd/b6TL4mGTh2kPznuHvt4ayTmfLsa4vlg83/kO/SzqB9RENnDkkcKsseXC0d/Dra8/E/EiABEiABEiCBy48ABaDBY2o69734T0kUy87tUhEQIMW3jBRz6kH7zh7eez4Vm9ksuv5f8YSGv2/na/GVba9+KIJ1mU1Wk8SPv1mem/uy/UifXb9YNm/aJKcPnrKn2yOx7Ev0qBhZuWiNhAdGGEyEzZEACZAACZAACRhNgALQQOKtTubaP+zw+uqglF8dImfXrBdfPPL1Xb1cTOXl9rj8Z1+S0kE3/dte6ePaBX+cJ1kHMuXWxLHyUtIKl7z+Vn8ZNWOMhHQLlXG/GC/r33tFhifGS97+PJGyyuwY+SuvDZTRY26TWRPmSKd2TbuVnEunGEECJEACJEACJPCTIUABaNBQ6JIu7W8fJebj30hpdIxcmHq3tJ90p5hzc8Rmscj5WXOkEFZ8fWvtkQq/pEX3ybGd2SK2i1m++CLFJa9+xftJ6i5U4ytvv71BXp6P9f1KK7OZ8dl7TGcZOTpBZk+cK1d2DHQpzwgSIAESIAESIIHLnwAFoEFjXNGps1R07izlwd3E5tNa/O+faW+5ZFC8FDz9RymP6F5rT1TQDZgQJ5kfHb0o/PC4tu+IWLlv2gMy9Nrh9jIHs/bLuvfWyvbtyXLsy2wp/656T17BZz4dewfILQkjZXZiknTrfHWt7TCSBEiABEiABEjAcwhQABo11l5eUoKt3awrloqpqEjKO3eR8489KcW3javzQ49WrVrhQ2F8oYGRUuH3wsPLsVOIVda9v1aeWfGkZOw9IiV5NZd20Z06rooJkcE33iQz7pjJ9/qMGmO2QwIkQAIkQAJuQoACsLkHymYTXdev7UNzxSvjiNgg6HR5l/Pz5ovNv90ltb7qqbWSciRFtu74QBImDpP8o+cufrVbWdrUxiRBfbrKDQMGyq9GTJKBvQdx6ZZLIstMJEACJEACJOCZBCgAm3jczelpYrpQKGUxPxPLh/8n1ueWiPfnn9lbKY2JlQLssFHWN7ZGqznfnpCVf1kqdw6dKFEhve1pBRcK5JXNf5K/bNog6bvTpOxbx9cbSLaIXNGrk/Qb0F/GDhsvI64f9ZPbmq3GATJAAiRAAiRAAiTwkyJAAdhEw2EqyBfrkqfFd+VSvOvXRSo6Boh36gF77eXYv7fw/iQpmjRZBMu8qNl1YIes3rhcPk7eJWcPY60+vLZ36Fi6BF0RLNs+3CI5X54QKbZntf9n7mCWntdHypgRt8vUkb+W9m2bb1u46lbpIwESIAESIAESuBwJeJQAjIiI8Mc7dTvLyspGZWZmfu08oD179gyy2WzrEdcFNhd2wuHDh08756nVj0e8lr9ulDbzHxRz3kn7B7r6Za/astAwufD72VJ0xwTM2mHaDmb3Vx/LuPGjpeQs3tvrgAgfWP0YN19k2yqs3edk2oa1lWsH95fJt02XW64byce6TmzoJQESIAESIAESaDgBjxGAEH/XQ/ytAqoeteGC+FtqMpnWHDp0aH337t2nwv8i8k2sLW+NuPh48d++vSrKBF9ZZC/7si4lY27HxxtecvJMrrz38bvy0Wfb5MC+/VJSDPGns3snq4rZPaa2JrmyV6AMGTpMZoz7nfQI7lkzA0MkQAIkQAIkQAIk0AQEVK94hIGo+xME4BoIvdcwAxjvPAMYHx/vlZOTcxozfgGAoWuomHv06HEGYd0PzWlNFVdUWJIP/y5+k7E5yCqLOtpkT9EFaePvJ95FXvL9iXNS8b1rFbo1W/vQ9hIR1UOui7tBRg5MkNjucZzlc0V8STEm/CVfcYWfnD6dL5iUpWlBAhyLFoRfS9Mcj1qgtFAUx6KFwNfSrI5Fp07+HqOBakGgi4t4hjly5Mh0PVIIO5cBP3bsWIC3tzc+ra0Se6rYvo+KiuqUmpr6g3k6raXa9LpK5OsikUItnVMIezGtQJ/pVhoVe37d/CTsmgjpF9tfRtyYIDdEDaTYcwCiSwIkQAIkQAIkYCgBjxGAdVFt3bp1q3JsxfYDg+X6irAAX90m3flNQj+80tfJR4JCgiQmOk7ieveTm2Lj7Y9ydT0/muYjoL/m1DjciyH+3xIEHGPgcFuiD2yzmoBjHBxudQp9RhNwjIHDNbp9tldNgGNQzcJjfJgBzAoLC8O8XbXRR8CIP4sYh0rTR8D4NFcufrJbnZU+EiABEiABEiABEnB7Ag7B4/YH0pgDSE5OLsO7gR9B9CVqPXAnwUmGdZkWRBwNCZAACZAACZAACbg1AU8UgFWfCEDorcbHIaN0BCEAfwfnLsQdhDsN26/dq/E0JEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJGA4AZddMQzvgZs22LNnzzvw5fBCdN8b+wavxx7Ci9z0UNy+2/hyezbGQvdvhmPb07Vr13t0aR+3PzA3PgCcH8+g+wE4L6a58WG4fdexB3oCFqHX65QV58YH2BHpPrc/KDc9AJwTkzAG/60XKYzJ+zg35rrpobhtt3E++IP9TmwHO0q3g0U4CuH/wQG1gz1YWFg4+fjx4xfc9gDr2XFPXAamnohcs4eGhnbBOfwMto8bjP2Cr4F/MJaTGeqakzHNTQDirx/amFxaWtoPYxENEeidm5urS/rQtBABjMkQnBOTW6h5NltJAIIjFDe35WazOQHnRh+cG7EYm1sIyHgCwcHBvjgnXsB43AQR3lfvGRifm43viee2CLF3vYo/EOjhoIDweviT9D4ON81qtc53pHmCSwHYgFG2WCxDcQJvxT7BZ1BcF4teh4vr+AZUxSKNJIAt/HTHlpnZ2dnYkdluvsTY1NjppTKejgEEsH92RzTzOM6HJwxojk3UQQDnwW1IfistLS0Xbjl+sI738fHZXUcRJjUTgXbt2umuUib8ULWGhIRY4PfCtctjZpqaCWu9qsU16dewv0WhHC0IQRgMxx/iT0WhYDx0JtCj7uPcC1hHvv6mK4qccCqWg4ut/jHRGEzg6NGjGWhSrYSHh3eGMxP2Lg3TGE8AN7gV+FU9DwupX42LrfEdYItVBHBNikCgGLN+78MNxGOvd/HY0aNmOKpgtLAHkwUFGIcFmI09BHse58ZHGRkZn7Rwtzyqecy8TtcDxjjYL0wYgxr3cdxLcpCmcR5jOAPYgKHGhdWFG/6YKhpQFYs0EYHIyMgQXFi3obqV+EW3o4mqZTX1IICL593I/nV6enpyPYoxazMRwDXJC3Y4BPkkCPLr0Ex/jBF/HDUT77qqxfVJH8FPxVh08/PzC8I9pAJjMaeuMkxrXgK13cfRokfdx12ETPMiv2xqP46TOchxNPAHwn/cEaZrLAG8fxmDG9xOtLoM4u8pY1tna04E7oR/GG5sX+CceAwX2NEYm+ed0uk1kAD4n0RzWyHIv8VsUzH878D2N7ALbKqSAB4vDofXPhYpKSml8L8CGw9L00IEcH7oPbvqPo5HwoGVcS3UI+ObpQBsAHPMNH2IYjfjD6ZTXFycN/z/gRveew2oikUaSUDHAOz1EddMiL9ljayOxRtBAPyHwUbDxuJCugDj8nd+ddoIoI0sCv6bUMXQsLAw/cLRjDEZjri9jayWxRtGQN9NHh4dHd0GxTEMpgS4KQ2riqWaggAe+X6Des7jB+sgrQ+zs9PgeNR9nAJQR76eRl+qxsmchD+Yrfn5+Qfg/wLv1vytntUwexMQgBifhWr8YBfozBPsXnxdt6gJqmYVJODWBCDEP8O16SmcIztxXhzEwXyD69Ratz4oN+08fghtwVisLyoqSsFY7MMTC0tJSQmfVrTMeNoczWIcfgUx/nTl+dEP9/QFjjS6JEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJNDMBLhZZzMDZvUkQAK1E8B6jWuwNloMUvU6dA1sNmwhrK7TlYB17Jz320ZU/Q12IvknSq3GOmxv/Fhp5J2BNcGmIp8u7u4Fuxd7G/9XVlZW3o+Vbcp0rEn2KPpxDGv2rQEjXZzWD/4XmrIN1kUCJEACepGjIQESIAHDCUDUqLixG4ieTCzKOgVbln3iiDPShfh7DqIrDlt2JWCHgP/XttGnhd7e3snx8fF9kpOTy4zqD4TvQkdbEMi6S8ERR5guCZAACTQVAQrApiLJekiABBpDwOTl5VXjiQQEWBYq/BS2N+wzEENT4FbN5iF9C8KvQTCtw0zZtfC/iDz+cMvhPoZZv7fhdxgT8q9C4ErswHBHdnZ2kSMhPDy8G8TfvQj3cIg/TUO9j0IYHj516lRrBAtQfgncgbBWtRCssyFY30W8CrZo1BGAdrvAnwkhOVXrQr9CkW850toiXvcdzYSovDM1NfUMyoUhvAK2K6wNVveyXob4tfAfQZkM1Dca/iL0owThKahrnraJOBWoT8DxQZkHNExDAiRAAvUhwK3g6kOLeUmABIwmcAgCpzfsq3U0rPvcvgO7WPNCfI2FWFocFBSkQk03XvWGgHoNXgvSxziLP03HVmkqHk8jLVPDzgYi8k2ItYJKgRmOPANh+6LOR7Ft1ONOeW+AgJ2AtF6IO4o6X9I09Olu5NuA+BthwxFVgcfKkzUN5k3UsxnxUfDHw97bq1evq+HaDWZIN8Dzd9il6McS5F2KumZoImYl9ce7Pq7m/tcKhIYESKDeBDgDWG9kLEACJGAggR0/1lZEREQ08qi4e0fzYuYtA46KLYHwU+dJiKcAiKdI+Cs04gdGZ9/q/DEMMfY52nkAs253I28YhN0AuDqrZzcIb4BQPKkBzNKtRFsfqx99ehhlhsDOQR8ikC8S7o4+ffp0KC4u7oe9xH9eme80XBWPOrOnjovx8fFZh71kH9cZy5ycHG3/S9TvIlpdCjKCBEiABGohQAFYCxRGkQAJ/DQIQEjpRyF2A+Fkg3V+TGzRBDxSLcWsn4q4KgMRFQkhlqURKLIR/lOwOgs4GLZGXpTfjVm5Dph9656WllbjfTvM/G2EaFuEMvqYdinsEoQ3q8CD+zzCdoM2qt4RxExgK7RVrgnoxxvIZ0X6mwhugW2DsAmPoTW/DfVU9QUCMxwiLw99QZKr2b9//3kI2nWYXZyO1AGo50XXXIwhARIggUsjUOev3kurgrlIgARIwBACpyB6YrUlfbcOjn5BLFar9RAcFUcjNQzRFQZnF4SYfYYOYuxTPEL9A+IsyPOg5nE2OnMHgbYMInINRGBgZZoZeR9De1EQiGlwh8K+jxk3fRy7C/7bUcbsVM+tISEh7TWM9u6Bo49u1fwS+Z5AubfgnkN4CKwZM4r5cHej79PgSmRkpM5QJqMPelzORoVi1Q91HJM+8p0CG4p+bIJLQwIkQAINIkAB2CBsLEQCJNDEBKpmwpzqrREH0aUiTh+n7od/CfxbNW9KSkopxNWtEFBJSNsH/59hE9PT07+F66hDZ+QmI5xU+T6fFq0yEGT3IW1TWVnZB6hjr7aB+oIxGxcPgViCWTf9kKMfROEBpO1B+3mwAajLr7KSXIvF8jekpSEcgHpmVcbPhfumloEwfB1l/omw/bk06pwI/wik7UPaNvgfQp8PwHU2mxGYgTyPaGTlDOXX6MtqDdOQAAmQAAmQAAmQAAm0AAGIs4Wwq4xoGo+Jg9HW8aioqI5GtMc2SIAELl8CnAG8fMeWR0YCJHAZEcBs43zMSu7BIT2EWckzl9Gh8VBIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwB0I/AuCIUT658URYwAAAABJRU5ErkJggg==\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x129ad9630>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "colors = [\"red\", \"green\", \"blue\", \"black\"]\n", "linestyles = [\"--\", \"-\"]\n", "for i, col in enumerate(data.columns):\n", " ax.plot(np.arange(50)/5, data[col], label = col, color = colors[i%4], linestyle = linestyles[int(i/4)])\n", "ax.set_xlabel(\"Truck Capacity\")\n", "ax.set_ylabel(\"Ratio to Centralized Optimal Solution\")\n", "ax.set_title(\"Cost of Decentralization under different policies\")\n", "ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) Reproduce in matplotlib the provided plot stocks.png Use the provided datafiles ny_temps.txt, yahoo_data.txt, and google_data.txt. Provide your new plot and the Python code." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XAZRVyt8B4Kzm+PPKNt+YPh34+GN9Za9fF/u+v2jQAOje3bc6zp1z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"text/plain": [ "<matplotlib.figure.Figure at 0x10cfb3fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ny_temps = np.loadtxt(\"hw_2_data/ny_temps.txt\", skiprows=1)\n", "yahoo_data = np.loadtxt(\"hw_2_data/yahoo_data.txt\", skiprows=1)\n", "google_data = np.loadtxt(\"hw_2_data/google_data.txt\", skiprows=1)\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "\n", "yahoo = ax1.plot(yahoo_data[:,0], yahoo_data[:,1], color = 'purple', \n", " linestyle = '-', label = \"Yahoo!Stock Value\")\n", "google = ax1.plot(google_data[:,0], google_data[:,1], color = 'blue', \n", " linestyle = '-', label = \"Google Stock Value\")\n", "\n", "# add secondary y axis\n", "ax2 = ax1.twinx()\n", "temps = ax2.plot(ny_temps[:,0], ny_temps[:,1], color = 'red', \n", " linestyle = ':', label = \"NY Mon. High Temp\")\n", "\n", "# add legend together\n", "data = yahoo + google + temps\n", "labs = [l.get_label() for l in data]\n", "ax1.legend(data, labs, loc=(0.03,.45), prop={'size':7}, frameon=False)\n", "\n", "ax1.set_title(\"New York Temperature, Google, and Yahoo!\", \n", " size = 16, family='Times New Roman', fontweight=\"bold\")\n", "ax1.set_xlabel(\"Date (MJD)\")\n", "ax1.set_ylabel(\"Value (Dollars)\")\n", "ax2.set_ylabel(\"Temperature ($^\\circ$F)\")\n", "ax1.set_xlim(48800, 55620)\n", "ax1.set_ylim(-20, 780)\n", "ax1.minorticks_on()\n", "ax2.set_ylim(-150,100)\n", "ax2.minorticks_on()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "3) Make a generic \"Brushing\" graph" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "from matplotlib.patches import Rectangle\n", "\n", "import numpy as np\n", "import plotly\n", "import pandas as pd\n", "import datashader\n", "import seaborn as sns\n", "import sys\n", "import os\n", "\n", "from bokeh.io import output_file, show\n", "from bokeh.layouts import gridplot\n", "from bokeh.models import ColumnDataSource\n", "from bokeh.plotting import figure\n", "\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "flowers = pd.read_table(\"hw_2_data/flowers.csv\", sep=\",\").set_index(\"species\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class DrawClass:\n", "\n", " def __init__(self, data, colors):\n", " \"\"\" Initialize the brusher plots\n", " \n", " data: pd.DataFrame - figure will have NxN subplots where N\n", " is the number of features/columns \n", " colors: np.ndarray - the colors group each row into categories \n", " accordingly\n", " \"\"\"\n", " self.data = data\n", " self.colors = colors\n", " self.size = data.shape[1]\n", " self.fig, self.axes = plt.subplots(self.size, self.size)\n", " self.ax_data = {}\n", " self.ax_dict = {}\n", " self.active = np.array(data.shape[0])\n", " \n", " self.rect = None\n", " self.current_axes = None\n", " self.xy0 = None\n", " self.xy1 = None\n", " \n", " \n", " for x_ix, x_data in enumerate(self.data.columns):\n", " for y_ix, y_data in enumerate(self.data.columns):\n", " ax_temp = self.axes[y_ix, x_ix]\n", " scatterplot = ax_temp.scatter(data[x_data], data[y_data], alpha = 0.4)\n", " \n", " ax_temp.set_xlim(self.data[x_data].min(), self.data[x_data].max())\n", " ax_temp.set_ylim(self.data[y_data].min(), self.data[y_data].max())\n", " \n", " ax_temp.xaxis.set_ticks([])\n", " ax_temp.yaxis.set_ticks([])\n", " \n", " scatterplot.set_color(colors)\n", " \n", " self.ax_data[x_ix, y_ix] = scatterplot\n", " self.ax_dict[str(ax_temp)] = (x_data, y_data)\n", " \n", " \n", " self.cids = {}\n", " self.cids['button_press_event'] = self.fig.canvas.mpl_connect('button_press_event', self.onclick)\n", " self.cids['button_release_event'] = self.fig.canvas.mpl_connect('button_release_event', self.offclick)\n", " \n", " \n", " self.fig.show()\n", " self.flush()\n", " \n", " def flush(self):\n", " \"\"\"\n", " Desciption:\n", " Flush std out and draw canvas - to make sure everything is written right now\n", " \"\"\"\n", " sys.stdout.flush()\n", " self.fig.canvas.draw()\n", " \n", " \n", " def onclick(self, event):\n", " if event.x is None or event.y is None:\n", " return\n", " \n", " #self.active = np.ones(self.data.shape[0])\n", " #self.update_colors(active = self.active.values)\n", " if self.rect is not None:\n", " self.rect.remove()\n", " self.ax0 = event.inaxes\n", " self.xy0 = (event.xdata, event.ydata)\n", " self.flush()\n", " \n", " def offclick(self, event):\n", " if event.xdata is None or event.ydata is None: return\n", " if event.inaxes != self.ax0: return\n", " \n", " self.xy1 = (event.xdata, event.ydata)\n", " \n", " # Make a rectangular, finding upper left point, width, height\n", " xmin = min(self.xy0[0], self.xy1[0])\n", " xmax = max(self.xy0[0], self.xy1[0])\n", " ymin = min(self.xy0[1], self.xy1[1])\n", " ymax = max(self.xy0[1], self.xy1[1])\n", " \n", " width = xmax - xmin\n", " height = ymax - ymin\n", " area = width * height\n", " \n", " self.rect = Rectangle((xmin, ymin), width, height, color = 'k', alpha = 0.1)\n", " self.ax0.add_patch(self.rect)\n", " self.update_state(xmin, xmax, ymin, ymax)\n", " if area < 0.01:\n", " self.update_colors()\n", " self.flush()\n", "\n", "\n", " def update_state(self, xmin, xmax, ymin, ymax):\n", " # find out column names\n", " x_label, y_label = self.ax_dict[str(self.ax0)]\n", " # get indices for active datas\n", " self.active = (self.data[x_label] > xmin) & (self.data[x_label] < xmax)\n", " self.active = self.active & (self.data[y_label] > ymin) & (self.data[y_label] < ymax)\n", "\n", " # update the colors\n", " self.update_colors(active = self.active.values)\n", "\n", " def update_colors(self, active = None):\n", " \n", " # update colors\n", " colors = self.colors.copy()\n", " if active is not None:\n", " colors[~active] = (0, 0, 0, 0.1)\n", " \n", " # set the colors for each axis\n", " for (x, y), data in self.ax_data.items():\n", " data.set_color(colors)" ] }, { "cell_type": "code", "execution_count": 22, "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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" }, { "data": { "text/plain": [ "<__main__.DrawClass at 0x1260f6940>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "color_map = {\n", " 'setosa': (0.6, 0, 0, 0.4),\n", " 'versicolor': (0, 0.6, 0, 0.4),\n", " 'virginica': (0, 0, 0.6, 0.4)\n", "}\n", "colors = np.array([color_map[x] for x in flowers.index]) \n", "DrawClass(data = flowers, colors = colors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I discussed HW with Kevin Li, Ying Cao, Tugce, Jing Dai" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [py3]", "language": "python", "name": "Python [py3]" }, "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
jakevdp/multiband_LS	examples/SuperSmoother.ipynb	1	95020	{ "metadata": { "name": "", "signature": "sha256:a5b6bcca693d9011979ed21dc6da16fc816aee1c9e1571cdee97312dae303101" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SuperSmoother Example\n", "\n", "This is an example of doing periodic analysis with the SuperSmoother" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# use seaborn's default plotting styles for matplotlib\n", "import seaborn; seaborn.set()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Data\n", "\n", "The [astroML](http://astroml.org) project includes a data fetcher for light curves from the LINEAR survey.\n", "We'll use one of the light curves as an example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from astroML.datasets import fetch_LINEAR_sample\n", "data = fetch_LINEAR_sample()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[10003298 10004892 10013411 ..., 9984569 9987252 999528]\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "lcid = 10022663\n", "t, y, dy = data[lcid].T" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots()\n", "ax.errorbar(t, y, dy, fmt='o');\n", "ax.invert_yaxis()\n", "ax.set_xlabel('MJD')\n", "ax.set_ylabel('magnitude')\n", "ax.set_title('LINEAR object {0}'.format(lcid));" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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2pJU6C2Zb1NDkTw28IndBnPbTbFAg0IB2YhGJy+yZ0yNviyRNgYBIBqmzYLYNDg3z4KZt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\nrXKlTafrOOvHzrj09/d1NCO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"text": [ "<matplotlib.figure.Figure at 0x10aace110>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing the Periodogram\n", "\n", "Now we import the functions to compute the periodogram:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# imports are from up one directory\n", "import sys; import os; sys.path.append(os.path.abspath('..'))\n", "\n", "from multiband_LS import SuperSmoother" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "model = SuperSmoother().fit(t, y, dy)\n", "model.optimizer.period_range = (0.6, 0.65)\n", "print(\"best period: {0:.4f}\".format(model.best_period))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Finding optimal frequency:\n", " - Using omega_step = 0.00064\n", " - Computing periods at 1262 steps from 0.60 to 0.65\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Zooming-in on 5 candidate peaks:\n", " - Computing periods at 1010 steps\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "best period: 0.6160\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "period = np.linspace(model.best_period - 0.005,\n", " model.best_period + 0.005, 500)\n", "\n", "P = model.periodogram(period)\n", "plt.plot(period, P)\n", "plt.xlabel('period (days)'); plt.ylabel('Lomb-Scargle Power');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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LoEmFgNzn2jUN0LAoBqAeMq47qJkCoFKhzUkrqGkB4Jz/vwA+A+AUAKcC+DfO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\n0dHAAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10abd5090>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "t_fit = np.linspace(0, model.best_period, 500)\n", "\n", "plt.errorbar(t % model.best_period, y, dy, fmt='o')\n", "y_fit = model.predict(t_fit)\n", "plt.plot(t_fit, y_fit, '-k')\n", "plt.gca().invert_yaxis()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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rghUZ5SrZAYSFBJZVPQsICmHA8P8jKiqK9+bMIC3t8wr3lgc1IbyHBGw/cv58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\nSUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10a1cc5d0>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's our result." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] } ], "metadata": {} } ] }	bsd-2-clause
FeitengLab/EmotionMap	2StockEmotion/2. 数据可视化（用于目视检查数据问题）.ipynb	2	2782	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "检查结果：数据检查完毕，这次没有发现有坐标异常点（有的注释是之前初步检查时发现的，彼时信息不全，当信息补充完整后，问题数据应该都被删掉了）" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from matplotlib import cm\n", "plt.rcParams['figure.figsize'] = (16 ,12)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df = pd.read_csv('Tokyo.txt', delimiter='\\s+')\n", "for i in [10, 15, 18, 20, 25]:\n", " fig, ax = plt.subplots()\n", " cmap = cm.get_cmap('BuGn')\n", " ax = df.plot.hexbin(x='lon', y='lat', gridsize=i, sharex=False, ax=ax, cmap=cmap, vmin=0, vmax=10000)\n", " plt.title('tokyo')\n", " plt.show()\n", "# fig.savefig('tokyo{}.png'.format(i))\n", "# 运行此程序发现有一个东经138度的异常点，手工去除后，重新运行\n", "# 最红的几个点，从左下到右上三个点分别为涩谷区，东京证券交易所，不明\n", "# 中心点： 35.68, 139.78\n", "# 35.66, 139.7 35.7, 139.77 35.715, 139.8" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "df2 = pd.read_csv('London.txt', delimiter='\\s+')\n", "for i in [10, 15, 18, 20, 25]:\n", " cmap = cm.get_cmap('BuGn')\n", " fig, ax = plt.subplots()\n", " df2.plot.hexbin(x='lon', y='lat', gridsize=i, sharex=False, ax=ax, cmap=cmap, vmin=0, vmax=50000)\n", " # df2.plot.scatter(x='lon', y='lat', sharex=False) #用于检测异常值，共检测到至少三个异常点，需要剔除\n", " plt.title('london')\n", " plt.show()\n", "# fig.savefig('london{}.png'.format(i))\n", "# 中心点： 51.515, -0.10\n", "# 51.51, -0.13 周围名胜很多，位于白金汉宫和大英博物馆的中间" ] } ], "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
scholer/cy-rest-python	basic/RECOMB2014_demo-final.ipynb	3	217564	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![](http://cl.ly/YS6s/cyrest.001.png)\n", "\n", "by [Keiichiro Ono](http://keiono.github.io/)\n", "\n", "----\n", "\n", "# Goal: Scalable & Reproducible Analysis\n", "__Make Cytoscape external tool friendly__\n", "\n", "## Cytoscape Session File = Result\n", "\n", "\n", "## IPython Notebook + cyREST = Process\n", "\n", "----\n", "## New to IPython Notebook?\n", "__There is a great Nature article about this technology:__\n", "\n", "### [Interactive notebooks: Sharing the code](http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261)\n", "The free IPython notebook makes data analysis easier to record, understand and reproduce.\n", "\n", "by Helen Shen 05 November 2014\n", "\n", "----\n", "\n", "# Core Idea: Access Cytoscape via tools/languages of your choice\n", "\n", "![](http://cl.ly/YTR0/rest2.png)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Cytoscape service is located at: http://localhost:1234/v1/\n", "\n", "\n", "Available Styles are: http://localhost:1234/v1/styles\n", "\n", "\n", "\n" ] }, { "data": { "text/plain": [ "<Response [200]>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import requests # Best REST client for Python!\n", "import json # Standard toolkit to do something with JSON in Python\n", "import networkx as nx # Popular python library for network analysis\n", "from IPython.display import Image # Library to display images in IPython Notebook\n", "import pandas as pd # Pandas - de-facto standard tool for data analysis in Python \n", "from py2cytoscape import util # Small utility to convert Python objects to Cytoscape-friendly ones.\n", "\n", "# Default Port Number for cyREST\n", "PORT_NUMBER = 1234\n", "\n", "# Header for posting data to the server as JSON\n", "HEADERS = {'Content-Type': 'application/json'}\n", "\n", "# The base URL to talking to local instance of Cytoscape\n", "BASE_URL = 'http://localhost:' + str(PORT_NUMBER) + '/v1/'\n", "styles_url = BASE_URL + 'styles'\n", "\n", "print('\\n\\nCytoscape service is located at: ' + BASE_URL + '\\n\\n')\n", "print('Available Styles are: ' + styles_url + '\\n\\n\\n')\n", "\n", "# Give a fresh start... Delete all networks\n", "requests.delete(BASE_URL + 'session')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save some mouse clicks...\n", "\n", "## What's the meaning of the following code?\n", "* Delete all current networks\n", "* Generate scale-free network with NetworkX\n", "* Send it to Cytoscape" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_scale_free_graph(size, index):\n", " scale_free_graph = nx.scale_free_graph(size)\n", " scale_free_graph.graph['name'] = 'Scale-Free Graph ' + str(index)\n", " return scale_free_graph\n", "\n", "# Create 10 scale-free networks on-the-fly\n", "networks = []\n", "\n", "suids = []\n", "\n", "for i in range(5):\n", " network = create_scale_free_graph(100, i)\n", " networks.append(network)\n", " \n", " # Send generated network to Cytoscape\n", " res1 = requests.post(BASE_URL + 'networks', data=json.dumps(util.from_networkx(network)), headers=HEADERS)\n", " res1_dict = json.loads(res1.content)\n", " new_suid = res1_dict['networkSUID']\n", " suids.append(new_suid)\n", " \n", " # Apply layout!\n", " requests.get(BASE_URL + 'apply/layouts/force-directed/' + str(new_suid))\n", " requests.get(BASE_URL + 'apply/styles/Directed/' + str(new_suid))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "The network Image is available here: http://localhost:1234/v1/networks/89996/views/first.png\n", "\n", "\n", "\n" ] }, { "data": { "image/png": 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JqAYKFjQbR0MM+8wKen1pKR1ojZ1Os+9L5GVLQ5lRXd23b5+ptFb22eFehQQR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\nJXv4TD7Zb/0FfSutHoYsg7YT3rficDgcDscKgVUpaFZ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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "network_image_url = BASE_URL+ 'networks/' + str(suids[0]) + '/views/first.png'\n", "\n", "print('\\n\\nThe network Image is available here: ' + network_image_url + '\\n\\n\\n')\n", "Image(network_image_url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use External Services\n", "----\n", "\n", "## What is this (messy) code doing?\n", "* Search KEGG Disease database to get list of cancer types\n", "* Get list of pathways related to those cancers\n", "* Feed the list of KEGG pathways to Cytoscape and visualize them\n", "\n", "## Agents working in this workflow\n", "* __Cytoscape__ - Visualizer & KEGG data reader\n", "* __IPython Notebook__ - Conductor\n", "* __KEGG Pathway Database__ - External Service\n", "* __KEGG Disease Database__ - External Service\n", "* __KEGG API__ - External Service" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ds:H00013</td>\n", " <td>Small cell lung cancer</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ds:H00014</td>\n", " <td>Non-small cell lung cancer</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ds:H00016</td>\n", " <td>Oral cancer</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ds:H00017</td>\n", " <td>Esophageal cancer</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ds:H00018</td>\n", " <td>Gastric cancer</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>ds:H00019</td>\n", " <td>Pancreatic cancer</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>ds:H00020</td>\n", " <td>Colorectal cancer</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>ds:H00022</td>\n", " <td>Bladder cancer</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>ds:H00023</td>\n", " <td>Testicular cancer</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>ds:H00024</td>\n", " <td>Prostate cancer</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id name\n", "0 ds:H00013 Small cell lung cancer\n", "1 ds:H00014 Non-small cell lung cancer\n", "2 ds:H00016 Oral cancer\n", "3 ds:H00017 Esophageal cancer\n", "4 ds:H00018 Gastric cancer\n", "5 ds:H00019 Pancreatic cancer\n", "6 ds:H00020 Colorectal cancer\n", "7 ds:H00022 Bladder cancer\n", "8 ds:H00023 Testicular cancer\n", "9 ds:H00024 Prostate cancer" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import io\n", "\n", "# KEGG API\n", "KEGG_API_URL = 'http://rest.kegg.jp/'\n", "\n", "# Find information about 'cancer' from KEGG disease database.\n", "query = 'cancer'\n", "\n", "res = requests.get(KEGG_API_URL + '/find/disease/' + query)\n", "pathway_list = res.content.decode('utf8')\n", "\n", "disease_df = pd.read_csv(io.StringIO(pathway_list), delimiter='\\t', header=None, names=['id', 'name'])\n", "disease_df.head(10)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "disease_ids = disease_df['id']\n", "disease_urls = disease_ids.apply(lambda x: KEGG_API_URL + 'get/' + x)\n", "\n", "def disease_parser(entry):\n", " lines = entry.split('\\n')\n", " data = {}\n", " \n", " last_key = None\n", " for line in lines:\n", " if '///' in line:\n", " return data\n", " \n", " parts = line.split(' ')\n", " if parts[0] is not None and len(parts[0]) != 0:\n", " last_key = parts[0]\n", " data[parts[0]] = line.replace(parts[0], '').strip()\n", " else:\n", " last_val = data[last_key]\n", " data[last_key] = last_val + '\|' + line.strip()\n", " return data \n", "\n", "result = []\n", "for url in disease_urls:\n", " res = requests.get(url)\n", " rows = disease_parser(res.content)\n", " result.append(rows)\n", "disease_df = pd.DataFrame(result)\n", "pathways = disease_df['PATHWAY'].dropna().unique()\n", "\n", "p_urls = []\n", "for pathway in pathways:\n", " entries = pathway.split('\|')\n", " for en in entries:\n", " url = KEGG_API_URL + 'get/' + en.split(' ')[0].split('(')[0] + '/kgml'\n", " p_urls.append(url)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "disease_df = pd.DataFrame(result)\n", "pathways = disease_df['PATHWAY'].dropna().unique()\n", "\n", "p_urls = []\n", "for pathway in pathways:\n", " entries = pathway.split('\|')\n", " for en in entries:\n", " url = KEGG_API_URL + 'get/' + en.split(' ')[0].split('(')[0] + '/kgml'\n", " p_urls.append(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import all cancer pathways into Cytoscape from KEGG Pathway DB" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def create_from_list(network_list):\n", " server_res = requests.post(BASE_URL + 'networks?source=url&collection=' + query, data=json.dumps(network_list), headers=HEADERS)\n", " return json.loads(server_res.content)\n", "\n", "requests.delete(BASE_URL + 'networks')\n", "\n", "url_list = list(set(p_urls))\n", "pathway_suids = create_from_list(url_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Embedded Visualization with Cytoscape.js" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "if (window['cytoscape'] === undefined) {\n", " var paths = {\n", " cytoscape: 'http://cytoscape.github.io/cytoscape.js/api/cytoscape.js-latest/cytoscape.min'\n", " };\n", "\n", " require.config({\n", " paths: paths\n", " });\n", "\n", " require(['cytoscape'], function (cytoscape) {\n", " console.log('---------- Loading Cytoscape.js Module -----------');\n", " window['cytoscape'] = cytoscape;\n", "\n", " var event = document.createEvent(\"HTMLEvents\");\n", " event.initEvent(\"load_cytoscape\", true, false);\n", " window.dispatchEvent(event);\n", " });\n", "}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<!DOCTYPE html>\n", "<html>\n", "\n", "<head>\n", " <meta charset=utf-8 />\n", " <style type=\"text/css\">\n", " body {\n", " font: 14px helvetica neue, helvetica, arial, sans-serif;\n", " }\n", "\n", " #cya769a0b5-d8aa-45e7-945d-844517bc6478 {\n", " height: 700px;\n", " width: 1098px;\n", " position: absolute;\n", " left: 4px;\n", " top: 5px;\n", " background: white;\n", " }\n", " </style>\n", "\n", " <script>\n", " (function() {\n", " function render() {\n", " $('#cya769a0b5-d8aa-45e7-945d-844517bc6478').cytoscape({\n", " elements: {\n", " nodes: 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One line below will prevent that. -->\n", " <div id=\"dummy\" style=\"width:1098px;height:700px\">\n", "</body>\n", "\n", "</html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Package to render networks in Cytoscape.js\n", "from py2cytoscape import cytoscapejs as cyjs\n", "\n", "res1 = requests.get(BASE_URL + 'styles/KEGG Style.json')\n", "kegg_style = res1.json()\n", "\n", "# Load local network files\n", "kegg_url = BASE_URL + 'networks/' + str(pathway_suids[3]['networkSUID'][0])\n", "net1 = requests.get(kegg_url + '/views/first')\n", "\n", "# Render it!\n", "cyjs.render(net1.json(), style=kegg_style[0]['style'], background='white')" ] } ], "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
mhjabreel/euler-project	euler-007 10001st prime.ipynb	2	70064	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "primes = []\n", "\n", "(2..1000000).lazy.each do \|i\| \n", " primes << i if primes.all? {\|prime\| (i % prime) != 0 }\n", " break if primes.count == 10001\n", "end\n", "\n", "puts primes" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 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104119, 104123, 104147, 104149, 104161, 104173, 104179, 104183, 104207, 104231, 104233, 104239, 104243, 104281, 104287, 104297, 104309, 104311, 104323, 104327, 104347, 104369, 104381, 104383, 104393, 104399, 104417, 104459, 104471, 104473, 104479, 104491, 104513, 104527, 104537, 104543, 104549, 104551, 104561, 104579, 104593, 104597, 104623, 104639, 104651, 104659, 104677, 104681, 104683, 104693, 104701, 104707, 104711, 104717, 104723, 104729, 104743]\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "puts primes.last" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "104743\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "outputs": [] } ] } ] }	mit
mediagit2016/workcamp-maschinelles-lernen-grundlagen	18-05-14-ml-workcamp/sensor-daten-10/Projekt-Sensordaten-Daten Skalieren-Workcamp-ML.ipynb	1	63756	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>WorkCamp # Maschinelles Lernen - ## Grundlagen - ###2018</h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Praktische Übung</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Beispiel xx # Arbeiten mit Sensordaten ## Einlesen und Skalierung von Daten</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problemstellung:<br>\n", "In diesem Jupyter Notebook , werden Sie in einer Fallstudie die Aufbereitung von Daten durch Skalierung, Normalisierung, Skalenänderung und Binärisierung kennenlernen. Dies ist für einige Algorithmen für Maschinelles Lernen notwendig.\n", "Nach Abschluss dieses Notebooks sollten Sie wissen:\n", "<ul>\n", "<li>Wie man ein Vorhersagemodellierungsproblem auf Basis einer Fragestelluung zur Classiﬁcation durchgehend abarbeitet.\n", "<li>Wie man bisher unbekannte Daten in panda DataFrames lädt: (csv, xlsx, xls, xml, json, hdf5 etc.).\n", "<li>Wie man unbekannte Daten mit einer deskriptiven Statistik in python analysiert.\n", "<li>Wie man unbekannte Daten mit python Bibliotheken visualisiert.\n", "<li>Wie man erzeugte Plots, speichert und dokumentiert.\n", "<li>Wie man Datentransformationen verwendet, um die Performance des Modells zu verbessern, zum Beispiel Normalisierung oder Standardisierung.\n", "<li>Wie man Algorithmus-, oder Hyperparameter-Tuning verwendet, um die Modell-Leistung zu verbessern.\n", "<li>Wie man Ensemble-Methoden verwendet und eine Abstimmung der Parameter zur Verbesserung der Modell-Performance durchführt.\n", "<li>Wie man die Kreuz-Validierung zur Beurteilung der Performance von ML-Algorithmen einsetzt.\n", "<li> Auf welcher Basis eine Beurteilung der verwendetn Classification Algorithmen stattfindet. (Classification Matrix, Confusion Matrix)\n", "</ul>\n", "Die Module und Bibliotheken stehen alle in der <b>Anaconda scikit-learn</b> Umgebung zum Maschinellen Lernen direkt zur Verfügung.<br>\n", "<b>Arbeiten mit Zeitreihen:</b><br>\n", "Insbesondere beim arbeiten mit Zeitreihen (timeseries) wird, falls notwendig, statsmodels und dessen Klassen, Bibliotheken und Module nachgeladen.<br>\n", "<b>Tipp:</b><br>\n", "<b>Falls in Ihrer Version statsmodels nicht vorhanden ist, mit: !pip install statsmodels in einer Jupyter Zelle\n", "nachinstallieren.</b><br>\n", "Informationen zu statsmodels finden Sie hier: http://www.statsmodels.org/<br>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "########Eventuell Strukturbild einbauen ########" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "########Evtl. nochmals Vorgehen als Ablaufmodell#########" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Laden der entsprechenden Module (kann etwas dauern !)\n", "# Wir laden die Module offen, damit man einmal sieht, was da alles benötigt wird\n", "# Allerdings aufpassen, dann werden die Module anderst angesprochen wie beim Standard\n", "# zum Beispiel pyplot und nicht plt\n", "from matplotlib import pyplot\n", "pyplot.rcParams[\"figure.figsize\"] = (15,12)\n", "%matplotlib inline\n", "import numpy as np #wird allerdings nicht benötigt\n", "from pandas import read_csv\n", "from pandas import set_option\n", "from pandas.plotting import scatter_matrix\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import ExtraTreesClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problem Beschreibung:<br>\n", "Der Fokus dieses Projektes liegt auf dem Datensatz \"sensordaten-10.csv\". Das Problem ist die Vorhersage von guten und schlechten Werkstücken aus den 10 Sensordaten. Jedes Muster ist ein Satz von 10 Zahlen. Die Sensoren decken unterschiedliche Wertebereiche ab.Das Label, das jeder Datenreihe zugeordnet ist, enthält 0 oder 1. Wenn das Werkstück die Beurteilung gut hat steht eine 1 in der Spalte Label, sonst eine 0.<br>\n", "<b>Aufgabe:</b><br>\n", "Laden Sie die Daten und verschaffen Sie sich einen ersten Überblick<br>\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "#Laden der Daten [12100 Datensätze mit 10 Sensoren und einer Spalte Label (12100x11)Matrix]\n", "url = 'sensordaten-10.csv'\n", "datensatz = read_csv(url, sep=';', header=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Beschreibende Statistik</h3>" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(12100, 11)\n" ] } ], "source": [ "# Ausgabe df.shape\n", "print(datensatz.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sens-1 float64\n", "Sens-2 float64\n", "Sens-3 float64\n", "Sens-4 float64\n", "Sens-5 float64\n", "Sens-6 float64\n", "Sens-7 float64\n", "Sens-8 float64\n", "Sens-9 float64\n", "Sens-10 float64\n", "Label int64\n", "dtype: object\n" ] } ], "source": [ "# Ausgabe df.dtypes\n", "# Spalte enthält die Classifikation R oder M\n", "set_option('display.max_rows', 50)\n", "print(datensatz.dtypes)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Sens-1 Sens-2 Sens-3 Sens-4 Sens-5 Sens-6 Sens-7 Sens-8 Sens-9 Sens-10 Label\n", "0 15.31 25.31 75.31 109.23 63.07 0.65 159.60 23.69 41.53 59.21 1\n", "1 16.76 26.76 76.76 115.05 77.62 1.38 181.44 47.48 77.22 118.70 1\n", "2 18.62 28.62 78.62 122.48 96.20 2.31 209.29 46.78 76.16 116.94 1\n", "3 18.52 28.52 78.52 122.08 95.21 2.26 207.82 41.68 68.52 104.20 1\n", "4 18.93 28.93 78.93 123.74 99.34 2.47 214.01 34.61 57.92 86.54 1\n", "5 17.28 27.28 77.28 117.12 82.81 1.64 189.21 46.71 76.07 116.78 1\n", "6 18.09 28.09 78.09 120.35 90.88 2.04 201.31 42.68 70.02 106.71 1\n", "7 19.00 29.00 79.00 124.00 100.00 2.50 215.00 31.19 52.78 77.96 1\n", "8 17.78 27.78 77.78 119.11 87.79 1.89 196.68 43.80 71.71 109.51 0\n", "9 17.83 27.83 77.83 119.31 88.27 1.91 197.40 47.71 77.57 119.28 1\n", "10 16.53 26.53 76.53 114.11 75.26 1.26 177.89 60.44 96.66 151.09 0\n", "11 19.23 29.23 79.23 124.90 102.26 2.61 218.39 55.07 88.60 137.67 1\n", "12 17.33 27.33 77.33 117.30 83.25 1.66 189.88 49.71 80.57 124.28 1\n", "13 16.86 26.86 76.86 115.45 78.62 1.43 182.92 38.00 63.00 94.99 1\n", "14 19.18 29.18 79.18 124.72 101.79 2.59 217.68 42.50 69.75 106.26 1\n", "15 17.10 27.10 77.10 116.41 81.02 1.55 186.53 49.83 80.75 124.58 0\n", "16 19.37 29.37 79.37 125.49 103.73 2.69 220.59 39.42 65.13 98.55 1\n", "17 17.47 27.47 77.47 117.87 84.67 1.73 192.01 44.87 73.30 112.16 1\n", "18 17.83 27.83 77.83 119.32 88.30 1.91 197.44 45.00 73.50 112.50 1\n", "19 18.46 28.46 78.46 121.85 94.62 2.23 206.93 29.64 50.45 74.09 1\n" ] } ], "source": [ "# Ausgabe df.head mit vergösserter display width\n", "set_option('display.width', 100)\n", "print(datensatz.head(20))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Sens-1 Sens-2 Sens-3 Sens-4 Sens-5 Sens-6 Sens-7 \\\n", "count 12100.0000 12100.0000 12100.0000 12100.0000 12100.0000 12100.0000 12100.0000 \n", "mean 18.0005 28.0005 78.0005 120.0021 90.0052 2.0006 200.0078 \n", "std 1.0067 1.0067 1.0067 4.0267 10.0667 0.5028 15.1000 \n", "min 13.9900 23.9900 73.9900 103.9600 49.9100 0.1300 139.8600 \n", "25% 17.3100 27.3100 77.3100 117.2400 83.1100 1.6600 189.6600 \n", "50% 18.0000 28.0000 78.0000 119.9900 89.9800 2.0000 199.9650 \n", "75% 18.6700 28.6700 78.6700 122.6800 96.7100 2.3400 210.0600 \n", "max 21.7400 31.7400 81.7400 134.9700 127.4200 3.8700 256.1200 \n", "\n", " Sens-8 Sens-9 Sens-10 Label \n", "count 12100.0000 12100.0000 12100.0000 12100.0000 \n", "mean 44.0383 72.0574 110.0942 0.9230 \n", "std 7.9944 11.9916 19.9872 0.2666 \n", "min 11.9300 23.8900 29.8100 0.0000 \n", "25% 38.7900 64.1875 96.9700 1.0000 \n", "50% 44.0400 72.0600 110.0950 1.0000 \n", "75% 49.3300 80.0000 123.3300 1.0000 \n", "max 86.9200 136.3700 217.2900 1.0000 \n" ] } ], "source": [ "# Ausgabe df.describe() mit 4 Nachkomma Stellen\n", "set_option('precision', 4)\n", "print(datensatz.describe())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Label\n", "0 932\n", "1 11168\n", "dtype: int64\n" ] } ], "source": [ "# Ausgabe der Klassen Verteilung in der Spalte 60\n", "print(datensatz.groupby('Label').size())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Visualisierung der Daten</h3>" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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7k/zSasbX9/p7jDPJ+iTXJ7kxyV+udowthj393h+b5H8m+WyL81+MKc73JLkn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\nUdcewDnA7cCnmx1zIvD1wM303uAfAU4ad5wrqNcb6X3Y3wn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JUkeYAEqSJElSR5gASpIk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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe910358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Ausgabe Histogramm\n", "pyplot.rcParams[\"figure.figsize\"] = (15,12)\n", "datensatz.hist()\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Standardisierung der Daten</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standardisierung ist eine nützliche Technik, um Attribute mit einer beliebigen Gauss-Normalverteilung N( , ) in eine Standard-Gaußsche Verteilung N(0,1) mit einem Mittelwert von 0 und einer Standardabweichung von 1 zu transformieren. Die Standardisierung ist am besten geeignet für Algorithmen, die eine Gaußsche Verteilung in den Eingangsvariablen annehmen und besser mit skalierten Daten arbeiten, wie z.B. Lineare Regression, Logistische Regression und Lineare Diskriminanzanalyse. Wir können Daten mit scikit-learn mit der StandardScaler-Klasse standardisieren." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-2.673 -2.673 -2.673 -2.675 -2.676 -2.686 -2.676 -2.545 -2.546 -2.546]\n", " [-1.232 -1.232 -1.232 -1.23 -1.23 -1.234 -1.23 0.431 0.431 0.431]\n", " [ 0.615 0.615 0.615 0.615 0.615 0.615 0.615 0.343 0.342 0.343]\n", " [ 0.516 0.516 0.516 0.516 0.517 0.516 0.517 -0.295 -0.295 -0.295]\n", " [ 0.923 0.923 0.923 0.928 0.927 0.934 0.927 -1.179 -1.179 -1.179]\n", " [-0.716 -0.716 -0.716 -0.716 -0.715 -0.717 -0.715 0.334 0.335 0.335]\n", " [ 0.089 0.089 0.089 0.086 0.087 0.078 0.086 -0.17 -0.17 -0.169]\n", " [ 0.993 0.993 0.993 0.993 0.993 0.993 0.993 -1.607 -1.608 -1.608]\n", " [-0.219 -0.219 -0.219 -0.222 -0.22 -0.22 -0.22 -0.03 -0.029 -0.029]\n", " [-0.169 -0.169 -0.169 -0.172 -0.172 -0.18 -0.173 0.459 0.46 0.46 ]]\n" ] } ], "source": [ "# Standardisierung der Daten (Mittelwert = 0 , Standardabweichung = 1)\n", "# mit from sklearn.preprocessing import StandardScaler wurde die Klasse in Zelle 1 bereits geladen\n", "#Laden des Moduls set_printoptions\n", "from numpy import set_printoptions\n", "# Übergabe der Werte in datensatz an ein array\n", "array = datensatz.values\n", "# Aufteilen des arrays in abhängige Variable Y und unabhängige Variable X\n", "X = array[:,0:10]\n", "Y = array[:,10]\n", "scaler = StandardScaler().fit(X)\n", "rescaledX = scaler.transform(X)\n", "# Ausgabe einer Kurzfassung der Daten 0:10\n", "set_printoptions(precision=3)\n", "print(rescaledX[0:10,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Normalisierung der Daten</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Die Normalisierung in scikit-learn bezieht sich auf die Skalierung jeder Beobachtung (Zeile) auf eine Länge von 1 (eine Einheitsnorm oder ein Vektor mit der Länge von 1 in der linearen Algebra). Diese Vorverarbeitungsmethode kann für spärliche Datensätze (viele Nullen) mit Attributen unterschiedlicher Größenordnung nützlich sein, wenn Algorithmen verwendet werden, die Eingabewerte gewichten, wie Neuronale Netze und Algorithmen mit Entfernungsmessungen wie k-Nearest Neighbors. Wir können Daten in Python in scikit-learn mit der Normalizer class normalisieren." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.066 0.109 0.325 0.471 0.272 0.003 0.689 0.102 0.179 0.255]\n", " [ 0.059 0.094 0.269 0.403 0.272 0.005 0.636 0.166 0.271 0.416]\n", " [ 0.06 0.092 0.252 0.393 0.309 0.007 0.672 0.15 0.244 0.375]\n", " [ 0.061 0.094 0.259 0.403 0.314 0.007 0.686 0.138 0.226 0.344]\n", " [ 0.063 0.096 0.262 0.411 0.33 0.008 0.712 0.115 0.193 0.288]\n", " [ 0.059 0.094 0.265 0.402 0.284 0.006 0.649 0.16 0.261 0.401]\n", " [ 0.061 0.094 0.262 0.404 0.305 0.007 0.676 0.143 0.235 0.359]\n", " [ 0.064 0.097 0.265 0.416 0.335 0.008 0.721 0.105 0.177 0.261]\n", " [ 0.06 0.094 0.264 0.404 0.298 0.006 0.668 0.149 0.243 0.372]\n", " [ 0.059 0.092 0.258 0.396 0.293 0.006 0.656 0.158 0.258 0.396]]\n" ] } ], "source": [ "# Normalisierung der Daten (Vektorlänge 1)\n", "# Laden des Moduls Normalizer\n", "from sklearn.preprocessing import Normalizer\n", "#Übergabe der Werte in datensatz an ein array2\n", "array2 = datensatz.values\n", "# Aufteilen des arrays in abhängige Variable Y und unabhängige Variable X\n", "X = array2[:,0:10]\n", "Y = array2[:,10]\n", "scaler = Normalizer().fit(X)\n", "normalizedX = scaler.transform(X)\n", "# Ausgabe einer Kurzfassung der Daten 0:10\n", "set_printoptions(precision=3)\n", "print(normalizedX[0:10,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Binärisierung der Daten</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wir können die Daten mit einer binären Schwelle (threshold) transformieren. Alle Werte oberhalb der Schwelle werden mit 1 und alle gleich oder kleiner mit 0 gekennzeichnet. Dies wird als Binärisierung der Daten. Es kann nützlich sein, wenn wir scharfe Trennungen haben wollen. Es ist auch im Feature Engineering nützlich, wenn neue Festures mit großer Bedeutung hinzugefügt werden sollen. Wir können neue binäre Attribute in Python in scikit-learn mit der Binarizer class erstellen.\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 1. 1. 1. 0. 1. 0. 0. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]\n", " [ 0. 0. 1. 1. 1. 0. 1. 0. 1. 1.]]\n" ] } ], "source": [ "# Binärisierung der Daten \n", "# Laden des Moduls Binarizer aus sklearn.preprocessing\n", "from sklearn.preprocessing import Binarizer\n", "# Übergabe der Werte in datensatz an ein array3\n", "array3 = datensatz.values\n", "# Aufteilen des arrays in abhängige Variable Y und unabhängige Variable X\n", "X = array3[:,0:10]\n", "Y = array3[:,10]\n", "# Einstellen des threshold() für den Binarizer. Wir wählen 50\n", "binarizer = Binarizer(threshold=50.0).fit(X)\n", "binaryX = binarizer.transform(X)\n", "# Ausgabe einer Kurzfassung der Daten 0:10\n", "set_printoptions(precision=3)\n", "print(binaryX[0:10,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Skalierung der Daten</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wenn die Daten aus Attributen mit unterschiedlichen Größenordnungen bestehen, soe wie hier im Datensatz der Sensordaten, können sich viele Algorithmen des maschinellen Lernens durch die Neuskalierung der Attribute im Ergebnis verbessern. Oft wird dies als Normalisierung bezeichnet. Die Attribute werden dann in den Bereich zwischen 0 und 1 skaliert. Dies ist nützlich zum Beispiel für Optimierungsalgorithmen, die im Kern von maschinellen Lernalgorithmen wie dem Gradientenabstieg (gradient descent) verwendet werden. Dies ist auch nützlich bei Algorithmen, wie Regression und Neuronale Netze, die Eingaben gewichten und Algorithmen, die Entfernungsmessungen verwenden, wie zum Beipiel k-Nearest Neighbors. Wir können die Daten in scikit-learn mit der MinMaxScaler-Klasse neu skalieren." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.17 0.17 0.17 0.17 0.17 0.14 0.17 0.16 0.16 0.16]\n", " [ 0.36 0.36 0.36 0.36 0.36 0.33 0.36 0.47 0.47 0.47]\n", " [ 0.6 0.6 0.6 0.6 0.6 0.58 0.6 0.46 0.46 0.46]\n", " [ 0.58 0.58 0.58 0.58 0.58 0.57 0.58 0.4 0.4 0.4 ]\n", " [ 0.64 0.64 0.64 0.64 0.64 0.63 0.64 0.3 0.3 0.3 ]\n", " [ 0.42 0.42 0.42 0.42 0.42 0.4 0.42 0.46 0.46 0.46]\n", " [ 0.53 0.53 0.53 0.53 0.53 0.51 0.53 0.41 0.41 0.41]\n", " [ 0.65 0.65 0.65 0.65 0.65 0.63 0.65 0.26 0.26 0.26]\n", " [ 0.49 0.49 0.49 0.49 0.49 0.47 0.49 0.42 0.43 0.43]\n", " [ 0.5 0.5 0.5 0.5 0.49 0.48 0.49 0.48 0.48 0.48]]\n" ] } ], "source": [ "# Skalierung der Daten \n", "# Laden des Moduls MinMaxScaler aus sklearn.preprocessing\n", "from sklearn.preprocessing import MinMaxScaler\n", "# Übergabe der Werte in datensatz an ein array4\n", "array4 = datensatz.values\n", "# Aufteilen des arrays in abhängige Variable Y und unabhängige Variable X\n", "X = array4[:,0:10]\n", "Y = array4[:,10]\n", "# Einstellen der feature_range() im MinMaxScaler\n", "scaler = MinMaxScaler(feature_range=(0, 1))\n", "rescaledX = scaler.fit_transform(X)\n", "# Ausgabe einer Kurzfassung der Daten 0:10 mit precision=2\n", "set_printoptions(precision=2)\n", "print(rescaledX[0:10,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Weiteres Beispiel</h3>" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.64 0.848 0.15 0.907 -0.693 0.204 0.468 1.426]\n", " [-0.845 -1.123 -0.161 0.531 -0.693 -0.684 -0.365 -0.191]\n", " [ 1.234 1.944 -0.264 -1.288 -0.693 -1.103 0.604 -0.106]\n", " [-0.845 -0.998 -0.161 0.155 0.123 -0.494 -0.921 -1.042]\n", " [-1.142 0.504 -1.505 0.907 0.766 1.41 5.485 -0.02 ]\n", " [ 0.343 -0.153 0.253 -1.288 -0.693 -0.811 -0.818 -0.276]\n", " [-0.251 -1.342 -0.988 0.719 0.071 -0.126 -0.676 -0.616]\n", " [ 1.828 -0.184 -3.573 -1.288 -0.693 0.42 -1.02 -0.361]\n", " [-0.548 2.382 0.046 1.535 4.022 -0.189 -0.948 1.681]\n", " [ 1.234 0.128 1.39 -1.288 -0.693 -4.06 -0.724 1.766]]\n" ] } ], "source": [ "# Standardisierung der Daten (Mittelwert = 0 , Standardabweichung = 1)\n", "from sklearn.preprocessing import StandardScaler\n", "from pandas import read_csv\n", "from numpy import set_printoptions\n", "# Übergabe des Dateinamens an die Variable Dateiname\n", "dateiname = 'pima-indians-diabetes.data.csv'\n", "# Festlegen der Spalten Namen für den DataFrame\n", "namen = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n", "# Einlesen der Daten in einen panda DataFrame mit read_csv()\n", "df = read_csv(dateiname, names=namen)\n", "# Übergabe der Werte in df an ein array5\n", "array5 = df.values\n", "# Aufteilen des arrays in abhängige Variable Y und unabhängige Variable X - hier steht die Klasse in Spalte 9\n", "X = array5[:,0:8]\n", "Y = array5[:,8]\n", "scaler = StandardScaler().fit(X)\n", "rescaledX = scaler.transform(X)\n", "# summarize transformed data\n", "set_printoptions(precision=3)\n", "print(rescaledX[0:10,:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Weiterführende Links:</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<ul>\n", "<li> https://www.stuttgart.ihk.de\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Weiterführende Literatur:</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<ul>\n", "<li> https://www.stuttgart.ihk.de\n", "</ul>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<b>Ansprechpartner IHK-Region Stuttgart:</b><br>\n", "Dipl. Wirtsch-Ing. R. Rank" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
eecs445-f16/umich-eecs445-f16	discussion03_linear-regression-naive-bayes/discussion03_linear-regression-naive-bayes.ipynb	1	12090	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "$$ \\text{LaTeX command declarations here.}\n", "\\newcommand{\\R}{\\mathbb{R}}\n", "\\renewcommand{\\vec}[1]{\\mathbf{#1}}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# EECS445 Machine Learning\n", "## Discussion 03: Linear Regression & Naive Bayes\n", "Written by Zhao Fu; Edited by Chansoo Lee" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Linear Regression: Notations\n", "\n", "- Let vector $\\vec{x}_n \\in \\mathbb{R}^D$ denote the $n\\text{th}$ data. $D$ denotes number of attributes in dataset.\n", "- Let vector $\\phi(\\vec{x}_n) \\in \\mathbb{R}^M$ denote features for data $\\vec{x}_n$. $\\phi_j(\\vec{x}_n)$ denotes the $j\\text{th}$ feature for data $x_n$.\n", "- Feature $\\phi(\\vec{x}_n)$ is the artificial features which represents the preprocessing step. $\\phi(\\vec{x}_n)$ is usually some combination of transformations of $\\vec{x}_n$. For example, $\\phi(\\vec{x})$ could be vector constructed by $[\\vec{x}_n^T, \\cos(\\vec{x}_n)^T, \\exp(\\vec{x}_n)^T]^T$. If we do nothing to $\\vec{x}_n$, then $\\phi(\\vec{x}_n)=\\vec{x}_n$.\n", "- Continuous-valued label vector $t \\in \\mathbb{R}^N$ (target values). $t_n \\in \\mathbb{R}$ denotes the target value for $i\\text{th}$ data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Recall: Least Squares Error Function\n", "- We will find the solution $\\vec{w}$ to linear regression by minimizing a cost/objective function.\n", "- When the objective function is sum of squared errors (sum differences between target $t$ and prediction $y$ over entire training data), this approach is also called least squares.\n", "- The objective function is \n", "$$\n", "\\begin{aligned}\n", "E(\\vec{w}) \n", "&= \\frac12 \\sum_{n=1}^N (y(\\vec{x}_n, \\vec{w}) - t_n)^2 \\\\\n", "&= \\frac12 \\sum_{n=1}^N \\left( \\sum_{j=0}^{M-1} w_j\\phi_j(\\vec{x}_n) - t_n \\right)^2 \\\\\n", "&= \\frac12 \\sum_{n=1}^N \\left( \\vec{w}^T \\phi(\\vec{x}_n) - t_n \\right)^2 \\\\\n", "\\end{aligned}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Exercise 3.1\n", "Consider a data set in which each data point $t_n$ is associated with a weighting factor $r_n > 0$, so that the sum-of-squares error function becomes\n", "$$\n", "E(\\vec{w}; \\vec{r}) = \\frac12 \\sum_{n=1}^N r_n\\left(\\vec{w}^T \\phi(\\vec{x}_n) - t_n \\right)^2\n", "$$\n", "\n", "Find an expression for the solution $\\vec{w}^$ that minimizes this error function. Give two\n", "alternative interpretations of the weighted sum-of-squares error function in terms of\n", "(i) data dependent noise variance and (ii) replicated data points.\n", "\n", "#### Solution (Matrix Calculus)\n", "\n", "We can write the above error function as \n", "$E(\\vec{w};\\vec{r}) = \\frac{1}{2} \\\|S(\\Phi \\vec{w} - t)\\\|_2^2$\n", "where $S$ is an $N$-by-$N$ diagonal matrix with entries $\\sqrt{r_n}$. Note $S^\\top S = S S^\\top$ is a diagonal matrix ($N$-by-$N$) with entries $r_n$. We denote $R = S^\\top S$. We also know that\n", "\n", "Similarly to hands-on lecture 4, $\\frac{1}{2}\\\|S(\\Phi \\vec{w} - t)\\\|_2^2 = \\frac{1}{2}(\\vec{w}^\\top (S \\Phi)^\\top S^\\top (S \\Phi) \\vec{w} - 2\\vec{w}^\\top \\Phi^\\top S^\\top S t - t^\\top t)$, and thus\n", "$$\\frac{\\partial E}{\\partial \\vec{w}} = \\Phi^\\top R \\Phi \\vec{w} - \\Phi^\\top R t.$$\n", "\n", "By setting it equal to 0, we have the closed form solution $\\vec{w} = (\\Phi^\\top R \\Phi)^{-1}\\Phi^\\top Rt$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Model Selection: Cross Validation\n", "Suppose we are using the following linear regression model to fit a dataset\n", "$$h_\\theta(x) = \\sum_{i=0}^{k}{\\theta_i \\phi_i(x)}$$\n", "where $\\phi_i(x) = x^i$, and wish to decide if $k$ should be $0, 1, \\dots,$ or $10$. How can we automatically select a model?\n", "\n", "For the sake of concreteness, we assume we have some finite set of models $M = \\{M_1,\\dots, M_d\\}$ that we’re trying to select among.\n", "For instance, in our first example above, the model $M_i$ would be an $i$th order polynomial regression model." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Model Selection: Hold-out Cross Validation\n", "\n", "Suppose we have a training dataset $S$.\n", "\n", "In hold-out cross validation(also called simple cross validation), we do the following:\n", "1. Randomly split $S$ into $S_{train}$ (say, 70% of the data) and $S_{cv}$ (the remaining 30%). Here, $S_{cv}$ is called the hold-out cross validation set.\n", "2. Train each model $M_i$ on $S_{train}$ only, to get some hypothesis.\n", "3. Select and output the hypothesis that had the smallest error $\\epsilon_{S_{cv}}$ on the hold out cross validation set. (Recall, $\\epsilon_{S_{cv}}$ denotes the empirical error on the set of examples in Scv.)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Model Selection: K-Fold Cross Validation\n", "\n", "Here is a method, called k-fold cross validation, that holds out less data each time:\n", "1. Randomly split $S$ into $k$ disjoint subsets of $m/k$ training examples each. Let’s call these subsets $S_1,\\dots, S_k$.\n", "2. For each model $M_i$, we evaluate it as follows:\n", "For $j = 1,\\dots, k$\n", "Train the model $M_i$ on $S_1 \\cup \\cdots \\cup S_{j−1} \\cup S_{j+1} \\cup \\cdots S_k$ (i.e., train on all the data except $S_j$) to get some hypothesis $h_{ij}$. Test the hypothesis $h_{ij}$ on $S_j$, to get $\\epsilon_{S_j}(h_{ij})$. The estimated generalization error of model $M_i$ is then calculated as the average of the $\\epsilon_{S_j}(h_{ij})$, which is $ \\frac{1}{k}\\sum_{j}{\\epsilon_{S_j}(h_{ij})}$.\n", "3. Pick the model $M_i$ with the lowest estimated generalization error, and retrain that model on the entire training set $S$. The resulting hypothesis is then output as our final answer.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Bayesian Linear Regression\n", "Recall Maximum Likelihood Estimator(MLE)* for least square regression:\n", "\n", "Given $\\{ \\vec{x}_n, t_n \\}_{n=1}^N$, we want to find $\\vec{w}_{ML}$ that maximizes data likelihood function\n", "$$\n", "\\vec{w}_{ML}\n", "=\\arg \\max p(\\vec{t}\|\\mathcal{X},\\vec{w},\\beta)\n", "=\\arg \\max \\prod_{n=1}^N \\mathcal{N}(t_n\|\\vec{w}^T\\phi(\\vec{x}_n),\\beta^{-1})\n", "$$\n", "and by derivation we have shown in lecture $\\vec{w}_{ML}$ is equivalent to the least squares solution $\\hat{\\vec{w}} = \\Phi^\\dagger \\vec{t}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Bayesian Linear Regression\n", "Recall MAP Estimator for least square regression:\n", "\n", "$$\n", "\\begin{aligned}\n", "\\vec{w}_{MAP} \n", "&= \\arg \\max p(\\vec{w}\|\\vec{t}, \\mathcal{X},\\beta) & & (\\text{Posteriori Probability})\\\\\n", "&= \\arg \\max \\frac{p(\\vec{w}, \\vec{t}, \\mathcal{X},\\beta)}{p(\\mathcal{X}, t, \\beta)} \\\\\n", "&= \\arg \\max \\frac{p(\\vec{t}\|\\vec{w}, \\mathcal{X},\\beta) p(\\vec{w}, \\mathcal{X}, \\beta)}{p(\\mathcal{X}, t, \\beta)} \\\\\n", "&= \\arg \\max p(\\vec{t}\|\\vec{w}, \\mathcal{X},\\beta) p(\\vec{w}, \\mathcal{X}, \\beta) & & (p(X, t, \\beta) \\text{ is irrelevant to} \\ \\vec{w})\\\\\n", "&= \\arg \\max p(\\vec{t}\|\\vec{w}, \\mathcal{X},\\beta) p(\\vec{w}) p(\\mathcal{X}) p(\\beta) & & (\\text{Independence}) \\\\\n", "&= \\arg \\max p(\\vec{t}\|\\vec{w}, \\mathcal{X},\\beta) p(\\vec{w}) & & (\\text{Likelihood} \\times \\text{Prior})\n", "\\end{aligned}\n", "$$\n", "Here, we assume $\\vec{w} \\sim \\mathcal{N}(\\vec{0}, \\alpha^{-1}I)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Exercise 3.2:\n", "Suppose that we have already observed $N$ data points, the posterior distribution over $\\vec{w}$ can be regarded as the prior for the next observation. So we can predict the next data point $(\\vec{x}_{N+1}, t_{N+1})$ by maximize $p(t_{N+1}\|\\vec{x}_{N+1}, \\vec{X}, \\alpha, \\beta)$. Derive the full expression for the posterior of the new data point.\n", "\n", "We can also predict the expectation value of $t_{N+1}$ given $\\vec{x}_{N+1}$ by $\\mathbb{E}[t_{N+1}\|\\vec{x}_{N+1}]$ using the posterior probability." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Review: Naive Bayes\n", "\n", "- The essence of Naive Bayes is the conditionally independence assumption\n", " $$\n", " P(\\vec{x} \| y = c) = \\prod_{d=1}^D P(x_d \| y=c)\n", " $$\n", " i.e., given the label, all features are independent.\n", " \n", "- The full generative model of Naive Bayes is:\n", " $$\n", " \\begin{align}\n", " y &\\sim \\mathrm{Categorical}(\\pi) \\\\\n", " x_d \| y=c &\\sim \\mathrm{Categorical}(\\theta_{cd}) \\quad \\forall\\, d = 1,\\dots,D\n", " \\end{align}\n", " $$\n", " with parameters:\n", " - $P(y = c) = \\pi_c$, $\\forall c = 1,\\dots,C $\n", " - $\\sum_{c=1}^C \\pi_c = 1$ and $\\pi_c \\geq 0, \\forall c=1,\\dots,C$\n", " - $P(x_d = m\| y = c) = \\theta_{cdm}$ for every $d = 1,\\dots,D, m = 1, \\dots, M, c = 1, \\dots, C$\n", " - $\\sum_{m=1}^M \\theta_{cdm} = 1$\n", "\n", "- Conditional indepence assumption\n", " - Conditional independence: for any $i \\neq j$, $P(X_i \| X_j, Y) = P(X_i \| Y)$\n", " - The implication is: $P(X_1, \\ldots, X_n \| Y) = P(X_1 \| X_2, \\ldots, X_n, Y) P(X_2,\\ldots, X_n \| Y)$.\n", " - By the Bayes theorem, \n", "$$P(Y\| X_1, \\ldots, X_n) = \\frac{P(Y)}{P(X_1, \\ldots, X_n)} P(X_1,\\ldots,X_n \| Y) = \\frac{P(Y)\\prod_i P(X_i \| Y)}{P(X_1,\\ldots, X_n)}$$\n", " - When computing the MAP estimate $P(Y \| X_1,\\ldots, X_n)$, we can simply compare the numerator.\n", "- Parameter $\\pi$ and $\\theta$ are learned from training data.\n", " - $\\hat{\\pi}_c = \\frac{N_c}{N}$\n", " - $\\hat{\\theta}_{cdm} = \\frac{N_{cdm}}{N_c}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Common problems\n", "1. What if not all the words appear in a category, in which case we have $N_{cdm} = 0$?\n", "2. What if some attributes are continues variables, not discrete?" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ledeprogram/algorithms	class5/homework/benzaquen_mercy_5_4.ipynb	1	94136	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using data from this FiveThirtyEight post, write code to calculate the correlation of the responses from the poll.\n", "Respond to the story in your PR. Is this a good example of data journalism? Why or why not?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "import statsmodels.formula.api as smf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_excel(\"Iran_data_3.xlsx\")" ] }, { "cell_type": "code", "execution_count": 214, "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>Subject</th>\n", " <th>Sentiment</th>\n", " <th>Total</th>\n", " <th>Dem</th>\n", " <th>Rep</th>\n", " <th>Ind</th>\n", " <th>Men</th>\n", " <th>Women</th>\n", " <th>White</th>\n", " <th>Black</th>\n", " <th>...</th>\n", " <th>No Degree</th>\n", " <th>Under Age 35</th>\n", " <th>35-54</th>\n", " <th>55+</th>\n", " <th>65+</th>\n", " <th>Under $50k</th>\n", " <th>$50k+</th>\n", " <th>Lib</th>\n", " <th>Cons</th>\n", " <th>Party</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Obama</td>\n", " <td>Approve</td>\n", " <td>0.44</td>\n", " <td>0.78</td>\n", " <td>0.10</td>\n", " <td>0.37</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.35</td>\n", " <td>0.85</td>\n", " <td>...</td>\n", " <td>0.43</td>\n", " <td>0.56</td>\n", " <td>0.35</td>\n", " <td>0.45</td>\n", " <td>0.43</td>\n", " <td>0.48</td>\n", " <td>0.42</td>\n", " <td>0.69</td>\n", " <td>0.24</td>\n", " <td>0.15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Obama</td>\n", " <td>Disapprove</td>\n", " <td>0.50</td>\n", " <td>0.17</td>\n", " <td>0.86</td>\n", " <td>0.53</td>\n", " <td>0.53</td>\n", " <td>0.47</td>\n", " <td>0.60</td>\n", " <td>0.07</td>\n", " <td>...</td>\n", " <td>0.52</td>\n", " <td>0.36</td>\n", " <td>0.60</td>\n", " <td>0.50</td>\n", " <td>0.52</td>\n", " <td>0.46</td>\n", " <td>0.52</td>\n", " <td>0.24</td>\n", " <td>0.72</td>\n", " <td>0.82</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Obama</td>\n", " <td>(Don't know)</td>\n", " <td>0.06</td>\n", " <td>0.05</td>\n", " <td>0.04</td>\n", " <td>0.10</td>\n", " <td>0.05</td>\n", " <td>0.06</td>\n", " <td>0.05</td>\n", " <td>0.08</td>\n", " <td>...</td>\n", " <td>0.06</td>\n", " <td>0.08</td>\n", " <td>0.04</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.06</td>\n", " <td>0.04</td>\n", " <td>0.02</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Negotiations</td>\n", " <td>Very confident</td>\n", " <td>0.17</td>\n", " <td>0.29</td>\n", " <td>0.04</td>\n", " <td>0.15</td>\n", " <td>0.17</td>\n", " <td>0.17</td>\n", " <td>0.11</td>\n", " <td>0.37</td>\n", " <td>...</td>\n", " <td>0.14</td>\n", " <td>0.18</td>\n", " <td>0.13</td>\n", " <td>0.19</td>\n", " <td>0.18</td>\n", " <td>0.18</td>\n", " <td>0.16</td>\n", " <td>0.24</td>\n", " <td>0.10</td>\n", " <td>0.06</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Negotiations</td>\n", " <td>Somewhat confident</td>\n", " <td>0.31</td>\n", " <td>0.49</td>\n", " <td>0.13</td>\n", " <td>0.29</td>\n", " <td>0.28</td>\n", " <td>0.35</td>\n", " <td>0.29</td>\n", " <td>0.46</td>\n", " <td>...</td>\n", " <td>0.29</td>\n", " <td>0.39</td>\n", " <td>0.29</td>\n", " <td>0.29</td>\n", " <td>0.26</td>\n", " <td>0.34</td>\n", " <td>0.31</td>\n", " <td>0.48</td>\n", " <td>0.19</td>\n", " <td>0.11</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Negotiations</td>\n", " <td>Not very confident</td>\n", " <td>0.19</td>\n", " <td>0.11</td>\n", " <td>0.26</td>\n", " <td>0.22</td>\n", " <td>0.18</td>\n", " <td>0.19</td>\n", " <td>0.21</td>\n", " <td>0.06</td>\n", " <td>...</td>\n", " <td>0.22</td>\n", " <td>0.23</td>\n", " <td>0.20</td>\n", " <td>0.15</td>\n", " <td>0.14</td>\n", " <td>0.16</td>\n", " <td>0.20</td>\n", " <td>0.16</td>\n", " <td>0.22</td>\n", " <td>0.16</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Negotiations</td>\n", " <td>Not at all confident</td>\n", " <td>0.31</td>\n", " <td>0.08</td>\n", " <td>0.57</td>\n", " <td>0.32</td>\n", " <td>0.35</td>\n", " <td>0.27</td>\n", " <td>0.37</td>\n", " <td>0.05</td>\n", " <td>...</td>\n", " <td>0.32</td>\n", " <td>0.17</td>\n", " <td>0.38</td>\n", " <td>0.34</td>\n", " <td>0.40</td>\n", " <td>0.29</td>\n", " <td>0.32</td>\n", " <td>0.11</td>\n", " <td>0.47</td>\n", " <td>0.67</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Negotiations</td>\n", " <td>(Don't know)</td>\n", " <td>0.02</td>\n", " <td>0.04</td>\n", " <td>0.01</td>\n", " <td>0.02</td>\n", " <td>0.02</td>\n", " <td>0.02</td>\n", " <td>0.02</td>\n", " <td>0.05</td>\n", " <td>...</td>\n", " <td>0.03</td>\n", " <td>0.03</td>\n", " <td>0.00</td>\n", " <td>0.03</td>\n", " <td>0.02</td>\n", " <td>0.03</td>\n", " <td>0.01</td>\n", " <td>0.01</td>\n", " <td>0.02</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Deal</td>\n", " <td>Favor</td>\n", " <td>0.47</td>\n", " <td>0.60</td>\n", " <td>0.34</td>\n", " <td>0.44</td>\n", " <td>0.46</td>\n", " <td>0.47</td>\n", " <td>0.45</td>\n", " <td>0.54</td>\n", " <td>...</td>\n", " <td>0.45</td>\n", " <td>0.51</td>\n", " <td>0.46</td>\n", " <td>0.45</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.48</td>\n", " <td>0.61</td>\n", " <td>0.35</td>\n", " <td>0.35</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Deal</td>\n", " <td>Oppose</td>\n", " <td>0.43</td>\n", " <td>0.26</td>\n", " <td>0.60</td>\n", " <td>0.49</td>\n", " <td>0.48</td>\n", " <td>0.39</td>\n", " <td>0.46</td>\n", " <td>0.31</td>\n", " <td>...</td>\n", " <td>0.45</td>\n", " <td>0.37</td>\n", " <td>0.47</td>\n", " <td>0.44</td>\n", " <td>0.49</td>\n", " <td>0.45</td>\n", " <td>0.41</td>\n", " <td>0.28</td>\n", " <td>0.56</td>\n", " <td>0.61</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Deal</td>\n", " <td>(Don't know)</td>\n", " <td>0.10</td>\n", " <td>0.14</td>\n", " <td>0.06</td>\n", " <td>0.07</td>\n", " <td>0.06</td>\n", " <td>0.14</td>\n", " <td>0.09</td>\n", " <td>0.15</td>\n", " <td>...</td>\n", " <td>0.10</td>\n", " <td>0.12</td>\n", " <td>0.07</td>\n", " <td>0.11</td>\n", " <td>0.10</td>\n", " <td>0.09</td>\n", " <td>0.11</td>\n", " <td>0.11</td>\n", " <td>0.09</td>\n", " <td>0.04</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>11 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " Subject Sentiment Total Dem Rep Ind Men Women \\\n", "0 Obama Approve 0.44 0.78 0.10 0.37 0.41 0.47 \n", "1 Obama Disapprove 0.50 0.17 0.86 0.53 0.53 0.47 \n", "2 Obama (Don't know) 0.06 0.05 0.04 0.10 0.05 0.06 \n", "3 Negotiations Very confident 0.17 0.29 0.04 0.15 0.17 0.17 \n", "4 Negotiations Somewhat confident 0.31 0.49 0.13 0.29 0.28 0.35 \n", "5 Negotiations Not very confident 0.19 0.11 0.26 0.22 0.18 0.19 \n", "6 Negotiations Not at all confident 0.31 0.08 0.57 0.32 0.35 0.27 \n", "7 Negotiations (Don't know) 0.02 0.04 0.01 0.02 0.02 0.02 \n", "8 Deal Favor 0.47 0.60 0.34 0.44 0.46 0.47 \n", "9 Deal Oppose 0.43 0.26 0.60 0.49 0.48 0.39 \n", "10 Deal (Don't know) 0.10 0.14 0.06 0.07 0.06 0.14 \n", "\n", " White Black ... No Degree Under Age 35 35-54 55+ 65+ \\\n", "0 0.35 0.85 ... 0.43 0.56 0.35 0.45 0.43 \n", "1 0.60 0.07 ... 0.52 0.36 0.60 0.50 0.52 \n", "2 0.05 0.08 ... 0.06 0.08 0.04 0.05 0.05 \n", "3 0.11 0.37 ... 0.14 0.18 0.13 0.19 0.18 \n", "4 0.29 0.46 ... 0.29 0.39 0.29 0.29 0.26 \n", "5 0.21 0.06 ... 0.22 0.23 0.20 0.15 0.14 \n", "6 0.37 0.05 ... 0.32 0.17 0.38 0.34 0.40 \n", "7 0.02 0.05 ... 0.03 0.03 0.00 0.03 0.02 \n", "8 0.45 0.54 ... 0.45 0.51 0.46 0.45 0.41 \n", "9 0.46 0.31 ... 0.45 0.37 0.47 0.44 0.49 \n", "10 0.09 0.15 ... 0.10 0.12 0.07 0.11 0.10 \n", "\n", " Under $50k $50k+ Lib Cons Party \n", "0 0.48 0.42 0.69 0.24 0.15 \n", "1 0.46 0.52 0.24 0.72 0.82 \n", "2 0.05 0.05 0.06 0.04 0.02 \n", "3 0.18 0.16 0.24 0.10 0.06 \n", "4 0.34 0.31 0.48 0.19 0.11 \n", "5 0.16 0.20 0.16 0.22 0.16 \n", "6 0.29 0.32 0.11 0.47 0.67 \n", "7 0.03 0.01 0.01 0.02 0.00 \n", "8 0.47 0.48 0.61 0.35 0.35 \n", "9 0.45 0.41 0.28 0.56 0.61 \n", "10 0.09 0.11 0.11 0.09 0.04 \n", "\n", "[11 rows x 21 columns]" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create a new df just with data for Approve of Obama" ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_obama_approve = df[df['Sentiment'] == 'Approve']" ] }, { "cell_type": "code", "execution_count": 216, "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>Subject</th>\n", " <th>Sentiment</th>\n", " <th>Total</th>\n", " <th>Dem</th>\n", " <th>Rep</th>\n", " <th>Ind</th>\n", " <th>Men</th>\n", " <th>Women</th>\n", " <th>White</th>\n", " <th>Black</th>\n", " <th>...</th>\n", " <th>No Degree</th>\n", " <th>Under Age 35</th>\n", " <th>35-54</th>\n", " <th>55+</th>\n", " <th>65+</th>\n", " <th>Under $50k</th>\n", " <th>$50k+</th>\n", " <th>Lib</th>\n", " <th>Cons</th>\n", " <th>Party</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Obama</td>\n", " <td>Approve</td>\n", " <td>0.44</td>\n", " <td>0.78</td>\n", " <td>0.1</td>\n", " <td>0.37</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.35</td>\n", " <td>0.85</td>\n", " <td>...</td>\n", " <td>0.43</td>\n", " <td>0.56</td>\n", " <td>0.35</td>\n", " <td>0.45</td>\n", " <td>0.43</td>\n", " <td>0.48</td>\n", " <td>0.42</td>\n", " <td>0.69</td>\n", " <td>0.24</td>\n", " <td>0.15</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " Subject Sentiment Total Dem Rep Ind Men Women White Black \\\n", "0 Obama Approve 0.44 0.78 0.1 0.37 0.41 0.47 0.35 0.85 \n", "\n", " ... No Degree Under Age 35 35-54 55+ 65+ Under $50k $50k+ \\\n", "0 ... 0.43 0.56 0.35 0.45 0.43 0.48 0.42 \n", "\n", " Lib Cons Party \n", "0 0.69 0.24 0.15 \n", "\n", "[1 rows x 21 columns]" ] }, "execution_count": 216, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_obama_approve" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del df_obama_approve['Sentiment']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create a new df just with data for In favor of the Iran deal" ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_favor_iran_deal = df[df['Sentiment'] == 'Favor']" ] }, { "cell_type": "code", "execution_count": 219, "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>Subject</th>\n", " <th>Sentiment</th>\n", " <th>Total</th>\n", " <th>Dem</th>\n", " <th>Rep</th>\n", " <th>Ind</th>\n", " <th>Men</th>\n", " <th>Women</th>\n", " <th>White</th>\n", " <th>Black</th>\n", " <th>...</th>\n", " <th>No Degree</th>\n", " <th>Under Age 35</th>\n", " <th>35-54</th>\n", " <th>55+</th>\n", " <th>65+</th>\n", " <th>Under $50k</th>\n", " <th>$50k+</th>\n", " <th>Lib</th>\n", " <th>Cons</th>\n", " <th>Party</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8</th>\n", " <td>Deal</td>\n", " <td>Favor</td>\n", " <td>0.47</td>\n", " <td>0.6</td>\n", " <td>0.34</td>\n", " <td>0.44</td>\n", " <td>0.46</td>\n", " <td>0.47</td>\n", " <td>0.45</td>\n", " <td>0.54</td>\n", " <td>...</td>\n", " <td>0.45</td>\n", " <td>0.51</td>\n", " <td>0.46</td>\n", " <td>0.45</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.48</td>\n", " <td>0.61</td>\n", " <td>0.35</td>\n", " <td>0.35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " Subject Sentiment Total Dem Rep Ind Men Women White Black \\\n", "8 Deal Favor 0.47 0.6 0.34 0.44 0.46 0.47 0.45 0.54 \n", "\n", " ... No Degree Under Age 35 35-54 55+ 65+ Under $50k $50k+ \\\n", "8 ... 0.45 0.51 0.46 0.45 0.41 0.47 0.48 \n", "\n", " Lib Cons Party \n", "8 0.61 0.35 0.35 \n", "\n", "[1 rows x 21 columns]" ] }, "execution_count": 219, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_favor_iran_deal" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": false }, "outputs": [], "source": [ "del df_favor_iran_deal['Sentiment']" ] }, { "cell_type": "code", "execution_count": 221, "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>Subject</th>\n", " <th>Total</th>\n", " <th>Dem</th>\n", " <th>Rep</th>\n", " <th>Ind</th>\n", " <th>Men</th>\n", " <th>Women</th>\n", " <th>White</th>\n", " <th>Black</th>\n", " <th>College Degree</th>\n", " <th>No Degree</th>\n", " <th>Under Age 35</th>\n", " <th>35-54</th>\n", " <th>55+</th>\n", " <th>65+</th>\n", " <th>Under $50k</th>\n", " <th>$50k+</th>\n", " <th>Lib</th>\n", " <th>Cons</th>\n", " <th>Party</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8</th>\n", " <td>Deal</td>\n", " <td>0.47</td>\n", " <td>0.6</td>\n", " <td>0.34</td>\n", " <td>0.44</td>\n", " <td>0.46</td>\n", " <td>0.47</td>\n", " <td>0.45</td>\n", " <td>0.54</td>\n", " <td>0.5</td>\n", " <td>0.45</td>\n", " <td>0.51</td>\n", " <td>0.46</td>\n", " <td>0.45</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.48</td>\n", " <td>0.61</td>\n", " <td>0.35</td>\n", " <td>0.35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Subject Total Dem Rep Ind Men Women White Black College Degree \\\n", "8 Deal 0.47 0.6 0.34 0.44 0.46 0.47 0.45 0.54 0.5 \n", "\n", " No Degree Under Age 35 35-54 55+ 65+ Under $50k $50k+ Lib Cons \\\n", "8 0.45 0.51 0.46 0.45 0.41 0.47 0.48 0.61 0.35 \n", "\n", " Party \n", "8 0.35 " ] }, "execution_count": 221, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_favor_iran_deal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combine the the two sub df" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "collapsed": false }, "outputs": [], "source": [ "obama_approve_favor_deal = df_obama_approve.append(df_favor_iran_deal)" ] }, { "cell_type": "code", "execution_count": 223, "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>Subject</th>\n", " <th>Total</th>\n", " <th>Dem</th>\n", " <th>Rep</th>\n", " <th>Ind</th>\n", " <th>Men</th>\n", " <th>Women</th>\n", " <th>White</th>\n", " <th>Black</th>\n", " <th>College Degree</th>\n", " <th>No Degree</th>\n", " <th>Under Age 35</th>\n", " <th>35-54</th>\n", " <th>55+</th>\n", " <th>65+</th>\n", " <th>Under $50k</th>\n", " <th>$50k+</th>\n", " <th>Lib</th>\n", " <th>Cons</th>\n", " <th>Party</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Obama</td>\n", " <td>0.44</td>\n", " <td>0.78</td>\n", " <td>0.10</td>\n", " <td>0.37</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.35</td>\n", " <td>0.85</td>\n", " <td>0.47</td>\n", " <td>0.43</td>\n", " <td>0.56</td>\n", " <td>0.35</td>\n", " <td>0.45</td>\n", " <td>0.43</td>\n", " <td>0.48</td>\n", " <td>0.42</td>\n", " <td>0.69</td>\n", " <td>0.24</td>\n", " <td>0.15</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Deal</td>\n", " <td>0.47</td>\n", " <td>0.60</td>\n", " <td>0.34</td>\n", " <td>0.44</td>\n", " <td>0.46</td>\n", " <td>0.47</td>\n", " <td>0.45</td>\n", " <td>0.54</td>\n", " <td>0.50</td>\n", " <td>0.45</td>\n", " <td>0.51</td>\n", " <td>0.46</td>\n", " <td>0.45</td>\n", " <td>0.41</td>\n", " <td>0.47</td>\n", " <td>0.48</td>\n", " <td>0.61</td>\n", " <td>0.35</td>\n", " <td>0.35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Subject Total Dem Rep Ind Men Women White Black College Degree \\\n", "0 Obama 0.44 0.78 0.10 0.37 0.41 0.47 0.35 0.85 0.47 \n", "8 Deal 0.47 0.60 0.34 0.44 0.46 0.47 0.45 0.54 0.50 \n", "\n", " No Degree Under Age 35 35-54 55+ 65+ Under $50k $50k+ Lib Cons \\\n", "0 0.43 0.56 0.35 0.45 0.43 0.48 0.42 0.69 0.24 \n", "8 0.45 0.51 0.46 0.45 0.41 0.47 0.48 0.61 0.35 \n", "\n", " Party \n", "0 0.15 \n", "8 0.35 " ] }, "execution_count": 223, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obama_approve_favor_deal" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del obama_approve_favor_deal['Subject']" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del obama_approve_favor_deal['Total']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Transpose so we can have out column names as rows" ] }, { "cell_type": "code", "execution_count": 226, "metadata": { "collapsed": false }, "outputs": [], "source": [ "obama_approve_favor_deal_transpose = obama_approve_favor_deal.transpose()" ] }, { "cell_type": "code", "execution_count": 227, "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>Approve_Obama</th>\n", " <th>Favor_Deal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Dem</th>\n", " <td>0.78</td>\n", " <td>0.60</td>\n", " </tr>\n", " <tr>\n", " <th>Rep</th>\n", " <td>0.10</td>\n", " <td>0.34</td>\n", " </tr>\n", " <tr>\n", " <th>Ind</th>\n", " <td>0.37</td>\n", " <td>0.44</td>\n", " </tr>\n", " <tr>\n", " <th>Men</th>\n", " <td>0.41</td>\n", " <td>0.46</td>\n", " </tr>\n", " <tr>\n", " <th>Women</th>\n", " <td>0.47</td>\n", " <td>0.47</td>\n", " </tr>\n", " <tr>\n", " <th>White</th>\n", " <td>0.35</td>\n", " <td>0.45</td>\n", " </tr>\n", " <tr>\n", " <th>Black</th>\n", " <td>0.85</td>\n", " <td>0.54</td>\n", " </tr>\n", " <tr>\n", " <th>College Degree</th>\n", " <td>0.47</td>\n", " <td>0.50</td>\n", " </tr>\n", " <tr>\n", " <th>No Degree</th>\n", " <td>0.43</td>\n", " <td>0.45</td>\n", " </tr>\n", " <tr>\n", " <th>Under Age 35</th>\n", " <td>0.56</td>\n", " <td>0.51</td>\n", " </tr>\n", " <tr>\n", " <th>35-54</th>\n", " <td>0.35</td>\n", " <td>0.46</td>\n", " </tr>\n", " <tr>\n", " <th>55+</th>\n", " <td>0.45</td>\n", " <td>0.45</td>\n", " </tr>\n", " <tr>\n", " <th>65+</th>\n", " <td>0.43</td>\n", " <td>0.41</td>\n", " </tr>\n", " <tr>\n", " <th>Under $50k</th>\n", " <td>0.48</td>\n", " <td>0.47</td>\n", " </tr>\n", " <tr>\n", " <th>$50k+</th>\n", " <td>0.42</td>\n", " <td>0.48</td>\n", " </tr>\n", " <tr>\n", " <th>Lib</th>\n", " <td>0.69</td>\n", " <td>0.61</td>\n", " </tr>\n", " <tr>\n", " <th>Cons</th>\n", " <td>0.24</td>\n", " <td>0.35</td>\n", " </tr>\n", " <tr>\n", " <th>Party</th>\n", " <td>0.15</td>\n", " <td>0.35</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Approve_Obama Favor_Deal\n", "Dem 0.78 0.60\n", "Rep 0.10 0.34\n", "Ind 0.37 0.44\n", "Men 0.41 0.46\n", "Women 0.47 0.47\n", "White 0.35 0.45\n", "Black 0.85 0.54\n", "College Degree 0.47 0.50\n", "No Degree 0.43 0.45\n", "Under Age 35 0.56 0.51\n", "35-54 0.35 0.46\n", "55+ 0.45 0.45\n", "65+ 0.43 0.41\n", "Under $50k 0.48 0.47\n", "$50k+ 0.42 0.48\n", "Lib 0.69 0.61\n", "Cons 0.24 0.35\n", "Party 0.15 0.35" ] }, "execution_count": 227, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obama_approve_favor_deal_transpose.columns = [\"Approve_Obama\",\"Favor_Deal\"]\n", "obama_approve_favor_deal_transpose" ] }, { "cell_type": "code", "execution_count": 228, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 229, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1097d9780>" ] }, "execution_count": 229, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RlZWVEAgE8PT0hKOjIx49eoR+/fph/vz5mDVrljHyEhERNTq9H/CJjY1FUlIS5s+fj7S0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\nMYiLi0NCQoLO72r2l2Ffe+0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gLy8PTk5OWm0BAQGYO3cufv75Z6SmpkIsFmvuvwKAs7Mz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\n/qcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a6707b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "obama_approve_favor_deal_transpose.plot(kind='scatter', x= 'Approve_Obama', y='Favor_Deal')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate Correlation Coefficient" ] }, { "cell_type": "code", "execution_count": 230, "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>Approve_Obama</th>\n", " <th>Favor_Deal</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Approve_Obama</th>\n", " <td>1.000000</td>\n", " <td>0.913868</td>\n", " </tr>\n", " <tr>\n", " <th>Favor_Deal</th>\n", " <td>0.913868</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Approve_Obama Favor_Deal\n", "Approve_Obama 1.000000 0.913868\n", "Favor_Deal 0.913868 1.000000" ] }, "execution_count": 230, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obama_approve_favor_deal_transpose.corr()" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept 0.305280\n", "Approve_Obama 0.355619\n", "dtype: float64" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = smf.ols(formula='Favor_Deal~Approve_Obama',data=obama_approve_favor_deal_transpose).fit()\n", "lm.params" ] }, { "cell_type": "code", "execution_count": 233, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10acf1e10>" ] }, "execution_count": 233, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HxdW3b1+4urpKlE2fPh08Hk/iXCsqKsKZM2egqamJpUuXStTv0qULJkyYIHP7LMvKvI6o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\nd3fCw8ONJuP8+fO16+/x9anvS3crui737t3LX/7yF1xcXGj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"text/plain": [ "<matplotlib.figure.Figure at 0x10ad3ae10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "intercept, slope = lm.params\n", "ax = obama_approve_favor_deal_transpose.plot(kind='scatter', x= 'Approve_Obama', y='Favor_Deal')\n", "plt.plot(obama_approve_favor_deal_transpose[\"Approve_Obama\"],slope*obama_approve_favor_deal_transpose[\"Approve_Obama\"]+intercept,\"-\",color=\"red\")\n", "ax.set_title(\"Feelings on Obama predict feelings on Iran deal\")\n", "ax.set_ylabel('Favor Iran deal')\n", "ax.set_xlabel(\"Approve of Obama\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
KirstieJane/NORA_WhitakerVendetti_DevSci2017	JUPYTER_NOTEBOOKS/VISAN_Figures.ipynb	1	1637156	null	mit
Juanlu001/poliastro	docs/source/examples/CZML Tutorial.ipynb	1	64181	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualize orbital data with Cesium\n", "\n", "Poliastro allows users to easily convert orbital data to CZML, a JSON format primarily used in applications running Cesium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dependencies\n", "\n", "You will only need poliastro (obviously) and czml3, a library for easily creating and using CZML packets\n", "\n", "``pip install poliastro czml3``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Our first example: The Molniya orbit\n", "\n", "We'll start off by using one of the readily usable poliastro examples. Of course, you can use any poliastro ``Orbit`` object." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: AstropyDeprecationWarning: astropy.extern.six will be removed in 4.0, use the six module directly if it is still needed [astropy.extern.six]\n" ] } ], "source": [ "from poliastro.czml.extract_czml import CZMLExtractor" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from poliastro.examples import molniya" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To initialize the extractor, you'll only need the starting and ending epoch of the time period you wish to visualize and the number of sample points. The larger the sample point size, the more accurate the trajectory and the bigger your packets. Finding that sweet spot between reasonable package size and visual accuracy depends on the specific orbit. Generally, you'll need a bigger sample for faster satellites. You could also \"break\" your orbit into different parts and define the sample size individually (for example, this could be useful when the satellite accelerates within a certain time interval).\n", "\n", "For this specific example, we're only interested in a single orbital period." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "start_epoch = molniya.epoch\n", "end_epoch = molniya.epoch + molniya.period\n", "N = 80" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "extractor = CZMLExtractor(\n", " start_epoch,\n", " end_epoch,\n", " N\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add an orbit you can simply call ``add_orbit`` and pass your ``Orbit`` along with an optional precision parameter (``rtol``). However, there are also many optional parameters you can pass to the extractor to specify the visual characteristics of your trajectory:\n", "\n", "#### Id parameters:\n", "\n", "``id_name``: The orbit id name\n", "\n", "``id_description``: The orbit's description\n", "\n", "#### Path parameters:\n", "\n", "``path_width``: The trajectorie's width. It's defined in pixels and defaults to ``1.0``\n", "\n", "``path_show``: Whether the trajectorie's path is visible (true by default)\n", "\n", "``path_color``: The trajectorie's color, a simple list with the rgba values (e.g. ``[45, 30, 50, 255]``)\n", "\n", "#### Label parameters:\n", "\n", "``label_text``: The label text; the text that appears besides the orbit. \n", "\n", "``label_show``: Whether the label is visible (true by default)\n", "\n", "``label_fill_color``: The fill color of the label , a simple list with the rgba values\n", "\n", "``label_outline_color``: The fill color of the label , a simple list with the rgba values\n", "\n", "``label_font``: The font properties (CSS syntax)\n", "\n", "#### Groundtrack parameters:\n", "\n", "``show_groundtrack``: Whether the groundtrack is visible (true by default)\n", "\n", "``groundtrack_lead_time``: The time the animation is ahead of the real-time groundtrack\n", "\n", "``groundtrack_trail_time``: The time the animation is behind the real-time groundtrack\n", "\n", "``groundtrack_width``: The groundtrack width\n", "\n", "``groundtrack_color``: The groundtrack color. By default, it is set to the trajectory's color\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "extractor.add_orbit(molniya,\n", " id_name=\"MolniyaOrbit\",\n", " path_width=2,\n", " label_text=\"Molniya\", \n", " label_fill_color=[125, 80, 120, 255]\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now export the extractor packets by simply calling ``extractor.packets`` and load it to the Cesium app as described [here](https://github.com/poliastro/cesium-app)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{\n", " \"id\": \"document\",\n", " \"version\": \"1.0\",\n", " \"name\": \"document_packet\",\n", " \"clock\": {\n", " \"interval\": \"2000-01-01T12:00:00.000/2000-01-01T23:59:35.108\",\n", " \"currentTime\": \"2000-01-01T12:00:00.000\",\n", " \"multiplier\": 60,\n", " \"range\": \"LOOP_STOP\",\n", " \"step\": \"SYSTEM_CLOCK_MULTIPLIER\"\n", " }\n", " }, {\n", " \"id\": \"custom_properties\",\n", " \"properties\": {\n", " \"custom_attractor\": true,\n", " \"ellipsoid\": [\n", " {\n", " \"array\": [\n", " 6378136.6,\n", " 6378136.6,\n", " 6356751.9\n", " ]\n", " }\n", " ],\n", " \"map_url\": [\n", " \"https://upload.wikimedia.org/wikipedia/commons/c/c4/Earthmap1000x500compac.jpg\"\n", " ],\n", " \"scene3D\": true\n", " }\n", " }, {\n", " \"id\": 0,\n", " \"name\": \"MolniyaOrbit\",\n", " \"availability\": \"2000-01-01T12:00:00Z/2000-01-01T23:59:35Z\",\n", " \"position\": {\n", " \"epoch\": \"2000-01-01T12:00:00.000\",\n", " \"interpolationAlgorithm\": \"LAGRANGE\",\n", " \"interpolationDegree\": 5,\n", " \"referenceFrame\": \"INERTIAL\",\n", " \"cartesian\": [\n", " 0.0,\n", " 10140093.639301956,\n", " -800580.7545708627,\n", " -1598722.8245590802,\n", " 539.6888535268,\n", " 12583736.532830043,\n", " 608614.3053631423,\n", " 1215374.6836679683,\n", " 1079.3777070536,\n", " 14295507.86717956,\n", " 1989670.1402512235,\n", " 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38857.5974539309,\n", " -15852598.670961218,\n", " 3766095.5927112834,\n", " 7520719.114421264,\n", " 39397.2863074578,\n", " -14787403.229959672,\n", " 2479017.267440332,\n", " 4950483.090312828,\n", " 39936.9751609846,\n", " -13280284.57641061,\n", " 1120227.7306741788,\n", " 2237043.085919334,\n", " 40476.6640145114,\n", " -11138571.623096671,\n", " -286874.6960856143,\n", " -572875.5304221162,\n", " 41016.3528680382,\n", " -8054972.095609328,\n", " -1656088.279449939,\n", " -3307131.8748607785,\n", " 41556.041721565,\n", " -3666476.7099878453,\n", " -2716113.2463438613,\n", " -5423952.819531379,\n", " 42095.7305750919,\n", " 1741220.9792386417,\n", " -2919128.3756440603,\n", " -5829364.664732453,\n", " 42635.4194286187,\n", " 6613709.608181573,\n", " -2104083.683650703,\n", " -4201758.024570373,\n", " 43175.1082821455,\n", " 10140093.63930242,\n", " -800580.7545706416,\n", " -1598722.8245586387,\n", " 43714.7971356723,\n", " 12583736.53283038,\n", " 608614.3053633778,\n", " 1215374.683668438\n", " ]\n", " },\n", " \"billboard\": {\n", " \"show\": true,\n", " \"image\": \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAADJSURBVDhPnZHRDcMgEEMZjVEYpaNklIzSEfLfD4qNnXAJSFWfhO7w2Zc0Tf9QG2rXrEzSUeZLOGm47WoH95x3Hl3jEgilvDgsOQUTqsNl68ezEwn1vae6lceSEEYvvWNT/Rxc4CXQNGadho1NXoJ+9iaqc2xi2xbt23PJCDIB6TQjOC6Bho/sDy3fBQT8PrVhibU7yBFcEPaRxOoeTwbwByCOYf9VGp1BYI1BA+EeHhmfzKbBoJEQwn1yzUZtyspIQUha85MpkNIXB7GizqDEECsAAAAASUVORK5CYII=\"\n", " },\n", " \"label\": {\n", " \"text\": \"Molniya\",\n", " \"font\": \"11pt Lucida Console\",\n", " \"style\": \"FILL\",\n", " \"fillColor\": {\n", " \"rgba\": [\n", " 125,\n", " 80,\n", " 120,\n", " 255\n", " ]\n", " },\n", " \"outlineColor\": {\n", " \"rgba\": [\n", " 255,\n", " 255,\n", " 0,\n", " 255\n", " ]\n", " },\n", " \"outlineWidth\": 1.0\n", " },\n", " \"path\": {\n", " \"width\": 2,\n", " \"resolution\": 120,\n", " \"material\": {\n", " \"solidColor\": {\n", " \"color\": {\n", " \"rgba\": [\n", " 255,\n", " 255,\n", " 0,\n", " 255\n", " ]\n", " }\n", " }\n", " }\n", " }\n", " }]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "extractor.packets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Landing on Mars\n", "\n", "You can customize the attractor of your orbit by defining any valid ellipsoid with the help of poliastro's ``Body`` class. For your convenience, poliastro offers a pre-defined list of all the major planetary bodies of the solar system so you can simply import them." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from poliastro.bodies import Mars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, when defining a new attractor you want to be able to identify something other than it's shape. For this reason, the extractor allows you to easily set the UV map by simply providing a valid URL." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "mars_uv = \"https://upload.wikimedia.org/wikipedia/commons/f/fd/Mars_2020_LandingSites_Final_8-full.jpg\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: AstropyDeprecationWarning: The truth value of a Quantity is ambiguous. In the future this will raise a ValueError. [astropy.units.quantity]\n" ] } ], "source": [ "extractor = CZMLExtractor(\n", " start_epoch,\n", " end_epoch,\n", " N,\n", " attractor=Mars,\n", " pr_map=mars_uv\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{\n", " \"id\": \"document\",\n", " \"version\": \"1.0\",\n", " \"name\": \"document_packet\",\n", " \"clock\": {\n", " \"interval\": \"2000-01-01T12:00:00.000/2000-01-01T23:59:35.108\",\n", " \"currentTime\": \"2000-01-01T12:00:00.000\",\n", " \"multiplier\": 60,\n", " \"range\": \"LOOP_STOP\",\n", " \"step\": \"SYSTEM_CLOCK_MULTIPLIER\"\n", " }\n", " }, {\n", " \"id\": \"custom_properties\",\n", " \"properties\": {\n", " \"custom_attractor\": true,\n", " \"ellipsoid\": [\n", " {\n", " \"array\": [\n", " 3396190.0,\n", " 3396190.0,\n", " 3376220.0\n", " ]\n", " }\n", " ],\n", " \"map_url\": \"https://upload.wikimedia.org/wikipedia/commons/f/fd/Mars_2020_LandingSites_Final_8-full.jpg\",\n", " \"scene3D\": true\n", " }\n", " }]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "extractor.packets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Return to Flatland\n", "\n", "Instead of a 3D globe you may want to visualize your orbit as a 2D projection instead. In this case you can simply set ``scene3D`` to ``false`` and Cesium will automatically render the scene's orthographic projection. This can be of use when plotting animated groundtracks as we'll see in the next section" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "extractor = CZMLExtractor(\n", " start_epoch,\n", " end_epoch,\n", " N,\n", " scene3D=False\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[{\n", " \"id\": \"document\",\n", " \"version\": \"1.0\",\n", " \"name\": \"document_packet\",\n", " \"clock\": {\n", " \"interval\": \"2000-01-01T12:00:00.000/2000-01-01T23:59:35.108\",\n", " \"currentTime\": \"2000-01-01T12:00:00.000\",\n", " \"multiplier\": 60,\n", " \"range\": \"LOOP_STOP\",\n", " \"step\": \"SYSTEM_CLOCK_MULTIPLIER\"\n", " }\n", " }, {\n", " \"id\": \"custom_properties\",\n", " \"properties\": {\n", " \"custom_attractor\": true,\n", " \"ellipsoid\": [\n", " {\n", " \"array\": [\n", " 6378136.6,\n", " 6378136.6,\n", " 6356751.9\n", " ]\n", " }\n", " ],\n", " \"map_url\": [\n", " \"https://upload.wikimedia.org/wikipedia/commons/c/c4/Earthmap1000x500compac.jpg\"\n", " ],\n", " \"scene3D\": false\n", " }\n", " }]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "extractor.packets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground track plotting\n", "\n", "Another useful feature the extractor offers, is the ability to plot the ground track of an orbit. You can set the groundtrack by setting the aforementioned ``groundtrack_show`` parameter to true. Note that this also works in 2D view. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "extractor = CZMLExtractor(\n", " start_epoch,\n", " end_epoch,\n", " N\n", " )" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "extractor.add_orbit(molniya,\n", " groundtrack_show=True,\n", " groundtrack_lead_time=20,\n", " groundtrack_trail_time=20\n", " )" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{\n", " \"id\": \"document\",\n", " \"version\": \"1.0\",\n", " \"name\": \"document_packet\",\n", " \"clock\": {\n", " \"interval\": \"2000-01-01T12:00:00.000/2000-01-01T23:59:35.108\",\n", " \"currentTime\": \"2000-01-01T12:00:00.000\",\n", " \"multiplier\": 60,\n", " \"range\": \"LOOP_STOP\",\n", " \"step\": \"SYSTEM_CLOCK_MULTIPLIER\"\n", " }\n", " }, {\n", " \"id\": \"custom_properties\",\n", " \"properties\": {\n", " \"custom_attractor\": true,\n", " \"ellipsoid\": [\n", " 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"extractor.packets" ] }, { "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 }	mit
joommf/tutorial	workshops/2017-04-24-Intermag2017/tutorial4_current_induced_dw_motion.ipynb	1	184043	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import oommfc as oc\n", "import discretisedfield as df\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Tutorial 4 - Current induced domain wall motion\n", "\n", "In this tutorial we show how spin transfer torque (STT) can be included in micromagnetic simulations. To illustrate that, we will try to move a domain wall pair using spin-polarised current.\n", "\n", "Let us simulate a two-dimensional sample with length $L = 500 \\,\\text{nm}$, width $w = 20 \\,\\text{nm}$ and discretisation cell $(2.5 \\,\\text{nm}, 2.5 \\,\\text{nm}, 2.5 \\,\\text{nm})$. The material parameters are:\n", "\n", "- exchange energy constant $A = 15 \\,\\text{pJ}\\,\\text{m}^{-1}$,\n", "- Dzyaloshinskii-Moriya energy constant $D = 3 \\,\\text{mJ}\\,\\text{m}^{-2}$,\n", "- uniaxial anisotropy constant $K = 0.5 \\,\\text{MJ}\\,\\text{m}^{-3}$ with easy axis $\\mathbf{u}$ in the out of plane direction $(0, 0, 1)$,\n", "- gyrotropic ratio $\\gamma = 2.211 \\times 10^{5} \\,\\text{m}\\,\\text{A}^{-1}\\,\\text{s}^{-1}$, and\n", "- Gilbert damping $\\alpha=0.3$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Definition of parameters\n", "L = 500e-9 # sample length (m)\n", "w = 20e-9 # sample width (m)\n", "d = 2.5e-9 # discretisation cell size (m)\n", "Ms = 5.8e5 # saturation magnetisation (A/m)\n", "A = 15e-12 # exchange energy constant (J/)\n", "D = 3e-3 # Dzyaloshinkii-Moriya energy constant (J/m2)\n", "K = 0.5e6 # uniaxial anisotropy constant (J/m3)\n", "u = (0, 0, 1) # easy axis\n", "gamma = 2.211e5 # gyromagnetic ratio (m/As)\n", "alpha = 0.3 # Gilbert damping\n", "\n", "# Mesh definition\n", "p1 = (0, 0, 0)\n", "p2 = (L, w, d)\n", "cell = (d, d, d)\n", "mesh = oc.Mesh(p1=p1, p2=p2, cell=cell)\n", "\n", "# Micromagnetic system definition\n", "system = oc.System(name=\"domain_wall_pair\")\n", "system.hamiltonian = oc.Exchange(A=A) + \\\n", " oc.DMI(D=D, kind=\"interfacial\") + \\\n", " oc.UniaxialAnisotropy(K=K, u=u)\n", "system.dynamics = oc.Precession(gamma=gamma) + oc.Damping(alpha=alpha)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Because we want to move a DW pair, we need to initialise the magnetisation in an appropriate way before we relax the system." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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E5AfAF/zn3wYuj+7bB0xkecEwjJlLAhxU1c2jv1DVzwCfaeIZvwr8\nraoOTTQBhmEYs4l+oBPoAso1vj9R4/OT0XUl+n+F6kFn2Yc3DKNFUNXDwCMi8hIAcWwa52OuY4JL\niGCdLcMwZh9/AbwFuA141+gvVfVJICcitTpijVgHPOXXxQ3DmD5E5A7gq8B6EXlMRG7E7eO80R8e\n/SBwzTietxJYBnxpommyZUTDMGYNIvKbwJCq3i4iOeArInKFqv7rqFu/AFwC/PM4o9gG7HzqKTUM\nY7pQ1evqfHX1BJ/3I2DJhBOE+dkyDKMFEZFzgdep6g3jCHMO8PrxhDEMw2gGW0Y0DKPlUNVvAff6\n2a9mmYtbnjQMw5hUbGbLMAzDMAxjCrGZLcMwDMMwjCnEOluGYRiGYRhTiHW2DMMwDMMwphDrbBmG\nYRiGYUwh1tkyDMMwDMOYQqyzZRiGYRiGMYX8f6Fe4vPhzrD0AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x110ca4ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def m_value(pos):\n", " x, y, z = pos\n", " if 20e-9 < x < 40e-9:\n", " return (0, 1e-8, -1)\n", " else:\n", " return (0, 1e-8, 1)\n", " # We have added the y-component of 1e-8 to the magnetisation to be able to \n", " # plot the vector field. This will not be necessary in the long run.\n", " \n", "system.m = df.Field(mesh, value=m_value, norm=Ms)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now, we can relax the magnetisation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/4/24 9:51: Calling OOMMF (domain_wall_pair/domain_wall_pair.mif) ... [6.8s]\n" ] }, { "data": { "image/png": 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XAQbx5uEF60LZir9ESz7k1PUeRO+MOJ2NUAW9nUrXpZG/nIUfTRsHhe9E/lJT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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11c6b2668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "md = oc.MinDriver()\n", "md.drive(system)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now we can add the STT term to the dynamics equation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "ux = 400 # velocity in x direction (m/s)\n", "beta = 0.5 # non-adiabatic STT parameter\n", "\n", "system.dynamics += oc.STT(u=(ux, 0, 0), beta=beta) # please notice the use of `+=` operator" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "And drive the system for half a nano second:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/4/24 9:51: Calling OOMMF (domain_wall_pair/domain_wall_pair.mif) ... [5.0s]\n" ] }, { "data": { "image/png": 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y2jLefHER9c1pejb2suBvS8h0DbBhWTtd63t4+eE3WD57JSLC8/fOpJgL2/fB\nGx5n/M5jiMVc7rn2T0yYOp5hY4Zw3w8eYvErYVLVvqKDNQvXcfu378aNx5iy/0T6OjIc/alDuO/6\nhzni1P34xIVHM25KmJDrQDP9wTd44aE3SG7XSsdAkVWLM3Q2BHQVFXpkA2+O2MRbQ4ayS9pn0oiF\nLOwZz25t7XhK0xE0Mq5uHb1BmrENReJ14JanAhnOOX8kpSwYmY0j4UOkl7sdhYeYAUq9XwptNliM\nn7uxJs6GfItdxyF6AyI5Sp37I1JCguWUuz5ube9RAvswGuSux+gw7gflV2w8nYFPgEKR9ZeiFAxI\niZI4pJRLwnQQS+5OW+sPSSb24r1gW5KtXyilvgM8DfYxBqjsofV+o7v3K8Ri42itOx7K0ymbIg1m\nPSgoS4CLi0bImzCLLUhAUsUB8EiiyBG+QxKOEmgJcAifig0pxHQBBlQD0IWYfsSO0hi9BscOxulg\nKeHTnSC60z6FCqJ7gABj1mO8bnvtwmr9dXXUQdskaABE2ZEng9GrqqMi2p9XncTU/mw7qiSI3gRS\nwEgRE4QjE1ovx9GOvXYWYiWNv9A+TVbkPIzuwNgOMQgW41Y4ebPsUKtg/JW245IwoBKg9bqaUZEa\nTt4sBC/kFGwIh15R9onXhImtVEbE5tZwmrsZJ5E8IgVMEF4R6GUoy8n4c2o4Lajh1BmOJkkHJihY\nTm9thdMKW/+QEwRovR5jR0U25zR7kJNeX30iH+S0xiZjYII336an4mC7SR5T5SRWT26VUyWhMd48\nxAwgVT2V0LIJE5QtpyVb4ATGX2Y747AtQk4bMKbP1mdhdfhYe28gBNbO1oHJhHLBekBbTnZUJJhf\n7cSNPxvk0DBx1h0gOYzkMYGynFbi6MqoyOacsMmG6E6QEkbKmMCOiugloFW13Ux1hG+J7YxtW+Bj\n9AZMZaQnWFzDadYgp2ANmH7bFhsBHY4u2ZFFEyyocgr9sETFD5EsIsqOwglGr6qO2OjNbLYyqlTR\nUxEjJUxgR9300s04Ddrs4poYsQnwwtE8O4qsg8XVNRuDnAQTrK5pi3bC2LIO43VZucERV+3PRqRi\nsxursSW0481ji3kPsSXQy4hXY0stp1o/7LCxpb06SrA5p9drOC0D04cgtr01Wq9GV+Pe61Ub0qVn\nMNafgtL0MGkMFuEHi3Bdw+kfuZmDDw4oZoaxbGMrrywbz+L1E/iv1z9C0AxOs09izAATV/YSPJbG\ny4UPy+tBbZDwAAAgAElEQVTu2A4BkgmQOkXdTg0cdOAkdtp/B17xOvjq5P2Y8coyFpdyTE64/O6G\np1j8/EJWzVpOueCx55G7cPe1f6avo7/a/m0jWzn67EP5+UW3MnR0G3cs/QV7HLkr9173AHsePZUJ\nU8cx74WFnHDRR2loradrQy/FfIn6ljqyvTnWL21n5wMnkc8U6N7Qy84HTiLTNQBxYZeDJ7PurQ24\nMZeJe03grdeWM2L8MJLpBCveXMPUQ6cA0L2hl10O2ol8pkC56LHrIZNZt2QDftln7JTRtK8I/TuR\nitPXGca0ZF2SrvU9ZLqztAxvontDL0/99jnO+074oPfjC37JlAMmcfGvPs+X9/s2pXwZlKJleDPN\nw5p44c+v88DNz7HduCEMnTKO3Q7ZiUM+uhuJjh68dX189aoT+P3i+Zw0ZQqr21fz+IrZrJ2boHXK\nSvJD63i8uDOfGf8ai4M2xpoBmhtz9Js6mmNFPFziypAzqxkC5Pp+iOvEUbIGbdfvBoU7QrtCo8vP\noQgIgjX24dWODFsdmWC1jUkGcAEdTkk7LdbWO0GFV/uStLYueDYVKqIRwBdDThxcEco4pJShwRlK\nfd1xKKeFwsDPiLX+EPc9TCVuS7K1G3btFZtPhB69zaW8B8Ri4/H9lZSDZSSljyQgODgoDJqEAk9A\nQrcEoGSnH4p2mFGArD0uoIlJ2PllTR8OBo0wEGwKGxVN3j4BFsUQt1NweRyUnb/OGg8ThngKdprE\nF4NyEiB5SgIx5QIBRYmDlBAgLwECGAxFkaocKgmE2bJjm7QgMXutkBcfATSGkh2JCUSsnE+ROI6t\nR14cDIJBKFSmYRDK2ARUIKbiQECBBMoO7ecl5BPKhaM2AYKnEiBQFnArnEiAlDHv4GRs3Qxiw27I\nSSyn+FY4KWvQW+dUkau0t0EoS8xyEmIqAQSUSECFk9VSpS3Ehn9PJUFym+tpM07aupxQlLAbCMSg\nnCRI8W2cEgjFKidsW5REWf1WOHkUieHYts1b/Uq1bmFblK1thpySllMcbJ3yQo1+PWv3g5zKAjEV\ns5ySVNbC5EVXbXZzTgmQAiVRuEpV9VTxm4qeBEPJRrCtc3KrnPLibZFTXKVq9FSycmozmx3kNGh7\nFU6hzVpOxtRw0lXbC/VUoCTg2unmosTDkesamw05vd0Py5Rwcez93s0PfRHEciqSwLGx4+1++E5O\ngzZbIL4Zp4rNFu1vvhgcG1s280NJgpTfwclU9WRAJXhnbNmaH9bGlsQW/XBLsWVLnIrEa2xPavQk\ng7Zn27ssTnVpQ0HiGAqA4Ks4CBQCxW9nH8zhExajGwKeWTWOl1dOYXb7WGIYRk/sItFYRjUaxpf7\nqH9I0bu8GckPbvKgW2K0HTfA2L3q2NDUQV9mCA/3t5O9fxEjn+3j0xNm4wz4pDoGeLr7bihXpr9D\nrFqwju2njmfHvSbgKJjz7AJGTxzBMecexphJI+nt6EcpxTlXnEbTkAbiiTifvup0Eqmw/7jk9i9X\nt+P4+Yxrqvf92Yvfqx4fdmo4GzB9+nR+MTO8Zp9jBz/7+/3Hvl09/vFf/7N6fPjpB1WPT704XBaw\n9zG7V3+76v5/rx5f98SV4VplpfjJ81cT+AGxeIxb5/8UHWjSDWmufugypuw/kVgizoTdxjFszBAO\nOWV/Fr60lEWvhLMpKEXHhn42ZTWLFmzkvttnUBrXiJ92eeKK31OcHOe+efOZMA7cUZuIjdbsEe9j\nu6GrKQw0MCneydB0jnXlIQx1A8pOgCtxWhxN0cCwxtBmEzGNKGHAGLT4OIAnBkeFfUalD9AYfBX6\nQ0nCtAogJxXLg6wOH1J9BG2n6UtIdWlFwWyiOiUuGUQgKw5JCcgrhwBFANQpQ50SlHRSyP8O151A\nPDG1ujxlW7EtydYZwA4i4r3rle8Dmhu/ijFZjOlB+8vQpenE/Bm4yiUtgi8GF4VGqsvbFWmEIpUF\n7wpQzjAwHYAQ2CmgSuAAECeNMfmwQzZhZx2gCWzH7dn1MgC+lKg+7dlgZxACOxriE+DbDqhsp/cA\nPFOsypVMziZ3gi+hnMdgk4ZyxpY9WJ62T8Yeplonr+Yl0dryyqZUDfK+HSXw0fhb4ORJrlqGSBFT\n5dRvywtgi5wKg5xkkJOxwXNzTplBTuadnHwM/hY51chJGfMOTqbKqVzDqWxHJEJOpRpOfVauVk+Z\nwbao5WTyg3oyW9JTLadiDaeslTP4dv2YV+OMZel7Byeg2t6hXMFem6+Ry1Y5lW0Sb5Catni77VU4\n5Wv0tCVOfuW2lGXAdtxv19O7cerfjNOgXC2n/BY41dhTjZ62xMmz5YbHtZwK72iL0GYrZWRqytiS\nngTftpcn1QH7t9ne1vwwb88X3iYn1fsNcqrY3rv7Yckmh1v3w5oyamyvEtU247Q1m31XTtsSW97J\nqdYPPVPDaQvtXa6pW6nGvnOmh3X9bVz3/Aks696OOe1jWNw5Ctcx7DNmFfuNXM7y18ezafc422UK\nxOfF6Fkxnm4/htkBaPOYMLaHMaPWkiltx/rR8Eq+nq5FrTSs0LTOXENzV8gz3bsRJeEeVeOmjmPU\n2FZcRzHQk2Xt4o187PzDuWDaGYgIXet7uP5zN7PLQZNwXYeRO45A2X2phoxstftrUU20INwPSwca\nN2bXttqER2uN64a/1Z7f0rHWGsdxQrl3uRbY7N5bLE9rYvGwy48lYtX6DhnVRroxjeM47H74zkzY\nbTxHnHEgh592AD+68FdM3HN7Ju65PXsetSu+b1g6ZzVvTF/MwjfX05crEwypI9GdJDU8xqZOB3/E\ncNJDC7w0JkGqs4EDmvtY67Th+62MTmxgbTnJiHiAq3wCHBKOAxImyjkJwm05gCzhA1wJE5qvgrzp\nra4Ez9l1jT6BXa1V8a3Q1sOX4cJ7meqDceUlDVBqGCKbqIWI4CtFULPcXAsoHFIqTqrlR7iJPVAq\njetux3vBtuyEtwBoeU93/W9gQ8eBtHceS2/32ajc93D9F3FQxJRDnZug0UnSbN9cSdnpQ7AL0Inj\n2Bw3mTzC/hYj7u4IQMIuqANIpsLFzwpFMnWkPY7hOmEDxmO7DF6bPHzwODF4nIhX5mxT1cXkg79B\nMnnYFo/jsSm2vGYq+W4ysV/1fKrm2kTyYHukiLnhgkFHjaRiccnEwdVrk6naeh5gjxwcpy1sCXdi\nzX0Pranb4PFg/ZM4qj6sb3z3mmtrOCUG5SpvEyma7NufkIgPtneqtm7JQ2o4jbechlMxx1pOm9fz\noKqc64R7yLnuDlu47+Y6S8T3qRzhqIaQU2y3wWtTtXoaLC8em2pLa0CReNu93i43WHbMLvoPOYX2\nmEgcWHNtbRtWfndwnfBtKNfZvno+tRmnGruo2nIcxy5Cj8d23SKPZKJGzupSUY+yfpOI713DqcYW\nNuM00XIaUuWUrOGUqrWhqp4cXGek5TSWis1ujdOgD8Rw7DrLmLvzVjjV2mxlNCCNsssH4rV+mNiK\nft2dAFC0MuiHB9RcW+uHtbY32nIazRb9cLN67m+PXBzVajnttJVra/W0pz2qjS171MgNXqusTcPW\nYsv+1fNbjy1jgLfHlsERlM1sfTNOLVvgNKjfwXajJn4N7v9YiTci8Nhbh/KlB85nWXcYgzOlNN84\n/AnOOXw6y5dtx/JnJsJAjPR9rWQeG0X7iuHk2xxajuzgvP94nNO/9BySKvNKy/a87I9m3bNjUHcK\n437XybCH1hDvGkz4x+01hl0vOZLkl/bj7KvPoDxmOLmmBhINdQzbZTz5UsBtV93PGeO+wr/tcSlz\npy/iwRuf4u7vP8BnJ1/M7Zffw5LXl3PpMdP4/bQ/ICL89a4XefXxcHXN3x56jcuPuxaA2y+/h99d\nFW6Jcflx3+dvD72GMYazRn2ezrVdeCWf83b8CgDP3PkC3zvzJwD86uu/474fhFuXfP2wq5j97Hy8\nkscZIz5HpnuAJa8v5wt7/AcAj/zqKX7yuV8B8KMLbuLh/wq3ILlor0tZ9MpSsn05Tht6IeVi+GDx\n8y/ewu2X30MhV+R7Z/6Er+z3LZ66YzoP/+ppfnj+TXz14Kv43B7fZPSkkehYnFuv+QuP3jGDX9/0\nLLMWtnPkZw6je59hlMemSTcbuo9xSaYDxsY3MXWHFTR3l9nDX8a+7jpmbWhhnMqgpcxKbyjjEwZN\ngEOMOIqyGEqiKYjGY3CvAF2zO4C2o+X5cnON7VT6JacmltX4fU2fE7e+E3MnUbHvePIAKx3KaoSU\n0hTt+RhCkxIaFaCEEh79/V+np/MoujcdSGDXOW4rtiXZagHeUko99R63fviHMHL444wa8SLDhj8L\njdMIEieE2akIWgxGBC2aOC5l4wMKoUyYaGlcGwjceNgRKBLEbYBx3LE4KnyTKJ22C5NxSadOCI/c\ncSTsordEYk9cFS7Qq687jYqCGho+Y+9bXw1cifguxGNhp5BMHopDmKQ01J9ny1DU1YWLux3VRiIZ\n1i2R3IN4bEcrd2y1Q6+vu9DKOdSlTw7r5owkbpOXRHKfanBMp0+g0vnVpy+wpcVJpT4GQCy2A3Eb\nuJPJA6r86+vOrHKqr6twSleT1ER8CrF42Hmnkofi2LfT6us/XdVVfd3ZllNLNeAnErsTs2/jpVLH\nVJ2gvv6zW+A0gniiltPYd3Kqq3CKkU6Gixxj7oRqZ5pM7I9rg3d9+vQtcEqRSh0FQDw+mXh8N9sW\nB1eTlPr02YOc6s+xnJqryV0isRux+E5W7qhqh95Q5aSoqzvNchpOwtpgIrEXMTf8IkI6dTyVzq++\n7nNWLkY6fbzlNI54xfaS+1UTr7r0qVTctKHufFtaklTyaMtppxpOB1aTlPr6QU4NVmeOaqx2sPHE\nVGLxyVbuiHeUAYr6ujOs3DCSyVpO2wOQSn28mljXVW3WJW3fOou5Y97GKexE0+lTquXV11f0myCZ\n/EhYt9jEanBMpg6sJin19Z96JycaqolHIr4r8Xjoh+nkESjq7LW1fnim5TSEhA22ieSexGI72PI+\nVuXUUMOpLnVSeOSMJpHYx7bFvrhOuC1Auu7kGk4XVjmlkh+1nHYknrCckgfiqDbbbmdRtdmG8yot\nX+OHtbHl8Gpsqa/hVHmY2nps+cg2xJb9rFxtbDmRwdjyWVtanFTK+mFsB+IJG1tq9FSXPosK6usq\n/tRaTZCTyX1xnSG2jFPIlpP8aPrx/H7WAbSkCzTWewxpGsBTDr9982Aeue1IZFnIG6MotimKRxbY\n/7zZXP7FeznwsMU8sWwqj337EDY9OBS5vZlh95UZOaNA3cYiifEG+UQrnSc1kz1mKOW9h/JWJsvC\n2RsoTO/g+5fey6K/vM7cO19g1jPz2fDmKh755VM8dOOTDGzqp5QrIyK4MYf1Szayz7G7M36XsUzc\newf+80+XcPwXjkUpxUc+fTj7fyK09wM/uQ/fuutiAD71rZM565thW3/rzq9x4An74DgON8+9nuHj\nhpFIxbnp1esAOPrsQ/nGLRcBcME1n+LUr4cvb333ocvY6+ipJFIJbl3wM5qHNjF5v4n8+NnvAPDR\nzx7Jl34W6uiin5zPxy4IY951T17JLgfuRGNrA7ct+CnJdBiPv/Dj8/jc988hmUpwzLmHc8y5h5Ht\nzWF0OCq0fM4qutZ385dfPcOzd80gKAc8/NsXWfDsAua9vJxrvvkH3O4ysYnDaB/fTAv19OyTZv0u\nTSRamxhzaAcMMYxIZfj05JfRgWLHeCd7pjrIGpehrkO9Ey7rqMMl6cRoVQkUUKfeOeHW5YV+ptKH\nU31os31cPDaJeHyK/e0ElI2z6Uqf6wzFjW1v7fSYqi/Ebf8WSx6FAlw1jDIKF2hC06KEpB3gclGk\nVB2trTczZPiztA17rPpQva3YlmnE77ynO/43kS8+Trn8KoE3l7QyxJWD4FSnXVylUE6MeqNQThJf\nAkqiSapwjQjBShwUvv8GMRygRKDtwlvdBZTs+coia1NdbIzJIXbhfbgI2kcB2mRsyFFo3YsCHPzB\nfWPEoxJoRXwU4cZ1RrLVuWSRQji9SQB2qwlMUH3dXcSvsjSSwSEcVq3uKyu+LQeU8av1hLJdT6SA\nDA6gMFRnfcWvliemMgsNIvlq3bTJWk4GqGwV4KFUhVOAknBgVUyuupDbmHzIScKFseGPfnXtRtgW\ndlG06bcvHwimMm1TUzdMAMazcrWcspaTYCrvZ4hXvTacYrF1poRLOJFsTKbKqdLGGB9Rdg2Y8auc\njORxCdcE1HKSSlvVcAKNY5d6G12pW8VebH3smgCMt0VOQp9tixo9GS9su0p7VrYwkGJ1vZgxuS1w\n8hBVaW+NEmt7pji44N7qV21WtxpOoqlMvxszMGizJpwmUwRIrZ6q+i0Peqa1WezAf6VuqipXaxeD\netImU9Vv5dVtEX8z/SoJ/dDoQo3t5ar+NOiHgz5pJMCxm2toU+uHxUFONXYx2BYejp0Eq7VZqXAS\nv6qnzeXKg7an+wf9cDNOlWtDP1S2jat10xX96qr+w/ur6j0qsUVqYgv2N0VNGdp/b7HF/P3YIpKr\niS01dTOVeBGgxNiaetUylI2LDkG4tg7ABCgaUfTgqBaakh5XHPUEyfqduW7xIp5Yvwt9G+tpTOVJ\nrXfxhgmBBjemqUuX2bmtk2MPf5W5hQncMOej+E820LQoIB4aIklVJrV7AbNbjDVDmij01jM804iz\nME+8s0S6rh4vyJFa3o/fk0UyOUqebSut2fuoXYnHFK3DmygXPIrZIsPHDaWUL/ORcw/jpYdf56zL\nTsJ1HXL9ebabED6U+55PPBEm6o7j0DrcPvg01VU1VfkNYOiotupxZR8sN+bSPNQ+BDbXD8qNGJxc\nGjKy9R1yyXSymkg1tTVusYyho4dUpxVjiRjFfIl0fYqjzj6U3vY+hoxqZfSkkcx88DUOOmFf0o0p\n6hpT/Nc372Hp6+HWKFo0/hoPR4M4AfWbypi4UCx4NCeS6FFxVnvD6KSZfcd10pluo0ky7KU24MWF\nBqMZl8wjohAV+llMhS+95cSQxKEgPi4KIxBT4TrJrOezXQruXdnDRRMgwMWx6YujWkD32GMXF4NB\noXUHDirczsXYl9lMFpewvy77y8K4EizDxSEh/aAcChJ+ykmUVDwFDWgpUOq7iFhsZxLJA3EaLiLm\nDOr23eBOmzZtiyeUUmratGlMmzZtzZb+aq95v3DTTTdNO/e8XhLxXahPfRzHdFKiAVcyYZBVYMSg\nUGjr2DEUeco0qgQehgQlYkpR0Juoo4RC0LKOuGh800FKSrgoyqaLhHTh4lDSXcRMFzHyGNONg48O\nOkkxgIPCMz2kTBdxpSjrPuJmI0mgbHpwpB/H9OKYfhQGbfpISR8uDp7pJak3hqNwuoeYaSdJgGe6\ncSSHMj3EpB8HCeVMNy4OZdNL0mwkjkPJ9BEzm4hTJDA9OJQQ3U2SfhQK3/STNl3EUJRMLwm9gSSK\nsunDNV3EJIuRHhQBWveQkn5cFL7pI6k3EVNOlVMC8HQPjvRVeSkMWr+dUztxHMqmwsmwsfMAths2\nA2V6iUlf2O6mn5TpIVbDKVGV20SCMr7pwaGI6G4S9KMI1zGlTZfVU9/bOHUSkzxGuiynblLSb/XU\nT1p3EFcuJd1bo6c+HNvWym6WaEx/Dae+zTnpjSQx+KYXJQO4phdHemzi3U/a9OBU2sK0k8ChaHqJ\nmQ4SePi6K+RkukkQJn2B9JMyXVX9VjiVTC+u6SROHm26UfgY3UOyyqmPlOkkptzN5MqmF8f04MoA\nmB4UGh30kKZGrsJJ9xI37SQQPNODkgyO6bOcQtvr6tyZMcNexjN9JIy1WdNHzLTXcCogpouEhHry\npaInq1+9gaS1WddsIk7BcvIQ3UPS1s2vcnIo6x4SeiMJFJ7pxTHdxCQLEnIyQS8pMlaul6TpCP3C\n6rfCKbTX/ion8zY/rHKybZHEt36YB9NDXPpwNrM9h7Lpq+HUS8x0Wk49KMpb5oSirMPykoBn/dCt\ncgow1g8rcknTQVyFZcR1hVNv6Iemxg/fHlssp41dBzB62As2tvTY2NK79diiN2xjbOmrxpay9JPQ\n60Pbk34c00lcBjDSO2iz9Fq/yAzGPTOAq9eSRuObAJFOHN2NkoFwq2lTQJk1JCXFtOVjeXLFeMob\n6sE4lL0k5UaH2A4FRu/ZzlgnwzEff5U1ySE8+fL+rHl2DPVPKOo2amo3dAz2DFh6QBOFbJL4sgSp\ntxwaX+umfl4vsXyA6R7A7SviiY/OZHE8DTEHNxmnriHF6pXd7HTQFCQwPP7b6eT7cgwd3cbjtz3L\nmEnb8Zsr7uW5e2dy/Bc+wjVn/YxZz8zj0FMP4N7rHuSNp+ey59FTeeL251i/tJ0JU8fx2C3PEE/G\naR3ezAM/f4ztp44lnojzwM8fY8oBk1izZg3zn1jC5P0m0r2xl3nTFzJ28ijmz1hMqVCmZVgTz9z5\nAqMnjSQWj/HIzU8zed8d0Vrz6M3PMHm/ifS09zHzgVfZcc/tmffCQtYsWs+YSSN55OanyWcKjNh+\nGL/40q0opRg2dghXnnAd2Z4sE3Ybzxf3uoxXHp3FpL134Npzb6BzXQ+zn1/Es/e/TCKVoLOnQFE7\nTNl/R7Ij2jBDmxi760jempyk3OZSrivRc7yDTnnsNLydPfZdzDBT4IihCzh8+EK8ossxQ9cyMl4k\nIMmOSR+l7BIeJ2aTckW9E8cB4o5DCgdwaXYSDBjNTmlNgKbfT7JHQx6Nxuh+YhJuX5SUTQiCEY+0\nbsdFUdK9JEwHDkWM6SKBwTMbSIlHXDmUTSd1eASmlwQKRznEbL36jUNMSXV9mFEQV8OprzsPJzYG\nz3uVVPpEHKeZq6++un3atGm3vFt+8/c+RD2d8LuGf6n92LRSKgEcCpwPPC8iv9vWZOrdMHnyZFm8\neA4DhT/Sn7sVCVbS4CRxJSCt3HB3Xss+EEPSieEbDUoRVw69ukSrk8QYQxafZidJwfg4yiGpXPpM\niWYnyf9v78zD7Kzqw//5nne9y9yZO1uSyWSdSWayQhYSQkKAgOyLINiCiKJW24q/WqpUq7ZErVYq\n7tqq1VprXbCo1A1cAAVZDYuQQAgJ2ZdJZp+5d+7yvuf3x/vO5CZMwiQ1TcJzPs+TJ/feec+95/Oe\n5Z77vud8D1rTqcvUiY3WmgEC0sqmGEabfTpi0R8WSYsNCD06oFZsNJpeLWREg9YMEeIrm0JYxhGF\nEkV/WCQj0eqfXq2pEQENfWjSIoQ6pEyIpxyGwhKeWIgoesMiWXEIgR4t1AiI1vQTkhJFqMO4wC1y\nYYmk2PFnDOcNejRUi0a0ZpCQZHx+RKJfDwOxk0bo1SFZiX6LDzvp2Ckx4iSo+FwMO/VoTVYEtKYX\noUpA65DH1r2H0+d++jBOUCMSO2lSEg2Y9ahOZWrFQaPp0XJYp8GwSGrEafRyylc42SJYI05Rum6t\nqRMh1Jr+CqfiKOXUFxapOYxTqEMC0bhikw9L+GIjIvSGJWriy+NR+Ubne5CQpFgHnIvRnEKgDyFD\neIBTMSxjxU4DYYEqcQgRerWmNnbqQ8jETgVCfOVQCEs4YqFip+fXvY8lc26nR0M2Kl4GKpzKaDz1\ncqdsnLfeuM4St6eXO5ViJw5w6kWojp1yI+UbbdZrj+oUtYX9Tjp2OrAd9oVFqg+qs1pD/wHt8OVO\nfXE5HaodHrrOVji9YjsskBbn5e0QIcNo7fDwfctD6/6G0+fcfkDfEjn9MfsWoVrCMTpF51sQerCo\nooyKnVxlUQoDbBSWKPq1RrTiAxvO5bcvtFDu3z/J3HLLtM3fTJMeYP09rQx2JinWaJyu6HpjY+M+\nps7cRXlcyI6BanZ0Zin0J0j3hJT3eaT7irh7y1g7B1GD0VVxDfTNqUI1Z2jUCbp29JNxHdJ7B+nu\n6Ke2IUNjNsGGNRvxfZv+jm7QUNOY4cqbLuS7n/gRub48fsrjynddxJ2f+jGX/cUF/Mn7XsvHr/sM\nb/34G5i5qIXv3/5jLn7bKlLVKR7+8e9ZeslClFI89+gGZi2Nplm89OxWps2dzP3330/rxDaaZ0wg\nCAL2butk/NRGOnd1k65J4iU8dm3aw4Q4wv2OF3cxsTWaC7nlue1MmdWM1poXn3yJGQunUxwqsv7x\njcw7cxb7dnax7qH1rLx6GWsfWs/D//M41/7dVdxx213c8437ePs/38AX/+ob9HX2c/brl7FjUwcv\nPrkZgOb2ifR0DiKWYsbSGax7citlESadNp0dPTlydkhyRjVbS/2Ua0J0VYFys6Y53UuL38n4cT2M\nkz7GSy/N6W6cUJFUIZPdYGTVsCLuM9FYogh09L/S0BuWqLN8dpVz1NkeJR2yLW8xMyEjq599ZTMY\nFknEfWuvhjqR6GoYmhpRlHRAKJAQm56wQEYclBZ6KJIVj35dIiFWvGoxoIzQH4YgIRbRin5XiFbe\nikM6dQNV6XdgWVF5iMgarfX+CcqH4HCDLR94C/AGYBrQAySI7pf9AviS1vrJV/qAI6Gtbab+2X0O\nOhykyluAXXqcoRBSonBEARpr5CJ/NGm+rAOUWCiim3523FmFKoOt+ynqMpY4WGjyuPgUQENB1eHp\nrqjzVHU4upshXcZWCWxdIo+LRwGFkJMsCd1NqDXlOF1JB6CyOLqPIR3iiIWFJkeSBIOgIa9qK9LV\n4+lOijpAVAZHD5IHXKJZETmqSNA3kjdfd1HWIaGqx9VdFHSApZLYukAeC5cyFkJOakjoqEMYUnUk\ndBdlHcTphp18bF0mh4cf30Y90CnK28udbCxCciRIkIudos842OnRdbdw5pzbYyeJz0WaBP1x3qJz\nUdaaUNVVOKWw9dBBTtUkdM9B6Q52isppNCetNUVVjz/iVIujew9y8vHJIxpyqo7kiFNUvlE5VePo\ngcM6+bqbQGuCOF3klMbW+cM4VZZTA67uYkgHsVNx1LoXOUX14kCnAFu52DogRxI/CloS14ueUepe\nDb97NikAACAASURBVI7uJ4/GRY3U2SeefQfL53yKofgzIqcoXUEHKJXG0XnyCC5RYOEjc3Lw4ttL\nw05oPVLXI6c6HN1zkFNUToevs8NOIS7D7TBFgoGRvPkHlW/kVIWjcwc6kSFB70FO+9thlLfhdljp\nVE1C90Y/wuJ0Ud7q474lwFZe3A4P5TRK30KIw+H7lofWvZcz53xyTH3LaE5H0reUdIAepW8ZxCUR\n19lBqknRS6g1RZXF1z1RWAqVwdED5LTGFxsbTS8Wm3oz/OdzC9ieT/FSqRptaXI5Dz9ZoKo3YOip\nGiSObebV5Dl14Qbq2rrZWGhga3cNezfW0vhryE20sfo16b4i9FngFrF29KEG4qkMtqAci3BcmqF0\nguZMkuLuPvo37YWBQZpmT6Rjw25S1UlOWTGTB+94iCCORVbdUIVtKaobMlz4lnNobm1iy7qt+Cmf\npZcsxPFsOrZ2MqmtKZpfXA5IpH3KpQDLUvHqwuj2lFKK4e9dEeH+++/nrLPOGnl+KIZXGFamrXwc\nhuH+x0EYHa8U+YE8yaoExaESHVv3Mrm9mZ69faz55dNMbp+IRrjrC3ezZd02yqWQnn19dO3uBSWs\nev0yVDrJAz98nNJAnmB8llTCo9g3hHXhNNxe6OzqR59jEfYmsDJdNLX2sLlUR/u4F2lJdDEkNmfU\n7yAVFvFUjtmJMo6KLhpZYhGGIVoEG0WfHiIjPkNhCRHBE5t94SB1KkkQavp1QNZyyOsylvi4lBnQ\n4EsU2jlHmiQDhFpTUjV4ujduF0kcXSCH4BOiNZQljccgBV2OQkvoKMqBhVDS5SgUkg7YE7gkVBlb\nQjLOXErhbsKwn4b6b+N7Z/zvB1sHHCTiAPVAXmvd80rHHy1tbW360TW3YNkz2NH1IaxgG54UEBSe\nONiUUfYp2OU/EFoNKL0PEESq0WFXtP8WUUwaiQOaHZoUMLw6xYGRxaOjkQDi+Q1SBSPL0Uf7jIp9\nmSTN8JYrSBIqloq/nCiu0Ms/w2dkDsyouAxvrYFkYGT5//+FU4rhYKaPPvsels795DF2qnh9VJJA\nboxOw/OLDvQ4wK/ymBEq3qsyb5KAimXzY3eKYg8dmopzJdUwErKi4tyO5lRZvw9wqtw3jJHXHn32\n5qj8Ks/bKzpV1LEjKqcK5yOqs5Xl+8pOo9bZyno/KpVOlXXolcqp0qky3ZE4HV07fHTtLSydc9th\nPqOyHVae72PTt4ikkeG+Tmpg5Cvj5XVWk0DI8+ueSdzT20YiyHH3xjb6BhNUbRH8jv1ruGynjF87\nhL98H9sHspR2J6hZr0mu16ggejccTaE9QE8rYg1AV18GX5fJbhuiUEgDIY41hLO1jO4vE/oBanvU\npsRWNM2dwvatXVh7OvCSHrl8iYvffDbdu7t56KdP0jqnmez4ap741bO89+t/zu1v/RfchMuHf/he\nPnjZJ6hrynL9B1/Hl/76G0xoGcfFbz2X/7j1Dia1NbHy6tP51kd/wClnzWb6KZP57j/dxevfezn2\nxIDv33IPb7/ten7y1V/Rs6eXP7vter72/m/j+g7XfeAqvvw3/0n7klbmnzWLb976fV77rovo6+zn\nN3c8zJs+/Hoe+O9H2b5hFzd+5E/44ed/TveeXt5+2/V8c/X3KeZL/Nltb+Bzf/k1qmpT/PntN/CR\n138ay7H48I9u4Zbz/xE/5bHquhXc/e+/4abPvomXnt/JT//jAcZNrCHZ1MDmTZ2E+TyFaXXotE91\nwuKlmULJgRqrTHBqH6GtmRl0snj6S9hWwISgl9Z0B44KqMLCRUiqAE9slAhlHeCIHV0QQaGIwpC4\nYlEOAxCFEugpW9TY0d7GfWEN1Va81VN0Xeyg2re/3wslVdGeRiNqYzr+X4CAKiTsJxCHMhaWztEZ\nQGdoU2UJjpSoy/wdjkqSSr0eJYkxD7bGshoRrXVJa73rWA60hikEQ2zqeBOChStFEI+SFpSU6cVB\n6V0MUsatugVBU/KvArudAI2b/SpaPMoyEafqQ9EbeueDdxEAVuqdBFYLAQ5W4rUEJAhUM3byzdGx\nzlKIV7wp/wpCey4hYPmXEEg9ARksbyUhFqHVivKvjNMtBydaaWP5ryWwZhCisNyVBFJNQB2Wdx4h\ngrZmoeLVj+KejXbi1ZOJKwnUlChv7hkEpAhkHJZ/MVqDtucjcd7EOw9tn4rWYPlXEKgmAnws93QC\nPAI1CStxdZy3xeBGK1OUdzGhPSd2Op9AGgiownKXE2ITWtNR8UoynGUQL0VX/mWxk2B5KwmkhoAs\nlncOIUJotY1E6BX3rAqnKwisqYTYFU6Nozu556LthWitsRPDTl6FUzNW4srol5yzEHHj0B3exYT2\n3PhcnE+gGglIY7nLCbAJrWlY/msP4TQzcnLPIZAsATVY3lmEKEK7DfEui9K5Z6KdpXH5XkloTSPA\nwnKXEUiaQBqwvAtjp7lIXN8ip0Wx05UEqjl2WkaAT6CasPzLY6cFiHte7HRhhdNFBGocASks93RC\nHEI1BSsx7LQU4nAkyr+U0GqPnPxhp+r9TtYM1IjTCrSzbMQJ8QixovMmVbHT+YSAtuYg3sWx0yp0\nHP7CTlxFoCYR4MbllCCUJqzE5WgN2JVO5xPa82OnS2OnZHwuYqeR9nQauGeNlNN+p3MJpC52Gm6H\nLRV1dgXaOSN2uoLQaonb4QoCyRBIPZY/3A5nI94lFe1wcVxnryJQkyvaYZJAxmP5l8Xlewrinj/i\npJ1TYqfLCNQEAhJxOpdQTcZKVDoNt8NLCK3ZsdN5FX3LigqnK+J0Z0T9C6D8ywms1pf1LSKZ2Km9\nom+pbIdXVfQty0bpW+Yh3gWx02sq+pbLKvqWJfvb4UjeFo7UPXFfQ2i3RX2Lu5RAaglII3bUFwaq\nGRlelm+1oeOVXGJNJJQMq2p2cr63g7uen0vfUAIJIT8xYOI1m5h7wzM4M/uRi/exfarDzqebUI9W\nUfcQpNcND7QgzGiCq/MMNHvs66lD8kka9wZUP1BA1pfxKeBv7MVaX8CeVAV19shAC0AHmiAIWXFm\nK159LVYqwXV/ewU7N+3hifvW8ZbV1/DiU5t5/O6nmbu8jXUPradULDPYm+OX//kbcv159m7vZPy0\nRnr39dPXOcCi8+fTubMbP+Uz54w2unZ109QyjuYZTQz25mieMQHbtenZ28fsM9qob6qlv3uQZZcu\nQmvI1FWxYNU8Ond10zilnmnzptDfPciE6eNoahlHz94+2k9rpa4py95tnZx+2SKCcki+f4hFr5lP\nT0cfYRDS0FzHQM8gnTu7Wfu7KN9DgwX+49bvAzA0WGDL2m1c9/4r+Pz/+wZ7t+wlW5ukrydPYV8P\n510yl+pxWRJ7cmT29tPfMUD2D2Vaunykz6X/yVqKa1O8sLOR721ewL07ZvJIZzu/7p3F7/NTeKy/\nkRfySXJx8Oew4i6VHS9DQQQnXvZhqeG9XCClQophtDRExXEutVQj8aporFZCaypag3LmUpa6qG9x\nFhLE39XiXbG/b43bhZP+G0qSRZPBznyMUGssbyVDzhyEAlbyBgIUvjOROlvRHUBZJtLZ91EGCo+A\nHvNW0VH7PaKjjzmajr7Pkk2/icHybkQS9IaQdlspkCCdvBKNpiSNWKomilDtnoGWNCUcHP81hHhg\nT8byV0VR3t3liD2bog5x0+8ENY5AUvg1HyOQFNqaiJv5u+gyt7sEK3EpJR1ip24EezZlbPzs7YSq\ngVDV4Ge/RBkfbbfgZt4bfYa/CuWfH6WruhmxWynj4df+C6FkCa16/OznKWOjndnY6b+MokT7lyPe\nCko6xM18BG1PIZAkfu1XCKQKbU3Az346WhPpLMBOvjEK6pq8DnFPowT42U+irSZCqcKv/WrkZE/C\nr/mn2GkZVvJ1Ud7SbwdnXuz0uf1OtV+mjIe2WnAzH4idzkV5F1LSIU76ZsRui5yy/0Kohp2+SBkH\n7cxGqcbY6VKUd3bsdCvamkqZ2ElVo63xsZMC5xTs5I2x058iXuTk1XwSbTUTjDil0VYzfs1tlNCx\n0zVxOb0FcU6hhIWf/SyhaiRU1fi1XybAR1vTcTIfipy8s1D+JbHTuxG7nTIufu0XCVVt7PSlyMlu\nw616d+x0Cco7J0qX+SDamkYQO4VSTWiNw89+hhJW5JR6S+x0DeItpYTGq7ktdkrH5Rs7ZT8ZXSNw\nl2AlXx85JW9EnAWUUPjZz6DVOAJVjV/7FcokCO1puJl/GHGy/Mtip3ehnVmUcfCzXyRU9YRWbezk\ngt2GU3Vz7HQRyj83dno/iEcZH7/2y4RSQ6ga8LOfi+qsOw8n/fYoXfIqxD2Dkg4jJ3tS7BSVU2g3\n4dfcHjstxkpdFzu9KXYS/Oyn0GrCiFMgSUJ7Cm71h6M6652J5V8R19m/AGe4HX4hclJZ/Oy/xE4z\ncareEzudX+H0PrTdSpnEQU5foIyFdubgpN8RpUtcVdEOP4a2J0d9xHA7tJvws8NOi7BT18dO14Oz\nKG6Ht6OtCQSSqXCajJv5eOy0AitxZez0DnDmUMbCz34+dqrBz/5r3Le04mb+dn879C+InKreg9gz\nXta3iDVplL7ligqnDx/Yt6jMQX3LQuzkDXGdvbaib/lURd/ytbhvmRydCx2Cu3ykzjpV7wRnAWVs\nvNqvRz96VA1+/R2U8NHOLJzsZ6N0iWuQuC9zsv9G2V7Mv29cyCeev4im1AALG7qoqy1hZUt0dGX4\n/T1z6N9TRe6RBhLP+9SGeSaVu0imchRnlQjai5RnlelrdBl8IU1txxDj1hbRz1jYGwdweguooRC2\n5XHmVjGwoJbcQAFXh1it9VBbTfXCFub/2Uqqkw4P3/MMM0+bRr5nkO984i42r9vOP9/9fn7z/UdG\nvqk6tnfy3KNRnKXzrl/JymuiHy6v++tLaFkwFYDL//ICGprr8JIuq65bzpTZzbi+w5KLFtC2eDp+\n2mfO8nb8pEdT63jGT2ukdcE0Zi2dgWVbtC2ezszFLWRq00yfN5mZi6bTunAaVdkU7UtaOeXsuSSq\nEkybP4XFF57KuKkNpKtTrLhqKY2T66nKpll17XKSmQStC6ax6trlFIdKXPv+K0dCVCw8by7NbVFY\nhbUPb0A5Nhe8aSWP3v0Hujr6ufzNK6EqxS9/uIYLLp9PyxVzKSRdMrmQcFkCp97F3ZtjavtusnqA\nGX4nV9c9zSzdxWnVGzm76gVmWPs4O7OHU9P9+DIc3z2KAh9qTU5rCmiKOmRbEM3H7ijZPJWvphAG\n/KR3CrtLabYVarht66UMhVByL0ClozZiZT4A7krKCF79dwmtCVHdq/smAR7amoabeU/cXy5Hucso\nanBSb0SrJkKVxE1eRZEQVBWOdzYBLglvITldIiUOAR4T3QY6S10o7zJyhd+SK/z2iEY3x3SwJSIX\nish6EXlRRN43hhRMbvgvNvXfg7aXUFCzcO1WvNQ7yYUDVFfdRL/U4icuILTG0RuWcN25lO1plONR\nbl6y4MwDSdOvyyi7BZzpDOgyIi6BPYNiHJSwqMZHVzBE0a/LiD0DsSZGj1UdOO3khncIt1soxZ8x\npGrRdjsiyTjddMSeGqdLo512huKYMyV7GoEdBYTMkQanHVHZON0kxG6NHouNttspxPGVStYkAjuK\n7TSIDU4bYjVGx1oTwJ7JwPCvBHsmxTg2zrATUOHUvN/JbiM3vG+kPYNSHFR0SNWhnXZEEhVOU+J0\nVWinnXwcw6lsTaccx/DJUwV2O4gVp5uM2C2xkxs7NcROkyucnNipIXZqip3iMAd2G0VruJwmjDgN\n6ADsGYjVFKerj528UZzqYycvzltLhVMmdsrsd4oDpOYlA3Y7ojJxuikVTl5Uvqo+dppCEMcVy+GN\n4jSDAR3ud4qDfBatiRVOGuyZFU4N4LRF5wgI7JmU4nhdBdUQ1z23wmly7FQDdntUJkDZbqlwqo7O\nhaqK001F7OmxUwItPkNx7KMDnfz4XNTFeWtG7Bn0D4dYsNsoxvGzDnSS2GlCnK4xdrIrymlS7DQu\ndrLjvLUi9qTYKRu3w3ScrpWSHV0VGZJs7JQexSmJttsZimNQHdgOk3E7rI3T7XcSUXGdHR+fi+aK\nOqvAbkOs8bHTeLDbotdHyqn5ICdV4TTcDmtjp+TLnU64vqXtqPqWst1KMW6HeZWN+9kkA7qEONMR\nexoDuoSyahC3nWtm7ObBa9/FJ5dtZo9l0600dNv0P1GPDFkIQjqVZ/qqF8mfNkTHuVBeWaQgHv3l\nNOUGSKkiifUW4Q4Ha/IgVmcfau/+W7FhqOj1FdYkYagKinmbMFAECWFfeZBnv/kwmzbsQaaMY8e6\nbZSLZXSoqcqm+f5nf87GJ1/CT3mc/adnsHt7F1uf28H5bz6b3/7gcTq27GPumbP42dfvB61pahnP\ngz94jFKhRKY+wxO/fhZEEEux9pEXCAJNIVfkpWe3EgQhuzZ10Lu3j707Otny/A669/SwbcNutr+4\nm77OfnZs2kPnrm52b+pgoDdP775+Xnx6M0ODBYJymRfWbKJYKFMcKvLsQ+tBYLAvx0M/eYJEVYKB\n7kHu/d7DTJwxgW0v7OSxXzzD8tcu4YU1L7F94x4uftsqlG3xnx+/i/W/3wS2jUolufPL97LovHmQ\nzXDH937Phme20D8+Qffsagb25Vmb6GXfEpuyuCROzePMzDNouYyv7SSZzNGHR1EUGwsJthVtSoQU\ndJlQa3rDEjlKdIcBA7pEZ1hiMCiTo8Q9/U1sLKbYGyh+3t/CtrKiU9fx8WUfoyAetjcX5UyJ6pM1\nHpyZDAczCuwZFOO+Zf93QCqu31MQZyYDugjio525FNR4lCQYklpCaxqut5i+MIfjLsZyFlKy51Jf\n/dfkgw6mZd/HzsFfkUjfTDpxEUfCKw62RORdIpJ9peNGSWcBXwQuAmYD14rI7MOn0jzVcTMpp4VJ\n6WXkSn9gUt0n6ct9g5R/PkoSFEvPkvDPoVh6DvBw7BaCYAdW/MUcht0oVRvtbA9RB6ULsaoNBBCv\nXNAEINbIJEPiSfjxk4rHI1JHehoMBoPBAIyso0dXPA4RHLQO+N2el/izNTPZPejjbnJwN7mIpXFS\nBVSmSL/lsrerGvdFl/419Qzdl8XfqkjtCbCe8HElYHBxie3zXMJtAfaEkFKLx+DUDPkzsuw9L02Y\nK5L8eR/ZLovBcTZ6Vw/23kG8Dd2Us1Wcfu0Kzlk0ib2bo7115y5vY96Z7fz2zkeZsWg6n/nNray5\ndy3LLjqVN3zwdWxbv5vlVyzm4Z88gVKKeSva+Psrb0fZFsnqBG+b/15KxYAdG3bzhuk3US4F/Prb\nv+PdZ/8DKOFzN32NfTu6QAnvWPS3PPbzp8j153nTrJvZt6OLDU+8xA1t7yYoh/zwC/fwd5f9Eyjh\nI9d+lm9//EeghOumvYsnfv0sQ/kC1067iZ49vQz2DfHOpR8glUlQKpX59J9/lSmzm2mYXM9Pv/pr\npsyaSG1TFj/pMW9FO1s37ObT9/09NXVpdu/q5ZZ/fQvTWuopK4uH7niIf/63Gxk/uR69q0D6qT1Y\nW3tIdds0rVUk15TpfjHNi882s/OFqazf1MyavVN4vH8K+woZ0hSZ7Pcz3i1SCKNobZuKFiFCIRTW\n9I9DIawtjOPZQjSw3x1mSFplStqilFOUChZb+kp87Ik7iOYLWuyfp3nwMEZGrp5JPE8w2pcTtC4i\n8c4oWuewrFqCsAuIAkuXg214cUDlQvEp0skrGCz8kurk1djWeCg/yeTqm9jYdRvd+QeOqPYfMs7W\nMKtXr74c+Nzq1atXrF69un/16tUbxxJba/Xq1acD87XWn7/11luD1atXZ4H2W2+99cFDpfnCFz5/\n6w1vX8X8xttBl0m4s6lOXIimRDpxGbbKolSadPIqRFxcZya+txDBxXXn4zgtiEriuUuxrEYsqwHP\nPR2lMtj2NDxvESIerj0Lx4l+abnuqdhWM0pVxenqsazxeO5pKEnjOC247imI+DjObBxnBkpSuN5C\nbKsJpWriz8hi2xPx3MWIJLHtVlx3Hkp8HHcujj0dpVJ47mIsazyWVYfrLUGpGmx7Mq67CCUejjMT\n15mNqCSuMx/bnoJIGs9bgqUasazGKG8qg21PxXMXRnmz2yucTsG2J1U4NRzkNB3XPRURr8IpHUUH\nt4edlqJULbY9EddbjEgKx27BdechksCNnUSl8NxFbNs2xPTp83G9pVgqi21PwnUXoySB48zAdeZE\n6Zz52PbU2Ok0bGtcXE5LKpyGy6kdx2nfX04vcxqH556GqHRUviNOs3CcmSiVwnUWYNsTUao6ityt\narHtJlx3MaKGnebHeZsT1SFJ4bqLsKwJWFZtFKXeqsW2m+PyPdhpHo497SCn+tipGtueMuLkOG24\nzizUSDlNrnBqjJy80xBVFTstiOteO44zM67fC7CsCierDtuaEJ1vlcJ2DuW0GMuegKWykZPa77Rl\nyw6mT1uA686pKN9pKJWO6+xoTosRcSucUvEOApOjdN4SLBWVkztSZyudonISSeK5p2JZzSiViXY6\niJ0OrLNRO3Sd2ThO60g5Re1w2KlufztUSRx7Bq47N0o30g7TB7RDz12Ckuq4HS4+TDuswveWolQD\nltWI656GVdkO8XHc/X3LfqdD9S3T/yh9y47tiunT54y9b5Ej6Vu8o+xbhss3dnKH22ENrrsEpeqw\n7GZcdyGiUnQUp3DvHs0Z9cKi2gw7En0MTe2FhGZoIEGmYZBS0aZ/XxVKh9SsD7G6hsO6Cl2zhK6Z\nNrXj+5lcHGCwv4YhUjidJboW2KT6B6h+tIRb0PRNTsFUn8RjO5FS/IXt2TRcNJM51VX85Ev3kByX\n5RN3vYeHH3yRR3/8BBdcv4LtmzvZ9OxWevf1ka6v5r7v/I7dm/fipn22r9/Blud2sOulDgq5Ih3b\n9rFz4x7SNUm6dvWwd3sniZRPfrBAT0cv5WKADjXFoRIzz5zMc/e+RHVDFXu3dVEqlJk0s4k9W/aS\n7x9i/NQGevb2ke8fwrJUNN8qV2CgexARIZHy2LNlH+ViQMOkOrY+v5NcX56axmq2rd9JT0cfxaEi\nOzfuYffmvQSBpnffAM88sJ7ufQNMmTeFNfc8zZ6dPbjpJNn6Ku7/2TOcev6pnHPpKTzw5DYevHcd\n561s5VEnT7Hax+kusv0ih/TUFFYiT+u5LzE+283c+i1cNOspZtftZHFqG01uL0VReAibyyl2BUlC\nhA1BirK22B2mGFQOJW3RGSQYCF16gwS7ghQhih1D1fRKgpSTQulz2T0wnYbkOCZlV+3vL93FI3XW\nfVl/mcZ14u8OifoWSzVG32vOApTKxN8Bc1GqCteZg+vOitqFuxDPnY+SNAlvEZ7Tim01Mq7qrZTD\nHqq8U/Dscf/7OFsHHBStRz0fuBFYDNwBfE1rvfEwaa4GLtRavy1+/kZgqdb6pkOlaWtr0+vXr3/F\n/BhOPO6//37OPvvs450Nw1Fiyu/k5dVUdnfvfIp/fPaH5Islhl6qotwZ77mqQrKTe5loC32/SlAM\nNHksSp7g15ZozAJli93PW2gHcg0B9U8O4EYXqAiTioEJCQptDrmaHJO/thfpK4PvkJ+QZOKKqVRt\nzrPl4Y24k+s4Y/E0dm7r5oXfrGXlVUvIjq/mR5/5KQDLLlvEI/c8g84PrxIWmlsb2f7CLkQJ5/zJ\nGdz7nd+hbMVFN57DT7/6a7ykx5lXnsav/utB6pvrGDe5jrUPvcCp58yh7aJmvnfLPVx98yXc+emf\nojW8+cPX8I2/jyavX//B1/Gtj96Jl/RYevECfvvfjzB9/mQK+RI7NuzivDeu5IE7H6WQK/An772c\nOz75Y7TW3PCh1/HNj9wJwNV/cyn/fftPADj90kU88tMoapOfSTBUCPBdxVAxQKXTfODLN/LZ93yb\ngdBiXG2Cc29cyTe/9RCW1lzx5yu466kX2NM3AFJENwi5KYp0Mke1LlDj9lNTM4BTU0ZJSKnoUBh0\nKXXaZFSC7ESPfWofoRQJCwq/kKHBq2Ju06/Ia0VBR1MnNFAOLPYVUjzb1cTT+5p5a9vp3DJ/FbY6\nsaaa/1FDP8RveArRYOtC4D7gdOCXWutbDnH8mAZbIvJ24O3x07lEG18bTj7qgX3HOxOGo8aU38mL\nKbuTG1N+JzdtWuuqVzroFfdGFJG/Am4gqgz/BrxXa12SaOO8DcCogy1gBzCp4nlz/NoBaK2/Anwl\n/qzfj2WEaDjxMGV3cmPK7+TFlN3JjSm/kxsR+f1YjhvLRtS1wFVa6y2VL2qtQxG59DDpHgdmiMg0\nokHWnwLXjSVTBoPBYDAYDK8WXnGwpbX+h8P87bnD/K0sIjcB9xAtHfi61nrtUeXSYDAYDAaD4SRl\nLFe2jhqt9c+Anx1Bklec0W84YTFld3Jjyu/kxZTdyY0pv5ObMZXfmCfIGwwGg8FgMBiOnBNrDaXB\nYDAYDAbDq4wTYrB15Nv6GE4UROTrItIhIiZkx0mGiEwSkftEZJ2IrI1XHhtOEkTEF5HHROTpuPxW\nH+88GY4MEbFE5EkR+cnxzovhyBCRzSLyjIg8NZYVicf9NmK8rc8LwGuA7USrGK/VWq87rhkzjAkR\nWQkMAN/UWs893vkxjB0RmQBM0Fo/ISJVwBrgtabtnRzEwaZTWusBEXGAB4G/0lo/8gpJDScIInIz\nUaDwjNb6cKv7DScYIrIZWKy1HlOMtBPhytYS4EWt9SatdRH4LnDFcc6TYYxorX8LdB3vfBiOHK31\nLq31E/HjfuA5YOLxzZVhrOiIgfipE/8zk3BPEkSkGbiEKH6l4VXOiTDYmghsq3i+HdPhGwz/p4jI\nVGAB8OjxzYnhSIhvQz0FdBDt6GHK7+ThM0RBwcNXOtBwQqKBX4nImngnnMNyIgy2DAbDcURE0sCd\nwLu11n3HOz+GsaO1DrTWpxLt0LFERMyt/JOAOCB4h9Z6zfHOi+GoWRG3vYuAd8ZTag7JiTDY0mHY\nNwAABBtJREFUGtO2PgaD4Y9PPNfnTuC/tNY/ON75MRwdWuseoj1rLzzeeTGMieXA5fG8n+8Cq0Tk\nW8c3S4YjQWu9I/6/A/gh0ZSoQ3IiDLZGtvUREZdoW5//Oc55Mhhe9cQTrL8GPKe1/tTxzo/hyBCR\nBhGpiR8niBYZPX98c2UYC1rr92utm7XWU4m+8+7VWl9/nLNlGCMikooXFSEiKeB84LAr8o/7YEtr\nXQaGt/V5DrjDbOtz8iAi3wEeBtpEZLuIvPV458kwZpYDbyT6Vf1U/O/i450pw5iZANwnIn8g+tH6\nS621CSFgMBx7xgEPisjTwGPAT7XWdx8uwXEP/WAwGAwGg8Hwaua4X9kyGAwGg8FgeDVjBlsGg8Fg\nMBgMxxAz2DIYDAaDwWA4hpjBlsFgMBgMBsMxxAy2DAaDwWAwvGoQka+LSIeIHDYcwxjf65yK1dpP\niciQiLz2iN/HrEY0GAyvNuK4U3cDq7TWwRjT3ATktNZfP6aZMxgMx5Q4mvsA8E2t9R9tVwURqQVe\nBJq11rkjSWuubBkMhlcjbwF+MNaBVszXgXcdo/wYDIb/I7TWvwW6Kl8TkRYRuTvey/ABEWk/ire+\nGvj5kQ60wAy2DAbDSYSInCYifxARP47ivPYQ+wG+AbgrTnO2iPxGRO4SkU0i8k8i8gYReUxEnhGR\nFoC4A90sIofddsNgMJyUfAV4l9Z6EfAe4EtH8R5/CnznaD7cPppEBoPBcDzQWj8uIv8DfBRIAN/S\nWh8wLyPe9mu61npzxcunALOIfu1uAv5Na71ERP6K6GrWu+Pjfg+cSRQV2mAwvAoQkTRwBvD9aJcy\nALz4b1cBHx4l2Q6t9QUV7zEBmEe0280RYwZbBoPhZOPDRNvTDAH/b5S/1wM9B732uNZ6F4CIbAR+\nEb/+DHBOxXEdwNHcXjAYDCcuCujRWp968B+01j8AfjCG93g98EOtdeloM2AwGAwnE3VAGqgC/FH+\nnh/l9ULF47DieciBPzr9OL3BYHiVoLXuA14SkWsAJOKUI3ybaznKW4hgBlsGg+Hk48vAh4D/Aj5x\n8B+11t2AJSKjDcReiZnA/3q5uMFgOH6IyHeAh4E2EdkuIm8lmsf51njz6LXAFUfwflOBScBvjjZP\n5jaiwWA4aRCRG4CS1vrbImIBD4nIKq31vQcd+gtgBfCrI/yI5cCt//ucGgyG44XW+tpD/OnCo3y/\nzcDEo84QJs6WwWB4FSIiC4G/1lq/8QjSLABuPpI0BoPBMBbMbUSDwfCqQ2v9BHBffPVrrNQT3Z40\nGAyGPyrmypbBYDAYDAbDMcRc2TIYDAaDwWA4hpjBlsFgMBgMBsMxxAy2DAaDwWAwGI4hZrBlMBgM\nBoPBcAwxgy2DwWAwGAyGY4gZbBkMBoPBYDAcQ/4/wplEArORJDkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11c868cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "td = oc.TimeDriver()\n", "td.drive(system, t=0.5e-9, n=100)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We see that the DW pair has moved to the positive $x$ direction." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Exercise\n", "\n", "Modify the code below (which is a copy of the example from above) to obtain one domain wall instead of a domain wall pair and move it using the same current." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11c918828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Definition of parameters\n", "L = 500e-9 # sample length (m)\n", "w = 20e-9 # sample width (m)\n", "d = 2.5e-9 # discretisation cell size (m)\n", "Ms = 5.8e5 # saturation magnetisation (A/m)\n", "A = 15e-12 # exchange energy constant (J/)\n", "D = 3e-3 # Dzyaloshinkii-Moriya energy constant (J/m2)\n", "K = 0.5e6 # uniaxial anisotropy constant (J/m3)\n", "u = (0, 0, 1) # easy axis\n", "gamma = 2.211e5 # gyromagnetic ratio (m/As)\n", "alpha = 0.3 # Gilbert damping\n", "\n", "# Mesh definition\n", "p1 = (0, 0, 0)\n", "p2 = (L, w, d)\n", "cell = (d, d, d)\n", "mesh = oc.Mesh(p1=p1, p2=p2, cell=cell)\n", "\n", "# Micromagnetic system definition\n", "system = oc.System(name=\"domain_wall\")\n", "system.hamiltonian = oc.Exchange(A=A) + \\\n", " oc.DMI(D=D, kind=\"interfacial\") + \\\n", " oc.UniaxialAnisotropy(K=K, u=u)\n", "system.dynamics = oc.Precession(gamma=gamma) + oc.Damping(alpha=alpha)\n", "\n", "def m_value(pos):\n", " x, y, z = pos\n", " if 20e-9 < x < 40e-9:\n", " return (0, 1e-8, -1)\n", " else:\n", " return (0, 1e-8, 1)\n", " # We have added the y-component of 1e-8 to the magnetisation to be able to \n", " # plot the vector field. This will not be necessary in the long run.\n", " \n", "system.m = df.Field(mesh, value=m_value, norm=Ms)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/4/24 9:52: Calling OOMMF (domain_wall/domain_wall.mif) ... [7.0s]\n" ] }, { "data": { "image/png": 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cBBjEm4cXrAtlK/4cLfmQU+d7EL0z4nQ2QhX0diqdl0b+chZ+NG0cFL4V+UtN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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119337ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "md = oc.MinDriver()\n", "md.drive(system)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/4/24 9:52: Calling OOMMF (domain_wall/domain_wall.mif) ... [4.6s]\n" ] }, { "data": { "image/png": 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4ZgkjJg4j05XljVfXsOT5FaxdsJ5sb455Ty1kw7ItaK15+aHXmbD/WLyyx/P3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\nqXhZUs1Ebj9K4riHMxvxehnXFZAE2ltMWXWgJElJmoicOfj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"text/plain": [ "<matplotlib.figure.Figure at 0x11c8cb0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ux = 400 # velocity in x direction (m/s)\n", "beta = 0.5 # non-adiabatic STT parameter\n", "\n", "system.dynamics += oc.STT(u=(ux, 0, 0), beta=beta)\n", "\n", "td = oc.TimeDriver()\n", "td.drive(system, t=0.5e-9, n=100)\n", "\n", "system.m.plot_slice(\"z\", 0);" ] } ], "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 }	bsd-3-clause
uthaipon/SkillsWorkshop2017	Week04/Assignment/Lesson06/mfahrbach_06.ipynb	2	139339	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>time</th>\n", " <th>gage_height</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2013-09-01</td>\n", " <td>00:00</td>\n", " <td>57</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2013-09-01</td>\n", " <td>00:15</td>\n", " <td>57</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2013-09-01</td>\n", " <td>00:30</td>\n", " <td>57</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2013-09-01</td>\n", " <td>00:45</td>\n", " <td>57</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2013-09-01</td>\n", " <td>01:00</td>\n", " <td>57</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date time gage_height\n", "0 2013-09-01 00:00 57\n", "1 2013-09-01 00:15 57\n", "2 2013-09-01 00:30 57\n", "3 2013-09-01 00:45 57\n", "4 2013-09-01 01:00 57" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/bouldercreek_09_2013.txt', skiprows=27, header=None, delim_whitespace=True, usecols=[2, 3, 5])\n", "df.columns = ['date', 'time', 'gage_height']\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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RVh3KxPgBvghoxyFS5oDF/Q+n80sxdVUc2rvaY/GTIbCx4l8PGd+Qbp7YOHMg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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10bdb4748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "list_numbers = [1.5, 4, 2.2, 5.7]\n", "plt.plot(list_numbers)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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VFTtOcCjnytJJqB0c3CxEbTlMca/Zl83H+0/yaL/WjIgMsMhjVtWYWLf/JIu3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\nSlZKJSmlehqdydyUUuFX/5z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"text/plain": [ "<matplotlib.figure.Figure at 0x10bed9cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([6.8, 4.3, 3.2, 8.1], list_numbers)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10bfac898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([6.8, 4.3, 3.2, 8.1], list_numbers, 'ro-.')\n", "plt.axis([0,10,0,6])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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WBzDvN/PITM+MOpTIlJsg3P3W8Fl9ASJSb3yX/x05s3O4LPuyep0cIIGrmMzsifCu6KL3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\n/awUg7gNuwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c01bcf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "t = np.arange(0., 10., 0.5)\n", "\n", "plt.plot(t, t, 'r--', t, t2, 'bs-', t, t3, 'g^:')\n", "\n", "plt.xlabel('This is the x axis')\n", "plt.ylabel('This is the y axis')\n", "plt.title('This is the figure title')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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Iy0wDIC0zjTHLxrD9oPUe5I8lCGNMyNiWvI2IsNw161aKKFggfTEtFpF/uw3V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\nCNJOyS0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c1874e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(t, t, 'r--', label='linear')\n", "plt.plot(t, t2, 'bs-', label='square')\n", "plt.plot(t, t3, 'g^:', label='cubic')\n", "\n", "plt.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "\n", "plt.xlabel('This is the x axis')\n", "plt.ylabel('This is the y axis')\n", "plt.title('This is the figure title')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c024518>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c17bf98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "plt.plot(t, t, 'r--', label='linear')\n", "\n", "plt.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "plt.title('This is figure 1')\n", "\n", "plt.show()\n", "\n", "plt.figure(2)\n", "plt.plot(t, t2, 'bs-', label='square')\n", "\n", "plt.legend(loc='upper left', shadow=True, fontsize='x-large')\n", "plt.title('This is figure 2')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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+cYcjIhFqzYCyWuDH7j43XMR+jplNC4/9xt3vTj/ZzPoDQ4FjgAOBF83sKPdG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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c0d85f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "\n", "plt.subplot(2,2,1) # two row, two columns, position 1\n", "plt.plot(t, t, 'r--', label='linear')\n", "\n", "plt.subplot(2,2,2) # two row, two columns, position 2\n", "plt.plot(t, t2, 'bs-', label='square')\n", "\n", "plt.subplot(2,2,3) # two row, two columns, position 3\n", "plt.plot(t, t**3, 'g^:', label='cubic')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge - Final Plot" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10c2e17f0>]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c2a4828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(df['gage_height'])" ] } ], "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 }	bsd-3-clause
timahutchinson/desispec	doc/nb/QA_Exposure.ipynb	1	8952	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# QA for an Exposures (v1.1)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# imports\n", "from desispec.qa import qa_exposure as dqaexp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "expid = 7\n", "night = '20160607'\n", "specprod_dir = '/Users/xavier/DESI/DESI_SCRATCH/redux/madrone/'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-r0-00000007.yaml\n", "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-r1-00000007.yaml\n", "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-b0-00000007.yaml\n", "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-b1-00000007.yaml\n", "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-z0-00000007.yaml\n", "INFO:qa.py:81:load_qa_frame: Loaded QA file /Users/xavier/DESI/DESI_SCRATCH/redux/madrone/exposures/20160607/00000007/qa-z1-00000007.yaml\n" ] } ], "source": [ "# Load data\n", "reload(dqaexp)\n", "qaexp = dqaexp.QA_Exposure(expid, night, 'dark', specprod_dir=specprod_dir)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'expid': 7,\n", " 'flavor': 'dark',\n", " 'frames': {'b0': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2,\n", " 'ZP_WAVE': 4800.0},\n", " 'QA': {'MAX_ZP_OFF': [0.4802616039991179, 0],\n", " 'NSTARS_FIBER': 12,\n", " 'RMS_ZP': 0.1449260023073517,\n", " 'ZP': 25.592318861231973}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.7977447509765625,\n", " 'NBAD_PCHI': 38,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 38,\n", " 'RESID_PER': [-28.585147666931153, 30.517656135559076]}}},\n", " 'b1': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 4800.0},\n", " 'QA': {'MAX_ZP_OFF': [0.5010662602690843, 0],\n", " 'NSTARS_FIBER': 13,\n", " 'RMS_ZP': 0.14132149782701103,\n", " 'ZP': 25.593496049967925}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.7645645141601562,\n", " 'NBAD_PCHI': 35,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 36,\n", " 'RESID_PER': [-28.651506423950195, 30.514743328094482]}}},\n", " 'r0': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 6500.0},\n", " 'QA': {'MAX_ZP_OFF': [0.5145173890571471, 0],\n", " 'NSTARS_FIBER': 12,\n", " 'RMS_ZP': 0.1552311138402372,\n", " 'ZP': 25.462437703942683}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.8249320983886719,\n", " 'NBAD_PCHI': 18,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 38,\n", " 'RESID_PER': [-38.79963073730468, 41.19881591796872]}}},\n", " 'r1': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 6500.0},\n", " 'QA': {'MAX_ZP_OFF': [0.5120047209460807, 0],\n", " 'NSTARS_FIBER': 13,\n", " 'RMS_ZP': 0.14398544844135472,\n", " 'ZP': 25.463873267968722}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.6858577728271484,\n", " 'NBAD_PCHI': 14,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 36,\n", " 'RESID_PER': [-38.262718200683594, 41.21645736694336]}}},\n", " 'z0': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 8250.0},\n", " 'QA': {'MAX_ZP_OFF': [0.46679589006055267, 0],\n", " 'NSTARS_FIBER': 12,\n", " 'RMS_ZP': 0.14446527967674802,\n", " 'ZP': 25.420884471125603}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.48260498046875,\n", " 'NBAD_PCHI': 38,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 38,\n", " 'RESID_PER': [-68.66947860717772, 72.949688720703]}}},\n", " 'z1': {'FLUXCALIB': {'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 8250.0},\n", " 'QA': {'MAX_ZP_OFF': [0.473581729721424, 0],\n", " 'NSTARS_FIBER': 13,\n", " 'RMS_ZP': 0.13706811678757105,\n", " 'ZP': 25.429825809028927}},\n", " 'SKYSUB': {'PARAM': {'PCHI_RESID': 0.05, 'PER_RESID': 95.0},\n", " 'QA': {'MED_RESID': 0.5413665771484375,\n", " 'NBAD_PCHI': 36,\n", " 'NREJ': 0,\n", " 'NSKY_FIB': 36,\n", " 'RESID_PER': [-69.9621726989746, 73.98566360473627]}}}},\n", " 'night': '20160607'}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qaexp.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FluxCalib QA" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/xavier/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called before pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", " warnings.warn(_use_error_msg)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Wrote QA FluxCalib Exposure file: qa-00000006-flux.pdf\n" ] } ], "source": [ "outfil = 'qa-00000006-flux.pdf'\n", "qaexp.fluxcalib(outfil)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'FLUXCALIB': {u'b': {u'ZP_RMS': 0.0}, u'r': {}, u'z': {}},\n", " 'expid': 2,\n", " 'flavor': 'dark',\n", " 'frames': {'b0': {u'FIBERFLAT': {u'PARAM': {'MAX_MEAN_OFF': 0.05,\n", " 'MAX_N_MASK': 20000,\n", " 'MAX_OFF': 0.15,\n", " 'MAX_RMS': 0.02,\n", " 'MAX_SCALE_OFF': 0.05},\n", " u'QA': {'MAX_MEANSPEC': 20036.989142187947,\n", " 'MAX_MEAN_OFF': 11.95263722228059,\n", " 'MAX_OFF': 31.86232340755261,\n", " 'MAX_RMS': 4.606179058912466,\n", " 'MAX_SCALE_OFF': 17.14729933910587,\n", " 'N_MASK': 141167}},\n", " u'FLUXCALIB': {u'PARAM': {'MAX_ZP_OFF': 0.2, 'ZP_WAVE': 4800.0},\n", " u'QA': {'MAX_ZP_OFF': [0.03162147678992966, 119],\n", " 'NSTARS_FIBER': 5,\n", " 'RMS_ZP': 0.010376131442290066,\n", " 'ZP': 25.234761071013523}},\n", " u'SKYSUB': {u'PARAM': {'PCHI_RESID': 0.05},\n", " u'QA': {'MED_RESID': 0.7486787818569205, 'NBAD_PCHI': 38, 'NSKY_FIB': 45}},\n", " 'camera': 'b0',\n", " u'file': '/Users/xavier/DESI/TST/dogwood/exposures/20150211/00000002/qa-b0-00000002.yaml',\n", " 'flavor': 'science'}},\n", " 'night': '20150211'}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qaexp.data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
guangtunbenzhu/BGT-Cosmology	Spectroscopy/.ipynb_checkpoints/eBOSSmeasure-talkmovie-checkpoint.ipynb	1	1343674	null	mit
richardotis/pycalphad-sandbox	myTDB-rotis-2.ipynb	1	181490	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Only needed in a Jupyter Notebook\n", "%matplotlib inline\n", "# Optional plot styling\n", "import matplotlib\n", "matplotlib.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from pycalphad import Database, binplot, calculate, Model, equilibrium\n", "import pycalphad.variables as v\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [] } ], "source": [ "db = Database('N.TDB')\n", "\n", "my_phases = ['Q']\n", "\n", "result = calculate(db, ['MG', 'AL', 'SI', 'CU'], 'Q', T=(300, 900, 0.5), output='heat_capacity')\n", "\n", "eq = equilibrium(db, ['MG', 'AL', 'SI', 'CU'], my_phases, {v.X('SI'): 7/21,v.X('MG'): 9/21,v.X('CU'): 2/21,\n", " v.T: (300,900,25), v.P: 101325}, output='heat_capacity')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IhHA4POucN9I4uxBCiDeequUwnK+Fwf5MhWxmBAqDNNtDLFKHOUUdIWgXKJo+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i6SEQRBOJmEhAS0Wi3BYNW86uLiYlwuF1armKoiXFjOqE7ioEGD2LFjB+np6dXfrFJSUkhLSxOV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\nY2OJjY3l5ptvZtWqVS51XnzxRRo2bEhkZCQ9e/YkJSXFpdxqt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"text/plain": [ "<matplotlib.figure.Figure at 0x16adf8dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(9,6))\n", "fig.gca().set_xlabel('Temperature (K)')\n", "fig.gca().set_ylabel('Isobaric Heat Capacity (J/mol-formula-K)')\n", "fig.gca().plot(result['T'], result['heat_capacity'])\n", "fig.gca().plot(eq['T'], np.squeeze(eq['heat_capacity'].values),'--k')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<xarray.Dataset>\n", "Dimensions: (P: 1, T: 1, X_CU: 1, X_MG: 1, X_SI: 1, component: 4, internal_dof: 10, vertex: 4)\n", "Coordinates:\n", " * P (P) float64 1.013e+05\n", " * T (T) float64 800.0\n", " * X_CU (X_CU) float64 1e-09\n", " * X_MG (X_MG) float64 1e-09\n", " * X_SI (X_SI) float64 1e-09\n", " * vertex (vertex) int64 0 1 2 3\n", " * component (component) object 'AL' 'CU' 'MG' 'SI'\n", " * internal_dof (internal_dof) int64 0 1 2 3 4 5 6 7 8 9\n", "Data variables:\n", " NP (P, T, X_CU, X_MG, X_SI, vertex) float64 0.1158 0.8842 nan nan\n", " MU (P, T, X_CU, X_MG, X_SI, component) float64 -1.256e+15 ...\n", " GM (P, T, X_CU, X_MG, X_SI) float64 -3.251e+04\n", " X (P, T, X_CU, X_MG, X_SI, vertex, component) float64 0.1429 ...\n", " Y (P, T, X_CU, X_MG, X_SI, vertex, internal_dof) float64 1.0 ...\n", " Phase (P, T, X_CU, X_MG, X_SI, vertex) object u'Q' u'Q' '' ''\n", "Attributes:\n", " solve_iterations: 1\n", " engine: pycalphad 0.3.2\n", " hull_iterations: 2\n", " created: 2016-02-23 12:30:39.448905\n" ] } ], "source": [ "print(eq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not sure why X_CU, Mg, Si=1e-09?\n", "\n", "Also above figure sounds like that the equilibrium heat capacity only follows one of the end member and there is no configuration entropy in it, I expect that the equilibrium one goes to higher value than all of the end-members due to configuration term $$ RT\\sum_{s}a^{(s)} \\sum_{i} y_{i}^{(s)} \\ln y_{i}^{(s)} $$ \n", "in CEF\n", "$$ G = \\sum_{I0}P_{I0}(Y).G^\\circ_{I0} + RT\\sum_{s}a^{(s)} \\sum_{i} y_{i}^{(s)} \\ln y_{i}^{(s)} + \\sum_{Z>0} \\sum_{IZ} P_{IZ}(Y).L_{IZ} $$\n", "\n", "Is it possible to print out the coefficient P, A, y in the equilibrium?\n", "\n", "If I can plot the energy (G) of equilibrium for whole range of composition versus all end-members, this will also show me how much is configuration entropy contribution:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [] }, { "ename": "ValueError", "evalue": "x and y must be the same size", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-27-63bf937ff6b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m v.T: 800, v.P: 101325},output='GM')\n\u001b[1;32m 13\u001b[0m ax.scatter(result.X.sel(component='AL'), eq.GM,\n\u001b[0;32m---> 14\u001b[0;31m marker='+', s=5, color=colorlist['Q'])\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/firebird/allPythonEnv/pycalphad031/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, args, kwargs)\u001b[0m\n\u001b[1;32m 1810\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1811\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1812\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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 1813\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1814\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/firebird/allPythonEnv/pycalphad031/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, kwargs)\u001b[0m\n\u001b[1;32m 3838\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3839\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3840\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" ] }, { "data": { "image/png": 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AAA8+SO/95JMWP+Z1z438++SPgQMHttq1WiQS58yZg4SEhCbX9O7dW/vaarUiNjYWer0e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\nDIlEREREJMKQSEREREQiDIlEREREJMKQSEREREQiDIlEREREJ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"text/plain": [ "<matplotlib.figure.Figure at 0x1161bb9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pycalphad.plot.utils import phase_legend\n", "legend_handles, colorlist = phase_legend(my_phases)\n", "\n", "fig = plt.figure(figsize=(9,6))\n", "ax = fig.gca()\n", "\n", "result = calculate(db, ['MG', 'AL', 'SI', 'CU'], 'Q', T=800, output='GM')\n", "ax.scatter(result.X.sel(component='AL'), result.GM,\n", " marker='.', s=5, color=colorlist['Q'])\n", "\n", "eq = equilibrium(db, ['MG', 'AL', 'SI', 'CU'], my_phases, {v.X('SI'): 7/21,v.X('MG'): 9/21,v.X('CU'): 2/21, \n", " v.T: 800, v.P: 101325},output='GM')\n", "ax.scatter(result.X.sel(component='AL'), eq.GM,\n", " marker='+', s=5, color=colorlist['Q'])\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
SolitonScientific/AtomicString	AStrings 3D.ipynb	1	4710712	null	mit
Lab41/pelops	pelops/analysis/splitDataset.ipynb	3	3823	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from random import shuffle\n", "import glob\n", "import shutil\n", "import tqdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_dir(path):\n", " if not os.path.exists(path):\n", " os.makedirs(path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def move_link(src,dst):\n", " real_src = os.path.realpath(src)\n", " #print(real_src,dst)\n", " os.symlink(real_src,dst)\n", " os.unlink(src)\n", " #os.rename(src,dst)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def prep_datasets(srcpath,destpath,percent=0.3):\n", " \n", " if percent >1 or percent < 0:\n", " print ('bad')\n", " raise ValueError('percent needs to be in [0,1]')\n", " found = 0\n", " moved = 0\n", " for image_class_filepath in tqdm.tqdm(glob.glob(os.path.join(srcpath, ''))):\n", " \n", " if os.path.isdir(image_class_filepath):\n", " image_class_num = int(os.path.basename(image_class_filepath))\n", " \n", " directory_name = os.path.join(destpath, '{}'.format(image_class_num))\n", " #print(directory_name)\n", " make_dir(directory_name)\n", " \n", " dir_contents = list()\n", " \n", " for filename in glob.glob(os.path.join(image_class_filepath, '')):\n", " found+=1\n", " dir_contents.append(filename)\n", " \n", " \n", " shuffle(dir_contents)\n", " threshold = int (percent * len(dir_contents))\n", " mixed = dir_contents[:threshold]\n", " for filename in mixed:\n", " moved +=1\n", " #print ('filename:',os.path.basename(filename))\n", " src = os.path.join(srcpath,'{}'.format(image_class_num),filename)\n", " #print(directory_name,filename)\n", " dst = os.path.join(directory_name,os.path.basename(filename))\n", " #print('src:{0}\\ndst:{1}'.format(src,dst))\n", " move_link(src,dst)\n", " print('total:',found,'moved:',moved,'remains:',found-moved)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train = '/local_data/dgrossman/keras/color/train'\n", "test = '/local_data/dgrossman/keras/color/test'\n", "validate = '/local_data/dgrossman/keras/color/validate'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prep_datasets(train,test,0.3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prep_datasets(test,validate,0.3)" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
ericmjl/hiv-resistance-prediction	old_notebooks/Tests with RNN.ipynb	1	27950	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using Theano backend.\n", "Downloading data from https://s3.amazonaws.com/text-datasets/nietzsche.txt\n", "606208/600901 [==============================] - 0s \n", "corpus length: 600893\n", "total chars: 57\n", "nb sequences: 200291\n", "Vectorization...\n", "Build model...\n", "\n", "--------------------------------------------------\n", "Iteration 1\n", "Epoch 1/1\n", "200291/200291 [==============================] - 220s - loss: 2.7521 \n", "\n", "----- diversity: 0.2\n", "----- Generating with seed: \" usage, to oppose th\"\n", " usage, to oppose the the the the the the the the the the the of pore the and and the the the the the the pore the the the the the the the the pore the the the the the the the the the the the the the the the the the the pores of the the the the the the the he the for the the ante the the the the the the the the the the the the the and of the the eres of the the the the the the the the core the the the son the the the\n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \" usage, to oppose th\"\n", " usage, to oppose the ther pove ches wis the ar the the ane the the the les beritit, in the lithe and sand bof intinl and ons raant aund tore of the te the the son eub evere the tot on the pentacop pent on the popeend bob the hact end on sut andy ins the ting thel and ot us of the the ang of res and and and o in the thimt ans on the lote the the the ersene of and ave and in the anter of the the the ante the the the t\n", "\n", "----- diversity: 1.0\n", "----- Generating with seed: \" usage, to oppose th\"\n", " usage, to oppose thorlit\n", "thel- fetere- foot tite ef:yr arsiand anl thalse, os sorethane tho he nes waltd3e,, wh anen bulgisune bnome bucwit the tiutl undendis\n", "riftel thig onvasedus id apethe gte-innos cat\n", "ind ortpeses barde, os mveuk, pokaud or bed fryucint rebpesity et av espiseve, fhe -rothiv olp luspthet hsusofel of mere-n-an\n", " teeckes of\n", "pcosshiruzt-inud co3 apvoot on tos whrae thol me avurags-en\n", "oxun torecsor te\n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \" usage, to oppose th\"\n", " usage, to oppose thas lunly pfenbt\" tild iaald, hem\n", "col \"9axw afl gmote,t or the dhag atuëlog theue\n", "lol an thy a0gs\n", "lfe migedfunubg\n", "-fyblavdevy whew aconndowulity asy\n", "es si, onuale whitq nt e bed prandgicl., w1eccoit this\n", "tto uwhurn!, h u nkegbe\n", "diworat ytibeelg[-.\n", " ho4 ece huesl\", paind bvy th evtaondmin woutl sup t elilte, be pole hurc\n", "le5ee seicn sesthoth-y gipslizsof thimusn\"-, aas gibatozouul\n", "oth\n", "o. percunislif\n", "\n", "--------------------------------------------------\n", "Iteration 2\n", "Epoch 1/1\n", "200291/200291 [==============================] - 220s - loss: 2.2788 \n", "\n", "----- diversity: 0.2\n", "----- Generating with seed: \"ynicism is the only\n", "\"\n", "ynicism is the only\n", "the sould the sor the some the sore and the enter the in the mere the sor the sor the rester and the sor the in the in the for the in the sore the onder the sore the sore the sore the sould the sould and and the in the rerest on the bere the exprest of the resting of the erong the sore the serition the ender in the tor the meraly of the soul of the enter the eres in the and the in the sore the in \n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \"ynicism is the only\n", "\"\n", "ynicism is the only\n", "conden the tore and in the fint the as nither and the for the paese the here and the rome the worce for the bemest ond the soull conderes and in the canle this sond and and the istict of the in dereed the ger the ment of the here the remest and sore the with we to the eren the a dere fouth in the in the horings the contice thir of the inder of\n", "the sourd endor the prering in hor the dester and the \n", "\n", "----- diversity: 1.0\n", "----- Generating with seed: \"ynicism is the only\n", "\"\n", "ynicism is the only\n", "utucosbed hicd, in the on the id dild on e1feulien aldif nos wreriguc tmecintar to praycy, the tad wrow lis our e atlige, to cencinde an of pherinxl wo ever nogling mentm\n", "e fox as fur meandutoons ce. dhel] ow the for the derse wonchmsupy at or\n", "the crenceg tourae os of carsong\n", "orty. to allong thels sumfeu atuan cane eno a phis ome fyemis at ouct renoaned to erofalien, in of dectimed. ncpryningm to \n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \"ynicism is the only\n", "\"\n", "ynicism is the only\n", "meloness, ax eveule \"vouth\n", "e\"pewi them or why cinnom midiy timud treeksl weane birgy al' atros the nat ynot aldueds\n", "tu thisses ip themes wevl for\n", "to4\n", "heowtery talthy chepress- u( wiadung the lresces\n", "ferdentit,-sitro. whonekss dust--asd g\"thele! atapite, at in dellonges\n", "s\n", "mpacurteor car, nhrrav\n", "porn.t acssiticy had _y maas tal\n", "onvere, un sacfer-7tonle.\"st--ouuleny ars\"d--st, s kisl meenticage to ho\n", "\n", "--------------------------------------------------\n", "Iteration 3\n", "Epoch 1/1\n", "200291/200291 [==============================] - 220s - loss: 2.0797 \n", "\n", "----- diversity: 0.2\n", "----- Generating with seed: \"and would like\n", "to pl\"\n", "and would like\n", "to plestion of the fremand and and and the mant in the freation of the which has in the mand and the sempinitions of the will the mand in the mand the sention of the manting of the sencint and and and the great of the in the sould the sention of the which he sore the which he sere the sention of the sunce of the sind and his in the sence of the man the sentention of the sence the mant and the mand the \n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \"and would like\n", "to pl\"\n", "and would like\n", "to pless of manilicaily of selfound and uthers and chanter in the concopred and sulligest of a preinger the serting of the resulucing is which whe desentund and which has extond and estund in the sacticinity in the shal of the while hif in the\n", "sigtation, a selfention, and in the beliged the ches in the for in the continity and himself the urestive what whing serfire of his all agest the coment in for t\n", "\n", "----- diversity: 1.0\n", "----- Generating with seed: \"and would like\n", "to pl\"\n", "and would like\n", "to plowed hime of\n", "the stivial do this of\n", "soul, the moeg alse whing.\n", "\n", "112. an everyebred shelan it sins,istectoxption more inmlong of deigion. ratualy a a by, and digon in e sal.\n", "\n", "the gan blou in in his\n", "shimsingikily, ly choisungast wich the sentains of the sage\n", "ount\n", "with lry the crusor: the revolutiom and heselhescoacj with vay steeist cuntlishling\"s, which te s9mear\" prind\n", "s2mestibges far ud she vast \n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \"and would like\n", "to pl\"\n", "and would like\n", "to pligh premighas of the tadlw this, \"ucre=pen,\n", "-ngrel\"leduglve thik mere chist, he\n", "ch_lonearte ancoliraaled heferem of\n", "lifome manise a: icec(uviereung theresaveralg; pearal hir, a gteil\n", "phed s1afieverhe\n", " u 'vonibt, ding 'leesgen\n", "in.estidlyits, which ve dreans whourone: ov realukal\n", "12imufed lemongiss\n", "mustificution of the edpretidy\n", "\n", "5\n", "t7\n", "opnalivif. it ethem, wo ho vis permadets thutiots me fremprifiry \n", "\n", "--------------------------------------------------\n", "Iteration 4\n", "Epoch 1/1\n", "200291/200291 [==============================] - 220s - loss: 1.9250 \n", "\n", "----- diversity: 0.2\n", "----- Generating with seed: \"yle; and these laws \"\n", "yle; and these laws the still to the beligion of the man the stand the strenged to the strenged the cantion of the whole the prosent of the most of the still the man the resting the man the presision of the presers of the man the pression of the senting the man be man who and all the strees and the man for the presers a man in the read condicions of the prosting the most and the still to the strent the presing the ma\n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \"yle; and these laws \"\n", "yle; and these laws the mast men all of the real, and stoll\n", "condured wat as preas be who dees in the later in the bely all that which is a condrulical end discoperal encertate, the nother will the ong in the the indistore of the presers of the cantion of his conter on the reath of the chest as the the more to the rade of the mant for and very a coldicion to fon ancomsting and and belight whind the has most free which\n", "\n", "----- diversity: 1.0\n", "----- Generating with seed: \"yle; and these laws \"\n", "yle; and these laws of it we their the geaes. saccherering stilly houre des schall they hind his himpone\n", "to\n", "the the prifourer widd, antwich,\" its salfor and the weral forevy,, bechiesone\n", "to doug qeansa; of there geal--that ase they tan or was, urenijuress in\n", "fumondel; pariy idjinctility, senfqueltent to the\n", "mistlidizumn seulicing the vare sums alafion.\n", "itsceess and this the ubagent, a prolonk,ry, neagity thacsed--thi\n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \"yle; and these laws \"\n", "yle; and these laws oreal hexope soqeinations.--qhear, anitole) the chap is nom one us ore inselved\n", "a phale manios\n", "iinterfout of the sall-storgre is net sterf locies he age is nathyr,ich. weorelys, sunsficiatine--o2 ctore of ghen\n", "vurgald waster siffices\n", "theysese whetecery a simstine, but .or\n", "thes\n", "\"m.-- the who its\n", "jodble poltio alf the\n", "fifcle that \"out., nothenbe that bat hersflanterrussines unow b!awshed. toud\n", "we ha\n", "\n", "--------------------------------------------------\n", "Iteration 5\n", "Epoch 1/1\n", "200291/200291 [==============================] - 220s - loss: 1.8003 \n", "\n", "----- diversity: 0.2\n", "----- Generating with seed: \"flict with this. a f\"\n", "flict with this. a fore of the sense of the sention of the sentions of the some the sention of the sention of the sentity of the sent the streng of the sentions of the sections of the sent of the sich of the some the sent of the sent the sention of the self-constines of the sentity of the sention of the sented of the sented the sentity of the section of the sentity of the seppect in the sention of the self-consting t\n", "\n", "----- diversity: 0.5\n", "----- Generating with seed: \"flict with this. a f\"\n", "flict with this. a fore is and not resure of the some him the deash of position of the sect of the sectard of\n", "the mearing of the strection, the beting of the suppicises and also the most to the armont the most of unaches and and not that is secred on the sent and constions of means in the sense the sentunt of such a many of a madity. he who condert deserves and hessence, and sunce of the indict. the senting of the ob\n", "\n", "----- diversity: 1.0\n", "----- Generating with seed: \"flict with this. a f\"\n", "flict with this. a fors lather, of the from be the subload equrite, itless, the stakness mays lavery the memal: toor intulians, god--a somethe\n", "\n", "ruct which it becuared that if a lond, conscsutionody of the mertus here, wat sthing, chands pain in alquef: who is werlde lisk\n", "bes, bo hi_sagnes and mading\n", "himself we\n", "detrices kest of\n", "itself their of thats themselves the geem speren mane ther prohoworal, were,\n", "thought as\n", "yhe\n", "\n", "----- diversity: 1.2\n", "----- Generating with seed: \"flict with this. a f\"\n", "flict with this. a faven\n", "in5erj.\"\n", "[1o] peal itsels and\n", "tyelt inthouthnes, sollowemeuh obnessitunce,\n", "hos cowarness in himself in remand\n", "not gife, the evismustly sheeg at it the red truess were obeing his of:\" ant to net.--ay moverity.\n", " äeking dveaken perkan somethed men of a cauls thmome\n", "jed og not)ibnets\n", "of the\n", "becomes he mostiy of\n", "the sacc--we peild,ing eypre innsush of perain that\"\"-plwy wearnness! a) the still tha\n", "\n", "--------------------------------------------------\n", "Iteration 6\n", "Epoch 1/1\n", " 12672/200291 [>.............................] - ETA: 207s - loss: 1.7109" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 0: GRID K520 (CNMeM is disabled)\n" ] }, { "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-2-903758401e83>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'-'\u001b[0m \u001b[1;33m\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Iteration'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnb_epoch\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[0m\u001b[0;32m 72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mstart_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmaxlen\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/home/ubuntu/anaconda3/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, show_accuracy, class_weight, sample_weight)\u001b[0m\n\u001b[0;32m 505\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m shuffle=shuffle, metrics=metrics)\n\u001b[0m\u001b[0;32m 508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 509\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\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[1;32m/home/ubuntu/anaconda3/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, metrics)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 227\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m 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358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/ubuntu/anaconda3/lib/python3.5/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m 857\u001b[0m \u001b[0mt0_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 858\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\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 860\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 861\u001b[0m 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node)\n\u001b[0m\u001b[0;32m 953\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mImportError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgof\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMissingGXX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 954\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from keras.models import Sequential\n", "from keras.layers.core import Dense, Activation, Dropout\n", "from keras.layers.recurrent import LSTM\n", "from keras.datasets.data_utils import get_file\n", "import numpy as np\n", "import random\n", "import sys\n", "\n", "'''\n", " Example script to generate text from Nietzsche's writings.\n", " At least 20 epochs are required before the generated text\n", " starts sounding coherent.\n", " It is recommended to run this script on GPU, as recurrent\n", " networks are quite computationally intensive.\n", " If you try this script on new data, make sure your corpus\n", " has at least ~100k characters. ~1M is better.\n", "'''\n", "\n", "path = get_file('nietzsche.txt', origin=\"https://s3.amazonaws.com/text-datasets/nietzsche.txt\")\n", "text = open(path).read().lower()\n", "print('corpus length:', len(text))\n", "\n", "chars = set(text)\n", "print('total chars:', len(chars))\n", "char_indices = dict((c, i) for i, c in enumerate(chars))\n", "indices_char = dict((i, c) for i, c in enumerate(chars))\n", "\n", "# cut the text in semi-redundant sequences of maxlen characters\n", "maxlen = 20\n", "step = 3\n", "sentences = []\n", "next_chars = []\n", "for i in range(0, len(text) - maxlen, step):\n", " sentences.append(text[i: i + maxlen])\n", " next_chars.append(text[i + maxlen])\n", "print('nb sequences:', len(sentences))\n", "\n", "print('Vectorization...')\n", "X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\n", "y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\n", "for i, sentence in enumerate(sentences):\n", " for t, char in enumerate(sentence):\n", " X[i, t, char_indices[char]] = 1\n", " y[i, char_indices[next_chars[i]]] = 1\n", "\n", "\n", "# build the model: 2 stacked LSTM\n", "print('Build model...')\n", "model = Sequential()\n", "model.add(LSTM(512, return_sequences=True, input_shape=(maxlen, len(chars))))\n", "model.add(Dropout(0.2))\n", "model.add(LSTM(512, return_sequences=False))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(len(chars)))\n", "model.add(Activation('softmax'))\n", "\n", "model.compile(loss='categorical_crossentropy', optimizer='rmsprop')\n", "\n", "\n", "def sample(a, temperature=1.0):\n", " # helper function to sample an index from a probability array\n", " a = np.log(a) / temperature\n", " a = np.exp(a) / np.sum(np.exp(a))\n", " return np.argmax(np.random.multinomial(1, a, 1))\n", "\n", "# train the model, output generated text after each iteration\n", "for iteration in range(1, 60):\n", " print()\n", " print('-' 50)\n", " print('Iteration', iteration)\n", " model.fit(X, y, batch_size=128, nb_epoch=1)\n", "\n", " start_index = random.randint(0, len(text) - maxlen - 1)\n", "\n", " for diversity in [0.2, 0.5, 1.0, 1.2]:\n", " print()\n", " print('----- diversity:', diversity)\n", "\n", " generated = ''\n", " sentence = text[start_index: start_index + maxlen]\n", " generated += sentence\n", " print('----- Generating with seed: \"' + sentence + '\"')\n", " sys.stdout.write(generated)\n", "\n", " for iteration in range(400):\n", " x = np.zeros((1, maxlen, len(chars)))\n", " for t, char in enumerate(sentence):\n", " x[0, t, char_indices[char]] = 1.\n", "\n", " preds = model.predict(x, verbose=0)[0]\n", " next_index = sample(preds, diversity)\n", " next_char = indices_char[next_index]\n", "\n", " generated += next_char\n", " sentence = sentence[1:] + next_char\n", "\n", " sys.stdout.write(next_char)\n", " sys.stdout.flush()\n", " print()" ] }, { "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
bnmnetp/CS341	Notebooks/CheckSum.ipynb	2	4577	{ "metadata": { "name": "", "signature": "sha256:15dcb56cedb573a1503e00196907fbebe8f53ca5400c2fc19407f319f3e8cdcb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Checksumming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hex to Binary to Decimal\n", "\n", "<table>\n", "<tr><td>bin </td><td> hex </td><td> decimal</td></tr>\n", "<tr><td>0000 </td><td> 0 </td><td> 0</td></tr>\n", "<tr><td>0001 </td><td> 1 </td><td> 1</td></tr>\n", "<tr><td>0010 </td><td> 2 </td><td> 2</td></tr>\n", "<tr><td>0011 </td><td> 3 </td><td> 3</td></tr>\n", "<tr><td>0100 </td><td> 4 </td><td> 4</td></tr>\n", "<tr><td>0101 </td><td> 5 </td><td> 5</td></tr>\n", "<tr><td>0110 </td><td> 6 </td><td> 6</td></tr>\n", "<tr><td>0111 </td><td> 7 </td><td> 7</td></tr>\n", "<tr><td>1000 </td><td> 8 </td><td> 8</td></tr>\n", "<tr><td>1001 </td><td> 9 </td><td> 9</td></tr>\n", "<tr><td>1010 </td><td> a </td><td> 10</td></tr>\n", "<tr><td>1011 </td><td> b </td><td> 11</td></tr>\n", "<tr><td>1100 </td><td> c </td><td> 12</td></tr>\n", "<tr><td>1101 </td><td> d </td><td> 13</td></tr>\n", "<tr><td>1110 </td><td> e </td><td> 14</td></tr>\n", "<tr><td>1111 </td><td> f </td><td> 15</td></tr>\n", "</table>\n", "\n", "Converting from binary to decimal:\n", "\n", "Starting on the right multiply each binary digit by $2^n$ where n is the position of the bit starting with bit 0 on the right. for example: $1 \\cdot 2^0 + 0 \\cdot 2^1 + 1 \\cdot 2^2 + 1 \\cdot 2^3 = 13$\n", "\n", "Converting from decimal to binary:\n", "\n", "Repeatedly divide by 2, the remainder becomes the next bit building from right to left. For example to convert 13 to binary:\n", "\n", "* 13 /2 = 6 remainder 1\n", "* 6 / 2 = 3 remainder 0\n", "* 3 / 2 = 1 remainder 1\n", "* 1 / 2 = 0 remainder 1\n", "\n", "Link to [ascii chart](http://www.ascii-code.com)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Computing a Checksum\n", "\n", "1. Add the integer value for the first character to the second\n", "2. If the number is > 255 then you must \"lop off\" the leftmost bit and add 1 to the result\n", "3. Continue this process with the rest of the numbers/characters\n", "\n", "When all of the letters of the message have been added then take the one's complement\n", "(flip all the bits)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking a checksum\n", "\n", "1. Repeat the same process (steps 1-3) as we did in Computing a Checksum (but not the one's complement part)\n", "2. Now add in the included checksum\n", "3. If all the bits are 1, that is, the sum is 255 then there are no errors!\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "1. Write a function to take a string and compute the sum of the letters in the string.\n", "2. Write a function to take the one's complement of that sum.\n", "3. combine 1 and 2 into a checksum function.\n", "4. Write a function that checks the checksum.\n", "5. TEST\n", "\n", "6. Find a team of 3 people. Each of you will send a message (maybe 2 or 3 random words) to another member of your team using the third member as an intermediary as follows:\n", " 1. make up a short message and compute a checksum for that message.\n", " 2. Mail the message and the checksum to team member 2 -- Team member 2 may choose to change one or more letters of the message or the checksum and will send a copy of the (potentially altered) message to team member 3.\n", " 3. Team member three will verify whether the message was altered or not.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Further Investigation\n", "\n", "* can checksums detect characters that are just out of order?\n", "* What happens if all the bits are one and you have a number larger than 255? Can that even happen?\n" ] } ], "metadata": {} } ] }	cc0-1.0
yasintoda/pysolar	pysolar/rest2-validation.ipynb	2	21147	{ "metadata": { "name": "", "signature": "sha256:7361226704a3fa0c9e784994a84c2ea1e0e141b623917a26db4be9b6a49315c4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import rest\n", "rest.get_broadband_direct_normal_irradiance(45.0)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "high-frequency\n", "0.10658232436\n", "0.836702348686\n", "-260.173914274\n", "-12.8235481008\n", "0.880961871382\n", "0.0\n", "low-frequency\n", "0.0357065199122\n", "-4.43131175029\n", "1.0\n", "1.0\n", "0.319475643881\n", "0.0\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "0.0" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "mRprime = linspace(0, 100, 1000)\n", "high = map(lambda mRprime: rest.get_rayleigh_transmittance('high-frequency', mRprime), mRprime)\n", "low = map(lambda mRprime: rest.get_rayleigh_transmittance('low-frequency', mRprime), mRprime)\n", "plot(mRprime, high)\n", "show()\n", "plot(mRprime, low)\n", "show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xb12d4c0c>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0xb646028c>" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
stephenpardy/PythonNotebooks	for_blog/Holidays.ipynb	1	56844	{ "cells": [ { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import bs4\n", "from bs4 import BeautifulSoup\n", "from mechanize import Browser\n", "from urllib2 import urlopen\n", "import re\n", "from datetime import datetime\n", "import time\n", "import sqlite3\n", "import json\n", "import time\n", "import requests\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import sys \n" ] }, { "cell_type": "code", "execution_count": 303, "metadata": { "collapsed": false }, "outputs": [], "source": [ "date = datetime(year=2016, month=3, day=19)\n", "url = \"https://en.wikipedia.org/wiki/{:s}_{:d}\".format(date.strftime(\"%B\"), 19) \n", "page = urlopen(url)\n", "contents = page.read()\n", "soup = BeautifulSoup(contents, 'html.parser')" ] }, { "cell_type": "code", "execution_count": 304, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<ul>\\n<li>Christian <a class=\"mw-redirect\" href=\"/wiki/Feast_day\" title=\"Feast day\">feast day</a>:\\n<ul>\\n<li><a href=\"/wiki/Saint_Joseph\" title=\"Saint Joseph\">Joseph of Nazareth</a> (<a href=\"/wiki/Western_Christianity\" title=\"Western Christianity\">Western Christianity</a>)</li>\\n<li><a href=\"/wiki/March_19_(Eastern_Orthodox_liturgics)\" title=\"March 19 (Eastern Orthodox liturgics)\">March 19 (Eastern Orthodox liturgics)</a></li>\\n</ul>\\n</li>\\n<li>Earliest day on which <a href=\"/wiki/Maundy_Thursday\" title=\"Maundy Thursday\">Maundy Thursday</a> can fall, while April 22 is the latest; celebrated on Thursday before <a href=\"/wiki/Easter\" title=\"Easter\">Easter</a>. (<a href=\"/wiki/Christianity\" title=\"Christianity\">Christianity</a>)</li>\\n<li><a href=\"/wiki/Flag_days_in_Finland#Days_on_which_flying_the_Finnish_flag_is_an_established_custom\" title=\"Flag days in Finland\">Minna Canth's Birthday</a> (<a href=\"/wiki/Finland\" title=\"Finland\">Finland</a>)</li>\\n<li><a href=\"/wiki/St_Joseph%27s_Day\" title=\"St Joseph's Day\">St Joseph's Day</a> (<a class=\"mw-redirect\" href=\"/wiki/Roman_Catholicism\" title=\"Roman Catholicism\">Roman Catholicism</a> and <a href=\"/wiki/Anglican_Communion\" title=\"Anglican Communion\">Anglican Communion</a>) related observances:\\n<ul>\\n<li><a href=\"/wiki/Father%27s_Day\" title=\"Father's Day\">Father's Day</a> (Spain, Portugal, Belgium, Italy, <a href=\"/wiki/Honduras\" title=\"Honduras\">Honduras</a>, and <a href=\"/wiki/Bolivia\" title=\"Bolivia\">Bolivia</a>)</li>\\n<li><a href=\"/wiki/Falles\" title=\"Falles\">Las Fallas</a>, celebrated on the week leading to March 19. (<a href=\"/wiki/Valencia\" title=\"Valencia\">Valencia</a>)</li>\\n<li><a href=\"/wiki/Mission_San_Juan_Capistrano\" title=\"Mission San Juan Capistrano\">\"Return of the Swallow\"</a>, annual observance of the <a href=\"/wiki/American_cliff_swallow\" title=\"American cliff swallow\">swallows</a>' return to <a href=\"/wiki/Mission_San_Juan_Capistrano\" title=\"Mission San Juan Capistrano\">Mission San Juan Capistrano</a> in <a href=\"/wiki/California\" title=\"California\">California</a>.</li>\\n</ul>\\n</li>\\n<li><a href=\"/wiki/Kashubian_Unity_Day\" title=\"Kashubian Unity Day\">Kashubian Unity Day</a> (<a href=\"/wiki/Poland\" title=\"Poland\">Poland</a>)</li>\\n<li>The first day of <a href=\"/wiki/Quinquatria\" title=\"Quinquatria\">Quinquatria</a>, held in honor of <a href=\"/wiki/Minerva\" title=\"Minerva\">Minerva</a>. (<a href=\"/wiki/Roman_Empire\" title=\"Roman Empire\">Roman Empire</a>)</li>\\n</ul>" ] }, "execution_count": 304, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup.find('span', {'id': 'Holidays_and_observances'}).find_next('ul')" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conn = sqlite3.connect('/Users/spardy/WikipediaDates.db')\n", "c = conn.cursor()\n", "\n", "for month in [3]:#xrange(2, 13):\n", " for day in [19]:#xrange(1, 32):\n", " try:\n", " date = datetime(year=2016, month=month, day=day)\n", " except ValueError:\n", " continue\n", "\n", " url = \"https://en.wikipedia.org/wiki/{:s}_{:d}\".format(date.strftime(\"%B\"), day) \n", " page = urlopen(url)\n", " contents = page.read()\n", " soup = BeautifulSoup(contents, 'html.parser')\n", "\n", " #loop over non-holidays\n", " for _id in ['Events', 'Births', 'Deaths']:\n", " #Create a table, dropping the old one if it exists\n", " table_name = \"{:s}{:d}{:d}\".format(_id, month, day)\n", " c.execute(\"DROP TABLE if exists %s\" % table_name)\n", " c.execute(\"CREATE TABLE %s (year, name)\" % table_name)\n", "\n", " entries = soup.find('span', {'id': _id}).find_next('ul')\n", " for entry in entries:\n", " if (entry is not None) and (entry.name is not None):\n", " m = re.match(r\".?([0-9]+) (.)\", entry.text)\n", " if m is not None:\n", " yr = m.groups()[0]\n", " name = m.groups()[1].replace('</li>', '')\n", " c.execute(\"INSERT INTO %s VALUES (?, ?)\" % table_name, (yr, name))\n", "\n", "\n", " holidays = soup.find('span', {'id': 'Holidays_and_observances'}).find_next('ul')\n", " table_name = \"{:s}{:d}{:d}\".format('Holidays', month, day)\n", " c.execute(\"DROP TABLE if exists %s\" % table_name)\n", " c.execute(\"CREATE TABLE %s (name)\" % table_name)\n", " for holiday in holidays.text.split('\\n'):\n", " if holiday != '':\n", " c.execute(\"INSERT INTO %s VALUES (?)\" % table_name, (holiday,)) \n", "\n", " conn.commit()\n", "\n", "conn.close()\n" ] }, { "cell_type": "code", "execution_count": 324, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataset = np.zeros((12, 31, 4))+np.nan\n", "\n", "with open('/Users/spardy/Code/Web/Blog/resources/wikipedia_calendar.csv', 'w') as f:\n", " f.write('Date,Events,Births,Deaths,Holidays\\n')\n", " with sqlite3.connect('/Users/spardy/WikipediaDates.db') as conn:\n", " c = conn.cursor()\n", " for month in xrange(1, 13):\n", " for day in xrange(1, 32):\n", " try:\n", " date = datetime(year=2016, month=month, day=day)\n", " except ValueError:\n", " continue\n", " f.write('2016-{:02d}-{:02d},'.format(month, day))\n", " for i, _id in enumerate(['Events', 'Births', 'Deaths']):\n", " table_name = \"{:s}{:d}{:d}\".format(_id, month, day)\n", " data = c.execute(\"SELECT count() FROM sqlite_master WHERE name ='%s' and type='table';\" % table_name).fetchall()\n", " if data[0][0] > 0:\n", " data = c.execute(\"SELECT FROM %s\" % table_name).fetchall()\n", " dataset[month-1, day-1, i] = len(data)\n", " f.write(\"{:d},\".format(len(data)))\n", " table_name = \"{:s}{:d}{:d}\".format('Holidays', month, day)\n", " data = c.execute(\"SELECT count() FROM sqlite_master WHERE name ='%s' and type='table';\" % table_name).fetchall()\n", " if data[0][0] > 0:\n", " data = c.execute(\"SELECT FROM %s\" % table_name).fetchall()\n", "\n", " if any(['Christian feast day:' in d for d in data]):\n", " dataset[month-1, day-1, 3] = len(data) - 1\n", " f.write(\"{:d}\".format(len(data) - 1))\n", " else:\n", " dataset[month-1, day-1, 3] = len(data)\n", " f.write(\"{:d}\".format(len(data)))\n", " \n", " f.write('\\n')\n", " \n", " #print \"On January {:d} there are {:d} Events, {:d} Births, {:d} Deaths, and {:d} Holidays\".format(day, nums)" ] }, { "cell_type": "code", "execution_count": 319, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(u'Christian feast day:',), (u'Abb\\xe1n',), (u'Heribert of Cologne',), (u'March 16 (Eastern Orthodox liturgics)',), (u'Day of the Book Smugglers (Lithuania)',), (u'Latvian Legion Day (Latvia)',), (u\"Saint Urho's Day (Finnish Americans and Finnish Canadians)\",)]\n", "True\n" ] } ], "source": [ "with sqlite3.connect('/Users/spardy/WikipediaDates.db') as conn:\n", " c = conn.cursor()\n", " table_name = \"{:s}{:d}{:d}\".format('Holidays', 3, 16)\n", " data = c.execute(\"SELECT count() FROM sqlite_master WHERE name ='%s' and type='table';\" % table_name).fetchall()\n", " if data[0][0] > 0:\n", " data = c.execute(\"SELECT * FROM %s\" % table_name).fetchall()\n", " print data\n", " print any(['Christian feast day:' in d for d in data])" ] }, { "cell_type": "code", "execution_count": 325, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11a5b43d0>]" ] }, "execution_count": 325, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lb0t/ydvfkjz1WgKlQak7qHegVDqhvNRBLIMs09rWmtJYi6de1H7JS+q+QGlv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\nqQttP8h5HnSQ/W1wkLf59Kc5k8IdURq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PWvIShDTEpScJfEpdlM7q1VEbQh6p5eI96Ul8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\ngICAgGWEQOoBAQEBywj/HxxPxhOjESBtAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a115590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plt.plot(dataset[:, :, 0].ravel())\n", "#plt.plot(dataset[:, :, 1].ravel())\n", "#plt.plot(dataset[:, :, 2].ravel())\n", "plt.plot(dataset[:, :, 3].ravel())" ] }, { "cell_type": "code", "execution_count": 326, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30.0 156.0\n", "198 0\n", "109.0 735.0\n", "59 88\n", "56.0 418.0\n", "59 88\n", "5.0 42.0\n", "95 217\n" ] } ], "source": [ "for i in xrange(4):\n", " print np.nanmin(dataset[:, :, i]), np.nanmax(dataset[:, :, i])\n", " print np.nanargmin(dataset[:, :, i].ravel()), np.nanargmax(dataset[:, :, i].ravel())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Old using EarthCalendar.net they do not allow this type of use" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "year = 2016\n", "end_pattern = 'Copyright'\n", "holidays = {}\n", "\n", "for month in xrange(1, 13):\n", " for day in xrange(1, 31):\n", " try:\n", " date = datetime(year=year, month=month, day=day)\n", " except ValueError:\n", " continue\n", " \n", " url = \"http://www.earthcalendar.net/_php/lookup.php?mode=date&m={:d}&d={:d}&y={:d}\".format(month,\n", " day,\n", " year) \n", " page = urlopen(url)\n", " contents = page.read()\n", " soup = BeautifulSoup(contents, 'html.parser')\n", "\n", " holidays[date] = {}\n", "\n", " for holiday, place in zip(soup.find_all('font', {\"face\": \"Tahoma\",\n", " \"size\": \"2\",\n", " \"color\": \"#000040\"})[::2],\n", " soup.find_all('font', {\"face\": \"Tahoma\",\n", " \"size\": \"2\",\n", " \"color\": \"#000040\"})[1::2]):\n", " if holiday.contents[0].find(end_pattern) > -1:\n", " break\n", "\n", " if isinstance(holiday.contents[0], bs4.element.Tag):\n", " if holiday.contents[0].name == 'a':\n", " _holiday = holiday.contents[0].contents[0]\n", " else:\n", " _holiday = holiday.contents[0]\n", " \n", " if isinstance(place.contents[0], bs4.element.Tag):\n", " if place.contents[0].name == 'select':\n", " place_list = place.contents[0].find_all('option')\n", " _place = [place_entry.contents[0] for place_entry in place_list]\n", " else:\n", " _place = place.contents[0]\n", " \n", " \n", " holidays[date][_holiday] = _place\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-02-13 00:00:00 0\n", "2016-12-29 00:00:00 0\n", "2016-04-20 00:00:00 0\n", "2016-10-22 00:00:00 0\n", "2016-02-29 00:00:00 0\n", "2016-02-20 00:00:00 0\n", "2016-01-23 00:00:00 0\n" ] } ], "source": [ "for day, holiday in holidays.iteritems():\n", " if len(holiday.keys()) == 0: \n", " print day, len(holiday.keys())" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-07-18 00:00:00 2\n", "2016-04-04 00:00:00 8\n", "2016-09-17 00:00:00 3\n", "2016-06-05 00:00:00 5\n", "2016-01-10 00:00:00 1\n", "2016-10-11 00:00:00 3\n", "2016-08-02 00:00:00 6\n", "2016-03-29 00:00:00 4\n", "2016-12-20 00:00:00 3\n", "2016-11-29 00:00:00 3\n", "2016-01-06 00:00:00 20\n", "2016-05-12 00:00:00 7\n", "2016-06-17 00:00:00 4\n", "2016-03-01 00:00:00 15\n", "2016-09-29 00:00:00 5\n", "2016-08-22 00:00:00 1\n", "2016-07-15 00:00:00 3\n", "2016-11-17 00:00:00 7\n", "2016-10-30 00:00:00 2\n", "2016-04-19 00:00:00 6\n", "2016-05-20 00:00:00 5\n", "2016-02-02 00:00:00 9\n", "2016-09-02 00:00:00 1\n", "2016-08-26 00:00:00 4\n", "2016-07-27 00:00:00 4\n", "2016-11-05 00:00:00 3\n", "2016-06-12 00:00:00 4\n", "2016-09-14 00:00:00 2\n", "2016-01-19 00:00:00 2\n", "2016-10-18 00:00:00 4\n", "2016-03-22 00:00:00 7\n", "2016-04-11 00:00:00 3\n", "2016-01-15 00:00:00 2\n", "2016-08-05 00:00:00 7\n", "2016-07-23 00:00:00 7\n", "2016-06-24 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"2016-03-07 00:00:00 1\n", "2016-06-19 00:00:00 6\n", "2016-12-14 00:00:00 1\n", "2016-08-20 00:00:00 7\n", "2016-02-16 00:00:00 2\n", "2016-05-02 00:00:00 6\n", "2016-07-13 00:00:00 3\n", "2016-11-23 00:00:00 2\n", "2016-04-17 00:00:00 2\n", "2016-03-11 00:00:00 2\n", "2016-02-04 00:00:00 3\n", "2016-05-22 00:00:00 4\n", "2016-11-11 00:00:00 16\n", "2016-08-24 00:00:00 3\n", "2016-07-25 00:00:00 12\n", "2016-01-29 00:00:00 1\n", "2016-04-29 00:00:00 5\n", "2016-09-08 00:00:00 17\n", "2016-06-14 00:00:00 7\n", "2016-10-20 00:00:00 4\n", "2016-03-20 00:00:00 5\n", "2016-02-08 00:00:00 4\n", "2016-12-29 00:00:00 0\n", "2016-01-09 00:00:00 2\n", "2016-04-09 00:00:00 5\n", "2016-10-08 00:00:00 4\n", "2016-07-21 00:00:00 5\n", "2016-06-26 00:00:00 5\n", "2016-03-24 00:00:00 2\n", "2016-09-20 00:00:00 1\n", "2016-08-15 00:00:00 27\n", "2016-05-11 00:00:00 1\n", "2016-12-09 00:00:00 2\n", "2016-02-23 00:00:00 4\n", "2016-01-05 00:00:00 2\n", "2016-11-24 00:00:00 2\n", "2016-03-12 00:00:00 8\n", "2016-12-05 00:00:00 6\n", "2016-06-22 00:00:00 3\n", "2016-02-27 00:00:00 1\n", "2016-08-19 00:00:00 7\n", "2016-07-02 00:00:00 2\n", "2016-04-20 00:00:00 0\n", "2016-11-12 00:00:00 4\n", "2016-09-01 00:00:00 10\n", "2016-01-26 00:00:00 4\n", "2016-10-27 00:00:00 5\n", "2016-02-15 00:00:00 5\n", "2016-05-19 00:00:00 4\n", "2016-01-22 00:00:00 2\n", "2016-07-30 00:00:00 3\n", "2016-06-01 00:00:00 11\n", "2016-03-17 00:00:00 4\n", "2016-10-15 00:00:00 7\n", "2016-09-13 00:00:00 2\n", "2016-08-06 00:00:00 5\n", "2016-12-16 00:00:00 6\n", "2016-01-02 00:00:00 11\n", "2016-10-03 00:00:00 4\n", "2016-04-12 00:00:00 3\n", "2016-03-05 00:00:00 4\n", "2016-12-12 00:00:00 8\n", "2016-09-25 00:00:00 5\n", "2016-06-29 00:00:00 3\n", "2016-05-04 00:00:00 9\n", "2016-02-18 00:00:00 1\n", "2016-08-10 00:00:00 4\n", "2016-07-11 00:00:00 6\n", "2016-11-21 00:00:00 5\n", "2016-03-09 00:00:00 1\n", "2016-02-06 00:00:00 4\n", "2016-11-09 00:00:00 5\n", "2016-08-30 00:00:00 6\n", "2016-05-24 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8\n", "2016-05-08 00:00:00 13\n", "2016-06-21 00:00:00 10\n", "2016-08-18 00:00:00 1\n", "2016-03-13 00:00:00 1\n", "2016-12-04 00:00:00 1\n", "2016-11-13 00:00:00 1\n", "2016-05-28 00:00:00 6\n", "2016-02-26 00:00:00 1\n", "2016-01-27 00:00:00 5\n", "2016-10-26 00:00:00 4\n", "2016-07-03 00:00:00 5\n", "2016-04-23 00:00:00 8\n", "2016-05-16 00:00:00 1\n", "2016-09-06 00:00:00 7\n", "2016-02-14 00:00:00 2\n", "2016-11-01 00:00:00 14\n", "2016-10-14 00:00:00 9\n", "2016-04-03 00:00:00 1\n", "2016-01-23 00:00:00 0\n", "2016-12-19 00:00:00 4\n", "2016-09-18 00:00:00 2\n", "2016-03-18 00:00:00 5\n", "2016-04-15 00:00:00 11\n", "2016-08-01 00:00:00 13\n", "2016-06-28 00:00:00 6\n", "2016-09-30 00:00:00 4\n", "2016-01-03 00:00:00 2\n", "2016-10-02 00:00:00 4\n", "2016-03-06 00:00:00 4\n", "2016-12-15 00:00:00 6\n", "2016-11-22 00:00:00 7\n", "2016-05-05 00:00:00 11\n", "2016-08-21 00:00:00 1\n", "2016-02-17 00:00:00 2\n", "2016-03-10 00:00:00 2\n", "2016-07-12 00:00:00 8\n", "2016-05-25 00:00:00 14\n", "2016-08-25 00:00:00 5\n", "2016-02-05 00:00:00 6\n", "2016-11-10 00:00:00 6\n", "2016-10-21 00:00:00 7\n", "2016-04-26 00:00:00 2\n", "2016-07-24 00:00:00 6\n", "2016-01-28 00:00:00 1\n", "2016-12-26 00:00:00 16\n", "2016-02-09 00:00:00 1\n", "2016-09-11 00:00:00 5\n", "2016-06-15 00:00:00 3\n", "2016-04-06 00:00:00 4\n", "2016-07-20 00:00:00 6\n", "2016-09-23 00:00:00 4\n", "2016-01-08 00:00:00 6\n", "2016-10-09 00:00:00 8\n", "2016-06-27 00:00:00 4\n", "2016-12-22 00:00:00 3\n", "2016-01-04 00:00:00 6\n", "2016-08-12 00:00:00 5\n", "2016-05-10 00:00:00 5\n", "2016-03-03 00:00:00 8\n", "2016-06-23 00:00:00 7\n", "2016-08-16 00:00:00 7\n", "2016-12-02 00:00:00 4\n", "2016-02-28 00:00:00 2\n", "2016-05-30 00:00:00 8\n", "2016-11-19 00:00:00 7\n", "2016-10-28 00:00:00 2\n", "2016-07-01 00:00:00 14\n", "2016-04-21 00:00:00 10\n", "2016-05-18 00:00:00 5\n", "2016-07-29 00:00:00 5\n", "2016-11-07 00:00:00 6\n", "2016-09-12 00:00:00 1\n", "2016-01-17 00:00:00 1\n", "2016-04-01 00:00:00 5\n", "2016-10-16 00:00:00 8\n", "2016-12-17 00:00:00 3\n", "2016-06-02 00:00:00 3\n", "2016-03-16 00:00:00 5\n", "2016-01-13 00:00:00 5\n", "2016-04-13 00:00:00 8\n", "2016-08-07 00:00:00 4\n", "2016-09-24 00:00:00 11\n", "2016-06-30 00:00:00 5\n", "2016-10-04 00:00:00 5\n", "2016-08-11 00:00:00 4\n", "2016-03-04 00:00:00 1\n", "2016-12-13 00:00:00 3\n", "2016-11-20 00:00:00 4\n", "2016-05-07 00:00:00 1\n", "2016-02-19 00:00:00 3\n", "2016-03-08 00:00:00 7\n", "2016-07-10 00:00:00 3\n", "2016-05-27 00:00:00 3\n", "2016-07-06 00:00:00 5\n", "2016-02-07 00:00:00 2\n", "2016-11-08 00:00:00 7\n", "2016-10-23 00:00:00 6\n", "2016-09-05 00:00:00 1\n", "2016-04-24 00:00:00 2\n", "2016-01-30 00:00:00 1\n", "2016-12-24 00:00:00 6\n", "2016-02-11 00:00:00 4\n", "2016-06-09 00:00:00 3\n" ] } ], "source": [ "for day, holiday in holidays.iteritems():\n", " print day, len(holiday.keys())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
adriaanvuik/solid_state_physics	weak_coupling_model.ipynb	1	1457750	null	bsd-2-clause
mprego/NBA	Player Analysis.ipynb	2	96847	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from Player.Players import Players\n", "from Regression.Reg_Model import Reg_Model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Creates list of players during 2015-2016 season\n", "Player_List = Players('2015-16').players" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#prepare model for subset of players\n", "p_subset_list=[]\n", "index=0\n", "for key in Player_List:\n", " p=Player_List[key]\n", " stats = p.get_stats('GAME_DATE', 'Game_ID', 'PTS', ['PTS', 'MIN', 'FGM', 'FGA', 'FG_PCT'], 5)\n", " if len(stats>0):\n", " stats=stats[stats['n_games']>=5]\n", " y=stats['PTS']\n", " x=stats[['PTS_avg_5', 'MIN_avg_5', 'FGM_avg_5', 'FGA_avg_5', 'FG_PCT_avg_5']]\n", "\n", " if len(y)>=5:\n", " Model = Reg_Model()\n", " Model.set_training(x,y)\n", " Model.calc_model()\n", " \n", " p.set_model('PTS', Model)\n", " p_subset_list.append(key)\n", " index += 1\n", " if index>50:\n", " break\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25.1372459769\n" ] } ], "source": [ "#average MSE for players\n", "import numpy as np\n", "print np.mean([Player_List[a].model_list['PTS'].mse for a in p_subset_list])\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Try to see how data looks like by position, season_exp\n", "\n", "exp = [0,4,8,12]\n", "positions = set([Player_List[x].desc['POSITION'][0] for x in Player_List])\n", "mse_by_seg = {}\n", "for pos in positions:\n", " for ex in exp:\n", " x =pd.DataFrame()\n", " y =pd.DataFrame()\n", "\n", " for key in Player_List:\n", " if Player_List[key].desc['POSITION'][0] == pos:\n", " if Player_List[key].desc['SEASON_EXP'][0]>=ex and Player_List[key].desc['SEASON_EXP'][0]<(ex+4):\n", " stats = Player_List[key].get_stats('GAME_DATE', 'Game_ID', 'PTS', ['PTS', 'MIN', 'FGM', 'FGA', 'FG_PCT'], 5)\n", " if len(stats>0):\n", " stats=stats[stats['n_games']>=5]\n", " y = y.append(stats[['PTS', 'PTS_avg_5']], ignore_index=True).reset_index(drop=True)\n", " x = x.append(stats[['PTS_avg_5', 'MIN_avg_5', 'FGM_avg_5', 'FGA_avg_5', 'FG_PCT_avg_5']], ignore_index=True).reset_index(drop=True)\n", " y=y['PTS']\n", " Model = Reg_Model()\n", " Model.set_training(x,y)\n", " Model.calc_model()\n", " segment_name = pos + '-' + str(ex)\n", " mse_by_seg[segment_name] = Model.mse\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Center-0': 22.610728761159205,\n", " 'Center-12': 25.92413444999081,\n", " 'Center-4': 24.467674796327085,\n", " 'Center-8': 23.067862858829894,\n", " 'Center-Forward-0': 28.22705822061172,\n", " 'Center-Forward-12': 23.856781022281453,\n", " 'Center-Forward-4': 23.601499472919901,\n", " 'Center-Forward-8': 26.803202564760195,\n", " 'Forward-0': 21.943651024993212,\n", " 'Forward-12': 25.033184497551531,\n", " 'Forward-4': 26.248815010853559,\n", " 'Forward-8': 32.156109394999504,\n", " 'Forward-Center-0': 20.192992779688502,\n", " 'Forward-Center-12': 17.147617829468807,\n", " 'Forward-Center-4': 24.039159936258216,\n", " 'Forward-Center-8': 18.086930030500124,\n", " 'Forward-Guard-0': 21.053376665428225,\n", " 'Forward-Guard-12': 35.742420453841113,\n", " 'Forward-Guard-4': 38.623104452242416,\n", " 'Forward-Guard-8': 30.304432650905145,\n", " 'Guard-0': 26.376897816201502,\n", " 'Guard-12': 26.656856246560299,\n", " 'Guard-4': 38.418152612763187,\n", " 'Guard-8': 32.076886962358309,\n", " 'Guard-Forward-0': 21.113916436620155,\n", " 'Guard-Forward-12': 26.417658693009415,\n", " 'Guard-Forward-4': 37.828767128174448,\n", " 'Guard-Forward-8': 39.811349626808955}" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mse_by_seg" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Repeat with 10 games\n", "\n", "exp = [0,4,8,12]\n", "positions = set([Player_List[x].desc['POSITION'][0] for x in Player_List])\n", "mse_by_seg = {}\n", "for pos in positions:\n", " for ex in exp:\n", " x =pd.DataFrame()\n", " y =pd.DataFrame()\n", "\n", " for key in Player_List:\n", " if Player_List[key].desc['POSITION'][0] == pos:\n", " if Player_List[key].desc['SEASON_EXP'][0]>=ex and Player_List[key].desc['SEASON_EXP'][0]<(ex+4):\n", " stats = Player_List[key].get_stats('GAME_DATE', 'Game_ID', 'PTS', ['PTS', 'MIN', 'FGM', 'FGA', 'FG_PCT'], 10)\n", " if len(stats>0):\n", " stats=stats[stats['n_games']>=5]\n", " y = y.append(stats[['PTS', 'PTS_avg_10']], ignore_index=True).reset_index(drop=True)\n", " x = x.append(stats[['PTS_avg_10', 'MIN_avg_10', 'FGM_avg_10', 'FGA_avg_10', 'FG_PCT_avg_10']], ignore_index=True).reset_index(drop=True)\n", " y=y['PTS']\n", " Model = Reg_Model()\n", " Model.set_training(x,y)\n", " Model.calc_model()\n", " segment_name = pos + '-' + str(ex)\n", " mse_by_seg[segment_name] = Model.mse\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Center-0': 22.894815485543415,\n", " 'Center-12': 24.962184167276178,\n", " 'Center-4': 23.993721154223024,\n", " 'Center-8': 22.478342832127819,\n", " 'Center-Forward-0': 27.624327800350159,\n", " 'Center-Forward-12': 24.478380503289653,\n", " 'Center-Forward-4': 23.503942895604482,\n", " 'Center-Forward-8': 25.640627242186042,\n", " 'Forward-0': 21.933376108956153,\n", " 'Forward-12': 23.626238147113625,\n", " 'Forward-4': 26.145119786480379,\n", " 'Forward-8': 31.036927084028953,\n", " 'Forward-Center-0': 19.641409643339919,\n", " 'Forward-Center-12': 16.957357169032804,\n", " 'Forward-Center-4': 22.650696005687482,\n", " 'Forward-Center-8': 17.517433102596634,\n", " 'Forward-Guard-0': 20.094454417342217,\n", " 'Forward-Guard-12': 33.984787243421493,\n", " 'Forward-Guard-4': 37.288507725551518,\n", " 'Forward-Guard-8': 29.293965399437479,\n", " 'Guard-0': 26.717902060845645,\n", " 'Guard-12': 26.888961490598497,\n", " 'Guard-4': 37.722386207848587,\n", " 'Guard-8': 32.122756991925449,\n", " 'Guard-Forward-0': 21.452515019666897,\n", " 'Guard-Forward-12': 26.521176526962218,\n", " 'Guard-Forward-4': 37.165507404428709,\n", " 'Guard-Forward-8': 39.313326861147686}" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mse_by_seg" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Now i want to see how i can add other variables to the model. probably want a model that takes into account: opposing team, current team\n", "from Schedule.Schedule import Schedule\n", "from Schedule.Stats import Stats" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sched_2015 = Schedule(b_dt = '10/1/2015')\n", "sched_2015.add_four_factors()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create last n statistics\n", "games = sched_2015.get_games()\n", "stats = Stats(games, 'avg', 'GAME_DATE', 'Home Team', 'Away Team', 'Pts_diff', ['Game_ID'])" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stats_10 = stats.get_lastn_stats(10)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1230\n", "1078\n", "1073\n" ] } ], "source": [ "print len(stats_10)\n", "\n", "stats_10 = stats_10[stats_10['H_10_games']==10]\n", "print len(stats_10)\n", "\n", "stats_10 = stats_10[stats_10['A_10_games']==10]\n", "print len(stats_10)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Create model based on team data\n", "\n", "exp = [0,4,8,12]\n", "positions = set([Player_List[x].desc['POSITION'][0] for x in Player_List])\n", "mse_by_seg = {}\n", "for pos in positions:\n", " for ex in exp:\n", " x =pd.DataFrame()\n", " y =pd.DataFrame()\n", "\n", " for key in Player_List:\n", " if Player_List[key].desc['POSITION'][0] == pos:\n", " if Player_List[key].desc['SEASON_EXP'][0]>=ex and Player_List[key].desc['SEASON_EXP'][0]<(ex+4):\n", " stats = Player_List[key].get_stats('GAME_DATE', 'Game_ID', 'PTS', ['PTS', 'MIN', 'FGM', 'FGA', 'FG_PCT'], 10)\n", " if len(stats>0):\n", " data = Player_List[key].game_logs\n", " for index, game in data.iterrows():\n", " splits = game['MATCHUP'].split(' ')\n", " if splits[1] == '@':\n", " data.set_value(index, 'Home', 0)\n", " else:\n", " data.set_value(index, 'Home', 1)\n", " data = data[['Game_ID', 'Home']]\n", " stats = pd.merge(stats, data, on='Game_ID')\n", " \n", " stats=stats[stats['n_games']>=5]\n", " \n", " col_list = {}\n", " for col in h_games.columns.values:\n", " if col[0:2]=='H_':\n", " new_col = 'Y_' + col[2:]\n", " elif col[0:2]=='A_':\n", " new_col = 'M_' + col[2:]\n", " else:\n", " new_col=col\n", " col_list[col]=new_col\n", " h_games.rename(columns=col_list,inplace=True)\n", " \n", " col_list = {}\n", " for col in a_games.columns.values:\n", " if col[0:2]=='H_':\n", " new_col = 'M_' + col[2:]\n", " elif col[0:2]=='A_':\n", " new_col = 'Y_' + col[2:]\n", " else:\n", " new_col=col\n", " col_list[col]=new_col\n", " a_games.rename(columns=col_list,inplace=True)\n", " \n", " stats_all = h_games.append(a_games, ignore_index=True).reset_index(drop=True)\n", " stats_all = stats_all[stats_all['n_games']>=5]\n", " y=stats_all[['PTS', 'PTS_avg_10']]\n", " x=stats_all[['PTS_avg_10', 'MIN_avg_10', 'FGM_avg_10', 'FGA_avg_10', 'FG_PCT_avg_10', 'M_BTB',\n", " 'M_FF_EFG_10', 'M_FF_FTFGA_10', 'M_FF_ORB_10',\n", " 'M_FF_TOV_10', 'M_O_FF_EFG_10',\n", " 'M_O_FF_FTFGA_10', 'M_O_FF_ORB_10',\n", " 'M_O_FF_TOV_10', 'M_O_PTS_10','M_O_WL_10',\n", " 'M_PTS_10', 'M_WL_10', \n", " 'Y_FF_EFG_10', 'Y_FF_FTFGA_10', 'Y_FF_ORB_10',\n", " 'Y_FF_TOV_10', 'Y_O_FF_EFG_10',\n", " 'Y_O_FF_FTFGA_10', 'Y_O_FF_ORB_10',\n", " 'Y_O_FF_TOV_10', 'Y_O_PTS_10', 'Y_O_WL_10',\n", " 'Y_PTS_10', 'Y_WL_10']]\n", " \n", " y=y['PTS']\n", " Model = Reg_Model()\n", " Model.set_training(x,y)\n", " Model.calc_model()\n", " segment_name = pos + '-' + str(ex)\n", " mse_by_seg[segment_name] = Model.mse\n", " \n", "#Gets Player stats data and adds home indicator\n" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Center-0': 35.607776070121069,\n", " 'Center-12': 36.734098606492829,\n", " 'Center-4': 34.353005818799886,\n", " 'Center-8': 36.530407809205123,\n", " 'Center-Forward-0': 35.466471616742233,\n", " 'Center-Forward-12': 35.169697656066639,\n", " 'Center-Forward-4': 35.925466881945688,\n", " 'Center-Forward-8': 34.403624846409656,\n", " 'Forward-0': 31.44473202531816,\n", " 'Forward-12': 35.886199229820335,\n", " 'Forward-4': 35.626281252210475,\n", " 'Forward-8': 33.625814104219103,\n", " 'Forward-Center-0': 33.622497158257808,\n", " 'Forward-Center-12': 35.179658205492991,\n", " 'Forward-Center-4': 36.191241145666559,\n", " 'Forward-Center-8': 35.496055934455072,\n", " 'Forward-Guard-0': 36.060665850860119,\n", " 'Forward-Guard-12': 35.775333757580036,\n", " 'Forward-Guard-4': 35.705490383611028,\n", " 'Forward-Guard-8': 35.34033317636294,\n", " 'Guard-0': 36.684563386342525,\n", " 'Guard-12': 36.786209034247406,\n", " 'Guard-4': 36.5919178938247,\n", " 'Guard-8': 37.265287748435128,\n", " 'Guard-Forward-0': 35.532370330950997,\n", " 'Guard-Forward-12': 34.282195910134206,\n", " 'Guard-Forward-4': 35.247674633998805,\n", " 'Guard-Forward-8': 35.321878475346161}" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mse_by_seg" ] }, { "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": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'Pts_diff', u'H_WL_10', u'H_O_WL_10', u'A_WL_10', u'A_O_WL_10',\n", " u'H_PTS_10', u'H_O_PTS_10', u'A_PTS_10', u'A_O_PTS_10', u'H_AST_10',\n", " u'H_O_AST_10', u'A_AST_10', u'A_O_AST_10', u'H_STL_10', u'H_O_STL_10',\n", " u'A_STL_10', u'A_O_STL_10', u'H_BLK_10', u'H_O_BLK_10', u'A_BLK_10',\n", " u'A_O_BLK_10', u'H_FF_EFG_10', u'H_O_FF_EFG_10', u'A_FF_EFG_10',\n", " u'A_O_FF_EFG_10', u'H_FF_ORB_10', u'H_O_FF_ORB_10', u'A_FF_ORB_10',\n", " u'A_O_FF_ORB_10', u'H_FF_FTFGA_10', u'H_O_FF_FTFGA_10',\n", " u'A_FF_FTFGA_10', u'A_O_FF_FTFGA_10', u'H_FF_TOV_10', u'H_O_FF_TOV_10',\n", " u'A_FF_TOV_10', u'A_O_FF_TOV_10', u'H_BTB', u'A_BTB', u'H_10_games',\n", " u'A_10_games', u'Game_ID'],\n", " dtype='object')" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats_10.columns\n", "#try to see what variables i have on tap\n" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2544\n", "76\n", "66\n" ] } ], "source": [ "#join player stats and game stats for 1 player\n", "from Player.Player import Player\n", "lebron = Player(f_name='Lebron', l_name='James')\n", "\n", "data = lebron.game_logs\n", "for index, game in data.iterrows():\n", " splits = game['MATCHUP'].split(' ')\n", " if splits[1] == '@':\n", " data.set_value(index, 'Home', 0)\n", " else:\n", " data.set_value(index, 'Home', 1)\n", "data = data[['Game_ID', 'Home']]\n", " \n", "stats = lebron.get_stats('GAME_DATE', 'Game_ID', 'PTS', ['PTS', 'MIN', 'FGM', 'FGA', 'FG_PCT'], 10)\n", "\n", "stats_v2 = pd.merge(stats, data, on='Game_ID')\n", "print len(stats_v2)\n", "stats_v3 = stats_v2[stats_v2['n_games']>=10]\n", "print len(stats_v3)\n", "\n" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33\n", "33\n", "33\n", "33\n" ] } ], "source": [ "h_games = stats_v3[stats_v3['Home']==1]\n", "print len(h_games)\n", "a_games = stats_v3[stats_v3['Home']==0]\n", "print len(a_games)\n", "\n", "h_games = pd.merge(h_games, stats_10, on='Game_ID')\n", "print len(h_games)\n", "a_games = pd.merge(a_games, stats_10, on='Game_ID')\n", "print len(a_games)\n", "\n" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n", "50\n", "50\n", "50\n" ] } ], "source": [ "print len(h_games.columns)\n", "col_list = {}\n", "for col in h_games.columns.values:\n", " if col[0:2]=='H_':\n", " new_col = 'Y_' + col[2:]\n", " elif col[0:2]=='A_':\n", " new_col = 'M_' + col[2:]\n", " else:\n", " new_col=col\n", " col_list[col]=new_col\n", "h_games.rename(columns=col_list,inplace=True)\n", "print len(h_games.columns)\n", "\n", "print len(a_games.columns)\n", "col_list = {}\n", "for col in a_games.columns.values:\n", " if col[0:2]=='H_':\n", " new_col = 'M_' + col[2:]\n", " elif col[0:2]=='A_':\n", " new_col = 'Y_' + col[2:]\n", " else:\n", " new_col=col\n", " col_list[col]=new_col\n", "a_games.rename(columns=col_list,inplace=True)\n", "print len(a_games.columns) " ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "66\n", "50\n", "50\n", "50\n" ] } ], "source": [ "stats_all = h_games.append(a_games, ignore_index=True).reset_index(drop=True)\n", "print len(stats_all)\n", "print len(a_games.columns)\n", "print len(a_games.columns)\n", "print len(stats_all.columns)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index([u'FGA_avg_10', u'FGM_avg_10', u'FG_PCT_avg_10', u'Game_ID', u'Home',\n", " u'MIN_avg_10', u'M_10_games', u'M_AST_10', u'M_BLK_10', u'M_BTB',\n", " u'M_FF_EFG_10', u'M_FF_FTFGA_10', u'M_FF_ORB_10', u'M_FF_TOV_10',\n", " u'M_O_AST_10', u'M_O_BLK_10', u'M_O_FF_EFG_10', u'M_O_FF_FTFGA_10',\n", " u'M_O_FF_ORB_10', u'M_O_FF_TOV_10', u'M_O_PTS_10', u'M_O_STL_10',\n", " u'M_O_WL_10', u'M_PTS_10', u'M_STL_10', u'M_WL_10', u'PTS',\n", " u'PTS_avg_10', u'Pts_diff', u'Y_10_games', u'Y_AST_10', u'Y_BLK_10',\n", " u'Y_BTB', u'Y_FF_EFG_10', u'Y_FF_FTFGA_10', u'Y_FF_ORB_10',\n", " u'Y_FF_TOV_10', u'Y_O_AST_10', u'Y_O_BLK_10', u'Y_O_FF_EFG_10',\n", " u'Y_O_FF_FTFGA_10', u'Y_O_FF_ORB_10', u'Y_O_FF_TOV_10', u'Y_O_PTS_10',\n", " u'Y_O_STL_10', u'Y_O_WL_10', u'Y_PTS_10', u'Y_STL_10', u'Y_WL_10',\n", " u'n_games'],\n", " dtype='object')" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats_all.columns" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stats_all = stats_all[stats_all['n_games']>4]\n", "y=stats_all['PTS']\n", "x=stats_all[['PTS_avg_10', 'MIN_avg_10', 'FGM_avg_10', 'FGA_avg_10', 'FG_PCT_avg_10', 'M_BTB']]" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stats_all = stats_all[stats_all['n_games']>4]\n", "y=stats_all['PTS']\n", "x=stats_all[['PTS_avg_10', 'MIN_avg_10', 'FGM_avg_10', 'FGA_avg_10', 'FG_PCT_avg_10', 'M_BTB',\n", " 'M_FF_EFG_10', 'M_FF_FTFGA_10', 'M_FF_ORB_10',\n", " 'M_FF_TOV_10', 'M_O_FF_EFG_10',\n", " 'M_O_FF_FTFGA_10', 'M_O_FF_ORB_10',\n", " 'M_O_FF_TOV_10', 'M_O_PTS_10','M_O_WL_10',\n", " 'M_PTS_10', 'M_WL_10', \n", " 'Y_FF_EFG_10', 'Y_FF_FTFGA_10', 'Y_FF_ORB_10',\n", " 'Y_FF_TOV_10', 'Y_O_FF_EFG_10',\n", " 'Y_O_FF_FTFGA_10', 'Y_O_FF_ORB_10',\n", " 'Y_O_FF_TOV_10', 'Y_O_PTS_10', 'Y_O_WL_10',\n", " 'Y_PTS_10', 'Y_WL_10']]\n" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'numpy.float64'> PTS_avg_10\n", "<type 'numpy.float64'> MIN_avg_10\n", "<type 'numpy.float64'> FGM_avg_10\n", "<type 'numpy.float64'> FGA_avg_10\n", "<type 'numpy.float64'> FG_PCT_avg_10\n", "<type 'numpy.float64'> M_BTB\n", "<type 'numpy.float64'> M_FF_EFG_10\n", "<type 'numpy.float64'> M_FF_FTFGA_10\n", "<type 'numpy.float64'> M_FF_ORB_10\n", "<type 'numpy.float64'> M_FF_TOV_10\n", "<type 'numpy.float64'> M_O_FF_EFG_10\n", "<type 'numpy.float64'> M_O_FF_FTFGA_10\n", "<type 'numpy.float64'> M_O_FF_ORB_10\n", "<type 'numpy.float64'> M_O_FF_TOV_10\n", "<type 'numpy.float64'> M_O_PTS_10\n", "<type 'numpy.float64'> M_O_WL_10\n", "<type 'numpy.float64'> M_PTS_10\n", "<type 'numpy.float64'> M_WL_10\n", "<type 'numpy.float64'> Y_FF_EFG_10\n", "<type 'numpy.float64'> Y_FF_FTFGA_10\n", "<type 'numpy.float64'> Y_FF_ORB_10\n", "<type 'numpy.float64'> Y_FF_TOV_10\n", "<type 'numpy.float64'> Y_O_FF_EFG_10\n", "<type 'numpy.float64'> Y_O_FF_FTFGA_10\n", "<type 'numpy.float64'> Y_O_FF_ORB_10\n", "<type 'numpy.float64'> Y_O_FF_TOV_10\n", "<type 'numpy.float64'> Y_O_PTS_10\n", "<type 'numpy.float64'> Y_O_WL_10\n", "<type 'numpy.float64'> Y_PTS_10\n", "<type 'numpy.float64'> Y_WL_10\n" ] } ], "source": [ "for index, row in x.iterrows():\n", " for col in x.columns.values:\n", " print type(x.ix[index, col]), col\n", " break\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 167, "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>PTS_avg_10</th>\n", " <th>MIN_avg_10</th>\n", " <th>FGM_avg_10</th>\n", " <th>FGA_avg_10</th>\n", " <th>FG_PCT_avg_10</th>\n", " <th>M_BTB</th>\n", " <th>M_FF_EFG_10</th>\n", " <th>M_FF_FTFGA_10</th>\n", " <th>M_FF_ORB_10</th>\n", " <th>M_FF_TOV_10</th>\n", " <th>...</th>\n", " <th>Y_FF_TOV_10</th>\n", " <th>Y_O_FF_EFG_10</th>\n", " <th>Y_O_FF_FTFGA_10</th>\n", " <th>Y_O_FF_FTFGA_10</th>\n", " <th>Y_O_FF_ORB_10</th>\n", " <th>Y_O_FF_TOV_10</th>\n", " <th>Y_O_PTS_10</th>\n", " <th>Y_O_WL_10</th>\n", " <th>Y_PTS_10</th>\n", " <th>Y_WL_10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>27.5</td>\n", " <td>36.6</td>\n", " <td>10.4</td>\n", " <td>20.7</td>\n", " <td>0.4992</td>\n", " <td>0</td>\n", " <td>0.499567</td>\n", " <td>0.216664</td>\n", " <td>0.222306</td>\n", " <td>0.157226</td>\n", " <td>...</td>\n", " <td>0.148223</td>\n", " <td>0.470509</td>\n", " <td>0.203110</td>\n", " <td>0.203110</td>\n", " <td>0.222303</td>\n", " <td>0.136481</td>\n", " <td>96.3</td>\n", " <td>0.2</td>\n", " <td>103.2</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>29.0</td>\n", " <td>37.0</td>\n", " <td>10.9</td>\n", " <td>20.7</td>\n", " <td>0.5376</td>\n", " <td>0</td>\n", " <td>0.529546</td>\n", " <td>0.191625</td>\n", " <td>0.197374</td>\n", " <td>0.152234</td>\n", " <td>...</td>\n", " <td>0.148204</td>\n", " <td>0.488033</td>\n", " <td>0.212365</td>\n", " <td>0.212365</td>\n", " <td>0.218215</td>\n", " <td>0.132230</td>\n", " <td>98.7</td>\n", " <td>0.2</td>\n", " <td>104.1</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>28.0</td>\n", " <td>36.9</td>\n", " <td>10.4</td>\n", " <td>20.3</td>\n", " <td>0.5225</td>\n", " <td>0</td>\n", " <td>0.479129</td>\n", " <td>0.177602</td>\n", " <td>0.239701</td>\n", " <td>0.144632</td>\n", " <td>...</td>\n", " <td>0.150807</td>\n", " <td>0.484885</td>\n", " <td>0.211972</td>\n", " <td>0.211972</td>\n", " <td>0.226658</td>\n", " <td>0.130724</td>\n", " <td>99.2</td>\n", " <td>0.2</td>\n", " <td>104.8</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>26.8</td>\n", " <td>37.8</td>\n", " <td>9.5</td>\n", " <td>18.9</td>\n", " <td>0.5090</td>\n", " <td>0</td>\n", " <td>0.474859</td>\n", " <td>0.173879</td>\n", " <td>0.263528</td>\n", " <td>0.141281</td>\n", " <td>...</td>\n", " <td>0.144950</td>\n", " <td>0.491311</td>\n", " <td>0.220018</td>\n", " <td>0.220018</td>\n", " <td>0.223203</td>\n", " <td>0.120231</td>\n", " <td>100.0</td>\n", " <td>0.3</td>\n", " <td>104.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>26.5</td>\n", " <td>37.9</td>\n", " <td>9.5</td>\n", " <td>18.8</td>\n", " <td>0.5110</td>\n", " <td>0</td>\n", " <td>0.477212</td>\n", " <td>0.227320</td>\n", " <td>0.207927</td>\n", " <td>0.159148</td>\n", " <td>...</td>\n", " <td>0.152920</td>\n", " <td>0.487855</td>\n", " <td>0.227795</td>\n", " <td>0.227795</td>\n", " <td>0.216491</td>\n", " <td>0.128803</td>\n", " <td>99.1</td>\n", " <td>0.3</td>\n", " <td>103.7</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>26.4</td>\n", " <td>38.4</td>\n", " <td>9.3</td>\n", " <td>19.7</td>\n", " <td>0.4808</td>\n", " <td>1</td>\n", " <td>0.492952</td>\n", " <td>0.207718</td>\n", " <td>0.270791</td>\n", " <td>0.152829</td>\n", " <td>...</td>\n", " <td>0.150907</td>\n", " <td>0.510943</td>\n", " <td>0.226956</td>\n", " <td>0.226956</td>\n", " <td>0.174840</td>\n", " <td>0.128924</td>\n", " <td>99.5</td>\n", " <td>0.5</td>\n", " <td>100.1</td>\n", " <td>0.5</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>25.2</td>\n", " <td>36.9</td>\n", " <td>9.4</td>\n", " <td>19.5</td>\n", " <td>0.4861</td>\n", " <td>1</td>\n", " <td>0.511610</td>\n", " <td>0.257289</td>\n", " <td>0.272205</td>\n", " <td>0.155198</td>\n", " <td>...</td>\n", " <td>0.146851</td>\n", " <td>0.504212</td>\n", " <td>0.187476</td>\n", " <td>0.187476</td>\n", " <td>0.178954</td>\n", " <td>0.141573</td>\n", " <td>94.7</td>\n", " <td>0.4</td>\n", " <td>98.3</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>26.6</td>\n", " <td>37.6</td>\n", " <td>9.8</td>\n", " <td>20.7</td>\n", " <td>0.4772</td>\n", " <td>0</td>\n", " <td>0.484252</td>\n", " <td>0.181076</td>\n", " <td>0.206482</td>\n", " <td>0.165533</td>\n", " <td>...</td>\n", " <td>0.153133</td>\n", " <td>0.500008</td>\n", " <td>0.203859</td>\n", " <td>0.203859</td>\n", " <td>0.183240</td>\n", " <td>0.144048</td>\n", " <td>94.4</td>\n", " <td>0.4</td>\n", " <td>97.0</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>27.4</td>\n", " <td>36.6</td>\n", " <td>10.1</td>\n", " <td>21.0</td>\n", " <td>0.4860</td>\n", " <td>0</td>\n", " <td>0.506599</td>\n", " <td>0.233844</td>\n", " <td>0.215693</td>\n", " <td>0.140396</td>\n", " <td>...</td>\n", " <td>0.152206</td>\n", " <td>0.480203</td>\n", " <td>0.213088</td>\n", " <td>0.213088</td>\n", " <td>0.199855</td>\n", " <td>0.152822</td>\n", " <td>92.7</td>\n", " <td>0.3</td>\n", " <td>97.9</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>24.7</td>\n", " <td>34.2</td>\n", " <td>9.6</td>\n", " <td>19.8</td>\n", " <td>0.4826</td>\n", " <td>1</td>\n", " <td>0.553993</td>\n", " <td>0.153726</td>\n", " <td>0.220251</td>\n", " <td>0.138155</td>\n", " <td>...</td>\n", " <td>0.139893</td>\n", " <td>0.461490</td>\n", " <td>0.187855</td>\n", " <td>0.187855</td>\n", " <td>0.245744</td>\n", " <td>0.155057</td>\n", " <td>90.1</td>\n", " <td>0.2</td>\n", " <td>96.1</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>24.3</td>\n", " <td>33.1</td>\n", " <td>9.3</td>\n", " <td>19.2</td>\n", " <td>0.4854</td>\n", " <td>1</td>\n", " <td>0.481696</td>\n", " <td>0.225793</td>\n", " <td>0.258966</td>\n", " <td>0.119578</td>\n", " <td>...</td>\n", " <td>0.135372</td>\n", " <td>0.446000</td>\n", " <td>0.196200</td>\n", " <td>0.196200</td>\n", " <td>0.227202</td>\n", " <td>0.151054</td>\n", " <td>88.0</td>\n", " <td>0.2</td>\n", " <td>96.0</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>24.9</td>\n", " <td>35.3</td>\n", " <td>9.2</td>\n", " <td>16.7</td>\n", " <td>0.5391</td>\n", " <td>0</td>\n", " <td>0.562627</td>\n", " <td>0.178130</td>\n", " <td>0.210621</td>\n", " <td>0.136214</td>\n", " <td>...</td>\n", " <td>0.125190</td>\n", " <td>0.501951</td>\n", " <td>0.203365</td>\n", " <td>0.203365</td>\n", " <td>0.206812</td>\n", " <td>0.153338</td>\n", " <td>94.5</td>\n", " <td>0.1</td>\n", " <td>105.7</td>\n", " <td>0.9</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>23.4</td>\n", " <td>34.6</td>\n", " <td>8.9</td>\n", " <td>16.1</td>\n", " <td>0.5470</td>\n", " <td>0</td>\n", " <td>0.550081</td>\n", " <td>0.213965</td>\n", " <td>0.208560</td>\n", " <td>0.117590</td>\n", " <td>...</td>\n", " <td>0.123726</td>\n", " <td>0.514379</td>\n", " <td>0.206122</td>\n", " <td>0.206122</td>\n", " <td>0.183531</td>\n", " <td>0.138839</td>\n", " <td>97.1</td>\n", " <td>0.2</td>\n", " <td>105.2</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>22.7</td>\n", " <td>35.2</td>\n", " <td>8.7</td>\n", " <td>16.0</td>\n", " <td>0.5388</td>\n", " <td>1</td>\n", " <td>0.475464</td>\n", " <td>0.226111</td>\n", " <td>0.286582</td>\n", " <td>0.153885</td>\n", " <td>...</td>\n", " <td>0.133384</td>\n", " <td>0.521024</td>\n", " <td>0.216385</td>\n", " <td>0.216385</td>\n", " <td>0.192174</td>\n", " <td>0.132995</td>\n", " <td>99.4</td>\n", " <td>0.2</td>\n", " <td>106.3</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>23.3</td>\n", " <td>36.1</td>\n", " <td>9.1</td>\n", " <td>17.6</td>\n", " <td>0.5159</td>\n", " <td>0</td>\n", " <td>0.489019</td>\n", " <td>0.279045</td>\n", " <td>0.261555</td>\n", " <td>0.158768</td>\n", " <td>...</td>\n", " <td>0.122879</td>\n", " <td>0.513931</td>\n", " <td>0.203931</td>\n", " <td>0.203931</td>\n", " <td>0.199441</td>\n", " <td>0.130612</td>\n", " <td>99.0</td>\n", " <td>0.3</td>\n", " <td>102.4</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>22.4</td>\n", " <td>35.9</td>\n", " <td>9.0</td>\n", " <td>16.9</td>\n", " <td>0.5347</td>\n", " <td>1</td>\n", " <td>0.462186</td>\n", " <td>0.194991</td>\n", " <td>0.228034</td>\n", " <td>0.155730</td>\n", " <td>...</td>\n", " <td>0.127162</td>\n", " <td>0.500647</td>\n", " <td>0.206562</td>\n", " <td>0.206562</td>\n", " <td>0.206065</td>\n", " <td>0.128368</td>\n", " <td>98.2</td>\n", " <td>0.3</td>\n", " <td>101.7</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>21.5</td>\n", " <td>35.7</td>\n", " <td>8.4</td>\n", " <td>15.9</td>\n", " <td>0.5561</td>\n", " <td>0</td>\n", " <td>0.536069</td>\n", " <td>0.208809</td>\n", " <td>0.234657</td>\n", " <td>0.121977</td>\n", " <td>...</td>\n", " <td>0.127083</td>\n", " <td>0.505940</td>\n", " <td>0.219319</td>\n", " <td>0.219319</td>\n", " <td>0.223344</td>\n", " <td>0.129886</td>\n", " <td>99.7</td>\n", " <td>0.3</td>\n", " <td>102.6</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>22.3</td>\n", " <td>35.9</td>\n", " <td>8.8</td>\n", " <td>17.1</td>\n", " <td>0.5476</td>\n", " <td>0</td>\n", " <td>0.506685</td>\n", " <td>0.177643</td>\n", " <td>0.237134</td>\n", " <td>0.114975</td>\n", " <td>...</td>\n", " <td>0.117767</td>\n", " <td>0.516311</td>\n", " <td>0.206092</td>\n", " <td>0.206092</td>\n", " <td>0.238405</td>\n", " <td>0.119710</td>\n", " <td>102.9</td>\n", " <td>0.3</td>\n", " <td>105.5</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>23.7</td>\n", " <td>36.4</td>\n", " <td>9.0</td>\n", " <td>17.8</td>\n", " <td>0.5429</td>\n", " <td>0</td>\n", " <td>0.516987</td>\n", " <td>0.174166</td>\n", " <td>0.258591</td>\n", " <td>0.117192</td>\n", " <td>...</td>\n", " <td>0.115784</td>\n", " <td>0.498176</td>\n", " <td>0.205249</td>\n", " <td>0.205249</td>\n", " <td>0.246340</td>\n", " <td>0.126183</td>\n", " <td>100.1</td>\n", " <td>0.3</td>\n", " <td>106.0</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>24.7</td>\n", " <td>37.2</td>\n", " <td>9.4</td>\n", " <td>18.6</td>\n", " <td>0.5396</td>\n", " <td>1</td>\n", " <td>0.498113</td>\n", " <td>0.232298</td>\n", " <td>0.240407</td>\n", " <td>0.147785</td>\n", " <td>...</td>\n", " <td>0.114397</td>\n", " <td>0.496082</td>\n", " <td>0.213131</td>\n", " <td>0.213131</td>\n", " <td>0.252590</td>\n", " <td>0.121911</td>\n", " <td>100.7</td>\n", " <td>0.3</td>\n", " <td>106.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>24.6</td>\n", " <td>36.8</td>\n", " <td>9.3</td>\n", " <td>18.5</td>\n", " <td>0.5367</td>\n", " <td>0</td>\n", " <td>0.467716</td>\n", " <td>0.260476</td>\n", " <td>0.212279</td>\n", " <td>0.137020</td>\n", " <td>...</td>\n", " <td>0.113767</td>\n", " <td>0.496722</td>\n", " <td>0.206302</td>\n", " <td>0.206302</td>\n", " <td>0.249404</td>\n", " <td>0.129001</td>\n", " <td>100.5</td>\n", " <td>0.3</td>\n", " <td>107.3</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>24.9</td>\n", " <td>36.5</td>\n", " <td>9.4</td>\n", " <td>18.0</td>\n", " <td>0.5505</td>\n", " <td>0</td>\n", " <td>0.478688</td>\n", " <td>0.144253</td>\n", " <td>0.226084</td>\n", " <td>0.117666</td>\n", " <td>...</td>\n", " <td>0.115725</td>\n", " <td>0.500765</td>\n", " <td>0.216941</td>\n", " <td>0.216941</td>\n", " <td>0.245515</td>\n", " <td>0.132248</td>\n", " <td>102.0</td>\n", " <td>0.2</td>\n", " <td>111.0</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>25.3</td>\n", " <td>36.8</td>\n", " <td>9.8</td>\n", " <td>19.8</td>\n", " <td>0.4976</td>\n", " <td>1</td>\n", " <td>0.500017</td>\n", " <td>0.179978</td>\n", " <td>0.207758</td>\n", " <td>0.127054</td>\n", " <td>...</td>\n", " <td>0.110027</td>\n", " <td>0.484693</td>\n", " <td>0.193221</td>\n", " <td>0.193221</td>\n", " <td>0.249019</td>\n", " <td>0.121179</td>\n", " <td>100.7</td>\n", " <td>0.2</td>\n", " <td>110.2</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>24.5</td>\n", " <td>36.9</td>\n", " <td>9.6</td>\n", " <td>20.0</td>\n", " <td>0.4816</td>\n", " <td>0</td>\n", " <td>0.506787</td>\n", " <td>0.212613</td>\n", " <td>0.203558</td>\n", " <td>0.148240</td>\n", " <td>...</td>\n", " <td>0.120057</td>\n", " <td>0.484157</td>\n", " <td>0.188102</td>\n", " <td>0.188102</td>\n", " <td>0.244088</td>\n", " <td>0.125257</td>\n", " <td>99.7</td>\n", " <td>0.3</td>\n", " <td>107.6</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>24.0</td>\n", " <td>36.1</td>\n", " <td>9.4</td>\n", " <td>19.2</td>\n", " <td>0.4934</td>\n", " <td>1</td>\n", " <td>0.481874</td>\n", " <td>0.190510</td>\n", " <td>0.233001</td>\n", " <td>0.137453</td>\n", " <td>...</td>\n", " <td>0.126511</td>\n", " <td>0.495859</td>\n", " <td>0.212590</td>\n", " <td>0.212590</td>\n", " <td>0.210023</td>\n", " <td>0.128140</td>\n", " <td>99.7</td>\n", " <td>0.4</td>\n", " <td>106.1</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>25.0</td>\n", " <td>35.9</td>\n", " <td>9.8</td>\n", " <td>19.3</td>\n", " <td>0.5094</td>\n", " <td>0</td>\n", " <td>0.497701</td>\n", " <td>0.187773</td>\n", " <td>0.198312</td>\n", " <td>0.119435</td>\n", " <td>...</td>\n", " <td>0.124162</td>\n", " <td>0.499950</td>\n", " <td>0.206226</td>\n", " <td>0.206226</td>\n", " <td>0.206942</td>\n", " <td>0.125992</td>\n", " <td>98.9</td>\n", " <td>0.3</td>\n", " <td>105.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>23.9</td>\n", " <td>35.1</td>\n", " <td>9.6</td>\n", " <td>18.8</td>\n", " <td>0.5092</td>\n", " <td>1</td>\n", " <td>0.490998</td>\n", " <td>0.239479</td>\n", " <td>0.253810</td>\n", " <td>0.108359</td>\n", " <td>...</td>\n", " <td>0.120721</td>\n", " <td>0.499354</td>\n", " <td>0.213335</td>\n", " <td>0.213335</td>\n", " <td>0.202350</td>\n", " <td>0.133407</td>\n", " <td>98.8</td>\n", " <td>0.3</td>\n", " <td>106.7</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>24.0</td>\n", " <td>35.0</td>\n", " <td>9.6</td>\n", " <td>18.8</td>\n", " <td>0.5092</td>\n", " <td>1</td>\n", " <td>0.507392</td>\n", " <td>0.281610</td>\n", " <td>0.229411</td>\n", " <td>0.120867</td>\n", " <td>...</td>\n", " <td>0.122777</td>\n", " <td>0.502464</td>\n", " <td>0.213915</td>\n", " <td>0.213915</td>\n", " <td>0.209833</td>\n", " <td>0.131625</td>\n", " <td>99.1</td>\n", " <td>0.3</td>\n", " <td>106.7</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>25.1</td>\n", " <td>34.4</td>\n", " <td>9.8</td>\n", " <td>18.6</td>\n", " <td>0.5225</td>\n", " <td>0</td>\n", " <td>0.523461</td>\n", " <td>0.214265</td>\n", " <td>0.270302</td>\n", " <td>0.127802</td>\n", " <td>...</td>\n", " <td>0.138722</td>\n", " <td>0.523263</td>\n", " <td>0.216308</td>\n", " <td>0.216308</td>\n", " <td>0.191493</td>\n", " <td>0.127752</td>\n", " <td>101.8</td>\n", " <td>0.3</td>\n", " <td>107.9</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>25.1</td>\n", " <td>34.0</td>\n", " <td>9.6</td>\n", " <td>18.3</td>\n", " <td>0.5221</td>\n", " <td>0</td>\n", " <td>0.513850</td>\n", " <td>0.203850</td>\n", " <td>0.225548</td>\n", " <td>0.129182</td>\n", " <td>...</td>\n", " <td>0.141852</td>\n", " <td>0.526350</td>\n", " <td>0.220012</td>\n", " <td>0.220012</td>\n", " <td>0.193307</td>\n", " <td>0.127679</td>\n", " <td>102.6</td>\n", " <td>0.3</td>\n", " <td>109.5</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>25.8</td>\n", " <td>37.8</td>\n", " <td>9.2</td>\n", " <td>18.9</td>\n", " <td>0.4931</td>\n", " <td>0</td>\n", " <td>0.490895</td>\n", " <td>0.242097</td>\n", " <td>0.192924</td>\n", " <td>0.135134</td>\n", " <td>...</td>\n", " <td>0.154381</td>\n", " <td>0.484181</td>\n", " <td>0.212985</td>\n", " <td>0.212985</td>\n", " <td>0.186484</td>\n", " <td>0.124358</td>\n", " <td>97.4</td>\n", " <td>0.4</td>\n", " <td>100.4</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>26.0</td>\n", " <td>37.9</td>\n", " <td>9.4</td>\n", " <td>19.4</td>\n", " <td>0.4910</td>\n", " <td>0</td>\n", " <td>0.499352</td>\n", " <td>0.159478</td>\n", " <td>0.250560</td>\n", " <td>0.139627</td>\n", " <td>...</td>\n", " <td>0.145788</td>\n", " <td>0.521514</td>\n", " <td>0.208587</td>\n", " <td>0.208587</td>\n", " <td>0.181335</td>\n", " <td>0.135017</td>\n", " <td>99.1</td>\n", " <td>0.4</td>\n", " <td>100.7</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>25.5</td>\n", " <td>36.8</td>\n", " <td>9.3</td>\n", " <td>18.8</td>\n", " <td>0.5053</td>\n", " <td>0</td>\n", " <td>0.488435</td>\n", " <td>0.179974</td>\n", " <td>0.220357</td>\n", " <td>0.132517</td>\n", " <td>...</td>\n", " <td>0.145893</td>\n", " <td>0.510460</td>\n", " <td>0.199264</td>\n", " <td>0.199264</td>\n", " <td>0.178954</td>\n", " <td>0.142974</td>\n", " <td>96.7</td>\n", " <td>0.4</td>\n", " <td>100.3</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>27.4</td>\n", " <td>36.4</td>\n", " <td>10.4</td>\n", " <td>21.6</td>\n", " <td>0.4894</td>\n", " <td>0</td>\n", " <td>0.553977</td>\n", " <td>0.208272</td>\n", " <td>0.275703</td>\n", " <td>0.150120</td>\n", " <td>...</td>\n", " <td>0.143586</td>\n", " <td>0.477901</td>\n", " <td>0.207630</td>\n", " <td>0.207630</td>\n", " <td>0.205230</td>\n", " <td>0.153326</td>\n", " <td>92.1</td>\n", " <td>0.3</td>\n", " <td>97.5</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>27.4</td>\n", " <td>36.5</td>\n", " <td>10.6</td>\n", " <td>22.2</td>\n", " <td>0.4879</td>\n", " <td>0</td>\n", " <td>0.492349</td>\n", " <td>0.185336</td>\n", " <td>0.289930</td>\n", " <td>0.161253</td>\n", " <td>...</td>\n", " <td>0.135863</td>\n", " <td>0.478922</td>\n", " <td>0.211049</td>\n", " <td>0.211049</td>\n", " <td>0.204843</td>\n", " <td>0.153602</td>\n", " <td>92.2</td>\n", " <td>0.4</td>\n", " <td>96.8</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>26.0</td>\n", " <td>35.5</td>\n", " <td>10.0</td>\n", " <td>21.3</td>\n", " <td>0.4732</td>\n", " <td>0</td>\n", " <td>0.494249</td>\n", " <td>0.209113</td>\n", " <td>0.231249</td>\n", " <td>0.159812</td>\n", " <td>...</td>\n", " <td>0.136621</td>\n", " <td>0.478841</td>\n", " <td>0.212322</td>\n", " <td>0.212322</td>\n", " <td>0.222971</td>\n", " <td>0.149996</td>\n", " <td>93.0</td>\n", " <td>0.4</td>\n", " <td>95.9</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>25.0</td>\n", " <td>35.3</td>\n", " <td>9.6</td>\n", " <td>20.3</td>\n", " <td>0.4732</td>\n", " <td>0</td>\n", " <td>0.511178</td>\n", " <td>0.181182</td>\n", " <td>0.287908</td>\n", " <td>0.139437</td>\n", " <td>...</td>\n", " <td>0.141937</td>\n", " <td>0.476667</td>\n", " <td>0.205337</td>\n", " <td>0.205337</td>\n", " <td>0.230927</td>\n", " <td>0.153199</td>\n", " <td>91.3</td>\n", " <td>0.3</td>\n", " <td>95.2</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>23.8</td>\n", " <td>33.3</td>\n", " <td>9.0</td>\n", " <td>18.8</td>\n", " <td>0.4823</td>\n", " <td>0</td>\n", " <td>0.491327</td>\n", " <td>0.172689</td>\n", " <td>0.207030</td>\n", " <td>0.124872</td>\n", " <td>...</td>\n", " <td>0.130860</td>\n", " <td>0.457739</td>\n", " <td>0.204153</td>\n", " <td>0.204153</td>\n", " <td>0.234538</td>\n", " <td>0.141172</td>\n", " <td>90.4</td>\n", " <td>0.2</td>\n", " <td>97.1</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>24.8</td>\n", " <td>33.7</td>\n", " <td>9.2</td>\n", " <td>19.0</td>\n", " <td>0.4868</td>\n", " <td>0</td>\n", " <td>0.468566</td>\n", " <td>0.226556</td>\n", " <td>0.203601</td>\n", " <td>0.148289</td>\n", " <td>...</td>\n", " <td>0.125971</td>\n", " <td>0.484857</td>\n", " <td>0.212728</td>\n", " <td>0.212728</td>\n", " <td>0.225350</td>\n", " <td>0.142850</td>\n", " <td>94.2</td>\n", " <td>0.2</td>\n", " <td>100.3</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>22.8</td>\n", " <td>32.9</td>\n", " <td>8.5</td>\n", " <td>17.5</td>\n", " <td>0.4841</td>\n", " <td>1</td>\n", " <td>0.501838</td>\n", " <td>0.188712</td>\n", " <td>0.258114</td>\n", " <td>0.178430</td>\n", " <td>...</td>\n", " <td>0.120507</td>\n", " <td>0.483505</td>\n", " <td>0.205612</td>\n", " <td>0.205612</td>\n", " <td>0.217136</td>\n", " <td>0.141988</td>\n", " <td>94.1</td>\n", " <td>0.2</td>\n", " <td>102.4</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>24.2</td>\n", " <td>34.1</td>\n", " <td>9.0</td>\n", " <td>18.0</td>\n", " <td>0.4935</td>\n", " <td>0</td>\n", " <td>0.519734</td>\n", " <td>0.182389</td>\n", " <td>0.236291</td>\n", " <td>0.129562</td>\n", " <td>...</td>\n", " <td>0.120624</td>\n", " <td>0.495065</td>\n", " <td>0.192882</td>\n", " <td>0.192882</td>\n", " <td>0.200691</td>\n", " <td>0.142913</td>\n", " <td>94.0</td>\n", " <td>0.2</td>\n", " <td>101.1</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>24.5</td>\n", " <td>34.5</td>\n", " <td>9.0</td>\n", " <td>17.5</td>\n", " <td>0.5055</td>\n", " <td>0</td>\n", " <td>0.550143</td>\n", " <td>0.192924</td>\n", " <td>0.251578</td>\n", " <td>0.134328</td>\n", " <td>...</td>\n", " <td>0.121949</td>\n", " <td>0.509321</td>\n", " <td>0.197374</td>\n", " <td>0.197374</td>\n", " <td>0.199205</td>\n", " <td>0.147210</td>\n", " <td>96.3</td>\n", " <td>0.2</td>\n", " <td>103.0</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>24.2</td>\n", " <td>34.3</td>\n", " <td>8.9</td>\n", " <td>16.6</td>\n", " <td>0.5199</td>\n", " <td>0</td>\n", " <td>0.547388</td>\n", " <td>0.268603</td>\n", " <td>0.237310</td>\n", " <td>0.166178</td>\n", " <td>...</td>\n", " <td>0.128874</td>\n", " <td>0.513886</td>\n", " <td>0.193419</td>\n", " <td>0.193419</td>\n", " <td>0.206259</td>\n", " <td>0.142732</td>\n", " <td>97.3</td>\n", " <td>0.2</td>\n", " <td>104.2</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>25.1</td>\n", " <td>35.1</td>\n", " <td>9.5</td>\n", " <td>17.3</td>\n", " <td>0.5429</td>\n", " <td>0</td>\n", " <td>0.476096</td>\n", " <td>0.175126</td>\n", " <td>0.215186</td>\n", " <td>0.157267</td>\n", " <td>...</td>\n", " <td>0.121878</td>\n", " <td>0.514714</td>\n", " <td>0.210349</td>\n", " <td>0.210349</td>\n", " <td>0.190313</td>\n", " <td>0.143612</td>\n", " <td>98.0</td>\n", " <td>0.2</td>\n", " <td>105.4</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>23.2</td>\n", " <td>35.8</td>\n", " <td>9.2</td>\n", " <td>16.5</td>\n", " <td>0.5805</td>\n", " <td>0</td>\n", " <td>0.508327</td>\n", " <td>0.192894</td>\n", " <td>0.293065</td>\n", " <td>0.126152</td>\n", " <td>...</td>\n", " <td>0.129834</td>\n", " <td>0.503505</td>\n", " <td>0.208389</td>\n", " <td>0.208389</td>\n", " <td>0.219334</td>\n", " <td>0.139971</td>\n", " <td>97.6</td>\n", " <td>0.3</td>\n", " <td>100.7</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>21.7</td>\n", " <td>34.9</td>\n", " <td>8.5</td>\n", " <td>15.9</td>\n", " <td>0.5620</td>\n", " <td>0</td>\n", " <td>0.500400</td>\n", " <td>0.236975</td>\n", " <td>0.237301</td>\n", " <td>0.141298</td>\n", " <td>...</td>\n", " <td>0.127686</td>\n", " <td>0.502697</td>\n", " <td>0.217522</td>\n", " <td>0.217522</td>\n", " <td>0.223412</td>\n", " <td>0.120305</td>\n", " <td>99.3</td>\n", " <td>0.3</td>\n", " <td>103.3</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>21.9</td>\n", " <td>35.6</td>\n", " <td>8.5</td>\n", " <td>16.4</td>\n", " <td>0.5500</td>\n", " <td>0</td>\n", " <td>0.470834</td>\n", " <td>0.227380</td>\n", " <td>0.201342</td>\n", " <td>0.121235</td>\n", " <td>...</td>\n", " <td>0.125128</td>\n", " <td>0.500436</td>\n", " <td>0.207717</td>\n", " <td>0.207717</td>\n", " <td>0.231659</td>\n", " <td>0.123508</td>\n", " <td>100.0</td>\n", " <td>0.2</td>\n", " <td>104.9</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>24.9</td>\n", " <td>36.2</td>\n", " <td>9.4</td>\n", " <td>18.4</td>\n", " <td>0.5351</td>\n", " <td>0</td>\n", " <td>0.531662</td>\n", " <td>0.229817</td>\n", " <td>0.316480</td>\n", " <td>0.144740</td>\n", " <td>...</td>\n", " <td>0.111102</td>\n", " <td>0.493661</td>\n", " <td>0.205919</td>\n", " <td>0.205919</td>\n", " <td>0.251788</td>\n", " <td>0.134757</td>\n", " <td>100.8</td>\n", " <td>0.2</td>\n", " <td>110.2</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>23.9</td>\n", " <td>36.5</td>\n", " <td>9.4</td>\n", " <td>19.6</td>\n", " <td>0.4843</td>\n", " <td>0</td>\n", " <td>0.517653</td>\n", " <td>0.289349</td>\n", " <td>0.239187</td>\n", " <td>0.115065</td>\n", " <td>...</td>\n", " <td>0.119510</td>\n", " <td>0.486334</td>\n", " <td>0.192114</td>\n", " <td>0.192114</td>\n", " <td>0.243180</td>\n", " <td>0.133370</td>\n", " <td>99.7</td>\n", " <td>0.3</td>\n", " <td>107.3</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>24.7</td>\n", " <td>35.7</td>\n", " <td>9.9</td>\n", " <td>19.1</td>\n", " <td>0.5171</td>\n", " <td>0</td>\n", " <td>0.520403</td>\n", " <td>0.209262</td>\n", " <td>0.249088</td>\n", " <td>0.163916</td>\n", " <td>...</td>\n", " <td>0.140320</td>\n", " <td>0.501883</td>\n", " <td>0.209263</td>\n", " <td>0.209263</td>\n", " <td>0.204833</td>\n", " <td>0.135969</td>\n", " <td>98.6</td>\n", " <td>0.4</td>\n", " <td>105.0</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>24.3</td>\n", " <td>35.7</td>\n", " <td>9.5</td>\n", " <td>18.8</td>\n", " <td>0.5047</td>\n", " <td>0</td>\n", " <td>0.500086</td>\n", " <td>0.268688</td>\n", " <td>0.254992</td>\n", " <td>0.112913</td>\n", " <td>...</td>\n", " <td>0.142634</td>\n", " <td>0.510177</td>\n", " <td>0.222666</td>\n", " <td>0.222666</td>\n", " <td>0.190242</td>\n", " <td>0.131874</td>\n", " <td>100.2</td>\n", " <td>0.4</td>\n", " <td>106.4</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>24.2</td>\n", " <td>35.7</td>\n", " <td>9.3</td>\n", " <td>18.7</td>\n", " <td>0.4968</td>\n", " <td>0</td>\n", " <td>0.526488</td>\n", " <td>0.185804</td>\n", " <td>0.192554</td>\n", " <td>0.123057</td>\n", " <td>...</td>\n", " <td>0.138050</td>\n", " <td>0.523164</td>\n", " <td>0.229471</td>\n", " <td>0.229471</td>\n", " <td>0.174363</td>\n", " <td>0.131776</td>\n", " <td>101.8</td>\n", " <td>0.4</td>\n", " <td>106.9</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>24.4</td>\n", " <td>35.1</td>\n", " <td>9.1</td>\n", " <td>18.0</td>\n", " <td>0.5068</td>\n", " <td>1</td>\n", " <td>0.480995</td>\n", " <td>0.226236</td>\n", " <td>0.250478</td>\n", " <td>0.157824</td>\n", " <td>...</td>\n", " <td>0.129188</td>\n", " <td>0.518749</td>\n", " <td>0.232225</td>\n", " <td>0.232225</td>\n", " <td>0.170840</td>\n", " <td>0.129095</td>\n", " <td>101.2</td>\n", " <td>0.3</td>\n", " <td>109.5</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>25.5</td>\n", " <td>35.1</td>\n", " <td>9.6</td>\n", " <td>18.2</td>\n", " <td>0.5290</td>\n", " <td>0</td>\n", " <td>0.497613</td>\n", " <td>0.207681</td>\n", " <td>0.195665</td>\n", " <td>0.129238</td>\n", " <td>...</td>\n", " <td>0.129503</td>\n", " <td>0.517704</td>\n", " <td>0.219618</td>\n", " <td>0.219618</td>\n", " <td>0.171316</td>\n", " <td>0.127516</td>\n", " <td>100.2</td>\n", " <td>0.3</td>\n", " <td>106.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>25.0</td>\n", " <td>35.7</td>\n", " <td>9.4</td>\n", " <td>18.4</td>\n", " <td>0.5075</td>\n", " <td>0</td>\n", " <td>0.548096</td>\n", " <td>0.241649</td>\n", " <td>0.251403</td>\n", " <td>0.137479</td>\n", " <td>...</td>\n", " <td>0.135081</td>\n", " <td>0.513204</td>\n", " <td>0.211189</td>\n", " <td>0.211189</td>\n", " <td>0.177882</td>\n", " <td>0.128227</td>\n", " <td>99.2</td>\n", " <td>0.2</td>\n", " <td>107.8</td>\n", " <td>0.8</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>25.8</td>\n", " <td>34.7</td>\n", " <td>9.8</td>\n", " <td>18.7</td>\n", " <td>0.5241</td>\n", " <td>0</td>\n", " <td>0.532848</td>\n", " <td>0.202947</td>\n", " <td>0.225973</td>\n", " <td>0.132072</td>\n", " <td>...</td>\n", " <td>0.139733</td>\n", " <td>0.531975</td>\n", " <td>0.210012</td>\n", " <td>0.210012</td>\n", " <td>0.198069</td>\n", " <td>0.128530</td>\n", " <td>102.7</td>\n", " <td>0.3</td>\n", " <td>108.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>26.0</td>\n", " <td>34.6</td>\n", " <td>10.0</td>\n", " <td>18.3</td>\n", " <td>0.5504</td>\n", " <td>0</td>\n", " <td>0.506343</td>\n", " <td>0.193964</td>\n", " <td>0.223461</td>\n", " <td>0.135502</td>\n", " <td>...</td>\n", " <td>0.130458</td>\n", " <td>0.531374</td>\n", " <td>0.204812</td>\n", " <td>0.204812</td>\n", " <td>0.200797</td>\n", " <td>0.122287</td>\n", " <td>102.5</td>\n", " <td>0.3</td>\n", " <td>108.0</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>25.8</td>\n", " <td>33.8</td>\n", " <td>9.9</td>\n", " <td>17.7</td>\n", " <td>0.5707</td>\n", " <td>0</td>\n", " <td>0.537897</td>\n", " <td>0.182900</td>\n", " <td>0.185007</td>\n", " <td>0.127174</td>\n", " <td>...</td>\n", " <td>0.137958</td>\n", " <td>0.515277</td>\n", " <td>0.200390</td>\n", " <td>0.200390</td>\n", " <td>0.243610</td>\n", " <td>0.131315</td>\n", " <td>100.2</td>\n", " <td>0.4</td>\n", " <td>104.0</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>64</th>\n", " <td>26.7</td>\n", " <td>35.7</td>\n", " <td>10.7</td>\n", " <td>19.2</td>\n", " <td>0.5705</td>\n", " <td>0</td>\n", " <td>0.515139</td>\n", " <td>0.192341</td>\n", " <td>0.232533</td>\n", " <td>0.137465</td>\n", " <td>...</td>\n", " <td>0.141561</td>\n", " <td>0.501659</td>\n", " <td>0.220391</td>\n", " <td>0.220391</td>\n", " <td>0.245818</td>\n", " <td>0.135026</td>\n", " <td>102.1</td>\n", " <td>0.3</td>\n", " <td>107.8</td>\n", " <td>0.7</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>26.1</td>\n", " <td>34.8</td>\n", " <td>10.4</td>\n", " <td>18.1</td>\n", " <td>0.5983</td>\n", " <td>0</td>\n", " <td>0.489478</td>\n", " <td>0.184269</td>\n", " <td>0.219702</td>\n", " <td>0.124917</td>\n", " <td>...</td>\n", " <td>0.135055</td>\n", " <td>0.496816</td>\n", " <td>0.203889</td>\n", " <td>0.203889</td>\n", " <td>0.237000</td>\n", " <td>0.135240</td>\n", " <td>99.9</td>\n", " <td>0.3</td>\n", " <td>108.6</td>\n", " <td>0.7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>66 rows × 33 columns</p>\n", "</div>" ], "text/plain": [ " PTS_avg_10 MIN_avg_10 FGM_avg_10 FGA_avg_10 FG_PCT_avg_10 M_BTB \\\n", "0 27.5 36.6 10.4 20.7 0.4992 0 \n", "1 29.0 37.0 10.9 20.7 0.5376 0 \n", "2 28.0 36.9 10.4 20.3 0.5225 0 \n", "3 26.8 37.8 9.5 18.9 0.5090 0 \n", "4 26.5 37.9 9.5 18.8 0.5110 0 \n", "5 26.4 38.4 9.3 19.7 0.4808 1 \n", "6 25.2 36.9 9.4 19.5 0.4861 1 \n", "7 26.6 37.6 9.8 20.7 0.4772 0 \n", "8 27.4 36.6 10.1 21.0 0.4860 0 \n", "9 24.7 34.2 9.6 19.8 0.4826 1 \n", "10 24.3 33.1 9.3 19.2 0.4854 1 \n", "11 24.9 35.3 9.2 16.7 0.5391 0 \n", "12 23.4 34.6 8.9 16.1 0.5470 0 \n", "13 22.7 35.2 8.7 16.0 0.5388 1 \n", "14 23.3 36.1 9.1 17.6 0.5159 0 \n", "15 22.4 35.9 9.0 16.9 0.5347 1 \n", "16 21.5 35.7 8.4 15.9 0.5561 0 \n", "17 22.3 35.9 8.8 17.1 0.5476 0 \n", "18 23.7 36.4 9.0 17.8 0.5429 0 \n", "19 24.7 37.2 9.4 18.6 0.5396 1 \n", "20 24.6 36.8 9.3 18.5 0.5367 0 \n", "21 24.9 36.5 9.4 18.0 0.5505 0 \n", "22 25.3 36.8 9.8 19.8 0.4976 1 \n", "23 24.5 36.9 9.6 20.0 0.4816 0 \n", "24 24.0 36.1 9.4 19.2 0.4934 1 \n", "25 25.0 35.9 9.8 19.3 0.5094 0 \n", "26 23.9 35.1 9.6 18.8 0.5092 1 \n", "27 24.0 35.0 9.6 18.8 0.5092 1 \n", "28 25.1 34.4 9.8 18.6 0.5225 0 \n", "29 25.1 34.0 9.6 18.3 0.5221 0 \n", ".. ... ... ... ... ... ... \n", "36 25.8 37.8 9.2 18.9 0.4931 0 \n", "37 26.0 37.9 9.4 19.4 0.4910 0 \n", "38 25.5 36.8 9.3 18.8 0.5053 0 \n", "39 27.4 36.4 10.4 21.6 0.4894 0 \n", "40 27.4 36.5 10.6 22.2 0.4879 0 \n", "41 26.0 35.5 10.0 21.3 0.4732 0 \n", "42 25.0 35.3 9.6 20.3 0.4732 0 \n", "43 23.8 33.3 9.0 18.8 0.4823 0 \n", "44 24.8 33.7 9.2 19.0 0.4868 0 \n", "45 22.8 32.9 8.5 17.5 0.4841 1 \n", "46 24.2 34.1 9.0 18.0 0.4935 0 \n", "47 24.5 34.5 9.0 17.5 0.5055 0 \n", "48 24.2 34.3 8.9 16.6 0.5199 0 \n", "49 25.1 35.1 9.5 17.3 0.5429 0 \n", "50 23.2 35.8 9.2 16.5 0.5805 0 \n", "51 21.7 34.9 8.5 15.9 0.5620 0 \n", "52 21.9 35.6 8.5 16.4 0.5500 0 \n", "53 24.9 36.2 9.4 18.4 0.5351 0 \n", "54 23.9 36.5 9.4 19.6 0.4843 0 \n", "55 24.7 35.7 9.9 19.1 0.5171 0 \n", "56 24.3 35.7 9.5 18.8 0.5047 0 \n", "57 24.2 35.7 9.3 18.7 0.4968 0 \n", "58 24.4 35.1 9.1 18.0 0.5068 1 \n", "59 25.5 35.1 9.6 18.2 0.5290 0 \n", "60 25.0 35.7 9.4 18.4 0.5075 0 \n", "61 25.8 34.7 9.8 18.7 0.5241 0 \n", "62 26.0 34.6 10.0 18.3 0.5504 0 \n", "63 25.8 33.8 9.9 17.7 0.5707 0 \n", "64 26.7 35.7 10.7 19.2 0.5705 0 \n", "65 26.1 34.8 10.4 18.1 0.5983 0 \n", "\n", " M_FF_EFG_10 M_FF_FTFGA_10 M_FF_ORB_10 M_FF_TOV_10 ... \\\n", "0 0.499567 0.216664 0.222306 0.157226 ... \n", "1 0.529546 0.191625 0.197374 0.152234 ... \n", "2 0.479129 0.177602 0.239701 0.144632 ... \n", "3 0.474859 0.173879 0.263528 0.141281 ... \n", "4 0.477212 0.227320 0.207927 0.159148 ... \n", "5 0.492952 0.207718 0.270791 0.152829 ... \n", "6 0.511610 0.257289 0.272205 0.155198 ... \n", "7 0.484252 0.181076 0.206482 0.165533 ... \n", "8 0.506599 0.233844 0.215693 0.140396 ... \n", "9 0.553993 0.153726 0.220251 0.138155 ... \n", "10 0.481696 0.225793 0.258966 0.119578 ... \n", "11 0.562627 0.178130 0.210621 0.136214 ... \n", "12 0.550081 0.213965 0.208560 0.117590 ... \n", "13 0.475464 0.226111 0.286582 0.153885 ... \n", "14 0.489019 0.279045 0.261555 0.158768 ... \n", "15 0.462186 0.194991 0.228034 0.155730 ... \n", "16 0.536069 0.208809 0.234657 0.121977 ... \n", "17 0.506685 0.177643 0.237134 0.114975 ... \n", "18 0.516987 0.174166 0.258591 0.117192 ... \n", "19 0.498113 0.232298 0.240407 0.147785 ... \n", "20 0.467716 0.260476 0.212279 0.137020 ... \n", "21 0.478688 0.144253 0.226084 0.117666 ... \n", "22 0.500017 0.179978 0.207758 0.127054 ... \n", "23 0.506787 0.212613 0.203558 0.148240 ... \n", "24 0.481874 0.190510 0.233001 0.137453 ... \n", "25 0.497701 0.187773 0.198312 0.119435 ... \n", "26 0.490998 0.239479 0.253810 0.108359 ... \n", "27 0.507392 0.281610 0.229411 0.120867 ... \n", "28 0.523461 0.214265 0.270302 0.127802 ... \n", "29 0.513850 0.203850 0.225548 0.129182 ... \n", ".. ... ... ... ... ... \n", "36 0.490895 0.242097 0.192924 0.135134 ... \n", "37 0.499352 0.159478 0.250560 0.139627 ... \n", "38 0.488435 0.179974 0.220357 0.132517 ... \n", "39 0.553977 0.208272 0.275703 0.150120 ... \n", "40 0.492349 0.185336 0.289930 0.161253 ... \n", "41 0.494249 0.209113 0.231249 0.159812 ... \n", "42 0.511178 0.181182 0.287908 0.139437 ... \n", "43 0.491327 0.172689 0.207030 0.124872 ... \n", "44 0.468566 0.226556 0.203601 0.148289 ... \n", "45 0.501838 0.188712 0.258114 0.178430 ... \n", "46 0.519734 0.182389 0.236291 0.129562 ... \n", "47 0.550143 0.192924 0.251578 0.134328 ... \n", "48 0.547388 0.268603 0.237310 0.166178 ... \n", "49 0.476096 0.175126 0.215186 0.157267 ... \n", "50 0.508327 0.192894 0.293065 0.126152 ... \n", "51 0.500400 0.236975 0.237301 0.141298 ... \n", "52 0.470834 0.227380 0.201342 0.121235 ... \n", "53 0.531662 0.229817 0.316480 0.144740 ... \n", "54 0.517653 0.289349 0.239187 0.115065 ... \n", "55 0.520403 0.209262 0.249088 0.163916 ... \n", "56 0.500086 0.268688 0.254992 0.112913 ... \n", "57 0.526488 0.185804 0.192554 0.123057 ... \n", "58 0.480995 0.226236 0.250478 0.157824 ... \n", "59 0.497613 0.207681 0.195665 0.129238 ... \n", "60 0.548096 0.241649 0.251403 0.137479 ... \n", "61 0.532848 0.202947 0.225973 0.132072 ... \n", "62 0.506343 0.193964 0.223461 0.135502 ... \n", "63 0.537897 0.182900 0.185007 0.127174 ... \n", "64 0.515139 0.192341 0.232533 0.137465 ... \n", "65 0.489478 0.184269 0.219702 0.124917 ... \n", "\n", " Y_FF_TOV_10 Y_O_FF_EFG_10 Y_O_FF_FTFGA_10 Y_O_FF_FTFGA_10 \\\n", "0 0.148223 0.470509 0.203110 0.203110 \n", "1 0.148204 0.488033 0.212365 0.212365 \n", "2 0.150807 0.484885 0.211972 0.211972 \n", "3 0.144950 0.491311 0.220018 0.220018 \n", "4 0.152920 0.487855 0.227795 0.227795 \n", "5 0.150907 0.510943 0.226956 0.226956 \n", "6 0.146851 0.504212 0.187476 0.187476 \n", "7 0.153133 0.500008 0.203859 0.203859 \n", "8 0.152206 0.480203 0.213088 0.213088 \n", "9 0.139893 0.461490 0.187855 0.187855 \n", "10 0.135372 0.446000 0.196200 0.196200 \n", "11 0.125190 0.501951 0.203365 0.203365 \n", "12 0.123726 0.514379 0.206122 0.206122 \n", "13 0.133384 0.521024 0.216385 0.216385 \n", "14 0.122879 0.513931 0.203931 0.203931 \n", "15 0.127162 0.500647 0.206562 0.206562 \n", "16 0.127083 0.505940 0.219319 0.219319 \n", "17 0.117767 0.516311 0.206092 0.206092 \n", "18 0.115784 0.498176 0.205249 0.205249 \n", "19 0.114397 0.496082 0.213131 0.213131 \n", "20 0.113767 0.496722 0.206302 0.206302 \n", "21 0.115725 0.500765 0.216941 0.216941 \n", "22 0.110027 0.484693 0.193221 0.193221 \n", "23 0.120057 0.484157 0.188102 0.188102 \n", "24 0.126511 0.495859 0.212590 0.212590 \n", "25 0.124162 0.499950 0.206226 0.206226 \n", "26 0.120721 0.499354 0.213335 0.213335 \n", "27 0.122777 0.502464 0.213915 0.213915 \n", "28 0.138722 0.523263 0.216308 0.216308 \n", "29 0.141852 0.526350 0.220012 0.220012 \n", ".. ... ... ... ... \n", "36 0.154381 0.484181 0.212985 0.212985 \n", "37 0.145788 0.521514 0.208587 0.208587 \n", "38 0.145893 0.510460 0.199264 0.199264 \n", "39 0.143586 0.477901 0.207630 0.207630 \n", "40 0.135863 0.478922 0.211049 0.211049 \n", "41 0.136621 0.478841 0.212322 0.212322 \n", "42 0.141937 0.476667 0.205337 0.205337 \n", "43 0.130860 0.457739 0.204153 0.204153 \n", "44 0.125971 0.484857 0.212728 0.212728 \n", "45 0.120507 0.483505 0.205612 0.205612 \n", "46 0.120624 0.495065 0.192882 0.192882 \n", "47 0.121949 0.509321 0.197374 0.197374 \n", "48 0.128874 0.513886 0.193419 0.193419 \n", "49 0.121878 0.514714 0.210349 0.210349 \n", "50 0.129834 0.503505 0.208389 0.208389 \n", "51 0.127686 0.502697 0.217522 0.217522 \n", "52 0.125128 0.500436 0.207717 0.207717 \n", "53 0.111102 0.493661 0.205919 0.205919 \n", "54 0.119510 0.486334 0.192114 0.192114 \n", "55 0.140320 0.501883 0.209263 0.209263 \n", "56 0.142634 0.510177 0.222666 0.222666 \n", "57 0.138050 0.523164 0.229471 0.229471 \n", "58 0.129188 0.518749 0.232225 0.232225 \n", "59 0.129503 0.517704 0.219618 0.219618 \n", "60 0.135081 0.513204 0.211189 0.211189 \n", "61 0.139733 0.531975 0.210012 0.210012 \n", "62 0.130458 0.531374 0.204812 0.204812 \n", "63 0.137958 0.515277 0.200390 0.200390 \n", "64 0.141561 0.501659 0.220391 0.220391 \n", "65 0.135055 0.496816 0.203889 0.203889 \n", "\n", " Y_O_FF_ORB_10 Y_O_FF_TOV_10 Y_O_PTS_10 Y_O_WL_10 Y_PTS_10 Y_WL_10 \n", "0 0.222303 0.136481 96.3 0.2 103.2 0.8 \n", "1 0.218215 0.132230 98.7 0.2 104.1 0.8 \n", "2 0.226658 0.130724 99.2 0.2 104.8 0.8 \n", "3 0.223203 0.120231 100.0 0.3 104.8 0.7 \n", "4 0.216491 0.128803 99.1 0.3 103.7 0.7 \n", "5 0.174840 0.128924 99.5 0.5 100.1 0.5 \n", "6 0.178954 0.141573 94.7 0.4 98.3 0.6 \n", "7 0.183240 0.144048 94.4 0.4 97.0 0.6 \n", "8 0.199855 0.152822 92.7 0.3 97.9 0.7 \n", "9 0.245744 0.155057 90.1 0.2 96.1 0.8 \n", "10 0.227202 0.151054 88.0 0.2 96.0 0.8 \n", "11 0.206812 0.153338 94.5 0.1 105.7 0.9 \n", "12 0.183531 0.138839 97.1 0.2 105.2 0.8 \n", "13 0.192174 0.132995 99.4 0.2 106.3 0.8 \n", "14 0.199441 0.130612 99.0 0.3 102.4 0.7 \n", "15 0.206065 0.128368 98.2 0.3 101.7 0.7 \n", "16 0.223344 0.129886 99.7 0.3 102.6 0.7 \n", "17 0.238405 0.119710 102.9 0.3 105.5 0.7 \n", "18 0.246340 0.126183 100.1 0.3 106.0 0.7 \n", "19 0.252590 0.121911 100.7 0.3 106.8 0.7 \n", "20 0.249404 0.129001 100.5 0.3 107.3 0.7 \n", "21 0.245515 0.132248 102.0 0.2 111.0 0.8 \n", "22 0.249019 0.121179 100.7 0.2 110.2 0.8 \n", "23 0.244088 0.125257 99.7 0.3 107.6 0.7 \n", "24 0.210023 0.128140 99.7 0.4 106.1 0.6 \n", "25 0.206942 0.125992 98.9 0.3 105.8 0.7 \n", "26 0.202350 0.133407 98.8 0.3 106.7 0.7 \n", "27 0.209833 0.131625 99.1 0.3 106.7 0.7 \n", "28 0.191493 0.127752 101.8 0.3 107.9 0.7 \n", "29 0.193307 0.127679 102.6 0.3 109.5 0.7 \n", ".. ... ... ... ... ... ... \n", "36 0.186484 0.124358 97.4 0.4 100.4 0.6 \n", "37 0.181335 0.135017 99.1 0.4 100.7 0.6 \n", "38 0.178954 0.142974 96.7 0.4 100.3 0.6 \n", "39 0.205230 0.153326 92.1 0.3 97.5 0.7 \n", "40 0.204843 0.153602 92.2 0.4 96.8 0.6 \n", "41 0.222971 0.149996 93.0 0.4 95.9 0.6 \n", "42 0.230927 0.153199 91.3 0.3 95.2 0.7 \n", "43 0.234538 0.141172 90.4 0.2 97.1 0.8 \n", "44 0.225350 0.142850 94.2 0.2 100.3 0.8 \n", "45 0.217136 0.141988 94.1 0.2 102.4 0.8 \n", "46 0.200691 0.142913 94.0 0.2 101.1 0.8 \n", "47 0.199205 0.147210 96.3 0.2 103.0 0.8 \n", "48 0.206259 0.142732 97.3 0.2 104.2 0.8 \n", "49 0.190313 0.143612 98.0 0.2 105.4 0.8 \n", "50 0.219334 0.139971 97.6 0.3 100.7 0.7 \n", "51 0.223412 0.120305 99.3 0.3 103.3 0.7 \n", "52 0.231659 0.123508 100.0 0.2 104.9 0.8 \n", "53 0.251788 0.134757 100.8 0.2 110.2 0.8 \n", "54 0.243180 0.133370 99.7 0.3 107.3 0.7 \n", "55 0.204833 0.135969 98.6 0.4 105.0 0.6 \n", "56 0.190242 0.131874 100.2 0.4 106.4 0.6 \n", "57 0.174363 0.131776 101.8 0.4 106.9 0.6 \n", "58 0.170840 0.129095 101.2 0.3 109.5 0.7 \n", "59 0.171316 0.127516 100.2 0.3 106.8 0.7 \n", "60 0.177882 0.128227 99.2 0.2 107.8 0.8 \n", "61 0.198069 0.128530 102.7 0.3 108.8 0.7 \n", "62 0.200797 0.122287 102.5 0.3 108.0 0.7 \n", "63 0.243610 0.131315 100.2 0.4 104.0 0.6 \n", "64 0.245818 0.135026 102.1 0.3 107.8 0.7 \n", "65 0.237000 0.135240 99.9 0.3 108.6 0.7 \n", "\n", "[66 rows x 33 columns]" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Model = Reg_Model()\n", "Model.set_training(x,y)\n", "Model.calc_model()" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "34.265750089800278" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Model.mse" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'gbm'" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Model.model_type" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
bakanchevn/DBCourseMirea2017	Неделя 4/Лаб 4-1.ipynb	2	12978	{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sql extension is already loaded. To reload it, use:\n", " %reload_ext sql\n" ] }, { "data": { "text/plain": [ "'Connected: [email protected]'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%load_ext sql\n", "%sql sqlite:///chinook.db" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n", "0 rows affected.\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "Pragma foreign_key=on;\n", "DROP TABLE if exists Customers;\n", "CREATE TABLE Customers (cust_ID integer(0,0) NOT NULL Primary key, cust_name varchar(0,0), cust_info varchar(0,0)); \n", "\n", "DROP TABLE if exists Invoices;\n", "CREATE TABLE Invoices (invoice_ID integer(0,0) NOT NULL Primary key,\n", " customer_id varchar(0,0) NOT NULL,\n", " order_sum integer(0,0),\n", " invoice_date integer(0,0) NOT NULL,\n", " FOREIGN KEY (customer_id) REFERENCES Customers(cust_ID));\n", "\n", "DROP TABLE if exists Products;\n", "CREATE TABLE Products (product_id integer(0,0) NOT NULL Primary key,\n", " product_name varchar(0,0) NOT NULL, \n", " price inteder(0,0));\n", "\n", "DROP TABLE if exists Invoice_details;\n", "CREATE TABLE Invoice_details (invoice_ID integer(0,0) NOT NULL, \n", " product_id integer(0,0) NOT NULL,\n", " qty integer(0,0) NOT NULL,\n", " primary key (product_id, invoice_id)\n", " FOREIGN KEY (product_id) REFERENCES Products(product_id)\n", " FOREIGN KEY (invoice_ID) REFERENCES Invoices(invoice_ID));\n" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sqlite3\n", "db=sqlite3.connect('chinook.db')\n", "def insert_customer(name, info):\n", " cur = db.cursor()\n", " cur.execute( '''\n", " SELECT COALESCE(MAX(cust_ID)+1, 1) FROM Customers''')\n", " ID = cur.fetchone()[0]\n", " cur.execute('''\n", " INSERT INTO Customers(cust_ID, cust_name, cust_info) VALUES(?,?,?)''', (ID,name,info))\n", " db.commit()" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [], "source": [ "insert_customer('Microsoft', 'just another OS')\n", "insert_customer('Google', 'lmgfy')" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>cust_ID</th>\n", " <th>cust_name</th>\n", " <th>cust_info</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Microsoft</td>\n", " <td>just another OS</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>Google</td>\n", " <td>lmgfy</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, 'Microsoft', 'just another OS'), (2, 'Google', 'lmgfy')]" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "select * from customers" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def insert_products(name,price):\n", " cur=db.cursor()\n", " cur.execute('''select coalesce(max(product_id)+1,1) from products''')\n", " id=cur.fetchone()[0]\n", " cur.execute('''insert into products(product_id, product_name,price) values(?,?,?)''', (id,name,price))\n", " db.commit()" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [], "source": [ "insert_products('Win10',25000)\n", "insert_products('Google glass', 2000)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>product_id</th>\n", " <th>product_name</th>\n", " <th>price</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>Win10</td>\n", " <td>25000</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>Google glass</td>\n", " <td>2000</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, 'Win10', 25000), (2, 'Google glass', 2000)]" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%sql select * from products" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Написать функцию для добавления заказа. \n", "Вход параметр_1 - имя клиента, параметр_2 - список продуктов вида [['a',1],['b',2]]\n", "1 шаг - проверка, что есть такие продукты и клиены \n", "2 шаг - добавление в таблицы invoices and inv-det " ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def check_client(name):\n", " cur = db.cursor()\n", " cur.execute('''select cust_id from customers where cust_name = ?''', (name,))\n", " try:\n", " cli_id = cur.fetchone()[0]\n", " return cli_id\n", " except TypeError:\n", " print('Такого клиента не существует')\n", " return -1\n", " \n", " # Проверяем, есть ли такой клиент " ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def check_products(product_list):\n", " cur = db.cursor()\n", " res = []\n", " for a, qty in product_list:\n", " cur.execute('''select product_id from products where product_name = ? ''', (a, ))\n", " try: \n", " pr_id = cur.fetchone()[0]\n", " res.append([pr_id, qty])\n", " except TypeError:\n", " print('Такой продукт не существует')\n", " return -1 \n", " return res" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def add_invoice(customer_id, invoice_date='2017-04-24'):\n", " cur = db.cursor()\n", " cur.execute('''select coalesce(max(invoice_id) + 1, 1) from invoices''')\n", " inv_id = cur.fetchone()[0]\n", " cur.execute('''insert into invoices(invoice_id, customer_id, order_sum, invoice_date)\n", " VALUES(?,?,NULL,?)''', (inv_id, customer_id, invoice_date))\n", " db.commit()\n", " return inv_id" ] }, { "cell_type": "code", "execution_count": 190, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_products(product_list, inv_id):\n", " cur = db.cursor()\n", " for name, qty in product_list:\n", " cur.execute('''insert into invoice_details(invoice_id, product_id, qty) \n", " values (?,?,?)''', (inv_id, name, qty))\n", " db.commit()\n", " return 1\n", " \n" ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_invoice(inv_id):\n", " cur = db.cursor()\n", " cur.execute('''\n", " select sum(ii.qtyprice)\n", " from invoice_details ii\n", " inner join products p\n", " on ii.product_id = p.product_id \n", " where invoice_id = ?''', (str(inv_id)))\n", " sum_order = cur.fetchone()[0]\n", " cur.execute('''Update invoices set order_sum = ? where invoice_id = ?''', (sum_order, inv_id))\n", " db.commit()\n", " " ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_order(client_name, product_list):\n", " client_id = check_client(client_name)\n", " if client_id==-1:\n", " return -1 \n", " prod_list = check_products(product_list)\n", " if prod_list == -1:\n", " return -2\n", " inv_id = add_invoice(client_id)\n", " add_products(prod_list, inv_id)\n", " update_invoice(inv_id)\n", " db.commit()\n", " " ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [], "source": [ "add_order('Google', b)" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>invoice_ID</th>\n", " <th>customer_id</th>\n", " <th>order_sum</th>\n", " <th>invoice_date</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>260000</td>\n", " <td>2017-04-24</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, '2', 260000, '2017-04-24')]" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql \n", "SELECT \n", "from invoices" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done.\n" ] }, { "data": { "text/html": [ "<table>\n", " <tr>\n", " <th>invoice_ID</th>\n", " <th>product_id</th>\n", " <th>qty</th>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>5</td>\n", " </tr>\n", "</table>" ], "text/plain": [ "[(1, 1, 10), (1, 2, 5)]" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%sql\n", "select * \n", "from invoice_Details" ] }, { "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 }	gpl-3.0
SimonBiggs/electroninserts_bundle	Update model data.ipynb	1	699365	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Update model data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing all the packages to be used in this notebook\n", "\n", "The following cell imports all the modules and functions required. Of note are `lines 18 - 20` which import the custom `electroninserts` functions. These custom functions are located within [electroninserts.py](../edit/electroninserts.py) which can be perused to ensure it is running as you would expect." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:22.419277", "start_time": "2016-05-11T22:37:21.755521" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All modules and functions successfully imported.\n" ] } ], "source": [ "import os\n", "\n", "import dicom\n", "\n", "from glob import glob\n", "\n", "import zipfile\n", "import time\n", "import shutil\n", "import datetime\n", "import re\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from IPython.core.display import display, Markdown\n", "\n", "from bokeh.io import output_notebook, output_file, reset_output\n", "from bokeh.plotting import show, save\n", "\n", "from electroninserts import (\n", " parameterise_single_insert, display_parameterisation,\n", " convert2_ratio_perim_area, interactive, fallback_scatter)\n", "\n", "print(\"All modules and functions successfully imported.\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already up-to-date: version_information in c:\\users\\sbiggs\\appdata\\local\\continuum\\anaconda3\\envs\\electroninserts\\lib\\site-packages\n" ] }, { "data": { "application/json": { "Software versions": [ { "module": "Python", "version": "3.5.2 64bit [MSC v.1900 64 bit (AMD64)]" }, { "module": "IPython", "version": "5.0.0" }, { "module": "OS", "version": "Windows 7 6.1.7601 SP1" }, { "module": "dicom", "version": "0.9.9" }, { "module": "electroninserts", "version": "0.1.0" }, { "module": "re", "version": "2.2.1" }, { "module": "numpy", "version": "1.11.1" }, { "module": "pandas", "version": "0.18.1" }, { "module": "matplotlib", "version": "1.5.1" }, { "module": "bokeh", "version": "0.11.1" }, { "module": "version_information", "version": "1.0.3" } ] }, "text/html": [ "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.5.2 64bit [MSC v.1900 64 bit (AMD64)]</td></tr><tr><td>IPython</td><td>5.0.0</td></tr><tr><td>OS</td><td>Windows 7 6.1.7601 SP1</td></tr><tr><td>dicom</td><td>0.9.9</td></tr><tr><td>electroninserts</td><td>0.1.0</td></tr><tr><td>re</td><td>2.2.1</td></tr><tr><td>numpy</td><td>1.11.1</td></tr><tr><td>pandas</td><td>0.18.1</td></tr><tr><td>matplotlib</td><td>1.5.1</td></tr><tr><td>bokeh</td><td>0.11.1</td></tr><tr><td>version_information</td><td>1.0.3</td></tr><tr><td colspan='2'>Fri Aug 05 16:13:58 2016 AUS Eastern Standard Time</td></tr></table>" ], "text/latex": [ "\\begin{tabular}{\|l\|l\|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 3.5.2 64bit [MSC v.1900 64 bit (AMD64)] \\\\ \\hline\n", "IPython & 5.0.0 \\\\ \\hline\n", "OS & Windows 7 6.1.7601 SP1 \\\\ \\hline\n", "dicom & 0.9.9 \\\\ \\hline\n", "electroninserts & 0.1.0 \\\\ \\hline\n", "re & 2.2.1 \\\\ \\hline\n", "numpy & 1.11.1 \\\\ \\hline\n", "pandas & 0.18.1 \\\\ \\hline\n", "matplotlib & 1.5.1 \\\\ \\hline\n", "bokeh & 0.11.1 \\\\ \\hline\n", "version_information & 1.0.3 \\\\ \\hline\n", "\\hline \\multicolumn{2}{\|l\|}{Fri Aug 05 16:13:58 2016 AUS Eastern Standard Time} \\\\ \\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Software versions\n", "Python 3.5.2 64bit [MSC v.1900 64 bit (AMD64)]\n", "IPython 5.0.0\n", "OS Windows 7 6.1.7601 SP1\n", "dicom 0.9.9\n", "electroninserts 0.1.0\n", "re 2.2.1\n", "numpy 1.11.1\n", "pandas 0.18.1\n", "matplotlib 1.5.1\n", "bokeh 0.11.1\n", "version_information 1.0.3\n", "Fri Aug 05 16:13:58 2016 AUS Eastern Standard Time" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# !pip install --upgrade version_information\n", "# %load_ext version_information\n", "# %version_information dicom, electroninserts, re, numpy, pandas, matplotlib, bokeh, version_information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recording and loading the data\n", "The following cells outline both your data itself the specific details of the data collection and definition. You can either write your own contents into the cells, or you can drag and drop the relevant files from your computer via windows explorer into the cell itself.\n", "\n", "Once you have input your data to please consider uploading this data to http://simonbiggs.net/electrondata for the purpose of furthering this project." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Archive old files to minimise confusion\n", "old_data_files = glob('.csv')\n", "old_detail_files = glob('.yaml')\n", "old_report_files = glob('.html')\n", "old_zip_files = glob('.zip')\n", "\n", "old_files = np.concatenate([\n", " old_data_files, old_detail_files, old_report_files, old_zip_files])\n", "\n", "for data_file in old_files:\n", " os.rename(data_file, \"archive/{}\".format(data_file))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:38.443967", "start_time": "2016-05-11T22:37:38.440895" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing details.yaml\n" ] } ], "source": [ "%%writefile details.yaml\n", "%YAML 1.1\n", "---\n", "Machine: Elekta Synergy Linac with Agility MLCs\n", "\n", "R50 (cm):\n", " 6 MeV: 2.45\n", " 9 MeV: 3.47\n", " 12 MeV: 4.72\n", " 15 MeV: 5.8\n", " 18 MeV: 7.2\n", "\n", "Applicator factors:\n", " 6 MeV:\n", " 6x6 cm: 0.871\n", " 10x10 cm: 1.000\n", " 14x14 cm: 1.029\n", " 20x20 cm: 1.022\n", " 25x25 cm: 1.026\n", " 9 MeV:\n", " 6x6 cm: 0.919\n", " 10x10 cm: 1.000\n", " 14x14 cm: 0.981\n", " 20x20 cm: 0.977\n", " 25x25 cm: 0.971\n", " 12 MeV:\n", " 6x6 cm: 0.971\n", " 10x10 cm: 1.000\n", " 14x14 cm: 0.990\n", " 20x20 cm: 0.985\n", " 25x25 cm: 0.983\n", " 15 MeV:\n", " 6x6 cm: 0.980\n", " 10x10 cm: 1.000\n", " 14x14 cm: 0.982\n", " 20x20 cm: 0.988\n", " 25x25 cm: 0.987\n", " 18 MeV:\n", " 6x6 cm: 1.011\n", " 10x10 cm: 1.000\n", " 14x14 cm: 0.983\n", " 20x20 cm: 0.979\n", " 25x25 cm: 0.967\n", "\n", "SSD factors:\n", " 100 SSD: 1.000\n", "\n", "Applicator sizes at isocentre:\n", " 6x6 cm: 6.3x6.3 cm\n", " 10x10 cm: 10.5x10.5 cm\n", " 14x14 cm: 14.7x14.7 cm\n", " 20x20 cm: 21.1x21.1 cm\n", " 25x25 cm: 26.3x26.3 cm\n", "\n", "Definition of modelled factor: >\n", " The factor given here is the insert factor defined as the portion of the \n", " electron output factor that is dependent on the insert alone. The electron \n", " output factor is defined as per AAPM TG 25 as the ratio of dose per \n", " monitor unit at dmax.\n", "\n", "Measurement details: >\n", " Insert factors were measured in RW3 with an Advanced Markus on an Elekta \n", " Agility linac. When the depth of maximum dose was shifted from the reference \n", " depth this depth was searched for to a 1 mm resolution. All depth differences \n", " took into account stopping power ratio corrections as per the protocol in \n", " IAEA TRS 398.\n", "\n", "Insert shielding material: Cerrobend\n", "\n", "Software license of accompanying data: AGPLv3+" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:38.538336", "start_time": "2016-05-11T22:37:38.445055" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing data.csv\n" ] } ], "source": [ "%%writefile data.csv\n", "Label,Width (cm @ 100SSD),Length (cm @ 100SSD),Energy (MeV),Applicator (cm),SSD (cm),Insert factor (dose insert / dose open)\n", "Simon 3cm circle,3.15,3.16,12,10,100,0.9294\n", "Simon 3x5cm oval,3.16,5.25,12,10,100,0.9346\n", "Simon 3x13cm oval,3.17,13.64,12,10,100,0.9533\n", "Simon 3x6.5cm oval,3.17,6.83,12,10,100,0.9488\n", "Simon 3x9cm oval,3.17,9.43,12,10,100,0.9488\n", "P4,3.55,7.7,12,10,100,0.9443\n", "Simon #57 cutout,3.66,5.04,12,10,100,0.9434\n", "Simon #112 cutout,3.71,4.36,12,10,100,0.9488\n", "Simon 4cm circle,4.2,4.21,12,10,100,0.956\n", "Simon 4x10cm oval,4.21,10.51,12,10,100,0.9709\n", "Simon 4x13cm oval,4.21,13.65,12,10,100,0.9756\n", "Simon 4x6.5cm oval,4.21,6.82,12,10,100,0.9606\n", "Simon 4x8cm oval,4.21,8.41,12,10,100,0.9709\n", "Simon #20 cutout,4.38,5.47,12,10,100,0.9634\n", "P10,4.48,7.29,12,10,100,0.9606\n", "Simon #14 cutout,4.59,5.67,12,10,100,0.9588\n", "Simon #3 cutout,4.59,6.54,12,10,100,0.9681\n", "Simon #38 cutout,4.67,6.28,12,10,100,0.9737\n", "Simon #22 cutout,5.21,11.4,12,10,100,0.9881\n", "Simon 5cm circle,5.25,5.26,12,10,100,0.9709\n", "Simon 5x10cm oval,5.26,10.52,12,10,100,0.9881\n", "Simon 5x13cm oval,5.26,13.66,12,10,100,0.9872\n", "Simon 5x8cm oval,5.26,8.41,12,10,100,0.9833\n", "Simon #104 cutout,5.34,9.64,12,10,100,0.993\n", "P40,5.43,11.02,12,10,100,0.9872\n", "Simon #19 cutout,5.72,11.6,12,10,100,0.999\n", "Simon #83 cutout,5.86,8.62,12,10,100,0.9891\n", "Simon #58 cutout,6,7.98,12,10,100,0.9911\n", "P3,6.04,9.22,12,10,100,0.999\n", "Simon #33 cutout,6.08,6.64,12,10,100,0.993\n", "Simon 6cm circle,6.3,6.33,12,10,100,0.9862\n", "Simon #43 cutout,6.31,8.24,12,10,100,0.9921\n", "Simon #82 cutout,6.41,8.69,12,10,100,0.999\n", "Simon #16 cutout,6.53,10.99,12,10,100,1\n", "Simon #109 cutout,6.54,8.41,12,10,100,0.993\n", "Simon #106 cutout,6.64,9.81,12,10,100,0.999\n", "Simon #34 cutout,6.78,10.98,12,10,100,1.007\n", "P22,6.9,10.25,12,10,100,0.999\n", "Simon #41 cutout,7.08,10.77,12,10,100,1.005\n", "Simon #6 cutout,7.18,11.27,12,10,100,0.999\n", "P38_1,7.21,9.03,12,10,100,1.0101\n", "Simon 7cm circle,7.36,7.37,12,10,100,1.003\n", "Simon #73 cutout,7.56,10.05,12,10,100,1.004\n", "P12_1,7.6,10.26,12,10,100,1.0142\n", "Simon #70 cutout,7.64,8.99,12,10,100,1.003\n", "Simon #18 cutout,7.82,10.85,12,10,100,1.002\n", "Simon #32 cutout,8.06,11.85,12,10,100,1.007\n", "Simon 8cm circle,8.4,8.42,12,10,100,1.007\n", "Simon 9cm circle,9.45,9.47,12,10,100,1.0081\n", "Simon 5cm_15MeV,4.99,5,15,10,100,0.9747\n", "P36_2,5.07,9.15,15,10,100,0.9911\n", "Simon 5.3x12.5cm_15MeV,5.26,12.45,15,10,100,0.9921\n", "Simon 6.1cm_15MeV,6.09,6.1,15,10,100,0.9794\n", "Simon 6.7x12cm_15MeV,6.76,12,15,10,100,0.999\n", "P5_2,6.97,9.79,15,10,100,0.996\n", "Simon 7.25cm_15MeV,7.23,7.26,15,10,100,1.003\n", "P28,7.4,9.25,15,10,100,1\n", "P37_1,7.73,9.66,15,10,100,1.005\n", "Simon 8.3cm_15MeV,8.28,8.3,15,10,100,1.005\n", "Simon 8.5x10.9cm_15MeV,8.5,10.85,15,10,100,1.005\n", "Simon 9.5cm_15MeV,9.49,9.5,15,10,100,1.0091\n", "P31_1,4.9,9.4,18,10,100,0.9911\n", "P36_1,4.9,9.4,18,10,100,0.994\n", "Simon 5cm_18MeV,4.99,5,18,10,100,0.9823\n", "Simon 5.3x12.5cm_18MeV,5.26,12.45,18,10,100,0.995\n", "Simon 6.1cm_18MeV,6.09,6.1,18,10,100,0.9881\n", "P37_4,6.67,9.28,18,10,100,1.0081\n", "Simon 6.7x12cm_18MeV,6.76,12,18,10,100,0.999\n", "P12_2,6.83,9.19,18,10,100,1.005\n", "P13,7.1,8.79,18,10,100,0.997\n", "Simon 7.25cm_18MeV,7.23,7.26,18,10,100,1.001\n", "Simon 8.3cm_18MeV,8.28,8.3,18,10,100,1.003\n", "Simon 8.5x10.9cm_18MeV,8.5,10.85,18,10,100,1.004\n", "P42,9.14,11.32,18,10,100,0.998\n", "Simon 9.5cm_18MeV,9.49,9.5,18,10,100,1.0081\n", "P62,3.99,6.51,6,10,100,0.9643\n", "P50,4.1,5.99,6,10,100,0.9662\n", "Simon 5cm_6MeV,4.99,5,6,10,100,0.9766\n", "Simon 5.3x12.5cm_6MeV,5.26,12.45,6,10,100,0.996\n", "P7,5.69,7.58,6,10,100,0.995\n", "Appears to be a standard 6cm P5_1,6,6,6,10,100,0.9887\n", "P9_1,5.79,7.2,6,10,100,0.9852\n", "Simon 6.1cm_6MeV,6.09,6.1,6,10,100,0.9901\n", "P24,6.47,8.25,6,10,100,0.995\n", "Simon 6.7x12cm_6MeV,6.76,12,6,10,100,0.998\n", "Simon 7.25cm_6MeV,7.23,7.26,6,10,100,1.007\n", "P35_2,7.43,10.3,6,10,100,0.994\n", "Simon 8.3cm_6MeV,8.28,8.3,6,10,100,1.006\n", "P6,8.5,10.73,6,10,100,0.999\n", "Simon 8.5x10.9cm_6MeV,8.5,10.85,6,10,100,1.003\n", "Simon 9.5cm_6MeV,9.49,9.5,6,10,100,1.005\n", "P46,4.37,6.21,9,10,100,0.9606\n", "Simon 5cm_9MeV,4.99,5,9,10,100,0.9588\n", "P53,5.09,6.32,9,10,100,0.9756\n", "Simon 5.3x12.5cm_9MeV,5.26,12.45,9,10,100,0.9901\n", "P25,5.38,8.23,9,10,100,0.9852\n", "P8,5.64,6.96,9,10,100,0.9775\n", "Simon 6.1cm_9MeV,6.09,6.1,9,10,100,0.9823\n", "P9_2,6.29,6.69,9,10,100,0.993\n", "P34_3,6.72,10.23,9,10,100,0.997\n", "Simon 6.7x12cm_9MeV,6.76,12,9,10,100,1\n", "Simon 7.25cm_9MeV,7.23,7.26,9,10,100,1.005\n", "Simon 8.3cm_9MeV,8.28,8.3,9,10,100,1.004\n", "P34_2,8.37,10.6,9,10,100,0.997\n", "Simon 8.5x10.9cm_9MeV,8.5,10.85,9,10,100,1.005\n", "P17,8.6,9.81,9,10,100,1.0111\n", "Simon 9.5cm_9MeV,9.49,9.5,9,10,100,1.007\n", "P61,4.69,14.39,12,14,100,0.9901\n", "P56,5.91,12.56,12,14,100,1.0091\n", "P15_1,7.4,13.47,12,14,100,1.0101\n", "P21,7.65,10.8,12,14,100,1.002\n", "P45,9.11,12.34,12,14,100,1.0091\n", "P19,7.08,8.84,15,14,100,1.0215\n", "P15_2,8.57,12.91,15,14,100,1.0215\n", "P27,9.25,11.42,15,14,100,1.0132\n", "P37_3,7.3,12.97,18,14,100,1.0132\n", "P38_2,8.69,11.49,18,14,100,1.0173\n", "P33,6.21,10.43,6,14,100,0.999\n", "P37_2,7.29,10.1,6,14,100,1.007\n", "P20,8.6,9.24,6,14,100,1.006\n", "P38_3,8.84,15.52,6,14,100,1.003\n", "P16,9.05,11.99,6,14,100,1.0111\n", "P35_1,9.28,10.58,6,14,100,1.0091\n", "P23,9.87,13.16,6,14,100,1.005\n", "P26,6.68,10.71,9,14,100,1.003\n", "P14_2,7.03,12.47,9,14,100,1.0173\n", "P34_1,7.66,11.25,9,14,100,1.006\n", "P18,8.6,10.32,9,14,100,1.0101\n", "P14_1,8.61,12.03,9,14,100,1.0163\n", "P31_2,10.65,17.74,18,20,100,1.0142\n", "P60,18.48,20.89,6,20,100,1.004\n", "P41,7.85,24.61,9,20,100,1.0111\n", "P32,9.3,22.7,9,20,100,1.0111\n", "P51,10,28.65,15,25,100,1.0142\n", "P49,2.69,4.21,6,6,100,0.8961\n", "P43,2.8,2.9,6,6,100,0.885\n", "P48,3.31,6.08,6,6,100,0.969\n", "P47,3.39,3.4,6,6,100,0.9083\n", "P35_3,3.48,4.78,6,6,100,0.939\n", "Standard 4cm circle P63,4,4,6,6,100,0.9425\n", "P59,4.1,5.89,6,6,100,0.9814\n", "Standard 5cm circle P29,5,5,6,6,100,0.9891\n", "P30,4.12,5.22,9,6,100,0.9597\n", "P2,4.43,5.97,9,6,100,0.9728\n", "P52,4.89,5.6,9,6,100,0.999\n", "Appears to be a standard 6cm P58,6,6,9,6,100,1.002\n", "P64,8.96,13.21,9,14,100,1.0121\n", "P65 parameterised by hand,8.4,10.7,15,14,100,1.003" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Timestamping your data and details files and optionally uploading these for others to make use of\n", "Now that you have created your details and data files these are to be timestamped for later reference. Please consider giving back to this project by uncommenting the final lines of this cell which will add these two newly created files to this public dropbox folder — http://simonbiggs.net/electrondata. To uncomment these lines, select them and press `Ctrl + /`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:41.712195", "start_time": "2016-05-11T22:37:38.539258" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'20160805161358_details.yaml'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a timestamp for data and reports and rename the data file\n", "timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d%H%M%S')\n", "\n", "data_filename = \"{}_data.csv\".format(timestamp)\n", "details_filename = \"{}_details.yaml\".format(timestamp)\n", "shutil.move(\"data.csv\", data_filename)\n", "shutil.move(\"details.yaml\", details_filename)\n", "\n", "# # Uncomment these lines to give back to this project\n", "# import dropbox\n", "\n", "# simonbiggs_electroninserts_accesstoken = '5_VQ4CH7dO0AAAAAAAAhYhsClbUdrEnWKvMMnWagVXqKDTlTdf45ZwlKBp6Q2Rhq'\n", "# dpx = dropbox.dropbox.Dropbox(simonbiggs_electroninserts_accesstoken)\n", "# with open(data_filename, 'rb') as file:\n", "# dpx.files_upload(file, \"/{}\".format(data_filename))\n", "# with open(details_filename, 'rb') as file:\n", "# dpx.files_upload(file, \"/{}\".format(details_filename))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once your csv has been created it can be loaded into a pandas DataFrame as such:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:41.739974", "start_time": "2016-05-11T22:37:41.713576" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Label</th>\n", " <th>Width (cm @ 100SSD)</th>\n", " <th>Length (cm @ 100SSD)</th>\n", " <th>Energy (MeV)</th>\n", " <th>Applicator (cm)</th>\n", " <th>SSD (cm)</th>\n", " <th>Insert factor (dose insert / dose open)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Simon 3cm circle</td>\n", " <td>3.15</td>\n", " <td>3.16</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9294</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Simon 3x5cm oval</td>\n", " <td>3.16</td>\n", " <td>5.25</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9346</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Simon 3x13cm oval</td>\n", " <td>3.17</td>\n", " <td>13.64</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9533</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Simon 3x6.5cm oval</td>\n", " <td>3.17</td>\n", " <td>6.83</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9488</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Simon 3x9cm oval</td>\n", " <td>3.17</td>\n", " <td>9.43</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9488</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>P4</td>\n", " <td>3.55</td>\n", " <td>7.70</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9443</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Simon #57 cutout</td>\n", " <td>3.66</td>\n", " <td>5.04</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9434</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Simon #112 cutout</td>\n", " <td>3.71</td>\n", " <td>4.36</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9488</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Simon 4cm circle</td>\n", " <td>4.20</td>\n", " <td>4.21</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9560</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Simon 4x10cm oval</td>\n", " <td>4.21</td>\n", " <td>10.51</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9709</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Simon 4x13cm oval</td>\n", " <td>4.21</td>\n", " <td>13.65</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9756</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Simon 4x6.5cm oval</td>\n", " <td>4.21</td>\n", " <td>6.82</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9606</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Simon 4x8cm oval</td>\n", " <td>4.21</td>\n", " <td>8.41</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9709</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Simon #20 cutout</td>\n", " <td>4.38</td>\n", " <td>5.47</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9634</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>P10</td>\n", " <td>4.48</td>\n", " <td>7.29</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9606</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Simon #14 cutout</td>\n", " <td>4.59</td>\n", " <td>5.67</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9588</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Simon #3 cutout</td>\n", " <td>4.59</td>\n", " <td>6.54</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9681</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Simon #38 cutout</td>\n", " <td>4.67</td>\n", " <td>6.28</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9737</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Simon #22 cutout</td>\n", " <td>5.21</td>\n", " <td>11.40</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9881</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Simon 5cm circle</td>\n", " <td>5.25</td>\n", " <td>5.26</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9709</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Simon 5x10cm oval</td>\n", " <td>5.26</td>\n", " <td>10.52</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9881</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Simon 5x13cm oval</td>\n", " <td>5.26</td>\n", " <td>13.66</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9872</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Simon 5x8cm oval</td>\n", " <td>5.26</td>\n", " <td>8.41</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9833</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Simon #104 cutout</td>\n", " <td>5.34</td>\n", " <td>9.64</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9930</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>P40</td>\n", " <td>5.43</td>\n", " <td>11.02</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9872</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Simon #19 cutout</td>\n", " <td>5.72</td>\n", " <td>11.60</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9990</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Simon #83 cutout</td>\n", " <td>5.86</td>\n", " <td>8.62</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9891</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Simon #58 cutout</td>\n", " <td>6.00</td>\n", " <td>7.98</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9911</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>P3</td>\n", " <td>6.04</td>\n", " <td>9.22</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9990</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Simon #33 cutout</td>\n", " <td>6.08</td>\n", " <td>6.64</td>\n", " <td>12</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9930</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>P37_2</td>\n", " <td>7.29</td>\n", " <td>10.10</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0070</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td>P20</td>\n", " <td>8.60</td>\n", " <td>9.24</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0060</td>\n", " </tr>\n", " <tr>\n", " <th>120</th>\n", " <td>P38_3</td>\n", " <td>8.84</td>\n", " <td>15.52</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0030</td>\n", " </tr>\n", " <tr>\n", " <th>121</th>\n", " <td>P16</td>\n", " <td>9.05</td>\n", " <td>11.99</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0111</td>\n", " </tr>\n", " <tr>\n", " <th>122</th>\n", " <td>P35_1</td>\n", " <td>9.28</td>\n", " <td>10.58</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0091</td>\n", " </tr>\n", " <tr>\n", " <th>123</th>\n", " <td>P23</td>\n", " <td>9.87</td>\n", " <td>13.16</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0050</td>\n", " </tr>\n", " <tr>\n", " <th>124</th>\n", " <td>P26</td>\n", " <td>6.68</td>\n", " <td>10.71</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0030</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>P14_2</td>\n", " <td>7.03</td>\n", " <td>12.47</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0173</td>\n", " </tr>\n", " <tr>\n", " <th>126</th>\n", " <td>P34_1</td>\n", " <td>7.66</td>\n", " <td>11.25</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0060</td>\n", " </tr>\n", " <tr>\n", " <th>127</th>\n", " <td>P18</td>\n", " <td>8.60</td>\n", " <td>10.32</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0101</td>\n", " </tr>\n", " <tr>\n", " <th>128</th>\n", " <td>P14_1</td>\n", " <td>8.61</td>\n", " <td>12.03</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0163</td>\n", " </tr>\n", " <tr>\n", " <th>129</th>\n", " <td>P31_2</td>\n", " <td>10.65</td>\n", " <td>17.74</td>\n", " <td>18</td>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>1.0142</td>\n", " </tr>\n", " <tr>\n", " <th>130</th>\n", " <td>P60</td>\n", " <td>18.48</td>\n", " <td>20.89</td>\n", " <td>6</td>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>1.0040</td>\n", " </tr>\n", " <tr>\n", " <th>131</th>\n", " <td>P41</td>\n", " <td>7.85</td>\n", " <td>24.61</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>1.0111</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>P32</td>\n", " <td>9.30</td>\n", " <td>22.70</td>\n", " <td>9</td>\n", " <td>20</td>\n", " <td>100</td>\n", " <td>1.0111</td>\n", " </tr>\n", " <tr>\n", " <th>133</th>\n", " <td>P51</td>\n", " <td>10.00</td>\n", " <td>28.65</td>\n", " <td>15</td>\n", " <td>25</td>\n", " <td>100</td>\n", " <td>1.0142</td>\n", " </tr>\n", " <tr>\n", " <th>134</th>\n", " <td>P49</td>\n", " <td>2.69</td>\n", " <td>4.21</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.8961</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>P43</td>\n", " <td>2.80</td>\n", " <td>2.90</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.8850</td>\n", " </tr>\n", " <tr>\n", " <th>136</th>\n", " <td>P48</td>\n", " <td>3.31</td>\n", " <td>6.08</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9690</td>\n", " </tr>\n", " <tr>\n", " <th>137</th>\n", " <td>P47</td>\n", " <td>3.39</td>\n", " <td>3.40</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9083</td>\n", " </tr>\n", " <tr>\n", " <th>138</th>\n", " <td>P35_3</td>\n", " <td>3.48</td>\n", " <td>4.78</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9390</td>\n", " </tr>\n", " <tr>\n", " <th>139</th>\n", " <td>Standard 4cm circle P63</td>\n", " <td>4.00</td>\n", " <td>4.00</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9425</td>\n", " </tr>\n", " <tr>\n", " <th>140</th>\n", " <td>P59</td>\n", " <td>4.10</td>\n", " <td>5.89</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9814</td>\n", " </tr>\n", " <tr>\n", " <th>141</th>\n", " <td>Standard 5cm circle P29</td>\n", " <td>5.00</td>\n", " <td>5.00</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9891</td>\n", " </tr>\n", " <tr>\n", " <th>142</th>\n", " <td>P30</td>\n", " <td>4.12</td>\n", " <td>5.22</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9597</td>\n", " </tr>\n", " <tr>\n", " <th>143</th>\n", " <td>P2</td>\n", " <td>4.43</td>\n", " <td>5.97</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9728</td>\n", " </tr>\n", " <tr>\n", " <th>144</th>\n", " <td>P52</td>\n", " <td>4.89</td>\n", " <td>5.60</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>0.9990</td>\n", " </tr>\n", " <tr>\n", " <th>145</th>\n", " <td>Appears to be a standard 6cm P58</td>\n", " <td>6.00</td>\n", " <td>6.00</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>100</td>\n", " <td>1.0020</td>\n", " </tr>\n", " <tr>\n", " <th>146</th>\n", " <td>P64</td>\n", " <td>8.96</td>\n", " <td>13.21</td>\n", " <td>9</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0121</td>\n", " </tr>\n", " <tr>\n", " <th>147</th>\n", " <td>P65 parameterised by hand</td>\n", " <td>8.40</td>\n", " <td>10.70</td>\n", " <td>15</td>\n", " <td>14</td>\n", " <td>100</td>\n", " <td>1.0030</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>148 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " Label Width (cm @ 100SSD) \\\n", "0 Simon 3cm circle 3.15 \n", "1 Simon 3x5cm oval 3.16 \n", "2 Simon 3x13cm oval 3.17 \n", "3 Simon 3x6.5cm oval 3.17 \n", "4 Simon 3x9cm oval 3.17 \n", "5 P4 3.55 \n", "6 Simon #57 cutout 3.66 \n", "7 Simon #112 cutout 3.71 \n", "8 Simon 4cm circle 4.20 \n", "9 Simon 4x10cm oval 4.21 \n", "10 Simon 4x13cm oval 4.21 \n", "11 Simon 4x6.5cm oval 4.21 \n", "12 Simon 4x8cm oval 4.21 \n", "13 Simon #20 cutout 4.38 \n", "14 P10 4.48 \n", "15 Simon #14 cutout 4.59 \n", "16 Simon #3 cutout 4.59 \n", "17 Simon #38 cutout 4.67 \n", "18 Simon #22 cutout 5.21 \n", "19 Simon 5cm circle 5.25 \n", "20 Simon 5x10cm oval 5.26 \n", "21 Simon 5x13cm oval 5.26 \n", "22 Simon 5x8cm oval 5.26 \n", "23 Simon #104 cutout 5.34 \n", "24 P40 5.43 \n", "25 Simon #19 cutout 5.72 \n", "26 Simon #83 cutout 5.86 \n", "27 Simon #58 cutout 6.00 \n", "28 P3 6.04 \n", "29 Simon #33 cutout 6.08 \n", ".. ... ... \n", "118 P37_2 7.29 \n", "119 P20 8.60 \n", "120 P38_3 8.84 \n", "121 P16 9.05 \n", "122 P35_1 9.28 \n", "123 P23 9.87 \n", "124 P26 6.68 \n", "125 P14_2 7.03 \n", "126 P34_1 7.66 \n", "127 P18 8.60 \n", "128 P14_1 8.61 \n", "129 P31_2 10.65 \n", "130 P60 18.48 \n", "131 P41 7.85 \n", "132 P32 9.30 \n", "133 P51 10.00 \n", "134 P49 2.69 \n", "135 P43 2.80 \n", "136 P48 3.31 \n", "137 P47 3.39 \n", "138 P35_3 3.48 \n", "139 Standard 4cm circle P63 4.00 \n", "140 P59 4.10 \n", "141 Standard 5cm circle P29 5.00 \n", "142 P30 4.12 \n", "143 P2 4.43 \n", "144 P52 4.89 \n", "145 Appears to be a standard 6cm P58 6.00 \n", "146 P64 8.96 \n", "147 P65 parameterised by hand 8.40 \n", "\n", " Length (cm @ 100SSD) Energy (MeV) Applicator (cm) SSD (cm) \\\n", "0 3.16 12 10 100 \n", "1 5.25 12 10 100 \n", "2 13.64 12 10 100 \n", "3 6.83 12 10 100 \n", "4 9.43 12 10 100 \n", "5 7.70 12 10 100 \n", "6 5.04 12 10 100 \n", "7 4.36 12 10 100 \n", "8 4.21 12 10 100 \n", "9 10.51 12 10 100 \n", "10 13.65 12 10 100 \n", "11 6.82 12 10 100 \n", "12 8.41 12 10 100 \n", "13 5.47 12 10 100 \n", "14 7.29 12 10 100 \n", "15 5.67 12 10 100 \n", "16 6.54 12 10 100 \n", "17 6.28 12 10 100 \n", "18 11.40 12 10 100 \n", "19 5.26 12 10 100 \n", "20 10.52 12 10 100 \n", "21 13.66 12 10 100 \n", "22 8.41 12 10 100 \n", "23 9.64 12 10 100 \n", "24 11.02 12 10 100 \n", "25 11.60 12 10 100 \n", "26 8.62 12 10 100 \n", "27 7.98 12 10 100 \n", "28 9.22 12 10 100 \n", "29 6.64 12 10 100 \n", ".. ... ... ... ... \n", "118 10.10 6 14 100 \n", "119 9.24 6 14 100 \n", "120 15.52 6 14 100 \n", "121 11.99 6 14 100 \n", "122 10.58 6 14 100 \n", "123 13.16 6 14 100 \n", "124 10.71 9 14 100 \n", "125 12.47 9 14 100 \n", "126 11.25 9 14 100 \n", "127 10.32 9 14 100 \n", "128 12.03 9 14 100 \n", "129 17.74 18 20 100 \n", "130 20.89 6 20 100 \n", "131 24.61 9 20 100 \n", "132 22.70 9 20 100 \n", "133 28.65 15 25 100 \n", "134 4.21 6 6 100 \n", "135 2.90 6 6 100 \n", "136 6.08 6 6 100 \n", "137 3.40 6 6 100 \n", "138 4.78 6 6 100 \n", "139 4.00 6 6 100 \n", "140 5.89 6 6 100 \n", "141 5.00 6 6 100 \n", "142 5.22 9 6 100 \n", "143 5.97 9 6 100 \n", "144 5.60 9 6 100 \n", "145 6.00 9 6 100 \n", "146 13.21 9 14 100 \n", "147 10.70 15 14 100 \n", "\n", " Insert factor (dose insert / dose open) \n", "0 0.9294 \n", "1 0.9346 \n", "2 0.9533 \n", "3 0.9488 \n", "4 0.9488 \n", "5 0.9443 \n", "6 0.9434 \n", "7 0.9488 \n", "8 0.9560 \n", "9 0.9709 \n", "10 0.9756 \n", "11 0.9606 \n", "12 0.9709 \n", "13 0.9634 \n", "14 0.9606 \n", "15 0.9588 \n", "16 0.9681 \n", "17 0.9737 \n", "18 0.9881 \n", "19 0.9709 \n", "20 0.9881 \n", "21 0.9872 \n", "22 0.9833 \n", "23 0.9930 \n", "24 0.9872 \n", "25 0.9990 \n", "26 0.9891 \n", "27 0.9911 \n", "28 0.9990 \n", "29 0.9930 \n", ".. ... \n", "118 1.0070 \n", "119 1.0060 \n", "120 1.0030 \n", "121 1.0111 \n", "122 1.0091 \n", "123 1.0050 \n", "124 1.0030 \n", "125 1.0173 \n", "126 1.0060 \n", "127 1.0101 \n", "128 1.0163 \n", "129 1.0142 \n", "130 1.0040 \n", "131 1.0111 \n", "132 1.0111 \n", "133 1.0142 \n", "134 0.8961 \n", "135 0.8850 \n", "136 0.9690 \n", "137 0.9083 \n", "138 0.9390 \n", "139 0.9425 \n", "140 0.9814 \n", "141 0.9891 \n", "142 0.9597 \n", "143 0.9728 \n", "144 0.9990 \n", "145 1.0020 \n", "146 1.0121 \n", "147 1.0030 \n", "\n", "[148 rows x 7 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(data_filename)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Singling out a single energy, applicator, ssd is as simple as the following:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:41.802357", "start_time": "2016-05-11T22:37:41.741048" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Label</th>\n", " <th>Width (cm @ 100SSD)</th>\n", " <th>Length (cm @ 100SSD)</th>\n", " <th>Energy (MeV)</th>\n", " <th>Applicator (cm)</th>\n", " <th>SSD (cm)</th>\n", " <th>Insert factor (dose insert / dose open)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>75</th>\n", " <td>P62</td>\n", " <td>3.99</td>\n", " <td>6.51</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9643</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>P50</td>\n", " <td>4.10</td>\n", " <td>5.99</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9662</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td>Simon 5cm_6MeV</td>\n", " <td>4.99</td>\n", " <td>5.00</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9766</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>Simon 5.3x12.5cm_6MeV</td>\n", " <td>5.26</td>\n", " <td>12.45</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9960</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>P7</td>\n", " <td>5.69</td>\n", " <td>7.58</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9950</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>Appears to be a standard 6cm P5_1</td>\n", " <td>6.00</td>\n", " <td>6.00</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9887</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>P9_1</td>\n", " <td>5.79</td>\n", " <td>7.20</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9852</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>Simon 6.1cm_6MeV</td>\n", " <td>6.09</td>\n", " <td>6.10</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9901</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td>P24</td>\n", " <td>6.47</td>\n", " <td>8.25</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9950</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>Simon 6.7x12cm_6MeV</td>\n", " <td>6.76</td>\n", " <td>12.00</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9980</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td>Simon 7.25cm_6MeV</td>\n", " <td>7.23</td>\n", " <td>7.26</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>1.0070</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>P35_2</td>\n", " <td>7.43</td>\n", " <td>10.30</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9940</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>Simon 8.3cm_6MeV</td>\n", " <td>8.28</td>\n", " <td>8.30</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>1.0060</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>P6</td>\n", " <td>8.50</td>\n", " <td>10.73</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>0.9990</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>Simon 8.5x10.9cm_6MeV</td>\n", " <td>8.50</td>\n", " <td>10.85</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>1.0030</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>Simon 9.5cm_6MeV</td>\n", " <td>9.49</td>\n", " <td>9.50</td>\n", " <td>6</td>\n", " <td>10</td>\n", " <td>100</td>\n", " <td>1.0050</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Label Width (cm @ 100SSD) \\\n", "75 P62 3.99 \n", "76 P50 4.10 \n", "77 Simon 5cm_6MeV 4.99 \n", "78 Simon 5.3x12.5cm_6MeV 5.26 \n", "79 P7 5.69 \n", "80 Appears to be a standard 6cm P5_1 6.00 \n", "81 P9_1 5.79 \n", "82 Simon 6.1cm_6MeV 6.09 \n", "83 P24 6.47 \n", "84 Simon 6.7x12cm_6MeV 6.76 \n", "85 Simon 7.25cm_6MeV 7.23 \n", "86 P35_2 7.43 \n", "87 Simon 8.3cm_6MeV 8.28 \n", "88 P6 8.50 \n", "89 Simon 8.5x10.9cm_6MeV 8.50 \n", "90 Simon 9.5cm_6MeV 9.49 \n", "\n", " Length (cm @ 100SSD) Energy (MeV) Applicator (cm) SSD (cm) \\\n", "75 6.51 6 10 100 \n", "76 5.99 6 10 100 \n", "77 5.00 6 10 100 \n", "78 12.45 6 10 100 \n", "79 7.58 6 10 100 \n", "80 6.00 6 10 100 \n", "81 7.20 6 10 100 \n", "82 6.10 6 10 100 \n", "83 8.25 6 10 100 \n", "84 12.00 6 10 100 \n", "85 7.26 6 10 100 \n", "86 10.30 6 10 100 \n", "87 8.30 6 10 100 \n", "88 10.73 6 10 100 \n", "89 10.85 6 10 100 \n", "90 9.50 6 10 100 \n", "\n", " Insert factor (dose insert / dose open) \n", "75 0.9643 \n", "76 0.9662 \n", "77 0.9766 \n", "78 0.9960 \n", "79 0.9950 \n", "80 0.9887 \n", "81 0.9852 \n", "82 0.9901 \n", "83 0.9950 \n", "84 0.9980 \n", "85 1.0070 \n", "86 0.9940 \n", "87 1.0060 \n", "88 0.9990 \n", "89 1.0030 \n", "90 1.0050 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy = 6\n", "applicator = 10\n", "ssd = 100\n", "\n", "reference = (\n", " (data['Energy (MeV)'] == energy) &\n", " (data['Applicator (cm)'] == applicator) &\n", " (data['SSD (cm)'] == ssd)\n", ")\n", "\n", "data[reference]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to test if a given combination has sufficient data:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:41.861621", "start_time": "2016-05-11T22:37:41.803447" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of data = 16\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "number_of_data = len(data[reference])\n", "print(\"Number of data = {}\".format(number_of_data))\n", "\n", "number_of_data >= 8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying model report given applicator / energy / ssd combination" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:43.645930", "start_time": "2016-05-11T22:37:41.862664" }, "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-banner\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"f522df3a-782c-4b31-910b-82055f80754e\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", "\n", " function run_callbacks() {\n", " 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cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.78 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.79 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.80 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.81 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.82 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.83 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.84 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.85 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.86 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.87 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.88 cm^-1\",\" 0.89 cm^-1\",\" 0.89 cm^-1\",\" 0.89 cm^-1\",\" 0.89 cm^-1\",\" 0.89 cm^-1\"],\"hover_width\":[\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 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cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 cm\",\" 9.4 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cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.6 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.7 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.8 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 9.9 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.0 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.1 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.2 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.3 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.4 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.5 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.6 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.7 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.8 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 10.9 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.0 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.1 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.2 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.3 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 cm\",\" 11.4 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cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.6 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.7 cm\",\" 11.8 cm\",\" 11.8 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cm^-1\",\" 0.48 cm^-1\",\" 0.47 cm^-1\",\" 0.47 cm^-1\",\" 0.46 cm^-1\",\" 0.46 cm^-1\",\" 0.45 cm^-1\",\" 0.45 cm^-1\",\" 0.44 cm^-1\",\" 0.44 cm^-1\",\" 0.43 cm^-1\",\" 0.43 cm^-1\",\" 0.43 cm^-1\",\" 0.42 cm^-1\",\" 0.42 cm^-1\",\" 0.42 cm^-1\",\" 0.41 cm^-1\",\" 0.41 cm^-1\",\" 0.40 cm^-1\",\" 0.59 cm^-1\",\" 0.58 cm^-1\",\" 0.57 cm^-1\",\" 0.56 cm^-1\",\" 0.56 cm^-1\",\" 0.55 cm^-1\",\" 0.54 cm^-1\",\" 0.53 cm^-1\",\" 0.53 cm^-1\",\" 0.52 cm^-1\",\" 0.51 cm^-1\",\" 0.50 cm^-1\",\" 0.50 cm^-1\",\" 0.49 cm^-1\",\" 0.49 cm^-1\",\" 0.48 cm^-1\",\" 0.48 cm^-1\",\" 0.47 cm^-1\",\" 0.46 cm^-1\",\" 0.46 cm^-1\",\" 0.46 cm^-1\",\" 0.45 cm^-1\",\" 0.45 cm^-1\",\" 0.44 cm^-1\",\" 0.44 cm^-1\",\" 0.43 cm^-1\",\" 0.43 cm^-1\",\" 0.43 cm^-1\",\" 0.42 cm^-1\",\" 0.42 cm^-1\",\" 0.41 cm^-1\",\" 0.41 cm^-1\"],\"hover_width\":[\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 3.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 3.9 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.0 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.1 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.2 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 9.5 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 4.3 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 9.4 cm\",\" 4.4 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 9.3 cm\",\" 4.5 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 9.2 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 4.6 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 9.1 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 9.0 cm\",\" 4.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 8.9 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 8.8 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 4.8 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 8.6 cm\",\" 8.7 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 8.5 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 8.3 cm\",\" 8.4 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 cm\",\" 8.1 cm\",\" 8.2 cm\",\" 4.9 cm\",\" 5.0 cm\",\" 5.1 cm\",\" 5.2 cm\",\" 5.3 cm\",\" 5.4 cm\",\" 5.5 cm\",\" 5.6 cm\",\" 5.7 cm\",\" 5.8 cm\",\" 5.9 cm\",\" 6.0 cm\",\" 6.1 cm\",\" 6.2 cm\",\" 6.3 cm\",\" 6.4 cm\",\" 6.5 cm\",\" 6.6 cm\",\" 6.7 cm\",\" 6.8 cm\",\" 6.9 cm\",\" 7.0 cm\",\" 7.1 cm\",\" 7.2 cm\",\" 7.3 cm\",\" 7.4 cm\",\" 7.5 cm\",\" 7.6 cm\",\" 7.7 cm\",\" 7.8 cm\",\" 7.9 cm\",\" 8.0 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(cm)\",\"formatter\":{\"id\":\"1b53fe85-e295-4d3f-ba85-7c9f4aee0390\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"d6858251-ae0e-4f90-9007-262d3ea85049\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"71814fb1-b4f7-45b1-903a-44251900e1ca\",\"type\":\"BasicTicker\"}},\"id\":\"3af08202-a6aa-4665-88ad-427c82aee0d3\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"8efbf851-094a-4c04-9453-4d0c690a4696\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"plot\":{\"id\":\"d6858251-ae0e-4f90-9007-262d3ea85049\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"9accd467-6176-4604-a4a1-da74b12ded0c\",\"type\":\"ResetTool\"},{\"attributes\":{\"callback\":null},\"id\":\"f7f65837-33de-44c7-b4c2-e123cb7a0110\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":null,\"plot\":{\"id\":\"d6858251-ae0e-4f90-9007-262d3ea85049\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"tooltips\":[[\"Label\",\" @Label\"],[\"Width\",\" @Width\"],[\"Length\",\" @Length\"],[\"Factor\",\" @Factor\"]]},\"id\":\"ed84205d-73e3-4ecb-a4e8-bc8b4123ce65\",\"type\":\"HoverTool\"},{\"attributes\":{\"plot\":{\"id\":\"d6858251-ae0e-4f90-9007-262d3ea85049\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"71814fb1-b4f7-45b1-903a-44251900e1ca\",\"type\":\"BasicTicker\"}},\"id\":\"272e5f93-e790-405f-81e2-350af896f839\",\"type\":\"Grid\"}],\"root_ids\":[\"d6858251-ae0e-4f90-9007-262d3ea85049\"]},\"title\":\"Bokeh Application\",\"version\":\"0.11.1\"}};\n", " var render_items = [{\"docid\":\"28fc320b-458a-4af1-866b-845d3b8bd0e7\",\"elementid\":\"c8a34e64-e08f-4ba6-be20-f7b552ea3694\",\"modelid\":\"d6858251-ae0e-4f90-9007-262d3ea85049\",\"notebook_comms_target\":\"621f4e99-2fe7-42db-b26d-a23d13b56d50\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " });\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", " \n", " if (window._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", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<p><code><Bokeh Notebook handle for <strong>In[11]</strong>></code></p>" ], "text/plain": [ "<bokeh.io._CommsHandle at 0xb1e3400>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reset_output()\n", "output_notebook()\n", "# output_file(\"test.html\", title=\"test\")\n", "\n", "energy = 9\n", "applicator = 6\n", "ssd = 100\n", "\n", "reference = (\n", " (data['Energy (MeV)'] == energy) &\n", " (data['Applicator (cm)'] == applicator) &\n", " (data['SSD (cm)'] == ssd)\n", ")\n", "\n", "input_dataframe = data[reference]\n", "\n", "label = np.array(input_dataframe['Label']).astype(str)\n", "width_data = np.array(input_dataframe['Width (cm @ 100SSD)']).astype(float)\n", "length_data = np.array(input_dataframe['Length (cm @ 100SSD)']).astype(float)\n", "factor_data = np.array(input_dataframe['Insert factor (dose insert / dose open)']).astype(float)\n", "\n", "figure = fallback_scatter(width_data, length_data, factor_data, label)\n", "\n", "show(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating reports" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:43.651711", "start_time": "2016-05-11T22:37:43.646899" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iterate over the following scenarios:\n", "Energy = [ 6 9 12 15 18]\n", "Applicator = [ 6 10 14 20 25]\n", "SSD = [100]\n" ] } ], "source": [ "energy_array = np.unique(data['Energy (MeV)'])\n", "applicator_array = np.unique(data['Applicator (cm)'])\n", "ssd_array = np.unique(data['SSD (cm)'])\n", "\n", "print(\"Iterate over the following scenarios:\")\n", "print(\"Energy = {}\".format(energy_array))\n", "print(\"Applicator = {}\".format(applicator_array))\n", "print(\"SSD = {}\".format(ssd_array))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:54.148459", "start_time": "2016-05-11T22:37:43.652565" }, "collapsed": false }, "outputs": [], "source": [ "# reset_output()\n", "# output_notebook()\n", "\n", "for energy in energy_array:\n", " for applicator in applicator_array:\n", " for ssd in ssd_array:\n", " \n", " reference = (\n", " (data['Energy (MeV)'] == energy) &\n", " (data['Applicator (cm)'] == applicator) &\n", " (data['SSD (cm)'] == ssd)\n", " )\n", " \n", " input_dataframe = data[reference]\n", " label = np.array(input_dataframe['Label']).astype(str)\n", " width_data = np.array(\n", " input_dataframe['Width (cm @ 100SSD)']).astype(float)\n", " length_data = np.array(\n", " input_dataframe['Length (cm @ 100SSD)']).astype(float)\n", " factor_data = np.array(\n", " input_dataframe['Insert factor (dose insert / dose open)']).astype(float)\n", " \n", " number_of_data = len(input_dataframe)\n", " filename = \"{}_{}energy_{}applicator_{}ssd_{}data.html\".format(\n", " timestamp, str(energy).zfill(2), str(applicator).zfill(2), \n", " str(ssd).zfill(3), str(number_of_data).zfill(2))\n", " title = \"{}MeV \| {}App \| {}SSD\".format(\n", " energy, applicator, ssd)\n", " \n", " reset_output()\n", " output_file(filename, title=title)\n", " \n", " if number_of_data >= 8:\n", " ratio_perim_area_data = convert2_ratio_perim_area(width_data, length_data)\n", "\n", " figure = interactive(\n", " width_data, length_data, ratio_perim_area_data, factor_data, label)\n", " else:\n", " figure = fallback_scatter(width_data, length_data, factor_data, label)\n", " \n", " save(figure)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2016-05-11T22:37:54.391408", "start_time": "2016-05-11T22:37:54.149595" }, "collapsed": false }, "outputs": [ { "data": { "text/markdown": [ "[To download reports: `Right click me > Save link as...`](20160805161358_reports.zip)" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "created_reports = glob(\"{}*.html\".format(timestamp))\n", "zip_filename = '{}_reports.zip'.format(timestamp)\n", "\n", "with zipfile.ZipFile(zip_filename, 'w', zipfile.ZIP_DEFLATED) as zipf:\n", " for file in created_reports:\n", " zipf.write(file)\n", " \n", " zipf.write(details_filename)\n", " zipf.write(data_filename)\n", "\n", "display(Markdown(\"[To download reports: `Right click me > Save link as...`]({}_reports.zip)\".format(timestamp)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copyright information\n", "Copyright © 2016 Simon Biggs\n", "\n", "This program is free software: you can redistribute it and/or modify\n", "it under the terms of the GNU Affero General Public License as published\n", "by the Free Software Foundation, either version 3 of the License, or\n", "(at your option) any later version.\n", "\n", "This program is distributed in the hope that it will be useful,\n", "but WITHOUT ANY WARRANTY; without even the implied warranty of\n", "MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", "GNU Affero General Public License for more details.\n", "\n", "You should have received a copy of the GNU Affero General Public License\n", "along with this program. If not, see <http://www.gnu.org/licenses/>." ] } ], "metadata": { "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": { "toc_cell": true, "toc_number_sections": true, "toc_section_display": "block", "toc_threshold": 6, "toc_window_display": false }, "toc_position": { "height": "508px", "left": "1655.38px", "right": "20px", "top": "120px", "width": "177px" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }	agpl-3.0
suchow/edge	edge_analysis_0.ipynb	1	134573	{ "metadata": { "name": "", "signature": "sha256:918b9f9cf6f42171620f830e38a4cc40bcfe43e9583b08088b12719ededfa1ca" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy\n", "import matplotlib\n", "from matplotlib import pylab, mlab, pyplot\n", "np = numpy\n", "plt = pyplot\n", "\n", "from IPython.core.pylabtools import figsize, getfigs\n", "\n", "%matplotlib inline\n", "from pylab import \n", "from numpy import \n", "\n", "import tabular\n", "from sklearn import svm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import tabular as tb\n", "X = tb.tabarray(SVfile = 'edge.csv')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Inferring delimiter to be ','\n", "Setting metadata attribute from dialect delimiter to equal specified value: ','\n", "Inferring names from the last header line (line 1 ).\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "X.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "(7975,)" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "fieldnames = X.metadata['names']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "print fieldnames" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['Year', 'Title', 'Link', 'Type', 'ThreadId', 'Male_Contributions', 'Female_Contributions', 'FemaleParticipation', 'NumberofAuthorContributions', 'DebateSize', 'Live', 'UniqueContributors', 'UniqueMaleContributors', 'UniqueFemaleContributors', 'UniqueFemaleParticipation', 'Id', 'Id_num', 'Role', 'TwoAuthors', 'Name', 'Male', 'Female', 'Academic', 'Limited_Information', 'Job_Title', 'Job_Title_S', 'Job_Title_S_num', 'Department', 'Department_S', 'Department_S_num', 'Discipline', 'Workplace', 'HavePhD', 'PhD_Field', 'PhD_Year', 'PreviousContributions', 'ContributionsThisYear', 'ThreadsThisYear', 'PreviousThreads', 'AuthorAndCommenter', 'PhD_Institution', 'Years_from_PhD', 'PhD_Institution_SR', 'PhD_Institution_SR_Bin', 'Workplace_SR', 'Workplace_SR_Bin', 'SR_Ranking_Dif', 'PhD_Institution_US_IR', 'PhD_Institution_US_IR_Bin', 'Workplace_US_IR', 'Workplace_US_IR_Bin', 'USA_I_Ranking_Dif', 'PhD_Institution_US', 'PhD_Institution_US_Bin', 'Workplace_US', 'Workplace_US_Bin', 'USA_Ranking_Dif', 'Total_Citations', 'H_Index', 'i10_Index', 'Citations_Year', 'Citations_Cumulative', 'AcademicHierarchyStrict', 'PreviousCitations', 'ContributionsbyAuthor', 'dummy_Natural Sciences', 'dummy_Social Sciences', 'dummy_Professions', 'dummy_Humanities', 'dummy_Formal Sciences', 'dummy_Physics', 'dummy_Anthropology', 'dummy_Earth Sciences', 'dummy_Biology', 'dummy_Psychology', 'dummy_Journalism, media studies and communication', 'dummy_Medicine', 'dummy_Philosophy', 'dummy_Space Sciences', 'dummy_Linguistics', 'dummy_Computer Sciences', 'dummy_Engineering', 'dummy_Arts', 'dummy_Business', 'dummy_Environmental Studies and Forrestry', 'dummy_Sociology', 'dummy_Mathematics', 'dummy_Asian Studies', 'dummy_Education', 'dummy_Political Science', 'dummy_Economics', 'dummy_Systems', 'dummy_History', 'dummy_Musics', 'dummy_Chemistry', 'dummy_Archeology', 'dummy_Architecture and Design', 'dummy_Law', 'dummy_Zoology', 'dummy_Literature', 'dummy_Divinity', 'Order', 'Text', 'Number_Characters', 'WC', 'WPS', 'Sixltr', 'Dic', 'Numerals', 'funct', 'pronoun', 'ppron', 'i', 'we', 'you', 'shehe', 'they', 'ipron', 'article', 'verb', 'auxverb', 'past', 'present', 'future', 'adverb', 'preps', 'conj', 'negate', 'quant', 'number', 'swear', 'social', 'family', 'friend', 'humans', 'affect', 'posemo', 'negemo', 'anx', 'anger', 'sad', 'cogmech', 'insight', 'cause', 'discrep', 'tentat', 'certain', 'inhib', 'incl', 'excl', 'percept', 'see', 'hear', 'feel', 'bio', 'body', 'health', 'sexual', 'ingest', 'relativ', 'motion', 'space', 'time', 'work', 'achieve', 'leisure', 'home', 'money', 'relig', 'death', 'assent', 'nonfl', 'filler', 'Period', 'Comma', 'Colon', 'SemiC', 'QMark', 'Exclam', 'Dash', 'Quote', 'Apostro', 'Parenth', 'OtherP', 'AllPct']\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Testing the stated hypotheses..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Hypothesis 1a: Higher status participants are more verbose than are lower status participants.\n", "# Hypothesis 1b: Higher status participants use more dominant language than do lower status participants.\n", "# Hypothesis 1c: Male participants are more verbose than are female participants.\n", "# Hypothesis 1d: Male participants use more dominant language than do female participants.\n", "# Hypothesis 2a: Gender and status interact, such that high status is a better predictor of verbosity for male scientists than for female scientists. \n", "# Hypothesis 2b: Gender and status interact, such that high status is a better predictor of dominant language among male scientists than among female scientists. \n", "# Hypothesis 3a: Very low and very high status participants are the least likely to be verbose.\n", "# Hypothesis 3b: Very low and very high status participants are the least likely to use dominant language.\n", "# *Hypothesis 4: Female participation correlates with the number of females in the discussion.\n", "# Hypothesis 5a: The effect of gender on verbosity will be stronger in live speech than in written text.\n", "# Hypothesis 5b: The effect of gender on use of dominant language will be stronger in live speech than in written text.\n", "\n", "# variables of interest = [\u2018status\u2019, \u2018verbosity\u2019, \u2018gender\u2019, \u2018linguistic dominance\u2019, \u2018modality\u2019, \u2018discussion index\u2019]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Hypothesis 1a: Higher status participants are more verbose than are lower status participants.\n", "\n", "## Status markers\n", "# 'H_Index',\n", "# 'i10_Index',\n", "# 'Citations_Year',\n", "# 'Citations_Cumulative',\n", "# 'AcademicHierarchyStrict',\n", "# 'PreviousCitations'\n", "\n", "## Word count\n", "# 'WC'\n", "\n", "def get_nonnan_indices(a):\n", " inds = [isnan(i) for i in a if isnan(i)==False]\n", " prop_nans = sum(inds)/len(a)\n", " return inds,prop_nans\n", "\n", "h_ind,i10_ind,cityear_ind,citcumul_ind = map(get_nonnan_indices,[X['H_Index'],X['i10_Index'],X['Citations_Year'],X['Citations_Cumulative']])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(12,5))\n", "ax = fig.add_subplot(1,2,1)\n", "plt.scatter(X['Citations_Year'],X['WC'])\n", "ax = fig.add_subplot(1,2,2)\n", "plt.scatter(X['Citations_Year'],X['WPS'])\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ "<matplotlib.collections.PathCollection at 0x10dc70c90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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wTK+//6KEc2TGhocvDWz2TMnTzPhvMy7gl5vAGbeZjYQBdkMoQXQdrw1eMwpn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\nn3AaNMzGknX34T3jH3DydIuP5bPbQ2I97qumZfCx3t6rZMNOwqnCAbF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"text": [ "<matplotlib.figure.Figure at 0x10d980390>" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "h = plt.hist(X['WC'],300)\n", "plt.xlim([0, 1000])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "(0, 1000)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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IufAYGxsTO8NfXTDwWKHiH8DmSDJ5Xn62nM0OSHf3BRECISPeCma1WLXVgK8NbyC0g72I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\n4Vc6227gR+D7CYaHh+ns7ArsgwQnT4a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M811Tt/daQ387jDEHjDGug0Ts+mmMmWeM+Yzz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\n", "text": [ "<matplotlib.figure.Figure at 0x10e510ad0>" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [ "A = X['Citations_Cumulative']\n", "cityear = A[~np.isnan(A)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "h = plt.hist(cityear,100)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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trKqZpj0DbFzmMVaIV/vLYbBKZ4blfp/CT1fV15L8bWBPkkP9G6uqel+oo7XhxPdiSJpM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\nZShIklqGgiSpZShIklqGgiSp9f8BdO1i/ZZPYwQAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10cb86090>" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# lower status cutoff\n", "np.percentile(cityear,33)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "4923.0" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "# medium status cutoff\n", "np.percentile(cityear,66)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "19077.0" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "phd = nansum(X['HavePhD'])/len(X['HavePhD'])\n", "usrank = X['USA_Ranking_Dif']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "def remove_nans(a):\n", " return a[~np.isnan(a)]\n", "\n", "h = plt.hist(remove_nans(usrank),50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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dZNUE6vpIkrkkDw20LVrXpN7vReqcuu0yybok93V/419N8raufTTrtKrG/gBeDTyjG74F\nuKUb3gDsAZ4JXAoc4GRvZTdwVTf8KWDjGOp8IXA5cB9w5UD7pcBDi8wzTXVO1fqcV/PNwB8u0L5Q\nzc+YxHba1XNOV8OlXU17gCsmVc+82v4dWD2v7f3AH3fD7zz+tzXmul4BvHjwb2Sxuib5fi9S59Rt\nl8CFwM93w8+lf271ilGt04n0BKpqV1U90T29H7ikG94E7Kiqo9W/qewAcHWSi4DnVdXubrrbgc1j\nqHNfVe1f7vRTWOdUrc8FLHQ1w0I1XzXWqp7sxE2OVXUUOH6T47SYvw6vBbZ3w9uZwPtaVZ8HvjOv\nebG6JvZ+L1InTNl2WVVHqmpPN/xD+jfYrmVE63Qavkr6TfQ/iQJcTP8msuMO0f/Pzm8/3LVP0mVd\nd7GX5OVd21qmq85pX59v7Q4J3jbQlV2s5klZ6CbHSW97xxVwT5IvJHlL17amqua64TlgzWRKe4rF\n6pq29xumeLtMcin93sv9jGidnrarg5Lsot+Nme9dVfXJbpp3A49X1R2nq46ns5w6F/BNYF1Vfac7\nBn9nkp89bUUydJ0TtUTN7wY+DPxp9/zPgA8ANy6yqElevTDNV078UlV9K8lPAruS7BscWVU1jffe\nLKOuSdY8tdtlkucCnwDeXlU/yMCNcStZp6ctBKrq1UuNT/J7wGvp3zdw3GFg3cDzS+in2GFOHjI6\n3n54HHUuMs/jwOPd8JeSfB1YP211MoH1OWi5NSf5W+B4kC1U88hrOwXz61nHkz9lTUxVfav799tJ\n/pl+l38uyYVVdaQ77PfoRIs8abG6pur9rqoT62uatsskz6QfAB+rqju75pGs00ldHbQReAewqar+\nd2DUXcANSc5Lchn9HevuqjoCfD/J1enH3xuAO5+y4NNc9omB5PnpfysqSX66q/Pfuj/KqamTKV6f\n3UZ73OuA41doLFjzOGub5wvA+vSvCDsPuL6rcaKSnJ/ked3wc4Br6K/Du4At3WRbGP/2t5jF6pqq\n93sat8vub/Q2YG9VfXBg1GjW6TjObi9wtvsR4D+AB7vHrQPj3kX/RMY+4NcG2l9C/w05APzlmOp8\nHf3jwT8CjgCf7tp/C/hqV/sXgV+fxjqnbX3Oq/l24CvAl7uNd83T1TypB/Aa+ldkHAC2TbqerqbL\n6F8BsqfbFrd17auBe4D9wN3AqgnUtoP+IdPHu+3yjUvVNan3e4E63zSN2yXwcuCJ7r0+vs/cOKp1\n6s1iktSwabg6SJI0IYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkN+38MM1X1znPS/AAA\nAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x10b5602d0>" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "# score people as 1 for belonging to the first bin, 0 for any other bin\n", "ranks = remove_nans(X['Workplace_SR_Bin'])\n", "h = plt.hist(ranks)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ADQPj1jO7op/p2oP9M/M//c7TLEuSVr5er0ev13t1e2JiYsE5p3sZ537g1q59K3DfQP9N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\n", "text": [ "<matplotlib.figure.Figure at 0x10d54d6d0>" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "# score people as 1 for belonging to the first bin, 0 for any other bin\n", "ranks = remove_nans(X['PhD_Institution_SR_Bin'])\n", "h = plt.hist(ranks)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10d5847d0>" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "# career stage = award +1 for being a chaired professor or professor (that is, 5 or 6)\n", "career_stage = remove_nans(X['AcademicHierarchyStrict'])\n", "h = plt.hist(career_stage)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ot5L+JHldkgPAMeDxqjrUd009+Wvgz4Cf9l3ImCjg0STfSvKBvovp0QbgB0nuSvKvSf42\nyXnzDTTsx1S3hXMv8NFuhd+kqvppVV0GXAT8epJBzyWNXJLfBV6oqv24mn3FlVV1OfBe4EPdVnCL\nVgHvAG6rqncA/wfsmG+gYT+GkrwBuA/4QlXd33c946D71fSrwK/0XUsPfhW4utun3gP8RpJ7eq6p\nV1X1fPfvD4AvM/vdWy2aBqar6l+643uZDf+fYdiPmcx+LPEO4FBVfbbvevqU5C1J1nTtc4HfBvb3\nW9XoVdXHq2p9VW0AbgD+oar+oO+6+pLkvCSru/Ybga1Ak1fyVdVR4Lkkl3RdvwU8Nd/YsfiD40n2\nAFcBv5DkOeATVXVXz2X15UrgfcCTSV4Jtp1V9XCPNfVlHbA7yeuYXZj8XVU91nNN46D/S+j6tRb4\ncvd1DauAL1bVI/2W1KuPAF9Mcg7w78AfzjdoLC69lCStLLdxJKkBhr0kNcCwl6QGGPaS1ADDXpIa\nYNhLUgMMe0lqgGEvSQ34f8B4B6Y5Jdo8AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10cbe3190>" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "def _normalize(x): \n", " x = x - x.mean(0)\n", " x = x/x.std(0)\n", " return x\n", "\n", "verbosity = _normalize(X['WC'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "career_status = X['AcademicHierarchyStrict'] + X['PhD_Institution_SR_Bin'] + X['Workplace_SR_Bin'] + X['HavePhD'] \n", "pub_status = X['Citations_Cumulative']\n", "\n", "c1 = (X['AcademicHierarchyStrict'] > 4) 1.0 \n", "c2 = (X['PhD_Institution_SR_Bin'] == 1) * 1.0\n", "c3 = (X['Workplace_SR_Bin'] == 1) * 1.0\n", "c4 = (X['HavePhD'] == 1) * 1.0\n", "\n", "c5 = (X['Citations_Cumulative'] > np.percentile(X['Citations_Cumulative'],1/3.0)) * 1.0\n", "c6 = (X['Citations_Cumulative'] > np.percentile(X['Citations_Cumulative'],2/3.0)) * 1.0\n", "\n", "c = c1 + c2 + c3 + c4 + c5 + c6" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "-c:9: RuntimeWarning: invalid value encountered in greater\n", "-c:10: RuntimeWarning: invalid value encountered in greater\n" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hist(c,6)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 84, "text": [ "(array([ 2227., 440., 1313., 767., 1885., 1343.]),\n", " array([ 0., 1., 2., 3., 4., 5., 6.]),\n", " <a list of 6 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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6v2moQiPJOuBPgK8zvB9ZGbrzSvJbwAer6j6Yel90pqCH5RP2XnA1BJJcBlwFfH+wlfRX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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10cbc8b10>" ] } ], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "# Jordan: 15:02, Thu Aug 27 2015: In the current analysis, anybody from whom we are missing data is awarded no points for that category.\n", "# This is biased towards low status. What we really want is for the people from whom we are missing data to be regarded as being\n", "# of average status. \n", "sum(np.isnan(X['Workplace_SR_Bin']))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 88, "text": [ "3721" ] } ], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "# Theodore thought that that that that \n", "# What I'm telling you is that // that (~b/c) (that)--> \"that\" that (that)--> student wrote was a 'that' and not a 'which' is the reason he failed." ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
obulpathi/datascience	scikit/Chapter 3/Parameter Selection with Pipelines.ipynb	1	5352	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Pipelines with Grid-Search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature selection and regression without pipelines" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import make_regression\n", "\n", "X, y = make_regression(random_state=42, effective_rank=90)\n", "print(X.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, train_size=.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_selection import SelectFpr, f_regression\n", "from sklearn.linear_model import Ridge\n", "\n", "fpr = SelectFpr(score_func=f_regression)\n", "fpr.fit(X_train, y_train)\n", "X_train_fpr = fpr.transform(X_train)\n", "X_test_fpr = fpr.transform(X_test)\n", "\n", "print(X_train_fpr.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ridge = Ridge()\n", "ridge.fit(X_train_fpr, y_train)\n", "ridge.score(X_test_fpr, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## With pipelines" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline\n", "\n", "pipe = make_pipeline(SelectFpr(score_func=f_regression), Ridge())\n", "\n", "pipe.fit(X_train, y_train)\n", "pipe.score(X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grid-Searching alpha in Ridge" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.grid_search import GridSearchCV\n", "# without pipeline:\n", "param_grid_no_pipeline = {'alpha': 10. np.arange(-3, 5)}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipe.named_steps.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# with pipeline\n", "param_grid = {'ridge__alpha': 10. np.arange(-3, 5)}\n", "grid = GridSearchCV(pipe, param_grid, cv=10)\n", "grid.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting parameters of the preprocessing steps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "param_grid = {'ridge__alpha': 10. ** np.arange(-3, 5),\n", " 'selectfpr__alpha': [0.01, 0.02, 0.05, 0.1, 0.3]}\n", "grid = GridSearchCV(pipe, param_grid, cv=10)\n", "grid.fit(X_train, y_train)\n", "grid.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid.best_params_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "final_selectfpr = grid.best_estimator_.named_steps['selectfpr']\n", "final_selectfpr.get_support()" ] }, { "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 }	apache-2.0
Naereen/notebooks	Demonstration_of_running_a_Jupyter_notebook_with_sudo_rights.ipynb	1	4740	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Demonstration-of-running-a-Jupyter-notebook-with-sudo-rights\" data-toc-modified-id=\"Demonstration-of-running-a-Jupyter-notebook-with-sudo-rights-1\"><span class=\"toc-item-num\">1  </span>Demonstration of running a Jupyter notebook with sudo rights</a></div><div class=\"lev2 toc-item\"><a href=\"#Without-sudo-rights\" data-toc-modified-id=\"Without-sudo-rights-11\"><span class=\"toc-item-num\">1.1  </span>Without sudo rights</a></div><div class=\"lev2 toc-item\"><a href=\"#With-sudo-rights\" data-toc-modified-id=\"With-sudo-rights-12\"><span class=\"toc-item-num\">1.2  </span>With sudo rights</a></div><div class=\"lev2 toc-item\"><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-13\"><span class=\"toc-item-num\">1.3  </span>Conclusion</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of running a Jupyter notebook with sudo rights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Without sudo rights\n", "\n", "The next cells were first ran when Jupyter was not running with sudo rights." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lecture des listes de paquets... Fait\n", "E: Le répertoire /var/lib/apt/lists/partial pour les listes n'existe pas. - Acquire (13: Permission non accordée)\n", "W: Problème de suppression du lien /var/cache/apt/pkgcache.bin - RemoveCaches (13: Permission non accordée)\n", "W: Problème de suppression du lien /var/cache/apt/srcpkgcache.bin - RemoveCaches (13: Permission non accordée)\n" ] } ], "source": [ "!apt update" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[sudo] Mot de passe de lilian : \n" ] } ], "source": [ "!sudo apt update" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see here, using `sudo ...` asks for a password, but it doesn't work from Jupyter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## With sudo rights\n", "The next cells were then ran when Jupyter was running with sudo rights." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Réception de:1 http://archive.canonical.com/ubuntu bionic InRelease [10,2 kB] m\n", "Réception de:2 http://ppa.launchpad.net/audio-recorder/ppa/ubuntu bionic InRelease [15,9 kB]\n", "Réception de:3 http://archive.canonical.com/ubuntu bionic/partner Sources [1 904 B]3mm\u001b[33m\n", "Réception de:4 http://archive.canonical.com/ubuntu bionic/partner i386 Packages [2 300 B]\n", "Réception de:5 http://archive.canonical.com/ubuntu bionic/partner amd64 Packages [2 304 B]\n", "Réception de:6 http://archive.canonical.com/ubuntu bionic/partner Translation-en [1 272 B]\n", "Réception de:7 http://ppa.launchpad.net/costales/anoise/ubuntu bionic InRelease [15,4 kB]\n", "0% [Connexion à fr.archive.ubuntu.com] [Attente des fichiers d'en-tête] [7 In\u001b[0mm\u001b[33m^C\n" ] } ], "source": [ "!apt update" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It works!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "It works!" ] } ], "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" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "11.6667px", "width": "251.667px" }, "navigate_menu": true, "number_sections": true, "sideBar": false, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }	mit
kbrose/rain	n-year/notebooks/calculating-new-definitions.ipynb	1	127525	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function, unicode_literals\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn.apionly as sns\n", "%matplotlib inline\n", "# These settings are for a dark theme, comment it out if you\n", "# are using the default theme.\n", "sns.set(rc={'text.color':'#cdd2e9',\n", " 'figure.facecolor': '#384151',\n", " 'axes.facecolor':'#262931',\n", " 'axes.labelcolor':'#cdd2e9',\n", " 'grid.color':'#3b3e45',\n", " 'xtick.color':'#cdd2e9',\n", " 'ytick.color':'#cdd2e9',\n", " 'xtick.labelsize':13,\n", " 'ytick.labelsize':13,\n", " 'axes.titlesize':16,\n", " 'legend.fontsize':14,\n", " 'figure.figsize':(11.5,8)})\n", "sns.set_palette(sns.color_palette(\"Set2\", 8))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading in dataset, verifying integrity" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The following code is adopted from Pat's Rolling Rain N-Year Threshold.pynb\n", "# Loading in hourly rain data from CSV, parsing the timestamp, and adding it\n", "# as an index so it's more useful\n", "\n", "rain_df = pd.read_csv('data/ohare_full_precip_hourly.csv')\n", "rain_df['datetime'] = pd.to_datetime(rain_df['datetime'])\n", "rain_df = rain_df.set_index(pd.DatetimeIndex(rain_df['datetime']))\n", "# Data does not really exist before 1973, data between 11/1991 and 8/1992 is all 0s...\n", "rain_df = rain_df[(rain_df['datetime'] > '1973') &\n", " ((rain_df['datetime'] < '19911101') \| (rain_df['datetime'] > '19920801'))]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kgMIRVZpz6zUqGTfUdFIAAIgKQSmQAAYeOV6SVLVoiuGUAIDF3FIL5JJkOhlB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\n1d4V0oO1HUdY8ANoDEN38EmPRkWpjBIm54iewo/6WmqHzkB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"text/plain": [ "<matplotlib.figure.Figure at 0x1ed148c7518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chi_rain_series = rain_df['HOURLYPrecip'].resample('1H').max().dropna()\n", "# We take maximum, because when there are multiple reports within the same hour,\n", "# the values are accumulated (and then reset at the next full hour). Thus we\n", "# want to take the maximum reading from any given hour.\n", "ax = chi_rain_series.resample('24H').sum().plot()\n", "_ = ax.set_title('Daily Precipitation (in) at O\\'Hare')" ] }, { "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>1-year</th>\n", " <th>2-year</th>\n", " <th>5-year</th>\n", " <th>10-year</th>\n", " <th>25-year</th>\n", " <th>50-year</th>\n", " <th>100-year</th>\n", " </tr>\n", " <tr>\n", " <th>Duration</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>10-day</th>\n", " <td>4.12</td>\n", " <td>4.95</td>\n", " <td>6.04</td>\n", " <td>6.89</td>\n", " <td>8.18</td>\n", " <td>9.38</td>\n", " <td>11.14</td>\n", " </tr>\n", " <tr>\n", " <th>5-day</th>\n", " <td>3.25</td>\n", " <td>3.93</td>\n", " <td>4.91</td>\n", " <td>5.70</td>\n", " <td>6.93</td>\n", " <td>8.04</td>\n", " <td>9.96</td>\n", " </tr>\n", " <tr>\n", " <th>72-hr</th>\n", " <td>2.93</td>\n", " <td>3.55</td>\n", " <td>4.44</td>\n", " <td>5.18</td>\n", " <td>6.32</td>\n", " <td>7.41</td>\n", " <td>8.78</td>\n", " </tr>\n", " <tr>\n", " <th>48-hr</th>\n", " <td>2.70</td>\n", " <td>3.30</td>\n", " <td>4.09</td>\n", " <td>4.81</td>\n", " <td>5.88</td>\n", " <td>6.84</td>\n", " <td>8.16</td>\n", " </tr>\n", " <tr>\n", " <th>24-hr</th>\n", " <td>2.51</td>\n", " <td>3.04</td>\n", " <td>3.80</td>\n", " <td>4.47</td>\n", " <td>5.51</td>\n", " <td>6.46</td>\n", " <td>7.58</td>\n", " </tr>\n", " <tr>\n", " <th>18-hr</th>\n", " <td>2.30</td>\n", " <td>2.79</td>\n", " <td>3.50</td>\n", " <td>4.11</td>\n", " <td>5.06</td>\n", " <td>5.95</td>\n", " <td>6.97</td>\n", " </tr>\n", " <tr>\n", " <th>12-hr</th>\n", " <td>2.18</td>\n", " <td>2.64</td>\n", " <td>3.31</td>\n", " <td>3.89</td>\n", " <td>4.79</td>\n", " <td>5.62</td>\n", " <td>6.59</td>\n", " </tr>\n", " <tr>\n", " <th>6-hr</th>\n", " <td>1.88</td>\n", " <td>2.28</td>\n", " <td>2.85</td>\n", " <td>3.35</td>\n", " <td>4.13</td>\n", " <td>4.85</td>\n", " <td>5.68</td>\n", " </tr>\n", " <tr>\n", " <th>3-hr</th>\n", " <td>1.60</td>\n", " <td>1.94</td>\n", " <td>2.43</td>\n", " <td>2.86</td>\n", " <td>3.53</td>\n", " <td>4.14</td>\n", " <td>4.85</td>\n", " </tr>\n", " <tr>\n", " <th>2-hr</th>\n", " <td>1.48</td>\n", " <td>1.79</td>\n", " <td>2.24</td>\n", " <td>2.64</td>\n", " <td>3.25</td>\n", " <td>3.82</td>\n", " <td>4.47</td>\n", " </tr>\n", " <tr>\n", " <th>1-hr</th>\n", " <td>1.18</td>\n", " <td>1.43</td>\n", " <td>1.79</td>\n", " <td>2.10</td>\n", " <td>2.59</td>\n", " <td>3.04</td>\n", " <td>3.56</td>\n", " </tr>\n", " <tr>\n", " <th>30-min</th>\n", " <td>0.93</td>\n", " <td>1.12</td>\n", " <td>1.41</td>\n", " <td>1.65</td>\n", " <td>2.04</td>\n", " <td>2.39</td>\n", " <td>2.80</td>\n", " </tr>\n", " <tr>\n", " <th>15-min</th>\n", " <td>0.68</td>\n", " <td>0.82</td>\n", " <td>1.03</td>\n", " <td>1.21</td>\n", " <td>1.49</td>\n", " <td>1.75</td>\n", " <td>2.05</td>\n", " </tr>\n", " <tr>\n", " <th>10-min</th>\n", " <td>0.55</td>\n", " <td>0.67</td>\n", " <td>0.84</td>\n", " <td>0.98</td>\n", " <td>1.21</td>\n", " <td>1.42</td>\n", " <td>1.67</td>\n", " </tr>\n", " <tr>\n", " <th>5-min</th>\n", " <td>0.30</td>\n", " <td>0.36</td>\n", " <td>0.46</td>\n", " <td>0.54</td>\n", " <td>0.66</td>\n", " <td>0.78</td>\n", " <td>0.91</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1-year 2-year 5-year 10-year 25-year 50-year 100-year\n", "Duration \n", "10-day 4.12 4.95 6.04 6.89 8.18 9.38 11.14\n", "5-day 3.25 3.93 4.91 5.70 6.93 8.04 9.96\n", "72-hr 2.93 3.55 4.44 5.18 6.32 7.41 8.78\n", "48-hr 2.70 3.30 4.09 4.81 5.88 6.84 8.16\n", "24-hr 2.51 3.04 3.80 4.47 5.51 6.46 7.58\n", "18-hr 2.30 2.79 3.50 4.11 5.06 5.95 6.97\n", "12-hr 2.18 2.64 3.31 3.89 4.79 5.62 6.59\n", "6-hr 1.88 2.28 2.85 3.35 4.13 4.85 5.68\n", "3-hr 1.60 1.94 2.43 2.86 3.53 4.14 4.85\n", "2-hr 1.48 1.79 2.24 2.64 3.25 3.82 4.47\n", "1-hr 1.18 1.43 1.79 2.10 2.59 3.04 3.56\n", "30-min 0.93 1.12 1.41 1.65 2.04 2.39 2.80\n", "15-min 0.68 0.82 1.03 1.21 1.49 1.75 2.05\n", "10-min 0.55 0.67 0.84 0.98 1.21 1.42 1.67\n", "5-min 0.30 0.36 0.46 0.54 0.66 0.78 0.91" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_year_threshes = pd.read_csv('data/n_year_definitions.csv')\n", "n_year_threshes = n_year_threshes.set_index('Duration')\n", "n_year_threshes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Rainfall Equivalent\n", "\n", "Verify that the precipitation amount is the \"rainfall\" equivalent, i.e. that snowfall has been melted into liquid water. We will verify this by plotting the total amount of precipitation reported each day from the [\"Snowpocalypse\" in 2011, where 21.2 inches of snow fell at O'Hare from 1/31/2011 to 2/2/2011](https://en.wikipedia.org/wiki/January_31_%E2%80%93_February_2,_2011_North_American_blizzard#Illinois). We see that each of these days has < 1 inch of precipitation reported, and the total number of inches reported is < 2." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total of 1.62 inches of precip reported.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1ed1904ce48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "chi_rain_series['20110130':'20110204'].resample('24H').sum().plot(figsize=(6,4))\n", "print('Total of {:.2f} inches of precip reported.'.format(\n", " chi_rain_series['20110130':'20110204'].sum()\n", " ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Rainfall vs. n-year storm threshold" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dur_str_to_hours = {\n", " '5-min':5/60.0,\n", " '10-min':10/60.0,\n", " '15-min':15/60.0,\n", " '30-min':0.5,\n", " '1-hr':1.0,\n", " '2-hr':2.0,\n", " '3-hr':3.0,\n", " '6-hr':6.0,\n", " '12-hr':12.0,\n", " '18-hr':18.0,\n", " '24-hr':24.0,\n", " '48-hr':48.0,\n", " '72-hr':72.0,\n", " '5-day':524.0,\n", " '10-day':1024.0\n", "}\n", "\n", "def plot_thresh(duration_str, n_years, ax=None):\n", " '''\n", " For a given duration and a given n, the number of years, plot the\n", " rolling amount of rain of the given duration, and the amount\n", " of rain in the given duration that constitutes an n-year storm.\n", " \n", " duration_str: duration as a string, see index of n_year_threshes\n", " n_years : number of years, must be column of n_year_threshes\n", " ax : optional, matplotlib axis object on which to plot\n", " \n", " >>> plot_thresh('48-hour', 100)\n", " >>> plot_thresh('5-day', 10)\n", " '''\n", " global rain_df\n", " global n_year_threshes\n", " global dur_str_to_hours\n", " \n", " if ax is None:\n", " ax = plt.gca()\n", " \n", " thresh = n_year_threshes.loc[duration_str, str(n_years) + '-year']\n", " \n", " duration = dur_str_to_hours[duration_str]\n", " duration = max(duration, 1) # cannot upsample to more frequent than hourly\n", " # TODO: want to throw warning?\n", " \n", " # Create plot\n", " rain_line = chi_rain_series.rolling(window=int(duration), min_periods=0).sum().plot(\n", " ax=ax, color=sns.color_palette()[0])\n", " \n", " x_limits = ax.get_xlim()\n", " \n", " ax.plot(x_limits, [thresh, thresh], color=sns.color_palette()[1])\n", " \n", " ax.set_ylim([0, ax.get_ylim()[1]])\n", " \n", " ax.legend(['moving cumulative rain', \n", " str(n_years) + '-year ' + duration_str + ' threshold'],\n", " loc='best')\n", " \n", " return ax" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T2jW65oar6v4uKTmop5550u9BAQAAwEcWi9EReITXjAIAAISVKqMD8ApJKQAA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\ngVqtRmZ6FtJT0wNWl3g3SkOUIahYuAidXZ1oOt+EuvpaWCwWyGQyjI2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"text/plain": [ "<matplotlib.figure.Figure at 0x1ed19209208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plot_thresh('24-hr', 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate new n-year storm definitions\n", "\n", "Essentially replicating [bulletin 70](http://www.isws.illinois.edu/atmos/statecli/RF/download.htm).\n", "\n", "One of the pre-processing steps requires finding all of the \"separate storm systems\" for the durations that are less than or equal to 24 hours. Quoting from Bulletin 70, Section 3: Independence of Observations:\n", "\n", "> As in any statistical analysis, the individual ob-\n", " servations or data points should be independent of\n", " each other. With a partial-duration series, one must\n", " be careful that the observations used are not meteor-\n", " ologically dependent; that is, they must be from sepa-\n", " rate storm systems. In the present study, data for\n", " precipitation durations of 24 hours or less were ob-\n", " tained from individual precipitation events, defined\n", " as precipitation periods in which there was no pre-\n", " cipitation for at least 6 hours before and 6 hours\n", " after the precipitation event (Huff, 1967); then, only\n", " the maximum value for the particular duration (6\n", " hours, 12 hours, etc.) within such a precipitation\n", " event was used. This ensures that the precipitation\n", " values are independent of each other and are derived\n", " from individual storms. For precipitation durations\n", " of 2 to 10 days, no time separation criteria were\n", " needed." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def get_independent_storms():\n", " '''\n", " TODO\n", " \n", " See page 21 of http://www.isws.illinois.edu/atmos/statecli/PDF/b70-all.pdf,\n", " Section 3: Independence of Observations, also quoted above.\n", " '''\n", " pass" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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in//3b5QUF2Plr8uRnHzVLsERERERUf2w1yIYNq8zefNmFj7/8lN7xEJEZLU+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\ncJUML6WcgwSASn/n+gk+Afi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"text/plain": [ "<matplotlib.figure.Figure at 0x1ed191e5dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps = np.linspace(0,1,1000)\n", "f, (ax1, ax2) = plt.subplots(1, 2, sharex=True, figsize=(11,4))\n", "\n", "ax1.set_title('Quantiles')\n", "ax1.plot(chi_rain_series.rolling(window=int(24), min_periods=0).sum().quantile(ps))\n", "ax1.hold(True)\n", "ax1.plot(chi_rain_series.resample('24H').sum().dropna().quantile(ps), '--')\n", "\n", "ax2.set_title('Difference')\n", "junk = ax2.plot(ps, \n", " chi_rain_series.rolling(window=int(24), min_periods=0).sum().quantile(ps) - \n", " chi_rain_series.resample('24H').sum().dropna().quantile(ps)\n", ")\n", "# We hope that these will match well. One is generated using (somewhat) independent\n", "# observations, and the other is generated using highly dependent observations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def new_recurrence_intervals():\n", " '''\n", " TODO\n", " '''\n", " global rain_df\n", " global n_year_threshes\n", " global dur_str_to_hours\n", " \n", " new_recur_ints = n_year_threshes.ix[:-4,:].copy(deep=True)\n", " \n", " for recurrence in new_recur_ints.columns:\n", " for dur_str in new_recur_ints.index:\n", " thresh = n_year_threshes.ix[dur_str, recurrence]\n", " dur = dur_str_to_hours[dur_str]\n", " rain = chi_rain_series.rolling(window=int(dur), min_periods=0).sum()\n", " # 24 * 365.25 = 8766 hours per year\n", " # rain.size * dur is the number of total hours we are looking at, so\n", " # rain.size * dur / 8766 is total number of years in dataset\n", " new_recur_ints.ix[dur_str, recurrence] = rain.size * dur / 8766. / sum(rain > thresh)\n", " \n", " return new_recur_ints" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_recur_ints = new_recurrence_intervals()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1-year 2-year 5-year 10-year 25-year 50-year 100-year\n", "Duration \n", "10-day 1.367616 2.363959 3.639560 6.122948 7.972638 11.844276 26.527516\n", "5-day 1.181166 2.177883 3.952641 4.574523 7.279120 9.619324 26.256827\n", "72-hr 1.088788 1.984449 3.557377 4.839816 5.603998 8.529842 13.425230\n", "48-hr 1.008591 1.940184 3.330963 4.960326 5.518861 8.168791 12.475971\n", "24-hr 1.150020 2.042198 3.586298 4.901274 5.624413 7.299770 19.793608\n", "18-hr 1.054577 1.778688 3.051189 4.084395 5.676108 5.984114 13.309495\n", "12-hr 1.146178 1.709747 2.875049 4.361303 5.848112 6.200407 16.082307\n", "6-hr 1.025167 1.726959 2.859077 4.084395 6.276022 8.577230 8.872997\n", "3-hr 1.063293 1.608231 3.063297 4.765128 7.568144 8.041153 9.189890\n", "2-hr 1.128583 1.715446 3.298935 5.360769 7.147692 8.577230 9.530256\n", "1-hr 0.875228 1.786923 3.898741 6.126593 8.577230 8.577230 10.721538\n" ] } ], "source": [ "print(new_recur_ints.to_string())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mpl-2.0
dtamayo/rebound	ipython_examples/AdvWHFast.ipynb	1	19640	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Advanced settings for WHFast: Extra speed, accuracy, and additional forces\n", "\n", "There are several performance enhancements one can make to WHFast. However, each one has pitfalls that an inexperienced user can unwittingly fall into. We therefore chose safe default settings that make the integrator difficult to misuse. This makes the default WHFast substantially slower and less accurate than it can be. Here we describe how to alter the integrator settings to improve WHFast's performance.\n", "\n", "TL;DR\n", "\n", "As long as \n", "\n", "1. you don't add, remove or otherwise modify particles between timesteps\n", "2. you get your outputs by passing a list of output times ahead of time and access the particles pointer between calls to `sim.integrate()` (see, e.g., the Visualization section of [WHFast.ipynb](WHFast.ipynb))\n", "\n", "you can set `sim.ri_whfast.safe_mode = 0` to get a substantial performance boost. Under the same stipulations, you can set `sim.ri_whfast.corrector = 11` to get much higher accuracy, at a nearly negligible loss of performance (as long as there are many timesteps between outputs).\n", "\n", "If you want to modify particles, or if the code breaks with these advanced settings, read below for details, and check out the Common mistake with WHFast section at the bottom of [WHFast.ipynb](WHFast.ipynb)." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The Wisdom-Holman algorithm\n", "\n", "In order to understand and apply the various integrator flags, we need to first understand the Wisdom-Holman scheme (see, e.g., Wisdom & Holman 1991, or Rein & Tamayo 2015 for more details).\n", "\n", "The Wisdom-Holman algorithm consists of alternating Keplerian steps that evolve particles on their two-body Keplerian orbits around the star with interaction steps that apply impulses to the particles' velocities from the interactions between bodies. The basic algorithm for a single timestep $dt$ is a Leapfrog Drift-Kick-Drift scheme with an interaction kick over the full $dt$ sandwiched between half timesteps of Keplerian drift:\n", "\n", "$H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2)$\n", "\n", "Timesteps like the one above are then concatenated over the full integration:\n", "\n", "$H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2)$ $H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2)$ ... $H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2)$" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Combining Kepler steps and synchronizing\n", "\n", "It turns out that Kepler steps take longer than interaction steps as long as you don't have many planets, so an obvious and important performance boost would be to combine adjacent Kepler half-steps into full ones, i.e.:\n", "\n", "$H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt) ... \\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2)$\n", "\n", "The issue is that if you were to, say, output the state of the particles as the simulation progressed, the positions would not correspond to anything real, since the beginning (or end) of one of the full $H_{Kepler}(dt)$ steps corresponds to some intermediate step in an abstract sequence of calculations for a given timestep. In order to get the particles' actual positions, we would have to calculate to the end the timestep we want the output for by splitting a full Kepler step back into two half-steps, e.g.,\n", "\n", "$H_{Kepler}(dt/2)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt)\\:H_{Interaction}(dt)\\:H_{Kepler}(dt/2) \\text{PRINT OUTPUT} H_{Kepler}(dt/2) H_{Interaction}(dt)\\:H_{Kepler}(dt)$...\n", "\n", "We call this step of reinserting half-Kepler steps to obtain the physical state of the particles synchronizing. This must be done whenever the actual states of the particles are required, e.g., before every output, or if one wanted to use the particles' states to compute additional changes to the particle orbits between timesteps. It is also necessary to synchronize each timestep whenever the MEGNO chaos indicator is being computed.\n", "\n", "Conversions between Jacobi and Inertial Coordinates\n", "\n", "It turns out that the most convenient coordinate system to work in for performing the Kepler steps is often Jacobi coordinates (see, e.g., 9.5.4 of Murray & Dermott). WHFast therefore works in Jacobi coordinates by default, converting to inertial coordinates when it needs to (e.g. for output, and for doing the direct gravity calculation in the interaction step, which is most easily done in inertial coordinates).\n", "\n", "One feature of WHFast is that it works in whatever inertial coordinate system you choose for your initial conditions. This means that whatever happens behind the scenes, the user always gets the particles' inertial coordinates at the front end. At the beginning of every timestep, WHFast therefore has to somehow obtain the Jacobi coordinates. The straightforward thing would be to convert from the inertial coordinates to Jacobi coordinates every timestep, but these conversions slow things down, and they represent extra operations that grow the round-off error.\n", "\n", "WHFast therefore stores the Jacobi coordinates internally throughout the time it is running, and only recalculates Jacobi coordinates from the inertial ones if told to do so. Since Jacobi coordinates reference particles to the center of mass of all the particles with indices lower than their own (typically all the particles interior to them), the main reason you would have to recalculate Jacobi coordinates is if between timesteps you choose to somehow change the particles' positions or velocities (give them kicks in addition to their mutual gravity), or change the particles' masses. \n", "\n", "Overriding the defaults\n", "\n", "Let's begin by importing rebound, and defining a simple function to reset rebound and initialize a new simulation with a test case," ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import rebound\n", "import numpy as np\n", "def test_case():\n", " sim = rebound.Simulation()\n", " sim.integrator = 'whfast'\n", " sim.add(m=1.) # add the Sun\n", " sim.add(m=3.e-6,e=0.99, a=1.) # add Earth\n", " sim.move_to_com()\n", " sim.dt = 0.2\n", " return sim" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "By default WHFast synchronizes and recalculates the Jacobi coordinates from the inertial ones every timestep. This guarantees that the user always gets physical particle states for output, and ensures reliable output if the user decides to, e.g., grow the particles' masses between timesteps. \n", "\n", "Now that you understand the pitfalls, if you want to boost WHFast's performance, you simply set" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sim = test_case()\n", "sim.ri_whfast.safe_mode = 0" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now it becomes the user's responsibility to appropriately synchronize and recalculate jacobi coordinates when needed. You can tell WHFast to recalculate Jacobi coordinates for a given timestep (say after you change a particle's mass) with the `sim.ri_whfast.recalculate_jacobi_this_timestep` flag. After it recalculates Jacobi coordinates, WHFast will reset this flag to zero, so you just set it each time you mess with the particles." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "safe_mode = 1\n", "---------------------------------\n", "REBOUND version: \t3.4.0\n", "REBOUND built on: \tMay 31 2017 11:53:50\n", "Number of particles: \t2\n", "Selected integrator: \twhfast\n", "Simulation time: \t6.2831853071795858e+05\n", "Current timestep: \t0.200000\n", "---------------------------------\n", "<rebound.Particle object, m=1.0 x=3.8952737650111065e-06 y=-4.025686466113066e-07 z=0.0 vx=2.1862005456099384e-06 vy=9.999414128270949e-08 vz=0.0>\n", "<rebound.Particle object, m=3e-06 x=-1.2984227143844442 y=0.1341896612527594 z=0.0 vx=-0.7287335151973472 vy=-0.0333313804272124 vz=0.0>\n", "---------------------------------\n", "Safe integration took 1.4043679237365723 seconds\n", "---------------------------------\n", "REBOUND version: \t3.4.0\n", "REBOUND built on: \tMay 31 2017 11:53:50\n", "Number of particles: \t2\n", "Selected integrator: \twhfast\n", "Simulation time: \t6.2831853071795858e+05\n", "Current timestep: \t0.200000\n", "---------------------------------\n", "<rebound.Particle object, m=1.0 x=3.89491388755522e-06 y=-4.0258518356289257e-07 z=0.0 vx=2.18648433924743e-06 vy=9.996480978405334e-08 vz=0.0>\n", "<rebound.Particle object, m=3e-06 x=-1.298304629185073 y=0.13419506118763086 z=0.0 vx=-0.7288281130824766 vy=-0.03332160326135111 vz=0.0>\n", "---------------------------------\n", "Manual integration took 0.8836901187896729 seconds\n" ] } ], "source": [ "import time\n", "Porb = 2np.pi # orbital period for Earth, using units of G = 1, solar masses, AU and yr/2pi\n", "\n", "sim = test_case()\n", "print(\"safe_mode = {0}\".format(sim.ri_whfast.safe_mode))\n", "start_time = time.time()\n", "sim.integrate(1.e5Porb)\n", "sim.status()\n", "print(\"Safe integration took {0} seconds\".format(time.time() - start_time))\n", "\n", "sim = test_case()\n", "sim.ri_whfast.safe_mode = 0\n", "start_time = time.time()\n", "sim.integrate(1.e5Porb)\n", "sim.status()\n", "print(\"Manual integration took {0} seconds\".format(time.time() - start_time))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "In our test case with a single planet, there is effectively no interaction step, and by combining Kepler steps we get almost the full factor of 2 speedup we expect. Because Kepler steps are expensive (by virtue of having to solve the transcendental Kepler equation), this will always be an important performance boost for few-planet cases.\n", "\n", "Note that one case where REBOUND needs to synchronize every timestep is if you're using the MEGNO chaos indicator. So if you call" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sim.init_megno()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "REBOUND will synchronize every timestep even if you set `sim.ri_whfast.safe_mode = 0` and never explicitly call `sim.integrator_synchronize()`." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "Modifying particles/forces\n", "\n", "Again, if performance is a factor in your simulations, you would not want to write a custom stepper in python that modifies the particles, since this will be very slow. You could either write a modified C version of `reb_integrate` in `src/librebound.c` (the flags are defined in `librebound.h`, and have the same name as the python ones, just without `sim.` in front), or you can use the REBOUNDXF library, which takes care of this for you and supports many typically used modifications. We again illustrate a simple scheme with python code:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sim = test_case()\n", "sim.ri_whfast.safe_mode = 0\n", "def integrate_mod(sim, t_final):\n", " while sim.t < t_final:\n", " sim.step()\n", " sim.particles[1].m += 1.e-10\n", " sim.ri_whfast.recalculate_jacobi_this_timestep = 1\n", " sim.integrator_synchronize()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here, because we grow the mass of the planet every timestep, we have to recalculate Jacobi coordinates every timestep (since they depend on the masses of the particles). We therefore manually set the flag to recalculate them the next timestep every time we make a change. Here we would actually get the same result if we just left `sim.ri_whfast.safe_mode = 1`, since when recalculating Jacobi coordinates, WHFast automatically has to synchronize in order to get real positions and velocities for the planets. In this case WHFast is therefore synchronizing and recalculating Jacobi coordinates every timestep.\n", "\n", "But imagine now that instead of growing the mass, we continually add an impulse to vx:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sim = test_case()\n", "sim.ri_whfast.safe_mode = 0\n", "def integrate_mod(sim, t_final):\n", " while sim.t < t_final:\n", " sim.step()\n", " sim.particles[1].vx += 1.e-10sim.dt\n", " sim.ri_whfast.recalculate_jacobi_this_timestep = 1\n", " sim.integrator_synchronize()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This would not give accurate results, because the `sim.particles[1].vx` we access after `sim.step()` isn't a physical velocity (it's missing a half-Kepler step). It's basically at an intermediate point in the calculation. In order to make this work, one would call `sim.integrator_synchronize()` between `sim.step()` and accessing `sim.particles[1].vx`, to ensure the velocity is physical." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Symplectic correctors\n", "\n", "Symplectic correctors make the Wisdom-Holman scheme higher order (without symplectic correctors it's second order). The great thing about them is that they only need to get applied when you synchronize. So if you just need to synchronize to output, and there are many timesteps between outputs, they represent a very small performance loss for a huge boost in accuracy (compare for example the green line (11th order corrector) to the red line (no corrector) in Fig. 4 of Rein & Tamayo 2015--beyond the right of the plot, where the round-off errors dominate, the two lines would rise in unison). We have implemented symplectic correctors up to order 11. You can set the order with (must be an odd number), e.g.," ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sim.ri_whfast.corrector = 11" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "By default, WHFast does not use correctors, i.e., sim.integrator_whfast_corrector = 0. This is because the default is also to synchronize every timestep. An Nth order corrector does N-1 Kepler steps of various sizes, so an 11th order corrector done every timestep would increase the number of Kepler steps by an order of magnitude, making WHFast unacceptably slow. So keep in mind that if you're doing modifications that require recalculating jacobi coordinates or synchronizing every timestep, you should turn off symplectic correctors (the default) unless you really need the accuracy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the internal coordinate system\n", "\n", "WHFast by default uses Jacobi coordinates internally. This works well for planetary systems which are stable and orbits are not crossing. However, in some cases a different coordinate system might perform better. WHFast also support so called democratic heliocentric coordinates and the so called WHDS coordinates. For mor information on these coordinates systems [see Hernandez and Dehnen (2016)](https://arxiv.org/abs/1612.05329). To select a different coordinate system, use the following syntax:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim.ri_whfast.coordinates = 'jacobi' #default\n", "sim.ri_whfast.coordinates = 'democraticheliocentric' \n", "sim.ri_whfast.coordinates = 'whds' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that symplectic corrector are only compatible with Jacobi coordinates because both democratic heliocentric and WHDS include a so called jump step." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Warning messages\n", "\n", "If you choose a timestep that is larger than the smallest dynamical timescale and WHFast has difficulties to solve the Kepler problem, you will receive a warning message." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rein/git/rebound/rebound/simulation.py:305: RuntimeWarning: WHFast convergence issue. Timestep is larger than at least one orbital period.\n", " warnings.warn(msg[1:], RuntimeWarning)\n" ] } ], "source": [ "sim = test_case()\n", "sim.dt = 1000.\n", "sim.integrate(1000.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
nickk752/presidential-data-science	Author Attribution.ipynb	1	391378	{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import string\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import nltk\n", "import glob\n", "import os\n", "import pandas as pd\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.cluster import KMeans\n", "from scipy.cluster.vq import whiten\n", "import re\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import make_moons, make_circles, make_classification\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.gaussian_process import GaussianProcessClassifier\n", "from sklearn.gaussian_process.kernels import RBF\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", "from sklearn.model_selection import cross_val_score\n", "%matplotlib inline\n", "\n", "def token_to_pos(ch):\n", " tokens = nltk.word_tokenize(ch)\n", " return [p[1] for p in nltk.pos_tag(tokens)]\n", " \n", "\n", "def corpustovector(corpus):\n", " # create feature vectors\n", " num_tweets = len(corpus)\n", " fvs_lexical = np.zeros((len(corpus), 3), np.float64)\n", " fvs_punct = np.zeros((len(corpus), 3), np.float64)\n", "\n", "\n", "\n", " for e, tw_text in enumerate(corpus):\n", " # note: the nltk.word_tokenize includes punctuation\n", " tokens = nltk.word_tokenize(tw_text.lower())\n", " words = word_tokenizer.tokenize(tw_text.lower())\n", " sentences = sentence_tokenizer.tokenize(tw_text)\n", " vocab = set(words)\n", " words_per_sentence = np.array([len(word_tokenizer.tokenize(s)) for s in sentences])\n", "\n", " # average number of words per sentence\n", " fvs_lexical[e, 0] = words_per_sentence.mean()\n", " # sentence length variation\n", " fvs_lexical[e, 1] = words_per_sentence.std()\n", " # Lexical diversity\n", " fvs_lexical[e, 2] = len(vocab) / float(len(words))\n", "\n", " # Commas per sentence\n", " fvs_punct[e, 0] = tokens.count(',') / float(len(sentences))\n", " # Exclamations per sentence\n", " fvs_punct[e, 1] = tokens.count('!') / float(len(sentences))\n", " # Colons per sentence\n", " fvs_punct[e, 2] = tokens.count(':') / float(len(sentences)) \n", " \n", " \n", " # apply whitening to decorrelate the features\n", " fvs_lexical = whiten(fvs_lexical)\n", " fvs_punct = whiten(fvs_punct)\n", " \n", " # get most common words in the whole book\n", " NUM_TOP_WORDS = 10\n", " \n", " translator = str.maketrans('', '', string.punctuation)\n", "\n", " all_text = ' '.join(corpus)\n", " all_tokens = nltk.word_tokenize(all_text.translate(translator))\n", " fdist = nltk.FreqDist(all_tokens)\n", " vocab = sorted(fdist, key=fdist.get, reverse=True)[:NUM_TOP_WORDS]\n", "\n", " # use sklearn to create the bag for words feature vector for each speech\n", " vectorizer = CountVectorizer(vocabulary=vocab, tokenizer=nltk.word_tokenize)\n", " fvs_bow = vectorizer.fit_transform(corpus).toarray().astype(np.float64)\n", "\n", " # normalise by dividing each row by its Euclidean norm\n", " fvs_bow /= np.c_[np.apply_along_axis(np.linalg.norm, 1, fvs_bow)]\n", " fvs_bow = np.nan_to_num(fvs_bow)\n", " \n", " \n", " tweets_pos = [token_to_pos(tw) for tw in corpus]\n", " \n", " # count frequencies for common POS types\n", " pos_list = ['NN', 'NNP', 'DT', 'IN', 'JJ', 'NNS']\n", " fvs_syntax = np.array([[tw.count(pos) for pos in pos_list]\n", " for tw in tweets_pos]).astype(np.float64)\n", "\n", "\n", " # normalise by dividing each row by number of tokens in the chapter\n", " fvs_syntax /= np.c_[np.array([len(tw) for tw in tweets_pos])]\n", " \n", " \n", " fvs = np.c_[fvs_lexical , fvs_punct , fvs_bow, fvs_syntax]\n", " cols=['mean-wps', 'std-wps', 'div-wps', 'commas','ats','colons','bow1','bow2','bow3','bow4','bow5','bow6','bow7','bow8','bow9','bow10','NN', 'NNP', 'DT', 'IN', 'JJ', 'NNS']\n", " dfCorpus = pd.DataFrame(fvs, columns=cols)\n", " print(dfCorpus.shape)\n", " return dfCorpus" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Tweets for Bernie-Sanders : 29\n", "Number of Tweets for Donald-Trump : 74\n", "(103, 22)\n" ] } ], "source": [ "nltk.data.path.append('N:\\\\nltk_data')\n", "sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n", "word_tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')\n", "\n", "def concat_tweets(tweets, n):\n", " concatenated = []\n", " for i in range(len(tweets) // n):\n", " appendable = ''\n", " for x in range(n):\n", " appendable += (tweets[i + xn] + ' ')\n", " concatenated.append(str(appendable)) \n", " return concatenated\n", "\n", "# Load data\n", "folder = './Campaign Speeches/2016/nltk'\n", "folder2 ='./Twitter/tweets/nltk/'\n", "tweets=[]\n", "labels = []\n", "row_labels = []\n", "for e, file in enumerate(os.listdir(folder)):\n", " with open(os.path.join(folder, file)) as f:\n", " newTweets = f.read().split('\\n')\n", " newTweets.pop()\n", " newTweetsConcat = concat_tweets(newTweets, 1)\n", " print('Number of Tweets for', \\\n", " file.strip('.txt'), ':', len(newTweetsConcat))\n", " tweets=tweets+newTweetsConcat\n", " labels.append(file.strip('.txt'))\n", " for i in range(len(newTweetsConcat)):\n", " row_labels.append(e)\n", "\n", "dfFeatures = corpustovector(tweets)\n", "df = pd.DataFrame()\n", "df['tweets'] = tweets\n", "df['label'] = row_labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train several machine learning classifiers on the tweet data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", " weights='uniform')\n", "Accuracy: 0.85 (+/- 0.00)\n", "\n", "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')\n", "Accuracy: 0.97 (+/- 0.00)\n", "\n", "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False)\n", "Accuracy: 0.88 (+/- 0.00)\n", "\n", "MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False)\n", "Accuracy: 0.91 (+/- 0.00)\n", "\n", "AdaBoostClassifier(algorithm='SAMME', base_estimator=None, learning_rate=1.0,\n", " n_estimators=20, random_state=None)\n", "Accuracy: 0.88 (+/- 0.00)\n", "\n", "GaussianNB(priors=None)\n", "Accuracy: 0.91 (+/- 0.00)\n", "\n" ] } ], "source": [ "X = dfFeatures\n", "y = df['label']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33)\n", "\n", "names= ['kNN', 'Decision Tree', 'Random Forest', 'MLP', 'AdaBoost', 'Gaussian']\n", "\n", "#Defines each classifier and puts them in an array\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " DecisionTreeClassifier(max_depth=5),\n", " RandomForestClassifier(),\n", " MLPClassifier(alpha=1, max_iter=1000),\n", " AdaBoostClassifier(n_estimators = 20, algorithm='SAMME'),\n", " GaussianNB()]\n", "\n", "allScores=[]\n", "# iterate over classifiers\n", "for name, clf in zip(names, classifiers):\n", " clf.fit(X_train, y_train)\n", " scores = clf.score(X_test, y_test)\n", " print(clf)\n", " print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() 2))\n", " allScores.append(scores.mean())\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use PCA to reduce the feature vector to 2 dimensions, train the machine learning classifiers on the result, and graph the visualization of each classifier in 2d space" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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YkVu5Y9qwYaxt9OSn3G5eyS+4ILrjamI0Q64nF13EW/kXXvDe/qelsfLATBMI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\nAAbo9esVkGOYArI0it27uQ2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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0b335c0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WODz1TTvSVHNJyaK2RqpbG2K2RJ8nn5M1q9n2yZuAOLFsbkM0NiE9ObByORhj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\n4FBKXrTU2tfjpqexmFPPrEPTLao3n2L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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0b165f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JmSCFNSMU1Q/fs8Np4s7JeCDnV0zQ8MBFek82koj6UIamYnsXldt7Mt20Neek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\nOEXDwYtUL9goYP7L0Bw8/Qp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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0cfd748>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IxZA+X7KhvrEJe90KGGHSI0UZTUYEcrTXTcvhCoKtObizw5QsbMRf2Dv2gecw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\nmSpjGlcga5qY8NfZc53bm/5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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0cb4ac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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44CwoRbR04KytINg7gO10ogfDSIeB4fNjljGeZme7XXG55nzGQikuHVSEnOaY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\nU0cCWWQl//Q2+j9oD7f9DJgJBsyEVBN9Jz67eHN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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a22edb38>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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FfsqIQAYwTPDlHHrjZSE5zvrtPw7DjqV2p978zXCueHAJ3uzulwPGHh1j9mte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\n1vp1qOZmhkzsz3HrlvJS6DS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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a4475320>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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a55vw489EUKlnnsH8zndQXR3pSphLFuG+51YIZp77nIjZ6E1JSF14Zm0vPnHx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\n20l6Qjk4mZ6TmFESUGLONR+uOB9OANr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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0c5fa90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA(n_components=2, svd_solver='full')\n", "pca.fit(X)\n", "pcavecs = pca.transform(X)\n", "\n", "#figure = plt.figure()\n", "i = 1\n", "# iterate over datasets\n", "h=.02\n", "# preprocess dataset, split into training and test part\n", "X = pcavecs\n", "y = df['label']\n", "X = StandardScaler().fit_transform(X)\n", "X_train, X_test, y_train, y_test = \\\n", " train_test_split(X, y, test_size=.4, random_state=42)\n", " \n", "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", " \n", "# just plot the dataset first\n", "cm = plt.cm.RdBu\n", "cm_bright = matplotlib.colors.ListedColormap(['#FF0000', '#0000FF'])\n", "ax = plt.subplot()\n", " \n", "# Plot the training points\n", "ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", "# and testing points\n", "ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n", "ax.set_xlim(xx.min(), xx.max())\n", "ax.set_ylim(yy.min(), yy.max())\n", "ax.set_xticks(())\n", "ax.set_yticks(())\n", "i += 1\n", " \n", "# iterate over classifiers\n", "for name, clf in zip(names, classifiers):\n", " plt.figure()\n", " ax = plt.subplot()\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", "\n", " \n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, x_max]x[y_min, y_max].\n", " if hasattr(clf, \"decision_function\"):\n", " Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) \n", " else:\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", " \n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n", " \n", " \n", " # Plot also the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n", " alpha=0.6)\n", " \n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n", " size=15, horizontalalignment='right')\n", " plt.title(name)\n", " \n", "plt.tight_layout()\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_politician_per_classifer(features, labels, classifer):\n", " lexical = ['mean-wps', 'std-wps', 'div-wps']\n", " punc = ['commas', 'ats', 'colons']\n", " bow = ['bow1','bow2','bow3','bow4','bow5','bow6','bow7','bow8','bow9','bow10']\n", " syntactic = ['NN', 'NNP', 'DT', 'IN', 'JJ', 'NNS']\n", "\n", " feature_sets = [lexical, punc, bow, syntactic]\n", " feature_set_names = ['Lexical', 'Punctuation', 'Bag of Words', 'Syntactic']\n", " \n", " accuracies = ''\n", " \n", " for feature_set, name in zip(feature_sets, feature_set_names):\n", " X_train, X_test, y_train, y_test = \\\n", " train_test_split(features[feature_set], labels, test_size=.4)\n", " clf = classifer\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", " score_str = \"%0.2f (+/- %0.2f)\" % (score.mean(), score.std() * 2)\n", " accuracy_str = str(name + ': ' + score_str)\n", " accuracies += (accuracy_str + '\\n')\n", " \n", " return accuracies" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "kNN :\n", "Lexical: 0.88 (+/- 0.00)\n", "Punctuation: 0.76 (+/- 0.00)\n", "Bag of Words: 0.88 (+/- 0.00)\n", "Syntactic: 0.83 (+/- 0.00)\n", "\n", "Decision Tree :\n", "Lexical: 0.79 (+/- 0.00)\n", "Punctuation: 0.76 (+/- 0.00)\n", "Bag of Words: 0.93 (+/- 0.00)\n", "Syntactic: 0.86 (+/- 0.00)\n", "\n", "Random Forest :\n", "Lexical: 0.79 (+/- 0.00)\n", "Punctuation: 0.67 (+/- 0.00)\n", "Bag of Words: 0.86 (+/- 0.00)\n", "Syntactic: 0.93 (+/- 0.00)\n", "\n", "MLP :\n", "Lexical: 0.86 (+/- 0.00)\n", "Punctuation: 0.81 (+/- 0.00)\n", "Bag of Words: 0.79 (+/- 0.00)\n", "Syntactic: 0.64 (+/- 0.00)\n", "\n", "AdaBoost :\n", "Lexical: 0.86 (+/- 0.00)\n", "Punctuation: 0.83 (+/- 0.00)\n", "Bag of Words: 0.88 (+/- 0.00)\n", "Syntactic: 0.83 (+/- 0.00)\n", "\n", "Gaussian :\n", "Lexical: 0.76 (+/- 0.00)\n", "Punctuation: 0.79 (+/- 0.00)\n", "Bag of Words: 0.86 (+/- 0.00)\n", "Syntactic: 0.93 (+/- 0.00)\n", "\n" ] } ], "source": [ "names= ['kNN', 'Decision Tree', 'Random Forest', 'MLP', 'AdaBoost', 'Gaussian']\n", "\n", "#Defines each classifier and puts them in an array\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " DecisionTreeClassifier(max_depth=5),\n", " RandomForestClassifier(),\n", " MLPClassifier(alpha=1, max_iter=1000),\n", " AdaBoostClassifier(n_estimators = 20, algorithm='SAMME'),\n", " GaussianNB()]\n", "\n", "for clf, name in zip(classifiers, names):\n", " print(name, str(':\\n'+ predict_politician_per_classifer(dfFeatures, row_labels, clf)))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now time to build the classifier for campaign speeches!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(103, 22)\n" ] } ], "source": [ "root='./Campaign Speeches/2016/nltk/'\n", "candidates=os.listdir(root)\n", "tweets=[]\n", "labels = []\n", "y=[]\n", "for e, file in enumerate(os.listdir(root)):\n", " with open(os.path.join(root, file)) as f:\n", " newTweets = f.read().split('\\n')\n", " newTweets.pop()\n", " tweets=tweets+newTweets\n", " for i in range(len(newTweets)):\n", " labels.append(file.strip('.txt'))\n", " y.append(e)\n", " \n", "all_text = ' '.join(tweets)\n", "\n", "dfFeatures = corpustovector(tweets)\n", "df=pd.DataFrame()\n", "df['tweets']=tweets\n", "df['label']=y\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", " weights='uniform')\n", "Accuracy: 0.79 (+/- 0.16)\n", "\n", "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')\n", "Accuracy: 0.86 (+/- 0.32)\n", "\n", "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n", " max_depth=19, max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=20, n_jobs=-1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=True)\n", "Accuracy: 0.88 (+/- 0.24)\n", "\n", "MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False)\n", "Accuracy: 0.88 (+/- 0.20)\n", "\n", "AdaBoostClassifier(algorithm='SAMME', base_estimator=None, learning_rate=1.0,\n", " n_estimators=20, random_state=None)\n", "Accuracy: 0.93 (+/- 0.14)\n", "\n", "GaussianNB(priors=None)\n", "Accuracy: 0.87 (+/- 0.17)\n", "\n" ] } ], "source": [ "X = dfFeatures\n", "y = df['label']\n", "\n", "\n", "names= ['kNN', 'Decision Tree', 'Random Forest', 'MLP', 'AdaBoost', 'gaus']\n", "\n", "#Defines each classifier and puts them in an array\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " DecisionTreeClassifier(max_depth=5),\n", " RandomForestClassifier(warm_start=True, n_jobs=-1, n_estimators =20, max_depth=19, max_features=None, criterion='entropy'),\n", " MLPClassifier(alpha=1, max_iter=1000),\n", " AdaBoostClassifier(n_estimators = 20, algorithm='SAMME'),\n", " GaussianNB()]\n", "\n", "\n", "allScores=[]\n", "# iterate over classifiers\n", "for name, clf in zip(names,\n", " classifiers):\n", " scores = cross_val_score(clf, X, y, cv=5)\n", " print(clf)\n", " print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n", " allScores.append(scores.mean())\n", " print()\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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YkVu5Y9qwYaxt9OSn3G5eyS+4ILrjamI0Q64nF13EW/kXXvDe/qelsfLATBMI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\nAAbo9esVkGOYArI0it27uQ2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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a0c97048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RINc2dHLH1x8Dpv6LPtFU15j84OFOfvg1L3t3OCgoMnnb+wK86fYAIsNXdvQd\np9jbJ47xf2dK8YJmWo5Wkmqd5JR0DVrpkXiuqfCMUzn1zx+g4te/RLoiiGllflGPh8Y3vY2y4nGa\n/vvqYWw/eQA5eBRVU435/ttQK5b2Pe1dWIPv+BEWVU+9h5wJW1eu5YI9WyjuasWM1VrurVlJS1HZ\nFEeWzKJBvv7VN6cX3Dh2twtn5by+f+vVWfoZ0cSQgqoadfHt30z60kO/PzmTBXo0JHrS8WuUWO0x\nnDj3tOax5ZfriYZtKNOGdeMKYpjMXV3LWde9TH2Hb8AU6Gz7sXTVn2DBt75K0aZnieQXcPK9H6Th\nrbdxfqltzD2SZedebB/8DwiGEKUs/9XlJPq9L6MuPCdpW9/xI9MqC3MHfLjCIbq9eRM2aWQwUY2T\nibimI1Fwh2K6vSejJdOJIaMS5EQSG9SkinQ2iUK2Eb/TgKGbKwW63bz04BX42pMHGQ17hDPXv4Kx\n+BUuubJ/wG46XXMzEhlzsyHbOz6Mse/ggMdVTTWRR36a9Fi8DjlbrIvJYjCLIM5gogqZC+tIyYaa\n8Mlk0mbqpROR+DJBidmgFuhkEu2EwSaz5HRV8+rTqweIMVgleEe21FCz6BUgWYgNXy8nn6jjRw/W\nsP1wORVzTN77MR/rrp15LRvlwCC3u0dqwTQtgzqGs3IenK7PKj95JAwnrIORahFMJYlCPBsy45Ey\n5gw5U9L5q1qk03Pv5lr+9lATdY9fg4oM/ptZWGzy5+0Jg2FKMe/ubzH3R9/HF7LjJMRveAvv46eI\nx8knvtzDDW8aXbmeUvDYb1w8dI+b7g6D8y4N8f5P+qmaN/qeEul6W4wU+9VvQdo6Bsabl0Pk2Ufh\n4FGkth7yc1GrlkNuzrBTpwu72inubCHkcNFQNmfcGw8NZRMMZxF4F9aMayyTSV8zp1koxJNmWYyW\ndD70bBdnw+8D08TMyeVDb8ln90uOIbZWnL8uxP/c39P3SPlvfsXiL34aW0JpiA8P9/FuPsT/UlRi\nsmFre3/SqBS5O7dT/PQT2Hp76Dp/Le1XXo2ZkzvgbD/8mpffP+Am4LcydcNQ5OQp7n+ig9KK0U0l\nV6ZCmdExCbJx728wfnx/cpN5twvztjeCCLL3AErEur/weoje9c8wf056UVaKC/Zuoby1EVEK07Ba\nXr6w5jI6x9juMi7CccGdzsI6UmazEMfJekFOJO5DZ/vg1FAoBX/9nZN7v++ltcngjLMifOjTPlad\nN3wPYEdxuXpoAAAgAElEQVRzI0v+7S4Kn38OUPSuWMXVR3/JS93pJ3MYhsJmgzUXhfnaj7vxxFyj\nc6+6CM+xgbfwftwU0oFyONmwtZ38QutCl/7xUcofeYhoTi7K7sDe1UFgwUKOfe6/UAl9E7o6hFvW\nFhEKJtsmDofijbf7+fBnRr6+Wzw7HvOK1KaJcffPMH69AWw2iEYx33ADau05GA9tQM2p6J973dEF\nxYWYn74LRAaI8oKG46w6uCtplhyAz+XlyYuvG1Wv5ESbIe7VZot9MNEkVk9MR4toPJlW3d4y8aGz\nXZwfvsfNPd/19mWQr2x38LHb8vn+Q12ctWYIUY5GWfXmm3DV12FEre1yd+/kSeMyFnKUDlJLwxQ1\nyyIsXhqlq8PgxFEbS1daAuJobUl7CgOTXHrwOYrx5lhibOvqpHTDo4Qqq1B2KxOP5uXhPlFL/tbN\ndF52Zd/+Rw/YcDjVAEEOh4WdWxxA5oI8QIh7htkhDYlfdLvbhfNj78N8/23Q0AQVpZCbg/HtH6Py\ncpNFtCAPaWiC1nYoLca7sIZQzFMGyPnUfw8QYwAPEa66ZQVm9dyMhCVVhGeLAEOyRwxaiEdKVghy\nOlJFOnWQELJHpCNh+MXdnj4xjhMMwE+/7eG793cPum/hpr/jaGnuE2MAUQqvLch75Jd8N/LRvsdt\nNkXN8gjLVvWLhsPZf6yu8y6g6NmnkZS7nmbK8LmLuPltfnp7hPxChetkPQJ9Yhwn6naTs/+VJEGu\nmGsSDg3MDsVQzFuYWU+NuD0BMc94FEIMyWVSi6otCyBy/IhlAdQkfBYcduu2ZUAgisTpfs7KecQv\noWEfxCKy2XBXzsVHstgm3oLHY4kzmyyJOFPZ1nSmkLWCnEpqzW42VXK0NhtEIuluZ4XD+4e+xO4T\nx5HowAzaEfLz9sv28OCRKE0NBoYBi5dGrGxYKTo7hIIikwWL+wWx9lNfoGDLi0gwgBGNYgJ+vHzY\n/r8sWx2h9oidr3w8l9JKk3fcUE61ObCdmhEKEy4tT4qlap7J2ReE2bXVQShBmJ1OePv7A0O+vgFC\nPEoSM6+4CB6rhWN3PxkTw2QRVJdcgPGzB1F5Of2VFi3tqJpqKCpIH+vrrkEdrU3yowHweuCMRXhT\nVumovhkSP22zUYQhvUc8XSbhZBvTRpBTSWdzvPDs1Ah0YcnglQZzq4fOIHtXrEoqzYoT8eaQf/Ma\nHnlDB+EQvLzZzu/vd9N8GlBCfpHi9g/3YDOs1o0AvqXL2fXHp5j3g++Qt3M7vurF7L/t4+RtvwL3\nq4rScoUY0NMl/PD+RSyZv5ZFdZsJVVSCYWDr6kLZbXSsu2JAPF/9326+/qlcNj3lRATyC03+7au9\nnLli6NenzOiYfeLB6oftkTCegI+GAw6qlcJ3/EifRaDOXYl5aC3Gpq0gBgiokiLMd75p0POYb7wR\neeo52HcI8QdQLicYBtFvfjbtezRbBTiR0On6vr+rb7426QeKWTyIN1qyYlBvIkit4oCJFegffd3D\n7+5Pti1cbsXXf9zNhZcPUf+rFCvfchO5r+zEFrQyM9NuJ1RRxY6nXsB09zeU6emI4m11kNP2KssX\n9ULN2WxvFyS1v24CjacMvvXZHMoqzSQ7tanB4DXXdvLPrd8hb8c2UBAuLePUP9+Jb/mKQY/n6wFf\nr1BSroYd4zIjkbEP2pFmNpdSzGmqZ35jXZ8t0ZVbwKHqpaz7+A1Aglg2NCH1p1C5OXDGYrAPM+PN\nNJEXtyPbdkFZCeb166FYLyiaKbN18s1wTKtBvYkgXQY9kRbHB/7dj8MJv/2Fm2BAKC4z+cjnfUOL\nMYAI++5/hPnf+xbljzyEhMO0Xn8Ttf/2uSQxVqbCm6sgN0Rv9WK2AbSaiDG0wHR3CoYxsEDA4VQ0\nd3mpv+uT2Lo6MYJBwiWlaTPBRLy54M1VA5r+p+ubIYaNba1R1g59BTIiMdMq6mpnQUMtAZfbWuxT\nKfJ7Ojm3J80kkapyVFX5wMcHwzBQl16AuvSCzLZv70Se2YSx5wCqqAB19TrUWWdm1erVk8Fsm3k3\nUczYDHk40mXQMPaGPKYJAT94vBP7nUz0Zg374L+r3V3Clz+WS1GpSeJmjacM3vyeAGuvCA+5gCxY\nP1zpno/7+vFJP+muXeIaeqP1kFMz5GVH95Lr6yYcG9HMy4XcBXMw2jrhe1+B3JxRnSdjWtqQF7Yh\nx+uQnXtRLgcUF0EohPT6MW/9J9TlF09sDFnGbOlJMVpmfYY8HIOV2o21m51hgHeC9QDom1ARqV7D\nttbIoKKcl6+45nVB/vp7Fzm5CrtD0d1hUDHPZM2FVva+aeOeQduH1rb5hnweLGGubfNx7+baAdcs\nHpcZibA5d/WoLAzvwpq+AbTaDU9iPxjua+JetdIqKTNsNmzeHKLB0MQKcm09tu/9DGX3QHMLkdpT\nRPIL6K5YgpnnRnKDlP5tC5E3vwd746sTF0eWEM+MtRiPD7NWkNMRF5x0NdCJZEOpXRx77U7IXY0y\nFccO2dmxxU5BkWLdNSHijsdrbglROc9k01MOfL3CJVcFufSqMAmOyJBimwnVxd6+65Xu+sSFeVtr\nBHJXjzhbjtcMV998LfaSIpxPv4BZXoJhs2F4c6C7B1VcOGgFxXiwOWcVi//wCE5HIZGiYvLqTmHL\ny8cIBXHX1+I/cxnK7aazs50jBxsJzl0NjK26JJvRYjz+aEFOQ6o4vfBs/+26qZjyUjuV4rWcW2Tw\npvfnsvFxaxkkm13x/z6Xw/d+1cXSlVFE4OwLIpx9wfCzBseCIf0+/WDWj2G3o0zF5tzVSY9nIlrx\nmuGQaWLuOYij249yO6G7BbEZRN/79mE98ExJjQ/AFgrhOXaEUNUcRATT68Xe1YXpcuNsacZ/5jLE\njCKAWVQ8ptea7ejBu4lBC3IGDJY9pquFhvFtDO/rgfZWg8Jiq3cEMKD/w6O/d/HsX5wEAzHTOjaj\n7j/el8ejz3eMl0YNS+IdRtz6ST/YJ4jR/9GL2xmJpLM24vWu3sVnwBf+DXPHHuTAEVRpMeZF50L5\n6NYViVSv6ft7W2t/CWOqDaQA5XQikQjK4SA4dz7O0w0YwSDRnByIRHCdbqD9iquI5hcM+1rPL7He\nmLFWoUw2iXXHmvElKwQ50O2m8eBcepoKcOf7qFh6ktzSwWe3ZQvD+dAw+gzaNOGJ3zt59i9OlLIE\ndt01IW54c5BUff35jyME/ANVt6dbOLSvf2r1ZBG/LrVt1rUY7hoMED5TJVVnpK4o0SfMl5yPumTY\ncZIk0opvq9kXg2Ef4tfLbqd9/bWU/PVPBKvmEs3No+esVeTt3kHUm4ujrYXW17yWpje/Y9BDJL7W\nlztjg57TSKD14N3EMuWCHOh28+pTZ2NGbNhdYTpPF9FxsoSaS/dROHfwddaylUSRHmw2IQz0We/d\nXJv0+Oa/O/jbBhdllSZ2uyIahY2PO8nJN7nqtSppgKzVHNidDawqj3B4ZKUepik0H6qi+XAVZtSg\naEEzlcvqcbhHbnfE/efhbIxU4pNJ0k2vjt8iX/mRa/HFp0xnQF8GnlAqKIZtyBrudDS94a3Yujop\n2Px8nz1S+4nP0H71dURzc5OaMmVCXKCVqdjebr32uEBnk7WRbqakZvyZckE+vX8e0YgNd541Bdfm\njBIJ2qnbWUPBnG3TupxzsNmEiT50aklZ/O9Tz14CUYNDzf1CaEYMXv2OnQd3/QMRuPTKFUAxTd5t\n2J0XEQkl1yTb7bBs1ciEtPalJbQeq8DhDSKGovHAXLpOF7Hsml3Y7KPrfRyvwsiEeJlcYpYYF93E\nRkBDkc7/HY34pkO5XJz6wF00v/FW7B3thMor+uyJsRCPLW5vpPOeB2M8Jt8MhR68mzymXJC7mwpx\neJKbpttdEQJdHqIhO3bXxA5ETTbpMuh0lQxtyoM9J4SR4D8qBYEuN9XFXo43RHn6kQZsriDFS/30\nHFlMtLkEFXHgdCoMG3zxez0MUaI8AH+Xh7bactwFvr4fQk+Bn0Cnl46TxZRUp+8mNxyZijFYGWOi\nz5qYJTor52FUr0nyedOjAOGX206OKDMfCeHSMsKlE7foaKr3nI7xmgk5FFqMJ5cpF2RnTgBfRy42\ne78omxEDw25i2CfX+5xsBhssBMivbKezoRhXbn/znrDfRV55O/U7F9F2aA6IAiXkVXSw9tYX6agr\n4/iBYgyPn4IzjvKT5/3scw7t3yb63YEuLyIDp0SLTeFrzx21IMPA0sGhSFe7nM7vHY743Uaqpx8n\nm8oXR8u2VpPzY774eAuzbiw/+Uy5IFcur+fQ31cStUexOaKYUSHY46ZqZe2gS93PBuasqqW7qYBA\nlxubM0o0bMOwKXJLu2nYW407vxexZg3TdbqQk7sWs/DCQ5QtOR07glDXTtp66lQhimfodmcYX0cO\nXc35OJxRckq7cLjDKFNw5428CX3i8Tdt3DNiAYwL7/Z2NWrLIfXuI17CmFq+OBzRoIOuIwuJ+j14\nKpvwzjk9rnbaaLL4eFldovc8XhlzvGmQLmubXKZckAsqO1h40QFO7lpMoNuBGIrKs05QdVb98DtP\nMyIhG20nyhFRFC9owuYY/Nbbk+9n+Wt20nykkt62PLyFPZTVnOboi0uxe4JIrBhABNx5Adpqy5h/\n7pEknzddPXWqf90XW9DOjt+vxd+ZA6aNIIqeljzyq9rxFvZSNK91HK/EyBgP7zfOUHclg9Fxqoht\nv74cZQpmxIbNEaFgThvnvWXTmJOGkdg56RBDkqanp5KpDw0DvWhd1jb5TLkgA5QubKZ4QQuRgAOb\nMzLqwaNspvFgFa/86SLLZoix5pbNlC5uHHQfV06QeauT7xWjYTuGkSIColBKUFEDhrh26fzrOLXb\nlhDs9oIZHxi02np2NxVw9i1bpq2XX9vmy3i2YTqUgl2/v5hoqL95fTTsoONkCXU7F1O+pIHW42WE\nA07yKzsomteSsUjXtvnGNLEo7TJYscqUuBAPd2cxmJg7K+cNbKeZwkgEW1semZEVggzWOnFO7+hW\nRM52At1udv/xIsyUFaR3/O5irviXx3F6Mn/dRfNbaNw/D1tBv4UQ9jvxFnVjc2YumqmZ4ulX5w2I\nD8CwKSJ+JxSMLZMDy8udiAG2wbjn0zcm+cejobcln3DAOeBxM2KndlsNLUcrQAliM2k9XkFLWSVn\nXL4XY5ikIp4Zj1SME5tKQfrVVyLVawb12lP37zsGDDjOcCWFNW91ZRRzJBAcUthTyVToZ6LIZ40g\nT2eUguNbz6D2pTMJB5wUzGll2VW7ya+0lqc/vX9+3+SOVI4+v5y5q4+TW9aZkSdZceZJOk6W4O/0\nYtijqKiBYY+y4PwjY/I07a70bUKVKdgGeW4kDNWAaKIZU5Ysg2e7YZ8bR1U7NqclcEpBd3MBJbs7\neEPHH6jsaOJ0UTnPrLiCY+XWa07sMph5XXYaEU5DJF6BEhPjxCn2mew/UjJdK3Dgz9nghE7XU/PW\nm4bdbqQiD9PDD9eCPA68+tTZ1O9a1Jdhtp8oZ+uvruDi258mp6SHSNBu2QkpmBEbJ3Yspn7XIjyF\nPZz3lk199diD4XBHWH7NLtrrS+huLsCT56e4umnMdxfV5x2hu7GIaDjhIyEm3qIecop6Mz6OMiHY\n68bmiOJwJwv5aAf3MiUYhI2POXnhGSeRMJxzcZjvfPAmPv6/fx61KOeUdOPwBol2Jn9VxB7Bnefr\nE2Ow/Pw10Ze5c+svMCrC+FxeKjua+OeND/CLK9/O0YpFmGpkszczXZ17c+7qvkkvyowm7Qdgr82e\nSSZDMREiD1bFSLoa9myrINGCPEbCAQf1uxZjRpInZZgRG0dfXMaqm7ZRuriRY1uXYoZTL7egojai\nUehpyWfHo5dw8e3PDHtOmyNK6aImShc1jdvrqFhWT/vJYup31CA263bb6Qlx7htfyPgYHaeKOLFt\nCeGgA5RQOLeF6vOPDPCfJ8K6UAoe+JGHPTvsFJeZuD2w5e9Ojuy388Mv38hDO2oHrfkeChE45/Uv\n8tJD1qBeNGLDZjfJr2yz7lCSlyTkrZ2P0Ov0gMeasdftyUMhXPvKs3zGUZGxGI92de4BS2aNcjHZ\nmYbd7UpaDTvbhDiOFuQx4mvPxbBFBwiyUgadp4sAKJjTRuXSehoPzCUadhCfuJC8g0FPSz697Tkj\nykjHCxFYfs1uFl14iI5TxbhyghTOa8nYBvF3eDny/HLsrgjuvABKQcfJUpRpsOSy/RkdQyk4dcKg\nu0uYM98kvzDzCoZTJwz27bJTObd/qaryKpPGUwZ7d9i5fW01mzbuobbNN2Rv50SiYRtdpwtxuMNc\n/sHHaTo4l2Cvm6J5LRTMbeXgs6vwtebhzA0gAmZYWBiqxVfhwkH/3UGjOClrtKqGhhPjAQ39MxTU\nJBtCi3AfiX1QslWEE9GCPEY8Bb0DxNjCJLe0C7DEbuWN26hYdpKGPfNpOVZJJDjwpsswFGG/E6ZA\nkOO48/1U5p8c8X7NRysRwB4bWBQBV56fzoZigj3W4M+BffmYATdf+NdlRMIR7P2FC3R1Cvfe7eHE\nERuGzRLnq18X5LpbQhn9KLQ0GtZapinb2mzQUGfA2n7PNpOBvvpd1bz61DkgJigDd0Ev5735eTwJ\ng5uL1x7gyPPL8bXnIqJAoLvSS7GtDR+W4PcEI+SEAiy97Jxh7wrinm829bCYbiT23IDpIcKJaEEe\nI05viIrldTSmVCkYdpPFF/evGCEC5UsaKF/SwOHnlnNs8zLMaEpWDeSVd05W6ONKqNeFpFQWiICI\nov1kMadeWURvwMQwTO79vocly6Lc8XEfzthA/W9/7qbuqI2yKivDjUTgid+7mLfQZOU5w1ePFJeZ\nYDLAQohGoXLuyMooO04Vs//Jc5Lez97WPLY9fBnr3vdE3/Gd3hDLrtmFvzOHaMiOp7CHFxov4Nbn\nf4cpQsDpwRMKUJ1j0HzLm4c8Zzwz1mI8cqa7CCeiBXkcWPna7Tg9Iep2LMaM2sgp6mb5a3aQX5Fe\nXKsvOMzJPQsJ9bpiX3oTw26y/Nodk1KDHQ0bdJyyKjXc+T6K5rZhc4xtmnpeZQedp0rAkzwFHlE0\n7p+PzRGhODdCTyDCgcbjKLWQlzc7WHtFmK5OYf9uO2UV/XaD3Q7eHMULzzhYeU4EpeDAKzY2PeWk\nu1NYeV6ES9aH+3pEz1tocsaKCAf22CkpNzEMaGs2KC41WZGBoCdyYluNFXsiyiDY7aarsZCCWPUM\nWOLvLey/o9kzbzkPr72Fmi17cLQYVNaUoz56K90XDL7UqxbjkZMqwtOhgiITtCCPA4ZNsezq3Zxx\n+R4a9i6g9XgFzUcqcef7ySkeaOg53GEuec+T1O1cTMuRKlx5PqrPP0LhnLYJjzXkd3Jw4yoCPW7r\nNlsJDd4gS6/aPaZKjZLqJloOV+Hv9OJwhzCjNqJhG5VL62g8NLeveiTXbacnEOHV+lPs2lrO2ivC\nhIKxaSgpGmizg7/XUujn/ubgD79y4/YqHE7466Mudj0Z4Jvl36So8TCdl17Be977ZjZsKGTjX5wY\nBqy9IsyNbw4mLVWViV0R7HXDgK7TIIYi7Bu89rau3YcZtrP9wBvwh+/g7LNKMZVw+dEQN10QGrBQ\nwHgsADupdPcgW3ciJ06i5lehLjwH8vMmNYTp5gmPFC3I40QkZGPL/evxd+QSDdsRw6RuRw1n37yF\n8jMaBmzvcEdYvPYgi9cenNQ4T+1ZQLDHjSe/f2JJoNtN/e6FY4rF7oyy9KrdtBytpONkCQ53iLIl\nDbhy/TQenJdkJeS67XT4Dfa1tANuiksVRWUm3V1CXn5sVRQFPZ3CNTeF8fvg8UddlJSbxBaapqjn\nFF2PH2I/h7nZ3EDxM0/y228F+D/fp7A5IBIWwiHhxrcEB8Q6XKVF2ZIG2utLUNHkr4cZtVGQ8KOZ\nbtpzQegyCvIrKF9qZfvRqGLjX1xUzTe5YJ0lwH11xcpk9d4NGfd0nlKaWrF99yfW2oUuB8b23fDk\nc0Q/8QEoL52w084kOyITtCCPEydersHXntvnOyrTQJkGrzx2Aevv+lPWNEpqP1GGMye51tmVE6Sj\nrhR10cGMqyqiESNWYWLVKotYbVMrl9dTudyqKIiGDboaC7G7wgS6PLjz/VY1QlRwGU5aI9u4d/Mc\nbl9bza3vDfCTb3toahDsdgiFhOqaKBdeHqbxlIEZpU+MUYqCFzehTDc7OZub2cAT/iv4iv8jBDEg\n9v098Iqdz38ojx883DWia+TO86NMg8RqGLFHqLl0X19tdVyMEwfqgkH43AdzKU6wXmw2a+XvTU85\nuWBdJMmeSF0JJZsx/vQ3VK8PKq2WowqguQ1jwxOY7xt8hZTRMNtEOBEtyOPE6f3z0049VqbQ3VyQ\n5DtOJWKYsVmDCbO4FH21x5nQVldC7UtnYEYNUIIrz8+SS/fjTsi6u5vzObLpLKJhG2bUoLc9l7Df\nidMbwlQKT56PlpdX8PBuk2WfsXP+ugj//tVeXt7soK1FWLI8yspzIjhdkJuvME1BKas1qK2nGyMc\nIkAhVVh3H/+PT+IjJynOSETYt8vO6ZPGkAN7wV4XnSeLcXhCiM3klT9fACrBXxCTvLJOFl+cfAeR\nWjURCQmmKQN6jdgdCr9PJqV/8UQhu/ehSgqTHywuQHbvHziSOgpmqic8UrQgjxP2wfpIKBnzgNl4\nUrakgYa9C/qa0CsFwR4PlUvrM/pO+bs8HNu8FIc73Oc5h3qdHN50Fiuu344YVvZ8ZNNZiGHizre2\nceX68Xd6qVpxnBPbzqDpdDGIwlSKT33Y5G3vtvP+Twa45nUDfeySMsVZZ4fZt9NOaaXCZrPTZeYg\nwHU8AUAjFWnjtTsUXR1C5dz0r+fwc8s5+uIyxFAoExQCZpoa8aZC/F0ePPn+QTu0eXMV8xdFaW4w\nKCjuF+XONmH9DdZdSbqVUKYFHg+EI1bKHycStSbAjFKMU+8QZlMmPBhakMeJBeceoet0ytRjTNz5\nPnKKs2fB1srl9VbP44bivgb3BZXtVK2oy2j/9hOWX5j4I+PMCeHv8HLouRWYYRsOT5BIyIanoF9c\nbXaTaNjOgY2rCfvduLyBvmnHwYDBww+GuOZ1NpYsT//jdev7AvzuPjc7X3Igkkt1YS6f7/gy1eoE\nADfyGIdZQoiUQTcFC5ekP2bzkUqObVmKMm0oM2GH1Ek7gGGPEujy0hK12pCmXU1b4I3vCvB/3/TS\ndEqwOxShoFBeFeUTl7xMQc/07JgHYF55CcaGJ1BVZdZagqaJtLRh/tN1IzpOYiasBXggWpDHiYpl\n9bTXl1C/c3Hf7b/dFeacNz2fVesC2uwmS9btx9+RQ7DHjTM3gLewN+MYIyHHgG3DfgdttWW01ZWi\nonYMWwRE4co5aXU9U9ByrIJAtydmBSgC3VY1hjMniNNlEvC5ue8vDfzX8vTr03lz4LYPBXh9dxC/\nHypDC1j1jmaCTblEI3BX9Afc63gfHTgJhyxLxuWBj37Bx4e+k76yonNrCSsjr3CaKgK46KIQS4wH\nirIZtdFua8SmBl/9REIhVh9+hm+c18kz6iocQeGsmh7WndOB1zO9W8qqq9dhtrRibHnZEuSoiXnJ\nBahrLhtyv3ije21HZIYW5HFCBJZfu4uFFx6ivb4EV06Q4gVNA0q5sgER8Bb14h3FjMCCqnaaDs2x\nbEPA15FD24lSS2ijloiZUTugaDlWRllNE4EuD4EuD/2lZNZ24YATuzsUWzZKcfDwEeDcIc+fk6fI\nyYOwmsvr1+zFfGILpZEGtnIhXUYhZy6PEIkI5ZVRbr0jwPef3pB2qvSixuN8+PTP+SEfpoh2vAQ4\nwXz2EhfbxAG9MCtvaOXC688cdOqz59ABVt56M0YwgE2ZXBSNYF6/HvO2j4/ZX80KHHbUbW8kesNV\n0NwGpcVQUjTo5joTHh1akMcZT4EvaXrtTCO/op2ieS2015XS05ZHMEloExFCvV4aD8zBNA3S2QCg\niATtIODN85Nb2dxXJxzu8XLrudfR0WawdFWkb4Avzu5tdv7+pIdAcH3/gwE4tM/OL//awVOnj/H9\np60G/KlibItGeOvm39NdkI+/yU0vuZjYWEAdjVTSQjE2TxBlCnZPkI9/2s6Nb3YgMkgfCqVYfsc7\ncLS3IqrfOzb+9nfUhWtQN1w9/IWdLhQXWf/FMU3o7gG3G19Dsu2lhXjkaEHWjAgxYNHFB/AW97D7\njxeQXoz7tiYSdCD2wbxTwYza8OT7WHnjdvIrnICTjlNF7Nl0Jv/zXIc12Ba1sWi1ne99x44rJsov\nPuMgmKZTaTgS5aOfPUjRygOD1hvPaT+NJxigpyKHs9t28WTkNbgJEMWgilN0lxisWB/k7m/FfwGG\nHpR1HzyAs7kpSYwBxB/AeOQxomMV5CO1GH95GoJh1FWXotasyIqsW/YewPz5g0hHFxgG3XYbO1zV\nmEa63i6aTNCCrBkxhqGIhuwYdhMzPNyXTxAEFRtATD6QyarXbaGsprFvFptpCrUvnUFeruqrXFEq\nwtHd8O6PvcL17y4GYH9bBUpcoFLOL4qyQoO5Q0z+UDExM2yKq5Y/QUttMXt61xDESTjXRtmCDrq9\nLwED++cOOJapkIAPY7BG9oGBE1NGgvHrDRj/81OrosE04ZHHMF+7HvPzH5sSUY57wuaxOtw/+y3K\n4+LU6V7ENHEHA1SXmBybv2TS45opaEHWjApnTjBDPRAUiqL5ls0hceFScNZ1O6g4I3lNwWCXh0jI\nntSoXwTyckz8p8t54VmrbCxkO47IP5EqgyJC+ZmnADje6qO3bi6cXoTdFWLuqlpyS7s5VVRJg82D\nq7OTgCuHty96gObgEwQ6vDyz9jI6l+Zwoj2UUd9mZUZZfslKcDiA5JW5lcuFeu36gTu1dXDwvt08\nsHkFPUVzed1tdq68tGfg9Wxpw/juT5FQQilgIIDx142oG69GnZf5AqZjJdUTlucOUdrRQtBl9X1W\nhqNj8/gAABmnSURBVIHf7aG8rYm6qmoiia38NBmjBVkzJD3N+Rz8xwo6T5bgyvNTc8l+KpaeonTR\naWz2KNGQnfT+cByT/LIuDFuUssWnCXS7UQiFc1uZu+r4gK0NRxSUDJhrYJoGxQVCpScfmzOKUWTi\nuWE7e/9yHmL0i/yaN2zG4Q5zotVP/RPrCTbOIRK0gZjUbltCxaVbKFx2hP8795+4v/dF9u46ivRA\nrjSx+fwr6FrqRaR/yamh6J/osZvo1/4D2ye+BFETCYdRXjdq4QLMN78ueadXD3Pfu3fzkdC3CWMn\ngp1fbPVzw5VtPPDtV5Nes7ywDWxpLKFAEHnyHxMuyKl9I6B/HbsVQT9RW+rdiaAEHJGwFuRRogVZ\nMyg9LXls/uV6omEbYBDyudn95wtZ2rObBecd5YK3/50dj15KsMdtTfKIgjJtMWtCrCWgCntYceM2\nXv7NOgJdXjAs66KkuqlPx4M9Lo6/dAZtJ8rxFvbg8AYIxUry4lOt/R1eOupLOPzcCgybyfxzD3PG\nFXspq2mg9XgFhs2kZGFTX31057H5RFvmEQnGTqIMVNSgfcsl3Pv95eTmKw4F30HRvj3YAn4+9PgJ\nWnMKocOfNAg4WJYcnwIdn+ihLjmfyO9+hrHhr9DUirr4PNSVl4Aj+SvW9Zkfc1foRQL0dzzqVTk8\n/pzBE8+XcP261v6NHfb0toQIOCdG8DJt3tOZW0BuU3eS8BpmFCUGQWdmi59qBqIFWTMoh/6xok+M\n45hhO4f+vop5a46RW9rNuvf/ld7WPKIRG3nlnXSdLuT41jMI+VxULa9nzqpaXv7tpQR7YtUYsXLc\nup01FFS1UzivlRd/cY217qBprdAhhknJwkaC3R4QRdjvoqc1H0wrI4uaBie2L8GMGiy/ZjeVy5Ib\n6te2+eg+Wk3AN1DMbA7Fy5vtXP6aMMrlpuec8wH4+sVw7+bavlVFEok3jhcjVtY3WLvMqnLMO981\n+AVt7+TJE2fhIJwkyAC9EQ+/eaIySZDVuoss3zgVpwPzxmsGP88IGG2dcGNpJeVtjbgDfsIOB4Zp\nYo9EODqvRg/qjQEtyJpB6TxVQroqCqWEQLcHb6E1/Tq3tH8mYuGcdtbcsrXv38EeFx31pVbmnIAZ\ntnP8pTPJO95JOODo7x0Ry2Q7T5Vw0bs2Egk42PfXc/vEuG//iJ36nYs584q9aaemrzunir8eU32r\nfRfQwSKO0azm43SlXyLz9rXVSXXG92620sP4is0qQRtH1S7TbselgsgA5xsMonhcKa8jL4foNz+L\n7VNfBUPAVGCamHe+E5aOftr1eDTvCTtc7DnjbKqaT1LU3Y7f5aWhbA4d+cWjjkujBVkzBO783lhm\nm4wyBacns97J4aDT8njTVI6F/U5ajlUkN/KJEQnZaa8tpeV4Jd3N6WfviUDI50qq+45ntze9JcjG\nx10E/Sbf4V/5AD8hhBNnb4i2v7yV45d+0+qCPwRxcU7MiCPVa6wnR7NuXV4O1559EnPHwNfrtkd4\n1z8NbNOqLl9L5G8PIc++CKEQat2FUFE2ipNbxMV4PGqEQ04XtXMXo0uNx48snEemyRZqLn0VI6WG\n2LBHqFpRO2Al6cHwFvVg2AeqsRhRymoacLjTC7syhYZ9CxAjis0ZgTRZJaJw5fqpa/dR1+7jRJuP\njgOLCT37dr5wVy7z/n97Zx7dVnmm8d93tdjyvslb7NiJ7ZA9wUlKIAHSQIZDWUIL09CFBhooA5TS\nToeWM+2ctswUSnro0Dml01LKMj2FUlqgbVIKpaSBwGRwyG7H4IV4ifclXmJZy73f/CFLsSx5iW3J\ncvz9/nKu7r26Pkd5/On53vd5Cz08oO3kDp7ExiDJ9GJjkOw//Jb5jz3C8UNm7ropiSuWpHHTxhT+\n8FwMMsTbaMOE21x3eEppbTE/uI+X0ncQTz8J9BHHGWKFk/u/eJKLVo4yvisxAXndFuSN10SNGCvC\ng5ChPoGjkJxTJC++9ZEwPo5iPE6fSqPlRB5Ck+QsbSApzLGejUcL+PDNlege72Zd7oo6llx52JtR\nMUFaKnM5tutjQ3GdGppJxxLr4uIvvkHnR1lUvFYaEMokTDrWuEFS8zoZ7LMx0JWIayCG4dUcwuxh\n0aXlFF5URV3XABs/vpz3nsul4nW7t6oCb710s8wmU7YFPZPLlkgyPQw6zt4z1ib5/F0D3HpvcMeJ\n4fFM31QPXaf/rXJ2vWXnTHo+W240Mz8nRJfLNONqaaTmhV0REWOTx0Neaz0pfd30xSfRkF2A2xLa\nKpoL3LDnpfellGvHO08J8izixBsraTyy0D/vTTMZLFxfSdHGynGunBqGIXD2x2KJdWG2Ti5KtLcl\nhZNlJTh64kgvbGX+mlqsNhdSQtXe5dSVlaCZdQx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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1a07400>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5U6xvfI6/7v0yvcFCONQ5U5A0TJORUGp9aaAgTGHNAMMdpSnVBppus2b3ynZ4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\n3YgavnX5qISszKtp70kO/uimWUNTmVR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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1a6bf60>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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q1DtDSAmDhxfS+8JqzGgAf+kAlRt3EKyafqqdsuggX3jye2xs24+Qkv1lDdwx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\n2L+1iJPb87FMjbqVYRpfNog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E7/6Yn4KiqYdaS1siLROzo3XSB8kcGM4qtuPjjCeLO57f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\nQShU7zmMzRW/EdGi1U2k5AzRdLSMwLAT/5ADb3+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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1ae0160>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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MhH7nR9EOHRu/fV0txq+/B6R3Sqh4MtH7QpGdLPrCkGSQ6c5yja/m8Lt/WT5O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\nM1o4xzbB9zScQh8aZMlGHxd5DuAnB4n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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1af4198>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6HCOc7iXbwRy2jD+Mdv2XoJ/yYbt8Em3wDxzFxCV6zoq9jg+6lqxbguwyhaJA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\nIu4l+8Ncd1dFq89qv8SqwuDLw9w8N8D6NR6MiHU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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1bc0f98>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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h9u1DL1iAdeut6NUjb1M/ZDspQWW/0Yjrv76H0XQM+6K12HfcBhVlk3ofA0E1\ncMGv1KZELsnq6g7DUBJO00jbEO41ScQnN1KhpPUYi7a/QqDPuQ7owEsFKJWur1EP/qsMTaDYSlu7\nuvyRn1Lc0YI35gSQmwR5hLiP2zGwsOKw89n0t/Ku2/smt3zvi3zg3+/iA/9+F+c8+zsMa/QZNUI9\nJo9+vZ5YyCQWTv6EXDzxb4swo97Bz999Ww4P/sw2ZSiUx82bP/sN4YbFWP4AOj8fXVZG/Oc/Rzc0\noHbtwvX3fw+7d6Pz8+HIEVz/9E+oF17IaBuu73wXz0c/gfnyK6hjzRgPP4brvR+BjqnpMzwRVCuG\nTE47HQZnTK9nsEYlRDqSNjliz/OFPPOjGqL9JihYdWUnl36oCVcWN8bzhoPc/s3Psmjnq1guN654\njE1vv4Wt8/8l3QxMABgum4LyOPMawkT6TNqP+Ji3cGhH/1nPP4E7PrKfyUeEFexku16V9hqriqMH\neftD3yccyKO7rAIzEWfts7/DtCxeuvLP0u+HzcWg0xd273NFnHl155AvScP7r2azZhVZchqv/el5\nfPv2YkTCBJetBJeLNYB5//1orxfKy52FS0vRLhfmffeROO+8saeo6OnB9fWvDxmAoeIJdDAMj2yE\nD71zSso/k/1TUpsSmZA5SHLA0e15rP9/Cwh1u7ESBlbcYMfTpTx1T01W63nP97/E4h2v4IlF8Yf6\nccdjXLj+l3ww8mN0mpO+MmDx2l7qzujHl+8kTKh75HeWyCh3RTWx6KMAt8dm2UU9I54/84UnsVwu\nooF8UArL7aG7tIIzXnwKVyz9vZSiIRMrTQ3SihtEgiMHIeR7XEN+UmtWs1K7UorIaUsJnXEWyuMG\nnCbA7q07oLh46LIFBahjx5xZaMda5fbtaYepq1gMc8NGEvWrR9y+fjI8VbVDalTTSWpTYiwSUDng\nxf+rIBEbeigSMYPdm4qJBDM7RJ5wiDNeeGpETccbjXD1n+7jyjuPYrptTI+NYdqgbCoWhfAmg0lr\np8Evv3Tkvas2XnsrUa9/aPkw2cFKjnvrWHZpNzUrgyNeV9zWRNQ39HW2y4VhJfCF+9O+jwVn9mN6\nRrYzmm5N/ZnpX5MqNayAWW8GVIZCGYro/Fq6WrtoC6bs31AIXVY26oXDg6qqnJlsh9FKoRc2DPZT\nTUdQwfQ3+xUvXTwkqIQYIE18OaC7Of1N3QxTE+py48sb/86t3kho1GaiQH8Pq67opv6sfvZtKSKR\nULQf8BME8BvTAAAgAElEQVTuM7ESCjQEu9yU14cprRu5rVcvuZb6PVu54E+/JuFyg61pd1fwpYvv\n4d2X7cd0wcb/raKv3UNJTZTll3ZTWhOlqX4JK17dTCxlLjZ3LELMFyCUVzhiOwBVp4VYtKaXgy8X\nEk/Oaej2Wixe10vlaIMwRpHaFDjbzYDtN97Mgm99FW2atOl8jHCY8lA31ic/Oe4MtHrRIuy1azFe\neAGVGlR+P4lPfQqYvlF/MznaL3WknzT5CcjyQt3KJav0bd94cBqLc2r6w7fq2Pt8EdoeWltyeS0+\ncu8OXN4MjpHWfP6vrqako2XIw7YyeO2iq7n/018b8ng0aLBrYzFHtuajDFi4uo9ll3TjHmNbxe3N\n1O/dRm9JOQeXrQalOL4rwAu/rMDts3H7baJBE23BJR9sZqG/kRv/5+u441GC+UV4YhG84SDPvuP9\n7D77otHfig37Xihi+9MlKGDlFZ0sWdfLVE2KnXqh8EyGVcFLW6j45QN4W5uJF5fScsPN9FxyBWtq\n04f1EF1duD/wAYwNG8DtBreb+Le/jX3LLWkXH7jgdyqHps/URb5yce/Jb1pmkpCAmh6dR708+HdL\niEeNwQECLq/Fee9q5byb2jJez/JXN/Ghb34WVzyGoW0SLjcxr59//+aDdFZmfy3OeLSGP32vlnhU\n4QmcaJYL9bgoXxDhwltbKOpo4exNj1NzaDe9xWW8ceHbOHLaGVNelokYPqPFRMLK1dFO2fo/kPfm\nG8TL59Fx9TsIrTx99BdojYrFnOujlMp+SqXWVlRXF3rx4nGbBqdrVoqBoJrua6dkqqSTlwTUHNN+\n2Mem+6to2hMgUJRg7btaWXFZd9Zz41Uf3MUVv/1f5jUd5sCKc3j2+vfTU1Y5LWVOxBSPfqOe/LL4\nkHJaCecmje/43JFp2e50mEitytXZwaIv3Y3Z20OiqAgjHMaMRDh658foveiyrLY/6Ylqx1p3qNe5\nd31rG66ug1CQN+l1znRtSkLq5CIBJaad1vD4t+vA0EOaBiP9JoXlMS69vXkWSzcx2QRVxS9+Rtkf\nHiFWfWK0pREOo+Jx9v7H99Fud9bbn46gUhs2YP7wh6jebmckzOmLsN9zPfjS931mYyZqUxJSJ58Z\nm4tPnLqUguWXdvHa78uhKIHLo4mHDeJhk+WXjhx2PhdkM7Aib/s2rMKiIY/Zfj+enm5cXR3EK6qy\n3v7wef8mFFItLZi/+hXq5ZdBKdSePejFi9F19RANo97Yiy55BXX9hdmvexhPVe20D6AYHOGHBNWp\nRgJqHOvXT899eqbC2942dUOKJ2rhOc7Q710bSgh2uAgUJzjrmhYqFmU34i4XDYRV6sXAqUEVq6jE\n23QMKz/lOjHLQiuFlZf+2rFMDQTVkPn+MtHRgetv/xbV24suLUW9/jqqpQUKC9F5eeD1oxfUY27c\nROwD78PVdWBS5YSZmy5JRvmdek7ZgMo0eOpKcvd21Zm+h8kGWddxD13HvZTWRikeNnO5UtBwbj8L\nz+7HSihMt57ye0rNttGCqvPq6yh6aQtGMIidlweJBN7mJjrf8nbsSQbUAGWokRPTjsF4/HFUTw86\nOUGtAsjLQ+3d6zzmcjmzrrvcqN6+KR+SPhO1KQmpU8dJHVBjncBzOXgylcl7aOwKjbkfxgqvRFTx\nu2/Wc3R7PoZLY8UV9av7uPazR0ZMwaQMcHky78+ci0YGlZdPfOzTVD1wH57mJjAMOt52La033zal\n282m2c948010wYnn9bx5qJ4e55tEOAwFBc4Fwnl50LAE5XYPXjslISVyzUkxSGK0E/DJEELTqbEr\n/ezl4ATXU/9dzfYnS4fMgm56bFZf084lH5h7AyCm2sCACmVb/MXSAqy8PGz/9H/mBgZSpAsq8/vf\nx1i/Hl1d7TwQi2E89xz09GBffDEkEqholMTnPoe+7MRIw6m+bkoGT4ixnBSj+MJ9Jj3NHjx+m5Lq\nKMpIH0YSROOzLUXH4Qo6D1WA0pQvaqG0ri3txa+NXSG0hl0/vg2dGFnJ9gQsPvrT7TNQ6rlhNi78\nHW20nzp0CNdnPoP2+6GoCOJxVGMjuqYGSkrQFRXY73gHeuXK9OudY0ElITU3zemA0hp2bShi08Mm\nSmm0VngK+pm35g0aqkdOGCrGpjXs37yC7mNluLxx0JCIuilf1Ez92n1p+4y0hvVffzdpp0E3LFb+\n5c8m3bcV7jPp73DjK7AoKBs5B+BcM5tBNSSktm3DvOce1JEj4PViXXst9p//edoJZ9OuU0JKTLM5\nOcx8oHYUbi2j9aUyXHkhCvxOIEX7ConvPguq35zNIs5J/W1FdB8rxVcYGgwjly9Ox6FKKpcex188\nsqlPKSiu6aD7WBlDQ0pTWtdOXUlgRG0208DSNmx/qoR9LxShlPN35Wkh1tzQNuZUS7lueB/VTIRU\n6iCKgZDSZ5xB4j//E/r7wedzpkbKZp2BwjnVLyV9UievWQ2o0Zrr9m1fSF5A4/GfqC158iP0tRUR\nDXrxZjB5qjgh2OmMKEutKQ38HurOTxtQACve9hov/uxybMtAWybKsDBdNive6hy34U2rmQbWkW35\n7HmuiPyyOIbp1Naa9wTY/mQpq6/tmMhbzCmpM6nD9Nem0g5JV8oZEDHRdUpIiRww4wE1/CSWrv/I\nipsoY9goMQUojW3JHUKy5fbHRrlhoXaa/HBCIthRQCLqJlDSjycQo7Cyh4v+Yj1HXl5CX2sxhVVd\nLDh3H76CSLqVDTmWw0cPpobV/pcK8eZZGMnvH0pBXmmcw6/nc/pbO7O6SWMuy/e4Rr2Gajqkq01N\nan0pt/GAyTf5SUiJbM1IQGUSSqlKF7Rx+KViXN4Tc7zFoy48/hi+grl/AehMK5rficuTIBbyOGEF\nxIJePIEYBRXdRINe9m1aSaQn4ASZhsrljdSccQR/YZhlV27Leptj1a6O7Q8wv6p0yPPKAG0r7ISC\nkySgYOab/aY6pGBqa1MSUiIb0xZQ2YZSqrL6NjqPVNDXWoRh2mhboUybxRfsOukuAp0JLo/F0su3\nceiFpYR7nIlC88p6WXjeXgxTc+jF04j2+fEVOuGvbUXzzgXkl/VTXNM5JWVIPf5tRb0c2FqO6YtS\nv8yDy2MT6TMpnh/F7Rt5s8KTwXQ3+0WjsPVFN6ZLc+aaBC73BGahGIOElJgNUzqKbzKhNJxtKXqa\nSulrLcITiFJS1y59T5OkNcRCXpTSuP0xlIJo0Mubv1+DtyA8JPxjIQ8F83pZcsmOKS3D/s3L2f/c\ncnRKU623rBNPQZB3fTZMSfXIO8eebAZG+01VSG16ws2XP53vDDjBmSjiq/f0sXpdYsxrpiZiKkf4\nyei+U9eMDjNPDSa5Jmn6BDvz2fmn1XQenofpsqg58yCnXbYd0zXxWkek18/2x8/GVzi0XykeceMv\nCrLsiqkbNdnbXMwL91+OPfzaKsNi8U2P4C3pG3woF+YZnG5TEVStTQa3XllMNDK0acEf0PxmSxd5\nBRrb0nS0KVaU5lNb6wTYZEhIicma9mHmEkozKxr0suV/ryQRdQEGCcuk8bXFBDsKOfc9mye8Xm9B\nGE9elHjEjdt3YsBEIuKm9PTMb5aYiaaddWkHuZimjT9YS80i5yySOsDiZA6q1EEUEw2p9b/1YI/y\n/WTDeg/nXxbjZ/cEOLTXQClYON/Fpz5tceaZE+/nm4vNfWJuynpI3Pr1rw+ePOpKAhJOUyTYmU/H\noXnEQukvpmx8bRFWwiT1kNkJF51H5tHfMYnhxAoaztuLtgwivX4ifT4ivQEKKrspWzi1AaVtNXjH\n4LTPJQ18rgautUr9zJ1shvdNZauvWxFP0ypqJaCvR/GjbwdoPGgyb75mXpVNa1+cr3zZpKUluWBX\nF2rvXujtHbmSMQwf4TcZJ2ZDXzHpdaVTvHQxDTNz3bSYYlnVoHp7QpQjNaapFI+4ee1XF9DTVIph\n2tgJkwXn7mPpFduG9An1NpWgrZFtM8rQHHl5MZ68KEXVnZQ3tGQ9kCR/Xi+rrnmFzsZy4mGn76lw\nfheGMbWj6aqWH6Xx9UXY8aEfO20bzFvSlPY1A5+1uVqrioUNEjGFv8BKO60UTG4AxbrL4vz6p37C\nwy5lUwZU11u8uNFNxfyBKpYirwBajifY9LTFezrvwVi/HgwDtMa64Qbs97/f+TsDc6kmBcigiTko\nqxqUx2VIOE2xbb9bS/exMuyEi0TUg51suju+behJqqCyG8O0Rrzeirk4tq2e/ZtW8sbD57PlJ1di\nxbPvZPAEYlQtO07d6kMU13ROeTgBFNd0Urf6AIYrAcpGGRaGK8Hyt74+7gCY1Nr68BpVf6eLzmNe\nErHcGeIZCxu89HA5j/3HAtb/Vx1PfK+WtkO+MV8zkdrU2ecnWHtxDH/gRDufP2BzzbujlJZplBp5\nHF0u4JEH6Hv4EXRVFXr+fHRFBeYvfuEEVhbmSk1q8KaHUpOaU7IaJFE0f7G+4PavT2NxTi3xiJun\n//MdaWtG+fN6uOiOJwb/jvT52PzfV5OIuThx1e3AsTtxYjZMi4bzd7HkkumbRXqyepuLad07H2Xa\nzF9xlEBJMOt1NHaFsKJu2l9fRYFaiDI0hglnvK2DhWf3T32hs/T8zyto3hcgrzSOYUA0aJCIGVx5\n5/Fx5x3MdvCEbcPTf/Dwx4e9uNya626OcuGVcfp6FF/5TD4l5bYTSjj9iy3HFA/GbiZQE0B7vMzL\nS06F1NcHfj+Je+7J+v1O9cCJ6Rw0IbWo2ZfpIAmZlmEWJaKutN9wAeLhofOn+QoinPe+pymu7QB0\nsjalGT5FhG2ZHH8zt78mFlZ1s+SSnSy+cPeEwgmcGlV8z1lEOkrpCjfTFWrB7bd47XfldBzxTmid\nWkO41yTca5LF97YR+jtdtOwLOFM5Jf+HefNstA2HXx//RoaZ1qRC/ZCIOy1yV70jxjf+p49//UE/\nF13lXOBeWKx52w1R2psNutoVvd2K5mMGy1dGKfIEByePbQsmA9PrRXV3T+g9T3VNSvqjBOTYZLGn\nGl9hGJc3Tmz4sGtlU9bQMmL5gope1r3vGbSGaL+XjT+4Fntkq98o0xqdXKJBL73NJRSXhlHKRX80\nQXNrOyX5lRx8tYCyBU6ToZWAzqM+7ISipCaKx59+yFtfh5tXfltO13En3EpropzzzvYJzbIeDZoo\ngxF9gaZbE+zMbOLW1JAaXpN69XkX37g7n6ZGA9MFV98Y5VNfCOJN04L4lutj1DVYvPCsh2gEzjov\nwdnr4oS/tRLfoQMkysoHw1i1tzv3jJqgqZ6/T/qjhNSgZpFSsOqaVwf7ZMBponP74mNeIKsU+Aqi\n5JX1caKZz2G4ElSffmgaS50bnH62E7eXz/e6yPe6aO/oYuc2Z8LZruMe/vifdWz+WRXPP1TB49+p\no/HNkX2oiZhi8/2V9LZ5yC+Lk18Wp6fVw+afVZKIZ5/2BeVxUBorMfRxK2owryG7qbryPS7u23J4\nsDa1f5fJXXcUcvSQiWUpYlHFHx/28qVPpq+ZKQXLz7D44MfD3Pm5MOsujePxQsttHwSl8DQdx93b\nTe++Q+i8PKxbb836/Q7ZXrImNVnSHyVAAmrWVSxpYt37n2b+ykaKqtupX7uHi/5iPf7C8U9kZ77z\nBdz+GKY7DsrGdMcpqOymYd2eGSj52LR2ZgOZTFPZWHwFYUyPNWJAiEf78Fe28fhjW3n42x60TTJ0\nEngCFq/8toL+zqE11tYDfiJ9LgJFCZRyTuqBogSRXhet+/0jth3uNWk/4iXUk34wisdvs+KyLoKd\nHsJ9JrGwQV+bm/zyOHWnZ98/llqbeuAeH7Fh40liUcWWZz20NmX+3zlS38CBr3yD9mvfSWjREtpu\nvJmX//5fYOBOvJOgAoVTOmhiOgyElMht0sSXAworezjz+peyfl1+eR+XffQPtOyuIdIboKi6k9L6\n1lmdr1BraD9UQdOb9cRDHrwFEWrOPEhJ7dTM6TfAMDX15+7jwPPLiUc0hsvGirkIlPSzdFUPwc4q\nGuMe2rraqM0rA8Dl0aDh+K4ASy88cd1PNGhipwlSrZ3nBtgWbF1fyqFXC50badqKujP6WX1dO+aw\n/0mnXdBLQXmcAy8WEQ0bLDq3l4Y1fbh9E0vsgYt6X37DwrZHHmC3R9N01EgZUj6+Q+H5PB/6EJ3a\nYKknwRpXfEonmZ2q4edlZ0/fTBODTX1aj2yTFbNOAipHWHGTvc+u4tibC9GWQfniJpZfuXVwAtfR\nmG6L6tOPzFApx9d+qILDLyzFHYjiLQxjxVzs37yS0y57k6KqiXXAj6akroMV+a/TfqCSWMhL0fxO\nSuvbMd0WdsLE7zJJAEePOk1+tbVloCARHVrTKKqMOZO4p5yjdHL8SVHliatgD7xUyIGXisgvjzmX\nDtlw+I18lre/xlde/xilbU20V9Xx6Ps/zY5zLiERMzBcmtKaKPWr+/EGJjcRbr7HReVpQXoaAyQS\nQ0+m8ZiifnG6Dsn0drxucu//C6AUeHya3W+6eP4ZDx/7hyAvM/mQmgv9UcVLFxPd/BJn7j5MIBIk\n7A3QWLWAzuLyKd2OmDgJqBygNbzyi4vpOV6CbTmHpGV3DV2N87jkzsdxeRPjrCE3aA1Nb9bjDkRx\neZyTpcubQNuKpu0LpjygAAIlQRace2DE4/nlvSilCZgeDJdNfzRBY2MHxb4qKhcPDf2SmijVK0Ic\n25GHN8/Z19GgSe2qICU1J9rT9r1YhL/oxMg8ZcCC2CHaH49SYR9GAfMb9/O+b/4d6xa8ye5jdcQj\nBqZHs/lnVfzZ3YeoXTWxUYsDzr+xgz0byiBhMDAaxuvXXHdzhOLSzGpntg3/979+8gpsAs7k9hQW\naVqOG2x51sNbr49OSU1qIKQma+Ai3qnmPtrMvD2HiHji9Gg/LivO0kO72FO/jM6SeVO+PZE96YPK\nAT1NJfQ2nwgnALRBIubi+PYFs1ewLGlbEQ95MN1Dv8mbngThnolf4K21MxVUX2sRVnzsj6wzC76z\nPyuWHSUW8hLp9eOK5ZPozyOat5PSuqGdOErBmhtaOef6NvJLExSUJTjn+nbW3NA2pNUnHjYwXUND\noGH/G0RsHzpl6OTP4+9h/4EK4hETUFgxg3jE5Pf/Vp9+1GWGrLjiuQersBMqeUNPjc9vc8enQnzq\nC+nvipxOZ7uir0cNhtOA/ELNm6+6Bu/QO1Vydeh53uvbsb0ebI+bggJFwuUm7nazoDl3WiROdVKD\nygH9bUVoRn77teMueppKgZE1hFykDI23IIIVcw2p9SWibvJKJ3bxbLjXz/5NK4n2+0Bpp+9p7R5K\n60beGj7S62fPhlXEQyeugyqs7iBQFERbJkXVnbi8MR74WjWx3gJWX+xnybo+CsrjmC5YeHb/mBf5\nzl8W5NiOfPJKnaHnyrbpjhWylhcxUo7fvXyIsE4zWjCuaD3op2pJZiP5wn0mfW0eCiuj+PJsNj9Q\nyb4tRdiJEyGtNcRjGc9OBIA/Oe7Dthi8qzFAPApFxc77UIaa0lpULjb1ubp6sQI+PH4fsR6nppcw\nXfgjYemTyhESUDkgr7Q/7aVLhitBQXnPjJdnopSCmjMPsn/zSrStMD0JElE3dsKk+vTsLzrRNuzb\nuJJE1D3YF2fFDQ5uWYa/6LUhIx21hgNblpGIuk7ceFFDz9Fyyhe2UVLbQev+Snb88iKshInGZtMD\nsHtTMVf91XHmLUx/G/tUKy7rpu2gn/52N6bHGZThctnckfjxkOUMRulr0pldomYl4Kkf1rBrQwmG\nqbESiuWXdrH3uWISsaFJFI0ofnmvnw9+fPzyD8gr0Jx9fpxXnnMzr8rGMCAWhUhEccnbhs48ezI3\n9SXKijG7erADTmIX5EO4O0HYF5BwyhHSxJcDimvb8RcHUUZq+4/GMG2qz5xbVxOW1HZy2qXb8RaE\nkzWnPpZduZX88r7xXzxMsKNw8Nb0A0y3c/LvOlJOImbS11pELOwh2u8j3JM3ZFmlnObFtn1VNO2s\nYdcTq8EGbyCGL5DA8MRpO6p4+Tfz0BmMX8grSXDFncdYdVUnlYvDrLiim+vf+yaV3qGzvn/A9RM8\nrpFTjLt9NhWLxq89Pf9gFbs2lGDFnaZBO2Gw4+lS4pH0/117J/Ad5sb3RThzTZz2FoP2FoNQv+Km\nD0ZYuurEZzAXm/pg6q6NCp69CiOWwAhF8BQUYMTiuONxjlTNnWb1k53UoHKAUrD2tmfZ8cdzaN1T\ng9aK4poOVr39FTz+uXeH2aL5XRTN75r0eizLSFvlUEDTzlq2P34uytDOqMdFTWlDJtzrp3XvfAyX\njR03QYFyhTBdNh63IpKAw7sShPtMAkXjdxD58mxOu+BEbWCrfieFniBX//IeAv299BaU4r11IQve\nCHHoNRM7oTDdTtXp+r87POqM5gO0hjceK0XH7eQ7HRhWqBh+UfaAeUuyH3jhD8AHPx6huzNKsE9R\nXmXjTTND1FQ39U3WVNai4vMr6L76EvJefRNXZw9Rr4feC5bQFS6bkvWLyZPJYnOMtkFrhWFO0xWu\nc0gi6mLro+fh9sUwkncN1jZ0NZYT7g2gUweVGAn8RWEKK7px+50+oljITeueGkY0FCibQEk/SiXn\nQ/QHWXj9E7z9utMnVM5In8nj36mldaubmOnFE7C56q+OkleS4Oj2fPwFCZZc0JPRMPOa3W9ScPcz\nPMitJHBxmHqOUocTVBoMUGi0baAMjctjc/NXDhCo7ctoclnv0UYW/Nu/ULzxaazCIo7d8de0/Pnt\nYzZpTeVt43Wod9J9UdN5F16ZTHZmyGSxc5QykHBKcnkT1J293xmJ1+ejr7WQ49vrCXUVDA0nANtF\nuCuf3tZiwt0Bwj1+Oo/MI20VTIMdN7FtsC2D6qWtGO7EhG+K+NuvLuTItgJCVoBEzCTU7eax/6jH\ndGvW3NDGqqu6Mgqn+Yf2cN0vvku5v4suSjCxOINt1OGMKvMXJqhZFmTpxd2ULwyz4vIubvvm3sFm\nw/Eml3W3tXDW9Vcy79GH8XR24D90gIZ//QINX/yHMV+Xa0190znDhMgtElAip81b3MLyt7xBUXUH\nPYM3bRz9hBnuysOKK8oWtIEaOdu7QxGPuklEPJQ1tNKwbs+Qe02BM4/f8z+v4A/fqmPTT6tGvZdT\n51EvbQf9Q0bWgTPQ4dVHs7vg85xNjxHz+ilY6WIJ++iihDB+TmMvhmFRUh1lweo+rvnkUd7373t5\n28eOUlLtNAEPTIc0lup7f4gRCqJSxrqb4RBVD/0Ud1vruK9/+Vj2/YjDTdVcfTB98/Q11IM7HqOg\nvwdfNLu5E8XUkoASOS+vtJ9E2Isad2SVAgy6j88jHnPhLwoPG3gywKZh3S7Ofe9Gznrni4MXFQ+E\n1KMPHWDDffNpO+zHdGu6mj1sur+Kpr0j5+Xr73APNj+m0rZBT3N2t/0obT1OxB8gvzTOrec8QZE3\nSIdRTszlY/6SfgrK46y6cvS+vYGJZUdT+OLzmLGRfZq2x0tg99jNZROpRRlPPYXrgx/EfeutGL/5\njXOFcFLO1qJsTcneQ5yz4yVWHtjO6l2vsvTgTgxrEhewiQmTgBJzQqg7D21n+nHVBDvy8RWEcPtj\nKMMefBxlU3fOARZftIeiypHD3+pKAnTvXURbRzeBogSmW+MvsPD4LbY/WTpi8tvyheG0Fw+bbpva\nM7K79qu1ZiH+kPOa6rIOPn/h97l9+a85d8EuzrmxgyvvPEZ+6fiziowWUuGGxdjmyAluVTxGtCaz\nE36mtSjX3XfjvukmzIcewnz4Ydx33IH71ltB65yuRXn3H6bo4FHcpV4iXh9hr4/Snk7qmw5N6XZE\nZiSgxJxQtrDVuS1JRhQldR3YcRfli5oorm3HVxDEWxCidEEby9+ydcxXm8FSTO/QmoYnYNPX5h4x\nE0SgyOKsa9pxeVOGZ5saj99m9TXtI9bd0ehl6x9L2belcMStPF695BpMK0F+TyeGlaA80sK17vUs\n/qBi8Xn9ePPG78caq6nv+F98FJ28SeEA2+Ohb/W5RBpGn93b3d6G/eNH2fSPW3n2V3FC40xaofbv\nx/ze91Ch0GADqwoGMf70J4xnnhn3PWRqOmpRgZ37MMpKTlz5rBQRr5d5nS0oe3JzKYrsSUCJnKA1\ntO2vYvtj57DryTPpaxv6LbvmjMN4AtGhTXbGwF2Fh6wJly9G3TkHKFnQTrTfj+myyCvvpbCym2VX\nbsUwxh6E4isI41O+wUlmARJRhb/QGjLzwoBLPtDMlXceo7w+TH5ZjFVXdvLn/75nyLB1bcMf/7OW\nB+86jQ33VbP+v+r40V+uoP3wib6ttuqFPPKBz9JctxhfqJ/eknk8dstHOXD62nH333DpalGhZSvY\n9YOfEKmpxfZ6sT0eOq96O7t+eP+o66l46H7Wn/8Ab/vKe/nnn5/H1+/KY0G1YtOm0Zv8jCefTD8q\nMBjE+P3vB/+cquuippKKRmFYLVMrhaFtDAmoGSfXQYlZpzW89usL6DxUgRV3g7JpfG0Ry656gwVn\nHwScEX0XfOhJDjy3jNY9NZieBAvO3UeoK49DLy5j4OaFLm+ctX/+LKapWXjebiK9Z9G0YwG2ZVDe\n0ILbN/IOuVbCoKtxHqAprWtn/soj7Nu0CtuMc/RoB1Xzygn3ujjz6g62PVHKvi1F+AoSnPX2DmpW\nhFAKVl7ezcrLR58Md9fGYvY+XzRsJgjNI1+r50Pf2z14Pm+tbeCx2z4xqf05cGuOdLovvYJXNr6G\nu70VK5CHnTf6Lei9Rw7R8k8P81Xr90RIGSQSgXffaHPkaDzttVO6oGDESR4AtxtdXAxM3XVRMLXT\nH0Xrawls3wvKmVmirx88iRj9/gKsdO9JTCsJKDHr2vbOPxFOANrAThjsfnI1VcuP4kle1+Txx1h+\n1TaWX7VtyOsXnreP7qNluH1xSuraBi+G3frIOtoPVGEnnI952/75dB8r5+I7/zh4AXTbvireeGTd\nwO9CrNwAABgdSURBVFVGKKVZfeMWFl2wi2NbF9LTE8AuU5zxtg62/KKS7iYviagJSnPgpSIufG8z\n57xzZFPecNueKHVeN4Qi3OOio9FL+YJo2tdNRrrbxTubVcTnVY77+vLf/YavJN5PmJEjGO1YnKee\nMrjmmpG1Cvsd74BPpAlZlwv7ttuGPDTZOfqmevqj8OlL8R45RqAvSDQaxRexsQ2Tg7WLZPqjWSBN\nfGLWNe+qPRFOKZRh03m4YtzXe/OiVC47Tmn9iXAKduYPCScAtIEVN2l8rQGAaL+X139zPlbMTSLm\ndv6Nenj1VxeSP6+H0697mfNvfglz5aNE+twnwglAKxJRg80PVBHpH/+btRUb5b+aYtwZ2icik2Hn\n4zGiEcLajybN+9OayGjT/xUUEHv4YXRREbqgAF1YiPb7id9zD3rRosHFpnKwxFSxA366rn8Lfeef\nTbi8lKOVdbyxbDXBwNTcxFFkR2pQYtY5t+ewSfd9Kd0Q7kz0tRaljN47wU646DnuTGXTtLMu7exB\nCmjZXUvd6oMYpk20q5jNG9xpakBgujVNuwM0nDv26Lbll3bR0egbbOJbyXYu5xl6KaGyeglWmlrK\nbOt86zXc9IN7eDR2Pf0MPUHH8XD55SObSwfoSy4heuQIxoYNEIthX3YZ5I/enDhZU9nMp70eIiuW\n0G0qjsmsErNKalBi1tWceShtECmlKVvYMqF1Bkr60Xrkx1uZFvnlyVsrRN3Y1sjQsS1FIuqmv72A\nbb9bS++ra4j35ZMuzbQN3rzxr5E5422dlC8M4/HG+TG38xJr+Saf4x77Tr78V2+l5sDQk6vWjBjS\nPhHjzS4xluDpZ3HerSVcaTxDHs7wdxdxfK4Yn/h8kP2hcYace73Yb30r9nXXTWs4Tdc1UQMX7YrZ\nIzUoMeuKazpZdOFODmxe6dR6kk3959z0HOYEa1CFlT0UzOumt2Vg9gmHYdjUnbMfgPKGFg69sAwr\nPvS/gTI0xbVt7Nu4CpTGE4hiuuOMiCGl8eZZzF+aftz1ns1FbPlFJf0dbuY1hLnkA02s3fIHbnns\nlwSs5AwFMefnjq99kq/84HGiURcb7pufnM1cUbMyyJV3HqO0Nvs+qrEGS2Tq8Bf/hS9d9yI3//DH\nPN54NubKBbzlI0UsWmplNAP8eFSgcMruFSVOPhJQpzgrbtC6t5pov5/i2naKqyc/C/lELL5wNzVn\nHKbjUCWmO8G8xc0j7sybrXPfs4kdfzyHlt01oBX583pZdc0rg/eRKqruZN5px2nbWz0YUqY7QdWK\nxsH+Kl9hmJ6mEgzbjekLY0X8oDQKRaA4zrs+fzDtDOVvPF7Kxp/MH2wWPLYjn4e/sogv1DxAwBoZ\naP5gHzWHdvMf915H897AYL/U0R15/PwflnD7f+3KaLb16RBcex4Na8/jrwcfkVkVCoK9VHS0EHf9\n/+3de3CcV3nH8e95371qV1ppdZcs62bJsny3EztOSJyAG0gCSaAphCRjmDakLZ1Ch3+4DMx0ytBS\npnTaYaY0bYFSmDQBSjIllNRJcRKDExzbiaPYli+y7jdL1n3v+76nf6wkS9ZK1kq70mp1Pv8EK9Lq\nVbD00znnOc9jobewnIjVdvMPUhKmAmodmxjI4cQzBzGNWNWcpku8lVfZ9bE3bnpXKBUc2UHKtydv\n09/qiLLzoROYhsA0tOmWRlOEgB0fOcHVS2X0NFWCkJRvb6dwUy9DHYWxabVBK4FhF6YpQDNx5kRx\neyNYHCb1B0bjrmxMA44/UzLnzCoaEvj7rv+5i3JG8bCRDqxCMtqh0d/inF00IQVGRNB0xMv+P5g9\nd2qx5q3mUxInJdsvnmFjXztCSqQQbL/0Lie2H2DAe/OCHiUxKqDWKSnh7ecPEAnYmNpTM0y41l5E\n59vVVO5dG2PmF0PTJZoe/7d+IaC4vofi+p7pt0WCFnzX3ATHsvANuUFqTI27CITBuyGE0xMlMBb/\n2ycwZpkz+Xbys/FD4zBVtiv8Q/hzvM1uNEw0JJ8wf8IpsSfuasyIaAy0zu0DuBjJ2OZbSDJmRSWD\nrWQD+btTM4JjpqKhfjb2tWOZaiky+Xvcre+9yUt3PBC3lZSydCqg1in/iIvgmJMbu32bEQvdZ1IX\nUFJC77kKOk5uIhKyUry5m+r9F+NeoF0Nfc1lNL24D6HJydLwqXBi+p/9LU7Kt/gorot/9mR3G/MO\nJnyh7DGsE3D+Wg0bzXakphOSVr5R/tdUahJpzr1ro9tMimrTr6u20MT0rKhMVl3J9Iyoir6O6+F0\ng4KRAa7ml6zgk2U+FVDrlSnmvXe4+KasiWt+ZSfd71ZPn/m0nXDRd66C2//o5TlbcCst5LPT9OK+\n2Xen4oiGNZw5UTbO0wzWYpXs/NAgZ17Kn7XNZ7G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"text/plain": [ "<matplotlib.figure.Figure at 0x1a8a1c32f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA(n_components=2, svd_solver='full')\n", "pca.fit(dfFeatures)\n", "pcavecs = pca.transform(dfFeatures)\n", "#print(pcavecs)\n", "\n", "\n", "i = 1\n", "# iterate over datasets\n", "h=.02\n", "# preprocess dataset, split into training and test part\n", "X = pcavecs\n", "y = df['label']\n", "X = StandardScaler().fit_transform(X)\n", "X_train, X_test, y_train, y_test = \\\n", " train_test_split(X, y, test_size=.4, random_state=42)\n", "\n", "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", "# just plot the dataset first\n", "cm = plt.cm.RdBu\n", "cm_bright = matplotlib.colors.ListedColormap(['#FF0000', '#0000FF'])\n", "ax = plt.subplot()\n", "\n", "# Plot the training points\n", "ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", "# and testing points\n", "ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n", "ax.set_xlim(xx.min(), xx.max())\n", "ax.set_ylim(yy.min(), yy.max())\n", "ax.set_xticks(())\n", "ax.set_yticks(())\n", "i += 1\n", "\n", "# iterate over classifiers\n", "for name, clf in zip(names, classifiers):\n", " plt.figure()\n", " ax = plt.subplot()\n", " #ax.set_title(name+' '+sub+', '+' vs '.join(candidates))\n", " ax.set_title(name+', '+' vs '.join(candidates))\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", "\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, x_max]x[y_min, y_max].\n", " if hasattr(clf, \"decision_function\"):\n", " Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) \n", " else:\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n", "\n", "\n", " # Plot also the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n", " # and testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n", " alpha=0.6)\n", "\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n", " size=15, horizontalalignment='right')\n", " i += 1\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
yashdeeph709/Algorithms	PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Statements Assessment Test-checkpoint.ipynb	2	3794	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Statements Assessment Test\n", "Lets test your knowledge!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____\n", "Use for, split(), and if to create a Statement that will print out words that start with 's':" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "st = 'Print only the words that start with s in this sentence'" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "______\n", "Use range() to print all the even numbers from 0 to 10." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Code Here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "Use List comprehension to create a list of all numbers between 1 and 50 that are divisble by 3." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Code in this cell\n", "[]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____\n", "Go through the string below and if the length of a word is even print \"even!\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "st = 'Print every word in this sentence that has an even number of letters'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Code in this cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____\n", "Write a program that prints the integers from 1 to 100. But for multiples of three print \"Fizz\" instead of the number, and for the multiples of five print \"Buzz\". For numbers which are multiples of both three and five print \"FizzBuzz\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Code in this cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____\n", "Use List Comprehension to create a list of the first letters of every word in the string below:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "st = 'Create a list of the first letters of every word in this string'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Code in this cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Great Job!" ] } ], "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
ZettelGeist/zettelgeist	jupyter/zdemo.ipynb	1	29099	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "YAML support is provided by PyYAML at http://pyyaml.org/. This notebook depends on it." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import yaml\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell provides an initial example of a note in our system. \n", "\n", "A note is nothing more than a YAML document. The idea of notetaking is to keep it simple, so a note should make no assumptions about formatting whatsoever.\n", "\n", "In our current thinking, we have the following sections:\n", "\n", "- title: an optional title (text)\n", "- tags: one or more keywords (text, sequence of text, no nesting)\n", "- mentions: one or more mentions (text, sequence of text, no nesting)\n", "- outline: one or more items (text, sequence of text, nesting is permitted)\n", "- dates (numeric text, sequence, must follow established historical ways of representing dates)\n", "- text (text from the source as multiline string)\n", "- bibtex, ris, or inline (text for the bibliographic item; will be syntax checked)\n", "- bibkey (text, a hopefully unique identifier for referring to this source in other Zettels)\n", "- cite: Used to cite a bibkey from the same or other notes. In addition, the citation may be represented as a list, where the first item is the bibkey and subsequent items are pages or ranges of page numbers. See below for a good example of how this will work.\n", "- note (any additional details that you wish to hide from indexing)\n", "\n", "In most situations, freeform text is permitted. If you need to do crazy things, you must put quotes around the text so YAML can process it. However, words separated by whitespace and punctuation seems to work fine in most situations.\n", "\n", "These all are intended to be string data, so there are no restrictions on what can be in any field; however, we will likely limit tags, mentions, dates in some way as we go forward. Fields such as bibtex, ris, or inline are also subject to validity checking." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the document to the console (nothing special here)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "myFirstZettel=\"\"\"\n", "title: First BIB Note for Castells\n", "tags:\n", " - Castells\n", " - Network Society\n", " - Charles Babbage is Awesome\n", " - Charles Didn't do Everything\n", "mentions:\n", " - gkt\n", " - dbdennis\n", "dates: 2016\n", "cite:\n", " - Castells Rise 2016\n", " - ii-iv\n", " - 23-36\n", "outline:\n", " - Introduction\n", " - - Computers\n", " - People\n", " - Conclusions\n", " - - Great Ideas of Computing\n", "\n", "text: \|\n", " Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eleifend est sed diam maximus rutrum. Quisque sit amet imperdiet odio, id tristique libero. Aliquam viverra convallis mauris vel tristique. Cras ac dolor non risus porttitor molestie vel at nisi. Donec vitae finibus quam. Phasellus vehicula urna sed nibh condimentum, ultrices interdum velit eleifend. Nam suscipit dolor eu rutrum fringilla. Sed pulvinar purus purus, sit amet venenatis enim convallis a. Duis fringilla nisl sit amet erat lobortis dictum. Nunc fringilla arcu nec ex blandit, a gravida purus commodo. Vivamus lacinia tellus dui, vel maximus lacus ornare id.\n", "\n", " Vivamus euismod justo sit amet luctus bibendum. Integer non mi ullamcorper enim fringilla vulputate sit amet in urna. Nullam eu sodales ipsum. Curabitur id convallis ex. Duis a condimentum lorem. Nulla et urna massa. Duis in nibh eu elit lobortis vehicula. Mauris congue mauris mollis metus lacinia, ut suscipit mi egestas. Donec luctus ante ante, eget viverra est mollis vitae.\n", "\n", " Vivamus in purus in erat dictum scelerisque. Aliquam dictum quis ligula ac euismod. Mauris elementum metus vel scelerisque feugiat. Vivamus bibendum massa eu pellentesque sodales. Nulla nec lacus dolor. Donec scelerisque, nibh sed placerat gravida, nunc turpis tristique nibh, ac feugiat enim massa ut eros. Nulla finibus, augue egestas hendrerit accumsan, tellus augue tempor eros, in sagittis dolor turpis nec mi. Nunc fringilla mi non malesuada aliquet.\n", "\n", "bibkey:\n", " Castells Rise 1996\n", "bibtex: \|\n", " @book{castells_rise_1996,\n", " address = {Cambridge, Mass.},\n", " series = {Castells, {Manuel}, 1942- {Information} age . v},\n", " title = {The rise of the network society},\n", " isbn = {978-1-55786-616-5},\n", " language = {eng},\n", " publisher = {Blackwell Publishers},\n", " author = {Castells, Manuel},\n", " year = {1996},\n", " keywords = {Information networks., Information society., Information technology Economic aspects., Information technology Social aspects., Technology and civilization.}\n", " }\n", "\n", "note:\n", " George likes this new format.\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "title: First BIB Note for Castells\n", "tags:\n", " - Castells\n", " - Network Society\n", " - Charles Babbage is Awesome\n", " - Charles Didn't do Everything\n", "mentions:\n", " - gkt\n", " - dbdennis\n", "dates: 2016\n", "cite:\n", " - Castells Rise 2016\n", " - ii-iv\n", " - 23-36\n", "outline:\n", " - Introduction\n", " - - Computers\n", " - People\n", " - Conclusions\n", " - - Great Ideas of Computing\n", "\n", "text: \|\n", " Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eleifend est sed diam maximus rutrum. Quisque sit amet imperdiet odio, id tristique libero. Aliquam viverra convallis mauris vel tristique. Cras ac dolor non risus porttitor molestie vel at nisi. Donec vitae finibus quam. Phasellus vehicula urna sed nibh condimentum, ultrices interdum velit eleifend. Nam suscipit dolor eu rutrum fringilla. Sed pulvinar purus purus, sit amet venenatis enim convallis a. Duis fringilla nisl sit amet erat lobortis dictum. Nunc fringilla arcu nec ex blandit, a gravida purus commodo. Vivamus lacinia tellus dui, vel maximus lacus ornare id.\n", "\n", " Vivamus euismod justo sit amet luctus bibendum. Integer non mi ullamcorper enim fringilla vulputate sit amet in urna. Nullam eu sodales ipsum. Curabitur id convallis ex. Duis a condimentum lorem. Nulla et urna massa. Duis in nibh eu elit lobortis vehicula. Mauris congue mauris mollis metus lacinia, ut suscipit mi egestas. Donec luctus ante ante, eget viverra est mollis vitae.\n", "\n", " Vivamus in purus in erat dictum scelerisque. Aliquam dictum quis ligula ac euismod. Mauris elementum metus vel scelerisque feugiat. Vivamus bibendum massa eu pellentesque sodales. Nulla nec lacus dolor. Donec scelerisque, nibh sed placerat gravida, nunc turpis tristique nibh, ac feugiat enim massa ut eros. Nulla finibus, augue egestas hendrerit accumsan, tellus augue tempor eros, in sagittis dolor turpis nec mi. Nunc fringilla mi non malesuada aliquet.\n", "\n", "bibkey:\n", " Castells Rise 1996\n", "bibtex: \|\n", " @book{castells_rise_1996,\n", " address = {Cambridge, Mass.},\n", " series = {Castells, {Manuel}, 1942- {Information} age . v},\n", " title = {The rise of the network society},\n", " isbn = {978-1-55786-616-5},\n", " language = {eng},\n", " publisher = {Blackwell Publishers},\n", " author = {Castells, Manuel},\n", " year = {1996},\n", " keywords = {Information networks., Information society., Information technology Economic aspects., Information technology Social aspects., Technology and civilization.}\n", " }\n", "\n", "note:\n", " George likes this new format.\n", "\n" ] } ], "source": [ "print(myFirstZettel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows how to load just the YAML portion of the document, resulting in a Python dictionary data structure. Observe that the Python dictionary has { key : value, ... }. So we can extract the YAML fields from the Python dictionary data structure.\n", "\n", "Notice that when you write a YAML list of mentions, there is a nested Python list ['gkt', 'dbdennis']." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "doc = yaml.load(myFirstZettel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Closing the loop, the following shows how to iterate the keys of the data structure." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mentions = ['gkt', 'dbdennis']\n", "dates = 2016\n", "outline = ['Introduction', ['Computers', 'People'], 'Conclusions', ['Great Ideas of Computing']]\n", "bibtex = @book{castells_rise_1996,\n", " address = {Cambridge, Mass.},\n", " series = {Castells, {Manuel}, 1942- {Information} age . v},\n", " title = {The rise of the network society},\n", " isbn = {978-1-55786-616-5},\n", " language = {eng},\n", " publisher = {Blackwell Publishers},\n", " author = {Castells, Manuel},\n", " year = {1996},\n", " keywords = {Information networks., Information society., Information technology Economic aspects., Information technology Social aspects., Technology and civilization.}\n", "}\n", "\n", "title = First BIB Note for Castells\n", "bibkey = Castells Rise 1996\n", "tags = ['Castells', 'Network Society', 'Charles Babbage is Awesome', \"Charles Didn't do Everything\"]\n", "cite = ['Castells Rise 2016', 'ii-iv', '23-36']\n", "note = George likes this new format.\n", "text = Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eleifend est sed diam maximus rutrum. Quisque sit amet imperdiet odio, id tristique libero. Aliquam viverra convallis mauris vel tristique. Cras ac dolor non risus porttitor molestie vel at nisi. Donec vitae finibus quam. Phasellus vehicula urna sed nibh condimentum, ultrices interdum velit eleifend. Nam suscipit dolor eu rutrum fringilla. Sed pulvinar purus purus, sit amet venenatis enim convallis a. Duis fringilla nisl sit amet erat lobortis dictum. Nunc fringilla arcu nec ex blandit, a gravida purus commodo. Vivamus lacinia tellus dui, vel maximus lacus ornare id.\n", "\n", "Vivamus euismod justo sit amet luctus bibendum. Integer non mi ullamcorper enim fringilla vulputate sit amet in urna. Nullam eu sodales ipsum. Curabitur id convallis ex. Duis a condimentum lorem. Nulla et urna massa. Duis in nibh eu elit lobortis vehicula. Mauris congue mauris mollis metus lacinia, ut suscipit mi egestas. Donec luctus ante ante, eget viverra est mollis vitae.\n", "\n", "Vivamus in purus in erat dictum scelerisque. Aliquam dictum quis ligula ac euismod. Mauris elementum metus vel scelerisque feugiat. Vivamus bibendum massa eu pellentesque sodales. Nulla nec lacus dolor. Donec scelerisque, nibh sed placerat gravida, nunc turpis tristique nibh, ac feugiat enim massa ut eros. Nulla finibus, augue egestas hendrerit accumsan, tellus augue tempor eros, in sagittis dolor turpis nec mi. Nunc fringilla mi non malesuada aliquet.\n", "\n" ] } ], "source": [ "for key in doc.keys():\n", " print(key, \"=\", doc[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this shows how to get any particular item of interest. In this case, we're extracting the bibtex key so we can do something with the embedded BibTeX (e.g. print it)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Castells Rise 1996\n", "@book{castells_rise_1996,\n", " address = {Cambridge, Mass.},\n", " series = {Castells, {Manuel}, 1942- {Information} age . v},\n", " title = {The rise of the network society},\n", " isbn = {978-1-55786-616-5},\n", " language = {eng},\n", " publisher = {Blackwell Publishers},\n", " author = {Castells, Manuel},\n", " year = {1996},\n", " keywords = {Information networks., Information society., Information technology Economic aspects., Information technology Social aspects., Technology and civilization.}\n", "}\n", "\n" ] } ], "source": [ "print(doc['bibkey'])\n", "print(doc['bibtex'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adapted from http://stackoverflow.com/questions/12472338/flattening-a-list-recursively. There really must be a nicer way to do stuff like this. I will rewrite this using a walker so we can have custom processing of the list items." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def flatten(item):\n", " if type(item) != type([]):\n", " return [str(item)]\n", " if item == []:\n", " return item\n", " if isinstance(item[0], list):\n", " return flatten(item[0]) + flatten(item[1:])\n", " return item[:1] + flatten(item[1:])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['George was here']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flatten(\"George was here\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A', 'B', 'C', 'D', 'E']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flatten(['A', ['B', 'C'], ['D', ['E']]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are onto some `sqlite3` explorations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ordinarily, I would use some sort of mapping framework to handle database operations. However, it's not clear the FTS support is part of any ORM (yet). I will continue to research but since there is likely only one table, it might not be worth the trouble." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Next we will actually add the Zettel to the database and do a test query. Almost there." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CREATE VIRTUAL TABLE zettels USING fts4(mentions,dates,outline,bibtex,inline,title,bibkey,ris,tags,cite,note,text,summary)\n", "{ 'bibkey': 'Castells Rise 1996',\n", " 'bibtex': '@book{castells_rise_1996,\\n'\n", " ' address = {Cambridge, Mass.},\\n'\n", " ' series = {Castells, {Manuel}, 1942- {Information} age . v},\\n'\n", " ' title = {The rise of the network society},\\n'\n", " ' isbn = {978-1-55786-616-5},\\n'\n", " ' language = {eng},\\n'\n", " ' publisher = {Blackwell Publishers},\\n'\n", " ' author = {Castells, Manuel},\\n'\n", " ' year = {1996},\\n'\n", " ' keywords = {Information networks., Information society., '\n", " 'Information technology Economic aspects., Information technology '\n", " 'Social aspects., Technology and civilization.}\\n'\n", " '}\\n',\n", " 'cite': ['Castells Rise 2016', 'ii-iv', '23-36'],\n", " 'dates': 2016,\n", " 'mentions': ['gkt', 'dbdennis'],\n", " 'note': 'George likes this new format.',\n", " 'outline': [ 'Introduction',\n", " ['Computers', 'People'],\n", " 'Conclusions',\n", " ['Great Ideas of Computing']],\n", " 'tags': [ 'Castells',\n", " 'Network Society',\n", " 'Charles Babbage is Awesome',\n", " \"Charles Didn't do Everything\"],\n", " 'text': 'Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam '\n", " 'eleifend est sed diam maximus rutrum. Quisque sit amet imperdiet '\n", " 'odio, id tristique libero. Aliquam viverra convallis mauris vel '\n", " 'tristique. Cras ac dolor non risus porttitor molestie vel at nisi. '\n", " 'Donec vitae finibus quam. Phasellus vehicula urna sed nibh '\n", " 'condimentum, ultrices interdum velit eleifend. Nam suscipit dolor '\n", " 'eu rutrum fringilla. Sed pulvinar purus purus, sit amet venenatis '\n", " 'enim convallis a. Duis fringilla nisl sit amet erat lobortis '\n", " 'dictum. Nunc fringilla arcu nec ex blandit, a gravida purus '\n", " 'commodo. Vivamus lacinia tellus dui, vel maximus lacus ornare id.\\n'\n", " '\\n'\n", " 'Vivamus euismod justo sit amet luctus bibendum. Integer non mi '\n", " 'ullamcorper enim fringilla vulputate sit amet in urna. Nullam eu '\n", " 'sodales ipsum. Curabitur id convallis ex. Duis a condimentum lorem. '\n", " 'Nulla et urna massa. Duis in nibh eu elit lobortis vehicula. Mauris '\n", " 'congue mauris mollis metus lacinia, ut suscipit mi egestas. Donec '\n", " 'luctus ante ante, eget viverra est mollis vitae.\\n'\n", " '\\n'\n", " 'Vivamus in purus in erat dictum scelerisque. Aliquam dictum quis '\n", " 'ligula ac euismod. Mauris elementum metus vel scelerisque feugiat. '\n", " 'Vivamus bibendum massa eu pellentesque sodales. Nulla nec lacus '\n", " 'dolor. Donec scelerisque, nibh sed placerat gravida, nunc turpis '\n", " 'tristique nibh, ac feugiat enim massa ut eros. Nulla finibus, augue '\n", " 'egestas hendrerit accumsan, tellus augue tempor eros, in sagittis '\n", " 'dolor turpis nec mi. Nunc fringilla mi non malesuada aliquet.\\n',\n", " 'title': 'First BIB Note for Castells'}\n", "INSERT INTO zettels VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)\n", "dict_keys(['mentions', 'dates', 'outline', 'bibtex', 'inline', 'bibkey', 'ris', 'title', 'cite', 'tags', 'note', 'text', 'summary'])\n", "[ 'gkt,dbdennis',\n", " '2016',\n", " 'Introduction,Computers,People,Conclusions,Great Ideas of Computing',\n", " '@book{castells_rise_1996,\\n'\n", " ' address = {Cambridge, Mass.},\\n'\n", " ' series = {Castells, {Manuel}, 1942- {Information} age . v},\\n'\n", " ' title = {The rise of the network society},\\n'\n", " ' isbn = {978-1-55786-616-5},\\n'\n", " ' language = {eng},\\n'\n", " ' publisher = {Blackwell Publishers},\\n'\n", " ' author = {Castells, Manuel},\\n'\n", " ' year = {1996},\\n'\n", " ' keywords = {Information networks., Information society., Information '\n", " 'technology Economic aspects., Information technology Social aspects., '\n", " 'Technology and civilization.}\\n'\n", " '}\\n',\n", " '',\n", " 'Castells Rise 1996',\n", " '',\n", " 'First BIB Note for Castells',\n", " 'Castells Rise 2016,ii-iv,23-36',\n", " \"Castells,Network Society,Charles Babbage is Awesome,Charles Didn't do \"\n", " 'Everything',\n", " 'George likes this new format.',\n", " 'Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eleifend est '\n", " 'sed diam maximus rutrum. Quisque sit amet imperdiet odio, id tristique '\n", " 'libero. Aliquam viverra convallis mauris vel tristique. Cras ac dolor non '\n", " 'risus porttitor molestie vel at nisi. Donec vitae finibus quam. Phasellus '\n", " 'vehicula urna sed nibh condimentum, ultrices interdum velit eleifend. Nam '\n", " 'suscipit dolor eu rutrum fringilla. Sed pulvinar purus purus, sit amet '\n", " 'venenatis enim convallis a. Duis fringilla nisl sit amet erat lobortis '\n", " 'dictum. Nunc fringilla arcu nec ex blandit, a gravida purus commodo. '\n", " 'Vivamus lacinia tellus dui, vel maximus lacus ornare id.\\n'\n", " '\\n'\n", " 'Vivamus euismod justo sit amet luctus bibendum. Integer non mi ullamcorper '\n", " 'enim fringilla vulputate sit amet in urna. Nullam eu sodales ipsum. '\n", " 'Curabitur id convallis ex. Duis a condimentum lorem. Nulla et urna massa. '\n", " 'Duis in nibh eu elit lobortis vehicula. Mauris congue mauris mollis metus '\n", " 'lacinia, ut suscipit mi egestas. Donec luctus ante ante, eget viverra est '\n", " 'mollis vitae.\\n'\n", " '\\n'\n", " 'Vivamus in purus in erat dictum scelerisque. Aliquam dictum quis ligula ac '\n", " 'euismod. Mauris elementum metus vel scelerisque feugiat. Vivamus bibendum '\n", " 'massa eu pellentesque sodales. Nulla nec lacus dolor. Donec scelerisque, '\n", " 'nibh sed placerat gravida, nunc turpis tristique nibh, ac feugiat enim '\n", " 'massa ut eros. Nulla finibus, augue egestas hendrerit accumsan, tellus '\n", " 'augue tempor eros, in sagittis dolor turpis nec mi. Nunc fringilla mi non '\n", " 'malesuada aliquet.\\n',\n", " '']\n" ] } ], "source": [ "import sqlite3\n", "\n", "# This is for showing data structures only.\n", "\n", "import pprint\n", "printer = pprint.PrettyPrinter(indent=2)\n", "\n", "class SQLiteFTS(object): \n", " def __init__(self, db_name, table_name, field_names):\n", " self.db_name = db_name\n", " self.conn = sqlite3.connect(db_name)\n", " self.cursor = self.conn.cursor()\n", " \n", " self.table_name = table_name\n", " self.fts_field_names = field_names\n", " self.fts_field_refs = ['?'] * len(self.fts_field_names) # for sqlite insert template generation\n", " self.fts_field_init = [''] * len(self.fts_field_names)\n", " self.fts_fields = dict(zip(self.fts_field_names, self.fts_field_refs))\n", " self.fts_default_record = dict(zip(self.fts_field_names, self.fts_field_init))\n", "\n", " def bind(self, doc):\n", " self.record = self.fts_default_record.copy()\n", " for k in doc.keys():\n", " if k in self.record.keys():\n", " self.record[k] = doc[k]\n", " else:\n", " print(\"Unknown fts field %s\" % k)\n", " self.record.update(doc)\n", " \n", " def drop_table(self):\n", " self.conn.execute(\"DROP TABLE IF EXISTS %s\" % self.table_name)\n", "\n", " def create_table(self):\n", " sql_fields = \",\".join(self.fts_default_record.keys())\n", " print(\"CREATE VIRTUAL TABLE zettels USING fts4(%s)\" % sql_fields)\n", " self.conn.execute(\"CREATE VIRTUAL TABLE zettels USING fts4(%s)\" % sql_fields) \n", " \n", " def insert_into_table(self):\n", " sql_params = \",\".join(self.fts_fields.values())\n", " #printer.pprint(self.record)\n", " #printer.pprint(self.record.values())\n", " sql_insert_values = [ \",\".join(flatten(value)) for value in list(self.record.values())]\n", " print(\"INSERT INTO zettels VALUES (%s)\" % sql_params)\n", " print(self.record.keys())\n", " printer.pprint(sql_insert_values)\n", " self.conn.execute(\"INSERT INTO zettels VALUES (%s)\" % sql_params, sql_insert_values)\n", "\n", " def done(self):\n", " self.conn.commit()\n", " self.conn.close()\n", " \n", "sql = SQLiteFTS('zettels.db', 'zettels', ['title', 'tags', 'mentions', 'outline', 'cite', 'dates', 'summary', 'text', 'bibkey', 'bibtex', 'ris', 'inline', 'note' ])\n", "\n", "#doc_keys = list(doc.keys())\n", "#doc_keys.sort()\n", "#rec_keys = list(sql.record.keys())\n", "#rec_keys.sort()\n", "#print(\"doc keys %s\" % doc_keys)\n", "#print(\"record keys %s\" % rec_keys)\n", "\n", "sql.drop_table()\n", "sql.create_table()\n", "printer.pprint(doc)\n", "sql.bind(doc)\n", "sql.insert_into_table()\n", "sql.done()\n", "\n", "#sql_insert_values = [ str(field) for field in sql.record.values()]\n", "#print(sql_insert_values)\n", "\n", "#print(record)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"xyz.txt\") as datafile:\n", " text = datafile.read()\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "@misc{blahblahblah,\n", " title = {In Depth - In Depth: Ray Kurzweil - Book {TV}},\n", " url = {http://www.booktv.org/Program/7515/In+Depth+Ray+Kurzweil.aspx},\n", " urldate = {2011-02-11},\n", " keywords = {{AddedToZettels}},\n", " file = {In Depth - In Depth\\: Ray Kurzweil - Book TV:/Users/dbdennis/Library/Application Support/Zotero/Profiles/duztnovb.default/zotero/storage/TWWBX3QV/In+Depth+Ray+Kurzweil.html:text/html}\n", "}\n", "\n" ] } ], "source": [ "print(text)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "bibkey = 'blahblahblah'\n", "bibtex = text\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bibkey: blahblahblah\n", "bibtex: \|\n", " @misc{blahblahblah,\n", " title = {In Depth - In Depth: Ray Kurzweil - Book {TV}},\n", " url = {http://www.booktv.org/Program/7515/In+Depth+Ray+Kurzweil.aspx},\n", " urldate = {2011-02-11},\n", " keywords = {{AddedToZettels}},\n", " file = {In Depth - In Depth\\: Ray Kurzweil - Book TV:/Users/dbdennis/Library/Application Support/Zotero/Profiles/duztnovb.default/zotero/storage/TWWBX3QV/In+Depth+Ray+Kurzweil.html:text/html}\n", " }\n", "\n" ] } ], "source": [ "import yaml\n", "from collections import OrderedDict\n", "\n", "class quoted(str): pass\n", "\n", "def quoted_presenter(dumper, data):\n", " return dumper.represent_scalar('tag:yaml.org,2002:str', data, style='\"')\n", "yaml.add_representer(quoted, quoted_presenter)\n", "\n", "class literal(str): pass\n", "\n", "def literal_presenter(dumper, data):\n", " return dumper.represent_scalar('tag:yaml.org,2002:str', data, style='\|')\n", "yaml.add_representer(literal, literal_presenter)\n", "\n", "def ordered_dict_presenter(dumper, data):\n", " return dumper.represent_dict(data.items())\n", "yaml.add_representer(OrderedDict, ordered_dict_presenter)\n", "\n", "d = OrderedDict(bibkey=bibkey, bibtex=literal(bibtex))\n", "print(yaml.dump(d))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
feststelltaste/software-analytics	courses/20190918_Uni_Leipzig/Identifying Modularization Options based on Code Changes (Demo Presentation).ipynb	1	4232811	null	gpl-3.0
mcmartins/francy	notebooks/francy-numericalsgps.ipynb	1	67033	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Numerical semigroups with `francy`\n", "\n", "## Load `francy` Package" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 1, "metadata": { "text/plain": "" }, "output_type": "execute_result" }, { "data": { "text/plain": [ "true" ] }, "execution_count": 2, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "LoadPackage(\"francy\");\n", "LoadPackage(\"num\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drawing Apéry sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example draws the Apéry set of a numerical semigrup with respect to its multiplicity. By passing over a node with the mouse, the set of factorizations with respect to the minimal generating system of the numerical semigroup is displayed. Clicking a node produces a message with the same information." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( arg... ) ... end" ] }, "execution_count": 3, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "apery:=function(arg)\n", " local ap,c,hasse, s, n, r, graphHasse, aps, es, canvas, i, order, showfacts, message;\n", " # rel is a list of lists with two elements representin a binary relation\n", " # hasse(rel) removes from rel the pairs [x,y] such that there exists\n", " # z with [x,z],[z,y] in rel\n", " hasse:=function(rel)\n", " local dom, out;\n", " dom:=Flat(rel);\n", " out:=Filtered(rel, p-> ForAny(dom, x->([p[1],x] in rel) and ([x,p[2]] in rel)));\n", " return Difference(rel,out);\n", " end;\n", "\n", " order:=function(x)\n", " return Maximum(LengthsOfFactorizationsElementWRTNumericalSemigroup(x,s));\n", " end;\n", "\n", " showfacts:=function(x)\n", " message := FrancyMessage(Concatenation(String(x), \" factors as \"), \n", " String(FactorizationsElementWRTNumericalSemigroup(x,s)));\n", " SetFrancyId(message, Concatenation(\"message-for-\", String(x)));\n", " Add(canvas, message);\n", " return Draw(canvas);\n", " end;\n", " if Length(arg)=1 then\n", " s:=arg[1];\n", " n:=MultiplicityOfNumericalSemigroup(s);\n", " fi;\n", " if Length(arg)=2 then\n", " s:=arg[1];\n", " n:=arg[2];\n", " fi;\n", " if Length(arg)>2 then\n", " Error(\"The number of arguments must be one or two\");\n", " fi;\n", " \n", " graphHasse := Graph(GraphType.UNDIRECTED);\n", " #SetSimulation(graphHasse,true);\n", " #SetDrag(graphHasse,true);\n", " ap:=AperyList(s,n);\n", " c:=Cartesian([1..n],[1..n]);\n", " c:=Filtered(c, p-> ap[p[2]]<>ap[p[1]]);\n", " c:=Filtered(c, p-> ap[p[1]]-ap[p[2]] in s);\n", " c:=hasse(c);\n", " aps:=[];\n", " for i in [1..n] do\n", " aps[i]:=Shape(ShapeType!.CIRCLE, String(ap[i]));\n", " SetLayer(aps[i],-order(ap[i]));\n", " Add(aps[i],Callback(showfacts,[ap[i]]));\n", " Add(aps[i],FrancyMessage(Concatenation(\"{\",\n", " JoinStringsWithSeparator(List(FactorizationsElementWRTNumericalSemigroup(ap[i],s), \n", " f->Concatenation(\"(\",JoinStringsWithSeparator(f,\",\"),\")\")),\",\"),\")\")));\n", " Add(graphHasse,aps[i]);\n", " od;\n", " for r in c do\n", " Add(graphHasse,Link(aps[r[1]],aps[r[2]]));\n", " od;\n", " canvas:=Canvas(\"Apery\");\n", " Add(canvas,graphHasse);\n", " return Draw(canvas); \n", "end;" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F45\",\"height\" : 600,\"title\" : \"Apery\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F1\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F2\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F2\",\"size\" : 10,\"title\" : \"0\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F4\" : {\"type\" : \"default\",\"id\" : \"F4\",\"text\" : \"{(0,0,0))\",\"title\" : \"\"}},\"callbacks\" : {\"F3\" : {\"func\" : \"unknown\",\"id\" : \"F3\",\"trigger\" : \"click\",\"knownArgs\" : [\"0\"],\"requiredArgs\" : {}}}},\"F5\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F5\",\"size\" : 10,\"title\" : \"31\",\"layer\" : -1,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F7\" : {\"type\" : \"default\",\"id\" : \"F7\",\"text\" : \"{(0,0,1))\",\"title\" : \"\"}},\"callbacks\" : {\"F6\" : {\"func\" : \"unknown\",\"id\" : \"F6\",\"trigger\" : \"click\",\"knownArgs\" : [\"31\"],\"requiredArgs\" : {}}}},\"F8\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F8\",\"size\" : 10,\"title\" : \"62\",\"layer\" : -2,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F10\" : {\"type\" : \"default\",\"id\" : \"F10\",\"text\" : \"{(0,0,2))\",\"title\" : \"\"}},\"callbacks\" : {\"F9\" : {\"func\" : \"unknown\",\"id\" : \"F9\",\"trigger\" : \"click\",\"knownArgs\" : [\"62\"],\"requiredArgs\" : {}}}},\"F11\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F11\",\"size\" : 10,\"title\" : \"93\",\"layer\" : -3,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F13\" : {\"type\" : \"default\",\"id\" : \"F13\",\"text\" : \"{(0,0,3))\",\"title\" : \"\"}},\"callbacks\" : {\"F12\" : {\"func\" : \"unknown\",\"id\" : \"F12\",\"trigger\" : \"click\",\"knownArgs\" : [\"93\"],\"requiredArgs\" : {}}}},\"F14\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F14\",\"size\" : 10,\"title\" : \"54\",\"layer\" : -2,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F16\" : {\"type\" : \"default\",\"id\" : \"F16\",\"text\" : \"{(0,2,0))\",\"title\" : \"\"}},\"callbacks\" : {\"F15\" : {\"func\" : \"unknown\",\"id\" : \"F15\",\"trigger\" : \"click\",\"knownArgs\" : [\"54\"],\"requiredArgs\" : {}}}},\"F17\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F17\",\"size\" : 10,\"title\" : \"85\",\"layer\" : -3,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F19\" : {\"type\" : \"default\",\"id\" : \"F19\",\"text\" : \"{(0,2,1))\",\"title\" : \"\"}},\"callbacks\" : {\"F18\" : {\"func\" : \"unknown\",\"id\" : \"F18\",\"trigger\" : \"click\",\"knownArgs\" : [\"85\"],\"requiredArgs\" : {}}}},\"F20\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F20\",\"size\" : 10,\"title\" : \"116\",\"layer\" : -4,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F22\" : {\"type\" : \"default\",\"id\" : \"F22\",\"text\" : \"{(0,2,2))\",\"title\" : \"\"}},\"callbacks\" : {\"F21\" : {\"func\" : \"unknown\",\"id\" : \"F21\",\"trigger\" : \"click\",\"knownArgs\" : [\"116\"],\"requiredArgs\" : {}}}},\"F23\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F23\",\"size\" : 10,\"title\" : \"27\",\"layer\" : -1,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F25\" : {\"type\" : \"default\",\"id\" : \"F25\",\"text\" : \"{(0,1,0))\",\"title\" : \"\"}},\"callbacks\" : {\"F24\" : {\"func\" : \"unknown\",\"id\" : \"F24\",\"trigger\" : \"click\",\"knownArgs\" : [\"27\"],\"requiredArgs\" : {}}}},\"F26\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F26\",\"size\" : 10,\"title\" : \"58\",\"layer\" : -2,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F28\" : {\"type\" : \"default\",\"id\" : \"F28\",\"text\" : \"{(0,1,1))\",\"title\" : \"\"}},\"callbacks\" : {\"F27\" : {\"func\" : \"unknown\",\"id\" : \"F27\",\"trigger\" : \"click\",\"knownArgs\" : [\"58\"],\"requiredArgs\" : {}}}},\"F29\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F29\",\"size\" : 10,\"title\" : \"89\",\"layer\" : -3,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F31\" : {\"type\" : \"default\",\"id\" : \"F31\",\"text\" : \"{(0,1,2))\",\"title\" : \"\"}},\"callbacks\" : {\"F30\" : {\"func\" : \"unknown\",\"id\" : \"F30\",\"trigger\" : \"click\",\"knownArgs\" : [\"89\"],\"requiredArgs\" : {}}}}},\"links\" : {\"F32\" : {\"id\" : \"F32\",\"source\" : \"F5\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F2\",\"color\" : \"\",\"invisible\" : false},\"F33\" : {\"id\" : \"F33\",\"source\" : \"F8\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F5\",\"color\" : \"\",\"invisible\" : false},\"F34\" : {\"id\" : \"F34\",\"source\" : \"F11\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F8\",\"color\" : \"\",\"invisible\" : false},\"F35\" : {\"id\" : \"F35\",\"source\" : \"F14\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F23\",\"color\" : \"\",\"invisible\" : false},\"F36\" : {\"id\" : \"F36\",\"source\" : \"F17\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F14\",\"color\" : \"\",\"invisible\" : false},\"F37\" : {\"id\" : \"F37\",\"source\" : \"F17\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F26\",\"color\" : \"\",\"invisible\" : false},\"F38\" : {\"id\" : \"F38\",\"source\" : \"F20\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F17\",\"color\" : \"\",\"invisible\" : false},\"F39\" : {\"id\" : \"F39\",\"source\" : \"F20\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F29\",\"color\" : \"\",\"invisible\" : false},\"F40\" : {\"id\" : \"F40\",\"source\" : \"F23\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F2\",\"color\" : \"\",\"invisible\" : false},\"F41\" : {\"id\" : \"F41\",\"source\" : \"F26\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F5\",\"color\" : \"\",\"invisible\" : false},\"F42\" : {\"id\" : \"F42\",\"source\" : \"F26\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F23\",\"color\" : \"\",\"invisible\" : false},\"F43\" : {\"id\" : \"F43\",\"source\" : \"F29\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F8\",\"color\" : \"\",\"invisible\" : false},\"F44\" : {\"id\" : \"F44\",\"source\" : \"F29\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F26\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 4, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "apery(NumericalSemigroup(10,51,27,31));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drawing sons of numerical semigroups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to draw the sons of a numerical semigroup in the tree of numerical semigroups. If we click on a node, then the sets of sons of that node are added to the canvas, and if the node is a leaf, a warning message is displayed.\n", "\n", "Passing the mouse over a node shows the set of minimal generators of the node." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( s ) ... end" ] }, "execution_count": 5, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "sons:=function(s)\n", " local gens, frb, desc, graphHasse, d, shpr, shp, canvas, sonsf, i, gn, lbl;\n", " \n", " \n", " sonsf:=function(s,n)\n", " local gens, frb, desc, d, shp, i, lbl, gn;\n", "\n", " frb:=FrobeniusNumber(s);\n", " gens:=Filtered(MinimalGenerators(s), x-> x>frb);\n", " desc:=List(gens, g->RemoveMinimalGeneratorFromNumericalSemigroup(g,s));\n", " gn:=Genus(s);\n", " i:=0;\n", " for d in desc do\n", " i:=i+1;\n", " lbl:=Concatenation(\"$\\\\langle\",JoinStringsWithSeparator(MinimalGenerators(d),\",\"),\"\\\\rangle$\");\n", " shp:=Shape(ShapeType!.CIRCLE, lbl);\n", " SetFrancyId(shp,lbl);\n", " SetLayer(shp,Genus(d));\n", " SetSize(shp,1);\n", " Add(shp,Callback(sonsf,[d,shp]));\n", " Add(shp,FrancyMessage(String(MinimalGenerators(d))));\n", " Add(graphHasse,shp);\n", " Add(graphHasse,Link(n,shp));\n", " od;\n", " if desc<>[] then \n", " return Draw(canvas);\n", " fi;\n", " Add(canvas, FrancyMessage(FrancyMessageType.WARNING, \"This semigroup is a leaf\"));\n", " return Draw(canvas);\n", " end;\n", " \n", " frb:=FrobeniusNumber(s);\n", " gens:=Filtered(MinimalGenerators(s), x-> x>frb);\n", " desc:=List(gens, g->RemoveMinimalGeneratorFromNumericalSemigroup(g,s));\n", " gn:=Genus(s);\n", "\n", " graphHasse := Graph(GraphType.UNDIRECTED);\n", " lbl:=Concatenation(\"$\\\\langle\",JoinStringsWithSeparator(MinimalGenerators(s),\",\"),\"\\\\rangle$\");\n", " shpr:=Shape(ShapeType!.CIRCLE, lbl);\n", " SetSize(shpr,1);\n", " SetFrancyId(shpr,lbl);\n", " Add(shpr,FrancyMessage(String(MinimalGenerators(s))));\n", " SetLayer(shpr,Genus(s));\n", " Add(graphHasse,shpr);\n", " i:=0;\n", " for d in desc do\n", " i:=i+1;\n", " lbl:=Concatenation(\"$\\\\langle\",JoinStringsWithSeparator(MinimalGenerators(d),\",\"),\"\\\\rangle$\");\n", " shp:=Shape(ShapeType!.CIRCLE, lbl);\n", " SetFrancyId(shp,lbl);\n", " SetLayer(shp,Genus(d));\n", " SetSize(shp,1);\n", " Add(shp,Callback(sonsf,[d,shp]));\n", " Add(shp,FrancyMessage(String(MinimalGenerators(d))));\n", " Add(graphHasse,shp);\n", " Add(graphHasse,Link(shpr,shp));\n", " od;\n", " canvas:=Canvas(\"Sons of a numerical semigroup\");\n", " SetTexTypesetting(canvas, true);\n", " Add(canvas,graphHasse);\n", " return Draw(canvas); \n", "end;" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F57\",\"height\" : 600,\"title\" : \"Sons of a numerical semigroup\",\"zoomToFit\" : true,\"texTypesetting\" : true,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F46\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"$\\\\langle3,5,7\\\\rangle$\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"$\\\\langle3,5,7\\\\rangle$\",\"size\" : 1,\"title\" : \"$\\\\langle3,5,7\\\\rangle$\",\"layer\" : 3,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F48\" : {\"type\" : \"default\",\"id\" : \"F48\",\"text\" : \"[ 3, 5, 7 ]\",\"title\" : \"\"}},\"callbacks\" : {}},\"$\\\\langle3,7,8\\\\rangle$\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"$\\\\langle3,7,8\\\\rangle$\",\"size\" : 1,\"title\" : \"$\\\\langle3,7,8\\\\rangle$\",\"layer\" : 4,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F51\" : {\"type\" : \"default\",\"id\" : \"F51\",\"text\" : \"[ 3, 7, 8 ]\",\"title\" : \"\"}},\"callbacks\" : {\"F50\" : {\"func\" : \"unknown\",\"id\" : \"F50\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"<object>\"],\"requiredArgs\" : {}}}},\"$\\\\langle3,5\\\\rangle$\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"$\\\\langle3,5\\\\rangle$\",\"size\" : 1,\"title\" : \"$\\\\langle3,5\\\\rangle$\",\"layer\" : 4,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F55\" : {\"type\" : \"default\",\"id\" : \"F55\",\"text\" : \"[ 3, 5 ]\",\"title\" : \"\"}},\"callbacks\" : {\"F54\" : {\"func\" : \"unknown\",\"id\" : \"F54\",\"trigger\" : \"click\",\"knownArgs\" : [\"<object>\",\"<object>\"],\"requiredArgs\" : {}}}}},\"links\" : {\"F52\" : {\"id\" : \"F52\",\"source\" : \"$\\\\langle3,5,7\\\\rangle$\",\"length\" : 0,\"weight\" : 0,\"target\" : \"$\\\\langle3,7,8\\\\rangle$\",\"color\" : \"\",\"invisible\" : false},\"F56\" : {\"id\" : \"F56\",\"source\" : \"$\\\\langle3,5,7\\\\rangle$\",\"length\" : 0,\"weight\" : 0,\"target\" : \"$\\\\langle3,5\\\\rangle$\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 6, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "sons(NumericalSemigroup(3,5,7));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tree of numerical semigroups\n", "\n", "Now we draw the sons of a numerical semigroup `s` in the tree of numerical semigroups up to level `l`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( s, l, generators ) ... end" ] }, "execution_count": 7, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "sonstree:=function(s,l,generators)\n", " local gens, frb, desc, graphTreee, d, shpr, shp, canvas, sonsf, lbl;\n", "\n", "\n", " sonsf:=function(s,n,lv)\n", " local gens, frb, desc, d, shp;\n", " if lv=0 then\n", " return ;\n", " fi;\n", " frb:=FrobeniusNumber(s);\n", " gens:=Filtered(generators(s), x-> x>frb);\n", " desc:=List(gens, g->RemoveMinimalGeneratorFromNumericalSemigroup(g,s));\n", " for d in desc do\n", " lbl:=Concatenation(\"$\\\\{\",JoinStringsWithSeparator(generators(d),\",\"),\"\\\\}$\");\n", " shp:=Shape(ShapeType!.CIRCLE, lbl);\n", " SetSize(shp,5);\n", " Add(graphTreee,shp);\n", " SetParentShape(shp,n);\n", " sonsf(d,shp,lv-1);\n", " od;\n", " if desc<>[] then\n", " return ;\n", " fi;\n", " #Add(canvas, FrancyMessage(FrancyMessageType.WARNING, \"This semigroup is a leaf\"));\n", " return ;\n", " end;\n", "\n", " frb:=FrobeniusNumber(s);\n", " gens:=Filtered(generators(s), x-> x>frb);\n", " desc:=List(gens, g->RemoveMinimalGeneratorFromNumericalSemigroup(g,s));\n", "\n", " graphTreee := Graph(GraphType.TREE);\n", " SetCollapsed(graphTreee,false);\n", " shpr:=Shape(ShapeType!.CIRCLE, \"S\");\n", " SetSize(shpr,5);\n", " Add(shpr,FrancyMessage(String(generators(s))));\n", " Add(graphTreee,shpr);\n", " canvas:=Canvas(\"Sons of a numerical semigroup\");\n", " SetTexTypesetting(canvas, true);\n", " Add(canvas,graphTreee);\n", " sonsf(s,shpr,l);\n", " return Draw(canvas);\n", "end;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Darker dots correspond either to leaves or to elements with highest genus. Blue nodes can be collapsed by clicking. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F61\",\"height\" : 600,\"title\" : \"Sons of a numerical semigroup\",\"zoomToFit\" : true,\"texTypesetting\" : true,\"menus\" : {},\"graph\" : {\"type\" : \"tree\",\"id\" : \"F58\",\"simulation\" : true,\"collapsed\" : false,\"nodes\" : {\"F59\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F59\",\"size\" : 5,\"title\" : \"S\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {\"F60\" : {\"type\" : \"default\",\"id\" : \"F60\",\"text\" : \"[ 1 ]\",\"title\" : \"\"}},\"callbacks\" : {}},\"F62\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F62\",\"size\" : 5,\"title\" : \"$\\\\{2,3\\\\}$\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"F59\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F63\" : 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\"$\\\\{2,13\\\\}$\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"F123\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F125\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F125\",\"size\" : 5,\"title\" : \"$\\\\{2,15\\\\}$\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"F124\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F126\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F126\",\"size\" : 5,\"title\" : \"$\\\\{2,17\\\\}$\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"F125\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {}},\"messages\" : {}}}" }, "execution_count": 9, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "sonstree(NumericalSemigroup(1),8,ArfCharactersOfArfNumericalSemigroup);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Oversemigroups" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( s ) ... end" ] }, "execution_count": 10, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "oversemigroups:=function(s)\n", " local ov, graphHasse, canvas,c,i,r,ovs,n,hasse,lbl;\n", " \n", " hasse:=function(rel)\n", " local dom, out;\n", " dom:=Flat(rel);\n", " out:=Filtered(rel, p-> ForAny(dom, x->([p[1],x] in rel) and ([x,p[2]] in rel)));\n", " return Difference(rel,out);\n", " end;\n", "\n", " ov:=OverSemigroupsNumericalSemigroup(s);\n", " n:=Length(ov);\n", " graphHasse := Graph(GraphType.UNDIRECTED);\n", " #SetSimulation(graphHasse,true);\n", " #SetDrag(graphHasse,true);\n", " c:=Cartesian([1..n],[1..n]);\n", " c:=Filtered(c, p-> p[2]<>p[1]);\n", " c:=Filtered(c, p-> IsSubset(ov[p[1]],ov[p[2]]));\n", " c:=hasse(c);\n", " ovs:=[];\n", " for i in [1..n] do\n", " lbl:=Concatenation(\"$\\\\langle\",JoinStringsWithSeparator(MinimalGenerators(ov[i]),\",\"),\"\\\\rangle$\");\n", "\n", "\n", " if IsIrreducible(ov[i]) then\n", " ovs[i]:=Shape(ShapeType!.DIAMOND, lbl);\n", " else\n", " ovs[i]:=Shape(ShapeType!.CIRCLE, lbl);\n", " fi;\n", " SetLayer(ovs[i],Genus(ov[i]));\n", " SetSize(ovs[i],2);\n", " Add(graphHasse,ovs[i]);\n", " od;\n", " for r in c do\n", " Add(graphHasse,Link(ovs[r[1]],ovs[r[2]]));\n", " od;\n", " canvas:=Canvas(\"Oversemigroups\");\n", " SetTexTypesetting(canvas, true);\n", " Add(canvas,graphHasse);\n", " return Draw(canvas); \n", "end;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Hasse diagram of the set of oversemigroups of the given numerical semigroup is displayed. Irreducible numerical semigroups are drawn as diamonds. 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\"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F139\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F139\",\"size\" : 2,\"title\" : \"$\\\\langle5,6,7,8,9\\\\rangle$\",\"layer\" : 4,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F140\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"diamond\",\"id\" : \"F140\",\"size\" : 2,\"title\" : \"$\\\\langle5,6,7,9\\\\rangle$\",\"layer\" : 5,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F141\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F141\",\"size\" : 2,\"title\" : \"$\\\\langle6,7,8,9,10,11\\\\rangle$\",\"layer\" : 5,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F142\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"diamond\",\"id\" : \"F142\",\"size\" : 2,\"title\" : \"$\\\\langle6,7,8,9,11\\\\rangle$\",\"layer\" : 6,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F143\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F143\",\"size\" : 2,\"title\" : \"$\\\\langle6,7,9,10,11\\\\rangle$\",\"layer\" : 6,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F144\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F144\",\"size\" : 2,\"title\" : \"$\\\\langle6,7,9,11\\\\rangle$\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F145\" : {\"id\" : \"F145\",\"source\" : \"F128\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F129\",\"color\" : \"\",\"invisible\" : false},\"F146\" : {\"id\" : \"F146\",\"source\" : \"F129\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F130\",\"color\" : \"\",\"invisible\" : false},\"F147\" : {\"id\" : \"F147\",\"source\" : \"F129\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F132\",\"color\" : \"\",\"invisible\" : false},\"F148\" : {\"id\" : \"F148\",\"source\" : \"F130\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F131\",\"color\" : \"\",\"invisible\" : false},\"F149\" : {\"id\" : \"F149\",\"source\" : \"F130\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F137\",\"color\" : \"\",\"invisible\" : false},\"F150\" : {\"id\" : \"F150\",\"source\" : \"F131\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F138\",\"color\" : \"\",\"invisible\" : false},\"F151\" : {\"id\" : \"F151\",\"source\" : \"F132\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F133\",\"color\" : \"\",\"invisible\" : false},\"F152\" : {\"id\" : \"F152\",\"source\" : \"F132\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F134\",\"color\" : \"\",\"invisible\" : false},\"F153\" : {\"id\" : \"F153\",\"source\" : \"F132\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F137\",\"color\" : \"\",\"invisible\" : false},\"F154\" : {\"id\" : \"F154\",\"source\" : \"F133\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F135\",\"color\" : \"\",\"invisible\" : false},\"F155\" : {\"id\" : \"F155\",\"source\" : \"F133\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F138\",\"color\" : \"\",\"invisible\" : false},\"F156\" : {\"id\" : \"F156\",\"source\" : \"F134\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F135\",\"color\" : \"\",\"invisible\" : false},\"F157\" : {\"id\" : \"F157\",\"source\" : \"F134\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F139\",\"color\" : \"\",\"invisible\" : false},\"F158\" : {\"id\" : \"F158\",\"source\" : \"F135\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F136\",\"color\" : \"\",\"invisible\" : false},\"F159\" : {\"id\" : \"F159\",\"source\" : \"F135\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F141\",\"color\" : \"\",\"invisible\" : false},\"F160\" : {\"id\" : \"F160\",\"source\" : \"F136\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F143\",\"color\" : \"\",\"invisible\" : false},\"F161\" : {\"id\" : \"F161\",\"source\" : \"F137\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F138\",\"color\" : \"\",\"invisible\" : false},\"F162\" : {\"id\" : \"F162\",\"source\" : \"F137\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F139\",\"color\" : \"\",\"invisible\" : false},\"F163\" : {\"id\" : \"F163\",\"source\" : \"F138\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F141\",\"color\" : \"\",\"invisible\" : false},\"F164\" : {\"id\" : \"F164\",\"source\" : \"F139\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F140\",\"color\" : \"\",\"invisible\" : false},\"F165\" : {\"id\" : \"F165\",\"source\" : \"F139\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F141\",\"color\" : \"\",\"invisible\" : false},\"F166\" : {\"id\" : \"F166\",\"source\" : \"F140\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F143\",\"color\" : \"\",\"invisible\" : false},\"F167\" : {\"id\" : \"F167\",\"source\" : \"F141\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F142\",\"color\" : \"\",\"invisible\" : false},\"F168\" : {\"id\" : \"F168\",\"source\" : \"F141\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F143\",\"color\" : \"\",\"invisible\" : false},\"F169\" : {\"id\" : \"F169\",\"source\" : \"F142\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F144\",\"color\" : \"\",\"invisible\" : false},\"F170\" : {\"id\" : \"F170\",\"source\" : \"F143\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F144\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 11, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "oversemigroups(NumericalSemigroup(6,7,9,11));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphs associated to elements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Eliahou graph of an element in a numerical semigroup is a graph whose vertices are the factorizations of the element, and to vertices are joined with an edge if they have common support." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( n, s ) ... end" ] }, "execution_count": 12, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "DrawEliahouGraph:=function(n,s)\n", " local graph, canvas, f, fs, c, nf, i, p;\n", " \n", " f:=FactorizationsElementWRTNumericalSemigroup(n,s);\n", " graph:=Graph(GraphType.UNDIRECTED);\n", " #SetShowNeighbours(graph,true);\n", " #SetSimulation(graph,true);\n", " #SetDrag(graph,true);\n", " nf:=Length(f);\n", " fs:=[];\n", " for i in [1..nf] do \n", " fs[i]:=Shape(ShapeType!.CIRCLE, Concatenation(\"(\",JoinStringsWithSeparator(f[i],\",\"),\")\"));\n", " SetLayer(fs[i],Sum(f[i]));\n", " SetSize(fs[i],1);\n", " Add(graph,fs[i]);\n", " od;\n", " c:=Cartesian([1..nf],[1..nf]);\n", " c:=Filtered(c,p->p[1]<p[2] and f[p[1]]f[p[2]]<>0);\n", " for p in c do \n", " Add(graph,Link(fs[p[1]],fs[p[2]]));\n", " od;\n", " canvas:=Canvas(\"Eliahou graph\");\n", " Add(canvas,graph);\n", " return Draw(canvas);\n", "end;" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Numerical semigroup with 3 generators" ] }, "execution_count": 13, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "s:=NumericalSemigroup(5,7,9);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F193\",\"height\" : 600,\"title\" : \"Eliahou graph\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F172\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F173\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F173\",\"size\" : 1,\"title\" : \"(7,2,0)\",\"layer\" : 9,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F174\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F174\",\"size\" : 1,\"title\" : \"(0,7,0)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F175\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F175\",\"size\" : 1,\"title\" : \"(8,0,1)\",\"layer\" : 9,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F176\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F176\",\"size\" : 1,\"title\" : \"(1,5,1)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F177\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F177\",\"size\" : 1,\"title\" : \"(2,3,2)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F178\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F178\",\"size\" : 1,\"title\" : \"(3,1,3)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F179\" : {\"id\" : \"F179\",\"source\" : \"F173\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F174\",\"color\" : \"\",\"invisible\" : false},\"F180\" : {\"id\" : \"F180\",\"source\" : \"F173\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F175\",\"color\" : \"\",\"invisible\" : false},\"F181\" : {\"id\" : \"F181\",\"source\" : \"F173\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F176\",\"color\" : \"\",\"invisible\" : false},\"F182\" : {\"id\" : \"F182\",\"source\" : \"F173\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F177\",\"color\" : \"\",\"invisible\" : false},\"F183\" : {\"id\" : \"F183\",\"source\" : \"F173\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F178\",\"color\" : \"\",\"invisible\" : false},\"F184\" : {\"id\" : \"F184\",\"source\" : \"F174\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F176\",\"color\" : \"\",\"invisible\" : false},\"F185\" : {\"id\" : \"F185\",\"source\" : \"F174\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F177\",\"color\" : \"\",\"invisible\" : false},\"F186\" : {\"id\" : \"F186\",\"source\" : \"F174\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F178\",\"color\" : \"\",\"invisible\" : false},\"F187\" : {\"id\" : \"F187\",\"source\" : \"F175\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F176\",\"color\" : \"\",\"invisible\" : false},\"F188\" : {\"id\" : \"F188\",\"source\" : \"F175\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F177\",\"color\" : \"\",\"invisible\" : false},\"F189\" : {\"id\" : \"F189\",\"source\" : \"F175\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F178\",\"color\" : \"\",\"invisible\" : false},\"F190\" : {\"id\" : \"F190\",\"source\" : \"F176\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F177\",\"color\" : \"\",\"invisible\" : false},\"F191\" : {\"id\" : \"F191\",\"source\" : \"F176\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F178\",\"color\" : \"\",\"invisible\" : false},\"F192\" : {\"id\" : \"F192\",\"source\" : \"F177\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F178\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 14, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "DrawEliahouGraph(49,s);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[ 14, 25, 27 ]" ] }, "execution_count": 15, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "BettiElements(s);" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F222\",\"height\" : 600,\"title\" : \"Eliahou graph\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F194\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F195\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F195\",\"size\" : 1,\"title\" : \"(11,0,0)\",\"layer\" : 11,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F196\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F196\",\"size\" : 1,\"title\" : \"(4,5,0)\",\"layer\" : 9,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F197\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F197\",\"size\" : 1,\"title\" : \"(5,3,1)\",\"layer\" : 9,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F198\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F198\",\"size\" : 1,\"title\" : \"(6,1,2)\",\"layer\" : 9,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F199\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F199\",\"size\" : 1,\"title\" : \"(0,4,3)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F200\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F200\",\"size\" : 1,\"title\" : \"(1,2,4)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F201\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F201\",\"size\" : 1,\"title\" : \"(2,0,5)\",\"layer\" : 7,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F202\" : {\"id\" : \"F202\",\"source\" : \"F195\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F196\",\"color\" : \"\",\"invisible\" : false},\"F203\" : {\"id\" : \"F203\",\"source\" : \"F195\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F197\",\"color\" : \"\",\"invisible\" : false},\"F204\" : {\"id\" : \"F204\",\"source\" : \"F195\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F198\",\"color\" : \"\",\"invisible\" : false},\"F205\" : {\"id\" : \"F205\",\"source\" : \"F195\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F200\",\"color\" : \"\",\"invisible\" : false},\"F206\" : {\"id\" : \"F206\",\"source\" : \"F195\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false},\"F207\" : {\"id\" : \"F207\",\"source\" : \"F196\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F197\",\"color\" : \"\",\"invisible\" : false},\"F208\" : {\"id\" : \"F208\",\"source\" : \"F196\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F198\",\"color\" : \"\",\"invisible\" : false},\"F209\" : {\"id\" : \"F209\",\"source\" : \"F196\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F199\",\"color\" : \"\",\"invisible\" : false},\"F210\" : {\"id\" : \"F210\",\"source\" : \"F196\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F200\",\"color\" : \"\",\"invisible\" : false},\"F211\" : {\"id\" : \"F211\",\"source\" : \"F196\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false},\"F212\" : {\"id\" : \"F212\",\"source\" : \"F197\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F198\",\"color\" : \"\",\"invisible\" : false},\"F213\" : {\"id\" : \"F213\",\"source\" : \"F197\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F199\",\"color\" : \"\",\"invisible\" : false},\"F214\" : {\"id\" : \"F214\",\"source\" : \"F197\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F200\",\"color\" : \"\",\"invisible\" : false},\"F215\" : {\"id\" : \"F215\",\"source\" : \"F197\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false},\"F216\" : {\"id\" : \"F216\",\"source\" : \"F198\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F199\",\"color\" : \"\",\"invisible\" : false},\"F217\" : {\"id\" : \"F217\",\"source\" : \"F198\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F200\",\"color\" : \"\",\"invisible\" : false},\"F218\" : {\"id\" : \"F218\",\"source\" : \"F198\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false},\"F219\" : {\"id\" : \"F219\",\"source\" : \"F199\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F200\",\"color\" : \"\",\"invisible\" : false},\"F220\" : {\"id\" : \"F220\",\"source\" : \"F199\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false},\"F221\" : {\"id\" : \"F221\",\"source\" : \"F200\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F201\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 16, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "DrawEliahouGraph(55,s);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( n, s ) ... end" ] }, "execution_count": 17, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "DrawRosalesGraph:=function(n,s)\n", " local graph, canvas, msg, msgs, c, nv, i, p;\n", " \n", " msg:=Filtered(MinimalGenerators(s), g->n-g in s);\n", " graph:=Graph(GraphType.UNDIRECTED);\n", " #SetSimulation(graph,true);\n", " #SetDrag(graph,true);\n", " nv:=Length(msg);\n", " msgs:=[];\n", " for i in [1..nv] do \n", " msgs[i]:=Shape(ShapeType!.CIRCLE, String(msg[i]));\n", " SetSize(msgs[i],1);\n", " Add(graph,msgs[i]);\n", " od;\n", " c:=Cartesian([1..nv],[1..nv]);\n", " c:=Filtered(c,p->p[1]<p[2] and n-(msg[p[1]]+msg[p[2]]) in s);\n", " for p in c do \n", " Add(graph,Link(msgs[p[1]],msgs[p[2]]));\n", " od;\n", " canvas:=Canvas(\"Rosales graph\");\n", " Add(canvas,graph);\n", " return Draw(canvas);\n", "end;" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Numerical semigroup with 3 generators" ] }, "execution_count": 18, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "s:=NumericalSemigroup(5,7,9);" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F230\",\"height\" : 600,\"title\" : \"Rosales graph\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F223\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F224\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F224\",\"size\" : 1,\"title\" : \"5\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F225\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F225\",\"size\" : 1,\"title\" : \"7\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F226\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F226\",\"size\" : 1,\"title\" : \"9\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F227\" : {\"id\" : \"F227\",\"source\" : \"F224\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F225\",\"color\" : \"\",\"invisible\" : false},\"F228\" : {\"id\" : \"F228\",\"source\" : \"F224\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F226\",\"color\" : \"\",\"invisible\" : false},\"F229\" : {\"id\" : \"F229\",\"source\" : \"F225\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F226\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 19, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "DrawRosalesGraph(49,s);" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.francy+json": "{\"version\" : \"1.2.4\",\"mime\" : \"application\\/vnd.francy+json\",\"canvas\" : {\"width\" : 800,\"id\" : \"F236\",\"height\" : 600,\"title\" : \"Rosales graph\",\"zoomToFit\" : true,\"texTypesetting\" : false,\"menus\" : {},\"graph\" : {\"type\" : \"undirected\",\"id\" : \"F231\",\"simulation\" : true,\"collapsed\" : true,\"nodes\" : {\"F232\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F232\",\"size\" : 1,\"title\" : \"3\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F233\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F233\",\"size\" : 1,\"title\" : \"5\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}},\"F234\" : {\"x\" : 0,\"y\" : 0,\"type\" : \"circle\",\"id\" : \"F234\",\"size\" : 1,\"title\" : \"7\",\"layer\" : 0,\"color\" : \"\",\"parent\" : \"\",\"menus\" : {},\"messages\" : {},\"callbacks\" : {}}},\"links\" : {\"F235\" : {\"id\" : \"F235\",\"source\" : \"F232\",\"length\" : 0,\"weight\" : 0,\"target\" : \"F234\",\"color\" : \"\",\"invisible\" : false}}},\"messages\" : {}}}" }, "execution_count": 20, "metadata": { "application/vnd.francy+json": {} }, "output_type": "execute_result" } ], "source": [ "DrawRosalesGraph(10,NumericalSemigroup(3,5,7));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphs of factorizations" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function( f ) ... end" ] }, "execution_count": 21, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "DrawFactorizationGraph:=function(f)\n", " local graph, canvas, fs, c, nf, i, p, ln, distance, Kruskal, tv;\n", "\n", " Kruskal := function(V, E)\n", " local trees, needed, v, e, i,j, nv;\n", "\n", " trees := List(V, v-> [v]);\n", " needed := [];\n", " nv:=Length(V);\n", " for e in E do\n", " i:=First([1..Length(trees)], k-> e[1] in trees[k]);\n", " j:=First([1..Length(trees)], k-> e[2] in trees[k]);\n", " if i<>j then\n", " trees[i]:=Union(trees[i], trees[j]);\n", " trees[j]:=[];\n", " Add(needed,e);\n", " fi;\n", " if Length(needed)=nv-1 then\n", " break;\n", " fi;\n", " od;\n", " return needed;\n", " end;\n", " \n", " distance := function(a,b)\n", " local k, gcd, i;\n", "\n", " k := Length(a);\n", " if k <> Length(b) then\n", " Error(\"The lengths of a and b are different.\\n\");\n", " fi;\n", "\n", "\n", " gcd := [];\n", " for i in [1..k] do\n", " Add(gcd, Minimum(a[i],b[i]));\n", " od;\n", " return(Maximum(Sum(a-gcd),Sum(b-gcd)));\n", "\n", " end;\n", "\n", " graph:=Graph(GraphType.UNDIRECTED);\n", "# SetSimulation(graph,true);\n", "#SetDrag(graph,true);\n", "#SetShowNeighbours(graph,true);\n", " nf:=Length(f);\n", " fs:=[];\n", " for i in [1..nf] do \n", " fs[i]:=Shape(ShapeType!.CIRCLE, Concatenation(\"(\",JoinStringsWithSeparator(f[i],\",\"),\")\"));\n", " SetLayer(fs[i],Sum(f[i]));\n", " SetSize(fs[i],1);\n", " Add(graph,fs[i]);\n", " od;\n", " c:=Cartesian([1..nf],[1..nf]);\n", " c:=Filtered(c,p->p[1]<p[2] and f[p[1]]f[p[2]]<>0);\n", " Sort(c,function(e,ee) return distance(f[e[1]],f[e[2]])<distance(f[ee[1]],f[ee[2]]); end);\n", " tv:=Kruskal(f,List(c,p->[f[p[1]],f[p[2]]]));\n", " for p in c do \n", " ln:=Link(fs[p[1]],fs[p[2]]);\n", " #SetWeight(ln, distance(f[p[1]],f[p[2]]));\n", " SetTitle(ln, String(distance(f[p[1]],f[p[2]])));\n", " if [f[p[1]],f[p[2]]] in tv then \n", " SetColor(ln,\"red\");\n", " fi;\n", " Add(graph,ln);\n", " od;\n", " canvas:=Canvas(\"Factorizations graph\");\n", " Add(canvas,graph);\n", " return Draw(canvas);\n", "end;" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[ [ 10, 2, 0 ], [ 5, 5, 0 ], [ 0, 8, 0 ], [ 11, 0, 1 ], [ 6, 3, 1 ], [ 1, 6, 1 ], [ 7, 1, 2 ], [ 2, 4, 2 ], [ 3, 2, 3 ], [ 4, 0, 4 ], [ 0, 1, 5 ] ]" ] }, "execution_count": 22, "metadata": { "text/plain": "" }, "output_type": "execute_result" } ], "source": [ "f:=FactorizationsElementWRTNumericalSemigroup(40,NumericalSemigroup(3,5,7));" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "GAP 4", "language": "gap", "name": "gap-4" }, "language_info": { "codemirror_mode": "gap", "file_extension": ".g", "mimetype": "text/x-gap", "name": "GAP 4", "nbconvert_exporter": "", "pygments_lexer": "gap", "version": "4.dev" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ddcampayo/ddcampayo.github.io	cursos_previos/Curso_CFD_OS_2019/notebooks/conveccion_1d.ipynb	1	46939	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convección en una dimensión" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import scipy as np\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parámetros" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "L = 1.0 # longitud del sistema 1D\n", "nx = 42 # nodos espaciales\n", "dx = L / (nx-2) # sí, quitamos dos nodos ...\n", "x = np.linspace( 0 , L , num=nx )\n", "\n", "T= 0.1 # tiempo total\n", "nt = 100 # pasos temporales\n", "dt = T / nt\n", "\n", "c = 1 # velocidad de la onda" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ¡Número de Courant !" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.04" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Co = c * dt / dx\n", "Co" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Condiciones iniciales" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "u0 = 1 * np.ones(nx) # todo uno\n", "x1 = L/4 ; n1 = int(x1 / dx)\n", "x2 = L/2 ; n2 = int(x2 / dx)\n", "u0[ n1 : n2 ] = 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f643a62c190>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZPinNePlhJV89VvLISYpOfo2kR2Y93tP7NQyBTr56dPLIydS4X/Dkk8f9is3y6KPS6tWT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\nPAC02P8Diy1vBrZXlXwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f643a6f9910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot( x , u0 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Un paso en el tiempo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recordemos que queremos implementar $u_i^{n+1} = u_i^n - \\mathrm{Co}/2 (u_{i+1}^n-u_{i-1}^n)$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "u = u0.copy()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "un = u.copy() # distribución actual\n", "\n", "i = 1\n", "u[i] = un[i] - (Co / 2.0) * (un[i+1] - _valor_izdo )\n", "\n", "\n", "\n", "for i in range( 2 , nx - 2 ): # Ahora queda claro por qué hemos quitado los extremos !!\n", " u[i] = un[i] - (Co / 2.0) * (un[i+1] - un[i-1])\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f643a591050>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f643a6ed610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x,u)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tiempo completo" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "u = u0.copy()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "for n in range(nt):\n", " un = u.copy()\n", " for i in range( 1 , nx - 1 ): \n", " u[i] = un[i] - (Co / 2.0) * (un[i+1] - un[i-1])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fb46945bb90>,\n", " <matplotlib.lines.Line2D at 0x7fb46945bd90>]" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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u06fDL3858McHli/3LXciUl2q5orX3ixY4Hvhu9+JZ8cOX7/ur/MmEMzk+7v5\ncrkhr5m8iJSjqkPeDL71Lbj++q478Tz1lF8caujQ4o8fOdK3qG3p8zRz+eE6YYJ/4Smn77mpCZqb\n/b1pRaS6VHXIg19294gjuu7E0/2m3WH0V5d3rvwLkKJYqGzFCn/7QvUri1Sfqg956LoTz+7d4U+6\nBvqry2/Z4v8iOOig8sZX7pWv6qwRqV4KeeDUU30ILlzoyzVz54Z/bH8hH1UdPIqZvOrxItVJIV/w\njW/A17/ua+Bjx4Z/XB5CXjN5keql2/8VnHACnH++77YpRX/ryq9YUVrppy/Tpw+8XOOcZvIi1cxc\nmGUUo9qZmUtyf6VqafHLJdQVWYumu927/bo5LS37Lhlw4olw881+qeNy7NzpX3x27Ch9obJXX/Xj\neOON8sYgIukxM5xzA2qdULmmm5EjSwt48DdxOPRQv4hYdx0dvt8+ilvtDR/uT95u3Fj6YzWLF6lu\nCvkI9FaXX7/eB3OxZYjDGugNRFSPF6luCvkI9FaXj3oGPdA2Ss3kRaqbQj4C9fX7zuSjvtXeQDts\nNJMXqW4K+Qj0Vq6JYyZfash3dPjZfxTnBUQknxTyEUgi5AfSRtnQAIcc4m8ALSLVSSEfgUmT4M03\n/S32wLdVrl//7l38IjFhgm/TbG4O/xjV40VEIR+Bmhp/Y/GGBv/5mjVw+OHhVrIMa9AgX/svpWSj\neryIKOQj0v3ka7krT/al1JKNZvIiopCPSPe6fFzhWurJV83kRUQhH5GshXxrK2za5MclItVLIR+R\n7hdERd0jHygl5F9+2ZeQeq6nIyLVRSEfkaAmv2OHXwxs6tTo9xG8kHR0FN9W9XgRAYV8ZMaP9yWS\npUv9CdJSV4sMY8QIvxrlpk3Ft1U9XkRAIR8ZMz/T/q//incGfdRRsGpV8e00kxcRUMhHato0+M1v\n4g3Xj30MvvIVaGrqfzvN5EUEFPKRmjYNtm6NN+QvvxwuuAAuvRTa2nrfpqnJXxk7aVJ84xCRfFDI\nR6i+3r+Pu0zyve/BsGHwuc/52/v1tGIFzJjhr5IVkeqmGIjQtGn+7lKHHRbvfmpq4Be/gKeeghtv\n3Pf7qseLSEA38o7QnDnw61/7k7BxGzkS7r0XTj3V/wXxvvd1fU/1eBEJaCYfocGDYcGC5PY3aZLv\n5rniCnjhha6vx7V2jojkj0I+504+GW65xc/kX3vN1+jjuuJWRPJH5ZoK8MEP+qtt3/c+uOMOqK31\nNxEXETE8ypfOAAAEaklEQVTXW3tGXDszc0nur5o4B5/+NDzxBEyeDA89lPaIRCQqZoZzbkBn+1Su\nqRBmsGiRv93f7Nlpj0ZEskIz+QqzZ49fwGzYsLRHIiJRKWcmr5p8hamtTXsEIpIlKteIiFQwhbyI\nSAVTyIuIVDCFvIhIBVPIi4hUMIW8iEgFU8inZPHixWkPITN0LLroWHTRsYiGQj4l+gHuomPRRcei\ni45FNBTyIiIVTCEvIlLBEl+7JrGdiYhUkIGuXZNoyIuISLJUrhERqWAKeRGRChZLyJvZBWb2ipmt\nMbP/2cv3a83sTjNba2bLzGxSHOPIghDH4ktmttLMXjCzP5rZYWmMMwnFjkW37T5gZp1mNifJ8SUp\nzLEwsw8VfjaWm9nPkx5jUkL8jhxmZg+b2XOF35ML0xhn3MzsVjPbamYv9bPNTYXcfMHMjg/1xM65\nSN/wLxwNwGRgCPACML3HNn8H3FL4+MPAnVGPIwtvIY/FmcDQwsefqeZjUdhuBPAo8AQwJ+1xp/hz\ncSTwLDCq8PmBaY87xWPxI+DKwsdHA41pjzumY3E6cDzwUh/fvxC4v/DxfwOeDPO8cczkTwbWOuc2\nOufagDuBi3tsczHws8LHdwPnxDCOLCh6LJxzjzrn3il8+iQwIeExJiXMzwXAN4FvA7uTHFzCwhyL\n/wH8wDnXDOCc25bwGJMS5lh0AqMKH48BtiQ4vsQ455YATf1scjFwe2Hbp4DRZjau2PPGEfITgM3d\nPn+VfYPr3W2ccx3AdjM7IIaxpC3MsejuCuD3sY4oPUWPhZnNBiY65yr1GATC/FxMA44ysyVm9oSZ\nnZ/Y6JIV5ljcAHzCzDYD9wGfT2hsWdPzWG0hxKQwK7f/G1D/ZyUxs48DJ+DLN1XHzAy4EfhU9y+n\nNJwsGIwv2cwDJgGPmdnMYGZfZT4K3Oac+99mdgrwc2BGymPKjThm8lvwP5SBiez759WrwGEAZlaD\nrzv+NYaxpC3MscDMzgW+BlxU+JO1EhU7FiPxv7iLzawROAW4p0JPvob9Hfmtc67TObcBWAPUJzO8\nRIU5FlcAvwRwzj0JDDWzA5MZXqZsoZCbBb3mSU9xhPzTwJFmNtnMaoGPAL/tsc29dM3YPgg8HMM4\nsqDosSiUKBYC73POvZXCGJPS77FwzjU75w52zk1xzh2BPz9xkXPuuZTGG6cwvyO/Ac4CKARaPbA+\n0VEmI8yx2AicC2BmRwP7VfA5CqPvv2B/C3wSoPAXzXbn3NZiTxh5ucY512FmVwEP4l9EbnXOvWxm\nNwBPO+fuA24F/q+ZrQXewv/HVpyQx+I7wHDgV4WSxUbn3CXpjToeIY/FXg+hQss1YY6Fc+4BMzvP\nzFYC7cDVzrn+TsrlUsifi6uBH5vZl/AnYT/V9zPml5ndAcwHxprZJuA6oBZwzrlFzrnfmdnfmFkD\nsBO4PNTzFtpxRESkAumKVxGRCqaQFxGpYAp5EZEKppAXEalgCnkRkQqmkBcRqWAKeRGRCqaQFxGp\nYP8fWSBSDw4ak/wAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb469537690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x , u , x , u0 , 'r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formulaciones alternativas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Qué pasa si probamos el algoritmo \"peor\"? $u_i^{n+1} = u_i^n - \\mathrm{Co} (u_{i}^n-u_{i-1}^n)$" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "u = u0.copy()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "for n in range(nt):\n", " un = u.copy()\n", " for i in range( 1 , nx ): \n", " u[i] = un[i] - Co * (un[i] - un[i-1])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fb469386790>,\n", " <matplotlib.lines.Line2D at 0x7fb469386990>]" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb469493cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x , u , x , u0 , 'r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reflexiones" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* ¿Qué hemos hecho en los bordes? (condiciones de contorno)\n", "* ¿Por qué los algoritmos difieren tantísimo?\n", "* ¿Cuántos parámetros hay realmente? (Pista: 1)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
mne-tools/mne-tools.github.io	dev/_downloads/524b9a6067f1a7bd3b66aa385465b921/20_source_alignment.ipynb	1	18952	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Source alignment and coordinate frames\n\nThis tutorial shows how to visually assess the spatial alignment of MEG sensor\nlocations, digitized scalp landmark and sensor locations, and MRI volumes. This\nalignment process is crucial for computing the forward solution, as is\nunderstanding the different coordinate frames involved in this process.\n\nLet's start out by loading some data.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os.path as op\n\nimport numpy as np\nimport nibabel as nib\nfrom scipy import linalg\n\nimport mne\nfrom mne.io.constants import FIFF\n\ndata_path = mne.datasets.sample.data_path()\nsubjects_dir = op.join(data_path, 'subjects')\nraw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')\ntrans_fname = op.join(data_path, 'MEG', 'sample',\n 'sample_audvis_raw-trans.fif')\nraw = mne.io.read_raw_fif(raw_fname)\ntrans = mne.read_trans(trans_fname)\nsrc = mne.read_source_spaces(op.join(subjects_dir, 'sample', 'bem',\n 'sample-oct-6-src.fif'))\n\n# Load the T1 file and change the header information to the correct units\nt1w = nib.load(op.join(data_path, 'subjects', 'sample', 'mri', 'T1.mgz'))\nt1w = nib.Nifti1Image(t1w.dataobj, t1w.affine)\nt1w.header['xyzt_units'] = np.array(10, dtype='uint8')\nt1_mgh = nib.MGHImage(t1w.dataobj, t1w.affine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. raw:: html\n\n <style>\n .pink {color:DarkSalmon; font-weight:bold}\n .blue {color:DeepSkyBlue; font-weight:bold}\n .gray {color:Gray; font-weight:bold}\n .magenta {color:Magenta; font-weight:bold}\n .purple {color:Indigo; font-weight:bold}\n .green {color:LimeGreen; font-weight:bold}\n .red {color:Red; font-weight:bold}\n </style>\n\n.. role:: pink\n.. role:: blue\n.. role:: gray\n.. role:: magenta\n.. role:: purple\n.. role:: green\n.. role:: red\n\n\n## Understanding coordinate frames\nFor M/EEG source imaging, there are three coordinate frames must be\nbrought into alignment using two 3D [transformation matrices](wiki_xform_)\nthat define how to rotate and translate points in one coordinate frame\nto their equivalent locations in another. The three main coordinate frames\nare:\n\n* :blue:`\"meg\"`: the coordinate frame for the physical locations of MEG\n sensors\n* :gray:`\"mri\"`: the coordinate frame for MRI images, and scalp/skull/brain\n surfaces derived from the MRI images\n* :pink:`\"head\"`: the coordinate frame for digitized sensor locations and\n scalp landmarks (\"fiducials\")\n\n\nEach of these are described in more detail in the next section.\n\nA good way to start visualizing these coordinate frames is to use the\n`mne.viz.plot_alignment` function, which is used for creating or inspecting\nthe transformations that bring these coordinate frames into alignment, and\ndisplaying the resulting alignment of EEG sensors, MEG sensors, brain\nsources, and conductor models. If you provide ``subjects_dir`` and\n``subject`` parameters, the function automatically loads the subject's\nFreesurfer MRI surfaces. Important for our purposes, passing\n``show_axes=True`` to `~mne.viz.plot_alignment` will draw the origin of each\ncoordinate frame in a different color, with axes indicated by different sized\narrows:\n\n* shortest arrow: (R)ight / X\n* medium arrow: forward / (A)nterior / Y\n* longest arrow: up / (S)uperior / Z\n\nNote that all three coordinate systems are RAS coordinate frames and\nhence are also `right-handed`_ coordinate systems. Finally, note that the\n``coord_frame`` parameter sets which coordinate frame the camera\nshould initially be aligned with. Let's have a look:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',\n subjects_dir=subjects_dir, surfaces='head-dense',\n show_axes=True, dig=True, eeg=[], meg='sensors',\n coord_frame='meg', mri_fiducials='estimated')\nmne.viz.set_3d_view(fig, 45, 90, distance=0.6, focalpoint=(0., 0., 0.))\nprint('Distance from head origin to MEG origin: %0.1f mm'\n % (1000 * np.linalg.norm(raw.info['dev_head_t']['trans'][:3, 3])))\nprint('Distance from head origin to MRI origin: %0.1f mm'\n % (1000 * np.linalg.norm(trans['trans'][:3, 3])))\ndists = mne.dig_mri_distances(raw.info, trans, 'sample',\n subjects_dir=subjects_dir)\nprint('Distance from %s digitized points to head surface: %0.1f mm'\n % (len(dists), 1000 * np.mean(dists)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coordinate frame definitions\n1. Neuromag/Elekta/MEGIN head coordinate frame (\"head\", :pink:`pink axes`)\n The head coordinate frame is defined through the coordinates of\n anatomical landmarks on the subject's head: usually the Nasion (`NAS`_),\n and the left and right preauricular points (`LPA`_ and `RPA`_).\n Different MEG manufacturers may have different definitions of the head\n coordinate frame. A good overview can be seen in the\n `FieldTrip FAQ on coordinate systems`_.\n\n For Neuromag/Elekta/MEGIN, the head coordinate frame is defined by the\n intersection of\n\n 1. the line between the LPA (:red:`red sphere`) and RPA\n (:purple:`purple sphere`), and\n 2. the line perpendicular to this LPA-RPA line one that goes through\n the Nasion (:green:`green sphere`).\n\n The axes are oriented as X origin\u2192RPA, Y origin\u2192NAS,\n Z origin\u2192upward (orthogonal to X and Y).\n\n .. note:: The required 3D coordinates for defining the head coordinate\n frame (NAS, LPA, RPA) are measured at a stage separate from\n the MEG data recording. There exist numerous devices to\n perform such measurements, usually called \"digitizers\". For\n example, see the devices by the company `Polhemus`_.\n\n2. MEG device coordinate frame (\"meg\", :blue:`blue axes`)\n The MEG device coordinate frame is defined by the respective MEG\n manufacturers. All MEG data is acquired with respect to this coordinate\n frame. To account for the anatomy and position of the subject's head, we\n use so-called head position indicator (HPI) coils. The HPI coils are\n placed at known locations on the scalp of the subject and emit\n high-frequency magnetic fields used to coregister the head coordinate\n frame with the device coordinate frame.\n\n From the Neuromag/Elekta/MEGIN user manual:\n\n The origin of the device coordinate system is located at the center\n of the posterior spherical section of the helmet with X axis going\n from left to right and Y axis pointing front. The Z axis is, again\n normal to the plane with positive direction up.\n\n .. note:: The HPI coils are shown as :magenta:`magenta spheres`.\n Coregistration happens at the beginning of the recording and\n the head\u2194meg transformation matrix is stored in\n ``raw.info['dev_head_t']``.\n\n3. MRI coordinate frame (\"mri\", :gray:`gray axes`)\n Defined by Freesurfer, the \"MRI surface RAS\" coordinate frame has its\n origin at the center of a 256\u00d7256\u00d7256 1mm anisotropic volume (though the\n center may not correspond to the anatomical center of the subject's\n head).\n\n .. note:: We typically align the MRI coordinate frame to the head\n coordinate frame through a [rotation and translation matrix](wiki_xform_), that we refer to in MNE as ``trans``.\n\n### A bad example\nLet's try using `~mne.viz.plot_alignment` by making ``trans`` the identity\ntransform. This (incorrectly!) equates the MRI and head coordinate frames.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "identity_trans = mne.transforms.Transform('head', 'mri')\nmne.viz.plot_alignment(raw.info, trans=identity_trans, subject='sample',\n src=src, subjects_dir=subjects_dir, dig=True,\n surfaces=['head-dense', 'white'], coord_frame='meg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A good example\nHere is the same plot, this time with the ``trans`` properly defined\n(using a precomputed transformation matrix).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mne.viz.plot_alignment(raw.info, trans=trans, subject='sample',\n src=src, subjects_dir=subjects_dir, dig=True,\n surfaces=['head-dense', 'white'], coord_frame='meg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the transformations\nLet's visualize these coordinate frames using just the scalp surface; this\nwill make it easier to see their relative orientations. To do this we'll\nfirst load the Freesurfer scalp surface, then apply a few different\ntransforms to it. In addition to the three coordinate frames discussed above,\nwe'll also show the \"mri_voxel\" coordinate frame. Unlike MRI Surface RAS,\n\"mri_voxel\" has its origin in the corner of the volume (the left-most,\nposterior-most coordinate on the inferior-most MRI slice) instead of at the\ncenter of the volume. \"mri_voxel\" is also not an RAS coordinate system:\nrather, its XYZ directions are based on the acquisition order of the T1 image\nslices.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# The head surface is stored in \"mri\" coordinate frame\n# (origin at center of volume, units=mm)\nseghead_rr, seghead_tri = mne.read_surface(\n op.join(subjects_dir, 'sample', 'surf', 'lh.seghead'))\n\n# To put the scalp in the \"head\" coordinate frame, we apply the inverse of\n# the precomputed `trans` (which maps head \u2192 mri)\nmri_to_head = linalg.inv(trans['trans'])\nscalp_pts_in_head_coord = mne.transforms.apply_trans(\n mri_to_head, seghead_rr, move=True)\n\n# To put the scalp in the \"meg\" coordinate frame, we use the inverse of\n# raw.info['dev_head_t']\nhead_to_meg = linalg.inv(raw.info['dev_head_t']['trans'])\nscalp_pts_in_meg_coord = mne.transforms.apply_trans(\n head_to_meg, scalp_pts_in_head_coord, move=True)\n\n# The \"mri_voxel\"\u2192\"mri\" transform is embedded in the header of the T1 image\n# file. We'll invert it and then apply it to the original `seghead_rr` points.\n# No unit conversion necessary: this transform expects mm and the scalp surface\n# is defined in mm.\nvox_to_mri = t1_mgh.header.get_vox2ras_tkr()\nmri_to_vox = linalg.inv(vox_to_mri)\nscalp_points_in_vox = mne.transforms.apply_trans(\n mri_to_vox, seghead_rr, move=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've transformed all the points, let's plot them. We'll use the\nsame colors used by `~mne.viz.plot_alignment` and use :green:`green` for the\n\"mri_voxel\" coordinate frame:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_head(renderer, points, color, opacity=0.95):\n renderer.mesh(points.T, triangles=seghead_tri, color=color,\n opacity=opacity)\n\n\nrenderer = mne.viz.backends.renderer.create_3d_figure(\n size=(600, 600), bgcolor='w', scene=False)\nadd_head(renderer, seghead_rr, 'gray')\nadd_head(renderer, scalp_pts_in_meg_coord, 'blue')\nadd_head(renderer, scalp_pts_in_head_coord, 'pink')\nadd_head(renderer, scalp_points_in_vox, 'green')\nmne.viz.set_3d_view(figure=renderer.figure, distance=800,\n focalpoint=(0., 30., 30.), elevation=105, azimuth=180)\nrenderer.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The relative orientations of the coordinate frames can be inferred by\nobserving the direction of the subject's nose. Notice also how the origin of\nthe :green:`mri_voxel` coordinate frame is in the corner of the volume\n(above, behind, and to the left of the subject), whereas the other three\ncoordinate frames have their origin roughly in the center of the head.\n\n### Example: MRI defacing\nFor a real-world example of using these transforms, consider the task of\ndefacing the MRI to preserve subject anonymity. If you know the points in\nthe \"head\" coordinate frame (as you might if you're basing the defacing on\ndigitized points) you would need to transform them into \"mri\" or \"mri_voxel\"\nin order to apply the blurring or smoothing operations to the MRI surfaces or\nimages. Here's what that would look like (we'll use the nasion landmark as a\nrepresentative example):\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the nasion:\nnasion = [p for p in raw.info['dig'] if\n p['kind'] == FIFF.FIFFV_POINT_CARDINAL and\n p['ident'] == FIFF.FIFFV_POINT_NASION][0]\nassert nasion['coord_frame'] == FIFF.FIFFV_COORD_HEAD\nnasion = nasion['r'] # get just the XYZ values\n\n# Transform it from head to MRI space (recall that `trans` is head \u2192 mri)\nnasion_mri = mne.transforms.apply_trans(trans, nasion, move=True)\n# Then transform to voxel space, after converting from meters to millimeters\nnasion_vox = mne.transforms.apply_trans(\n mri_to_vox, nasion_mri 1e3, move=True)\n# Plot it to make sure the transforms worked\nrenderer = mne.viz.backends.renderer.create_3d_figure(\n size=(400, 400), bgcolor='w', scene=False)\nadd_head(renderer, scalp_points_in_vox, 'green', opacity=1)\nrenderer.sphere(center=nasion_vox, color='orange', scale=10)\nmne.viz.set_3d_view(figure=renderer.figure, distance=600.,\n focalpoint=(0., 125., 250.), elevation=45, azimuth=180)\nrenderer.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining the head\u2194MRI ``trans`` using the GUI\nYou can try creating the head\u2194MRI transform yourself using\n:func:`mne.gui.coregistration`.\n\n* First you must load the digitization data from the raw file\n (``Head Shape Source``). The MRI data is already loaded if you provide the\n ``subject`` and ``subjects_dir``. Toggle ``Always Show Head Points`` to see\n the digitization points.\n* To set the landmarks, toggle ``Edit`` radio button in ``MRI Fiducials``.\n* Set the landmarks by clicking the radio button (LPA, Nasion, RPA) and then\n clicking the corresponding point in the image.\n* After doing this for all the landmarks, toggle ``Lock`` radio button. You\n can omit outlier points, so that they don't interfere with the finetuning.\n\n .. note:: You can save the fiducials to a file and pass\n ``mri_fiducials=True`` to plot them in\n :func:`mne.viz.plot_alignment`. The fiducials are saved to the\n subject's bem folder by default.\n* Click ``Fit Head Shape``. This will align the digitization points to the\n head surface. Sometimes the fitting algorithm doesn't find the correct\n alignment immediately. You can try first fitting using LPA/RPA or fiducials\n and then align according to the digitization. You can also finetune\n manually with the controls on the right side of the panel.\n* Click ``Save As...`` (lower right corner of the panel), set the filename\n and read it with :func:`mne.read_trans`.\n\nFor more information, see step by step instructions\n[in these slides](https://www.slideshare.net/mne-python/mnepython-coregistration).\nUncomment the following line to align the data yourself.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# mne.gui.coregistration(subject='sample', subjects_dir=subjects_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n## Alignment without MRI\nThe surface alignments above are possible if you have the surfaces available\nfrom Freesurfer. :func:`mne.viz.plot_alignment` automatically searches for\nthe correct surfaces from the provided ``subjects_dir``. Another option is\nto use a `spherical conductor model <eeg_sphere_model>`. It is\npassed through ``bem`` parameter.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sphere = mne.make_sphere_model(info=raw.info, r0='auto', head_radius='auto')\nsrc = mne.setup_volume_source_space(sphere=sphere, pos=10.)\nmne.viz.plot_alignment(\n raw.info, trans=trans, eeg='projected', bem=sphere, src=src, dig=True,\n surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'],\n coord_frame='meg', show_axes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to use :func:`mne.gui.coregistration`\nto warp a subject (usually ``fsaverage``) to subject digitization data, see\n[these slides](https://www.slideshare.net/mne-python/mnepython-scale-mri).\n\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
darshanbagul/ComputerVision	EdgeDetection-ZeroCrossings/EdgeDetectionByZeroCrossings.ipynb	1	1154014	null	gpl-3.0
fossdevil/Assignments	PRNN/Assignment/Assignment5/.ipynb_checkpoints/Assignment5-checkpoint.ipynb	1	684580	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import time\n", "from sklearn.svm import SVC\n", "from sklearn.neural_network import MLPClassifier\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as clrs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading Files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def readFile(fileName):\n", " f = open(fileName, \"r+\")\n", " \n", " X = []\n", " y = []\n", " \n", " for line in f.readlines():\n", " m = line.strip().split(\",\")\n", " if len(m) == 3:\n", " X.append([float(m[0]), float(m[1])])\n", " y.append(int(float(m[2])))\n", " \n", " X = np.asarray(X)\n", " y = np.asarray(y)\n", " \n", " f.close()\n", " \n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper Function to plot points" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plotPoints(X, y):\n", " colors = ['r','b','k','y']\n", " \n", " X0 = np.asarray([X[i] for i in range(len(y)) if y[i] == 0])\n", " X1 = np.asarray([X[i] for i in range(len(y)) if y[i] == 1])\n", " X2 = np.asarray([X[i] for i in range(len(y)) if y[i] == 2])\n", " X3 = np.asarray([X[i] for i in range(len(y)) if y[i] == 3])\n", "# print X0.shape, X1.shape, X2.shape, X3.shape\n", " \n", " x0 = plt.scatter(X0[:,0], X0[:,1], s = 5, color = colors[0])\n", " x1 = plt.scatter(X1[:,0], X1[:,1], s = 5, color = colors[1])\n", " x2 = plt.scatter(X2[:,0], X2[:,1], s = 5, color = colors[2])\n", " x3 = plt.scatter(X3[:,0], X3[:,1], s = 5, color = colors[3])\n", " \n", " plt.legend((x0,x1,x2,x3), ('Class-0','Class-1','Class-2','Class-3'), loc='upper right')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Draw Plots " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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yNezxs3a8bo1hfNzfO+iXc2BZ3rjFujqvlsFtCmL1NtXMIy4t/QLfdd07LnEZ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ScprDk/rwL0AYBpWhXJW13XMTQ0hAcPHoAxhsbGRiWWT2Q0bp4gFovB5/MhkUgoJaGZTAaJRAKm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\nrPlF54vh0AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe85b9bd0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X0, y0 = readFile(\"assignment-5/assign_5_data_0.txt\")\n", "plotPoints(X0,y0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XXpfLo7SwUFBoonRac47TpXMMymZVKkmN9glSQ7OzQX+CsBy8KqzXTydVq97q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9Xa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\nreX79ydctTp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ijOiJJ4imp+UQcDLNMmWSi+SaohFumpRNpcg0TUokEpROpwkAbdwIOnqU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\nx9VYf48cAW3axHzFW31ubdjm4sWLNDEx4cM98Xic3nn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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe85b6a56d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X10, y10 = readFile(\"assignment-5/assign_5_data_10.txt\")\n", "plotPoints(X10,y10)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lGIFsJZRU0aJDrovMMAwvmAzHYlCwHyrlM5VKsa7OYHd3PJyp5CykVfBTRw3D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aS+vt4rGV1fX5fh4WFpatJUn3l5eenQVFb5sGwMZ7nv3fxBKvX/sffuwXFd553g7z5AWUI/BAIE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cJMnMvPF6/xJbW9PadYnP179+okDT+ebNcxBUwSINd2VlGa+9dhnl5Q1GlbMsuwHIcL1RjpJH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\nmYOjM/a2ht/ZTMMdAxEvtTJcaPHsPLBXUWEj4KrpfYAah6OCwM/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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe8584c25d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X20, y20 = readFile(\"assignment-5/assign_5_data_20.txt\")\n", "plotPoints(X20,y20)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3L4fw9itHQOhLp++WESkxMLAPvb3r8XTqfjSsq4GTvF9lrYduyYO8AgouWipN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Chw+LcG5FAaJRoLUVTtcxHDtG2L0b+MWvK5hDM4aGVHDejKEhkUnS3Cw0BKqqYP/+Zly5woIo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\nLK2P0t0O88V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rSHFY/dWPBRnc6fZFYPiYzTgpWvZAIfunLnocGen0ZZhzc9JBTQzZmkhd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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe8583ba0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X40, y40 = readFile(\"assignment-5/assign_5_data_40.txt\")\n", "plotPoints(X40,y40)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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12BEyFRmGwVhVlWdaraYs+FGkFxGW0eMSlrG0uVEa2i+nXJdXr46EoqR27AAB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qA7d1b5fMH/0qd9377MLZ9clzfz86+fJIQ3blBUMqiwbxCuq1Q26dmnvkAXL12iKeJQ6q1HbXrv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\n9j9GgrtgyyC6tOTKPbHbI5e2hNLXnpzk2NlpQYn8xca8cBCZMjvX3Nnx4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a5de52IOAHC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\nMepiSB1GaVOZFYvF+PPQoTJzuZZ+ViZxZ8ATJ+422rrCalXxQ0XEgSLCcrm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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe8584d0410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X60, y60 = readFile(\"assignment-5/assign_5_data_60.txt\")\n", "plotPoints(X60,y60)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def findTestIndices(size, p = 0.25):\n", " testSize = (int)(p*size)\n", " indicesForTest = np.random.choice(size, testSize, replace=False)\n", " allIndices = np.arange(size)\n", " indicesForTrain = np.setdiff1d(allIndices, indicesForTest)\n", " print testSize\n", " return indicesForTrain, indicesForTest" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def readTrainTestData(X, y, indicesForTrain, indicesForTest):\n", " XTrain = []\n", " yTrain = []\n", " XTest = []\n", " yTest = []\n", " \n", " for i in indicesForTrain:\n", " XTrain.append(X[i])\n", " yTrain.append(y[i])\n", " \n", " for i in indicesForTest:\n", " XTest.append(X[i])\n", " yTest.append(y[i])\n", " \n", " return XTrain, yTrain, XTest, yTest" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1280\n" ] } ], "source": [ "cleanFile = \"assignment-5/assign_5_data_0.txt\"\n", "XClean, yClean = readFile(cleanFile)\n", "size = XClean.shape[0]\n", "indicesForTrain, indicesForTest = findTestIndices(size, 0.2)\n", "XClean, yClean, XTest, yTest = readTrainTestData(XClean, yClean, indicesForTrain, indicesForTest)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def NNAccuracies():\n", " noise = [0, 10, 20, 40, 60]\n", " trainFile = []\n", " for i in noise:\n", " trainFile.append(\"assignment-5/assign_5_data_\" + str(i) + \".txt\")\n", " \n", " NN = []\n", " NNScores = []\n", " NNConf = MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', alpha=0.0001, learning_rate_init=0.001, max_iter=2000, tol=0.0001, early_stopping=False)\n", " \n", " for i in range(len(trainFile)):\n", " XTotal, yTotal = readFile(trainFile[i])\n", " XTrain, yTrain, _, _ = readTrainTestData(XTotal, yTotal, indicesForTrain, indicesForTest)\n", " NN.append(NNConf.fit(XTrain, yTrain))\n", " NNScores.append(NN[i].score(XTest, yTest))\n", " \n", " return NNScores" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def svmAccuracies():\n", " noise = [0, 10, 20, 40, 60]\n", " trainFile = []\n", " for i in noise:\n", " trainFile.append(\"assignment-5/assign_5_data_\" + str(i) + \".txt\")\n", " \n", " svm = []\n", " svmScores = []\n", " svmC = SVC(C = 1e4, kernel='rbf', tol = 1e-5)\n", " for i in range(len(trainFile)):\n", " XTotal, yTotal = readFile(trainFile[i])\n", " XTrain, yTrain, _, _ = readTrainTestData(XTotal, yTotal, indicesForTrain, indicesForTest)\n", " svm.append(svmC.fit(XTrain, yTrain))\n", " svmScores.append(svm[i].score(XTest, yTest))\n", " \n", " return svmScores" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svmScores = svmAccuracies()\n", "NNScores = NNAccuracies()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print svmScores" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print NNScores" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
amitkaps/applied-machine-learning	Module-03g-Model-HyperParameterOpt.ipynb	1	26527	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to parameter tuning\n", "\n", "Hyper-parameters\n", "\n", "A machine learning model is a mathematical formula with a number of parameters that are learnt from the data. That is the crux of machine learning: fitting a model to the data.\n", "\n", "However, there is another kind of parameters that cannot be directly learned from the regular training process. These parameters express “higher-level” properties of the model such as its complexity or how fast it should learn. They are called hyperparameters. Hyperparameters are usually fixed before the actual training process begins.\n", "\n", "So, how are hyperparameters decided?\n", "\n", "Broadly speaking, this is done by setting different values for those hyperparameters, training different models, and deciding which ones work best by testing them\n", "\n", "So, to summarize. Hyperparameters:\n", "\n", "- Define higher level concepts about the model such as complexity, or capacity to learn.\n", "- Cannot be learned directly from the data in the standard model training process and need to be predefined.\n", "- Can be decided by setting different values, training different models, and choosing the values that test better\n", "\n", "Some examples of hyperparameters:\n", "\n", "- Number of leaves or depth of a tree\n", "- Number of latent factors in a matrix factorization\n", "- Learning rate (in many models)\n", "- Number of hidden layers in a deep neural network\n", "- Number of clusters in a k-means clustering\n", "\n", "source: [Quora](https://www.quora.com/What-are-hyperparameters-in-machine-learning)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Read the data \n", "#Read the data\n", "df = pd.read_csv(\"data/historical_loan.csv\")\n", "\n", "# refine the data\n", "df.years = df.years.fillna(np.mean(df.years))\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setup the features and target\n", "X = df.iloc[:,1:]\n", "y = df.iloc[:,0]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic checks\n", "\n", "Check if the columns are the same in train and test.\n", "\n", "What else will you check? [Discuss]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['amount', 'grade', 'years', 'ownership', 'income', 'age'], dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['amount', 'grade', 'years', 'ownership', 'income', 'age'], dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test.columns" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6181, 6) (1546, 6)\n" ] } ], "source": [ "print(X_train.shape, X_test.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train\n", "amount int64\n", "grade object\n", "years float64\n", "ownership object\n", "income float64\n", "age int64\n", "dtype: object\n", "\n", "test\n", "amount int64\n", "grade object\n", "years float64\n", "ownership object\n", "income float64\n", "age int64\n", "dtype: object\n" ] } ], "source": [ "print(\"train\")\n", "print(X_train.dtypes)\n", "print()\n", "print(\"test\")\n", "print(X_test.dtypes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The categorical data should be encoded.\n", "\n", "We saw LabelEncoder earlier. Now, we will use one-hot encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### One-hot encoding\n", "\n", "![](img/onehot.jpg)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "X_train_updated = pd.get_dummies(X_train)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6181, 6)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6181, 15)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_updated.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "amount 14500.0\n", "years 11.0\n", "income 64000.0\n", "age 35.0\n", "grade_A 1.0\n", "grade_B 0.0\n", "grade_C 0.0\n", "grade_D 0.0\n", "grade_E 0.0\n", "grade_F 0.0\n", "grade_G 0.0\n", "ownership_MORTGAGE 1.0\n", "ownership_OTHER 0.0\n", "ownership_OWN 0.0\n", "ownership_RENT 0.0\n", "Name: 303, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#print the first record\n", "X_train_updated.iloc[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise\n", "Apply one-hot encoding to test dataset and store in test_updated" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Code here" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_test_updated = pd.get_dummies(X_test)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1546, 6) (1546, 15)\n" ] } ], "source": [ "print(X_test.shape, X_test_updated.shape)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "amount 3000.0\n", "years 1.0\n", "income 49800.0\n", "age 22.0\n", "grade_A 1.0\n", "grade_B 0.0\n", "grade_C 0.0\n", "grade_D 0.0\n", "grade_E 0.0\n", "grade_F 0.0\n", "grade_G 0.0\n", "ownership_MORTGAGE 0.0\n", "ownership_OTHER 0.0\n", "ownership_OWN 0.0\n", "ownership_RENT 1.0\n", "Name: 2184, dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#print the first record\n", "X_test_updated.iloc[1]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6181, 15) (6181,)\n" ] } ], "source": [ "print(X_train_updated.shape, y_train.shape)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Let's build random forest model" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_rf = RandomForestClassifier(n_estimators=100,\n", " criterion=\"gini\",\n", " max_depth=5,\n", " min_samples_split=2,\n", " min_samples_leaf= 1,\n", " oob_score=True,\n", " n_jobs=-1\n", " )" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=5, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n", " oob_score=True, random_state=None, verbose=0, warm_start=False)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_rf.fit(X_train_updated, y_train)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.63873159682899205" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_rf.oob_score_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do cross validation and see what the generalization error is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-validation" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.metrics import roc_curve, auc" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_rf = RandomForestClassifier(n_estimators=100,\n", " criterion=\"gini\",\n", " max_depth=5,\n", " min_samples_split=2,\n", " min_samples_leaf= 1,\n", " oob_score=True,\n", " n_jobs=-1\n", " )" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 112 ms, sys: 64.7 ms, total: 176 ms\n", "Wall time: 2.18 s\n" ] } ], "source": [ "%%time\n", "\n", "#Or use %%timeit -n1 -r1 to time the cell\n", "\n", "cross_val_score_rf = cross_val_score(model_rf, \n", " X_train_updated, \n", " y_train, scoring=\"roc_auc\",\n", " cv=5,\n", " n_jobs=-1\n", " )" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.6969647 , 0.68786796, 0.69946444, 0.69435555, 0.67146693])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score_rf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.69002391398907892" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#What is the average cross validation score?\n", "np.mean(cross_val_score_rf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### grid-search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above was for some arbitrary chosen parameter value.\n", "\n", "How do we run the model on various choices of hyper-parameters?\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import classification_report" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Tuning hyper-parameters for roc_auc\n", "\n", "Best parameters set found on development set:\n", "\n", "{'max_depth': 6, 'n_estimators': 100}\n", "\n", "Grid scores on development set:\n", "\n", "0.684 (+/-0.022) for {'max_depth': 3, 'n_estimators': 50}\n", "0.684 (+/-0.022) for {'max_depth': 3, 'n_estimators': 100}\n", "0.687 (+/-0.018) for {'max_depth': 4, 'n_estimators': 50}\n", "0.687 (+/-0.022) for {'max_depth': 4, 'n_estimators': 100}\n", "0.687 (+/-0.016) for {'max_depth': 5, 'n_estimators': 50}\n", "0.690 (+/-0.021) for {'max_depth': 5, 'n_estimators': 100}\n", "0.691 (+/-0.022) for {'max_depth': 6, 'n_estimators': 50}\n", "0.692 (+/-0.020) for {'max_depth': 6, 'n_estimators': 100}\n", "\n", "Detailed classification report:\n", "\n", "The model is trained on the full development set.\n", "The scores are computed on the full evaluation set.\n", "\n", "AUC: 0.630219677953\n", " precision recall f1-score support\n", "\n", " 0 0.63 0.71 0.67 807\n", " 1 0.64 0.55 0.59 739\n", "\n", "avg / total 0.63 0.63 0.63 1546\n", "\n", "\n", "1 loop, best of 1: 22.9 s per loop\n" ] } ], "source": [ "%%timeit -n1 -r1\n", "\n", "# Set the parameters by cross-validation\n", "tuned_parameters = [{'n_estimators': [50,100], \n", " 'max_depth': [3, 4, 5, 6]\n", " }]\n", "\n", "scores = ['roc_auc']\n", "\n", "for score in scores:\n", " print(\"# Tuning hyper-parameters for %s\" % score)\n", " print()\n", "\n", " clf = GridSearchCV(RandomForestClassifier(n_jobs=-1), \n", " tuned_parameters, cv=5,\n", " scoring='%s' % score)\n", " clf\n", " clf.fit(X_train_updated, y_train)\n", "\n", " print(\"Best parameters set found on development set:\")\n", " print()\n", " print(clf.best_params_)\n", " print()\n", " print(\"Grid scores on development set:\")\n", " print()\n", " means = clf.cv_results_['mean_test_score']\n", " stds = clf.cv_results_['std_test_score']\n", " for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n", " print(\"%0.3f (+/-%0.03f) for %r\"\n", " % (mean, std * 2, params))\n", " print()\n", "\n", " print(\"Detailed classification report:\")\n", " print()\n", " print(\"The model is trained on the full development set.\")\n", " print(\"The scores are computed on the full evaluation set.\")\n", " print()\n", " y_true, y_pred = y_test, clf.predict(X_test_updated)\n", " \n", " false_positive_rate, true_positive_rate, thresholds = roc_curve(y_true, y_pred)\n", " roc_auc = auc(false_positive_rate, true_positive_rate)\n", " print(\"AUC:\", roc_auc)\n", " \n", " print(classification_report(y_true, y_pred))\n", " print()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise\n", "\n", "- For `max_depth` include - 6, 10\n", "- Add `min_samples_split`, `min_samples_leaf` to the grid search\n", "- In addition to `roc_auc`, add `precision` and `recall` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Challenges with `grid_search`\n", "\n", "Discuss" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Randomized Search" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "from scipy.stats import randint as sp_randint" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Tuning hyper-parameters for roc_auc\n", "\n", "Best parameters set found on development set:\n", "\n", "{'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 3, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n", "\n", "Grid scores on development set:\n", "\n", "0.702 (+/-0.024) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 7, 'min_samples_leaf': 5, 'min_samples_split': 4, 'n_estimators': 100}\n", "0.686 (+/-0.020) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': 4, 'max_features': 5, 'min_samples_leaf': 7, 'min_samples_split': 7, 'n_estimators': 50}\n", "0.687 (+/-0.016) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': 4, 'max_features': 8, 'min_samples_leaf': 9, 'min_samples_split': 2, 'n_estimators': 100}\n", "0.685 (+/-0.018) for {'bootstrap': False, 'criterion': 'gini', 'max_depth': 4, 'max_features': 8, 'min_samples_leaf': 7, 'min_samples_split': 2, 'n_estimators': 100}\n", "0.690 (+/-0.019) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': 6, 'max_features': 4, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n", "0.685 (+/-0.019) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 6, 'min_samples_leaf': 3, 'min_samples_split': 2, 'n_estimators': 50}\n", "0.703 (+/-0.024) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 1, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 100}\n", "0.682 (+/-0.023) for {'bootstrap': False, 'criterion': 'entropy', 'max_depth': 3, 'max_features': 10, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 100}\n", "0.697 (+/-0.023) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 9, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 50}\n", "0.684 (+/-0.018) for {'bootstrap': False, 'criterion': 'gini', 'max_depth': 3, 'max_features': 7, 'min_samples_leaf': 8, 'min_samples_split': 4, 'n_estimators': 100}\n", "0.686 (+/-0.019) for {'bootstrap': False, 'criterion': 'entropy', 'max_depth': 4, 'max_features': 5, 'min_samples_leaf': 7, 'min_samples_split': 7, 'n_estimators': 50}\n", "0.692 (+/-0.019) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': 6, 'max_features': 8, 'min_samples_leaf': 9, 'min_samples_split': 3, 'n_estimators': 100}\n", "0.693 (+/-0.020) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': 6, 'max_features': 4, 'min_samples_leaf': 7, 'min_samples_split': 8, 'n_estimators': 100}\n", "0.692 (+/-0.018) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': 6, 'max_features': 5, 'min_samples_leaf': 7, 'min_samples_split': 5, 'n_estimators': 100}\n", "0.703 (+/-0.021) for {'bootstrap': False, 'criterion': 'entropy', 'max_depth': None, 'max_features': 2, 'min_samples_leaf': 6, 'min_samples_split': 6, 'n_estimators': 50}\n", "0.692 (+/-0.019) for {'bootstrap': True, 'criterion': 'gini', 'max_depth': 6, 'max_features': 8, 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 100}\n", "0.689 (+/-0.020) for {'bootstrap': False, 'criterion': 'entropy', 'max_depth': 6, 'max_features': 6, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n", "0.701 (+/-0.022) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': None, 'max_features': 4, 'min_samples_leaf': 8, 'min_samples_split': 2, 'n_estimators': 100}\n", "0.704 (+/-0.026) for {'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 3, 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n", "0.687 (+/-0.020) for {'bootstrap': True, 'criterion': 'entropy', 'max_depth': 4, 'max_features': 2, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n", "\n", "Detailed classification report:\n", "\n", "The model is trained on the full development set.\n", "The scores are computed on the full evaluation set.\n", "\n", "1 loop, best of 1: 30.7 s per loop\n" ] } ], "source": [ "%%timeit -n1 -r1\n", "\n", "# Set the parameters by cross-validation\n", "tuned_parameters = { \"n_estimators\": [50,100], \n", " \"max_depth\": [3, 4, 6, None],\n", " \"max_features\": sp_randint(1, 11),\n", " \"min_samples_split\": sp_randint(2, 11),\n", " \"min_samples_leaf\": sp_randint(1, 11),\n", " \"bootstrap\": [True, False],\n", " \"criterion\": [\"gini\", \"entropy\"]\n", " }\n", "\n", "scores = ['roc_auc']\n", "\n", "\n", "n_iter_search = 20\n", "\n", "for score in scores:\n", " print(\"# Tuning hyper-parameters for %s\" % score)\n", " print()\n", "\n", " clf = RandomizedSearchCV(RandomForestClassifier(n_jobs=-1), \n", " param_distributions = tuned_parameters, \n", " n_iter = n_iter_search,\n", " n_jobs=-1,\n", " cv=5,\n", " scoring='%s' % score)\n", " clf.fit(X_train_updated, y_train)\n", "\n", " print(\"Best parameters set found on development set:\")\n", " print()\n", " print(clf.best_params_)\n", " print()\n", " print(\"Grid scores on development set:\")\n", " print()\n", " means = clf.cv_results_['mean_test_score']\n", " stds = clf.cv_results_['std_test_score']\n", " for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n", " print(\"%0.3f (+/-%0.03f) for %r\"\n", " % (mean, std * 2, params))\n", " print()\n", "\n", " print(\"Detailed classification report:\")\n", " print()\n", " print(\"The model is trained on the full development set.\")\n", " print(\"The scores are computed on the full evaluation set.\")\n", " print()\n", " y_true, y_pred = y_test, clf.predict(X_test_updated)\n", " \n", " #false_positive_rate, true_positive_rate, thresholds = roc_curve(y_true, y_pred)\n", " #roc_auc = auc(false_positive_rate, true_positive_rate)\n", " #print(\"AUC:\", roc_auc)\n", " \n", " #print(classification_report(y_true, y_pred))\n", " #print()\n" ] }, { "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
weikang9009/pysal	notebooks/viz/splot/esda_moran_matrix_viz.ipynb	5	363839	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualising the `esda` Moran Matrix with `splot`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`esda.moran.Moran_BV_matrix` offers you a tool to assess the relationship between multiple input variables and over space as bivariate and univariate Moran's I Statistics. `Moran_BV_matrix` returns a dictionary of `Moran_BV` objects which can be displayed and further analysed. In case you are not familiar with Moran Statistics, have a look at `splot`'s `esda_morans_viz.ipynb` notebook. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents\n", "\n", "* What to import?\n", "* Example 1: Working with arrays \n", "* Example 2: Working with a [geopandas.GeoDataFrame](http://geopandas.org/reference/geopandas.GeoDataFrame.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pysal.lib.weights.contiguity import Queen\n", "from pysal.lib import examples\n", "import pysal.lib as lp\n", "import geopandas as gpd\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Working with arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are generally two ways in which a `Moran_BV_matrix` and a `splot.esda.moran_facet` can be generated. The first of the two options is to use `np.arrays` representing the attributes of different variables and adding a list of variable names. This first option is a great choice in case you needed to calculate your weights separately with `pysal.lib.weights` and already have your values stored in an array. The second and more popular option is ot directly load a DataFrame. If you are unsure in how to work with `numpy` arrays or you already have your variables stored in a dataframe, we would recommend to use Example 2.\n", "\n", "In this example we will look at visualizing your results stored as a `np.array`. We know that we would like to examine all values for the variables named: `varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']`. We can pass in a list of these variable names separately with `varnames=varnames`. Additionally, we need to create an `np.array` containing the values of each individual variable separately with `vars = [np.array(f.by_col[var]) for var in varnames]`:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['SIDR74', 'SIDR79', 'NWR74', 'NWR79']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = gpd.read_file(examples.get_path(\"sids2.dbf\"))\n", "\n", "varnames = ['SIDR74', 'SIDR79', 'NWR74', 'NWR79']\n", "varnames" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.91659 , 0. , 1.568381, 1.968504, 6.333568, 4.820937,\n", " 0. , 0. , 4.132231, 0.620347, 1.932367, 3.596314,\n", " 2.393776, 2.570694, 1.834862, 4.988914, 1.831502, 1.271456,\n", " 0.755858, 2.066116, 1.331558, 0. , 0.788022, 1.429593,\n", " 0.843313, 1.421157, 2.782534, 4.531722, 1.264223, 2.007528,\n", " 1.989555, 0. , 2.734482, 1.66251 , 0. , 1.291156,\n", " 1.104667, 2.614379, 0.966417, 0.8285 , 0. , 1.452169,\n", " 1.399384, 5.050505, 0. , 2.569373, 1.570916, 1.215067,\n", " 2.971367, 0.651324, 2.748331, 0.868961, 1.197605, 1.500375,\n", " 0.947867, 0. , 2.600297, 4.444444, 4.597701, 2.220249,\n", " 4.010695, 2.71166 , 1.588983, 2.055076, 3.610108, 1.749781,\n", " 1.888218, 2.038169, 0.731886, 2.384738, 2.122241, 1.942502,\n", " 0. , 2.786291, 2.557545, 1.220324, 1.876173, 0. ,\n", " 1.322314, 1.845018, 1.94742 , 1.865855, 1.730104, 1.021711,\n", " 9.55414 , 4.685408, 0. , 1.610954, 1.451379, 0. ,\n", " 2.215406, 3.547672, 2.599032, 3.929522, 2.071251, 4.489338,\n", " 3.257329, 4.477612, 2.171553, 2.292526])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variable = [np.array(f[variable]) for variable in varnames]\n", "variable[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can open a file containing pre calculated spatial weights for \"sids2.dbf\". In case you don't have spatial weights, check out `pysal.lib.weights` which will provide you with many options calculating your own." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<pysal.lib.weights.weights.W at 0x1a1f854160>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w = lp.io.open(examples.get_path(\"sids2.gal\")).read()\n", "w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to import and generate our `Moran_BV_matrix`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(0, 1): <esda.moran.Moran_BV at 0x1047ff5f8>,\n", " (1, 0): <esda.moran.Moran_BV at 0x1047ff940>,\n", " (0, 2): <esda.moran.Moran_BV at 0x1047ff2e8>,\n", " (2, 0): <esda.moran.Moran_BV at 0x1a1f854080>,\n", " (0, 3): <esda.moran.Moran_BV at 0x1a1f842748>,\n", " (3, 0): <esda.moran.Moran_BV at 0x1a1f8555c0>,\n", " (1, 2): <esda.moran.Moran_BV at 0x1a1f854048>,\n", " (2, 1): <esda.moran.Moran_BV at 0x1a1f8546a0>,\n", " (1, 3): <esda.moran.Moran_BV at 0x1a1f85c8d0>,\n", " (3, 1): <esda.moran.Moran_BV at 0x1a1f85cb70>,\n", " (2, 3): <esda.moran.Moran_BV at 0x1a1f85cb38>,\n", " (3, 2): <esda.moran.Moran_BV at 0x1a1f85cb00>}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pysal.explore.esda.moran import Moran_BV_matrix\n", "\n", "matrix = Moran_BV_matrix(variable, w, varnames = varnames)\n", "matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualise our matrix with `splot.esda.moran_facet()`. You will see Univariate Moran objects with a grey background, surrounded by all possible bivariate combinations of your input dataset:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x864 with 16 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pysal.viz.splot.esda import moran_facet\n", "\n", "moran_facet(matrix)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: insert a DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, it is possible to generte your `Moran_BV_matrix` and a `moran_facet` using a `pandas` or `geopandas` DataFrame as input. Let's have a look at a simple example examining `columbus.shp` example data:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "path = examples.get_path('columbus.shp')\n", "gdf = gpd.read_file(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order for `moran_facet` to generate sensible results, it is recommended to extract all columns you would specifically like to analyse and generate a new GeoDataFrame:" ] }, { "cell_type": "code", "execution_count": 8, "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>HOVAL</th>\n", " <th>CRIME</th>\n", " <th>INC</th>\n", " <th>EW</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>80.467003</td>\n", " <td>15.725980</td>\n", " <td>19.531</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>44.567001</td>\n", " <td>18.801754</td>\n", " <td>21.232</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>26.350000</td>\n", " <td>30.626781</td>\n", " <td>15.956</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>33.200001</td>\n", " <td>32.387760</td>\n", " <td>4.477</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>23.225000</td>\n", " <td>50.731510</td>\n", " <td>11.252</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " HOVAL CRIME INC EW\n", "0 80.467003 15.725980 19.531 1.0\n", "1 44.567001 18.801754 21.232 0.0\n", "2 26.350000 30.626781 15.956 1.0\n", "3 33.200001 32.387760 4.477 0.0\n", "4 23.225000 50.731510 11.252 1.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variables2 = gdf[['HOVAL', 'CRIME', 'INC', 'EW']]\n", "variables2.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(49, 4)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variables2.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now generate our own spatial weights leveraging `pysal.lib` and create a second `matrix2` from our GeoDataFrame. Note that there is no list of `varnames` needed, this list will be automatically extracted from teh first row of your `gdf`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<pysal.lib.weights.contiguity.Queen at 0x1a1f82ac50>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w2 = Queen.from_shapefile(path)\n", "w2" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(0, 1): <esda.moran.Moran_BV at 0x1a20ddbcc0>,\n", " (1, 0): <esda.moran.Moran_BV at 0x1a20ddbb38>,\n", " (0, 2): <esda.moran.Moran_BV at 0x1a20ddbb00>,\n", " (2, 0): <esda.moran.Moran_BV at 0x1a20ddbc50>,\n", " (0, 3): <esda.moran.Moran_BV at 0x1a20bf2828>,\n", " (3, 0): <esda.moran.Moran_BV at 0x1a20c14390>,\n", " (1, 2): <esda.moran.Moran_BV at 0x1a20ddb160>,\n", " (2, 1): <esda.moran.Moran_BV at 0x1a20ddbfd0>,\n", " (1, 3): <esda.moran.Moran_BV at 0x1a20ddbf98>,\n", " (3, 1): <esda.moran.Moran_BV at 0x1a20ddbf60>,\n", " (2, 3): <esda.moran.Moran_BV at 0x1037e02b0>,\n", " (3, 2): <esda.moran.Moran_BV at 0x1037e0198>}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pysal.explore.esda.moran import Moran_BV_matrix\n", "\n", "matrix2 = Moran_BV_matrix(variables2, w2)\n", "matrix2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like in the first example we can now plot our data with a simple `splot` call:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x864 with 16 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from pysal.viz.splot.esda import moran_facet\n", "\n", "moran_facet(matrix2)\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
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/launching_into_ml/labs/TrainingWithXGBoostInCMLE.ipynb	1	19320	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XGBoost Training on Vertex AI Platform\n", "In this lab, you will learn to use the [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) to demonstrate how to train a model on AI Platform." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Objective\n", "### How to bring your model to AI Platform\n", "Getting your model ready for training can be done in 3 steps:\n", "1. Create your Python model file.\n", " 1. Add code to download your data from [Google Cloud Storage](https://cloud.google.com/storage) so that AI Platform can use it\n", " 1. Add code to export and save the model to [Google Cloud Storage](https://cloud.google.com/storage) once AI Platform finishes training the model\n", "1. Create a trainer package.\n", "1. Submit the training job." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites\n", "Before you jump in, let’s cover some of the different tools you’ll be using to get online prediction up and running on AI Platform. \n", "\n", "[Google Cloud Platform](https://cloud.google.com/) lets you build and host applications and websites, store data, and analyze data on Google's scalable infrastructure.\n", "\n", "[AI Platform](https://cloud.google.com/ml-engine/) is a managed service that enables you to easily build machine learning models that work on any type of data, of any size.\n", "\n", "[Google Cloud Storage](https://cloud.google.com/storage/) (GCS) is a unified object storage for developers and enterprises, from live data serving to data analytics/ML to data archiving.\n", "\n", "[Cloud SDK](https://cloud.google.com/sdk/) is a command line tool which allows you to interact with Google Cloud products. In order to run this notebook, make sure that Cloud SDK is [installed](https://cloud.google.com/sdk/downloads) in the same environment as your Jupyter kernel.\n", "\n", "Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/TrainingWithXGBoostInCMLE.ipynb).\n", "\n", "Make sure to enable the __`AI Platform Training & Prediction and Compute Engine APIs.`__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "* [Create a project on GCP](https://cloud.google.com/resource-manager/docs/creating-managing-projects)\n", "* [Enable AI Platform Training and Prediction and Compute Engine APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component&_ga=2.217405014.1312742076.1516128282-1417583630.1516128282)\n", "* [Install Cloud SDK](https://cloud.google.com/sdk/downloads)\n", "* [[Optional] Install XGBoost](http://xgboost.readthedocs.io/en/latest/build.html)\n", "* [[Optional] Install scikit-learn](http://scikit-learn.org/stable/install.html)\n", "* [[Optional] Install pandas](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)\n", "* [[Optional] Install Google API Python Client](https://github.com/google/google-api-python-client)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set the environment variables\n", "\n", "These variables will be needed for the following steps.\n", "* `TRAINER_PACKAGE_PATH` - The local path to the root directory of your training application. In this case: ./census_training/.\n", "* `MAIN_TRAINER_MODULE` - Specifies which file the AI Platform Training training service should run. This is formatted as `[YOUR_FOLDER_NAME.YOUR_PYTHON_FILE_NAME]`. In this case, `census_training.train`.\n", "* `JOB_DIR` - The path to a Cloud Storage location to use for your training job's output files. For example, `gs://$BUCKET_ID/xgboost_job_dir`.\n", "* `RUNTIME_VERSION` - You must specify a [AI Platform Training runtime version that supports scikit-learn](https://cloud.google.com/ai-platform/training/docs/runtime-version-list). In this example, 2.5.\n", "* `PYTHON_VERSION` - The Python version to use for the job. For this tutorial, specify Python 3.7.\n", "\n", "Replace:\n", "* `PROJECT_ID <YOUR_PROJECT_ID>` - Use the PROJECT_ID that matches your Google Cloud project.\n", "* `BUCKET_ID <YOUR_BUCKET_ID>` - The name of your Cloud Storage bucket.\n", "* `REGION` - The name of the region you're using to run your training job. Use one of the [available regions](https://cloud.google.com/ai-platform/training/docs/regions) for the AI Platform Training training service. Make sure your Cloud Storage bucket is in the same region. This tutorial uses `us-central1`.\n", "* `SCALE_TIER` - A predefined cluster specification for machines to run your training job. In this case, BASIC. You can also [use custom scale tiers](https://cloud.google.com/ai-platform/training/docs/machine-types#specifying_your_configuration) to define your own cluster configuration for training." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: PROJECT_ID=qwiklabs-gcp-03-796dbcf2fb19\n", "env: BUCKET_ID=qwiklabs-gcp-03-796dbcf2fb19\n", "env: REGION=us-central1\n", "env: TRAINER_PACKAGE_PATH=./census_training\n", "env: MAIN_TRAINER_MODULE=census_training.train\n", "env: JOB_DIR=gs://qwiklabs-gcp-03-796dbcf2fb19/xgb_job_dir\n", "env: RUNTIME_VERSION=2.5\n", "env: PYTHON_VERSION=3.7\n", "env: SCALE_TIER=BASIC\n" ] } ], "source": [ "%env PROJECT_ID <YOUR_PROJECT_ID>\n", "%env BUCKET_ID <YOUR_BUCKET_ID>\n", "%env REGION us-central1\n", "%env TRAINER_PACKAGE_PATH ./census_training\n", "%env MAIN_TRAINER_MODULE census_training.train\n", "%env JOB_DIR gs://<YOUR_BUCKET_ID>/xgb_job_dir\n", "%env RUNTIME_VERSION 2.5\n", "%env PYTHON_VERSION 3.7\n", "! mkdir census_training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data\n", "The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this sample\n", "uses for training is provided by the [UC Irvine Machine Learning\n", "Repository](https://archive.ics.uci.edu/ml/datasets/). We have hosted the data on a public GCS bucket `gs://cloud-samples-data/ml-engine/census/data/`. \n", "\n", " * Training file is `adult.data.csv`\n", " * Evaluation file is `adult.test.csv` (not used in this notebook)\n", "\n", "Note: Your typical development process with your own data would require you to upload your data to GCS so that AI Platform can access that data. However, in this case, we have put the data on GCS to avoid the steps of having you download the data from UC Irvine and then upload the data to GCS." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disclaimer\n", "This dataset is provided by a third party. Google provides no representation,\n", "warranty, or other guarantees about the validity or any other aspects of this dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Create your python model file\n", "\n", "First, we'll create the python model file (provided below) that we'll upload to AI Platform. This is similar to your normal process for creating a XGBoost model. However, there are two key differences:\n", "1. Downloading the data from GCS at the start of your file, so that AI Platform can access the data.\n", "1. Exporting/saving the model to GCS at the end of your file, so that you can use it for predictions.\n", "\n", "The code in this file loads the data into a pandas DataFrame and pre-processes the data with scikit-learn. This data is then loaded into a DMatrix and used to train a model. Lastly, the model is saved to a file that can be uploaded to [AI Platform's prediction service](https://cloud.google.com/ml-engine/docs/scikit/getting-predictions#deploy_models_and_versions).\n", "\n", "REPLACE Line 18: BUCKET_ID = 'true-ability-192918' with your GCS BUCKET_ID\n", "\n", "Note: In normal practice you would want to test your model locally on a small dataset to ensure that it works, before using it with your larger dataset on AI Platform. This avoids wasted time and costs." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing ./census_training/train.py\n" ] } ], "source": [ "%%writefile ./census_training/train.py\n", "# [START setup]\n", "import datetime\n", "import os\n", "import subprocess\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "import pandas as pd\n", "from google.cloud import storage\n", "import xgboost as xgb\n", "\n", "\n", "# TODO: REPLACE 'BUCKET_CREATED_ABOVE' with your GCS BUCKET_ID\n", "BUCKET_ID = 'true-ability-192918'\n", "# [END setup]\n", "\n", "# ---------------------------------------\n", "# 1. Add code to download the data from GCS (in this case, using the publicly hosted data).\n", "# AI Platform will then be able to use the data when training your model.\n", "# ---------------------------------------\n", "# [START download-data]\n", "census_data_filename = 'adult.data.csv'\n", "\n", "# Public bucket holding the census data\n", "bucket = storage.Client().bucket('cloud-samples-data')\n", "\n", "# Path to the data inside the public bucket\n", "data_dir = 'ml-engine/census/data/'\n", "\n", "# TODO 1a: Your code here\n", "\n", "# [END download-data]\n", "\n", "# ---------------------------------------\n", "# This is where your model code would go. Below is an example model using the census dataset.\n", "# ---------------------------------------\n", "\n", "# [START define-and-load-data]\n", "\n", "# these are the column labels from the census data files\n", "COLUMNS = (\n", " 'age',\n", " 'workclass',\n", " 'fnlwgt',\n", " 'education',\n", " 'education-num',\n", " 'marital-status',\n", " 'occupation',\n", " 'relationship',\n", " 'race',\n", " 'sex',\n", " 'capital-gain',\n", " 'capital-loss',\n", " 'hours-per-week',\n", " 'native-country',\n", " 'income-level'\n", ")\n", "# categorical columns contain data that need to be turned into numerical values before being used by XGBoost\n", "CATEGORICAL_COLUMNS = (\n", " 'workclass',\n", " 'education',\n", " 'marital-status',\n", " 'occupation',\n", " 'relationship',\n", " 'race',\n", " 'sex',\n", " 'native-country'\n", ")\n", "\n", "# Load the training census dataset\n", "with open(census_data_filename, 'r') as train_data:\n", " raw_training_data = pd.read_csv(train_data, header=None, names=COLUMNS)\n", "# remove column we are trying to predict ('income-level') from features list\n", "train_features = raw_training_data.drop('income-level', axis=1)\n", "# create training labels list\n", "train_labels = (raw_training_data['income-level'] == ' >50K')\n", "\n", "# [END define-and-load-data]\n", "\n", "# [START categorical-feature-conversion]\n", "# Since the census data set has categorical features, we need to convert\n", "# them to numerical values. \n", "# convert data in categorical columns to numerical values\n", "encoders = {col:LabelEncoder() for col in CATEGORICAL_COLUMNS}\n", "for col in CATEGORICAL_COLUMNS:\n", " train_features[col] = encoders[col].fit_transform(train_features[col])\n", "# [END categorical-feature-conversion]\n", "\n", "# [START load-into-dmatrix-and-train]\n", "# load data into DMatrix object\n", "dtrain = xgb.DMatrix(train_features, train_labels)\n", "# train model\n", "bst = xgb.train({}, dtrain, 20)\n", "# [END load-into-dmatrix-and-train]\n", "\n", "#TODO 1b: Your code here\n", "\n", "# Upload the model to GCS\n", "bucket = storage.Client().bucket(BUCKET_ID)\n", "blob = bucket.blob('{}/{}'.format(\n", " datetime.datetime.now().strftime('census_%Y%m%d_%H%M%S'),\n", " model))\n", "blob.upload_from_filename(model)\n", "# [END export-to-gcs]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Create a trainer package\n", "The easiest (and recommended) way to create a training application package uses gcloud to package and upload the application when you submit your training job. This method allows you to create a very simple file structure with only two files. For this tutorial, the file structure of your training application package should appear similar to the following:\n", "\n", "census_training/\n", " __init__.py\n", " train.py" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing ./census_training/__init__.py\n" ] } ], "source": [ "# TODO 2: Your code here\n", "# Note that __init__.py can be an empty file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more about [packaging a training application](https://cloud.google.com/ai-platform/training/docs/packaging-trainer#using_gcloud_to_package_and_upload_your_application_recommended)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Submit the training job\n", "Next we need to submit the job for training on AI Platform. We'll use gcloud to submit the job which has the following flags:\n", "\n", "* `job-name` - A name to use for the job (mixed-case letters, numbers, and underscores only, starting with a letter). In this case: `census_training_$(date +\"%Y%m%d_%H%M%S\")`\n", "* `job-dir` - The path to a Google Cloud Storage location to use for job output.\n", "* `package-path` - A packaged training application that is staged in a Google Cloud Storage location. If you are using the gcloud command-line tool, this step is largely automated.\n", "* `module-name` - The name of the main module in your trainer package. The main module is the Python file you call to start the application. If you use the gcloud command to submit your job, specify the main module name in the --module-name argument. Refer to Python Packages to figure out the module name.\n", "* `region` - The Google Cloud Compute region where you want your job to run. You should run your training job in the same region as the Cloud Storage bucket that stores your training data. Select a region from [here](https://cloud.google.com/ml-engine/docs/regions) or use the default '`us-central1`'.\n", "* `runtime-version` - The version of AI Platform to use for the job. If you don't specify a runtime version, the training service uses the default AI Platform runtime version 1.0. See the list of runtime versions for more information.\n", "* `python-version` - The Python version to use for the job. Python 3.5 is available with runtime version 1.4 or greater. If you don't specify a Python version, the training service uses Python 2.7.\n", "* `scale-tier` - A scale tier specifying the type of processing cluster to run your job on. This can be the CUSTOM scale tier, in which case you also explicitly specify the number and type of machines to use.\n", "\n", "Note: Check to make sure gcloud is set to the current PROJECT_ID" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Updated property [core/project].\n" ] } ], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Submit the training job." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Job [census_training_20210914_135137] submitted successfully.\n", "Your job is still active. You may view the status of your job with the command\n", "\n", " $ gcloud ai-platform jobs describe census_training_20210914_135137\n", "\n", "or continue streaming the logs with the command\n", "\n", " $ gcloud ai-platform jobs stream-logs census_training_20210914_135137\n", "jobId: census_training_20210914_135137\n", "state: QUEUED\n" ] } ], "source": [ "# TODO 3: Your code here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Optional] Cloud Logging\n", "You can view the logs for your training job:\n", "1. Go to https://console.cloud.google.com/\n", "1. Select \"Logging\" in left-hand pane\n", "1. Select \"Cloud ML Job\" resource from the drop-down\n", "1. In filter by prefix, use the value of $JOB_NAME to view the logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Optional] Verify Model File in GCS\n", "View the contents of the destination model folder to verify that model file has indeed been uploaded to GCS.\n", "\n", "Note: The model can take a few minutes to train and show up in GCS." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gs://qwiklabs-gcp-03-796dbcf2fb19/census_20210913_194824/model.bst\n" ] } ], "source": [ "! gsutil ls gs://$BUCKET_ID/census_*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next Steps:\n", "The AI Platform online prediction service manages computing resources in the cloud to run your models. Check out the [documentation pages](https://cloud.google.com/ml-engine/docs/scikit/) that describe the process to get online predictions from these exported models using AI Platform." ] } ], "metadata": { "environment": { "name": "tf2-gpu.2-6.m79", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m79" }, "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": 4 }	apache-2.0
GoogleCloudPlatform/asl-ml-immersion	notebooks/introduction_to_tensorflow/solutions/3_keras_sequential_api_vertex.ipynb	1	106000	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing the Keras Sequential API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learning Objectives\n", " 1. Build a DNN model using the Keras Sequential API\n", " 1. Learn how to use feature columns in a Keras model\n", " 1. Learn how to train a model with Keras\n", " 1. Learn how to save/load, and deploy a Keras model on GCP\n", " 1. Learn how to deploy and make predictions with the Keras model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [Keras sequential API](https://keras.io/models/sequential/) allows you to create Tensorflow models layer-by-layer. This is useful for building most kinds of machine learning models but it does not allow you to create models that share layers, re-use layers or have multiple inputs or outputs. \n", "\n", "In this lab, we'll see how to build a simple deep neural network model using the Keras sequential api and feature columns. Once we have trained our model, we will deploy it using Vertex AI and see how to call our model for online prediciton.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by importing the necessary libraries for this lab." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3.2\n" ] } ], "source": [ "import datetime\n", "import os\n", "import shutil\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "from google.cloud import aiplatform\n", "from matplotlib import pyplot as plt\n", "from tensorflow import keras\n", "from tensorflow.keras.callbacks import TensorBoard\n", "from tensorflow.keras.layers import Dense, DenseFeatures\n", "from tensorflow.keras.models import Sequential\n", "\n", "print(tf.__version__)\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load raw data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the taxifare dataset, using the CSV files that we created in the first notebook of this sequence. Those files have been saved into `../data`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 jupyter jupyter 123590 Jul 18 20:44 ../data/taxi-test.csv\n", "-rw-r--r-- 1 jupyter jupyter 579055 Jul 18 20:44 ../data/taxi-train.csv\n", "-rw-r--r-- 1 jupyter jupyter 123114 Jul 18 20:44 ../data/taxi-valid.csv\n" ] } ], "source": [ "!ls -l ../data/.csv" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==> ../data/taxi-test.csv <==\n", "6.0,2013-03-27 03:35:00 UTC,-73.977672,40.784052,-73.965332,40.801025,2,0\n", "19.3,2012-05-10 18:43:16 UTC,-73.954366,40.778924,-74.004094,40.723104,1,1\n", "7.5,2014-05-20 23:09:00 UTC,-73.999165,40.738377,-74.003473,40.723862,2,2\n", "12.5,2015-02-23 19:51:31 UTC,-73.9652099609375,40.76948165893555,-73.98949432373047,40.739742279052734,1,3\n", "10.9,2011-03-19 03:32:00 UTC,-73.99259,40.742957,-73.989908,40.711053,1,4\n", "7.0,2012-09-18 12:51:11 UTC,-73.971195,40.751566,-73.975922,40.756361,1,5\n", "19.0,2014-05-20 23:09:00 UTC,-73.998392,40.74517,-73.939845,40.74908,1,6\n", "8.9,2012-07-18 08:46:08 UTC,-73.997638,40.756541,-73.973303,40.762019,1,7\n", "4.5,2010-07-11 20:39:08 UTC,-73.976738,40.751321,-73.986671,40.74883,1,8\n", "7.0,2013-12-12 02:16:40 UTC,-73.985024,40.767537,-73.981273,40.779302,1,9\n", "\n", "==> ../data/taxi-train.csv <==\n", "11.3,2011-01-28 20:42:59 UTC,-73.999022,40.739146,-73.990369,40.717866,1,0\n", "7.7,2011-06-27 04:28:06 UTC,-73.987443,40.729221,-73.979013,40.758641,1,1\n", "10.5,2011-04-03 00:54:53 UTC,-73.982539,40.735725,-73.954797,40.778388,1,2\n", "16.2,2009-04-10 04:11:56 UTC,-74.001945,40.740505,-73.91385,40.758559,1,3\n", "33.5,2014-02-24 18:22:00 UTC,-73.993372,40.753382,-73.8609,40.732897,2,4\n", "6.9,2011-12-10 00:25:23 UTC,-73.996237,40.721848,-73.989416,40.718052,1,5\n", "6.1,2012-09-01 14:30:19 UTC,-73.977048,40.758461,-73.984899,40.744693,2,6\n", "9.5,2012-11-08 13:28:07 UTC,-73.969402,40.757545,-73.950049,40.776079,1,7\n", "9.0,2014-07-15 11:37:25 UTC,-73.979318,40.760949,-73.95767,40.773724,1,8\n", "3.3,2009-11-09 18:06:58 UTC,-73.955675,40.779154,-73.961172,40.772368,1,9\n", "\n", "==> ../data/taxi-valid.csv <==\n", "5.3,2012-01-03 19:21:35 UTC,-73.962627,40.763214,-73.973485,40.753353,1,0\n", "25.3,2010-09-27 07:30:15 UTC,-73.965799,40.794243,-73.927134,40.852261,3,1\n", "27.5,2015-05-19 00:40:02 UTC,-73.86344146728516,40.76899719238281,-73.96058654785156,40.76129913330078,1,2\n", "5.7,2010-04-29 12:28:00 UTC,-73.989255,40.738912,-73.97558,40.749172,1,3\n", "11.5,2013-06-23 06:08:09 UTC,-73.99731,40.763735,-73.955657,40.768141,1,4\n", "18.0,2014-10-14 18:52:03 UTC,-73.997995,40.761638,-74.008985,40.712442,1,5\n", "4.9,2010-04-29 12:28:00 UTC,-73.977315,40.766182,-73.970845,40.761462,5,6\n", "32.33,2014-02-24 18:22:00 UTC,-73.985358,40.761352,-73.92427,40.699145,1,7\n", "17.0,2015-03-26 02:48:58 UTC,-73.93981170654297,40.846473693847656,-73.97361755371094,40.786983489990234,1,8\n", "12.5,2013-04-09 09:39:13 UTC,-73.977323,40.753934,-74.00719,40.741472,1,9\n" ] } ], "source": [ "!head ../data/taxi.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use tf.data to read the CSV files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We wrote these functions for reading data from the csv files above in the [previous notebook](./2a_dataset_api.ipynb)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "CSV_COLUMNS = [\n", " \"fare_amount\",\n", " \"pickup_datetime\",\n", " \"pickup_longitude\",\n", " \"pickup_latitude\",\n", " \"dropoff_longitude\",\n", " \"dropoff_latitude\",\n", " \"passenger_count\",\n", " \"key\",\n", "]\n", "LABEL_COLUMN = \"fare_amount\"\n", "DEFAULTS = [[0.0], [\"na\"], [0.0], [0.0], [0.0], [0.0], [0.0], [\"na\"]]\n", "UNWANTED_COLS = [\"pickup_datetime\", \"key\"]\n", "\n", "\n", "def features_and_labels(row_data):\n", " label = row_data.pop(LABEL_COLUMN)\n", " features = row_data\n", "\n", " for unwanted_col in UNWANTED_COLS:\n", " features.pop(unwanted_col)\n", "\n", " return features, label\n", "\n", "\n", "def create_dataset(pattern, batch_size=1, mode=\"eval\"):\n", " dataset = tf.data.experimental.make_csv_dataset(\n", " pattern, batch_size, CSV_COLUMNS, DEFAULTS\n", " )\n", "\n", " dataset = dataset.map(features_and_labels)\n", "\n", " if mode == \"train\":\n", " dataset = dataset.shuffle(buffer_size=1000).repeat()\n", "\n", " # take advantage of multi-threading; 1=AUTOTUNE\n", " dataset = dataset.prefetch(1)\n", " return dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build a simple keras DNN model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use feature columns to connect our raw data to our keras DNN model. Feature columns make it easy to perform common types of feature engineering on your raw data. For example, you can one-hot encode categorical data, create feature crosses, embeddings and more. We'll cover these in more detail later in the course, but if you want to a sneak peak browse the official TensorFlow [feature columns guide](https://www.tensorflow.org/guide/feature_columns).\n", "\n", "In our case we won't do any feature engineering. However, we still need to create a list of feature columns to specify the numeric values which will be passed on to our model. To do this, we use `tf.feature_column.numeric_column()`\n", "\n", "We use a python dictionary comprehension to create the feature columns for our model, which is just an elegant alternative to a for loop." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "INPUT_COLS = [\n", " \"pickup_longitude\",\n", " \"pickup_latitude\",\n", " \"dropoff_longitude\",\n", " \"dropoff_latitude\",\n", " \"passenger_count\",\n", "]\n", "\n", "# Create input layer of feature columns\n", "feature_columns = {\n", " colname: tf.feature_column.numeric_column(colname) for colname in INPUT_COLS\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we create the DNN model. The Sequential model is a linear stack of layers and when building a model using the Sequential API, you configure each layer of the model in turn. Once all the layers have been added, you compile the model. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Build a keras DNN model using Sequential API\n", "model = Sequential(\n", " [\n", " DenseFeatures(feature_columns=feature_columns.values()),\n", " Dense(units=32, activation=\"relu\", name=\"h1\"),\n", " Dense(units=8, activation=\"relu\", name=\"h2\"),\n", " Dense(units=1, activation=\"linear\", name=\"output\"),\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, to prepare the model for training, you must configure the learning process. This is done using the compile method. The compile method takes three arguments:\n", "\n", "* An optimizer. This could be the string identifier of an existing optimizer (such as `rmsprop` or `adagrad`), or an instance of the [Optimizer class](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/optimizers).\n", "* A loss function. This is the objective that the model will try to minimize. It can be the string identifier of an existing loss function from the [Losses class](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses) (such as categorical_crossentropy or mse), or it can be a custom objective function.\n", "* A list of metrics. For any machine learning problem you will want a set of metrics to evaluate your model. A metric could be the string identifier of an existing metric or a custom metric function.\n", "\n", "We will add an additional custom metric called `rmse` to our list of metrics which will return the root mean square error. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Create a custom evalution metric\n", "def rmse(y_true, y_pred):\n", " return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))\n", "\n", "\n", "# Compile the keras model\n", "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[rmse, \"mse\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train your model, Keras provides two functions that can be used:\n", " 1. `.fit()` for training a model for a fixed number of epochs (iterations on a dataset).\n", " 2. `.train_on_batch()` runs a single gradient update on a single batch of data. \n", " \n", "The `.fit()` function works for various formats of data such as Numpy array, list of Tensors tf.data and Python generators. The `.train_on_batch()` method is for more fine-grained control over training and accepts only a single batch of data.\n", "\n", "Our `create_dataset` function above generates batches of training examples, so we can use `.fit`. \n", "\n", "We start by setting up some parameters for our training job and create the data generators for the training and validation data.\n", "\n", "We refer you the the blog post [ML Design Pattern #3: Virtual Epochs](https://medium.com/google-cloud/ml-design-pattern-3-virtual-epochs-f842296de730) for further details on why express the training in terms of `NUM_TRAIN_EXAMPLES` and `NUM_EVALS` and why, in this training code, the number of epochs is really equal to the number of evaluations we perform." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "TRAIN_BATCH_SIZE = 1000\n", "NUM_TRAIN_EXAMPLES = 10000 * 5 # training dataset will repeat, wrap around\n", "NUM_EVALS = 50 # how many times to evaluate\n", "NUM_EVAL_EXAMPLES = 10000 # enough to get a reasonable sample\n", "\n", "trainds = create_dataset(\n", " pattern=\"../data/taxi-train\", batch_size=TRAIN_BATCH_SIZE, mode=\"train\"\n", ")\n", "\n", "evalds = create_dataset(\n", " pattern=\"../data/taxi-valid\", batch_size=1000, mode=\"eval\"\n", ").take(NUM_EVAL_EXAMPLES // 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are various arguments you can set when calling the [.fit method](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model#fit). Here `x` specifies the input data which in our case is a `tf.data` dataset returning a tuple of (inputs, targets). The `steps_per_epoch` parameter is used to mark the end of training for a single epoch. Here we are training for NUM_EVALS epochs. Lastly, for the `callback` argument we specify a Tensorboard callback so we can inspect Tensorboard after training. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "1/1 [==============================] - ETA: 0s - loss: 459.8058 - rmse: 21.4431 - mse: 459.8058WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'ExpandDims_4:0' shape=(1000, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'ExpandDims_3:0' shape=(1000, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'ExpandDims_1:0' shape=(1000, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'ExpandDims:0' shape=(1000, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'ExpandDims_2:0' shape=(1000, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "1/1 [==============================] - 0s 380ms/step - loss: 459.8058 - rmse: 21.4431 - mse: 459.8058 - val_loss: 417.1967 - val_rmse: 20.4248 - val_mse: 417.1967\n", "Epoch 2/50\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n", "Instructions for updating:\n", "use `tf.profiler.experimental.stop` instead.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n", "Instructions for updating:\n", "use `tf.profiler.experimental.stop` instead.\n", "1/1 [==============================] - 0s 209ms/step - loss: 408.0704 - rmse: 20.2008 - mse: 408.0704 - val_loss: 372.5472 - val_rmse: 19.3002 - val_mse: 372.5472\n", "Epoch 3/50\n", "1/1 [==============================] - 0s 227ms/step - loss: 360.3557 - rmse: 18.9830 - mse: 360.3557 - val_loss: 331.1995 - val_rmse: 18.1980 - val_mse: 331.1995\n", "Epoch 4/50\n", "1/1 [==============================] - 0s 215ms/step - loss: 347.3665 - rmse: 18.6378 - mse: 347.3665 - val_loss: 297.4266 - val_rmse: 17.2441 - val_mse: 297.4266\n", "Epoch 5/50\n", "1/1 [==============================] - 0s 206ms/step - loss: 286.5241 - rmse: 16.9270 - mse: 286.5241 - val_loss: 263.9825 - val_rmse: 16.2456 - val_mse: 263.9825\n", "Epoch 6/50\n", "1/1 [==============================] - 0s 193ms/step - loss: 256.2915 - rmse: 16.0091 - mse: 256.2915 - val_loss: 236.8786 - val_rmse: 15.3884 - val_mse: 236.8786\n", "Epoch 7/50\n", "1/1 [==============================] - 0s 204ms/step - loss: 256.5312 - rmse: 16.0166 - mse: 256.5312 - val_loss: 212.8298 - val_rmse: 14.5825 - val_mse: 212.8298\n", "Epoch 8/50\n", "1/1 [==============================] - 0s 201ms/step - loss: 209.4233 - rmse: 14.4715 - mse: 209.4233 - val_loss: 190.6100 - val_rmse: 13.8029 - val_mse: 190.6100\n", "Epoch 9/50\n", "1/1 [==============================] - 0s 193ms/step - loss: 173.8032 - rmse: 13.1834 - mse: 173.8032 - val_loss: 173.2266 - val_rmse: 13.1577 - val_mse: 173.2266\n", "Epoch 10/50\n", "1/1 [==============================] - 0s 197ms/step - loss: 162.1626 - rmse: 12.7343 - mse: 162.1626 - val_loss: 156.3861 - val_rmse: 12.4996 - val_mse: 156.3861\n", "Epoch 11/50\n", "1/1 [==============================] - 0s 203ms/step - loss: 145.3596 - rmse: 12.0565 - mse: 145.3596 - val_loss: 143.3395 - val_rmse: 11.9601 - val_mse: 143.3395\n", "Epoch 12/50\n", "1/1 [==============================] - 0s 203ms/step - loss: 137.8060 - rmse: 11.7391 - mse: 137.8060 - val_loss: 133.4605 - val_rmse: 11.5499 - val_mse: 133.4605\n", "Epoch 13/50\n", "1/1 [==============================] - 0s 186ms/step - loss: 119.3317 - rmse: 10.9239 - mse: 119.3317 - val_loss: 125.3690 - val_rmse: 11.1937 - val_mse: 125.3690\n", "Epoch 14/50\n", "1/1 [==============================] - 0s 202ms/step - loss: 123.4050 - rmse: 11.1088 - mse: 123.4050 - val_loss: 118.4538 - val_rmse: 10.8764 - val_mse: 118.4538\n", "Epoch 15/50\n", "1/1 [==============================] - 0s 199ms/step - loss: 118.7843 - rmse: 10.8988 - mse: 118.7843 - val_loss: 115.4629 - val_rmse: 10.7350 - val_mse: 115.4629\n", "Epoch 16/50\n", "1/1 [==============================] - 0s 221ms/step - loss: 110.8544 - rmse: 10.5287 - mse: 110.8544 - val_loss: 113.8647 - val_rmse: 10.6289 - val_mse: 113.8647\n", "Epoch 17/50\n", "1/1 [==============================] - 0s 258ms/step - loss: 88.6581 - rmse: 9.4158 - mse: 88.6581 - val_loss: 110.6485 - val_rmse: 10.5062 - val_mse: 110.6485\n", "Epoch 18/50\n", "1/1 [==============================] - 0s 194ms/step - loss: 136.8709 - rmse: 11.6992 - mse: 136.8709 - val_loss: 110.7408 - val_rmse: 10.5055 - val_mse: 110.7408\n", "Epoch 19/50\n", "1/1 [==============================] - 0s 208ms/step - loss: 106.2396 - rmse: 10.3073 - mse: 106.2396 - val_loss: 108.7835 - val_rmse: 10.4049 - val_mse: 108.7835\n", "Epoch 20/50\n", "1/1 [==============================] - 0s 197ms/step - loss: 87.4391 - rmse: 9.3509 - mse: 87.4391 - val_loss: 113.3825 - val_rmse: 10.6276 - val_mse: 113.3825\n", "Epoch 21/50\n", "1/1 [==============================] - 0s 200ms/step - loss: 100.7617 - rmse: 10.0380 - mse: 100.7617 - val_loss: 114.2324 - val_rmse: 10.6659 - val_mse: 114.2324\n", "Epoch 22/50\n", "1/1 [==============================] - 0s 195ms/step - loss: 92.7291 - rmse: 9.6296 - mse: 92.7291 - val_loss: 115.1919 - val_rmse: 10.7205 - val_mse: 115.1919\n", "Epoch 23/50\n", "1/1 [==============================] - 0s 203ms/step - loss: 86.5144 - rmse: 9.3013 - mse: 86.5144 - val_loss: 117.7335 - val_rmse: 10.8400 - val_mse: 117.7335\n", "Epoch 24/50\n", "1/1 [==============================] - 0s 203ms/step - loss: 94.9213 - rmse: 9.7428 - mse: 94.9213 - val_loss: 119.8224 - val_rmse: 10.9329 - val_mse: 119.8224\n", "Epoch 25/50\n", "1/1 [==============================] - 0s 196ms/step - loss: 93.9629 - rmse: 9.6934 - mse: 93.9629 - val_loss: 119.8038 - val_rmse: 10.9380 - val_mse: 119.8038\n", "Epoch 26/50\n", "1/1 [==============================] - 0s 198ms/step - loss: 78.0710 - rmse: 8.8358 - mse: 78.0710 - val_loss: 120.3646 - val_rmse: 10.9522 - val_mse: 120.3646\n", "Epoch 27/50\n", "1/1 [==============================] - 0s 202ms/step - loss: 112.3472 - rmse: 10.5994 - mse: 112.3472 - val_loss: 122.0424 - val_rmse: 11.0315 - val_mse: 122.0424\n", "Epoch 28/50\n", "1/1 [==============================] - 0s 206ms/step - loss: 123.3582 - rmse: 11.1067 - mse: 123.3582 - val_loss: 120.6063 - val_rmse: 10.9622 - val_mse: 120.6063\n", "Epoch 29/50\n", "1/1 [==============================] - 0s 206ms/step - loss: 108.7708 - rmse: 10.4293 - mse: 108.7708 - val_loss: 123.2730 - val_rmse: 11.0951 - val_mse: 123.2730\n", "Epoch 30/50\n", "1/1 [==============================] - 0s 202ms/step - loss: 124.9369 - rmse: 11.1775 - mse: 124.9369 - val_loss: 123.0336 - val_rmse: 11.0849 - val_mse: 123.0336\n", "Epoch 31/50\n", "1/1 [==============================] - 0s 199ms/step - loss: 124.0933 - rmse: 11.1397 - mse: 124.0933 - val_loss: 122.9485 - val_rmse: 11.0797 - val_mse: 122.9485\n", "Epoch 32/50\n", "1/1 [==============================] - 0s 205ms/step - loss: 80.7687 - rmse: 8.9871 - mse: 80.7687 - val_loss: 121.2341 - val_rmse: 10.9870 - val_mse: 121.2341\n", "Epoch 33/50\n", "1/1 [==============================] - 0s 205ms/step - loss: 107.8542 - rmse: 10.3853 - mse: 107.8542 - val_loss: 121.9428 - val_rmse: 10.9864 - val_mse: 121.9428\n", "Epoch 34/50\n", "1/1 [==============================] - 0s 235ms/step - loss: 114.0660 - rmse: 10.6802 - mse: 114.0660 - val_loss: 119.4364 - val_rmse: 10.9115 - val_mse: 119.4364\n", "Epoch 35/50\n", "1/1 [==============================] - 0s 226ms/step - loss: 85.6417 - rmse: 9.2543 - mse: 85.6417 - val_loss: 118.7148 - val_rmse: 10.8906 - val_mse: 118.7148\n", "Epoch 36/50\n", "1/1 [==============================] - 0s 198ms/step - loss: 104.3050 - rmse: 10.2130 - mse: 104.3050 - val_loss: 118.7663 - val_rmse: 10.8810 - val_mse: 118.7663\n", "Epoch 37/50\n", "1/1 [==============================] - 0s 208ms/step - loss: 127.7845 - rmse: 11.3042 - mse: 127.7845 - val_loss: 118.6710 - val_rmse: 10.8670 - val_mse: 118.6710\n", "Epoch 38/50\n", "1/1 [==============================] - 0s 199ms/step - loss: 104.2268 - rmse: 10.2092 - mse: 104.2268 - val_loss: 115.3383 - val_rmse: 10.7196 - val_mse: 115.3383\n", "Epoch 39/50\n", "1/1 [==============================] - 0s 196ms/step - loss: 99.5264 - rmse: 9.9763 - mse: 99.5264 - val_loss: 113.7026 - val_rmse: 10.6488 - val_mse: 113.7026\n", "Epoch 40/50\n", "1/1 [==============================] - 0s 214ms/step - loss: 85.6453 - rmse: 9.2545 - mse: 85.6453 - val_loss: 115.0825 - val_rmse: 10.7220 - val_mse: 115.0825\n", "Epoch 41/50\n", "1/1 [==============================] - 0s 223ms/step - loss: 118.8601 - rmse: 10.9023 - mse: 118.8601 - val_loss: 113.5028 - val_rmse: 10.6275 - val_mse: 113.5028\n", "Epoch 42/50\n", "1/1 [==============================] - 0s 205ms/step - loss: 99.6453 - rmse: 9.9822 - mse: 99.6453 - val_loss: 110.8391 - val_rmse: 10.5131 - val_mse: 110.8391\n", "Epoch 43/50\n", "1/1 [==============================] - 0s 190ms/step - loss: 106.5154 - rmse: 10.3206 - mse: 106.5154 - val_loss: 110.8601 - val_rmse: 10.5010 - val_mse: 110.8601\n", "Epoch 44/50\n", "1/1 [==============================] - 0s 195ms/step - loss: 76.3681 - rmse: 8.7389 - mse: 76.3681 - val_loss: 112.8917 - val_rmse: 10.6131 - val_mse: 112.8917\n", "Epoch 45/50\n", "1/1 [==============================] - 0s 194ms/step - loss: 98.0120 - rmse: 9.9001 - mse: 98.0120 - val_loss: 111.2567 - val_rmse: 10.5443 - val_mse: 111.2567\n", "Epoch 46/50\n", "1/1 [==============================] - 0s 187ms/step - loss: 78.5080 - rmse: 8.8605 - mse: 78.5080 - val_loss: 111.4790 - val_rmse: 10.5444 - val_mse: 111.4790\n", "Epoch 47/50\n", "1/1 [==============================] - 0s 201ms/step - loss: 85.4436 - rmse: 9.2436 - mse: 85.4436 - val_loss: 110.7689 - val_rmse: 10.5095 - val_mse: 110.7689\n", "Epoch 48/50\n", "1/1 [==============================] - 0s 183ms/step - loss: 72.4617 - rmse: 8.5124 - mse: 72.4617 - val_loss: 110.0478 - val_rmse: 10.4685 - val_mse: 110.0478\n", "Epoch 49/50\n", "1/1 [==============================] - 0s 186ms/step - loss: 87.6100 - rmse: 9.3600 - mse: 87.6100 - val_loss: 109.9866 - val_rmse: 10.4647 - val_mse: 109.9866\n", "Epoch 50/50\n", "1/1 [==============================] - 0s 198ms/step - loss: 109.8545 - rmse: 10.4811 - mse: 109.8545 - val_loss: 109.9271 - val_rmse: 10.4758 - val_mse: 109.9271\n", "CPU times: user 26.3 s, sys: 6.99 s, total: 33.3 s\n", "Wall time: 21.5 s\n" ] } ], "source": [ "%%time\n", "steps_per_epoch = NUM_TRAIN_EXAMPLES // (TRAIN_BATCH_SIZE * NUM_EVALS)\n", "\n", "LOGDIR = \"./taxi_trained\"\n", "history = model.fit(\n", " x=trainds,\n", " steps_per_epoch=steps_per_epoch,\n", " epochs=NUM_EVALS,\n", " validation_data=evalds,\n", " callbacks=[TensorBoard(LOGDIR)],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### High-level model evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we've run data through the model, we can call `.summary()` on the model to get a high-level summary of our network. We can also plot the training and evaluation curves for the metrics we computed above. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_features (DenseFeature multiple 0 \n", "_________________________________________________________________\n", "h1 (Dense) multiple 192 \n", "_________________________________________________________________\n", "h2 (Dense) multiple 264 \n", "_________________________________________________________________\n", "output (Dense) multiple 9 \n", "=================================================================\n", "Total params: 465\n", "Trainable params: 465\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running `.fit` (or `.fit_generator`) returns a History object which collects all the events recorded during training. Similar to Tensorboard, we can plot the training and validation curves for the model loss and rmse by accessing these elements of the History object." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "RMSE_COLS = [\"rmse\", \"val_rmse\"]\n", "\n", "pd.DataFrame(history.history)[RMSE_COLS].plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "LOSS_COLS = [\"loss\", \"val_loss\"]\n", "\n", "pd.DataFrame(history.history)[LOSS_COLS].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making predictions with our model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make predictions with our trained model, we can call the [predict method](https://www.tensorflow.org/api_docs/python/tf/keras/Model#predict), passing to it a dictionary of values. The `steps` parameter determines the total number of steps before declaring the prediction round finished. Here since we have just one example, we set `steps=1` (setting `steps=None` would also work). Note, however, that if x is a `tf.data` dataset or a dataset iterator, and steps is set to None, predict will run until the input dataset is exhausted." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'pickup_longitude': <tf.Tensor 'ExpandDims_4:0' shape=(1, 1) dtype=float32>, 'pickup_latitude': <tf.Tensor 'ExpandDims_3:0' shape=(1, 1) dtype=float32>, 'dropoff_longitude': <tf.Tensor 'ExpandDims_1:0' shape=(1, 1) dtype=float32>, 'dropoff_latitude': <tf.Tensor 'ExpandDims:0' shape=(1, 1) dtype=float32>, 'passenger_count': <tf.Tensor 'ExpandDims_2:0' shape=(1, 1) dtype=float32>}\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'pickup_longitude': <tf.Tensor 'ExpandDims_4:0' shape=(1, 1) dtype=float32>, 'pickup_latitude': <tf.Tensor 'ExpandDims_3:0' shape=(1, 1) dtype=float32>, 'dropoff_longitude': <tf.Tensor 'ExpandDims_1:0' shape=(1, 1) dtype=float32>, 'dropoff_latitude': <tf.Tensor 'ExpandDims:0' shape=(1, 1) dtype=float32>, 'passenger_count': <tf.Tensor 'ExpandDims_2:0' shape=(1, 1) dtype=float32>}\n", "Consider rewriting this model with the Functional API.\n" ] }, { "data": { "text/plain": [ "array([[11.649432]], dtype=float32)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(\n", " x={\n", " \"pickup_longitude\": tf.convert_to_tensor([-73.982683]),\n", " \"pickup_latitude\": tf.convert_to_tensor([40.742104]),\n", " \"dropoff_longitude\": tf.convert_to_tensor([-73.983766]),\n", " \"dropoff_latitude\": tf.convert_to_tensor([40.755174]),\n", " \"passenger_count\": tf.convert_to_tensor([3.0]),\n", " },\n", " steps=1,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Export and deploy our model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, making individual predictions is not realistic, because we can't expect client code to have a model object in memory. For others to use our trained model, we'll have to export our model to a file, and expect client code to instantiate the model from that exported file. \n", "\n", "We'll export the model to a TensorFlow SavedModel format. Once we have a model in this format, we have lots of ways to \"serve\" the model, from a web application, from JavaScript, from mobile applications, etc." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs_4:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs_3:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs_1:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs_2:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs_4:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs_3:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs_1:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs_2:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs_4:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs_3:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs_1:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs_2:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs_4:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs_3:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs_1:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs_2:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs/pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs/pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs/dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs/dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs/passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs/pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs/pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs/dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs/dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs/passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs/pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs/pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs/dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs/dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs/passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'collections.OrderedDict'> input: OrderedDict([('pickup_longitude', <tf.Tensor 'inputs/pickup_longitude:0' shape=(None, 1) dtype=float32>), ('pickup_latitude', <tf.Tensor 'inputs/pickup_latitude:0' shape=(None, 1) dtype=float32>), ('dropoff_longitude', <tf.Tensor 'inputs/dropoff_longitude:0' shape=(None, 1) dtype=float32>), ('dropoff_latitude', <tf.Tensor 'inputs/dropoff_latitude:0' shape=(None, 1) dtype=float32>), ('passenger_count', <tf.Tensor 'inputs/passenger_count:0' shape=(None, 1) dtype=float32>)])\n", "Consider rewriting this model with the Functional API.\n", "INFO:tensorflow:Assets written to: ./export/savedmodel/20210718204705/assets\n", "INFO:tensorflow:Assets written to: ./export/savedmodel/20210718204705/assets\n" ] } ], "source": [ "OUTPUT_DIR = \"./export/savedmodel\"\n", "shutil.rmtree(OUTPUT_DIR, ignore_errors=True)\n", "TIMESTAMP = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n", "\n", "EXPORT_PATH = os.path.join(OUTPUT_DIR, TIMESTAMP)\n", "\n", "tf.saved_model.save(model, EXPORT_PATH) # with default serving function" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [ "flake8-noqa-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2021-07-18 20:47:17.573668: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0\n", "The given SavedModel SignatureDef contains the following input(s):\n", " inputs['dropoff_latitude'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: serving_default_dropoff_latitude:0\n", " inputs['dropoff_longitude'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: serving_default_dropoff_longitude:0\n", " inputs['passenger_count'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: serving_default_passenger_count:0\n", " inputs['pickup_latitude'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: serving_default_pickup_latitude:0\n", " inputs['pickup_longitude'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: serving_default_pickup_longitude:0\n", "The given SavedModel SignatureDef contains the following output(s):\n", " outputs['output_1'] tensor_info:\n", " dtype: DT_FLOAT\n", " shape: (-1, 1)\n", " name: StatefulPartitionedCall:0\n", "Method name is: tensorflow/serving/predict\n", "./export/savedmodel/20210718204705\n", "./export/savedmodel/20210718204705/assets\n", "./export/savedmodel/20210718204705/variables\n", "./export/savedmodel/20210718204705/variables/variables.data-00000-of-00001\n", "./export/savedmodel/20210718204705/variables/variables.index\n", "./export/savedmodel/20210718204705/saved_model.pb\n" ] } ], "source": [ "!saved_model_cli show \\\n", " --tag_set serve \\\n", " --signature_def serving_default \\\n", " --dir {EXPORT_PATH}\n", "\n", "!find {EXPORT_PATH}\n", "os.environ['EXPORT_PATH'] = EXPORT_PATH" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deploy our model to Vertex AI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we will deploy our trained model to Vertex AI and see how we can make online predicitons. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [ "flake8-noqa-line-1", "flake8-noqa-line-8-E501" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MODEL_DISPLAYNAME: taxifare-20210718204705\n" ] } ], "source": [ "PROJECT = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", "PROJECT = PROJECT[0]\n", "BUCKET = PROJECT\n", "REGION = \"us-central1\"\n", "MODEL_DISPLAYNAME = f\"taxifare-{TIMESTAMP}\"\n", "\n", "print(f\"MODEL_DISPLAYNAME: {MODEL_DISPLAYNAME}\")\n", "\n", "# from https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers\n", "SERVING_CONTAINER_IMAGE_URI = (\n", " \"us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-3:latest\"\n", ")\n", "\n", "os.environ[\"BUCKET\"] = BUCKET\n", "os.environ[\"REGION\"] = REGION" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [ "flake8-noqa-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bucket exists, let's not recreate it.\n" ] } ], "source": [ "%%bash\n", "# Create GCS bucket if it doesn't exist already...\n", "exists=$(gsutil ls -d \| grep -w gs://${BUCKET}/)\n", "\n", "if [ -n \"$exists\" ]; then\n", " echo -e \"Bucket exists, let's not recreate it.\"\n", "else\n", " echo \"Creating a new GCS bucket.\"\n", " gsutil mb -l ${REGION} gs://${BUCKET}\n", " echo \"\\nHere are your current buckets:\"\n", " gsutil ls\n", "fi" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "tags": [ "flake8-noqa-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Copying file://./export/savedmodel/20210718204705/saved_model.pb [Content-Type=application/octet-stream]...\n", "Copying file://./export/savedmodel/20210718204705/variables/variables.data-00000-of-00001 [Content-Type=application/octet-stream]...\n", "Copying file://./export/savedmodel/20210718204705/variables/variables.index [Content-Type=application/octet-stream]...\n", "/ [3 files][203.4 KiB/203.4 KiB] \n", "Operation completed over 3 objects/203.4 KiB. \n" ] } ], "source": [ "!gsutil cp -R $EXPORT_PATH gs://$BUCKET/$MODEL_DISPLAYNAME" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:google.cloud.aiplatform.models:Creating Model\n", "INFO:google.cloud.aiplatform.models:Create Model backing LRO: projects/619455089084/locations/us-central1/models/738458397493428224/operations/6118841857079246848\n", "INFO:google.cloud.aiplatform.models:Model created. Resource name: projects/619455089084/locations/us-central1/models/738458397493428224\n", "INFO:google.cloud.aiplatform.models:To use this Model in another session:\n", "INFO:google.cloud.aiplatform.models:model = aiplatform.Model('projects/619455089084/locations/us-central1/models/738458397493428224')\n" ] } ], "source": [ "uploaded_model = aiplatform.Model.upload(\n", " display_name=MODEL_DISPLAYNAME,\n", " artifact_uri=f\"gs://{BUCKET}/{MODEL_DISPLAYNAME}\",\n", " serving_container_image_uri=SERVING_CONTAINER_IMAGE_URI,\n", ")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:google.cloud.aiplatform.models:Creating Endpoint\n", "INFO:google.cloud.aiplatform.models:Create Endpoint backing LRO: projects/619455089084/locations/us-central1/endpoints/3751129053692690432/operations/3191502099288424448\n", "INFO:google.cloud.aiplatform.models:Endpoint created. Resource name: projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n", "INFO:google.cloud.aiplatform.models:To use this Endpoint in another session:\n", "INFO:google.cloud.aiplatform.models:endpoint = aiplatform.Endpoint('projects/619455089084/locations/us-central1/endpoints/3751129053692690432')\n", "INFO:google.cloud.aiplatform.models:Deploying model to Endpoint : projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n", "INFO:google.cloud.aiplatform.models:Deploy Endpoint model backing LRO: projects/619455089084/locations/us-central1/endpoints/3751129053692690432/operations/7219971965971333120\n", "INFO:google.cloud.aiplatform.models:Endpoint model deployed. Resource name: projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n" ] } ], "source": [ "MACHINE_TYPE = \"n1-standard-2\"\n", "\n", "endpoint = uploaded_model.deploy(\n", " machine_type=MACHINE_TYPE,\n", " accelerator_type=None,\n", " accelerator_count=None,\n", ")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "instance = {\n", " \"pickup_longitude\": -73.982683,\n", " \"pickup_latitude\": 40.742104,\n", " \"dropoff_longitude\": -73.983766,\n", " \"dropoff_latitude\": 40.755174,\n", " \"passenger_count\": 3.0,\n", "}" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Prediction(predictions=[[11.6494322]], deployed_model_id='8509533903730507776', explanations=None)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "endpoint.predict([instance])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cleanup\n", "\n", "When deploying a model to an endpoint for online prediction, the minimum `min-replica-count` is 1, and it is charged per node hour. So let's delete the endpoint to reduce unnecessary charges. Before we can delete the endpoint, we first undeploy all attached models... " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:google.cloud.aiplatform.models:Undeploying Endpoint model: projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n", "INFO:google.cloud.aiplatform.models:Undeploy Endpoint model backing LRO: projects/619455089084/locations/us-central1/endpoints/3751129053692690432/operations/673989907588317184\n", "INFO:google.cloud.aiplatform.models:Endpoint model undeployed. Resource name: projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n" ] }, { "data": { "text/plain": [ "<google.cloud.aiplatform.models.Endpoint object at 0x7ff90412ff10> \n", "resource name: projects/619455089084/locations/us-central1/endpoints/3751129053692690432" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "endpoint.undeploy_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...then delete the endpoint." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:google.cloud.aiplatform.base:Deleting Endpoint : projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n", "INFO:google.cloud.aiplatform.base:Delete Endpoint backing LRO: projects/619455089084/locations/us-central1/operations/3011358114193604608\n", "INFO:google.cloud.aiplatform.base:Endpoint deleted. . Resource name: projects/619455089084/locations/us-central1/endpoints/3751129053692690432\n" ] } ], "source": [ "endpoint.delete()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2021 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": { "environment": { "kernel": "python3", "name": "tf2-gpu.2-6.m82", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m82" }, "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" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
Ledoux/ShareYourSystem	Ouvaton/Rebooter.ipynb	1	12613	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Rebooter\n\n##Doc\n----\n\n\n> \n> The Rebooter\n> \n> \n\n----\n\n<small>\nView the Rebooter notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Rebooter.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -- coding: utf-8 --\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nThe Rebooter\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Objects.Concluder\"\nDecorationModuleStr=\"ShareYourSystem.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport copy\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass RebooterClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t'RebootingDoStrsList',\n\t\t'RebootingNameStrsList',\n\t\t'RebootingAllDoBool',\n\t\t'RebootingAllNameBool',\n\t\t'RebootingDoingIsBool',\n\t\t'RebootedWatchBoolKeyStrsList',\n\t\t'RebootingSetDoIsBool'\n\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_RebootingNameStrsList=None,\n\t\t\t\t\t\t_RebootingDoStrsList=None,\n\t\t\t\t\t\t_RebootingAllNameBool=True,\n\t\t\t\t\t\t_RebootingAllDoBool=True,\n\t\t\t\t\t\t_RebootingSetDoIsBool=True,\n\t\t\t\t\t\t_RebootedWatchBoolKeyStrsList=None,\n\t\t\t\t\t\t_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,_KwargVariablesDict)\n\n\tdef do_reboot(self):\n\n\t\t#set\n\t\tif self.RebootingAllNameBool:\n\n\t\t\t#filter\n\t\t\tself.RebootingNameStrsList=SYS.filterNone(\n\t\t\t\tmap(\n\t\t\t\t\tlambda __MroClass:\n\t\t\t\t\t__MroClass.NameStr \n\t\t\t\t\tif hasattr(__MroClass,'DoStr')\n\t\t\t\t\telse None,\n\t\t\t\t\tself.__class__.__mro__\n\t\t\t\t)\n\t\t\t)\n\n\t\t#set\n\t\tif self.RebootingAllDoBool:\n\n\t\t\t#filter\n\t\t\tself.RebootingDoStrsList=SYS.filterNone(\n\t\t\t\tmap(\n\t\t\t\t\tlambda __MroClass:\n\t\t\t\t\t__MroClass.DoStr \n\t\t\t\t\tif hasattr(__MroClass,'DoStr')\n\t\t\t\t\telse None,\n\t\t\t\t\tself.__class__.__mro__\n\t\t\t\t)\n\t\t\t)\n\t\t\n\t\t#debug\n\t\t'''\n\t\tself.debug(\n\t\t\t\t\t('self.',self,[\n\t\t\t\t\t\t'RebootingDoStrsList',\n\t\t\t\t\t\t'RebootingNameStrsList'\n\t\t\t\t\t\t])\n\t\t\t\t)\n\t\t'''\n\t\t\n\t\t#map\n\t\tmap(\n\t\t\t\tlambda __RebootingNameStr:\n\t\t\t\tself.setSwitch(\n\t\t\t\t\t__RebootingNameStr,\n\t\t\t\t\tself.RebootingDoStrsList\n\t\t\t\t),\n\t\t\t\tself.RebootingNameStrsList\n\t\t\t)\n\n\n\t\t#Check\n\t\tif self.RebootingSetDoIsBool:\n\n\t\t\t#debug\n\t\t\t'''\n\t\t\tself.debug(('self.',self,['RebootingNameStrsList']))\n\t\t\t'''\n\n\t\t\t#map\n\t\t\tmap(\n\t\t\t\t\tlambda __RebootingClass:\n\t\t\t\t\tself.setDone(\n\t\t\t\t\t\t__RebootingClass\n\t\t\t\t\t) \n\t\t\t\t\t#if hasattr(__RebootingClass,'DoneAttributeVariablesOrderedDict')\n\t\t\t\t\t#else None,\n\t\t\t\t\t,map(\n\t\t\t\t\t\t\tlambda __RebootingClassStr:\n\t\t\t\t\t\t\tgetattr(\n\t\t\t\t\t\t\t\tSYS,\n\t\t\t\t\t\t\t\t__RebootingClassStr\n\t\t\t\t\t\t\t) \n\t\t\t\t\t\t\t#if hasattr(SYS,__RebootingClassStr)\n\t\t\t\t\t\t\t#else None\n\t\t\t\t\t\t\t,map(SYS.getClassStrWithNameStr,self.RebootingNameStrsList)\n\t\t\t\t\t\t)\n\t\t\t\t)\n\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Rebooter sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Objects/Rebooter\" target=\"_blank\">Github</a>\n</small>\n\n", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.", "cell_type": "markdown", "prompt_number": 2, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "cell_type": "code", "prompt_number": 3, "language": "python", "input": [ "#ImportModules\n", "import ShareYourSystem as SYS\n", "from ShareYourSystem.Classors import Classer\n", "from ShareYourSystem.Objects import Rebooter\n", "\n", "#Definition \n", "@Classer.ClasserClass(\n", "{\n", " 'ClassingSwitchMethodStrsList':['make']\n", "})\n", "class MakerClass(Rebooter.RebooterClass):\n", "\n", " #Definition\n", " RepresentingKeyStrsList=[\n", " 'MakingMyFloat',\n", " 'MadeMyInt'\n", " ]\n", "\n", " def default_init(self,\n", " _MakingMyFloat=0.,\n", " _MadeMyInt=0,\n", " _KwarVariablesDict\n", " ):\n", " Rebooter.RebooterClass.__init__(self,_KwarVariablesDict)\n", "\n", " def do_make(self):\n", " \n", " #print\n", " print('I am in the do_make of the Maker')\n", "\n", " #cast\n", " self.MadeMyInt=int(self.MakingMyFloat)\n", "\n", "#Definition\n", "@Classer.ClasserClass({\n", " 'ClassingSwitchMethodStrsList':[\"make\"]\n", "})\n", "class BuilderClass(MakerClass):\n", "\n", " #Definition\n", " RepresentingKeyStrsList=[\n", " ]\n", "\n", " def default_init(self,\n", " _KwarVariablesDict\n", " ):\n", " MakerClass.__init__(self,_KwarVariablesDict)\n", "\n", " def mimic_make(self):\n", " \n", " #print\n", " print('I am in the mimic_make of the Builder')\n", "\n", " #call the parent method\n", " MakerClass.make(self)\n", "\n", " #cast\n", " self.MadeMyInt+=10\n", "\n", " def do_build(self):\n", " pass\n", "\n", "\n", "#Definition an instance\n", "MyBuilder=BuilderClass()\n", "\n", "#Print\n", "print('Before make, MyBuilder is ')\n", "SYS._print(MyBuilder,{\n", " 'RepresentingKeyStrsList':[\n", " 'MakingMyFloat',\n", " 'MadeMyInt',\n", " ]\n", "})\n", "\n", "#make once\n", "MyBuilder.make(3.)\n", "\n", "#Print\n", "print('After the first make, MyBuilder is ')\n", "SYS._print(MyBuilder,{\n", " 'RepresentingKeyStrsList':[\n", " 'MakingMyFloat',\n", " 'MadeMyInt',\n", " ]\n", "})\n", "\n", "#make again\n", "MyBuilder.make(5.)\n", "\n", "#Print\n", "print('After the second make, MyBuilder is ')\n", "SYS._print(MyBuilder,{\n", " 'RepresentingKeyStrsList':[\n", " 'MakingMyFloat',\n", " 'MadeMyInt',\n", " ]\n", "})\n", "\n", "#make again\n", "print('Now we reboot')\n", "MyBuilder.reboot(\n", " #_NameStrsList=['Maker','Builder'],\n", " #_DoStrsList=['Make'],\n", " #_AllDoBool=True,\n", " #_AllNameBool=True,\n", " )\n", "\n", "#Print\n", "print('After the reboot, MyBuilder is ')\n", "SYS._print(MyBuilder,{\n", " 'RepresentingKeyStrsList':[\n", " 'MakingMyFloat',\n", " 'MadeMyInt',\n", " ]\n", "})\n", "\n", "#make again\n", "MyBuilder.make(8.)\n", "\n", "#Definition the AttestedStr\n", "SYS._attest(\n", " [\n", " 'MyBuilder is '+SYS._str(\n", " MyBuilder,\n", " {\n", " 'RepresentingAlineaIsBool':False,\n", " 'RepresentingKeyStrsList':[\n", " 'MakingMyFloat',\n", " 'MadeMyInt',\n", " 'RebootedWatchBoolKeyStrsList'\n", " ]\n", " }\n", " )\n", " ]\n", ") \n" ], "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Before make, MyBuilder is \n", "< (BuilderClass), 4537218832>\n", " /{ \n", " / '<Base><Class>MadeMyInt' : 0\n", " / '<Base><Class>MakingMyFloat' : 0.0\n", " / '<New><Instance>IdInt' : 4537218832\n", " /}\n", "I am in the mimic_make of the Builder\n", "I am in the do_make of the Maker\n", "After the first make, MyBuilder is \n", "< (BuilderClass), 4537218832>\n", " /{ \n", " / '<New><Instance>IdInt' : 4537218832\n", " / '<Spe><Instance>MadeMyInt' : 13\n", " / '<Spe><Instance>MakingMyFloat' : 3.0\n", " /}\n", "After the second make, MyBuilder is \n", "< (BuilderClass), 4537218832>\n", " /{ \n", " / '<New><Instance>IdInt' : 4537218832\n", " / '<Spe><Instance>MadeMyInt' : 13\n", " / '<Spe><Instance>MakingMyFloat' : 3.0\n", " /}\n", "Now we reboot\n", "After the reboot, MyBuilder is \n", "< (BuilderClass), 4537218832>\n", " /{ \n", " / '<New><Instance>IdInt' : 4537218832\n", " / '<Spe><Instance>MadeMyInt' : 0\n", " / '<Spe><Instance>MakingMyFloat' : 3.0\n", " /}\n", "I am in the mimic_make of the Builder\n", "I am in the do_make of the Maker\n", "\n", "\n", "*Start of the Attest *\n", "\n", "MyBuilder is < (BuilderClass), 4537218832>\n", " /{ \n", " / '<New><Instance>IdInt' : 4537218832\n", " / '<Spe><Instance>MadeMyInt' : 18\n", " / '<Spe><Instance>MakingMyFloat' : 8.0\n", " / '<Spe><Instance>RebootedWatchBoolKeyStrsList' : []\n", " /}\n", "\n", "*End of the Attest ***\n", "\n", "\n" ] } ], "collapsed": false, "metadata": { "slideshow": { "slide_type": "-" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }	mit
shwetkm/shwetkm.github.io	2020-01-28-Interactive Bubble Plot.ipynb	1	69889	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 4, "hidden": true, "row": 0, "width": 4 }, "report_default": {} } } } }, "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=\"1001\">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 != null && id in Bokeh.index) {\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 id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\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[toinsert.length - 1].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[toinsert.length - 1].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[toinsert.length - 1]);\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(\"1001\");\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(\"1001\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.2.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-1.0.2.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.2.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.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(\"1001\")).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(\"1001\");\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(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.2.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-1.0.2.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.2.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.2.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.2.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(\"1001\")).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 numpy as np\n", "import pandas as pd\n", "import json\n", "from ipywidgets import interact\n", "from bokeh.plotting import figure, show, ColumnDataSource\n", "from bokeh.io import push_notebook, show, output_notebook\n", "from bokeh.plotting import figure\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "#your_local_path=\"F:/Study/Dashboard/Coffee/\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with open('Cafe.json') as f:\n", " i=1\n", " for line in f:\n", " coffee = pd.DataFrame(json.loads(line))\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "# coffee = pd.read_json('Cafe1.json')\n", "#coffee = coffee.sample(n=1000)\n", "#coffee.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "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>2013:10</th>\n", " <th>2013:11</th>\n", " <th>2013:12</th>\n", " <th>2013:9</th>\n", " <th>2014:1</th>\n", " <th>2014:10</th>\n", " <th>2014:11</th>\n", " <th>2014:12</th>\n", " <th>2014:2</th>\n", " <th>2014:3</th>\n", " <th>...</th>\n", " <th>2016:8</th>\n", " <th>2016:9</th>\n", " <th>2017:1</th>\n", " <th>2017:2</th>\n", " <th>2017:3</th>\n", " <th>2017:4</th>\n", " <th>2017:5</th>\n", " <th>2017:6</th>\n", " <th>2017:7</th>\n", " <th>2017:8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 31.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 32.0, 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'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 30.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 31.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 34.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 17.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 18.0, 'CostIncurredAtStore':...</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 147.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 160.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 212.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 223.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 239.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 257.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 268.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 293.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 304.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 317.0, 'CostIncurredAtStore'...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 14.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 15.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 28.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 30.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 32.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 16.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 17.0, 'CostIncurredAtStore':...</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 94.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 103.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 135.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 148.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 157.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 166.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 179.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 193.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 204.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 223.0, 'CostIncurredAtStore'...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 24.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 26.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 27.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 13.0, 'CostIncurredAtStore':...</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 92.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 100.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 116.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 121.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 123.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 134.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 142.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 151.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 155.0, 'CostIncurredAtStore'...</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>{'NoOfEmployees': 18.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 19.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 21.0, 'CostIncurredAtStore':...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 72.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 80.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 109.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 117.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 125.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 136.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 149.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 160.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 163.0, 'CostIncurredAtStore'...</td>\n", " <td>{'NoOfEmployees': 174.0, 'CostIncurredAtStore'...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>{'NoOfEmployees': 11.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 12.0, 'CostIncurredAtStore':...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 49.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 53.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 62.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 67.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 74.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 77.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 83.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 90.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 97.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 99.0, 'CostIncurredAtStore':...</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>{'NoOfEmployees': 34.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 36.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 49.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 52.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 55.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 59.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 60.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 61.0, 'CostIncurredAtStore':...</td>\n", " <td>{'NoOfEmployees': 66.0, 'CostIncurredAtStore':...</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7 rows × 48 columns</p>\n", "</div>" ], "text/plain": [ " 2013:10 \\\n", "0 {'NoOfEmployees': 12.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 12.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 12.0, 'CostIncurredAtStore':... \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2013:11 \\\n", "0 {'NoOfEmployees': 13.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 13.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 13.0, 'CostIncurredAtStore':... \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2013:12 \\\n", "0 {'NoOfEmployees': 14.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 14.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 14.0, 'CostIncurredAtStore':... \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2013:9 \\\n", "0 {'NoOfEmployees': 11.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 11.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 11.0, 'CostIncurredAtStore':... \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2014:1 \\\n", "0 {'NoOfEmployees': 15.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 15.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 15.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 11.0, 'CostIncurredAtStore':... \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2014:10 \\\n", "0 {'NoOfEmployees': 31.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 30.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 28.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 24.0, 'CostIncurredAtStore':... \n", "4 {'NoOfEmployees': 18.0, 'CostIncurredAtStore':... \n", "5 NaN \n", "6 NaN \n", "\n", " 2014:11 \\\n", "0 {'NoOfEmployees': 32.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 31.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 30.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 26.0, 'CostIncurredAtStore':... \n", "4 {'NoOfEmployees': 19.0, 'CostIncurredAtStore':... \n", "5 {'NoOfEmployees': 11.0, 'CostIncurredAtStore':... \n", "6 NaN \n", "\n", " 2014:12 \\\n", "0 {'NoOfEmployees': 34.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 34.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 32.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 27.0, 'CostIncurredAtStore':... \n", "4 {'NoOfEmployees': 21.0, 'CostIncurredAtStore':... \n", "5 {'NoOfEmployees': 12.0, 'CostIncurredAtStore':... \n", "6 NaN \n", "\n", " 2014:2 \\\n", "0 {'NoOfEmployees': 16.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 17.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 16.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 12.0, 'CostIncurredAtStore':... \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " 2014:3 \\\n", "0 {'NoOfEmployees': 17.0, 'CostIncurredAtStore':... \n", "1 {'NoOfEmployees': 18.0, 'CostIncurredAtStore':... \n", "2 {'NoOfEmployees': 17.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 13.0, 'CostIncurredAtStore':... \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", "5 ... \n", "6 ... \n", "\n", " 2016:8 \\\n", "0 {'NoOfEmployees': 114.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 147.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 94.0, 'CostIncurredAtStore':... \n", "3 {'NoOfEmployees': 92.0, 'CostIncurredAtStore':... \n", "4 {'NoOfEmployees': 72.0, 'CostIncurredAtStore':... \n", "5 {'NoOfEmployees': 49.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 34.0, 'CostIncurredAtStore':... \n", "\n", " 2016:9 \\\n", "0 {'NoOfEmployees': 122.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 160.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 103.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 100.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 80.0, 'CostIncurredAtStore':... \n", "5 {'NoOfEmployees': 53.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 36.0, 'CostIncurredAtStore':... \n", "\n", " 2017:1 \\\n", "0 {'NoOfEmployees': 146.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 212.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 135.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 116.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 109.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 62.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 49.0, 'CostIncurredAtStore':... \n", "\n", " 2017:2 \\\n", "0 {'NoOfEmployees': 157.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 223.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 148.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 121.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 117.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 67.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 52.0, 'CostIncurredAtStore':... \n", "\n", " 2017:3 \\\n", "0 {'NoOfEmployees': 170.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 239.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 157.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 123.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 125.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 74.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 55.0, 'CostIncurredAtStore':... \n", "\n", " 2017:4 \\\n", "0 {'NoOfEmployees': 176.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 257.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 166.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 134.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 136.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 77.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 59.0, 'CostIncurredAtStore':... \n", "\n", " 2017:5 \\\n", "0 {'NoOfEmployees': 190.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 268.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 179.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 142.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 149.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 83.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 60.0, 'CostIncurredAtStore':... \n", "\n", " 2017:6 \\\n", "0 {'NoOfEmployees': 206.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 293.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 193.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 151.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 160.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 90.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 61.0, 'CostIncurredAtStore':... \n", "\n", " 2017:7 \\\n", "0 {'NoOfEmployees': 215.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 304.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 204.0, 'CostIncurredAtStore'... \n", "3 {'NoOfEmployees': 155.0, 'CostIncurredAtStore'... \n", "4 {'NoOfEmployees': 163.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 97.0, 'CostIncurredAtStore':... \n", "6 {'NoOfEmployees': 66.0, 'CostIncurredAtStore':... \n", "\n", " 2017:8 \n", "0 {'NoOfEmployees': 222.0, 'CostIncurredAtStore'... \n", "1 {'NoOfEmployees': 317.0, 'CostIncurredAtStore'... \n", "2 {'NoOfEmployees': 223.0, 'CostIncurredAtStore'... \n", "3 NaN \n", "4 {'NoOfEmployees': 174.0, 'CostIncurredAtStore'... \n", "5 {'NoOfEmployees': 99.0, 'CostIncurredAtStore':... \n", "6 NaN \n", "\n", "[7 rows x 48 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stdf = coffee['Monthly Data'].apply(json.loads).apply(pd.Series)\n", "stdf.reset_index(drop=True)\n", "#stdf.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "coffee = coffee[['Store ID', 'Name', 'Brand', 'Store Number', 'Phone Number',\n", " 'Ownership Type', 'Street Combined', 'Street 1', 'Street 2', 'Street 3',\n", " 'City', 'State', 'Country', 'Coordinates', 'Latitude', 'Longitude',\n", " 'Start Date', 'Monthly Data']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 14, "hidden": true, "row": 0, "width": 12 }, "report_default": {} } } } }, "outputs": [], "source": [ "#len(coffee.Country.unique())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": {} } } } }, "outputs": [], "source": [ "stdf.columns = range(0,len(stdf.columns))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 4, "hidden": true, "row": 0, "width": 4 }, "report_default": {} } } } }, "outputs": [], "source": [ "stdf['City'] = coffee['City']\n", "stdf['Name'] = coffee['Name']\n", "stdf_city = stdf[stdf['City'] == 'Pune']\n", "#len(stdf_city['Name'].unique())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 4, "height": 10, "hidden": true, "row": 0, "width": 4 }, "report_default": {} } } } }, "outputs": [], "source": [ "#stdf_city[0].apply(pd.Series)" ] }, { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 5, "hidden": false, "row": 0, "width": 3 }, "report_default": {} } } } }, "source": [ "# Cost Incurred vs Revenue Plot for stores in Pune\n", "#### Bubble size represents Profits" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 3, "height": 17, "hidden": false, "row": 0, "width": 9 }, "report_default": {} } } } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/shwetakamal/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/api.py:107: RuntimeWarning: '<' not supported between instances of 'str' and 'int', sort order is undefined for 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for <strong>In[11]</strong>></code></p>" ], "text/plain": [ "<bokeh.io.notebook.CommsHandle at 0x7f5d16586e90>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bokeh.palettes import Spectral11\n", "from bokeh.models import HoverTool\n", "from bokeh.models import LinearInterpolator, CategoricalColorMapper\n", "from bokeh.models import ColumnDataSource\n", "\n", "sss = stdf_city[0].apply(pd.Series)\n", "Profit = sss['Profit']\n", "source = ColumnDataSource(dict(\n", " x= sss['CostIncurredAtStore'],\n", " y= sss['RevenueOfStore'],\n", " profit = sss['Profit'],\n", " profit_scaled = Profit.apply(lambda x: abs(x/5)),\n", " stores = stdf_city['Name'], \n", " emp = sss['NoOfEmployees'],\n", " \n", " ))\n", "\n", "\n", "def update_m (month):\n", " sss = stdf_city[month].apply(pd.Series)\n", " Profit = sss['Profit']\n", " new_data = dict(\n", " x= sss['CostIncurredAtStore'],\n", " y= sss['RevenueOfStore'],\n", " stores = stdf_city['Name'], \n", " emp = sss['NoOfEmployees'],\n", " profit = Profit,\n", " profit_scaled = Profit.apply(lambda x: abs(x/5))\n", " \n", " )\n", " source.data = new_data #updating the source data with the newdata i.e data of each year\n", " p1.title.text = str(month) #updating the title \n", " push_notebook() #push this into chart\n", "\n", "size_mapper = LinearInterpolator( #this is to give size for each data point according to their population\n", " x=[5, 15],\n", " y = [3,5]\n", ")\n", "\n", "#to give color to each type of data point\n", "color_mapper = CategoricalColorMapper(\n", " factors = list(stdf_city['Name'].unique()), #this tells the compiler to color the continents\n", " palette = Spectral11,)\n", "\n", "hover = HoverTool(tooltips = [(\"Employee Count\",\"@emp\"),(\"Profit\",\"@profit\"), (\"Revenue\",\"@y\"), (\"Cost Incurred\",\"@x\")], #when u hover mouse on data points\n", " show_arrow=False)\n", "\n", "\n", "\n", "PLOT_OPTS = dict( #the dimensions of figure is given\n", " height =450,\n", " width = 750,\n", " x_axis_type='log',\n", " x_range=[1300, 30000],\n", " y_range=(1500,30000)\n", ")\n", "\n", "\n", "p1 = figure( #how do u want the overall dimensions of fig\n", " title = str('2013 October Cost Incurred vs Revenue'),toolbar_location='above', #title should always be in string format\n", " title_location = 'above',\n", " tools=[hover],\n", " **PLOT_OPTS)\n", "\n", "\n", "p1.circle(\n", " x='x',y='y', #these have been wriiten before and is being called for the sake of hovering to work and is defined in update function\n", " size={'field':'profit_scaled'}, #we cant use the size of data point as population as the china population is one billion and all the pixels gets filled and hence we use a mapper and give the rangee of X and y axis\n", " color = {'field':'stores','transform':color_mapper}, #this will color all the regions defined by color_mapper\n", " legend='stores', #a legend of which color is what continent\n", " source=source, #what is the data source\n", " alpha=0.9) #how much of transparency of data pojint\n", "\n", "p1.legend.border_line_color = None #to remove the border\n", "p1.legend.location = (10,0) #this is going to take legend out of the chart box\n", "p1.right.append(p1.legend[0]) #this is going to place the legend to the right\n", "show(p1, notebook_handle=True) #notebook_handle will take the consideration of viewing each year that we defined in update" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 2, "hidden": false, "row": 5, "width": 3 }, "report_default": {} } } } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85c8270f348a412d8a87d5f9d93146b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=23, description='month', max=47), Output()), _dom_classes=('widget-inter…" ] }, "metadata": {}, "output_type": 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