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alexjj/pancham

pandas-cookbook/cookbook/Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb

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{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n", "plt.rcParams['figure.figsize'] = (15, 5)\n", "\n", "# This is necessary to show lots of columns in pandas 0.12. \n", "# Not necessary in pandas 0.13.\n", "pd.set_option('display.line_width', 5000) \n", "pd.set_option('display.max_columns', 60)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay! We're going back to our bike path dataset here. I live in Montreal, and I was curious about whether we're more of a commuter city or a biking-for-fun city -- do people bike more on weekends, or on weekdays?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.1 Adding a 'weekday' column to our dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to load up the data. We've done this before." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n", "bikes['Berri 1'].plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.axes.AxesSubplot at 0x1079e5090>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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aLlvWgjdPp20DOPP+V1x5NUInDwAAYoKehTP73wBgSULvIKlU+dDMXHDplegY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UAAAg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\nupD0rFy5EitWrMAjjzyCp59+OuR3ly1bNkReJT4tLS340Y9+hPb2dthsN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"text": [ "<matplotlib.figure.Figure at 0x1079de7d0>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next up, we're just going to look at the Berri bike path. Berri is a street in Montreal, with a pretty important bike path. I use it mostly on my way to the library now, but I used to take it to work sometimes when I worked in Old Montreal. \n", "\n", "So we're going to create a dataframe with just the Berri bikepath in it" ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes = bikes[['Berri 1']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes[:5]" ], "language": "python", "metadata": {}, "outputs": [ { "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>Berri 1</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " Berri 1\n", "Date \n", "2012-01-01 35\n", "2012-01-02 83\n", "2012-01-03 135\n", "2012-01-04 144\n", "2012-01-05 197\n", "\n", "[5 rows x 1 columns]" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to add a 'weekday' column. Firstly, we can get the weekday from the index. We haven't talked about indexes yet, but the index is what's on the left on the above dataframe, under 'Date'. It's basically all the days of the year." ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes.index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "<class 'pandas.tseries.index.DatetimeIndex'>\n", "[2012-01-01, ..., 2012-11-05]\n", "Length: 310, Freq: None, Timezone: None" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that actually some of the days are missing -- only 310 days of the year are actually there. Who knows why.\n", "\n", "Pandas has a bunch of really great time series functionality, so if we wanted to get the day of the month for each row, we could do it like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes.index.day" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n", " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1, 2, 3,\n", " 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n", " 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", " 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", " 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n", " 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n", " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1,\n", " 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n", " 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1, 2, 3, 4, 5,\n", " 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n", " 23, 24, 25, 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", " 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n", " 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n", " 29, 30, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n", " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1,\n", " 2, 3, 4, 5], dtype=int32)" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We actually want the weekday, though:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes.index.weekday" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n", " 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n", " 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n", " 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n", " 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n", " 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n", " 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n", " 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n", " 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n", " 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n", " 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n", " 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n", " 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n", " 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the days of the week, where 0 is Monday. I found out that 0 was Monday by checking on a calendar.\n", "\n", "Now that we know how to *get* the weekday, we can add it as a column in our dataframe like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "berri_bikes['weekday'] = berri_bikes.index.weekday\n", "berri_bikes[:5]" ], "language": "python", "metadata": {}, "outputs": [ { "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>Berri 1</th>\n", " <th>weekday</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2012-01-01</th>\n", " <td> 35</td>\n", " <td> 6</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-02</th>\n", " <td> 83</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-03</th>\n", " <td> 135</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-04</th>\n", " <td> 144</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>2012-01-05</th>\n", " <td> 197</td>\n", " <td> 3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 2 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " Berri 1 weekday\n", "Date \n", "2012-01-01 35 6\n", "2012-01-02 83 0\n", "2012-01-03 135 1\n", "2012-01-04 144 2\n", "2012-01-05 197 3\n", "\n", "[5 rows x 2 columns]" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.2 Adding up the cyclists by weekday" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This turns out to be really easy!\n", "\n", "Dataframes have a `.groupby()` method that is similar to SQL groupby, if you're familiar with that. I'm not going to explain more about it right now -- if you want to to know more, [the documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html) is really good.\n", "\n", "In this case, `berri_bikes.groupby('weekday').aggregate(sum)` means \"Group the rows by weekday and then add up all the values with the same weekday\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n", "weekday_counts" ], "language": "python", "metadata": {}, "outputs": [ { "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>Berri 1</th>\n", " </tr>\n", " <tr>\n", " <th>weekday</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 134298</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 135305</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 152972</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 160131</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 141771</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 101578</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 99310</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " Berri 1\n", "weekday \n", "0 134298\n", "1 135305\n", "2 152972\n", "3 160131\n", "4 141771\n", "5 101578\n", "6 99310\n", "\n", "[7 rows x 1 columns]" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's hard to remember what 0, 1, 2, 3, 4, 5, 6 mean, so we can fix it up and graph it:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", "weekday_counts" ], "language": "python", "metadata": {}, "outputs": [ { "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>Berri 1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Monday</th>\n", " <td> 134298</td>\n", " </tr>\n", " <tr>\n", " <th>Tuesday</th>\n", " <td> 135305</td>\n", " </tr>\n", " <tr>\n", " <th>Wednesday</th>\n", " <td> 152972</td>\n", " </tr>\n", " <tr>\n", " <th>Thursday</th>\n", " <td> 160131</td>\n", " </tr>\n", " <tr>\n", " <th>Friday</th>\n", " <td> 141771</td>\n", " </tr>\n", " <tr>\n", " <th>Saturday</th>\n", " <td> 101578</td>\n", " </tr>\n", " <tr>\n", " <th>Sunday</th>\n", " <td> 99310</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7 rows \u00d7 1 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " Berri 1\n", "Monday 134298\n", "Tuesday 135305\n", "Wednesday 152972\n", "Thursday 160131\n", "Friday 141771\n", "Saturday 101578\n", "Sunday 99310\n", "\n", "[7 rows x 1 columns]" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "weekday_counts.plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "<matplotlib.axes.AxesSubplot at 0x107bf1450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x107bed390>" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So it looks like Montrealers are commuter cyclists -- they bike much more during the week. Neat!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4.3 Putting it together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's put all that together, to prove how easy it is. 6 lines of magical pandas!\n", "\n", "If you want to play around, try changing `sum` to `max`, `numpy.median`, or any other function you like." ] }, { "cell_type": "code", "collapsed": false, "input": [ "bikes = pd.read_csv('../data/bikes.csv', \n", " sep=';', encoding='latin1', \n", " parse_dates=['Date'], dayfirst=True, \n", " index_col='Date')\n", "# Add the weekday column\n", "berri_bikes = bikes[['Berri 1']]\n", "berri_bikes['weekday'] = berri_bikes.index.weekday\n", "\n", "# Add up the number of cyclists by weekday, and plot!\n", "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n", "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", "weekday_counts.plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "<matplotlib.axes.AxesSubplot at 0x107a4ad10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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unlicense

pk-ai/training

machine-learning/deep-learning/udacity/ud730/1_notmnist.ipynb

1

68918

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5hIbr52I7Z7U" }, "source": [ "Deep Learning\n", "=============\n", "\n", "Assignment 1\n", "------------\n", "\n", "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n", "\n", "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "apJbCsBHl-2A" }, "outputs": [], "source": [ "# These are all the modules we'll be using later. Make sure you can import them\n", "# before proceeding further.\n", "from __future__ import print_function\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "import sys\n", "import tarfile\n", "from IPython.display import display, Image\n", "from scipy import ndimage\n", "from sklearn.linear_model import LogisticRegression\n", "from six.moves.urllib.request import urlretrieve\n", "from six.moves import cPickle as pickle\n", "\n", "# Config the matplotlib backend as plotting inline in IPython\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jNWGtZaXn-5j" }, "source": [ "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labeled examples. Given these sizes, it should be possible to train models quickly on any machine." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 186058, "status": "ok", "timestamp": 1444485672507, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "EYRJ4ICW6-da", "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found and verified ./notMNIST_large.tar.gz\n", "Found and verified ./notMNIST_small.tar.gz\n" ] } ], "source": [ "url = 'https://commondatastorage.googleapis.com/books1000/'\n", "last_percent_reported = None\n", "data_root = '.' # Change me to store data elsewhere\n", "\n", "def download_progress_hook(count, blockSize, totalSize):\n", " \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n", " slow internet connections. Reports every 5% change in download progress.\n", " \"\"\"\n", " global last_percent_reported\n", " percent = int(count * blockSize * 100 / totalSize)\n", "\n", " if last_percent_reported != percent:\n", " if percent % 5 == 0:\n", " sys.stdout.write(\"%s%%\" % percent)\n", " sys.stdout.flush()\n", " else:\n", " sys.stdout.write(\".\")\n", " sys.stdout.flush()\n", " \n", " last_percent_reported = percent\n", " \n", "def maybe_download(filename, expected_bytes, force=False):\n", " \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n", " dest_filename = os.path.join(data_root, filename)\n", " if force or not os.path.exists(dest_filename):\n", " print('Attempting to download:', filename) \n", " filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)\n", " print('\\nDownload Complete!')\n", " statinfo = os.stat(dest_filename)\n", " if statinfo.st_size == expected_bytes:\n", " print('Found and verified', dest_filename)\n", " else:\n", " raise Exception(\n", " 'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')\n", " return dest_filename\n", "\n", "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n", "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cC3p0oEyF8QT" }, "source": [ "Extract the dataset from the compressed .tar.gz file.\n", "This should give you a set of directories, labeled A through J." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 186055, "status": "ok", "timestamp": 1444485672525, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "H8CBE-WZ8nmj", "outputId": "ef6c790c-2513-4b09-962e-27c79390c762" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./notMNIST_large already present - Skipping extraction of ./notMNIST_large.tar.gz.\n", "['./notMNIST_large/A', './notMNIST_large/B', './notMNIST_large/C', './notMNIST_large/D', './notMNIST_large/E', './notMNIST_large/F', './notMNIST_large/G', './notMNIST_large/H', './notMNIST_large/I', './notMNIST_large/J']\n", "./notMNIST_small already present - Skipping extraction of ./notMNIST_small.tar.gz.\n", "['./notMNIST_small/A', './notMNIST_small/B', './notMNIST_small/C', './notMNIST_small/D', './notMNIST_small/E', './notMNIST_small/F', './notMNIST_small/G', './notMNIST_small/H', './notMNIST_small/I', './notMNIST_small/J']\n" ] } ], "source": [ "num_classes = 10\n", "np.random.seed(133)\n", "\n", "def maybe_extract(filename, force=False):\n", " root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz\n", " if os.path.isdir(root) and not force:\n", " # You may override by setting force=True.\n", " print('%s already present - Skipping extraction of %s.' % (root, filename))\n", " else:\n", " print('Extracting data for %s. This may take a while. Please wait.' % root)\n", " tar = tarfile.open(filename)\n", " sys.stdout.flush()\n", " tar.extractall(data_root)\n", " tar.close()\n", " data_folders = [\n", " os.path.join(root, d) for d in sorted(os.listdir(root))\n", " if os.path.isdir(os.path.join(root, d))]\n", " if len(data_folders) != num_classes:\n", " raise Exception(\n", " 'Expected %d folders, one per class. Found %d instead.' % (\n", " num_classes, len(data_folders)))\n", " print(data_folders)\n", " return data_folders\n", " \n", "train_folders = maybe_extract(train_filename)\n", "test_folders = maybe_extract(test_filename)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4riXK3IoHgx6" }, "source": [ "---\n", "Problem 1\n", "---------\n", "\n", "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Displaying images of train folders\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABt0lEQVR4nG2SO2iTURiGn3Ox1RZb\nqdCCkNStlgpCLbgUd6HiBRG1dGgdnBQddPaSpZMFXVwEu0StguIFHQQHFwfrIhpKHZIOraCCqWCS\n//I6hD/5/+g3nvc833nf832QlGNeVd3C8m8Z+isK9X0Y0zqzbfBorrjMzrn/orzV+CVpbSCFtjpM\n6gMjv2Odx3eKjju6DE+kT12dqGFXtbYbjqmhaVxW9FzUfRzbP0vvbBY1bP2oIzhPQYGmsqhjSqUe\njGW0EelFlrQs6RoeLM8VajKd1bInaOzFguOUGiqmRU9BzzCAof+r4vpYWzUMrGi6md0zr5puty05\nZrS2o+nCsi+SfuRbqOGNbtLtvffeb+GV6rqeoI4Dqk8kVx0nFao8iKH50CyVXC5pE26r9oX5Mws2\nAiz5DcVKVxyp1IvBYzg+9LPwzSsJ/Wf/FRONnLhnIwxuWS9TqQ1dKwr0vmngkDSbePXe+26uKoh0\nGIflodaHs585tqlArzEw+kuPsjOyPFWo+CCW0308yE7esAiROQc9X1Qeyq6joXdVgTbHuSDd7Vwa\nxw2FoYq+tjS4iLKieHx2vVwp/QV8b72CTbtCAgAAAABJRU5ErkJggg==\n", "text/plain": [ "<IPython.core.display.Image 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"<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABbUlEQVR4nMWSO2uUQRSGn7nI4oJB\nYmXAQFJuIRbCghDRQhC8NAHLYGEhCIo/wF7/gH/AUrAQvHSKiLE0BIwgsbAzRYi6rLoz57wW327y\nxRVb3/aZZ4Yz74FxIourplZs+HDCyFxVqb7LatUWe4m3tmUT2VwvT7Zg4Oj9n5q4by7STkiB3oZM\nkukuIaZ9OHa4qSK5tmfJgdiGbgwRwOgHpv2w9YQ34/0j/wNmCDEA7n+DUdbcMU1z8APnlo4MPjz/\nHDTtLr6Vy/X1PInMNY0k15cOAeLhR/1qtdaZJcKUeK85ur6S4E8zXyFj6ellm/aIcwScZ9YFCE0n\nYqcCxC1E5FIchpSTc4oIYtOCgAeqkutxDwLdO5KkkW6QgXD8xawlPJbXa4NjZ+c9Qs0b/e/N1Muu\nYhovpbmsaKc//vPE6U9yr7WUUqpVd631dvuIHLr9cW+Z7f31zoQFSPKDZy6cWJjpDr5tvnvy6ldk\n3MFvYX7tzG9mOVIAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution for Problem 1\n", "import random\n", "print('Displaying images of train folders')\n", "# Looping through train folders and displaying a random image of each folder\n", "for path in train_folders:\n", " image_file = os.path.join(path, random.choice(os.listdir(path)))\n", " display(Image(filename=image_file))\n", "\n", "print('Displaying images of test folders')\n", "# Looping through train folders and displaying a random image of each folder\n", "for path in test_folders:\n", " image_file = os.path.join(path, random.choice(os.listdir(path)))\n", " display(Image(filename=image_file))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PBdkjESPK8tw" }, "source": [ "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n", "\n", "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n", "\n", "A few images might not be readable, we'll just skip them." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 30 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 399874, "status": "ok", "timestamp": 1444485886378, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "h7q0XhG3MJdf", "outputId": "92c391bb-86ff-431d-9ada-315568a19e59" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./notMNIST_large/A.pickle already present - Skipping pickling.\n", "./notMNIST_large/B.pickle already present - Skipping pickling.\n", "./notMNIST_large/C.pickle already present - Skipping pickling.\n", "./notMNIST_large/D.pickle already present - Skipping pickling.\n", "./notMNIST_large/E.pickle already present - Skipping pickling.\n", "./notMNIST_large/F.pickle already present - Skipping pickling.\n", "./notMNIST_large/G.pickle already present - Skipping pickling.\n", "./notMNIST_large/H.pickle already present - Skipping pickling.\n", "./notMNIST_large/I.pickle already present - Skipping pickling.\n", "./notMNIST_large/J.pickle already present - Skipping pickling.\n", "./notMNIST_small/A.pickle already present - Skipping pickling.\n", "./notMNIST_small/B.pickle already present - Skipping pickling.\n", "./notMNIST_small/C.pickle already present - Skipping pickling.\n", "./notMNIST_small/D.pickle already present - Skipping pickling.\n", "./notMNIST_small/E.pickle already present - Skipping pickling.\n", "./notMNIST_small/F.pickle already present - Skipping pickling.\n", "./notMNIST_small/G.pickle already present - Skipping pickling.\n", "./notMNIST_small/H.pickle already present - Skipping pickling.\n", "./notMNIST_small/I.pickle already present - Skipping pickling.\n", "./notMNIST_small/J.pickle already present - Skipping pickling.\n" ] } ], "source": [ "image_size = 28 # Pixel width and height.\n", "pixel_depth = 255.0 # Number of levels per pixel.\n", "\n", "def load_letter(folder, min_num_images):\n", " \"\"\"Load the data for a single letter label.\"\"\"\n", " image_files = os.listdir(folder)\n", " dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n", " dtype=np.float32)\n", " print(folder)\n", " num_images = 0\n", " for image in image_files:\n", " image_file = os.path.join(folder, image)\n", " try:\n", " image_data = (ndimage.imread(image_file).astype(float) - \n", " pixel_depth / 2) / pixel_depth\n", " if image_data.shape != (image_size, image_size):\n", " raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n", " dataset[num_images, :, :] = image_data\n", " num_images = num_images + 1\n", " except IOError as e:\n", " print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n", " \n", " dataset = dataset[0:num_images, :, :]\n", " if num_images < min_num_images:\n", " raise Exception('Many fewer images than expected: %d < %d' %\n", " (num_images, min_num_images))\n", " \n", " print('Full dataset tensor:', dataset.shape)\n", " print('Mean:', np.mean(dataset))\n", " print('Standard deviation:', np.std(dataset))\n", " return dataset\n", " \n", "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n", " dataset_names = []\n", " for folder in data_folders:\n", " set_filename = folder + '.pickle'\n", " dataset_names.append(set_filename)\n", " if os.path.exists(set_filename) and not force:\n", " # You may override by setting force=True.\n", " print('%s already present - Skipping pickling.' % set_filename)\n", " else:\n", " print('Pickling %s.' % set_filename)\n", " dataset = load_letter(folder, min_num_images_per_class)\n", " try:\n", " with open(set_filename, 'wb') as f:\n", " pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n", " except Exception as e:\n", " print('Unable to save data to', set_filename, ':', e)\n", " \n", " return dataset_names\n", "\n", "train_datasets = maybe_pickle(train_folders, 45000)\n", "test_datasets = maybe_pickle(test_folders, 1800)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vUdbskYE2d87" }, "source": [ "---\n", "Problem 2\n", "---------\n", "\n", "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From Training dataset\n", "Showing a first image from pickle ./notMNIST_large/A.pickle\n", "Showing a first image from pickle ./notMNIST_large/B.pickle\n", "Showing a first image from pickle ./notMNIST_large/C.pickle\n", "Showing a first image from pickle ./notMNIST_large/D.pickle\n", "Showing a first image from pickle ./notMNIST_large/E.pickle\n", "Showing a first image from pickle ./notMNIST_large/F.pickle\n", "Showing a first image from pickle ./notMNIST_large/G.pickle\n", "Showing a first image from pickle ./notMNIST_large/H.pickle\n", "Showing a first image from pickle ./notMNIST_large/I.pickle\n", "Showing a first image from pickle ./notMNIST_large/J.pickle\n", "From Test Dataset\n", "Showing a first image from pickle ./notMNIST_small/A.pickle\n", "Showing a first image from pickle ./notMNIST_small/B.pickle\n", "Showing a first image from pickle ./notMNIST_small/C.pickle\n", "Showing a first image from pickle ./notMNIST_small/D.pickle\n", "Showing a first image from pickle ./notMNIST_small/E.pickle\n", "Showing a first image from pickle ./notMNIST_small/F.pickle\n", "Showing a first image from pickle ./notMNIST_small/G.pickle\n", "Showing a first image from pickle ./notMNIST_small/H.pickle\n", "Showing a first image from pickle ./notMNIST_small/I.pickle\n", "Showing a first image from pickle ./notMNIST_small/J.pickle\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff270ebb860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution for Problem 2\n", "def show_first_image(datasets):\n", " for pickl in datasets:\n", " print('Showing a first image from pickle ', pickl)\n", " try:\n", " with open(pickl, 'rb') as f:\n", " letter_set = pickle.load(f)\n", " plt.imshow(letter_set[0])\n", " except Exception as e:\n", " print('Unable to show image from pickle ', pickl, ':', e)\n", " raise\n", "print('From Training dataset')\n", "show_first_image(train_datasets)\n", "print('From Test Dataset')\n", "show_first_image(test_datasets)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cYznx5jUwzoO" }, "source": [ "---\n", "Problem 3\n", "---------\n", "Another check: we expect the data to be balanced across classes. Verify that.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape for Training set\n", "Shape of pickle ./notMNIST_large/A.pickle is (52909, 28, 28)\n", "Shape of pickle ./notMNIST_large/B.pickle is (52911, 28, 28)\n", "Shape of pickle ./notMNIST_large/C.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/D.pickle is (52911, 28, 28)\n", "Shape of pickle ./notMNIST_large/E.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/F.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/G.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/H.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/I.pickle is (52912, 28, 28)\n", "Shape of pickle ./notMNIST_large/J.pickle is (52911, 28, 28)\n", "Shape for Test set\n", "Shape of pickle ./notMNIST_small/A.pickle is (1872, 28, 28)\n", "Shape of pickle ./notMNIST_small/B.pickle is (1873, 28, 28)\n", "Shape of pickle ./notMNIST_small/C.pickle is (1873, 28, 28)\n", "Shape of pickle ./notMNIST_small/D.pickle is (1873, 28, 28)\n", "Shape of pickle ./notMNIST_small/E.pickle is (1873, 28, 28)\n", "Shape of pickle ./notMNIST_small/F.pickle is (1872, 28, 28)\n", "Shape of pickle ./notMNIST_small/G.pickle is (1872, 28, 28)\n", "Shape of pickle ./notMNIST_small/H.pickle is (1872, 28, 28)\n", "Shape of pickle ./notMNIST_small/I.pickle is (1872, 28, 28)\n", "Shape of pickle ./notMNIST_small/J.pickle is (1872, 28, 28)\n" ] } ], "source": [ "def show_dataset_shape(datasets):\n", " for pickl in datasets:\n", " try:\n", " with open(pickl, 'rb') as f:\n", " letter_set = pickle.load(f)\n", " print('Shape of pickle ', pickl, 'is', np.shape(letter_set))\n", " except Exception as e:\n", " print('Unable to show image from pickle ', pickl, ':', e)\n", " raise\n", "\n", "print('Shape for Training set')\n", "show_dataset_shape(train_datasets)\n", "print('Shape for Test set')\n", "show_dataset_shape(test_datasets)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LA7M7K22ynCt" }, "source": [ "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n", "\n", "Also create a validation dataset for hyperparameter tuning." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 411281, "status": "ok", "timestamp": 1444485897869, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "s3mWgZLpyuzq", "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training: (20000, 28, 28) (20000,)\n", "Validation: (1000, 28, 28) (1000,)\n", "Testing: (1000, 28, 28) (1000,)\n" ] } ], "source": [ "def make_arrays(nb_rows, img_size):\n", " if nb_rows:\n", " dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n", " labels = np.ndarray(nb_rows, dtype=np.int32)\n", " else:\n", " dataset, labels = None, None\n", " return dataset, labels\n", "\n", "def merge_datasets(pickle_files, train_size, valid_size=0):\n", " num_classes = len(pickle_files)\n", " valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n", " train_dataset, train_labels = make_arrays(train_size, image_size)\n", " vsize_per_class = valid_size // num_classes\n", " tsize_per_class = train_size // num_classes\n", " \n", " start_v, start_t = 0, 0\n", " end_v, end_t = vsize_per_class, tsize_per_class\n", " end_l = vsize_per_class+tsize_per_class\n", " for label, pickle_file in enumerate(pickle_files): \n", " try:\n", " with open(pickle_file, 'rb') as f:\n", " letter_set = pickle.load(f)\n", " # let's shuffle the letters to have random validation and training set\n", " np.random.shuffle(letter_set)\n", " if valid_dataset is not None:\n", " valid_letter = letter_set[:vsize_per_class, :, :]\n", " valid_dataset[start_v:end_v, :, :] = valid_letter\n", " valid_labels[start_v:end_v] = label\n", " start_v += vsize_per_class\n", " end_v += vsize_per_class\n", " \n", " train_letter = letter_set[vsize_per_class:end_l, :, :]\n", " train_dataset[start_t:end_t, :, :] = train_letter\n", " train_labels[start_t:end_t] = label\n", " start_t += tsize_per_class\n", " end_t += tsize_per_class\n", " except Exception as e:\n", " print('Unable to process data from', pickle_file, ':', e)\n", " raise\n", " \n", " return valid_dataset, valid_labels, train_dataset, train_labels\n", " \n", "\"\"\"\n", "train_size = 200000\n", "valid_size = 10000\n", "test_size = 10000\n", "\"\"\" \n", "train_size = 20000\n", "valid_size = 1000\n", "test_size = 1000\n", "\n", "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n", " train_datasets, train_size, valid_size)\n", "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n", "\n", "print('Training:', train_dataset.shape, train_labels.shape)\n", "print('Validation:', valid_dataset.shape, valid_labels.shape)\n", "print('Testing:', test_dataset.shape, test_labels.shape)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GPTCnjIcyuKN" }, "source": [ "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "6WZ2l2tN2zOL" }, "outputs": [], "source": [ "def randomize(dataset, labels):\n", " permutation = np.random.permutation(labels.shape[0])\n", " shuffled_dataset = dataset[permutation,:,:]\n", " shuffled_labels = labels[permutation]\n", " return shuffled_dataset, shuffled_labels\n", "train_dataset, train_labels = randomize(train_dataset, train_labels)\n", "test_dataset, test_labels = randomize(test_dataset, test_labels)\n", "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "puDUTe6t6USl" }, "source": [ "---\n", "Problem 4\n", "---------\n", "Convince yourself that the data is still good after shuffling!\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Printing Train, validation and test labels after shuffling\n", "[array([1], dtype=int32), array([5], dtype=int32), array([2], dtype=int32), array([0], dtype=int32), array([6], dtype=int32), array([8], dtype=int32), array([5], dtype=int32), array([3], dtype=int32), array([3], dtype=int32), array([3], dtype=int32)]\n", "[array([8], dtype=int32), array([2], dtype=int32), array([8], dtype=int32), array([3], dtype=int32), array([1], dtype=int32), array([5], dtype=int32), array([4], dtype=int32), array([1], dtype=int32), array([3], dtype=int32), array([5], dtype=int32)]\n", "[array([7], dtype=int32), array([0], dtype=int32), array([3], dtype=int32), array([1], dtype=int32), array([2], dtype=int32), array([2], dtype=int32), array([7], dtype=int32), array([2], dtype=int32), array([6], dtype=int32), array([4], dtype=int32)]\n" ] } ], "source": [ "print('Printing Train, validation and test labels after shuffling')\n", "def print_first_10_labels(labels):\n", " printing_labels = []\n", " for i in range(10):\n", " printing_labels.append(labels[[i]])\n", " print(printing_labels)\n", "print_first_10_labels(train_labels)\n", "print_first_10_labels(test_labels)\n", "print_first_10_labels(valid_labels)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tIQJaJuwg5Hw" }, "source": [ "Finally, let's save the data for later reuse:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "QiR_rETzem6C" }, "outputs": [], "source": [ "pickle_file = os.path.join(data_root, 'notMNIST.pickle')\n", "\n", "try:\n", " f = open(pickle_file, 'wb')\n", " save = {\n", " 'train_dataset': train_dataset,\n", " 'train_labels': train_labels,\n", " 'valid_dataset': valid_dataset,\n", " 'valid_labels': valid_labels,\n", " 'test_dataset': test_dataset,\n", " 'test_labels': test_labels,\n", " }\n", " pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n", " f.close()\n", "except Exception as e:\n", " print('Unable to save data to', pickle_file, ':', e)\n", " raise" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 413065, "status": "ok", "timestamp": 1444485899688, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "hQbLjrW_iT39", "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compressed pickle size: 690800512\n" ] } ], "source": [ "statinfo = os.stat(pickle_file)\n", "print('Compressed pickle size:', statinfo.st_size)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gE_cRAQB33lk" }, "source": [ "---\n", "Problem 5\n", "---------\n", "\n", "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n", "Measure how much overlap there is between training, validation and test samples.\n", "\n", "Optional questions:\n", "- What about near duplicates between datasets? (images that are almost identical)\n", "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L8oww1s4JMQx" }, "source": [ "---\n", "Problem 6\n", "---------\n", "\n", "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n", "\n", "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n", "\n", "Optional question: train an off-the-shelf model on all the data!\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "validation accuracy, 0.8244\n", "test accuracy, 0.8933\n" ] } ], "source": [ "logreg_model_clf = LogisticRegression()\n", "nsamples, nx, ny = train_dataset.shape\n", "d2_train_dataset = train_dataset.reshape((nsamples,nx*ny))\n", "logreg_model_clf.fit(d2_train_dataset, train_labels)\n", "from sklearn.metrics import accuracy_score\n", "nsamples, nx, ny = valid_dataset.shape\n", "d2_valid_dataset = valid_dataset.reshape((nsamples,nx*ny))\n", "print(\"validation accuracy,\", accuracy_score(valid_labels, logreg_model_clf.predict(d2_valid_dataset)))\n", "nsamples, nx, ny = test_dataset.shape\n", "d2_train_dataset = test_dataset.reshape((nsamples,nx*ny))\n", "print(\"test accuracy,\", accuracy_score(test_labels, logreg_model_clf.predict(d2_train_dataset)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "colab": { "default_view": {}, "name": "1_notmnist.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

flaviostutz/datascience-snippets

kaggle-sea-lion/07-train-lion-patches-single.ipynb

1

100711

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train sea lion classifier with a convnet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "INPUT_DIR = '../../input/kaggle-sea-lion/02/'\n", "OUTPUT_DIR = '../../output/kaggle-sea-lion/07/'\n", "IMAGE_DIMS = (84,84,3)#class0\n", "#IMAGE_DIMS = (84,84,3)#class1\n", "#IMAGE_DIMS = (56,56,3)#class2\n", "#IMAGE_DIMS = (42,42,3)#class3\n", "#IMAGE_DIMS = (26,26,3)#class4\n", "\n", "TRAIN_CLASS = 0\n", "SAVE_WEIGHTS_FILE = OUTPUT_DIR + 'last-weights-medium1-class0.h5'\n", "\n", "Y_CHANGE = [[1,2,3,4,5],[0]]\n", "TRAIN_WEIGHT_RAW = (1,0.2,0.2,0.2,0.2,0.2)\n", "TRAIN_WEIGHT = (1,1)\n", "TEST_WEIGHT_RAW = (1,0.2,0.2,0.2,0.2,0.2)\n", "TEST_WEIGHT = (1,1)\n", "\n", "SAVE_MODEL_FILE = None\n", "LOAD_WEIGHTS_FILE = SAVE_WEIGHTS_FILE\n", "LOAD_MODEL_FILE = None\n", "INPUT_DATASET_NAME = 'lion-patches-0px'\n", "\n", "RECREATE_OUTPUT_DIR = True\n", "RUN_TRAINING = True\n", "\n", "TRAIN_EPOCHS = 10\n", "INPUT_RANGE = 1\n", "DEBUG = False\n", "\n", "BATCH_SIZE=48" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "import sklearn\n", "import os\n", "import glob\n", "\n", "import keras\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras import models\n", "\n", "from modules.logging import logger\n", "import modules.utils as utils\n", "from modules.utils import Timer\n", "import modules.logging\n", "import modules.cnn as cnn\n", "import modules.lions as lions" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Prepare" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Prepare output dir" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:45,548 INFO Output dirs created\n" ] } ], "source": [ "utils.mkdirs(OUTPUT_DIR, dirs=['tf-logs','weights'], recreate=RECREATE_OUTPUT_DIR)\n", "modules.logging.setup_file_logger(OUTPUT_DIR + 'out.log')\n", "TF_LOGS_DIR = OUTPUT_DIR + 'tf-logs/'\n", "WEIGHTS_DIR = OUTPUT_DIR + 'weights/'\n", "input_dataset_path = INPUT_DIR + utils.dataset_name(INPUT_DATASET_NAME, IMAGE_DIMS)\n", "\n", "logger.info('Output dirs created')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Prepare train, validate and test data flows" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:45,589 INFO Using dataset ../../input/kaggle-sea-lion/02/lion-patches-0px-84-84.h5 as input\n", "2017-06-11 13:39:45,604 INFO preparing train data\n", "2017-06-11 13:39:45,606 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:45,607 INFO loading Y from raw dataset\n", "2017-06-11 13:39:45,609 INFO > [started] generator dump...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1600/2263" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:46,004 INFO > [done] generator dump (395.147 ms)\n", "2017-06-11 13:39:46,007 INFO raw sample class distribution\n", "2017-06-11 13:39:46,008 INFO 0: 78\n", "2017-06-11 13:39:46,010 INFO 1: 45\n", "2017-06-11 13:39:46,011 INFO 2: 675\n", "2017-06-11 13:39:46,013 INFO 3: 150\n", "2017-06-11 13:39:46,014 INFO 4: 281\n", "2017-06-11 13:39:46,015 INFO 5: 1034\n", "2017-06-11 13:39:46,017 INFO overall output samples per class: 90\n", "2017-06-11 13:39:46,018 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:46,020 INFO SETUP FLOW 0 0.7\n", "2017-06-11 13:39:46,021 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:46,023 INFO output distribution for this flow\n", "2017-06-11 13:39:46,024 INFO 0: 62 (1.15)\n", "2017-06-11 13:39:46,026 INFO 1: 12 (0.40)\n", "2017-06-11 13:39:46,027 INFO 2: 12 (0.03)\n", "2017-06-11 13:39:46,028 INFO 3: 12 (0.12)\n", "2017-06-11 13:39:46,029 INFO 4: 12 (0.06)\n", "2017-06-11 13:39:46,031 INFO 5: 12 (0.02)\n", "2017-06-11 13:39:46,034 INFO source range: 0-1836 (1836)\n", "2017-06-11 13:39:46,035 INFO output range: 0-125 (125)\n", "2017-06-11 13:39:46,036 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:46,038 INFO loading Y from raw dataset\n", "2017-06-11 13:39:46,039 INFO > [started] generator dump...\n", "2017-06-11 13:39:46,040 INFO starting new flow...\n", "2017-06-11 13:39:46,559 INFO > [done] generator dump (519.571 ms)\n", "2017-06-11 13:39:46,562 INFO raw sample class distribution\n", "2017-06-11 13:39:46,563 INFO 0: 67\n", "2017-06-11 13:39:46,564 INFO 1: 58\n", "2017-06-11 13:39:46,566 INFO overall output samples per class: 116\n", "2017-06-11 13:39:46,567 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:46,569 INFO SETUP FLOW 0 1\n", "2017-06-11 13:39:46,570 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:46,571 INFO output distribution for this flow\n", "2017-06-11 13:39:46,574 INFO 0: 115 (1.73)\n", "2017-06-11 13:39:46,575 INFO 1: 116 (2.00)\n", "2017-06-11 13:39:46,576 INFO source range: 0-123 (123)\n", "2017-06-11 13:39:46,577 INFO output range: 0-231 (231)\n", "2017-06-11 13:39:46,579 INFO train size=231 batches=5\n", "2017-06-11 13:39:46,580 INFO preparing valid data\n", "2017-06-11 13:39:46,582 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:46,583 INFO loading Y from raw dataset\n", "2017-06-11 13:39:46,585 INFO > [started] generator dump...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1600/2263" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:46,918 INFO > [done] generator dump (333.349 ms)\n", "2017-06-11 13:39:46,921 INFO raw sample class distribution\n", "2017-06-11 13:39:46,923 INFO 0: 78\n", "2017-06-11 13:39:46,924 INFO 1: 45\n", "2017-06-11 13:39:46,926 INFO 2: 675\n", "2017-06-11 13:39:46,927 INFO 3: 150\n", "2017-06-11 13:39:46,929 INFO 4: 281\n", "2017-06-11 13:39:46,931 INFO 5: 1034\n", "2017-06-11 13:39:46,932 INFO overall output samples per class: 90\n", "2017-06-11 13:39:46,934 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:46,935 INFO SETUP FLOW 0.7 0.85\n", "2017-06-11 13:39:46,936 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:46,938 INFO output distribution for this flow\n", "2017-06-11 13:39:46,939 INFO 0: 13 (1.15)\n", "2017-06-11 13:39:46,941 INFO 1: 2 (0.40)\n", "2017-06-11 13:39:46,942 INFO 2: 2 (0.03)\n", "2017-06-11 13:39:46,943 INFO 3: 2 (0.12)\n", "2017-06-11 13:39:46,945 INFO 4: 2 (0.06)\n", "2017-06-11 13:39:46,946 INFO 5: 2 (0.02)\n", "2017-06-11 13:39:46,949 INFO source range: 1588-1894 (306)\n", "2017-06-11 13:39:46,951 INFO output range: 126-152 (26)\n", "2017-06-11 13:39:46,952 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:46,954 INFO loading Y from raw dataset\n", "2017-06-11 13:39:46,955 INFO > [started] generator dump...\n", "2017-06-11 13:39:46,956 INFO starting new flow...\n", "2017-06-11 13:39:47,056 INFO > [done] generator dump (100.959 ms)\n", "2017-06-11 13:39:47,059 INFO raw sample class distribution\n", "2017-06-11 13:39:47,060 INFO 0: 15\n", "2017-06-11 13:39:47,062 INFO 1: 11\n", "2017-06-11 13:39:47,063 INFO overall output samples per class: 22\n", "2017-06-11 13:39:47,065 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:47,066 INFO SETUP FLOW 0 1\n", "2017-06-11 13:39:47,068 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:47,069 INFO output distribution for this flow\n", "2017-06-11 13:39:47,071 INFO 0: 22 (1.47)\n", "2017-06-11 13:39:47,072 INFO 1: 22 (2.00)\n", "2017-06-11 13:39:47,074 INFO source range: 0-25 (25)\n", "2017-06-11 13:39:47,075 INFO output range: 0-44 (44)\n", "2017-06-11 13:39:47,076 INFO SETUP FLOW 0 25\n", "2017-06-11 13:39:47,078 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:47,079 INFO output distribution for this flow\n", "2017-06-11 13:39:47,080 INFO 0: 2225 (1.15)\n", "2017-06-11 13:39:47,081 INFO 1: 450 (0.40)\n", "2017-06-11 13:39:47,082 INFO 2: 450 (0.03)\n", "2017-06-11 13:39:47,083 INFO 3: 450 (0.12)\n", "2017-06-11 13:39:47,085 INFO 4: 450 (0.06)\n", "2017-06-11 13:39:47,086 INFO 5: 450 (0.02)\n", "2017-06-11 13:39:47,089 INFO source range: 0-2262 (2262)\n", "2017-06-11 13:39:47,091 INFO output range: 0-4475 (4475)\n", "2017-06-11 13:39:47,092 INFO valid size=44 batches=1\n", "2017-06-11 13:39:47,094 INFO preparing test data\n", "2017-06-11 13:39:47,095 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:47,097 INFO loading Y from raw dataset\n", "2017-06-11 13:39:47,098 INFO > [started] generator dump...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1600/2263" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,423 INFO > [done] generator dump (324.211 ms)\n", "2017-06-11 13:39:47,426 INFO raw sample class distribution\n", "2017-06-11 13:39:47,427 INFO 0: 78\n", "2017-06-11 13:39:47,429 INFO 1: 45\n", "2017-06-11 13:39:47,430 INFO 2: 675\n", "2017-06-11 13:39:47,431 INFO 3: 150\n", "2017-06-11 13:39:47,433 INFO 4: 281\n", "2017-06-11 13:39:47,434 INFO 5: 1034\n", "2017-06-11 13:39:47,436 INFO overall output samples per class: 90\n", "2017-06-11 13:39:47,437 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:47,438 INFO SETUP FLOW 0.85 1\n", "2017-06-11 13:39:47,440 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:47,441 INFO output distribution for this flow\n", "2017-06-11 13:39:47,442 INFO 0: 13 (1.15)\n", "2017-06-11 13:39:47,444 INFO 1: 2 (0.40)\n", "2017-06-11 13:39:47,445 INFO 2: 2 (0.03)\n", "2017-06-11 13:39:47,446 INFO 3: 2 (0.12)\n", "2017-06-11 13:39:47,448 INFO 4: 2 (0.06)\n", "2017-06-11 13:39:47,449 INFO 5: 2 (0.02)\n", "2017-06-11 13:39:47,452 INFO source range: 1869-2090 (221)\n", "2017-06-11 13:39:47,453 INFO output range: 153-179 (26)\n", "2017-06-11 13:39:47,455 INFO loading input data for class distribution analysis...\n", "2017-06-11 13:39:47,457 INFO loading Y from raw dataset\n", "2017-06-11 13:39:47,458 INFO > [started] generator dump...\n", "2017-06-11 13:39:47,459 INFO starting new flow...\n", "2017-06-11 13:39:47,516 INFO > [done] generator dump (58.177 ms)\n", "2017-06-11 13:39:47,520 INFO raw sample class distribution\n", "2017-06-11 13:39:47,522 INFO 0: 16\n", "2017-06-11 13:39:47,523 INFO 1: 10\n", "2017-06-11 13:39:47,524 INFO overall output samples per class: 20\n", "2017-06-11 13:39:47,526 INFO augmentation/undersampling ratio per class\n", "2017-06-11 13:39:47,527 INFO SETUP FLOW 0 1\n", "2017-06-11 13:39:47,528 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:47,529 INFO output distribution for this flow\n", "2017-06-11 13:39:47,530 INFO 0: 20 (1.25)\n", "2017-06-11 13:39:47,532 INFO 1: 20 (2.00)\n", "2017-06-11 13:39:47,533 INFO source range: 0-25 (25)\n", "2017-06-11 13:39:47,535 INFO output range: 0-40 (40)\n", "2017-06-11 13:39:47,536 INFO SETUP FLOW 0 25\n", "2017-06-11 13:39:47,537 INFO calculating source range according to start/end range of the desired output..\n", "2017-06-11 13:39:47,538 INFO output distribution for this flow\n", "2017-06-11 13:39:47,540 INFO 0: 2225 (1.15)\n", "2017-06-11 13:39:47,541 INFO 1: 450 (0.40)\n", "2017-06-11 13:39:47,543 INFO 2: 450 (0.03)\n", "2017-06-11 13:39:47,544 INFO 3: 450 (0.12)\n", "2017-06-11 13:39:47,547 INFO 4: 450 (0.06)\n", "2017-06-11 13:39:47,549 INFO 5: 450 (0.02)\n", "2017-06-11 13:39:47,552 INFO source range: 0-2262 (2262)\n", "2017-06-11 13:39:47,553 INFO output range: 0-4475 (4475)\n", "2017-06-11 13:39:47,555 INFO test size=40 batches=1\n" ] } ], "source": [ "logger.info('Using dataset ' + input_dataset_path + ' as input')\n", "h5file = h5py.File(input_dataset_path, 'r')\n", "\n", "#used for image augmentation (creating new images for balancing)\n", "image_augmentation_generator = ImageDataGenerator(\n", " featurewise_center=False,\n", " samplewise_center=False,\n", " featurewise_std_normalization=False,\n", " samplewise_std_normalization=False,\n", " zca_whitening=False,\n", " rotation_range=359,\n", " width_shift_range=0,\n", " height_shift_range=0,\n", " shear_range=0,\n", " horizontal_flip=True,\n", " vertical_flip=True)\n", "\n", "#applied to all images during training\n", "image_randomize_generator = ImageDataGenerator(\n", " featurewise_center=False,\n", " samplewise_center=False,\n", " featurewise_std_normalization=False,\n", " samplewise_std_normalization=False,\n", " zca_whitening=False,\n", " rotation_range=359,\n", " width_shift_range=0,\n", " height_shift_range=0,\n", " shear_range=0,\n", " horizontal_flip=True,\n", " vertical_flip=True)\n", "\n", "logger.info('preparing train data')\n", "train_batch_generator = utils.BatchGeneratorXYH5(h5file, start_ratio=0, end_ratio=INPUT_RANGE)\n", "train_balance_generator = utils.ClassBalancerGeneratorXY(train_batch_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TRAIN_WEIGHT_RAW,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0, end_ratio=0.7)\n", "train_image_generator = utils.ImageAugmentationXYGenerator(train_balance_generator, image_randomize_generator)\n", "train_generator = utils.ClassBalancerGeneratorXY(train_image_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TRAIN_WEIGHT,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0, end_ratio=1,\n", " change_y=Y_CHANGE)\n", "#train_generator = utils.ChangeXYGenerator(train_image_generator, categorical_to_boolean)\n", "logger.info('train size=' + str(train_generator.size) + ' batches=' + str(train_generator.nr_batches))\n", "\n", "\n", "logger.info('preparing valid data')\n", "valid_batch_generator = utils.BatchGeneratorXYH5(h5file, start_ratio=0, end_ratio=INPUT_RANGE)\n", "valid_balance_generator = utils.ClassBalancerGeneratorXY(valid_batch_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TEST_WEIGHT_RAW,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0.7, end_ratio=0.85)\n", "valid_generator = utils.ClassBalancerGeneratorXY(valid_balance_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TEST_WEIGHT,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0, end_ratio=1,\n", " change_y=Y_CHANGE)\n", "logger.info('valid size=' + str(valid_generator.size) + ' batches=' + str(valid_generator.nr_batches))\n", "\n", "\n", "\n", "logger.info('preparing test data')\n", "test_batch_generator = utils.BatchGeneratorXYH5(h5file, start_ratio=0, end_ratio=INPUT_RANGE)\n", "test_balance_generator = utils.ClassBalancerGeneratorXY(test_batch_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TEST_WEIGHT_RAW,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0.85, end_ratio=1)\n", "test_generator = utils.ClassBalancerGeneratorXY(test_balance_generator,\n", " image_augmentation=image_augmentation_generator,\n", " output_weight=TEST_WEIGHT,\n", " max_augmentation_ratio=1,\n", " max_undersampling_ratio=1,\n", " enforce_max_ratios=False,\n", " batch_size=BATCH_SIZE,\n", " start_ratio=0, end_ratio=1,\n", " change_y=Y_CHANGE)\n", "logger.info('test size=' + str(test_generator.size) + ' batches=' + str(test_generator.nr_batches))\n", "\n", "#FIXME when using 1 on end ratio size and nr_batches gets negative (h5 batch generator, not balancer...)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "#logger.info('INPUT DATASET DATA')\n", "#dataset_path = INPUT_DIR + utils.dataset_name(INPUT_DATASET_NAME, IMAGE_DIMS)\n", "#with h5py.File(input_dataset_path, 'r') as h5file:\n", "# logger.info('generator')\n", "# input_generator = utils.BatchGeneratorXYH5(h5file, start_ratio=0.001, end_ratio=0.0012, batch_size=64)\n", "# X, Y = utils.dump_xy_to_array(input_generator.flow(), input_generator.size, x=True, y=True)\n", "# utils.show_images(X, image_labels=utils.onehot_to_label(Y), group_by_label=False, cols=10, is_bgr=True, size=2)\n", "#\n", "# logger.info('x ' + str(np.shape(X)))\n", "# logger.info('y ' + str(np.shape(Y)))\n", "# logger.info(str(utils.class_distribution(Y)))\n", "\n", "if(DEBUG):\n", " logger.info('BALANCE GENERATOR DATA')\n", " #dataset_path = INPUT_DIR + utils.dataset_name(INPUT_DATASET_NAME, IMAGE_DIMS)\n", " X_train, Y_train = utils.dump_xy_to_array(train_generator.flow(), train_generator.size, x=False, y=True)\n", " logger.info('y ' + str(np.shape(Y_train)))\n", " #logger.info(str(utils.class_distribution(Y_train)))\n", "\n", " for xs,ys in train_generator.flow():\n", " utils.show_images(xs, image_labels=ys, cols=10, is_bgr=True, size=2)\n", " break" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Prepare CNN model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,595 INFO Load CNN model\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/lions.py:164: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", kernel_initializer=\"glorot_uniform\", padding=\"same\")`\n", " model.add(convolutional.Conv2D(64, (3, 3), activation='relu', padding='same', init='glorot_uniform'))\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/lions.py:166: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", kernel_initializer=\"glorot_uniform\", padding=\"same\")`\n", " model.add(convolutional.Conv2D(128, (3, 3), activation='relu', padding='same', init='glorot_uniform'))\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/lions.py:168: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", kernel_initializer=\"glorot_uniform\", padding=\"same\")`\n", " model.add(convolutional.Conv2D(256, (3, 3), activation='relu', padding='same', init='glorot_uniform'))\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/lions.py:173: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1024, activation=\"relu\", kernel_initializer=\"glorot_uniform\")`\n", " model.add(core.Dense(1024, activation='relu', init='glorot_uniform'))\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/lions.py:175: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1024, activation=\"relu\", kernel_initializer=\"glorot_uniform\")`\n", " model.add(core.Dense(1024, activation='relu', init='glorot_uniform'))\n", "2017-06-11 13:39:47,756 INFO loaded model from function convnet_medium1_single\n", "2017-06-11 13:39:47,761 INFO Model prepared\n" ] } ], "source": [ "logger.info('Load CNN model')\n", "#model = lions.convnet_alexnet2_lion_keras(IMAGE_DIMS)\n", "\n", "model = None\n", "if(LOAD_MODEL_FILE!=None and os.path.isfile(LOAD_MODEL_FILE)):\n", " with open(LOAD_MODEL_FILE, 'r') as model_file:\n", " my = model_file.read()\n", " model = models.model_from_yaml(my)\n", " logger.info('loaded model from file ' + LOAD_MODEL_FILE)\n", "else:\n", " model = lions.convnet_medium1_boolean(IMAGE_DIMS)\n", " logger.info('loaded model from function convnet_medium1_single')\n", " \n", "\n", "if(LOAD_WEIGHTS_FILE!=None and os.path.isfile(LOAD_WEIGHTS_FILE)):\n", " model.load_weights(LOAD_WEIGHTS_FILE)\n", " logger.info('Loaded model weights from ' + LOAD_WEIGHTS_FILE)\n", "\n", "logger.info('Model prepared')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,772 INFO Starting CNN training...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_1/kernel:0 is illegal; using conv2d_1/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,978 INFO Summary name conv2d_1/kernel:0 is illegal; using conv2d_1/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_1/kernel:0 is illegal; using conv2d_1/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,984 INFO Summary name conv2d_1/kernel:0 is illegal; using conv2d_1/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_1/bias:0 is illegal; using conv2d_1/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,988 INFO Summary name conv2d_1/bias:0 is illegal; using conv2d_1/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_1/bias:0 is illegal; using conv2d_1/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:47,995 INFO Summary name conv2d_1/bias:0 is illegal; using conv2d_1/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_2/kernel:0 is illegal; using conv2d_2/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,001 INFO Summary name conv2d_2/kernel:0 is illegal; using conv2d_2/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_2/kernel:0 is illegal; using conv2d_2/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,007 INFO Summary name conv2d_2/kernel:0 is illegal; using conv2d_2/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_2/bias:0 is illegal; using conv2d_2/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,011 INFO Summary name conv2d_2/bias:0 is illegal; using conv2d_2/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_2/bias:0 is illegal; using conv2d_2/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,018 INFO Summary name conv2d_2/bias:0 is illegal; using conv2d_2/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_3/kernel:0 is illegal; using conv2d_3/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,023 INFO Summary name conv2d_3/kernel:0 is illegal; using conv2d_3/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_3/kernel:0 is illegal; using conv2d_3/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,029 INFO Summary name conv2d_3/kernel:0 is illegal; using conv2d_3/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_3/bias:0 is illegal; using conv2d_3/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,033 INFO Summary name conv2d_3/bias:0 is illegal; using conv2d_3/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name conv2d_3/bias:0 is illegal; using conv2d_3/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,040 INFO Summary name conv2d_3/bias:0 is illegal; using conv2d_3/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_1/kernel:0 is illegal; using dense_1/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,046 INFO Summary name dense_1/kernel:0 is illegal; using dense_1/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_1/kernel:0 is illegal; using dense_1/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,055 INFO Summary name dense_1/kernel:0 is illegal; using dense_1/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_1/bias:0 is illegal; using dense_1/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,059 INFO Summary name dense_1/bias:0 is illegal; using dense_1/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_1/bias:0 is illegal; using dense_1/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,066 INFO Summary name dense_1/bias:0 is illegal; using dense_1/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_2/kernel:0 is illegal; using dense_2/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,072 INFO Summary name dense_2/kernel:0 is illegal; using dense_2/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_2/kernel:0 is illegal; using dense_2/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,077 INFO Summary name dense_2/kernel:0 is illegal; using dense_2/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_2/bias:0 is illegal; using dense_2/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,081 INFO Summary name dense_2/bias:0 is illegal; using dense_2/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_2/bias:0 is illegal; using dense_2/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,088 INFO Summary name dense_2/bias:0 is illegal; using dense_2/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_3/kernel:0 is illegal; using dense_3/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,094 INFO Summary name dense_3/kernel:0 is illegal; using dense_3/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_3/kernel:0 is illegal; using dense_3/kernel_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,103 INFO Summary name dense_3/kernel:0 is illegal; using dense_3/kernel_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_3/bias:0 is illegal; using dense_3/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,107 INFO Summary name dense_3/bias:0 is illegal; using dense_3/bias_0 instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Summary name dense_3/bias:0 is illegal; using dense_3/bias_0 instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:48,114 INFO Summary name dense_3/bias:0 is illegal; using dense_3/bias_0 instead.\n", "/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:9: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<generator..., steps_per_epoch=5, validation_data=<generator..., verbose=1, epochs=10, callbacks=[<keras.ca..., validation_steps=1)`\n", "2017-06-11 13:39:48,735 INFO starting new flow...\n", "2017-06-11 13:39:48,738 INFO starting new flow...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "Epoch 1/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 1.1947 - acc: 0.5990 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:39:58,966 INFO starting new flow...\n", "2017-06-11 13:39:58,968 INFO starting new flow...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 00000: val_acc improved from -inf to 0.25000, saving model to ../../output/kaggle-sea-lion/07/weights/weights-00-0.25.h5\n", "5/5 [==============================] - 12s - loss: 1.1029 - acc: 0.5292 - val_loss: 0.6992 - val_acc: 0.2500\n", "5/5 [==============================] - 12s - loss: 1.1029 - acc: 0.5292 - val_loss: 0.6992 - val_acc: 0.2500\n", "Epoch 2/10\n", "Epoch 2/10\n", "4/5 [=======================>......] - ETA: 1s - loss: 0.6893 - acc: 0.5365\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00001: val_acc improved from 0.25000 to 0.35417, saving model to ../../output/kaggle-sea-lion/07/weights/weights-01-0.35.h5\n", "5/5 [==============================] - 11s - loss: 0.6761 - acc: 0.5958 - val_loss: 0.7925 - val_acc: 0.3542\n", "5/5 [==============================] - 12s - loss: 0.6761 - acc: 0.5958 - val_loss: 0.7925 - val_acc: 0.3542\n", "Epoch 3/10\n", "Epoch 3/10\n", "4/5 [=======================>......] - ETA: 1s - loss: 0.6275 - acc: 0.6562\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00002: val_acc improved from 0.35417 to 0.75000, saving model to ../../output/kaggle-sea-lion/07/weights/weights-02-0.75.h5\n", "5/5 [==============================] - 12s - loss: 0.6161 - acc: 0.6583 - val_loss: 0.5861 - val_acc: 0.7500\n", "5/5 [==============================] - 12s - loss: 0.6161 - acc: 0.6583 - val_loss: 0.5861 - val_acc: 0.7500\n", "Epoch 4/10\n", "Epoch 4/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.6611 - acc: 0.6615\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00003: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.6723 - acc: 0.6250 - val_loss: 0.6176 - val_acc: 0.6458\n", "5/5 [==============================] - 11s - loss: 0.6723 - acc: 0.6250 - val_loss: 0.6176 - val_acc: 0.6458\n", "Epoch 5/10\n", "Epoch 5/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.6367 - acc: 0.6250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00004: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.6287 - acc: 0.6375 - val_loss: 0.7277 - val_acc: 0.4167\n", "5/5 [==============================] - 11s - loss: 0.6287 - acc: 0.6375 - val_loss: 0.7277 - val_acc: 0.4167\n", "Epoch 6/10\n", "Epoch 6/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.6058 - acc: 0.6510\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00005: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.5948 - acc: 0.6708 - val_loss: 0.6680 - val_acc: 0.6042\n", "5/5 [==============================] - 11s - loss: 0.5948 - acc: 0.6708 - val_loss: 0.6680 - val_acc: 0.6042\n", "Epoch 7/10\n", "Epoch 7/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.6184 - acc: 0.6146\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00006: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.6096 - acc: 0.6167 - val_loss: 0.6454 - val_acc: 0.5833\n", "5/5 [==============================] - 11s - loss: 0.6096 - acc: 0.6167 - val_loss: 0.6454 - val_acc: 0.5833\n", "Epoch 8/10\n", "Epoch 8/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.5230 - acc: 0.6771\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00007: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.5276 - acc: 0.6625 - val_loss: 0.8927 - val_acc: 0.5833\n", "5/5 [==============================] - 11s - loss: 0.5276 - acc: 0.6625 - val_loss: 0.8927 - val_acc: 0.5833\n", "Epoch 9/10\n", "Epoch 9/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.6056 - acc: 0.7396\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00008: val_acc did not improve\n", "5/5 [==============================] - 10s - loss: 0.5998 - acc: 0.7375 - val_loss: 0.6058 - val_acc: 0.6875\n", "5/5 [==============================] - 10s - loss: 0.5998 - acc: 0.7375 - val_loss: 0.6058 - val_acc: 0.6875\n", "Epoch 10/10\n", "Epoch 10/10\n", "4/5 [=======================>......] - ETA: 2s - loss: 0.5036 - acc: 0.7448\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bEpoch 00009: val_acc did not improve\n", "5/5 [==============================] - 11s - loss: 0.4638 - acc: 0.7625 - val_loss: 0.6294 - val_acc: 0.6875\n", "5/5 [==============================] - 11s - loss: 0.4638 - acc: 0.7625 - val_loss: 0.6294 - val_acc: 0.6875\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:41:45,547 INFO Saved last weights to ../../output/kaggle-sea-lion/07/last-weights-medium1-class0.h5\n" ] } ], "source": [ "if(RUN_TRAINING):\n", " logger.info('Starting CNN training...')\n", " history = model.fit_generator(train_generator.flow(),\n", " steps_per_epoch = train_generator.nr_batches,\n", " nb_epoch = TRAIN_EPOCHS,\n", " callbacks = cnn.get_callbacks_keras(model, WEIGHTS_DIR, TF_LOGS_DIR),\n", " validation_data = valid_generator.flow(), \n", " validation_steps = valid_generator.nr_batches,\n", " verbose = 1)\n", "\n", " if(SAVE_MODEL_FILE!=None):\n", " with open(SAVE_MODEL_FILE, 'w') as model_file:\n", " model_file.write(model.to_yaml())\n", " logger.info('Saved last model to ' + SAVE_MODEL_FILE)\n", " \n", " if(SAVE_WEIGHTS_FILE!=None):\n", " model.save_weights(SAVE_WEIGHTS_FILE)\n", " logger.info('Saved last weights to ' + SAVE_WEIGHTS_FILE)\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Epoch accuracy/loss" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:41:45,559 INFO Training info\n" ] }, { "data": { "image/png": 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wzyDarnhXmz3rm1Mb8PISrunWihFdW7LmYA6zfjrAP77Zw6zlB5gyqB2/GxxH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\nay/KaLHA57/X5uXx7+r9cWcRFgVTPoU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"text/plain": [ "<matplotlib.figure.Figure at 0x7f028c8e6198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if(RUN_TRAINING):\n", " logger.info('Training info')\n", " cnn.show_training_info_keras(history)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Confusion matrix" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-06-11 13:41:45,812 INFO Evaluating model performance (40 samples)...\n", "2017-06-11 13:41:45,821 INFO starting new flow...\n", "2017-06-11 13:41:45,824 INFO starting new flow...\n", "2017-06-11 13:41:46,875 INFO Accuracy: 0.854166686535 - Loss: 0.325673669577\n", "2017-06-11 13:41:46,876 INFO Predicting Y for detailed analysis...\n", "/notebooks/datascience-snippets/kaggle-sea-lion/modules/cnn.py:59: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if(self.y_ds==None):\n", "2017-06-11 13:41:48,362 INFO Accuracy: 0.5\n", "2017-06-11 13:41:48,364 INFO Number of test samples: 40\n", "2017-06-11 13:41:48,365 INFO Kappa score: 0.137931034483 (-1 bad; 0 just luck; 1 great)\n", "2017-06-11 13:41:48,367 INFO \n", " precision recall f1-score support\n", "\n", " any 1.00 0.17 0.29 24\n", " male 0.44 1.00 0.62 16\n", "\n", "avg / total 0.78 0.50 0.42 40\n", "\n", "2017-06-11 13:41:48,368 INFO Accuracy per class:\n", "2017-06-11 13:41:48,370 INFO any: 100.0%\n", "2017-06-11 13:41:48,371 INFO male: 44.4%\n", "2017-06-11 13:41:48,372 INFO Confusion matrix:\n", "2017-06-11 13:41:48,374 INFO \n", "[[ 4 20]\n", " [ 0 16]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f02401b93c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cnn.evaluate_dataset_keras(test_generator.flow(), \n", " test_generator.nr_batches, \n", " test_generator.size, \n", " model, \n", " class_labels=['any', 'male'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "if(DEBUG):\n", " a = test_generator.flow()\n", " cnn.show_predictions(a, 50, model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

analyticsguru/NUPredict480FinalProject

predict480FinalProjectDataPrep.ipynb

1

125571

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#supress warning message\n", "import warnings; warnings.simplefilter(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#import some extension libraries\n", "import pandas as pd #import pandas library\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "superstore = pd.ExcelFile(r'/Users/ishmaelamin/Documents/Northwestern_University/2016_Fall/Predict_480/480_Group_Final_Project/Superstore_Sales_Data.xlsx')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sheet_1 = pd.read_excel(superstore,sheetname=0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sheet_2 = pd.read_excel(superstore,sheetname=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['External', 'Internal']\n", "There are 2 sheets in the workbook\n" ] } ], "source": [ "#display worksheet names in the workbook\n", "read_sheets_name = superstore.sheet_names\n", "print(read_sheets_name)\n", "\n", "#count sheets in the workbook\n", "print(\"There are %d sheets in the workbook\" % len(read_sheets_name))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "403" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Display the recordsin the first sheet\n", "sheet_1.head()\n", "len(sheet_1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "18" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(sheet_2)" ] }, { "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>id</th>\n", " <th>Product_Type_ID</th>\n", " <th>Order_Date</th>\n", " <th>Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>152.48</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>2010-12-22</td>\n", " <td>152.48</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>2012-10-28</td>\n", " <td>152.48</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>2012-09-24</td>\n", " <td>152.48</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>2010-04-20</td>\n", " <td>152.48</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Product_Type_ID Order_Date Price\n", "0 1 1 2009-03-22 152.48\n", "1 2 1 2010-12-22 152.48\n", "2 3 1 2012-10-28 152.48\n", "3 4 1 2012-09-24 152.48\n", "4 5 1 2010-04-20 152.48" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sheet_1.ix[:,0:4].head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#sheet_1[[\"id\",\"Order_Date\"]].head()\n", "sheet_1.set_index('Product_Type_ID',inplace=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sheet_2.set_index('Product_Type_ID',inplace=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: 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<td>Adesso</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Price Customer_Segment Product_Category \\\n", "Product_Type_ID \n", "1 1 2009-03-22 152.48 Enterprise Technology \n", 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" </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>CPU</td>\n", " <td>50</td>\n", " <td>200</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Graphics/video card</td>\n", " <td>35</td>\n", " <td>75</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Speakers</td>\n", " <td>15</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Wi-Fi</td>\n", " <td>20</td>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Sound card</td>\n", " <td>15</td>\n", " <td>40</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Internal_Product_Name Internal_Cost Internal_Sale_Price\n", "Product_Type_ID \n", "1 CPU 50 200\n", "2 Graphics/video card 35 75\n", "3 Speakers 15 25\n", "6 Wi-Fi 20 50\n", "7  Sound card 15 40" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sheet_2.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#result = pd.merge(sheet_1, sheet_2, how='left', on=['Product_Type_ID'])\n", "internal = sheet_1.join(sheet_2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "403" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(internal)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "internal = internal[[\"id\",\"Order_Date\",\"Internal_Cost\",\"Internal_Sale_Price\",\"Customer_Segment\",\"Product_Category\",\"Product_Sub_Category\",\"Product_Type\",\"Internal_Product_Name\",\"Vendor\"]]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " 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"text/plain": [ " id Order_Date Internal_Cost Internal_Sale_Price \\\n", "Product_Type_ID \n", "1 1 2009-03-22 50 200 \n", "1 2 2010-12-22 50 200 \n", "1 3 2012-10-28 50 200 \n", "1 4 2012-09-24 50 200 \n", "1 5 2010-04-20 50 200 \n", "\n", " Customer_Segment Product_Category Product_Sub_Category \\\n", "Product_Type_ID \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "\n", " Product_Type Internal_Product_Name Vendor \n", "Product_Type_ID \n", "1 CPU CPU Adesso \n", "1 CPU CPU Adesso \n", "1 CPU CPU Adesso \n", "1 CPU CPU Adesso \n", "1 CPU CPU Adesso " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "internal.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "internal['Business_Type'] = pd.Series('internal', index=internal.index)" ] }, { "cell_type": "code", "execution_count": 19, "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>Order_Date</th>\n", " <th>Internal_Cost</th>\n", " <th>Internal_Sale_Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Internal_Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Internal_Cost Internal_Sale_Price \\\n", "Product_Type_ID \n", "1 1 2009-03-22 50 200 \n", "1 2 2010-12-22 50 200 \n", "1 3 2012-10-28 50 200 \n", "1 4 2012-09-24 50 200 \n", "1 5 2010-04-20 50 200 \n", "\n", " Customer_Segment Product_Category Product_Sub_Category \\\n", "Product_Type_ID \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "1 Enterprise Technology Computer Components \n", "\n", " Product_Type Internal_Product_Name Vendor Business_Type \n", "Product_Type_ID \n", "1 CPU CPU Adesso internal \n", "1 CPU CPU Adesso internal \n", "1 CPU CPU Adesso internal \n", "1 CPU CPU Adesso internal \n", "1 CPU CPU Adesso internal " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "internal.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "internal.columns = [\"id\",\"Order_Date\",\"Cost\",\"Price\",\"Customer_Segment\",\"Product_Category\",\"Product_Sub_Category\",\"Product_Type\",\"Product_Name\",\"Vendor\",\"Business_Type\"]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>Order_Date</th>\n", " <th>Cost</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Cost Price Customer_Segment Product_Category \\\n", "Product_Type_ID \n", "1 1 2009-03-22 50 200 Enterprise Technology \n", "1 2 2010-12-22 50 200 Enterprise Technology \n", "1 3 2012-10-28 50 200 Enterprise Technology \n", "1 4 2012-09-24 50 200 Enterprise Technology \n", "1 5 2010-04-20 50 200 Enterprise Technology \n", "\n", " Product_Sub_Category Product_Type Product_Name Vendor \\\n", "Product_Type_ID \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "\n", " Business_Type \n", "Product_Type_ID \n", "1 internal \n", "1 internal \n", "1 internal \n", "1 internal \n", "1 internal " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "internal.head()\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "external=sheet_1" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# append column to both datasets that distinguish them as either internal or external\n", "external['Business_Type'] = pd.Series('external', index=external.index)" ] }, { "cell_type": "code", "execution_count": 24, "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>Order_Date</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Price Customer_Segment Product_Category \\\n", "Product_Type_ID \n", "1 1 2009-03-22 152.48 Enterprise Technology \n", "1 2 2010-12-22 152.48 Enterprise Technology \n", "1 3 2012-10-28 152.48 Enterprise Technology \n", "1 4 2012-09-24 152.48 Enterprise Technology \n", "1 5 2010-04-20 152.48 Enterprise Technology \n", "\n", " Product_Sub_Category Product_Type Product_Name Vendor \\\n", "Product_Type_ID \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "\n", " Business_Type \n", "Product_Type_ID \n", "1 external \n", "1 external \n", "1 external \n", "1 external \n", "1 external " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "external.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Add cost column so that the two dataframes can be concatenated\n", "external['Cost'] = pd.Series(None, index=external.index)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>Order_Date</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " <th>Cost</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Price Customer_Segment Product_Category \\\n", "Product_Type_ID \n", "1 1 2009-03-22 152.48 Enterprise Technology \n", "1 2 2010-12-22 152.48 Enterprise Technology \n", "1 3 2012-10-28 152.48 Enterprise Technology \n", "1 4 2012-09-24 152.48 Enterprise Technology \n", "1 5 2010-04-20 152.48 Enterprise Technology \n", "\n", " Product_Sub_Category Product_Type Product_Name Vendor \\\n", "Product_Type_ID \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "1 Computer Components CPU Adesso CPU Adesso \n", "\n", " Business_Type Cost \n", "Product_Type_ID \n", "1 external NaN \n", "1 external NaN \n", "1 external NaN \n", "1 external NaN \n", "1 external NaN " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "external.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# get a list of columns\n", "cols = list(external)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['id',\n", " 'Order_Date',\n", " 'Cost',\n", " 'Price',\n", " 'Customer_Segment',\n", " 'Product_Category',\n", " 'Product_Sub_Category',\n", " 'Product_Type',\n", " 'Product_Name',\n", " 'Vendor',\n", " 'Business_Type']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# move the column to head of list using index, pop and insert\n", "cols.insert(2, cols.pop(cols.index('Cost')))\n", "cols" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>Order_Date</th>\n", " <th>Cost</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>NaN</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>NaN</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>NaN</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>NaN</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>NaN</td>\n", " <td>152.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>Adesso CPU</td>\n", " <td>Adesso</td>\n", " <td>external</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Cost Price Customer_Segment \\\n", "Product_Type_ID \n", "1 1 2009-03-22 NaN 152.48 Enterprise \n", "1 2 2010-12-22 NaN 152.48 Enterprise \n", "1 3 2012-10-28 NaN 152.48 Enterprise \n", "1 4 2012-09-24 NaN 152.48 Enterprise \n", "1 5 2010-04-20 NaN 152.48 Enterprise \n", "\n", " Product_Category Product_Sub_Category Product_Type \\\n", "Product_Type_ID \n", "1 Technology Computer Components CPU \n", "1 Technology Computer Components CPU \n", "1 Technology Computer Components CPU \n", "1 Technology Computer Components CPU \n", "1 Technology Computer Components CPU \n", "\n", " Product_Name Vendor Business_Type \n", "Product_Type_ID \n", "1 Adesso CPU Adesso external \n", "1 Adesso CPU Adesso external \n", "1 Adesso CPU Adesso external \n", "1 Adesso CPU Adesso external \n", "1 Adesso CPU Adesso external " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use ix to reorder\n", "external = external.ix[:, cols]\n", "external.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>Order_Date</th>\n", " <th>Cost</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id Order_Date Cost Price Customer_Segment Product_Category \\\n", "Product_Type_ID \n", "1 1 2009-03-22 50 200 Enterprise Technology \n", "1 2 2010-12-22 50 200 Enterprise Technology \n", "1 3 2012-10-28 50 200 Enterprise Technology \n", "1 4 2012-09-24 50 200 Enterprise Technology \n", "1 5 2010-04-20 50 200 Enterprise Technology \n", "\n", " Product_Sub_Category Product_Type Product_Name Vendor \\\n", "Product_Type_ID \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "1 Computer Components CPU CPU Adesso \n", "\n", " Business_Type \n", "Product_Type_ID \n", "1 internal \n", "1 internal \n", "1 internal \n", "1 internal \n", "1 internal " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "internal.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Preserve the value of product_type_id as a column\n", "internal['Product_Type_ID1'] = internal.index" ] }, { "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>id</th>\n", " <th>Order_Date</th>\n", " <th>Cost</th>\n", " <th>Price</th>\n", " <th>Customer_Segment</th>\n", " <th>Product_Category</th>\n", " <th>Product_Sub_Category</th>\n", " <th>Product_Type</th>\n", " <th>Product_Name</th>\n", " <th>Vendor</th>\n", " <th>Business_Type</th>\n", " <th>Product_Type_ID1</th>\n", " </tr>\n", " <tr>\n", " <th>Product_Type_ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2009-03-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2010-12-22</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>3</td>\n", " <td>2012-10-28</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4</td>\n", " <td>2012-09-24</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>CPU</td>\n", " <td>CPU</td>\n", " <td>Adesso</td>\n", " <td>internal</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2010-04-20</td>\n", " <td>50</td>\n", " <td>200</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer 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<td>Canon P1-DHIII Palm graphics card</td>\n", " <td>Canon</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>18</td>\n", " <td>2011-12-07</td>\n", " <td>NaN</td>\n", " <td>17.98</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Graphics/video card</td>\n", " <td>Canon P1-DHIII Palm graphics card</td>\n", " <td>Canon</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>19</td>\n", " <td>2011-12-02</td>\n", " <td>NaN</td>\n", " <td>17.98</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Graphics/video card</td>\n", " <td>Canon P1-DHIII Palm graphics card</td>\n", " <td>Canon</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>20</td>\n", " <td>2010-07-03</td>\n", " <td>NaN</td>\n", " <td>17.98</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Graphics/video card</td>\n", " <td>Canon P1-DHIII Palm graphics card</td>\n", " <td>Canon</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>21</td>\n", " <td>2009-04-07</td>\n", " <td>NaN</td>\n", " <td>40.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Graphics/video card</td>\n", " <td>Keytronic Designer graphics card</td>\n", " <td>Keytronic</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>22</td>\n", " <td>2011-04-17</td>\n", " <td>NaN</td>\n", " <td>40.48</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Graphics/video card</td>\n", " <td>Keytronic Designer graphics card</td>\n", " <td>Keytronic</td>\n", " <td>external</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23</td>\n", " <td>2012-12-21</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>24</td>\n", " <td>2011-03-12</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>25</td>\n", " <td>2011-04-20</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>26</td>\n", " <td>2012-05-07</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>27</td>\n", " <td>2011-07-22</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>28</td>\n", " <td>2010-01-10</td>\n", " <td>NaN</td>\n", " <td>20.97</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>29</td>\n", " <td>2009-05-13</td>\n", " <td>NaN</td>\n", " <td>30.42</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>Wi-Fi</td>\n", " <td>Microsoft WiFi</td>\n", " <td>Microsoft</td>\n", " <td>external</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>776</th>\n", " <td>776</td>\n", " <td>2012-05-06</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Brother</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>777</th>\n", " <td>777</td>\n", " <td>2012-07-13</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Brother</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>778</th>\n", " <td>778</td>\n", " <td>2012-05-11</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>779</th>\n", " <td>779</td>\n", " <td>2010-01-09</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>780</th>\n", " <td>780</td>\n", " <td>2011-02-06</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>781</th>\n", " <td>781</td>\n", " <td>2012-03-30</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>782</th>\n", " <td>782</td>\n", " <td>2011-09-02</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>783</th>\n", " <td>783</td>\n", " <td>2009-03-23</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>784</th>\n", " <td>784</td>\n", " <td>2012-03-05</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Canon</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>785</th>\n", " <td>785</td>\n", " <td>2011-11-16</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Hewlett Packard</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>786</th>\n", " <td>786</td>\n", " <td>2010-02-27</td>\n", " <td>345.0</td>\n", " <td>500.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>RAM</td>\n", " <td>RAM</td>\n", " <td>Hewlett Packard</td>\n", " <td>internal</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>787</th>\n", " <td>787</td>\n", " <td>2011-11-16</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>788</th>\n", " <td>788</td>\n", " <td>2012-03-03</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>789</th>\n", " <td>789</td>\n", " <td>2010-05-10</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>790</th>\n", " <td>790</td>\n", " <td>2011-09-15</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>791</th>\n", " <td>791</td>\n", " <td>2011-02-03</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>792</th>\n", " <td>792</td>\n", " <td>2009-09-19</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>793</th>\n", " <td>793</td>\n", " <td>2012-04-04</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>794</th>\n", " <td>794</td>\n", " <td>2012-10-27</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>795</th>\n", " <td>795</td>\n", " <td>2009-12-01</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>796</th>\n", " <td>796</td>\n", " <td>2010-09-19</td>\n", " <td>10.0</td>\n", " <td>50.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>microprocessor</td>\n", " <td>microprocessor</td>\n", " <td>Unknown</td>\n", " <td>internal</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>797</th>\n", " <td>797</td>\n", " <td>2010-06-14</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Micro Innovations</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>798</th>\n", " <td>798</td>\n", " <td>2011-06-25</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Micro Innovations</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>799</th>\n", " <td>799</td>\n", " <td>2010-12-05</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Micro Innovations</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>800</th>\n", " <td>800</td>\n", " <td>2010-02-22</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Microsoft</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>801</th>\n", " <td>801</td>\n", " <td>2011-08-16</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Microsoft</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>802</th>\n", " <td>802</td>\n", " <td>2010-11-01</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>Microsoft</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>803</th>\n", " <td>803</td>\n", " <td>2010-06-14</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>PC Concepts</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>804</th>\n", " <td>804</td>\n", " <td>2012-02-10</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>PC Concepts</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>805</th>\n", " <td>805</td>\n", " <td>2010-04-19</td>\n", " <td>35.0</td>\n", " <td>45.00</td>\n", " <td>Enterprise</td>\n", " <td>Technology</td>\n", " <td>Computer Components</td>\n", " <td>harddrive</td>\n", " <td>harddrive</td>\n", " <td>PC Concepts</td>\n", " <td>internal</td>\n", " <td>21</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>806 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " index Order_Date Cost Price Customer_Segment Product_Category \\\n", "0 0 2009-03-22 NaN 152.48 Enterprise Technology \n", "1 1 2010-12-22 NaN 152.48 Enterprise Technology \n", "2 2 2012-10-28 NaN 152.48 Enterprise Technology \n", "3 3 2012-09-24 NaN 152.48 Enterprise Technology \n", "4 4 2010-04-20 NaN 152.48 Enterprise Technology \n", "5 5 2011-12-24 NaN 120.97 Enterprise Technology \n", "6 6 2009-09-17 NaN 120.97 Enterprise Technology \n", "7 7 2012-05-21 NaN 120.97 Enterprise Technology \n", "8 8 2010-06-07 NaN 140.99 Enterprise Technology \n", "9 9 2012-03-02 NaN 150.98 Enterprise Technology \n", "10 10 2010-08-30 NaN 150.98 Enterprise Technology \n", "11 11 2012-04-11 NaN 150.98 Enterprise Technology \n", "12 12 2010-03-08 NaN 150.98 Enterprise Technology \n", "13 13 2009-01-26 NaN 150.98 Enterprise Technology \n", "14 14 2009-03-25 NaN 150.98 Enterprise Technology \n", "15 15 2012-05-07 NaN 150.98 Enterprise Technology \n", "16 16 2010-08-15 NaN 17.98 Enterprise Technology \n", "17 17 2012-05-28 NaN 17.98 Enterprise Technology \n", "18 18 2011-12-07 NaN 17.98 Enterprise Technology \n", "19 19 2011-12-02 NaN 17.98 Enterprise Technology \n", "20 20 2010-07-03 NaN 17.98 Enterprise Technology \n", "21 21 2009-04-07 NaN 40.48 Enterprise Technology \n", "22 22 2011-04-17 NaN 40.48 Enterprise Technology \n", "23 23 2012-12-21 NaN 20.97 Enterprise Technology \n", "24 24 2011-03-12 NaN 20.97 Enterprise Technology \n", "25 25 2011-04-20 NaN 20.97 Enterprise Technology \n", "26 26 2012-05-07 NaN 20.97 Enterprise Technology \n", "27 27 2011-07-22 NaN 20.97 Enterprise Technology \n", "28 28 2010-01-10 NaN 20.97 Enterprise Technology \n", "29 29 2009-05-13 NaN 30.42 Enterprise Technology \n", ".. ... ... ... ... ... ... \n", "776 776 2012-05-06 345.0 500.00 Enterprise Technology \n", "777 777 2012-07-13 345.0 500.00 Enterprise Technology \n", "778 778 2012-05-11 345.0 500.00 Enterprise Technology \n", "779 779 2010-01-09 345.0 500.00 Enterprise Technology \n", "780 780 2011-02-06 345.0 500.00 Enterprise Technology \n", "781 781 2012-03-30 345.0 500.00 Enterprise Technology \n", "782 782 2011-09-02 345.0 500.00 Enterprise Technology \n", "783 783 2009-03-23 345.0 500.00 Enterprise Technology \n", "784 784 2012-03-05 345.0 500.00 Enterprise Technology \n", "785 785 2011-11-16 345.0 500.00 Enterprise Technology \n", "786 786 2010-02-27 345.0 500.00 Enterprise Technology \n", "787 787 2011-11-16 10.0 50.00 Enterprise Technology \n", "788 788 2012-03-03 10.0 50.00 Enterprise Technology \n", "789 789 2010-05-10 10.0 50.00 Enterprise Technology \n", "790 790 2011-09-15 10.0 50.00 Enterprise Technology \n", "791 791 2011-02-03 10.0 50.00 Enterprise Technology \n", "792 792 2009-09-19 10.0 50.00 Enterprise Technology \n", "793 793 2012-04-04 10.0 50.00 Enterprise Technology \n", "794 794 2012-10-27 10.0 50.00 Enterprise Technology \n", "795 795 2009-12-01 10.0 50.00 Enterprise Technology \n", "796 796 2010-09-19 10.0 50.00 Enterprise Technology \n", "797 797 2010-06-14 35.0 45.00 Enterprise Technology \n", "798 798 2011-06-25 35.0 45.00 Enterprise Technology \n", "799 799 2010-12-05 35.0 45.00 Enterprise Technology \n", "800 800 2010-02-22 35.0 45.00 Enterprise Technology \n", "801 801 2011-08-16 35.0 45.00 Enterprise Technology \n", "802 802 2010-11-01 35.0 45.00 Enterprise Technology \n", "803 803 2010-06-14 35.0 45.00 Enterprise Technology \n", "804 804 2012-02-10 35.0 45.00 Enterprise Technology \n", "805 805 2010-04-19 35.0 45.00 Enterprise Technology \n", "\n", " Product_Sub_Category Product_Type \\\n", "0 Computer Components CPU \n", "1 Computer Components CPU \n", "2 Computer Components CPU \n", "3 Computer Components CPU \n", "4 Computer Components CPU \n", "5 Computer Components Graphics/video card \n", "6 Computer Components Graphics/video card \n", "7 Computer Components Graphics/video card \n", "8 Computer Components Graphics/video card \n", "9 Computer Components Graphics/video card \n", "10 Computer Components Graphics/video card \n", "11 Computer Components Graphics/video card \n", "12 Computer Components Graphics/video card \n", "13 Computer Components Graphics/video card \n", "14 Computer Components Graphics/video card \n", "15 Computer Components Graphics/video card \n", "16 Computer Components Graphics/video card \n", "17 Computer Components Graphics/video card \n", "18 Computer Components Graphics/video card \n", "19 Computer Components Graphics/video card \n", "20 Computer Components Graphics/video card \n", "21 Computer Components Graphics/video card \n", "22 Computer Components Graphics/video card \n", "23 Computer Components Wi-Fi \n", "24 Computer Components Wi-Fi \n", "25 Computer Components Wi-Fi \n", "26 Computer Components Wi-Fi \n", "27 Computer Components Wi-Fi \n", "28 Computer Components Wi-Fi \n", "29 Computer Components Wi-Fi \n", ".. ... ... \n", "776 Computer Components RAM \n", "777 Computer Components RAM \n", "778 Computer Components RAM \n", "779 Computer Components RAM \n", "780 Computer Components RAM \n", "781 Computer Components RAM \n", "782 Computer Components RAM \n", "783 Computer Components RAM \n", "784 Computer Components RAM \n", "785 Computer Components RAM \n", "786 Computer Components RAM \n", "787 Computer Components microprocessor \n", "788 Computer Components microprocessor \n", "789 Computer Components microprocessor \n", "790 Computer Components microprocessor \n", "791 Computer Components microprocessor \n", "792 Computer Components microprocessor \n", "793 Computer Components microprocessor \n", "794 Computer Components microprocessor \n", "795 Computer Components microprocessor \n", "796 Computer Components microprocessor \n", "797 Computer Components harddrive \n", "798 Computer Components harddrive \n", "799 Computer Components harddrive \n", "800 Computer Components harddrive \n", "801 Computer Components harddrive \n", "802 Computer Components harddrive \n", "803 Computer Components harddrive \n", "804 Computer Components harddrive \n", "805 Computer Components harddrive \n", "\n", " Product_Name Vendor Business_Type \\\n", "0 Adesso CPU Adesso external \n", "1 Adesso CPU Adesso external \n", "2 Adesso CPU Adesso external \n", "3 Adesso CPU Adesso external \n", "4 Adesso CPU Adesso external \n", "5 Canon BP1200DH 12-Digit graphics card Canon external \n", "6 Canon BP1200DH graphics card Canon external \n", "7 Canon BP1200DH graphics card Canon external \n", "8 Canon MP100DHII graphics card Canon external \n", "9 Canon MP41DH graphics card Canon external \n", "10 Canon MP41DH graphics card Canon external \n", "11 Canon MP41DH graphics card Canon external \n", "12 Canon MP41DH graphics card Canon external \n", "13 Canon MP41DH graphics card Canon external \n", "14 Canon MP41DH graphics card Canon external \n", "15 Canon MP41DH graphics card Canon external \n", "16 Canon P1-DHIII Palm graphics card Canon external \n", "17 Canon P1-DHIII Palm graphics card Canon external \n", "18 Canon P1-DHIII Palm graphics card Canon external \n", "19 Canon P1-DHIII Palm graphics card Canon external \n", "20 Canon P1-DHIII Palm graphics card Canon external \n", "21 Keytronic Designer graphics card Keytronic external \n", "22 Keytronic Designer graphics card Keytronic external \n", "23 Microsoft WiFi Microsoft external \n", "24 Microsoft WiFi Microsoft external \n", "25 Microsoft WiFi Microsoft external \n", "26 Microsoft WiFi Microsoft external \n", "27 Microsoft WiFi Microsoft external \n", "28 Microsoft WiFi Microsoft external \n", "29 Microsoft WiFi Microsoft external \n", ".. ... ... ... \n", "776 RAM Brother internal \n", "777 RAM Brother internal \n", "778 RAM Canon internal \n", "779 RAM Canon internal \n", "780 RAM Canon internal \n", "781 RAM Canon internal \n", "782 RAM Canon internal \n", "783 RAM Canon internal \n", "784 RAM Canon internal \n", "785 RAM Hewlett Packard internal \n", "786 RAM Hewlett Packard internal \n", "787 microprocessor Unknown internal \n", "788 microprocessor Unknown internal \n", "789 microprocessor Unknown internal \n", "790 microprocessor Unknown internal \n", "791 microprocessor Unknown internal \n", "792 microprocessor Unknown internal \n", "793 microprocessor Unknown internal \n", "794 microprocessor Unknown internal \n", "795 microprocessor Unknown internal \n", "796 microprocessor Unknown internal \n", "797 harddrive Micro Innovations internal \n", "798 harddrive Micro Innovations internal \n", "799 harddrive Micro Innovations internal \n", "800 harddrive Microsoft internal \n", "801 harddrive Microsoft internal \n", "802 harddrive Microsoft internal \n", "803 harddrive PC Concepts internal \n", "804 harddrive PC Concepts internal \n", "805 harddrive PC Concepts internal \n", "\n", " Product_Type_ID \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 \n", "5 2 \n", "6 2 \n", "7 2 \n", "8 2 \n", "9 2 \n", "10 2 \n", "11 2 \n", "12 2 \n", "13 2 \n", "14 2 \n", "15 2 \n", "16 2 \n", "17 2 \n", "18 2 \n", "19 2 \n", "20 2 \n", "21 2 \n", "22 2 \n", "23 6 \n", "24 6 \n", "25 6 \n", "26 6 \n", "27 6 \n", "28 6 \n", "29 6 \n", ".. ... \n", "776 19 \n", "777 19 \n", "778 19 \n", "779 19 \n", "780 19 \n", "781 19 \n", "782 19 \n", "783 19 \n", "784 19 \n", "785 19 \n", "786 19 \n", "787 20 \n", "788 20 \n", "789 20 \n", "790 20 \n", "791 20 \n", "792 20 \n", "793 20 \n", "794 20 \n", "795 20 \n", "796 20 \n", "797 21 \n", "798 21 \n", "799 21 \n", "800 21 \n", "801 21 \n", "802 21 \n", "803 21 \n", "804 21 \n", "805 21 \n", "\n", "[806 rows x 12 columns]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.reset_index()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#export to file .csv\n", "result.to_csv(r'/Users/ishmaelamin/Documents/Northwestern_University/2016_Fall/Predict_480/superstore_businesstypes.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

wd15/extremefill2D

notebooks/fig3a_sim.ipynb

1

1599

{ "metadata": { "name": "fig3a_sim_nx200" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "!date\n", "\n", "from tools import batch_launch\n", " \n", "kPluses = np.logspace(1, 4, 20)[[5, 10, 15, 19]][::-1]\n", "kMinuses = np.logspace(6, 9, 20)[[5, 10, 15, 19]]\n", " \n", "reason = \"Figure 3(a) Nx=200\"\n", " \n", "for kPlus in kPluses:\n", " for kMinus in kMinuses:\n", " batch_launch(reason=reason,\n", " tags=['fig3a_nx200'],\n", " kPlus=kPlus,\n", " kMinus=kMinus,\n", " rboundary=50e-6 / np.sqrt(np.pi),\n", " Nx=200,\n", " spacing_ratio=1.2,\n", " CFL=0.1,\n", " levelset_update_ncell=5,\n", " data_frequency=100,\n", " appliedPotential=-0.25,\n", " bulkSuppressor=0.02)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fri Nov 1 19:01:52 EDT 2013\r\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

rafaelscnunes/COS738-AutomaticPatentClassification

GitHub/classification-TF-1gram.ipynb

1

303043

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic Patent Classification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "plt.style.use(\"classic\")\n", "sns.set()\n", "from time import time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pre-processing..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing data into pandas.DataFrame" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Importing data...\n", "Number of occurences on X_title: 347032\n", "Number of occurences on X_resume: 347032\n", "Number of occurences on y: 347032\n", "done in 1.687s.\n" ] } ], "source": [ "### Importing data into pandas.DataFrame\n", "# LOAD DATA DIRECTLY FROM .CSV INTO PYTHON_LIST()\n", "\n", "t0 = time()\n", "print('Importing data...')\n", "try:\n", " dataset = open('dataset_ipc_first.csv', 'r', encoding='latin-1')\n", "except:\n", " dataset = open('./output/dataset_ipc_first.csv', 'r', encoding='latin-1')\n", "else:\n", " pass\n", "\n", "if dataset:\n", " IPC_level1_classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n", " X_title, X_resume, y = [], [], []\n", " header = dataset.readline()\n", " if header[:-1] == 'title|resume|ipc':\n", " for line in dataset:\n", " line = line[:-1].split('|')\n", " if line[2][0:1] in IPC_level1_classes:\n", " X_title.append(line[0]) # title\n", " X_resume.append(line[1]) # resume\n", " y.append(line[2][0:1]) # only first level IPC class (A..H)\n", " print('Number of occurences on X_title: ', len(X_title))\n", " print('Number of occurences on X_resume: ', len(X_resume))\n", " print('Number of occurences on y: ', len(y))\n", " categories = pd.DataFrame(y, columns = ['ipc_level1'])\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "\n", "## REDUCE SAMPLE SIZE\n", "# reducing number of rows to 5000\n", "# X_title = X_title[0:4999]\n", "# X_resume = X_resume[0:4999]\n", "# y = y[0:4999]\n", "# categories = categories[0:4999]\n", "# print('Number of samples has been reduced to 5.000 during test phase. Must comment this section to run a valid experiment')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fedeab33860>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total count of patent requests: 347032\n", "\n", "Best guess is the category: A 0.243577\n", "Name: ipc_level1, dtype: float64\n" ] } ], "source": [ "### first look on data\n", "plt.figure(figsize=(12,6))\n", "ax = sns.countplot(x=\"ipc_level1\", data=categories.sort_values('ipc_level1'))\n", "plt.ylabel('Patent requests', fontsize=12)\n", "plt.xlabel('First level IPC', fontsize=12)\n", "plt.xticks(rotation='horizontal')\n", "plt.show()\n", "print('Total count of patent requests: ', len(categories))\n", "print()\n", "best_guess = categories.ipc_level1.value_counts().nlargest(n=1) / len(categories)\n", "print('Best guess is the category: %s' % best_guess)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting features..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vectorization (Term Frequency - TF)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting features...\n", "Title vector matrix shape: (347032, 64585)\n", "Resume vector matrix shape: (347032, 254990)\n", "done in 42.052s.\n" ] } ], "source": [ "### Vectorization\n", "from time import time\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "t0 = time()\n", "print('Extracting features...')\n", "vec = CountVectorizer(ngram_range = (1, 1), max_df = .95, min_df = 1)\n", "\n", "X_title_features = vec.fit_transform(X_title)\n", "print('Title vector matrix shape: ', X_title_features.shape)\n", "\n", "X_resume_features = vec.fit_transform(X_resume)\n", "print('Resume vector matrix shape: ', X_resume_features.shape)\n", "\n", "print('done in %0.3fs.' % (time() - t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reducing deminsionality" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selecting most relevant features...\n", "Title most relevant features matrix shape: (347032, 200)\n", "Resume most relevant features matrix shape: (347032, 800)\n", "done in 630.008s.\n" ] } ], "source": [ "### Reducing deminsionality with SVD\n", "from sklearn.decomposition import TruncatedSVD\n", "\n", "t0 = time()\n", "print('Selecting most relevant features...')\n", "\n", "svd = TruncatedSVD(n_components = 200)\n", "X_title_svd = svd.fit_transform(X_title_features)\n", "print('Title most relevant features matrix shape: ', X_title_svd.shape)\n", "\n", "svd = TruncatedSVD(n_components = 800)\n", "X_resume_svd = svd.fit_transform(X_resume_features)\n", "print('Resume most relevant features matrix shape: ', X_resume_svd.shape)\n", "\n", "print('done in %0.3fs.' % (time() - t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categories encoding" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Encoding labels...\n", "Encoded classes: ['A' 'B' 'C' 'D' 'E' 'F' 'G' 'H']\n", "done in 0.072s.\n" ] } ], "source": [ "### Encoding each category into one exclusive integer\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "t0 = time()\n", "print('Encoding labels...')\n", "l_enc = LabelEncoder()\n", "y_encoded = l_enc.fit_transform(y)\n", "print('Encoded classes: ', l_enc.classes_)\n", "print('done in %0.3fs.' % (time() - t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spliting train and test samples..." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating sampling...\n", "X_train matrix shape: (277625, 1000)\n", "X_test matrix shape: (69407, 1000)\n", "y_train matrix shape: (277625,)\n", "y_test matrix shape: (69407,)\n", "done in 2.374s.\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "t0 = time()\n", "print('Creating sampling...')\n", "X_concatenated = np.concatenate((X_title_svd, X_resume_svd), axis=1)\n", "X_train, X_test, y_train, y_test = train_test_split(X_concatenated, y_encoded, test_size=0.2, random_state=583)\n", "print('X_train matrix shape: ', X_train.shape)\n", "print('X_test matrix shape: ', X_test.shape)\n", "print('y_train matrix shape: ', y_train.shape)\n", "print('y_test matrix shape: ', y_test.shape)\n", "print('done in %0.3fs.' % (time() - t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing measurement..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score\n", "\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", "# if normalize:\n", "# cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "# print(\"Normalized confusion matrix\")\n", "# else:\n", "# print('Confusion matrix, without normalization')\n", "\n", "# print(cm)\n", "\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " fmt = '.2f' if normalize else 'd'\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, format(cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training, predicting & measuring against best guess..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Naïve Bayes - Gaussian" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Naïve Bayes (Gaussian) model...\n", "done in 5.340s.\n", "Predicting classes using Naïve Bayes (Gaussian) model...\n", "done in 5.396s.\n", "Accuracy: 0.372512858934\n", "52.93% better than best guess.\n" ] }, { "data": { "image/png": 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2DBw4UD/pSUuB4FHvHMF5X9aWWbj6xzhyBY0gOjbh7Sd8gLDQ9L977J8UgKud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\n+7iT38cdgK4dQ/ikf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"text/plain": [ "<matplotlib.figure.Figure at 0x7fedfe729d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.model_selection import cross_val_predict\n", "\n", "t0 = time()\n", "print('Training Naïve Bayes (Gaussian) model...')\n", "classifier = GaussianNB()\n", "classifier.fit(X_train, y_train)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "t0 = time()\n", "print('Predicting classes using Naïve Bayes (Gaussian) model...')\n", "y_pred = classifier.predict(X_test)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "# Printing accuracy\n", "acc = accuracy_score(y_test, y_pred)\n", "print('Accuracy: ', str(acc))\n", "comparision = (acc - best_guess[0])/best_guess[0]*100\n", "print('%0.2f%% better than best guess.' % comparision)\n", "\n", "# Making the Confusion Matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "np.set_printoptions(precision=2)\n", "class_names = ['A','B','C','D','E','F','G','H']\n", "plt.figure(figsize=(12,6))\n", "plot_confusion_matrix(cm, classes=class_names, title='Confusion matrix')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Naïve Bayes - Bernoulli" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Naïve Bayes (Bernoulli) model...\n", "done in 7.783s.\n", "Predicting classes using Naïve Bayes (Bernoulli) model...\n", "done in 1.818s.\n", "Accuracy: 0.547754549253\n", "124.88% better than best guess.\n" ] }, { "data": { "image/png": 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tTef+JS0tjcTERFJSkgFITkoiMTH9mm3YpDl6evosmDWFxIQEbt24zqqlC/Hr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\ne8EUdS1BVa+aAJQpU4bKVat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+3XTjm9aUHLiN2MTMf8E1qxyd3kyr5UP6N93CNibce5ag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\njiQ4QgghhDA4kuCINCM8PFzfI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"text/plain": [ "<matplotlib.figure.Figure at 0x7fed9c1d7710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.naive_bayes import BernoulliNB\n", "\n", "t0 = time()\n", "print('Training Naïve Bayes (Bernoulli) model...')\n", "classifier = BernoulliNB()\n", "classifier.fit(X_train, y_train)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "t0 = time()\n", "print('Predicting classes using Naïve Bayes (Bernoulli) model...')\n", "y_pred = classifier.predict(X_test)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "# Printing accuracy\n", "acc = accuracy_score(y_test, y_pred)\n", "print('Accuracy: ', str(acc))\n", "comparision = (acc - best_guess[0])/best_guess[0]*100\n", "print('%0.2f%% better than best guess.' % comparision)\n", "\n", "# Making the Confusion Matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "np.set_printoptions(precision=2)\n", "class_names = ['A','B','C','D','E','F','G','H']\n", "plt.figure(figsize=(12,6))\n", "plot_confusion_matrix(cm, classes=class_names, title='Confusion matrix')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Forest" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Random Forest Classifier...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 3.4min\n", "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.3min finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "done in 682.415s.\n", "Predicting classes using Random Forest Classifier...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=16)]: Done 18 tasks | elapsed: 0.1s\n", "[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed: 0.6s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "done in 0.910s.\n", "Accuracy: 0.55471350152\n", "127.74% better than best guess.\n" ] }, { "data": { "image/png": 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IIYRIV2TmRwghhEiD5JyfpEnyI4QQQqRBkvwkLU0ue21YNItudcvQunx+2lYu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\nzg5cvXKF3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fed9c96cdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "t0 = time()\n", "print('Training Random Forest Classifier...')\n", "classifier = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', n_jobs=-1, verbose=1)\n", "classifier.fit(X_train, y_train)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "t0 = time()\n", "print('Predicting classes using Random Forest Classifier...')\n", "y_pred = classifier.predict(X_test)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "# Printing accuracy\n", "acc = accuracy_score(y_test, y_pred)\n", "print('Accuracy: ', str(acc))\n", "comparision = (acc - best_guess[0])/best_guess[0]*100\n", "print('%0.2f%% better than best guess.' % comparision)\n", "\n", "# Making the Confusion Matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "np.set_printoptions(precision=2)\n", "class_names = ['A','B','C','D','E','F','G','H']\n", "plt.figure(figsize=(12,6))\n", "plot_confusion_matrix(cm, classes=class_names, title='Confusion matrix')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi-layer Perceptron" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Multi-layer Perceptron Classifier...\n", "done in 1946.618s.\n", "Predicting classes using Multi-layer Perceptron Classifier...\n", "done in 0.664s.\n", "Accuracy: 0.640612618324\n", "163.00% better than best guess.\n" ] }, { "data": { "image/png": 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iC8m0V4Yk8yOEEEKIXEUyP0IIIUROJGt+MiTBjxBCCJETybRXhiQsFEIIIUSu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SeSn1et/H4p3Xv2xszMjGY+vnR9vxdOTgVRqVTUb9AIb29vjhw+pK2/9eefGDpiFB6FCpMvXz4G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\nBYuZMm2m3vuS2bkT9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"text/plain": [ "<matplotlib.figure.Figure at 0x7feda691cd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "\n", "t0 = time()\n", "print('Training Multi-layer Perceptron Classifier...')\n", "classifier = MLPClassifier(hidden_layer_sizes=(100, 50), verbose=False)\n", "classifier.fit(X_train, y_train)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "t0 = time()\n", "print('Predicting classes using Multi-layer Perceptron Classifier...')\n", "y_pred = classifier.predict(X_test)\n", "print('done in %0.3fs.' % (time() - t0))\n", "\n", "# Printing accuracy\n", "acc = accuracy_score(y_test, y_pred)\n", "print('Accuracy: ', str(acc))\n", "comparision = (acc - best_guess[0])/best_guess[0]*100\n", "print('%0.2f%% better than best guess.' % comparision)\n", "\n", "# Making the Confusion Matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "np.set_printoptions(precision=2)\n", "class_names = ['A','B','C','D','E','F','G','H']\n", "plt.figure(figsize=(12,6))\n", "plot_confusion_matrix(cm, classes=class_names, title='Confusion matrix')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

CompPhysics/MachineLearning

doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/KNNeighbors Example from Sci-Kit Learn Website.ipynb

1

67660

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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fJHWCwykllVJm3gbcVrPuqqrXp3c8KEnC/klS+3knTpIkSZJKxCROkiRJkkrE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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a1bd9c080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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wfCr68HOH08HIr0ay7ut1uLJctP1HWxotvzh4f4eTkSO/Yt26r3G5smjb9h80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\nsNlsDJ44mD2/7MGV5aLleS1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"text/plain": [ "<matplotlib.figure.Figure at 0x1a18ee6a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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q/3YeD02BJcDYfv24+7LLmLZgAV194y7yjXteWlq55mMnXEcr9ujTgwXTFrDr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\n8b82qc01Sjt17AkyqSgfHCOpWy8xbCm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"text/plain": [ "<matplotlib.figure.Figure at 0x1a1b22e358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "from sklearn import neighbors, datasets\n", "from sklearn.neighbors import KNeighborsRegressor\n", "\n", "n_neighbors = 15\n", "\n", "# import some data to play with\n", "iris = datasets.load_iris()\n", "\n", "# we only take the first two features. We could avoid this ugly\n", "# slicing by using a two-dim dataset\n", "X = iris.data[:, :2]\n", "y = iris.target\n", "\n", "h = .02 # step size in the mesh\n", "\n", "# Create color maps\n", "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", "\n", "for weights in ['uniform', 'distance']:\n", " # we create an instance of Neighbours Classifier and fit the data.\n", " clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n", " clf.fit(X, y)\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", " 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.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", " Z = clf.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, cmap=cmap_light)\n", "\n", " # Plot also the training points\n", " plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,\n", " edgecolor='k', s=20)\n", " plt.xlim(xx.min(), xx.max())\n", " plt.ylim(yy.min(), yy.max())\n", " plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n", " % (n_neighbors, weights))\n", "\n", "plt.show()\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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

cc0-1.0

birdsarah/bokeh-miscellany

old/slider_example/Gapminder homage 0_3 html with population - better slice.ipynb

1

2256903

gpl-2.0

luwei0917/awsemmd_script

notebook/GlpG_paper/apr_week2_Fourth.ipynb

1

4681388

mit

mattgiguere/doglodge

code/.ipynb_checkpoints/bf_qt_scraping-checkpoint.ipynb

1

14699

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# bf_qt_scraping\n", "\n", "This notebook describes how hotel data can be scraped using PyQT.\n", "\n", "The items we want to extract are:\n", "- the hotels for a given city\n", "- links to each hotel page\n", "- text hotel summary\n", "- text hotel description\n", "\n", "Once the links for each hotel are determined, I then want to extract the following items pertaining to each review:\n", "- title\n", "- author\n", "- text\n", "- rating\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys \n", "from PyQt4.QtGui import * \n", "from PyQt4.QtCore import * \n", "from PyQt4.QtWebKit import * \n", "from lxml import html " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Render(QWebPage): \n", " def __init__(self, url): \n", " self.app = QApplication(sys.argv) \n", " QWebPage.__init__(self) \n", " self.loadFinished.connect(self._loadFinished) \n", " self.mainFrame().load(QUrl(url)) \n", " self.app.exec_() \n", "\n", " def _loadFinished(self, result): \n", " self.frame = self.mainFrame() \n", " self.app.quit() \n", " \n", " def update_url(self, url):\n", " self.mainFrame().load(QUrl(url)) \n", " self.app.exec_() \n", " " ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = 'http://www.bringfido.com/lodging/city/new_haven_ct_us' \n", "#This does the magic.Loads everything\n", "r = Render(url) \n", "#result is a QString.\n", "result = r.frame.toHtml()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# result" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#QString should be converted to string before processed by lxml\n", "formatted_result = str(result.toAscii())" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Next build lxml tree from formatted_result\n", "tree = html.fromstring(formatted_result)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method HtmlElement.text_content of <Element html at 0x10c181c00>>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree.text_content" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "#Now using correct Xpath we are fetching URL of archives\n", "archive_links = tree.xpath('//*[@id=\"results_list\"]/div')\n", "print archive_links" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = 'http://pycoders.com/archive/' \n", "r = Render(url) \n", "result = r.frame.toHtml()\n", "\n", "#QString should be converted to string before processed by lxml\n", "formatted_result = str(result.toAscii())\n", "\n", "tree = html.fromstring(formatted_result)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "#Now using correct Xpath we are fetching URL of archives\n", "archive_links = tree.xpath('//*[@class=\"campaign\"]/a/@href')\n", "\n", "# for lnk in archive_links:\n", "# print(lnk)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Now the Hotels" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url = 'http://www.bringfido.com/lodging/city/new_haven_ct_us' \n", "r = Render(url) \n", "result = r.frame.toHtml()\n", "\n", "#QString should be converted to string before processed by lxml\n", "formatted_result = str(result.toAscii())\n", "\n", "tree = html.fromstring(formatted_result)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<Element div at 0x10adf9788>, <Element div at 0x10adf97e0>, <Element div at 0x10adf9838>, <Element div at 0x10adf9890>, <Element div at 0x10adf98e8>]\n", "\n", "La Quinta Inn & Suites New Haven\n", "La Quinta Inn & Suites New HavenNew Haven, CT, USLa Quinta Inn & Suites New Haven is pet friendly! Up to two pets of any size are allowed in each room for no additional fee or deposit.Hotel Overview | Map | Photos | Guest ReviewsLow Rates from$75 (no pet fee)VISIT WEBSITE\n", "*************************\n", "Omni New Haven Hotel at Yale\n", "Omni New Haven Hotel at YaleNew Haven, CT, USOmni New Haven Hotel At Yale welcomes a maximum of two dogs, 25lbs or less, per guest room for an additioanl $50 per stay. Dogs over 25lbs require prior approval from the manager. Please note that ...Hotel Overview | Map | Photos | Guest ReviewsLow Rates from$219 + pet feeCHECK RATES\n", "*************************\n", "Premiere Hotel & Suites\n", "Premiere Hotel & SuitesNew Haven, CT, USPremiere Hotel And Suites allows up to two dogs (50 lbs or less) per guest room for an additional fee of $75 per stay. Larger dogs may be permitted with prior management approval.Hotel Overview | Map | Photos | Guest ReviewsLow Rates from$110 + pet feeCHECK RATES\n", "*************************\n", "Econo Lodge Conference Center New Haven\n", "Econo Lodge Conference Center New HavenNew Haven, CT, USEcono Lodge Conference Center welcomes up to two pets, 25lbs or less, in a limited number of pet-friendly rooms, for an additional $10 per pet, per night.Hotel Overview | Map | Photos | Guest ReviewsLow Rates from$56 + pet feeCHECK RATES\n", "*************************\n", "The Study at Yale\n", "The Study at YaleNew Haven, CT, USThe Study At Yale allows up to two dogs (50 lbs or less) for an additional fee of $50 per pet per stay.Hotel Overview | Map | Photos | Guest ReviewsLow Rates from$189 + pet feeCHECK RATES\n", "*************************\n" ] } ], "source": [ "#Now using correct Xpath we are fetching URL of archives\n", "archive_links = tree.xpath('//*[@id=\"results_list\"]/div')\n", "\n", "print(archive_links)\n", "print('')\n", "\n", "for lnk in archive_links:\n", " print(lnk.xpath('div[2]/h1/a/text()')[0])\n", " print(lnk.text_content())\n", " print('*'*25)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now Get the Links" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/lodging/70449/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=75.01\n", "*************************\n", "/lodging/70451/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=219\n", "*************************\n", "/lodging/70452/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=109.65\n", "*************************\n", "/lodging/70447/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=55.95\n", "*************************\n", "/lodging/106805/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=189\n", "*************************\n" ] } ], "source": [ "links = []\n", "for lnk in archive_links:\n", " print(lnk.xpath('div/h1/a/@href')[0])\n", " links.append(lnk.xpath('div/h1/a/@href')[0])\n", " print('*'*25)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/lodging/70449/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=75.01'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lnk.xpath('//*/div/h1/a/@href')[0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/lodging/70449/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=75.01',\n", " '/lodging/70451/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=219',\n", " '/lodging/70452/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=109.65',\n", " '/lodging/70447/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=55.95',\n", " '/lodging/106805/?cid=14745&ar=&dt=&rm=1&ad=1&ch=0&dg=1&rt=189']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "links" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Reviews\n", "\n", "Next, we want to step through each page, and scrape the reviews for each hotel." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url_base = 'http://www.bringfido.com' \n", "r.update_url(url_base+links[0]) \n", "result = r.frame.toHtml()\n", "\n", "#QString should be converted to string before processed by lxml\n", "formatted_result = str(result.toAscii())\n", "\n", "tree = html.fromstring(formatted_result)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<Element div at 0x10d9ecdb8>, <Element div at 0x10d9ece10>]\n", "\n", "5\n", "3\n" ] } ], "source": [ "hotel_description = tree.xpath('//*[@class=\"body\"]/text()')\n", "\n", "details = tree.xpath('//*[@class=\"address\"]/text()')\n", "\n", "address = details[0]\n", "csczip = details[1]\n", "phone = details[2]\n", "\n", "#Now using correct Xpath we are fetching URL of archives\n", "reviews = tree.xpath('//*[@class=\"review_container\"]')\n", "\n", "texts = []\n", "titles = []\n", "authors = []\n", "ratings = []\n", "\n", "print(reviews)\n", "print('')\n", "for rev in reviews:\n", " titles.append(rev.xpath('div/div[1]/text()')[0])\n", " authors.append(rev.xpath('div/div[2]/text()')[0])\n", " texts.append(rev.xpath('div/div[3]/text()')[0])\n", " ratings.append(rev.xpath('div[2]/img/@src')[0].split('/')[-1][0:1])\n", " print(rev.xpath('div[2]/img/@src')[0].split('/')[-1][0:1])\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Great value and no pet fee', 'Getting old']" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titles" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['\\nErin\\nin Washington, DC\\n', '\\nLawrence\\nin Pittsfield, MA\\n']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "authors" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['My 75lb dog and I received a lovely welcome from two young gentlemen working at the desk the evening of 8/1/14. Check-in was very easy and even though this hotel is close to the highway and Ikea, there is a big patch of grass/bushes out front for dogs to relieve themselves --I didn\\'t see bags so bring your own. Yes, I agree with review that it is an older place but my room with one queen bed was plenty clean and comfortable. When I travel with my dog, the less fancy the better. :). Also, it made it nice that there was a dog friendly restaurant less than 10 minute drive away...see review for \"Basta\". I definitely feel good about spending just over $100 at this hotel which was just a stop along the way in my travels. Recommend to other dog owners. (P.s. This site says small pets only but that is not up to date. The policy attached on this site shows that they take any size). ',\n", " 'I stayed at this La Quinta and felt that it was getting up there in age and a little run down. It was decently clean, although not fully clean. The staff were fairly helpful.']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "texts" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['5', '3']" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

beangoben/lerningMachin

Python/Variables.ipynb

1

13506

{ "metadata": { "name": "", "signature": "sha256:d290d607f154fe7ba32e0579a79168cbf04ae90c48548ebabc7ebe786014e5c9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Variables" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "\u00bfQu\u00e9 es una variable?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Podemos pensar en una variable como un contenedor para almacenar datos.\n", "- Formalmente son espacios en la memoria de nuestra computadora que almacenan datos." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "\u00bfQu\u00e9 tipo de datos pueden almacenar?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los tres tipos simples de datos son:\n", "- N\u00fameros:\n", " - Enteros\n", " - Reales (punto flotante)\n", " - Complejos\n", "- Cadenas de texto\n", "- Valores booleanos\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "\u00bfC\u00f3mo es que se definen las variables?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "B\u00e1sicamente, utilizaremos el operador de asignaci\u00f3n = (igual), para definir el tipo de dato que guardar\u00e1 la variable. Respecto al nombre, los nombres deben comenzar con letras y no debe haber espacios entre el nombre." ] }, { "cell_type": "code", "collapsed": false, "input": [ "miperro = 5\n", "MiPerro = 2.3\n", "Mi_Perro = 2 + 4j\n", "\n", "print(type(miperro))\n", "print(type(MiPerro))\n", "print(type(Mi_Perro))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'int'>\n", "<type 'float'>\n", "<type 'complex'>\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "Mi_Perro = 4\n", "print(Mi_Perro)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "Mi_Perro = \"Firulais\"\n", "miperro = 'Rocky'\n", "\n", "print(Mi_Perro)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Firulais\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "print(miperro)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Rocky\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print(type(Mi_Perro))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'str'>\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "spiderman = True\n", "venom = False\n", "\n", "print(type(spiderman))\n", "print(spiderman)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'bool'>\n", "True\n" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Operaciones aritm\u00e9ticas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las operaciones aritm\u00e9ticas que podemos realizar con variables num\u00e9ricas son:\n", "- Suma (+)\n", "- Resta (-)\n", "- Multiplicaci\u00f3n (*)\n", "- Divisi\u00f3n (/)\n", "- Divisi\u00f3n entera (//)\n", "- Residuo (%)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 3\n", "y = 5\n", "z = x + y\n", "print(z)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "8\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "z = z - x\n", "print(z)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "x = x * y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "z = z / y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "print(x)\n", "print(z)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "15\n", "1\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "z = 3.2\n", "x = 2\n", "z = z // x\n", "print(z)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "x = 5 % 3\n", "print(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n" ] } ], "prompt_number": 24 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Operaciones con cadenas de texto" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las operaciones son:\n", "- Concatenar (+)\n", "- Repetir texto (*)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mejor_heroe = \"Spiderman\"\n", "texto = \"El mejor h\u00e9roe es: \"\n", "heroe = texto + mejor_heroe\n", "print(heroe)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "El mejor h\u00e9roe es: Spiderman\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "mejor_heroe = mejor_heroe*11\n", "print(mejor_heroe)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "SpidermanSpidermanSpidermanSpidermanSpidermanSpidermanSpidermanSpidermanSpidermanSpidermanSpiderman\n" ] } ], "prompt_number": 33 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Observaciones:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- \u00bfQu\u00e9 sucede cuando operamos variables de distinto tipo?\n", "- \u00bfC\u00f3mo puedo cambiar el tipo de variables para operar?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 3\n", "print(type(x))\n", "y = 2.8\n", "print(type(y))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'int'>\n", "<type 'float'>\n" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "print(x+y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5.8\n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "perro = \"Mirlo\"\n", "print(perro+x)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "cannot concatenate 'str' and 'int' objects", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-36-b309818b85ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mperro\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Mirlo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperro\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: cannot concatenate 'str' and 'int' objects" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "x = 2\n", "y = \"3\"\n", "print(x+y)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-37-1432fe02b301>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"3\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "print(type(y))\n", "y = int(y)\n", "print(type(y))\n", "\n", "print(x+y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'str'>\n", "<type 'int'>\n", "5\n" ] } ], "prompt_number": 38 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ejercicios:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Definir una variable EDAD en 50, imprimir la edad y tipo de variable.\n", "- Pedir al usuario que ingrese la edad y guardarla en la variable EDAD, redefiniendola. Imprimir tipo.\n", "- \u00bfLa variable que ingres\u00f3 el usuario es entera? Si no, convi\u00e9rtela a entera e imprime nuevamente el tipo de variable.\n", "- Y no lo olvides... \u00a1No dejes de compartir tus ejercicios y las dudas que te surjan con nosotros!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "texto = \"Mucho gusto, \"\n", "nombre = raw_input(\"\u00bfCu\u00e1l es tu nombre?\")\n", "\n", "print(type(nombre))\n", "print(texto+nombre)" ], "language": "python", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "\u00bfCu\u00e1l es tu nombre?Rodolfo\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "<type 'str'>\n", "Mucho gusto, Rodolfo\n" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }

gpl-3.0

tayebzaidi/HonorsThesisTZ

ThesisCode/testing/LightCurve Align plots.ipynb

1

39521

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import numpy as np\n", "import sys\n", "import matplotlib.pyplot as plt\n", "sys.path.append('../gen_lightcurves/gp_smoothed')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '../gen_lightcurves/gp_smoothed/SN2009an_gpsmoothed.json'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-cd6edd752fec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maligned_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0maligned_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0maligned_lc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlc_path\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlc_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mlc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../gen_lightcurves/gp_smoothed/SN2009an_gpsmoothed.json'" ] } ], "source": [ "aligned_path = '../gen_lightcurves/gp_smoothed_aligned/'\n", "lc_path = '../gen_lightcurves/gp_smoothed/'\n", "\n", "#Select random lightcurve after looking\n", "obj = 'SN2009an'\n", "aligned_name = obj+'_aligned_lc.json'\n", "lc_name = obj+'_gpsmoothed.json'\n", "\n", "with open(aligned_path+aligned_name, 'r') as f:\n", " aligned_lc = json.load(f)\n", "#with open(lc_path+lc_name, 'r') as f:\n", "# lc = json.load(f)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['bsplinemag', 'dmag', 'goodstatus', 'kernel', 'mag', 'mjd', 'modeldate', 'modelerr', 'modelmag', 'shift', 'type'])\n" ] } ], "source": [ "mag_aligned = aligned_lc['g']['modelmag']\n", "mjd_aligned = aligned_lc['g']['modeldate']\n", "print(aligned_lc['g'].keys())\n", "shift = aligned_lc['g']['shift']\n", "mag_orig = aligned_lc['g']['mag']\n", "mjd_orig = aligned_lc['g']['mjd']\n", "\n", "mag = mag_aligned\n", "mjd = np.array(mjd_aligned) + shift\n", "mag_err = aligned_lc['g']['dmag']" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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HEal2LJYPie0tLS3N5ebm+o5RJjMDiOnDkACbdmyi6cNNGdBhANlnxd76siLR\nyMzmOOfSfOeIpGjvbIif3u7+dHc2521m0ZBFu38nEam6yna29mjLPqubUpcBHQbw8vyXdShSRCQG\nDE4bzOINi8n5Nsd3FJFqRYO2VMngLoPZXrCdF758wXcUERGpwAXtLqBeaj0t9ScSYRq0pUqOb3I8\n3Q7txujZo2P+kKqISLxLrZHKHzr9gTe/epPvt3zvO45ItRG2QdvMnjWzdWa2oMRrATNbY2Zzg4/T\ny/jst2Y2P/ie6D6Brxob0mUIS35awn+//a/vKCISAurt+HZN2jUUukKe/vxp31FEqo1w7tF+Duhb\nyusjnHOdgo93y/n8ScH3xM3FQbG+HmtJgUCAyztdDgHo3ao3ZoaZEQgEfEcTkap7DvX2HuKpt18a\n+RIE4M7MO3d3tnpbJLySwvXFzrmPzaxluL5f/AoEAvzud7/jhJ4n4IocKSkpfDTlI9LT031HE5Eq\nUm/Ht0AgQK2ja3HzwJuhAFJTU5kyZYp6WySMfJyjfZ2ZzQseojy4jPc44EMzm2Nm5S5gamZZZpZr\nZrnr168PfdoQiof1WEvKycnBFTpwkJeXR05Oju9IIhIeIevtWOpsiL/e3rliJxQW/zk/P1+9LRJm\nYV1HO7hnZKJzrn3weSNgA8WF/DegiXPuD6V8rqlz7nszawhMAoY55z6uaHvRviZrvKzHusv06dPp\n0aMHRa4IkuDjjz6mZ4+evmOJxKRoWUc7kr0d7Z0N8dnbu49E1kzho490JFKkKqJyHW3n3FrnXKFz\nrgjIBrqW8b7vg/9dB7xZ1vvEn0AgQEZGBkVFRcV//e6EXj176Vw/kTij3o4fu3p715HIHTt2kJGR\nod4WCaOIDtpm1qTE03OBBaW8p7aZHbDrz0Cf0t4nfgUCgd0XCRUUFtB8RHN6P99bhS0SZ9Tb8aNk\nb1/0+kXUfaAuv+b/qt4WCaNwLu83DpgOtDGz1WZ2FfBgcPmnecBJwJ+C721qZruuZG8EfGpmXwKz\ngHecc++HK6fsv8SERK7pfA1TvpnCkg1LfMcRkSpSb1cfQ9KGsGnHJl5d8KrvKCJxLaznaEdatJ/v\nF2/n+pW0dutaDhtxGEO7DGVE3xG+44jEnGg5RzuSor2zIX572zlHhyc7UDOpJrmDcnf/niJSOVF5\njnZ1F0/rse6tUZ1GnNf2PMbOHcuv+b/6jiMiEhLx2ttmxpAuQ/j8h8+ZtWaW7zgicUuDtoTMkLQh\nbM7brENL4rUfAAAgAElEQVSRIiIxYGDHgdRJrsOTuU/6jiIStzRoR1C8rce6tx7Ne9C+YXtGzR4V\nl3uARKT6iefePqDmAQzsOJBXF7zKT9t+8h1HJC5p0I6g7OxssrOzfccIGzNjSNoQvvjxCx2KFJG4\nEO+9PThtMHmFeYydO9Z3FJG4pEFbQurSjpdyQPIBjM4d7TuKiIhUoEOjDvRs3pMnc58svvmYiISU\nBm0JqQNqHsBlx17GawteY8O2Db7jiIhIBYZ0GcKKjSv4cPmHvqOIxB0N2hJyuw9FfqFDkSIi0a7/\nMf1pWLsho2frSKRIqGnQlpBr17AdJ7Y4kSdzn6SwqNB3HBERKUdyYjKDjh/ExKUT+XbTt77jiMQV\nDdoRFK/rsZZmSJchfLPpGz5Y/oHvKCIiVVZdejurcxZmxlO5T/mOIhJXNGhLWJxz9Dk0rtOYUbNH\n+Y4iIiIVaH5Qc85qcxZPf/E0Owp2+I4jEjc0aEdQPK/HurfkxGSyjs/ivWXvsWLjCt9xRESqpDr1\n9pC0IWzYtoHXF73uO4pI3LB4OiSWlpbmcnNzfccok5kBVIvDkABrfllDi0dbcEP6DTx46oO+44hE\nNTOb45xL850jkqK9s6F69XaRK+KYUcdQL7Ue06+a7juOSFSrbGdrj7aEzaEHHso5R5/DM188w/ad\n233HERGRciRYAkPShjBj9Qw+/+Fz33FE4oIGbQmroV2G8vP2n3lt4Wu+o4iISAUu73Q5tWrU0lJ/\nIiGiQVvCKrNlJsfUP0YXRYqIxIC6KXUZ0GEAr8x/hY3bN/qOIxLzNGhLWJkZQ7sMJff7XGatmeU7\njoiIVGBol6FsL9jO2Lm66ZjI/tKgHUHVZT3WvQ08diB1kuvwxKwnfEcREdkn1bG3j218LD2a92DU\n7FEUuSLfcURimgZtCbsDax7I5cdezmsLX2P9r+t9xxERkQpc1+U6Vmxcwftfv+87ikhM06AdQdVp\nPda9DekyhPzCfJ7+/GnfUUREKq269va5x5yrm46JhIDW0Y6g6rQea2l6v9Cbr3/+mhXXryAxIdF3\nHJGoonW0o1N17u1AToC7p97NsmHLOKLeEb7jiEQVraMtUWdol6F8t/k7Jiyd4DuKiIhUIKtzFokJ\niVrqT2Q/aNCWiDmrzVk0O7CZDkWKiMSApgc0pf8x/Xl27rNs27nNdxyRmKRBWyImKSGJwWmDmbxi\nMl+t/8p3HBERqcB1Xa5j045NvDzvZd9RRGKSBm2JqEHHDyI5MVl7tUVEYkCP5j04ttGxPDH7iWp5\nnrrI/tKgHUHVcT3WvTWo3YCL2l/E818+zy95v/iOIyJSrure22bGdV2vY97aeXz63ae+44jEHA3a\nEnHDug5ja/5Wnp/7vO8oIiJSgUs6XELdlLo8Putx31FEYo4G7Qiqruux7i2taRrdDu3GE7Of0F3H\nRCSqqbehVo1aXHXcVbzx1Rus+WWN7zgiMUWDdgRlZ2eTnZ3tO0ZUuK7rdSz9aSmTV0z2HUVEpEzq\n7WJDugyhyBXxz9x/+o4iElM0aIsX57c9n0a1G+lQpIhIDGh1cCvObH0mT815iryCPN9xRGKGBm3x\nomZSTa7pfA3vLH2H5T8v9x1HREQqMKzrMNZvW89rC1/zHUUkZmjQFm+uTbuWxIRELfUnIhIDTml1\nCsfUP4bHZz1erVdiEdkXGrTFmyYHNOH8tufzzBfPsDV/q+84IiJSjl1L/eV+n8vMNTN9xxGJCRq0\nI6i6r8damuu7Xc8veb/wwpcv+I4iIvIb6u09XXbsZRxY80BGzhzpO4pITNCgLV51O7QbaU3TdChS\nRCQG1Emuwx86/YF/LfoX32/53ncckagXtkHbzJ41s3VmtmCv14eZ2RIzW2hmD5bx2b7B93xtZreE\nK2OkaT3W3zIzru96PYs3LGbSikm+44hUa+rt31Jv/9Z1Xa+jsKhQS/2JVIKFay+imfUCtgIvOOfa\nB187CbgNOMM5l2dmDZ1z6/b6XCKwFDgVWA3MBi52zi2qaJtpaWkuNzc3xL9J6JgZgPbc7iWvII8W\nj7YgrWkaEy+Z6DuOiBdmNsc5l+Y5Q0R7O9o7G9TbZek3rh8zV89k1Z9WUTOppu84IhFX2c4O2x5t\n59zHwM97vTwYeMA5lxd8z7rffBC6Al8751Y45/KBV4Gzw5VT/Lv/nvtZe9Na3hnwDma2+xEIBHxH\nE6lW1NtSGYFAgImXTGT9X9aTUiNFnS1SjkoN2lbsUjO7I/i8uZl1rcL2WgM9zWymmU01sy6lvOdQ\nYFWJ56uDr5WVLcvMcs0sd/369VWIJL4FAgEmTJ4ASYBBamoq06ZNU2mLVFEIOxtC3Nvq7NgXCAT4\n7LPP1NkilVDZPdqjgXTg4uDzLUBVFj9OAg4GugM3AeNt13G5/9n7OUCZx+ycc2Occ2nOubQGDRpU\nIZJEg/mz5kMh4CA/P5+cnBzfkURiWag6G0Lc2+rs+DB16lQoABzk5eeps0XKUNlBu5tzbiiwA8A5\ntxFIrsL2VgNvuGKzgCKgfinvOazE82aALm2Oc5mZmcXnQhokJCWQmZnpO5JILAtVZ4N6W0qRmZlJ\nQkICGFiiqbNFylDZQXtn8GIXB2BmDSgu2331FnBy8DtaU1z8G/Z6z2zgKDM73MySgYuAt6uwraij\n9VjLlp6ezrEdj6VmRk0aDm5I125VPcotIoSus0G9rd4uRXp6Oh07duSg4w/CXeY4tG2ZZ3iKVGuV\nHbRHAm8CDc3sXuBT4L7yPmBm44DpQBszW21mVwHPAq2CS0e9ClzunHNm1tTM3gVwzhUA1wEfAF8B\n451zC6vwu0mMCAQCmBlz584l77M81jy6hqTEJJ3vJ1J1+9zZoN6WyinZ2ZvnbKbo6SJa1G2hzhYp\nRaWX9zOzo4HeFJ+LN8U591U4g1VFtC8VtWst1jFjxnhOEr0Kigpo9VgrjjrkKKZcNsV3HJGICfXy\nfurs0FBvV+yCf13ApBWTWP2n1dROru07jkhEVLazyx20zaxeeR92zu29DJRX0V7aWo+1cv7+6d+5\nZcotfHntl3Rs1NF3HJGICMWgrc4OPfV2xaatmsYJz57A6NNHM7jLYN9xRCIiVOtozwFyg/9dT/EN\nCZYF/zxnf0OKlGZQ50GkJqXy2IzHfEcRiTXqbIm49GbpdGnahcdmPkaRq+qlACLxqdxB2zl3uHOu\nFcXn3fVzztV3zh0CnAm8EYmAUv3US61H24VtefacZ/e4gY1uiCBSPnW2+GBmHPb5YSwZtoTEhER1\ntkgJSZV8Xxfn3LW7njjn3jOzv4UpkwgvPvYibdu0pfHMxqQuTuXll18mPT3ddyyRWKHOloga98Q4\nWh7Zkq0zt1I/t746WySosquObDCz282spZm1MLPbgJ/CGUyqt2MaHEM3140fJ/3IN998Q+/evZk+\nfbrvWCKxQp0tEZWcmMxZtc5iy5tb1NkiJVR20L4YaEDxclFvAQ353x3HpJK0Huu+abu1bfHdItHd\nIkX2kTo7RNTblddgXQN1tsheKjVoO+d+ds4Nd84dF3wMj7ar1yX+XN3/akgEDJKTk3XnMZFKUmeL\nD6f3Ob14qjCokVxDnS1CJQdtM/uvmX209yPc4eJNVlbW7jVZpXyBQIATTjgBCgAH27dvJyMjQxfW\niFSCOjt01NuVEwgEyMjIKN6j7WDH9h3qbBEqecMaM+tc4mkKcB5Q4Jz7S7iCVUW0r8mq9Vj33Y6C\nHTQf0Zxuzbox4eIJvuOIhE0ob1ijzg4d9fa+O+OVM5jz/RxW/nElNZNq+o4jEhahWkcbAOfcnBKP\nz5xzNwDd9julSAVSklIYnDaYiUsnsvSnpb7jiMQEdbb49Kfuf2Ltr2t5Zf4rvqOIeFfZU0fqlXjU\nN7PfAY3DnE0EgCFdhlAzsSYjpo/wHUUkJqizxafeh/emY6OOjJgxQkcCpNqr7KojJe82Nh24Ebgq\nXKFESmpUpxGXdryU5758jg3bNviOIxIL1NnijZlxQ/cbmL9uPpNWTPIdR8Sryg7axzjnWgXvOnaU\nc64PMDucwURKuiH9BnYU7ODJ2U/6jiISC9TZ4tXFHS6mSZ0mPDz9Yd9RRLyq7KA9rZTXtBL9PtJ6\nrFXXtkFbTjvyNJ6Y/QQ7Cnb4jiMS7dTZIaLerprkxGSGdR3Gh8s/ZP7a+b7jiHhT7qBtZo2DV6+n\nmtlxZnZ88JEJ1IpIQpGgG9NvZN2v63h53su+o4hEJXW2RJNr0q6hVo1aPDLjEd9RRLypaI/274CH\ngGbAI8DDwccNwK3hjRZ/tB7r/vn4+Y8hAFd3vhoz2/3QOq0iu6mzQ0y9XXUj/z6Sbbdt47lznlNn\nS7VV2XW0z3PO/TsCefZLtK/JqvVY99+LX77IZY9fRqPpjXjz6TdJT0/3HUkkJEK8jrY6O0TU2/tn\n+c/LOfKmIzlo7kG898R76myJG5Xt7KQKvuRS59xLQEszu2HvnzvndDxIIqrFlhbwPKwtXEvv3r2Z\nMmWKilskSJ0t0WbdknXwAmwu3KzOlmqpolNHagf/Wwc4oJSHSER99slnu2/Lnp+fT05Oju9IItFE\nnS1RJScnZ3dn5+XlqbOl2il3j7Zz7qngf++KTByR8mVmZpKQkECRK4LE4uciUkydLdGmZGe7REeP\nXj18RxKJqMreGbKBmd1qZmPM7Nldj3CHEykpEAiQkZFBUVEROCjMLyQjI0MX1ojsRZ0t0WDvznY7\nHb169FJnS7VS2YshpwGfUHyXscJdr0fbxTaxcGGNhMbqX1Zz+GOHMzhtMCNPG+k7jsh+C/HFkOps\niSpFroh2o9uRmpTKnKw5uy8yFYlVIbkYsoRazrmb9zOTSMg0O7AZl3S4hGe+eIY7T7yTQ2od4juS\nSDRRZ0tUSbAEbky/kUETBvHRNx/Ru1Vv35FEIqKyd4acaGanhzVJNaD1WEPrpoyb2LZzG6Nmj/Id\nRSTaqLNDRL0dOgM7DqRJnSb8/bO/+44iEjGVPXVkC8VXs+cBOwEDnHPuwPDG2zfRfhhS67GGXr9x\n/Zi+ajor/7iS2sm1K/6ASJQK8akj6uwQUW+H1oOfPcjNk28md1AunZt29h1HpMoq29mV2qPtnDvA\nOZfgnEt1zh0YfB5VhS3V080n3MxP23/i2S90nZfILupsiVbXdL6GA2seyIPTHvQdRSQiKrvqyPGl\nPI4ws8qe4y0SFj2a9yDjsAwenv4wOwt3+o4jEhXU2RKtDko5iMFpg3l90ess/3m57zgiYVfZc7RH\nAzOA7OBjBvAqsNTM+oQpm0il3HzCzazcvJLxC8f7jiISLdTZErWGdxtOUkISD017yHcUkbCr7KD9\nLXCcc66zc64z0AlYAJwC6PiPeHVm6zNp26Atf//s7zqPUqTYt6izJUo1OaAJVxx7BWPnjuXHrT/6\njiMSVpUdtI92zi3c9cQ5t4jiEl8RnljxyTmnQTAMEiyBW064hfnr5vPOsnd8xxGJBursEFFvh8dN\nJ9zEzqKdPDrjUd9RRMKqsoP2EjN70sxODD5GU3wIsibFV7SLeHVR+4toWbcl935yr/5SFFFnS5Q7\nst6RXNDuAkbPHs3G7Rt9xxEJm8oO2lcAXwN/BP4ErAi+thM4KRzB4pHWYw2fGok1+EvGX5ixegZT\nV071HUfEtytQZ4eEejt8/trjr2zJ36J7IUhcq9Q62rEi2tdk1Xqs4bWjYActH21Jx0Yd+XDgh77j\niOyTUK6jHSuivbNBvR1uuheCxKqQrqNtZkeZ2etmtsjMVux67H9MkdBJSUrhhvQbmLRiErnfR/df\n3iLhpM6WWPHXHn/lp+0/kf15tu8oImFR2VNHxgJPAgUUH3Z8AXixvA+Y2bNmts7MFuz1+jAzW2Jm\nC82s1KvfzexbM5tvZnPNTBOTVNq1addSN6Uu935yr+8oIj7tc2eDelsiL+OwDE5scSL/mPYP8gry\nfMcRCbnKDtqpzrkpFJ9qstI5FwBOruAzzwF9S75gZicBZwMdnXPtgPIW0TzJOdepuh1Klf1zYM0D\nGd5tOG8tfov5a+f7jiPiS1U6G9Tb4sHtvW7n+y3fM3buWN9RREKusoP2DjNLAJaZ2XVmdi7QsLwP\nOOc+Bn7e6+XBwAPOubzge9bta2CRilzf7XoOSD5Ae7WlOtvnzgb1tvjR+/DedG/WnQc+fUB3+JW4\nU9lB+49ALeB6oDMwELi8CttrDfQ0s5lmNtXMupTxPgd8aGZzzKzcy73NLMvMcs0sd/369VWIFDla\njzUy6qXWY2iXoYxfOJ7FGxb7jiPiQ6g6G0Lc27HU2aDejgQz4/aet7Ny80pemveS7zgiIVWpQds5\nN9s5t9U5t9o5d6Vzrr9zbkYVtpcEHAx0B24CxtuuS7r3dIJz7njgNGComfUqJ9sY51yacy6tQYMG\nVYgk8eiG9BtISUrh/k/v9x1FJOJC2NkQ4t5WZ0tpTj/qdI5rfBz3fXofBUUFvuOIhExSeT80s7fL\n+7lz7qx93N5q4A1XvHtglpkVAfWBPXZrOOe+D/53nZm9CXQFPt7HbUWdXWuxjhkzxnOS+NegdgOu\nTbuWkTNHckevOzii3hG+I4mEXRg6G9TbgHo73MyM23vdznnjz+O1Ba8xoOMA35FEQqLcdbTNbD2w\nChgHzAT22IvhnCv3ziBm1hKY6JxrH3x+LdDUOXeHmbUGpgDNXYkQZlYbSHDObQn+eRJwt3Pu/Yp+\nmWhfk1XrsUbWD1t+oNXIVlzS/hKeOfsZ33FEyhWKdbT3t7OD39GSCPV2tHc2qLcjqcgVcew/j2Vn\n4U4WDllIYkKi70giZQrVOtqNgVuB9sBjwKnABufc1EoM2eOA6UAbM1ttZlcBzwKtgktHvQpc7pxz\nZtbUzN4NfrQR8KmZfQnMAt6pzJAtsrcmBzThms7X8PyXz7Nio5YQlmqhyp0N6m3xK8ESuPPEO1ny\n0xJeW/ia7zgiIVHpO0OaWU3gYuAfFO+peDycwaoi2veOaM9I5GmvtsSKUN8ZUp0dGurtyNJebYkV\nIbszpJnVNLP+wEvAUGAk8Mb+RxQJP+3VlupGnS2xTHu1Jd6UO2ib2fPANOB44C7nXBfn3N+cc2si\nkk4kBG4+4WZqJNbg3o+1rrbEN3W2xIP+x/SnfcP23D31bgqLCn3HEdkvFe3RHkjxGqrDgWlm9kvw\nscXMfgl/vPii9Vj9eOrhp9hx+w6ePedZzGz3IxAI+I4mEmrq7BBTb0fe3XfdzYIhC1gybAlJiUnq\nbIlplT5HOxbEwvl+4scPW37g0OGHUnthbT589EPS09N9RxLZQ6jP0Y4F6mwpS5ErovaltSlYXcBH\n931EzxN6+o4ksoeQnaMtoZOVlbV7TVaJrG8XfAsvwNbZWzn55JOZPn2670giEgPU237MnDGTvNfy\nKPikgN69e6uzJWZp0I6g7OxssrOzfceolnJycnCFDhzk5eeRk5PjO5KIxAD1th8lO3vnzp1M/miy\n70giVaJBW6qFzMxMEhISwMAlOA7tcKjvSCIiUobdnQ2QAJsbb/YbSKSKNGhL3AsEAmRkZFBUVAQO\nKIDL+12uC2tERKLQHp0NUAAPX/0wt/3fbX6DiVSBBm2Je4FAYPfKAc457pl6DwSg79V9fUcTEZG9\n7N3ZOd/kQAAOPu1g39FE9pkGbal2hncfTqPajbhl8i1atktEJMqd2PJE+h7Zl/s+uY9NOzb5jiOy\nTzRoR5DWY40OdZLr8H+9/o+pK6fy4fIPfccRkSim3o4O9518Hxt3bOShaQ/5jiKyTzRoS7U0qPMg\nDq97OH+d8leKXJHvOCIiUo7jmhzHRe0vYsSMEfy49UffcUQqTYN2BGk91uiRnJhMp8Wd+OLaL0hM\nSNQdI0WkVOrt6NFgVgO23baNJgc0UWdLzNCdISPIzAB0GDJKFLkiWt/cmuUjlkMBpKamMmXKFN01\nUrzQnSGjk3o7upz70Lm89de31Nnine4MKVKBBEugZ1FPKCx+np+frxvZiIhEsWO2HqPOlpiiQVuq\ntUH9B0EiYJCcnExmZqbvSCIiUoZ+v+uHJRgYJNVIUmdL1EvyHUDEp4yMDFp3bc3S1KWcd9Z5OgQp\nIhLF0tPT6dCpAwvrLKR1Rmu6d+/uO5JIubRHW6qtQCCAmbF02lKYAi8Nf0kX1oiIRKldnT1vzjwK\npxYy//75JCQkqLMlquliSBFg5aaVtHmiDee3O58Xz33RdxyphnQxpEjlFRQV0PHJjhQUFbBgyAKS\nE5N9R5JqRhdDiuyDsY+OJe//8nip/0taNkpEJMolJSRx3OLjWHb9Mmom1VRvS9TSOdoRtGst1jFj\nxnhOInsLBAL0OKkHp55yKhRCaoqWjRIR9XY0e/nxl1lebzkz75up5f4kammPdgRlZ2eTnZ3tO4aU\nYfa02VAAOMjLz9OyUSKi3o5y3Qu6a7k/iWoatEWCMjMzSUhIAAOX4Ejvob0iIiLR7MIzLyyeZAxq\n1Kih5f4k6mjQFqH41JGMjAyKiorAgdvpOKnXSTrXT0QkSu3qbQoBBzt27CAjI0O9LVFFg7YIxYXt\nnNv9OH/8+aTck8KVf7zSdzQRESlFyd4eOWMkBOCNRW9o0JaookFbpBQP9XkIw7jxwxt9RxERkQoM\n7jKYDg07cMOHN7B953bfcUR206AdQbv+5S3Rr/lBzbm15638+6t/M3nFZN9xRMQT9XZsSEpI4vHT\nHufbTd/y4GcP+o4jspsGbZEy/Dnjz7Q6uBXXv3c9Owt3+o4jIiLlOLHliVzY7kIe+OwBvtn4je84\nIoAG7YjKysravSarRL+UpBQe/d2jfLXhK0bMGOE7joh4oN6OLQ/1eYhES2T4+8N9RxEBdAv2iDIz\nAB2GjDFnv3o2k1dMZtGQRbSo28J3HIlTugV7dFJvx56Hpj3ETZNu4q0L3+Lso8/2HUfilG7BLhIi\nI/uOBNAeEhGRGDC823DaN2zP9e9fz6/5v/qOI9WcBm2RCrSo24I7et3Bf5b8hwlLJviOIyIi5aiR\nWIMnz3iS7zZ/x91T7/YdR6o5DdoilfCn9D/RtkFbhr03jK35W33HERGRcvRo3oMrO13JIzMeYf7a\n+b7jSDUWtkHbzJ41s3VmtqDEa6+Z2dzg41szm1vGZ/ua2RIz+9rMbglXRpHKSk5M5qkzn2Ll5pXc\n8d87fMcRCQv1tsSTB099kLopdbl6wtUUFhX6jiPVVDj3aD8H9C35gnPuQudcJ+dcJ+DfwBt7f8jM\nEoFRwGlAW+BiM2sbxpwRo/VYY1uP5j24tvO1PDbzMWavme07jkg4PId6ew/q7dhVv1Z9Huv7GLPW\nzGLU7FG+40g1FbZB2zn3MfBzaT+z4su4LwDGlfLjrsDXzrkVzrl84FVAlw1LVDhoxkEU3VlE12Zd\nMbPdD93yV+KBelvizZLXl0AAhncfrs4WL5I8bbcnsNY5t6yUnx0KrCrxfDXQLSKpwmzXWqxjxozx\nnESq6oF7HqBuu7r89bK/QgGkpqYyZcoU0tPTfUcTCTf1tsScu+66i2NPOJbzzjgPCiE1RZ0tkeXr\nYsiLKX2vCICV8lqZx+3MLMvMcs0sd/369SEJFy7Z2dlkZ2f7jiH7yX3rIHi6X35+Pjk5OV7ziERI\nSHo7ljob1NvxYMmcJVAAOMjLz1NnS0RFfNA2sySgP/BaGW9ZDRxW4nkz4Puyvs85N8Y5l+acS2vQ\noEHogoqUITMzkwRLAAOX4OjRq4fvSCJhFcreVmdLpGVmZpKQUNzZRQlFtO/S3nckqUZ87NE+BVjs\nnFtdxs9nA0eZ2eFmlgxcBLwdsXQiFUhPT6djx440aNeAosuKmMEM35FEwk29LTFrV2cfeuShJF+Z\nTPb6bF3gKhETzuX9xgHTgTZmttrMrgr+6CL2OvxoZk3N7F0A51wBcB3wAfAVMN45tzBcOUX2RSAQ\nwMyYO3cu6xesh2fgLz3+wnV/uc53NJH9pt6WeFOys9csW0P+mHwmXDKB/oP7+44m1YTF07/q0tLS\nXG5uru8YZSq+aB/9SzqO/Lj1R9qNbscRBx/BZ3/4jBqJNXxHkhhlZnOcc2m+c0RStHc2qLfjTWFR\nIb2e68Wi9YuYP3g+zQ5s5juSxKjKdrbuDBlBWo81/jSu05inznyK2d/P5r5P7vMdR0RCTL0dXxIT\nEnn+nOfJL8znyv9cSZEr8h1J4pwGbZH99Pu2v+fSjpfyt4//phvZiIhEuSPrHckjfR5h8orJjJql\nG9lIeGnQjqCsrKzda7JKfHn8tMdpckATLn3zUrbt3OY7joiEiHo7PmV1zuL0o07nL5P/wuINi33H\nkTimc7QjSOf6xbePvvmI3i/05prO1/DPM//pO47EGJ2jHZ3U2/Hrx60/0n50e5of1JzpV02nZlJN\n35EkhugcbZEIO/nwk7kp4yaemvMUb3z1hu84IiJSjsZ1GjP27LF88eMX3DrlVt9xJE5p0BYJoXtO\nvoe0pmlc/fbVrNq8quIPiIiIN/3a9GNol6E8MuMR3v/6fd9xJA5p0BYJoeTEZF7p/wr5hfkMfHMg\nhUWFviOJiEg5/nHqP2jfsD2Xv3U5a7eu9R1H4owGbZEQO+qQoxh9xmimrpzK3VPv9h1HRETKkVoj\nlXHnjeOXvF8Y8MYA7SCRkNKgHUFaj7X6uOzYy7ii0xX87eO/MWn5JN9xRKSK1NvVQ/uG7XnitCeY\n8s0U7vn4Ht9xJI5o0BYJk1Gnj6Jtg7YMeGMA32/53nccEREpxx+O+wMDOw7krql3MWXFFN9xJE5o\n0I4grcdavdSqUYt/nf8vtu3cxkWvX8TOwp2+I4nIPlJvVx9mxpNnPMnR9Y/mkjcuYc0va3xHkjig\ndbQjSOuxVk+vzH+FAW8M4E/d/8Qjv3vEdxyJUlpHOzqpt6ufResX0TW7Kx0bdSTnihySE5N9R5Io\npHW0RaLEJR0u4fqu1zNixgjGzR/nO46IiJSjbYO2PHv2s0xfPZ0bPrjBdxyJcRq0RSLgoT4P0aN5\nDwYdFHQAAB/PSURBVK6ecDXz1873HUdERMpxQbsLuDH9RkbNHsXzc5/3HUdimAZtkQiokViD8b8f\nz0E1D+Kc187hp20/+Y4kIiLleOCUBzip5Ulc+861zF4z23cciVEatEUipMkBTXjzwjdZ88safv+v\n3+viSBGRKJaUkMT488fTuE5jzn71bK0eJVWiQTuCtB6rdGvWjex+2eR8m8Pw94f7jiMiFVBvV2/1\na9Xn7YveZkv+Fs559Ry279zuO5LEGA3aIhE28NiB3JRxE0/mPsmoWaN8xxERkXJ0aNSBl859idzv\nc7nq7av0Dy/ZJxq0I0jrscou9/e+n36t+3H9+9fz7rJ3fccRkTKotwXg7KPP5r7e9zFuwTju+O8d\nvuNIDNE62hGk9VilpK35W+k1thfLfl7GJ1d+QqfGnXxHEo+0jnZ0Um/LLs45Bk0YxDNfPMPYs8dy\nRacrfEcSj7SOtkiUq5Nch4mXTKRuSl3OfOVMVv+y2nckEREpw647R57S6hQGTRik27RLpWjQFvGo\n6QFNeeeSd9iSv4W+L/Vl4/aNviOJiEgZaiTW4PXzX+fo+kdz7mvn8sUPX/iOJFFOg7aIZx0bdeSt\nC99i2c/LOOvVs3RVu8j/t3f3cTbW+R/HX5+ZwbidxCgahaiolXuGCiF3yxYpNtutLNvWb9vd3+6q\ntlDttrXblp/8ivJLJUoRKYad3HUzRsqgpSVUKJQSZTAz398f58IYZzTMOee6zsz7+Xich3Nznet8\nDte8feZ7Xdf3EgmwlOQU5l03j9OST6P31N5s+maT3yVJgKnRFgmArg278sJVL/DOZ+8w5NUh5BXk\n+V2SiIgUI61GGhlDMziYf5CeL/Rkx74dfpckAaVGO4Y0H6ucyKALBzGu9zhmfzybm2ffTIEr8Lsk\nkXJPuS3FaZralLk/n8u277bR84WeOvRPwlKjLRIgv273a+7vej/Pr36eO+bdof/gRUQCrGP9jsy6\ndhb/3vVv+r7Yl30H9/ldkgSMGu0Y0nysUhJ3X3o3v0//PU+seIK7Mu9Ssy3iI+W2/JiejXsy/erp\nLN+2nCunX0luXq7fJUmAaB7tGNJ8rFJSzjlGvjGSp1Y+xZ8v+zNju471uySJMs2jHUzKbSmp53Ke\n48bXbqRn457MunYWyUnJfpckUVTSzE6KRTEicnLMjAl9J5BXkMf9S+8n0RK5r8t9fpclIiLFuP7i\n68kryOOWObcw8OWBzLxmJpWSKvldlvhMjbZIQCVYAhP7TSTf5TN6yWgA7u1875ERNhERCZabW95M\nfkE+w+cOZ+DLA3nlmlc0sl3OqdEWCbAES+Dpfk8DMHrJaHLzcvlLt7+o2RYRCahbW9+Kw/HLub+k\n/7T+vDb4NapUqOJ3WeITNdoiAZeYkMgz/Z8hOTGZh955iB8O/cBjvR5Tsy0iElDDWw+nYmJFbplz\nC72n9mbukLlUr1Td77LEB2q0Y0gn08ipSrAEJvSdQHJSMo8tf4x9B/fxVL+nSErQj7BINCm35VTd\n2OJGkpOSGTpzKN2e68ab171J7Sq1/S5LYkz/S4vECTPj0Z6PUr1Sde5fej/f5H7DiwNf1PF/IiIB\nNfiiwVSpUIVrZlzDpf93KQuGLqB+Sn2/y5IYito82mY22cx2mtnaQs+9ZGarvNsWM1tVzHu3mNka\nb7lgz/10EjQfq5SWmTG261ge7/U4s9bPovfU3uzJ3eN3WVJGKLePp9yW0up/fn8yhmawfe92Ok3u\nxLpd6/wuSWIoavNom9llwD7gOefcRWFe/wewxzl33ATBZrYFaOOc++pkPjPoc7JqPlaJpKmrp3Lj\n7BtpWrspb173Jmk10vwuSUohCPNoxzq3g57ZoNyWyPnwiw/pNbUXh/IPMXvwbC4951K/S5JSKGlm\nR21E2zm3FNgd7jULJdc1wLRofb5IWXdd8+uYd908tny7hQ5Pd2D1jtV+lyRxTrktEj0t67Yk65Ys\n6lStQ/fnu/PS2pf8LkliwK9LsF8K7HDObSjmdQcsMLOVZqZ9diLF6N6oO2/f/DYAl0y+hDf+84bP\nFUkZptwWKaWGNRvyzs3v0O6sdgx+dTAPLn1Qe0vKOL8a7SGceFSkk3OuFdAbuM3bnRmWmQ03s/fN\n7P1du3ZFuk6RwGt+RnOyhmXR+PTG9JvWj0ffe1TBLdEQkdxWZkt5V6tKLRb+YiE//8nPuWfRPfxi\n1i/Izcv1uyyJkpg32maWBAwAit1n4pzb7v25E5gFtDvBshOdc22cc21SU1MjXa5IXEirkcaym5Yx\noOkAfrfgdwybM0zBLRETydxWZotAclIyL1z1Ag90fYCpa6bSdUpXtu/d7ndZEgV+jGh3B9Y757aG\ne9HMqppZ9cP3gSuAteGWjTfOOY00StRUrViVlwe9zJ8v+zOTV02m87Od2fpd2B8zkZOl3BaJMDPj\n7svu5pVBr7BmxxpaT2zNO5+943dZEmHRnN5vGvAecL6ZbTWzW7yXBlNk96OZ1TOzN72HZwBvm1kO\nkA284ZybH606RcqSBEtgbNexzLxmJut2raP1xNYs2bLE77IkTii3RWJvYLOBZA3LolrFanSZ0oUn\nsp/QL3dlSNSm9/ND0KeKOjwX68SJE32uRMqD9V+t58rpV7Jh9wYe6PoAf7zkjySYX6dlyI8JwvR+\nsRb0zAbltsTOt7nfMnTmUN7Y8AaDLxrMxJ9O1GXbA6ykma1GO4Y0H6vE2t4Dexk+dzjT106nd+Pe\nPHfVc7oEcECp0Q4m5bbEUoEr4G9v/417Ft1D49MbM2PQDJqf0dzvsiQM3+fRFhH/Va9UnRcHvMiE\nPhPI3JxJ8/9tTuamTL/LEhGRMBIsgVGXjiLz+ky+O/Ad7Sa1Y3z2eP2iF8fUaIuUcWbGyLYjWT5s\nOSnJKfR4vgd/XPhHDuYf9Ls0EREJo0uDLuSMyKFbo27cPu92+k3rx87vd/pdlpwCNdoi5USLM1uw\ncvhKhrcezsPvPky7Se10NUkRkYCqU7UOc4fMZVyvcfxr07+4aMJFzFo3y++y5CSp0RYpR6pUqMKT\nP32SOYPn8OW+L2kzsQ0Pvf0QeQV5fpcmIiJFmBm3t7+dlcNXUj+lPgNeHsANr93AN/u/8bs0KSE1\n2jGk+VglKPqd34+1v1rLzy74GaMyR9Hh6Q7kfJnjd1kigaPcliC4sM6FZN2Sxb2X3cvU1VNpNqGZ\nRrfjhBptkXKqdpXazBg0gxmDZvD5d5/TZlIb7nnrHu7+892Y2XG30aNH+12yiEi5VSGxAmO6jiH7\n1mzOrHYmA14ewKAZg/jdqN8pswMsye8CyhPNxypBdHWzq+naoCu/XfBbHlz2II1SGzFvwzxGDRrF\nnj17mDp1Kunp6X6XKeIL5bYETau6rcgels0j7z7C2CVjqVijIuOyxjF5xGRldgBpHu0Y0nysEnSL\nNi9i5Bsj+XjVxzAFyIPKlSuTmZmp4I4yzaMdTMptCbKNuzdy25u3sWDxAmV2jGkebRE5aV0bdiVn\nRA49EnpAfui53AO5LMhc4G9hIiJynManN2b+dfMZXG3wMZk9e/5sfwuTI9Roi8gxKiVVYsxNY0KX\nazdwCY7xO8fzzAfPkF+Q73d5IiJSiJlxx7V3HJPZ//zyn9zz1j3sPbDX7/LKPTXaInKc9PR0mjdv\nTsMGDXny5SdpfHFjhr0+jBZPteD1j1/XbnQRkQApnNmvzH2FgVcM5MFlD9L4fxozPns8B/IO+F1i\nuaVGW0SOMXr0aMyMVatWsXnzZkYMGEHWsCyu3nE1uXm59J/en46TO5K5KVMNt4iIz4pm9tW9rmba\n1dMYtmcYzVKbcfu82zlv/HlM/nCyrpngA50MKSIldij/EFNypjBmyRi2freVTvU7cW/ne+nRqMeR\nk8bk1OhkSBGJNOcc/9r0L+5+625WbF9Bo5qNGHXJKK6/+HoqJlb0u7y4ppMhRSTiKiRWYFirYWy4\nfQNP9HmCz/Z8Rs8XetL+6fa88u9XSnQM9+HRF835KiISXWZGj3N7sHzYcuYMnkOtyrW49fVbaTyu\nMY9lPca+g/tKtB7l9qnTiHYMaT5WKWsO5h9kyqopPPzuw2zcvZFza57LnR3u5PqLr6d6peonfG+X\nLl0AWLx4cfQLjQMa0Q4m5baUJc45Mj7J4K9v/5Wlny6lZnJNRrQZwa/a/oq0Gmk/+n7l9lElzWw1\n2jGk+VilrMovyOe19a/x8LsPk70tmxqVanBzi5sZ0WYE59c+P+x7FNjHUqMdTMptKauytmbxyLuP\nMGvdLBIsgYHNBnJb29u49OxLiz0UULl9lA4dEZGYSUxIZGCzgSwftpysW7Lo26Qv41eM54InLqDr\nlK5MXzud3Lxcv8sUERFPh7QOvHrNq3xyxyf8psNvyNiYQednO3PhhAt5LOsxvvrhK79LLBPUaItI\nRLVPa8+LA1/k8zs/5y+X/4Ut325hyKtDqPuPuoycO5KsrVkaHRQRCYiGNRvy9yv+zrbfbmNy/8nU\nqFSDOzPupN4/6jHw5YHM+XgOB/MP+l1m3NKhIzGkXZBSHhW4AjI3ZTIlZwqvrnuV3LxcGtVsRN6H\neaTuTGXF3BWasQQdOhJUym0pj1bvWM2zq57lhdUvsOuHXdRMrsnApgPJnpzNaXtOY8miJX6X6Dsd\nox1ACmwp7/bk7mHmuplMWzuNhZ8sBAtdQnjABQO48oIraXdWOxITEv0u0xdqtINJuS3l2aH8Q2R8\nksG0tdOYvX423x/6ngoHK3B9++u56oKruLzh5VSuUNnvMn2hRltEAq1jj458lfoVDfs05K3Nb5FX\nkEdqlVT6NOlD78a96XFuD06vfLrfZcaMGm0RCbIfDv1A25+35avUr9hffz97D+6lclJlujfqTt8m\nfenZuCcNTmvgd5kxU9LMTopFMSIiRVU8VJF62+uRMTSDb/Z/w/yN85m7YS5zPp7DlJwpJFgCbeu1\npVvDbnRr1I2O9TuSnJTsd9kiIuVSlQpVSN2VSuquVDLGZbB4y2Lm/mcur//ndV7/z+sAnF/rfLo3\n6k63ht3o0qALNSvX9Llq/2lEO4Y0H6vIUcVNE5VXkMeKbSvI+CSDjE8yWLFtBfkun0qJleiQ1oHO\n53TmsnMuo31ae6pVrBb7wqNEI9rBpNwWOSpcbjvn+Pjrj5m/cT4Zn2Sw9NOl/HDoBwzj4jMvpvM5\nnel8Tmc6nd2JOlXr+FN4FOjQkQDSsX4iR7Vs2ZI9e/YwdepU0tPTi13uuwPfsfTTpSzavIglny7h\nwy8/pMAVkGiJtKzbko5pHelYvyPp9dOpX6N+2BMrR48ezZgxY457/r777gvMlc3UaAeTclvkqJLk\n9sH8g2Rvy+atzW+x5NMlvPf5e+zP2w/AebXOo1P9TqSnpdOxfkeapjYlwY6fAK8sZbYa7RhSYIuE\nvPfee1xyySUUFBRQuXJlMjMzT9hsF7Yndw9ZW7N4+7O3WfbZMrK3ZR8J8brV6tIhrQPtz2pP+7T2\ntK7b+pgrVJa0ufeDGu1gUm6LhJxqbh/MP8j729/nnc/eYdlny3j383f5ev/XANSoVIN2Z7ULZfZZ\n7Wl3VjvOqHbGkfeWhczWMdoiEnOLFy+moKAAgIMHD7J48eISh2hKcgo9G/ekZ+OeQOis+NU7VvPu\n5++yfNtylm9bzqz1swAwjKapTWlbry21vq5FzuocXIGjW7duDBkyhMmTJx+3/iCNmIiIBMWp5nbF\nxIp0rB/a8/jfnf4b5xwbd288ktlZW7N46O2HyHf5AJydcjZt67Wlzu46x2R2ZmYmGRkZgR/pLkqN\ntojEXJcuXUhISKCgoICKFSseOe7vVFRIrEDreq1pXa81t3M7AF//8DUrtq9g+dblrNi+gnkb57Fz\n/k7wBiX3H9jPmkNrmPj+RJ6870mqfl+VpYuWRuCbiYiUTZHKbTOjSa0mNKnVhBta3ACEZjT58IsP\nWb5tOdnbslmxfQWb5mw6JrNHPjGSG267gaU3LeWum+8iMT8xLi4Fr0NHYki7IEWOiuUuQeccr/3r\nNQb2HogrcCRUSKDyzZX5/szvAbACo3Vaa1rXbU2ruq1oVbcVF9W5KKaznOjQkWBSboscFcvcnr9o\nPn169DmS2bVG1GLX6btCLzqo8kMVrupw1ZHMbnlmS1KSU6JaU2E6dCSAFNQiR6WkpJCSkhKT4+7M\njKt6XMXFP7n4yH8SHTp0YNM3m+g3oh97q++lxrk1eOmjl3hq5VMAJCUk0Sy1GS3PbEnPc3sy5CdD\nol6nBI9yW+SoWOZ2r669jsns9PR0vtz3JSu3r+S2v9zGvmr7WLxlMVPXTD3ynkY1G9HyzJa0qtuK\nP13yp7AnWsaa/xWIiETZ6NGjMTNWrVrF5s2b6dixIwkJCTw/7nkqbahEhUUVeKDJA+z+w2423bGJ\nGYNm8IeOf+Cs6mcxf+N85m2cV6L1F70F9ZhBEZEgC5fZZsaTf3+Svuf1pWZOTWq8WYMZ6TPY8fsd\nzL9uPg9e/iCt6rZi1ZerePqDp0/YZMcys3XoSAxpPlaRo4qbRzuWSnoW/YG8A1RKqhSRdRVHh44E\nk3Jb5Kh4ye3cvNwfPfQvVpmtEe0YmjRpEpMmTfK7DBHxhDuLPpwfa7JPZl0SX5TbIsFSkqwtyfk1\nscpsNdoiUm4dPoseKPXsJ5Fcl4iIhBeprI1VZke10TazyWa208zWFnquhZllmdkqM3vfzNoV894b\nzGyDd7shmnWKSPmUnp5O8+bNadiw4UnvNozmuvyizBaRoItU1sYqs6M9ov0s0KvIcw8DY5xzLYB7\nvcfHMLPTgfuA9kA74D4zqxndUkWkPEpJSeHss88uVcgWd+JOHJ4M+SzKbBEJuNLmdiwzO6qNtnNu\nKbC76NNADe9+CrA9zFt7Agudc7udc98ACzk+/EVEAmH06NE45467xVujrcwWkfIglpntxzHavwEe\nMbPPgb8Do8IscxbweaHHW73njmNmw73dme/v2rUr4sVG0uF/SJHy7PBIwpIlS1iyZImmwgu+cpvZ\noNwWAeV2afjRaI8E7nTO1QfuBJ4Js4yFeS5s0jnnJjrn2jjn2qSmpkawTBGJhqCM/uo/jhJTZouU\nc8rtUxf1ebTNrAEw1zl3kfd4D3Cac85Z6Nq2e5xzNYq8ZwjQxTn3S+/xU8Bi59y0E31W0Odk1Xys\nIlKcoMyjrcw+lnJbRMIJ8jza24HO3v3LgQ1hlskArjCzmt4JNVd4z8U1zccqInGo3GY2KLdFpHSS\norlyM5sGdAFqm9lWQmel3wo8bmZJQC4w3Fu2DTDCOTfMObfbzO4HVnirGuucK3qCjoiIRJAyW0Qk\nsnQJ9hgK7XVFJ9aIyHGCcuhILAU9s0G5LSLhBfnQERERERGRMk+NtoiIiIhIFET1GG05lnY9iojE\nF+W2iJSGRrRFRERERKJAjXYMDR8+/MicrCIiEnzKbREpDc06EkM6e11EiqNZR4JJuS0i4WjWERER\nERERH6nRFhERERGJAjXaIiIiIiJRoEZbRERERCQKytTJkGa2C/ge+MrvWkqhNqrfT6rfX+W5/nOc\nc6mRLCbovMz+1O86SqA8b5d+i+faQfX7LZr1lyizy1SjDWBm78fzmfuq31+q31+qX4Io3v9d47n+\neK4dVL/fglC/Dh0REREREYkCNdoiIiIiIlFQFhvtiX4XUEqq31+q31+qX4Io3v9d47n+eK4dVL/f\nfK+/zB2jLSIiIiISBGVxRFtERERExHeBbbTNbIuZrTGzVWb2fpHXfm9mzsxqe49TzOx1M8sxs4/M\n7KZCy/7NzNZ6t2sLPd/QzJab2QYze8nMKvpYf00zm2Vmq80s28wuKrRsLzP72Mw2mtmf4rD+yWa2\n08zWFlnH6Wa20Kt/oZnVDFr9ZlbfzBaZ2Tpvu/qvOKs/2Xt8+OdiTKF1xMX2472eaGYfmtnceKu/\nuPVEe/uRyCguf4MqXN7G07ZWXObGy3coLnOjnVeRVjRz46n+cJnr+/bjnAvkDdgC1A7zfH0gg9Dc\nq7W95+4C/ubdTwV2AxWBvsBCIAmoCrwP1PCWexkY7N1/EhjpY/2PAPd59y8AMr37icAnQCPv++QA\nzeKlfu/xZUArYG2R9TwM/Mm7/6fD/35Bqh+oC7Ty7lcH/lPo7z8e6jegmne/ArAc6BBP24/33G+B\nF4G5hZ6Li/pPsJ6obj+6RWQbKDZ/g3oLl7fxtK0Vl7nx8h2Ky9xo51UUvscxmRtP9YfLXL+3n8CO\naJ/AP4E/AIUPLndAdTMzoBqhRjuP0A/oEudcnnPue0JB2ctb7nLgFe/9U4Arfay/GZAJ4JxbDzQw\nszOAdsBG59wm59xBYDrwsziqH+fcUkL/HkX9jFDdEND6nXNfOOc+8J7fC6wDzvLeEw/1O+fcPm+Z\nCt7NxdP2Y2ZphH5hfvrwwvFU/wn4tf1IyYXNX59rOqFi8jZutrUTZG5cfIfiMhf/8uqkFc1cn/M2\nUnzdfoLcaDtggZmtNLPhAGbWH9jmnMspsux4oCmwHVgD/JdzroBQY93bzKp4u3m7EhqRqgV865zL\n896/laMNlB/15wADvGXaAecAaV5Nnxda7nCd8VL/iZzhnPsCQuEK1Ilk8US4fjNrALQkNEIRN/V7\nuwBXATuBhc655cTX9vMYoca2oNDy8VT/cevxRHv7kdIrLn/jTVxua0UyN26+Q9HMJbRXJNp5FUlF\nMzcWeRtJ4TLX1+0nKZYfdpI6Oee2m1kdYKGZrQfuBq4Is2xPYBWh37rO9ZZf5pxbYGZtgXeBXcB7\nhEa6Lcw6Ij39ysnU/xDwuPfDuQb48EfqjJf6/RSx+s2sGvAq8Bvn3HfRLx2IUP3OuXyghZmdBsyy\n0PHDO8KsI3Dbj5n9FNjpnFtpZl0KLR9P2/9x6/FGHSX4YrGdSRhFMzc0qBofimYuoUHA4xaLbVUl\nU0zmxtvPQbjs9lVgG23n3Hbvz51mNgvoDDQEcrwfujTgA28E6SbgIRc6AGejmW0mdKxktnPuQeBB\nADN7EdhA6Lr3p5lZkvdbWhqh0XBf6nfOfel9h8O7aTZ7tyqERuAPO1xnvNR/IjvMrK5z7gszq0vo\nt//A1W9mFQgF/lTn3Mx4q7/Q+r41s8VAL+AfxMf2Mxjob2Z9gGSghpm9APwiTuoPt552wFKivP1I\nRGwlfP7Gm7ja1orJ3Lj6DnBM5nYgynkVQZ0okrmERrjjpf7iMtfX7SeQh46YWVUzq374PqFRpBXO\nuTrOuQbOuQaEQrCV95/cZ0A3b/kzgPOBTd4unFre882B5sACryFfBFztfeQNwGy/6jez0+zoWbzD\ngKXeyOkKoImFzvitSKjxmBNH9Z/IHK/uwNbvNU3PAOucc4/GYf2p3qgKZlYZ6A6sj5ftxzk3yjmX\n5i0/GHjLOTc0XuovZj2HZ4OI2vYjERM2f32u6VTEzbZ2gsyNi+9QTOauI4p5FUnFZO51xEn9J8hc\nf7cfF4CzRIveCJ3lnePdPgLuDrPMFo6e9V8PWEBot+1aYKj3fDLwb++WBbQo8hnZwEZgBlDJx/rT\nCY20rwdmAjULLdeH0JnXnxReTxzVPw34AjhEqDm5xXu+FqETyDZ4f54etPqBSwjtIltN6NCkVUCf\nOKq/OaHDGFZ7Pxf3xtv2U2j5Lhw760jg6z/ReqK5/egWuRvF5G9Qb+HyNp62teIyN16+Q3GZG828\niuJ3OZK58VJ/cZnr9/ajK0OKiIiIiERBIA8dERERERGJd2q0RURERESiQI22iIiIiEgUqNEWERER\nEYkCNdoiIiIiIlGgRlvimpnlm9kqM/vIzHLM7LdmFrXt2kLeMrMa3mNnZs8Xej3JzHaZ2Vzv8Y1m\nNt67P9rMtnn1bjCzmWbWrNB7p5tZk2jVLiISz2Kd9yKRoA1U4t1+51wL59yFQA9Cc67eF8XP6wPk\nuKMX5PkeuMi7OAFeDdtO8P5/evU2AV4C3jKzVO+1/wX+EI2iRUTKgFjnvUipqdGWMsM5txMYDvza\nG3luYGbLzOwD79YRwMyeN7OfHX6fmU01s/5mdqGZZXsjJquLGV2+juOvKjUP6OvdH0LoohElqfcl\nQhda+rn31DKgu5kllfQ7i4iURzHKe5FSU6MtZYpzbhOh7boOsBPo4ZxrBVwLjPMWexq4CcDMUoCO\nwJvACOBx51wLoA2hK6sV1QlYWeS56cBgM0smdGWw5SdR8gfABV7tBYSuvHXxSbxfRKRcikHei5Sa\nGm0pi8z7swIwyczWELpsbDMA59wSoLGZ1SE0Av2qcy4PeA+4y8z+CJzjnNsfZt2nO+f2Fn7CObca\naOCt681TrPWwnUC9k1yHiEh5Fc28Fyk1NdpSpphZIyCfUMN6J7CD0AhxG6BioUWfJ3QYyE3A/wE4\n514E+gP7gQwzuzzMR+QVc/LNHODvlPCwkUJaAusKPU72Pl9ERE4gBnkvUmpqtKXM8E4qfBIY75xz\nQArwhXdIxi+AxEKLPwv8BsA595H3/kbAJufcOEKNc/MwH/Mx0CjM85OBsc65NSdR70DgCo5tzs8D\nPirpOkREyqMY5b1IqemkK4l3lc1sFaHdhnmERi4e9V6bALxqZoOARYRmCAHAObfDzNYBrxVa17XA\nUDM7BHwJjA3zeW8AXQgdS32Ec24r8HiY5ZOAA4Ue32lmQ4GqwFrgcufcLgAzO4PQWfVflOB7i4iU\nN7HOe5FSs9AvgiLli5lVAdYArZxze07ifXWB55xzPUq4/D+BDc65CSVY9k7gO+fcMyWtR0RETuxU\n814kEnToiJQ7ZtYdWA/8z8mGrjfaPOnwBWt+5HPmEdodObWEq/8WmHIy9YiISPFKk/cikaARbRER\nERGRKNCItoiIiIhIFKjRFhERERGJAjXaIiIiIiJRoEZbRERERCQK1GiLiIiIiESBGm0RERERkSj4\nfxgMr/DNur8kAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd9ffdd47f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,6))\n", "\n", "ax1 = fig.add_subplot(121)\n", "ax2 = fig.add_subplot(122)\n", "\n", "#print(np.array(mjd_orig)-shift)\n", "\n", "ax1.plot(mjd, mag, color='green')\n", "ax1.errorbar(mjd_orig, mag_orig, fmt='o', ecolor='k', yerr=mag_err, color='k', markersize=3,capsize=3)\n", "ax1.axvline(x=shift, linewidth=2, linestyle='dashed', color='black')\n", "ax1.set_title('Pre-Alignment')\n", "ax1.set_ylabel('Magnitude')\n", "ax1.set_xlabel('Days (MJD)')\n", "ax1.invert_yaxis()\n", "\n", "\n", "ax2.plot(mjd_aligned, mag_aligned, color = 'green')\n", "ax2.errorbar(np.array(mjd_orig)-shift, mag_orig, fmt='o', ecolor='k', yerr=mag_err, color='k', markersize=3,capsize=3)\n", "ax2.axvline(linewidth=2, linestyle='dashed', color='black')\n", "#ax2.axvline(x=mjd_aligned[0], linewidth=0.5, linestyle='dashed', color='black')\n", "#ax2.axvline(x=mjd_aligned[-1], linewidth=0.5, linestyle='dashed', color='black')\n", "ax2.set_ylabel('Magnitude')\n", "ax2.set_xlabel('Days')\n", "ax2.set_title('Post-Alignment')\n", "ax2.invert_yaxis()\n", "\n", "plt.show()\n", "#plt.savefig('LCAlign.pdf')\n", "#aligned_lc['g'].keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

tritemio/multispot_paper

out_notebooks/usALEX-5samples-E-corrected-all-ph-out-12d.ipynb

1

469298

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Executed:** Mon Mar 27 11:39:24 2017\n", "\n", "**Duration:** 7 seconds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# usALEX-5samples - Template\n", "\n", "> *This notebook is executed through [8-spots paper analysis](8-spots paper analysis.ipynb).*\n", "> *For a direct execution, uncomment the cell below.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ph_sel_name = \"None\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_id = \"12d\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data_id = \"7d\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load software and filenames definitions" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.5.9).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import *" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_notebook()\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data folder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_dir = './data/singlespot/'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "data_dir = os.path.abspath(data_dir) + '/'\n", "assert os.path.exists(data_dir), \"Path '%s' does not exist.\" % data_dir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List of data files:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',\n", " '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',\n", " '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',\n", " '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',\n", " '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from glob import glob\n", "file_list = sorted(f for f in glob(data_dir + '*.hdf5') if '_BKG' not in f)\n", "## Selection for POLIMI 2012-11-26 datatset\n", "labels = ['17d', '27d', '7d', '12d', '22d']\n", "files_dict = {lab: fname for lab, fname in zip(labels, file_list)}\n", "files_dict" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'12d'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initial loading of the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = loader.photon_hdf5(filename=files_dict[data_id])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the **leakage coefficient** from disk:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Leakage coefficient: 0.10029\n" ] } ], "source": [ "leakage_coeff_fname = 'results/usALEX - leakage coefficient DexDem.csv'\n", "leakage = np.loadtxt(leakage_coeff_fname)\n", "\n", "print('Leakage coefficient:', leakage)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the **direct excitation coefficient** ($d_{exAA}$) from disk:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Direct excitation coefficient (dir_ex_aa): 0.06062\n" ] } ], "source": [ "dir_ex_coeff_fname = 'results/usALEX - direct excitation coefficient dir_ex_aa.csv'\n", "dir_ex_aa = np.loadtxt(dir_ex_coeff_fname)\n", "\n", "print('Direct excitation coefficient (dir_ex_aa):', dir_ex_aa)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the **gamma-factor** ($\\gamma$) from disk:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gamma-factor: 1.020526\n" ] } ], "source": [ "gamma_fname = 'results/usALEX - gamma factor - all-ph.csv'\n", "gamma = np.loadtxt(gamma_fname)\n", "\n", "print('Gamma-factor:', gamma)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Update `d` with the correction coefficients:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.leakage = leakage\n", "d.dir_ex = dir_ex_aa\n", "d.gamma = gamma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Laser alternation selection\n", "\n", "At this point we have only the timestamps and the detector numbers:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([42190, 56253, 56263]), array([47999942928, 47999959323, 48000016759]))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.ph_times_t[0][:3], d.ph_times_t[0][-3:]#, d.det_t" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First and last timestamps: 42,190 48,000,016,759\n", "Total number of timestamps: 1,912,555\n" ] } ], "source": [ "print('First and last timestamps: {:10,} {:10,}'.format(d.ph_times_t[0][0], d.ph_times_t[0][-1]))\n", "print('Total number of timestamps: {:10,}'.format(d.ph_times_t[0].size))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should check if everithing is OK with an alternation histogram:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { 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ISJgUSImIiIiESYGUiIiISJgUSImIiIiESYGUiIiISJgUSImIiIiESYGUiIiI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\nJCmkhBBCCCGc9P8ldDN76O1+zwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ca73240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_alternation_hist(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the plot looks good we can apply the parameters with:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Total photons (after ALEX selection): 1,642,933\n", "# D photons in D+A excitation periods: 404,153\n", "# A photons in D+A excitation periods: 1,238,780\n", "# D+A photons in D excitation period: 1,043,678\n", "# D+A photons in A excitation period: 599,255\n", "\n" ] } ], "source": [ "loader.alex_apply_period(d)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D+A photons in D-excitation period: 1,043,678\n", "D+A photons in A-excitation period: 599,255\n" ] } ], "source": [ "print('D+A photons in D-excitation period: {:10,}'.format(d.D_ex[0].sum()))\n", "print('D+A photons in A-excitation period: {:10,}'.format(d.A_ex[0].sum()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measurements infos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the measurement data is in the `d` variable. We can print it:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "singlespot_007_dsDNA_12d_3nM_green100u_red40u G1.021 Lk10.029 dir6.1" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or check the **measurements duration**:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "599.99949153749992" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.time_max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the background using automatic threshold:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x122e59ac8>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KEBQ0xuL9PU0UXSKE3wKfvDk7IO13q1evd+nQ4W38/dta5JkwYTSnTp0kNjaG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\n3d+BX4VKRETkUo67TsCbiz+EkiXj26UDcel3CqB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"text/plain": [ "<matplotlib.figure.Figure at 0x122d6c940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d, timetrace_bg)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([1960.0483965382143],\n", " [406.04619715470801],\n", " [902.31032918273252],\n", " [628.25703861394027])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst search and selection" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Calculating burst periods ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Counting D and A ph and calculating FRET ... \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying background correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying leakage correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying direct excitation correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [DONE Counting D/A]\n" ] } ], "source": [ "d.burst_search(L=10, m=10, F=7, ph_sel=Ph_sel('all'))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n" ] }, { "data": { "image/png": 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0Oh10Oh26du2K1NRUd5ZDRESkSC7fcp87d67079jYWMTGxgIA1Go1MjMzXf30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\niEhhFHO0vCcmCSAiIvJGigh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"text/plain": [ "<matplotlib.figure.Figure at 0x1044aa6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(d.ph_sel)\n", "dplot(d, hist_fret);" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# if data_id in ['7d', '27d']:\n", "# ds = d.select_bursts(select_bursts.size, th1=20)\n", "# else:\n", "# ds = d.select_bursts(select_bursts.size, th1=30)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_bursts_all = ds.num_bursts[0]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def select_and_plot_ES(fret_sel, do_sel):\n", " ds_fret= ds.select_bursts(select_bursts.ES, **fret_sel)\n", " ds_do = ds.select_bursts(select_bursts.ES, **do_sel)\n", " bpl.plot_ES_selection(ax, **fret_sel)\n", " bpl.plot_ES_selection(ax, **do_sel) \n", " return ds_fret, ds_do" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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yI5UKKa01K99z2XunBFKKTgK3kgVTnmel8qrlU00JludVyWroCo9xz+N4LLc+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\nyhqszJXlIOhZBO2I6HetwU87FNaxWhx57foSx4vBj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"text/plain": [ "<matplotlib.figure.Figure at 0x1044f9be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1)\n", "\n", "if data_id == '7d':\n", " fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True) \n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", " \n", "elif data_id == '12d':\n", " fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '17d':\n", " fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False)\n", " do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)\n", "\n", "elif data_id == '22d':\n", " fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) \n", "\n", "elif data_id == '27d':\n", " fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False)\n", " do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True)\n", " ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel) " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(329, 948)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n_bursts_do = ds_do.num_bursts[0]\n", "n_bursts_fret = ds_fret.num_bursts[0]\n", "\n", "n_bursts_do, n_bursts_fret" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D-only fraction: 0.257635082224\n" ] } ], "source": [ "d_only_frac = 1.*n_bursts_do/(n_bursts_do + n_bursts_fret)\n", "print('D-only fraction:', d_only_frac)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Mp556iunTp/O5z32OWbNmcdBBBw1az4cKLTddEyA+IcWPk7TKahpeUr5qZeLt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\nYhQ0clTl3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"text/plain": [ "<matplotlib.figure.Figure at 0x12089dbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9Cimtxx5PMxo/7mas5lKrB90lY9yN1hsyJpWhL8nYmNrdu2iQUR9BWo8SiFk8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\nL2iONjuaEARHmx1VfVxxtJ1SXHP0Y0dXkM+M7JCTURpIJBJ49NFHcer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"text/plain": [ "<matplotlib.figure.Figure at 0x12089da58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Donor Leakage fit" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bandwidth = 0.03\n", "\n", "E_range_do = (-0.1, 0.15)\n", "E_ax = np.r_[-0.2:0.401:0.0002]\n", "\n", "E_pr_do_kde = bext.fit_bursts_kde_peak(ds_do, bandwidth=bandwidth, weights='size', \n", " x_range=E_range_do, x_ax=E_ax, save_fitter=True)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all: E_peak = 1.54%\n" ] }, { "data": { "image/png": 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FCBiZNSQC4mCVBwWFsclGtIKCU253YuZQY7vaZfkJIcTQkb80MeRUv8bh4z6yEnrvFgK5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\ne5cAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x121877438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, bins=np.r_[E_ax.min(): E_ax.max(): bandwidth])\n", "plt.xlim(-0.3, 0.5)\n", "print(\"%s: E_peak = %.2f%%\" % (ds.ph_sel, E_pr_do_kde*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst sizes" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nt_th1 = 50" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x121d47e80>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZMwcNGzaU+nAqqEOWgg/x4hgZGeHq1auSx0+fPoWhoSEMDQ1llgP5A8NiYmLw3XfflaiO\nd9ugIN4uXbogIiKiyDYtcOfOnULJTZ06dbB06VJ4eHigQ4cOkvKDBw8iLS0N1tbWEAQB2tra2Ldv\nH7755psSxfquvLw8uLq64tmzZ9i9ezd0dXWRlpYm8/UUJTIyEoMGDYJIJELv3r3x+vVrvHr1CsB/\n542sX5yDBw/Gtm3b8Pz582I/BAtoaWnJPecKEta8vDzo6upK9YgUdOfKKtfS0io21vcZGxvj2bNn\nkkTs6dOnMDIygqGhYaHygvOqIO6cnJwSvdbiXs+7X17Af3+rs2fPIj09Hc7OzhAEAW/fvoW9vT32\n7dsHQ0NDpKamwsDAQNLNq6enJ3Wskr63evfuDQ0NDdy7dw/t27fH7t27IQgCRCIRnJyc4OTkhIsX\nL2Lr1q3Q0NAo8hd4YGAgduzYgZ07dxbqHQDyu+/f7al6+vQpTE1NkZ2djRYtWmDOnDlYtmwZAgIC\nShT7+4rrITh+/Hih5FzW+wfI/9FTcOkkIyMDP/74o+RLOysrS+pfIP990LlzZ5w8eRL+/v44ffo0\nvLy8ZMYRGBiILVu2oE6dOti/fz+SkpJgb28vSWYKzmVBECT/l/e3lddD8OrVKwwZMkQyiHb16tVo\n2rQpjhw5gqysLISFheHZs2cQiUSoU6cO3rx5g969e0uS8+HDhyMmJkatEwK1usvg1q1bmD9/Pry9\nvfH777+rOhysX78eBw4cwMiRI+Hp6SlztLkslpaWiIyMRG5uLjIyMnDy5MlC23Tv3l0ySvX69esY\nPXo0srOzMXjwYNSvXx/Tp0/HiBEjJJnq5cuX8ezZM2RnZyM8PByffvopunXrhsOHDyM7OxtZWVk4\ndOgQunfvDi0trWI/XHv27Ilz585BLBbj9evXOHr0KHr37i23HMj/gm7VqlWJroP16NEDERERyM3N\nRXJyMq5cuVKqNv37779x9OhRfPbZZ4We69evH9q2bYsjR44AyM/Enz9/jlOnTiEqKgrHjx/HunXr\n8PvvvyMvL6/YWN/n4+ODjIwMbN++XfJhVr9+fTRs2BDnzp0DALkjvt/166+/SmI8f/48GjZsiDp1\n6hS7X6dOnZCQkIDIyEhYWVmVKGZLS0vJL8FTp07hxYsXhbapU6cOmjRpgoiICADA0aNHMXPmTLnl\nshSX6Pbp00fyBXL58mXo6uqiUaNG6Nu3r8zy+vXr486dOwCAY8eOlei1Fvd65P2tbGxscOzYMYSG\nhuLAgQPQ1NREaGgotLW10bdvX0liERYWhk8++QQikUgqPlljB2S1y82bN5GTk4NWrVqha9euMDEx\nwYoVK6S+9E6fPl1sD0RERAR27dqFoKAgmckAkN/eERERyMrKwvPnz3HhwgXJdXMTExOMHTsWeXl5\nCvs8vXbtmqQnpoCs9w8AbNiwQdJrsnPnTqlLehcuXACQf0mywPz58xEbG4uxY8dizpw5RX7+Ojo6\nok2bNvj999+xcOFCTJw4EaGhoejcuTMEQZCcA6GhoZL2KenftsCjR48we/Zs5OXl4enTpzh69CgG\nDBiAffv24eDBgzhw4ADGjRsHR0dHTJw4ER999BGio6ORlZWFt2/f4vTp02jXrl2x9aiSWvUQZGZm\nws3NDfr6+pg1axZGjx6t0ngcHR0xf/58hIaGQktLC8uWLQMg/5dSQXnfvn1x9epV2Nvbo169ejA0\nNJT8qing4eGBJUuW4ODBg9DQ0MDGjRuho6ODOXPmYPLkyahZsyb09fWxZs0a3L17F40aNcL8+fOR\nkpKCIUOGoG/fvgDyP3xGjx6N3Nxc2NjYYODAgcjNzYWhoSGmTZsmt3vUyMgIc+bMwcSJE5GTkwMH\nBwd8/PHHACC3/OHDhzA2Ni5R2w0cOBDXrl3D8OHD0bhxY0lX3sSJEzFv3rxCbQrkd9UV9JjUqlUL\nGzduROPGjWW2uYeHh+QD/+DBgxg9erQk8wcAa2trrF27FsePH5d5yUYesViMoKAgNG/eXJKMiEQi\nhIaGYu3atVi0aBEEQUDbtm2LPZaPjw8WLVqE7du3o27dupIxHSX5pd27d2+kpKTIHDwpi7u7O9zc\n3BAUFIQ2bdpIXTJ417p167B06VJs2bJFck20qPL3Yy0udicnJ3h6esLW1hba2tpYvXp1keXjx4/H\n3LlzYWdnhx49ekiNMygJeXGX5G/17mtxcXGBm5sbDh48iLp160ouB33++edwc3PD/v37ixyTUjCG\nQCQS4e3bt/juu+8k57Kfnx9++OEHjBkzBlpaWsjNzUXPnj2xZs2aIl/bTz/9hDdv3mDmzJmSHgZv\nb28IgoDNmzfjp59+grm5ueQ23bdv3+Kbb76BgYGB1HE8PT0xbdo0WFlZISAgAEZGRlKD38rj0aNH\nUp8JRb1/FixYAFdXV2zcuBEtW7aU+iWemJiIYcOGwdDQEHp6egCAL774Ah4eHtiyZQu0tbUlg4s9\nPDxgbW2N/v37S/bPy8tDZmYm6tSpg9jYWKlbSkUiEeLj42FnZ4eGDRtK2r2kf9sCbdq0wYABAzBi\nxAgIggBXV1c0b95c7va9evVCXFwc7OzsoK2tDUtLS5V/pxVLQXcvSPn2228Ff39/yeM///xTGD58\nuDB48GDBzc1NcjuOIOTfquHs7Cy5daMyunr1quR2uJycHGHs2LHCnTt3yny8CxcuSN3aRfS+3bt3\nS27RvH37tjBq1CgVR0Tq6ObNm0JgYKCqw5BS2lsdjx07JrmtkSqWQnsI7t+/Dy8vL1y9elWSpaem\npsLT0xP79+9H48aN4eXlha1bt8LFxQV///03TExMsGvXLsyYMQMvX76U3P5WmbRs2RJ+fn7YuXMn\nBEHAqFGjYGZmpvQ4Hj58iK+//lrql5Dw/780Nm3aVGR2qw7Hrwhr167F2bNnJTEWxGdpaSk1ylxd\nj/+uXbt24cCBA4V+pZuYmGDEiBGYN28eNDQ0UKNGDXh7e1do3coUHh4uuWWzgCAI0NPTkwxQo7JJ\nSUmRDH6srHJzcyU9pFSxRIJQzEXBcli7di3atm2L6OhofPzxx5g0aRIOHjyIY8eOwc/PDwBw+/Zt\nzJ49G3/++SfOnTuHPXv2wNDQEDVq1MCCBQsUFRoRERG9Q6E9BK6urgDyb4kqkJycLHXNycjISDLS\nuWfPnujZs6ciQyIiIiIZlD6oUFaHRElu4ZKlLCPIqfR69Mjvuj1/XmGdSfSO92+bI8VgOysP21p5\nyjq/B6CChMDY2FhqatKUlBQYGRmV+Xipqa8qIiwqQm5ubWhpabCtlcTAQJdtrQRsZ+VhWytPo0Zl\nH3en9HkIevXqhZiYGDx+/BhA/kpz70/rS0RERMql9B4CAwMDeHt7Y9asWcjNzYWZmZncmaGIiIhI\nORR6l4Gi5eXlsRtKCQYPzr9kEBaWoepQqgV2ryoH21l52NbKU6kuGRAREZH6YUJARERETAiIiIiI\nCQERERGBCQERERGBCQERERGBCQEREREA4LPPRiA+/n9Kr/fmzRtYvXqF0ut9n9InJiIiInqXWJyO\ngAB/3LgRi/btzeHk5Aw9PX1Vh6U0Z878hV69VL+kMxMCIiJSGbE4HTY2VkhIiAcAhIQEIzDQHxER\nxyskKbh69Qp+/nkrDA2NcPduAnR0dLBsmQ+aNm2Ge/f+xapVy5GdnYUPP2yF7OzsIo/19dczMG3a\nDHTq1EXu8+3adcD1638jJSUZQ4YMx9Sp0yEIAjZt+h43b97Aq1evoKmpCQ8PL5iamgEALl++CGfn\naXj9+jXWrPHG//53B3p6+mjQoAFatzbFlClflLsdSoIJARERVbhdu3YgKCgAAKClpYnc3Lcyt0tO\nTkZi4mOpsoSEePTt27PIhe/Gj3fC5MnTShTLrVtxcHVdjA8++BC+vuvx22+7sXDhIixf7oHx450w\ncKANYmIuIyrqaAlfnXxPn6bghx+2IzX1GRwcRmLEiFF48iQJ6elp+OmnnQCAn3/eir17A+Hh4YWk\npEQYGBigRo0a+PHHzdDR0UFg4H6kpaVh2rSJaN3atNwxlRQTAiIiUpnXrzNLVV4WjRs3wQcffAgA\nMDU1Q3T0X3jxQoy7dxMwYMBgAECXLl3RrFnzQvvm5ubiiy8mQSQS4dGjR1i92hu1a9eCjc1wjB07\nvtD2lpa9AAAGBg2hr18fYnE62rfvgHr16uLAgf149OgRrly5iCZNmgLIv1zw6ae9AQAXLpzD3Lmu\nAID69eujb1+rCmuDkmBCQEREFW7y5GmSX/BFrWXg5+eL5cuXFCr/+ut5mD17ToXEUqNGDcn/RSIR\n8lfwEf3//wWIRCIAgIaGZqF9tbS0sHPnb/8fU9GXDGTXJeDs2TPYtOl7jB8/Ef36WcHY2BgxMVcA\nAGfPnoan54r/r18DgpAn2V9DQ1Tm11wWvMuAiIhUxsnJGa1bm0iVmZiYwsnJWaH11qtXD6ambXDk\nyGEAwI0bsXj48H6R+xQkDqV16dIF9OnTD3Z2o2BiYobTp/9CXt5bZGRkICcnB/XrNwCQ37sQEREG\nAHjx4gVOnz5V5jrLgj0ERESkMnp6+oiIOI6AAH/ExV1Hu3YdlHaXwdKl3li1ajmCg/fggw8+QNOm\nzYrcftOmrUU+X/jLO/+xnd0oeHktxpQp5yESacDcvCOuXLmECxfOonv3npKtnZwmY926VXB2Hod6\n9fRgbNwYNWrULNNrKwsuf0zF4vLHysWlYpWD7aw8bOuSOXYsAvXrN0DXrhbIzc3FrFnT8PnnM6WS\nhuKUZ/lj9hAQERGpgVatTLBu3Ups2eKLnJwc9OtnXapkoLyYEBAREamB1q1NsHXrLyqrn4MKiYiI\niAkBERERMSEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEg\nIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIi\nMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEg\nIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiMCEgIiIiAFqqDoBKTixOR0CAP27ciEX79uZwcnKG\nnp6+qsMiIqIqgAlBJSEWp8PGxgoJCfEAgJCQYAQG+iMi4jiTAiIiKjdeMqgkAgL8JclAgYSEeAQE\n+KsoIiIiqkqYEFQSN27EyiyPi7uu5EiIiKgqYkJQSbRvby6zvF27DkqOhIiIqiImBJXE0KG2hco0\nNDTQv7+VCqIhIqKqhglBJXHs2BEAgJPTFIwePRZffDELGhoacHWdh9zcXBVHR0RElR3vMqgEBEFA\nUFAgWrT4AOvWbYCGRn4e17RpMyxbthjr16/Bt98uVnGURERUmbGHoBK4fv1v3Lx5A2PHjpckAwAw\nc+ZX6NfPChs2rMP582dVGCEREVV2TAgqgaCgXwEA48Y5SpVraGhg8+af0KCBAWbN+hzp6WmqCI+I\niKoAJgRqLisrCyEhwejduy9atPig0PNGRkbYtGkLHj9+BDu7oZg5cxr8/HwhFqerIFoiIqqsmBCo\nucjIcKSlpRXqHXhXt27doaenh1u34hASEozly5fAxsaKSQEREZUYEwI1FxT0K+rWrYdhw0bI3SYg\nwB9isViqjLMYEhFRaahVQhAXF4eFCxdi5cqVWLZsmarDUbmkpEScOBGFkSNHoXbt2nK3kzeL4alT\nxxUVGhERVTFqlRCkp6dj0aJFWLRoEZKTk5GRkaHqkFQqOHgP8vLyirxcAMifxfDUqRNwdZ2Lx48f\nws/Pl+MLiIhILpEgCIKiK3Fzc0Pbtm0xadIkAEBUVBQ2btyInJwcdO7cGV5eXtDR0ZFsv3fvXojF\nYkyfPr3I4+bl5SE19ZVCY1cVQRDQs2cXaGhoIDr6MkQikdxt318JEQBatmyFtm3bISzsEDQ1tfD2\n7X+TF7VubVKqVRIHD64NLS0NhIVV7wRNWQwMdKvsea1O2M7Kw7ZWnkaN6pZ5X4X2ENy/fx9Tp05F\nZGSkpCw1NRWenp7Ytm0bIiIiULNmTWzduhUA8ObNGyxbtgwNGjQoNhmo6i5evIC7dxMwbtzEIpMB\nANDT00fc0PCMAAAgAElEQVRExHF4eq7A6NFj4em5AkePnsTOnYEYN85RKhkAOL6AiIgKU+hMhXv3\n7sWoUaNgZGQkKTtz5gw6d+6Mxo0bAwAcHBzw9ddfw8XFBWvWrMGtW7eQmZmJY8eOYdGiRdDXL9mv\n2Kpmz55foaGhgbFjx5Voez09fcyePadQeXZ2tsztf/99H+zs7NG8eQuIxekICPDHjRuxaN/eHE5O\nziXuPSAioqpBoQmBq6srACA6OlpSlpycDGNjY8ljIyMjPHnyBACwdOlSRYZTabx69QoHDoTAymoA\njI0bl+tY7dubIyQkuFB5XNx1fPJJe3Tr1h337v2Lp09TAAAhIcEIDPQv1SUFIiKq/JS+loGsIQua\nmpplOpZIJIKBgW55Q1Ir6enp+PbbOXj1KgPGxobQ1MwpVy/JnDlfYs+eAPzzzz+SsjZt2uCHH37A\nwYMH8csvv+DVK+lrewkJ8QgJCcKCBQsAAFpa+Zcsqlpbq6uqeF6rI7az8rCtKwelJwTGxsaIi4uT\nPE5JSZG6pFAagiBUqYEq7w8O/PXXX3Hu3Ply/lrXRljYnwgI8Edc3HW0a9dBcknA3NwCjx4lITR0\nf6G9Ll68Imnb3Nz8QYVVqa3VGQdgKQfbWXnY1sqjtoMKZenVqxdiYmLw+PFjAEBwcDCsra2VHYZa\nCgjwl7pTAKiYAYAF4wt+/PFnzJ49Ryq56NCho8x9zMzalKtOIiKqXJSeEBgYGMDb2xuzZs3C0KFD\n8ezZM3z99dfKDkMtyZtgKC7uusLqdHJyRuvWJoXKw8MPIzU1VWH1EhGRelHKJYNVq1ZJPe7Xrx/6\n9eunjKorFXkDANu166CwOgtuWXz3kgIArFjhiWHDBmDbtp1ITm6FN2/ewM8viHcgEBFVUUqZmEhR\nqtrERGJxOgYM6IP79+9JykxMTHHkSJTSv4TDww9jxowpyM3Nxdu3Z/+/tHupJzWi0uP1VuVgOysP\n21p5KtUYApJPT08f/v6/AQA6dDCHp+cKlSQDADB06HA4Ok7C27dvpco5qRERUdWk9LsMqGh169YD\nANjbfyZzoiFlSk+XveaBIsc0EBGRarCHgOSSt2iSIsc0EBGRajAhILlk3YFgYmIKJydnFUVERESK\nwoSA5Cq4A6FJk6aoXbs2AGD+fDcOKCQiqoKYEFCR9PT0YWRkjI8++gh6evrYuXO7qkMiIiIFYEJA\nJaKpqYmpUz/HxYvnceHCeVWHQ0REFYwJAZXYtGkzUaNGDfzww0ZVh0JERBWMCQGVmKGhIcaNm4iI\niHDcuXNb1eEQEVEFYkKgZtR94shZs2ZDQ0MDW7ZsUnUoRERUgZgQqCmRSKTqEGRq1ao1hg+3w/79\ne5GY+FjV4RARUQUpNiHIyspCbGz+Kny//vorPDw8kJiYqPDASH3Nnj0HOTk52LbtR1WHQkREFaTY\nhMDV1RVRUVGIjY2Fv78/jI2N4eHhoYzYSE116tQFvXv3xe7dOyEWy57emIiIKpdiE4JHjx5h7ty5\niIqKgr29PWbPng2xWKyM2EiNffXVHGRkvIS//y+qDoWIiCpAsQlBbm4uAOCvv/6CpaUlsrKy8OoV\nl7Gs7vr3t8ZHH7XFhg3rMH36ZPj5+bK3gIioEis2IejXrx/69OmDmjVrolOnTnBwcMCQIUOUERup\nsRcvxHj+PBWvXr3CgQMhWL58CWxsrCRJgVicDj8/X8ycOY3JAhFRJVDs8sdz587F+PHjYWhoCABY\nvXo1WrZsqfDASL0FBPgjJSVZqiwhIR7OzhPQp09//PLLNsnzISHBCAz0R0TEca6DQESkportIXBw\ncICxsTE0NPI3/eijjzBmzBiFB0bq7caNWJnlZ8+ewerVK2QmCwEB/soIjYiIykBuD8HUqVNx48YN\nZGRkwMLCQlL+9u1bfPzxx0oJjtRX+/bmCAkJLlTu5uaBmJgrOHr0SKHn4uKuKyM0IiIqA7kJwaZN\nm5Ceno4lS5bA29v7vx20tNCoUSOlBEfqy8nJGYGB/khIiJeUmZiYYtq06dDR8ZeZELRr10GZIRIR\nUSnITQjq1KmDOnXqYOfOncjMzETt2rURGxuLe/fuYciQIZJLCFQ96enpIyLiOAIC/BEXdx3t2nWA\nk5Mz9PT0ZSYLjRoZwsnJWYURExFRUYodVLh582bcu3cP8+fPx6xZs2BiYoLz589j5cqVyoiv2lH3\ntQzepaenj9mz58gsL0gWbtyIxdGjEWjSpAkHFBIRqbFif+afOHECPj4+iIiIwPDhw+Hv74/bt7nS\nnaKp61oGJVWQLGzdugNTp36Bv/++htu3b6k6LCIikqNE/f41a9ZEdHQ0LC0tAeSvb0BUUuPHOwIA\nfvstQMWREBGRPMUmBE2aNMH8+fORkJCAnj17wt3dHa1atVJGbFRFtG5tiu7deyI4OAjZ2dmqDoeI\niGQoNiFYvXo1+vTpg4CAAOjo6KB9+/ZYvXq1MmKjKsTRcRJSU1Nx9GiEqkMhIiIZSjQxkZ2dHZo3\nbw4AcHR0hK6ursIDo6pl+HA76OrWQVAQLxsQEamjYhOCRo0aIS4uThmxUBVWp04djBw5ClFRx5CU\nlKjqcIiI6D3FJgQPHjzA6NGj0bFjR1hYWKBbt25SMxcSldSECU7Iy8vDvn1Bqg6FiIjeU+w8BAEB\n7OKlitG1qwVMTc3w228BcHGZV+lvrSQiqkqKTQjkzTnQtGnTCg+GqjaRSITx452wfPkSnD9/Fj17\nfqrqkIiI6P8VmxDs2rVL8v+cnBzcuXMHFhYWsLa2VmRcVEWNHTsePj7L8NtvAUwIiIjUSKkvGTx6\n9Ahr165VWEBUtRkaGmLgQBscOnQAK1euRd269QptIxanS6Y9bt/eXLJGAhERKU6pVyhq1qwZ7t69\nq4hYCJVrLYOymjDBCZmZmThwIKTQc2JxOmxsrLB8+RKEhARj+fIlsLGxglicroJIiYiqj2J7CHbv\n3i35vyAIuHXrFurWravQoKjyr2VQlAEDBqFhw0b4/vu1iI4+LdULsHXrFqlVEgEgISEeAQH+mD17\nDnsPiIgUpNiE4NYt6QVpGjRoABcXF4UFRFXfq1cZyM3NxbNnTxESEoyQkGCsXeuDWrVqIS0tTeY+\na9Z449ixCNy8eQNisRgAEBISjMBAf0REHGdSQERUTsUmBKtWrVJGHFSNBAT4Iz1d+ov/zZs3+OCD\nlmjXrgPOnPmr0D4tWnyIW7duSpKBAu/2HhARUdnJHUOQmZmJ7du3448//kBGRgamTZuGTp06wdHR\nEY8fP1ZmjFTF3LgRK7O8ffsO2LnzV7RubSJVbmJiivDwY7CyGiBzv7i46xUeIxFRdSM3IVi6dCmu\nXr2K0NBQODo6wszMDL///jv69OkDLy8vZcZIVUz79uYyy9u16wA9PX1ERByHp+cKjB49Fp6eK3Dk\nSBT09PSL3I+IiMpH7iWDmzdvIiwsDK9fv0afPn2wcOFCaGhooHXr1hg2bJgyY6QqxsnJGYGB/lKD\nB01MTOHk5AwA0NPTl3kJQNZ+tWrVwsSJkxQfNBFRFSc3IdDSyn+qVq1aaNKkCTQ0/utM0NbWVnxk\nVGUV9AIEBPgjLu462rXrUKK7Bd7fLyXlCU6f/gtXrlyCtfUgJUVPRFQ1yU0I3k0A3v0/ULVviSPl\nkNcLUJr9Xr58AQuLjli+3BP9+llDU1OzosMkIqo25CYEDx8+xOzZswv9XxAEPHr0SDnRERWhbt16\nWLDADe7uCxEcvAfjxjmqOiQiokpLbkKwePFiyf/fX7dgwADZo72JlM3JaQp++mkLVq/2hp3dKNSq\nVUvVIRERVUpyEwJ7e3tlxkFUJjo6OvDwWIbPP3fG9u1b4eIyV9UhERFVSqVey4BI3djajkSXLp/A\n13c9UlNTVR0OEVGlxIRAzVSHxY0qmkgkgqfnCrx8+QIbN65TdThERJVSsQnBn3/+KfP/pFi8kaN0\nLC17wcpqALZv3wonp3Hw8/PlColERKUgNyFwdHSEr68v1q9fj+fPnwMAfvjhB6UFRlQaYnE6/ve/\nf5CXl4fIyHAum0xEVEpyEwJfX1+YmJggNTUVX331FQYNGoSHDx/ip59+wuXLl5UZI1GxAgL88fDh\nA6mygoWPiIioeHITApFIhGHDhqFJkyYICgpCREQEDA0NYWBggJCQEGXGSFQseQsmhYQEIzMzU8nR\nEBFVPnITAhcXF4wcORLPnj1DUFAQ/v77b2hpaWHMmDFYuXKlMmMkKpa8hY9u3IhFjx6dsXv3TqSm\npsLPzxczZ07jGAMiovfITQgCAwMRFBSEGjVqICsrC/v27cODBw8wZswY+Pj4KDNGomI5OTnLXDZ5\n8+YfoauriwUL5sDc3AzLly9BSEgwxxgQEb1H7sREQP7CRq1bt8bkyZMBAKmpqfj+++8RExOjjNiI\nSqyoBZNGj3bA9OlTcPjwQal9CsYYlGVNBSKiqqbIhAAAtm3bVuj/ffr0UVxERGUkb8EkLS0t6Ojo\nyNwnLu66osMiIqoUODERVQvyxhi0a9dByZEQEaknJgRULcgaY6CtrYNRo8aoKCIiIvXChICqhYIx\nBp6eKzB69FiMGvUZcnKy4eXlwemiiYhQgjEEpFz8clKc98cY6Ovr45dftsPCoiemTZuuwsiIiFSP\nPQRqSsTFDBTOy2slunT5BJ6e7rhy5ZKqwyEiUin2EFC1VaNGDfz8824MGNAbU6c6wdFxEu7eTUD7\n9uaSWxaJiKoL9hBQtdasWXOsW7cRSUmJ+O671Zy0iIiqLSYEVO3dv3+/UBkXRiKi6oaXDKjak7cw\nUkVMWiQWpyMgwB83bsTyUgQRqTW17CFITk7G+PHjkZiYqOpQqBqQN2lRixYflOu4YnE6bGysuH4C\nEVUKapcQvH79Gj///DPq1aun6lCompA1aREABAbuxtWrV8p83IAAfyQkxEuV8VIEEakrpV0ycHNz\nQ9u2bTFp0iQAQFRUFDZu3IicnBx07twZXl5e0NHRQa1atbB48WK4u7srKzSq5mQtjGRmZoavv54J\nO7shWLlyHdLT00vd7a/ISxFERBVN4QnB/fv34eXlhatXr6Jt27YA8ldN9PT0xP79+9G4cWN4eXlh\n69atcHFxUXQ4RDLJWhgpMvIkHB0/w7x5X0vKQkKCERjoj4iI48UmBTVq1JBZzvUTiEgdKfySwd69\nezFq1CjY2NhIys6cOYPOnTujcePGAAAHBwf88ccfig6FqFQ+/LAlRo36rFB5Sbr9U1NTcfRoBDQ1\npXPuhg0bwsnJuULjJCKqCArvIXB1dQUAREdHS8qSk5NhbGwseWxkZITk5GSp/VatWlXssUUiEQwM\ndCsoUvWQllYbAKCrW0NtXpuWVv6sieoSjzI9eHBXZnl8/C257SEIAmbMcEZq6jOEhobin3/+wbVr\n1xAdHY2kpCRkZqahVaumcuusiue1OmI7Kw/bunJQyW2Hsubr19TULNNxUlNfVURIaiMtLf/1vHqV\npTavLTe3NrS0NNQmHmUyNW0rs7xlS1O57bF3728IDQ3FjBlf4dNPrfHpp9YAgLt3E2Bl1QsTJkxE\nRMRxuZcUDAx0q2VbKxvbWXnY1srTqFHdMu+rkrsMjI2NkZKSInmckpICIyMjVYSitriWgXqQdwfC\nuXPRyM7OLlT+4MF9uLsvRJs2H2Hx4qVSz7Vq1RorVqxCXNx1rFtXfA8YEZEyqSQh6NWrF2JiYvD4\n8WMAQHBwMKytrVURClGR3l822dNzOWbO/ArHj/+Jzz93lkoK8vLy4OIyC1lZb7Bly3bUrFmz0PEm\nTnTGoEE22Lx5A86fP6fMl0JEVCSVXDIwMDCAt7c3Zs2ahdzcXJiZmZVozACRKrx/B4IgCNDW1sHm\nzRswZcpEdO/eAzdvxuHly5c4e/YMFi9eig4dOso8lkgkwvff+6Fv3+748svPMXGiM/755w5nMSQi\nlRMJsi7oVxJ5eXlV7rrU3bvx6NGjC7y9V2P69C9VHQ4AYPDg/DEEYWEZqg5FbQiCgEWLXLFjx09S\n5TVr1sS1a7fRoEGDIvcPDt6Lr776QqqsdWsTREQcR6tWTavcea2OeF1bedjWylPpxhAQVXYikQhN\nmxa+U+DNmzf47beAYvdPTn5SqIyzGBKRKjEhICqjGzdkzzhYkpkIOYshEakbJgREZSRvUaSSzERY\nnn2JiBSBCQFRGcm6JdHExLREMxGWZ18iIkVgQkBURoVvSVyBI0eiSnSnQMG+Li5zAQB9+1qVeF8i\nIkVQyW2HRFWFrEWRSrPv4sXLsGfPb9DQEDEZICKVYg8BkQqJRCJ07WqBK1cuIy8vT9XhEFE1xoSA\nSMW6deuOFy/E+OefO6oOhYiqMSYEaqYSzxNFZdStW3cAwKVLF1QcCRFVZ0wI1BQXN6o+zM07Qltb\nG5cvX1R1KERUjTEhIFKxmjVrwty8E3sIiEilmBAQqYFu3bojPv5/eP48VdWhEFE1xYSASA1062YB\nALhy5ZKKIyGi6ooJAZEa+G9gIccREJFqMCEgUgPGxo3RvHkLjiMgIpVhQkCkJrp27YarV68gNzdX\n1aEQUTXEhIBITXTr1h2ZmZmIjZW9NDIRkSIxISBSEwXjCM6dO6fiSIioOmJCQKQm2rZtj1q1auHs\n2bOqDoWIqiEmBERqQltbG507f4Lz58+rOhQiqoaYEKgZLmVQvXXr1h337t3DkydJqg6FiKoZLVUH\nQLJxLYPqqWCCokuXLsLW1k7mNmJxOgIC/HHjRizatzeHk5Mz9PT0y113ccdVVL1EpB6YEBCpkU8+\nyU8ILl+WnRCIxemwsbFCQkI8ACAkJBiBgf6IiDheri/n4o6rqHqJSH3wkgGRGjEwMICZmZncCYoC\nAvwlX8oFEhLiERDgX6565R33s8/sMGfOl/jsMzuF1EtE6oM9BERqpmfPnggKCkJWVhZq1Kgh9dyN\nG7LnKIiLu16uOuUdNzb2b9y+fQvZ2dkKqZeI1Ad7CIjUTI8ePZCdnY3Y2GuFnmvf3lzmPu3adShX\nnfKO6+HhhQcPUuDh4aWQeolIfTAhIFIzlpaWAGQvdNS0aVOZ+zRr1qxcdTo5OcPYuLFUmYmJKZyc\nnCXPN27cRO7zRFT5MSEgUjNt27ZF3br1Co0juH//Hlxd56FZs+ZwdXXH6NFjsXChO5o2bQZX17l4\n8OB+mevU09NH9+49oampCTs7e3h6rsCRI1GSAYN6evrw9l4DALC07FXoeSKq/DiGgEjNaGho4JNP\nuuLSpQsQBAEikQhv3rzBtGmT8Pp1JvbvP4iOHTtLth8wYBBsbQfj888n4dCho4XGHZRETk4OTp48\njv79rbF9u+yBgrq6ugCAmTNnw8ZmaNleHBGpLfYQEKmhbt26IyUlGQ8fPgAAeHi4ITb2GlauXCeV\nDABA586fYMWK1bh27SqWLHErU33R0achFqdj2LAR5Y6diCon9hAQqaGChY4uXbqA8+fPYvfuXzB2\n7Hg4OU2Wuf3kydNw8eJ57Nq1AxYWPTBmjEOp6gsLOwQNDQ0MHsxf/kTVFRMCIjVkamoKAPD0XIy0\ntFSYmbXB2rUb5M5gKRKJ8N13vrhxIxbz57vg+vW/kZycXKIZBfPy8nDkyGH06GGJhg0bKuT1yMPZ\nD4nUBxMCNSNwMYNqLz09HWPG5M9S+PRpMgDgzZs3yMnJBlBb7n66urrw9f0RQ4ZY4ccf/QCUbEbB\ny5cvISUlGS4ucyv2hRSDsx8SqReOIVBTXMug+vr5558LzQr44MH9Es0KePbsmUJJZXEzCoaF/QEA\nGDrUtgzRlp2iZl0korJhQkCkZq5dKzwhEVCyWQFLO5OhIAgICzuETp06o1mz5iUPsgIoatZFIiob\nJgREaqZTp04yy0syK2BpZzKMi7uBBw/uKb13AFDcrItEVDZMCIjUzOeff47WrU2kyko6K6CTk3Oh\nfZs1ay5334LLBaq43bBjx8KJD2c/JFIdDiokUjP6+vqIiDiOgAB/xMVdR7t2HUo8+l5P7799Y2Iu\nIzLyCBo1MkS9enoytw8PPwQzszYwNTUr9tgVOa5FEAR8//1a1KpVCzNnzkZAwC7k5uZy9kMiFWJC\nQKSG9PT0MXv2nHLvu379GqxZ44MjR8IwdOhwqe3u3o3HrVs38c03C8odb2lFRR1FdPRpzJu3EG5u\nS/D69Wts3erHu2yIVIiXDIiqsJkzZ8PQ0Aje3kuRm5sr9VxY2GEAwLBhpRs/UN4v7bdv32L5ck80\nbNgQX32Vn7h06pQ/++Lff8seUElEiseEgKgK09XVhavrIsTH/w+BgbulngsP/wPNmjWHubnsQYyK\nsnfvb7h9+xbmz/8WdevWA/DfeAImBESqw4SAqIqbMMEJpqZmWLt2JTIyMgAASUmJuHLlMoYOHa7U\nOS8yMzOxZo0PWrZsBSenKZLyli1bo27devj776tKi4WIpDEhIKritLS0sGTJcjx9moKtW/NnMAwP\nL7hcoNy7C7Zv/xFJSYlYvHgpdHR0JOUaGhowN+/IhIBIhZgQEFUDgwcPQffuPeHn54uUlBSEhx9C\nw4YNYWHRQ2kxpKamYtOmDejS5RPY2o4s9HzHjp3x4MF9PH+eqrSYiOg/TAiIqgGRSISlS1cgM/MV\nHBxG4syZv9C8+QfIyHip8LrF4nT4+fnC1nYQXr58gfnz3WRepqjK4wgK2mDmzGnw8/OFWJxeJeuk\nyo23HaoZ3nZFimJqagZdXV3Exd0AAFy9egU2NlYKXUzo/QWMAMDT0x0WFt0L1dmxY/6dBrGx19C/\nv7VC4lEFVSzixIWjqCzYQ6C2uLgRVayAAH+8evVKqkzRiwmVZgGjli1boV49PVy7VrXGEahiEScu\nHEVlwYSAqJpQxWJCpalTJBKhY8dOiI2tWpcM1L3diQowISCqJsq7mFBZbk8sbZ3m5p3w8OEDpKZW\nnYGFqljEiQtHUVkwISCqJmQtfKToxYRKW+d/MxZWncsGTk7OMDIykipTRbu3bm3ChaOoSEwIiKqJ\ngoWPPD1XYPTosfD0XKHwxYQK6mzWrDn09PSLrbNg1sSqlBDo6emjc+dPJI/nzXNVWrtbWw+UlH37\n7WIOKKQi8S4DomqkPIsmladOY+PG0NevX2zdH37YEnp6+lXq1sPXr1/jr79OSR5PmfK5Ur6Y9fT0\n0bPnp4iKOgYAOHXqBEaOHK3weqnyYg8BEakNkUgEc/NOVaqH4NSpE8jMfAUzszYqi8HMrA0iIsIK\nLXBF9C4mBESkVjp16ozHjx/h6dOnqg6lQoSHH4KWlhYGDBisshiGDrVFamoqLl48r7IYSP0xISCi\nUlH05FkFMxbGxlb+XoKcnBxERoajd+++0NPTU1kcQ4YMg0gkQljYHyqLgdQfEwIiUisFMxZWhXEE\n585FIy0tTemLSL3PyMgYXbtaIDz8MGdDJbmYEBCRWmnR4gPo6+tXiRkLw8MPQSQSwcZmmKpDwdCh\ntnj8+FGVGp9BFYsJgZph9k7VXf6MhZ0r/YyFeXl5CA8/DAuLHjA0NFR1OBg6dDgAICzskIojIXXF\nhEBNlWVWOCJ1VdpEt2PHzkhMfIyUlBQFRaR4MTGX8eRJEoYNs5UqV2bS/25dLVu2Qrt2HTiOgORi\nQkBEJVLeJLU0+/+38mHl7d4ODz8MIL+rXl0MHToc8fH/wz//3FF1KKSGmBAQkdopuNOgso4jEAQB\nYWF/oEOHjmjR4gNVhyNRMLiRvQQkCxMCIlI7zZu3QIMGDSrtOIJbt27i33/vFrpcoCoFvTMff9wW\nLVu24jgCkokJARGpnYIZCytrD0F4eP4XrjpdLgDy23XoUFvExl7Dw4cPVB0OqRkmBESkljp16oIn\nT5KQnPxE1aGUWljYIbRubYI2bT5SdSiFFPRaFCQtRAXUKiF4+vQp5s2bBx8fH2zcuFHV4RCRClXW\nlQ/v3fsXcXHXMWzYCLW8W6hLl64wNm7MywZUiFolBHv37sXYsWOxePFiJCUlITExUdUhEZGKdOpU\nOWcs/O/uguEqjkQ2DQ0NDBkyDBcunKvUt3VSxVNKQuDm5obdu3dLHkdFRcHW1hY2NjZwd3dHdnY2\nACAlJQVNmjQBABgbGyM5OVkZ4RGRGmratBkMDAwqXQ9BWNgfaNKkKTp16qLqUOQaNmwEBEFAZGS4\nqkMhNaLQhOD+/fuYOnUqIiMjJWWpqanw9PTEtm3bEBERgZo1a+Knn34CADRr1gxJSUkAgCdPnsDI\nyEiR4RGRGhOJRGjbtj3Ono3GzJnT4OfnC7E4vUT7isXp8PPzLfV+5SEWp2P1am9cunQBTZs2w8uX\nLxReZ1n17Pkp6tWrhx9/3KzUNiL1pqXIg+/duxejRo2S+mI/c+YMOnfujMaNGwMAHBwcMHv2bHz9\n9df47LPP4O3tjaNHj+KDDz6Q9BYQUfUjFqfjxo1YZGS8REhIMEJCghEY6I+IiOPQ09Mvcj8bGysk\nJMQDQIn3K2+s79Z56dIF2NhYKbTO8sjMfAVBEBAf/z/Ex/9PKW1E6k+hPQSurq4YPlz6OlpycjKM\njY0lj42MjCSXBurXr4/169djyZIl+PLLLxUZGhGVkbKm3g0I8EdaWppUWUJCPAIC/Ivdr+CLuTT7\nlUdp6lSH9UoCAvzx8uVLqTJFtxGpP4X2EMgi682gqalZpmNpaGigUaO65Q1JrTRq1E0tPjDeFRNT\n8L+q1dbqTB3P6zFjRpT53Lx8+WKp94mPvyWzPCHhdpHtU5r9KqqdS1LnypXLsXLl8gqpr6S8vZfB\n23tZofKytm15qOM5TdJEghK+fdzd3fHxxx9j0qRJOHjwIKKiorBp0yYAwJ07d+Di4iI1zoCIiIiU\nS+m3Hfbq1QsxMTF4/PgxACA4OBjW1tbKDoOIiIjeofRLBgYGBvD29sasWbOQm5sLMzMzrFq1Stlh\nEBER0TuUcsmAiIiI1JtazVRIREREqqH0SwYVJSoqChs3bkROTg46d+4MLy8v6OjoqDqsKsHT0xPR\n0dG8g6EAAAgYSURBVNGoV68eAMDS0hKzZ8/GokWLcOfOHYhEInh4eKBnz54qjrTycnNzQ9u2bTFp\n0iS8fv1abtvyPC+/d9v62bNnsLa2RqtWrSTPb9q0Cc2bN2dbl8O+ffsQEBAATU1NNGjQAMuXL4eB\ngQHP6womq51r1qxZcee0UAk9e/ZMsLS0FBITEwVBEIRly5YJvr6+Ko6q6hgxYoQQHx8vVbZq1Sph\nxYoVgiAIwv3794U+ffoIL1++VEV4ldq9e/eEKVOmCJ06dRL8/f0FQZDftjzPy0dWWx87dkz45ptv\nCm3Lti67mzdvClZWVsKLFy8EQRCE3377TZg0aRLP6wr2fjsHBgYKkyZNqtBzulJeMpA12+Eff/yh\n4qiqhlevXuHevXvYuHEjRowYAXd3d4jFYkRFRWHMmDEAgBYtWqBDhw6IiopScbSVT8HsnTY2NpKy\n99vW3NwcUVFRPM/LSVZbX716FYmJiXBwcMCYMWNw9OhRAPxMKQ9dXV14e3ujbt38eQbat2+PxMRE\nHD9+nOd1BXq/nTt06ICkpKQKPacr5SWDomY7pPJJSUnBp59+Cg8PDxgZGWH16tVYvHgxUlJS2OYV\nwNXVFQAQHR0tKXv/fDY0NJS0Ldu87GS1tY6ODoYMGYLJkyfj3r17mDhxIlq0aMHPlHJo0aIFWrRo\nAQDIycnBhg0bMGTIEPj7+/O8rkCy2tnGxgaampoVdk5XyoRAqMDZDklay5YtsWXLFsnjWbNmoVev\nXnj79m2hbTU0KmUHk9rJy8srVKahoSGzzXmel8+cOXMk///www8xZMgQREVFQUur8Ech27p00tPT\nMXfuXOjq6sLFxQW//PJLoW14Xpffu+08Z84cqbYr7zldKT/RjY2NpdbxTklJ4cqIFeTmzZsID/9v\nSVRBEKChoYFmzZrh6dOnkvL3ewyo7Jo0aSKzbXmeV7xdu3YhNTVV8lgQBGhra7Oty+nevXtwcHCA\nqakp/Pz8oKWlxfNaAd5t582bN0NTU7NCz+lKmRBwtkPFEQQBK1euxLNnzwAAO3fuxODBg2FtbY29\ne/cCAB4+fIhr167h008/VWWoVYaVlRX27dsHQLpteZ5XvIsXL+LXX38FACQlJeHo0aMYNGgQ27oc\nnj59CicnJzg5OWHRokWScmtra57XFej9dhaJRAAq9pyutBMTnTx5Et9//73UbIe1atVSdVhVQnBw\nMHbt2oW8vDyYmprCx8cHGhoa8PT0xJ07dwAA8+bNg5WVlYojrbzeXd/j1atXctuW53n5vdvWKSkp\nWLJkCRITEyEIAmbPni0ZdMi2LpsNGzbgl19+gYmJieRybq1atbBjxw4sWbKE53UFkdfOmzZtgoeH\nR4Wc05U2ISAiIqKKUykvGRAREVHFYkJARERETAiIiIiICQERERGBCQERERGBCQERERGBCQFRlfb4\n8WO0bdsW9vb2GDlyJGxtbeHk5ISEhIQKq8PDw0Nyr3lJbNq0CYcPH66w+omoYnAeAqIq7PHjx7C3\nt8fFixclZb/88gvOnTuH7du3V0gdVlZW2LJlCz766KMKOR4RqUalXNyIiMpGEASIxWIYGhoCAEJD\nQ/Hnn3/ihx9+AAD4+fnh5cuXcHd3h5WVFTp27Ig7d+5g6dKlOHv2LE6ePAktLS00a9YMa9aswbZt\n25CSkoK5c+di06ZNMDU1ldQVHx8PDw8P5ObmQhAEzJo1CwMGDJDMHNipUyd4enpCJBJBEAQ8fPgQ\n1tbWWLt2LY79X3t3D9JKFoZx/K8EFI0RbSJYiY0WCWJjEQwGY2GTGAs7QW0kGiwigoLxC8FSQRHU\nMoIQxMTGFKJFwI9CrEMMKNjExsJoFDVmC7nD9a7L3WX3suB9ft3MOWdgTvXMnJnz7u+zvr5OPp/H\nYrEQCoWor6//X+ZM5HehQCDyxd3f3+Pz+SgUCtze3vL4+Eg4HDbav+2J/hmbzcbi4iKZTIaxsTES\niQTwvo1qKpViZGSEWCzG0tLShzAA73Uwuru76enp4eLigkgkgtvtNtrtdjuxWAyAk5MTZmZmGB8f\n5+rqitXVVcLhMGazmbOzMwKBAPF4/L+cFhH5gQKByBdnNpuJRqPG8f7+Pv39/RwcHPx0bHNzM/Be\ny762thafz4fT6aS9vR273W70+2zl0eVyEQqFOD09xeFwfCg9/L1UKsXExAQbGxtUV1cTj8fJZDL0\n9vYa1314eODu7g6LxfKP7l1E/j59VCjym+no6KCoqIh0Om28rv/m5eXlQ9/S0lLgvY791tYW09PT\nlJSUMDo6alRY+ytut5u9vT3a2to4Pj7G4/GQy+U+9Lm5ucHv9zM3N2e8YSgUCrS2thKNRonFYsRi\nMSKRiMKAyC+mQCDyxf349H5+fs7z8zN1dXVUVVWRTqd5fX3l6emJo6OjT6+RTCbp6uqisbGRoaEh\nvF4vyWQSAJPJRD6f/9OYYDDI4eEhHo+H2dlZstks2WzWaM/lcgwODjIwMIDT6TTOt7S0kEgkuL6+\nBmBnZ4e+vr5/Ow0i8hNaMhD54nK5HD6fD4C3tzdMJhPLy8tUVFTgcDiw2Wx0dnZSU1NDU1OTMe77\nbwsaGhpwuVx4vV7Ky8uprKxkfn4eeK97HwwGWVhYMJYYAPx+P5OTk4TDYYqLixkeHsZqtRrtm5ub\nXF5esru7y/b2NoVCAavVytraGlNTUwQCAQDKyspYWVn5pXMkIvrtUERERNCSgYiIiKBAICIiIigQ\niIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgI8AdbJdlYWQMAlwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x121c94b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_fret, hist_size, which='all', add_naa=False)\n", "xlim(-0, 250)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_nt = np.arange(35, 120)\n", "nt_th = np.zeros(Th_nt.size)\n", "for i, th in enumerate(Th_nt):\n", " ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th)\n", " nt_th[i] = (ds_nt.nd[0] + ds_nt.na[0]).mean() - th" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x121c6eba8>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7zIkOJaHMViHi8zxJYVfHk0dfM6O6lceDC9OgCGGeHOlW0M9swv5YHN6Vu4ZW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\nRui8IjrPI4SQwWBIJL12m4AfvNCMd791YN55Kjz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"text/plain": [ "<matplotlib.figure.Figure at 0x121c6e940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plot(Th_nt, nt_th)\n", "plt.axvline(nt_th1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "22.002399108774739" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]\n", "nt_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fret fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')\n", "E_fitter = ds_fret.E_fitter" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Value Min Max Stderr Vary Expr\n", "amplitude 0.9798 -inf inf 0.01839 True None\n", "center 0.727 -1 2 0.001995 True None\n", "fwhm 0.2168 -inf inf 0.004698 False 2.3548200*sigma\n", "height 4.246 -inf inf 0.07968 False 0.3989423*amplitude/max(1.e-15, sigma)\n", "sigma 0.09206 0 inf 0.001995 True None\n" ] } ], "source": [ "E_fitter.fit_res[0].params.pretty_print()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 73.98 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>97.9768</td>\n", " <td>72.7043</td>\n", " <td>9.20616</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 97.9768 72.7043 9.20616" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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OkYJciBwTDAa5446foej0/OSBP/ODs+cxNy+Phx76TyZNmsQHrzxBe1sTHx+IZjqqEEKI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\njc2z+ta3vsXixYtHJrEQIywYMfjpb9/EH1FxjbuMv5cH2VU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"text/plain": [ "<matplotlib.figure.Figure at 0x121c4e400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(E_fitter, ax=ax[0])\n", "mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde*100))\n", "display(E_fitter.params*100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Weighted mean of $E$ of each burst:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.71692524])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_m(weights='size')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (no weights):" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.72720534])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gaussian fit (using burst size as weights):" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.72752382])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.73980000000002688,\n", " 0.7270431068081753,\n", " 0.092061568620043,\n", " 0.0029900207471414606,\n", " 0.0019951146629595459)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E_kde_w = E_fitter.kde_max_pos[0]\n", "E_gauss_w = E_fitter.params.loc[0, 'center']\n", "E_gauss_w_sig = E_fitter.params.loc[0, 'sigma']\n", "E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "E_gauss_w_fiterr = E_fitter.fit_res[0].params['center'].stderr\n", "E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err, E_gauss_w_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stoichiometry fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max position of the Kernel Density Estimation (KDE):" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True)\n", "S_fitter = ds_fret.S_fitter" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])\n", "S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all\n", "KDE peak 57.58 \n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>amplitude</th>\n", " <th>center</th>\n", " <th>sigma</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>100.911</td>\n", " <td>55.8745</td>\n", " <td>10.5387</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " amplitude center sigma\n", "0 100.911 55.8745 10.5387" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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yvKkOoU+kD4iz2vWVYNW4YeD6gMr6ME2tUQ5Xm4zLi7fDiT7gRAePhojU7yVi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\n7lrEyJGjePnlv1BR0b76QVKCCV3XOVjX/u8EMoIlhLhwuq7z1Vd7cDp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"text/plain": [ "<matplotlib.figure.Figure at 0x122e596a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(S_fitter, ax=ax[0])\n", "mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1])\n", "print('%s\\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde*100))\n", "display(S_fitter.params*100)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.57580000000002229,\n", " 0.5587446737031131,\n", " 0.10538745942473593,\n", " 0.003422825560022943,\n", " 0.0038226710502243782)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S_kde = S_fitter.kde_max_pos[0]\n", "S_gauss = S_fitter.params.loc[0, 'center']\n", "S_gauss_sig = S_fitter.params.loc[0, 'sigma']\n", "S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0]))\n", "S_gauss_fiterr = S_fitter.fit_res[0].params['center'].stderr\n", "S_kde, S_gauss, S_gauss_sig, S_gauss_err, S_gauss_fiterr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Maximum likelihood fit for a Gaussian population is the mean:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.56475103353830014, 0.10159734277479977)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S = ds_fret.S[0]\n", "S_ml_fit = (S.mean(), S.std())\n", "S_ml_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Computing the weighted mean and weighted standard deviation we get:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.54995920736208226, 0.098099152805091572]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.)\n", "S_mean = np.dot(weights, S)/weights.sum()\n", "S_std_dev = np.sqrt(\n", " np.dot(weights, (S - S_mean)**2)/weights.sum())\n", "S_wmean_fit = [S_mean, S_std_dev]\n", "S_wmean_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save data to file" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample = data_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following string contains the list of variables to be saved. When saving, the order of the variables is preserved." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "variables = ('sample n_bursts_all n_bursts_do n_bursts_fret '\n", " 'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err E_gauss_w_fiterr '\n", " 'S_kde S_gauss S_gauss_sig S_gauss_err S_gauss_fiterr '\n", " 'E_pr_do_kde nt_mean\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just a trick to format the different variables:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample,n_bursts_all,n_bursts_do,n_bursts_fret,E_kde_w,E_gauss_w,E_gauss_w_sig,E_gauss_w_err,E_gauss_w_fiterr,S_kde,S_gauss,S_gauss_sig,S_gauss_err,S_gauss_fiterr,E_pr_do_kde,nt_mean\n", "\n", "12d, 1307, 329, 948, 0.739800, 0.727043, 0.092062, 0.002990, 0.001995, 0.575800, 0.558745, 0.105387, 0.003423, 0.003823, 0.015400, 22.002399\n", "\n" ] } ], "source": [ "variables_csv = variables.replace(' ', ',')\n", "fmt_float = '{%s:.6f}'\n", "fmt_int = '{%s:d}'\n", "fmt_str = '{%s}'\n", "fmt_dict = {**{'sample': fmt_str}, \n", " **{k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}\n", "var_dict = {name: eval(name) for name in variables.split()}\n", "var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\\n'\n", "data_str = var_fmt.format(**var_dict)\n", "\n", "print(variables_csv)\n", "print(data_str)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NOTE: The file name should be the notebook name but with .csv extension\n", "with open('results/usALEX-5samples-E-corrected-all-ph.csv', 'a') as f:\n", " f.seek(0, 2)\n", " if f.tell() == 0:\n", " f.write(variables_csv)\n", " f.write(data_str)" ] } ], "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": 0 }

mit

tritemio/multispot_paper

out_notebooks/usALEX-5samples-PR-raw-dir_ex_aa-fit-out-AexAem-17d.ipynb

1

628102

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Executed:** Mon Mar 27 11:38:07 2017\n", "\n", "**Duration:** 10 seconds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# usALEX-5samples - Template\n", "\n", "> *This notebook is executed through [8-spots paper analysis](8-spots paper analysis.ipynb).*\n", "> *For a direct execution, uncomment the cell below.*" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ph_sel_name = \"AexAem\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_id = \"17d\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ph_sel_name = \"all-ph\"\n", "# data_id = \"7d\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load software and filenames definitions" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Optimized (cython) burst search loaded.\n", " - Optimized (cython) photon counting loaded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------\n", " You are running FRETBursts (version 0.5.9).\n", "\n", " If you use this software please cite the following paper:\n", "\n", " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", "\n", "--------------------------------------------------------------\n" ] } ], "source": [ "from fretbursts import *" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_notebook()\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data folder:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_dir = './data/singlespot/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that the folder exists:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "data_dir = os.path.abspath(data_dir) + '/'\n", "assert os.path.exists(data_dir), \"Path '%s' does not exist.\" % data_dir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List of data files in `data_dir`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from glob import glob" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',\n", " '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',\n", " '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5',\n", " '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',\n", " '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',\n", " '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/2012-12-06_001_4 Channel Dark Count.hdf5']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file_list = sorted(f for f in glob(data_dir + '*.hdf5') if '_BKG' not in f)\n", "file_list" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Selection for POLIMI 2012-12-6 dataset\n", "# file_list.pop(2)\n", "# file_list = file_list[1:-2]\n", "# display(file_list)\n", "# labels = ['22d', '27d', '17d', '12d', '7d']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Selection for P.E. 2012-12-6 dataset\n", "# file_list.pop(1)\n", "# file_list = file_list[:-1]\n", "# display(file_list)\n", "# labels = ['22d', '27d', '17d', '12d', '7d']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Selection for POLIMI 2012-11-26 datatset\n", "labels = ['17d', '27d', '7d', '12d', '22d']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'12d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/007_dsDNA_12d_3nM_green100u_red40u.hdf5',\n", " '17d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/004_dsDNA_17d_green100u_red40u.hdf5',\n", " '22d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/008_dsDNA_22d_500pM_green100u_red40u.hdf5',\n", " '27d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/005_dsDNA_27d_green100u_red40u.hdf5',\n", " '7d': '/Users/anto/Google Drive/notebooks/multispot_paper/data/singlespot/006_dsDNA_7d_green100u_red40u.hdf5'}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files_dict = {lab: fname for lab, fname in zip(labels, file_list)}\n", "files_dict" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('17d', 'AexAem')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ph_sel_map = {'all-ph': Ph_sel('all'), 'AexAem': Ph_sel(Aex='Aem')}\n", "ph_sel = ph_sel_map[ph_sel_name]\n", "\n", "data_id, ph_sel_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initial loading of the data:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = loader.photon_hdf5(filename=files_dict[data_id])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Laser alternation selection\n", "\n", "At this point we have only the timestamps and the detector numbers:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([array([ 1954, 2562, 3108, ..., 48000428736,\n", " 48000442778, 48000447993])],\n", " [array([1, 1, 1, ..., 1, 0, 0], dtype=uint32)])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.ph_times_t, d.det_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should check if everithing is OK with an alternation histogram:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9h23lW4LLm8s2HVH/KEcUUY7aidedY0/l7JRzcLkceDy1b79vF9m+vu4Nem9P\nDu/tyan3eKfG1b1Q1Tup9n5Ue6p289auNyhyFwbmYx3aJvksfnna1UDgIpx18FKcZdUuWVhU+9zM\n+SI0eKRHZ3B5lyvAsnjq68cprt7HP7fND67f7Kl7k86UyNoJ9fO2PEmlt+510hhnLFeffi0AD228\nj5KaEp7bOg+gzuNxuD+dcx8G8Ny2+eQd2MGf18+s877K28+aWeexEDnWWmWQ8po+vtv/Dd+HXgYP\neunb5+ssX336tfRM7MXOyp3sOPADxsGPGfxh/3d8XbY5uF2lr7Le8BTvSmBA2iDyK/PZWLKeHw58\nj31v4LRyjVlDu4h2dIqtvdwXZY8kyhHNnqrdvPjNcyH7uyBtEJd1vjy43Dc5i0tPGcGeqj289O0/\nAfhg73s4bI7gqfVPCj8Obn9eu/P541l3NfwDEJGwbSzZwMq85cdsf90SutMtoXvInMjmirJHkxnb\nJaQ9yZXUrP4pESlMOXNqSHuMIwa7rel/BqyDNyHOO7CDR798ELs98JrkskXQObYLlmVxYYdhIf28\nppeBB/8A/MumhyjzlPH45kcBcPuqcNqc9DlsDleEPeJHxzVDAtSsc+6vsxxhj8QwDBJdiVRG1p4F\nrPCU4zO9lHlKcfvdeM2697ASOVZaZZACGNFpDLGn175rJM4ZuKdTkiuZ0+JPD9k+1hmH3ebg1LjT\nODXutGC7zbDVCVKHXN7lCvql9g9pX1/8XzaWrOfr0k18Xdr4X6mZsZ1x+6sPft+F7glnUFpTyn/3\nrePjgg/5uDA3+G7eSEck7SLbE+uM4/ozDr5rz/SR6EpkffF/gyGtylfJip2vNXpcETkyX5d9RV5l\n7ZnrQ2eRr+o6mcyYwHPPZ/qCl7Z+al3iTuXmntPC7u+yR9Al7tSjqsFuc2BiUVyzD5thYLc5sBsH\n7wllGAw/ZVSj/Q0M/KaP/Mradz7HOuOYdNov693+5p6/xfrR5HOAyAbOLh06k3XI/C1P8V3FN8zd\n9HCjdYkcrVYbpGIdsSRHhL6fo19q/3oDUFNe27GElXnLA++Ua0RmTGcmnnZVcHnngR1s3/8D7Q6e\nst5S/nVw4ufOwy4pdok7lYs7Die/che7q2pfSBwOOz6fnwRnYAJrpD0yOEnea3rpFn8GQzvUvt25\nyF2oICVyjH1U8AFv7loV0t49oQe9kwKfjefxe/imYutPXdoJIcoRxf3n1QaScM6s/fhMUlNiHDFH\ntP2PdYkE9W09AAAgAElEQVQ9lUh7ZEi7sxln4ESOxEn/GxVhjyDelRBcttvtRNojcdnru6lL4Br/\n4df5z2t3fp31pmXW+1fUoVNPHWNO4be9/xBsDvdUv4gce2M7jyctqnYGc4do3a/tWPmpz+ZdcsqI\nn/R4cvI66YPU+e1/zvnH8N1xNuPgzeREpNXp176/3sQhIkdE/+KLiIiIhElBSkRERCRMJ/2lvdZq\nX3URObtrP3U9JaIdZ6ec04IViYiInHwUpFqp7Qd+4Jmvnwgun51yjoKUiIjIT0xBqpWJdcZyXfcb\ngstuv5uXv/tXC1YkIiJy8lKQamWiHNGM6DQ6uFxWU6ogJSIi0kI02VxEREQkTApSIiIiImFqdpC6\n/fbbefHFFwHYt28fZ511FuPHjw/+n5eXB0BOTg5jxoxh+PDh3HHHHXg8gQ+KdLvdTJ8+nZEjRzJq\n1CjWrl0b3HdDfUREREROZE0GqR07dnDdddfx5ptvBts2bNjARRddxNKlS4P/d+rUieLiYmbOnMn8\n+fNZvXo1kZGRzJs3D4C5c+eSkpLCqlWrePrpp7n99ts5cOBAvX2eeeaZ4zdiERERkWOkySC1YMEC\nLr/8coYPHx5sW79+Pbt372bSpElMmDCBt94K3M8oNzeXrKwsMjIyAJg0aRLLly8HYM2aNUyYMAGA\nzMxM+vbtS05OTqN9RERERE5kTb5r77bbbgPgo48+Cra5XC5GjBjBtddey/bt27n66qvJzMykoKCA\n9PTaD/xMS0tj7969ACHrUlNTKSgoAAjpc6hdRERE5EQW1u0Ppk2bFvy+S5cujBgxgpycHByO0N3Z\n7XYATNMMWWez2fD7/Q32aUxUtJOEhKgjKfuEZBhGg+Pw+h2kpMTgsrsa7l9dg81mEOEKbHssWV4P\n3rJIDIezyW0bG0dr0lbGAW1nLI2Nw/J5caZEYzgbfo40R1SUE5stcJyGnkcev5NConDam34+NORk\neExaiwqb0dIlSBsRVpB6/vnnGTNmDCkpKQBYloXT6SQtLY3NmzcHtyssLCQtLQ2AjIwMioqKSExM\nDK7LysrC7/c32Kcx7iov5eXucMo/oSQkRDU4Dq/ppZhKXHZvg/3LaqowTYsaj4/i4spjWpvl9WKV\nV2M4fE1u29g4WpO2Mg5oO2NpbByWz4dRXIXhbPg50hxutxfTtCgvd1NM/c8jj99DeYUbp63p50ND\nTobHpLUwTaulS5A2IqzbH6xbt46XXnoJgD179vDWW29xySWXMHDgQD7//HPy8/MBWLRoEcOGDQMg\nOzubhQsXApCXl8eGDRsYMGBAvX2ys7OPemAiIiIix1tYZ6Tuuece7r77bsaMGYNlWcyYMYMuXboA\nMHv2bKZMmYLP56N79+7MmTMHgFtvvZWZM2cyenTgrtyzZs0iKSmp0T4iIiIiJ7JmB6nDw01qamrw\ntgY/NnToUIYOHRrSHhMTw6OPPnpEfUREREROZLqzuYiIiEiYFKREREREwqQgJSIiIhImBSkRERGR\nMClIiYiIiIRJQUpEREQkTApSIiIiImFSkBIREREJk4KUiIiISJjC+ogYERGRk8X/vPk/lNWUHbf9\nJ0Yk8uylzx63/cvxpTNSIiIijSirKaO0uvS47Lu0uvS4hjQ5/nRGSkREpAlJkUksHrv4mO93wvIJ\nzd42Pz+fiy++mDPOOAMAn89HVFQUN910ExdddNExr+1I/Oc//+Ghhx7C4/Fgs9n4/e9/zwUXXAAE\nPqs3NzcXm83GoEGDuO222wBYtWoVf/7zn0lPTwcgPj6eF154AYAnn3yS119/HdM0ufLKK/n1r39d\n73Hnz5/PSy+9REpKCgCnn346Dz/8MB6PhzvvvJMtW7YAMH78eK677joAPv/8c2bPnk11dTWZmZk8\n9NBDxMfHhz12BSkREZFWIjY2lqVLlwaXt23bxnXXXUdSUhJZWVktUpPf72fatGnMmzeP3r17s3Xr\nViZPnswHH3xAbm4uX3zxBStWrMCyLK688kreeecdhg0bxvr165k6dSpXXXVVnf2tWbOG999/n2XL\nluH3+5k8eTI9e/akX79+IcfesGEDs2fPZvDgwXXa//3vf+PxeFixYgVVVVWMGjWK/v3707VrV6ZP\nn85TTz1Fr169ePbZZ7nvvvt48MEHwx6/Lu2JiIi0Ut27d2fy5MnBMzm7d+/mN7/5DWPHjuWyyy7j\ntddeA2DdunX86le/4ne/+x1jx45l4sSJ7Ny5s8k+48ePZ9KkSVxxxRUUFhYyfvz4kBqqq6v54x//\nSO/evQHo1q0blmVRXl6OaZpUV1dTU1NDdXU1Ho+HyMhIIBCCcnJyGD9+PNdffz3btm0DICcnh9Gj\nR+NyuYiKimLs2LEsX7683vFv2LCBBQsWcNlll3HLLbewZ88eACzLwu124/P5cLvdmKaJy+Vi48aN\nJCcn06tXLwCuvPJK3njjDfx+f9iPgYKUiIhIK9ajRw++/fZbAH7/+9+TnZ3N8uXLee6555g7dy4b\nN24EYOPGjfzv//4vy5cv5+yzz+bZZ59tss+3337Lk08+ycKFC0lNTa1zNuyQmJgYxo4dG1z+29/+\nRteuXUlLS+OSSy4hMzOTQYMGMXToUDp16sTAgQMBaNeuHb/5zW9YunQpV155JTfccAPV1dUUFBQE\nL/cBpKWlsXfv3pDjVlRU0KtXL37729+ybNkyzjvvPG6++WYAfvWrX1FRUcHAgQMZNmwYl1xyCV27\ndqWwsJC0tLTgPmJjY3E4HJSUlIT981eQEhERaeWioqJwu91s3LiRK664AggElUsuuYSPPvoIgFNO\nOYWuXbsCcOaZZ1JaWtpkn44dO9KuXbtm1WBZFg8++CBvvPEGjz/+OAAvv/wylZWV5Obmkpubi2VZ\nPPHEEwA8/fTTwct1F198MQkJCWzatAnLskL2bbfbQ9ri4+P5+9//Trdu3QC49tprycvLY+/evTz+\n+OOceuqpfPzxx7z//vts3LiRpUuXYppmvXXXt//mUpASERFpxTZt2kT37t3rDQmmaeLz+QCCl9QA\nDMPAsqwm+0RFRTWrBrfbzZQpU9i8eXPw7BXAe++9x2WXXUZkZCQRERFMnDiRtWvXUlZWxnPPPRdy\nXKfTSXp6OkVFRcH2wsJC0tPTWbNmDePGjWP8+PHcfffd5OXlsXDhwuB2lmVhWRYOh4P33nuPCRMm\nYLPZiI+PZ+zYsaxdu5b09HQKCwuDfQ4cOABAYmJis8ZZH002F5GTztdlX7G3andweVdlXgtWI61B\naXXpEb3D7kj2mxSZ1Oztf3y2ZtOmTSxYsIBnn32WmJgYevfuzYIFC7jqqqsoKiri7bff5tFHH603\nMAFh9amvpilTppCSksKTTz5Z5+xOr169ePvttxk9ejQQmP/Ut29foqOjee655+jZsyf9+/fnww8/\npKamht69e1NcXMy8efOYMGECpmmyYsUKbr75ZoYMGVLn3YmFhYU88sgjnHvuuXTt2pWFCxdyxhln\n0K5dO3r16sXq1as599xz8Xg8vP/++wwZMoSzzjqL4uJivvzyS/r06cPChQsZPHgwNlv455UUpETk\npPPu7rd5d09OS5chrURiRPhnK5qSFJl0RPuvqqoKTvg2DIPo6GgefPBBunfvDsAjjzzCPffcw8sv\nv4xpmvzv//4v5513HuvWrWtwnw8//DCzZs1qsk9hYSE33nhjyDypjz/+mE8//ZSuXbvyi1/8Iljb\nI488wk033cT999/PyJEjcblc9OnTh2nTpuFyuXjyySe57777qKmpISYmhr/97W/Y7XYuuugitm7d\nyi9+8Qu8Xi9jx45lyJAhIXWnpqbywAMPMH36dEzTJDU1lUcffRSAO+64g3vvvZcRI0bgcDgYPHgw\nv/zlLzEMg8cff5xZs2ZRU1ND+/btefjhh5v986+PYdV3MfIE99UVg/jumitJPuP8li7lqCUkRFFe\n7q53ndf00i3+DFx2V4P9y2pK+U3uNZydcg53nn3PMa3N8nqxdnyL4Wg6bzc2jtakrYwD2s5YGhuH\n5fNhdD4dw+k8on0+9dVjvLsnh7Gdx9Muon2wvV/qBSRHJNfbx+P38E3FVpy2IzvW4U6Gx6S1KJt3\nL6MeXtHSZUgboDNSInLSGpA6mNPiu7Z0GSLSimmyuYiIiEiYFKREREREwqQgJSIiIhImBSkRERGR\nMGmyuYiISCNK7pyGub/iuO3fFhdP8n2PHbf9y/GlM1IiIiKNMPdXYJaXHZ99l5cd15Amx5/OSImI\niDTBlpBIu8f/ecz3u2/qr8Pq53a7GTx4MBdffDH333//Ma6qYV9++SVLly5l5syZjW731Vdfce+9\n9+J2u4mPj+eOO+6gZ8+eACxcuJDnn38ev9/PsGHD+MMf/gBAcXExt912G3v37iUiIoL777+fHj16\nhOzb7/dz//33B28Y2rdvX/70pz/hcrlYtWoVf/7zn4MfehwfH88LL7wAwJw5c8jNzcVmszFo0CBu\nu+02AH744QdmzJhBRUUFiYmJPPLII2RkZDT7Z6IzUiIiIq3MypUrueCCC3jnnXcoLS39yY77zTff\n1PmsuobccsstXHvttSxbtozZs2czffp0vF4vW7ZsYf78+SxYsIA33niD77//nldffRWAe+65h0GD\nBrFy5UpmzpzJ1KlT6/0A45deeomCggKWL1/OihUrqK6u5tlnnwVg/fr1TJ06laVLl7J06dJgiHrn\nnXf44osvWLFiBa+99hqfffYZ77zzDgC/+93vuP7661m5ciVXX311MNg1l4KUiIhIK/PKK68watQo\n+vfvzyuvvBJsf/LJJxk+fDhjxozhj3/8I16vF5/Px+zZs7n00ksZNWoUDz74IAAVFRX84Q9/4Be/\n+AWXXXYZTz/9NAD5+fnBM0WXXXYZl19+OZs3b6awsJC//e1vfPrpp/z5z38GYNy4cXU+YBigtLSU\noqIihg8fDkDnzp2Ji4tjw4YNrFmzhuzsbOLi4rDZbEyYMIHly5fj8/n44IMPmDAh8HmGZ599NrGx\nsfz3v/8NGXuvXr347W9/i2EYAPTs2ZPduwOfnblhwwZycnIYP348119/Pdu2bQMCH4hcXV1NTU0N\n1dXVeDweIiMj2bt3b3C8ACNGjOCbb75h7969zX4sFKRERERakc2bN7Njxw6GDh3K2LFjeeWVV/D7\n/eTk5LB69WpeffVVVqxYgWEYrFy5kpdffpnvv/+elStXsmzZMrZt28ann37KnDlz6NevH0uWLGHx\n4sV8/vnnrFq1CoBdu3YxbNgwli1bxo033sj//d//kZqaytSpU+nXrx933303AK+99hrt27evU19S\nUhIZGRm8/vrrwXq/++47ioqKKCgoCF52A0hLS2Pv3r2UlZVht9uJjY0NrktNTaWgoCBk/Oeddx6n\nn346AHv27OHFF19kxIgRALRr147f/OY3LF26lCuvvJIbbriBmpoaLrnkEjIzMxk0aBBDhw6lU6dO\nDBw4kIKCAlJTU+vsPzU1VUFKRESkrVqwYAHDhw/H5XIxePBgPB4Pq1evZu3atQwfPpzo6GgAHnjg\nAcaNG8fHH3/M2LFjcTgcOBwOnnvuOfr168d7773HCy+8wLhx45g4cSI7d+5k69atAKSkpHDppZcC\ncOmll1JWVkZeXl6za3zqqadYunQpl112GUuWLKFfv344HI56L9XZ7XZM06x3nc3WcEzZsmULV199\nNZMnT2bAgAEAPP300/Tr1w+Aiy++mISEBL788kteeeUVKisryc3NJTc3F8uyeOKJJzBNs959N3bc\nH9NkcxERkVaisrKS119/nZiYGLKzs7EsC5/Px7/+9S+ysrLqbFtSUoLP58Pxow+eLygowOVyYVkW\nTz31FJ06dQKgrKyMiIgISkpKsNvtdfqYphnS1hjTNHnuueeCy2PGjCEzM5P09PQ6c6wKCwtJT08n\nJSUFv99PZWUlMTExddbdddddbNq0CcMwmDp1KhdeeCHvvvsuM2bMYMaMGYwZMwYIXFJ89dVXuf76\n6+vU4XQ6effdd7nsssuIjIwEYOLEifz9739nwoQJIXO+Dh23uRSkREREmmCWl4X9Drum9mtLSGz2\n9suWLaNjx46sWLEi2JaXl8fw4cMZPHgwb731Ftdddx2RkZE8+OCDdOvWjZ///OesWrWK0aNHA/CH\nP/yBq666igEDBvDCCy9w1113UVlZyeTJk7nhhhs455xzKCwsZO3atfTv359Vq1aRkZFBhw4dsNvt\n+Hy+JuucOXMmN954IxdeeCHvvPMOlmUF34E3depUbrzxRuLi4liyZAnZ2dnY7XaGDh3KokWLuPba\na9m4cSNlZWX06dMnJCCuXbuW22+/nWeeeabOupiYGP7xj3/Qs2dP+vfvz4cffkhNTQ29e/emV69e\nvP3228GfQU5ODn379iU9PZ2OHTvy9ttvc/HFF/Pmm2/SqVOnkMt9jVGQEhERaYQtLv747Tsh8Yj2\nv2jRIq655po6bZ06dWLkyJFs376dUaNGccUVVwCQlZXFr38dCH+7du1i/PjxAAwbNowRI0bQr18/\nZs+ezZgxY/D5fIwcOZIxY8aQn59PVFQUixcv5oEHHiA2Npa5c+cCgUngTz75JLfddhsPPfQQ48aN\n4+9//3vIPKl7772Xu+66i7/85S8kJSXx1FNPAdCjRw9uuOEGrr76anw+HxdccAFXXnklAHfffTd3\n3nknS5YsweFw8Je//CXkbBrAY489FjyGZVkYhsF5553HnXfeyRNPPMF9991HTU0NMTExPPHEE9jt\ndm666Sbuv/9+Ro4cicvlok+fPkybNg2ARx99lLvvvpvHHnuM2NhYHn744WY/HgCGVd9FyRPcV1cM\n4rtrriT5jPNbupSjlpAQRXm5u951XtNLt/gzcNldDfYvqynlN7nXcHbKOdx59j3HtDbL68Xa8S1G\nPb/IP9bYOFqTtjIOaDtjaWwcls+H0fl0DKfziPb51FeP8e6eHB782V85Lb5rs/p4/B6+qdiK03Zk\nxzrcyfCYtBZl8+5l1MMrmt7wJJSfn8+4ceP47LPPWrqUVkGTzUVERKSOQ7cWkKYpSImIiEhQx44d\ng3cNl6YpSImIiIiESUFKREREJEwKUiIiIiJhUpASERERCZOClIiIiEiYFKREREREwqQ7m7cRG0s2\ncM37VwaXz0zsxe1n3d2CFYmIiLR9ClKtnM2wkxqVFlw2LT/7qvdR7a9uwapERERODgpSrVy8K54n\nL/h7cLnSW8m1H1zVghWJiIicPJo9R+r222/nxRdfBMDtdjN9+nRGjhzJqFGjWLt2bXC7nJwcxowZ\nw/Dhw7njjjvweDxh9xERERE5kTUZpHbs2MF1113Hm2++GWx77LHHSElJYdWqVTz99NPcfvvtHDhw\ngOLiYmbOnMn8+fNZvXo1kZGRzJs3D4C5c+c2u88zzzxz/EYsIiIicow0GaQWLFjA5ZdfzvDhw4Nt\nOTk5TJgwAYDMzEz69u1LTk4Oubm5ZGVlkZGRAcCkSZNYvnw5AGvWrDniPiIiIiInsibnSN12220A\nfPTRR8G2goIC0tPTg8upqakUFBQA1GlPS0tj7969R9znULuIiIjIiSysyeamaYa02Ww2/H5/SLvd\nbg+7T2Oiop0kJEQ1p9wTmmEYDY7D63eQkhKDy+5q9v4iPBY2m0GEK9D3aFheD96ySAyHs8ltGxtH\na9JWxgFtZyyNjcPyeXGmRGM4m/8cAYiMcmKzGSQmRpGS1LznicfvpJAonPamnw8NORkek9aiwma0\ndAnSRoQVpDp06EBRURGJiYkAFBYWkpWVhd/vZ/PmzcHtCgsLSUsLvDU/IyPjiPs0xl3lpbzcHU75\nJ5SEhKgGx+E1vRRTicvubfb+Kr1VmKZFjcdHcXHlUdVmeb1Y5dUYDl+T2zY2jtakrYwD2s5YGhuH\n5fNhFFdhOJv/HAGodnsxTYuyMjfFZvOeJx6/h/IKN05b08+HhpwMj0lrYZpWS5cgbURYdza/6KKL\nWLhwIQB5eXls2LCBAQMGMHDgQD7//HPy8/MBWLRoEcOGDQMgOzu72X2ys7OPemAiIiIix1tYZ6Ru\nvfVWZs6cyejRowGYNWsWSUlJAMyePZspU6bg8/no3r07c+bMCbuPiIiIyIms2UHq8HATExPDo48+\nWu92Q4cOZejQoSHt4fQREREROZHpQ4tFREREwqQgJSIiIhImBSkRERGRMClIiYiIiIRJQUpEREQk\nTApSIiIiImFSkBIREREJk4KUiIiISJgUpERERETCpCAlIiIiEiYFKREREZEwKUiJiIiIhElBSkRE\nRCRMClIiIiIiYVKQEhEREQmTgpSIiIhImBSkRERERMKkICUiIiISJgUpERERkTApSImIiIiESUFK\nREREJEwKUiIiIiJhUpASERERCZOClIiIiEiYFKREREREwqQgJSIiIhImBSkRERGRMClIiYiIiIRJ\nQUpEREQkTApSIiIiImFSkBIREREJk4KUiIiISJgUpERERETCpCAlIiIiEiYFKREREZEwKUiJiIiI\nhElBSkRERCRMClIiIiIiYVKQEhEREQmTgpSIiIhImBSkRERERMKkICUiIiISJkdLFyAicrztrdrD\nfm9FcLnisO9FRI6GgpSItHkLfvg3uXvfb+kyRKQNUpASkZNG/9QBxDpjg8txrvgWrEZE2gIFKRE5\naUw89So6xWa2dBki0oYcVZCaOXMmH330EfHxgb/qLrjgAm655RZmzJjB1q1bMQyDu+66i/79+wOQ\nk5PD3Llz8Xq9ZGVlMWvWLFwuF263u8E+IiIiIieqowpSX3zxBfPnz6dr167BtgceeICUlBRWrVrF\nzp07mTx5MitXrqSmpoaZM2eyePFiMjIymDVrFvPmzePWW29l7ty59faJjY1t5OgiIiIiLSvs2x9U\nVlayfft25s6dy9ixY7njjjsoLy8nJyeHCRMmAJCZmUnfvn3JyckhNzeXrKwsMjIyAJg0aRLLly8H\nYM2aNXX69OnTh5ycnKMdm4iIiMhxFXaQKiwsZMCAAdx1110sX76chIQE7rzzTgoLC0lPTw9ul5qa\nSkFBAQUFBXXa09LS2Lt3L0C96woKCsItTUREROQnEfalvVNPPZWnnnoquDxlyhQGDhyI3+8P2dZm\ns9XbbrfbATBNs94+jYmKdpKQEHWkZZ9wDMNocBxev4OUlBhcdlez9xfhsbDZDCJcgb5Hw/J68JZF\nYjicTW7b2Dhak7YyDmg7Y2lsHJbPizMlGsPZ+HMkMtKBzWaQmBRFSnx4zwuP30khUTjtTT8fGnIy\nPCatRYXNaOkSpI0IO0h99dVXbN++nZEjRwJgWRY2m42MjAyKiopITEwEAmeusrKy8Pv9bN68Odi/\nsLCQtLQ0gAb7NMZd5aW83B1u+SeMhISoBsfhNb0UU4nL7m32/iq9VZimRY3HR3Fx5VHVZnm9WOXV\nGA5fk9s2No7WpK2MA9rOWBobh+XzYRRXYTgbf45UV/swTYuyUjfF3vCeFx6/h/IKN05b08+HhpwM\nj0lrYZpWS5cgbUTYl/Ysy+L+++9n3759APzzn//k0ksvJTs7mwULFgCQl5fHhg0bGDBgAAMHDuTz\nzz8nPz8fgEWLFjFs2DAAsrOzWbhwYUgfERERkRNZ2GekevXqxbRp07jmmmswTZNu3bpx3333YbPZ\nmDlzJqNHjwZg1qxZJCUlATB79mymTJmCz+eje/fuzJkzB4Bbb721wT4iIiIiJ6qjuv3BxIkTmThx\nYkj7o48+Wu/2Q4cOZejQoSHtMTExDfYREREROVGFfWlPRERE5GSnICUiIiISJgUpERERkTApSImI\niIiESUFKREREJEwKUiIiIiJhUpASERERCZOClIiIiEiYFKREREREwqQgJSIiIhImBSkRERGRMClI\niYiIiIRJQUpEREQkTApSIiIiImFytHQBcnx8X/EtMz77fXC5U2xnppx5awtWJCIi0vYoSLVRbr+b\nbyq21TYYRssVIyIi0kYpSLUx0Y5oXr7w1eCyx6zhmvevasGKRERE2i4FqTbGMAwcRu3DalpmC1Yj\nIiLStmmyuYiIiEiYFKREREREwqQgJSIiIhImBSkRERGRMClIiYiIiIRJQUpEREQkTApSIiIiImFS\nkBIREREJk4KUiIiISJgUpERERETCpCAlIiIiEiYFKREREZEwKUiJiIiIhElBSkRERCRMClIiIiIi\nYVKQEhEREQmTgpSIiIhImBSkRERERMLkaOkCRESOta/LvqK4uii4XOQuaMFqRKQtU5A6Sbh9VXxV\nuim4HGmP4rT4ri1YkcjxsypvOZ8UftzSZYjISUBB6iSxqzKPP30+I7h8atxpPHT+3BasSOT4G9t5\nPPHO+OBygiuxBasRkbZIQaqNsxk2RnQaHVy2LIvVu1ZS5illyQ8Lgu1xrngu6TgCgO37f8DCxPL5\nsGp2Yfgc2A0bmREdAFhZ+h5us7rOcYpLShkWPaBOW9fIzGMyhgP+SnyWH7thx4YNm2HDjoHNsGHD\nRr6nkDJ/RUi/rhGdiLZHhbRblnVM6jqkzFdBobc4pL2dM5lkR0JIe7VZQ5kvtN4YezRx9hhMy8TE\nDFlvYGA37E3W4zG9bK/JJ84ewX53TbA91h5FB1dak/2PhQr/AXyWH7AwLQsr8BvF/9/encfHdO+P\nH3+dyU6QWBJrUJTcayu/S0u4lShBEonrWlrJLfqgvVWqC4LYEkmLB6pB6S1SrSKx3CTWCqoutVO+\nlq+oRMSSIMQkEZnJ+f0ROTcjE0uqMuP7fvaP5nzmLJ/3+Zwz8/b5zJyPqhaiFpWiAvUe1OdxbXLt\nfib5aoG2fFh/EiedI7pCFa6cR7EpOi9/re2Nk+1/2/yNur7UrlTnWYcnhBAaSaRecLY6W4a9PEJb\nLk6ksvKzWP3b9ybrXs+7BkB86obilcFoAEUB4PWqHQHYnX2g1HEURSHpxi8mZY0c6ml/p+SnM7BG\nb8RPCJ4AABdBSURBVEBhzc1NNHSoiw4dCgqKovDf/4oShnP3LuLp1ARnm8oc0v9arthtFBtedmyE\njaJDQYfNg8TraM7/mF2/XeU/41XwCluv7eW28a6W1BgfJDbF/y9UC8k03KKSzolOVV4h6c5+VMwn\nAi85NND+7unShW7VOnIy93+ZfeVfZtdv4fQSZ/N+KzOm/tV7AnBPvc+Nglv8P+dWZBbcIj5rJ00c\ni451/f5NMg23UBTFJEFp6tiQ8XVHkFOYS1jafJx1lR+cm6L/dIqiJapQlAyNcBsAwK7sgzQtkRjH\nZG6gjl0tbVlF5VrBDXpU8wJg+529ZcZQUttKngAczz1j9vU3a/pR+Z4jX6fFmd+BqkKWrXaNujm6\nU92hBjmGnCc6vhBC/F6K+qz/ef4cnB7QhQv/GET15h0quiq/W7VqTty5k2f2tYLCAppVbY69jf0z\nO56qqqW+OzL31Odm133dvRtk32a3/pDZ1z+uMwyA9be207xqI+7nGwDYmf2L2fV/r9p2Nalu66Il\nNSoqRtVIIYWk5l+hXeU/U9fejcSsXU+8z0YO9UjJT9eWH04+itk8SDB06MhX75vdl4LCG9U6s+fu\nIe4V5ptdx9W2KlkleqM6OrfhRO7ZMtdv6tiQ5HupTxyPSX0UBZ+qr3HLcKfM5NFGsQFVxWimB+xJ\n2Ct2ANwv0VtUUrvKf9aSNAWlKHVWdOy/e8zs+sXJd1lt0tG5DY46BwD+J/c8vap64eTWkKXnvzK7\nvy9fW/LMeqTuG+9zPvscdjq7cu/jUfe7NXkR4ri9ZAZ9ZidUdDXEC0B6pP6PURSF19xNh+DC7GaU\n6lGx1znQonIz1NRk+tTwplA1/aDVKToaPhjq61iljckb60j3QSbrnsz9X3IK81BKlBWi0sG5FQoK\n6oOjqw+GgIqHvRRFMdlPDVuXJxraeqtmgNZzVJRoqSa9SUWJmBFX22o46OxJvpfKxXuXAXCqZE9e\n7n2MGGldqTm17WppPTTFsgzZZBbcKnVcN7vquNhWZZjb30zO5yH9SZZmrEGHDqNaSFUbZwACXL0J\nqO6DUTVyXzWU2p+tYoOdYovemGvSS5VtvMvZvN9o8OD8AxhVI9Vtq9Gu8p+0MtdqzhToVbKNehZc\n/bbU/hs71OetWgHasqqqD86Vke9vJGD30Lk+l3eR7i6dtGUnnSMdnFsDkHwvtdQ1YqPYlDm8O9yt\nf6n1AVxsi77PdCznDHpjUa9SpUr25OYWJa+vVWmLrfLfty3VYECp05R8CkjRXyy1v5LDfEII8UeQ\nHqkK9rx7pJ6GWlCAmpqMYvv4fPtF+BcqvDhxwIsTy6PiUA0GlIZNUezK30v0pKRH6r9ehDikR0o8\nK/JATiGEEEKIcpJESgghhBCinCSREkIIIYQoJ4tJpJKSkvD398fX15fQ0FDu3zf/yyghhBBCCEth\nEYnUzZs3mTJlCkuXLmXr1q04Ojry1Vfmf84shBBCCGEpLCKR2rt3L6+88gp16hQ972XgwIHEx8dX\ncK2EEEIIIR7NIp4jdf36dWrXrq0tu7u7c/162bO1/+bpjuJclYJC8w8BtCYFRtsy4zAWln620HNn\nNJTxzG5TqqEA1WAB9f2dXpQ44MWJ5ZFxGJ9vfL/3nnzU/W5NXoQ4cho9mymshLCIRMrsk6Rtyn7w\not/0MqaLsFa1H79Khanb6fHrPOD4B1bjeXpR4oAXJxZLiaNe7a6/fyeWfL8/DWuP4+Unf28T4lEs\nYmivdu3aZGRkaMsZGRm4uz+fyVWFEEIIIcrLIhIpLy8vjh49Snp60fxasbGx+Pj4VHCthBBCCCEe\nzWKmiNm9ezdz587FYDDw8ssvExUVhZOTzJMlhBBCCMtlMYmUEEIIIYS1sYihPSGEEEIIaySJlBBC\nCCFEOVldImVNU8lMmTIFHx8fgoKCCAoKYvbs2eTl5TF27Fh69+5Nnz592L9/v7a+JcY2YcIEvv32\nW4By1f1R2zxvJWO5ceMGbdq00domKCiItLQ0i45l7dq1+Pv7ExgYyLBhw7h8+bLVtom5WKyxTb7+\n+mv69OmDn5+fVi9rbBNzcVhjexT77rvvCAwMfGydLD0OYSVUK3Ljxg21U6dO6pUrV1RVVdVp06ap\nX3zxRQXXqmwBAQFqcnKySVlUVJQaHh6uqqqqpqamql27dlXv3r1rcbGlpKSoQ4cOVdu2bavGxMQ8\ndd0XLFigqqqqRkZGmt2momP58ccf1Q8//LDUupYay+nTp1Vvb281OztbVVVVXbVqlRoSEmKVbfJw\nLN9//70aEhJidW1y+PBh1c/PT83Pz1dVVVVHjx6t/utf/7K6NjEXxzfffGN17VHs5MmTapcuXdTA\nwMBH1snS4xDWw6p6pKxpKpmcnBxSUlKYP38+AQEBhIaGcufOHZKSkujfvz8AHh4etG7dmqSkJIuL\nbc2aNfTr1w9fX1+trDx137lzp8k2rVq1IikpqcJjOXbsGFeuXGHgwIH079+f7du3A4++xioylsqV\nKxMREUGVKlUAaNmyJVeuXClVJ2tok4djadWqFVevXrW6Nmnfvj0bN27E3t4evV7PrVu3cHFxsbr7\nxFwc1apVs7r2ALh79y7Tpk3j448/1sqs8R4R1sUinmz+pJ52KpmKlJGRQefOnZk8eTLu7u589tln\nTJo0iYyMDJMY3NzctBgsKbZx48YB8J///Ecre/j8P6ru165dM7tNRcRlLhZ7e3t69erF22+/TUpK\nCkOGDMHDw8NsfS0hFg8PDzw8iqa0KCgoYN68efTq1YuYmBiraxNzsfj6+mJjY2NVbQJFMzDExcUx\na9Ys3N3d6d69O9OnT7e6NjEXx4oVK6yuPSZNmsQ///lPnJ2dtTJrfd8S1sOqEin1KaeSqUiNGzdm\n0aJF2vJ7772Hl5cXRqOx1Lo6nc5suaXFVlhYWKrscXUva5uKNmbMGO3vRo0a0atXL5KSkrC1LX1L\nWFIst2/fZuzYsVSuXJnRo0ezbNkys3WyhjYpGcuYMWNMrndrapP+/fvTv39/Zs+ezfjx482+T1lD\nmxTHMWvWLMaPH89XX32lvWYN7fHtt9/i5uaGt7c3Bw4c0MpfpPctYZms6sqwpqlkTp8+zebNm7Vl\nVVXR6XTUr1+fzMxMrby4h8oaYqtbt+5T171OnTpmt6loK1as4ObNm9qyqqrY2dlZdCwpKSkMHDiQ\nZs2aER0dja2trdW2SclYvvzyS2xsbKyuTS5evMivv/6qLQcGBnLmzJky62QtcQQFBXHmzBmra4+E\nhAQOHDhAYGAgYWFhXLx4kUGDBlntPSKsh1UlUtY0lYyqqkRGRnLjxg0Ali9fTs+ePfHx8WHNmjUA\npKWlcfz4cTp37mwVsXl7e7N27Vrg8XXv3r07AD4+Pma3qWgHDx7ku+++A+Dq1ats376dHj16WGws\nmZmZBAcHExwczMSJE7XysupkqXGYi0VRFMD62iQ9PZ3x48eTm5sLQGJiIh07dnyqe9yS4zh06BAr\nV64ErKM9YmNjSUhIYOPGjURERNC4cWNWr179Qr1vCctkdU82t6apZGJjY1mxYgWFhYU0a9aMmTNn\notPpmDJlCufOnQPgo48+wtvbG7DM2EJDQ/H09CQkJIScnJynrvujtqnIWDIyMggLC+PKlSuoqsqo\nUaO0L6NbYizz5s1j2bJlNG3aVBs6cnJy4ptvviEsLMyq2qSsWBYsWMDkyZOtpk0Ali1bxrp167C1\ntaV58+aEhYWV6x63xDjy8vKs6h4p6eDBg0RFRbFhwwarf98Sls/qEikhhBBCCEthVUN7QgghhBCW\nRBIpIYQQQohykkRKCCGEEKKcJJESQgghhCgnSaSEEEIIIcpJEilRbqGhoQQFBREYGEiLFi0ICAgg\nMDCQDz74gIyMDIYMGfKH12HYsGHo9fo//DglTZgwgRYtWnDq1CmT8p9++okWLVqwceNGrWz37t0M\nGjSIXr164e/vz9ixY0lLS9NeDw4OxsfHh6CgIPr27Yufnx+ff/45BoMBKPoZd/Es9iUFBwdrc395\ne3tz9uxZs3XV6/XMmDGDHj160LdvX/r168f333//2Bh79uzJP/7xD5OyknU5efIkM2bMeOx+noXY\n2FjtmT6rV682+zT3ijJ58mQOHjz4VNuUbDshhPWzqilihGWJiorS/vb09GTVqlUmc1wVP1zxj7Rv\n374//BgPUxSFunXrkpiYSMuWLbXy+Ph4atasqS1v3ryZ6Oho5s6dS4sWLQDYunUrgwcPJjY2Vpss\nddKkSdozavLz8/n4448JDw9n+vTp2vHKo6CggJCQEF5//XU2b96Mra0tWVlZfPLJJ1y+fJnx48eb\n3W7//v24u7uTmprK+fPnadasmUnsAOfPnzd5KvQf6ejRo3h6egIwaNCg53LMJxUREVHRVRBCVDBJ\npMQz8fDjyNLT0wkMDOTQoUNER0eTlpZGSkoKmZmZdOrUiebNm7NlyxYyMjIIDw/ntddeIzs7m/Dw\ncH777TcMBgO+vr689957GI1Gpk6dyqlTp9DpdLRs2ZIZM2YwefJkAN58801WrlzJkSNHWLp0KQUF\nBWRlZfHWW28xfPhwNmzYwI8//oher+fq1au89NJL+Pn5sXbtWtLS0vjoo48ICAggOjqa5ORkrl+/\nzq1bt2jbti3h4eHY29uXird3795s2rSJCRMmAJCTk8O5c+do06aNts78+fOZPn26lkQB+Pr6cvz4\ncZYsWcK0adNKnTsHBwfCwsLw8fHh008//V1tsmXLFhwdHRk9erRW5urqypw5c+jWrRtDhw7Fzc2t\n1HarV6+mW7duZGZmEhMTUypZyMzM5Msvv0Sv1xMeHk5YWBhr1qzRnuZdu3Ztpk6diru7O8HBwVSr\nVo2UlBTeeecdFixYQFBQEPv27SMjI4ORI0cyYMAA8vLymDp1KpcuXSIrKwtXV1fmz5/P6dOn2blz\nJ/v27aNKlSqkp6eTnZ3NxIkTOXv2LBEREWRnZ2NnZ8eYMWPo2rUrGzZsICkpCaPRSGpqKm5ubsyb\nNw9XV1eTOEJDQwG4cOECt2/fxtvbW2vPHTt2sGTJEoxGI1WrViUsLIwmTZoQGhpKVlYW6enp+Pn5\nsXfvXt5++218fHzYvHkzS5YsAaBmzZqEhYXRqFEjMjIymDBhApmZmdSrV487d+78rnYVQlgWGdoT\nf5iSPSknTpwgJiaGxMRENm3ahF6vZ9WqVbz77rvah09UVBQdO3Zk3bp1xMXFcfToUTZv3syxY8dI\nTk5m48aNxMXFAUWJWvEH/KpVq6hWrRoxMTHMmTOHdevWERMTw7x58ygoKADg2LFjzJs3j+3bt3Ph\nwgUOHjzIypUriYqKYsGCBVo9f/31VxYvXszWrVvR6/VlDiPVqlWLxo0b88svvwCwbds2evbsqcWc\nlZXFpUuXaNeuXaltX331VY4fP17meXN3d6dKlSpcvHjxic+1OSdOnDB7fFdXV5o0aWIyv1qxGzdu\nsHv3bvr06UNAQACbNm0q9cFfq1YtRo8eTceOHQkLC+PAgQNs3bqVNWvWsH79enr06MGkSZNM4klM\nTNSGBY1GIz/88APR0dFERUVRWFjInj17qFGjBqtXr2bbtm14eHiwdu1avL298fb2Zvjw4QQFBQFF\n15XRaOT9999n5MiRxMfHaxMGX716FYDDhw8zc+ZMNm/eTKVKlYiNjTV7jpKTk1m5ciXx8fEcOXKE\nhIQEUlJSWLhwIcuXL2f9+vWMGjWKUaNGmWyXkJDAyJEjteULFy4QGRnJ0qVL+fe//01AQAAffPAB\nADNmzKBDhw4kJCTw6aefkpKS8rimE0JYEemREs9Fp06dcHR0BIo+yLt06QKAh4eH9kG9e/duTp06\npQ0J5uXlce7cOby8vMjOzubNN9/Ey8uLkJAQ6tWrV+oYixcvZteuXWzcuJHk5GSMRiP5+fkAtG7d\nmho1agBFE5J6eXlpx8/Oztb20bt3b1xcXADo168fy5cv59133y11LEVR8Pf3Jz4+nldffZX4+Him\nT5/OrFmztNcVRaGgoAAHBweTbe/fv//Y86UoCpUqVdLq/zBVVbWZ6h+1j+JE8mFl1SEuLo727dvj\n5uaGm5sbHh4erFmzhhEjRpR5nJ9++okLFy4wYMAAVFVFVVXu3bunvf5wMtetWzegaDj43r175Obm\n0rNnT+rXr8/KlStJTU3lxIkTVK9evcxjFicjxdfRSy+9RPv27Tl06BAALVu21Lb39PTk1q1bZvfT\nr18/rX38/f3Zu3cv2dnZXL16leDgYK23MCcnR7tOzCWnBw4coEuXLtqkt3379mXmzJlcu3aNffv2\nab2nTZo04S9/+UuZcQkhrI8kUuKZeNz3eOzs7EyWbW1LX3qFhYUsWrSIBg0aAHD79m0cHBxwcnIi\nISGBQ4cOsX//foYOHcrUqVO1CUahKOkKCgrC19eXdu3a0a9fP7Zt2/ZUxwdMkhNVVdHpyu60feON\nN5g7dy5paWnk5+fTsGFD7TUXFxcaNmzI4cOHef311022O3jwoMkQ4MPS09PJy8ujQYMGqKpqdijo\n5s2bWsJXlrZt27JixYpS5RkZGVy+fJlWrVqZlKuqSmxsLHl5efj4+KCqKrm5ufzwww+88847ZR5H\nVVX+9re/MWbMGAAMBgO3b9/WXi9OoIs9nFhCUa9iXFwcwcHB9O3bFwcHB+0L9+YYjcZSw8lGoxGD\nwYCiKCbHVBSl1LrFSl4HJdu7a9euWlIMcP36dapWrWo2Hii6ds2VGQwGdDqdyeuPS4CFENZFhvbE\nM/Espmzs3LkzMTExQFEPQHBwMDt27GDXrl2MGDGCjh07MnbsWLy8vDh//jxQ9EFoMBhISUkhLy+P\n0aNH89e//pU9e/YARR+uTyMpKYnc3FwMBgPr169/5ESlzs7OtG/fntDQUAICAkq9/sknnxAZGcmZ\nM2e0ssTERLZs2WIyLFSSXq8nKiqKIUOGYG9vT5MmTYCiXp9iP//8M3q9nj/96U+PjMXX1xdbW1vm\nzJmjJSU3b95k3LhxDBgwQOs9KbZnzx5yc3PZvXs3SUlJ7Ny5kx07dpCTk8P27dtN1rWxsdH22alT\nJxITE7Ven8WLFzNu3LhH1q1Y8XWzd+9e+vfvT1BQEA0aNGDPnj1a29nY2JRqx8aNG2t1hqKhtSNH\njtChQ4cnOm6xTZs2UVBQQF5eHgkJCfj4+NChQwf27Nmj/bpy/fr1pX7B+LBXX32Vn3/+mevXrwOw\nceNGatSoQf369enSpYs2JJ2Wlsbhw4efqo5CCMsmPVLimXiaX5aVte7kyZOJiIjA398fg8FA7969\n8ff3x2g0smvXLnr37o2TkxN169bVHq3Qo0cPBg8ezNKlS+ncuTO+vr5UqVKFpk2b4uHhQWpq6lPV\n1dXVleHDh3P79m28vLwIDg5+ZCwBAQGMGTOGhQsXltp39+7dqVy5MpGRkWRlZWkzzK9atYq6detq\n60VFRREdHa199+eNN97g/fff1/a3ePFiIiMjmTt3LkajkZo1a/L111+bfAl+8ODBWs+LoijMnj0b\nHx8fli9fzhdffIGfnx92dnbodDr+/ve/m300xdq1axk0aJDJfp2dnbUv83/44Ydaedu2bVm4cCHj\nxo1j1qxZBAcHa8mGm5sbn332mdlzXdbysGHDmDJlCnFxceh0Olq3bs2lS5eAouG7zz//3KT3yM7O\njoULFxIREcHs2bPR6XRERkZSv359bXjvSTg4ODB48GD0ej19+/bVejmnTJmifS+qUqVKREdHm92+\nuP5NmzZl4sSJjBgxgsLCQlxcXFi0aBEAYWFhTJw4EX9/f+rUqUPz5s2fuH5CCMunqM+iK0GIF0B0\ndDR3797Vfs0lXmyhoaF4enoSEhJS0VURQlgxGdoTQgghhCgn6ZESQgghhCgn6ZESQgghhCgnSaSE\nEEIIIcpJEikhhBBCiHKSREoIIYQQopwkkRJCCCGEKKf/D0A8Q7y8TiAzAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ca3ce48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_alternation_hist(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the plot looks good we can apply the parameters with:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Total photons (after ALEX selection): 2,453,382\n", "# D photons in D+A excitation periods: 756,184\n", "# A photons in D+A excitation periods: 1,697,198\n", "# D+A photons in D excitation period: 1,462,400\n", "# D+A photons in A excitation period: 990,982\n", "\n" ] } ], "source": [ "loader.alex_apply_period(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measurements infos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the measurement data is in the `d` variable. We can print it:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "singlespot_004_dsDNA_17d_green100u_red40u G1.000" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or check the **measurements duration**:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "600.00559991249997" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.time_max" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the background using automatic threshold:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Calculating BG rates ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] } ], "source": [ "d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x121395c88>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KCiIzMxMnJycaNmxImzZtijIkoYDmzFmo6hCEYrR/f5CqQxCEQpWYmICLS9Yg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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1043e3630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(d, timetrace_bg)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([2286.0248813678868],\n", " [613.94010117705795],\n", " [914.22216629179422],\n", " [742.22554889663013])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Burst search and selection" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lmfit version: 0.9.5\n" ] } ], "source": [ "from mpl_toolkits.axes_grid1 import AxesGrid\n", "import lmfit\n", "print('lmfit version:', lmfit.__version__)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert d.dir_ex == 0\n", "assert d.leakage == 0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " - Performing burst search (verbose=False) ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Recomputing background limits for AexAem ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Recomputing background limits for all ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Fixing burst data to refer to ph_times_m ... " ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Calculating burst periods ..." ] }, { "name": "stdout", "output_type": "stream", "text": [ "[DONE]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Counting D and A ph and calculating FRET ... \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying background correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying leakage correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - Applying direct excitation correction.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " [DONE Counting D/A]\n" ] } ], "source": [ "d.burst_search(m=10, F=6, ph_sel=ph_sel)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AexAem [18675]\n" ] } ], "source": [ "print(d.ph_sel, d.num_bursts)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([4456])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_sa = d.select_bursts(select_bursts.naa, th1=30)\n", "ds_sa.num_bursts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preliminary selection and plots" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([608])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask = (d.naa[0] - np.abs(d.na[0] + d.nd[0])) > 30\n", "ds_saw = d.select_bursts_mask_apply([mask])\n", "\n", "ds_sas0 = ds_sa.select_bursts(select_bursts.S, S2=0.10)\n", "ds_sas = ds_sa.select_bursts(select_bursts.S, S2=0.15)\n", "ds_sas2 = ds_sa.select_bursts(select_bursts.S, S2=0.20)\n", "ds_sas3 = ds_sa.select_bursts(select_bursts.S, S2=0.25)\n", "\n", "ds_st = d.select_bursts(select_bursts.size, add_naa=True, th1=30)\n", "ds_sas.num_bursts" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DH55TJqnL5Yr90i4sLGTTpk0N/vBB01+l9dX9Cs7OzmbatGk88MADeL1ezpw5w69//WuW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\n77+LmJ9FOJzwgxdgZBRKRQCEywXbtiN8PoTPjzxxvPW3aLQt79btaYl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ZOGYnqFYNTssi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I0gZCQkNDQkBJopQMzqQ9IkMNlJyczHU34KWZOIRWMoMTOKMDDy5uu48dYx5OSlfStCcvDtDC/2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\nnS0sgiDpAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1044ea5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dx = ds_sas0\n", "size = dx.na[0] + dx.nd[0]\n", "s_hist, s_bins = np.histogram(size, bins=np.r_[-15 : 25 : 1], density=True)\n", "s_ax = s_bins[:-1] + 0.5*(s_bins[1] - s_bins[0])\n", "plot(s_ax, s_hist, '-o', alpha=0.5)\n", "\n", "dx = ds_sas\n", "size = dx.na[0] + dx.nd[0]\n", "s_hist, s_bins = np.histogram(size, bins=np.r_[-15 : 25 : 1], density=True)\n", "s_ax = s_bins[:-1] + 0.5*(s_bins[1] - s_bins[0])\n", "plot(s_ax, s_hist, '-o', alpha=0.5)\n", "\n", "dx = ds_sas2\n", "size = dx.na[0] + dx.nd[0]\n", "s_hist, s_bins = np.histogram(size, bins=np.r_[-15 : 25 : 1], density=True)\n", "s_ax = s_bins[:-1] + 0.5*(s_bins[1] - s_bins[0])\n", "plot(s_ax, s_hist, '-o', alpha=0.5)\n", "\n", "dx = ds_sas3\n", "size = dx.na[0] + dx.nd[0]\n", "s_hist, s_bins = np.histogram(size, bins=np.r_[-15 : 25 : 1], density=True)\n", "s_ax = s_bins[:-1] + 0.5*(s_bins[1] - s_bins[0])\n", "plot(s_ax, s_hist, '-o', alpha=0.5)\n", "\n", "plt.title('(nd + na) for A-only population using different S cutoff');" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ee+zBcn//9tuvGTiwH6NH38W9997J3Xffxn/+Mwmz2VwrxzeZjIwf/9hF78do\nNDJq1HDWr/+53N+WLFnIfffdWfa4oCCf/v2vZvTou8pumZkZABw6dIDHHnuQ++67k7FjHyIrK/Oi\nYxO+o9bU/FYT8+fPZ/jw4QDk5eVx+eWXc/PNN5fd0tLSqtxeWl5C1AKr1YrZbCIqKpo1a76jS5du\nFdbr2rU7L7/8WtnjCROe4vPPP+fmm++46BiKi4vZv3/fRe9nxoz/YDKVHyu1f/8+5s//hKioqLKy\n3bv/pHfva5gyZbpHXavVytNPP8FLL02jS5duLF26mFmzXuPVV2dddHzCN2qz5bV7927mzJlDTEwM\nADt27CA1NZVZs2r++ZDkJcRFeuaZJzh+/Dh2u5X77ruTU6dOER0dTdu27ejTp1+l21mtViwWCwkJ\nCQC8/PIUWrduy8iRtwPw+OMPM2rUnbRq1YbHH3+Y5OTG5OfnMXv2R0ybNoVTp7JQqVT06tWb++9/\nmFdemYrZbOLhh+9j9uyPmT37HTZu3IBWqyUpKZkXXphCQEAAzzzzBA88MIa2bduVi+mzzz4jOTmZ\n4mKDR7nRaGTmzOk88shYFi78rKx89+5dnDp1iocfvg+328Xdd99L376pbNr0O02bNitL4kOG3Ei3\nbt0v9qUWPlRbvQ1LSkp46aWXeOqpp5g7dy4A27dvJzMzk1GjRuFyuXjooYcYMGBAlfuR5CXERXrt\ntTf58svP0ev1DB8+gjFj7mf69JlERkaWq7tt22ZGj74Lt1vh1KksEhOTuPbaa7HZqj7GqVNZTJ36\nKm3btuO7774hICCAjz6aj81mZcaMqVgsFv71r0ncd99dzJ79MTk52axevYqlS78BYPbsdzh69DDt\n21/Ga6+9WeEx9u/fy+rVq5k58x2efPJRj7/NmPEf/vGPBwgJCfEo1+v19O9/PbfddidpaScZO/ZB\nkpMbk55+koiISKZNe4kjRw6TkJDA2LHjz+NVFf6mtnobTpw4kUcffZTQ0NCyMp1Ox6BBg7j33ns5\nfvw4d999NykpKbRrV/4HVlk8tRKNEJe4w4cP0apVG9xuN4WFBRUmLig9bfjRR58xd+7nfPPNWq68\nsgfjx1f/pa7X68taSh07dmbnzu3885+P89VXi3n44bEEBQV51I+NjSMxMYnRo+9izpx36dOnL+3b\nX1bp/k0mI6++Oo0ZM2agOWdyukWLviA2No7eva9BURSPvz3wwCPcdlvpNbAmTVJITR3Ahg2/4HQ6\n2bRpI6OLzDu1AAAgAElEQVRG3cVHH82nR4+eTJr0r2qfp/BfGnXNb5X59NNPiY+PJzU11eOz9OST\nT3LvvfcC0KxZMwYOHMi6deuqjEdaXkJcpGeeeYLt27dy4MB+XC4nRUWFjB59F/fd92CVpw3VajVD\nh97Egw/+vazs7H9op9NRdl+vDyi736hRY774Yilbt25iy5ZNPPTQP3jlldeJijozk7parea99z5k\n9+4/2bLlD6ZMeYHbbruDW28dVWEsf/zxOyUlRh5//HGcThfp6em89dbrmEwmvv/+W+x2G/fddycW\ni4Xc3BzGjLmf9977kIULP2PAgEFlx1YUN1qtlujoGFq0aEWrVq0BGDRoKLNmvYrT6USrla+d+qg2\nWl4rV67EarUyfPhwzGYzOTk53HHHHdxwww0MGzas7BoYUO3nRD5FQlykl16axpNPPsoHH3zK118v\np7CwgHvuua/Cuue2XH799Sc6dOgAQGRkJIcOHQAgKyuTw4cPV7jdV18tYv/+vTz//Iv07Nmbw4cP\nkZZ2ktjYONxuFwCHDh3k5Zdf4v33P6Zjx04oisLhw4cqfQ6pqdeRmnodMTEh5Oebyq639e7dlxtu\nGFxWb/v2rfzvf6/z3nsfArBjxzZKSkp44IFHyM4+xc8//8hbb71PcHAw7777FkePHqFFi5b88stP\ntGrVWhJXPVYb17wWLVpUdn/Tpk1Mnz6dBQsW8Oijj2IwGHjiiSfIysri+++/55NPPqlyX/JJEuIi\n/fnnLjp2vPz0/Z0MHnxjpXW3b9/K6NF3oSgKDoeTpKQkXnnlFQBuuWUUU6ZM5J57bjvd2aFr2XYq\n1ZlfvQMHDmHr1k3cffdtBAQE0KpVa1JTr0etVtO6dVv+8Y/bmTPnE66++hruu+9OgoNDCAsL47nn\nJgFU2WGjouNV5emnJ/DKK1P5+99HoSgK48b9kyZNUgD497+n8/LLU3A47AQHh/DSS9NqtE/hn+py\nnNdLL73EpEmTGDZsGIqi8Pzzz9O8efMqt1Ep5/4UrEdyc0t8HQJA2a9VfyHxVE3iqZw/xRIXF3ZB\n29144418+OFn1Vf0En96TaE0HvUFNKM2dR1W47o9tq087/2fL2l5CSGEqJa/zbAhyUsIIUS1ajpz\nhrdI8hJCCFEtjbS8hBBC1DeynpcQQoh6R9bzEkIIUe9Iy0sIIUS9o1L716gqSV5CCCGqpZKWlxBC\niPpGpZKWlxBCiHrG31peXg/nX//6F59++mmFf3vnnXcYNGgQN9xwAx9//LGXIxNCCFEZtVqp8c0r\n8XjlKMCJEycYPXo03333XYV/X7duHT///DPLly9n2bJlrFq1ij/++MNb4QkhhKiCSl3zmzd47bTh\nwoULueWWW8qWPD/X2rVrGTp0KHq9HiidXHPFihVcddVV3gpRCCFEJfytt6HXWl7PPvssQ4cOrfTv\n2dnZJCYmlj1OSEjg1KlT3ghNCCFENS7Zlld1KlqZ5dzlyM8VERGEVuv7q4gqlYqYmBBfh1FG4qma\nxFM5f4rlYvjTc/C317Sma7WdSwYpVyIxMZHc3Nyyxzk5OR4tsYoYDJa6DqtG/HG9HomnchJP5fwp\nlgtdzwvwm+cA/vWaQmk8F5LA/K2rvN/k0v79+7NixQpsNhsWi4WVK1fSv39/X4clhBACOW3oYd26\ndfz444/85z//ITU1lQMHDnDrrbficDi48cYb6du3ry/DE0IIcZq/ddjwevKaPn162f3U1FRSU1PL\nHo8ZM4YxY8Z4OyQhhBDVqO0W1fz581m8eDHLli3DYrHw/PPPc+DAAVQqFS+88AI9e/ascnu/ueYl\nhBDCf9Xm4OPdu3czZ84cYmJiAHjjjTeIiYnhm2++4eTJk9xzzz2sWrWK0NDQyuOptWiEEEI0WLV1\nzaukpISXXnqJp556qqxs3bp1jBgxAoCUlBQ6derE2rVrq9yPtLyEEEJUq7Z6G06cOJFHH33Uo1VV\n0Tjf7OzsKvcjLS8hhBDVqo2W16effkp8fDypqakeY3vdbne5uupqBpZJy0sIIUS1aqO34cqVK7Fa\nrQwfPhyz2UxOTg633347ycnJ5ObmEhkZCZSO8+3SpUuV+5KWlxBCiGqp1TW/VWbRokWsXLmSZcuW\nMXXqVJo3b84XX3xBamoqX375JQBpaWns2LGDq6++usp4pOUlhBCiWnU5zuvxxx9n8uTJZfPfTpky\nhaioqCq3keQlhBCiWrU9zqtHjx4sXboUgJCQEGbOnHle20vyEkIIUS1/m9tQkpcQQohqyazyQggh\n6p1Lfm5DIYQQ9Y+3ZouvKUleQgghqqe5sEUs64okLyGEENVSqSV5CSGEqG80/nXe0L+iEUIIIWpA\nWl5CCCGqJ6cNhRBC1Dcq6bAhhBCi3pGWlxBCiHpHWl5CCCHqG+kqL4QQov7xs67ykryEEA3K2cvL\ni9ojLS8hhKhDkrzqiFzzEkKIuiPJq45I8hJCiLojyatu1OZpww8++IBly5ahUqno1KkTU6ZMobi4\nmP79+9OiRYuyem+99RZNmjSpcB+SvIQQDYokrzpSSy2vrVu3smLFCpYuXYper+eJJ55g/vz5pKSk\nkJqayqxZs2q0H//qPiKEEMI/qdU1v1WhW7duLFu2DL1ej9FopKCggIiICLZv305mZiajRo1ixIgR\nfP/991WHU5vPrSpr165l2LBhDBw4kAkTJmC328vVmT59OkOGDGHYsGG8+uqr3gpNCNGASMurbqg0\nqhrfqqPRaFi8eDGpqakUFRVx3XXXodfrGTRoEAsXLuS///0v//73v9m/f3+l+/BK8srPz2fy5MnM\nmTOH1atXExgYyPvvv+9RZ82aNezcuZOVK1eybNkyNm/ezJo1a7wRnhCiAZHkVUfUqprfamDEiBFs\n2rSJPn368Nxzz/HEE09w7733AtCsWTMGDhzIunXrKg+nNp5TddavX0+XLl1ISkoCYNSoUaxYscKj\njtvtxmq1YrPZsFqt2O12AgICvBGeEKIBcbvdvg6hYdKoan6rwrFjx9i1a1fZ45tvvpl9+/Yxd+5c\n8vPzPepqtZV3y/BK8srOziYxMbHscUJCAtnZ2R51BgwYQEpKCn369KFfv340adKEPn36eCM8IUQD\nUtElCXHxVGpVjW9VycjI4LnnnsNsNgPw9ddfc9VVV7F582bmzZsHQFZWFt9//z3XX399pfvxSm/D\niprxGo3G4/GCBQswmUysX78elUrFP//5T95++23Gjh1b6X4jIoLQan3f50SlUhETE+LrMMpIPFWT\neCrnT7FcKLvd7lfPwd9eU5XqAnsN1tL0UL1792bkyJGMHDkSrVZL27ZtmTRpEhaLhUmTJjFs2DAU\nReH555+nefPmle7HK8krMTGRPXv2lD3OyckhISHBo85PP/3ETTfdRGBgIAAjR47kgw8+qDJ5GQyW\nugn4PMXEhJCfb/J1GGUknqpJPJXzp1ji4sIuaDubzUZenvHCv6RrmT+9plAazwW9NrU4SHn06NGM\nHj3aoywsLIzZs2fXeB9eabb07t2bbdu2kZGRAcCiRYvo37+/R53LLruMH374AbfbjdvtZu3atXTu\n3Nkb4QkhGhC3243T6fR1GA1ObZ02rC1eSV4xMTFMnTqVMWPGMHjwYPLy8nj88cdZt24dkyZNAuCR\nRx4hJiaGwYMHM3z4cNxuN0888YQ3whNCNDA2m83XITQ8tdRho7Z4bYaNfv360a9fP4+y1NRUUlNT\nAdDr9bz00kveCkcI0YCZTEZCQ0N9HUbD4mezyvu+t4MQQtQyg8Hg6xAanNocpFwbJHldYtxuhSNH\nC2p1n0ePFeB2K+XKXC4ZbyN8w2Ao8nUIDU8tTQ9VW2Ri3ktIWrqBLxbt5viJIlq3iub2kZ1ITLjw\nUyu5eSYWLtrNvgN5NGkczh23dSIkRM+XS3azZ28ujZLDueO2jn7VTVg0fG63m6IiSV61Tk4bCl+w\n2128+voGjp8o/ac+dLiAV2auv6h9/veN39h3IA+AtPRiXpu1gdff+o09e3MByMgsZuabv1FQ6B9D\nGsSlocCq5sSJ474Oo+GRlpfwBbeilDu153Re3Gk91znbKwq4nEq5MrfLjZ8MuRGXgLDoBE6dyvJ1\nGA2Pl5JSTUnyEkI0KKERMWRlZfo6jIZHThuK2uJ0uln9/SH+2JRebV29TsPVPZuUtYDUahX9+jS7\nqONf06cp6rM+0C2aR5GUFIrmrLIruyUTHn5hEyzv2p3NilX7sdnODDjdvTeHZSv3Y7XKIFRRsfDY\nJI4dOyoT9NY2OW0oasPRYwXMX7CL7JzSaWd+35zOXbd3JjYmuML6arWKO0d15m89GvPjz8cZcF1L\nmjSOuKgYbhzSjiu7NmL5yn0UFlk5eqwQgNiYYGJighlwXUvatYlFr9cCNR80WlJi47OFf/Ln7tLJ\nmzdtzmD4je3YvvMUO3aeAmDzlgxuH9mRTh0TqtqVuAQFR8SSZTJy/PhRWrRo5etwGg4/O23oX9GI\nGvvt97SyxAVw8FA+e/flVLtdi+bR3H9v14tOXH9JTgqjfbs4MjJLysry8s3ExgTRrk3sBe3zyLHC\nssQFUFhkZc2PR8sSF0CRwcq6n49deOCiwYpOaEy+ycXGjb/5OpSGpZbX87rocLxyFCGE8JLIuMYk\ntezE779v8HUoDYufnTaU5FVPVTQr9MXMon1uT8TzqlfDw9b0GBWp6LmpKyi7mGOIhqNRmy7s27eX\n3NxcX4fScEjyErVh+LB2ZR0wdDo1w4a0pdffmpz3fpwuN6t+Psb46T/z7S/HK50Vw+1WWPfTMSZM\nXsOq1QdxOF24FYVVu7L4eH8OTTrGo9OqUamgZYsodu46xbKV+7HbXSiKwq9bMnjqlV9Y+O1BbHZX\npfH8viOLT1cdoHmbWAIDSi/JNm8aSWGBhZYtoggKLC27onMid9/puerAn7uzefE/P/LpZzsoMcrE\nrJey5FaXExwcwsqVy3wdSsPhZ6cNVUpFK0XWE7m5JdVX8gJfrtdz7HghYaEBxMae6ahR03jcboUX\n/7eRzLOunTVKCGXK438r19J59fUNnDh5ZtaC+LgQrG1j2HfqzHuQGKilmcFGWlrxmViig4hrEc2O\nfWd+AUdHBDL5sasIC9F7HGPOwj/5Y9eZ61rhwVpSwgM5fOTMdFYhwTpuHd6Bq3o09tj2yyV7+PnX\n4x71nny8J8lJ5deE8sf1lfwlHn+K5ULX83ri3R+xO5wkFm1izZrv+fTTBYSGXti+aoM/vaZQGo/6\nAlpH7t+eqXFdda/Xznv/50taXvVc82ZRHonrfLgVxSNxAWRkG6no50xmVrHH45xcEycKzB5lp6xO\nCgqsHmX5BRZOZnr+yCgwWDFbHOWOkXbKs16x2UlxiWcLymR2EBdXfrqpzMzicvWKiqzl6omGz1RS\niLnEQN++/bDb7fzf/82ucDV3cZ7ktKEQQtQdl8OGRqPhix0mwtr2Y86nC9i1a4evw6r3VCpVjW/e\nIMmrATqZVlRuPsHMrBLy8j1bSmqViviYII+y2PAAcnKNHmV5eWaiIj3rhUcHERek8yiLDNERlOjZ\nKgqJDCAiKtCjLDRUT3axZ4uqyOog4JxYQoK0BJ8zwDkoSEthkedzs1gcBAZ6DlkMDNQSEeG5rdXm\nZH8tz6jfkB06nI/VVv8Gg4dHJxIRm0RQSDiX9RpCdFJTXn/9VaxWaYlflFpseX3wwQcMGTKEoUOH\nMmHCBOx2OxaLhfHjxzN48GCGDBnCxo0bq9yH5qV6vAKk2Wz3dQgABAfrsVRwGszbrDYny7/ez4dz\nt/HrhpNo1CqSk8JY9e1B5i3Yya8bToBKRfNmkajVpb+Q+l7ZCJUKTmaW0CYxlPyMYtZvOInT5Sal\ncQRrfjzKx59ux2x20LJFNCazncSOCRxzuTGV2OmQFIbB4aJlkwgy7S7SdGqat47BlWciuV0cJ1Vw\nymSnVfNIrCYHKc0jOeV08dPuUxQZ7bRpFM6aI/n8e+1hjjpctGgZhbvISrPkMIwmB1mFVpq3jMJp\ncdKkcThms4NNmzPIyTXRskU0u3Zn894HW0hLL6Z500hcLjeXdYhnzIPdiT/r9OK2PTm8OW87azem\ncfRkEc0bhxMcqKvi1fQef/n8QGksmZnFfPrZTpZ/fYBNWzKIjgoiKdH714xCQi5sZpbVm4+j0pS+\ntyq1mqikZpzcsY709DR6977Gay2Dv/jT+wul8VzQa3DqR1CpanRTJadWuputW7cye/ZsvvrqK+65\n5x5WrlxJdnY269atIzAwkNmzZ9OnTx8ee+wxRo4ciV6vr3A/krxqgb98OL9avo+ffjleOkGuS2H/\nwTyOnyxi89bM0gly3QoHD+UTGKilRfNoADQaNe1aRIPFwaZN6bjdCm5F4fCRAk6cLGLjH6VligIF\nhRaSO8azM8eE63RZvsFKy6aR7Cgw43QrKECWzUnT5tHsSzfgdJWW5ZbYado0gr1ZJThdpdcfjp4y\nsifPxPIj+dhPl2VbnTRLCCXtYAGO0xP/FhTbSE4K5eTRQhyO0rLMrBIOHc7n519PYD/de7HIYCWl\ncQSPPdzDoyV2IrOY/360FauttF5GtpGDxwvpe06nD1/xl88PlMbyysxfOXgoHwCr1cm2HVl07BBP\nZERgNVvXrtpIXgBqjZY7B3Tn888/xWq10LXrlV5NYP70/sJFJK/s80heSZUnr+TkZEaNGoVOp8No\nNLJ48WKuuOIKFi1axNNPP01sbCwRERFs3boVlUpFu3btKtyPnDZsQCqay62icU8VlVV0OdtV0bYV\nVKzoWrhSwR4r2rbiY1QQXw2fR0XbVlSvouOKUhW+Xq76/Xp169adJ598hkWLFjJnzrvSgeNC1OJp\nQ41Gw+LFi0lNTaWoqIjrrruO7OxsEhMTy+okJCSQnZ1d6T4keQkhGhRTSSGm4jM3c4mB4mIDvXpd\nzZgxj7Fo0RdMmzYFm03GAp6XWh7nNWLECDZt2sQ111zDc889V+EPiqq69Evy8nMHD+Xz2Re7arSg\n45XdGnl0my9dJVkhJvpMR4gmjcPpeJnnZLZpp0o4nmP06IIed3o/8fFnymJig3EEaEg+a/LfuPAA\ndHYXTc86pRQXokPjUmgSe2bb6DA9+lhoelaHjqhQPcFRCq1iz8QXFaQlLEmhabMz11jCQnSowwNo\n0jSyrCwkRIc6Oogmzc6UBQfr0Os1bN/puZZTQmww3Tudec7BQVqu65niUcdqdbJk2V42/pF2pszm\nZOmKfWzYePKS+qXet08zgoLOnHbtckXSRa247W0uhw2n3Vp202g0zPklk5mr9rPDmkLT3nfyxfJV\njBs3RmbgOB+11PI6duwYu3btKns8fPhw9u3bR1JSksf7kZOT49ESO5cMUq4FdTEI0W53seDLP9m0\nJQMAvV7DjUPacm3f5lVu53C4+Hn9Cf78M5vDp3vWaTQqWjSP4vJOifTt06xsGRNFUfhy9SHW/nYS\nl1tBo4K2yeFoXG6OHCvA7eb0jBnR2IN1bLbacSqgRuGyuFB0boWD6QacbkAFLVpFo9KqOXnozPWq\ndo3CCYrSckhbgOP0R62FOxyNQ83JgGJslNZrHBCNRtFg0GfjoPTaVNOSCJRTGg4WmjGfvtbVISqI\nEAX2mmyYTpe1jwoiwuEiI6MYs7m0d1zHDvHcfUdnwsLOXDfZfSif7Xtz+MetHXE7z8zysXtvDp8v\n3IXBUPpLvHXLaHr+rQkrVx2g8PRYsRbNo/jHXVdc8Ji6qvjTINa/YikusbHq24N07pTAZe3jfRLL\nxQxSVuuCqqxz6sR+8v5YgN3u4J//fIZevXpf0LFqwp/eX7jwQcrKvqk1rqtq/0Klf1u/fj3Tpk1j\nyZIlBAcHM2vWLLKysoiJicHhcPDCCy+QlpbGnXfeyYoVK4iKiqpwP9Ly8lOFhZayxAWlyWzdT9XP\noq7TaRgysE1Z4oLS6xUnThq4tm9zj/W3XG6F79efKLv+41Jgb0Yxh4+WJi4ovZ51+EgBW+wO/lok\n2Y2KP3NNHDtlpGwxZQWOHiog57ihLHEB7M8o5lSIsSxxARxVF1MQYSlLXADptgIILyxLXAAnwgzk\nuF1liQtgb6GFLK2qLHEB7Cu0YLI4yxIXlCaktHTPgcsdW8dwz03tiTqn48Hvf6SVJS6AQ0cKWL/h\nRFniAjh6rJCDh/O4VISHBXDHbZ18lrjqWmRcI159dRZdunTlxRcnMmPGfygpKa5+w0tZLZ027N27\nNyNHjmTkyJHcdNNNZGVlMWnSJMaOHUthYSFDhw5lzJgxTJkypdLEBbKelxDiEhUWFsbEiS/Sp09f\n3nzzde6//+88/PCjpKZe7/Xu9PVCLc6cMXr0aEaPHl2ufObMmTXehyQvP+WuoLeeWlOzf6jSUe6e\nvQDVahWKonj8U1bUg0+lVqFSq1DO7l2mKt2es8pUKB6tuDNl5ePRacqXVfTjrKIPo6aCbVUVHENV\nQT1FXf75OZ0VTDxc0Yz1mvIHqb8n2C8tppJC1NqqByTbLCaKiw0AXH75Fcya9RYff/wh06ZNYcmS\nRTzwwMO0bOm5kGV4eMSlndT8bDFKGedVC2p7HMfuPdl8Mm8HcTEhqDUqrFYn7dvFMfrvXQgJrnjA\n3tkiIoJIaRzOiZMGSox2EhuF4w7V88eubJo3CiciLICjaQbenr+DkCAdocE6jGYHsY3DsbeMxNk4\nnFiNBmuRlcimEZg7xaMN0hETpKPY7CA5NpD4xnpU0Rpi9HqKi+0kJgSS2DkATRzEBARSVOigQ3Mt\nT/7DTq+WZnAFc6zIRVxwAE2idLg1DuICgym0OmgcrmNwRxWJURbCtaFkGR1EB+ppk6BH36SEhOAw\nTmU5CQ8LIKZFFFmKQtPoYIqKrMREa7lqgBpnexNJkeHkHbESGqun5chwtqrSCdXqSQ4Kp8RoY+Gi\n3XyxaDehoXoS4kMwWp18/MNh/sgspmnjcEpyTQSfnvh32OA22GwuTqYZCAzQ0LRpJJu3ZqDVamia\nElmrX2L+NA7In2K50HFeS3/aUTqu0eWs9KZWq9mRaWPjoXw2Hspj20kjSkwbQhJbs3P7FhYs+Jy1\n2w6zrziIzSeM/LzrOD1axhAYeP5j3fzpNYWLGOdl+K3m47yirq79wM8hHTZqQW1ekF2xaj/f/XCk\n7LFOq2bY0Lb079fivONxudws/PoA6zanl7Ua1GoVfbol88uWDI+yFl0TWW+wlI3FUqvgqvhQ/sgx\nlpWpgJ5NQ9lTVIjr9MYqoFt8OAfMBWVlADe3COWK1idBdaalcyS7CSsOG3EoZ8r+lhhFWMQp3Gdd\n/9K74vizpBCHcub6V4wlibW7VFjPajn1bB6IrlE6duXMta7GSgwn7Eas7jNl7VXx7PuiAJP5zBdI\n+04J7DTZKTnrS6VDo3DG39TBo5PH/gN5zJ2/nZKSMz+UWreK5smxPSt9/c+XP13Q96dY6rLDRlUU\nt5sjO39l189LcdjMtO1+PS2u6MOEW7sRERFZ/Q7O4U+vKVxEh40T/61xXVXTp897/+dLThv6mZxz\nZnl3ON2EhVbf2qqIRqNGrVV7nO5yuxWyck3lykxuxWMQsVsBo0rlUaYAVrfLI0kpgE3t9igDCAxx\neCQuADdOj8QFgNrukbgAXCqHR+ICMLtdWJ2e/3BWHCiK59x7Fp0dq9WzLMdg8khcANl5Js796VNk\ncXgkLoDY2GCPxAXl3yPRsKjUalp16Uuzy67iwOY17N34DQc2r6G18nfuvPMegoIuPDHWaxWdr/ch\n/4pGCCH8hFYfyGVXD+XGx16jxeW9+fLLBfzjH3ewYsVSHA7/OQ3oNSp1zW9e4LXktXbtWoYNG8bA\ngQPLZhE+16pVq7jlllsYOnQoTz/99CX5AWmUHO7xOCBAQ2xM9WOL7HYXO/88VW5qn+T4EI+OFVqN\nisAArUeZRqMiSKdBe3aZSkWISuVRplZBEBp0Z5WpAL1Ki+6s0xAqQKfWoFLO6kWhQIhOjV7t2bMi\nXK9Fe05vi/jAAALUnicF4kLVhOo96yWEaAhSe06uq3cFEqzxLAsJDiA0zLP1GhoZSESIZ72oYD0F\nBs8L/RkFZiIiPa9zREUFkZfnOUO/aLgCgkLodM1w3nvvA3r16s0777zJ/fffww8/rMblqnxV8AbH\nz9bz8so1r/z8fG688UYWL15MUlJSWf/9cePGldXZtWsX48aNY9GiRcTFxTF+/Hg6depUYXfKvzTE\na14ABw/l8cWi3SQlhjHilg7lliM51569OSxcspv8fAtNUyJ45MEehJ/1ZX0io5h5y/fhciuYLQ7y\niqwkxASh1ahBqyY/SEOG2UFCZBCaEB0oCtoiKzm5ZmKig1HFBuFUFAIMNrJzTMTGBBKcEoBdcWO1\naDhRaCcxXEvjBDeKysmAdmZiwgvREUiINgA0Tk4W6ci0GcEVRFpeGAabgxtaOUiMKMDlCiDLGIHJ\nZadnfAQpEXaKrGq+OqqQZjbTNkyHWmvAYQtgy95kjuW5uONvZto2ycTu0rMrN5qTJgvmtEi27HYQ\nFqym25UqjBojOnsYe7PNBCtqWp7Sk3/MSGCHOH632AnWqLksUEd2tpHGIXqOHykkQK/hpv4tuOry\nJD7/5Ri/7M4mQKOiY2gAeenFJMSHcORoITqdmoHXt+K61JZotRf3z+pP10T8KZYLveb1wCtfodbW\n7qk9m8XEE4PbER4eQWZmBgsWzGfDhl9JTEzmtttup0+fvmjO6hp7ds9Ef3pN4SKueWW+XeO6quSx\n573/8+WV5LV8+XJ++OEH3n679Mnv37+fsWPHsmbNmrI606ZNIyIigrFjS590QUEBTqeT+PjKB0k2\n1OQFpdehzu2KXpHiEhsTJq3xKAsL1TNj6vUeZRabk7H//tGjTK2G3ITyqxIHmR3luoXrtGqPwccA\nrsahGO2eZTPvzEFRe84Zl2YMxeT0bNF0j9HhVnu+ZpeFN0On89z222M2Stye64u1DQ9CURs8yj5Y\n2i2dmDIAACAASURBVII9GZ7H6Hy5nhMmz21jXLHsy/GcaqunC04cK/IoS2kXw75zVoDuFhrA8dOz\nrf/l9pEd6XN1Uy6GP325+VMsF5q87pu6AJWm9mfA1weFePTSK87LYt/v35J+YBuhUXG0+9sgmrTr\nhtNmYcKIK8s6d/jTawoXkbyy3q1xXVXSo+e9//PllQ4bNZkt+MSJE7Rt25ZHHnmEU6dO0bVrV559\n9llvhOeXapK4oJLZ1iv4OVLx3io5RkU/Zyoocyvlt1epys8nr1R0HFUFY8wqqlZBmVqlcO7Jmgpn\nuy9fVHEsFajppPP1uLNugxUenXhRvQ1rKjAljPiUNhhyM9i9fgXbvvuMg5t/oE3363G5utT58b3O\nzzpseCV5VfQPrjln9KnT6WT9+vXMnTuX0NBQnnvuOd58802ee+65SvcbERF00adsaoNKpSImpnwL\nxhsqGkwLlIvHYj2PFXFVlPvmr2liUalV5bet4BAatapcgqjouWg0as7NVBqNGtc5nRa12vKjlCsu\nK3+MiurpKtpWV37bkJCAi37vffn5OZc/xXIx9HrvdaSOa9SUa0c9TlFOOrt+WcHW1fP4Z/pvjB49\nmiFDhvjda3rBYxT9bJCyV97hxMRE9uzZU/Y4JyeHhATPmc3j4+Pp1KkTERERAAwbNozZs2dXuV+D\nofqZ1r3hYk4LGI12Vqw6QEqTCK7u2eS8P1gul5vUfs356ZfjuN0K+gANjVpFM2/pbgZc3RStRo3N\n7mL52iO0aRbFkZOFuNyg06lp3DKaZL2avfkmHG7QqeDKAB1qnZZdJVYcLgWNGto0Kn1PjmQVY3eW\nns5s1joGp17NwTwTNlfppL79u6jZnx9Ji8h8NGoniqIisyQOk1uLWrHjVrkAhR7RkcTqFXJsNlyU\nJtXGmiQwuXAHqVFr3ChuBXeelatsJfyq0mE63Z2+Y3gMiXrIsllwKKWnGE8UxpPQ0kpWsYa8otJM\nd203HY1TLJiOack2nj5GQBR2C8SF6Mk1lXYYahMbQpGiEB4TRHF+6eepaXI4GpOd+FA9OcbSes0T\nQjFoVET+P3tvHidXVeb/v+9e+9b73uktC0mAsG8SAiLqYFREdIQRZxzAGb7Ol9HvjIiMjjIyjP5k\nZNQZxl1QBEERFJhhDZCArCH7nk466X2p7trv+vujmq663RXSWUhCqE9e9Uc/ee6559a9t55zzvN5\nPqfaT3ySKt/VWUFrc9h177duH+HZ53ZxycUdU+SbHTtHeXpFNxdf1E5TY3jGPTyWlpWOpb4c7LIh\ngK4fwGDtMMEXqeXMD11D28lLiY6+xr/8yzf53vd+wFVXfYoLLriEQODI70ZdChUV/oMLYMfYzOuI\nETaWL1/OvffeS0NDA7fccguqqrqWBZ944gm++93vcs899+D3+/nqV7+K1+vlxhtv3Ge77/Sc12ur\n+/j1b9aSSuVZlW1zolx91UlUxA5cuXzP3gke/t+tbB1IMj5Zl9RQE+Dic5p56KkdjEyKzFbFvEQr\nfOxO64wk8j/+VWGN2pBGZtsYY6P5H/Bo1IuvLUJ/ymBwkoEXC2rUVPvY5UBfMn9szKvQVScTaRwi\nbuXzS0FZ4+Soj00TOoO5/Pfil1TOqPBzVl0aWc4LoDq2SiYbpVYCbTL/ZTsKOTuAPLAVWc8vLduC\nh+3+TqLVHlQlf88dR2bvRIDfbrfZkczbNFGmMhejoTZDQhwDQBYkzIlq3tjmY/NgniHoVUS6Kvxk\nTJstw/nzqpLAyT4NZTjDjp58Pk2RRVrbIqREka29+T7LksCi6gAXLKjm9FMLOzFbls2v7l3Liy/t\nAfLLvsuWziGdNnjhTz04Tt629LxWLvvIAte9O5YCxrHUl2OJsHEgeJPckc1mefjh3/P0049jWQ7v\ne9/7ufTS5VRUVE75Hg3JqYPOeQ3/ZNa+QuW+iXaHC0dk5lVRUcEtt9zC5z73OUzTpKuri1tvvZWn\nnnqKp59+mm984xtcdNFF9Pf3c8UVV2DbNgsWLOBLX/rSkejeUcNLr+ydClyQVy7ftXv8oIJXY0OI\nQMzH+LaCmvzegSQrX+udClwAQ6MZ/NX+qcAFMDSeo1kQ6R0tzGTHxjL4zPBU4AIYTeSI1AfoK9pb\nbDRjEAhZU4ELIGHm6E76pwIXQMrS8SmeqcAFIIg6UcVBo0A7FwUDKR2fClwAopOlUUpjKIXvShBM\nkkYhcAHkbBMtmiYhFsgXpmNhiVk2Dxa+q4xhM5TS6S36DnTLYY9u4vQUiCCGaTM4mKK3aH3TtBy6\nEzlX4IL8suybgQvyhJsVz3VjFKnf27bDM891zwheZRxeWEYO2z56GoRv7h8mCALUvoeTLj+djS8/\nxc9+8xA/vPtemuefTtdpF+LxBV3EjmMex9jM64gtDC9dupSlS5e6bMuWLWPZsmVTf1955ZVceeWV\nR6pLZZRRxnGII0XYmC1UNcqSC69g8Xs+zI43nmPTi//DE794idrWBayf7+Gss855Zwj+vluDVxkz\nIZdQiZdK2HI5E03b/60qRUYQSymmlyJalDhvqZWFUsL2JQkZpTro7EOpfUYDJVossbgtz/La5BJG\npYRNEmB6pkQSRQTbcp0+TzZxXN+tZdkzlPxlWcSyHFfhuCyJWJadJ6JMwjTtGbYyjj/IikbXqRfR\nseQCeja+wrrnH+IrX/lHFixYyOWX52vFDmY574jhGOtbWVX+MOBgVaPnz6+aVC6PEwpp/PkVi1ly\nUt3U/ycSOe75zTp++es1SKJAa0vkLSn08+ZEEQSBnXvG0RSROY1hdvTE6WiJMJHUURWRunmVvGoY\nLKgNkkzoKJJIe2eYdVqGuTUhMsNZJFGkZnENf8JiXm2ITEpHFATOO93HnMV7aAxE2DNZ7nRWp0Yu\nvJdmb4ysnf8OLqiIcWVjgrAcZWfaxAHOr67hfc0Kgh3BdNKAjZyLEUjtxXQiCJIIOBgTMuK6ZzHl\nKgRVBkwMpQE104dth7C8HsBCsCqpCwo0+kJsH7fI2SanVYa5rHOMOk+AwYyI7pjUaVEi/iRdVRoD\nYxpJ3eY9nT4+eNoeFtXL9I16SeQs5lf5GdEtYtV+whakMwYtLWH6PRKxmJeALJHMmrTVB4kLsGr7\nCO3VAaJ+lfUbBvnhT18lFPQQCKgkUzrz5lZy7V+dypmnN7Jn7wTx8SyNDSFEUeDFl/bQUB8kFvWy\nbfsod3z/RVY8t4v6+uBBLRkfThxLCugHqyr/2MvdCNNUVo4mpMkBy5sQBJFIdSNN80/jrz+ylJ6e\n3dxzzy954on/QZIkWlpaUZS3r/8Hqyov5NYjCOKsPngWvQ09n9afsqr8oeNQk9x7eyeIRb14vYUH\ndnw8yy23PUu6SFC2sz3G//0/+1czn0gb/NPtK0kUHVsZ87K3xstIUQ6mxiOh+gzGzMJ8o1KQyaZk\n+osKkmOqxEfOHkMIFK7R0j3sGtdIF+WwfKLC59v8NPoKfhOGTEqtps5fGGiYFugjA3itws7ENhrW\n3hTywIaCTfZgtyxAMfsL5xVVcnPORvUUcnFZU2DbmEVjuJAnMy2JVweqGbcKRcW2JSJlqwlFewvt\nWSJPvdLByu5Cn2UBTg94eLW/8HxJApxYH+K1vYXrFQX4YHWQ11buphiXfqCLSy7uLJzXdvjdQxtd\nO2ELApx+WgMvvbzXNVv75McXce7ZzRwtHA+EjUNVlT/cUFW5JPsxm07whQ/OIxyO0N29k/vvv5cn\nn3wcr9fLpZd+mMsuu5xQaCY79VBxsIQNJu6dvW/oigNv/wBRXjY8BjBdzxAgmzVdgQtgZHR2pQGV\nMZ8rcAGMp3RGDPdIdjhn4VXdL9WwY5Kapt4+qltIgYxL+11Ss1iqRbExbRvUedyz4ZBiEpw2mZAl\nwHarYYjksCeG3DYzizPNT7J1pGmbTHpkh8aA+7yyZCGLhqtGTJRsamMZ0kWHS5KNYbsLyUwHktPe\nbcuBzLTCNNuB4ZGZGofTa4xEUUCaNmN2HBgZycxQMhkdK2smHipmsxnlkUROkTCNmRqIxRtiRqNR\n/vqvr+Oyyz7Oo4/+gQceuI/f/ObXvP/9H2T58o/sk9RxRNmK5ZxXGWWUUcbbh6PNNpwO0ZExjZkz\nrwIrsc/9H5HTWXTZIra99gw//vVv+eEvf0P7iecx94yLUT2FYmc9kzyybMXDGLzuu+8+7rrrLiRJ\nIhaL8fWvfx2Px8OFF15IW1th78I77riDpqamkm2Ug9cxgHXrB6itDbrU4/1+lapKH0NF6uWxmJeB\ngSQ1NYG3bG/HjlFqox76xwqjz9oKH4Km0J0rzMjaNBlFhN12YdbSGtPQfTJb+gt+7aqE1RdEqCvQ\nyKOyn46AzPqJwjJag8fH3qyHZl/h2LjhwchCVdEqjqkLWGIQqYheb+kKtieKlCnMvqxwPVa4EXms\nQH23HC/O0Bg0FL4ry5aR8GMxNmUTbA/Nfh8j8UL/FBQcR8FxCuogtqVQV2Mi9jhTclchj0BNvcXW\nvoLNJ4tELAcRB3uSUOKRReSQhiAKUzJdqioykcjOIGA0NoSQJRFzMvehKCKtLRF27Y5P0ellWaSx\n4fAvE73bcOyxDUsvG74VPL4gJ1/4cRaeeylbXn6CjS8+xq4NL7Hw3EvpPOVCJPko/HQfJsLGxo0b\nufPOO3nwwQcJBoP86le/4qabbuKqq65i2bJl3H777bNqp0zYOAw42CT3yGiaX9y9mj8+tpWVL+wG\nQWBOa56UoaoS55zVjCDk/WprAuzYOcbKF3qwLJs5rdEZ7LREMsev71vHvb9Zh57U6WqJoDvQVBtk\n194JnOEMiyr95GSBEx2B4c0j5PqyLIgEMfwCC5tVhu0xbC3N4gY/mQmBU7MOiTWDdL+uE0hXEGmy\nmBMOIcnjhLQMXYEYY1mHUyrChDwT7MqlGdXDxBSHLWk/6zJx9qRHGUtrRFUFUgZSpgdZzGKI1WCZ\nmGMCUt9WJCuOEWwBB4yF5yMsaEcMKeihdkinMHM+pPE+5NGdWKMGVjCKQQBHSKLIOSS7Est2kJ0q\nPLJE1Jdljj/KeBZUIYju6ExYSbxyBMkWGcuEeGnEJC7GWdQqIege5jZKVDSNMiaN09WmoJoaTYKG\nPJCmr2eCVq9KIKRRF9Jwkjo7htOEG0PE/ArVEQ+G6bBx0zCvr+mnvq5AwKivD7HkpDr6BpLUVAf4\nm2tO55ST61lycj1j8QzRiJfPXXMaHe2xw/JMHizKhI3Dj+mEjQM6Vlaobp5L+8lLMXMZ1q/8A93r\nXiBYUYM3GOXsrko8ngMTIT5YwgbmlvyobzYfZd4+m8lkMpx22mm0t7dP2R544AF8Ph+bN2/m/vvv\n59577yUSibh8pqNM2DgMONgk950/eoU169wCxX919RIX4xDgZ3ev5uVX9rpsn/rEYs4+0z2dvvf+\ndTz7/C6XrXV+FZt2uxXY586JsHmaivqCiyNsl9y2zt4Q21YNu2zLPx0hPNedm1KdSoaMUZetWo2S\nw63APk+J0K66jzXiEvKe1102vXEJUpt7NwFrIIO6eYXLlquZj9PplhmzTD+S7L4XI2kPzw64+4Ll\n54Vp+aqw5KM36Va1D+oeuh905yuCQZX+ae9+UBVRd024bJom8Z3bLmF/OJZIEsdSX453wsbBIDE2\nyGuP38PeratpnLuE//7ml2hubj2gNg6asJF9ePa+nktn5WYYBtdeey0LFy5EkiTC4TBXX3013d3d\nXHnllfzoRz9i3rzSgbC8bHgUMetRQ8nxxUzbbMchs2yu9DlmKzl/BFBCP7gkSl1vyaso4Tjbqy15\njnfssLCMYxXBaDXvufzz9Gx6hZce+Tmf//zn+NKX/okzz9w/C/mQcZgJG/F4nBtuuAG/38/f/d3f\nucTaW1tbueSSS3jqqafKwauMMsp4d+CdwjY8FFQ0dHDuxz6PZ++zfPnLX+RDH/oIV1756an6sLeF\nhXgYi5S7u7u59tprOf/887nxxhsRBIGf/exnXHrppVRUVEz5yW+R2yvnvA4DDjZPEA5r7Ng5RmqS\n1n7KyfW859wW1Gnb3YfDHrp3xUlMKpy3NkcYGkrRUB8iFNJcfrt7xhmfyC99zZ9XxcXL5tA/kiY+\naWtuCJJTRUKaPNVeQ0OQCY+HiqhI0py0+QLggWBGYSKeP7axOUBkIVSGNCzyPw4KQboTImFFwyTv\n5xMDOI6AT/JiTPr5xAD1AS+qoyJNahlaBHC0IJYuIuXyZAtLjmDtmcCONSBNcjIsQ8HesA1H8CHZ\n+aViSwqR2zCOHatDCAoIAtimgriuG1sKIvjyS++2rTKug2l6SFn5vmiSh6zuRRN9jBtv9k/FTkaJ\nyD5STgYH8EkKUrqCqOwjMZantfu9CpG2CBUVfsYnstgO+DSZOREP1WEPyfEctu3g8cjUdFbQk8gx\ntyGEJIlkcib3/89WNu0YpaM5kt/JusTzo+sWDz+yhTXrBmhri5bcnuXtwvGQ8/rdM6txHLAt85j4\niDgYhn7Y21VUDbXhJCw5wKMP38/DT62i267n+Q29nN5esc9c2EHnvJwds895iR37bGZoaIhPfOIT\n/OVf/iV/+7d/O9WX//7v/6a3t5czzzyTvr4+brvtNr7whS8QjUZLtnNQOS/HcdiwYQPNzc0Eg0dP\n5v+dnvMCMEyLp57eSXNzmPlzq/bpZ1k2Tzy1gzfW9rNrMocligIXXdDG8ksL02rbdnh1dR+ikA+G\nb9qeeWkPz20YZONQnuEnOA4n1ATJemRezRYGAad3KITCOtsnCnmytrEgVV4La/7IFEuvwxfBtCWe\nGx6Z2uBxcThCo09krKgwuEIOMyfio94/wZv8EifjQdYdVDk5tRJhjNmY23YjrHkZwcrnB6wTz4L2\nDuQVDyJMBlWrpg1LCqE/9jxCLh8snbPOQD7/RNTnHkHU87VwRtsikktOo8fox54UfbLMEAOpAG/E\n42Qna7vCkh9bD/D8JpNULv8qNEcVWqokVq5zmMjmbU2aTBsCr5sWE5PJ93qvQjsC/TvipCa3WKkM\nqdTFvGxKGcRz+fNWhz0sP6WeR57cSXxSDLgi4uGajy+ioyXien52do/x01+8PlXTFw5pfPrKk5jb\nVVAifztRznkdfhzOnNe+MNK7k2d/811k1cNZy6/hq586b58U+oPOeVmPz95Xeu8+/+v222/nJz/5\nCR0dHVNL9V6vlzvuuIOvfOUr9Pb24jgO119/PZdcsu+c8ayCV39/PzfccAPXXXcd55xzDldddRU7\nduxAlmX+8z//k8WLF8/+og4jjofgdSAYGUnzT9942mULhTR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9v8HreMC7SR5Kz6b4++Un\nTWkWptNpbrvtX/DlBrntju8wd+7MXYhjsYMrx3lHFimXUUYZZbxT8G4ibGTTCUKhMOFwhL6+Xm6+\n+UbGxkb59re/y/z5pRmqkcjBBq93ac7rySef5NJLL+WSSy7hxhtvfEtB389//vPceuutR6prRw3j\niZnSTYI4c8RoWTNHPOPjM1WzS9lKceHlEueQSjwKgj1TzVxyZvqZ9uxsTon2HKHE+EmZWdPklNhm\nwRGkGZdX2iZPbZj5JmRRRmR6OYGINE1RVBFFhOnHChLyND9NllzF4JAv/lan2TyKRG6aPFdaN/FM\n2z1bU0RXATuAblho0/xkWUSdpjovSwIezf29SpIw6x0IynjnYe3aN7j++usA+N737txn4DoUOI4z\n68+RgPS1r33ta2/3SUZGRrjmmmu46667+NznPsfKlStZv349Z5xxxgzfu+66i9///vfMnTuX8847\n7y3bTaePDUX7A906vW8oxX/es4b7Ht2Cadp0NIcZHstw56/X8saOUTpboyQnsoRDGtWVfla+2EM2\na+aLWCey/Pzu1dx7/3rSKZ22OTESiRx3/eoN7rlvLYmkzgkn1DA8nOLue97gTy/20NEaJWVYeDWZ\nplo/23eM0dUUJm3ZeBSJxoYQr+1I0xkL42gGiiBSqcd49oUcraEIalBHFETUiToefclhTjCM5tUR\nBYEmX5jnB+MoTpTmUP5HeetoJb/cEcexwjT481qMfRNhVg2PgRUjrNoI2GhCE4GAhilXIeQyCI6F\n7utEbKvCquuA3mEwchgnn4N5Zh2GEESyZAQsDCuEbPZjEcRGQcBAl+sRQhImQbBEBEzSTogxdYCs\nvwo1IyBaGdIV8+ipUmiPeBjLeEkYBieEo9RFxumqFsllA8QzJhe3B/j7pUka/H4SOQXdyWHrlfxx\nq0NAU4iqPsZzOu3+ML3dDnLIQ4NfZWIiR1tHhIEGEU+lQr3mZSyh01UbhHiWFS/toTrmpbbKzwvb\nR/inB9ZiyyItMR9jiRyndFTwDx9bRLCoYH3t5mG++4vVZB2H5toA8bEsTY0hRAHMtEFjU4jRiRzz\n2qL83V+czAXntZJKG+zZO0F7W4zPXXMaVZX7H3Ef6LP8dsLvP7jC7Mde7kaQjp3lUUkSsay3Z9Zi\n6DlGNq/iO9+5jUWLFvPNb36LWOythb19PnW/2y+VQtZaS36RfP8fj7TwgNs/UBwRwsbvf/97Hn/8\ncb73ve8BsGnTJq6//nqeeMKtUrx27Vq+9a1vccopp5BOp7nxxhvfst13ImGjbyjF1/7jBcyi2VRb\nU4jdfUnMImmnpkovY3snMM2CX0N9kKHhtCsnVlsTYCyeIZcr2Kqr/UyM58jmCksVobDGhCSSLfIL\nBFSGYx5SRcnkoFdEEGEiVehL0CcghlUGJwo2vyaw5MQJ4mZhtheQReZWKPRmCsW9XknkjCofQ7nC\nvZIR+UBTDR61MPhwbLByCrK38MNpGzaZeBohahb5CXiTHlQKxb2OAznfHGRvrsjPYTxtoEtFOSIb\n0olK4sqE69jusTpGLXd7Z4QbmVc7RjF+vTbMo91uW0Ouhpc3undXPq0jyOqUu9j6NNvP5tXuYuYT\nTm/g2R53Pu1jJ9dz5TmtLtvzr/Xy0wfWu2yLm8Ns2ehu7+xzmvnU5YtctsGhFFWVvln/WB0PhI13\ni6q8nk2z8sH/wh7azFVXfYa/+IvPzEot/mB3Uo7rs989JKJ+8oDbP1AckZzXwMAAtbW1U3/X1NQw\nMODe2j2RSPC1r32N//iP/+C3v509q+WdhmzOdAUugGTKcAUugIxhuwIXQDKpuwJX/ljdFbje9CsO\nXACZjEl22vJSOm2QCrpHqImMPUNTM5F2cGR3X1I5h8w0RfekaTM6/byWTcJ020xspq2m5TdgVSUo\n2spSVEQIeYBkkZ+TJ3UUHyuAMK1BQRQwhWkzCDG/MeD0YxXJoliIXhCgLjzzx2aihMp+roQtW+Kt\nMo2ZI++JUjOcEku6pXYaMEosJQv2TFt11cHlN97JON43o5RkmfjgHl58+Efk0gn+/Zv/zAUXvP1b\nkBzOOq/77ruPu+66C0mSiMVifP3rX6eiooIvf/nLbN68GUEQ+MpXvsJZZ521zzaOSPAqNbkr3uAS\n4KabbuK6666jvr5+1u2Gw17k6b+CRwGCIFBRMbsfidHEzB8iqcQ1SCVGRmKJrXzFWY6oD2aZYFoL\nMH2Lk1I7C4sCs8nrilKJay5xfZIkzmhOlgSm7XqCVKI9WRbR7Zm2abEvn6vSp/vNzM/JJXJGSgm/\nUjsfl8o3lfLzetUZz5LfN3O75lLPvaYps34O94UDeZaPVVhGDvkYWja0dfOwkQuymQRnVk3wk8d/\nwtmLOrn55pvp7Ow8oPf7YH8LDhfbcOPGjdx55508+OCDBINB7rnnHm666Sbmz59PRUUFjzzyCLt3\n7+aqq67ij3/8I4FA6c0zj0jwqq2tZf36wrLH4OAgNTUFuvDAwACrV6+mp6eH73//+wwPD2PbNrZt\nc9NNN+2z3fHxg5M5Odw4kKUWCYfGmgB7BvKzCUGA+XOiyJJIT1+iYOuI0SvkhXvfxLyuSvr6k3Tv\nKiw1xeoC+EIa/XsLy1RNDSGSKZ29RTVH9XUBooLArsFCP+dU+6nyK2woGtm3V+XFercV1SYt7FSR\nfCIvbStEgkXNMjVagF3pQl9qPUE8tkiawvLdvKCf5oDKlqJ75VgBXuyFsxsK30vPhEJ/yuGUGmeK\nqJDRJZI5B69WsGH6MBwPihOfCp6mEMYwZGS1EFANU8I2Jw3C5Etn+/EJEkmEqRcxmfWxa1hD8oog\n5K9PsD2s2O1wUauAOrn/2HhOQZMtVFFAn5zh+EQVWQNNEcgZeVtIk1HGbTRRIDfpF9RE1JiDZ4tE\nNp2Pul6PhCdr4pdFUpOzbp8sEu+Js6sn7tobrDrmIeBTSE7uDaapEp1tMfp3x0kk8/dOVSWam0KH\nvOR3PCwbHq9sQ1PP8srj9/Jq7xo+/vFP8tnPXocsy4xO20R2f6io8B9UADtcMy+/388tt9xCMJi/\nvwsXLuQnP/kJfX193HHHHQA0NzezaNEinnzySZYvX16ynSOS8xoZGWH58uXce++9NDQ0cMstt6Cq\nKv/wD/9Q0v973/seiUTiuMx5AViWzeOrdvPq+kGu+EAXHc2RvNrDC7t5ee0Al1/SSVdrFNt2ePb5\nbl56ZS8fvnQeXZ2V2LbDyhd288zKXTg+lV19CQQBOhvD5MYyaKo0FfDa5kRJpXQ8Hpldk6KwrW1R\nJgyboCiwazIINnfE2BNU8fkVNk8WynbVBMjaOm0LTHona6lq5SjduzXmNBv0TQrZNnrD5CwTv6yy\nKz2GAzT5QkS8OufXCQS0QRDAsaJsHZfYlfCwOTmKA7T6wnyoxcPaEYfXxoZwcGjwhPhgswdZ0EnZ\nfSA4yE6YoKwSdPx4iCMIDqbtxzFymJ4okjeHIDhYpgfbEclaGSyhHwQbxwpg2Q4CHkQ5jiBYCAQZ\nSIms2h3gmd1pdNumyuNhYY2DJcisG0ug2xaVmpePtXtIGgLPDY6iOxYByUsm6SUx4WPLQI60aRHz\nqIRNFS0Le/uSpHMmkYBKRbMXX5XNGOOkTZOgrBId8OL0Q/9wmmTaIOhXqGiLYhg2Ez0TJFI6Qb/C\nxy7p4twlhVWIZNrg/se2kMqYfPKDc4lFPKTTBr//wybGx7NcftkJVMQOfdfl4yF4HY85r/HhXp5/\n4Psk40P829du4n3vO3jV9oPNeQ1nfz5r30rPp2flZxgG1157LQsXLuTnP/85K1asIBLJb330jW98\ng5qaGq655pqSxx4xhY1nnnmG73znO5imSVdXF7feeisvvPACTz/9NN/4xjdcvsd78Doc+Olv1/P8\nq70u24K6IDu27V+lvJSt5rR6Xhtzz2TPO0NlQB102eb4Y+xMuY+d44uyM+0mMny4JcC8KndB7dbR\nGh7rc9+zOb4oI4b72NNjQeZXunOiIaeaOtldWqATQfBP2/lYl8nQ47JhBUFyn3dPPMw3X3C7VXk1\nMrKbfBHTNDTNPbKVLA/PvTFt3y9RIrvF3T9NE/Cf6rYJDmQfYwZKKfz/59eWzaDBv90oB6/Dj0MN\nXt3rXuClR35OMFbDaR/4NF/91HmEw5H9H7gPHGzwGsr+dNa+VZ7P7NcnHo9zww034Pf7+fd//3dO\nOukknn/+eVfwqqur47Of/WzJ449YkfLSpUtZunSpy7Zs2TKWLVs2w/f6668/Qr0qo4wyjje8kwkb\nxVJPhp7ljafuZ9f6F5iz+FxOvOBjWMbM2tAjhcM5z+nu7ubaa6/l/PPP58tf/jIA9fX1DA0NTQWv\nwcFBTj755H22UVbYKKOMMo4rvFPloYqlnrZs2cztt38PcWKC2/75q5x33num/N6UgjrSOFwKG0ND\nQ1x11VVce+21XHnllVP2Cy+8kPvuu4+bbrqJnp4eVq9ezde//vV9tlMOXkcBtu3w9IqdvLG2n48u\nX0BrS+klANt2eH7Vrsmc13w62gu7n77/Pa0Mj2XYtGMMQYCOxjCZ8QxNjSF69uTJG61tUcYMi6bm\nMD2TOa85rRGSSZ2W5vBUHqyxI8YeWaCzNsDWN3NedQH64jqNjVF69fyyXpMvjCVkafVH6E7l82X1\nWpj+EWiMxNiTyy8n1ntCvLgHar01RPwDIIBtRYjrFu2+GNvTeb86LUhENQgqMXal83mwKjWI6djo\nejWqMgSCg+SEsMQsKTuET0jmc16OH1uRsXU/spJAEBwM08OuZAq/XIlPG0UQbEzTz6M7PcyP+WiP\nDSEINgIBNBkuagmwoieNYdtUejwsqQPdibB6ZALDsYkoHmTDQ0T1kxJHMRwLv+QBx8epLV7W79XJ\nmBZRj0LEUtFaveztS5DOWYT9ChWVfrymxbg2TtoyCSgKnRUa1nsFNq7MkkwbBHwKLZU+TNNmb7xg\n+9j7Ol1Lhum0wYMPbyKd1vnYR04gEvGQyRg89MfNjE9kufwjJxCNHjtLZUcT71TCRjadwO8P8NBD\nD3L33T9j4cLF3H77913ktqOJwzXzuvvuu4nH4zzwwAPcf//9AHi9Xn784x9z880382d/9mcA/PM/\n/zPRaHSf7ZRV5Q8DDiRPMD6R5Qd3vsyeSXagIMB7l7Wz/FK3gGY6bfC9/3qJXbvjU37vObeFj1/m\nrlx//pW9PLNiJ31FrMS5XRXEDdvFLOyqD+BkzKnABtDSEmakys+aolHhvIgXTbPYninkfk6o16ir\nzdJflJuqUsKkJ7ys7Sucoz3mIRIQ2DRaOMdZdR5ObRZ4caiQJ/MKQbyihiEMT7EDNQJoogdbLNgq\nVR9dIQlF7kcQ84+pYkcIUovkTU9JaVmmxkhGZGtqF2/S+f2iF90I8fOt47xZYtUe1PhEh0bcHihi\nG/rZNFBJMDiAIOYdbctDbzzKGwPjGJOMwZhH4YQaL+viYxh23s/raHgmali9MTHFNgx7ZOZGfawd\nTpKZPHE4IHLWqSqGfxBzso5N1hXE1ZXsfGOUzCSL0ONTWLykno++v4ugv0CP37Z9lB/99NUpZqFH\nk3nvRW2seG4XExP5ZSRVlfjUJxZz6pLZl5qUQjnndfgx2+A10tdNes3v2LlzO1df/Vk+/vFPHlRu\nan842JxXX/rOWfvW+a494PYPFEe/SOpdhng8OxW4IJ+kX7dhcIZfIpGbClxv+q1dN9NvUWeFK3AB\n7N6bcAUugF1DaVfgAtjdM+EKXACb4hl2ZNykhfW9OYZN9zmGjHG2DbnzCttHs+yZcJM+XujLsnYa\nESTjJFDklKtOLEcSj5J22Yb1NLKUmgpcAIYYB81xaUBKco7eTJziOrSUneGpvQ7FtcHbEzmGcrqr\nXiXgSTGnMjMVuABEKUtWt6cCF8Bo1mAk7UwFLoCMkCOXtqYCF8B41iQpOFOBC2A8aaOIxlTgAjBV\nA0kxpgIXQDZtEPXKrsAF0L1rbCpwQb7QfePGoanABaDrFlu2DlPGOw+O47BzzUqe+Pm/kEgkuOOO\n/+ITn/jU2xK4DgW2M/vPkUB52bCMMsoo4yghl0nxymO/YNeGl2hdeBb/37/eSE1N7f4PPAqwnGMn\njwjl4PW2YnQsQzikudQfNE1GkgSXUrzfN1MNIJszZ/ipPhndsFy5kEzWQFFEjKKRvtcrkzJt9GKb\nJuFokktKyutV8EsCqaJzBCSBgCiRsAt+HlFEMhVMqTDSlxwZjySRKdrpWRFEFMct8aRKAoropnsr\ngkBAEskUzYoERwDH/TgKCJiWTLGQum0LJHSIFrlatoNhSRTrWjlOXmvR3Z5DxpAoVsRyHDBLqN0H\n1Jkvqm3MfF0UeaafWkLtw8zJMG0lSyyhlD9TnCt/fdPh889U3XBKjNSHR9JUVhx6/dc7Ccc62/BN\nRuFQzxZefvQXWKbBmR/6LJX1bXg8nn01c9RxpGZUs8URUZV/u3CsqsobhsWj/7OVn/z8dVav6aeh\nPjSVTA8EVE5cXEtv3wQTEznee2E7f37F4qkAZ1k2Tz6zg7t+tYZQSKMy5iOR0mlYVMM23WTF+gHq\nYj6qwx5WPNfNT+9ajc+rUF3jZ3wiR2tXJQMZA02VaKgOEJ/IMq8hRHoojaJI1NcFGZ/I0tJZwZjp\nEE4YtFT76TMtlngUtN0TaEMmLZUBBjHp8AZJDTv07/bQFAihe5OE9Gq27/SRykJbOMhYLkuDJ8LI\niEb/sERbOEiKFIuq/XTV6mTtHFVKhJSV4YSQnwsacjQFE0SlKH0ZHZ8YImdLDOsZInIMmwwVSpBq\nL4yZOQS7Co+UYiwT5eXBIGvicQw9SI3Ppjep8FB3hq3JFB6hAo+cQXQCjOs+VHWcjmCMPSmdSs1L\npU/l9XgSTawhoqXJGAFeHQyxKTFBUIohizqa4MVxvNjKCAtiEfoSNhpe7LFKXtpq0KBWInkzeAUN\ne6ySjWMp5jcGSacsQh6F+oiHDX0J5tUGyZkWAU2mMebjTxszRMxK/BEdxZDJvBBjzdoJWtqiCJaN\noohUzYny4vpBevoSdLREMAyLX923lqdXdNPSHAYnL0d1xeUL+fCl82htjrCzO44jQH17Ba9uHWZr\nd5z2pjAiAvf/bgN3/eoNtmwbobUlSiAwM+Dt71k+mjhYVfnfPbMaxwHbMo+Jj4iDYejYlkk2Nc61\nF7Sy940nWfnHuzlzyWK+c9ttLF96KuctqCcUCh8GKbe3xsGqyo/mXsNBmNUnqp3yNvTcjTJh4zBg\nepL7zh+9wpp1hSJbQYAb/s9ZtLcV2IKO4zA+niMScY+0fvnrNax60V1k23BKPWt63fmqc6v8bFzj\nLuRtX1jN+p1ulfJS6uMdC6tZN81vQWeMDVvdxcdtJ9Xw8oA7/3XK/BBr4u781/xIlJd3u3NsHzpF\nxlPpvo7TohG6Kva4bHvG61kx5L62Ln+UiM/dZ8GqYGPCfd6oHGV3ZsyluFavRRAk97WJdphVwwlX\nrqveEyRtJbGLbNVqAK+adPkJeoifPRFw5b/qQipZMeWyxTSVsT0WepHAcsQrkzFsckW2oCYR7E25\n9vTyevIj8Uy2kH/UVImQaZEo0sJUFYl/+vL5LlahrlvcfMcLDBflFWVZpEGTGCjKe8qSyNduXko0\n8tZEhjJh4/CjmLAx2LOV5Bu/o7d3L9de+zd86EMfeduD1XQcLGFj6/gPZ+3bGf7rA27/QFFeNnwb\nkMm6R66OwwyVd0EQZgQuoOSoV7dn1ldkMjPZS9PV6oEZavX79Cuhem6UOFY3Zx5rlFhPKFUTIooz\nCzUtZ6bNEUr0pYSumm5bM6RCLcea8VBbjjVDVNSwbFfgArCY6adb1ozryxoWhjLNZroDF0DGsMhN\n+77SuoU6bWeATNacIXKc060Zz4xuWHg87qtTVYncNCabadpkpo1JTct2LS2XcWThOA7bXnuGV/73\nbk5d0MEPfvBDWlvnHO1uHRDscs6rjDLKKOPdg2xqgud+90P2bl1Nx8lL+bd/+SJVVdVHu1sHjGMt\n53VscTGPEezYOcazz+/CPsi7teiEGuSipH19XZDa6tKy/sUYmFRyLyZkVFb6WNJRgVa0pUZFQEVT\nJTSt4BcOaXhEAW+xLaCiyiJeb2GMEgyoKIqIv8gW8Cl4RMGlZO73KXgkkVCxnyYRSNlEtIKfT5YI\n5mxiRbv++hQRx5HwioWZpSxI9I57sK0CecBxJAbiPnxiYYlHQsSyFGQKfgIiaV3DJxZsIiKqoBGS\n/EU2AV33oREoOlYgJHuo0QIuv4jioUIJFvmBV/QREEMUwyv56aj0uPxaoh7mRNzbhjQHfLRP2ztr\nTqWfjmq3rb02QPO0ovSOrhitc9y2toYgTU1uJYUTFlShqm5yyeu749TWu/s8ry3GiYtqXbO5jvYY\nwaA757Vj5+ghPedl7B+929fw0A++zGhfN8v+/IssXvpRVHX/ucdjEZYjzPpzJFDOeRUhr2KwkVUv\n9uA40NQY4s+vWExz01vLsZTKE/T3J7j/wY3M7axg2dI5JfebehOmafPY49t4/MntmKZNJKwRiXiZ\nN7eSS97bgaJIDE9k+fkT28klsgyuH8LQLUJBjYoKL5Iksmt3HMOw8QdVqlui2JZN/44xcjkTv0+h\npiYAksCOkQxZ3cLvlamvDiDYDoO742TTBl6fQlVzBEsW2JHMkcxZeDWZ+oYggumQ2TBINqGj+mSi\n59RgygLxN+KkxnUUTaJiUTW6F5zgCGlHxyOJnNXsQ9McXtmlM5wx8MoC15woE9VMvvucny3DNpok\ncMliiXAwR9rKkrazSAicGImgSCarBkz2ZrOIwMnRGB7JZNw0mDDzgrk1agU502LTAPQkcojAe+cE\nqA7qmI5Bxknnd1s2KhjIWCiyxZieHyjUazEMxyRpWAzoKcBhcTiGVzLZMiizaTSfS6qmiuFRGVk1\n2ZvK3+uuWICcaWNNCOzsy/dlbk2AnGUjILBzeNKvJoBu2TiaxJZ4ngW32KuijWSw61U22/lzdIle\n5L05vJZDz85JVZPGEIIgcPFF7Zx8Yt3UMxNP6/zouZ08vzUvxNwZ1AimTd57VjNnnZT327FzlIf+\nsJkzTm/krDOaXM/57x7ayAt/yj/nzU1hPvnxRTQ3hY+LnNdnb/stonz0cl6SLGOZOmuffZDtr6+g\nae4STrzwE2heP3omyY0fO/WQhHUPFQeb81oz+uNZ+y6O/dUBt3+gKAevIrz6Wi8/+cXrLtvihTVc\n+9lT3/K4Q33h9/ZO8M1/e85lq67y89WblrpsIyNp/ukbT7tsgYBKMulmXfp8Cum0O3emqBKJaQrl\nkgBKciZjc7QuMCOXVDuUnpEDE2JeMtN2ca64VCFpus8d9XgYy06jLiciDE1ji15+dpaU4/4e03qA\nYd1tWxQOMG657/3gUBW7Em5yyRUnaOQEN3kjnomyN+u2haUIPdNsIb2arXE3kaTaqWb9oPscXUqI\n9bvdfVlQH2RDr9s2tzXCmiH3dZzS4mXzmJuEcmpaZfer7mL0Cy+Yw0eXL3DZ/vBGHz96bqfL9t4F\nNfztsnb2h1de3ctP71rtsp24uIZr/vLU4yJ4feaWexCko0M517MpLl8S4847f8DQ0CCf/ey1fOxj\nH2ZsrLAzwZFgFL4VDjZ4rR75yax9T6r4ywNu/0BRznmVUUYZxxWOlrah4zhseOER/vnhR+no6OSb\n3/wWDQ2NRKN+bPuduVRYjGNtdbkcvMooo4wyDhG5TIo//eHH9Gx6heuuvpLrrrseRZmJo+FzAAAg\nAElEQVQpPvBOxrGmsFEmbExi4+Yh/ueJbXS2x6YS3Z0dsRmCuYeC3T3jfPvfV/Lb328gV0SDrqsN\nctmHF+DR8mOJqkofqipx7/3rpmj3/QNJ7v71Gtrbong8+eW/WNRLNOKhfU4Uny9/bDTiobYmQNuc\nKH5//uWJhDXqawPMq/ETnCRgRAIqHZU+5rRGCE4Wr/orNGKXNDBnSYRwOG8Le2QWRn1UtcUITVL7\ngwGVv/jUiVxzxSIqJ2uOvAGFytPq0EcqCQp5m1+WOaE6SG1EosqbLzj1SjJXtM/jb07tpDE4Wbit\nClx2qoQqSYSkSVKGIZF+qZahRz0EE3myheRIBDJ1vL7dj6jn85CCI2Ema9AthzpfnhwhIdAZivB8\nt0Qum/dzHJF4soqecZuYMplvcASIN7B2s4+AUTHZnkA4V8vAGNR7wlO2aruGvUMCLYHgpM1hgRwg\nNZBhbpGCxYK6IGMpg66awJTfSWEv4tYxTvYWRt8Loz5GenU6vYWlscWWRmZ3irY5BSXtk0+qY9nS\nNtdz9Or6AV58ZidLwl7EyQXeRX6V4T/t4Zlnd+6XgLFoYQ0XLWtDnNSH7OqsYPkHD99z/m7D8J5t\nPPrDmxnes51zL7uev/iLzxx3gQuOPW3Dcs4LuOe+tTy/avfU31WVPi5a1sa5Z7fM6vjZ5An+94lt\nPPzIlqkflmjEw/XXnU5tbeHHKx7P8tuHNrD6jf4pWahwSOPMMxp58qmdmFY+5xTwqzQ3h9mydWSq\njsvvU2hpibB168hUbsrnVf5/9t47XK7quvv/nDO9tzu3zO39qgskRK8xNqaDwd0Y14SA49dO4tfB\nP8cmxfxSHDuxEyDGDWMbbIIpJqYKZAQCSaBeb+99ej9n5rx/zNXMnJkRukgChND3ee7zaJb22aft\nmbX3Xt/1XbS2OOnt85NekKcxGbU0t7sZ6J0nvZBvZDRoaVjr5XV9iuTCsUaNyArFwsiueRILjtag\n03DWsmpuuKIb8wIzMS1leODFIX4/GSKy0J9eI3DhMj1hwzTxTM75agSBZVYfVzf0YNfnHJmczfLU\n8BBhYy9JJSc9JSDgiFWz8eEkwXAuJiaKAivOczJoSONPphfawZkNdsYTUfypVN7W7XAzk4zjTyXz\ntjU1DuYyMeZShUTeDrOH3QMaJqMFyavV9RYiUorJWKFdt8vB0ISG0WCh3fJqM9npOOOzhThGR4Od\nsAAj/sKxndUWTDMJxouqRzc22gm6jAwVxb86fTbcI2HGhgpxt3qfjauu6GbFMnU5jP/81Q5e31OI\nidXVWLFrBIZ7CxW0mxodfPnWs8pywkoxPhFmciqqUqI/GWJebzVho7hg5NDuTWx75gE8De2su/xm\nRFEsI2ScSM8Ujj7mtWn6Z4tue3bNzW+6/zeLU9uGwIGDajXu2bk4ba3uw7Q+Ohzs86tmxIFgkunZ\nmMp5OZ1GshlFpWcYCqfo7Z3POy6AaCxNKimrEpBjcYlUSlaRKuIJiVQ6k3dckEuIlZNy3nFBLoE6\nZhBIFiWxJjNZZFnOOy6AlJTBYNHlHRfkaP2OWiuR4YI6RzqjkJQzxLUF4kZGUdBpyTsuAK0osqTW\nwIZAwTEoKIT8ct5xQa6uWcCfwe9KF7WD6aiMX06pbGEpnXdch2wTMYmwoFa2n4mlmYyqv8CTQYkI\n6naTkRSjQTXRZTyQIlvkuAAm5uL4NeptldH5OJZJNcFjfDzCrKwmufRORGgYVhNGxicidHdWUYp9\nfWoVlMnpKOkSes3IaIhEQjqi86r32akvodmfDHgri1EeKhhpsVj5yU9+xNgrj/PZj9/Apz/9WTSa\n3Dh5p4pFvtWooG3wjuKU8zqFUziFkwpvJWEjGY9gNJr4wQ++zyuvvMzf/M03ufTSy96Sc51oONEU\nNt7zMa9kSsZkUu9P60sSgN8MZDnL1Fz5FoG9JEFUFAUsFZTBbTa1GKkggM1urGArb6fXlV+z3VYu\nblqpnaGCmrlOW94uW4HiqyTLpao0iljWYzYjki3ZpY5EynXUtXpNPh6Tvz6tBk3Juc0VbAaxgk3Q\noC1RcDdoNGXq72adFkOJzarXYCxRp7caNJhKkoWtRi3mEpvdpMNSMrasFh02g3rO6DDpsJaMBbNZ\nR6gklSAel8oqEJiMWmxW9Ts2GrXoSt5xMiUzP69eLZ7Cm4eUTvKP/3gHW7du5h//8Z/eM44LTryY\n13taVX77zinu/tEW5v25bcJQOMmypdXc8sUz8LgXX0bikBL3/gE///GL7Tz+/CDxhERHsxPtwg/f\niuU1WK16BgYDNNbb+bMvnEFLc3mi4rIlXpxOIwODAWprrPzp59fyJxe14XaZGBgM4PWa+cJn1/K+\ni9vwVlkYGAzgsBtwOIwMDgVpaXaiKApOh5HP37yGS/+kHV+djYHBAGaLjiq3mf7BQE6lnFxcrLrK\nwvCOWdpdFmSHDpNWQ2tMw8DOeZp9NjSiiF6vwdtg56VBP/vGQnT67GTSGe7/9Q5eerqPDrsBnEY0\nGoEOq4EdB6JUiXbcnpyjNkar+f3WNK+Nh+j0WNAJ8NNn+vjlU6PoknbqGzQogkIqWMMfeiWqfFbc\nWQ0ZSaHlNCcHzBG8JhMuowEpm+XMJhM2zxSNdgNi1kJSzlCvd7NtOIVVsFBn15HKyvhws2WLhBiz\n0FyrJ6WkqRXreGEbWEQDjU4DUUliWbWNyVQIi06Hz2omkpZYVmMjYZqjzpvBLTrwxzKstptI7JnF\ngkCDz0YwLtHT4GDSH8coCDTXWPHH01yxqo6vfbCHi9c1EI6mGZ+J0dHsJBBKYkjINDfamUtILPNa\nUUbCpAWBtgY7oWCS5g4P40aRp7ZPYtRraKuzsXnLOHffuxUpkqa1xYk/kmbdilr+4qbVXHheM3JG\nYXgkyOpVddzyhTOwF01utu+c4q4fbeHZ5wdQFGhtcZVNDkrH8omAo1WVf3LLEILm+BMmMrLEiw/9\ngNjcGP/yL99nxYpVizruRHqmcPSq8v3hHSiwqL82++oj9vf1r3+diYkJVq1axdzcHGeffTZPP/00\nDz74IA8++CDnnnsuDsfht2Dfs4SNbTsmufenr6tsa06r47OfPv1N9+XxWHh12zh33rNFZV/W4eGr\nn1H3F4ulMZt1Rxw88XguZlH8IxOPSxgMGpVax+xcjL/7zgsUa/e6XEa+/Y2L844TcpWZv/Ht51Tx\nNINeJJNVkIvEY7V6kbTVQLoo+VirEZBdRmJFcTKdRqAhnFZV8xU1AvHlXuZjhS+qRgSPz8poUVxL\nFKBHhvGilYAgKDSusTEYLMSrBEWhy6NnNKVeMVy+QiReknw82N/Ivhl1uw6tlX2j6lVwd4ednZPq\nuNY5XTr6Iur+1jabGU2rY6HeA9Xs3xFQ2dqWVrGrpEL1Jy9p58ozGlS23z55kCdfHC65FjcHSmJY\nXd0etk2qx/VZNTZ6t0+qbBdc0MJHrl+mskWj6bKyJ69vn+THP1OP83POauQTH11JJZxI5IITSVVe\nyWbZ+Lu7GO/dzr3/8W+sW3fWoo89kZ4pHD1h45mx+xbd9tKGmw77f8PDw9xxxx1s27aNr3zlK9x0\n0008++yzPPHEE3zve99b9DneszGvdKqCmvkxuPFiUsQhpCrYKm0VVoK5QoHKSja9TkOp6Hw6nVU5\nLsgpbGRKIq5pKVt2z3I6i1Siei5nFNIlx0oZRUX6AMhmFKSSdpksJNJqW1YpfzaKIpAoWUgrgoBU\nYfdWI2YolQCRKqjdpysszFPpCsUeK7x4RagwPiqo7GcqCLVX/lmosN1aoT+pQrtS1XjIOfZSVKrX\nVfqOAFVB0pMRx6sYZTGrcM/Gxxndt4V1V3yG7u73ZlrB8doOfPDBB7n++uupqSkwabdt28bExAQf\n+chHyGQyfPGLX+T973//G/bznnVep3AKp3By4niwDQ+xCu12B5s3v8rG/S9w+1e/xBVXXH3SsgmP\nhOOVpPy1r30NgJdeeilv0+v1fPCDH+Tmm29maGiIT37ykzQ1NdHTc/iJwnuWsNHc5KC2pqA0bjJq\nWbWi5g2OeGPUVVlobSjQjnVakXWL6E+Wszyzvr+Mrl+KTCbL8xsG2b23kOOTzSq8tm0iH78CQICG\nNjdbd6sLVeq0IqevrlO1q1/qxbfEq2rX3Omho0kdi2voctPSWKJc7rVQ1+xULSga21x0GXX5xFmA\nDq+VdptRZeuxGahxmSgOu7RUW6hO6yheaDVaLRgzFjRFJ2nKmvHvsCLIRUM34MQe1aIr2opt0Blx\nCgKGIvp6s1VPbUzGXLTtWm8zkE0ZMYiFM9coBpT9egyZwtzOqTFjbNdgthXZPCYEi5qU4XIaOZhI\nEyyKccyGkkynJNXKyOoykqixYHUW4joOsw5bSsZTpORvXyBfOFwF0o7VqmfZEnVJjXBC4pevjDBe\nVJQyHpcYHApQVZREbTRoyGSyTEy++S33VDrD4+sHGBgNHblxCWQ5yzPP9XOw943H+fGA3V2Lo6ru\nmP5s7hrsdgfZbJa77/4h73vfB/jYxz6Fw+F8R3UJ30m8lYSNL3/5y9x8880AtLS0cNlll7F+/fo3\nPOY9u/KqrbVx+9fO55n1A0xNRbjumiU47Ecv5ulyGLn9T9exYcsYe/v83HhZJ9WeNyZ99A34+fWD\nu5haqFZ8xhofN1y/rIx5NjwS5JcP7GJ8oZry6pW1nHduE4/9/gAjCz8kzU0OskBMFNkx4GfHgJ+V\n3VV86uoluJ1GNBqRz918Omfvm+X36/uZN2rYMZeLEbWurMEaSSOLAgcWBGUba62g1xC169kxH4dI\nipZGB+Z0BmNCZrg/F/upb3FhyipkDRp6xyMwHWNJtQXZa0LRaTg4HYXZKM1OUy5HLC7T3x9gEqh1\nmbAYNIgakd7xMMzEqHEZMdcbkEQ9eyZigEStzUmjK4tlQqb/QJAJBar2OFh6ocjMlJHX90RRCOCx\nG3A2GNGl4MD+EJMKeGx6nB4LlpTMUH+A3qxCq8OIvtmObDOwdybKeCSF02Slo1rEMisxsCfIfCaK\nba+e5rPsaGpEeoNhMoofy0U6GuccxKMG9gTi9E+EsZo0dFSbyZp07AklGOqf56XRIDevqUcOpnj4\n5WFSUhaTUUO7z0PKrGV3UuLgfAxTrYXVHR7MkxEmhoMcnI2h12s4rcONpNMwNRpmz0wcvU6kvcdL\nndPIdVcvUW0hr983w09fGiKSlHlk2zjXn15Pmyjyu0f3EYmmcySadjeZrMLcbIztO6fYtWeaSy5s\n5YrLuyqySkux68Acv3hsH/PBJI+u7+fCMxq44bJOTIYj/4T09fv51YM785Wdz1hbzw3XLS0b5yci\n/vM//wOtVsuXvvR/3rNO6xDeSqr8z372M6666io8Hk/eptW+8dh6z668ADQakcsu7eDmT512TI7r\nEERR4OIzG7n1E6uO6LgAnl0/kHdcAFtem+BgkVLCITy/YTDvuCDHHvvDk715xwUwPBJCtOiZKFJu\n2Hlgjq171CuwpUu8NCyrZniuQG4YnI0h2g2MTBWuZXQqCnY9fUWkiiF/HJNGYLiIoDA+E0Ow6hkY\nL8zkp2ZimLLkHNcCJoIJzKkM/WOFa54KJBBFIee4FjAdSJKNCuyZKtzHVCRNakakb38wH6Ob8yfp\n26LltT3R/JpuPpwiPSOxvz+Ub+ePpJHCKQZ6C0nigVASaS7OrulIPt4VTMhMTmXp3RnIxwYjMYmJ\nPSn2B0L5djFZYsaZYPtcLB/fi6YyDEbSbPPH87HBaDrDva+O8usNg6QWkr8T6Qx7Z6JsjRaUTBJy\nlk3BOEO98/lYVDqdYWTvLGNDwXySeFrKsm80xHXXLimLff785Zzjglws8sEtYzzy+H4iCxUDMhmF\nvn4/wWBCZXtm/QCzs4ujzz/2/ADzC2QaRYEXNo8xPB4+wlE5PLO+P++4ALZsHa84zk807Nmziw0b\nnudLX/oKNtvJl8z9ZpFRFv/3ZrF582buv/9+ACYnJ3n66ae59NJL3/CY9+zK6xRO4RROThwPwkYy\nHuW++37LihUrOffc84/Tlb278VauvL797W/zzW9+k6uuugpFUbj99ttpbW19w2PeNuf13HPP8f3v\nfx9JkjjttNO44447VBVFM5kM3/nOd9i8eTMAK1eu5Fvf+ta7turoYrDooVBpu2KRB1dqttjdj0rb\nJEKFHiueY5HnXfy1LPLYRbY73KHlk8bFnmRx5zhWVHr+lc5U+Z4X167ieY/l3S2yv+OF40HYmB3Z\nz9jB/fz7v//Xe3678BCOd/LxnXfemf93dXU199xzz5s6/m3ZNpyfn+dv//Zv+e///m+efPJJjEYj\nd999t6rN/fffz/T0NI899hiPP/44yWSSe++99+24vCMinpC475G9fPsHm+gr0aA7ElIpmYcf3cff\n37mBfQdmVf/3oeuWsnSBMKHTiVz5wS5WLi+QPCQ5wxNPHqS/fz6f0KzRCLR3epiKS7QsqI+LYi6m\nEZqK0LFAGhFFgUvPbeL8tfX5/jJZhd/vmGT9oJ+uRgcCuR+RS0/z8flrl3LhGfUIQs7W2ewkNBJm\nhdeCKOR+gJbU2hiSZNo63fn8s5Z2F/vNWlq7PXlbe6ODyGSU1V4L2gVbT5WFxFiY5XV2tAskijaf\njSkpQ3eLE92Crb3GijweYa3diH7B1uU0kZ2O0tXiQq/LDdnGRjuBGoHubifGBTWJ5loLCQ909zjz\nChhNXgspRaG1w52Pz3S3urjtumX8xTkt2BaUVBodRjBq8K2ozqtYNNXZuOXaVXy6axk2XW4S5dWb\n0U0aWeox41ggatQ6jLhcJnq8FlwLZIsGh5Fvvb+Lv7xuGZ4FlZNal4mv37Ccb7+vk5oF8obXomO1\n1YCnpwrXAinD4TRSvdSLp9GO55DNpqe1wc4P7t/O5MLWsD+S4vuP7KVKK1DnWGhn1LLKZkTnNFFd\nnVPZt1h0tLW60Os11BTZPvHRlSrSUiUkEhIPPrSb1HycxoW2Rr2GJQ12fvPAzkURMG64bpl6nF/e\nVSY4fDxxPAgb04N7aG1tZ/nyyvlw70W8lduGR4O3JUn50Ucf5ZlnnuGHP/whAPv37+e2227j2Wef\nzbfZunUrTqeTjo4OAH784x8zODjIP/zDPxy23+NdSbkSxqYifPcnrxOOLaiZC3D1JW1cXVSx9nBJ\niPPzcb73g00EihJvL7qghRtLkkt37JrCV2fDW2XJ26LRNN/995eZKYphtbW6mEtlmAkV+uv0WUkG\nkswWxbDauqq48dqlNPkKSZ5yJsv/fWgX/cX9uUz8+cXtdBSJs/aPBPn5I/sYL4pXeX020h4jA0Xn\naLbq0Zt07C5SOWkyaGkKpxkYLcRC3NUWnFVmBvYWHLfbY8LQYGN3URyk2qKnRRDpL4qF2KtMuOps\nDO8qMCyddgOebjvbMoVjXVotTXozO+KF8zo0WtokEzsHCknFdqOWa9c08Mmrl+D35+4lmJS455UR\nNgz686suu0bkIy1urj2vJe+Q47LEr7f1sv6F2Xxul9GgobXDw9a5KIfSzIxagSt7arjp9Hp0C6zG\nZDrDqwdmOWdJNbqF/LuknOG+TSO8uHksL6as0wisqbaybSZKauEXQCfC6XV2Dvb5SS/EzrQagUsu\naOHJXVMkFvK4RFFgRZuLsX1zJBO5+JcowKoWF8P988QXbIIAa0+v54brKxMmisfy5FSE//ivVwuJ\n6AJ0LvEyORYiWiSc/L5L2rju6iVlfZWi0jh/I7xTScpSKslD//rnfOMv/4KPfvQTR93PIZwsScq/\n7P3lott+ovPYn9uR8LZsG05PT1NbW5v/XFNTw/S0mkiwdu3a/L8nJye57777+M53vvN2XN4bYsaf\nyDsuyAWrF0sVDoSSKscFMDAYKGu3akVtmS0WS6scF8BcIMGMpE5snQ4kSc+pg+7T42GV44JcIL+/\npL/hUFLluADam5xMlJS6n52IEBbVc5zhaBpNSSL0SErG6FerV/hnYlCSFOufT6BxqwkyM7E07rT6\n3sJzCcwlm07BcArFmIWiWwnIMnaLOpE3lJGJJNWSPOGkTHWNRbUN5DTqkBVFtV0YzmRx1ttU6iZm\nrQ5pVqNKSk6mMsSzWYrzo5OygtusyzsuyK1ULix5x0atBieoqgBIGYWQVsw7LgApC3Epm3dckEsa\n7x0P5x0X5NImMuF03nFBbpsnnpLzjgty4zebVRbF9Jufj6sUVFAgEUqqHBfA4FD5mK6ESuP8RMTU\n0F6yWZl16858py/lhMJ7spJypcXdofIBpdi/fz+33norn/rUpzj33HPfsF+Hw1SmJHG8USqUC6DT\nafF4CrNHQRBUnw9hdq6cyaXVihXbliJVQRmhkh7d4TTqSs8Rr6DSUKnd4ZD7wVdKbOXtxEXGByrN\n/DSaxdkqvXPtItvZbMay96XXl38NrFZD2bOpVGKkEs3cYtEv6rmaTOUOpFRQ93C2is+gwrVodeXt\nDAbNYa+v+NmUCkIf9hzaw/f3TqHSO10s/OO9OKp8dHY243Qe+30d7vfhncLRxvDekyVRamtr2bNn\nT/7zzMyMShrkEJ5//nluv/12br/9dq666qoj9hsKJY7Y5lghKEpO26/4zSlZpqYj+S2gw20LyHIG\nvV6jkuix2fRlbScmI7hcRkzGAgV6eCSI0aAhWbRqcdgNhKNp1UzaYtAimrXE4wWbx20qO8fUbBSX\nTiRQNIN3mXX0DgdwFyXPBoNJHBY9wWhhdu2w6dFb9EwWbVfaTVp0eo1K79CuETGbdISLjjWZdFjs\nBvxFx5oMGkyiwFTR9Zl0ItoSJX+DXszHuPI2nYglrv4W6QUBYwm5TCuIiCU/+hqNwMR8DEVR8s9H\nzmTRxtQrCQ3gn4wwX1NYvWaziqr6NYCAgrFkVSmiEJ2OqZ6/oigMDQdpbXGp2iYC5ZMbnZxFUBSU\noh8YXSaLgIJStAqtNElQdAKCKKAUTZFtVgOiKKhqyaWzCtMzEZXDHxkN4auzUVNjy1/79HQEjUZQ\nyYppNQJaraDSw7RZy8f0+ESYKo8ZwyLywA6Ho902jEUCJBJHzzacHu3D4vTi98fIZI5d4PdE3DY8\nGgd2ojmvt0VV3uv18r3vfY8PfOAD2O127r77bjo7O1Urq02bNvFXf/VX3HXXXVx44YWL6vdYVeUX\nA7fDyNrlNYxPR0mmZJrr7ezp87Nl1zR1Xgtet/mwqtE2q4Ez1viYnYsTjqS47uoerrt6aX7gJFMy\njzy+n/t/vZNXNo/hcpnwuM089sQBHvztboxGLfU+G7FYmis+2MUnP7qSC85oIBRJMz0bo8dnY2Y0\nhEYj0tjgIBxJcdmlHXz5tnNIL6y0MpksTz/Xz8/u24YllKaj1sa0lGGpz85UKMkfdk1h1GlorbKw\n4Y+D/Oinr0FCpr3JQSCW5qIzG7ntE6v54MpaMopC71SEJT4b/phEMi6xrNrKfDLNGqMezVCISEyi\no8lBKJKiqc3FPAozSYmOFhexUJK2BjuJlEx0OsayWhtzcob2aitJKctYWqaj1UkikKTFZ0OSssz4\nE3Q2u4jG07R4LYjxNIEDQZbZrQRMCq2iCVN/ipneEEvtNiIGhTqrlVRCR280TWeTAymWptZrQTHr\neO7ALBOBBF3VVobHwvzgF9vp3zvLCqeJqE6kSa+lYS7Jlq0TTMzE6Gh2MD4f51/+Zw/bB/y019lQ\nlBzRokoRGevzs8xmJG3S4NVqaQulef21CQbHQrQ3OQkGEvzop6/zxJO9DAwGaG1xEY6kuPenr7Pt\n1TE6XUa0Fj0mg4ZGvZaB/gCtHjN6oxazVqQDgaEDczRWWTCadWiMWlyNdvZOhGnwmLGYtGi1ItU+\nGzvnYrgbbLj1WvSCwGeuX8ZVf9LOyuU1jI+HkbNZvK0udvT72bZ3loZaGzqNwK9+s4vf/s8etu+Y\noqnRAQo88NtdPPFkL26XCY/HjCxlaGp0MDAUwOEw4q2yIEkZbrhuKVdd0Z0f04mkxMOP7OOXD+xk\n85ZxPG7zEUkhh8PRqsr/7oXtue3RjPym/5KxEPLIq1z1vvM555zzjwvT8GRRld86u2vRqvJrvG89\n0eVtU5V/4YUX+Ld/+zdkWaarq4s777yTTZs28fzzz/P3f//3fPSjH2VwcBCfz4eiKAiCwNq1a/nG\nN75x2D7fDsJGMf7hrlcZLFEQ/+afn8malXVHnFklk3LZttO9P3udbSVq4R3tbvr61UrjN318FWeu\nU6uU3//ATja9MqqyffhDy7jw/BbVTO+Rx/fzzHP9qna1ZzXwWknNsQusBvq3T6ls77+0nWuuUGuL\nPfL6OD97Wa2OvtZlZnCvmknZ1uVh15T6/ayotTFwUJ2c2tTjYVtIPUte4TQxsU/NYuvw2RgrSWxt\naHXSV6IkX9fq5LWSna02h5HxCfW1tNmNhA6o+6vxmJnxx1VixVUeEyNyRmXzWHSkpmIqm92qIxqX\nVSsci0mL4k+obHq9SKakWrZWK5Ky6FXxL41GwJbOkCyqlSaKAskWB5GilbcogKHKTCAuqWx3feI0\napwF0oKiKHzz319mcrZYyT9HvJmaUsc46322sufVUG9nrCQp+Uu3nElPt7ra810/2sLuPTMq221/\nto4lPWoZssXgnSBsJOMRtv36W3zuc1/k2ms/dFR9lOJEXHkdDWHj7r2LJ2z82dKThLABcNFFF3HR\nRRepbJdccgmXXHIJAA888MDbdSlHjWyFiKVcQRm8EirFSyodW8kmVBpnFeYclSZTlfqrdB9Spfuo\nNK2pcI5K/R2TrcK9ZSvIt5eq5ANUEJdHLpXdp/L9ZjLlKvtSBVsmq5Sr8ctK2b3IchahxCZVUvKX\ns8gl95fJKGRKrjGbVdTb1+SC6HK23CaWxAAFQSh7Xoqy+DGYqfD8K8VbF9vfiYx0Oo1ef3SrvpMZ\nJ9q24SmFjVM4hVM4hSLo9XrS6dSRG77HcKI5r5NC2/DlbRM8tXG44uywGK9tm5CkznwAACAASURB\nVODpZ/tUM8HtOyZ58uleJPnINY7OPd2nIhAs7XBTU6XWMDzYO89jT+xXbfccDmtP96l06pqbnJx5\nRj0WS8HW2GCnpdlVduzqVXUqJqS3yszgUJBQ0Rbc+HycybSsqqrrcZuwJGU8Ref1WvToyCmiF7db\nvlStXO4PJpkcDFJTdF6XWYdeAK+7sE1js+jR6kRqXUU2kw7FoqXGW2BdWUw6bLJCY9F5rQYN9izU\nVxfamQxaRLeJuvoCrd9o0GKx6nMCwgsw6DVYbXo6iq7FoBFxWQy0Vxe104o4PSaaitT4tVoRl9dM\nS2ORTSNQXW2ho66wfaURBXxVFlqLqmCLokBdk53WNpfK1lpno621YBOEXK5ee5srv0oWBOju8dLZ\n7FTZWttd+Lo8qtV0a7ub9iqLSo2/u85Gu82Ipsi4xGth02vjqtXc9h2TVDsM+QRxgLZ6G26XKU88\nAmhtc2N3m9EVjfOGRgcWpxG9vrAf29Xpoba2PJa1bk29apehrdVFQ706HWNoOMjDj+4jFnvrY9ZH\nA6PRSDy+OM3H9xLeSlX5o8G7upLy3gPT/PThvRxYyJ1qrLXymeuX0VzyZQkEE/zqgV3s3Z+Ly9TW\nWLnmym5eemU0vz9f7bXw8Y+uoLPdwxthLpDgf57uZXWPlzNX5UqMeDwWRkeD/ObhPWzZOg6A02Hk\nIzcuVylmVEIkkuKRx/fT3OTgvHOaEUWBaDTNo7/fj6/OxoXntxyWDh+PSzz2xH4mp6IMDPrJZnPb\nkx+5cTkDsTS/3zyKnFEwakWWW/RopCyDQznhWa1Bg3t1LYICc9smyUhZtFqBlmYXrS0uLv9Ap+rH\n6qmNwzz6XD+pdAaNRsC3xIuigZn986RTGUQROpucZESB3lCCuJRFFKC7wYFGr2HffJyYlEFE4TSH\nGX1CYnY4SCImgQC1q+uQDBpie2ZIRtIgQEtnFZJBw8FMhmA6g6AonG41YI5KjIdThBbiPB1NDrJa\nkYFslvkFRmBngx1Fp2EqmmJ+od0ylwltVmFKzjK98MO52qTHGkkznZaZWWBJdlVZMGQUZmSZ6UjO\n1lZrRSOKhGNpphcmCO0eMyYFprUCowv9ddmNOBMy6UCC2YVYUr3Phl6vIZ6QmJ7OxT58dTYMBg0R\nBcYWUip81RaMRi1+o4b+SG7m32rS4YvJJBSF4YWkbm+1BYPLiBKXGF9ICPfUWNDUWtCG00wM5lRg\n6rwWbnh/O5s2juRL6birzJirLAgpmdGF743LZcTlNJLWiPRN5q7ZbTNQY9YhiwK9C+VTnFY9DXYD\n56xr4MySStHFCIWSPPr7/bS3uTnnrMY8OSCdzvA/j+zlpU0jKApYLXo+dN1S1hWpwBTjaGNen/+n\nhxG1RxfzSiViBDbfz/LlK/nKV/76qPooxckS8/ruzsXHvP5y5Vsf83pb2IZvFZ56cZA/bhnPfw5H\n02hEkZUlQeTXt02yfsNg/nM0liYQSHKgiAAQi0uk0xl1zasKMJt0rF1eQ0Nt4YtlNuvZvXea3z26\nL29LpmQCgQTnnNX4hv0ZDFpWrailualQJ0iv17ByeQ2tLa43ZAXpdBoaGxz89uG9+TiKLGc50O9n\ny2w0PwOSswrzcobocEFtPZtRiE9EiI5H8tTqbBb8gQRf+dLZZXlE//rj1/KJsooC4ZkYUjCZV0JX\nFJgPJgnqNcQO2YC5cIq4XkNoIWFYQWAyJeOcjRMpSoCNTkWxJmXCRUndwfk40UY7M4fICILApJTB\nrgjMFbXzh1JkfFYmiij6/nAKq03PWNE5ZpMyDpuekaLV6ZScocqozTsQgPm4hNNjYqTIFoim8doN\nDBclegcSErY6K31FNbTmUzJNGpHJoYKMWCSSpspjZrRIeSQSTeOstTFQRJaIxCSsjQ72BQv9BeUs\n1Q4TwyOFxPh4TKLGZmCoyJaISfiMOoaLzhuNS4Tm4/QeKJBfEnEJj0WvapdMyljdJvqKlPwT6QwW\nh5GBorpfyXQGT7WVD1/5xpWEjUYtq1bW0tToUI3fyakov3pwV/5zWsowNRXhwvNbKvbzTrANRVFk\n377dmEjygQ9cflTnL8XJwjbcOLV4tuG5tW892/BUzOsUTuEUTirY3bXHJA9lr/IxPd17HK/o5MDx\nqqR8vPAuj3lVSNSsSJFb1KFUptcdQ3eL7K/Szu2id3MXeR9vRtm7siJ5hWuseOpK7Y5hZ7ric6hg\nq2Q6+kMXf3BFLPbYRdqEo7+5RY+jyg+h/FIqtVr0+H13RCgcHh/T01On4l4lONFiXu/qbUOPQw8K\nDI6FUBSFHp+dkd5cDKalxYlmYV/XV2dDqxEZHA6QzSqsW1vPxz68HKtFz8BQztba4mRiMkIonKKt\n1fWmZKfMZj1GoxaH3UD/oB9ZztLU4CAcSTM5FaF9QdG7EvYP+Pn3+7bTNxKko9mJUa+lr9/PXf+9\nhd4+P+2troo0+0MwGrQ0NjgYGAyQTMrUVFuwWvTU6TRk9BricpYGqx5fOovLYUSrEUgkZepqrXz+\nM2tYt6aeoeEgsZiEx51Lkt76+gQtzU5sVgMzszF+8vNt6BUwmnVEkzJuh4HqKjMGnQazSUcsLuGy\n66ldiBVZbXrCSRmnRU99lRldRsFm1hFKyTiNWjqsBmJ6DW6Ljngohd1u4OMfXsHFF7UyMRnBH0hg\nseipaXORDaVyiiEpGbtW5HQEJH+CqioLwVgak1FDa72D1FycGo+ZuZSMWSuy2qRHGg1T57EwLWew\niALnypA9ME9TnY1xOYNZgHMzkBoMUF9tZSYpo9cKdNU7mA4mafJamI+k0GlF2lucjCYkmr0WAqEk\nOhFOc5iI9fpprrEwlZbRiAJLfXYG0hma6m1Ep6OIGgHfylr60xmaGuyEZ2MIQi6fb3YqQovPznw0\nBQJ0tTiZn4jSUW1lOi0jCArLOuyMG9K0NNgJTyZQgNZON8NxieZ6O5FgEgVo6XAzkpRobrATDabI\nZpWcTZJpaHKSmI/nx/l8IEFjg4NINNeusd3NbFymyWcjGpfIZBTqO9yM6kTqG+ykgykymSyreqr4\n1DVLMJsKZJ9dB+b4/s+3MTYdpaPZiV6nYd+BWf7rni2MjIZob3Oj12vYP+DnJ7/bR1WtlUxCRpIy\ntLe5uekTqytKsMHRbxs+uWUIQXP0yhhZJcvU3o2ceeY5FZWA3ixOlm3DFyYXv214Yd2K43a9h8O7\nmrBxKEl5YibKfb/YzmiRYG6118L//avzMBbJ08zOxQiGkipSxtx8nPt/vYPevkJisMtl4q+/cs6i\nqysXB2QjkRS/enAXO3cXhIetFj1fvu0sfHXqAPSjz/Xz2PqB/GezUcu5S7xsfGk4P7E2GrXc+qfr\nVKy1SkilZB763V5e2TyWzzfSLZSOP7hnpmDTilx4QQtXX9Gd1w2U5SyPPL6fP24cyucCaTQCF5zX\nzIsvjeTZmaIo0LWihj1DwXyelCgKLOtwc2AwkI+JCQL0LPWyayqSryIsCLCizc3B0RDJBUkpAbig\nq4qbL+vCVPSD+MyGQR774xDxIsbmkg43073zxIuScTuXeBmcixMtsnV1upmciBIpYrJ1tbsIDAVV\nMbbWbg/zU1HCoYKtudPDEAr+othZh8/OVCbDXJGty2PG0OvHXyRC7Gt2MuIxMl18DpcRJSoxVRSf\na3CbsM3EVaLLdXU2kgaNKoG4uspMepmZkUjBVms2YJmCoWI1focRm1FLf1EVgCq7AZdZT29RkrjH\nZqBVyjJYJAztcBhw+OwcLIqduRxGzK0Odhb15zTp+OyaBi4oiQc/8L8HeOalkfxnm0XH2ja3Knne\nYtax/IwGNmwtxKZNRi3XnN/C+y5secMf0XeCsAGQjEXY8+g/8/nP38INN3z4qPs5hJOFsPGt1361\n6LZ3rPn4m+7/zeKkiHn5qq3MlShGzMzGSKVklfPyVlnKyjFUecwES5TfA4EEsZi0aOdVDJvNQKJE\nzTwaSxMIJsqc10iJikE8KTMyFlbtCCWTMnPzsSM6L4NBi0aj1rCTpCxyXFLb5CwWs04leKvVihiN\nWlUSayajMDoWVqUVZLMKyZSsSvDNZhWS6YxK9VxRIJHK5B3XIVsyKecdF+RmaGlRUDkuAE+VReW4\nAFKRlMpxQe65RktssXBa5bgAYsGkynEBxPwJleOCHEvOr1H/mM5HU8yVpGDMhZOYStXz52JM69TH\nTkZSEFJfy2QgQaJE3X9yMkLKqhbpnZmLk4ip+5uKp7AG1XPNmVCSRFr9NZ4Lp8qSmecjKVxJdTpI\nKJQia1dfXyCUJJlUf0eCCYnqChJPpeM3EpMYLVHhiMUlRibUtkRSxlVlfsuKPB5rMUqtTkdM62H3\n7p3HxXmdLKiQ6/+O4qRwXqdwCqdwCodwrIQNgKr6dvr6TpE2inEqSfktQCCYwOlQr5IcDkNZnCkc\nSTFeMgsEqCpZjVksOkymI/v1REJiqKSycjKpXu1BjvpuL9nXT6cz+UrBh6DTinhLkp61WhGnQ/1F\nlOUsvX1qXb5MJlsWDxdFgWqvpcyWTmfKAupVHrOKqCEIoDdoy2y1VeayvLNaj1mVJAtg0mnKbHUu\nkypJFkAPSJJ6RRCJpVSJswBGk67sfZpNOgylNrMWY4k6vcmmL3ufZqtBFbsBsFr1WEviiw6zDnuJ\nrcZlxmFXv0+7w4jLrO7PbTXgKllRue0GnE71WPV4zLhLxq/TbsBjVJ/DZdTjsqn7c1h0OEpiQ3az\nDmfJtdhMWqwl12I267CVPgOzjpqSazHrNczH1KvUZFLOV7A+BINeg7GkP71Og7Hk2eu0ItGS1bEk\nZ+jtV4/pdxLO2iYmJsaIxU6c7b53Gse7kvLXv/517rvvPgASiQRf+cpXuPzyy7niiivYtGnTEY9/\nVxM2IpEk6zcM8uOfvU44kqK9zU0slua8c5r5wmfX5EuMZLMKL740zH//+DU2vDhMPC7R1lYgZaw9\n3YfVomNwMMDa03382RfOOOKW4dbXxrn73q08v2GQmdkYLU1Odu+d4a4fbWFkNExbixNJyrKkp4o/\n/+IZ1BRtu+zeM81dP9rCUJ+f1kYHGQE6m538n0+fxrlnNtJQb2dgKEhLk5NbvnhGTul7AQd757nr\nR1t47vlBBgcDtLa6mJyKcs+9W9mzb5aGejsGQ07J4s++cAbnndNEV4eHoeEgVqses1nH9p3T7D84\nlyNlLDjVhno7S3q8jIyGMBi1mKosHJiMUF1jxW7SYbfp+fxn1nDxeS2s7vEyMhnGZNByy8dW8r5z\nmjltqZexqQiCIOB1megfDFBjNeB0mzAbddx6RTdXndnEWd1exubjZDJZ2vUaBl6b5LVtE9RUW9Ho\nNPz4od08tXEEhzVHAMkqCvU1Fg6OhDA5jNRXmclkstQ2Odk/EcFq1uGrtiDLWeqaneyai2Gw6Wmu\nMiOlM/hWuNhpSqLUm2k3m5DiMg1Lq3k9mEBxGmj3WkklJOq6q9gZSaLTiLTV2YgnZdoaHewLxBFF\ngc5qK9FUhk+e3cQtl3Rw3tlNJFMyU9NRGrqr2JuUEKUsHfV2wimZnlobQ3Mx0hqB7lobsbhEZ72d\n8bkYEb2G7gYH8XCK1hYXMzNR5Fia9lYX4bhMc4ebXkEhEpJZUmcnKkt0eWxMRxOEjRl6vHai4TTt\njQ7G0xnmJZklPjuxuES310J2IEhiMkpnq4uwnKXba0UZDuGfj9PR5iaekGhudBCNpfBPR3OK/1KG\nM5bX8K2/OIfz2t14rAb2T0Zo9ZpJpDOs3z/LTDjJkjo7u3dPc/e9WxgfCtLe7CCVVWiosSHJGUbn\n4rQ2O1HSGWp8NgJOA/2hFG31dgQ5S603Nzl7dec0I5MROpucDA8HuftHW3nu+UFGRkK0tbgYnIvR\n4nNwNDhWwgbk2JJTezeybt2Z1Na+ce7nkXCyEDaeHls8YeP9DYcnbAwPD/PVr36VjRs3cvbZZ7Nq\n1Sq++93vYjQaueeeezj//PO59dZbufHGG9HrD1809V1N2PjNQzt56Hd7VbYLzm/mIx9arrJtenWU\n+3+9U2U7+8wGPvmxVSqbJGUqFv4rxb4Ds/zwrs0qW3OTQ5VICtDe5uKrf3GOyjY6FuL//9eNKlt9\nvY3b//oC9bXImbJCh4Fggv/v2+tVNofdQDiSUiuc2/R85+/epxqgiYTEX/3N06pjdTqR7//LB1W2\nbFbhlm89S7GWqoDCD//2EtWKMpvNCdEWszIVReGv/umPBCPqWfU9/3Ap2pLvyh13bmBmWq1m7mp2\nMllSwNNXbWaiRDm+scbKaMmxNe0uBkpiSa2rHfRF1baepIneMXWspttn50DJirytzcXeknPcenEb\nly5TVwP+yVMHebqkMkBnh5tdk+pznFZlYV/JKn2ty1Smxl+31sfWoDqetqrLxt6I+vp6THZ2DajP\ncYbNwMQ2dWWA9nYX/f3qSsdtLS4GSqofr15Zyxc+u0ZFLtgy6Ocfn9ivarfcZiBQco66JgeD8+pr\nrqu10ldSsqjRacA/or7mWreJYEllcpfXQp9W4Ll/vZKjwbGoyh9CIhZmx4N38MlPfpoPf/hjx9TX\nyULY+OqmxRM2/u3swxM2/vmf/5mlS5fy0ksvsWTJEm666SYuvfRSfvCDH9DTk0uAv+2227j00ku5\n5pprDtvPuzrmlamUUFDBVEm5vJIi+WIc1+GOrWSrNC2odC1KhUBopQq92UrnraBwnsmWV0sVKkhM\nVbpmURTKlv0KQtlWoSiW2wRBqBjUFUWh7GEolVTiK6rOl/eXqfBgKylTVorZV2y32By2SgUgK9kq\nvfcK3VVsV0m1f5G5aUqFm1MqjbdF5uJVrFJQaZxXem8Vrq+S9GglPdJMJgsVxv9iEYsEELVHX4wS\ncjJR9fUN7N27h1AoiN3ueMsIJu8WHK+Y19e+9jUAXnrppbxtenqa2trCxLCmpobp6emyY4vxrnZe\np3AKp3AKpThWtiGARqNhQnay/eUdJBu38jc3rMXhcB75wJMYbyVhI1thlnqk1eG7mrAxOBRQERKc\nTgP+QIKRom0IfyDB/oNzqmquTqfxsGKgxQhHUjz40G4OHCxow0WiKbbtmKSxoSD+a7XoMJt1NDYW\nbGazjvPOaVL1F09KvLh9ksY2d95mNGi54LzmI15LOp3hxZeHVZR5rVbAV2ulva3YJnL5ZZ1lx+t1\nGi44r1mlXN7e5mL9C4Nlq8EPnNecJ1uIAiypt7H+hcH8LDmbVfjjxiH+8HRvnkqvKAobtozhq7Go\nSBkdzU7+sGGQdBEp49UtY7jdJpVyeWuLi2qzDkORraPOSpVRi7GIlNHaYMduzVUePoSWejtuQcBS\n1K7ZZ8OQ0mLRFNmMFrQmHbYi1f4ljQ4uWVWH01LYW29zGPD4k3iK+uuusbK8Xh2DGRoLEZ2J4Sk6\nts5tQidn8RbZfA4jWb1IVRFRo85uQNGIuIsIOh63CVs4TaOxcH21Jh2WKYl6TVEFAVGHdS5Nc9E5\nvDYDF55RT3NT4QfW6jSSdJvxNheu22rXg1VPbUPBZrMZOGudWoMzHE+zZe+MSqHfqs8lpdd2FMav\nyaTFbtLSXqQwb9Rr8GpFlhZVFTBpRWqMOtqK4rd6nYjHbaapqD+dXoOnzsqy6qOrvgw5tqGjqu6Y\n/6rqO4gGZ9Aajm0L8mTBW6mw4fP5mJ0tbKHPzMyoVmKV8K5eeW3fMYUoCnS0uxGEXKmF3Xtm2LN3\nhgvOa8btMvHEk72k05mFH2s3TY12rry8u4wRWIqXXxnl4Uf3kkjI/HHjMGes8dHR7uGx3+8ntpBb\n1NriRKMRmZqOcnBB5Lel2Um118L11yxRKQds2zvDLx7bR2ghHtTa6cFnN3D9VUvK2GelOHBwjl8+\nsJP5hdyiep8Nk1HLfCDBwYXkal+djSqPmWuv7mHpkpqyPXZRFPjIDcs5a10Dv//DQWZnY/T2+ent\n87N56xif+vgq6n0553vjZV2cvbqOh5/qJTgRYfDgPIMH59ny2jhXXNbJs+sHGR7JxW+2bB3niiu6\nefqVUfoWYn4epxGP00g8KdM3HKRvOMgfN49xw6XtvLxxOP+snA4jVR4TqXSGwYUYjMNuwF1vI5OU\nGV24N5vNQGujnWSWfCVrm0VHV50NSc7mbVazjtZGOxGjlgMzUQglsRq09NRbkFKwfzAXb7EatSyp\ntXFRt5cLV+S+IGd2e3nghQHm+/wMH5gnpIDDoKFzqZfT1tTz/mU1+W0jWc7y4P8e5PnNoyhK7kd4\nZbMDyaDlwFiISX8CnVZkVYMdWadh/1SEiVASnUagp82FOSoxOBjgQEZBqxFYsrIW4mkGBgPM+xOI\nosDZS7zIBg0j/QF65SyiKHBajwu0AiP7AvRKOdsZbS7qOz187MxGDDoN5y6rYePLw2zqnWdfIM7w\nRBgBWHJaLZZEhoHpKHNDQQQBOnu8NHlMXHtljyrX7o+7p/j5c/3EFnLtVjTYEXUiY5NR9gZy7717\ndS2OaJrpySgHe3PvqavZiWjUMj8epm9BDHh1i5OM08jcZDQ/VpvrbRh0GmbmE+xfSJpu6nBj0YhM\nhVPsG1LHwN4pmO1uFEUhnThx4lXvJN7Kldcll1zCb37zG77xjW8wOjrK9u3b+bu/+7s3POZdvfKC\n3Cqgr9/P2FgYqUj1fMOLwzyzfoB0uqB63j/g55KL2o7ouADWvzBAoqjc+pbXJlj/wkDecQEMDgWJ\nxdNEi9QXhoaDnH1mQ5nkzQubx/KOC2BwIsJpa+qP6LggRziZL0qKHZ+IoCgQCBT29ScmIyzpqaLm\nCDPW5iYn9T47s0XEiNGxMNt3qoPwDbU2unx2potIC1NTUTa8OJx3XADTMzHWvzySd1yQU5dXFBgr\nUkyfCyR4vshxAQRDSTLZXDL0IYTDKUjKjBb1F4mkSCdlBosSYCMxiUQqk3dckFNRj8elnOM6ZEvJ\nBAIS+8cLRIFoUiYgZfKOC8Bs0HL5ah9DvfP5WFQqlSE+HOIDy2tV8Y5IPM36V0fz7dJSlonxKPtG\nQ/lZpyRnGRwPs2cinI/lSRmFvTNR+vr8+XijnFHY1e+nr79gy2YVRvbMMNofyK9ss1mF/r1+xg8E\n8+M8m1UY6PNz/WofhoV4rSgKXHBeC8MJieSh7wOwdyrKVCSVTxJXFDg4GuKCC1rLksT/d+t43nEB\n9I6FiUXSKtuB6SiJpKyivI8MB8nE0kSKvg+jQ0GUmES4yDY8HkHOKAQjBfr9yGSUtCgQCJ84RSAN\n5tx36ZTzyiGbFRb992bxpS99Cb/fz5VXXsktt9zCHXfcgcv1xsIM7+qV1ymcwimcQimOB2EDIB4O\nks3IJBOxxQscn8TIZo4vYeXOO+/M/9tisfDd7373TR1/0jivSmw6sSITbHGDsBKzqJKt0jkqXUvl\nk5SbMpmsSroJqFiMstI5FsuGqhQHXawqfsVrqaRCX8lW6dhKp11kf5Uec0X1/ArWSs+q0thY7Duv\n9ExFIXdmRWXL3UvxqXI2QXV+QSg/TyUbHCbYvcjnVel9Vj620j0v7rlWtJWf4rix+Y4HYQMgHvGj\nKMphi8G+13A8nunxxLs6Sfm3D++mttbK5z9zOuef28z4RIRAMElNtYXPffp0Ljh/wRZI4HabcNbZ\n2bh9Cl+1hSpX5SCsP5Dg57/cgd8fp9prIRROYbcZqKuzEYmkqK2xEgqlcNgNfOIjK3n/+9qJRNNM\nTESw2Qx87MMrVNWTI9EUD/52D4O9c/jq7fjDKcwmLfUtTp7bN4PdrKelxko8LvHQ7/byywd3YTHr\naKi357/MnR0e4nGJsfEQRoOWa67q4Zoru0mlM4yMhtDrNVx1RTcXnJerxHykpMjWFhfZrMLwSBCN\nRqS1w83mvnkyWYX2RgfZrMLTz/Xz7PoBWpqdhBe2d/7k4jZuvH4ZRqM2H6Nqb3MzMRaitclBOCGR\nVRS6mxzMDAVpaXIQScooWYUL1zXwqeuW4nQYGRgK5JKU21xMTUVpanKQSMjIcpbWdjdj4TRNDXZS\nCQlZztLS5mIikqLJ5yCVzmkrrltZw+c+tJzGOiv9wyFSUoa2RgfhQJKmKjNJjUBKztLjMaPtD9Ds\nNJLQa0jKWZbU2YjOxzkwGqSr3o7ZoGXvvll+/svtVC1o7iWTMg0NdqIagd2983Q0ObAsbK8ZDVo6\nm50MjIaIJSQaaiyY0hmqzTpEi55YSqbTZ+Or1y7jgh4vvdMRQgmZRqeRhlAat16D0WIglpCorTJT\nZdRiMWqwWPTEYhJerwVzlQW9UYvdqicSk6h2m6g26TAoCjaniUhMosplpNaiY/Mro/jq7LiLyBWr\n2tyMzcWYDaXw2Ay06TQokTQer4VQNI3HaeTzNyyns7lA8JiLpvjeUwcZDCZodBgJRNM4zDq6zHrS\nExGq6+34Y2kcFh1fuKyLKy5sIxBMMjUVxWLR4WtxMRVN0VhrJRhIYjbnbJPBBM11NvyhFGajlrYa\nK3NjYZoaHfgjKUwGLY0rXPRbJTrsVoL+BAa9hg9f/sZFLw+H53fPYbK5MJptx/Q3PbyP4PQoqy6+\ngXO7vRiNb17rFE6eJOXf9e1FUYRF/V3XvuwtuHI13tVJyi9s6Kenuyq/UlEUhb37ZunuqsonzyqK\nwpPrB3h0wyBSUcTx+vd3cMWFrar+BgYD/OCuV/NxMoCeLg8DQwHS6cLsdvWqWj75sZV5BQ+Px8Km\nV4ZpqHdgLpLlmZmN8a/fe0kVJ2vpruJASiaaKsQP1ra4mNs1rYoVrFpRwxc/t1Z1fUPDQZwOoypO\nNjIawmrV4y5yxotNihyfCPMf929nrkigts5rwZCQmCqKdbldJr74uTU0FjHU5ubi3PPjrUwUJePa\nnUasVj0TRXEom8PAbbeeTUN1gRUaDCX5yc9ep79I4dxi0eOut9NbFNeymrT4PGYOFvVnMWn5xFU9\nnLmqoHoQS0j8+KHd7NhfYIUajFpaGuwM7yjE8vRGLVWn17Gr6LwGncifinyv4wAAIABJREFU1Dt4\ndVNBCV2nE2nt9rKzqJ1OK/Llm05jSXuBGSfJWR58ZC+bXhrOL680GpGLr+ji2ovbCgSPTJZfP9PH\nK0/15tsJokDPsmr275nJ50kJAvQsq2HnUEAVHF/V4aZ393Qhz0+AJctqOLhvNs8AFQS4/polXHJR\nG8X4341DPPXoPuQikeS1Zzby8Q8tU0lr9U5H+OYje/JxMoAzvBbmtk2RLop1LV3j4+YbV2Apksx6\n9bVx7n/iIPGiMd3daGdsNq6Kk3U02gmMhokVxckau9z01wkEs4XvXJfWhH84wUN/fxlHg+ORpAzw\n3P3/hN5o4YzLP81fXtFz1FT5kyVJ+VNP/nbRbX9x2Y1vuv83i3f1tuGypdWqz4IgVLSZ7QaV4wKY\nnCkfTIFAQuW4IKeAXey4IEdbNxnVQe6uzqqy/sKRlMpxAURiaaIliZlTczES0RL18Sm1ugPkmIyl\nKJaOerOoqbGqHBfA5GwMU4kygj+QKCOCVFWZCQTUqgrhYJKspL63SChFfYm+otNhzK/mDiEWS6Mp\nmZ1GEzLhEnX5WELGU7Jqtph0JFPq95ZKymRLlN/TSZlkTH2OlJRlskRJQ5KyxGV1f5KcZa7kfnVa\nEa2iqPYFM5ksDlFUzWy1GhFbOqNqp2QVpLikSvBVFEhksmWsrnRCVieoKyDF06oEX0VBNeE4BLde\nq3JcAEo6U6YJORdNqxwXQDwuqRwXQCaSUjkuAJfHrHJcAJFkRuW4AKLRtMpxAYTjKYJZ9XdpDplQ\nQn3s243w/BQzwwc4+9ovvqPXcSLheMe8jhXvaud1CqdwCqfwVmDfK3/AbHfR1LOWdCpx5APeAzjR\nYl7veqr8YuB1m8pUyrXZbNkqy+02lSmX19Xayqj1dbXlRfIOHJwrqzdltxmwWNSzSp/Xgr1E8buu\n2lJGra+rW1yS5vBIkHn/kcuVj42HVQUQIReEry1Rsa/zWlQJ3ZDbNpyeUc/qpwIJ7NXqFZXHbaKm\nRm1z2A1MlKwig8Ek9hJVdrPDgMVRoo5u0VNXsmqzmnXYLWqxzmhcKlOSNxu1ZfdhNGqpL7lfo15T\n9j51eg2mkneu04kEkrKKVCHJ5er8Wo2I16s+B+RWuaUK/QaDpsxm0ogUC+8LKJh0okqhXxByBSyL\ndSUFgbL7BQhHkqpkcMjpDpYiNhPFVEJMcGgEDCXPVW/WEy9ZIduteqyVxnTJ2Df/v/bOPK6pM/v/\nnySQEAIJYd93RMAFFPcNwV1RsLj0N9Xa6b5M69SZ+drxq7ZfF9o6o+1UW8fWpY7WtaK1qFVxGxVr\n1Yqgghs7su8Est7fH0jgkgskIQvg8369eL3Iw3PvPbm54dz7nHM+x84K1u0+d3cHG4jbrWJ4iqxh\n30F3ZW1oqKtCQ63+P0VPMvDo1gX0GzYJMmkjZI2aT7TPI8ZMldeHXh3zaumkrA2lFRLsPZ6J0tIG\n2LKAwsJaONjzMf+FMAwIa02wqKpqxMEf76LoaR3mJ4QhLMQZ1TVNOHzkLvLyaxjnHz2eiRu3imBr\nw0X8nBCMGOap/nt9vQxJP93H/cwyzI0LReQQd9Q3yvHDxWzcfFSOP0QFYPwAF0gkcvyUnInbacWY\nE9sfI4d7dhpUbWyU49jPmbh8NQ+WlhzMmBqE6Cg/cDhs2hp7k1SB5BMPcOG/OeBwWJgcE4ApkwLU\n2okKhQonL+Xg9NVcTBvri6njfMACkHL+CU6nPIaHuxA5udVQqShMnOCLqZMDcer3pzh6LQ9KpQqh\nLraozirHuFHemD41CBYWbFy6kovkk1lwdxMiL78GcrkSY0Z5I3ZGMK7fKETyyQeQyhTw9xXjaWk9\nxP5iPKyVolGmRH83W1QW1iEyxAkJU4NgzbfEtdtPcfDkAwwKdkTCtH60f5RXbhXh0KkHqGuQw89T\niMqaJvT3t8eCGf0gsuHhTkYJDv14F74+dkiID4VIZIVHpQ3415EM+LrY4OWYQDgIebifWYYDhzNg\nZctDeZMCVbVSeLvZQtIoh5WIh3KFChV1UoR4ifDqlCDUVUhw4FAGysolcHezhVyuhFjMx8KEAbQO\nAm3Jy6/BvoPpUChUkCuUKCuTwNlZAK4lBwqFCiqKQmlpA5ycBIDAEkolBa5MieKSejg6WD9zJCy8\nOH8A/HzFKCmtx4HDGairk2HhvAEIaKPcUlxch/2HMvDwcSXs7KwgtOWiqUmJhfMGILhf6xJ3WXkD\nDhy+i/uZZRAKebAOckB+owx+jQoUZFfD1pYLRwdrVFQ3QeAsQHZhHYQCLubPaC5kb6GuQYaDJx/g\n7qMKLJgRjBGDXFEvkePwLw/xe2YZ7DxskfW0DtY8DgJteKjJqUZcbH+MGuEFiVyBfXfzcLWgHC+G\neSPG1wVSuQpeHvrFmF5Zuw8sjn7JFU0NNai8cRA2NrZYv/5zdWyoO9qGfSXmNS/piNZzD8XP1Xn/\nuvLcOK8WVv7fOVr7dgD45H8nwrHdHTlTyjrT2MZ/XcXjJ3SF7qV/GomgAIeu96eiNHpeMc1j4ocD\nd3ClTZIBACycNwDjxvjQvixHjt1Dyvls2ry42f0xOTqgy+OeOvMQx5Mf0Ma8wl1xu11sZdoQdyyZ\nTJekunw1F/sOZtDGAvzFGufKI8QRd9p1sh4W5IBlc+mdAZjsy3xSiQ3bb9LGgnzssPyNYZ1u6+Ag\nQGlZvca5Lyqtx8ov6X2ExGIrFLSLUdrzLcFq9z5sbLj4bO1kdIVcrsSf/3aKlirPYrWIGtNT5S0t\nORqrAxvWT6ElBQHNxcrt07lXfnIOle1idKv/PgHO7WKX6z67REu6AZqf4NrHzxz9xSgoof8D/p/X\nI9HPl15IyvQ5bfgxAzfb9Z9bOicEI/vT49Ptvw9OTporHNrQnYSN1OPbIS/8HVu37oCvr1/XG2hB\nX3FeLxxO0nrujwnxOu9fV56LZcO2MCpgM/hvJgfCNMasEq/l/hjqR7RxXB0el+F9MKmya2sfUzUO\n43EZLdSujkrbWyfGc8+ojq7dtkznnunOmvEYTGNaCrqx2SyN90xRmvukKOZ9Mta7MbwXJruZNtb6\n/WnZIYHpXDPfHzNc+2aup8rJSMWT2xfx1lvvGcxx9SV62rKhyZxXSkoKYmNjMW3aNHz00UeQyWQa\nc7Zs2YLp06dj6tSp2Llzp6lMIxAIzzk1ZYW4nrwLfoPGYuLEGHOb0yN5Lp1XRUUFVq1ahW3btuHU\nqVOwsrLC1q1baXPOnTuHixcv4tixYzh69CiSk5Px66+/GtyWyTEB6gQMFgsYM8obdiL91sd/u1kI\nLpcD2zbt1SPC3dQCt8Zk5HAvOLVZ6vRwt8W9rHKaFiEADBvqDtc2AXofbxGtiLozBg90oaXiu7na\nYMpwTwS6tS7nuNtbY2yY5v76BzsisE1NlKODNaLG+yG4X+tyqoM9H5NHeWNQm6UnB1seYsLdtbLP\n202IoWGtS09CAReTRnl3skXHNDbKceHCE/TzEqkfUPg8C8wc64tJ4W7qMSsuB7NGemHCuFaFfi6X\ngymTA7s8hlyhxMlfHiIwwB6cZ1kZFhw24meHYNqUIFg8e2ppiU1OnRKoTrZgs1mImegHnha6nEDz\ndd6SbMFiAWNHe0PMoKM5aaI/+PzWfY4f64OYif4QtFmajBzqjsljvGmxxsgBLvBy025Zb3KEO+zb\nfEcCnQW4c71AY1nTUGibsNEkqUOTpA61lcU4v38jbMTOCBmpX23Z84BSydL6xxSYJOZ17NgxnDlz\nBps3bwYAZGZm4r333sPZs2fVc1asWIHg4GAsXrwYALB7925kZWVh3bp1He5Xn5gX0JztdurMQwyP\n9KS1GNGWmtom7N6ThsxnrVIEAkv4eIkQNcEPYSHOXWxtOORyJU6dfoQHjyrw5FlBrQWHjfg5IYga\n76uep1SqkHL+CaysLDB2tI9OcjcqFYVLV3IhkyoRM7E5IURFUUi5/RT1TXLEDvdS/9Nl4tr1AjRI\n5Bg/xlvd7PP6jUIUl9Rh2uQgdXZn6v1S5JTWI36UD60FijbcySrDvUeViI32V6tgdEb7GER6Rgn2\nHkhH3bPaM1dPIdy87fDCtCCIhc3/8B8W1eJiejHiR/nA4VnGXF5+Da6k5mHKpAA42GtmGLYlO6cK\nu/emqTM+HR2t4eEuxOyZwQgLbe4CUFJaj5Tz2Yga7wv3Z46hrLwBZ889wfixPjrfFFVVN+KXM48w\nYpgn/Hw7vs5r66Q4+ctDRAx2w6iR3qioaEB9vQwnfnmIQQNd0P9Zgke9RI7j559gQJADBvbTrGvs\njEapAgf/m438h5V4+kxxnsvlYM6sYESNpy/RyWRK/HAwHX/98zidjtGCNgkbsqYGfDgnHDY2tli7\n9mM8efIY//jHF3B0dDJ448m+EvOa8Z/jWs89sShW5/3riknqvLTpkllSUoIJEybQ5ly8eNEo9tjZ\nWWHhvIF6b59fUKt2XADQ0CCHQkmZ1HEBzQH98WN9cOrMI/WYQqnCiV8e0pwXh8PGlEldPxkwwWaz\nEDXOlz7GYmFyhHZPRyOHe2p8eZl6qY0KccYoPc/foGAnDAp20mtboLm/WF2bouniglpMjfJTOy4A\nCHIXIqid8/D2EsHbS7vrKO1OMa1UobxcghGRnrT0dhdnG/y/BfT9OTkK8OJ8/a5VsR1fq+tcaMvD\nggR6goyNDRfzX6BL/NhYW+LFmcF62cLnWSAmxAWJP7cmAMlkSly6nKvhvGrrpPjtRqFexwGa+3l1\nlbDRJKmDUCjCoUP7cfduOjZs+BIBAZp98Ait9LQ6L5M4L6aHOw6Ho/Oc9ohEfFqti6kQ2mre1Vla\ncuDgIGCYbVzYHTz1mMOWjmCxWD3aHi5X82tgY2NlUJutGJ4Ira0t4eAg6FHnx5i21Ndr6vu1lHa0\nRWmAxlFMn2lbVHILpKVdx48/HsDy5f+DqKhR3T5mR/SkzxfQXwD5uXRerq6uuHv3rvp1aWkpXFxc\nNObo2kmzpkZzzVwuV6qXpzob6w41tZrHVShUGksDprClqqoR7aXLOWygrKzeYGrYKhUFiqK6zISk\nKAoKpUpdQ9aCtssmhj43HdHeHqVSMyWzUSIzqM3ydinvACCVKlBR0dCjlpWMaUtVNXMxffvj1dZ2\nv52JTNa5vFRVeQk+2/UFYmKmIipqmlHPf0/6fAGob5h0pafJQ5lEVd7JyQmbNm3C1KlTIRQKsXXr\nVgQFBWHMmDHqOSwWCz/88ANmz54NuVyOzz77DAsWLICvr2+H+5W00eBTKFQ4ffYRtn53AzU1TQjw\nF4PNYuHsuSfY+t1vqKxqRICffbf+OVIUhSup+Tj04114uguhVDardAT3c0B+QS1yc6sR4GcPKysL\n/Hq9AF9v+w2PnlTB308MPt8SN24W4uttv+HBw3L4+Yo16nV0Jf1BOb45kA6hvTWsuRxIGuTw8mhe\n2rpxqwjeXiKIhPolo7SQm1eNf393Axcv58LdzbbD2E5hUS227biJlHNP4OJsAyfH1jvNrlS1S8sa\nsOP73/HzqQewF/MZFUwMSXt7+vdzRJNUgbz8GggElpj/wgAMj/Toskj8x6P38P2e2wCau2p3drPg\n7y8Gh81GTm4VLCzYmDU9GJOi/bXqAmBKjGXLzd+LsPuHNLg42YDNaVbtHzzIBUsWhWvohPL5lugX\n6AAvL/2KlE/9lgMWp+PvlkqpwKWDX8LNXoA1az6FpWX3vodd0ZM+X0B/VfldNx5prSr/8lDjL8Ga\nrEj5woUL2LhxIxQKBfr164fExESkpqbi/PnzWLNmDQDgm2++QXJyMuRyOWbPno1333230322Tdj4\n7J+XkZff2n1XJORBLOYjJ7e166+tLQ8rl4+HoJ28kLZ8t+sWfr/9VP3aimcBT08hHj2uVI/xeBz0\nD3ZCWpvOxFwuBwNCnXGrzbaWlmwsfW8Uo9iuNvx8/gmSzj5Wv2azWRjsLcL9u6W0sVdfjkB4GwV2\nXfjtZiG+33ObVuMz/4UwTGgXA0tLL8Z3O2/R6n7mzApWx9k6u/N89LgSX33zq7pjMADETPTD3Dmh\netmsDR3Zk19QA7GYD5surg+JRI41n15s7vr8DB9vEf724dguj11a1gAOh0W7CehJd+bGsOXwkbs4\nfylH/drSgo0X4kMxboxPp9vpW6T82mdHwLbQjHlxLJoXmu5dTcb91JPYu30bwsOH6HUMXehJny+g\nf8JG1NZTWs+98JbxszZNJswbFRWFqKgo2lh0dDSio6PVr99++228/fbbeu2/rJx+cdTUStXt11uo\nq5NCKlNCoOfyc3m7YzRJFWhop8AulSo1LlSZTKlhn1yuQk03lkfK2qUZq1QUpO2U1VUqqlvpyBWV\njRrFqeUVmks/VVWNGgWrTPOYqK5pojkuoDmZwRy0bfnSGTK5kua4AO1tdnbqObEPU1HW7lqQK1R6\n30BqA1Mzypbswurqany48xKWvfsmBg+OMJoNfZHnMuZFIBAIpoIp27BJUgcbG1ts3LgB/v7+WLz4\nFYOmwz8PGDLmtWrVKly5cgVCYXOYY/To0fjrX/+q0z76jDxU+75WLs4CeHnSU5vt7fkoKNGvNqyq\nuhG2NnSla5GIp3G3bmurOSYQWNKKPoHmTLOKdnfrEokct9OediCn00pTkwIsJUVT++FxOfDyEtLi\nLlxLDurqZYwJCdrg5mpDUyRns1mMtUYuLjY0NX42mwVPD+1qkpwcrWlFsiwW8xNQ2p1i1LfreZae\nUUJLcQeAjLslqKmhP9HevVeK6uruJwG0wONxNJ6gHB2sUVys37XV06muaULGvdKuJ3ZA+8+Tz7dA\nRYWky+vc0Fy6dAF376bjgw/+AgsLct+uK4ZU2EhLS8O2bduQlJSEpKQknR0XYKKEDWPRNmFjeKQH\n7OyskF9Qi0nR/liyKAIjh3vBXsxHbn41PHzskFcrRertYpRWSBDkI9ZoyMeESkXh3IVsfLfrFopL\n6uHjLQKbxcKIYZ54/Y9DETnEAwPCXJD1oBxDh7jhjT9GYugQd/j5iJGTVwU3VxvU1UnxtKQBnh5C\nWFqy4eZqi4YGGdIySvDwYQV8fexw934Z/v3dDaReL0Dmg3L4+thptEkBgFu3n+Kbb3/Dk4cVcHew\nhrWQh0AfOyxdHIHIcDcMDHPG0+J6CG24UKlUuHu/DGnpJfD0sIVYrJtYqauLDYZEuKOktB4iEQ9v\nvz4MIf0166mcHAUYNtQd5eUS8PkWePPVSAwa2Jop2lnA2k5khZEjvFBT2wSKovDaK0NpqvzFJfXY\n/v0tnE55jNRr+RAILMHjcrBj9+84deYRrlzLA59vCWu+JXbtuY2TvzzC1Wv54PIsYCPgYvfe20g+\n9RBXU/NhacmBj7cdBILuBdAtLTgYM9oLbDYLJaUN8HQXIju3GldS8yBXKOHnK9Zao7Kr82Nq2tqi\nUlG4cCkH3+24hWvXC5CXVwN/PRKN+gU5ICjAAbl51XB2EkDSKEd6RimyHpbDx5v5OgcAgUC/tihM\nCRvSxgZcPPJvjB49FvHxCXrtV1960ucL6J+w8W1qttYJG6+N8u9wPw0NDfjnP/+Jp0+fYuvWrUhL\nS8OwYcNgZaVbclmfU5WnKErjg7l4PR+7j2XSxkZHuOHVdoWZTKSlF2NbO+Xy0BBHvPvmCPVrBwcB\nysvrNY776HEFNn11jTbm4myNklL6E5ezkzVKyyTt5gmw6u9RtLGKSglW/d952pjQlovENXQ1cy7P\nAq+/fYw2xuNxsPEz/YOoTOdV23naBqyZtv30H/9FfkEtbczdzVZDCd3TQ4iCQvo8by8RLYkHAN56\nLRJRE/wNFkDffygd/72SRxtLiA/FxAnaC7v2pIB+W1sy7pXim22/0f7ev58j/vTOCKZNu6SgsAaJ\nGy7Txpiu8xYMqSqf/t9jKL/zC/7zn/1wddUvgUlfetLnC+ifsDH8Hylaz73+l471IbOzs7Fhwwas\nXr0aLi4uSExMRGFhoVqBSVv63LMz4z9YhjGtXTbjPO0UyRklwBnV1hkOy3RcRnFuBluYNu3mLYq2\nd2rdiSNouy2z6rmWY7qbpTO993awHYznrztvzjQxpoa6KrAtWpeJKUqFe1dPYHFCrMkdV1/CUAkb\nfn5++Prrr9Wv33nnHYwdOxYqlUonp9pnYl4EAoEANGcbKmRNUMiaIKmtQD/qAZwEbCxa9Iq5Tevd\nqCjtfzrh3r17OHHiROtuVSpwOBydnwb7vPMqLKpF+q0i+LdpT+/uLEDUCM9OtmolwN8eQ9p0jHVw\n4CMmquP13LZ4ugsxaoSn+uHI1oYLGwEXAf6tIqm2tjxMnxqE8WNbVcptbLiYPlWzyE8kskLMRH91\nUgafb4FZ0/tpzLO2tsS0KYFqwVwelwNvLxF+PvkAcoWm0kMLMpkSx45n4uhP9zWaIBoTlYpCSmoe\ndvx4F7XtkjKmTAqEsE3r+AB/Maz5lhA+i5OwWM36iVMnB9JU04dFemDq5EA42LcuHw2NcNdKiDnt\nTjG+3XmT1pAx424Jvt1xU2O5cvRIb1oSS/9+jhg8kK4e8/BRBbZtv4HcvGr0RErLGvDdrlu4+XsR\nbdzPV4zIIa0alvZiPmImanftM+HsJKBd5wJB8+d47kI2Y28wfRHau0Lk6AaRoxts7Jxw5sxpTJo0\nBc7OptUe7WuwVZTWP51BURTWr1+P8vJmfdidO3diypQpOtvT52JebTmenIXTKY/VXwwfXzFCBrtg\n+ni/TpXQmbh3vwzZuVWYHB1Ay6wDul7TfvykEqfPPsajx5VokjbL1ri52iAgwB5zZvZXB8Bz86rx\n++2nmBwT0GkdTGFRLX79rQCTowMYg90t9pSU1OOnE1l48qQKtc+y8pydBFiyKBw+3vTi6CfZVdj1\nn99R8azLtL2Yj5cXhSOwTWt5fens/JRVSvDNvjvILWr+LK35Flg0OwTDB7UmfDQ2yvHTz1l49KRS\n7Tx4PA6CAuwxKSZA3bVaKlXgdMpjBAc5oF9Qs+q5TKbE6ZTHCPATq5NNOrKnqUmBXXtuIz2jWTTa\ngsPG5En+KHpah7Q7zWMcDgtTYgIwa0arQG1zYkM2bG15GDbUgza+Z18afv2tWWSWxQLGjfHREME1\nZ0zk7LnHOH7igbrWbvAgV7y0cBAtKSMzqxyPnlRiSozmta8PuXnVOHX6IR49roLkWSKDl6cQf3x5\nCC2L0xAxr+z0q3h8fhe+/XaX2YR3+0rMa8SaM1rP/XVl513FDx06hF27dkGlUiEoKAjr1q2Dra1u\nn3efi3m15cq1fNodXW5OFV57OUJnxwUAoSFOCA3RT7k8wN8eStUjteMCgKfF9YidGUz7J+Hjbafh\nVJjwcBdqpUDh4mIDsR0ftXWtah+lZQ3IelCucZx7mWVqxwUAlVWNuHuv1CDOqzOeFNSqHRcASBoV\nuH6nmOa8+HxLhIU549KVXPWYVKqETK5SOy4A4PEsEDuDrnrO5XIYn06ZqK5pUjsuoFmhP/VaPqpr\nWtPxlUoKV6/l05wXm81CNMPTuEyuVDsuoDmEdPlqnobzMiep1wtoReJpd4oxbXIgvK1b09v7Bzui\nf7BuLVA6w8fbDmwOW+24gOZODTm51QYv4s7JSEVwcAhRjDcAXT1R6cK8efMwb968bu2jzy8bEgiE\n54uWZpSVxXkofJiGmJjOnwII2mGoZUOD2WOSo5gJy3btUlgsgKOF0jpFUWhq6lyVuru2tBxHXxqb\ntKsbYUrgs7BgWvoxz+oxpdIsoG5bGK0eYzh/TPO6g1Kp0jhfFhZsjTGOBVurGA2bxVJ3TG7B0DZ3\nB4qiGFchTNFmyJifZ0vCxuPbF+HhIMC0aTMMst/nHZaK0vrHFPSZImUmIsJdUVEpQUlJA7y9RHjz\n1Ui4tGn+x0RhUS2+3XkLyaceQGjD00opQpsixAGhzpDLlMjLr4FQyIOriw0uX82HBYfVvIyiZfuS\n2jopfjiYjr3770CppODnZwdOu/Vra2suyssbcOjIXVy5mocAf3vU18vA5XGQEB/6LGjefLzGJjmS\njt3H+Qs5CPC3h0QiB4fDgp+vGLd+f4qa2iYE+HdPjZ/p/CiVKpxOeYyjR+/B10MIOZo7A0wd64MF\nM4I1/qk6OljD21OEJ9lVaJIqMGGcLxbMG6DRfkVfe67fKMD3/7kNe3s+rPmWaJDIMTTCDa++PARD\nI9yRl1+D2lopfH3s0NAgx83fi+DlKYKdqOPCSg6HjcGDXFH0tBZVVU0I6e+Et16PhMCaHs80RxFr\ny3VeXi6Bl4cIVdVNcHW1wbIPxtAaZBqL0BBnqJQq5OZVQ2jLwx8WDsKQcHqDU32LlM9nlINvK8ad\nC0cwbng4pk41r/PqK0XKu84+BIuCVj+vxPQhVXlj0FXCRgt5+TXw9BB26SAePKzAV9/8SrurHj9W\nM8DeHl0Cshn3SvHdzpuQy1ufOEJDnPDum8O73LauXopP1l1AY2PrU6G3lwj/s4yuZi4Q8PDe0p9R\n39Dq3B0d+PjL0jG0BA+VisL/fpyCmjYis7a2XLBZLNqYUMjD2tXROqlGtIXp/HyxORUPH7VV47fA\nG69Hon+gQ/vNachkSpRXSODupn/blPb2HE66h/MXs9WvWSxg/gsDMH5sq+q5SkVh38F0XL2WT5v3\n2itDET6o875zFEUhL7+mw3imqQP6WQ/KsXnrddp1PiTCDUteCoezs61JbSkuaVZvad8WBehewoZU\nKsORTe9jw5rVmDHD+C3pO6OvJGxELT/R9aRnXPjU+DcMfTpho4X2uocdUVcn1VgOMqQmHtCcLt/W\ncelyDJlUSXNcQHOSQXsUCiXNcQFAbZ1MIzORoiiakwKAujqZxjJZba0USiWFLhpb60T79yyVKiDU\nQmmcy+V0y3Ex20JX3qcowMqK/mbZbJbGMi9Fadc4kcViaZWIYyqYrnOlUqX3zUl3MNZTXtGjNLDY\nbEREDDXK/p9HTBXL0pbnwnkRCITni8KHt+HkFQRra+bmqQTdMVWuP+ceAAAOE0lEQVQsS1t6TvS4\nB+DsLICNTevdP4vVXKhpSIRCHhwc6Lpr2hTOAs0p466u9DtVpm25XAsNRf0AhnksFkujGaafr53G\nPn287TQSD7qLvx89Bd/ZSQBbG+P1eOoMX18x7WnTRsCFi7PmE0H72CTfysLoXZ+NgbOzDa3hpjGu\nc3PS1FCLkpz7sHf1NbcpfYqelm3YpxM2dEUktMLoEV5okMjBYrHw5quRGBrh3uV2ugRk+VaWGDPK\nGwCF+noZXn4pnLFGiAlLSw7GjvKGpSUHlVWNeHH+QMYaJltbHiIGu8Kab4my8gYkxIcy1oWxWCyM\nGuEFkZCH4pJ6xM3ujwUJAzBqhBfEYis8fVqP2Jn9sHDewG4tKTGdn8GDXOHlKUJ+fg0mjPfFksXh\n4PON2469I3v8/cQYEOaC/IIahIY44c3XIhnrjXy87RA+yBWFRbUICLDH228MM8gSpqkD+iKRFUaP\npF/nLckSPSm5QN+EDaGiBOdPJ2P1svfg7x9o9r5dPemcAvonbOw9kaV1wsai6cFd77CbPBcJG8am\nJwZkiT0dQ+zpmJ5ki74JG7t378ZXX23B0aMnwOWa52m+LT3pnAL6J2xM/9Oxric94+RXc3Tev66Q\nmBeBQOhT5OTkwNvbp0c4rr5ET4t5EedFIBD6FDk5OfD19TW3GX0Okm1IIPRi6uqkSPopE/ZiK0yd\nHNit4m2CccjLy8PgwZHmNqPPwVYS50Ug9Epu3ynG3v13IJE0B99v3CrCq0uGwMtTuzpCgmkoLy+H\nq2vnheME3elpy4YkVZ5A0JKMuyVqxwUAZeUSZOdUmdEiQkfY23eu1ELQHbZKpfWPKSBPXgQCoc8h\nFhu3lc/zCIl5EQi9FC5X8+vCNEYwP3Z2PUeOq69AYl4EQi/lhbgQiO2scOKXh+DzLZAQH4Yh4W7m\nNovAgJUVv+tJBJ3oaTEv4rwIBC3hcNiYHBOAyCHu4PMtYWVFvj49FVLjZXjIsiGB0MsRi8ldfU+G\ny9VP/ojQOYZ0XikpKfjiiy8gl8sRERGBTz75ROcbDpJtSCAQ+hQ8nn6aiITOYSsprX86o6KiAqtW\nrcK2bdtw6tQpWFlZYevWrbrbo+8bIRAIhJ6IQKApqkzoPiwVpfVPZ1y+fBkRERFwc2uOFy9YsAA/\n/fSTzvaQZUMCgdCnWLVqlblN6JMYatmwpKSEVkTu4uKCkpISnffTq52XvqrTxqAn2QIQe7qC2NMx\nPckWfRg9erS5TdCgt59TAPj+6CKD7IepkQlHjzbtZNmQQCAQCCbD1dUVpaWl6telpaVwcXHReT/E\neREIBALBZIwdOxa3bt1CYWEhAODQoUOIiYnReT+9uhklgUAgEHofFy5cwMaNG6FQKNCvXz8kJiaC\nz9etBIU4LwKBQCD0OsiyIYFAIBB6HcR5EQgEAqHXQZyXlqSkpCA2NhbTpk3DRx99BJlMpjFny5Yt\nmD59OqZOnYqdO3ea1R6lUok1a9YgNjYWsbGxWLFiBaPNprKnLe+//z4SExONZou29iQnJ2Pu3LmY\nNWsW/vKXv0AulzPsyXT2JCYmYubMmYiNjcXnn39uNFvasnz5cuzevZvxb6a8ng2BLtegITl48CBi\nY2MRFxeHP/7xjygoKEBjYyP+/Oc/Y8aMGZg5cyZSU1NNbueePXsQFxcHAD3CHoNDEbqkvLycGj16\nNFVUVERRFEV9/PHH1Jdffkmbk5KSQs2bN4+SSqWURCKhXnjhBeratWtms2fXrl3Uu+++S6lUKoqi\nKOrDDz+ktmzZYjZ7Wti9ezc1cuRIav369UaxRVt70tLSqAkTJlClpaUURVHU0qVLqe3bt5vNnjNn\nzlALFiyglEolpVAoqISEBOrMmTNGsYeiKConJ4d65ZVXqPDwcOr777/X+Lspr2dDoMs1aEju3btH\nRUdHU7W1tRRFUdQPP/xALV68mEpMTKTWrFlDURRF5ebmUuPHj6fq6upMZmd6ejo1btw4Ki4ujqIo\nilq/fr1Z7TEG5MlLC7SRM0lJScGsWbPA5XLB5/Mxe/ZsvSRPDGVPWFgYli5dqhYoDQ0NRVFRkdns\nAYD09HScOXMGCxcuNIoduthz/PhxJCQkwMnJCQCwcuVKzJo1y2z2qFQqNDU1QSqVoqmpCTKZzKga\nfQcOHMDcuXMxbdo0xr+b8no2BIaSHNIVgUCAtWvXwta2uQh5wIABKCoqwrlz55CQkAAA8Pb2xqBB\ng5CSkmISO+vq6vDxxx9j2bJl6jFz2mMsiPPSAm3kTJjmFBcXm82eyMhIBAYGAgCePn2K3bt3Y/r0\n6Wazp+UL9emnn+pVTW9oe3JzcyGVSvHWW28hLi4OmzdvhlAoNJs9U6ZMgbe3N8aNG4eoqCh4eXlh\n3LhxRrEHAP72t7916qxNeT0bAkNJDumKt7c3Ro0aBQCQy+XYtGkTpk+frmGPs7MzSkpKTGLnihUr\n8M4776gdEqB5fkxpj7EgzksLKC3kTLSZY0p7WsjMzMRLL72ERYsWYcyYMWazZ8WKFXjrrbfg7u5u\nFBt0tUehUODy5cv47LPP8OOPP6K2thZffvml2ezZt28fGhoacPnyZVy+fBkURWHz5s1GsUcbTHk9\nGwJz21tdXY033ngD1tbWeP/996FUKjXmsNlso9u5e/duODs7Izo6mnYslUplFnuMCXFeWqCNnImr\nqyvKyspoc9re0ZjaHgA4f/48XnnlFSxduhSvvfaaUWzRxp6SkhLcvn0bX3/9NeLi4rB//34cP34c\n69atM4s9QPOd5/jx4yESicDhcBAbG4u0tDSz2XPhwgXMmTMHVlZW4PF4mDdvHi2obmpMeT0bAkNJ\nDulDTk4OFixYgKCgIGzevBkWFhZwd3dnPH/GtvP48eP49ddfERcXh5UrVyI7OxsLFy40mz3GhDgv\nLdBGziQmJgY//fQTpFIpGhsbcfz4cb0kTwxlT2pqKpYvX46vv/4asbGxRrFDW3tcXFxw6dIlJCUl\n4ejRo1i4cKE6A9Ic9gDApEmTcO7cOdTX14OiKKSkpGDgwIFmsycsLAxnzpyBSqWCSqVCSkoKBg0a\nZBR7tMGU17MhMJTkkK6UlZVh0aJFWLRoEf7+97+rx2NiYnDw4EEAQH5+Pm7fvo0xY8YY3c5Dhw7h\n+PHjOHr0KNauXQs/Pz/s378f0dHRZrHHmBCFDS1hkjNJTU3F+fPnsWbNGgDAN998g+TkZMjlcsye\nPRvvvvuu2exZuHAhsrOz4e7uDoqiwGKxEBkZaTSHoc35aWHz5s2oq6vDRx99ZBRbtLVnz5492Ldv\nH1QqFUJDQ7FmzRpYW1ubxR6ZTIb169fj2rVr4HK5GDhwIFauXAkrKyuj2NPCRx99hJCQECxevBjn\nzp0z2/VsCAwhOaQrmzZtwo4dOxAYGKheguPz+di+fTtWrlyJrKwsAMCHH36I6Ohok9p5/fp1JCYm\nIikpCQ0NDVi1apVZ7TE0xHkRCAQCoddBlg0JBAKB0OsgzotAIBAIvQ7ivAgEAoHQ6yDOi0AgEAi9\nDuK8CAQCgdDrsDC3AQRCZxQWFmLy5MkIDg5Wp/xTFAV7e3vs2LHD3OYR+ih/+MMf8K9//QsUReGD\nDz7A3r17aX9PSkrCunXr4OXlBZVKBYVCgdDQUHzyyScGKbeor6/H+++/T67xTiDOi9DjsbGxQVJS\nkrnNIDwnNDY2oqGhAQ4ODkhOTsbw4cMZ540cOZIm4fXOO+9g7969eP3117ttQ3V1NTIyMrq9n74M\ncV4EAoHwjDfeeAOPHz+GVCpFXFwcioqK4ODggLCwMEyaNKnD7RobG9HY2KiWVmpb/A0AixYtwpIl\nS9C/f38sWrQI3t7eKCsrw4EDB7B8+XJ1x4eJEyfiT3/6E1auXIn6+nosWLAABw4cwKZNm3DhwgVY\nWFjA09MTn3/+uVG7DvQGiPMi9Hjq6+sRHx8PAOqlw2nTpuHNN980s2WEvsa2bduwa9cu8Hg8vPji\ni3jxxRexZcsW2Nvba8y9du0a4uPjoVKpUFRUBHd3d7VqRWcUFRXhq6++QlhYGI4dOwYej4cjR46g\nqakJK1asgEQiwdq1axEfH48DBw6guLgYSUlJuHTpEoBmVY+srCyzyof1BIjzIvR4yLIhwZRkZWVh\n/vz5UKlUqKioYHRcAH3ZUKVSYcOGDfjggw+wffv2TvfP4/EQFhYGABgyZAg2bdqEV199FWPGjMGy\nZctgbW2Nqqoq9XxnZ2d4eHggPj4e48ePR0xMzHPvuACSbUggEAhq3njjDZw8eRKrV6/GrFmzUFlZ\nifj4eJw9e7bT7dhsNhISEnDz5k31WFvlPblcrv697XKfl5cXTp8+jcWLF6OkpATz5s3DnTt3NPa9\nb98+rF69GjweD8uWLcOePXu6+1Z7PeTJi9DjIfKbBFOxceNGLFmyBIcPH8bhw4dRUVHR4fJ0++vy\n7Nmz6icqsViM+/fvAwAKCgrUgrjtt9u7dy8yMjKQmJiICRMmIDMzEzk5OXBxcVH3BMvMzMTy5ctx\n4MABhIeHQ6VSITMz06DvuzdCnBehxyORSNQxL6A17rVr1y6IRCIzWkboa9y6dQsRERHq3+fOndvh\n3OvXryM+Ph4URUEul8PDwwOff/45AOCll17CsmXLMGvWLPj7+9MyFlkslvr3uLg4XLt2DTNnzgSP\nx0P//v0xffp0cDgchISEIDY2FocPH8bEiRMxZ84cCAQCiEQirF271khnoPdAVOUJBAKB0OsgMS8C\ngUAg9DqI8yIQCARCr4M4LwKBQCD0OojzIhAIBEKvgzgvAoFAIPQ6iPMiEAgEQq+DOC8CgUAg9Dr+\nP3PZKygCvHErAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1044f8b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dx = ds_sa\n", "\n", "alex_jointplot(dx);" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1228cf908>" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mmSwLCwtDWFgYAEAQBCQlJWHbtm1M7kRNUNZpUaG0Psd7Va0WelF0UERE5Grs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\nu1dfrqiGTqtvVNeWe/WrarX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"text/plain": [ "<matplotlib.figure.Figure at 0x12338ce10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dplot(ds_sa, hist_S)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A-direct excitation fitting\n", "\n", "To extract the A-direct excitation coefficient we need to fit the \n", "S values for the A-only population.\n", "\n", "The S value for the A-only population is fitted with different methods:\n", "- Histogram git with 2 Gaussians or with 2 asymmetric Gaussians \n", "(an asymmetric Gaussian has right- and left-side of the peak\n", "decreasing according to different sigmas).\n", "- KDE maximum\n", "\n", "In the following we apply these methods using different selection\n", "or weighting schemes to reduce amount of FRET population and make\n", "fitting of the A-only population easier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Even selection\n", "\n", "Here A-only and FRET population are evenly selected." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dx = ds_sa\n", "\n", "bin_width = 0.03\n", "bandwidth = 0.03\n", "bins = np.r_[-0.2 : 1 : bin_width]\n", "x_kde = np.arange(bins.min(), bins.max(), 0.0002)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Gauss]: 0.11949144651970635\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [KDE]: 0.0834236186349\n" ] } ], "source": [ "## Weights\n", "weights = None\n", "\n", "## Histogram fit\n", "fitter_g = mfit.MultiFitter(dx.S)\n", "fitter_g.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_g.fit_histogram(model = mfit.factory_two_gaussians(p1_center=0.1, p2_center=0.4))\n", "S_hist_orig = fitter_g.hist_pdf\n", "\n", "S_2peaks = fitter_g.params.loc[0, 'p1_center']\n", "dir_ex_S2p = S_2peaks/(1 - S_2peaks)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S2p)\n", "\n", "## KDE\n", "fitter_g.calc_kde(bandwidth=bandwidth)\n", "fitter_g.find_kde_max(x_kde, xmin=0, xmax=0.15)\n", "\n", "S_peak = fitter_g.kde_max_pos[0]\n", "dir_ex_S_kde = S_peak/(1 - S_peak)\n", "print('Fitted direct excitation (na/naa) [KDE]: ', dir_ex_S_kde)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Vopf63wLemt3bcl/VlBXROhybvsKx6SutY3gNu93Ou39aQHH2KZ55+hlef/1N\nxqIwpKqatWu/5KFZ8wgIjiDz699RUFRK7X8vVcoOLEIIrUiddx27QyW/3E5MSMOafmEHlsggPSZD\n3Q4sF74WwpNJh1x4nQ8//IATR/cyetKPmTRpSr2fKYrCC7/+BXfOfAVHTQkZ3/6JnBIr4QF1C3uE\nEEJ4t8IKBzaH2miHvF8nE2EBehQFokP0VNQ4MOoUqf/C40mHXHiVEyeO8e67i4mKT2bKXQ82eoyv\nUWHew6OYOu0BrPn7KTn9DfcOCZBbKgshRBtw4cZAMaENO+S+RoUHhwUyMsmPkUl+xEcYuSHKIPVf\neDzpkAuvoaoqv/zlbHLzcumYNJj2oU2vSfY1Kvxm9k/p0bUTp7/7B8czSloxqRBCCHfJ/e+CzgvT\nUy7la1QY3MWHaYMCGNXDl7MFNqotjlZOKcSVkQ65B1BiY1FiY7WO4fE2b97I4cOHiO/ai+4DJta7\nXNnYOTSZTDzz1JNUFGby0u/mt3ZcIYRwkjrvOrlldiKCdBj1daPeNpuNY8cOs3HjetavX8X27Vsp\nKMgH6qawWO0qR7KsWkYW4rJadKdO4V6G1/6odQSP53A4+P3vF6IoCrc/MBdfHz2h/v/7PNnUORw6\nZAgmA+z5dg1HzzxPzy7SIAohWp/Uede4sKCzZ4e63bOqqir5z3+2UF5eRnBwMEFBQeTn55KdnUlS\nUi969epLO389B9ItDIg3oSgydUV4JhkhF17hyy//zZkzp+jXrz/BnW4kKljfosKqKApPPPEUdruV\n37zwQiskFUII4S6FFQ7sDpXoED0WSy1bt36N2VzBjTfexG23TWLEiLFMnDiFDh1iOX78CIcO7adf\nZxNFlXYyiuxaxxeiSdIhF15h5cpPCA1tx+yn51Fe0/jq+qbM/OkMQtpFsue7jZSWe89epUIIIeq7\nsI1hdLCOXbt+wGyuYPDgYcTHd3EO0vj4+DJkyAhiYzty8uQxgu0ZGHQK+9NrtYwuRLOkQy483okT\nx0lLS+PnP3+Kjol1t+2+kg65Tqdjyo/uxWqt5ZU33nJXTCGEEG6WW2ZDpyjUlmWSnZ1J9+49iIvr\n1OA4nU7HoEFDCA0N5eihPXQJq+FUng1zjSzuFJ5JOuTC461c+Sk6ncLkyT8ir+zCdldXtvxh3i+f\nIig0kh27dqPBzWmFEEK4QG6pnfAAB4cP7SEwMIjevfs2eazBYGTQoGGoqoqhZB8Oh4OD5y2tmFaI\nlpMOuQeFzqfOAAAgAElEQVSwLfwttoW/1TiFZ6qoKGfr1i0MGzaCqKgocsvs+Bp1hPjVnz9+uXMY\nHBzEPQ/9nOLCXLbtPuLWzEIIcSmp89fOZlcpNDsIqD1LbW0t/fqloNc3PzgTEhJKz559qKooJFyX\nw4EMCw6HDMoIzyMdcg+gHjuGeuyY1jE80tat32Cz2Rg//nagbv5gTEjDBZ0tOYcz7kkF4OOV/3ZP\nWCGEaILU+WtXUGHHbqvBUnyK8PAI2rdv2a5Z3bv3ICAgEP/KY1RUWziTb3NzUiGunHTIhUdbtuxD\nFEUhOTkFc42DihrHFc0fv9gNnTvQPakvO7//hvIquWwphBDeJLfMjqHyLDps9OnTv8VbGOr1evr0\nSUavVuNbnca+dKn/wvNIh1x4rMzM8+zZs5OammoMBsNF88evrkMOkHr7BKrMpbzx0bes3VfFjjO1\n1Fjl8qUQQni6nGILxqp0oiIiiIyMvqLHxsV1IiwsnMDaM3x3ooJ//WCW+i88inTIhcdavnwptbW1\nTJlyJ4qiOLe7utoRcoDbbxtFVXUNn3y0mGNZFrYer2bpdrMUZSGE8HDZWWkYFQuJiT2u+LGKotC1\ne2+qaizUlpzl2+M1Uv+FR5EOuQdQBg9GGTxY6xgeRVVV1q9fjY+PL3fffR9Qd7nS36QjyLfhZcqW\nnsMzxT6YTL6UnNtBXlEZAMWVdg5kyCVMIYT7SJ2/Nhabg6rCM/j6BRAb2/GqniPXEondEExHYzr5\n5RYcDqn/wnNc2d5xwi0Mv3ha6wge5+TJk2RlZdKtWyIdO3ZCVdUmF3RCy89hfrmd7gNuY+f6xezZ\n+gkTpz4C1C0WEkIId5E6f21OnctGsZUT1ykZne7qxhILKhzYArvhV7ubUGs2hRUJRIXopf4LjyAj\n5MIjffHFFxgMRiZPvgMAc41KZa3jmuaPA0QF6+k36j4UnY7MAxuclyojg67teYUQQrjPqdOnQdGT\n1L3rVT9HVLAeu2979EY/YvXp5JTaQJX6LzyDdMiFR/ruu++4+eaRPP74zwFcMn8coF8nE51j2xMS\n2YnK/ONkFVQRHqCnXyfTNWcWQgjherW1tRQVZKH6tqd9mN9VP0+/TibCAo3YAuIJNphRLEXo9YrU\nf+ERpEMuPM65c2mkp6dz880jndNTXNUh9zUqPDgskDFjx2E0Gqk4v5O7bwrA19iy7bOEEEK0royM\nNCxWOyFRCeh1V1+rL9T/wf0SCfIzkBSYSUKEQeq/8AjSIRceZ9u2bwEYPnyE83u5ZXYCfHQE+l77\nW9bXqPDy83NI6NyJ2oIjpMlNIoQQwiOpqsqZM6exKn60j4m55ufzNSoMSwqhf48EYox5nM2twFzj\ncEFSIa6NdMg9gHXWI1hnPaJ1DI/x/ffbiY2NJSHhBoB6CzqbcqXnsF27MPr37UXe2T3sOVeDqsq2\nV0II95E6f3VKSoopKinF5teR9qGu24fihhu64eejoKs6L7usCI8gHXJPUFFR95+gpKSYw4cPMXTo\nUOd0lYoalWrLZe7QeRXncPDgIdirCjl1Jo2sElllL4RwI6nzVyUt7Qw2B9j9OxF9jVMWLxYeHkFY\nSDAB1kz2p9did8igjNDWZT9urlixgo8++gi9Xk9YWBi/+93viIuLq3fM+PHjMZlM6PV1fywzZ85k\nwoQJ7kks2rTt2/9Devo5zp496/yeq+aPX+rGGwfj/9f3yT2zl33nuhEXJruACnEpaQOEVux2O+fP\nn0Pxi8RgCiAi0HVjiIqiEB9/AwXF+8g3F3My148eHWRxp9BOsz2QY8eOsWTJElatWkVQUBDLly/n\n+eef5x//+IfzmLKyMsxmM9u2bXN7WNH2ffHFOoB6jXluaV2H3JWjIwBdunSlXbtQCk9t5UTunYyu\ncbhkjroQbYW0AUJLublZWK1WanzjiArWo7uGBZ2N6dw5gUOHD+BryWTfuUjpkAtNNdv7CAgIYOHC\nhQQFBQHQp08fcnJy6h2zf/9+fH19mTFjBqmpqbzzzjs4HLJAQlw5m83Grl078Pf3Z/jw4QDUWFW2\nn6rhfJGNI1lWl97iWKfTYbfbObjjSyorStgv8wiFqEfaAKGls2lpVFoUduWGU1blcPkt7v39A4iJ\njiHQnkVmsYW8Mpm6KLTTbIe8U6dODBkyBACr1cqiRYsaXIasqalh6NChvPfee/zzn//k+++/Z9my\nZe5L3Abp7rwL3Z13aR1Dc0ePHqa0tIQuXboSFhZGjVXlo20V7E6rpbJWZevxapZuNzdalK/2HN58\n80hU1UHOoXUcyLDIPEIhLiJtgOtInb8y5eYaDp86T54lknwznC2wNln/r0XHjvH46m3oawvZl17r\n0ucW4kooagu2lygtLWXOnDkEBATw5ptvOucJNmbjxo0sW7aMv//9700eY7HYMBi8Z2qAoiheuwuH\nN2V/+eWXefXVV3n++ed57rnn2Ha8ktW7Kth1ppqEKBOdI4wA3NI3gGGJ/i55zYyMDHr16kXyjcMY\n+/hH3Dk4iD6dfK/pOb3pnF/MW3OD92W/2lt/a0XaAO96f13grbkBVm8+xIE928kx3cjx0nBu7OJH\ngI/OpfUf6m46tHTpUmp8OlLqn8Kc28Pw97n696Y3n3Nvze5tuZuq/5ddxXbu3Dkee+wxRo4cyXPP\nPefc+eKCTZs2ERkZSb9+/YC6LeoMhuaftqysuqW5PUJ4eABFRZVax7gq3pR9z559DBt2M1Om3IOq\nqpzKMHPsfDV2m4NAo4Oq6ropJaczISnCNX98AQHhREVFc+rYQcbYbWw5UEaHgGu7bOlN5/xi3pob\nvC97ZGSQ1hFaTNoA73t/XeCtuQHOp5/B5tBzvCgEk1FFsduoqnZt/b8gPDyKrNxMyu09+OZAKYO7\n+FzDc3nvOffW7N6Wu6n63+zHwIKCAqZPn8706dOZN29eg0IMkJmZyaJFi7DZbNTW1rJs2TJZXS+u\nWHFxEefOnePmm0cSFRUFgMUOJZV22ofq8TP9770XGeTaxZ39+6dQUVFOQPUpdp6p4aNtFew4U+vy\nS6NCeBtpA4QWqqqqsFXmU2CPwepQ6BJlgP++9Vxd/wHi4jqjx0oQRazeU8mavZXSBohW1+wwxtKl\nSyktLeWzzz7j008/BcDPz49HH32ULVu28NJLLzF9+nQyMzNJTU3FbrczYcIEpk6d2irhRduxf/9e\nAFJSbgTAalPJKbURGqCnY/j/3qbhAXr6dXLtSvj77nuQ4yeOs+/YOc6b4qiy1JBTZufQeQsPDguU\n2yqL65a0AUILmZnpGPSQY+tAVLCeIL+6sUN31H+A2Ng4du3WUVGcyZGiIBwqhAVapQ0QrapFc8hd\nraDAu26O4O7LIWp5GQBKcIjLn9tbLuW8/vqrbNr0JZ9/vh5/f38O5MCXe0sZ18ePGisUVNiJDKor\nxo0Vx2s5hxaLhVvGTyD0hqFEDP4FJZUOBt5gwqhXGJnkd8WXL73lnF/KW3OD92X3pikr7nA9tgHu\nrPNN8ba/iws2bvyCcwU1VISOZsANvlTUqM3Wf1f4ZO1GcvIL2WoeTaCvnl5xdWuWrrQN8NZzDt6b\n3dtyN1X/5U4oHsD2+EwAjMs+1jiJNlRVZd++PfTs2Rt/f39KKu18d6KWTuEG+nY0NXqZ/FLXcg5N\nJhPRnXqQkXaQnrfoKDLbKatyEBGkp6BCtsESQly7673Ot1R5eRnZeUXU+iUyqqc/KfFXP5/7Sjh8\no1HUbGL9y8kwh+BwgE6HtAGi1XjPMnfRZmVnZ3H06BFsNhuqqvL1kRpU4JZefi3qjLtCn379qaoo\nRK2s22O52lJ34cgd8xWFEEI07tSZs5hrVSLaJ9DfDdNTmtKhQywAYfoCVFRpA0Srkw650Nzu3bso\nLS3FbndwOs/G2QIrN3XzI6IVC+Gk0YMw6BSKMg9h0CtU1apum68ohBCiIVVVOXgiDZs+mNQhsS6/\nM2dzbuwehsEnmEA1H4Aqi0PaANGqpEMuNLdly9eAys0jRrP5aDVBvjpG9HDdPrMt0adXDwz2CjJ3\n/4uuUUY6hBl4QBbzCCFEqzmalofZXEH72M50/O99J1qLr1FhUK9OhJnMtA+y0C3aKG2AaFXSIRea\ncjgc7Nz5AwaTHwfNiRzNsjCkmw8+xtZ9axqNRvz9/Ug7uZ9RPUyE+OkwypVKIYRwqxqryo4ztazZ\nW8n6bSdRUBhzY1dNsnSMiyXIV0ev8FIig/XSGRetShZ1egD9k09pHUEzR0+cIie/GJ+AdhwtjyHY\n38GuM7UM6+W4oudxxTkcMOBGPvtsBTmnduDwG0BxpUPmDwohXOJ6rvNNqbGqLN1uprjSTk6JlbDy\n8+gCwggMCNQkT0REFEajAX9bPkXmBE0yiOuXjJB7AN1NQ9HdNFTrGJpYu2kHOoOJdt1vBUXHDZFG\nSqoc7EmruaLnccU5vO228QDs//5LAIpkdb0QwkWu5zrflAMZFoor7VhsKmXFuZh0VpSAOA5kWDTJ\no9PpiIyMQVdbSInZhs0uNwYSrUc65EJTBw/uIyTqBiKHPEX7UD3+PnWXCPPKbK2eZcyYWzEYDJw4\nUneTokLzlY3SCyGEaLn88rpBj9wyO+3Ixsegx+7XQdOtBqOiotGpVhRrKUXSBohWJB1yoRm73U5u\n+hEC2/dEURSig/83PSQ6pPVnU5lMJhITk6iuqsDHICPkQgjhTlH/rfnlZgsR+nzwjwadSdOpglFR\nMRj0CjpLIYVmaQNE65EOudDMmTOn0TlqCI7phcmg4G+qGx0PD9AzIMFXk0z33vsgPj6+mKyFFFbI\n6IgQQrhLv04mAn10mKy5GHUO7L5xmm81GBwcQoC/L/raAoqkDRCtSBZ1egDH0SMA6Hr20jhJ6zp0\n6AAK0KtPPzp09KVLtPF/t0c26biSG+G66hz27t0HgPLco+g7RGJ3qOhbcS9cIUTbdL3W+eb4GhWG\ndvehJC2XAKOR/v3iSY7303R3E0VRaB/TnqyiNArLLYA2g0Pi+iMdcg9gf/l3AOius1sq7927B1Ux\nERYdz219/OjR4epHRVx1Dnv06IVOp1CQcYSo9iNkpxUhhEtcr3X+cjILqghWCknu0Y2burXu/Sea\nEhUVg+HoGQoKC4FgreOI64RMWRGaUFWV7du/JT39LIWZJ+gc4RmfDf38/OjatTuZZ+tGswplHrkQ\nQriFqqpkZKRj0kNCfLzWcZyioqLr7thcnodVdloRrUQ65EIT6ennKC4uRm/yp0diV/xNnvNW7NWr\nD1kZZzCX5skqeyGEcJNCswNrRSb+/r5ERcVoHccpICAQf/8AdJYiiqUNEK3Ec3pB4rpy6NBBqqur\naRfTlW6xQVrHqcdoNJCRfpaze9bLCLkQQrjJycxydJYiOneKR1E8a61OREQUOmsp+eXa7Ikurj/S\nIRea2LNnF7UWCx26DSTeQ6arXHDrrXU3CMo59YOsshdCCDc5cy4dnaLQo2u81lEaiG0fBaqd7Lwi\nraOI64Rn9YSuU4Y/vK51hFalqio7d/6AwehH3A296dDu2hdNuvIcxscnEBISSl7GcUqqHNjsKga9\nZ43eCCG8y/VW5y/HalcpK8ggwM+fiIhIreM0EBsThU5RKCwsAOK0jiOuA9Ih9wBKXEetI7Sq3Nwc\nampq6DfqAW4aNNgl2wq6+hx269adXbt3UWUuobgy0HkDCyGEuBrXW52/nFOZpWApITa+p8dNV4G6\n/ciNRhMVpQVaRxHXCZmyIlrdoUMHsKs6EvrfSs/4MK3jNGrQoJtAVTm9fxNFcrc2IYRwqeOnzwHQ\nq1u8pjmaoigKgSERWKuKsNhk6qJwP+mQi1Z38OABbKqeiLhE4iONWsdp1JQpdxLXsTM2S43csVMI\nIVwsPzcDk28gsTERWkdpUkREJDgsZOaVah1FXAekQy5a3aFDBwmN6UpkSACh/p75FkxMTCK2Q3vK\nck/JCLkQQrhQdn4J1upSImM6eeR0lQs6xEQDkJGdp3EScT3wzN7Qdcbx1QYcX23QOkarKCwsJDMr\nk+D2vUiIdN0SBlefQ0VRSErqSWnOSfLLbC57XiHE9el6qvOXc/hkGgCJXRM0TtK8zh3CQdFTUCDz\nyIX7SYfcA9j/8QH2f3ygdYxWcejQAYqKS6ipLCXehR1yd5zDXr16Y6s1k3H+PDa5W5sQ4hpcT3W+\nOaqqkpWZDsZgEjuGax2nWUF+RnQ+oZSXFWodRVwHpEMuWtWhQwcpLS2lOPcsHcM8e5Ofnj17YdBB\n/vmjFFfKPHIhhLhWJSUlVFVWEBrR0Su2kw0IDqe22kxtba3WUUQbd9kO+YoVK5g8eTJ33HEHP/nJ\nT8jMzGxwzOLFi5kwYQLjxo3jgw9kBEA0bc+eXaiqQmLPfvgYPbsYd+uWSKW5lOM71sgdO8V1S9oA\n4UpHT6XhUFW6JMRrHaVF2rWLwO6AgkIZJRfu1WyH/NixYyxZsoTly5ezatUqbrnlFp5//vl6x2ze\nvJmtW7eyevVqVq1axfr169mxY4dbQwvvVFFRzvHjx9Gb/Ejp11vrOJfl5+eHDpWsU7spMssIubj+\nSBsgXElVVc6lp+MwhJDYqZ3WcVokJiocUMnIztc6imjjmu2QBwQEsHDhQoKCggDo06cPOTk59Y75\n+uuvmTRpEiaTCT8/P1JTU1mzZo37EguvdfjwIcxV1RhNvowY3E/rOC3SvXt3aipLOJORq3UUIVqd\ntAHClYqLi6isNGMK6UhYgHfMmG0fHoSq8yVfRsiFmzX7F9GpUyeGDBkCgNVqZdGiRUyYMKHeMXl5\necTExDi/jo6OJjdXOi9XwrjsY4zLPtY6htsdPLgfm6oQe0NfeiREu/S53XUOBw4cBMAP2za6/LmF\n8HTSBrjO9VLnm5N27hwWO3Tq6NnbHV4sIliPw9SOspJiVFUW9wv3adGqutLSUubMmUNAQAC/+MUv\n6v2ssTeoXt/8bcZDQvwwGLzj0zHUbYEXHh6gdYyr4knZjx0/QvuE/rzwxt+JiAhs9lhPyT116hTe\nfGsRp4/8QEjoUy1ahOQp2a+Ut+YG787uDaQN8M73lyflVlWVjKxMdL5hDOzVnvBwn2aP95Ts4UBg\nSBS20nyMRjshISHNHu8pua+Gt2b31tyXumyH/Ny5czz22GOMHDmS5557rsGn2piYmHp7dObn59cb\nLWlMWVn1VcbVRnh4AEVFlVrHuCqekr2qqor9B44QlngrEb72y2bylNydOycSEBhMWVEuJ9MriA5p\nvqMBnpP9SnlrbvC+7JGRQVpHaDFpA7zv/XWBJ+UuKiqkpLQCi29PQgxWioqav7+DJ2X39Q+hvMDB\nmTMZdO58Q7PHelLuK+Wt2b0td1P1v9khioKCAqZPn8706dOZN29eo5eYxo4dy5o1a6itraW6upq1\na9cyduxY16QWbcb+g4cprbRjDUyksMJOjdU7Lv3pdDrG3z4Vh91OQblV6zhCtCppA4Qr1FhVtu8/\nQ2mVg0p98x/WPFFURDh2B+QXyDxy4T7NjpAvXbqU0tJSPvvsMz799FOgbueJRx99lC1btvDSSy8x\nZswYTpw4wdSpU7FaraSmpjJy5MhWCS+8Q41V5W+rd1BR48AU2YudZ2s5nWfjwWGB+Hr41ocAKf16\n8/XW/3D8dAa9O3bXOo4QrUbaAHGtaqwqH22rwHwug2JrEOWVPizdbvaa+g8QFerDMUMQuflyx07h\nPs12yOfMmcOcOXMa/dmYMWOc///444/z+OOPuzbZdcT+wV8B0D/8U42TuMeBDAunTxxC7x9OdFQU\nAMWVdg5kWBjcpfl5hC3lznPYv29vdIrC4SOHuWu0dMjF9UPaANdp63W+KQcyLJSWFaO3VlHs6EaE\nv87l9d/dwgP1OIztKC3Nwm63odd79k3thHfynlU1bZhj01c4Nn2ldQy3yS6qJvvcUSrzjlN6Yp3z\n+wUuvNmOO89h9+5JmAx6Tp086pbnF0K0fW29zjclv9yOoTobq12lytCeIN+6bocr67+7RQTpcJhC\nsdodlJSUaB1HtFHSIRduV1lwitqqCgwmH8LCo5zfjwy6/AJJT+Dr60tc5y6knz6Mze4dc9+FEMIT\nRAbpsFdmY3YEERYSDMqF73tH/QfwM+nwDQjHZlcpLpZ55MI9pEMu3C7j9CEcthp8ffyI7NgDgPAA\nPf06mTRO1nI6Ry1nD27hxDnZX1kIIVoqIbQKxVZJiRpDZHBdJ9zb6j9AeFgoNlVPcXGR1lFEGyUd\ncuFWqqqyc89+jHqVrjd0ZkBiNCOT/HjAixb0APTq1RuALzZs0DiJEEJ4j/SMdIx6SOoaT99OJq+s\n/wCRQQas+hAK5Y6dwk2kQ+4BlNhYlNhYrWO4xbn8Ws6dPozJoDB8UF8m9fdncBcflxdjd5/DiePH\nAbDj+21uew0hRNvVlut8c06cSUc1BnHvzTFuq/+tITxQh8PYjnKzmZqaGq3jiDZIlgp7AMNrf9Q6\ngtts2H4Uu7WGB+6fzuTJU9z2Ou4+h4MG9Mdo8uXMqSNufR0hRNvUlut8UwqLSygvLyckugcRXjRn\nvDERQXU7rdhqz1JSUkT79tffhyvhXjJCLtymotrBzj378TPpeOC+++jdu4/Wka6axa4QFB5Hfk4m\n209Ues2NjYQQQit7jpzDoar0T0rQOso1C/BRyKoMoqzKwZ4TudIGCJeTDrlwmwPnLeSmHyYyPIT4\neO8tyDVWlaXbzYR3H4veP5w1206xdLtZCrIQQjRBVVXSMtLRm4LokxCmdZxrUmNV+WxXFefLjZht\nJk6fz5c2QLicdMiFW9jsKvvSaijNPkpKv77odN77VjuQYaG40k58n7HoTQHkpJ903thCCCFEQyfO\nl2CtLqNjXCf0eu+t//C/NsDfpKPcEYLOUkqx2SZtgHAp7/4rER7rZK6VrMyz6O2V9OvXX+s41yS/\nvO4GFrHx3VAUHTnpxwDvurGFEEK0pr1H0lBQGNDbe6+OXnChDQj01VFmCwGHBcVeKW2AcCnpkHsA\n28LfYlv4W41TuNbecxbKso6CvRaz2UxVVZVbX8+d5zDqv3vnhgT64x+eQH5mXYfcm25sIYTQVlus\n800pr3ZQmJ9JQGAg7SO9e7oKXNQG+OkwqyHYHaCzlkgbIFxKOuQeQD12DPXYMa1juExuqY3sUhu2\noqPU1tawdOk/sFrde2nPneewXycTYQF6FAXCYpOoKDxPgL7a625sIYTQTlur883ZfbIYxVpK94TO\nKIr3bXF4qQttQJCfjkpCsTsgkHJpA4RLSYdcuNzedAsKUJh5BJPJRFxcR0JCQrWOddV8jQoPDgtk\nZJIfvZK6gKUM/6LvvHIvXSGEcCebXeXo6XSMeoUe3eK1juMSF9qAMT396Brjj2IMJCGkQtoA4VLS\nIRcuU2NV2Xq8mlV7qqgoPE9pSRFWq5WePXtpHe2a+RoVBnfx4d4xidRUFLBhw3qtIwkhhMeosars\nOFPLe1sqqCg6T0hQIO3aef90lQsutAGTkv3R+7WjtLQYu13mkAvXkRsDCZe4sDXgwfO15JXZMBce\noqCkmhB/Ez179tY6nssMu7EvRpMvJ04c1jqKEEJ4hAv1v7jSzrH0UhLVUkrpTq0NfI1ap3OtjuEG\ndhrbUWPJoqyslLCwcK0jiTZCOuQeQBk8WOsI1+xAhoUis53cMjv+PjrMOUexOUDvG9wqI+StdQ4N\nBj1RHTqTe/4MFosVk6mNtTZCCLdoC3W+KRe2BTTXOPCx5mL0UajUx3Agw8LgLj5ax3Op2HZ6FJ8w\nLNVQXFwoHXLhMtIh9wCGXzytdYRrll9up6LGQa1VJSFCz/fph+iYOIgnn32N+PgAt79+a57Dnr36\nknXuBF9u2c7kcaNa7XWFEN6rLdT5plzYFrCgwkGYLhe90ReLsV2b3BbQqFeIiWxHabFCcXEhkKh1\nJNFGyBxy4RJRwXpKKh0A+NgLqaooJKpzb6JDTV59U6DGjB01GkXR8e33O7WOIoQQmruwLWBlZRWh\n+lJU/w6gKG12W8BOESasuhDyCoq0jiLakLbVUxKa6dfJRK1Vxd9HR2lW3fzqbol92+S2UNPumEhk\nbDeKy2u1jiKEEJrr18mEr1HBz5aDXqdg9+1AeIC+TdZ/gI5hBhymUEpKy7BY5G6dwjWkQy5cwmJT\nuSHKwMgkX9SSY4QGmJh9z8A2uS2Un58fnRO6cfrkURwOVes4QgihKV+jwo0JPnT2zyfQ35fhfWJ5\nYFhgm6z/8N955KYwLDYoKZFRcuEa0iEXLnE234pBr3B7P3+q8w4zsH8vQoP8tI7lNr169aY4P4Oz\n2SVaRxFCCM2l51cSoitlYK94burq22Y74wAGvUJURDgWu0pRUaHWcUQbIR1yD2Cd9QjWWY9oHeOa\nnMm34WtU0Nfmk5uby7lzabz99p9a7fVb+xzemFy3leP23Uda7TWFEN6rLdT5plhsKjk5mfgYIC6u\nk9ZxWkWnmBBsGMnJlw65cA3pkHuCioq6/7yU1a6SUWQjIcLI/v17cTgcFBUVYrW24ty6Vj6HQwf2\nQaco7D0g+5ELIVrAy+t8c9ILbSjV2fj7mYiMjNY6TqvoFGHEYWxHXn4hqipTF8W1a/G2h3PnzqVn\nz5489NBDDX42fvx4TCYTen3diuqZM2cyYcIE16UUHu18kQ2rXSUhysCaDXux2+2YTCZ69eqjdTS3\niYqKxlZTzua1H+KYPwudru1enhVC6r9ozqnsSvS1hSR079rmdtVqSodQPYqpHVXVBVRVVRIQEKh1\nJOHlLtshT09PZ8GCBezbt4+ePXs2+HlZWRlms5lt27a5JaDwfGfzbSgoxEfo2bdvD2FhYVRUVNCr\nV9u5Q+elFEUhLCyMtLOnyCqupWOEr9aRhHA5qf/iclRV5VxGJia9Snyn62O6CtTNIw8LD6ciXaW4\nuFA65OKaXfaj7Mcff8ydd97J+PHjG/35/v378fX1ZcaMGaSmpvLOO+/gcDhcHlR4JlVVOZNvpX2o\nnl/VXzUAACAASURBVMLcDEpLSzEYDAQHhxAbG6d1PLdK7p+Mw25j/catWkcRwi2k/ovLyS93YKnI\nws/XRFRUjNZxWlXnDpHYVZXMXJlHLq7dZUfIf/3rXwOwffv2Rn9eU1PD0KFD+c1vfoPVamXmzJmE\nhIQwffp01yZtw3R33qV1hKtWZHZQVu2gb0cT+/fvBeD221MJDAxEUVpvGocW5/CW0SP57JPlfLt1\nM7PuG9fqry+Eu0n9dx1vrvPNOZ1Thd5SQMfOnZ3Tlq4XN8QEsF8fQLZ0yIULtHgOeVPGjRvHuHF1\nnRGTycSMGTNYtmxZswU5JMQPg8F75pkpikJ4uBtv/z5zhtue2t3ZjxdW4e9nYmBSCG98dojQ0GBm\nz/7ZNc8jvOLcbjyHTZk2bQo/f/JxTh8/QGg7f/T/nUfu9veLm3hrbvDu7N7sauo/XKdtgAY1qjX+\nLs7nnsaoV0np192lr+UNf9MhoSprAyMor8ihXTs/dDqdV+Ruirdm99bcl7rmDvmmTZuIjIykX79+\nQN0UBoOh+actK6u+1pdtVeHhARQVVWod46q4O/u+U2b0qgOdpYodO3bRp08/Skqu/d/XO865Qpfu\nvSittnH0bDkd2tW9770je0Pemhu8L3tkZJDWEVziauo/SBvQWtydu7LWQW7mOYINegICwl36Wt5y\nzgMCQ6nIzuDs2SzatQvzmtyN8dbs3pa7qfp/zUMUmZmZLFq0CJvNRm1tLcuWLZMV9teJaouDrBI7\nN0QZOH36FGazmeTkFK1jtapJk6Zgs1RzLL1Y6yhCtDqp/9e3M7m16GvzaB/ToUUfxNqiuJgIHKpK\nRk6B1lGEl7uqDvnmzZuZP38+ANOnT6dr166kpqaSmppKcnIyU6dOdWlI4ZnOFdpwqCo3RBrZt28P\nAP37X18d8oH9e6NTFHbulf3IxfVB6r+44PjZTBTVTlKXzlpH0UzXjhGAQka2dMjFtWnxR9rf//73\nzv8fM2YMY8aMAUCv1/Ob3/zG9cmuI2p5GQBKcIjGSa7M2Xwbep1C5wgDS3bvIiQkhMLCQqKjY/D3\n92/VLFqdw549e2MywJGjh7E7RjvnkQvRlkj9v3beWuebYneo5Oeex8eoIy6ube+o1ZzYMB8whVBY\nVKR1FOHlvGdVTRtme3wmtsdnah3jijgcKmcLbHQMM2CzVHPkyEFiY+OYN+9X7Nmzq9XzaHUOIyMj\niY6KIu/8cXJK7a3++kII7+CNdb4554v+P3v3HV9Vff9x/HXO3SN7LwiEGfYSGYpAVbSA4rZubR21\namvdq9Vqra11VKmjv5ZqxbrqQHCAIHuvsCEkhOw9bu4e5/z+iCyZgdyc3OT7fDz6aOGenPvm2+T7\n/eTc7/CjuitJTErFYDBoHUczOlkiKiYBt9OB39+Op1MLnY4oyIXTUtkUwuNXyEnWk5e3iWAwhMXS\n8lS8f/8BGqdrX31696Zo13reX97AmgIfXr/Yh1kQhM5tR0E5qAH6dOHpKgckJiTgCyh8sLSMFbvd\neAOq1pGECNQ1V2EIZ6ygOghAz2Q9//lyDbIs4XI5SU1NJTExUeN07ccbUMkvd9NcW8TipUtBN5nC\nepgx1IjZIKavCILQOZWUFKPXyfTK7jqncx6LN6Cypz4KX1CloKQKh5yCWQ5x/Ti7GAOEVhFPyIXT\nUlAdIMGmI9Yqs27dGnr37kthYQEDBgzUOlq7yiv2k9p3HBJQvnspigJ1zSHyisVHl4IgdE71ziC+\n5nLi4pMxmUxax9FUXrGfgGQjiBHJ1zKPvN4lxgCh9URBLrRas0eh2hGiZ4qesrJSKisr6dkzh0DA\nT25u1yrIqx0huueeiyTraC7bgtPbMl2lplnMJxcEoXPatrccSfGT00NMV6l2hJBkCb8uHkOwEVUV\nY4BwesSUlQ5Ad899Wkc4Zd6AypxNbvZUBOiXZmDl9jUAXHTRVG6//S5Am4/otGrD5GgdRrMVe1wa\nzvp9OLwKqUBSVNc6QloQhBOLpH7+eLwBlbxiPyvyCjGHIKd7ltaRNJccrWNnOaimOGR3JR6XA3RW\nMQYIrSaekHcA8tljkc8eq3WMk/IGVN5b4WThdg8NLoWd5QFmf7kCm81O3779sNujsNvtmmTTqg2H\ndDMSb9ORlj0AJeChbP8eEqN0DOlmbPcsgiB0XJHSzx/Pgf5/0XYXweZyHGo8n25SuvwCxgNjgN6a\nAIC3uYYEmxgDhNYTBblwyvKK/dQ2h2h0K8RaZUJBH/vzN5PRaziy3DW/lcwGievH2bn+upuIT+mJ\nwVvOLefFiMU8giB0KnnFfupdITyOGvT4USwZYq40h8aAnwxLQ6/TkWRs5DqxoFM4DV2zihJOS7Wj\npRgPKSrxdpmKws2Egn4y+pyldTRNmQ0S108fR0KMCX/tTpo8YttDQRA6l2pHy5xoxVUKSJhj0gEx\nVxpaxoDxfW3ExcWj89eLYlw4LaIgF05ZcrSO2uYQsiQRb9NRunsNkiQz5uwxWkfTnMVioW+fPlQV\nb2N/bVDrOIIgCG0qOVqHElKwBCrx6hLRGcyAWC9zuITEJAI+J9UNbq2jCBFIFOQdgLJjO8qO7VrH\nOKncDAOegEqcTUaWFEr3rKFbziDy13zK3/72kqbZOkIbjhw2hOa6UrYXVGuaQxCEjqcj9FFnYkg3\nI7K/GpkAqrXl6biYK32kbunJAOzZX6lxEiESiYK8Awg99wyh557ROsZJVTaGGJBh4IJBFqIDhRhC\nDm6YMZFlSxdSVLRP02wdoQ2HDBmKUSexbsMmVLVrL3QSBOFIHaGPOhNmg0SqvhKjQWZIv2wm9LOI\nudI/0qd7ChJQVlmldRQhAomCXDhluyoCmA0yM0bYMNSvx2aSGZjbj8rKSgYOHKx1PM31759Lc0Ml\nK75+h3qXmEcuCELn4fMHcdSXkZCYxuWj4xidYxLF+I9E2SyYrFE01NdqHUWIQKIgF05JIKRSUBWg\nR5Iek0Fi1aqV9OjRg8rKCgAGDx6icULtxcbGYTToKd+7idJ6sdBJEITOY/PuYlQlSM/sbK2jdGiJ\nSSkE3I043F179xmh9URBLpySfTVB/CGVvmkGSktLKC7ez5gx49m6dQuyLHW5EzqPp3+//rgcdewo\nqNA6iiAIQpvZs7cQJD3Dc8XpnCfSPTMVUNizv0brKEKEEQW5cEp2lwfQyxI5yQYWL14EwLnnTqBv\n3/5MmzYDq9WqccKOYcyYsUioLJw/T+sogiAIbcLl9tBUX0F0QiZ2i1jEeSK5vVoWvBaXi8X9Quvo\ntQ4ggP6FF7WOcEKBkEpBdYCeyS3TVRYvXkRmZhY9e/YiJ6e31vGAjtOGF100lZdefpHdW1fg8NxJ\ntEX8zisIQsfpo07Hxh2FqKpC3149tY7S4aUmxaE3mKirE0/IhdYR1UIHIGVmIWVmaR3juA5MV+mT\namDfvkL27y9i4sTJSFLHWdDTUdqwX7/+pGdk4fM0U1ov9iMXBKFFR+mjTkdBYQHorAzpk6F1lA5P\nkiSiYxPxNtfh9YvF/cKpEwW5cFLHmq4yYcJEjVN1XNdeczU+t4N9leJwCEEQIltdfQPO5kbikrtj\nMYqS4VSkJieD6mdvab3WUYQIIn66hBMKhFT2/jBdxaiHJUu+p2fPnnTvnq11tA5r9Fkj0RFk3aYt\nWkcRBEE4I5u256OqKv375GgdJWL06pYKQFGp2I9cOHWiIBdOqLA6SOCH3VX27s2nrKyUCRMm4XK5\n2LNnN4oiPpL7sVGjRmHUSezesQm3+MhSEIQIFQwG2b+/ENWUyMDseK3jRIzM1AR0Oj3VNaIgF06d\nKMg7AGX+Nyjzv9E6xjHtrjg0XWXBgm8BmDhxMhs2rOPuu29nzZpVGids0ZHaMCUlhcysTCr35VEm\n9iMXBIGO1UedqqL9RXi8fpLTe2EShwCdMp1Ohz06AZejlkBQPJQRTo0oyDuA0DuzCL0zS+sYRzl8\ndxWUAAsXzmfw4KGkpaWzceN6ZFli0KCOcSBQR2vDUcOHUblvC9v2VmodRRCEDqCj9VGnYuvO3Siy\niYF9xN7jrZWSnAIhD0WVDq2jCBHilAvyRx55hHffffeYr82cOZOLLrqICy+8kFmzIqvDEY7v8Okq\nq1atwOFwcNFFFwOwadNG+vbtj91u1zhlx2Q1G3DUlvD1vE+1jiIIZ0z0/11PfX0dtXX1qLbu9E41\naR0n4vTMSgGgoFg8lBFOzUkL8v3793Prrbfy7bffHvP1RYsWsWTJEr744gs+//xz5s2bx5o1a9o8\nqND+dlcEMOhapqt8++1XWK1Wxo+fQFVVJeXlZQwdOlzriB3W9OkzkCTYun4J/qCqdRxBOC2i/++6\n8vfuwRdUyeiWI6arnIbsjCRkWaayWhwQJJyakxbkH374IZdddhlTpkw55usLFy5k6tSpGI1GLBYL\n06dPZ86cOW0eVGhf/uAP01WS9DTW17B+/VomTpyM2Wxm27aW3UOGDx+hccqOq3v3bOLiE6gu2UFZ\ng9iPXIhMov/vmjweD3sL9xE0pZHbLUbrOBHJYDBgjYrH2ViNooiHMsLJnbQgf+ihh5g6depxX6+q\nqiI1NfXgn1NSUqisFB/RRDJvQOWLDS62l/rxB1U+++JzVBUuvngaAJMmnc+//z2b3NyBGift2AYO\nGITH2cC6rXu1jiIIp0X0/12PN6Dy3apt1DoCVCg9yYgTB3qfruSkZNSgi9Iap9ZRhAhwxj9pqnr0\nb346ne6EXxMTY0Gvj5z1pJIkkZBgC98bfDM3bLdubXavX+G/ixpZujtAs1+iuM7Hpx9+zphhQxgz\n5tAUlcTEvuGIe1Cr2zyMbdhaB7JfMv0ili5bzJLvv+GuK0dqHeukwv59HkaRnD2SnU7/D110DNCg\njzqd/v/9BdXU799LTTCeJjWer7YH+PmkWMztfChQpP5MH557UP/uFBXuory2gWG5qSf5Su11hjaP\nZGdckKemplJTU3Pwz9XV1Uc8MTmWpibPmb5tu0pIsFFX59I6xmlpbfY1BT72VXioavATZ5XZuW4B\nDkcTfUZMa9c26AxtfsEF00l79S2qap1UVTvR6zr2PMzO0OaRIikpSusIbeJ0+n8QY0B7OZ3+v3L/\nLiS/nxJ/NslxCiXVHhZthtE57buwszO0eUpsDKqqsrewhLoB3TROdnKdoc0jwfH6/zP+lXfy5MnM\nmTMHn8+Hx+Phyy+/ZPLkyWd6W0Ej1Y4QNY4QIUUlKVpm19o52GKSSO19ttbRIk58fDz9c4dQVrCJ\nikYxj1zofET/37lUNnjRu/bSHIrCKSeRYG/5tKOmWZyncDpMJhMWWyxNDdXH/DRJEA53WgX5okWL\nePLJJwGYNGkS5513HpdffjmXXnopEyZMYMKECW0aUmg/yVEyFU0hLAYZV+l6mmqK6XvWNFJijVpH\ni0hjx5yFs7GKTTuKtI4iCG1C9P+dl85ZgBr0U+DvRUq0HvmHCiEp6uTTkIRjS0xMRvE7qGqIrE+F\nhPYnqRr82lZT09zeb3lGIu3jkMO1NvvOcj9Pf9ZISrRM3ie/xdlYxW2Pvcstk5KQ1QAPP3w/l1xy\nGeedNymMqTtPm+dt3c41N9/O4J/8nPtv/xlDuhkxd9AtxDpLm0eCzjJl5XSJMaB9tCa33+9j7tzP\nya+3sKr5LIb3MGE2SCTYdFw3zt7u/VZnafM1WwpZsXIZxvSzGTUgW4wBYRBpucM2ZUU4c6FZ/yQ0\n659axwBgR1mAEdlGck3bcNUWMOPya7hlUhJmg8S2bVvYtm0rXq9X65hH6UhteIA3oLKmJhMMdnZv\nXc/inR7eW+HEGxAfXQpCV9MR+6jD7dy5jUAwgD4+l7G9zQzPNjGhn0WTYryz8AZUNlXZ8QdVyiuq\nWLJLjAHC8YmCvI15AyprCnx8ucnNmgLfKf3gKd/NR/lufjukO7FGt0JhdZBBmQY2LXqXbqlx/Obn\nVx3sjNesWQ3AqFGjtYx5TB2lDQ+XV+yn0aNij0mkevtcGhsc1LtC5BX7tY4mCEKYHG8M6Ih91AEO\nRxP5+bvR29IImRK5crSNqUOtjM4xiWL8DOQV+3H4jQRkO/pgHYAYA4TjEgV5G/IGVN5b4WTJLg87\ny/1H/TZ8OsV6e9q034eKSt2eBRQUFHDDDTdjtVoPvr5u3RpycnJISEjQMGXkqHa0LIRKTM9BCXjY\nuvpzQCyQEoTO6mRjgKJ2vDFAVVU2bVqPJEnUGXKJt+nIThR7j7eFA2NA0JCARW3C7w8AYgwQjk0U\n5G0or9hPvSuE16/S6FbwBVTqnC2/DZ+so9ZaIKSytcRPstnLZx/Oolu37kydesnB1ysrKygpKeas\ns8ZomDKyJEe3LIQaMv5yAPZvXQSIBVKC0FkdGAMcHoVmj0IgqFL/wxigqCp1TqXDjQElJfuprq4k\nIb0PjQELw7obkSTxVLwtHBgDJHPLQyy/qx4QY4BwbOLX4DZU7QihKLD1hxMuAWRJoqIxxOKdOopq\ngpgMEvE2GbNROvjR1fCT3Lc97CwL4A2oVKycRVNTIw8//Bh6/aFvD0mSueyyKxk//lwNU0aWId2M\nbC3xQ2p3TFGJNJRuI8GmY0g3sWONIHRG1Y4QTW6FbaWHpiToZYlqR4h0RwhvQKW6KURilA5ZPjR9\nob33+D7A6/WwadM67HY7NVIvjDoYkCn6p7ZyYAwI+BKgCRRPLQmZ6WIMEI5JFORtKDlax4LGIP6g\nSlaCHllq+QgzOUqmtD5I+Q97UZfUSwzOMmIxStQ0h5AyMjTNraoqG4p8NBRvZN3yr7nggilHzRNP\nSUnhrrt+pVHCk9O6DY/FbJC4fpydvGI/c3oNo2DzAnqbdmM2dPxTOwVBaL3kKJn9tUH0OonsRD2+\ngIo3oBJlkSmzpOLVq+RXBahpDpGbYUSStJu+oKoqGzasxe/3M2j4OXyyRWVYdzFnvC0dGgOMLKm1\nkmCoF4tkheMSBXkb6pOqp8GlEGWW6RavB4mDW0blFfv5focHh1dhV3mAHWV+BmUZSYrSof/zS5rm\nLq0Psb+smg3zXiExMZFf/vIeTfOcDq3b8HjMBonROSZuvu5qni/YwLoNmzl3jCjIBaEzspplggpk\nxOlIiWmZlnBwDBj8Eiu3u+nWGKK4Lkh+ZYA+qQbNpi8UFu6lvLyUPn36UdQcA/gY1l08uW1rB8aA\n/O2pNNbsR1KDgEHrWEIHJAryNpRX7Kd/uoGBmUZkWSIpSndwz9EDH13JMvRNM7CzPMC+SjdKRjGf\nfrqHiooKHI5GFEXFZDKRkJBIVlYWPXvmkJ3dE1kO33T/dXudLP34OXT+Zh577GXs9q69R3I4XHnJ\nxbz19ptsysvTOoogCGGgKCpr9/oY08vEyB4mGtwKiXaZTGsDRQXFyE43CYEAJrMVOTqKIoeVeLs2\nU9jq62vZvHk9cXHx9Ok3hH8scdEtQU+imNscNmkpKTRU76OgpIrcnEyt4wgdkCjI20izR2FjkZ9e\nKUamD7cd9fqBj6427/exfuMmSvd+zcZNK/nVv73E2WSczmasVisWixVFUQgGDx21HhUVxdChwznn\nnAmMHj3miJ1PzlRDs59/znwOZ9UeHn/ofgYPHtpm9xYOibHpyRlwNrs2f0dzs4OoqGitIwmC0Ia2\nlQaoc4W4YKCFId2MFBUVsH37Fko8h05ojFVVTH6VaL1Kr1gzvlA66/NzGZ8b3245PR43K1cuQ6/X\nM2bMOeyubJnbPiJbm3nsXUWv7mns2Ar7SspFQS4ckyjI28jKvV6Cisq5fY/fqW3fsp5/vfV39u0r\nRK/XM3LECEgczlnDBvLtO4/R1NSE3+8nNjaWkSNHMXjwMJzOZvLyNrF27WqWLVuC0Whk9OgxTJ58\nPqNGjcZoPP2nK16vl4effJriXSu56fqfMX36jKOuURSF+fO/YfTos4mLa79BozMaOXo8O9Z9y6pV\nq7jgggu1jiMIQhsJhFRW5HuJs+rok6ywbNkiqqoqsdujGDZsFKmpaVitNkKhEM3NDqqrqygtLaag\npJBNKwupK8lgzLCBJCUlh3WHE7/fx9Kli/B6PZxzzkSsVhsb9zuJtsjkJItyIJwyk6NAH0VNTbXW\nUYQOSvwEtoE6Z4itJQH6pRlJjT26SWtqavjd7x5n0aIFyLLMI488wSWXzCA6Oobvd3hZX+Tjopv+\ngOqtY8/eQir35bHguwUMGzaCK6+8hiuvvAav18vatatZsuR7Vq9eybJlS7Db7Zx77nlMnnw+AwcO\nPmpaizegsmK3m/wSN8nRuiOO7C0oKuahx59i2449DBx/BXffedcxB4Jdu3by17++wJ133s3ll18V\nngbsIsaeNZLZBhOLli4TBbkgdCKb9vtp9ipcmCuxdMkCmpubyc0dRP/+A4/ol2VZJj4+gfj4BPr1\ny2VQTT1fLNlGcWkxjrpyDJY45NjedMvsxtBs8xkv/vP6FdYU+Kh2hEiwhnCVrMDhaGLYiLEUOeP5\nareTvP0+Lh9lQ5bFQsNw0skStphkPA378Pt9GI3iEwnhSKIgbwMr9vgAGN/n6B+w+fO/5pFHHqCu\nro7k5BRuvfXnXHPNdQefbE/MNZP11nMUVAf46qcPkjl4OImDr2B4sIGzzk45eB+z2cy5557Hueee\nh9vtZsWKpSxcuIBvvpnHV1/NJTExkUmTfsKkST+hZ89e+ILw3gon7pCMyxVAJ8PWEj/n93Ly5ZxP\nmTX7I7wBieQxd9Nj7BRmr3Rx/TFWf69cuQyAcePOCVfztYngs78HQP/E77WMcULZKVaSsnJZ8O3X\nPPHII9jtdq0jCYJwhrwBldV7fSTbgpTvWonL5WTMmHPIzOx2xHXH6qNSkuL52dTx/HtxLZsK88ly\nl6CvW01l0RbydvTiZ+cPIMp6ep+CegMq/13USFGlBznkwdy4GqPSzLizR7O4OJF6l4fd5QHqXQrb\nSgOclXPmvwAIJ5aSkkJhXQEl5VXkZHc7+RcIXYooyM9QZWOQXRV+hnYzEW8/tCBGURRefvlF/v73\nV5Ekmeuuu4GHH36cmJjYI75ekiQSy3ajNIfYXxskpECCXcZuimN3jY7R0S0da16xn2pHCKvkZNyA\nJM4/fwrnnz+F+vo6Fi9exMKFC/joow/46KMPSExMxJ6UQ1UgBYdix+cPEvQ04K3N5+XGAiRJxZo2\njF5n3YohOoNEu+6Y++Gqqsry5cvIyckhNTWt3dr0dKg7d2od4aSSo3XYohPZUVnKRx99wK23/lzr\nSIIgnKF1hT68/iA9lI04nc3HLMbh+H2U3SzTN9PO4j29KAn2oF90FfHKPvw1W/jsi90Mye1Dt+w+\n7Kk1UO0IHfVp5/Fs3u9jXYGX+soScvRbUAiyQx3CplXxeAMe9DqJepdCUpSM06douh96V9EjK43C\nHbCvpEIU5MJRREF+hpbs9mLQSYztfagjc7vdPP/8M6xatZLBg4fxwAOPMG7c+OPeI6iomA0S0ZaW\n/cpL68Ggk3D7VQw6WJXvw+VXaKjcx7ezHuSsSVfz8hO3YTG2fPx52WVXctllV1JaWsLixYvYunUL\n363aQn2jA50soZNUVMAcm0n3oReRlns+zcbuhEKQEa/nwCeqP94Pt7h4P2Vlpdxww81haLmuR6+T\nmPzT61j79Zt89dUcUZALQoRzehXW7/ORFNqF11PNoEHDjlmMn4w3AP3TDewohy0NaUAqqcY6+hv2\ns2X7dlZs2E5An0DQkslOcxpbS8zH/ETz8Fxz1tWir99BrrEcRbZQphuFX43D7VGRJfAEFPQypMe1\nlAHiOPfw65ZkRdHHUFlVqXUUoQMSBfkZKKoNsr82yOgcM3ZzS1Xb2NjAI4/8loKCAn72sxu4+ebb\nTrploV4nIaEyKNOI26/S6FZocCk4PArvLGumqDaIzSQTpYsiNrU3K775N48Gq3nxmYePOE0zMzOL\nS6+4AX0vN54BHhqaXPROCREKhjBZo9EbzEzoZwFgyS7PUTl+vB9uWVkpFovlhL9MCK2T2zuT6IQM\ntmzJw+/3n9GiXEEQtLVqr4+QuxqTv5CMbt3o2zf3tO6THK0jyiIzqoeJZq/ywxiQzD45lTJ/E0Fn\nESmGSkyeGoy6Lbga4lgopTOibwpRUdEYDEZUVcXjcbN7fzXrt+8n0VGBZJDA1oNAdH+y5Ja+JilK\nd8ziWxznHn52s4zRnoSruQCv14PZbNE6ktCBiIL8NHgDKnn7fXy8zo2EyuCslk3+GxrqefDB31BS\nsp/f/vZhpky5+JTuZzVKePwSSGA1SVhNOgZlGrnqbBsfrnbiD0KDW6HSbSd67JMEjW+x9Puveeqp\nJh569Gl2V8tUO0KgqhTWBPEH4Yqz7OyuMOFT9bg9Lcc4H35s+9YSP/WuQ53ysY50Hzt2PB9//IUo\nGttQeqyOHoMnsW3xu3z88Qdcd92NWkcSBKGVvAGV5Xs8fLK6iYG6TdjiLYwYMfq0d0g5cE5FvStE\njFUmxiozvLuOS0dY+XCNkY1F0ZS4c7Er1STrq0gM1VO2byvu6h0oqorHrxIIqQRDLTu+6PR6Bvbr\nTbWuF+WOQ/13gk3HjJE2PlnrOmn/L4RHUmIqlY0FVFVV0b17ttZxhA5EFOSt5A2ovLfCSX5lgPxK\nP90T9fxvnZvzc5q55aYr0el0PPbY75g4cfIp31N39tkkhGBCPws1zaEjDhQalGWi3qXQQwWHV6Gy\nMQSj7iIqNoGvFn1ElfVbeg05j8rGEPvrgkRbZJ68JJbeqQbOyjFT1CSzt5Qj7gkcPNL9x+/3YyZT\nZMwplEaP1jrCKUmP0zNs8o3sWPY+y5YtEQW5IESYA2PA6r1eotw7UMweqvRjUOUTF7Qn6qMOHbF+\ndJ88oocJp09BUQzUOjMpb0xjtztEb4ufYbFeKuscqHKAaqdCo8+IzR7DY5f1ICnGiC3KwqLNjUfd\n81T7f6HtZWWmULG3ZT9yUZALhxMFeSvlFfupd7YcfWzUS6TH6ikpr2Tary+jqaGG++77bauKQE6t\nzQAAIABJREFUcQD9vfejB47VXR/+5CTaIhNtkRmebWLAlF/w3/lnUaFkU1/gQ1FVYiwyfVL11LsU\noKWTH9fXSr9E9aj7HjjOt7PQ33u/1hFOSZRZIiOjOyPPu5KqqnwxbUUQIkxesZ/S+iBuZz39TKWo\n1kwaST7posiT9VHH65MPHwOSo3UkR+swyBI9k6NZsstLQXUMsiShqCqZ8XpSE/QU1qokxYDZKB/z\nnp2t/48kWQlmVhvjKa8oR1XVsO47L0QWUZC3UrUjRINbwe1XyE4yEPQ7+fCFq3HWV3PbrT/n/vsf\nbNP3O9HTjCbPIFbk+6hxhLCZJDLj9UiSWJzTkUmSREacjtR+57L7u41s2LCOMWPGaR1LEIRTVO0I\nUd4QoIduJwa9Hn90y7zxcPW7JxoDgopKSIUmt0JajI44uxzWLMKZS4nRoZpTcbt30tTUSGxsnNaR\nhA5CFOStlByto7whiF6WSLQqfPzn63DUlTFpyuU8/fRzYXnP4z3NSInRk2APkGA/ctHomS7OcTia\nMJnMETNdJdKkxelJ7TWaPYsMfP/9QlGQC0IEiTJLKM5SYo2NhKJzUXUtC/PCuSjyeGNAZryBjLgg\nGXFHvrdYoNlx6WSJ+KQ0XPt2UFlZIQpy4aATb/8hHCUtRocvCEnRMmvnvkJD1T56DzqHf/z99XbP\nMqSbkXjboY7X53ZQuX3BGS/OmT37P1x55SU0NzvONKJwDOmxOoxmG70HnsXKlctxu91aRxIE4RQF\ngwGy9XuQDTaCthxAu0WRPx4DtMwinLqM5DgCmCgtL9U6itCBiIK8lbaW+hnazUhq41dU7l7CpVff\nxrxP/4vF2P5NeeCjzAn9LORmGHHu+Ji8+a+x4JsvTvuewWCQRYsW0LNnDlFR0W2YVjggNUaHTpbI\nGTwRj8fDZ5/9T+tIgiCcgkBIZdeuHSRY/AwZPJzcTAsT+lm47gR7gofTj8cALbMIpy4z3kDIlExV\nVQ2BQEDrOEIHIaastEKzR2FneQBjwyYWff0vzj17BC88+/gRe4GfjsCdLQfEGN78v1Z/7eEfZU7u\n+0seqs3n9ddfITU1jVGjWr/zyJo1q2hsbOS22+5o9ddq6UzasL3pdRLJ0TrchhGUlZUyc+YrXHfd\nDVrHEgThJDburUd17CU9PY3Jo3q2akFeuPoosUAz8qTH6lBMyfg9pVRXV5KRkaV1JKEDEE/IW2FD\nkY/66v3M/+BPpKSk8tRTT59xMQ5Ac3PLf86QxWLhmWf+SGJiEs899zTFxftbfY9vvvkKs9nMueee\nd8Z52lUbtWF7SY/V0eTTMXLkaMrKSlm3bo3WkQRBOAFVVdm0aSM6SWXCmJGt3x0jwvooIXyiLDKW\nmBT8IaisLNc6jtBBnLQgX7hwIdOmTWPKlCk8+uij+P3+o66ZMmUK06dPZ8aMGcyYMYOvv/46LGG1\n5AuorMgrZv4/7sGgg2eeeZ6YmFitYx0lLi6eZ575I6FQiP/+971Wfa3L5WLjxvVMmDARq9UapoQC\nQPoPi7CmX307AG+99Xct4wjCcYkxoMXGXaUEnOVkZfcWC/GEM5aZYMWvi6e8vBRVPXprYqHrOeHj\n3bq6Op566ik++eQT0tLSePrpp3nzzTe59957D17T1NSE0+lk+fLlYQ+rpY37XMx+4XqcdcX89ld/\npUePnlpHOq6cnN68+OKrrc5os9l4//2P8fvFnLZwS49t+dHL6DuW5OQUli9fSjAYbJtPXAShjYgx\noIWiKGzatAFJZ+K80cO0jiN0AmmxOvaaUnG6dlJXV0tiYpLWkQSNnfAJ+fLlyxk2bBhpaWkAXH31\n1cyZM+eIazZv3ozZbObmm29m+vTpvP766yiKEr7EGlAUlYfuu4mmmmKmTbuEa6+9XutIJ9W3b7/T\nOnAmJiaWpCTRMYRbtEXCZpIpbwxx8cXT8Xq9fP65WNwpdCxiDGixYu1WPO4muuUMIsom5msLZy49\nTkfIlIY/pFJeXqJ1HKEDOOHjuKqqKlJTUw/+OSUlhaqqqiOu8Xq9jB07lieeeIJAIMDtt99OTEwM\nN9zQeRap/fLXj1KwdTm9+uTyt7+90eb3ly+7os3v2dVEWhseOCCouC7Ifff9hvXr17B27WquuOJq\nraMJwkFiDACv18Pa9RtQDbGcN7Lfad8n0vooIbxSonXIRhsYYykrK2HQoGHi1M4u7oQF+bHmNel0\nR+55euGFF3LhhRcCYDQaufnmm5k9e/YJO+OYGAt6fWSsJ121ahX/mfUmUbGJfP/dNyQnhWErwNtv\nbvt7/kCSJBISbKiqSkVFBenp6WF7r7Z0IPcpC2MbttapZu/XXaK0yUVsSjJXXXUFH3zwAU1NVfTs\nqc10qFa3eQcSydk7MjEGwPyF63B7/PQdPoEeWWfQ/2vQR0Xyz0WkZm9N7p7pARxlmXi9u5BlP/Hx\n8WFOd2Jdoc07shMW5KmpqWzfvv3gn6urq0lJSTnimu+++46kpCSGDBkCtHTgJ5sH29TkOd287aqq\nqpLfPvQ4SVm5/OaxF9DJJurqXFrHapWEBBt1dS5ee+0VFi9eyOuvv0Va2pFFudPZjNls6VDzlw/k\njkSnmt0mBXF7/GwvbOYnP/kps2e/zzvvzObee+9vh5RH6wpt3lEkJUVpHeGUdPUxoK6uhrytu1Bt\n3RjaIymivscg8n4uDhep2VuTO0YfYq8/AXMgxLZtuxkwYHCY051YV2jzjuB4/f8JH1GMHz+ejRs3\nUlZWBsDHH3/M5MmTj7imtLSUl19+mWAwiM/nY/bs2Vx00UVtFFs7DQ4Xd9//CPsqHIy84g9cfM4Q\nrSOdkfPPvxCPx8OTTz6Ky3XkN+6sWf/khhuuPurvhfCKtcmUN4T4fIOLUm8Sw0eczfz53+B0iq3R\nhI6hK48BHr/Ct0vWUOeS8McMJMYaGU/0hciRECVT6rDQ4LeRt7MQj79zrb0QWueEPUxCQgLPPvss\nd911FxdffDG1tbXcc889LFq0iCeffBKAG264gV69ejF9+nSmT5/OsGHDuPzyy9slfLi4fSF+fv8z\n7NydT/yIOzEm9OXT9W68gcjdmqhfv/488MDD7N9fxJ/+9OzBRVfNzQ4WLPiG7Owe2GyR/5FPpPAG\nVD5c7aKmOcSO8gBLdnkw9rgIl9vNrFkd/3AjoWvoqmOAN6Dyn2+2UVldR4EvB4ffyHsrnBE9Bggd\nizegsmy3j6K6ECW+NOoam3jv+1LxPdaFnXSOwnnnncd55513xN9NmjSJSZMmAS3zCZ944omwhNPK\n0y/8jT1blpM6+BLsvSeSmWCg3hUkr9gflhPRVEcTAFJ0TJvf+3CTJp3Pvn37+OCD2bz33jvceOMt\nfP75p3g8Hq688pqwvne4tVcbtpW8Yj/1rhBRZpnKxhDBENjSh9DQ5OL111/hpptuIzo6DOsVBKGV\nuuIYsD6/EW/NNhwhO036Hgywy9S7Amc0BkRaHyWEV16xH7dPwaiXqAqkkqrfjbNuP3nFKeLk1S5K\nfAb3I6+//irv/98LWOOysA+8gUS7DpuppZlqmkNhec/gXbcTvOv2sNz7x2655edcfvmV/OQnF+B2\nu/nss0/o27cfw4aNaJf3D5f2bMO2UO1o+V6Ks8qoqFQ7QkiSxLgLf4bX6+WFF57VOKEgdE2qqlK4\newP+QIA9gYFkxhuQaNn94kzGgEjro4TwqnaEQGoZA2o9FgK6BHTeMqod4hyQrkoU5IdZuHABL730\nAlZ7NKnnPYZBL9Mj+dCHCElRuhN8dWSQZZk77/wV6ekZ5OVtwuVyct11N4rtltpZcnTL91KcTcZq\nlClvCKKq8LObfklsbByffvoxTqdT45SC0PWUlhbjc5RT7M/GZIs/ot/vDGOA0DEcGAPS4/SoqFSG\n0pEUH9ZQjcbJBK2IgvwH+/YVcs89dyDLOu547C0s0ankJBsw6lsK1QSbjiHdWn/QTkc2Zsw4Zs2a\nzdlnj9U6SpczpJuReJsOJMiI0+ELqviDKiN6Wrn++ptwu9389a8vaB1TELoUr9fDho3r8GOl0dCX\nnGQ9Pzwc75RjgKCdA2OA1SQRb9dR6ExGJ+vQu/drHU3QSMfZ505DTqeTq666FLfbzX0PP0cgbhhX\n9zOQHqenpjlEr0wb2TEKZkPne4qcnp6hdYQuyWyQuH6cnbxiP1VNQQx6ieQYHSY93Hffb3n33VnM\nn/81Dz30GBaLReu4gtDpqarKunWrqGvy4Ikaw4OT43H71E4/BgjaOHwMSInWsbFIIjEhm5rq/bhc\nTmw2u9YRhXbW5Z+Qh0IhXnjhOWRZ5oabfo6p95VEmWUuGmJldI6JqUOtjOtr7dQd8bx5X/Lcc093\nuuOuOzqzQWJ0jonpw23MGGmjya1QWB3EaDTyyiszsVpt/O9/H2kdUxC6hL17d7O/tJwmYy/69Uxn\nUKaxy4wBgjYOjAE3nRPFWTlmKpXuKIpKQUG+1tEEDXTpglxVVWbOfJXVq1dyxx13c9Ylj+H0KUwZ\nbGnXzld3z33o7rmv3d7vx0pLi1m8eBGvvfbyMU/miwRat+GZGpJlxGyQWFPgA2DSpJ8wYMBAPvzw\nferr6zROJwidW2NjA5vzNuEIxWBO7M9PBrT9p1KR3kcJ4TU6x4RXigJTPPv27SUUCs8mEkLH1aUL\n8g8/fJ8vv/yCyZPPZ8yFN7Kzws+w7iZ6JBnaNYd89ljkdprHXVhYwLJlS44ovH/xi7uYPPl85s6d\nw3vvvdMuOdpae7ZhOJgMEkO7mShtCFLWEESSJO644268Xi/vvPMvreMJQqfl9/tYtWopDg+4ooZz\n0RBbWB7IRHofJYRXTrKeBLuOWqk7Pr+P4uJ9WkcS2lmXLcg///xT/vF/b5HecwjZ597Nv5a6sJtk\nJvQzax0tbBRF4dVX/8oLLzxHQ0P9wb+XZZkHHniEkSNH8e67s5g7d46GKbuu4dlG9LLE2h+ekvfv\nn8vEiZP5+uu5bN++TeN0gtD5ePwKX8xfyr6KJvKDgxjQPa7dH8gIAoAkSZzV04RTTkORrezatV1M\nI+1iumRB/pe/PM8DD9yHas2kzwWP8s22IPmVAdx+BSUyZ2yckm+++YodO7Zz/fU3ER+fcMRrer2e\nJ598hsGDhxAbG6tRwq7NbpYZmGkkvypA7Q/7Hd95590oisptt92I2+3WOKEgdB7egMq7c9dSUVHO\nLldPilzJFNcHxUmJgmZyMwxEWfQ0G3vhdDZTWip2XOlKulxBPnPmq8yc+SomaxTjrnySOq+RBleI\njHgditpyelZnVFVVxVtvzaR792wuv/yqY15jtVp58cVXGT/+3HZOJxwwqqcRCYl1hS1PyePjE5gw\nYSIVFWU8+OCvNU4nCJ3HkvW78NfvpjaYRHGwF71T9Tg8SqcdA4SOTydLjOhhokHKQJXN7Ny5TTwl\n70K6VEH+2msv85e/PE9MTCz3P/8JTimZwuogdpNMVnzLDpDhOo3zRJQd21F2bA/re7z88p/x+bw8\n/PDjGAzH/0g2Ug8Iao82bA9xNh190wzsKA/Q7GnpiB999Emysroxb94c5s37UuOEghD5KirKKN6z\nnoZANFu9g8mM1xNlCe+JzJ2ljxLCa0iWEbNRj9vSB4fDQVFRgdaRhHbSZQryN9+cyV//+gIxMbF8\n9NHnhCyZFNYEiLJIDMg0IP/QElqcxBZ67hlCzz0T1ve47bY7uPfe39K7d59Wf63T2RyGRG2rPdqw\nvZzV00hIUVlf1PKUXK/X8/bb72AwGHj44d9QV1ercUJBiFx1dTWsWLkMV8jMFu9wMuLNdEsI/4nM\nnamPEsLnwAL/aiUTvcnOtm1bCAQCWscS2kGXKMg//fRjPvnkQ/r06csXX3xDobcbtc4QWQl6BmQY\n0es672mcB/Tu3YeLL57a6q8rLy/jlluu5+OPPwhDKuFYUmP1pMXqmLfJzafrXawp8NGzd39+85sH\naW5u5pFHHojY7SkFQUt1dbV8v3gRNU4Jb+wYBnSPpluiOI1T6FiGZxsBmcJAX6oa3HyzbJNY29AF\ndOqTOhVF4Y03Xufzz//HoEGD+d3Tz7Noj8zeqpbtDX99oZmtpQFqmkMkRbV0xOL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"text/plain": [ "<matplotlib.figure.Figure at 0x123abe400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "\n", "mfit.plot_mfit(fitter_g, ax=ax[0])\n", "ax[0].set_title('2-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks*100))\n", "\n", "mfit.plot_mfit(fitter_g, ax=ax[1], plot_model=False, plot_kde=True)\n", "ax[1].set_title('KDE fit (S_fit = %.2f %%)' % (S_peak*100));" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Gauss]: 0.07037038847309614\n" ] } ], "source": [ "## 2-Asym-Gaussian\n", "fitter_ag = mfit.MultiFitter(dx.S)\n", "fitter_ag.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_ag.fit_histogram(model = mfit.factory_two_asym_gaussians(p1_center=0.1, p2_center=0.4))\n", "#print(fitter_ag.fit_obj[0].model.fit_report())\n", "\n", "S_2peaks_a = fitter_ag.params.loc[0, 'p1_center']\n", "dir_ex_S2pa = S_2peaks_a/(1 - S_2peaks_a)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S2pa)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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o2rUr//rXv9Dr9aW2WbRoERs3bsRsNvPAAw/w6KOP1lpgIaC4GC/dm0NargmA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kFYWDi/+MWv7Npu3/4OlZU3Jhh+fhri4gai082wa5udnYXR2N7ltTFj7mfMmPu7vGYwGHjjjT/Z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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x123d65f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "\n", "mfit.plot_mfit(fitter_g, ax=ax[0])\n", "ax[0].set_title('2-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks*100))\n", "\n", "mfit.plot_mfit(fitter_ag, ax=ax[1])\n", "ax[1].set_title('2-Asym-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks_a*100));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Zero threshold on nd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select bursts with:\n", "\n", "$$n_d < 0$$." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Gauss]: 0.03467701820142223\n" ] } ], "source": [ "dx = ds_sa.select_bursts(select_bursts.nd, th1=-100, th2=0)\n", "\n", "fitter = bext.bursts_fitter(dx, 'S')\n", "fitter.fit_histogram(model = mfit.factory_gaussian(center=0.1))\n", "S_1peaks_th = fitter.params.loc[0, 'center']\n", "dir_ex_S1p = S_1peaks_th/(1 - S_1peaks_th)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S1p)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-0.1, 0.6)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zMTJySAhxXSosBFlZWTz//PM8+eST7ojH5das+QqHw86AAQOL7w14U2ugSI8e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\nbWrRs7nZ6eNn56ucS7VzPs3O+VQHaTk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"text/plain": [ "<matplotlib.figure.Figure at 0x123d690b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mfit.plot_mfit(fitter)\n", "plt.xlim(-0.1, 0.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selection 1\n", "\n", "Bursts are weighted using $w = f(S)$, where the function $f(S)$ is a\n", "Gaussian fitted to the $S$ histogram of the FRET population." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Gauss]: 0.09496065168631854\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [KDE]: 0.0827197921178\n" ] } ], "source": [ "dx = ds_sa\n", "\n", "## Weights\n", "weights = 1 - mfit.gaussian(dx.S[0], fitter_g.params.loc[0, 'p2_center'], fitter_g.params.loc[0, 'p2_sigma'])\n", "weights[dx.S[0] >= fitter_g.params.loc[0, 'p2_center']] = 0\n", "\n", "## Histogram fit\n", "fitter_w1 = mfit.MultiFitter(dx.S)\n", "fitter_w1.weights = [weights]\n", "fitter_w1.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_w1.fit_histogram(model = mfit.factory_two_gaussians(p1_center=0.1, p2_center=0.4))\n", "S_2peaks_w1 = fitter_w1.params.loc[0, 'p1_center']\n", "dir_ex_S2p_w1 = S_2peaks_w1/(1 - S_2peaks_w1)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S2p_w1)\n", "\n", "## KDE\n", "fitter_w1.calc_kde(bandwidth=bandwidth)\n", "fitter_w1.find_kde_max(x_kde, xmin=0, xmax=0.15)\n", "S_peak_w1 = fitter_w1.kde_max_pos[0]\n", "dir_ex_S_kde_w1 = S_peak_w1/(1 - S_peak_w1)\n", "print('Fitted direct excitation (na/naa) [KDE]: ', dir_ex_S_kde_w1)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_weights(x, weights, ax):\n", " ax2 = ax.twinx()\n", " x_sort = x.argsort()\n", " ax2.plot(x[x_sort], weights[x_sort], color='k', lw=4, alpha=0.4)\n", " ax2.set_ylabel('Weights');" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RXPnvPX36FGlpl5x+r5Z8DkmSpBtJa+WXYWGdGTgwzuG13bt/kfMTJElqFllJ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\nLPYldPoiHG2YvH6QoSDqn3cgU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"text/plain": [ "<matplotlib.figure.Figure at 0x123d80080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "mfit.plot_mfit(fitter_w1, ax=ax[0])\n", "mfit.plot_mfit(fitter_g, ax=ax[0], plot_model=False, plot_kde=False)\n", "plot_weights(dx.S[0], weights, ax=ax[0])\n", "ax[0].set_title('2-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks_w1*100))\n", "\n", "mfit.plot_mfit(fitter_w1, ax=ax[1], plot_model=False, plot_kde=True)\n", "mfit.plot_mfit(fitter_g, ax=ax[1], plot_model=False, plot_kde=False)\n", "plot_weights(dx.S[0], weights, ax=ax[1])\n", "ax[1].set_title('KDE fit (S_fit = %.2f %%)' % (S_peak_w1*100));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selection 2\n", "\n", "Bursts are here weighted using weights $w$:\n", "\n", "$$w = n_{aa} - |n_a + n_d|$$" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Gauss]: 0.08731556606975518\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [KDE]: 0.0773540185305\n" ] } ], "source": [ "## Weights\n", "sizes = dx.nd[0] + dx.na[0] #- dir_ex_S_kde_w3*dx.naa[0]\n", "weights = dx.naa[0] - abs(sizes)\n", "weights[weights < 0] = 0\n", "\n", "## Histogram\n", "fitter_w4 = mfit.MultiFitter(dx.S)\n", "fitter_w4.weights = [weights]\n", "fitter_w4.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_w4.fit_histogram(model = mfit.factory_two_gaussians(p1_center=0.1, p2_center=0.4))\n", "S_2peaks_w4 = fitter_w4.params.loc[0, 'p1_center']\n", "dir_ex_S2p_w4 = S_2peaks_w4/(1 - S_2peaks_w4)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S2p_w4)\n", "\n", "## KDE\n", "fitter_w4.calc_kde(bandwidth=bandwidth)\n", "fitter_w4.find_kde_max(x_kde, xmin=0, xmax=0.15)\n", "S_peak_w4 = fitter_w4.kde_max_pos[0]\n", "dir_ex_S_kde_w4 = S_peak_w4/(1 - S_peak_w4)\n", "print('Fitted direct excitation (na/naa) [KDE]: ', dir_ex_S_kde_w4)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iLbr2CPn5+V9lZqB270Lr3Ufyv+j0pEMufE9ervvvui5XnqX5+bkX8AQGom/e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S3AnDqVT37+SS677LKgHK9Tgnju59JYi7zWKdE0jTFjxnL6dBFFRYUh0QMNjywrK5AuV8g0KBS9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\nFui60T0rQ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HA7U6ZeyqM/eZxd2z7nf37/WxYufJHp0y/l+9//T3JHjuNkbdx57LOTsdQ0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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1043e3588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))\n", "\n", "mfit.plot_mfit(fitter_w4, ax=ax[0])\n", "mfit.plot_mfit(fitter_g, ax=ax[0], plot_model=False, plot_kde=False)\n", "#plot_weights(dx.S[0], weights, ax=ax[0])\n", "ax[0].set_title('2-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks_w4*100))\n", "\n", "mfit.plot_mfit(fitter_w4, ax=ax[1], plot_model=False, plot_kde=True)\n", "mfit.plot_mfit(fitter_g, ax=ax[1], plot_model=False, plot_kde=False)\n", "#plot_weights(dx.S[0], weights, ax=ax[1])\n", "ax[1].set_title('KDE fit (S_fit = %.2f %%)' % (S_peak_w4*100));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selection 3\n", "\n", "Bursts are here selected according to:\n", "\n", "$$n_{aa} - |n_a + n_d| > 30$$" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1056]\n", "Fitted direct excitation (na/naa) [2-Gauss]: 0.07289517598153095\n", "Fitted direct excitation (na/naa) [KDE]: 0.0713520462824\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitted direct excitation (na/naa) [2-Asym-Gauss]: 0.1743774277810383\n" ] } ], "source": [ "mask = (d.naa[0] - np.abs(d.na[0] + d.nd[0])) > 30\n", "ds_saw = d.select_bursts_mask_apply([mask])\n", "print(ds_saw.num_bursts)\n", "\n", "dx = ds_saw\n", "\n", "## Weights\n", "weights = None\n", "\n", "## 2-Gaussians\n", "fitter_w5 = mfit.MultiFitter(dx.S)\n", "fitter_w5.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_w5.fit_histogram(model = mfit.factory_two_gaussians(p1_center=0.1, p2_center=0.4))\n", "S_2peaks_w5 = fitter_w5.params.loc[0, 'p1_center']\n", "dir_ex_S2p_w5 = S_2peaks_w5/(1 - S_2peaks_w5)\n", "print('Fitted direct excitation (na/naa) [2-Gauss]:', dir_ex_S2p_w5)\n", "\n", "## KDE\n", "fitter_w5.calc_kde(bandwidth=bandwidth)\n", "fitter_w5.find_kde_max(x_kde, xmin=0, xmax=0.15)\n", "S_peak_w5 = fitter_w5.kde_max_pos[0]\n", "S_2peaks_w5_fiterr = fitter_w5.fit_res[0].params['p1_center'].stderr\n", "dir_ex_S_kde_w5 = S_peak_w5/(1 - S_peak_w5)\n", "print('Fitted direct excitation (na/naa) [KDE]: ', dir_ex_S_kde_w5)\n", "\n", "## 2-Asym-Gaussians\n", "fitter_w5a = mfit.MultiFitter(dx.S)\n", "fitter_w5a.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])\n", "fitter_w5a.fit_histogram(model = mfit.factory_two_asym_gaussians(p1_center=0.05, p2_center=0.3))\n", "S_2peaks_w5a = fitter_w5a.params.loc[0, 'p1_center']\n", "dir_ex_S2p_w5a = S_2peaks_w5a/(1 - S_2peaks_w5a)\n", "#print(fitter_w5a.fit_obj[0].model.fit_report(min_correl=0.5))\n", "print('Fitted direct excitation (na/naa) [2-Asym-Gauss]:', dir_ex_S2p_w5a)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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pDBgwACEhIZg7dy4mTZpkGdWv7mcxZswYPP300/jtt9/svk5Jc+bMQa9evRAT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UkRueZK7Stf+QoAe/funfL1RCLBTTfdxJ//+Z+TSqX4/Oc/j8fj4eGHH86vpQXgwoUupITaxpVU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\n/OpLjIw9mEaNhrMnwO5031Kf2DhE2nBESIhfNNfW1vL8808TH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"text/plain": [ "<matplotlib.figure.Figure at 0x12166a2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 3, figsize=(19, 4.5))\n", "\n", "mfit.plot_mfit(fitter_w5, ax=ax[0])\n", "mfit.plot_mfit(fitter_g, ax=ax[0], plot_model=False, plot_kde=False)\n", "ax[0].set_title('2-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks_w5*100))\n", "\n", "mfit.plot_mfit(fitter_w5, ax=ax[1], plot_model=False, plot_kde=True)\n", "mfit.plot_mfit(fitter_g, ax=ax[1], plot_model=False, plot_kde=False)\n", "ax[1].set_title('KDE fit (S_fit = %.2f %%)' % (S_peak_w5*100));\n", "\n", "mfit.plot_mfit(fitter_w5a, ax=ax[2])\n", "mfit.plot_mfit(fitter_g, ax=ax[2], plot_model=False, plot_kde=False)\n", "ax[2].set_title('2-Asym-Gaussians fit (S_fit = %.2f %%)' % (S_2peaks_w5a*100));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Save data to file" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sample = data_id\n", "n_bursts_aa = ds_sas.num_bursts[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following string contains the list of variables to be saved. When saving, the order of the variables is preserved." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "variables = ('sample n_bursts_aa dir_ex_S1p dir_ex_S_kde dir_ex_S2p dir_ex_S2pa '\n", " 'dir_ex_S2p_w1 dir_ex_S_kde_w1 dir_ex_S_kde_w4 dir_ex_S_kde_w5 dir_ex_S2p_w5 dir_ex_S2p_w5a '\n", " 'S_2peaks_w5 S_2peaks_w5_fiterr\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just a trick to format the different variables:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample,n_bursts_aa,dir_ex_S1p,dir_ex_S_kde,dir_ex_S2p,dir_ex_S2pa,dir_ex_S2p_w1,dir_ex_S_kde_w1,dir_ex_S_kde_w4,dir_ex_S_kde_w5,dir_ex_S2p_w5,dir_ex_S2p_w5a,S_2peaks_w5,S_2peaks_w5_fiterr\n", "\n", "17d, 608, 0.034677, 0.083424, 0.119491, 0.070370, 0.094961, 0.082720, 0.077354, 0.071352, 0.072895, 0.174377, 0.067942, 0.003098\n", "\n" ] } ], "source": [ "variables_csv = variables.replace(' ', ',')\n", "fmt_float = '{%s:.6f}'\n", "fmt_int = '{%s:d}'\n", "fmt_str = '{%s}'\n", "fmt_dict = {**{'sample': fmt_str}, \n", " **{k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}\n", "var_dict = {name: eval(name) for name in variables.split()}\n", "var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\\n'\n", "data_str = var_fmt.format(**var_dict)\n", "\n", "print(variables_csv)\n", "print(data_str)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# NOTE: The file name should be the notebook name but with .csv extension\n", "with open('results/usALEX-5samples-PR-raw-dir_ex_aa-fit-%s.csv' % ph_sel_name, 'a') as f:\n", " f.seek(0, 2)\n", " if f.tell() == 0:\n", " f.write(variables_csv)\n", " f.write(data_str)" ] } ], "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": 0 }

mit

Juan-Mateos/coll_int_ai_case

notebooks/ml_topic_analysis_exploration.ipynb

1

999465

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prototype pipeline for the analysis of ML arxiv data\n", "\n", "We query arxiv to get papers, and then run them against Crossref event data to find social media discussion and Microsoft Academic Knowledge to find institutional affiliations\n", "\n", "```\n", "Query Arxiv -> Paper repository -> Analysis -> Topic model -> Classify\n", " | |\n", " | |----> Social network analysis of researchers\n", " | |----> Geocoding of institutions (via GRID?)\n", " |\n", " Extract author data from Google Scholar ----> Geocode institution via Google Places API?\n", " | |\n", " Enrich paper data with MAK(?) |---> Spatial and network analysis\n", " |\n", " Obtain Crossref Event data\n", " \n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preamble" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "#Some imports\n", "import time\n", "#import xml.etree.ElementTree as etree\n", "from lxml import etree\n", "import feedparser\n", "\n", "#Imports\n", "#Key imports are loaded from my profile (see standard_imports.py in src folder).\n", "\n", "#Paths\n", "\n", "#Paths\n", "top = os.path.dirname(os.getcwd())\n", "\n", "#External data (to download the GRID database)\n", "ext_data = os.path.join(top,'data/external')\n", "\n", "#Interim data (to place seed etc)\n", "int_data = os.path.join(top,'data/interim')\n", "\n", "#Figures\n", "fig_path = os.path.join(top,'reports')\n", "\n", "#Models\n", "mod_path = os.path.join(top,'models')\n", "\n", "\n", "#Get date for saving files\n", "today = datetime.datetime.today()\n", "\n", "today_str = \"_\".join([str(x) for x in [today.day,today.month,today.year]])\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Get Arxiv data about machine learning\n", "\n", "* Write a APi querier and extract papers with the terms machine learning or artificial intelligence. Get 2000 results... and play nice!\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Arxiv_querier():\n", " '''\n", " This class takes as an input a query and the number of results, and returns all the parsed results.\n", " Includes routines to deal with multiple pages of results.\n", "\n", " '''\n", " \n", " def __init__(self,base_url=\"http://export.arxiv.org/api/query?\"):\n", " '''\n", " Initialise\n", " '''\n", " \n", " self.base_url = base_url\n", " \n", " def query(self,query_string,max_results=100,wait_time=3):\n", " '''\n", " Query the base url\n", " \n", " '''\n", " #Attribute query string\n", " \n", " #Load base URL\n", " base_url = self.base_url\n", " \n", " #Prepare query string\n", " processed_query = re.sub(' ','+',query_string)\n", " \n", " self.query_string=\"_\".join(query_string.split(\" \"))\n", " \n", " start=0\n", " pages = 0\n", " \n", " #Run the query and store results for as long as the number of results is bigger than the max results\n", " keep_running = True\n", " \n", " result_store = []\n", " \n", " while keep_running==True:\n", " pages +=1\n", " print(pages)\n", " \n", " #Query url (NB the start arg, which will change as we go through different\n", " #pages)\n", " query_url = base_url+'search_query=all:{q}&start={s}&max_results={max_res}'.format(\n", " q=processed_query,s=start,max_res=max_results)\n", " \n", " \n", " #Download\n", " source = requests.get(query_url)\n", " \n", " #Parse the xml and get the entries (papers)\n", " parsed = feedparser.parse(source.content)\n", " \n", " #Extract entries\n", " entries = parsed['entries']\n", " \n", " #If the number of entries is bigger than the maximum number of results\n", " #this means we need to go to another page. We do that by offseting the\n", " #start with max results\n", " \n", " result_store.append(entries)\n", " \n", " if len(entries)==max_results:\n", " start+=max_results\n", " \n", " #If we have less than max results this means we have run out of \n", " #results and we toggle the keep_running switch off.\n", " if len(entries)<max_results:\n", " keep_running=False\n", " \n", " time.sleep(wait_time)\n", " \n", " #Save results in a flat list\n", " self.entry_results = [x for el in result_store for x in el]\n", " \n", " def extract_data(self):\n", " '''\n", " Here we extract data from the entries \n", " \n", " '''\n", " \n", " #Load entries\n", " entries = self.entry_results\n", " \n", " #Create df\n", " output = pd.concat([pd.DataFrame({\n", " 'query':self.query_string,\n", " 'id':x['id'],\n", " 'link':x['link'],\n", " 'title':x['title'],\n", " 'authors':\", \".join([el['name'] for el in x['authors']]),\n", " 'summary':x['summary'],\n", " 'updated':x['updated'],\n", " 'published':x['published'],\n", " 'category':x['arxiv_primary_category']['term'],\n", " 'pdf':str([el['href'] for el in x['links'] if el['type']=='application/pdf'][0]\n", " )},index=[0]) for x in entries]).reset_index(drop=True)\n", " \n", " output['year_published'] = [x.split(\"-\")[0] for x in output['published']]\n", " \n", " self.output_df = output" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "artificial intelligence\n", "run 0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "16\n", "17\n", "18\n", "19\n", "20\n", "21\n", "22\n", "23\n", "run 1\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "16\n", "17\n", "18\n", "19\n", "20\n", "21\n", "22\n", "23\n", "run 2\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "machine learning\n", "run 0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "16\n", "17\n", "18\n", "run 1\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "14\n", "15\n", "run 2\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "deep learning\n", "run 0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "run 1\n", "1\n", "2\n", "run 2\n", "1\n" ] } ], "source": [ "query_terms = ['artificial intelligence','machine learning','deep learning']\n", "\n", "\n", "#There are some inconsistencies in the number of results so we run the query three times for each\n", "#term and remove duplicated results\n", "\n", "def extract_arxiv_data(term,max_results=1000,wait_time=10, tests=3):\n", " '''\n", " This function initialises the Arxiv_querier class, extracts the data and outputs it\n", " \n", " '''\n", " print(term)\n", " \n", " collected = []\n", " \n", " #We collect the data thrice\n", " for i in np.arange(tests):\n", " print('run'+ ' ' +str(i))\n", " initialised = Arxiv_querier()\n", " initialised.query(term,max_results,wait_time)\n", " initialised.extract_data()\n", " out = initialised.output_df\n", " collected.append(out)\n", " \n", " #We concatenate the dfs and remove the duplicates.\n", " \n", " output = pd.concat(collected)\n", " output_no_dupes = output.drop_duplicates('id')\n", " \n", " #Return both\n", " return([output,output_no_dupes])\n", "\n", "\n", "arxiv_ai_results_three = [extract_arxiv_data(term=q) for q in query_terms]" ] }, { "cell_type": "code", "execution_count": 373, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(38999, 11)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>authors</th>\n", " <th>category</th>\n", " <th>id</th>\n", " <th>link</th>\n", " <th>pdf</th>\n", " <th>published</th>\n", " <th>query</th>\n", " <th>summary</th>\n", " <th>title</th>\n", " <th>updated</th>\n", " <th>year_published</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Tshilidzi Marwala</td>\n", " <td>q-fin.GN</td>\n", " <td>http://arxiv.org/abs/1509.01213v1</td>\n", " <td>http://arxiv.org/abs/1509.01213v1</td>\n", " <td>http://arxiv.org/pdf/1509.01213v1</td>\n", " <td>2015-07-01T16:26:21Z</td>\n", " <td>artificial_intelligence</td>\n", " <td>Artificial intelligence has impacted many aspe...</td>\n", " <td>Impact of Artificial Intelligence on Economic ...</td>\n", " <td>2015-07-01T16:26:21Z</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Dan Geiger, Prakash Shenoy</td>\n", " <td>cs.AI</td>\n", " <td>http://arxiv.org/abs/1304.3846v1</td>\n", " <td>http://arxiv.org/abs/1304.3846v1</td>\n", " <td>http://arxiv.org/pdf/1304.3846v1</td>\n", " <td>2013-04-13T20:44:25Z</td>\n", " <td>artificial_intelligence</td>\n", " <td>This is the Proceedings of the Thirteenth Conf...</td>\n", " <td>Proceedings of the Thirteenth Conference on Un...</td>\n", " <td>2013-04-13T20:44:25Z</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>David Heckerman, E. Mamdani</td>\n", " <td>cs.AI</td>\n", " <td>http://arxiv.org/abs/1304.3851v1</td>\n", " <td>http://arxiv.org/abs/1304.3851v1</td>\n", " <td>http://arxiv.org/pdf/1304.3851v1</td>\n", " <td>2013-04-13T21:03:12Z</td>\n", " <td>artificial_intelligence</td>\n", " <td>This is the Proceedings of the Ninth Conferenc...</td>\n", " <td>Proceedings of the Ninth Conference on Uncerta...</td>\n", " <td>2013-04-13T21:03:12Z</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Laveen Kanal, John Lemmer</td>\n", " <td>cs.AI</td>\n", " <td>http://arxiv.org/abs/1304.3859v1</td>\n", " <td>http://arxiv.org/abs/1304.3859v1</td>\n", " <td>http://arxiv.org/pdf/1304.3859v1</td>\n", " <td>2013-04-13T21:37:12Z</td>\n", " <td>artificial_intelligence</td>\n", " <td>This is the Proceedings of the Second Conferen...</td>\n", " <td>Proceedings of the Second Conference on Uncert...</td>\n", " <td>2013-04-13T21:37:12Z</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Michael Swan Laufer</td>\n", " <td>cs.AI</td>\n", " <td>http://arxiv.org/abs/1311.0716v1</td>\n", " <td>http://arxiv.org/abs/1311.0716v1</td>\n", " <td>http://arxiv.org/pdf/1311.0716v1</td>\n", " <td>2013-10-30T14:19:49Z</td>\n", " <td>artificial_intelligence</td>\n", " <td>In this paper, I put forward that in many inst...</td>\n", " <td>Artificial Intelligence in Humans</td>\n", " <td>2013-10-30T14:19:49Z</td>\n", " <td>2013</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " authors category id \\\n", "0 Tshilidzi Marwala q-fin.GN http://arxiv.org/abs/1509.01213v1 \n", "1 Dan Geiger, Prakash Shenoy cs.AI http://arxiv.org/abs/1304.3846v1 \n", "2 David Heckerman, E. Mamdani cs.AI http://arxiv.org/abs/1304.3851v1 \n", "3 Laveen Kanal, John Lemmer cs.AI http://arxiv.org/abs/1304.3859v1 \n", "4 Michael Swan Laufer cs.AI http://arxiv.org/abs/1311.0716v1 \n", "\n", " link pdf \\\n", "0 http://arxiv.org/abs/1509.01213v1 http://arxiv.org/pdf/1509.01213v1 \n", "1 http://arxiv.org/abs/1304.3846v1 http://arxiv.org/pdf/1304.3846v1 \n", "2 http://arxiv.org/abs/1304.3851v1 http://arxiv.org/pdf/1304.3851v1 \n", "3 http://arxiv.org/abs/1304.3859v1 http://arxiv.org/pdf/1304.3859v1 \n", "4 http://arxiv.org/abs/1311.0716v1 http://arxiv.org/pdf/1311.0716v1 \n", "\n", " published query \\\n", "0 2015-07-01T16:26:21Z artificial_intelligence \n", "1 2013-04-13T20:44:25Z artificial_intelligence \n", "2 2013-04-13T21:03:12Z artificial_intelligence \n", "3 2013-04-13T21:37:12Z artificial_intelligence \n", "4 2013-10-30T14:19:49Z artificial_intelligence \n", "\n", " summary \\\n", "0 Artificial intelligence has impacted many aspe... \n", "1 This is the Proceedings of the Thirteenth Conf... \n", "2 This is the Proceedings of the Ninth Conferenc... \n", "3 This is the Proceedings of the Second Conferen... \n", "4 In this paper, I put forward that in many inst... \n", "\n", " title updated \\\n", "0 Impact of Artificial Intelligence on Economic ... 2015-07-01T16:26:21Z \n", "1 Proceedings of the Thirteenth Conference on Un... 2013-04-13T20:44:25Z \n", "2 Proceedings of the Ninth Conference on Uncerta... 2013-04-13T21:03:12Z \n", "3 Proceedings of the Second Conference on Uncert... 2013-04-13T21:37:12Z \n", "4 Artificial Intelligence in Humans 2013-10-30T14:19:49Z \n", "\n", " year_published \n", "0 2015 \n", "1 2013 \n", "2 2013 \n", "3 2013 \n", "4 2013 " ] }, "execution_count": 373, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_papers = pd.concat([x[1] for x in arxiv_ai_results_three]).drop_duplicates('id').reset_index(drop=True)\n", "print(all_papers.shape)\n", "all_papers.head()" ] }, { "cell_type": "code", "execution_count": 374, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_papers.to_csv(int_data+'/{today}_ai_papers.csv'.format(today=today_str),index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Some exploratory analysis" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n", " warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n" ] } ], "source": [ "from nltk.corpus import stopwords\n", "from nltk.tokenize import word_tokenize, sent_tokenize, RegexpTokenizer, PunktSentenceTokenizer\n", "from nltk.stem import WordNetLemmatizer, SnowballStemmer, PorterStemmer\n", "import scipy\n", "import ast\n", "import string as st\n", "from bs4 import BeautifulSoup\n", "\n", "import gensim\n", "from gensim.models.coherencemodel import CoherenceModel\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from itertools import product\n", "\n", "stopwords_c = stopwords.words('english')\n", "stemmer = PorterStemmer()\n", "lemmatizer= WordNetLemmatizer()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Read papers\n", "all_papers = pd.read_csv(int_data+'/19_8_2017_ai_papers.csv'.format(today=today_str))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11079f198>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wFdAxtoodAxS4+wlmNjM+fsnMWgDrgA7A1QDuPi7OZyZQEGdb4O4nxPKxqdNV\npm/fvr5o0aIaxS4iIiIJGjQIVq6Ed96BFml12jUaZrbY3ftWN106R2V2MLP94uOWwDeAFcAc4PQ4\n2Qjgifh4enxOrH/WQ/Y3HRgej9o8GOgOLAAWAt3jUZ57Eg4QmJ7eYoqIiEhOeP11ePbZcPmlJpaU\n1UQ6a6YTMDmOM2sGPOzuT5nZ68BUM7sOeBW4O05/N3Cfma0ENhISLdx9uZk9DLwO7AAudfedAGY2\nGpgJNAcmufvyjC2hiIiIJG/CBMjLg4suqn7aJqzGXZnZQl2ZIiIiOeLjjyE/H04/He69N+loEpGx\nrkwRERGROpk8GT79VNfFTIMSMxEREak/u3aFM/337w99q20wavI0+k5ERETqz6xZ8NZbcP/9SUeS\nE9RiJiIiIvVnwgQ44IAwvkyqpcRMRERE6se778JTT8GoUeGITKmWEjMRERGpH3fcAc2awcUXJx1J\nzlBiJiIiIpm3dWu4YPmwYdC5c9LR5AwlZiIiIpJ5U6fCpk06RUYNKTETERGRzHKHW2+FI46A445L\nOpqcotNliIiISGa99BIsWQJ/+hOYJR1NTlGLmYiIiGTWrbfCvvvCOeckHUnOUWImIiIimbN2LTz6\nKFx4IbRunXQ0OUeJmYiIiGTOXXfBjh1wySVJR5KTlJiJiIhIZmzfHsaVDRkC3bsnHU1OUmImIiIi\nmTFtGqxbB5ddlnQkOUuJmYiIiGTGhAnQrVtoMZNaUWImIiIidbdkCbzwAlx6abgMk9SK1pyIiIjU\n3W23QcuW4WhMqTUlZiIiIlI3GzfCAw/AuedC27ZJR5PTlJiJiIhI3dxzD2zbFroxpU6UmImIiEjt\n7dwJt98OAwZA795JR5PzlJiJiIhI7f3977BqFYwenXQkjYISMxEREam9W2+FTp1g2LCkI2kUqk3M\nzKyLmc0xs9fNbLmZXR7LC8ys0MyWxNvQlNeMNbOVZvammZ2QUj4klq00s6tTyg82s5dj+UNmtmem\nF1REREQy7K23YOZM+P73YY89ko6mUUinxWwHMMbdewH9gUvNrFesu9Hd+8TbDIBYNxw4HBgC3G5m\nzc2sOXAbcCLQCzg7ZT6/jfM6BNgEXJSh5RMREZH6cvvtISEbNSrpSBqNahMzd1/r7q/Ex58AK4D8\nKl5yKjDV3Yvd/V1gJdAv3la6+yp33w5MBU41MwO+DjwaXz8ZOK22CyQiIiINYMuWcDTm6adDx45J\nR9No1GgBnowVAAAgAElEQVSMmZl1Bb4MvByLRpvZa2Y2ycxKTlySD3yQ8rI1sayy8nbAR+6+o0y5\niIiIZKtJk2DzZl0XM8PSTszMrDXwGPBDd98M3AF8EegDrAXG10uEpWMYZWaLzGxRUVFRfb+diIiI\nVGTtWvjlL+FrX4P+/ZOOplFJKzEzsz0ISdkD7v44gLt/6O473X0XMJHQVQlQCHRJeXnnWFZZ+X+A\n/cysRZnyctz9Lnfv6+59O3TokE7oIiIikmmXXw6ffQZ/+hOYJR1No5LOUZkG3A2scPc/ppR3Spls\nGLAsPp4ODDezPDM7GOgOLAAWAt3jEZh7Eg4QmO7uDswBTo+vHwE8UbfFEhERkXrx5JPwyCPwi1/A\noYcmHU2j06L6SfgKcB7wLzNbEst+Sjiqsg/gwGrgYgB3X25mDwOvE47ovNTddwKY2WhgJtAcmOTu\ny+P8rgKmmtl1wKuERFBERESyySefhMsuHXEE/PjHSUfTKFlosMo9ffv29UWLFiUdhoiISNPxwx/C\nLbfAP/8JxxyTdDQ5xcwWu3vf6qbTmf9FRESkegsWhKTskkuUlNUjJWYiIiJStc8/h5Ej4QtfgN/8\nJuloGrV0xpiJiIhIU/bHP8Jrr8G0adCmTdLRNGpqMRMREZHKvfMOFBSEi5Sfpgvz1DclZiIiIlIx\n990XKL/11qSjaRLUlSkiIiIVu/9+mDULbrsN8nW1xIagFjMREREpr6gIrrgCjj02tJpJg1BiJiIi\nIuWNGRMuUn7XXdBM6UJD0ZoWERGR0p55Bu67D666Cg4/POlomhQlZiIiIrLb1q2h67J7d/jZz5KO\npsnR4H8RERHZ7de/hlWrYM4c2GuvpKNpctRiJiIiIsHSpfCHP8B3vwsDByYdTZOkxExERERg585w\n2aV27eD3v086miZLXZkiIiICEybAwoXw4IOw//5JR9NkqcVMRESkqXv//TDQ/8QT4ayzko6mSVNi\nJiIi0pS5wyWXhPvbbwezpCNq0tSVKSIi0pQ9+ij87W8wfjx07Zp0NE2eWsxERESaqk2b4LLL4Mgj\n4Qc/SDoaQS1mIiIiTdfVV4drYs6YAS2UEmQDtZiJiIg0Rc8/H66DecUVocVMsoISMxERkaamuBhG\njQpjyn71q6SjkRRqtxQREWlqxo2DN9+Ep5+GvfdOOhpJoRYzERGRpmTFCvjNb+A734EhQ5KORspQ\nYiYiItJU7NoVujBbt4Ybb0w6GqlAtYmZmXUxszlm9rqZLTezy2P5/mb2jJm9He/bxnIzs1vMbKWZ\nvWZmR6bMa0Sc/m0zG5FSfpSZ/Su+5hYznd1OREQk4/78Z3jhhXDOsgMOSDoaqUA6LWY7gDHu3gvo\nD1xqZr2Aq4HZ7t4dmB2fA5wIdI+3UcAdEBI54BrgaKAfcE1JMhenGZnyOrWtioiIZNLatfCTn8DA\ngXDBBUlHI5WodvC/u68F1sbHn5jZCiAfOBUYGCebDMwFrorlU9zdgflmtp+ZdYrTPuPuGwHM7Blg\niJnNBdq4+/xYPgU4DXg6M4tYf8bOGluubMBBAxjafSjFO4opmFtQrn5wt8EM6jaIzcWbGTdvXLn6\nod2HMuCgAWzYuoHxL44vVz+s5zD65fejcHMhExZMKFd/1hFn0adjH1ZtWsXExRPL1Z/f+3x6dujJ\niqIVTFk6pVz9yKNG0q1tN5asW8JDyx4qVz+632jy2+SzoHAB01ZMK1c/5tgxtG/VnnnvzWPG2zPK\n1Y8dMJY2eW2YvWo2s1bNKldfMLCAvBZ5zHh7BvPem1euftzgsM6mrZjGgsIFperyWuRRMLAAgKnL\nprJ03dJS9W3y2jB2QNhmk5dM5o0Nb5Sqb9+qPWOOHQPAxMUTWbVpVan6/Db5jO43GoAJCyZQuLmw\nVH23tt0YedRIAMa/OJ4NWzeUqu/Rvgcj+oSG4nHzxrG5eHOp+t4dezP8iOFhPcwtoHhHcan6fvn9\nGNZzGKB9T/ue9r1U2vcKgGr2PXdWn/dN8rdu4ZYRh7Jh9k+BprPv5ZIajTEzs67Al4GXgQNj0gaw\nDjgwPs4HPkh52ZpYVlX5mgrKK3r/UWa2yMwWFRUV1SR0ERGRpmvCBLrOXsTs87/Khs77Jx2NVMFC\nw1YaE5q1Bp4Drnf3x83sI3ffL6V+k7u3NbOngBvc/YVYPpvQkjYQ2Mvdr4vlvwC2EVrabnD3wbF8\nAHCVu59cVTx9+/b1RYsW1WhhRUREmpznnoNBg+Dkk+Hxx6GZjvtLgpktdve+1U2X1tYxsz2Ax4AH\n3P3xWPxh7KIk3q+P5YVAl5SXd45lVZV3rqBcRERE6uL99+GMM6B7d5gyRUlZDkjnqEwD7gZWuPsf\nU6qmAyVHVo4AnkgpPz8endkf+Dh2ec4EjjeztnHQ//HAzFi32cz6x/c6P2VeIiIiUhvbtsG3vhXO\n8v/Xv0KbNklHJGlI58z/XwHOA/5lZkti2U+BG4CHzewi4D3gzFg3AxgKrAS2AhcCuPtGM7sWWBin\n+3XJgQDAJcC9QEvCoP+sH/gvIiKStdzh+9+HxYth+nQ47LCkI5I0pXNU5gtAZecVK3eoQzwa89JK\n5jUJmFRB+SLgiOpiERERkTTcemvouvzVr+CUU5KORmpAnc0iIiKNydy58KMfwamnws9/nnQ0UkNK\nzERERBoLDfbPedpiIiIijcG2bTBsGGzfrsH+OSydwf8iIiKSzdzh4ovhlVc02D/HqcVMREQk191y\nC9x3nwb7NwJKzERERHLZnDkwZgycdpoG+zcCSsxERERy1XvvwZlnhsH+kydrsH8joC0oIiKSi0rO\n7K/B/o2KBv+LiIjkGncYNQpefVWD/RsZJWYiIiK55uab4f774de/hpNPTjoaySB1ZYqIiOSSOXPg\nyivDYP+f/SzpaCTDlJiJiIjkipLB/oceqjP7N1LaoiIiIrlg69ZwZv/PPw+D/ffZJ+mIpB5ojJmI\niEi2Kxnsv2QJPPlkaDGTRkmJmYiISLa7+WZ44AG49lo46aSko5F6pK5MERGRbFYy2H/YMPjpT5OO\nRuqZEjMREZFsVTLY/7DDdGb/JkJbWEREJBtpsH+TpDFmIiIi2SZ1sP9TT4VrYUqToMRMREQk29xy\nSxjsf911MHRo0tFIA1JXpoiISDb55z/DYP9TT4WxY5OORhqYEjMREZFssX59GOx/0EFw770a7N8E\nqStTREQkG+zYAcOHw8aNMH8+7Ldf0hFJAqpNxc1skpmtN7NlKWUFZlZoZkvibWhK3VgzW2lmb5rZ\nCSnlQ2LZSjO7OqX8YDN7OZY/ZGZ7ZnIBRUREcsIvfxnOWXbHHdC7d9LRSELSaSO9FxhSQfmN7t4n\n3mYAmFkvYDhweHzN7WbW3MyaA7cBJwK9gLPjtAC/jfM6BNgEXFSXBRIREck5Tz4J48bByJFwwQVJ\nRyMJqjYxc/fngY1pzu9UYKq7F7v7u8BKoF+8rXT3Ve6+HZgKnGpmBnwdeDS+fjJwWg2XQUREJHet\nWgXnnQdHHhmOxpQmrS6jCkeb2Wuxq7NtLMsHPkiZZk0sq6y8HfCRu+8oUy4iItL4bdsG3/52GOT/\n6KOw115JRyQJq21idgfwRaAPsBYYn7GIqmBmo8xskZktKioqaoi3FBERqT+XXRZOInvffXDwwUlH\nI1mgVomZu3/o7jvdfRcwkdBVCVAIdEmZtHMsq6z8P8B+ZtaiTHll73uXu/d1974dOnSoTegiIiLZ\nYdIkuPtu+PnP4aSTko5GskStEjMz65TydBhQcsTmdGC4meWZ2cFAd2ABsBDoHo/A3JNwgMB0d3dg\nDnB6fP0I4InaxCQiIpIzXn0VLr0UBg+GgoKko5EsUu15zMzsQWAg0N7M1gDXAAPNrA/gwGrgYgB3\nX25mDwOvAzuAS919Z5zPaGAm0ByY5O7L41tcBUw1s+uAV4G7M7Z0IiIi2WbTJjj9dGjfHv7yF2je\nPOmIJItYaLTKPX379vVFixYlHYaIiEj6du2C006Dv/8dnn8e+vdPOiJpIGa22N37VjedzvwvIiLS\nUH73u3DOsltuUVImFdJFuERERBrCs8/Cz34WLrs0enTS0UiWUmImIiJS3woLQ0J22GEwcSKYJR2R\nZCklZiIiIvXp88/hzDNh61Z47DFo3TrpiCSLaYyZiIhIffrJT+DFF2HqVOjZM+loJMupxUxERKS+\nPPII3HQT/OAHcNZZSUcjOUCJmYiISH144w347nfhmGPg979POhrJEUrMREREMm3LlnBx8pYt4eGH\nYc89k45IcoTGmImIiGSSO4waFVrM/vEP6Nw56YgkhygxExERyaTbb4cHH4Trr4dBg5KORnKMujJF\nREQyZf58uOIKOPlkuPrqpKORHKTETEREJBOKiuCMMyA/H6ZMgWb6iZWaU1emiIhIXe3cCeecE5Kz\nF1+Etm2TjkhylBIzERGRuioogGeeCZdbOvLIpKORHKZ2VhERkbq48Ua47rpwzrKLLko6GslxSsxE\nRERq69Zb4Uc/CmPL7rxTFyeXOlNiJiIiUhu33x4utTRsGDzwALTQ6CCpOyVmIiIiNXXXXXDppfDN\nb4aLk++xR9IRSSOhxExERKQmJk2Ciy+Gk07S5ZYk45SYiYiIpGvyZPje92DIEHj0UcjLSzoiaWSU\nmImIiKTj/vvhwgth8GB4/HHYa6+kI5JGSImZiIhIdaZOhREj4Gtfg7/+FVq2TDoiaaSUmImIiFTl\nkUfg3HNhwACYPh1atUo6ImnEqk3MzGySma03s2UpZfub2TNm9na8bxvLzcxuMbOVZvaamR2Z8poR\ncfq3zWxESvlRZvav+JpbzHQSGBERyRKPPw5nnw3HHANPPQV77510RNLIpdNidi8wpEzZ1cBsd+8O\nzI7PAU4EusfbKOAOCIkccA1wNNAPuKYkmYvTjEx5Xdn3EhERaXhPPAFnnQX9+sGMGdC6ddIRSRNQ\nbWLm7s8DG8sUnwpMjo8nA6ellE/xYD6wn5l1Ak4AnnH3je6+CXgGGBLr2rj7fHd3YErKvERERJLx\n1FPhbP5HHQV//zvss0/SEUkTUdvTFB/o7mvj43XAgfFxPvBBynRrYllV5WsqKM8JY2eNLVc24KAB\nDO0+lOIdxRTMLShXP7jbYAZ1G8Tm4s2MmzeuXP3Q7kMZcNAANmzdwPgXx5erH9ZzGP3y+1G4uZAJ\nCyaUqz/riLPo07EPqzatYuLiieXqz+99Pj079GRF0QqmLJ1Srn7kUSPp1rYbS9Yt4aFlD5WrH91v\nNPlt8llQuIBpK6aVqx9z7Bjat2rPvPfmMePtGeXqxw4YS5u8NsxeNZtZq2aVqy8YWEBeizxmvD2D\nee/NK1c/bnBYZ9NWTGNB4YJSdXkt8igYWADA1GVTWbpuaan6NnltGDsgbLPJSybzxoY3StW3b9We\nMceOAWDi4oms2rSqVH1+m3xG9xsNwIQFEyjcXFiqvlvbbow8aiQA418cz4atG0rV92jfgxF9Qi/+\nuHnj2Fy8uVR97469GX7E8LAe5hZQvKO4VH2//H4M6zkM0L6nfU/7XqpM73uHLlzFedc8xrqDO3D3\n1cfwPT4hnzba93J438sldR78H1u6PAOxVMvMRpnZIjNbVFRU1BBvKSIiTcghi9/l3Gse48OD2nP3\nDcP5rLVOiSENy0JeVc1EZl2Bp9z9iPj8TWCgu6+N3ZFz3f0wM7szPn4wdbqSm7tfHMvvBObG2xx3\n7xHLz06drip9+/b1RYsW1WRZRUREKvfss+Fs/ocdBrNnQ7t2SUckjYiZLXb3vtVNV9sWs+lAyZGV\nI4AnUsrPj0dn9gc+jl2eM4HjzaxtHPR/PDAz1m02s/7xaMzzU+YlIiLSMJ57Dk4+Gbp3h1mzlJRJ\nYqodY2ZmDxJavNqb2RrC0ZU3AA+b2UXAe8CZcfIZwFBgJbAVuBDA3Tea2bXAwjjdr9295ICCSwhH\nfrYEno43ERGRhjFvXmgpO/jgkJS1b590RNKEpdWVmY3UlSkiInX24otwwgnQuTPMnQsHHljtS0Rq\no767MkVERHLbyy+Hi5F/4QthfJmSMskCSsxERKTpWbgQjj8+JGPPPgudOiUdkQigxExERJoS93AR\n8uOPD2PJ5syB/Jw5faY0AUrMRESkaVi5MgzyHzYMunQJLWWdOycdlUgpSsxERKRx27oVfvELOPxw\n+Oc/4aab4JVX4KCDko5MpJzaXpJJREQku7mHC5H/8Ifw3ntw3nnwu99Bx45JRyZSKbWYiYhI45Pa\nbbnPPuEEslOmKCmTrKfETEREGo+tW+GXvwzdli+8ADfeGLotjzsu6chE0qKuTBERyX3uMH06XH55\n6LY85xz4/e91GgzJOWoxExGR3LZyZbjO5WmnhW7LuXPh/vuVlElOUmImIiK5KbXbct48+OMfQ7fl\n//5v0pGJ1Jq6MkVEJLeUdFv+8IewerW6LXNM16v/VqfXr77hpAxFkp3UYiYiIrnjnXd2d1vuvbe6\nLaXRUWImIiLZL7Xb8vnnYfx4ePVVdVtKo6OuTBERyV47dsDjj8NVV4Vuy+98J3RbfuELSUcmUi+U\nmImISPbZtAnuvhtuvRXefx969QoXHB84MOnIROqVEjMREckeb74Jt9wC994bui8HDoSbb4ZTToHm\nzZOOTqTeKTETEZFkucMzz4SLiz/9NOy5ZzjS8gc/gD59ko5OpEEpMRMRkWRs3Qr33RdayF5/HQ48\nEH71K7j44vBYJA2N7fQbSsxERKRhrVkDt90Gd90FGzfCkUeGC4yfeSbk5SUdnUiilJiJiEjDmD8/\ndFc++mjovhw2LJwk9itfAbOkoxPJCkrMRESk/nz+eUjEbroJFiyAffeFK66ASy+Frl2Tjk4k6ygx\nExGRzNuwIXRV3nYb/PvfcOih4fH550Pr1klHJ5K1lJiJiEhm7NwJL7wQLpF0//3w2Wdw/PHw5z/D\nCSdAM11sRqQ6dUrMzGw18AmwE9jh7n3NbH/gIaArsBo40903mZkBNwNDga3ABe7+SpzPCODncbbX\nufvkusQlIiINZOdOmDcPHnkEHnsMPvwQWrWCESPC6S569Uo6QpGckokWs6+5+4aU51cDs939BjO7\nOj6/CjgR6B5vRwN3AEfHRO4aoC/gwGIzm+7umzIQm4iIZNrOneF6lY88Ei6X9OGH0LIlnHRSOLJy\n6NBwgXERqbH66Mo8FRgYH08G5hISs1OBKe7uwHwz28/MOsVpn3H3jQBm9gwwBHiwHmITEZHa2LGj\ndDK2fn1oGTvpJDjjDCVjIhlS18TMgX+YmQN3uvtdwIHuvjbWrwNKzhKYD3yQ8to1sayy8nLMbBQw\nCuC//uu/6hi6iIhUaccOeO653clYUVFIxk4+OSRjJ56oZEwkw+qamH3V3QvN7ADgGTN7I7XS3T0m\nbRkRE7+7APr27Zux+YqISLRjB8yduzsZ27AhJF+pyVirVklHKQ2ksZ1VPxfUKTFz98J4v97MpgH9\ngA/NrJO7r41dlevj5IVAl5SXd45lhezu+iwpn1uXuEREpAZ27IA5c0IyNm1a6WTszDNhyBAlYyIN\npNaJmZntDTRz90/i4+OBXwPTgRHADfH+ifiS6cBoM5tKGPz/cUzeZgK/MbO2cbrjgbG1jUtERNJQ\nWAizZ4fb3/4G//lPSMZOOWV3y1jLlklHKdLk1KXF7EBgWjgLBi2Av7j7381sIfCwmV0EvAecGaef\nQThVxkrC6TIuBHD3jWZ2LbAwTvfrkgMBREQkQzZuDF2UJcnYm2+G8nbtwrnGzjgjtIwpGRNJVK0T\nM3dfBfSuoPw/wKAKyh24tJJ5TQIm1TYWEREpY+vWcLLXkkTslVfC9Sn33huOOw5GjoRBg+C//1sn\nfhXJIjrzv4hIY7BjByxcuDsRe/FF2L4d9tgD+veHa64JiVi/frDnnklHK9R9YD1ocH1jpMRMRCQX\nucOyZbsTseeeg08+CXV9+sBll4VEbMAAXZuyCdFRlLlPiZmISC747DNYvjx0ST77bLitjwe9H3II\nfOc7IRH72tegfftkYxWRWlNiJiKSbdavhyVLYOnS3fdvvBEuhQTQsSN84xshERs0CHTCbZFGQ4mZ\niEhSdu6Et94qnYAtWQLr1u2epksX6N0bTjstdFH27h1ayMIR8SLSyCgxExFpCJs3w2uvlU7C/vWv\n0EUJYZB+r15wwgkh+erTJxwx2a5dsnGLSINSYiYikkmffhrOEfbGG+F+2bKQiK1atXuadu1C8nXJ\nJbuTsB49dLRkltPAemkISsxERGrKHdauDclX2dsHH+yerlmz0O141FFw0UUhCevdG/Lz1RUpIhVS\nYiYiUpniYnjnnYoTsJJTUwDss09o8Ro4EA47LDzu0SMkZXl5iYUvIrlHiZmING3bt4dWrtWr4d13\nd3dDvvFG6H7ctWv3tF26hITrggt2J189ekCnTmoBE5GMUGImIo3b55/DmjUh8SpJvlIfFxaGrskS\neXlw6KHw5S/D2WfvTr4OPVQnahWReqf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"text/plain": [ "<matplotlib.figure.Figure at 0x10aa89550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let's begin by looking at years\n", "\n", "#When where they published?\n", "\n", "#Year distribution\n", "year_pubs = all_papers['year_published'].value_counts()\n", "year_pubs.index = [int(x) for x in year_pubs.index]\n", "\n", "fig,ax = plt.subplots(figsize=(10,5))\n", "\n", "year_pubs_sorted = year_pubs[sorted(year_pubs.index)]\n", "year_pubs_subset = year_pubs_sorted[year_pubs_sorted.index>1991]\n", "\n", "ax.plot(np.arange(1993,2018),year_pubs_subset.cumsum(),color='red')\n", "ax.bar(np.arange(1993,2018),year_pubs_subset)\n", "ax.hlines(xmin=1993,xmax=2017,y=[10000,20000,30000,40000],colors='green',linestyles='dashed',alpha=0.7)\n", "\n", "\n", "ax.set_title(\"Papers on AI, ML and DL, total per year (bar) and cumulative (red)\",size=14)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "cs.AI 6983\n", "cs.LG 5043\n", "stat.ML 4799\n", "cs.CV 3216\n", "cs.CL 1466\n", "cs.NE 952\n", "cs.IT 613\n", "stat.ME 586\n", "quant-ph 578\n", "astro-ph 562\n", "math.OC 550\n", "math.ST 492\n", "cond-mat.mes-hall 489\n", "cs.RO 485\n", "cs.IR 450\n", "cs.SI 435\n", "cs.LO 413\n", "astro-ph.IM 391\n", "cs.NI 362\n", "cs.CY 338\n", "Name: category, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#What are the categories of the papers? Are we capturing what we think we are capturing\n", "#Top 20\n", "all_papers['category'].value_counts()[:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See <a href='https://arxiv.org/help/api/user-manual'>here</a> for abbreviations of categories.\n", "\n", "In a nutshell, AI is AI, LG is 'Learning', CV is 'Computer Vision', 'CL' is 'computation and language' and NE is 'Neural and Evolutionary computing'. SL.ML is kind of self-explanatory. We seem to be picking up the main things" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#NB do we want to remove hyphens?\n", "punct = re.sub('-','',st.punctuation)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def comp_sentence(sentence):\n", " '''\n", " Takes a sentence and pre-processes it.\n", " The output is the sentence as a bag of words\n", " \n", " '''\n", " #Remove line breaks and hyphens\n", " sentence = re.sub('\\n',' ',sentence)\n", " sentence = re.sub('-',' ',sentence)\n", " \n", " #Lowercase and tokenise\n", " text_lowered = [x.lower() for x in sentence.split(\" \")]\n", " \n", " #Remove signs and digits\n", " text_no_signs_digits = [\"\".join([x for x in el if x not in punct+st.digits]) for \n", " el in text_lowered]\n", " \n", " #Remove stop words, single letters\n", " text_stopped = [w for w in text_no_signs_digits if w not in stopwords_c and\n", " len(w)>1]\n", " \n", " #Stem\n", " text_lemmatised = [lemmatizer.lemmatize(w) for w in text_stopped]\n", " \n", " #Output\n", " return(text_lemmatised)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Process text\n", "clean_corpus = [comp_sentence(x) for x in all_papers['summary']]" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "model 38373\n", "data 28715\n", "method 28506\n", "algorithm 27870\n", "learning 26955\n", "problem 24803\n", "network 23526\n", "based 21769\n", "system 18944\n", "approach 18448\n", "result 17895\n", "paper 17894\n", "show 17434\n", "using 16476\n", "time 13776\n", "two 12595\n", "set 12269\n", "also 11862\n", "new 11756\n", "feature 11753\n", "state 11549\n", "used 11492\n", "proposed 11445\n", "function 11360\n", "one 11321\n", "information 11137\n", "performance 11013\n", "machine 10880\n", "present 10670\n", "task 10076\n", "dtype: int64" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We remove rate words\n", "word_freqs = pd.Series([x for el in clean_corpus for x in el]).value_counts()\n", "\n", "word_freqs[:30]" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['certifiable', 'bake', 'aeraulic', 'spdvcfp', 'sobre', 'stitched',\n", " 'undithering', 'bookkeeping', 'recycle', 'lssol'],\n", " dtype='object')" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rare_words = word_freqs.index[word_freqs<=2]\n", "rare_words[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lots of the rare words seem to be typos and so forth. We remove them" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Removing rare words\n", "clean_corpus_no_rare = [[x for x in el if x not in rare_words] for el in clean_corpus]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 NLP (topic modelling & word embeddings)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Identify 2-grams (frequent in science!)\n", "bigram_transformer = gensim.models.Phrases(clean_corpus_no_rare)\n", "\n", "#Train the model on the corpus\n", "\n", "#Let's do a bit of grid search\n", "\n", "#model = gensim.models.Word2Vec(bigram_transformer[clean_corpus], size=360, window=15, min_count=2, iter=20)" ] }, { "cell_type": "code", "execution_count": 472, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('cybersecurity', 0.5190048813819885),\n", " ('ethic', 0.5039341449737549),\n", " ('ai', 0.5019052624702454),\n", " ('ethical_issue', 0.496062695980072),\n", " ('agi', 0.48768138885498047),\n", " ('astrobiology', 0.4816303849220276),\n", " ('technological_advance', 0.48043808341026306),\n", " ('deception', 0.4778890013694763),\n", " ('appreciation', 0.474555104970932),\n", " ('ethical', 0.4712696373462677)]" ] }, "execution_count": 472, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('ai_safety')" ] }, { "cell_type": "code", "execution_count": 470, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('computational_complexity', 0.5796849727630615),\n", " ('treewidth', 0.4251006245613098),\n", " ('ulbc', 0.3759896457195282),\n", " ('qrd', 0.37181398272514343),\n", " ('communication_cost', 0.36393606662750244),\n", " ('tree_width', 0.3622165024280548),\n", " ('worst_case', 0.35874366760253906),\n", " ('running_time', 0.35821208357810974),\n", " ('computational_cost', 0.3545072078704834),\n", " ('polynomial', 0.3506772816181183)]" ] }, "execution_count": 470, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('complexity')" ] }, { "cell_type": "code", "execution_count": 475, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('open_source', 0.7268495559692383),\n", " ('source_code', 0.7222407460212708),\n", " ('python', 0.705554723739624),\n", " ('released', 0.671886682510376),\n", " ('repository', 0.6573259830474854),\n", " ('downloaded', 0.6564698219299316),\n", " ('freely_available', 0.6537964344024658),\n", " ('download', 0.653651237487793),\n", " ('open_sourced', 0.6528508067131042),\n", " ('made_publicly', 0.6449865102767944)]" ] }, "execution_count": 475, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.most_similar('github')" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Create 3 different dictionaries and bows depending on word sizes\n", "\n", "def remove_words_below_threshold(corpus,threshold):\n", " '''\n", " Takes a list of terms and removes any which are below a threshold of occurrences\n", " \n", " '''\n", " #Produce token frequencies\n", " token_frequencies = pd.Series([x for el in corpus for x in el]).value_counts()\n", " \n", " #Identify tokens to drop (below a threshold)\n", " tokens_to_drop = token_frequencies.index[token_frequencies<=threshold]\n", " \n", " #Processed corpus\n", " processed_corpus = [[x for x in el if x not in tokens_to_drop] for el in corpus]\n", " \n", " #Dictionary\n", " dictionary = gensim.corpora.Dictionary(processed_corpus)\n", " corpus_bow = [dictionary.doc2bow(x) for x in processed_corpus]\n", " \n", " return([dictionary,corpus_bow,processed_corpus])" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/gensim/models/phrases.py:248: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n", " warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n" ] } ], "source": [ "#Initial model run to see what comes out.\n", "\n", "#Transform corpus to bigrams\n", "transformed_corpus = bigram_transformer[clean_corpus]\n", "\n", "corpora_to_process = {str(x):remove_words_below_threshold(transformed_corpus,x) for x in [1,2,5,10]}" ] }, { "cell_type": "code", "execution_count": 534, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100_2\n", "training\n", "coherence\n", "100_5\n", "training\n", "coherence\n", "200_2\n", "training\n", "coherence\n", "200_5\n", "training\n", "coherence\n", "300_2\n", "training\n", "coherence\n", "300_5\n", "training\n", "coherence\n" ] } ], "source": [ "#Need to turn this into a function.\n", "#Topic modelling\n", "\n", "#Parameters for Grid search.\n", "lda_params = list(product([100,200,300],[2,5]))\n", "\n", "#Model container\n", "lda_models = []\n", "\n", "for x in lda_params:\n", " #Print stage\n", " print('{x}_{y}'.format(x=x[0],y=x[1]))\n", " \n", " #Load corpus and dict\n", " \n", " dictionary = corpora_to_process[str(x[1])][0]\n", " corpus_bow = corpora_to_process[str(x[1])][1]\n", " corpus = corpora_to_process[str(x[1])][2]\n", " \n", " print('training')\n", " #Train model\n", " mod = gensim.models.LdaModel(corpus_bow,num_topics=x[0],id2word=dictionary,\n", " passes=10,iterations=50)\n", " \n", " print('coherence')\n", " #Extract coherence\n", " cm = CoherenceModel(mod,texts=corpus,\n", " dictionary=dictionary,coherence='u_mass')\n", " \n", " #Get value\n", " try:\n", " coherence_value = cm.get_coherence()\n", " except:\n", " print('coherence_error')\n", " coherence_value='error'\n", " \n", " \n", " lda_models.append([x,mod,[coherence_value,cm]])" ] }, { "cell_type": "code", "execution_count": 535, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(mod_path+'/{t}_ai_topic_models.p'.format(t=today_str),'wb') as outfile:\n", " pickle.dump(lda_models,outfile)" ] }, { "cell_type": "code", "execution_count": 536, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1dd2ecba8>" ] }, "execution_count": 536, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ts2bNLO8vnhSUZcUMTj8d5syBQw+Fyy6D7t1hwYJk90xERCQh+vXrx5VXXvm3ocuslC1b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\nmYhIkm3aBAsX7phZmz/f5/Sn22uvHee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"text/plain": [ "<matplotlib.figure.Figure at 0x1d6b06ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualiase model performance\n", "\n", "model_eval = pd.DataFrame([[x[0][0],x[0][1],x[2][0]] for x in lda_models],columns=['topics','word_lim','coherence'])\n", "\n", "fig,ax = plt.subplots(figsize=(10,5))\n", "\n", "cols = ['red','green','blue']\n", "legs = []\n", "\n", "for num,x in enumerate(set(model_eval['word_lim'])):\n", " \n", " subset = model_eval.loc[[z == x for z in model_eval['word_lim']],:]\n", " \n", " ax.plot(subset.loc[:,'topics'],subset.loc[:,'coherence'],color=cols[num-1])\n", " \n", " legs.append([cols[num-1],x]) \n", "\n", "ax.legend(labels=[x[1] for x in legs],title='Min word count')\n", "ax.set_title('Model performance with different parameters')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(mod_path+'/19_8_2017_ai_topic_models.p','rb') as infile:\n", " lda_models = pickle.load(infile)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " topic_term_dists = topic_term_dists.ix[topic_order]\n" ] }, { "data": { "text/html": [ "\n", "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n", "\n", "\n", "<div id=\"ldavis_el1008174796761449978178010\"></div>\n", "<script type=\"text/javascript\">\n", "\n", "var ldavis_el1008174796761449978178010_data = {\"topic.order\": [58, 10, 6, 43, 19, 14, 74, 41, 100, 47, 42, 55, 68, 71, 92, 57, 99, 22, 65, 16, 37, 63, 62, 13, 82, 56, 21, 38, 88, 11, 39, 78, 94, 73, 96, 27, 69, 40, 91, 5, 66, 20, 67, 83, 24, 54, 81, 17, 87, 28, 52, 46, 32, 98, 61, 84, 4, 29, 1, 2, 12, 86, 36, 77, 25, 93, 7, 45, 48, 30, 60, 64, 23, 8, 33, 18, 35, 90, 70, 95, 50, 59, 26, 34, 89, 97, 15, 85, 31, 72, 79, 9, 53, 80, 76, 75, 3, 49, 44, 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1.134779 1 21 -0.236785 0.076950\n", "62 1.133391 1 22 -0.092988 0.156009\n", "61 1.109935 1 23 0.075456 -0.013181\n", "12 1.089505 1 24 -0.151512 0.095938\n", "81 1.085638 1 25 -0.169000 0.195092\n", "55 1.026907 1 26 -0.042567 -0.157955\n", "20 1.009117 1 27 0.084602 -0.030518\n", "37 1.006234 1 28 0.069181 0.005695\n", "87 1.004119 1 29 0.066171 0.021986\n", "10 0.961697 1 30 0.016595 -0.056606\n", "... ... ... ... ... ...\n", "59 0.621650 1 71 0.072462 0.020894\n", "63 0.617379 1 72 0.073947 -0.018010\n", "22 0.602663 1 73 0.095199 0.024136\n", "7 0.601415 1 74 0.081566 -0.037070\n", "32 0.594665 1 75 0.105633 0.013422\n", "17 0.590822 1 76 0.073865 -0.020691\n", "34 0.589498 1 77 0.089203 0.048090\n", "89 0.579089 1 78 0.100897 0.005771\n", "69 0.574985 1 79 0.097867 0.045831\n", "94 0.568627 1 80 0.100071 0.001992\n", "49 0.562043 1 81 0.078735 -0.060240\n", "58 0.561302 1 82 0.096802 -0.007927\n", "25 0.547226 1 83 0.061313 -0.004066\n", "33 0.544455 1 84 0.089689 -0.024055\n", "88 0.532335 1 85 0.013223 -0.033567\n", "96 0.525940 1 86 0.092876 -0.005805\n", "14 0.524782 1 87 0.113231 0.006488\n", "84 0.486804 1 88 0.078345 0.026652\n", "30 0.454526 1 89 0.093417 0.041953\n", "71 0.449260 1 90 0.085412 -0.007730\n", "78 0.448688 1 91 0.085361 0.030713\n", "8 0.442412 1 92 0.115548 0.010375\n", "52 0.437397 1 93 0.092537 0.046841\n", "79 0.434545 1 94 0.091790 -0.030886\n", "75 0.418577 1 95 0.106265 0.004918\n", "74 0.417252 1 96 0.118586 0.011142\n", "2 0.409716 1 97 0.061522 0.068070\n", "48 0.392069 1 98 0.083636 0.001688\n", "43 0.368782 1 99 0.010547 0.056894\n", "50 0.314575 1 100 0.121422 0.008994\n", "\n", "[100 rows x 5 columns], topic_info= Category Freq Term Total loglift \\\n", "term \n", "26957 Default 14303.000000 none 14303.000000 30.0000 \n", "3677 Default 11475.000000 simulation 11475.000000 29.0000 \n", "23790 Default 22826.000000 potential 22826.000000 28.0000 \n", "22152 Default 9216.000000 pay_per 9216.000000 27.0000 \n", "13557 Default 14031.000000 algorithm 14031.000000 26.0000 \n", "26730 Default 8429.000000 given 8429.000000 25.0000 \n", "11662 Default 12144.000000 intelligent 12144.000000 24.0000 \n", "4561 Default 11907.000000 scale 11907.000000 23.0000 \n", "11790 Default 6886.000000 includes 6886.000000 22.0000 \n", "25161 Default 6518.000000 phenomenon 6518.000000 21.0000 \n", "21865 Default 9229.000000 continuous_variable 9229.000000 20.0000 \n", "3540 Default 7351.000000 also 7351.000000 19.0000 \n", "27309 Default 6013.000000 assignment 6013.000000 18.0000 \n", "17577 Default 7972.000000 task 7972.000000 17.0000 \n", "25545 Default 5651.000000 extract 5651.000000 16.0000 \n", "19531 Default 5157.000000 neural_network 5157.000000 15.0000 \n", "13918 Default 7119.000000 furthermore 7119.000000 14.0000 \n", "3414 Default 6003.000000 however 6003.000000 13.0000 \n", "25197 Default 5056.000000 classifier 5056.000000 12.0000 \n", "6813 Default 6295.000000 efficient 6295.000000 11.0000 \n", "82 Default 4801.000000 particle 4801.000000 10.0000 \n", "12672 Default 4509.000000 kernel 4509.000000 9.0000 \n", "18725 Default 4749.000000 architecture 4749.000000 8.0000 \n", "13334 Default 4274.000000 aiming 4274.000000 7.0000 \n", "16604 Default 4079.000000 primary 4079.000000 6.0000 \n", "26959 Default 4038.000000 alzheimers_disease 4038.000000 5.0000 \n", "11452 Default 4546.000000 constraint 4546.000000 4.0000 \n", "6771 Default 3878.000000 offer 3878.000000 3.0000 \n", "5849 Default 4460.000000 diagrammatic 4460.000000 2.0000 \n", "18819 Default 4432.000000 discus 4432.000000 1.0000 \n", "... ... ... ... ... ... \n", "17234 Topic100 182.245516 infor_mation 183.442564 5.7552 \n", "8340 Topic100 165.014677 flight 166.211726 5.7545 \n", "12802 Topic100 156.569540 remains_challenging 157.766588 5.7541 \n", "28501 Topic100 144.313941 metabolism 145.510990 5.7534 \n", "662 Topic100 144.132953 wfomc 145.330001 5.7534 \n", "10007 Topic100 135.332363 kinematics 136.529411 5.7529 \n", "27379 Topic100 134.329411 cost_sensitive 135.526460 5.7528 \n", "26908 Topic100 130.515186 c 131.712234 5.7526 \n", "28764 Topic100 102.776411 lying 103.973459 5.7501 \n", "4326 Topic100 95.768089 route 96.965137 5.7493 \n", "25649 Topic100 88.083652 mathematical_formalism 89.280700 5.7482 \n", "23090 Topic100 86.240798 ing 87.437847 5.7479 \n", "21134 Topic100 183.462091 query 186.161992 5.7471 \n", "6455 Topic100 78.766327 accessibility 79.963375 5.7466 \n", "649 Topic100 78.233606 abductive 79.430655 5.7465 \n", "14741 Topic100 67.985918 tr 69.182967 5.7443 \n", "6721 Topic100 66.972675 stage 68.169724 5.7440 \n", "28572 Topic100 55.924315 representational_power 57.121363 5.7405 \n", "8416 Topic100 54.911756 sda 56.108805 5.7401 \n", "2347 Topic100 49.963788 mcp 51.160836 5.7380 \n", "180 Topic100 49.327962 maxcut 50.525010 5.7377 \n", "7135 Topic100 48.167797 holonomic 49.364845 5.7372 \n", "10810 Topic100 47.897984 likelihood_estimate 49.095032 5.7370 \n", "7540 Topic100 70.580487 class_imbalance 73.079225 5.7269 \n", "22073 Topic100 90.231868 possibility_necessity 104.233044 5.6175 \n", "5738 Topic100 143.113604 unobservable 189.082029 5.4832 \n", "14816 Topic100 185.544566 phenomenal 263.961770 5.4092 \n", "2440 Topic100 266.593030 clause 499.591608 5.1336 \n", "9067 Topic100 57.517837 travel_time 63.048216 5.6699 \n", "16994 Topic100 65.948288 working 123.543173 5.1340 \n", "\n", " logprob \n", "term \n", "26957 30.0000 \n", "3677 29.0000 \n", "23790 28.0000 \n", "22152 27.0000 \n", "13557 26.0000 \n", "26730 25.0000 \n", "11662 24.0000 \n", "4561 23.0000 \n", "11790 22.0000 \n", "25161 21.0000 \n", "21865 20.0000 \n", "3540 19.0000 \n", "27309 18.0000 \n", "17577 17.0000 \n", "25545 16.0000 \n", "19531 15.0000 \n", "13918 14.0000 \n", "3414 13.0000 \n", "25197 12.0000 \n", "6813 11.0000 \n", "82 10.0000 \n", "12672 9.0000 \n", "18725 8.0000 \n", "13334 7.0000 \n", "16604 6.0000 \n", "26959 5.0000 \n", "11452 4.0000 \n", "6771 3.0000 \n", "5849 2.0000 \n", "18819 1.0000 \n", "... ... \n", "17234 -4.0154 \n", "8340 -4.1147 \n", "12802 -4.1673 \n", "28501 -4.2488 \n", "662 -4.2500 \n", "10007 -4.3130 \n", "27379 -4.3205 \n", "26908 -4.3493 \n", "28764 -4.5882 \n", "4326 -4.6588 \n", "25649 -4.7425 \n", "23090 -4.7636 \n", "21134 -4.0087 \n", "6455 -4.8543 \n", "649 -4.8611 \n", "14741 -5.0015 \n", "6721 -5.0165 \n", "28572 -5.1968 \n", "8416 -5.2150 \n", "2347 -5.3095 \n", "180 -5.3223 \n", "7135 -5.3461 \n", "10810 -5.3517 \n", "7540 -4.9640 \n", "22073 -4.7184 \n", "5738 -4.2571 \n", "14816 -3.9975 \n", "2440 -3.6350 \n", "9067 -5.1687 \n", "16994 -5.0319 \n", "\n", "[4755 rows x 6 columns], token_table= Topic Freq Term\n", "term \n", "16267 65 0.995152 abc\n", "22873 21 0.992341 abc_boost\n", "649 100 0.981989 abductive\n", "17324 20 0.990350 abductive_framework\n", "4192 21 0.971908 abductive_reasoning\n", "22300 42 0.734455 abelian\n", "22300 64 0.143782 abelian\n", "22300 66 0.116580 abelian\n", "19112 13 0.954569 ability_generalize\n", "18689 59 0.990206 ability_handle\n", "10963 1 0.024690 able\n", "10963 2 0.127795 able\n", "10963 3 0.120271 able\n", "10963 4 0.002119 able\n", "10963 5 0.002225 able\n", "10963 6 0.077779 able\n", "10963 8 0.003709 able\n", "10963 9 0.045777 able\n", "10963 11 0.137331 able\n", "10963 13 0.009113 able\n", "10963 14 0.000106 able\n", "10963 15 0.019286 able\n", "10963 16 0.087740 able\n", "10963 17 0.005510 able\n", "10963 18 0.009749 able\n", "10963 19 0.020239 able\n", "10963 20 0.007841 able\n", "10963 21 0.013776 able\n", "10963 22 0.030836 able\n", "10963 24 0.026385 able\n", "... ... ... ...\n", "6257 87 0.994456 workload\n", "9773 48 0.991478 workplace\n", "7507 32 0.999195 world\n", "25922 76 0.996063 wormhole\n", "11256 16 0.973657 worse\n", "11129 52 0.993336 worst_case\n", "575 11 0.293142 would\n", "575 60 0.180771 would\n", "575 84 0.429941 would\n", "575 88 0.092828 would\n", "11300 49 0.990625 would_allow\n", "8146 45 0.991808 written\n", "14758 67 0.996995 wrong\n", "22167 96 0.985545 wrt\n", "27284 7 0.917223 www\n", "27284 88 0.078619 www\n", "7403 84 0.986883 xml\n", "24672 32 0.986645 xxn\n", "1264 76 0.998494 yang\n", "10766 18 0.806116 year\n", "10766 26 0.192106 year\n", "19652 13 0.199029 yet\n", "19652 56 0.101260 yet\n", "19652 89 0.694854 yet\n", "14127 44 0.991120 yield_superior\n", "531 5 0.993355 yielded\n", "13045 46 0.996974 younger\n", "5807 13 0.983611 zero_shot\n", "24306 94 0.990779 zhang\n", "1687 41 0.976062 zone\n", "\n", "[10974 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[58, 10, 6, 43, 19, 14, 74, 41, 100, 47, 42, 55, 68, 71, 92, 57, 99, 22, 65, 16, 37, 63, 62, 13, 82, 56, 21, 38, 88, 11, 39, 78, 94, 73, 96, 27, 69, 40, 91, 5, 66, 20, 67, 83, 24, 54, 81, 17, 87, 28, 52, 46, 32, 98, 61, 84, 4, 29, 1, 2, 12, 86, 36, 77, 25, 93, 7, 45, 48, 30, 60, 64, 23, 8, 33, 18, 35, 90, 70, 95, 50, 59, 26, 34, 89, 97, 15, 85, 31, 72, 79, 9, 53, 80, 76, 75, 3, 49, 44, 51])" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "check_model= lda_models[1][1]\n", "\n", "#Explore topics via LDAvis\n", "import pyLDAvis.gensim\n", "\n", "pyLDAvis.enable_notebook()\n", "pyLDAvis.gensim.prepare(\n", " #Insert best model/corpus/topics here \n", " check_model, \n", " corpora_to_process[str(5)][1],\n", " corpora_to_process[str(5)][0])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Can we extract the relevant terms for the topics as in Sievert and Shirley in order to name them?\n", "\n", "#First - create a matrix with top 30 terms per topic\n", "top_30_kws = [check_model.get_topic_terms(topicid=n,topn=1000) for n in np.arange(0,100)]\n", "\n", "#Keyword df where the columns are tokens and the rows are topics\n", "top_30_kws_df = pd.concat([pd.DataFrame([x[1] for x in el],\n", " index=[x[0] for x in el]) for el in top_30_kws],\n", " axis=1).fillna(0).T.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#This is the dictionary\n", "selected_dictionary = corpora_to_process[str(5)][0]\n", "\n", "#Total number of terms in the document\n", "total_terms = np.sum([vals for vals in selected_dictionary.dfs.values()])\n", "\n", "#Appearances of different terms\n", "document_freqs = pd.Series([v for v in selected_dictionary.dfs.values()],\n", " index=[k for k in selected_dictionary.dfs.keys()])[top_30_kws_df.columns]/total_terms\n", "\n", "#Normalise the terms (divide the vector of probabilities of each keywords in each topic by the totals)\n", "top_30_kws_normalised = top_30_kws_df.apply(lambda x: x/document_freqs,axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Now we want to extract, for each topic, the relevance score.\n", "\n", "def relevance_score(prob_in_topic,prob_in_corpus,id2word_lookup,lambda_par = 0.6):\n", " '''\n", " Combines the probabilities using the definition in Sievert and Shirley and returns the top 5 named\n", " #terms for each topic \n", " '''\n", " #Create dataframe\n", " combined = pd.concat([prob_in_topic,prob_in_corpus],axis=1)\n", " \n", " combined.columns=['prob_in_topic','prob_in_corpus']\n", " \n", " #Create relevance metric\n", " combined['relevance'] = lambda_par*combined['prob_in_topic'] + (1-lambda_par)*combined['prob_in_corpus']\n", " \n", " #Top words\n", " top_ids = list(combined.sort_values('relevance',ascending=False).index[:5])\n", " \n", " #Top words\n", " top_words = \"_\".join([id2word_lookup[this_id] for this_id in top_ids])\n", " \n", " return(top_words)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "relevance_scores = [relevance_score(top_30_kws_df.iloc[n,:],\n", " top_30_kws_normalised.iloc[n,:],\n", " dictionary.id2token,lambda_par=0.6) for n in np.arange(len(top_30_kws_df))]" ] }, { "cell_type": "code", "execution_count": 601, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5min 51s, sys: 2min 56s, total: 8min 47s\n", "Wall time: 8min 59s\n" ] } ], "source": [ "%%time\n", "#Create a df with the topic predictions.\n", "paper_preds = check_model[corpora_to_process[str(5)][1]]\n", "\n", "paper_topics_df = pd.concat([pd.DataFrame([x[1] for x in el],index=[x[0] for x in el]) for el in paper_preds],\n", " axis=1).T\n", "\n", "#Replace NAs with zeros and drop pointless index\n", "paper_topics_df.fillna(value=0,inplace=True)\n", "paper_topics_df.reset_index(drop=True,inplace=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "paper_topics_df.columns = relevance_scores\n", "\n", "paper_topics_df.to_csv(int_data+'/{t}_paper_topic_mix.csv'.format(t=today_str),index=False)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#paper_topics_df = pd.read_csv(int_data+'/{t}_paper_topic_mix.csv')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1561f7e48>" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CZK+nmZnZ2sAFWDOdfPLJvP322zXPL774Yq666ioGDRrEtddey4UXXgjA4YcfznnnnceO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\nN8nuKVz92dSX7PqSN0TEOaX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"text/plain": [ "<matplotlib.figure.Figure at 0x1569c32e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Quick test of Deep learning papers\n", "\n", "#These are papers with a topic that seems to capture deep learning\n", "dl_papers = [x>0.05 for x in paper_topics_df['network_training_model_deep_deep_learning']]\n", "\n", "dl_papers_metadata = pd.concat([pd.Series(dl_papers),all_papers],axis=1)\n", "\n", "paper_frequencies = pd.crosstab(dl_papers_metadata.year_published,dl_papers_metadata[0])\n", "\n", "paper_frequencies.columns=['no_dl','dl']\n", "\n", "\n", "fig,ax = plt.subplots(figsize=(10,5))\n", "\n", "paper_frequencies.plot.bar(stacked=True,ax=ax)\n", "ax.set_title('Number of papers in the DL \\'topic\\'')\n", "ax.legend(labels=['Not ANN/DL related','NN/DL topic >0.05'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of this is interesting. Doesn't seem to be picking up the policy related terms (safety, discrimination)\n", "\n", "Next stages - focus on policy related terms. Can we look for papers in keyword dictionaries identified through the word embeddings?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtain Google Scholar data" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "68083" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#How many authors are there in the data? Can we collect all their institutions from Google Scholar\n", "\n", "paper_authors = pd.Series([x for el in all_papers['authors'] for x in el.split(\", \")])\n", "\n", "paper_authors_unique = paper_authors.drop_duplicates()\n", "\n", "len(paper_authors_unique)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 68,000 authors. It might take a while to get their data from Google Scholar" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x2159e2278>" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x213d217f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Top authors and frequencies\n", "\n", "authors_freq = paper_authors.value_counts()\n", "\n", "fig,ax=plt.subplots(figsize=(10,3))\n", "\n", "ax.hist(authors_freq,bins=30)\n", "ax.set_title('Distribution of publications')" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "count 68083.000000\n", "mean 1.859701\n", "std 2.795677\n", "min 1.000000\n", "25% 1.000000\n", "50% 1.000000\n", "75% 2.000000\n", "max 150.000000\n", "dtype: float64\n" ] }, { "data": { "text/plain": [ "9856" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Pretty skewed distribution!\n", "print(authors_freq.describe())\n", "\n", "np.sum(authors_freq>2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Less than 10,000 authors with 3+ papers in the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_scholar_data(" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 178 µs, sys: 1e+03 ns, total: 179 µs\n", "Wall time: 182 µs\n" ] } ], "source": [ "%%time\n", "#Test run\n", "import scholarly\n", "\n", "@ratelim.patient(max_calls=30,time_interval=60)\n", "def get_scholar_data(scholarly_object):\n", " '''''' \n", " try:\n", " scholarly_object = next(scholarly_object)\n", " metadata = {}\n", " metadata['name']=scholarly_object.name\n", " metadata['affiliation'] = scholarly_object.affiliation\n", " metadata['interests'] = scholarly_object.interests\n", " return(metadata)\n", " \n", " except:\n", " return('nothing')\n", " \n", "\n", "#Extract information from each query (it is a generator)\n", "#Get data\n", "\n", "#ml_author_gscholar=[]\n", "\n", "for num,x in enumerate(paper_authors_unique[1484:]):\n", " if num % 100 == 0:\n", " print(str(num)+\":\"+x) \n", "\n", " result = get_scholar_data(scholarly.search_author(x))\n", " ml_author_gscholar.append(result)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1484" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ml_author_gscholar)" ] }, { "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": 2 }

mit

jmschrei/pomegranate

tutorials/old/Tutorial_7_Parallelization.ipynb

1

83938

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pomegranate and parallelization\n", "\n", "pomegranate supports parallelization through a set of built in functions based off of joblib. All computationally intensive functions in pomegranate are implemented in cython with the global interpreter lock (GIL) released, allowing for multithreading to be used for efficient parallel processing. The following functions can be called for parallelization:\n", "\n", "1. fit\n", "2. summarize\n", "3. predict\n", "4. predict_proba\n", "5. predict_log_proba\n", "6. log_probability\n", "7. probability\n", "\n", "These functions can all be simply parallelized by passing in `n_jobs=X` to the method calls. This tutorial will demonstrate how to use those calls. First we'll look at a simple multivariate Gaussian mixture model, and compare its performance to sklearn. Then we'll look at a hidden Markov model with Gaussian emissions, and lastly we'll look at a mixture of Gaussian HMMs. These can all utilize the build-in parallelization that pomegranate has.\n", "\n", "Let's dive right in!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from sklearn.mixture import GaussianMixture\n", "from pomegranate import *\n", "import seaborn, time\n", "seaborn.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_dataset(n_samples, n_dim, n_classes, alpha=1):\n", " \"\"\"Create a random dataset with n_samples in each class.\"\"\"\n", " \n", " X = numpy.concatenate([numpy.random.normal(i*alpha, 1, size=(n_samples, n_dim)) for i in range(n_classes)])\n", " y = numpy.concatenate([numpy.zeros(n_samples) + i for i in range(n_classes)])\n", " idx = numpy.arange(X.shape[0])\n", " numpy.random.shuffle(idx)\n", " return X[idx], y[idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. General Mixture Models\n", "\n", "pomegranate has a very efficient implementation of mixture models, particularly Gaussian mixture models. Lets take a look at how fast pomegranate is versus sklearn, and then see how much faster parallelization can get it to be." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jmschr/anaconda2/lib/python2.7/site-packages/sklearn/mixture/base.py:237: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n", " % (init + 1), ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 37 s per loop\n", "\n", "pomegranate GMM\n", "1 loop, best of 3: 23.6 s per loop\n", "\n", "pomegranate GMM (4 jobs)\n", "1 loop, best of 3: 10.6 s per loop\n" ] } ], "source": [ "n, d, k = 1000000, 5, 3\n", "X, y = create_dataset(n, d, k)\n", "\n", "print \"sklearn GMM\"\n", "%timeit GaussianMixture(n_components=k, covariance_type='full', max_iter=15, tol=1e-10).fit(X)\n", "print \n", "print \"pomegranate GMM\"\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=15, stop_threshold=1e-10)\n", "print\n", "print \"pomegranate GMM (4 jobs)\"\n", "%timeit GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, n_jobs=4, max_iterations=15, stop_threshold=1e-10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like on a large dataset not only is pomegranate faster than sklearn at performing 15 iterations of EM on 3 million 5 dimensional datapoints with 3 clusters, but the parallelization is able to help in speeding things up. \n", "\n", "Lets now take a look at the time it takes to make predictions using GMMs. Lets fit the model to a small amount of data, and then predict a larger amount of data drawn from the same underlying distributions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "d, k = 25, 2\n", "X, y = create_dataset(1000, d, k)\n", "a = GaussianMixture(k, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=25)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sklearn GMM\n", "1 loop, best of 3: 2.12 s per loop\n", "\n", "pomegranate GMM\n", "1 loop, best of 3: 1.68 s per loop\n", "\n", "pomegranate GMM (4 jobs)\n", "1 loop, best of 3: 1.59 s per loop\n" ] } ], "source": [ "del X, y\n", "n = 1000000\n", "X, y = create_dataset(n, d, k)\n", "\n", "print \"sklearn GMM\"\n", "%timeit -n 1 a.predict_proba(X)\n", "print\n", "print \"pomegranate GMM\"\n", "%timeit -n 1 b.predict_proba(X)\n", "print\n", "print \"pomegranate GMM (4 jobs)\"\n", "%timeit -n 1 b.predict_proba(X, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like pomegranate can be slightly slower than sklearn when using a single processor, but that it can be parallelized to get faster performance. At the same time, predictions at this level happen so quickly (millions per second) that this may not be the most reliable test for parallelization.\n", "\n", "To ensure that we're getting the exact same results just faster, lets subtract the predictions from each other and make sure that the sum is equal to 0." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print (b.predict_proba(X) - b.predict_proba(X, n_jobs=4)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, no difference between the two.\n", "\n", "Lets now make sure that pomegranate and sklearn are learning basically the same thing. Lets fit both models to some 2 dimensional 2 component data and make sure that they both extract the underlying clusters by plotting them." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7f4e62e02210>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VjqIO6c5RyWQODEEQBJEdSJsTIW0mrOBYyxQAOO+883Deeec5OSRB9BrivZ4n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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e64e5be50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d, k = 2, 2\n", "X, y = create_dataset(1000, d, k, alpha=2)\n", "a = GaussianMixture(k, n_init=1, max_iter=25).fit(X)\n", "b = GeneralMixtureModel.from_samples(MultivariateGaussianDistribution, k, X, max_iterations=25)\n", "\n", "y1, y2 = a.predict(X), b.predict(X)\n", "\n", "plt.figure(figsize=(16,6))\n", "plt.subplot(121)\n", "plt.title(\"sklearn clusters\", fontsize=14)\n", "plt.scatter(X[y1==0, 0], X[y1==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y1==1, 0], X[y1==1, 1], color='c', edgecolor='c')\n", "\n", "plt.subplot(122)\n", "plt.title(\"pomegranate clusters\", fontsize=14)\n", "plt.scatter(X[y2==0, 0], X[y2==0, 1], color='m', edgecolor='m')\n", "plt.scatter(X[y2==1, 0], X[y2==1, 1], color='c', edgecolor='c')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like we're getting the same basic results for the two. The two algorithms are initialized a bit differently, and so it can be difficult to directly compare the results between them, but it looks like they're getting roughly the same results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Multivariate Gaussian HMM\n", "\n", "Now let's move on to training a hidden Markov model with multivariate Gaussian emissions with a diagonal covariance matrix. We'll randomly generate some Gaussian distributed numbers and use pomegranate with either one or four threads to fit our model to the data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "1 loop, best of 1: 22.1 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 1: 12.9 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 1: 8.37 s per loop\n" ] } ], "source": [ "X = numpy.random.randn(1000, 500, 50)\n", "\n", "print \"pomegranate Gaussian HMM (1 job)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5, n_jobs=2)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit -n 1 -r 1 HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=5, n_jobs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All we had to do was pass in the n_jobs parameter to the fit function in order to get a speed improvement. It looks like we're getting a really good speed improvement, as well! This is mostly because the HMM algorithms perform a lot more operations than the other models, and so spend the vast majority of time with the GIL released. You may not notice as strong speedups when using a MultivariateGaussianDistribution because BLAS uses multithreaded operations already internally, even when only one job is specified.\n", "\n", "Now lets look at the prediction function to make sure the we're getting speedups there as well. You'll have to use a wrapper function to parallelize the predictions for a HMM because it returns an annotated sequence rather than a single value like a classic machine learning model might." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Gaussian HMM (1 job)\n", "1 loop, best of 3: 3.22 s per loop\n", "\n", "pomegranate Gaussian HMM (2 jobs)\n", "1 loop, best of 3: 1.93 s per loop\n" ] } ], "source": [ "model = HiddenMarkovModel.from_samples(NormalDistribution, 5, X, max_iterations=2, verbose=False)\n", "\n", "print \"pomegranate Gaussian HMM (1 job)\"\n", "%timeit predict_proba(model, X)\n", "print\n", "print \"pomegranate Gaussian HMM (2 jobs)\"\n", "%timeit predict_proba(model, X, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, we're getting a really good speedup on that as well! Looks like the parallel processing is more efficient with a bigger, more complex model, than with a simple one. This can make sense, because all inference/training is more complex, and so there is more time with the GIL released compared to with the simpler operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Mixture of Hidden Markov Models\n", "\n", "Let's stack another layer onto this model by making it a mixture of these hidden Markov models, instead of a single one. At this point we're sticking a multivariate Gaussian HMM into a mixture and we're going to train this big thing in parallel." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def create_model(mus):\n", " n = mus.shape[0]\n", " \n", " starts = numpy.zeros(n)\n", " starts[0] = 1.\n", " \n", " ends = numpy.zeros(n)\n", " ends[-1] = 0.5\n", " \n", " transition_matrix = numpy.zeros((n, n))\n", " distributions = []\n", " \n", " for i in range(n):\n", " transition_matrix[i, i] = 0.5\n", " \n", " if i < n - 1:\n", " transition_matrix[i, i+1] = 0.5\n", " \n", " distribution = IndependentComponentsDistribution([NormalDistribution(mu, 1) for mu in mus[i]])\n", " distributions.append(distribution)\n", " \n", " model = HiddenMarkovModel.from_matrix(transition_matrix, distributions, starts, ends)\n", " return model\n", " \n", "\n", "def create_mixture(mus):\n", " hmms = [create_model(mu) for mu in mus]\n", " return GeneralMixtureModel(hmms)\n", "\n", "n, d = 50, 10\n", "mus = [(numpy.random.randn(d, n)*0.2 + numpy.random.randn(n)*2).T for i in range(2)]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Mixture of Gaussian HMMs (1 job)\n", "1 loop, best of 3: 3 s per loop\n", "\n", "pomegranate Mixture of Gaussian HMMs (2 jobs)\n", "1 loop, best of 3: 1.83 s per loop\n" ] } ], "source": [ "model = create_mixture(mus)\n", "X = numpy.random.randn(400, 150, d)\n", "\n", "print \"pomegranate Mixture of Gaussian HMMs (1 job)\"\n", "%timeit model.fit(X, max_iterations=5)\n", "print\n", "\n", "model = create_mixture(mus)\n", "print \"pomegranate Mixture of Gaussian HMMs (2 jobs)\"\n", "%timeit model.fit(X, max_iterations=5, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're getting a really nice speed improvement when training this complex model. Let's take a look now at the time it takes to do inference with it." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pomegranate Mixture of Gaussian HMMs (1 job)\n", "1 loop, best of 3: 598 ms per loop\n", "\n", "pomegranate Mixture of Gaussian HMMs (2 jobs)\n", "1 loop, best of 3: 411 ms per loop\n" ] } ], "source": [ "model = create_mixture(mus)\n", "\n", "print \"pomegranate Mixture of Gaussian HMMs (1 job)\"\n", "%timeit model.predict_proba(X)\n", "print\n", "\n", "model = create_mixture(mus)\n", "print \"pomegranate Mixture of Gaussian HMMs (2 jobs)\"\n", "%timeit model.predict_proba(X, n_jobs=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're getting a good speed improvement here too through parallelization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "Hopefully you'll find pomegranate useful in your work! Parallelization should allow you to train complex models faster than before. Keep in mind though that there is an overhead to using parallel processing, and so it's possible that on some smaller examples it does not work as well. In general, the bigger the dataset, the closer to a linear speedup you'll get with pomegranate.\n", "\n", "If you have any interesting examples of how you've used pomegranate in your work, I'd love to hear about them. In addition I'd like to hear any feedback you may have on features you'd like to see. Please shoot me an email. Good luck!" ] } ], "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": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

arsenovic/galgebra

examples/ipython/inner_product.ipynb

1

21533

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "from sympy import Symbol, symbols, sin, cos, Rational, expand, simplify, collect, S\n", "from galgebra.printer import Eprint, Get_Program, Print_Function, Format\n", "from galgebra.ga import Ga, one, zero\n", "from galgebra.mv import Nga\n", "Format()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "X = (x, y, z) = symbols('x y z')\n", "o3d = Ga('e_x e_y e_z', g=[1, 1, 1], coords=X)\n", "(ex, ey, ez) = o3d.mv()\n", "grad = o3d.grad" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "c = o3d.mv('c', 'scalar')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "a = o3d.mv('a', 'vector')\n", "b = o3d.mv('b', 'vector')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "A = o3d.mv('A','mv')\n", "B = o3d.mv('B','mv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The inner product of blades in GAlgebra is zero if either operand is a scalar:\n", "\n", "$$\\begin{split}\\begin{aligned}\n", " {\\boldsymbol{A}}_{r}{\\wedge}{\\boldsymbol{B}}_{s} &\\equiv {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{r+s}} \\\\\n", " {\\boldsymbol{A}}_{r}\\cdot{\\boldsymbol{B}}_{s} &\\equiv {\\left \\{ { \\begin{array}{cc}\n", " r\\mbox{ and }s \\ne 0: & {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{{\\left |{r-s}\\right |}}} \\\\\n", " r\\mbox{ or }s = 0: & 0 \\end{array}} \\right \\}}\n", " \\end{aligned}\\end{split}$$\n", " \n", "This definition comes from _David Hestenes and Garret Sobczyk, “Clifford Algebra to Geometric Calculus,” Kluwer Academic Publishers, 1984_.\n", "\n", "In some other literature, the inner product is defined without the exceptional case for scalar part and the definition above is known as \"the modified Hestenes inner product\" (this name comes from the source code of [GAViewer](http://www.geometricalgebra.net/gaviewer_download.html))." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c|a" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a|c" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c|A" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A|c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$ab=a \\wedge b + a \\cdot b$ holds for vectors:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( a^{x} b^{x} + a^{y} b^{y} + a^{z} b^{z}\\right ) + \\left ( a^{x} b^{y} - a^{y} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( a^{x} b^{z} - a^{z} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( a^{y} b^{z} - a^{z} b^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( a^{x} b^{x} + a^{y} b^{y} + a^{z} b^{z}\\right ) + \\left ( a^{x} b^{y} - a^{y} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( a^{x} b^{z} - a^{z} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( a^{y} b^{z} - a^{z} b^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a*b" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( a^{x} b^{y} - a^{y} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( a^{x} b^{z} - a^{z} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( a^{y} b^{z} - a^{z} b^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( a^{x} b^{y} - a^{y} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( a^{x} b^{z} - a^{z} b^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( a^{y} b^{z} - a^{z} b^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a^b" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} a^{x} b^{x} + a^{y} b^{y} + a^{z} b^{z} \\end{equation*}" ], "text/plain": [ "a^{x} b^{x} + a^{y} b^{y} + a^{z} b^{z}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a|b" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(a*b)-(a^b)-(a|b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$aA=a \\wedge A + a \\cdot A$ holds for the products between vectors and multivectors:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( A^{x} a^{x} + A^{y} a^{y} + A^{z} a^{z}\\right ) + \\left ( A a^{x} - A^{xy} a^{y} - A^{xz} a^{z}\\right ) \\boldsymbol{e}_{x} + \\left ( A a^{y} + A^{xy} a^{x} - A^{yz} a^{z}\\right ) \\boldsymbol{e}_{y} + \\left ( A a^{z} + A^{xz} a^{x} + A^{yz} a^{y}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} a^{z} - A^{x} a^{y} + A^{y} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} a^{y} - A^{x} a^{z} + A^{z} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} a^{x} - A^{y} a^{z} + A^{z} a^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xy} a^{z} - A^{xz} a^{y} + A^{yz} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( A^{x} a^{x} + A^{y} a^{y} + A^{z} a^{z}\\right ) + \\left ( A a^{x} - A^{xy} a^{y} - A^{xz} a^{z}\\right ) \\boldsymbol{e}_{x} + \\left ( A a^{y} + A^{xy} a^{x} - A^{yz} a^{z}\\right ) \\boldsymbol{e}_{y} + \\left ( A a^{z} + A^{xz} a^{x} + A^{yz} a^{y}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} a^{z} - A^{x} a^{y} + A^{y} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} a^{y} - A^{x} a^{z} + A^{z} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} a^{x} - A^{y} a^{z} + A^{z} a^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xy} a^{z} - A^{xz} a^{y} + A^{yz} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a*A" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} A a^{x} \\boldsymbol{e}_{x} + A a^{y} \\boldsymbol{e}_{y} + A a^{z} \\boldsymbol{e}_{z} + \\left ( - A^{x} a^{y} + A^{y} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{x} a^{z} + A^{z} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{y} a^{z} + A^{z} a^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xy} a^{z} - A^{xz} a^{y} + A^{yz} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "A a^{x} \\boldsymbol{e}_{x} + A a^{y} \\boldsymbol{e}_{y} + A a^{z} \\boldsymbol{e}_{z} + \\left ( - A^{x} a^{y} + A^{y} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{x} a^{z} + A^{z} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{y} a^{z} + A^{z} a^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xy} a^{z} - A^{xz} a^{y} + A^{yz} a^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a^A" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( A^{x} a^{x} + A^{y} a^{y} + A^{z} a^{z}\\right ) + \\left ( - A^{xy} a^{y} - A^{xz} a^{z}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xy} a^{x} - A^{yz} a^{z}\\right ) \\boldsymbol{e}_{y} + \\left ( A^{xz} a^{x} + A^{yz} a^{y}\\right ) \\boldsymbol{e}_{z} + A^{xyz} a^{z} \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} - A^{xyz} a^{y} \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + A^{xyz} a^{x} \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( A^{x} a^{x} + A^{y} a^{y} + A^{z} a^{z}\\right ) + \\left ( - A^{xy} a^{y} - A^{xz} a^{z}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xy} a^{x} - A^{yz} a^{z}\\right ) \\boldsymbol{e}_{y} + \\left ( A^{xz} a^{x} + A^{yz} a^{y}\\right ) \\boldsymbol{e}_{z} + A^{xyz} a^{z} \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} - A^{xyz} a^{y} \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + A^{xyz} a^{x} \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a|A" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} 0 \\end{equation*}" ], "text/plain": [ " 0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(a*A)-(a^A)-(a|A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$AB=A \\wedge B + A \\cdot B$ does NOT hold for the products between multivectors and multivectors:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( A B - A^{xyz} B^{xyz} - A^{xy} B^{xy} - A^{xz} B^{xz} + A^{x} B^{x} - A^{yz} B^{yz} + A^{y} B^{y} + A^{z} B^{z}\\right ) + \\left ( A B^{x} - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} + A^{x} B - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A B^{y} + A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} + A^{y} B - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( A B^{z} - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz} + A^{z} B\\right ) \\boldsymbol{e}_{z} + \\left ( A B^{xy} + A^{xyz} B^{z} + A^{xy} B - A^{xz} B^{yz} + A^{x} B^{y} + A^{yz} B^{xz} - A^{y} B^{x} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( A B^{xz} - A^{xyz} B^{y} + A^{xy} B^{yz} + A^{xz} B + A^{x} B^{z} - A^{yz} B^{xy} - A^{y} B^{xyz} - A^{z} B^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A B^{yz} + A^{xyz} B^{x} - A^{xy} B^{xz} + A^{xz} B^{xy} + A^{x} B^{xyz} + A^{yz} B + A^{y} B^{z} - A^{z} B^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A B^{xyz} + A^{xyz} B + A^{xy} B^{z} - A^{xz} B^{y} + A^{x} B^{yz} + A^{yz} B^{x} - A^{y} B^{xz} + A^{z} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( A B - A^{xyz} B^{xyz} - A^{xy} B^{xy} - A^{xz} B^{xz} + A^{x} B^{x} - A^{yz} B^{yz} + A^{y} B^{y} + A^{z} B^{z}\\right ) + \\left ( A B^{x} - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} + A^{x} B - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A B^{y} + A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} + A^{y} B - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( A B^{z} - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz} + A^{z} B\\right ) \\boldsymbol{e}_{z} + \\left ( A B^{xy} + A^{xyz} B^{z} + A^{xy} B - A^{xz} B^{yz} + A^{x} B^{y} + A^{yz} B^{xz} - A^{y} B^{x} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( A B^{xz} - A^{xyz} B^{y} + A^{xy} B^{yz} + A^{xz} B + A^{x} B^{z} - A^{yz} B^{xy} - A^{y} B^{xyz} - A^{z} B^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A B^{yz} + A^{xyz} B^{x} - A^{xy} B^{xz} + A^{xz} B^{xy} + A^{x} B^{xyz} + A^{yz} B + A^{y} B^{z} - A^{z} B^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( A B^{xyz} + A^{xyz} B + A^{xy} B^{z} - A^{xz} B^{y} + A^{x} B^{yz} + A^{yz} B^{x} - A^{y} B^{xz} + A^{z} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A*B" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( - A^{xyz} B^{xyz} - A^{xy} B^{xy} - A^{xz} B^{xz} + A^{x} B^{x} - A^{yz} B^{yz} + A^{y} B^{y} + A^{z} B^{z}\\right ) + \\left ( - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{z} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{y} - A^{y} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{x} + A^{x} B^{xyz}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( - A^{xyz} B^{xyz} - A^{xy} B^{xy} - A^{xz} B^{xz} + A^{x} B^{x} - A^{yz} B^{yz} + A^{y} B^{y} + A^{z} B^{z}\\right ) + \\left ( - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{z} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{y} - A^{y} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{x} + A^{x} B^{xyz}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A|B" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( - A^{xz} B^{yz} + A^{yz} B^{xz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( A^{xy} B^{yz} - A^{yz} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{xy} B^{xz} + A^{xz} B^{xy}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( - A^{xz} B^{yz} + A^{yz} B^{xz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( A^{xy} B^{yz} - A^{yz} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{xy} B^{xz} + A^{xz} B^{xy}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(A*B)-(A^B)-(A|B)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{equation*} \\left ( A B - 2 A^{xyz} B^{xyz} - 2 A^{xy} B^{xy} - 2 A^{xz} B^{xz} + 2 A^{x} B^{x} - 2 A^{yz} B^{yz} + 2 A^{y} B^{y} + 2 A^{z} B^{z}\\right ) + \\left ( - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{z} + A^{xz} B^{yz} - A^{x} B^{y} - A^{yz} B^{xz} + A^{y} B^{x} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{y} - A^{xy} B^{yz} - A^{x} B^{z} + A^{yz} B^{xy} - A^{y} B^{xyz} + A^{z} B^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{x} + A^{xy} B^{xz} - A^{xz} B^{xy} + A^{x} B^{xyz} - A^{y} B^{z} + A^{z} B^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{xy} B^{z} + A^{xz} B^{y} - A^{x} B^{yz} - A^{yz} B^{x} + A^{y} B^{xz} - A^{z} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} \\end{equation*}" ], "text/plain": [ "\\left ( A B - 2 A^{xyz} B^{xyz} - 2 A^{xy} B^{xy} - 2 A^{xz} B^{xz} + 2 A^{x} B^{x} - 2 A^{yz} B^{yz} + 2 A^{y} B^{y} + 2 A^{z} B^{z}\\right ) + \\left ( - A^{xyz} B^{yz} + A^{xy} B^{y} + A^{xz} B^{z} - A^{yz} B^{xyz} - A^{y} B^{xy} - A^{z} B^{xz}\\right ) \\boldsymbol{e}_{x} + \\left ( A^{xyz} B^{xz} - A^{xy} B^{x} + A^{xz} B^{xyz} + A^{x} B^{xy} + A^{yz} B^{z} - A^{z} B^{yz}\\right ) \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{xy} - A^{xy} B^{xyz} - A^{xz} B^{x} + A^{x} B^{xz} - A^{yz} B^{y} + A^{y} B^{yz}\\right ) \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{z} + A^{xz} B^{yz} - A^{x} B^{y} - A^{yz} B^{xz} + A^{y} B^{x} + A^{z} B^{xyz}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y} + \\left ( - A^{xyz} B^{y} - A^{xy} B^{yz} - A^{x} B^{z} + A^{yz} B^{xy} - A^{y} B^{xyz} + A^{z} B^{x}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{z} + \\left ( A^{xyz} B^{x} + A^{xy} B^{xz} - A^{xz} B^{xy} + A^{x} B^{xyz} - A^{y} B^{z} + A^{z} B^{y}\\right ) \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z} + \\left ( - A^{xy} B^{z} + A^{xz} B^{y} - A^{x} B^{yz} - A^{yz} B^{x} + A^{y} B^{xz} - A^{z} B^{xy}\\right ) \\boldsymbol{e}_{x}\\wedge \\boldsymbol{e}_{y}\\wedge \\boldsymbol{e}_{z}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(A<B)+(A|B)+(A>B)-A*B" ] }, { "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

jbliss1234/ML

t81_558_class4_class_reg.ipynb

1

126785

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# T81-558: Applications of Deep Neural Networks\n", "**Class 4: Classification and Regression**\n", "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n", "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Binary Classification, Classification and Regression\n", "\n", "* **Binary Classification** - Classification between two possibilities (positive and negative). Common in medical testing, does the person have the disease (positive) or not (negative).\n", "* **Classification** - Classification between more than 2. The iris dataset (3-way classification).\n", "* **Regression** - Numeric prediction. How many MPG does a car get?\n", "\n", "In this class session we will look at some visualizations for all three.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature Vector Encoding\n", "\n", "These are exactly the same feature vector encoding functions from [Class 3](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class3_training.ipynb). They must be defined for this class as well. For more information, refer to class 3." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)\n", "def encode_text_dummy(df,name):\n", " dummies = pd.get_dummies(df[name])\n", " for x in dummies.columns:\n", " dummy_name = \"{}-{}\".format(name,x)\n", " df[dummy_name] = dummies[x]\n", " df.drop(name, axis=1, inplace=True)\n", "\n", "# Encode text values to indexes(i.e. [1],[2],[3] for red,green,blue).\n", "def encode_text_index(df,name):\n", " le = preprocessing.LabelEncoder()\n", " df[name] = le.fit_transform(df[name])\n", " return le.classes_\n", "\n", "# Encode a numeric column as zscores\n", "def encode_numeric_zscore(df,name,mean=None,sd=None):\n", " if mean is None:\n", " mean = df[name].mean()\n", "\n", " if sd is None:\n", " sd = df[name].std()\n", "\n", " df[name] = (df[name]-mean)/sd\n", "\n", "# Convert all missing values in the specified column to the median\n", "def missing_median(df, name):\n", " med = df[name].median()\n", " df[name] = df[name].fillna(med)\n", "\n", "# Convert a Pandas dataframe to the x,y inputs that TensorFlow needs\n", "def to_xy(df,target):\n", " result = []\n", " for x in df.columns:\n", " if x != target:\n", " result.append(x)\n", "\n", " # find out the type of the target column. Is it really this hard? :(\n", " target_type = df[target].dtypes\n", " target_type = target_type[0] if hasattr(target_type, '__iter__') else target_type\n", " \n", " # Encode to int for classification, float otherwise. TensorFlow likes 32 bits.\n", " if target_type in (np.int64, np.int32):\n", " # Classification\n", " return df.as_matrix(result).astype(np.float32),df.as_matrix([target]).astype(np.int32)\n", " else:\n", " # Regression\n", " return df.as_matrix(result).astype(np.float32),df.as_matrix([target]).astype(np.float32)\n", " \n", "# Nicely formatted time string\n", "def hms_string(sec_elapsed):\n", " h = int(sec_elapsed / (60 * 60))\n", " m = int((sec_elapsed % (60 * 60)) / 60)\n", " s = sec_elapsed % 60\n", " return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Toolkit: Visualization Functions\n", "\n", "This class will introduce 3 different visualizations that can be used with the two different classification type neural networks and regression neural networks.\n", "\n", "* **Confusion Matrix** - For any type of classification neural network.\n", "* **ROC Curve** - For binary classification.\n", "* **Lift Curve** - For regression neural networks.\n", "\n", "The code used to produce these visualizations is shown here:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import roc_curve, auc\n", "\n", "# Plot a confusion matrix.\n", "# cm is the confusion matrix, names are the names of the classes.\n", "def plot_confusion_matrix(cm, names, title='Confusion matrix', cmap=plt.cm.Blues):\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(names))\n", " plt.xticks(tick_marks, names, rotation=45)\n", " plt.yticks(tick_marks, names)\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')\n", " \n", "\n", "# Plot an ROC. pred - the predictions, y - the expected output.\n", "def plot_roc(pred,y):\n", " fpr, tpr, _ = roc_curve(y_test, pred)\n", " roc_auc = auc(fpr, tpr)\n", "\n", " plt.figure()\n", " plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n", " plt.plot([0, 1], [0, 1], 'k--')\n", " plt.xlim([0.0, 1.0])\n", " plt.ylim([0.0, 1.05])\n", " plt.xlabel('False Positive Rate')\n", " plt.ylabel('True Positive Rate')\n", " plt.title('Receiver Operating Characteristic (ROC)')\n", " plt.legend(loc=\"lower right\")\n", " plt.show()\n", " \n", "# Plot a lift curve. pred - the predictions, y - the expected output.\n", "def chart_regression(pred,y):\n", " t = pd.DataFrame({'pred' : pred.flatten(), 'y' : y_test.flatten()})\n", " t.sort_values(by=['y'],inplace=True)\n", "\n", " a = plt.plot(t['y'].tolist(),label='expected')\n", " b = plt.plot(t['pred'].tolist(),label='prediction')\n", " plt.ylabel('output')\n", " plt.legend()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Binary Classification\n", "\n", "\n", "Binary classification is used to create a model that classifies between only two classes. These two classes are often called \"positive\" and \"negative\". Consider the following program that uses the [wcbreast_wdbc dataset](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/datasets_wcbc.ipynb) to classify if a breast tumor is cancerous (malignant) or not (benign). The iris dataset is not binary, because there are three classes (3 types of iris).\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py:281: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n", " out.itemset((i, self.y[sample]), 1.0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step #50, epoch #3, avg. train loss: 2.53698, avg. val loss: 2.35609\n", "Step #100, epoch #7, avg. train loss: 0.55002, avg. val loss: 0.54495\n", "Step #150, epoch #10, avg. train loss: 0.48565, avg. val loss: 0.49846\n", "Step #200, epoch #14, avg. train loss: 0.45980, avg. val loss: 0.49158\n", "Step #250, epoch #17, avg. train loss: 0.43307, avg. val loss: 0.45604\n", "Step #300, epoch #21, avg. train loss: 0.40331, avg. val loss: 0.44432\n", "Step #350, epoch #25, avg. train loss: 0.36997, avg. val loss: 0.44423\n", "Step #400, epoch #28, avg. train loss: 0.36701, avg. val loss: 0.44364\n", "Step #450, epoch #32, avg. train loss: 0.34160, avg. val loss: 0.44327\n", "Step #500, epoch #35, avg. train loss: 0.35113, avg. val loss: 0.44534\n", "Step #550, epoch #39, avg. train loss: 0.32387, avg. val loss: 0.43548\n", "Step #600, epoch #42, avg. train loss: 0.33891, avg. val loss: 0.42804\n", "Final accuracy: 0.8471001757469244\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stopping. Best step:\n", " step 429 with loss 0.3980848491191864\n" ] } ], "source": [ "import os\n", "import pandas as pd\n", "from sklearn.cross_validation import train_test_split\n", "import tensorflow.contrib.learn as skflow\n", "import numpy as np\n", "from sklearn import metrics\n", "\n", "path = \"./data/\"\n", " \n", "filename = os.path.join(path,\"wcbreast_wdbc.csv\") \n", "df = pd.read_csv(filename,na_values=['NA','?'])\n", "\n", "# Encode feature vector\n", "df.drop('id',axis=1,inplace=True)\n", "encode_numeric_zscore(df,'mean_radius')\n", "encode_text_index(df,'mean_texture') \n", "encode_text_index(df,'mean_perimeter')\n", "encode_text_index(df,'mean_area')\n", "encode_text_index(df,'mean_smoothness')\n", "encode_text_index(df,'mean_compactness')\n", "encode_text_index(df,'mean_concavity')\n", "encode_text_index(df,'mean_concave_points')\n", "encode_text_index(df,'mean_symmetry')\n", "encode_text_index(df,'mean_fractal_dimension')\n", "encode_text_index(df,'se_radius')\n", "encode_text_index(df,'se_texture')\n", "encode_text_index(df,'se_perimeter')\n", "encode_text_index(df,'se_area')\n", "encode_text_index(df,'se_smoothness')\n", "encode_text_index(df,'se_compactness')\n", "encode_text_index(df,'se_concavity')\n", "encode_text_index(df,'se_concave_points')\n", "encode_text_index(df,'se_symmetry')\n", "encode_text_index(df,'se_fractal_dimension')\n", "encode_text_index(df,'worst_radius')\n", "encode_text_index(df,'worst_texture')\n", "encode_text_index(df,'worst_perimeter')\n", "encode_text_index(df,'worst_area')\n", "encode_text_index(df,'worst_smoothness')\n", "encode_text_index(df,'worst_compactness')\n", "encode_text_index(df,'worst_concavity')\n", "encode_text_index(df,'worst_concave_points')\n", "encode_text_index(df,'worst_symmetry')\n", "encode_text_index(df,'worst_fractal_dimension')\n", "diagnosis = encode_text_index(df,'diagnosis')\n", "num_classes = len(diagnosis)\n", "\n", "# Create x & y for training\n", "\n", "# Create the x-side (feature vectors) of the training\n", "x, y = to_xy(df,'diagnosis')\n", " \n", "# Split into train/test\n", "x_train, x_test, y_train, y_test = train_test_split( \n", " x, y, test_size=0.25, random_state=42) \n", " \n", "# Create a deep neural network with 3 hidden layers of 10, 20, 10\n", "classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=num_classes,\n", " steps=10000)\n", "\n", "# Early stopping\n", "early_stop = skflow.monitors.ValidationMonitor(x_test, y_test,\n", " early_stopping_rounds=200, print_steps=50, n_classes=num_classes)\n", " \n", "# Fit/train neural network\n", "classifier.fit(x_train, y_train, early_stop)\n", "\n", "# Measure accuracy\n", "score = metrics.accuracy_score(y, classifier.predict(x))\n", "print(\"Final accuracy: {}\".format(score))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confusion Matrix\n", "\n", "The confusion matrix is a common visualization for both binary and larger classification problems. Often a model will have difficulty differentiating between two classes. For example, a neural network might be really good at telling the difference between cats and dogs, but not so good at telling the difference between dogs and wolves. The following code generates a confusion matrix:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[67 22]\n", " [ 3 51]]\n", "Normalized confusion matrix\n", "[[ 0.75 0.25]\n", " [ 0.06 0.94]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f299c153080>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f298c0f6940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "from sklearn import svm, datasets\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "\n", "pred = classifier.predict(x_test)\n", " \n", "# Compute confusion matrix\n", "cm = confusion_matrix(y_test, pred)\n", "np.set_printoptions(precision=2)\n", "print('Confusion matrix, without normalization')\n", "print(cm)\n", "plt.figure()\n", "plot_confusion_matrix(cm, diagnosis)\n", "\n", "# Normalize the confusion matrix by row (i.e by the number of samples\n", "# in each class)\n", "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "print('Normalized confusion matrix')\n", "print(cm_normalized)\n", "plt.figure()\n", "plot_confusion_matrix(cm_normalized, diagnosis, title='Normalized confusion matrix')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above two confusion matrixes show the same network. The bottom (normalized) is the type you will normally see. Notice the two labels. The label \"B\" means benign (no cancer) and the label \"M\" means malignant (cancer). The left-right (x) axis are the predictions, the top-bottom) are the expected outcomes. A perfect model (that never makes an error) has a dark blue diagonal that runs from top-left to bottom-right. \n", "\n", "To read, consider the top-left square. This square indicates \"true labeled\" of B and also \"predicted label\" of B. This is good! The prediction matched the truth. The blueness of this box represents how often \"B\" is classified correct. It is not darkest blue. This is because the square to the right(which is off the perfect diagonal) has some color. This square indicates truth of \"B\" but prediction of \"M\". The white square, at the bottom-left, indicates a true of \"M\" but predicted of \"B\". The whiteness indicates this rarely happens. \n", "\n", "Your conclusion from the above chart is that the model sometimes classifies \"B\" as \"M\" (a false negative), but never mis-classifis \"M\" as \"B\". Always look for the dark diagonal, this is good!\n", "\n", "### ROC Curves\n", "\n", "ROC curves can be a bit confusing. However, they are very common. It is important to know how to read them. Even their name is confusing. Do not worry about their name, it comes from electrical engineering (EE).\n", "\n", "Binary classification is common in medical testing. Often you want to diagnose if someone has a disease. This can lead to two types of errors, know as false positives and false negatives:\n", "\n", "* **False Positive** - Your test (neural network) indicated that the patient had the disease; however, the patient did not have the disease.\n", "* **False Negative** - Your test (neural network) indicated that the patient did not have the disease; however, the patient did have the disease.\n", "* **True Positive** - Your test (neural network) correctly identified that the patient had the disease.\n", "* **True Negative** - Your test (neural network) correctly identified that the patient did not have the disease.\n", "\n", "Types of errors:\n", "\n", "![Type of Error](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/class_4_errors.png \"Type of Error\")\n", "\n", "Neural networks classify in terms of probbility of it being positive. However, at what probability do you give a positive result? Is the cutoff 50%? 90%? Where you set this cutoff is called the threshold. Anything above the cutoff is positive, anything below is negative. Setting this cutoff allows the model to be more sensative or specific:\n", "\n", "![Sensitivity vs. Specificity](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/class_4_t1vst2.png \"Sensitivity vs. Specificity\")\n", "\n", "The following shows a more sensitive cutoff:\n", "\n", "![Sensitive Cutoff ](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/class_4_spec_cut.png \"Sensitive Cutoff\")\n", "\n", "**An ROC curve measures how good a model is regardless of the cutoff.** The following shows how to read a ROC chart:\n", "\n", "\n", "![Reading a ROC Chart](https://raw.githubusercontent.com/jeffheaton/t81_558_deep_learning/master/images/class_4_roc.png \"Reading a ROC Chart\")\n", "\n", "The following code shows an ROC chart for the breast cancer neural network. The area under the curve (AUC) is also an important measure. The larger the AUC, the better." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hnAvcgXPvaONavRoyM+Hyy72OxBhjSk8wzVwn49xu9GTgXeAU4IOQRhVlrO9D\nfFJV3nnnHWbNmuV1KMaERDAJIkdVDwHXAq+p6v1A3dCGFT1y+z707et1JCacli9fzkUXXcQbb7xB\n9erVvQ7HmJAIJkEcFpEewE3A5+68cqELKbrMmgVVq9pNgeLFwYMHeeaZZ2jfvj3dunVj7ty5tGjR\nwuuwjAmJYEZz7QfcjTPc92oRaQyMC21Y0cP6PsSXK6+8kjJlyvDzzz/TsGFDr8MxJqSCveVoWSB3\nDOKVqno4pFH5jyHiWjHl9n1YtgxOPNHraEw4bNy4kTp16ljTVRM1QtIPwmfjHYCxwEacPhC1ROQm\nVZ1dnB3GkokToUMHSw7xpG5dq34z8SOYOoiXgMtU9TxVbQ9cDrwS2rCiQ27xkok9f/zxBwcPHvQ6\nDGM8FUyCKK+qS3InVHUpEPcNOn/7DRYvhiuu8DoSU5pycnIYOXIkLVq0YM6cOV6HY4yngqmk/kVE\n3gTec6f7YIP1MWYM9OplfR9iydKlS+nfvz+HDh1ixowZpKSkeB2SMZ4K5griTmA1MMh9rMbpTR23\n7L4PseXw4cM89dRTdOjQgeuvv57Zs2dbcjCGIq4gRCQFOBmYpKrPhyekyPfdd1ClivV9iBVlyjjD\njWVkZFC/fn2PozEmchQ2WN8/ce4c9wtwDvB/qvpOGGMrGE/ENHO99VZISYEHHvA6EmOMKVxJmrkW\nliAygTaqukdETgCmquo5JYizRCIlQVjfB2NMNAnJaK7AAVXdA6CqW4pYN25Y34fotXHjRnr16sW6\ndeu8DsWYqFDYSf8kn/tQTwJO9r03dbgCjDTW9yH65OTkMHz4cFq1akXTpk050bK7MUEprJL6ugLT\nr4cykGhgfR+iz+LFi+nfvz8JCQnMnDmTM844w+uQjIkaAROEqs4IZyDRYMwYuOEG6/sQLbKysujS\npQuPPfZYXpIwxgQvqMH6SrQDkS7AyzjFWaNUdWiA9c4B5gDXq+pRRVheV1Ln5ECTJjB+PJx9tmdh\nmGO0b98+KlWq5HUYxngmVJXUJSYiCThFU5cCzYBeInJagPX+BXwZynhK4rvv4Ljj4KyzvI7EHAtL\nDsYUX9AJQkQqFGP7bYAVqrrWvSvdh0A3P+v9A5gA/FmMfYSF3fchcqkq6enpXodhTMwpMkGISBsR\nWQSscKdbishrQW6/LrDeZ3oDBW5XKiJ1gKtV9Q2c4cQjzu7dMHky9OnjdSSmoHXr1nHllVdyxx13\nsGPHDq/IYj8hAAAdV0lEQVTDMSamBHMF8SpwBbAVQFUXABeWYgwvAw/7TEdckvjkEzj/fKhVy+tI\nTK7s7GxeeeUVzjrrLNq1a0dGRgbVqlXzOixjYkowo7kmqOraAnfQyg5y+xuBBj7T9dx5vloDH4qz\ng+OBriJySFWnFNzYkCFD8p6npqaSmpoaZBglM3o0/P3vYdmVCcK6devo0aMHFStWZPbs2TRt2tTr\nkIyJGGlpaaSlpZXKtopsxSQiE4GhwJs4YzL9AzhPVXsUuXGRMsCvQCdgEzAP6OXeU8Lf+u8Cn0VS\nK6Y1a+Ccc2DDBqhQnFoYU+r27t3LxIkT6dOnjzVdNaYIIb3lKHAXTjFTA2Az8LU7r0iqmi0iA4Dp\nHGnmulRE7nAW68iCLwk68jDJ7ftgySFyVK5cmZtuusnrMIyJeSHvB1FavLiCsL4P3lNVxJqOGVNs\nIb2CEJG38PPLXlX7F2eH0SQ93fo+eEVVGTduHMOHD2fmzJl592wwxoRPMEVMX/s8rwhcQ/6mqzHL\n+j54Y82aNdx1111s3LiRt99+25KDMR455iImt9dzuqq2D01IAfcb1iKm3Ps+LF1qzVvD5fDhw7zy\nyis899xzPPjggwwcOJBy5cp5HZYxUS3UldQFNQZifrxk6/sQfmlpaUydOpW5c+fSpEkTr8MxJu4F\n08w1iyN1EAnANmCwqn4c4tgKxhHWK4iLLnL6PlxXcNBzE1JWKW1M6QrJLUfdDQtQnyOd23K8GlI1\nnAlizRpo3Ro2brTmrcaY6Bay0VzdM/JUVc12H9HRJraErO9DaG3ZsoVPP/3U6zCMMUUIphvqfBE5\nM+SRRIicHLutaKioKmPGjCElJYUffvjB63CMMUUIWEktImVV9TBwJvCjiKwC9uAMpqeqGpO9A9LT\noXJl6xhX2latWsWdd97J1q1b+d///sfZdoCNiXiFXUHMc/9eBTQFLgN6AN3dvzHJ+j6UvokTJ9K2\nbVsuvfRS5s2bZ8nBmCgRsJJaRDJUNWKKlsJRSb1nD9SrB0uWQO3aId1VXFm/fj2HDh3ipJNO8joU\nY+JOqPpBnCAiDwRaqKr/Kc4OI9knn8B551lyKG3169f3OgRjTDEUliDKAFWIwBv4hMro0XBXUOPU\nmkD2799PxYoVvQ7DGFMKCiti+iWSKqJDXcS0dq1TMW19H4pn8+bN3HfffVSuXJlRo0Z5HY4xxhWq\nfhBxc+UA1vehuFSVd955h5SUFBo2bMhrrwV7u3JjTKQrrIipU9ii8JiqU7z00UdeRxJdVqxYQf/+\n/dm9ezfTp0+nVatWXodkjClFAa8gVHVbOAPxUno6VKpkfR+O1WeffcZVV13F3LlzLTkYE4PsjnLA\nbbfB6afDgw+GZPPGGOOZkA3WF0lClSCs74MxJpaFbLC+eGB9H4o2ZcoUpk2b5nUYxpgwi/sEMXo0\n9O3rdRSRadOmTXTv3p0HH3yQKlWqeB2OMSbM4jpBrF0LCxbAlVd6HUlkycnJYcSIEbRo0YLTTjuN\nBQsW0KFDB6/DMsaEWXFuORozxoyB668H6/ibX79+/Vi2bBnffPMNKSkpXodjjPFI3FZSq0KTJvDh\nh3DOOaW22Ziwfv166tSpQ5kyZbwOxRhTQqEarC+mpac7Vw6tW3sdSeSxwfWMMRDHdRB23wfYsWMH\ne/bs8ToMY0yEissEsWeP07z1xhu9jsQ7n3zyCc2aNbPmq8aYgOKyiGnSJGjfPj77PmzcuJEBAwaw\ndOlSPvjgAy644AKvQzLGRKi4vILILV6KJ6rK8OHDadWqFS1btmTBggWWHIwxhYq7K4i1a2H+/Pjr\n+yAibN26lZkzZ3LGGWd4HY4xJgrEXTPXp5+GTZtg2LBSCMoYYyKcNXMNUu59H8aN8zoSY4yJfHFV\nBzF7tnPHuFju+5CVlcUdd9xBZmam16EYY6JcXCWIWO77oKp89NFHNGvWjHLlyllnN2NMiYW8iElE\nugAv4ySjUao6tMDy3sDD7uQu4C5VXVTacezZAxMnOvd9iDXr1q3j7rvvZs2aNUyYMIH27dt7HZIx\nJgaE9ApCRBKA14FLgWZALxE5rcBqq4ELVLUl8DTwVihimTQJzj039vo+HDhwgI4dO9K2bVt++eUX\nSw7GmFIT6iuINsAKVV0LICIfAt2AZbkrqOpcn/XnAnVDEcjo0dC/fyi27K0KFSqwaNEiu1+DMabU\nhboOoi6w3md6A4UngL8BpT72w9q1kJEBV11V2luODJYcjDGhEDHNXEXkQuBW4PxA6wwZMiTveWpq\nKqmpqUFte+zY2Ljvw08//cTZZ5+NxGItuzGmVKSlpZGWllYq2wppRzkRaQcMUdUu7vRgQP1UVLcA\nJgJdVHVVgG0Vq6OcKpxyCnzwAbRpc8wvjwhbt27lwQcfZMaMGcyZM4d69ep5HZIxJkqUpKNcqIuY\nfgSaiEhDESkP3ABM8V1BRBrgJIebAiWHkpg9G8qXj86bAqkqH3zwAc2bNycxMZHMzExLDsaYsAlp\nEZOqZovIAGA6R5q5LhWRO5zFOhJ4HEgGhotTdnJIVUvtt3609n3YunUrN954I7///juffvopbaL1\n8scYE7VieiymvXuhXj1YvBjq1AlRYCFy6NAh3nnnHfr160e5cuW8DscYE6VKUsQU0wni/fedx9Sp\nIQrKGGMiXCTXQXgqHu/7YIwxpSVmE8S6dfDLL5Hf92H69Om0b9+evXv3eh2KMcbkEzH9IEpbpPd9\n2LJlCw888ADp6ekMHz6cypUrex2SMcbkE5NXELn3fYjE4iVVZcyYMTRv3pyaNWuyePFiunbt6nVY\nxhhzlJi8gpgzB8qVi8y+D/Pnz+eVV15h6tSpnH322V6HY4wxAcVkK6bbb3d6Tw8aFOKgiiknJ4eE\nhJi8eDPGRBhr5upj716oWxcyM6Ov74MxxpQ2a+bqY9IkaNfO++Swe/duJk+e7G0QxhhTAjGXICKh\ncnrq1Kk0b96cKVOmEC1XaLGgUaNGiIg97BGXj0aNGpX6dyqmipjWrYMzz4SNG71p3rp582buu+8+\n5s2bx4gRI7j44ovDH0QcExFLyCZuBfr8u/OtiGnsWOjZ05vkkJaWRkpKCg0bNmTRokWWHIwxUS9m\nriBU4dRT4b33oG3bMAbm+vPPP/n9999p1apV+HduALuCMPEtFFcQMdMPYs4cKFvWu5sC1axZk5o1\na3qzc2OMCYGYKWIK530fDh06FPqdGGOMx2IiQezdCxMnwo03hnY/u3bt4p577qF79+6h3ZExcWDJ\nkiWcE4nDHUSgP//8kzPOOCPsP05jIkFMnuzUO9StG7p9TJkyhWbNmrFnzx7efffd0O3IxKxGjRpR\nuXJlEhMTqV27NjfddBO7du3Kt86cOXPo1KkTiYmJJCUl0a1bN5YuXZpvnV27dnHffffRsGFDEhMT\nOeWUU3jggQfYtm1bON9OiT3xxBMMitThDoJ08OBB+vXrR7Vq1ahTpw4vvfRSoeuPHDmSJk2aUL16\nddq0acPs2bPzluXeWjj3Ua5cObp16wY4RdgXXXQRI0aMCOn7OYqqRsXDCdW/zp1VP/ww4OIS+f33\n37V79+56yimn6DfffBOanZh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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29d26a6fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred = classifier.predict_proba(x_test)\n", "pred = pred[:,1] # Only positive cases\n", "# print(pred[:,1])\n", "plot_roc(pred,y_test)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification\n", "\n", "We've already seen multi-class classification, with the iris dataset. Confusion matrixes work just fine with 3 classes. The following code generates a confusion matrix for iris." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py:281: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n", " out.itemset((i, self.y[sample]), 1.0)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Step #50, epoch #12, avg. train loss: 0.42043, avg. val loss: 0.43100\n", "Step #100, epoch #25, avg. train loss: 0.09383, avg. val loss: 0.16211\n", "Step #150, epoch #37, avg. train loss: 0.05107, avg. val loss: 0.14559\n", "Step #200, epoch #50, avg. train loss: 0.04197, avg. val loss: 0.16927\n", "Step #250, epoch #62, avg. train loss: 0.02741, avg. val loss: 0.15594\n", "Step #300, epoch #75, avg. train loss: 0.02923, avg. val loss: 0.15539\n", "Step #350, epoch #87, avg. train loss: 0.02136, avg. val loss: 0.16504\n", "Step #400, epoch #100, avg. train loss: 0.02007, avg. val loss: 0.15931\n", "Step #450, epoch #112, avg. train loss: 0.02117, avg. val loss: 0.16234\n", "Step #500, epoch #125, avg. train loss: 0.02170, avg. val loss: 0.15867\n", "Step #550, epoch #137, avg. train loss: 0.01714, avg. val loss: 0.15081\n", "Step #600, epoch #150, avg. train loss: 0.01578, avg. val loss: 0.15566\n", "Step #650, epoch #162, avg. train loss: 0.01815, avg. val loss: 0.16091\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stopping. Best step:\n", " step 464 with loss 0.030616959556937218\n" ] }, { "data": { "text/plain": [ "TensorFlowDNNClassifier(batch_size=32, class_weight=None, clip_gradients=5.0,\n", " config=None, continue_training=False, dropout=None,\n", " hidden_units=[10, 20, 10], learning_rate=0.1, n_classes=3,\n", " optimizer='Adagrad', steps=10000, verbose=1)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pandas as pd\n", "from sklearn.cross_validation import train_test_split\n", "import tensorflow.contrib.learn as skflow\n", "import numpy as np\n", "\n", "path = \"./data/\"\n", " \n", "filename = os.path.join(path,\"iris.csv\") \n", "df = pd.read_csv(filename,na_values=['NA','?'])\n", "\n", "# Encode feature vector\n", "encode_numeric_zscore(df,'petal_w')\n", "encode_numeric_zscore(df,'petal_l')\n", "encode_numeric_zscore(df,'sepal_w')\n", "encode_numeric_zscore(df,'sepal_l')\n", "species = encode_text_index(df,\"species\")\n", "num_classes = len(species)\n", "\n", "# Create x & y for training\n", "\n", "# Create the x-side (feature vectors) of the training\n", "x, y = to_xy(df,'species')\n", " \n", "# Split into train/test\n", "x_train, x_test, y_train, y_test = train_test_split( \n", " x, y, test_size=0.25, random_state=45) \n", " # as much as I would like to use 42, it gives a perfect result, and a boring confusion matrix!\n", " \n", "# Create a deep neural network with 3 hidden layers of 10, 20, 10\n", "classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=num_classes,\n", " steps=10000)\n", "\n", "# Early stopping\n", "early_stop = skflow.monitors.ValidationMonitor(x_test, y_test,\n", " early_stopping_rounds=200, print_steps=50, n_classes=num_classes)\n", " \n", "# Fit/train neural network\n", "classifier.fit(x_train, y_train, early_stop)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[14 0 0]\n", " [ 0 9 0]\n", " [ 0 3 12]]\n", "Normalized confusion matrix\n", "[[ 1. 0. 0. ]\n", " [ 0. 1. 0. ]\n", " [ 0. 0.2 0.8]]\n" ] }, { "data": { "image/png": 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rL+R0G20w8CdJbaS87debXJ8QQg9olfRCvwu6tp+kwpC/EEJr6yvpg2r6XdAN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\nPmr7n/n5CsA1wHds3ybpU6S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"text/plain": [ "<matplotlib.figure.Figure at 0x7f29744527b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f298c05fa58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "from sklearn import svm, datasets\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "\n", "\n", "\n", "pred = classifier.predict(x_test)\n", " \n", "# Compute confusion matrix\n", "cm = confusion_matrix(y_test, pred)\n", "np.set_printoptions(precision=2)\n", "print('Confusion matrix, without normalization')\n", "print(cm)\n", "plt.figure()\n", "plot_confusion_matrix(cm, species)\n", "\n", "# Normalize the confusion matrix by row (i.e by the number of samples\n", "# in each class)\n", "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "print('Normalized confusion matrix')\n", "print(cm_normalized)\n", "plt.figure()\n", "plot_confusion_matrix(cm_normalized, species, title='Normalized confusion matrix')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the strong diagonal? Iris is easy. See the light blue near the bottom? Sometimes virginica is confused for versicolor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression\n", "\n", "We've already seen regression with the MPG dataset. Regression uses its own set of visualizations, one of the most common is the lift chart. The following code generates a lift chart." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #50, epoch #5, avg. train loss: 40.25010, avg. val loss: 35.26281\n", "Step #100, epoch #10, avg. train loss: 6.67342, avg. val loss: 6.36329\n", "Step #150, epoch #15, avg. train loss: 3.44723, avg. val loss: 3.21085\n", "Step #200, epoch #20, avg. train loss: 1.99451, avg. val loss: 1.88279\n", "Step #250, epoch #25, avg. train loss: 1.46245, avg. val loss: 1.39983\n", "Step #300, epoch #30, avg. train loss: 1.22336, avg. val loss: 1.14907\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stopping. Best step:\n", " step 109 with loss 0.603066623210907\n" ] }, { "data": { "text/plain": [ "TensorFlowDNNRegressor(batch_size=32, clip_gradients=5.0, config=None,\n", " continue_training=False, dropout=None,\n", " hidden_units=[50, 25, 10], learning_rate=0.1, n_classes=0,\n", " optimizer='Adagrad', steps=5000, verbose=1)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow.contrib.learn as skflow\n", "import pandas as pd\n", "import os\n", "import numpy as np\n", "from sklearn import metrics\n", "from scipy.stats import zscore\n", "\n", "path = \"./data/\"\n", "\n", "filename_read = os.path.join(path,\"auto-mpg.csv\")\n", "df = pd.read_csv(filename_read,na_values=['NA','?'])\n", "\n", "# create feature vector\n", "missing_median(df, 'horsepower')\n", "df.drop('name',1,inplace=True)\n", "encode_numeric_zscore(df, 'horsepower')\n", "encode_numeric_zscore(df, 'weight')\n", "encode_numeric_zscore(df, 'cylinders')\n", "encode_numeric_zscore(df, 'displacement')\n", "encode_numeric_zscore(df, 'acceleration')\n", "encode_text_dummy(df, 'origin')\n", "\n", "# Encode to a 2D matrix for training\n", "x,y = to_xy(df,['mpg'])\n", "\n", "# Split into train/test\n", "x_train, x_test, y_train, y_test = train_test_split(\n", " x, y, test_size=0.25, random_state=42)\n", "\n", "# Create a deep neural network with 3 hidden layers of 50, 25, 10\n", "regressor = skflow.TensorFlowDNNRegressor(hidden_units=[50, 25, 10], steps=5000)\n", "\n", "# Early stopping\n", "early_stop = skflow.monitors.ValidationMonitor(x_test, y_test,\n", " early_stopping_rounds=200, print_steps=50)\n", "\n", "# Fit/train neural network\n", "regressor.fit(x_train, y_train, early_stop)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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px20/cvbGWTzze3Lo4iHyx3lTPL7jOTAwkMDAQJtdy67rMRhjhgGvAjFAXsAN+AOoBfiJ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\nWH1yNcteXUZZ97KpOt+JE9C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"text/plain": [ "<matplotlib.figure.Figure at 0x7f299e373198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred = regressor.predict(x_test)\n", "\n", "chart_regression(pred,y_test)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "To generate a lift chart, perform the following activities:\n", "\n", "* Sort the data by expected output. Plot the blue line above.\n", "* For every point on the x-axis plot the predicted value for that same data point. This is the green line above.\n", "* The x-axis is just 0 to 100% of the dataset. The expected always starts low and ends high.\n", "* The y-axis is ranged according to the values predicted.\n", "\n", "Reading a lift chart:\n", "* The expected and predict lines should be close. Notice where one is above the ot other.\n", "* The above chart is the most accurate on lower MPG." ] }, { "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": 0 }

apache-2.0

MilweeScience/Turner

index_turner.ipynb

1

4245

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 7th Grade Comprehensive Science Jupyter Notebooks\n", "\n", "\n", "## <span style=\"color:green\">beta.mybinder.org/repo/MilweeScience/Turner</span>\n", "\n", "\n", "\n", "### What is Jupyter Notebooks?\n", "Jupyter Notebooks is a program that simplifies coding into “blocks” that allows code to be sectioned into chunks that are more easily\n", "interpreted and meaningful to students and teachers. Jupyter Notebooks also allows the use of section headings, in-code help\n", "comments, and easy reset options which make it a great tool for sharing code for others to manipulate and explore. Jupyter\n", "Notebooks uses a type of code called Python, this coding language is used in a wide variety of applications including- web\n", "development, app development, and most importantly- analysis of experimental results in scientific research.\n", "\n", "### Why are we doing this?\n", "\n", "This coding activity uses the same programing language and system used by many scientists around the world. The goal of this activity is to introduce you to the ways that coding is used in the real world. Organizations like CERN in Geneva, Switzerland use Jupyter Notebooks to analyze data from their particle colliders as they study the composition of atoms (other groups include- NASA, Google, & IBM). Each unit this year has a Jupyter Notebook associated with it, each explores different scientific concepts that are\n", "part of that unit for your practice and introduces you to new coding functions like creating graphs, customizing graphs, analyzing\n", "data, creating data charts, and sorting large amounts of information.\n", "\n", "Since this is the first time many of you will work with code, the changes you will be making are small. Others of you, who have coded before, will find opportunity to look into different ways code can be used. Each lesson provides an opportunity for “unstructured coding” where you can explore new ideas you might have or work with the information in a new way. Know that coding is often a community effort in which many people contribute, don’t be afraid to ask for help or offer it.\n", "\n", "### [Jupyter Notebooks Quick Start Guide](https://github.com/SCPSscience/booklet/raw/master/JupyterNotebooksStudentGuide.pdf)\n", "\n", "- [Intro to Jupyter](./Intro_to_Jupyter.ipynb) is a 5-minute guide of how to use our activities. If you've never programmed before, this is a great place to start. From CodinginK12.org and Adam LaMee at UCF.\n", "\n", "### Unit 3 - Beyond Our World\n", "- [Properties of Stars](./Stars_Turner.ipynb) - Unit 3 - Beyond Our World [Propterites of Stars Coding Booklet](https://github.com/SCPSscience/booklet/raw/master/Stars.pdf)\n", "\n", "\n", "\n", "### Previous Jupyter Notebooks\n", "\n", "#### Unit 2 - Interaction of Spheres\n", "- [Global & Local Temperature](./Global_Local_Temp_Turner.ipynb) - Unit 2 - Interactions of Spheres (Atmosphere & Hydrosphere) \n", " [Global & Local Temperature Coding Booklet](https://github.com/SCPSscience/booklet/raw/master/Temps.pdf)\n", "\n", "#### Unit 1 - Change Over Time \n", "- [Weathering & Erosion](./Erosion_Turner.ipynb) Unit 1 - Change Over Time (Weathering & Erosion) \n", " [Erosion Coding Booklet](https://github.com/SCPSscience/booklet/raw/master/Erosion.pdf)\n", "\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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

blagasz/python-ann

example.ipynb

1

2057

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import neurolab as nl\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create train samples\n", "x = np.linspace(-7, 7, 20)\n", "y = np.sin(x) * 0.5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "size = len(x)\n", "\n", "inp = x.reshape(size,1)\n", "tar = y.reshape(size,1)\n", "\n", "# Create network with 2 layers and random initialized\n", "net = nl.net.newff([[-7, 7]],[5, 1])\n", "\n", "# Train network\n", "# default train method is\n", "# BroydenFletcherGoldfarbShanno (BFGS) method Using scipy.optimize.fmin_bfgs\n", "error = net.train(inp, tar, epochs=500, show=100, goal=0.02)\n", "\n", "# Simulate network\n", "out = net.sim(inp)\n", "\n", "# Plot result\n", "import pylab as pl\n", "pl.subplot(211)\n", "pl.plot(error)\n", "pl.xlabel('Epoch number')\n", "pl.ylabel('error (default SSE)')\n", "\n", "x2 = np.linspace(-6.0,6.0,150)\n", "y2 = net.sim(x2.reshape(x2.size,1)).reshape(x2.size)\n", "\n", "y3 = out.reshape(size)\n", "\n", "pl.subplot(212)\n", "pl.plot(x2, y2, '-',x , y, '.', x, y3, 'p')\n", "pl.legend(['train target', 'net output'])\n", "pl.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

michhar/csvconf2016

csvconfv2 MHarris-python-kernel.ipynb

1

1283756

mit

jobar8/interpies

notebooks/Create_Globes_with_Basemap_and_Cartopy.ipynb

1

8557458

bsd-3-clause

hadibakalim/deepLearning

01.neural_network/00.intro_and_general/.ipynb_checkpoints/00.intro-checkpoint.ipynb

1

1354

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Neural Networks\n", "\n", "### What is all?\n", "\n", "\n", "Scikit-learn\n", "- An extremely popular Machine Learning library for python.\n", "\n", "Perceptrons\n", "- The simplest form of a neural network.\n", "\n", "Gradient Descent\n", "- A process by which Machine Learning algorithms learn to improve themselves based on the accuracy of their predictions. \n", "\n", "Backpropagation\n", "- The process by which neural networks learn how to improve individual parameters. \n", "\n", "Numpy\n", "- An extremely popular library for scientific computing in python.\n", "\n", "Tensorflow\n", "- One of the most popular python libraries for creating neural networks. It is maintained by Google.\n", "\n", "We'll learn more about those." ] } ], "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

SteveDiamond/cvxpy

examples/notebooks/WWW/max_entropy.ipynb

2

5361

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Entropy maximization\n", "\n", "A derivative work by Judson Wilson, 6/2/2014.<br>\n", "Adapted from the CVX example of the same name, by Joëlle Skaf, 4/24/2008.\n", "\n", "## Introduction\n", "\n", "Consider the linear inequality constrained entropy maximization problem:\n", " $$\\begin{array}{ll}\n", " \\mbox{maximize} & -\\sum_{i=1}^n x_i \\log(x_i) \\\\\n", " \\mbox{subject to} & \\sum_{i=1}^n x_i = 1 \\\\\n", " & Fx \\succeq g,\n", " \\end{array}$$\n", "where the variable is $x \\in \\mathbf{{\\mbox{R}}}^{n}$. \n", "\n", "This problem can be formulated in CVXPY using the `entr` atom.\n", "\n", "## Generate problem data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cvxpy as cp\n", "import numpy as np\n", "\n", "# Make random input repeatable. \n", "np.random.seed(0) \n", "\n", "# Matrix size parameters.\n", "n = 20\n", "m = 10\n", "p = 5\n", "\n", "# Generate random problem data.\n", "tmp = np.random.rand(n)\n", "A = np.random.randn(m, n)\n", "b = A.dot(tmp)\n", "F = np.random.randn(p, n)\n", "g = F.dot(tmp) + np.random.rand(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Formulate and solve problem" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n", "\n", "It pcost dcost gap pres dres k/t mu step sigma IR | BT\n", " 0 +0.000e+00 -1.183e+01 +7e+01 1e+00 4e-01 1e+00 1e+00 --- --- 0 0 - | - - \n", " 1 -1.045e+01 -1.419e+01 +2e+01 2e-01 1e-01 4e-01 3e-01 0.7833 5e-02 1 1 1 | 2 1\n", " 2 -5.864e+00 -6.905e+00 +4e+00 7e-02 3e-02 1e-01 5e-02 0.7833 5e-02 1 1 1 | 2 1\n", " 3 -5.590e+00 -6.018e+00 +1e+00 3e-02 1e-02 5e-02 2e-02 0.6266 5e-02 1 1 1 | 2 2\n", " 4 -5.525e+00 -5.621e+00 +3e-01 7e-03 3e-03 1e-02 5e-03 0.7818 1e-02 1 1 1 | 1 1\n", " 5 -5.490e+00 -5.530e+00 +1e-01 3e-03 1e-03 5e-03 2e-03 0.6266 5e-02 1 1 1 | 2 2\n", " 6 -5.484e+00 -5.493e+00 +3e-02 7e-04 3e-04 1e-03 4e-04 0.7833 1e-02 1 1 1 | 1 1\n", " 7 -5.482e+00 -5.485e+00 +1e-02 3e-04 1e-04 4e-04 2e-04 0.6266 5e-02 2 1 1 | 2 2\n", " 8 -5.481e+00 -5.482e+00 +2e-03 6e-05 3e-05 1e-04 4e-05 0.7833 1e-02 1 1 1 | 1 1\n", " 9 -5.481e+00 -5.481e+00 +1e-03 3e-05 1e-05 4e-05 2e-05 0.6266 5e-02 1 1 1 | 2 2\n", "10 -5.481e+00 -5.481e+00 +2e-04 6e-06 3e-06 9e-06 3e-06 0.7833 1e-02 2 1 1 | 1 1\n", "11 -5.481e+00 -5.481e+00 +9e-05 2e-06 1e-06 4e-06 1e-06 0.6266 5e-02 1 0 0 | 2 2\n", "12 -5.481e+00 -5.481e+00 +2e-05 5e-07 2e-07 8e-07 3e-07 0.7833 1e-02 1 0 0 | 1 1\n", "13 -5.481e+00 -5.481e+00 +8e-06 2e-07 1e-07 3e-07 1e-07 0.6266 5e-02 2 0 0 | 2 2\n", "14 -5.481e+00 -5.481e+00 +2e-06 5e-08 2e-08 7e-08 3e-08 0.7833 1e-02 1 0 0 | 1 1\n", "15 -5.481e+00 -5.481e+00 +7e-07 2e-08 9e-09 3e-08 1e-08 0.6266 5e-02 0 0 0 | 2 2\n", "16 -5.481e+00 -5.481e+00 +2e-07 4e-09 2e-09 7e-09 3e-09 0.7833 9e-03 0 0 0 | 1 1\n", "17 -5.481e+00 -5.481e+00 +6e-08 2e-09 8e-10 3e-09 1e-09 0.6266 5e-02 1 0 0 | 2 2\n", "18 -5.481e+00 -5.481e+00 +1e-08 4e-10 2e-10 6e-10 2e-10 0.7833 9e-03 1 0 0 | 1 1\n", "\n", "OPTIMAL (within feastol=4.0e-10, reltol=2.7e-09, abstol=1.5e-08).\n", "Runtime: 0.003436 seconds.\n", "\n", "\n", "The optimal value is: 5.480901488005442\n", "\n", "The optimal solution is:\n", "[0.43483319 0.66111715 0.49201039 0.3603062 0.3841663 0.30283659\n", " 0.4173023 0.79107794 0.76667303 0.38292364 1.2479328 0.50416987\n", " 0.68053833 0.67163957 0.13877258 0.5248668 0.08418897 0.56927148\n", " 0.50000247 0.78291311]\n" ] } ], "source": [ "# Entropy maximization.\n", "x = cp.Variable(shape=n)\n", "obj = cp.Maximize(cp.sum(cp.entr(x)))\n", "constraints = [A*x == b,\n", " F*x <= g ]\n", "prob = cp.Problem(obj, constraints)\n", "prob.solve(solver=cp.ECOS, verbose=True)\n", "\n", "# Print result.\n", "print(\"\\nThe optimal value is:\", prob.value)\n", "print('\\nThe optimal solution is:')\n", "print(x.value)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

agmarrugo/sensors-actuators

notebooks/Ex_2_3.ipynb

1

31939

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The transfer function\n", "\n", "__Analytic form of transfer function__. In certain cases the transfer function is available as an analytic expression. One common transfer function used for resistance temperature sensors (to be discussed in __Chapter 3__) is the Callendar– Van Duzen equation. It gives the resistance of the sensor at a temperature T as\n", "\n", " \n", "$$R(T)=R_{0}(1+AT+BT^2+C(T-100)T^3) \\enspace,$$\n", "\n", "where the constants A, B, and C are determined by direct measurement of resistance for the specific material used in the sensor and $R_0$ is the temperature of the sensor at 0 ºC. Typical temperatures used for calibration are the oxygen point (-182.962 ºC; the equilibrium between liquid oxygen and its vapor), the triple point of water (0.01 ºC; the point of equilibrium temperature between ice, liquid water, and water vapor), the steam point (100 ºC; the equilibrium point between water and vapor), the zinc point (419.58 ºC; the equilibrium point between solid and liquid zinc), the silver point (961.93 ºC), and the gold point (1064.43 ºC), as well as others. Consider a platinum resistance sensor with a nominal resistance of 25 $\\Omega $ at 0 C. To calibrate the sensor its resistance is measured at the oxygen point as 6.2 $\\Omega $, at the steam point as 35.6 $\\Omega $, and at the zinc point as 66.1 $\\Omega $. Calculate the coefficients A, B, and C and plot the transfer function between -200 ºC and 600 ºC.\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution ###\n", "\n", "In order to obtain the sensor calibration, several measurements at different temperatures where taken:\n", "\n", "1. 6.2 $\\Omega $ at a temperature of -182.962 ºC (oxygen point).\n", "\n", "2. 35.6 $\\Omega $ at a temperature of 100 ºC (steam point).\n", "\n", "3. 66.1 $\\Omega $ at a temperature of 419.58 ºC (zinc point)\n", "\n", "Let's plot the points," ] }, { "cell_type": "code", "execution_count": 1, "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": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from math import log, exp\n", "%matplotlib inline\n", "from scipy.interpolate import InterpolatedUnivariateSpline\n", "\n", "\n", "T_exp = np.array([-182.962,100,419.58]);# Celcius\n", "R_exp = np.array([6.2 ,35.6,66.1])# Ohm\n", "plt.plot(T_exp,R_exp,'*');\n", "plt.ylabel('Resistance of the sensor [Ohm]')\n", "plt.xlabel('Temperature [C]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reordering the Callendar-Van Duzen equation we obtain the following\n", "\n", "$$ AT+BT^2+C(T-100)T^3 =\\frac{R(T)}{R_0}-1 \\enspace,$$\n", "\n", "which we can write in matrix form as $Mx=p$, where\n", "\n", "$$\\begin{bmatrix} T_1 & T_1^2 & (T_1-100)T_1^3 \\\\ T_2 & T_2^2 & (T_2-100)T_2^3 \\\\ T_3 & T_3^2 & (T_3-100)T_3^3\\end{bmatrix} \\begin{bmatrix} A\\\\ B \\\\ C\\end{bmatrix} = \\begin{bmatrix} \\frac{R(T_1)}{R_0}-1 \\\\ \\frac{R(T_2)}{R_0}-1 \\\\ \\frac{R(T_3)}{R_0}-1\\end{bmatrix} \\enspace.$$\n", "\n", "Because $M$ is square we can solve by computing $M^{-1}$ directly.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "M\n", "[[-1.830e+02 3.348e+04 1.733e+09]\n", " [ 1.000e+02 1.000e+04 0.000e+00]\n", " [ 4.196e+02 1.760e+05 2.361e+10]]\n", "\n", "\n", "p\n", "[[-0.752]\n", " [ 0.424]\n", " [ 1.644]]\n", "\n", "\n", "x\n", "[[ 4.160e-03]\n", " [ 8.031e-07]\n", " [-1.028e-11]]\n" ] } ], "source": [ "R0=25;\n", "M=np.array([[T_exp[0],(T_exp[0])**2,(T_exp[0]-100)*(T_exp[0])**3],[T_exp[1],(T_exp[1])**2,(T_exp[1]-100)*(T_exp[1])**3],[T_exp[2],(T_exp[2])**2,(T_exp[2]-100)*(T_exp[2])**3]]);\n", "p=np.array([[(R_exp[0]/R0)-1],[(R_exp[1]/R0)-1],[(R_exp[2]/R0)-1]]);\n", "x = np.linalg.solve(M,p) #solve linear equations system\n", "\n", "np.set_printoptions(precision=3)\n", "\n", "print('M')\n", "print(M)\n", "print('\\n')\n", "print('p')\n", "print(p)\n", "print('\\n')\n", "print('x')\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have found the coeffiecients $A$, $B$, and $C$ necessary to describe the sensor's transfer function. Now we plot it from -200 C a 600 C." ] }, { "cell_type": "code", "execution_count": 3, "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": [ "A=x[0];B=x[1];C=x[2];\n", "T_range= np.arange(start = -200, stop = 601, step = 1);\n", "R_funT= R0*(1+A[0]*T_range+B[0]*(T_range)**2+C[0]*(T_range-100)*(T_range)**3);\n", "plt.plot(T_range,R_funT,T_exp[0],R_exp[0],'ro',T_exp[1],R_exp[1],'ro',T_exp[2],R_exp[2],'ro');\n", "plt.ylabel('Sensor resistance [Ohm]')\n", "plt.xlabel('Temperature [C]')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We see the fit is accurate. Note that our approach is also valid if we have more experimental points, in which case the system of equations $Mx=p$ is solved in the [Least-Squares sense.](http://math.mit.edu/~gs/linearalgebra/ila0403.pdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> This page was written in the [IPython Jupyter Notebook](https://jupyter.org/). To download the notebook click on this option at the top menu or get it from the [github repo](https://github.com/agmarrugo/sensors-actuators)." ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

dougbrose/data-science

ch17_fig7.ipynb

1

32680

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Ch17 Figure7" ] }, { "cell_type": "code", "execution_count": 1, "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>id</th>\n", " <th>gender</th>\n", " <th>test taker</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>male</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>female</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>male</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>female</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>female</td>\n", " <td>N</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id gender test taker\n", "0 0 male Y\n", "1 1 female Y\n", "2 2 male N\n", "3 3 female Y\n", "4 4 female N" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# I once worked for an organization that was trying to figure out why more men than women were participating in their medical studies. They got a report from the labs that men were 60% more likely to participate in a medical study. The data science team was tasked with trying to figure out why this was the case.\n", "\n", "data = []\n", "\n", "for i in range(5000):\n", " \n", " if rd.random() <= .4:\n", " gender = 'male'\n", " else:\n", " gender = 'female'\n", " \n", " if gender == 'male':\n", " if rd.random() <= .8:\n", " tested = 'Y'\n", " else:\n", " tested = 'N'\n", " else:\n", " if rd.random () <= .3:\n", " tested = 'Y'\n", " else:\n", " tested = 'N'\n", " data.append([i, gender, tested])\n", "\n", "df = pd.DataFrame(data, columns=['id', 'gender', 'test taker'])\n", "# df.to_csv('csv_output/ch17_fig6.csv', index=False)\n", "df = pd.read_csv('csv_output/ch17_fig7.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Xl4czZ87A0tKSs0SIXmBqtRp37txBu3btSr1+6ElF030VCgV8fX0xZcoUFBQU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\nxhhjjDmk/wJ6MfKNeOa2+gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xbca9be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv('csv_output/ch17_fig7.csv')\n", "\n", "%matplotlib inline\n", "sns.set_style(\"white\")\n", "cm = sns.color_palette('Blues', 2)\n", "\n", "f, ax = plt.subplots(1,2, figsize=(8,4))\n", "ax1 = ax[1]\n", "ax = ax[0]\n", "\n", "dgb = df.groupby(['gender', 'test taker']).id.count().reset_index().sort_values('id', ascending=False)\n", "# print(dgb[(dgb['took test'] == 'Y') & (dgb['gender']=='male')].id.values / dgb[(dgb['took test'] == 'Y') & (dgb['gender']=='female')].id.values)\n", "ax.bar(left= np.arange(2), height=[dgb[dgb['gender']=='male'].id.sum(), dgb[dgb['gender']=='female'].id.sum()], color=cm[0]);\n", "ax.bar(left= np.arange(2), height=[dgb[(dgb['gender']=='male') & (dgb['test taker']=='Y')].id.sum(), dgb[(dgb['gender']=='female') & (dgb['test taker']=='Y')].id.sum()], color=cm[1]);\n", "\n", "ax.legend(['non-test taker', 'test taker'], loc='upper left')\n", "ax.set_title('male and female \\n (test taker versus non-test takers)')\n", "ax.set_xticks(np.arange(2)+.5);\n", "ax.set_xticklabels(['male', 'female'])\n", "ax.set_ylabel('total count')\n", "\n", "ax1.bar(left=np.arange(2), height=[dgb[(dgb['gender']=='male') & (dgb['test taker']=='Y')].id.sum(), 0], color=cm[0])\n", "ax1.bar(left=np.arange(2), height=[dgb[(dgb['gender']=='female') & (dgb['test taker']=='Y')].id.sum(), 0], color=cm[1])\n", "ax1.bar(left=np.arange(2), height=[0, dgb[(dgb['gender']=='female') & (dgb['test taker']=='N')].id.sum()], color=cm[1])\n", "ax1.bar(left=np.arange(2), height=[0, dgb[(dgb['gender']=='male') & (dgb['test taker']=='N')].id.sum()], color=cm[0])\n", "\n", "ax1.set_xticks(np.arange(2)+.5);\n", "ax1.set_xticklabels(['test-taker', 'non-test taker'])\n", "ax1.set_title('test taker versus non-test takers \\n (male and female)');\n", "ax1.legend(['male', 'female'], loc='upper left')\n", "\n", "f.tight_layout()\n", "f.savefig('svg_output/ch17_fig7.svg', format='svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can pivot the table two ways, on the left is by male and female, about 80% of male are test takers versus only 30% of female are test takers. On the right is by test takers and non-test takers, 60% of test takers are male versus only 15% of non-test takers are male." ] } ], "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

nukui-s/TripletEmbedding

workspace/FB40k.ipynb

1

674560

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from knowledge import FBManager\n", "from tripletembed.model import TripletEmbedding\n", "import numpy as np\n", "import pandas as pd\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fbm = FBManager(\"data/FB15k/freebase_mtr100_mte100-train.txt\", \"data/FB15k/freebase_mtr100_mte100-test.txt\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s_train, o_train, p_train = fbm.get_train_data()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_entity = fbm.n_inst\n", "n_relation = fbm.n_prop" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = TripletEmbedding(n_entity, n_relation, 30, 15, [30], sigma=10e-6, learning_rate=10e-3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "fit() missing 1 required positional argument: 'labels'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-650079e5212a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: fit() missing 1 required positional argument: 'labels'" ] } ], "source": [ "model.fit(s_train, o_train, p_train)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s_train_negative = []\n", "o_train_negative = []\n", "p_train_negtive = []\n", "n_negative = 5\n", "for s, o, p in zip(s_train, o_train, p_train):\n", " s_train_negative += [s] * n_negative\n", " o_train_negative += [o] * n_negative\n", " nn = 0\n", " while nn < n_negative:\n", " p_n = np.random.randint(0, n_relation)\n", " if p_n != p:\n", " nn += 1\n", " p_train_negtive.append(p_n)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s_train_pn = np.append(s_train, s_train_negative)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "o_train_pn = np.append(o_train, o_train_negative)\n", "p_train_pn = np.append(p_train, p_train_negtive)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "label_train = np.array([1] * s_train.size + [-1] * len(s_train_negative))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------------\n", "The 1th epoch\n", "-----------------------------------\n", "The 1th loop: cost = 504.518310546875\n", "The 2th loop: cost = 512.5032958984375\n", "The 3th loop: cost = 470.38311767578125\n", "The 4th loop: cost = 429.51934814453125\n", "The 5th loop: cost = 399.04278564453125\n", "The 6th loop: cost = 399.1201171875\n", "The 7th loop: cost = 379.96417236328125\n", "The 8th loop: cost = 361.59674072265625\n", "The 9th loop: cost = 346.20184326171875\n", "The 10th loop: cost = 330.44744873046875\n", "The 11th loop: cost = 292.53521728515625\n", "The 12th loop: cost = 263.1124267578125\n", "The 13th loop: cost = 239.34449768066406\n", "The 14th loop: cost = 235.19032287597656\n", "The 15th loop: cost = 227.84298706054688\n", "The 16th loop: cost = 215.45706176757812\n", "The 17th loop: cost = 196.17251586914062\n", "The 18th loop: cost = 188.94265747070312\n", "The 19th loop: cost = 170.55369567871094\n", "The 20th loop: cost = 151.47779846191406\n", "The 21th loop: cost = 149.2867431640625\n", "The 22th loop: cost = 135.72515869140625\n", "The 23th loop: cost = 130.00238037109375\n", "The 24th loop: cost = 119.0959701538086\n", "The 25th loop: cost = 108.8670654296875\n", "The 26th loop: cost = 105.76089477539062\n", "The 27th loop: cost = 98.104736328125\n", "The 28th loop: cost = 86.80149841308594\n", "The 29th loop: cost = 85.65464782714844\n", "The 30th loop: cost = 81.37913513183594\n", "The 31th loop: cost = 75.26498413085938\n", "The 32th loop: cost = 73.86626434326172\n", "The 33th loop: cost = 76.01923370361328\n", "The 34th loop: cost = 67.87803649902344\n", "The 35th loop: cost = 73.10264587402344\n", "The 36th loop: cost = 64.417724609375\n", "The 37th loop: cost = 65.26161193847656\n", "The 38th loop: cost = 69.29402923583984\n", "The 39th loop: cost = 63.77220153808594\n", "The 40th loop: cost = 59.62183380126953\n", "The 41th loop: cost = 52.886390686035156\n", "The 42th loop: cost = 64.72412109375\n", "The 43th loop: cost = 58.579708099365234\n", "The 44th loop: cost = 71.71578979492188\n", "The 45th loop: cost = 53.90542984008789\n", "The 46th loop: cost = 53.74812316894531\n", "The 47th loop: cost = 58.96876525878906\n", "The 48th loop: cost = 58.57863998413086\n", "The 49th loop: cost = 68.13856506347656\n", "The 50th loop: cost = 72.68238830566406\n", "The 51th loop: cost = 62.60625076293945\n", "The 52th loop: cost = 59.09061813354492\n", "The 53th loop: cost = 41.403812408447266\n", "The 54th loop: cost = 60.544212341308594\n", "The 55th loop: cost = 60.153011322021484\n", "The 56th loop: cost = 62.318450927734375\n", "The 57th loop: cost = 55.151065826416016\n", "The 58th loop: cost = 48.05994415283203\n", "The 59th loop: cost = 61.2239990234375\n", "The 60th loop: cost = 68.91248321533203\n", "The 61th loop: cost = 80.27778625488281\n", "The 62th loop: cost = 61.01100158691406\n", "The 63th loop: cost = 56.48251724243164\n", "The 64th loop: cost = 60.390804290771484\n", "The 65th loop: cost = 52.880592346191406\n", "The 66th loop: cost = 54.648014068603516\n", "The 67th loop: cost = 56.99512481689453\n", "The 68th loop: cost = 47.738182067871094\n", "The 69th loop: cost = 62.325950622558594\n", "The 70th loop: cost = 55.238983154296875\n", "The 71th loop: cost = 51.42212677001953\n", "The 72th loop: cost = 49.61557388305664\n", "The 73th loop: cost = 51.041961669921875\n", "The 74th loop: cost = 60.839744567871094\n", "The 75th loop: cost = 61.245487213134766\n", "The 76th loop: cost = 52.55101013183594\n", "The 77th loop: cost = 56.35783004760742\n", "The 78th loop: cost = 61.34859848022461\n", "The 79th loop: cost = 61.23749923706055\n", "The 80th loop: cost = 64.23701477050781\n", "The 81th loop: cost = 58.835205078125\n", "The 82th loop: cost = 48.7919807434082\n", "The 83th loop: cost = 62.66324996948242\n", "The 84th loop: cost = 57.90226745605469\n", "The 85th loop: cost = 76.69105529785156\n", "The 86th loop: cost = 56.16305923461914\n", "The 87th loop: cost = 55.74243927001953\n", "The 88th loop: cost = 53.386024475097656\n", "The 89th loop: cost = 55.71824264526367\n", "The 90th loop: cost = 60.439910888671875\n", "The 91th loop: cost = 47.928470611572266\n", "The 92th loop: cost = 60.600948333740234\n", "The 93th loop: cost = 58.54194641113281\n", "The 94th loop: cost = 49.653236389160156\n", "The 95th loop: cost = 60.330543518066406\n", "The 96th loop: cost = 56.212318420410156\n", "The 97th loop: cost = 64.994873046875\n", "The 98th loop: cost = 56.49315643310547\n", "The 99th loop: cost = 56.77306365966797\n", "The 100th loop: cost = 60.954586029052734\n", "The 101th loop: cost = 54.805843353271484\n", "The 102th loop: cost = 60.639652252197266\n", "The 103th loop: cost = 64.96250915527344\n", "The 104th loop: cost = 41.04366683959961\n", "The 105th loop: cost = 63.18170166015625\n", "The 106th loop: cost = 58.05141830444336\n", "The 107th loop: cost = 55.40951919555664\n", "The 108th loop: cost = 63.591285705566406\n", "The 109th loop: cost = 62.63954162597656\n", "The 110th loop: cost = 52.49071502685547\n", "The 111th loop: cost = 69.59734344482422\n", "The 112th loop: cost = 52.25860595703125\n", "The 113th loop: cost = 43.73462677001953\n", "The 114th loop: cost = 64.02922058105469\n", "The 115th loop: cost = 54.85686492919922\n", "The 116th loop: cost = 61.55963134765625\n", "The 117th loop: cost = 51.61452102661133\n", "The 118th loop: cost = 54.027732849121094\n", "The 119th loop: cost = 50.54658508300781\n", "The 120th loop: cost = 71.96353149414062\n", "The 121th loop: cost = 63.14869689941406\n", "The 122th loop: cost = 64.88845825195312\n", "The 123th loop: cost = 69.59849548339844\n", "The 124th loop: cost = 62.51258850097656\n", "The 125th loop: cost = 69.74482727050781\n", "The 126th loop: cost = 63.50651931762695\n", "The 127th loop: cost = 51.120994567871094\n", "The 128th loop: cost = 55.375274658203125\n", "The 129th loop: cost = 56.27374267578125\n", "The 130th loop: cost = 59.30000305175781\n", "The 131th loop: cost = 54.486480712890625\n", "The 132th loop: cost = 65.95797729492188\n", "The 133th loop: cost = 56.59408950805664\n", "The 134th loop: cost = 66.48857116699219\n", "The 135th loop: cost = 49.54538345336914\n", "The 136th loop: cost = 64.45541381835938\n", "The 137th loop: cost = 55.927764892578125\n", "The 138th loop: cost = 61.72763442993164\n", "The 139th loop: cost = 65.52244567871094\n", "The 140th loop: cost = 68.9594955444336\n", "The 141th loop: cost = 62.207786560058594\n", "The 142th loop: cost = 56.87116241455078\n", "The 143th loop: cost = 53.185302734375\n", "The 144th loop: cost = 55.799827575683594\n", "The 145th loop: cost = 61.88337326049805\n", "The 146th loop: cost = 57.76947784423828\n", "The 147th loop: cost = 56.63722229003906\n", "The 148th loop: cost = 56.025543212890625\n", "The 149th loop: cost = 46.5152587890625\n", "The 150th loop: cost = 66.38294982910156\n", "The 151th loop: cost = 50.78179168701172\n", "The 152th loop: cost = 66.07234954833984\n", "The 153th loop: cost = 66.81523132324219\n", "The 154th loop: cost = 52.40244674682617\n", "The 155th loop: cost = 59.965694427490234\n", "The 156th loop: cost = 58.33021545410156\n", "The 157th loop: cost = 60.124786376953125\n", "The 158th loop: cost = 71.0118408203125\n", "The 159th loop: cost = 68.70465087890625\n", "The 160th loop: cost = 54.64329147338867\n", "The 161th loop: cost = 47.46503448486328\n", "The 162th loop: cost = 56.8016357421875\n", "The 163th loop: cost = 74.61666107177734\n", "The 164th loop: cost = 53.05900573730469\n", "The 165th loop: cost = 51.4404296875\n", "The 166th loop: cost = 43.441322326660156\n", "The 167th loop: cost = 74.22590637207031\n", "The 168th loop: cost = 60.40146255493164\n", "The 169th loop: cost = 66.98688507080078\n", "The 170th loop: cost = 65.01145935058594\n", "The 171th loop: cost = 63.669525146484375\n", "The 172th loop: cost = 46.90760803222656\n", "The 173th loop: cost = 66.03075408935547\n", "The 174th loop: cost = 62.53619384765625\n", "The 175th loop: cost = 62.61521911621094\n", "The 176th loop: cost = 55.008460998535156\n", "The 177th loop: cost = 53.284461975097656\n", "The 178th loop: cost = 60.45365905761719\n", "The 179th loop: cost = 49.17298889160156\n", "The 180th loop: cost = 62.80970764160156\n", "The 181th loop: cost = 53.69514465332031\n", "The 182th loop: cost = 58.50763702392578\n", "The 183th loop: cost = 54.24440383911133\n", "The 184th loop: cost = 58.369956970214844\n", "The 185th loop: cost = 63.4810905456543\n", "The 186th loop: cost = 42.858062744140625\n", "The 187th loop: cost = 59.9437141418457\n", "The 188th loop: cost = 65.43756103515625\n", "The 189th loop: cost = 51.30718231201172\n", "The 190th loop: cost = 65.73023223876953\n", "The 191th loop: cost = 58.860904693603516\n", "The 192th loop: cost = 64.50897216796875\n", "The 193th loop: cost = 56.61001205444336\n", "The 194th loop: cost = 61.334495544433594\n", "The 195th loop: cost = 72.34967041015625\n", "The 196th loop: cost = 62.494625091552734\n", "The 197th loop: cost = 73.3383560180664\n", "The 198th loop: cost = 64.64531707763672\n", "The 199th loop: cost = 55.58013153076172\n", "The 200th loop: cost = 57.183067321777344\n", "The 201th loop: cost = 73.1240463256836\n", "The 202th loop: cost = 53.789974212646484\n", "The 203th loop: cost = 50.70671844482422\n", "The 204th loop: cost = 63.60955047607422\n", "The 205th loop: cost = 50.10216522216797\n", "The 206th loop: cost = 59.71902084350586\n", "The 207th loop: cost = 61.32907485961914\n", "The 208th loop: cost = 65.83685302734375\n", "The 209th loop: cost = 62.41783905029297\n", "The 210th loop: cost = 45.002479553222656\n", "The 211th loop: cost = 52.55767822265625\n", "The 212th loop: cost = 69.43832397460938\n", "The 213th loop: cost = 67.85517883300781\n", "The 214th loop: cost = 50.1734733581543\n", "The 215th loop: cost = 61.89696502685547\n", "The 216th loop: cost = 64.19698333740234\n", "The 217th loop: cost = 81.766357421875\n", "The 218th loop: cost = 53.024505615234375\n", "The 219th loop: cost = 59.650665283203125\n", "The 220th loop: cost = 60.22945785522461\n", "The 221th loop: cost = 55.752479553222656\n", "The 222th loop: cost = 54.064979553222656\n", "The 223th loop: cost = 69.17664337158203\n", "The 224th loop: cost = 65.38150024414062\n", "The 225th loop: cost = 52.41304397583008\n", "The 226th loop: cost = 45.638763427734375\n", "The 227th loop: cost = 51.951393127441406\n", "The 228th loop: cost = 49.19563293457031\n", "The 229th loop: cost = 49.35859680175781\n", "The 230th loop: cost = 58.35985565185547\n", "The 231th loop: cost = 60.47540283203125\n", "The 232th loop: cost = 58.1234016418457\n", "The 233th loop: cost = 58.846099853515625\n", "The 234th loop: cost = 68.73612213134766\n", "The 235th loop: cost = 60.34048843383789\n", "The 236th loop: cost = 63.240386962890625\n", "The 237th loop: cost = 64.62705993652344\n", "The 238th loop: cost = 59.183937072753906\n", "The 239th loop: cost = 54.7056770324707\n", "The 240th loop: cost = 50.8890495300293\n", "The 241th loop: cost = 61.95606231689453\n", "The 242th loop: cost = 73.02836608886719\n", "The 243th loop: cost = 57.38334655761719\n", "The 244th loop: cost = 63.90660095214844\n", "The 245th loop: cost = 52.20075988769531\n", "The 246th loop: cost = 52.77891159057617\n", "The 247th loop: cost = 52.32957458496094\n", "The 248th loop: cost = 58.113746643066406\n", "The 249th loop: cost = 58.65835952758789\n", "The 250th loop: cost = 60.83181381225586\n", "The 251th loop: cost = 48.445579528808594\n", "The 252th loop: cost = 65.2576904296875\n", "The 253th loop: cost = 60.700538635253906\n", "The 254th loop: cost = 64.58747100830078\n", "The 255th loop: cost = 47.112770080566406\n", "The 256th loop: cost = 57.13096618652344\n", "The 257th loop: cost = 55.112213134765625\n", "The 258th loop: cost = 50.946598052978516\n", "The 259th loop: cost = 52.57554626464844\n", "The 260th loop: cost = 38.77876281738281\n", "The 261th loop: cost = 56.707000732421875\n", "The 262th loop: cost = 58.71125793457031\n", "The 263th loop: cost = 51.460052490234375\n", "The 264th loop: cost = 53.10332489013672\n", "The 265th loop: cost = 65.37247467041016\n", "The 266th loop: cost = 52.024566650390625\n", "The 267th loop: cost = 55.146697998046875\n", "The 268th loop: cost = 72.01290893554688\n", "The 269th loop: cost = 60.41651153564453\n", "The 270th loop: cost = 51.11931610107422\n", "The 271th loop: cost = 69.84495544433594\n", "The 272th loop: cost = 79.4796142578125\n", "The 273th loop: cost = 70.91451263427734\n", "The 274th loop: cost = 47.42708969116211\n", "The 275th loop: cost = 57.718666076660156\n", "The 276th loop: cost = 55.419578552246094\n", "The 277th loop: cost = 56.22295379638672\n", "The 278th loop: cost = 46.01203536987305\n", "The 279th loop: cost = 51.634010314941406\n", "The 280th loop: cost = 71.2332763671875\n", "The 281th loop: cost = 56.9830322265625\n", "The 282th loop: cost = 60.007972717285156\n", "The 283th loop: cost = 56.203399658203125\n", "The 284th loop: cost = 67.49736022949219\n", "The 285th loop: cost = 57.86808395385742\n", "The 286th loop: cost = 58.611656188964844\n", "The 287th loop: cost = 47.498687744140625\n", "The 288th loop: cost = 59.76153564453125\n", "The 289th loop: cost = 63.114131927490234\n", "The 290th loop: cost = 65.88532257080078\n", "The 291th loop: cost = 56.496978759765625\n", "The 292th loop: cost = 73.53567504882812\n", "The 293th loop: cost = 63.26496124267578\n", "The 294th loop: cost = 75.81116485595703\n", "The 295th loop: cost = 59.62079620361328\n", "The 296th loop: cost = 63.02192687988281\n", "The 297th loop: cost = 63.74041748046875\n", "The 298th loop: cost = 53.398155212402344\n", "The 299th loop: cost = 57.54885482788086\n", "The 300th loop: cost = 79.3299331665039\n", "The 301th loop: cost = 52.14875793457031\n", "The 302th loop: cost = 56.937828063964844\n", "The 303th loop: cost = 49.46385192871094\n", "The 304th loop: cost = 57.621116638183594\n", "The 305th loop: cost = 53.18559646606445\n", "The 306th loop: cost = 63.219451904296875\n", "The 307th loop: cost = 58.36524200439453\n", "The 308th loop: cost = 66.24996948242188\n", "The 309th loop: cost = 66.86483764648438\n", "The 310th loop: cost = 64.42019653320312\n", "The 311th loop: cost = 56.318992614746094\n", "The 312th loop: cost = 60.409217834472656\n", "The 313th loop: cost = 41.883453369140625\n", "The 314th loop: cost = 54.28166961669922\n", "The 315th loop: cost = 61.33435821533203\n", "The 316th loop: cost = 56.30052947998047\n", "The 317th loop: cost = 68.86705017089844\n", "The 318th loop: cost = 61.00946807861328\n", "The 319th loop: cost = 62.60044860839844\n", "The 320th loop: cost = 56.9019889831543\n", "The 321th loop: cost = 68.91487884521484\n", "The 322th loop: cost = 66.39886474609375\n", "The 323th loop: cost = 71.94099426269531\n", "The 324th loop: cost = 60.78113555908203\n", "The 325th loop: cost = 84.19796752929688\n", "The 326th loop: cost = 56.651832580566406\n", "The 327th loop: cost = 51.57605743408203\n", "The 328th loop: cost = 59.427032470703125\n", "The 329th loop: cost = 61.68263244628906\n", "The 330th loop: cost = 64.54528045654297\n", "The 331th loop: cost = 55.15398406982422\n", "The 332th loop: cost = 62.501800537109375\n", "The 333th loop: cost = 70.41109466552734\n", "The 334th loop: cost = 52.672332763671875\n", "The 335th loop: cost = 44.40882873535156\n", "The 336th loop: cost = 53.98280334472656\n", "The 337th loop: cost = 62.327171325683594\n", "The 338th loop: cost = 58.57856369018555\n", "The 339th loop: cost = 58.67051315307617\n", "The 340th loop: cost = 49.004371643066406\n", "The 341th loop: cost = 56.28368377685547\n", "The 342th loop: cost = 54.04119873046875\n", "The 343th loop: cost = 54.74010467529297\n", "The 344th loop: cost = 51.65306854248047\n", "The 345th loop: cost = 70.55154418945312\n", "The 346th loop: cost = 53.091278076171875\n", "The 347th loop: cost = 62.30476379394531\n", "The 348th loop: cost = 56.800559997558594\n", "The 349th loop: cost = 72.47523498535156\n", "The 350th loop: cost = 52.22042465209961\n", "The 351th loop: cost = 52.860633850097656\n", "The 352th loop: cost = 67.42330932617188\n", "The 353th loop: cost = 60.117591857910156\n", "The 354th loop: cost = 62.50587844848633\n", "The 355th loop: cost = 62.7447509765625\n", "The 356th loop: cost = 57.48484802246094\n", "The 357th loop: cost = 67.18836975097656\n", "The 358th loop: cost = 45.176788330078125\n", "The 359th loop: cost = 63.59034729003906\n", "The 360th loop: cost = 72.98168182373047\n", "The 361th loop: cost = 50.336692810058594\n", "The 362th loop: cost = 59.82227325439453\n", "The 363th loop: cost = 52.53252410888672\n", "The 364th loop: cost = 62.481727600097656\n", "The 365th loop: cost = 72.44904327392578\n", "The 366th loop: cost = 48.20222854614258\n", "The 367th loop: cost = 76.11503601074219\n", "The 368th loop: cost = 58.48685836791992\n", "The 369th loop: cost = 56.952213287353516\n", "The 370th loop: cost = 60.675575256347656\n", "The 371th loop: cost = 50.61027526855469\n", "The 372th loop: cost = 51.048301696777344\n", "The 373th loop: cost = 64.39750671386719\n", "The 374th loop: cost = 61.342384338378906\n", "The 375th loop: cost = 49.462791442871094\n", "The 376th loop: cost = 54.925228118896484\n", "The 377th loop: cost = 53.37159729003906\n", "The 378th loop: cost = 70.70702362060547\n", "The 379th loop: cost = 69.30181884765625\n", "The 380th loop: cost = 72.95569610595703\n", "The 381th loop: cost = 62.847660064697266\n", "The 382th loop: cost = 41.937950134277344\n", "The 383th loop: cost = 61.9610595703125\n", "The 384th loop: cost = 67.49378967285156\n", "The 385th loop: cost = 51.21946716308594\n", "The 386th loop: cost = 66.99583435058594\n", "The 387th loop: cost = 56.33025360107422\n", "The 388th loop: cost = 54.27286148071289\n", "The 389th loop: cost = 59.96839141845703\n", "The 390th loop: cost = 64.24610137939453\n", "The 391th loop: cost = 60.47202682495117\n", "The 392th loop: cost = 53.694366455078125\n", "The 393th loop: cost = 48.444190979003906\n", "The 394th loop: cost = 51.31563949584961\n", "The 395th loop: cost = 52.52301025390625\n", "The 396th loop: cost = 49.179847717285156\n", "The 397th loop: cost = 60.20166778564453\n", "The 398th loop: cost = 55.87204360961914\n", "The 399th loop: cost = 53.739341735839844\n", "The 400th loop: cost = 51.51846694946289\n", "The 401th loop: cost = 58.99949645996094\n", "The 402th loop: cost = 65.6956787109375\n", "The 403th loop: cost = 56.236663818359375\n", "The 404th loop: cost = 61.408607482910156\n", "The 405th loop: cost = 64.99950408935547\n", "The 406th loop: cost = 51.39939880371094\n", "The 407th loop: cost = 51.66535949707031\n", "The 408th loop: cost = 57.044525146484375\n", "The 409th loop: cost = 65.37239837646484\n", "The 410th loop: cost = 65.73298645019531\n", "The 411th loop: cost = 70.53868865966797\n", "The 412th loop: cost = 59.567325592041016\n", "The 413th loop: cost = 50.74287033081055\n", "The 414th loop: cost = 52.38886260986328\n", "The 415th loop: cost = 48.10936737060547\n", "The 416th loop: cost = 63.80977249145508\n", "The 417th loop: cost = 55.369041442871094\n", "The 418th loop: cost = 61.18834686279297\n", "The 419th loop: cost = 61.45041275024414\n", "The 420th loop: cost = 62.102516174316406\n", "The 421th loop: cost = 64.35285186767578\n", "The 422th loop: cost = 54.57212829589844\n", "The 423th loop: cost = 59.85585021972656\n", "The 424th loop: cost = 60.87288284301758\n", "The 425th loop: cost = 64.96694946289062\n", "The 426th loop: cost = 79.42605590820312\n", "The 427th loop: cost = 65.56221008300781\n", "The 428th loop: cost = 67.82475280761719\n", "The 429th loop: cost = 61.9061279296875\n", "The 430th loop: cost = 63.901607513427734\n", "The 431th loop: cost = 53.7420768737793\n", "The 432th loop: cost = 56.78802490234375\n", "The 433th loop: cost = 69.89092254638672\n", "The 434th loop: cost = 57.62702178955078\n", "The 435th loop: cost = 51.04869079589844\n", "The 436th loop: cost = 56.82929229736328\n", "The 437th loop: cost = 61.78980255126953\n", "The 438th loop: cost = 69.14830017089844\n", "The 439th loop: cost = 78.70620727539062\n", "The 440th loop: cost = 69.90959167480469\n", "The 441th loop: cost = 57.648712158203125\n", "The 442th loop: cost = 57.787071228027344\n", "The 443th loop: cost = 53.140663146972656\n", "The 444th loop: cost = 64.56298828125\n", "The 445th loop: cost = 51.46482849121094\n", "The 446th loop: cost = 66.99726867675781\n", "The 447th loop: cost = 73.65345764160156\n", "The 448th loop: cost = 58.2562141418457\n", "The 449th loop: cost = 65.26129150390625\n", "The 450th loop: cost = 62.07122039794922\n", "The 451th loop: cost = 52.15208435058594\n", "The 452th loop: cost = 53.203739166259766\n", "The 453th loop: cost = 63.45155715942383\n", "The 454th loop: cost = 65.78656005859375\n", "The 455th loop: cost = 57.80524444580078\n", "The 456th loop: cost = 68.7646484375\n", "The 457th loop: cost = 55.653778076171875\n", "The 458th loop: cost = 67.73624420166016\n", "The 459th loop: cost = 51.20273971557617\n", "The 460th loop: cost = 79.47012329101562\n", "The 461th loop: cost = 67.248779296875\n", "The 462th loop: cost = 54.243141174316406\n", "The 463th loop: cost = 69.71170043945312\n", "The 464th loop: cost = 74.31404113769531\n", "The 465th loop: cost = 59.16432571411133\n", "The 466th loop: cost = 64.0733642578125\n", "The 467th loop: cost = 56.76310348510742\n", "The 468th loop: cost = 56.71773910522461\n", "The 469th loop: cost = 57.49497604370117\n", "The 470th loop: cost = 60.461612701416016\n", "The 471th loop: cost = 49.59332275390625\n", "The 472th loop: cost = 66.24231719970703\n", "The 473th loop: cost = 57.99989318847656\n", "The 474th loop: cost = 55.8396110534668\n", "The 475th loop: cost = 59.182743072509766\n", "The 476th loop: cost = 69.91580963134766\n", "The 477th loop: cost = 60.09026336669922\n", "The 478th loop: cost = 53.76929473876953\n", "The 479th loop: cost = 59.99827194213867\n", "The 480th loop: cost = 74.02774810791016\n", "The 481th loop: cost = 47.240440368652344\n", "The 482th loop: cost = 61.24058532714844\n", "The 483th loop: cost = 75.38238525390625\n", "The 484th loop: cost = 61.895530700683594\n", "The 485th loop: cost = 49.636817932128906\n", "The 486th loop: cost = 60.888370513916016\n", "The 487th loop: cost = 70.17607116699219\n", "The 488th loop: cost = 67.58502197265625\n", "The 489th loop: cost = 57.104087829589844\n", "The 490th loop: cost = 62.337825775146484\n", "The 491th loop: cost = 70.24437713623047\n", "The 492th loop: cost = 45.097381591796875\n", "The 493th loop: cost = 55.23890686035156\n", "The 494th loop: cost = 70.31510925292969\n", "The 495th loop: cost = 61.193050384521484\n", "The 496th loop: cost = 58.248287200927734\n", "The 497th loop: cost = 80.89998626708984\n", "The 498th loop: cost = 50.94919967651367\n", "The 499th loop: cost = 50.39392852783203\n", "The 500th loop: cost = 57.632930755615234\n", "The 501th loop: cost = 56.60594177246094\n", "The 502th loop: cost = 57.11473083496094\n", "The 503th loop: cost = 70.07777404785156\n", "The 504th loop: cost = 67.85508728027344\n", "The 505th loop: cost = 68.64115905761719\n", "The 506th loop: cost = 56.64015579223633\n", "The 507th loop: cost = 64.13667297363281\n", "The 508th loop: cost = 63.512115478515625\n", "The 509th loop: cost = 55.24945831298828\n", "The 510th loop: cost = 56.624847412109375\n", "The 511th loop: cost = 52.10301208496094\n", "The 512th loop: cost = 57.34687805175781\n", "The 513th loop: cost = 63.86498260498047\n", "The 514th loop: cost = 69.96769714355469\n", "The 515th loop: cost = 61.37192153930664\n", "The 516th loop: cost = 62.847023010253906\n", "The 517th loop: cost = 66.02042388916016\n", "The 518th loop: cost = 68.6531982421875\n", "The 519th loop: cost = 61.26521301269531\n", "The 520th loop: cost = 60.33008575439453\n", "The 521th loop: cost = 51.29029083251953\n", "The 522th loop: cost = 60.039939880371094\n", "The 523th loop: cost = 59.184783935546875\n", "The 524th loop: cost = 58.15022277832031\n", "The 525th loop: cost = 58.483375549316406\n", "The 526th loop: cost = 60.966121673583984\n", "The 527th loop: cost = 72.34638977050781\n", "The 528th loop: cost = 60.39653015136719\n", "The 529th loop: cost = 68.69203186035156\n", "The 530th loop: cost = 61.15922927856445\n", "The 531th loop: cost = 70.59251403808594\n", "The 532th loop: cost = 65.35353088378906\n", "The 533th loop: cost = 56.540225982666016\n", "The 534th loop: cost = 59.055747985839844\n", "The 535th loop: cost = 59.1749267578125\n", "The 536th loop: cost = 54.085205078125\n", "The 537th loop: cost = 63.40549850463867\n", "The 538th loop: cost = 42.748291015625\n", "The 539th loop: cost = 58.585777282714844\n", "The 540th loop: cost = 50.61857223510742\n", "The 541th loop: cost = 71.06787872314453\n", "The 542th loop: cost = 62.978946685791016\n", "The 543th loop: cost = 74.17735290527344\n", "The 544th loop: cost = 56.25099182128906\n", "The 545th loop: cost = 59.522926330566406\n", "The 546th loop: cost = 62.45022201538086\n", "The 547th loop: cost = 58.42803192138672\n", "The 548th loop: cost = 72.1705322265625\n", "The 549th loop: cost = 44.12153625488281\n", "The 550th loop: cost = 68.760498046875\n", "The 551th loop: cost = 54.0662841796875\n", "The 552th loop: cost = 80.45494079589844\n", "The 553th loop: cost = 58.75825500488281\n", "The 554th loop: cost = 54.352447509765625\n", "The 555th loop: cost = 60.642005920410156\n", "The 556th loop: cost = 64.08287048339844\n", "The 557th loop: cost = 60.579898834228516\n", "The 558th loop: cost = 65.767822265625\n", "The 559th loop: cost = 53.41667175292969\n", "The 560th loop: cost = 65.25418090820312\n", "The 561th loop: cost = 80.45996856689453\n", "The 562th loop: cost = 61.74468994140625\n", "The 563th loop: cost = 56.25495147705078\n", "The 564th loop: cost = 51.27354431152344\n", "The 565th loop: cost = 71.54791259765625\n", "The 566th loop: cost = 52.218231201171875\n", "The 567th loop: cost = 67.7701187133789\n", "The 568th loop: cost = 58.72058868408203\n", "The 569th loop: cost = 57.52311706542969\n", "The 570th loop: cost = 50.801368713378906\n", "The 571th loop: cost = 59.65142822265625\n", "The 572th loop: cost = 62.30064392089844\n", "The 573th loop: cost = 62.51397705078125\n", "The 574th loop: cost = 68.09825897216797\n", "The 575th loop: cost = 77.41152954101562\n", "The 576th loop: cost = 47.0838623046875\n", "The 577th loop: cost = 51.62647247314453\n", "The 578th loop: cost = 58.8077278137207\n", "The 579th loop: cost = 69.68424224853516\n", "The 580th loop: cost = 56.07255554199219\n", "The 581th loop: cost = 49.02943801879883\n", "The 582th loop: cost = 60.594627380371094\n", "The 583th loop: cost = 56.7382926940918\n", "The 584th loop: cost = 76.00526428222656\n", "The 585th loop: cost = 60.4931526184082\n", "The 586th loop: cost = 73.86618041992188\n", "The 587th loop: cost = 62.68733215332031\n", "The 588th loop: cost = 71.23324584960938\n", "The 589th loop: cost = 69.49091339111328\n", "The 590th loop: cost = 58.35586929321289\n", "The 591th loop: cost = 59.25462341308594\n", "The 592th loop: cost = 56.268028259277344\n", "The 593th loop: cost = 66.7418441772461\n", "The 594th loop: cost = 56.07438278198242\n", "The 595th loop: cost = 66.64259338378906\n", "The 596th loop: cost = 71.79057312011719\n", "The 597th loop: cost = 57.91887283325195\n", "The 598th loop: cost = 58.218475341796875\n", "The 599th loop: cost = 69.68529510498047\n", "The 600th loop: cost = 60.03227996826172\n", "The 601th loop: cost = 65.75436401367188\n", "The 602th loop: cost = 54.062076568603516\n", "The 603th loop: cost = 69.48331451416016\n", "The 604th loop: cost = 59.94526290893555\n", "The 605th loop: cost = 55.194908142089844\n", "The 606th loop: cost = 53.00829315185547\n", "The 607th loop: cost = 65.32778930664062\n", "The 608th loop: cost = 57.99774932861328\n", "The 609th loop: cost = 51.34892654418945\n", "The 610th loop: cost = 66.9803466796875\n", "The 611th loop: cost = 67.1240005493164\n", "The 612th loop: cost = 63.84356689453125\n", "The 613th loop: cost = 66.71812438964844\n", "The 614th loop: cost = 55.672279357910156\n", "The 615th loop: cost = 55.00539779663086\n", "The 616th loop: cost = 51.232574462890625\n", "The 617th loop: cost = 54.306819915771484\n", "The 618th loop: cost = 50.473487854003906\n", "The 619th loop: cost = 64.15399169921875\n", "The 620th loop: cost = 58.174583435058594\n", "The 621th loop: cost = 64.03218841552734\n", "The 622th loop: cost = 66.32664489746094\n", "The 623th loop: cost = 63.723114013671875\n", "The 624th loop: cost = 60.903907775878906\n", "The 625th loop: cost = 60.85920715332031\n", "The 626th loop: cost = 55.818119049072266\n", "The 627th loop: cost = 66.95893859863281\n", "The 628th loop: cost = 38.20664978027344\n", "The 629th loop: cost = 71.58421325683594\n", "The 630th loop: cost = 48.27686309814453\n", "The 631th loop: cost = 69.1531982421875\n", "The 632th loop: cost = 58.666446685791016\n", "The 633th loop: cost = 65.63478088378906\n", "The 634th loop: cost = 56.98198318481445\n", "The 635th loop: cost = 64.3348388671875\n", "The 636th loop: cost = 63.461856842041016\n", "The 637th loop: cost = 43.935821533203125\n", "The 638th loop: cost = 69.95521545410156\n", "The 639th loop: cost = 68.69161224365234\n", "The 640th loop: cost = 60.268890380859375\n", "The 641th loop: cost = 61.413490295410156\n", "The 642th loop: cost = 65.15187072753906\n", "The 643th loop: cost = 53.44505310058594\n", "The 644th loop: cost = 62.41278839111328\n", "The 645th loop: cost = 46.294532775878906\n", "The 646th loop: cost = 67.42387390136719\n", "The 647th loop: cost = 61.07854461669922\n", "The 648th loop: cost = 73.98352813720703\n", "The 649th loop: cost = 65.99217224121094\n", "The 650th loop: cost = 59.94572830200195\n", "The 651th loop: cost = 62.613006591796875\n", "The 652th loop: cost = 61.46123504638672\n", "The 653th loop: cost = 67.50709533691406\n", "The 654th loop: cost = 49.087589263916016\n", "The 655th loop: cost = 61.872352600097656\n", "The 656th loop: cost = 64.57543182373047\n", "The 657th loop: cost = 57.32863235473633\n", "The 658th loop: cost = 62.63363265991211\n", "The 659th loop: cost = 46.70751190185547\n", "The 660th loop: cost = 76.9337158203125\n", "The 661th loop: cost = 59.239009857177734\n", "The 662th loop: cost = 53.61960983276367\n", "The 663th loop: cost = 50.59546661376953\n", "The 664th loop: cost = 50.6583251953125\n", "The 665th loop: cost = 48.126304626464844\n", "The 666th loop: cost = 71.30963134765625\n", "The 667th loop: cost = 58.226383209228516\n", "The 668th loop: cost = 61.32634735107422\n", "The 669th loop: cost = 53.244049072265625\n", "The 670th loop: cost = 46.89533996582031\n", "The 671th loop: cost = 67.13848114013672\n", "The 672th loop: cost = 45.29656219482422\n", "The 673th loop: cost = 51.92171096801758\n", "The 674th loop: cost = 70.42264556884766\n", "The 675th loop: cost = 69.201171875\n", "The 676th loop: cost = 65.133056640625\n", "The 677th loop: cost = 41.463478088378906\n", "The 678th loop: cost = 54.47877502441406\n", "The 679th loop: cost = 60.22745132446289\n", "The 680th loop: cost = 63.39464569091797\n", "The 681th loop: cost = 71.66073608398438\n", "The 682th loop: cost = 63.49876403808594\n", "The 683th loop: cost = 63.885986328125\n", "The 684th loop: cost = 53.97930908203125\n", "The 685th loop: cost = 53.71277618408203\n", "The 686th loop: cost = 58.6013069152832\n", "The 687th loop: cost = 41.68779373168945\n", "The 688th loop: cost = 49.010746002197266\n", "The 689th loop: cost = 60.974769592285156\n", "The 690th loop: cost = 72.07582092285156\n", "The 691th loop: cost = 65.27897644042969\n", "The 692th loop: cost = 64.99346923828125\n", "The 693th loop: cost = 59.63922882080078\n", "The 694th loop: cost = 59.39740753173828\n", "The 695th loop: cost = 68.08775329589844\n", "The 696th loop: cost = 55.31932830810547\n", "The 697th loop: cost = 61.07698059082031\n", "The 698th loop: cost = 46.134422302246094\n", "The 699th loop: cost = 64.85006713867188\n", "The 700th loop: cost = 57.90285873413086\n", "The 701th loop: cost = 66.18937683105469\n", "The 702th loop: cost = 72.18919372558594\n", "The 703th loop: cost = 59.234825134277344\n", "The 704th loop: cost = 47.85526657104492\n", "The 705th loop: cost = 52.019317626953125\n", "The 706th loop: cost = 54.92610168457031\n", "The 707th loop: cost = 60.27098083496094\n", "The 708th loop: cost = 58.87619400024414\n", "The 709th loop: cost = 45.42478942871094\n", "The 710th loop: cost = 58.34151077270508\n", "The 711th loop: cost = 55.48933792114258\n", "The 712th loop: cost = 71.05677795410156\n", "The 713th loop: cost = 59.74193572998047\n", "The 714th loop: cost = 37.54643249511719\n", "The 715th loop: cost = 80.42985534667969\n", "The 716th loop: cost = 57.14447021484375\n", "The 717th loop: cost = 67.66773223876953\n", "The 718th loop: cost = 68.91903686523438\n", "The 719th loop: cost = 65.44161987304688\n", "The 720th loop: cost = 68.22990417480469\n", "The 721th loop: cost = 64.72222900390625\n", "The 722th loop: cost = 57.422183990478516\n", "The 723th loop: cost = 66.78669738769531\n", "The 724th loop: cost = 65.4714126586914\n", "The 725th loop: cost = 68.05103302001953\n", "The 726th loop: cost = 56.37992477416992\n", "The 727th loop: cost = 57.16956329345703\n", "The 728th loop: cost = 75.71006774902344\n", "The 729th loop: cost = 62.112545013427734\n", "The 730th loop: cost = 67.58999633789062\n", "The 731th loop: cost = 63.30324935913086\n", "The 732th loop: cost = 61.19719314575195\n", "The 733th loop: cost = 58.23362731933594\n", "The 734th loop: cost = 63.64529800415039\n", "The 735th loop: cost = 65.17549133300781\n", "The 736th loop: cost = 63.595184326171875\n", "The 737th loop: cost = 49.758644104003906\n", "The 738th loop: cost = 50.80361557006836\n", "The 739th loop: cost = 68.29106140136719\n", "The 740th loop: cost = 60.763607025146484\n", "The 741th loop: cost = 68.22676086425781\n", "The 742th loop: cost = 61.309688568115234\n", "The 743th loop: cost = 62.917362213134766\n", "The 744th loop: cost = 75.09770965576172\n", "The 745th loop: cost = 61.024314880371094\n", "The 746th loop: cost = 54.196990966796875\n", "The 747th loop: cost = 61.83252716064453\n", "The 748th loop: cost = 66.11692810058594\n", "The 749th loop: cost = 62.017818450927734\n", "The 750th loop: cost = 56.23231506347656\n", "The 751th loop: cost = 62.06393051147461\n", "The 752th loop: cost = 49.205806732177734\n", "The 753th loop: cost = 53.65357971191406\n", "The 754th loop: cost = 64.93054962158203\n", "The 755th loop: cost = 61.22738265991211\n", "The 756th loop: cost = 67.45997619628906\n", "The 757th loop: cost = 64.15496063232422\n", "The 758th loop: cost = 68.32644653320312\n", "The 759th loop: cost = 60.04298400878906\n", "The 760th loop: cost = 62.0593376159668\n", "The 761th loop: cost = 56.68632507324219\n", "The 762th loop: cost = 58.930908203125\n", "The 763th loop: cost = 54.44331359863281\n", "The 764th loop: cost = 61.146785736083984\n", "The 765th loop: cost = 57.17908477783203\n", "The 766th loop: cost = 53.15829849243164\n", "The 767th loop: cost = 51.718204498291016\n", "The 768th loop: cost = 63.34617614746094\n", "The 769th loop: cost = 66.93527221679688\n", "The 770th loop: cost = 58.1405029296875\n", "The 771th loop: cost = 64.19816589355469\n", "The 772th loop: cost = 54.4716796875\n", "The 773th loop: cost = 57.41063690185547\n", "The 774th loop: cost = 62.47785186767578\n", "The 775th loop: cost = 53.74163818359375\n", "The 776th loop: cost = 68.90060424804688\n", "The 777th loop: cost = 61.4940299987793\n", "The 778th loop: cost = 60.39570236206055\n", "The 779th loop: cost = 50.22677230834961\n", "The 780th loop: cost = 61.65438461303711\n", "The 781th loop: cost = 54.52874755859375\n", "The 782th loop: cost = 59.485389709472656\n", "The 783th loop: cost = 66.88592529296875\n", "The 784th loop: cost = 67.03627014160156\n", "The 785th loop: cost = 54.66975402832031\n", "The 786th loop: cost = 61.661346435546875\n", "The 787th loop: cost = 75.10149383544922\n", "The 788th loop: cost = 69.43092346191406\n", "The 789th loop: cost = 51.339149475097656\n", "The 790th loop: cost = 61.96920394897461\n", "The 791th loop: cost = 51.087974548339844\n", "The 792th loop: cost = 61.53076171875\n", "The 793th loop: cost = 65.4311294555664\n", "The 794th loop: cost = 56.23487091064453\n", "The 795th loop: cost = 60.406494140625\n", "The 796th loop: cost = 55.39744567871094\n", "The 797th loop: cost = 50.36665725708008\n", "The 798th loop: cost = 67.6755142211914\n", "The 799th loop: cost = 64.37461853027344\n", "The 800th loop: cost = 62.26978302001953\n", "The 801th loop: cost = 53.94294357299805\n", "The 802th loop: cost = 64.11386108398438\n", "The 803th loop: cost = 50.923641204833984\n", "The 804th loop: cost = 47.649009704589844\n", "The 805th loop: cost = 64.34355163574219\n", "The 806th loop: cost = 66.38040924072266\n", "The 807th loop: cost = 62.37858963012695\n", "The 808th loop: cost = 49.105247497558594\n", "The 809th loop: cost = 45.98721694946289\n", "The 810th loop: cost = 63.36442184448242\n", "The 811th loop: cost = 59.92629623413086\n", "The 812th loop: cost = 83.91084289550781\n", "The 813th loop: cost = 65.12483215332031\n", "The 814th loop: cost = 69.02963256835938\n", "The 815th loop: cost = 50.91722106933594\n", "The 816th loop: cost = 66.24784851074219\n", "The 817th loop: cost = 73.09918212890625\n", "The 818th loop: cost = 64.25627136230469\n", "The 819th loop: cost = 65.4522933959961\n", "The 820th loop: cost = 63.73858642578125\n", "The 821th loop: cost = 65.91897583007812\n", "The 822th loop: cost = 61.23744201660156\n", "The 823th loop: cost = 50.933692932128906\n", "The 824th loop: cost = 60.705108642578125\n", "The 825th loop: cost = 68.72078704833984\n", "The 826th loop: cost = 58.554588317871094\n", "The 827th loop: cost = 49.912174224853516\n", "The 828th loop: cost = 44.60301208496094\n", "The 829th loop: cost = 56.841552734375\n", "The 830th loop: cost = 51.98286819458008\n", "The 831th loop: cost = 55.92287826538086\n", "The 832th loop: cost = 64.57353210449219\n", "The 833th loop: cost = 69.2176513671875\n", "The 834th loop: cost = 70.87045288085938\n", "The 835th loop: cost = 56.65779495239258\n", "The 836th loop: cost = 67.15110778808594\n", "The 837th loop: cost = 57.070404052734375\n", "The 838th loop: cost = 75.57210540771484\n", "The 839th loop: cost = 55.62517166137695\n", "The 840th loop: cost = 56.249359130859375\n", "The 841th loop: cost = 52.69063186645508\n", "The 842th loop: cost = 48.82676696777344\n", "The 843th loop: cost = 49.72417449951172\n", "The 844th loop: cost = 63.58631896972656\n", "The 845th loop: cost = 56.05778884887695\n", "The 846th loop: cost = 62.1140251159668\n", "The 847th loop: cost = 69.35237121582031\n", "The 848th loop: cost = 47.67665100097656\n", "The 849th loop: cost = 57.0883903503418\n", "The 850th loop: cost = 48.27513885498047\n", "The 851th loop: cost = 65.086181640625\n", "The 852th loop: cost = 61.36898422241211\n", "The 853th loop: cost = 71.80052947998047\n", "The 854th loop: cost = 63.02532958984375\n", "The 855th loop: cost = 73.88853454589844\n", "The 856th loop: cost = 47.680904388427734\n", "The 857th loop: cost = 62.33522033691406\n", "The 858th loop: cost = 69.010986328125\n", "The 859th loop: cost = 56.182655334472656\n", "The 860th loop: cost = 55.85148620605469\n", "The 861th loop: cost = 54.8725700378418\n", "The 862th loop: cost = 68.279052734375\n", "The 863th loop: cost = 66.4348373413086\n", "The 864th loop: cost = 56.9456787109375\n", "The 865th loop: cost = 59.1224365234375\n", "The 866th loop: cost = 69.16487121582031\n", "The 867th loop: cost = 54.2742919921875\n", "The 868th loop: cost = 70.26422119140625\n", "The 869th loop: cost = 53.97895431518555\n", "The 870th loop: cost = 64.64531707763672\n", "The 871th loop: cost = 63.29986572265625\n", "The 872th loop: cost = 56.98324203491211\n", "The 873th loop: cost = 68.33441162109375\n", "The 874th loop: cost = 54.708839416503906\n", "The 875th loop: cost = 58.45317840576172\n", "The 876th loop: cost = 62.20846939086914\n", "The 877th loop: cost = 59.191627502441406\n", "The 878th loop: cost = 56.92621612548828\n", "The 879th loop: cost = 59.84974670410156\n", "The 880th loop: cost = 70.20066833496094\n", "The 881th loop: cost = 65.56111907958984\n", "The 882th loop: cost = 64.94681549072266\n", "The 883th loop: cost = 77.34417724609375\n", "The 884th loop: cost = 57.733646392822266\n", "The 885th loop: cost = 58.76427459716797\n", "The 886th loop: cost = 49.93281173706055\n", "The 887th loop: cost = 49.478248596191406\n", "The 888th loop: cost = 46.49585723876953\n", "The 889th loop: cost = 48.68468475341797\n", "The 890th loop: cost = 56.58705139160156\n", "The 891th loop: cost = 70.24996948242188\n", "The 892th loop: cost = 55.87091064453125\n", "The 893th loop: cost = 59.62804412841797\n", "The 894th loop: cost = 69.14401245117188\n", "The 895th loop: cost = 53.957916259765625\n", "The 896th loop: cost = 63.89292526245117\n", "The 897th loop: cost = 61.09254837036133\n", "The 898th loop: cost = 56.0994758605957\n", "The 899th loop: cost = 71.83291625976562\n", "The 900th loop: cost = 61.2396240234375\n", "The 901th loop: cost = 68.42977905273438\n", "The 902th loop: cost = 52.64563751220703\n", "The 903th loop: cost = 56.49386215209961\n", "The 904th loop: cost = 57.34581756591797\n", "The 905th loop: cost = 63.30569076538086\n", "The 906th loop: cost = 62.596622467041016\n", "The 907th loop: cost = 59.814327239990234\n", "The 908th loop: cost = 75.24529266357422\n", "The 909th loop: cost = 68.0927505493164\n", "The 910th loop: cost = 48.11941146850586\n", "The 911th loop: cost = 57.07241439819336\n", "The 912th loop: cost = 62.239479064941406\n", "The 913th loop: cost = 59.561309814453125\n", "The 914th loop: cost = 49.362892150878906\n", "The 915th loop: cost = 62.81575393676758\n", "The 916th loop: cost = 50.42255401611328\n", "The 917th loop: cost = 61.126182556152344\n", "The 918th loop: cost = 56.42289733886719\n", "The 919th loop: cost = 68.28976440429688\n", "The 920th loop: cost = 62.858306884765625\n", "The 921th loop: cost = 78.13157653808594\n", "The 922th loop: cost = 71.02197265625\n", "The 923th loop: cost = 69.19700622558594\n", "The 924th loop: cost = 63.77534484863281\n", "The 925th loop: cost = 63.01936721801758\n", "The 926th loop: cost = 58.603370666503906\n", "The 927th loop: cost = 63.34837341308594\n", "The 928th loop: cost = 60.11640548706055\n", "The 929th loop: cost = 59.91712951660156\n", "The 930th loop: cost = 57.25080871582031\n", "The 931th loop: cost = 67.50154113769531\n", "The 932th loop: cost = 64.50100708007812\n", "The 933th loop: cost = 43.21034622192383\n", "The 934th loop: cost = 53.940574645996094\n", "The 935th loop: cost = 58.334205627441406\n", "The 936th loop: cost = 57.553348541259766\n", "The 937th loop: cost = 59.115631103515625\n", "The 938th loop: cost = 58.896812438964844\n", "The 939th loop: cost = 46.563995361328125\n", "The 940th loop: cost = 61.96669006347656\n", "The 941th loop: cost = 55.14076232910156\n", "The 942th loop: cost = 46.44012451171875\n", "The 943th loop: cost = 66.70188903808594\n", "The 944th loop: cost = 81.39678955078125\n", "The 945th loop: cost = 55.811737060546875\n", "The 946th loop: cost = 67.44404602050781\n", "The 947th loop: cost = 54.424072265625\n", "The 948th loop: cost = 60.43465805053711\n", "The 949th loop: cost = 50.793678283691406\n", "The 950th loop: cost = 61.299259185791016\n", "The 951th loop: cost = 57.87677764892578\n", "The 952th loop: cost = 65.32917785644531\n", "The 953th loop: cost = 72.41001892089844\n", "The 954th loop: cost = 54.71955871582031\n", "The 955th loop: cost = 59.4691162109375\n", "The 956th loop: cost = 60.67284393310547\n", "The 957th loop: cost = 52.91923904418945\n", "The 958th loop: cost = 58.12800598144531\n", "The 959th loop: cost = 57.872432708740234\n", "The 960th loop: cost = 63.551422119140625\n", "The 961th loop: cost = 62.6978645324707\n", "The 962th loop: cost = 60.784629821777344\n", "The 963th loop: cost = 57.44264221191406\n", "The 964th loop: cost = 71.31773376464844\n", "The 965th loop: cost = 56.04027557373047\n", "The 966th loop: cost = 55.48683547973633\n", "The 967th loop: cost = 56.5162239074707\n", "The 968th loop: cost = 55.73860168457031\n", "The 969th loop: cost = 61.803871154785156\n", "The 970th loop: cost = 55.829872131347656\n", "The 971th loop: cost = 54.08140563964844\n", "The 972th loop: cost = 59.373138427734375\n", "The 973th loop: cost = 63.93317413330078\n", "The 974th loop: cost = 64.42060852050781\n", "The 975th loop: cost = 47.82915115356445\n", "The 976th loop: cost = 50.5689582824707\n", "The 977th loop: cost = 57.651248931884766\n", "The 978th loop: cost = 66.16445922851562\n", "The 979th loop: cost = 74.29376220703125\n", "The 980th loop: cost = 61.98996353149414\n", "The 981th loop: cost = 62.01898956298828\n", "The 982th loop: cost = 57.71089172363281\n", "The 983th loop: cost = 51.97475051879883\n", "The 984th loop: cost = 60.671546936035156\n", "The 985th loop: cost = 59.50635528564453\n", "The 986th loop: cost = 63.471580505371094\n", "The 987th loop: cost = 62.408409118652344\n", "The 988th loop: cost = 69.78659057617188\n", "The 989th loop: cost = 67.35987854003906\n", "The 990th loop: cost = 54.530181884765625\n", "The 991th loop: cost = 59.639217376708984\n", "The 992th loop: cost = 52.804603576660156\n", "The 993th loop: cost = 62.968082427978516\n", "The 994th loop: cost = 55.95489501953125\n", "The 995th loop: cost = 59.95271301269531\n", "The 996th loop: cost = 45.920772552490234\n", "The 997th loop: cost = 55.171730041503906\n", "The 998th loop: cost = 44.48847198486328\n", "The 999th loop: cost = 68.05784606933594\n", "The 1000th loop: cost = 70.53245544433594\n", "The 1001th loop: cost = 59.67778778076172\n", "The 1002th loop: cost = 41.53125762939453\n", "The 1003th loop: cost = 80.38250732421875\n", "The 1004th loop: cost = 59.51887512207031\n", "The 1005th loop: cost = 64.48574829101562\n", "The 1006th loop: cost = 59.80024719238281\n", "The 1007th loop: cost = 65.88877868652344\n", "The 1008th loop: cost = 58.31904983520508\n", "The 1009th loop: cost = 53.951454162597656\n", "The 1010th loop: cost = 66.2021484375\n", "The 1011th loop: cost = 64.30992126464844\n", "The 1012th loop: cost = 47.41436004638672\n", "The 1013th loop: cost = 52.14464569091797\n", "The 1014th loop: cost = 51.500152587890625\n", "The 1015th loop: cost = 58.41123580932617\n", "The 1016th loop: cost = 56.946685791015625\n", "The 1017th loop: cost = 51.76750183105469\n", "The 1018th loop: cost = 56.20977783203125\n", "The 1019th loop: cost = 64.29962158203125\n", "The 1020th loop: cost = 57.19969177246094\n", "The 1021th loop: cost = 55.269615173339844\n", "The 1022th loop: cost = 59.3597412109375\n", "The 1023th loop: cost = 62.07836151123047\n", "The 1024th loop: cost = 61.340911865234375\n", "The 1025th loop: cost = 56.00966262817383\n", "The 1026th loop: cost = 71.41490173339844\n", "The 1027th loop: cost = 55.13294982910156\n", "The 1028th loop: cost = 64.23201751708984\n", "The 1029th loop: cost = 61.42510223388672\n", "The 1030th loop: cost = 60.153228759765625\n", "The 1031th loop: cost = 68.85577392578125\n", "The 1032th loop: cost = 60.25579833984375\n", "The 1033th loop: cost = 66.29611206054688\n", "The 1034th loop: cost = 49.054786682128906\n", "The 1035th loop: cost = 64.96907043457031\n", "The 1036th loop: cost = 57.16764831542969\n", "The 1037th loop: cost = 54.67646789550781\n", "The 1038th loop: cost = 55.975257873535156\n", "The 1039th loop: cost = 54.5573844909668\n", "The 1040th loop: cost = 46.528812408447266\n", "The 1041th loop: cost = 64.84136199951172\n", "The 1042th loop: cost = 62.420562744140625\n", "The 1043th loop: cost = 52.21882629394531\n", "The 1044th loop: cost = 54.90477752685547\n", "The 1045th loop: cost = 50.376976013183594\n", "The 1046th loop: cost = 45.48408508300781\n", "The 1047th loop: cost = 64.57859802246094\n", "The 1048th loop: cost = 49.56245040893555\n", "The 1049th loop: cost = 53.323143005371094\n", "The 1050th loop: cost = 72.79967498779297\n", "The 1051th loop: cost = 68.30572509765625\n", "The 1052th loop: cost = 53.90464782714844\n", "The 1053th loop: cost = 67.35489654541016\n", "The 1054th loop: cost = 54.641273498535156\n", "The 1055th loop: cost = 54.7531852722168\n", "The 1056th loop: cost = 46.877052307128906\n", "The 1057th loop: cost = 59.982269287109375\n", "The 1058th loop: cost = 69.17561340332031\n", "The 1059th loop: cost = 48.926170349121094\n", "The 1060th loop: cost = 55.89366912841797\n", "The 1061th loop: cost = 63.23236083984375\n", "The 1062th loop: cost = 55.48900604248047\n", "The 1063th loop: cost = 50.88222885131836\n", "The 1064th loop: cost = 60.6281623840332\n", "The 1065th loop: cost = 51.06094741821289\n", "The 1066th loop: cost = 58.6422119140625\n", "The 1067th loop: cost = 53.18262481689453\n", "The 1068th loop: cost = 52.866493225097656\n", "The 1069th loop: cost = 56.6112174987793\n", "The 1070th loop: cost = 63.996803283691406\n", "The 1071th loop: cost = 58.010581970214844\n", "The 1072th loop: cost = 54.52090072631836\n", "The 1073th loop: cost = 58.788597106933594\n", "The 1074th loop: cost = 57.24165725708008\n", "The 1075th loop: cost = 60.228511810302734\n", "The 1076th loop: cost = 59.18363952636719\n", "The 1077th loop: cost = 54.81862258911133\n", "The 1078th loop: cost = 52.981895446777344\n", "The 1079th loop: cost = 63.330623626708984\n", "The 1080th loop: cost = 46.858909606933594\n", "The 1081th loop: cost = 52.657958984375\n", "The 1082th loop: cost = 48.493980407714844\n", "The 1083th loop: cost = 52.307674407958984\n", "The 1084th loop: cost = 54.15790557861328\n", "The 1085th loop: cost = 63.087196350097656\n", "The 1086th loop: cost = 47.58373260498047\n", "The 1087th loop: cost = 56.04606246948242\n", "The 1088th loop: cost = 58.244544982910156\n", "The 1089th loop: cost = 61.104286193847656\n", "The 1090th loop: cost = 52.307228088378906\n", "The 1091th loop: cost = 47.57084655761719\n", "The 1092th loop: cost = 44.70101547241211\n", "The 1093th loop: cost = 72.42650604248047\n", "The 1094th loop: cost = 61.34455108642578\n", "The 1095th loop: cost = 53.71638107299805\n", "The 1096th loop: cost = 49.781700134277344\n", "The 1097th loop: cost = 56.59100341796875\n", "The 1098th loop: cost = 64.35298156738281\n", "The 1099th loop: cost = 53.23152160644531\n", "The 1100th loop: cost = 58.24668502807617\n", "The 1101th loop: cost = 65.7840576171875\n", "The 1102th loop: cost = 55.613494873046875\n", "The 1103th loop: cost = 59.13389587402344\n", "The 1104th loop: cost = 64.89373779296875\n", "The 1105th loop: cost = 64.3617935180664\n", "The 1106th loop: cost = 61.44481658935547\n", "The 1107th loop: cost = 55.60468292236328\n", "The 1108th loop: cost = 56.41590881347656\n", "The 1109th loop: cost = 64.43582153320312\n", "The 1110th loop: cost = 61.731689453125\n", "The 1111th loop: cost = 67.06341552734375\n", "The 1112th loop: cost = 61.27488708496094\n", "The 1113th loop: cost = 67.5822982788086\n", "The 1114th loop: cost = 74.3511734008789\n", "The 1115th loop: cost = 59.96082305908203\n", "The 1116th loop: cost = 61.45637893676758\n", "The 1117th loop: cost = 50.507568359375\n", "The 1118th loop: cost = 59.265586853027344\n", "The 1119th loop: cost = 56.627349853515625\n", "The 1120th loop: cost = 51.59701919555664\n", "The 1121th loop: cost = 51.946659088134766\n", "The 1122th loop: cost = 73.88554382324219\n", "The 1123th loop: cost = 66.05226135253906\n", "The 1124th loop: cost = 60.45265197753906\n", "The 1125th loop: cost = 63.66703414916992\n", "The 1126th loop: cost = 69.83381652832031\n", "The 1127th loop: cost = 64.09909057617188\n", "The 1128th loop: cost = 58.9312629699707\n", "The 1129th loop: cost = 45.14511489868164\n", "The 1130th loop: cost = 61.806427001953125\n", "The 1131th loop: cost = 58.89716720581055\n", "The 1132th loop: cost = 54.416717529296875\n", "The 1133th loop: cost = 57.39431381225586\n", "The 1134th loop: cost = 63.43439483642578\n", "The 1135th loop: cost = 65.17219543457031\n", "The 1136th loop: cost = 47.8333740234375\n", "The 1137th loop: cost = 50.33228302001953\n", "The 1138th loop: cost = 58.0689582824707\n", "The 1139th loop: cost = 63.05192565917969\n", "The 1140th loop: cost = 57.14886474609375\n", "The 1141th loop: cost = 58.65302276611328\n", "The 1142th loop: cost = 47.629730224609375\n", "The 1143th loop: cost = 52.84025955200195\n", "The 1144th loop: cost = 47.20423126220703\n", "The 1145th loop: cost = 50.15015411376953\n", "The 1146th loop: cost = 62.42198181152344\n", "The 1147th loop: cost = 67.754150390625\n", "The 1148th loop: cost = 66.35328674316406\n", "The 1149th loop: cost = 51.05760192871094\n", "The 1150th loop: cost = 68.10780334472656\n", "The 1151th loop: cost = 61.56123352050781\n", "The 1152th loop: cost = 72.18379211425781\n", "The 1153th loop: cost = 72.21971893310547\n", "The 1154th loop: cost = 56.29073715209961\n", "The 1155th loop: cost = 53.62086868286133\n", "The 1156th loop: cost = 64.1050796508789\n", "The 1157th loop: cost = 67.460205078125\n", "The 1158th loop: cost = 59.40196990966797\n", "The 1159th loop: cost = 60.5482292175293\n", "The 1160th loop: cost = 54.58235168457031\n", "The 1161th loop: cost = 59.66780090332031\n", "The 1162th loop: cost = 58.746429443359375\n", "The 1163th loop: cost = 58.84193801879883\n", "The 1164th loop: cost = 47.395751953125\n", "The 1165th loop: cost = 48.715797424316406\n", "The 1166th loop: cost = 59.3375244140625\n", "The 1167th loop: cost = 51.96466827392578\n", "The 1168th loop: cost = 64.51947021484375\n", "The 1169th loop: cost = 56.02638626098633\n", "The 1170th loop: cost = 66.23075866699219\n", "The 1171th loop: cost = 50.2327880859375\n", "The 1172th loop: cost = 49.460357666015625\n", "The 1173th loop: cost = 53.02739334106445\n", "The 1174th loop: cost = 53.324462890625\n", "The 1175th loop: cost = 57.073089599609375\n", "The 1176th loop: cost = 49.78734588623047\n", "The 1177th loop: cost = 66.96429443359375\n", "The 1178th loop: cost = 56.694862365722656\n", "The 1179th loop: cost = 54.639652252197266\n", "The 1180th loop: cost = 61.199363708496094\n", "The 1181th loop: cost = 59.84676742553711\n", "The 1182th loop: cost = 62.92343521118164\n", "The 1183th loop: cost = 58.72932052612305\n", "The 1184th loop: cost = 70.95773315429688\n", "The 1185th loop: cost = 66.60601043701172\n", "The 1186th loop: cost = 54.448768615722656\n", "The 1187th loop: cost = 53.23824691772461\n", "The 1188th loop: cost = 61.988197326660156\n", "The 1189th loop: cost = 58.30550765991211\n", "The 1190th loop: cost = 51.21533203125\n", "The 1191th loop: cost = 56.292396545410156\n", "The 1192th loop: cost = 49.71620178222656\n", "The 1193th loop: cost = 52.355491638183594\n", "The 1194th loop: cost = 46.947784423828125\n", "The 1195th loop: cost = 52.53321075439453\n", "The 1196th loop: cost = 73.19686889648438\n", "The 1197th loop: cost = 71.52253723144531\n", "The 1198th loop: cost = 54.83235549926758\n", "The 1199th loop: cost = 57.74955368041992\n", "The 1200th loop: cost = 55.75181579589844\n", "The 1201th loop: cost = 51.431053161621094\n", "The 1202th loop: cost = 75.84233856201172\n", "The 1203th loop: cost = 55.535160064697266\n", "The 1204th loop: cost = 67.41946411132812\n", "The 1205th loop: cost = 69.81982421875\n", "The 1206th loop: cost = 57.88508987426758\n", "The 1207th loop: cost = 66.83551025390625\n", "The 1208th loop: cost = 58.47534942626953\n", "The 1209th loop: cost = 58.89982604980469\n", "The 1210th loop: cost = 58.67546844482422\n", "The 1211th loop: cost = 52.89727783203125\n", "The 1212th loop: cost = 49.29059600830078\n", "The 1213th loop: cost = 55.09043502807617\n", "The 1214th loop: cost = 57.484004974365234\n", "The 1215th loop: cost = 70.06522369384766\n", "The 1216th loop: cost = 45.73365020751953\n", "The 1217th loop: cost = 55.763023376464844\n", "The 1218th loop: cost = 57.771507263183594\n", "The 1219th loop: cost = 55.61885070800781\n", "The 1220th loop: cost = 63.12248992919922\n", "The 1221th loop: cost = 61.78954315185547\n", "The 1222th loop: cost = 73.94276428222656\n", "The 1223th loop: cost = 50.972843170166016\n", "The 1224th loop: cost = 59.03339767456055\n", "The 1225th loop: cost = 66.9010009765625\n", "The 1226th loop: cost = 53.70118713378906\n", "The 1227th loop: cost = 59.24808883666992\n", "The 1228th loop: cost = 58.99201583862305\n", "The 1229th loop: cost = 60.06867980957031\n", "The 1230th loop: cost = 59.233673095703125\n", "The 1231th loop: cost = 55.139404296875\n", "The 1232th loop: cost = 67.18162536621094\n", "The 1233th loop: cost = 72.26152038574219\n", "The 1234th loop: cost = 65.99895477294922\n", "The 1235th loop: cost = 65.2813720703125\n", "The 1236th loop: cost = 62.87017822265625\n", "The 1237th loop: cost = 53.61428451538086\n", "The 1238th loop: cost = 49.53129577636719\n", "The 1239th loop: cost = 60.681766510009766\n", "The 1240th loop: cost = 57.870025634765625\n", "The 1241th loop: cost = 53.6787109375\n", "The 1242th loop: cost = 60.191314697265625\n", "The 1243th loop: cost = 56.159515380859375\n", "The 1244th loop: cost = 60.42697525024414\n", "The 1245th loop: cost = 55.065757751464844\n", "The 1246th loop: cost = 57.51548767089844\n", "The 1247th loop: cost = 60.396053314208984\n", "The 1248th loop: cost = 70.68004608154297\n", "The 1249th loop: cost = 56.66409683227539\n", "The 1250th loop: cost = 57.52223205566406\n", "The 1251th loop: cost = 66.0789794921875\n", "The 1252th loop: cost = 56.270103454589844\n", "The 1253th loop: cost = 59.73916244506836\n", "The 1254th loop: cost = 55.63677978515625\n", "The 1255th loop: cost = 49.50762939453125\n", "The 1256th loop: cost = 60.7760124206543\n", "The 1257th loop: cost = 58.336402893066406\n", "The 1258th loop: cost = 45.993247985839844\n", "The 1259th loop: cost = 58.99393081665039\n", "The 1260th loop: cost = 55.16814422607422\n", "The 1261th loop: cost = 53.17484664916992\n", "The 1262th loop: cost = 54.61152648925781\n", "The 1263th loop: cost = 58.77251052856445\n", "The 1264th loop: cost = 61.34413528442383\n", "The 1265th loop: cost = 68.38687896728516\n", "The 1266th loop: cost = 50.16913604736328\n", "The 1267th loop: cost = 68.032958984375\n", "The 1268th loop: cost = 60.61595916748047\n", "The 1269th loop: cost = 50.76340866088867\n", "The 1270th loop: cost = 59.644866943359375\n", "The 1271th loop: cost = 55.30363464355469\n", "The 1272th loop: cost = 51.44886779785156\n", "The 1273th loop: cost = 67.85736846923828\n", "The 1274th loop: cost = 59.95703887939453\n", "The 1275th loop: cost = 60.87386703491211\n", "The 1276th loop: cost = 51.377479553222656\n", "The 1277th loop: cost = 56.300689697265625\n", "The 1278th loop: cost = 82.71623229980469\n", "The 1279th loop: cost = 61.341453552246094\n", "The 1280th loop: cost = 65.09407806396484\n", "The 1281th loop: cost = 59.300262451171875\n", "The 1282th loop: cost = 65.1250991821289\n", "The 1283th loop: cost = 58.298641204833984\n", "The 1284th loop: cost = 58.52513885498047\n", "The 1285th loop: cost = 58.705841064453125\n", "The 1286th loop: cost = 64.31663513183594\n", "The 1287th loop: cost = 52.55366134643555\n", "The 1288th loop: cost = 59.364418029785156\n", "The 1289th loop: cost = 61.73701095581055\n", "The 1290th loop: cost = 56.755027770996094\n", "The 1291th loop: cost = 69.00615692138672\n", "The 1292th loop: cost = 58.194786071777344\n", "The 1293th loop: cost = 52.297454833984375\n", "The 1294th loop: cost = 45.43070602416992\n", "The 1295th loop: cost = 57.46755599975586\n", "The 1296th loop: cost = 65.44454956054688\n", "The 1297th loop: cost = 52.62928771972656\n", "The 1298th loop: cost = 56.29328918457031\n", "The 1299th loop: cost = 50.72528839111328\n", "The 1300th loop: cost = 54.601593017578125\n", "The 1301th loop: cost = 65.57902526855469\n", "The 1302th loop: cost = 49.087249755859375\n", "The 1303th loop: cost = 52.76103210449219\n", "The 1304th loop: cost = 64.74253845214844\n", "The 1305th loop: cost = 67.65219116210938\n", "The 1306th loop: cost = 61.50743103027344\n", "The 1307th loop: cost = 57.443565368652344\n", "The 1308th loop: cost = 67.93089294433594\n", "The 1309th loop: cost = 58.53643798828125\n", "The 1310th loop: cost = 56.73991775512695\n", "The 1311th loop: cost = 52.859405517578125\n", "The 1312th loop: cost = 72.4078140258789\n", "The 1313th loop: cost = 63.793827056884766\n", "The 1314th loop: cost = 50.791011810302734\n", "The 1315th loop: cost = 62.994293212890625\n", "The 1316th loop: cost = 62.7293815612793\n", "The 1317th loop: cost = 60.491737365722656\n", "The 1318th loop: cost = 63.26020050048828\n", "The 1319th loop: cost = 61.69902038574219\n", "The 1320th loop: cost = 54.03439712524414\n", "The 1321th loop: cost = 61.09184265136719\n", "The 1322th loop: cost = 60.1639404296875\n", "The 1323th loop: cost = 52.06697463989258\n", "The 1324th loop: cost = 49.03740310668945\n", "The 1325th loop: cost = 60.5168571472168\n", "The 1326th loop: cost = 46.492034912109375\n", "The 1327th loop: cost = 76.89872741699219\n", "The 1328th loop: cost = 63.7972412109375\n", "The 1329th loop: cost = 58.768104553222656\n", "The 1330th loop: cost = 66.18115234375\n", "The 1331th loop: cost = 52.38014602661133\n", "The 1332th loop: cost = 56.63323211669922\n", "The 1333th loop: cost = 58.80302810668945\n", "The 1334th loop: cost = 53.37629699707031\n", "The 1335th loop: cost = 56.030757904052734\n", "The 1336th loop: cost = 53.74563217163086\n", "The 1337th loop: cost = 67.46761322021484\n", "The 1338th loop: cost = 56.197479248046875\n", "The 1339th loop: cost = 59.951873779296875\n", "The 1340th loop: cost = 54.11863708496094\n", "The 1341th loop: cost = 52.602989196777344\n", "The 1342th loop: cost = 58.402099609375\n", "The 1343th loop: cost = 48.377445220947266\n", "The 1344th loop: cost = 61.439735412597656\n", "The 1345th loop: cost = 47.5366096496582\n", "The 1346th loop: cost = 47.631202697753906\n", "The 1347th loop: cost = 49.054283142089844\n", "The 1348th loop: cost = 58.67985153198242\n", "The 1349th loop: cost = 47.623374938964844\n", "The 1350th loop: cost = 68.31398010253906\n", "The 1351th loop: cost = 64.96099853515625\n", "The 1352th loop: cost = 50.18110656738281\n", "The 1353th loop: cost = 59.754459381103516\n", "The 1354th loop: cost = 54.50922393798828\n", "The 1355th loop: cost = 50.51131057739258\n", "The 1356th loop: cost = 67.74066162109375\n", "The 1357th loop: cost = 63.84165573120117\n", "The 1358th loop: cost = 66.14535522460938\n", "The 1359th loop: cost = 59.27650451660156\n", "The 1360th loop: cost = 61.43416213989258\n", "The 1361th loop: cost = 56.596927642822266\n", "The 1362th loop: cost = 48.32181167602539\n", "The 1363th loop: cost = 59.810733795166016\n", "The 1364th loop: cost = 52.85003662109375\n", "The 1365th loop: cost = 62.40825271606445\n", "The 1366th loop: cost = 54.89218521118164\n", "The 1367th loop: cost = 57.972389221191406\n", "The 1368th loop: cost = 59.272216796875\n", "The 1369th loop: cost = 64.24421691894531\n", "The 1370th loop: cost = 58.273868560791016\n", "The 1371th loop: cost = 58.018550872802734\n", "The 1372th loop: cost = 56.22516632080078\n", "The 1373th loop: cost = 64.75650787353516\n", "The 1374th loop: cost = 60.80619812011719\n", "The 1375th loop: cost = 58.6468505859375\n", "The 1376th loop: cost = 64.54669189453125\n", "The 1377th loop: cost = 57.58282470703125\n", "The 1378th loop: cost = 71.39202117919922\n", "The 1379th loop: cost = 67.11467742919922\n", "The 1380th loop: cost = 46.959877014160156\n", "The 1381th loop: cost = 60.715667724609375\n", "The 1382th loop: cost = 56.131656646728516\n", "The 1383th loop: cost = 64.98792266845703\n", "The 1384th loop: cost = 58.1580810546875\n", "The 1385th loop: cost = 54.90192794799805\n", "The 1386th loop: cost = 51.549583435058594\n", "The 1387th loop: cost = 69.15231323242188\n", "The 1388th loop: cost = 60.316925048828125\n", "The 1389th loop: cost = 51.549766540527344\n", "The 1390th loop: cost = 50.76136779785156\n", "The 1391th loop: cost = 54.44950866699219\n", "The 1392th loop: cost = 78.60768127441406\n", "The 1393th loop: cost = 57.507606506347656\n", "The 1394th loop: cost = 62.24787902832031\n", "The 1395th loop: cost = 61.579376220703125\n", "The 1396th loop: cost = 56.79821014404297\n", "The 1397th loop: cost = 72.32394409179688\n", "The 1398th loop: cost = 60.7939338684082\n", "The 1399th loop: cost = 56.915916442871094\n", "The 1400th loop: cost = 62.77470397949219\n", "The 1401th loop: cost = 54.42594909667969\n", "The 1402th loop: cost = 53.791194915771484\n", "The 1403th loop: cost = 54.054603576660156\n", "The 1404th loop: cost = 53.662353515625\n", "The 1405th loop: cost = 56.830322265625\n", "The 1406th loop: cost = 54.71159362792969\n", "The 1407th loop: cost = 59.94721221923828\n", "The 1408th loop: cost = 60.887657165527344\n", "The 1409th loop: cost = 79.93499755859375\n", "The 1410th loop: cost = 59.23561096191406\n", "The 1411th loop: cost = 64.35012817382812\n", "The 1412th loop: cost = 55.48113250732422\n", "The 1413th loop: cost = 51.103370666503906\n", "The 1414th loop: cost = 56.2242546081543\n", "The 1415th loop: cost = 55.066802978515625\n", "The 1416th loop: cost = 59.65393829345703\n", "The 1417th loop: cost = 44.632625579833984\n", "The 1418th loop: cost = 64.91221618652344\n", "The 1419th loop: cost = 46.67633819580078\n", "The 1420th loop: cost = 59.385162353515625\n", "The 1421th loop: cost = 48.71098327636719\n", "The 1422th loop: cost = 53.891197204589844\n", "The 1423th loop: cost = 61.569828033447266\n", "The 1424th loop: cost = 58.202903747558594\n", "The 1425th loop: cost = 61.52233123779297\n", "The 1426th loop: cost = 53.2332878112793\n", "The 1427th loop: cost = 51.4688720703125\n", "The 1428th loop: cost = 55.38529586791992\n", "The 1429th loop: cost = 51.708091735839844\n", "The 1430th loop: cost = 54.795753479003906\n", "The 1431th loop: cost = 55.29362487792969\n", "The 1432th loop: cost = 54.58142852783203\n", "The 1433th loop: cost = 57.69198989868164\n", "The 1434th loop: cost = 66.2611083984375\n", "The 1435th loop: cost = 57.144493103027344\n", "The 1436th loop: cost = 61.435386657714844\n", "The 1437th loop: cost = 50.9417724609375\n", "The 1438th loop: cost = 58.308746337890625\n", "The 1439th loop: cost = 56.11656188964844\n", "The 1440th loop: cost = 54.31501770019531\n", "The 1441th loop: cost = 44.20619583129883\n", "The 1442th loop: cost = 49.93584442138672\n", "The 1443th loop: cost = 59.772762298583984\n", "The 1444th loop: cost = 56.99492645263672\n", "The 1445th loop: cost = 47.55059051513672\n", "The 1446th loop: cost = 62.67384719848633\n", "The 1447th loop: cost = 57.656471252441406\n", "The 1448th loop: cost = 55.42152786254883\n", "The 1449th loop: cost = 56.43583679199219\n", "The 1450th loop: cost = 51.73816680908203\n", "The 1451th loop: cost = 61.72105026245117\n", "The 1452th loop: cost = 64.53897094726562\n", "The 1453th loop: cost = 74.06769561767578\n", "The 1454th loop: cost = 62.158836364746094\n", "The 1455th loop: cost = 54.11064147949219\n", "The 1456th loop: cost = 56.42951965332031\n", "The 1457th loop: cost = 61.39631652832031\n", "The 1458th loop: cost = 51.07374572753906\n", "The 1459th loop: cost = 55.800472259521484\n", "The 1460th loop: cost = 64.28298950195312\n", "The 1461th loop: cost = 69.75177764892578\n", "The 1462th loop: cost = 50.44721984863281\n", "The 1463th loop: cost = 65.69099426269531\n", "The 1464th loop: cost = 51.537498474121094\n", "The 1465th loop: cost = 54.992515563964844\n", "The 1466th loop: cost = 63.780128479003906\n", "The 1467th loop: cost = 64.36068725585938\n", "The 1468th loop: cost = 57.097557067871094\n", "The 1469th loop: cost = 50.543479919433594\n", "The 1470th loop: cost = 71.23480987548828\n", "The 1471th loop: cost = 54.68382263183594\n", "The 1472th loop: cost = 60.5316047668457\n", "The 1473th loop: cost = 47.053260803222656\n", "The 1474th loop: cost = 67.38846588134766\n", "The 1475th loop: cost = 58.344017028808594\n", "The 1476th loop: cost = 58.90248107910156\n", "The 1477th loop: cost = 61.7861213684082\n", "The 1478th loop: cost = 47.48060989379883\n", "The 1479th loop: cost = 51.67660903930664\n", "The 1480th loop: cost = 70.39181518554688\n", "The 1481th loop: cost = 50.54363250732422\n", "The 1482th loop: cost = 65.19490051269531\n", "The 1483th loop: cost = 48.981239318847656\n", "The 1484th loop: cost = 56.97077941894531\n", "The 1485th loop: cost = 57.86662292480469\n", "The 1486th loop: cost = 52.16526412963867\n", "The 1487th loop: cost = 62.1218147277832\n", "The 1488th loop: cost = 49.973655700683594\n", "The 1489th loop: cost = 50.25248718261719\n", "The 1490th loop: cost = 56.6966552734375\n", "The 1491th loop: cost = 71.94023895263672\n", "The 1492th loop: cost = 44.32207489013672\n", "The 1493th loop: cost = 60.350738525390625\n", "The 1494th loop: cost = 58.814884185791016\n", "The 1495th loop: cost = 57.44318771362305\n", "The 1496th loop: cost = 54.207061767578125\n", "The 1497th loop: cost = 58.22136688232422\n", "The 1498th loop: cost = 63.71519088745117\n", "The 1499th loop: cost = 57.97937774658203\n", "The 1500th loop: cost = 52.61752700805664\n", "The 1501th loop: cost = 60.653404235839844\n", "The 1502th loop: cost = 55.90187454223633\n", "The 1503th loop: cost = 48.88345718383789\n", "The 1504th loop: cost = 62.512508392333984\n", "The 1505th loop: cost = 56.75198745727539\n", "The 1506th loop: cost = 79.58645629882812\n", "The 1507th loop: cost = 49.81819152832031\n", "The 1508th loop: cost = 53.212860107421875\n", "The 1509th loop: cost = 58.943538665771484\n", "The 1510th loop: cost = 59.5760498046875\n", "The 1511th loop: cost = 53.865936279296875\n", "The 1512th loop: cost = 51.975257873535156\n", "The 1513th loop: cost = 55.80708694458008\n", "The 1514th loop: cost = 51.215415954589844\n", "The 1515th loop: cost = 60.5804328918457\n", "The 1516th loop: cost = 55.11835479736328\n", "The 1517th loop: cost = 62.64133834838867\n", "The 1518th loop: cost = 56.48487091064453\n", "The 1519th loop: cost = 56.90604019165039\n", "The 1520th loop: cost = 47.38726806640625\n", "The 1521th loop: cost = 63.93125534057617\n", "The 1522th loop: cost = 61.41979217529297\n", "The 1523th loop: cost = 60.4498291015625\n", "The 1524th loop: cost = 60.25480651855469\n", "The 1525th loop: cost = 56.73627471923828\n", "The 1526th loop: cost = 57.4033203125\n", "The 1527th loop: cost = 53.315460205078125\n", "The 1528th loop: cost = 55.61452865600586\n", "The 1529th loop: cost = 60.372676849365234\n", "The 1530th loop: cost = 55.70330810546875\n", "The 1531th loop: cost = 57.941650390625\n", "The 1532th loop: cost = 45.792259216308594\n", "The 1533th loop: cost = 57.07474136352539\n", "The 1534th loop: cost = 63.88867950439453\n", "The 1535th loop: cost = 64.44949340820312\n", "The 1536th loop: cost = 50.96879196166992\n", "The 1537th loop: cost = 54.15962600708008\n", "The 1538th loop: cost = 58.885704040527344\n", "The 1539th loop: cost = 62.85259246826172\n", "The 1540th loop: cost = 66.70594024658203\n", "The 1541th loop: cost = 63.04331970214844\n", "The 1542th loop: cost = 76.8386001586914\n", "The 1543th loop: cost = 57.21723937988281\n", "The 1544th loop: cost = 60.84443283081055\n", "The 1545th loop: cost = 65.41686248779297\n", "The 1546th loop: cost = 56.54060363769531\n", "The 1547th loop: cost = 56.23175048828125\n", "The 1548th loop: cost = 54.823402404785156\n", "The 1549th loop: cost = 63.74346160888672\n", "The 1550th loop: cost = 49.74784851074219\n", "The 1551th loop: cost = 53.15992736816406\n", "The 1552th loop: cost = 61.070438385009766\n", "The 1553th loop: cost = 43.06879806518555\n", "The 1554th loop: cost = 45.43415832519531\n", "The 1555th loop: cost = 41.47693634033203\n", "The 1556th loop: cost = 76.21083068847656\n", "The 1557th loop: cost = 44.25404357910156\n", "The 1558th loop: cost = 63.65453338623047\n", "The 1559th loop: cost = 49.100093841552734\n", "The 1560th loop: cost = 62.542144775390625\n", "The 1561th loop: cost = 55.125274658203125\n", "The 1562th loop: cost = 58.7459831237793\n", "The 1563th loop: cost = 55.240440368652344\n", "The 1564th loop: cost = 61.85707092285156\n", "The 1565th loop: cost = 53.159690856933594\n", "The 1566th loop: cost = 58.90488052368164\n", "The 1567th loop: cost = 57.91590118408203\n", "The 1568th loop: cost = 53.448204040527344\n", "The 1569th loop: cost = 54.87403106689453\n", "The 1570th loop: cost = 57.68522644042969\n", "The 1571th loop: cost = 51.6839599609375\n", "The 1572th loop: cost = 60.50337219238281\n", "The 1573th loop: cost = 60.31679916381836\n", "The 1574th loop: cost = 64.51397705078125\n", "The 1575th loop: cost = 47.33094024658203\n", "The 1576th loop: cost = 43.916770935058594\n", "The 1577th loop: cost = 56.77259063720703\n", "The 1578th loop: cost = 53.720703125\n", "The 1579th loop: cost = 64.17499542236328\n", "The 1580th loop: cost = 57.519004821777344\n", "The 1581th loop: cost = 56.684165954589844\n", "The 1582th loop: cost = 64.38050842285156\n", "The 1583th loop: cost = 44.68255615234375\n", "The 1584th loop: cost = 54.29717254638672\n", "The 1585th loop: cost = 53.66984939575195\n", "The 1586th loop: cost = 58.55752944946289\n", "The 1587th loop: cost = 65.13185119628906\n", "The 1588th loop: cost = 60.96660614013672\n", "The 1589th loop: cost = 59.25159454345703\n", "The 1590th loop: cost = 58.41014862060547\n", "The 1591th loop: cost = 55.73649215698242\n", "The 1592th loop: cost = 64.94819641113281\n", "The 1593th loop: cost = 66.60013580322266\n", "The 1594th loop: cost = 53.090675354003906\n", "The 1595th loop: cost = 56.98577880859375\n", "The 1596th loop: cost = 66.85862731933594\n", "The 1597th loop: cost = 59.63820266723633\n", "The 1598th loop: cost = 57.380577087402344\n", "The 1599th loop: cost = 59.00050354003906\n", "The 1600th loop: cost = 47.87547302246094\n", "The 1601th loop: cost = 50.343353271484375\n", "The 1602th loop: cost = 60.07731246948242\n", "The 1603th loop: cost = 57.898311614990234\n", "The 1604th loop: cost = 44.077877044677734\n", "The 1605th loop: cost = 66.57064819335938\n", "The 1606th loop: cost = 62.85400390625\n", "The 1607th loop: cost = 53.63539123535156\n", "The 1608th loop: cost = 50.8233642578125\n", "The 1609th loop: cost = 63.34130859375\n", "The 1610th loop: cost = 64.18116760253906\n", "The 1611th loop: cost = 55.635894775390625\n", "The 1612th loop: cost = 57.3298225402832\n", "The 1613th loop: cost = 64.41798400878906\n", "The 1614th loop: cost = 48.125396728515625\n", "The 1615th loop: cost = 52.895790100097656\n", "The 1616th loop: cost = 58.37322998046875\n", "The 1617th loop: cost = 54.746795654296875\n", "The 1618th loop: cost = 49.110843658447266\n", "The 1619th loop: cost = 58.988922119140625\n", "The 1620th loop: cost = 60.995506286621094\n", "The 1621th loop: cost = 57.613162994384766\n", "The 1622th loop: cost = 51.79254913330078\n", "The 1623th loop: cost = 53.94593811035156\n", "The 1624th loop: cost = 61.842552185058594\n", "The 1625th loop: cost = 58.43215560913086\n", "The 1626th loop: cost = 52.39208221435547\n", "The 1627th loop: cost = 54.874229431152344\n", "The 1628th loop: cost = 52.3167610168457\n", "The 1629th loop: cost = 65.05073547363281\n", "The 1630th loop: cost = 53.53423309326172\n", "The 1631th loop: cost = 49.710487365722656\n", "The 1632th loop: cost = 67.05326843261719\n", "The 1633th loop: cost = 61.84723663330078\n", "The 1634th loop: cost = 53.98448181152344\n", "The 1635th loop: cost = 54.19169616699219\n", "The 1636th loop: cost = 63.82378387451172\n", "The 1637th loop: cost = 60.02036666870117\n", "The 1638th loop: cost = 48.6473388671875\n", "The 1639th loop: cost = 69.31503295898438\n", "The 1640th loop: cost = 58.41022491455078\n", "The 1641th loop: cost = 49.88030242919922\n", "The 1642th loop: cost = 61.65850067138672\n", "The 1643th loop: cost = 47.057552337646484\n", "The 1644th loop: cost = 57.858577728271484\n", "The 1645th loop: cost = 63.943084716796875\n", "The 1646th loop: cost = 42.53137969970703\n", "The 1647th loop: cost = 59.64506149291992\n", "The 1648th loop: cost = 53.657352447509766\n", "The 1649th loop: cost = 58.409786224365234\n", "The 1650th loop: cost = 60.271759033203125\n", "The 1651th loop: cost = 52.373260498046875\n", "The 1652th loop: cost = 51.277496337890625\n", "The 1653th loop: cost = 48.5341682434082\n", "The 1654th loop: cost = 53.62181091308594\n", "The 1655th loop: cost = 68.00353240966797\n", "The 1656th loop: cost = 59.70591735839844\n", "The 1657th loop: cost = 47.733116149902344\n", "The 1658th loop: cost = 51.11882019042969\n", "The 1659th loop: cost = 49.95332717895508\n", "The 1660th loop: cost = 61.77572250366211\n", "The 1661th loop: cost = 59.69068145751953\n", "The 1662th loop: cost = 60.44753646850586\n", "The 1663th loop: cost = 48.99018859863281\n", "The 1664th loop: cost = 50.70428466796875\n", "The 1665th loop: cost = 57.15825271606445\n", "The 1666th loop: cost = 48.86840057373047\n", "The 1667th loop: cost = 53.72289276123047\n", "The 1668th loop: cost = 62.14615249633789\n", "The 1669th loop: cost = 67.91070556640625\n", "The 1670th loop: cost = 69.32131958007812\n", "The 1671th loop: cost = 55.941444396972656\n", "The 1672th loop: cost = 61.46852111816406\n", "The 1673th loop: cost = 57.797000885009766\n", "The 1674th loop: cost = 59.14766311645508\n", "The 1675th loop: cost = 61.019920349121094\n", "The 1676th loop: cost = 55.680301666259766\n", "The 1677th loop: cost = 65.36056518554688\n", "The 1678th loop: cost = 53.30707550048828\n", "The 1679th loop: cost = 57.09080505371094\n", "The 1680th loop: cost = 45.61821746826172\n", "The 1681th loop: cost = 53.45210647583008\n", "The 1682th loop: cost = 51.306400299072266\n", "The 1683th loop: cost = 53.77891159057617\n", "The 1684th loop: cost = 52.7633056640625\n", "The 1685th loop: cost = 52.33252716064453\n", "The 1686th loop: cost = 54.47411346435547\n", "The 1687th loop: cost = 48.20399856567383\n", "The 1688th loop: cost = 55.308677673339844\n", "The 1689th loop: cost = 50.09598922729492\n", "The 1690th loop: cost = 51.08728790283203\n", "The 1691th loop: cost = 61.09849166870117\n", "The 1692th loop: cost = 62.174072265625\n", "The 1693th loop: cost = 59.89643478393555\n", "The 1694th loop: cost = 57.05656433105469\n", "The 1695th loop: cost = 60.69178771972656\n", "The 1696th loop: cost = 50.06062316894531\n", "The 1697th loop: cost = 55.144100189208984\n", "The 1698th loop: cost = 48.10580062866211\n", "The 1699th loop: cost = 55.52605438232422\n", "The 1700th loop: cost = 52.31432342529297\n", "The 1701th loop: cost = 52.11412811279297\n", "The 1702th loop: cost = 46.82514190673828\n", "The 1703th loop: cost = 44.267478942871094\n", "The 1704th loop: cost = 57.87457275390625\n", "The 1705th loop: cost = 59.0587043762207\n", "The 1706th loop: cost = 68.04035949707031\n", "The 1707th loop: cost = 63.421180725097656\n", "The 1708th loop: cost = 51.952693939208984\n", "The 1709th loop: cost = 51.114418029785156\n", "The 1710th loop: cost = 49.44706726074219\n", "The 1711th loop: cost = 62.75971984863281\n", "The 1712th loop: cost = 64.11054229736328\n", "The 1713th loop: cost = 64.57308197021484\n", "The 1714th loop: cost = 54.092857360839844\n", "The 1715th loop: cost = 57.985633850097656\n", "The 1716th loop: cost = 58.73372268676758\n", "The 1717th loop: cost = 54.29640197753906\n", "The 1718th loop: cost = 55.15073776245117\n", "The 1719th loop: cost = 56.014591217041016\n", "The 1720th loop: cost = 63.660484313964844\n", "The 1721th loop: cost = 54.964691162109375\n", "The 1722th loop: cost = 53.99402618408203\n", "The 1723th loop: cost = 55.84427261352539\n", "The 1724th loop: cost = 47.173160552978516\n", "The 1725th loop: cost = 53.39208221435547\n", "The 1726th loop: cost = 61.58571243286133\n", "The 1727th loop: cost = 49.46929168701172\n", "The 1728th loop: cost = 45.38502883911133\n", "The 1729th loop: cost = 44.82139587402344\n", "The 1730th loop: cost = 50.617759704589844\n", "The 1731th loop: cost = 42.398658752441406\n", "The 1732th loop: cost = 68.58200073242188\n", "The 1733th loop: cost = 47.03360366821289\n", "The 1734th loop: cost = 56.4303092956543\n", "The 1735th loop: cost = 51.9884033203125\n", "The 1736th loop: cost = 49.83910369873047\n", "The 1737th loop: cost = 50.66413879394531\n", "The 1738th loop: cost = 46.71482849121094\n", "The 1739th loop: cost = 56.58324432373047\n", "The 1740th loop: cost = 57.11488723754883\n", "The 1741th loop: cost = 50.79617691040039\n", "The 1742th loop: cost = 56.17917251586914\n", "The 1743th loop: cost = 53.56201171875\n", "The 1744th loop: cost = 45.27582550048828\n", "The 1745th loop: cost = 54.204185485839844\n", "The 1746th loop: cost = 63.79689025878906\n", "The 1747th loop: cost = 53.006324768066406\n", "The 1748th loop: cost = 50.20155334472656\n", "The 1749th loop: cost = 69.0411605834961\n", "The 1750th loop: cost = 51.50462341308594\n", "The 1751th loop: cost = 57.959190368652344\n", "The 1752th loop: cost = 56.81563949584961\n", "The 1753th loop: cost = 68.63549041748047\n", "The 1754th loop: cost = 53.441429138183594\n", "The 1755th loop: cost = 67.6128158569336\n", "The 1756th loop: cost = 52.61298370361328\n", "The 1757th loop: cost = 58.06978225708008\n", "The 1758th loop: cost = 58.044525146484375\n", "The 1759th loop: cost = 55.801204681396484\n", "The 1760th loop: cost = 50.163818359375\n", "The 1761th loop: cost = 48.82716369628906\n", "The 1762th loop: cost = 49.99400329589844\n", "The 1763th loop: cost = 53.82721710205078\n", "The 1764th loop: cost = 64.62135314941406\n", "The 1765th loop: cost = 47.33829879760742\n", "The 1766th loop: cost = 59.77949905395508\n", "The 1767th loop: cost = 59.95301055908203\n", "The 1768th loop: cost = 55.266151428222656\n", "The 1769th loop: cost = 57.459617614746094\n", "The 1770th loop: cost = 52.54075241088867\n", "The 1771th loop: cost = 55.377899169921875\n", "The 1772th loop: cost = 51.230308532714844\n", "The 1773th loop: cost = 56.28548812866211\n", "The 1774th loop: cost = 52.194427490234375\n", "The 1775th loop: cost = 62.88113021850586\n", "The 1776th loop: cost = 55.911346435546875\n", "The 1777th loop: cost = 44.844505310058594\n", "The 1778th loop: cost = 53.49354553222656\n", "The 1779th loop: cost = 48.31525421142578\n", "The 1780th loop: cost = 58.470458984375\n", "The 1781th loop: cost = 60.4601936340332\n", "The 1782th loop: cost = 56.880043029785156\n", "The 1783th loop: cost = 43.12306213378906\n", "The 1784th loop: cost = 63.860801696777344\n", "The 1785th loop: cost = 49.316070556640625\n", "The 1786th loop: cost = 54.611854553222656\n", "The 1787th loop: cost = 48.862342834472656\n", "The 1788th loop: cost = 57.002540588378906\n", "The 1789th loop: cost = 59.04440689086914\n", "The 1790th loop: cost = 55.607791900634766\n", "The 1791th loop: cost = 64.10972595214844\n", "The 1792th loop: cost = 58.477596282958984\n", "The 1793th loop: cost = 55.600772857666016\n", "The 1794th loop: cost = 58.84467697143555\n", "The 1795th loop: cost = 60.144771575927734\n", "The 1796th loop: cost = 57.23030471801758\n", "The 1797th loop: cost = 42.54633331298828\n", "The 1798th loop: cost = 55.538761138916016\n", "The 1799th loop: cost = 70.3809585571289\n", "The 1800th loop: cost = 49.10356903076172\n", "The 1801th loop: cost = 46.34538269042969\n", "The 1802th loop: cost = 47.29194641113281\n", "The 1803th loop: cost = 65.27954864501953\n", "The 1804th loop: cost = 56.9766960144043\n", "The 1805th loop: cost = 43.43717956542969\n", "The 1806th loop: cost = 52.71540451049805\n", "The 1807th loop: cost = 60.82569122314453\n", "The 1808th loop: cost = 61.87969207763672\n", "The 1809th loop: cost = 59.42439651489258\n", "The 1810th loop: cost = 45.200294494628906\n", "The 1811th loop: cost = 49.19115447998047\n", "The 1812th loop: cost = 51.78327178955078\n", "The 1813th loop: cost = 52.09596252441406\n", "The 1814th loop: cost = 58.50654602050781\n", "The 1815th loop: cost = 64.24798583984375\n", "The 1816th loop: cost = 51.95899200439453\n", "The 1817th loop: cost = 57.727745056152344\n", "The 1818th loop: cost = 58.185054779052734\n", "The 1819th loop: cost = 57.677703857421875\n", "The 1820th loop: cost = 60.396846771240234\n", "The 1821th loop: cost = 53.51512908935547\n", "The 1822th loop: cost = 53.5514030456543\n", "The 1823th loop: cost = 51.525726318359375\n", "The 1824th loop: cost = 55.493194580078125\n", "The 1825th loop: cost = 57.818809509277344\n", "The 1826th loop: cost = 47.960533142089844\n", "The 1827th loop: cost = 57.28740692138672\n", "The 1828th loop: cost = 53.494781494140625\n", "The 1829th loop: cost = 54.35435485839844\n", "The 1830th loop: cost = 52.74903869628906\n", "The 1831th loop: cost = 48.80363845825195\n", "The 1832th loop: cost = 76.62091827392578\n", "The 1833th loop: cost = 44.15645980834961\n", "The 1834th loop: cost = 50.87416458129883\n", "The 1835th loop: cost = 60.56359100341797\n", "The 1836th loop: cost = 58.51089096069336\n", "The 1837th loop: cost = 46.053009033203125\n", "The 1838th loop: cost = 47.89598846435547\n", "The 1839th loop: cost = 61.07366180419922\n", "The 1840th loop: cost = 54.15507125854492\n", "The 1841th loop: cost = 55.41721725463867\n", "The 1842th loop: cost = 46.919715881347656\n", "The 1843th loop: cost = 48.43112564086914\n", "The 1844th loop: cost = 46.48086166381836\n", "The 1845th loop: cost = 57.49370574951172\n", "The 1846th loop: cost = 47.39439392089844\n", "The 1847th loop: cost = 48.882286071777344\n", "The 1848th loop: cost = 62.95085525512695\n", "The 1849th loop: cost = 44.34961700439453\n", "The 1850th loop: cost = 45.940147399902344\n", "The 1851th loop: cost = 44.88158416748047\n", "The 1852th loop: cost = 47.33454132080078\n", "The 1853th loop: cost = 52.015785217285156\n", "The 1854th loop: cost = 56.26224136352539\n", "The 1855th loop: cost = 55.56420135498047\n", "The 1856th loop: cost = 47.490455627441406\n", "The 1857th loop: cost = 64.26866149902344\n", "The 1858th loop: cost = 49.106903076171875\n", "The 1859th loop: cost = 52.009010314941406\n", "The 1860th loop: cost = 64.59088134765625\n", "The 1861th loop: cost = 49.09376525878906\n", "The 1862th loop: cost = 51.17938232421875\n", "The 1863th loop: cost = 52.88988494873047\n", "The 1864th loop: cost = 56.19795608520508\n", "The 1865th loop: cost = 63.662322998046875\n", "The 1866th loop: cost = 55.24715805053711\n", "The 1867th loop: cost = 59.510066986083984\n", "The 1868th loop: cost = 49.11750030517578\n", "The 1869th loop: cost = 53.292320251464844\n", "The 1870th loop: cost = 57.23411560058594\n", "The 1871th loop: cost = 55.194557189941406\n", "The 1872th loop: cost = 54.55879592895508\n", "The 1873th loop: cost = 50.012237548828125\n", "The 1874th loop: cost = 62.753177642822266\n", "The 1875th loop: cost = 52.10976791381836\n", "The 1876th loop: cost = 58.56367492675781\n", "The 1877th loop: cost = 49.246742248535156\n", "The 1878th loop: cost = 52.50680923461914\n", "The 1879th loop: cost = 50.72336959838867\n", "The 1880th loop: cost = 56.592933654785156\n", "The 1881th loop: cost = 60.639312744140625\n", "The 1882th loop: cost = 57.974212646484375\n", "The 1883th loop: cost = 55.4471321105957\n", "The 1884th loop: cost = 57.85612487792969\n", "The 1885th loop: cost = 56.14948272705078\n", "The 1886th loop: cost = 59.19966125488281\n", "The 1887th loop: cost = 53.34126663208008\n", "The 1888th loop: cost = 57.108131408691406\n", "The 1889th loop: cost = 56.814170837402344\n", "The 1890th loop: cost = 61.3914680480957\n", "The 1891th loop: cost = 46.93743133544922\n", "The 1892th loop: cost = 46.358619689941406\n", "The 1893th loop: cost = 46.0323486328125\n", "The 1894th loop: cost = 50.210548400878906\n", "The 1895th loop: cost = 67.43817901611328\n", "The 1896th loop: cost = 47.70733642578125\n", "The 1897th loop: cost = 74.93755340576172\n", "The 1898th loop: cost = 53.38593673706055\n", "The 1899th loop: cost = 51.00464630126953\n", "The 1900th loop: cost = 41.59368896484375\n", "The 1901th loop: cost = 70.6417236328125\n", "The 1902th loop: cost = 56.36894226074219\n", "The 1903th loop: cost = 60.54115295410156\n", "The 1904th loop: cost = 60.99485778808594\n", "The 1905th loop: cost = 59.47438430786133\n", "The 1906th loop: cost = 57.735652923583984\n", "The 1907th loop: cost = 58.02519226074219\n", "The 1908th loop: cost = 57.90898895263672\n", "The 1909th loop: cost = 56.395774841308594\n", "The 1910th loop: cost = 48.653926849365234\n", "The 1911th loop: cost = 63.47568130493164\n", "The 1912th loop: cost = 55.808250427246094\n", "The 1913th loop: cost = 54.14191818237305\n", "The 1914th loop: cost = 50.98035430908203\n", "The 1915th loop: cost = 50.3975830078125\n", "The 1916th loop: cost = 52.00437545776367\n", "The 1917th loop: cost = 52.15613555908203\n", "The 1918th loop: cost = 46.45165252685547\n", "The 1919th loop: cost = 76.65850067138672\n", "The 1920th loop: cost = 44.89659881591797\n", "The 1921th loop: cost = 52.789405822753906\n", "The 1922th loop: cost = 56.016475677490234\n", "The 1923th loop: cost = 64.66213989257812\n", "The 1924th loop: cost = 52.88436508178711\n", "The 1925th loop: cost = 47.85742950439453\n", "The 1926th loop: cost = 51.96209716796875\n", "The 1927th loop: cost = 62.2182731628418\n", "The 1928th loop: cost = 46.979820251464844\n", "The 1929th loop: cost = 67.42015075683594\n", "The 1930th loop: cost = 41.66670227050781\n", "The 1931th loop: cost = 57.80272674560547\n", "The 1932th loop: cost = 51.71770477294922\n", "The 1933th loop: cost = 62.16358184814453\n", "The 1934th loop: cost = 58.38933563232422\n", "The 1935th loop: cost = 65.3524169921875\n", "The 1936th loop: cost = 54.39496994018555\n", "The 1937th loop: cost = 51.10840606689453\n", "The 1938th loop: cost = 47.039306640625\n", "The 1939th loop: cost = 65.06465148925781\n", "The 1940th loop: cost = 46.417686462402344\n", "The 1941th loop: cost = 51.27204513549805\n", "The 1942th loop: cost = 45.89261245727539\n", "The 1943th loop: cost = 57.886173248291016\n", "The 1944th loop: cost = 58.832435607910156\n", "The 1945th loop: cost = 41.66614532470703\n", "The 1946th loop: cost = 54.382850646972656\n", "The 1947th loop: cost = 58.16175842285156\n", "The 1948th loop: cost = 44.494903564453125\n", "The 1949th loop: cost = 65.24504089355469\n", "The 1950th loop: cost = 59.594764709472656\n", "The 1951th loop: cost = 55.49223709106445\n", "The 1952th loop: cost = 57.79345703125\n", "The 1953th loop: cost = 55.61428451538086\n", "The 1954th loop: cost = 54.71671676635742\n", "The 1955th loop: cost = 50.394229888916016\n", "The 1956th loop: cost = 43.236454010009766\n", "The 1957th loop: cost = 55.080196380615234\n", "The 1958th loop: cost = 50.318992614746094\n", "The 1959th loop: cost = 39.18497085571289\n", "The 1960th loop: cost = 70.32398986816406\n", "The 1961th loop: cost = 60.770347595214844\n", "The 1962th loop: cost = 41.92522048950195\n", "The 1963th loop: cost = 53.654300689697266\n", "The 1964th loop: cost = 53.943851470947266\n", "The 1965th loop: cost = 68.16947937011719\n", "The 1966th loop: cost = 64.74409484863281\n", "The 1967th loop: cost = 45.406982421875\n", "The 1968th loop: cost = 51.945716857910156\n", "The 1969th loop: cost = 58.28767013549805\n", "The 1970th loop: cost = 55.07075119018555\n", "The 1971th loop: cost = 53.07346725463867\n", "The 1972th loop: cost = 57.605167388916016\n", "The 1973th loop: cost = 52.77693176269531\n", "The 1974th loop: cost = 60.52864074707031\n", "The 1975th loop: cost = 64.56497192382812\n", "The 1976th loop: cost = 49.186973571777344\n", "The 1977th loop: cost = 43.71672439575195\n", "The 1978th loop: cost = 52.13330078125\n", "The 1979th loop: cost = 67.0129623413086\n", "The 1980th loop: cost = 50.71509552001953\n", "The 1981th loop: cost = 64.41697692871094\n", "The 1982th loop: cost = 51.59259033203125\n", "The 1983th loop: cost = 41.374000549316406\n", "The 1984th loop: cost = 52.88572692871094\n", "The 1985th loop: cost = 65.81563568115234\n", "The 1986th loop: cost = 54.32568359375\n", "The 1987th loop: cost = 46.917381286621094\n", "The 1988th loop: cost = 55.66033172607422\n", "The 1989th loop: cost = 54.25821304321289\n", "The 1990th loop: cost = 63.63724899291992\n", "The 1991th loop: cost = 48.1624755859375\n", "The 1992th loop: cost = 55.439788818359375\n", "The 1993th loop: cost = 57.6021614074707\n", "The 1994th loop: cost = 56.22185516357422\n", "The 1995th loop: cost = 42.329193115234375\n", "The 1996th loop: cost = 50.07520294189453\n", "The 1997th loop: cost = 50.39137268066406\n", "The 1998th loop: cost = 54.90657043457031\n", "The 1999th loop: cost = 59.67256164550781\n", "The 2000th loop: cost = 56.2178955078125\n", "The 2001th loop: cost = 49.62093734741211\n", "The 2002th loop: cost = 59.094696044921875\n", "The 2003th loop: cost = 50.49163818359375\n", "The 2004th loop: cost = 46.30514907836914\n", "The 2005th loop: cost = 54.767974853515625\n", "The 2006th loop: cost = 57.273372650146484\n", "The 2007th loop: cost = 48.925453186035156\n", "The 2008th loop: cost = 55.433631896972656\n", "The 2009th loop: cost = 53.13875961303711\n", "The 2010th loop: cost = 53.41530227661133\n", "The 2011th loop: cost = 51.54645538330078\n", "The 2012th loop: cost = 50.97624206542969\n", "The 2013th loop: cost = 55.57267379760742\n", "The 2014th loop: cost = 46.89054489135742\n", "The 2015th loop: cost = 50.516727447509766\n", "The 2016th loop: cost = 54.045265197753906\n", "The 2017th loop: cost = 61.022361755371094\n", "The 2018th loop: cost = 63.957096099853516\n", "The 2019th loop: cost = 57.7100944519043\n", "The 2020th loop: cost = 55.35779571533203\n", "The 2021th loop: cost = 55.829856872558594\n", "The 2022th loop: cost = 57.504173278808594\n", "The 2023th loop: cost = 53.32611083984375\n", "The 2024th loop: cost = 56.7174072265625\n", "The 2025th loop: cost = 51.21491241455078\n", "The 2026th loop: cost = 49.15035629272461\n", "The 2027th loop: cost = 57.264381408691406\n", "The 2028th loop: cost = 61.188987731933594\n", "The 2029th loop: cost = 53.85018539428711\n", "The 2030th loop: cost = 40.671966552734375\n", "The 2031th loop: cost = 62.84314727783203\n", "The 2032th loop: cost = 42.102569580078125\n", "The 2033th loop: cost = 51.58503341674805\n", "The 2034th loop: cost = 57.71136474609375\n", "The 2035th loop: cost = 49.82799530029297\n", "The 2036th loop: cost = 47.92631530761719\n", "The 2037th loop: cost = 51.134769439697266\n", "The 2038th loop: cost = 49.98298645019531\n", "The 2039th loop: cost = 51.90728759765625\n", "The 2040th loop: cost = 48.193824768066406\n", "The 2041th loop: cost = 54.85517120361328\n", "The 2042th loop: cost = 51.27006912231445\n", "The 2043th loop: cost = 53.11994552612305\n", "The 2044th loop: cost = 55.85995101928711\n", "The 2045th loop: cost = 49.37566375732422\n", "The 2046th loop: cost = 53.63661193847656\n", "The 2047th loop: cost = 65.11638641357422\n", "The 2048th loop: cost = 45.83867645263672\n", "The 2049th loop: cost = 42.70574188232422\n", "The 2050th loop: cost = 65.03953552246094\n", "The 2051th loop: cost = 55.18622589111328\n", "The 2052th loop: cost = 52.17554473876953\n", "The 2053th loop: cost = 50.69389343261719\n", "The 2054th loop: cost = 60.5383415222168\n", "The 2055th loop: cost = 53.89773178100586\n", "The 2056th loop: cost = 43.63706970214844\n", "The 2057th loop: cost = 64.62715148925781\n", "The 2058th loop: cost = 57.23080825805664\n", "The 2059th loop: cost = 53.21739959716797\n", "The 2060th loop: cost = 48.799102783203125\n", "The 2061th loop: cost = 56.263885498046875\n", "The 2062th loop: cost = 61.22966003417969\n", "The 2063th loop: cost = 52.968875885009766\n", "The 2064th loop: cost = 51.07183074951172\n", "The 2065th loop: cost = 47.523223876953125\n", "The 2066th loop: cost = 55.06897735595703\n", "The 2067th loop: cost = 63.12869644165039\n", "The 2068th loop: cost = 56.44576644897461\n", "The 2069th loop: cost = 55.6440315246582\n", "The 2070th loop: cost = 53.680702209472656\n", "The 2071th loop: cost = 49.033775329589844\n", "The 2072th loop: cost = 62.142303466796875\n", "The 2073th loop: cost = 48.348182678222656\n", "The 2074th loop: cost = 53.76264953613281\n", "The 2075th loop: cost = 47.25768280029297\n", "The 2076th loop: cost = 47.059566497802734\n", "The 2077th loop: cost = 51.280860900878906\n", "The 2078th loop: cost = 51.68586349487305\n", "The 2079th loop: cost = 46.06945037841797\n", "The 2080th loop: cost = 51.02423095703125\n", "The 2081th loop: cost = 53.887569427490234\n", "The 2082th loop: cost = 50.09455108642578\n", "The 2083th loop: cost = 55.2316780090332\n", "The 2084th loop: cost = 57.167415618896484\n", "The 2085th loop: cost = 64.69729614257812\n", "The 2086th loop: cost = 59.99671173095703\n", "The 2087th loop: cost = 51.2313346862793\n", "The 2088th loop: cost = 49.97233963012695\n", "The 2089th loop: cost = 51.866477966308594\n", "The 2090th loop: cost = 49.965431213378906\n", "The 2091th loop: cost = 58.74833679199219\n", "The 2092th loop: cost = 53.52206039428711\n", "The 2093th loop: cost = 57.13204574584961\n", "The 2094th loop: cost = 48.03303146362305\n", "The 2095th loop: cost = 51.82728576660156\n", "The 2096th loop: cost = 50.353118896484375\n", "The 2097th loop: cost = 58.80474853515625\n", "The 2098th loop: cost = 53.63988494873047\n", "The 2099th loop: cost = 52.24030303955078\n", "The 2100th loop: cost = 55.8070068359375\n", "The 2101th loop: cost = 56.05864715576172\n", "The 2102th loop: cost = 52.14449691772461\n", "The 2103th loop: cost = 54.32188415527344\n", "The 2104th loop: cost = 58.71619415283203\n", "The 2105th loop: cost = 51.16835021972656\n", "The 2106th loop: cost = 47.83587646484375\n", "The 2107th loop: cost = 53.16212463378906\n", "The 2108th loop: cost = 53.18550109863281\n", "The 2109th loop: cost = 48.02357482910156\n", "The 2110th loop: cost = 54.35554504394531\n", "The 2111th loop: cost = 49.2984619140625\n", "The 2112th loop: cost = 60.65274429321289\n", "The 2113th loop: cost = 70.98274993896484\n", "The 2114th loop: cost = 50.82770538330078\n", "The 2115th loop: cost = 60.92864990234375\n", "The 2116th loop: cost = 51.56816101074219\n", "The 2117th loop: cost = 55.156036376953125\n", "The 2118th loop: cost = 43.689247131347656\n", "The 2119th loop: cost = 53.43077087402344\n", "The 2120th loop: cost = 46.89241027832031\n", "The 2121th loop: cost = 62.61559295654297\n", "The 2122th loop: cost = 62.508277893066406\n", "The 2123th loop: cost = 62.819129943847656\n", "The 2124th loop: cost = 50.01279067993164\n", "The 2125th loop: cost = 44.575828552246094\n", "The 2126th loop: cost = 45.43897247314453\n", "The 2127th loop: cost = 53.10143280029297\n", "The 2128th loop: cost = 58.11267852783203\n", "The 2129th loop: cost = 57.46727752685547\n", "The 2130th loop: cost = 50.13752365112305\n", "The 2131th loop: cost = 48.14674377441406\n", "The 2132th loop: cost = 63.03715515136719\n", "The 2133th loop: cost = 53.17890167236328\n", "The 2134th loop: cost = 60.02687072753906\n", "The 2135th loop: cost = 46.91223907470703\n", "The 2136th loop: cost = 53.05670928955078\n", "The 2137th loop: cost = 43.57481384277344\n", "The 2138th loop: cost = 61.18003463745117\n", "The 2139th loop: cost = 45.07634735107422\n", "The 2140th loop: cost = 56.66107940673828\n", "The 2141th loop: cost = 50.56919860839844\n", "The 2142th loop: cost = 48.180030822753906\n", "The 2143th loop: cost = 56.75924301147461\n", "The 2144th loop: cost = 55.21041488647461\n", "The 2145th loop: cost = 56.56419372558594\n", "The 2146th loop: cost = 57.318538665771484\n", "The 2147th loop: cost = 54.69820022583008\n", "The 2148th loop: cost = 56.06182098388672\n", "The 2149th loop: cost = 53.1285400390625\n", "The 2150th loop: cost = 54.547637939453125\n", "The 2151th loop: cost = 59.09128952026367\n", "The 2152th loop: cost = 55.48813247680664\n", "The 2153th loop: cost = 49.16951370239258\n", "The 2154th loop: cost = 64.46414947509766\n", "The 2155th loop: cost = 49.824554443359375\n", "The 2156th loop: cost = 51.54893493652344\n", "The 2157th loop: cost = 61.21821594238281\n", "The 2158th loop: cost = 54.71160125732422\n", "The 2159th loop: cost = 48.87261962890625\n", "The 2160th loop: cost = 47.91426467895508\n", "The 2161th loop: cost = 44.25946044921875\n", "The 2162th loop: cost = 50.882049560546875\n", "The 2163th loop: cost = 54.43519592285156\n", "The 2164th loop: cost = 50.02214431762695\n", "The 2165th loop: cost = 61.23338317871094\n", "The 2166th loop: cost = 48.481361389160156\n", "The 2167th loop: cost = 49.258872985839844\n", "The 2168th loop: cost = 45.517295837402344\n", "The 2169th loop: cost = 45.090797424316406\n", "The 2170th loop: cost = 58.52570724487305\n", "The 2171th loop: cost = 53.72911071777344\n", "The 2172th loop: cost = 53.76045227050781\n", "The 2173th loop: cost = 49.33511734008789\n", "The 2174th loop: cost = 58.103363037109375\n", "The 2175th loop: cost = 54.38521194458008\n", "The 2176th loop: cost = 50.82122802734375\n", "The 2177th loop: cost = 49.99778366088867\n", "The 2178th loop: cost = 51.691627502441406\n", "The 2179th loop: cost = 51.19710922241211\n", "The 2180th loop: cost = 64.2650146484375\n", "The 2181th loop: cost = 56.37187194824219\n", "The 2182th loop: cost = 55.33357620239258\n", "The 2183th loop: cost = 54.524330139160156\n", "The 2184th loop: cost = 59.128135681152344\n", "The 2185th loop: cost = 54.99882507324219\n", "The 2186th loop: cost = 49.80889892578125\n", "The 2187th loop: cost = 54.398460388183594\n", "The 2188th loop: cost = 41.30247497558594\n", "The 2189th loop: cost = 63.63471221923828\n", "The 2190th loop: cost = 60.38459014892578\n", "The 2191th loop: cost = 55.193748474121094\n", "The 2192th loop: cost = 54.537322998046875\n", "The 2193th loop: cost = 48.17455291748047\n", "The 2194th loop: cost = 61.31787872314453\n", "The 2195th loop: cost = 51.74888610839844\n", "The 2196th loop: cost = 60.955501556396484\n", "The 2197th loop: cost = 64.62614440917969\n", "The 2198th loop: cost = 50.02184295654297\n", "The 2199th loop: cost = 57.213897705078125\n", "The 2200th loop: cost = 63.03199005126953\n", "The 2201th loop: cost = 57.5344352722168\n", "The 2202th loop: cost = 57.331844329833984\n", "The 2203th loop: cost = 51.602012634277344\n", "The 2204th loop: cost = 55.03424072265625\n", "The 2205th loop: cost = 46.43027114868164\n", "The 2206th loop: cost = 45.86895751953125\n", "The 2207th loop: cost = 40.46982192993164\n", "The 2208th loop: cost = 58.408729553222656\n", "The 2209th loop: cost = 58.90611267089844\n", "The 2210th loop: cost = 49.64299011230469\n", "The 2211th loop: cost = 64.58076477050781\n", "The 2212th loop: cost = 61.92765426635742\n", "The 2213th loop: cost = 51.901702880859375\n", "The 2214th loop: cost = 52.560848236083984\n", "The 2215th loop: cost = 51.85475540161133\n", "The 2216th loop: cost = 57.52526092529297\n", "The 2217th loop: cost = 51.24650573730469\n", "The 2218th loop: cost = 55.817718505859375\n", "The 2219th loop: cost = 56.61030197143555\n", "The 2220th loop: cost = 56.681915283203125\n", "The 2221th loop: cost = 57.66897201538086\n", "The 2222th loop: cost = 53.15432357788086\n", "The 2223th loop: cost = 50.51331329345703\n", "The 2224th loop: cost = 47.13663864135742\n", "The 2225th loop: cost = 41.51751708984375\n", "The 2226th loop: cost = 55.92213439941406\n", "The 2227th loop: cost = 58.53765869140625\n", "The 2228th loop: cost = 56.13743591308594\n", "The 2229th loop: cost = 59.851829528808594\n", "The 2230th loop: cost = 47.848323822021484\n", "The 2231th loop: cost = 55.645015716552734\n", "The 2232th loop: cost = 45.57284927368164\n", "The 2233th loop: cost = 48.56062316894531\n", "The 2234th loop: cost = 43.93560028076172\n", "The 2235th loop: cost = 58.778846740722656\n", "The 2236th loop: cost = 55.56269454956055\n", "The 2237th loop: cost = 49.85785675048828\n", "The 2238th loop: cost = 46.867244720458984\n", "The 2239th loop: cost = 46.18125534057617\n", "The 2240th loop: cost = 51.69740295410156\n", "The 2241th loop: cost = 50.37306213378906\n", "The 2242th loop: cost = 43.58066940307617\n", "The 2243th loop: cost = 42.02259826660156\n", "The 2244th loop: cost = 65.17080688476562\n", "The 2245th loop: cost = 58.5555305480957\n", "The 2246th loop: cost = 58.3878288269043\n", "The 2247th loop: cost = 51.262481689453125\n", "The 2248th loop: cost = 53.106109619140625\n", "The 2249th loop: cost = 63.213531494140625\n", "The 2250th loop: cost = 51.732452392578125\n", "The 2251th loop: cost = 58.973167419433594\n", "The 2252th loop: cost = 50.14705276489258\n", "The 2253th loop: cost = 49.40318298339844\n", "The 2254th loop: cost = 53.99805450439453\n", "The 2255th loop: cost = 57.4857292175293\n", "The 2256th loop: cost = 48.83412170410156\n", "The 2257th loop: cost = 62.5098762512207\n", "The 2258th loop: cost = 51.16631317138672\n", "The 2259th loop: cost = 49.19298553466797\n", "The 2260th loop: cost = 53.6617546081543\n", "The 2261th loop: cost = 56.16725158691406\n", "The 2262th loop: cost = 56.45608139038086\n", "The 2263th loop: cost = 50.052764892578125\n", "The 2264th loop: cost = 51.17552185058594\n", "The 2265th loop: cost = 55.35508728027344\n", "The 2266th loop: cost = 56.11249542236328\n", "The 2267th loop: cost = 54.26136779785156\n", "The 2268th loop: cost = 47.88042449951172\n", "The 2269th loop: cost = 54.90258026123047\n", "The 2270th loop: cost = 47.12511444091797\n", "The 2271th loop: cost = 55.3790397644043\n", "The 2272th loop: cost = 52.631378173828125\n", "The 2273th loop: cost = 45.1117057800293\n", "The 2274th loop: cost = 53.323795318603516\n", "The 2275th loop: cost = 55.57180404663086\n", "The 2276th loop: cost = 57.01128387451172\n", "The 2277th loop: cost = 50.5407600402832\n", "The 2278th loop: cost = 43.34141540527344\n", "The 2279th loop: cost = 42.26457214355469\n", "The 2280th loop: cost = 56.190818786621094\n", "The 2281th loop: cost = 46.52385711669922\n", "The 2282th loop: cost = 65.27499389648438\n", "The 2283th loop: cost = 54.027835845947266\n", "The 2284th loop: cost = 46.04600143432617\n", "The 2285th loop: cost = 69.79804229736328\n", "The 2286th loop: cost = 49.70874786376953\n", "The 2287th loop: cost = 45.346954345703125\n", "The 2288th loop: cost = 47.299888610839844\n", "The 2289th loop: cost = 46.30223846435547\n", "The 2290th loop: cost = 58.045528411865234\n", "The 2291th loop: cost = 48.669898986816406\n", "The 2292th loop: cost = 57.26182556152344\n", "The 2293th loop: cost = 61.147579193115234\n", "The 2294th loop: cost = 44.69189453125\n", "The 2295th loop: cost = 45.79692077636719\n", "The 2296th loop: cost = 48.624759674072266\n", "The 2297th loop: cost = 56.603233337402344\n", "The 2298th loop: cost = 53.61925506591797\n", "The 2299th loop: cost = 50.1165657043457\n", "The 2300th loop: cost = 46.542083740234375\n", "The 2301th loop: cost = 52.26652526855469\n", "The 2302th loop: cost = 64.3239517211914\n", "The 2303th loop: cost = 44.80473327636719\n", "The 2304th loop: cost = 47.529972076416016\n", "The 2305th loop: cost = 46.105472564697266\n", "The 2306th loop: cost = 46.97843551635742\n", "The 2307th loop: cost = 53.99650573730469\n", "The 2308th loop: cost = 57.094940185546875\n", "The 2309th loop: cost = 55.84064483642578\n", "The 2310th loop: cost = 42.35581970214844\n", "The 2311th loop: cost = 52.03585433959961\n", "The 2312th loop: cost = 52.09193801879883\n", "The 2313th loop: cost = 60.32351303100586\n", "The 2314th loop: cost = 47.170475006103516\n", "The 2315th loop: cost = 52.705718994140625\n", "The 2316th loop: cost = 55.829124450683594\n", "The 2317th loop: cost = 46.59239196777344\n", "The 2318th loop: cost = 56.94963836669922\n", "The 2319th loop: cost = 50.178855895996094\n", "The 2320th loop: cost = 55.497093200683594\n", "The 2321th loop: cost = 43.55125045776367\n", "The 2322th loop: cost = 54.139404296875\n", "The 2323th loop: cost = 39.09194564819336\n", "The 2324th loop: cost = 51.64493942260742\n", "The 2325th loop: cost = 60.08344268798828\n", "The 2326th loop: cost = 53.13429260253906\n", "The 2327th loop: cost = 59.452430725097656\n", "The 2328th loop: cost = 55.29216003417969\n", "The 2329th loop: cost = 51.33622360229492\n", "The 2330th loop: cost = 53.10560989379883\n", "The 2331th loop: cost = 45.425262451171875\n", "The 2332th loop: cost = 58.38372802734375\n", "The 2333th loop: cost = 53.31882858276367\n", "The 2334th loop: cost = 51.2237548828125\n", "The 2335th loop: cost = 52.67854690551758\n", "The 2336th loop: cost = 51.35799026489258\n", "The 2337th loop: cost = 47.448486328125\n", "The 2338th loop: cost = 50.8632926940918\n", "The 2339th loop: cost = 46.50160217285156\n", "The 2340th loop: cost = 47.58340072631836\n", "The 2341th loop: cost = 48.80467987060547\n", "The 2342th loop: cost = 61.856300354003906\n", "The 2343th loop: cost = 53.31229782104492\n", "The 2344th loop: cost = 57.474308013916016\n", "The 2345th loop: cost = 43.650054931640625\n", "The 2346th loop: cost = 52.19432067871094\n", "The 2347th loop: cost = 49.914581298828125\n", "The 2348th loop: cost = 53.255409240722656\n", "The 2349th loop: cost = 47.82825469970703\n", "The 2350th loop: cost = 59.21173095703125\n", "The 2351th loop: cost = 55.182579040527344\n", "The 2352th loop: cost = 43.83850860595703\n", "The 2353th loop: cost = 57.06837844848633\n", "The 2354th loop: cost = 49.353240966796875\n", "The 2355th loop: cost = 51.635311126708984\n", "The 2356th loop: cost = 52.26972198486328\n", "The 2357th loop: cost = 47.27070999145508\n", "The 2358th loop: cost = 60.04815673828125\n", "The 2359th loop: cost = 56.76176834106445\n", "The 2360th loop: cost = 60.5906867980957\n", "The 2361th loop: cost = 48.929229736328125\n", "The 2362th loop: cost = 47.044944763183594\n", "The 2363th loop: cost = 51.71086883544922\n", "The 2364th loop: cost = 47.671104431152344\n", "The 2365th loop: cost = 45.298095703125\n", "The 2366th loop: cost = 45.297245025634766\n", "The 2367th loop: cost = 45.44432067871094\n", "The 2368th loop: cost = 52.248802185058594\n", "The 2369th loop: cost = 51.76841735839844\n", "The 2370th loop: cost = 44.59697723388672\n", "The 2371th loop: cost = 44.95130920410156\n", "The 2372th loop: cost = 47.78706359863281\n", "The 2373th loop: cost = 61.29535675048828\n", "The 2374th loop: cost = 62.23660659790039\n", "The 2375th loop: cost = 57.755706787109375\n", "The 2376th loop: cost = 48.211856842041016\n", "The 2377th loop: cost = 50.72148895263672\n", "The 2378th loop: cost = 52.96442794799805\n", "The 2379th loop: cost = 54.11327362060547\n", "The 2380th loop: cost = 49.01959991455078\n", "The 2381th loop: cost = 56.219512939453125\n", "The 2382th loop: cost = 53.77157974243164\n", "The 2383th loop: cost = 54.33566665649414\n", "The 2384th loop: cost = 52.427093505859375\n", "The 2385th loop: cost = 55.922550201416016\n", "The 2386th loop: cost = 49.8551139831543\n", "The 2387th loop: cost = 56.782249450683594\n", "The 2388th loop: cost = 51.16984939575195\n", "The 2389th loop: cost = 66.39656829833984\n", "The 2390th loop: cost = 47.79338836669922\n", "The 2391th loop: cost = 50.48663330078125\n", "The 2392th loop: cost = 49.61628723144531\n", "The 2393th loop: cost = 43.09464645385742\n", "The 2394th loop: cost = 53.363075256347656\n", "The 2395th loop: cost = 51.0375862121582\n", "The 2396th loop: cost = 56.59838104248047\n", "The 2397th loop: cost = 45.1237907409668\n", "The 2398th loop: cost = 39.825469970703125\n", "The 2399th loop: cost = 59.16919708251953\n", "The 2400th loop: cost = 66.28045654296875\n", "The 2401th loop: cost = 47.36286163330078\n", "The 2402th loop: cost = 50.085445404052734\n", "The 2403th loop: cost = 56.45562744140625\n", "The 2404th loop: cost = 53.928531646728516\n", "The 2405th loop: cost = 49.47734451293945\n", "The 2406th loop: cost = 40.748409271240234\n", "The 2407th loop: cost = 57.853485107421875\n", "The 2408th loop: cost = 42.63290786743164\n", "The 2409th loop: cost = 48.10783004760742\n", "The 2410th loop: cost = 58.128509521484375\n", "The 2411th loop: cost = 59.30113220214844\n", "The 2412th loop: cost = 46.59374237060547\n", "The 2413th loop: cost = 46.26472854614258\n", "The 2414th loop: cost = 60.872230529785156\n", "The 2415th loop: cost = 59.5211181640625\n", "The 2416th loop: cost = 44.672393798828125\n", "The 2417th loop: cost = 62.00739288330078\n", "The 2418th loop: cost = 51.0684814453125\n", "The 2419th loop: cost = 52.80176544189453\n", "The 2420th loop: cost = 46.818695068359375\n", "The 2421th loop: cost = 53.03568649291992\n", "The 2422th loop: cost = 50.62220764160156\n", "The 2423th loop: cost = 44.31690216064453\n", "The 2424th loop: cost = 50.13534164428711\n", "The 2425th loop: cost = 55.698753356933594\n", "The 2426th loop: cost = 45.53683090209961\n", "The 2427th loop: cost = 67.2379150390625\n", "The 2428th loop: cost = 50.9796257019043\n", "The 2429th loop: cost = 50.66800308227539\n", "The 2430th loop: cost = 57.85115051269531\n", "The 2431th loop: cost = 46.851314544677734\n", "The 2432th loop: cost = 50.32035827636719\n", "The 2433th loop: cost = 54.939510345458984\n", "The 2434th loop: cost = 45.48198699951172\n", "The 2435th loop: cost = 51.286224365234375\n", "The 2436th loop: cost = 44.53241729736328\n", "The 2437th loop: cost = 52.71562194824219\n", "The 2438th loop: cost = 49.56354904174805\n", "The 2439th loop: cost = 57.41097640991211\n", "The 2440th loop: cost = 56.38499450683594\n", "The 2441th loop: cost = 54.067047119140625\n", "The 2442th loop: cost = 51.92763137817383\n", "The 2443th loop: cost = 58.9908447265625\n", "The 2444th loop: cost = 63.75748825073242\n", "The 2445th loop: cost = 56.552154541015625\n", "The 2446th loop: cost = 55.552669525146484\n", "The 2447th loop: cost = 49.57417297363281\n", "The 2448th loop: cost = 55.06937026977539\n", "The 2449th loop: cost = 51.26466369628906\n", "The 2450th loop: cost = 58.52777099609375\n", "The 2451th loop: cost = 45.42150115966797\n", "The 2452th loop: cost = 58.70610809326172\n", "The 2453th loop: cost = 54.93401336669922\n", "The 2454th loop: cost = 56.16674041748047\n", "The 2455th loop: cost = 51.34490966796875\n", "The 2456th loop: cost = 58.3126335144043\n", "The 2457th loop: cost = 42.080421447753906\n", "The 2458th loop: cost = 55.707183837890625\n", "The 2459th loop: cost = 51.90776824951172\n", "The 2460th loop: cost = 51.66162872314453\n", "The 2461th loop: cost = 54.57059097290039\n", "The 2462th loop: cost = 45.216148376464844\n", "The 2463th loop: cost = 52.887367248535156\n", "The 2464th loop: cost = 59.91909408569336\n", "The 2465th loop: cost = 48.61357879638672\n", "The 2466th loop: cost = 59.50715637207031\n", "The 2467th loop: cost = 53.87969207763672\n", "The 2468th loop: cost = 48.89688491821289\n", "The 2469th loop: cost = 48.01838302612305\n", "The 2470th loop: cost = 53.5849723815918\n", "The 2471th loop: cost = 45.569671630859375\n", "The 2472th loop: cost = 46.109840393066406\n", "The 2473th loop: cost = 50.398006439208984\n", "The 2474th loop: cost = 51.79369354248047\n", "The 2475th loop: cost = 43.351444244384766\n", "The 2476th loop: cost = 58.98101043701172\n", "The 2477th loop: cost = 48.79833221435547\n", "The 2478th loop: cost = 52.145591735839844\n", "The 2479th loop: cost = 42.478271484375\n", "The 2480th loop: cost = 52.828208923339844\n", "The 2481th loop: cost = 56.29944610595703\n", "The 2482th loop: cost = 50.27090072631836\n", "The 2483th loop: cost = 50.10340881347656\n", "The 2484th loop: cost = 47.608604431152344\n", "The 2485th loop: cost = 61.39678192138672\n", "The 2486th loop: cost = 47.57999038696289\n", "The 2487th loop: cost = 57.61677551269531\n", "The 2488th loop: cost = 40.96601104736328\n", "The 2489th loop: cost = 51.99279022216797\n", "The 2490th loop: cost = 62.65547561645508\n", "The 2491th loop: cost = 49.187232971191406\n", "The 2492th loop: cost = 47.20922088623047\n", "The 2493th loop: cost = 41.590179443359375\n", "The 2494th loop: cost = 48.96763229370117\n", "The 2495th loop: cost = 48.0009765625\n", "The 2496th loop: cost = 62.880455017089844\n", "The 2497th loop: cost = 54.0234489440918\n", "The 2498th loop: cost = 42.87586212158203\n", "The 2499th loop: cost = 58.7552375793457\n", "The 2500th loop: cost = 43.58380126953125\n", "The 2501th loop: cost = 52.829856872558594\n", "The 2502th loop: cost = 46.04452896118164\n", "The 2503th loop: cost = 52.987850189208984\n", "The 2504th loop: cost = 54.96894836425781\n", "The 2505th loop: cost = 46.91758346557617\n", "The 2506th loop: cost = 52.214752197265625\n", "The 2507th loop: cost = 40.159400939941406\n", "The 2508th loop: cost = 66.85011291503906\n", "The 2509th loop: cost = 58.93684387207031\n", "The 2510th loop: cost = 49.74578094482422\n", "The 2511th loop: cost = 41.47126007080078\n", "The 2512th loop: cost = 59.97161865234375\n", "The 2513th loop: cost = 52.168766021728516\n", "The 2514th loop: cost = 57.39688491821289\n", "The 2515th loop: cost = 66.47313690185547\n", "The 2516th loop: cost = 48.999149322509766\n", "The 2517th loop: cost = 49.367488861083984\n", "The 2518th loop: cost = 43.30513000488281\n", "The 2519th loop: cost = 60.129913330078125\n", "The 2520th loop: cost = 64.90951538085938\n", "The 2521th loop: cost = 50.262516021728516\n", "The 2522th loop: cost = 61.83650207519531\n", "The 2523th loop: cost = 48.321895599365234\n", "The 2524th loop: cost = 58.644134521484375\n", "The 2525th loop: cost = 49.87861633300781\n", "The 2526th loop: cost = 63.35945129394531\n", "The 2527th loop: cost = 55.772377014160156\n", "The 2528th loop: cost = 52.2266845703125\n", "The 2529th loop: cost = 46.97581481933594\n", "The 2530th loop: cost = 54.03605651855469\n", "The 2531th loop: cost = 46.22995376586914\n", "The 2532th loop: cost = 52.432228088378906\n", "The 2533th loop: cost = 42.916343688964844\n", "The 2534th loop: cost = 50.75596237182617\n", "The 2535th loop: cost = 43.69963455200195\n", "The 2536th loop: cost = 55.668479919433594\n", "The 2537th loop: cost = 50.889930725097656\n", "The 2538th loop: cost = 60.22903823852539\n", "The 2539th loop: cost = 67.98612976074219\n", "The 2540th loop: cost = 63.48709487915039\n", "The 2541th loop: cost = 50.014461517333984\n", "The 2542th loop: cost = 48.23137283325195\n", "The 2543th loop: cost = 55.32974624633789\n", "The 2544th loop: cost = 58.65581512451172\n", "The 2545th loop: cost = 51.61863708496094\n", "The 2546th loop: cost = 55.29236602783203\n", "The 2547th loop: cost = 58.140071868896484\n", "The 2548th loop: cost = 46.318824768066406\n", "The 2549th loop: cost = 54.78413772583008\n", "The 2550th loop: cost = 47.287574768066406\n", "The 2551th loop: cost = 47.960609436035156\n", "The 2552th loop: cost = 40.33696746826172\n", "The 2553th loop: cost = 45.533935546875\n", "The 2554th loop: cost = 57.65814208984375\n", "The 2555th loop: cost = 48.12371063232422\n", "The 2556th loop: cost = 53.2332763671875\n", "The 2557th loop: cost = 46.53581237792969\n", "The 2558th loop: cost = 52.44306945800781\n", "The 2559th loop: cost = 52.452056884765625\n", "The 2560th loop: cost = 50.0827751159668\n", "The 2561th loop: cost = 49.72051239013672\n", "The 2562th loop: cost = 49.842742919921875\n", "The 2563th loop: cost = 49.79722595214844\n", "The 2564th loop: cost = 49.174705505371094\n", "The 2565th loop: cost = 58.322418212890625\n", "The 2566th loop: cost = 44.81184387207031\n", "The 2567th loop: cost = 61.326751708984375\n", "The 2568th loop: cost = 50.47442626953125\n", "The 2569th loop: cost = 48.70722961425781\n", "The 2570th loop: cost = 50.16288375854492\n", "The 2571th loop: cost = 42.59492492675781\n", "The 2572th loop: cost = 42.24079895019531\n", "The 2573th loop: cost = 42.17191696166992\n", "The 2574th loop: cost = 64.0232162475586\n", "The 2575th loop: cost = 59.078285217285156\n", "The 2576th loop: cost = 53.64908981323242\n", "The 2577th loop: cost = 59.59667205810547\n", "The 2578th loop: cost = 61.12403106689453\n", "The 2579th loop: cost = 48.30061340332031\n", "The 2580th loop: cost = 55.318763732910156\n", "The 2581th loop: cost = 52.511260986328125\n", "The 2582th loop: cost = 49.43711853027344\n", "The 2583th loop: cost = 53.05647277832031\n", "The 2584th loop: cost = 64.00468444824219\n", "The 2585th loop: cost = 55.82054901123047\n", "The 2586th loop: cost = 59.063255310058594\n", "The 2587th loop: cost = 47.6025390625\n", "The 2588th loop: cost = 47.51498031616211\n", "The 2589th loop: cost = 55.531341552734375\n", "The 2590th loop: cost = 53.175445556640625\n", "The 2591th loop: cost = 41.53797912597656\n", "The 2592th loop: cost = 50.370269775390625\n", "The 2593th loop: cost = 45.421409606933594\n", "The 2594th loop: cost = 50.43669128417969\n", "The 2595th loop: cost = 46.59333038330078\n", "The 2596th loop: cost = 48.74013137817383\n", "The 2597th loop: cost = 49.117279052734375\n", "The 2598th loop: cost = 46.682228088378906\n", "The 2599th loop: cost = 55.26294708251953\n", "The 2600th loop: cost = 53.642547607421875\n", "The 2601th loop: cost = 54.13035583496094\n", "The 2602th loop: cost = 48.34016418457031\n", "The 2603th loop: cost = 42.43559265136719\n", "The 2604th loop: cost = 52.13014221191406\n", "The 2605th loop: cost = 60.173213958740234\n", "The 2606th loop: cost = 47.16707992553711\n", "The 2607th loop: cost = 44.666568756103516\n", "The 2608th loop: cost = 44.018592834472656\n", "The 2609th loop: cost = 47.015594482421875\n", "The 2610th loop: cost = 43.28300094604492\n", "The 2611th loop: cost = 48.35664367675781\n", "The 2612th loop: cost = 50.079002380371094\n", "The 2613th loop: cost = 56.09336853027344\n", "The 2614th loop: cost = 41.65161895751953\n", "The 2615th loop: cost = 42.997066497802734\n", "The 2616th loop: cost = 39.35974884033203\n", "The 2617th loop: cost = 45.16892623901367\n", "The 2618th loop: cost = 46.76333999633789\n", "The 2619th loop: cost = 44.02839279174805\n", "The 2620th loop: cost = 51.125526428222656\n", "The 2621th loop: cost = 53.24859619140625\n", "The 2622th loop: cost = 44.88471221923828\n", "The 2623th loop: cost = 44.77142333984375\n", "The 2624th loop: cost = 50.94084930419922\n", "The 2625th loop: cost = 53.38928985595703\n", "The 2626th loop: cost = 47.43224334716797\n", "The 2627th loop: cost = 50.05937194824219\n", "The 2628th loop: cost = 57.18454360961914\n", "The 2629th loop: cost = 44.885520935058594\n", "The 2630th loop: cost = 38.264957427978516\n", "The 2631th loop: cost = 54.27668762207031\n", "The 2632th loop: cost = 47.606117248535156\n", "The 2633th loop: cost = 52.56423568725586\n", "The 2634th loop: cost = 53.054466247558594\n", "The 2635th loop: cost = 53.65544891357422\n", "The 2636th loop: cost = 53.50968551635742\n", "The 2637th loop: cost = 48.52790069580078\n", "The 2638th loop: cost = 49.82355499267578\n", "The 2639th loop: cost = 51.06670379638672\n", "The 2640th loop: cost = 53.59563064575195\n", "The 2641th loop: cost = 45.58644485473633\n", "The 2642th loop: cost = 59.453792572021484\n", "The 2643th loop: cost = 54.3712043762207\n", "The 2644th loop: cost = 54.39537811279297\n", "The 2645th loop: cost = 54.51909637451172\n", "The 2646th loop: cost = 53.01679229736328\n", "The 2647th loop: cost = 61.945213317871094\n", "The 2648th loop: cost = 45.417015075683594\n", "The 2649th loop: cost = 41.9122314453125\n", "The 2650th loop: cost = 46.19527816772461\n", "The 2651th loop: cost = 44.544795989990234\n", "The 2652th loop: cost = 46.055694580078125\n", "The 2653th loop: cost = 56.424110412597656\n", "The 2654th loop: cost = 48.29051971435547\n", "The 2655th loop: cost = 43.25776290893555\n", "The 2656th loop: cost = 60.893043518066406\n", "The 2657th loop: cost = 50.74985885620117\n", "The 2658th loop: cost = 48.52660369873047\n", "The 2659th loop: cost = 52.40088653564453\n", "The 2660th loop: cost = 55.96470642089844\n", "The 2661th loop: cost = 38.008724212646484\n", "The 2662th loop: cost = 48.45707702636719\n", "The 2663th loop: cost = 50.1649169921875\n", "The 2664th loop: cost = 47.238258361816406\n", "The 2665th loop: cost = 48.73603057861328\n", "The 2666th loop: cost = 60.52764892578125\n", "The 2667th loop: cost = 43.73206329345703\n", "The 2668th loop: cost = 54.416603088378906\n", "The 2669th loop: cost = 46.87015914916992\n", "The 2670th loop: cost = 60.62983703613281\n", "The 2671th loop: cost = 50.54310607910156\n", "The 2672th loop: cost = 51.536773681640625\n", "The 2673th loop: cost = 51.783348083496094\n", "The 2674th loop: cost = 54.82974624633789\n", "The 2675th loop: cost = 46.126163482666016\n", "The 2676th loop: cost = 52.18769836425781\n", "The 2677th loop: cost = 44.59413146972656\n", "The 2678th loop: cost = 61.323970794677734\n", "The 2679th loop: cost = 56.944515228271484\n", "The 2680th loop: cost = 51.717185974121094\n", "The 2681th loop: cost = 48.933170318603516\n", "The 2682th loop: cost = 42.592777252197266\n", "The 2683th loop: cost = 46.31575012207031\n", "The 2684th loop: cost = 39.57162857055664\n", "The 2685th loop: cost = 58.46977996826172\n", "The 2686th loop: cost = 50.12593078613281\n", "The 2687th loop: cost = 48.201263427734375\n", "The 2688th loop: cost = 48.38972473144531\n", "The 2689th loop: cost = 58.4713134765625\n", "The 2690th loop: cost = 41.9002685546875\n", "The 2691th loop: cost = 46.80945587158203\n", "The 2692th loop: cost = 52.26212692260742\n", "The 2693th loop: cost = 62.42164611816406\n", "The 2694th loop: cost = 48.27485656738281\n", "The 2695th loop: cost = 61.85261535644531\n", "The 2696th loop: cost = 53.896270751953125\n", "The 2697th loop: cost = 51.435020446777344\n", "The 2698th loop: cost = 52.44590377807617\n", "The 2699th loop: cost = 58.74909210205078\n", "The 2700th loop: cost = 53.43108367919922\n", "The 2701th loop: cost = 47.867942810058594\n", "The 2702th loop: cost = 42.32060241699219\n", "The 2703th loop: cost = 44.44004821777344\n", "The 2704th loop: cost = 54.15597915649414\n", "The 2705th loop: cost = 59.737579345703125\n", "The 2706th loop: cost = 56.951080322265625\n", "The 2707th loop: cost = 54.05659484863281\n", "The 2708th loop: cost = 55.15805435180664\n", "The 2709th loop: cost = 53.781734466552734\n", "The 2710th loop: cost = 56.378299713134766\n", "The 2711th loop: cost = 48.13111114501953\n", "The 2712th loop: cost = 44.56134796142578\n", "The 2713th loop: cost = 45.56475830078125\n", "The 2714th loop: cost = 49.69314193725586\n", "The 2715th loop: cost = 48.98115539550781\n", "The 2716th loop: cost = 44.647552490234375\n", "The 2717th loop: cost = 51.758460998535156\n", "The 2718th loop: cost = 50.0134162902832\n", "The 2719th loop: cost = 58.08733367919922\n", "The 2720th loop: cost = 41.77537536621094\n", "The 2721th loop: cost = 54.51630401611328\n", "The 2722th loop: cost = 52.08448028564453\n", "The 2723th loop: cost = 48.15452194213867\n", "The 2724th loop: cost = 46.56655502319336\n", "The 2725th loop: cost = 54.439842224121094\n", "The 2726th loop: cost = 48.521026611328125\n", "The 2727th loop: cost = 46.30241394042969\n", "The 2728th loop: cost = 54.054649353027344\n", "The 2729th loop: cost = 48.536285400390625\n", "The 2730th loop: cost = 60.703102111816406\n", "The 2731th loop: cost = 54.81679916381836\n", "The 2732th loop: cost = 44.19395065307617\n", "The 2733th loop: cost = 53.62510681152344\n", "The 2734th loop: cost = 47.38996124267578\n", "The 2735th loop: cost = 53.43330383300781\n", "The 2736th loop: cost = 49.34831619262695\n", "The 2737th loop: cost = 45.114219665527344\n", "The 2738th loop: cost = 59.125633239746094\n", "The 2739th loop: cost = 64.8551025390625\n", "The 2740th loop: cost = 62.7860221862793\n", "The 2741th loop: cost = 52.52580261230469\n", "The 2742th loop: cost = 57.56610107421875\n", "The 2743th loop: cost = 57.39507293701172\n", "The 2744th loop: cost = 51.396690368652344\n", "The 2745th loop: cost = 43.37242889404297\n", "The 2746th loop: cost = 54.03105926513672\n", "The 2747th loop: cost = 48.921775817871094\n", "The 2748th loop: cost = 56.18563461303711\n", "The 2749th loop: cost = 45.71205139160156\n", "The 2750th loop: cost = 45.60023498535156\n", "The 2751th loop: cost = 51.62158966064453\n", "The 2752th loop: cost = 49.330787658691406\n", "The 2753th loop: cost = 53.498924255371094\n", "The 2754th loop: cost = 44.41364288330078\n", "The 2755th loop: cost = 62.200889587402344\n", "The 2756th loop: cost = 48.07353973388672\n", "The 2757th loop: cost = 56.589988708496094\n", "The 2758th loop: cost = 48.4913215637207\n", "The 2759th loop: cost = 52.661285400390625\n", "The 2760th loop: cost = 52.96292495727539\n", "The 2761th loop: cost = 52.94313430786133\n", "The 2762th loop: cost = 48.941139221191406\n", "The 2763th loop: cost = 50.79646301269531\n", "The 2764th loop: cost = 45.42768859863281\n", "The 2765th loop: cost = 52.74911117553711\n", "The 2766th loop: cost = 43.05857849121094\n", "The 2767th loop: cost = 42.92791748046875\n", "The 2768th loop: cost = 54.090118408203125\n", "The 2769th loop: cost = 48.20545196533203\n", "The 2770th loop: cost = 48.630577087402344\n", "The 2771th loop: cost = 54.52170944213867\n", "The 2772th loop: cost = 48.55290222167969\n", "The 2773th loop: cost = 51.79914855957031\n", "The 2774th loop: cost = 42.54932403564453\n", "The 2775th loop: cost = 58.245018005371094\n", "The 2776th loop: cost = 49.842933654785156\n", "The 2777th loop: cost = 49.25808334350586\n", "The 2778th loop: cost = 48.90382385253906\n", "The 2779th loop: cost = 41.36375427246094\n", "The 2780th loop: cost = 42.56529998779297\n", "The 2781th loop: cost = 42.77570724487305\n", "The 2782th loop: cost = 58.1096076965332\n", "The 2783th loop: cost = 47.560508728027344\n", "The 2784th loop: cost = 51.6557502746582\n", "The 2785th loop: cost = 49.615413665771484\n", "The 2786th loop: cost = 49.66898727416992\n", "The 2787th loop: cost = 51.805816650390625\n", "The 2788th loop: cost = 47.58331298828125\n", "The 2789th loop: cost = 52.63319778442383\n", "The 2790th loop: cost = 48.37234115600586\n", "The 2791th loop: cost = 60.104984283447266\n", "The 2792th loop: cost = 53.25611877441406\n", "The 2793th loop: cost = 47.91374588012695\n", "The 2794th loop: cost = 45.81282424926758\n", "The 2795th loop: cost = 52.93242645263672\n", "The 2796th loop: cost = 52.51076889038086\n", "The 2797th loop: cost = 46.89215850830078\n", "The 2798th loop: cost = 49.445228576660156\n", "The 2799th loop: cost = 52.22047424316406\n", "The 2800th loop: cost = 43.349853515625\n", "The 2801th loop: cost = 51.69853973388672\n", "The 2802th loop: cost = 49.896175384521484\n", "The 2803th loop: cost = 45.02661895751953\n", "The 2804th loop: cost = 59.177452087402344\n", "The 2805th loop: cost = 49.15748596191406\n", "The 2806th loop: cost = 58.71573257446289\n", "The 2807th loop: cost = 50.37876510620117\n", "The 2808th loop: cost = 35.9644775390625\n", "The 2809th loop: cost = 52.63432312011719\n", "The 2810th loop: cost = 62.090232849121094\n", "The 2811th loop: cost = 53.86566162109375\n", "The 2812th loop: cost = 54.79875946044922\n", "The 2813th loop: cost = 46.83986282348633\n", "The 2814th loop: cost = 53.28684997558594\n", "The 2815th loop: cost = 52.87471008300781\n", "The 2816th loop: cost = 47.093421936035156\n", "The 2817th loop: cost = 55.53510284423828\n", "The 2818th loop: cost = 45.030792236328125\n", "The 2819th loop: cost = 45.18682861328125\n", "The 2820th loop: cost = 52.342376708984375\n", "The 2821th loop: cost = 51.214813232421875\n", "The 2822th loop: cost = 51.71424865722656\n", "The 2823th loop: cost = 59.321468353271484\n", "The 2824th loop: cost = 45.687110900878906\n", "The 2825th loop: cost = 53.6738395690918\n", "The 2826th loop: cost = 46.83686065673828\n", "The 2827th loop: cost = 37.89657211303711\n", "The 2828th loop: cost = 51.05742645263672\n", "The 2829th loop: cost = 49.387245178222656\n", "The 2830th loop: cost = 50.31462478637695\n", "The 2831th loop: cost = 49.728641510009766\n", "The 2832th loop: cost = 46.86601257324219\n", "The 2833th loop: cost = 48.027076721191406\n", "The 2834th loop: cost = 51.25175857543945\n", "The 2835th loop: cost = 51.808319091796875\n", "The 2836th loop: cost = 38.96088409423828\n", "The 2837th loop: cost = 42.975154876708984\n", "The 2838th loop: cost = 52.15675354003906\n", "The 2839th loop: cost = 51.63067626953125\n", "The 2840th loop: cost = 46.28119659423828\n", "The 2841th loop: cost = 65.96466827392578\n", "The 2842th loop: cost = 51.249412536621094\n", "The 2843th loop: cost = 47.80740737915039\n", "The 2844th loop: cost = 55.039886474609375\n", "The 2845th loop: cost = 49.65032196044922\n", "The 2846th loop: cost = 50.1472053527832\n", "The 2847th loop: cost = 45.217262268066406\n", "The 2848th loop: cost = 46.191864013671875\n", "The 2849th loop: cost = 50.233455657958984\n", "The 2850th loop: cost = 56.782249450683594\n", "The 2851th loop: cost = 44.85295104980469\n", "The 2852th loop: cost = 57.185447692871094\n", "The 2853th loop: cost = 54.20368957519531\n", "The 2854th loop: cost = 47.45524597167969\n", "The 2855th loop: cost = 55.733154296875\n", "The 2856th loop: cost = 56.22783660888672\n", "The 2857th loop: cost = 39.60712432861328\n", "The 2858th loop: cost = 39.38422393798828\n", "The 2859th loop: cost = 61.93208312988281\n", "The 2860th loop: cost = 45.15374755859375\n", "The 2861th loop: cost = 50.3454475402832\n", "The 2862th loop: cost = 53.27217102050781\n", "The 2863th loop: cost = 46.377471923828125\n", "The 2864th loop: cost = 36.868473052978516\n", "The 2865th loop: cost = 39.975067138671875\n", "The 2866th loop: cost = 53.31927490234375\n", "The 2867th loop: cost = 47.249855041503906\n", "The 2868th loop: cost = 43.195030212402344\n", "The 2869th loop: cost = 61.47844314575195\n", "The 2870th loop: cost = 47.278358459472656\n", "The 2871th loop: cost = 56.11615753173828\n", "The 2872th loop: cost = 50.76284408569336\n", "The 2873th loop: cost = 43.97164535522461\n", "The 2874th loop: cost = 47.0560188293457\n", "The 2875th loop: cost = 45.89286422729492\n", "The 2876th loop: cost = 47.922019958496094\n", "The 2877th loop: cost = 52.31205749511719\n", "The 2878th loop: cost = 59.80327606201172\n", "The 2879th loop: cost = 47.146671295166016\n", "The 2880th loop: cost = 45.19062042236328\n", "The 2881th loop: cost = 42.87554168701172\n", "The 2882th loop: cost = 58.406524658203125\n", "The 2883th loop: cost = 52.44450378417969\n", "The 2884th loop: cost = 57.0537109375\n", "The 2885th loop: cost = 41.96985626220703\n", "The 2886th loop: cost = 50.913822174072266\n", "The 2887th loop: cost = 52.80543518066406\n", "The 2888th loop: cost = 61.4124641418457\n", "The 2889th loop: cost = 47.94685363769531\n", "The 2890th loop: cost = 44.78770446777344\n", "The 2891th loop: cost = 45.68878936767578\n", "The 2892th loop: cost = 47.66144943237305\n", "The 2893th loop: cost = 54.374969482421875\n", "The 2894th loop: cost = 54.98772430419922\n", "The 2895th loop: cost = 50.11575698852539\n", "The 2896th loop: cost = 46.73239517211914\n", "The 2897th loop: cost = 52.57171630859375\n", "The 2898th loop: cost = 54.3577880859375\n", "The 2899th loop: cost = 54.99632263183594\n", "The 2900th loop: cost = 56.515987396240234\n", "The 2901th loop: cost = 60.863609313964844\n", "The 2902th loop: cost = 53.715606689453125\n", "The 2903th loop: cost = 48.95781707763672\n", "The 2904th loop: cost = 48.42386245727539\n", "The 2905th loop: cost = 53.326499938964844\n", "The 2906th loop: cost = 50.72425079345703\n", "The 2907th loop: cost = 53.77305221557617\n", "The 2908th loop: cost = 56.26641082763672\n", "The 2909th loop: cost = 52.08214569091797\n", "The 2910th loop: cost = 54.67578125\n", "The 2911th loop: cost = 51.58668518066406\n", "The 2912th loop: cost = 59.45020294189453\n", "The 2913th loop: cost = 55.569610595703125\n", "The 2914th loop: cost = 50.38850021362305\n", "The 2915th loop: cost = 40.8402099609375\n", "The 2916th loop: cost = 41.9521484375\n", "The 2917th loop: cost = 49.58430480957031\n", "The 2918th loop: cost = 50.71370315551758\n", "The 2919th loop: cost = 58.006263732910156\n", "The 2920th loop: cost = 55.40044021606445\n", "The 2921th loop: cost = 53.31666564941406\n", "The 2922th loop: cost = 55.386070251464844\n", "The 2923th loop: cost = 50.59410095214844\n", "The 2924th loop: cost = 58.357906341552734\n", "The 2925th loop: cost = 60.16554260253906\n", "The 2926th loop: cost = 43.06847381591797\n", "The 2927th loop: cost = 52.67764663696289\n", "The 2928th loop: cost = 47.69072341918945\n", "The 2929th loop: cost = 46.53782272338867\n", "The 2930th loop: cost = 63.158203125\n", "The 2931th loop: cost = 57.15822219848633\n", "The 2932th loop: cost = 47.589683532714844\n", "The 2933th loop: cost = 52.4828987121582\n", "The 2934th loop: cost = 46.987060546875\n", "The 2935th loop: cost = 41.276161193847656\n", "The 2936th loop: cost = 34.12978744506836\n", "The 2937th loop: cost = 59.454715728759766\n", "The 2938th loop: cost = 62.29176712036133\n", "The 2939th loop: cost = 52.373443603515625\n", "The 2940th loop: cost = 52.55731201171875\n", "The 2941th loop: cost = 63.579833984375\n", "The 2942th loop: cost = 57.31675720214844\n", "The 2943th loop: cost = 48.38904571533203\n", "The 2944th loop: cost = 49.451175689697266\n", "The 2945th loop: cost = 53.35637664794922\n", "The 2946th loop: cost = 46.647613525390625\n", "The 2947th loop: cost = 59.373802185058594\n", "The 2948th loop: cost = 50.52743911743164\n", "The 2949th loop: cost = 45.445552825927734\n", "The 2950th loop: cost = 46.39883804321289\n", "The 2951th loop: cost = 50.240699768066406\n", "The 2952th loop: cost = 56.386199951171875\n", "The 2953th loop: cost = 50.7602653503418\n", "The 2954th loop: cost = 54.85805892944336\n", "The 2955th loop: cost = 55.71398162841797\n", "The 2956th loop: cost = 55.41442108154297\n", "The 2957th loop: cost = 45.690887451171875\n", "The 2958th loop: cost = 52.83223342895508\n", "The 2959th loop: cost = 47.784034729003906\n", "The 2960th loop: cost = 47.69139862060547\n", "The 2961th loop: cost = 36.82933807373047\n", "The 2962th loop: cost = 49.19670867919922\n", "The 2963th loop: cost = 57.23799133300781\n", "The 2964th loop: cost = 45.464500427246094\n", "The 2965th loop: cost = 43.798866271972656\n", "The 2966th loop: cost = 61.20518112182617\n", "The 2967th loop: cost = 39.34381866455078\n", "The 2968th loop: cost = 36.98426818847656\n", "The 2969th loop: cost = 46.86773681640625\n", "The 2970th loop: cost = 51.183284759521484\n", "The 2971th loop: cost = 51.77382278442383\n", "The 2972th loop: cost = 45.672119140625\n", "The 2973th loop: cost = 58.526580810546875\n", "The 2974th loop: cost = 38.19220733642578\n", "The 2975th loop: cost = 47.07544708251953\n", "The 2976th loop: cost = 51.351722717285156\n", "The 2977th loop: cost = 51.52521514892578\n", "The 2978th loop: cost = 57.079307556152344\n", "The 2979th loop: cost = 55.64630889892578\n", "The 2980th loop: cost = 48.16766357421875\n", "The 2981th loop: cost = 45.441524505615234\n", "The 2982th loop: cost = 46.96162414550781\n", "The 2983th loop: cost = 48.00688171386719\n", "The 2984th loop: cost = 52.470314025878906\n", "The 2985th loop: cost = 51.33928680419922\n", "The 2986th loop: cost = 50.6358757019043\n", "The 2987th loop: cost = 50.48640441894531\n", "The 2988th loop: cost = 45.09580993652344\n", "The 2989th loop: cost = 66.72195434570312\n", "The 2990th loop: cost = 49.02667999267578\n", "The 2991th loop: cost = 53.923622131347656\n", "The 2992th loop: cost = 42.67240905761719\n", "The 2993th loop: cost = 56.07337951660156\n", "The 2994th loop: cost = 49.36119842529297\n", "The 2995th loop: cost = 55.95043182373047\n", "The 2996th loop: cost = 45.831787109375\n", "The 2997th loop: cost = 47.32185363769531\n", "The 2998th loop: cost = 49.469642639160156\n", "The 2999th loop: cost = 56.98445510864258\n", "The 3000th loop: cost = 50.0916862487793\n", "The 3001th loop: cost = 51.34638214111328\n", "The 3002th loop: cost = 66.03399658203125\n", "The 3003th loop: cost = 52.856475830078125\n", "The 3004th loop: cost = 48.199562072753906\n", "The 3005th loop: cost = 52.73527526855469\n", "The 3006th loop: cost = 40.68440246582031\n", "The 3007th loop: cost = 50.772335052490234\n", "The 3008th loop: cost = 53.725826263427734\n", "The 3009th loop: cost = 43.769432067871094\n", "The 3010th loop: cost = 48.672767639160156\n", "The 3011th loop: cost = 41.1827278137207\n", "The 3012th loop: cost = 49.254581451416016\n", "The 3013th loop: cost = 50.46428298950195\n", "The 3014th loop: cost = 47.43035125732422\n", "The 3015th loop: cost = 51.19276428222656\n", "The 3016th loop: cost = 46.21952819824219\n", "The 3017th loop: cost = 52.60553741455078\n", "The 3018th loop: cost = 40.38288879394531\n", "The 3019th loop: cost = 59.69771194458008\n", "The 3020th loop: cost = 55.01719665527344\n", "The 3021th loop: cost = 47.98735809326172\n", "The 3022th loop: cost = 44.05006790161133\n", "The 3023th loop: cost = 52.74965286254883\n", "The 3024th loop: cost = 53.03553771972656\n", "The 3025th loop: cost = 49.71513748168945\n", "The 3026th loop: cost = 50.013519287109375\n", "The 3027th loop: cost = 40.9918327331543\n", "The 3028th loop: cost = 53.33926010131836\n", "The 3029th loop: cost = 47.24393081665039\n", "The 3030th loop: cost = 48.69601821899414\n", "The 3031th loop: cost = 48.04924011230469\n", "The 3032th loop: cost = 55.743072509765625\n", "The 3033th loop: cost = 56.17277145385742\n", "The 3034th loop: cost = 47.50230026245117\n", "The 3035th loop: cost = 61.12919616699219\n", "The 3036th loop: cost = 39.0037841796875\n", "The 3037th loop: cost = 48.64410400390625\n", "The 3038th loop: cost = 51.33994674682617\n", "The 3039th loop: cost = 43.97734832763672\n", "The 3040th loop: cost = 50.17404556274414\n", "The 3041th loop: cost = 48.70457458496094\n", "The 3042th loop: cost = 53.08940887451172\n", "The 3043th loop: cost = 53.149192810058594\n", "The 3044th loop: cost = 52.079410552978516\n", "The 3045th loop: cost = 38.25590515136719\n", "The 3046th loop: cost = 45.314369201660156\n", "The 3047th loop: cost = 42.36894226074219\n", "The 3048th loop: cost = 45.82362365722656\n", "The 3049th loop: cost = 50.68061828613281\n", "The 3050th loop: cost = 49.03349304199219\n", "The 3051th loop: cost = 52.890567779541016\n", "The 3052th loop: cost = 50.46367645263672\n", "The 3053th loop: cost = 48.09539031982422\n", "The 3054th loop: cost = 57.029239654541016\n", "The 3055th loop: cost = 55.69211196899414\n", "The 3056th loop: cost = 56.60753631591797\n", "The 3057th loop: cost = 49.31609344482422\n", "The 3058th loop: cost = 47.90137481689453\n", "The 3059th loop: cost = 49.35138702392578\n", "The 3060th loop: cost = 51.140220642089844\n", "The 3061th loop: cost = 47.878868103027344\n", "The 3062th loop: cost = 43.00343322753906\n", "The 3063th loop: cost = 51.74458312988281\n", "The 3064th loop: cost = 54.06242370605469\n", "The 3065th loop: cost = 50.35840606689453\n", "The 3066th loop: cost = 62.92392349243164\n", "The 3067th loop: cost = 50.823822021484375\n", "The 3068th loop: cost = 57.27970886230469\n", "The 3069th loop: cost = 41.06528091430664\n", "The 3070th loop: cost = 50.520835876464844\n", "The 3071th loop: cost = 53.02140808105469\n", "The 3072th loop: cost = 49.60833740234375\n", "The 3073th loop: cost = 54.1063117980957\n", "The 3074th loop: cost = 47.96100616455078\n", "The 3075th loop: cost = 46.191078186035156\n", "The 3076th loop: cost = 50.94141387939453\n", "The 3077th loop: cost = 50.35446548461914\n", "The 3078th loop: cost = 45.47357177734375\n", "The 3079th loop: cost = 51.444549560546875\n", "The 3080th loop: cost = 42.16449737548828\n", "The 3081th loop: cost = 52.28584289550781\n", "The 3082th loop: cost = 54.317787170410156\n", "The 3083th loop: cost = 53.63726806640625\n", "The 3084th loop: cost = 58.41228485107422\n", "The 3085th loop: cost = 46.58086395263672\n", "The 3086th loop: cost = 55.616947174072266\n", "The 3087th loop: cost = 48.753089904785156\n", "The 3088th loop: cost = 47.55803680419922\n", "The 3089th loop: cost = 46.74678039550781\n", "The 3090th loop: cost = 41.71559524536133\n", "The 3091th loop: cost = 59.96364974975586\n", "The 3092th loop: cost = 52.03009796142578\n", "The 3093th loop: cost = 38.14642333984375\n", "The 3094th loop: cost = 51.98906707763672\n", "The 3095th loop: cost = 43.821678161621094\n", "The 3096th loop: cost = 51.57575988769531\n", "The 3097th loop: cost = 48.450836181640625\n", "The 3098th loop: cost = 47.667816162109375\n", "The 3099th loop: cost = 47.639427185058594\n", "The 3100th loop: cost = 55.96437072753906\n", "The 3101th loop: cost = 55.41572189331055\n", "The 3102th loop: cost = 43.74335479736328\n", "The 3103th loop: cost = 55.58216857910156\n", "The 3104th loop: cost = 49.68738555908203\n", "The 3105th loop: cost = 41.40064239501953\n", "The 3106th loop: cost = 50.59970474243164\n", "The 3107th loop: cost = 49.555023193359375\n", "The 3108th loop: cost = 62.00863265991211\n", "The 3109th loop: cost = 53.79505157470703\n", "The 3110th loop: cost = 50.00541687011719\n", "The 3111th loop: cost = 50.12293243408203\n", "The 3112th loop: cost = 44.91941452026367\n", "The 3113th loop: cost = 51.26176452636719\n", "The 3114th loop: cost = 47.63323974609375\n", "The 3115th loop: cost = 55.746559143066406\n", "The 3116th loop: cost = 55.0023307800293\n", "The 3117th loop: cost = 51.53271484375\n", "The 3118th loop: cost = 51.43378448486328\n", "The 3119th loop: cost = 59.25534439086914\n", "The 3120th loop: cost = 53.82990264892578\n", "The 3121th loop: cost = 60.380062103271484\n", "The 3122th loop: cost = 54.097965240478516\n", "The 3123th loop: cost = 50.37931442260742\n", "The 3124th loop: cost = 58.70732879638672\n", "The 3125th loop: cost = 52.063228607177734\n", "The 3126th loop: cost = 49.17719268798828\n", "The 3127th loop: cost = 43.216880798339844\n", "The 3128th loop: cost = 60.442161560058594\n", "The 3129th loop: cost = 54.613365173339844\n", "The 3130th loop: cost = 63.77098083496094\n", "The 3131th loop: cost = 44.66085433959961\n", "The 3132th loop: cost = 47.37140655517578\n", "The 3133th loop: cost = 48.39073181152344\n", "The 3134th loop: cost = 44.459896087646484\n", "The 3135th loop: cost = 46.39575958251953\n", "The 3136th loop: cost = 57.706790924072266\n", "The 3137th loop: cost = 45.922508239746094\n", "The 3138th loop: cost = 52.03211212158203\n", "The 3139th loop: cost = 54.03972625732422\n", "The 3140th loop: cost = 45.006141662597656\n", "The 3141th loop: cost = 41.11918640136719\n", "The 3142th loop: cost = 54.956214904785156\n", "The 3143th loop: cost = 43.607513427734375\n", "The 3144th loop: cost = 47.617271423339844\n", "The 3145th loop: cost = 45.42044448852539\n", "The 3146th loop: cost = 47.33953857421875\n", "The 3147th loop: cost = 45.92177200317383\n", "The 3148th loop: cost = 42.5904426574707\n", "The 3149th loop: cost = 49.391605377197266\n", "The 3150th loop: cost = 49.1339225769043\n", "The 3151th loop: cost = 49.43865966796875\n", "The 3152th loop: cost = 55.798606872558594\n", "The 3153th loop: cost = 48.57688903808594\n", "The 3154th loop: cost = 55.04964828491211\n", "The 3155th loop: cost = 53.4598388671875\n", "The 3156th loop: cost = 53.050506591796875\n", "The 3157th loop: cost = 48.116943359375\n", "The 3158th loop: cost = 57.20448303222656\n", "The 3159th loop: cost = 47.56128692626953\n", "The 3160th loop: cost = 54.29677963256836\n", "The 3161th loop: cost = 44.57814025878906\n", "The 3162th loop: cost = 54.79899597167969\n", "The 3163th loop: cost = 43.520423889160156\n", "The 3164th loop: cost = 49.65719223022461\n", "The 3165th loop: cost = 48.02509307861328\n", "The 3166th loop: cost = 45.42400360107422\n", "The 3167th loop: cost = 50.77063751220703\n", "The 3168th loop: cost = 60.39044952392578\n", "The 3169th loop: cost = 49.738311767578125\n", "The 3170th loop: cost = 37.784854888916016\n", "The 3171th loop: cost = 54.039581298828125\n", "The 3172th loop: cost = 46.69355010986328\n", "The 3173th loop: cost = 50.077362060546875\n", "The 3174th loop: cost = 38.21845245361328\n", "The 3175th loop: cost = 44.03118133544922\n", "The 3176th loop: cost = 43.42930603027344\n", "The 3177th loop: cost = 44.53319549560547\n", "The 3178th loop: cost = 44.96940612792969\n", "The 3179th loop: cost = 52.905765533447266\n", "The 3180th loop: cost = 50.17002868652344\n", "The 3181th loop: cost = 50.867454528808594\n", "The 3182th loop: cost = 55.88288116455078\n", "The 3183th loop: cost = 52.29835510253906\n", "The 3184th loop: cost = 47.992713928222656\n", "The 3185th loop: cost = 50.05595779418945\n", "The 3186th loop: cost = 60.89759063720703\n", "The 3187th loop: cost = 46.517356872558594\n", "The 3188th loop: cost = 53.360145568847656\n", "The 3189th loop: cost = 43.69348907470703\n", "The 3190th loop: cost = 45.05675506591797\n", "The 3191th loop: cost = 51.1995735168457\n", "The 3192th loop: cost = 44.41779708862305\n", "The 3193th loop: cost = 52.02486801147461\n", "The 3194th loop: cost = 45.11551284790039\n", "The 3195th loop: cost = 43.20964050292969\n", "The 3196th loop: cost = 51.80181884765625\n", "The 3197th loop: cost = 43.37699890136719\n", "The 3198th loop: cost = 57.19273376464844\n", "The 3199th loop: cost = 57.0292854309082\n", "The 3200th loop: cost = 47.83355712890625\n", "The 3201th loop: cost = 45.91114044189453\n", "The 3202th loop: cost = 43.22719955444336\n", "The 3203th loop: cost = 52.630775451660156\n", "The 3204th loop: cost = 51.356651306152344\n", "The 3205th loop: cost = 55.27903747558594\n", "The 3206th loop: cost = 46.53688049316406\n", "The 3207th loop: cost = 41.506141662597656\n", "The 3208th loop: cost = 48.26888656616211\n", "The 3209th loop: cost = 53.23735809326172\n", "The 3210th loop: cost = 52.81664276123047\n", "The 3211th loop: cost = 46.615631103515625\n", "The 3212th loop: cost = 46.26458740234375\n", "The 3213th loop: cost = 46.87321472167969\n", "The 3214th loop: cost = 49.59454345703125\n", "The 3215th loop: cost = 51.76752471923828\n", "The 3216th loop: cost = 54.6826057434082\n", "The 3217th loop: cost = 59.996482849121094\n", "The 3218th loop: cost = 45.15801239013672\n", "The 3219th loop: cost = 56.11248016357422\n", "The 3220th loop: cost = 37.35758972167969\n", "The 3221th loop: cost = 44.20166778564453\n", "The 3222th loop: cost = 41.64291763305664\n", "The 3223th loop: cost = 55.537811279296875\n", "The 3224th loop: cost = 50.14438247680664\n", "The 3225th loop: cost = 53.436439514160156\n", "The 3226th loop: cost = 53.977943420410156\n", "The 3227th loop: cost = 55.34842300415039\n", "The 3228th loop: cost = 62.393714904785156\n", "The 3229th loop: cost = 48.004947662353516\n", "The 3230th loop: cost = 56.00807189941406\n", "The 3231th loop: cost = 51.9578971862793\n", "The 3232th loop: cost = 47.33446502685547\n", "The 3233th loop: cost = 51.2161979675293\n", "The 3234th loop: cost = 50.160499572753906\n", "The 3235th loop: cost = 46.61600875854492\n", "The 3236th loop: cost = 44.7192268371582\n", "The 3237th loop: cost = 42.67338562011719\n", "The 3238th loop: cost = 57.23735046386719\n", "The 3239th loop: cost = 54.10426330566406\n", "The 3240th loop: cost = 63.12434005737305\n", "The 3241th loop: cost = 59.966121673583984\n", "The 3242th loop: cost = 50.60863494873047\n", "The 3243th loop: cost = 52.56080627441406\n", "The 3244th loop: cost = 54.68388366699219\n", "The 3245th loop: cost = 51.85728454589844\n", "The 3246th loop: cost = 57.464168548583984\n", "The 3247th loop: cost = 54.519710540771484\n", "The 3248th loop: cost = 53.5190315246582\n", "The 3249th loop: cost = 44.610355377197266\n", "The 3250th loop: cost = 53.24116516113281\n", "The 3251th loop: cost = 45.57717514038086\n", "The 3252th loop: cost = 56.84496307373047\n", "The 3253th loop: cost = 50.28267288208008\n", "The 3254th loop: cost = 45.11023712158203\n", "The 3255th loop: cost = 42.71941375732422\n", "The 3256th loop: cost = 52.53681564331055\n", "The 3257th loop: cost = 52.744049072265625\n", "The 3258th loop: cost = 53.7340087890625\n", "The 3259th loop: cost = 58.44355773925781\n", "The 3260th loop: cost = 55.48588180541992\n", "The 3261th loop: cost = 61.59734344482422\n", "The 3262th loop: cost = 51.64811706542969\n", "The 3263th loop: cost = 48.51439666748047\n", "The 3264th loop: cost = 56.112335205078125\n", "The 3265th loop: cost = 58.54572677612305\n", "The 3266th loop: cost = 54.45185089111328\n", "The 3267th loop: cost = 64.30491638183594\n", "The 3268th loop: cost = 55.15790557861328\n", "The 3269th loop: cost = 49.13920211791992\n", "The 3270th loop: cost = 51.48859405517578\n", "The 3271th loop: cost = 45.155479431152344\n", "The 3272th loop: cost = 48.647735595703125\n", "The 3273th loop: cost = 44.870330810546875\n", "The 3274th loop: cost = 36.86540603637695\n", "The 3275th loop: cost = 58.372032165527344\n", "The 3276th loop: cost = 58.788551330566406\n", "The 3277th loop: cost = 56.79636001586914\n", "The 3278th loop: cost = 58.147178649902344\n", "The 3279th loop: cost = 56.653099060058594\n", "The 3280th loop: cost = 61.0630989074707\n", "The 3281th loop: cost = 46.82170867919922\n", "The 3282th loop: cost = 48.479888916015625\n", "The 3283th loop: cost = 41.77104568481445\n", "The 3284th loop: cost = 48.817466735839844\n", "The 3285th loop: cost = 56.78425598144531\n", "The 3286th loop: cost = 45.4925422668457\n", "The 3287th loop: cost = 56.39423751831055\n", "The 3288th loop: cost = 70.76728820800781\n", "The 3289th loop: cost = 50.37340545654297\n", "The 3290th loop: cost = 57.922271728515625\n", "The 3291th loop: cost = 52.610084533691406\n", "The 3292th loop: cost = 48.081912994384766\n", "The 3293th loop: cost = 41.171844482421875\n", "The 3294th loop: cost = 43.376922607421875\n", "The 3295th loop: cost = 59.28137969970703\n", "The 3296th loop: cost = 55.304115295410156\n", "The 3297th loop: cost = 52.07386016845703\n", "The 3298th loop: cost = 46.563758850097656\n", "The 3299th loop: cost = 41.77610397338867\n", "The 3300th loop: cost = 59.11713409423828\n", "The 3301th loop: cost = 43.959651947021484\n", "The 3302th loop: cost = 43.71677017211914\n", "The 3303th loop: cost = 57.01118087768555\n", "The 3304th loop: cost = 62.32305145263672\n", "The 3305th loop: cost = 47.709449768066406\n", "The 3306th loop: cost = 55.90122985839844\n", "The 3307th loop: cost = 38.47422409057617\n", "The 3308th loop: cost = 53.86032485961914\n", "The 3309th loop: cost = 44.53191375732422\n", "The 3310th loop: cost = 51.90967559814453\n", "The 3311th loop: cost = 42.14229202270508\n", "The 3312th loop: cost = 54.98695373535156\n", "The 3313th loop: cost = 41.877838134765625\n", "The 3314th loop: cost = 50.8816032409668\n", "The 3315th loop: cost = 44.82483673095703\n", "The 3316th loop: cost = 50.37700653076172\n", "The 3317th loop: cost = 43.1071662902832\n", "The 3318th loop: cost = 42.08513641357422\n", "The 3319th loop: cost = 58.001319885253906\n", "The 3320th loop: cost = 45.144405364990234\n", "The 3321th loop: cost = 52.57931900024414\n", "The 3322th loop: cost = 44.95177459716797\n", "The 3323th loop: cost = 42.05012512207031\n", "The 3324th loop: cost = 44.70834732055664\n", "The 3325th loop: cost = 42.378910064697266\n", "The 3326th loop: cost = 47.8101921081543\n", "The 3327th loop: cost = 47.26678466796875\n", "The 3328th loop: cost = 48.70137023925781\n", "The 3329th loop: cost = 55.22044372558594\n", "The 3330th loop: cost = 57.588775634765625\n", "The 3331th loop: cost = 55.48008346557617\n", "The 3332th loop: cost = 53.41720962524414\n", "The 3333th loop: cost = 47.23176193237305\n", "The 3334th loop: cost = 54.63618087768555\n", "The 3335th loop: cost = 37.488826751708984\n", "The 3336th loop: cost = 41.20045471191406\n", "The 3337th loop: cost = 49.480587005615234\n", "The 3338th loop: cost = 50.765445709228516\n", "The 3339th loop: cost = 53.95263671875\n", "The 3340th loop: cost = 48.47007751464844\n", "The 3341th loop: cost = 57.805389404296875\n", "The 3342th loop: cost = 51.286529541015625\n", "The 3343th loop: cost = 62.07814025878906\n", "The 3344th loop: cost = 51.238487243652344\n", "The 3345th loop: cost = 66.55912780761719\n", "The 3346th loop: cost = 41.812591552734375\n", "The 3347th loop: cost = 48.096614837646484\n", "The 3348th loop: cost = 49.41423034667969\n", "The 3349th loop: cost = 48.01547622680664\n", "The 3350th loop: cost = 55.2139778137207\n", "The 3351th loop: cost = 40.996307373046875\n", "The 3352th loop: cost = 51.53001403808594\n", "The 3353th loop: cost = 56.18745040893555\n", "The 3354th loop: cost = 40.08961868286133\n", "The 3355th loop: cost = 50.37138366699219\n", "The 3356th loop: cost = 54.369117736816406\n", "The 3357th loop: cost = 45.272544860839844\n", "The 3358th loop: cost = 57.523155212402344\n", "The 3359th loop: cost = 57.88334274291992\n", "The 3360th loop: cost = 58.57386016845703\n", "The 3361th loop: cost = 52.604373931884766\n", "The 3362th loop: cost = 50.737884521484375\n", "The 3363th loop: cost = 43.15377426147461\n", "The 3364th loop: cost = 48.06389617919922\n", "The 3365th loop: cost = 51.351829528808594\n", "The 3366th loop: cost = 53.409751892089844\n", "The 3367th loop: cost = 47.941917419433594\n", "The 3368th loop: cost = 51.102027893066406\n", "The 3369th loop: cost = 58.2489128112793\n", "The 3370th loop: cost = 45.34264373779297\n", "The 3371th loop: cost = 39.42722702026367\n", "The 3372th loop: cost = 43.88450622558594\n", "The 3373th loop: cost = 54.32815933227539\n", "The 3374th loop: cost = 43.566383361816406\n", "The 3375th loop: cost = 57.552391052246094\n", "The 3376th loop: cost = 47.907371520996094\n", "The 3377th loop: cost = 53.49560546875\n", "The 3378th loop: cost = 48.42079544067383\n", "The 3379th loop: cost = 54.34152603149414\n", "The 3380th loop: cost = 47.88314437866211\n", "The 3381th loop: cost = 37.843421936035156\n", "The 3382th loop: cost = 60.70864486694336\n", "The 3383th loop: cost = 48.366790771484375\n", "The 3384th loop: cost = 44.143699645996094\n", "The 3385th loop: cost = 50.817832946777344\n", "The 3386th loop: cost = 47.150108337402344\n", "The 3387th loop: cost = 50.718467712402344\n", "The 3388th loop: cost = 60.218841552734375\n", "The 3389th loop: cost = 51.009796142578125\n", "The 3390th loop: cost = 43.45378875732422\n", "The 3391th loop: cost = 64.75782775878906\n", "The 3392th loop: cost = 56.22511672973633\n", "The 3393th loop: cost = 48.416358947753906\n", "The 3394th loop: cost = 51.87066650390625\n", "The 3395th loop: cost = 49.87045669555664\n", "The 3396th loop: cost = 50.51711654663086\n", "The 3397th loop: cost = 55.75000762939453\n", "The 3398th loop: cost = 62.12396240234375\n", "The 3399th loop: cost = 51.361106872558594\n", "The 3400th loop: cost = 44.375858306884766\n", "The 3401th loop: cost = 57.905372619628906\n", "The 3402th loop: cost = 58.64469909667969\n", "The 3403th loop: cost = 52.41718292236328\n", "The 3404th loop: cost = 47.30419158935547\n", "The 3405th loop: cost = 50.899940490722656\n", "The 3406th loop: cost = 47.370601654052734\n", "The 3407th loop: cost = 63.48662185668945\n", "The 3408th loop: cost = 50.658172607421875\n", "The 3409th loop: cost = 52.36517333984375\n", "The 3410th loop: cost = 49.55264663696289\n", "The 3411th loop: cost = 44.74151611328125\n", "The 3412th loop: cost = 46.867469787597656\n", "The 3413th loop: cost = 50.89652633666992\n", "The 3414th loop: cost = 52.354248046875\n", "The 3415th loop: cost = 40.13407897949219\n", "The 3416th loop: cost = 39.352699279785156\n", "The 3417th loop: cost = 46.85384750366211\n", "The 3418th loop: cost = 53.86024475097656\n", "The 3419th loop: cost = 58.15632629394531\n", "The 3420th loop: cost = 48.88294982910156\n", "The 3421th loop: cost = 43.67326354980469\n", "The 3422th loop: cost = 48.66306686401367\n", "The 3423th loop: cost = 48.89735412597656\n", "The 3424th loop: cost = 44.188846588134766\n", "The 3425th loop: cost = 59.82680130004883\n", "The 3426th loop: cost = 42.98059844970703\n", "The 3427th loop: cost = 55.565582275390625\n", "The 3428th loop: cost = 46.99071502685547\n", "The 3429th loop: cost = 58.27290344238281\n", "The 3430th loop: cost = 55.226505279541016\n", "The 3431th loop: cost = 61.573272705078125\n", "The 3432th loop: cost = 46.73125457763672\n", "The 3433th loop: cost = 50.97932434082031\n", "The 3434th loop: cost = 46.70915222167969\n", "The 3435th loop: cost = 48.23650360107422\n", "The 3436th loop: cost = 58.497623443603516\n", "The 3437th loop: cost = 47.62127685546875\n", "The 3438th loop: cost = 53.767093658447266\n", "The 3439th loop: cost = 43.741065979003906\n", "The 3440th loop: cost = 60.217491149902344\n", "The 3441th loop: cost = 59.38299560546875\n", "The 3442th loop: cost = 41.875022888183594\n", "The 3443th loop: cost = 45.080108642578125\n", "The 3444th loop: cost = 42.979103088378906\n", "The 3445th loop: cost = 47.77265167236328\n", "The 3446th loop: cost = 46.275108337402344\n", "The 3447th loop: cost = 45.48303985595703\n", "The 3448th loop: cost = 42.046714782714844\n", "The 3449th loop: cost = 49.93601608276367\n", "The 3450th loop: cost = 46.62151336669922\n", "The 3451th loop: cost = 40.936378479003906\n", "The 3452th loop: cost = 43.61967086791992\n", "The 3453th loop: cost = 42.548091888427734\n", "The 3454th loop: cost = 55.36532974243164\n", "The 3455th loop: cost = 51.460975646972656\n", "The 3456th loop: cost = 57.23200225830078\n", "The 3457th loop: cost = 43.12047576904297\n", "The 3458th loop: cost = 58.51295471191406\n", "The 3459th loop: cost = 52.43749237060547\n", "The 3460th loop: cost = 45.78324508666992\n", "The 3461th loop: cost = 54.5705680847168\n", "The 3462th loop: cost = 50.156982421875\n", "The 3463th loop: cost = 54.12998962402344\n", "The 3464th loop: cost = 57.480987548828125\n", "The 3465th loop: cost = 47.9951171875\n", "The 3466th loop: cost = 41.7574462890625\n", "The 3467th loop: cost = 39.113372802734375\n", "The 3468th loop: cost = 45.62104034423828\n", "The 3469th loop: cost = 54.902748107910156\n", "The 3470th loop: cost = 49.23442459106445\n", "The 3471th loop: cost = 39.906742095947266\n", "The 3472th loop: cost = 38.96707534790039\n", "The 3473th loop: cost = 47.02415084838867\n", "The 3474th loop: cost = 51.934486389160156\n", "The 3475th loop: cost = 61.85744094848633\n", "The 3476th loop: cost = 35.86937713623047\n", "The 3477th loop: cost = 53.84012985229492\n", "The 3478th loop: cost = 51.6473388671875\n", "The 3479th loop: cost = 46.107261657714844\n", "The 3480th loop: cost = 55.067935943603516\n", "The 3481th loop: cost = 43.806556701660156\n", "The 3482th loop: cost = 48.704715728759766\n", "The 3483th loop: cost = 55.780296325683594\n", "The 3484th loop: cost = 55.68556594848633\n", "The 3485th loop: cost = 48.347320556640625\n", "The 3486th loop: cost = 48.96868896484375\n", "The 3487th loop: cost = 47.337303161621094\n", "The 3488th loop: cost = 55.29463577270508\n", "The 3489th loop: cost = 55.168975830078125\n", "The 3490th loop: cost = 43.29467010498047\n", "The 3491th loop: cost = 47.846004486083984\n", "The 3492th loop: cost = 53.19905090332031\n", "The 3493th loop: cost = 55.487953186035156\n", "The 3494th loop: cost = 53.188392639160156\n", "The 3495th loop: cost = 49.76963806152344\n", "The 3496th loop: cost = 65.9139175415039\n", "The 3497th loop: cost = 60.061012268066406\n", "The 3498th loop: cost = 47.525856018066406\n", "The 3499th loop: cost = 50.472049713134766\n", "The 3500th loop: cost = 43.13682556152344\n", "The 3501th loop: cost = 57.78126525878906\n", "The 3502th loop: cost = 54.51905059814453\n", "The 3503th loop: cost = 51.437591552734375\n", "The 3504th loop: cost = 50.46186065673828\n", "The 3505th loop: cost = 54.18742752075195\n", "The 3506th loop: cost = 41.790245056152344\n", "The 3507th loop: cost = 53.03693389892578\n", "The 3508th loop: cost = 44.29015350341797\n", "The 3509th loop: cost = 58.676326751708984\n", "The 3510th loop: cost = 45.9217643737793\n", "The 3511th loop: cost = 46.72755432128906\n", "The 3512th loop: cost = 48.53731155395508\n", "The 3513th loop: cost = 39.797088623046875\n", "The 3514th loop: cost = 45.20950698852539\n", "The 3515th loop: cost = 63.27479553222656\n", "The 3516th loop: cost = 45.531585693359375\n", "The 3517th loop: cost = 59.862369537353516\n", "The 3518th loop: cost = 48.1717529296875\n", "The 3519th loop: cost = 42.29103088378906\n", "The 3520th loop: cost = 47.44564437866211\n", "The 3521th loop: cost = 52.382972717285156\n", "The 3522th loop: cost = 47.772029876708984\n", "The 3523th loop: cost = 42.9255256652832\n", "The 3524th loop: cost = 50.28395080566406\n", "The 3525th loop: cost = 42.5392951965332\n", "The 3526th loop: cost = 52.88036346435547\n", "The 3527th loop: cost = 45.76384735107422\n", "The 3528th loop: cost = 46.59532165527344\n", "The 3529th loop: cost = 47.40834426879883\n", "The 3530th loop: cost = 45.7294921875\n", "The 3531th loop: cost = 49.90732192993164\n", "The 3532th loop: cost = 45.659324645996094\n", "The 3533th loop: cost = 46.84265899658203\n", "The 3534th loop: cost = 45.48460388183594\n", "The 3535th loop: cost = 59.12767791748047\n", "The 3536th loop: cost = 53.348724365234375\n", "The 3537th loop: cost = 33.024383544921875\n", "The 3538th loop: cost = 46.534915924072266\n", "The 3539th loop: cost = 39.96504211425781\n", "The 3540th loop: cost = 38.33820724487305\n", "The 3541th loop: cost = 47.07744598388672\n", "The 3542th loop: cost = 49.92059326171875\n", "The 3543th loop: cost = 40.16590881347656\n", "The 3544th loop: cost = 42.87652587890625\n", "The 3545th loop: cost = 52.06077575683594\n", "The 3546th loop: cost = 54.427635192871094\n", "The 3547th loop: cost = 45.83966827392578\n", "The 3548th loop: cost = 48.18955993652344\n", "The 3549th loop: cost = 53.42312240600586\n", "The 3550th loop: cost = 50.55925750732422\n", "The 3551th loop: cost = 47.92444610595703\n", "The 3552th loop: cost = 44.42974853515625\n", "The 3553th loop: cost = 46.11595153808594\n", "The 3554th loop: cost = 46.05527114868164\n", "The 3555th loop: cost = 42.81834411621094\n", "The 3556th loop: cost = 51.470420837402344\n", "The 3557th loop: cost = 56.03667449951172\n", "The 3558th loop: cost = 41.0345573425293\n", "The 3559th loop: cost = 48.02901840209961\n", "The 3560th loop: cost = 52.50893020629883\n", "The 3561th loop: cost = 50.94442367553711\n", "The 3562th loop: cost = 61.14765548706055\n", "The 3563th loop: cost = 52.08073425292969\n", "The 3564th loop: cost = 44.60455322265625\n", "The 3565th loop: cost = 53.84375762939453\n", "The 3566th loop: cost = 42.00965881347656\n", "The 3567th loop: cost = 51.89258575439453\n", "The 3568th loop: cost = 48.482749938964844\n", "The 3569th loop: cost = 41.73258972167969\n", "The 3570th loop: cost = 47.994049072265625\n", "The 3571th loop: cost = 58.04022216796875\n", "The 3572th loop: cost = 51.607521057128906\n", "The 3573th loop: cost = 46.284027099609375\n", "The 3574th loop: cost = 50.01028060913086\n", "The 3575th loop: cost = 42.197654724121094\n", "The 3576th loop: cost = 54.25917053222656\n", "The 3577th loop: cost = 54.664886474609375\n", "The 3578th loop: cost = 54.2891731262207\n", "The 3579th loop: cost = 54.09170150756836\n", "The 3580th loop: cost = 52.140872955322266\n", "The 3581th loop: cost = 57.85245132446289\n", "The 3582th loop: cost = 47.981605529785156\n", "The 3583th loop: cost = 46.87162780761719\n", "The 3584th loop: cost = 52.24491500854492\n", "The 3585th loop: cost = 49.06996154785156\n", "The 3586th loop: cost = 44.81043243408203\n", "The 3587th loop: cost = 50.7301025390625\n", "The 3588th loop: cost = 58.15452575683594\n", "The 3589th loop: cost = 45.604530334472656\n", "The 3590th loop: cost = 47.051429748535156\n", "The 3591th loop: cost = 46.855751037597656\n", "The 3592th loop: cost = 37.57460021972656\n", "The 3593th loop: cost = 42.808349609375\n", "The 3594th loop: cost = 41.28851318359375\n", "The 3595th loop: cost = 50.093170166015625\n", "The 3596th loop: cost = 47.66069030761719\n", "The 3597th loop: cost = 50.456111907958984\n", "The 3598th loop: cost = 63.862213134765625\n", "The 3599th loop: cost = 52.87956237792969\n", "The 3600th loop: cost = 37.52838134765625\n", "The 3601th loop: cost = 50.76597213745117\n", "The 3602th loop: cost = 48.430152893066406\n", "The 3603th loop: cost = 52.905189514160156\n", "The 3604th loop: cost = 52.33842086791992\n", "The 3605th loop: cost = 48.872230529785156\n", "The 3606th loop: cost = 59.23616027832031\n", "The 3607th loop: cost = 49.504798889160156\n", "The 3608th loop: cost = 47.712162017822266\n", "The 3609th loop: cost = 46.67778015136719\n", "The 3610th loop: cost = 42.392181396484375\n", "The 3611th loop: cost = 56.08607482910156\n", "The 3612th loop: cost = 51.076812744140625\n", "The 3613th loop: cost = 54.360206604003906\n", "The 3614th loop: cost = 58.962730407714844\n", "The 3615th loop: cost = 40.58375549316406\n", "The 3616th loop: cost = 42.91236114501953\n", "The 3617th loop: cost = 46.40928268432617\n", "The 3618th loop: cost = 38.42710494995117\n", "The 3619th loop: cost = 36.96196365356445\n", "The 3620th loop: cost = 43.34121322631836\n", "The 3621th loop: cost = 37.903053283691406\n", "The 3622th loop: cost = 61.10196304321289\n", "The 3623th loop: cost = 40.818782806396484\n", "The 3624th loop: cost = 51.29362869262695\n", "The 3625th loop: cost = 50.4942512512207\n", "The 3626th loop: cost = 56.01622772216797\n", "The 3627th loop: cost = 47.949607849121094\n", "The 3628th loop: cost = 46.17192077636719\n", "The 3629th loop: cost = 40.55694580078125\n", "The 3630th loop: cost = 49.67230224609375\n", "The 3631th loop: cost = 45.84898376464844\n", "The 3632th loop: cost = 46.572505950927734\n", "The 3633th loop: cost = 43.191429138183594\n", "The 3634th loop: cost = 50.731964111328125\n", "The 3635th loop: cost = 46.80438995361328\n", "The 3636th loop: cost = 42.696922302246094\n", "The 3637th loop: cost = 52.18453598022461\n", "The 3638th loop: cost = 49.8712158203125\n", "The 3639th loop: cost = 45.24565887451172\n", "The 3640th loop: cost = 60.842384338378906\n", "The 3641th loop: cost = 43.71345901489258\n", "The 3642th loop: cost = 59.03062438964844\n", "The 3643th loop: cost = 48.3057975769043\n", "The 3644th loop: cost = 53.55821990966797\n", "The 3645th loop: cost = 44.920326232910156\n", "The 3646th loop: cost = 53.959877014160156\n", "The 3647th loop: cost = 49.23955535888672\n", "The 3648th loop: cost = 40.41499328613281\n", "The 3649th loop: cost = 47.1783332824707\n", "The 3650th loop: cost = 45.43174743652344\n", "The 3651th loop: cost = 49.68023681640625\n", "The 3652th loop: cost = 47.960853576660156\n", "The 3653th loop: cost = 52.79557418823242\n", "The 3654th loop: cost = 56.844703674316406\n", "The 3655th loop: cost = 57.76471710205078\n", "The 3656th loop: cost = 54.66748809814453\n", "The 3657th loop: cost = 47.00666809082031\n", "The 3658th loop: cost = 49.53361511230469\n", "The 3659th loop: cost = 44.56458282470703\n", "The 3660th loop: cost = 43.17052459716797\n", "The 3661th loop: cost = 54.012451171875\n", "The 3662th loop: cost = 50.194950103759766\n", "The 3663th loop: cost = 42.75102615356445\n", "The 3664th loop: cost = 49.73904037475586\n", "The 3665th loop: cost = 46.249839782714844\n", "The 3666th loop: cost = 46.23643112182617\n", "The 3667th loop: cost = 54.947574615478516\n", "The 3668th loop: cost = 57.02306365966797\n", "The 3669th loop: cost = 50.312049865722656\n", "The 3670th loop: cost = 40.50829315185547\n", "The 3671th loop: cost = 37.4569206237793\n", "The 3672th loop: cost = 45.182769775390625\n", "The 3673th loop: cost = 51.14510726928711\n", "The 3674th loop: cost = 64.93373107910156\n", "The 3675th loop: cost = 48.238162994384766\n", "The 3676th loop: cost = 45.99475860595703\n", "The 3677th loop: cost = 43.05337142944336\n", "The 3678th loop: cost = 44.809722900390625\n", "The 3679th loop: cost = 44.76026153564453\n", "The 3680th loop: cost = 55.856502532958984\n", "The 3681th loop: cost = 39.88322448730469\n", "The 3682th loop: cost = 59.582130432128906\n", "The 3683th loop: cost = 54.6033935546875\n", "The 3684th loop: cost = 45.05305480957031\n", "The 3685th loop: cost = 45.47637176513672\n", "The 3686th loop: cost = 50.76178741455078\n", "The 3687th loop: cost = 45.363372802734375\n", "The 3688th loop: cost = 51.41471862792969\n", "The 3689th loop: cost = 53.12408447265625\n", "The 3690th loop: cost = 47.26293182373047\n", "The 3691th loop: cost = 45.25737762451172\n", "The 3692th loop: cost = 49.05943298339844\n", "The 3693th loop: cost = 49.628456115722656\n", "The 3694th loop: cost = 44.271026611328125\n", "The 3695th loop: cost = 33.36174011230469\n", "The 3696th loop: cost = 42.27499008178711\n", "The 3697th loop: cost = 47.531097412109375\n", "The 3698th loop: cost = 47.50172805786133\n", "The 3699th loop: cost = 58.852935791015625\n", "The 3700th loop: cost = 43.040252685546875\n", "The 3701th loop: cost = 46.45928192138672\n", "The 3702th loop: cost = 38.33152770996094\n", "The 3703th loop: cost = 46.698272705078125\n", "The 3704th loop: cost = 48.68525314331055\n", "The 3705th loop: cost = 47.272830963134766\n", "The 3706th loop: cost = 39.495994567871094\n", "The 3707th loop: cost = 51.39064407348633\n", "The 3708th loop: cost = 48.587833404541016\n", "The 3709th loop: cost = 50.83541488647461\n", "The 3710th loop: cost = 45.878543853759766\n", "The 3711th loop: cost = 63.545257568359375\n", "The 3712th loop: cost = 55.30635452270508\n", "The 3713th loop: cost = 45.42499923706055\n", "The 3714th loop: cost = 56.98162078857422\n", "The 3715th loop: cost = 50.206764221191406\n", "The 3716th loop: cost = 42.44166564941406\n", "The 3717th loop: cost = 49.878135681152344\n", "The 3718th loop: cost = 59.90306854248047\n", "The 3719th loop: cost = 52.16950225830078\n", "The 3720th loop: cost = 45.507266998291016\n", "The 3721th loop: cost = 48.95088577270508\n", "The 3722th loop: cost = 52.28167724609375\n", "The 3723th loop: cost = 42.92607879638672\n", "The 3724th loop: cost = 41.73131561279297\n", "The 3725th loop: cost = 47.74805450439453\n", "The 3726th loop: cost = 37.386322021484375\n", "The 3727th loop: cost = 42.26564025878906\n", "The 3728th loop: cost = 55.63334655761719\n", "The 3729th loop: cost = 42.63579177856445\n", "The 3730th loop: cost = 41.253517150878906\n", "The 3731th loop: cost = 50.18122100830078\n", "The 3732th loop: cost = 56.931671142578125\n", "The 3733th loop: cost = 42.968475341796875\n", "The 3734th loop: cost = 42.71612548828125\n", "The 3735th loop: cost = 52.48347473144531\n", "The 3736th loop: cost = 54.04222106933594\n", "The 3737th loop: cost = 38.743431091308594\n", "The 3738th loop: cost = 56.73768615722656\n", "The 3739th loop: cost = 55.10509490966797\n", "The 3740th loop: cost = 50.40642166137695\n", "The 3741th loop: cost = 49.538330078125\n", "The 3742th loop: cost = 47.99580383300781\n", "The 3743th loop: cost = 44.95647430419922\n", "The 3744th loop: cost = 51.920188903808594\n", "The 3745th loop: cost = 60.347633361816406\n", "The 3746th loop: cost = 52.11982727050781\n", "The 3747th loop: cost = 46.48126983642578\n", "The 3748th loop: cost = 55.77565002441406\n", "The 3749th loop: cost = 43.45665740966797\n", "The 3750th loop: cost = 44.188507080078125\n", "The 3751th loop: cost = 50.10444641113281\n", "The 3752th loop: cost = 45.7296028137207\n", "The 3753th loop: cost = 47.47148132324219\n", "The 3754th loop: cost = 51.77503967285156\n", "The 3755th loop: cost = 59.55424880981445\n", "The 3756th loop: cost = 47.93344497680664\n", "The 3757th loop: cost = 48.70691680908203\n", "The 3758th loop: cost = 40.28178024291992\n", "The 3759th loop: cost = 41.94203186035156\n", "The 3760th loop: cost = 58.72554016113281\n", "The 3761th loop: cost = 47.93232727050781\n", "The 3762th loop: cost = 44.374332427978516\n", "The 3763th loop: cost = 53.01886749267578\n", "The 3764th loop: cost = 46.71348571777344\n", "The 3765th loop: cost = 36.1112174987793\n", "The 3766th loop: cost = 44.34369659423828\n", "The 3767th loop: cost = 53.17253112792969\n", "The 3768th loop: cost = 43.35524368286133\n", "The 3769th loop: cost = 51.02305603027344\n", "The 3770th loop: cost = 46.51836395263672\n", "The 3771th loop: cost = 55.100643157958984\n", "The 3772th loop: cost = 46.834991455078125\n", "The 3773th loop: cost = 43.935298919677734\n", "The 3774th loop: cost = 45.200862884521484\n", "The 3775th loop: cost = 49.985408782958984\n", "The 3776th loop: cost = 47.71312713623047\n", "The 3777th loop: cost = 62.39928436279297\n", "The 3778th loop: cost = 44.6835823059082\n", "The 3779th loop: cost = 43.33993911743164\n", "The 3780th loop: cost = 61.14793014526367\n", "The 3781th loop: cost = 47.756370544433594\n", "The 3782th loop: cost = 44.331451416015625\n", "The 3783th loop: cost = 46.752197265625\n", "The 3784th loop: cost = 43.16193389892578\n", "The 3785th loop: cost = 49.25838088989258\n", "The 3786th loop: cost = 46.23969268798828\n", "The 3787th loop: cost = 60.67558288574219\n", "The 3788th loop: cost = 52.123931884765625\n", "The 3789th loop: cost = 42.592708587646484\n", "The 3790th loop: cost = 52.73128890991211\n", "The 3791th loop: cost = 53.401668548583984\n", "The 3792th loop: cost = 44.854469299316406\n", "The 3793th loop: cost = 53.62730407714844\n", "The 3794th loop: cost = 50.80287551879883\n", "The 3795th loop: cost = 53.19789123535156\n", "The 3796th loop: cost = 45.753334045410156\n", "The 3797th loop: cost = 41.679443359375\n", "The 3798th loop: cost = 43.5303840637207\n", "The 3799th loop: cost = 61.55203628540039\n", "The 3800th loop: cost = 55.97209930419922\n", "The 3801th loop: cost = 53.264617919921875\n", "The 3802th loop: cost = 37.55659103393555\n", "The 3803th loop: cost = 56.42864990234375\n", "The 3804th loop: cost = 52.31835174560547\n", "The 3805th loop: cost = 45.33110809326172\n", "The 3806th loop: cost = 54.708229064941406\n", "The 3807th loop: cost = 41.173404693603516\n", "The 3808th loop: cost = 32.96639633178711\n", "The 3809th loop: cost = 50.892948150634766\n", "The 3810th loop: cost = 56.748043060302734\n", "The 3811th loop: cost = 48.305503845214844\n", "The 3812th loop: cost = 41.1761360168457\n", "The 3813th loop: cost = 45.7436408996582\n", "The 3814th loop: cost = 47.67610168457031\n", "The 3815th loop: cost = 39.501686096191406\n", "The 3816th loop: cost = 40.9229736328125\n", "The 3817th loop: cost = 57.6221809387207\n", "The 3818th loop: cost = 47.319801330566406\n", "The 3819th loop: cost = 52.23528289794922\n", "The 3820th loop: cost = 52.38660430908203\n", "The 3821th loop: cost = 46.00575256347656\n", "The 3822th loop: cost = 44.577415466308594\n", "The 3823th loop: cost = 46.05149841308594\n", "The 3824th loop: cost = 50.23808288574219\n", "The 3825th loop: cost = 48.82343673706055\n", "The 3826th loop: cost = 54.74041748046875\n", "The 3827th loop: cost = 43.46742248535156\n", "The 3828th loop: cost = 47.61642837524414\n", "The 3829th loop: cost = 49.2840690612793\n", "The 3830th loop: cost = 41.73421096801758\n", "The 3831th loop: cost = 52.436859130859375\n", "The 3832th loop: cost = 61.7120475769043\n", "The 3833th loop: cost = 47.888694763183594\n", "The 3834th loop: cost = 50.61432647705078\n", "The 3835th loop: cost = 53.202125549316406\n", "The 3836th loop: cost = 50.50483703613281\n", "The 3837th loop: cost = 41.296592712402344\n", "The 3838th loop: cost = 43.13301467895508\n", "The 3839th loop: cost = 45.05510330200195\n", "The 3840th loop: cost = 46.296958923339844\n", "The 3841th loop: cost = 49.997962951660156\n", "The 3842th loop: cost = 52.561771392822266\n", "The 3843th loop: cost = 43.85481643676758\n", "The 3844th loop: cost = 34.86890411376953\n", "The 3845th loop: cost = 47.67115020751953\n", "The 3846th loop: cost = 45.478431701660156\n", "The 3847th loop: cost = 53.139408111572266\n", "The 3848th loop: cost = 48.20597839355469\n", "The 3849th loop: cost = 52.714019775390625\n", "The 3850th loop: cost = 35.31290817260742\n", "The 3851th loop: cost = 53.7707633972168\n", "The 3852th loop: cost = 51.761924743652344\n", "The 3853th loop: cost = 42.89595413208008\n", "The 3854th loop: cost = 56.7931022644043\n", "The 3855th loop: cost = 46.134498596191406\n", "The 3856th loop: cost = 45.9578857421875\n", "The 3857th loop: cost = 38.160491943359375\n", "The 3858th loop: cost = 49.99559783935547\n", "The 3859th loop: cost = 61.45451354980469\n", "The 3860th loop: cost = 47.55279541015625\n", "The 3861th loop: cost = 39.566688537597656\n", "The 3862th loop: cost = 50.41791534423828\n", "The 3863th loop: cost = 47.704986572265625\n", "The 3864th loop: cost = 42.94392395019531\n", "The 3865th loop: cost = 52.95021057128906\n", "The 3866th loop: cost = 53.43975830078125\n", "The 3867th loop: cost = 53.25749206542969\n", "The 3868th loop: cost = 42.35250473022461\n", "The 3869th loop: cost = 44.881996154785156\n", "The 3870th loop: cost = 44.294456481933594\n", "The 3871th loop: cost = 43.547733306884766\n", "The 3872th loop: cost = 57.307960510253906\n", "The 3873th loop: cost = 43.89840316772461\n", "The 3874th loop: cost = 43.64781951904297\n", "The 3875th loop: cost = 58.164794921875\n", "The 3876th loop: cost = 47.76824188232422\n", "The 3877th loop: cost = 48.76953887939453\n", "The 3878th loop: cost = 42.35006332397461\n", "The 3879th loop: cost = 46.81977844238281\n", "The 3880th loop: cost = 43.459747314453125\n", "The 3881th loop: cost = 51.517845153808594\n", "The 3882th loop: cost = 52.078460693359375\n", "The 3883th loop: cost = 41.025367736816406\n", "The 3884th loop: cost = 46.8993034362793\n", "The 3885th loop: cost = 56.41221618652344\n", "The 3886th loop: cost = 46.35094451904297\n", "The 3887th loop: cost = 56.42109298706055\n", "The 3888th loop: cost = 44.65168762207031\n", "The 3889th loop: cost = 48.20927047729492\n", "The 3890th loop: cost = 46.93071746826172\n", "The 3891th loop: cost = 55.374114990234375\n", "The 3892th loop: cost = 48.55162811279297\n", "The 3893th loop: cost = 47.00352096557617\n", "The 3894th loop: cost = 46.05162048339844\n", "The 3895th loop: cost = 62.299930572509766\n", "The 3896th loop: cost = 54.017478942871094\n", "The 3897th loop: cost = 43.12659454345703\n", "The 3898th loop: cost = 41.53266143798828\n", "The 3899th loop: cost = 43.84458923339844\n", "The 3900th loop: cost = 39.36437225341797\n", "The 3901th loop: cost = 53.05107498168945\n", "The 3902th loop: cost = 53.87010192871094\n", "The 3903th loop: cost = 52.44725036621094\n", "The 3904th loop: cost = 59.127593994140625\n", "The 3905th loop: cost = 43.75090026855469\n", "The 3906th loop: cost = 43.784095764160156\n", "The 3907th loop: cost = 53.52406311035156\n", "The 3908th loop: cost = 42.96937561035156\n", "The 3909th loop: cost = 38.74726104736328\n", "The 3910th loop: cost = 57.05088806152344\n", "The 3911th loop: cost = 49.20491409301758\n", "The 3912th loop: cost = 38.85182189941406\n", "The 3913th loop: cost = 46.14826965332031\n", "The 3914th loop: cost = 48.80303955078125\n", "The 3915th loop: cost = 47.44806671142578\n", "The 3916th loop: cost = 51.74232482910156\n", "The 3917th loop: cost = 49.95478820800781\n", "The 3918th loop: cost = 50.52381134033203\n", "The 3919th loop: cost = 51.991729736328125\n", "The 3920th loop: cost = 50.52893829345703\n", "The 3921th loop: cost = 42.6383056640625\n", "The 3922th loop: cost = 51.69273376464844\n", "The 3923th loop: cost = 48.40318298339844\n", "The 3924th loop: cost = 47.524898529052734\n", "The 3925th loop: cost = 45.36632537841797\n", "The 3926th loop: cost = 38.951622009277344\n", "The 3927th loop: cost = 46.52332305908203\n", "The 3928th loop: cost = 32.23997497558594\n", "The 3929th loop: cost = 43.34291076660156\n", "The 3930th loop: cost = 53.459144592285156\n", "The 3931th loop: cost = 45.10075759887695\n", "The 3932th loop: cost = 54.8266716003418\n", "The 3933th loop: cost = 40.07353210449219\n", "The 3934th loop: cost = 41.23168182373047\n", "The 3935th loop: cost = 51.54762649536133\n", "The 3936th loop: cost = 49.208839416503906\n", "The 3937th loop: cost = 42.764644622802734\n", "The 3938th loop: cost = 52.54903793334961\n", "The 3939th loop: cost = 51.444915771484375\n", "The 3940th loop: cost = 49.930877685546875\n", "The 3941th loop: cost = 52.2008056640625\n", "The 3942th loop: cost = 49.83771514892578\n", "The 3943th loop: cost = 48.05284118652344\n", "The 3944th loop: cost = 58.48625946044922\n", "The 3945th loop: cost = 48.9180908203125\n", "The 3946th loop: cost = 50.119415283203125\n", "The 3947th loop: cost = 45.92903137207031\n", "The 3948th loop: cost = 59.02109146118164\n", "The 3949th loop: cost = 40.090370178222656\n", "The 3950th loop: cost = 45.440425872802734\n", "The 3951th loop: cost = 43.18916320800781\n", "The 3952th loop: cost = 61.037200927734375\n", "The 3953th loop: cost = 49.46283721923828\n", "The 3954th loop: cost = 39.59418869018555\n", "The 3955th loop: cost = 54.86309051513672\n", "The 3956th loop: cost = 50.52033996582031\n", "The 3957th loop: cost = 54.94425964355469\n", "The 3958th loop: cost = 49.863433837890625\n", "The 3959th loop: cost = 41.64147186279297\n", "The 3960th loop: cost = 41.072608947753906\n", "The 3961th loop: cost = 49.482383728027344\n", "The 3962th loop: cost = 48.077362060546875\n", "The 3963th loop: cost = 51.819671630859375\n", "The 3964th loop: cost = 40.184471130371094\n", "The 3965th loop: cost = 50.587493896484375\n", "The 3966th loop: cost = 42.8381233215332\n", "The 3967th loop: cost = 50.79261016845703\n", "The 3968th loop: cost = 45.08041000366211\n", "The 3969th loop: cost = 47.70255661010742\n", "The 3970th loop: cost = 52.46844482421875\n", "The 3971th loop: cost = 39.447837829589844\n", "The 3972th loop: cost = 50.500511169433594\n", "The 3973th loop: cost = 59.746334075927734\n", "The 3974th loop: cost = 55.366371154785156\n", "The 3975th loop: cost = 45.97066116333008\n", "The 3976th loop: cost = 45.58805847167969\n", "The 3977th loop: cost = 41.467105865478516\n", "The 3978th loop: cost = 52.67786407470703\n", "The 3979th loop: cost = 41.13465881347656\n", "The 3980th loop: cost = 53.879188537597656\n", "The 3981th loop: cost = 39.43491744995117\n", "The 3982th loop: cost = 45.302581787109375\n", "The 3983th loop: cost = 57.00524139404297\n", "The 3984th loop: cost = 51.78046798706055\n", "The 3985th loop: cost = 43.92894744873047\n", "The 3986th loop: cost = 42.097999572753906\n", "The 3987th loop: cost = 55.2961540222168\n", "The 3988th loop: cost = 55.12576675415039\n", "The 3989th loop: cost = 42.26624298095703\n", "The 3990th loop: cost = 43.55729675292969\n", "The 3991th loop: cost = 41.54557800292969\n", "The 3992th loop: cost = 52.98008346557617\n", "The 3993th loop: cost = 42.2180061340332\n", "The 3994th loop: cost = 54.75154113769531\n", "The 3995th loop: cost = 51.348846435546875\n", "The 3996th loop: cost = 59.33512878417969\n", "The 3997th loop: cost = 43.38048553466797\n", "The 3998th loop: cost = 43.83048629760742\n", "The 3999th loop: cost = 40.05133819580078\n", "The 4000th loop: cost = 41.968082427978516\n", "The 4001th loop: cost = 50.44276809692383\n", "The 4002th loop: cost = 44.00468444824219\n", "The 4003th loop: cost = 42.02623748779297\n", "The 4004th loop: cost = 41.08024215698242\n", "The 4005th loop: cost = 46.6769905090332\n", "The 4006th loop: cost = 69.96501159667969\n", "The 4007th loop: cost = 54.96234130859375\n", "The 4008th loop: cost = 40.03091812133789\n", "The 4009th loop: cost = 53.33007049560547\n", "The 4010th loop: cost = 52.42475128173828\n", "The 4011th loop: cost = 52.725677490234375\n", "The 4012th loop: cost = 43.517921447753906\n", "The 4013th loop: cost = 43.60923767089844\n", "The 4014th loop: cost = 52.793697357177734\n", "The 4015th loop: cost = 52.09947204589844\n", "The 4016th loop: cost = 44.48811340332031\n", "The 4017th loop: cost = 54.277557373046875\n", "The 4018th loop: cost = 44.453369140625\n", "The 4019th loop: cost = 44.61579132080078\n", "The 4020th loop: cost = 41.05357360839844\n", "The 4021th loop: cost = 42.06836700439453\n", "The 4022th loop: cost = 41.614723205566406\n", "The 4023th loop: cost = 50.51628494262695\n", "The 4024th loop: cost = 52.45246887207031\n", "The 4025th loop: cost = 49.529754638671875\n", "The 4026th loop: cost = 43.57319259643555\n", "The 4027th loop: cost = 42.32048797607422\n", "The 4028th loop: cost = 39.59707260131836\n", "The 4029th loop: cost = 51.918853759765625\n", "The 4030th loop: cost = 55.49592208862305\n", "The 4031th loop: cost = 37.94575500488281\n", "The 4032th loop: cost = 38.76872253417969\n", "The 4033th loop: cost = 53.48979187011719\n", "The 4034th loop: cost = 51.30536651611328\n", "The 4035th loop: cost = 46.75011444091797\n", "The 4036th loop: cost = 48.311790466308594\n", "The 4037th loop: cost = 48.855224609375\n", "The 4038th loop: cost = 60.745262145996094\n", "The 4039th loop: cost = 48.39645767211914\n", "The 4040th loop: cost = 52.65205764770508\n", "The 4041th loop: cost = 49.50721740722656\n", "The 4042th loop: cost = 47.5147819519043\n", "The 4043th loop: cost = 47.97598648071289\n", "The 4044th loop: cost = 49.68816375732422\n", "The 4045th loop: cost = 49.39323043823242\n", "The 4046th loop: cost = 59.5901985168457\n", "The 4047th loop: cost = 46.13543701171875\n", "The 4048th loop: cost = 51.243804931640625\n", "The 4049th loop: cost = 36.52527618408203\n", "The 4050th loop: cost = 51.05998229980469\n", "The 4051th loop: cost = 44.976436614990234\n", "The 4052th loop: cost = 37.87052917480469\n", "The 4053th loop: cost = 41.381507873535156\n", "The 4054th loop: cost = 46.86598205566406\n", "The 4055th loop: cost = 47.7609748840332\n", "The 4056th loop: cost = 39.28034973144531\n", "The 4057th loop: cost = 50.99982833862305\n", "The 4058th loop: cost = 59.446624755859375\n", "The 4059th loop: cost = 46.478843688964844\n", "The 4060th loop: cost = 55.507225036621094\n", "The 4061th loop: cost = 49.486732482910156\n", "The 4062th loop: cost = 48.011932373046875\n", "The 4063th loop: cost = 47.94419479370117\n", "The 4064th loop: cost = 43.0557861328125\n", "The 4065th loop: cost = 47.57978057861328\n", "The 4066th loop: cost = 59.1981201171875\n", "The 4067th loop: cost = 43.49162292480469\n", "The 4068th loop: cost = 48.78651809692383\n", "The 4069th loop: cost = 46.775360107421875\n", "The 4070th loop: cost = 45.824623107910156\n", "The 4071th loop: cost = 39.58507537841797\n", "The 4072th loop: cost = 39.476375579833984\n", "The 4073th loop: cost = 50.7384033203125\n", "The 4074th loop: cost = 39.0484733581543\n", "The 4075th loop: cost = 53.236167907714844\n", "The 4076th loop: cost = 52.7337760925293\n", "The 4077th loop: cost = 37.11150360107422\n", "The 4078th loop: cost = 57.252418518066406\n", "The 4079th loop: cost = 44.83427429199219\n", "The 4080th loop: cost = 45.421791076660156\n", "The 4081th loop: cost = 39.36056900024414\n", "The 4082th loop: cost = 51.810733795166016\n", "The 4083th loop: cost = 48.778297424316406\n", "The 4084th loop: cost = 37.03839111328125\n", "The 4085th loop: cost = 51.893341064453125\n", "The 4086th loop: cost = 53.01342010498047\n", "The 4087th loop: cost = 42.154964447021484\n", "The 4088th loop: cost = 52.40847396850586\n", "The 4089th loop: cost = 46.569541931152344\n", "The 4090th loop: cost = 47.68156433105469\n", "The 4091th loop: cost = 53.27996063232422\n", "The 4092th loop: cost = 43.152774810791016\n", "The 4093th loop: cost = 41.36091613769531\n", "The 4094th loop: cost = 61.3553466796875\n", "The 4095th loop: cost = 37.230491638183594\n", "The 4096th loop: cost = 53.01704025268555\n", "The 4097th loop: cost = 59.49018859863281\n", "The 4098th loop: cost = 54.206390380859375\n", "The 4099th loop: cost = 42.42259216308594\n", "The 4100th loop: cost = 44.19610595703125\n", "The 4101th loop: cost = 60.270206451416016\n", "The 4102th loop: cost = 50.088897705078125\n", "The 4103th loop: cost = 44.836849212646484\n", "The 4104th loop: cost = 51.167945861816406\n", "The 4105th loop: cost = 43.191688537597656\n", "The 4106th loop: cost = 40.47882843017578\n", "The 4107th loop: cost = 48.20033264160156\n", "The 4108th loop: cost = 47.974647521972656\n", "The 4109th loop: cost = 47.148468017578125\n", "The 4110th loop: cost = 47.06731414794922\n", "The 4111th loop: cost = 47.1048469543457\n", "The 4112th loop: cost = 49.79490661621094\n", "The 4113th loop: cost = 47.27491760253906\n", "The 4114th loop: cost = 43.34513854980469\n", "The 4115th loop: cost = 43.470306396484375\n", "The 4116th loop: cost = 53.54524612426758\n", "The 4117th loop: cost = 52.133018493652344\n", "The 4118th loop: cost = 46.8753547668457\n", "The 4119th loop: cost = 53.362937927246094\n", "The 4120th loop: cost = 42.448760986328125\n", "The 4121th loop: cost = 48.12036895751953\n", "The 4122th loop: cost = 56.48876953125\n", "The 4123th loop: cost = 50.917930603027344\n", "The 4124th loop: cost = 54.05793762207031\n", "The 4125th loop: cost = 50.17816925048828\n", "The 4126th loop: cost = 49.45000076293945\n", "The 4127th loop: cost = 42.776893615722656\n", "The 4128th loop: cost = 42.24982833862305\n", "The 4129th loop: cost = 51.909080505371094\n", "The 4130th loop: cost = 49.46425247192383\n", "The 4131th loop: cost = 51.66465759277344\n", "The 4132th loop: cost = 41.67664337158203\n", "The 4133th loop: cost = 41.87261199951172\n", "The 4134th loop: cost = 47.022247314453125\n", "The 4135th loop: cost = 41.99585723876953\n", "The 4136th loop: cost = 48.59679412841797\n", "The 4137th loop: cost = 42.7242431640625\n", "The 4138th loop: cost = 53.62774658203125\n", "The 4139th loop: cost = 37.55037307739258\n", "The 4140th loop: cost = 47.872222900390625\n", "The 4141th loop: cost = 52.391544342041016\n", "The 4142th loop: cost = 49.428924560546875\n", "The 4143th loop: cost = 53.15147399902344\n", "The 4144th loop: cost = 49.85433578491211\n", "The 4145th loop: cost = 56.961917877197266\n", "The 4146th loop: cost = 48.90357208251953\n", "The 4147th loop: cost = 43.87744903564453\n", "The 4148th loop: cost = 52.95349884033203\n", "The 4149th loop: cost = 50.58320236206055\n", "The 4150th loop: cost = 44.300758361816406\n", "The 4151th loop: cost = 41.58527374267578\n", "The 4152th loop: cost = 47.324947357177734\n", "The 4153th loop: cost = 32.919795989990234\n", "The 4154th loop: cost = 50.283775329589844\n", "The 4155th loop: cost = 34.368682861328125\n", "The 4156th loop: cost = 42.29573059082031\n", "The 4157th loop: cost = 59.326316833496094\n", "The 4158th loop: cost = 52.48833465576172\n", "The 4159th loop: cost = 44.292728424072266\n", "The 4160th loop: cost = 48.104042053222656\n", "The 4161th loop: cost = 45.23516845703125\n", "The 4162th loop: cost = 44.55188751220703\n", "The 4163th loop: cost = 50.66282272338867\n", "The 4164th loop: cost = 44.55034255981445\n", "The 4165th loop: cost = 51.440547943115234\n", "The 4166th loop: cost = 49.8052864074707\n", "The 4167th loop: cost = 45.871341705322266\n", "The 4168th loop: cost = 56.14640808105469\n", "The 4169th loop: cost = 57.347381591796875\n", "The 4170th loop: cost = 52.78651809692383\n", "The 4171th loop: cost = 46.22560119628906\n", "The 4172th loop: cost = 51.56937026977539\n", "The 4173th loop: cost = 43.973854064941406\n", "The 4174th loop: cost = 42.802616119384766\n", "The 4175th loop: cost = 41.598968505859375\n", "The 4176th loop: cost = 55.34624099731445\n", "The 4177th loop: cost = 51.615028381347656\n", "The 4178th loop: cost = 47.9598503112793\n", "The 4179th loop: cost = 44.875694274902344\n", "The 4180th loop: cost = 51.938846588134766\n", "The 4181th loop: cost = 48.816383361816406\n", "The 4182th loop: cost = 41.52824401855469\n", "The 4183th loop: cost = 45.42414093017578\n", "The 4184th loop: cost = 54.118309020996094\n", "The 4185th loop: cost = 50.03761291503906\n", "The 4186th loop: cost = 44.25941467285156\n", "The 4187th loop: cost = 52.33428955078125\n", "The 4188th loop: cost = 58.13974380493164\n", "The 4189th loop: cost = 57.432640075683594\n", "The 4190th loop: cost = 48.26816940307617\n", "The 4191th loop: cost = 44.981231689453125\n", "The 4192th loop: cost = 49.35734558105469\n", "The 4193th loop: cost = 46.302818298339844\n", "The 4194th loop: cost = 51.586219787597656\n", "The 4195th loop: cost = 55.351287841796875\n", "The 4196th loop: cost = 50.072509765625\n", "The 4197th loop: cost = 42.14808654785156\n", "The 4198th loop: cost = 53.16722106933594\n", "The 4199th loop: cost = 46.77063751220703\n", "The 4200th loop: cost = 52.83332061767578\n", "The 4201th loop: cost = 58.59624481201172\n", "The 4202th loop: cost = 49.939208984375\n", "The 4203th loop: cost = 41.65351104736328\n", "The 4204th loop: cost = 43.98472595214844\n", "The 4205th loop: cost = 46.13750457763672\n", "The 4206th loop: cost = 42.67106628417969\n", "The 4207th loop: cost = 50.734012603759766\n", "The 4208th loop: cost = 41.83415985107422\n", "The 4209th loop: cost = 49.55067443847656\n", "The 4210th loop: cost = 36.872352600097656\n", "The 4211th loop: cost = 39.95784378051758\n", "The 4212th loop: cost = 43.08671188354492\n", "The 4213th loop: cost = 52.65648651123047\n", "The 4214th loop: cost = 36.37156295776367\n", "The 4215th loop: cost = 42.070709228515625\n", "The 4216th loop: cost = 42.374908447265625\n", "The 4217th loop: cost = 40.606407165527344\n", "The 4218th loop: cost = 61.59342956542969\n", "The 4219th loop: cost = 53.921451568603516\n", "The 4220th loop: cost = 52.43480682373047\n", "The 4221th loop: cost = 51.48487091064453\n", "The 4222th loop: cost = 53.07904815673828\n", "The 4223th loop: cost = 49.594627380371094\n", "The 4224th loop: cost = 53.091854095458984\n", "The 4225th loop: cost = 55.72605895996094\n", "The 4226th loop: cost = 46.49513626098633\n", "The 4227th loop: cost = 50.44542694091797\n", "The 4228th loop: cost = 48.94355010986328\n", "The 4229th loop: cost = 56.2342529296875\n", "The 4230th loop: cost = 50.5931282043457\n", "The 4231th loop: cost = 44.8928337097168\n", "The 4232th loop: cost = 48.757347106933594\n", "The 4233th loop: cost = 44.38988494873047\n", "The 4234th loop: cost = 45.1068229675293\n", "The 4235th loop: cost = 51.15031051635742\n", "The 4236th loop: cost = 45.324012756347656\n", "The 4237th loop: cost = 46.43888854980469\n", "The 4238th loop: cost = 48.278717041015625\n", "The 4239th loop: cost = 45.677425384521484\n", "The 4240th loop: cost = 45.357337951660156\n", "The 4241th loop: cost = 47.784908294677734\n", "The 4242th loop: cost = 32.88184356689453\n", "The 4243th loop: cost = 46.099815368652344\n", "The 4244th loop: cost = 56.18476104736328\n", "The 4245th loop: cost = 54.322906494140625\n", "The 4246th loop: cost = 49.67052459716797\n", "The 4247th loop: cost = 57.703086853027344\n", "The 4248th loop: cost = 48.99341583251953\n", "The 4249th loop: cost = 49.325538635253906\n", "The 4250th loop: cost = 47.75847244262695\n", "The 4251th loop: cost = 38.52102279663086\n", "The 4252th loop: cost = 46.050132751464844\n", "The 4253th loop: cost = 42.95429992675781\n", "The 4254th loop: cost = 47.62199401855469\n", "The 4255th loop: cost = 47.977378845214844\n", "The 4256th loop: cost = 48.98625946044922\n", "The 4257th loop: cost = 40.5628776550293\n", "The 4258th loop: cost = 49.67282485961914\n", "The 4259th loop: cost = 45.57465362548828\n", "The 4260th loop: cost = 52.77988815307617\n", "The 4261th loop: cost = 44.19142532348633\n", "The 4262th loop: cost = 53.26973342895508\n", "The 4263th loop: cost = 49.73651123046875\n", "The 4264th loop: cost = 50.525150299072266\n", "The 4265th loop: cost = 43.24646759033203\n", "The 4266th loop: cost = 46.529747009277344\n", "The 4267th loop: cost = 42.190269470214844\n", "The 4268th loop: cost = 43.81099319458008\n", "The 4269th loop: cost = 46.67700958251953\n", "The 4270th loop: cost = 48.767791748046875\n", "The 4271th loop: cost = 37.54259490966797\n", "The 4272th loop: cost = 47.32701110839844\n", "The 4273th loop: cost = 55.017154693603516\n", "The 4274th loop: cost = 44.516502380371094\n", "The 4275th loop: cost = 63.12831115722656\n", "The 4276th loop: cost = 54.30084228515625\n", "The 4277th loop: cost = 44.53317642211914\n", "The 4278th loop: cost = 47.962493896484375\n", "The 4279th loop: cost = 46.091888427734375\n", "The 4280th loop: cost = 41.458106994628906\n", "The 4281th loop: cost = 40.3160514831543\n", "The 4282th loop: cost = 49.73706817626953\n", "The 4283th loop: cost = 65.9955825805664\n", "The 4284th loop: cost = 56.193519592285156\n", "The 4285th loop: cost = 56.64064025878906\n", "The 4286th loop: cost = 47.181129455566406\n", "The 4287th loop: cost = 41.429290771484375\n", "The 4288th loop: cost = 50.29355239868164\n", "The 4289th loop: cost = 45.88547134399414\n", "The 4290th loop: cost = 51.85946273803711\n", "The 4291th loop: cost = 47.070213317871094\n", "The 4292th loop: cost = 56.750579833984375\n", "The 4293th loop: cost = 46.92131042480469\n", "The 4294th loop: cost = 54.18195724487305\n", "The 4295th loop: cost = 52.794593811035156\n", "The 4296th loop: cost = 46.37309265136719\n", "The 4297th loop: cost = 52.1085205078125\n", "The 4298th loop: cost = 41.419715881347656\n", "The 4299th loop: cost = 39.29612731933594\n", "The 4300th loop: cost = 47.03254318237305\n", "The 4301th loop: cost = 45.52141571044922\n", "The 4302th loop: cost = 43.97077178955078\n", "The 4303th loop: cost = 49.85307312011719\n", "The 4304th loop: cost = 40.45587158203125\n", "The 4305th loop: cost = 57.33150100708008\n", "The 4306th loop: cost = 36.571022033691406\n", "The 4307th loop: cost = 37.770042419433594\n", "The 4308th loop: cost = 44.25114822387695\n", "The 4309th loop: cost = 47.06488037109375\n", "The 4310th loop: cost = 46.98331832885742\n", "The 4311th loop: cost = 45.42060089111328\n", "The 4312th loop: cost = 54.57556915283203\n", "The 4313th loop: cost = 58.498023986816406\n", "The 4314th loop: cost = 63.685821533203125\n", "The 4315th loop: cost = 53.165557861328125\n", "The 4316th loop: cost = 45.21028137207031\n", "The 4317th loop: cost = 51.484764099121094\n", "The 4318th loop: cost = 46.44929504394531\n", "The 4319th loop: cost = 49.679344177246094\n", "The 4320th loop: cost = 55.314788818359375\n", "The 4321th loop: cost = 40.596622467041016\n", "The 4322th loop: cost = 56.35601043701172\n", "The 4323th loop: cost = 54.665340423583984\n", "The 4324th loop: cost = 45.50910186767578\n", "The 4325th loop: cost = 45.07823181152344\n", "The 4326th loop: cost = 36.29434585571289\n", "The 4327th loop: cost = 38.835731506347656\n", "The 4328th loop: cost = 41.26344299316406\n", "The 4329th loop: cost = 42.015541076660156\n", "The 4330th loop: cost = 55.97246551513672\n", "The 4331th loop: cost = 40.837989807128906\n", "The 4332th loop: cost = 55.63050079345703\n", "The 4333th loop: cost = 40.404273986816406\n", "The 4334th loop: cost = 59.8038330078125\n", "The 4335th loop: cost = 46.000244140625\n", "The 4336th loop: cost = 48.027557373046875\n", "The 4337th loop: cost = 56.293663024902344\n", "The 4338th loop: cost = 54.48624801635742\n", "The 4339th loop: cost = 47.90227508544922\n", "The 4340th loop: cost = 48.606842041015625\n", "The 4341th loop: cost = 48.437294006347656\n", "The 4342th loop: cost = 57.138771057128906\n", "The 4343th loop: cost = 45.75033950805664\n", "The 4344th loop: cost = 38.184715270996094\n", "The 4345th loop: cost = 48.0926628112793\n", "The 4346th loop: cost = 49.21549987792969\n", "The 4347th loop: cost = 55.19970703125\n", "The 4348th loop: cost = 51.834938049316406\n", "The 4349th loop: cost = 53.55425262451172\n", "The 4350th loop: cost = 41.17520523071289\n", "The 4351th loop: cost = 44.900978088378906\n", "The 4352th loop: cost = 42.30011749267578\n", "The 4353th loop: cost = 49.83671951293945\n", "The 4354th loop: cost = 49.75497055053711\n", "The 4355th loop: cost = 64.3648681640625\n", "The 4356th loop: cost = 53.43232727050781\n", "The 4357th loop: cost = 53.30681610107422\n", "The 4358th loop: cost = 46.582489013671875\n", "The 4359th loop: cost = 37.2421875\n", "The 4360th loop: cost = 49.679283142089844\n", "The 4361th loop: cost = 46.9058723449707\n", "The 4362th loop: cost = 47.66870880126953\n", "The 4363th loop: cost = 50.8956413269043\n", "The 4364th loop: cost = 47.37743377685547\n", "The 4365th loop: cost = 44.553688049316406\n", "The 4366th loop: cost = 44.41075134277344\n", "The 4367th loop: cost = 56.63252258300781\n", "The 4368th loop: cost = 46.01463317871094\n", "The 4369th loop: cost = 45.469905853271484\n", "The 4370th loop: cost = 48.747406005859375\n", "The 4371th loop: cost = 43.47062683105469\n", "The 4372th loop: cost = 52.70032501220703\n", "The 4373th loop: cost = 49.04872131347656\n", "The 4374th loop: cost = 51.622039794921875\n", "The 4375th loop: cost = 64.48794555664062\n", "The 4376th loop: cost = 48.888221740722656\n", "The 4377th loop: cost = 42.38007736206055\n", "The 4378th loop: cost = 50.72312545776367\n", "The 4379th loop: cost = 46.47276306152344\n", "The 4380th loop: cost = 50.41037368774414\n", "The 4381th loop: cost = 53.68080520629883\n", "The 4382th loop: cost = 46.49358367919922\n", "The 4383th loop: cost = 48.4259147644043\n", "The 4384th loop: cost = 45.746395111083984\n", "The 4385th loop: cost = 54.35661315917969\n", "The 4386th loop: cost = 60.48649215698242\n", "The 4387th loop: cost = 45.29955291748047\n", "The 4388th loop: cost = 35.049339294433594\n", "The 4389th loop: cost = 48.219947814941406\n", "The 4390th loop: cost = 50.522705078125\n", "The 4391th loop: cost = 56.05635070800781\n", "The 4392th loop: cost = 50.35972595214844\n", "The 4393th loop: cost = 43.1805305480957\n", "The 4394th loop: cost = 46.897979736328125\n", "The 4395th loop: cost = 46.300140380859375\n", "The 4396th loop: cost = 50.084136962890625\n", "The 4397th loop: cost = 42.31501770019531\n", "The 4398th loop: cost = 52.83451843261719\n", "The 4399th loop: cost = 53.54548645019531\n", "The 4400th loop: cost = 52.00872039794922\n", "The 4401th loop: cost = 48.36222839355469\n", "The 4402th loop: cost = 56.66929244995117\n", "The 4403th loop: cost = 53.94194030761719\n", "The 4404th loop: cost = 41.81682586669922\n", "The 4405th loop: cost = 58.4802131652832\n", "The 4406th loop: cost = 57.604000091552734\n", "The 4407th loop: cost = 56.534000396728516\n", "The 4408th loop: cost = 46.104408264160156\n", "The 4409th loop: cost = 47.06561279296875\n", "The 4410th loop: cost = 51.272613525390625\n", "The 4411th loop: cost = 43.086639404296875\n", "The 4412th loop: cost = 47.997615814208984\n", "The 4413th loop: cost = 51.925357818603516\n", "The 4414th loop: cost = 48.17854309082031\n", "The 4415th loop: cost = 42.01490020751953\n", "The 4416th loop: cost = 52.087013244628906\n", "The 4417th loop: cost = 54.34125900268555\n", "The 4418th loop: cost = 56.076385498046875\n", "The 4419th loop: cost = 41.56028747558594\n", "The 4420th loop: cost = 44.50016784667969\n", "The 4421th loop: cost = 45.28067398071289\n", "The 4422th loop: cost = 52.438297271728516\n", "The 4423th loop: cost = 38.64105987548828\n", "The 4424th loop: cost = 41.782569885253906\n", "The 4425th loop: cost = 52.67388153076172\n", "The 4426th loop: cost = 43.09938430786133\n", "The 4427th loop: cost = 54.56435012817383\n", "The 4428th loop: cost = 35.97589111328125\n", "The 4429th loop: cost = 51.415855407714844\n", "The 4430th loop: cost = 47.277992248535156\n", "The 4431th loop: cost = 46.332366943359375\n", "The 4432th loop: cost = 47.965232849121094\n", "The 4433th loop: cost = 53.04538345336914\n", "The 4434th loop: cost = 47.92680358886719\n", "The 4435th loop: cost = 45.23736572265625\n", "The 4436th loop: cost = 54.12152862548828\n", "The 4437th loop: cost = 52.82966232299805\n", "The 4438th loop: cost = 38.05194854736328\n", "The 4439th loop: cost = 46.97394943237305\n", "The 4440th loop: cost = 46.62842559814453\n", "The 4441th loop: cost = 48.095176696777344\n", "The 4442th loop: cost = 55.631004333496094\n", "The 4443th loop: cost = 52.292877197265625\n", "The 4444th loop: cost = 40.354736328125\n", "The 4445th loop: cost = 50.1463508605957\n", "The 4446th loop: cost = 52.23234558105469\n", "The 4447th loop: cost = 47.559906005859375\n", "The 4448th loop: cost = 57.306156158447266\n", "The 4449th loop: cost = 49.11823654174805\n", "The 4450th loop: cost = 39.67274856567383\n", "The 4451th loop: cost = 39.50352478027344\n", "The 4452th loop: cost = 56.231380462646484\n", "The 4453th loop: cost = 46.956398010253906\n", "The 4454th loop: cost = 44.402137756347656\n", "The 4455th loop: cost = 48.268245697021484\n", "The 4456th loop: cost = 45.39562225341797\n", "The 4457th loop: cost = 54.738182067871094\n", "The 4458th loop: cost = 44.56182861328125\n", "The 4459th loop: cost = 47.80194854736328\n", "The 4460th loop: cost = 51.8414306640625\n", "The 4461th loop: cost = 53.42582321166992\n", "The 4462th loop: cost = 39.23182678222656\n", "The 4463th loop: cost = 47.839698791503906\n", "The 4464th loop: cost = 54.35049057006836\n", "The 4465th loop: cost = 44.56993103027344\n", "The 4466th loop: cost = 50.40477752685547\n", "The 4467th loop: cost = 45.36564636230469\n", "The 4468th loop: cost = 46.80513000488281\n", "The 4469th loop: cost = 48.40690231323242\n", "The 4470th loop: cost = 42.655967712402344\n", "The 4471th loop: cost = 42.72035598754883\n", "The 4472th loop: cost = 59.288002014160156\n", "The 4473th loop: cost = 38.88114547729492\n", "The 4474th loop: cost = 50.458740234375\n", "The 4475th loop: cost = 55.73036575317383\n", "The 4476th loop: cost = 47.367645263671875\n", "The 4477th loop: cost = 49.54433059692383\n", "The 4478th loop: cost = 50.73689651489258\n", "The 4479th loop: cost = 46.125755310058594\n", "The 4480th loop: cost = 39.07023239135742\n", "The 4481th loop: cost = 48.87983322143555\n", "The 4482th loop: cost = 44.486534118652344\n", "The 4483th loop: cost = 46.2724494934082\n", "The 4484th loop: cost = 46.445579528808594\n", "The 4485th loop: cost = 38.806297302246094\n", "The 4486th loop: cost = 65.5495834350586\n", "The 4487th loop: cost = 43.214168548583984\n", "The 4488th loop: cost = 48.1043701171875\n", "The 4489th loop: cost = 45.29426574707031\n", "The 4490th loop: cost = 48.361412048339844\n", "The 4491th loop: cost = 44.29829406738281\n", "The 4492th loop: cost = 46.160560607910156\n", "The 4493th loop: cost = 40.91765594482422\n", "The 4494th loop: cost = 65.7828598022461\n", "The 4495th loop: cost = 44.017112731933594\n", "The 4496th loop: cost = 39.248653411865234\n", "The 4497th loop: cost = 46.990360260009766\n", "The 4498th loop: cost = 45.509857177734375\n", "The 4499th loop: cost = 39.75066375732422\n", "The 4500th loop: cost = 46.69670104980469\n", "The 4501th loop: cost = 48.536888122558594\n", "The 4502th loop: cost = 52.034751892089844\n", "The 4503th loop: cost = 46.020851135253906\n", "The 4504th loop: cost = 34.62016296386719\n", "The 4505th loop: cost = 54.610633850097656\n", "The 4506th loop: cost = 55.88967514038086\n", "The 4507th loop: cost = 42.60384750366211\n", "The 4508th loop: cost = 41.512535095214844\n", "The 4509th loop: cost = 59.874637603759766\n", "The 4510th loop: cost = 52.990543365478516\n", "The 4511th loop: cost = 47.93214416503906\n", "The 4512th loop: cost = 50.787315368652344\n", "The 4513th loop: cost = 59.40790939331055\n", "The 4514th loop: cost = 44.964054107666016\n", "The 4515th loop: cost = 43.774681091308594\n", "The 4516th loop: cost = 51.08281707763672\n", "The 4517th loop: cost = 51.57533645629883\n", "The 4518th loop: cost = 45.04580307006836\n", "The 4519th loop: cost = 44.705360412597656\n", "The 4520th loop: cost = 44.79821014404297\n", "The 4521th loop: cost = 56.69365310668945\n", "The 4522th loop: cost = 40.81841278076172\n", "The 4523th loop: cost = 45.96452331542969\n", "The 4524th loop: cost = 51.92137908935547\n", "The 4525th loop: cost = 46.17927551269531\n", "The 4526th loop: cost = 43.8001823425293\n", "The 4527th loop: cost = 63.435455322265625\n", "The 4528th loop: cost = 51.84739685058594\n", "The 4529th loop: cost = 43.14957809448242\n", "The 4530th loop: cost = 50.282745361328125\n", "The 4531th loop: cost = 52.09648513793945\n", "The 4532th loop: cost = 40.12934875488281\n", "The 4533th loop: cost = 50.951210021972656\n", "The 4534th loop: cost = 47.150394439697266\n", "The 4535th loop: cost = 43.123329162597656\n", "The 4536th loop: cost = 48.41688537597656\n", "The 4537th loop: cost = 50.09303283691406\n", "The 4538th loop: cost = 50.395477294921875\n", "The 4539th loop: cost = 49.92253112792969\n", "The 4540th loop: cost = 57.30266571044922\n", "The 4541th loop: cost = 42.18809127807617\n", "The 4542th loop: cost = 48.712890625\n", "The 4543th loop: cost = 48.01167297363281\n", "The 4544th loop: cost = 48.623619079589844\n", "The 4545th loop: cost = 43.3914794921875\n", "The 4546th loop: cost = 49.853389739990234\n", "The 4547th loop: cost = 42.808258056640625\n", "The 4548th loop: cost = 48.826751708984375\n", "The 4549th loop: cost = 49.232032775878906\n", "The 4550th loop: cost = 59.93559646606445\n", "The 4551th loop: cost = 47.90401077270508\n", "The 4552th loop: cost = 47.812625885009766\n", "The 4553th loop: cost = 49.947322845458984\n", "The 4554th loop: cost = 55.26899337768555\n", "The 4555th loop: cost = 48.88225555419922\n", "The 4556th loop: cost = 48.55452346801758\n", "The 4557th loop: cost = 51.338199615478516\n", "The 4558th loop: cost = 42.419193267822266\n", "The 4559th loop: cost = 47.11973190307617\n", "The 4560th loop: cost = 39.60258102416992\n", "The 4561th loop: cost = 42.66294479370117\n", "The 4562th loop: cost = 65.65451049804688\n", "The 4563th loop: cost = 50.6027717590332\n", "The 4564th loop: cost = 46.80426788330078\n", "The 4565th loop: cost = 52.655967712402344\n", "The 4566th loop: cost = 47.035301208496094\n", "The 4567th loop: cost = 42.867610931396484\n", "The 4568th loop: cost = 43.08872985839844\n", "The 4569th loop: cost = 44.84394073486328\n", "The 4570th loop: cost = 42.285675048828125\n", "The 4571th loop: cost = 46.06901931762695\n", "The 4572th loop: cost = 46.362831115722656\n", "The 4573th loop: cost = 51.1326904296875\n", "The 4574th loop: cost = 45.017494201660156\n", "The 4575th loop: cost = 47.058780670166016\n", "The 4576th loop: cost = 52.816463470458984\n", "The 4577th loop: cost = 53.52302932739258\n", "The 4578th loop: cost = 46.68951416015625\n", "The 4579th loop: cost = 45.729469299316406\n", "The 4580th loop: cost = 47.350990295410156\n", "The 4581th loop: cost = 46.253273010253906\n", "The 4582th loop: cost = 43.682716369628906\n", "The 4583th loop: cost = 55.4222412109375\n", "The 4584th loop: cost = 44.4329833984375\n", "The 4585th loop: cost = 58.670616149902344\n", "The 4586th loop: cost = 48.69729995727539\n", "The 4587th loop: cost = 43.51099395751953\n", "The 4588th loop: cost = 45.239830017089844\n", "The 4589th loop: cost = 43.69470977783203\n", "The 4590th loop: cost = 34.67621612548828\n", "The 4591th loop: cost = 48.1053466796875\n", "The 4592th loop: cost = 41.490234375\n", "The 4593th loop: cost = 40.14373779296875\n", "The 4594th loop: cost = 50.425148010253906\n", "The 4595th loop: cost = 47.46916961669922\n", "The 4596th loop: cost = 46.572715759277344\n", "The 4597th loop: cost = 46.26852035522461\n", "The 4598th loop: cost = 48.890872955322266\n", "The 4599th loop: cost = 52.74934005737305\n", "The 4600th loop: cost = 54.4864501953125\n", "The 4601th loop: cost = 41.44099426269531\n", "The 4602th loop: cost = 48.232723236083984\n", "The 4603th loop: cost = 50.34724807739258\n", "The 4604th loop: cost = 52.246177673339844\n", "The 4605th loop: cost = 50.31759262084961\n", "The 4606th loop: cost = 44.081153869628906\n", "The 4607th loop: cost = 53.406105041503906\n", "The 4608th loop: cost = 50.12080001831055\n", "The 4609th loop: cost = 56.379066467285156\n", "The 4610th loop: cost = 48.56279373168945\n", "The 4611th loop: cost = 52.406890869140625\n", "The 4612th loop: cost = 50.118324279785156\n", "The 4613th loop: cost = 45.446983337402344\n", "The 4614th loop: cost = 50.65497589111328\n", "The 4615th loop: cost = 42.93837356567383\n", "The 4616th loop: cost = 38.08648681640625\n", "The 4617th loop: cost = 50.86779022216797\n", "The 4618th loop: cost = 44.76711654663086\n", "The 4619th loop: cost = 44.94533157348633\n", "The 4620th loop: cost = 46.468650817871094\n", "The 4621th loop: cost = 42.21646499633789\n", "The 4622th loop: cost = 33.086971282958984\n", "The 4623th loop: cost = 42.50264358520508\n", "The 4624th loop: cost = 37.159019470214844\n", "The 4625th loop: cost = 39.64329528808594\n", "The 4626th loop: cost = 42.532318115234375\n", "The 4627th loop: cost = 57.20936584472656\n", "The 4628th loop: cost = 35.06656265258789\n", "The 4629th loop: cost = 38.87754821777344\n", "The 4630th loop: cost = 47.126075744628906\n", "The 4631th loop: cost = 43.04431915283203\n", "The 4632th loop: cost = 50.53315734863281\n", "The 4633th loop: cost = 53.93090057373047\n", "The 4634th loop: cost = 51.3030891418457\n", "The 4635th loop: cost = 50.4621696472168\n", "The 4636th loop: cost = 52.37809371948242\n", "The 4637th loop: cost = 48.29601287841797\n", "The 4638th loop: cost = 55.294677734375\n", "The 4639th loop: cost = 45.94390869140625\n", "The 4640th loop: cost = 47.60224151611328\n", "The 4641th loop: cost = 45.17848205566406\n", "The 4642th loop: cost = 47.67784118652344\n", "The 4643th loop: cost = 45.7356071472168\n", "The 4644th loop: cost = 43.07542419433594\n", "The 4645th loop: cost = 43.22712707519531\n", "The 4646th loop: cost = 41.07223892211914\n", "The 4647th loop: cost = 53.73668670654297\n", "The 4648th loop: cost = 57.37683868408203\n", "The 4649th loop: cost = 66.20674133300781\n", "The 4650th loop: cost = 47.30968475341797\n", "The 4651th loop: cost = 39.5102424621582\n", "The 4652th loop: cost = 44.16469955444336\n", "The 4653th loop: cost = 43.51813507080078\n", "The 4654th loop: cost = 55.30187225341797\n", "The 4655th loop: cost = 44.17253494262695\n", "The 4656th loop: cost = 41.72272872924805\n", "The 4657th loop: cost = 44.35551452636719\n", "The 4658th loop: cost = 49.407859802246094\n", "The 4659th loop: cost = 44.30393981933594\n", "The 4660th loop: cost = 58.107810974121094\n", "The 4661th loop: cost = 41.56352996826172\n", "The 4662th loop: cost = 43.94786834716797\n", "The 4663th loop: cost = 45.353538513183594\n", "The 4664th loop: cost = 43.34719467163086\n", "The 4665th loop: cost = 48.32670974731445\n", "The 4666th loop: cost = 40.60182189941406\n", "The 4667th loop: cost = 52.34902572631836\n", "The 4668th loop: cost = 44.66876983642578\n", "The 4669th loop: cost = 58.707550048828125\n", "The 4670th loop: cost = 48.382965087890625\n", "The 4671th loop: cost = 43.87453842163086\n", "The 4672th loop: cost = 50.92560577392578\n", "The 4673th loop: cost = 39.42250061035156\n", "The 4674th loop: cost = 50.98920440673828\n", "The 4675th loop: cost = 46.54144287109375\n", "The 4676th loop: cost = 50.44340515136719\n", "The 4677th loop: cost = 48.55405044555664\n", "The 4678th loop: cost = 41.28324890136719\n", "The 4679th loop: cost = 46.89230728149414\n", "The 4680th loop: cost = 46.072479248046875\n", "The 4681th loop: cost = 36.64739227294922\n", "The 4682th loop: cost = 46.186676025390625\n", "The 4683th loop: cost = 52.543434143066406\n", "The 4684th loop: cost = 46.01964569091797\n", "The 4685th loop: cost = 46.99586486816406\n", "The 4686th loop: cost = 49.94099807739258\n", "The 4687th loop: cost = 48.706390380859375\n", "The 4688th loop: cost = 48.62706756591797\n", "The 4689th loop: cost = 43.615447998046875\n", "The 4690th loop: cost = 47.129173278808594\n", "The 4691th loop: cost = 46.406497955322266\n", "The 4692th loop: cost = 50.650333404541016\n", "The 4693th loop: cost = 47.97076416015625\n", "The 4694th loop: cost = 50.48809814453125\n", "The 4695th loop: cost = 47.64931869506836\n", "The 4696th loop: cost = 41.8236083984375\n", "The 4697th loop: cost = 47.7386589050293\n", "The 4698th loop: cost = 37.954925537109375\n", "The 4699th loop: cost = 40.72895050048828\n", "The 4700th loop: cost = 44.18731689453125\n", "The 4701th loop: cost = 52.59156036376953\n", "The 4702th loop: cost = 56.33702850341797\n", "The 4703th loop: cost = 50.37396240234375\n", "The 4704th loop: cost = 54.23390197753906\n", "The 4705th loop: cost = 44.09740447998047\n", "The 4706th loop: cost = 47.88755798339844\n", "The 4707th loop: cost = 40.12218475341797\n", "The 4708th loop: cost = 46.359642028808594\n", "The 4709th loop: cost = 45.84163284301758\n", "The 4710th loop: cost = 42.40126037597656\n", "The 4711th loop: cost = 55.728271484375\n", "The 4712th loop: cost = 41.13802719116211\n", "The 4713th loop: cost = 39.193824768066406\n", "The 4714th loop: cost = 41.7060432434082\n", "The 4715th loop: cost = 50.72172927856445\n", "The 4716th loop: cost = 49.946678161621094\n", "The 4717th loop: cost = 48.78337478637695\n", "The 4718th loop: cost = 45.31224060058594\n", "The 4719th loop: cost = 50.76585006713867\n", "The 4720th loop: cost = 47.54003143310547\n", "The 4721th loop: cost = 42.405250549316406\n", "The 4722th loop: cost = 53.7098388671875\n", "The 4723th loop: cost = 46.3593864440918\n", "The 4724th loop: cost = 40.370262145996094\n", "The 4725th loop: cost = 55.41947937011719\n", "The 4726th loop: cost = 39.72125244140625\n", "The 4727th loop: cost = 45.82949447631836\n", "The 4728th loop: cost = 49.765926361083984\n", "The 4729th loop: cost = 55.94779968261719\n", "The 4730th loop: cost = 55.259490966796875\n", "The 4731th loop: cost = 38.02467346191406\n", "The 4732th loop: cost = 43.94590759277344\n", "The 4733th loop: cost = 57.42711639404297\n", "The 4734th loop: cost = 41.72547912597656\n", "The 4735th loop: cost = 43.81529998779297\n", "The 4736th loop: cost = 48.1971549987793\n", "The 4737th loop: cost = 45.061309814453125\n", "The 4738th loop: cost = 41.96364974975586\n", "The 4739th loop: cost = 48.7471923828125\n", "The 4740th loop: cost = 42.86931610107422\n", "The 4741th loop: cost = 46.30429458618164\n", "The 4742th loop: cost = 43.64823913574219\n", "The 4743th loop: cost = 53.818321228027344\n", "The 4744th loop: cost = 55.39847946166992\n", "The 4745th loop: cost = 44.20380401611328\n", "The 4746th loop: cost = 48.92693328857422\n", "The 4747th loop: cost = 51.91170120239258\n", "The 4748th loop: cost = 44.93971633911133\n", "The 4749th loop: cost = 47.25617218017578\n", "The 4750th loop: cost = 56.91219711303711\n", "The 4751th loop: cost = 43.67816925048828\n", "The 4752th loop: cost = 44.780479431152344\n", "The 4753th loop: cost = 49.13523864746094\n", "The 4754th loop: cost = 55.13812255859375\n", "The 4755th loop: cost = 49.2341194152832\n", "The 4756th loop: cost = 51.87038040161133\n", "The 4757th loop: cost = 42.74400329589844\n", "The 4758th loop: cost = 41.563385009765625\n", "The 4759th loop: cost = 43.097412109375\n", "The 4760th loop: cost = 39.735877990722656\n", "The 4761th loop: cost = 48.44062042236328\n", "The 4762th loop: cost = 42.82002258300781\n", "The 4763th loop: cost = 50.14408874511719\n", "The 4764th loop: cost = 36.94987487792969\n", "The 4765th loop: cost = 50.586734771728516\n", "The 4766th loop: cost = 55.319114685058594\n", "The 4767th loop: cost = 45.25667190551758\n", "The 4768th loop: cost = 46.288543701171875\n", "The 4769th loop: cost = 38.978519439697266\n", "The 4770th loop: cost = 52.924652099609375\n", "The 4771th loop: cost = 51.08464050292969\n", "The 4772th loop: cost = 49.11030578613281\n", "The 4773th loop: cost = 42.359352111816406\n", "The 4774th loop: cost = 47.64796829223633\n", "The 4775th loop: cost = 52.58430480957031\n", "The 4776th loop: cost = 51.93551254272461\n", "The 4777th loop: cost = 57.783294677734375\n", "The 4778th loop: cost = 46.78425598144531\n", "The 4779th loop: cost = 40.95849609375\n", "The 4780th loop: cost = 40.66999816894531\n", "The 4781th loop: cost = 43.55881118774414\n", "The 4782th loop: cost = 39.447540283203125\n", "The 4783th loop: cost = 50.029747009277344\n", "The 4784th loop: cost = 49.6943359375\n", "The 4785th loop: cost = 51.54969024658203\n", "The 4786th loop: cost = 40.641456604003906\n", "The 4787th loop: cost = 37.11767578125\n", "The 4788th loop: cost = 45.39918518066406\n", "The 4789th loop: cost = 57.687232971191406\n", "The 4790th loop: cost = 50.12356948852539\n", "The 4791th loop: cost = 45.10509490966797\n", "The 4792th loop: cost = 57.36919021606445\n", "The 4793th loop: cost = 47.29688262939453\n", "The 4794th loop: cost = 41.99190902709961\n", "The 4795th loop: cost = 50.55712127685547\n", "The 4796th loop: cost = 43.72578811645508\n", "The 4797th loop: cost = 47.625457763671875\n", "The 4798th loop: cost = 51.880977630615234\n", "The 4799th loop: cost = 51.67304611206055\n", "The 4800th loop: cost = 48.08283233642578\n", "The 4801th loop: cost = 49.391563415527344\n", "The 4802th loop: cost = 42.24091339111328\n", "The 4803th loop: cost = 41.10736846923828\n", "The 4804th loop: cost = 44.02102279663086\n", "The 4805th loop: cost = 32.38468933105469\n", "The 4806th loop: cost = 48.297264099121094\n", "The 4807th loop: cost = 51.87995529174805\n", "The 4808th loop: cost = 51.040184020996094\n", "The 4809th loop: cost = 59.0783576965332\n", "The 4810th loop: cost = 38.809242248535156\n", "The 4811th loop: cost = 49.55756378173828\n", "The 4812th loop: cost = 49.815486907958984\n", "The 4813th loop: cost = 42.90500259399414\n", "The 4814th loop: cost = 49.269065856933594\n", "The 4815th loop: cost = 56.25100326538086\n", "The 4816th loop: cost = 42.75484085083008\n", "The 4817th loop: cost = 45.514732360839844\n", "The 4818th loop: cost = 55.3516960144043\n", "The 4819th loop: cost = 54.09439468383789\n", "The 4820th loop: cost = 49.453102111816406\n", "The 4821th loop: cost = 43.63262176513672\n", "The 4822th loop: cost = 48.73588562011719\n", "The 4823th loop: cost = 61.001895904541016\n", "The 4824th loop: cost = 48.022918701171875\n", "The 4825th loop: cost = 53.79574203491211\n", "The 4826th loop: cost = 49.86244583129883\n", "The 4827th loop: cost = 47.15739440917969\n", "The 4828th loop: cost = 42.97453689575195\n", "The 4829th loop: cost = 41.434486389160156\n", "The 4830th loop: cost = 49.48176193237305\n", "The 4831th loop: cost = 49.05101013183594\n", "The 4832th loop: cost = 41.19972229003906\n", "The 4833th loop: cost = 50.133445739746094\n", "The 4834th loop: cost = 58.06934356689453\n", "The 4835th loop: cost = 51.142459869384766\n", "The 4836th loop: cost = 52.23367691040039\n", "The 4837th loop: cost = 47.889766693115234\n", "The 4838th loop: cost = 45.92435073852539\n", "The 4839th loop: cost = 45.520896911621094\n", "The 4840th loop: cost = 54.595699310302734\n", "The 4841th loop: cost = 50.156036376953125\n", "The 4842th loop: cost = 56.051971435546875\n", "The 4843th loop: cost = 50.02510070800781\n", "The 4844th loop: cost = 48.805511474609375\n", "The 4845th loop: cost = 49.945919036865234\n", "The 4846th loop: cost = 48.55299758911133\n", "The 4847th loop: cost = 42.22689437866211\n", "The 4848th loop: cost = 50.47807693481445\n", "The 4849th loop: cost = 49.68973159790039\n", "The 4850th loop: cost = 45.33992004394531\n", "The 4851th loop: cost = 40.743492126464844\n", "The 4852th loop: cost = 57.42185592651367\n", "The 4853th loop: cost = 51.02147674560547\n", "The 4854th loop: cost = 58.403106689453125\n", "The 4855th loop: cost = 49.12320327758789\n", "The 4856th loop: cost = 43.121673583984375\n", "The 4857th loop: cost = 48.666419982910156\n", "The 4858th loop: cost = 58.743438720703125\n", "The 4859th loop: cost = 55.01066207885742\n", "The 4860th loop: cost = 47.83669662475586\n", "The 4861th loop: cost = 46.48860168457031\n", "The 4862th loop: cost = 43.445289611816406\n", "The 4863th loop: cost = 44.85797119140625\n", "The 4864th loop: cost = 47.27449417114258\n", "The 4865th loop: cost = 49.107147216796875\n", "The 4866th loop: cost = 37.71526336669922\n", "The 4867th loop: cost = 47.05139923095703\n", "The 4868th loop: cost = 54.57699203491211\n", "The 4869th loop: cost = 50.45466995239258\n", "The 4870th loop: cost = 42.09138107299805\n", "The 4871th loop: cost = 41.555137634277344\n", "The 4872th loop: cost = 51.86962127685547\n", "The 4873th loop: cost = 48.36371994018555\n", "The 4874th loop: cost = 44.72040939331055\n", "The 4875th loop: cost = 43.28794860839844\n", "The 4876th loop: cost = 47.77690887451172\n", "The 4877th loop: cost = 47.907630920410156\n", "The 4878th loop: cost = 51.52068328857422\n", "The 4879th loop: cost = 57.42008972167969\n", "The 4880th loop: cost = 44.41614532470703\n", "The 4881th loop: cost = 41.21377944946289\n", "The 4882th loop: cost = 45.372291564941406\n", "The 4883th loop: cost = 49.05760955810547\n", "The 4884th loop: cost = 37.11820602416992\n", "The 4885th loop: cost = 59.471221923828125\n", "The 4886th loop: cost = 39.807525634765625\n", "The 4887th loop: cost = 44.777587890625\n", "The 4888th loop: cost = 44.631500244140625\n", "The 4889th loop: cost = 45.992774963378906\n", "The 4890th loop: cost = 47.76179885864258\n", "The 4891th loop: cost = 42.39552307128906\n", "The 4892th loop: cost = 49.97216033935547\n", "The 4893th loop: cost = 46.12200164794922\n", "The 4894th loop: cost = 45.945213317871094\n", "The 4895th loop: cost = 47.06891632080078\n", "The 4896th loop: cost = 53.54365539550781\n", "The 4897th loop: cost = 46.488433837890625\n", "The 4898th loop: cost = 40.065792083740234\n", "The 4899th loop: cost = 42.9622802734375\n", "The 4900th loop: cost = 55.183738708496094\n", "The 4901th loop: cost = 55.76049041748047\n", "The 4902th loop: cost = 50.763999938964844\n", "The 4903th loop: cost = 46.132755279541016\n", "The 4904th loop: cost = 35.686439514160156\n", "The 4905th loop: cost = 48.18235778808594\n", "The 4906th loop: cost = 49.53296661376953\n", "The 4907th loop: cost = 46.70639419555664\n", "The 4908th loop: cost = 54.67738723754883\n", "The 4909th loop: cost = 41.79747009277344\n", "The 4910th loop: cost = 49.87825012207031\n", "The 4911th loop: cost = 46.949195861816406\n", "The 4912th loop: cost = 40.29497528076172\n", "The 4913th loop: cost = 59.826873779296875\n", "The 4914th loop: cost = 36.23440170288086\n", "The 4915th loop: cost = 49.25731658935547\n", "The 4916th loop: cost = 59.51399230957031\n", "The 4917th loop: cost = 39.280364990234375\n", "The 4918th loop: cost = 40.83282470703125\n", "The 4919th loop: cost = 45.21562194824219\n", "The 4920th loop: cost = 47.715087890625\n", "The 4921th loop: cost = 42.948883056640625\n", "The 4922th loop: cost = 46.761749267578125\n", "The 4923th loop: cost = 47.71894073486328\n", "The 4924th loop: cost = 49.830841064453125\n", "The 4925th loop: cost = 39.472938537597656\n", "The 4926th loop: cost = 51.534080505371094\n", "The 4927th loop: cost = 38.821781158447266\n", "The 4928th loop: cost = 43.33172607421875\n", "The 4929th loop: cost = 42.82963562011719\n", "The 4930th loop: cost = 48.58056640625\n", "The 4931th loop: cost = 43.397422790527344\n", "The 4932th loop: cost = 36.192901611328125\n", "The 4933th loop: cost = 52.362571716308594\n", "The 4934th loop: cost = 47.33103942871094\n", "The 4935th loop: cost = 40.61561584472656\n", "The 4936th loop: cost = 60.888206481933594\n", "The 4937th loop: cost = 46.555240631103516\n", "The 4938th loop: cost = 45.488441467285156\n", "The 4939th loop: cost = 46.85002517700195\n", "The 4940th loop: cost = 43.12915802001953\n", "The 4941th loop: cost = 48.603153228759766\n", "The 4942th loop: cost = 50.920372009277344\n", "The 4943th loop: cost = 54.94486618041992\n", "The 4944th loop: cost = 51.4913215637207\n", "The 4945th loop: cost = 52.358543395996094\n", "The 4946th loop: cost = 42.17137908935547\n", "The 4947th loop: cost = 49.80690383911133\n", "The 4948th loop: cost = 48.77360916137695\n", "The 4949th loop: cost = 41.414546966552734\n", "The 4950th loop: cost = 47.8679313659668\n", "The 4951th loop: cost = 41.484764099121094\n", "The 4952th loop: cost = 49.31798553466797\n", "The 4953th loop: cost = 48.21434020996094\n", "The 4954th loop: cost = 41.459373474121094\n", "The 4955th loop: cost = 50.18634796142578\n", "The 4956th loop: cost = 53.527259826660156\n", "The 4957th loop: cost = 42.02438735961914\n", "The 4958th loop: cost = 52.227516174316406\n", "The 4959th loop: cost = 37.433448791503906\n", "The 4960th loop: cost = 47.93475341796875\n", "The 4961th loop: cost = 41.71209716796875\n", "The 4962th loop: cost = 58.39419937133789\n", "The 4963th loop: cost = 48.03580856323242\n", "The 4964th loop: cost = 51.4493408203125\n", "The 4965th loop: cost = 46.85498809814453\n", "The 4966th loop: cost = 47.13404846191406\n", "The 4967th loop: cost = 50.54920959472656\n", "The 4968th loop: cost = 40.4135627746582\n", "The 4969th loop: cost = 40.07521057128906\n", "The 4970th loop: cost = 45.158931732177734\n", "The 4971th loop: cost = 44.24326705932617\n", "The 4972th loop: cost = 42.97209167480469\n", "The 4973th loop: cost = 50.37409210205078\n", "The 4974th loop: cost = 56.540252685546875\n", "The 4975th loop: cost = 41.822105407714844\n", "The 4976th loop: cost = 45.64039993286133\n", "The 4977th loop: cost = 54.385963439941406\n", "The 4978th loop: cost = 43.902137756347656\n", "The 4979th loop: cost = 44.61693572998047\n", "The 4980th loop: cost = 48.01423645019531\n", "The 4981th loop: cost = 43.358402252197266\n", "The 4982th loop: cost = 45.94751739501953\n", "The 4983th loop: cost = 40.556854248046875\n", "The 4984th loop: cost = 46.609954833984375\n", "The 4985th loop: cost = 49.40568161010742\n", "The 4986th loop: cost = 46.765785217285156\n", "The 4987th loop: cost = 45.57978820800781\n", "The 4988th loop: cost = 39.76844787597656\n", "The 4989th loop: cost = 48.029991149902344\n", "The 4990th loop: cost = 45.780494689941406\n", "The 4991th loop: cost = 43.7115478515625\n", "The 4992th loop: cost = 53.699703216552734\n", "The 4993th loop: cost = 38.98609924316406\n", "The 4994th loop: cost = 41.44050598144531\n", "The 4995th loop: cost = 49.20771026611328\n", "The 4996th loop: cost = 47.62738800048828\n", "The 4997th loop: cost = 46.70026779174805\n", "The 4998th loop: cost = 41.949668884277344\n", "The 4999th loop: cost = 45.05567932128906\n", "The 5000th loop: cost = 39.71369171142578\n", "The 5001th loop: cost = 42.046939849853516\n", "The 5002th loop: cost = 48.32026672363281\n", "The 5003th loop: cost = 43.707489013671875\n", "The 5004th loop: cost = 42.030189514160156\n", "The 5005th loop: cost = 45.8485107421875\n", "The 5006th loop: cost = 49.92353439331055\n", "The 5007th loop: cost = 38.01750183105469\n", "The 5008th loop: cost = 45.92045974731445\n", "The 5009th loop: cost = 46.191829681396484\n", "The 5010th loop: cost = 46.50180435180664\n", "The 5011th loop: cost = 45.67585754394531\n", "The 5012th loop: cost = 43.35761260986328\n", "The 5013th loop: cost = 45.07402801513672\n", "The 5014th loop: cost = 48.92655944824219\n", "The 5015th loop: cost = 41.91413116455078\n", "The 5016th loop: cost = 38.80615234375\n", "The 5017th loop: cost = 50.34058380126953\n", "The 5018th loop: cost = 45.587371826171875\n", "The 5019th loop: cost = 46.875030517578125\n", "The 5020th loop: cost = 43.751651763916016\n", "The 5021th loop: cost = 53.004859924316406\n", "The 5022th loop: cost = 43.417449951171875\n", "The 5023th loop: cost = 50.63523864746094\n", "The 5024th loop: cost = 54.130977630615234\n", "The 5025th loop: cost = 51.61008071899414\n", "The 5026th loop: cost = 42.76653289794922\n", "The 5027th loop: cost = 55.86669921875\n", "The 5028th loop: cost = 49.24414825439453\n", "The 5029th loop: cost = 55.65864562988281\n", "The 5030th loop: cost = 45.88195037841797\n", "The 5031th loop: cost = 45.04615020751953\n", "The 5032th loop: cost = 45.68492126464844\n", "The 5033th loop: cost = 47.211830139160156\n", "The 5034th loop: cost = 54.18519592285156\n", "The 5035th loop: cost = 52.881919860839844\n", "The 5036th loop: cost = 49.164588928222656\n", "The 5037th loop: cost = 40.54400634765625\n", "The 5038th loop: cost = 35.889198303222656\n", "The 5039th loop: cost = 48.79957580566406\n", "The 5040th loop: cost = 41.11888122558594\n", "The 5041th loop: cost = 46.63042068481445\n", "The 5042th loop: cost = 45.94602966308594\n", "The 5043th loop: cost = 54.6975212097168\n", "The 5044th loop: cost = 48.596473693847656\n", "The 5045th loop: cost = 49.37196731567383\n", "The 5046th loop: cost = 54.44541931152344\n", "The 5047th loop: cost = 50.379066467285156\n", "The 5048th loop: cost = 47.82917785644531\n", "The 5049th loop: cost = 51.0028076171875\n", "The 5050th loop: cost = 46.604679107666016\n", "The 5051th loop: cost = 51.89985656738281\n", "The 5052th loop: cost = 47.270721435546875\n", "The 5053th loop: cost = 45.99658203125\n", "The 5054th loop: cost = 45.32848358154297\n", "The 5055th loop: cost = 49.10710144042969\n", "The 5056th loop: cost = 46.19611358642578\n", "The 5057th loop: cost = 41.63154983520508\n", "The 5058th loop: cost = 44.8470344543457\n", "The 5059th loop: cost = 50.80103302001953\n", "The 5060th loop: cost = 44.16945266723633\n", "The 5061th loop: cost = 49.498966217041016\n", "The 5062th loop: cost = 47.29302978515625\n", "The 5063th loop: cost = 50.726890563964844\n", "The 5064th loop: cost = 52.12548828125\n", "The 5065th loop: cost = 43.42627716064453\n", "The 5066th loop: cost = 48.91075897216797\n", "The 5067th loop: cost = 42.77716064453125\n", "The 5068th loop: cost = 45.3997802734375\n", "The 5069th loop: cost = 52.72370147705078\n", "The 5070th loop: cost = 50.36186981201172\n", "The 5071th loop: cost = 46.74848556518555\n", "The 5072th loop: cost = 49.00170135498047\n", "The 5073th loop: cost = 55.288551330566406\n", "The 5074th loop: cost = 49.247188568115234\n", "The 5075th loop: cost = 53.413822174072266\n", "The 5076th loop: cost = 41.095314025878906\n", "The 5077th loop: cost = 54.784202575683594\n", "The 5078th loop: cost = 51.40977096557617\n", "The 5079th loop: cost = 44.96320343017578\n", "The 5080th loop: cost = 41.860801696777344\n", "The 5081th loop: cost = 44.253021240234375\n", "The 5082th loop: cost = 37.38432312011719\n", "The 5083th loop: cost = 48.39081573486328\n", "The 5084th loop: cost = 41.64019775390625\n", "The 5085th loop: cost = 49.48305130004883\n", "The 5086th loop: cost = 48.898956298828125\n", "The 5087th loop: cost = 47.239410400390625\n", "The 5088th loop: cost = 40.42583465576172\n", "The 5089th loop: cost = 35.62216567993164\n", "The 5090th loop: cost = 49.23174285888672\n", "The 5091th loop: cost = 53.91961669921875\n", "The 5092th loop: cost = 47.92527770996094\n", "The 5093th loop: cost = 45.05722427368164\n", "The 5094th loop: cost = 48.677791595458984\n", "The 5095th loop: cost = 43.52128982543945\n", "The 5096th loop: cost = 36.85607147216797\n", "The 5097th loop: cost = 43.63111877441406\n", "The 5098th loop: cost = 50.55625915527344\n", "The 5099th loop: cost = 51.22129821777344\n", "The 5100th loop: cost = 43.02452850341797\n", "The 5101th loop: cost = 52.081119537353516\n", "The 5102th loop: cost = 44.751956939697266\n", "The 5103th loop: cost = 46.05158233642578\n", "The 5104th loop: cost = 43.72306442260742\n", "The 5105th loop: cost = 41.408782958984375\n", "The 5106th loop: cost = 43.25267791748047\n", "The 5107th loop: cost = 54.79680633544922\n", "The 5108th loop: cost = 52.6917724609375\n", "The 5109th loop: cost = 33.502586364746094\n", "The 5110th loop: cost = 56.783538818359375\n", "The 5111th loop: cost = 48.10279846191406\n", "The 5112th loop: cost = 45.97328567504883\n", "The 5113th loop: cost = 46.065032958984375\n", "The 5114th loop: cost = 50.82847595214844\n", "The 5115th loop: cost = 48.581756591796875\n", "The 5116th loop: cost = 43.183265686035156\n", "The 5117th loop: cost = 41.45911407470703\n", "The 5118th loop: cost = 51.741485595703125\n", "The 5119th loop: cost = 51.17852783203125\n", "The 5120th loop: cost = 47.761962890625\n", "The 5121th loop: cost = 57.22309112548828\n", "The 5122th loop: cost = 48.099525451660156\n", "The 5123th loop: cost = 46.62679672241211\n", "The 5124th loop: cost = 35.346221923828125\n", "The 5125th loop: cost = 57.3285026550293\n", "The 5126th loop: cost = 36.27898025512695\n", "The 5127th loop: cost = 43.7105598449707\n", "The 5128th loop: cost = 47.49143981933594\n", "The 5129th loop: cost = 45.449344635009766\n", "The 5130th loop: cost = 43.075042724609375\n", "The 5131th loop: cost = 37.469444274902344\n", "The 5132th loop: cost = 48.46889114379883\n", "The 5133th loop: cost = 47.097354888916016\n", "The 5134th loop: cost = 48.58530807495117\n", "The 5135th loop: cost = 49.948062896728516\n", "The 5136th loop: cost = 44.80096435546875\n", "The 5137th loop: cost = 43.92464065551758\n", "The 5138th loop: cost = 52.39778518676758\n", "The 5139th loop: cost = 45.298973083496094\n", "The 5140th loop: cost = 44.69190216064453\n", "The 5141th loop: cost = 42.565101623535156\n", "The 5142th loop: cost = 47.70262145996094\n", "The 5143th loop: cost = 40.156005859375\n", "The 5144th loop: cost = 51.13350296020508\n", "The 5145th loop: cost = 38.23772048950195\n", "The 5146th loop: cost = 49.92279815673828\n", "The 5147th loop: cost = 43.35955047607422\n", "The 5148th loop: cost = 48.11145782470703\n", "The 5149th loop: cost = 40.77189636230469\n", "The 5150th loop: cost = 46.332427978515625\n", "The 5151th loop: cost = 47.5351448059082\n", "The 5152th loop: cost = 45.386260986328125\n", "The 5153th loop: cost = 50.326168060302734\n", "The 5154th loop: cost = 46.56977844238281\n", "The 5155th loop: cost = 40.51016616821289\n", "The 5156th loop: cost = 43.749752044677734\n", "The 5157th loop: cost = 46.5528678894043\n", "The 5158th loop: cost = 39.720542907714844\n", "The 5159th loop: cost = 49.470035552978516\n", "The 5160th loop: cost = 44.95505905151367\n", "The 5161th loop: cost = 45.32786178588867\n", "The 5162th loop: cost = 51.462799072265625\n", "The 5163th loop: cost = 49.64443588256836\n", "The 5164th loop: cost = 49.25708770751953\n", "The 5165th loop: cost = 42.37727355957031\n", "The 5166th loop: cost = 46.39888381958008\n", "The 5167th loop: cost = 48.722652435302734\n", "The 5168th loop: cost = 42.58915328979492\n", "The 5169th loop: cost = 58.56975173950195\n", "The 5170th loop: cost = 44.20234680175781\n", "The 5171th loop: cost = 58.19425964355469\n", "The 5172th loop: cost = 38.6002311706543\n", "The 5173th loop: cost = 55.74119186401367\n", "The 5174th loop: cost = 40.46166229248047\n", "The 5175th loop: cost = 44.08885192871094\n", "The 5176th loop: cost = 48.98896026611328\n", "The 5177th loop: cost = 41.116634368896484\n", "The 5178th loop: cost = 37.180171966552734\n", "The 5179th loop: cost = 47.301719665527344\n", "The 5180th loop: cost = 53.19102096557617\n", "The 5181th loop: cost = 41.6044921875\n", "The 5182th loop: cost = 43.2005615234375\n", "The 5183th loop: cost = 47.14619445800781\n", "The 5184th loop: cost = 47.89702606201172\n", "The 5185th loop: cost = 41.37002944946289\n", "The 5186th loop: cost = 44.878440856933594\n", "The 5187th loop: cost = 42.712562561035156\n", "The 5188th loop: cost = 50.05498504638672\n", "The 5189th loop: cost = 57.61601257324219\n", "The 5190th loop: cost = 44.59710693359375\n", "The 5191th loop: cost = 44.54391098022461\n", "The 5192th loop: cost = 47.15351104736328\n", "The 5193th loop: cost = 38.49114227294922\n", "The 5194th loop: cost = 46.762821197509766\n", "The 5195th loop: cost = 45.93586349487305\n", "The 5196th loop: cost = 47.32878112792969\n", "The 5197th loop: cost = 56.56458282470703\n", "The 5198th loop: cost = 48.33841323852539\n", "The 5199th loop: cost = 50.334564208984375\n", "The 5200th loop: cost = 42.30205535888672\n", "The 5201th loop: cost = 47.1253776550293\n", "The 5202th loop: cost = 50.37891387939453\n", "The 5203th loop: cost = 48.703857421875\n", "The 5204th loop: cost = 62.91215515136719\n", "The 5205th loop: cost = 47.34025573730469\n", "The 5206th loop: cost = 47.70586013793945\n", "The 5207th loop: cost = 45.311248779296875\n", "The 5208th loop: cost = 47.454742431640625\n", "The 5209th loop: cost = 41.312828063964844\n", "The 5210th loop: cost = 45.40144729614258\n", "The 5211th loop: cost = 42.482261657714844\n", "The 5212th loop: cost = 48.26935958862305\n", "The 5213th loop: cost = 39.25893020629883\n", "The 5214th loop: cost = 42.56159973144531\n", "The 5215th loop: cost = 46.76263427734375\n", "The 5216th loop: cost = 49.74855041503906\n", "The 5217th loop: cost = 37.35204315185547\n", "The 5218th loop: cost = 57.567970275878906\n", "The 5219th loop: cost = 52.23060607910156\n", "The 5220th loop: cost = 41.620670318603516\n", "The 5221th loop: cost = 46.86180114746094\n", "The 5222th loop: cost = 48.882659912109375\n", "The 5223th loop: cost = 52.27613067626953\n", "The 5224th loop: cost = 47.017574310302734\n", "The 5225th loop: cost = 46.54148864746094\n", "The 5226th loop: cost = 50.73128128051758\n", "The 5227th loop: cost = 39.75448226928711\n", "The 5228th loop: cost = 56.76829147338867\n", "The 5229th loop: cost = 35.69884490966797\n", "The 5230th loop: cost = 51.039756774902344\n", "The 5231th loop: cost = 42.9228630065918\n", "The 5232th loop: cost = 45.23168182373047\n", "The 5233th loop: cost = 46.034645080566406\n", "The 5234th loop: cost = 45.50672149658203\n", "The 5235th loop: cost = 49.15928649902344\n", "The 5236th loop: cost = 37.08768844604492\n", "The 5237th loop: cost = 33.27734375\n", "The 5238th loop: cost = 41.49591827392578\n", "The 5239th loop: cost = 42.67558288574219\n", "The 5240th loop: cost = 38.52338790893555\n", "The 5241th loop: cost = 37.34782791137695\n", "The 5242th loop: cost = 42.7788200378418\n", "The 5243th loop: cost = 45.395084381103516\n", "The 5244th loop: cost = 42.41879653930664\n", "The 5245th loop: cost = 42.02317810058594\n", "The 5246th loop: cost = 49.427730560302734\n", "The 5247th loop: cost = 49.64793395996094\n", "The 5248th loop: cost = 40.469017028808594\n", "The 5249th loop: cost = 44.8485107421875\n", "The 5250th loop: cost = 47.87525939941406\n", "The 5251th loop: cost = 52.171630859375\n", "The 5252th loop: cost = 42.694068908691406\n", "The 5253th loop: cost = 48.94178009033203\n", "The 5254th loop: cost = 42.80781555175781\n", "The 5255th loop: cost = 50.680908203125\n", "The 5256th loop: cost = 43.69441604614258\n", "The 5257th loop: cost = 56.22735595703125\n", "The 5258th loop: cost = 54.967933654785156\n", "The 5259th loop: cost = 42.6998176574707\n", "The 5260th loop: cost = 47.075157165527344\n", "The 5261th loop: cost = 46.98855209350586\n", "The 5262th loop: cost = 42.98875427246094\n", "The 5263th loop: cost = 46.17591094970703\n", "The 5264th loop: cost = 48.27547073364258\n", "The 5265th loop: cost = 54.483192443847656\n", "The 5266th loop: cost = 37.75005340576172\n", "The 5267th loop: cost = 35.61286163330078\n", "The 5268th loop: cost = 48.73646545410156\n", "The 5269th loop: cost = 42.771522521972656\n", "The 5270th loop: cost = 53.83287048339844\n", "The 5271th loop: cost = 53.72570037841797\n", "The 5272th loop: cost = 50.16743469238281\n", "The 5273th loop: cost = 36.407649993896484\n", "The 5274th loop: cost = 41.545188903808594\n", "The 5275th loop: cost = 46.99012756347656\n", "The 5276th loop: cost = 46.723228454589844\n", "The 5277th loop: cost = 55.083343505859375\n", "The 5278th loop: cost = 41.685646057128906\n", "The 5279th loop: cost = 49.23843765258789\n", "The 5280th loop: cost = 50.54657745361328\n", "The 5281th loop: cost = 40.122535705566406\n", "The 5282th loop: cost = 43.352752685546875\n", "The 5283th loop: cost = 56.3657341003418\n", "The 5284th loop: cost = 48.99568176269531\n", "The 5285th loop: cost = 39.492431640625\n", "The 5286th loop: cost = 48.84502410888672\n", "The 5287th loop: cost = 41.14898681640625\n", "The 5288th loop: cost = 55.857059478759766\n", "The 5289th loop: cost = 45.63741683959961\n", "The 5290th loop: cost = 43.53527069091797\n", "The 5291th loop: cost = 43.57917022705078\n", "The 5292th loop: cost = 35.422035217285156\n", "The 5293th loop: cost = 39.67681884765625\n", "The 5294th loop: cost = 51.21332550048828\n", "The 5295th loop: cost = 48.70081329345703\n", "The 5296th loop: cost = 39.629234313964844\n", "The 5297th loop: cost = 44.445960998535156\n", "The 5298th loop: cost = 37.199039459228516\n", "The 5299th loop: cost = 50.177371978759766\n", "The 5300th loop: cost = 44.227237701416016\n", "The 5301th loop: cost = 41.652366638183594\n", "The 5302th loop: cost = 52.23108673095703\n", "The 5303th loop: cost = 44.64207077026367\n", "The 5304th loop: cost = 45.36274719238281\n", "The 5305th loop: cost = 54.197608947753906\n", "The 5306th loop: cost = 40.767059326171875\n", "The 5307th loop: cost = 40.41217803955078\n", "The 5308th loop: cost = 46.877655029296875\n", "The 5309th loop: cost = 45.17959976196289\n", "The 5310th loop: cost = 44.13777542114258\n", "The 5311th loop: cost = 43.15827178955078\n", "The 5312th loop: cost = 42.314903259277344\n", "The 5313th loop: cost = 45.61181640625\n", "The 5314th loop: cost = 55.41531753540039\n", "The 5315th loop: cost = 36.6829833984375\n", "The 5316th loop: cost = 43.26409149169922\n", "The 5317th loop: cost = 32.73005676269531\n", "The 5318th loop: cost = 52.34067153930664\n", "The 5319th loop: cost = 42.94741439819336\n", "The 5320th loop: cost = 50.14472961425781\n", "The 5321th loop: cost = 40.39146423339844\n", "The 5322th loop: cost = 45.577842712402344\n", "The 5323th loop: cost = 46.84308624267578\n", "The 5324th loop: cost = 43.952423095703125\n", "The 5325th loop: cost = 38.18681335449219\n", "The 5326th loop: cost = 40.79551696777344\n", "The 5327th loop: cost = 50.425167083740234\n", "The 5328th loop: cost = 44.16465377807617\n", "The 5329th loop: cost = 48.23925018310547\n", "The 5330th loop: cost = 45.57244873046875\n", "The 5331th loop: cost = 46.52949523925781\n", "The 5332th loop: cost = 46.13130187988281\n", "The 5333th loop: cost = 43.15635681152344\n", "The 5334th loop: cost = 48.10850143432617\n", "The 5335th loop: cost = 45.735191345214844\n", "The 5336th loop: cost = 47.057464599609375\n", "The 5337th loop: cost = 44.428192138671875\n", "The 5338th loop: cost = 44.95692443847656\n", "The 5339th loop: cost = 45.168785095214844\n", "The 5340th loop: cost = 42.122798919677734\n", "The 5341th loop: cost = 48.84600067138672\n", "The 5342th loop: cost = 48.402164459228516\n", "The 5343th loop: cost = 42.17037582397461\n", "The 5344th loop: cost = 40.52819061279297\n", "The 5345th loop: cost = 39.47177505493164\n", "The 5346th loop: cost = 46.38981628417969\n", "The 5347th loop: cost = 48.66340255737305\n", "The 5348th loop: cost = 43.381587982177734\n", "The 5349th loop: cost = 46.128379821777344\n", "The 5350th loop: cost = 40.647705078125\n", "The 5351th loop: cost = 44.583953857421875\n", "The 5352th loop: cost = 50.46107482910156\n", "The 5353th loop: cost = 55.1177978515625\n", "The 5354th loop: cost = 45.159568786621094\n", "The 5355th loop: cost = 43.66532897949219\n", "The 5356th loop: cost = 42.958839416503906\n", "The 5357th loop: cost = 45.14439392089844\n", "The 5358th loop: cost = 39.803367614746094\n", "The 5359th loop: cost = 35.37885665893555\n", "The 5360th loop: cost = 47.18084716796875\n", "The 5361th loop: cost = 46.00663757324219\n", "The 5362th loop: cost = 45.4036979675293\n", "The 5363th loop: cost = 53.353599548339844\n", "The 5364th loop: cost = 41.51961898803711\n", "The 5365th loop: cost = 42.16825866699219\n", "The 5366th loop: cost = 43.92310333251953\n", "The 5367th loop: cost = 39.63614273071289\n", "The 5368th loop: cost = 46.010597229003906\n", "The 5369th loop: cost = 49.68274688720703\n", "The 5370th loop: cost = 41.700130462646484\n", "The 5371th loop: cost = 46.884342193603516\n", "The 5372th loop: cost = 45.87366485595703\n", "The 5373th loop: cost = 42.46100997924805\n", "The 5374th loop: cost = 52.45281982421875\n", "The 5375th loop: cost = 36.163265228271484\n", "The 5376th loop: cost = 42.5080451965332\n", "The 5377th loop: cost = 42.81608581542969\n", "The 5378th loop: cost = 35.18769836425781\n", "The 5379th loop: cost = 49.01805877685547\n", "The 5380th loop: cost = 46.918819427490234\n", "The 5381th loop: cost = 44.07828903198242\n", "The 5382th loop: cost = 40.81415939331055\n", "The 5383th loop: cost = 41.1815185546875\n", "The 5384th loop: cost = 43.25068664550781\n", "The 5385th loop: cost = 42.24783706665039\n", "The 5386th loop: cost = 39.867469787597656\n", "The 5387th loop: cost = 50.952919006347656\n", "The 5388th loop: cost = 49.04132080078125\n", "The 5389th loop: cost = 47.37498474121094\n", "The 5390th loop: cost = 48.78340530395508\n", "The 5391th loop: cost = 37.991661071777344\n", "The 5392th loop: cost = 49.91224670410156\n", "The 5393th loop: cost = 50.73394012451172\n", "The 5394th loop: cost = 41.57825469970703\n", "The 5395th loop: cost = 37.40755081176758\n", "The 5396th loop: cost = 48.915771484375\n", "The 5397th loop: cost = 48.896385192871094\n", "The 5398th loop: cost = 37.21215057373047\n", "The 5399th loop: cost = 42.861759185791016\n", "The 5400th loop: cost = 49.8808708190918\n", "The 5401th loop: cost = 50.686241149902344\n", "The 5402th loop: cost = 42.770931243896484\n", "The 5403th loop: cost = 41.22223663330078\n", "The 5404th loop: cost = 54.10096740722656\n", "The 5405th loop: cost = 35.031654357910156\n", "The 5406th loop: cost = 48.699100494384766\n", "The 5407th loop: cost = 41.09865188598633\n", "The 5408th loop: cost = 42.179656982421875\n", "The 5409th loop: cost = 39.45707702636719\n", "The 5410th loop: cost = 40.74853515625\n", "The 5411th loop: cost = 47.73078918457031\n", "The 5412th loop: cost = 42.23982238769531\n", "The 5413th loop: cost = 51.7719612121582\n", "The 5414th loop: cost = 38.44639205932617\n", "The 5415th loop: cost = 41.04701232910156\n", "The 5416th loop: cost = 43.11140060424805\n", "The 5417th loop: cost = 43.31444549560547\n", "The 5418th loop: cost = 42.80409240722656\n", "The 5419th loop: cost = 42.37099838256836\n", "The 5420th loop: cost = 44.23527526855469\n", "The 5421th loop: cost = 56.54925537109375\n", "The 5422th loop: cost = 42.07487487792969\n", "The 5423th loop: cost = 47.6015739440918\n", "The 5424th loop: cost = 55.66990280151367\n", "The 5425th loop: cost = 45.644996643066406\n", "The 5426th loop: cost = 37.74374771118164\n", "The 5427th loop: cost = 40.71821212768555\n", "The 5428th loop: cost = 45.98023986816406\n", "The 5429th loop: cost = 47.23450469970703\n", "The 5430th loop: cost = 50.78946304321289\n", "The 5431th loop: cost = 47.4673957824707\n", "The 5432th loop: cost = 41.26664352416992\n", "The 5433th loop: cost = 45.15669250488281\n", "The 5434th loop: cost = 39.643768310546875\n", "The 5435th loop: cost = 46.436729431152344\n", "The 5436th loop: cost = 42.66759490966797\n", "The 5437th loop: cost = 47.014808654785156\n", "The 5438th loop: cost = 43.00349426269531\n", "The 5439th loop: cost = 42.54669189453125\n", "The 5440th loop: cost = 40.689308166503906\n", "The 5441th loop: cost = 51.592369079589844\n", "The 5442th loop: cost = 41.86909484863281\n", "The 5443th loop: cost = 43.78636932373047\n", "The 5444th loop: cost = 42.440185546875\n", "The 5445th loop: cost = 43.79618835449219\n", "The 5446th loop: cost = 42.519195556640625\n", "The 5447th loop: cost = 46.275753021240234\n", "The 5448th loop: cost = 47.38549041748047\n", "The 5449th loop: cost = 39.91712188720703\n", "The 5450th loop: cost = 42.37372589111328\n", "The 5451th loop: cost = 35.16040802001953\n", "The 5452th loop: cost = 44.58794403076172\n", "The 5453th loop: cost = 41.0103759765625\n", "The 5454th loop: cost = 47.807613372802734\n", "The 5455th loop: cost = 48.882301330566406\n", "The 5456th loop: cost = 45.73075866699219\n", "The 5457th loop: cost = 41.611053466796875\n", "The 5458th loop: cost = 48.51403045654297\n", "The 5459th loop: cost = 45.936378479003906\n", "The 5460th loop: cost = 47.54866027832031\n", "The 5461th loop: cost = 46.43043899536133\n", "The 5462th loop: cost = 42.37034606933594\n", "The 5463th loop: cost = 39.14421463012695\n", "The 5464th loop: cost = 45.16960144042969\n", "The 5465th loop: cost = 37.92919921875\n", "The 5466th loop: cost = 47.99245834350586\n", "The 5467th loop: cost = 41.47917938232422\n", "The 5468th loop: cost = 42.23088073730469\n", "The 5469th loop: cost = 43.55908203125\n", "The 5470th loop: cost = 46.42192840576172\n", "The 5471th loop: cost = 45.361759185791016\n", "The 5472th loop: cost = 55.30084991455078\n", "The 5473th loop: cost = 53.058509826660156\n", "The 5474th loop: cost = 52.97647476196289\n", "The 5475th loop: cost = 43.06436538696289\n", "The 5476th loop: cost = 51.66156005859375\n", "The 5477th loop: cost = 55.624820709228516\n", "The 5478th loop: cost = 48.784820556640625\n", "The 5479th loop: cost = 47.744667053222656\n", "The 5480th loop: cost = 48.18285369873047\n", "The 5481th loop: cost = 48.033721923828125\n", "The 5482th loop: cost = 47.63853073120117\n", "The 5483th loop: cost = 51.05320358276367\n", "The 5484th loop: cost = 52.33818817138672\n", "The 5485th loop: cost = 40.94383239746094\n", "The 5486th loop: cost = 40.84645080566406\n", "The 5487th loop: cost = 44.234954833984375\n", "The 5488th loop: cost = 49.98322677612305\n", "The 5489th loop: cost = 39.49749755859375\n", "The 5490th loop: cost = 41.28895568847656\n", "The 5491th loop: cost = 45.689998626708984\n", "The 5492th loop: cost = 38.362754821777344\n", "The 5493th loop: cost = 58.31199645996094\n", "The 5494th loop: cost = 41.07572937011719\n", "The 5495th loop: cost = 49.877723693847656\n", "The 5496th loop: cost = 45.966678619384766\n", "The 5497th loop: cost = 45.3102912902832\n", "The 5498th loop: cost = 48.239479064941406\n", "The 5499th loop: cost = 51.3912239074707\n", "The 5500th loop: cost = 43.35466766357422\n", "The 5501th loop: cost = 50.19109344482422\n", "The 5502th loop: cost = 46.361419677734375\n", "The 5503th loop: cost = 57.01896667480469\n", "The 5504th loop: cost = 38.51226806640625\n", "The 5505th loop: cost = 45.84312438964844\n", "The 5506th loop: cost = 47.758453369140625\n", "The 5507th loop: cost = 48.17359924316406\n", "The 5508th loop: cost = 49.39751052856445\n", "The 5509th loop: cost = 36.842079162597656\n", "The 5510th loop: cost = 50.50395965576172\n", "The 5511th loop: cost = 52.592933654785156\n", "The 5512th loop: cost = 42.0877685546875\n", "The 5513th loop: cost = 56.45018768310547\n", "The 5514th loop: cost = 47.21390151977539\n", "The 5515th loop: cost = 32.054779052734375\n", "The 5516th loop: cost = 39.56024932861328\n", "The 5517th loop: cost = 45.47184371948242\n", "The 5518th loop: cost = 43.117679595947266\n", "The 5519th loop: cost = 46.09217834472656\n", "The 5520th loop: cost = 49.29120635986328\n", "The 5521th loop: cost = 40.714263916015625\n", "The 5522th loop: cost = 37.88359069824219\n", "The 5523th loop: cost = 50.87010192871094\n", "The 5524th loop: cost = 39.48954391479492\n", "The 5525th loop: cost = 47.89570236206055\n", "The 5526th loop: cost = 43.28600311279297\n", "The 5527th loop: cost = 46.83983612060547\n", "The 5528th loop: cost = 42.70317459106445\n", "The 5529th loop: cost = 49.9835205078125\n", "The 5530th loop: cost = 45.3040885925293\n", "The 5531th loop: cost = 40.75736999511719\n", "The 5532th loop: cost = 50.857215881347656\n", "The 5533th loop: cost = 42.60172653198242\n", "The 5534th loop: cost = 47.57270431518555\n", "The 5535th loop: cost = 39.23044967651367\n", "The 5536th loop: cost = 44.734596252441406\n", "The 5537th loop: cost = 49.108306884765625\n", "The 5538th loop: cost = 47.98789978027344\n", "The 5539th loop: cost = 42.00095748901367\n", "The 5540th loop: cost = 42.487937927246094\n", "The 5541th loop: cost = 44.369075775146484\n", "The 5542th loop: cost = 42.2970085144043\n", "The 5543th loop: cost = 37.66093826293945\n", "The 5544th loop: cost = 37.99312210083008\n", "The 5545th loop: cost = 51.372467041015625\n", "The 5546th loop: cost = 39.67589569091797\n", "The 5547th loop: cost = 47.711021423339844\n", "The 5548th loop: cost = 44.13432693481445\n", "The 5549th loop: cost = 39.406089782714844\n", "The 5550th loop: cost = 53.407135009765625\n", "The 5551th loop: cost = 36.881134033203125\n", "The 5552th loop: cost = 52.332427978515625\n", "The 5553th loop: cost = 45.515079498291016\n", "The 5554th loop: cost = 40.0087890625\n", "The 5555th loop: cost = 40.67654800415039\n", "The 5556th loop: cost = 48.47801971435547\n", "The 5557th loop: cost = 42.118133544921875\n", "The 5558th loop: cost = 50.802894592285156\n", "The 5559th loop: cost = 46.64255142211914\n", "The 5560th loop: cost = 48.209598541259766\n", "The 5561th loop: cost = 41.23723602294922\n", "The 5562th loop: cost = 48.677513122558594\n", "The 5563th loop: cost = 52.94673538208008\n", "The 5564th loop: cost = 49.66541290283203\n", "The 5565th loop: cost = 32.565242767333984\n", "The 5566th loop: cost = 51.22206115722656\n", "The 5567th loop: cost = 38.940887451171875\n", "The 5568th loop: cost = 44.8391227722168\n", "The 5569th loop: cost = 57.82197189331055\n", "The 5570th loop: cost = 45.445152282714844\n", "The 5571th loop: cost = 48.10655975341797\n", "The 5572th loop: cost = 46.19287872314453\n", "The 5573th loop: cost = 39.65402603149414\n", "The 5574th loop: cost = 43.012542724609375\n", "The 5575th loop: cost = 40.259765625\n", "The 5576th loop: cost = 43.47913360595703\n", "The 5577th loop: cost = 42.426475524902344\n", "The 5578th loop: cost = 50.422569274902344\n", "The 5579th loop: cost = 39.00151824951172\n", "The 5580th loop: cost = 40.565372467041016\n", "The 5581th loop: cost = 43.30026626586914\n", "The 5582th loop: cost = 38.275482177734375\n", "The 5583th loop: cost = 36.68763732910156\n", "The 5584th loop: cost = 49.86958694458008\n", "The 5585th loop: cost = 38.0698127746582\n", "The 5586th loop: cost = 45.86742401123047\n", "The 5587th loop: cost = 42.12778854370117\n", "The 5588th loop: cost = 45.83511734008789\n", "The 5589th loop: cost = 45.536163330078125\n", "The 5590th loop: cost = 35.96733856201172\n", "The 5591th loop: cost = 49.99184799194336\n", "The 5592th loop: cost = 47.40288543701172\n", "The 5593th loop: cost = 42.83259963989258\n", "The 5594th loop: cost = 41.188438415527344\n", "The 5595th loop: cost = 45.271690368652344\n", "The 5596th loop: cost = 51.79205322265625\n", "The 5597th loop: cost = 43.14570999145508\n", "The 5598th loop: cost = 41.272159576416016\n", "The 5599th loop: cost = 37.193294525146484\n", "The 5600th loop: cost = 41.509613037109375\n", "The 5601th loop: cost = 47.13655090332031\n", "The 5602th loop: cost = 45.70811080932617\n", "The 5603th loop: cost = 48.506019592285156\n", "The 5604th loop: cost = 49.638031005859375\n", "The 5605th loop: cost = 44.981178283691406\n", "The 5606th loop: cost = 38.74440002441406\n", "The 5607th loop: cost = 47.48769760131836\n", "The 5608th loop: cost = 43.285701751708984\n", "The 5609th loop: cost = 49.73579406738281\n", "The 5610th loop: cost = 34.816917419433594\n", "The 5611th loop: cost = 46.27659606933594\n", "The 5612th loop: cost = 59.203887939453125\n", "The 5613th loop: cost = 44.01995849609375\n", "The 5614th loop: cost = 54.8360595703125\n", "The 5615th loop: cost = 49.97078323364258\n", "The 5616th loop: cost = 47.71214294433594\n", "The 5617th loop: cost = 43.996788024902344\n", "The 5618th loop: cost = 45.215187072753906\n", "The 5619th loop: cost = 50.74285125732422\n", "The 5620th loop: cost = 53.76465606689453\n", "The 5621th loop: cost = 33.2152099609375\n", "The 5622th loop: cost = 36.72100830078125\n", "The 5623th loop: cost = 44.64087677001953\n", "The 5624th loop: cost = 39.33296585083008\n", "The 5625th loop: cost = 48.44560241699219\n", "The 5626th loop: cost = 47.19971466064453\n", "The 5627th loop: cost = 50.078582763671875\n", "The 5628th loop: cost = 52.36955261230469\n", "The 5629th loop: cost = 36.30620193481445\n", "The 5630th loop: cost = 47.57917785644531\n", "The 5631th loop: cost = 49.562828063964844\n", "The 5632th loop: cost = 39.85276794433594\n", "The 5633th loop: cost = 51.24974060058594\n", "The 5634th loop: cost = 48.759220123291016\n", "The 5635th loop: cost = 44.06073760986328\n", "The 5636th loop: cost = 47.93594741821289\n", "The 5637th loop: cost = 48.87405014038086\n", "The 5638th loop: cost = 44.456504821777344\n", "The 5639th loop: cost = 43.20155334472656\n", "The 5640th loop: cost = 41.137657165527344\n", "The 5641th loop: cost = 38.55077362060547\n", "The 5642th loop: cost = 50.91877746582031\n", "The 5643th loop: cost = 47.97402572631836\n", "The 5644th loop: cost = 35.83142852783203\n", "The 5645th loop: cost = 38.5943603515625\n", "The 5646th loop: cost = 41.52211380004883\n", "The 5647th loop: cost = 42.03498840332031\n", "The 5648th loop: cost = 55.300052642822266\n", "The 5649th loop: cost = 37.97346115112305\n", "The 5650th loop: cost = 41.533714294433594\n", "The 5651th loop: cost = 51.967098236083984\n", "The 5652th loop: cost = 45.785247802734375\n", "The 5653th loop: cost = 39.78356170654297\n", "The 5654th loop: cost = 48.03939437866211\n", "The 5655th loop: cost = 40.08697509765625\n", "The 5656th loop: cost = 43.312278747558594\n", "The 5657th loop: cost = 54.0213623046875\n", "The 5658th loop: cost = 40.94281005859375\n", "The 5659th loop: cost = 42.094024658203125\n", "The 5660th loop: cost = 37.64629364013672\n", "The 5661th loop: cost = 50.48747634887695\n", "The 5662th loop: cost = 49.89933776855469\n", "The 5663th loop: cost = 48.258644104003906\n", "The 5664th loop: cost = 33.79412841796875\n", "The 5665th loop: cost = 49.10440444946289\n", "The 5666th loop: cost = 42.9425048828125\n", "The 5667th loop: cost = 55.845367431640625\n", "The 5668th loop: cost = 44.23292922973633\n", "The 5669th loop: cost = 39.98002624511719\n", "The 5670th loop: cost = 48.366294860839844\n", "The 5671th loop: cost = 47.11904525756836\n", "The 5672th loop: cost = 42.161964416503906\n", "The 5673th loop: cost = 51.722251892089844\n", "The 5674th loop: cost = 44.05128479003906\n", "The 5675th loop: cost = 42.44194793701172\n", "The 5676th loop: cost = 48.37090301513672\n", "The 5677th loop: cost = 43.69766616821289\n", "The 5678th loop: cost = 50.67522430419922\n", "The 5679th loop: cost = 37.116477966308594\n", "The 5680th loop: cost = 53.187904357910156\n", "The 5681th loop: cost = 43.58407211303711\n", "The 5682th loop: cost = 38.59556579589844\n", "The 5683th loop: cost = 47.949710845947266\n", "The 5684th loop: cost = 51.62053680419922\n", "The 5685th loop: cost = 44.0260009765625\n", "The 5686th loop: cost = 39.99651336669922\n", "The 5687th loop: cost = 36.51509094238281\n", "The 5688th loop: cost = 42.23310089111328\n", "The 5689th loop: cost = 42.90617370605469\n", "The 5690th loop: cost = 44.71711349487305\n", "The 5691th loop: cost = 41.88932418823242\n", "The 5692th loop: cost = 43.81291961669922\n", "The 5693th loop: cost = 42.61802291870117\n", "The 5694th loop: cost = 43.672645568847656\n", "The 5695th loop: cost = 41.76768493652344\n", "The 5696th loop: cost = 41.02153778076172\n", "The 5697th loop: cost = 43.2916145324707\n", "The 5698th loop: cost = 41.643043518066406\n", "The 5699th loop: cost = 39.250335693359375\n", "The 5700th loop: cost = 49.933345794677734\n", "The 5701th loop: cost = 48.02643585205078\n", "The 5702th loop: cost = 46.979393005371094\n", "The 5703th loop: cost = 50.937068939208984\n", "The 5704th loop: cost = 43.22053527832031\n", "The 5705th loop: cost = 47.515716552734375\n", "The 5706th loop: cost = 46.57213592529297\n", "The 5707th loop: cost = 45.80109405517578\n", "The 5708th loop: cost = 48.24909973144531\n", "The 5709th loop: cost = 46.66773986816406\n", "The 5710th loop: cost = 44.907997131347656\n", "The 5711th loop: cost = 46.724700927734375\n", "The 5712th loop: cost = 43.75741195678711\n", "The 5713th loop: cost = 45.86867141723633\n", "The 5714th loop: cost = 49.40627670288086\n", "The 5715th loop: cost = 39.75676727294922\n", "The 5716th loop: cost = 48.29933166503906\n", "The 5717th loop: cost = 39.45972442626953\n", "The 5718th loop: cost = 46.61614990234375\n", "The 5719th loop: cost = 53.69147491455078\n", "The 5720th loop: cost = 42.518394470214844\n", "The 5721th loop: cost = 46.71332931518555\n", "The 5722th loop: cost = 40.2723274230957\n", "The 5723th loop: cost = 39.56471633911133\n", "The 5724th loop: cost = 40.997276306152344\n", "The 5725th loop: cost = 40.21690368652344\n", "The 5726th loop: cost = 43.19969940185547\n", "The 5727th loop: cost = 48.1483268737793\n", "The 5728th loop: cost = 34.51124572753906\n", "The 5729th loop: cost = 39.70331954956055\n", "The 5730th loop: cost = 53.626976013183594\n", "The 5731th loop: cost = 37.87385177612305\n", "The 5732th loop: cost = 47.067073822021484\n", "The 5733th loop: cost = 44.29032897949219\n", "The 5734th loop: cost = 45.18004608154297\n", "The 5735th loop: cost = 43.31893539428711\n", "The 5736th loop: cost = 41.51245880126953\n", "The 5737th loop: cost = 55.937774658203125\n", "The 5738th loop: cost = 50.5451774597168\n", "The 5739th loop: cost = 41.888885498046875\n", "The 5740th loop: cost = 51.33137893676758\n", "The 5741th loop: cost = 45.99285125732422\n", "The 5742th loop: cost = 53.13029861450195\n", "The 5743th loop: cost = 43.15001678466797\n", "The 5744th loop: cost = 39.645782470703125\n", "The 5745th loop: cost = 38.54435348510742\n", "The 5746th loop: cost = 32.4527587890625\n", "The 5747th loop: cost = 46.505184173583984\n", "The 5748th loop: cost = 41.31650161743164\n", "The 5749th loop: cost = 47.67498016357422\n", "The 5750th loop: cost = 47.59477996826172\n", "The 5751th loop: cost = 41.020286560058594\n", "The 5752th loop: cost = 35.5737190246582\n", "The 5753th loop: cost = 53.54936218261719\n", "The 5754th loop: cost = 55.616153717041016\n", "The 5755th loop: cost = 50.10853576660156\n", "The 5756th loop: cost = 49.41319274902344\n", "The 5757th loop: cost = 44.934814453125\n", "The 5758th loop: cost = 48.76374435424805\n", "The 5759th loop: cost = 53.162574768066406\n", "The 5760th loop: cost = 44.981224060058594\n", "The 5761th loop: cost = 41.859092712402344\n", "The 5762th loop: cost = 41.96174621582031\n", "The 5763th loop: cost = 47.9078483581543\n", "The 5764th loop: cost = 34.68055725097656\n", "The 5765th loop: cost = 34.79847717285156\n", "The 5766th loop: cost = 42.08188247680664\n", "The 5767th loop: cost = 42.90496063232422\n", "The 5768th loop: cost = 41.42823791503906\n", "The 5769th loop: cost = 34.85060119628906\n", "The 5770th loop: cost = 40.55708694458008\n", "The 5771th loop: cost = 51.98540496826172\n", "The 5772th loop: cost = 43.81214904785156\n", "The 5773th loop: cost = 46.076698303222656\n", "The 5774th loop: cost = 41.029144287109375\n", "The 5775th loop: cost = 45.18074417114258\n", "The 5776th loop: cost = 48.96357345581055\n", "The 5777th loop: cost = 43.80414581298828\n", "The 5778th loop: cost = 42.762245178222656\n", "The 5779th loop: cost = 51.506343841552734\n", "The 5780th loop: cost = 38.51104736328125\n", "The 5781th loop: cost = 49.34288024902344\n", "The 5782th loop: cost = 40.056915283203125\n", "The 5783th loop: cost = 45.51866912841797\n", "The 5784th loop: cost = 33.89114761352539\n", "The 5785th loop: cost = 50.07756805419922\n", "The 5786th loop: cost = 42.90122604370117\n", "The 5787th loop: cost = 50.40312957763672\n", "The 5788th loop: cost = 38.968109130859375\n", "The 5789th loop: cost = 47.587608337402344\n", "The 5790th loop: cost = 40.37006759643555\n", "The 5791th loop: cost = 48.79559326171875\n", "The 5792th loop: cost = 48.14303207397461\n", "The 5793th loop: cost = 49.7586669921875\n", "The 5794th loop: cost = 38.042667388916016\n", "The 5795th loop: cost = 43.13154602050781\n", "The 5796th loop: cost = 38.075687408447266\n", "The 5797th loop: cost = 39.48249816894531\n", "The 5798th loop: cost = 44.18334197998047\n", "The 5799th loop: cost = 43.988258361816406\n", "The 5800th loop: cost = 43.716835021972656\n", "The 5801th loop: cost = 44.90346145629883\n", "The 5802th loop: cost = 49.905277252197266\n", "The 5803th loop: cost = 43.493194580078125\n", "The 5804th loop: cost = 51.833587646484375\n", "The 5805th loop: cost = 47.0029296875\n", "The 5806th loop: cost = 44.14653778076172\n", "The 5807th loop: cost = 44.26786804199219\n", "The 5808th loop: cost = 43.55055618286133\n", "The 5809th loop: cost = 45.65447998046875\n", "The 5810th loop: cost = 35.87397003173828\n", "The 5811th loop: cost = 50.63601303100586\n", "The 5812th loop: cost = 31.485397338867188\n", "The 5813th loop: cost = 44.09061050415039\n", "The 5814th loop: cost = 43.908023834228516\n", "The 5815th loop: cost = 47.642822265625\n", "The 5816th loop: cost = 40.45317459106445\n", "The 5817th loop: cost = 55.078346252441406\n", "The 5818th loop: cost = 49.323001861572266\n", "The 5819th loop: cost = 46.531944274902344\n", "The 5820th loop: cost = 47.36300277709961\n", "The 5821th loop: cost = 48.73274612426758\n", "The 5822th loop: cost = 40.61741638183594\n", "The 5823th loop: cost = 43.28480529785156\n", "The 5824th loop: cost = 43.243934631347656\n", "The 5825th loop: cost = 41.45326614379883\n", "The 5826th loop: cost = 42.0182991027832\n", "The 5827th loop: cost = 56.66718673706055\n", "The 5828th loop: cost = 40.451637268066406\n", "The 5829th loop: cost = 52.015499114990234\n", "The 5830th loop: cost = 39.817787170410156\n", "The 5831th loop: cost = 35.79192352294922\n", "The 5832th loop: cost = 39.65126419067383\n", "The 5833th loop: cost = 45.91111373901367\n", "The 5834th loop: cost = 44.91315841674805\n", "The 5835th loop: cost = 44.440147399902344\n", "The 5836th loop: cost = 62.103004455566406\n", "The 5837th loop: cost = 61.80565643310547\n", "The 5838th loop: cost = 46.833824157714844\n", "The 5839th loop: cost = 41.17682647705078\n", "The 5840th loop: cost = 43.5501594543457\n", "The 5841th loop: cost = 34.36729431152344\n", "The 5842th loop: cost = 44.963409423828125\n", "The 5843th loop: cost = 43.10099411010742\n", "The 5844th loop: cost = 46.44591522216797\n", "The 5845th loop: cost = 42.84555435180664\n", "The 5846th loop: cost = 53.9764518737793\n", "The 5847th loop: cost = 45.74806213378906\n", "The 5848th loop: cost = 47.66248321533203\n", "The 5849th loop: cost = 39.374488830566406\n", "The 5850th loop: cost = 52.769386291503906\n", "The 5851th loop: cost = 32.355751037597656\n", "The 5852th loop: cost = 41.07847595214844\n", "The 5853th loop: cost = 36.349334716796875\n", "The 5854th loop: cost = 46.92533493041992\n", "The 5855th loop: cost = 43.367286682128906\n", "The 5856th loop: cost = 52.41455841064453\n", "The 5857th loop: cost = 39.275909423828125\n", "The 5858th loop: cost = 40.18156433105469\n", "The 5859th loop: cost = 51.82362747192383\n", "The 5860th loop: cost = 50.7786750793457\n", "The 5861th loop: cost = 42.883567810058594\n", "The 5862th loop: cost = 40.793861389160156\n", "The 5863th loop: cost = 43.88751220703125\n", "The 5864th loop: cost = 40.095481872558594\n", "The 5865th loop: cost = 49.860023498535156\n", "The 5866th loop: cost = 44.58368682861328\n", "The 5867th loop: cost = 38.69439697265625\n", "The 5868th loop: cost = 48.50395584106445\n", "The 5869th loop: cost = 42.70868682861328\n", "The 5870th loop: cost = 49.10426712036133\n", "The 5871th loop: cost = 50.06584548950195\n", "The 5872th loop: cost = 41.63157653808594\n", "The 5873th loop: cost = 43.799163818359375\n", "The 5874th loop: cost = 36.43878936767578\n", "The 5875th loop: cost = 36.15854263305664\n", "The 5876th loop: cost = 61.3699836730957\n", "The 5877th loop: cost = 39.55074691772461\n", "The 5878th loop: cost = 50.29609680175781\n", "The 5879th loop: cost = 49.16019058227539\n", "The 5880th loop: cost = 47.574588775634766\n", "The 5881th loop: cost = 37.99900817871094\n", "The 5882th loop: cost = 41.783294677734375\n", "The 5883th loop: cost = 46.55583953857422\n", "The 5884th loop: cost = 39.931766510009766\n", "The 5885th loop: cost = 54.863521575927734\n", "The 5886th loop: cost = 41.691566467285156\n", "The 5887th loop: cost = 44.1487922668457\n", "The 5888th loop: cost = 42.19880676269531\n", "The 5889th loop: cost = 41.60693359375\n", "The 5890th loop: cost = 54.61897277832031\n", "The 5891th loop: cost = 41.26246643066406\n", "The 5892th loop: cost = 42.67567443847656\n", "The 5893th loop: cost = 46.71599578857422\n", "The 5894th loop: cost = 49.59611892700195\n", "The 5895th loop: cost = 41.82941436767578\n", "The 5896th loop: cost = 38.596824645996094\n", "The 5897th loop: cost = 38.09455108642578\n", "The 5898th loop: cost = 36.84562683105469\n", "The 5899th loop: cost = 38.96943664550781\n", "The 5900th loop: cost = 45.839874267578125\n", "The 5901th loop: cost = 42.776737213134766\n", "The 5902th loop: cost = 41.89082336425781\n", "The 5903th loop: cost = 39.69349670410156\n", "The 5904th loop: cost = 46.79362106323242\n", "The 5905th loop: cost = 44.69491958618164\n", "The 5906th loop: cost = 40.385704040527344\n", "The 5907th loop: cost = 44.7674560546875\n", "The 5908th loop: cost = 39.97992706298828\n", "The 5909th loop: cost = 47.07252502441406\n", "The 5910th loop: cost = 52.706031799316406\n", "The 5911th loop: cost = 48.375816345214844\n", "The 5912th loop: cost = 44.90009307861328\n", "The 5913th loop: cost = 46.40091323852539\n", "The 5914th loop: cost = 37.47954559326172\n", "The 5915th loop: cost = 51.58283233642578\n", "The 5916th loop: cost = 43.45061492919922\n", "The 5917th loop: cost = 49.233726501464844\n", "The 5918th loop: cost = 43.19512939453125\n", "The 5919th loop: cost = 49.64580154418945\n", "The 5920th loop: cost = 56.436275482177734\n", "The 5921th loop: cost = 43.6319580078125\n", "The 5922th loop: cost = 37.745819091796875\n", "The 5923th loop: cost = 42.8125\n", "The 5924th loop: cost = 49.60448455810547\n", "The 5925th loop: cost = 46.43888854980469\n", "The 5926th loop: cost = 42.38688278198242\n", "The 5927th loop: cost = 48.37168884277344\n", "The 5928th loop: cost = 39.636573791503906\n", "The 5929th loop: cost = 50.360321044921875\n", "The 5930th loop: cost = 46.40507125854492\n", "The 5931th loop: cost = 39.23506546020508\n", "The 5932th loop: cost = 44.238800048828125\n", "The 5933th loop: cost = 47.160587310791016\n", "The 5934th loop: cost = 41.92125701904297\n", "The 5935th loop: cost = 50.63140106201172\n", "The 5936th loop: cost = 48.14207458496094\n", "The 5937th loop: cost = 42.158447265625\n", "The 5938th loop: cost = 47.337913513183594\n", "The 5939th loop: cost = 43.763450622558594\n", "The 5940th loop: cost = 39.214195251464844\n", "The 5941th loop: cost = 37.738426208496094\n", "The 5942th loop: cost = 39.73817825317383\n", "The 5943th loop: cost = 46.732093811035156\n", "The 5944th loop: cost = 43.665245056152344\n", "The 5945th loop: cost = 42.115318298339844\n", "The 5946th loop: cost = 42.696006774902344\n", "The 5947th loop: cost = 46.071990966796875\n", "The 5948th loop: cost = 30.800785064697266\n", "The 5949th loop: cost = 52.7802619934082\n", "The 5950th loop: cost = 48.90653991699219\n", "The 5951th loop: cost = 52.970272064208984\n", "The 5952th loop: cost = 52.918251037597656\n", "The 5953th loop: cost = 43.57578659057617\n", "The 5954th loop: cost = 38.79045867919922\n", "The 5955th loop: cost = 42.739383697509766\n", "The 5956th loop: cost = 43.6226692199707\n", "The 5957th loop: cost = 50.96493911743164\n", "The 5958th loop: cost = 36.36143493652344\n", "The 5959th loop: cost = 41.60923767089844\n", "The 5960th loop: cost = 50.1515998840332\n", "The 5961th loop: cost = 48.61869812011719\n", "The 5962th loop: cost = 42.30045700073242\n", "The 5963th loop: cost = 44.22809600830078\n", "The 5964th loop: cost = 38.151878356933594\n", "The 5965th loop: cost = 42.64564895629883\n", "The 5966th loop: cost = 50.650291442871094\n", "The 5967th loop: cost = 39.73592758178711\n", "The 5968th loop: cost = 54.307823181152344\n", "The 5969th loop: cost = 36.49463653564453\n", "The 5970th loop: cost = 43.6417350769043\n", "The 5971th loop: cost = 45.93328094482422\n", "The 5972th loop: cost = 36.753700256347656\n", "The 5973th loop: cost = 42.428688049316406\n", "The 5974th loop: cost = 44.423404693603516\n", "The 5975th loop: cost = 40.390777587890625\n", "The 5976th loop: cost = 50.94938659667969\n", "The 5977th loop: cost = 43.79100036621094\n", "The 5978th loop: cost = 45.71916198730469\n", "The 5979th loop: cost = 54.41820526123047\n", "The 5980th loop: cost = 38.936485290527344\n", "The 5981th loop: cost = 46.663482666015625\n", "The 5982th loop: cost = 34.754722595214844\n", "The 5983th loop: cost = 40.52538299560547\n", "The 5984th loop: cost = 44.47447967529297\n", "The 5985th loop: cost = 34.90304946899414\n", "The 5986th loop: cost = 41.623077392578125\n", "The 5987th loop: cost = 44.57094955444336\n", "The 5988th loop: cost = 36.923458099365234\n", "The 5989th loop: cost = 51.154972076416016\n", "The 5990th loop: cost = 35.14850616455078\n", "The 5991th loop: cost = 43.767574310302734\n", "The 5992th loop: cost = 41.846473693847656\n", "The 5993th loop: cost = 41.833892822265625\n", "The 5994th loop: cost = 47.604042053222656\n", "The 5995th loop: cost = 39.35913848876953\n", "The 5996th loop: cost = 46.44019317626953\n", "The 5997th loop: cost = 40.961551666259766\n", "The 5998th loop: cost = 41.542259216308594\n", "The 5999th loop: cost = 41.068538665771484\n", "The 6000th loop: cost = 47.248023986816406\n", "The 6001th loop: cost = 49.83718490600586\n", "The 6002th loop: cost = 42.61852264404297\n", "The 6003th loop: cost = 43.75917053222656\n", "The 6004th loop: cost = 41.759239196777344\n", "The 6005th loop: cost = 49.64656066894531\n", "The 6006th loop: cost = 50.686134338378906\n", "The 6007th loop: cost = 46.79241943359375\n", "The 6008th loop: cost = 49.919822692871094\n", "The 6009th loop: cost = 36.70800018310547\n", "The 6010th loop: cost = 41.71832275390625\n", "The 6011th loop: cost = 44.46589660644531\n", "The 6012th loop: cost = 44.57442092895508\n", "The 6013th loop: cost = 44.45795440673828\n", "The 6014th loop: cost = 48.83154296875\n", "The 6015th loop: cost = 45.49391174316406\n", "The 6016th loop: cost = 40.514408111572266\n", "The 6017th loop: cost = 38.301910400390625\n", "The 6018th loop: cost = 51.54881286621094\n", "The 6019th loop: cost = 44.1379508972168\n", "The 6020th loop: cost = 50.060272216796875\n", "The 6021th loop: cost = 40.06248474121094\n", "The 6022th loop: cost = 48.412086486816406\n", "The 6023th loop: cost = 49.96452713012695\n", "The 6024th loop: cost = 48.19770050048828\n", "The 6025th loop: cost = 43.06367492675781\n", "The 6026th loop: cost = 45.59151077270508\n", "The 6027th loop: cost = 42.392311096191406\n", "The 6028th loop: cost = 47.52558898925781\n", "The 6029th loop: cost = 43.79976272583008\n", "The 6030th loop: cost = 46.705135345458984\n", "The 6031th loop: cost = 49.532386779785156\n", "The 6032th loop: cost = 43.54805374145508\n", "The 6033th loop: cost = 42.71797180175781\n", "The 6034th loop: cost = 42.256107330322266\n", "The 6035th loop: cost = 47.12105178833008\n", "The 6036th loop: cost = 44.951080322265625\n", "The 6037th loop: cost = 40.348426818847656\n", "The 6038th loop: cost = 55.693607330322266\n", "The 6039th loop: cost = 37.777915954589844\n", "The 6040th loop: cost = 40.16068649291992\n", "The 6041th loop: cost = 49.901939392089844\n", "The 6042th loop: cost = 43.40700149536133\n", "The 6043th loop: cost = 48.47584915161133\n", "The 6044th loop: cost = 58.348175048828125\n", "The 6045th loop: cost = 36.18183898925781\n", "The 6046th loop: cost = 43.73139190673828\n", "The 6047th loop: cost = 49.60369110107422\n", "The 6048th loop: cost = 52.84697723388672\n", "The 6049th loop: cost = 45.34737014770508\n", "The 6050th loop: cost = 42.456607818603516\n", "The 6051th loop: cost = 50.36851501464844\n", "The 6052th loop: cost = 40.06598663330078\n", "The 6053th loop: cost = 47.793006896972656\n", "The 6054th loop: cost = 41.8952522277832\n", "The 6055th loop: cost = 44.518402099609375\n", "The 6056th loop: cost = 47.46058654785156\n", "The 6057th loop: cost = 49.86854934692383\n", "The 6058th loop: cost = 41.13975524902344\n", "The 6059th loop: cost = 49.89592742919922\n", "The 6060th loop: cost = 43.24021911621094\n", "The 6061th loop: cost = 41.34261703491211\n", "The 6062th loop: cost = 31.54532814025879\n", "The 6063th loop: cost = 47.20388412475586\n", "The 6064th loop: cost = 45.952247619628906\n", "The 6065th loop: cost = 42.6158561706543\n", "The 6066th loop: cost = 49.90626525878906\n", "The 6067th loop: cost = 39.10507583618164\n", "The 6068th loop: cost = 53.542972564697266\n", "The 6069th loop: cost = 43.116546630859375\n", "The 6070th loop: cost = 43.54651641845703\n", "The 6071th loop: cost = 40.58884811401367\n", "The 6072th loop: cost = 44.70317459106445\n", "The 6073th loop: cost = 39.92150115966797\n", "The 6074th loop: cost = 34.385475158691406\n", "The 6075th loop: cost = 44.27655029296875\n", "The 6076th loop: cost = 39.70161437988281\n", "The 6077th loop: cost = 40.90909194946289\n", "The 6078th loop: cost = 51.526161193847656\n", "The 6079th loop: cost = 47.885093688964844\n", "The 6080th loop: cost = 42.66365051269531\n", "The 6081th loop: cost = 40.60110855102539\n", "The 6082th loop: cost = 52.41426086425781\n", "The 6083th loop: cost = 40.62797546386719\n", "The 6084th loop: cost = 42.586952209472656\n", "The 6085th loop: cost = 42.1600341796875\n", "The 6086th loop: cost = 48.93560791015625\n", "The 6087th loop: cost = 40.41187286376953\n", "The 6088th loop: cost = 48.99378204345703\n", "The 6089th loop: cost = 50.823638916015625\n", "The 6090th loop: cost = 36.61701202392578\n", "The 6091th loop: cost = 47.55714416503906\n", "The 6092th loop: cost = 45.97468185424805\n", "The 6093th loop: cost = 45.348506927490234\n", "The 6094th loop: cost = 43.20207595825195\n", "The 6095th loop: cost = 49.8455924987793\n", "The 6096th loop: cost = 39.97887420654297\n", "The 6097th loop: cost = 37.140769958496094\n", "The 6098th loop: cost = 45.00640106201172\n", "The 6099th loop: cost = 42.935821533203125\n", "The 6100th loop: cost = 46.729698181152344\n", "The 6101th loop: cost = 42.132530212402344\n", "The 6102th loop: cost = 38.22114181518555\n", "The 6103th loop: cost = 46.11997604370117\n", "The 6104th loop: cost = 38.946815490722656\n", "The 6105th loop: cost = 43.07421875\n", "The 6106th loop: cost = 43.33354568481445\n", "The 6107th loop: cost = 41.227081298828125\n", "The 6108th loop: cost = 48.026023864746094\n", "The 6109th loop: cost = 39.11021041870117\n", "The 6110th loop: cost = 41.40362548828125\n", "The 6111th loop: cost = 49.48042678833008\n", "The 6112th loop: cost = 44.55091094970703\n", "The 6113th loop: cost = 43.85665512084961\n", "The 6114th loop: cost = 37.044227600097656\n", "The 6115th loop: cost = 49.43741989135742\n", "The 6116th loop: cost = 48.652137756347656\n", "The 6117th loop: cost = 41.70077133178711\n", "The 6118th loop: cost = 51.9456787109375\n", "The 6119th loop: cost = 41.94704055786133\n", "The 6120th loop: cost = 42.079856872558594\n", "The 6121th loop: cost = 37.30082702636719\n", "The 6122th loop: cost = 40.518898010253906\n", "The 6123th loop: cost = 41.297584533691406\n", "The 6124th loop: cost = 47.80137634277344\n", "The 6125th loop: cost = 47.76155090332031\n", "The 6126th loop: cost = 45.64891815185547\n", "The 6127th loop: cost = 48.29499053955078\n", "The 6128th loop: cost = 46.53881072998047\n", "The 6129th loop: cost = 45.14757537841797\n", "The 6130th loop: cost = 41.78798294067383\n", "The 6131th loop: cost = 48.051658630371094\n", "The 6132th loop: cost = 49.81883239746094\n", "The 6133th loop: cost = 43.65370178222656\n", "The 6134th loop: cost = 41.509178161621094\n", "The 6135th loop: cost = 45.75236129760742\n", "The 6136th loop: cost = 44.565513610839844\n", "The 6137th loop: cost = 52.58116912841797\n", "The 6138th loop: cost = 41.282691955566406\n", "The 6139th loop: cost = 40.427276611328125\n", "The 6140th loop: cost = 45.582515716552734\n", "The 6141th loop: cost = 41.44233322143555\n", "The 6142th loop: cost = 44.78498840332031\n", "The 6143th loop: cost = 46.17588806152344\n", "The 6144th loop: cost = 43.60334777832031\n", "The 6145th loop: cost = 51.79833221435547\n", "The 6146th loop: cost = 43.939701080322266\n", "The 6147th loop: cost = 49.73112869262695\n", "The 6148th loop: cost = 51.701629638671875\n", "The 6149th loop: cost = 49.48438262939453\n", "The 6150th loop: cost = 50.12937927246094\n", "The 6151th loop: cost = 45.43718719482422\n", "The 6152th loop: cost = 44.25851821899414\n", "The 6153th loop: cost = 51.23870086669922\n", "The 6154th loop: cost = 38.653804779052734\n", "The 6155th loop: cost = 46.486026763916016\n", "The 6156th loop: cost = 37.65367889404297\n", "The 6157th loop: cost = 39.783878326416016\n", "The 6158th loop: cost = 47.52669143676758\n", "The 6159th loop: cost = 40.09087371826172\n", "The 6160th loop: cost = 48.521488189697266\n", "The 6161th loop: cost = 32.900115966796875\n", "The 6162th loop: cost = 28.019432067871094\n", "The 6163th loop: cost = 45.72026824951172\n", "The 6164th loop: cost = 54.6776123046875\n", "The 6165th loop: cost = 58.82146453857422\n", "The 6166th loop: cost = 36.733848571777344\n", "The 6167th loop: cost = 42.668792724609375\n", "The 6168th loop: cost = 46.448516845703125\n", "The 6169th loop: cost = 46.75169372558594\n", "The 6170th loop: cost = 40.04171371459961\n", "The 6171th loop: cost = 50.18948745727539\n", "The 6172th loop: cost = 47.90003967285156\n", "The 6173th loop: cost = 45.03080749511719\n", "The 6174th loop: cost = 45.99162292480469\n", "The 6175th loop: cost = 45.64262008666992\n", "The 6176th loop: cost = 45.43288040161133\n", "The 6177th loop: cost = 46.86388397216797\n", "The 6178th loop: cost = 37.52747344970703\n", "The 6179th loop: cost = 56.02748107910156\n", "The 6180th loop: cost = 42.245391845703125\n", "The 6181th loop: cost = 38.2398681640625\n", "The 6182th loop: cost = 51.27616882324219\n", "The 6183th loop: cost = 39.74895095825195\n", "The 6184th loop: cost = 39.547393798828125\n", "The 6185th loop: cost = 41.89866256713867\n", "The 6186th loop: cost = 43.02269744873047\n", "The 6187th loop: cost = 47.29330825805664\n", "The 6188th loop: cost = 39.38395309448242\n", "The 6189th loop: cost = 43.52808380126953\n", "The 6190th loop: cost = 39.67304611206055\n", "The 6191th loop: cost = 49.19176483154297\n", "The 6192th loop: cost = 44.33081817626953\n", "The 6193th loop: cost = 47.75897216796875\n", "The 6194th loop: cost = 39.58832931518555\n", "The 6195th loop: cost = 45.98081588745117\n", "The 6196th loop: cost = 50.41023254394531\n", "The 6197th loop: cost = 46.33714294433594\n", "The 6198th loop: cost = 39.386451721191406\n", "The 6199th loop: cost = 48.93007278442383\n", "The 6200th loop: cost = 45.740325927734375\n", "The 6201th loop: cost = 41.6925048828125\n", "The 6202th loop: cost = 52.2405891418457\n", "The 6203th loop: cost = 42.70318603515625\n", "The 6204th loop: cost = 52.426456451416016\n", "The 6205th loop: cost = 53.52143859863281\n", "The 6206th loop: cost = 44.53385925292969\n", "The 6207th loop: cost = 41.809993743896484\n", "The 6208th loop: cost = 43.44322967529297\n", "The 6209th loop: cost = 45.090633392333984\n", "The 6210th loop: cost = 47.12576675415039\n", "The 6211th loop: cost = 51.32766342163086\n", "The 6212th loop: cost = 37.96205139160156\n", "The 6213th loop: cost = 39.00470733642578\n", "The 6214th loop: cost = 46.75698471069336\n", "The 6215th loop: cost = 48.48902893066406\n", "The 6216th loop: cost = 41.167999267578125\n", "The 6217th loop: cost = 49.85154724121094\n", "The 6218th loop: cost = 47.05545425415039\n", "The 6219th loop: cost = 48.77126693725586\n", "The 6220th loop: cost = 43.12324142456055\n", "The 6221th loop: cost = 50.459720611572266\n", "The 6222th loop: cost = 49.0536003112793\n", "The 6223th loop: cost = 42.806758880615234\n", "The 6224th loop: cost = 42.205970764160156\n", "The 6225th loop: cost = 53.16084671020508\n", "The 6226th loop: cost = 46.4958381652832\n", "The 6227th loop: cost = 41.388641357421875\n", "The 6228th loop: cost = 50.112125396728516\n", "The 6229th loop: cost = 43.47074890136719\n", "The 6230th loop: cost = 46.085357666015625\n", "The 6231th loop: cost = 35.60235595703125\n", "The 6232th loop: cost = 41.92335510253906\n", "The 6233th loop: cost = 46.3231315612793\n", "The 6234th loop: cost = 47.58369445800781\n", "The 6235th loop: cost = 39.95532989501953\n", "The 6236th loop: cost = 52.03976058959961\n", "The 6237th loop: cost = 41.86590576171875\n", "The 6238th loop: cost = 40.392478942871094\n", "The 6239th loop: cost = 46.30967330932617\n", "The 6240th loop: cost = 46.485816955566406\n", "The 6241th loop: cost = 49.262237548828125\n", "The 6242th loop: cost = 47.39149475097656\n", "The 6243th loop: cost = 48.82309341430664\n", "The 6244th loop: cost = 54.627723693847656\n", "The 6245th loop: cost = 35.438316345214844\n", "The 6246th loop: cost = 44.130916595458984\n", "The 6247th loop: cost = 34.26947021484375\n", "The 6248th loop: cost = 48.31198501586914\n", "The 6249th loop: cost = 47.16095733642578\n", "The 6250th loop: cost = 46.53607177734375\n", "The 6251th loop: cost = 42.557167053222656\n", "The 6252th loop: cost = 46.772884368896484\n", "The 6253th loop: cost = 39.448883056640625\n", "The 6254th loop: cost = 42.76722717285156\n", "The 6255th loop: cost = 44.17033386230469\n", "The 6256th loop: cost = 39.90424346923828\n", "The 6257th loop: cost = 42.93998718261719\n", "The 6258th loop: cost = 49.66254425048828\n", "The 6259th loop: cost = 39.00731658935547\n", "The 6260th loop: cost = 51.75375747680664\n", "The 6261th loop: cost = 40.43093490600586\n", "The 6262th loop: cost = 47.55076599121094\n", "The 6263th loop: cost = 39.0770263671875\n", "The 6264th loop: cost = 37.11532211303711\n", "The 6265th loop: cost = 51.66304016113281\n", "The 6266th loop: cost = 39.512123107910156\n", "The 6267th loop: cost = 46.07975769042969\n", "The 6268th loop: cost = 44.89692687988281\n", "The 6269th loop: cost = 35.763092041015625\n", "The 6270th loop: cost = 53.386512756347656\n", "The 6271th loop: cost = 38.774620056152344\n", "The 6272th loop: cost = 42.98192596435547\n", "The 6273th loop: cost = 44.704429626464844\n", "The 6274th loop: cost = 44.88703918457031\n", "The 6275th loop: cost = 41.237876892089844\n", "The 6276th loop: cost = 40.55160903930664\n", "The 6277th loop: cost = 48.30622100830078\n", "The 6278th loop: cost = 34.55292510986328\n", "The 6279th loop: cost = 41.4642219543457\n", "The 6280th loop: cost = 46.61097717285156\n", "The 6281th loop: cost = 38.831478118896484\n", "The 6282th loop: cost = 43.51902770996094\n", "The 6283th loop: cost = 50.40384292602539\n", "The 6284th loop: cost = 53.212974548339844\n", "The 6285th loop: cost = 43.11854553222656\n", "The 6286th loop: cost = 40.11767578125\n", "The 6287th loop: cost = 37.17323303222656\n", "The 6288th loop: cost = 46.83808898925781\n", "The 6289th loop: cost = 44.378013610839844\n", "The 6290th loop: cost = 55.36417770385742\n", "The 6291th loop: cost = 53.15409851074219\n", "The 6292th loop: cost = 42.95696258544922\n", "The 6293th loop: cost = 43.854515075683594\n", "The 6294th loop: cost = 48.62519836425781\n", "The 6295th loop: cost = 43.93191909790039\n", "The 6296th loop: cost = 42.402099609375\n", "The 6297th loop: cost = 41.822532653808594\n", "The 6298th loop: cost = 39.60872268676758\n", "The 6299th loop: cost = 48.0740966796875\n", "The 6300th loop: cost = 36.95228576660156\n", "The 6301th loop: cost = 43.708946228027344\n", "The 6302th loop: cost = 39.12236785888672\n", "The 6303th loop: cost = 38.040977478027344\n", "The 6304th loop: cost = 50.42560577392578\n", "The 6305th loop: cost = 44.26250457763672\n", "The 6306th loop: cost = 40.61891555786133\n", "The 6307th loop: cost = 44.667762756347656\n", "The 6308th loop: cost = 38.177642822265625\n", "The 6309th loop: cost = 48.936580657958984\n", "The 6310th loop: cost = 44.05473327636719\n", "The 6311th loop: cost = 36.95161819458008\n", "The 6312th loop: cost = 38.346954345703125\n", "The 6313th loop: cost = 37.92412567138672\n", "The 6314th loop: cost = 41.27821731567383\n", "The 6315th loop: cost = 53.22533416748047\n", "The 6316th loop: cost = 43.99542236328125\n", "The 6317th loop: cost = 45.272483825683594\n", "The 6318th loop: cost = 45.52287673950195\n", "The 6319th loop: cost = 33.324859619140625\n", "The 6320th loop: cost = 52.543365478515625\n", "The 6321th loop: cost = 43.03853225708008\n", "The 6322th loop: cost = 45.85374069213867\n", "The 6323th loop: cost = 48.265533447265625\n", "The 6324th loop: cost = 39.03974151611328\n", "The 6325th loop: cost = 49.146217346191406\n", "The 6326th loop: cost = 37.60198211669922\n", "The 6327th loop: cost = 49.903175354003906\n", "The 6328th loop: cost = 43.94007873535156\n", "The 6329th loop: cost = 46.3441047668457\n", "The 6330th loop: cost = 49.2271842956543\n", "The 6331th loop: cost = 43.68699264526367\n", "The 6332th loop: cost = 48.0513801574707\n", "The 6333th loop: cost = 41.2769775390625\n", "The 6334th loop: cost = 37.254112243652344\n", "The 6335th loop: cost = 49.39855194091797\n", "The 6336th loop: cost = 41.999385833740234\n", "The 6337th loop: cost = 55.45747756958008\n", "The 6338th loop: cost = 34.1046142578125\n", "The 6339th loop: cost = 43.01152420043945\n", "The 6340th loop: cost = 44.65387725830078\n", "The 6341th loop: cost = 46.082637786865234\n", "The 6342th loop: cost = 40.18341827392578\n", "The 6343th loop: cost = 36.76844787597656\n", "The 6344th loop: cost = 45.35084533691406\n", "The 6345th loop: cost = 42.29182052612305\n", "The 6346th loop: cost = 42.27128982543945\n", "The 6347th loop: cost = 43.45719909667969\n", "The 6348th loop: cost = 29.803316116333008\n", "The 6349th loop: cost = 51.501792907714844\n", "The 6350th loop: cost = 38.883087158203125\n", "The 6351th loop: cost = 45.72272491455078\n", "The 6352th loop: cost = 40.465885162353516\n", "The 6353th loop: cost = 42.41734313964844\n", "The 6354th loop: cost = 34.318931579589844\n", "The 6355th loop: cost = 49.532691955566406\n", "The 6356th loop: cost = 47.74247741699219\n", "The 6357th loop: cost = 33.994224548339844\n", "The 6358th loop: cost = 53.33159255981445\n", "The 6359th loop: cost = 50.303749084472656\n", "The 6360th loop: cost = 49.867130279541016\n", "The 6361th loop: cost = 42.902671813964844\n", "The 6362th loop: cost = 54.634254455566406\n", "The 6363th loop: cost = 52.49060821533203\n", "The 6364th loop: cost = 48.37812042236328\n", "The 6365th loop: cost = 46.021705627441406\n", "The 6366th loop: cost = 45.214473724365234\n", "The 6367th loop: cost = 46.807212829589844\n", "The 6368th loop: cost = 45.99213409423828\n", "The 6369th loop: cost = 44.453060150146484\n", "The 6370th loop: cost = 50.22430419921875\n", "The 6371th loop: cost = 47.48609924316406\n", "The 6372th loop: cost = 35.406185150146484\n", "The 6373th loop: cost = 39.362449645996094\n", "The 6374th loop: cost = 35.92928695678711\n", "The 6375th loop: cost = 41.30729675292969\n", "The 6376th loop: cost = 49.56784439086914\n", "The 6377th loop: cost = 40.44770431518555\n", "The 6378th loop: cost = 44.704532623291016\n", "The 6379th loop: cost = 38.89654541015625\n", "The 6380th loop: cost = 45.280487060546875\n", "The 6381th loop: cost = 58.49178695678711\n", "The 6382th loop: cost = 54.66210174560547\n", "The 6383th loop: cost = 50.801666259765625\n", "The 6384th loop: cost = 43.202842712402344\n", "The 6385th loop: cost = 39.071617126464844\n", "The 6386th loop: cost = 44.9867057800293\n", "The 6387th loop: cost = 41.89946365356445\n", "The 6388th loop: cost = 45.953025817871094\n", "The 6389th loop: cost = 47.42072296142578\n", "The 6390th loop: cost = 40.95323181152344\n", "The 6391th loop: cost = 43.56515884399414\n", "The 6392th loop: cost = 42.93877029418945\n", "The 6393th loop: cost = 48.60015106201172\n", "The 6394th loop: cost = 40.91185760498047\n", "The 6395th loop: cost = 35.864990234375\n", "The 6396th loop: cost = 44.012481689453125\n", "The 6397th loop: cost = 49.8081169128418\n", "The 6398th loop: cost = 48.669769287109375\n", "The 6399th loop: cost = 48.22412109375\n", "The 6400th loop: cost = 42.931495666503906\n", "The 6401th loop: cost = 53.00231170654297\n", "The 6402th loop: cost = 39.596893310546875\n", "The 6403th loop: cost = 43.90205383300781\n", "The 6404th loop: cost = 46.53506088256836\n", "The 6405th loop: cost = 38.39437484741211\n", "The 6406th loop: cost = 39.99671173095703\n", "The 6407th loop: cost = 44.6468505859375\n", "The 6408th loop: cost = 44.69010925292969\n", "The 6409th loop: cost = 39.19422912597656\n", "The 6410th loop: cost = 45.75284957885742\n", "The 6411th loop: cost = 32.041046142578125\n", "The 6412th loop: cost = 40.18666076660156\n", "The 6413th loop: cost = 43.14806365966797\n", "The 6414th loop: cost = 39.08636474609375\n", "The 6415th loop: cost = 42.43473815917969\n", "The 6416th loop: cost = 45.60458755493164\n", "The 6417th loop: cost = 56.0485954284668\n", "The 6418th loop: cost = 42.187896728515625\n", "The 6419th loop: cost = 51.286651611328125\n", "The 6420th loop: cost = 52.69300842285156\n", "The 6421th loop: cost = 38.032554626464844\n", "The 6422th loop: cost = 42.22544479370117\n", "The 6423th loop: cost = 43.32563018798828\n", "The 6424th loop: cost = 44.71886444091797\n", "The 6425th loop: cost = 49.21796417236328\n", "The 6426th loop: cost = 39.326637268066406\n", "The 6427th loop: cost = 47.3306770324707\n", "The 6428th loop: cost = 47.1033821105957\n", "The 6429th loop: cost = 45.134483337402344\n", "The 6430th loop: cost = 40.935462951660156\n", "The 6431th loop: cost = 48.47474670410156\n", "The 6432th loop: cost = 45.079830169677734\n", "The 6433th loop: cost = 47.270416259765625\n", "The 6434th loop: cost = 34.133888244628906\n", "The 6435th loop: cost = 48.57746124267578\n", "The 6436th loop: cost = 38.7415657043457\n", "The 6437th loop: cost = 45.767112731933594\n", "The 6438th loop: cost = 42.056793212890625\n", "The 6439th loop: cost = 46.17136764526367\n", "The 6440th loop: cost = 48.332374572753906\n", "The 6441th loop: cost = 39.285335540771484\n", "The 6442th loop: cost = 46.376007080078125\n", "The 6443th loop: cost = 39.672080993652344\n", "The 6444th loop: cost = 42.08449935913086\n", "The 6445th loop: cost = 53.70536804199219\n", "The 6446th loop: cost = 37.200130462646484\n", "The 6447th loop: cost = 46.199771881103516\n", "The 6448th loop: cost = 44.25053405761719\n", "The 6449th loop: cost = 38.70596694946289\n", "The 6450th loop: cost = 50.162109375\n", "The 6451th loop: cost = 40.411277770996094\n", "The 6452th loop: cost = 37.24519348144531\n", "The 6453th loop: cost = 40.43575668334961\n", "The 6454th loop: cost = 43.28583908081055\n", "The 6455th loop: cost = 43.0438232421875\n", "The 6456th loop: cost = 40.751502990722656\n", "The 6457th loop: cost = 36.88872528076172\n", "The 6458th loop: cost = 45.163116455078125\n", "The 6459th loop: cost = 41.2898063659668\n", "The 6460th loop: cost = 48.23662185668945\n", "The 6461th loop: cost = 40.87245559692383\n", "The 6462th loop: cost = 55.877655029296875\n", "The 6463th loop: cost = 40.027645111083984\n", "The 6464th loop: cost = 45.871826171875\n", "The 6465th loop: cost = 43.20071792602539\n", "The 6466th loop: cost = 53.86151123046875\n", "The 6467th loop: cost = 38.682159423828125\n", "The 6468th loop: cost = 37.016300201416016\n", "The 6469th loop: cost = 44.11470031738281\n", "The 6470th loop: cost = 42.44459915161133\n", "The 6471th loop: cost = 41.568939208984375\n", "The 6472th loop: cost = 45.73310470581055\n", "The 6473th loop: cost = 40.154449462890625\n", "The 6474th loop: cost = 50.5247688293457\n", "The 6475th loop: cost = 42.442657470703125\n", "The 6476th loop: cost = 44.35663604736328\n", "The 6477th loop: cost = 48.787803649902344\n", "The 6478th loop: cost = 39.60947036743164\n", "The 6479th loop: cost = 44.03189468383789\n", "The 6480th loop: cost = 51.46611022949219\n", "The 6481th loop: cost = 41.855228424072266\n", "The 6482th loop: cost = 55.129302978515625\n", "The 6483th loop: cost = 37.206233978271484\n", "The 6484th loop: cost = 47.12001037597656\n", "The 6485th loop: cost = 45.76212692260742\n", "The 6486th loop: cost = 38.70486068725586\n", "The 6487th loop: cost = 45.807010650634766\n", "The 6488th loop: cost = 36.26802444458008\n", "The 6489th loop: cost = 44.36692810058594\n", "The 6490th loop: cost = 45.756919860839844\n", "The 6491th loop: cost = 47.08097457885742\n", "The 6492th loop: cost = 41.472991943359375\n", "The 6493th loop: cost = 49.64704132080078\n", "The 6494th loop: cost = 48.25493240356445\n", "The 6495th loop: cost = 42.94719314575195\n", "The 6496th loop: cost = 42.589107513427734\n", "The 6497th loop: cost = 38.78528594970703\n", "The 6498th loop: cost = 35.17049789428711\n", "The 6499th loop: cost = 54.124813079833984\n", "The 6500th loop: cost = 50.629554748535156\n", "The 6501th loop: cost = 38.09766387939453\n", "The 6502th loop: cost = 35.114376068115234\n", "The 6503th loop: cost = 42.27758026123047\n", "The 6504th loop: cost = 51.02088928222656\n", "The 6505th loop: cost = 42.296295166015625\n", "The 6506th loop: cost = 44.652374267578125\n", "The 6507th loop: cost = 42.98844528198242\n", "The 6508th loop: cost = 39.21144104003906\n", "The 6509th loop: cost = 40.19781494140625\n", "The 6510th loop: cost = 46.869956970214844\n", "The 6511th loop: cost = 48.700828552246094\n", "The 6512th loop: cost = 40.47691345214844\n", "The 6513th loop: cost = 40.76108169555664\n", "The 6514th loop: cost = 45.183326721191406\n", "The 6515th loop: cost = 40.54124069213867\n", "The 6516th loop: cost = 41.03086853027344\n", "The 6517th loop: cost = 38.43696594238281\n", "The 6518th loop: cost = 46.26942825317383\n", "The 6519th loop: cost = 45.40220642089844\n", "The 6520th loop: cost = 46.27115249633789\n", "The 6521th loop: cost = 44.40105056762695\n", "The 6522th loop: cost = 48.71902084350586\n", "The 6523th loop: cost = 44.141197204589844\n", "The 6524th loop: cost = 50.165260314941406\n", "The 6525th loop: cost = 32.596988677978516\n", "The 6526th loop: cost = 39.84810256958008\n", "The 6527th loop: cost = 41.764076232910156\n", "The 6528th loop: cost = 40.06980514526367\n", "The 6529th loop: cost = 49.89130401611328\n", "The 6530th loop: cost = 41.4361686706543\n", "The 6531th loop: cost = 44.99049377441406\n", "The 6532th loop: cost = 54.606075286865234\n", "The 6533th loop: cost = 39.506980895996094\n", "The 6534th loop: cost = 41.73495101928711\n", "The 6535th loop: cost = 50.38140869140625\n", "The 6536th loop: cost = 43.835166931152344\n", "The 6537th loop: cost = 40.33129119873047\n", "The 6538th loop: cost = 45.6987190246582\n", "The 6539th loop: cost = 39.48858642578125\n", "The 6540th loop: cost = 48.3737907409668\n", "The 6541th loop: cost = 41.54490661621094\n", "The 6542th loop: cost = 41.252281188964844\n", "The 6543th loop: cost = 40.20998001098633\n", "The 6544th loop: cost = 45.249267578125\n", "The 6545th loop: cost = 40.72364807128906\n", "The 6546th loop: cost = 41.26721954345703\n", "The 6547th loop: cost = 44.54348373413086\n", "The 6548th loop: cost = 53.79680633544922\n", "The 6549th loop: cost = 46.251075744628906\n", "The 6550th loop: cost = 42.3993034362793\n", "The 6551th loop: cost = 55.66448974609375\n", "The 6552th loop: cost = 41.038787841796875\n", "The 6553th loop: cost = 41.5629997253418\n", "The 6554th loop: cost = 55.5325927734375\n", "The 6555th loop: cost = 43.469566345214844\n", "The 6556th loop: cost = 37.509742736816406\n", "The 6557th loop: cost = 49.542903900146484\n", "The 6558th loop: cost = 46.187095642089844\n", "The 6559th loop: cost = 37.50712585449219\n", "The 6560th loop: cost = 43.24470901489258\n", "The 6561th loop: cost = 37.67485046386719\n", "The 6562th loop: cost = 44.3922004699707\n", "The 6563th loop: cost = 43.779151916503906\n", "The 6564th loop: cost = 42.37327194213867\n", "The 6565th loop: cost = 33.465599060058594\n", "The 6566th loop: cost = 44.60771179199219\n", "The 6567th loop: cost = 39.969810485839844\n", "The 6568th loop: cost = 49.44165802001953\n", "The 6569th loop: cost = 46.10230255126953\n", "The 6570th loop: cost = 37.23982238769531\n", "The 6571th loop: cost = 34.29611587524414\n", "The 6572th loop: cost = 42.693565368652344\n", "The 6573th loop: cost = 45.510215759277344\n", "The 6574th loop: cost = 42.70805358886719\n", "The 6575th loop: cost = 46.5430908203125\n", "The 6576th loop: cost = 40.937286376953125\n", "The 6577th loop: cost = 47.1253662109375\n", "The 6578th loop: cost = 43.388160705566406\n", "The 6579th loop: cost = 45.308998107910156\n", "The 6580th loop: cost = 56.57599639892578\n", "The 6581th loop: cost = 50.979766845703125\n", "The 6582th loop: cost = 48.61471176147461\n", "The 6583th loop: cost = 45.647220611572266\n", "The 6584th loop: cost = 40.69097900390625\n", "The 6585th loop: cost = 40.39363479614258\n", "The 6586th loop: cost = 43.35017013549805\n", "The 6587th loop: cost = 43.71057891845703\n", "The 6588th loop: cost = 52.11851501464844\n", "The 6589th loop: cost = 35.6453971862793\n", "The 6590th loop: cost = 47.50471115112305\n", "The 6591th loop: cost = 39.73583984375\n", "The 6592th loop: cost = 39.7889518737793\n", "The 6593th loop: cost = 46.05939483642578\n", "The 6594th loop: cost = 45.41431427001953\n", "The 6595th loop: cost = 47.34447479248047\n", "The 6596th loop: cost = 45.01390838623047\n", "The 6597th loop: cost = 44.148128509521484\n", "The 6598th loop: cost = 41.92896270751953\n", "The 6599th loop: cost = 42.005733489990234\n", "The 6600th loop: cost = 42.723487854003906\n", "The 6601th loop: cost = 37.59709930419922\n", "The 6602th loop: cost = 38.698219299316406\n", "The 6603th loop: cost = 46.584136962890625\n", "The 6604th loop: cost = 41.35359191894531\n", "The 6605th loop: cost = 45.802207946777344\n", "The 6606th loop: cost = 43.507850646972656\n", "The 6607th loop: cost = 35.42268371582031\n", "The 6608th loop: cost = 39.51727294921875\n", "The 6609th loop: cost = 45.28498077392578\n", "The 6610th loop: cost = 46.20549774169922\n", "The 6611th loop: cost = 33.98963928222656\n", "The 6612th loop: cost = 53.17005920410156\n", "The 6613th loop: cost = 43.279090881347656\n", "The 6614th loop: cost = 38.397579193115234\n", "The 6615th loop: cost = 32.99810028076172\n", "The 6616th loop: cost = 38.22241973876953\n", "The 6617th loop: cost = 51.182533264160156\n", "The 6618th loop: cost = 50.02056121826172\n", "The 6619th loop: cost = 46.26677322387695\n", "The 6620th loop: cost = 50.759830474853516\n", "The 6621th loop: cost = 42.05387878417969\n", "The 6622th loop: cost = 35.11572265625\n", "The 6623th loop: cost = 44.928985595703125\n", "The 6624th loop: cost = 46.362831115722656\n", "The 6625th loop: cost = 47.15114212036133\n", "The 6626th loop: cost = 42.57192611694336\n", "The 6627th loop: cost = 37.60918426513672\n", "The 6628th loop: cost = 34.95051574707031\n", "The 6629th loop: cost = 39.23236846923828\n", "The 6630th loop: cost = 47.19239807128906\n", "The 6631th loop: cost = 35.879207611083984\n", "The 6632th loop: cost = 34.112491607666016\n", "The 6633th loop: cost = 36.02053451538086\n", "The 6634th loop: cost = 44.84405517578125\n", "The 6635th loop: cost = 44.92630386352539\n", "The 6636th loop: cost = 41.521751403808594\n", "The 6637th loop: cost = 47.489295959472656\n", "The 6638th loop: cost = 42.564273834228516\n", "The 6639th loop: cost = 41.3785285949707\n", "The 6640th loop: cost = 46.9510498046875\n", "The 6641th loop: cost = 39.64190673828125\n", "The 6642th loop: cost = 48.964637756347656\n", "The 6643th loop: cost = 48.20168685913086\n", "The 6644th loop: cost = 43.03239440917969\n", "The 6645th loop: cost = 49.628868103027344\n", "The 6646th loop: cost = 42.25804901123047\n", "The 6647th loop: cost = 43.71302032470703\n", "The 6648th loop: cost = 51.010101318359375\n", "The 6649th loop: cost = 36.49677276611328\n", "The 6650th loop: cost = 48.783294677734375\n", "The 6651th loop: cost = 37.45075988769531\n", "The 6652th loop: cost = 43.02962112426758\n", "The 6653th loop: cost = 40.81572723388672\n", "The 6654th loop: cost = 39.37689971923828\n", "The 6655th loop: cost = 42.43986129760742\n", "The 6656th loop: cost = 34.60838317871094\n", "The 6657th loop: cost = 29.77360725402832\n", "The 6658th loop: cost = 34.881263732910156\n", "The 6659th loop: cost = 38.908660888671875\n", "The 6660th loop: cost = 37.3167610168457\n", "The 6661th loop: cost = 55.883644104003906\n", "The 6662th loop: cost = 43.38098907470703\n", "The 6663th loop: cost = 36.26015090942383\n", "The 6664th loop: cost = 45.28218078613281\n", "The 6665th loop: cost = 33.90672302246094\n", "The 6666th loop: cost = 41.64527893066406\n", "The 6667th loop: cost = 29.89874267578125\n", "The 6668th loop: cost = 46.182281494140625\n", "The 6669th loop: cost = 42.72230529785156\n", "The 6670th loop: cost = 49.60214614868164\n", "The 6671th loop: cost = 47.955631256103516\n", "The 6672th loop: cost = 50.00608825683594\n", "The 6673th loop: cost = 37.448455810546875\n", "The 6674th loop: cost = 43.085205078125\n", "The 6675th loop: cost = 46.30914306640625\n", "The 6676th loop: cost = 43.52434158325195\n", "The 6677th loop: cost = 43.790679931640625\n", "The 6678th loop: cost = 37.393592834472656\n", "The 6679th loop: cost = 37.58265686035156\n", "The 6680th loop: cost = 46.70843505859375\n", "The 6681th loop: cost = 39.3916015625\n", "The 6682th loop: cost = 41.017005920410156\n", "The 6683th loop: cost = 52.44291687011719\n", "The 6684th loop: cost = 48.182411193847656\n", "The 6685th loop: cost = 45.76812744140625\n", "The 6686th loop: cost = 39.8734130859375\n", "The 6687th loop: cost = 40.6824951171875\n", "The 6688th loop: cost = 50.3166389465332\n", "The 6689th loop: cost = 37.58112335205078\n", "The 6690th loop: cost = 32.863677978515625\n", "The 6691th loop: cost = 39.268653869628906\n", "The 6692th loop: cost = 40.08530044555664\n", "The 6693th loop: cost = 45.22768020629883\n", "The 6694th loop: cost = 33.66728210449219\n", "The 6695th loop: cost = 42.73912048339844\n", "The 6696th loop: cost = 47.34183120727539\n", "The 6697th loop: cost = 44.95709228515625\n", "The 6698th loop: cost = 42.390052795410156\n", "The 6699th loop: cost = 50.69904327392578\n", "The 6700th loop: cost = 49.19187927246094\n", "The 6701th loop: cost = 42.517086029052734\n", "The 6702th loop: cost = 48.338924407958984\n", "The 6703th loop: cost = 36.713844299316406\n", "The 6704th loop: cost = 48.217594146728516\n", "The 6705th loop: cost = 36.10603713989258\n", "The 6706th loop: cost = 38.23899841308594\n", "The 6707th loop: cost = 41.07007598876953\n", "The 6708th loop: cost = 54.221397399902344\n", "The 6709th loop: cost = 43.94598388671875\n", "The 6710th loop: cost = 44.392826080322266\n", "The 6711th loop: cost = 49.50089645385742\n", "The 6712th loop: cost = 36.798851013183594\n", "The 6713th loop: cost = 45.962913513183594\n", "The 6714th loop: cost = 42.57243347167969\n", "The 6715th loop: cost = 44.883514404296875\n", "The 6716th loop: cost = 50.729957580566406\n", "The 6717th loop: cost = 31.362136840820312\n", "The 6718th loop: cost = 40.939857482910156\n", "The 6719th loop: cost = 34.79521179199219\n", "The 6720th loop: cost = 44.0255241394043\n", "The 6721th loop: cost = 46.10780715942383\n", "The 6722th loop: cost = 40.49906921386719\n", "The 6723th loop: cost = 37.65180587768555\n", "The 6724th loop: cost = 44.529388427734375\n", "The 6725th loop: cost = 46.851043701171875\n", "The 6726th loop: cost = 46.28778076171875\n", "The 6727th loop: cost = 37.55889892578125\n", "The 6728th loop: cost = 38.69670867919922\n", "The 6729th loop: cost = 34.4627685546875\n", "The 6730th loop: cost = 48.65935516357422\n", "The 6731th loop: cost = 54.233055114746094\n", "The 6732th loop: cost = 41.92554473876953\n", "The 6733th loop: cost = 45.65717315673828\n", "The 6734th loop: cost = 43.80210876464844\n", "The 6735th loop: cost = 36.50668716430664\n", "The 6736th loop: cost = 48.008697509765625\n", "The 6737th loop: cost = 38.0671272277832\n", "The 6738th loop: cost = 43.21523666381836\n", "The 6739th loop: cost = 49.228641510009766\n", "The 6740th loop: cost = 43.37691116333008\n", "The 6741th loop: cost = 43.100704193115234\n", "The 6742th loop: cost = 37.754947662353516\n", "The 6743th loop: cost = 52.6361198425293\n", "The 6744th loop: cost = 46.656982421875\n", "The 6745th loop: cost = 39.088829040527344\n", "The 6746th loop: cost = 41.73091125488281\n", "The 6747th loop: cost = 32.911376953125\n", "The 6748th loop: cost = 39.40001678466797\n", "The 6749th loop: cost = 48.847633361816406\n", "The 6750th loop: cost = 48.36741638183594\n", "The 6751th loop: cost = 43.71138381958008\n", "The 6752th loop: cost = 37.16277313232422\n", "The 6753th loop: cost = 39.32630157470703\n", "The 6754th loop: cost = 34.51439666748047\n", "The 6755th loop: cost = 38.38927459716797\n", "The 6756th loop: cost = 37.26781463623047\n", "The 6757th loop: cost = 43.1629638671875\n", "The 6758th loop: cost = 45.74463653564453\n", "The 6759th loop: cost = 44.174495697021484\n", "The 6760th loop: cost = 41.37382125854492\n", "The 6761th loop: cost = 43.51824951171875\n", "The 6762th loop: cost = 39.37291717529297\n", "The 6763th loop: cost = 36.970184326171875\n", "The 6764th loop: cost = 59.6815299987793\n", "The 6765th loop: cost = 43.609336853027344\n", "The 6766th loop: cost = 46.14775085449219\n", "The 6767th loop: cost = 39.91941833496094\n", "The 6768th loop: cost = 46.43584442138672\n", "The 6769th loop: cost = 32.888397216796875\n", "The 6770th loop: cost = 39.1075439453125\n", "The 6771th loop: cost = 40.381412506103516\n", "The 6772th loop: cost = 44.541656494140625\n", "The 6773th loop: cost = 51.89612579345703\n", "The 6774th loop: cost = 38.99779510498047\n", "The 6775th loop: cost = 48.63905715942383\n", "The 6776th loop: cost = 39.769004821777344\n", "The 6777th loop: cost = 44.71006774902344\n", "The 6778th loop: cost = 51.59954833984375\n", "The 6779th loop: cost = 51.26862716674805\n", "The 6780th loop: cost = 40.29456329345703\n", "The 6781th loop: cost = 49.884185791015625\n", "The 6782th loop: cost = 48.03055191040039\n", "The 6783th loop: cost = 44.654273986816406\n", "The 6784th loop: cost = 46.51676559448242\n", "The 6785th loop: cost = 40.79279708862305\n", "The 6786th loop: cost = 42.23074722290039\n", "The 6787th loop: cost = 44.75157928466797\n", "The 6788th loop: cost = 40.76148223876953\n", "The 6789th loop: cost = 46.115970611572266\n", "The 6790th loop: cost = 46.06798553466797\n", "The 6791th loop: cost = 43.38882064819336\n", "The 6792th loop: cost = 53.03371810913086\n", "The 6793th loop: cost = 37.311248779296875\n", "The 6794th loop: cost = 33.90606689453125\n", "The 6795th loop: cost = 42.082244873046875\n", "The 6796th loop: cost = 40.622406005859375\n", "The 6797th loop: cost = 40.30438232421875\n", "The 6798th loop: cost = 38.330078125\n", "The 6799th loop: cost = 56.156494140625\n", "The 6800th loop: cost = 36.55654525756836\n", "The 6801th loop: cost = 46.40093994140625\n", "The 6802th loop: cost = 48.05168533325195\n", "The 6803th loop: cost = 46.904544830322266\n", "The 6804th loop: cost = 39.10031509399414\n", "The 6805th loop: cost = 43.34276580810547\n", "The 6806th loop: cost = 46.013450622558594\n", "The 6807th loop: cost = 43.312217712402344\n", "The 6808th loop: cost = 42.96711349487305\n", "The 6809th loop: cost = 48.3861083984375\n", "The 6810th loop: cost = 43.25096893310547\n", "The 6811th loop: cost = 39.04685592651367\n", "The 6812th loop: cost = 49.3763427734375\n", "The 6813th loop: cost = 44.23332977294922\n", "The 6814th loop: cost = 47.834842681884766\n", "The 6815th loop: cost = 39.361534118652344\n", "The 6816th loop: cost = 41.2689094543457\n", "The 6817th loop: cost = 41.86742401123047\n", "The 6818th loop: cost = 41.80946731567383\n", "The 6819th loop: cost = 48.50695037841797\n", "The 6820th loop: cost = 41.23486328125\n", "The 6821th loop: cost = 44.321990966796875\n", "The 6822th loop: cost = 49.32106018066406\n", "The 6823th loop: cost = 38.921165466308594\n", "The 6824th loop: cost = 39.97135925292969\n", "The 6825th loop: cost = 41.14686965942383\n", "The 6826th loop: cost = 35.51784133911133\n", "The 6827th loop: cost = 40.066322326660156\n", "The 6828th loop: cost = 52.525474548339844\n", "The 6829th loop: cost = 50.08625411987305\n", "The 6830th loop: cost = 36.130584716796875\n", "The 6831th loop: cost = 41.86635208129883\n", "The 6832th loop: cost = 44.352508544921875\n", "The 6833th loop: cost = 46.806556701660156\n", "The 6834th loop: cost = 35.03185272216797\n", "The 6835th loop: cost = 52.914283752441406\n", "The 6836th loop: cost = 44.07221221923828\n", "The 6837th loop: cost = 31.96955108642578\n", "The 6838th loop: cost = 39.53974914550781\n", "The 6839th loop: cost = 48.325439453125\n", "The 6840th loop: cost = 35.60057830810547\n", "The 6841th loop: cost = 45.43803405761719\n", "The 6842th loop: cost = 43.42348098754883\n", "The 6843th loop: cost = 49.79836654663086\n", "The 6844th loop: cost = 47.52777862548828\n", "The 6845th loop: cost = 46.467071533203125\n", "The 6846th loop: cost = 43.50983428955078\n", "The 6847th loop: cost = 39.201438903808594\n", "The 6848th loop: cost = 36.349456787109375\n", "The 6849th loop: cost = 49.24493408203125\n", "The 6850th loop: cost = 52.519351959228516\n", "The 6851th loop: cost = 41.36896514892578\n", "The 6852th loop: cost = 45.569786071777344\n", "The 6853th loop: cost = 36.18601608276367\n", "The 6854th loop: cost = 50.149375915527344\n", "The 6855th loop: cost = 42.72193145751953\n", "The 6856th loop: cost = 43.63240051269531\n", "The 6857th loop: cost = 48.364776611328125\n", "The 6858th loop: cost = 39.00932312011719\n", "The 6859th loop: cost = 38.57832717895508\n", "The 6860th loop: cost = 42.600791931152344\n", "The 6861th loop: cost = 45.942726135253906\n", "The 6862th loop: cost = 46.06257629394531\n", "The 6863th loop: cost = 37.66992950439453\n", "The 6864th loop: cost = 45.02195358276367\n", "The 6865th loop: cost = 44.805625915527344\n", "The 6866th loop: cost = 44.389930725097656\n", "The 6867th loop: cost = 37.69430923461914\n", "The 6868th loop: cost = 45.48276138305664\n", "The 6869th loop: cost = 42.77743148803711\n", "The 6870th loop: cost = 42.93241882324219\n", "The 6871th loop: cost = 42.776004791259766\n", "The 6872th loop: cost = 43.816410064697266\n", "The 6873th loop: cost = 45.181419372558594\n", "The 6874th loop: cost = 43.674468994140625\n", "The 6875th loop: cost = 50.871273040771484\n", "The 6876th loop: cost = 44.164695739746094\n", "The 6877th loop: cost = 43.73719787597656\n", "The 6878th loop: cost = 44.19483947753906\n", "The 6879th loop: cost = 38.683597564697266\n", "The 6880th loop: cost = 39.48809051513672\n", "The 6881th loop: cost = 44.53184509277344\n", "The 6882th loop: cost = 42.83794403076172\n", "The 6883th loop: cost = 46.01903533935547\n", "The 6884th loop: cost = 41.70937728881836\n", "The 6885th loop: cost = 39.204193115234375\n", "The 6886th loop: cost = 35.644615173339844\n", "The 6887th loop: cost = 38.84283447265625\n", "The 6888th loop: cost = 39.17873001098633\n", "The 6889th loop: cost = 44.84442138671875\n", "The 6890th loop: cost = 36.76526641845703\n", "The 6891th loop: cost = 42.67692565917969\n", "The 6892th loop: cost = 40.348052978515625\n", "The 6893th loop: cost = 47.576072692871094\n", "The 6894th loop: cost = 40.702491760253906\n", "The 6895th loop: cost = 38.008750915527344\n", "The 6896th loop: cost = 49.275665283203125\n", "The 6897th loop: cost = 44.13512420654297\n", "The 6898th loop: cost = 48.861942291259766\n", "The 6899th loop: cost = 50.375892639160156\n", "The 6900th loop: cost = 41.740230560302734\n", "The 6901th loop: cost = 36.75604248046875\n", "The 6902th loop: cost = 44.68601608276367\n", "The 6903th loop: cost = 43.814849853515625\n", "The 6904th loop: cost = 43.23168182373047\n", "The 6905th loop: cost = 42.128543853759766\n", "The 6906th loop: cost = 47.51959228515625\n", "The 6907th loop: cost = 46.250328063964844\n", "The 6908th loop: cost = 38.59022521972656\n", "The 6909th loop: cost = 43.955081939697266\n", "The 6910th loop: cost = 38.41007995605469\n", "The 6911th loop: cost = 37.77306365966797\n", "The 6912th loop: cost = 44.59359359741211\n", "The 6913th loop: cost = 43.794151306152344\n", "The 6914th loop: cost = 39.28205871582031\n", "The 6915th loop: cost = 48.140342712402344\n", "The 6916th loop: cost = 39.19096755981445\n", "The 6917th loop: cost = 29.695812225341797\n", "The 6918th loop: cost = 37.668609619140625\n", "The 6919th loop: cost = 39.67860412597656\n", "The 6920th loop: cost = 40.00227355957031\n", "The 6921th loop: cost = 47.191673278808594\n", "The 6922th loop: cost = 39.751190185546875\n", "The 6923th loop: cost = 42.087425231933594\n", "The 6924th loop: cost = 52.73042297363281\n", "The 6925th loop: cost = 39.392555236816406\n", "The 6926th loop: cost = 41.70285415649414\n", "The 6927th loop: cost = 32.63699722290039\n", "The 6928th loop: cost = 46.18730545043945\n", "The 6929th loop: cost = 42.904624938964844\n", "The 6930th loop: cost = 45.22895812988281\n", "The 6931th loop: cost = 40.14812469482422\n", "The 6932th loop: cost = 45.473052978515625\n", "The 6933th loop: cost = 40.334869384765625\n", "The 6934th loop: cost = 47.8870964050293\n", "The 6935th loop: cost = 43.28638458251953\n", "The 6936th loop: cost = 48.14027404785156\n", "The 6937th loop: cost = 36.58416748046875\n", "The 6938th loop: cost = 37.77776336669922\n", "The 6939th loop: cost = 46.541900634765625\n", "The 6940th loop: cost = 50.20381546020508\n", "The 6941th loop: cost = 41.146949768066406\n", "The 6942th loop: cost = 42.38226318359375\n", "The 6943th loop: cost = 42.33695983886719\n", "The 6944th loop: cost = 45.81281280517578\n", "The 6945th loop: cost = 48.58964538574219\n", "The 6946th loop: cost = 41.03474426269531\n", "The 6947th loop: cost = 43.94867706298828\n", "The 6948th loop: cost = 50.27720642089844\n", "The 6949th loop: cost = 39.69758605957031\n", "The 6950th loop: cost = 39.92378234863281\n", "The 6951th loop: cost = 44.85545349121094\n", "The 6952th loop: cost = 35.64220428466797\n", "The 6953th loop: cost = 46.347145080566406\n", "The 6954th loop: cost = 51.893699645996094\n", "The 6955th loop: cost = 29.117971420288086\n", "The 6956th loop: cost = 47.38820266723633\n", "The 6957th loop: cost = 47.03302764892578\n", "The 6958th loop: cost = 38.146942138671875\n", "The 6959th loop: cost = 43.208030700683594\n", "The 6960th loop: cost = 36.827354431152344\n", "The 6961th loop: cost = 48.885459899902344\n", "The 6962th loop: cost = 40.97200393676758\n", "The 6963th loop: cost = 43.60691452026367\n", "The 6964th loop: cost = 35.09514617919922\n", "The 6965th loop: cost = 37.27309799194336\n", "The 6966th loop: cost = 42.58297348022461\n", "The 6967th loop: cost = 36.48109436035156\n", "The 6968th loop: cost = 43.88666534423828\n", "The 6969th loop: cost = 47.153324127197266\n", "The 6970th loop: cost = 35.82215118408203\n", "The 6971th loop: cost = 40.243900299072266\n", "The 6972th loop: cost = 35.475669860839844\n", "The 6973th loop: cost = 36.441463470458984\n", "The 6974th loop: cost = 36.140567779541016\n", "The 6975th loop: cost = 41.24713897705078\n", "The 6976th loop: cost = 55.404518127441406\n", "The 6977th loop: cost = 41.269020080566406\n", "The 6978th loop: cost = 48.38566207885742\n", "The 6979th loop: cost = 40.59492492675781\n", "The 6980th loop: cost = 48.71243667602539\n", "The 6981th loop: cost = 53.29857635498047\n", "The 6982th loop: cost = 52.28699493408203\n", "The 6983th loop: cost = 43.56575393676758\n", "The 6984th loop: cost = 45.83220672607422\n", "The 6985th loop: cost = 53.926902770996094\n", "The 6986th loop: cost = 38.34607696533203\n", "The 6987th loop: cost = 42.6641731262207\n", "The 6988th loop: cost = 40.49192810058594\n", "The 6989th loop: cost = 42.158042907714844\n", "The 6990th loop: cost = 36.60865783691406\n", "The 6991th loop: cost = 37.36096954345703\n", "The 6992th loop: cost = 40.384910583496094\n", "The 6993th loop: cost = 38.378273010253906\n", "The 6994th loop: cost = 43.95648956298828\n", "The 6995th loop: cost = 39.733367919921875\n", "The 6996th loop: cost = 31.202810287475586\n", "The 6997th loop: cost = 48.995826721191406\n", "The 6998th loop: cost = 55.878231048583984\n", "The 6999th loop: cost = 38.57122802734375\n", "The 7000th loop: cost = 35.52412033081055\n", "The 7001th loop: cost = 41.309600830078125\n", "The 7002th loop: cost = 39.93838882446289\n", "The 7003th loop: cost = 36.42406463623047\n", "The 7004th loop: cost = 41.107059478759766\n", "The 7005th loop: cost = 44.144264221191406\n", "The 7006th loop: cost = 35.695526123046875\n", "The 7007th loop: cost = 41.411582946777344\n", "The 7008th loop: cost = 44.9427604675293\n", "The 7009th loop: cost = 42.89018249511719\n", "The 7010th loop: cost = 39.81414794921875\n", "The 7011th loop: cost = 39.75895690917969\n", "The 7012th loop: cost = 34.27977752685547\n", "The 7013th loop: cost = 40.838619232177734\n", "The 7014th loop: cost = 40.37919998168945\n", "The 7015th loop: cost = 43.946189880371094\n", "The 7016th loop: cost = 41.43567657470703\n", "The 7017th loop: cost = 45.52901077270508\n", "The 7018th loop: cost = 50.474647521972656\n", "The 7019th loop: cost = 35.51847839355469\n", "The 7020th loop: cost = 43.52368927001953\n", "The 7021th loop: cost = 34.02577590942383\n", "The 7022th loop: cost = 40.720584869384766\n", "The 7023th loop: cost = 45.61873245239258\n", "The 7024th loop: cost = 39.18112564086914\n", "The 7025th loop: cost = 38.40780258178711\n", "The 7026th loop: cost = 50.107383728027344\n", "The 7027th loop: cost = 37.47511291503906\n", "The 7028th loop: cost = 48.903568267822266\n", "The 7029th loop: cost = 42.96039581298828\n", "The 7030th loop: cost = 38.759220123291016\n", "The 7031th loop: cost = 50.166038513183594\n", "The 7032th loop: cost = 42.924583435058594\n", "The 7033th loop: cost = 42.1866340637207\n", "The 7034th loop: cost = 44.04393768310547\n", "The 7035th loop: cost = 41.61176300048828\n", "The 7036th loop: cost = 44.96448516845703\n", "The 7037th loop: cost = 40.484771728515625\n", "The 7038th loop: cost = 43.49690246582031\n", "The 7039th loop: cost = 38.10762023925781\n", "The 7040th loop: cost = 46.22584533691406\n", "The 7041th loop: cost = 50.54402160644531\n", "The 7042th loop: cost = 33.4250602722168\n", "The 7043th loop: cost = 41.4849853515625\n", "The 7044th loop: cost = 45.87076187133789\n", "The 7045th loop: cost = 44.71906280517578\n", "The 7046th loop: cost = 43.037872314453125\n", "The 7047th loop: cost = 37.72808837890625\n", "The 7048th loop: cost = 48.697166442871094\n", "The 7049th loop: cost = 41.094207763671875\n", "The 7050th loop: cost = 50.3912353515625\n", "The 7051th loop: cost = 33.962345123291016\n", "The 7052th loop: cost = 40.46327209472656\n", "The 7053th loop: cost = 42.03392028808594\n", "The 7054th loop: cost = 44.63776397705078\n", "The 7055th loop: cost = 39.624610900878906\n", "The 7056th loop: cost = 50.88720703125\n", "The 7057th loop: cost = 39.37907791137695\n", "The 7058th loop: cost = 40.627418518066406\n", "The 7059th loop: cost = 51.403778076171875\n", "The 7060th loop: cost = 53.16973114013672\n", "The 7061th loop: cost = 39.35707092285156\n", "The 7062th loop: cost = 35.12966537475586\n", "The 7063th loop: cost = 40.94237518310547\n", "The 7064th loop: cost = 44.403221130371094\n", "The 7065th loop: cost = 49.438507080078125\n", "The 7066th loop: cost = 45.211273193359375\n", "The 7067th loop: cost = 33.85519790649414\n", "The 7068th loop: cost = 38.462677001953125\n", "The 7069th loop: cost = 45.821353912353516\n", "The 7070th loop: cost = 35.743263244628906\n", "The 7071th loop: cost = 46.842002868652344\n", "The 7072th loop: cost = 45.545654296875\n", "The 7073th loop: cost = 50.22065734863281\n", "The 7074th loop: cost = 43.9299201965332\n", "The 7075th loop: cost = 48.062843322753906\n", "The 7076th loop: cost = 48.804351806640625\n", "The 7077th loop: cost = 45.13658905029297\n", "The 7078th loop: cost = 39.29204559326172\n", "The 7079th loop: cost = 42.251792907714844\n", "The 7080th loop: cost = 36.72795867919922\n", "The 7081th loop: cost = 37.74797058105469\n", "The 7082th loop: cost = 40.25181579589844\n", "The 7083th loop: cost = 33.568580627441406\n", "The 7084th loop: cost = 33.31159210205078\n", "The 7085th loop: cost = 50.17133331298828\n", "The 7086th loop: cost = 37.92744445800781\n", "The 7087th loop: cost = 46.038230895996094\n", "The 7088th loop: cost = 47.06397247314453\n", "The 7089th loop: cost = 38.26863098144531\n", "The 7090th loop: cost = 37.78578186035156\n", "The 7091th loop: cost = 49.38218688964844\n", "The 7092th loop: cost = 46.756011962890625\n", "The 7093th loop: cost = 35.58559799194336\n", "The 7094th loop: cost = 43.00710678100586\n", "The 7095th loop: cost = 39.55023193359375\n", "The 7096th loop: cost = 43.284523010253906\n", "The 7097th loop: cost = 40.1497802734375\n", "The 7098th loop: cost = 36.17448425292969\n", "The 7099th loop: cost = 47.35948181152344\n", "The 7100th loop: cost = 43.37861633300781\n", "The 7101th loop: cost = 40.45531463623047\n", "The 7102th loop: cost = 43.37290954589844\n", "The 7103th loop: cost = 36.97111511230469\n", "The 7104th loop: cost = 37.912410736083984\n", "The 7105th loop: cost = 46.00131607055664\n", "The 7106th loop: cost = 37.233009338378906\n", "The 7107th loop: cost = 43.147125244140625\n", "The 7108th loop: cost = 40.09558868408203\n", "The 7109th loop: cost = 38.31459426879883\n", "The 7110th loop: cost = 38.68309020996094\n", "The 7111th loop: cost = 42.372802734375\n", "The 7112th loop: cost = 45.127227783203125\n", "The 7113th loop: cost = 40.855995178222656\n", "The 7114th loop: cost = 41.374839782714844\n", "The 7115th loop: cost = 41.88749694824219\n", "The 7116th loop: cost = 41.56329345703125\n", "The 7117th loop: cost = 41.647926330566406\n", "The 7118th loop: cost = 34.88645553588867\n", "The 7119th loop: cost = 46.4729118347168\n", "The 7120th loop: cost = 43.84298324584961\n", "The 7121th loop: cost = 37.937896728515625\n", "The 7122th loop: cost = 34.380615234375\n", "The 7123th loop: cost = 49.18878936767578\n", "The 7124th loop: cost = 40.900577545166016\n", "The 7125th loop: cost = 40.591400146484375\n", "The 7126th loop: cost = 46.5928955078125\n", "The 7127th loop: cost = 39.65399932861328\n", "The 7128th loop: cost = 37.40934753417969\n", "The 7129th loop: cost = 40.13641357421875\n", "The 7130th loop: cost = 40.23712921142578\n", "The 7131th loop: cost = 41.062217712402344\n", "The 7132th loop: cost = 45.02922821044922\n", "The 7133th loop: cost = 40.899654388427734\n", "The 7134th loop: cost = 47.633426666259766\n", "The 7135th loop: cost = 47.94345474243164\n", "The 7136th loop: cost = 46.98298645019531\n", "The 7137th loop: cost = 41.98527526855469\n", "The 7138th loop: cost = 54.62561798095703\n", "The 7139th loop: cost = 38.181671142578125\n", "The 7140th loop: cost = 38.176109313964844\n", "The 7141th loop: cost = 40.285621643066406\n", "The 7142th loop: cost = 41.84313201904297\n", "The 7143th loop: cost = 49.58491897583008\n", "The 7144th loop: cost = 44.17146301269531\n", "The 7145th loop: cost = 38.610477447509766\n", "The 7146th loop: cost = 42.93013381958008\n", "The 7147th loop: cost = 52.34562683105469\n", "The 7148th loop: cost = 51.584537506103516\n", "The 7149th loop: cost = 44.596900939941406\n", "The 7150th loop: cost = 45.71653747558594\n", "The 7151th loop: cost = 44.11589050292969\n", "The 7152th loop: cost = 46.129215240478516\n", "The 7153th loop: cost = 42.912689208984375\n", "The 7154th loop: cost = 37.32376480102539\n", "The 7155th loop: cost = 38.64607620239258\n", "The 7156th loop: cost = 44.489830017089844\n", "The 7157th loop: cost = 47.09290313720703\n", "The 7158th loop: cost = 44.686885833740234\n", "The 7159th loop: cost = 33.31672286987305\n", "The 7160th loop: cost = 46.741722106933594\n", "The 7161th loop: cost = 45.68468475341797\n", "The 7162th loop: cost = 43.25829315185547\n", "The 7163th loop: cost = 34.5496826171875\n", "The 7164th loop: cost = 45.98501205444336\n", "The 7165th loop: cost = 36.935569763183594\n", "The 7166th loop: cost = 48.443031311035156\n", "The 7167th loop: cost = 32.169090270996094\n", "The 7168th loop: cost = 38.56523895263672\n", "The 7169th loop: cost = 40.350616455078125\n", "The 7170th loop: cost = 37.5452995300293\n", "The 7171th loop: cost = 41.698081970214844\n", "The 7172th loop: cost = 40.18063735961914\n", "The 7173th loop: cost = 54.47692108154297\n", "The 7174th loop: cost = 38.36006164550781\n", "The 7175th loop: cost = 40.69708251953125\n", "The 7176th loop: cost = 44.88483428955078\n", "The 7177th loop: cost = 35.13800048828125\n", "The 7178th loop: cost = 41.009342193603516\n", "The 7179th loop: cost = 38.14231491088867\n", "The 7180th loop: cost = 35.6728515625\n", "The 7181th loop: cost = 39.182071685791016\n", "The 7182th loop: cost = 49.0542106628418\n", "The 7183th loop: cost = 40.275146484375\n", "The 7184th loop: cost = 46.26667785644531\n", "The 7185th loop: cost = 43.171295166015625\n", "The 7186th loop: cost = 46.80325698852539\n", "The 7187th loop: cost = 39.60137939453125\n", "The 7188th loop: cost = 50.350433349609375\n", "The 7189th loop: cost = 41.864444732666016\n", "The 7190th loop: cost = 36.6978645324707\n", "The 7191th loop: cost = 35.00465393066406\n", "The 7192th loop: cost = 42.83186340332031\n", "The 7193th loop: cost = 51.4622688293457\n", "The 7194th loop: cost = 42.847076416015625\n", "The 7195th loop: cost = 38.54448318481445\n", "The 7196th loop: cost = 42.43992614746094\n", "The 7197th loop: cost = 42.923805236816406\n", "The 7198th loop: cost = 33.89347457885742\n", "The 7199th loop: cost = 46.609588623046875\n", "The 7200th loop: cost = 43.29697799682617\n", "The 7201th loop: cost = 42.888893127441406\n", "The 7202th loop: cost = 45.460140228271484\n", "The 7203th loop: cost = 38.76460266113281\n", "The 7204th loop: cost = 39.40032958984375\n", "The 7205th loop: cost = 42.95401382446289\n", "The 7206th loop: cost = 41.45149230957031\n", "The 7207th loop: cost = 34.92324447631836\n", "The 7208th loop: cost = 43.540138244628906\n", "The 7209th loop: cost = 40.87269973754883\n", "The 7210th loop: cost = 29.84210205078125\n", "The 7211th loop: cost = 38.91107177734375\n", "The 7212th loop: cost = 43.053314208984375\n", "The 7213th loop: cost = 38.66624450683594\n", "The 7214th loop: cost = 47.26743698120117\n", "The 7215th loop: cost = 35.38561248779297\n", "The 7216th loop: cost = 51.37305450439453\n", "The 7217th loop: cost = 48.85272216796875\n", "The 7218th loop: cost = 35.46086120605469\n", "The 7219th loop: cost = 39.31010055541992\n", "The 7220th loop: cost = 39.797542572021484\n", "The 7221th loop: cost = 47.588722229003906\n", "The 7222th loop: cost = 44.39476776123047\n", "The 7223th loop: cost = 38.41175842285156\n", "The 7224th loop: cost = 42.020809173583984\n", "The 7225th loop: cost = 41.254947662353516\n", "The 7226th loop: cost = 37.42719268798828\n", "The 7227th loop: cost = 30.2338924407959\n", "The 7228th loop: cost = 40.80890655517578\n", "The 7229th loop: cost = 38.833457946777344\n", "The 7230th loop: cost = 43.0753288269043\n", "The 7231th loop: cost = 39.02488708496094\n", "The 7232th loop: cost = 42.74382019042969\n", "The 7233th loop: cost = 38.404258728027344\n", "The 7234th loop: cost = 34.12870788574219\n", "The 7235th loop: cost = 40.623992919921875\n", "The 7236th loop: cost = 50.72404479980469\n", "The 7237th loop: cost = 43.93824768066406\n", "The 7238th loop: cost = 47.663551330566406\n", "The 7239th loop: cost = 39.9659309387207\n", "The 7240th loop: cost = 45.75797653198242\n", "The 7241th loop: cost = 34.7130126953125\n", "The 7242th loop: cost = 38.16217041015625\n", "The 7243th loop: cost = 42.932579040527344\n", "The 7244th loop: cost = 45.173500061035156\n", "The 7245th loop: cost = 37.12140655517578\n", "The 7246th loop: cost = 39.7814826965332\n", "The 7247th loop: cost = 37.643341064453125\n", "The 7248th loop: cost = 39.252418518066406\n", "The 7249th loop: cost = 32.41157913208008\n", "The 7250th loop: cost = 46.86228561401367\n", "The 7251th loop: cost = 56.07906723022461\n", "The 7252th loop: cost = 41.30048751831055\n", "The 7253th loop: cost = 44.699241638183594\n", "The 7254th loop: cost = 36.4221076965332\n", "The 7255th loop: cost = 47.82423400878906\n", "The 7256th loop: cost = 37.838600158691406\n", "The 7257th loop: cost = 37.93669128417969\n", "The 7258th loop: cost = 35.64811325073242\n", "The 7259th loop: cost = 48.129920959472656\n", "The 7260th loop: cost = 35.79123306274414\n", "The 7261th loop: cost = 41.403194427490234\n", "The 7262th loop: cost = 43.78223419189453\n", "The 7263th loop: cost = 47.89590072631836\n", "The 7264th loop: cost = 42.56979751586914\n", "The 7265th loop: cost = 41.49072265625\n", "The 7266th loop: cost = 46.14174270629883\n", "The 7267th loop: cost = 42.067962646484375\n", "The 7268th loop: cost = 44.829036712646484\n", "The 7269th loop: cost = 43.80852508544922\n", "The 7270th loop: cost = 43.14077377319336\n", "The 7271th loop: cost = 51.72575378417969\n", "The 7272th loop: cost = 48.53171157836914\n", "The 7273th loop: cost = 41.245758056640625\n", "The 7274th loop: cost = 47.0184326171875\n", "The 7275th loop: cost = 40.41217803955078\n", "The 7276th loop: cost = 38.317020416259766\n", "The 7277th loop: cost = 43.235084533691406\n", "The 7278th loop: cost = 42.8963623046875\n", "The 7279th loop: cost = 42.06258773803711\n", "The 7280th loop: cost = 44.55182647705078\n", "The 7281th loop: cost = 40.42858123779297\n", "The 7282th loop: cost = 45.17244338989258\n", "The 7283th loop: cost = 48.315338134765625\n", "The 7284th loop: cost = 56.314849853515625\n", "The 7285th loop: cost = 46.37251281738281\n", "The 7286th loop: cost = 48.87078857421875\n", "The 7287th loop: cost = 37.53591537475586\n", "The 7288th loop: cost = 45.926475524902344\n", "The 7289th loop: cost = 41.34143829345703\n", "The 7290th loop: cost = 44.97980499267578\n", "The 7291th loop: cost = 49.25028610229492\n", "The 7292th loop: cost = 41.656654357910156\n", "The 7293th loop: cost = 50.19347381591797\n", "The 7294th loop: cost = 35.854862213134766\n", "The 7295th loop: cost = 39.17881774902344\n", "The 7296th loop: cost = 33.7581787109375\n", "The 7297th loop: cost = 39.53700637817383\n", "The 7298th loop: cost = 35.55257797241211\n", "The 7299th loop: cost = 50.443965911865234\n", "The 7300th loop: cost = 41.698081970214844\n", "The 7301th loop: cost = 47.21501159667969\n", "The 7302th loop: cost = 52.68687438964844\n", "The 7303th loop: cost = 43.69111633300781\n", "The 7304th loop: cost = 41.97840118408203\n", "The 7305th loop: cost = 45.765838623046875\n", "The 7306th loop: cost = 39.745628356933594\n", "The 7307th loop: cost = 41.05172348022461\n", "The 7308th loop: cost = 36.28044509887695\n", "The 7309th loop: cost = 41.83802032470703\n", "The 7310th loop: cost = 44.140960693359375\n", "The 7311th loop: cost = 45.927024841308594\n", "The 7312th loop: cost = 38.49816131591797\n", "The 7313th loop: cost = 44.538238525390625\n", "The 7314th loop: cost = 48.49260330200195\n", "The 7315th loop: cost = 38.94920349121094\n", "The 7316th loop: cost = 52.13081359863281\n", "The 7317th loop: cost = 35.50276565551758\n", "The 7318th loop: cost = 45.084861755371094\n", "The 7319th loop: cost = 46.60572052001953\n", "The 7320th loop: cost = 48.23673629760742\n", "The 7321th loop: cost = 37.66365051269531\n", "The 7322th loop: cost = 49.654109954833984\n", "The 7323th loop: cost = 34.898468017578125\n", "The 7324th loop: cost = 43.055023193359375\n", "The 7325th loop: cost = 48.970458984375\n", "The 7326th loop: cost = 35.06483459472656\n", "The 7327th loop: cost = 36.75722122192383\n", "The 7328th loop: cost = 39.57585525512695\n", "The 7329th loop: cost = 32.486812591552734\n", "The 7330th loop: cost = 29.53266716003418\n", "The 7331th loop: cost = 46.56435012817383\n", "The 7332th loop: cost = 41.83175277709961\n", "The 7333th loop: cost = 40.428245544433594\n", "The 7334th loop: cost = 38.99700927734375\n", "The 7335th loop: cost = 46.42305374145508\n", "The 7336th loop: cost = 38.50042724609375\n", "The 7337th loop: cost = 52.93136978149414\n", "The 7338th loop: cost = 49.3165283203125\n", "The 7339th loop: cost = 52.513458251953125\n", "The 7340th loop: cost = 37.78744888305664\n", "The 7341th loop: cost = 36.45404815673828\n", "The 7342th loop: cost = 39.45281219482422\n", "The 7343th loop: cost = 39.681373596191406\n", "The 7344th loop: cost = 48.03865432739258\n", "The 7345th loop: cost = 41.07933807373047\n", "The 7346th loop: cost = 42.35847473144531\n", "The 7347th loop: cost = 38.16135025024414\n", "The 7348th loop: cost = 44.390167236328125\n", "The 7349th loop: cost = 40.471900939941406\n", "The 7350th loop: cost = 39.00738525390625\n", "The 7351th loop: cost = 33.483638763427734\n", "The 7352th loop: cost = 41.69173812866211\n", "The 7353th loop: cost = 36.88697052001953\n", "The 7354th loop: cost = 43.858726501464844\n", "The 7355th loop: cost = 50.47322082519531\n", "The 7356th loop: cost = 33.20003890991211\n", "The 7357th loop: cost = 31.54371452331543\n", "The 7358th loop: cost = 39.796142578125\n", "The 7359th loop: cost = 39.76640319824219\n", "The 7360th loop: cost = 52.510467529296875\n", "The 7361th loop: cost = 37.92340087890625\n", "The 7362th loop: cost = 41.050071716308594\n", "The 7363th loop: cost = 41.22276306152344\n", "The 7364th loop: cost = 38.10419845581055\n", "The 7365th loop: cost = 38.74803924560547\n", "The 7366th loop: cost = 35.0710334777832\n", "The 7367th loop: cost = 40.6693229675293\n", "The 7368th loop: cost = 47.2855224609375\n", "The 7369th loop: cost = 45.81260299682617\n", "The 7370th loop: cost = 48.333194732666016\n", "The 7371th loop: cost = 34.07875442504883\n", "The 7372th loop: cost = 45.54279327392578\n", "The 7373th loop: cost = 46.37442398071289\n", "The 7374th loop: cost = 42.58705520629883\n", "The 7375th loop: cost = 46.438289642333984\n", "The 7376th loop: cost = 43.86599349975586\n", "The 7377th loop: cost = 48.435203552246094\n", "The 7378th loop: cost = 42.6402587890625\n", "The 7379th loop: cost = 36.214447021484375\n", "The 7380th loop: cost = 36.98554229736328\n", "The 7381th loop: cost = 41.87284851074219\n", "The 7382th loop: cost = 58.31212615966797\n", "The 7383th loop: cost = 39.49092102050781\n", "The 7384th loop: cost = 48.82695770263672\n", "The 7385th loop: cost = 38.68602752685547\n", "The 7386th loop: cost = 46.256378173828125\n", "The 7387th loop: cost = 43.24282455444336\n", "The 7388th loop: cost = 46.988983154296875\n", "The 7389th loop: cost = 35.78935241699219\n", "The 7390th loop: cost = 44.169281005859375\n", "The 7391th loop: cost = 41.7678108215332\n", "The 7392th loop: cost = 33.6436653137207\n", "The 7393th loop: cost = 44.01225662231445\n", "The 7394th loop: cost = 36.429237365722656\n", "The 7395th loop: cost = 35.42135238647461\n", "The 7396th loop: cost = 38.69196319580078\n", "The 7397th loop: cost = 42.639183044433594\n", "The 7398th loop: cost = 36.546241760253906\n", "The 7399th loop: cost = 44.390533447265625\n", "The 7400th loop: cost = 39.113075256347656\n", "The 7401th loop: cost = 48.17314147949219\n", "The 7402th loop: cost = 33.39589309692383\n", "The 7403th loop: cost = 41.66437530517578\n", "The 7404th loop: cost = 37.5811767578125\n", "The 7405th loop: cost = 41.39301681518555\n", "The 7406th loop: cost = 55.290306091308594\n", "The 7407th loop: cost = 40.296852111816406\n", "The 7408th loop: cost = 53.27600860595703\n", "The 7409th loop: cost = 50.54356384277344\n", "The 7410th loop: cost = 44.7601203918457\n", "The 7411th loop: cost = 43.040016174316406\n", "The 7412th loop: cost = 49.711265563964844\n", "The 7413th loop: cost = 39.63038635253906\n", "The 7414th loop: cost = 38.98857116699219\n", "The 7415th loop: cost = 38.73440170288086\n", "The 7416th loop: cost = 44.86449432373047\n", "The 7417th loop: cost = 48.12775802612305\n", "The 7418th loop: cost = 36.449485778808594\n", "The 7419th loop: cost = 39.17512130737305\n", "The 7420th loop: cost = 35.02088165283203\n", "The 7421th loop: cost = 40.763099670410156\n", "The 7422th loop: cost = 33.220497131347656\n", "The 7423th loop: cost = 37.828636169433594\n", "The 7424th loop: cost = 44.296302795410156\n", "The 7425th loop: cost = 58.480445861816406\n", "The 7426th loop: cost = 41.76172637939453\n", "The 7427th loop: cost = 39.07838439941406\n", "The 7428th loop: cost = 45.13681411743164\n", "The 7429th loop: cost = 37.48637008666992\n", "The 7430th loop: cost = 45.659934997558594\n", "The 7431th loop: cost = 49.73871612548828\n", "The 7432th loop: cost = 39.19023895263672\n", "The 7433th loop: cost = 38.23414611816406\n", "The 7434th loop: cost = 41.3592529296875\n", "The 7435th loop: cost = 48.86149215698242\n", "The 7436th loop: cost = 55.82593536376953\n", "The 7437th loop: cost = 40.73042297363281\n", "The 7438th loop: cost = 46.43721008300781\n", "The 7439th loop: cost = 45.10912322998047\n", "The 7440th loop: cost = 51.50807189941406\n", "The 7441th loop: cost = 34.22646713256836\n", "The 7442th loop: cost = 39.598960876464844\n", "The 7443th loop: cost = 35.088905334472656\n", "The 7444th loop: cost = 34.074378967285156\n", "The 7445th loop: cost = 44.96885681152344\n", "The 7446th loop: cost = 50.22780227661133\n", "The 7447th loop: cost = 40.39717102050781\n", "The 7448th loop: cost = 58.42230987548828\n", "The 7449th loop: cost = 36.80582809448242\n", "The 7450th loop: cost = 51.23269271850586\n", "The 7451th loop: cost = 27.176109313964844\n", "The 7452th loop: cost = 45.30040740966797\n", "The 7453th loop: cost = 37.776588439941406\n", "The 7454th loop: cost = 38.302799224853516\n", "The 7455th loop: cost = 42.66733169555664\n", "The 7456th loop: cost = 40.80183792114258\n", "The 7457th loop: cost = 39.846900939941406\n", "The 7458th loop: cost = 36.84618377685547\n", "The 7459th loop: cost = 41.55095672607422\n", "The 7460th loop: cost = 34.881622314453125\n", "The 7461th loop: cost = 40.27616500854492\n", "The 7462th loop: cost = 44.308387756347656\n", "The 7463th loop: cost = 36.480289459228516\n", "The 7464th loop: cost = 43.88482666015625\n", "The 7465th loop: cost = 35.15960693359375\n", "The 7466th loop: cost = 49.582847595214844\n", "The 7467th loop: cost = 36.40554428100586\n", "The 7468th loop: cost = 41.2652473449707\n", "The 7469th loop: cost = 37.37856674194336\n", "The 7470th loop: cost = 41.45823287963867\n", "The 7471th loop: cost = 46.14033508300781\n", "The 7472th loop: cost = 43.36705780029297\n", "The 7473th loop: cost = 48.60784149169922\n", "The 7474th loop: cost = 52.21875\n", "The 7475th loop: cost = 41.35361099243164\n", "The 7476th loop: cost = 47.830204010009766\n", "The 7477th loop: cost = 45.89637756347656\n", "The 7478th loop: cost = 48.86337661743164\n", "The 7479th loop: cost = 32.79978561401367\n", "The 7480th loop: cost = 45.091182708740234\n", "The 7481th loop: cost = 39.884613037109375\n", "The 7482th loop: cost = 36.49768829345703\n", "The 7483th loop: cost = 44.156822204589844\n", "The 7484th loop: cost = 41.09473419189453\n", "The 7485th loop: cost = 43.895973205566406\n", "The 7486th loop: cost = 43.46112823486328\n", "The 7487th loop: cost = 47.04258728027344\n", "The 7488th loop: cost = 39.4417610168457\n", "The 7489th loop: cost = 46.38315963745117\n", "The 7490th loop: cost = 40.59403991699219\n", "The 7491th loop: cost = 44.53306579589844\n", "The 7492th loop: cost = 43.10285949707031\n", "The 7493th loop: cost = 41.467613220214844\n", "The 7494th loop: cost = 44.11115264892578\n", "The 7495th loop: cost = 40.19132614135742\n", "The 7496th loop: cost = 35.95229721069336\n", "The 7497th loop: cost = 40.130252838134766\n", "The 7498th loop: cost = 44.90280532836914\n", "The 7499th loop: cost = 49.30884552001953\n", "The 7500th loop: cost = 36.558536529541016\n", "The 7501th loop: cost = 35.75482177734375\n", "The 7502th loop: cost = 47.81588363647461\n", "The 7503th loop: cost = 46.98241424560547\n", "The 7504th loop: cost = 36.44530487060547\n", "The 7505th loop: cost = 42.544837951660156\n", "The 7506th loop: cost = 37.29841613769531\n", "The 7507th loop: cost = 41.552764892578125\n", "The 7508th loop: cost = 42.38072204589844\n", "The 7509th loop: cost = 44.54563522338867\n", "The 7510th loop: cost = 39.64955139160156\n", "The 7511th loop: cost = 45.032615661621094\n", "The 7512th loop: cost = 38.371803283691406\n", "The 7513th loop: cost = 44.497711181640625\n", "The 7514th loop: cost = 36.617637634277344\n", "The 7515th loop: cost = 33.55303192138672\n", "The 7516th loop: cost = 46.412437438964844\n", "The 7517th loop: cost = 38.43401336669922\n", "The 7518th loop: cost = 46.400779724121094\n", "The 7519th loop: cost = 44.305397033691406\n", "The 7520th loop: cost = 44.544158935546875\n", "The 7521th loop: cost = 44.55775451660156\n", "The 7522th loop: cost = 35.02779006958008\n", "The 7523th loop: cost = 43.86308288574219\n", "The 7524th loop: cost = 40.755149841308594\n", "The 7525th loop: cost = 39.49622344970703\n", "The 7526th loop: cost = 36.00605392456055\n", "The 7527th loop: cost = 47.97373962402344\n", "The 7528th loop: cost = 42.732566833496094\n", "The 7529th loop: cost = 41.47262191772461\n", "The 7530th loop: cost = 42.79011917114258\n", "The 7531th loop: cost = 44.51920700073242\n", "The 7532th loop: cost = 39.63298797607422\n", "The 7533th loop: cost = 36.118343353271484\n", "The 7534th loop: cost = 44.4582633972168\n", "The 7535th loop: cost = 30.957523345947266\n", "The 7536th loop: cost = 35.017127990722656\n", "The 7537th loop: cost = 41.28508758544922\n", "The 7538th loop: cost = 44.72087097167969\n", "The 7539th loop: cost = 42.23119354248047\n", "The 7540th loop: cost = 37.4649772644043\n", "The 7541th loop: cost = 41.837982177734375\n", "The 7542th loop: cost = 35.137516021728516\n", "The 7543th loop: cost = 37.55479049682617\n", "The 7544th loop: cost = 45.608131408691406\n", "The 7545th loop: cost = 44.074153900146484\n", "The 7546th loop: cost = 50.81086730957031\n", "The 7547th loop: cost = 43.26122283935547\n", "The 7548th loop: cost = 43.658538818359375\n", "The 7549th loop: cost = 37.83970642089844\n", "The 7550th loop: cost = 33.758113861083984\n", "The 7551th loop: cost = 41.54553985595703\n", "The 7552th loop: cost = 52.89361572265625\n", "The 7553th loop: cost = 43.1917724609375\n", "The 7554th loop: cost = 44.23137664794922\n", "The 7555th loop: cost = 43.22488021850586\n", "The 7556th loop: cost = 47.643096923828125\n", "The 7557th loop: cost = 48.323890686035156\n", "The 7558th loop: cost = 34.45705795288086\n", "The 7559th loop: cost = 35.23116683959961\n", "The 7560th loop: cost = 37.061241149902344\n", "The 7561th loop: cost = 41.35492706298828\n", "The 7562th loop: cost = 39.57068634033203\n", "The 7563th loop: cost = 55.1861686706543\n", "The 7564th loop: cost = 43.99415588378906\n", "The 7565th loop: cost = 40.626243591308594\n", "The 7566th loop: cost = 42.72191619873047\n", "The 7567th loop: cost = 36.92456817626953\n", "The 7568th loop: cost = 41.06657791137695\n", "The 7569th loop: cost = 37.69172668457031\n", "The 7570th loop: cost = 42.7154541015625\n", "The 7571th loop: cost = 46.64754867553711\n", "The 7572th loop: cost = 39.41297149658203\n", "The 7573th loop: cost = 38.11774444580078\n", "The 7574th loop: cost = 41.89009475708008\n", "The 7575th loop: cost = 36.789146423339844\n", "The 7576th loop: cost = 42.9846076965332\n", "The 7577th loop: cost = 37.981224060058594\n", "The 7578th loop: cost = 38.655757904052734\n", "The 7579th loop: cost = 41.94255065917969\n", "The 7580th loop: cost = 37.117698669433594\n", "The 7581th loop: cost = 45.002986907958984\n", "The 7582th loop: cost = 43.08601760864258\n", "The 7583th loop: cost = 50.5054931640625\n", "The 7584th loop: cost = 42.46122741699219\n", "The 7585th loop: cost = 46.71002197265625\n", "The 7586th loop: cost = 43.33628845214844\n", "The 7587th loop: cost = 41.633506774902344\n", "The 7588th loop: cost = 41.434932708740234\n", "The 7589th loop: cost = 35.88872146606445\n", "The 7590th loop: cost = 45.11039733886719\n", "The 7591th loop: cost = 39.78217315673828\n", "The 7592th loop: cost = 44.58247375488281\n", "The 7593th loop: cost = 40.788482666015625\n", "The 7594th loop: cost = 39.251426696777344\n", "The 7595th loop: cost = 49.120540618896484\n", "The 7596th loop: cost = 34.905479431152344\n", "The 7597th loop: cost = 44.50196075439453\n", "The 7598th loop: cost = 42.657020568847656\n", "The 7599th loop: cost = 42.06430435180664\n", "The 7600th loop: cost = 39.74361038208008\n", "The 7601th loop: cost = 43.96346664428711\n", "The 7602th loop: cost = 44.02955627441406\n", "The 7603th loop: cost = 38.172786712646484\n", "The 7604th loop: cost = 46.134254455566406\n", "The 7605th loop: cost = 46.163604736328125\n", "The 7606th loop: cost = 43.86726379394531\n", "The 7607th loop: cost = 41.93426513671875\n", "The 7608th loop: cost = 41.827247619628906\n", "The 7609th loop: cost = 46.65452575683594\n", "The 7610th loop: cost = 34.413726806640625\n", "The 7611th loop: cost = 49.91019821166992\n", "The 7612th loop: cost = 37.157562255859375\n", "The 7613th loop: cost = 41.28901672363281\n", "The 7614th loop: cost = 44.206172943115234\n", "The 7615th loop: cost = 38.643341064453125\n", "The 7616th loop: cost = 38.38999938964844\n", "The 7617th loop: cost = 43.921485900878906\n", "The 7618th loop: cost = 34.324275970458984\n", "The 7619th loop: cost = 40.07281494140625\n", "The 7620th loop: cost = 41.04370880126953\n", "The 7621th loop: cost = 40.01749038696289\n", "The 7622th loop: cost = 41.040287017822266\n", "The 7623th loop: cost = 44.49347686767578\n", "The 7624th loop: cost = 44.00749969482422\n", "The 7625th loop: cost = 33.747676849365234\n", "The 7626th loop: cost = 55.42388916015625\n", "The 7627th loop: cost = 35.09522247314453\n", "The 7628th loop: cost = 39.049232482910156\n", "The 7629th loop: cost = 57.72941970825195\n", "The 7630th loop: cost = 43.99681854248047\n", "The 7631th loop: cost = 48.38816452026367\n", "The 7632th loop: cost = 41.283050537109375\n", "The 7633th loop: cost = 45.94890594482422\n", "The 7634th loop: cost = 46.82170104980469\n", "The 7635th loop: cost = 36.971160888671875\n", "The 7636th loop: cost = 40.77174377441406\n", "The 7637th loop: cost = 37.80729293823242\n", "The 7638th loop: cost = 49.90512466430664\n", "The 7639th loop: cost = 38.155967712402344\n", "The 7640th loop: cost = 41.968753814697266\n", "The 7641th loop: cost = 38.53057098388672\n", "The 7642th loop: cost = 45.100276947021484\n", "The 7643th loop: cost = 42.31360626220703\n", "The 7644th loop: cost = 45.1399040222168\n", "The 7645th loop: cost = 41.905487060546875\n", "The 7646th loop: cost = 34.429473876953125\n", "The 7647th loop: cost = 33.943119049072266\n", "The 7648th loop: cost = 35.04216003417969\n", "The 7649th loop: cost = 41.081077575683594\n", "The 7650th loop: cost = 40.27197265625\n", "The 7651th loop: cost = 48.34552764892578\n", "The 7652th loop: cost = 36.91867446899414\n", "The 7653th loop: cost = 38.12079620361328\n", "The 7654th loop: cost = 49.67069625854492\n", "The 7655th loop: cost = 45.19013977050781\n", "The 7656th loop: cost = 43.92641830444336\n", "The 7657th loop: cost = 35.827274322509766\n", "The 7658th loop: cost = 42.22052764892578\n", "The 7659th loop: cost = 44.02208709716797\n", "The 7660th loop: cost = 40.12151336669922\n", "The 7661th loop: cost = 44.27394104003906\n", "The 7662th loop: cost = 40.696407318115234\n", "The 7663th loop: cost = 46.79011154174805\n", "The 7664th loop: cost = 43.35797119140625\n", "The 7665th loop: cost = 46.3684196472168\n", "The 7666th loop: cost = 46.198158264160156\n", "The 7667th loop: cost = 41.16301727294922\n", "The 7668th loop: cost = 39.860633850097656\n", "The 7669th loop: cost = 37.46559524536133\n", "The 7670th loop: cost = 46.079742431640625\n", "The 7671th loop: cost = 49.418174743652344\n", "The 7672th loop: cost = 39.44397735595703\n", "The 7673th loop: cost = 36.34081268310547\n", "The 7674th loop: cost = 35.081138610839844\n", "The 7675th loop: cost = 44.62409973144531\n", "The 7676th loop: cost = 36.88981628417969\n", "The 7677th loop: cost = 38.317665100097656\n", "The 7678th loop: cost = 33.94684600830078\n", "The 7679th loop: cost = 50.062286376953125\n", "The 7680th loop: cost = 35.16552734375\n", "The 7681th loop: cost = 47.26850128173828\n", "The 7682th loop: cost = 50.159271240234375\n", "The 7683th loop: cost = 35.16215515136719\n", "The 7684th loop: cost = 42.764442443847656\n", "The 7685th loop: cost = 47.22276306152344\n", "The 7686th loop: cost = 38.898773193359375\n", "The 7687th loop: cost = 42.319854736328125\n", "The 7688th loop: cost = 36.96886444091797\n", "The 7689th loop: cost = 36.72047424316406\n", "The 7690th loop: cost = 34.24134063720703\n", "The 7691th loop: cost = 43.01525115966797\n", "The 7692th loop: cost = 37.420616149902344\n", "The 7693th loop: cost = 45.08098220825195\n", "The 7694th loop: cost = 42.11764144897461\n", "The 7695th loop: cost = 44.145389556884766\n", "The 7696th loop: cost = 43.711631774902344\n", "The 7697th loop: cost = 43.72773361206055\n", "The 7698th loop: cost = 47.86309814453125\n", "The 7699th loop: cost = 49.05778503417969\n", "The 7700th loop: cost = 41.62287521362305\n", "The 7701th loop: cost = 42.49920654296875\n", "The 7702th loop: cost = 46.03605651855469\n", "The 7703th loop: cost = 34.7994499206543\n", "The 7704th loop: cost = 49.12901306152344\n", "The 7705th loop: cost = 40.264888763427734\n", "The 7706th loop: cost = 47.71070861816406\n", "The 7707th loop: cost = 47.36231994628906\n", "The 7708th loop: cost = 44.909549713134766\n", "The 7709th loop: cost = 45.088340759277344\n", "The 7710th loop: cost = 41.44404602050781\n", "The 7711th loop: cost = 48.94764709472656\n", "The 7712th loop: cost = 41.41535949707031\n", "The 7713th loop: cost = 39.48202133178711\n", "The 7714th loop: cost = 43.87953186035156\n", "The 7715th loop: cost = 43.101036071777344\n", "The 7716th loop: cost = 40.92204666137695\n", "The 7717th loop: cost = 39.970245361328125\n", "The 7718th loop: cost = 41.35588073730469\n", "The 7719th loop: cost = 42.2606201171875\n", "The 7720th loop: cost = 29.937828063964844\n", "The 7721th loop: cost = 33.00028610229492\n", "The 7722th loop: cost = 36.95317077636719\n", "The 7723th loop: cost = 37.541961669921875\n", "The 7724th loop: cost = 38.034915924072266\n", "The 7725th loop: cost = 38.3045768737793\n", "The 7726th loop: cost = 41.28080749511719\n", "The 7727th loop: cost = 50.49530029296875\n", "The 7728th loop: cost = 33.99711990356445\n", "The 7729th loop: cost = 34.596900939941406\n", "The 7730th loop: cost = 49.35798645019531\n", "The 7731th loop: cost = 43.540794372558594\n", "The 7732th loop: cost = 37.13624572753906\n", "The 7733th loop: cost = 45.3249397277832\n", "The 7734th loop: cost = 48.295989990234375\n", "The 7735th loop: cost = 39.043914794921875\n", "The 7736th loop: cost = 37.77814483642578\n", "The 7737th loop: cost = 46.32495880126953\n", "The 7738th loop: cost = 40.008670806884766\n", "The 7739th loop: cost = 48.88964080810547\n", "The 7740th loop: cost = 39.77802276611328\n", "The 7741th loop: cost = 41.06782531738281\n", "The 7742th loop: cost = 39.703651428222656\n", "The 7743th loop: cost = 39.34563064575195\n", "The 7744th loop: cost = 42.83534240722656\n", "The 7745th loop: cost = 43.06415557861328\n", "The 7746th loop: cost = 36.80593490600586\n", "The 7747th loop: cost = 34.851341247558594\n", "The 7748th loop: cost = 45.121986389160156\n", "The 7749th loop: cost = 38.85452651977539\n", "The 7750th loop: cost = 38.85559844970703\n", "The 7751th loop: cost = 44.024742126464844\n", "The 7752th loop: cost = 42.021697998046875\n", "The 7753th loop: cost = 33.763301849365234\n", "The 7754th loop: cost = 42.21100616455078\n", "The 7755th loop: cost = 33.64347839355469\n", "The 7756th loop: cost = 39.35810089111328\n", "The 7757th loop: cost = 45.86381149291992\n", "The 7758th loop: cost = 33.25717544555664\n", "The 7759th loop: cost = 40.21518325805664\n", "The 7760th loop: cost = 39.92647171020508\n", "The 7761th loop: cost = 41.212188720703125\n", "The 7762th loop: cost = 47.632198333740234\n", "The 7763th loop: cost = 47.657127380371094\n", "The 7764th loop: cost = 42.16552734375\n", "The 7765th loop: cost = 33.509944915771484\n", "The 7766th loop: cost = 38.478763580322266\n", "The 7767th loop: cost = 40.41920471191406\n", "The 7768th loop: cost = 42.89421844482422\n", "The 7769th loop: cost = 49.08416748046875\n", "The 7770th loop: cost = 33.97028350830078\n", "The 7771th loop: cost = 46.95611572265625\n", "The 7772th loop: cost = 44.942657470703125\n", "The 7773th loop: cost = 36.98905944824219\n", "The 7774th loop: cost = 39.938743591308594\n", "The 7775th loop: cost = 41.783653259277344\n", "The 7776th loop: cost = 43.043296813964844\n", "The 7777th loop: cost = 35.61143493652344\n", "The 7778th loop: cost = 42.51164245605469\n", "The 7779th loop: cost = 44.849578857421875\n", "The 7780th loop: cost = 38.66591262817383\n", "The 7781th loop: cost = 51.41611862182617\n", "The 7782th loop: cost = 42.59336471557617\n", "The 7783th loop: cost = 34.71403884887695\n", "The 7784th loop: cost = 35.7988395690918\n", "The 7785th loop: cost = 37.75603485107422\n", "The 7786th loop: cost = 47.442771911621094\n", "The 7787th loop: cost = 39.19679641723633\n", "The 7788th loop: cost = 37.778831481933594\n", "The 7789th loop: cost = 45.83265686035156\n", "The 7790th loop: cost = 41.138771057128906\n", "The 7791th loop: cost = 41.03022766113281\n", "The 7792th loop: cost = 50.70472717285156\n", "The 7793th loop: cost = 51.85105895996094\n", "The 7794th loop: cost = 52.17178726196289\n", "The 7795th loop: cost = 40.488975524902344\n", "The 7796th loop: cost = 41.33710861206055\n", "The 7797th loop: cost = 41.33961868286133\n", "The 7798th loop: cost = 39.643898010253906\n", "The 7799th loop: cost = 36.811458587646484\n", "The 7800th loop: cost = 34.045372009277344\n", "The 7801th loop: cost = 48.30303955078125\n", "The 7802th loop: cost = 42.54451370239258\n", "The 7803th loop: cost = 44.393741607666016\n", "The 7804th loop: cost = 37.72271728515625\n", "The 7805th loop: cost = 40.79617691040039\n", "The 7806th loop: cost = 35.187278747558594\n", "The 7807th loop: cost = 34.065433502197266\n", "The 7808th loop: cost = 37.14624786376953\n", "The 7809th loop: cost = 43.61796951293945\n", "The 7810th loop: cost = 36.33726501464844\n", "The 7811th loop: cost = 40.133323669433594\n", "The 7812th loop: cost = 49.17938995361328\n", "The 7813th loop: cost = 44.882083892822266\n", "The 7814th loop: cost = 40.10101318359375\n", "The 7815th loop: cost = 48.02525329589844\n", "The 7816th loop: cost = 31.10565185546875\n", "The 7817th loop: cost = 35.374855041503906\n", "The 7818th loop: cost = 40.3994140625\n", "The 7819th loop: cost = 51.624122619628906\n", "The 7820th loop: cost = 39.97490692138672\n", "The 7821th loop: cost = 41.808528900146484\n", "The 7822th loop: cost = 40.45185470581055\n", "The 7823th loop: cost = 32.33289337158203\n", "The 7824th loop: cost = 48.48334503173828\n", "The 7825th loop: cost = 40.939701080322266\n", "The 7826th loop: cost = 42.77995300292969\n", "The 7827th loop: cost = 37.539642333984375\n", "The 7828th loop: cost = 34.5200309753418\n", "The 7829th loop: cost = 31.74905014038086\n", "The 7830th loop: cost = 34.43061065673828\n", "The 7831th loop: cost = 47.22649383544922\n", "The 7832th loop: cost = 38.94480514526367\n", "The 7833th loop: cost = 36.755680084228516\n", "The 7834th loop: cost = 47.147926330566406\n", "The 7835th loop: cost = 26.264719009399414\n", "The 7836th loop: cost = 49.61579895019531\n", "The 7837th loop: cost = 44.17152404785156\n", "The 7838th loop: cost = 39.86125183105469\n", "The 7839th loop: cost = 36.2646484375\n", "The 7840th loop: cost = 47.10466003417969\n", "The 7841th loop: cost = 46.80186462402344\n", "The 7842th loop: cost = 37.10536193847656\n", "The 7843th loop: cost = 41.07044982910156\n", "The 7844th loop: cost = 39.63666534423828\n", "The 7845th loop: cost = 39.888153076171875\n", "The 7846th loop: cost = 38.40815734863281\n", "The 7847th loop: cost = 38.58546447753906\n", "The 7848th loop: cost = 44.44392013549805\n", "The 7849th loop: cost = 39.14148712158203\n", "The 7850th loop: cost = 33.023292541503906\n", "The 7851th loop: cost = 38.50246810913086\n", "The 7852th loop: cost = 42.95878219604492\n", "The 7853th loop: cost = 45.687049865722656\n", "The 7854th loop: cost = 42.60588836669922\n", "The 7855th loop: cost = 33.37574005126953\n", "The 7856th loop: cost = 45.459808349609375\n", "The 7857th loop: cost = 49.484344482421875\n", "The 7858th loop: cost = 42.63689041137695\n", "The 7859th loop: cost = 40.75212860107422\n", "The 7860th loop: cost = 40.86790466308594\n", "The 7861th loop: cost = 40.076053619384766\n", "The 7862th loop: cost = 36.9920654296875\n", "The 7863th loop: cost = 43.52238464355469\n", "The 7864th loop: cost = 41.626441955566406\n", "The 7865th loop: cost = 42.795745849609375\n", "The 7866th loop: cost = 39.871429443359375\n", "The 7867th loop: cost = 50.61787414550781\n", "The 7868th loop: cost = 36.69178009033203\n", "The 7869th loop: cost = 35.30683898925781\n", "The 7870th loop: cost = 48.15713882446289\n", "The 7871th loop: cost = 34.058502197265625\n", "The 7872th loop: cost = 43.86811065673828\n", "The 7873th loop: cost = 38.8834342956543\n", "The 7874th loop: cost = 41.978965759277344\n", "The 7875th loop: cost = 47.54301452636719\n", "The 7876th loop: cost = 35.46834945678711\n", "The 7877th loop: cost = 48.22771453857422\n", "The 7878th loop: cost = 40.77617645263672\n", "The 7879th loop: cost = 37.542381286621094\n", "The 7880th loop: cost = 46.36874771118164\n", "The 7881th loop: cost = 37.131404876708984\n", "The 7882th loop: cost = 41.64042663574219\n", "The 7883th loop: cost = 48.99340057373047\n", "The 7884th loop: cost = 43.08804702758789\n", "The 7885th loop: cost = 46.2869873046875\n", "The 7886th loop: cost = 44.5722541809082\n", "The 7887th loop: cost = 45.129512786865234\n", "The 7888th loop: cost = 46.726375579833984\n", "The 7889th loop: cost = 40.464012145996094\n", "The 7890th loop: cost = 39.48980712890625\n", "The 7891th loop: cost = 38.763763427734375\n", "The 7892th loop: cost = 46.30138397216797\n", "The 7893th loop: cost = 50.03455352783203\n", "The 7894th loop: cost = 43.03852844238281\n", "The 7895th loop: cost = 47.601402282714844\n", "The 7896th loop: cost = 35.182334899902344\n", "The 7897th loop: cost = 45.276397705078125\n", "The 7898th loop: cost = 37.4908332824707\n", "The 7899th loop: cost = 36.1841926574707\n", "The 7900th loop: cost = 41.34272766113281\n", "The 7901th loop: cost = 48.09807586669922\n", "The 7902th loop: cost = 38.87286376953125\n", "The 7903th loop: cost = 48.06790542602539\n", "The 7904th loop: cost = 37.56798553466797\n", "The 7905th loop: cost = 43.961708068847656\n", "The 7906th loop: cost = 45.22999572753906\n", "The 7907th loop: cost = 44.5831184387207\n", "The 7908th loop: cost = 35.71009826660156\n", "The 7909th loop: cost = 42.67547607421875\n", "The 7910th loop: cost = 33.31282043457031\n", "The 7911th loop: cost = 46.194984436035156\n", "The 7912th loop: cost = 44.10646057128906\n", "The 7913th loop: cost = 33.69063186645508\n", "The 7914th loop: cost = 34.98174285888672\n", "The 7915th loop: cost = 38.16438674926758\n", "The 7916th loop: cost = 48.0051155090332\n", "The 7917th loop: cost = 41.50897979736328\n", "The 7918th loop: cost = 37.92821502685547\n", "The 7919th loop: cost = 34.996864318847656\n", "The 7920th loop: cost = 39.496490478515625\n", "The 7921th loop: cost = 41.70228958129883\n", "The 7922th loop: cost = 33.650306701660156\n", "The 7923th loop: cost = 41.12504959106445\n", "The 7924th loop: cost = 38.86365509033203\n", "The 7925th loop: cost = 44.9005012512207\n", "The 7926th loop: cost = 41.37318420410156\n", "The 7927th loop: cost = 37.04521942138672\n", "The 7928th loop: cost = 44.531089782714844\n", "The 7929th loop: cost = 40.87005615234375\n", "The 7930th loop: cost = 41.253074645996094\n", "The 7931th loop: cost = 37.7960319519043\n", "The 7932th loop: cost = 43.15589141845703\n", "The 7933th loop: cost = 40.668556213378906\n", "The 7934th loop: cost = 40.55552673339844\n", "The 7935th loop: cost = 50.420570373535156\n", "The 7936th loop: cost = 36.07831954956055\n", "The 7937th loop: cost = 39.39185333251953\n", "The 7938th loop: cost = 41.407344818115234\n", "The 7939th loop: cost = 30.870468139648438\n", "The 7940th loop: cost = 34.58150863647461\n", "The 7941th loop: cost = 38.39805603027344\n", "The 7942th loop: cost = 39.90412139892578\n", "The 7943th loop: cost = 38.19808578491211\n", "The 7944th loop: cost = 37.80965805053711\n", "The 7945th loop: cost = 32.43669891357422\n", "The 7946th loop: cost = 50.90134811401367\n", "The 7947th loop: cost = 39.56658935546875\n", "The 7948th loop: cost = 48.979312896728516\n", "The 7949th loop: cost = 40.080291748046875\n", "The 7950th loop: cost = 40.881317138671875\n", "The 7951th loop: cost = 33.79553985595703\n", "The 7952th loop: cost = 44.59778594970703\n", "The 7953th loop: cost = 42.61311340332031\n", "The 7954th loop: cost = 48.549530029296875\n", "The 7955th loop: cost = 36.84661865234375\n", "The 7956th loop: cost = 37.36980438232422\n", "The 7957th loop: cost = 51.17414093017578\n", "The 7958th loop: cost = 44.73467254638672\n", "The 7959th loop: cost = 39.63661193847656\n", "The 7960th loop: cost = 33.5797004699707\n", "The 7961th loop: cost = 42.28290557861328\n", "The 7962th loop: cost = 43.30331039428711\n", "The 7963th loop: cost = 36.59099578857422\n", "The 7964th loop: cost = 40.79052734375\n", "The 7965th loop: cost = 41.269561767578125\n", "The 7966th loop: cost = 43.71406555175781\n", "The 7967th loop: cost = 43.93454360961914\n", "The 7968th loop: cost = 28.739355087280273\n", "The 7969th loop: cost = 35.973716735839844\n", "The 7970th loop: cost = 41.81167984008789\n", "The 7971th loop: cost = 29.312049865722656\n", "The 7972th loop: cost = 47.780879974365234\n", "The 7973th loop: cost = 47.85749816894531\n", "The 7974th loop: cost = 42.663909912109375\n", "The 7975th loop: cost = 42.48811721801758\n", "The 7976th loop: cost = 47.31203079223633\n", "The 7977th loop: cost = 42.788185119628906\n", "The 7978th loop: cost = 41.816070556640625\n", "The 7979th loop: cost = 38.02506637573242\n", "The 7980th loop: cost = 43.04443359375\n", "The 7981th loop: cost = 46.366539001464844\n", "The 7982th loop: cost = 40.611236572265625\n", "The 7983th loop: cost = 38.17710494995117\n", "The 7984th loop: cost = 40.07335662841797\n", "The 7985th loop: cost = 40.31040954589844\n", "The 7986th loop: cost = 39.089805603027344\n", "The 7987th loop: cost = 40.321067810058594\n", "The 7988th loop: cost = 49.50773620605469\n", "The 7989th loop: cost = 28.355186462402344\n", "The 7990th loop: cost = 42.31189727783203\n", "The 7991th loop: cost = 46.63975524902344\n", "The 7992th loop: cost = 28.61994743347168\n", "The 7993th loop: cost = 49.589019775390625\n", "The 7994th loop: cost = 35.8608512878418\n", "The 7995th loop: cost = 39.01150894165039\n", "The 7996th loop: cost = 34.03252410888672\n", "The 7997th loop: cost = 40.514373779296875\n", "The 7998th loop: cost = 44.366634368896484\n", "The 7999th loop: cost = 34.81854248046875\n", "The 8000th loop: cost = 37.95954132080078\n", "The 8001th loop: cost = 39.85020446777344\n", "The 8002th loop: cost = 51.11888885498047\n", "The 8003th loop: cost = 43.33952331542969\n", "The 8004th loop: cost = 41.495643615722656\n", "The 8005th loop: cost = 37.97357940673828\n", "The 8006th loop: cost = 38.931392669677734\n", "The 8007th loop: cost = 56.540565490722656\n", "The 8008th loop: cost = 42.90834045410156\n", "The 8009th loop: cost = 39.726951599121094\n", "The 8010th loop: cost = 45.68824005126953\n", "The 8011th loop: cost = 41.73885726928711\n", "The 8012th loop: cost = 46.35272979736328\n", "The 8013th loop: cost = 32.98712921142578\n", "The 8014th loop: cost = 41.980186462402344\n", "The 8015th loop: cost = 51.367393493652344\n", "The 8016th loop: cost = 38.924560546875\n", "The 8017th loop: cost = 39.56575012207031\n", "The 8018th loop: cost = 39.16222381591797\n", "The 8019th loop: cost = 48.526649475097656\n", "The 8020th loop: cost = 42.77066421508789\n", "The 8021th loop: cost = 35.36110305786133\n", "The 8022th loop: cost = 38.845436096191406\n", "The 8023th loop: cost = 39.975399017333984\n", "The 8024th loop: cost = 33.64154052734375\n", "The 8025th loop: cost = 36.85359573364258\n", "The 8026th loop: cost = 30.613311767578125\n", "The 8027th loop: cost = 39.129730224609375\n", "The 8028th loop: cost = 32.219417572021484\n", "The 8029th loop: cost = 40.47709655761719\n", "The 8030th loop: cost = 53.606834411621094\n", "The 8031th loop: cost = 45.584251403808594\n", "The 8032th loop: cost = 41.340545654296875\n", "The 8033th loop: cost = 33.65019226074219\n", "The 8034th loop: cost = 52.05354309082031\n", "The 8035th loop: cost = 42.87890625\n", "The 8036th loop: cost = 43.76367950439453\n", "The 8037th loop: cost = 40.563201904296875\n", "The 8038th loop: cost = 44.42098617553711\n", "The 8039th loop: cost = 38.20964050292969\n", "The 8040th loop: cost = 38.75981903076172\n", "The 8041th loop: cost = 49.861000061035156\n", "The 8042th loop: cost = 35.63591766357422\n", "The 8043th loop: cost = 38.98200988769531\n", "The 8044th loop: cost = 49.322486877441406\n", "The 8045th loop: cost = 37.43439483642578\n", "The 8046th loop: cost = 38.64000701904297\n", "The 8047th loop: cost = 44.37812805175781\n", "The 8048th loop: cost = 34.14628219604492\n", "The 8049th loop: cost = 44.52292251586914\n", "The 8050th loop: cost = 35.01292037963867\n", "The 8051th loop: cost = 32.1634635925293\n", "The 8052th loop: cost = 30.834716796875\n", "The 8053th loop: cost = 43.40456771850586\n", "The 8054th loop: cost = 38.798370361328125\n", "The 8055th loop: cost = 39.776023864746094\n", "The 8056th loop: cost = 35.853797912597656\n", "The 8057th loop: cost = 39.88664627075195\n", "The 8058th loop: cost = 50.43218231201172\n", "The 8059th loop: cost = 33.14282989501953\n", "The 8060th loop: cost = 44.902748107910156\n", "The 8061th loop: cost = 40.153785705566406\n", "The 8062th loop: cost = 35.326316833496094\n", "The 8063th loop: cost = 33.18717956542969\n", "The 8064th loop: cost = 40.64054870605469\n", "The 8065th loop: cost = 40.74761199951172\n", "The 8066th loop: cost = 40.65073776245117\n", "The 8067th loop: cost = 49.48786544799805\n", "The 8068th loop: cost = 44.705589294433594\n", "The 8069th loop: cost = 39.220062255859375\n", "The 8070th loop: cost = 45.67335510253906\n", "The 8071th loop: cost = 43.697959899902344\n", "The 8072th loop: cost = 42.4401741027832\n", "The 8073th loop: cost = 37.17242431640625\n", "The 8074th loop: cost = 33.92654800415039\n", "The 8075th loop: cost = 47.72814178466797\n", "The 8076th loop: cost = 45.98252868652344\n", "The 8077th loop: cost = 40.73786163330078\n", "The 8078th loop: cost = 41.49022674560547\n", "The 8079th loop: cost = 31.361562728881836\n", "The 8080th loop: cost = 38.041465759277344\n", "The 8081th loop: cost = 37.53889846801758\n", "The 8082th loop: cost = 43.1165657043457\n", "The 8083th loop: cost = 46.184776306152344\n", "The 8084th loop: cost = 35.23998260498047\n", "The 8085th loop: cost = 36.254791259765625\n", "The 8086th loop: cost = 33.34379577636719\n", "The 8087th loop: cost = 30.75940704345703\n", "The 8088th loop: cost = 48.070594787597656\n", "The 8089th loop: cost = 49.38973617553711\n", "The 8090th loop: cost = 36.34821701049805\n", "The 8091th loop: cost = 37.63450622558594\n", "The 8092th loop: cost = 43.53388977050781\n", "The 8093th loop: cost = 34.298614501953125\n", "The 8094th loop: cost = 35.79373550415039\n", "The 8095th loop: cost = 40.094024658203125\n", "The 8096th loop: cost = 37.501976013183594\n", "The 8097th loop: cost = 43.1328239440918\n", "The 8098th loop: cost = 43.03108215332031\n", "The 8099th loop: cost = 49.34547805786133\n", "The 8100th loop: cost = 42.42483139038086\n", "The 8101th loop: cost = 37.511192321777344\n", "The 8102th loop: cost = 41.3000373840332\n", "The 8103th loop: cost = 40.96022033691406\n", "The 8104th loop: cost = 41.97128677368164\n", "The 8105th loop: cost = 41.805419921875\n", "The 8106th loop: cost = 37.402442932128906\n", "The 8107th loop: cost = 38.86848068237305\n", "The 8108th loop: cost = 33.39851379394531\n", "The 8109th loop: cost = 39.60807800292969\n", "The 8110th loop: cost = 44.67962646484375\n", "The 8111th loop: cost = 36.66877365112305\n", "The 8112th loop: cost = 47.07109069824219\n", "The 8113th loop: cost = 40.43067169189453\n", "The 8114th loop: cost = 41.38395309448242\n", "The 8115th loop: cost = 35.26387023925781\n", "The 8116th loop: cost = 40.10926818847656\n", "The 8117th loop: cost = 42.67290496826172\n", "The 8118th loop: cost = 39.29903030395508\n", "The 8119th loop: cost = 46.43352127075195\n", "The 8120th loop: cost = 39.446739196777344\n", "The 8121th loop: cost = 44.653175354003906\n", "The 8122th loop: cost = 30.080690383911133\n", "The 8123th loop: cost = 40.83283996582031\n", "The 8124th loop: cost = 42.23808670043945\n", "The 8125th loop: cost = 38.883522033691406\n", "The 8126th loop: cost = 43.89564514160156\n", "The 8127th loop: cost = 41.499908447265625\n", "The 8128th loop: cost = 42.9527473449707\n", "The 8129th loop: cost = 44.044822692871094\n", "The 8130th loop: cost = 40.34931182861328\n", "The 8131th loop: cost = 35.080528259277344\n", "The 8132th loop: cost = 47.00339889526367\n", "The 8133th loop: cost = 39.752132415771484\n", "The 8134th loop: cost = 39.37054443359375\n", "The 8135th loop: cost = 38.691680908203125\n", "The 8136th loop: cost = 37.29917907714844\n", "The 8137th loop: cost = 35.99561309814453\n", "The 8138th loop: cost = 45.668216705322266\n", "The 8139th loop: cost = 48.868751525878906\n", "The 8140th loop: cost = 35.431678771972656\n", "The 8141th loop: cost = 35.23055648803711\n", "The 8142th loop: cost = 34.2795524597168\n", "The 8143th loop: cost = 34.31867599487305\n", "The 8144th loop: cost = 38.9931526184082\n", "The 8145th loop: cost = 35.018821716308594\n", "The 8146th loop: cost = 38.76491165161133\n", "The 8147th loop: cost = 32.3995361328125\n", "The 8148th loop: cost = 33.965614318847656\n", "The 8149th loop: cost = 37.76961135864258\n", "The 8150th loop: cost = 46.96466064453125\n", "The 8151th loop: cost = 46.07296371459961\n", "The 8152th loop: cost = 36.64726257324219\n", "The 8153th loop: cost = 41.92645263671875\n", "The 8154th loop: cost = 42.86525344848633\n", "The 8155th loop: cost = 43.46405029296875\n", "The 8156th loop: cost = 44.602413177490234\n", "The 8157th loop: cost = 43.39849090576172\n", "The 8158th loop: cost = 42.067909240722656\n", "The 8159th loop: cost = 40.334346771240234\n", "The 8160th loop: cost = 39.221412658691406\n", "The 8161th loop: cost = 39.4616813659668\n", "The 8162th loop: cost = 33.438323974609375\n", "The 8163th loop: cost = 44.241214752197266\n", "The 8164th loop: cost = 35.091182708740234\n", "The 8165th loop: cost = 56.070945739746094\n", "The 8166th loop: cost = 44.21064376831055\n", "The 8167th loop: cost = 45.62563705444336\n", "The 8168th loop: cost = 39.03240966796875\n", "The 8169th loop: cost = 43.777870178222656\n", "The 8170th loop: cost = 37.71656036376953\n", "The 8171th loop: cost = 37.653724670410156\n", "The 8172th loop: cost = 41.87281799316406\n", "The 8173th loop: cost = 40.221351623535156\n", "The 8174th loop: cost = 46.7548828125\n", "The 8175th loop: cost = 40.40265655517578\n", "The 8176th loop: cost = 40.90915298461914\n", "The 8177th loop: cost = 42.03038787841797\n", "The 8178th loop: cost = 41.37483596801758\n", "The 8179th loop: cost = 44.34869384765625\n", "The 8180th loop: cost = 39.40608215332031\n", "The 8181th loop: cost = 39.29925537109375\n", "The 8182th loop: cost = 42.08641052246094\n", "The 8183th loop: cost = 40.15459442138672\n", "The 8184th loop: cost = 47.54808044433594\n", "The 8185th loop: cost = 44.142547607421875\n", "The 8186th loop: cost = 32.161617279052734\n", "The 8187th loop: cost = 32.375022888183594\n", "The 8188th loop: cost = 31.531545639038086\n", "The 8189th loop: cost = 39.304405212402344\n", "The 8190th loop: cost = 34.93247604370117\n", "The 8191th loop: cost = 37.741973876953125\n", "The 8192th loop: cost = 47.463050842285156\n", "The 8193th loop: cost = 42.0517578125\n", "The 8194th loop: cost = 37.72883605957031\n", "The 8195th loop: cost = 43.325157165527344\n", "The 8196th loop: cost = 38.33219909667969\n", "The 8197th loop: cost = 55.52208709716797\n", "The 8198th loop: cost = 39.08344650268555\n", "The 8199th loop: cost = 36.10014343261719\n", "The 8200th loop: cost = 39.24827575683594\n", "The 8201th loop: cost = 42.766334533691406\n", "The 8202th loop: cost = 39.75794219970703\n", "The 8203th loop: cost = 35.46056365966797\n", "The 8204th loop: cost = 40.16950225830078\n", "The 8205th loop: cost = 47.59634017944336\n", "The 8206th loop: cost = 43.714508056640625\n", "The 8207th loop: cost = 45.89784240722656\n", "The 8208th loop: cost = 44.01758575439453\n", "The 8209th loop: cost = 35.23516845703125\n", "The 8210th loop: cost = 42.49273681640625\n", "The 8211th loop: cost = 42.91107177734375\n", "The 8212th loop: cost = 43.49626159667969\n", "The 8213th loop: cost = 41.86610412597656\n", "The 8214th loop: cost = 37.334434509277344\n", "The 8215th loop: cost = 48.32553482055664\n", "The 8216th loop: cost = 42.88648223876953\n", "The 8217th loop: cost = 48.89744567871094\n", "The 8218th loop: cost = 48.77727508544922\n", "The 8219th loop: cost = 40.9293212890625\n", "The 8220th loop: cost = 45.77191162109375\n", "The 8221th loop: cost = 46.77356719970703\n", "The 8222th loop: cost = 37.70214080810547\n", "The 8223th loop: cost = 39.329490661621094\n", "The 8224th loop: cost = 40.432228088378906\n", "The 8225th loop: cost = 44.60050582885742\n", "The 8226th loop: cost = 50.99339294433594\n", "The 8227th loop: cost = 32.79011154174805\n", "The 8228th loop: cost = 36.440879821777344\n", "The 8229th loop: cost = 33.090476989746094\n", "The 8230th loop: cost = 38.22815704345703\n", "The 8231th loop: cost = 50.70420837402344\n", "The 8232th loop: cost = 39.68389129638672\n", "The 8233th loop: cost = 49.798805236816406\n", "The 8234th loop: cost = 28.061267852783203\n", "The 8235th loop: cost = 38.04474639892578\n", "The 8236th loop: cost = 39.913387298583984\n", "The 8237th loop: cost = 33.729759216308594\n", "The 8238th loop: cost = 31.25548553466797\n", "The 8239th loop: cost = 35.027835845947266\n", "The 8240th loop: cost = 41.908973693847656\n", "The 8241th loop: cost = 43.01349639892578\n", "The 8242th loop: cost = 37.82896423339844\n", "The 8243th loop: cost = 39.76968002319336\n", "The 8244th loop: cost = 37.23362731933594\n", "The 8245th loop: cost = 39.44544219970703\n", "The 8246th loop: cost = 43.91460418701172\n", "The 8247th loop: cost = 37.99998474121094\n", "The 8248th loop: cost = 33.885101318359375\n", "The 8249th loop: cost = 42.89909362792969\n", "The 8250th loop: cost = 47.742881774902344\n", "The 8251th loop: cost = 43.46635055541992\n", "The 8252th loop: cost = 35.27235412597656\n", "The 8253th loop: cost = 44.35026168823242\n", "The 8254th loop: cost = 39.838722229003906\n", "The 8255th loop: cost = 40.339195251464844\n", "The 8256th loop: cost = 41.27385711669922\n", "The 8257th loop: cost = 34.66637420654297\n", "The 8258th loop: cost = 43.37187957763672\n", "The 8259th loop: cost = 41.202701568603516\n", "The 8260th loop: cost = 33.923336029052734\n", "The 8261th loop: cost = 39.30223846435547\n", "The 8262th loop: cost = 44.539764404296875\n", "The 8263th loop: cost = 40.11570739746094\n", "The 8264th loop: cost = 46.374000549316406\n", "The 8265th loop: cost = 62.26313781738281\n", "The 8266th loop: cost = 30.50092124938965\n", "The 8267th loop: cost = 34.59556198120117\n", "The 8268th loop: cost = 35.382667541503906\n", "The 8269th loop: cost = 39.10906219482422\n", "The 8270th loop: cost = 32.32448959350586\n", "The 8271th loop: cost = 35.91424560546875\n", "The 8272th loop: cost = 36.5439338684082\n", "The 8273th loop: cost = 32.44438934326172\n", "The 8274th loop: cost = 41.730194091796875\n", "The 8275th loop: cost = 40.58332443237305\n", "The 8276th loop: cost = 32.07223892211914\n", "The 8277th loop: cost = 34.377655029296875\n", "The 8278th loop: cost = 33.99501419067383\n", "The 8279th loop: cost = 30.24596405029297\n", "The 8280th loop: cost = 42.89993667602539\n", "The 8281th loop: cost = 40.546531677246094\n", "The 8282th loop: cost = 39.60742950439453\n", "The 8283th loop: cost = 34.38349914550781\n", "The 8284th loop: cost = 44.42516326904297\n", "The 8285th loop: cost = 51.172698974609375\n", "The 8286th loop: cost = 52.53226089477539\n", "The 8287th loop: cost = 39.0241813659668\n", "The 8288th loop: cost = 46.446197509765625\n", "The 8289th loop: cost = 46.13359832763672\n", "The 8290th loop: cost = 41.717864990234375\n", "The 8291th loop: cost = 39.21064758300781\n", "The 8292th loop: cost = 45.42780303955078\n", "The 8293th loop: cost = 41.16826248168945\n", "The 8294th loop: cost = 43.50371551513672\n", "The 8295th loop: cost = 34.909236907958984\n", "The 8296th loop: cost = 42.5384407043457\n", "The 8297th loop: cost = 42.794029235839844\n", "The 8298th loop: cost = 30.80773162841797\n", "The 8299th loop: cost = 39.87626647949219\n", "The 8300th loop: cost = 33.528045654296875\n", "The 8301th loop: cost = 48.06273651123047\n", "The 8302th loop: cost = 37.189064025878906\n", "The 8303th loop: cost = 38.906227111816406\n", "The 8304th loop: cost = 54.29794692993164\n", "The 8305th loop: cost = 41.77488708496094\n", "The 8306th loop: cost = 31.66189956665039\n", "The 8307th loop: cost = 38.78902053833008\n", "The 8308th loop: cost = 39.62294006347656\n", "The 8309th loop: cost = 50.402645111083984\n", "The 8310th loop: cost = 50.4304084777832\n", "The 8311th loop: cost = 37.6046142578125\n", "The 8312th loop: cost = 45.75002670288086\n", "The 8313th loop: cost = 36.56878662109375\n", "The 8314th loop: cost = 40.5458869934082\n", "The 8315th loop: cost = 37.762428283691406\n", "The 8316th loop: cost = 53.17292785644531\n", "The 8317th loop: cost = 41.89630889892578\n", "The 8318th loop: cost = 44.12548828125\n", "The 8319th loop: cost = 47.966957092285156\n", "The 8320th loop: cost = 40.504154205322266\n", "The 8321th loop: cost = 42.65278625488281\n", "The 8322th loop: cost = 44.054832458496094\n", "The 8323th loop: cost = 43.72111511230469\n", "The 8324th loop: cost = 42.21708679199219\n", "The 8325th loop: cost = 35.75035858154297\n", "The 8326th loop: cost = 34.667137145996094\n", "The 8327th loop: cost = 43.07524871826172\n", "The 8328th loop: cost = 40.09016418457031\n", "The 8329th loop: cost = 50.49781036376953\n", "The 8330th loop: cost = 38.72472381591797\n", "The 8331th loop: cost = 40.26887893676758\n", "The 8332th loop: cost = 37.74620819091797\n", "The 8333th loop: cost = 34.749969482421875\n", "The 8334th loop: cost = 41.65233612060547\n", "The 8335th loop: cost = 40.85350799560547\n", "The 8336th loop: cost = 34.53369140625\n", "The 8337th loop: cost = 36.91371154785156\n", "The 8338th loop: cost = 41.753787994384766\n", "The 8339th loop: cost = 38.52210998535156\n", "The 8340th loop: cost = 36.325294494628906\n", "The 8341th loop: cost = 43.6866455078125\n", "The 8342th loop: cost = 40.54637145996094\n", "The 8343th loop: cost = 41.44470977783203\n", "The 8344th loop: cost = 36.77460479736328\n", "The 8345th loop: cost = 41.66236114501953\n", "The 8346th loop: cost = 31.10249900817871\n", "The 8347th loop: cost = 45.13893127441406\n", "The 8348th loop: cost = 38.753318786621094\n", "The 8349th loop: cost = 35.7904052734375\n", "The 8350th loop: cost = 39.462371826171875\n", "The 8351th loop: cost = 42.22768020629883\n", "The 8352th loop: cost = 37.557926177978516\n", "The 8353th loop: cost = 43.06092071533203\n", "The 8354th loop: cost = 36.058414459228516\n", "The 8355th loop: cost = 43.843753814697266\n", "The 8356th loop: cost = 28.764650344848633\n", "The 8357th loop: cost = 40.84449005126953\n", "The 8358th loop: cost = 41.65966796875\n", "The 8359th loop: cost = 39.20738220214844\n", "The 8360th loop: cost = 39.05658721923828\n", "The 8361th loop: cost = 43.546653747558594\n", "The 8362th loop: cost = 43.941104888916016\n", "The 8363th loop: cost = 36.72021484375\n", "The 8364th loop: cost = 43.255149841308594\n", "The 8365th loop: cost = 47.33439254760742\n", "The 8366th loop: cost = 37.02891159057617\n", "The 8367th loop: cost = 40.09103775024414\n", "The 8368th loop: cost = 46.03642654418945\n", "The 8369th loop: cost = 36.505126953125\n", "The 8370th loop: cost = 36.93639373779297\n", "The 8371th loop: cost = 44.701053619384766\n", "The 8372th loop: cost = 44.71361541748047\n", "The 8373th loop: cost = 39.89207458496094\n", "The 8374th loop: cost = 40.82179260253906\n", "The 8375th loop: cost = 41.315330505371094\n", "The 8376th loop: cost = 39.24536895751953\n", "The 8377th loop: cost = 39.38594055175781\n", "The 8378th loop: cost = 40.46531295776367\n", "The 8379th loop: cost = 42.496009826660156\n", "The 8380th loop: cost = 40.67873001098633\n", "The 8381th loop: cost = 44.800106048583984\n", "The 8382th loop: cost = 34.806396484375\n", "The 8383th loop: cost = 39.41978454589844\n", "The 8384th loop: cost = 35.874786376953125\n", "The 8385th loop: cost = 46.079368591308594\n", "The 8386th loop: cost = 29.181495666503906\n", "The 8387th loop: cost = 48.33993148803711\n", "The 8388th loop: cost = 49.58637237548828\n", "The 8389th loop: cost = 45.282554626464844\n", "The 8390th loop: cost = 42.96668243408203\n", "The 8391th loop: cost = 40.32451629638672\n", "The 8392th loop: cost = 36.006568908691406\n", "The 8393th loop: cost = 33.97209930419922\n", "The 8394th loop: cost = 41.179473876953125\n", "The 8395th loop: cost = 47.005374908447266\n", "The 8396th loop: cost = 46.453887939453125\n", "The 8397th loop: cost = 42.94466781616211\n", "The 8398th loop: cost = 35.106117248535156\n", "The 8399th loop: cost = 43.30097961425781\n", "The 8400th loop: cost = 46.33222198486328\n", "The 8401th loop: cost = 42.567787170410156\n", "The 8402th loop: cost = 36.091461181640625\n", "The 8403th loop: cost = 38.363609313964844\n", "The 8404th loop: cost = 43.06521224975586\n", "The 8405th loop: cost = 44.35559844970703\n", "The 8406th loop: cost = 36.13118362426758\n", "The 8407th loop: cost = 40.04894256591797\n", "The 8408th loop: cost = 41.401702880859375\n", "The 8409th loop: cost = 30.760759353637695\n", "The 8410th loop: cost = 48.64405822753906\n", "The 8411th loop: cost = 43.76129913330078\n", "The 8412th loop: cost = 28.720870971679688\n", "The 8413th loop: cost = 40.986419677734375\n", "The 8414th loop: cost = 43.345455169677734\n", "The 8415th loop: cost = 38.85863494873047\n", "The 8416th loop: cost = 37.780059814453125\n", "The 8417th loop: cost = 47.51035690307617\n", "The 8418th loop: cost = 39.46992492675781\n", "The 8419th loop: cost = 41.468597412109375\n", "The 8420th loop: cost = 41.172119140625\n", "The 8421th loop: cost = 45.2509765625\n", "The 8422th loop: cost = 46.553375244140625\n", "The 8423th loop: cost = 38.22000503540039\n", "The 8424th loop: cost = 41.87413024902344\n", "The 8425th loop: cost = 35.555320739746094\n", "The 8426th loop: cost = 36.404273986816406\n", "The 8427th loop: cost = 41.002532958984375\n", "The 8428th loop: cost = 42.208309173583984\n", "The 8429th loop: cost = 39.55889892578125\n", "The 8430th loop: cost = 40.85157012939453\n", "The 8431th loop: cost = 39.10045623779297\n", "The 8432th loop: cost = 38.482582092285156\n", "The 8433th loop: cost = 34.927162170410156\n", "The 8434th loop: cost = 47.081626892089844\n", "The 8435th loop: cost = 42.35511016845703\n", "The 8436th loop: cost = 37.64370346069336\n", "The 8437th loop: cost = 47.04453659057617\n", "The 8438th loop: cost = 40.428855895996094\n", "The 8439th loop: cost = 34.71475601196289\n", "The 8440th loop: cost = 44.299835205078125\n", "The 8441th loop: cost = 36.42450714111328\n", "The 8442th loop: cost = 36.40791320800781\n", "The 8443th loop: cost = 44.41216278076172\n", "The 8444th loop: cost = 39.18342971801758\n", "The 8445th loop: cost = 38.742225646972656\n", "The 8446th loop: cost = 39.73949432373047\n", "The 8447th loop: cost = 34.549949645996094\n", "The 8448th loop: cost = 45.324623107910156\n", "The 8449th loop: cost = 50.796974182128906\n", "The 8450th loop: cost = 41.714805603027344\n", "The 8451th loop: cost = 38.560997009277344\n", "The 8452th loop: cost = 37.410213470458984\n", "The 8453th loop: cost = 40.76155471801758\n", "The 8454th loop: cost = 37.71065139770508\n", "The 8455th loop: cost = 48.664154052734375\n", "The 8456th loop: cost = 44.90754699707031\n", "The 8457th loop: cost = 39.54907989501953\n", "The 8458th loop: cost = 48.144935607910156\n", "The 8459th loop: cost = 40.02320098876953\n", "The 8460th loop: cost = 36.37781524658203\n", "The 8461th loop: cost = 41.53913879394531\n", "The 8462th loop: cost = 41.48307800292969\n", "The 8463th loop: cost = 33.1429443359375\n", "The 8464th loop: cost = 42.72191619873047\n", "The 8465th loop: cost = 44.2333984375\n", "The 8466th loop: cost = 32.310665130615234\n", "The 8467th loop: cost = 40.73535919189453\n", "The 8468th loop: cost = 40.088924407958984\n", "The 8469th loop: cost = 45.589088439941406\n", "The 8470th loop: cost = 38.3834228515625\n", "The 8471th loop: cost = 32.90424346923828\n", "The 8472th loop: cost = 32.71290969848633\n", "The 8473th loop: cost = 37.14496612548828\n", "The 8474th loop: cost = 39.267852783203125\n", "The 8475th loop: cost = 46.21746826171875\n", "The 8476th loop: cost = 37.20917892456055\n", "The 8477th loop: cost = 38.82421875\n", "The 8478th loop: cost = 41.752967834472656\n", "The 8479th loop: cost = 43.30888366699219\n", "The 8480th loop: cost = 47.748390197753906\n", "The 8481th loop: cost = 42.39514923095703\n", "The 8482th loop: cost = 39.34538269042969\n", "The 8483th loop: cost = 36.58599853515625\n", "The 8484th loop: cost = 38.79862976074219\n", "The 8485th loop: cost = 41.706764221191406\n", "The 8486th loop: cost = 39.89091491699219\n", "The 8487th loop: cost = 43.36217498779297\n", "The 8488th loop: cost = 34.353816986083984\n", "The 8489th loop: cost = 40.424827575683594\n", "The 8490th loop: cost = 39.263893127441406\n", "The 8491th loop: cost = 44.229583740234375\n", "The 8492th loop: cost = 39.780452728271484\n", "The 8493th loop: cost = 42.10370635986328\n", "The 8494th loop: cost = 33.844818115234375\n", "The 8495th loop: cost = 42.341766357421875\n", "The 8496th loop: cost = 40.393455505371094\n", "The 8497th loop: cost = 44.102020263671875\n", "The 8498th loop: cost = 39.052894592285156\n", "The 8499th loop: cost = 44.00971221923828\n", "The 8500th loop: cost = 45.68877410888672\n", "The 8501th loop: cost = 39.002769470214844\n", "The 8502th loop: cost = 41.176902770996094\n", "The 8503th loop: cost = 36.44552993774414\n", "The 8504th loop: cost = 40.47168731689453\n", "The 8505th loop: cost = 46.17703628540039\n", "The 8506th loop: cost = 40.294593811035156\n", "The 8507th loop: cost = 35.40852355957031\n", "The 8508th loop: cost = 42.04612731933594\n", "The 8509th loop: cost = 34.58823013305664\n", "The 8510th loop: cost = 32.93077850341797\n", "The 8511th loop: cost = 39.524784088134766\n", "The 8512th loop: cost = 37.0328369140625\n", "The 8513th loop: cost = 36.86394119262695\n", "The 8514th loop: cost = 37.20109558105469\n", "The 8515th loop: cost = 54.52275085449219\n", "The 8516th loop: cost = 34.66046905517578\n", "The 8517th loop: cost = 36.51383972167969\n", "The 8518th loop: cost = 42.632572174072266\n", "The 8519th loop: cost = 38.8487434387207\n", "The 8520th loop: cost = 38.37580108642578\n", "The 8521th loop: cost = 48.6013298034668\n", "The 8522th loop: cost = 35.97233963012695\n", "The 8523th loop: cost = 48.66181182861328\n", "The 8524th loop: cost = 37.091705322265625\n", "The 8525th loop: cost = 40.36674499511719\n", "The 8526th loop: cost = 38.626686096191406\n", "The 8527th loop: cost = 42.20960235595703\n", "The 8528th loop: cost = 41.26465606689453\n", "The 8529th loop: cost = 38.08434295654297\n", "The 8530th loop: cost = 35.69970703125\n", "The 8531th loop: cost = 41.87926483154297\n", "The 8532th loop: cost = 43.60697555541992\n", "The 8533th loop: cost = 30.287628173828125\n", "The 8534th loop: cost = 39.615806579589844\n", "The 8535th loop: cost = 36.95652770996094\n", "The 8536th loop: cost = 41.03284454345703\n", "The 8537th loop: cost = 41.07624053955078\n", "The 8538th loop: cost = 26.99698257446289\n", "The 8539th loop: cost = 40.207366943359375\n", "The 8540th loop: cost = 38.44289779663086\n", "The 8541th loop: cost = 43.593902587890625\n", "The 8542th loop: cost = 34.323516845703125\n", "The 8543th loop: cost = 33.51716613769531\n", "The 8544th loop: cost = 55.05925750732422\n", "The 8545th loop: cost = 38.77149963378906\n", "The 8546th loop: cost = 44.37873077392578\n", "The 8547th loop: cost = 41.50336456298828\n", "The 8548th loop: cost = 36.48674774169922\n", "The 8549th loop: cost = 33.35780334472656\n", "The 8550th loop: cost = 41.05113983154297\n", "The 8551th loop: cost = 34.2813606262207\n", "The 8552th loop: cost = 48.66217041015625\n", "The 8553th loop: cost = 34.56465148925781\n", "The 8554th loop: cost = 57.05294418334961\n", "The 8555th loop: cost = 39.62883377075195\n", "The 8556th loop: cost = 41.341796875\n", "The 8557th loop: cost = 42.26911544799805\n", "The 8558th loop: cost = 45.282169342041016\n", "The 8559th loop: cost = 42.41712951660156\n", "The 8560th loop: cost = 45.299705505371094\n", "The 8561th loop: cost = 38.59345245361328\n", "The 8562th loop: cost = 37.35198211669922\n", "The 8563th loop: cost = 38.37903594970703\n", "The 8564th loop: cost = 41.144439697265625\n", "The 8565th loop: cost = 42.229705810546875\n", "The 8566th loop: cost = 45.41035842895508\n", "The 8567th loop: cost = 37.26264572143555\n", "The 8568th loop: cost = 43.430206298828125\n", "The 8569th loop: cost = 35.99946212768555\n", "The 8570th loop: cost = 43.990169525146484\n", "The 8571th loop: cost = 39.069122314453125\n", "The 8572th loop: cost = 44.205047607421875\n", "The 8573th loop: cost = 48.05440139770508\n", "The 8574th loop: cost = 43.16513442993164\n", "The 8575th loop: cost = 43.46718215942383\n", "The 8576th loop: cost = 34.80311965942383\n", "The 8577th loop: cost = 37.70387268066406\n", "The 8578th loop: cost = 40.109580993652344\n", "The 8579th loop: cost = 38.80928421020508\n", "The 8580th loop: cost = 51.75049591064453\n", "The 8581th loop: cost = 39.77195739746094\n", "The 8582th loop: cost = 49.9627571105957\n", "The 8583th loop: cost = 46.345237731933594\n", "The 8584th loop: cost = 46.26189041137695\n", "The 8585th loop: cost = 41.097599029541016\n", "The 8586th loop: cost = 31.393585205078125\n", "The 8587th loop: cost = 40.544593811035156\n", "The 8588th loop: cost = 41.773658752441406\n", "The 8589th loop: cost = 41.5627326965332\n", "The 8590th loop: cost = 34.50736999511719\n", "The 8591th loop: cost = 46.054447174072266\n", "The 8592th loop: cost = 39.76783752441406\n", "The 8593th loop: cost = 33.78779220581055\n", "The 8594th loop: cost = 45.383522033691406\n", "The 8595th loop: cost = 37.03231430053711\n", "The 8596th loop: cost = 33.58781814575195\n", "The 8597th loop: cost = 42.95740509033203\n", "The 8598th loop: cost = 37.272464752197266\n", "The 8599th loop: cost = 44.93668746948242\n", "The 8600th loop: cost = 50.859535217285156\n", "The 8601th loop: cost = 47.841705322265625\n", "The 8602th loop: cost = 31.91261100769043\n", "The 8603th loop: cost = 42.01554489135742\n", "The 8604th loop: cost = 35.748878479003906\n", "The 8605th loop: cost = 42.540550231933594\n", "The 8606th loop: cost = 47.05663299560547\n", "The 8607th loop: cost = 37.368934631347656\n", "The 8608th loop: cost = 41.603912353515625\n", "The 8609th loop: cost = 37.33409118652344\n", "The 8610th loop: cost = 36.10786437988281\n", "The 8611th loop: cost = 38.10620880126953\n", "The 8612th loop: cost = 42.69989776611328\n", "The 8613th loop: cost = 38.23516082763672\n", "The 8614th loop: cost = 44.4332160949707\n", "The 8615th loop: cost = 38.54829788208008\n", "The 8616th loop: cost = 45.76457977294922\n", "The 8617th loop: cost = 38.89518356323242\n", "The 8618th loop: cost = 41.73325729370117\n", "The 8619th loop: cost = 39.10879135131836\n", "The 8620th loop: cost = 41.22200012207031\n", "The 8621th loop: cost = 41.329124450683594\n", "The 8622th loop: cost = 39.872314453125\n", "The 8623th loop: cost = 32.70490264892578\n", "The 8624th loop: cost = 39.473899841308594\n", "The 8625th loop: cost = 36.16804122924805\n", "The 8626th loop: cost = 41.44947052001953\n", "The 8627th loop: cost = 42.250762939453125\n", "The 8628th loop: cost = 37.337669372558594\n", "The 8629th loop: cost = 40.68533706665039\n", "The 8630th loop: cost = 31.711179733276367\n", "The 8631th loop: cost = 36.76408386230469\n", "The 8632th loop: cost = 41.6201286315918\n", "The 8633th loop: cost = 36.13837814331055\n", "The 8634th loop: cost = 37.05915451049805\n", "The 8635th loop: cost = 52.44695281982422\n", "The 8636th loop: cost = 43.02027893066406\n", "The 8637th loop: cost = 41.13812255859375\n", "The 8638th loop: cost = 31.27413558959961\n", "The 8639th loop: cost = 37.9575309753418\n", "The 8640th loop: cost = 36.3835563659668\n", "The 8641th loop: cost = 44.077392578125\n", "The 8642th loop: cost = 46.05588150024414\n", "The 8643th loop: cost = 34.91920471191406\n", "The 8644th loop: cost = 46.908573150634766\n", "The 8645th loop: cost = 37.799560546875\n", "The 8646th loop: cost = 35.03451156616211\n", "The 8647th loop: cost = 35.41328430175781\n", "The 8648th loop: cost = 38.21124267578125\n", "The 8649th loop: cost = 38.146514892578125\n", "The 8650th loop: cost = 38.574195861816406\n", "The 8651th loop: cost = 40.2724609375\n", "The 8652th loop: cost = 33.11738586425781\n", "The 8653th loop: cost = 41.60244369506836\n", "The 8654th loop: cost = 38.720123291015625\n", "The 8655th loop: cost = 37.58549499511719\n", "The 8656th loop: cost = 40.30695724487305\n", "The 8657th loop: cost = 47.560630798339844\n", "The 8658th loop: cost = 47.80427932739258\n", "The 8659th loop: cost = 34.793216705322266\n", "The 8660th loop: cost = 39.769901275634766\n", "The 8661th loop: cost = 38.38953399658203\n", "The 8662th loop: cost = 44.547279357910156\n", "The 8663th loop: cost = 38.313232421875\n", "The 8664th loop: cost = 42.34409713745117\n", "The 8665th loop: cost = 44.844852447509766\n", "The 8666th loop: cost = 37.86357116699219\n", "The 8667th loop: cost = 43.243507385253906\n", "The 8668th loop: cost = 39.494041442871094\n", "The 8669th loop: cost = 37.58226013183594\n", "The 8670th loop: cost = 50.9930419921875\n", "The 8671th loop: cost = 36.277462005615234\n", "The 8672th loop: cost = 33.76240539550781\n", "The 8673th loop: cost = 32.98820495605469\n", "The 8674th loop: cost = 41.34960174560547\n", "The 8675th loop: cost = 38.26641082763672\n", "The 8676th loop: cost = 49.545536041259766\n", "The 8677th loop: cost = 52.68986511230469\n", "The 8678th loop: cost = 37.94977951049805\n", "The 8679th loop: cost = 42.172935485839844\n", "The 8680th loop: cost = 37.41065979003906\n", "The 8681th loop: cost = 44.542518615722656\n", "The 8682th loop: cost = 39.801124572753906\n", "The 8683th loop: cost = 45.19689178466797\n", "The 8684th loop: cost = 46.061378479003906\n", "The 8685th loop: cost = 33.06346130371094\n", "The 8686th loop: cost = 33.60049057006836\n", "The 8687th loop: cost = 47.212867736816406\n", "The 8688th loop: cost = 42.69785690307617\n", "The 8689th loop: cost = 37.35865020751953\n", "The 8690th loop: cost = 38.4736328125\n", "The 8691th loop: cost = 39.22528839111328\n", "The 8692th loop: cost = 39.01091003417969\n", "The 8693th loop: cost = 39.69731140136719\n", "The 8694th loop: cost = 36.3618049621582\n", "The 8695th loop: cost = 33.07716369628906\n", "The 8696th loop: cost = 43.041236877441406\n", "The 8697th loop: cost = 36.735862731933594\n", "The 8698th loop: cost = 45.189449310302734\n", "The 8699th loop: cost = 41.55205535888672\n", "The 8700th loop: cost = 32.694881439208984\n", "The 8701th loop: cost = 49.598121643066406\n", "The 8702th loop: cost = 37.432159423828125\n", "The 8703th loop: cost = 29.249216079711914\n", "The 8704th loop: cost = 38.96940612792969\n", "The 8705th loop: cost = 39.32929992675781\n", "The 8706th loop: cost = 33.00416564941406\n", "The 8707th loop: cost = 37.41809844970703\n", "The 8708th loop: cost = 42.65899658203125\n", "The 8709th loop: cost = 51.45204162597656\n", "The 8710th loop: cost = 45.55810546875\n", "The 8711th loop: cost = 35.831851959228516\n", "The 8712th loop: cost = 37.44862365722656\n", "The 8713th loop: cost = 37.889183044433594\n", "The 8714th loop: cost = 41.01087951660156\n", "The 8715th loop: cost = 31.841625213623047\n", "The 8716th loop: cost = 36.62525177001953\n", "The 8717th loop: cost = 38.42560577392578\n", "The 8718th loop: cost = 41.98188781738281\n", "The 8719th loop: cost = 34.45068359375\n", "The 8720th loop: cost = 41.881309509277344\n", "The 8721th loop: cost = 39.43540954589844\n", "The 8722th loop: cost = 35.98140335083008\n", "The 8723th loop: cost = 29.795225143432617\n", "The 8724th loop: cost = 41.18914031982422\n", "The 8725th loop: cost = 43.585079193115234\n", "The 8726th loop: cost = 41.044586181640625\n", "The 8727th loop: cost = 41.90932846069336\n", "The 8728th loop: cost = 32.391563415527344\n", "The 8729th loop: cost = 40.928306579589844\n", "The 8730th loop: cost = 43.983741760253906\n", "The 8731th loop: cost = 39.32002639770508\n", "The 8732th loop: cost = 37.52239990234375\n", "The 8733th loop: cost = 38.148651123046875\n", "The 8734th loop: cost = 38.3473014831543\n", "The 8735th loop: cost = 41.003597259521484\n", "The 8736th loop: cost = 42.811187744140625\n", "The 8737th loop: cost = 43.00606155395508\n", "The 8738th loop: cost = 31.00444221496582\n", "The 8739th loop: cost = 45.992431640625\n", "The 8740th loop: cost = 38.299827575683594\n", "The 8741th loop: cost = 39.310882568359375\n", "The 8742th loop: cost = 37.473487854003906\n", "The 8743th loop: cost = 43.519432067871094\n", "The 8744th loop: cost = 54.55946731567383\n", "The 8745th loop: cost = 43.61784362792969\n", "The 8746th loop: cost = 42.0626220703125\n", "The 8747th loop: cost = 42.1160888671875\n", "The 8748th loop: cost = 29.002376556396484\n", "The 8749th loop: cost = 42.1448974609375\n", "The 8750th loop: cost = 35.85546112060547\n", "The 8751th loop: cost = 36.73151397705078\n", "The 8752th loop: cost = 38.657554626464844\n", "The 8753th loop: cost = 39.1467399597168\n", "The 8754th loop: cost = 38.74748992919922\n", "The 8755th loop: cost = 33.58778762817383\n", "The 8756th loop: cost = 47.90239715576172\n", "The 8757th loop: cost = 39.61025619506836\n", "The 8758th loop: cost = 33.919918060302734\n", "The 8759th loop: cost = 33.303245544433594\n", "The 8760th loop: cost = 34.23972702026367\n", "The 8761th loop: cost = 40.568458557128906\n", "The 8762th loop: cost = 40.43439483642578\n", "The 8763th loop: cost = 33.712493896484375\n", "The 8764th loop: cost = 37.324974060058594\n", "The 8765th loop: cost = 35.943058013916016\n", "The 8766th loop: cost = 43.00885772705078\n", "The 8767th loop: cost = 37.91638946533203\n", "The 8768th loop: cost = 46.95838165283203\n", "The 8769th loop: cost = 36.92591094970703\n", "The 8770th loop: cost = 38.976505279541016\n", "The 8771th loop: cost = 41.49922180175781\n", "The 8772th loop: cost = 42.274932861328125\n", "The 8773th loop: cost = 45.26264190673828\n", "The 8774th loop: cost = 33.9088020324707\n", "The 8775th loop: cost = 37.81692123413086\n", "The 8776th loop: cost = 45.10099792480469\n", "The 8777th loop: cost = 45.404483795166016\n", "The 8778th loop: cost = 34.35961151123047\n", "The 8779th loop: cost = 43.946983337402344\n", "The 8780th loop: cost = 43.537105560302734\n", "The 8781th loop: cost = 38.38001251220703\n", "The 8782th loop: cost = 36.218116760253906\n", "The 8783th loop: cost = 37.230987548828125\n", "The 8784th loop: cost = 37.589569091796875\n", "The 8785th loop: cost = 39.8525390625\n", "The 8786th loop: cost = 40.92128372192383\n", "The 8787th loop: cost = 33.108642578125\n", "The 8788th loop: cost = 43.215946197509766\n", "The 8789th loop: cost = 32.079444885253906\n", "The 8790th loop: cost = 39.78783416748047\n", "The 8791th loop: cost = 40.236915588378906\n", "The 8792th loop: cost = 42.24803161621094\n", "The 8793th loop: cost = 32.544456481933594\n", "The 8794th loop: cost = 38.12812042236328\n", "The 8795th loop: cost = 44.82512664794922\n", "The 8796th loop: cost = 47.353248596191406\n", "The 8797th loop: cost = 40.62470626831055\n", "The 8798th loop: cost = 42.59785079956055\n", "The 8799th loop: cost = 43.58560562133789\n", "The 8800th loop: cost = 31.77933120727539\n", "The 8801th loop: cost = 40.12017059326172\n", "The 8802th loop: cost = 43.361976623535156\n", "The 8803th loop: cost = 40.081687927246094\n", "The 8804th loop: cost = 47.11488723754883\n", "The 8805th loop: cost = 33.59187316894531\n", "The 8806th loop: cost = 45.31490707397461\n", "The 8807th loop: cost = 46.470458984375\n", "The 8808th loop: cost = 45.07210159301758\n", "The 8809th loop: cost = 43.358070373535156\n", "The 8810th loop: cost = 46.64161682128906\n", "The 8811th loop: cost = 39.629573822021484\n", "The 8812th loop: cost = 40.256431579589844\n", "The 8813th loop: cost = 36.23573303222656\n", "The 8814th loop: cost = 38.35328674316406\n", "The 8815th loop: cost = 44.46907424926758\n", "The 8816th loop: cost = 47.89427185058594\n", "The 8817th loop: cost = 36.89571762084961\n", "The 8818th loop: cost = 38.290428161621094\n", "The 8819th loop: cost = 31.58371353149414\n", "The 8820th loop: cost = 34.76948165893555\n", "The 8821th loop: cost = 41.781593322753906\n", "The 8822th loop: cost = 41.950042724609375\n", "The 8823th loop: cost = 40.119056701660156\n", "The 8824th loop: cost = 46.01087188720703\n", "The 8825th loop: cost = 40.58177185058594\n", "The 8826th loop: cost = 49.1421012878418\n", "The 8827th loop: cost = 29.578777313232422\n", "The 8828th loop: cost = 42.350006103515625\n", "The 8829th loop: cost = 31.7117919921875\n", "The 8830th loop: cost = 34.61128234863281\n", "The 8831th loop: cost = 32.56647491455078\n", "The 8832th loop: cost = 40.06270217895508\n", "The 8833th loop: cost = 35.78679656982422\n", "The 8834th loop: cost = 38.34600067138672\n", "The 8835th loop: cost = 28.519392013549805\n", "The 8836th loop: cost = 39.115455627441406\n", "The 8837th loop: cost = 39.000732421875\n", "The 8838th loop: cost = 42.196929931640625\n", "The 8839th loop: cost = 46.332908630371094\n", "The 8840th loop: cost = 38.156890869140625\n", "The 8841th loop: cost = 38.071556091308594\n", "The 8842th loop: cost = 39.45561599731445\n", "The 8843th loop: cost = 33.87821960449219\n", "The 8844th loop: cost = 52.24201965332031\n", "The 8845th loop: cost = 43.9518928527832\n", "The 8846th loop: cost = 39.12553405761719\n", "The 8847th loop: cost = 36.5322151184082\n", "The 8848th loop: cost = 37.07276916503906\n", "The 8849th loop: cost = 31.065942764282227\n", "The 8850th loop: cost = 44.87651062011719\n", "The 8851th loop: cost = 41.2677116394043\n", "The 8852th loop: cost = 31.79705047607422\n", "The 8853th loop: cost = 39.200313568115234\n", "The 8854th loop: cost = 46.91948699951172\n", "The 8855th loop: cost = 44.00880432128906\n", "The 8856th loop: cost = 37.170223236083984\n", "The 8857th loop: cost = 37.862796783447266\n", "The 8858th loop: cost = 44.53907775878906\n", "The 8859th loop: cost = 31.49504852294922\n", "The 8860th loop: cost = 37.22760772705078\n", "The 8861th loop: cost = 44.755592346191406\n", "The 8862th loop: cost = 37.91747283935547\n", "The 8863th loop: cost = 40.40761184692383\n", "The 8864th loop: cost = 40.01103973388672\n", "The 8865th loop: cost = 29.72923469543457\n", "The 8866th loop: cost = 30.375995635986328\n", "The 8867th loop: cost = 45.15705108642578\n", "The 8868th loop: cost = 43.18238830566406\n", "The 8869th loop: cost = 34.28852081298828\n", "The 8870th loop: cost = 35.133914947509766\n", "The 8871th loop: cost = 32.51371765136719\n", "The 8872th loop: cost = 33.32408905029297\n", "The 8873th loop: cost = 41.47206115722656\n", "The 8874th loop: cost = 33.790977478027344\n", "The 8875th loop: cost = 30.005970001220703\n", "The 8876th loop: cost = 36.83720397949219\n", "The 8877th loop: cost = 33.427852630615234\n", "The 8878th loop: cost = 40.45372772216797\n", "The 8879th loop: cost = 45.023590087890625\n", "The 8880th loop: cost = 41.04520797729492\n", "The 8881th loop: cost = 32.3915901184082\n", "The 8882th loop: cost = 34.883602142333984\n", "The 8883th loop: cost = 42.714378356933594\n", "The 8884th loop: cost = 45.971805572509766\n", "The 8885th loop: cost = 38.889793395996094\n", "The 8886th loop: cost = 32.46561050415039\n", "The 8887th loop: cost = 40.107913970947266\n", "The 8888th loop: cost = 38.785667419433594\n", "The 8889th loop: cost = 35.872440338134766\n", "The 8890th loop: cost = 44.53252029418945\n", "The 8891th loop: cost = 30.681896209716797\n", "The 8892th loop: cost = 39.31794738769531\n", "The 8893th loop: cost = 36.025028228759766\n", "The 8894th loop: cost = 45.499717712402344\n", "The 8895th loop: cost = 38.88665008544922\n", "The 8896th loop: cost = 43.86048889160156\n", "The 8897th loop: cost = 44.9974365234375\n", "The 8898th loop: cost = 45.54005813598633\n", "The 8899th loop: cost = 38.39091110229492\n", "The 8900th loop: cost = 35.25513458251953\n", "The 8901th loop: cost = 29.73220443725586\n", "The 8902th loop: cost = 29.502574920654297\n", "The 8903th loop: cost = 35.45909118652344\n", "The 8904th loop: cost = 36.168785095214844\n", "The 8905th loop: cost = 43.350372314453125\n", "The 8906th loop: cost = 35.48710632324219\n", "The 8907th loop: cost = 41.067298889160156\n", "The 8908th loop: cost = 49.15333557128906\n", "The 8909th loop: cost = 36.26893997192383\n", "The 8910th loop: cost = 34.85666275024414\n", "The 8911th loop: cost = 39.49934387207031\n", "The 8912th loop: cost = 41.414093017578125\n", "The 8913th loop: cost = 46.959022521972656\n", "The 8914th loop: cost = 39.05071258544922\n", "The 8915th loop: cost = 39.299495697021484\n", "The 8916th loop: cost = 42.291744232177734\n", "The 8917th loop: cost = 37.189300537109375\n", "The 8918th loop: cost = 37.07408142089844\n", "The 8919th loop: cost = 45.641841888427734\n", "The 8920th loop: cost = 35.28846740722656\n", "The 8921th loop: cost = 40.30394744873047\n", "The 8922th loop: cost = 32.56242370605469\n", "The 8923th loop: cost = 43.00891876220703\n", "The 8924th loop: cost = 41.40834045410156\n", "The 8925th loop: cost = 40.498138427734375\n", "The 8926th loop: cost = 40.20533752441406\n", "The 8927th loop: cost = 49.50645446777344\n", "The 8928th loop: cost = 39.85026931762695\n", "The 8929th loop: cost = 39.40106964111328\n", "The 8930th loop: cost = 41.10588836669922\n", "The 8931th loop: cost = 38.351104736328125\n", "The 8932th loop: cost = 53.054115295410156\n", "The 8933th loop: cost = 47.689693450927734\n", "The 8934th loop: cost = 36.792938232421875\n", "The 8935th loop: cost = 36.68691635131836\n", "The 8936th loop: cost = 34.53207778930664\n", "The 8937th loop: cost = 42.187034606933594\n", "The 8938th loop: cost = 48.64680480957031\n", "The 8939th loop: cost = 36.78844451904297\n", "The 8940th loop: cost = 31.924148559570312\n", "The 8941th loop: cost = 35.29931640625\n", "The 8942th loop: cost = 45.223297119140625\n", "The 8943th loop: cost = 35.554908752441406\n", "The 8944th loop: cost = 42.19805908203125\n", "The 8945th loop: cost = 34.791141510009766\n", "The 8946th loop: cost = 41.354156494140625\n", "The 8947th loop: cost = 41.980533599853516\n", "The 8948th loop: cost = 41.515892028808594\n", "The 8949th loop: cost = 49.27125930786133\n", "The 8950th loop: cost = 40.424041748046875\n", "The 8951th loop: cost = 48.430381774902344\n", "The 8952th loop: cost = 31.262981414794922\n", "The 8953th loop: cost = 38.803565979003906\n", "The 8954th loop: cost = 37.37266159057617\n", "The 8955th loop: cost = 34.37683868408203\n", "The 8956th loop: cost = 33.42347717285156\n", "The 8957th loop: cost = 49.366546630859375\n", "The 8958th loop: cost = 37.112548828125\n", "The 8959th loop: cost = 44.89274597167969\n", "The 8960th loop: cost = 38.35924530029297\n", "The 8961th loop: cost = 31.849401473999023\n", "The 8962th loop: cost = 39.527015686035156\n", "The 8963th loop: cost = 34.0003776550293\n", "The 8964th loop: cost = 35.12480926513672\n", "The 8965th loop: cost = 33.87480545043945\n", "The 8966th loop: cost = 39.19463348388672\n", "The 8967th loop: cost = 41.50176239013672\n", "The 8968th loop: cost = 35.602481842041016\n", "The 8969th loop: cost = 34.342552185058594\n", "The 8970th loop: cost = 40.37702560424805\n", "The 8971th loop: cost = 38.655609130859375\n", "The 8972th loop: cost = 47.17915344238281\n", "The 8973th loop: cost = 36.54643630981445\n", "The 8974th loop: cost = 40.584068298339844\n", "The 8975th loop: cost = 44.29267883300781\n", "The 8976th loop: cost = 32.23244094848633\n", "The 8977th loop: cost = 35.682586669921875\n", "The 8978th loop: cost = 44.54491424560547\n", "The 8979th loop: cost = 37.08245086669922\n", "The 8980th loop: cost = 37.16442108154297\n", "The 8981th loop: cost = 39.58305358886719\n", "The 8982th loop: cost = 37.77375411987305\n", "The 8983th loop: cost = 38.474609375\n", "The 8984th loop: cost = 31.70598602294922\n", "The 8985th loop: cost = 41.07441329956055\n", "The 8986th loop: cost = 33.93242645263672\n", "The 8987th loop: cost = 34.69891357421875\n", "The 8988th loop: cost = 45.82403564453125\n", "The 8989th loop: cost = 41.671661376953125\n", "The 8990th loop: cost = 44.63764953613281\n", "The 8991th loop: cost = 34.14323425292969\n", "The 8992th loop: cost = 43.71527099609375\n", "The 8993th loop: cost = 48.990901947021484\n", "The 8994th loop: cost = 39.51995086669922\n", "The 8995th loop: cost = 32.646846771240234\n", "The 8996th loop: cost = 38.20041275024414\n", "The 8997th loop: cost = 37.812191009521484\n", "The 8998th loop: cost = 42.60900115966797\n", "The 8999th loop: cost = 46.56031036376953\n", "The 9000th loop: cost = 37.913978576660156\n", "The 9001th loop: cost = 36.19845962524414\n", "The 9002th loop: cost = 47.79314422607422\n", "The 9003th loop: cost = 36.3365364074707\n", "The 9004th loop: cost = 34.03666687011719\n", "The 9005th loop: cost = 38.45940017700195\n", "The 9006th loop: cost = 46.432212829589844\n", "The 9007th loop: cost = 41.78982925415039\n", "The 9008th loop: cost = 33.598846435546875\n", "The 9009th loop: cost = 42.499671936035156\n", "The 9010th loop: cost = 35.545616149902344\n", "The 9011th loop: cost = 39.27938461303711\n", "The 9012th loop: cost = 45.58720397949219\n", "The 9013th loop: cost = 45.129451751708984\n", "The 9014th loop: cost = 44.553321838378906\n", "The 9015th loop: cost = 36.75636672973633\n", "The 9016th loop: cost = 33.97393798828125\n", "The 9017th loop: cost = 38.92195510864258\n", "The 9018th loop: cost = 39.37338638305664\n", "The 9019th loop: cost = 37.25234603881836\n", "The 9020th loop: cost = 28.996253967285156\n", "The 9021th loop: cost = 42.55998992919922\n", "The 9022th loop: cost = 45.00947570800781\n", "The 9023th loop: cost = 41.104408264160156\n", "The 9024th loop: cost = 40.460533142089844\n", "The 9025th loop: cost = 30.101455688476562\n", "The 9026th loop: cost = 32.67731857299805\n", "The 9027th loop: cost = 37.52627182006836\n", "The 9028th loop: cost = 39.12870788574219\n", "The 9029th loop: cost = 44.444725036621094\n", "The 9030th loop: cost = 44.58767318725586\n", "The 9031th loop: cost = 40.44745635986328\n", "The 9032th loop: cost = 35.800132751464844\n", "The 9033th loop: cost = 36.161720275878906\n", "The 9034th loop: cost = 29.91193389892578\n", "The 9035th loop: cost = 39.909481048583984\n", "The 9036th loop: cost = 44.77702331542969\n", "The 9037th loop: cost = 34.81068420410156\n", "The 9038th loop: cost = 41.30891799926758\n", "The 9039th loop: cost = 41.33291244506836\n", "The 9040th loop: cost = 41.47150421142578\n", "The 9041th loop: cost = 35.0313835144043\n", "The 9042th loop: cost = 33.53543472290039\n", "The 9043th loop: cost = 36.55929183959961\n", "The 9044th loop: cost = 45.16581344604492\n", "The 9045th loop: cost = 39.09263610839844\n", "The 9046th loop: cost = 39.592411041259766\n", "The 9047th loop: cost = 40.67626190185547\n", "The 9048th loop: cost = 45.11863327026367\n", "The 9049th loop: cost = 39.26713562011719\n", "The 9050th loop: cost = 41.13330841064453\n", "The 9051th loop: cost = 38.552223205566406\n", "The 9052th loop: cost = 44.691646575927734\n", "The 9053th loop: cost = 35.97779846191406\n", "The 9054th loop: cost = 37.31805419921875\n", "The 9055th loop: cost = 38.92261505126953\n", "The 9056th loop: cost = 42.21832275390625\n", "The 9057th loop: cost = 47.08845901489258\n", "The 9058th loop: cost = 37.96616744995117\n", "The 9059th loop: cost = 29.71088409423828\n", "The 9060th loop: cost = 36.55319595336914\n", "The 9061th loop: cost = 43.937965393066406\n", "The 9062th loop: cost = 40.3224983215332\n", "The 9063th loop: cost = 38.530540466308594\n", "The 9064th loop: cost = 31.15650177001953\n", "The 9065th loop: cost = 35.04209899902344\n", "The 9066th loop: cost = 45.8417854309082\n", "The 9067th loop: cost = 44.667999267578125\n", "The 9068th loop: cost = 35.80760192871094\n", "The 9069th loop: cost = 44.10104751586914\n", "The 9070th loop: cost = 27.01819610595703\n", "The 9071th loop: cost = 40.24860382080078\n", "The 9072th loop: cost = 38.15035629272461\n", "The 9073th loop: cost = 34.90432357788086\n", "The 9074th loop: cost = 36.82853698730469\n", "The 9075th loop: cost = 37.64033126831055\n", "The 9076th loop: cost = 36.365116119384766\n", "The 9077th loop: cost = 42.76865005493164\n", "The 9078th loop: cost = 48.22852325439453\n", "The 9079th loop: cost = 37.26776885986328\n", "The 9080th loop: cost = 32.54311752319336\n", "The 9081th loop: cost = 36.08879852294922\n", "The 9082th loop: cost = 38.77647018432617\n", "The 9083th loop: cost = 36.03274154663086\n", "The 9084th loop: cost = 42.049415588378906\n", "The 9085th loop: cost = 33.75680923461914\n", "The 9086th loop: cost = 36.86614227294922\n", "The 9087th loop: cost = 47.75254821777344\n", "The 9088th loop: cost = 39.50794219970703\n", "The 9089th loop: cost = 34.03712844848633\n", "The 9090th loop: cost = 39.102378845214844\n", "The 9091th loop: cost = 35.45545959472656\n", "The 9092th loop: cost = 33.08646011352539\n", "The 9093th loop: cost = 37.01744079589844\n", "The 9094th loop: cost = 31.053577423095703\n", "The 9095th loop: cost = 42.412742614746094\n", "The 9096th loop: cost = 44.72291564941406\n", "The 9097th loop: cost = 43.59114074707031\n", "The 9098th loop: cost = 39.32124328613281\n", "The 9099th loop: cost = 40.697113037109375\n", "The 9100th loop: cost = 35.943302154541016\n", "The 9101th loop: cost = 38.700843811035156\n", "The 9102th loop: cost = 39.38922119140625\n", "The 9103th loop: cost = 42.54311752319336\n", "The 9104th loop: cost = 43.65438461303711\n", "The 9105th loop: cost = 44.14946746826172\n", "The 9106th loop: cost = 40.31978988647461\n", "The 9107th loop: cost = 39.656761169433594\n", "The 9108th loop: cost = 47.10086441040039\n", "The 9109th loop: cost = 40.128868103027344\n", "The 9110th loop: cost = 31.177780151367188\n", "The 9111th loop: cost = 40.191490173339844\n", "The 9112th loop: cost = 32.78192138671875\n", "The 9113th loop: cost = 41.757415771484375\n", "The 9114th loop: cost = 35.77798843383789\n", "The 9115th loop: cost = 39.48155975341797\n", "The 9116th loop: cost = 36.09197235107422\n", "The 9117th loop: cost = 34.53800582885742\n", "The 9118th loop: cost = 42.43055725097656\n", "The 9119th loop: cost = 35.21125030517578\n", "The 9120th loop: cost = 40.476959228515625\n", "The 9121th loop: cost = 42.854270935058594\n", "The 9122th loop: cost = 31.92971420288086\n", "The 9123th loop: cost = 37.47272872924805\n", "The 9124th loop: cost = 47.61317443847656\n", "The 9125th loop: cost = 29.466861724853516\n", "The 9126th loop: cost = 44.969024658203125\n", "The 9127th loop: cost = 38.72551345825195\n", "The 9128th loop: cost = 48.257530212402344\n", "The 9129th loop: cost = 37.37836456298828\n", "The 9130th loop: cost = 37.743797302246094\n", "The 9131th loop: cost = 45.121177673339844\n", "The 9132th loop: cost = 38.25511169433594\n", "The 9133th loop: cost = 43.30133056640625\n", "The 9134th loop: cost = 31.290130615234375\n", "The 9135th loop: cost = 35.60413360595703\n", "The 9136th loop: cost = 33.651702880859375\n", "The 9137th loop: cost = 32.3990478515625\n", "The 9138th loop: cost = 36.13356018066406\n", "The 9139th loop: cost = 41.93338394165039\n", "The 9140th loop: cost = 43.86632537841797\n", "The 9141th loop: cost = 45.05052947998047\n", "The 9142th loop: cost = 50.210018157958984\n", "The 9143th loop: cost = 46.95569610595703\n", "The 9144th loop: cost = 38.321922302246094\n", "The 9145th loop: cost = 38.122230529785156\n", "The 9146th loop: cost = 50.67568588256836\n", "The 9147th loop: cost = 38.016387939453125\n", "The 9148th loop: cost = 33.53843307495117\n", "The 9149th loop: cost = 41.0953254699707\n", "The 9150th loop: cost = 32.2573356628418\n", "The 9151th loop: cost = 36.71454620361328\n", "The 9152th loop: cost = 38.95710754394531\n", "The 9153th loop: cost = 44.88285827636719\n", "The 9154th loop: cost = 39.9183235168457\n", "The 9155th loop: cost = 29.912601470947266\n", "The 9156th loop: cost = 41.92988204956055\n", "The 9157th loop: cost = 44.80071258544922\n", "The 9158th loop: cost = 39.45672607421875\n", "The 9159th loop: cost = 36.12934875488281\n", "The 9160th loop: cost = 41.91928482055664\n", "The 9161th loop: cost = 40.794334411621094\n", "The 9162th loop: cost = 38.558509826660156\n", "The 9163th loop: cost = 29.849140167236328\n", "The 9164th loop: cost = 38.17095184326172\n", "The 9165th loop: cost = 34.950035095214844\n", "The 9166th loop: cost = 44.46598815917969\n", "The 9167th loop: cost = 33.151580810546875\n", "The 9168th loop: cost = 46.18296813964844\n", "The 9169th loop: cost = 41.864009857177734\n", "The 9170th loop: cost = 42.375450134277344\n", "The 9171th loop: cost = 37.60898208618164\n", "The 9172th loop: cost = 36.33872985839844\n", "The 9173th loop: cost = 37.409461975097656\n", "The 9174th loop: cost = 34.136863708496094\n", "The 9175th loop: cost = 41.60095977783203\n", "The 9176th loop: cost = 38.21470642089844\n", "The 9177th loop: cost = 34.84056854248047\n", "The 9178th loop: cost = 27.965545654296875\n", "The 9179th loop: cost = 41.52143096923828\n", "The 9180th loop: cost = 45.394287109375\n", "The 9181th loop: cost = 40.14735412597656\n", "The 9182th loop: cost = 45.52568817138672\n", "The 9183th loop: cost = 34.032371520996094\n", "The 9184th loop: cost = 37.86566162109375\n", "The 9185th loop: cost = 44.98529815673828\n", "The 9186th loop: cost = 32.93488311767578\n", "The 9187th loop: cost = 41.41365432739258\n", "The 9188th loop: cost = 33.13134765625\n", "The 9189th loop: cost = 40.34779739379883\n", "The 9190th loop: cost = 32.05909729003906\n", "The 9191th loop: cost = 33.904541015625\n", "The 9192th loop: cost = 31.398204803466797\n", "The 9193th loop: cost = 36.712562561035156\n", "The 9194th loop: cost = 34.84220886230469\n", "The 9195th loop: cost = 42.12971115112305\n", "The 9196th loop: cost = 52.061553955078125\n", "The 9197th loop: cost = 33.98828887939453\n", "The 9198th loop: cost = 37.797142028808594\n", "The 9199th loop: cost = 36.85432434082031\n", "The 9200th loop: cost = 40.33296203613281\n", "The 9201th loop: cost = 42.07345962524414\n", "The 9202th loop: cost = 38.076499938964844\n", "The 9203th loop: cost = 44.290916442871094\n", "The 9204th loop: cost = 52.62683868408203\n", "The 9205th loop: cost = 37.90720748901367\n", "The 9206th loop: cost = 43.12945556640625\n", "The 9207th loop: cost = 28.9542179107666\n", "The 9208th loop: cost = 35.3304328918457\n", "The 9209th loop: cost = 38.07173156738281\n", "The 9210th loop: cost = 40.46321487426758\n", "The 9211th loop: cost = 38.35768508911133\n", "The 9212th loop: cost = 42.21351623535156\n", "The 9213th loop: cost = 48.085174560546875\n", "The 9214th loop: cost = 31.150177001953125\n", "The 9215th loop: cost = 32.699951171875\n", "The 9216th loop: cost = 38.198524475097656\n", "The 9217th loop: cost = 38.210548400878906\n", "The 9218th loop: cost = 43.6468505859375\n", "The 9219th loop: cost = 42.501914978027344\n", "The 9220th loop: cost = 44.256221771240234\n", "The 9221th loop: cost = 38.71357727050781\n", "The 9222th loop: cost = 38.54481506347656\n", "The 9223th loop: cost = 37.612728118896484\n", "The 9224th loop: cost = 36.567962646484375\n", "The 9225th loop: cost = 39.9754753112793\n", "The 9226th loop: cost = 43.74273681640625\n", "The 9227th loop: cost = 40.87028503417969\n", "The 9228th loop: cost = 38.18170928955078\n", "The 9229th loop: cost = 36.58747863769531\n", "The 9230th loop: cost = 41.52701950073242\n", "The 9231th loop: cost = 45.056495666503906\n", "The 9232th loop: cost = 40.77141571044922\n", "The 9233th loop: cost = 42.89269256591797\n", "The 9234th loop: cost = 41.61232376098633\n", "The 9235th loop: cost = 43.638587951660156\n", "The 9236th loop: cost = 38.10443878173828\n", "The 9237th loop: cost = 42.67596435546875\n", "The 9238th loop: cost = 46.796268463134766\n", "The 9239th loop: cost = 42.12786102294922\n", "The 9240th loop: cost = 36.41215896606445\n", "The 9241th loop: cost = 37.453941345214844\n", "The 9242th loop: cost = 36.041996002197266\n", "The 9243th loop: cost = 42.94734191894531\n", "The 9244th loop: cost = 31.409934997558594\n", "The 9245th loop: cost = 41.821563720703125\n", "The 9246th loop: cost = 36.07426834106445\n", "The 9247th loop: cost = 39.151611328125\n", "The 9248th loop: cost = 39.114959716796875\n", "The 9249th loop: cost = 39.02351379394531\n", "The 9250th loop: cost = 40.50798034667969\n", "The 9251th loop: cost = 37.87401580810547\n", "The 9252th loop: cost = 43.51948547363281\n", "The 9253th loop: cost = 42.49720001220703\n", "The 9254th loop: cost = 37.44911193847656\n", "The 9255th loop: cost = 33.869503021240234\n", "The 9256th loop: cost = 39.57712936401367\n", "The 9257th loop: cost = 29.31247329711914\n", "The 9258th loop: cost = 45.77549743652344\n", "The 9259th loop: cost = 41.449462890625\n", "The 9260th loop: cost = 39.8867073059082\n", "The 9261th loop: cost = 50.076908111572266\n", "The 9262th loop: cost = 34.591705322265625\n", "The 9263th loop: cost = 39.488922119140625\n", "The 9264th loop: cost = 33.97151184082031\n", "The 9265th loop: cost = 43.165592193603516\n", "The 9266th loop: cost = 39.33008575439453\n", "The 9267th loop: cost = 42.4520378112793\n", "The 9268th loop: cost = 43.154720306396484\n", "The 9269th loop: cost = 33.92791748046875\n", "The 9270th loop: cost = 44.62815856933594\n", "The 9271th loop: cost = 39.84440612792969\n", "The 9272th loop: cost = 42.13086700439453\n", "The 9273th loop: cost = 41.968711853027344\n", "The 9274th loop: cost = 28.089950561523438\n", "The 9275th loop: cost = 34.00032424926758\n", "The 9276th loop: cost = 43.602516174316406\n", "The 9277th loop: cost = 39.641998291015625\n", "The 9278th loop: cost = 35.585662841796875\n", "The 9279th loop: cost = 40.54058837890625\n", "The 9280th loop: cost = 44.321144104003906\n", "The 9281th loop: cost = 42.367279052734375\n", "The 9282th loop: cost = 37.69309997558594\n", "The 9283th loop: cost = 40.96104431152344\n", "The 9284th loop: cost = 36.11738586425781\n", "The 9285th loop: cost = 36.402984619140625\n", "The 9286th loop: cost = 34.6612434387207\n", "The 9287th loop: cost = 46.70857238769531\n", "The 9288th loop: cost = 36.28312683105469\n", "The 9289th loop: cost = 41.727256774902344\n", "The 9290th loop: cost = 41.603172302246094\n", "The 9291th loop: cost = 31.354904174804688\n", "The 9292th loop: cost = 44.64217758178711\n", "The 9293th loop: cost = 38.931640625\n", "The 9294th loop: cost = 41.51910400390625\n", "The 9295th loop: cost = 34.43242645263672\n", "The 9296th loop: cost = 40.969940185546875\n", "The 9297th loop: cost = 30.07370376586914\n", "The 9298th loop: cost = 37.09226989746094\n", "The 9299th loop: cost = 36.328731536865234\n", "The 9300th loop: cost = 43.46554183959961\n", "The 9301th loop: cost = 39.06696319580078\n", "The 9302th loop: cost = 36.84436798095703\n", "The 9303th loop: cost = 45.79629898071289\n", "The 9304th loop: cost = 41.8571891784668\n", "The 9305th loop: cost = 42.018428802490234\n", "The 9306th loop: cost = 38.15575408935547\n", "The 9307th loop: cost = 37.20979690551758\n", "The 9308th loop: cost = 37.49928665161133\n", "The 9309th loop: cost = 30.892898559570312\n", "The 9310th loop: cost = 47.59172821044922\n", "The 9311th loop: cost = 31.261138916015625\n", "The 9312th loop: cost = 35.46122741699219\n", "The 9313th loop: cost = 34.2697639465332\n", "The 9314th loop: cost = 33.39353942871094\n", "The 9315th loop: cost = 50.45628356933594\n", "The 9316th loop: cost = 40.10577392578125\n", "The 9317th loop: cost = 35.87183380126953\n", "The 9318th loop: cost = 49.915557861328125\n", "The 9319th loop: cost = 33.947017669677734\n", "The 9320th loop: cost = 41.06376647949219\n", "The 9321th loop: cost = 36.25431823730469\n", "The 9322th loop: cost = 34.42521667480469\n", "The 9323th loop: cost = 42.24238586425781\n", "The 9324th loop: cost = 39.702964782714844\n", "The 9325th loop: cost = 41.14734649658203\n", "The 9326th loop: cost = 33.616294860839844\n", "The 9327th loop: cost = 47.478397369384766\n", "The 9328th loop: cost = 44.427520751953125\n", "The 9329th loop: cost = 36.63243865966797\n", "The 9330th loop: cost = 43.22641372680664\n", "The 9331th loop: cost = 33.6811408996582\n", "The 9332th loop: cost = 46.447509765625\n", "The 9333th loop: cost = 40.46062469482422\n", "The 9334th loop: cost = 35.975440979003906\n", "The 9335th loop: cost = 33.444149017333984\n", "The 9336th loop: cost = 34.19541931152344\n", "The 9337th loop: cost = 33.01944351196289\n", "The 9338th loop: cost = 39.93281936645508\n", "The 9339th loop: cost = 36.43328857421875\n", "The 9340th loop: cost = 33.552818298339844\n", "The 9341th loop: cost = 43.59967041015625\n", "The 9342th loop: cost = 44.700870513916016\n", "The 9343th loop: cost = 40.28486251831055\n", "The 9344th loop: cost = 38.73353576660156\n", "The 9345th loop: cost = 42.32217788696289\n", "The 9346th loop: cost = 46.081581115722656\n", "The 9347th loop: cost = 41.39576721191406\n", "The 9348th loop: cost = 37.19145965576172\n", "The 9349th loop: cost = 43.31060028076172\n", "The 9350th loop: cost = 50.545101165771484\n", "The 9351th loop: cost = 46.46592712402344\n", "The 9352th loop: cost = 35.39802169799805\n", "The 9353th loop: cost = 44.16492462158203\n", "The 9354th loop: cost = 41.396240234375\n", "The 9355th loop: cost = 38.3557243347168\n", "The 9356th loop: cost = 34.93727111816406\n", "The 9357th loop: cost = 40.74736785888672\n", "The 9358th loop: cost = 27.061037063598633\n", "The 9359th loop: cost = 49.19645309448242\n", "The 9360th loop: cost = 42.76764678955078\n", "The 9361th loop: cost = 35.931114196777344\n", "The 9362th loop: cost = 33.33843994140625\n", "The 9363th loop: cost = 40.29844665527344\n", "The 9364th loop: cost = 32.982887268066406\n", "The 9365th loop: cost = 48.02842712402344\n", "The 9366th loop: cost = 33.159019470214844\n", "The 9367th loop: cost = 34.98463439941406\n", "The 9368th loop: cost = 29.75546646118164\n", "The 9369th loop: cost = 35.357398986816406\n", "The 9370th loop: cost = 31.489885330200195\n", "The 9371th loop: cost = 34.03279113769531\n", "The 9372th loop: cost = 27.039325714111328\n", "The 9373th loop: cost = 43.09394073486328\n", "The 9374th loop: cost = 30.102209091186523\n", "The 9375th loop: cost = 45.746673583984375\n", "The 9376th loop: cost = 43.25328826904297\n", "The 9377th loop: cost = 40.15380859375\n", "The 9378th loop: cost = 34.616432189941406\n", "The 9379th loop: cost = 37.71324920654297\n", "The 9380th loop: cost = 28.086578369140625\n", "The 9381th loop: cost = 45.95178985595703\n", "The 9382th loop: cost = 40.28638458251953\n", "The 9383th loop: cost = 33.22051239013672\n", "The 9384th loop: cost = 37.13579177856445\n", "The 9385th loop: cost = 42.2487907409668\n", "The 9386th loop: cost = 42.45834732055664\n", "The 9387th loop: cost = 34.602439880371094\n", "The 9388th loop: cost = 45.039344787597656\n", "The 9389th loop: cost = 36.875083923339844\n", "The 9390th loop: cost = 44.346458435058594\n", "The 9391th loop: cost = 41.79312515258789\n", "The 9392th loop: cost = 36.722312927246094\n", "The 9393th loop: cost = 35.613075256347656\n", "The 9394th loop: cost = 35.86806869506836\n", "The 9395th loop: cost = 35.96407699584961\n", "The 9396th loop: cost = 48.44361877441406\n", "The 9397th loop: cost = 38.92280578613281\n", "The 9398th loop: cost = 43.41639709472656\n", "The 9399th loop: cost = 36.05657958984375\n", "The 9400th loop: cost = 30.56341552734375\n", "The 9401th loop: cost = 45.60392761230469\n", "The 9402th loop: cost = 37.024173736572266\n", "The 9403th loop: cost = 42.181068420410156\n", "The 9404th loop: cost = 45.65129852294922\n", "The 9405th loop: cost = 34.46426773071289\n", "The 9406th loop: cost = 35.14775085449219\n", "The 9407th loop: cost = 30.199419021606445\n", "The 9408th loop: cost = 43.09237289428711\n", "The 9409th loop: cost = 38.23017120361328\n", "The 9410th loop: cost = 38.10208511352539\n", "The 9411th loop: cost = 42.90146255493164\n", "The 9412th loop: cost = 28.899085998535156\n", "The 9413th loop: cost = 42.51641845703125\n", "The 9414th loop: cost = 38.71173858642578\n", "The 9415th loop: cost = 34.13459777832031\n", "The 9416th loop: cost = 44.668548583984375\n", "The 9417th loop: cost = 33.76189041137695\n", "The 9418th loop: cost = 33.45197296142578\n", "The 9419th loop: cost = 33.50151443481445\n", "The 9420th loop: cost = 41.969749450683594\n", "The 9421th loop: cost = 39.78157424926758\n", "The 9422th loop: cost = 33.45237350463867\n", "The 9423th loop: cost = 41.60182189941406\n", "The 9424th loop: cost = 42.73063278198242\n", "The 9425th loop: cost = 38.23682403564453\n", "The 9426th loop: cost = 46.522796630859375\n", "The 9427th loop: cost = 43.48723602294922\n", "The 9428th loop: cost = 47.20988464355469\n", "The 9429th loop: cost = 41.26146697998047\n", "The 9430th loop: cost = 38.93503952026367\n", "The 9431th loop: cost = 33.74443435668945\n", "The 9432th loop: cost = 35.637420654296875\n", "The 9433th loop: cost = 43.38749694824219\n", "The 9434th loop: cost = 29.236234664916992\n", "The 9435th loop: cost = 29.337186813354492\n", "The 9436th loop: cost = 42.09040069580078\n", "The 9437th loop: cost = 39.1226806640625\n", "The 9438th loop: cost = 38.10110092163086\n", "The 9439th loop: cost = 44.820980072021484\n", "The 9440th loop: cost = 37.32002258300781\n", "The 9441th loop: cost = 44.43507766723633\n", "The 9442th loop: cost = 37.73625946044922\n", "The 9443th loop: cost = 42.5001220703125\n", "The 9444th loop: cost = 35.00013732910156\n", "The 9445th loop: cost = 33.26951599121094\n", "The 9446th loop: cost = 39.308494567871094\n", "The 9447th loop: cost = 42.57963562011719\n", "The 9448th loop: cost = 31.42436981201172\n", "The 9449th loop: cost = 35.30902099609375\n", "The 9450th loop: cost = 40.00871276855469\n", "The 9451th loop: cost = 41.723236083984375\n", "The 9452th loop: cost = 37.847434997558594\n", "The 9453th loop: cost = 32.33330535888672\n", "The 9454th loop: cost = 34.336002349853516\n", "The 9455th loop: cost = 40.42182922363281\n", "The 9456th loop: cost = 44.369171142578125\n", "The 9457th loop: cost = 32.028438568115234\n", "The 9458th loop: cost = 37.5742073059082\n", "The 9459th loop: cost = 42.30547332763672\n", "The 9460th loop: cost = 40.49286651611328\n", "The 9461th loop: cost = 26.795515060424805\n", "The 9462th loop: cost = 42.18663024902344\n", "The 9463th loop: cost = 28.687162399291992\n", "The 9464th loop: cost = 38.46883773803711\n", "The 9465th loop: cost = 36.665260314941406\n", "The 9466th loop: cost = 42.06806945800781\n", "The 9467th loop: cost = 38.155029296875\n", "The 9468th loop: cost = 44.28679656982422\n", "The 9469th loop: cost = 36.892269134521484\n", "The 9470th loop: cost = 39.7236213684082\n", "The 9471th loop: cost = 29.999839782714844\n", "The 9472th loop: cost = 35.63159942626953\n", "The 9473th loop: cost = 32.598968505859375\n", "The 9474th loop: cost = 44.300437927246094\n", "The 9475th loop: cost = 29.646411895751953\n", "The 9476th loop: cost = 38.781768798828125\n", "The 9477th loop: cost = 40.18714904785156\n", "The 9478th loop: cost = 33.05223083496094\n", "The 9479th loop: cost = 43.57708740234375\n", "The 9480th loop: cost = 45.36193084716797\n", "The 9481th loop: cost = 32.640625\n", "The 9482th loop: cost = 39.69495391845703\n", "The 9483th loop: cost = 45.218841552734375\n", "The 9484th loop: cost = 36.6734619140625\n", "The 9485th loop: cost = 40.17047119140625\n", "The 9486th loop: cost = 36.73199462890625\n", "The 9487th loop: cost = 44.61871337890625\n", "The 9488th loop: cost = 35.609893798828125\n", "The 9489th loop: cost = 37.79257583618164\n", "The 9490th loop: cost = 33.64369201660156\n", "The 9491th loop: cost = 43.45238494873047\n", "The 9492th loop: cost = 35.143280029296875\n", "The 9493th loop: cost = 41.03208923339844\n", "The 9494th loop: cost = 45.01775360107422\n", "The 9495th loop: cost = 56.19884490966797\n", "The 9496th loop: cost = 36.778907775878906\n", "The 9497th loop: cost = 40.84330368041992\n", "The 9498th loop: cost = 44.99199295043945\n", "The 9499th loop: cost = 42.330543518066406\n", "The 9500th loop: cost = 36.26584243774414\n", "The 9501th loop: cost = 39.23828125\n", "The 9502th loop: cost = 37.3475227355957\n", "The 9503th loop: cost = 41.30017852783203\n", "The 9504th loop: cost = 39.000022888183594\n", "The 9505th loop: cost = 38.37831497192383\n", "The 9506th loop: cost = 28.35598373413086\n", "The 9507th loop: cost = 32.128631591796875\n", "The 9508th loop: cost = 35.749542236328125\n", "The 9509th loop: cost = 33.516448974609375\n", "The 9510th loop: cost = 43.08030700683594\n", "The 9511th loop: cost = 43.357337951660156\n", "The 9512th loop: cost = 40.766021728515625\n", "The 9513th loop: cost = 35.506893157958984\n", "The 9514th loop: cost = 37.027976989746094\n", "The 9515th loop: cost = 40.7028694152832\n", "The 9516th loop: cost = 38.12574768066406\n", "The 9517th loop: cost = 39.54373550415039\n", "The 9518th loop: cost = 39.696163177490234\n", "The 9519th loop: cost = 41.339359283447266\n", "The 9520th loop: cost = 42.435081481933594\n", "The 9521th loop: cost = 37.08725357055664\n", "The 9522th loop: cost = 37.23802185058594\n", "The 9523th loop: cost = 41.830867767333984\n", "The 9524th loop: cost = 35.93736267089844\n", "The 9525th loop: cost = 38.9080810546875\n", "The 9526th loop: cost = 42.55622863769531\n", "The 9527th loop: cost = 38.628562927246094\n", "The 9528th loop: cost = 44.41012191772461\n", "The 9529th loop: cost = 35.23018264770508\n", "The 9530th loop: cost = 37.89532470703125\n", "The 9531th loop: cost = 44.80152893066406\n", "The 9532th loop: cost = 41.847320556640625\n", "The 9533th loop: cost = 38.49544906616211\n", "The 9534th loop: cost = 31.40544891357422\n", "The 9535th loop: cost = 40.13739013671875\n", "The 9536th loop: cost = 36.75431823730469\n", "The 9537th loop: cost = 29.47282600402832\n", "The 9538th loop: cost = 34.71357727050781\n", "The 9539th loop: cost = 44.758968353271484\n", "The 9540th loop: cost = 38.49997329711914\n", "The 9541th loop: cost = 38.459266662597656\n", "The 9542th loop: cost = 32.190765380859375\n", "The 9543th loop: cost = 34.09503936767578\n", "The 9544th loop: cost = 35.184730529785156\n", "The 9545th loop: cost = 39.80438232421875\n", "The 9546th loop: cost = 33.89122772216797\n", "The 9547th loop: cost = 44.21684265136719\n", "The 9548th loop: cost = 47.53667068481445\n", "The 9549th loop: cost = 40.88872528076172\n", "The 9550th loop: cost = 33.98638916015625\n", "The 9551th loop: cost = 48.1400146484375\n", "The 9552th loop: cost = 44.17111587524414\n", "The 9553th loop: cost = 42.58086395263672\n", "The 9554th loop: cost = 40.03517150878906\n", "The 9555th loop: cost = 42.46106719970703\n", "The 9556th loop: cost = 40.60105895996094\n", "The 9557th loop: cost = 34.818443298339844\n", "The 9558th loop: cost = 49.6126823425293\n", "The 9559th loop: cost = 39.312164306640625\n", "The 9560th loop: cost = 45.07861328125\n", "The 9561th loop: cost = 37.262123107910156\n", "The 9562th loop: cost = 40.84870910644531\n", "The 9563th loop: cost = 38.52655792236328\n", "The 9564th loop: cost = 44.68540954589844\n", "The 9565th loop: cost = 30.343975067138672\n", "The 9566th loop: cost = 40.74165725708008\n", "The 9567th loop: cost = 32.00666809082031\n", "The 9568th loop: cost = 37.475318908691406\n", "The 9569th loop: cost = 36.7268180847168\n", "The 9570th loop: cost = 37.88422393798828\n", "The 9571th loop: cost = 34.76664352416992\n", "The 9572th loop: cost = 47.932594299316406\n", "The 9573th loop: cost = 36.844520568847656\n", "The 9574th loop: cost = 36.395591735839844\n", "The 9575th loop: cost = 45.50068283081055\n", "The 9576th loop: cost = 33.34916687011719\n", "The 9577th loop: cost = 34.49527359008789\n", "The 9578th loop: cost = 43.54302215576172\n", "The 9579th loop: cost = 41.28541564941406\n", "The 9580th loop: cost = 37.00193405151367\n", "The 9581th loop: cost = 39.18766784667969\n", "The 9582th loop: cost = 37.00284957885742\n", "The 9583th loop: cost = 39.14315414428711\n", "The 9584th loop: cost = 41.214237213134766\n", "The 9585th loop: cost = 38.943824768066406\n", "The 9586th loop: cost = 39.39312744140625\n", "The 9587th loop: cost = 35.77799987792969\n", "The 9588th loop: cost = 37.21250534057617\n", "The 9589th loop: cost = 44.06049728393555\n", "The 9590th loop: cost = 42.25172424316406\n", "The 9591th loop: cost = 41.68397903442383\n", "The 9592th loop: cost = 42.588958740234375\n", "The 9593th loop: cost = 29.572372436523438\n", "The 9594th loop: cost = 40.050167083740234\n", "The 9595th loop: cost = 31.53036117553711\n", "The 9596th loop: cost = 40.5235481262207\n", "The 9597th loop: cost = 42.096134185791016\n", "The 9598th loop: cost = 41.47687530517578\n", "The 9599th loop: cost = 36.0107421875\n", "The 9600th loop: cost = 32.57842254638672\n", "The 9601th loop: cost = 40.990966796875\n", "The 9602th loop: cost = 46.94084167480469\n", "The 9603th loop: cost = 42.95104217529297\n", "The 9604th loop: cost = 35.959556579589844\n", "The 9605th loop: cost = 44.77911376953125\n", "The 9606th loop: cost = 47.956748962402344\n", "The 9607th loop: cost = 40.59442138671875\n", "The 9608th loop: cost = 39.22296905517578\n", "The 9609th loop: cost = 42.075035095214844\n", "The 9610th loop: cost = 43.25605773925781\n", "The 9611th loop: cost = 38.21661376953125\n", "The 9612th loop: cost = 35.306739807128906\n", "The 9613th loop: cost = 37.591331481933594\n", "The 9614th loop: cost = 41.42005157470703\n", "The 9615th loop: cost = 39.29596710205078\n", "The 9616th loop: cost = 35.754920959472656\n", "The 9617th loop: cost = 43.98155212402344\n", "The 9618th loop: cost = 33.8994026184082\n", "The 9619th loop: cost = 34.43073654174805\n", "The 9620th loop: cost = 40.108055114746094\n", "The 9621th loop: cost = 33.19169998168945\n", "The 9622th loop: cost = 35.29106140136719\n", "The 9623th loop: cost = 36.29972457885742\n", "The 9624th loop: cost = 33.03300094604492\n", "The 9625th loop: cost = 32.36577606201172\n", "The 9626th loop: cost = 34.190731048583984\n", "The 9627th loop: cost = 32.987396240234375\n", "The 9628th loop: cost = 43.468719482421875\n", "The 9629th loop: cost = 40.53399658203125\n", "The 9630th loop: cost = 39.60630798339844\n", "The 9631th loop: cost = 36.45488739013672\n", "The 9632th loop: cost = 34.498558044433594\n", "The 9633th loop: cost = 34.43024444580078\n", "The 9634th loop: cost = 43.917625427246094\n", "The 9635th loop: cost = 40.42759704589844\n", "The 9636th loop: cost = 41.264404296875\n", "The 9637th loop: cost = 31.5445556640625\n", "The 9638th loop: cost = 40.64882278442383\n", "The 9639th loop: cost = 33.68548583984375\n", "The 9640th loop: cost = 36.04989242553711\n", "The 9641th loop: cost = 39.94231414794922\n", "The 9642th loop: cost = 33.829742431640625\n", "The 9643th loop: cost = 35.92433166503906\n", "The 9644th loop: cost = 37.00309753417969\n", "The 9645th loop: cost = 32.60129165649414\n", "The 9646th loop: cost = 32.77446365356445\n", "The 9647th loop: cost = 36.75334930419922\n", "The 9648th loop: cost = 52.64977264404297\n", "The 9649th loop: cost = 40.91779327392578\n", "The 9650th loop: cost = 36.19812774658203\n", "The 9651th loop: cost = 37.803741455078125\n", "The 9652th loop: cost = 33.200531005859375\n", "The 9653th loop: cost = 41.52570343017578\n", "The 9654th loop: cost = 30.97350311279297\n", "The 9655th loop: cost = 43.756839752197266\n", "The 9656th loop: cost = 38.795902252197266\n", "The 9657th loop: cost = 34.56065368652344\n", "The 9658th loop: cost = 37.59466552734375\n", "The 9659th loop: cost = 37.91743469238281\n", "The 9660th loop: cost = 42.90264892578125\n", "The 9661th loop: cost = 34.001914978027344\n", "The 9662th loop: cost = 39.53968811035156\n", "The 9663th loop: cost = 32.86030960083008\n", "The 9664th loop: cost = 39.40875244140625\n", "The 9665th loop: cost = 32.326141357421875\n", "The 9666th loop: cost = 38.47021484375\n", "The 9667th loop: cost = 38.535667419433594\n", "The 9668th loop: cost = 34.07212829589844\n", "The 9669th loop: cost = 37.998741149902344\n", "The 9670th loop: cost = 36.76229476928711\n", "The 9671th loop: cost = 40.62460708618164\n", "The 9672th loop: cost = 40.48168182373047\n", "The 9673th loop: cost = 43.5047607421875\n", "The 9674th loop: cost = 32.52157974243164\n", "The 9675th loop: cost = 38.91804122924805\n", "The 9676th loop: cost = 45.68385314941406\n", "The 9677th loop: cost = 33.209903717041016\n", "The 9678th loop: cost = 45.389495849609375\n", "The 9679th loop: cost = 36.769691467285156\n", "The 9680th loop: cost = 34.942710876464844\n", "The 9681th loop: cost = 42.40666198730469\n", "The 9682th loop: cost = 41.8930549621582\n", "The 9683th loop: cost = 48.01441192626953\n", "The 9684th loop: cost = 38.407142639160156\n", "The 9685th loop: cost = 34.23613739013672\n", "The 9686th loop: cost = 35.26170349121094\n", "The 9687th loop: cost = 44.545040130615234\n", "The 9688th loop: cost = 35.39664840698242\n", "The 9689th loop: cost = 39.5942497253418\n", "The 9690th loop: cost = 31.98857879638672\n", "The 9691th loop: cost = 44.809295654296875\n", "The 9692th loop: cost = 39.182777404785156\n", "The 9693th loop: cost = 35.33641815185547\n", "The 9694th loop: cost = 37.65153503417969\n", "The 9695th loop: cost = 32.432796478271484\n", "The 9696th loop: cost = 38.846580505371094\n", "The 9697th loop: cost = 30.019548416137695\n", "The 9698th loop: cost = 35.60443878173828\n", "The 9699th loop: cost = 42.381202697753906\n", "The 9700th loop: cost = 40.132530212402344\n", "The 9701th loop: cost = 30.20656967163086\n", "The 9702th loop: cost = 33.8607177734375\n", "The 9703th loop: cost = 45.37010955810547\n", "The 9704th loop: cost = 38.84602737426758\n", "The 9705th loop: cost = 45.98297119140625\n", "The 9706th loop: cost = 40.863502502441406\n", "The 9707th loop: cost = 47.20805740356445\n", "The 9708th loop: cost = 32.720001220703125\n", "The 9709th loop: cost = 43.04017639160156\n", "The 9710th loop: cost = 37.31940841674805\n", "The 9711th loop: cost = 34.213356018066406\n", "The 9712th loop: cost = 36.15662384033203\n", "The 9713th loop: cost = 38.99754333496094\n", "The 9714th loop: cost = 36.72233581542969\n", "The 9715th loop: cost = 38.002681732177734\n", "The 9716th loop: cost = 41.15308380126953\n", "The 9717th loop: cost = 44.789093017578125\n", "The 9718th loop: cost = 36.64676284790039\n", "The 9719th loop: cost = 34.532623291015625\n", "The 9720th loop: cost = 28.352285385131836\n", "The 9721th loop: cost = 45.293434143066406\n", "The 9722th loop: cost = 43.06444549560547\n", "The 9723th loop: cost = 41.498313903808594\n", "The 9724th loop: cost = 41.09702682495117\n", "The 9725th loop: cost = 35.50408935546875\n", "The 9726th loop: cost = 48.0738525390625\n", "The 9727th loop: cost = 42.53617477416992\n", "The 9728th loop: cost = 38.84605407714844\n", "The 9729th loop: cost = 30.302425384521484\n", "The 9730th loop: cost = 35.716712951660156\n", "The 9731th loop: cost = 40.02197265625\n", "The 9732th loop: cost = 39.100833892822266\n", "The 9733th loop: cost = 47.39851760864258\n", "The 9734th loop: cost = 44.28926467895508\n", "The 9735th loop: cost = 39.79460144042969\n", "The 9736th loop: cost = 37.289222717285156\n", "The 9737th loop: cost = 37.406715393066406\n", "The 9738th loop: cost = 29.91278076171875\n", "The 9739th loop: cost = 38.87898254394531\n", "The 9740th loop: cost = 35.60134506225586\n", "The 9741th loop: cost = 49.50718307495117\n", "The 9742th loop: cost = 37.806846618652344\n", "The 9743th loop: cost = 39.55406951904297\n", "The 9744th loop: cost = 46.54593276977539\n", "The 9745th loop: cost = 27.863431930541992\n", "The 9746th loop: cost = 37.8957633972168\n", "The 9747th loop: cost = 41.587799072265625\n", "The 9748th loop: cost = 38.81500244140625\n", "The 9749th loop: cost = 33.989013671875\n", "The 9750th loop: cost = 39.68983459472656\n", "The 9751th loop: cost = 39.289161682128906\n", "The 9752th loop: cost = 32.77019500732422\n", "The 9753th loop: cost = 45.59872055053711\n", "The 9754th loop: cost = 39.41949462890625\n", "The 9755th loop: cost = 29.835853576660156\n", "The 9756th loop: cost = 33.36128234863281\n", "The 9757th loop: cost = 44.08922576904297\n", "The 9758th loop: cost = 46.12342834472656\n", "The 9759th loop: cost = 44.811119079589844\n", "The 9760th loop: cost = 40.78929901123047\n", "The 9761th loop: cost = 41.236061096191406\n", "The 9762th loop: cost = 35.550785064697266\n", "The 9763th loop: cost = 26.93898582458496\n", "The 9764th loop: cost = 37.70811462402344\n", "The 9765th loop: cost = 30.25103187561035\n", "The 9766th loop: cost = 37.17918014526367\n", "The 9767th loop: cost = 37.331581115722656\n", "The 9768th loop: cost = 49.57171630859375\n", "The 9769th loop: cost = 35.40398406982422\n", "The 9770th loop: cost = 36.433197021484375\n", "The 9771th loop: cost = 40.99264144897461\n", "The 9772th loop: cost = 32.045082092285156\n", "The 9773th loop: cost = 40.946189880371094\n", "The 9774th loop: cost = 34.685726165771484\n", "The 9775th loop: cost = 43.9257926940918\n", "The 9776th loop: cost = 44.94942092895508\n", "The 9777th loop: cost = 35.08818054199219\n", "The 9778th loop: cost = 42.71827697753906\n", "The 9779th loop: cost = 33.70726013183594\n", "The 9780th loop: cost = 36.94414520263672\n", "The 9781th loop: cost = 42.273963928222656\n", "The 9782th loop: cost = 36.838600158691406\n", "The 9783th loop: cost = 34.51239776611328\n", "The 9784th loop: cost = 34.656654357910156\n", "The 9785th loop: cost = 40.035369873046875\n", "The 9786th loop: cost = 33.410301208496094\n", "The 9787th loop: cost = 31.285667419433594\n", "The 9788th loop: cost = 32.951576232910156\n", "The 9789th loop: cost = 34.795352935791016\n", "The 9790th loop: cost = 39.2990608215332\n", "The 9791th loop: cost = 49.36509704589844\n", "The 9792th loop: cost = 42.79209899902344\n", "The 9793th loop: cost = 45.36566162109375\n", "The 9794th loop: cost = 35.10910415649414\n", "The 9795th loop: cost = 43.68212127685547\n", "The 9796th loop: cost = 35.94062805175781\n", "The 9797th loop: cost = 46.4205322265625\n", "The 9798th loop: cost = 35.236114501953125\n", "The 9799th loop: cost = 37.13226318359375\n", "The 9800th loop: cost = 36.41119384765625\n", "The 9801th loop: cost = 31.93439483642578\n", "The 9802th loop: cost = 42.231101989746094\n", "The 9803th loop: cost = 38.32126235961914\n", "The 9804th loop: cost = 31.19906997680664\n", "The 9805th loop: cost = 60.07262420654297\n", "The 9806th loop: cost = 34.75176239013672\n", "The 9807th loop: cost = 36.32101058959961\n", "The 9808th loop: cost = 33.150146484375\n", "The 9809th loop: cost = 45.71544647216797\n", "The 9810th loop: cost = 39.83050537109375\n", "The 9811th loop: cost = 47.26176452636719\n", "The 9812th loop: cost = 43.47835159301758\n", "The 9813th loop: cost = 38.5040283203125\n", "The 9814th loop: cost = 40.703224182128906\n", "The 9815th loop: cost = 44.60791015625\n", "The 9816th loop: cost = 33.127685546875\n", "The 9817th loop: cost = 33.929962158203125\n", "The 9818th loop: cost = 27.840408325195312\n", "The 9819th loop: cost = 37.775447845458984\n", "The 9820th loop: cost = 39.413055419921875\n", "The 9821th loop: cost = 34.173423767089844\n", "The 9822th loop: cost = 34.06373977661133\n", "The 9823th loop: cost = 41.2939338684082\n", "The 9824th loop: cost = 40.25242614746094\n", "The 9825th loop: cost = 39.815528869628906\n", "The 9826th loop: cost = 35.047821044921875\n", "The 9827th loop: cost = 38.119911193847656\n", "The 9828th loop: cost = 46.228702545166016\n", "The 9829th loop: cost = 47.78974151611328\n", "The 9830th loop: cost = 36.06829833984375\n", "The 9831th loop: cost = 48.095916748046875\n", "The 9832th loop: cost = 36.777870178222656\n", "The 9833th loop: cost = 43.710662841796875\n", "The 9834th loop: cost = 34.79026794433594\n", "The 9835th loop: cost = 29.57447052001953\n", "The 9836th loop: cost = 36.444740295410156\n", "The 9837th loop: cost = 42.46959686279297\n", "The 9838th loop: cost = 39.448890686035156\n", "The 9839th loop: cost = 31.600860595703125\n", "The 9840th loop: cost = 47.059852600097656\n", "The 9841th loop: cost = 34.643333435058594\n", "The 9842th loop: cost = 45.73127365112305\n", "The 9843th loop: cost = 25.733325958251953\n", "The 9844th loop: cost = 33.68402862548828\n", "The 9845th loop: cost = 46.912437438964844\n", "The 9846th loop: cost = 39.08064270019531\n", "The 9847th loop: cost = 36.159019470214844\n", "The 9848th loop: cost = 38.84857177734375\n", "The 9849th loop: cost = 36.57777404785156\n", "The 9850th loop: cost = 35.184959411621094\n", "The 9851th loop: cost = 42.33354568481445\n", "The 9852th loop: cost = 29.56744384765625\n", "The 9853th loop: cost = 31.2514705657959\n", "The 9854th loop: cost = 36.14055633544922\n", "The 9855th loop: cost = 44.397464752197266\n", "The 9856th loop: cost = 42.50204086303711\n", "The 9857th loop: cost = 44.21248245239258\n", "The 9858th loop: cost = 33.77891540527344\n", "The 9859th loop: cost = 42.12818908691406\n", "The 9860th loop: cost = 36.60529327392578\n", "The 9861th loop: cost = 47.47402572631836\n", "The 9862th loop: cost = 40.919036865234375\n", "The 9863th loop: cost = 43.29814910888672\n", "The 9864th loop: cost = 40.02105712890625\n", "The 9865th loop: cost = 35.838134765625\n", "The 9866th loop: cost = 34.785560607910156\n", "The 9867th loop: cost = 41.45759582519531\n", "The 9868th loop: cost = 40.631832122802734\n", "The 9869th loop: cost = 37.063011169433594\n", "The 9870th loop: cost = 44.077945709228516\n", "The 9871th loop: cost = 40.69874954223633\n", "The 9872th loop: cost = 44.115684509277344\n", "The 9873th loop: cost = 47.16767883300781\n", "The 9874th loop: cost = 44.60930633544922\n", "The 9875th loop: cost = 37.91688537597656\n", "The 9876th loop: cost = 39.11265182495117\n", "The 9877th loop: cost = 43.934593200683594\n", "The 9878th loop: cost = 34.34791564941406\n", "The 9879th loop: cost = 37.39424133300781\n", "The 9880th loop: cost = 37.70379638671875\n", "The 9881th loop: cost = 40.16844177246094\n", "The 9882th loop: cost = 43.265464782714844\n", "The 9883th loop: cost = 39.904327392578125\n", "The 9884th loop: cost = 33.63810729980469\n", "The 9885th loop: cost = 45.1481819152832\n", "The 9886th loop: cost = 36.159358978271484\n", "The 9887th loop: cost = 38.07084655761719\n", "The 9888th loop: cost = 36.60687255859375\n", "The 9889th loop: cost = 37.826385498046875\n", "The 9890th loop: cost = 36.971778869628906\n", "The 9891th loop: cost = 43.55581283569336\n", "The 9892th loop: cost = 40.193172454833984\n", "The 9893th loop: cost = 35.154640197753906\n", "The 9894th loop: cost = 39.306861877441406\n", "The 9895th loop: cost = 47.55513000488281\n", "The 9896th loop: cost = 37.785316467285156\n", "The 9897th loop: cost = 37.34926223754883\n", "The 9898th loop: cost = 36.29230499267578\n", "The 9899th loop: cost = 38.1198616027832\n", "The 9900th loop: cost = 42.40103530883789\n", "The 9901th loop: cost = 46.89464569091797\n", "The 9902th loop: cost = 38.944095611572266\n", "The 9903th loop: cost = 38.58812713623047\n", "The 9904th loop: cost = 31.347095489501953\n", "The 9905th loop: cost = 38.60408020019531\n", "The 9906th loop: cost = 41.18183135986328\n", "The 9907th loop: cost = 37.971797943115234\n", "The 9908th loop: cost = 40.87615203857422\n", "The 9909th loop: cost = 42.01902770996094\n", "The 9910th loop: cost = 44.722808837890625\n", "The 9911th loop: cost = 49.891143798828125\n", "The 9912th loop: cost = 45.77598190307617\n", "The 9913th loop: cost = 40.599037170410156\n", "The 9914th loop: cost = 36.30198287963867\n", "The 9915th loop: cost = 37.65924072265625\n", "The 9916th loop: cost = 42.56865692138672\n", "The 9917th loop: cost = 44.438865661621094\n", "The 9918th loop: cost = 42.727012634277344\n", "The 9919th loop: cost = 44.110939025878906\n", "The 9920th loop: cost = 43.759666442871094\n", "The 9921th loop: cost = 42.48270797729492\n", "The 9922th loop: cost = 41.904483795166016\n", "The 9923th loop: cost = 41.26722717285156\n", "The 9924th loop: cost = 35.762603759765625\n", "The 9925th loop: cost = 37.371002197265625\n", "The 9926th loop: cost = 34.01274108886719\n", "The 9927th loop: cost = 42.82762908935547\n", "The 9928th loop: cost = 38.378318786621094\n", "The 9929th loop: cost = 36.09992218017578\n", "The 9930th loop: cost = 43.65594482421875\n", "The 9931th loop: cost = 42.25977325439453\n", "The 9932th loop: cost = 31.482486724853516\n", "The 9933th loop: cost = 43.77809143066406\n", "The 9934th loop: cost = 34.013633728027344\n", "The 9935th loop: cost = 33.5751953125\n", "The 9936th loop: cost = 33.286170959472656\n", "The 9937th loop: cost = 48.90735626220703\n", "The 9938th loop: cost = 38.46910095214844\n", "The 9939th loop: cost = 40.388519287109375\n", "The 9940th loop: cost = 43.31803894042969\n", "The 9941th loop: cost = 38.79204177856445\n", "The 9942th loop: cost = 34.48809814453125\n", "The 9943th loop: cost = 37.886940002441406\n", "The 9944th loop: cost = 34.28899002075195\n", "The 9945th loop: cost = 36.593414306640625\n", "The 9946th loop: cost = 47.87156295776367\n", "The 9947th loop: cost = 40.362361907958984\n", "The 9948th loop: cost = 37.21690368652344\n", "The 9949th loop: cost = 36.05809783935547\n", "The 9950th loop: cost = 40.82745361328125\n", "The 9951th loop: cost = 25.932498931884766\n", "The 9952th loop: cost = 35.84196472167969\n", "The 9953th loop: cost = 41.98323440551758\n", "The 9954th loop: cost = 42.33378982543945\n", "The 9955th loop: cost = 40.802040100097656\n", "The 9956th loop: cost = 35.921024322509766\n", "The 9957th loop: cost = 41.58497619628906\n", "The 9958th loop: cost = 47.3036003112793\n", "The 9959th loop: cost = 30.914981842041016\n", "The 9960th loop: cost = 33.277015686035156\n", "The 9961th loop: cost = 44.593509674072266\n", "The 9962th loop: cost = 45.44081115722656\n", "The 9963th loop: cost = 31.92669677734375\n", "The 9964th loop: cost = 33.6871337890625\n", "The 9965th loop: cost = 29.600126266479492\n", "The 9966th loop: cost = 45.96115493774414\n", "The 9967th loop: cost = 37.31585693359375\n", "The 9968th loop: cost = 37.98550033569336\n", "The 9969th loop: cost = 39.66796112060547\n", "The 9970th loop: cost = 37.396522521972656\n", "The 9971th loop: cost = 40.73110580444336\n", "The 9972th loop: cost = 35.125816345214844\n", "The 9973th loop: cost = 47.663330078125\n", "The 9974th loop: cost = 32.466712951660156\n", "The 9975th loop: cost = 35.37537384033203\n", "The 9976th loop: cost = 40.349830627441406\n", "The 9977th loop: cost = 42.417972564697266\n", "The 9978th loop: cost = 35.095550537109375\n", "The 9979th loop: cost = 33.018028259277344\n", "The 9980th loop: cost = 40.67808532714844\n", "The 9981th loop: cost = 49.910682678222656\n", "The 9982th loop: cost = 36.91895294189453\n", "The 9983th loop: cost = 41.60586166381836\n", "The 9984th loop: cost = 41.26750946044922\n", "The 9985th loop: cost = 32.087467193603516\n", "The 9986th loop: cost = 39.78807067871094\n", "The 9987th loop: cost = 44.65877151489258\n", "The 9988th loop: cost = 33.46959686279297\n", "The 9989th loop: cost = 41.72019958496094\n", "The 9990th loop: cost = 36.13581085205078\n", "The 9991th loop: cost = 38.33403015136719\n", "The 9992th loop: cost = 35.23911666870117\n", "The 9993th loop: cost = 39.81338882446289\n", "The 9994th loop: cost = 36.800819396972656\n", "The 9995th loop: cost = 39.99892807006836\n", "The 9996th loop: cost = 40.118408203125\n", "The 9997th loop: cost = 38.40652084350586\n", "The 9998th loop: cost = 45.97956085205078\n", "The 9999th loop: cost = 37.921173095703125\n", "The 10000th loop: cost = 38.46497344970703\n", "The 10001th loop: cost = 34.01976013183594\n", "The 10002th loop: cost = 34.039031982421875\n", "The 10003th loop: cost = 37.41211700439453\n", "The 10004th loop: cost = 41.7489013671875\n", "The 10005th loop: cost = 36.766334533691406\n", "The 10006th loop: cost = 39.879119873046875\n", "The 10007th loop: cost = 33.8226203918457\n", "The 10008th loop: cost = 44.16876220703125\n", "The 10009th loop: cost = 38.939598083496094\n", "The 10010th loop: cost = 43.97517013549805\n", "The 10011th loop: cost = 31.49547576904297\n", "The 10012th loop: cost = 32.47258377075195\n", "The 10013th loop: cost = 44.0548095703125\n", "The 10014th loop: cost = 39.62506866455078\n", "The 10015th loop: cost = 36.760894775390625\n", "The 10016th loop: cost = 38.66531753540039\n", "The 10017th loop: cost = 39.75421142578125\n", "The 10018th loop: cost = 39.277557373046875\n", "The 10019th loop: cost = 45.26525115966797\n", "The 10020th loop: cost = 35.778076171875\n", "The 10021th loop: cost = 37.542205810546875\n", "The 10022th loop: cost = 39.57221603393555\n", "The 10023th loop: cost = 36.548336029052734\n", "The 10024th loop: cost = 33.747066497802734\n", "The 10025th loop: cost = 43.34529495239258\n", "The 10026th loop: cost = 36.31358337402344\n", "The 10027th loop: cost = 39.64590072631836\n", "The 10028th loop: cost = 41.027931213378906\n", "The 10029th loop: cost = 37.52491760253906\n", "The 10030th loop: cost = 35.81232452392578\n", "The 10031th loop: cost = 33.60285186767578\n", "The 10032th loop: cost = 38.47850799560547\n", "The 10033th loop: cost = 39.54277801513672\n", "The 10034th loop: cost = 36.40489959716797\n", "The 10035th loop: cost = 29.553617477416992\n", "The 10036th loop: cost = 37.531822204589844\n", "The 10037th loop: cost = 44.445106506347656\n", "The 10038th loop: cost = 41.530086517333984\n", "The 10039th loop: cost = 38.48677062988281\n", "The 10040th loop: cost = 35.63103485107422\n", "The 10041th loop: cost = 33.54083251953125\n", "The 10042th loop: cost = 43.711151123046875\n", "The 10043th loop: cost = 43.372772216796875\n", "The 10044th loop: cost = 37.00628662109375\n", "The 10045th loop: cost = 38.43501281738281\n", "The 10046th loop: cost = 38.63870620727539\n", "The 10047th loop: cost = 44.297245025634766\n", "The 10048th loop: cost = 41.961666107177734\n", "The 10049th loop: cost = 41.895145416259766\n", "The 10050th loop: cost = 37.576942443847656\n", "The 10051th loop: cost = 35.13002395629883\n", "The 10052th loop: cost = 37.382911682128906\n", "The 10053th loop: cost = 34.46966552734375\n", "The 10054th loop: cost = 44.73594665527344\n", "The 10055th loop: cost = 40.09455871582031\n", "The 10056th loop: cost = 30.60641860961914\n", "The 10057th loop: cost = 34.07749938964844\n", "The 10058th loop: cost = 44.46617126464844\n", "The 10059th loop: cost = 40.289215087890625\n", "The 10060th loop: cost = 42.23004913330078\n", "The 10061th loop: cost = 38.32813262939453\n", "The 10062th loop: cost = 31.828269958496094\n", "The 10063th loop: cost = 37.39993667602539\n", "The 10064th loop: cost = 30.73122787475586\n", "The 10065th loop: cost = 33.70214080810547\n", "The 10066th loop: cost = 32.548362731933594\n", "The 10067th loop: cost = 40.498939514160156\n", "The 10068th loop: cost = 41.385040283203125\n", "The 10069th loop: cost = 34.682228088378906\n", "The 10070th loop: cost = 35.6588134765625\n", "The 10071th loop: cost = 27.980976104736328\n", "The 10072th loop: cost = 36.42900466918945\n", "The 10073th loop: cost = 34.91681671142578\n", "The 10074th loop: cost = 29.220314025878906\n", "The 10075th loop: cost = 37.31922912597656\n", "The 10076th loop: cost = 39.108516693115234\n", "The 10077th loop: cost = 36.22852325439453\n", "The 10078th loop: cost = 35.34547424316406\n", "The 10079th loop: cost = 36.632625579833984\n", "The 10080th loop: cost = 37.504295349121094\n", "The 10081th loop: cost = 47.967281341552734\n", "The 10082th loop: cost = 39.41484451293945\n", "The 10083th loop: cost = 43.1881217956543\n", "The 10084th loop: cost = 28.265134811401367\n", "The 10085th loop: cost = 39.709136962890625\n", "The 10086th loop: cost = 35.535247802734375\n", "The 10087th loop: cost = 35.19424819946289\n", "The 10088th loop: cost = 43.316978454589844\n", "The 10089th loop: cost = 33.87335205078125\n", "The 10090th loop: cost = 41.39691162109375\n", "The 10091th loop: cost = 31.491539001464844\n", "The 10092th loop: cost = 49.63913345336914\n", "The 10093th loop: cost = 40.762001037597656\n", "The 10094th loop: cost = 40.009273529052734\n", "The 10095th loop: cost = 40.7714958190918\n", "The 10096th loop: cost = 36.517391204833984\n", "The 10097th loop: cost = 28.91680145263672\n", "The 10098th loop: cost = 44.40619659423828\n", "The 10099th loop: cost = 40.619407653808594\n", "The 10100th loop: cost = 35.36443328857422\n", "The 10101th loop: cost = 40.89779281616211\n", "The 10102th loop: cost = 45.68973922729492\n", "The 10103th loop: cost = 36.20629119873047\n", "The 10104th loop: cost = 38.33702850341797\n", "The 10105th loop: cost = 34.89563751220703\n", "The 10106th loop: cost = 52.86162185668945\n", "The 10107th loop: cost = 36.14710998535156\n", "The 10108th loop: cost = 39.918357849121094\n", "The 10109th loop: cost = 34.00211715698242\n", "The 10110th loop: cost = 36.57171630859375\n", "The 10111th loop: cost = 30.724597930908203\n", "The 10112th loop: cost = 38.470985412597656\n", "The 10113th loop: cost = 42.17250061035156\n", "The 10114th loop: cost = 39.79088592529297\n", "The 10115th loop: cost = 48.28971481323242\n", "The 10116th loop: cost = 31.899381637573242\n", "The 10117th loop: cost = 39.754737854003906\n", "The 10118th loop: cost = 39.52061080932617\n", "The 10119th loop: cost = 37.864662170410156\n", "The 10120th loop: cost = 42.926361083984375\n", "The 10121th loop: cost = 35.84735870361328\n", "The 10122th loop: cost = 33.80646514892578\n", "The 10123th loop: cost = 34.014102935791016\n", "The 10124th loop: cost = 43.795860290527344\n", "The 10125th loop: cost = 40.93581008911133\n", "The 10126th loop: cost = 31.802663803100586\n", "The 10127th loop: cost = 35.3485107421875\n", "The 10128th loop: cost = 44.68197250366211\n", "The 10129th loop: cost = 48.924076080322266\n", "The 10130th loop: cost = 36.71091079711914\n", "The 10131th loop: cost = 31.720563888549805\n", "The 10132th loop: cost = 42.478797912597656\n", "The 10133th loop: cost = 43.93500518798828\n", "The 10134th loop: cost = 32.595176696777344\n", "The 10135th loop: cost = 36.92925262451172\n", "The 10136th loop: cost = 34.999908447265625\n", "The 10137th loop: cost = 39.561370849609375\n", "The 10138th loop: cost = 34.764976501464844\n", "The 10139th loop: cost = 41.96363830566406\n", "The 10140th loop: cost = 42.87693405151367\n", "The 10141th loop: cost = 37.71177673339844\n", "The 10142th loop: cost = 46.694610595703125\n", "The 10143th loop: cost = 43.24348449707031\n", "The 10144th loop: cost = 38.87762451171875\n", "The 10145th loop: cost = 33.666839599609375\n", "The 10146th loop: cost = 40.774417877197266\n", "The 10147th loop: cost = 39.756927490234375\n", "The 10148th loop: cost = 43.00910568237305\n", "The 10149th loop: cost = 36.28042221069336\n", "The 10150th loop: cost = 40.42568588256836\n", "The 10151th loop: cost = 38.931304931640625\n", "The 10152th loop: cost = 33.919376373291016\n", "The 10153th loop: cost = 27.341087341308594\n", "The 10154th loop: cost = 35.51295471191406\n", "The 10155th loop: cost = 44.485984802246094\n", "The 10156th loop: cost = 33.62461471557617\n", "The 10157th loop: cost = 40.19813537597656\n", "The 10158th loop: cost = 49.28268051147461\n", "The 10159th loop: cost = 40.56391143798828\n", "The 10160th loop: cost = 35.452091217041016\n", "The 10161th loop: cost = 43.319393157958984\n", "The 10162th loop: cost = 42.413246154785156\n", "The 10163th loop: cost = 35.57862854003906\n", "The 10164th loop: cost = 33.75228500366211\n", "The 10165th loop: cost = 39.85442352294922\n", "The 10166th loop: cost = 35.286376953125\n", "The 10167th loop: cost = 38.986881256103516\n", "The 10168th loop: cost = 35.12471389770508\n", "The 10169th loop: cost = 42.19765090942383\n", "The 10170th loop: cost = 36.89718246459961\n", "The 10171th loop: cost = 37.386436462402344\n", "The 10172th loop: cost = 42.866943359375\n", "The 10173th loop: cost = 38.99456787109375\n", "The 10174th loop: cost = 40.23811340332031\n", "The 10175th loop: cost = 38.438228607177734\n", "The 10176th loop: cost = 29.50716781616211\n", "The 10177th loop: cost = 35.68122100830078\n", "The 10178th loop: cost = 45.99693298339844\n", "The 10179th loop: cost = 46.61614990234375\n", "The 10180th loop: cost = 38.5777587890625\n", "The 10181th loop: cost = 39.624542236328125\n", "The 10182th loop: cost = 36.357444763183594\n", "The 10183th loop: cost = 39.2730827331543\n", "The 10184th loop: cost = 43.02793884277344\n", "The 10185th loop: cost = 36.46867752075195\n", "The 10186th loop: cost = 37.41740417480469\n", "The 10187th loop: cost = 42.22919845581055\n", "The 10188th loop: cost = 31.16572380065918\n", "The 10189th loop: cost = 27.610231399536133\n", "The 10190th loop: cost = 36.465293884277344\n", "The 10191th loop: cost = 37.39678192138672\n", "The 10192th loop: cost = 38.884307861328125\n", "The 10193th loop: cost = 39.973236083984375\n", "The 10194th loop: cost = 35.84800720214844\n", "The 10195th loop: cost = 42.080078125\n", "The 10196th loop: cost = 40.8154296875\n", "The 10197th loop: cost = 41.00969696044922\n", "The 10198th loop: cost = 30.373699188232422\n", "The 10199th loop: cost = 38.259178161621094\n", "The 10200th loop: cost = 42.22805404663086\n", "The 10201th loop: cost = 34.84134292602539\n", "The 10202th loop: cost = 39.11310577392578\n", "The 10203th loop: cost = 33.24436569213867\n", "The 10204th loop: cost = 40.545467376708984\n", "The 10205th loop: cost = 37.82436752319336\n", "The 10206th loop: cost = 39.781402587890625\n", "The 10207th loop: cost = 41.99479293823242\n", "The 10208th loop: cost = 37.61122512817383\n", "The 10209th loop: cost = 41.24824905395508\n", "The 10210th loop: cost = 38.764156341552734\n", "The 10211th loop: cost = 29.344467163085938\n", "The 10212th loop: cost = 39.248268127441406\n", "The 10213th loop: cost = 36.44535446166992\n", "The 10214th loop: cost = 34.108551025390625\n", "The 10215th loop: cost = 34.83766555786133\n", "The 10216th loop: cost = 41.801292419433594\n", "The 10217th loop: cost = 36.373619079589844\n", "The 10218th loop: cost = 35.90235137939453\n", "The 10219th loop: cost = 39.056739807128906\n", "The 10220th loop: cost = 41.24488067626953\n", "The 10221th loop: cost = 40.41485595703125\n", "The 10222th loop: cost = 36.347015380859375\n", "The 10223th loop: cost = 38.44861602783203\n", "The 10224th loop: cost = 45.635955810546875\n", "The 10225th loop: cost = 36.12762451171875\n", "The 10226th loop: cost = 45.21599578857422\n", "The 10227th loop: cost = 47.02548599243164\n", "The 10228th loop: cost = 34.000282287597656\n", "The 10229th loop: cost = 39.6661376953125\n", "The 10230th loop: cost = 34.55528259277344\n", "The 10231th loop: cost = 38.639122009277344\n", "The 10232th loop: cost = 42.43958282470703\n", "The 10233th loop: cost = 38.66807174682617\n", "The 10234th loop: cost = 42.03839111328125\n", "The 10235th loop: cost = 36.6982421875\n", "The 10236th loop: cost = 41.649566650390625\n", "The 10237th loop: cost = 39.31169509887695\n", "The 10238th loop: cost = 36.00810241699219\n", "The 10239th loop: cost = 35.522098541259766\n", "The 10240th loop: cost = 37.221038818359375\n", "The 10241th loop: cost = 34.97819900512695\n", "The 10242th loop: cost = 38.003662109375\n", "The 10243th loop: cost = 37.359596252441406\n", "The 10244th loop: cost = 43.73664093017578\n", "The 10245th loop: cost = 40.083656311035156\n", "The 10246th loop: cost = 40.647003173828125\n", "The 10247th loop: cost = 41.59680938720703\n", "The 10248th loop: cost = 34.43486022949219\n", "The 10249th loop: cost = 39.94116973876953\n", "The 10250th loop: cost = 41.246009826660156\n", "The 10251th loop: cost = 36.34342956542969\n", "The 10252th loop: cost = 37.08904266357422\n", "The 10253th loop: cost = 41.01229476928711\n", "The 10254th loop: cost = 30.877243041992188\n", "The 10255th loop: cost = 36.5771484375\n", "The 10256th loop: cost = 39.98448944091797\n", "The 10257th loop: cost = 39.4979248046875\n", "The 10258th loop: cost = 35.34872055053711\n", "The 10259th loop: cost = 38.598976135253906\n", "The 10260th loop: cost = 40.74579620361328\n", "The 10261th loop: cost = 40.10506820678711\n", "The 10262th loop: cost = 40.29740905761719\n", "The 10263th loop: cost = 42.581172943115234\n", "The 10264th loop: cost = 42.40855407714844\n", "The 10265th loop: cost = 38.62293243408203\n", "The 10266th loop: cost = 39.197792053222656\n", "The 10267th loop: cost = 43.97748565673828\n", "The 10268th loop: cost = 43.95454406738281\n", "The 10269th loop: cost = 32.41016387939453\n", "The 10270th loop: cost = 37.961891174316406\n", "The 10271th loop: cost = 44.63784408569336\n", "The 10272th loop: cost = 43.08049392700195\n", "The 10273th loop: cost = 36.8519401550293\n", "The 10274th loop: cost = 33.73945236206055\n", "The 10275th loop: cost = 43.60279846191406\n", "The 10276th loop: cost = 39.00511932373047\n", "The 10277th loop: cost = 37.86220932006836\n", "The 10278th loop: cost = 35.127098083496094\n", "The 10279th loop: cost = 36.03395080566406\n", "The 10280th loop: cost = 37.503761291503906\n", "The 10281th loop: cost = 32.415687561035156\n", "The 10282th loop: cost = 41.744720458984375\n", "The 10283th loop: cost = 37.360145568847656\n", "The 10284th loop: cost = 38.8095703125\n", "The 10285th loop: cost = 43.723472595214844\n", "The 10286th loop: cost = 45.914268493652344\n", "The 10287th loop: cost = 32.92505645751953\n", "The 10288th loop: cost = 44.03871154785156\n", "The 10289th loop: cost = 28.743282318115234\n", "The 10290th loop: cost = 36.042198181152344\n", "The 10291th loop: cost = 39.896324157714844\n", "The 10292th loop: cost = 36.53784942626953\n", "The 10293th loop: cost = 40.83622741699219\n", "The 10294th loop: cost = 44.513370513916016\n", "The 10295th loop: cost = 33.681968688964844\n", "The 10296th loop: cost = 38.80091857910156\n", "The 10297th loop: cost = 38.90400695800781\n", "The 10298th loop: cost = 34.443389892578125\n", "The 10299th loop: cost = 37.71588134765625\n", "The 10300th loop: cost = 39.79259490966797\n", "The 10301th loop: cost = 33.445098876953125\n", "The 10302th loop: cost = 36.91270446777344\n", "The 10303th loop: cost = 44.237831115722656\n", "The 10304th loop: cost = 34.78181838989258\n", "The 10305th loop: cost = 41.2700309753418\n", "The 10306th loop: cost = 42.459991455078125\n", "The 10307th loop: cost = 42.174251556396484\n", "The 10308th loop: cost = 35.386314392089844\n", "The 10309th loop: cost = 44.97423553466797\n", "The 10310th loop: cost = 40.58197021484375\n", "The 10311th loop: cost = 34.813724517822266\n", "The 10312th loop: cost = 33.71581268310547\n", "The 10313th loop: cost = 37.46092987060547\n", "The 10314th loop: cost = 41.228904724121094\n", "The 10315th loop: cost = 40.64572525024414\n", "The 10316th loop: cost = 34.8409538269043\n", "The 10317th loop: cost = 38.7576904296875\n", "The 10318th loop: cost = 38.63134002685547\n", "The 10319th loop: cost = 37.44793701171875\n", "The 10320th loop: cost = 33.755584716796875\n", "The 10321th loop: cost = 39.39586639404297\n", "The 10322th loop: cost = 46.04557800292969\n", "The 10323th loop: cost = 41.90117263793945\n", "The 10324th loop: cost = 39.945831298828125\n", "The 10325th loop: cost = 33.60975646972656\n", "The 10326th loop: cost = 40.70915222167969\n", "The 10327th loop: cost = 35.277793884277344\n", "The 10328th loop: cost = 40.26698303222656\n", "The 10329th loop: cost = 48.38878631591797\n", "The 10330th loop: cost = 35.55415344238281\n", "The 10331th loop: cost = 35.88885498046875\n", "The 10332th loop: cost = 29.320697784423828\n", "The 10333th loop: cost = 47.716251373291016\n", "The 10334th loop: cost = 37.90199661254883\n", "The 10335th loop: cost = 39.45249938964844\n", "The 10336th loop: cost = 37.595237731933594\n", "The 10337th loop: cost = 34.784847259521484\n", "The 10338th loop: cost = 36.29641342163086\n", "The 10339th loop: cost = 39.11170196533203\n", "The 10340th loop: cost = 33.62908935546875\n", "The 10341th loop: cost = 33.99498748779297\n", "The 10342th loop: cost = 37.56129837036133\n", "The 10343th loop: cost = 46.68901824951172\n", "The 10344th loop: cost = 34.139251708984375\n", "The 10345th loop: cost = 37.57685852050781\n", "The 10346th loop: cost = 38.04938888549805\n", "The 10347th loop: cost = 32.830936431884766\n", "The 10348th loop: cost = 33.806190490722656\n", "The 10349th loop: cost = 38.693824768066406\n", "The 10350th loop: cost = 35.32392120361328\n", "The 10351th loop: cost = 31.356597900390625\n", "The 10352th loop: cost = 44.66551971435547\n", "The 10353th loop: cost = 44.582237243652344\n", "The 10354th loop: cost = 36.937015533447266\n", "The 10355th loop: cost = 36.783573150634766\n", "The 10356th loop: cost = 37.24931335449219\n", "The 10357th loop: cost = 39.95949172973633\n", "The 10358th loop: cost = 35.32630920410156\n", "The 10359th loop: cost = 41.194313049316406\n", "The 10360th loop: cost = 31.574962615966797\n", "The 10361th loop: cost = 33.40852355957031\n", "The 10362th loop: cost = 39.308929443359375\n", "The 10363th loop: cost = 35.54844665527344\n", "The 10364th loop: cost = 43.38871765136719\n", "The 10365th loop: cost = 33.834861755371094\n", "The 10366th loop: cost = 38.235450744628906\n", "The 10367th loop: cost = 35.72840118408203\n", "The 10368th loop: cost = 37.76641845703125\n", "The 10369th loop: cost = 36.495994567871094\n", "The 10370th loop: cost = 38.22760772705078\n", "The 10371th loop: cost = 39.5825309753418\n", "The 10372th loop: cost = 30.69091033935547\n", "The 10373th loop: cost = 37.574424743652344\n", "The 10374th loop: cost = 35.87127685546875\n", "The 10375th loop: cost = 43.7988166809082\n", "The 10376th loop: cost = 40.85634231567383\n", "The 10377th loop: cost = 36.86135482788086\n", "The 10378th loop: cost = 35.137046813964844\n", "The 10379th loop: cost = 38.77362060546875\n", "The 10380th loop: cost = 50.559547424316406\n", "The 10381th loop: cost = 41.10200500488281\n", "The 10382th loop: cost = 49.23788833618164\n", "The 10383th loop: cost = 42.66014862060547\n", "The 10384th loop: cost = 36.993343353271484\n", "The 10385th loop: cost = 33.15115737915039\n", "The 10386th loop: cost = 40.97707748413086\n", "The 10387th loop: cost = 33.7302360534668\n", "The 10388th loop: cost = 40.60389709472656\n", "The 10389th loop: cost = 37.264930725097656\n", "The 10390th loop: cost = 33.94990539550781\n", "The 10391th loop: cost = 42.741920471191406\n", "The 10392th loop: cost = 33.991207122802734\n", "The 10393th loop: cost = 35.98577880859375\n", "The 10394th loop: cost = 32.98191833496094\n", "The 10395th loop: cost = 38.783714294433594\n", "The 10396th loop: cost = 43.288631439208984\n", "The 10397th loop: cost = 36.78334426879883\n", "The 10398th loop: cost = 35.768741607666016\n", "The 10399th loop: cost = 38.33842849731445\n", "The 10400th loop: cost = 40.89876937866211\n", "The 10401th loop: cost = 42.86030960083008\n", "The 10402th loop: cost = 36.5322265625\n", "The 10403th loop: cost = 44.354637145996094\n", "The 10404th loop: cost = 38.89644241333008\n", "The 10405th loop: cost = 39.24824142456055\n", "The 10406th loop: cost = 34.50826644897461\n", "The 10407th loop: cost = 48.62689208984375\n", "The 10408th loop: cost = 46.43622589111328\n", "The 10409th loop: cost = 51.13666915893555\n", "The 10410th loop: cost = 40.98805236816406\n", "The 10411th loop: cost = 33.45301055908203\n", "The 10412th loop: cost = 35.738792419433594\n", "The 10413th loop: cost = 35.687374114990234\n", "The 10414th loop: cost = 43.95188522338867\n", "The 10415th loop: cost = 29.71627426147461\n", "The 10416th loop: cost = 36.80546569824219\n", "The 10417th loop: cost = 35.39649963378906\n", "The 10418th loop: cost = 45.374977111816406\n", "The 10419th loop: cost = 36.09245300292969\n", "The 10420th loop: cost = 31.967370986938477\n", "The 10421th loop: cost = 32.88028335571289\n", "The 10422th loop: cost = 41.26319122314453\n", "The 10423th loop: cost = 36.98313903808594\n", "The 10424th loop: cost = 24.892215728759766\n", "The 10425th loop: cost = 39.55424880981445\n", "The 10426th loop: cost = 42.76880645751953\n", "The 10427th loop: cost = 36.454654693603516\n", "The 10428th loop: cost = 42.90301513671875\n", "The 10429th loop: cost = 42.25873565673828\n", "The 10430th loop: cost = 40.607627868652344\n", "The 10431th loop: cost = 42.739376068115234\n", "The 10432th loop: cost = 40.61147689819336\n", "The 10433th loop: cost = 38.51210021972656\n", "The 10434th loop: cost = 32.02159881591797\n", "The 10435th loop: cost = 32.58217239379883\n", "The 10436th loop: cost = 36.889915466308594\n", "The 10437th loop: cost = 42.916725158691406\n", "The 10438th loop: cost = 36.22248458862305\n", "The 10439th loop: cost = 28.821725845336914\n", "The 10440th loop: cost = 36.15248107910156\n", "The 10441th loop: cost = 34.139503479003906\n", "The 10442th loop: cost = 37.46413803100586\n", "The 10443th loop: cost = 29.51079559326172\n", "The 10444th loop: cost = 38.461692810058594\n", "The 10445th loop: cost = 41.62700653076172\n", "The 10446th loop: cost = 36.833351135253906\n", "The 10447th loop: cost = 40.190452575683594\n", "The 10448th loop: cost = 30.280712127685547\n", "The 10449th loop: cost = 32.89125442504883\n", "The 10450th loop: cost = 45.3975715637207\n", "The 10451th loop: cost = 36.022499084472656\n", "The 10452th loop: cost = 44.51639938354492\n", "The 10453th loop: cost = 35.66535186767578\n", "The 10454th loop: cost = 31.15825080871582\n", "The 10455th loop: cost = 42.30646514892578\n", "The 10456th loop: cost = 43.55907440185547\n", "The 10457th loop: cost = 41.938568115234375\n", "The 10458th loop: cost = 43.520023345947266\n", "The 10459th loop: cost = 35.5383186340332\n", "The 10460th loop: cost = 33.968143463134766\n", "The 10461th loop: cost = 39.385398864746094\n", "The 10462th loop: cost = 34.33721923828125\n", "The 10463th loop: cost = 37.53130340576172\n", "The 10464th loop: cost = 31.00244140625\n", "The 10465th loop: cost = 36.47339630126953\n", "The 10466th loop: cost = 37.02313995361328\n", "The 10467th loop: cost = 36.376129150390625\n", "The 10468th loop: cost = 36.81787109375\n", "The 10469th loop: cost = 26.74371337890625\n", "The 10470th loop: cost = 36.80290985107422\n", "The 10471th loop: cost = 34.78388595581055\n", "The 10472th loop: cost = 43.46798324584961\n", "The 10473th loop: cost = 38.53007507324219\n", "The 10474th loop: cost = 35.209266662597656\n", "The 10475th loop: cost = 38.157997131347656\n", "The 10476th loop: cost = 33.644256591796875\n", "The 10477th loop: cost = 42.64097595214844\n", "The 10478th loop: cost = 45.74212646484375\n", "The 10479th loop: cost = 34.988807678222656\n", "The 10480th loop: cost = 34.14069747924805\n", "The 10481th loop: cost = 39.033905029296875\n", "The 10482th loop: cost = 45.90620422363281\n", "The 10483th loop: cost = 38.715824127197266\n", "The 10484th loop: cost = 38.70039367675781\n", "The 10485th loop: cost = 51.61353302001953\n", "The 10486th loop: cost = 39.53607940673828\n", "The 10487th loop: cost = 49.21092987060547\n", "The 10488th loop: cost = 44.290645599365234\n", "The 10489th loop: cost = 43.99604797363281\n", "The 10490th loop: cost = 43.0345573425293\n", "The 10491th loop: cost = 37.73344802856445\n", "The 10492th loop: cost = 44.77938461303711\n", "The 10493th loop: cost = 39.571998596191406\n", "The 10494th loop: cost = 42.64320373535156\n", "The 10495th loop: cost = 38.25434494018555\n", "The 10496th loop: cost = 38.05388641357422\n", "The 10497th loop: cost = 36.664085388183594\n", "The 10498th loop: cost = 36.28764724731445\n", "The 10499th loop: cost = 35.01598358154297\n", "The 10500th loop: cost = 41.480682373046875\n", "The 10501th loop: cost = 30.359153747558594\n", "The 10502th loop: cost = 30.902498245239258\n", "The 10503th loop: cost = 27.61569595336914\n", "The 10504th loop: cost = 39.68694305419922\n", "The 10505th loop: cost = 29.633544921875\n", "The 10506th loop: cost = 44.83734893798828\n", "The 10507th loop: cost = 34.94428253173828\n", "The 10508th loop: cost = 37.886043548583984\n", "The 10509th loop: cost = 36.08130645751953\n", "The 10510th loop: cost = 42.546287536621094\n", "The 10511th loop: cost = 37.96734619140625\n", "The 10512th loop: cost = 46.043357849121094\n", "The 10513th loop: cost = 29.55206298828125\n", "The 10514th loop: cost = 35.54591369628906\n", "The 10515th loop: cost = 37.761314392089844\n", "The 10516th loop: cost = 35.64116668701172\n", "The 10517th loop: cost = 35.58611297607422\n", "The 10518th loop: cost = 36.2115592956543\n", "The 10519th loop: cost = 32.356754302978516\n", "The 10520th loop: cost = 29.44353485107422\n", "The 10521th loop: cost = 41.67790985107422\n", "The 10522th loop: cost = 29.328155517578125\n", "The 10523th loop: cost = 40.62580108642578\n", "The 10524th loop: cost = 43.82482147216797\n", "The 10525th loop: cost = 40.28138732910156\n", "The 10526th loop: cost = 32.73876190185547\n", "The 10527th loop: cost = 31.67668342590332\n", "The 10528th loop: cost = 31.78633689880371\n", "The 10529th loop: cost = 37.61548614501953\n", "The 10530th loop: cost = 41.58073043823242\n", "The 10531th loop: cost = 34.031471252441406\n", "The 10532th loop: cost = 43.347900390625\n", "The 10533th loop: cost = 34.04775619506836\n", "The 10534th loop: cost = 35.56112289428711\n", "The 10535th loop: cost = 42.407684326171875\n", "The 10536th loop: cost = 31.44552993774414\n", "The 10537th loop: cost = 41.20716857910156\n", "The 10538th loop: cost = 43.732452392578125\n", "The 10539th loop: cost = 34.83512878417969\n", "The 10540th loop: cost = 40.60292053222656\n", "The 10541th loop: cost = 38.393550872802734\n", "The 10542th loop: cost = 33.75028991699219\n", "The 10543th loop: cost = 27.336315155029297\n", "The 10544th loop: cost = 36.82919692993164\n", "The 10545th loop: cost = 40.617713928222656\n", "The 10546th loop: cost = 36.26533889770508\n", "The 10547th loop: cost = 36.878021240234375\n", "The 10548th loop: cost = 44.5514030456543\n", "The 10549th loop: cost = 33.122154235839844\n", "The 10550th loop: cost = 31.909744262695312\n", "The 10551th loop: cost = 44.685909271240234\n", "The 10552th loop: cost = 34.739501953125\n", "The 10553th loop: cost = 32.56769561767578\n", "The 10554th loop: cost = 36.28205871582031\n", "The 10555th loop: cost = 50.61383819580078\n", "The 10556th loop: cost = 41.8457145690918\n", "The 10557th loop: cost = 41.190284729003906\n", "The 10558th loop: cost = 40.98527526855469\n", "The 10559th loop: cost = 45.57378387451172\n", "The 10560th loop: cost = 39.8746337890625\n", "The 10561th loop: cost = 37.64717483520508\n", "The 10562th loop: cost = 37.70594024658203\n", "The 10563th loop: cost = 29.321178436279297\n", "The 10564th loop: cost = 35.35535430908203\n", "The 10565th loop: cost = 39.37114334106445\n", "The 10566th loop: cost = 43.548439025878906\n", "The 10567th loop: cost = 37.52330017089844\n", "The 10568th loop: cost = 36.873252868652344\n", "The 10569th loop: cost = 37.39631652832031\n", "The 10570th loop: cost = 34.126617431640625\n", "The 10571th loop: cost = 31.107425689697266\n", "The 10572th loop: cost = 30.187721252441406\n", "The 10573th loop: cost = 42.53593826293945\n", "The 10574th loop: cost = 33.61897277832031\n", "The 10575th loop: cost = 38.084564208984375\n", "The 10576th loop: cost = 30.54056739807129\n", "The 10577th loop: cost = 34.99810791015625\n", "The 10578th loop: cost = 44.96941375732422\n", "The 10579th loop: cost = 40.85210418701172\n", "The 10580th loop: cost = 34.857147216796875\n", "The 10581th loop: cost = 37.60929870605469\n", "The 10582th loop: cost = 29.67081642150879\n", "The 10583th loop: cost = 34.439537048339844\n", "The 10584th loop: cost = 35.75492858886719\n", "The 10585th loop: cost = 37.422515869140625\n", "The 10586th loop: cost = 45.50965881347656\n", "The 10587th loop: cost = 37.14646530151367\n", "The 10588th loop: cost = 45.652183532714844\n", "The 10589th loop: cost = 40.19755554199219\n", "The 10590th loop: cost = 44.26698303222656\n", "The 10591th loop: cost = 41.883872985839844\n", "The 10592th loop: cost = 35.7740364074707\n", "The 10593th loop: cost = 39.43218994140625\n", "The 10594th loop: cost = 33.343711853027344\n", "The 10595th loop: cost = 34.14790725708008\n", "The 10596th loop: cost = 41.71574401855469\n", "The 10597th loop: cost = 34.47279357910156\n", "The 10598th loop: cost = 35.55940246582031\n", "The 10599th loop: cost = 39.00791931152344\n", "The 10600th loop: cost = 33.17597579956055\n", "The 10601th loop: cost = 35.853668212890625\n", "The 10602th loop: cost = 37.950714111328125\n", "The 10603th loop: cost = 38.54160690307617\n", "The 10604th loop: cost = 35.20263671875\n", "The 10605th loop: cost = 38.477752685546875\n", "The 10606th loop: cost = 44.84938430786133\n", "The 10607th loop: cost = 37.720680236816406\n", "The 10608th loop: cost = 49.85150909423828\n", "The 10609th loop: cost = 43.10437774658203\n", "The 10610th loop: cost = 41.048641204833984\n", "The 10611th loop: cost = 42.26612091064453\n", "The 10612th loop: cost = 38.663509368896484\n", "The 10613th loop: cost = 44.19282150268555\n", "The 10614th loop: cost = 40.18016815185547\n", "The 10615th loop: cost = 38.33112335205078\n", "The 10616th loop: cost = 44.91820526123047\n", "The 10617th loop: cost = 35.13670349121094\n", "The 10618th loop: cost = 35.593727111816406\n", "The 10619th loop: cost = 34.368865966796875\n", "The 10620th loop: cost = 37.15459442138672\n", "The 10621th loop: cost = 30.960847854614258\n", "The 10622th loop: cost = 35.38166427612305\n", "The 10623th loop: cost = 35.68486785888672\n", "The 10624th loop: cost = 43.52935791015625\n", "The 10625th loop: cost = 42.80377197265625\n", "The 10626th loop: cost = 25.541259765625\n", "The 10627th loop: cost = 33.75825881958008\n", "The 10628th loop: cost = 33.02643585205078\n", "The 10629th loop: cost = 35.383155822753906\n", "The 10630th loop: cost = 30.606775283813477\n", "The 10631th loop: cost = 30.649555206298828\n", "The 10632th loop: cost = 43.4691162109375\n", "The 10633th loop: cost = 33.83354187011719\n", "The 10634th loop: cost = 38.54316711425781\n", "The 10635th loop: cost = 42.628028869628906\n", "The 10636th loop: cost = 34.92604064941406\n", "The 10637th loop: cost = 45.891334533691406\n", "The 10638th loop: cost = 38.50462341308594\n", "The 10639th loop: cost = 35.05324172973633\n", "The 10640th loop: cost = 35.778648376464844\n", "The 10641th loop: cost = 31.390512466430664\n", "The 10642th loop: cost = 34.54875183105469\n", "The 10643th loop: cost = 37.603424072265625\n", "The 10644th loop: cost = 35.248138427734375\n", "The 10645th loop: cost = 39.85447692871094\n", "The 10646th loop: cost = 42.10129928588867\n", "The 10647th loop: cost = 36.18496322631836\n", "The 10648th loop: cost = 38.62651824951172\n", "The 10649th loop: cost = 40.34953308105469\n", "The 10650th loop: cost = 35.707706451416016\n", "The 10651th loop: cost = 36.55072021484375\n", "The 10652th loop: cost = 42.39324951171875\n", "The 10653th loop: cost = 34.81745147705078\n", "The 10654th loop: cost = 51.634307861328125\n", "The 10655th loop: cost = 28.01146125793457\n", "The 10656th loop: cost = 32.25987243652344\n", "The 10657th loop: cost = 28.60457420349121\n", "The 10658th loop: cost = 34.21135711669922\n", "The 10659th loop: cost = 27.146778106689453\n", "The 10660th loop: cost = 38.23727035522461\n", "The 10661th loop: cost = 41.95179748535156\n", "The 10662th loop: cost = 35.08940887451172\n", "The 10663th loop: cost = 45.26152801513672\n", "The 10664th loop: cost = 39.218807220458984\n", "The 10665th loop: cost = 38.481971740722656\n", "The 10666th loop: cost = 30.278911590576172\n", "The 10667th loop: cost = 37.95799255371094\n", "The 10668th loop: cost = 40.837364196777344\n", "The 10669th loop: cost = 32.71797561645508\n", "The 10670th loop: cost = 36.63224792480469\n", "The 10671th loop: cost = 38.5649299621582\n", "The 10672th loop: cost = 36.91587448120117\n", "The 10673th loop: cost = 32.60693359375\n", "The 10674th loop: cost = 33.392242431640625\n", "The 10675th loop: cost = 37.99951171875\n", "The 10676th loop: cost = 35.500099182128906\n", "The 10677th loop: cost = 35.05779266357422\n", "The 10678th loop: cost = 36.368927001953125\n", "The 10679th loop: cost = 32.349578857421875\n", "The 10680th loop: cost = 45.55967330932617\n", "The 10681th loop: cost = 41.890953063964844\n", "The 10682th loop: cost = 33.55540466308594\n", "The 10683th loop: cost = 29.265987396240234\n", "The 10684th loop: cost = 44.84996032714844\n", "The 10685th loop: cost = 38.669761657714844\n", "The 10686th loop: cost = 42.3955078125\n", "The 10687th loop: cost = 34.55038070678711\n", "The 10688th loop: cost = 36.5098762512207\n", "The 10689th loop: cost = 30.343412399291992\n", "The 10690th loop: cost = 33.0284309387207\n", "The 10691th loop: cost = 41.16176986694336\n", "The 10692th loop: cost = 39.011905670166016\n", "The 10693th loop: cost = 32.54483413696289\n", "The 10694th loop: cost = 36.95399475097656\n", "The 10695th loop: cost = 47.715702056884766\n", "The 10696th loop: cost = 35.15312957763672\n", "The 10697th loop: cost = 29.704387664794922\n", "The 10698th loop: cost = 33.353946685791016\n", "The 10699th loop: cost = 38.908443450927734\n", "The 10700th loop: cost = 41.15868377685547\n", "The 10701th loop: cost = 32.157989501953125\n", "The 10702th loop: cost = 32.631107330322266\n", "The 10703th loop: cost = 40.31761169433594\n", "The 10704th loop: cost = 44.071754455566406\n", "The 10705th loop: cost = 34.167266845703125\n", "The 10706th loop: cost = 34.804237365722656\n", "The 10707th loop: cost = 42.507110595703125\n", "The 10708th loop: cost = 38.73883056640625\n", "The 10709th loop: cost = 27.915637969970703\n", "The 10710th loop: cost = 36.3101806640625\n", "The 10711th loop: cost = 38.792945861816406\n", "The 10712th loop: cost = 36.531105041503906\n", "The 10713th loop: cost = 37.516510009765625\n", "The 10714th loop: cost = 41.92483139038086\n", "The 10715th loop: cost = 37.6118049621582\n", "The 10716th loop: cost = 45.57602310180664\n", "The 10717th loop: cost = 36.68031311035156\n", "The 10718th loop: cost = 33.34504318237305\n", "The 10719th loop: cost = 43.049015045166016\n", "The 10720th loop: cost = 32.037071228027344\n", "The 10721th loop: cost = 40.35088348388672\n", "The 10722th loop: cost = 36.61518096923828\n", "The 10723th loop: cost = 37.721885681152344\n", "The 10724th loop: cost = 31.802738189697266\n", "The 10725th loop: cost = 40.47008514404297\n", "The 10726th loop: cost = 43.14196014404297\n", "The 10727th loop: cost = 26.464998245239258\n", "The 10728th loop: cost = 34.084136962890625\n", "The 10729th loop: cost = 28.975921630859375\n", "The 10730th loop: cost = 31.051340103149414\n", "The 10731th loop: cost = 36.36409378051758\n", "The 10732th loop: cost = 44.73439025878906\n", "The 10733th loop: cost = 51.26939392089844\n", "The 10734th loop: cost = 34.29115295410156\n", "The 10735th loop: cost = 29.75481414794922\n", "The 10736th loop: cost = 44.04148864746094\n", "The 10737th loop: cost = 43.72229766845703\n", "The 10738th loop: cost = 35.15763473510742\n", "The 10739th loop: cost = 48.53254699707031\n", "The 10740th loop: cost = 32.94619369506836\n", "The 10741th loop: cost = 46.43552017211914\n", "The 10742th loop: cost = 35.283843994140625\n", "The 10743th loop: cost = 39.05704879760742\n", "The 10744th loop: cost = 39.929840087890625\n", "The 10745th loop: cost = 38.58817672729492\n", "The 10746th loop: cost = 31.760967254638672\n", "The 10747th loop: cost = 38.116050720214844\n", "The 10748th loop: cost = 39.726707458496094\n", "The 10749th loop: cost = 40.93854904174805\n", "The 10750th loop: cost = 36.188907623291016\n", "The 10751th loop: cost = 32.9237060546875\n", "The 10752th loop: cost = 34.66827392578125\n", "The 10753th loop: cost = 40.14324951171875\n", "The 10754th loop: cost = 31.464935302734375\n", "The 10755th loop: cost = 30.760175704956055\n", "The 10756th loop: cost = 34.48796463012695\n", "The 10757th loop: cost = 33.23052978515625\n", "The 10758th loop: cost = 42.11688232421875\n", "The 10759th loop: cost = 34.74263381958008\n", "The 10760th loop: cost = 49.03907775878906\n", "The 10761th loop: cost = 34.236907958984375\n", "The 10762th loop: cost = 32.786460876464844\n", "The 10763th loop: cost = 31.63983154296875\n", "The 10764th loop: cost = 39.63610076904297\n", "The 10765th loop: cost = 34.662841796875\n", "The 10766th loop: cost = 33.15230178833008\n", "The 10767th loop: cost = 32.67955780029297\n", "The 10768th loop: cost = 45.885231018066406\n", "The 10769th loop: cost = 47.36776351928711\n", "The 10770th loop: cost = 31.039432525634766\n", "The 10771th loop: cost = 39.05379104614258\n", "The 10772th loop: cost = 42.045101165771484\n", "The 10773th loop: cost = 43.28770446777344\n", "The 10774th loop: cost = 39.2231330871582\n", "The 10775th loop: cost = 39.27086639404297\n", "The 10776th loop: cost = 31.890897750854492\n", "The 10777th loop: cost = 44.522640228271484\n", "The 10778th loop: cost = 37.41333770751953\n", "The 10779th loop: cost = 38.476348876953125\n", "The 10780th loop: cost = 37.512489318847656\n", "The 10781th loop: cost = 39.72303009033203\n", "The 10782th loop: cost = 36.012672424316406\n", "The 10783th loop: cost = 37.62104415893555\n", "The 10784th loop: cost = 38.289485931396484\n", "The 10785th loop: cost = 29.35151481628418\n", "The 10786th loop: cost = 44.851470947265625\n", "The 10787th loop: cost = 33.549560546875\n", "The 10788th loop: cost = 37.66114044189453\n", "The 10789th loop: cost = 29.574975967407227\n", "The 10790th loop: cost = 41.4681282043457\n", "The 10791th loop: cost = 40.0762939453125\n", "The 10792th loop: cost = 39.814109802246094\n", "The 10793th loop: cost = 31.233257293701172\n", "The 10794th loop: cost = 35.6065788269043\n", "The 10795th loop: cost = 40.19846725463867\n", "The 10796th loop: cost = 35.74132537841797\n", "The 10797th loop: cost = 45.1646728515625\n", "The 10798th loop: cost = 32.63542556762695\n", "The 10799th loop: cost = 35.40039825439453\n", "The 10800th loop: cost = 43.40807342529297\n", "The 10801th loop: cost = 34.71674346923828\n", "The 10802th loop: cost = 35.93824768066406\n", "The 10803th loop: cost = 31.405460357666016\n", "The 10804th loop: cost = 32.94562530517578\n", "The 10805th loop: cost = 36.16777801513672\n", "The 10806th loop: cost = 33.7964973449707\n", "The 10807th loop: cost = 43.082557678222656\n", "The 10808th loop: cost = 33.32427215576172\n", "The 10809th loop: cost = 34.04156494140625\n", "The 10810th loop: cost = 39.32228088378906\n", "The 10811th loop: cost = 34.285316467285156\n", "The 10812th loop: cost = 38.121978759765625\n", "The 10813th loop: cost = 35.899147033691406\n", "The 10814th loop: cost = 38.954952239990234\n", "The 10815th loop: cost = 38.718788146972656\n", "The 10816th loop: cost = 37.56847381591797\n", "The 10817th loop: cost = 28.94818878173828\n", "The 10818th loop: cost = 31.548826217651367\n", "The 10819th loop: cost = 34.91841125488281\n", "The 10820th loop: cost = 29.911975860595703\n", "The 10821th loop: cost = 31.931982040405273\n", "The 10822th loop: cost = 40.45964050292969\n", "The 10823th loop: cost = 35.21263885498047\n", "The 10824th loop: cost = 36.503578186035156\n", "The 10825th loop: cost = 34.27635192871094\n", "The 10826th loop: cost = 32.449588775634766\n", "The 10827th loop: cost = 45.1553955078125\n", "The 10828th loop: cost = 33.85788345336914\n", "The 10829th loop: cost = 38.1047477722168\n", "The 10830th loop: cost = 40.54363250732422\n", "The 10831th loop: cost = 32.925437927246094\n", "The 10832th loop: cost = 35.32638931274414\n", "The 10833th loop: cost = 35.68284606933594\n", "The 10834th loop: cost = 44.25936508178711\n", "The 10835th loop: cost = 32.551944732666016\n", "The 10836th loop: cost = 32.451316833496094\n", "The 10837th loop: cost = 39.04037094116211\n", "The 10838th loop: cost = 30.830629348754883\n", "The 10839th loop: cost = 40.68531799316406\n", "The 10840th loop: cost = 38.440818786621094\n", "The 10841th loop: cost = 36.70393753051758\n", "The 10842th loop: cost = 41.70855712890625\n", "The 10843th loop: cost = 33.74916076660156\n", "The 10844th loop: cost = 42.72175598144531\n", "The 10845th loop: cost = 33.63848114013672\n", "The 10846th loop: cost = 36.8212890625\n", "The 10847th loop: cost = 42.13995361328125\n", "The 10848th loop: cost = 35.13553237915039\n", "The 10849th loop: cost = 40.35580062866211\n", "The 10850th loop: cost = 31.170963287353516\n", "The 10851th loop: cost = 34.26348876953125\n", "The 10852th loop: cost = 34.645503997802734\n", "The 10853th loop: cost = 33.29474639892578\n", "The 10854th loop: cost = 31.50792121887207\n", "The 10855th loop: cost = 41.18376159667969\n", "The 10856th loop: cost = 28.908512115478516\n", "The 10857th loop: cost = 35.73628234863281\n", "The 10858th loop: cost = 45.451087951660156\n", "The 10859th loop: cost = 36.970272064208984\n", "The 10860th loop: cost = 27.4224910736084\n", "The 10861th loop: cost = 25.04372787475586\n", "The 10862th loop: cost = 34.71001052856445\n", "The 10863th loop: cost = 32.277767181396484\n", "The 10864th loop: cost = 44.49342346191406\n", "The 10865th loop: cost = 40.46105194091797\n", "The 10866th loop: cost = 51.758140563964844\n", "The 10867th loop: cost = 29.37397003173828\n", "The 10868th loop: cost = 36.18665313720703\n", "The 10869th loop: cost = 30.286624908447266\n", "The 10870th loop: cost = 33.997886657714844\n", "The 10871th loop: cost = 37.917213439941406\n", "The 10872th loop: cost = 42.277000427246094\n", "The 10873th loop: cost = 36.03959655761719\n", "The 10874th loop: cost = 37.83961486816406\n", "The 10875th loop: cost = 41.03169250488281\n", "The 10876th loop: cost = 33.63671112060547\n", "The 10877th loop: cost = 37.56615447998047\n", "The 10878th loop: cost = 34.105262756347656\n", "The 10879th loop: cost = 35.37257385253906\n", "The 10880th loop: cost = 33.48785400390625\n", "The 10881th loop: cost = 36.707923889160156\n", "The 10882th loop: cost = 39.80816650390625\n", "The 10883th loop: cost = 35.4141731262207\n", "The 10884th loop: cost = 37.80963897705078\n", "The 10885th loop: cost = 52.658531188964844\n", "The 10886th loop: cost = 40.78877258300781\n", "The 10887th loop: cost = 41.574851989746094\n", "The 10888th loop: cost = 28.2291259765625\n", "The 10889th loop: cost = 47.66979217529297\n", "The 10890th loop: cost = 41.460594177246094\n", "The 10891th loop: cost = 37.06101989746094\n", "The 10892th loop: cost = 45.14508056640625\n", "The 10893th loop: cost = 44.85889434814453\n", "The 10894th loop: cost = 42.649147033691406\n", "The 10895th loop: cost = 36.28156661987305\n", "The 10896th loop: cost = 41.92831039428711\n", "The 10897th loop: cost = 34.053062438964844\n", "The 10898th loop: cost = 44.488624572753906\n", "The 10899th loop: cost = 40.15732192993164\n", "The 10900th loop: cost = 34.81721115112305\n", "The 10901th loop: cost = 38.43290710449219\n", "The 10902th loop: cost = 39.21269989013672\n", "The 10903th loop: cost = 36.179969787597656\n", "The 10904th loop: cost = 46.272666931152344\n", "The 10905th loop: cost = 35.682891845703125\n", "The 10906th loop: cost = 44.52928924560547\n", "The 10907th loop: cost = 28.059755325317383\n", "The 10908th loop: cost = 40.060142517089844\n", "The 10909th loop: cost = 44.24213409423828\n", "The 10910th loop: cost = 34.275390625\n", "The 10911th loop: cost = 30.683582305908203\n", "The 10912th loop: cost = 35.2591438293457\n", "The 10913th loop: cost = 32.40230941772461\n", "The 10914th loop: cost = 35.798004150390625\n", "The 10915th loop: cost = 34.32941436767578\n", "The 10916th loop: cost = 38.077693939208984\n", "The 10917th loop: cost = 49.22174835205078\n", "The 10918th loop: cost = 38.340335845947266\n", "The 10919th loop: cost = 34.64813232421875\n", "The 10920th loop: cost = 46.260494232177734\n", "The 10921th loop: cost = 31.54229164123535\n", "The 10922th loop: cost = 47.24448013305664\n", "The 10923th loop: cost = 34.05367660522461\n", "The 10924th loop: cost = 49.73568344116211\n", "The 10925th loop: cost = 36.251991271972656\n", "The 10926th loop: cost = 35.591949462890625\n", "The 10927th loop: cost = 35.307655334472656\n", "The 10928th loop: cost = 33.608055114746094\n", "The 10929th loop: cost = 47.984249114990234\n", "The 10930th loop: cost = 36.76136016845703\n", "The 10931th loop: cost = 33.50503158569336\n", "The 10932th loop: cost = 33.42131805419922\n", "The 10933th loop: cost = 37.508872985839844\n", "The 10934th loop: cost = 49.026405334472656\n", "The 10935th loop: cost = 34.71497344970703\n", "The 10936th loop: cost = 38.9901237487793\n", "The 10937th loop: cost = 27.075454711914062\n", "The 10938th loop: cost = 37.849571228027344\n", "The 10939th loop: cost = 31.44274139404297\n", "The 10940th loop: cost = 34.72373580932617\n", "The 10941th loop: cost = 29.05763816833496\n", "The 10942th loop: cost = 36.15377426147461\n", "The 10943th loop: cost = 35.25003433227539\n", "The 10944th loop: cost = 27.985389709472656\n", "The 10945th loop: cost = 29.4664306640625\n", "The 10946th loop: cost = 31.562759399414062\n", "The 10947th loop: cost = 38.528297424316406\n", "The 10948th loop: cost = 43.61738204956055\n", "The 10949th loop: cost = 37.575828552246094\n", "The 10950th loop: cost = 51.870948791503906\n", "The 10951th loop: cost = 36.624267578125\n", "The 10952th loop: cost = 38.48334503173828\n", "The 10953th loop: cost = 33.86000061035156\n", "The 10954th loop: cost = 36.23553466796875\n", "The 10955th loop: cost = 44.53245162963867\n", "The 10956th loop: cost = 32.408653259277344\n", "The 10957th loop: cost = 35.892852783203125\n", "The 10958th loop: cost = 44.73603820800781\n", "The 10959th loop: cost = 38.15159606933594\n", "The 10960th loop: cost = 32.037193298339844\n", "The 10961th loop: cost = 30.979778289794922\n", "The 10962th loop: cost = 35.069297790527344\n", "The 10963th loop: cost = 41.77399444580078\n", "The 10964th loop: cost = 30.322010040283203\n", "The 10965th loop: cost = 39.10565948486328\n", "The 10966th loop: cost = 37.0491943359375\n", "The 10967th loop: cost = 30.305015563964844\n", "The 10968th loop: cost = 44.54848861694336\n", "The 10969th loop: cost = 32.99004364013672\n", "The 10970th loop: cost = 33.35673141479492\n", "The 10971th loop: cost = 30.75580596923828\n", "The 10972th loop: cost = 40.183868408203125\n", "The 10973th loop: cost = 45.935569763183594\n", "The 10974th loop: cost = 37.867919921875\n", "The 10975th loop: cost = 33.90496826171875\n", "The 10976th loop: cost = 41.728431701660156\n", "The 10977th loop: cost = 40.86215591430664\n", "The 10978th loop: cost = 31.835105895996094\n", "The 10979th loop: cost = 34.78302764892578\n", "The 10980th loop: cost = 36.19281005859375\n", "The 10981th loop: cost = 36.526771545410156\n", "The 10982th loop: cost = 35.46424865722656\n", "The 10983th loop: cost = 39.04267120361328\n", "The 10984th loop: cost = 36.045509338378906\n", "The 10985th loop: cost = 40.39246368408203\n", "The 10986th loop: cost = 41.50763702392578\n", "The 10987th loop: cost = 33.459571838378906\n", "The 10988th loop: cost = 39.570533752441406\n", "The 10989th loop: cost = 36.3731689453125\n", "The 10990th loop: cost = 43.93104553222656\n", "The 10991th loop: cost = 43.2998046875\n", "The 10992th loop: cost = 39.77214813232422\n", "The 10993th loop: cost = 40.67057418823242\n", "The 10994th loop: cost = 41.47856140136719\n", "The 10995th loop: cost = 38.227108001708984\n", "The 10996th loop: cost = 40.07624053955078\n", "The 10997th loop: cost = 33.66254425048828\n", "The 10998th loop: cost = 39.08545684814453\n", "The 10999th loop: cost = 39.605995178222656\n", "The 11000th loop: cost = 41.13871765136719\n", "The 11001th loop: cost = 37.482425689697266\n", "The 11002th loop: cost = 40.54178237915039\n", "The 11003th loop: cost = 37.751853942871094\n", "The 11004th loop: cost = 41.2027587890625\n", "The 11005th loop: cost = 36.87049865722656\n", "The 11006th loop: cost = 33.002479553222656\n", "The 11007th loop: cost = 37.26351547241211\n", "The 11008th loop: cost = 39.44523239135742\n", "The 11009th loop: cost = 33.704498291015625\n", "The 11010th loop: cost = 44.214935302734375\n", "The 11011th loop: cost = 39.22540283203125\n", "The 11012th loop: cost = 51.29701614379883\n", "The 11013th loop: cost = 35.726051330566406\n", "The 11014th loop: cost = 42.372581481933594\n", "The 11015th loop: cost = 47.899627685546875\n", "The 11016th loop: cost = 33.01002502441406\n", "The 11017th loop: cost = 34.88361358642578\n", "The 11018th loop: cost = 37.87086486816406\n", "The 11019th loop: cost = 35.383392333984375\n", "The 11020th loop: cost = 40.980430603027344\n", "The 11021th loop: cost = 38.229122161865234\n", "The 11022th loop: cost = 40.03839874267578\n", "The 11023th loop: cost = 34.13806915283203\n", "The 11024th loop: cost = 43.475242614746094\n", "The 11025th loop: cost = 26.45555305480957\n", "The 11026th loop: cost = 42.11764144897461\n", "The 11027th loop: cost = 43.0003662109375\n", "The 11028th loop: cost = 32.338775634765625\n", "The 11029th loop: cost = 39.57182312011719\n", "The 11030th loop: cost = 38.974647521972656\n", "The 11031th loop: cost = 35.59600830078125\n", "The 11032th loop: cost = 37.090030670166016\n", "The 11033th loop: cost = 39.17210388183594\n", "The 11034th loop: cost = 41.71747589111328\n", "The 11035th loop: cost = 44.10671615600586\n", "The 11036th loop: cost = 39.443992614746094\n", "The 11037th loop: cost = 35.04644775390625\n", "The 11038th loop: cost = 34.46709060668945\n", "The 11039th loop: cost = 37.12474822998047\n", "The 11040th loop: cost = 44.341041564941406\n", "The 11041th loop: cost = 33.872833251953125\n", "The 11042th loop: cost = 37.744293212890625\n", "The 11043th loop: cost = 41.630252838134766\n", "The 11044th loop: cost = 39.24642562866211\n", "The 11045th loop: cost = 32.764404296875\n", "The 11046th loop: cost = 34.792362213134766\n", "The 11047th loop: cost = 31.454919815063477\n", "The 11048th loop: cost = 39.557151794433594\n", "The 11049th loop: cost = 50.746482849121094\n", "The 11050th loop: cost = 29.696847915649414\n", "The 11051th loop: cost = 34.205772399902344\n", "The 11052th loop: cost = 46.952064514160156\n", "The 11053th loop: cost = 38.73755645751953\n", "The 11054th loop: cost = 39.269195556640625\n", "The 11055th loop: cost = 38.35670471191406\n", "The 11056th loop: cost = 37.2174186706543\n", "The 11057th loop: cost = 33.130455017089844\n", "The 11058th loop: cost = 33.46745300292969\n", "The 11059th loop: cost = 45.822418212890625\n", "The 11060th loop: cost = 28.975257873535156\n", "The 11061th loop: cost = 39.115928649902344\n", "The 11062th loop: cost = 31.891361236572266\n", "The 11063th loop: cost = 35.740936279296875\n", "The 11064th loop: cost = 36.17451477050781\n", "The 11065th loop: cost = 34.0386962890625\n", "The 11066th loop: cost = 25.976158142089844\n", "The 11067th loop: cost = 26.733570098876953\n", "The 11068th loop: cost = 30.537736892700195\n", "The 11069th loop: cost = 29.835128784179688\n", "The 11070th loop: cost = 45.992759704589844\n", "The 11071th loop: cost = 32.18585205078125\n", "The 11072th loop: cost = 45.68489456176758\n", "The 11073th loop: cost = 28.044811248779297\n", "The 11074th loop: cost = 30.139257431030273\n", "The 11075th loop: cost = 32.161903381347656\n", "The 11076th loop: cost = 40.19646072387695\n", "The 11077th loop: cost = 38.005393981933594\n", "The 11078th loop: cost = 34.07949447631836\n", "The 11079th loop: cost = 41.185546875\n", "The 11080th loop: cost = 38.98097229003906\n", "The 11081th loop: cost = 36.085140228271484\n", "The 11082th loop: cost = 37.28951644897461\n", "The 11083th loop: cost = 33.02119827270508\n", "The 11084th loop: cost = 38.87511444091797\n", "The 11085th loop: cost = 38.07302474975586\n", "The 11086th loop: cost = 32.3906135559082\n", "The 11087th loop: cost = 33.400360107421875\n", "The 11088th loop: cost = 35.32327651977539\n", "The 11089th loop: cost = 28.454357147216797\n", "The 11090th loop: cost = 37.42943572998047\n", "The 11091th loop: cost = 34.25323486328125\n", "The 11092th loop: cost = 34.6120491027832\n", "The 11093th loop: cost = 40.51597595214844\n", "The 11094th loop: cost = 35.76349639892578\n", "The 11095th loop: cost = 36.3871955871582\n", "The 11096th loop: cost = 32.25096893310547\n", "The 11097th loop: cost = 43.105628967285156\n", "The 11098th loop: cost = 38.29064178466797\n", "The 11099th loop: cost = 36.84769058227539\n", "The 11100th loop: cost = 31.289152145385742\n", "The 11101th loop: cost = 33.908058166503906\n", "The 11102th loop: cost = 42.68757247924805\n", "The 11103th loop: cost = 45.07478332519531\n", "The 11104th loop: cost = 28.755264282226562\n", "The 11105th loop: cost = 43.635135650634766\n", "The 11106th loop: cost = 44.78181457519531\n", "The 11107th loop: cost = 39.61717224121094\n", "The 11108th loop: cost = 34.3211669921875\n", "The 11109th loop: cost = 35.70317840576172\n", "The 11110th loop: cost = 35.971824645996094\n", "The 11111th loop: cost = 39.997657775878906\n", "The 11112th loop: cost = 37.25994110107422\n", "The 11113th loop: cost = 35.634010314941406\n", "The 11114th loop: cost = 34.622772216796875\n", "The 11115th loop: cost = 34.30436706542969\n", "The 11116th loop: cost = 37.510475158691406\n", "The 11117th loop: cost = 32.13880920410156\n", "The 11118th loop: cost = 36.31159973144531\n", "The 11119th loop: cost = 37.20038604736328\n", "The 11120th loop: cost = 38.65050506591797\n", "The 11121th loop: cost = 41.69760513305664\n", "The 11122th loop: cost = 45.40113067626953\n", "The 11123th loop: cost = 42.6104621887207\n", "The 11124th loop: cost = 42.21924591064453\n", "The 11125th loop: cost = 35.34222412109375\n", "The 11126th loop: cost = 33.29991149902344\n", "The 11127th loop: cost = 37.63512420654297\n", "The 11128th loop: cost = 32.465087890625\n", "The 11129th loop: cost = 36.62673568725586\n", "The 11130th loop: cost = 40.904869079589844\n", "The 11131th loop: cost = 32.08660888671875\n", "The 11132th loop: cost = 29.476543426513672\n", "The 11133th loop: cost = 32.695220947265625\n", "The 11134th loop: cost = 36.76438522338867\n", "The 11135th loop: cost = 33.942291259765625\n", "The 11136th loop: cost = 38.269168853759766\n", "The 11137th loop: cost = 31.822303771972656\n", "The 11138th loop: cost = 32.91606521606445\n", "The 11139th loop: cost = 32.57085418701172\n", "The 11140th loop: cost = 37.8668327331543\n", "The 11141th loop: cost = 38.43492126464844\n", "The 11142th loop: cost = 46.177818298339844\n", "The 11143th loop: cost = 34.97227478027344\n", "The 11144th loop: cost = 44.9188232421875\n", "The 11145th loop: cost = 43.05781555175781\n", "The 11146th loop: cost = 43.140480041503906\n", "The 11147th loop: cost = 41.40717315673828\n", "The 11148th loop: cost = 41.918907165527344\n", "The 11149th loop: cost = 34.91509246826172\n", "The 11150th loop: cost = 35.23797607421875\n", "The 11151th loop: cost = 41.79894256591797\n", "The 11152th loop: cost = 37.34820556640625\n", "The 11153th loop: cost = 37.44648742675781\n", "The 11154th loop: cost = 38.00912857055664\n", "The 11155th loop: cost = 33.23746109008789\n", "The 11156th loop: cost = 30.84051513671875\n", "The 11157th loop: cost = 34.469940185546875\n", "The 11158th loop: cost = 44.2808837890625\n", "The 11159th loop: cost = 34.01055145263672\n", "The 11160th loop: cost = 39.58490753173828\n", "The 11161th loop: cost = 38.924163818359375\n", "The 11162th loop: cost = 39.27897262573242\n", "The 11163th loop: cost = 28.876054763793945\n", "The 11164th loop: cost = 32.06309127807617\n", "The 11165th loop: cost = 27.453914642333984\n", "The 11166th loop: cost = 46.138389587402344\n", "The 11167th loop: cost = 34.47270202636719\n", "The 11168th loop: cost = 43.248435974121094\n", "The 11169th loop: cost = 30.541751861572266\n", "The 11170th loop: cost = 38.739925384521484\n", "The 11171th loop: cost = 32.577735900878906\n", "The 11172th loop: cost = 38.384727478027344\n", "The 11173th loop: cost = 34.22110366821289\n", "The 11174th loop: cost = 42.04228210449219\n", "The 11175th loop: cost = 34.992698669433594\n", "The 11176th loop: cost = 37.37273406982422\n", "The 11177th loop: cost = 31.05630111694336\n", "The 11178th loop: cost = 28.289844512939453\n", "The 11179th loop: cost = 36.80344772338867\n", "The 11180th loop: cost = 40.87147521972656\n", "The 11181th loop: cost = 38.17512893676758\n", "The 11182th loop: cost = 29.808347702026367\n", "The 11183th loop: cost = 30.583200454711914\n", "The 11184th loop: cost = 37.60568618774414\n", "The 11185th loop: cost = 34.14414978027344\n", "The 11186th loop: cost = 41.784095764160156\n", "The 11187th loop: cost = 43.26370620727539\n", "The 11188th loop: cost = 35.594722747802734\n", "The 11189th loop: cost = 36.25788879394531\n", "The 11190th loop: cost = 33.453468322753906\n", "The 11191th loop: cost = 34.06412124633789\n", "The 11192th loop: cost = 38.739768981933594\n", "The 11193th loop: cost = 32.91132354736328\n", "The 11194th loop: cost = 32.25156021118164\n", "The 11195th loop: cost = 34.25405502319336\n", "The 11196th loop: cost = 36.51545715332031\n", "The 11197th loop: cost = 38.35908508300781\n", "The 11198th loop: cost = 33.93134307861328\n", "The 11199th loop: cost = 34.65636444091797\n", "The 11200th loop: cost = 34.939300537109375\n", "The 11201th loop: cost = 37.119258880615234\n", "The 11202th loop: cost = 41.517127990722656\n", "The 11203th loop: cost = 29.326370239257812\n", "The 11204th loop: cost = 47.644432067871094\n", "The 11205th loop: cost = 39.07278823852539\n", "The 11206th loop: cost = 37.14595031738281\n", "The 11207th loop: cost = 43.30635070800781\n", "The 11208th loop: cost = 34.80902099609375\n", "The 11209th loop: cost = 33.70191955566406\n", "The 11210th loop: cost = 43.612545013427734\n", "The 11211th loop: cost = 40.346431732177734\n", "The 11212th loop: cost = 33.856163024902344\n", "The 11213th loop: cost = 40.73484802246094\n", "The 11214th loop: cost = 31.704660415649414\n", "The 11215th loop: cost = 36.32109069824219\n", "The 11216th loop: cost = 38.01762390136719\n", "The 11217th loop: cost = 29.315078735351562\n", "The 11218th loop: cost = 37.08679962158203\n", "The 11219th loop: cost = 40.413326263427734\n", "The 11220th loop: cost = 36.54944610595703\n", "The 11221th loop: cost = 30.3851261138916\n", "The 11222th loop: cost = 41.68573760986328\n", "The 11223th loop: cost = 37.77808380126953\n", "The 11224th loop: cost = 39.095314025878906\n", "The 11225th loop: cost = 31.68175506591797\n", "The 11226th loop: cost = 37.277462005615234\n", "The 11227th loop: cost = 38.94598388671875\n", "The 11228th loop: cost = 32.506614685058594\n", "The 11229th loop: cost = 44.14918518066406\n", "The 11230th loop: cost = 41.23542785644531\n", "The 11231th loop: cost = 31.361591339111328\n", "The 11232th loop: cost = 38.00300598144531\n", "The 11233th loop: cost = 27.872955322265625\n", "The 11234th loop: cost = 33.912418365478516\n", "The 11235th loop: cost = 32.18278121948242\n", "The 11236th loop: cost = 29.642749786376953\n", "The 11237th loop: cost = 33.44588088989258\n", "The 11238th loop: cost = 43.98800277709961\n", "The 11239th loop: cost = 32.96863555908203\n", "The 11240th loop: cost = 32.70063781738281\n", "The 11241th loop: cost = 33.77749252319336\n", "The 11242th loop: cost = 44.0140380859375\n", "The 11243th loop: cost = 33.36082458496094\n", "The 11244th loop: cost = 36.13454818725586\n", "The 11245th loop: cost = 38.67584991455078\n", "The 11246th loop: cost = 36.15442657470703\n", "The 11247th loop: cost = 44.641700744628906\n", "The 11248th loop: cost = 32.5962028503418\n", "The 11249th loop: cost = 36.86827850341797\n", "The 11250th loop: cost = 32.79705810546875\n", "The 11251th loop: cost = 30.581043243408203\n", "The 11252th loop: cost = 38.588775634765625\n", "The 11253th loop: cost = 28.00684356689453\n", "The 11254th loop: cost = 39.46929168701172\n", "The 11255th loop: cost = 43.05793762207031\n", "The 11256th loop: cost = 31.64078140258789\n", "The 11257th loop: cost = 29.11567497253418\n", "The 11258th loop: cost = 35.605918884277344\n", "The 11259th loop: cost = 29.862796783447266\n", "The 11260th loop: cost = 45.56431579589844\n", "The 11261th loop: cost = 43.251434326171875\n", "The 11262th loop: cost = 45.004676818847656\n", "The 11263th loop: cost = 41.399993896484375\n", "The 11264th loop: cost = 35.600242614746094\n", "The 11265th loop: cost = 38.17420196533203\n", "The 11266th loop: cost = 33.85211181640625\n", "The 11267th loop: cost = 36.353515625\n", "The 11268th loop: cost = 38.27714538574219\n", "The 11269th loop: cost = 42.31742858886719\n", "The 11270th loop: cost = 32.168907165527344\n", "The 11271th loop: cost = 41.580448150634766\n", "The 11272th loop: cost = 33.83811950683594\n", "The 11273th loop: cost = 30.623510360717773\n", "The 11274th loop: cost = 34.675846099853516\n", "The 11275th loop: cost = 47.39092254638672\n", "The 11276th loop: cost = 35.08012008666992\n", "The 11277th loop: cost = 29.448944091796875\n", "The 11278th loop: cost = 34.67745590209961\n", "The 11279th loop: cost = 31.068443298339844\n", "The 11280th loop: cost = 36.041290283203125\n", "The 11281th loop: cost = 31.00723648071289\n", "The 11282th loop: cost = 40.667659759521484\n", "The 11283th loop: cost = 38.94084548950195\n", "The 11284th loop: cost = 46.590354919433594\n", "The 11285th loop: cost = 42.268184661865234\n", "The 11286th loop: cost = 41.17172622680664\n", "The 11287th loop: cost = 38.983734130859375\n", "The 11288th loop: cost = 37.15936279296875\n", "The 11289th loop: cost = 33.188758850097656\n", "The 11290th loop: cost = 40.387489318847656\n", "The 11291th loop: cost = 37.283538818359375\n", "The 11292th loop: cost = 28.65064239501953\n", "The 11293th loop: cost = 42.55641555786133\n", "The 11294th loop: cost = 37.84960174560547\n", "The 11295th loop: cost = 40.575199127197266\n", "The 11296th loop: cost = 36.35350799560547\n", "The 11297th loop: cost = 42.81523895263672\n", "The 11298th loop: cost = 39.22298812866211\n", "The 11299th loop: cost = 47.993629455566406\n", "The 11300th loop: cost = 35.58808898925781\n", "The 11301th loop: cost = 26.930082321166992\n", "The 11302th loop: cost = 43.17155456542969\n", "The 11303th loop: cost = 45.23683166503906\n", "The 11304th loop: cost = 38.016300201416016\n", "The 11305th loop: cost = 38.780235290527344\n", "The 11306th loop: cost = 38.87743377685547\n", "The 11307th loop: cost = 39.62397003173828\n", "The 11308th loop: cost = 35.971763610839844\n", "The 11309th loop: cost = 35.89137268066406\n", "The 11310th loop: cost = 34.867469787597656\n", "The 11311th loop: cost = 37.33767318725586\n", "The 11312th loop: cost = 47.94807434082031\n", "The 11313th loop: cost = 32.87808609008789\n", "The 11314th loop: cost = 38.20337677001953\n", "The 11315th loop: cost = 38.5020866394043\n", "The 11316th loop: cost = 36.64678955078125\n", "The 11317th loop: cost = 34.941856384277344\n", "The 11318th loop: cost = 39.32273864746094\n", "The 11319th loop: cost = 35.667762756347656\n", "The 11320th loop: cost = 35.7647819519043\n", "The 11321th loop: cost = 35.28540802001953\n", "The 11322th loop: cost = 45.81785202026367\n", "The 11323th loop: cost = 37.25035858154297\n", "The 11324th loop: cost = 49.14545822143555\n", "The 11325th loop: cost = 35.39877700805664\n", "The 11326th loop: cost = 40.70167922973633\n", "The 11327th loop: cost = 34.051387786865234\n", "The 11328th loop: cost = 45.80612564086914\n", "The 11329th loop: cost = 39.67847442626953\n", "The 11330th loop: cost = 35.76308822631836\n", "The 11331th loop: cost = 34.76129913330078\n", "The 11332th loop: cost = 35.646480560302734\n", "The 11333th loop: cost = 36.378353118896484\n", "The 11334th loop: cost = 35.504817962646484\n", "The 11335th loop: cost = 34.61769104003906\n", "The 11336th loop: cost = 32.61836242675781\n", "The 11337th loop: cost = 30.353761672973633\n", "The 11338th loop: cost = 38.593902587890625\n", "The 11339th loop: cost = 42.94561004638672\n", "The 11340th loop: cost = 30.490570068359375\n", "The 11341th loop: cost = 31.638607025146484\n", "The 11342th loop: cost = 44.0604362487793\n", "The 11343th loop: cost = 40.22032165527344\n", "The 11344th loop: cost = 39.00550842285156\n", "The 11345th loop: cost = 42.5103759765625\n", "The 11346th loop: cost = 35.844051361083984\n", "The 11347th loop: cost = 39.539710998535156\n", "The 11348th loop: cost = 38.58269500732422\n", "The 11349th loop: cost = 40.207855224609375\n", "The 11350th loop: cost = 46.401390075683594\n", "The 11351th loop: cost = 35.42889404296875\n", "The 11352th loop: cost = 39.17295837402344\n", "The 11353th loop: cost = 43.071781158447266\n", "The 11354th loop: cost = 38.25713348388672\n", "The 11355th loop: cost = 41.17713928222656\n", "The 11356th loop: cost = 38.444358825683594\n", "The 11357th loop: cost = 32.51740646362305\n", "The 11358th loop: cost = 41.348167419433594\n", "The 11359th loop: cost = 39.86895751953125\n", "The 11360th loop: cost = 37.944908142089844\n", "The 11361th loop: cost = 43.04383850097656\n", "The 11362th loop: cost = 33.56060791015625\n", "The 11363th loop: cost = 34.717918395996094\n", "The 11364th loop: cost = 43.23344802856445\n", "The 11365th loop: cost = 39.36518096923828\n", "The 11366th loop: cost = 39.28838348388672\n", "The 11367th loop: cost = 42.323577880859375\n", "The 11368th loop: cost = 33.76325607299805\n", "The 11369th loop: cost = 31.056529998779297\n", "The 11370th loop: cost = 35.28147888183594\n", "The 11371th loop: cost = 44.33550262451172\n", "The 11372th loop: cost = 32.26792907714844\n", "The 11373th loop: cost = 39.4750862121582\n", "The 11374th loop: cost = 41.77029037475586\n", "The 11375th loop: cost = 43.241355895996094\n", "The 11376th loop: cost = 39.74955368041992\n", "The 11377th loop: cost = 31.372343063354492\n", "The 11378th loop: cost = 31.944753646850586\n", "The 11379th loop: cost = 29.839427947998047\n", "The 11380th loop: cost = 50.46336364746094\n", "The 11381th loop: cost = 32.926055908203125\n", "The 11382th loop: cost = 38.13991928100586\n", "The 11383th loop: cost = 33.19953155517578\n", "The 11384th loop: cost = 35.622398376464844\n", "The 11385th loop: cost = 42.34032440185547\n", "The 11386th loop: cost = 30.86072540283203\n", "The 11387th loop: cost = 35.94695281982422\n", "The 11388th loop: cost = 28.387210845947266\n", "The 11389th loop: cost = 41.757381439208984\n", "The 11390th loop: cost = 32.661964416503906\n", "The 11391th loop: cost = 20.55645751953125\n", "The 11392th loop: cost = 45.24344253540039\n", "The 11393th loop: cost = 43.26603317260742\n", "The 11394th loop: cost = 29.194934844970703\n", "The 11395th loop: cost = 34.648136138916016\n", "The 11396th loop: cost = 38.93719482421875\n", "The 11397th loop: cost = 41.79347229003906\n", "The 11398th loop: cost = 38.947654724121094\n", "The 11399th loop: cost = 36.3089599609375\n", "The 11400th loop: cost = 43.10069274902344\n", "The 11401th loop: cost = 40.94682312011719\n", "The 11402th loop: cost = 44.187870025634766\n", "The 11403th loop: cost = 44.381263732910156\n", "The 11404th loop: cost = 42.009742736816406\n", "The 11405th loop: cost = 39.7916259765625\n", "The 11406th loop: cost = 37.9029655456543\n", "The 11407th loop: cost = 46.692665100097656\n", "The 11408th loop: cost = 33.991233825683594\n", "The 11409th loop: cost = 37.113529205322266\n", "The 11410th loop: cost = 35.34968185424805\n", "The 11411th loop: cost = 35.087162017822266\n", "The 11412th loop: cost = 37.73075485229492\n", "The 11413th loop: cost = 44.14311981201172\n", "The 11414th loop: cost = 38.20506286621094\n", "The 11415th loop: cost = 36.53355407714844\n", "The 11416th loop: cost = 34.963287353515625\n", "The 11417th loop: cost = 35.47706604003906\n", "The 11418th loop: cost = 44.301761627197266\n", "The 11419th loop: cost = 34.116695404052734\n", "The 11420th loop: cost = 29.564977645874023\n", "The 11421th loop: cost = 28.581384658813477\n", "The 11422th loop: cost = 26.94924545288086\n", "The 11423th loop: cost = 32.202003479003906\n", "The 11424th loop: cost = 40.73615264892578\n", "The 11425th loop: cost = 36.471858978271484\n", "The 11426th loop: cost = 40.36773681640625\n", "The 11427th loop: cost = 31.93587875366211\n", "The 11428th loop: cost = 33.379234313964844\n", "The 11429th loop: cost = 45.22771453857422\n", "The 11430th loop: cost = 39.64668655395508\n", "The 11431th loop: cost = 35.146385192871094\n", "The 11432th loop: cost = 34.73887634277344\n", "The 11433th loop: cost = 41.28896713256836\n", "The 11434th loop: cost = 36.32592010498047\n", "The 11435th loop: cost = 42.376461029052734\n", "The 11436th loop: cost = 31.08157730102539\n", "The 11437th loop: cost = 34.824127197265625\n", "The 11438th loop: cost = 38.016273498535156\n", "The 11439th loop: cost = 36.16685485839844\n", "The 11440th loop: cost = 37.051597595214844\n", "The 11441th loop: cost = 43.28158187866211\n", "The 11442th loop: cost = 44.12322998046875\n", "The 11443th loop: cost = 42.4153938293457\n", "The 11444th loop: cost = 37.277076721191406\n", "The 11445th loop: cost = 42.128143310546875\n", "The 11446th loop: cost = 43.62660217285156\n", "The 11447th loop: cost = 47.349395751953125\n", "The 11448th loop: cost = 36.0670280456543\n", "The 11449th loop: cost = 33.195770263671875\n", "The 11450th loop: cost = 35.15483093261719\n", "The 11451th loop: cost = 35.20222473144531\n", "The 11452th loop: cost = 35.21924591064453\n", "The 11453th loop: cost = 35.10614013671875\n", "The 11454th loop: cost = 42.74651336669922\n", "The 11455th loop: cost = 34.4035758972168\n", "The 11456th loop: cost = 37.144989013671875\n", "The 11457th loop: cost = 39.53954315185547\n", "The 11458th loop: cost = 41.65924835205078\n", "The 11459th loop: cost = 34.90869903564453\n", "The 11460th loop: cost = 34.44655990600586\n", "The 11461th loop: cost = 46.48207092285156\n", "The 11462th loop: cost = 33.739654541015625\n", "The 11463th loop: cost = 31.087568283081055\n", "The 11464th loop: cost = 34.38957214355469\n", "The 11465th loop: cost = 38.18254470825195\n", "The 11466th loop: cost = 34.363502502441406\n", "The 11467th loop: cost = 37.18149185180664\n", "The 11468th loop: cost = 32.448509216308594\n", "The 11469th loop: cost = 39.03736877441406\n", "The 11470th loop: cost = 36.02440643310547\n", "The 11471th loop: cost = 33.03419494628906\n", "The 11472th loop: cost = 34.27082824707031\n", "The 11473th loop: cost = 31.15452003479004\n", "The 11474th loop: cost = 37.58135986328125\n", "The 11475th loop: cost = 36.89544677734375\n", "The 11476th loop: cost = 38.74833679199219\n", "The 11477th loop: cost = 33.65935134887695\n", "The 11478th loop: cost = 35.771087646484375\n", "The 11479th loop: cost = 31.664579391479492\n", "The 11480th loop: cost = 38.263431549072266\n", "The 11481th loop: cost = 31.552637100219727\n", "The 11482th loop: cost = 33.01925277709961\n", "The 11483th loop: cost = 32.12111282348633\n", "The 11484th loop: cost = 38.66163635253906\n", "The 11485th loop: cost = 36.30496597290039\n", "The 11486th loop: cost = 41.74824523925781\n", "The 11487th loop: cost = 38.73646545410156\n", "The 11488th loop: cost = 38.24805450439453\n", "The 11489th loop: cost = 33.33412551879883\n", "The 11490th loop: cost = 35.93511962890625\n", "The 11491th loop: cost = 30.908748626708984\n", "The 11492th loop: cost = 37.859642028808594\n", "The 11493th loop: cost = 32.225013732910156\n", "The 11494th loop: cost = 32.73274230957031\n", "The 11495th loop: cost = 40.151649475097656\n", "The 11496th loop: cost = 38.43534469604492\n", "The 11497th loop: cost = 32.827423095703125\n", "The 11498th loop: cost = 40.378238677978516\n", "The 11499th loop: cost = 36.797760009765625\n", "The 11500th loop: cost = 28.55345916748047\n", "The 11501th loop: cost = 35.98884582519531\n", "The 11502th loop: cost = 30.20797348022461\n", "The 11503th loop: cost = 37.534996032714844\n", "The 11504th loop: cost = 32.546875\n", "The 11505th loop: cost = 41.91438674926758\n", "The 11506th loop: cost = 27.64490509033203\n", "The 11507th loop: cost = 32.60003662109375\n", "The 11508th loop: cost = 37.71567153930664\n", "The 11509th loop: cost = 30.973613739013672\n", "The 11510th loop: cost = 33.19233703613281\n", "The 11511th loop: cost = 50.48499298095703\n", "The 11512th loop: cost = 36.304718017578125\n", "The 11513th loop: cost = 38.74812698364258\n", "The 11514th loop: cost = 37.8516845703125\n", "The 11515th loop: cost = 32.019737243652344\n", "The 11516th loop: cost = 43.83958435058594\n", "The 11517th loop: cost = 33.71773910522461\n", "The 11518th loop: cost = 37.986732482910156\n", "The 11519th loop: cost = 31.899627685546875\n", "The 11520th loop: cost = 37.50048065185547\n", "The 11521th loop: cost = 37.64482879638672\n", "The 11522th loop: cost = 30.178329467773438\n", "The 11523th loop: cost = 38.43134307861328\n", "The 11524th loop: cost = 37.66432571411133\n", "The 11525th loop: cost = 35.262603759765625\n", "The 11526th loop: cost = 35.3076286315918\n", "The 11527th loop: cost = 35.62330627441406\n", "The 11528th loop: cost = 31.132482528686523\n", "The 11529th loop: cost = 41.7130241394043\n", "The 11530th loop: cost = 38.717464447021484\n", "The 11531th loop: cost = 36.120574951171875\n", "The 11532th loop: cost = 35.63316345214844\n", "The 11533th loop: cost = 31.198474884033203\n", "The 11534th loop: cost = 31.904842376708984\n", "The 11535th loop: cost = 41.60508728027344\n", "The 11536th loop: cost = 31.862071990966797\n", "The 11537th loop: cost = 27.106685638427734\n", "The 11538th loop: cost = 35.49658203125\n", "The 11539th loop: cost = 31.132444381713867\n", "The 11540th loop: cost = 37.3983268737793\n", "The 11541th loop: cost = 34.20450210571289\n", "The 11542th loop: cost = 32.795433044433594\n", "The 11543th loop: cost = 39.434078216552734\n", "The 11544th loop: cost = 42.20330047607422\n", "The 11545th loop: cost = 37.056827545166016\n", "The 11546th loop: cost = 41.01622009277344\n", "The 11547th loop: cost = 31.79498291015625\n", "The 11548th loop: cost = 43.52698516845703\n", "The 11549th loop: cost = 39.2474365234375\n", "The 11550th loop: cost = 37.25453186035156\n", "The 11551th loop: cost = 46.21586990356445\n", "The 11552th loop: cost = 37.04925537109375\n", "The 11553th loop: cost = 38.38404083251953\n", "The 11554th loop: cost = 43.290157318115234\n", "The 11555th loop: cost = 35.804832458496094\n", "The 11556th loop: cost = 30.494678497314453\n", "The 11557th loop: cost = 35.14737319946289\n", "The 11558th loop: cost = 39.82778549194336\n", "The 11559th loop: cost = 41.87901306152344\n", "The 11560th loop: cost = 35.65121841430664\n", "The 11561th loop: cost = 32.08275604248047\n", "The 11562th loop: cost = 43.31813430786133\n", "The 11563th loop: cost = 42.949974060058594\n", "The 11564th loop: cost = 39.3632926940918\n", "The 11565th loop: cost = 33.51688003540039\n", "The 11566th loop: cost = 45.483726501464844\n", "The 11567th loop: cost = 41.673458099365234\n", "The 11568th loop: cost = 26.822975158691406\n", "The 11569th loop: cost = 37.41122817993164\n", "The 11570th loop: cost = 36.9205207824707\n", "The 11571th loop: cost = 44.010162353515625\n", "The 11572th loop: cost = 44.7322883605957\n", "The 11573th loop: cost = 35.68800735473633\n", "The 11574th loop: cost = 31.442445755004883\n", "The 11575th loop: cost = 42.384376525878906\n", "The 11576th loop: cost = 35.826045989990234\n", "The 11577th loop: cost = 39.20784378051758\n", "The 11578th loop: cost = 41.57767105102539\n", "The 11579th loop: cost = 42.05855941772461\n", "The 11580th loop: cost = 41.88623809814453\n", "The 11581th loop: cost = 35.919517517089844\n", "The 11582th loop: cost = 34.109901428222656\n", "The 11583th loop: cost = 34.84228515625\n", "The 11584th loop: cost = 36.27833938598633\n", "The 11585th loop: cost = 36.20137023925781\n", "The 11586th loop: cost = 34.878814697265625\n", "The 11587th loop: cost = 36.67879104614258\n", "The 11588th loop: cost = 45.824806213378906\n", "The 11589th loop: cost = 37.213260650634766\n", "The 11590th loop: cost = 35.85610580444336\n", "The 11591th loop: cost = 40.979217529296875\n", "The 11592th loop: cost = 29.9858455657959\n", "The 11593th loop: cost = 41.06141662597656\n", "The 11594th loop: cost = 36.578277587890625\n", "The 11595th loop: cost = 31.458419799804688\n", "The 11596th loop: cost = 37.28451156616211\n", "The 11597th loop: cost = 32.768699645996094\n", "The 11598th loop: cost = 39.38977813720703\n", "The 11599th loop: cost = 41.1672248840332\n", "The 11600th loop: cost = 31.254310607910156\n", "The 11601th loop: cost = 34.79479217529297\n", "The 11602th loop: cost = 34.631919860839844\n", "The 11603th loop: cost = 32.83728790283203\n", "The 11604th loop: cost = 32.226463317871094\n", "The 11605th loop: cost = 37.99068832397461\n", "The 11606th loop: cost = 40.645328521728516\n", "The 11607th loop: cost = 39.49396514892578\n", "The 11608th loop: cost = 43.68106460571289\n", "The 11609th loop: cost = 35.378875732421875\n", "The 11610th loop: cost = 38.66681671142578\n", "The 11611th loop: cost = 29.00457000732422\n", "The 11612th loop: cost = 34.670806884765625\n", "The 11613th loop: cost = 27.33194351196289\n", "The 11614th loop: cost = 35.86052322387695\n", "The 11615th loop: cost = 32.157691955566406\n", "The 11616th loop: cost = 38.848060607910156\n", "The 11617th loop: cost = 36.02067565917969\n", "The 11618th loop: cost = 56.195213317871094\n", "The 11619th loop: cost = 40.34479522705078\n", "The 11620th loop: cost = 33.844505310058594\n", "The 11621th loop: cost = 31.922544479370117\n", "The 11622th loop: cost = 31.52212905883789\n", "The 11623th loop: cost = 29.999710083007812\n", "The 11624th loop: cost = 40.38533020019531\n", "The 11625th loop: cost = 42.011192321777344\n", "The 11626th loop: cost = 34.576114654541016\n", "The 11627th loop: cost = 34.05820846557617\n", "The 11628th loop: cost = 37.265892028808594\n", "The 11629th loop: cost = 36.5717658996582\n", "The 11630th loop: cost = 31.405763626098633\n", "The 11631th loop: cost = 42.7508544921875\n", "The 11632th loop: cost = 38.88106918334961\n", "The 11633th loop: cost = 38.86641311645508\n", "The 11634th loop: cost = 38.3773307800293\n", "The 11635th loop: cost = 42.276336669921875\n", "The 11636th loop: cost = 30.991039276123047\n", "The 11637th loop: cost = 39.81864547729492\n", "The 11638th loop: cost = 39.33319091796875\n", "The 11639th loop: cost = 34.022552490234375\n", "The 11640th loop: cost = 30.67219352722168\n", "The 11641th loop: cost = 29.9940185546875\n", "The 11642th loop: cost = 39.88018035888672\n", "The 11643th loop: cost = 44.63715744018555\n", "The 11644th loop: cost = 38.770263671875\n", "The 11645th loop: cost = 46.558929443359375\n", "The 11646th loop: cost = 36.2748908996582\n", "The 11647th loop: cost = 38.77465057373047\n", "The 11648th loop: cost = 43.15493392944336\n", "The 11649th loop: cost = 32.178123474121094\n", "The 11650th loop: cost = 40.33287048339844\n", "The 11651th loop: cost = 32.02004623413086\n", "The 11652th loop: cost = 33.967891693115234\n", "The 11653th loop: cost = 38.31654739379883\n", "The 11654th loop: cost = 34.977195739746094\n", "The 11655th loop: cost = 33.348323822021484\n", "The 11656th loop: cost = 36.83073425292969\n", "The 11657th loop: cost = 36.68574523925781\n", "The 11658th loop: cost = 27.958473205566406\n", "The 11659th loop: cost = 36.6870002746582\n", "The 11660th loop: cost = 37.83085250854492\n", "The 11661th loop: cost = 35.372013092041016\n", "The 11662th loop: cost = 37.850921630859375\n", "The 11663th loop: cost = 41.49191665649414\n", "The 11664th loop: cost = 34.26153564453125\n", "The 11665th loop: cost = 40.96628952026367\n", "The 11666th loop: cost = 38.85255432128906\n", "The 11667th loop: cost = 37.203094482421875\n", "The 11668th loop: cost = 46.35191345214844\n", "The 11669th loop: cost = 34.40387725830078\n", "The 11670th loop: cost = 33.11266326904297\n", "The 11671th loop: cost = 41.67967224121094\n", "The 11672th loop: cost = 38.852195739746094\n", "The 11673th loop: cost = 40.48955535888672\n", "The 11674th loop: cost = 42.8392219543457\n", "The 11675th loop: cost = 34.490081787109375\n", "The 11676th loop: cost = 31.744447708129883\n", "The 11677th loop: cost = 39.64723205566406\n", "The 11678th loop: cost = 42.55082702636719\n", "The 11679th loop: cost = 35.417152404785156\n", "The 11680th loop: cost = 42.96907043457031\n", "The 11681th loop: cost = 30.96151351928711\n", "The 11682th loop: cost = 32.99406433105469\n", "The 11683th loop: cost = 35.38688659667969\n", "The 11684th loop: cost = 37.13737487792969\n", "The 11685th loop: cost = 37.92814636230469\n", "The 11686th loop: cost = 33.673274993896484\n", "The 11687th loop: cost = 37.8614501953125\n", "The 11688th loop: cost = 49.424232482910156\n", "The 11689th loop: cost = 42.54201126098633\n", "The 11690th loop: cost = 38.540550231933594\n", "The 11691th loop: cost = 36.7662353515625\n", "The 11692th loop: cost = 42.633338928222656\n", "The 11693th loop: cost = 36.940284729003906\n", "The 11694th loop: cost = 32.6273078918457\n", "The 11695th loop: cost = 37.6129150390625\n", "The 11696th loop: cost = 34.956581115722656\n", "The 11697th loop: cost = 27.510852813720703\n", "The 11698th loop: cost = 34.65850067138672\n", "The 11699th loop: cost = 35.09529495239258\n", "The 11700th loop: cost = 43.68065643310547\n", "The 11701th loop: cost = 35.62367630004883\n", "The 11702th loop: cost = 38.09233093261719\n", "The 11703th loop: cost = 34.803955078125\n", "The 11704th loop: cost = 32.416770935058594\n", "The 11705th loop: cost = 32.91496658325195\n", "The 11706th loop: cost = 35.09624481201172\n", "The 11707th loop: cost = 42.39609909057617\n", "The 11708th loop: cost = 38.38383102416992\n", "The 11709th loop: cost = 43.44576644897461\n", "The 11710th loop: cost = 34.63617706298828\n", "The 11711th loop: cost = 35.59941482543945\n", "The 11712th loop: cost = 38.95148468017578\n", "The 11713th loop: cost = 33.871482849121094\n", "The 11714th loop: cost = 34.047000885009766\n", "The 11715th loop: cost = 32.186065673828125\n", "The 11716th loop: cost = 42.65849304199219\n", "The 11717th loop: cost = 39.94990539550781\n", "The 11718th loop: cost = 26.961383819580078\n", "The 11719th loop: cost = 37.480133056640625\n", "The 11720th loop: cost = 34.56604766845703\n", "The 11721th loop: cost = 34.74885940551758\n", "The 11722th loop: cost = 43.82647705078125\n", "The 11723th loop: cost = 37.26521682739258\n", "The 11724th loop: cost = 47.868568420410156\n", "The 11725th loop: cost = 43.46440124511719\n", "The 11726th loop: cost = 30.844606399536133\n", "The 11727th loop: cost = 45.56346130371094\n", "The 11728th loop: cost = 39.92089080810547\n", "The 11729th loop: cost = 40.57180404663086\n", "The 11730th loop: cost = 41.114227294921875\n", "The 11731th loop: cost = 42.688194274902344\n", "The 11732th loop: cost = 35.96009826660156\n", "The 11733th loop: cost = 34.962493896484375\n", "The 11734th loop: cost = 40.327415466308594\n", "The 11735th loop: cost = 30.71731185913086\n", "The 11736th loop: cost = 42.27480697631836\n", "The 11737th loop: cost = 32.324920654296875\n", "The 11738th loop: cost = 37.041107177734375\n", "The 11739th loop: cost = 33.268455505371094\n", "The 11740th loop: cost = 45.90129470825195\n", "The 11741th loop: cost = 40.7662353515625\n", "The 11742th loop: cost = 33.859981536865234\n", "The 11743th loop: cost = 35.521018981933594\n", "The 11744th loop: cost = 38.75910186767578\n", "The 11745th loop: cost = 47.580108642578125\n", "The 11746th loop: cost = 29.932537078857422\n", "The 11747th loop: cost = 35.21976852416992\n", "The 11748th loop: cost = 27.294933319091797\n", "The 11749th loop: cost = 30.088768005371094\n", "The 11750th loop: cost = 39.111228942871094\n", "The 11751th loop: cost = 32.73664093017578\n", "The 11752th loop: cost = 43.82469940185547\n", "The 11753th loop: cost = 33.44017028808594\n", "The 11754th loop: cost = 38.09281921386719\n", "The 11755th loop: cost = 39.9379997253418\n", "The 11756th loop: cost = 41.50262451171875\n", "The 11757th loop: cost = 31.22178077697754\n", "The 11758th loop: cost = 32.93617248535156\n", "The 11759th loop: cost = 37.9885368347168\n", "The 11760th loop: cost = 34.148746490478516\n", "The 11761th loop: cost = 51.835060119628906\n", "The 11762th loop: cost = 31.715255737304688\n", "The 11763th loop: cost = 32.347557067871094\n", "The 11764th loop: cost = 39.16197204589844\n", "The 11765th loop: cost = 44.07886505126953\n", "The 11766th loop: cost = 48.42160415649414\n", "The 11767th loop: cost = 45.86161422729492\n", "The 11768th loop: cost = 36.23015594482422\n", "The 11769th loop: cost = 34.774375915527344\n", "The 11770th loop: cost = 33.13389587402344\n", "The 11771th loop: cost = 31.006824493408203\n", "The 11772th loop: cost = 35.955352783203125\n", "The 11773th loop: cost = 40.78193664550781\n", "The 11774th loop: cost = 30.64959716796875\n", "The 11775th loop: cost = 31.752670288085938\n", "The 11776th loop: cost = 39.168113708496094\n", "The 11777th loop: cost = 31.123497009277344\n", "The 11778th loop: cost = 38.724586486816406\n", "The 11779th loop: cost = 39.89814758300781\n", "The 11780th loop: cost = 26.918312072753906\n", "The 11781th loop: cost = 35.5113525390625\n", "The 11782th loop: cost = 38.203575134277344\n", "The 11783th loop: cost = 38.162567138671875\n", "The 11784th loop: cost = 33.546390533447266\n", "The 11785th loop: cost = 41.545867919921875\n", "The 11786th loop: cost = 46.33448791503906\n", "The 11787th loop: cost = 33.389251708984375\n", "The 11788th loop: cost = 38.984642028808594\n", "The 11789th loop: cost = 34.89289855957031\n", "The 11790th loop: cost = 39.848121643066406\n", "The 11791th loop: cost = 35.002113342285156\n", "The 11792th loop: cost = 33.92778778076172\n", "The 11793th loop: cost = 34.46269989013672\n", "The 11794th loop: cost = 38.65247344970703\n", "The 11795th loop: cost = 37.15656661987305\n", "The 11796th loop: cost = 35.176002502441406\n", "The 11797th loop: cost = 35.5460205078125\n", "The 11798th loop: cost = 35.433326721191406\n", "The 11799th loop: cost = 34.92903137207031\n", "The 11800th loop: cost = 34.67267608642578\n", "The 11801th loop: cost = 33.18254470825195\n", "The 11802th loop: cost = 35.865264892578125\n", "The 11803th loop: cost = 34.837188720703125\n", "The 11804th loop: cost = 29.400680541992188\n", "The 11805th loop: cost = 26.13409423828125\n", "The 11806th loop: cost = 33.750064849853516\n", "The 11807th loop: cost = 29.473434448242188\n", "The 11808th loop: cost = 38.582157135009766\n", "The 11809th loop: cost = 44.730655670166016\n", "The 11810th loop: cost = 32.38532638549805\n", "The 11811th loop: cost = 41.662994384765625\n", "The 11812th loop: cost = 31.237682342529297\n", "The 11813th loop: cost = 30.977760314941406\n", "The 11814th loop: cost = 34.877708435058594\n", "The 11815th loop: cost = 37.73517608642578\n", "The 11816th loop: cost = 35.314186096191406\n", "The 11817th loop: cost = 42.732933044433594\n", "The 11818th loop: cost = 33.97699737548828\n", "The 11819th loop: cost = 37.188072204589844\n", "The 11820th loop: cost = 32.581199645996094\n", "The 11821th loop: cost = 41.29971694946289\n", "The 11822th loop: cost = 39.54883575439453\n", "The 11823th loop: cost = 34.624786376953125\n", "The 11824th loop: cost = 27.758935928344727\n", "The 11825th loop: cost = 32.31672668457031\n", "The 11826th loop: cost = 43.49028778076172\n", "The 11827th loop: cost = 36.264007568359375\n", "The 11828th loop: cost = 32.80788803100586\n", "The 11829th loop: cost = 37.636009216308594\n", "The 11830th loop: cost = 34.12993240356445\n", "The 11831th loop: cost = 37.158599853515625\n", "The 11832th loop: cost = 32.48186111450195\n", "The 11833th loop: cost = 38.215431213378906\n", "The 11834th loop: cost = 34.55376434326172\n", "The 11835th loop: cost = 37.87846374511719\n", "The 11836th loop: cost = 37.16436767578125\n", "The 11837th loop: cost = 39.080902099609375\n", "The 11838th loop: cost = 33.82036209106445\n", "The 11839th loop: cost = 40.278377532958984\n", "The 11840th loop: cost = 38.964324951171875\n", "The 11841th loop: cost = 40.21216583251953\n", "The 11842th loop: cost = 47.372772216796875\n", "The 11843th loop: cost = 30.004179000854492\n", "The 11844th loop: cost = 39.525997161865234\n", "The 11845th loop: cost = 34.5338134765625\n", "The 11846th loop: cost = 33.51201248168945\n", "The 11847th loop: cost = 39.44429016113281\n", "The 11848th loop: cost = 28.241336822509766\n", "The 11849th loop: cost = 40.168540954589844\n", "The 11850th loop: cost = 40.16730499267578\n", "The 11851th loop: cost = 35.48579025268555\n", "The 11852th loop: cost = 38.23621368408203\n", "The 11853th loop: cost = 30.54134750366211\n", "The 11854th loop: cost = 33.594635009765625\n", "The 11855th loop: cost = 39.54414749145508\n", "The 11856th loop: cost = 35.686439514160156\n", "The 11857th loop: cost = 34.762821197509766\n", "The 11858th loop: cost = 35.64457702636719\n", "The 11859th loop: cost = 44.25175476074219\n", "The 11860th loop: cost = 30.02138328552246\n", "The 11861th loop: cost = 32.24192810058594\n", "The 11862th loop: cost = 32.0233039855957\n", "The 11863th loop: cost = 30.185569763183594\n", "The 11864th loop: cost = 28.158533096313477\n", "The 11865th loop: cost = 36.15561294555664\n", "The 11866th loop: cost = 30.91257095336914\n", "The 11867th loop: cost = 36.66880798339844\n", "The 11868th loop: cost = 32.725669860839844\n", "The 11869th loop: cost = 46.45738220214844\n", "The 11870th loop: cost = 27.338279724121094\n", "The 11871th loop: cost = 42.348731994628906\n", "The 11872th loop: cost = 35.02693557739258\n", "The 11873th loop: cost = 41.009422302246094\n", "The 11874th loop: cost = 37.86228942871094\n", "The 11875th loop: cost = 33.483497619628906\n", "The 11876th loop: cost = 32.29895782470703\n", "The 11877th loop: cost = 48.346744537353516\n", "The 11878th loop: cost = 38.048240661621094\n", "The 11879th loop: cost = 40.74268341064453\n", "The 11880th loop: cost = 41.534297943115234\n", "The 11881th loop: cost = 42.917755126953125\n", "The 11882th loop: cost = 40.89239501953125\n", "The 11883th loop: cost = 43.44059753417969\n", "The 11884th loop: cost = 38.44260787963867\n", "The 11885th loop: cost = 35.12190628051758\n", "The 11886th loop: cost = 39.57073974609375\n", "The 11887th loop: cost = 34.91376495361328\n", "The 11888th loop: cost = 35.34565734863281\n", "The 11889th loop: cost = 39.05859375\n", "The 11890th loop: cost = 36.66839599609375\n", "The 11891th loop: cost = 35.65019226074219\n", "The 11892th loop: cost = 39.92067337036133\n", "The 11893th loop: cost = 35.001068115234375\n", "The 11894th loop: cost = 28.232975006103516\n", "The 11895th loop: cost = 39.50962829589844\n", "The 11896th loop: cost = 37.36427307128906\n", "The 11897th loop: cost = 30.798738479614258\n", "The 11898th loop: cost = 27.41452407836914\n", "The 11899th loop: cost = 44.098331451416016\n", "The 11900th loop: cost = 36.373287200927734\n", "The 11901th loop: cost = 33.91714859008789\n", "The 11902th loop: cost = 37.09300231933594\n", "The 11903th loop: cost = 31.00786781311035\n", "The 11904th loop: cost = 25.184898376464844\n", "The 11905th loop: cost = 46.15178680419922\n", "The 11906th loop: cost = 30.28670310974121\n", "The 11907th loop: cost = 30.08679962158203\n", "The 11908th loop: cost = 43.751216888427734\n", "The 11909th loop: cost = 30.675888061523438\n", "The 11910th loop: cost = 27.491004943847656\n", "The 11911th loop: cost = 36.87621307373047\n", "The 11912th loop: cost = 37.46556854248047\n", "The 11913th loop: cost = 40.3404541015625\n", "The 11914th loop: cost = 27.735185623168945\n", "The 11915th loop: cost = 44.696861267089844\n", "The 11916th loop: cost = 34.18537521362305\n", "The 11917th loop: cost = 41.65164566040039\n", "The 11918th loop: cost = 34.655941009521484\n", "The 11919th loop: cost = 36.495269775390625\n", "The 11920th loop: cost = 35.72491455078125\n", "The 11921th loop: cost = 36.67705535888672\n", "The 11922th loop: cost = 29.541736602783203\n", "The 11923th loop: cost = 33.946495056152344\n", "The 11924th loop: cost = 34.94453048706055\n", "The 11925th loop: cost = 33.10002899169922\n", "The 11926th loop: cost = 36.500152587890625\n", "The 11927th loop: cost = 28.347511291503906\n", "The 11928th loop: cost = 38.23248291015625\n", "The 11929th loop: cost = 33.58354187011719\n", "The 11930th loop: cost = 32.02595901489258\n", "The 11931th loop: cost = 27.712989807128906\n", "The 11932th loop: cost = 50.5945930480957\n", "The 11933th loop: cost = 39.627708435058594\n", "The 11934th loop: cost = 38.72922134399414\n", "The 11935th loop: cost = 40.870521545410156\n", "The 11936th loop: cost = 38.451072692871094\n", "The 11937th loop: cost = 45.261444091796875\n", "The 11938th loop: cost = 39.209693908691406\n", "The 11939th loop: cost = 31.793502807617188\n", "The 11940th loop: cost = 30.169326782226562\n", "The 11941th loop: cost = 38.20854949951172\n", "The 11942th loop: cost = 46.39039611816406\n", "The 11943th loop: cost = 34.223506927490234\n", "The 11944th loop: cost = 25.14672088623047\n", "The 11945th loop: cost = 34.722190856933594\n", "The 11946th loop: cost = 35.005897521972656\n", "The 11947th loop: cost = 35.42713928222656\n", "The 11948th loop: cost = 31.616968154907227\n", "The 11949th loop: cost = 41.21244430541992\n", "The 11950th loop: cost = 43.111351013183594\n", "The 11951th loop: cost = 33.001590728759766\n", "The 11952th loop: cost = 33.517677307128906\n", "The 11953th loop: cost = 39.43259048461914\n", "The 11954th loop: cost = 36.81322479248047\n", "The 11955th loop: cost = 41.644020080566406\n", "The 11956th loop: cost = 35.05615997314453\n", "The 11957th loop: cost = 37.35792541503906\n", "The 11958th loop: cost = 39.07598876953125\n", "The 11959th loop: cost = 46.00672912597656\n", "The 11960th loop: cost = 25.89096450805664\n", "The 11961th loop: cost = 43.35817337036133\n", "The 11962th loop: cost = 31.89974021911621\n", "The 11963th loop: cost = 35.01768493652344\n", "The 11964th loop: cost = 34.43791198730469\n", "The 11965th loop: cost = 33.503395080566406\n", "The 11966th loop: cost = 43.52499008178711\n", "The 11967th loop: cost = 33.441497802734375\n", "The 11968th loop: cost = 37.97364044189453\n", "The 11969th loop: cost = 33.450321197509766\n", "The 11970th loop: cost = 38.003875732421875\n", "The 11971th loop: cost = 38.17085266113281\n", "The 11972th loop: cost = 37.64654541015625\n", "The 11973th loop: cost = 37.09629440307617\n", "The 11974th loop: cost = 33.038047790527344\n", "The 11975th loop: cost = 36.477333068847656\n", "The 11976th loop: cost = 44.760475158691406\n", "The 11977th loop: cost = 34.53176498413086\n", "The 11978th loop: cost = 42.89196014404297\n", "The 11979th loop: cost = 31.20916175842285\n", "The 11980th loop: cost = 29.081279754638672\n", "The 11981th loop: cost = 33.977874755859375\n", "The 11982th loop: cost = 32.56642532348633\n", "The 11983th loop: cost = 33.91039276123047\n", "The 11984th loop: cost = 33.291770935058594\n", "The 11985th loop: cost = 37.2160758972168\n", "The 11986th loop: cost = 39.20852279663086\n", "The 11987th loop: cost = 33.581787109375\n", "The 11988th loop: cost = 35.43651580810547\n", "The 11989th loop: cost = 34.47217559814453\n", "The 11990th loop: cost = 35.24993133544922\n", "The 11991th loop: cost = 43.00630187988281\n", "The 11992th loop: cost = 34.9521598815918\n", "The 11993th loop: cost = 42.01757049560547\n", "The 11994th loop: cost = 37.175636291503906\n", "The 11995th loop: cost = 38.11043930053711\n", "The 11996th loop: cost = 30.117393493652344\n", "The 11997th loop: cost = 28.850217819213867\n", "The 11998th loop: cost = 33.73979187011719\n", "The 11999th loop: cost = 33.28878402709961\n", "The 12000th loop: cost = 37.21430206298828\n", "The 12001th loop: cost = 32.984039306640625\n", "The 12002th loop: cost = 26.149951934814453\n", "The 12003th loop: cost = 42.65558624267578\n", "The 12004th loop: cost = 39.90383529663086\n", "The 12005th loop: cost = 27.528966903686523\n", "The 12006th loop: cost = 26.389793395996094\n", "The 12007th loop: cost = 37.78333282470703\n", "The 12008th loop: cost = 34.517852783203125\n", "The 12009th loop: cost = 30.6717472076416\n", "The 12010th loop: cost = 35.37044143676758\n", "The 12011th loop: cost = 34.53556442260742\n", "The 12012th loop: cost = 35.1357421875\n", "The 12013th loop: cost = 40.92623519897461\n", "The 12014th loop: cost = 36.60173416137695\n", "The 12015th loop: cost = 29.703441619873047\n", "The 12016th loop: cost = 32.675262451171875\n", "The 12017th loop: cost = 37.054874420166016\n", "The 12018th loop: cost = 40.28770065307617\n", "The 12019th loop: cost = 39.24251174926758\n", "The 12020th loop: cost = 35.060279846191406\n", "The 12021th loop: cost = 33.369361877441406\n", "The 12022th loop: cost = 34.03185272216797\n", "The 12023th loop: cost = 39.009803771972656\n", "The 12024th loop: cost = 36.829246520996094\n", "The 12025th loop: cost = 41.86292266845703\n", "The 12026th loop: cost = 38.83240509033203\n", "The 12027th loop: cost = 32.50213623046875\n", "The 12028th loop: cost = 39.02406692504883\n", "The 12029th loop: cost = 38.408729553222656\n", "The 12030th loop: cost = 32.54383850097656\n", "The 12031th loop: cost = 33.427303314208984\n", "The 12032th loop: cost = 34.34204864501953\n", "The 12033th loop: cost = 32.034114837646484\n", "The 12034th loop: cost = 42.082298278808594\n", "The 12035th loop: cost = 38.35997009277344\n", "The 12036th loop: cost = 48.26898956298828\n", "The 12037th loop: cost = 32.61829376220703\n", "The 12038th loop: cost = 36.77890396118164\n", "The 12039th loop: cost = 32.396053314208984\n", "The 12040th loop: cost = 29.219417572021484\n", "The 12041th loop: cost = 30.34919548034668\n", "The 12042th loop: cost = 32.806663513183594\n", "The 12043th loop: cost = 39.08646011352539\n", "The 12044th loop: cost = 26.04561424255371\n", "The 12045th loop: cost = 45.194602966308594\n", "The 12046th loop: cost = 33.25996398925781\n", "The 12047th loop: cost = 31.841808319091797\n", "The 12048th loop: cost = 40.71595764160156\n", "The 12049th loop: cost = 35.9183349609375\n", "The 12050th loop: cost = 35.728641510009766\n", "The 12051th loop: cost = 34.823760986328125\n", "The 12052th loop: cost = 32.6478271484375\n", "The 12053th loop: cost = 36.15431213378906\n", "The 12054th loop: cost = 32.0533447265625\n", "The 12055th loop: cost = 34.40134048461914\n", "The 12056th loop: cost = 37.615989685058594\n", "The 12057th loop: cost = 29.44571304321289\n", "The 12058th loop: cost = 29.5411376953125\n", "The 12059th loop: cost = 35.30432891845703\n", "The 12060th loop: cost = 35.614017486572266\n", "The 12061th loop: cost = 33.97321701049805\n", "The 12062th loop: cost = 40.237205505371094\n", "The 12063th loop: cost = 33.5417594909668\n", "The 12064th loop: cost = 31.781042098999023\n", "The 12065th loop: cost = 39.48945999145508\n", "The 12066th loop: cost = 44.205196380615234\n", "The 12067th loop: cost = 32.99919128417969\n", "The 12068th loop: cost = 43.25775909423828\n", "The 12069th loop: cost = 28.971786499023438\n", "The 12070th loop: cost = 25.397857666015625\n", "The 12071th loop: cost = 26.725460052490234\n", "The 12072th loop: cost = 38.030704498291016\n", "The 12073th loop: cost = 34.77546691894531\n", "The 12074th loop: cost = 44.46348190307617\n", "The 12075th loop: cost = 28.16826057434082\n", "The 12076th loop: cost = 33.38487243652344\n", "The 12077th loop: cost = 36.93291473388672\n", "The 12078th loop: cost = 31.866504669189453\n", "The 12079th loop: cost = 28.158206939697266\n", "The 12080th loop: cost = 42.79285430908203\n", "The 12081th loop: cost = 36.91634750366211\n", "The 12082th loop: cost = 34.96440887451172\n", "The 12083th loop: cost = 35.92277526855469\n", "The 12084th loop: cost = 36.447975158691406\n", "The 12085th loop: cost = 35.50212097167969\n", "The 12086th loop: cost = 34.6575927734375\n", "The 12087th loop: cost = 37.26781463623047\n", "The 12088th loop: cost = 33.211578369140625\n", "The 12089th loop: cost = 33.537784576416016\n", "The 12090th loop: cost = 29.345378875732422\n", "The 12091th loop: cost = 33.95082473754883\n", "The 12092th loop: cost = 37.58427047729492\n", "The 12093th loop: cost = 39.63334655761719\n", "The 12094th loop: cost = 40.970489501953125\n", "The 12095th loop: cost = 36.62969207763672\n", "The 12096th loop: cost = 33.5929069519043\n", "The 12097th loop: cost = 33.711517333984375\n", "The 12098th loop: cost = 29.44536781311035\n", "The 12099th loop: cost = 37.252498626708984\n", "The 12100th loop: cost = 35.899879455566406\n", "The 12101th loop: cost = 35.409400939941406\n", "The 12102th loop: cost = 39.33035659790039\n", "The 12103th loop: cost = 44.09199523925781\n", "The 12104th loop: cost = 36.94506072998047\n", "The 12105th loop: cost = 32.5218391418457\n", "The 12106th loop: cost = 38.84137725830078\n", "The 12107th loop: cost = 39.64146041870117\n", "The 12108th loop: cost = 32.08195877075195\n", "The 12109th loop: cost = 40.69495391845703\n", "The 12110th loop: cost = 38.53480529785156\n", "The 12111th loop: cost = 39.239776611328125\n", "The 12112th loop: cost = 39.55332946777344\n", "The 12113th loop: cost = 35.989898681640625\n", "The 12114th loop: cost = 30.130821228027344\n", "The 12115th loop: cost = 42.845298767089844\n", "The 12116th loop: cost = 31.083112716674805\n", "The 12117th loop: cost = 38.678382873535156\n", "The 12118th loop: cost = 32.49797439575195\n", "The 12119th loop: cost = 40.985931396484375\n", "The 12120th loop: cost = 28.227746963500977\n", "The 12121th loop: cost = 38.079166412353516\n", "The 12122th loop: cost = 34.201393127441406\n", "The 12123th loop: cost = 35.322532653808594\n", "The 12124th loop: cost = 25.94852066040039\n", "The 12125th loop: cost = 37.421688079833984\n", "The 12126th loop: cost = 40.24907302856445\n", "The 12127th loop: cost = 33.81781768798828\n", "The 12128th loop: cost = 29.69870376586914\n", "The 12129th loop: cost = 29.69405746459961\n", "The 12130th loop: cost = 35.22389221191406\n", "The 12131th loop: cost = 42.37697219848633\n", "The 12132th loop: cost = 33.15117645263672\n", "The 12133th loop: cost = 38.911251068115234\n", "The 12134th loop: cost = 35.83830261230469\n", "The 12135th loop: cost = 32.9472541809082\n", "The 12136th loop: cost = 43.03345489501953\n", "The 12137th loop: cost = 37.52493667602539\n", "The 12138th loop: cost = 40.08743667602539\n", "The 12139th loop: cost = 31.41461181640625\n", "The 12140th loop: cost = 31.759685516357422\n", "The 12141th loop: cost = 36.43187713623047\n", "The 12142th loop: cost = 34.3505744934082\n", "The 12143th loop: cost = 41.55851745605469\n", "The 12144th loop: cost = 33.28581237792969\n", "The 12145th loop: cost = 37.201324462890625\n", "The 12146th loop: cost = 35.30476379394531\n", "The 12147th loop: cost = 35.06888198852539\n", "The 12148th loop: cost = 34.215171813964844\n", "The 12149th loop: cost = 28.299240112304688\n", "The 12150th loop: cost = 36.084232330322266\n", "The 12151th loop: cost = 42.870399475097656\n", "The 12152th loop: cost = 37.39923858642578\n", "The 12153th loop: cost = 31.083721160888672\n", "The 12154th loop: cost = 36.447967529296875\n", "The 12155th loop: cost = 29.914386749267578\n", "The 12156th loop: cost = 34.97174835205078\n", "The 12157th loop: cost = 41.930686950683594\n", "The 12158th loop: cost = 31.28218650817871\n", "The 12159th loop: cost = 43.99595642089844\n", "The 12160th loop: cost = 27.480680465698242\n", "The 12161th loop: cost = 36.119422912597656\n", "The 12162th loop: cost = 30.06857681274414\n", "The 12163th loop: cost = 36.64588928222656\n", "The 12164th loop: cost = 49.47819519042969\n", "The 12165th loop: cost = 28.854759216308594\n", "The 12166th loop: cost = 30.010616302490234\n", "The 12167th loop: cost = 28.472503662109375\n", "The 12168th loop: cost = 43.061553955078125\n", "The 12169th loop: cost = 37.35414123535156\n", "The 12170th loop: cost = 25.372835159301758\n", "The 12171th loop: cost = 34.021148681640625\n", "The 12172th loop: cost = 37.376853942871094\n", "The 12173th loop: cost = 30.715839385986328\n", "The 12174th loop: cost = 31.083436965942383\n", "The 12175th loop: cost = 37.79546356201172\n", "The 12176th loop: cost = 38.052711486816406\n", "The 12177th loop: cost = 31.422313690185547\n", "The 12178th loop: cost = 35.24217224121094\n", "The 12179th loop: cost = 43.3148193359375\n", "The 12180th loop: cost = 38.548179626464844\n", "The 12181th loop: cost = 33.244964599609375\n", "The 12182th loop: cost = 37.42292022705078\n", "The 12183th loop: cost = 44.25780487060547\n", "The 12184th loop: cost = 36.01905059814453\n", "The 12185th loop: cost = 33.652931213378906\n", "The 12186th loop: cost = 38.325889587402344\n", "The 12187th loop: cost = 37.97142028808594\n", "The 12188th loop: cost = 40.218772888183594\n", "The 12189th loop: cost = 34.79051208496094\n", "The 12190th loop: cost = 42.067481994628906\n", "The 12191th loop: cost = 29.480546951293945\n", "The 12192th loop: cost = 25.864946365356445\n", "The 12193th loop: cost = 32.014488220214844\n", "The 12194th loop: cost = 35.10424041748047\n", "The 12195th loop: cost = 29.608631134033203\n", "The 12196th loop: cost = 41.01261901855469\n", "The 12197th loop: cost = 43.69647216796875\n", "The 12198th loop: cost = 36.71440887451172\n", "The 12199th loop: cost = 44.845794677734375\n", "The 12200th loop: cost = 39.538551330566406\n", "The 12201th loop: cost = 33.36972427368164\n", "The 12202th loop: cost = 39.27841567993164\n", "The 12203th loop: cost = 37.91822814941406\n", "The 12204th loop: cost = 32.22206115722656\n", "The 12205th loop: cost = 29.199066162109375\n", "The 12206th loop: cost = 41.57820129394531\n", "The 12207th loop: cost = 43.332740783691406\n", "The 12208th loop: cost = 33.1002082824707\n", "The 12209th loop: cost = 41.606910705566406\n", "The 12210th loop: cost = 35.949302673339844\n", "The 12211th loop: cost = 36.15740966796875\n", "The 12212th loop: cost = 40.82916259765625\n", "The 12213th loop: cost = 25.368406295776367\n", "The 12214th loop: cost = 41.053367614746094\n", "The 12215th loop: cost = 32.23530197143555\n", "The 12216th loop: cost = 34.41118621826172\n", "The 12217th loop: cost = 30.00864601135254\n", "The 12218th loop: cost = 35.73744583129883\n", "The 12219th loop: cost = 24.965198516845703\n", "The 12220th loop: cost = 30.403152465820312\n", "The 12221th loop: cost = 41.40557861328125\n", "The 12222th loop: cost = 46.8392448425293\n", "The 12223th loop: cost = 22.991722106933594\n", "The 12224th loop: cost = 43.83546447753906\n", "The 12225th loop: cost = 34.494850158691406\n", "The 12226th loop: cost = 34.292327880859375\n", "The 12227th loop: cost = 31.894454956054688\n", "The 12228th loop: cost = 35.11974334716797\n", "The 12229th loop: cost = 37.64482116699219\n", "The 12230th loop: cost = 32.8321647644043\n", "The 12231th loop: cost = 32.83329772949219\n", "The 12232th loop: cost = 39.069793701171875\n", "The 12233th loop: cost = 38.79305648803711\n", "The 12234th loop: cost = 39.087059020996094\n", "The 12235th loop: cost = 33.48236083984375\n", "The 12236th loop: cost = 47.527366638183594\n", "The 12237th loop: cost = 35.209678649902344\n", "The 12238th loop: cost = 30.130443572998047\n", "The 12239th loop: cost = 32.939971923828125\n", "The 12240th loop: cost = 32.88665771484375\n", "The 12241th loop: cost = 39.32501983642578\n", "The 12242th loop: cost = 35.698936462402344\n", "The 12243th loop: cost = 43.23261260986328\n", "The 12244th loop: cost = 35.12664794921875\n", "The 12245th loop: cost = 28.109806060791016\n", "The 12246th loop: cost = 27.74727439880371\n", "The 12247th loop: cost = 28.348495483398438\n", "The 12248th loop: cost = 51.38641357421875\n", "The 12249th loop: cost = 34.71085739135742\n", "The 12250th loop: cost = 37.014713287353516\n", "The 12251th loop: cost = 33.990760803222656\n", "The 12252th loop: cost = 36.32638931274414\n", "The 12253th loop: cost = 31.84588050842285\n", "The 12254th loop: cost = 48.02516555786133\n", "The 12255th loop: cost = 34.87690734863281\n", "The 12256th loop: cost = 44.82263946533203\n", "The 12257th loop: cost = 34.86780548095703\n", "The 12258th loop: cost = 36.79474639892578\n", "The 12259th loop: cost = 32.59211730957031\n", "The 12260th loop: cost = 41.955387115478516\n", "The 12261th loop: cost = 34.538818359375\n", "The 12262th loop: cost = 31.4677734375\n", "The 12263th loop: cost = 35.99638366699219\n", "The 12264th loop: cost = 37.674537658691406\n", "The 12265th loop: cost = 40.987327575683594\n", "The 12266th loop: cost = 41.946876525878906\n", "The 12267th loop: cost = 37.96169662475586\n", "The 12268th loop: cost = 31.31356430053711\n", "The 12269th loop: cost = 38.36956787109375\n", "The 12270th loop: cost = 41.777320861816406\n", "The 12271th loop: cost = 33.350868225097656\n", "The 12272th loop: cost = 36.991180419921875\n", "The 12273th loop: cost = 37.31602478027344\n", "The 12274th loop: cost = 37.786041259765625\n", "The 12275th loop: cost = 30.460796356201172\n", "The 12276th loop: cost = 35.82746124267578\n", "The 12277th loop: cost = 44.77525329589844\n", "The 12278th loop: cost = 29.561565399169922\n", "The 12279th loop: cost = 40.02653121948242\n", "The 12280th loop: cost = 34.19047164916992\n", "The 12281th loop: cost = 36.948814392089844\n", "The 12282th loop: cost = 42.091339111328125\n", "The 12283th loop: cost = 38.95002746582031\n", "The 12284th loop: cost = 36.95692825317383\n", "The 12285th loop: cost = 41.934688568115234\n", "The 12286th loop: cost = 36.435707092285156\n", "The 12287th loop: cost = 32.305137634277344\n", "The 12288th loop: cost = 39.698692321777344\n", "The 12289th loop: cost = 30.722272872924805\n", "The 12290th loop: cost = 37.70191955566406\n", "The 12291th loop: cost = 38.65983581542969\n", "The 12292th loop: cost = 33.47132110595703\n", "The 12293th loop: cost = 30.161697387695312\n", "The 12294th loop: cost = 40.78551483154297\n", "The 12295th loop: cost = 40.749839782714844\n", "The 12296th loop: cost = 37.47685241699219\n", "The 12297th loop: cost = 42.5022087097168\n", "The 12298th loop: cost = 34.20198059082031\n", "The 12299th loop: cost = 46.86238098144531\n", "The 12300th loop: cost = 38.80498504638672\n", "The 12301th loop: cost = 34.081268310546875\n", "The 12302th loop: cost = 37.37535095214844\n", "The 12303th loop: cost = 38.81560516357422\n", "The 12304th loop: cost = 38.72380828857422\n", "The 12305th loop: cost = 31.169382095336914\n", "The 12306th loop: cost = 34.83204650878906\n", "The 12307th loop: cost = 31.385347366333008\n", "The 12308th loop: cost = 39.17601776123047\n", "The 12309th loop: cost = 33.03031921386719\n", "The 12310th loop: cost = 32.78141784667969\n", "The 12311th loop: cost = 39.853729248046875\n", "The 12312th loop: cost = 34.126651763916016\n", "The 12313th loop: cost = 31.734525680541992\n", "The 12314th loop: cost = 38.28440856933594\n", "The 12315th loop: cost = 28.224702835083008\n", "The 12316th loop: cost = 35.40986633300781\n", "The 12317th loop: cost = 34.25225067138672\n", "The 12318th loop: cost = 31.820697784423828\n", "The 12319th loop: cost = 38.45002365112305\n", "The 12320th loop: cost = 38.197017669677734\n", "The 12321th loop: cost = 38.5123291015625\n", "The 12322th loop: cost = 39.807106018066406\n", "The 12323th loop: cost = 30.14144515991211\n", "The 12324th loop: cost = 37.29279327392578\n", "The 12325th loop: cost = 38.53266906738281\n", "The 12326th loop: cost = 35.008792877197266\n", "The 12327th loop: cost = 33.215370178222656\n", "The 12328th loop: cost = 33.064544677734375\n", "The 12329th loop: cost = 36.97106170654297\n", "The 12330th loop: cost = 38.51502990722656\n", "The 12331th loop: cost = 34.99036407470703\n", "The 12332th loop: cost = 27.370594024658203\n", "The 12333th loop: cost = 37.25050354003906\n", "The 12334th loop: cost = 35.16783905029297\n", "The 12335th loop: cost = 33.552162170410156\n", "The 12336th loop: cost = 33.81170654296875\n", "The 12337th loop: cost = 38.18207550048828\n", "The 12338th loop: cost = 41.95703887939453\n", "The 12339th loop: cost = 40.454627990722656\n", "The 12340th loop: cost = 42.90217590332031\n", "The 12341th loop: cost = 38.036258697509766\n", "The 12342th loop: cost = 33.65824890136719\n", "The 12343th loop: cost = 30.693222045898438\n", "The 12344th loop: cost = 34.592674255371094\n", "The 12345th loop: cost = 34.82158660888672\n", "The 12346th loop: cost = 36.23740005493164\n", "The 12347th loop: cost = 43.345680236816406\n", "The 12348th loop: cost = 37.152835845947266\n", "The 12349th loop: cost = 33.92694091796875\n", "The 12350th loop: cost = 26.501171112060547\n", "The 12351th loop: cost = 33.30602264404297\n", "The 12352th loop: cost = 38.319175720214844\n", "The 12353th loop: cost = 36.584251403808594\n", "The 12354th loop: cost = 32.67756271362305\n", "The 12355th loop: cost = 44.60763168334961\n", "The 12356th loop: cost = 36.92936706542969\n", "The 12357th loop: cost = 33.210784912109375\n", "The 12358th loop: cost = 41.739742279052734\n", "The 12359th loop: cost = 41.53117370605469\n", "The 12360th loop: cost = 35.290340423583984\n", "The 12361th loop: cost = 43.27976608276367\n", "The 12362th loop: cost = 38.19533920288086\n", "The 12363th loop: cost = 34.73640441894531\n", "The 12364th loop: cost = 31.624679565429688\n", "The 12365th loop: cost = 37.7831916809082\n", "The 12366th loop: cost = 35.964141845703125\n", "The 12367th loop: cost = 44.265323638916016\n", "The 12368th loop: cost = 34.31187438964844\n", "The 12369th loop: cost = 41.35469436645508\n", "The 12370th loop: cost = 36.27033233642578\n", "The 12371th loop: cost = 38.1336784362793\n", "The 12372th loop: cost = 35.691192626953125\n", "The 12373th loop: cost = 31.276939392089844\n", "The 12374th loop: cost = 34.750953674316406\n", "The 12375th loop: cost = 38.845703125\n", "The 12376th loop: cost = 31.765085220336914\n", "The 12377th loop: cost = 42.74275588989258\n", "The 12378th loop: cost = 39.91276168823242\n", "The 12379th loop: cost = 42.675777435302734\n", "The 12380th loop: cost = 40.29961013793945\n", "The 12381th loop: cost = 37.15755844116211\n", "The 12382th loop: cost = 38.91874694824219\n", "The 12383th loop: cost = 35.582908630371094\n", "The 12384th loop: cost = 34.674041748046875\n", "The 12385th loop: cost = 39.225181579589844\n", "The 12386th loop: cost = 34.62750244140625\n", "The 12387th loop: cost = 36.827796936035156\n", "The 12388th loop: cost = 40.7447509765625\n", "The 12389th loop: cost = 35.28449249267578\n", "The 12390th loop: cost = 30.622600555419922\n", "The 12391th loop: cost = 39.65711975097656\n", "The 12392th loop: cost = 36.30784225463867\n", "The 12393th loop: cost = 33.857566833496094\n", "The 12394th loop: cost = 41.478111267089844\n", "The 12395th loop: cost = 37.24132537841797\n", "The 12396th loop: cost = 36.66648864746094\n", "The 12397th loop: cost = 35.8355598449707\n", "The 12398th loop: cost = 35.84266662597656\n", "The 12399th loop: cost = 36.96527862548828\n", "The 12400th loop: cost = 31.730545043945312\n", "The 12401th loop: cost = 36.9930534362793\n", "The 12402th loop: cost = 28.69209098815918\n", "The 12403th loop: cost = 37.309967041015625\n", "The 12404th loop: cost = 32.989768981933594\n", "The 12405th loop: cost = 41.752838134765625\n", "The 12406th loop: cost = 28.786048889160156\n", "The 12407th loop: cost = 28.131595611572266\n", "The 12408th loop: cost = 36.64620590209961\n", "The 12409th loop: cost = 38.29242706298828\n", "The 12410th loop: cost = 42.374664306640625\n", "The 12411th loop: cost = 37.36726379394531\n", "The 12412th loop: cost = 40.13932418823242\n", "The 12413th loop: cost = 24.64468002319336\n", "The 12414th loop: cost = 41.07568359375\n", "The 12415th loop: cost = 37.11032485961914\n", "The 12416th loop: cost = 39.04424285888672\n", "The 12417th loop: cost = 27.73638153076172\n", "The 12418th loop: cost = 39.98680877685547\n", "The 12419th loop: cost = 30.187786102294922\n", "The 12420th loop: cost = 40.62165451049805\n", "The 12421th loop: cost = 34.31577682495117\n", "The 12422th loop: cost = 41.6090087890625\n", "The 12423th loop: cost = 27.341197967529297\n", "The 12424th loop: cost = 38.263824462890625\n", "The 12425th loop: cost = 27.3797607421875\n", "The 12426th loop: cost = 39.38418197631836\n", "The 12427th loop: cost = 39.739044189453125\n", "The 12428th loop: cost = 30.613637924194336\n", "The 12429th loop: cost = 31.360363006591797\n", "The 12430th loop: cost = 29.42760467529297\n", "The 12431th loop: cost = 34.13273620605469\n", "The 12432th loop: cost = 38.60417556762695\n", "The 12433th loop: cost = 39.78711700439453\n", "The 12434th loop: cost = 32.747684478759766\n", "The 12435th loop: cost = 26.890592575073242\n", "The 12436th loop: cost = 31.574451446533203\n", "The 12437th loop: cost = 29.950897216796875\n", "The 12438th loop: cost = 37.53569030761719\n", "The 12439th loop: cost = 42.183162689208984\n", "The 12440th loop: cost = 45.96385955810547\n", "The 12441th loop: cost = 35.633201599121094\n", "The 12442th loop: cost = 41.872676849365234\n", "The 12443th loop: cost = 32.25684356689453\n", "The 12444th loop: cost = 30.882274627685547\n", "The 12445th loop: cost = 38.53276062011719\n", "The 12446th loop: cost = 34.173240661621094\n", "The 12447th loop: cost = 39.98613739013672\n", "The 12448th loop: cost = 36.419883728027344\n", "The 12449th loop: cost = 43.83440399169922\n", "The 12450th loop: cost = 41.240966796875\n", "The 12451th loop: cost = 31.22390365600586\n", "The 12452th loop: cost = 35.38274383544922\n", "The 12453th loop: cost = 29.976669311523438\n", "The 12454th loop: cost = 32.144187927246094\n", "The 12455th loop: cost = 35.692657470703125\n", "The 12456th loop: cost = 41.741722106933594\n", "The 12457th loop: cost = 31.755290985107422\n", "The 12458th loop: cost = 31.904739379882812\n", "The 12459th loop: cost = 39.9425048828125\n", "The 12460th loop: cost = 38.856815338134766\n", "The 12461th loop: cost = 31.818870544433594\n", "The 12462th loop: cost = 35.28377914428711\n", "The 12463th loop: cost = 32.545021057128906\n", "The 12464th loop: cost = 37.07710266113281\n", "The 12465th loop: cost = 34.77980041503906\n", "The 12466th loop: cost = 29.318466186523438\n", "The 12467th loop: cost = 45.23762893676758\n", "The 12468th loop: cost = 32.166194915771484\n", "The 12469th loop: cost = 30.142528533935547\n", "The 12470th loop: cost = 38.14886474609375\n", "The 12471th loop: cost = 29.897769927978516\n", "The 12472th loop: cost = 31.27417755126953\n", "The 12473th loop: cost = 41.80508804321289\n", "The 12474th loop: cost = 52.631126403808594\n", "The 12475th loop: cost = 34.1697998046875\n", "The 12476th loop: cost = 35.84258270263672\n", "The 12477th loop: cost = 30.049348831176758\n", "The 12478th loop: cost = 41.88481140136719\n", "The 12479th loop: cost = 35.21482467651367\n", "The 12480th loop: cost = 39.88849639892578\n", "The 12481th loop: cost = 43.84791564941406\n", "The 12482th loop: cost = 38.43251037597656\n", "The 12483th loop: cost = 37.200321197509766\n", "The 12484th loop: cost = 34.42085266113281\n", "The 12485th loop: cost = 31.97351837158203\n", "The 12486th loop: cost = 37.55059814453125\n", "The 12487th loop: cost = 34.130184173583984\n", "The 12488th loop: cost = 34.871315002441406\n", "The 12489th loop: cost = 42.03987121582031\n", "The 12490th loop: cost = 35.74826431274414\n", "The 12491th loop: cost = 32.80081558227539\n", "The 12492th loop: cost = 37.243038177490234\n", "The 12493th loop: cost = 32.01492691040039\n", "The 12494th loop: cost = 28.266374588012695\n", "The 12495th loop: cost = 46.85993576049805\n", "The 12496th loop: cost = 34.39924240112305\n", "The 12497th loop: cost = 32.12289047241211\n", "The 12498th loop: cost = 38.47320556640625\n", "The 12499th loop: cost = 40.61715316772461\n", "The 12500th loop: cost = 32.91783905029297\n", "The 12501th loop: cost = 32.995059967041016\n", "The 12502th loop: cost = 33.278865814208984\n", "The 12503th loop: cost = 30.55506134033203\n", "The 12504th loop: cost = 34.595001220703125\n", "The 12505th loop: cost = 39.326271057128906\n", "The 12506th loop: cost = 33.736305236816406\n", "The 12507th loop: cost = 34.75555419921875\n", "The 12508th loop: cost = 25.431407928466797\n", "The 12509th loop: cost = 29.398487091064453\n", "The 12510th loop: cost = 35.929481506347656\n", "The 12511th loop: cost = 43.58100891113281\n", "The 12512th loop: cost = 48.53529739379883\n", "The 12513th loop: cost = 31.840429306030273\n", "The 12514th loop: cost = 35.10771179199219\n", "The 12515th loop: cost = 37.212554931640625\n", "The 12516th loop: cost = 45.75497055053711\n", "The 12517th loop: cost = 37.205413818359375\n", "The 12518th loop: cost = 33.133487701416016\n", "The 12519th loop: cost = 32.60962677001953\n", "The 12520th loop: cost = 40.42324447631836\n", "The 12521th loop: cost = 36.58133316040039\n", "The 12522th loop: cost = 35.76677703857422\n", "The 12523th loop: cost = 30.24584197998047\n", "The 12524th loop: cost = 34.335044860839844\n", "The 12525th loop: cost = 35.6851806640625\n", "The 12526th loop: cost = 43.70674133300781\n", "The 12527th loop: cost = 36.11947250366211\n", "The 12528th loop: cost = 37.8840217590332\n", "The 12529th loop: cost = 35.95102310180664\n", "The 12530th loop: cost = 35.67308044433594\n", "The 12531th loop: cost = 39.89018249511719\n", "The 12532th loop: cost = 32.92185592651367\n", "The 12533th loop: cost = 38.12077331542969\n", "The 12534th loop: cost = 39.844322204589844\n", "The 12535th loop: cost = 29.025808334350586\n", "The 12536th loop: cost = 34.980735778808594\n", "The 12537th loop: cost = 33.47919464111328\n", "The 12538th loop: cost = 33.18674087524414\n", "The 12539th loop: cost = 30.789203643798828\n", "The 12540th loop: cost = 30.763246536254883\n", "The 12541th loop: cost = 34.56417465209961\n", "The 12542th loop: cost = 27.578845977783203\n", "The 12543th loop: cost = 33.68742752075195\n", "The 12544th loop: cost = 35.13790512084961\n", "The 12545th loop: cost = 34.97587585449219\n", "The 12546th loop: cost = 26.817861557006836\n", "The 12547th loop: cost = 33.86800003051758\n", "The 12548th loop: cost = 32.9119873046875\n", "The 12549th loop: cost = 30.72766876220703\n", "The 12550th loop: cost = 35.075439453125\n", "The 12551th loop: cost = 42.247650146484375\n", "The 12552th loop: cost = 39.97366714477539\n", "The 12553th loop: cost = 30.455585479736328\n", "The 12554th loop: cost = 37.612056732177734\n", "The 12555th loop: cost = 39.75273132324219\n", "The 12556th loop: cost = 33.96391296386719\n", "The 12557th loop: cost = 30.958620071411133\n", "The 12558th loop: cost = 37.03547668457031\n", "The 12559th loop: cost = 37.70269775390625\n" ] } ], "source": [ "model.fit(s_train_pn, o_train_pn, p_train_pn, label_train)" ] }, { "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

rayjustinhuang/DataAnalysisandMachineLearning

Linear Programming with OR-Tools.ipynb

1

7762

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Programming with OR-Tools\n", "\n", "In this notebook, we do some basic LP solving with Google's OR-Tools. Problems used will be examples in Hamdy Taha's Operations Research: An Introduction, 9th Edition, which I have in paperback." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from ortools.linear_solver import pywraplp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reddy Mikks model\n", "\n", "Given the following variables:\n", "\n", "$\\begin{aligned}\n", "x_1 = \\textrm{Tons of exterior paint produced daily} \\newline\n", "x_2 = \\textrm{Tons of interior paint produced daily}\n", "\\end{aligned}$\n", "\n", "and knowing that we want to maximize the profit, where \\$5000 is the profit from exterior paint and \\$4000 is the profit from a ton of interior paint, the Reddy Mikks model is:\n", "\n", "$$\\textrm{Maximize } z = 5x_1 + 4x_2$$\n", "subject to\n", "$$6x_1 + 4x_2 \\le 24$$\n", "$$x_1 + 2x_2 \\le 6$$\n", "$$-x_1 + x_2 \\le 1$$\n", "$$x_2 \\le 2$$\n", "$$x_1, x_2 \\ge 0$$" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The company should produce 3.0 tons of exterior paint\n", "The company should produce 1.5 tons of interior paint\n", "The optimal profit is 21.0 thousand USD\n" ] } ], "source": [ "reddymikks = pywraplp.Solver('Reddy_Mikks', pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)\n", "\n", "x1 = reddymikks.NumVar(0, reddymikks.infinity(), 'x1')\n", "x2 = reddymikks.NumVar(0, reddymikks.infinity(), 'x2')\n", "\n", "reddymikks.Add(6*x1 + 4*x2 <= 24)\n", "reddymikks.Add(x1 + 2*x2 <= 6)\n", "reddymikks.Add(-x1 + x2 <= 1)\n", "reddymikks.Add(x2 <= 2)\n", "\n", "profit = reddymikks.Objective()\n", "profit.SetCoefficient(x1, 5)\n", "profit.SetCoefficient(x2, 4)\n", "profit.SetMaximization()\n", "\n", "status = reddymikks.Solve()\n", "\n", "if status not in [reddymikks.OPTIMAL, reddymikks.FEASIBLE]:\n", " raise Exception('No feasible solution found')\n", " \n", "print(\"The company should produce\",round(x1.solution_value(),2),\"tons of exterior paint\")\n", "print(\"The company should produce\",round(x2.solution_value(),2),\"tons of interior paint\")\n", "print(\"The optimal profit is\", profit.Value(), 'thousand USD')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More simple problems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A company that operates 10 hours a day manufactures two products on three sequential processes. The following data characterizes the problem:" ] }, { "cell_type": "code", "execution_count": 11, "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>Process 1</th>\n", " <th>Process 2</th>\n", " <th>Process 3</th>\n", " <th>Unit profit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Product 1</th>\n", " <td>10</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>Product 2</th>\n", " <td>5</td>\n", " <td>20</td>\n", " <td>10</td>\n", " <td>30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Process 1 Process 2 Process 3 Unit profit\n", "Product 1 10 6 8 20\n", "Product 2 5 20 10 30" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "problemdata = pd.DataFrame({'Process 1': [10, 5], 'Process 2':[6, 20], 'Process 3':[8, 10], 'Unit profit':[20, 30]})\n", "problemdata.index = ['Product 1', 'Product 2']\n", "\n", "problemdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where there are 10 hours a day dedicated to production. Process times are given in minutes per unit while profit is given in USD.\n", "\n", "The optimal mix of the two products would be characterized by the following model:\n", "\n", "$\\begin{aligned}\n", "x_1 = \\textrm{Units of product 1} \\newline\n", "x_2 = \\textrm{Units of product 2}\n", "\\end{aligned}$\n", "\n", "$$\\textrm{Maximize } z = 20x_1 + 30x_2$$\n", "\n", "subject to\n", "\n", "$$\\begin{array}{rcl}\n", "10x_1 + 5x_2 \\le 600 \\newline\n", "6x_1 + 20x_2 \\le 600 \\newline\n", "8x_1 + 10x_2 \\le 600 \\newline\n", "x_1, x_2 \\ge 0\n", "\\end{array}$$\n", "\n", "(we will assume that continuous solution values are acceptable for this problem)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The company should produce 52.94 units of product 1\n", "The company should produce 14.12 units of product 2\n", "The optimal profit is 1482.35 USD\n" ] } ], "source": [ "simpleprod = pywraplp.Solver('Simple_Production', pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)\n", "\n", "x1 = simpleprod.NumVar(0, simpleprod.infinity(), 'x1')\n", "x2 = simpleprod.NumVar(0, simpleprod.infinity(), 'x2')\n", "\n", "for i in problemdata.columns[:-1]:\n", " simpleprod.Add(problemdata.loc[problemdata.index[0], i]*x1 + problemdata.loc[problemdata.index[1], i]*x2 <= 600)\n", "\n", "profit = simpleprod.Objective()\n", "profit.SetCoefficient(x1, 20)\n", "profit.SetCoefficient(x2, 30)\n", "profit.SetMaximization()\n", "\n", "status = simpleprod.Solve()\n", "\n", "if status not in [simpleprod.OPTIMAL, simpleprod.FEASIBLE]:\n", " raise Exception('No feasible solution found')\n", " \n", "print(\"The company should produce\",round(x1.solution_value(),2),\"units of product 1\")\n", "print(\"The company should produce\",round(x2.solution_value(),2),\"units of product 2\")\n", "print(\"The optimal profit is\", round(profit.Value(),2), 'USD')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

RaRe-Technologies/gensim

docs/notebooks/nmslibtutorial.ipynb

5

301875

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Similarity Queries using Nmslib Tutorial" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial is about using the ([Non-Metric Space Library (NMSLIB)](https://github.com/nmslib/nmslib \"Link to nmslib repo\")) library for similarity queries with a Word2Vec model built with gensim.\n", "\n", "## Why use Nmslib?\n", "The current implementation for finding k nearest neighbors in a vector space in gensim has linear complexity via brute force in the number of indexed documents, although with extremely low constant factors. The retrieved results are exact, which is an overkill in many applications: approximate results retrieved in sub-linear time may be enough. Nmslib can find approximate nearest neighbors much faster.\n", "Compared to annoy, nmslib has more parameteres to control the build and query time and accuracy. Nmslib can achieve faster and more accurate nearest neighbors search than annoy. This figure shows a comparison between annoy and nmslib indexer with differents parameters. This shows nmslib is better than annoy.\n", "\n", "\n", "## Prerequisites\n", "Additional libraries needed for this tutorial:\n", "- nmslib\n", "- annoy\n", "- psutil\n", "- matplotlib\n", "\n", "## Outline\n", "1. Download Text8 Corpus\n", "2. Build Word2Vec Model\n", "3. Construct NmslibIndex with model & make a similarity query\n", "4. Verify & Evaluate performance\n", "5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n", "6. Work with Google's word2vec C formats" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPython 3.6.0\n", "IPython 7.5.0\n", "\n", "gensim 3.7.3\n", "numpy 1.16.2\n", "scipy 1.2.1\n", "psutil 5.6.3\n", "matplotlib 3.1.0\n", "\n", "compiler : GCC 4.2.1 Compatible Apple LLVM 10.0.0 (clang-1000.11.45.5)\n", "system : Darwin\n", "release : 18.2.0\n", "machine : x86_64\n", "processor : i386\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "# pip install watermark\n", "%reload_ext watermark\n", "%watermark -v -m -p gensim,numpy,scipy,psutil,matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Download Text8 Corpus" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2019-06-27 13:48:42-- http://mattmahoney.net/dc/text8.zip\n", "Resolving mattmahoney.net... 67.195.197.75\n", "Connecting to mattmahoney.net|67.195.197.75|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 31344016 (30M) [application/zip]\n", "Saving to: 'text8.zip'\n", "\n", "text8.zip 100%[=====================>] 29.89M 327KB/s in 98s \n", "\n", "2019-06-27 13:50:21 (313 KB/s) - 'text8.zip' saved [31344016/31344016]\n", "\n", "Archive: text8.zip\n", " inflating: text8 \n" ] } ], "source": [ "import os.path\n", "if not os.path.isfile('text8'):\n", " !wget -c http://mattmahoney.net/dc/text8.zip\n", " !unzip text8.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import & Set up Logging\n", "I'm not going to set up logging due to the verbose input displaying in notebooks, but if you want that, uncomment the lines in the cell below." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "LOGS = False\n", "\n", "if LOGS:\n", " import logging\n", " logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Build Word2Vec Model" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Word2Vec(vocab=71290, size=100, alpha=0.05)\n" ] } ], "source": [ "from gensim.models import Word2Vec, KeyedVectors\n", "from gensim.models.word2vec import Text8Corpus\n", "\n", "# Using params from Word2Vec_FastText_Comparison\n", "\n", "params = {\n", " 'alpha': 0.05,\n", " 'size': 100,\n", " 'window': 5,\n", " 'iter': 5,\n", " 'min_count': 5,\n", " 'sample': 1e-4,\n", " 'sg': 1,\n", " 'hs': 0,\n", " 'negative': 5\n", "}\n", "\n", "model = Word2Vec(Text8Corpus('text8'), **params)\n", "print(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the [Word2Vec tutorial](word2vec.ipynb) for how to initialize and save this model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Comparing the traditional implementation, Annoy and Nmslib approximation" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Set up the model and vector that we are using in the comparison\n", "from gensim.similarities.index import AnnoyIndexer\n", "from gensim.similarities.nmslib import NmslibIndexer\n", "\n", "model.init_sims()\n", "annoy_index = AnnoyIndexer(model, 300)\n", "nmslib_index = NmslibIndexer(model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('the', 1.0), ('of', 0.700629323720932), ('in', 0.6932138800621033), ('which', 0.6132444441318512), ('for', 0.6099657416343689)]\n", "[('the', 0.9999999701976776), ('of', 0.91037717461586), ('in', 0.9058823883533478), ('a', 0.8834112882614136), ('and', 0.8790014386177063)]\n", "[('the', 1.0000001192092896), ('of', 0.82075434923172), ('in', 0.811764657497406), ('a', 0.7668224573135376), ('and', 0.7580028772354126)]\n" ] } ], "source": [ "# Dry run to make sure both indices are fully in RAM\n", "vector = model.wv.syn0norm[0]\n", "print(model.most_similar([vector], topn=5, indexer=annoy_index))\n", "print(model.most_similar([vector], topn=5, indexer=nmslib_index))\n", "print(model.most_similar([vector], topn=5))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def avg_query_time(annoy_index=None, queries=1000):\n", " \"\"\"\n", " Average query time of a most_similar method over 1000 random queries,\n", " uses annoy if given an indexer\n", " \"\"\"\n", " total_time = 0\n", " for _ in range(queries):\n", " rand_vec = model.wv.syn0norm[np.random.randint(0, len(model.wv.vocab))]\n", " start_time = time.clock()\n", " model.most_similar([rand_vec], topn=5, indexer=annoy_index)\n", " total_time += time.clock() - start_time\n", " return total_time / queries" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gensim (s/query):\t0.00560\n", "Annoy (s/query):\t0.00099\n", "Nmslib (s/query):\t0.00046\n", "\n", "Nmslib is 12.16 times faster on average on this particular run\n", "\n", "Nmslib is 2.15 times faster on average than annoy on this particular run\n" ] } ], "source": [ "queries = 10000\n", "\n", "gensim_time = avg_query_time(queries=queries)\n", "annoy_time = avg_query_time(annoy_index, queries=queries)\n", "nmslib_time = avg_query_time(nmslib_index, queries=queries)\n", "print(\"Gensim (s/query):\\t{0:.5f}\".format(gensim_time))\n", "print(\"Annoy (s/query):\\t{0:.5f}\".format(annoy_time))\n", "print(\"Nmslib (s/query):\\t{0:.5f}\".format(nmslib_time))\n", "speed_improvement_gensim = gensim_time / nmslib_time\n", "speed_improvement_annoy = annoy_time / nmslib_time\n", "print (\"\\nNmslib is {0:.2f} times faster on average on this particular run\".format(speed_improvement_gensim))\n", "print (\"\\nNmslib is {0:.2f} times faster on average than annoy on this particular run\".format(speed_improvement_annoy))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Construct Nmslib Index with model & make a similarity query\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an indexer\n", "An instance of `NmslibIndexer` needs to be created in order to use Nmslib in gensim. The `NmslibIndexer` class is located in `gensim.similarities.nmslib`\n", "\n", "`NmslibIndexer()` takes three parameters:\n", "\n", "**`model`**: A `Word2Vec` or `Doc2Vec` model\n", "\n", "**`index_params`**: Parameters for building nmslib indexer. `index_params` effects the build time and the index size. The example is `{'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}`. Increasing the value of `M` and `efConstruction` improves the accuracy of search. However this also leads to longer indexing times. `indexThreadQty` is the number of thread. \n", "\n", "**`query_time_params`**: Parameters for querying on nmslib indexer. `query_time_params` effects the query time and the search accuracy. The example is `{'efSearch': 100}`. A larger `efSearch` will give more accurate results, but larger query time. \n", "\n", "More information can be found [here](https://github.com/nmslib/nmslib/blob/master/manual/methods.md). The relationship between parameters, build/query time, and accuracy will be investigated later in the tutorial. \n", "\n", "Now that we are ready to make a query, lets find the top 5 most similar words to \"science\" in the Text8 corpus. To make a similarity query we call `Word2Vec.most_similar` like we would traditionally, but with an added parameter, `indexer`. The only supported indexerers in gensim as of now are Annoy and Nmslib. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate Neighbors\n", "('science', 1.0000000596046448)\n", "('fiction', 0.8769421577453613)\n", "('protoscience', 0.8432635962963104)\n", "('multidisciplinary', 0.835610032081604)\n", "('sciences', 0.8348604440689087)\n", "('astrobiology', 0.8340338170528412)\n", "('actuarial', 0.8339103162288666)\n", "('interdisciplinary', 0.8327268362045288)\n", "('xenobiology', 0.8318319618701935)\n", "('criminology', 0.8261869251728058)\n", "('futurists', 0.82555091381073)\n", "\n", "Normal (not nmslib-indexed) Neighbors\n", "('science', 0.9999998807907104)\n", "('fiction', 0.7538841962814331)\n", "('protoscience', 0.6865270733833313)\n", "('multidisciplinary', 0.6712199449539185)\n", "('sciences', 0.6697208881378174)\n", "('astrobiology', 0.6680675148963928)\n", "('actuarial', 0.6678205132484436)\n", "('interdisciplinary', 0.6654534339904785)\n", "('xenobiology', 0.663663923740387)\n", "('vernor', 0.6569585800170898)\n", "('criminology', 0.652373731136322)\n" ] } ], "source": [ "# Building nmslib indexer\n", "nmslib_index = NmslibIndexer(model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})\n", "# Derive the vector for the word \"science\" in our model\n", "vector = model[\"science\"]\n", "# The instance of AnnoyIndexer we just created is passed \n", "approximate_neighbors = model.most_similar([vector], topn=11, indexer=nmslib_index)\n", "\n", "# Neatly print the approximate_neighbors and their corresponding cosine similarity values\n", "print(\"Approximate Neighbors\")\n", "for neighbor in approximate_neighbors:\n", " print(neighbor)\n", "\n", "normal_neighbors = model.most_similar([vector], topn=11)\n", "print(\"\\nNormal (not nmslib-indexed) Neighbors\")\n", "for neighbor in normal_neighbors:\n", " print(neighbor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Analyzing the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The closer the cosine similarity of a vector is to 1, the more similar that word is to our query, which was the vector for \"science\". In this case the results are almostly same." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Verify & Evaluate performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Persisting Indexes\n", "You can save and load your indexes from/to disk to prevent having to construct them each time. This will create two files on disk, _fname_ and _fname.d_. Both files are needed to correctly restore all attributes. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "fname = '/tmp/mymodel.index'\n", "\n", "# Persist index to disk\n", "nmslib_index.save(fname)\n", "\n", "# Load index back\n", "if os.path.exists(fname):\n", " nmslib_index2 = NmslibIndexer.load(fname)\n", " nmslib_index2.model = model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('science', 1.0000000596046448)\n", "('fiction', 0.8769421577453613)\n", "('protoscience', 0.8432635962963104)\n", "('multidisciplinary', 0.835610032081604)\n", "('sciences', 0.8348604440689087)\n", "('astrobiology', 0.8340338170528412)\n", "('actuarial', 0.8339103162288666)\n", "('interdisciplinary', 0.8327268362045288)\n", "('xenobiology', 0.8318319618701935)\n", "('criminology', 0.8261869251728058)\n", "('futurists', 0.82555091381073)\n" ] } ], "source": [ "# Results should be identical to above\n", "vector = model[\"science\"]\n", "approximate_neighbors2 = model.most_similar([vector], topn=11, indexer=nmslib_index2)\n", "for neighbor in approximate_neighbors2:\n", " print(neighbor)\n", " \n", "assert approximate_neighbors == approximate_neighbors2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be sure to use the same model at load that was used originally, otherwise you will get unexpected behaviors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Save memory by memory-mapping indices saved to disk\n", "\n", "Nmslib library has a useful feature that indices can be memory-mapped from disk. It saves memory when the same index is used by several processes.\n", "\n", "Below are two snippets of code. First one has a separate index for each process. The second snipped shares the index between two processes via memory-mapping. The second example uses less total RAM as it is shared." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Remove verbosity from code below (if logging active)\n", "\n", "if LOGS:\n", " logging.disable(logging.CRITICAL)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from multiprocessing import Process\n", "import psutil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bad Example: Two processes load the Word2vec model from disk and create there own Nmslib indices from that model. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Id: 36262\n", "\n", "Memory used by process 36262: pmem(rss=600539136, vms=6052294656, pfaults=170185, pageins=2)\n", "---\n", "Process Id: 36263\n", "\n", "Memory used by process 36263: pmem(rss=600539136, vms=6052294656, pfaults=170155, pageins=0)\n", "---\n", "CPU times: user 547 ms, sys: 308 ms, total: 856 ms\n", "Wall time: 59.2 s\n" ] } ], "source": [ "%%time\n", "\n", "model.save('/tmp/mymodel.pkl')\n", "\n", "def f(process_id):\n", " print('Process Id: {}'.format(os.getpid()))\n", " process = psutil.Process(os.getpid())\n", " new_model = Word2Vec.load('/tmp/mymodel.pkl')\n", " vector = new_model[\"science\"]\n", " nmslib_index = NmslibIndexer(new_model, {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 10})\n", " approximate_neighbors = new_model.most_similar([vector], topn=5, indexer=nmslib_index)\n", " print('\\nMemory used by process {}: {}\\n---'.format(os.getpid(), process.memory_info()))\n", "\n", "# Creating and running two parallel process to share the same index file.\n", "p1 = Process(target=f, args=('1',))\n", "p1.start()\n", "p1.join()\n", "p2 = Process(target=f, args=('2',))\n", "p2.start()\n", "p2.join()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Good example. Two processes load both the Word2vec model and index from disk and memory-map the index\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process Id: 36265\n", "\n", "Memory used by process 36265: pmem(rss=417345536, vms=5981540352, pfaults=129428, pageins=0)\n", "---\n", "Process Id: 36266\n", "\n", "Memory used by process 36266: pmem(rss=417579008, vms=5981540352, pfaults=129528, pageins=0)\n", "---\n", "CPU times: user 581 ms, sys: 266 ms, total: 847 ms\n", "Wall time: 3.74 s\n" ] } ], "source": [ "%%time\n", "\n", "model.save('/tmp/mymodel.pkl')\n", "\n", "def f(process_id):\n", " print('Process Id: {}'.format(os.getpid()))\n", " process = psutil.Process(os.getpid())\n", " new_model = Word2Vec.load('/tmp/mymodel.pkl')\n", " vector = new_model[\"science\"]\n", " nmslib_index = NmslibIndexer.load('/tmp/mymodel.index')\n", " nmslib_index.model = new_model\n", " approximate_neighbors = new_model.most_similar([vector], topn=5, indexer=nmslib_index)\n", " print('\\nMemory used by process {}: {}\\n---'.format(os.getpid(), process.memory_info()))\n", "\n", "# Creating and running two parallel process to share the same index file.\n", "p1 = Process(target=f, args=('1',))\n", "p1.start()\n", "p1.join()\n", "p2 = Process(target=f, args=('2',))\n", "p2.start()\n", "p2.join()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Build dataset of Initialization times and accuracy measures" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "exact_results = [element[0] for element in model.most_similar([model.wv.syn0norm[0]], topn=100)]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# For calculating query time\n", "queries = 1000" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def create_evaluation_graph(x_values, y_values_init, y_values_accuracy, y_values_query, param_name):\n", " plt.figure(1, figsize=(12, 6))\n", " plt.subplot(231)\n", " plt.plot(x_values, y_values_init)\n", " plt.title(\"{} vs initalization time\".format(param_name))\n", " plt.ylabel(\"Initialization time (s)\")\n", " plt.xlabel(param_name)\n", " plt.subplot(232)\n", " plt.plot(x_values, y_values_accuracy)\n", " plt.title(\"{} vs accuracy\".format(param_name))\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(param_name)\n", " plt.tight_layout()\n", " plt.subplot(233)\n", " plt.plot(y_values_init, y_values_accuracy)\n", " plt.title(\"Initialization time vs accuracy\")\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(\"Initialization time (s)\")\n", " plt.tight_layout()\n", " plt.subplot(234)\n", " plt.plot(x_values, y_values_query)\n", " plt.title(\"{} vs query time\".format(param_name))\n", " plt.ylabel(\"query time\")\n", " plt.xlabel(param_name)\n", " plt.tight_layout()\n", " plt.subplot(235)\n", " plt.plot(y_values_query, y_values_accuracy)\n", " plt.title(\"query time vs accuracy\")\n", " plt.ylabel(\"% accuracy\")\n", " plt.xlabel(\"query time (s)\")\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def evaluate_nmslib_performance(parameter, is_parameter_query, parameter_start, parameter_end, parameter_step):\n", " nmslib_x_values = []\n", " nmslib_y_values_init = []\n", " nmslib_y_values_accuracy = []\n", " nmslib_y_values_query = []\n", " index_params = {'M': 100, 'indexThreadQty': 10, 'efConstruction': 100, 'post': 0}\n", " query_params = {'efSearch': 100}\n", " \n", " for x in range(parameter_start, parameter_end, parameter_step):\n", " nmslib_x_values.append(x)\n", " start_time = time.time()\n", " if is_parameter_query:\n", " query_params[parameter] = x\n", " else:\n", " index_params[parameter] = x\n", " nmslib_index = NmslibIndexer(model\n", " , index_params\n", " , query_params)\n", " nmslib_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=nmslib_index)\n", " top_words = [result[0] for result in approximate_results]\n", " nmslib_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " nmslib_y_values_query.append(avg_query_time(nmslib_index, queries=queries))\n", " create_evaluation_graph(nmslib_x_values,\n", " nmslib_y_values_init, \n", " nmslib_y_values_accuracy, \n", " nmslib_y_values_query, \n", " parameter)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter M\n", "evaluate_nmslib_performance(\"M\", False, 50, 401, 50)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter efConstruction\n", "evaluate_nmslib_performance(\"efConstruction\", False, 50, 1001, 100)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate nmslib indexer, changing the parameter efSearch\n", "evaluate_nmslib_performance(\"efSearch\", True, 50, 401, 100)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Evaluate annoy indexer, changing the parameter num_tree\n", "annoy_x_values = []\n", "annoy_y_values_init = []\n", "annoy_y_values_accuracy = []\n", "annoy_y_values_query = []\n", "\n", "for x in range(100, 401, 50):\n", " annoy_x_values.append(x)\n", " start_time = time.time()\n", " annoy_index = AnnoyIndexer(model, x)\n", " annoy_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=annoy_index)\n", " top_words = [result[0] for result in approximate_results]\n", " annoy_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " annoy_y_values_query.append(avg_query_time(annoy_index, queries=queries))\n", "create_evaluation_graph(annoy_x_values,\n", " annoy_y_values_init, \n", " annoy_y_values_accuracy, \n", " annoy_y_values_query, \n", " \"num_tree\")" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# nmslib indexer changing the parameter M, efConstruction, efSearch\n", "nmslib_y_values_init = []\n", "nmslib_y_values_accuracy = []\n", "nmslib_y_values_query = []\n", "\n", "for M in [100, 200]:\n", " for efConstruction in [100, 200]:\n", " for efSearch in [100, 200]:\n", " start_time = time.time()\n", " nmslib_index = NmslibIndexer(model, \n", " {'M': M, 'indexThreadQty': 10, 'efConstruction': efConstruction, 'post': 0},\n", " {'efSearch': efSearch})\n", " nmslib_y_values_init.append(time.time() - start_time)\n", " approximate_results = model.most_similar([model.wv.syn0norm[0]], topn=100, indexer=nmslib_index)\n", " top_words = [result[0] for result in approximate_results]\n", " nmslib_y_values_accuracy.append(len(set(top_words).intersection(exact_results)))\n", " nmslib_y_values_query.append(avg_query_time(nmslib_index, queries=queries))\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Make a comparison between annoy and nmslib indexer\n", "plt.figure(1, figsize=(12, 6))\n", "plt.subplot(121)\n", "plt.scatter(nmslib_y_values_init, nmslib_y_values_accuracy, label=\"nmslib\", color='r', marker='o')\n", "plt.scatter(annoy_y_values_init, annoy_y_values_accuracy, label=\"annoy\", color='b', marker='x')\n", "plt.legend()\n", "plt.title(\"Initialization time vs accuracy. Upper left is better.\")\n", "plt.ylabel(\"% accuracy\")\n", "plt.xlabel(\"Initialization time (s)\")\n", "plt.subplot(122)\n", "plt.scatter(nmslib_y_values_query, nmslib_y_values_accuracy, label=\"nmslib\", color='r', marker='o')\n", "plt.scatter(annoy_y_values_query, annoy_y_values_accuracy, label=\"annoy\", color='b', marker='x')\n", "plt.legend()\n", "plt.title(\"Query time vs accuracy. Upper left is better.\")\n", "plt.ylabel(\"% accuracy\")\n", "plt.xlabel(\"Query time (s)\")\n", "plt.xlim(min(nmslib_y_values_query+annoy_y_values_query), max(nmslib_y_values_query+annoy_y_values_query))\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Work with Google word2vec files\n", "\n", "Our model can be exported to a word2vec C format. There is a binary and a plain text word2vec format. Both can be read with a variety of other software, or imported back into gensim as a `KeyedVectors` object." ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "# To export our model as text\n", "model.wv.save_word2vec_format('/tmp/vectors.txt', binary=False)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "71290 100\n", "the -0.20548718 0.19478682 -0.15149663 -0.31142342 0.014471135 -0.17996445 -0.07373469 0.09573618 -0.06328416 0.15571225 0.021413572 -0.12776679 -0.16940169 0.15807933 -0.21688043 0.074471496 0.08091913 0.07911484 0.31909388 -0.12297766 0.16993207 -0.02962172 0.08481803 0.12566781 0.02949822 -0.009697897 0.10780254 -0.102594994 -0.03935867 0.2679534 -0.061677158 -0.26071545 0.16285498 0.051780242 -0.1697231 0.24037386 0.0726078 -0.090416454 0.0776138 -0.06611322 0.057015926 0.07859522 -0.1910579 0.2974446 -0.033308737 -0.07360004 0.10797568 0.3595622 0.26797494 -0.062491674 0.21733648 -0.08524646 -0.06860078 -0.01714756 0.1305319 -0.09754544 -0.11249808 0.27328265 0.0041686473 -0.09874534 -0.30283058 0.111191 -0.026302295 -0.095534325 -0.0907799 -0.09120328 0.00068672217 0.31802058 -0.03345536 0.103762306 0.068564445 0.07402255 0.013657822 0.020439595 0.14985266 -0.13516407 0.36674532 0.0077319355 0.24709526 0.07666927 -0.11271039 0.02220251 -0.0235162 0.06409378 -0.10098407 0.23384795 0.094924204 0.061178204 0.19992544 0.29211295 0.004201066 0.12897494 -0.07837112 0.06269808 0.18545003 0.10452479 0.093281575 -0.24360427 0.01996208 0.35048977\n", "of -0.19580448 0.209091 -0.099757686 -0.21272708 0.022300478 -0.21946296 -0.19841547 0.09413904 0.08382151 0.20281556 0.12914163 -0.062828615 -0.24957581 0.29098126 -0.12465087 0.09803499 0.08116001 0.06726196 0.2615518 -0.035177294 0.06914535 -0.09427601 0.0011476864 0.23290296 0.0034714406 0.009549841 0.03912403 0.11168332 -0.10166585 0.34404838 -0.08050078 -0.3933344 0.17402974 -0.15764657 -0.21314052 0.20686424 0.034598276 0.16018851 0.14357902 0.046236232 0.075825214 0.029642927 -0.059190348 0.4163471 -0.1367429 -0.017528763 0.19181107 0.2601198 -0.020112848 -0.23402186 0.2841525 -0.10974068 -0.002565893 0.00070757867 0.13032512 -0.002393167 -0.14120881 0.22138755 0.027622899 0.06942904 -0.39498508 0.1133777 0.19053803 -0.062439334 -0.025348661 -0.11142109 0.015062763 0.3285828 -0.09184951 0.2661699 -0.11710489 -0.15770112 -0.12773664 0.15360866 0.08832063 -0.20914252 0.32392043 -0.023845093 0.3131217 0.08974748 -0.11354328 -0.2037927 -0.06780317 0.20184614 -0.13539118 0.2029387 0.07701099 -0.048417546 0.09797926 0.284204 0.036153372 0.17912139 -0.118080124 -0.025121484 0.10947146 0.09291596 0.1244357 0.006804844 0.025120731 0.28958535\n" ] } ], "source": [ "from smart_open import open\n", "# View the first 3 lines of the exported file\n", "\n", "# The first line has the total number of entries and the vector dimension count. \n", "# The next lines have a key (a string) followed by its vector.\n", "with open('/tmp/vectors.txt') as myfile:\n", " for i in range(3):\n", " print(myfile.readline().strip())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# To import a word2vec text model\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# To export our model as binary\n", "model.wv.save_word2vec_format('/tmp/vectors.bin', binary=True)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# To import a word2vec binary model\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# To create and save Nmslib Index from a loaded `KeyedVectors` object \n", "nmslib_index = NmslibIndexer(wv, \n", " {'M': 100, 'indexThreadQty': 1, 'efConstruction': 100}, {'efSearch': 100})\n", "nmslib_index.save('/tmp/mymodel.index')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate Neighbors\n", "('cat', 1.0)\n", "('cats', 0.8335747122764587)\n", "('meow', 0.8298084139823914)\n", "('leopardus', 0.8215900659561157)\n", "('albino', 0.8207193315029144)\n", "('poodle', 0.8168523907661438)\n", "('saimiri', 0.8167656660079956)\n", "('squirrel', 0.8138661682605743)\n", "('sighthound', 0.8134040832519531)\n", "('proboscis', 0.8130573630332947)\n", "('eared', 0.812840610742569)\n", "\n", "Normal (not Nmslib-indexed) Neighbors\n", "('cat', 1.0)\n", "('cats', 0.6671494245529175)\n", "('meow', 0.6596168279647827)\n", "('leopardus', 0.6431801319122314)\n", "('albino', 0.6414386034011841)\n", "('poodle', 0.633704662322998)\n", "('saimiri', 0.633531391620636)\n", "('squirrel', 0.6277321577072144)\n", "('sighthound', 0.6268081665039062)\n", "('proboscis', 0.6261147260665894)\n", "('eared', 0.6256811618804932)\n" ] } ], "source": [ "# Load and test the saved word vectors and saved nmslib index\n", "wv = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True)\n", "nmslib_index = NmslibIndexer.load('/tmp/mymodel.index')\n", "nmslib_index.model = wv\n", "\n", "vector = wv[\"cat\"]\n", "approximate_neighbors = wv.most_similar([vector], topn=11, indexer=nmslib_index)\n", "# Neatly print the approximate_neighbors and their corresponding cosine similarity values\n", "print(\"Approximate Neighbors\")\n", "for neighbor in approximate_neighbors:\n", " print(neighbor)\n", "\n", "normal_neighbors = wv.most_similar([vector], topn=11)\n", "print(\"\\nNormal (not Nmslib-indexed) Neighbors\")\n", "for neighbor in normal_neighbors:\n", " print(neighbor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recap\n", "In this notebook we used the Nmslib module to build an indexed approximation of our word embeddings. To do so, we did the following steps:\n", "1. Download Text8 Corpus\n", "2. Build Word2Vec Model\n", "3. Construct NmslibIndex with model & make a similarity query\n", "4. Verify & Evaluate performance\n", "5. Evaluate relationship of parameters to initialization/query time and accuracy, compared with annoy\n", "6. Work with Google's word2vec C formats" ] } ], "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 }

lgpl-2.1

dipanjanS/BerkeleyX-CS100.1x-Big-Data-with-Apache-Spark

Week 2 - Introduction to Apache Spark/lab1_word_count_student.ipynb

1

33229

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# + \n", "# **Word Count Lab: Building a word count application**\n", "#### This lab will build on the techniques covered in the Spark tutorial to develop a simple word count application. The volume of unstructured text in existence is growing dramatically, and Spark is an excellent tool for analyzing this type of data. In this lab, we will write code that calculates the most common words in the [Complete Works of William Shakespeare](http://www.gutenberg.org/ebooks/100) retrieved from [Project Gutenberg](http://www.gutenberg.org/wiki/Main_Page). This could also be scaled to find the most common words on the Internet.\n", "#### ** During this lab we will cover: **\n", "#### *Part 1:* Creating a base RDD and pair RDDs\n", "#### *Part 2:* Counting with pair RDDs\n", "#### *Part 3:* Finding unique words and a mean value\n", "#### *Part 4:* Apply word count to a file\n", "#### Note that, for reference, you can look up the details of the relevant methods in [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 1: Creating a base RDD and pair RDDs **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### In this part of the lab, we will explore creating a base RDD with `parallelize` and using pair RDDs to count words." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1a) Create a base RDD **\n", "#### We'll start by generating a base RDD by using a Python list and the `sc.parallelize` method. Then we'll print out the type of the base RDD." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pyspark.rdd.RDD'>\n" ] } ], "source": [ "wordsList = ['cat', 'elephant', 'rat', 'rat', 'cat']\n", "wordsRDD = sc.parallelize(wordsList, 4)\n", "# Print out the type of wordsRDD\n", "print type(wordsRDD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1b) Pluralize and test **\n", "#### Let's use a `map()` transformation to add the letter 's' to each string in the base RDD we just created. We'll define a Python function that returns the word with an 's' at the end of the word. Please replace `<FILL IN>` with your solution. If you have trouble, the next cell has the solution. After you have defined `makePlural` you can run the third cell which contains a test. If you implementation is correct it will print `1 test passed`.\n", "#### This is the general form that exercises will take, except that no example solution will be provided. Exercises will include an explanation of what is expected, followed by code cells where one cell will have one or more `<FILL IN>` sections. The cell that needs to be modified will have `# TODO: Replace <FILL IN> with appropriate code` on its first line. Once the `<FILL IN>` sections are updated and the code is run, the test cell can then be run to verify the correctness of your solution. The last code cell before the next markdown section will contain the tests." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cats\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def makePlural(word):\n", " \"\"\"Adds an 's' to `word`.\n", "\n", " Note:\n", " This is a simple function that only adds an 's'. No attempt is made to follow proper\n", " pluralization rules.\n", "\n", " Args:\n", " word (str): A string.\n", "\n", " Returns:\n", " str: A string with 's' added to it.\n", " \"\"\"\n", " return word + 's'\n", "\n", "print makePlural('cat')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cats\n" ] } ], "source": [ "# One way of completing the function\n", "def makePlural(word):\n", " return word + 's'\n", "\n", "print makePlural('cat')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# Load in the testing code and check to see if your answer is correct\n", "# If incorrect it will report back '1 test failed' for each failed test\n", "# Make sure to rerun any cell you change before trying the test again\n", "from test_helper import Test\n", "# TEST Pluralize and test (1b)\n", "Test.assertEquals(makePlural('rat'), 'rats', 'incorrect result: makePlural does not add an s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1c) Apply `makePlural` to the base RDD **\n", "#### Now pass each item in the base RDD into a [map()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.map) transformation that applies the `makePlural()` function to each element. And then call the [collect()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.collect) action to see the transformed RDD." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cats', 'elephants', 'rats', 'rats', 'cats']\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "pluralRDD = wordsRDD.map(makePlural)\n", "print pluralRDD.collect()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Apply makePlural to the base RDD(1c)\n", "Test.assertEquals(pluralRDD.collect(), ['cats', 'elephants', 'rats', 'rats', 'cats'],\n", " 'incorrect values for pluralRDD')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1d) Pass a `lambda` function to `map` **\n", "#### Let's create the same RDD using a `lambda` function." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cats', 'elephants', 'rats', 'rats', 'cats']\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "pluralLambdaRDD = wordsRDD.map(lambda word: word + 's')\n", "print pluralLambdaRDD.collect()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Pass a lambda function to map (1d)\n", "Test.assertEquals(pluralLambdaRDD.collect(), ['cats', 'elephants', 'rats', 'rats', 'cats'],\n", " 'incorrect values for pluralLambdaRDD (1d)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1e) Length of each word **\n", "#### Now use `map()` and a `lambda` function to return the number of characters in each word. We'll `collect` this result directly into a variable." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4, 9, 4, 4, 4]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "pluralLengths = (pluralRDD\n", " .map(lambda word: len(word))\n", " .collect())\n", "print pluralLengths" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Length of each word (1e)\n", "Test.assertEquals(pluralLengths, [4, 9, 4, 4, 4],\n", " 'incorrect values for pluralLengths')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1f) Pair RDDs **\n", "#### The next step in writing our word counting program is to create a new type of RDD, called a pair RDD. A pair RDD is an RDD where each element is a pair tuple `(k, v)` where `k` is the key and `v` is the value. In this example, we will create a pair consisting of `('<word>', 1)` for each word element in the RDD.\n", "#### We can create the pair RDD using the `map()` transformation with a `lambda()` function to create a new RDD." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('cat', 1), ('elephant', 1), ('rat', 1), ('rat', 1), ('cat', 1)]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "wordPairs = wordsRDD.map(lambda word: (word, 1))\n", "print wordPairs.collect()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Pair RDDs (1f)\n", "Test.assertEquals(wordPairs.collect(),\n", " [('cat', 1), ('elephant', 1), ('rat', 1), ('rat', 1), ('cat', 1)],\n", " 'incorrect value for wordPairs')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 2: Counting with pair RDDs **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now, let's count the number of times a particular word appears in the RDD. There are multiple ways to perform the counting, but some are much less efficient than others.\n", "#### A naive approach would be to `collect()` all of the elements and count them in the driver program. While this approach could work for small datasets, we want an approach that will work for any size dataset including terabyte- or petabyte-sized datasets. In addition, performing all of the work in the driver program is slower than performing it in parallel in the workers. For these reasons, we will use data parallel operations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2a) `groupByKey()` approach **\n", "#### An approach you might first consider (we'll see shortly that there are better ways) is based on using the [groupByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.groupByKey) transformation. As the name implies, the `groupByKey()` transformation groups all the elements of the RDD with the same key into a single list in one of the partitions. There are two problems with using `groupByKey()`:\n", " + #### The operation requires a lot of data movement to move all the values into the appropriate partitions.\n", " + #### The lists can be very large. Consider a word count of English Wikipedia: the lists for common words (e.g., the, a, etc.) would be huge and could exhaust the available memory in a worker.\n", " \n", "#### Use `groupByKey()` to generate a pair RDD of type `('word', iterator)`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rat: [1, 1]\n", "elephant: [1]\n", "cat: [1, 1]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "# Note that groupByKey requires no parameters\n", "wordsGrouped = wordPairs.groupByKey()\n", "for key, value in wordsGrouped.collect():\n", " print '{0}: {1}'.format(key, list(value))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST groupByKey() approach (2a)\n", "Test.assertEquals(sorted(wordsGrouped.mapValues(lambda x: list(x)).collect()),\n", " [('cat', [1, 1]), ('elephant', [1]), ('rat', [1, 1])],\n", " 'incorrect value for wordsGrouped')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2b) Use `groupByKey()` to obtain the counts **\n", "#### Using the `groupByKey()` transformation creates an RDD containing 3 elements, each of which is a pair of a word and a Python iterator.\n", "#### Now sum the iterator using a `map()` transformation. The result should be a pair RDD consisting of (word, count) pairs." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('rat', 2), ('elephant', 1), ('cat', 2)]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "wordCountsGrouped = wordsGrouped.map(lambda (k,v): (k, sum(v)))\n", "print wordCountsGrouped.collect()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Use groupByKey() to obtain the counts (2b)\n", "Test.assertEquals(sorted(wordCountsGrouped.collect()),\n", " [('cat', 2), ('elephant', 1), ('rat', 2)],\n", " 'incorrect value for wordCountsGrouped')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2c) Counting using `reduceByKey` **\n", "#### A better approach is to start from the pair RDD and then use the [reduceByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.reduceByKey) transformation to create a new pair RDD. The `reduceByKey()` transformation gathers together pairs that have the same key and applies the function provided to two values at a time, iteratively reducing all of the values to a single value. `reduceByKey()` operates by applying the function first within each partition on a per-key basis and then across the partitions, allowing it to scale efficiently to large datasets." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('rat', 2), ('elephant', 1), ('cat', 2)]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "# Note that reduceByKey takes in a function that accepts two values and returns a single value\n", "\n", "wordCounts = wordPairs.reduceByKey(lambda a,b: a+b)\n", "print wordCounts.collect()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Counting using reduceByKey (2c)\n", "Test.assertEquals(sorted(wordCounts.collect()), [('cat', 2), ('elephant', 1), ('rat', 2)],\n", " 'incorrect value for wordCounts')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2d) All together **\n", "#### The expert version of the code performs the `map()` to pair RDD, `reduceByKey()` transformation, and `collect` in one statement." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('rat', 2), ('elephant', 1), ('cat', 2)]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "wordCountsCollected = (wordsRDD\n", " .map(lambda word: (word, 1))\n", " .reduceByKey(lambda a,b: a+b)\n", " .collect())\n", "print wordCountsCollected" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST All together (2d)\n", "Test.assertEquals(sorted(wordCountsCollected), [('cat', 2), ('elephant', 1), ('rat', 2)],\n", " 'incorrect value for wordCountsCollected')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 3: Finding unique words and a mean value **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3a) Unique words **\n", "#### Calculate the number of unique words in `wordsRDD`. You can use other RDDs that you have already created to make this easier." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "uniqueWords = wordsRDD.map(lambda word: (word, 1)).distinct().count()\n", "print uniqueWords" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Unique words (3a)\n", "Test.assertEquals(uniqueWords, 3, 'incorrect count of uniqueWords')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3b) Mean using `reduce` **\n", "#### Find the mean number of words per unique word in `wordCounts`.\n", "#### Use a `reduce()` action to sum the counts in `wordCounts` and then divide by the number of unique words. First `map()` the pair RDD `wordCounts`, which consists of (key, value) pairs, to an RDD of values." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "1.67\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "from operator import add\n", "\n", "totalCount = (wordCounts\n", " .map(lambda (a,b): b)\n", " .reduce(add))\n", "average = totalCount / float(wordCounts.distinct().count())\n", "print totalCount\n", "print round(average, 2)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Mean using reduce (3b)\n", "Test.assertEquals(round(average, 2), 1.67, 'incorrect value of average')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 4: Apply word count to a file **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### In this section we will finish developing our word count application. We'll have to build the `wordCount` function, deal with real world problems like capitalization and punctuation, load in our data source, and compute the word count on the new data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4a) `wordCount` function **\n", "#### First, define a function for word counting. You should reuse the techniques that have been covered in earlier parts of this lab. This function should take in an RDD that is a list of words like `wordsRDD` and return a pair RDD that has all of the words and their associated counts." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('rat', 2), ('elephant', 1), ('cat', 2)]\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "def wordCount(wordListRDD):\n", " \"\"\"Creates a pair RDD with word counts from an RDD of words.\n", "\n", " Args:\n", " wordListRDD (RDD of str): An RDD consisting of words.\n", "\n", " Returns:\n", " RDD of (str, int): An RDD consisting of (word, count) tuples.\n", " \"\"\"\n", " return (wordListRDD\n", " .map(lambda a : (a,1))\n", " .reduceByKey(lambda a,b: a+b))\n", "print wordCount(wordsRDD).collect()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST wordCount function (4a)\n", "Test.assertEquals(sorted(wordCount(wordsRDD).collect()),\n", " [('cat', 2), ('elephant', 1), ('rat', 2)],\n", " 'incorrect definition for wordCount function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4b) Capitalization and punctuation **\n", "#### Real world files are more complicated than the data we have been using in this lab. Some of the issues we have to address are:\n", " + #### Words should be counted independent of their capitialization (e.g., Spark and spark should be counted as the same word).\n", " + #### All punctuation should be removed.\n", " + #### Any leading or trailing spaces on a line should be removed.\n", " \n", "#### Define the function `removePunctuation` that converts all text to lower case, removes any punctuation, and removes leading and trailing spaces. Use the Python [re](https://docs.python.org/2/library/re.html) module to remove any text that is not a letter, number, or space. Reading `help(re.sub)` might be useful." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hi you\n", "no underscore\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "import re\n", "def removePunctuation(text):\n", " \"\"\"Removes punctuation, changes to lower case, and strips leading and trailing spaces.\n", "\n", " Note:\n", " Only spaces, letters, and numbers should be retained. Other characters should should be\n", " eliminated (e.g. it's becomes its). Leading and trailing spaces should be removed after\n", " punctuation is removed.\n", "\n", " Args:\n", " text (str): A string.\n", "\n", " Returns:\n", " str: The cleaned up string.\n", " \"\"\"\n", " return re.sub(\"[^a-zA-Z0-9 ]\", \"\", text.strip(\" \").lower())\n", "print removePunctuation('Hi, you!')\n", "print removePunctuation(' No under_score!')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Capitalization and punctuation (4b)\n", "Test.assertEquals(removePunctuation(\" The Elephant's 4 cats. \"),\n", " 'the elephants 4 cats',\n", " 'incorrect definition for removePunctuation function')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4c) Load a text file **\n", "#### For the next part of this lab, we will use the [Complete Works of William Shakespeare](http://www.gutenberg.org/ebooks/100) from [Project Gutenberg](http://www.gutenberg.org/wiki/Main_Page). To convert a text file into an RDD, we use the `SparkContext.textFile()` method. We also apply the recently defined `removePunctuation()` function using a `map()` transformation to strip out the punctuation and change all text to lowercase. Since the file is large we use `take(15)`, so that we only print 15 lines." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: 1609\n", "1: \n", "2: the sonnets\n", "3: \n", "4: by william shakespeare\n", "5: \n", "6: \n", "7: \n", "8: 1\n", "9: from fairest creatures we desire increase\n", "10: that thereby beautys rose might never die\n", "11: but as the riper should by time decease\n", "12: his tender heir might bear his memory\n", "13: but thou contracted to thine own bright eyes\n", "14: feedst thy lights flame with selfsubstantial fuel\n" ] } ], "source": [ "# Just run this code\n", "import os.path\n", "baseDir = os.path.join('data')\n", "inputPath = os.path.join('cs100', 'lab1', 'shakespeare.txt')\n", "fileName = os.path.join(baseDir, inputPath)\n", "\n", "shakespeareRDD = (sc\n", " .textFile(fileName, 8)\n", " .map(removePunctuation))\n", "print '\\n'.join(shakespeareRDD\n", " .zipWithIndex() # to (line, lineNum)\n", " .map(lambda (l, num): '{0}: {1}'.format(num, l)) # to 'lineNum: line'\n", " .take(15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4d) Words from lines **\n", "#### Before we can use the `wordcount()` function, we have to address two issues with the format of the RDD:\n", " + #### The first issue is that that we need to split each line by its spaces.\n", " + #### The second issue is we need to filter out empty lines.\n", " \n", "#### Apply a transformation that will split each element of the RDD by its spaces. For each element of the RDD, you should apply Python's string [split()](https://docs.python.org/2/library/string.html#string.split) function. You might think that a `map()` transformation is the way to do this, but think about what the result of the `split()` function will be." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'zwaggerd', u'zounds', u'zounds', u'zounds', u'zounds']\n", "928908\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "shakespeareWordsRDD = shakespeareRDD.flatMap(lambda a: a.split(\" \"))\n", "shakespeareWordCount = shakespeareWordsRDD.count()\n", "print shakespeareWordsRDD.top(5)\n", "print shakespeareWordCount" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n", "1 test passed.\n" ] } ], "source": [ "# TEST Words from lines (4d)\n", "# This test allows for leading spaces to be removed either before or after\n", "# punctuation is removed.\n", "Test.assertTrue(shakespeareWordCount == 927631 or shakespeareWordCount == 928908,\n", " 'incorrect value for shakespeareWordCount')\n", "Test.assertEquals(shakespeareWordsRDD.top(5),\n", " [u'zwaggerd', u'zounds', u'zounds', u'zounds', u'zounds'],\n", " 'incorrect value for shakespeareWordsRDD')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4e) Remove empty elements **\n", "#### The next step is to filter out the empty elements. Remove all entries where the word is `''`." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "882996\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "shakeWordsRDD = shakespeareWordsRDD.filter(lambda word: len(word) > 0)\n", "shakeWordCount = shakeWordsRDD.count()\n", "print shakeWordCount" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Remove empty elements (4e)\n", "Test.assertEquals(shakeWordCount, 882996, 'incorrect value for shakeWordCount')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4f) Count the words **\n", "#### We now have an RDD that is only words. Next, let's apply the `wordCount()` function to produce a list of word counts. We can view the top 15 words by using the `takeOrdered()` action; however, since the elements of the RDD are pairs, we need a custom sort function that sorts using the value part of the pair.\n", "#### You'll notice that many of the words are common English words. These are called stopwords. In a later lab, we will see how to eliminate them from the results.\n", "#### Use the `wordCount()` function and `takeOrdered()` to obtain the fifteen most common words and their counts." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the: 27361\n", "and: 26028\n", "i: 20681\n", "to: 19150\n", "of: 17463\n", "a: 14593\n", "you: 13615\n", "my: 12481\n", "in: 10956\n", "that: 10890\n", "is: 9134\n", "not: 8497\n", "with: 7771\n", "me: 7769\n", "it: 7678\n" ] } ], "source": [ "# TODO: Replace <FILL IN> with appropriate code\n", "top15WordsAndCounts = wordCount(shakeWordsRDD).takeOrdered(15, lambda (a,b): -b)\n", "print '\\n'.join(map(lambda (w, c): '{0}: {1}'.format(w, c), top15WordsAndCounts))" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 test passed.\n" ] } ], "source": [ "# TEST Count the words (4f)\n", "Test.assertEquals(top15WordsAndCounts,\n", " [(u'the', 27361), (u'and', 26028), (u'i', 20681), (u'to', 19150), (u'of', 17463),\n", " (u'a', 14593), (u'you', 13615), (u'my', 12481), (u'in', 10956), (u'that', 10890),\n", " (u'is', 9134), (u'not', 8497), (u'with', 7771), (u'me', 7769), (u'it', 7678)],\n", " 'incorrect value for top15WordsAndCounts')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

guillaume-chevalier/LSTM-Human-Activity-Recognition

LSTM.ipynb

1

213498

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <a title=\"Activity Recognition\" href=\"https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\" > LSTMs for Human Activity Recognition</a>\n", "\n", "Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:\n", "- WALKING,\n", "- WALKING_UPSTAIRS,\n", "- WALKING_DOWNSTAIRS,\n", "- SITTING,\n", "- STANDING,\n", "- LAYING.\n", "\n", "Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. [Other research](https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.names) on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. \n", "\n", "Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series. \n", "\n", "## Video dataset overview\n", "\n", "Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:\n", "\n", "<p align=\"center\">\n", " <a href=\"http://www.youtube.com/watch?feature=player_embedded&v=XOEN9W05_4A\n", "\" target=\"_blank\"><img src=\"http://img.youtube.com/vi/XOEN9W05_4A/0.jpg\" \n", "alt=\"Video of the experiment\" width=\"400\" height=\"300\" border=\"10\" /></a>\n", " <a href=\"https://youtu.be/XOEN9W05_4A\"><center>[Watch video]</center></a>\n", "</p>\n", "\n", "## Details about the input data\n", "\n", "I will be using an LSTM on the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this:\n", "\n", "> The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. \n", "\n", "That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could fork my code on using a [Butterworth Low-Pass Filter (LPF) in Python](https://github.com/guillaume-chevalier/filtering-stft-and-laplace-transform) and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.\n", "\n", "## What is an RNN?\n", "\n", "As explained in [this article](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. **In our case, the \"many to one\" architecture is used**: we accept time series of [feature vectors](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2) (one vector per [time step](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2)) to convert them to a probability vector at the output for classification. Note that a \"one to one\" architecture would be a standard feedforward neural network. \n", "\n", "> <a href=\"https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm\" ><img src=\"https://raw.githubusercontent.com/Neuraxio/Machine-Learning-Figures/master/rnn-architectures.png\" /></a>\n", "> [Learn more on RNNs](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm)\n", "\n", "## What is an LSTM?\n", "\n", "An LSTM is an improved RNN. It is more complex, but easier to train, avoiding what is called the vanishing gradient problem. I recommend [this course](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm) for you to learn more on LSTMs.\n", "\n", "> [Learn more on LSTMs](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_lstm)\n", "\n", "\n", "## Results \n", "\n", "Scroll on! Nice visuals awaits. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# All Includes\n", "\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf # Version 1.0.0 (some previous versions are used in past commits)\n", "from sklearn import metrics\n", "\n", "import os" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Useful Constants\n", "\n", "# Those are separate normalised input features for the neural network\n", "INPUT_SIGNAL_TYPES = [\n", " \"body_acc_x_\",\n", " \"body_acc_y_\",\n", " \"body_acc_z_\",\n", " \"body_gyro_x_\",\n", " \"body_gyro_y_\",\n", " \"body_gyro_z_\",\n", " \"total_acc_x_\",\n", " \"total_acc_y_\",\n", " \"total_acc_z_\"\n", "]\n", "\n", "# Output classes to learn how to classify\n", "LABELS = [\n", " \"WALKING\", \n", " \"WALKING_UPSTAIRS\", \n", " \"WALKING_DOWNSTAIRS\", \n", " \"SITTING\", \n", " \"STANDING\", \n", " \"LAYING\"\n", "] \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's start by downloading the data: " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/ubuntu/pynb/LSTM-Human-Activity-Recognition\n", "data\t LSTM_files LSTM_OLD.ipynb README.md\n", "LICENSE LSTM.ipynb lstm.py\t screenlog.0\n", "/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data\n", "download_dataset.py source.txt\n", "\n", "Downloading...\n", "--2017-05-24 01:49:53-- https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip\n", "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249\n", "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 60999314 (58M) [application/zip]\n", "Saving to: ‘UCI HAR Dataset.zip’\n", "\n", "100%[======================================>] 60,999,314 1.69MB/s in 38s \n", "\n", "2017-05-24 01:50:31 (1.55 MB/s) - ‘UCI HAR Dataset.zip’ saved [60999314/60999314]\n", "\n", "Downloading done.\n", "\n", "Extracting...\n", "Extracting successfully done to /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data/UCI HAR Dataset.\n", "/home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data\n", "download_dataset.py __MACOSX source.txt UCI HAR Dataset UCI HAR Dataset.zip\n", "/home/ubuntu/pynb/LSTM-Human-Activity-Recognition\n", "data\t LSTM_files LSTM_OLD.ipynb README.md\n", "LICENSE LSTM.ipynb lstm.py\t screenlog.0\n", "\n", "Dataset is now located at: data/UCI HAR Dataset/\n" ] } ], "source": [ "# Note: Linux bash commands start with a \"!\" inside those \"ipython notebook\" cells\n", "\n", "DATA_PATH = \"data/\"\n", "\n", "!pwd && ls\n", "os.chdir(DATA_PATH)\n", "!pwd && ls\n", "\n", "!python download_dataset.py\n", "\n", "!pwd && ls\n", "os.chdir(\"..\")\n", "!pwd && ls\n", "\n", "DATASET_PATH = DATA_PATH + \"UCI HAR Dataset/\"\n", "print(\"\\n\" + \"Dataset is now located at: \" + DATASET_PATH)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing dataset:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "TRAIN = \"train/\"\n", "TEST = \"test/\"\n", "\n", "\n", "# Load \"X\" (the neural network's training and testing inputs)\n", "\n", "def load_X(X_signals_paths):\n", " X_signals = []\n", " \n", " for signal_type_path in X_signals_paths:\n", " file = open(signal_type_path, 'r')\n", " # Read dataset from disk, dealing with text files' syntax\n", " X_signals.append(\n", " [np.array(serie, dtype=np.float32) for serie in [\n", " row.replace(' ', ' ').strip().split(' ') for row in file\n", " ]]\n", " )\n", " file.close()\n", " \n", " return np.transpose(np.array(X_signals), (1, 2, 0))\n", "\n", "X_train_signals_paths = [\n", " DATASET_PATH + TRAIN + \"Inertial Signals/\" + signal + \"train.txt\" for signal in INPUT_SIGNAL_TYPES\n", "]\n", "X_test_signals_paths = [\n", " DATASET_PATH + TEST + \"Inertial Signals/\" + signal + \"test.txt\" for signal in INPUT_SIGNAL_TYPES\n", "]\n", "\n", "X_train = load_X(X_train_signals_paths)\n", "X_test = load_X(X_test_signals_paths)\n", "\n", "\n", "# Load \"y\" (the neural network's training and testing outputs)\n", "\n", "def load_y(y_path):\n", " file = open(y_path, 'r')\n", " # Read dataset from disk, dealing with text file's syntax\n", " y_ = np.array(\n", " [elem for elem in [\n", " row.replace(' ', ' ').strip().split(' ') for row in file\n", " ]], \n", " dtype=np.int32\n", " )\n", " file.close()\n", " \n", " # Substract 1 to each output class for friendly 0-based indexing \n", " return y_ - 1\n", "\n", "y_train_path = DATASET_PATH + TRAIN + \"y_train.txt\"\n", "y_test_path = DATASET_PATH + TEST + \"y_test.txt\"\n", "\n", "y_train = load_y(y_train_path)\n", "y_test = load_y(y_test_path)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additionnal Parameters:\n", "\n", "Here are some core parameter definitions for the training. \n", "\n", "For example, the whole neural network's structure could be summarised by enumerating those parameters and the fact that two LSTM are used one on top of another (stacked) output-to-input as hidden layers through time steps. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Some useful info to get an insight on dataset's shape and normalisation:\n", "(X shape, y shape, every X's mean, every X's standard deviation)\n", "(2947, 128, 9) (2947, 1) 0.0991399 0.395671\n", "The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.\n" ] } ], "source": [ "# Input Data \n", "\n", "training_data_count = len(X_train) # 7352 training series (with 50% overlap between each serie)\n", "test_data_count = len(X_test) # 2947 testing series\n", "n_steps = len(X_train[0]) # 128 timesteps per series\n", "n_input = len(X_train[0][0]) # 9 input parameters per timestep\n", "\n", "\n", "# LSTM Neural Network's internal structure\n", "\n", "n_hidden = 32 # Hidden layer num of features\n", "n_classes = 6 # Total classes (should go up, or should go down)\n", "\n", "\n", "# Training \n", "\n", "learning_rate = 0.0025\n", "lambda_loss_amount = 0.0015\n", "training_iters = training_data_count * 300 # Loop 300 times on the dataset\n", "batch_size = 1500\n", "display_iter = 30000 # To show test set accuracy during training\n", "\n", "\n", "# Some debugging info\n", "\n", "print(\"Some useful info to get an insight on dataset's shape and normalisation:\")\n", "print(\"(X shape, y shape, every X's mean, every X's standard deviation)\")\n", "print(X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))\n", "print(\"The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility functions for training:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def LSTM_RNN(_X, _weights, _biases):\n", " # Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters. \n", " # Moreover, two LSTM cells are stacked which adds deepness to the neural network. \n", " # Note, some code of this notebook is inspired from an slightly different \n", " # RNN architecture used on another dataset, some of the credits goes to \n", " # \"aymericdamien\" under the MIT license.\n", "\n", " # (NOTE: This step could be greatly optimised by shaping the dataset once\n", " # input shape: (batch_size, n_steps, n_input)\n", " _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size\n", " # Reshape to prepare input to hidden activation\n", " _X = tf.reshape(_X, [-1, n_input]) \n", " # new shape: (n_steps*batch_size, n_input)\n", " \n", " # ReLU activation, thanks to Yu Zhao for adding this improvement here:\n", " _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])\n", " # Split data because rnn cell needs a list of inputs for the RNN inner loop\n", " _X = tf.split(_X, n_steps, 0) \n", " # new shape: n_steps * (batch_size, n_hidden)\n", "\n", " # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow\n", " lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n", " lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)\n", " lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)\n", " # Get LSTM cell output\n", " outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)\n", "\n", " # Get last time step's output feature for a \"many-to-one\" style classifier, \n", " # as in the image describing RNNs at the top of this page\n", " lstm_last_output = outputs[-1]\n", " \n", " # Linear activation\n", " return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']\n", "\n", "\n", "def extract_batch_size(_train, step, batch_size):\n", " # Function to fetch a \"batch_size\" amount of data from \"(X|y)_train\" data. \n", " \n", " shape = list(_train.shape)\n", " shape[0] = batch_size\n", " batch_s = np.empty(shape)\n", "\n", " for i in range(batch_size):\n", " # Loop index\n", " index = ((step-1)*batch_size + i) % len(_train)\n", " batch_s[i] = _train[index] \n", "\n", " return batch_s\n", "\n", "\n", "def one_hot(y_, n_classes=n_classes):\n", " # Function to encode neural one-hot output labels from number indexes \n", " # e.g.: \n", " # one_hot(y_=[[5], [0], [3]], n_classes=6):\n", " # return [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n", " \n", " y_ = y_.reshape(len(y_))\n", " return np.eye(n_classes)[np.array(y_, dtype=np.int32)] # Returns FLOATS\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's get serious and build the neural network:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "\n", "# Graph input/output\n", "x = tf.placeholder(tf.float32, [None, n_steps, n_input])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", "\n", "# Graph weights\n", "weights = {\n", " 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n", " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))\n", "}\n", "biases = {\n", " 'hidden': tf.Variable(tf.random_normal([n_hidden])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", "}\n", "\n", "pred = LSTM_RNN(x, weights, biases)\n", "\n", "# Loss, optimizer and evaluation\n", "l2 = lambda_loss_amount * sum(\n", " tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()\n", ") # L2 loss prevents this overkill neural network to overfit the data\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer\n", "\n", "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hooray, now train the neural network:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-19-3339689e51f6>:9: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", "Instructions for updating:\n", "Use `tf.global_variables_initializer` instead.\n", "Training iter #1500: Batch Loss = 5.416760, Accuracy = 0.15266665816307068\n", "PERFORMANCE ON TEST SET: Batch Loss = 4.880829811096191, Accuracy = 0.05632847175002098\n", "Training iter #30000: Batch Loss = 3.031930, Accuracy = 0.607333242893219\n", "PERFORMANCE ON TEST SET: Batch Loss = 3.0515167713165283, Accuracy = 0.6067186594009399\n", "Training iter #60000: Batch Loss = 2.672764, Accuracy = 0.7386666536331177\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.780435085296631, Accuracy = 0.7027485370635986\n", "Training iter #90000: Batch Loss = 2.378301, Accuracy = 0.8366667032241821\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.6019773483276367, Accuracy = 0.7617915868759155\n", "Training iter #120000: Batch Loss = 2.127290, Accuracy = 0.9066667556762695\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.3625404834747314, Accuracy = 0.8116728663444519\n", "Training iter #150000: Batch Loss = 1.929805, Accuracy = 0.9380000233650208\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.306251049041748, Accuracy = 0.8276212215423584\n", "Training iter #180000: Batch Loss = 1.971904, Accuracy = 0.9153333902359009\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.0835530757904053, Accuracy = 0.8771631121635437\n", "Training iter #210000: Batch Loss = 1.860249, Accuracy = 0.8613333702087402\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.9994492530822754, Accuracy = 0.8788597583770752\n", "Training iter #240000: Batch Loss = 1.626292, Accuracy = 0.9380000233650208\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.879166603088379, Accuracy = 0.8944689035415649\n", "Training iter #270000: Batch Loss = 1.582758, Accuracy = 0.9386667013168335\n", "PERFORMANCE ON TEST SET: Batch Loss = 2.0341007709503174, Accuracy = 0.8361043930053711\n", "Training iter #300000: Batch Loss = 1.620352, Accuracy = 0.9306666851043701\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.8185184001922607, Accuracy = 0.8639293313026428\n", "Training iter #330000: Batch Loss = 1.474394, Accuracy = 0.9693333506584167\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.7638503313064575, Accuracy = 0.8747878670692444\n", "Training iter #360000: Batch Loss = 1.406998, Accuracy = 0.9420000314712524\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.5946787595748901, Accuracy = 0.902273416519165\n", "Training iter #390000: Batch Loss = 1.362515, Accuracy = 0.940000057220459\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.5285792350769043, Accuracy = 0.9046487212181091\n", "Training iter #420000: Batch Loss = 1.252860, Accuracy = 0.9566667079925537\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.4635565280914307, Accuracy = 0.9107565879821777\n", "Training iter #450000: Batch Loss = 1.190078, Accuracy = 0.9553333520889282\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.442753553390503, Accuracy = 0.9093992710113525\n", "Training iter #480000: Batch Loss = 1.159610, Accuracy = 0.9446667432785034\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.4130011796951294, Accuracy = 0.8971834778785706\n", "Training iter #510000: Batch Loss = 1.100551, Accuracy = 0.9593333601951599\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.3075592517852783, Accuracy = 0.9117745757102966\n", "Training iter #540000: Batch Loss = 1.123470, Accuracy = 0.9240000247955322\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.2605488300323486, Accuracy = 0.9165251851081848\n", "Training iter #570000: Batch Loss = 1.103454, Accuracy = 0.909333348274231\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.2327136993408203, Accuracy = 0.9009160399436951\n", "Training iter #600000: Batch Loss = 1.083368, Accuracy = 0.8966666460037231\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.2683708667755127, Accuracy = 0.8890395164489746\n", "Training iter #630000: Batch Loss = 0.939185, Accuracy = 0.9700000882148743\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.2147629261016846, Accuracy = 0.8866642713546753\n", "Training iter #660000: Batch Loss = 0.881242, Accuracy = 0.9806667566299438\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.1068334579467773, Accuracy = 0.9151678681373596\n", "Training iter #690000: Batch Loss = 0.831674, Accuracy = 0.9853334426879883\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.0885852575302124, Accuracy = 0.9121139645576477\n", "Training iter #720000: Batch Loss = 0.866615, Accuracy = 0.9573334455490112\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.0513516664505005, Accuracy = 0.9158465266227722\n", "Training iter #750000: Batch Loss = 0.858979, Accuracy = 0.940000057220459\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.0598633289337158, Accuracy = 0.9063453674316406\n", "Training iter #780000: Batch Loss = 0.750040, Accuracy = 0.9593334197998047\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.010966420173645, Accuracy = 0.9155071973800659\n", "Training iter #810000: Batch Loss = 0.732136, Accuracy = 0.9620000123977661\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.9865696430206299, Accuracy = 0.9161858558654785\n", "Training iter #840000: Batch Loss = 0.758945, Accuracy = 0.9406667351722717\n", "PERFORMANCE ON TEST SET: Batch Loss = 1.0347753763198853, Accuracy = 0.8958262205123901\n", "Training iter #870000: Batch Loss = 0.710809, Accuracy = 0.9660000205039978\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.9786491990089417, Accuracy = 0.893111526966095\n", "Training iter #900000: Batch Loss = 0.705978, Accuracy = 0.9553333520889282\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.9204542636871338, Accuracy = 0.9002374410629272\n", "Training iter #930000: Batch Loss = 0.759181, Accuracy = 0.9066667556762695\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.9086415767669678, Accuracy = 0.9036307334899902\n", "Training iter #960000: Batch Loss = 0.705333, Accuracy = 0.9286667108535767\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.850454568862915, Accuracy = 0.9080419540405273\n", "Training iter #990000: Batch Loss = 0.599754, Accuracy = 0.9693333506584167\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.8451057076454163, Accuracy = 0.9114353656768799\n", "Training iter #1020000: Batch Loss = 0.585689, Accuracy = 0.9700000286102295\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.8170899152755737, Accuracy = 0.9110959768295288\n", "Training iter #1050000: Batch Loss = 0.553970, Accuracy = 0.984000027179718\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.8555561304092407, Accuracy = 0.9114352464675903\n", "Training iter #1080000: Batch Loss = 0.601349, Accuracy = 0.9693334102630615\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.8512595891952515, Accuracy = 0.8781810998916626\n", "Training iter #1110000: Batch Loss = 0.601967, Accuracy = 0.937999963760376\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.7551606297492981, Accuracy = 0.9087206721305847\n", "Training iter #1140000: Batch Loss = 0.597223, Accuracy = 0.9353333711624146\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.7431289553642273, Accuracy = 0.909060001373291\n", "Training iter #1170000: Batch Loss = 0.523300, Accuracy = 0.9500000476837158\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.745741605758667, Accuracy = 0.9093992710113525\n", "Training iter #1200000: Batch Loss = 0.500816, Accuracy = 0.9600000381469727\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6978224515914917, Accuracy = 0.9138105511665344\n", "Training iter #1230000: Batch Loss = 0.495834, Accuracy = 0.9546667337417603\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6866210699081421, Accuracy = 0.9178825616836548\n", "Training iter #1260000: Batch Loss = 0.480467, Accuracy = 0.9813334345817566\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6883729100227356, Accuracy = 0.9100779294967651\n", "Training iter #1290000: Batch Loss = 0.516874, Accuracy = 0.9326666593551636\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6925369501113892, Accuracy = 0.9032914042472839\n", "Training iter #1320000: Batch Loss = 0.570053, Accuracy = 0.9080000519752502\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.743996798992157, Accuracy = 0.8978621363639832\n", "Training iter #1350000: Batch Loss = 0.491792, Accuracy = 0.9580000638961792\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6543726921081543, Accuracy = 0.8951475024223328\n", "Training iter #1380000: Batch Loss = 0.423705, Accuracy = 0.9760000705718994\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6256207227706909, Accuracy = 0.91788250207901\n", "Training iter #1410000: Batch Loss = 0.399226, Accuracy = 0.9840000867843628\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6232836246490479, Accuracy = 0.9205971360206604\n", "Training iter #1440000: Batch Loss = 0.415493, Accuracy = 0.972000002861023\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.6083709001541138, Accuracy = 0.9104173183441162\n", "Training iter #1470000: Batch Loss = 0.499316, Accuracy = 0.9306666851043701\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5882848501205444, Accuracy = 0.9117745757102966\n", "Training iter #1500000: Batch Loss = 0.478666, Accuracy = 0.9346666932106018\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5803182125091553, Accuracy = 0.91652512550354\n", "Training iter #1530000: Batch Loss = 0.366041, Accuracy = 0.968666672706604\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5783829689025879, Accuracy = 0.9114352464675903\n", "Training iter #1560000: Batch Loss = 0.377644, Accuracy = 0.9506667256355286\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5899279117584229, Accuracy = 0.9070240259170532\n", "Training iter #1590000: Batch Loss = 0.485060, Accuracy = 0.9133333563804626\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.7430599927902222, Accuracy = 0.8649473190307617\n", "Training iter #1620000: Batch Loss = 0.386228, Accuracy = 0.9633333683013916\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5264637470245361, Accuracy = 0.9070240259170532\n", "Training iter #1650000: Batch Loss = 0.416933, Accuracy = 0.9193333983421326\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5343363881111145, Accuracy = 0.914489209651947\n", "Training iter #1680000: Batch Loss = 0.421477, Accuracy = 0.9300000667572021\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5374469757080078, Accuracy = 0.9243297576904297\n", "Training iter #1710000: Batch Loss = 0.403527, Accuracy = 0.9300000071525574\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5439008474349976, Accuracy = 0.905666708946228\n", "Training iter #1740000: Batch Loss = 0.331851, Accuracy = 0.9753334522247314\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5405154228210449, Accuracy = 0.9093992710113525\n", "Training iter #1770000: Batch Loss = 0.337737, Accuracy = 0.9780000448226929\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5582258701324463, Accuracy = 0.9026126861572266\n", "Training iter #1800000: Batch Loss = 0.332086, Accuracy = 0.9600000381469727\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5655900835990906, Accuracy = 0.8995587825775146\n", "Training iter #1830000: Batch Loss = 0.400998, Accuracy = 0.9480000734329224\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.47865116596221924, Accuracy = 0.9144891500473022\n", "Training iter #1860000: Batch Loss = 0.364531, Accuracy = 0.9493333697319031\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.49166250228881836, Accuracy = 0.9158465266227722\n", "Training iter #1890000: Batch Loss = 0.316529, Accuracy = 0.9593334197998047\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5186017751693726, Accuracy = 0.9104173183441162\n", "Training iter #1920000: Batch Loss = 0.309109, Accuracy = 0.9626667499542236\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5222393274307251, Accuracy = 0.9002374410629272\n", "Training iter #1950000: Batch Loss = 0.427720, Accuracy = 0.9193333387374878\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5457150340080261, Accuracy = 0.9070240259170532\n", "Training iter #1980000: Batch Loss = 0.330174, Accuracy = 0.9526667594909668\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.5917137861251831, Accuracy = 0.8812350034713745\n", "Training iter #2010000: Batch Loss = 0.371541, Accuracy = 0.906000018119812\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.53951495885849, Accuracy = 0.8802171349525452\n", "Training iter #2040000: Batch Loss = 0.382413, Accuracy = 0.9206666946411133\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.42567864060401917, Accuracy = 0.9324736595153809\n", "Training iter #2070000: Batch Loss = 0.342763, Accuracy = 0.9326667189598083\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.4292983412742615, Accuracy = 0.9273836612701416\n", "Training iter #2100000: Batch Loss = 0.259442, Accuracy = 0.9873334169387817\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.44131210446357727, Accuracy = 0.9273836612701416\n", "Training iter #2130000: Batch Loss = 0.284630, Accuracy = 0.9593333601951599\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.46982717514038086, Accuracy = 0.9093992710113525\n", "Training iter #2160000: Batch Loss = 0.299012, Accuracy = 0.9686667323112488\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.48389002680778503, Accuracy = 0.9138105511665344\n", "Training iter #2190000: Batch Loss = 0.287106, Accuracy = 0.9700000286102295\n", "PERFORMANCE ON TEST SET: Batch Loss = 0.4670214056968689, Accuracy = 0.9216151237487793\n", "Optimization Finished!\n", "FINAL RESULT: Batch Loss = 0.45611169934272766, Accuracy = 0.9165252447128296\n" ] } ], "source": [ "# To keep track of training's performance\n", "test_losses = []\n", "test_accuracies = []\n", "train_losses = []\n", "train_accuracies = []\n", "\n", "# Launch the graph\n", "sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "# Perform Training steps with \"batch_size\" amount of example data at each loop\n", "step = 1\n", "while step * batch_size <= training_iters:\n", " batch_xs = extract_batch_size(X_train, step, batch_size)\n", " batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))\n", "\n", " # Fit training using batch data\n", " _, loss, acc = sess.run(\n", " [optimizer, cost, accuracy],\n", " feed_dict={\n", " x: batch_xs, \n", " y: batch_ys\n", " }\n", " )\n", " train_losses.append(loss)\n", " train_accuracies.append(acc)\n", " \n", " # Evaluate network only at some steps for faster training: \n", " if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):\n", " \n", " # To not spam console, show training accuracy/loss in this \"if\"\n", " print(\"Training iter #\" + str(step*batch_size) + \\\n", " \": Batch Loss = \" + \"{:.6f}\".format(loss) + \\\n", " \", Accuracy = {}\".format(acc))\n", " \n", " # Evaluation on the test set (no learning made here - just evaluation for diagnosis)\n", " loss, acc = sess.run(\n", " [cost, accuracy], \n", " feed_dict={\n", " x: X_test,\n", " y: one_hot(y_test)\n", " }\n", " )\n", " test_losses.append(loss)\n", " test_accuracies.append(acc)\n", " print(\"PERFORMANCE ON TEST SET: \" + \\\n", " \"Batch Loss = {}\".format(loss) + \\\n", " \", Accuracy = {}\".format(acc))\n", "\n", " step += 1\n", "\n", "print(\"Optimization Finished!\")\n", "\n", "# Accuracy for test data\n", "\n", "one_hot_predictions, accuracy, final_loss = sess.run(\n", " [pred, accuracy, cost],\n", " feed_dict={\n", " x: X_test,\n", " y: one_hot(y_test)\n", " }\n", ")\n", "\n", "test_losses.append(final_loss)\n", "test_accuracies.append(accuracy)\n", "\n", "print(\"FINAL RESULT: \" + \\\n", " \"Batch Loss = {}\".format(final_loss) + \\\n", " \", Accuracy = {}\".format(accuracy))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training is good, but having visual insight is even better:\n", "\n", "Okay, let's plot this simply in the notebook for now." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ok4/nnnvOCkq9vb1p166dLd2hQ4cKXbY7P//8s/WLAsA777yDt7c3AH5+frz5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\naJG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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdfded7cdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# (Inline plots: )\n", "%matplotlib inline\n", "\n", "font = {\n", " 'family' : 'Bitstream Vera Sans',\n", " 'weight' : 'bold',\n", " 'size' : 18\n", "}\n", "matplotlib.rc('font', **font)\n", "\n", "width = 12\n", "height = 12\n", "plt.figure(figsize=(width, height))\n", "\n", "indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))\n", "plt.plot(indep_train_axis, np.array(train_losses), \"b--\", label=\"Train losses\")\n", "plt.plot(indep_train_axis, np.array(train_accuracies), \"g--\", label=\"Train accuracies\")\n", "\n", "indep_test_axis = np.append(\n", " np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),\n", " [training_iters]\n", ")\n", "plt.plot(indep_test_axis, np.array(test_losses), \"b-\", label=\"Test losses\")\n", "plt.plot(indep_test_axis, np.array(test_accuracies), \"g-\", label=\"Test accuracies\")\n", "\n", "plt.title(\"Training session's progress over iterations\")\n", "plt.legend(loc='upper right', shadow=True)\n", "plt.ylabel('Training Progress (Loss or Accuracy values)')\n", "plt.xlabel('Training iteration')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## And finally, the multi-class confusion matrix and metrics!" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing Accuracy: 91.65252447128296%\n", "\n", "Precision: 91.76286479743305%\n", "Recall: 91.65252799457076%\n", "f1_score: 91.6437546304815%\n", "\n", "Confusion Matrix:\n", "[[466 2 26 0 2 0]\n", " [ 5 441 25 0 0 0]\n", " [ 1 0 419 0 0 0]\n", " [ 1 1 0 396 87 6]\n", " [ 2 1 0 87 442 0]\n", " [ 0 0 0 0 0 537]]\n", "\n", "Confusion matrix (normalised to % of total test data):\n", "[[ 15.81269073 0.06786563 0.88225317 0. 0.06786563 0. ]\n", " [ 0.16966406 14.96437073 0.84832031 0. 0. 0. ]\n", " [ 0.03393281 0. 14.21784878 0. 0. 0. ]\n", " [ 0.03393281 0.03393281 0. 13.43739319 2.95215464\n", " 0.20359688]\n", " [ 0.06786563 0.03393281 0. 2.95215464 14.99830341 0. ]\n", " [ 0. 0. 0. 0. 0. 18.22192001]]\n", "Note: training and testing data is not equally distributed amongst classes, \n", "so it is normal that more than a 6th of the data is correctly classifier in the last category.\n" ] }, { "data": { "image/png": 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V5pYiMzMzM7OqGdkxcxx0BLcUmZmZmZlZpTkoMrNKk/ROSV2SPj3UZRkOJK2V6+ueVpZ3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\nv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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdfd4635e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Results\n", "\n", "predictions = one_hot_predictions.argmax(1)\n", "\n", "print(\"Testing Accuracy: {}%\".format(100*accuracy))\n", "\n", "print(\"\")\n", "print(\"Precision: {}%\".format(100*metrics.precision_score(y_test, predictions, average=\"weighted\")))\n", "print(\"Recall: {}%\".format(100*metrics.recall_score(y_test, predictions, average=\"weighted\")))\n", "print(\"f1_score: {}%\".format(100*metrics.f1_score(y_test, predictions, average=\"weighted\")))\n", "\n", "print(\"\")\n", "print(\"Confusion Matrix:\")\n", "confusion_matrix = metrics.confusion_matrix(y_test, predictions)\n", "print(confusion_matrix)\n", "normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n", "\n", "print(\"\")\n", "print(\"Confusion matrix (normalised to % of total test data):\")\n", "print(normalised_confusion_matrix)\n", "print(\"Note: training and testing data is not equally distributed amongst classes, \")\n", "print(\"so it is normal that more than a 6th of the data is correctly classifier in the last category.\")\n", "\n", "# Plot Results: \n", "width = 12\n", "height = 12\n", "plt.figure(figsize=(width, height))\n", "plt.imshow(\n", " normalised_confusion_matrix, \n", " interpolation='nearest', \n", " cmap=plt.cm.rainbow\n", ")\n", "plt.title(\"Confusion matrix \\n(normalised to % of total test data)\")\n", "plt.colorbar()\n", "tick_marks = np.arange(n_classes)\n", "plt.xticks(tick_marks, LABELS, rotation=90)\n", "plt.yticks(tick_marks, LABELS)\n", "plt.tight_layout()\n", "plt.ylabel('True label')\n", "plt.xlabel('Predicted label')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Outstandingly, **the final accuracy is of 91%**! And it can peak to values such as 93.25%, at some moments of luck during the training, depending on how the neural network's weights got initialized at the start of the training, randomly. \n", "\n", "This means that the neural networks is almost always able to correctly identify the movement type! Remember, the phone is attached on the waist and each series to classify has just a 128 sample window of two internal sensors (a.k.a. 2.56 seconds at 50 FPS), so it amazes me how those predictions are extremely accurate given this small window of context and raw data. I've validated and re-validated that there is no important bug, and the community used and tried this code a lot. (Note: be sure to report something in the issue tab if you find bugs, otherwise [Quora](https://www.quora.com/), [StackOverflow](https://stackoverflow.com/questions/tagged/tensorflow?sort=votes&pageSize=50), and other [StackExchange](https://stackexchange.com/sites#science) sites are the places for asking questions.)\n", "\n", "I specially did not expect such good results for guessing between the labels \"SITTING\" and \"STANDING\". Those are seemingly almost the same thing from the point of view of a device placed at waist level according to how the dataset was originally gathered. Thought, it is still possible to see a little cluster on the matrix between those classes, which drifts away just a bit from the identity. This is great.\n", "\n", "It is also possible to see that there was a slight difficulty in doing the difference between \"WALKING\", \"WALKING_UPSTAIRS\" and \"WALKING_DOWNSTAIRS\". Obviously, those activities are quite similar in terms of movements. \n", "\n", "I also tried my code without the gyroscope, using only the 3D accelerometer's 6 features (and not changing the training hyperparameters), and got an accuracy of 87%. In general, gyroscopes consumes more power than accelerometers, so it is preferable to turn them off. \n", "\n", "\n", "## Improvements\n", "\n", "In [another open-source repository of mine](https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs), the accuracy is pushed up to nearly 94% using a special deep LSTM architecture which combines the concepts of bidirectional RNNs, residual connections, and stacked cells. This architecture is also tested on another similar activity dataset. It resembles the nice architecture used in \"[Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://arxiv.org/pdf/1609.08144.pdf)\", without an attention mechanism, and with just the encoder part - as a \"many to one\" architecture instead of a \"many to many\" to be adapted to the Human Activity Recognition (HAR) problem. I also worked more on the problem and came up with the [LARNN](https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network), however it's complicated for just a little gain. Thus the current, original activity recognition project is simply better to use for its outstanding simplicity. \n", "\n", "If you want to learn more about deep learning, I have also built a list of the learning ressources for deep learning which have revealed to be the most useful to me [here](https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources). \n", "\n", "\n", "## References\n", "\n", "The [dataset](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) can be found on the UCI Machine Learning Repository: \n", "\n", "> Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.\n", "\n", "## Citation\n", "\n", "Copyright (c) 2016 Guillaume Chevalier. To cite my code, you can point to the URL of the GitHub repository, for example: \n", "\n", "> Guillaume Chevalier, LSTMs for Human Activity Recognition, 2016, \n", "> https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n", "\n", "My code is available for free and even for private usage for anyone under the [MIT License](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/blob/master/LICENSE), however I ask to cite for using the code. \n", "\n", "Here is the BibTeX citation code: \n", "```\n", "@misc{chevalier2016lstms,\n", " title={LSTMs for human activity recognition},\n", " author={Chevalier, Guillaume},\n", " year={2016}\n", "}\n", "```\n", "\n", "## Extra links\n", "\n", "### Connect with me\n", "\n", "- [LinkedIn](https://ca.linkedin.com/in/chevalierg)\n", "- [Twitter](https://twitter.com/guillaume_che)\n", "- [GitHub](https://github.com/guillaume-chevalier/)\n", "- [Quora](https://www.quora.com/profile/Guillaume-Chevalier-2)\n", "- [YouTube](https://www.youtube.com/c/GuillaumeChevalier)\n", "- [Dev/Consulting](http://www.neuraxio.com/en/)\n", "\n", "### Liked this project? Did it help you? Leave a [star](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/stargazers), [fork](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/network/members) and share the love!\n", "\n", "This activity recognition project has been seen in:\n", "\n", "- [Hacker News 1st page](https://news.ycombinator.com/item?id=13049143)\n", "- [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow#tutorials)\n", "- [TensorFlow World](https://github.com/astorfi/TensorFlow-World#some-useful-tutorials)\n", "- And more.\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[NbConvertApp] Converting notebook LSTM.ipynb to markdown\n", "[NbConvertApp] Support files will be in LSTM_files/\n", "[NbConvertApp] Making directory LSTM_files\n", "[NbConvertApp] Making directory LSTM_files\n", "[NbConvertApp] Writing 38654 bytes to LSTM.md\n" ] } ], "source": [ "# Let's convert this notebook to a README automatically for the GitHub project's title page:\n", "!jupyter nbconvert --to markdown LSTM.ipynb\n", "!mv LSTM.md README.md" ] } ], "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

openai/openai-python

examples/embeddings/Get_embeddings.ipynb

1

2512

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Get embeddings\n", "\n", "The function `get_embedding` will give us an embedding for an input text." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12288" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import openai\n", "\n", "embedding = openai.Embedding.create(input=\"Sample document text goes here\", engine=\"text-similarity-davinci-001\")['data'][0]['embedding']\n", "len(embedding)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1024\n" ] } ], "source": [ "import openai\n", "from tenacity import retry, wait_random_exponential, stop_after_attempt\n", "\n", "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))\n", "def get_embedding(text: str, engine=\"text-similarity-davinci-001\") -> List[float]:\n", "\n", " # replace newlines, which can negatively affect performance.\n", " text = text.replace(\"\\n\", \" \")\n", "\n", " return openai.Embedding.create(input=[text], engine=engine)[\"data\"][0][\"embedding\"]\n", "\n", "embedding = get_embedding(\"Sample query text goes here\", engine=\"text-search-ada-query-001\")\n", "print(len(embedding))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1024\n" ] } ], "source": [ "embedding = get_embedding(\"Sample document text goes here\", engine=\"text-search-ada-doc-001\")\n", "print(len(embedding))" ] } ], "metadata": { "interpreter": { "hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8" }, "kernelspec": { "display_name": "Python 3.7.3 64-bit ('base': conda)", "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" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }

mit

telegraphic/allantools

examples/gradev-demo.ipynb

1

45517

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## GRADEV: gap robust allan deviation\n", "\n", "Notebook setup & package imports" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pylab as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gap robust allan deviation comparison\n", "Compute the GRADEV of a white phase noise. Compares two different\n", "scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def example1():\n", " \"\"\"\n", " Compute the GRADEV of a white phase noise. Compares two different \n", " scenarios. 1) The original data and 2) ADEV estimate with gap robust ADEV.\n", " \"\"\"\n", " N = 1000\n", " f = 1\n", " y = np.random.randn(1,N)[0,:]\n", " x = np.linspace(1,len(y),len(y))\n", " x_ax, y_ax, err_l,err_h, ns = allan.gradev(y,f,x)\n", " plt.errorbar(x_ax, y_ax,yerr=[err_l,err_h],label='GRADEV, no gaps')\n", " \n", " \n", " y[np.floor(0.4*N):np.floor(0.6*N)] = np.NaN # Simulate missing data\n", " x_ax, y_ax, err_l,err_h, ns = allan.gradev(y,f,x)\n", " plt.errorbar(x_ax, y_ax,yerr=[err_l,err_h], label='GRADEV, with gaps')\n", " plt.xscale('log')\n", " plt.yscale('log')\n", " plt.grid()\n", " plt.legend()\n", " plt.xlabel('Tau / s')\n", " plt.ylabel('Overlapping Allan deviation')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", 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= np.NaN # Simulate missing data\n", " x_ax, y_ax, err_l, err, ns = allan.gradev(y,f,x)\n", " plt.loglog(x_ax, y_ax,'g.',label=\"With gaps\")\n", " plt.grid()\n", " plt.legend()\n", " plt.xlabel('Tau / s')\n", " plt.ylabel('Overlapping Allan deviation')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXGWd7/HPN+ksJiwJS1hkCSiLQRAhoAhoEEFAEAEB\n", "2UZREGfAfUauVzSJMs7AeB1n5DIXHAVFBQICCogg0TABRdlBSRSEsMUkQAgRQpJO+nf/OKfT1ZWq\n", "7trrnKrv+/XqV7rPOXXOkzyp+vXz/J5FEYGZmVmlRrS7AGZmli8OHGZmVhUHDjMzq4oDh5mZVcWB\n", "w8zMquLAYWZmVXHgMDOzqjhwmJlZVXraXYByJI0HLgZWAXMi4sdtLpKZmZHtFsexwKyI+Djw/nYX\n", "xszMEi0NHJK+J2mxpEeKjh8mab6kxySdmx5+PfBM+v3aVpbTzMzKa3WL4zLgsMIDkkYCF6XHpwAn\n", "SXoT8CywbXpZlltGZmZdpaUfyBExF3ip6PC+wOMRsSAieoGrgKOB64DjJF0M/KyV5TQzs/KykBwv\n", 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"python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

badlands-model/BayesLands

Examples/etopo/etopoGen.ipynb

1

5162950

gpl-3.0

tonyfast/tidy-harness

harness/ext/base.ipynb

1

4259

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "try:\n", " from ..base import AttributeObject\n", "except:\n", " from harness.python.base import AttributeObject\n", "\n", "from toolz.curried import *\n", "import importlib, inspect, jinja2.ext\n", "\n", "__all__ = ['HarnessExtension']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class HarnessExtension(jinja2.ext.Extension):\n", " \"\"\"Use the jinja2 extension framework to create build Harness templates.\n", " imports - string object that can load a module.\n", " module_ - a derived object containing the main module of the extension.\n", " \"\"\"\n", " \n", " imports = None \n", " mixin = None\n", " alias = \"\"\n", " \n", " def __init__(self, environment):\n", " self.function = identity\n", " if self.imports:\n", " self.module_= importlib.import_module(self.imports)\n", "\n", " def pipe(self, dataframe, attr,):\n", " function = None\n", " try: # to access the attribute from the module\n", " function = getattr(self.module_, attr)\n", " except AttributeError: pass\n", "\n", " try: # to access the attriubte from the class superceding the module\n", " function = getattr(self, attr)\n", " except AttributeError: pass\n", "\n", " if not(function is None):\n", " if not callable(function):\n", " return function # which is a non-callable datatype\n", " value = AttributeObject(\n", " func=function, \n", " callback=partial(self.callback, dataframe),\n", " arguments=self.arguments(function),\n", " keywords=self.keywords(dataframe),\n", " )\n", " return value\n", " \n", " \n", " def callback(self, dataframe, value):\n", " \n", " return value\n", " \n", " def arguments(self, function):\n", " try:\n", " return inspect.getfullargspec(function).args\n", " except TypeError: pass\n", " return []\n", " \n", " def keywords(self, dataframe):\n", " return {}\n", " \n", " def __dir__(self):\n", " return list(\n", " concatv(\n", " super().__dir__(), \n", " dir(self.module_) if hasattr(self, 'module_') else []\n", " )\n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class JinjaExtension(HarnessExtension):\n", " alias = 'jinja2'\n", " def __init__(self, environment):\n", " super().__init__(environment)\n", " self.env = environment\n", " self.module_ = environment\n", " \n", " def callback(self, dataframe, value):\n", " if isinstance(value, jinja2.Template):\n", " value = value.render(df=dataframe)\n", " if isinstance(value, jinja2.BaseLoader):\n", " return dataframe\n", " return value\n", " \n", " def add_template(self, **kwargs):\n", " # This will break with a custom environment\n", " first(self.env.loader.loaders).mapping.update(**kwargs)\n", " return self.env.loader" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

ethen8181/machine-learning

model_selection/partial_dependence/partial_dependence.ipynb

1

342015

{ "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=\"#Partial-Dependence-Plot\" data-toc-modified-id=\"Partial-Dependence-Plot-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Partial Dependence Plot</a></span><ul class=\"toc-item\"><li><span><a href=\"#Individual-Conditional-Expectation-(ICE)-Plot\" data-toc-modified-id=\"Individual-Conditional-Expectation-(ICE)-Plot-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Individual Conditional Expectation (ICE) Plot</a></span></li><li><span><a href=\"#Implementation\" data-toc-modified-id=\"Implementation-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Implementation</a></span></li></ul></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>" ] }, { "cell_type": 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os.getcwd()\n", "os.chdir(os.path.join('..', '..', 'notebook_format'))\n", "\n", "from formats import load_style\n", "load_style(css_style = 'custom2.css', plot_style = False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ethen 2018-01-24 16:15:24 \n", "\n", "CPython 3.5.2\n", "IPython 6.2.1\n", "\n", "numpy 1.13.3\n", "pandas 0.21.1\n", "matplotlib 2.1.0\n", "sklearn 0.19.1\n" ] } ], "source": [ "os.chdir(path)\n", "\n", "# 1. magic for inline plot\n", "# 2. magic to print version\n", "# 3. magic so that the notebook will reload external python modules\n", "# 4. magic to enable retina (high resolution) plots\n", "# https://gist.github.com/minrk/3301035\n", "%matplotlib inline\n", "%load_ext watermark\n", "%load_ext autoreload\n", "%autoreload 2\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "\n", "%watermark -a 'Ethen' -d -t -v -p numpy,pandas,matplotlib,sklearn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Partial Dependence Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "During the talk, [Youtube: PyData - Random Forests Best Practices for the Business World](https://www.youtube.com/watch?v=E7VLE-U07x0&index=51&list=PLGVZCDnMOq0oqs6RTJk4zZde86DZrgnzm), one of the best practices that the speaker mentioned when using tree-based models is to check for directional relationships. When using non-linear machine learning algorithms, such as popular tree-based models random forest and gradient boosted trees, it can be hard to understand the relations between predictors and model outcome as they do not give us handy coefficients like linear-based models. For example, in terms of random forest, all we get is the feature importance. Although based on that information, we can tell which feature is significantly influencing the outcome based on the importance calculation, it does not inform us in which direction is the predictor influencing outcome. In this notebook, we'll be exploring **Partial dependence plot (PDP)**, a model agnostic technique that gives us an approximate directional influence for a given feature that was used in the model. Note much of the explanation is \"borrowed\" from the blog post at the following link, [Blog: Introducing PDPbox](https://towardsdatascience.com/introducing-pdpbox-2aa820afd312), this documentation aims to improve upon it by giving a cleaner implementation.\n", "\n", "**Partial dependence plot (PDP)** aims to visualize the marginal effect of a given predictor towards the model outcome by plotting out the average model outcome in terms of different values of the predictor. Let's first gain some intuition of how it works with a made up example. Assume we have a data set that only contains three data points and three features (A, B, C) as shown below.\n", "\n", "<img src=\"img/pd1.png\" width=\"30%\" height=\"30%\">\n", "\n", "If we wish to see how feature A is influencing the prediction Y, what PDP does is to generate a new data set as follow. (here we assume that feature A only has three unique values: A1, A2, A3)\n", "\n", "<img src=\"img/pd2.png\" width=\"30%\" height=\"30%\">\n", "\n", "We then perform the prediction as usual with this new set of data. As we can imagine, PDP would generate **num_rows * num_grid_points** (here, the number of grid point equals the number of unique values of the target feature, more on this later) number of predictions and average them for each unique value of Feature A.\n", "\n", "<img src=\"img/pd3.png\" width=\"30%\" height=\"30%\">\n", "\n", "In the end, PDP would only plot out the average predictions for each unique value of our target feature.\n", "\n", "<img src=\"img/pd4.png\" width=\"30%\" height=\"30%\">\n", "\n", "Let's now formalize this idea with some notation. The partial dependence function is defined as:\n", "\n", "$$\n", "\\begin{align}\n", "\\hat{f}_{x_S}(x_S) = E_{x_C} \\left[ f(x_S, x_C) \\right]\n", "\\end{align}\n", "$$\n", "\n", "The term $x_S$ denotes the set of features for which the partial dependence function should be plotting and $x_C$ are all other features that were used in the machine learning model $f$. In other words, if there were $p$ predictors, $S$ is a subset of our $p$ predictors, $S \\subset \\left\\{ x_1, x_2, \\ldots, x_p \\right\\}$, $C$ would be complementing $S$ such that $S \\cup C = \\left\\{x_1, x_2, \\ldots, x_p\\right\\}$. The function above is then estimated by calculating averages in the training data, which is also known as Monte Carlo method:\n", "\n", "$$\n", "\\begin{align}\n", "\\hat{f}_{x_S}(x_S) = \\frac{1}{n} \\sum_{i=1}^n f(x_S, x_{Ci})\n", "\\end{align}\n", "$$\n", "\n", "Where $\\left\\{x_{C1}, x_{C2}, \\ldots, x_{CN}\\right\\}$ are the values of $X_C$ occurring over all observations in the training data. In other words, in order to calculate the partial dependence of a given variable (or variables), the entire training set must be utilized for every set of joint values. For classification, where the machine learning model outputs probabilities, the partial dependence function displays the probability for a certain class given different values for features $x_s$, a straightforward way to handle multi-class problems is to plot one line per class." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual Conditional Expectation (ICE) Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an extension of a PDP, ICE plot visualizes the relationship between a feature and the predicted responses for each observation. While a PDP visualizes the averaged relationship between features and predicted responses, a set of ICE plots disaggregates the averaged information and visualizes an individual dependence for each observation. Hence, instead of only plotting out the average predictions, ICEbox displays all individual lines. (three lines in total in this case)\n", "\n", "<img src=\"img/pd5.png\" width=\"30%\" height=\"30%\">\n", "\n", "The authors of the [Paper: A. Goldstein, A. Kapelner, J. Bleich, E. Pitkin Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation](https://arxiv.org/abs/1309.6392) claims with everything displayed in its raw state, any interesting discovers wouldn’t be shielded because of the averaging inherented with PDP. A vivid example from the paper is shown below:\n", "\n", "<img src=\"img/pd6.png\" width=\"50%\" height=\"50%\">\n", "\n", "In this example, if we only look at the PDP in Figure b, we would think that on average, the feature X2 is not meaningfully associated with the our target response variable Y. However, if judging from the scatter plot showed in Figure a, this conclusion is plainly wrong. Now if we were to plot out the individual estimated conditional expectation curves, everything becomes more obvious.\n", "\n", "<img src=\"img/pd7.png\" width=\"30%\" height=\"30%\">\n", "\n", "After having an understand of the procedure for PDP and ICE plot, we can observe that:\n", "\n", "- PDP is a global method, it takes into account all instances and makes a statement about the global relationship of a feature with the predicted outcome.\n", "- One of the main advantage of PDP is that it can be used to interpret the result of any \"black box\" learning methods.\n", "- PDP can be quite computationally expensive when the data set becomes large.\n", "- Owing to the limitations of computer graphics, and human perception, the size of the subsets $x_S$ must be small (l ≈ 1,2,3). There are of course a large number of such subsets, but only those chosen from among the usually much smaller set of highly relevant predictors are likely to be informative.\n", "- PDP can obfuscate relationship that comes from interactions. PDPs show us how the average relationship between feature $x_S$ and $\\hat{y}$ looks like. This works well only in cases where the interactions between $x_S$ and the remaining features $x_C$ are weak. In cases where interactions do exist, the ICE plot may give a lot more insight of the underlying relationship." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll be using the [titanic dataset](https://www.kaggle.com/c/titanic/data) (details of the dataset is listed in the link) to test our implementation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dimension: (891, 12)\n", "features: Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n", " 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n", " dtype='object')\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we download the training data and store it\n", "# under the `data` directory\n", "data_dir = Path('data')\n", "data_path = data_dir / 'train.csv'\n", "data = pd.read_csv(data_path)\n", "print('dimension: ', data.shape)\n", "print('features: ', data.columns)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# some naive feature engineering\n", "data['Age'] = data['Age'].fillna(data['Age'].median())\n", "data['Embarked'] = data['Embarked'].fillna('S')\n", "data['Sex'] = data['Sex'].apply(lambda x: 1 if x == 'male' else 0)\n", "data = pd.get_dummies(data, columns = ['Embarked'])\n", "\n", "# features/columns that are used\n", "label = data['Survived']\n", "features = [\n", " 'Pclass', 'Sex',\n", " 'Age', 'SibSp',\n", " 'Parch', 'Fare',\n", " 'Embarked_C', 'Embarked_Q', 'Embarked_S']\n", "data = data[features]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " data, label, test_size = 0.2, random_state = 1234, stratify = label)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 2 important features:\n", "Fare\n", "Sex\n" ] } ], "source": [ "# fit a baseline random forest model and show its top 2 most important features\n", "rf = RandomForestClassifier(n_estimators = 50, random_state = 1234)\n", "rf.fit(X_train, y_train)\n", "\n", "print('top 2 important features:')\n", "imp_index = np.argsort(rf.feature_importances_)\n", "print(features[imp_index[-1]])\n", "print(features[imp_index[-2]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aforementioned, tree-based models lists out the top important features, but it is not clear whether they have a positive or negative impact on the result. This is where tools such as partial dependence plots can aid us communicate the results better to others." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e6ddf28>" ] }, "metadata": { "image/png": { "height": 535, "width": 944 } }, "output_type": "display_data" } ], "source": [ "from partial_dependence import PartialDependenceExplainer\n", "plt.rcParams['figure.figsize'] = 16, 9\n", "\n", "\n", "# we specify the feature name and its type to fit the partial dependence\n", "# result, after fitting the result, we can call .plot to visualize it\n", "# since this is a binary classification model, when we call the plot\n", "# method, we tell it which class are we targeting, in this case 1 means\n", "# the passenger did indeed survive (more on centered argument later)\n", "pd_explainer = PartialDependenceExplainer(estimator = rf, verbose = 0)\n", "pd_explainer.fit(data, feature_name = 'Sex', feature_type = 'cat')\n", "pd_explainer.plot(centered = False, target_class = 1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hopefully, we can agree that the partial dependence plot makes intuitive sense, as for the categorical feature `Sex`, 1 indicates that the passenger was a male. And we know that during the titanic accident, the majority of the survivors were female passenger, thus the plot is telling us male passengers will on average have around 40% chance lower of surviving when compared with female passengers. Also instead of only plotting the \"partial dependence\" plot, the plot also fills between the standard deviation range. This is essentially borrowing the idea from ICE plot that only plotting the average may obfuscate the relationship.\n", "\n", "Centered plot can be useful when we are not interested in seeing the absolute change of a predicted value, but rather the difference in prediction compared to a fixed point of the feature range." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e59ac18>" ] }, "metadata": { "image/png": { "height": 535, "width": 944 } }, "output_type": "display_data" } ], "source": [ "# centered = True is actually the default\n", "pd_explainer.plot(centered = True, target_class = 1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can perform the same process for numerical features such as `Fare`. We know that more people from the upper class survived, and people from the upper class generally have to pay more Fare to get onboard the titanic. The partial dependence plot below also depicts this trend." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e7b4e48>" ] }, "metadata": { "image/png": { "height": 535, "width": 944 } }, "output_type": "display_data" } ], "source": [ "pd_explainer.fit(data, feature_name = 'Fare', feature_type = 'num')\n", "pd_explainer.plot(target_class = 1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you prefer to create your own visualization, you can call the `results_` attribute to access the partial dependence result. And for those that are interested in the implementation details, the code can be obtained at the following [link](https://github.com/ethen8181/machine-learning/tree/master/model_selection/partial_dependence/partial_dependence.py).\n", "\n", "We'll conclude our discussion on parital dependence plot by providing a link to another blog that showcases this method's usefulness in ensuring the behavior of the new machine learning model does intuitively and logically match our intuition and does not differ significantly from a baseline model. [Blog: Using Partial Dependence to Compare Sort Algorithms](http://techblog.hotwire.com/2016/06/13/partial-dependence-compare-sort/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Blog: Introducing PDPbox](https://towardsdatascience.com/introducing-pdpbox-2aa820afd312)\n", "- [Online Book: Partial Dependence Plot (PDP)](https://christophm.github.io/interpretable-ml-book/pdp.html)\n", "- [Mathworks Documentation: plotPartialDependence](https://www.mathworks.com/help/stats/regressiontree.plotpartialdependence.html?requestedDomain=true#mw_79dadf51-f451-45a9-a801-2e9ccec37aae)\n", "- [Github: PDPbox - python partial dependence plot toolbox](https://github.com/SauceCat/PDPbox)" ] } ], "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" }, "toc": { "nav_menu": { "height": "12px", "width": "252px" }, "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": "block", "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Elucidation/ChessboardDetect

Chessboard Detect.ipynb

1

1672098

mit

eshlykov/mipt-day-after-day

statistics/hw-13/hw-13.3.ipynb

1

38520

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Теоретическое домашнее задание 13\n", "### Задача 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Используя метод линейной регрессии, постройте приближение функции $f$ многочленом третьей степени по следующим данным:_\n", "\n", "|$f$|3.9|5.0|5.7|6.5|7.1|7.6|7.8|8.1|8.4| \n", "|---|---|---|---|---|---|---|---|---|---|\n", "|$x$|4.0|5.2|6.1|7.0|7.9|8.6|8.9|9.5|9.9|" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import scipy\n", "from scipy.linalg import inv\n", "import matplotlib.pyplot\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Решение.**_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Ясно, что нам нужна модель $y=\\theta_0 + \\theta_1 x + \\theta_2 x^2 + \\theta_3 x^3$_." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = 9 # Размер выборки\n", "k = 4 # Количество параметров" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Рассмотрим отклик._" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 3.9]\n", " [ 5. ]\n", " [ 5.7]\n", " [ 6.5]\n", " [ 7.1]\n", " [ 7.6]\n", " [ 7.8]\n", " [ 8.1]\n", " [ 8.4]]\n" ] } ], "source": [ "Y = numpy.array([3.9, 5.0, 5.7, 6.5, 7.1, 7.6, 7.8, 8.1, 8.4]).reshape(n, 1)\n", "print(Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Рассмотрим регрессор._" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 4. 16. 64. ]\n", " [ 1. 5.2 27.04 140.608]\n", " [ 1. 6.1 37.21 226.981]\n", " [ 1. 7. 49. 343. ]\n", " [ 1. 7.9 62.41 493.039]\n", " [ 1. 8.6 73.96 636.056]\n", " [ 1. 8.9 79.21 704.969]\n", " [ 1. 9.5 90.25 857.375]\n", " [ 1. 9.9 98.01 970.299]]\n" ] } ], "source": [ "x = numpy.array([4.0, 5.2, 6.1, 7.0, 7.9, 8.6, 8.9, 9.5, 9.9])\n", "X = numpy.ones((n, k))\n", "X[:, 1] = x\n", "X[:, 2] = x ** 2\n", "X[:, 3] = x ** 3\n", "print(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Воспользуемся классической формулой для получения оценки._" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ -1.37289441e-01]\n", " [ 1.07154906e+00]\n", " [ -1.14849960e-02]\n", " [ -1.00145913e-03]]\n" ] } ], "source": [ "Theta = inv(X.T @ X) @ X.T @ Y\n", "print(Theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Построим график полученной функции и нанесем точки выборки._" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = numpy.linspace(3.5, 10.4, 1000)\n", "y = Theta[0] + x * Theta[1] + x ** 2 * Theta[2] + x ** 3 * Theta[3]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ihUKS2/3xcztzWe2IF/rv7ElGlZWVAm6pGPR4jUPzfCEt8ldrni+sICdoASgi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\nZ58dcLTVTetTAwkZSh4AAAAA8BVlD4B3VbMtHSx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"text/plain": [ "<matplotlib.figure.Figure at 0x18a4e0cc390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matplotlib.pyplot.figure(figsize=(20, 8))\n", "matplotlib.pyplot.plot(x, y, color='turquoise', label='Предсказание', linewidth=2.5)\n", "matplotlib.pyplot.scatter(X[:, 1], Y, s=40.0, label='Выборка', color='blue', alpha=0.5)\n", "matplotlib.pyplot.legend()\n", "matplotlib.pyplot.title('Функция $f(x)$')\n", "matplotlib.pyplot.grid()\n", "matplotlib.pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Вывод**. Кубический многочлен, полученный методом линейной регресии, отлично приближает данную функцию. По графику видно, однако, что ее может хорошо приблизить и линейный многочлен._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<font color=\"#808080\">_ Странно, что в этом задании ничего больше не требуют, но что просили, то я и сделал. Даже график построил._</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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

unlicense

zaqwes8811/micro-apps

self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/Supporting_Notebooks/Converting-Multivariate-Equations-to-Univariate.ipynb

1

8125

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <style>\n", " .output_wrapper, .output {\n", " height:auto !important;\n", " max-height:100000px; \n", " }\n", " .output_scroll {\n", " box-shadow:none !important;\n", " webkit-box-shadow:none !important;\n", " }\n", " </style>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#format the book\n", "from __future__ import division, print_function\n", "%matplotlib inline\n", "import sys\n", "sys.path.insert(0, '..')\n", "import book_format\n", "book_format.set_style()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Converting the Multivariate Equations to the Univariate Case\n", "\n", "The multivariate Kalman filter equations do not resemble the equations for the univariate filter. However, if we use one dimensional states and measurements the equations do reduce to the univariate equations. This section will provide you with a strong intuition into what the Kalman filter equations are actually doing. While reading this section is not required to understand the rest of the book, I recommend reading this section carefully as it should make the rest of the material easier to understand.\n", "\n", "Here are the multivariate equations for the prediction. \n", "\n", "$$\n", "\\begin{aligned}\n", "\\mathbf{\\bar{x}} &= \\mathbf{F x} + \\mathbf{B u} \\\\\n", "\\mathbf{\\bar{P}} &= \\mathbf{FPF}^\\mathsf{T} + \\mathbf Q\n", "\\end{aligned}\n", "$$\n", "\n", "For a univariate problem the state $\\mathbf x$ only has one variable, so it is a $1\\times 1$ matrix. Our motion $\\mathbf{u}$ is also a $1\\times 1$ matrix. Therefore, $\\mathbf{F}$ and $\\mathbf B$ must also be $1\\times 1$ matrices. That means that they are all scalars, and we can write\n", "\n", "$$\\bar{x} = Fx + Bu$$\n", "\n", "Here the variables are not bold, denoting that they are not matrices or vectors. \n", "\n", "Our state transition is simple - the next state is the same as this state, so $F=1$. The same holds for the motion transition, so, $B=1$. Thus we have\n", "\n", "$$x = x + u$$\n", "\n", "which is equivalent to the Gaussian equation from the last chapter\n", "\n", "$$ \\mu = \\mu_1+\\mu_2$$\n", "\n", "Hopefully the general process is clear, so now I will go a bit faster on the rest. We have\n", "\n", "$$\\mathbf{\\bar{P}} = \\mathbf{FPF}^\\mathsf{T} + \\mathbf Q$$\n", "\n", "Again, since our state only has one variable $\\mathbf P$ and $\\mathbf Q$ must also be $1\\times 1$ matrix, which we can treat as scalars, yielding \n", "\n", "$$\\bar{P} = FPF^\\mathsf{T} + Q$$\n", "\n", "We already know $F=1$. The transpose of a scalar is the scalar, so $F^\\mathsf{T} = 1$. This yields\n", "\n", "$$\\bar{P} = P + Q$$\n", "\n", "which is equivalent to the Gaussian equation of \n", "\n", "$$\\sigma^2 = \\sigma_1^2 + \\sigma_2^2$$\n", "\n", "This proves that the multivariate prediction equations are performing the same math as the univariate equations for the case of the dimension being 1.\n", "\n", "These are the equations for the update step:\n", "\n", "$$\n", "\\begin{aligned}\n", "\\mathbf{K}&= \\mathbf{\\bar{P}H}^\\mathsf{T} (\\mathbf{H\\bar{P}H}^\\mathsf{T} + \\mathbf R)^{-1} \\\\\n", "\\textbf{y} &= \\mathbf z - \\mathbf{H \\bar{x}}\\\\\n", "\\mathbf x&=\\mathbf{\\bar{x}} +\\mathbf{K\\textbf{y}} \\\\\n", "\\mathbf P&= (\\mathbf{I}-\\mathbf{KH})\\mathbf{\\bar{P}}\n", "\\end{aligned}\n", "$$\n", "\n", "As above, all of the matrices become scalars. $H$ defines how we convert from a position to a measurement. Both are positions, so there is no conversion, and thus $H=1$. Let's substitute in our known values and convert to scalar in one step. The inverse of a 1x1 matrix is the reciprocal of the value so we will convert the matrix inversion to division.\n", "\n", "$$\n", "\\begin{aligned}\n", "K &=\\frac{\\bar{P}}{\\bar{P} + R} \\\\\n", "y &= z - \\bar{x}\\\\\n", "x &=\\bar{x}+Ky \\\\\n", "P &= (1-K)\\bar{P}\n", "\\end{aligned}\n", "$$\n", "\n", "Before we continue with the proof, I want you to look at those equations to recognize what a simple concept these equations implement. The residual $y$ is nothing more than the measurement minus the prediction. The gain $K$ is scaled based on how certain we are about the last prediction vs how certain we are about the measurement. We choose a new state $x$ based on the old value of $x$ plus the scaled value of the residual. Finally, we update the uncertainty based on how certain we are about the measurement. Algorithmically this should sound exactly like what we did in the last chapter.\n", "\n", "Let's finish off the algebra to prove this. Recall that the univariate equations for the update step are:\n", "\n", "$$\n", "\\begin{aligned}\n", "\\mu &=\\frac{\\sigma_1^2 \\mu_2 + \\sigma_2^2 \\mu_1} {\\sigma_1^2 + \\sigma_2^2}, \\\\\n", "\\sigma^2 &= \\frac{1}{\\frac{1}{\\sigma_1^2} + \\frac{1}{\\sigma_2^2}}\n", "\\end{aligned}\n", "$$\n", "\n", "Here we will say that $\\mu_1$ is the state $x$, and $\\mu_2$ is the measurement $z$. Thus it follows that that $\\sigma_1^2$ is the state uncertainty $P$, and $\\sigma_2^2$ is the measurement noise $R$. Let's substitute those in.\n", "\n", "$$\\begin{aligned} \\mu &= \\frac{Pz + Rx}{P+R} \\\\\n", "\\sigma^2 &= \\frac{1}{\\frac{1}{P} + \\frac{1}{R}}\n", "\\end{aligned}$$\n", "\n", "I will handle $\\mu$ first. The corresponding equation in the multivariate case is\n", "\n", "$$\n", "\\begin{aligned}\n", "x &= x + Ky \\\\\n", "&= x + \\frac{P}{P+R}(z-x) \\\\\n", "&= \\frac{P+R}{P+R}x + \\frac{Pz - Px}{P+R} \\\\\n", "&= \\frac{Px + Rx + Pz - Px}{P+R} \\\\\n", "&= \\frac{Pz + Rx}{P+R}\n", "\\end{aligned}\n", "$$\n", "\n", "Now let's look at $\\sigma^2$. The corresponding equation in the multivariate case is\n", "\n", "$$ \n", "\\begin{aligned}\n", "P &= (1-K)P \\\\\n", "&= (1-\\frac{P}{P+R})P \\\\\n", "&= (\\frac{P+R}{P+R}-\\frac{P}{P+R})P \\\\\n", "&= (\\frac{P+R-P}{P+R})P \\\\\n", "&= \\frac{RP}{P+R}\\\\\n", "&= \\frac{1}{\\frac{P+R}{RP}}\\\\\n", "&= \\frac{1}{\\frac{R}{RP} + \\frac{P}{RP}} \\\\\n", "&= \\frac{1}{\\frac{1}{P} + \\frac{1}{R}}\n", "\\quad\\blacksquare\n", "\\end{aligned}\n", "$$\n", "\n", "We have proven that the multivariate equations are equivalent to the univariate equations when we only have one state variable. I'll close this section by recognizing one quibble - I hand waved my assertion that $H=1$ and $F=1$. In general we know this is not true. For example, a digital thermometer may provide measurement in volts, and we need to convert that to temperature, and we use $H$ to do that conversion. I left that issue out to keep the explanation as simple and streamlined as possible. It is very straightforward to add that generalization to the equations above, redo the algebra, and still have the same results.\\\\\\" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

idemello/idemello.github.io

proj2.ipynb

1

1654031

mit

wcmckee/wcmckee-notebook

sortbooks.ipynb

1

11660

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"import os" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "listkindy = list(os.listdir(os.getcwd()))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "os.listdir(os.getcwd)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "coercing to Unicode: need string or buffer, builtin_function_or_method found", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-7-277901f21ef6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: coercing to Unicode: need string or buffer, builtin_function_or_method found" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "gdir = ('/home/public/github/')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "os.listdir('/home/public/github/')" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "OSError", "evalue": "[Errno 2] No such file or directory: '/home/public/github/'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-ee89d493c687>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/home/public/github/'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mOSError\u001b[0m: [Errno 2] No such file or directory: '/home/public/github/'" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "dabook = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "os.mkdir('notebooks')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "import shutil" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "os.getcwd()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "'/var/www/wcmckee-notebook'" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "for filz in os.listdir(gdir):\n", " print filz\n", " for fila in os.listdir(gdir + filz):\n", " print fila\n", " if 'ipynb' in fila:\n", " dabook.append(os.getcwd() + '/' + filz + '/' + fila)\n", " # shutil.copy(fila, '/home/public/notebooks')\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "ename": "OSError", "evalue": "[Errno 2] No such file or directory: '/home/public/github/'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-390feb717b63>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mfilz\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgdir\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[1;32mprint\u001b[0m \u001b[0mfilz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfila\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgdir\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfilz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mfila\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'ipynb'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfila\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mOSError\u001b[0m: [Errno 2] No such file or directory: '/home/public/github/'" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "#for da in dabook:\n", "# if 'checkpoints' or '.html' in da:\n", "# dabook.remove(da) # removes too many, filter so it only \n", " #removes files i don't want. if they dont end\n", " #with .ipynb, i dont want to see them. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "dabook" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "[]" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

zipeiyang/liupengyuan.github.io

chapter2/homework/computer/5-17/201611680890-5.17.ipynb

13

4450

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "\n", "def win():\n", " print(\n", " '''\n", " ======恭喜你，你赢了=======\n", " \n", " \n", " .\"\". .\"\",\n", " | | / /\n", " | | / /\n", " | | / /\n", " | |/ ;-._ \n", " } ` _/ / ;\n", " | /` ) / /\n", " | / /_/\\_/\\\n", " |/ / |\n", " ( ' \\ '- |\n", " \\ `. /\n", " | |\n", " | |\n", " \n", " ======恭喜你，你赢了=======\n", " '''\n", " )\n", " return\n", " \n", "def lose():\n", " print(\n", " '''\n", " ======YOU LOSE=======\n", " \n", " \n", " \n", "\n", " .-\" \"-.\n", " / \\\n", " | |\n", " |, .-. .-. ,|\n", " | )(__/ \\__)( |\n", " |/ /\\ \\|\n", " (@_ (_ ^^ _)\n", " _ ) \\_______\\__|IIIIII|__/__________________________\n", " (_)@8@8{}<________|-\\IIIIII/-|___________________________>\n", " )_/ \\ /\n", " (@ `--------`\n", " \n", " \n", " \n", " ======YOU LOSE=======\n", " '''\n", " )\n", " return\n", " \n", "def get_ch_table(line):\n", " ch_table = []\n", " for ch in line:\n", " if ch not in ch_table:\n", " ch_table.append(ch)\n", " return ch_table\n", "\n", "def idiom_robot(file_name):\n", " with open(file_name) as fh:\n", " text = fh.read()\n", " idioms = text.split()\n", " idiom = random.choice(idioms)\n", " chs = get_ch_table(text.replace(r'\\n', ''))\n", "\n", " guess_ch_table = [ch for ch in idiom]\n", " while len(guess_ch_table) < 6:\n", " ch = random.choice(chs)\n", " if ch not in guess_ch_table:\n", " guess_ch_table.append(ch)\n", " \n", " random.shuffle(guess_ch_table)\n", " \n", " for i in range(0,6,2):\n", " print(guess_ch_table[i], guess_ch_table[i+1])\n", " \n", " return idiom\n", "\n", "def main():\n", " filename = r'd:\\temp\\idioms_correct.txt'\n", " score = 10\n", " while score >= 0:\n", " real_idiom = idiom_robot(filename)\n", " answer_idiom = input('请输入猜测成语，回车结束，直接回车表示退出游戏：')\n", " if answer_idiom == real_idiom:\n", " print('答对了，加十分')\n", " score += 10\n", " print('你当前的分数是：', score)\n", " if score == 100:\n", " win()\n", " return\n", " elif answer_idiom == '':\n", " print('退出游戏。')\n", " print('你最后的分数是：', score)\n", " return\n", " else:\n", " score -= 10\n", " print('答错了，减十分')\n", " print('成语其实是：', real_idiom)\n", " print('你当前的分数是：', score)\n", " else:\n", " lose()\n", " return\n", "\n", "if __name__ == '__main()__':\n", " main()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ecabreragranado/OpticaFisicaII

OCT/OCT.ipynb

1

12016

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Tomografía de coherencia óptica. Puntos básicos." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "*La tomografía de coherencia óptica (TCO) es una técnica de imagen tomográfica óptica, no invasiva e interferométrica, que ofrece una penetración de milímetros (aproximadamente 2-3 mm en el tejido o material de que se trate) con resolución axial y lateral de escala micrométrica. La técnica fue demostrada por primera vez en 1991 con una resolución axial de ~30µm. En 2001 la TCO alcanzó una resolución submicrométrica debido a la introducción de fuentes de luz de banda amplia (fuentes que emiten longitudes de onda sobre un rango de ~100 nm). Ahora la TCO es una técnica de imagen ampliamente aceptada, especialmente en oftalmología, otras aplicaciones biomédicas, y la conservación de obras de arte.*\n", "\n", "Esta es la definición que [aparece en la Wikipedia](https://es.wikipedia.org/wiki/Tomograf%C3%ADa_de_coherencia_%C3%B3ptica), pero ¿en qué consiste?. ¿Y en qué se relaciona con lo estudiado durante el curso hasta ahora?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Interferencia de baja coherencia" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Cuando hemos estudiado la interferencia entre dos haces, hemos hecho hincapié en que para que poder visualizarla la diferencia de camino $\\Delta$ entre los dos haces que interfieren, ha de ser menor que la longitud de coherencia $l_c$ de la radiación. En caso contrario, el desfase relativo entre las dos ondas varía aleatoriamente y el promedio temporal en el tiempo de observación es nulo. Hasta ahora hemos visto una longitud de coherencia pequeña como un factor limitante en experimentos de interferometría. Sin embargo, puede ser un elemento que podemos utilizar para alcanzar una gran resolución en nuestras medidas.\n", "\n", "Antes de ver la interferencia de baja coherencia primeramente hay que recordar la relación entre la longitud de coherencia y la anchura espectral de la fuente. Pulsos de luz muy anchos espectralmente poseen una longitud de coherencia pequeña, mientras que el caso límite de radiación monocromática tendría una longitud de coherencia infinita. En particular, la relación entre ambas magnitudes es la siguiente:\n", "\n", "$$ l_c = \\frac{\\lambda^2 }{\\Delta \\lambda}$$\n", "\n", "Se observa así que cuando hablamos de interferencia con radiación de baja coherencia, hablamos igualmente de interferencia con fuentes de espectro ancho, como pueden ser diodos superluminiscentes o pulsos láser de fs.\n", "\n", "\n", "En la interferometría de baja coherencia, efectivamente la luz utilizada posee una longitud de coherencia pequeña, del orden de micras o incluso menor. Esto tiene como consecuencia que si construimos un interferómetro utilizando esta radiación, la diferencia de camino entre los haces que interfieren ha de ser como máximo de ese orden para observar dicha interferencia. O dicho de otro modo, cuando obtenemos interferencia sabemos que la diferencia de camino entre los dos haces es del orden de micras o menor. Por tanto, podemos utilizar este método para medir de forma muy precisa distancias.\n", "\n", "\n", "Además, utilizando radiación en la región del infrarrojo ($\\sim 800$ nm) la longitud de penetración en los medios biológicos es del orden de mm. En concreto, en el caso del ojo se consigue penetrar hasta la retina ya que esta radiación no es absorbida por el agua presente en la córnea y en los humores acuoso y vítreo." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### ¿En qué consiste?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "El principio y montaje para la tomografía de coherencia óptica se puede observar en la siguiente figura:\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Como se observa en la figura, la radiación con una longitud de coherencia baja (ancho espectro) ilumina un interferómetro de Michelson, en donde en uno de sus brazos colocamos la muestra (o el ojo) y en el otro un espejo móvil. Las diferencias de camino entre el haz reflejado en el espejo y cada uno de los haces reflejados en las superficies de la muestra serán:\n", "\n", "$$\\Delta_1 = 2 z - 2 H$$\n", "\n", "$$\\Delta_2 = 2 z - 2 (H+ n L_1)$$\n", "\n", "$$\\Delta_3 = 2 z - 2 (H + n L_2)$$\n", "\n", "donde se ha supuesto que $z$ es la distancia entre el espejo móvil y el divisor de haz, $H$ la distancia entre la primera superficie de la muestra y el divisor, $n$ el índice de refracción dentro de la muestra, y $L_{1,2}$ las distancias de las superficies maś alejadas de la muestra con respecto a la primera.\n", "\n", "El movimiento de este espejo permitirá igualar los caminos ópticos de los haces reflejados en el espejo móvil y en cada una de las superficies de las que consta la muestra. La irradiancia medida por el detector en función del desplazamiento del espejo se puede apreciar en la figura superior derecha. Se puede observar que aparecen, en el ejemplo mostrado, 3 zonas de interferencia, con máximos y mínimos, correspondientes a cada una de las 3 superficies de las que consta la muestra. La altura de cada zona depende de la reflectancia de esa superficie mientras que la anchura es proporcional a la longitud de coherencia de la fuente.\n", "\n", "Si pensamos en que movemos el espejo desde una zona cercana al divisor de haz del interferómetro, veríamos primero que al mover el espejo la irradiancia se mantiene constante. Esto es debido a que el camino óptico recorrido por el haz reflejado en el espejo es muy diferente a los caminos ópticos de los haces reflejados por cualquiera de las 3 superficies de la muestra, y por tanto no vemos interferencia ($I_{total} = I_1 + I_2$). \n", "\n", "Al seguir moviendo el espejo, llegará un punto en el que la diferencia de camino entre el haz reflejado en el espejo y el haz reflejado por la primera superficie de la muestra sea menor que $l_c$. Entonces veremos interferencia y la irradiancia detectada oscilará entre unos valores máximos y mínimos a medida que movemos el espejo. \n", "\n", "Si continuamos moviendo el espejo alejándonos del divisor de haz, nos saldremos de la zona en la que la diferencia de camino entre el haz reflejado en el espejo y el haz reflejado por la primera superficie de la muestra es menor que $l_c$, volviendo a una irradiancia constante. Un posterior movimiento del espejo provocará que se cumpla esta condición (diferencia de camino menor que $l_c$) pero entre el haz reflejado por el espejo y el reflejado por la segunda superficie. Entonces veremos la segunda zona de interferencia en la señal captada por el detector. Lo mismo ocurrirá con la tercera superficie." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "----\n", "\n", "Veamos un ejemplo numérico en donde definimos dos trenes de pulsos con una diferencia de camino que depende de su posición" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Figura para visualizar la interferencia en OCT" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function __main__.oct>" ] }, "execution_count": 4, "metadata": { }, "output_type": "execute_result" }, { "data": { "image/png": "011e5515ded4db60bd77910d366510367d4a8b59" } } ], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "\n", "%matplotlib inline\n", "from scipy.interpolate import interp1d\n", "import ipywidgets as widg\n", "\n", "num_pulsos = 2\n", "c0 = 3e8\n", "twidth = 2e-14\n", "t = np.linspace(0,twidth*25,15000)\n", "t01 = np.linspace(2,5,num_pulsos)*4*twidth\n", "A01 = np.linspace(10,4,num_pulsos)\n", "t02 = [8*twidth]\n", "Lambda = 9.5e-7\n", "wpulse = 2*np.pi*c0/Lambda\n", "k = 2.0*np.pi/Lambda\n", "\n", "z = 3.2*Lambda #lo que se mueve un espejo. Luego, en tren2, vendrá multiplicado por 2\n", "tren1 = np.sum(np.asarray([A01[i]*np.exp(-((t-t01[i])/twidth)**2)*np.exp(1.0j*(-wpulse*t)) \n", " for i in range(len(t01))]),axis=0)\n", "zshow = np.linspace(0,c0*twidth*10,900)\n", "\n", "int_array = np.zeros(np.shape(zshow))\n", "dt = t[1]-t[0]\n", "t_int = t[-1]\n", "i1 = np.sum(np.real(tren1)**2)*dt/t_int #+ np.sum(np.real(tren2)**2)*dt/t_int)\n", "print(i1*2) # i1 + i2\n", "for i,zz in enumerate(zshow):\n", " tren2 = np.sum(np.asarray([np.exp(-((t-(t00 + 2*zz/c0 - twidth*4.5))/twidth)**2)*np.exp(1.0j*(k*2*zz-wpulse*t))\n", " for t00 in t02]),axis=0)\n", " #Interferencia\n", " etotal = tren1+tren2\n", " int_array[i] = np.sum(np.real(etotal)**2)*dt/t_int\n", "fun_int = interp1d(zshow,int_array)\n", "\n", "def oct(z,zshow,int_array,tren1,fun_int):\n", " z=z*1e-6\n", " tren2 = np.sum(np.asarray([A01[0]*np.exp(-((t-(t00 + 2*z/c0 - twidth*4.5))/twidth)**2)*np.exp(1.0j*(k*2*z-wpulse*t))\n", " for t00 in t02]),axis=0)\n", " fig, ax = plt.subplots(1,3,figsize=(21,6))\n", " ax[0].plot(t*1e15,np.real(tren1),t*1e15,np.real(tren2))\n", " ax[0].set_title('Solapamiento temporal pulsos')\n", " ax[0].set_xlabel('t (fs)')\n", " ax[0].set_ylabel('E (u.a)')\n", " ax[1].plot(zshow*1e6,int_array)\n", " ax[1].set_title('Solapamiento temporal pulsos')\n", " ax[1].set_xlabel(r'z ($\\mu$m)')\n", " ax[1].set_ylabel('I (u.a)')\n", " ax[2].plot(z*1e6,fun_int(z),'go',alpha=0.5)\n", " ax[2].plot(zshow*1e6,int_array)\n", " ax[2].set_xlim(0,25)\n", " ax[2].set_title('Zoom')\n", " ax[2].set_xlabel(r'z ($\\mu$m)')\n", " ax[2].set_ylabel('I (u.a)')\n", " \n", "\n", "widg.interact(oct,z=(0,25,1),zshow=widg.fixed(zshow),int_array=widg.fixed(int_array),tren1=widg.fixed(tren1),fun_int=widg.fixed(fun_int))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "**De la figura**\n", "\n", "* Relacionar la interfranja con la longitud de onda (Nota: $Int$ = 2 $\\lambda$) \n", "\n", "* Relacionar anchura de la zona de interferencia con l$_c$\n", "\n", "* Relacionar valor cte con i1 + i2 + i3\n", "\n", "* ¿Por qué la segunda zona tiene un valor menor? " ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] } ], "metadata": { "kernelspec": { "display_name": "T - Python 3 (Ubuntu Linux)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

dsevilla/bdge

pig-hive/hive.ipynb

1

1606

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NoSQL (Hive)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install apache-airflow[async]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install ppextensions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install --upgrade pip\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install pyhive[hive]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext ppextensions.ppmagics" ] }, { "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

MMaus/mutils

mmnotebooks/old/AnkleSlip - temporary NB.ipynb

1

86853

{ "metadata": { "name": "AnkleSlip - temporary NB" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## load data from temporary file" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import mutils.io as mio\n", "dat = mio.mload('tmp.dat')\n", "sdt_kin_r = dat['sdt_kin_r']\n", "sdt_kin_r_noIC = dat['sdt_kin_r_noIC']\n", "sdt_param_r = dat['sdt_param_r']\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "## TESTING - DELETE ME!\n", "#sdt_kin_r.shape\n", "s_kin_reord_r = hstack([sdt_kin_r[:, [0,20,21]], sdt_kin_r_noIC])\n", "p1 = dot(sdt_param_r.T, pinv(s_kin_reord_r.T))\n", "# alternatively, compute predictors using the lstsq-function\n", "#p2, _,_, _ = lstsq(sdt_kin_r, sdt_param_r, )\n", "#p2 = p2.T\n", "#allclose(p1, p2) # results in \"True\"\n", "\n", "u,s,v = svd(p1, full_matrices = False)\n", "# the rows of v span the space where our features live" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "qv,rv = qr(v) # rv spans the same subspace as v\n", "# TEST:\n", "#prv = dot(rv.T, rv) # projector on \"rv\" subspace\n", "#print allclose(prv, dot(prv, prv)) # True -> is a projector\n", "#print allclose(dot(prv, v.T), v.T) # True -> projects into the same subspace as v\n", "rv2 = rv.copy()\n", "# make \"identity\" 3x3 block\n", "rv2[1,:] -= rv2[2, :] * rv2[1,2] / rv2[2,2]\n", "rv2[0,:] -= rv2[1, :] * rv2[0,1] / rv2[1,1]\n", "rv2[0,:] -= rv2[2, :] * rv2[0,2] / rv2[2,2]\n", "\n", "# projector on last two rows (-space), and projector to orth. kompl.\n", "p2 = dot(rv2[3:,:].T, rv2[3:, :])\n", "ap = eye(41) - p2\n", "\n", "# can we remove anything from the first 3 components?\n", "rv3 = rv2.copy()\n", "rv3[:3,:] = dot(ap,rv3[:3,:].T).T\n", "print \"are data unchanged?\", allclose(rv2, rv3)\n", "\n", "# visualize factors\n", "figure()\n", "pcolor(rv2)\n", "colorbar()\n", "clim(-1,1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "are data unchanged? True\n" ] }, { "output_type": "display_data", "png": 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} ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# alternative approach:\n", "\n", "* first: compute everything that can be predicted using CoM state information\n", "* secondly: compute two \"best\" remaining predictors\n", "* (third): analyze these predictors" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# this variant does not incorporate bootstrap!\n", "#dat = mio.mload('tmp.dat')\n", "#sdt_kin_r = dat['sdt_kin_r']\n", "#sdt_kin_r_noIC = dat['sdt_kin_r_noIC']\n", "#sdt_param_r = dat['sdt_param_r']\n", "#s_kin_reord_r\n", "s_kin_CoM = s_kin_reord_r[:, :3]\n", "s_kin_noCoM = s_kin_reord_r[:, 3:]\n", "\n", "p_c = dot(sdt_param_r.T, pinv(s_kin_CoM.T)) # predictor matrix for CoM state\n", "\n", "prediction_c = dot(p_c, s_kin_CoM.T).T\n", "remainder = sdt_param_r - prediction_c\n", "\n", "# compute the annihilator matrix for prediction_c\n", "# this \n", "#M = dot(p_c, pinv(p_c)) # projector on columnspace of p_c\n", "#remainder2 = dot((eye(5) - M), sdt_param_r.T).T\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "# TODO: use constrained least squares!\n", "# Here: use the version from wikipedia :)\n", "Y = sdt_param_r\n", "X = s_kin_reord_r\n", "print Y.shape, X.shape\n", "beta = dot(pinv(X), Y)\n", "# formulate constraint in this syntax:\n", "# H0: Q.T beta = c\n", "Q = vstack([zeros(3,3), one((38,3))])\n", "print dot(Q.T, beta)\n", "betaC = 0\n", "#beta1 = hstack([eye(3), zeros(3,38)])\n", "#beta2 = " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1957, 5) (1957, 41)\n", "[[-0.07578836 0.1006066 0.65345936 0.18273519 -0.46722666]\n", " [ 0.15721321 0.05710575 -0.1216022 -0.40659151 0.13378005]\n", " [ 0.06111183 -0.12740712 0.11897112 0.04168046 -0.0549717 ]]\n" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "import mutils.statistics as st\n", "figure(12, figsize=(16,8))\n", "clf()\n", "fac_vars = st.find_factors(s_kin_noCoM.T, remainder.T)\n", "facs = st.find_factors(s_kin_noCoM.T, remainder.T, 2)\n", "subplot(2,1,1)\n", "plot(fac_vars, 'kd--')\n", "title('how much of the predictable variance\\ncan be predicted using k factors')\n", "xlabel('# of factors')\n", "ylabel('relative predictable variance')\n", "ylim(0,1)\n", "gca().set_xticks(arange(5))\n", "gca().set_xticklabels(arange(5) + 1)\n", "subplot(2,1,2)\n", "pcolor(facs.T), colorbar(), clim(-1,1)\n", "noCoM_lbls = ['r_anl_y - com_y', 'r_anl_x - com_x', 'r_mtv_z - r_anl_z', 'r_mtv_x - r_anl_x', 'r_kne_y - com_y',\n", " 'r_elb_y - com_y', 'r_elb_x - com_x', 'r_wrl_z - com_z', 'r_wrl_x - com_x', 'cvii_y - com_y',\n", " 'l_anl_y - com_y', 'l_anl_x - com_x', 'l_mtv_z - l_anl_z', 'l_mtv_x - l_anl_x', 'l_kne_y - com_y',\n", " 'l_elb_y - com_y', 'l_elb_x - com_x', 'l_wrl_z - com_z', 'l_wrl_x - com_x', ]\n", "all_lbls = noCoM_lbls + ['v_' + x for x in noCoM_lbls]\n", "gca().set_yticks([.5, 1.5])\n", "gca().set_xticks(arange(len(all_lbls)) + .5)\n", "_ = gca().set_xticklabels(all_lbls, rotation=90)\n", "title('weight on factors')\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 51, "text": [ "<matplotlib.text.Text at 0x9e4a0d0>" ] }, { "output_type": "display_data", "png": 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ev//977rrrrts6idPnqzt27fb1C9YsED33HOPTf0f/vAHJSQk2NT/4x//UGRk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\ntaILFy4w6+Tl5dGaNWto4MCBN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} ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

pycrystem/pycrystem

doc/demos/02 GaAs Nanowire - Phase Mapping - Orientation Mapping.ipynb

1

198354

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phase/Orientation Mapping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial demonstrates how to achieve phase and orientation mapping via scanning electron diffraction using both pattern and vector matching." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data was acquired from a GaAs nanowire displaying polymorphism between zinc blende and wurtzite structures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This functionaility has been checked to run in pyxem-0.13.0 (Feb 2021). Bugs are always possible, do not trust the code blindly, and if you experience any issues please report them here: https://github.com/pyxem/pyxem-demos/issues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. <a href='#loa'> Load & Inspect Data</a>\n", "2. <a href='#pre'> Pre-processing</a>\n", "3. <a href='#tem'> Template matching</a>\n", " 1. <a href='#tema'> [Build Template Library]</a>\n", " 2. <a href='#temb'>[Indexing]</a>\n", "4. <a href='#vec'> Vector Matching</a>\n", " 1. <a href='#veca'> [Build Vector Library]</a>\n", " 2. <a href='#vecb'>[Indexing Vectors]</a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import pyxem and other required libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:hyperspy.api:The ipywidgets GUI elements are not available, probably because the hyperspy_gui_ipywidgets package is not installed.\n", "WARNING:hyperspy_gui_traitsui:The module://ipykernel.pylab.backend_inline matplotlib backend is not supported by the installed traitsui version and the ETS toolkit has been set to null. To set the ETS toolkit independently from the matplotlib backend, set it before importing matplotlib. See http://hyperspy.readthedocs.io/en/stable/user_guide/getting_started.html for more information.\n", "WARNING:hyperspy_gui_traitsui:The traitsui GUI elements are not available.\n", "WARNING:silx.opencl.common:Unable to import pyOpenCl. Please install it from: http://pypi.python.org/pypi/pyopencl\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import diffpy.structure\n", "import pyxem as pxm\n", "import hyperspy.api as hs\n", "\n", "accelarating_voltage = 200 # kV\n", "camera_length = 0.2 # m\n", "diffraction_calibration = 0.032 # px / Angstrom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='loa'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Loading and Inspection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the demo data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ElectronDiffraction2D, title: GaAs NW (110 zone), dimensions: (20, 45|144, 144)>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp = hs.load('./data/02/polymorphic_nanowire.hdf5')\n", "dp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set data type, scale intensity range and set calibration" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dp.data = dp.data.astype('float64')\n", "dp.data *= 1 / dp.data.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect metadata" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details open>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " Acquisition_instrument\n", " </li></summary>\n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details closed>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " TEM\n", " </li></summary>\n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details closed>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " Detector\n", " </li></summary>\n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details closed>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " Diffraction\n", " </li></summary>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>camera_length = 10.0</li></ul>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>exposure_time = 10.0</li></ul>\n", " </details></ul></details></ul>\n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>rocking_angle = 12.217304763960307</li></ul>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>rocking_frequency = 100.0</li></ul>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>scan_rotation = 1.0</li></ul>\n", " </details></ul></details></ul><ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details open>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " General\n", " </li></summary>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>title = GaAs NW (110 zone)</li></ul>\n", " </details></ul><ul style=\"margin: 0px; list-style-position: outside;\">\n", " <details open>\n", " <summary style=\"display: list-item;\">\n", " <li style=\"display: inline;\">\n", " Signal\n", " </li></summary>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>binned = False</li></ul>\n", " \n", " <ul style=\"margin: 0px; list-style-position: outside;\">\n", " <li style='margin-left:1em; padding-left: 0.5em'>signal_type = electron_diffraction</li></ul>\n", " </details></ul>" ], "text/plain": [ "├── Acquisition_instrument\n", "│ └── TEM\n", "│ ├── Detector\n", "│ │ └── Diffraction\n", "│ │ ├── camera_length = 10.0\n", "│ │ └── exposure_time = 10.0\n", "│ ├── rocking_angle = 12.217304763960307\n", "│ ├── rocking_frequency = 100.0\n", "│ └── scan_rotation = 1.0\n", "├── General\n", "│ └── title = GaAs NW (110 zone)\n", "└── Signal\n", " ├── binned = False\n", " └── signal_type = electron_diffraction" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp.metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot an interactive virtual image to inspect data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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yYKz/h4jsINo0Kncnt1bAq4XaTbxBOg4Ud9ojTpI9C1iqqufF7Y8h5xJPRHYCneIs83Vz9IXAM6r6gzioMzQnkHMU8PVCbbtBOqmgyPOQQ4n2iywTkYos9Xeq6l4bDRVCVRfH16cVk68jNNp7JogbpOPkoar3E20NUaiMaTuq2j7v9dVEW0hUCTdIJxVkZaWOG6STCtwgE8K7775r6o0bNzb1Hj16mPpTTz1l6qH1nlY2ggo2bbL3W92+3drUKrz2dfPmzaYeyuPasWNHUx80KH9D5IhQNoTTTjvN1GvVCk9Ld+6818bJALzzzjumfsYZZ5h6oe/VSYFBOo7fD+k4To3gHtJJBVnxkG6QTirIikH6kNVxEkTiPeRXvvIVUw/tcjVmzBhTD+269NJL5pb33HDDDcE+DR8+3NR79epl6qHsBqE+zZw509RD38Wzzz5r6uXl5aYe8jbt27c3dQivKS4pKTH10Praiy66KNhGIbLiIRNvkI4D2TFIH7I6ToJwD+kkHp+HdBynRnAP6aSCrHjIxBvktm3bTH3SpEmm3qdPH1MP3SXfqFEjUy+UP/Saa64x9YkTJ5p6aWmpqYd2udq6daupjxo1ytRDu26F9FDGg9B3BNCvXz9TD61l3b17t6mH1gFXRlYM0oesjpMgEu8hHQey4yHdIJ1UkBWD9CGr4yQI95BO4snSPGTiDTIUESwrKzP10PrNUE7TUKQztD4UoEGDBvvUp1WrVpl6KPoaymLQrVs3U1+9erWpN2zY0NRDP+5jjrFyAEeE1u/+8Ic/NPXJkyebeuj/40Qk3iAdB7JzDekG6aSCrBikB3UcJ0G4h3RSQVY8pBukkwqyYpA+ZHWcBJF4D9mzZ09TD6W5CCUHrl27tqmHFlS3bt062Ke1a9ea+o4dO0y9efPmph5aaB3qU2jrt9C2dtOmTTP1hQsXmnqh9Bp16tg/lWXLlpl63bp1TT30HRUiS/OQ7iEdJ0Ek3kM6DmTnGtIN0kkFWTFIH7I6ToJwD+mkgqx4yMQbZN++fU19+vTppt6qVStTD6UCCSUfPv3004N9CkVHv/51e/v4ESNGmPqWLVtM/eyzzzb1ULLiI4880tRD292FtuwL1Q/w7W9/29TnzJlj6k2bNjX1UBR88ODBwbazROIN0nHAPaTjJAafh3Qcp0ZwD+mkgqx4SDdIJxW4QSaEu+++29RPOeUUU583b56pn3POOaYeStVx1113BfsUSqWxc+dOU+/QoYOph/par149U7/99ttN/dxzzzX1UPqTs846y9Rr1QpfwYSSN4fWuIbSmRx77LHBNpwUGKTjQHY8pAd1HCdBuId0UkFWPKQbpJN4fB7ScZwaIfEe8uCDDzb1devW7ZP+/PPPm3poO7rQulSAgQMHmvq9995r6p06dTL17t27m/q4ceNM/aGHHjL1UAaAdu3amXooYrphwwZTBxg9erSp//jHPzb1BQsWmHoow0BlZMVDJt4gHQeyY5A+ZHWcBOEe0kkF7iEdxyk67iGdVJAVD5l4gxQRU2/WrJmpl5SUmHpoXefGjRtNvUWLFsE+PfDAA6Yeigg//PDDph66q75Xr16m/sEHH5h6KNdp6Lt7+eWXTT30nRZqY+jQoaZ+xx13mHro/1MIn4d0HKdGSLyHdBzwIavjJIqsGKQPWR0nQbiHdFJBVjxk4g0yFCl84YUXTD20Q1QoanrUUUeZ+uLFi4N9CkVTQ3Vdcsklpv7444+beijXab9+/Uw9tE43lNN2yJAhpv7kk0+aOsB3vvMdU589e7ap/+Y3vzH1jh07mvpVV10VbDtLJN4gHQfcQzpOYvB5SMdxagT3kE4qyIqHdIN0UoEbZEJ4++23Tf1nP/uZqQ8fPtzUQxHEUH7Xk046KdinN99809RDP5p//OMfpt64cWNT79Onj6mHctQ2bNjQ1EM5ZwcNGmTqofyxAGvXrjX10BrXUF2h3LVOROIN0nEgOx7SgzqOkyDcIJ3EUzHtUV2PyhCRniLyiYiUi0iZiDydd/w5EVEROTp+/d8isi0uu1FELsop+yMR2R4/XqqsbTdIJxUU0yCBcuB6VS0BjgQGiMgAiIwV6AXsyik/AzhGVesDvwIeicvWBW4FzgQOAk6rqCeEG6Tj5KGqM1R1VPz8Y2AtUJHLcwxwXV75h1R1SfzyUaA0fn4VsEFVJ6jqFmACcH2hthMf1OnSpYuphyKOPXv2NPUePXqYenl5ualPmTIl2KclS5aYemhnp48//tjUQ1HWZ555xtRPPvlkUw9FLkO5ZWfNmmXqY8eONXUI7/gVigiH1uMOGzYs2EYhqjmo01ZEtuS8HqOql1sFRaQ30AwYKSJ3AKtV9UkRsRciw++BiqS0HYA1OceWEHnXIIk3SMeBajfIJap6QmWFRKQFMBa4BygDvgccU6D8jcBZQOfP2zEfsjqOgYiUArOBV1T1ZuBU4ADgfRHZCdQG5ohIl7j8hcAvgQGqWpFKfh5wSE61bQE7uVOMG6STCoocZRVgFrBUVc+L2x+jqrVUtY6q1iEK6nRS1XdF5CTgcaJAUO4qkBFAYxHpIyINiIz6j4XadoN0nL0ZCrQDOsbTGdtE5JYC5YcTXf79Ni67BUBVy4GfA68C64FJqvpcoYb9GtJJPMW+/UpV7wfur6RMnZzn9l3X0bHbgNuq2nbiDTKUMeD73/++qY8fP97UQ3eqT5s2zdRDO1YBtG7d2tSXLl1q6pdeeqmp169f39RDkeXJkyebeiiyHMpFW1ZWZuqFIqChz3bYYYeZemiHsPvuu8/Uzz///GDb4EvnHMepARLvIR0HsuMh3SCdVJAVg/Qhq+MkCPeQTirIiodMvEGG1oe2bNlyn/Tp06ebeiiiOXPmzGCfvva1r5l6aFep3bt3m3poTWxozWporemHH35o6qEo7gUXXGDqc+fONXUI51+tXbu2qa9bt87Uf/jDHwbbcFJgkI6TpTSQbpBOKsiKQXpQx3EShHtIJxVkxUO6QTqpwA0yIYQihb/61a9MffDgwaYeilDOnz/f1Ddu3Bjs0+jRo009tE5z1apVph6Kpr766qum3qpVK1P/6le/auqhvKyhqG8oMgpw/fV25ol77rnH1EO5YkPZEM4+++xg21mi6AYpIgcBrYBtRHdu278Ox8nBPWQ1IiKNiZL7XALUA1YD9YEWIjIF+KOqvlaMvjhOkimWh3yK6O7pPqq6PveAiBwPXC4i7VT14SL1x0kRPg9ZzajqmQWOTQPsmxIdJ8YNcj8hIl2Jkv3k3nE9ptj9cJwkUlSDFJG/AF2JsnlVBHOUKPmsyYQJE0y9a9eupv7iiy+G2jb10Jk3tKsTwKmnnmrqM2bMMPXQetkPPvjA1Js0aWLqoWjqo48+aur33nuvqY8bN87UQ2trAX7yk5+Yeui7OPzww01969atwTYK4R5y/3CSqoZzYzhOgKwYZLGXzr0lIm6QjhOg2B5yBJFRriDa0EQAVVV7/Ok4MVnxkMU2yIeBy4F3+fQa0nGcmGIb5OrKEsU6Tj4+D7n/mC4io4DniYasQOFpj9AuSv369TP11atXm/r7779v6qGMBKWlpaYO0Lx58316T6jt9u3bm/q77767T/WEIsIDBthbEZaUlJj6VVddZeoAv//97039gAMOMPVQ5Ld///7BNgrhBrl/KCUyxLNytILTHo6TJYpqkKo6pJjtOV8c3EPuB0SkGfDv7L1S5+pi9sNJH26Q+4dngUnAK3x2j3bHcSi+QR4Qb37pOPuEe8j9wwsi0l9V/17VNwwaNMjUQ3fDh3KOhta+hqKyoR2uIJx9oE2bNqb+pS99ydRD0dTQmtVQ/tVQpPOGG24w9TFj7BhaaCcwCK/HDb3nrrvuMvXf/e53wTac4hvk94Afikg5sINPV+ocWOR+OCnC5yH3E6raqJjtOV8csmKQRVlcLiJtKzkuImLv/Ok4GaJYHvJXIlKLKMo6jU9z6rQHTgfOAH4KfFSk/jgpIyseslgpPC6Kb7u6FLgaaAlsBd4D/g7coar2PtuOgxtktaOqc4Af7ev76tata+q33nqrqYd2oProI9v5tmjRwtQL3T1//vnnm3ooChrKANCgQQNTP/jgg009tK63R48epr506VJT79ixo6l3797d1AFWrFhh6lOnTjX1J5980tTr1asXbMNJQaJkx4HseEjfbMdxEoR7SCfxZGkesqgeUkS+IiIN4ueXici9ImIvb3GcHCqMsjoeSabYQ9b7ga0ichwwDFhElGfHcRyKb5A7NTpFnQf8QVX/G/DVO06lZMVDFvsacpOI/AC4DDglXixgz2vEhKYSRoywHWsocW/fvn1N/bHHHjP15cuXB/sUWnheVmZPpc6dO9fU16xZY+qbN2829dCWcKHpkFD9oWmVUDJpgH/961+mfs4555j6okWLTP2II44ItlGIpBtSdVFsD/lvRCk8vqmqK4DDAHujR8fJIMVeXL4CuDfn9Yf4NaRTBbLiIYu1P+QbqtpbRDYRJbXacwi//cpx9lCstay9478ewHH2mTQEY6qLYs9D7hVZEZEri9kHJ514lHX/cIuIXAj8P6Ah8GeiIM9fQ29o2rSpqa9du9bUQ4uaa9eubeoNGzY09VDEFGDLli2mvn79elNv27atqYcWhYe2bAt95tNOO83Ut23bZuqhxeWh1B4Axx9/vKmHvtfQdnShCLITUewo66lEiwHeAd4ARqnqN4rcByeFuIfcPxwE9CQyysOANiIimvRvyalxsvITKbaHnAK8rKr9gBOBVsDkIvfBcRJLsT1k33juEVXdBnxXRE4pch+cFJIVD1nshQEfishBwFFEOXUcx8mh2Ht7fIsoN+thRIGdk4C3ADszMDB//nxT79atm6mHkgyHUnWMGjXK1EPJhyEcvQy9J7S+NhTJDW0XF0pbEopoHnrooaZeXl5u6qFUI4Xquu+++0z9pptuMvWnnnrK1L/xjXBsLw3BmOqi2NeQ3yO6dvxAVU8HugPri9wHJ4VkJcpabIMsq8guJyIlqjoX6FDkPjhOYil2UOcjEWkCPAOME5FPgA+K3AcnhSTds1UXxQ7qVOyEc6uIvAY0Buy8jY6TgxvkfkZVJ9RU246TVBKfdS60TvOtt94y9dC2c61atTL1I4880tQbN24c7NPrr79u6qEEyqFkwhs2bNinPoWyHoSSGO/aZe+Ju2PHDlMvFFkObdvXs2dPU1+5cqWph9bvVkZWPKTnZXWcBFHs269uiBcGOE6Vqc4pj6R72mJ7yBbA2yLyhIj0k0JZlRwnBzfI/YCq/pho2dzDwFXAAhH5hYjYe347TsYo+jVkfKvVivixk+iWrKdE5JfF7ouTHorpIUWkp4h8IiLlIlImIk/nHX9ORFREjo5fi4i8IyLbRWSbiAzOKftQrG8XkYcqa7vYa1m/B1wBrCHKFvCfqrojzs+6ANhrAeTChQvNuo4++mhTnzJliqk/8MADpt6pUydTD63dhHDkN7TuNpTTdODAgaa+bNkyU1+1apWp796929QPPNDOHRaKEoe+CwhvI9eyZUtTf++990z9kEMOCbZRiCIPNcuB61V1lIi0BD4QkQGq+ryI9AR6Abkh7J8ArYESov1PHwJGiUg7YAjRarTdxCNCVV0SarjYHvJgYKCqnq2qT6rqDgBV3Q18vch9cRwTVZ2hqqPi5x8Da4GKs9UY4Lq8twwCRmvEw0C9eLuMG4EFqro4NsIFRFtoBCn2NeRPVdVcKqeq9inVcaj2IWtbEdmS83g01K6I9AaaASNF5A5gtarmJ25qCuSmj98MdAXaArnDneWxFiTxCwMcZz+wRFVPqKyQiLQAxgL3AGVEdysdsz875gsDnMRTE/OQIlIKzAZeUdWbiRK0HQC8LyI7gdrAHBHpwmeHtBBlVJwJLCG6tqygVawFcYN0UkGRo6wCzAKWqup5cftjVLWWqtZR1TpEQZ1Oqvou8AQwKI62fhPYrqozgPuAo0SkrYi0JZrys+/ojkn8kPXKK+08yk8//bSph6KB8+bNM/XQ3fxjx44N9in0T23evLmpX3HFFaY+c+ZMUz/oIHsxUyjKGmLTpk2mfsstt5j6M888E6wrtJNWSA/lkA1lbkgYQ4F2QJmIVKSHuFNVbwuU/xlwIbCdyFD/HUBVF8fXpxXh9xGqurhQw4k3SMeB4k57qOr9RJsLFypTJ+e5Al0C5a4mmgqpEm6QTipI+pK36sKvIR0nQbiHdFKBe0jHcYpO4j3kyJEjTX3WrFmmHlpnGtrx6fPkKA1lH2jdurWph9bjhqKmF154oamH1uMed9xxph76DLfffruphzIMQPizhSK53bt3N/XPs/tVGm6bqi4Sb5COAz5kdRynBnAP6aSCrHhIN0gnFWTFIH3I6jgJIvEe8sMPPzT1a6+91tRDEc3S0lJTD+VAfeKJJ4J9Wr58uamH1m/WqWN/zZdccompv/rqq6Ye2iFq3Lhxpr54sb1sMrRb1saNG00dwhHYUDQ1lH0g9P+pDPeQjuMUncR7SMfxeUjHSRhZMUgfsjpOgnAP6aSCrHjIxBtkKEdpmzZtTP2Xv7TzLffu3dvUf/GLX5j6scceG+xTKN/p3LlzTf2ss84y9dAuVH369DH1UIaB0I4MoXabNWtm6qHoLsC2bdtMvWHDhqYeyiTQtGnTYBuFyIpB+pDVcRJE4j2k44B7SMdxagD3kE7i8XlIx0kYbpAJ4ZNPPjH10JrIUJ7VI444wtT79+9v6sOHDw/26Vvf+pap16plXwGE7pIP7UK1fft2U58wYYKpd+7c2dRXrlxp6qEdqBYtWmTqAD169DD1v//976berl07U69fv76pX3DBBcG2s0TiDdJxwD2k4ySKrBikR1kdJ0G4h3RSgXtIx3GKjiT9zHP55ZebHZwzZ44l06tXL1NfsGCBqYfWdYbW0EJ4l6vQ+s3QOtAuXcz9WXjwwQdN/aKLLjL1rl27mvr8+fNNPRQNPuecc0wd4KOPPjL1FStWmPrBBx9s6qG1xp988om9IDeqS88+++xg3/aV0aNHT6vKhq01gQ9ZnVSQdMdRXfiQ1XEShHtIJxW4h3Qcp+i4h3RSQVY8ZOINskGDBqYeiix26NDB1E855RRTf+yxx0w9FEkFmDRpkqlfd911pr5161ZTD909/9BDD5l6KEdtWVmZqYeiuDNmzDD1UH5XgHXr1pl66P8wceJEUz/ppJOCbRQiKwbpQ1bHSRCJ95CO4/dDOk7CyIpB+pDVcRKEe0gnFWTFQybeIHfv3m3qobveQ3vejx8/3tRXr15t6jfddFOwT/PmzTP1UDQ1tHa0vLzc1EeOHGnqtWvXNvX27dubeigy2rdvX1N/7rnnTB3CGQNCEdtQhobQul4nIvEG6TjgHtJxEkOWoqwe1HGcBOEe0kkFWfGQbpBOKnCDTAjnnnuuqYfuqt+1a5epn3HGGaYeykX6t7/9LdinK6+80tRDP5pQ/tXLLrvM1KdOnWrqoYjwW2+9Zeqh/LG//vWvTf3oo4829ULsa5aEUM5ZJyLxBuk4kB0P6UEdx0kQ7iGdVJAVD+kG6SQen4d0HKdGSLyHPO6440w9tK6zTZs2pj527FhTHzRokKm/+eabwT6FdqEKZSvYsGGDqT/88MOmfumll5p6aCewuXPnmnrou7j44otN/dFHHzV1gDPPPNPUQ7liQ7tiHXjggcE2CpEVD5l4g3QcyI5B+pDVcRKEe0gnFbiHdByn6LiHdFJBVjykG6STeLI0D5l4gxw9erSpn3feeab+4osvmvrpp59u6qHF66H0GgD169c39X/961+mfuihh5p6vXr1TD201V4oLUaLFi1MPZRYuWHDhqYeWjQP0K1bN1OfPXu2qQ8cONDUx4wZE2zDSYFBOg74kNVxEkVWDNKjrI6TINxDOqnAPaTjOEUn8R5y2rRpph5KyRHayi2URuOll14y9dCCbYDDDjtsn/QvfelLpv7qq6+aeigJ9Pz58029bdu2pt6kSRNT37x5s6mH+g/wyCOPmPoNN9xg6i+88IKpDx06NNhGIYrpIUWkJzAWOABQ4EVVvVBEJgInxsW2AGeq6nQRORx4G2gMCPCYqn4zrush4Kr4PcNV9ZpCbbuHdBJPxTxkdT2qQDlwvaqWAEcCA0RkAHCJqpaqainwOlCRYv7PwJJY7wYMEZEGItIOGAJ0BI6O9baFGnaDdJw8VHWGqo6Kn38MrAU6qeqynGINibwn8d+GIiJAM2AnUAbcCCxQ1cWqugRYAAwr1Hbih6yOAzUX1BGR3kRGNjJ+/QZwEpHRdYuLDQHmxFot4Gequiv2hrlGvBxoW6g995BOKqjmIWtbEdmS8zDvzBaRFkTXkvdUeEdV7a2qdYiGrA/ERYcB7xM5uDOAH4lI68/zOd0gnSyyRFUb5Dwuzy8gIqXAbOAVVb3ZqONWIk8JcAXwsEaMBzYBZwJLgFzDbBVrQSTp8zsjRowwO9iuXTuz/HvvvWfqoQhlaJ1pKEUIhJMAH3744aYeWuMaio6Wlpaa+qxZs0w91NdWrVqZeseOHU195cqVpg7h1BuhKPW1115r6m+88YapDxs2TEJtN2rUSLt37x7s274yadKkaap6Quh4fC24ENioqt1z9L6q+kr8/EngZFU9TERmAWtU9TQR6QzMBI4hGsLOA46Kq1gAdFDVxaG2/RrScfZmKNAOKBORirPvncC1ItKUKIizAfhafOxyYJyIlMWvH1TV+QDxcLjCG4woZIzgBumkhGKO5FT1fuB+49BtgfLTAXPyWFWvBq6uattukE7iydL9kB7UcZwE4R7SSQVZ8ZCJN8gFCxaYem6kc/DgwWzfvh1V5aijjjKzDIQioMuWLTP1Ll26BPu0bt06Ux8/frypN2jQwNRXrFhh6qG+hta4rl27dp/6E1qn26tXL1MHuOWWW0w9lH3g6aefNvXQOtrKcINMGS+88AJlZWWUlZVVXthxEsoXxiAtGjVqxPnnn8/y5ctp2bIlW7Zs4fnnn2fXrl00atSI8847j8WLF9OmTRs++ugjpk6dyplnnknDhg15++23g57HKT5Z8ZBfiKCOqtK/f38GDhzIscce+5ljTZo0YcaMGTz22GOUl5fTvn37zxybNGkS99xzD82aNaNbt27cf//9vPjii3vV4zjF4AvhIZ999lm2bt1K/fr16d+/P+vWrWP58uUAbNy4kTVr1iAirFq16jMrTjZu3LjnOm7lypUsXLgQiK7tQtd9Ts3gHjJFbN26FYCysjIWLVr0meVwO3fu3PN89+7d1Kr16UfetWvXnuequue1qn6mnFOz1MD9kDVG4j1kz549Tf3xxx8HoKSkBBGhrKyMkpIS+vXrx/jx41m5ciXl5eXs2rWLlStX0rx58z3/kN27d+95vmnTJgB27NjBtm3b2LRpE3Xr1mXHjh0sXbrUbDu0Hd1VV11l6i+//LKph9bdbt++3dRbtmxp6qEcsqFhd/PmzU09tN0dhNe/hjI3bNmyxdRDWQyciMQbZGU0btyYG2+8EYgWWc+ZMyc4VeKkl6R7tuoi9Qa5atUqfvCDH+x5nXsHxfr16/ntb3+753XuXRcbN25k5MiRe17nbla6bt06/vznP++nHjufh6wYpF8oOU6CSKWHHDBgAAMGDChqm3feeWdR23M+i3tIx3GKTuI95Jo1a/bSHnnkEf7yl7+Y5Y877jhTD0UJQ+tSQ1FCgBNPPNHUQ3fcL1q0yNT79u1r6tOnTzf1UGaAUIaB0NRNaA1tRcTZIrT7VWh3squvtm8BHDduXLCNQmTFQybeIB0nDfOH1YUPWR0nQbiHdFJBVjykG6STCrJikD5kdZwEkXgPGYogXnnllaYeuoM9xOTJk009tD4U4MwzzzT10JK90J34s2fPNvVQZoBVq1aZ+kEHHWTqRxxxhKmvXr3a1AutZQ1FbPv372/quaugcglFuyvDPaTjOEUn8R7ScSA7HtIN0kk8Pg/pOE6N4B7SSQVZ8ZCJ3/1KRFYDH9R0P5z9ShtVbRY6WFpaqqHdzj4Pc+bMKbj7VU2SeA9Z6B/lOF80Em+QjgPZGbJ6UMdxEoR7SCcVuId09jsi8uY+ln9KRKoluiEir4iIveYuYWQpL6sbZA2iquHtpvKI966vXdmW2PvAo8C3q6kup5pwg6wCInKiiMwUkfoi0kBEZovIXlmIReQZEZkWH78m1tqIyAIROUREaonIJBE5Kz62Of7bUkQmisg7IjJLRPoY3bgUeDanrc0icoeIzBCRKSLSItaHi8j9sbZYRE4Tkb+IyHsiMjynvueAS6rtS9rPuId09qCqbxP9gG8HfgmMVNVZRtGrVfV44ATguyLSVFU/AO4m2rN+GDBHVf+R977BwFhV7QYcB7xj1P0VYFrO6wbAFFU9DpgI/HvOsYOAk4Eb437fB3QGuohIt/gzfQKUiEjTqnwHNU1WDNKDOlXnNuBtoAz4bqDMd0Xkgvj54cBRwFpV/bOIXAQMBboZ73sb+IuI1AWeUdV3jDItgdz7prYDL8TPpwG594Q9r6oqIu8CK1X1XQARmQ205VODXwW0AnzfvYTgHrLqNAUaAo2A+vkHReQ0oC9wcuy1pleUE5EDgMPionvdsKmqE4FTgGXAcBG5wmh/W167O/TT0/0uPntyrbiZc3fO84rXueXqx/Umnqx4SDfIqvMg8BPgMaIhaD6NgU9UdauIdAROyjl2d/y+W4A/5b9RRNoQebI/AX8Gehj1vwe0N/TPhYgIcCiwpLrqdP7v+JC1CsQea4eqjhKR2sCbIvJVVR2fU+xlYKiIvAfMA6bE7z0VOBH4iqruEpELRWSIqj6S897TgP8UkR3AZsDykC/G5V6ppo91PNE16M5KSyaApHu26iLxi8udCBEpBV4jNuxqqO+3wHOq+ur/uXP7mZKSEj3ssMMqL1hFFi9enNjF5T5kTQmqug34KdC6mqqclQZjzBo+ZE0Rqjq2Guva61o2yWRlJOcG6aSCrBikD1kdJ0G4h3RSgXtIx3GKjntIJxVkxUO6QTqJJw1L3qoLH7I6ToJwD+mkgqx4SDdIJxVkxSB9yOo4CcI9pJMK3EM6jlN03EM6qSArHtIN0kk8Pg/pOE6N4B7SSQVZ8ZBukE4qyIpB+pDVcRKEe0gnFbiHdByn6LiHdFJBVjykG6STeHwe0nGcGsEN0kkFxdxsR0R6isgnIlIuImUi8nSsTxSRbfFjjYh0z3nP92O9TETW5+g/EpHt8eOlytp2g3RSQZF3vyoHrlfVEuBIYICIDAAuUdVSVS0FXgdGwp7Nku4GTlfV+kCvWK8L3Eq0VeBBwGlxPUHcIB0nD1Wdoaqj4ucfE+2f2UlVl+UUawhUWPfdwP+q6pT4PXNi/Spgg6pOUNUtwATg+kJtu0E6qaCm9ocUkd5AMz71hm+IyE6inci+ERfrADQRkfUiskVEHszR1+RUt4Rog9wgbpBOFmkbG07F41GrkIi0AMYC91R4R1Xtrap1iIasD8RFaxPtTH0U0B0YIiJnfZ6O+bSHkwqqedpjSWXb0cXb/80GXlHVm40itxIZJUQ7X69T1dXAahF5HzgbmEs0bK2gLbC8ULvuIZ3EU53D1SpGWQWYBSxV1fNy9L45xYbx6XD0D0BXESkRkaZAG2ASMAJoLCJ9RKQBcCrwx0Jtu4d0nL0ZCrQDykRkW6zdCVwbG5wCG4CvAajqiyLyJrAxPvaSqj4DICI/B14FBHhNVZ8r1LDvoOwkntq1a2uDBg2qrb5NmzYldgdl95BOKsiK4/BrSMdJEO4hnVTgHtJxnKLjHtJJBVnxkG6QTuLx+yEdx6kR3EM6qSArHtIN0kkFWTFIH7I6ToJwD+mkAveQjuMUHfeQTirIiod0g3QSj89DOo5TI7iHdFJBVjykG6STCrJikD5kdZwE4R7SSQXuIR3HKTruIZ1UkBUP6QbppIGxwCHVWN+ayovUDJ4G0nEShF9DOk6CcIN0nAThBuk4CcIN0nEShBuk4ySI/w86EcCmB/o1mQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 211.2x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x392.727 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 211.2x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "roi = hs.roi.CircleROI(cx=72, cy=72, r_inner=0, r=2)\n", "dp.plot_integrated_intensity(roi=roi, cmap='viridis')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='pre'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Pre-processing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply affine transformation to correct for off axis camera geometry" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f6f016ec4dbc40e6bf3660cbc3617014", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "scale_x = 0.995\n", "scale_y = 1.031\n", "offset_x = 0.631\n", "offset_y = -0.351\n", "dp.apply_affine_transformation(np.array([[scale_x, 0, offset_x],\n", " [0, scale_y, offset_y],\n", " [0, 0, 1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform difference of gaussian background subtraction with various parameters on one selected diffraction pattern and plot to identify good parameters" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from pyxem.utils.expt_utils import investigate_dog_background_removal_interactive" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " \r" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 144x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x392.727 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dp_test_area = dp.inav[0, 0]\n", "\n", "gauss_stddev_maxs = np.arange(2, 12, 0.2) # min, max, step\n", "gauss_stddev_mins = np.arange(1, 4, 0.2) # min, max, step\n", "\n", "investigate_dog_background_removal_interactive(dp_test_area,\n", " gauss_stddev_maxs,\n", " gauss_stddev_mins)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove background using difference of gaussians method with parameters identified above" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 0.4s\n" ] } ], "source": [ "dp = dp.subtract_diffraction_background('difference of gaussians',\n", " min_sigma=2, max_sigma=8,\n", " lazy_result=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform further adjustments to the data ranges" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "dp.data -= dp.data.min()\n", "dp.data *= 1 / dp.data.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set diffraction calibration and scan calibration" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dp = pxm.signals.ElectronDiffraction2D(dp) #this is needed because of a bug in the code\n", "dp.set_diffraction_calibration(diffraction_calibration)\n", "dp.set_scan_calibration(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='tem'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Pattern Matching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pattern matching generates a database of simulated diffraction patterns and then compares all simulated patterns against each experimental pattern to find the best match" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import generators required for simulation and indexation" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from diffsims.libraries.structure_library import StructureLibrary\n", "from diffsims.generators.diffraction_generator import DiffractionGenerator\n", "from diffsims.generators.library_generator import DiffractionLibraryGenerator\n", "\n", "from diffsims.generators.zap_map_generator import get_rotation_from_z_to_direction\n", "from diffsims.generators.rotation_list_generators import get_grid_around_beam_direction\n", "\n", "from pyxem.generators.indexation_generator import TemplateIndexationGenerator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1. Define Library of Structures & Orientations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the crystal phases to be included in the simulated library" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "structure_zb = diffpy.structure.loadStructure('./data/02/GaAs_mp-2534_conventional_standard.cif')\n", "structure_wz = diffpy.structure.loadStructure('./data/02/GaAs_mp-8883_conventional_standard.cif')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a basic rotations list. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "za110c = get_rotation_from_z_to_direction(structure_zb, [1,1,0])\n", "rot_list_cubic = get_grid_around_beam_direction(beam_rotation=za110c, resolution=1, angular_range=(0,180))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "za110h = get_rotation_from_z_to_direction(structure_wz, [1,1,0])\n", "rot_list_hex = get_grid_around_beam_direction(beam_rotation=za110h, resolution=1, angular_range=(0,180))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct a StructureLibrary defining crystal structures and orientations for which diffraction will be simulated " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "struc_lib = StructureLibrary(['ZB','WZ'],\n", " [structure_zb,structure_wz],\n", " [rot_list_cubic,rot_list_hex])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='temb'></a>\n", "### 3.2. Simulate Diffraction for all Structures & Orientations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a diffsims DiffractionGenerator with diffraction simulation parameters" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "diff_gen = DiffractionGenerator(accelerating_voltage=accelarating_voltage)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize a diffsims DiffractionLibraryGenerator" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "lib_gen = DiffractionLibraryGenerator(diff_gen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calulate library of diffraction patterns for all phases and unique orientations" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " \r" ] } ], "source": [ "target_pattern_dimension_pixels = dp.axes_manager.signal_shape[0]\n", "half_size = target_pattern_dimension_pixels // 2\n", "reciprocal_radius = diffraction_calibration*(half_size - 1)\n", "\n", "diff_lib = lib_gen.get_diffraction_library(struc_lib,\n", " calibration=diffraction_calibration,\n", " reciprocal_radius=reciprocal_radius,\n", " half_shape=(half_size, half_size),\n", " max_excitation_error=1/10,\n", " with_direct_beam=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally, save the library for later use." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#diff_lib.pickle_library('./GaAs_cubic_hex.pickle')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If saved, the library can be loaded as follows" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#from diffsims.libraries.diffraction_library import load_DiffractionLibrary\n", "#diff_lib = load_DiffractionLibrary('./GaAs_cubic_hex.pickle', safety=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='temb'></a>\n", "### 3.3. Pattern Matching Indexation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize `TemplateIndexationGenerator` with the experimental data and diffraction library and perform correlation, returning the `n_largest` matches with highest correlation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-block alert-warning\"><b>Note:</b> This workflow has been changed from previous version, make sure you have pyxem 0.13.0 or later installed</div>" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0cf69059248a48e4ba8d3854ce4fea3a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "indexer = TemplateIndexationGenerator(dp, diff_lib)\n", "indexation_results = indexer.correlate(n_largest=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the solutions via a plotting (can be slow, so we don't run by default)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "if False:\n", " indexation_results.plot_best_matching_results_on_signal(dp, diff_lib)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get crystallographic map from indexation results" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "833156e2ed494f3cbe28a6d5ecf16a68", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8936c79ea191474b824e3cfa63618e18", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "974bb32cf36747bab7ab0f588328b569", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "crystal_map = indexation_results.to_crystal_map()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "crystal_map is now a CrystalMap object, which comes from orix, see their documentation for details. Below we lift their code to plot a phase map" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "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": [ "from matplotlib import pyplot as plt\n", "from orix import plot\n", "\n", "fig, ax = plt.subplots(subplot_kw=dict(projection=\"plot_map\"))\n", "im = ax.plot_map(crystal_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='vec'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Vector Matching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-block alert-danger\"><b>Note:</b> This workflow is less well developed than the template matching one, and may well be broken</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vector matching generates a database of vector pairs (magnitues and inter-vector angles) and then compares all theoretical values against each measured diffraction vector pair to find the best match" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import generators required for simulation and indexation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "from diffsims.generators.library_generator import VectorLibraryGenerator\n", "from diffsims.libraries.structure_library import StructureLibrary\n", "from diffsims.libraries.vector_library import load_VectorLibrary\n", "\n", "from pyxem.generators.indexation_generator import VectorIndexationGenerator\n", "\n", "from pyxem.generators.subpixelrefinement_generator import SubpixelrefinementGenerator\n", "from pyxem.signals.diffraction_vectors import DiffractionVectors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='veca'></a>\n", "### 4.1. Define Library of Structures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define crystal structure for which to determine theoretical vector pairs" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "structure_zb = diffpy.structure.loadStructure('./data/02/GaAs_mp-2534_conventional_standard.cif')\n", "structure_wz = diffpy.structure.loadStructure('./data/02/GaAs_mp-8883_conventional_standard.cif')\n", "\n", "structure_library = StructureLibrary(['ZB', 'WZ'],\n", " [structure_zb, structure_wz],\n", " [[], []])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize VectorLibraryGenerator with structures to be considered" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "vlib_gen = VectorLibraryGenerator(structure_library)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determine VectorLibrary with all vectors within given reciprocal radius" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "reciprocal_radius = diffraction_calibration*(half_size - 1)/2" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.1360000000000001" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reciprocal_radius" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2/2 [00:01<00:00, 1.56it/s]\n" ] } ], "source": [ "vec_lib = vlib_gen.get_vector_library(reciprocal_radius)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally, save the library for later use" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "#vec_lib.pickle_library('./GaAs_cubic_hex_vectors.pickle')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#vec_lib = load_VectorLibrary('./GaAs_cubic_hex_vectors.pickle',safety=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2. Find Diffraction Peaks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tune peak finding parameters interactively" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6efa804175834775be959ef848ddaffc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<BaseSignal, title: , dimensions: (20, 45|)>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp.find_peaks(interactive=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform peak finding on the data with parameters from above" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "792949da33a846bba837b578f48f898a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=900), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "peaks = dp.find_peaks(method='difference_of_gaussian',\n", " min_sigma=0.005,\n", " max_sigma=5.0,\n", " sigma_ratio=2.0,\n", " threshold=0.06,\n", " overlap=0.8,\n", " interactive=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "coaxing peaks back into a DiffractionVectors" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "peaks = DiffractionVectors(peaks).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`peaks` now contain the 2D positions of the diffraction spots on the detector. The vector matching method works in 3D coordinates, which are found by projecting the detector positions back onto the Ewald sphere. Because the methods that follow are slow, we constrain ourselves to looking at a smaller subset of the data" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "peaks = peaks.inav[:2,:2]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "peaks.calculate_cartesian_coordinates?" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:hyperspy.signal:The function you applied does not take into account the difference of units and of scales in-between axes.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "81bbfbb23cd44f17acb793469e7aa52f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/carter/anaconda3/lib/python3.7/site-packages/pyxem/utils/vector_utils.py:57: RuntimeWarning: invalid value encountered in sqrt\n", " k_z = np.sqrt(1 / (wavelength ** 2) - np.sum(k_xy ** 2, axis=1)) - 1 / wavelength\n" ] } ], "source": [ "peaks.calculate_cartesian_coordinates(accelerating_voltage=accelarating_voltage,\n", " camera_length=camera_length)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='vecb'></a>\n", "### 4.3. Vector Matching Indexation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize `VectorIndexationGenerator` with the experimental data and vector library and perform indexation using `n_peaks_to_index` and returning the `n_best` indexation results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-block alert-danger\"><b>Alert: This code no longer works on this example, and may even be completely broken. Caution is advised.</b> </div>" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "#indexation_generator = VectorIndexationGenerator(peaks, vec_lib)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "#indexation_results = indexation_generator.index_vectors(mag_tol=3*diffraction_calibration,\n", "# angle_tol=4, # degree\n", "# index_error_tol=0.2,\n", "# n_peaks_to_index=7,\n", "# n_best=5,\n", "# show_progressbar=True)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#indexation_results.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Refine all crystal orientations for improved phase reliability and orientation reliability maps." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "#refined_results = indexation_generator.refine_n_best_orientations(indexation_results,\n", "# accelarating_voltage=accelarating_voltage,\n", "# camera_length=camera_length,\n", "# index_error_tol=0.2,\n", "# vary_angles=True,\n", "# vary_scale=True,\n", "# method=\"leastsq\")\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get crystallographic map from optimized indexation results." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "#crystal_map = refined_results.get_crystallographic_map()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the objections documentation for further details" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "#crystal_map?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

zpenoyre/illustris

apiTestNotebook.ipynb

1

47392

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "import illustrisAPI.data as iApi\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#example of getting subhalo data from z=1 in illustris 3\n", "haloFirstSub=iApi.getHaloField('GroupFirstSub',snapshot=85,simulation='Illustris-3')\n", "haloMass=iApi.getHaloField('GroupMass',snapshot=85,simulation='Illustris-3')\n", "subVelDisp=iApi.getSubhaloField('SubhaloVelDisp',snapshot=85,simulation='Illustris-3')\n", "subBhMass=iApi.getSubhaloField('SubhaloBHMass',snapshot=85,simulation='Illustris-3')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Zephyr/Astro/anaconda/lib/python3.5/site-packages/matplotlib/scale.py:103: RuntimeWarning: divide by zero encountered in log\n", " return np.divide(np.log(a, out=a), np.log(self.base), out=a)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c879f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#example plots of stellar component of the black hole sigma relation for central galaxies\n", "bigGalaxies=np.argwhere(haloMass>100)\n", "mSigma=plt.gca()\n", "mSigma.scatter(subVelDisp[haloFirstSub[bigGalaxies]],subBhMass[haloFirstSub[bigGalaxies]])\n", "mSigma.set_yscale('log')\n", "mSigma.set_xscale('log')\n", "mSigma.set_ylim(1e-5,1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "#example of getting data from a particular galaxy (from online)\n", "fields=[\n", " [4,'Coordinates'],\n", " [4,'Velocities'],\n", " [4,'Masses']\n", "]\n", "data=iApi.getGalaxy(150,fields)\n", "rStar=data[0][:,:]\n", "vStar=data[1][:,:]\n", "mStar=data[2][:]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Zephyr/Astro/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:46: RuntimeWarning: divide by zero encountered in log\n" ] }, { "data": { "image/png": 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nsZW/4Ls+hfWIKJvOqxqTLnyYxuaHnOuwrthLX4jWFXvcV3n/iMpcvsbj/d9G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\ntm34prX7dvJZ8byNfO1Ey0n8uvx+bff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"text/plain": [ "<matplotlib.figure.Figure at 0x11286a588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#example plots of stellar component of the galaxy\n", "fig = plt.figure(figsize=(4,12))\n", "grid=matplotlib.gridspec.GridSpec(3,1)\n", "#moves to center of mass frame\n", "com=findCenterMass(rStar,mStar)\n", "rStar=rStar-com\n", "vom=findCenterVelocity(vStar,mStar)\n", "vStar=vStar-vom\n", "#reorients with z-axis along direction of galaxy angular momentum\n", "jUnit=findPlane(rStar,vStar)\n", "rStar=projectPos(jUnit,rStar)\n", "vStar=projectPos(jUnit,vStar)\n", "#plot map of projected density\n", "nPixels=32\n", "edgeDist=10\n", "plotMap(rStar,mStar,mStar,nPixels,edgeDist,grid,0,0,useMean=0,projection=0)\n", "plotMap(rStar,mStar,mStar,nPixels,edgeDist,grid,0,1,useMean=0,projection=1)\n", "plotMap(rStar,mStar,mStar,nPixels,edgeDist,grid,0,2,useMean=0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# routines used for plotting and data handling in the examples\n", "def findCenterMass(rStar,mStar):\n", " com=np.zeros(3)\n", " for i in range(3):\n", " com[i]=np.sum(rStar[:,i]*mStar[:])/np.sum(mStar[:])\n", " return com\n", "def findCenterVelocity(vStar,mStar):\n", " vom=np.zeros(3)\n", " for i in range(3):\n", " vom[i]=np.sum(vStar[:,i]*mStar[:])/np.sum(mStar[:])\n", " return vom\n", "def findPlane(rStar,vStar):\n", " jStar=np.cross(rStar,vStar)\n", " j=np.sum(jStar,axis=0)\n", " j=j/np.linalg.norm(j)\n", " return j\n", "def projectPos(j,r):\n", " z=j[0]*r[:,0]+j[1]*r[:,1]+j[2]*r[:,2]\n", " n=np.array([0,-j[2],j[1]])\n", " n=n/np.linalg.norm(n)\n", " m=np.cross(j,n)\n", " x=n[0]*r[:,0]+n[1]*r[:,1]+n[2]*r[:,2]\n", " y=m[0]*r[:,0]+m[1]*r[:,1]+m[2]*r[:,2]\n", " return np.vstack((x,y,z)).T\n", "def findMap(r,m,data,nPixels,edgeDist,mean=1,projectAxis=2):\n", " pixels=np.zeros((nPixels,nPixels))\n", " vertAxis=(projectAxis+1)%3\n", " horzAxis=(projectAxis+2)%3\n", " for i in range(nPixels):\n", " for j in range(nPixels):\n", " inPix=np.argwhere((edgeDist*(((2*i)/nPixels)-1)<r[:,vertAxis])\n", " &(edgeDist*(((2*j)/nPixels)-1)<r[:,horzAxis])\n", " &(edgeDist*(((2*(i+1))/nPixels)-1)>r[:,vertAxis])\n", " &(edgeDist*(((2*(j+1))/nPixels)-1)>r[:,horzAxis]))\n", " if inPix.size==0:\n", " continue\n", " if mean==1: #return the mass weighted mean\n", " pixels[i,j]=np.sum(data[inPix]*m[inPix])/np.sum(m[inPix])\n", " if mean==0:\n", " pixels[i,j]=np.sum(data[inPix])\n", " return pixels\n", "def plotMap(r,m,data,nPixels,edgeDist,grid,iPlot,jPlot,logPlot=1,useMean=1,projection=2):\n", " pixels = findMap(r,m,data,nPixels,edgeDist,mean=useMean,projectAxis=projection)\n", " plot = plt.subplot(grid[jPlot,iPlot])\n", " if logPlot==1:\n", " plot.imshow(np.log(pixels), interpolation ='none', aspect = 'auto')\n", " else:\n", " plot.imshow(pixels, interpolation ='none', aspect = 'auto')" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

evanbiederstedt/RRBSfun

QC_filtered50K/regression_methylation_weighted_10August2016_avgReadCpgs_gtreql3.8CpG_filter50K.ipynb

1

4918858

mit

Tahsin-Mayeesha/Udacity-Machine-Learning-Nanodegree

projects/boston_housing/boston_housing.ipynb

1

201477

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Model Evaluation & Validation\n", "## Project 1: Predicting Boston Housing Prices\n", "\n", "Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!\n", "\n", "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. \n", "\n", ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a *good fit* could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.\n", "\n", "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing). The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:\n", "- 16 data points have an `'MDEV'` value of 50.0. These data points likely contain **missing or censored values** and have been removed.\n", "- 1 data point has an `'RM'` value of 8.78. This data point can be considered an **outlier** and has been removed.\n", "- The features `'RM'`, `'LSTAT'`, `'PTRATIO'`, and `'MDEV'` are essential. The remaining **non-relevant features** have been excluded.\n", "- The feature `'MDEV'` has been **multiplicatively scaled** to account for 35 years of market inflation.\n", "\n", "Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Boston housing dataset has 489 data points with 4 variables each.\n" ] } ], "source": [ "# Import libraries necessary for this project\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import visuals as vs # Supplementary code\n", "from sklearn.cross_validation import ShuffleSplit\n", "\n", "# Pretty display for notebooks\n", "%matplotlib inline\n", "\n", "# Load the Boston housing dataset\n", "data = pd.read_csv('housing.csv')\n", "prices = data['MDEV']\n", "features = data.drop('MDEV', axis = 1)\n", " \n", "# Success\n", "print \"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Exploration\n", "In this first section of this project, you will make a cursory investigation about the Boston housing data and provide your observations. Familiarizing yourself with the data through an explorative process is a fundamental practice to help you better understand and justify your results.\n", "\n", "Since the main goal of this project is to construct a working model which has the capability of predicting the value of houses, we will need to separate the dataset into **features** and the **target variable**. The **features**, `'RM'`, `'LSTAT'`, and `'PTRATIO'`, give us quantitative information about each data point. The **target variable**, `'MDEV'`, will be the variable we seek to predict. These are stored in `features` and `prices`, respectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Calculate Statistics\n", "For your very first coding implementation, you will calculate descriptive statistics about the Boston housing prices. Since `numpy` has already been imported for you, use this library to perform the necessary calculations. These statistics will be extremely important later on to analyze various prediction results from the constructed model.\n", "\n", "In the code cell below, you will need to implement the following:\n", "- Calculate the minimum, maximum, mean, median, and standard deviation of `'MDEV'`, which is stored in `prices`.\n", " - Store each calculation in their respective variable." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Statistics for Boston housing dataset:\n", "\n", "Minimum price: $105,000.00\n", "Maximum price: $1,024,800.00\n", "Mean price: $454,342.94\n", "Median price $438,900.00\n", "Standard deviation of prices: $165,171.13\n" ] } ], "source": [ "# TODO: Minimum price of the data\n", "minimum_price = prices.min()\n", "\n", "# TODO: Maximum price of the data\n", "maximum_price = prices.max()\n", "\n", "# TODO: Mean price of the data\n", "mean_price = prices.mean()\n", "\n", "# TODO: Median price of the data\n", "median_price = prices.median()\n", "\n", "# TODO: Standard deviation of prices of the data\n", "std_price = prices.std(ddof=0)\n", "\n", "# Show the calculated statistics\n", "print \"Statistics for Boston housing dataset:\\n\"\n", "print \"Minimum price: ${:,.2f}\".format(minimum_price)\n", "print \"Maximum price: ${:,.2f}\".format(maximum_price)\n", "print \"Mean price: ${:,.2f}\".format(mean_price)\n", "print \"Median price ${:,.2f}\".format(median_price)\n", "print \"Standard deviation of prices: ${:,.2f}\".format(std_price)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1 - Feature Observation\n", "As a reminder, we are using three features from the Boston housing dataset: `'RM'`, `'LSTAT'`, and `'PTRATIO'`. For each data point (neighborhood):\n", "- `'RM'` is the average number of rooms among homes in the neighborhood.\n", "- `'LSTAT'` is the percentage of all Boston homeowners who have a greater net worth than homeowners in the neighborhood.\n", "- `'PTRATIO'` is the ratio of students to teachers in primary and secondary schools in the neighborhood.\n", "\n", "_Using your intuition, for each of the three features above, do you think that an increase in the value of that feature would lead to an **increase** in the value of `'MDEV'` or a **decrease** in the value of `'MDEV'`? Justify your answer for each._ \n", "**Hint:** Would you expect a home that has an `'RM'` value of 6 be worth more or less than a home that has an `'RM'` value of 7?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "* RM is the average number of rooms among homes in the neighborhood. The price of a three room house is likely to be less than the price of a five/six room house. It's likely that the increase in room number will lead to an increase in the house price.\n", "\n", "* LSTAT is the percentage of the Boston homeowners who had a greater networth than the homeowners in the neighborhood. If the neighborhood is poor, then it's likely the LSTAT will be high as more Boston homeowners will have a higher networth than the homeowners of the respective neighborhood. In a poor neighborhood, the house price is low. It's likely that increase in LSTAT will lead to a decrease in the house price MDEV.\n", "\n", "* PTRATIO is the ratio of students to teachers in the primary and secondary school in the neighborhood. If PTRATIO is 12/1 it means 1 teacher has to attend to at least 10 students. In poor neighborhoods there's often not enough fund in schools to hire more teachers. Thus I'd assume higher PTRATIO will correspond to a poor neighborhood where the house prices will be low.\n", "\n", "To confirm my intuition I created a pairplot of MDEV against the three values, RM, LSTAT and PTRATIO. RM shows a positive relationship, LSTAT shows a negative one and PTRATIO also shows a strict negative relationship after 20 and moderative negative relationship before 20 with some occassional upper end prices." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x39ca048>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EovFpn+tK+whIJGsJ43wTOeVBaipKePGtq1rb9brmLCcrIbT8MXBUUZT/XQhRD/wKyA2s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\n0JPiT8fR0FLN9OLQ35wquXF+TSnL31bLzp1tA3Jy4mI0ayX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"text/plain": [ "<matplotlib.figure.Figure at 0x74effd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(data,x_vars = [\"RM\",\"LSTAT\",\"PTRATIO\"], y_vars = [\"MDEV\"],kind = \"scatter\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "## Developing a Model\n", "In this second section of the project, you will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Define a Performance Metric\n", "It is difficult to measure the quality of a given model without quantifying its performance over training and testing. This is typically done using some type of performance metric, whether it is through calculating some type of error, the goodness of fit, or some other useful measurement. For this project, you will be calculating the [*coefficient of determination*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination), R², to quantify your model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how \"good\" that model is at making predictions. \n", "\n", "The values for R² range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the **target variable**. A model with an R² of 0 always fails to predict the target variable, whereas a model with an R² of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the **features**. *A model can be given a negative R² as well, which indicates that the model is no better than one that naively predicts the mean of the target variable.*\n", "\n", "For the `performance_metric` function in the code cell below, you will need to implement the following:\n", "- Use `r2_score` from `sklearn.metrics` to perform a performance calculation between `y_true` and `y_predict`.\n", "- Assign the performance score to the `score` variable." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Import 'r2_score'\n", "\n", "from sklearn.metrics import r2_score\n", "\n", "def performance_metric(y_true, y_predict):\n", " \"\"\" Calculates and returns the performance score between \n", " true and predicted values based on the metric chosen. \"\"\"\n", " \n", " # TODO: Calculate the performance score between 'y_true' and 'y_predict'\n", " score = r2_score(y_true,y_predict)\n", " \n", " # Return the score\n", " return score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2 - Goodness of Fit\n", "Assume that a dataset contains five data points and a model made the following predictions for the target variable:\n", "\n", "| True Value | Prediction |\n", "| :-------------: | :--------: |\n", "| 3.0 | 2.5 |\n", "| -0.5 | 0.0 |\n", "| 2.0 | 2.1 |\n", "| 7.0 | 7.8 |\n", "| 4.2 | 5.3 |\n", "*Would you consider this model to have successfully captured the variation of the target variable? Why or why not?* \n", "\n", "Run the code cell below to use the `performance_metric` function and calculate this model's coefficient of determination." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model has a coefficient of determination, R^2, of 0.923.\n" ] } ], "source": [ "# Calculate the performance of this model\n", "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n", "print \"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer:** \n", "\n", "Given R^2 captures the percentage of squared correlation between the predicted and actual values of the target variable and it ranges from 0 to 1 where close to 1 means better performance, and given this model had a R^2 score of 0.923, it appears that the model has been able to successfully capture the variation of the target variable.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Shuffle and Split Data\n", "Your next implementation requires that you take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset.\n", "\n", "For the code cell below, you will need to implement the following:\n", "- Use `train_test_split` from `sklearn.cross_validation` to shuffle and split the `features` and `prices` data into training and testing sets.\n", " - Split the data into 80% training and 20% testing.\n", " - Set the `random_state` for `train_test_split` to a value of your choice. This ensures results are consistent.\n", "- Assign the train and testing splits to `X_train`, `X_test`, `y_train`, and `y_test`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training and testing split was successful.\n" ] } ], "source": [ "# TODO: Import 'train_test_split'\n", "\n", "from sklearn.cross_validation import train_test_split\n", "\n", "# TODO: Shuffle and split the data into training and testing subsets\n", "X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size = 0.2, random_state = 0)\n", "\n", "# Success\n", "print \"Training and testing split was successful.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3 - Training and Testing\n", "*What is the benefit to splitting a dataset into some ratio of training and testing subsets for a learning algorithm?* \n", "**Hint:** What could go wrong with not having a way to test your model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "If we train and test our model on the same data set, the model will most likely to be good at predicting as it has already seen the data. But it would give a false sense of high performance(with respect to the evaluation metric we choose). The end goal of a machine learning model is to make accurate predictions on the data it has not seen before. \n", "\n", "If we split the dataset into training and testing sets, then we can train the model on the training data and test it on the independent testing dataset. After assessing the model's performance on the testing data set that the model has not seen before, we can be more confident about it's performance. \n", "\n", "So we split the data set into training and testing subsets to avoid overfitting and to test the model's performance against a data set it has not seen before." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "## Analyzing Model Performance\n", "In this third section of the project, you'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, you'll investigate one particular algorithm with an increasing `'max_depth'` parameter on the full training set to observe how model complexity affects performance. Graphing your model's performance based on varying criteria can be beneficial in the analysis process, such as visualizing behavior that may not have been apparent from the results alone." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learning Curves\n", "The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R², the coefficient of determination. \n", "\n", "Run the code cell below and use these graphs to answer the following question." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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P/R6RRY9DKtm7LdfFCYUgkSDU1kpoxXLcd96244M+/NAmSmhu0oxxiqKMJ0VD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\ngxOH2bZxe8U8q4UNRESknhZdwycIajvv+bHnODIxG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"text/plain": [ "<matplotlib.figure.Figure at 0x39ca390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Produce learning curves for varying training set sizes and maximum depths\n", "vs.ModelLearning(features, prices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4 - Learning the Data\n", "*Choose one of the graphs above and state the maximum depth for the model. What happens to the score of the training curve as more training points are added? What about the testing curve? Would having more training points benefit the model?* \n", "**Hint:** Are the learning curves converging to particular scores?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "###### Chosen graph = max depth 1.\n", "\n", "It appears that both training and testing curve is showing lower scores compared to other graphs. \n", "\n", "* *What happens to the score of the training curve as more training points are added?*\n", "\n", "As more training points are being added, the score of the training curve goes down significantly. At first it went down to around 0.6 when there was about 50 data points, but after adding more data points the score went down even furture and levelled off near 0.4 after 100 data points.\n", "\n", "* *What about the testing curve?*\n", "\n", "In the graph with max depth 1, the testing accuracy is near 0.4 at maximum and then mostly stays there. It achieves the maximum accuracy with 50 data points, but after that as more data gets added it does nothing to improve the performance of the testing curve.\n", "\n", "* *Would having more training points benefit the model?*\n", "\n", "It seems like a classic case of underfitting as the model is too simple to capture the complexities inherent in the data set. This model has high bias. Adding more training points will not benefit the model as it will consistently under fit the data set because the model is just too simple to capture complexities of the process that developed the data.\n", "\n", "* *Are the learning curves converging to particular scores?*\n", "\n", "In this graph the scores seem to be converging to 0.4. In models with high bias, training and testing scores tend to converge to same score because the model fails to capture complexities above that level systematically. This is yet another evidence to claim that this model has high bias even adding lots of data points will not improve the performance of the model significantly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Complexity Curves\n", "The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the **learning curves**, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the `performance_metric` function. \n", "\n", "Run the code cell below and use this graph to answer the following two questions." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Lmb56u3khMGiq/bR6LfBnACnlw0KIw4ZsfwpogWKXstG6lhlAt+Nt2oTd1Ym7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\nvYlr/3sN/3zlPqJ2lM8d/AU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lcbNPKcWc5FzTId1gMBiqzJ4xcsuWzTRcdRlWOk3qS2fh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\nJ9xRREQkSkXV9b1S41M5IzU53DFERCSKRdWIr05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"text/plain": [ "<matplotlib.figure.Figure at 0xbc29978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.ModelComplexity(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 5 - Bias-Variance Tradeoff\n", "*When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance? How about when the model is trained with a maximum depth of 10? What visual cues in the graph justify your conclusions?* \n", "**Hint:** How do you know when a model is suffering from high bias or high variance?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "\n", "* *When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance?* \n", "\n", "When the model is trained with a maximum depth of one, the model suffers from high bias because the accuracy for both training and testing score is relatively low,around 0.4 for the testing score and 0.5 for the training score. \n", "\n", "This suggests the model failed to capture the complex relationships inherent in the data set and suffers from underfitting. This is a classic case of high bias. \n", "\n", "\n", "\n", "* *How about when the model is trained with a maximum depth of 10?* \n", "\n", "When the model is trained with a maximum depth of 10 the difference between training score and testing score is pretty high. Training score is showing as 'near accurate'/very close to one while testing score is around 0.7. \n", "\n", "This suggests that the model suffers from high variance consdering it has very good performance on training score because it has learnt the quirks of the training dataset and overfitted to it. \n", "\n", "However, it fails to generalize it's predictions to the testing dataset because of overfitting. This is a classic case of suffering from high variance. \n", "\n", "\n", "* *What visual cues in the graph justify your conclusions?*\n", "\n", "In max depth one the training score and testing score is pretty close and both of them are low scores. This was the visual cue for high bias because models with high bias converge to similar training and testing scores and underfits the data.\n", "\n", "In max depth 10 the visual cue was the big gap between training and testing score and the training score being close to one. This suggested the model has overfitted the training data and scored very high on it, while it failed to generalize to the testing data.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 6 - Best-Guess Optimal Model\n", "*Which maximum depth do you think results in a model that best generalizes to unseen data? What intuition lead you to this answer?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "I think the model with max depth 4 best generalizes to the unseen data because it has the highest testing score which is very close to 0.8 and that's also the depth where the difference between the training score and the testing score is really small. Until the depth of four, the testing score keeps rising.\n", "\n", "Above the depth of four, the training score of the model keeps increasing and goes to one but the testing score keeps decreasing while the gap between the training and testing scores also increases.This signals overfitting and a high variance model. Those models will not be able to generalize over independent data sets.\n", "\n", "Under the depth of four the gap between training and testing score is low, but the score it self is really low which signals underfitting and high bias. Those models will not be able to capture the complexities of the data sets for both training and testing sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "## Evaluating Model Performance\n", "In this final section of the project, you will construct a model and make a prediction on the client's feature set using an optimized model from `fit_model`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 7 - Grid Search\n", "*What is the grid search technique and how it can be applied to optimize a learning algorithm?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "Many Learning algorithms tend to have parameters that can be tuned for optimal model performance over a particular data set. However, figuring out the best combination of the parameters manually can be a lengthy process. \n", "\n", "Grid Search automates this process by trying out multiple combinations of the given parameters over an estimator/model to find out the best combination for those parameters under the chosen evaluation metric to determine the best model. This way we can optimize a learning algorithm by choosing the model with the best parameter combination returned by the grid search. Note that Grid Search will exhaustively try all combinations of the given parameters instead of a random sample of the parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 8 - Cross-Validation\n", "*What is the k-fold cross-validation training technique? What benefit does this technique provide for grid search when optimizing a model?* \n", "**Hint:** Much like the reasoning behind having a testing set, what could go wrong with using grid search without a cross-validated set?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "* What is the k-fold cross-validation training technique? \n", "\n", "When we train a model we want to use as much data as possible, but we also want to test our model using as much data as possible. As we split the data into training and testing sets, we have to make a trade off when we pick the amount of data that will go into the training set vs the amount of the data that will go to the testing set.\n", "\n", "K-Ford cross validation overcomes this problem by randomly splitting the data into k subsets. For k iterations, each time one subset of the data set is hold out for the testing and the rest goes into training the model. This way we can evaluate the model's performance k times and the average score over the k-iterations is likely to be a good predictor of the model's actual performance over new data sets when it will have to generalize it's predictions compared to the situation where we only split the data set into two subsets, training and testing and evaluate performance only once. This way the model learns from the whole data set and performs testing on the entire data sets leading to better performance and more accurate evaluations.\n", "\n", "* What benefit does this technique provide for grid search when optimizing a model?* \n", "\n", "When Grid Search technique is trying out different combinations of the given parameters for the model, if it evaluates the model only by splitting the data into two subsets, training and testing it can easily return an overfitted model which has learnt only the quirks of the training subset. Not to mention in this case we will not be using the whole data set for training and testing either, k-fold cross validation can be combined the grid search technique to ensure that the grid-search is choosing the model that best generalizes instead of the model that overfits.\n", "\n", "In this case we will provide the grid search with some parameters and a given model and the data set. The k-fold cross validation will evaluate the grid search's chosen model with some combination of the parameters in each iteration and return the average score. Grid Search will choose the model that performed best on k-fold cross validation and return that model with the chosen combination of the parameters.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation: Fitting a Model\n", "Your final implementation requires that you bring everything together and train a model using the **decision tree algorithm**. To ensure that you are producing an optimized model, you will train the model using the grid search technique to optimize the `'max_depth'` parameter for the decision tree. The `'max_depth'` parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction. Decision trees are part of a class of algorithms called *supervised learning algorithms*.\n", "\n", "For the `fit_model` function in the code cell below, you will need to implement the following:\n", "- Use [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) from `sklearn.tree` to create a decision tree regressor object.\n", " - Assign this object to the `'regressor'` variable.\n", "- Create a dictionary for `'max_depth'` with the values from 1 to 10, and assign this to the `'params'` variable.\n", "- Use [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) from `sklearn.metrics` to create a scoring function object.\n", " - Pass the `performance_metric` function as a parameter to the object.\n", " - Assign this scoring function to the `'scoring_fnc'` variable.\n", "- Use [`GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) from `sklearn.grid_search` to create a grid search object.\n", " - Pass the variables `'regressor'`, `'params'`, `'scoring_fnc'`, and `'cv_sets'` as parameters to the object. \n", " - Assign the `GridSearchCV` object to the `'grid'` variable." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.metrics import make_scorer\n", "from sklearn.grid_search import GridSearchCV\n", "\n", "def fit_model(X, y):\n", " \"\"\" Performs grid search over the 'max_depth' parameter for a \n", " decision tree regressor trained on the input data [X, y]. \"\"\"\n", " \n", " # Create cross-validation sets from the training data\n", " cv_sets = ShuffleSplit(X.shape[0], n_iter = 10, test_size = 0.20, random_state = 0)\n", "\n", " # TODO: Create a decision tree regressor object\n", " regressor = DecisionTreeRegressor()\n", "\n", " # TODO: Create a dictionary for the parameter 'max_depth' with a range from 1 to 10\n", " params = {\"max_depth\":(1,2,3,4,5,6,7,8,9,10)}\n", "\n", " # TODO: Transform 'performance_metric' into a scoring function using 'make_scorer' \n", " scoring_fnc = make_scorer(performance_metric) # as performance metric is R^2 which is a scoring function, not a loss function\n", " # greater_is_better defaults to true\n", "\n", " # TODO: Create the grid search object\n", " grid = GridSearchCV(regressor,params,cv = cv_sets, scoring = scoring_fnc)\n", "\n", " # Fit the grid search object to the data to compute the optimal model\n", " grid = grid.fit(X, y)\n", "\n", " # Return the optimal model after fitting the data\n", " return grid.best_estimator_\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making Predictions\n", "Once a model has been trained on a given set of data, it can now be used to make predictions on new sets of input data. In the case of a *decision tree regressor*, the model has learned *what the best questions to ask about the input data are*, and can respond with a prediction for the **target variable**. You can use these predictions to gain information about data where the value of the target variable is unknown — such as data the model was not trained on." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 9 - Optimal Model\n", "_What maximum depth does the optimal model have? How does this result compare to your guess in **Question 6**?_ \n", "\n", "Run the code block below to fit the decision tree regressor to the training data and produce an optimal model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter 'max_depth' is 4 for the optimal model.\n" ] } ], "source": [ "# Fit the training data to the model using grid search\n", "reg = fit_model(X_train, y_train)\n", "\n", "# Produce the value for 'max_depth'\n", "print \"Parameter 'max_depth' is {} for the optimal model.\".format(reg.get_params()['max_depth'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "It appears that the optimal model has the max depth of four. My initial guess was that choosing the model with max depth of four would be the optimal choice because the difference between training and testing score was small and the testing score was the highest near 0.8 at the max depth of four in the model complexity graph. \n", "\n", "However, since the grid search has choosen the model with max depth of four it verifies my assumption. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 10 - Predicting Selling Prices\n", "Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:\n", "\n", "| Feature | Client 1 | Client 2 | Client 3 |\n", "| :---: | :---: | :---: | :---: |\n", "| Total number of rooms in home | 5 rooms | 4 rooms | 8 rooms |\n", "| Household net worth (income) | Top 34th percent | Bottom 45th percent | Top 7th percent |\n", "| Student-teacher ratio of nearby schools | 15-to-1 | 22-to-1 | 12-to-1 |\n", "*What price would you recommend each client sell his/her home at? Do these prices seem reasonable given the values for the respective features?* \n", "**Hint:** Use the statistics you calculated in the **Data Exploration** section to help justify your response. \n", "\n", "Run the code block below to have your optimized model make predictions for each client's home." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted selling price for Client 1's home: $324,240.00\n", "Predicted selling price for Client 2's home: $189,123.53\n", "Predicted selling price for Client 3's home: $942,666.67\n" ] } ], "source": [ "# Produce a matrix for client data\n", "client_data = [[5, 34, 15], # Client 1\n", " [4, 55, 22], # Client 2\n", " [8, 7, 12]] # Client 3\n", "\n", "# Show predictions\n", "for i, price in enumerate(reg.predict(client_data)):\n", " print \"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "### Stats from the data exploration section :\n", "\n", "The statistics from the data exploration section looks like(all in USD) : \n", "* Minimum price: 105,000.00 \n", "* Maximum price: 1,024,800.00\n", "* Mean price: 454,342.94\n", "* Median price 438,900.00\n", "* Standard deviation of prices: 165,171.13\n", "\n", "### Interpretation :\n", "\n", "* The first client's house has 5 rooms, the household networth is top 34 percent and the student teacher ratio of nearby schools is 15 to 1. The model predicted his house should be sold for $324,240,00. The house has average number of rooms and similar to average PTRATIO, I believe the LSTAT number 34th percentile got this houses price down. The predicted price is pretty close to the median price though ( 438,900.00) so it's more or less reasonable.\n", "\n", "* The second client's house has four rooms, the household networth is around bottom 45 percent and the student teacher ratio of nearby schools is 22 to 1. From the data exploratory section we have noticed that the low household networth and high student teacher ratio is negatively correlated with the house price. The model has predicted the house price around 189,123.53 USD which is near the minimum. The maximum PTRATIO in this data set is also 22 and the maximum LSTAT is around 37. Given the house does seem to be owned by poor owners(with top 55% household networth greater than this household owners) and the PTRATIO is around the maximum(22), the model's predictions that this house will sale below the average house price( mean house price = 438,900.00 USD) and near the minimum price( 105,000.00) seems reasonable to me.\n", "\n", "* The third client's house has eight rooms, the house hold networth is around top 7 percent and PTRATIO is at the data set's minimum which is 12 to one. The maximum number of rooms in this data set also happens to be eight. Since the number of rooms is positively correlated with the price, the number of household networth and PTRATIO is negatively correlated, it appears that the models prediction this house will sale at the price of 942,666.57, which is near the maximum price (1,024,800.00) is a reasonable guess.\n", "\n", "* From the nearest neighbors algorithm(below) we can see that the numbers for the predictions made by the decision-tree-regressor are pretty close, at least between one standard deviation above/below the predictions made by nearest neighbors. Note that the nearest neighbors part was added from the pro-tips part of the last review." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "280980.0\n", "The predicted 5 nearest neighbors price for home 1 is: $315,840.00\n", "The predicted 5 nearest neighbors price for home 2 is: $280,980.00\n", "The predicted 5 nearest neighbors price for home 3 is: $808,920.00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", "C:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", "C:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n", "C:\\Users\\User\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] } ], "source": [ "from sklearn.neighbors import NearestNeighbors\n", "num_neighbors=5\n", "def nearest_neighbor_price(x):\n", " def find_nearest_neighbor_indexes(x, X): # x is your vector and X is the data set.\n", " neigh = NearestNeighbors( num_neighbors )\n", " neigh.fit(X)\n", " distance, indexes = neigh.kneighbors( x )\n", " return indexes\n", " indexes = find_nearest_neighbor_indexes(x, features)\n", " sum_prices = []\n", " for i in indexes:\n", " sum_prices.append(prices[i])\n", " neighbor_avg = np.mean(sum_prices)\n", " return neighbor_avg\n", "print nearest_neighbor_price( [4, 55, 22])\n", "index = 0 \n", "for i in client_data:\n", " val=nearest_neighbor_price(i)\n", " index += 1\n", " print \"The predicted {} nearest neighbors price for home {} is: ${:,.2f}\".format(num_neighbors,index, val)\n", " \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>RM</th>\n", " <th>LSTAT</th>\n", " <th>PTRATIO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>489.000000</td>\n", " <td>489.000000</td>\n", " <td>489.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>6.240288</td>\n", " <td>12.939632</td>\n", " <td>18.516564</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.643650</td>\n", " <td>7.081990</td>\n", " <td>2.111268</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>3.561000</td>\n", " <td>1.980000</td>\n", " <td>12.600000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.880000</td>\n", " <td>7.370000</td>\n", " <td>17.400000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>6.185000</td>\n", " <td>11.690000</td>\n", " <td>19.100000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6.575000</td>\n", " <td>17.120000</td>\n", " <td>20.200000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>8.398000</td>\n", " <td>37.970000</td>\n", " <td>22.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RM LSTAT PTRATIO\n", "count 489.000000 489.000000 489.000000\n", "mean 6.240288 12.939632 18.516564\n", "std 0.643650 7.081990 2.111268\n", "min 3.561000 1.980000 12.600000\n", "25% 5.880000 7.370000 17.400000\n", "50% 6.185000 11.690000 19.100000\n", "75% 6.575000 17.120000 20.200000\n", "max 8.398000 37.970000 22.000000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[[\"RM\",\"LSTAT\",\"PTRATIO\"]].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity\n", "An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted. Run the code cell below to run the `fit_model` function ten times with different training and testing sets to see how the prediction for a specific client changes with the data it's trained on." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trial 1: $324,240.00\n", "Trial 2: $324,450.00\n", "Trial 3: $346,500.00\n", "Trial 4: $420,622.22\n", "Trial 5: $413,334.78\n", "Trial 6: $411,931.58\n", "Trial 7: $344,750.00\n", "Trial 8: $407,232.00\n", "Trial 9: $352,315.38\n", "Trial 10: $316,890.00\n", "\n", "Range in prices: $103,732.22\n" ] } ], "source": [ "vs.PredictTrials(features, prices, fit_model, client_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 11 - Applicability\n", "*In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.* \n", "**Hint:** Some questions to answering:\n", "- *How relevant today is data that was collected from 1978?*\n", "- *Are the features present in the data sufficient to describe a home?*\n", "- *Is the model robust enough to make consistent predictions?*\n", "- *Would data collected in an urban city like Boston be applicable in a rural city?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer: **\n", "\n", "I believe this model should not be used in a real world setting. \n", "\n", "Some reasons :\n", "\n", "* The data is most probably not relevant today anymore given the house prices in US is reaching sky-high these days in urban areas. Having a housing data set from 2015-2016 would have made me more confident about the results.\n", "* The features seem insufficient to describe a home. The neighborhood zip codes, the last buying price of the house, market trends, when the house was built etc can be better predictors of the price.\n", "* The model doesn't seem robust to me given it's predictions ranged around 103,732.22. In a real world setting this model can undersale a house for 100,000 which might hurt the house owners.\n", "* The data collected in an urban city like Boston will definitely not be applicable in a rural city. I'd argue this model will not be able to predict similar urban city's like San Fransico's house prices because of the current trend of housing price going higher. \n", "\n", "The model, decision tree regressor is a reasonable selection to predict housing prices, but the data set we have trained the model on, is 20 years old and is not a good refletion of the current trends. Perhaps we should collect better data and train another model before making any prediction in this situation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

csdms/pymt

notebooks/sedflux3d_and_child.ipynb

1

444006

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Sedflux3D + CHILD\n", "* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/sedflux3d_and_child.ipynb\n", "* Install command: `$ conda install notebook pymt_sedflux pymt_child`\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Some magic to make plots appear within the notebook\n", "%matplotlib inline\n", "import numpy as np # In case we need to use numpy\n", "import pymt.models" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/huttone/anaconda/envs/child_sedflux/lib/python3.6/site-packages/pymt/utils/decorators.py:60: UserWarning: Call to deprecated function get_grid_size.\n", " name=func.__name__\n" ] } ], "source": [ "child = pymt.models.Child()\n", "sedflux = pymt.models.Sedflux3D()\n", "\n", "child_in, child_dir = child.setup(\n", " \"_child\",\n", " grid_node_spacing=500.0,\n", " grid_x_size=40000.0,\n", " grid_y_size=20000.0,\n", " run_duration=1e6,\n", ")\n", "sedflux_in, sedflux_dir = sedflux.setup(\n", " \"_sedflux\",\n", " river_bed_load_flux=0.0,\n", " river_suspended_load_concentration_0=0.1,\n", " river_suspended_load_concentration_1=0.1,\n", " run_duration=1e6 * 365.0,\n", ")\n", "\n", "child.initialize(child_in, dir=child_dir)\n", "sedflux.initialize(sedflux_in, dir=sedflux_dir)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "gid = child.var[\"land_surface__elevation\"].grid\n", "x, y = child.get_grid_x(gid), child.get_grid_y(gid)\n", "z = child.get_value(\"land_surface__elevation\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-100. , -100. , -100. , ..., 99.99964955,\n", " 99.56988064, 100.34489894])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_shore = 20000.0\n", "z[np.where(x > x_shore)] += 100.0\n", "z[np.where(x <= x_shore)] -= 100.0\n", "\n", "child.set_value(\"land_surface__elevation\", z)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "sedflux.set_value(\n", " \"bedrock_surface__elevation\", mapfrom=(\"land_surface__elevation\", child)\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "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": [ "child.quick_plot(\n", " \"land_surface__elevation\", edgecolors=\"k\", vmin=-200, vmax=200, cmap=\"BrBG_r\"\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "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": [ "sedflux.quick_plot(\"bedrock_surface__elevation\", vmin=-200, vmax=200, cmap=\"BrBG_r\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 4.])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sedflux.set_value(\"channel_exit_water_flow__speed\", 1.2)\n", "sedflux.set_value(\"channel_exit_x-section__mean_of_width\", 400.)\n", "sedflux.set_value(\"channel_exit_x-section__mean_of_depth\", 4.)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "now = child.time\n", "times = np.arange(now, now + 1000, 1.0)\n", "sedflux.update()\n", "child.update()\n", "for t in times:\n", " child.update_until(t, units=\"years\")\n", "\n", " sedflux.set_value(\"channel_water_sediment~bedload__mass_flow_rate\", mapfrom=child)\n", " sedflux.update_until(t, units=\"years\")\n", "\n", " z = child.get_value(\"land_surface__elevation\")\n", " child.set_value(\n", " \"land_surface__elevation\",\n", " mapfrom=(\"land-or-seabed_sediment_surface__elevation\", sedflux),\n", " nomap=np.where(z > 0.0),\n", " )" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "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": [ "child.quick_plot(\n", " \"land_surface__elevation\", edgecolors=\"k\", vmin=-200, vmax=200, cmap=\"BrBG_r\"\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "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": [ "child.quick_plot(\n", " \"land_surface__elevation\", edgecolors=\"k\", vmin=-200, vmax=200, cmap=\"BrBG_r\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

davofis/computational_seismology

05_pseudospectral/cheby_derivative_solution.ipynb

1

8155

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div style='background-image: url(\"../../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n", " <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px\">\n", " <div style=\"position: relative ; top: 50% ; transform: translatey(-50%)\">\n", " <div style=\"font-size: xx-large ; font-weight: 900 ; color: rgba(0 , 0 , 0 , 0.8) ; line-height: 100%\">Computational Seismology</div>\n", " <div style=\"font-size: large ; padding-top: 20px ; color: rgba(0 , 0 , 0 , 0.5)\">Numerical derivatives based on a derivative matrix</div>\n", " </div>\n", " </div>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seismo-Live: http://seismo-live.org\n", "\n", "##### Authors:\n", "* Fabian Linder ([@fablindner](https://github.com/fablindner))\n", "* Heiner Igel ([@heinerigel](https://github.com/heinerigel))\n", "* David Vargas ([@dvargas](https://github.com/davofis))\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Equations\n", "\n", "Calculating a derivative using the differentation theorem of the Fourier Transform is in the mathematical sense a convolution of the function $f(x)$ with $ik$, where $k$ is the wavenumber and $i$ the imaginary unit. This can also be formulated as a matrix-vector product involving so-called Toeplitz matrices. An elegant (but inefficient) way of performing a derivative operation on a space-dependent function described on the Chebyshev collocation points is by defining a derivative matrix $D_{ij}$\n", "\n", "$$ D_{ij} \\ = \\ -\\frac{2 N^2 + 1}{6} \\hspace{1.5cm} \\text{for i = j = N} $$\n", "$$ D_{ij} \\ = \\ -\\frac{1}{2} \\frac{x_i}{1-x_i^2} \\hspace{1.5cm} \\text{for i = j = 1,2,...,N-1} $$\n", "$$ D_{ij} \\ = \\ \\frac{c_i}{c_j} \\frac{(-1)^{i+j}}{x_i - x_j} \\hspace{1.5cm} \\text{for i $\\neq$ j =\n", "0,1,...,N}$$\n", "\n", "where $N+1$ is the number of Chebyshev collocation points $ \\ x_i = cos(i\\pi / N)$, $ \\ i=0,...,N$ and the $c_i$ are given as\n", "\n", "$$ c_i = 2 \\hspace{1.5cm} \\text{for i = 0 or N} $$\n", "$$ c_i = 1 \\hspace{1.5cm} \\text{otherwise} $$\n", "\n", "This differentiation matrix allows us to write the derivative of the function $f_i = f(x_i)$ (possibly depending on time) simply as\n", "\n", "$$\\partial_x u_i = D_{ij} \\ u_j$$\n", "\n", "where the right-hand side is a matrix-vector product, and the Einstein summation convention applies." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This is a configuration step for the exercise. Please run it before calculating the derivative!\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Show the plots in the Notebook.\n", "plt.switch_backend(\"nbagg\")" ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden", "solution2_first": true }, "source": [ "#### Exercise 1\n", "\n", "Define a python function call \"get_cheby_matrix(nx)\" that initializes the Chebyshev derivative matrix $D_{ij}$ " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "solution2": "hidden" }, "outputs": [], "source": [ "# Function for setting up the Chebyshev derivative matrix\n", "def get_cheby_matrix(nx):\n", " cx = np.zeros(nx+1)\n", " x = np.zeros(nx+1)\n", " for ix in range(0,nx+1):\n", " x[ix] = np.cos(np.pi * ix / nx)\n", " \n", " cx[0] = 2.\n", " cx[nx] = 2.\n", " cx[1:nx] = 1.\n", " \n", " D = np.zeros((nx+1,nx+1))\n", " for i in range(0, nx+1):\n", " for j in range(0, nx+1):\n", " if i==j and i!=0 and i!=nx:\n", " D[i,i]=-x[i]/(2.0*(1.0-x[i]*x[i]))\n", " else:\n", " D[i,j]=(cx[i]*(-1)**(i+j))/(cx[j]*(x[i]-x[j]))\n", " \n", " D[0,0] = (2.*nx**2+1.)/6.\n", " D[nx,nx] = -D[0,0]\n", " return D " ] }, { "cell_type": "markdown", "metadata": { "solution2": "hidden", "solution2_first": true }, "source": [ "#### Exercise 2\n", "\n", "Calculate the numerical derivative by applying the differentiation matrix $D_{ij}$. Define an arbitrary function (e.g. a Gaussian) and initialize its analytical derivative on the Chebyshev collocation points. Calculate the numerical derivative and the difference to the analytical solution. Vary the wavenumber content of the analytical function. Does it make a difference? Why is the numerical result not entirely exact?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "solution2": "hidden" }, "outputs": [], "source": [ "# Initialize arbitrary test function on Chebyshev collocation points\n", "nx = 200 # Number of grid points\n", "x = np.zeros(nx+1)\n", "for ix in range(0,nx+1):\n", " x[ix] = np.cos(ix * np.pi / nx) \n", "dxmin = min(abs(np.diff(x)))\n", "dxmax = max(abs(np.diff(x)))\n", "\n", "# Function example: Gaussian\n", "# Width of Gaussian\n", "s = .2 \n", "# Gaussian function (modify!)\n", "f = np.exp(-1/s**2 * x**2)\n", "\n", "# Initialize differentiation matrix\n", "D = get_cheby_matrix(nx)\n", "\n", "# Analytical derivative\n", "df_ana = -2/s**2 * x * np.exp(-1/s**2 * x**2)\n", "\n", "# Calculate numerical derivative using differentiation matrix\n", "df_num = D @ f\n", "\n", "# To make the error visible, it is multiply by 10^12\n", "df_err = 1e12*(df_ana - df_num)\n", "\n", "# Calculate error between analytical and numerical solution\n", "err = np.sum((df_num - df_ana)**2) / np.sum(df_ana**2) * 100\n", "print('Error: %s' %err)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "solution2": "hidden", "solution2_first": true }, "source": [ "#### Exercise 3\n", "\n", "Now that the numerical derivative is available, we can visually inspect our results. Make a plot of both, the analytical and numerical derivatives together with the difference error. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "solution2": "hidden" }, "outputs": [], "source": [ "# Plot analytical and numerical derivatives\n", "# ---------------------------------------------------------------\n", "\n", "plt.subplot(2,1,1)\n", "plt.plot(x, f, \"g\", lw = 1.5, label='Gaussian')\n", "plt.legend(loc='upper right', shadow=True)\n", "plt.xlabel('$x$') \n", "plt.ylabel('$f(x)$')\n", "\n", "plt.subplot(2,1,2)\n", "plt.plot(x, df_ana, \"b\", lw = 1.5, label='Analytical')\n", "plt.plot(x, df_num, 'k--', lw = 1.5, label='Numerical')\n", "plt.plot(x, df_err, \"r\", lw = 1.5, label='Difference')\n", "plt.legend(loc='upper right', shadow=True)\n", "plt.xlabel('$x$') \n", "plt.ylabel('$\\partial_x f(x)$')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

CoderDojoTC/python-minecraft

classroom-code/examples/hello_world.ipynb

1

480

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!/usr/bin/python\n", "import mcpi.minecraft as minecraft\n", "import mcpi.block as block\n", "\n", "# Connect to the Minecraft server\n", "world = minecraft.Minecraft.create()\n", "\n", "world.postToChat(\"Hello Minecraft!\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

mit

ES-DOC/esdoc-jupyterhub

notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb

1

164419

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "**MIP Era**: CMIP6 \n", "**Institute**: CCCR-IITM \n", "**Source ID**: SANDBOX-1 \n", "**Topic**: Ocean \n", "**Sub-Topics**: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n", "**Properties**: 133 (101 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:48" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-1', 'ocean')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Seawater Properties](#2.-Key-Properties---&gt;-Seawater-Properties) \n", "[3. Key Properties --&gt; Bathymetry](#3.-Key-Properties---&gt;-Bathymetry) \n", "[4. Key Properties --&gt; Nonoceanic Waters](#4.-Key-Properties---&gt;-Nonoceanic-Waters) \n", "[5. Key Properties --&gt; Software Properties](#5.-Key-Properties---&gt;-Software-Properties) \n", "[6. Key Properties --&gt; Resolution](#6.-Key-Properties---&gt;-Resolution) \n", "[7. Key Properties --&gt; Tuning Applied](#7.-Key-Properties---&gt;-Tuning-Applied) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid](#9.-Grid) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Discretisation --&gt; Horizontal](#11.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[12. Timestepping Framework](#12.-Timestepping-Framework) \n", "[13. Timestepping Framework --&gt; Tracers](#13.-Timestepping-Framework---&gt;-Tracers) \n", "[14. Timestepping Framework --&gt; Baroclinic Dynamics](#14.-Timestepping-Framework---&gt;-Baroclinic-Dynamics) \n", "[15. Timestepping Framework --&gt; Barotropic](#15.-Timestepping-Framework---&gt;-Barotropic) \n", "[16. Timestepping Framework --&gt; Vertical Physics](#16.-Timestepping-Framework---&gt;-Vertical-Physics) \n", "[17. Advection](#17.-Advection) \n", "[18. Advection --&gt; Momentum](#18.-Advection---&gt;-Momentum) \n", "[19. Advection --&gt; Lateral Tracers](#19.-Advection---&gt;-Lateral-Tracers) \n", "[20. Advection --&gt; Vertical Tracers](#20.-Advection---&gt;-Vertical-Tracers) \n", "[21. Lateral Physics](#21.-Lateral-Physics) \n", "[22. Lateral Physics --&gt; Momentum --&gt; Operator](#22.-Lateral-Physics---&gt;-Momentum---&gt;-Operator) \n", "[23. Lateral Physics --&gt; Momentum --&gt; Eddy Viscosity Coeff](#23.-Lateral-Physics---&gt;-Momentum---&gt;-Eddy-Viscosity-Coeff) \n", "[24. Lateral Physics --&gt; Tracers](#24.-Lateral-Physics---&gt;-Tracers) \n", "[25. Lateral Physics --&gt; Tracers --&gt; Operator](#25.-Lateral-Physics---&gt;-Tracers---&gt;-Operator) \n", "[26. Lateral Physics --&gt; Tracers --&gt; Eddy Diffusity Coeff](#26.-Lateral-Physics---&gt;-Tracers---&gt;-Eddy-Diffusity-Coeff) \n", "[27. Lateral Physics --&gt; Tracers --&gt; Eddy Induced Velocity](#27.-Lateral-Physics---&gt;-Tracers---&gt;-Eddy-Induced-Velocity) \n", "[28. Vertical Physics](#28.-Vertical-Physics) \n", "[29. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Details](#29.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Details) \n", "[30. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Tracers](#30.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Tracers) \n", "[31. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Momentum](#31.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Momentum) \n", "[32. Vertical Physics --&gt; Interior Mixing --&gt; Details](#32.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Details) \n", "[33. Vertical Physics --&gt; Interior Mixing --&gt; Tracers](#33.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Tracers) \n", "[34. Vertical Physics --&gt; Interior Mixing --&gt; Momentum](#34.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Momentum) \n", "[35. Uplow Boundaries --&gt; Free Surface](#35.-Uplow-Boundaries---&gt;-Free-Surface) \n", "[36. Uplow Boundaries --&gt; Bottom Boundary Layer](#36.-Uplow-Boundaries---&gt;-Bottom-Boundary-Layer) \n", "[37. Boundary Forcing](#37.-Boundary-Forcing) \n", "[38. Boundary Forcing --&gt; Momentum --&gt; Bottom Friction](#38.-Boundary-Forcing---&gt;-Momentum---&gt;-Bottom-Friction) \n", "[39. Boundary Forcing --&gt; Momentum --&gt; Lateral Friction](#39.-Boundary-Forcing---&gt;-Momentum---&gt;-Lateral-Friction) \n", "[40. Boundary Forcing --&gt; Tracers --&gt; Sunlight Penetration](#40.-Boundary-Forcing---&gt;-Tracers---&gt;-Sunlight-Penetration) \n", "[41. Boundary Forcing --&gt; Tracers --&gt; Fresh Water Forcing](#41.-Boundary-Forcing---&gt;-Tracers---&gt;-Fresh-Water-Forcing) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Ocean key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of ocean model code (NEMO 3.6, MOM 5.0,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OGCM\" \n", "# \"slab ocean\" \n", "# \"mixed layer ocean\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Basic approximations made in the ocean.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Primitive equations\" \n", "# \"Non-hydrostatic\" \n", "# \"Boussinesq\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the ocean component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# \"Salinity\" \n", "# \"U-velocity\" \n", "# \"V-velocity\" \n", "# \"W-velocity\" \n", "# \"SSH\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Seawater Properties \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Eos Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Wright, 1997\" \n", "# \"Mc Dougall et al.\" \n", "# \"Jackett et al. 2006\" \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Eos Functional Temp\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Temperature used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Eos Functional Salt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Salinity used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Practical salinity Sp\" \n", "# \"Absolute salinity Sa\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Eos Functional Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Depth or pressure used in EOS for sea water ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pressure (dbars)\" \n", "# \"Depth (meters)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.6. Ocean Specific Heat\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specific heat in ocean (cpocean) in J/(kg K)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.7. Ocean Reference Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Boussinesq reference density (rhozero) in kg / m3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Bathymetry \n", "*Properties of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Reference Dates\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Reference date of bathymetry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Present day\" \n", "# \"21000 years BP\" \n", "# \"6000 years BP\" \n", "# \"LGM\" \n", "# \"Pliocene\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the bathymetry fixed in time in the ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Ocean Smoothing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe any smoothing or hand editing of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Source\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe source of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Nonoceanic Waters \n", "*Non oceanic waters treatement in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Isolated Seas\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how isolated seas is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. River Mouth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how river mouth mixing or estuaries specific treatment is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Software Properties \n", "*Software properties of ocean code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Resolution \n", "*Resolution in the ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Range Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Number Of Vertical Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Is Adaptive Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. Thickness Level 1\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Thickness of first surface ocean level (in meters)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Brief description of conservation methodology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in the ocean by the numerical schemes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Enstrophy\" \n", "# \"Salt\" \n", "# \"Volume of ocean\" \n", "# \"Momentum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Consistency Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Corrected Conserved Prognostic Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Set of variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Was Flux Correction Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Does conservation involve flux correction ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid \n", "*Ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of grid in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Properties of vertical discretisation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Coordinates\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of vertical coordinates in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Z-coordinate\" \n", "# \"Z*-coordinate\" \n", "# \"S-coordinate\" \n", "# \"Isopycnic - sigma 0\" \n", "# \"Isopycnic - sigma 2\" \n", "# \"Isopycnic - sigma 4\" \n", "# \"Isopycnic - other\" \n", "# \"Hybrid / Z+S\" \n", "# \"Hybrid / Z+isopycnic\" \n", "# \"Hybrid / other\" \n", "# \"Pressure referenced (P)\" \n", "# \"P*\" \n", "# \"Z**\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Partial Steps\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Using partial steps with Z or Z* vertical coordinate in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Type of horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal grid type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Lat-lon\" \n", "# \"Rotated north pole\" \n", "# \"Two north poles (ORCA-style)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Staggering\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal grid staggering type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"N/a\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite difference\" \n", "# \"Finite volumes\" \n", "# \"Finite elements\" \n", "# \"Unstructured grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Timestepping Framework \n", "*Ocean Timestepping Framework*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Diurnal Cycle\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Diurnal cycle type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Via coupling\" \n", "# \"Specific treatment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Timestepping Framework --&gt; Tracers \n", "*Properties of tracers time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracers time stepping scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracers time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Timestepping Framework --&gt; Baroclinic Dynamics \n", "*Baroclinic dynamics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Baroclinic dynamics type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Preconditioned conjugate gradient\" \n", "# \"Sub cyling\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Baroclinic dynamics scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Time Step\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Baroclinic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Timestepping Framework --&gt; Barotropic \n", "*Barotropic time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Splitting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time splitting method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"split explicit\" \n", "# \"implicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Time Step\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Barotropic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Timestepping Framework --&gt; Vertical Physics \n", "*Vertical physics time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Details of vertical time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Advection \n", "*Ocean advection*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of advection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Advection --&gt; Momentum \n", "*Properties of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flux form\" \n", "# \"Vector form\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Scheme Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of ocean momemtum advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. ALE\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Using ALE for vertical advection ? (if vertical coordinates are sigma)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Advection --&gt; Lateral Tracers \n", "*Properties of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Flux Limiter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Monotonic flux limiter for lateral tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Effective Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Effective order of limited lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Passive Tracers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Passive tracers advected*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ideal age\" \n", "# \"CFC 11\" \n", "# \"CFC 12\" \n", "# \"SF6\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.6. Passive Tracers Advection\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is advection of passive tracers different than active ? if so, describe.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Advection --&gt; Vertical Tracers \n", "*Properties of vertical tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Flux Limiter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Monotonic flux limiter for vertical tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Lateral Physics \n", "*Ocean lateral physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lateral physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of transient eddy representation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Eddy active\" \n", "# \"Eddy admitting\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Lateral Physics --&gt; Momentum --&gt; Operator \n", "*Properties of lateral physics operator for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Direction of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Discretisation of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Lateral Physics --&gt; Momentum --&gt; Eddy Viscosity Coeff \n", "*Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Lateral physics momemtum eddy viscosity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Constant Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Variable Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.4. Coeff Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.5. Coeff Backscatter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Lateral Physics --&gt; Tracers \n", "*Properties of lateral physics for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Mesoscale Closure\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there a mesoscale closure in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Submesoscale Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Lateral Physics --&gt; Tracers --&gt; Operator \n", "*Properties of lateral physics operator for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Direction of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Discretisation of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Lateral Physics --&gt; Tracers --&gt; Eddy Diffusity Coeff \n", "*Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Lateral physics tracers eddy diffusity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Constant Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Variable Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Coeff Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.5. Coeff Backscatter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Lateral Physics --&gt; Tracers --&gt; Eddy Induced Velocity \n", "*Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV in lateral physics tracers in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"GM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Constant Val\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If EIV scheme for tracers is constant, specify coefficient value (M2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Flux Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV flux (advective or skew)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Added Diffusivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV added diffusivity (constant, flow dependent or none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Vertical Physics \n", "*Ocean Vertical Physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Details \n", "*Properties of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Langmuir Cells Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there Langmuir cells mixing in upper ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Tracers \n", "*Properties of boundary layer (BL) mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of boundary layer mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Closure Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant BL mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Momentum \n", "*Properties of boundary layer (BL) mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of boundary layer mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Closure Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant BL mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Vertical Physics --&gt; Interior Mixing --&gt; Details \n", "*Properties of interior mixing in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Convection Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of vertical convection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Non-penetrative convective adjustment\" \n", "# \"Enhanced vertical diffusion\" \n", "# \"Included in turbulence closure\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Tide Induced Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how tide induced mixing is modelled (barotropic, baroclinic, none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Double Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there double diffusion*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Shear Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there interior shear mixing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Vertical Physics --&gt; Interior Mixing --&gt; Tracers \n", "*Properties of interior mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of interior mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant interior mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Profile\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Vertical Physics --&gt; Interior Mixing --&gt; Momentum \n", "*Properties of interior mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of interior mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant interior mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.3. Profile\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Uplow Boundaries --&gt; Free Surface \n", "*Properties of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Free surface scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear implicit\" \n", "# \"Linear filtered\" \n", "# \"Linear semi-explicit\" \n", "# \"Non-linear implicit\" \n", "# \"Non-linear filtered\" \n", "# \"Non-linear semi-explicit\" \n", "# \"Fully explicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.3. Embeded Seaice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the sea-ice embeded in the ocean model (instead of levitating) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Uplow Boundaries --&gt; Bottom Boundary Layer \n", "*Properties of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Type Of Bbl\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diffusive\" \n", "# \"Acvective\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Lateral Mixing Coef\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Sill Overflow\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe any specific treatment of sill overflows*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Boundary Forcing \n", "*Ocean boundary forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of boundary forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Surface Pressure\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.3. Momentum Flux Correction\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.4. Tracers Flux Correction\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.5. Wave Effects\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how wave effects are modelled at ocean surface.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.6. River Runoff Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how river runoff from land surface is routed to ocean and any global adjustment done.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.7. Geothermal Heating\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how geothermal heating is present at ocean bottom.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Boundary Forcing --&gt; Momentum --&gt; Bottom Friction \n", "*Properties of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Non-linear\" \n", "# \"Non-linear (drag function of speed of tides)\" \n", "# \"Constant drag coefficient\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Boundary Forcing --&gt; Momentum --&gt; Lateral Friction \n", "*Properties of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Free-slip\" \n", "# \"No-slip\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Boundary Forcing --&gt; Tracers --&gt; Sunlight Penetration \n", "*Properties of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"1 extinction depth\" \n", "# \"2 extinction depth\" \n", "# \"3 extinction depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.2. Ocean Colour\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the ocean sunlight penetration scheme ocean colour dependent ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.3. Extinction Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe and list extinctions depths for sunlight penetration scheme (if applicable).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Boundary Forcing --&gt; Tracers --&gt; Fresh Water Forcing \n", "*Properties of surface fresh water forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. From Atmopshere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from atmos in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. From Sea Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from sea-ice in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Real salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.3. Forced Mode Restoring\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface salinity restoring in forced mode (OMIP)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

gpl-3.0

RobbieNesmith/PandasTutorial

Tutorial/Exercises-4.ipynb

2

218081

{ "cells": [ { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import seaborn as sbn\n", "sbn.set()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>body {\n", " margin: 0;\n", " font-family: Helvetica;\n", "}\n", "table.dataframe {\n", " border-collapse: collapse;\n", " border: none;\n", "}\n", "table.dataframe tr {\n", " border: none;\n", "}\n", "table.dataframe td, table.dataframe th {\n", " margin: 0;\n", " border: 1px solid white;\n", " padding-left: 0.25em;\n", " padding-right: 0.25em;\n", "}\n", "table.dataframe th:not(:empty) {\n", " background-color: #fec;\n", " text-align: left;\n", " font-weight: normal;\n", "}\n", "table.dataframe tr:nth-child(2) th:empty {\n", " border-left: none;\n", " border-right: 1px dashed #888;\n", "}\n", "table.dataframe td {\n", " border: 2px solid #ccf;\n", " background-color: #f4f4ff;\n", "}\n", "h3 {\n", " color: white;\n", " background-color: black;\n", " padding: 0.5em;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "css = open('style-table.css').read() + open('style-notebook.css').read()\n", "HTML('<style>{}</style>'.format(css))" ] }, { "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>title</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>The Rising Son</td>\n", " <td>1990</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Ashes of Kukulcan</td>\n", " <td>2016</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>The Thousand Plane Raid</td>\n", " <td>1969</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Crucea de piatra</td>\n", " <td>1993</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>The 86</td>\n", " <td>2015</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year\n", "0 The Rising Son 1990\n", "1 Ashes of Kukulcan 2016\n", "2 The Thousand Plane Raid 1969\n", "3 Crucea de piatra 1993\n", "4 The 86 2015" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None)\n", "titles.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>title</th>\n", " <th>year</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>character</th>\n", " <th>n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Suuri illusioni</td>\n", " <td>1985</td>\n", " <td>Homo $</td>\n", " <td>actor</td>\n", " <td>Guests</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Gangsta Rap: The Glockumentary</td>\n", " <td>2007</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Menace II Society</td>\n", " <td>1993</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Lew-Loc</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Porndogs: The Adventures of Sadie</td>\n", " <td>2009</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Bosco</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Stop Pepper Palmer</td>\n", " <td>2014</td>\n", " <td>Too $hort</td>\n", " <td>actor</td>\n", " <td>Himself</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title year name type character n\n", "0 Suuri illusioni 1985 Homo $ actor Guests 22\n", "1 Gangsta Rap: The Glockumentary 2007 Too $hort actor Himself NaN\n", "2 Menace II Society 1993 Too $hort actor Lew-Loc 27\n", "3 Porndogs: The Adventures of Sadie 2009 Too $hort actor Bosco 3\n", "4 Stop Pepper Palmer 2014 Too $hort actor Himself NaN" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)\n", "cast.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Define a year as a \"Superman year\" whose films feature more Superman characters than Batman. How many years in film history have been Superman years?" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Superman: 12\n" ] } ], "source": [ "both = cast[(cast.character=='Superman') | (cast.character == 'Batman')].groupby(['year','character']).size().unstack().fillna(0)\n", "diff = both.Superman - both.Batman\n", "print(\"Superman: \" + str(len(diff[diff>0])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### How many years have been \"Batman years\", with more Batman characters than Superman characters?" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batman: 24\n" ] } ], "source": [ "both = cast[(cast.character=='Superman') | (cast.character == 'Batman')].groupby(['year','character']).size().unstack().fillna(0)\n", "diff = both.Batman - both.Superman\n", "print(\"Batman: \" + str(len(diff[diff>0])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Plot the number of actor roles each year and the number of actress roles each year over the history of film." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c41e4c50>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dvU1cF8vkeveMpGvXAACiokIIDQ5s0XVbq11uOOhPnjyZdevW8cQTT7B+/XqmTp3qPf7M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\nl8qs8n59R0VmORUTy/HHAuhV3TmJ3W5M37UDBlL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f71c46b6320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cast.groupby(['year','type']).size().unstack().plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Plot the number of actor roles each year and the number of actress roles each year, but this time as a kind='area' plot." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c4166748>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3/7yGtJaf/MHZ+BLo1bz8DqInva8+fRERGRDKmypwW55un2c3pp1wJJQBn6hK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\no53xxWNVjMK4h2C7h2AaUzD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fH/WEHcE6Wzo12EI3xvlpeQ7jP9czCKsh/cQ30nnB3aWA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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f71c416d7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cast.groupby(['year','type']).size().unstack().plot(kind='area')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Plot the difference between the number of actor roles each year and the number of actress roles each year over the history of film." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "foo = cast.groupby(['year','type']).size().unstack().fillna(0)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c40f48d0>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3myTiVwqKa3C0upgwPIGahhZ2HS2Ho+WEh1h5Onu03xen6QqLSZm7iE/YebgM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\nmMsw+O8/HOPzQ2UAJMWGcc+4eAYmRdL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f71c45e7cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "foo['diff'] = foo['actor']-foo['actress']\n", "foo['diff'].plot()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Plot the fraction of roles that have been 'actor' roles each year in the hitsory of film." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c408a278>" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AmHWdxsb2sK8TKC8vfVjHj4Sz2w1AZ5cLVfUGbXtbJ2ZNo6OjGwCHo2vMr2M4\nxuO+xCK5L/3JPQlN7ktocl9Cc/vmtN0uT7/7M5wgHjZPX7x4MVVVVVRXV+NyuXj11VdZv3590DEl\nJSV88MEHADQ1NVFZWcn06dOHdO5wRPXweOA67YG25pTRcSGEiFu9c9pjODxusVjYuXMnO3bsQNM0\ntm7dSmlpKXv27AGgvLycBx54gIcffpgtW7ag6zoPPfQQWVlZACHPHSljs5AoDNqh1mn37T2u+ua6\nhRBCxB9jydcoq8cHbS1WVlZGWVlZ0GPl5eXG1zk5OTzxxBNDPnek7B3+TDu6+o5D8DptzSzV40II\nIYJ5xmOddjRxdLqwmE0kJ4YuVJtIQRuG9B0elzamQggR98ZlyVc0sTtdZKYmDKmifbwpioJJUcIO\nj0tzFSGEiF9GG9N46D0ezS1M/UwmJagQzdgwRNqYCiFE3BuXNqbRorPHg0fVo7Jy3M9sUoK25vQP\nCPgzbV3q0IQQIm75l3yN+YYh0cDfwjQzLXqDtsmkeDcM0TTMJsUYxpc5bSGEEC53HAVtf+V4Rkr0\nBm2zSfG1MdWNIXGQ6nEhhBC9c9pWSxwUotljINMOHB43hwjaqhSiCSFE3PJEqLlKTARtY4evKM60\nvYVoGlqfoN27HEwmtYUQIl7F1Zx2rGXaoYfHJ+rKxED+61AV/+vZTzlzuXWiL0UIMcmNW0e0aBDN\ne2n7mU0KbrfWL2ibpRBtQjW2ddFs72b+jOygx90ejVfer6Krx8PpZz9j9YJ8tt08W3YnEkKMiUgt\n+YqJoB3Nm4X4Ba7TDjWnLc1VJsa/vnicy3XtPPat68nPSjYeP3Gpha4eDyvm5NLW0cNHpxo4cr6J\n228s4cZFNrLTo69drhAidhkd0eIl006wmkhKiN7LNfuCtqrpQW3qpLnKxKmqa6eqzrsF3sGKWu75\nfInx3MenGgD4wvUzmFWYwXvHannh3Yu88M559h64wHULbVwzL4/uHpWOLjcuj8rnlk6N6tEeIUT0\nMjqijbKNafRGwQB2Z09UZ9ngW6fty7RN1t7+6GZZ8jVhDhytAUAB3jtWy51rZ2I2mXB7VI6cb2RK\nRiIlhRkoisLnlk7luoU2jlXZeX7/Wd47Xsd7x+uCXq/J3s3XN82fgJ9ECBHr4mZ4XNN1HE43JVMz\nJvpSwhpwyZciQXsi9LhVPjxZR3Z6IktKpnDgaA3HL7awbHYuxytb6OpRKVs2LaiXvdVi5tbrZrC8\nJJuKC821I3w1AAAgAElEQVRcbewgNdlKWpKV3/7hHB+erGfbzbNJToz6XxshRJSJ1PB41FePO7vc\naLoe9cOSZpPJGB43KSGWfMmc9rg6fLqBrh6VG5cUcvOKaUBv5v3xae/Q+LUL8kOea1IUls/OZfP1\nM7lp+TRWzc/n88um0uNSOXSyfnx+ACHEpOKOl3XasVCEBr3D46qmYZY57Ql34GgNCvD5pYXMKEin\n2JZGxYVmmtq6+OxcE7mZScwsGHql+NqlUzEpCu8cuYouH8CEEMMUNxuGxMJyL+jNqFW1b/W4978y\nPD5+apqcnKu2s3BmNrm+ivHPL5uKqun87JWT9LhUVs3PH9Y2r9npiSyfk8vl+g4qa9vH6tKFEJOU\n26NiUpSgJcEjEfVBO5YybaB/cxVZpz3u/ljhHQb/3LKpxmPXLbRhtZg4V20H4Nr5oYfGw7lpuff1\n3jlyNQJXKYSIJ25VwzLKvuMQA0E71jJt6G2oAqAoCiZFkUx7nFxt7OC9Y3WkJVtZMSfPeDwlycqq\ned5APdyhcb+Fs3LIzUzio1P1dHZ7InbNQojJz+3RRj00DjEQtGMl0w4K2n3W4Zl8O4CJsdHZ7eb1\njy7zg//4iJ1PfURHl5ubVkzt1y7w5hXTUIAbFhcMa2jcz6QolC2fisut8cGJusFPEEIIH7dHG3Xl\nOMTAki9HjATtUP3G/fzLwcTYeOaNs3x4sh6zyVv1fd0im5FVB5pdlMmj91/HlMykEb/X2qVTefGP\nlbx75CrrVxaN5rKFEHEkUpl21Adte4wPj0Nvi1MxNmqbO7FaTPzDn90w6E5wtpyUUb1XZmoCy2bn\n8unZRq42OZmWmzqq1xNCxAePRyMpwTz4gYOI/uHxDhfJiWYSrKP/YcdSuEzbpEj1+Fhqae8mJz1x\n3LZuXTnPO1f+yZmGcXk/IUTsc3vUUa/RhhgI2o5OV1Tvo+0XPKdt6vecDI+PDZdbpb3TTU7GyIe8\nh2tZaS5mk8KnZxrH7T2FELHN7dGwTvbqcU3Tae90Rf18NvQJ2n0zbRkeHzOt7T0A5GSM365cKUkW\nFs3K4XJDBw1tXRF5TXtHj1G/IYSYXHRdx+XRIpJpDzqnfeDAAR599FE0TWPr1q3cf//9Qc8/9dRT\nvPzyywCoqsqFCxf48MMPycjIYN26daSmpmI2m7FYLDz//PPDurj2Lje6Hv3z2QAmU+//DJPSvxBN\nqsfHRoujG4Cc9PHLtAGumZtHxYVmPjnTwG1rZgzpnKa2Lhrt3RTb0khNsgJwub6d1z66zMenGkhO\ntPA3f3KNzJMLMcn4R1rHPGirqsquXbt4+umnsdlsbN26lfXr11NaWmocs2PHDnbs2AHA22+/zS9/\n+UsyMno399i9ezdZWVkjujh7hzeLykyN/r2NzSH6jfuZTAoeVYL2WGiZgEwbYMWcXH71msInZxqH\nFLS7ejw88swn2Du82XRuZhIZqQlcrHEAkJ+VTENbFz/Z8xl/+6cryQvY+1sIEduMFqYRWPIV9hUq\nKiooLi6mqKgIq9XK5s2b2b9//4DHv/LKK2zevDnosdH0aXZ0+ivHrSN+jfESqt+48b00Vxkzzb5M\ne8o4zmkDpKckMK84i4s1DiPbD+el9yqxd7hYWjqFRbNy6HapXKxxML84i7/44lIefeA6vrRuNm0d\nLn6y5whtvg+sQojY51Ejs1kIDJJp19fXU1hYaHxvs9moqKgIeWxXVxcHDx7k+9//vvGYoihs374d\nk8lEeXk527ZtG9bF+bOSzLToz7RNgzRXkUK0sdHi8Aa37HEO2uCtIj9V1cqnZxu5ZdX0AY+raXLy\n1uFq8rKS+Pbdi7FazOi6TrdLDdrm89bVxTi7Pbzy/iUe/+0RvnHbAmYVpo+oEYwQInr4R1ot5tH/\nLocN2sP5Y/H2229zzTXXBA2N/+Y3vyE/P5+Wlha2b99OSUkJq1atGvA1srNTsFh6l3apeLtOTZ+a\nSV7e8NtO+o3m3KFKD/hgkZaaGPSeiQkWHJ3ucbmO4Yi26xmJjh5vO9G5s6aQkhSZEZmh3pdbrpvJ\nM2+cpaKyhS/ftjDkMbqu88+/O4aq6Txw91KmFoafKrr/nqWgKLzyXiU/+tVhCqek8vkV07hldTEF\nUyZurnsy/FsZC3JfQpP7Ekz11TylpyWO+t6EDdo2m43a2lrj+7q6Omw2W8hj9+3bx+233x70WH6+\ntytVTk4OGzZsoKKiImzQbm3tDPq+psG3m5JHpbFxZDsr5eWlj/jc4ejuchtf93S7g95T0zRUVRuX\n6xiq8bovo1HT5GRKZhKJYdbo1zU5SU604Gzvxtk++DD1YIZ7X2ZPy+TExWYuXGoOWTD5yZkGjpxr\nZHFJDrPyU4f02nevncnsqel8eKKeT8818tu3zvLiuxf4b7cvYGWITm9jLRb+rUwEuS+hyX3pr77J\nCYDqDh3LhhPIww6wL168mKqqKqqrq3G5XLz66qusX7++33Ht7e0cPnw46Lmuri46OjoA6Ozs5ODB\ng8ydO3fIFwax08IUgofE+xaimWXJ17A1tHXxvac+4vl3LoQ9rsXRzZRxLkILtHJeHroOh0M0Wulx\nq+zZfx6zSeErt8wd8siVoigsLc3l/i2L+Kc//xzfuG0+AD/9/XFe/ONFWYkgRIwZtzlti8XCzp07\n2bFjh7Hkq7S0lD179gBQXl4OwFtvvcXatWtJSuqdV2xqauLBBx8EvFXod9xxB2vXrh3WxflbmKbH\nXHOV/oVoMqc9POeutKHpOhUXmviTDaE/7HV2e+h2qePaWKWvNQttPP/OBfZ/Us1NK6YFLfd7+9Or\nNDu6uW1NMQUjbJ+amGDm88umUlKYwT//roKX3rvE5foOvnLLHGOvcCFEdHP7g/Z4bBhSVlZGWVlZ\n0GP+YO139913c/fddwc9Nn36dPbu3Tuqi3M4XaQmWSJSJj/WQu2hHficZNrD418K1djWTVNbV8gA\n1dLuX6M9cZl2VloiaxbaeP94HccuNLNsdi7g7dT22keXSUow84Xrh7aOO5yi/DS+941r+bcXj3Pk\nfBNHLzSxrDSXdSunsXBmTr9/c0KI6OHxRC7TjupoaHe6YqKxCoRfp202Keggw5rDcLHWYXx9qqo1\n5DFGY5UJzLQBNl7rrRx//aPLxmPvHq3B4XSxfmWR0UhltNKSrfzVl5axY/MCZhZkcOR8E4//9ij/\n+7mj8qFQiCjmz7StEagej9qg7VE1OrrcMTGfDYNsGOL7Xv6wDo3LrVLd0EFGijfYnbo8UNCemMYq\nfRXb0lk4M5vTl9uoqmvH7dF47dBlEq1mI6BHitlk4sYlhez8+ip2fn0V86ZncaKyhTcPX4no+wgh\nIsfj8S35GuvmKhOpvdNbjR0zmXbAsIfZFHxbJWgPz+X6DlRNZ/UCGxmpCZy61BqySU/v8PjEZtoA\nG68tBuCNjy9z8Fgtre093Lxi2pjWY8wqzOC/372Y9BQrLxy4SG2zc8zeSwgxcpEsRIvaoO2IkX20\n/cJtGOIfOpditKG5WGMHoGRqBgtmZGN3uqht7ux3nJFpZ0580F5SksPU3FQ+OtXAy+9VYrWYuHV1\nZLPsUDJSEvjarfNwezSe2ncKVdPG/D2FEMPTOzw+iYO23envOx4bQTuwEGjA4XGZ0x4S/3y2P2hD\n6Hlt/5x2dhR0zFMUhY3XTkfVdNo6XJQtmzpunfxWzsvnuoU2LtY4eO3Q5cFPEEKMq7goRLNPokzb\nH7Ql0x6aizUO0pKt5GUlDxK0e8hITYia1QXXL7KRkWLFYlbYtKZ4XN/7KxvmkpmWwN6Dlbx3rFY+\nIAoRRYzh8cm8n3ZvY5WJz6KGItyGIWaZ0x4yh9NFk72bkqkZKIpCXlYyuZlJnLncGnT/NF2npb1n\nQhur9GW1mPnLbcv5zpeWj3tFe1qylR2bF2BSFJ7ad4rHdn9CZUAFvhBi4rh9vcet5oG7Ow5V1AZt\newx1Q4Pg4fGBMm0J2oPzr88uKeztYT9/RjbObg9XGjqMx9o73XhULSqK0ALNKEhnXnH2hLz34llT\neOS+67h2fj4Xahz86JeHeeX9SxNyLUKIXv5M2xoPmfZkGB73F6JJ0B7cxdreIjS/hb4h8pNVLcZj\nxnx2FGXa0WBKZhL//a7F/PWXV5CRmsC+D6pwudWJviwh4lpczGk7jBam0b+XNoRfp63457RlnnFQ\n/kx71tTgTBuC57WNyvEoy7SjxfwZ2Vy/uIAet8rJAZrTCCHGhzselnzZnS7Skq0R+SHHw2AbhoBk\n2oPRdJ3KWge2nJSgLmJZaYkUTknh7JU2I2v0r9GeEgXLvaLVNXPyAPjsbOMEX4kQ8a13eHwyB+0O\nF5lpsTE0Dn3bmIZuriLV4+HVt3TS1aNSUth/m7qV8/JxuTWjVajRwnQC+45Hu5KpGWSkJnD0fNOA\nHxg1XeeNj6/w419/Smt7zzhfoRDxwT3Zh8fdHo3OHg8ZMbC7l1+44XGZ0x4aowhtama/525bU+yd\no/2wihZHd0ALU8m0B2IyKSyfPQVHp5sLvoY1gexOF//4n0fZs/8cZ660ceBozQRcpRCTX29HtEla\niGYs94qlTNsU2MZUmquMxIWa3qYqfSUnWri3rASXW+P5dy7Q0t6N2aTEzOqCibLCGCJvCnr8RGUL\n33/qEMcvtrBoVg4JFhMfnaoP2S5WCDE6bo9/ydckzbQdnb7K8cmSacvw+JCcvNRCUoKZ6flpIZ+/\ncUkhMwrS+fBkPZfrO8hKS+x3r0WwhTOzSbSa+fRcoxGQq+ra+afnj+Ls9lC+bjZ/uW0ZS2fnUtvc\nGbSsTggRGZ4I7qcdlUHb3hGLmbas0x6N+tZOGlq7WDAje8B5H5Oi8Ce3zAW8UygTvbtXLLBazCwp\nyaGhtYua5k66ejw8sfc4HlXnz+9dysbVxZgUhTUL8gE4dKp+gq9YiMln0m8YEouZdtigLXPagzp+\n0bsGe0nJlLDHzS7K5LpFNgCmyHz2kKwIqCL/9ZtnqW/tYtOaYpaW9t7rJSVTSEow89HJBhkiFyLC\n3JO9etze4dssJIYybRkeH50Tld6gvXhWzqDHfvGm2cywpbN8Tu5YX9aksHT2FEyKwmuHLvP+8Tpm\nFaZzz+dLgo5JsJpZMSePZke3URAohIgMf3OVSTunbY+xvuMwxOFxyWBCcns0TlW1UpCTQm5W8qDH\nZ6cn8v3t17J6gW0cri72pSZZmVecRWePh6QEMw/cuTjkMN2ahd77eeikDJELEUkeVcdkUiJSgxOV\nQTvWWpjCIG1MZU47rPPVbfS4VRaXDJ5li5G5YXEBigJf3zSf/AE+GC2cmU1qkoWPTzfIv1UhIsit\nahHbjdASkVeJMLvThaJAenJstDCFQdqYKjI8Hs6xyqHNZ4uRu2FxASvm5JKSNPDvlMVsYtX8fN49\nUsOZK23GtqhCiNHxqFpEhsYhijPt9JSEmFrOI5n2yB2/2ILVYmLe9KyJvpRJS1GUsAHbzz/l8M5n\nV6UgTYgIcXsil2lHZdC2O10x1zQjXKYtbUwH1treQ3VjB3OnZ5FgHf1es2J05k3PYlpuKh+fbuBn\nL5802i8KIUbOE8Hh8agL2j1ulW6XGlPz2dA30zaFfE4y7f6OVzYDsGQIVeNi7JlMCg99ZQWl0zL4\n8GQ9P9nzGe2+JZhCiJHxTOZM22hhGnNBW9qYjoSx1Evms6NGRkoCD5WvYNX8fM5W23nonw9Q2+yc\n6MsSIma5VR2rJTIjiYMG7QMHDrBp0yY2btzIk08+2e/5p556irvuuou77rqLO+64g4ULF+JwOIZ0\nbij2GKwcB1mnPRKapnOisoWcDO/WmyJ6JFjNfOvORWxaU8zVRie7fnmYT86MfIvPKw0dNLR2RvAK\nhYgdHlWLSAtTGKR6XFVVdu3axdNPP43NZmPr1q2sX7+e0tJS45gdO3awY8cOAN5++21++ctfkpGR\nMaRzQ4ndTFs6og1XVX07zm4P18zNMyrsRfQwKQrbbp7Notl5/J/ffsZPf3+MzdfP4O7PlQy5SLTH\nrfKbt84ZO4gtmJFN2fKpXDM3LyItHQdScaGJmYUZMdVVUUxOuq57h8cj9O89bNCuqKiguLiYoqIi\nADZv3sz+/fsHDLyvvPIKmzdvHtG5fvYYDdqBMWegQjQJ2sHOXmkDYH6xLC2KZjddU0RGopmfvnCM\nfR9U8dbhajJSrWSkJJCfncyfbpxHcmL/PyVXGjp4Yu9xaps7KcpLIzXJwqmqVk5VtZKWbGVZ6RSW\nz8ll0awckhIit/r0+MVm/vE/K1haOoW/+OKyIZ+n6zrHLjaTnpJAsS0taMpL1TTa2l1kZyQaH8KF\nGApV09GBBOs4BO36+noKCwuN7202GxUVFSGP7erq4uDBg3z/+98f9rmBYrGxCniX1JhNCqqmD7jk\nS4bHg/mD9lxZ6hX1puensfMbq3juD+epqmvH0eniUl07F2ocFE5J5fYbZgYdf+R8E//6++N4VI1b\nVhbxxZtLsVrM1DY7efdIDYdO1vPe8TreO16HxWzic0sL2XpTacjgP1yvflgFQMWFZi7VOZhZ0H+r\n11De+qSa37x1DoBEq5nSaRlkpiZytamDmqZOPKrG1ptK+cJ1M0Z9jSJ+eIy+45GZ0w77GzKcIcu3\n336ba665hoyMjGGf65ednYJL9Qa2mdOzyctLH/ZrhBKp1xmM2WxC1VRs+RlB2XZWnXe7w5TUxHG7\nlqGYyGvRNJ3zVx3kZyczf3behF1HKNH0/yha5OWlkwf89ddXG491drv55q43eOuTar582wIjW3a5\nVX6z/xyKAju/uYbViwqCXmfp/AK+remcr27joxN1HDhylbc/u8qxyhYe/OIyVs4feXvaM1UtnL7c\nRn5OCg0tnbz+cTX/3zfXDHre+eo2/vPtC2SmJXD9kqmcuNjMyUutACRYTMwsTKe6oYO3P7vKn35h\nIWbfUOdE/FvRdZ265k4Kc1PH/b2HSn6HevkTUavFFJH7EjZo22w2amtrje/r6uqw2UL/Qu3bt4/b\nb799ROf6tbZ2Uu+rUlV73DQ2tg/+EwwiLy89Iq8zFCbFO0ze3By8J3FHRzcADkf3uF3LYMbzvoRy\ntbGD9k4Xi2cVRM09gYm/L9Eo3D25+ZoiXnn/Ei/sP8uGVdMBeO3QZRpbu9i0uphZ+akDnpudbOHW\nVUWsXzGVV96/xL4PqvjBzz6kbPlUvnbrvBF98H/2tdMAfP3Wefz+wEUOnajjk+M1FNu8fyyb7d08\n88YZ5hVnc8uqIixmE109Hv7+Fx/jUTW++YUF3s58ZSW0d7ro7PaQl5WMyaTwzBtn+MOnV3nzg0us\nnJc3Yf9WPjvXyP/53TH+ZMNc1q8sGvf3H4z8DgVrbfdugGU1mwa8L8MJ5mEH2RcvXkxVVRXV1dW4\nXC5effVV1q9f3++49vZ2Dh8+HPTcUM/ty+7swWxSSI2hFqZ+ZpPSb2gcegvRVC3+GlW4PRpPvnyC\nj083BD3uHxqfVyxD47Fsw6oiEqwmXjt0GY+q0dHl5pX3L5GaZGHzDUMbRraYTdz1uRJ2fn0V0/JS\nefdIDReuDn+nsZomJ5+ebWRWYQbzi7O448aZALz8/iXAG7B//OynHL3QzHNvn+eHv/iY81ftPPNG\n73alga1001MSsOWkGKNmN6+YBsA7n1UP+9oiyb+N7Yt/vIiz2z2h1yIG59+Wc1yqxy0WCzt37mTH\njh1omsbWrVspLS1lz549AJSXlwPw1ltvsXbtWpKSkgY9dzDeFqbWmCz28Abt/v9j4rm5yuHTDXx4\nop6zV9pYOTfP+AN4RuazJ4X0lARuWj6NNz6+wvvH66htdtLZ42HbzbNJHULb1EDFtnTuuGEmT+w9\nwZkrrcwuyhzW+a8dugzAF64rRlEUFs/KYVZhOp+caaTiQjPPvHGGJns3m6+fQXunmwNHa3h09ycA\nlEzN6LddaV/T8tKYW5TJiUut1Ld0TtgQ8LlqOwDObg/73q9i27rZE3IdYmiMbTnHa8OQsrIyysrK\ngh7zB2u/u+++m7vvvntI54aj6zp2p4vCnOidqwlnoK3X4rmN6f5PvVlJi6OHYxebWTY7F13XOXul\njYzUBGzZg2/FKaLbrauL+cOn1bz0XiUOp4spGYmsXzltRK/l/xDnD0xD1eLo5oMTddhyUlgxx1sj\noSgKd9w4i39+voJ//M+jANy5dhZ3rp0FeDdR+dXrZ3A4XTywZdGQlqDddM00zlbbefdIDYvnjf/W\nsJ3dHq42dlAyNQN7h4u3PrnCumumGVvadvV4OFHZwrLZuRELEmJ0egvRJuEuX90uFZdbIzMttirH\n/QYaHjfHQUe0zm43iQnmoJGGyloHF2scTM1NpabJWzW8bHYujfZu2jpcrJon67Mng+z0RG5cUsi7\nR7xrse/5fOmIK2Wz0hKxZSdzrroNTdMHXA+u6zrvH6/j/FU7Da1d1DQ7UTWd29YUB52zrHQKM2zp\nVNW3BwVs8H5A2LVjta8v9NCud+XcfNJTznHwWC333bN0RD/jaFyssaPjXe8+LTeVJ18+ye8OXOSB\nLYs4V93Gz14+SZO9m5uWT+Vrm+aP+/WJ/twRrh6Pqo9iDl+P41htiDBYpj1Zp7S7ejw89G8f8E//\nWRE0BfDWYW+W/eX1c5hZkM7RC020OLo5e1mGxieb29YUYzYpFNvSWLNodBnonOlZdPWoXGnoGPCY\nNz6+wlP7TvHukRpOVXmrvK9fZOP6gEp18Gbb//OLS/nOl5YHBezA54fzx9RqMfG5pVPp6HJz0Ncw\nZjydv+odgZg9LZPVC23MLEjn0Ml6fvFfp/j7X39Ks72bzNQE3jlSw2dnR97BTkROpIfHoypo2zt8\njVViNNNevcDGmgX9/2BN9jntS3XtdPV4OF7ZYhT9OJwuPj5dT0FOCgtmZnPTimnoOhw4WiPrsyeh\n/OwUvveNa/nLbctHXY/i36LV/++kr7NXfMuzUhP43jdW8W/fKeN/P7iW++5YFPIPY1ZaIosiuCHN\nTcunogB7372AvaNnVK813L8J/mmD0mmZmBSFL/nmsw8crSUnPZG//soKvlO+HIvZxNP/dZq2UV6f\nGD1Hp7dYMFK9R6IqaBuNVWI00763rJQv3zKn3+O91eOTNWh7K33NJoWXDlZyorKFd4/W4FF11l0z\nDZOisGaBjeREMweO1nD6cispiRaK8tIm+MpFJE3PT4tIJ0P/h7mz1f2DtsPp4om9xwH41p2LmFmQ\nQeI4b+mam5XMtQvyuVhj52/+/QN+9+6FEVVxX7hq59v/+wC/eu00bo866PGqpnGxxkHhlBTSfKtr\n5hVnc/sNM7l5xTT+7purmVecTVFeGttuLqWjy81/7Ds1qaflYoG/537hlMjUakVV0DZamMZopj2Q\nyd7GtKrOu/bwvjsWYjIpPPnyCf7waTWJCWZuXOLtipeYYOa6RQW0dbhosnczpyhzyP2rRXzJzUwi\nOz2Rs1fa0AMCjqbp/PtLJ2jrcHHvTSXMm8D2t//t9oX82b1LSU60sO+DKv7m3z4whq6H6s3DV+hx\nq7xzpIZHdn8y6IYq1Q1Oetwqc/pU1d/z+RK+eus8UgKq9devLGJxSQ7HK1vYfzj0ErVq2cRlXDS0\ndgFQEKFNkaIqaMd6pj0Qo3p8kn7ivVTbTmqShWvn5/OldbNp73Rj73Bx4+KCoLaUNy3vrSieK+uz\nxQAURWHu9CzaO93UtfQGlVc+uMSpqlZWzMll0+riibtAvGvLb7thFn//wPXc8/kSOns8vPnxlSGf\n39Hl5tOzTRTkpPD5ZYVcru/g735xmL0HK3nhwEWeffMsu18/Q4uj2zjnnG/kYfa0wX93FEVhxxcW\nkJ5i5bm3zxvn+p290sbf/eJjHtn9CZ3dniFftxi+xjZv0LZJph07JvOctrPbTUNbFzML0lEUhfUr\ni1i9IB+LWenXrWl6fhqlU71tbucWSdAWA/MPkfvX89e3dvLK+1VkpyeyY/OCqFl1kGg1s/n6GeRm\nJnG8stlY3jOYQyfr8agan1tWyDduW8COzQtQVY29Byt55f1LvPVJNW9/dpWn/+u0Mdrgz+T7ZtoD\nyUxL5Ft3LkbX4ae/P258AGhq6+JfXjiGqum0d7p5+f3KEfzkYqga2rrITk+M2DROVAXtWN2WczCT\neXjcPzQ+s7C35/z9Wxbx///ZjSHncL6+aT5fWjebkqlD28RBxCdjvbZviPzZN8/hUTW+tG520DBw\nNFAUheWzc+nqUY0PGYP549EazCaFGxZ7p49uXFLIj+5bw//YupS/+coKfrD9WhbPyuFEZQuHTtYD\n3iK09BQr+cPobbBgRjZfWj8bh9PF/3nhGI5OF//8uwo6utx8ef0ccjOTeOtwddCIRl+6ro+64C5e\nuT0qrY4e8rMi148iqoK23enCYlYistNPNPFP3U7GQjR/0J5h6+0OZVKUASsli/LTuHV1cdRkSiI6\nTfUVW5290saRc00cu9jMghnZXDs/f6IvLaRlc3IBOHquadBjq+raudzQwdLSKUEJSm5mMstn5zKv\nOJtiWzpfvXUeCRYTe/af43J9O63tPcyeljns351bVhZx45ICqura+X+f/JDqRifrryliw7XT+dK6\n2aiazm/3nxvw/L0HK/nLf3mPU5dahvW+AhrbutGBvAg2kYqqoO1w9pCZmjDp/qD7G45MxirOSiPT\nll19ROQoisKcokyaHT388rXTmE0Kf7pxbtT+bZg3PYvkRDNHzjcFFc+F8scK7/ruzy2dGva4vKxk\n7lw7C0enm3954RjAsFu7gvdefu3WeZRMzcDZ7WHhzGzKb/EuFbtmbh7zi7M4eqGZ45XN/c49V91m\nLON81dcmVgxdg28+exJn2u6Y20d7KPq2MVU1jU/ONEyKDUSq6hykJVuZkpE0+MFCDIN/vbaj083G\n1dMjtmRmLFjMJhbNmkKTvZurTU7j8W6Xh5ffq+TUpRZ0XcflVvnwRD2ZaQksKR187fiGa6dTlJdG\nk06ara4AACAASURBVN07Hz1nCEVooVgtZv7H1qV8ef0c/uyuxUYioSgK5evnoCiwZ//5oDn5rh4P\nP3v5JAD52cmcqGzhauPADW9Ef42+yvHhTGkMJqqCtkfVyExNnOjLiLi+hWjvfFbDT39/nE/OxHbH\noo4uN41t3UYRmhCR5F9hkJ2eyB03zJzYixmCFbN9Q+Tne4fIn/vDeX7/x0r+Yc8Rvv8fH/PsW+fo\n7PFw4+LCkJsL9WUxm/j6pnkovq9nFIx8RCsjJYEN107vVxNQbEunbNlUapqc3p3PfA1cfvPWOZrs\n3Xzhuhlsu9mbmb85wPIxEZqRaUcwaEfd5HFGanQVmUSCv7mKP2ifqPTODTXbuwc8JxZU1fvms0fx\nh0SIgcywpXNvWQkLZuSQlBB1f6r6WVI6BUWBI+eb2Hz9TE5VtfLOkRqm5qZSlJfK4dONVPsy1c8t\nLRzy65ZOy+Srm+ah65FrhdnXtnWz0XwdCx995hOWlk6h4kIzM2zp3Ll2FiZFIS8riQ9O1HFvWQnp\nk2xZ7ljxr9GO5PB41P0mZEzmTFvXUTWN05e9vZL9S9xi1aVabye0mRK0xRhQFIXN18+c6MsYsrRk\nK3OmZXKu2k5jWxe/+K9TKArs2LyAWYUZNN/UzTtHrpKSaMGWM7xGG4E9DsZCUoKFb9w2nxuXFLD7\n9TNUXGjGajFx3x0Ljd3Pblk5nd/sP8c7n13ljhv793EX/TW0dZGaZInoioeoC9qTbbkXgH8UTNV0\nKmvb6XZ5Wxb6N0iJVcZyrwJZviUEeKvIz1bbefy5ozS2dXPbdcXM8i2HnJKZxL1lpRN8heHNKcri\ne9+4lveO1ZKbmczU3N46grVLC3nx4EX+8OlVNq2ZIVt/DkLTdJrauii2RTapibq7PjmDdu/weOCy\nCUesZ9p17aQlW8nJmHyjI0KMxHLfvHZ9SycFOSncGYMZqcVsomz5tH6brCQnWvjc0qnYnS4+OlU/\nQVcXO1rau1E1PaLz2RCFQXtSVo8HbBhyqqrVKCqJ5aDd0eWmyd7NzEIpQhPCr3BKKracFBRg+xfm\nkzDOm5mMtVtWFqEo8OIfL0rDlUH457PzIjifDTI8Pi4URcGkKHS7PNQ0OSm2pdPt8sRM0NZ1nY9O\nNfC7dy9gtZi4dn6+UYgi89lCBPvWlkXYnS7mTMJWvbm+teMv/rGSf3q+gr/5yjUkJkyuDyaRMhZr\ntCHKgnZGyuQdajWZFK40dKDrsGBmNheu2mlo60LT9Kje7aqprYvdb5zl2MVmYw7rpfcuGc/LfLYQ\nwSb7aoo7bphJY1sX7x2r499fOsGD9yyJ6r9hE2Us1mhDlAXtf/izGydtcYPJBP4tcxfOyKaxrQtd\n9w4zR+uUwKGT9Tz9X6dwuTUWzczmq7fOIyM1gaPnm/noVD0Op4v5sluXEHFFURS+vmk+re09HDnf\nxG/eOsdXNsyRabI+xmKNNkRZ0J6sARt6l32ZTQpzirL4zNej2OF0RWXQ/vBkHT97+SRJCRbuu30+\n1y2yGb+UaxbaWLPQNsFXKISYKBaziT+7awl//+tP2P9pNSVTM7h+ccFEX1ZUaWjtIsFqiviU7+SN\nklHGX4xWOi2TxASzEajtUbjs66NT9UbA/n/Kl3P94gL5FC2ECJKSZOHP711KotXMs2+dpS1EYVqP\nb3lrXycqW/jX3x+js9s91pc5IXRdp6Gti7ys5Ij/7ZSgPU78mfbCmdlAb5V8uGI0t0flyZdO8OIf\nL479BfocPt3Aky+dJCnBzF99aZmxxlQIIfrKy0rmizeX4uz2sPv1M8ZmKW6P5p3v/scDfHy6Ieic\nupZO/vXFYxw+08gfPr06EZc95to73fS41IgXoYEE7XHjL9RYOMO79jEjJXzQ9qga//biCT48Wc8f\nPr066M5BkdDV4+Fnr5wkwWriL7ctp3Tq8HcUEkLEl5tWTGN+sXfK79Cpejq73fzjfx7l0Ml6VE3n\nyZdOcMLXn6Lb5eFfXjhGV4+Kxayw/5Nq3J7Y3zipr7GazwYJ2uPGajGRmGA2trDMDJNpa5rOU/tO\nccS38UBHl3tcWp6ermrF7dHYsGo6s6dJwBZCDM6kKHzjCwtIsJr49Rtn+e5PD3KqqpUVc3L5iy8u\nQ1HgX353jIs1Dp5+9TQ1TU7Wryxi/coi7E4Xh05OvkYtDa2dQOSXe8EQCtEOHDjAo48+iqZpbN26\nlfvvv7/fMYcOHeKxxx7D4/GQnZ3N7t27AVi3bh2pqamYzWYsFgvPP/98xH+AWPGVW+aig9HH178x\nSt+gres6v3r9DIdO1jO7KJOSwgze+PgKVxudZKWN7XI4/6fhvp2QhBAinPysZLaWlfLsW+eorHFw\n84pp/MmGuZhMCg9sWcS/vnicv//1p3hUjdlFmXxp3WzsHS7e/LiaNz6+zI1LJlfdjNFYZQwy7bBB\nW1VVdu3axdNPP43NZmPr1q2sX7+e0tLe/rkOh4Mf/vCHPPXUUxQUFNDS0hL0Grt37yYrS5YFLfO1\nN/QbqBDt8JlGDhytoTg/jb/YupTjvh3BrjZ2jHkwPVHZQlKCmZKpMo8thBiedSuLaHH0UDw1kzXz\nco0gvHJePl+7dR6/fO0MmakJ/Pc7F2Mxm5iSmcS1C/I5dLKek5daJ1Wy0GgMjw9vY5ihCBu0Kyoq\nKC4upqioCIDNmzezf//+oKD98ssvs3HjRgoKvOX+OTnBN3485mJjUVKChQRr/1amlb6ds76yYS4p\nSVam5aUBUN3oHNPrabJ3Ud/axfLZucZogBBCDJVJUdi2bjZ5eek0NrYHPVe2fBq27BSmZCaRnd47\nYnjr6ukcOlnP6x9djnjQ1nTdWLUTTrfLg9ujRXS70WZ7NwowZQyahYUN2vX19RQW9u77arPZqKio\nCDqmqqoKj8fDV7/6VZxOJ1/72te46667AO8i/O3bt2MymSgvL2fbtm0R/wFiWUZKQr+gXd/inQsp\nmOL9hGbLTsZiVrja1DGm13Lykne70Mn0aVcIET3mz8ju99jMggzmTc/ieGULn5xpxO7s4Xy1nbaO\nHuYXZ7OkdAozCtKHFHwD1bV08r+e/ZTVC2yUr5/T7/kLV+0cOd/E6apWKmvbsVpNPPLf1pCTkTTi\nny9Qe5eb1GQrZlPkE6CwQXsocwwej4eTJ0/yi1/8gq6uLsrLy1m+fDkzZ87k2WefxWaz0dLSwvbt\n2ykpKWHVqlURu/hYl5mawKW69qBPhHUtnSQnWkhP9s55W8wmCnJSudrkHPInx5E44RuG9y9JE0KI\n8bBx9XTOXGnjp78/FvT46cttvHiwkowUK1vWzmLdNUVDej2P6l1u1tbh4o2PrzCnKJOV8/KN5z84\nXsfPXjkJeEcH8rKTqW/p5PWPrvDlW/oH+JHo6HKTlhy5PbQDhQ3aNpuN2tpa4/u6ujpstuBOWAUF\nBWRnZ5OUlERSUhKrVq3i9OnTzJw50zg2JyeHDRs2UFFRETZoZ2enYLFEvvl8Xl509gLOzU7hQo2D\n5NQkMlITUDWdxrZuZk3NID+/d1559vQsqhs70ExmbAH7246W/76oms7py63kZiWzZJ5tUhWEjES0\n/nuZSHJPQpP7Etpw7sstU9KorOugx62ycFYO82fmkJWexNGzjRw+Vc+Hx2t55o2zpKYm8n/bu/vo\nqOp73+PvSWbyQEIIIckECODJRB4EERAPykrMkkDAwuRiwEMspRpCkVMBKddyrkV6l4VFj3qkrpbV\nIhWxcijcLrkVe+RJ4AoHjyL1IGmJPPhATAxJICGQZIZMMrPvHyEjkTAQyBBm5vP6i9n57T17f9lr\nf+f327+HKRlp1zzem9uKKamo497Byfzti2r+sOM4o4f1ITE+mqNfVrN++zFioswsemwUw9MTsZjD\nmbvyPfYXlfNE7rCbnqHS4zFocDaRmtz9ijh0xv3iM2kPGzaMkpISysrKSE5OZtu2baxatapNmezs\nbJYvX47b7cblclFUVERBQQFOpxO3201sbCwOh4MDBw4wf/58nydz7lI3+c7U3vuV20WUpaXp5Muv\na+ibGMOZWifNbg+94iLbnHOv7i03UdHxSsxG0k19p8dj8O+7jnPnHQk8MLjl1+dXpy9Q52hiRHoi\nZ/3cDH+7u53vl66imLRPcWnfjcQlL7PtuuPO+osM7NOdgX26M25kH/5143/z6p//RuPFJh68p89V\nj3P863O8teckSfFRzH54MAeLK3lz53Fe+MPH/HDSYFZu+ATDMPjnqcOwWWNpqLsIwITR/di05yT/\nZ+dnTM289g8DX+qdTXiMluf75XHwFZeOJHOfSdtsNrNs2TIKCwu9Q75sNhubN28GID8/H5vNRmZm\nJrm5uYSFhfHoo4+Snp5OaWmpN0m73W7sdjsZGRnXfWKh4PIJVvomxnz7Pvs7PQ5TL3VG++ZMPaMG\n3lzS3vfpN7z/aTnvf1pO+P8Yyj8OsVKsoV4icptKSejGM/kjePGPh/nD9mNAy/oHkZetVW4YBufq\nGvn9fxRjMpmYax9KdKSZrBF9+NuX1Rw+eZbn3zhEo8vNEw8P5q472j7rHrynD3/5r1Ps+aSMSWP6\nExVx48ty1DtbpmbtkuZxgKysLLKystpsy8/Pb/O5sLCQwsLCNtv69evH1q1bO+EUg9d3pzKtuJS0\nkxPaju3rm9TSJH6zPcjrHC7+7/4viYoIx2Qy8fq2z+jdK4ajX9VgAoa001FERKSrpSbF8j9njOCl\nTYd5Y/sx3th+jLiYCBJ7RNHocnP2/EUam1rmOZ+a+Q/YLk0OZTKZeOLhwXx5+mPO17uYNKZ/uzX1\nyIhwxt+bytsHvmLfp+VM/Mf+N3yu9Y5LSbtbFyVt8R9v0r40Vrvy0oD8lIS2Ne1ecVFERYTzzdmb\nS9pb9n1Bw8Vm8rPvJK1fPCvfOMRvthRxrq6R/indO3XIg4hIZxqQ0p1/mTmKPZ+UcabWydnzTkoq\n6oi0hJPcM5rEHlGk9Ylj0pi2Cbd7twieyR/JydJaHhxx9ab1cfemsv3g1+z8+GvGjUq94VUn65wt\nz/Pu0f55nippd6G4bm1nRWttHrd+p3ncZDLRNymGr8rraGr23NDN9GX5Bf7zyGn6JsWQfW9fUqw9\nmPzAAN79sASAoXeoaVxEbm/9kmN54uHB3s+GYVxXx9m+iTH0vUYn3thoC1kj+rDrUCkfHa0g08e7\nc1+8NW0/NY9rFo0u1F7zeI+YCKIjr/wtlZoUi8cwvE3o12IYBs1uDx7D8HY+M4AfTBjoHTv4SGYa\nw9JakvVwW69OuCIRkVuns0e65NzXD5MJ9heV3/AxWt9pd1fzePC5fNGQpmYP1RcucudVFupo/ZVY\ndqaefsmxPo/ruNjMS5sOU1LZtqfi/XdZGdT/2/fWYWEmFuTdTUlFPempWiBEREJbQlwUQwb0pPjU\nOapqnTe04Eed07/vtFXT7kLRkWbM4SYuOFycqXViGGBNaH+u2m97kPt+r20YBht2Haekso4B1u4M\n7h/Pnak9uDutFzPGpV9R3mIOV8IWEbnk/rtapuQ+eLTihvZvbR7v3lW9x8V/TCYTcTEtU5l6h3td\nJWl/24Pc9zjqD49WcLC4ElvfOP7XzFF+mUZPRCRY3TsoiQ27jvPh0UqmjL2jw03w3w758k9HND3R\nu1hctwjONzRRcWlimavVtLt3i6BHTITPmnbVOQcbdp0gKiKcufahStgiIh0UHWlmRHoiFTWOK14x\nXo86p4vwMBPRkZ0/uycoaXe5uJgImt0eTp1uuTmsPtZf7ZsUQ/WFizgbm73bDMPA2djM2fNOXn2n\nmEaXmx9OHESSHxZfFxEJBfcPbZmC+6OjlR3et87RMu+4v6aDVvN4F2vtQX6irBYTkOwraSfGUnzq\nHP/79Y9xewwaXW6crmYuX/107LAU7h+a4uezFhEJXnen9SImyszB4kr+6aF0wsJMnCit5d93HWfs\nsN5XjAW/XL2jiZ5+WJKzlZJ2F2vtQX6+3kVijygsPhZMGXFnIh8eraCxyU2kJZyEuEiiI2PoFmmm\nW5SFpPgonzeTiIhcmzk8jPsGJ/P+p+V8VnKOc3WN/GHHMdwegz/9v89xNDbzSOY/XFGbbnZ7cDQ2\n0z/a9wifmzo3vx1ZrkvcZbOQXe19dqshA3ry66cz/X1KIiIh7/6hKbz/aTnrt39GzYVGukWamTlh\nIFsPfMV//Ncpmprd/NND6W0Sd8PFlleX/ppYBZS0u9zly8D5ep8tIiK3TnpqD3rFRVF94SLWntE8\n/eg9pCR0Y/CAnvzb5sPs/LgUj4c2a3DXX5qSOtaPU0KrI1oXi7tsAP61atoiInJrhJlMfH/CnTw0\nqi9LfzjaOxy3Z/dI/uX7o7D2jOa9v5Z6h3iB/1f4AiXtLnd5TftqY7RFROTWG3lnErNyBl2RhONi\nIrj70tTPVZcWeoKWnuPgv4lVQEm7y7VpHlfSFhEJCK1TnFbVfrseRL2fpzAFvdPucjHRFsJMJkwm\n6OXHYQIiItJ5Wofnnrm8pu30f01bSbuLhV1adjPSEq4ZzEREAkSSt6b9bdL2LsupmnZw++ljI/HT\n5DkiIuIHiT2iMdG2pl3vvNR7XDXt4ObP/2AREel8FnMYCXGRnDl/0bvt2+ZxDfkSERG5rSTFR3Ou\nrhFXkxtoaR63mMOIsPgvtSppi4iI3ABvZ7RLte16p38XCwElbRERkRvS2hmt9b12nbOJ7n7shAZK\n2iIiIjckuWfL3BpVtU6amt00utx+He4FStoiIiI3JPmymna989JiIX6cdxyUtEVERG7I5WO16xz+\nH+4F15G09+/fz6RJk8jJyWHt2rXtljl48CBTp05lypQpzJo1q0P7ioiIBKJuUWZioy1U1Tq9U5j6\nu3nc5zhtt9vN8uXLWb9+PVarlenTp5OdnY3NZvOWuXDhAr/4xS9Yt24dKSkp1NTUXPe+IiIigSwp\nPpqvK+u40NC6LGcX1rSLioro378/qampWCwWJk+ezJ49e9qU+ctf/kJOTg4pKSkAJCQkXPe+IiIi\ngSy5ZzRuj8HXVfVAFzePV1ZW0rt3b+9nq9VKZWVlmzIlJSWcP3+eWbNmkZeXx9tvv33d+4qIiASy\n1vfaX35zHuji5vHrGSDe3NxMcXExb7zxBk6nk/z8fEaMGOHXweUiIiK3g9Ye5Kcq6wD/9x73mbSt\nViunT5/2fq6oqMBqtbYpk5KSQs+ePYmKiiIqKorRo0dz7NgxUlJSrrnvd/Xs2Q2zOfxGrsOnpKTu\nnX7MYKC4tE9xuZJi0j7FpX2hFJc772gEwNXkAWBAajy9ekS3W7Yz4uIzaQ8bNoySkhLKyspITk5m\n27ZtrFq1qk2Z7Oxsli9fjtvtxuVyUVRUREFBAXfcccc19/2uc+ccPv9+I5KSunPmTF2nHzfQKS7t\nU1yupJi0T3FpX6jFJeI7jcqNDhdnXM1XlPMVl44kc59J22w2s2zZMgoLC/F4PEyfPh2bzcbmzZsB\nyM/Px2azkZmZSW5uLmFhYTz66KOkp6cDtLuviIhIsOgRG4HFHEZTs4eoiHAsZv9Of2IyDMPw6zd0\ngD9+nYXar77rpbi0T3G5kmLSPsWlfaEYl+deO0j52QYSe0Tx4j+PbbdMZ9W0NSOaiIjITWjtjObv\nxUJASVtEROSmtA77io32b89xUNIWERG5Ka3ravt7YhVQ0hYREbkprUlbzeMiIiK3uSEDejJ+dCqZ\n9/Tx+3f5HPIlIiIivpnDw/j++IG35LtU0xYREQkQStoiIiIBQklbREQkQChpi4iIBAglbRERkQCh\npC0iIhIglLRFREQChJK2iIhIgFDSFhERCRBK2iIiIgFCSVtERCRAKGmLiIgECCVtERGRAKGkLSIi\nEiCUtEVERAKEkraIiEiAUNIWEREJEEraIiIiAUJJW0REJECYr1Vg//79rFy5Eo/Hw/Tp05k7d26b\nvx88eJAf//jH9OvXD4AJEybw1FNPATBu3DhiYmIIDw/HbDbz1ltv+eESREREQoPPpO12u1m+fDnr\n16/HarUyffp0srOzsdlsbcrdd999rFmzpt1jbNiwgfj4+M47YxERkRDls3m8qKiI/v37k5qaisVi\nYfLkyezZs6dDX2AYxk2doIiIiLTwmbQrKyvp3bu397PVaqWysrJNGZPJxOHDh8nNzeVHP/oRn3/+\neZu/FRQUkJeXx5/+9KdOPnUREZHQ4rN53GQyXfMAd911F++//z7R0dHs27ePp556ip07dwKwadMm\nkpOTqampoaCggLS0NEaPHt05Zy4iIhJifCZtq9XK6dOnvZ8rKiqwWq1tysTGxnr/nZWVxfPPP09t\nbS3x8fEkJycDkJCQwIQJEygqKvKZtJOSut/QRVyLv44b6BSX9ikuV1JM2qe4tE9xaV9nxMVn8/iw\nYcMoKSmhrKwMl8vFtm3byM7OblPm7Nmz3vfWRUVFAMTHx+N0OqmvrwfA4XBw4MABBg4ceNMnLCIi\nEqp81rTNZjPLli2jsLDQO+TLZrOxefNmAPLz89m5cyebNm0iPDyc6OhoVq1aBbQk8/nz5wMtvdDt\ndjsZGRl+vhwREZHgZTLUvVtERCQgaEY0ERGRAKGkLSIiEiCUtEVERAJEwCXtZ599lrFjx2K3273b\njh07xowZM7Db7cybN8/bax3g1VdfJScnh0mTJnHgwAHv9r///e/Y7XZycnJYsWLFLb0Gf+hIXD74\n4APy8vKw2+3k5eXx0UcfefcJ5bi0Ki8vZ+TIkbz++uvebaEel9a/TZkyBbvdjsvlAoIrLh2JSWNj\nI4sXL8Zut/O9732PtWvXevcJppgAnD59mlmzZjF58mSmTJnCm2++CUBtbS0FBQVMnDiR2bNnc+HC\nBe8+ofDc7WhcOu25awSYQ4cOGUePHjWmTJni3ZaXl2ccOnTIMAzDeOutt4xXXnnFMAzDOHnypJGb\nm2u4XC6jtLTUGD9+vOHxeAzDMIxp06YZR44cMQzDMObMmWPs27fvFl9J5+pIXIqLi42qqirDMAzj\nxIkTRmZmpnefUI5LqwULFhhPP/20sW7dOu+2UI5LU1OTYbfbjWPHjhmGYRi1tbWG2+02DCO44tKR\nmGzZssX4yU9+YhiGYTidTuOhhx4yvvnmG8MwgismhmEYVVVVRnFxsWEYhlFfX2/k5OQYn3/+ufHC\nCy8Ya9euNQzDMF599VXjpZdeMgwjdJ67HY1LZz13A66mPXr0aOLi4tpsKykp8U7aMnbsWHbt2gXA\nnj17mDx5MhaLhdTUVPr378+RI0eoqqqioaGB4cOHAzB16lR27959ay+kk3UkLkOGDCEpKQmA9PR0\nGhsbaWpqCvm4AOzevZvU1FTS09O920I9Lh988AGDBg1i0KBBAPTo0YOwsLCgi0tHYpKUlITD4cDt\nduNwOLBYLMTGxgZdTKDlWocMGQJATEwMNpuNyspK9u7dyyOPPALAI4884r3OUHnudjQunfXcDbik\n3Z709HTvRe7YscM7i1tVVRUpKSnecikpKVRWVl6x3Wq1UlVVdWtP+ha4Wlwut3PnToYOHYrFYqGy\nsjKk49LQ0MBrr73GggUL2pQP9bh89dVXmEwmCgsLycvL47XXXgNCIy5Xi0lmZiaxsbFkZGSQnZ3N\nnDlziIuLC/qYlJWV8dlnnzF8+HCqq6tJTEwEIDExkerqaiA0n7vXE5fL3cxzNyiS9sqVK9m0aRN5\neXk0NDRgsVi6+pRuC9eKy8mTJ3n55Zd5/vnnu+gMu8bV4rJ69Woef/xxoqOjQ3J1uqvFxe1288kn\nn/Dyyy/zxz/+kd27d/Phhx9e19oEge5qMdm6dSuNjY0cOHCAPXv2sG7dOkpLS7v4bP2roaGBhQsX\nsnTp0jbTV0PLOhWhcD+0p6Nxudnnrs8Z0QJFWloa69atA1pqBfv27QNafrFUVFR4y1VUVJCSktLu\n9tZ50oPJ1eICLdc8f/58XnzxRfr16we0H69QiMv+/fuBlml4d+7cyUsvvURdXR1hYWFERkaSk5MT\nknFpvV969+7NfffdR3x8PAAPPvggxcXF5ObmBn1crnavHD58mPHjxxMeHk5CQgKjRo3i6NGj3Hvv\nvUEZk6amJhYuXEhubi7jx48HoFevXpw5c4akpCSqqqpISEgAQuu525G4QOc8d4Oipl1TUwOAx+Ph\nd7/7HY899hgA48aN491338XlclFaWkpJSQnDhw8nKSmJ2NhYjhw5gmEYbN261RvwYHK1uFy4cIG5\nc+fy05/+lJEjR3rLJycnh2Rc8vPzAdi4cSN79+5l7969PP7448ybN4+ZM2eG/P2SkZHBiRMnuHjx\nIs3NzRw6dIj09PSQiMvV7pW0tDRv71+Hw8GRI0dIS0sLypgYhsHSpUux2Ww88cQT3u3jxo3jz3/+\nMwBvv/229zpD5bnb0bh01nM34KYxXbx4MR9//DG1tbX06tWLBQsW4HA42LhxIwATJ05k8eLF3vJr\n1qxhy5YthIeHs3TpUjIzM4GWLvbPPvssFy9eJCsri+eee65LrqezdCQuv/3tb/n973/PgAEDvPu/\n/vrrJCQkhHRcLrd69WpiYmIoKCgAQvt+AXjnnXdYu3YtJpOJrKwsnnnmGSC44tKRmLhcLn72s59x\n/PhxPB4P06ZNY/bs2UBwxQTgr3/9Kz/4wQ8YNGiQt6l38eLFDB8+nEWLFnH69Gn69u3LK6+84u3I\nFwrP3Y7GpbOeuwGXtEVEREJVUDSPi4iIhAIlbRERkQChpC0iIhIglLRFREQChJK2iIhIgFDSFhER\nCRBK2iIiIgFCSVtERCRABMXc4yLSvscee4xFixYxZswYAAoLC7Hb7ezYsQOn04nD4WDx4sU88MAD\nfPHFF/z85z/HYrFQX1/PokWLyMjI4De/+Q1lZWWUl5ezZMkS7r777i6+KpHQpaQtEsRmzJjBli1b\nGDNmDDU1NZw6dYp3332XOXPmMGbMGM6cOcOMGTN47733qK6uZuHChYwZM4bDhw+zYsUKMjIydYqw\nRgAAAWpJREFUACgvL2fDhg1dfDUioqQtEsQefvhhfvWrX1FfX8+OHTvIzc1l/fr1OJ1OVq9eDYDF\nYqGmpobExERefPFFfv3rX9PU1ERtba33OPfcc09XXYKIXEZJWySIRUZGMnHiRLZt28b27dv55S9/\nycaNG1m9erV3qc1WS5YswW63k5eXx4kTJ5g3bx7Qsiaw2axHhcjtQB3RRILcjBkzePPNN4mIiCA1\nNZVRo0axbds2oGXpyZUrVwJQXV1Neno6gHdpRWhZglBEbg9K2iJBzmazER0dzbRp0wB47rnn2L17\nNzNnzuTJJ5/kgQceAGD27NksWbKE2bNnM2rUKOLj43nhhRcwmUzepQdFpGtpaU6RIFdWVsaTTz7J\nO++8Q3h4eFefjojcBL2oEglia9asYfv27axYsUIJWyQIqKYtIiISIPROW0REJEAoaYuIiAQIJW0R\nEZEAoaQtIiISIJS0RUREAoSStoiISID4/zXYVMMzwtHjAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f71c403f2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "foo['totalRoles'] = foo['actor']+foo['actress']\n", "foo['manFrac'] = foo['actor']/foo['totalRoles']\n", "foo['manFrac'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Plot the fraction of supporting (n=2) roles that have been 'actor' roles each year in the history of film." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f71c3fd87b8>" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JlV9Lmtxd7co87DvaibrTPXjs7qp5Xwfxr7nHBAD48p1LkJGiwUcnunDo7FX8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\nZd22xrEfuxSMQ3G2ZRhqlRJLi+NvJB2qxQWOwF3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H8uabbzJkyBAA3nrrLa5evUpCQoJq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\nRCxEwS0iImIhCm4RERELUXCLiIhYyP8BzZeBGXEMDuYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f71c40261d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "support = cast[cast.n==2]\n", "bar = support.groupby(['year','type']).size().unstack().fillna(0)\n", "bar['totalRoles'] = bar['actor']+bar['actress']\n", "bar['manFrac'] = bar['actor']/bar['totalRoles']\n", "bar['manFrac'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Build a plot with a line for each rank n=1 through n=3, where the line shows what fraction of that rank's roles were 'actor' roles for each year in the history of film." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'AxesSubplot' and 'AxesSubplot'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-84-69299e995b5c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'totalRoles'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'actor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'actress'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'manFrac'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'actor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'totalRoles'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'manFrac'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'manFrac'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbaz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'manFrac'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'AxesSubplot' and 'AxesSubplot'" ] }, { "data": { "image/png": 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JKS0NNI3Wt97o9zGGkx2HG2lq93PFnHwyU23cdfVkNF3nmXXH4wSnst5FIBhm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lNTLn83XZnc9sr5oYceoquri56eHkZHR2loaCAjI2PSPhkZGRw5cgSA7777joiICKKioqbl4ERE\nRILRba+cZ8+ezeuvv05hYSEej4dnnnmGxMREPv30UwDWrVtHeno6ra2tPPHEE4SFhbFr164ZOXAR\nEZFAFWL++oGxiIiI2EqPI4uIiPgZlbOIiIifUTmLiIj4Gb8t523btrFixQqcTqd32/nz58nPz8fp\ndFJUVMTg4KD3ZwcPHiQrK4vVq1dz+vRp7/YffvgBp9NJVlYWFRUVM3oOd4IvuZw5cwaXy4XT6cTl\ncvHVV195XxPMudx06dIlli5dyqFDh7zbAikXXzO5+bM1a9bgdDoZHR0FAisT8C2XGzduUFZWhtPp\n5Kmnnpq0vkCg5dLb28tzzz1HTk4Oa9as4fDhwwAMDAywadMmsrOzKSgo4Nq1a97XBPq462sm0zrm\nGj/V1tZmzp07Z9asWePd5nK5TFtbmzHGmM8++8zs37/fGGPMhQsXTG5urhkdHTXd3d0mMzPTeDwe\nY4wxeXl5pr293RhjzObNm01ra+sMn8n08iWXjo4O43a7jTHGdHZ2mpUrV3pfE8y53FRcXGxeeukl\nU1NT490WSLn4ksnY2JhxOp3m/PnzxhhjBgYGzPj4uDEmsDIxxrdc6urqzMsvv2yMMWZkZMQ8/vjj\n5rfffjPGBF4ubrfbdHR0GGOMGRwcNFlZWebnn382u3fvNlVVVcYYYw4ePGj27t1rjAmOcdfXTKZz\nzPXbK+dly5YRERExaVtXVxfLli0DYMWKFZw8eRKYmN87JycHh8NBfHw8CxcupL29HbfbzdDQEMnJ\nyQA8/fTTNDU1zeyJTDNfclm8eDHR0dEAJCUlcePGDcbGxoI+F4Cmpibi4+NJSkrybgu0XHzJ5MyZ\nMyxatIhFixYBcO+99zJr1qyAywR8yyU6Oprh4WHGx8cZHh7G4XAQHh4ekLlER0ezePFiAObOnUti\nYiJ9fX20tLR4109Yu3at9zyDYdz1NZPpHHP9tpynkpSU5D2hxsZG7+xlU83v3dfX97ftsbGxuN3u\nmT3oGWCVy61OnDjBgw8+iMPhoK+vL6hzGRoaorq6muLi4kn7B0MuVpn88ssvhISEUFhYiMvlorq6\nGgiOTMA6l5UrVxIeHk5aWhoZGRls3ryZiIiIgM+lp6eHH3/8keTkZPr7+70TS0VFRdHf3w8E37j7\n32Ryq/93zL2rynnnzp188sknuFwuhoaGcDgcdh+SX/inXC5cuMC+fft46623bDpCe1jlUllZycaN\nGwkLC/vbvPCBziqT8fFxvv32W/bt28fHH39MU1MTX375ZcDMk/9PrHI5evQoN27c4PTp0zQ3N1NT\nU0N3d7fNR3tnDQ0NUVJSQnl5OeHh4ZN+FhISEjTviVv5msl0jLm3nSHM3yQkJFBTUwNM/KXf2toK\nTPwV8tf5vePi4qbcHhMTM7MHPQOscoGJc37xxRfZs2cP999/PzB1XsGQy6lTp4CJpVBPnDjB3r17\n+eOPP5g1axZz5swhKysr4HOxeq8sWLCARx99lHnz5gHw2GOP0dHRQW5ubsBnAtbvlbNnz5KZmUlo\naCiRkZGkpKRw7tw5HnnkkYDMZWxsjJKSEnJzc8nMzARg/vz5XLlyhejoaNxuN5GRkUDwjLu+ZALT\nN+beVVfOV69eBcDj8fD++++zfv16AFatWkV9fT2jo6N0d3fT1dVFcnIy0dHRhIeH097ejjGGo0eP\nesMNJFa5XLt2jRdeeIFXX32VpUuXevePiYkJylzWrVsHQG1tLS0tLbS0tLBx40aKiorYsGFDULxf\nrN4raWlpdHZ2cv36df7880/a2tpISkoKikzA+r2SkJDgfeJ2eHiY9vZ2EhISAjIXYwzl5eUkJiby\n/PPPe7evWrWKzz//HIAjR454zzMYxl1fM5nOMddvp+8sKyvj66+/ZmBggPnz51NcXMzw8DC1tbUA\nZGdnU1ZW5t3/wIED1NXVERoaSnl5OStXrgQmHl/ftm0b169fJz09ne3bt9tyPtPFl1zee+89Pvjg\nAx544AHv6w8dOkRkZGRQ53KryspK5s6dy6ZNm4DAer/4msmxY8eoqqoiJCSE9PR0XnnlFSCwMgHf\nchkdHeW1117jp59+wuPxkJeXR0FBARB4uXzzzTc8++yzLFq0yHubtqysjOTkZEpLS+nt7eW+++5j\n//793gfqAn3c9TWT6Rxz/bacRUREgtVddVtbREQkGKicRURE/IzKWURExM+onEVERPyMyllERMTP\nqJxFRET8jMpZRETEz6icRURE/MxdNbe2iExt/fr1lJaWkpqaCkBhYSFOp5PGxkZGRkYYHh6mrKyM\n5cuXc/HiRd544w0cDgeDg4OUlpaSlpbGu+++S09PD5cuXWLr1q08/PDDNp+VSPBSOYsEgPz8fOrq\n6khNTeXq1av8+uuv1NfXs3nzZlJTU7ly5Qr5+fl88cUX9Pf3U1JSQmpqKmfPnqWiooK0tDQALl26\nxEcffWTz2YiIylkkADz55JO8/fbbDA4O0tjYSG5uLh9++CEjIyNUVlYC4HA4uHr1KlFRUezZs4d3\n3nmHsbExBgYGvL9nyZIldp2CiNxC5SwSAObMmUN2djYNDQ0cP36cXbt2UVtbS2VlpXcZyJu2bt2K\n0+nE5XLR2dlJUVERMLEu7ezZGhJE/IEeCBMJEPn5+Rw+fJh77rmH+Ph4UlJSaGhoACaWRNy5cycA\n/f39JCUlAXiX/IOJ5fFExD+onEUCRGJiImFhYeTl5QGwfft2mpqa2LBhA1u2bGH58uUAFBQUsHXr\nVgoKCkhJSWHevHns3r2bkJAQ77J4ImIvLRkpEiB6enrYsmULx44dIzQ01O7DEZH/gz5gEgkABw4c\n4Pjx41RUVKiYRQKArpxFRET8jD5zFhER8TMqZxERET+jchYREfEzKmcRERE/o3IWERHxMypnERER\nP/NvVMQYo3OB5tkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f71c3d31080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thirdWheel = cast[cast.n==3]\n", "baz = thirdWheel.groupby(['year','type']).size().unstack().fillna(0)\n", "baz['totalRoles'] = baz['actor']+baz['actress']\n", "baz['manFrac'] = baz['actor']/baz['totalRoles']\n", "foo['manFrac'].plot() + (bar['manFrac'].plot() + baz['manFrac'].plot())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

FerdinandKlingenberg/TestAvSentinel-2Python

Resample.ipynb

1

8516

{ "cells": [ { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import modules\n", "import ndvi_algo\n", "import snappy\n", "from snappy import ProductIO\n", "from snappy import GPF\n", "from snappy import jpy\n", "import sys\n", "\n", "import numpy as np\n", "import string" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading...\n", "Product: S2A_MSIL1C_20170523T104031_N0205_R008_T32VNM_20170523T104025, 10980 x 10980 pixels, None\n", "Bands: ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12', 'view_zenith_mean', 'view_azimuth_mean', 'sun_zenith', 'sun_azimuth', 'view_zenith_B1', 'view_azimuth_B1', 'view_zenith_B2', 'view_azimuth_B2', 'view_zenith_B3', 'view_azimuth_B3', 'view_zenith_B4', 'view_azimuth_B4', 'view_zenith_B5', 'view_azimuth_B5', 'view_zenith_B6', 'view_azimuth_B6', 'view_zenith_B7', 'view_azimuth_B7', 'view_zenith_B8', 'view_azimuth_B8', 'view_zenith_B8A', 'view_azimuth_B8A', 'view_zenith_B9', 'view_azimuth_B9', 'view_zenith_B10', 'view_azimuth_B10', 'view_zenith_B11', 'view_azimuth_B11', 'view_zenith_B12', 'view_azimuth_B12']\n" ] } ], "source": [ "# Import files\n", "print(\"Reading...\")\n", "product = ProductIO.readProduct('/home/fklingenberg/Documents/Mosaicing_python/Data/S2A_MSIL1C_20170523T104031_N0205_R008_T32VNM_20170523T104025.SAFE/MTD_MSIL1C.xml')\n", "width = product.getSceneRasterWidth()\n", "height = product.getSceneRasterHeight()\n", "name = product.getName()\n", "description = product.getDescription()\n", "band_names = product.getBandNames()\n", "\n", "print(\"Product: %s, %d x %d pixels, %s\" % (name, width, height, description))\n", "print(\"Bands: %s\" % (list(band_names)))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Java class 'org.esa.snap.core.dataop.resamp' not found", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-65-81bedccde91b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mBandDescriptor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mResampling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.esa.snap.core.dataop.resamp.Resampling'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mresamp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.esa.snap.core.dataop.resamp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: Java class 'org.esa.snap.core.dataop.resamp' not found" ] } ], "source": [ "GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()\n", "HashMap = jpy.get_type('java.util.HashMap')\n", "BandDescriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')\n", "Resampling = jpy.get_type('org.esa.snap.core.dataop.resamp.Resampling')\n", "resamp = jpy.get_type('org.esa.snap.core.dataop.resamp')\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "resamplingMethod = Resampling.NEAREST_NEIGHBOUR.getName();\n", "#bruktIndex = Resampling.createIndex()\n", "indexxxx=Resample.computeIndex()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'targetBand11index' 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-63-9c1dd7e80a36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtargetBand11index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'targetBand11index' is not defined" ] } ], "source": [ "targetBand11index.computeIndex();" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'BandDescriptor' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-17a25204b9c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# NDSI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtargetBand1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBandDescriptor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtargetBand1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'band_1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtargetBand1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'float32'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtargetBand1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'(B3 - B11) / (B3 + B11)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'BandDescriptor' is not defined" ] } ], "source": [ "# NDSI\n", "targetBand1 = BandDescriptor()\n", "targetBand1.name = 'band_1'\n", "targetBand1.type = 'float32'\n", "targetBand1.expression = '(B3 - B11) / (B3 + B11)'" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# NDVI\n", "targetBand2 = BandDescriptor()\n", "targetBand2.name = 'band_2'\n", "targetBand2.type = 'float32'\n", "targetBand2.expression = '(B8 - B4) / (B8 + B4)'" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing...\n", "Done.\n" ] } ], "source": [ "# Til fil\n", "targetBands = jpy.array('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)\n", "targetBands[0] = targetBand2\n", "#targetBands[1] = targetBand1\n", "\n", "parameters = HashMap()\n", "parameters.put('targetBands', targetBands)\n", "\n", "result = GPF.createProduct('BandMaths', parameters, product)\n", "\n", "print(\"Writing...\")\n", "\n", "ProductIO.writeProduct(result, 'snappy_bmaths_output.dim', 'BEAM-DIMAP')\n", "\n", "print(\"Done.\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

Ledoux/ShareYourSystem

Ouvaton/Hdformater.ipynb

1

7247

{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Hdformater\n\n##Doc\n----\n\n\n> \n> An Hdformater instance maps an apply and so \"grinds\" a MappingArgDictsList \n> to a method.\n> \n> \n\n----\n\n<small>\nView the Hdformater notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Hdformater.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\nAn Hdformater instance maps an apply and so \"grinds\" a MappingArgDictsList \nto a method.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Interfacers.Writer\"\nDecorationModuleStr=\"ShareYourSystem.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport importlib\nimport os\nimport sys\n#from ShareYourSystem.Functers import Alerter\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass(**{'SwitchingUnboundMethodStr':'hdformat'})\nclass HdformaterClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'HdformatingModuleStr',\n\t\t\t\t\t\t\t\t\t'HdformatingFileKeyStr',\n\t\t\t\t\t\t\t\t\t'HdformatedFileVariable',\n\t\t\t\t\t\t\t\t\t'HdformatedStr'\n\t\t\t\t\t\t\t\t]\n\n\t#@Hooker.HookerClass(**{'HookingAfterVariablesList':[{'CallingVariable':BaseClass.__init__}]})\n\tdef default_init(self,\n\t\t\t_HdformatingModuleStr=\"tables\",\n\t\t\t_HdformatingFileKeyStr=\"\", \t\t\t\n\t\t\t_HdformatedFileVariable=None, \n\t\t\t_HdformatedFilePathStr=\"\",\t\t\n\t\t\t_HdformatedStr=\"\", \t\t\t\n\t\t\t**_KwargVariablesDict\n\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\t#@Alerter.AlerterClass()\t\t\n\t#@Switcher.SwitcherClass()\n\tdef do_hdformat(self):\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,[\n\t\t\t\t\t\t\t\t\t'HdformatingFileKeyStr'\n\t\t\t\t\t\t\t\t]))\n\t\t'''\n\t\t\n\t\t#Check\n\t\tif self.HdformatedFileVariable==None:\n\n\t\t\t#folder first\n\t\t\tself.folder()\n\n\t\t\t#set\n\t\t\tself.HdformatedFilePathStr=self.FolderingPathStr+self.HdformatingFileKeyStr\n\t\t\t\n\t\t\t#Maybe we have to import\n\t\t\tif self.HdformatingModuleStr not in sys.modules:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('We import first the hdf module')\n\t\t\t\t'''\n\n\t\t\t\t#Import\n\t\t\t\timportlib.import_module(self.HdformatingModuleStr)\n\n\t\t\t#Check\n\t\t\tif self.HdformatingFileKeyStr!=\"\":\n\n\t\t\t\t#Check for first write\n\t\t\t\tif os.path.isfile(self.HdformatedFilePathStr)==False:\n\n\t\t\t\t\t#debug\n\t\t\t\t\t'''\n\t\t\t\t\tself.debug('We create the file first')\n\t\t\t\t\t'''\n\n\t\t\t\t\t#Create the file \n\t\t\t\t\tself.HdformatedFileVariable=sys.modules[self.HdformatingModuleStr].File(\n\t\t\t\t\t\t\t\t\t\tself.HdformatedFilePathStr,'w')\n\t\t\t\t\t\t\n\t\t\t\t\t#Close it\n\t\t\t\t\tself.HdformatedFileVariable.close()\n\n\t\t\t\tif self.HdformatedFileVariable==None or ( \n\t\t\t\t\t(self.HdformatingModuleStr=='tables' and self.HdformatedFileVariable.isopen==0\n\t\t\t\t\t\t) or (self.HdformatingModuleStr=='h5py' and self.HdformatedFileVariable.mode=='c') ):\n\n\t\t\t\t\t#debug\n\t\t\t\t\t'''\n\t\t\t\t\tself.debug('We open the file')\n\t\t\t\t\t'''\n\n\t\t\t\t\t#Open the HdformatedFileVariable\n\t\t\t\t\tself.HdformatedFileVariable=sys.modules[self.HdformatingModuleStr].File(\n\t\t\t\t\t\tself.HdformatedFilePathStr,'r+')\n\n\t\t#Return self\n\t\t#return self\n\n\tdef hdfview(self):\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['HdformatingFilePathStr']))\n\t\t'''\n\n\t\tif self.HdformatedFilePathStr!=\"\":\n\t\t\n\t\t\t#set the HdformatedStr\n\t\t\tself.HdformatedStr=os.popen(\n\t\t\t\t\t\t\t\t\t\tSYS.h5lsPathStr+' -dlr '+self.HdformatedFilePathStr\n\t\t\t\t\t\t\t\t).read()\n\t\t\n\t\t#Return self\n\t\treturn self\n\n\tdef hdfclose(self):\n\n\t\t#Close the HdformatedFileVariable\n\t\tif self.HdformatedFileVariable!=None:\n\t\t\tself.HdformatedFileVariable.close()\n\n\t\t#Return self\n\t\treturn self\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Hdformater sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Interfacers/Hdformater\" 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": [ "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "from ShareYourSystem.Interfacers import Hdformater\n", "\n", "#Definition a Hdformater that writes an empty hdf file\n", "MyHdformater=Hdformater.HdformaterClass().hdformat(\n", " _FileKeyStr='Hdformats.hdf5',\n", " **{\n", " 'FolderingPathStr':Hdformater.LocalFolderPathStr\n", "}\n", ").hdfview().hdfclose()\n", "\n", "#Definition the AttestedStr\n", "SYS._attest(\n", " [\n", " 'MyHdformater.HdformatedStr is '+str(\n", " MyHdformater.HdformatedStr)\n", " ]\n", ") \n", "\n", "#Print\n", "\n", "\n", "\n", "\n" ], "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "*****Start of the Attest *****\n", "\n", "MyHdformater.HdformatedStr is / Group\n", "\n", "\n", "*****End of the Attest *****\n", "\n", "\n" ] } ], "collapsed": false, "metadata": { "slideshow": { "slide_type": "-" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }

mit

dsg-bielefeld/deep_disfluency

demos/demo.ipynb

1

11674

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no installed deep_disfluency package, pathing to source\n" ] } ], "source": [ "try:\n", " import deep_disfluency\n", "except ImportError:\n", " print \"no installed deep_disfluency package, pathing to source\"\n", " import sys\n", " sys.path.append(\"../\")\n", "from deep_disfluency.tagger.deep_tagger import DeepDisfluencyTagger" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Disfluency tagging on pre-segmented utterances\n", "tags repair structure incrementally and other edit terms <e/>\n", "(Hough and Schlangen Interspeech 2015 with an RNN)\n", "\n", "Initializing Tagger\n", "Processing args from config number 21 ...\n", "Intializing model from args...\n", "Using the cpu\n", "Warning: not using GPU, might be a bit slow\n", "\tAdjust Theano config file ($HOME/.theanorc)\n", "loading tag to index maps...\n", "Initializing model of type elman ...\n", "Loading saved weights from ../deep_disfluency/experiments/021/epoch_40\n", "No POS tagger specified,loading default CRF switchboard one\n", "Not using timing data\n", "Loading decoder...\n", "loading swbd_disf1_021 Markov model\n", "No timing model given\n", "Markov Model ready mode:\n", "constraint only\n" ] } ], "source": [ "# Initialize the tagger from the config file with a config number\n", "# and saved model directory\n", "MESSAGE = \"\"\"1. Disfluency tagging on pre-segmented utterances\n", "tags repair structure incrementally and other edit terms <e/>\n", "(Hough and Schlangen Interspeech 2015 with an RNN)\n", "\"\"\"\n", "print MESSAGE\n", "disf = DeepDisfluencyTagger(\n", " config_file=\"../deep_disfluency/experiments/experiment_configs.csv\",\n", " config_number=21,\n", " saved_model_dir=\"../deep_disfluency/experiments/021/epoch_40\"\n", " )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tagging...\n", "['<f/>']\n", "['<f/>']\n", "['<e/>']\n", "['<rms id=\"3\"/>', '<i id=\"3\"/><e/>', '<rps id=\"3\"/><rpnsub id=\"3\"/>']\n", "['<f/>']\n", "final tags:\n", "john \t<f/>\n", "likes \t<rms id=\"3\"/>\n", "uh \t<i id=\"3\"/><e/>\n", "loves \t<rps id=\"3\"/><rpnsub id=\"3\"/>\n", "mary \t<f/>\n" ] } ], "source": [ "# Tag each word incrementally\n", "# Notice the incremental diff\n", "# Set diff_only to False if you want the whole utterance's tag each time\n", "with_pos = False\n", "print \"tagging...\"\n", "if with_pos:\n", " # if POS is provided use this:\n", " print disf.tag_new_word(\"john\", pos=\"NNP\")\n", " print disf.tag_new_word(\"likes\", pos=\"VBP\")\n", " print disf.tag_new_word(\"uh\", pos=\"UH\")\n", " print disf.tag_new_word(\"loves\", pos=\"VBP\")\n", " print disf.tag_new_word(\"mary\", pos=\"NNP\")\n", "else:\n", " # else the internal POS tagger tags the words incrementally\n", " print disf.tag_new_word(\"john\")\n", " print disf.tag_new_word(\"likes\")\n", " print disf.tag_new_word(\"uh\")\n", " print disf.tag_new_word(\"loves\")\n", " print disf.tag_new_word(\"mary\")\n", "print \"final tags:\"\n", "for w, t in zip(\"john likes uh loves mary\".split(), disf.output_tags):\n", " print w, \"\\t\", t\n", "disf.reset() # resets the whole tagger for new utterance" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************\n", "2. Joint disfluency tagger and utterance semgenter\n", "Simple disf tags <e/>, <i/> and repair onsets <rps\n", "LSTM simple from Hough and Schlangen EACL 2017\n", "Initializing Tagger\n", "Processing args from config number 35 ...\n", "Intializing model from args...\n", "Using the cpu\n", "Warning: not using GPU, might be a bit slow\n", "\tAdjust Theano config file ($HOME/.theanorc)\n", "loading tag to index maps...\n", "Initializing model of type lstm ...\n", "Loading saved weights from ../deep_disfluency/experiments/035/epoch_6\n", "No POS tagger specified,loading default CRF switchboard one\n", "No timer specified, using default switchboard one\n", "Loading decoder...\n", "loading swbd_disf1_uttseg_simple_033 Markov model\n", "Markov Model ready mode:\n", "constraint only\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/julian/.local/lib/python2.7/site-packages/sklearn/base.py:311: UserWarning: Trying to unpickle estimator LogisticRegression from version 0.18.1 when using version 0.19.1. This might lead to breaking code or invalid results. Use at your own risk.\n", " UserWarning)\n", "/home/julian/.local/lib/python2.7/site-packages/sklearn/base.py:311: UserWarning: Trying to unpickle estimator StandardScaler from version 0.18.1 when using version 0.19.1. This might lead to breaking code or invalid results. Use at your own risk.\n", " UserWarning)\n" ] } ], "source": [ "# More complex set-up:\n", "print \"\\n\", \"*\" * 30\n", "MESSAGE = \"\"\"2. Joint disfluency tagger and utterance semgenter\n", "Simple disf tags <e/>, <i/> and repair onsets <rps\n", "LSTM simple from Hough and Schlangen EACL 2017\"\"\"\n", "print MESSAGE\n", "disf = DeepDisfluencyTagger(\n", " config_file=\"../deep_disfluency/experiments/experiment_configs.csv\",\n", " config_number=35,\n", " saved_model_dir=\"../deep_disfluency/experiments/035/epoch_6\",\n", " use_timing_data=True\n", " )\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tagging...\n", "['<f/><tc/>']\n", "['<f/><cc/>']\n", "['<e/><i/><cc/>']\n", "['<rps id=\"3\"/><cc/>']\n", "['<f/><cc/>']\n", "['<f/><ct/>', '<f/><tc/>']\n", "final tags:\n", "john \t<f/><tc/>\n", "likes \t<f/><cc/>\n", "uh \t<e/><i/><cc/>\n", "loves \t<rps id=\"3\"/><cc/>\n", "mary \t<f/><ct/>\n", "yeah \t<f/><tc/>\n" ] } ], "source": [ "print \"tagging...\"\n", "print disf.tag_new_word(\"john\", pos=\"NNP\", timing=0.3)\n", "print disf.tag_new_word(\"likes\", pos=\"VBP\", timing=0.3)\n", "print disf.tag_new_word(\"uh\", pos=\"UH\", timing=0.3)\n", "print disf.tag_new_word(\"loves\", pos=\"VBP\", timing=0.3)\n", "print disf.tag_new_word(\"mary\", pos=\"NNP\", timing=0.3)\n", "print disf.tag_new_word(\"yeah\", pos=\"UH\", timing=2.0)\n", "print \"final tags:\"\n", "for w, t in zip(\"john likes uh loves mary yeah\".split(), disf.output_tags):\n", " print w, \"\\t\", t\n", "disf.reset() # resets the whole tagger for next dialogue or turn" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************\n", "3. Joint disfluency tagger and utterance semgenter\"\n", "Full complex tag set with disfluency structure\"\n", "LSTM complex from Hough and Schlangen EACL 2017\n", "Initializing Tagger\n", "Processing args from config number 36 ...\n", "Intializing model from args...\n", "Using the cpu\n", "Warning: not using GPU, might be a bit slow\n", "\tAdjust Theano config file ($HOME/.theanorc)\n", "loading tag to index maps...\n", "Initializing model of type lstm ...\n", "Loading saved weights from ../deep_disfluency/experiments/036/epoch_15\n", "No POS tagger specified,loading default CRF switchboard one\n", "No timer specified, using default switchboard one\n", "Loading decoder...\n", "loading swbd_disf1_uttseg_034 Markov model\n", "Markov Model ready mode:\n", "constraint only\n" ] } ], "source": [ "print \"\\n\", \"*\" * 30\n", "MESSAGE = \"\"\"3. Joint disfluency tagger and utterance semgenter\"\n", "Full complex tag set with disfluency structure\"\n", "LSTM complex from Hough and Schlangen EACL 2017\"\"\"\n", "print MESSAGE\n", "disf = DeepDisfluencyTagger(\n", " config_file=\"../deep_disfluency/experiments/experiment_configs.csv\",\n", " config_number=36,\n", " saved_model_dir=\"../deep_disfluency/experiments/036/epoch_15\",\n", " use_timing_data=True\n", " )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tagging...\n", "['<f/><tc/>']\n", "['<e/><cc/>']\n", "['<rms id=\"2\"/><rms id=\"1\"/><tc/>', '<rm id=\"2\"/><rps id=\"1\"/><rpnsub id=\"1\"/><cc/>', '<rps id=\"2\"/><rpnsub id=\"2\"/><cc/>']\n", "['<f/><cc/>']\n", "['<f/><cc/>']\n", "['<f/><ct/>', '<f/><tt/>']\n", "final tags:\n", "i \t<rms id=\"2\"/><rms id=\"1\"/><tc/>\n", "uh \t<rm id=\"2\"/><rps id=\"1\"/><rpnsub id=\"1\"/><cc/>\n", "i \t<rps id=\"2\"/><rpnsub id=\"2\"/><cc/>\n", "love \t<f/><cc/>\n", "mary \t<f/><ct/>\n", "yeah \t<f/><tt/>\n" ] } ], "source": [ "print \"tagging...\"\n", "print disf.tag_new_word(\"i\", pos=\"PRP\", timing=0.3)\n", "print disf.tag_new_word(\"uh\", pos=\"UH\", timing=0.3)\n", "print disf.tag_new_word(\"i\", pos=\"PRP\", timing=0.3)\n", "print disf.tag_new_word(\"love\", pos=\"VBP\", timing=0.3)\n", "print disf.tag_new_word(\"mary\", pos=\"NNP\", timing=0.3)\n", "print disf.tag_new_word(\"yeah\", pos=\"UH\", timing=2.0)\n", "print \"final tags:\"\n", "for w, t in zip(\"i uh i love mary yeah\".split(), disf.output_tags):\n", " print w, \"\\t\", t\n", "disf.reset() # resets the whole tagger" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

cbpygit/pypmj

examples/Post process management.ipynb

1

5014

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and configuration" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append('..')\n", "import pypmj as jpy\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project set-up" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "project = jpy.JCMProject('scattering/mie/mie2D_post_processes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the simulation set" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mie_keys = {'constants' :{},\n", " 'parameters': {},\n", " 'geometry': {'radius':np.linspace(0.3, 0.5, 40)}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the `SimulationSet` can be initialized." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simuset = jpy.SimulationSet(project, mie_keys, duplicate_path_levels=0,\n", " storage_folder='mie2D_test_pp',\n", " storage_base=os.path.abspath('tmp_storage_folder'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simuset.make_simulation_schedule()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simuset.use_only_resources('localhost')\n", "simuset.resource_manager.resources.set_m_n_for_all(4,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running a single simulation with additional post process(es)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pp_file = os.path.join(project.working_dir, 'flux_computation.jcmp')\n", "results, logs = simuset.solve_single_simulation(0, run_post_process_files=pp_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print simuset.simulations[0].logs['Out']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using a processing function" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def read_scs(pp):\n", " results = {} #must be a dict\n", " results['SCS'] = pp[0]['ElectromagneticFieldEnergyFlux'][0][0].real\n", " return results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simuset.simulations[0].process_results(processing_func=read_scs, overwrite=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "'SCS' in simuset.simulations[0]._results_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running all simulations with (an) additional post process(es)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "simuset.run(N=10, processing_func=read_scs, run_post_process_files=pp_file)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_context('notebook')\n", "\n", "data = simuset.get_store_data().sort_values(by='radius')\n", "data.plot(x='radius', y='SCS', title='Results of the simulation')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

camilogavo/Colombian_Energy_Forecasting

Model_over_all_serie.ipynb

1

344983

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Carga de paquetes a utilizar en este documento\n", "library(forecast)\n", "library(dplyr)\n", "library(ggplot2)\n", "library(readr)\n", "library(tidyr)\n", "library(lubridate)\n", "library(XML)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Carga datos iniciales\n", "\n", "datos<-read.table('files/MEM2.csv',header=TRUE,sep=',') #cargo la serie de datos\n", "\n", "current_price<-datos$Precio_Corriente \n", "current_price<-ts(current_price,frequency = 7,start=c(1,4)) #construyo la serie corriente // start=c(num_semana,num_dia)\n", "\n", "\n", "constant_price<-datos$Precio_Constante_2008\n", "constant_price<-ts(constant_price,frequency = 7,start=c(1,4)) #construyo la serie constante // start=c(num_semana,num_dia)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Construir un vector con la fechas de los precios\n", "b<-strsplit(as.character(datos$Fecha),split = \"/\")\n", "\n", "date<-c()#construyo un vector para almacenar las fechas\n", "for(i in seq(1:(length(datos$Fecha)))){\n", " \n", " date<-c(date,paste(b[[i]][3],b[[i]][2],b[[i]][1],sep = \"-\")) #Lleno elvector en el orden YYYY-mm-dd\n", "\n", "}\n", "datos<-cbind(date,datos) #Uno el resultado a mi dataframe de datos" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Identificar los anos bisciestos y el total de semanas\n", "last_year<-as.Date(datos$date[length(datos$date)-1]) #Doy formato de fecha al ultimo dato\n", "last_year<-as.numeric(format(last_year,\"%Y\")) #extraer el año del ultimo dato\n", "leap_years<-length(seq(from = 1996,to = last_year,by = 4)) #Total dias bisiestos de la serie\n", "\n", "total_weeks<-round(length(constant_price+leap_years)/7,digits = 0)+1 #Totalde semanas, sabiendo que la serie empieza un miercoles" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Remover outliers de la serie de precios constantes\n", "const_price_wo<-tsclean(constant_price) #Se remueven los outliers bajo el metodo de sumpsmu\n", "\n", "#A continuacion vamos a identificar los datos que fueron tratados para esta transformacion\n", "outliers_const_price<-rep(0,length(constant_price)) #construyo el vector para almacenar los outliers\n", "for(item in 1:length((constant_price))){\n", " if(constant_price[item]!=const_price_wo[item]){\n", " outliers_const_price[item]<-constant_price[item]\n", " } \n", " \n", "}\n", "\n", "#Agregar datos constantes sin outliers al dataset\n", "\n", "datos$Precios_Const_wo<-const_price_wo\n", "\n", "\n", "\n", "\n", "\n", "# Transformacion Logaritmo Natural y Remocion de outliers. \n", "ln_const_price<-log(constant_price) # Se transforman los datos en log natural\n", "ln_const_wo<-tsclean(ln_const_price) #Se remueven los outliers bajo el metodo de sumpsmu\n", "## A continuacion vamos a identificar los datos que fueron tratados para esta transformacion\n", "outliers_ln<-rep(0,length(ln_const_price)) #construyo el vector para almacenar los outliers\n", "for(item in 1:length((ln_const_price))){\n", " if(ln_const_price[item]!=ln_const_wo[item]){\n", " outliers_ln[item]<-ln_const_price[item]\n", " } \n", " \n", "}\n", "\n", "#Agregar la transformacion Ln al dataset\n", "datos$Ln_P<-ln_const_price\n", "datos$Ln_Pwo<-ln_const_wo\n", "\n", "\n", "#--------------------------------------------------------\n", "\n", "#Transformacion Log10 y remocion outliers\n", "log10_const_price<-log10(constant_price) # Se transforman los datos en log10\n", "log10_const_wo<-tsclean(log10_const_price) #Se remueven los outliers bajo el metodo de sumpsmu\n", "##A continuacion vamos a identificar los datos que fueron tratados para esta transformacion\n", "outliers_log10<-rep(0,length(log10_const_price)) #construyo el vector para almacenar los outliers\n", "for(item in 1:length((log10_const_price))){\n", " if(log10_const_price[item]!=log10_const_wo[item]){\n", " outliers_log10[item]<-log10_const_price[item]\n", " } \n", " \n", "}\n", "\n", "#Agregar la transformacion Logaritmo Natural al dataset\n", "datos$Log10_P<-log10_const_price\n", "datos$Log10_Pwo<-log10_const_wo\n", "\n", "#--------------------------------------------------------\n", "\n", "#Transformacion raiz cuadrada y remocion outliers\n", "sqrt_const_price<-sqrt(constant_price) # Se transforman los datos en raiz cuadrada\n", "sqrt_const_wo<-tsclean(sqrt_const_price) #Se remueven los outliers bajo el metodo de sumpsmu\n", "\n", "#A continuacion vamos a identificar los datos que fueron tratados para esta transformacion\n", "outliers_sqrt<-rep(0,length(sqrt_const_price)) #construyo el vector para almacenar los outliers\n", "for(item in 1:length((sqrt_const_price))){\n", " if(sqrt_const_price[item]!=sqrt_const_wo[item]){\n", " outliers_sqrt[item]<-sqrt_const_price[item]\n", " } \n", " \n", "}\n", "\n", "#Agregar la transformacion Raiz Cuadrada al dataset\n", "datos$Sqrt_P<-sqrt_const_price\n", "datos$Sqrt_Pwo<-sqrt_const_wo\n", "\n", "\n", "#-------------------------------------------------------------------\n", "trescuartos_const_price<-(constant_price)^(3/4) # Se transforman los datos en raiz cuadrada\n", "trescuartos_const_wo<-tsclean(trescuartos_const_price) #Se remueven los outliers bajo el metodo de sumpsmu\n", "\n", "#A continuacion vamos a identificar los datos que fueron tratados para esta transformacion\n", "outliers_trescuartos<-rep(0,length(trescuartos_const_price)) #construyo el vector para almacenar los outliers\n", "for(item in 1:length((trescuartos_const_price))){\n", " if(trescuartos_const_price[item]!=trescuartos_const_wo[item]){\n", " outliers_trescuartos[item]<-trescuartos_const_price[item]\n", " } \n", " \n", "}\n", "\n", "#Agregar la transformacion Elevado a la 3/4 al dataset\n", "datos$Root4_P3<-trescuartos_const_price\n", "datos$Root4_Pwo3<-trescuartos_const_wo\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modelo sobre toda la serie" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m1<-auto.arima(const_price_wo,allowdrift = TRUE)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Series: const_price_wo \n", "ARIMA(2,1,1)(2,0,0)[7] \n", "\n", "Coefficients:\n", " ar1 ar2 ma1 sar1 sar2\n", " 0.8014 0.0826 -0.9634 0.1451 0.1172\n", "s.e. 0.0128 0.0118 0.0061 0.0115 0.0114\n", "\n", "sigma^2 estimated as 122.9: log likelihood=-30542.12\n", "AIC=61096.25 AICc=61096.26 BIC=61138.16" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>ar1</dt>\n", "\t\t<dd>0.801356796785399</dd>\n", "\t<dt>ar2</dt>\n", "\t\t<dd>0.0825550741650454</dd>\n", "\t<dt>ma1</dt>\n", "\t\t<dd>-0.963421406155061</dd>\n", "\t<dt>sar1</dt>\n", "\t\t<dd>0.14508348805542</dd>\n", "\t<dt>sar2</dt>\n", "\t\t<dd>0.11724252702671</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[ar1] 0.801356796785399\n", "\\item[ar2] 0.0825550741650454\n", "\\item[ma1] -0.963421406155061\n", "\\item[sar1] 0.14508348805542\n", "\\item[sar2] 0.11724252702671\n", "\\end{description*}\n" ], "text/markdown": [ "ar1\n", ": 0.801356796785399ar2\n", ": 0.0825550741650454ma1\n", ": -0.963421406155061sar1\n", ": 0.14508348805542sar2\n", ": 0.11724252702671\n", "\n" ], "text/plain": [ " ar1 ar2 ma1 sar1 sar2 \n", " 0.80135680 0.08255507 -0.96342141 0.14508349 0.11724253 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m1\n", "coefficients(m1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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uadKWp+WN84assW6RtH70yMJmw7axQN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\nzwnnVPk30hF56stRbE6HSNdJN0hHS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wQIGVevKiuNgiT7knAYqHsuwAChUe5dmO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\nSZV6PzhjHethH32Wqe2o/iV5hG5Q8mu8r+MRO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NNoo12NOdJ5Pd0y6E91UXd8d9AvqU69e7wXeoMJlf0XgL7yt8YNAn2o3uTSeuzLun55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\nwhpwJHwG7tO+/cF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D2UuicfG0NZ9kPx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\nQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x=const_price_wo,y=fitted(m1),type='p',\n", " ylab='Forecasted',xlab='Real')\n", "lines(x=seq(1:length(const_price_wo)),y=seq(1:length(const_price_wo)),col='red',lwd=2)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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qFkEIAABCAAAQhAAALrSeDBu6F35uEQ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\nfZKLJFrTG7KVx9JrWO5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aaIrbWbpkQm8VMF51zHSXInrBOY+MfgEbPjeTo+TvJ4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DU2eslR/RjHSACps/NWrqkT1BjhCAQKcJ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\nc+dbQvejjw5LXxtEKVKon6FU8Czz+7x79PsFC9mto8cqUVc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JKeTWGnUwXPErQHiTUSYQk1sUXvSMGc5HGMs5zljAI4C8flaXq+PCoI4d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\nPSSOknp0TkaCMFmUAuFb9HgopBTHmanG8shxjwkD7qYVRvfBhnnWp6U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Kg/sQMJwxlWJLZZnHSJhp8uURMJXk5mENdppwwAxZsKN/FzxC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\nAAEEEBhKAQKkATusn9P2h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TVd6O1xmJ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\nDSGjtKj/AAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(const_price_wo,type='l')\n", "lines(fitted(m1),col='red')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

steve-federowicz/om

examples/.ipynb_checkpoints/Untitled5-checkpoint.ipynb

2

181

{ "metadata": { "name": "", "signature": "sha256:421f621054b55c4937bb9cdce02c50b7d5c4525468b667f50ee49eef8ebcc38d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }

mit

joaquimargente/qisskit-ipynb

QuantumClient.ipynb

1

4110

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# USER, PLEASE SET CONFIG:\n", "token = \"_TOKEN_\"\n", "config = {\n", " \"url\": 'https://quantumexperience.ng.bluemix.net/api'\n", "}\n", "# ---- UTILS -----\n", "import sys\n", "if sys.version_info.major > 2: # Python 3\n", " from IBMQuantumExperience.IBMQuantumExperience import IBMQuantumExperience\n", "else: # Python 2 \n", " from IBMQuantumExperience import IBMQuantumExperience\n", "from IPython.display import Image, display\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "exec(\"experiment-conf.py\")\n", "%matplotlib inline\n", "api = IBMQuantumExperience(token)\n", "def showImageCode(idCode):\n", " if (idCode):\n", " code = api.get_image_code(idCode)\n", " if (code.get('error', None)):\n", " print(\"Failed to recover the Code\")\n", " else:\n", " display(Image(code['url']))\n", " else:\n", " print(\"Invalid IdCode\")\n", "def printBars(values, labels):\n", " N = len(values)\n", " ind = np.arange(N) # the x locations for the groups\n", " width = 0.35 # the width of the bars\n", " fig, ax = plt.subplots()\n", " rects1 = ax.bar(ind, values, width, color='r')\n", " # add some text for labels, title and axes ticks\n", " ax.set_ylabel('Probabilities')\n", " ax.set_xticks(ind + (width/2.))\n", " ax.set_xticklabels(labels)\n", " def autolabel(rects):\n", " # attach some text labels\n", " for rect in rects:\n", " height = rect.get_height()\n", " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", " '%f' % float(height),\n", " ha='center', va='bottom')\n", " autolabel(rects1)\n", " plt.show()\n", "def showResultsByExecution(executionRaw):\n", " result = executionRaw.get('result', {})\n", " data = result.get('data', {})\n", " print('Execution in ' + executionRaw.get('deviceRunType', 'Unknown') + ' at ' + executionRaw.get('endDate', 'Unknown'))\n", " if (data.get('p', None)):\n", " values = data['p']['values']\n", " labels = data['p']['labels']\n", " printBars(values, labels)\n", " else:\n", " print(\"Not plotted. Results are: \"+str(executionRaw))\n", "def showResultsByIdExecution(idExecution):\n", " execution = api.get_result_from_execution(idExecution)\n", " if (execution.get('measure', None)):\n", " values = execution['measure']['values']\n", " labels = execution['measure']['labels']\n", " printBars(values, labels)\n", " else:\n", " print(\"Not plotted. Results are: \"+str(execution))\n", "def showLastCodes():\n", " codes = api.get_last_codes()\n", " for code in codes:\n", " print(\"--------------------------------\")\n", " print(\"Code \" + code.get('name', 'Unknown'))\n", " print(\" \")\n", " showImageCode(code.get('id', None))\n", " print(\"------- Executions -------------\")\n", " for execution in code.get('executions', []):\n", " showResultsByExecution(execution)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

dataminingapp/dataminingapp-lectures

Lecture-4/Distance-Functions.ipynb

2

15481

{ "metadata": { "celltoolbar": "Raw Cell Format", "name": "", "signature": "sha256:d9c1b07d11a11d66b255616a18846d56b29fb1c35f5f1a34f3fd14a8e7d99bcc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Distance and similarity functions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import sklearn as sk\n", "import sklearn.datasets as sk_data\n", "import sklearn.metrics as metrics\n", "import seaborn as sns\n", "\n", "import time\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Brief intro to numpy: http://www.numpy.org/\n", "\n", "\n", "or for more references: http://docs.scipy.org/doc/numpy/reference/index.html" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Why numpy?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "def trad_version():\n", " t1 = time.time()\n", " X = range(10000000)\n", " Y = range(10000000)\n", " Z = []\n", " for i in range(len(X)):\n", " Z.append(X[i] + Y[i])\n", " return time.time() - t1\n", "\n", "def numpy_version():\n", " t1 = time.time()\n", " X = np.arange(10000000)\n", " Y = np.arange(10000000)\n", " Z = X + Y\n", " return time.time() - t1\n", "\n", "\n", "traditional_time = trad_version()\n", "numpy_time = numpy_version()\n", "print \"Traditional time = \"+ str(traditional_time)\n", "print \"Numpy time = \"+ str(numpy_time)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Arrays in numpy" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#1-dimensional arrays\n", "x = np.array([2,5,18,14,4])\n", "print \"\\n Deterministic 1-dimensional array \\n\"\n", "print x\n", "\n", "x = np.random.rand(5)\n", "print \"\\n Random 1-dimensional array \\n\"\n", "print x\n", "\n", "#2-dimensional arrays\n", "x = np.array([[2,5,18,14,4], [12,15,1,2,8]])\n", "print \"\\n Deterministic 2-dimensional array \\n\"\n", "print x\n", "\n", "x = np.random.rand(5,5)\n", "print \"\\n Random 2-dimensional array \\n\"\n", "print x\n", "print x.shape\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Manipulating and aggregating arrays" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.random.rand(5)\n", "print x\n", "print x+1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Aggregates" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.random.rand(2,4)\n", "print x\n", "print np.mean(x)\n", "print np.mean(x,0)\n", "print np.std(x)\n", "print np.std(x,1)\n", "print np.median(x)\n", "print np.median(x,1)\n", "print np.sum(x)\n", "print np.sum(x,1)\n", "print np.prod(x)\n", "print np.prod(x,1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Generating synthetic data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Random data are the simplest data one can generate. Other types of data following different distributions can be generated with functions extensively discussed below.\n", "\n", "http://docs.scipy.org/doc/numpy/reference/routines.random.html" ] }, { "cell_type": "code", "collapsed": false, "input": [ "A = np.random.rand(2,15)\n", "print (A)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "B = np.random.randint(2,size = (2,15))\n", "print B" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Euclidean distance" ] }, { "cell_type": "code", "collapsed": false, "input": [ "D = np.sqrt(np.sum(np.square(A[0,:]-A[1,:])))\n", "print D" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_euclidean_dist(x,y):\n", " return np.sqrt(np.sum(np.square(x-y)))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "d1 = my_euclidean_dist(A[0,:],A[1,:])\n", "d2 = my_euclidean_dist(B[0,:],B[1,:])\n", "print d1\n", "print d2" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Becoming more effective and efficient using scikit-learn, a set of libraries for data mining, data analysis and machine learning\n", "\n", "http://scikit-learn.org/stable/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generating data with specific structure using **sklearn.datasets**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "X, y = sk_data.make_blobs(n_samples=100, centers=3, n_features=2,center_box=(-30.0, 30.0),random_state=0)\n", "print X.shape, y.shape, type(y)\n", "\n", "plt.plot(X[y==1,0],X[y==1,1],'bo')\n", "plt.plot(X[y==0,0],X[y==0,1],'go')\n", "plt.plot(X[y==2,0],X[y==2,1],'ro')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "euclidean_dists = metrics.euclidean_distances(X)\n", "# print euclidean_dists.shape\n", "\n", "z = y\n", "idx = np.argsort(z)\n", "rearranged_dists = euclidean_dists[idx,:][:,idx]\n", "\n", "\n", "\n", "# Plot the matrices in a single row, using fig, (ax1, ax2) = plt.subplots(1,2,figsize=(15,10))\n", "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(15,10))\n", "sns.heatmap(euclidean_dists, xticklabels=False, yticklabels=False, linewidths=0, ax=ax1, square=True, cbar=False)\n", "sns.heatmap(rearranged_dists, xticklabels=False, yticklabels=False, linewidths=0, ax=ax2, square=True, cbar=False)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of generating data using **sklearn.datasets**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data, rows, columns = sk_data.make_biclusters(\n", " shape=(300, 50), n_clusters=2, noise=0.5,\n", " shuffle=False, random_state=0)\n", "#data[data>5] = 1\n", "sns.heatmap(data, xticklabels=False, yticklabels=False, linewidths=0)\n", "print type(data)\n", "print data.shape\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "newd = np.reshape(data,data.shape[0]*data.shape[1])\n", "plt.hist(newd)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "data[data<40] = 0\n", "data[data>=40] = 1\n", "sns.heatmap(data, xticklabels=False, yticklabels=False, linewidths=0)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "shuffled_data, row_idx = sk.utils.shuffle(data, np.arange(data.shape[0]), random_state=100)\n", "shuffled_data, col_idx = sk.utils.shuffle(shuffled_data.T, np.arange(data.shape[1]), random_state=100)\n", "shuffled_data = shuffled_data.T\n", "sns.heatmap(shuffled_data, xticklabels=False, yticklabels=False, linewidths=0)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "jacc_dists = metrics.pairwise_distances(data,Y=None,metric='jaccard', n_jobs=1)\n", "\n", "sns.heatmap(jacc_dists, xticklabels=False, yticklabels=False, linewidths=0)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "jacc_dists = metrics.pairwise_distances(shuffled_data,Y=None,metric='jaccard', n_jobs=1)\n", "\n", "\n", "y = [ row_idx.tolist().index(i) for i in range(len(row_idx))]\n", "\n", "rearranged_dists = jacc_dists[y,:][:,y]\n", "\n", "\n", "sns.heatmap(rearranged_dists, xticklabels=False, yticklabels=False, linewidths=0)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can compute pairwise distances using the **sklearn.metrics** functions summarized here:\n", "http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Code for setting the style of the notebook\n", "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../theme/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<link href='http://fonts.googleapis.com/css?family=EB+Garamond' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " .code_cell {\n", " width: 105ex !important ;\n", " margin-bottom: 15px !important;\n", " }\n", " div.cell {\n", " margin-left: auto;\n", " margin-right: auto;\n", " width: 70%;\n", " } \n", " div.cell.selected {\n", " border: thin rgba(171, 171, 171, 0.5) dashed;\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'EB Garamond', serif;\n", " }\n", " h3 {\n", " font-family: 'EB Garamond', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " h4 {\n", " font-family: 'EB Garamond', serif;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " div.text_cell_render {\n", " font-family: 'EB Garamond',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 140%;\n", " }\n", " div.input_area {\n", " border-color: rgba(0,0,0,0.10) !important;\n", " background: #fafafa;\n", " }\n", " .CodeMirror {\n", " font-family: \"Source Code Pro\";\n", " font-size: 90%;\n", " }\n", " .prompt {\n", " display: None;\n", " }\n", " .output {\n", " padding-left: 50px;\n", " padding-top: 5px;\n", " }\n", " .output_wrapper {\n", " padding-left: 5px;\n", " padding-top: inherit;\n", " }\n", " div.output_scroll {\n", " width: inherit;\n", " }\n", " .inner_cell {\n", " padding-left: 5px;\n", " }\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", " line-height: 100%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #CD2305;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " .warning {\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x3924d68>" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }

mit

santiago-salas-v/lit-imp

CO2 Equilibria.ipynb

1

34745

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CO2 Equilibria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Atm. pressure (func. of altitude)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$dp = -\\rho g dz$\n", "\n", "${{dp}\\over{dz}} = -\\rho g = -{{\\rho_0}\\over{p_0}} g p \n", "\\text{ .. Boyle-Mariotte i.g. }p/p_0 = \\rho/\\rho_0$\n", "\n", "${{1}\\over{p}}{{dp}\\over{dz}} = -{{1}\\over{p_0/(\\rho_0\\times g)}} \n", "\\equiv -{{1}\\over{H}} [=] 1/m \\text{...(Skalenhöhe)}$\n", "\n", "$\\int{{{1}\\over{p}}dp} = \\int{-{{1}\\over{H}}dz}$\n", "\n", "$ln(p) + c1 = -z/H$\n", "\n", "$p(z) = e^{c1} e^{-z/H}$\n", "\n", "$p(0) = p_0 \\rightarrow p(0) = e^{c1}e^0 = e^{c1} = p_0$\n", "\n", "$p(z) = p_0 e^{-z/H}$\n", "\n", "=============================================\n", "\n", "Hg: $p=\\rho g z$\n", "\n", "$z=760mm=0.760m, \\rho=13,595.098kg/m^3, g=9.80665m/s^2 \\rightarrow p=p_0=101325Pa$\n", "\n", "\n", "N2/O2/Ar: $p=p_0 e^{-z/H}$\n", "\n", "$p/p_0 = 1 \\rightarrow 0 = -z/H$\n", "\n", "$\\lim_{p\\rightarrow 0}{p/p_0} = +\\inf$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy.constants import *\n", "def interp_1(x3, x_1_2, y_1_2):\n", " x1, x2, y1, y2 = x_1_2[0], x_1_2[1], y_1_2[0], y_1_2[0]\n", " return (y2-y1)/(x2-x1)*(x3-x2) + y2\n", "\n", "comps = np.array(['N2', 'O2', 'Ar'])\n", "mol_pct = np.array([0.7812, 0.2096, 0.0092])\n", "mm = np.array([28.013, 31.999, 39.948])\n", "w_pct = np.multiply(mol_pct, mm)/sum(np.multiply(mol_pct, mm))\n", "# 1dm^3 = 1dm^3*(1m/10dm)^3*(100cm/m)^3*(1mL/cm^3) = 10^3mL = 1L\n", "# 0.1MPa, 300K\n", "dens_moldm3 = 1/np.array([24.854, 24.928, 24.928]) # mol/dm^3\n", "dens_gml = np.multiply(mm, dens_moldm3)/1000.0 # g/mL, kg/L\n", "dens_kgm3 = 1000.0*dens_gml # kg/m^3\n", "rho_hg = 13.596*interp_1(293.15, [294, 292], [997.983, 998.392]) # kg/m3\n", "rho_a = sum(w_pct*dens_kgm3) # kg/m^3\n", "delta_z = rho_hg/rho_a*760.0/1000.0\n", "print 'delta_z=' + '%.1f' % (delta_z/1000.0) + ' km'\n", "print 'p_0=' + '%g' % (rho_hg*g*761.4855/1000.0) + ' Pa' # kg/m^3*m/s^2*m [=] (kg m/s^2)/m^2 [=] Pa\n", "z = np.array(range(0,16500))\n", "p_0 = 101325.0 \n", "H = p_0/(g*rho_a)\n", "print 'H = ' + '%.1f' % (H/1000.0) + ' km'\n", "a = plt.plot(z,1.0*np.exp(-z/H))\n", "plt.xlabel('z, m')\n", "plt.yticks([1/float(x) for x in range(1,7,1)], ['$p_0$'] + ['$1/' + str(x) + 'p_0$' for x in range(2,7,1)]);\n", "print '99.9999% of atmosphere weight: ' + '%.1f' % (np.log(0.1/101325.0)*-H/1000.0) + ' km'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 - CO2 absorption\n", "### (Henley 3.10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def print_latex(str_x):\n", " ax = plt.axes([0,0,0.1,0.2])\n", " plt.cla()\n", " plt.text(0.5, 0.5, '$%s$'%str_x, size=20)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " ax.set_frame_on(0)\n", " plt.show()\n", "\n", "z = 91.44 # m\n", "p_at_top = 101.325*np.exp(-z/H) # kPa\n", "rho_w = interp_1(293.15, [294, 292], [997.983, 998.392]) # kg/m^3\n", "delta_p = rho_w * g * z # kg/m^3*m/s^2*m [=] (kg m/s^2)/m^2 [=] Pa\n", "\n", "print 'air pressure at the top:'\n", "print_latex('p = 101.325kPa\\\\times e^{\\\\frac{-91.44}{%g' % H + \\\n", " '}} = %g' % p_at_top + 'kPa')\n", "print 'p to lift water:'\n", "print_latex('\\\\rho g z = ' + '%g' % (delta_p/1000.0) + ' kPa')\n", "print_latex('p_{CO2} = ' + '%g' % (delta_p/1000.0) + 'kPa + 68.8kPa = ' \\\n", " + '%g' % (delta_p/1000.0 + 68.8) + 'kPa')\n", "print \"Henry's law application:\"\n", "print_latex('\\\\mu_{id.\\ soln.}=\\\\mu_{id.\\ g.}')\n", "print_latex('\\\\mu_i^* + RTln(x_i) = \\mu_i^{\\\\circ} + RTln(p_i)')\n", "print_latex('p_i/x_i = exp \\\\left(\\\\frac{\\\\mu_i^*-\\\\mu_i^{\\\\circ}}{RT} \\\\right) \\\\equiv K_i = \\\\mathcal{H}_i')\n", "print_latex('CO_2, 298.15K: \\ \\\\mathcal{H}_{CO_2} = 1670bar = 167000kPa')\n", "p_co2 = delta_p/1000.0 + 68.8 # kPa\n", "h_co2 = 167000.0 # 1670.0bar = 167000.0 kPa\n", "#h_co2 = 1666.6666666666667*100 # correct for exercise\n", "x_co2_max = p_co2/h_co2\n", "x_co2 = np.arange(0.0,x_co2_max*(1+1/20.0),x_co2_max/20.0)\n", "n_co2 = 101325.0*1000.0*(1/3.280839895013123)**3/(8.314*273.15) # 28.31m^3 (STP)\n", "mm_co2 = 12+2*16\n", "mm_h2o = 2*1 + 16\n", "n_h2o = n_co2*(1-x_co2[-1])/x_co2[-1]\n", "#n_h2o = n_co2*(1-0.0057)/0.0057\n", "m_co2 = n_co2 * mm_co2 / 1000.0 # kg\n", "m_h2o = n_h2o * mm_h2o / 1000.0 # kg\n", "x1_co2 = p_at_top/100.0/1670.0\n", "y1_co2 = 1.0\n", "n_s0 = (1.0 - x1_co2)/(x_co2_max - x1_co2) * n_co2\n", "m_h2o_0 = n_s0 * (1 - x_co2_max) * mm_h2o / 1000.0 # kg\n", "ax = plt.axes([0.0,0.0,1.0,1.0])\n", "plt.cla()\n", "plt.subplot(211)\n", "plt.plot(x_co2, h_co2 * x_co2)\n", "plt.plot([x_co2[-1], x1_co2],\n", " [p_co2, p_at_top], \n", " marker='<', color='r', linestyle='--' )\n", "plt.xlabel('$x_{CO_2}$', size=20)\n", "plt.ylabel('$p_{CO_2}, kPa$', size=20)\n", "plt.subplot(212)\n", "plt.plot(x_co2, h_co2 * x_co2 / 101.325)\n", "plt.plot([x_co2[-1], x1_co2],\n", " [p_co2/101.325, p_at_top/101.325], \n", " marker='<', color='r', linestyle='--' )\n", "plt.xlabel('$x_{CO_2}$', size=20)\n", "plt.ylabel('$p_{CO_2}, atm$', size=20)\n", "plt.show()\n", "print_latex('x_{CO_2} = ' + '%g' % x_co2[-1])\n", "print_latex('n \\\\times x_{CO_2} = n_{CO_2}')\n", "print_latex('n \\\\times (1-x_{CO_2}) = n_{H_2O}')\n", "print_latex('n_{CO_2} \\\\times \\\\frac{1-x_{CO_2}}{x_{CO_2}} = n_{H_2O}')\n", "print 'total CO2 to absorb'\n", "print_latex('n_{CO_2} = \\\\frac{101325 Pa\\\\times 28.31m^3}' + \\\n", " '{8.314\\\\frac{Pa \\\\times m^3}{mol K}\\\\times 273.15K} = ' + \\\n", " '%g' % n_co2 + 'gmol') # n = PV/RT\n", "print_latex('m_{CO_2} = n_{CO_2}/M_{CO_2} = ' + '%g' % m_co2 + 'kg')\n", "print 'water to absorb calc.kg of CO2 '\n", "print_latex('n_{H_2O} = \\\\frac{1-' + '%g' % x_co2[-1] + '}' + \\\n", " '{' + '%g' % x_co2[-1] + '} \\\\times ' + '%g' % n_co2 + 'mol = '+ \\\n", " '%g' % n_h2o + ' mol')\n", "print_latex('m_{H_2O} = n_{H_2O}/M_{H_2O} = ' + '%g' % m_h2o + 'kg')\n", "print 'Actual balance to account for pressure difference: '\n", "print_latex('(1)\\\\ x_{CO_2 1} n_{s 1} + y_{CO_2 1} n_{g 1} = x_{CO_2, 0} n_{s 0}')\n", "print_latex('(2)\\\\ n_{s 1} + n_{g 1} = n_{s 0}')\n", "print_latex('y_{CO_2 1} = p_{CO_2 1}/P = 1.01325/1.01325 = 1.0')\n", "print_latex('x_{CO_2 1} = p_{CO_2 1}/ \\\\mathcal{H}_{CO_2} ' + \\\n", " '= \\\\frac{1.01325}{1670} = ' + '%g' % x1_co2)\n", "print ''\n", "print_latex('x_{CO_2 1} (n_{s 0}-n_{g 1}) + n_{g 1} = x_{CO_2, 0} n_{s 0}')\n", "print_latex('n_{s 0} = \\\\frac{1-x_{CO_2 1}}{x_{CO_2, 0}-x_{CO_2 1}}\\\\times n_{g 1}' + \\\n", " '= \\\\frac{1-%g' % x1_co2 + '}{ %g' % x_co2_max + \\\n", " '-%g' % x1_co2 + '}\\\\times %g' % n_co2 + 'mol' + '=%g' % n_s0 + 'mol')\n", "print_latex('m_{H_2O, 0} = n_{s 0}(1-x_{CO_2 0})/MM_{H_2O} = %g' % m_h2o_0 + 'kg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 - CO2 equilibria in ac. solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 - Partial equilibria (closed system) - Stumm, Werner Aquatic Chemistry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aequilibria\n", "\n", "$\n", "\\begin{array}{ccccccc}\n", "(1) &\\ H_2CO_3 & & \\rightleftharpoons & CO_2(ac) & +H_2O & pK = -2.821023053\\\\\n", "(2) &\\ H_2CO_3 & + H_2O & \\rightleftharpoons & HCO_3^{-} & + H_3O^{+} & pK_{H_2CO_3} = 3.539912399\\\\\n", "(3) &\\ HCO_3^{-} & + H_2O & \\rightleftharpoons & CO_3^{2-} & + H_3O^{+} & pK_2 = 10.32991986\\\\\n", "(4) &\\ 2 H_2O & & \\rightleftharpoons & H_3O^{+} & + HO^{-} & pK_w = 13.99602524\\\\\n", "\\end{array}\n", "$\n", "\n", "### Species\n", "$\n", "[H_2CO_3*] \\equiv [H_2CO_3] + [CO_2(ac)] \\\\\n", "K = \\frac{[CO_2(ac)]}{[H_2CO_3]} \\\\\n", "K_{H_2CO_3} = \\frac{[HCO_3^{(-)}][H_3O^{(+)}]}{[H_2CO_3]}\\\\\n", "K_1 = \\frac{[HCO_3^{(-)}][H_3O^{(+)}]}{[H_2CO_3*]} = \n", " \\frac{[HCO_3^{(-)}][H_3O^{(+)}]}{[H_2CO_3] + [CO_2]} = \n", " \\frac{K_{H_2CO_3}}{1 + K}\\\\\n", "\\rightarrow pK_1 = pK_{H_2CO_3} - log_{10}(1 + K)\n", "$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print_latex('pK_1=' + '%1.4g' % (3.54 + np.log10(1 + 10 ** 2.821)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "dpi_res = 250\n", "\n", "npoints = 200\n", "figure = plt.figure(dpi= dpi_res)\n", "ax = plt.axes()\n", "# HA + H2O <<==>> H3O(+) + A(-)\n", "# 2H2O <<==>> H3O(+) + HO(-)\n", "pKa = 6.0\n", "pKw = 14.0\n", "pCT = 3.0\n", "x = np.array([14 * x / float(npoints) for x in range(npoints + 1)])\n", "\n", "ax.plot(x, -pCT - x - np.log10(10 ** -pKa + 10 ** -x))\n", "ax.plot(x, -pCT - pKa - np.log10(10 ** -pKa + 10 ** -x))\n", "ax.plot(x, -x, '--')\n", "ax.plot(x, +x - pKw, '--')\n", "ax.set_ylim(top=0)\n", "ax.set_xlabel('pH')\n", "ax.set_ylabel('-log Concentration (molar)')\n", "ax.legend(['HA', 'A-', 'H+', 'OH-'], loc='best')\n", "y_lim = ax.get_ylim()\n", "ax.plot([pKa, pKa], y_lim, '--', color='gray')\n", "plt.text(pKa, y_lim[1], 'pKa = ' + str(pKa))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 - Complete equilibria (open system)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aequilibria\n", "\n", "$\n", "\\begin{array}{ccccccc}\n", "(1) &\\ CO_2(ac) & + H_2O & \\rightleftharpoons & CO_2(g) & & \\mathcal{H_1} = 1670bar \\\\\n", "(2) &\\ CO_2(ac) & + H_2O & \\rightleftharpoons & H_2CO_3 & & pK_2 = 2.821023053\\\\\n", "(3) &\\ H_2CO_3 & + H_2O & \\rightleftharpoons & HCO_3^{-} & + H_3O^{+} & pK_3 = 3.539912399\\\\\n", "(4) &\\ HCO_3^{-} & + H_2O & \\rightleftharpoons & CO_3^{2-} & + H_3O^{+} & pK_4 = 10.32991986\\\\\n", "(5) &\\ 2 H_2O & & \\rightleftharpoons & H_3O^{+} & + HO^{-} & pK_w = 13.99602524\\\\\n", "(6) &\\ NaHCO_3 & & \\rightarrow & HCO_3^{-} & + Na^{+} & \\\\\n", "\\end{array}\n", "$\n", "\n", "### Reactor (Batch)\n", "\n", "#### Case 1: Constant V\n", "![constant V](./utils/V_const.png \"Constant V\")\n", "\n", "#### Case 2: Constant P\n", "![constant V](./utils/P_const.png \"Constant P\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model\n", "\n", "Material Balances (n)\n", "\n", "$n_i = n_{0,i} + \\sum_j{(\\nu_{ij}\\times \\xi_i)}$\n", "\n", "Equilibrium constant expressions ($n_r$)\n", "\n", "$K_j(T) = \\left [ \\prod_i{(x_i)^{\\nu_{ij}}} \\right ]_{eq.}$\n", "\n", "EOS (gas phase)\n", "\n", "* Case 1, $V_g$ = const. $P = \\sum_i{\\frac{n_{i,g} RT}{V_g}} = \\frac{RT}{V_g} \\sum_i{n_{i,g}}$\n", "\n", " Fixed V, P increases dep. on $p_{CO_2}$\n", "\n", "* Case 2, P = const. $V_g = \\sum_i{\\frac{n_{i,g} RT}{P}} = \\frac{RT}{P} \\sum_i{n_{i,g}}$\n", " \n", " Fixed P, V increases dep. on $p_{CO_2}$\n", "\n", " Inerts: $n_{N_2} = n_{N_2, 0};n_{O_2} = n_{O_2, 0}$\n", " \n", " $p_{N_2} = \\frac{RT}{V_g}n_{N_2}=\\frac{RT}{V_g}n_{N_2,0}\n", " =p_{N_2,0}\\times V_{g0}/V_g$\n", " \n", " $p_{O_2} = \\frac{RT}{V_g}n_{O_2}=\\frac{RT}{V_g}n_{O_2,0}\n", " =p_{O_2,0}\\times V_{g0}/V_g$\n", " \n", " Note: $p_{O_2}$ & $p_{N_2}$ are not constant; $n_{O_2}$ & $n_{N_2}$ are.\n", " \n", " $\\begin{array}{cc}\n", " P = const = & p_{CO_2} + p_{N_2} + p_{O_2}\\\\\n", " = & p_{CO_2} + p_{N_2,0}V_{g0}/V_g + p_{O_2,0}V_{g0}/V_g\\\\\n", " \\rightarrow V_g = & \\left( \\frac{p_{N_2,0} + p_{O_2,0}}{P - p_{CO_2}} \\right) V_{g0}& \\\\\n", " \\end{array}$\n", "\n", "Phase rule: *F* = 2 + (*N* - *R*) - *P* = 2 + (6 - 5) - 2 = 1 (fix T = 298.15K)\n", "\n", "### Initial condition\n", "\n", "0.1% NaHCO3 solution with pH 7.0 water\n", "\n", "$xw_{Na^{(+)},0} + xw_{HCO3^{(-)},0} = 0.1\\%$\n", "\n", "$\\frac{xw_{Na^{(+)},0}}{M_{Na^{(+)}}} = \\frac{xw_{HCO3^{(-)},0}}{M_{HCO3^{(-)}}}$\n", "\n", "$\\Rightarrow xw_{Na^{(+)}, 0} = \\frac{0.1\\%}{1+44/23}=0.034328\\%$\n", "\n", "$\\Rightarrow xw_{HCO3^{(-)}, 0} = \\frac{0.1\\%}{1+23/44}=0.065672\\%$\n", "\n", "$x_{Na^{(+)}, 0} = \\frac{0.00034324/23}{0.000343284/23+0.000656716/44+0.999/18} = 0.000268778$\n", "\n", "$x_{HCO3^{(-)}, 0} = \\frac{0.000656716/44}{0.000343284/23+0.000656716/44+0.999/18} = 0.000268778$\n", "\n", "$pH_0 = 13.99568/2 = 6.99784$\n", "\n", "$x_i/c_i = x_0 M_0 / \\rho_0$ ($x_0, M_0, \\rho_0$, solvent)\n", "\n", "Initial pH=13.99568/2 = 6.99784, mole fraction:\n", "\n", "$x_{H_3O^{(+)},0} = \\frac{c_{H_3O^{(+)},0}}{M_0 \\rho_0}\\times\\left(1 - \\sum_{i\\neq 0}{x_i}\\right) = \\frac{c_{H_3O^{(+)},0}}{M_0 \\rho_0}\\times\\left(1 -x_{H_3O^{(+)},0} - x_{HO^{(-)},0} - x_{Na^{(+)}, 0} - x_{HCO3^{(-)}, 0} \\right)$\n", "\n", "$x_{HO^{(-)},0} = \\frac{10^{-13.99568}/c_{H_3O^{(+)},0}}{M_0 \\rho_0}\\times\\left(1 - \\sum_{i\\neq 0}{x_i}\\right) = \\frac{10^{-13.99568}}{c_{H_3O^{(+)},0}M_0 \\rho_0}\\times\\left(1 -x_{H_3O^{(+)},0} - x_{HO^{(-)},0} - x_{Na^{(+)}, 0} - x_{HCO3^{(-)}, 0} \\right)$\n", "\n", "System:\n", "\n", "$\n", "\\begin{align}\n", "\\left(1 + \\frac{M_0}{\\rho_0} 10^{-pH_0} \\right) & x_{H_3O^{(+)},0} + & \\left(\\frac{M_0}{\\rho_0} 10^{-pH_0} \\right) & x_{HO^{(-)},0} = & \\frac{M_0}{\\rho_0} \\left(1-x_{Na^{(+)},0}-x_{H_3O^{(+)},0} \\right) \\\\\n", "\\left(\\frac{M_0}{\\rho_0} 10^{pH_0-13.99568} \\right) & x_{H_3O^{(+)},0} + & \\left(1 + \\frac{M_0}{\\rho_0} 10^{pH_0-13.99568} \\right) & x_{HO^{(-)},0} = & \\frac{M_0}{\\rho_0} \\frac{10^{-13.99568}}{c_{H_3O^{(+)},0}} \\left(1-x_{Na^{(+)},0}-x_{H_3O^{(+)},0} \\right) \\\\\n", "\\end{align}\n", "$\n", "\n", "$\\Rightarrow x_{H_3O^{(+)},0} = 1.8082E-09; x_{HO^{(-)},0} = 1.8082E-09$\n", "\n", "### Case 1: $V_l$, $V_g$ = const. \n", "\n", "$V_l/V_g \\equiv \\phi = 1.0$\n", "\n", "$P = p_{N2}+ p_{O2} + p_{CO2}$\n", "\n", "#### Input\n", "\n", "| # | Var | val |\n", "| - | - | - |\n", "| - | $V_g$ | 300mL = const. |\n", "| - | $T$ | 298.15K |\n", "| 0 | $x_{H2O,0}$ | 9.99571E-01 |\n", "| 1 | $x_{H_3O^{(+)},0}$ | 1.80820E-09 |\n", "| 2 | $x_{HO^{(-)},0}$ | 1.80820E-09 |\n", "| 3 | $x_{HCO3^{(-)},0}$ | 2.14408E-04 |\n", "| 4 | $x_{Na^{(+)},0}$ | 2.14408E-04 |\n", "| 5 | $x_{CO3^{(2-)},0}$ | 0 |\n", "| 6 | $x_{CO_2(ac),0}$ | 0 |\n", "| 7 | $x_{H_2CO_3,0}$ | 0 |\n", "| 8 | $p_{CO_2(g),0}$ | 0 kPa |\n", "\n", "#### Solution\n", "$\n", "x_{CO_2} = {{C_{CO_2}}\\over{C_{H_2O}+C_{H_3O^{(+)}}+C_{HO^{(-)}}+C_{HCO_3^{(-)}}+C_{Na^{(+)}}+C_{CO_3^{(2-)}}+C_{CO_2}+C_{H_2CO_3} }} \n", "$\n", "\n", "$V_l/V_g = \\phi = 1.0$\n", "\n", "---\n", "\n", "$\n", "\\begin{array}{ccccccccccc}\n", "0 = & 1670\\times 100kPa & - & {{p_{CO_2}}\\over{x_{CO_2}}} &&&&&&\\\\\n", "0 = & 10^{-2.821023053} & - & {{C_{H_2CO_3}}\\over{C_{CO_2}}} &&&&&&\\\\\n", "0 = & 10^{-3.539912399} & - & {{C_{H_3O^{(+)}} \\times C_{HCO_3^{(-)}}}\\over{C_{H_2CO_3}}} &&&&&&\\\\\n", "0 = & 10^{-10.32991986} & - & {{C_{H_3O^{(+)}} \\times C_{CO_3^{(2-)}}}\\over{C_{HCO_3^{(-)}}}} &&&&&&\\\\\n", "0 = & 10^{-13.99602524} & - & C_{H_3O^{(+)}} C_{HO^{(-)}} &&&&&&\\\\\n", "0 = & -C_{0,H_2O} & + & C_{H_2O} & - &(&- \\xi_0 & -\\xi_1& -\\xi_2& -\\xi_3 & -2\\xi_4 &)\\\\\n", "0 = & -C_{0,H_3O^{(+)}} & + & C_{H_3O^{(+)}} & - & (&&&+\\xi_2&+\\xi_3&+\\xi_4&)\\\\\n", "0 = & -C_{0,HO^{(-)}} & + & C_{HO^{(-)}} & - & (&&&&&+\\xi_4&)\\\\\n", "0 = & -C_{0,HCO_3^{(-)}} & + & C_{HCO_3^{(-)}} & - & (&&&+\\xi_2&-\\xi_3&&)\\\\\n", "0 = & -C_{0,Na^{(+)}} & + & C_{Na^{(+)}} & - & (&&&0&&&)\\\\\n", "0 = & -C_{0,CO_3^{(2-)}} & + & C_{CO_3^{(2-)}} & - & (&&&&+\\xi_3&&)\\\\\n", "0 = & -C_{0,CO_2(ac)} & + & C_{CO_2(ac)} & - &(&-\\xi_0&-\\xi_1&&&&)\\\\\n", "0 = & -C_{0,H2_CO_3} & + & C_{H2_CO_3} & - &(&&+\\xi_1&-\\xi_2&&&)\\\\\n", "0 = & -{p_{0,CO_2(g)}\\over{RT \\left({{V_l}\\over{V_g}}\\right)}} & + & {p_{CO_2(g)}\\over{RT \\left({{V_l}\\over{V_g}}\\right)}} & - &(&\\xi_0&&&&&)\\\\\n", "\\end{array}\n", "$\n", "\n", "### Case 2: P = const. \n", "\n", "$P = p_{N2}+ p_{O2} + p_{CO2} = 101.325 kPa$\n", "\n", "$V_l/V_g = {{V_l}\\over{\\frac{RT}{P} \\sum_i{n_{i,g}}}} = \n", "{{V_l}\\over{\\frac{p_{O2,0}+p_{N2,0}}{P - p_{CO2}}V_{g0}}} = \n", "{{V_l}\\over{V_{g0}}} \\times \\frac{P - p_{CO2}}{p_{O2,0}+p_{N2,0}}\n", "$\n", "\n", "#### Input\n", "\n", "| # | Var | val |\n", "| - | - | - |\n", "| - | $P$ | 101.325kPa = const. |\n", "| - | $T$ | 298.15K |\n", "| 0 | $x_{H2O,0}$ | 9.99571E-01 |\n", "| 1 | $x_{H_3O^{(+)},0}$ | 1.80820E-09 |\n", "| 2 | $x_{HO^{(-)},0}$ | 1.80820E-09 |\n", "| 3 | $x_{HCO3^{(-)},0}$ | 2.14408E-04 |\n", "| 4 | $x_{Na^{(+)},0}$ | 2.14408E-04 |\n", "| 5 | $x_{CO3^{(2-)},0}$ | 0 |\n", "| 6 | $x_{CO_2(ac),0}$ | 0 |\n", "| 7 | $x_{H_2CO_3,0}$ | 0 |\n", "| 8 | $p_{CO_2(g),0}$ | 0 kPa |\n", "\n", "#### Solution\n", "$\n", "x_{CO_2} = {{C_{CO_2}}\\over{C_{H_2O}+C_{H_3O^{(+)}}+C_{HO^{(-)}}+C_{HCO_3^{(-)}}+C_{Na^{(+)}}+C_{CO_3^{(2-)}}+C_{CO_2}+C_{H_2CO_3} }} \n", "$\n", "\n", "$V_l/V_{g0} = \\phi = 1.0$\n", "\n", "---\n", "\n", "$\n", "\\begin{array}{ccccccccccc}\n", "0 = & 1670\\times 100kPa & - & {{p_{CO_2}}\\over{x_{CO_2}}} &&&&&&\\\\\n", "0 = & 10^{-2.821023053} & - & {{C_{H_2CO_3}}\\over{C_{CO_2}}} &&&&&&\\\\\n", "0 = & 10^{-3.539912399} & - & {{C_{H_3O^{(+)}} \\times C_{HCO_3^{(-)}}}\\over{C_{H_2CO_3}}} &&&&&&\\\\\n", "0 = & 10^{-10.32991986} & - & {{C_{H_3O^{(+)}} \\times C_{CO_3^{(2-)}}}\\over{C_{HCO_3^{(-)}}}} &&&&&&\\\\\n", "0 = & 10^{-13.99602524} & - & C_{H_3O^{(+)}} C_{HO^{(-)}} &&&&&&\\\\\n", "0 = & -C_{0,H_2O} & + & C_{H_2O} & - &(&- \\xi_0 & -\\xi_1& -\\xi_2& -\\xi_3 & -2\\xi_4 &)\\\\\n", "0 = & -C_{0,H_3O^{(+)}} & + & C_{H_3O^{(+)}} & - & (&&&+\\xi_2&+\\xi_3&+\\xi_4&)\\\\\n", "0 = & -C_{0,HO^{(-)}} & + & C_{HO^{(-)}} & - & (&&&&&+\\xi_4&)\\\\\n", "0 = & -C_{0,HCO_3^{(-)}} & + & C_{HCO_3^{(-)}} & - & (&&&+\\xi_2&-\\xi_3&&)\\\\\n", "0 = & -C_{0,Na^{(+)}} & + & C_{Na^{(+)}} & - & (&&&0&&&)\\\\\n", "0 = & -C_{0,CO_3^{(2-)}} & + & C_{CO_3^{(2-)}} & - & (&&&&+\\xi_3&&)\\\\\n", "0 = & -C_{0,CO_2(ac)} & + & C_{CO_2(ac)} & - &(&-\\xi_0&-\\xi_1&&&&)\\\\\n", "0 = & -C_{0,H2_CO_3} & + & C_{H2_CO_3} & - &(&&+\\xi_1&-\\xi_2&&&)\\\\\n", "0 = & -{p_{0,CO_2(g)}\\over{RT \n", "\\left({{V_l}\\over{V_{g0}}}\\right)}} \n", "& + & {p_{CO_2(g)}\\over{RT \n", "\\left({{V_l}\\over{V_{g0}}}\\right) \\times \n", "\\left(\\frac{P - p_{CO_2}}{p_{O_2, 0} + p_{N_2, 0}} \\right)}} \n", "& - &(&\\xi_0&&&&&)\\\\\n", "\\end{array}\n", "$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import numpy as np\n", "from scipy import linalg\n", "from scipy.optimize import root, fsolve\n", "eps = np.finfo(float).eps\n", "\n", "comps = np.array([\n", " 'H2O', 'H3O(+)', 'HO(-)', 'HCO3(-)', 'Na(+)', \n", " 'CO3(2-)', 'CO2', 'H2CO3', 'CO2'\n", " ])\n", "\n", "mm = np.array([\n", " 18, 19, 17, 61, 23, 60, 44, 62, 44\n", " ], dtype=float)\n", "\n", "pkw = 13.99602524\n", "mm0 = mm[0]\n", "rho0 = 1.0\n", "ph0 = pkw/2\n", "xw0nahco3 = 0.001\n", "p0n2 = 78.12/(78.12 + 20.96)*101.325\n", "p0o2 = 20.96/(78.12 + 20.96)*101.325\n", "\n", "x = np.ones_like(mm)*eps\n", "x[3] = xw0nahco3/(mm[3]+mm[4])/(xw0nahco3/(mm[3]+mm[4])+xw0nahco3/(mm[3]+mm[4])+(1-xw0nahco3)/mm[0])\n", "x[4] = xw0nahco3/(mm[3]+mm[4])/(xw0nahco3/(mm[3]+mm[4])+xw0nahco3/(mm[3]+mm[4])+(1-xw0nahco3)/mm[0])\n", "\n", "\n", "a = np.array([\n", " [1 + mm0/rho0*10**(-ph0)/1000.0, mm0/rho0*10**(-ph0)/1000.0],\n", " [mm0/rho0*10**(ph0-pkw)/1000.0,1 + mm0/rho0*10**(ph0-pkw)/1000.0]\n", " ])\n", "b = np.array([\n", " [mm0/rho0*10**(-ph0)/1000.0*(1 - x[3] - x[4])],\n", " [mm0/rho0*10**(ph0-pkw)/1000.0*(1 - x[3] - x[4])]\n", " ])\n", "\n", "x[1:2+1] = linalg.inv(a).dot(b).flatten()\n", "x[0] = 1 - sum(x[1:])\n", "\n", "print 'mole frac.'\n", "print x\n", "print 'mass frac.'\n", "print np.multiply(x, mm)/sum(np.multiply(x, mm))\n", "\n", "c0 = np.ones([len(mm)-1,])*eps\n", "c0 = x[:8]/x[0]*rho0/mm0*1000.0\n", "p0co2 = 10**-3.5*101.325 # 10^-3.5atm = 10^-3.5*101.325 kPa\n", "c0[6] = p0co2 / (1670.0 *100) * (sum(c0)-c0[6]) / (1 - p0co2 / (1670.0 *100))\n", "xi = np.ones(5)*eps\n", "\n", "x0 = np.append(np.append(c0, p0co2), xi)\n", "\n", "def eq_set(x):\n", " # x, [nh2o, nh30, nho, nhco3, nna, nco3, nco2, nh2co3, pco2,\n", " # xi1, xi2, xi3, xi4, xi5]\n", " c = x[:8]\n", " xco2 = c[6] / sum(c)\n", " pco2 = x[8]\n", " xi = x[9:]\n", " return np.array([\n", " 1670.0*100*xco2 - pco2, # Hco2 = pco2/xco2\n", " 10**-2.821023053*c[6] - c[7], # 10^-pK2 = ch2co3/cco2\n", " 10**-3.539912399*c[7] - c[1]*c[3], # 10^-pK3 = chco3*ch3o/ch2co3\n", " 10**-10.32991986*c[3] - c[1]*c[5], # 10^-pK4 = cco3*ch3o/chco3\n", " 10**-13.99602524 - c[1]*c[2], # 10^-pKw = ch3o*cho \n", " c[0] - c0[0] - (- xi[0] - xi[1] - xi[2] - xi[3] - 2*xi[4]),\n", " c[1] - c0[1] - (+ xi[2] + xi[3] + xi[4]),\n", " c[2] - c0[2] - (+ xi[4]),\n", " c[3] - c0[3] - (+ xi[2] - xi[3]),\n", " c[4] - c0[4] - (+ 0),\n", " c[5] - c0[5] - (+ xi[3]),\n", " c[6] - c0[6] - (- xi[0] - xi[1]),\n", " c[7] - c0[7] - (+ xi[1] - xi[2]),\n", " pco2 - p0co2 - (+ xi[0])*8.314*298.15\n", " ]).T\n", "\n", "def jac_eq_set(x):\n", " j = np.zeros([len(x), len(x)])\n", " \n", " c = x[:8]\n", " xco2 = c[6] / sum(c)\n", " pco2 = x[8]\n", " xi = x[9:]\n", " \n", " d_f1_dc = np.zeros([9, ])\n", " \n", " d_f1_dc[0:6] = 1670.0*100*(-1.0/sum(c)**2)*c[6]\n", " d_f1_dc[6] = sum([c_ for i, c_ in enumerate(c) if i!=6])/sum(c)**2\n", " d_f1_dc[7] = 1670.0*100*(-1.0/sum(c)**2)*c[6]\n", " d_f1_dc[8] = -1.0\n", " \n", " j[0, :8+1] = d_f1_dc\n", "\n", " j[1, 6], j[1, 7] = 10**-2.821023053, -1\n", " j[2, 1], j[2, 3], j[2, 7] = -c[3], -c[1], 10**-3.539912399\n", " j[3, 1], j[3, 3], j[3, 5] = -c[5], 10**-10.32991986, -c[1]\n", " j[4, 1], j[4, 2] = -c[2], -c[1]\n", " for ind in range(8+1):\n", " j[ind + 5, ind] = +1\n", " j[5, 9:14] = np.array([+1, +1, +1, +1, +2])\n", " j[6, 11:14] = np.array([-1, -1, -1])\n", " j[7, 13] = -1\n", " j[8, 11:13] = np.array([-1, +1])\n", " j[10, 12] = -1\n", " j[11, 9:11] = +1\n", " j[12, 10:12] = np.array([-1, +1])\n", " j[13, 9] = -1*8.314*298.15\n", " \n", " return j\n", "\n", "#res = root(eq_set, x0, jac=jac_eq_set, tol=1e-10)\n", "res = root(eq_set, x0, jac=False, tol=1e-10)\n", "#res = root(eq_set, x0, method='broyden1', jac=jac_eq_set, tol=1e-10, options={'line_search': 'wolfe'})\n", "#res = root(eq_set, res.x, jac=False, tol=1e-12, method='broyden1')\n", "for item in res.keys():\n", " if item not in ['qtf', 'r', 'jac', 'fjac']:\n", " print str(item) + ': ' + str(getattr(res, str(item)))\n", "print '||f||: ' + str(np.sqrt(res.fun.T.dot(res.fun)))\n", "print ''\n", "print_latex('\\\\bf{pH}: ' + str(-np.log10(res.x[1])))\n", "print_latex('\\\\bf{pOH}: ' + str(-np.log10(res.x[2])) + \\\n", " ', pH+pOH=' + str(-np.log10(res.x[1])+-np.log10(res.x[2])))\n", "print_latex('\\\\bf{p_{CO2}}:' + str(res.x[8]) + ' kPa')\n", "print_latex('\\\\Sigma{c_i\\\\times z_i}:' + \\\n", " str(res.x[1]-res.x[2]-res.x[3]+res.x[4]-2*res.x[5]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jac_eq_set(res.x)[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print 'x0'\n", "for index, num in enumerate(x0):\n", " if index < 8:\n", " print 'C' + str(index) + '=' + '%.20e' % num + ','\n", " elif index == 8:\n", " print 'pco2' + '=' + '%.20e' % num + ','\n", " else:\n", " print 'x' + str(index-9) + '=' + '%.20e' % num + ','" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "print 'x:'\n", "for index, num in enumerate(res.x):\n", " if index < 8:\n", " print 'C' + str(index) + '=' + '%.20e' % num + ','\n", " elif index == 8:\n", " print 'pco2' + '=' + '%.20e' % num + ','\n", " else:\n", " print 'x' + str(index-9) + '=' + '%.20e' % num + ','" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numerik import nr_ls\n", "import logging\n", "\n", "logger = logging.getLogger()\n", "fhandler = logging.FileHandler(filename='./logs/CO2_Equilibria.log')\n", "formatter = logging.Formatter('%(asctime)s;%(message)s')\n", "fhandler.setFormatter(formatter)\n", "logger.addHandler(fhandler)\n", "logger.setLevel(logging.DEBUG)\n", "\n", "def notify_status_func(progress_k, stop_value, k,\n", " j_it_backtrack, lambda_ls, accum_step,\n", " x, diff, f_val, j_val, lambda_ls_y,\n", " method_loops):\n", " g_min = np.nan\n", " g1 = np.nan\n", " y = lambda_ls_y\n", " pr_str =';k=' + str(k) + \\\n", " ';backtrack=' + str(j_it_backtrack) + \\\n", " ';lambda_ls=' + str(lambda_ls) + \\\n", " ';accum_step=' + str(accum_step) + \\\n", " ';stop=' + str(stop_value) + \\\n", " ';X=' + '[' + ','.join(map(str, x.T.A1)) + ']' + \\\n", " ';||X(k)-X(k-1)||=' + str((diff.T * diff).item()) + \\\n", " ';f(X)=' + '[' + ','.join(map(str, f_val.T.A1)) + ']' + \\\n", " ';||f(X)||=' + str(np.sqrt((f_val.T * f_val).item())) + \\\n", " ';j(X)=' + str(j_val.tolist()) + \\\n", " ';Y=' + '[' + ','.join(map(str, y.T.A1)) + ']' + \\\n", " ';||Y||=' + str(np.sqrt((y.T * y).item())) + \\\n", " ';g=' + str(g_min) + \\\n", " ';|g-g1|=' + str(abs(g_min - g1))\n", " logging.debug(pr_str)\n", "\n", "progress_k, stop, outer_it_k, outer_it_j, \\\n", " lambda_ls, accum_step, x, \\\n", " diff, f_val, lambda_ls_y, \\\n", " method_loops = \\\n", " nr_ls(x0=np.matrix(x0).T,\n", " f=lambda x: np.matrix(eq_set(x)).T,\n", " j=lambda x: np.matrix(jac_eq_set(x)),\n", " tol=1e-12,\n", " max_it=1000,\n", " inner_loop_condition=lambda x_vec:\n", " all([item >= 0 for item in\n", " x_vec[0:9]]),\n", " notify_status_func=notify_status_func,\n", " method_loops=[0, 0],\n", " process_func_handle=None)\n", "\n", "x = x.A1\n", "x_nrls = x\n", "f_val = f_val.A1\n", " \n", "print '||f||: ' + str(np.sqrt(f_val.T.dot(f_val)))\n", "print_latex('\\\\bf{pH}: ' + str(-np.log10(x[1])))\n", "print_latex('\\\\bf{pOH}: ' + str(-np.log10(x[2])) + \\\n", " ', pH+pOH=' + str(-np.log10(x[1])+-np.log10(x[2])))\n", "print_latex('\\\\bf{p_{CO2}}:' + str(x[8]) + ' kPa')\n", "print_latex('\\\\Sigma{c_i\\\\times z_i}:' + \\\n", " str(x[1]-x[2]-x[3]+x[4]-2*x[5]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "print 'x:'\n", "for index, num in enumerate(x):\n", " if index < 8:\n", " print 'C' + str(index) + '=' + '%.20e' % num + ','\n", " elif index == 8:\n", " print 'pco2' + '=' + '%.20e' % num + ','\n", " else:\n", " print 'x' + str(index-9) + '=' + '%.20e' % num + ','" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def eq_set_const_p(x):\n", " # x, [nh2o, nh30, nho, nhco3, nna, nco3, nco2, nh2co3, pco2,\n", " # xi1, xi2, xi3, xi4, xi5]\n", " c = x[:8]\n", " xco2 = c[6] / sum(c)\n", " pco2 = x[8]\n", " xi = x[9:]\n", " f = eq_set(x)[0][0]\n", " f[13] = pco2 - p0co2 * (101.325 - pco2) / (p0n2 + p0o2) - \\\n", " (+ xi[0])*8.314*298.15 * \\\n", " (101.325 - pco2) / (p0n2 + p0o2)\n", " return f\n", "\n", "def jac_eq_set_const_p(x):\n", " #if type(x) == np.matrixlib.defmatrix.matrix:\n", " # x = x.A1\n", " j = jac_eq_set(x)\n", " \n", " c = x[:8]\n", " xco2 = c[6] / sum(c)\n", " pco2 = x[8]\n", " xi = x[9:]\n", " j[13, 8] = +p0co2 / (p0n2 + p0o2) +1 + \\\n", " xi[0]*8.314*298.15 / (p0n2 + p0o2)\n", " j[13, 9] = -1*8.314*298.15 * \\\n", " (101.325 - pco2) / (p0n2 + p0o2)\n", " \n", " return j\n", "\n", "progress_k, stop, outer_it_k, outer_it_j, \\\n", " lambda_ls, accum_step, x, \\\n", " diff, f_val, lambda_ls_y, \\\n", " method_loops = \\\n", " nr_ls(x0=np.matrix(x0).T,\n", " f=lambda x: np.matrix(eq_set_const_p(x)).T,\n", " j=lambda x: np.matrix(jac_eq_set_const_p(x)),\n", " tol=1e-14,\n", " max_it=1000,\n", " inner_loop_condition=lambda x_vec:\n", " all([item >= 0 for item in\n", " x_vec[0:9]]),\n", " notify_status_func=notify_status_func,\n", " method_loops=[0, 0],\n", " process_func_handle=None)\n", "\n", "x = x.A1\n", "f_val = f_val.A1\n", " \n", "print '||f||: ' + str(np.sqrt(f_val.T.dot(f_val)))\n", "print_latex('\\\\bf{pH}: ' + str(-np.log10(x[1])))\n", "print_latex('\\\\bf{pOH}: ' + str(-np.log10(x[2])) + \\\n", " ', pH+pOH=' + str(-np.log10(x[1])+-np.log10(x[2])))\n", "print_latex('\\\\bf{p_{CO2}}:' + str(x[8]) + ' kPa')\n", "print_latex('\\\\Sigma{c_i\\\\times z_i}:' + \\\n", " str(x[1]-x[2]-x[3]+x[4]-2*x[5]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "print 'x:'\n", "for index, num in enumerate(x):\n", " if index < 8:\n", " print 'C' + str(index) + '=' + '%.20e' % num + ','\n", " elif index == 8:\n", " print 'pco2' + '=' + '%.20e' % num + ','\n", " else:\n", " print 'x' + str(index-9) + '=' + '%.20e' % num + ','" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x=[10**-x for x in range(14+1)]\n", "plt.plot(range(20),np.log(range(20)) )\n", "plt.plot(np.arange(-2,2,0.03),1/np.arange(-2,2,0.03),\n", " np.arange(-2,2,0.03),1/(1+np.arange(-2,2,0.03)),\n", " np.arange(-2,2,0.03),1/(2+np.arange(-2,2,0.03))\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.arange(-2,2,0.03)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

daviddesancho/Cossio

notebooks/cossio_brownian_cython_calibrate.ipynb

1

1162260

mit

wholden/ArduiArcStepper

.ipynb_checkpoints/TestingArduinoMotorErrors-checkpoint.ipynb

1

27636

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from arduinostepper import arduinostepper as ardstep" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import serial" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser = serial.Serial('COM3',57600,timeout=0.005)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "motor = ardstep.arduinoMotor(ser,verbose=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ardstep.motordict['sample']['handle'] = motor" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def checksum(msg):\n", " chk = 0\n", " for c in msg:\n", " try:\n", " chk -= ord(c)\n", " except TypeError:\n", " chk -= c\n", " return bytes([chk % 256])\n", "\n", "def append_checksum(msg):\n", " return msg.encode('ascii') + checksum(msg)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser.close()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'\\x89PX\\xed')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0xed'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hex(237)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] }, { "data": { "text/plain": [ "'0'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "motor.readAndParse()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n", "43\n", "833\n", "OK\n", "789\n", "0\n", "OK\n", "44\n", "833\n", "OK\n", "791\n", "0\n", "OK\n", "44\n", "833\n", "OK\n", "790\n", "0\n", "OK\n", "43\n", "833\n", "OK\n", "790\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-22-e88b04aeea6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mardstep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgo_to_degree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mardstep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgo_to_degree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32mC:\\Users\\holde\\Documents\\GitHub\\ArduiArcStepper\\arduinostepper\\arduinostepper.py\u001b[0m in \u001b[0;36mgo_to_degree\u001b[0;34m(degree, blockuntilcomplete)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\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[1;32m 95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgo_to_mm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mblockuntilcomplete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for i in range(100):\n", " ardstep.go_to_degree(25)\n", " ardstep.go_to_degree(0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n", "46\n", "1666\n" ] } ], "source": [ "ardstep.go_to_degree(50)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'\\x00'" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checksum(append_checksum('X1234556'))" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'\\xa8'" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checksum('X')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a=append_checksum('X0')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def wrap_message(msg):\n", " return b'<'+msg+b'>'" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'<X0x>'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wrap_message(append_checksum('X0'))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(append_checksum('X0'))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(wrap_message(append_checksum('X500')))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n" ] }, { "data": { "text/plain": [ "'OK'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "motor.readAndParse()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = 'X'" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'X'" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.encode('ascii')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'\\xa8'" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checksum('X')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'x'" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bytes([120])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'X\\xa8')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'X')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ChecksumError\n" ] }, { "data": { "text/plain": [ "'ChecksumError'" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "motor.readAndParse()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "88" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ord('X')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0xa8'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hex(168)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'X\\xa8'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b'X\\xa8'" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'¨'" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chr(168)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "168" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-88 % 256" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser.close()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'<X500')" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'X500>')" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'<X1500>')" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'<X100>')" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(b'<PX>')" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n" ] }, { "data": { "text/plain": [ "'100'" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "motor.readAndParse()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "motor.waittime=0.1" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser.readline?" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ser.write(wrap_message(append_checksum('PX')))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "''" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "motor.readAndParse()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'PXX'" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "append_checksum('PX')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "b'\\x00'" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checksum('PXX')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ser.close()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-31-c894e1fd446a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrap_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mappend_checksum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'X900'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmotor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadAndParse\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 5\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\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[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for i in range(20):\n", " ser.write(wrap_message(append_checksum('X900')))\n", " time.sleep(0.25)\n", " print(motor.readAndParse())\n", " time.sleep(1)\n", " ser.write(wrap_message(append_checksum('X0')))\n", " time.sleep(0.25)\n", " print(motor.readAndParse())\n", " time.sleep(1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n", "OK\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-61-dd96292cbc76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmotor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadAndParse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\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[1;32m 6\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrap_message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb'X0'\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 7\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for i in range(20):\n", " ser.write(wrap_message((b'X900')))\n", " time.sleep(0.25)\n", " print(motor.readAndParse())\n", " time.sleep(1)\n", " ser.write(wrap_message((b'X0')))\n", " time.sleep(0.25)\n", " print(motor.readAndParse())\n", " time.sleep(1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OK\n", "44\n", "333\n", "OK\n", "290\n", "0\n", "OK\n", "42\n", "333\n", "OK\n", "289\n", "0\n", "OK\n", "43\n", "333\n", "OK\n", "290\n", "0\n", "OK\n", "42\n", "333\n", "OK\n", "292\n", "0\n", "OK\n", "42\n", "333\n", "OK\n", "291\n", "0\n", "OK\n", "44\n", "333\n", "OK\n", "289\n", "0\n", "OK\n", "44\n", "333\n", "OK\n", "290\n", "0\n", "OK\n", "41\n", "333\n", "OK\n", "286\n", "0\n", "OK\n", "44\n", "333\n", "OK\n", "290\n", "0\n", "OK\n", "43\n", "333\n", "OK\n", "289\n", "0\n", "OK\n", "42\n", "333\n", "OK\n", "287\n", "0\n", "OK\n", "41\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-19-dbfa8c6d1870>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mardstep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgo_to_degree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mardstep\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgo_to_degree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32mC:\\Users\\holde\\Documents\\GitHub\\ArduiArcStepper\\arduinostepper\\arduinostepper.py\u001b[0m in \u001b[0;36mgo_to_degree\u001b[0;34m(degree, blockuntilcomplete)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\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[1;32m 79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgo_to_mm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistance\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mblockuntilcomplete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for i in range(20):\n", " ardstep.go_to_degree(10)\n", " ardstep.go_to_degree(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ser.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "`" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

megatharun/basic-python-for-researcher

Tutorial 3 - Conditional Expression.ipynb

2

17382

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <span style=\"color: #B40486\">BASIC PYTHON FOR RESEARCHERS</span>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_by_ [**_Megat Harun Al Rashid bin Megat Ahmad_**](https://www.researchgate.net/profile/Megat_Harun_Megat_Ahmad) \n", "last updated: April 14, 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------\n", "## _<span style=\"color: #29088A\">3. Conditional Expressions</span>_\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$Python$ conditional expressions include the <span style=\"color: #0000FF\">$if/elif/else$</span> statement. In addition the <span style=\"color: #0000FF\">$for$</span> and <span style=\"color: #0000FF\">$while$</span> statements can be used in conditional looping. $Python$ also has the <span style=\"color: #0000FF\">$enumerate$&#40; &#41;</span> function for conditional looping." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "### **_3.1 The <span style=\"color: #0000FF\">if/else</span> condition_**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$if/else$</span> conditional expression allows statement to be executed if a condition is fulfilled:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The remainder of 56 divided by 3 is 2\n" ] } ], "source": [ "# Calculate the remainder of a divisional operation\n", "x = 56\n", "y = 3\n", "z = x % y # Modulo operation\n", "\n", "if z > 0:\n", " print (\"The remainder of %d divided by %d is %d\" % (x,y,z))\n", "else:\n", " print \"There's no remainder\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The $z = x$ &#37; $y$ operation means that the remainder of $x$ when divided with $y$ will be assigned to $z$. The <span style=\"color: #0000FF\">$if/else$</span> conditional expression above resulted in the printing of the variable $z$ if its value is positive, _i.e._ if $x$ when divided with $y$ has a remainder. If this condition is not fulfilled, then the text \"_There's no remainder_\" will be printed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statements inside conditional expression must be indented with space (not tab). The indentation must be consistent throughout the condition." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "### **_3.2 The <span style=\"color: #0000FF\">if/elif/else</span> condition_**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$if/elif/else$</span> conditional expression allows multiple conditions to be applied." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45 is less than 55\n" ] } ], "source": [ "# Compare the values of two integers\n", "\n", "int1 = 45\n", "int2 = 55\n", "\n", "if int1 > int2:\n", " print \"%d is larger than %d\" % (int1,int2)\n", "elif int1 == int2:\n", " print \"%d is equal to %d\" % (int1,int2)\n", "else:\n", " print \"%d is less than %d\" % (int1,int2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example above, the first condition always use the <span style=\"color: #0000FF\">$if$</span> condition expression (_i.e._ $int1$ > $int2$). Only if this is not fulfilled will the second condition be evaluated _i.e._ the <span style=\"color: #0000FF\">$elif$</span> condition expression. If this condition is also not fulfilled, then the <span style=\"color: #0000FF\">$else$</span> condition statement will be executed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In multiple conditional expressions, all the conditions will be evaluated in sequence. When one of the condition is fulfilled, the sequential evaluation will stop and the statement for that conditions will be executed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the conditional operators that can be used in a conditional expression:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "|Condition|Function|\n", "|---|---|\n", "|>|more than|\n", "|<|less than|\n", "|>=|equal or more than|\n", "|<=|equal or less than|\n", "|==|equal to|\n", "|!=|not equal to|\n", "|and|more than one conditional operations are true|\n", "|or|either one conditional operations is true|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, the multiple conditional expressions format is: \n", " \n", "_if (condition/s 1):_\n", ">_statement 1.1_ \n", "_statement 1.2_ \n", "......\n", "\n", "_elif (condition/s 2):_\n", ">_statement 2.1_ \n", "...... \n", "\n", "_elif (condition/s 3):_ \n", ">_statement 3.1_ \n", "...... \n", "\n", "...... \n", "...... \n", "...... \n", "\n", "\n", "_else:_ \n", ">_statement_ \n", "......" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The statement in each conditional expression can also be a conditional expression." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### <span style=\"color: #F5DA81; background-color: #610B4B\">Example 3.1</span>: Determine the maximum and minimum of three different integers: 34,12,67." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum integer is 67\n", "Minimum integer is 12\n" ] } ], "source": [ "x = 34\n", "y = 12\n", "z = 67\n", "\n", "if x > y:\n", " if y > z:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % z\n", " elif z > x:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % y\n", " else:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % y\n", " \n", "else: # y > x\n", " if x > z:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % z\n", " elif z > y:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % x\n", " else:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % x\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### <span style=\"color: #F5DA81; background-color: #610B4B\">Example 3.2</span>: Use only one type of conditional operator for Exercise 3.1." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum integer is 67\n", "Minimum integer is 12\n" ] } ], "source": [ "x = 34\n", "y = 12\n", "z = 67\n", "\n", "if x > y > z:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % z\n", "\n", "elif x > z > y:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % y\n", " \n", "elif y > x > z:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % z\n", "\n", "elif y > z > x:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % x\n", "\n", "elif z > x > y:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % y\n", "\n", "else:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### <span style=\"color: #F5DA81; background-color: #610B4B\">Exercise 3.1</span>: What if two or all integers have the same value. Try this and run the codes that solve Examples 3.1 and 3.2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Codes in Example 3.1 seems more robust but 3.2 can be made more robust adding `'>='` instead of `'>'`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum integer is 99\n", "Minimum integer is 78\n" ] } ], "source": [ "x = 78\n", "y = 78\n", "z = 99\n", "\n", "if x >= y >= z:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % z\n", "\n", "elif x >= z >= y:\n", " print 'Maximum integer is %d' % x\n", " print 'Minimum integer is %d' % y\n", " \n", "elif y >= x >= z:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % z\n", "\n", "elif y >= z >= x:\n", " print 'Maximum integer is %d' % y\n", " print 'Minimum integer is %d' % x\n", "\n", "elif z >= x >= y:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % y\n", "\n", "else:\n", " print 'Maximum integer is %d' % z\n", " print 'Minimum integer is %d' % x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "### **_3.3 The <span style=\"color: #0000FF\">for</span> and <span style=\"color: #0000FF\">while</span> conditions_**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$for$</span> and <span style=\"color: #0000FF\">$while$</span> functions can be used to do repetitive action. The indentation with space (not tab) for statements inside the loop is also applied and consistent throughout the condition." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "for i in range(0,5,1):\n", " print i" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the variable $i$ will be assigned the value of $0$ and cyclically incremented $5$ times by adding the integer $1$ to it each time. Only integer values are accepted in the parenthesis of the _range_ statement. The first integer is the intial value of the $i$ variable, the second integer indicates (not-inclusive) the limiting value of the $i$ variable and the third integer represent the integer added to the variable $i$ for each cycles." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n", "14\n", "20\n", "26\n", "32\n" ] } ], "source": [ "for i in range(4,17,3):\n", " print i*2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conditional looping can be nested as examplified below:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 x 6 = 6\n", "1 x 7 = 7\n", "1 x 8 = 8\n", "1 x 9 = 9\n", "1 x 10 = 10\n", "2 x 6 = 12\n", "2 x 7 = 14\n", "2 x 8 = 16\n", "2 x 9 = 18\n", "2 x 10 = 20\n", "3 x 6 = 18\n", "3 x 7 = 21\n", "3 x 8 = 24\n", "3 x 9 = 27\n", "3 x 10 = 30\n", "4 x 6 = 24\n", "4 x 7 = 28\n", "4 x 8 = 32\n", "4 x 9 = 36\n", "4 x 10 = 40\n", "5 x 6 = 30\n", "5 x 7 = 35\n", "5 x 8 = 40\n", "5 x 9 = 45\n", "5 x 10 = 50\n" ] } ], "source": [ "for i in range(1,6,1):\n", " for j in range(6,11,1):\n", " print '%d x %d = %d' % (i,j,i*j)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to loop into the elements of a string (_i.e._ a $list$)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N\n", "u\n", "m\n", "p\n", "y\n" ] } ], "source": [ "for name in 'Numpy':\n", " print name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$while$</span> function works similarly like <span style=\"color: #0000FF\">$for$</span> but initialization of the variable is performed before the <span style=\"color: #0000FF\">$while$</span> statement and incrementing process is carried out as part of the loop argument. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "6\n", "12\n", "18\n", "24\n" ] } ], "source": [ "z = 0\n", "while z < 27:\n", " print z\n", " z = z + 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "### **_3.3 The <span style=\"color: #0000FF\">$enumerate$&#40; &#41;</span> function_**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$enumerate$&#40; &#41;</span> function will make the <span style=\"color: #0000FF\">$for$</span> looping condition looking more comprehensible. The argument for this function is a $list$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 \tN\n", "1 \tu\n", "2 \tm\n", "3 \tp\n", "4 \ty\n" ] } ], "source": [ "for i,j in enumerate('Numpy'):\n", " print i, '\\t', j" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 'N')\n", "(1, 'u')\n", "(2, 'm')\n", "(3, 'p')\n", "(4, 'y')\n" ] } ], "source": [ "for item in enumerate('Numpy'):\n", " print item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <span style=\"color: #0000FF\">$enumerate$&#40; &#41;</span> function allows the extraction of both the default position and its element of a $list$. In the first example, the two variables $i$ and $j$ will be assigned the $list$ default positional number and its element, respectively. In the second example, the variable $item$ will be assigned a tuple that consists the pair of default positional number and its element of the $list$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default positional number can be initiated to a different number. This can be done by passing the initial number as another argument in the <span style=\"color: #0000FF\">$enumerate$&#40; &#41;</span> function." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 'N')\n", "(6, 'u')\n", "(7, 'm')\n", "(8, 'p')\n", "(9, 'y')\n" ] } ], "source": [ "for item in enumerate('Numpy',5):\n", " print item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More on conditional expressions and looping can be found on https://docs.python.org/2/tutorial/controlflow.html" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

artistic-2.0

barjacks/pythonrecherche

11 pandas plotting, re, selenum practice/02 Reading local files.ipynb

1

31491

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "from bs4 import BeautifulSoup\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read files int the relevant folder" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "lst = os.listdir('files/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# But let's look at one single file first" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "text = open('files/1.htm',\"r\") \n", "text_soup = BeautifulSoup(text.read(), 'html.parser')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Go and look at the files to get the correct tags\n", "date = text_soup.find('tbody').find_all('td', {'class':'tdcol1'})\n", "nr = text_soup.find('tbody').find_all('td', {'class':'tdcol2'})\n", "status = text_soup.find('tbody').find_all('td', {'class':'tdcol3'})\n", "meldung = text_soup.find('tbody').find_all('td')[3::5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Put it all together" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "whole_list = []\n", "\n", "for elem in lst:\n", " \n", " text = open('files/' + elem,\"r\") \n", " text_soup = BeautifulSoup(text.read(), 'html.parser')\n", " \n", " date = text_soup.find('tbody').find_all('td', {'class':'tdcol1'})\n", " nr = text_soup.find('tbody').find_all('td', {'class':'tdcol2'})\n", " status = text_soup.find('tbody').find_all('td', {'class':'tdcol3'})\n", " meldung = text_soup.find('tbody').find_all('td')[3::5]\n", " \n", " mini_list = []\n", " \n", " for d,n,s,m in zip(date, nr, status, meldung):\n", " \n", " mini_dict = {'Datum': d.text,\n", " 'Nummer': n.text,\n", " 'Status': s.text,\n", " 'Meldung': m.text}\n", " mini_list.append(mini_dict)\n", " \n", " whole_list += mini_list" ] }, { "cell_type": "code", "execution_count": 43, "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>Datum</th>\n", " <th>Meldung</th>\n", " <th>Nummer</th>\n", " <th>Status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>07.07.2017</td>\n", " <td>Restaurant Bar ADONIS GmbH, AarauSHAB-Nr. 130 ...</td>\n", " <td>3615849</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>07.07.2017</td>\n", " <td>Hertel Wilhelm, HorgenbergSHAB-Nr. 130 Konkur...</td>\n", " <td>3577217</td>\n", " <td>Konkurse;Konkursamtliche Grundstücksteigerung</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>07.07.2017</td>\n", " <td>Jossen Ulrich, NatersSHAB-Nr. 130 Konkursamt ...</td>\n", " <td>3615897</td>\n", " <td>Konkurse;Konkursamtliche Grundstücksteigerung</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>05.07.2017</td>\n", " <td>Erdogan Cuma, LangenthalSHAB-Nr. 128 Konkursa...</td>\n", " <td>3607085</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>05.07.2017</td>\n", " <td>GT Gastro AG, HergiswilSHAB-Nr. 128 Konkursam...</td>\n", " <td>3609623</td>\n", " <td>Konkurse;Konkursamtliche Grundstücksteigerung</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>30.06.2017</td>\n", " <td>Habermacher Alfred, GontenschwilSHAB-Nr. 125 ...</td>\n", " <td>3609797</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>30.06.2017</td>\n", " <td>LE RELAIS FLEURI Sàrl, Villars-Ste-CroixSHAB-N...</td>\n", " <td>3610131</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>30.06.2017</td>\n", " <td>Vilhena Ferreira Andreia Sofia, Le Châtelard-p...</td>\n", " <td>3589305</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>30.06.2017</td>\n", " <td>Dos Santos Sardico Antonio José, Dietikon, zur...</td>\n", " <td>3603515</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>28.06.2017</td>\n", " <td>Ekici Ali, Oberwil BLSHAB-Nr. 123 Zivilrechts...</td>\n", " <td>3600873</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>19.06.2015</td>\n", " <td>Restaurant Trung-Hoa GmbH, SpreitenbachSHAB-Nr...</td>\n", " <td>2210701</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>19.06.2015</td>\n", " <td>adRem AG, GersauSHAB-Nr. 116 Konkursamt Hochdorf</td>\n", " <td>2213195</td>\n", " <td>Konkurse;Konkursamtliche Grundstücksteigerung</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>15.06.2015</td>\n", " <td>Restaurant zum Bären Stampfli GmbH, WilSHAB-Nr...</td>\n", " <td>2199253</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>12.06.2015</td>\n", " <td>Restaurant Bar Siri GmbH, ZürichSHAB-Nr. 111 ...</td>\n", " <td>2177343</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>05.06.2015</td>\n", " <td>Lonia GmbH, ElggSHAB-Nr. 106 Konkursamt Elgg</td>\n", " <td>2183049</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>05.06.2015</td>\n", " <td>EBI Zeynur Sàrl en liquidation, CressierSHAB-N...</td>\n", " <td>2174931</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>29.05.2015</td>\n", " <td>Asian Restaurant und Take-Away GmbH, Unterkulm...</td>\n", " <td>2169461</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>27.05.2015</td>\n", " <td>Restaurant St. Gervais GmbH, Biel/BienneSHAB-N...</td>\n", " <td>2140713</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>27.05.2015</td>\n", " <td>Fondation Gilberte de Courgenay, Hôtel de la G...</td>\n", " <td>2169511</td>\n", " <td>Konkurse;Konkursamtliche Grundstücksteigerung</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>15.05.2015</td>\n", " <td>Saffron Asian Restaurant GmbH, KaiseraugstSHAB...</td>\n", " <td>2140687</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>17.07.2015</td>\n", " <td>Lonia GmbH, ElggSHAB-Nr. 136 Konkursamt Elgg</td>\n", " <td>2266413</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>17.07.2015</td>\n", " <td>\"Le Panorama Sàrl\", AyentSHAB-Nr. 136 Office ...</td>\n", " <td>2271803</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>16.07.2015</td>\n", " <td>Swann Hotel et Restaurant S.A., Laax GRSHAB-Nr...</td>\n", " <td>2258493</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>15.07.2015</td>\n", " <td>Restaurant Gundeldingerhof AG, BaselSHAB-Nr. 1...</td>\n", " <td>2255595</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>15.07.2015</td>\n", " <td>Hotel-Restaurant Post AG Lyss, LyssSHAB-Nr. 13...</td>\n", " <td>2258289</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>15.07.2015</td>\n", " <td>Restaurant St. Gervais GmbH, Biel/BienneSHAB-N...</td>\n", " <td>2260821</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>10.07.2015</td>\n", " <td>Restaurant Neuhüsli Christen Jürg und Biberste...</td>\n", " <td>2255565</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>01.07.2015</td>\n", " <td>Restaurant La Calèche GmbH, BernSHAB-Nr. 124 ...</td>\n", " <td>2230487</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>30.06.2015</td>\n", " <td>RESTAURANT LA MAISON GRISE SÀRL, TroinexSHAB-N...</td>\n", " <td>2221763</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>26.06.2015</td>\n", " <td>Restaurant Neuhüsli Christen Jürg und Biberste...</td>\n", " <td>2224777</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>409</th>\n", " <td>14.12.2015</td>\n", " <td>Zemp Junior Conditorei-Restaurant GmbH, Laufen...</td>\n", " <td>2535583</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>410</th>\n", " <td>09.12.2015</td>\n", " <td>Restaurant zum Tempel AG, ThunSHAB-Nr. 239 Ko...</td>\n", " <td>2513091</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>411</th>\n", " <td>04.12.2015</td>\n", " <td>CASA DELLA PIZZA SARL, LausanneSHAB-Nr. 236 O...</td>\n", " <td>2505447</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>412</th>\n", " <td>27.11.2015</td>\n", " <td>Restaurant Ochsen Sempach GmbH, SempachSHAB-Nr...</td>\n", " <td>2494789</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>413</th>\n", " <td>20.11.2015</td>\n", " <td>Restaurant zur alten Post GmbH Aarburg, Aarbur...</td>\n", " <td>2490227</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>414</th>\n", " <td>20.11.2015</td>\n", " <td>Zemp Junior Conditorei-Restaurant GmbH, Laufen...</td>\n", " <td>2492411</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>415</th>\n", " <td>18.11.2015</td>\n", " <td>Aurora8 Services GmbH, HergiswilSHAB-Nr. 224 ...</td>\n", " <td>2484521</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>416</th>\n", " <td>13.11.2015</td>\n", " <td>HOTEL RESTAURANT LA POSTE SARL, ST-CERGUESHAB-...</td>\n", " <td>2468983</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>417</th>\n", " <td>11.11.2015</td>\n", " <td>Restaurant zum Tempel AG, ThunSHAB-Nr. 219 Ko...</td>\n", " <td>2466181</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>418</th>\n", " <td>06.11.2015</td>\n", " <td>Espressomania GmbH, Holzhäusern, Gde. RischSHA...</td>\n", " <td>2453841</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>419</th>\n", " <td>29.01.2016</td>\n", " <td>AL.KO BP SA, VuarrensSHAB-Nr. 20 Office des f...</td>\n", " <td>2618369</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>420</th>\n", " <td>29.01.2016</td>\n", " <td>Restaurant zur alten Post GmbH Aarburg, Aarbur...</td>\n", " <td>2618171</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>421</th>\n", " <td>22.01.2016</td>\n", " <td>GL Gastro AG, GiswilSHAB-Nr. 15 Konkursamt Ob...</td>\n", " <td>2603189</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>422</th>\n", " <td>20.01.2016</td>\n", " <td>Restaurant zum Tempel AG, ThunSHAB-Nr. 13 Kon...</td>\n", " <td>2600329</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>423</th>\n", " <td>20.01.2016</td>\n", " <td>Café-Restaurant Bad Frutigen AG, FrutigenSHAB-...</td>\n", " <td>2594343</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>424</th>\n", " <td>15.01.2016</td>\n", " <td>Restaurant Alpina Alpnach GmbH in Liquidation,...</td>\n", " <td>2592019</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>425</th>\n", " <td>08.01.2016</td>\n", " <td>LA QUENOUILLE SARL, MONT-SUR-ROLLESHAB-Nr. 5 ...</td>\n", " <td>2551297</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>426</th>\n", " <td>08.01.2016</td>\n", " <td>Café-Restaurant les Masses, Pascal MALHERBES, ...</td>\n", " <td>2582637</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>427</th>\n", " <td>30.12.2015</td>\n", " <td>Gastro TotalRegional GmbH, TäuffelenSHAB-Nr. 2...</td>\n", " <td>2558007</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>428</th>\n", " <td>18.12.2015</td>\n", " <td>Restaurant Eintracht Arn GmbH, HorgenSHAB-Nr. ...</td>\n", " <td>2538665</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>429</th>\n", " <td>02.03.2017</td>\n", " <td>Restaurant Viva GmbH\\nOser Philipp, Bottmingen...</td>\n", " <td>3381287</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>430</th>\n", " <td>27.02.2017</td>\n", " <td>Zucol Christian, RapperswilSHAB-Nr. 40 Konkur...</td>\n", " <td>3365105</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>431</th>\n", " <td>24.02.2017</td>\n", " <td>Restaurant Mediterraneo Pizzeria GmbH, Lenzbur...</td>\n", " <td>3365153</td>\n", " <td>Konkurse;Vorläufige Konkursanzeige</td>\n", " </tr>\n", " <tr>\n", " <th>432</th>\n", " <td>24.02.2017</td>\n", " <td>Restaurant Bar ADONIS GmbH, AarauSHAB-Nr. 39 ...</td>\n", " <td>3353871</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>433</th>\n", " <td>24.02.2017</td>\n", " <td>Kassegger + Mielke Restaurant Rose, Rüschlikon...</td>\n", " <td>3365123</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>434</th>\n", " <td>24.02.2017</td>\n", " <td>Restaurant Pizzeria Swiss Leone GmbH, Gossau Z...</td>\n", " <td>3364903</td>\n", " <td>Konkurse;Einstellung des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>435</th>\n", " <td>24.02.2017</td>\n", " <td>Genossenschaft Arbeiten in der Giesserei in Li...</td>\n", " <td>3362477</td>\n", " <td>Konkurse;Kollokationsplan und Inventar</td>\n", " </tr>\n", " <tr>\n", " <th>436</th>\n", " <td>22.02.2017</td>\n", " <td>Villar Benigno, ZollikofenSHAB-Nr. 37 Konkurs...</td>\n", " <td>3341851</td>\n", " <td>Konkurse;Schluss des Konkursverfahrens</td>\n", " </tr>\n", " <tr>\n", " <th>437</th>\n", " <td>17.02.2017</td>\n", " <td>Serifeg Dalila, FleurierSHAB-Nr. 34 Office de...</td>\n", " <td>3333147</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " <tr>\n", " <th>438</th>\n", " <td>17.02.2017</td>\n", " <td>Arnold Philipp, BaarSHAB-Nr. 34 Konkursamt Ho...</td>\n", " <td>3344613</td>\n", " <td>Konkurse;Konkurspublikation/Schuldenruf</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>439 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " Datum Meldung Nummer \\\n", "0 07.07.2017 Restaurant Bar ADONIS GmbH, AarauSHAB-Nr. 130 ... 3615849 \n", "1 07.07.2017 Hertel Wilhelm, HorgenbergSHAB-Nr. 130 Konkur... 3577217 \n", "2 07.07.2017 Jossen Ulrich, NatersSHAB-Nr. 130 Konkursamt ... 3615897 \n", "3 05.07.2017 Erdogan Cuma, LangenthalSHAB-Nr. 128 Konkursa... 3607085 \n", "4 05.07.2017 GT Gastro AG, HergiswilSHAB-Nr. 128 Konkursam... 3609623 \n", "5 30.06.2017 Habermacher Alfred, GontenschwilSHAB-Nr. 125 ... 3609797 \n", "6 30.06.2017 LE RELAIS FLEURI Sàrl, Villars-Ste-CroixSHAB-N... 3610131 \n", "7 30.06.2017 Vilhena Ferreira Andreia Sofia, Le Châtelard-p... 3589305 \n", "8 30.06.2017 Dos Santos Sardico Antonio José, Dietikon, zur... 3603515 \n", "9 28.06.2017 Ekici Ali, Oberwil BLSHAB-Nr. 123 Zivilrechts... 3600873 \n", "10 19.06.2015 Restaurant Trung-Hoa GmbH, SpreitenbachSHAB-Nr... 2210701 \n", "11 19.06.2015 adRem AG, GersauSHAB-Nr. 116 Konkursamt Hochdorf 2213195 \n", "12 15.06.2015 Restaurant zum Bären Stampfli GmbH, WilSHAB-Nr... 2199253 \n", "13 12.06.2015 Restaurant Bar Siri GmbH, ZürichSHAB-Nr. 111 ... 2177343 \n", "14 05.06.2015 Lonia GmbH, ElggSHAB-Nr. 106 Konkursamt Elgg 2183049 \n", "15 05.06.2015 EBI Zeynur Sàrl en liquidation, CressierSHAB-N... 2174931 \n", "16 29.05.2015 Asian Restaurant und Take-Away GmbH, Unterkulm... 2169461 \n", "17 27.05.2015 Restaurant St. Gervais GmbH, Biel/BienneSHAB-N... 2140713 \n", "18 27.05.2015 Fondation Gilberte de Courgenay, Hôtel de la G... 2169511 \n", "19 15.05.2015 Saffron Asian Restaurant GmbH, KaiseraugstSHAB... 2140687 \n", "20 17.07.2015 Lonia GmbH, ElggSHAB-Nr. 136 Konkursamt Elgg 2266413 \n", "21 17.07.2015 \"Le Panorama Sàrl\", AyentSHAB-Nr. 136 Office ... 2271803 \n", "22 16.07.2015 Swann Hotel et Restaurant S.A., Laax GRSHAB-Nr... 2258493 \n", "23 15.07.2015 Restaurant Gundeldingerhof AG, BaselSHAB-Nr. 1... 2255595 \n", "24 15.07.2015 Hotel-Restaurant Post AG Lyss, LyssSHAB-Nr. 13... 2258289 \n", "25 15.07.2015 Restaurant St. Gervais GmbH, Biel/BienneSHAB-N... 2260821 \n", "26 10.07.2015 Restaurant Neuhüsli Christen Jürg und Biberste... 2255565 \n", "27 01.07.2015 Restaurant La Calèche GmbH, BernSHAB-Nr. 124 ... 2230487 \n", "28 30.06.2015 RESTAURANT LA MAISON GRISE SÀRL, TroinexSHAB-N... 2221763 \n", "29 26.06.2015 Restaurant Neuhüsli Christen Jürg und Biberste... 2224777 \n", ".. ... ... ... \n", "409 14.12.2015 Zemp Junior Conditorei-Restaurant GmbH, Laufen... 2535583 \n", "410 09.12.2015 Restaurant zum Tempel AG, ThunSHAB-Nr. 239 Ko... 2513091 \n", "411 04.12.2015 CASA DELLA PIZZA SARL, LausanneSHAB-Nr. 236 O... 2505447 \n", "412 27.11.2015 Restaurant Ochsen Sempach GmbH, SempachSHAB-Nr... 2494789 \n", "413 20.11.2015 Restaurant zur alten Post GmbH Aarburg, Aarbur... 2490227 \n", "414 20.11.2015 Zemp Junior Conditorei-Restaurant GmbH, Laufen... 2492411 \n", "415 18.11.2015 Aurora8 Services GmbH, HergiswilSHAB-Nr. 224 ... 2484521 \n", "416 13.11.2015 HOTEL RESTAURANT LA POSTE SARL, ST-CERGUESHAB-... 2468983 \n", "417 11.11.2015 Restaurant zum Tempel AG, ThunSHAB-Nr. 219 Ko... 2466181 \n", "418 06.11.2015 Espressomania GmbH, Holzhäusern, Gde. RischSHA... 2453841 \n", "419 29.01.2016 AL.KO BP SA, VuarrensSHAB-Nr. 20 Office des f... 2618369 \n", "420 29.01.2016 Restaurant zur alten Post GmbH Aarburg, Aarbur... 2618171 \n", "421 22.01.2016 GL Gastro AG, GiswilSHAB-Nr. 15 Konkursamt Ob... 2603189 \n", "422 20.01.2016 Restaurant zum Tempel AG, ThunSHAB-Nr. 13 Kon... 2600329 \n", "423 20.01.2016 Café-Restaurant Bad Frutigen AG, FrutigenSHAB-... 2594343 \n", "424 15.01.2016 Restaurant Alpina Alpnach GmbH in Liquidation,... 2592019 \n", "425 08.01.2016 LA QUENOUILLE SARL, MONT-SUR-ROLLESHAB-Nr. 5 ... 2551297 \n", "426 08.01.2016 Café-Restaurant les Masses, Pascal MALHERBES, ... 2582637 \n", "427 30.12.2015 Gastro TotalRegional GmbH, TäuffelenSHAB-Nr. 2... 2558007 \n", "428 18.12.2015 Restaurant Eintracht Arn GmbH, HorgenSHAB-Nr. ... 2538665 \n", "429 02.03.2017 Restaurant Viva GmbH\\nOser Philipp, Bottmingen... 3381287 \n", "430 27.02.2017 Zucol Christian, RapperswilSHAB-Nr. 40 Konkur... 3365105 \n", "431 24.02.2017 Restaurant Mediterraneo Pizzeria GmbH, Lenzbur... 3365153 \n", "432 24.02.2017 Restaurant Bar ADONIS GmbH, AarauSHAB-Nr. 39 ... 3353871 \n", "433 24.02.2017 Kassegger + Mielke Restaurant Rose, Rüschlikon... 3365123 \n", "434 24.02.2017 Restaurant Pizzeria Swiss Leone GmbH, Gossau Z... 3364903 \n", "435 24.02.2017 Genossenschaft Arbeiten in der Giesserei in Li... 3362477 \n", "436 22.02.2017 Villar Benigno, ZollikofenSHAB-Nr. 37 Konkurs... 3341851 \n", "437 17.02.2017 Serifeg Dalila, FleurierSHAB-Nr. 34 Office de... 3333147 \n", "438 17.02.2017 Arnold Philipp, BaarSHAB-Nr. 34 Konkursamt Ho... 3344613 \n", "\n", " Status \n", "0 Konkurse;Kollokationsplan und Inventar \n", "1 Konkurse;Konkursamtliche Grundstücksteigerung \n", "2 Konkurse;Konkursamtliche Grundstücksteigerung \n", "3 Konkurse;Konkurspublikation/Schuldenruf \n", "4 Konkurse;Konkursamtliche Grundstücksteigerung \n", "5 Konkurse;Vorläufige Konkursanzeige \n", "6 Konkurse;Einstellung des Konkursverfahrens \n", "7 Konkurse;Einstellung des Konkursverfahrens \n", "8 Konkurse;Schluss des Konkursverfahrens \n", "9 Konkurse;Vorläufige Konkursanzeige \n", "10 Konkurse;Einstellung des Konkursverfahrens \n", "11 Konkurse;Konkursamtliche Grundstücksteigerung \n", "12 Konkurse;Einstellung des Konkursverfahrens \n", "13 Konkurse;Einstellung des Konkursverfahrens \n", "14 Konkurse;Vorläufige Konkursanzeige \n", "15 Konkurse;Kollokationsplan und Inventar \n", "16 Konkurse;Schluss des Konkursverfahrens \n", "17 Konkurse;Kollokationsplan und Inventar \n", "18 Konkurse;Konkursamtliche Grundstücksteigerung \n", "19 Konkurse;Einstellung des Konkursverfahrens \n", "20 Konkurse;Einstellung des Konkursverfahrens \n", "21 Konkurse;Schluss des Konkursverfahrens \n", "22 Konkurse;Vorläufige Konkursanzeige \n", "23 Konkurse;Konkurspublikation/Schuldenruf \n", "24 Konkurse;Einstellung des Konkursverfahrens \n", "25 Konkurse;Schluss des Konkursverfahrens \n", "26 Konkurse;Einstellung des Konkursverfahrens \n", "27 Konkurse;Schluss des Konkursverfahrens \n", "28 Konkurse;Einstellung des Konkursverfahrens \n", "29 Konkurse;Vorläufige Konkursanzeige \n", ".. ... \n", "409 Konkurse;Einstellung des Konkursverfahrens \n", "410 Konkurse;Konkurspublikation/Schuldenruf \n", "411 Konkurse;Einstellung des Konkursverfahrens \n", "412 Konkurse;Vorläufige Konkursanzeige \n", "413 Konkurse;Vorläufige Konkursanzeige \n", "414 Konkurse;Vorläufige Konkursanzeige \n", "415 Konkurse;Konkurspublikation/Schuldenruf \n", "416 Konkurse;Schluss des Konkursverfahrens \n", "417 Konkurse;Vorläufige Konkursanzeige \n", "418 Konkurse;Konkurspublikation/Schuldenruf \n", "419 Konkurse;Einstellung des Konkursverfahrens \n", "420 Konkurse;Einstellung des Konkursverfahrens \n", "421 Konkurse;Konkurspublikation/Schuldenruf \n", "422 Konkurse;Kollokationsplan und Inventar \n", "423 Konkurse;Schluss des Konkursverfahrens \n", "424 Konkurse;Vorläufige Konkursanzeige \n", "425 Konkurse;Konkurspublikation/Schuldenruf \n", "426 Konkurse;Einstellung des Konkursverfahrens \n", "427 Konkurse;Konkurspublikation/Schuldenruf \n", "428 Konkurse;Vorläufige Konkursanzeige \n", "429 Konkurse;Vorläufige Konkursanzeige \n", "430 Konkurse;Kollokationsplan und Inventar \n", "431 Konkurse;Vorläufige Konkursanzeige \n", "432 Konkurse;Konkurspublikation/Schuldenruf \n", "433 Konkurse;Einstellung des Konkursverfahrens \n", "434 Konkurse;Einstellung des Konkursverfahrens \n", "435 Konkurse;Kollokationsplan und Inventar \n", "436 Konkurse;Schluss des Konkursverfahrens \n", "437 Konkurse;Konkurspublikation/Schuldenruf \n", "438 Konkurse;Konkurspublikation/Schuldenruf \n", "\n", "[439 rows x 4 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(whole_list)" ] } ], "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": 2 }

mit

stitchfix/d3-jupyter-tutorial

3d_meshing.ipynb

1

57578

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D Visualization of a Convex Hull with D3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides a simple example of convex hull visualization using D3." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### D3 Graph Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See accompanying d3_lib.py and the js and css folders." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from IPython.core.display import HTML\n", "import d3_lib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".axis path,\n", ".axis line {\n", " fill: none;\n", " stroke: grey;\n", " stroke-width: 1;\n", " shape-rendering: crispEdges;\n", "}\n", "\n", "\n", ".dot {\n", " stroke-width: 0.5;\n", " stroke: #000;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(d3_lib.set_styles(['basic_axis','3d_viewer']))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script src=\"lib/d3/d3.min.js\"></script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML('<script src=\"lib/d3/d3.min.js\"></script>')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def points_d3(points):\n", " return [ {\"x\": d[0], \"y\": d[1], \"z\": d[2]} for d in points ]\n", "\n", "def triangles_d3(points,triangles_vertices):\n", " triangles = []\n", " for tv in triangles_vertices:\n", " triangles.append( {\"x1\": points[tv[0]][0], \n", " \"y1\": points[tv[0]][1], \n", " \"z1\": points[tv[0]][2], \n", " \"x2\": points[tv[1]][0], \n", " \"y2\": points[tv[1]][1], \n", " \"z2\": points[tv[1]][2], \n", " \"x3\": points[tv[2]][0], \n", " \"y3\": points[tv[2]][1], \n", " \"z3\": points[tv[2]][2] } )\n", " \n", " return triangles\n", "\n", "def graph_points_triangles(objs):\n", " data = []\n", " for obj in objs:\n", " points, triangles_vertices = obj[0], obj[1]\n", " data.append( {\"points\": points_d3(points), \n", " \"triangles\": triangles_d3(points, triangles_vertices)} )\n", " return HTML(d3_lib.draw_graph('3d_viewer',{'data':data}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Meshing and Volume Calculations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import random\n", "from scipy.spatial import ConvexHull" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_mesh(points):\n", " hull = ConvexHull(points)\n", " indices = hull.simplices\n", " return indices, hull.vertices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Randomly Sampled Points on a Cylinder" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cylinder_points_and_hull_given_sample_size(sample_size):\n", " points = []\n", " for i in range(sample_size/2):\n", " x = random.uniform(-1,1)\n", " z = random.uniform(0,1)\n", " s = (-1.0, 1.0)[random.uniform(0,1) < 0.5]\n", " y = s * (1 - x**2) ** (0.5)\n", " points.append(np.array([x,y,z]))\n", " for z in range(0,2):\n", " for i in range(n/4):\n", " x = random.uniform(-1,1)\n", " s = (-1.0, 1.0)[random.uniform(0,1) < 0.5]\n", " y = s * random.uniform(0,1) * (1 - x**2) ** (0.5)\n", " points.append(np.array([x,y,z]))\n", " points = np.array(points)\n", " triangles_vertices, hull_points = compute_mesh(points)\n", " return points, hull_points, triangles_vertices" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.2788536 , 0.96033363, 0.02501076],\n", " [-0.55357852, -0.83279698, 0.73647121],\n", " [ 0.78435914, 0.62030698, 0.08693883]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random.seed(42)\n", "n = 100\n", "points, hull_vertices, triangles_vertices = cylinder_points_and_hull_given_sample_size(n)\n", "points[:3]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[42, 77, 99],\n", " [ 4, 69, 99],\n", " [ 4, 42, 99]], dtype=int32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "triangles_vertices[:3]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " <div id='maindiv9039285714'></div>\n", " \n", " <script>\n", " \n", "var el_width = 960,\n", " el_height = 500;\n", "\n", "\n", "var margin = {top: 20, right: 20, bottom: 20, left: 20},\n", " width = el_width * 0.75 - margin.left - margin.right,\n", " height = el_width * 0.75 - margin.top - margin.bottom;\n", "\n", "d3.select(\"#maindiv9039285714\").selectAll(\"svg\").remove();\n", "var svg = d3.select(\"#maindiv9039285714\").append(\"svg\")\n", " .attr(\"width\", width + margin.left + margin.right)\n", " .attr(\"height\", height + margin.top + margin.bottom);\n", "\n", "\n", "// viewport division lines and labels\n", "\n", "var divider_coords = [{\"x1\": width / 2.0, \"x2\": width / 2.0, \"y1\": 0, \"y2\": height},\n", " {\"x1\": 0, \"x2\": width, \"y1\": height / 2.0, \"y2\": height / 2.0}];\n", " \n", "var divider_label_config = [ {\"text\": \"Top\", \"x\": 10, \"y\": 10},\n", " {\"text\": \"Front\", \"x\": 10, \"y\": (height/2.0) + 10},\n", " {\"text\": \"Right\", \"x\": (width/2.0) + 10, \"y\": (height/2.0) + 10} ];\n", "\n", "var divider_g = svg.append(\"g\")\n", " .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\");\n", "\n", "divider_g.selectAll(\".divider\")\n", " .data(divider_coords)\n", " .enter().append(\"line\")\n", " .attr(\"class\", \"divider\")\n", " .attr(\"x1\", function(d) { return d.x1; })\n", " .attr(\"x2\", function(d) { return d.x2; })\n", " .attr(\"y1\", function(d) { return d.y1; })\n", " .attr(\"y2\", function(d) { return d.y2; })\n", " .style(\"stroke\", \"black\")\n", " .style(\"stoke-width\", 2);\n", "\n", "divider_g.selectAll(\".dividerlabel\")\n", " .data(divider_label_config)\n", " .enter().append(\"text\")\n", " .attr(\"class\", \"dividerlabel\")\n", " .attr(\"x\", function(d) { return d.x; })\n", " .attr(\"y\", function(d) { return d.y; })\n", " .attr(\"dy\", \".35em\")\n", " .text(function(d) { return d.text; });\n", "\n", "\n", "// viewport setup\n", "\n", "var viewport_padding = 20;\n", "\n", "var viewport_config = [\n", " {\"h\": [viewport_padding, (width / 2.0) - viewport_padding], \n", " \"v\": [(height / 2.0) - viewport_padding, viewport_padding], \n", " \"hdim\": \"x\", \"vdim\": \"y\", \"zdim\": \"z\"},\n", " {\"h\": [viewport_padding, (width / 2.0) - viewport_padding], \n", " \"v\": [height - viewport_padding, (height / 2.0) + viewport_padding], \n", " \"hdim\": \"x\", \"vdim\": \"z\", \"zdim\": \"y\"},\n", " {\"h\": [(width / 2.0) + viewport_padding, width - viewport_padding], \n", " \"v\": [height - viewport_padding, (height / 2.0) + viewport_padding], \n", " \"hdim\": \"y\", \"vdim\": \"z\", \"zdim\": \"x\"}\n", " ]\n", "\n", "var viewport = [];\n", "var h = [];\n", "var v = [];\n", "for (var i=0; i<viewport_config.length; i++) {\n", " h.push( d3.scale.linear().range(viewport_config[i][\"h\"]) );\n", " v.push( d3.scale.linear().range(viewport_config[i][\"v\"]) );\n", " viewport.push( svg.append(\"g\")\n", " .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\") );\n", "}\n", "\n", "\n", "\n", "//d3.csv(\"data.csv\", function(error, data) {\n", "// if (error) throw error;\n", "var data = [{'points': [{'y': 0.96033362509449738, 'x': 0.2788535969157675, 'z': 0.025010755222666936}, {'y': -0.83279698492220877, 'x': -0.5535785237023545, 'z': 0.7364712141640124}, {'y': 0.62030697779354571, 'x': 0.78435913540969082, 'z': 0.086938832629416152}, {'y': -0.34005496703815297, 'x': -0.94040556112385931, 'z': 0.21863797480360336}, {'y': -0.321445559912099, 'x': -0.94692806063227275, 'z': 0.19883765068664849}, {'y': -0.99595233484578105, 'x': 0.089882961206433354, 'z': 0.2204406220406967}, {'y': -0.78550058556350333, 'x': 0.6188609133556533, 'z': 0.0064987596780610168}, {'y': 0.91813023074877442, 'x': 0.39627878997645372, 'z': 0.34025051651799187}, {'y': 0.40475279667093927, 'x': 0.9144261444135624, 'z': 0.33659454511262676}, {'y': -0.59114234930571974, 'x': -0.80656724633307197, 'z': 0.84749436634745978}, {'y': 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'y2': -0.52526857682898909, 'y1': -0.0017565578905829238, 'x3': -0.98066060078332007, 'y3': 0.17283812625908226, 'x1': 0.97446612726300863, 'z1': 1.0, 'z2': 1.0, 'z3': 1.0}, {'x2': 0.86941251547285447, 'y2': 0.35386099621531025, 'y1': -0.0017565578905829238, 'x3': 0.6342082702309233, 'y3': 0.51290711369531117, 'x1': 0.97446612726300863, 'z1': 1.0, 'z2': 1.0, 'z3': 1.0}, {'x2': -0.85491377101368538, 'y2': 0.51796833656954533, 'y1': -0.0017565578905829238, 'x3': -0.98066060078332007, 'y3': 0.17283812625908226, 'x1': 0.97446612726300863, 'z1': 1.0, 'z2': 1.0, 'z3': 1.0}, {'x2': -0.4695991704991973, 'y2': -0.77769712454013062, 'y1': -0.0017565578905829238, 'x3': 0.16835518891794243, 'y3': -0.84054850710793583, 'x1': 0.97446612726300863, 'z1': 1.0, 'z2': 1.0, 'z3': 1.0}, {'x2': 0.66748990927961405, 'y2': -0.45546679006371843, 'y1': -0.0017565578905829238, 'x3': 0.16835518891794243, 'y3': -0.84054850710793583, 'x1': 0.97446612726300863, 'z1': 1.0, 'z2': 1.0, 'z3': 1.0}]}];\n", "\n", " data[0].points.forEach(function(d) {\n", " d.x = +d.x;\n", " d.y = +d.y;\n", " d.z = +d.z;\n", " });\n", "\n", " data[0].triangles.forEach(function(d) {\n", " d.x1 = +d.x1;\n", " d.y1 = +d.y1;\n", " d.z1 = +d.z1;\n", " d.x2 = +d.x2;\n", " d.y2 = +d.y2;\n", " d.z2 = +d.z2;\n", " d.x3 = +d.x3;\n", " d.y3 = +d.y3;\n", " d.z3 = +d.z3;\n", " });\n", "\n", " for (var i=0; i<viewport_config.length; i++) {\n", " \n", " // viewport scales\n", " \n", " var hdim_pts_extent = [99999,-99999];\n", " var vdim_pts_extent = [99999,-99999];\n", " if (data[0].points.length > 0) {\n", " hdim_pts_extent = d3.extent(data[0].points, function(d) { return d[viewport_config[i][\"hdim\"]]; });\n", " vdim_pts_extent = d3.extent(data[0].points, function(d) { return d[viewport_config[i][\"vdim\"]]; });\n", " }\n", "\n", " var hdim_tri1_extent = [99999,-99999];\n", " var vdim_tri1_extent = [99999,-99999];\n", " var hdim_tri2_extent = [99999,-99999];\n", " var vdim_tri2_extent = [99999,-99999];\n", " var hdim_tri3_extent = [99999,-99999];\n", " var vdim_tri3_extent = [99999,-99999];\n", " if (data[0].triangles.length > 0) {\n", " hdim_tri1_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"hdim\"] + \"1\"]; });\n", " vdim_tri1_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"vdim\"] + \"1\"]; });\n", " hdim_tri2_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"hdim\"] + \"2\"]; });\n", " vdim_tri2_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"vdim\"] + \"2\"]; });\n", " hdim_tri3_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"hdim\"] + \"3\"]; });\n", " vdim_tri3_extent = d3.extent(data[0].triangles, function(d) { return d[viewport_config[i][\"vdim\"] + \"3\"]; });\n", " }\n", " \n", " var hdim_extent = [ Math.min( hdim_pts_extent[0], hdim_tri1_extent[0], hdim_tri2_extent[0], hdim_tri3_extent[0] ) , \n", " Math.max( hdim_pts_extent[1], hdim_tri1_extent[1], hdim_tri2_extent[1], hdim_tri3_extent[1] )];\n", "\n", " var vdim_extent = [ Math.min( vdim_pts_extent[0], vdim_tri1_extent[0], vdim_tri2_extent[0], vdim_tri3_extent[0] ) , \n", " Math.max( vdim_pts_extent[1], vdim_tri1_extent[1], vdim_tri2_extent[1], vdim_tri3_extent[1] )];\n", " \n", " var hdim_size = hdim_extent[1] - hdim_extent[0];\n", " var vdim_size = vdim_extent[1] - vdim_extent[0];\n", " \n", " if (hdim_size > vdim_size) {\n", " h[i].domain(hdim_extent);\n", " v[i].domain([ vdim_extent[0] - (hdim_size - vdim_size) / 2 , vdim_extent[1] + (hdim_size - vdim_size) / 2 ]);\n", " } else {\n", " h[i].domain([ hdim_extent[0] - (vdim_size - hdim_size) / 2 , hdim_extent[1] + (vdim_size - hdim_size) / 2 ]);\n", " v[i].domain(vdim_extent);\n", " }\n", "\n", "\n", "\n", " // triangles\n", " \n", " var check_intercept = function(x0,y0,x1,y1,x2,y2,x3,y3) {\n", " \n", " // check if parallel, if so return false\n", " if ( Math.abs((y1-y0)/(x1-x0)) == Math.abs((y3-y2)/(x3-x2)) ) { \n", " // note: should probably be checked for overlap\n", " return [false, 0, 0]; \n", " } else {\n", " var c1 = ( (y2-y0)/y1 - (x2-x0)/x1 ) / ( x3/x1 - y3/y1 );\n", " var c0 = (x2-x0)/x1 + c1 * (x3/x1);\n", " if ( (c0 > 0) && (c0 < 1) && (c1 > 0) && (c1 < 1) ) {\n", " return [true, c0, c1];\n", " }\n", " }\n", " return [false, 0, 0];\n", " }\n", " \n", " // z-dimension sorting\n", " data[0].triangles.sort(function(a,b) {\n", "\n", " // for convenience\n", " var x = viewport_config[i][\"hdim\"];\n", " var y = viewport_config[i][\"vdim\"];\n", " var z = viewport_config[i][\"zdim\"];\n", " \n", " // check for overlap in x-y plane\n", " var lines = [[1,2],[2,3],[3,1]];\n", " lines.forEach(function(line_a){\n", " lines.forEach(function(line_b){\n", "\n", " // does line_a intersect line_b ?\n", " var ci = check_intercept( a[x + line_a[0]], a[y + line_a[0]],\n", " a[x + line_a[1]], a[y + line_a[1]],\n", " b[x + line_b[0]], b[y + line_b[0]],\n", " b[x + line_b[1]], b[y + line_b[1]] );\n", " if (ci[0]) {\n", " var c0 = ci[1];\n", " var c1 = ci[2];\n", " var z_int_a = a[z + line_a[0]] + c0 * a[z + line_a[1]];\n", " var z_int_b = b[z + line_b[0]] + c1 * b[z + line_b[1]];\n", " if (z_int_a > z_int_b) {\n", " return 1;\n", " } else if (z_int_a < z_int_b) {\n", " return -1;\n", " } else {\n", " return 0;\n", " }\n", " }\n", " });\n", " });\n", "\n", " // if no overlap, return 0\n", " return 0;\n", " });\n", " \n", " viewport[i].selectAll(\".triangle\")\n", " .data(data[0].triangles)\n", " .enter().append(\"polygon\")\n", " .attr(\"class\", function(d,j) { return \"triangle d\" + j; })\n", " .attr(\"points\", function(d) {\n", " var s = \"\";\n", " for (var j=1; j<4; j++) {\n", " s += h[i](d[viewport_config[i][\"hdim\"] + j]) + \",\";\n", " s += v[i](d[viewport_config[i][\"vdim\"] + j]) + \" \";\n", " }\n", " s = s.substring(0, s.length - 1);\n", " return s;\n", " })\n", " .style(\"fill\", \"grey\")\n", " .style(\"stroke\", \"black\")\n", " .style(\"stroke-width\", 0.5)\n", " .style(\"fill-opacity\", 0.8)\n", " .on(\"click\", function(event) {\n", " var class_name = d3.select(this).attr(\"class\");\n", " d3.selectAll(\".triangle\").style(\"fill\", 'grey');\n", " d3.selectAll(\".\" + class_name.replace(\"triangle \",\"\")).style(\"fill\", 'red');\n", " });\n", "\n", " // points\n", "\n", " var points = viewport[i].selectAll(\".dot\")\n", " .data(data[0].points)\n", " .enter().append(\"circle\")\n", " .attr(\"class\", \"dot\")\n", " .attr(\"r\", 1.5)\n", " .attr(\"cx\", function(d) { return h[i](d[viewport_config[i][\"hdim\"]]); })\n", " .attr(\"cy\", function(d) { return v[i](d[viewport_config[i][\"vdim\"]]); })\n", " .style(\"fill\", \"steelblue\");\n", "\n", " }\n", "\n", "//});\n", "\n", "\n", " </script>\n", "\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph_points_triangles([[points, triangles_vertices]])" ] } ], "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

junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3

CaseStudies/NumberConceptDevelopment.ipynb

1

2485569

gpl-3.0

zhongyuanzhou/FCH808.github.io

Theano/Theano - Deep NNet dropout & rectifiers.ipynb

4

8169

{ "metadata": { "name": "", "signature": "sha256:9990c95b3c79dff37ddc76d4ef9f7b88390aa7307f4f175145c2c079585fc530" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import theano\n", "from theano import tensor as T\n", "from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n", "import numpy as np\n", "from load import mnist\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "srng = RandomStreams()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "def floatX(X):\n", " return np.asarray(X, dtype=theano.config.floatX)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "def init_weights(shape):\n", " return theano.shared(floatX(np.random.randn(*shape) * 0.01))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "## Rectifier: If X below 0, return 0; else return X.\n", "def rectify(X):\n", " return T.maximum(X, 0.)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "## Numerically stable softmax\n", "def softmax(X):\n", " e_x = T.exp(X - X.max(axis=1).dimshuffle(0, 'x'))\n", " return e_x / e_x.sum(axis=1).dimshuffle(0, 'x')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):\n", " grads = T.grad(cost=cost, wrt=params)\n", " updates = []\n", " for p, g in zip(params, grads):\n", " ## A running average of the magnitude of the gradient\n", " acc = theano.shared(p.get_value() * 0.)\n", " acc_new = rho * acc + (1 - rho) * g ** 2\n", " ## Scale the gradient based on the running average\n", " gradient_scaling = T.sqrt(acc_new + epsilon)\n", " g = g / gradient_scaling\n", " updates.append((acc, acc_new))\n", " updates.append((p, p - lr * g))\n", " return updates" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "## Randomly drop values and scale the rest\n", "def dropout(X, p=0.):\n", " if p > 0:\n", " retain_prob = 1 - p\n", " X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)\n", " X /= retain_prob\n", " return X" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "## 1. Noise injected into model\n", "## 2. Rectifiers now used\n", "## 3. 2 hidden layers\n", "def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):\n", " X = dropout(X, p_drop_input)\n", " h = rectify(T.dot(X, w_h))\n", "\n", " h = dropout(h, p_drop_hidden)\n", " h2 = rectify(T.dot(h, w_h2))\n", "\n", " h2 = dropout(h2, p_drop_hidden)\n", " py_x = softmax(T.dot(h2, w_o))\n", " \n", " return h, h2, py_x" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "train_x, test_x, train_y, test_y = mnist(onehot=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "X = T.fmatrix()\n", "Y = T.fmatrix()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "w_h = init_weights((784, 625))\n", "w_h2 = init_weights((625, 625))\n", "w_o = init_weights((625, 10))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "noise_h, noise_h2, noise_py_x = model(X, w_h, w_h2, w_o, 0.2, 0.5)\n", "h, h2, py_x = model(X, w_h, w_h2, w_o, 0., 0.)\n", "y_x = T.argmax(py_x, axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y))\n", "params = [w_h, w_h2, w_o]\n", "updates = RMSprop(cost, params, lr=0.001)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)\n", "predict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "def run_model(max_iter=100):\n", " for i in range(max_iter):\n", " for start, end in zip(range(0, len(train_x), 128), range(128, len(train_x), 128)):\n", " cost = train(train_x[start:end], train_y[start:end])\n", " print np.mean(np.argmax(test_y, axis=1) == predict(test_x))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "run_model(10)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.9696\n", "0.972" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.974" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.971" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9751" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9738" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9758" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9763" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9773" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.9769" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

mvdbosch/AtosCodexDemo

Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb

1

170171

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Atos Codex - Data Scientist Workbench\n", "### Explore the CBS Crime and Demographics Dataset\n", "First check some of the environment specs and see what we have here" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "processor\t: 0\n", "model name\t: Intel(R) Xeon(R) CPU E5620 @ 2.40GHz\n", "processor\t: 1\n", "model name\t: Intel(R) Xeon(R) CPU E5620 @ 2.40GHz\n", "processor\t: 2\n", "model name\t: Intel(R) Xeon(R) CPU E5620 @ 2.40GHz\n", "processor\t: 3\n", "model name\t: Intel(R) Xeon(R) CPU E5620 @ 2.40GHz\n" ] } ], "source": [ "%%bash\n", "cat /proc/cpuinfo | grep 'processor\\|model name'" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " total used free shared buff/cache available\n", "Mem: 15 6 3 0 5 8\n", "Swap: 0 0 0\n" ] } ], "source": [ "%%bash\n", "free -g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Python packages" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import pandas as pd\n", "import geopandas as gpd\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from ipywidgets.widgets import interact, Text\n", "from IPython.display import display\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set Jupyter Notebook graphical parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use the notebook definition for interactive embedded graphics\n", "# %matplotlib notebook\n", "\n", "# use the inline definition for static embedded graphics\n", "%matplotlib inline \n", "\n", "rcParam = {\n", " 'figure.figsize': (12,6),\n", " 'font.weight': 'bold',\n", " 'axes.labelsize': 20.0,\n", " 'axes.titlesize': 20.0,\n", " 'axes.titleweight': 'bold',\n", " 'legend.fontsize': 14,\n", " 'xtick.labelsize': 14,\n", " 'ytick.labelsize': 14,\n", "}\n", "\n", "for key in rcParam:\n", " mpl.rcParams[key] = rcParam[key]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read the combines CBS dataset\n", "This is the file that we downladen & merged using Talend Open Studio for Big Data. (Note: please check the file path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cbs_data = pd.read_csv('combined_data.csv',sep=',',na_values=['NA','.'],error_bad_lines=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's inspect the contents of this file by looking at the first 5 rows.\n", "\n", "As you can see, this file has a lot of columns. For a description of the fieldnames, please see the description file" ] }, { "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>Regiocode</th>\n", " <th>Regionaam</th>\n", " <th>Inwoners</th>\n", " <th>Vermogen_Vernielingen_en_Geweld</th>\n", " <th>Vermogensmisdrijven</th>\n", " <th>Diefstal</th>\n", " <th>Fietsendiefstal</th>\n", " <th>Diefstal_overige_vervoersmiddelen</th>\n", " <th>Diefstal_uit_vanaf_vervoersmiddelen</th>\n", " <th>Zakkenrollerij_Straatroof_Beroving</th>\n", " <th>...</th>\n", " <th>AV50ATTRAC</th>\n", " <th>AF_PODIUM</th>\n", " <th>AV5_PODIUM</th>\n", " <th>AV10PODIUM</th>\n", " <th>AV20PODIUM</th>\n", " <th>AF_POP</th>\n", " <th>OPP_TOT</th>\n", " <th>OPP_LAND</th>\n", " <th>OPP_WATER</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>BU00030000</td>\n", " <td>Appingedam-Centrum</td>\n", " <td>2410</td>\n", " <td>177</td>\n", " <td>101</td>\n", " <td>96</td>\n", " <td>39</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>11.0</td>\n", " <td>5.3</td>\n", " <td>0.1</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>24.5</td>\n", " <td>90</td>\n", " <td>84</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BU00030001</td>\n", " <td>Appingedam-West</td>\n", " <td>3160</td>\n", " <td>88</td>\n", " <td>38</td>\n", " <td>31</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>11.7</td>\n", " <td>6.3</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>23.7</td>\n", " <td>163</td>\n", " <td>158</td>\n", " <td>5</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>BU00030002</td>\n", " <td>Appingedam-Oost</td>\n", " <td>5860</td>\n", " <td>211</td>\n", " <td>121</td>\n", " <td>116</td>\n", " <td>27</td>\n", " <td>6</td>\n", " <td>14</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>11.0</td>\n", " <td>5.1</td>\n", " <td>0.4</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>25.1</td>\n", " <td>295</td>\n", " <td>284</td>\n", " <td>11</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>BU00030007</td>\n", " <td>Verspr. huizen Damsterdiep en Eemskanaal</td>\n", " <td>360</td>\n", " <td>14</td>\n", " <td>12</td>\n", " <td>11</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>11.6</td>\n", " <td>7.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>23.7</td>\n", " <td>559</td>\n", " <td>540</td>\n", " <td>19</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>BU00030008</td>\n", " <td>Verspr. huizen ten zuiden van Eemskanaal</td>\n", " <td>95</td>\n", " <td>10</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>13.5</td>\n", " <td>7.7</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.1</td>\n", " <td>22.9</td>\n", " <td>582</td>\n", " <td>554</td>\n", " <td>28</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 216 columns</p>\n", "</div>" ], "text/plain": [ " Regiocode Regionaam Inwoners \\\n", "0 BU00030000 Appingedam-Centrum 2410 \n", "1 BU00030001 Appingedam-West 3160 \n", "2 BU00030002 Appingedam-Oost 5860 \n", "3 BU00030007 Verspr. huizen Damsterdiep en Eemskanaal 360 \n", "4 BU00030008 Verspr. huizen ten zuiden van Eemskanaal 95 \n", "\n", " Vermogen_Vernielingen_en_Geweld Vermogensmisdrijven Diefstal \\\n", "0 177 101 96 \n", "1 88 38 31 \n", "2 211 121 116 \n", "3 14 12 11 \n", "4 10 2 2 \n", "\n", " Fietsendiefstal Diefstal_overige_vervoersmiddelen \\\n", "0 39 2 \n", "1 2 2 \n", "2 27 6 \n", "3 2 1 \n", "4 1 0 \n", "\n", " Diefstal_uit_vanaf_vervoersmiddelen Zakkenrollerij_Straatroof_Beroving \\\n", "0 6 4 \n", "1 3 0 \n", "2 14 2 \n", "3 0 0 \n", "4 0 0 \n", "\n", " ... AV50ATTRAC AF_PODIUM AV5_PODIUM AV10PODIUM AV20PODIUM AF_POP \\\n", "0 ... 11.0 5.3 0.1 1.0 1.0 24.5 \n", "1 ... 11.7 6.3 0.0 1.0 1.0 23.7 \n", "2 ... 11.0 5.1 0.4 1.0 1.0 25.1 \n", "3 ... 11.6 7.0 0.0 1.0 1.0 23.7 \n", "4 ... 13.5 7.7 0.0 1.0 1.1 22.9 \n", "\n", " OPP_TOT OPP_LAND OPP_WATER id \n", "0 90 84 5 0 \n", "1 163 158 5 1 \n", "2 295 284 11 2 \n", "3 559 540 19 3 \n", "4 582 554 28 4 \n", "\n", "[5 rows x 216 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbs_data.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cbs_data_2015 = cbs_data.loc[cbs_data['YEAR'] == 2015];\n", "#list(cbs_data_2015)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will subset the entire 2010-2015 into just the year 2015.\n", "\n", "In the table below you will see summary statistics" ] }, { "cell_type": "code", "execution_count": 182, "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>Inwoners</th>\n", " <th>Vermogen_Vernielingen_en_Geweld</th>\n", " <th>Vermogensmisdrijven</th>\n", " <th>Diefstal</th>\n", " <th>Fietsendiefstal</th>\n", " <th>Diefstal_overige_vervoersmiddelen</th>\n", " <th>Diefstal_uit_vanaf_vervoersmiddelen</th>\n", " <th>Zakkenrollerij_Straatroof_Beroving</th>\n", " <th>Woninginbraak_incl_schuur_tuin</th>\n", " <th>Diefstal_inbraak_niet_residentiele_gebouwen</th>\n", " <th>...</th>\n", " <th>AV50ATTRAC</th>\n", " <th>AF_PODIUM</th>\n", " <th>AV5_PODIUM</th>\n", " <th>AV10PODIUM</th>\n", " <th>AV20PODIUM</th>\n", " <th>AF_POP</th>\n", " <th>OPP_TOT</th>\n", " <th>OPP_LAND</th>\n", " <th>OPP_WATER</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>...</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>2808.629355</td>\n", " <td>103.882353</td>\n", " <td>74.540263</td>\n", " <td>65.772701</td>\n", " <td>13.794118</td>\n", " <td>4.416048</td>\n", " <td>12.323529</td>\n", " <td>2.459737</td>\n", " <td>12.900343</td>\n", " <td>8.300114</td>\n", " <td>...</td>\n", " <td>25.731810</td>\n", " <td>5.505397</td>\n", " <td>1.295060</td>\n", " <td>3.633067</td>\n", " <td>11.621074</td>\n", " <td>12.220160</td>\n", " <td>127.193604</td>\n", " <td>121.849800</td>\n", " <td>5.332096</td>\n", " <td>6053.459737</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2197.816441</td>\n", " <td>131.950154</td>\n", " <td>101.114718</td>\n", " <td>93.461735</td>\n", " <td>28.204311</td>\n", " <td>7.148261</td>\n", " <td>17.676344</td>\n", " <td>9.995283</td>\n", " <td>15.971073</td>\n", " <td>19.342237</td>\n", " <td>...</td>\n", " <td>9.454629</td>\n", " <td>4.867955</td>\n", " <td>1.944618</td>\n", " <td>4.824405</td>\n", " <td>11.474965</td>\n", " <td>8.921645</td>\n", " <td>199.973329</td>\n", " <td>190.713978</td>\n", " <td>25.065847</td>\n", " <td>3342.892412</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>55.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.500000</td>\n", " <td>0.300000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.300000</td>\n", " <td>4.000000</td>\n", " <td>4.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1340.000000</td>\n", " <td>33.000000</td>\n", " <td>23.000000</td>\n", " <td>19.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>0.000000</td>\n", " <td>4.000000</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>19.000000</td>\n", " <td>1.800000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>4.400000</td>\n", " <td>5.200000</td>\n", " <td>38.000000</td>\n", " <td>37.000000</td>\n", " <td>0.000000</td>\n", " <td>3186.250000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2190.000000</td>\n", " <td>65.000000</td>\n", " <td>45.000000</td>\n", " <td>38.000000</td>\n", " <td>5.000000</td>\n", " <td>2.000000</td>\n", " <td>7.000000</td>\n", " <td>0.000000</td>\n", " <td>9.000000</td>\n", " <td>3.000000</td>\n", " <td>...</td>\n", " <td>25.000000</td>\n", " <td>4.100000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>8.000000</td>\n", " <td>10.250000</td>\n", " <td>72.500000</td>\n", " <td>70.000000</td>\n", " <td>0.000000</td>\n", " <td>6103.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3595.000000</td>\n", " <td>126.000000</td>\n", " <td>89.000000</td>\n", " <td>77.000000</td>\n", " <td>14.000000</td>\n", " <td>5.000000</td>\n", " <td>15.000000</td>\n", " <td>2.000000</td>\n", " <td>16.000000</td>\n", " <td>8.000000</td>\n", " <td>...</td>\n", " <td>32.000000</td>\n", " <td>7.800000</td>\n", " <td>2.000000</td>\n", " <td>4.500000</td>\n", " <td>14.000000</td>\n", " <td>17.300000</td>\n", " <td>136.000000</td>\n", " <td>132.000000</td>\n", " <td>3.000000</td>\n", " <td>8883.750000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>26755.000000</td>\n", " <td>2529.000000</td>\n", " <td>2031.000000</td>\n", " <td>1934.000000</td>\n", " <td>661.000000</td>\n", " <td>113.000000</td>\n", " <td>204.000000</td>\n", " <td>306.000000</td>\n", " <td>242.000000</td>\n", " <td>352.000000</td>\n", " <td>...</td>\n", " <td>49.000000</td>\n", " <td>56.200000</td>\n", " <td>33.200000</td>\n", " <td>48.000000</td>\n", " <td>63.000000</td>\n", " <td>60.600000</td>\n", " <td>3160.000000</td>\n", " <td>3106.000000</td>\n", " <td>852.000000</td>\n", " <td>12000.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 209 columns</p>\n", "</div>" ], "text/plain": [ " Inwoners Vermogen_Vernielingen_en_Geweld Vermogensmisdrijven \\\n", "count 3502.000000 3502.000000 3502.000000 \n", "mean 2808.629355 103.882353 74.540263 \n", "std 2197.816441 131.950154 101.114718 \n", "min 55.000000 1.000000 1.000000 \n", "25% 1340.000000 33.000000 23.000000 \n", "50% 2190.000000 65.000000 45.000000 \n", "75% 3595.000000 126.000000 89.000000 \n", "max 26755.000000 2529.000000 2031.000000 \n", "\n", " Diefstal Fietsendiefstal Diefstal_overige_vervoersmiddelen \\\n", "count 3502.000000 3502.000000 3502.000000 \n", "mean 65.772701 13.794118 4.416048 \n", "std 93.461735 28.204311 7.148261 \n", "min 0.000000 0.000000 0.000000 \n", "25% 19.000000 2.000000 1.000000 \n", "50% 38.000000 5.000000 2.000000 \n", "75% 77.000000 14.000000 5.000000 \n", "max 1934.000000 661.000000 113.000000 \n", "\n", " Diefstal_uit_vanaf_vervoersmiddelen \\\n", "count 3502.000000 \n", "mean 12.323529 \n", "std 17.676344 \n", "min 0.000000 \n", "25% 3.000000 \n", "50% 7.000000 \n", "75% 15.000000 \n", "max 204.000000 \n", "\n", " Zakkenrollerij_Straatroof_Beroving Woninginbraak_incl_schuur_tuin \\\n", "count 3502.000000 3502.000000 \n", "mean 2.459737 12.900343 \n", "std 9.995283 15.971073 \n", "min 0.000000 0.000000 \n", "25% 0.000000 4.000000 \n", "50% 0.000000 9.000000 \n", "75% 2.000000 16.000000 \n", "max 306.000000 242.000000 \n", "\n", " Diefstal_inbraak_niet_residentiele_gebouwen ... AV50ATTRAC \\\n", "count 3502.000000 ... 3502.000000 \n", "mean 8.300114 ... 25.731810 \n", "std 19.342237 ... 9.454629 \n", "min 0.000000 ... 0.500000 \n", "25% 1.000000 ... 19.000000 \n", "50% 3.000000 ... 25.000000 \n", "75% 8.000000 ... 32.000000 \n", "max 352.000000 ... 49.000000 \n", "\n", " AF_PODIUM AV5_PODIUM AV10PODIUM AV20PODIUM AF_POP \\\n", "count 3502.000000 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 5.505397 1.295060 3.633067 11.621074 12.220160 \n", "std 4.867955 1.944618 4.824405 11.474965 8.921645 \n", "min 0.300000 0.000000 0.000000 0.000000 0.300000 \n", "25% 1.800000 0.000000 1.000000 4.400000 5.200000 \n", "50% 4.100000 1.000000 2.000000 8.000000 10.250000 \n", "75% 7.800000 2.000000 4.500000 14.000000 17.300000 \n", "max 56.200000 33.200000 48.000000 63.000000 60.600000 \n", "\n", " OPP_TOT OPP_LAND OPP_WATER id \n", "count 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 127.193604 121.849800 5.332096 6053.459737 \n", "std 199.973329 190.713978 25.065847 3342.892412 \n", "min 4.000000 4.000000 0.000000 0.000000 \n", "25% 38.000000 37.000000 0.000000 3186.250000 \n", "50% 72.500000 70.000000 0.000000 6103.500000 \n", "75% 136.000000 132.000000 3.000000 8883.750000 \n", "max 3160.000000 3106.000000 852.000000 12000.000000 \n", "\n", "[8 rows x 209 columns]" ] }, "execution_count": 182, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbs_data_2015.describe()\n", "#cbs_data_2015.YEAR.describe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Inwoners</th>\n", " <th>Vermogen_Vernielingen_en_Geweld</th>\n", " <th>Vermogensmisdrijven</th>\n", " <th>Diefstal</th>\n", " <th>Fietsendiefstal</th>\n", " <th>Diefstal_overige_vervoersmiddelen</th>\n", " <th>Diefstal_uit_vanaf_vervoersmiddelen</th>\n", " <th>Zakkenrollerij_Straatroof_Beroving</th>\n", " <th>Woninginbraak_incl_schuur_tuin</th>\n", " <th>Diefstal_inbraak_niet_residentiele_gebouwen</th>\n", " <th>...</th>\n", " <th>AV50ATTRAC</th>\n", " <th>AF_PODIUM</th>\n", " <th>AV5_PODIUM</th>\n", " <th>AV10PODIUM</th>\n", " <th>AV20PODIUM</th>\n", " <th>AF_POP</th>\n", " <th>OPP_TOT</th>\n", " <th>OPP_LAND</th>\n", " <th>OPP_WATER</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>...</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>2808.629355</td>\n", " <td>103.882353</td>\n", " <td>74.540263</td>\n", " <td>65.772701</td>\n", " <td>13.794118</td>\n", " <td>4.416048</td>\n", " <td>12.323529</td>\n", " <td>2.459737</td>\n", " <td>12.900343</td>\n", " <td>8.300114</td>\n", " <td>...</td>\n", " <td>25.731810</td>\n", " <td>5.505397</td>\n", " <td>1.295060</td>\n", " <td>3.633067</td>\n", " <td>11.621074</td>\n", " <td>12.220160</td>\n", " <td>127.193604</td>\n", " <td>121.849800</td>\n", " <td>5.332096</td>\n", " <td>6053.459737</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2197.816441</td>\n", " <td>131.950154</td>\n", " <td>101.114718</td>\n", " <td>93.461735</td>\n", " <td>28.204311</td>\n", " <td>7.148261</td>\n", " <td>17.676344</td>\n", " <td>9.995283</td>\n", " <td>15.971073</td>\n", " <td>19.342237</td>\n", " <td>...</td>\n", " <td>9.454629</td>\n", " <td>4.867955</td>\n", " <td>1.944618</td>\n", " <td>4.824405</td>\n", " <td>11.474965</td>\n", " <td>8.921645</td>\n", " <td>199.973329</td>\n", " <td>190.713978</td>\n", " <td>25.065847</td>\n", " <td>3342.892412</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>55.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.500000</td>\n", " <td>0.300000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.300000</td>\n", " <td>4.000000</td>\n", " <td>4.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1340.000000</td>\n", " <td>33.000000</td>\n", " <td>23.000000</td>\n", " <td>19.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>0.000000</td>\n", " <td>4.000000</td>\n", " <td>1.000000</td>\n", " <td>...</td>\n", " <td>19.000000</td>\n", " <td>1.800000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>4.400000</td>\n", " <td>5.200000</td>\n", " <td>38.000000</td>\n", " <td>37.000000</td>\n", " <td>0.000000</td>\n", " <td>3186.250000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2190.000000</td>\n", " <td>65.000000</td>\n", " <td>45.000000</td>\n", " <td>38.000000</td>\n", " <td>5.000000</td>\n", " <td>2.000000</td>\n", " <td>7.000000</td>\n", " <td>0.000000</td>\n", " <td>9.000000</td>\n", " <td>3.000000</td>\n", " <td>...</td>\n", " <td>25.000000</td>\n", " <td>4.100000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>8.000000</td>\n", " <td>10.250000</td>\n", " <td>72.500000</td>\n", " <td>70.000000</td>\n", " <td>0.000000</td>\n", " <td>6103.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>3595.000000</td>\n", " <td>126.000000</td>\n", " <td>89.000000</td>\n", " <td>77.000000</td>\n", " <td>14.000000</td>\n", " <td>5.000000</td>\n", " <td>15.000000</td>\n", " <td>2.000000</td>\n", " <td>16.000000</td>\n", " <td>8.000000</td>\n", " <td>...</td>\n", " <td>32.000000</td>\n", " <td>7.800000</td>\n", " <td>2.000000</td>\n", " <td>4.500000</td>\n", " <td>14.000000</td>\n", " <td>17.300000</td>\n", " <td>136.000000</td>\n", " <td>132.000000</td>\n", " <td>3.000000</td>\n", " <td>8883.750000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>26755.000000</td>\n", " <td>2529.000000</td>\n", " <td>2031.000000</td>\n", " <td>1934.000000</td>\n", " <td>661.000000</td>\n", " <td>113.000000</td>\n", " <td>204.000000</td>\n", " <td>306.000000</td>\n", " <td>242.000000</td>\n", " <td>352.000000</td>\n", " <td>...</td>\n", " <td>49.000000</td>\n", " <td>56.200000</td>\n", " <td>33.200000</td>\n", " <td>48.000000</td>\n", " <td>63.000000</td>\n", " <td>60.600000</td>\n", " <td>3160.000000</td>\n", " <td>3106.000000</td>\n", " <td>852.000000</td>\n", " <td>12000.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 209 columns</p>\n", "</div>" ], "text/plain": [ " Inwoners Vermogen_Vernielingen_en_Geweld Vermogensmisdrijven \\\n", "count 3502.000000 3502.000000 3502.000000 \n", "mean 2808.629355 103.882353 74.540263 \n", "std 2197.816441 131.950154 101.114718 \n", "min 55.000000 1.000000 1.000000 \n", "25% 1340.000000 33.000000 23.000000 \n", "50% 2190.000000 65.000000 45.000000 \n", "75% 3595.000000 126.000000 89.000000 \n", "max 26755.000000 2529.000000 2031.000000 \n", "\n", " Diefstal Fietsendiefstal Diefstal_overige_vervoersmiddelen \\\n", "count 3502.000000 3502.000000 3502.000000 \n", "mean 65.772701 13.794118 4.416048 \n", "std 93.461735 28.204311 7.148261 \n", "min 0.000000 0.000000 0.000000 \n", "25% 19.000000 2.000000 1.000000 \n", "50% 38.000000 5.000000 2.000000 \n", "75% 77.000000 14.000000 5.000000 \n", "max 1934.000000 661.000000 113.000000 \n", "\n", " Diefstal_uit_vanaf_vervoersmiddelen \\\n", "count 3502.000000 \n", "mean 12.323529 \n", "std 17.676344 \n", "min 0.000000 \n", "25% 3.000000 \n", "50% 7.000000 \n", "75% 15.000000 \n", "max 204.000000 \n", "\n", " Zakkenrollerij_Straatroof_Beroving Woninginbraak_incl_schuur_tuin \\\n", "count 3502.000000 3502.000000 \n", "mean 2.459737 12.900343 \n", "std 9.995283 15.971073 \n", "min 0.000000 0.000000 \n", "25% 0.000000 4.000000 \n", "50% 0.000000 9.000000 \n", "75% 2.000000 16.000000 \n", "max 306.000000 242.000000 \n", "\n", " Diefstal_inbraak_niet_residentiele_gebouwen ... AV50ATTRAC \\\n", "count 3502.000000 ... 3502.000000 \n", "mean 8.300114 ... 25.731810 \n", "std 19.342237 ... 9.454629 \n", "min 0.000000 ... 0.500000 \n", "25% 1.000000 ... 19.000000 \n", "50% 3.000000 ... 25.000000 \n", "75% 8.000000 ... 32.000000 \n", "max 352.000000 ... 49.000000 \n", "\n", " AF_PODIUM AV5_PODIUM AV10PODIUM AV20PODIUM AF_POP \\\n", "count 3502.000000 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 5.505397 1.295060 3.633067 11.621074 12.220160 \n", "std 4.867955 1.944618 4.824405 11.474965 8.921645 \n", "min 0.300000 0.000000 0.000000 0.000000 0.300000 \n", "25% 1.800000 0.000000 1.000000 4.400000 5.200000 \n", "50% 4.100000 1.000000 2.000000 8.000000 10.250000 \n", "75% 7.800000 2.000000 4.500000 14.000000 17.300000 \n", "max 56.200000 33.200000 48.000000 63.000000 60.600000 \n", "\n", " OPP_TOT OPP_LAND OPP_WATER id \n", "count 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 127.193604 121.849800 5.332096 6053.459737 \n", "std 199.973329 190.713978 25.065847 3342.892412 \n", "min 4.000000 4.000000 0.000000 0.000000 \n", "25% 38.000000 37.000000 0.000000 3186.250000 \n", "50% 72.500000 70.000000 0.000000 6103.500000 \n", "75% 136.000000 132.000000 3.000000 8883.750000 \n", "max 3160.000000 3106.000000 852.000000 12000.000000 \n", "\n", "[8 rows x 209 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbs_data_2015 = cbs_data_2015.dropna();\n", "cbs_data_2015.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Description of some of the demographic features of this dataset" ] }, { "cell_type": "code", "execution_count": 15, "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>POSTCODE</th>\n", " <th>DEK_PERC</th>\n", " <th>OAD</th>\n", " <th>STED</th>\n", " <th>AANT_INW</th>\n", " <th>AANT_MAN</th>\n", " <th>AANT_VROUW</th>\n", " <th>P_00_14_JR</th>\n", " <th>P_15_24_JR</th>\n", " <th>P_25_44_JR</th>\n", " <th>...</th>\n", " <th>AV50ATTRAC</th>\n", " <th>AF_PODIUM</th>\n", " <th>AV5_PODIUM</th>\n", " <th>AV10PODIUM</th>\n", " <th>AV20PODIUM</th>\n", " <th>AF_POP</th>\n", " <th>OPP_TOT</th>\n", " <th>OPP_LAND</th>\n", " <th>OPP_WATER</th>\n", " <th>id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>...</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " <td>3502.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>5091.974586</td>\n", " <td>1.199315</td>\n", " <td>1276.281839</td>\n", " <td>3.191319</td>\n", " <td>2809.084809</td>\n", " <td>1383.221017</td>\n", " <td>1424.384637</td>\n", " <td>16.961736</td>\n", " <td>11.333524</td>\n", " <td>23.747287</td>\n", " <td>...</td>\n", " <td>25.731810</td>\n", " <td>5.505397</td>\n", " <td>1.295060</td>\n", " <td>3.633067</td>\n", " <td>11.621074</td>\n", " <td>12.220160</td>\n", " <td>127.193604</td>\n", " <td>121.849800</td>\n", " <td>5.332096</td>\n", " <td>6053.459737</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>2390.316317</td>\n", " <td>0.732967</td>\n", " <td>837.932645</td>\n", " <td>1.251542</td>\n", " <td>2189.350731</td>\n", " <td>1072.449722</td>\n", " <td>1119.185300</td>\n", " <td>4.576896</td>\n", " <td>3.028968</td>\n", " <td>5.674533</td>\n", " <td>...</td>\n", " <td>9.454629</td>\n", " <td>4.867955</td>\n", " <td>1.944618</td>\n", " <td>4.824405</td>\n", " <td>11.474965</td>\n", " <td>8.921645</td>\n", " <td>199.973329</td>\n", " <td>190.713978</td>\n", " <td>25.065847</td>\n", " <td>3342.892412</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1023.000000</td>\n", " <td>1.000000</td>\n", " <td>31.000000</td>\n", " <td>1.000000</td>\n", " <td>260.000000</td>\n", " <td>130.000000</td>\n", " <td>130.000000</td>\n", " <td>2.000000</td>\n", " <td>3.000000</td>\n", " <td>7.000000</td>\n", " <td>...</td>\n", " <td>0.500000</td>\n", " <td>0.300000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.300000</td>\n", " <td>4.000000</td>\n", " <td>4.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3062.500000</td>\n", " <td>1.000000</td>\n", " <td>641.000000</td>\n", " <td>2.000000</td>\n", " <td>1345.000000</td>\n", " <td>655.000000</td>\n", " <td>680.000000</td>\n", " <td>14.000000</td>\n", " <td>10.000000</td>\n", " <td>20.000000</td>\n", " <td>...</td>\n", " <td>19.000000</td>\n", " <td>1.800000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>4.400000</td>\n", " <td>5.200000</td>\n", " <td>38.000000</td>\n", " <td>37.000000</td>\n", " <td>0.000000</td>\n", " <td>3186.250000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5171.000000</td>\n", " <td>1.000000</td>\n", " <td>1160.000000</td>\n", " <td>3.000000</td>\n", " <td>2205.000000</td>\n", " <td>1080.000000</td>\n", " <td>1115.000000</td>\n", " <td>17.000000</td>\n", " <td>11.000000</td>\n", " <td>23.000000</td>\n", " <td>...</td>\n", " <td>25.000000</td>\n", " <td>4.100000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>8.000000</td>\n", " <td>10.250000</td>\n", " <td>72.500000</td>\n", " <td>70.000000</td>\n", " <td>0.000000</td>\n", " <td>6103.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6968.500000</td>\n", " <td>1.000000</td>\n", " <td>1736.250000</td>\n", " <td>4.000000</td>\n", " <td>3595.000000</td>\n", " <td>1763.750000</td>\n", " <td>1823.750000</td>\n", " <td>19.000000</td>\n", " <td>12.000000</td>\n", " <td>26.000000</td>\n", " <td>...</td>\n", " <td>32.000000</td>\n", " <td>7.800000</td>\n", " <td>2.000000</td>\n", " <td>4.500000</td>\n", " <td>14.000000</td>\n", " <td>17.300000</td>\n", " <td>136.000000</td>\n", " <td>132.000000</td>\n", " <td>3.000000</td>\n", " <td>8883.750000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>9991.000000</td>\n", " <td>6.000000</td>\n", " <td>5820.000000</td>\n", " <td>5.000000</td>\n", " <td>26535.000000</td>\n", " <td>13200.000000</td>\n", " <td>13500.000000</td>\n", " <td>42.000000</td>\n", " <td>49.000000</td>\n", " <td>50.000000</td>\n", " <td>...</td>\n", " <td>49.000000</td>\n", " <td>56.200000</td>\n", " <td>33.200000</td>\n", " <td>48.000000</td>\n", " <td>63.000000</td>\n", " <td>60.600000</td>\n", " <td>3160.000000</td>\n", " <td>3106.000000</td>\n", " <td>852.000000</td>\n", " <td>12000.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 181 columns</p>\n", "</div>" ], "text/plain": [ " POSTCODE DEK_PERC OAD STED AANT_INW \\\n", "count 3502.000000 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 5091.974586 1.199315 1276.281839 3.191319 2809.084809 \n", "std 2390.316317 0.732967 837.932645 1.251542 2189.350731 \n", "min 1023.000000 1.000000 31.000000 1.000000 260.000000 \n", "25% 3062.500000 1.000000 641.000000 2.000000 1345.000000 \n", "50% 5171.000000 1.000000 1160.000000 3.000000 2205.000000 \n", "75% 6968.500000 1.000000 1736.250000 4.000000 3595.000000 \n", "max 9991.000000 6.000000 5820.000000 5.000000 26535.000000 \n", "\n", " AANT_MAN AANT_VROUW P_00_14_JR P_15_24_JR P_25_44_JR \\\n", "count 3502.000000 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 1383.221017 1424.384637 16.961736 11.333524 23.747287 \n", "std 1072.449722 1119.185300 4.576896 3.028968 5.674533 \n", "min 130.000000 130.000000 2.000000 3.000000 7.000000 \n", "25% 655.000000 680.000000 14.000000 10.000000 20.000000 \n", "50% 1080.000000 1115.000000 17.000000 11.000000 23.000000 \n", "75% 1763.750000 1823.750000 19.000000 12.000000 26.000000 \n", "max 13200.000000 13500.000000 42.000000 49.000000 50.000000 \n", "\n", " ... AV50ATTRAC AF_PODIUM AV5_PODIUM AV10PODIUM \\\n", "count ... 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean ... 25.731810 5.505397 1.295060 3.633067 \n", "std ... 9.454629 4.867955 1.944618 4.824405 \n", "min ... 0.500000 0.300000 0.000000 0.000000 \n", "25% ... 19.000000 1.800000 0.000000 1.000000 \n", "50% ... 25.000000 4.100000 1.000000 2.000000 \n", "75% ... 32.000000 7.800000 2.000000 4.500000 \n", "max ... 49.000000 56.200000 33.200000 48.000000 \n", "\n", " AV20PODIUM AF_POP OPP_TOT OPP_LAND OPP_WATER \\\n", "count 3502.000000 3502.000000 3502.000000 3502.000000 3502.000000 \n", "mean 11.621074 12.220160 127.193604 121.849800 5.332096 \n", "std 11.474965 8.921645 199.973329 190.713978 25.065847 \n", "min 0.000000 0.300000 4.000000 4.000000 0.000000 \n", "25% 4.400000 5.200000 38.000000 37.000000 0.000000 \n", "50% 8.000000 10.250000 72.500000 70.000000 0.000000 \n", "75% 14.000000 17.300000 136.000000 132.000000 3.000000 \n", "max 63.000000 60.600000 3160.000000 3106.000000 852.000000 \n", "\n", " id \n", "count 3502.000000 \n", "mean 6053.459737 \n", "std 3342.892412 \n", "min 0.000000 \n", "25% 3186.250000 \n", "50% 6103.500000 \n", "75% 8883.750000 \n", "max 12000.000000 \n", "\n", "[8 rows x 181 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cbs_data_2015.iloc[:,35:216].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to make a label and a set of features out of our data\n", "\n", "Labelling: The relative amount of money and property crimes ( Vermogensmisdrijven_rel)\n", "\n", "Features : All neighbourhood demographic columns in the dataset" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labels = cbs_data_2015[\"Vermogensmisdrijven_rel\"].values\n", "columns = list(cbs_data_2015.iloc[:,37:215])" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [], "source": [ "features = cbs_data_2015[list(columns)];\n", "features = features.apply(lambda columns : pd.to_numeric(columns, errors='ignore'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect our labels and features" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 10. 18. 10. 8. 10. 9. 110. 29. 17.]\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>OAD</th>\n", " <th>STED</th>\n", " <th>AANT_INW</th>\n", " <th>AANT_MAN</th>\n", " <th>AANT_VROUW</th>\n", " <th>P_00_14_JR</th>\n", " <th>P_15_24_JR</th>\n", " <th>P_25_44_JR</th>\n", " <th>P_45_64_JR</th>\n", " <th>P_65_EO_JR</th>\n", " <th>...</th>\n", " <th>AV20ATTRAC</th>\n", " <th>AV50ATTRAC</th>\n", " <th>AF_PODIUM</th>\n", " <th>AV5_PODIUM</th>\n", " <th>AV10PODIUM</th>\n", " <th>AV20PODIUM</th>\n", " <th>AF_POP</th>\n", " <th>OPP_TOT</th>\n", " <th>OPP_LAND</th>\n", " <th>OPP_WATER</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>55982</th>\n", " <td>1106.0</td>\n", " <td>3.0</td>\n", " <td>2315</td>\n", " <td>1075</td>\n", " <td>1235</td>\n", " <td>10.0</td>\n", " <td>10.0</td>\n", " <td>20.0</td>\n", " <td>29.0</td>\n", " <td>31.0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>11.0</td>\n", " <td>5.3</td>\n", " <td>0.1</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>24.5</td>\n", " <td>90</td>\n", " <td>84</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>55983</th>\n", " <td>834.0</td>\n", " <td>4.0</td>\n", " <td>3175</td>\n", " <td>1570</td>\n", " <td>1600</td>\n", " <td>18.0</td>\n", " <td>10.0</td>\n", " <td>21.0</td>\n", " <td>33.0</td>\n", " <td>18.0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>11.7</td>\n", " <td>6.3</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>23.7</td>\n", " <td>163</td>\n", " <td>158</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>55984</th>\n", " <td>1040.0</td>\n", " <td>3.0</td>\n", " <td>5965</td>\n", " <td>2880</td>\n", " <td>3085</td>\n", " <td>16.0</td>\n", " <td>11.0</td>\n", " <td>23.0</td>\n", " <td>27.0</td>\n", " <td>23.0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>11.0</td>\n", " <td>5.1</td>\n", " <td>0.4</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>25.1</td>\n", " <td>295</td>\n", " <td>284</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>55988</th>\n", " <td>746.0</td>\n", " <td>4.0</td>\n", " <td>7800</td>\n", " <td>3870</td>\n", " <td>3930</td>\n", " <td>17.0</td>\n", " <td>12.0</td>\n", " <td>21.0</td>\n", " <td>30.0</td>\n", " <td>20.0</td>\n", " <td>...</td>\n", " <td>2.5</td>\n", " <td>18.9</td>\n", " <td>9.9</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>6.0</td>\n", " <td>9.9</td>\n", " <td>313</td>\n", " <td>308</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>55996</th>\n", " <td>163.0</td>\n", " <td>5.0</td>\n", " <td>2170</td>\n", " <td>1075</td>\n", " <td>1095</td>\n", " <td>16.0</td>\n", " <td>9.0</td>\n", " <td>22.0</td>\n", " <td>29.0</td>\n", " <td>23.0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>11.3</td>\n", " <td>12.1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>48.1</td>\n", " <td>451</td>\n", " <td>445</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 178 columns</p>\n", "</div>" ], "text/plain": [ " OAD STED AANT_INW AANT_MAN AANT_VROUW P_00_14_JR P_15_24_JR \\\n", "55982 1106.0 3.0 2315 1075 1235 10.0 10.0 \n", "55983 834.0 4.0 3175 1570 1600 18.0 10.0 \n", "55984 1040.0 3.0 5965 2880 3085 16.0 11.0 \n", "55988 746.0 4.0 7800 3870 3930 17.0 12.0 \n", "55996 163.0 5.0 2170 1075 1095 16.0 9.0 \n", "\n", " P_25_44_JR P_45_64_JR P_65_EO_JR ... AV20ATTRAC AV50ATTRAC \\\n", "55982 20.0 29.0 31.0 ... 1.0 11.0 \n", "55983 21.0 33.0 18.0 ... 1.0 11.7 \n", "55984 23.0 27.0 23.0 ... 1.0 11.0 \n", "55988 21.0 30.0 20.0 ... 2.5 18.9 \n", "55996 22.0 29.0 23.0 ... 0.0 11.3 \n", "\n", " AF_PODIUM AV5_PODIUM AV10PODIUM AV20PODIUM AF_POP OPP_TOT \\\n", "55982 5.3 0.1 1.0 1.0 24.5 90 \n", "55983 6.3 0.0 1.0 1.0 23.7 163 \n", "55984 5.1 0.4 1.0 1.0 25.1 295 \n", "55988 9.9 0.0 1.0 6.0 9.9 313 \n", "55996 12.1 0.0 0.0 1.0 48.1 451 \n", "\n", " OPP_LAND OPP_WATER \n", "55982 84 5 \n", "55983 158 5 \n", "55984 284 11 \n", "55988 308 5 \n", "55996 445 6 \n", "\n", "[5 rows x 178 columns]" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(labels[1:10])\n", "features.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature selection using Randomized Lasso\n", "Import Randomized Lasso from the Python Scikit-learn package" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import RandomizedLasso" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run Randomized Lasso, with 3000 resampling and 100 iterations." ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 3000 out of 3000 | elapsed: 8.1min finished\n" ] }, { "data": { "text/plain": [ "RandomizedLasso(alpha='aic', eps=2.2204460492503131e-16, fit_intercept=True,\n", " max_iter=100, memory=Memory(cachedir=None), n_jobs=1,\n", " n_resampling=3000, normalize=True, pre_dispatch='3*n_jobs',\n", " precompute='auto', random_state=None, sample_fraction=0.75,\n", " scaling=0.5, selection_threshold=0.25, verbose=True)" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rlasso = RandomizedLasso(alpha='aic',verbose =True,normalize =True,n_resampling=3000,max_iter=100)\n", "rlasso.fit(features, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Features sorted by their score\n", "In the table below the top10 best features (i.e. columns) are shown with their score" ] }, { "cell_type": "code", "execution_count": 189, "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>Score</th>\n", " <th>FeatureName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0000</td>\n", " <td>A_BED_GI</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.0000</td>\n", " <td>AV1_CAFTAR</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.9997</td>\n", " <td>AF_TREINST</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.9970</td>\n", " <td>AF_ZIEK_E</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.9957</td>\n", " <td>G_ELEK_HOE</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.9940</td>\n", " <td>P_HUKO_ONB</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.9937</td>\n", " <td>P_00_14_JR</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.9840</td>\n", " <td>G_GAS_HOEK</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.9823</td>\n", " <td>G_GAS_KO</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0.9813</td>\n", " <td>BEV_DICHTH</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Score FeatureName\n", "0 1.0000 A_BED_GI\n", "1 1.0000 AV1_CAFTAR\n", "2 0.9997 AF_TREINST\n", "3 0.9970 AF_ZIEK_E\n", "4 0.9957 G_ELEK_HOE\n", "5 0.9940 P_HUKO_ONB\n", "6 0.9937 P_00_14_JR\n", "7 0.9840 G_GAS_HOEK\n", "8 0.9823 G_GAS_KO\n", "9 0.9813 BEV_DICHTH" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfResults = pd.DataFrame.from_dict(sorted(zip(map(lambda x: round(x, 4), rlasso.scores_), list(features)), reverse=True))\n", "dfResults.columns = ['Score', 'FeatureName']\n", "dfResults.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because in the beginning of the lasso results table, a lot of high-scoring features are present, we want \n", "to check how the scores are devided across all features" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81ae72a6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfResults.plot('FeatureName', 'Score', kind='bar', color='navy')\n", "ax1 = plt.axes()\n", "x_axis = ax1.axes.get_xaxis()\n", "x_axis.set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatterplot\n", "Let's inspect one of the top variables and make a scatterplot for this one" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SDeYOYN9+BaJddbNRj5v8LD6tEgETBGl++e+vpF6rkqx/DDgpIh7dr2C0Uzcb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\nqm6soC2Esh60gyPxhSq34wthyrangT0lbVaVSR/Iv55cHn3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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81ae1df828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(y=pd.to_numeric(cbs_data_2015['Vermogensmisdrijven_rel']),x=pd.to_numeric(cbs_data_2015['A_BED_GI']));\n", "plt.ylabel('Vermogensmisdrijven_rel')\n", "plt.xlabel('A_BED_GI ( Bedrijfsvestigingen: Handel en horeca )')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 188, "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>Score</th>\n", " <th>FeatureName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>168</th>\n", " <td>0.0283</td>\n", " <td>AF_DAGLMD</td>\n", " </tr>\n", " <tr>\n", " <th>169</th>\n", " <td>0.0270</td>\n", " <td>AV5_ONDVRT</td>\n", " </tr>\n", " <tr>\n", " <th>170</th>\n", " <td>0.0260</td>\n", " <td>AANT_INK</td>\n", " </tr>\n", " <tr>\n", " <th>171</th>\n", " <td>0.0233</td>\n", " <td>P_LAAGINKH</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>0.0173</td>\n", " <td>AV20WARENH</td>\n", " </tr>\n", " <tr>\n", " <th>173</th>\n", " <td>0.0120</td>\n", " <td>AUTO_TOT</td>\n", " </tr>\n", " <tr>\n", " <th>174</th>\n", " <td>0.0117</td>\n", " <td>AANT_MAN</td>\n", " </tr>\n", " <tr>\n", " <th>175</th>\n", " <td>0.0093</td>\n", " <td>A_BEDV</td>\n", " </tr>\n", " <tr>\n", " <th>176</th>\n", " <td>0.0063</td>\n", " <td>AANT_VROUW</td>\n", " </tr>\n", " <tr>\n", " <th>177</th>\n", " <td>0.0003</td>\n", " <td>AANT_INW</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Score FeatureName\n", "168 0.0283 AF_DAGLMD\n", "169 0.0270 AV5_ONDVRT\n", "170 0.0260 AANT_INK\n", "171 0.0233 P_LAAGINKH\n", "172 0.0173 AV20WARENH\n", "173 0.0120 AUTO_TOT\n", "174 0.0117 AANT_MAN\n", "175 0.0093 A_BEDV\n", "176 0.0063 AANT_VROUW\n", "177 0.0003 AANT_INW" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfResults.tail(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also inspect one of the worst variables (Perc% of Low income households) and plot this one too" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T4EB3f2/eQB3AzP63mc2puv4GdgXiDxCNsldUJpaeCEyNL3+35vexZjYrvrw0/r0VWJe3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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f81adfda128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(y=pd.to_numeric(cbs_data_2015['Vermogensmisdrijven_rel']),x=pd.to_numeric(cbs_data_2015['P_LAAGINKH']));\n", "plt.ylabel('Vermogensmisdrijven_rel')\n", "plt.xlabel('Perc. Laaginkomen Huish.')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try-out another hypothese (e.g. Perc% of divorced vs. Rel% Domestic and Sexual violence crimes)" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8VctYumiIAJYuGuL8VcsGdga3hBNMS4hBO/O9ormadV15ROwBPAF4ZGZe076Q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\nD3UiYEmSJGlQNE3Wh4BrgacCa6naNh4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f81ae190550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(y=pd.to_numeric(cbs_data_2015['Gewelds_en_seksuele_misdrijven_rel']),x=pd.to_numeric(cbs_data_2015['P_GESCHEID']));\n", "plt.ylabel('Gewelds_en_seksuele_misdrijven_rel')\n", "plt.xlabel('Perc_Gescheiden')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

ramseylab/networkscompbio

class08_components_python3.ipynb

1

5066

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CS446/546 - Class Session 8 - Components\n", "\n", "In this class session we are going to find the number of proteins that are in the giant component of the (undirected) protein-protein interaction network, using igraph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from igraph import Graph\n", "from igraph import summary\n", "import pandas\n", "import numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 1: load in the SIF file (refer to Class 6 exercise) into a data frame `sif_data`, using the `pandas.read_csv` function, and name the columns `species1`, `interaction_type`, and `species2`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "sif_data = pandas.read_csv(\"shared/pathway_commons.sif\",\n", " sep=\"\\t\", names=[\"species1\",\"interaction_type\",\"species2\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 2: restrict the interactions to protein-protein undirected (\"in-complex-with\", \"interacts-with\"), by using the `isin` function and then using `[` to index rows into the data frame. Call the returned ata frame `interac_ppi`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "interaction_types_ppi = set([\"interacts-with\",\n", " \"in-complex-with\"])\n", "interac_ppi = sif_data[sif_data.interaction_type.isin(interaction_types_ppi)].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 3: restrict the data frame to only the unique interaction pairs of proteins (ignoring the interaction type), and call that data frame `interac_ppi_unique`. Make an igraph `Graph` object from `interac_ppi_unique` using `Graph.TupleList`, `values`, and `tolist`. Call `summary` on the `Graph` object. Refer to the notebooks for the in-class exercises in Class sessions 3 and 6." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IGRAPH UN-- 17531 475553 -- \n", "+ attr: name (v)\n" ] } ], "source": [ "boolean_vec = interac_ppi['species1'] > interac_ppi['species2']\n", "interac_ppi.loc[boolean_vec, ['species1', 'species2']] = interac_ppi.loc[boolean_vec, ['species2', 'species1']].values\n", " \n", "interac_ppi_unique = interac_ppi[[\"species1\",\"species2\"]].drop_duplicates() \n", "\n", "\n", "ppi_igraph = Graph.TupleList(interac_ppi_unique.values.tolist(), directed=False)\n", "summary(ppi_igraph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 4: Map the components of the network using the `igraph.Graph.clusters` method. That method returns a `igraph.clustering.VertexClustering` object. Call the `sizes` method on that `VertexClustering` object, to get a list of sizes of the components. What is the giant component size?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17524" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# call the `clusters` method on the `ppi_igraph` object, and assign the \n", "# resulting `VertexClustering` object to have object name `ppi_components`\n", "ppi_components = ppi_igraph.clusters()\n", "\n", "# call the `sizes` method on the `ppi_components` object, and assign the\n", "# resulting list object to have the name `ppi_component_sizes`.\n", "ppi_component_sizes = ppi_components.sizes()\n", "\n", "# make a `numpy.array` initialized by `ppi_component_sizes`, and find its \n", "# maximum value using the `max` method on the `numpy.array` class\n", "numpy.array(ppi_component_sizes).max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Advanced code-spellunking question: go to the GitHub repo for igraph (https://github.com/igraph), and find the code components.c. For the weakly connected components, is it doing a BFS or DFS?" ] }, { "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": 1 }

apache-2.0

ES-DOC/esdoc-jupyterhub

notebooks/miroc/cmip6/models/sandbox-3/land.ipynb

1

173500

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: MIROC \n", "**Source ID**: SANDBOX-3 \n", "**Topic**: Land \n", "**Sub-Topics**: Soil, Snow, Vegetation, Energy Balance, Carbon Cycle, Nitrogen Cycle, River Routing, Lakes. \n", "**Properties**: 154 (96 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/land?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**: 2018-02-20 15:02:41" ], "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', 'miroc', 'sandbox-3', 'land')" ], "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 --&gt; Conservation Properties](#2.-Key-Properties---&gt;-Conservation-Properties) \n", "[3. Key Properties --&gt; Timestepping Framework](#3.-Key-Properties---&gt;-Timestepping-Framework) \n", "[4. Key Properties --&gt; Software Properties](#4.-Key-Properties---&gt;-Software-Properties) \n", "[5. Grid](#5.-Grid) \n", "[6. Grid --&gt; Horizontal](#6.-Grid---&gt;-Horizontal) \n", "[7. Grid --&gt; Vertical](#7.-Grid---&gt;-Vertical) \n", "[8. Soil](#8.-Soil) \n", "[9. Soil --&gt; Soil Map](#9.-Soil---&gt;-Soil-Map) \n", "[10. Soil --&gt; Snow Free Albedo](#10.-Soil---&gt;-Snow-Free-Albedo) \n", "[11. Soil --&gt; Hydrology](#11.-Soil---&gt;-Hydrology) \n", "[12. Soil --&gt; Hydrology --&gt; Freezing](#12.-Soil---&gt;-Hydrology---&gt;-Freezing) \n", "[13. Soil --&gt; Hydrology --&gt; Drainage](#13.-Soil---&gt;-Hydrology---&gt;-Drainage) \n", "[14. Soil --&gt; Heat Treatment](#14.-Soil---&gt;-Heat-Treatment) \n", "[15. Snow](#15.-Snow) \n", "[16. Snow --&gt; Snow Albedo](#16.-Snow---&gt;-Snow-Albedo) \n", "[17. Vegetation](#17.-Vegetation) \n", "[18. Energy Balance](#18.-Energy-Balance) \n", "[19. Carbon Cycle](#19.-Carbon-Cycle) \n", "[20. Carbon Cycle --&gt; Vegetation](#20.-Carbon-Cycle---&gt;-Vegetation) \n", "[21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis](#21.-Carbon-Cycle---&gt;-Vegetation---&gt;-Photosynthesis) \n", "[22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration](#22.-Carbon-Cycle---&gt;-Vegetation---&gt;-Autotrophic-Respiration) \n", "[23. Carbon Cycle --&gt; Vegetation --&gt; Allocation](#23.-Carbon-Cycle---&gt;-Vegetation---&gt;-Allocation) \n", "[24. Carbon Cycle --&gt; Vegetation --&gt; Phenology](#24.-Carbon-Cycle---&gt;-Vegetation---&gt;-Phenology) \n", "[25. Carbon Cycle --&gt; Vegetation --&gt; Mortality](#25.-Carbon-Cycle---&gt;-Vegetation---&gt;-Mortality) \n", "[26. Carbon Cycle --&gt; Litter](#26.-Carbon-Cycle---&gt;-Litter) \n", "[27. Carbon Cycle --&gt; Soil](#27.-Carbon-Cycle---&gt;-Soil) \n", "[28. Carbon Cycle --&gt; Permafrost Carbon](#28.-Carbon-Cycle---&gt;-Permafrost-Carbon) \n", "[29. Nitrogen Cycle](#29.-Nitrogen-Cycle) \n", "[30. River Routing](#30.-River-Routing) \n", "[31. River Routing --&gt; Oceanic Discharge](#31.-River-Routing---&gt;-Oceanic-Discharge) \n", "[32. Lakes](#32.-Lakes) \n", "[33. Lakes --&gt; Method](#33.-Lakes---&gt;-Method) \n", "[34. Lakes --&gt; Wetlands](#34.-Lakes---&gt;-Wetlands) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land surface key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code (e.g. MOSES2.2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.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. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the processes modelled (e.g. dymanic vegation, prognostic albedo, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.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": [ "### 1.4. Land Atmosphere Flux Exchanges\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Fluxes exchanged with the atmopshere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_atmosphere_flux_exchanges') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"water\" \n", "# \"energy\" \n", "# \"carbon\" \n", "# \"nitrogen\" \n", "# \"phospherous\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Atmospheric Coupling Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of land surface coupling with the Atmosphere model component, which may be different for different quantities (e.g. dust: semi-implicit, water vapour: explicit)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.atmospheric_coupling_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Land Cover\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Types of land cover defined in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bare soil\" \n", "# \"urban\" \n", "# \"lake\" \n", "# \"land ice\" \n", "# \"lake ice\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Land Cover Change\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe how land cover change is managed (e.g. the use of net or gross transitions)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.land_cover_change') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.8. Tiling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general tiling procedure used in the land surface (if any). Include treatment of physiography, land/sea, (dynamic) vegetation coverage and orography/roughness*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Conservation Properties \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Energy\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how energy is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.energy') \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. Water\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how water is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.water') \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. Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how carbon is conserved globally and to what level (e.g. within X [units]/year)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.conservation_properties.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": [ "# 3. Key Properties --&gt; Timestepping Framework \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dependent On Atmosphere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a time step dependent on the frequency of atmosphere coupling?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestep_dependent_on_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overall timestep of land surface model (i.e. time between calls)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestepping Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of time stepping method and associated time step(s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.key_properties.timestepping_framework.timestepping_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Software Properties \n", "*Software properties of land surface code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.land.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": [ "### 4.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.land.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": [ "### 4.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.land.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": [ "# 5. Grid \n", "*Land surface grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Horizontal \n", "*The horizontal grid in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the horizontal grid (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.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. Matches Atmosphere Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the horizontal grid match the atmosphere?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.horizontal.matches_atmosphere_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": [ "# 7. Grid --&gt; Vertical \n", "*The vertical grid in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general structure of the vertical grid in the soil (not including any tiling)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Total Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The total depth of the soil (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.grid.vertical.total_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Soil \n", "*Land surface soil*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of soil in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.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. Heat Water Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the coupling between heat and water in the soil*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_water_coupling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Number Of Soil layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.number_of_soil layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the soil scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Soil --&gt; Soil Map \n", "*Key properties of the land surface soil map*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of soil map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.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": [ "### 9.2. Structure\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil structure map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.structure') \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. Texture\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil texture map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.texture') \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. Organic Matter\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil organic matter map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.organic_matter') \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. Albedo\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil albedo map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Water Table\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil water table map, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.water_table') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.7. Continuously Varying Soil Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the soil properties vary continuously with depth?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.continuously_varying_soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.8. Soil Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil depth map*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.soil_map.soil_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Soil --&gt; Snow Free Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow free albedo prognostic?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.prognostic') \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": [ "### 10.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, describe the dependancies on snow free albedo calculations*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"soil humidity\" \n", "# \"vegetation state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Direct Diffuse\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, describe the distinction between direct and diffuse albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.direct_diffuse') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"distinction between direct and diffuse albedo\" \n", "# \"no distinction between direct and diffuse albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.4. Number Of Wavelength Bands\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If prognostic, enter the number of wavelength bands used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.snow_free_albedo.number_of_wavelength_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": [ "# 11. Soil --&gt; Hydrology \n", "*Key properties of the land surface soil hydrology*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of the soil hydrological model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.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": [ "### 11.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river soil hydrology in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil hydrology tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Number Of Ground Water Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of soil layers that may contain water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.number_of_ground_water_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.6. Lateral Connectivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe the lateral connectivity between tiles*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.lateral_connectivity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"perfect connectivity\" \n", "# \"Darcian flow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.7. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The hydrological dynamics scheme in the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bucket\" \n", "# \"Force-restore\" \n", "# \"Choisnel\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Soil --&gt; Hydrology --&gt; Freezing \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Number Of Ground Ice Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How many soil layers may contain ground ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.number_of_ground_ice_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Ice Storage Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of ice storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.ice_storage_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Permafrost\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of permafrost, if any, within the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.freezing.permafrost') \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. Soil --&gt; Hydrology --&gt; Drainage \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General describe how drainage is included in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.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": [ "### 13.2. Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Different types of runoff represented by the land surface model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.hydrology.drainage.types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gravity drainage\" \n", "# \"Horton mechanism\" \n", "# \"topmodel-based\" \n", "# \"Dunne mechanism\" \n", "# \"Lateral subsurface flow\" \n", "# \"Baseflow from groundwater\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Soil --&gt; Heat Treatment \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description of how heat treatment properties are defined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.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": [ "### 14.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of soil heat scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the soil heat treatment tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Vertical Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the typical vertical discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.vertical_discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Heat Storage\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the method of heat storage*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.heat_storage') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Force-restore\" \n", "# \"Explicit diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe processes included in the treatment of soil heat*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.soil.heat_treatment.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"soil moisture freeze-thaw\" \n", "# \"coupling with snow temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Snow \n", "*Land surface snow*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of snow in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.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. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Number Of Snow Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The number of snow levels used in the land surface scheme/model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.number_of_snow_layers') \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. Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow density*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Water Equivalent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the snow water equivalent*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.water_equivalent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Heat Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of the heat content of snow*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.heat_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.7. Temperature\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow temperature*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.temperature') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.8. Liquid Water Content\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of the treatment of snow liquid water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.liquid_water_content') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.9. Snow Cover Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify cover fractions used in the surface snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_cover_fractions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ground snow fraction\" \n", "# \"vegetation snow fraction\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.10. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Snow related processes in the land surface scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"snow interception\" \n", "# \"snow melting\" \n", "# \"snow freezing\" \n", "# \"blowing snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.11. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the snow scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Snow --&gt; Snow Albedo \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of snow-covered land albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"prescribed\" \n", "# \"constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Functions\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If prognostic, *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.snow.snow_albedo.functions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation type\" \n", "# \"snow age\" \n", "# \"snow density\" \n", "# \"snow grain type\" \n", "# \"aerosol deposition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Vegetation \n", "*Land surface vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vegetation in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.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": [ "### 17.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of vegetation scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Dynamic Vegetation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there dynamic evolution of vegetation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.dynamic_vegetation') \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": [ "### 17.4. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vegetation tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.5. Vegetation Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Vegetation classification used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vegetation types\" \n", "# \"biome types\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.6. Vegetation Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of vegetation types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"broadleaf tree\" \n", "# \"needleleaf tree\" \n", "# \"C3 grass\" \n", "# \"C4 grass\" \n", "# \"vegetated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.7. Biome Types\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of biome types in the classification, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biome_types') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"evergreen needleleaf forest\" \n", "# \"evergreen broadleaf forest\" \n", "# \"deciduous needleleaf forest\" \n", "# \"deciduous broadleaf forest\" \n", "# \"mixed forest\" \n", "# \"woodland\" \n", "# \"wooded grassland\" \n", "# \"closed shrubland\" \n", "# \"opne shrubland\" \n", "# \"grassland\" \n", "# \"cropland\" \n", "# \"wetlands\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.8. Vegetation Time Variation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How the vegetation fractions in each tile are varying with time*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_time_variation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed (not varying)\" \n", "# \"prescribed (varying from files)\" \n", "# \"dynamical (varying from simulation)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.9. Vegetation Map\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If vegetation fractions are not dynamically updated , describe the vegetation map used (common name and reference, if possible)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.vegetation_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.10. Interception\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is vegetation interception of rainwater represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.interception') \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": [ "### 17.11. Phenology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic (vegetation map)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.12. Phenology Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation phenology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.phenology_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": [ "### 17.13. Leaf Area Index\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.14. Leaf Area Index Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of leaf area index*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.leaf_area_index_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": [ "### 17.15. Biomass\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biomass *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.16. Biomass Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biomass*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biomass_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": [ "### 17.17. Biogeography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.18. Biogeography Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation biogeography*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.biogeography_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": [ "### 17.19. Stomatal Resistance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify what the vegetation stomatal resistance depends on*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"light\" \n", "# \"temperature\" \n", "# \"water availability\" \n", "# \"CO2\" \n", "# \"O3\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.20. Stomatal Resistance Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of the treatment of vegetation stomatal resistance*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.stomatal_resistance_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": [ "### 17.21. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the vegetation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.vegetation.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Energy Balance \n", "*Land surface energy balance*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of energy balance in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the energy balance tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Number Of Surface Temperatures\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The maximum number of distinct surface temperatures in a grid cell (for example, each subgrid tile may have its own temperature)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.number_of_surface_temperatures') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.4. Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify the formulation method for land surface evaporation, from soil and vegetation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"alpha\" \n", "# \"beta\" \n", "# \"combined\" \n", "# \"Monteith potential evaporation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Describe which processes are included in the energy balance scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.energy_balance.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"transpiration\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Carbon Cycle \n", "*Land surface carbon cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of carbon cycle in land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.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": [ "### 19.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the carbon cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of carbon cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Anthropogenic Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Describe the treament of the anthropogenic carbon pool*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.anthropogenic_carbon') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grand slam protocol\" \n", "# \"residence time\" \n", "# \"decay time\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the carbon scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Carbon Cycle --&gt; Vegetation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Forest Stand Dynamics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of forest stand dyanmics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.forest_stand_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Carbon Cycle --&gt; Vegetation --&gt; Photosynthesis \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for photosynthesis (e.g. type of photosynthesis, distinction between C3 and C4 grasses, Nitrogen depencence, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.photosynthesis.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Carbon Cycle --&gt; Vegetation --&gt; Autotrophic Respiration \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Maintainance Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for maintainence respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.maintainance_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Growth Respiration\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the general method used for growth respiration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.autotrophic_respiration.growth_respiration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Carbon Cycle --&gt; Vegetation --&gt; Allocation \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the allocation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Allocation Bins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify distinct carbon bins used in allocation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_bins') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"leaves + stems + roots\" \n", "# \"leaves + stems + roots (leafy + woody)\" \n", "# \"leaves + fine roots + coarse roots + stems\" \n", "# \"whole plant (no distinction)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Allocation Fractions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the fractions of allocation are calculated*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.allocation.allocation_fractions') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"function of vegetation type\" \n", "# \"function of plant allometry\" \n", "# \"explicitly calculated\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Carbon Cycle --&gt; Vegetation --&gt; Phenology \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the phenology scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.phenology.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Carbon Cycle --&gt; Vegetation --&gt; Mortality \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the general principle behind the mortality scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.vegetation.mortality.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Carbon Cycle --&gt; Litter \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.litter.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Carbon Cycle --&gt; Soil \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Number Of Carbon Pools\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.number_of_carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Carbon Pools\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the carbon pools used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.carbon_pools') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the general method used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.soil.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Carbon Cycle --&gt; Permafrost Carbon \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Is Permafrost Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is permafrost included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.is_permafrost_included') \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": [ "### 28.2. Emitted Greenhouse Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the GHGs emitted*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.emitted_greenhouse_gases') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.3. Decomposition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List the decomposition methods used*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.decomposition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 28.4. Impact On Soil Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the impact of permafrost on soil properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.carbon_cycle.permafrost_carbon.impact_on_soil_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Nitrogen Cycle \n", "*Land surface nitrogen cycle*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the nitrogen cycle in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the notrogen cycle tiling, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of nitrogen cycle in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 29.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the nitrogen scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.nitrogen_cycle.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. River Routing \n", "*Land surface river routing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of river routing in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.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": [ "### 30.2. Tiling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the river routing, if any.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.tiling') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of river routing scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Grid Inherited From Land Surface\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the grid inherited from land surface?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_inherited_from_land_surface') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.5. Grid Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *General description of grid, if not inherited from land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.grid_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": [ "### 30.6. Number Of Reservoirs\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Enter the number of reservoirs*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.number_of_reservoirs') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.7. Water Re Evaporation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *TODO*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.water_re_evaporation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"flood plains\" \n", "# \"irrigation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.8. Coupled To Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is river routing coupled to the atmosphere model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_atmosphere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.9. Coupled To Land\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the coupling between land and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.coupled_to_land') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.10. Quantities Exchanged With Atmosphere\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If couple to atmosphere, which quantities are exchanged between river routing and the atmosphere model components?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.quantities_exchanged_with_atmosphere') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.11. Basin Flow Direction Map\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of basin flow direction map is being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.basin_flow_direction_map') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"adapted for other periods\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.12. Flooding\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the representation of flooding, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.flooding') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.13. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the river routing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. River Routing --&gt; Oceanic Discharge \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Discharge Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify how rivers are discharged to the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.discharge_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"direct (large rivers)\" \n", "# \"diffuse\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Quantities Transported\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Quantities that are exchanged from river-routing to the ocean model component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.river_routing.oceanic_discharge.quantities_transported') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Lakes \n", "*Land surface lakes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lakes in the land surface*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.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": [ "### 32.2. Coupling With Rivers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are lakes coupled to the river routing model component?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.coupling_with_rivers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time step of lake scheme in seconds*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Quantities Exchanged With Rivers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If coupling with rivers, which quantities are exchanged between the lakes and rivers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.quantities_exchanged_with_rivers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"heat\" \n", "# \"water\" \n", "# \"tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Vertical Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the vertical grid of lakes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.vertical_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List the prognostic variables of the lake scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Lakes --&gt; Method \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Ice Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is lake ice included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.ice_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of lake albedo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which dynamics of lakes are treated? horizontal, vertical, etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"No lake dynamics\" \n", "# \"vertical\" \n", "# \"horizontal\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Dynamic Lake Extent\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is a dynamic lake extent scheme included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.dynamic_lake_extent') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.5. Endorheic Basins\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basins not flowing to ocean included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.method.endorheic_basins') \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": [ "# 34. Lakes --&gt; Wetlands \n", "*TODO*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Description\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the treatment of wetlands, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.land.lakes.wetlands.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": [ "### \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

hzuosit/ctci_answer_python

Chapter_4/graph_class.ipynb

2

775

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Graph class\n", "from graph_node_class import node\n", "class graph:\n", " def __init__(self,root = None):\n", " self.root = root\n", " ajlist = {}" ] } ], "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

ES-DOC/esdoc-jupyterhub

notebooks/nuist/cmip6/models/sandbox-2/landice.ipynb

1

37248

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "**MIP Era**: CMIP6 \n", "**Institute**: NUIST \n", "**Source ID**: SANDBOX-2 \n", "**Topic**: Landice \n", "**Sub-Topics**: Glaciers, Ice. \n", "**Properties**: 30 (21 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/landice?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 16:54:34" ], "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', 'nuist', 'sandbox-2', 'landice')" ], "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 --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Grid](#3.-Grid) \n", "[4. Glaciers](#4.-Glaciers) \n", "[5. Ice](#5.-Ice) \n", "[6. Ice --&gt; Mass Balance](#6.-Ice---&gt;-Mass-Balance) \n", "[7. Ice --&gt; Mass Balance --&gt; Basal](#7.-Ice---&gt;-Mass-Balance---&gt;-Basal) \n", "[8. Ice --&gt; Mass Balance --&gt; Frontal](#8.-Ice---&gt;-Mass-Balance---&gt;-Frontal) \n", "[9. Ice --&gt; Dynamics](#9.-Ice---&gt;-Dynamics) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Land ice key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of land surface model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_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": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of land surface model code*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.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. Ice Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Specify how ice albedo is modelled*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.ice_albedo') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prescribed\" \n", "# \"function of ice age\" \n", "# \"function of ice density\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Atmospheric Coupling Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which variables are passed between the atmosphere and ice (e.g. orography, ice mass)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.atmospheric_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Oceanic Coupling Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which variables are passed between the ocean and ice*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.oceanic_coupling_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which variables are prognostically calculated in the ice model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice velocity\" \n", "# \"ice thickness\" \n", "# \"ice temperature\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of land ice code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.landice.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.landice.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&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**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.landice.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. Grid \n", "*Land ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the grid in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Adaptive Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is an adative grid being used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.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": [ "### 3.3. Base Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The base resolution (in metres), before any adaption*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.base_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": [ "### 3.4. Resolution Limit\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If an adaptive grid is being used, what is the limit of the resolution (in metres)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.resolution_limit') \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. Projection\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *The projection of the land ice grid (e.g. albers_equal_area)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.grid.projection') \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. Glaciers \n", "*Land ice glaciers*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of glaciers in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.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": [ "### 4.2. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the treatment of glaciers, if any*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.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": [ "### 4.3. Dynamic Areal Extent\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Does the model include a dynamic glacial extent?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.glaciers.dynamic_areal_extent') \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": [ "# 5. Ice \n", "*Ice sheet and ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of the ice sheet and ice shelf in the land ice scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.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": [ "### 5.2. Grounding Line Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the technique used for modelling the grounding line in the ice sheet-ice shelf coupling*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.grounding_line_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"grounding line prescribed\" \n", "# \"flux prescribed (Schoof)\" \n", "# \"fixed grid size\" \n", "# \"moving grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Ice Sheet\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are ice sheets simulated?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_sheet') \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": [ "### 5.4. Ice Shelf\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are ice shelves simulated?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.ice_shelf') \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. Ice --&gt; Mass Balance \n", "*Description of the surface mass balance treatment*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Surface Mass Balance\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how and where the surface mass balance (SMB) is calulated. Include the temporal coupling frequeny from the atmosphere, whether or not a seperate SMB model is used, and if so details of this model, such as its resolution*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.surface_mass_balance') \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. Ice --&gt; Mass Balance --&gt; Basal \n", "*Description of basal melting*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Bedrock\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of basal melting over bedrock*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.bedrock') \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. Ocean\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of basal melting over the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.basal.ocean') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Ice --&gt; Mass Balance --&gt; Frontal \n", "*Description of claving/melting from the ice shelf front*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Calving\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of calving from the front of the ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.calving') \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. Melting\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the implementation of melting from the front of the ice shelf*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.mass_balance.frontal.melting') \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. Ice --&gt; Dynamics \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General description if ice sheet and ice shelf dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.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": [ "### 9.2. Approximation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Approximation type used in modelling ice dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.approximation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SIA\" \n", "# \"SAA\" \n", "# \"full stokes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Adaptive Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there an adaptive time scheme for the ice scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.adaptive_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep (in seconds) of the ice scheme. If the timestep is adaptive, then state a representative timestep.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.landice.ice.dynamics.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": [ "### \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

GoogleCloudPlatform/vertex-ai-samples

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb

1

33266

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, "outputs": [], "source": [ "# Copyright 2022 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", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "JAPoU8Sm5E6e" }, "source": [ "<table align=\"left\">\n", "\n", " <td>\n", " <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n", " View on GitHub\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n", " <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n", " Open in Vertex AI Workbench\n", " </a>\n", " </td> \n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "j9gUDU_3vV9d" }, "source": [ "# Vertex AI: Track parameters and metrics for custom training jobs" ] }, { "cell_type": "markdown", "metadata": { "id": "tvgnzT1CKxrO" }, "source": [ "## Overview\n", "\n", "This notebook demonstrates how to track metrics and parameters for Vertex AI custom training jobs, and how to perform detailed analysis using this data.\n", "\n", "### Dataset\n", "\n", "This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone\n", "### Objective\n", "\n", "In this notebook, you will learn how to use Vertex AI SDK for Python to:\n", "\n", " * Track training parameters and prediction metrics for a custom training job.\n", " * Extract and perform analysis for all parameters and metrics within an Experiment.\n", "\n", "### Costs \n", "\n", "\n", "This tutorial uses billable components of Google Cloud:\n", "\n", "* Vertex AI\n", "* Cloud Storage\n", "\n", "Learn about [Vertex AI\n", "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n", "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n", "Calculator](https://cloud.google.com/products/calculator/)\n", "to generate a cost estimate based on your projected usage." ] }, { "cell_type": "markdown", "metadata": { "id": "ze4-nDLfK4pw" }, "source": [ "### Set up your local development environment\n", "\n", "**If you are using Colab or Vertex AI Workbench**, your environment already meets\n", "all the requirements to run this notebook. You can skip this step." ] }, { "cell_type": "markdown", "metadata": { "id": "gCuSR8GkAgzl" }, "source": [ "**Otherwise**, make sure your environment meets this notebook's requirements.\n", "You need the following:\n", "\n", "* The Google Cloud SDK\n", "* Git\n", "* Python 3\n", "* virtualenv\n", "* Jupyter notebook running in a virtual environment with Python 3\n", "\n", "The Google Cloud guide to [Setting up a Python development\n", "environment](https://cloud.google.com/python/setup) and the [Jupyter\n", "installation guide](https://jupyter.org/install) provide detailed instructions\n", "for meeting these requirements. The following steps provide a condensed set of\n", "instructions:\n", "\n", "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n", "\n", "1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n", "\n", "1. [Install\n", " virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n", " and create a virtual environment that uses Python 3. Activate the virtual environment.\n", "\n", "1. To install Jupyter, run `pip install jupyter` on the\n", "command-line in a terminal shell.\n", "\n", "1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n", "\n", "1. Open this notebook in the Jupyter Notebook Dashboard." ] }, { "cell_type": "markdown", "metadata": { "id": "i7EUnXsZhAGF" }, "source": [ "### Install additional packages\n", "\n", "Install additional package dependencies not installed in your notebook environment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IaYsrh0Tc17L" }, "outputs": [], "source": [ "import os\n", "\n", "# The Google Cloud Notebook product has specific requirements\n", "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n", "\n", "# Google Cloud Notebook requires dependencies to be installed with '--user'\n", "USER_FLAG = \"\"\n", "if IS_GOOGLE_CLOUD_NOTEBOOK:\n", " USER_FLAG = \"--user\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qblyW_dcyOQA" }, "outputs": [], "source": [ "! pip3 install -U tensorflow $USER_FLAG\n", "! python3 -m pip install {USER_FLAG} google-cloud-aiplatform --upgrade\n", "! pip3 install scikit-learn {USER_FLAG}\n" ] }, { "cell_type": "markdown", "metadata": { "id": "hhq5zEbGg0XX" }, "source": [ "### Restart the kernel\n", "\n", "After you install the additional packages, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EzrelQZ22IZj" }, "outputs": [], "source": [ "# Automatically restart kernel after installs\n", "import os\n", "\n", "if not os.getenv(\"IS_TESTING\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "lWEdiXsJg0XY" }, "source": [ "## Before you begin\n", "\n", "### Select a GPU runtime\n", "\n", "**Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select \"Runtime --> Change runtime type > GPU\"**" ] }, { "cell_type": "markdown", "metadata": { "id": "BF1j6f9HApxa" }, "source": [ "### Set up your Google Cloud project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", "\n", "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n", "\n", "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n", "\n", "1. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands." ] }, { "cell_type": "markdown", "metadata": { "id": "WReHDGG5g0XY" }, "source": [ "#### Set your project ID\n", "\n", "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oM1iC_MfAts1" }, "outputs": [], "source": [ "import os\n", "\n", "PROJECT_ID = \"\"\n", "\n", "# Get your Google Cloud project ID from gcloud\n", "if not os.getenv(\"IS_TESTING\"):\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID: \", PROJECT_ID)" ] }, { "cell_type": "markdown", "metadata": { "id": "qJYoRfYng0XZ" }, "source": [ "Otherwise, set your project ID here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riG_qUokg0XZ" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None:\n", " PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "XsnuGoJM9mUw" }, "source": [ "Set gcloud config to your project ID." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TL9QIaVd9hvm" }, "outputs": [], "source": [ "!gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "06571eb4063b" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "697568e92bd6" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "dr--iN2kAylZ" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "**If you are using Vertex AI Workbench**, your environment is already\n", "authenticated. Skip this step." ] }, { "cell_type": "markdown", "metadata": { "id": "sBCra4QMA2wR" }, "source": [ "**If you are using Colab**, run the cell below and follow the instructions\n", "when prompted to authenticate your account via oAuth.\n", "\n", "**Otherwise**, follow these steps:\n", "\n", "1. In the Cloud Console, go to the [**Create service account key**\n", " page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n", "\n", "2. Click **Create service account**.\n", "\n", "3. In the **Service account name** field, enter a name, and\n", " click **Create**.\n", "\n", "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n", "into the filter box, and select\n", " **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n", "\n", "5. Click *Create*. A JSON file that contains your key downloads to your\n", "local environment.\n", "\n", "6. Enter the path to your service account key as the\n", "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PyQmSRbKA8r-" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your GCP account. This provides access to your\n", "# Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# If on Google Cloud Notebooks, then don't execute this code\n", "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this notebook locally, replace the string below with the\n", " # path to your service account key and run this cell to authenticate your GCP\n", " # account.\n", " elif not os.getenv(\"IS_TESTING\"):\n", " %env GOOGLE_APPLICATION_CREDENTIALS ''" ] }, { "cell_type": "markdown", "metadata": { "id": "zgPO1eR3CYjk" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "\n", "When you submit a training job using the Cloud SDK, you upload a Python package\n", "containing your training code to a Cloud Storage bucket. Vertex AI runs\n", "the code from this package. In this tutorial, Vertex AI also saves the\n", "trained model that results from your job in the same bucket. Using this model artifact, you can then\n", "create Vertex AI model and endpoint resources in order to serve\n", "online predictions.\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all\n", "Cloud Storage buckets.\n", "\n", "You may also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Make sure to [choose a region where Vertex AI services are\n", "available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions). You may\n", "not use a Multi-Regional Storage bucket for training with Vertex AI." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MzGDU7TWdts_" }, "outputs": [], "source": [ "BUCKET_URI = \"gs://[your-bucket-name]\" # @param {type:\"string\"}\n", "REGION = \"[your-region]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cf221059d072" }, "outputs": [], "source": [ "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n", " BUCKET_URI = \"gs://\" + PROJECT_ID + \"-aip-\" + TIMESTAMP\n", "\n", "if REGION == \"[your-region]\":\n", " REGION = \"us-central1\"" ] }, { "cell_type": "markdown", "metadata": { "id": "-EcIXiGsCePi" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NIq7R4HZCfIc" }, "outputs": [], "source": [ "! gsutil mb -l $REGION $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "ucvCsknMCims" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vhOb7YnwClBb" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "XoEqT2Y4DJmf" }, "source": [ "### Import libraries and define constants" ] }, { "cell_type": "markdown", "metadata": { "id": "Y9Uo3tifg1kx" }, "source": [ "Import required libraries.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pRUOFELefqf1" }, "outputs": [], "source": [ "import pandas as pd\n", "from google.cloud import aiplatform\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", "from tensorflow.python.keras.utils import data_utils" ] }, { "cell_type": "markdown", "metadata": { "id": "O8XJZB3gR8eL" }, "source": [ "## Initialize Vertex AI and set an _experiment_\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xtXZWmYqJ1bh" }, "source": [ "Define experiment name." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JIOrI-hoJ46P" }, "outputs": [], "source": [ "EXPERIMENT_NAME = \"\" # @param {type:\"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "jWQLXXNVN4Lv" }, "source": [ "If EXEPERIMENT_NAME is not set, set a default one below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q1QInYWOKsmo" }, "outputs": [], "source": [ "if EXPERIMENT_NAME == \"\" or EXPERIMENT_NAME is None:\n", " EXPERIMENT_NAME = \"my-experiment-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "DKIsYVjj56_X" }, "source": [ "Initialize the *client* for Vertex AI." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Wrlk2B2nJ7-X" }, "outputs": [], "source": [ "aiplatform.init(\n", " project=PROJECT_ID,\n", " location=REGION,\n", " staging_bucket=BUCKET_URI,\n", " experiment=EXPERIMENT_NAME,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "6PlilQPFeS_h" }, "source": [ "## Tracking parameters and metrics in Vertex AI custom training jobs" ] }, { "cell_type": "markdown", "metadata": { "id": "9nokDKBAxwV8" }, "source": [ "This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V_T10yTTqcS_" }, "outputs": [], "source": [ "!wget https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv\n", "!gsutil cp abalone_train.csv {BUCKET_URI}/data/\n", "\n", "gcs_csv_path = f\"{BUCKET_URI}/data/abalone_train.csv\"" ] }, { "cell_type": "markdown", "metadata": { "id": "35QVNhACqcTJ" }, "source": [ "### Create a managed tabular dataset from a CSV\n", "\n", "A Managed dataset can be used to create an AutoML model or a custom model. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4OfCqaYRqcTJ" }, "outputs": [], "source": [ "ds = aiplatform.TabularDataset.create(display_name=\"abalone\", gcs_source=[gcs_csv_path])\n", "\n", "ds.resource_name" ] }, { "cell_type": "markdown", "metadata": { "id": "VcEOYYolqcTN" }, "source": [ "### Write the training script\n", "\n", "Run the following cell to create the training script that is used in the sample custom training job." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OauJqJmJqcTO" }, "outputs": [], "source": [ "%%writefile training_script.py\n", "\n", "import pandas as pd\n", "import argparse\n", "import os\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "parser = argparse.ArgumentParser()\n", "parser.add_argument('--epochs', dest='epochs',\n", " default=10, type=int,\n", " help='Number of epochs.')\n", "parser.add_argument('--num_units', dest='num_units',\n", " default=64, type=int,\n", " help='Number of unit for first layer.')\n", "args = parser.parse_args()\n", "# uncomment and bump up replica_count for distributed training\n", "# strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()\n", "# tf.distribute.experimental_set_strategy(strategy)\n", "\n", "col_names = [\"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\", \"Viscera weight\", \"Shell weight\", \"Age\"]\n", "target = \"Age\"\n", "\n", "def aip_data_to_dataframe(wild_card_path):\n", " return pd.concat([pd.read_csv(fp.numpy().decode(), names=col_names)\n", " for fp in tf.data.Dataset.list_files([wild_card_path])])\n", "\n", "def get_features_and_labels(df):\n", " return df.drop(target, axis=1).values, df[target].values\n", "\n", "def data_prep(wild_card_path):\n", " return get_features_and_labels(aip_data_to_dataframe(wild_card_path))\n", "\n", "\n", "model = tf.keras.Sequential([layers.Dense(args.num_units), layers.Dense(1)])\n", "model.compile(loss='mse', optimizer='adam')\n", "\n", "model.fit(*data_prep(os.environ[\"AIP_TRAINING_DATA_URI\"]),\n", " epochs=args.epochs ,\n", " validation_data=data_prep(os.environ[\"AIP_VALIDATION_DATA_URI\"]))\n", "print(model.evaluate(*data_prep(os.environ[\"AIP_TEST_DATA_URI\"])))\n", "\n", "# save as Vertex AI Managed model\n", "tf.saved_model.save(model, os.environ[\"AIP_MODEL_DIR\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "Yp2clkOJSDhR" }, "source": [ "### Launch a custom training job and track its trainig parameters on Vertex AI ML Metadata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "btb6d48lqcTT" }, "outputs": [], "source": [ "job = aiplatform.CustomTrainingJob(\n", " display_name=\"train-abalone-dist-1-replica\",\n", " script_path=\"training_script.py\",\n", " container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest\",\n", " requirements=[\"gcsfs==0.7.1\"],\n", " model_serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "k_QorXXztzPH" }, "source": [ "Start a new experiment run to track training parameters and start the training job. Note that this operation will take around 10 mins." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oVTORjQpJ7-Y" }, "outputs": [], "source": [ "aiplatform.start_run(\"custom-training-run-1\") # Change this to your desired run name\n", "parameters = {\"epochs\": 10, \"num_units\": 64}\n", "aiplatform.log_params(parameters)\n", "\n", "model = job.run(\n", " ds,\n", " replica_count=1,\n", " model_display_name=\"abalone-model\",\n", " args=[f\"--epochs={parameters['epochs']}\", f\"--num_units={parameters['num_units']}\"],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "5vhDsMJNqcTW" }, "source": [ "### Deploy Model and calculate prediction metrics" ] }, { "cell_type": "markdown", "metadata": { "id": "O-uCOL3Naap4" }, "source": [ "Deploy model to Google Cloud. This operation will take 10-20 mins." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y9GH72wWqcTX" }, "outputs": [], "source": [ "endpoint = model.deploy(machine_type=\"n1-standard-4\")" ] }, { "cell_type": "markdown", "metadata": { "id": "JY-5skFhasWs" }, "source": [ "Once model is deployed, perform online prediction using the `abalone_test` dataset and calculate prediction metrics." ] }, { "cell_type": "markdown", "metadata": { "id": "saw50bqwa-dR" }, "source": [ "Prepare the prediction dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ABZQmqsWISQv" }, "outputs": [], "source": [ "def read_data(uri):\n", " dataset_path = data_utils.get_file(\"abalone_test.data\", uri)\n", " col_names = [\n", " \"Length\",\n", " \"Diameter\",\n", " \"Height\",\n", " \"Whole weight\",\n", " \"Shucked weight\",\n", " \"Viscera weight\",\n", " \"Shell weight\",\n", " \"Age\",\n", " ]\n", " dataset = pd.read_csv(\n", " dataset_path,\n", " names=col_names,\n", " na_values=\"?\",\n", " comment=\"\\t\",\n", " sep=\",\",\n", " skipinitialspace=True,\n", " )\n", " return dataset\n", "\n", "\n", "def get_features_and_labels(df):\n", " target = \"Age\"\n", " return df.drop(target, axis=1).values, df[target].values\n", "\n", "\n", "test_dataset, test_labels = get_features_and_labels(\n", " read_data(\n", " \"https://storage.googleapis.com/download.tensorflow.org/data/abalone_test.csv\"\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "_HphZ38obJeB" }, "source": [ "Perform online prediction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eXD-OvsrKmCt" }, "outputs": [], "source": [ "prediction = endpoint.predict(test_dataset.tolist())\n", "prediction" ] }, { "cell_type": "markdown", "metadata": { "id": "TDKiv_O7bNwE" }, "source": [ "Calculate and track prediction evaluation metrics." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cj0fHucbKopn" }, "outputs": [], "source": [ "mse = mean_squared_error(test_labels, prediction.predictions)\n", "mae = mean_absolute_error(test_labels, prediction.predictions)\n", "\n", "aiplatform.log_metrics({\"mse\": mse, \"mae\": mae})" ] }, { "cell_type": "markdown", "metadata": { "id": "CCGmesdIbbHf" }, "source": [ "### Extract all parameters and metrics created during this experiment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KlcEBou-Pl4Z" }, "outputs": [], "source": [ "aiplatform.get_experiment_df()" ] }, { "cell_type": "markdown", "metadata": { "id": "WTHvPMweMlP1" }, "source": [ "### View data in the Cloud Console" ] }, { "cell_type": "markdown", "metadata": { "id": "F19_5lw0MqXv" }, "source": [ "Parameters and metrics can also be viewed in the Cloud Console. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GmN9vE9pqqzt" }, "outputs": [], "source": [ "print(\"Vertex AI Experiments:\")\n", "print(\n", " f\"https://console.cloud.google.com/ai/platform/experiments/experiments?folder=&organizationId=&project={PROJECT_ID}\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "TpV-iwP9qw9c" }, "source": [ "## Cleaning up\n", "\n", "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial:\n", "Training Job\n", "Model\n", "Cloud Storage Bucket\n", "\n", "* Vertex AI Dataset\n", "* Training Job\n", "* Model\n", "* Endpoint\n", "* Cloud Storage Bucket\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rwPZoZISHhaY" }, "outputs": [], "source": [ "# Warning: Setting this to true will delete everything in your bucket\n", "delete_bucket = False\n", "\n", "# Delete dataset\n", "ds.delete()\n", "\n", "# Delete the training job\n", "job.delete()\n", "\n", "# Undeploy model from endpoint\n", "endpoint.undeploy_all()\n", "\n", "# Delete the endpoint\n", "endpoint.delete()\n", "\n", "# Delete the model\n", "model.delete()\n", "\n", "\n", "if delete_bucket or os.getenv(\"IS_TESTING\"):\n", " ! gsutil -m rm -r $BUCKET_URI" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "sdk-metric-parameter-tracking-for-custom-jobs.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

dnxbjyj/python-basic

libs/ConfigParser/handout.ipynb

1

7504

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 用ConfigParser模块读写conf配置文件" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ConfigParser是Python内置的一个读取配置文件的模块，用它来读取和修改配置文件非常方便，本文介绍一下它的基本用法。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据准备" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "假设当前目录下有一个名为`sys.conf`的配置文件，其内容如下：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "[db]\n", "db_host=127.0.0.1\n", "db_port=22\n", "db_user=root\n", "db_pass=root123\n", "\n", "[concurrent]\n", "thread = 10\n", "processor = 20\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "注：配置文件中，各个配置项其实是用等号'='隔开的键值对，这个等号两边如果有空白符，在处理的时候都会被自动去掉。但是key之前不能存在空白符，否则会报错。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 配置文件介绍" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "配置文件即conf文件，其文件结构多为键值对的文件结构，比如上面的sys.conf文件。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "conf文件有2个层次结构，`[]`中的文本是section的名称，下面的键值对列表是item，代表每个配置项的键和值。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 初始化ConfigParser实例" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ConfigParser" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "cf = ConfigParser.ConfigParser()\n", "cf.read('./sys.conf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 读取所有的section列表" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "section即`[]`中的内容。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "【Output】\n", "['db', 'concurrent']\n" ] } ], "source": [ "s = cf.sections()\n", "print '【Output】'\n", "print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 读取指定section下options key列表" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "options即某个section下的每个键值对的key." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "【Output】\n", "['thread', 'processor']\n" ] } ], "source": [ "opt = cf.options('concurrent')\n", "print '【Output】'\n", "print opt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 获取指定section下的键值对字典列表" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "【Output】\n", "[('thread', '10'), ('processor', '20')]\n" ] } ], "source": [ "items = cf.items('concurrent')\n", "print '【Output】'\n", "print items" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 按照指定数据类型读取配置值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "cf对象有get()、getint()、getboolean()、getfloat()四种方法来读取不同数据类型的配置项的值。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "【Output】\n", "127.0.0.1 22 10\n" ] } ], "source": [ "db_host = cf.get('db','db_host')\n", "db_port = cf.getint('db','db_port')\n", "thread = cf.getint('concurrent','thread')\n", "\n", "print '【Output】'\n", "print db_host,db_port,thread" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 修改某个配置项的值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "比如要修改一下数据库的密码，可以这样修改：" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cf.set('db','db_pass','newpass')\n", "# 修改完了要写入才能生效\n", "with open('sys.conf','w') as f:\n", " cf.write(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 添加一个section" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "cf.add_section('log')\n", "cf.set('log','name','mylog.log')\n", "cf.set('log','num',100)\n", "cf.set('log','size',10.55)\n", "cf.set('log','auto_save',True)\n", "cf.set('log','info','%(bar)s is %(baz)s!')\n", "\n", "# 同样的，要写入才能生效\n", "with open('sys.conf','w') as f:\n", " cf.write(f)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "执行上面代码后，sys.conf文件多了一个section，内容如下：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```bash\n", "[log]\n", "name = mylog.log\n", "num = 100\n", "size = 10.55\n", "auto_save = True\n", "info = %(bar)s is %(baz)s!\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 移除某个section" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cf.remove_section('log')\n", "\n", "# 同样的，要写入才能生效\n", "with open('sys.conf','w') as f:\n", " cf.write(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 移除某个option" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cf.remove_option('db','db_pass')\n", "\n", "# 同样的，要写入才能生效\n", "with open('sys.conf','w') as f:\n", " cf.write(f)" ] } ], "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

damienstanton/tensorflownotes

6_lstm.ipynb

1

42631

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8tQJd2YSCfWR" }, "source": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "D7tqLMoKF6uq" }, "source": [ "Deep Learning\n", "=============\n", "\n", "Assignment 6\n", "------------\n", "\n", "After training a skip-gram model in `5_word2vec.ipynb`, the goal of this notebook is to train a LSTM character model over [Text8](http://mattmahoney.net/dc/textdata) data." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "MvEblsgEXxrd" }, "outputs": [], "source": [ "# These are all the modules we'll be using later. Make sure you can import them\n", "# before proceeding further.\n", "from __future__ import print_function\n", "import os\n", "import numpy as np\n", "import random\n", "import string\n", "import tensorflow as tf\n", "import zipfile\n", "from six.moves import range\n", "from six.moves.urllib.request import urlretrieve" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 5993, "status": "ok", "timestamp": 1445965582896, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "RJ-o3UBUFtCw", "outputId": "d530534e-0791-4a94-ca6d-1c8f1b908a9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found and verified text8.zip\n" ] } ], "source": [ "url = 'http://mattmahoney.net/dc/'\n", "\n", "def maybe_download(filename, expected_bytes):\n", " \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n", " if not os.path.exists(filename):\n", " filename, _ = urlretrieve(url + filename, filename)\n", " statinfo = os.stat(filename)\n", " if statinfo.st_size == expected_bytes:\n", " print('Found and verified %s' % filename)\n", " else:\n", " print(statinfo.st_size)\n", " raise Exception(\n", " 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n", " return filename\n", "\n", "filename = maybe_download('text8.zip', 31344016)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 5982, "status": "ok", "timestamp": 1445965582916, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "Mvf09fjugFU_", "outputId": "8f75db58-3862-404b-a0c3-799380597390" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data size 100000000\n" ] } ], "source": [ "def read_data(filename):\n", " f = zipfile.ZipFile(filename)\n", " for name in f.namelist():\n", " return tf.compat.as_str(f.read(name))\n", " f.close()\n", " \n", "text = read_data(filename)\n", "print('Data size %d' % len(text))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ga2CYACE-ghb" }, "source": [ "Create a small validation set." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 6184, "status": "ok", "timestamp": 1445965583138, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "w-oBpfFG-j43", "outputId": "bdb96002-d021-4379-f6de-a977924f0d02" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "99999000 ons anarchists advocate social relations based upon voluntary as\n", "1000 anarchism originated as a term of abuse first used against earl\n" ] } ], "source": [ "valid_size = 1000\n", "valid_text = text[:valid_size]\n", "train_text = text[valid_size:]\n", "train_size = len(train_text)\n", "print(train_size, train_text[:64])\n", "print(valid_size, valid_text[:64])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Zdw6i4F8glpp" }, "source": [ "Utility functions to map characters to vocabulary IDs and back." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 6276, "status": "ok", "timestamp": 1445965583249, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "gAL1EECXeZsD", "outputId": "88fc9032-feb9-45ff-a9a0-a26759cc1f2e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 26 0 Unexpected character: ï\n", "0\n", "a z \n" ] } ], "source": [ "vocabulary_size = len(string.ascii_lowercase) + 1 # [a-z] + ' '\n", "first_letter = ord(string.ascii_lowercase[0])\n", "\n", "def char2id(char):\n", " if char in string.ascii_lowercase:\n", " return ord(char) - first_letter + 1\n", " elif char == ' ':\n", " return 0\n", " else:\n", " print('Unexpected character: %s' % char)\n", " return 0\n", " \n", "def id2char(dictid):\n", " if dictid > 0:\n", " return chr(dictid + first_letter - 1)\n", " else:\n", " return ' '\n", "\n", "print(char2id('a'), char2id('z'), char2id(' '), char2id('ï'))\n", "print(id2char(1), id2char(26), id2char(0))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lFwoyygOmWsL" }, "source": [ "Function to generate a training batch for the LSTM model." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 6473, "status": "ok", "timestamp": 1445965583467, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "d9wMtjy5hCj9", "outputId": "3dd79c80-454a-4be0-8b71-4a4a357b3367" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ons anarchi', 'when milita', 'lleria arch', ' abbeys and', 'married urr', 'hel and ric', 'y and litur', 'ay opened f', 'tion from t', 'migration t', 'new york ot', 'he boeing s', 'e listed wi', 'eber has pr', 'o be made t', 'yer who rec', 'ore signifi', 'a fierce cr', ' two six ei', 'aristotle s', 'ity can be ', ' and intrac', 'tion of the', 'dy to pass ', 'f certain d', 'at it will ', 'e convince ', 'ent told hi', 'ampaign and', 'rver side s', 'ious texts ', 'o capitaliz', 'a duplicate', 'gh ann es d', 'ine january', 'ross zero t', 'cal theorie', 'ast instanc', ' dimensiona', 'most holy m', 't s support', 'u is still ', 'e oscillati', 'o eight sub', 'of italy la', 's the tower', 'klahoma pre', 'erprise lin', 'ws becomes ', 'et in a naz', 'the fabian ', 'etchy to re', ' sharman ne', 'ised empero', 'ting in pol', 'd neo latin', 'th risky ri', 'encyclopedi', 'fense the a', 'duating fro', 'treet grid ', 'ations more', 'appeal of d', 'si have mad']\n", "['ists advoca', 'ary governm', 'hes nationa', 'd monasteri', 'raca prince', 'chard baer ', 'rgical lang', 'for passeng', 'the nationa', 'took place ', 'ther well k', 'seven six s', 'ith a gloss', 'robably bee', 'to recogniz', 'ceived the ', 'icant than ', 'ritic of th', 'ight in sig', 's uncaused ', ' lost as in', 'cellular ic', 'e size of t', ' him a stic', 'drugs confu', ' take to co', ' the priest', 'im to name ', 'd barred at', 'standard fo', ' such as es', 'ze on the g', 'e of the or', 'd hiver one', 'y eight mar', 'the lead ch', 'es classica', 'ce the non ', 'al analysis', 'mormons bel', 't or at lea', ' disagreed ', 'ing system ', 'btypes base', 'anguages th', 'r commissio', 'ess one nin', 'nux suse li', ' the first ', 'zi concentr', ' society ne', 'elatively s', 'etworks sha', 'or hirohito', 'litical ini', 'n most of t', 'iskerdoo ri', 'ic overview', 'air compone', 'om acnm acc', ' centerline', 'e than any ', 'devotional ', 'de such dev']\n", "[' a']\n", "['an']\n" ] } ], "source": [ "batch_size=64\n", "num_unrollings=10\n", "\n", "class BatchGenerator(object):\n", " def __init__(self, text, batch_size, num_unrollings):\n", " self._text = text\n", " self._text_size = len(text)\n", " self._batch_size = batch_size\n", " self._num_unrollings = num_unrollings\n", " segment = self._text_size // batch_size\n", " self._cursor = [ offset * segment for offset in range(batch_size)]\n", " self._last_batch = self._next_batch()\n", " \n", " def _next_batch(self):\n", " \"\"\"Generate a single batch from the current cursor position in the data.\"\"\"\n", " batch = np.zeros(shape=(self._batch_size, vocabulary_size), dtype=np.float)\n", " for b in range(self._batch_size):\n", " batch[b, char2id(self._text[self._cursor[b]])] = 1.0\n", " self._cursor[b] = (self._cursor[b] + 1) % self._text_size\n", " return batch\n", " \n", " def next(self):\n", " \"\"\"Generate the next array of batches from the data. The array consists of\n", " the last batch of the previous array, followed by num_unrollings new ones.\n", " \"\"\"\n", " batches = [self._last_batch]\n", " for step in range(self._num_unrollings):\n", " batches.append(self._next_batch())\n", " self._last_batch = batches[-1]\n", " return batches\n", "\n", "def characters(probabilities):\n", " \"\"\"Turn a 1-hot encoding or a probability distribution over the possible\n", " characters back into its (mostl likely) character representation.\"\"\"\n", " return [id2char(c) for c in np.argmax(probabilities, 1)]\n", "\n", "def batches2string(batches):\n", " \"\"\"Convert a sequence of batches back into their (most likely) string\n", " representation.\"\"\"\n", " s = [''] * batches[0].shape[0]\n", " for b in batches:\n", " s = [''.join(x) for x in zip(s, characters(b))]\n", " return s\n", "\n", "train_batches = BatchGenerator(train_text, batch_size, num_unrollings)\n", "valid_batches = BatchGenerator(valid_text, 1, 1)\n", "\n", "print(batches2string(train_batches.next()))\n", "print(batches2string(train_batches.next()))\n", "print(batches2string(valid_batches.next()))\n", "print(batches2string(valid_batches.next()))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "KyVd8FxT5QBc" }, "outputs": [], "source": [ "def logprob(predictions, labels):\n", " \"\"\"Log-probability of the true labels in a predicted batch.\"\"\"\n", " predictions[predictions < 1e-10] = 1e-10\n", " return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\n", "\n", "def sample_distribution(distribution):\n", " \"\"\"Sample one element from a distribution assumed to be an array of normalized\n", " probabilities.\n", " \"\"\"\n", " r = random.uniform(0, 1)\n", " s = 0\n", " for i in range(len(distribution)):\n", " s += distribution[i]\n", " if s >= r:\n", " return i\n", " return len(distribution) - 1\n", "\n", "def sample(prediction):\n", " \"\"\"Turn a (column) prediction into 1-hot encoded samples.\"\"\"\n", " p = np.zeros(shape=[1, vocabulary_size], dtype=np.float)\n", " p[0, sample_distribution(prediction[0])] = 1.0\n", " return p\n", "\n", "def random_distribution():\n", " \"\"\"Generate a random column of probabilities.\"\"\"\n", " b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n", " return b/np.sum(b, 1)[:,None]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "K8f67YXaDr4C" }, "source": [ "Simple LSTM Model." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "Q5rxZK6RDuGe" }, "outputs": [], "source": [ "num_nodes = 64\n", "\n", "graph = tf.Graph()\n", "with graph.as_default():\n", " \n", " # Parameters:\n", " # Input gate: input, previous output, and bias.\n", " ix = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", " im = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", " ib = tf.Variable(tf.zeros([1, num_nodes]))\n", " # Forget gate: input, previous output, and bias.\n", " fx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", " fm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", " fb = tf.Variable(tf.zeros([1, num_nodes]))\n", " # Memory cell: input, state and bias. \n", " cx = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", " cm = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", " cb = tf.Variable(tf.zeros([1, num_nodes]))\n", " # Output gate: input, previous output, and bias.\n", " ox = tf.Variable(tf.truncated_normal([vocabulary_size, num_nodes], -0.1, 0.1))\n", " om = tf.Variable(tf.truncated_normal([num_nodes, num_nodes], -0.1, 0.1))\n", " ob = tf.Variable(tf.zeros([1, num_nodes]))\n", " # Variables saving state across unrollings.\n", " saved_output = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n", " saved_state = tf.Variable(tf.zeros([batch_size, num_nodes]), trainable=False)\n", " # Classifier weights and biases.\n", " w = tf.Variable(tf.truncated_normal([num_nodes, vocabulary_size], -0.1, 0.1))\n", " b = tf.Variable(tf.zeros([vocabulary_size]))\n", " \n", " # Definition of the cell computation.\n", " def lstm_cell(i, o, state):\n", " \"\"\"Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf\n", " Note that in this formulation, we omit the various connections between the\n", " previous state and the gates.\"\"\"\n", " input_gate = tf.sigmoid(tf.matmul(i, ix) + tf.matmul(o, im) + ib)\n", " forget_gate = tf.sigmoid(tf.matmul(i, fx) + tf.matmul(o, fm) + fb)\n", " update = tf.matmul(i, cx) + tf.matmul(o, cm) + cb\n", " state = forget_gate * state + input_gate * tf.tanh(update)\n", " output_gate = tf.sigmoid(tf.matmul(i, ox) + tf.matmul(o, om) + ob)\n", " return output_gate * tf.tanh(state), state\n", "\n", " # Input data.\n", " train_data = list()\n", " for _ in range(num_unrollings + 1):\n", " train_data.append(\n", " tf.placeholder(tf.float32, shape=[batch_size,vocabulary_size]))\n", " train_inputs = train_data[:num_unrollings]\n", " train_labels = train_data[1:] # labels are inputs shifted by one time step.\n", "\n", " # Unrolled LSTM loop.\n", " outputs = list()\n", " output = saved_output\n", " state = saved_state\n", " for i in train_inputs:\n", " output, state = lstm_cell(i, output, state)\n", " outputs.append(output)\n", "\n", " # State saving across unrollings.\n", " with tf.control_dependencies([saved_output.assign(output),\n", " saved_state.assign(state)]):\n", " # Classifier.\n", " logits = tf.nn.xw_plus_b(tf.concat(0, outputs), w, b)\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(\n", " logits, tf.concat(0, train_labels)))\n", "\n", " # Optimizer.\n", " global_step = tf.Variable(0)\n", " learning_rate = tf.train.exponential_decay(\n", " 10.0, global_step, 5000, 0.1, staircase=True)\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", " gradients, v = zip(*optimizer.compute_gradients(loss))\n", " gradients, _ = tf.clip_by_global_norm(gradients, 1.25)\n", " optimizer = optimizer.apply_gradients(\n", " zip(gradients, v), global_step=global_step)\n", "\n", " # Predictions.\n", " train_prediction = tf.nn.softmax(logits)\n", " \n", " # Sampling and validation eval: batch 1, no unrolling.\n", " sample_input = tf.placeholder(tf.float32, shape=[1, vocabulary_size])\n", " saved_sample_output = tf.Variable(tf.zeros([1, num_nodes]))\n", " saved_sample_state = tf.Variable(tf.zeros([1, num_nodes]))\n", " reset_sample_state = tf.group(\n", " saved_sample_output.assign(tf.zeros([1, num_nodes])),\n", " saved_sample_state.assign(tf.zeros([1, num_nodes])))\n", " sample_output, sample_state = lstm_cell(\n", " sample_input, saved_sample_output, saved_sample_state)\n", " with tf.control_dependencies([saved_sample_output.assign(sample_output),\n", " saved_sample_state.assign(sample_state)]):\n", " sample_prediction = tf.nn.softmax(tf.nn.xw_plus_b(sample_output, w, b))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 41 }, { "item_id": 80 }, { "item_id": 126 }, { "item_id": 144 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 199909, "status": "ok", "timestamp": 1445965877333, "user": { "color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "6f6f07b359200c46", "userId": "102167687554210253930" }, "user_tz": 420 }, "id": "RD9zQCZTEaEm", "outputId": "5e868466-2532-4545-ce35-b403cf5d9de6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n", "Average loss at step 0 : 3.29904174805 learning rate: 10.0\n", "Minibatch perplexity: 27.09\n", "================================================================================\n", "srk dwmrnuldtbbgg tapootidtu xsciu sgokeguw hi ieicjq lq piaxhazvc s fht wjcvdlh\n", "lhrvallvbeqqquc dxd y siqvnle bzlyw nr rwhkalezo siie o deb e lpdg storq u nx o\n", "meieu nantiouie gdys qiuotblci loc hbiznauiccb cqzed acw l tsm adqxplku gn oaxet\n", "unvaouc oxchywdsjntdh zpklaejvxitsokeerloemee htphisb th eaeqseibumh aeeyj j orw\n", "ogmnictpycb whtup otnilnesxaedtekiosqet liwqarysmt arj flioiibtqekycbrrgoysj\n", "================================================================================\n", "Validation set perplexity: 19.99\n", "Average loss at step 100 : 2.59553678274 learning rate: 10.0\n", "Minibatch perplexity: 9.57\n", "Validation set perplexity: 10.60\n", "Average loss at step 200 : 2.24747137785 learning rate: 10.0\n", "Minibatch perplexity: 7.68\n", "Validation set perplexity: 8.84\n", "Average loss at step 300 : 2.09438110709 learning rate: 10.0\n", "Minibatch perplexity: 7.41\n", "Validation set perplexity: 8.13\n", "Average loss at step 400 : 1.99440989017 learning rate: 10.0\n", "Minibatch perplexity: 6.46\n", "Validation set perplexity: 7.58\n", "Average loss at step 500 : 1.9320810616 learning rate: 10.0\n", "Minibatch perplexity: 6.30\n", "Validation set perplexity: 6.88\n", "Average loss at step 600 : 1.90935629249 learning rate: 10.0\n", "Minibatch perplexity: 7.21\n", "Validation set perplexity: 6.91\n", "Average loss at step 700 : 1.85583009005 learning rate: 10.0\n", "Minibatch perplexity: 6.13\n", "Validation set perplexity: 6.60\n", "Average loss at step 800 : 1.82152368546 learning rate: 10.0\n", "Minibatch perplexity: 6.01\n", "Validation set perplexity: 6.37\n", "Average loss at step 900 : 1.83169809818 learning rate: 10.0\n", "Minibatch perplexity: 7.20\n", "Validation set perplexity: 6.23\n", "Average loss at step 1000 : 1.82217029214 learning rate: 10.0\n", "Minibatch perplexity: 6.73\n", "================================================================================\n", "le action b of the tert sy ofter selvorang previgned stischdy yocal chary the co\n", "le relganis networks partucy cetinning wilnchan sics rumeding a fulch laks oftes\n", "hian andoris ret the ecause bistory l pidect one eight five lack du that the ses\n", "aiv dromery buskocy becomer worils resism disele retery exterrationn of hide in \n", "mer miter y sught esfectur of the upission vain is werms is vul ugher compted by\n", "================================================================================\n", "Validation set perplexity: 6.07\n", "Average loss at step 1100 : 1.77301145077 learning rate: 10.0\n", "Minibatch perplexity: 6.03\n", "Validation set perplexity: 5.89\n", "Average loss at step 1200 : 1.75306463003 learning rate: 10.0\n", "Minibatch perplexity: 6.50\n", "Validation set perplexity: 5.61\n", "Average loss at step 1300 : 1.72937195778 learning rate: 10.0\n", "Minibatch perplexity: 5.00\n", "Validation set perplexity: 5.60\n", "Average loss at step 1400 : 1.74773373723 learning rate: 10.0\n", "Minibatch perplexity: 6.48\n", "Validation set perplexity: 5.66\n", "Average loss at step 1500 : 1.7368799901 learning rate: 10.0\n", "Minibatch perplexity: 5.22\n", "Validation set perplexity: 5.44\n", "Average loss at step 1600 : 1.74528762937 learning rate: 10.0\n", "Minibatch perplexity: 5.85\n", "Validation set perplexity: 5.33\n", "Average loss at step 1700 : 1.70881183743 learning rate: 10.0\n", "Minibatch perplexity: 5.33\n", "Validation set perplexity: 5.56\n", "Average loss at step 1800 : 1.67776108027 learning rate: 10.0\n", "Minibatch perplexity: 5.33\n", "Validation set perplexity: 5.29\n", "Average loss at step 1900 : 1.64935536742 learning rate: 10.0\n", "Minibatch perplexity: 5.29\n", "Validation set perplexity: 5.15\n", "Average loss at step 2000 : 1.69528644681 learning rate: 10.0\n", "Minibatch perplexity: 5.13\n", "================================================================================\n", "vers soqually have one five landwing to docial page kagan lower with ther batern\n", "ctor son alfortmandd tethre k skin the known purated to prooust caraying the fit\n", "je in beverb is the sournction bainedy wesce tu sture artualle lines digra forme\n", "m rousively haldio ourso ond anvary was for the seven solies hild buil s to te\n", "zall for is it is one nine eight eight one neval to the kime typer oene where he\n", "================================================================================\n", "Validation set perplexity: 5.25\n", "Average loss at step 2100 : 1.68808053017 learning rate: 10.0\n", "Minibatch perplexity: 5.17\n", "Validation set perplexity: 5.01\n", "Average loss at step 2200 : 1.68322490931 learning rate: 10.0\n", "Minibatch perplexity: 5.09\n", "Validation set perplexity: 5.15\n", "Average loss at step 2300 : 1.64465074301 learning rate: 10.0\n", "Minibatch perplexity: 5.51\n", "Validation set perplexity: 5.00\n", "Average loss at step 2400 : 1.66408578038 learning rate: 10.0\n", "Minibatch perplexity: 5.86\n", "Validation set perplexity: 4.80\n", "Average loss at step 2500 : 1.68515402555 learning rate: 10.0\n", "Minibatch perplexity: 5.75\n", "Validation set perplexity: 4.82\n", "Average loss at step 2600 : 1.65405208349 learning rate: 10.0\n", "Minibatch perplexity: 5.38\n", "Validation set perplexity: 4.85\n", "Average loss at step 2700 : 1.65706222177 learning rate: 10.0\n", "Minibatch perplexity: 5.46\n", "Validation set perplexity: 4.78\n", "Average loss at step 2800 : 1.65204829812 learning rate: 10.0\n", "Minibatch perplexity: 5.06\n", "Validation set perplexity: 4.64\n", "Average loss at step 2900 : 1.65107253551 learning rate: 10.0\n", "Minibatch perplexity: 5.00\n", "Validation set perplexity: 4.61\n", "Average loss at step 3000 : 1.6495274055 learning rate: 10.0\n", "Minibatch perplexity: 4.53\n", "================================================================================\n", "ject covered in belo one six six to finsh that all di rozial sime it a the lapse\n", "ble which the pullic bocades record r to sile dric two one four nine seven six f\n", " originally ame the playa ishaps the stotchational in a p dstambly name which as\n", "ore volum to bay riwer foreal in nuily operety can and auscham frooripm however \n", "kan traogey was lacous revision the mott coupofiteditey the trando insended frop\n", "================================================================================\n", "Validation set perplexity: 4.76\n", "Average loss at step 3100 : 1.63705502152 learning rate: 10.0\n", "Minibatch perplexity: 5.50\n", "Validation set perplexity: 4.76\n", "Average loss at step 3200 : 1.64740695596 learning rate: 10.0\n", "Minibatch perplexity: 4.84\n", "Validation set perplexity: 4.67\n", "Average loss at step 3300 : 1.64711504817 learning rate: 10.0\n", "Minibatch perplexity: 5.39\n", "Validation set perplexity: 4.57\n", "Average loss at step 3400 : 1.67113256454 learning rate: 10.0\n", "Minibatch perplexity: 5.56\n", "Validation set perplexity: 4.71\n", "Average loss at step 3500 : 1.65637169957 learning rate: 10.0\n", "Minibatch perplexity: 5.03\n", "Validation set perplexity: 4.80\n", "Average loss at step 3600 : 1.66601825476 learning rate: 10.0\n", "Minibatch perplexity: 4.63\n", "Validation set perplexity: 4.52\n", "Average loss at step 3700 : 1.65021387935 learning rate: 10.0\n", "Minibatch perplexity: 5.50\n", "Validation set perplexity: 4.56\n", "Average loss at step 3800 : 1.64481814981 learning rate: 10.0\n", "Minibatch perplexity: 4.60\n", "Validation set perplexity: 4.54\n", "Average loss at step 3900 : 1.642069453 learning rate: 10.0\n", "Minibatch perplexity: 4.91\n", "Validation set perplexity: 4.54\n", "Average loss at step 4000 : 1.65179730773 learning rate: 10.0\n", "Minibatch perplexity: 4.77\n", "================================================================================\n", "k s rasbonish roctes the nignese at heacle was sito of beho anarchys and with ro\n", "jusar two sue wletaus of chistical in causations d ow trancic bruthing ha laters\n", "de and speacy pulted yoftret worksy zeatlating to eight d had to ie bue seven si\n", "s fiction of the feelly constive suq flanch earlied curauking bjoventation agent\n", "quen s playing it calana our seopity also atbellisionaly comexing the revideve i\n", "================================================================================\n", "Validation set perplexity: 4.58\n", "Average loss at step 4100 : 1.63794238806 learning rate: 10.0\n", "Minibatch perplexity: 5.47\n", "Validation set perplexity: 4.79\n", "Average loss at step 4200 : 1.63822438836 learning rate: 10.0\n", "Minibatch perplexity: 5.30\n", "Validation set perplexity: 4.54\n", "Average loss at step 4300 : 1.61844664574 learning rate: 10.0\n", "Minibatch perplexity: 4.69\n", "Validation set perplexity: 4.54\n", "Average loss at step 4400 : 1.61255454302 learning rate: 10.0\n", "Minibatch perplexity: 4.67\n", "Validation set perplexity: 4.54\n", "Average loss at step 4500 : 1.61543365479 learning rate: 10.0\n", "Minibatch perplexity: 4.83\n", "Validation set perplexity: 4.69\n", "Average loss at step 4600 : 1.61607327104 learning rate: 10.0\n", "Minibatch perplexity: 5.18\n", "Validation set perplexity: 4.64\n", "Average loss at step 4700 : 1.62757282495 learning rate: 10.0\n", "Minibatch perplexity: 4.24\n", "Validation set perplexity: 4.66\n", "Average loss at step 4800 : 1.63222063541 learning rate: 10.0\n", "Minibatch perplexity: 5.30\n", "Validation set perplexity: 4.53\n", "Average loss at step 4900 : 1.63678096652 learning rate: 10.0\n", "Minibatch perplexity: 5.43\n", "Validation set perplexity: 4.64\n", "Average loss at step 5000 : 1.610340662 learning rate: 1.0\n", "Minibatch perplexity: 5.10\n", "================================================================================\n", "in b one onarbs revieds the kimiluge that fondhtic fnoto cre one nine zero zero \n", " of is it of marking panzia t had wap ironicaghni relly deah the omber b h menba\n", "ong messified it his the likdings ara subpore the a fames distaled self this int\n", "y advante authors the end languarle meit common tacing bevolitione and eight one\n", "zes that materly difild inllaring the fusts not panition assertian causecist bas\n", "================================================================================\n", "Validation set perplexity: 4.69\n", "Average loss at step 5100 : 1.60593637228 learning rate: 1.0\n", "Minibatch perplexity: 4.69\n", "Validation set perplexity: 4.47\n", "Average loss at step 5200 : 1.58993269444 learning rate: 1.0\n", "Minibatch perplexity: 4.65\n", "Validation set perplexity: 4.39\n", "Average loss at step 5300 : 1.57930587292 learning rate: 1.0\n", "Minibatch perplexity: 5.11\n", "Validation set perplexity: 4.39\n", "Average loss at step 5400 : 1.58022856832 learning rate: 1.0\n", "Minibatch perplexity: 5.19\n", "Validation set perplexity: 4.37\n", "Average loss at step 5500 : 1.56654450059 learning rate: 1.0\n", "Minibatch perplexity: 4.69\n", "Validation set perplexity: 4.33\n", "Average loss at step 5600 : 1.58013380885 learning rate: 1.0\n", "Minibatch perplexity: 5.13\n", "Validation set perplexity: 4.35\n", "Average loss at step 5700 : 1.56974959254 learning rate: 1.0\n", "Minibatch perplexity: 5.00\n", "Validation set perplexity: 4.34\n", "Average loss at step 5800 : 1.5839582932 learning rate: 1.0\n", "Minibatch perplexity: 4.88\n", "Validation set perplexity: 4.31\n", "Average loss at step 5900 : 1.57129439116 learning rate: 1.0\n", "Minibatch perplexity: 4.66\n", "Validation set perplexity: 4.32\n", "Average loss at step 6000 : 1.55144061089 learning rate: 1.0\n", "Minibatch perplexity: 4.55\n", "================================================================================\n", "utic clositical poopy stribe addi nixe one nine one zero zero eight zero b ha ex\n", "zerns b one internequiption of the secordy way anti proble akoping have fictiona\n", "phare united from has poporarly cities book ins sweden emperor a sass in origina\n", "quulk destrebinist and zeilazar and on low and by in science over country weilti\n", "x are holivia work missincis ons in the gages to starsle histon one icelanctrotu\n", "================================================================================\n", "Validation set perplexity: 4.30\n", "Average loss at step 6100 : 1.56450940847 learning rate: 1.0\n", "Minibatch perplexity: 4.77\n", "Validation set perplexity: 4.27\n", "Average loss at step 6200 : 1.53433164835 learning rate: 1.0\n", "Minibatch perplexity: 4.77\n", "Validation set perplexity: 4.27\n", "Average loss at step 6300 : 1.54773445129 learning rate: 1.0\n", "Minibatch perplexity: 4.76\n", "Validation set perplexity: 4.25\n", "Average loss at step 6400 : 1.54021131516 learning rate: 1.0\n", "Minibatch perplexity: 4.56\n", "Validation set perplexity: 4.24\n", "Average loss at step 6500 : 1.56153374553 learning rate: 1.0\n", "Minibatch perplexity: 5.43\n", "Validation set perplexity: 4.27\n", "Average loss at step 6600 : 1.59556478739 learning rate: 1.0\n", "Minibatch perplexity: 4.92\n", "Validation set perplexity: 4.28\n", "Average loss at step 6700 : 1.58076951623 learning rate: 1.0\n", "Minibatch perplexity: 4.77\n", "Validation set perplexity: 4.30\n", "Average loss at step 6800 : 1.6070714438 learning rate: 1.0\n", "Minibatch perplexity: 4.98\n", "Validation set perplexity: 4.28\n", "Average loss at step 6900 : 1.58413293839 learning rate: 1.0\n", "Minibatch perplexity: 4.61\n", "Validation set perplexity: 4.29\n", "Average loss at step 7000 : 1.57905534983 learning rate: 1.0\n", "Minibatch perplexity: 5.08\n", "================================================================================\n", "jague are officiencinels ored by film voon higherise haik one nine on the iffirc\n", "oshe provision that manned treatists on smalle bodariturmeristing the girto in s\n", "kis would softwenn mustapultmine truativersakys bersyim by s of confound esc bub\n", "ry of the using one four six blain ira mannom marencies g with fextificallise re\n", " one son vit even an conderouss to person romer i a lebapter at obiding are iuse\n", "================================================================================\n", "Validation set perplexity: 4.25\n" ] } ], "source": [ "num_steps = 7001\n", "summary_frequency = 100\n", "\n", "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " print('Initialized')\n", " mean_loss = 0\n", " for step in range(num_steps):\n", " batches = train_batches.next()\n", " feed_dict = dict()\n", " for i in range(num_unrollings + 1):\n", " feed_dict[train_data[i]] = batches[i]\n", " _, l, predictions, lr = session.run(\n", " [optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)\n", " mean_loss += l\n", " if step % summary_frequency == 0:\n", " if step > 0:\n", " mean_loss = mean_loss / summary_frequency\n", " # The mean loss is an estimate of the loss over the last few batches.\n", " print(\n", " 'Average loss at step %d: %f learning rate: %f' % (step, mean_loss, lr))\n", " mean_loss = 0\n", " labels = np.concatenate(list(batches)[1:])\n", " print('Minibatch perplexity: %.2f' % float(\n", " np.exp(logprob(predictions, labels))))\n", " if step % (summary_frequency * 10) == 0:\n", " # Generate some samples.\n", " print('=' * 80)\n", " for _ in range(5):\n", " feed = sample(random_distribution())\n", " sentence = characters(feed)[0]\n", " reset_sample_state.run()\n", " for _ in range(79):\n", " prediction = sample_prediction.eval({sample_input: feed})\n", " feed = sample(prediction)\n", " sentence += characters(feed)[0]\n", " print(sentence)\n", " print('=' * 80)\n", " # Measure validation set perplexity.\n", " reset_sample_state.run()\n", " valid_logprob = 0\n", " for _ in range(valid_size):\n", " b = valid_batches.next()\n", " predictions = sample_prediction.eval({sample_input: b[0]})\n", " valid_logprob = valid_logprob + logprob(predictions, b[1])\n", " print('Validation set perplexity: %.2f' % float(np.exp(\n", " valid_logprob / valid_size)))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pl4vtmFfa5nn" }, "source": [ "---\n", "Problem 1\n", "---------\n", "\n", "You might have noticed that the definition of the LSTM cell involves 4 matrix multiplications with the input, and 4 matrix multiplications with the output. Simplify the expression by using a single matrix multiply for each, and variables that are 4 times larger.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4eErTCTybtph" }, "source": [ "---\n", "Problem 2\n", "---------\n", "\n", "We want to train a LSTM over bigrams, that is pairs of consecutive characters like 'ab' instead of single characters like 'a'. Since the number of possible bigrams is large, feeding them directly to the LSTM using 1-hot encodings will lead to a very sparse representation that is very wasteful computationally.\n", "\n", "a- Introduce an embedding lookup on the inputs, and feed the embeddings to the LSTM cell instead of the inputs themselves.\n", "\n", "b- Write a bigram-based LSTM, modeled on the character LSTM above.\n", "\n", "c- Introduce Dropout. For best practices on how to use Dropout in LSTMs, refer to this [article](http://arxiv.org/abs/1409.2329).\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Y5tapX3kpcqZ" }, "source": [ "---\n", "Problem 3\n", "---------\n", "\n", "(difficult!)\n", "\n", "Write a sequence-to-sequence LSTM which mirrors all the words in a sentence. For example, if your input is:\n", "\n", " the quick brown fox\n", " \n", "the model should attempt to output:\n", "\n", " eht kciuq nworb xof\n", " \n", "Refer to the lecture on how to put together a sequence-to-sequence model, as well as [this article](http://arxiv.org/abs/1409.3215) for best practices.\n", "\n", "---" ] } ], "metadata": { "colab": { "default_view": {}, "name": "6_lstm.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "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

google/floq-client

samples/notebooks/Floq_Client_Colab_Tutorial.ipynb

1

16285

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Floq Client Colab Tutorial", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "BA4x-JwReATi" }, "source": [ "Copyright 2021 Floq authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "metadata": { "id": "r_T_EAyqeM--" }, "source": [ "#@title Copyright 2021 Floq authors, All Rights Reserved.\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "BRRw-AIWZqlc" }, "source": [ "## Setup" ] }, { "cell_type": "code", "metadata": { "id": "4OaF0QTPKp9v" }, "source": [ "!pip install floq_client --quiet" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "IaugKX-sZW_E" }, "source": [ "# Imports\n", "import numpy as np\n", "import sympy\n", "\n", "import cirq\n", "import floq.client" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qvL0neTKZzMT" }, "source": [ "## Floq simulation" ] }, { "cell_type": "code", "metadata": { "id": "m_8G8NmsKRKO" }, "source": [ "nrows = 10\n", "ncols = 2\n", "qubits = cirq.GridQubit.rect(nrows, ncols) # 20 qubits\n", "parameters = sympy.symbols([f'a{idx}' for idx in range(nrows * ncols)])\n", "circuit = cirq.Circuit(cirq.HPowGate(exponent=p).on(q) for p, q in zip(parameters, qubits))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "o1FtrWaZLyaE" }, "source": [ "### New observable compatible with Floq" ] }, { "cell_type": "markdown", "metadata": { "id": "Q2ZVJ0LjMeWn" }, "source": [ "Floq accepts observables in the type of `cirq.ops.linear_combinations.PauliSum` only" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "52uVoaB0LxmX", "outputId": "0f94b8f9-a72c-4cc5-95d9-2de50e2af729" }, "source": [ "observables = []\n", "for i in range(nrows):\n", " for j in range(ncols):\n", " if i < nrows - 1:\n", " observables.append(cirq.Z(qubits[i*ncols + j]) * cirq.Z(qubits[(i + 1)*ncols + j]))\n", " # Z[i * ncols + j] * Z[(i + 1) * ncols + j]\n", " if j < ncols - 1:\n", " observables.append(cirq.Z(qubits[i*ncols + j]) * cirq.Z(qubits[i*ncols + j+1]))\n", " # Z[i * ncols + j] * Z[i * ncols + (j + 1)]\n", "len(observables)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "28" ] }, "metadata": { "tags": [] }, "execution_count": 10 } ] }, { "cell_type": "code", "metadata": { "id": "o07zMbZDhGIh" }, "source": [ "import copy\n", "\n", "def sum_pauli_strings(obs):\n", " m = copy.deepcopy(obs[0])\n", " for o in obs[1:]:\n", " m += o\n", " return m\n", "\n", "def split_observables(obs):\n", "# hack: split observables into many buckets with at most 26 terms\n", " obs_buckets = [obs[s:s+25] for s in range(0, len(obs), 25)]\n", " measure = []\n", " for obs in obs_buckets:\n", " measure.append(sum_pauli_strings(obs))\n", " return measure" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "u7LvljJiQUxz" }, "source": [ "measure = split_observables(observables)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NkHtjJHeQoyh", "outputId": "896b4799-0502-4449-9c7c-a535598ca2e0" }, "source": [ "[len(m) for m in measure]" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[25, 3]" ] }, "metadata": { "tags": [] }, "execution_count": 18 } ] }, { "cell_type": "code", "metadata": { "id": "Gjs3WK33WTGh" }, "source": [ "# These two results should have the same number of Pauli string terms\n", "assert sum_pauli_strings(observables) == sum_pauli_strings(measure)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5IxZpC4gL8cM" }, "source": [ "### Padding qubits" ] }, { "cell_type": "markdown", "metadata": { "id": "lxL4w9P1MFAU" }, "source": [ "Because Floq's minimum number of qubits is 26, we need to pad it. This will be changed in the future." ] }, { "cell_type": "code", "metadata": { "id": "GoOTklQWMEO_" }, "source": [ "def pad_circuit(circ, qubits):\n", " return circ + cirq.Circuit([cirq.I(q) for q in qubits])\n", "\n", "def get_pad_qubits(circ):\n", " num = len(circ.all_qubits())\n", " return [cirq.GridQubit(num, pad) for pad in range(26 - num)]" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0PWqXf0kMN_-" }, "source": [ "pad_qubits = get_pad_qubits(circuit)\n", "padded_circuit = pad_circuit(circuit, pad_qubits)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 898 }, "id": "Xo_LA2xlMl6o", "outputId": "07d1e49e-0cde-41bf-a7e2-12ef87878dc7" }, "source": [ "padded_circuit" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "<pre style=\"overflow: auto; white-space: pre;\">(0, 0): ────H^a0────────\n", "\n", "(0, 1): ────H^a1────────\n", "\n", "(1, 0): ────H^a2────────\n", "\n", "(1, 1): ────H^a3────────\n", "\n", "(2, 0): ────H^a4────────\n", "\n", "(2, 1): ────H^a5────────\n", "\n", "(3, 0): ────H^a6────────\n", "\n", "(3, 1): ────H^a7────────\n", "\n", "(4, 0): ────H^a8────────\n", "\n", "(4, 1): ────H^a9────────\n", "\n", "(5, 0): ────H^a10───────\n", "\n", "(5, 1): ────H^a11───────\n", "\n", "(6, 0): ────H^a12───────\n", "\n", "(6, 1): ────H^a13───────\n", "\n", "(7, 0): ────H^a14───────\n", "\n", "(7, 1): ────H^a15───────\n", "\n", "(8, 0): ────H^a16───────\n", "\n", "(8, 1): ────H^a17───────\n", "\n", "(9, 0): ────H^a18───────\n", "\n", "(9, 1): ────H^a19───────\n", "\n", "(20, 0): ───────────I───\n", "\n", "(20, 1): ───────────I───\n", "\n", "(20, 2): ───────────I───\n", "\n", "(20, 3): ───────────I───\n", "\n", "(20, 4): ───────────I───\n", "\n", "(20, 5): ───────────I───</pre>" ], "text/plain": [ "(0, 0): ────H^a0────────\n", "\n", "(0, 1): ────H^a1────────\n", "\n", "(1, 0): ────H^a2────────\n", "\n", "(1, 1): ────H^a3────────\n", "\n", "(2, 0): ────H^a4────────\n", "\n", "(2, 1): ────H^a5────────\n", "\n", "(3, 0): ────H^a6────────\n", "\n", "(3, 1): ────H^a7────────\n", "\n", "(4, 0): ────H^a8────────\n", "\n", "(4, 1): ────H^a9────────\n", "\n", "(5, 0): ────H^a10───────\n", "\n", "(5, 1): ────H^a11───────\n", "\n", "(6, 0): ────H^a12───────\n", "\n", "(6, 1): ────H^a13───────\n", "\n", "(7, 0): ────H^a14───────\n", "\n", "(7, 1): ────H^a15───────\n", "\n", "(8, 0): ────H^a16───────\n", "\n", "(8, 1): ────H^a17───────\n", "\n", "(9, 0): ────H^a18───────\n", "\n", "(9, 1): ────H^a19───────\n", "\n", "(20, 0): ───────────I───\n", "\n", "(20, 1): ───────────I───\n", "\n", "(20, 2): ───────────I───\n", "\n", "(20, 3): ───────────I───\n", "\n", "(20, 4): ───────────I───\n", "\n", "(20, 5): ───────────I───" ] }, "metadata": { "tags": [] }, "execution_count": 23 } ] }, { "cell_type": "code", "metadata": { "id": "lCG1Gw48MJf8" }, "source": [ "values = np.random.random(len(parameters))\n", "resolver = {s: v for s, v in zip(parameters, values)}\n", "print(resolver)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "XGTlxl6mMcIR" }, "source": [ "## Using Floq simulator" ] }, { "cell_type": "markdown", "metadata": { "id": "NJS68iN9Nf1o" }, "source": [ "Before going further, please **FORK THIS COLAB NOTEBOOK**, and **DO NOT SHARE YOUR API KEY WITH OTHERS PLEASE**" ] }, { "cell_type": "markdown", "metadata": { "id": "cgUmMYogWnsw" }, "source": [ "### Create & start a Floq instance" ] }, { "cell_type": "code", "metadata": { "id": "mHMKORPyLdxS" }, "source": [ "# Please specify your API_KEY\n", "API_KEY = \"\" #@param {type:\"string\"}" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "e-myF57Ibzes" }, "source": [ "!floq-client \"$API_KEY\" worker start" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "R51cZ4hbMmit" }, "source": [ "client = floq.client.CirqClient(API_KEY)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qu_KpPZYYS6K" }, "source": [ "### Expectation values from the circuit and measurements" ] }, { "cell_type": "code", "metadata": { "id": "WC_Q90CpMZBJ" }, "source": [ "energy = client.simulator.simulate_expectation_values(padded_circuit, measure, resolver)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3ZPG_TIxRpe7" }, "source": [ "# energy shows expectation values on each Pauli sum in measure.\n", "energy" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "rJmyrkkNbOhf" }, "source": [ "# Here is the total energy\n", "sum(energy)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gZKvtE8rbr28" }, "source": [ "### Samples from the circuit" ] }, { "cell_type": "code", "metadata": { "id": "PspLm1ZDWqlC" }, "source": [ "niter = 100\n", "samples = client.simulator.run(padded_circuit, resolver, niter)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Bx7LNZmyZJtY" }, "source": [ "samples" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UbscoejqdheC" }, "source": [ "### Stop the Floq instance" ] }, { "cell_type": "code", "metadata": { "id": "au7BJp1WdjjU" }, "source": [ "!floq-client \"$API_KEY\" worker stop" ], "execution_count": null, "outputs": [] } ] }

apache-2.0

m2dsupsdlclass/lectures-labs

labs/09_triplet_loss/triplet_loss_totally_looks_like.ipynb

1

21667

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Triplet Loss on Totally Looks Like dataset\n", "\n", "This notebook is inspired from [this Keras tutorial](https://keras.io/examples/vision/siamese_network/) by Hazem Essam and Santiago L. Valdarrama.\n", "\n", "The goal is to showcase the use of siamese networks and triplet loss to do representation learning using a CNN. It will also showcase data generators and data augmentation techniques.\n", "\n", "### Dataset\n", "\n", "The dataset considered is the [Totally Looks Like](https://sites.google.com/view/totally-looks-like-dataset) dataset, consisting of pairs of web curated similar looking images:\n", "\n", "Image pair 1 | Image pair 2\n", ":-------------------------:|:-------------------------:\n", " | \n", "\n", "The goal is to extract generic human perceptual representation through a CNN. The next cell downloads the dataset and unzips it (run it asap, it will download a few hundead megabytes)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import os.path as op\n", "from urllib.request import urlretrieve\n", "from pathlib import Path\n", "\n", "URL = \"https://github.com/m2dsupsdlclass/lectures-labs/releases/download/totallylookslike/dataset_totally.zip\"\n", "FILENAME = \"dataset_totally.zip\"\n", "\n", "if not op.exists(FILENAME):\n", " print('Downloading %s to %s...' % (URL, FILENAME))\n", " urlretrieve(URL, FILENAME)\n", "\n", "import zipfile\n", "if not op.exists(\"anchors\"):\n", " print('Extracting image files...')\n", " with zipfile.ZipFile(FILENAME, 'r') as zip_ref:\n", " zip_ref.extractall('.')\n", "\n", "home_dir = Path(Path.home())\n", "anchor_images_path = Path(\"./anchors\")\n", "positive_images_path = Path(\"./positives\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use mostly TensorFlow functions to open and process images:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def open_image(filename, target_shape = (256, 256)):\n", " \"\"\" Load the specified file as a JPEG image, preprocess it and\n", " resize it to the target shape.\n", " \"\"\"\n", " image_string = tf.io.read_file(filename)\n", " image = tf.image.decode_jpeg(image_string, channels=3)\n", " image = tf.image.convert_image_dtype(image, tf.float32)\n", " image = tf.image.resize(image, target_shape)\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "# Careful to sort images folders so that the anchor and positive images correspond.\n", "anchor_images = sorted([str(anchor_images_path / f) for f in os.listdir(anchor_images_path)])\n", "positive_images = sorted([str(positive_images_path / f) for f in os.listdir(positive_images_path)])\n", "\n", "anchor_count = len(anchor_images)\n", "positive_count = len(positive_images)\n", "\n", "print(f\"number of anchors: {anchor_count}, positive: {positive_count}\")\n", "\n", "anchor_dataset_files = tf.data.Dataset.from_tensor_slices(anchor_images)\n", "anchor_dataset = anchor_dataset_files.map(open_image)\n", "positive_dataset_files = tf.data.Dataset.from_tensor_slices(positive_images)\n", "positive_dataset = positive_dataset_files.map(open_image)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt \n", "\n", "def visualize(img_list):\n", " \"\"\"Visualize a list of images\"\"\"\n", " def show(ax, image):\n", " ax.imshow(image)\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " fig = plt.figure(figsize=(6, 18))\n", " \n", " num_imgs = len(img_list)\n", " \n", " axs = fig.subplots(1, num_imgs)\n", " for i in range(num_imgs):\n", " show(axs[i], img_list[i])\n", "\n", "# display the first element of our dataset\n", "anc = next(iter(anchor_dataset))\n", "pos = next(iter(positive_dataset))\n", "visualize([anc, pos])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import layers\n", "\n", "# data augmentations\n", "data_augmentation = tf.keras.Sequential([\n", " layers.RandomFlip(\"horizontal\"),\n", " # layers.RandomRotation(0.15), # you may add random rotations\n", " layers.RandomCrop(224, 224)\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate the list of negative images, let's randomize the list of available images (anchors and positives) and concatenate them together.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "\n", "rng = np.random.RandomState(seed=42)\n", "rng.shuffle(anchor_images)\n", "rng.shuffle(positive_images)\n", "\n", "negative_images = anchor_images + positive_images\n", "np.random.RandomState(seed=32).shuffle(negative_images)\n", "\n", "negative_dataset_files = tf.data.Dataset.from_tensor_slices(negative_images)\n", "negative_dataset_files = negative_dataset_files.shuffle(buffer_size=4096)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Build final triplet dataset\n", "dataset = tf.data.Dataset.zip((anchor_dataset_files, positive_dataset_files, negative_dataset_files))\n", "dataset = dataset.shuffle(buffer_size=1024)\n", "\n", "# preprocess function\n", "def preprocess_triplets(anchor, positive, negative):\n", " return (\n", " data_augmentation(open_image(anchor)),\n", " data_augmentation(open_image(positive)),\n", " data_augmentation(open_image(negative)),\n", " )\n", "\n", "# The map function is lazy, it is not evaluated on the spot, \n", "# but each time a batch is sampled.\n", "dataset = dataset.map(preprocess_triplets)\n", "\n", "# Let's now split our dataset in train and validation.\n", "train_dataset = dataset.take(round(anchor_count * 0.8))\n", "val_dataset = dataset.skip(round(anchor_count * 0.8))\n", "\n", "# define the batch size\n", "train_dataset = train_dataset.batch(32, drop_remainder=False)\n", "train_dataset = train_dataset.prefetch(8)\n", "\n", "val_dataset = val_dataset.batch(32, drop_remainder=False)\n", "val_dataset = val_dataset.prefetch(8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can visualize a triplet and display its shape:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "anc_batch, pos_batch, neg_batch = next(train_dataset.take(1).as_numpy_iterator())\n", "print(anc_batch.shape, pos_batch.shape, neg_batch.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "idx = np.random.randint(0, 32)\n", "visualize([anc_batch[idx], pos_batch[idx], neg_batch[idx]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Build the embedding network, starting from a resnet and adding a few layers. The output should have a dimension $d= 128$ or $d=256$. Edit the following code, and you may use the next cell to test your code.\n", "\n", "Bonus: Try to freeze the weights of the ResNet." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import Model, layers\n", "from tensorflow.keras import optimizers, losses, metrics, applications\n", "from tensorflow.keras.applications import resnet\n", "\n", "input_img = layers.Input((224,224,3))\n", "\n", "output = input_img # change that line and edit this code!\n", "\n", "embedding = Model(input_img, output, name=\"Embedding\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = embedding(np.random.randn(1,224,224,3))\n", "output.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following can be run to get the same architecture as we have:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import Model, layers\n", "from tensorflow.keras import optimizers, losses, metrics, applications\n", "from tensorflow.keras.applications import resnet\n", "\n", "input_img = layers.Input((224,224,3))\n", "\n", "base_cnn = resnet.ResNet50(weights=\"imagenet\", input_shape=(224,224,3), include_top=False)\n", "resnet_output = base_cnn(input_img)\n", "\n", "flatten = layers.Flatten()(resnet_output)\n", "dense1 = layers.Dense(512, activation=\"relu\")(flatten)\n", "# The batch normalization layer enables to normalize the activations\n", "# over the batch\n", "dense1 = layers.BatchNormalization()(dense1)\n", "dense2 = layers.Dense(256, activation=\"relu\")(dense1)\n", "dense2 = layers.BatchNormalization()(dense2)\n", "output = layers.Dense(256)(dense2)\n", "\n", "embedding = Model(input_img, output, name=\"Embedding\")\n", "\n", "trainable = False\n", "for layer in base_cnn.layers:\n", " if layer.name == \"conv5_block1_out\":\n", " trainable = True\n", " layer.trainable = trainable" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def preprocess(x):\n", " \"\"\" we'll need to preprocess the input before passing them\n", " to the resnet for better results. This is the same preprocessing\n", " that was used during the training of ResNet on ImageNet.\n", " \"\"\"\n", " return resnet.preprocess_input(x * 255.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Our goal is now to build the positive and negative distances from 3 inputs images: the anchor, the positive, and the negative one $‖f(A) - f(P)‖²$ $‖f(A) - f(N)‖²$. You may define a specific Layer using the [Keras subclassing API](https://keras.io/guides/making_new_layers_and_models_via_subclassing/), or any other method.\n", "\n", "You will need to run the Embedding model previously defined, don't forget to apply the preprocessing function defined above!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "anchor_input = layers.Input(name=\"anchor\", shape=(224, 224, 3))\n", "positive_input = layers.Input(name=\"positive\", shape=(224, 224, 3))\n", "negative_input = layers.Input(name=\"negative\", shape=(224, 224, 3))\n", "\n", "distances = [anchor_input, positive_input] # TODO: Change this code to actually compute the distances\n", "\n", "siamese_network = Model(\n", " inputs=[anchor_input, positive_input, negative_input], outputs=distances\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solution: run the following cell to get the exact same method as we have." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class DistanceLayer(layers.Layer):\n", " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", "\n", " def call(self, anchor, positive, negative):\n", " ap_distance = tf.reduce_sum(tf.square(anchor - positive), -1)\n", " an_distance = tf.reduce_sum(tf.square(anchor - negative), -1)\n", " return (ap_distance, an_distance)\n", "\n", "\n", "anchor_input = layers.Input(name=\"anchor\", shape=(224, 224, 3))\n", "positive_input = layers.Input(name=\"positive\", shape=(224, 224, 3))\n", "negative_input = layers.Input(name=\"negative\", shape=(224, 224, 3))\n", "\n", "distances = DistanceLayer()(\n", " embedding(preprocess(anchor_input)),\n", " embedding(preprocess(positive_input)),\n", " embedding(preprocess(negative_input)),\n", ")\n", "\n", "siamese_network = Model(\n", " inputs=[anchor_input, positive_input, negative_input], outputs=distances\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The final triplet model\n", "Once we are able to produce the distances, we may wrap it into a new Keras Model which includes the computation of the loss. The following implementation uses a subclassing of the Model class, redefining a few functions used internally during `model.fit`: `call`, `train_step`, `test_step` " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class TripletModel(Model):\n", " \"\"\"The Final Keras Model with a custom training and testing loops.\n", "\n", " Computes the triplet loss using the three embeddings produced by the\n", " Siamese Network.\n", "\n", " The triplet loss is defined as:\n", " L(A, P, N) = max(‖f(A) - f(P)‖² - ‖f(A) - f(N)‖² + margin, 0)\n", " \"\"\"\n", "\n", " def __init__(self, siamese_network, margin=0.5):\n", " super(TripletModel, self).__init__()\n", " self.siamese_network = siamese_network\n", " self.margin = margin\n", " self.loss_tracker = metrics.Mean(name=\"loss\")\n", "\n", " def call(self, inputs):\n", " return self.siamese_network(inputs)\n", "\n", " def train_step(self, data):\n", " # GradientTape is a context manager that records every operation that\n", " # you do inside. We are using it here to compute the loss so we can get\n", " # the gradients and apply them using the optimizer specified in\n", " # `compile()`.\n", " with tf.GradientTape() as tape:\n", " loss = self._compute_loss(data)\n", "\n", " # Storing the gradients of the loss function with respect to the\n", " # weights/parameters.\n", " gradients = tape.gradient(loss, self.siamese_network.trainable_weights)\n", "\n", " # Applying the gradients on the model using the specified optimizer\n", " self.optimizer.apply_gradients(\n", " zip(gradients, self.siamese_network.trainable_weights)\n", " )\n", "\n", " # Let's update and return the training loss metric.\n", " self.loss_tracker.update_state(loss)\n", " return {\"loss\": self.loss_tracker.result()}\n", "\n", " def test_step(self, data):\n", " loss = self._compute_loss(data)\n", " self.loss_tracker.update_state(loss)\n", " return {\"loss\": self.loss_tracker.result()}\n", "\n", " def _compute_loss(self, data):\n", " # The output of the network is a tuple containing the distances\n", " # between the anchor and the positive example, and the anchor and\n", " # the negative example.\n", " ap_distance, an_distance = self.siamese_network(data)\n", "\n", " loss = ap_distance - an_distance\n", " loss = tf.maximum(loss + self.margin, 0.0)\n", " return loss\n", "\n", " @property\n", " def metrics(self):\n", " # We need to list our metrics here so the `reset_states()` can be\n", " # called automatically.\n", " return [self.loss_tracker]\n", "\n", "\n", "siamese_model = TripletModel(siamese_network)\n", "siamese_model.compile(optimizer=optimizers.Adam(0.0001))\n", "siamese_model.fit(train_dataset, epochs=10, validation_data=val_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "embedding.save('best_model.h5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# uncomment to get a pretrained model\n", "url_pretrained = \"https://github.com/m2dsupsdlclass/lectures-labs/releases/download/totallylookslike/best_model.h5\"\n", "urlretrieve(url_pretrained, \"best_model.h5\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "loaded_model = tf.keras.models.load_model('best_model.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find most similar images in test dataset\n", "\n", "The `negative_images` list was built by concatenating all possible images, both anchors and positive. We can reuse these to form a bank of possible images to query from.\n", "\n", "We will first compute all embeddings of these images. To do so, we build a `tf.Dataset` and apply the few functions: `open_img` and `preprocess`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from functools import partial\n", "\n", "open_img = partial(open_image, target_shape=(224,224))\n", "all_img_files = tf.data.Dataset.from_tensor_slices(negative_images)\n", "dataset = all_img_files.map(open_img).map(preprocess).take(1024).batch(32, drop_remainder=False).prefetch(8)\n", "all_embeddings = loaded_model.predict(dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can build a `most_similar` function which takes an image path as input and return the `topn` most similar images through the embedding representation. It would be possible to use another metric, such as the cosine similarity here." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "random_img = np.random.choice(negative_images)\n", "\n", "def most_similar(img, topn=5):\n", " img_batch = tf.expand_dims(open_image(img, target_shape=(224, 224)), 0)\n", " new_emb = loaded_model.predict(preprocess(img_batch))\n", " dists = tf.sqrt(tf.reduce_sum((all_embeddings - new_emb)**2, -1)).numpy()\n", " idxs = np.argsort(dists)[:topn]\n", " return [(negative_images[idx], dists[idx]) for idx in idxs]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(random_img)\n", "most_similar(random_img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "random_img = np.random.choice(negative_images)\n", "visualize([open_image(im) for im, _ in most_similar(random_img)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this is not a rigorous evaluation, as we are using the images from the training set for both the query and the possible images. You may try with a completely different picture!\n", "\n", "### Going further\n", "\n", "In order to improve the training efficiency, hard negative mining would be most relevant in that case." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "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": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }

mit

mne-tools/mne-tools.github.io

0.22/_downloads/243172b1ef6a2d804d3245b8c0a927ef/plot_60_maxwell_filtering_sss.ipynb

2

19280

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Signal-space separation (SSS) and Maxwell filtering\n\nThis tutorial covers reducing environmental noise and compensating for head\nmovement with SSS and Maxwell filtering.\n :depth: 2\n\nAs usual we'll start by importing the modules we need, loading some\n`example data <sample-dataset>`, and cropping it to save on memory:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport mne\nfrom mne.preprocessing import find_bad_channels_maxwell\n\nsample_data_folder = mne.datasets.sample.data_path()\nsample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',\n 'sample_audvis_raw.fif')\nraw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)\nraw.crop(tmax=60)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background on SSS and Maxwell filtering\n\nSignal-space separation (SSS) :footcite:`TauluKajola2005,TauluSimola2006`\nis a technique based on the physics\nof electromagnetic fields. SSS separates the measured signal into components\nattributable to sources *inside* the measurement volume of the sensor array\n(the *internal components*), and components attributable to sources *outside*\nthe measurement volume (the *external components*). The internal and external\ncomponents are linearly independent, so it is possible to simply discard the\nexternal components to reduce environmental noise. *Maxwell filtering* is a\nrelated procedure that omits the higher-order components of the internal\nsubspace, which are dominated by sensor noise. Typically, Maxwell filtering\nand SSS are performed together (in MNE-Python they are implemented together\nin a single function).\n\nLike `SSP <tut-artifact-ssp>`, SSS is a form of projection. Whereas SSP\nempirically determines a noise subspace based on data (empty-room recordings,\nEOG or ECG activity, etc) and projects the measurements onto a subspace\northogonal to the noise, SSS mathematically constructs the external and\ninternal subspaces from `spherical harmonics`_ and reconstructs the sensor\nsignals using only the internal subspace (i.e., does an oblique projection).\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Maxwell filtering was originally developed for Elekta Neuromag\u00ae systems,\n and should be considered *experimental* for non-Neuromag data. See the\n Notes section of the :func:`~mne.preprocessing.maxwell_filter` docstring\n for details.</p></div>\n\nThe MNE-Python implementation of SSS / Maxwell filtering currently provides\nthe following features:\n\n- Basic bad channel detection\n (:func:`~mne.preprocessing.find_bad_channels_maxwell`)\n- Bad channel reconstruction\n- Cross-talk cancellation\n- Fine calibration correction\n- tSSS\n- Coordinate frame translation\n- Regularization of internal components using information theory\n- Raw movement compensation (using head positions estimated by MaxFilter)\n- cHPI subtraction (see :func:`mne.chpi.filter_chpi`)\n- Handling of 3D (in addition to 1D) fine calibration files\n- Epoch-based movement compensation as described in\n :footcite:`TauluKajola2005` through :func:`mne.epochs.average_movements`\n- **Experimental** processing of data from (un-compensated) non-Elekta\n systems\n\n\n## Using SSS and Maxwell filtering in MNE-Python\n\nFor optimal use of SSS with data from Elekta Neuromag\u00ae systems, you should\nprovide the path to the fine calibration file (which encodes site-specific\ninformation about sensor orientation and calibration) as well as a crosstalk\ncompensation file (which reduces interference between Elekta's co-located\nmagnetometer and paired gradiometer sensor units).\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fine_cal_file = os.path.join(sample_data_folder, 'SSS', 'sss_cal_mgh.dat')\ncrosstalk_file = os.path.join(sample_data_folder, 'SSS', 'ct_sparse_mgh.fif')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we perform SSS we'll look for bad channels \u2014 ``MEG 2443`` is quite\nnoisy.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>It is critical to mark bad channels in ``raw.info['bads']`` *before*\n calling :func:`~mne.preprocessing.maxwell_filter` in order to prevent\n bad channel noise from spreading.</p></div>\n\nLet's see if we can automatically detect it.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw.info['bads'] = []\nraw_check = raw.copy()\nauto_noisy_chs, auto_flat_chs, auto_scores = find_bad_channels_maxwell(\n raw_check, cross_talk=crosstalk_file, calibration=fine_cal_file,\n return_scores=True, verbose=True)\nprint(auto_noisy_chs) # we should find them!\nprint(auto_flat_chs) # none for this dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><h4>Note</h4><p>`~mne.preprocessing.find_bad_channels_maxwell` needs to operate on\n a signal without line noise or cHPI signals. By default, it simply\n applies a low-pass filter with a cutoff frequency of 40 Hz to the\n data, which should remove these artifacts. You may also specify a\n different cutoff by passing the ``h_freq`` keyword argument. If you\n set ``h_freq=None``, no filtering will be applied. This can be\n useful if your data has already been preconditioned, for example\n using :func:`mne.chpi.filter_chpi`,\n :func:`mne.io.Raw.notch_filter`, or :meth:`mne.io.Raw.filter`.</p></div>\n\nNow we can update the list of bad channels in the dataset.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bads = raw.info['bads'] + auto_noisy_chs + auto_flat_chs\nraw.info['bads'] = bads" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We called `~mne.preprocessing.find_bad_channels_maxwell` with the optional\nkeyword argument ``return_scores=True``, causing the function to return a\ndictionary of all data related to the scoring used to classify channels as\nnoisy or flat. This information can be used to produce diagnostic figures.\n\nIn the following, we will generate such visualizations for\nthe automated detection of *noisy* gradiometer channels.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Only select the data forgradiometer channels.\nch_type = 'grad'\nch_subset = auto_scores['ch_types'] == ch_type\nch_names = auto_scores['ch_names'][ch_subset]\nscores = auto_scores['scores_noisy'][ch_subset]\nlimits = auto_scores['limits_noisy'][ch_subset]\nbins = auto_scores['bins'] # The the windows that were evaluated.\n# We will label each segment by its start and stop time, with up to 3\n# digits before and 3 digits after the decimal place (1 ms precision).\nbin_labels = [f'{start:3.3f} \u2013 {stop:3.3f}'\n for start, stop in bins]\n\n# We store the data in a Pandas DataFrame. The seaborn heatmap function\n# we will call below will then be able to automatically assign the correct\n# labels to all axes.\ndata_to_plot = pd.DataFrame(data=scores,\n columns=pd.Index(bin_labels, name='Time (s)'),\n index=pd.Index(ch_names, name='Channel'))\n\n# First, plot the \"raw\" scores.\nfig, ax = plt.subplots(1, 2, figsize=(12, 8))\nfig.suptitle(f'Automated noisy channel detection: {ch_type}',\n fontsize=16, fontweight='bold')\nsns.heatmap(data=data_to_plot, cmap='Reds', cbar_kws=dict(label='Score'),\n ax=ax[0])\n[ax[0].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')\n for x in range(1, len(bins))]\nax[0].set_title('All Scores', fontweight='bold')\n\n# Now, adjust the color range to highlight segments that exceeded the limit.\nsns.heatmap(data=data_to_plot,\n vmin=np.nanmin(limits), # bads in input data have NaN limits\n cmap='Reds', cbar_kws=dict(label='Score'), ax=ax[1])\n[ax[1].axvline(x, ls='dashed', lw=0.25, dashes=(25, 15), color='gray')\n for x in range(1, len(bins))]\nax[1].set_title('Scores > Limit', fontweight='bold')\n\n# The figure title should not overlap with the subplots.\nfig.tight_layout(rect=[0, 0.03, 1, 0.95])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\"><h4>Note</h4><p>You can use the very same code as above to produce figures for\n *flat* channel detection. Simply replace the word \"noisy\" with\n \"flat\", and replace ``vmin=np.nanmin(limits)`` with\n ``vmax=np.nanmax(limits)``.</p></div>\n\nYou can see the un-altered scores for each channel and time segment in the\nleft subplots, and thresholded scores \u2013 those which exceeded a certain limit\nof noisiness \u2013 in the right subplots. While the right subplot is entirely\nwhite for the magnetometers, we can see a horizontal line extending all the\nway from left to right for the gradiometers. This line corresponds to channel\n``MEG 2443``, which was reported as auto-detected noisy channel in the step\nabove. But we can also see another channel exceeding the limits, apparently\nin a more transient fashion. It was therefore *not* detected as bad, because\nthe number of segments in which it exceeded the limits was less than 5,\nwhich MNE-Python uses by default.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>You can request a different number of segments that must be\n found to be problematic before\n `~mne.preprocessing.find_bad_channels_maxwell` reports them as bad.\n To do this, pass the keyword argument ``min_count`` to the\n function.</p></div>\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Obviously, this algorithm is not perfect. Specifically, on closer inspection\nof the raw data after looking at the diagnostic plots above, it becomes clear\nthat the channel exceeding the \"noise\" limits in some segments without\nqualifying as \"bad\", in fact contains some flux jumps. There were just not\n*enough* flux jumps in the recording for our automated procedure to report\nthe channel as bad. So it can still be useful to manually inspect and mark\nbad channels. The channel in question is ``MEG 2313``. Let's mark it as bad:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw.info['bads'] += ['MEG 2313'] # from manual inspection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After that, performing SSS and Maxwell filtering is done with a\nsingle call to :func:`~mne.preprocessing.maxwell_filter`, with the crosstalk\nand fine calibration filenames provided (if available):\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_sss = mne.preprocessing.maxwell_filter(\n raw, cross_talk=crosstalk_file, calibration=fine_cal_file, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see the effect, we can plot the data before and after SSS / Maxwell\nfiltering.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw.pick(['meg']).plot(duration=2, butterfly=True)\nraw_sss.pick(['meg']).plot(duration=2, butterfly=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that channels marked as \"bad\" have been effectively repaired by SSS,\neliminating the need to perform `interpolation <tut-bad-channels>`.\nThe heartbeat artifact has also been substantially reduced.\n\nThe :func:`~mne.preprocessing.maxwell_filter` function has parameters\n``int_order`` and ``ext_order`` for setting the order of the spherical\nharmonic expansion of the interior and exterior components; the default\nvalues are appropriate for most use cases. Additional parameters include\n``coord_frame`` and ``origin`` for controlling the coordinate frame (\"head\"\nor \"meg\") and the origin of the sphere; the defaults are appropriate for most\nstudies that include digitization of the scalp surface / electrodes. See the\ndocumentation of :func:`~mne.preprocessing.maxwell_filter` for details.\n\n\n## Spatiotemporal SSS (tSSS)\n\nAn assumption of SSS is that the measurement volume (the spherical shell\nwhere the sensors are physically located) is free of electromagnetic sources.\nThe thickness of this source-free measurement shell should be 4-8 cm for SSS\nto perform optimally. In practice, there may be sources falling within that\nmeasurement volume; these can often be mitigated by using Spatiotemporal\nSignal Space Separation (tSSS) :footcite:`TauluSimola2006`.\ntSSS works by looking for temporal\ncorrelation between components of the internal and external subspaces, and\nprojecting out any components that are common to the internal and external\nsubspaces. The projection is done in an analogous way to\n`SSP <tut-artifact-ssp>`, except that the noise vector is computed\nacross time points instead of across sensors.\n\nTo use tSSS in MNE-Python, pass a time (in seconds) to the parameter\n``st_duration`` of :func:`~mne.preprocessing.maxwell_filter`. This will\ndetermine the \"chunk duration\" over which to compute the temporal projection.\nThe chunk duration effectively acts as a high-pass filter with a cutoff\nfrequency of $\\frac{1}{\\mathtt{st\\_duration}}~\\mathrm{Hz}$; this\neffective high-pass has an important consequence:\n\n- In general, larger values of ``st_duration`` are better (provided that your\n computer has sufficient memory) because larger values of ``st_duration``\n will have a smaller effect on the signal.\n\nIf the chunk duration does not evenly divide your data length, the final\n(shorter) chunk will be added to the prior chunk before filtering, leading\nto slightly different effective filtering for the combined chunk (the\neffective cutoff frequency differing at most by a factor of 2). If you need\nto ensure identical processing of all analyzed chunks, either:\n\n- choose a chunk duration that evenly divides your data length (only\n recommended if analyzing a single subject or run), or\n\n- include at least ``2 * st_duration`` of post-experiment recording time at\n the end of the :class:`~mne.io.Raw` object, so that the data you intend to\n further analyze is guaranteed not to be in the final or penultimate chunks.\n\nAdditional parameters affecting tSSS include ``st_correlation`` (to set the\ncorrelation value above which correlated internal and external components\nwill be projected out) and ``st_only`` (to apply only the temporal projection\nwithout also performing SSS and Maxwell filtering). See the docstring of\n:func:`~mne.preprocessing.maxwell_filter` for details.\n\n\n## Movement compensation\n\nIf you have information about subject head position relative to the sensors\n(i.e., continuous head position indicator coils, or :term:`cHPI <HPI>`), SSS\ncan take that into account when projecting sensor data onto the internal\nsubspace. Head position data can be computed using\n:func:`mne.chpi.compute_chpi_locs` and :func:`mne.chpi.compute_head_pos`,\nor loaded with the:func:`mne.chpi.read_head_pos` function. The\n`example data <sample-dataset>` doesn't include cHPI, so here we'll\nload a :file:`.pos` file used for testing, just to demonstrate:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "head_pos_file = os.path.join(mne.datasets.testing.data_path(), 'SSS',\n 'test_move_anon_raw.pos')\nhead_pos = mne.chpi.read_head_pos(head_pos_file)\nmne.viz.plot_head_positions(head_pos, mode='traces')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cHPI data file could also be passed as the ``head_pos`` parameter of\n:func:`~mne.preprocessing.maxwell_filter`. Not only would this account for\nmovement within a given recording session, but also would effectively\nnormalize head position across different measurement sessions and subjects.\nSee `here <example-movement-comp>` for an extended example of applying\nmovement compensation during Maxwell filtering / SSS. Another option is to\napply movement compensation when averaging epochs into an\n:class:`~mne.Evoked` instance, using the :func:`mne.epochs.average_movements`\nfunction.\n\nEach of these approaches requires time-varying estimates of head position,\nwhich is obtained from MaxFilter using the ``-headpos`` and ``-hp``\narguments (see the MaxFilter manual for details).\n\n\n## Caveats to using SSS / Maxwell filtering\n\n1. There are patents related to the Maxwell filtering algorithm, which may\n legally preclude using it in commercial applications. More details are\n provided in the documentation of\n :func:`~mne.preprocessing.maxwell_filter`.\n\n2. SSS works best when both magnetometers and gradiometers are present, and\n is most effective when gradiometers are planar (due to the need for very\n accurate sensor geometry and fine calibration information). Thus its\n performance is dependent on the MEG system used to collect the data.\n\n\n## References\n\n.. footbibliography::\n\n\n.. LINKS\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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

dragly/doconce

doc/pub/ipynb/info.ipynb

3

20895

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- dom:TITLE: Special DocOnce features for Jupyter notebooks -->\n", "# Special DocOnce features for Jupyter notebooks\n", "<!-- dom:AUTHOR: Hans Petter Langtangen at Simula & University of Oslo -->\n", "<!-- Author: --> **Hans Petter Langtangen**, Simula and University of Oslo\n", "\n", "Date: **May 25, 2015**\n", "\n", "DocOnce enables turning book chapters, manuals, research papers, and in fact\n", "any type of document into Jupyter Notebooks (formerly knowns as IPython\n", "Notebooks). This note outlines some special features that one should\n", "be aware of and that can be used to tune the notebook typesetting.\n", "\n", "# Interactive sessions\n", "\n", "By default, interactive Python sessions in `!bc pyshell` and `!bc ipy`\n", "environments are (for the `ipynb` format)\n", "split such that the output is removed and each\n", "input part is a separate cell. This means that when executing\n", "all the cells, one recreates the entire interactive session\n", "with all the output. Below is an example.\n", "\n", "## Solving the world's simplest differential equation\n", "<div id=\"ipynb:de:simplest\"></div>\n", "\n", "Let us explore SymPy to solve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "y' = y,\\quad y(0)=y_0 = 2\\thinspace .\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "init_printing(use_latex=True) # find the best method to print\n", "t = symbols('t', real=True, positive=True)\n", "y = symbols('y', cls=Function)\n", "# Solve differential equation using dsolve\n", "eq = diff(y(t), t) - y(t)\n", "print eq" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sol = dsolve(eq)\n", "print sol" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = sol.rhs # grab right-hand side of equation\n", "# Determine integration constant C1 from initial condition\n", "C1 = symbols('C1')\n", "y0 = 2\n", "eq = y.subs(t, 0) - y0 # equation for initial condition\n", "print eq" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sol = solve(eq, C1) # solve wrt C1\n", "print sol" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = y.subs(C1, sol[0]) # insert C1=2 in solution\n", "print y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print latex(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The DocOnce input syntax of the first part of the above session looks like\n", "this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " !bc pyshell\n", " >>> from sympy import *\n", " >>> t = symbols('t', real=True, positive=True)\n", " >>> y = symbols('y', cls=Function)\n", " >>> # Solve differential equation using dsolve\n", " >>> eq = diff(y(t), t) - y(t)\n", " >>> print eq\n", " -y(t) + Derivative(y(t), t)\n", " >>> sol = dsolve(eq)\n", " >>> print sol\n", " y(t) == C1*exp(t)\n", " ...\n", " ...\n", " !ec\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is, the interactive session looks exactly as it does in the terminal\n", "window with the primitive Python shell.\n", "\n", "We can, alternatively, use IPython syntax:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " !bc ipy\n", " In [1]: from sympy import *\n", " In [2]: t = symbols('t', real=True, positive=True)\n", " In [3]: y = symbols('y', cls=Function)\n", " In [4]: # Solve differential equation using dsolve\n", " In [5]: eq = diff(y(t), t) - y(t)\n", " In [6]: print eq\n", " Out[6]: -y(t) + Derivative(y(t), t)\n", " In [7]: sol = dsolve(eq)\n", " In [8]: print sol\n", " Out[8]: y(t) == C1*exp(t)\n", " !ec\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This last `ipy` environment results in exactly the same interactive session\n", "in all formats except `ipynb` where the output is removed and the\n", "input is split over *two* cells. In format **ipynb** the above\n", "block is rendered as" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "t = symbols('t', real=True, positive=True)\n", "y = symbols('y', cls=Function)\n", "# Solve differential equation using dsolve\n", "eq = diff(y(t), t) - y(t)\n", "print eq" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sol = dsolve(eq)\n", "print sol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is an option `--ipynb_split_pyshell=off` that can be given to\n", "`doconce format ipynb` when compiling documents and that turns off\n", "the behaviour that interactive sessions are split into multiple\n", "cells. The result is then one single cell, and if we have\n", "\"printing\" as in `>>> eq`, there will be no output, except from\n", "the last one, in the output field in the notebook. This is usually\n", "not the behavior you want.\n", "\n", "\n", "## Showing an interactive session as pure text\n", "\n", "Sometimes one wants to show an interactive session exactly as it looks like,\n", "with the input and the output.\n", "This can be done in the notebook by using the `pyshell-t`\n", "or `ipy-t` environments (`-t` for *plain text* display).\n", "With this DocOnce input," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " !bc pyshell-t\n", " >>> a = 1\n", " >>> b = 2\n", " >>> a + b\n", " 3\n", " !ec\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we get the plain text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```Python\n", " >>> a = 1\n", " >>> b = 2\n", " >>> a + b\n", " 3\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `-t` postfix can be used for any code that you want to display as\n", "text rather than as an executable cell. For example, with `!bc pycod-t`\n", "we create verbatim text and not a cell:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```Python\n", " import MySpecialModule as m\n", " print m.main()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary code blocks\n", "\n", "For ordinary book and manual writing, interactive sessions are used\n", "when the results of statements are important. Otherwise one applies\n", "standard code blocks. A standard code block can for example be" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " !bc pycod\n", " x = np.linspace(0, 4*np.pi, 501)\n", " y = np.exp(-0.5*x)*np.sin(np.pi*x)\n", " plt.plot(x, y)\n", " !ec\n", " The result of this code segment appears in Figure ref{myfig}.\n", " \n", " FIGURE: [fig/myfig, width=500 frac=0.8] Plot. label{myfig}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This snippet turns out fine in all formats, except the notebook.\n", "The problem with notebooks is two-fold:\n", "\n", "1. The snippet does not run without import of `numpy` and `matplotlib`.\n", "\n", "2. The snippet results in a plot automatically, and with the figure\n", " in addittion, we get two plots.\n", "\n", "The remedy for problem 1 is to use *hidden code blocks*, notified as\n", "`!bc pyhid` for Python code and `!bc Xhid` in general for language `X`:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " !bc pyhid\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", " !ec\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Such code blocks are invisible in all formats except for `ipynb`.\n", "Books, manuals, and research papers will very often contain code\n", "snippets that do not run without (extensive) extra code. This exatra\n", "code must be provided in hidden code blocks for successful conversion\n", "to notebooks. If these cells take too much attention (that is probably\n", "why they were left out of the text...), one can insert some comments\n", "to explain that fact.\n", "\n", "Problem 2 is solved using one of the preprocessors and an if test on\n", "the format, e.g., with Preprocess:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " # #if FORMAT != 'ipynb'\n", " The result of this code segment appears in Figure ref{myfig}.\n", " \n", " FIGURE: [fig/myfig, width=500 frac=0.8] Plot. label{myfig}\n", " # #endif\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can write the complete code segment with a preceding hidden\n", "block for import and the if test. Below is the rendering of\n", "this in the format **ipynb**.\n", "\n", "<!-- Note: since Preprocess is used in verbatim block above, we must compile -->\n", "<!-- this document with --no_preprocess and therefore use Mako if test -->\n", "<!-- to test on the format -->" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.linspace(0, 4*np.pi, 501)\n", "y = np.exp(-0.5*x)*np.sin(np.pi*x)\n", "plt.plot(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `%matplotlib inline` is automatically inserted before the\n", "first import of `matplotlib` in a DocOnce-generated notebook such\n", "that all plots are inlined.\n", "\n", "(Hidden code blocks are also relevant for RunestoneInteractive\n", "books, which are made from Sphinx output.)\n", "\n", "# Other special options for notebooks\n", "\n", "## Figures and movies\n", "\n", "Figures and movies can be implemented in several ways in notebooks, depending\n", "on the value of the options `--ipynb_figure=` and `ipynb_movie=`. For\n", "the former we have the values\n", "\n", " * `md`: plain Markdown syntax for a figure, with no possibility to adjust\n", " the size (default)\n", "\n", " * `imgtag`: `<img ...>` tag in HTML taking the specified width into account\n", "\n", " * `Image`: Python notebook cell with `Image` object\n", "\n", "Below is an example\n", "with `--ipynb_figure=imgtag`.\n", "\n", "<!-- dom:FIGURE: [https://raw.githubusercontent.com/hplgit/doconce/master/doc/src/manual/fig/wave1D.png, width=400] -->\n", "<!-- begin figure -->\n", "\n", "<p></p>\n", "<img src=\"https://raw.githubusercontent.com/hplgit/doconce/master/doc/src/manual/fig/wave1D.png\" width=400>\n", "\n", "<!-- end figure -->\n", "\n", "\n", "\n", "For the movies we have the values\n", "\n", " * `md`: raw HTML code with `iframe` tag - not relevant for the notebook\n", "\n", " * `HTML`: raw HTML code with `iframe` tag\n", " embedded in the `HTML` object from the notebook (default)\n", "\n", " * `HTML-YouTube`: as `HTML` but use an `IPython.display.YouTubeVideo`\n", " object to display YouTube videos\n", "\n", " * `ipynb`: use `IPython.display.YouTubeVideo` object for YouTube videos,\n", " and use an `HTML` object with `video` tag for local movies\n", "\n", "Below is an example\n", "with `--ipynb_movie=ipynb`.\n", "Execute the cell to create the YouTube video object.\n", "\n", "<!-- dom:MOVIE: [http://youtu.be/PtJrPEIHNJw, width=640 height=480] -->\n", "<!-- begin movie -->" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo(\"PtJrPEIHNJw\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- end movie -->\n", "\n", "\n", "\n", "## Admonitions\n", "\n", "Markdown has no support for admonitions while DocOnce has extensive\n", "support. Some methods for simulating admonitions in notebooks have\n", "therefore been implemented. These are specified by the `--ipynb_admon=`\n", "command-line option.\n", "\n", " * `quote`: typeset admon as Markdown quote (special font and gray vertical bar on\n", " the left)\n", "\n", " * `paragraph`: typeset admon as a plain paragraph with a heading if any (default)\n", "\n", " * `hrule`: use a horozontal rule to surround the heading and the text\n", "\n", "Note that quotes in `!bc quote` environments\n", "are always typeset as Markdown quotes.\n", "\n", "Here are two examples\n", "typeset with `--ipynb_admon=hrule`.\n", "\n", "\n", "\n", "<hr/>\n", "**Notebooks have limited support for typesetting admons.**\n", "\n", "Admon environments must be simulated using Markdown quote environment,\n", "a plain paragraph, or decorations with HTML `<hr>` hrules.\n", "<hr/>\n", "\n", "\n", "\n", "\n", "\n", "<hr/>\n", "**Splitting documents.**\n", "\n", "Formats like `html` and `sphinx` support splitting documents into multiple\n", "web pages. The `!split` specification has no support in notebooks. This\n", "means that long documents *must* be long notebooks.\n", "<hr/>\n", "\n", "\n", "\n", "# Equation references\n", "\n", "Markdown (and thereby the Jupyter Notebook)\n", "does not support references to equations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"eq1\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", "a = 1,\n", "\\label{eq1} \\tag{1}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"eq2\"></div>\n", "\n", "$$\n", "\\begin{equation} \n", "b =2\n", "\\label{eq2} \\tag{2}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can we refer to [(1)](#eq1) and [(2)](#eq2) in format `ipynb`?\n", "Yes, in DocOnce-extended `ipynb` format, but not when writing\n", "notebooks interactively in the browser and using Markdown with LaTeX.\n", "\n", "# Translating notebooks to DocOnce\n", "\n", "## The `ipynb2doconce` tool\n", "\n", "There is a program `doconce ipynb2doconce` for translating a notebook\n", "file to DocOnce format:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Terminal> doconce ipynb2doconce notebook.ipynb\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output file `notebook.do.txt` should be an ordinary DocOnce file.\n", "With an optional argument `--cell_delimiter`, comment lines a la `# ---------- code cell` act in the as delimiter between the *markdown* and *code* cells in the\n", "`.do.txt` file.\n", "\n", "## Special comment annotations in the `.ipynb` file\n", "\n", "In order to translate a DocOnce document to the Jupyter Notebook format\n", "and back again, some information needs to be coded as comments\n", "in the notebook and such that it can be brought back to DocOnce syntax.\n", "For example, index workds (`idx`) and labels are coded in comments, so\n", "are the original DocOnce figure and movie commands (which contain more\n", "information than what the notebook normally can make use of).\n", "Comments in the `.ipynb` file starting with `dom:` (DocOnce Metadata)\n", "contain coded information that can be translated back to DocOnce (see\n", "below for examples).\n", "\n", "It is possible to write ordinary DocOnce documents with index words, labels,\n", "cross references, etc.; translate the document to a notebook; edit\n", "and expand the notebook interactively; and convert the notebook to\n", "a DocOnce document again - with preservation of index words, labels, etc.,\n", "despite the fact that the notebook does not know about such syntax\n", "and construction. If you want to insert title, authors, date,\n", "index words, labels,\n", "figures, or movies in the notebook, use these types of constructions\n", "while writing a markdown cell in the notebook:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <!-- dom:TITLE: Here goes the title -->\n", " # Here goes the title\n", " <!-- dom:AUTHOR: HPL Email:[email protected] at Simula & UiO -->\n", " <!-- Author: --> **HPL** (email: `[email protected]`), Simula and UiO\n", " \n", " <!-- dom:AUTHOR: Kaare Dump at Segfault, Cyberspace -->\n", " <!-- Author: --> **Kaare Dump**, Segfault, Cyberspace\n", " \n", " Date: **May 25, 2015**\n", " \n", " # Section heading\n", " <!-- dom:\\label{mysubsec} -->\n", " <!-- dom:idx{headigs} -->\n", " <!-- dom:idx{section} -->\n", " \n", " Some text...\n", " \n", " <!-- dom:FIGURE: [fig/myfig, width=500 frac=0.6] This is a caption. -->\n", " <!-- begin figure -->\n", " <p>This is a caption.</p>\n", " <img src=\"fig/myfig.png\" width=500>\n", " <!-- end figure -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to respect whitespace exactly as shown in this example.\n", "The `dom:` syntax must be the valid DocOnce syntax used when translating\n", "the document back to DocOnce format, while the Markdown lines coming\n", "after are the ones that can been seen in the present version of\n", "the notebook (these are removed forever when converting the notebook to\n", "DocOnce format).\n", "\n", "\n", "\n", "<hr/>\n", "**Mako code is lost!**\n", "\n", "Any Preprocess or Mako code one has in a DocOnce document is lost forever when\n", "converting to a Jupyter Notebook.\n", "<hr/>\n", "\n", "\n", "\n", "As a specific example on generating a notebook and converting it back,\n", "consider the [`example.do.txt`](https://github.com/hplgit/doconce/blob/master/doc/src/ipynb/example.do.txt) file:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Terminal> cp example.do.txt tmp1.do.txt\n", " Terminal> doconce format ipynb tmp1\n", " Terminal> cp tmp1.ipynb tmp2.ipynb\n", " Terminal> doconce ipynb2doconce tmp2.ipynb\n", " Terminal> doconce diff tmp1.do.txt tmp2.do.txt\n", " Termonal> google-chrome tmp_diff_tmp2.do.txt.html\n" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

atulsingh0/MachineLearning

BMLSwPython/01_GettingStarted_withPython.ipynb

1

224623

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import\n", "import numpy as np\n", "import scipy as sp\n", "import timeit\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comparing the time**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total time taken: -0.006210096431396868\n" ] } ], "source": [ "start = timeit.timeit()\n", "\n", "X = range(1000)\n", "\n", "pySum = sum([n*n for n in X])\n", "\n", "end = timeit.timeit()\n", "\n", "print(\"Total time taken: \", end-start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Learning Scipy **" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.00000000e+00 2.27200000e+03]\n", " [ 2.00000000e+00 nan]\n", " [ 3.00000000e+00 1.38600000e+03]]\n", "743\n" ] } ], "source": [ "# reading the web data \n", "\n", "data = sp.genfromtxt(\"data/web_traffic.tsv\", delimiter=\"\\t\")\n", "print(data[:3])\n", "print(len(data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Preprocessing and Cleaning the data **" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "8\n" ] } ], "source": [ "X = data[:, 0]\n", "y = data[:, 1]\n", "\n", "# checking for nan values\n", "print(sum(np.isnan(X)))\n", "print(sum(np.isnan(y)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Filtering the nan data **" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "0\n" ] } ], "source": [ "X = X[~np.isnan(y)]\n", "y = y[~np.isnan(y)]\n", "\n", "# checking for nan values\n", "print(sum(np.isnan(X)))\n", "print(sum(np.isnan(y)))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x297739d8710>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cdjM7Xz6wXuWc+1Y0zxskfc7M3uScO9DFbQQAAECf9b3GV9LzJe0ws0+Z2ZSZ7TKz/wyC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\n2LDXrMpcx+BWlRVjGsr1WX6symM/+GD2k/w0zr+qyWmjgsk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Ovana0CYppSdSSpenlLal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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x29773758b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(X, y, '.b')\n", "ax.margins(0.2)\n", "plt.xticks([w*24*7 for w in range(0, 6)], [\"week %d\" %w for w in range(0, 6)])\n", "ax.set_xlabel(\"Week\")\n", "ax.set_ylabel(\"Hits / Week\")\n", "ax.set_title(\"Web Traffic over weeks\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Choosing the right model and learning algorithm **" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# creating a error calc fuction\n", "def error(f, x, y):\n", " return np.sum((f(x) - y)**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Linear 1-d model **" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.59619213 989.02487106]\n", "[ 3.17389767e+08]\n", "Error : 317389767.34\n" ] }, { "data": { "image/png": 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X0Ucf3eR5d+7cSbdu3RgxYgRLly5t/cVkkAJfERERkRZs3pz4cb6de9u2bezfv5/hw4c3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\n2+Y27nzuTo4cP5K1zIThE7h25rU0NzWzZMoS6gbVZS1TyI2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7lq+ytCyzfKkxdDSGM799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"text/plain": [ "<matplotlib.figure.Figure at 0x29773758b70>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sp's polyfit func do the same\n", "fp1, residuals, rank, sv, rcond = sp.polyfit(X, y, 1, full=True)\n", "\n", "print(fp1)\n", "print(residuals)\n", "\n", "# generating the one order function\n", "f1 = sp.poly1d(fp1)\n", "\n", "# checking error\n", "print(\"Error : \",error(f1, X, y))\n", "\n", "x1 = np.array([-100, np.max(X)+100])\n", "y1 = f1(x1)\n", "\n", "ax.plot(x1, y1, c='g', linewidth=2)\n", "ax.legend([\"data\", \"d = %i\" % f1.order], loc='best')\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " $$ f(x) = 2.59619213 * x + 989.02487106 $$ \n", " \n", " ** Polynomial 2-d **" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.05322215e-02 -5.26545650e+00 1.97476082e+03]\n", "Error : 179983507.878\n" ] }, { "data": { "image/png": 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HBQsW1P1h4kiBr4iIiEg1ioqq3k62a69Zs4Zt27bRq1evCsdycnLKbc+ZM4cTTzyRPffc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"text/plain": [ "<matplotlib.figure.Figure at 0x29773758b70>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sp's polyfit func do the same\n", "fp2 = sp.polyfit(X, y, 2)\n", "\n", "print(fp2)\n", "\n", "# generating the 2 order function\n", "f2= sp.poly1d(fp2)\n", "\n", "# checking error\n", "print(\"Error : \",error(f2, X, y))\n", "\n", "x1= np.linspace(-100, np.max(X)+100, 2000)\n", "y2= f2(x1)\n", "\n", "ax.plot(x1, y2, c='r', linewidth=2)\n", "ax.legend([\"data\", \"d = %i\" % f1.order, \"d = %i\" % f2.order], loc='best')\n", "fig" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "$$ f(x) = 0.0105322215 * x^2 - 5.26545650 * x + 1974.6082 $$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "What if we want to regress two response output instead of one, As we can see in the graph that there is a steep change in data between week 3 and 4, so let's draw two reponses line, one for the data between week0 and week3.5 and second for week3.5 to week5" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we are going to divide the data on time so\n", "div = 3.5*7*24\n", "\n", "X1 = X[X<=div]\n", "Y1 = y[X<=div]\n", "\n", "X2 = X[X>div]\n", "Y2 = y[X>div]\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error inflection = 135015350.586215\n" ] } ], "source": [ "# now plotting the both data\n", "\n", "fa = sp.poly1d(sp.polyfit(X1, Y1, 1))\n", "fb = sp.poly1d(sp.polyfit(X2, Y2, 1))\n", "\n", "fa_error = error(fa, X1, Y1)\n", "fb_error = error(fb, X2, Y2)\n", "print(\"Error inflection = %f\" % (fa_error + fb_error))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Oh8PW/ZVdhlHhm07d0zBjS4rOsXLlStauXUu/fv1qbMvPz29U4durVy969eqV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tqsr6/pC0J2tqUEcX6+6y8+puu5bRs0Lc3hPuYwgXdoRbbN+I4rrXld/JPZ3M9Zmtn0462/uJZ1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\nXrZI3AqlB887j29kAFyQNzaZv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"text/plain": [ "<matplotlib.figure.Figure at 0x29773758b70>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1 = np.linspace(-100, X1[-1]+100, 1000)\n", "x2 = np.linspace(X1[-10], X2[-1]+100, 1000)\n", "\n", "ya = fa(x1)\n", "yb = fb(x2)\n", "\n", "ax.plot(x1, ya, c='#800000', linewidth=2) # brown\n", "ax.plot(x2, yb, c='#FFA500', linewidth=2) # orange\n", "ax.grid(True)\n", "\n", "\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we choose that function with degree 2 is best fit for our data and want to predict that if everything will go same then when we will hit the 100000 count ??\n", "\n", "$$ 0 = f(x) - 100000 = 0.0105322215 * x^2 - 5.26545650 * x + 1974.6082 - 100000 $$ \n", "\n", "SciPy's optimize module has the function \n", "fsolve that achieves this, when providing an initial starting position with parameter \n", "x0. As every entry in our input data file corresponds to one hour, and we have 743 of \n", "them, we set the starting position to some value after that. Let fbt2 be the winning \n", "polynomial of degree 2." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2\n", "0.01053 x - 5.265 x + 1975\n" ] } ], "source": [ "print(f2)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2\n", "0.01053 x - 5.265 x - 9.803e+04\n", "100,000 hits/hour expected at week 19.708090\n" ] } ], "source": [ "print(f2 - 100000)\n", "\n", "# import \n", "from scipy.optimize import fsolve\n", "\n", "reached_max = fsolve(f2-100000, x0=800)/(7*24)\n", "print(\"100,000 hits/hour expected at week %f\" % reached_max[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

NicholasBermuda/transit-fitting

notebooks/demo.ipynb

2

1507454

mit

Prooffreader/intro_machine_learning

08_Dimensionality_Reduction.ipynb

1

53609

{ "metadata": { "name": "", "signature": "sha256:55da80124d4fb776789c8deec8f1a94af945058ee2ed7d6eb47aed6679e8e09f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to data analysis using machine learning #\n", "\n", "## 08. Dimensionality reduction with Principal Component Analysis (PCA) ##\n", "\n", "by David Taylor, [www.prooffreader.com](http://www.prooffreader.com) (blog) [www.dtdata.io](http://dtdata.io) (hire me!)\n", "\n", "For links to more material including a slideshow explaining all this stuff in further detail, please see the front page of [this GitHub repo.](https://github.com/Prooffreader/intro_machine_learning)\n", "\n", "This is notebook 8 of 8. \n", "\n", "[[01]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/01_The_Dataset.ipynb) [[02]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/02_Clustering_KMeans.ipynb) [[03]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/03_Clustering_OtherAlgos.ipynb) [[04]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/04_Classification_kNN.ipynb) [[05]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/05_Classification_OtherAlgos.ipynb) [[06]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/06_Classification_Decision_Trees.ipynb) [[07]](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/07_Classification_Random_Forest.ipynb) **[08]**\n", "***\n", "\n", "In the [previous notebook](http://nbviewer.ipython.org/github/Prooffreader/intro_machine_learning/blob/master/07_Classification_Random_Forest.ipynb), we finished our look at Supervised Learning. Now we see a way to address the Curse of Dimensionality by reducing our features using PCA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Import libraries and load data #" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", "from statsmodels.sandbox.tools.tools_pca import pcasvd\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "df = pd.read_csv('fruit.csv')\n", "\n", "fruitnames = {1: 'Orange', 2: 'Pear', 3: 'Apple'}\n", "colors = {1: '#e09028', 2: '#55aa33', 3: '#cc3333'}\n", "fruitlist = ['Orange', 'Pear', 'Apple']\n", "\n", "numerical_columns = ['elongatedness', 'weight', 'sweetness', 'acidity', 'color_id']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Run PCA #\n", "\n", "Scikit-learn has a PCA module, but instead I'm using statsmodels's ``pcasvd`` function in order to make a biplot." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# code taken from http://okomestudio.net/biboroku/?p=2292\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "def biplot(plt, pca, labels=None, colors=None,\n", " xpc=1, ypc=2, scale=1):\n", " \"\"\"Generate biplot from the result of pcasvd of statsmodels.\n", "\n", " Parameters\n", " ----------\n", " plt : object\n", " An existing pyplot module reference.\n", "\n", " pca : tuple\n", " The result from statsmodels.sandbox.tools.tools_pca.pcasvd.\n", "\n", " labels : array_like, optional\n", " Labels for each observation.\n", "\n", " colors : array_like, optional\n", " Colors for each observation.\n", "\n", " xpc, ypc : int, optional\n", " The principal component number for x- and y-axis. Defaults to\n", " (xpc, ypc) = (1, 2).\n", "\n", " scale : float\n", " The variables are scaled by lambda ** scale, where lambda =\n", " singular value = sqrt(eigenvalue), and the observations are\n", " scaled by lambda ** (1 - scale). Must be in [0, 1].\n", "\n", " Returns\n", " -------\n", " None.\n", "\n", " \"\"\"\n", " xpc, ypc = (xpc - 1, ypc - 1)\n", " xreduced, factors, evals, evecs = pca\n", " singvals = np.sqrt(evals)\n", " \n", " # data\n", " xs = factors[:, xpc] * singvals[xpc]**(1. - scale)\n", " ys = factors[:, ypc] * singvals[ypc]**(1. - scale)\n", " \n", " colors = 'k' if colors is None else colors\n", " plt.scatter(xs, ys, c=colors, marker='o', alpha=0.4)\n", " \n", " # variables\n", " tvars = np.dot(np.eye(factors.shape[0], factors.shape[1]),\n", " evecs) * singvals**scale\n", " \n", " for i, col in enumerate(xreduced.columns.values):\n", " x, y = tvars[i][xpc], tvars[i][ypc]\n", " plt.arrow(0, 0, x, y, color='r',\n", " width=0.002, head_width=0.05)\n", " plt.text(x* 1.4, y * 1.4, col, color='r', ha='center', va='center')\n", " \n", " plt.title('Principal Component Analysis with biplot')\n", " plt.xlabel('PC{}'.format(xpc + 1))\n", " plt.ylabel('PC{}'.format(ypc + 1))\n", " \n", " return xs, ys " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "columns = ['elongatedness', 'weight', 'sweetness', 'acidity', 'color_id']\n", "\n", "data = df[columns]\n", "classes = df['fruit_id']\n", "data = (data - data.mean()) / data.std() # pca data must be normalized\n", "pca = pcasvd(data, keepdim=0, demean=False)\n", "\n", "colors = [['#e09028', '#55aa33', '#cc3333'][i-1] for i in classes]\n", "\n", "plt.figure(1)\n", "xs, ys = biplot(plt, pca, labels=classes, colors=colors,\n", " xpc=1, ypc=2)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Ce5Mdg9GA0ceI0+W8QSyK26GhoYGzZ8/idrtJSkq66akWkpOTOXYwlh2Hcgjy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\nwWDA7Xbz0VtvYS4oIK6xkeytW9n85psD7uwUGhqKzmZj+dSpRIWE0CI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"text": [ "<matplotlib.figure.Figure at 0x87a9ef0>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. Do 100 runs of Random Forest Classifier using two principal components and show confusion matrix #" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df_pca = pd.DataFrame({'PC1': xs,\n", " 'PC2': ys,\n", " 'fruit_id': classes})\n", "df_pca.sort('fruit_id', inplace=True)\n", "\n", "# parameters\n", "# I did not make a function because I wanted to preserve the \n", "# IPython output of the confusion matrix in the same cell.\n", "reps=100\n", "features = ['PC1', 'PC2']\n", "title_suffix='with two principal components'\n", "\n", "import time\n", "start = time.time()\n", "for i in range(reps):\n", " df_pca['is_train'] = np.random.uniform(0, 1, len(df_pca)) <= .75 # randomly assign training and testing set\n", " train, test = df_pca[df_pca['is_train']==True], df_pca[df_pca['is_train']==False]\n", " y, _ = pd.factorize(train['fruit_id'])\n", " clf = RandomForestClassifier(n_jobs=2)\n", " clf = clf.fit(train[features], y)\n", " preds = clf.predict(test[features])\n", " test_result = pd.crosstab(np.array([fruitnames[x] for x in test['fruit_id']]), \n", " np.array([fruitnames[x+1] for x in preds]), rownames=['actual'], colnames=['predicted'])\n", " if i == 0:\n", " final_result = test_result[:]\n", " else:\n", " final_result += test_result\n", "confmatrix = np.array(final_result)\n", "correct = 0\n", "for i in range(confmatrix.shape[0]):\n", " correct += confmatrix[i,i]\n", "accuracy = correct/confmatrix.sum()\n", "print('{} runs {}\\nFeatures: {}\\nAccuracy: {}\\ntime: {} sec'.format(reps, title_suffix, features, accuracy, int(time.time()-start)))\n", "final_result" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100 runs with two principal components\n", "Features: ['PC1', 'PC2']\n", "Accuracy: 0.9665188470066519\n", "time: 23 sec\n" ] }, { "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>predicted</th>\n", " <th>Apple</th>\n", " <th>Orange</th>\n", " <th>Pear</th>\n", " </tr>\n", " <tr>\n", " <th>actual</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Apple</th>\n", " <td> 1146</td>\n", " <td> 8</td>\n", " <td> 92</td>\n", " </tr>\n", " <tr>\n", " <th>Orange</th>\n", " <td> 8</td>\n", " <td> 1481</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>Pear</th>\n", " <td> 42</td>\n", " <td> 0</td>\n", " <td> 1732</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "predicted Apple Orange Pear\n", "actual \n", "Apple 1146 8 92\n", "Orange 8 1481 1\n", "Pear 42 0 1732" ] } ], "prompt_number": 4 } ], "metadata": {} } ] }

mit

tynbl/tynbl.github.io

docs/python-xxxy-2/00/01/coding_utf8_example.ipynb

2

1065

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# coding:utf-8\n", "\n", "'''\n", "Created on Dec 3, 2016\n", "\n", "@author: Bin Liang\n", "'''\n", "\n", "\n", "def run_main():\n", " \"\"\"\n", " main function\n", " \"\"\"\n", " chinese_str = 'Python数据分析'\n", " print chinese_str\n", " \n", " other_str = 'naïve'\n", " print other_str\n", "\n", "\n", "if __name__ == '__main__':\n", " run_main()" ] } ], "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

justanr/notebooks

fun.itertools.ipynb

1

9650

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Originally, I saw this issue in a post on /r/learnpython:\n", "\n", " '''\n", " if (n%a==0) or (n%b==0) or (n%c==0) or (n%ab==0) or (n%ac==0) or (n%bc==0) or (n%abc==0):\n", " print \"Yes.\"\n", " else:\n", " print \"No.\"\n", " '''\n", "\n", "The original poster was seeking advice on why this was throwing a `NameError`. Turns out, what he really wanted was to find out if n divided evenly by a, b, c or any combination of a, b and c.\n", "\n", "My first suggestion was to use any/all." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# pick a number, any numbers\n", "n = 15\n", "a = 1\n", "b = 2\n", "c = 3\n", "\n", "if any(not n%x for x in [a,b,c, a*b, a*c, c*b, a*b*c]):\n", " print(\"Yes.\")\n", "else:\n", " print(\"No.\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Yes.\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which works. But could you imagine being the guy who gets this code and is tasked with adding `d = 4` to this mix? Forbid `e = 5` or `f = 6`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import itertools as it\n", "\n", "def powerset(iterable):\n", " '''Courtesy of the itertools documentation.'''\n", " s = list(iterable)\n", " return it.chain.from_iterable(it.combinations(s,r) for r in range(1, len(s)+1))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enter itertools: a tool box of fun. This function returns powersets. That is given `range(1,4)` it'll output `(1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)`. The original itertools documentation actually has `range(0, len(s)+1)` but that causes the first set to be `(,)` -- which I consider to be useless in this instance (despite being a valid set result). So, let's rewrite that conditional." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from functools import partial, reduce\n", "from operator import mul\n", "\n", "f = partial(reduce, mul)\n", "\n", "if any(not n%f(x) for x in powerset(range(1,4))):\n", " print(\"Yes.\")\n", "else:\n", " print(\"No.\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Yes.\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now imagine being the guy tasked to extend the range from 3 to 6. You change one integer: `range(1,7)`. However, we'll likely want to do something with the valid results from that test. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "def do_thing(*stuff):\n", " '''A dummy functions.'''\n", " print(\"Yes.\")\n", "\n", "sets, selectors = it.tee(powerset(range(1,4)))\n", "selectors = (not n%f(x) for x in selectors)\n", "wanted = list(it.compress(sets, selectors))\n", "\n", "if any(wanted):\n", " do_thing()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Yes.\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "How this works is that `it.tee` produces a number of iterables from the original iterable -- you can change the number by passing a second value to tee. However, you must be careful with tee. If you advance the original iterable, you advanced the tee'd iterables as well -- most likely prematurely. But, since we're generating the iterable directly into it instead of passing a stored iterable, we can't do that (well, you could but that's black magic).\n", "\n", "What compress does is accept an iterable of data and an iterable of truthy values. The same result could be had with either `filter` or `it.filterfalse`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "filterd, filterfd = it.tee(powerset(range(1,4)))\n", "\n", "filterd = list(filter(lambda x: not n%f(x), filterd))\n", "filterfd = list(it.filterfalse(lambda x: n%f(x), filterfd))\n", "\n", "print(wanted == filterd == filterfd)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use itertools to divide up iterable based on a variety of things:\n", "\n", "* into values that are True/False based on a key.\n", "* into a set of values until a false one, a set of values after (and including) the first false one" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def partition(iterable, key):\n", " t, f = it.tee(iterable)\n", " return filter(key, t), it.filterfalse(key, f)\n", "\n", "def divide(iterable, key):\n", " t, f = it.tee(iterable)\n", " return it.takewhile(key, t), it.dropwhile(key, f)\n", "\n", "parted = partition(powerset(range(1,4)), lambda x: not n%f(x))\n", "divided = divide(powerset(range(1,4)), lambda x: not n%f(x))\n", "\n", "print(\"-\"*20)\n", "print(\"partition(powerset(range(1,4)), lambda x: not n%f(x))\", list(map(list, parted)), sep=\"\\n\")\n", "print(\"-\"*20)\n", "print(\"divide(powerset(range(1,4)), lambda x: not n%f(x))\", list(map(list, divided)), sep=\"\\n\")\n", "print(\"-\"*20)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--------------------\n", "partition(powerset(range(1,4)), lambda x: not n%f(x))\n", "[[(1,), (3,), (1, 3)], [(2,), (1, 2), (2, 3), (1, 2, 3)]]\n", "--------------------\n", "divide(powerset(range(1,4)), lambda x: not n%f(x))\n", "[[(1,)], [(2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]]\n", "--------------------\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also divy up the sets based on their product by way of `collections.defaultdict` and `it.groupby`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import defaultdict\n", "\n", "store = defaultdict(list)\n", "for k, g in it.groupby(powerset(range(1,4)), key=f):\n", " # g will be a grouper iterable that must be consumed\n", " store[k].extend(g)\n", "\n", "print(store)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "defaultdict(<class 'list'>, {1: [(1,)], 2: [(2,), (1, 2)], 3: [(3,), (1, 3)], 6: [(2, 3), (1, 2, 3)]})\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retrieving the desired information from this store is a little more involved, however." ] }, { "cell_type": "code", "collapsed": false, "input": [ "wanted = [v for k, v in store.items() if not n%k]\n", "\n", "print('-'*20)\n", "print(\"Raw list:\", wanted)\n", "print('-'*20)\n", "print(\"it.chain(*wanted)\", list(it.chain(*wanted)))\n", "print('-'*20)\n", "print(\"it.chain.from_iterable(wanted)\", list(it.chain.from_iterable(wanted)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "--------------------\n", "Raw list: [[(1,)], [(3,), (1, 3)]]\n", "--------------------\n", "it.chain(*wanted) [(1,), (3,), (1, 3)]\n", "--------------------\n", "it.chain.from_iterable(wanted) [(1,), (3,), (1, 3)]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference between `it.chain(*wanted)` and `it.chain.from_iterable(wanted)` is that the former is eager and the later is lazy. In this particular instance, we're consuming the iterator immediately either way and it's a basic list. However, with more expensive operations -- say we had a list of generators that each returned the results of floating point math -- you'd want the results to be as lazy as possible." ] } ], "metadata": {} } ] }

mit

mjbrodzik/ExploringCETB

Reading CETB files.ipynb

1

4812100

apache-2.0

chongxi/spiketag

spiketag/analysis/tests/Decoding_dusty.ipynb

1

2994418

bsd-3-clause

uwkejia/Clean-Energy-Outlook

examples/Demo.ipynb

2

7734

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ceo import data_cleaning\n", "from ceo import missing_data\n", "from ceo import svr_prediction\n", "from ceo import ridge_prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### datacleaning\n", "\n", "* The datacleaning module is used to clean and organize the data into 51 CSV files corresponding to the 50 states of the US and the District of Columbia. \n", "* The wrapping function **`clean_all_data`** takes all the data sets as input and sorts the data in to CSV files of the states.\n", "* The CSVs are stored in the **`Cleaned Data`** directory which is under the **`Data`** directory. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "data_cleaning.clean_all_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### missing_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The missing_data module is used to estimate the missing data of the GDP (from 1960 - 1962) and determine the values of the predictors (from 2016-2020).\n", "* The wrapping function **`predict_all`** takes the CSV files of the states as input and stores the predicted missing values in the same CSV files.\n", "* The CSVs generated replace the previous CSV files in the **`Cleaned Data`** directory which is under the **`Data`** directory." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "missing_data.predict_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ridge_prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The ridge_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using ridge regression.\n", "* The wrapping function ridge_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under Ridge Regression folder under the Predicted Data directory." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ridge_prediction.ridge_predict_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### svr_prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The svr_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using Support Vector Regression\n", "* The wrapping function SVR_predict_all takes the CSV files of the states as input and stores the future values of the energies in another CSV file under SVR folder under the Predicted Data directory." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "svr_prediction.SVR_predict_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualizations is done using Tableau software. The Tableau workbook for the predicted data is included in the repository. The Tableau dashboard created for this data is illustrated below:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "<div class='tableauPlaceholder' id='viz1489609724011' style='position: relative'><noscript><a href='#'><img alt='Clean Energy Production in the contiguous United States(in million kWh) ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz' style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='path' value='shared&#47;PB87S38NW' /> <param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /></object></div> <script type='text/javascript'> var divElement = document.getElementById('viz1489609724011'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='1004px';vizElement.style.height='869px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%HTML\n", "\n", "<div class='tableauPlaceholder' id='viz1489609724011' style='position: relative'><noscript><a href='#'><img alt='Clean Energy Production in the contiguous United States(in million kWh) ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz' style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='path' value='shared&#47;PB87S38NW' /> <param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;PB&#47;PB87S38NW&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /></object></div> <script type='text/javascript'> var divElement = document.getElementById('viz1489609724011'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='1004px';vizElement.style.height='869px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); </script>" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

moizumi99/CVBookExercise

Chapter-1/CV Book Ch1 Ex 6.ipynb

1

23325

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import *\n", "from numpy import random\n", "from scipy.ndimage import filters\n", "from PIL import *\n", "from pylab import *\n", "from scipy.ndimage import measurements, morphology\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "im = array(Image.open('data/houses.png').convert('L'))\n", "im = 1*(im<128)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of objects 45\n" ] } ], "source": [ "labels, nbr_objects = measurements.label(im)\n", "print \"Number of objects\", nbr_objects" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f163e8e9690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure(figsize=(6, 12))\n", "imshow(labels)\n", "show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f163c259fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure()\n", "hist(labels.flatten())\n", "axis([5, 45, 0, 20000])\n", "show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

unlicense

mne-tools/mne-tools.github.io

0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb

2

10377

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Visualize source leakage among labels using a circular graph\n\nThis example computes all-to-all pairwise leakage among 68 regions in\nsource space based on MNE inverse solutions and a FreeSurfer cortical\nparcellation. Label-to-label leakage is estimated as the correlation among the\nlabels' point-spread functions (PSFs). It is visualized using a circular graph\nwhich is ordered based on the locations of the regions in the axial plane.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Olaf Hauk <[email protected]>\n# Martin Luessi <[email protected]>\n# Alexandre Gramfort <[email protected]>\n# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery)\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import (read_inverse_operator,\n make_inverse_resolution_matrix,\n get_point_spread)\n\nfrom mne.viz import circular_layout, plot_connectivity_circle\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load forward solution and inverse operator\n\nWe need a matching forward solution and inverse operator to compute\nresolution matrices for different methods.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nsubjects_dir = data_path + '/subjects'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif'\nforward = mne.read_forward_solution(fname_fwd)\n# Convert forward solution to fixed source orientations\nmne.convert_forward_solution(\n forward, surf_ori=True, force_fixed=True, copy=False)\ninverse_operator = read_inverse_operator(fname_inv)\n\n# Compute resolution matrices for MNE\nrm_mne = make_inverse_resolution_matrix(forward, inverse_operator,\n method='MNE', lambda2=1. / 3.**2)\nsrc = inverse_operator['src']\ndel forward, inverse_operator # save memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read and organise labels for cortical parcellation\n\nGet labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labels = mne.read_labels_from_annot('sample', parc='aparc',\n subjects_dir=subjects_dir)\nn_labels = len(labels)\nlabel_colors = [label.color for label in labels]\n# First, we reorder the labels based on their location in the left hemi\nlabel_names = [label.name for label in labels]\nlh_labels = [name for name in label_names if name.endswith('lh')]\n\n# Get the y-location of the label\nlabel_ypos = list()\nfor name in lh_labels:\n idx = label_names.index(name)\n ypos = np.mean(labels[idx].pos[:, 1])\n label_ypos.append(ypos)\n\n# Reorder the labels based on their location\nlh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))]\n\n# For the right hemi\nrh_labels = [label[:-2] + 'rh' for label in lh_labels]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute point-spread function summaries (PCA) for all labels\n\nWe summarise the PSFs per label by their first five principal components, and\nuse the first component to evaluate label-to-label leakage below.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Compute first PCA component across PSFs within labels.\n# Note the differences in explained variance, probably due to different\n# spatial extents of labels.\nn_comp = 5\nstcs_psf_mne, pca_vars_mne = get_point_spread(\n rm_mne, src, labels, mode='pca', n_comp=n_comp, norm=None,\n return_pca_vars=True)\nn_verts = rm_mne.shape[0]\ndel rm_mne" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can show the explained variances of principal components per label. Note\nhow they differ across labels, most likely due to their varying spatial\nextent.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with np.printoptions(precision=1):\n for [name, var] in zip(label_names, pca_vars_mne):\n print(f'{name}: {var.sum():.1f}% {var}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output shows the summed variance explained by the first five principal\ncomponents as well as the explained variances of the individual components.\n\n## Evaluate leakage based on label-to-label PSF correlations\n\nNote that correlations ignore the overall amplitude of PSFs, i.e. they do\nnot show which region will potentially be the bigger \"leaker\".\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# get PSFs from Source Estimate objects into matrix\npsfs_mat = np.zeros([n_labels, n_verts])\n# Leakage matrix for MNE, get first principal component per label\nfor [i, s] in enumerate(stcs_psf_mne):\n psfs_mat[i, :] = s.data[:, 0]\n# Compute label-to-label leakage as Pearson correlation of PSFs\n# Sign of correlation is arbitrary, so take absolute values\nleakage_mne = np.abs(np.corrcoef(psfs_mat))\n\n# Save the plot order and create a circular layout\nnode_order = lh_labels[::-1] + rh_labels # mirror label order across hemis\nnode_angles = circular_layout(label_names, node_order, start_pos=90,\n group_boundaries=[0, len(label_names) / 2])\n# Plot the graph using node colors from the FreeSurfer parcellation. We only\n# show the 200 strongest connections.\nfig = plt.figure(num=None, figsize=(8, 8), facecolor='black')\nplot_connectivity_circle(leakage_mne, label_names, n_lines=200,\n node_angles=node_angles, node_colors=label_colors,\n title='MNE Leakage', fig=fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most leakage occurs for neighbouring regions, but also for deeper regions\nacross hemispheres.\n\n## Save the figure (optional)\n\nMatplotlib controls figure facecolor separately for interactive display\nversus for saved figures. Thus when saving you must specify ``facecolor``,\nelse your labels, title, etc will not be visible::\n\n >>> fname_fig = data_path + '/MEG/sample/plot_label_leakage.png'\n >>> fig.savefig(fname_fig, facecolor='black')\n\n## Plot PSFs for individual labels\n\nLet us confirm for left and right lateral occipital lobes that there is\nindeed no leakage between them, as indicated by the correlation graph.\nWe can plot the summary PSFs for both labels to examine the spatial extent of\ntheir leakage.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# left and right lateral occipital\nidx = [22, 23]\nstc_lh = stcs_psf_mne[idx[0]]\nstc_rh = stcs_psf_mne[idx[1]]\n\n# Maximum for scaling across plots\nmax_val = np.max([stc_lh.data, stc_rh.data])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Point-spread function for the lateral occipital label in the left hemisphere\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "brain_lh = stc_lh.plot(subjects_dir=subjects_dir, subject='sample',\n hemi='both', views='caudal',\n clim=dict(kind='value',\n pos_lims=(0, max_val / 2., max_val)))\nbrain_lh.add_text(0.1, 0.9, label_names[idx[0]], 'title', font_size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and in the right hemisphere.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "brain_rh = stc_rh.plot(subjects_dir=subjects_dir, subject='sample',\n hemi='both', views='caudal',\n clim=dict(kind='value',\n pos_lims=(0, max_val / 2., max_val)))\nbrain_rh.add_text(0.1, 0.9, label_names[idx[1]], 'title', font_size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both summary PSFs are confined to their respective hemispheres, indicating\nthat there is indeed low leakage between these two regions.\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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

xpmethod/middlemarch-critical-histories

old/e1/e1c-dehyphenate.ipynb

2

31442

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiment 1-C dehyphenate\n", "\n", "Improvements to the text matcher. Stemming, etc. Strip sequences of hyphen plus space." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2import json\n", "\n", "from collections import Counter\n", "import pandas as pd\n", "from nltk.corpus import names\n", "import nltk\n", "import re \n", "import os\n", "import difflib \n", "import logging\n", "import itertools\n", "from nltk.util import ngrams \n", "from nltk.stem import LancasterStemmer\n", "from difflib import SequenceMatcher\n", "from string import punctuation\n", "from termcolor import colored\n", "from IPython.display import clear_output\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the data. \n", "with open('txt/middlemarch.json') as f: \n", " rawCriticism = f.readlines()\n", "\n", "# Parse the data. \n", "data = [json.loads(line) for line in rawCriticism]\n", "\n", "# Load Middlemarch\n", "with open('middlemarch.txt') as f: \n", " rawMM = f.read()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Text: \n", " def __init__(self, raw_text, label, removeStopwords=True): \n", " if type(raw_text) == list: \n", " # JSTOR critical works come in lists, where each item represents a page. \n", " self.text = re.sub(r'- ',r'',' \\n '.join(raw_text))\n", " else: \n", " self.text = raw_text\n", " self.label = label\n", " self.tokens = self.getTokens(removeStopwords)\n", " self.trigrams = self.ngrams(3)\n", " \n", " def getTokens(self, removeStopwords=True): \n", " \"\"\" Tokenizes the text, breaking it up into words, removing punctuation. \"\"\"\n", " tokenizer = nltk.RegexpTokenizer('[a-zA-Z]\\w+\\'?\\w*') # A custom regex tokenizer. \n", " #tokenizer = nltk.RegexpTokenizer('\\w+|\\$[\\d\\.]+|\\S+') # A custom regex tokenizer. \n", " spans = list(tokenizer.span_tokenize(self.text))\n", " # Take note of how many spans there are in the text\n", " #print(spans)\n", " self.length = spans[-1][-1] \n", " tokens = tokenizer.tokenize(self.text)\n", " tokens = [ token.lower() for token in tokens ] # make them lowercase\n", " stemmer = LancasterStemmer()\n", " tokens = [ stemmer.stem(token) for token in tokens ]\n", " if not removeStopwords: \n", " self.spans = spans\n", " return tokens\n", " tokenSpans = list(zip(tokens, spans)) # zip it up\n", " stopwords = nltk.corpus.stopwords.words('english') # get stopwords\n", " tokenSpans = [ token for token in tokenSpans if token[0] not in stopwords ] # remove stopwords from zip\n", " self.spans = [ x[1] for x in tokenSpans ] # unzip; get spans\n", " return [ x[0] for x in tokenSpans ] # unzip; get tokens\n", " \n", " def ngrams(self, n): \n", " \"\"\" Returns ngrams for the text.\"\"\"\n", " return list(ngrams(self.tokens, n))\n", "\n", "class Matcher: \n", " def __init__(self, textObjA, textObjB, threshold=5, ngramSize=3, removeStopwords=True):\n", " \"\"\"\n", " Takes as input two Text() objects, and matches between them.\n", " \"\"\"\n", " self.threshold = threshold\n", " self.ngramSize = ngramSize\n", " \n", " #self.textA, self.textB = Text(fileA, removeStopwords=removeStopwords), \\\n", " # Text(fileB, removeStopwords=removeStopwords)\n", " self.textA = textObjA\n", " self.textB = textObjB \n", " \n", " self.textAgrams = self.textA.ngrams(ngramSize)\n", " self.textBgrams = self.textB.ngrams(ngramSize)\n", "\n", " self.locationsA = []\n", " self.locationsB = []\n", "\n", " def getContext(self, text, start, length, context): \n", " match = self.getTokensText(text, start, length)\n", " before = self.getTokensText(text, start-context, context)\n", " after = self.getTokensText(text, start+length, context)\n", " match = colored(match, 'red')\n", " out = \" \".join([before, match, after])\n", " out = out.replace('\\n', ' ') # Replace newlines with spaces. \n", " out = re.sub('\\s+', ' ', out)\n", " return out\n", "\n", " def getTokensText(self, text, start, length): \n", " \"\"\" Looks up the passage in the original text, using its spans. \"\"\"\n", " matchTokens = text.tokens[start:start+length]\n", " spans = text.spans[start:start+length]\n", " if len(spans) == 0: \n", " # Don't try to get text or context beyond the end of a text. \n", " passage = \"\"\n", " else: \n", " passage = text.text[spans[0][0]:spans[-1][-1]]\n", " return passage \n", "\n", " def getLocations(self, text, start, length, asPercentages=False): \n", " \"\"\" Gets the numeric locations of the match. \"\"\"\n", " spans = text.spans[start:start+length]\n", " if asPercentages: \n", " locations = (spans[0][0]/text.length, spans[-1][-1]/text.length)\n", " else: \n", " locations = (spans[0][0], spans[-1][-1])\n", " return locations\n", "\n", " def getMatch(self, match, textA, textB, context): \n", " length = match.size + self.ngramSize - 1 # offset according to nGram size \n", " wordsA = self.getContext(textA, match.a, length, context)\n", " wordsB = self.getContext(textB, match.b, length, context)\n", " spansA = self.getLocations(textA, match.a, length)\n", " spansB = self.getLocations(textB, match.b, length)\n", " self.locationsA.append(spansA)\n", " self.locationsB.append(spansB)\n", " line1 = ('%s: %s %s' % (colored(textA.label, 'green'), spansA, wordsA) )\n", " line2 = ('%s: %s %s' % (colored(textB.label, 'green'), spansB, wordsB) )\n", " return line1 + '\\n' + line2\n", "\n", " def match(self): \n", " \"\"\"\n", " This does the main work of finding matching n-gram sequences between\n", " the texts.\n", " \"\"\"\n", " sequence = SequenceMatcher(None,self.textAgrams,self.textBgrams)\n", " matchingBlocks = sequence.get_matching_blocks()\n", "\n", " # Only return the matching sequences that are higher than the \n", " # threshold given by the user. \n", " highMatchingBlocks = [match for match in matchingBlocks if match.size > self.threshold]\n", " \n", " numBlocks = len(highMatchingBlocks)\n", " self.numMatches = numBlocks\n", " \n", " if numBlocks > 0: \n", " print('%s total matches found.' % numBlocks, flush=True)\n", "\n", " for num, match in enumerate(highMatchingBlocks): \n", " print('match: ', match)\n", " out = self.getMatch(match, self.textA, self.textB, 5)\n", " print('\\n')\n", " print('match %s:' % (num+1), flush=True)\n", " print(out, flush=True)\n", "\n", " return self.numMatches, self.locationsA, self.locationsB" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mm = Text(rawMM, 'Middlemarch')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test1 = Text(data[0][0]['ocr'], 'test1')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'labo'" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stemmer = LancasterStemmer()\n", "stemmer.stem('labourer')" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20 total matches found.\n", "match: Match(a=550, b=959, size=37)\n", "\n", "\n", "match 1:\n", "\u001b[32mMiddlemarch\u001b[0m: (5809, 6218) interest in gimp and artificial protrusions of drapery \u001b[31mmind was theoretic, and yearned by its nature after some lofty conception of the world which might frankly include the parish of Tipton and her own rule of conduct there; she was enamoured of intensity and greatness, and rash in embracing whatever seemed to her to have those aspects; likely to seek martyrdom, to make retractations, and then to incur martyrdom after all in a quarter where she had not sought\u001b[0m Certainly such elements in the character of a marriageable girl\n", "\u001b[32mtest1\u001b[0m: (10452, 10861) closely, as the first description of Dorothea shows \u001b[31mmind was theoretic, and yearned by its nature after some lofty conception of the world which might frankly include the parish of Tipton and her own rule of conduct there; she was enamoured of intensity and greatness, and rash in embracing whatever seemed to her to have those aspects; likely to seek martyrdom, to make retractations, and then to incur martyrdom after all in a quarter where she had not sought\u001b[0m central issue of marriage is raised immediately\n", "match: Match(a=820, b=1007, size=9)\n", "\n", "\n", "match 2:\n", "\u001b[32mMiddlemarch\u001b[0m: (8751, 8851) might lead her at last to refuse all \u001b[31myoung lady of some birth and fortune, who knelt suddenly down on a brick floor by the side of a sick\u001b[0m laborer and prayed fervidly as if she thought herself living\n", "\u001b[32mtest1\u001b[0m: (10990, 11090) immediately-\"And how should Dorothea not marry?\" (p. 6)-with its obvious answer \u001b[31myoung lady of some birth and fortune, who knelt suddenly down on a brick floor by the side of a sick\u001b[0m labourer and prayed fervidly 3George Eliot's\n", "match: Match(a=834, b=1063, size=13)\n", "\n", "\n", "match 3:\n", "\u001b[32mMiddlemarch\u001b[0m: (8890, 9046) side of a sick laborer and prayed fervidly \u001b[31mthought herself living in the time of the Apostles--who had strange whims of fasting like a Papist, and of sitting up at night to read old theological books\u001b[0m wife might awaken you some fine\n", "\u001b[32mtest1\u001b[0m: (11578, 11733) parenthetically in my text. 74 NINETEENTH-CENTURY FICTION \u001b[31mthought herself living in the time of the Apostles-who had strange whims of fasting like a Papist, and of sitting up at night to read old theological books\u001b[0m Although the parallel relaxes, it remains the organizing\n", "match: Match(a=5333, b=1137, size=6)\n", "\n", "\n", "match 4:\n", "\u001b[32mMiddlemarch\u001b[0m: (57013, 57100) energetically, forgetting her previous small vexations \u001b[31mthink we deserve to be beaten out of our beautiful houses with a scourge of small cords\u001b[0m all of us who let tenants live\n", "\u001b[32mtest1\u001b[0m: (12400, 12487) Dorothea's cottages on his own estate, her enthusiastic reply \u001b[31mthink we deserve to be beaten out of our beautiful houses with a scourge of small cords\u001b[0m earlier, with Casaubon overhearing, she had cried out at dinner\n", "match: Match(a=7776, b=1245, size=10)\n", "\n", "\n", "match 5:\n", "\u001b[32mMiddlemarch\u001b[0m: (83868, 83999) letter, to look at it critically as a profession of love \u001b[31mwhole soul was possessed by the fact that a fuller life was opening before her: she was a neophyte about to enter on a higher grade\u001b[0m going to have room for the energies which stirred\n", "\u001b[32mtest1\u001b[0m: (13702, 13833) letter of proposal as what we might term a \"call \u001b[31mwhole soul was possessed by the fact that a fuller life was opening before her: she was a neophyte about to enter on a higher grade\u001b[0m This is the Dorothea of \"Miss Brooke.\" Whatever\n", "match: Match(a=10885, b=2632, size=73)\n", "\n", "\n", "match 6:\n", "\u001b[32mMiddlemarch\u001b[0m: (116900, 117594) little of women by following them about in their pony-phaetons \u001b[31mEven with a microscope directed on a water-drop we find ourselves making interpretations which turn out to be rather coarse; for whereas under a weak lens you may seem to see a creature exhibiting an active voracity into which other smaller creatures actively play as if they were so many animated tax-pennies, a stronger lens reveals to you certain tiniest hairlets which make vortices for these victims while the swallower waits passively at his receipt of custom. In this way, metaphorically speaking, a strong lens applied to Mrs. Cadwallader's match-making will show a play of minute causes producing what may be called thought and speech vortices to bring her the sort of food she needed\u001b[0m life was rurally simple, quite free\n", "\u001b[32mtest1\u001b[0m: (29273, 29966) affords a glimpse of the convergent paths of the stories \u001b[31mEven with a microscope directed on a water-drop we find ourselves making interpretations which turn out to be rather coarse; for whereas under a weak lens you may seem to see a creature exhibiting an active voracity into which other smaller creatures actively play as if they were so many animated tax-pennies, a stronger lens reveals to you certain tiniest hairlets which make vortices for these victims while the swallower waits passively at his receipt of custom. In this way, metaphorically speaking, a strong lens applied to Mrs. Cadwallader's match-making will show a play of minute causes producing what may be called thought and speech vortices to bring her the sort of food she needed\u001b[0m images of the microscope and of microorganisms have their origin\n", "match: Match(a=17853, b=3119, size=12)\n", "\n", "\n", "match 7:\n", "\u001b[32mMiddlemarch\u001b[0m: (192301, 192441) fine young women to purplefaced bachelors \u001b[31mLydgate was less ripe, and might possibly have experience before him which would modify his opinion as to the most excellent things in woman\u001b[0m Miss Brooke, however, was not again seen by either\n", "\u001b[32mtest1\u001b[0m: (34576, 34716) Lydgate's and Chichely's comparison of the two women \u001b[31mLydgate was less ripe, and might possibly have experience before him which would modify his opinion as to the most excellent things in woman\u001b[0m motto comparing the two figures of ivory\n", "match: Match(a=18112, b=3188, size=40)\n", "\n", "\n", "match 8:\n", "\u001b[32mMiddlemarch\u001b[0m: (195148, 195588) laughs for bird-notes, and blue eyes for a heaven \u001b[31mCertainly nothing at present could seem much less important to Lydgate than the turn of Miss Brooke's mind, or to Miss Brooke than the qualities of the woman who had attracted this young surgeon. But any one watching keenly the stealthy convergence of human lots, sees a slow preparation of effects from one life on another, which tells like a calculated irony on the indifference or the frozen stare with which we look at our unintroduced\u001b[0m neighbor. Destiny stands by sarcastic with our dramatis\n", "\u001b[32mtest1\u001b[0m: (35325, 35764) other's lives but on the part they have already \u001b[31mCertainly nothing at present could seem much less important to Lydgate than the turn of Miss Brooke's mind, or to Miss Brooke than the qualities of the woman who had attracted this young surgeon. But any one watching keenly the stealthy convergence of human lots, sees a slow preparation of effects from one life on another, which tells like a calculated irony on the indifference or the frozen stare with which we look at our unintroduced\u001b[0m neighbour. Destiny stands by sarcastic with our dramatis\n", "match: Match(a=37410, b=3393, size=11)\n", "\n", "\n", "match 9:\n", "\u001b[32mMiddlemarch\u001b[0m: (402604, 402726) would think of Mistress Second-Cousin \u001b[31mantique form animated by Christian sentiment--a sort of Christian Antigone--sensuous force controlled by spiritual passion\u001b[0m Yes, and that your painting her was the chief outcome\n", "\u001b[32mtest1\u001b[0m: (37559, 37679) religious depth for which her Puritanism does not account \u001b[31mAntique form animated by Christian sentiment-a sort of Christian Antigone-sensuous force controlled by spiritual passion\u001b[0m None of this serves to advance Middlemarch\n", "match: Match(a=38246, b=3772, size=41)\n", "\n", "\n", "match 10:\n", "\u001b[32mMiddlemarch\u001b[0m: (411725, 412177) expect people to be deeply moved by what is not unusual \u001b[31melement of tragedy which lies in the very fact of frequency, has not yet wrought itself into the coarse emotion of mankind; and perhaps our frames could hardly bear much of it. If we had a keen vision and feeling of all ordinary human life, it would be like hearing the grass grow and the squirrel's heart beat, and we should die of that roar which lies on the other side of silence. As it is, the quickest of us walk about well wadded with stupidity\u001b[0m However, Dorothea was crying, and if she had been required to state\n", "\u001b[32mtest1\u001b[0m: (41887, 42337) cannot suppose we will regard as tragic \u001b[31melement of tragedy which lies in the very fact of frequency, has not yet wrought itself into the coarse emotion of mankind; and perhaps our frames could hardly bear much of it. If we had a keen vision and feeling of all ordinary human life, it would be like hearing the grass grow and the squirrel's heart beat, and we should die of that roar which lies on the other side of silence. As it is, the quickest of us walk about well wadded with stupidity\u001b[0m end of the next chapter, after her quarrel\n", "match: Match(a=41626, b=3830, size=54)\n", "\n", "\n", "match 11:\n", "\u001b[32mMiddlemarch\u001b[0m: (449403, 450049) some new motive is born. Today \u001b[31mbegun to see that she had been under a wild illusion in expecting a response to her feeling from Mr. Casaubon, and she had felt the waking of a presentiment that there might be a sad consciousness in his life which made as great a need on his side as on her own. We are all of us born in moral stupidity, taking the world as an udder to feed our supreme selves: Dorothea had early begun to emerge from that stupidity, but yet it had been easier to her to imagine how she would devote herself to Mr. Casaubon, and become wise and strong in his strength and wisdom, than to conceive with that distinctness which is no longer reflection but feeling\u001b[0m idea wrought back to the directness of sense\n", "\u001b[32mtest1\u001b[0m: (42522, 43167) good of the world\" is thoroughly laid: To-day \u001b[31mbegun to see that she had been under a wild illusion in expecting a response to her feeling from Mr. Casaubon, and she had felt the waking of a presentiment that there might be a sad consciousness in his life which made as great a need on his side as on her own. We are all of us born in moral stupidity, taking the world as an udder to feed our supreme selves: Dorothea had early begun to emerge from that stupidity, but yet it had been easier to her to imagine how she would devote herself to Mr. Casaubon, and become wise and strong in his strength and wisdom, than to conceive with that distinctness which is no longer reflection but feeling\u001b[0m equivalent centre of self, whence the lights\n", "match: Match(a=41690, b=3886, size=9)\n", "\n", "\n", "match 12:\n", "\u001b[32mMiddlemarch\u001b[0m: (450145, 450244) directness of sense, like the solidity of objects \u001b[31mequivalent centre of self, whence the lights and shadows must always fall with a certain difference\u001b[0m CHAPTER XXII. \"Nous câusames longtemps\n", "\u001b[32mtest1\u001b[0m: (43187, 43286) conceive with that distinctness which is no longer reflection but feeling \u001b[31mequivalent centre of self, whence the lights and shadows must always fall with a certain difference\u001b[0m pp. 15657) Given this major development\n", "match: Match(a=145645, b=4006, size=7)\n", "\n", "\n", "match 13:\n", "\u001b[32mMiddlemarch\u001b[0m: (1575265, 1575374) cruel!\" said Dorothea, clasping her hands \u001b[31mwould you not like to be the one person who believed in that man's innocence, if the rest of the world belied\u001b[0m Besides, there is a man's character beforehand\n", "\u001b[32mtest1\u001b[0m: (44587, 44696) practical action on behalf of a friend and neighbor \u001b[31mWould you not like to be the one person who believed in that man's innocence, if the rest of the world belied\u001b[0m People glorify all sorts of bravery\n", "match: Match(a=145739, b=4015, size=9)\n", "\n", "\n", "match 14:\n", "\u001b[32mMiddlemarch\u001b[0m: (1576340, 1576437) would all stand by him and bring him out of his trouble \u001b[31mPeople glorify all sorts of bravery except the bravery they might show on behalf of their nearest\u001b[0m neighbors.\" Dorothea's eyes had a moist brightness\n", "\u001b[32mtest1\u001b[0m: (44706, 44803) man's innocence, if the rest of the world belied \u001b[31mPeople glorify all sorts of bravery except the bravery they might show on behalf of their nearest\u001b[0m neighbours\" (p. 538). Her instinctive understanding of what is tragic in Lydgate's\n", "match: Match(a=152207, b=4095, size=69)\n", "\n", "\n", "match 15:\n", "\u001b[32mMiddlemarch\u001b[0m: (1648982, 1649704) like me.\" As Lydgate rode away, he thought \u001b[31mThis young creature has a heart large enough for the Virgin Mary. She evidently thinks nothing of her own future, and would pledge away half her income at once, as if she wanted nothing for herself but a chair to sit in from which she can look down with those clear eyes at the poor mortals who pray to her. She seems to have what I never saw in any woman before--a fountain of friendship towards men--a man can make a friend of her. Casaubon must have raised some heroic hallucination in her. I wonder if she could have any other sort of passion for a man? Ladislaw?--there was certainly an unusual feeling between them. And Casaubon must have had a notion of it. Well--her love might help a man more than her money\u001b[0m Dorothea on her side had immediately formed a plan\n", "\u001b[32mtest1\u001b[0m: (45636, 46348) Miss Brooke. As he rides away he thinks \u001b[31mThis young creature has a heart large enough for the Virgin Mary. She evidently thinks nothing of her own future, and would pledge away half her income at once, as if she wanted nothing for herself but a chair to sit in from which she can look down with those clear eyes at the poor mortals who pray to her. She seems to have what I never saw in any woman before-a fountain of friendship towards men-a man can make a friend of her. Casaubon must have raised some heroic hallucination in her. I wonder if she could have any other sort of passion for a man? Ladislaw?-there was certainly an unusual feeling between them. And Casaubon must have had a notion of it. Well-her love might help a man more than her money\u001b[0m NINETEENTH-CENTURY FICTION What Dorothea has thought\n", "match: Match(a=155785, b=4230, size=9)\n", "\n", "\n", "match 16:\n", "\u001b[32mMiddlemarch\u001b[0m: (1688955, 1689089) consciousness that only sees another's lot as an accident \u001b[31mbegan now to live through that yesterday morning deliberately again, forcing herself to dwell on every detail and its possible meaning\u001b[0m alone in that scene? Was it her event only? She forced herself to think\n", "\u001b[32mtest1\u001b[0m: (47128, 47262) During her night watch in the cold room \u001b[31mbegan now to live through that yesterday morning deliberately again, forcing herself to dwell on every detail and its possible meaning\u001b[0m All the active thought with which she had before been representing\n", "match: Match(a=155864, b=4241, size=35)\n", "\n", "\n", "match 17:\n", "\u001b[32mMiddlemarch\u001b[0m: (1689907, 1690307) once shown her the truer measure of things \u001b[31mAll the active thought with which she had before been representing to herself the trials of Lydgate's lot, and this young marriage union which, like her own, seemed to have its hidden as well as evident troubles--all this vivid sympathetic experience returned to her now as a power: it asserted itself as acquired knowledge asserts itself and will not let us see as we saw in the day of our ignorance\u001b[0m said to her own irremediable grief, that it should make her more\n", "\u001b[32mtest1\u001b[0m: (47267, 47666) dwell on every detail and its possible meaning \u001b[31mAll the active thought with which she had before been representing to herself the trials of Lydgate's lot, and this young marriage union which, like her own, seemed to have its hidden as well as evident troubles-all this vivid sympathetic experience returned to her now as a power: it asserted itself as acquired knowledge asserts itself and will not let us see as we saw in the day of our ignorance\u001b[0m this victory of knowledge over self\n", "match: Match(a=157584, b=4387, size=12)\n", "\n", "\n", "match 18:\n", "\u001b[32mMiddlemarch\u001b[0m: (1708999, 1709140) anxiety, could only seize her language brokenly \u001b[31mmean, marriage drinks up all our power of giving or getting any blessedness in that sort of love. I know it may be very dear--but it murders\u001b[0m marriage--and then the marriage stays with us like\n", "\u001b[32mtest1\u001b[0m: (48916, 49055) those we were married to, it would be no use \u001b[31mmean, marriage drinks up all our power of giving or getting any blessedness in that sort of love. I know it may be very dear-but it murders\u001b[0m marriageand then the marriage stays with us like\n", "match: Match(a=157599, b=4402, size=15)\n", "\n", "\n", "match 19:\n", "\u001b[32mMiddlemarch\u001b[0m: (1709168, 1709342) know it may be very dear--but it murders our marriage \u001b[31mmarriage stays with us like a murder--and everything else is gone. And then our husband--if he loved and trusted us, and we have not helped him, but made a curse in his life\u001b[0m voice had sunk very low: there was a dread\n", "\u001b[32mtest1\u001b[0m: (49081, 49252) know it may be very dear-but it murders our marriageand \u001b[31mmarriage stays with us like a murder-and everything else is gone. And then our husband-if he loved and trusted us, and we have not helped him, but made a curse in his life\u001b[0m pp. 583-84) Here is the true reason for her overlong mourning\n", "match: Match(a=165399, b=4489, size=25)\n", "\n", "\n", "match 20:\n", "\u001b[32mMiddlemarch\u001b[0m: (1793142, 1793447) spent itself in channels which had no great name on the earth \u001b[31meffect of her being on those around her was incalculably diffusive: for the growing good of the world is partly dependent on unhistoric acts; and that things are not so ill with you and me as they might have been, is half owing to the number who lived faithfully a hidden life, and rest in unvisited tombs\u001b[0m \n", "\u001b[32mtest1\u001b[0m: (50020, 50325) real value of lives like Dorothea's \u001b[31meffect of her being on those around her was incalculably diffusive: for the growing good of the world is partly dependent on unhistoric acts; and that things are not so ill with you and me as they might have been, is half owing to the number who lived faithfully a hidden life, and rest in unvisited tombs\u001b[0m joining of \"Miss Brooke\" and \"Middlemarch\" suggested\n" ] }, { "data": { "text/plain": [ "(20,\n", " [(5809, 6218),\n", " (8751, 8851),\n", " (8890, 9046),\n", " (57013, 57100),\n", " (83868, 83999),\n", " (116900, 117594),\n", " (192301, 192441),\n", " (195148, 195588),\n", " (402604, 402726),\n", " (411725, 412177),\n", " (449403, 450049),\n", " (450145, 450244),\n", " (1575265, 1575374),\n", " (1576340, 1576437),\n", " (1648982, 1649704),\n", " (1688955, 1689089),\n", " (1689907, 1690307),\n", " (1708999, 1709140),\n", " (1709168, 1709342),\n", " (1793142, 1793447)],\n", " [(10452, 10861),\n", " (10990, 11090),\n", " (11578, 11733),\n", " (12400, 12487),\n", " (13702, 13833),\n", " (29273, 29966),\n", " (34576, 34716),\n", " (35325, 35764),\n", " (37559, 37679),\n", " (41887, 42337),\n", " (42522, 43167),\n", " (43187, 43286),\n", " (44587, 44696),\n", " (44706, 44803),\n", " (45636, 46348),\n", " (47128, 47262),\n", " (47267, 47666),\n", " (48916, 49055),\n", " (49081, 49252),\n", " (50020, 50325)])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Matcher(mm, test1).match()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

revspete/self-driving-car-nd

sem1/p2-traffic-sign-classifier/Traffic_Sign_Classifier.ipynb

1

2033519

mit

allentran/reinforcement-learning

MC/Off-Policy MC Control with Weighted Importance Sampling.ipynb

1

461646

{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import gym\n", "import matplotlib\n", "import numpy as np\n", "import sys\n", "\n", "from collections import defaultdict\n", "if \"../\" not in sys.path:\n", " sys.path.append(\"../\") \n", "from lib.envs.blackjack import BlackjackEnv\n", "from lib import plotting\n", "\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "env = BlackjackEnv()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_random_policy(nA):\n", " \"\"\"\n", " Creates a random policy function.\n", " \n", " Args:\n", " nA: Number of actions in the environment.\n", " \n", " Returns:\n", " A function that takes an observation as input and returns a vector\n", " of action probabilities\n", " \"\"\"\n", " A = np.ones(nA, dtype=float) / nA\n", " def policy_fn(observation):\n", " return A\n", " return policy_fn" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_greedy_policy(Q):\n", " \"\"\"\n", " Creates a greedy policy based on Q values.\n", " \n", " Args:\n", " Q: A dictionary that maps from state -> action values\n", " \n", " Returns:\n", " A function that takes an observation as input and returns a vector\n", " of action probabilities.\n", " \"\"\"\n", " \n", " def policy_fn(observation):\n", " probs = np.zeros(len(Q[observation]))\n", " optimal_action = np.argmax(Q[observation])\n", " probs[optimal_action] = 1.\n", " return probs\n", " return policy_fn" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mc_control_importance_sampling(env, num_episodes, behavior_policy, discount_factor=1.0):\n", " \"\"\"\n", " Monte Carlo Control Off-Policy Control using Weighted Importance Sampling.\n", " Finds an optimal greedy policy.\n", " \n", " Args:\n", " env: OpenAI gym environment.\n", " num_episodes: Number of episodes to sample.\n", " behavior_policy: The behavior to follow while generating episodes.\n", " A function that given an observation returns a vector of probabilities for each action.\n", " discount_factor: Gamma discount factor.\n", " \n", " Returns:\n", " A tuple (Q, policy).\n", " Q is a dictionary mapping state -> action values.\n", " policy is a function that takes an observation as an argument and returns\n", " action probabilities. This is the optimal greedy policy.\n", " \"\"\"\n", " \n", " # The final action-value function.\n", " # A dictionary that maps state -> action values\n", " Q = defaultdict(lambda: np.zeros(env.action_space.n))\n", " C = defaultdict(lambda: np.zeros(env.action_space.n))\n", " \n", " # Our greedily policy we want to learn\n", " target_policy = create_greedy_policy(Q)\n", " \n", " for _ in xrange(num_episodes):\n", " state = env.reset()\n", " experience = []\n", " while True:\n", " probs = behavior_policy(state)\n", " action = np.random.choice(np.arange(len(probs)), p=probs)\n", " next_state, reward, done, _ = env.step(action)\n", " experience.append((state, action, reward))\n", " if done:\n", " break\n", " state = next_state\n", " \n", " G = 0.\n", " W = 1.\n", " for state, action, reward in experience[::-1]:\n", " \n", " G = discount_factor * G + reward\n", " C[state][action] += W\n", " Q[state][action] += (W / C[state][action]) * (G - Q[state][action])\n", " p_action_target = target_policy(state)[action]\n", " if p_action_target <= 0:\n", " break\n", " W = W * p_action_target / behavior_policy(state)[action]\n", " \n", " return Q, target_policy" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "random_policy = create_random_policy(env.action_space.n)\n", "Q, policy = mc_control_importance_sampling(env, num_episodes=500000, behavior_policy=random_policy)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [ { "data": { "image/png": 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/rx9++EGGYahfv34+j7v88st12WWXaeHChTp48KCuuOIKHT16VCtWrFCTJk1y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zbQR08gZ94nCf3qRziVFV6Xk3T+uXdgXn4VoV/8ALfWuwk2XrAP76P23/4B/eKR5Nz2aURhEeTk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\nH7T6Wsw9q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jyvfqXzHN3EuXgcMlKHG+gJmq4W+mqDA9g3/B3yxAH0XWu9LidpHMCefidU\nn0bbsjjlHyJIgNLd+Ep34wDGxVdhPvT3uK4gsn0j0VVLcCorunACScHffwfn1Elqn34qpe0v0l5u\nPsFvPImxbz3a0V1eVxMXbecqnP7Dsf1dv5O1reC4Mwvkua7bbnsLdY2jdBtCNn6li3SqJcFUYKxk\nlKYZxvFQIUH8VMCkKJlHjVsl2YQQGLF69LL9zY+5QHTYROrMQmrDH/2udWqhO4CcQoxtSxNWc5NY\n/xHY/Uc0ziy2ogk/fnfk5BYSuWoOxrHtaKEKYr0uwrruHvTFf1ItKVrgAPaNDyCP7UHfvd7rcpLG\nAaxrFyArTqBtW+Z1OY3h8YEtaAe24EiJdsl4/J9/HCdmE920msi65bg1cSx6GehB8O+fIPz+20RX\nvpessjOS7NGLwFe/i751MVr5Ea/LiZuwLbR9m3Aum4KrJSYmaQqNz21V0dTruDPBsaIoSiqpwFjJ\nKIWFhXEHxkrrVMCkKJlHjVvFKyY2vn1rz5stGBt4OeGCflTXn//717mF7gail+5E2LFElNssOmgU\nTt/BjWGxCjITItZrIPbwSZgH1yMjtQAYFYegaDDW7E+jv/cnhJXYf8dM1hwWH92Fvnej1+UkjQNY\n193T2J4kDXszS8dB7lkHe9bh6Cb6iIn4rv4ObkMDkXXLiW5chVvf+t0N+rDLyLv7Ier+9O/Yh/al\nsPL0JwcMIfi5r6GvfR1ZVe51OZ0mD2xGXnwVdm4g4ccWQqDrevOM444Gx+r6TlEUr6jAWMkogUBA\ntaToBBUwKUpmamnsqnGreEECxplSZDjU/JjV+yKivYdxpu7838lOLXQndYRuoh9L7C3M0aFjcHr0\nw7f5bYRjJ/TY3VV08Gic4qGY+1ZdMFvbOHMEcLFmNYXGaja3A9hzH0Q7vBNt3yavy0kaB7Bm34s8\nthctA3ozSyuK3LkCY+cKHDMHfdRk/DOuxwmFiKxeSnTLWoh+1LrGvG4e/gnTCD39FG71WQ8rTz/6\nyNEU3P0QxoqXEHVxzNZOQ8J10XYsxxk7B1c3k3OOcxbIOzc4bnq8teBYzURWlA8JIJ3GQxqVkmgq\nMFYySjAYVDOM26CCYUXJTGrsKunO58Ywjm5v/n8r2JfIoFGcqr3wd7RTC931GoS+dx0igb/z0eGT\ncPMK8W15J64+ykrrIpfPwPXnNobFrTynxpmj4LgfzTTuxv2iG8Pih9AObkM7sMXrcpLGAaw59yMP\nb0fLwBnUMtqA3LIYA3ByCzAmTcG+4RacyjOEV76HOeZqhM9H6Gffh5j6EORcxqTp5N94G8ay5xCR\n7Fj4Tx7fhxx5NXagKKnnaSk4tiyruYVFez2Lz6WuFxVFSQYVGCsZJRAIUFnZ8X6I2UqFS52nniPF\nS2rsKpnIFA7m/g3NAaGdW0h02ETKay/ctlML3fnyELEo+pk4Q+Y2RC6dBqYfc9u7CQ2huysHSWTC\nTciGasyD69udTGOcLQWcxtD43T92y9DYkRL7xgfQ9m9FO7jV63KSxhESa859yP2bsyIUl/Uh5Ma3\nG8PjQBH6J+/HlRp1//srFRZ/jO/6W8mdOAVj6V+y6m4CAWib38OdcguO4U/++c4Jjpv6HDf1Oo4n\nNFYURUk0FRgrGaWwsJBDhw55XUbKqHBJUTKTGrtKtpBCYITOoIVOA+CYuURHTuNU/YWRYWcWugOg\nsARz67uJKBeAyBUzATC3L87muwRTxjH9RMbdhH7qIHplaYf3Mz784MCac29jaBzteIuSTOdI2diG\nYu8mtEPb298hQzlSawyL96zPyp/TGT0TrfwQ2p71cOf9xA7up/7F30NM9efOWfAAOUOGoi9bmJXt\nfrTKEzg1Z3CK+qfsnE0L5DUt2O44DrbdsedWXV8q3YqUjV/pIp1qSbDs/cmUrNSZHsaZ8ALadHHw\nca7rZkT9itJdqbGrZDvTtTAPbgDA1U0il82gIqLjtDCBuGeeQFYcjiukdfKLkDUVyAT1vQxfORts\nC/OD91VYnAB2QRGR8fMxSrfHFRY30atOolcexZp9L64vJwkVpp/GsPghtD0bsjJEbeJoOtb19yN3\nrcnKn9O6ej4i1oC+bSkyUkfO8ufIydcJfOOHaIOGel2ep/Iffoyckn7oK17KyrC4ib7pXWS0IeXn\nbWpJoWkamqY1P94081hdXyqKkioqMFYySqb3MFbhkqJkJjV2le7IEC5m6Q6EHcMVkshlMzhrmcSs\nC9PixoXuauJb6A4gvwhzT9cXyHKAhrE3IhtCmHtWqbA4AWJ9hxK7YibmgbVo8c4aP4deXYZ+5vCH\noXFuAitMP47Usec+jLZ7LdrhnV6XkzSObjb2LN6+HO3IB16Xk3DW+BsQmoa+adF5f0uMPWvxb36L\n/Pu/QM4n7gWptXqMbBV47HuYhou+7g0E2X39I2rPIiuOg0fXeecGx03/39Tn+OPBsboWVRQlGVRg\nrGSUzsww9oIKlxQlM6mxqyiNhBDokVq0iiO4CCKXXkM1uYRjLY+DAtNFVMbZg7hHCdrJ/XGFzC1x\ngOj4m9GqT2Hub7+/rtK+6MXjsQdfgblvFTLa9YWs9JpyjIqDWLM/jevPS0CF6ceROva8B9F2rUY7\nssvrcpLGMXxYc+5FblmCdmyv1+UknHXlTMgt+DAQvZCsqyZn2Z/I6duLwDd+gCwekOoSvaHrBJ/4\nMUZVKca2pd3m76y+ZTGaB7OMz9V0/anrOrquNy+S11JwrCjdgYvAFWn0lcV/EVVgrGSUYDBIVVVi\nbltNhEwLl1qqVel+0vF3M5Vc1824sasoqebDxrd/LQDRYROoMwqpi7Q8Nnrma4iqE/EtdCclmHkY\nh7d1qU4HiE68Fa3iCOahzF9wy2sOjW093EBPzH2rEXbierVqNacwTh9oDI1z8hN23HTg6Dr2vIfQ\ndqxEO7rb63KSxjFzsGbfi9z0HtrJA16Xk3DW5VOhZ1+MNX9rd/asufN9/B8speDhx/DPvQOy+Ro7\nN5/CJ/4Z49AW9L0bva4mpUSkHnlsL8Tx+pZMTQvkfTw4VteuiqIkgwqMlYwSDAY9mWGswiVFyUxq\n7CpK/HRALz+IjNQTG3gF4YJ+VIdbHi/NC93VxtmyoOcgjAObutT/0pGSyNWfQDuxF+No9t7+nyqO\nlEQn3oqM1mMc3pSU28210GmMU/uwZt2Dm1OQ8ON7wdFN7BsfQtu+PCtn3DZx/HlYs+9BW/82Wln2\nLUBtDZ8AxUMwVr2C6OD1gayuwLfsj+QMu5iCrz2JLOqT5CpTT/bsTeG3nkLfsRTtaPbOnG+LtnMl\nWqTrd1ok0seDY3VNqyhKMqjAWMkogUCAuro6YklanViFS4qSuVoav2rsKkr8DCeCcXwXVp+LiPa+\nmDP1rY+hTi10p/sRgFZ+sNM1OlInMuk2jCPbMU7s6fRxlEaOP4/I1bejnT6EUbY3qTdXaqEKjLI9\njaFxbiCJZ0o+Rzex5z6Atv19tOP7vC4naZzcAqzr7kZb+wby9FGvy0k4e+hoGHwpxsr4F3GTgG/L\nu+QcWEvBF76Nb+bc5BTpATlgCMGvfhdj3eto5Ue8Lsczwo4h929GOJYn52+6M64lTcGxuotU6VaE\nACHT6Ct7x58KjJWMkpOTg8/nIxQKdek4KhhWlMylxq+iJI9POJj712EH+xIZOIpTda2Pqc4sdAdA\n0QCMXZ1fmM7RTSJX34pxcBN6F0JnpZFdWExk7FyMw5vRq06k5JxaXSVG2a7G0DgvmJJzJppj+rHn\nPoi2ZSna8f1el5M0Tl4Qa+YCtFV/a1wALMvYA0bgXDIWY8ULCLvzgaA8cwLf0mfJHTOO/C9/FxHs\nkcAqU08fOZrgZx/DWPESsuqU1+V4Ttu/GRlOr1nG51LXv4qiJIMKjJWMEwgELuhj3NDQQDh84RtW\nFSwpSuZS41dRUksKgV5dDq5LdNgEymvb3r4zC905OUFEfQit5nSnarR9uUQm3oKxdy16Fs50TLVY\n/+HELp2Cb/8atIbqlJ5bqzuLeWIH1nV34+ZnVrjmmH7sGz6Dtnkx2sns/dDCKeiJNf1OtJV/RZ4t\n87qchHOKh+JcMRVz+QsIq+t3L0rAt+ENck7sJPDl72JOmtnlY3rBnDSDggWfwVj2HKIufdaO8ZJw\nHbSdKxBW1OtSFEVRUkb3ugAlvS1fvpwlS5YQCoUoKSnhjjvuYNCgQe3ut2nTJp555hlGjRrFgw8+\nmLB6GhoaGDlyJLt27WLr1q2UlZVRVlZGdXU1CxYsYPLkyedtr0IlRUl/TaHwx8erGr+KklqmG8M4\nsZvIpTM4Vd/2/N/Ghe6Ox7XQHQDBvpgb3uhUfa6mE50wH3CR4bpOHUP5SHTEZJxAEebelV3qJd0V\nsr4a8/gOotctQF+yEBGKsxe2B5rD4k3voZUf9rqcpHGCvbCm3Iq+4kVEzRmvy0k4p/dA7DHXNobF\nsUhCj62VH8Z3+ijimvmYYydR94df49Z17e7IVPHdcBu5EyZjLP2LCkc/Rh7bixx5NXZBz5Se13Vd\npGx7np+6Zla6laZWEOkinWpJMOF28K/LiROpuUVNSR+bNm3iT3/6E3fddReDBw9m6dKlbN26lSee\neIL8/NZXt66srOQXv/gFRUVF5OXldSowDofDzWHwyZMnKSsro7y8vHlmsRCCoqIiiouL6devH8XF\nxQwdOpQePTJrhkqqCSHUBYXimdaCYUVJBfX370LnjklDuOSUbifW/1IqYj5iVutBsK4Jevlt5Mnd\n8fUuDvZBqwth7lkTd62uphMefzOO6cetKkML9Ma3+S0VHHeCA0TH3oiwYxhHtya1X3FHOf4CogOv\nRF/6HKImfUNjx5eLff39aBsWoWXxDHenR1+sq29GX/5iRoT48XJ6FGNPno+54gVEQzu3UnRRrP9w\nYsMm0PDqc8S2rkvquboqZ8ED5AwZir7qr559iJTu7KL+WJPn4xj+lJ0zFoshpUTTtFa3iUZVuJ9p\nSkpKvC4hY4U3vIVbe9brMpqJ/B74x9/odRlJoWYYK61atmwZU6ZMYeLEiQDcddddfPDBB6xdu5ZZ\ns2a1uI/jODz77LPMnTuXAwcOtNgmojWvvvoqx48fp6ysrMVgePz48RQXF/Ob3/yGW265hVtvvbXD\nx1ZBQSP1HCipoGYMK0p6aW9MCsAIh7D6XcJZy2wzLIYPF7orj3OhOwB/IcbWJXHs9WGdmk5k3Dzc\nnALciqNgx7BDZ4hcdT3+TW8hog1xH7O7cnSD6Pib0c4eRz+VPq0UZDiEWbqF6MwF6MueR1RXeF3S\nBRx/Hvac+9A2vIN2utTrcpLGKSrBmji38d8hC9sROIEi7CnzMVe8nPSwGMA4vhet/DDyxluJjZ9C\n3R//HcLp9zcr/7Nfx8z3oa94CYG6XmuNduY4TqgSp2dqwj517awoipdUYKy0yLZtSktLmTNnTvNj\nQgiGDx/O4cOHW93v7bffJj8/n0mTJnHgwIG4znn8+HE0TWPcuHHNs4b79OmDaZrnbef3+7u86J2i\nKF2ngmFFST8tjcv2xqRPOKAbVJNLONb2tnk+Da2hOv6F7noOQi/dGfeiUq5mEBk3l7AvgBGuBfvD\nPqOxMHZ9DZGrbsC36Q1163QHOLkBIlddj3H8A7Sa9FvESoZrMY9uJjrjTrT3X0BWda7PdTI4/nzs\n6+9DW/cWWkV8fbszidN7ANa46xtnetfXeF1Owjl5Qexr7sBY9VdEfep6dksrin/Vi2gXjUb7+g+o\nf+H3WHt2pOz87Qk89j30cBX6ujfS4o6DdKdvehdn+p04Zk7KztnSmh6K0l25QuCm05hIp1oSTAXG\nSotqa2txXfeC1hMFBQWcOtXym4yDBw+ydu1avvnNb3bqnI8++miHtgsGg1RXp3ZhFiW7qBnn8VHB\nsKKkn0SNS124CMNHna1RF25/3wKfizge50J3UkdoBnrprrj2awyL51Ejc8jTwD3zseuPSB0xIWDM\njY2hcZxhdHdiFQ0gNuJqjEMb0cLp+6G7jNRhHtlEdPonYflLyLPlXpeEk1OAPedetLVvoJ3J3hZ9\nTvEQrKuuRV/6l5TMvE01x5+PPXMB+trXkB7dymwc2oZ2fD/y9nuJHj1M/cL/gZiHH3bpOsFvPoV+\nci/63o3e1ZFhRKgSeeYETvHQtAiK1PW4oijJkr3dmZWkaekTzkgkwh//+EcWLFhAbm5uUs8fCASo\nqcm+WQ+Kkg6EEBeMcdd11cWoonikpTEJiRmXQggM00c0alHdgbC4caG7E/EvdNdrEPq+tXHd5twU\nFlcLPz5Dw62pgBZ+XhGuJRYLE7nqBlzZen/H7iw66AqsSybg27cqrcPiJjJaj3lkE/Y1d+D07Odp\nLU5uoDEsXvN6VofFdsnFWFfORF+SpWGx6ceedQ/6hrfQqr2duS6j9fhXLCTH5xD4xg/RLrrEm0Jy\n8yl84p8xDm5RYXEn6FsWo6l2SIqiZDk1w1hpUX5+PkIIamvPv2gMhUIUFBRcsH1FRQWVlZX813/9\nV/Mb2Kb/fv3rX+eJJ56gqKgoIbUVFhZy6NChhBxLUborNWtYUdKLF2PSlGBHY5ypb/8cuiYw3Rii\nNr4FsBxfHiIWRT9zvMP7uJpBZPw8qvFj2aALB7eh9Q+KRUMNVm4hXDkH35Z34g+0s1jksum4OfmY\ne1dm1PMio/WYh9cTveY2WPkKsiL1Ya2TG8SefQ/a6tfQzpal/PypYve/BPvyKY1hcaTe63ISztFN\n7Dn3oW9+F63ypNflNDP2bUA7vhdxz+eI7tlJw8vPQorukpBFvQl8+f+ib12MVn4kJefMNiJchzix\nD4aMApG8OXjNaw20MZNZXbsr3Y4QSR13cUuDOw2SRQXGSos0TWPgwIHs3buXK664Amh8Mdq3bx/T\np0+/YPu+ffvyrW9967zHXn/9dSKRCLfffjuFhYUJq03NMFaUjlPBsKKkn5ba4qR6TGpSAyE4Vdex\ngKIzC90BUFiCufXdDm9+blgciTn0LPDhnu1A2FxfhZVfhBh9HebW97r9ok0Oksi4echICPPguozs\nSypjYcyD64lOvQ1WvYpM4UJzTn4P7Os+hb7qVWSV920xksUadCnOyAnoS/6MiMbZlzwDOFLHnnM/\n+rb303KhQllfQ877f0a/bBrGN35A3bO/wT5+NLnnHHgRwc8+1tiaoyr9eplnEn3HCtx+w7D9eV6X\noiiKkhRpFMsr6WbmzJmsXr2adevWUV5ezsKFC4lGo0ycOBGAZ599ltdel0e7IgAAIABJREFUew0A\nXdcpLi4+7ysnJwe/309xcTGalrjbRAOBQNw9jFU4pmS7ZN62rihK57Q1Lr0kpQZCo7ymY2Fx40J3\nobgXunPyi5A1Fci6qg5t7+omkfE3UU0OkZiDz9QQsQaIRTp2wtozWKaf6OUzunVc7Og+Ilffhl59\nAvP4zowMi5tIK4J5cC32lJtx+g5KyTmdgg/D4pWvZHdYPOQKnBHj0Rdna1gssa+/H333GrSyg16X\n0ybjgxX4t79H/gNfxn/zXSCT8xZdHzma4MNfxVjxogqLE0BYMeSBLQjH2/75Xl9TKIqSvdQMY6VV\nY8aMoa6ujjfffJPa2lpKSkp49NFHmxfCq66uRibpgqYtwWBQzTBWui01Y1hR0k8mjUtN07EcQXVt\nxxdaalzoLr7ZeQ5AfhHmulc7tL2rm409i/ETidkA5Pt13NPxLbDn1lRgBfrAyKmYu1dmdFjaGU5+\nEZHR12KUbkerPeN1OQkhrSjmwXVEr74Zd+2baGXJa0vmFPTEvnYB+sq/IqsrknYer1kXj8EZcllj\nWGx5uOhakjiAPed+tAOb0Y7t8bqcDpGhSnzL/oQcfS3G156k7n+fxjmduFYo5qQZ5N14K8ay57Ky\n9YhXtH0bcYaOxs65sGVjInSkJYWidD8izdpApFMtiSXcDr6bOXEiexd6UDLLtm3b+MIXvsCKFSu8\nLkXJUC3dDp5uWgugFEXpnESM+0wfl7pu0BB1qAvbHd6nZ76Gr+Y4Mt7exT36o1WexDy4ud1tWwqL\nC3JNjHAV1J2N67xNRGExxpljmPs3dGr/TBTrMwRr2DjMgxuQkTqvy0k4R+pEL56MXP8W2snEzxh1\ngr2wp3+yMSyuyY6wvSXWiPE4/S9BX7oQYce8LifhGsPi+9CO7UXfv8nrcjrF6dGXyOjZhFcvJbL4\ntRYX/IyH74bbyJ0wGWP5i1n5AYHX7IEjiV11Ha5uJv7Yto3jOBiG0eo2lmXhOJnTo15pVFJS4nUJ\nGath0yLcDt69lgoir5CcsXO8LiMpVEsKJeOoGcZKNvGqlYSaqaAorcvGFi+6YRJqsOMKizu90J2U\nYOZiHN7W7rYthcVSgqnR6bAYwK0qw+o1iOjg0Z0+RiaJXjQG+6Ir8e1dlZVhMYB0LMwDq3Em3IDd\nf1hCj+0U9sae/kmMFS9nd1h86SSckmHoS5/LyrAYwL72brSyQxkbFgPIs+X4lv2R3CtGUfCVf0D2\n6PzC4TkLHiDvqnEYyxaqsDhJZOluZLi2/Q0VRVEyjAqMlYzT1MM4U9+0K91TNgZQipINWhqb2TQu\nhRDohklVbYxILL4ZSD3zBLKiEwvd9RyEcWATwmk7nP6oZ/FHYTFAMNeHU9X1W7Gdsyew+l1CrP/I\nLh8rXTlAeNR1OD36YO5blbUhYJPG0HgNzrg52AOGJ+SYTo++2NfcgbHiJUQovg9HMol1xVScPoM/\nDIu97bmaLNb0TyKry9F3r/W6lC6TgG/TW/iPbqHgS9/BnDo77mPkf/br5PTrh77ipXb/HiudJwBt\ny2JkR/vtx8F1XTXJQ1E+Tsr0+8pS2fuTKVkrEAhgWRYNDQ1el6JkqGTP3FXBsKKkp+42NoWQSM2g\nMhTFsuP7GTu90J3uR7iglbfdMqA5LHZ954XFpqEh7SgimpjXeOfscaxBV2AVD03I8dKJIyXRibcg\n7QjmoY2ILP09/rjmmcZjZ2EP7NqHAU6PYuxpn2i8VT/U+Rnt6c4aPQO3Zz/09xdmbXBoTb4VEa5F\n377c61ISSjt9FN/SP5F79VTyv/gEoiDYof0CX/s+Pt1CX/8GolsvA5oa2uljcd+NkyjZeg2jKIr3\nVGCsZBxN0ygoKKC6utrrUjKS+pQ6cbJ9ZqKSnbL9b0BrH9pA93pTJaWGkDqVNVE609qwwOciKuNb\n6A6AogEYu1e1OSvZ1U3CLYTFAAU5Om4CZhefy648RmzoWKxeAxN6XC85Zi6RSZ9AqziCcXJPFi+3\n0jLp2Jj7V+GMuRZ78GWdOoZT1A972m0Y77+AqE2fXoiJFhs7CzfQE+395xFZ2ufUmjAXgYu++b2s\nHAsSB/+6V8k5vY/AV/4fxrgprW+s6wSf+DHG2aPo25Zl5fORroyNi5AJ+rBTURQlHeheF6AonREM\nBqmurqZfv35el6J0A60tdNWdwidFSTdqXLZO03QsR1Bd27l+lT3zNUTVCYQbX7jk5ASR9TVoNadb\n3aYpLK5pISzO9Ru49TXgJP52efvMCRh+NcK20M6eTPjxU8kO9iF6+QyMI1vQ6rN3Vmx7pOtg7ltF\n9MrpIDW0Q9s7vK9T1B97yvzGsLgueycgxMZfD2YO2oqXsnYGujVmFvhz0Ne8mvXhqHbyAL7yI4hZ\ntxAbO5n6Z3+D21D/0Qa5+RR+4wfoe9ehle7yrtBuSoQqkZUncRJ4R4vrush2bndX1z1KtyMEbhpN\ngMnmyThqhrGSkQKBgFr4Tkk41U5CUdKPGpfx0XSDcMyluq5zvWw/WuiuEwt/Bfti7FrV6rddw9fq\nzGKAHENCKFkLjjnYZ48TvXQqdkGvJJ0j+WL9LiF22TR8+9d067C4icTB3LcaZ9RU7Iuv7NA+Tu8B\n2FNuxlj2fHaHxRPngm6irXw5e8PiUddAsAhj7WtZHxY3kY6Ff/VL+OtPUfD1H6BfflXj40W9KfzW\nU+jbl6iw2EP65veQkfr2N1QURckAaoaxkpGCwSBVVdl7+6CSXGpmoiKEUP/eaailsan+nTpON0xC\n9Vbci9udq2euQJ6Kf6E7J9gH7XQpMlLX4vddw0d43DyqHR9R68KwuDDfh1tzCpLZa9NxsM+WEb1i\nJua2d9HqMus6InrJpMbF7fauQiRhFnamagyNVxK9bAoIDW3/pla3dfoMwp44tzEsrg+lsMrUik2e\nD7aFtvpvWRukWiMnQu+BGCtezNpAvC3G0Z1oZQeQ8xcQm3wd5qAh6GtfQ1ad8rq0bk2E65AnDuAM\nuRxE1+bmqesfRVG8pmYYKxkpGAzGPcM4m28VUFqmZiYqSnpSYzOxhBDohklVbaxLYXGeT0MLd2Kh\nOwB/IcaBDS1+vzEsvqnVsFjXJZprQbi2E1XHybGwa04RHT0LJ6cg+edLAAcIX3U9bl4B5r41Kixu\ngYTGnsaXTsAePq7FbZziIR+GxQuzOyyeehvEwmhrX8/asNi+eAwMHIGx8qW4W+dkExkN41v/KsbQ\n4chD21RYnCb0HcvRIonrZdzWe1h1zaR0S0I0fiCTNl/Z+mqrAmMlQzX1MFYUUOGToqQrNTaTTwiJ\n1A0qQzEsu2vPaacXuus5CL10J8K+MMj8KCw2WwyLAQI5RsIXumuTFcWuqSBy5RxcX27qztsJjtSJ\nTLoN2VCNeWQLIpkzsDNcc2g8Yhz2iInnfc8pvgh7/A2NYXFDCj6Y8EjsmjugvgZt/dvZGxYPugz3\n4tEYy19EOC3/TekuXE0nOu0ORG0FbsnFXpejfEhYUeTBrQi7e/9+KoqS+VRgrGQk1cO4e1Lhk6Kk\nJzU2vSGlhpA6ldVRHKdrz3PjQncn41/oTuoITUdvoWema/jbDYtzfAYiUgdW5xbo6zQrgl13lshV\nN+Aa/tSeu4OcnAIiV9+GXr4P49QBr8vJCI2h8WqcS67EvvRqAJx+Q7HHz8FY+lzWhsUOEJ1xF6L6\nNNqmd7M3LC4ZhnvZpMY2FHbn+rRnC1cIYlMaZ5PL2gpcfz6u1LwuS/mQtncDMqp6GSuKktlUD2Ml\nI6kexp2XCeGN6jGsKOlL9RlOD5qmYzmC6tquB61dWuiu12D0PWsvmPnaGBbPazMsBsj1SdxTp+M/\nbyJEG7CEhDE34Nv0JiLVoXUbrJ4lxEZOwTi8Ea0he9snJIMEzANriF58NU6PPtCjb2NYHM7O8MYB\nrGs/hTxdirZ9udflJI3TZzDOlTMw338eEYt4XY6nXMCaMA9X05DVJz980MbpNQDt1BFPa1MaCddB\n+2A1zpXX4upGp47RdG2lWlIoyvlcJG4Xe4QnkpvF83Cz9ydTslpnehgr6amlmYlqVqKieE/NGk5f\nmm4QjrlU1yVmhl3PPIGs6MRCd748RCyCXnn8vMebw2K37bA4kGvihs6Alz1II3XEIvVErroeV6bH\nPIrowMuwhk/Ct2+1Cos7SQJa1QnoOxgAp9dA3CzsMegA1qxPI8sOZXdYXFSCPX4O5ooXEdHE9YbN\nVNboGTgFPT4KiwHqzuIMvsy7opQLyKMfIFPRm19RFCVJVGCsZKRAIKB6GGcYFT6lF/WcK03U2Pz/\n7N1pcFtnfuf77/OcBSBAAlwlkhIlkqI225Ila7EtyVt7d3eSdtztdKe3pHJT05OtX8ykaiqZykxV\nqt+kat5NqpKpmkoqk3Td25ObjjvxuK+77W7b7VW2ZFmWWha1UAt3cQNAAjjLc+4LNhUtlESCAA4A\nPp8qVrcpAOdPEgc4+J3/+T/VxbRsMlmf2VxxZiPGIwZGdvkL3QHQ1Il98ufXfeu6sNi9dY1SSiwZ\nwFz47+Uil8b1XfK7nwq9YyW//RBqbQ/26bcR3uruolwJp/MuVEsX9vnDWMMnUdv34T7z23ib9xAY\nlXFiYKUU4D3xDeTlzzBOvht2OSWjkm34D3we++0fIHKzYZcTOq9vD6q9Bzl58fp/yM4QNHeEU5S2\nKAEYH7+OLGFHvD5O0zStlHRgrFWlQjqM9RtqeejwSdMqk943q5sQAtOymc645N3ideQWutCdqm9B\nzowjrwl8AytKbu9zTKvbh8UAjXELNT1829uUk5ibwQsC8vc+EUonqgKye54Dy8Q++/6yZ0lr8xSQ\n790HkRjW+SMI5SM9B3vwU6wLHxN0bcJ9+rfw7n2EwK7M2dVLoYTEe+qbyIHjGKc+CLucklH1TfiH\nnsd6558Rc/rKQn/9Vvy+e2H83E3/JgEEBPWNZa9LuzVj/BIiM1XQfZcykkLTViUhKu+rRunAWKtK\neoZx+HT4pGmVSe+bi6vmn10IiTQtJtMunl+8n6Pghe4A6luwP3v/6vcCO0pu7+eZ9m3c24yhAIhY\nBsLNVd4c0tkpPMPCuecxyvlsUaZN/oFfx0iNYV/6tGYXLCs1hcTZ+hAiP4t5+dOb5mpLFPbIaayB\nDyGZxH3i67gPfIEgngyp4sIoKfGe/Aay/yhG/5GwyykZFWvAf+TLmO/9EJnRx/x+63q8ew7B6Jlb\nf4B35vDXby1nWdoSmEd+jHQKuIpnCar52EbTtMqnA2OtKukZxuVTq+GTPluv1QI9A7z2SWkgpMlk\nykGp4v1dTQNsClzormkdxvCZq2MsAruO3J7PM+1bdwyLAerrTIKZ0eVvtxwyE3jROM5dD5UlNFbx\nJvL7fxVr6CTWlYEybLE2KdPGuesRjMnLWGPnbhu6S8C6cpHIwIcYuLgPfwn3kS+jmtrLVG3hlDTx\nnvoW8rPDGOeOhV1OyahIDP+xr2IefgUjVcBrVI3xEy14e5+G8fO3//CeHkN19pWrLG2JZGoCOVU5\nV9RomqYtVW0M8dJWnUQioQPjIlsInW4MmnTwpGnh0/vn6mQYJp4SzGScoj92c8xA3iFYW4ySEuwY\n1vn5sGo+LH5uGWGxTTA7Dao4M5hLIUiN4yXXIrY8gH36vZJtx23diLdlL/a5w8i8ns1aKBVtwOnd\nhzl4EmNueZ2oRmoUIzWKqkvi3v8seC7GiXeRw2crrtNbmTbeE19HfvpzjEunwi6nZJRp4z/xNcwj\nP8aYGgm7nNAFdQ14D/4aTFxEKu+2t5VKoSJRAtNCeMVZFFUrDvPoa6jHfhMViS35PkEQ6AYXTVtE\nIEToa05cqxYX1V2gA2OtKjU2NpJOp/F9H8Mwwi6nqujgSdMql94/tQWGaZFzA2azxf/QH4sYGPlU\nYQvdNW/AOnsEEahlh8VSQMSEYHKygKrLK5gZxW3qgN77sM8V/7J/Z+O9qPZuIqffQfg62CmUl1iD\nt/5urAsfI525gh9HZmeIXDyKMqN4Ow7g3/swRv8R5PlPERVwckOZNt6TX0ceewNjsD/sckpGGSb+\nU9/EPPYGxpXLYZcTusCK4Bz6dZgZRi51EUzPRa3trunnSTUS2Qxy+Bxe13aElEULgvXxoaZVtx/9\n6Ef8y7/8C9PT03R3d/Pbv/3b9PUtfqXIa6+9xptvvsnFi/OLnvb29vLVr371lrcvhsqJ5TVtGaLR\nKJZl6S7j26jVURKaVgv0/qndjmnZZLI+s9nbd5MVKhEJEBMFLHRnRhFBgDF67pqw2FxSWAyQjEcI\npit0FMUigulhvDXduBvuLurj5u95DNXSgd3/rg6LV8Bt68Hv3IZ97sMVhcXXkl4O+/JxrMvHUN3b\ncZ/5bbx7DhJYkaI8fiGUHZ2fWXz09ZoOAZWU+E99C/PkOxij58MuJ3SBYeI+9CWYm1reFQizE6iN\nxX3N0oojSDSjggDP8/A8Tx/vadoq98477/C//tf/4sUXX+Qv/uIv2LhxI9/97ndvmXGdPHmSgwcP\n8l//63/lu9/9Li0tLXz3u99laqqwhTWXQgfGWlUSQpBMJpmZmbnzjVcBPcdU0yqXDoa1pRJCYFo2\nMxmXvLu8heiWqjle2EJ3ALR0YZ16B64Li5f2OJZpIJULRQr2ykVNDeGu24bXsWXljyUluX2/ggg8\n7PMfFvY30ABwunagku1Y5z4sSegulcIePjW/QF7zGtwnv4G77xmCuoaib+t2VCSG98TXkB++ijF8\nrqzbLicF+E9+C6P/o5oOxZcqEAL3wBcJ3BxymWNWZH6WINFS1oU7tTvz128hSLRiGAZSSoIlBMdL\nGUmhjyW1VUmIyvsqwMsvv8wTTzzBI488wrp16/jd3/1dIpEIP/3pTxe9/R/+4R/y1FNPsXHjRjo7\nO/n2t7+NUorjx4+v5Ld5Wzow1qrWapxjrLsSNa04SrG/3G7/1LQ7EUIgTYvJtIvrl+Y5Yxpgi8IW\nulN1SeTcDDI3u+ywGCBRZxFMV+eiP2pyELdnJ96ansIfw64jf//zGJOXsIZ+UXHzcauFAvKbHgDD\nwrpwtOSh+/wCeeeJDHyItATuYy/iPvTrqGRbSbcLoOrq8R7/Ksb7r2CMXSj59sIyHxZ/E2PgU8wL\nJ8IuJ3QB4O17jsAwkJnxAh9FETSuKWZZ2goEpoW/42GUaSOEwDAMTNO8KThWSp9E1LTVwvM8zp07\nx44dO65+TwjBjh07OH369JIeI5/P4/s+9fX1pSpTzzDWqlcymWR6enln3atFqeeYCiF0iKVpBdJz\nhrVik1KCMJlMOZTyaVToQncAJNdiHf9ZQWFxLGoR5FLgl2bERjn4E5ehby94Dubk4PLu29CKs+Mx\nrIvHMGYrf35zpVJInG2HMGbGMK8MlH375vQw5vQwKt6Ee+BXwMlhfPo2cvRC0U8AqFgC79EXMd77\nV+TEUJEfvXIowH/8axjDZzHPfRx2ORXB2/kIqqEJOXmx8AfJZ1Bd25DTY8UrTCuYt/tx/BsWu1sI\njhdCY9/38X0fpRSyiDOONU2rTOl0GqUUyWTyuu8nk0mGhpb2vv8P//APNDc3s3PnzlKUCOgO45pz\n45nJoaEh/vVf/zWkakormUxWfYex7hjWtMql90+tHAzDxA8MJtOlDYtXstCdSqxFpq6Qv/uRZYfF\nAHWWhNSVZW+30vgTg7jbHsRPLr1zz23fhHvPI9hn39Nh8QooM4pz1yOY4wOhhMXXkrNTRC4cwZoY\nwN/1KO7Tv4XffU/RVmxX9U3zYfE7L9V0WAygHnkROTGI+dkHYZdSEby+Paj2npWFxQCpcVR7d1Fq\n0lZGNa5BremGW7w+CCGQUmKa5tWF3H3fx/PufIJVH4tqq5aQlfdVzB9vCSeM/vmf/5l3332XP/7j\nP8Y0S9cHrDuMa8DCmUiY71Kam5vj5MmTfPDBB1y6dIlcLsdTTz2FbdshV1pchcwwDquzVnckalrl\n0vunFhbDtMi5AbPZ0i98logEiMECFroDqG9BKZ8pV+AtMyxOxm2C1DjUxERNhT81jHP3w9ifvI6R\nuX0A7PTtQzV3YPe/jaji7uqwqVgSp3sP5uVPMbKV0yggnTkil46hpIm3eSf+9vuRAycw+j9CeIXt\n06qhBe+hL2L8/J+QM9V/kuV2vIPPI2ansU68HXYpFcFfvxV/070wdmbFjyUBZVgEdh3Cya68OK0g\nAQJv37MoO3rH2y40KSx8VlVKXf3fa/9N07TK9bd/+7eMjl6/uPPBgwc5dOjQTbdtaGhASnlTnjUz\nM3NT1/GNfvjDH/LSSy/xZ3/2Z3R1da288NvQgXENWAiLh4aGOHnyJCdOnGBgYIB4PM6+fft44IEH\nai4shsqcYayDp+qh/yar02L7qH4uaGEwLZt01iPvlH5m4fxCd0OFzXtt7QbDYCqvlh0Wm4bERBHk\n0svfbqVSPv7UCM6OzxE59mPk3M0nrhXg3PskQoDd/y6iJsLycHjJdrzO7dgDRxFuZQZfUnnYgyfn\nxyus3YTbcw9idADzxLuI3OySH0cl2/AO/Crmm/8vIl3b3eje/Z9H+C7mscUX9llt/Nb1ePccgtH+\n4l3+6+VRnZswBj4t1iNqy+Rv249a5kKZ14bDC13Gvu8D85/59bgKTatcv/Vbv7Xk25qmSW9vL8eP\nH2fv3r3A/GfSTz/9lGefffaW9/vhD3/ID37wA/70T/+Unp7C19ZYcp0l34JWUrlcjuHhYU6ePMnR\no0eZmJigq6uL559/ni1bttDc3FyTYTGEO8NYB8OaVtn0Pnpr+ncQLiEEhmkxkynd4nbXWtFCd2aE\nIJZkeja/7LAYIBGzCCYvL/t+FU95+Kkx8vc+QeTo/4fMZf7tn6RJfu8XMNKjmCP9enG7FXDX9qEa\nO7DPH66KDm0JyLGzKEA1rcP93FcRM+OYn7x1xwBYNbXjPfB5zDf/EZGZKku9YfH2PAmmjfnBv+r9\nA1CJFry9T8P4+eLOikyPozZs14FxSIK6elTvvQRGYXHLwrHawqXmC/ONF64sXph9rGmrUQAEFXTi\npNA98fOf/zx/+Zd/SW9vL319fbz88svk83keffRRAP77f//vNDc385u/+ZsAvPTSS3z/+9/nO9/5\nDq2trVezsGg0SjR65ysZCqED4yqklCKXyzE4OMgHH3zAsWPHEEKwefNmnnrqKXp7e2ltbQ27zJJL\nJpNcurT8y2uXQ4dOmlbZ9D6qVRMhBNK0mEq7+Ko8z9FCF7oLTBu/YxvpWaegsLjONhHOHIHnLPu+\nVcFz8NMT5Hc9SfTIjxBOFhWtJ3/fM5hDpzBnRsKusKrlN+wCK4p1/qPCOuNDJAE5NYg5NYhf34J7\n6IuIfBb5yVvIK5dv2hdV63q8fU9hvvF9xOzyRq1VG2/nIxBvxHr3JR0WMx8qug/+GkxcRKrinhSR\nXh7VlCAQAqGPicrO2/fsTQvdFUoIgWmaV0dULHzpTmNNq24HDhwgnU7z/e9/n+npabq7u/nTP/1T\nEokEABMTE1enCQC8+uqreJ7Hf/tv/+26x/nyl7/Ml770pZLUqAPjKnTixAlef/11Lly4QHNzM/ff\nfz/btm1jy5YtWJZ19XZBENT0G0kikVj2DONb0aGTplU2vY9q1U5KCcJkMlXaxe2uVehCd4EVwV+7\nGdcPcD2/sG1HDYKxsYLuWzXcHP7cDPndT2OeO4q7eR/WwJGKmrNbbRTgbH4QmZ/DvPhx1YeKRmYC\nIzOBitTj7n0CXwUYv3gfefkzRBCg1mzAu+8JzJ/9P4i5Ghrdsghv+wFo6cB6+wd6TAvzr7POoRdg\nZhjp5Uu0EZ+gpRNxZbA0j68tyl+3GZUofvOWEALDMJBSXp1xrGladXv66ad5+umnF/23//Jf/st1\n//2Xf/mX5SjpOjowrkIfffQRAwMDtLS08Nhjj7F7925isX87gxkEQc2HxTDfYVzIDGM9w1TT5oW1\nCOTt6GBYq0WGYeIrwXSmvN22hSx0p6woau1mlDDJzM0Vtt2YTZCehCrrDC1Ifg43EifYfhD79DvI\nfObO99EWpaSJs+UgxtQQ5mRpryArN5nPELn4Mcq08bbtwb/nIIxdhLb1mD/9vxG52n7e+Jvvg85e\nrJ//Y9V1jJdCIA3cQy/A3BQyv/Q518uWncHfcBdSB8ZlExgW/s5HUFZkZY/zy+PexT7PLwTHC7ON\nNW3VEXL+q1JUUi1FpgPjKrR161YGBwfJZrP84Ac/4PXXX2fTpk10dXXR2dlJd3c3hmFcvf3CrKOV\neuutt/jpT39KOp2ms7OTF154gQ0bNix623fffZfDhw8zMjJ/Seb69ev5whe+cMvbFyKZTJJOpxkd\nHWVkZITh4WFGRkZIpVL80R/9kQ6Gb0P/HrRKoPdRbTUwTIucGzCbdcu63aYCFrpTdgy1ZhNp10YG\nuYL2RykFlgwI5sJZY6Ds4k3ISBx3dgaxcRf2mXcRSn+IXy5lx3D6HsAcOY2RvhJ2OSUjPQd78ARu\nch1qfR+4LqrnbuRnH9bs88bvvpug5x6sN/93zf6MyxEIgXvweQLfQZb6dXJ2iqC1u7Tb0K7j7f5c\n0UZR3Ik+ZtY0rdREsMRXmqGhoVLXoi3T0NAQn376KRcvXmR4eJjJyUmi0Sjt7e2sW7eOvr4+tm3b\nVpQB2EeOHOF73/seL774Ihs3buRnP/sZx44d40/+5E+or6+/6fZ///d/T09PDz09PZimyU9+8hOO\nHz/Of/pP/4lkMrns7SulmJqauhoKDw8Pc/78eaanp1Fq/sNwPB6nvb2djo4Onn/++etCc03Tblau\nDuNbdQ1rWq0zLZtM1iPnlLejzjSgtU4hh04t+ZJ+FYmj2nr5bKKBvtY5plOFdRc3N0QIpoaWPQaj\nKjW0ISIxvKkRIEBEG7AtE7v/Xd1FuQx+vBl34y7MS8cxcrU9lgHAbe0mSLRhXfyYQPl4a/oIognk\nmY+R/R/VVKjqr9+CuvsA9pv/G1Gr88yXIQC8/Z9HxRuQ0+X5bK3FKqIrAAAgAElEQVTaNmG98X1E\ntvb3rbCpxjW4B38dZa/8s/fCInfXjpu8ked5Vz8Ha9Wns7Mz7BKqVuYX76Eq6DVN1jVQv/2BsMso\nCd1hXKWCIKCzs/PqC83ExAQXLlxgcHCQCxcu8MEHH/D222+TSCSujq7YsWNHwaMq3njjDQ4cOMD+\n/fsBePHFFzl58iTvv/8+jz/++E23//rXv37df3/lK1/hk08+ob+/n7179952W9lsloGBgeu6hkdG\nRnCc+QPNhVC8s7OTy5cv8x//43+ko6OD+vr6mh/DoWmVTI+T0LR5QggM02Jm1sX1yv/8X+5Cdyra\ngN/aw8cjCe5pnyMzW1jYG7EMhJeH1RAWN3aANPCmhq9+K8ilcWQD9N2PfeZ9HRovgdu0DtW+GXvg\nI4RbojmuFcTp2AqRGNaFo4hAIQB77AwKibd+E17fLmT/EeSZI4gqD4JUew/qnkPYb+mweIG38xFU\nogk5cbF8G3Xn8Ndvwez/qHzbXIUCBN6+Z4sSFsPS1iLSx9faahUgCCpolYNKqqXYdGBcpW58A2lp\naaGlpYX77ruPVCrFlStXmJyc5PTp03z00Ue8//77BQfGvu9z6dIlnnzyyeu2v2XLFgYGBpb0GI7j\n4Pv+dbOWb2VwcJC//uu/xrZt1q5dS0dHB7t27aKjo4P29nYaGxsRQjA5Ocl3v/td/vqv/1oHxZpW\nRjoY1rRbWwiLp9Iuvir/PrHche5UXQKvpZtjIwla4y5KuXh+YUFVfZ1JML4KZmU2r4dA4c/cvKhf\nMJfGrUvApv3zobFe3OuWnPZtBIlWrHOHa6qr9lacrh0IwLx47KaPlhKFPdY/Hxxv2Iy3eTfy9BHk\n2aNVGRyr1vX49z0+31nsrIITSEvg9d2Hau9Bjp8r74ZT46j1W0AHxiXlb92HqmsIuwxN07Si0oFx\nFfM8j0wmgxDi6pgH3/dJJBIkEgl6e3vZuXMnzz333NX7FDLLOJPJEATBTaMnGhoaGFviCuj/8i//\nQjKZZMuWLXe87YYNG/jP//k/09zcfNt6E4kEjuOQy+Woq6tbUh2api2PnjOsaUsnpQRhMpFyCGs3\nWc5CdyrWiNe0gaNDCQDWJRym04V1edbX2QRzM1DjwZ9o3YBy86jM1C1vo7Ip3Hgj9O7DPvdBDfed\nFC7fvQcMA2vgI0SNv6cowO3Zg8xlMEf7b/t8kCjs0V8Gx91b8Tbfhzz9EfLsx1XTsa6a1uLvfxb7\n5/+IyBc22qbW+Ou24G/aDWP9Zd+2VB4qGiOQJkJ5Zd/+ahBE6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3FlBKf7vjuG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p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\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2sACsYXFrKGE2mzl79iw7d+5Eo9Hg4+PDhAkTiI6Oti4vvY87FxgYyHPPPcfChQt55JFH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\nAQA6nQ5hYWE4duwY7rrrLvj6+qK8vBx79uyBn58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DwWlyOwdH/2KHAwcO4NixY2hubsbY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\nAHDlyhVER0e7fByOMCYiIiIiIiIiIiK6SXK5HE8++eRN7aPRaKDRaLq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"text/plain": [ "<matplotlib.figure.Figure at 0x10f8dd610>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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X22+/7W3P8+KLL2rgwIF67LHHtHz5cl1xxRVyu93as2ePNm3apK+++sr7/fvq\nq69q0KBBuuuuu/T666/roosuUnR0tA4dOqSdO3dq165d+vDDD/MMFnS5XPriiy/UvHnzAleAAwAA\nIDDKbxQOAMBpGIahhx9+WB988IH69eun3377TbNnz9aCBQuUkZGhsWPH6vPPP1fPnj0LfPw111yj\nN998U3/++admz56tb7/9Vr169dLy5cvVuHHjPNt36tRJU6dOVXh4uN544w099dRTmjVrVr59+jrO\n6Z5DQff7uu/pp5/W+PHjlZmZqfj4eH322Wfq0aOHli9f7u0zWpTjn87ZZ5+tK6+8UoZhqF69eurS\npYvP7UzTVHx8vG688UYdPnxYs2fP1jfffKObbrpJb7/9thwOR6Hr6tSpk9544w01a9ZMy5cv17vv\nvqtzzz1XH3zwgerWrevzMf3799eqVavUt29f/fTTT5o7d67ef/997d+/X3379tX06dML/ZxP9/U4\nVdeuXfX666+rSZMmWrZsmd577z01btxYH3zwgc4666wCn/OZzodTP3/ppZf0zDPP6Nxzz9XKlSv1\nxhtvaOvWrbryyit11VVXebetUaOGZs2apUaNGmnRokV66qmn9NRTT51xaNqYMWMUHx+viy66SKtW\nrdIbb7yhlJQU3XXXXVq2bJmqVq16xjoLe5+vbYcPH66DBw/6bPEgSY8++qjefPNNtW3bVp999ple\neeUVLV26VDExMXrooYe0bNkyRUZG5nnMww8/rAsvvFA//PCD5s6dqyVLlsgwDE2ZMkULFizw1uhw\nOPTWW29p6NChOnjwoGbPnq3t27dr5MiRio+PP+2526dPH82aNUv79+/Xm2++qe+//17XXXedVqxY\n4bMftK/9DBo0SB988IGuu+467dy5U3PmzPGeu/369Ttj8OxyubR48WKZppnvDR232y3DMBQeHi6H\nw6F+/fopIiJCq1ev9vbYbtSokT766CPddtttSklJ0Zw5c7R48WIlJibqjjvuUExMjHd/dnh93333\nybIsvf/++5ozZ462b9+uhg0b6qmnnsp3zVy3bp2SkpJ00003nfZ5AAAAwP8Mq5BLOXz12QMAoCJa\nvHix7r33Xj3zzDMaMGBAsMsBKqxjx46pffv26tSpk15++eVglxN0hmEUepW23cP41N7h0v8NZHQ6\nnd6w2u12+7V1zZkMHz5cO3fu1ObNmwtc5Q4AQFlUr169YJdQZv18+0hl/PZrsMvwqnJeU13w6uxg\nlxEQrDBC9X60AAAgAElEQVQGAABAmRQbG6t//vOf+uCDD/S///0v2OWUOYVZ0W1ZlizLkmmaCg8P\nzxMiB8q3336rDRs2aMKECYTFAAAAQUAPYwAAiqEovVYBBM7IkSOVmZmphIQENWvWLNjllCkej0em\naRYpADZNUw6HQx6PJ0+fY39KSkrSgw8+qEGDBvl93wAAADgzAmMAAIoh0CvsABSO0+nUXXfdFewy\nyozc1y6PxyOPx+Ptt1zY65plWTIMQ2FhYZJOtrDweDx+q7F79+7q3r273/YHAADKB9M0ZDpC5/cw\n0wydWvyNwBgAgCIaOHCgBg4cGOwyAKBAdvh76gpg+3PDMGSapizLktvtlnRy9bCvnsZnEhYWJsuy\n/B4cAwAAIDgIjAEAAIAy6kzB8OnY7Sjs0NhecVxU9rHsFcelPSAPAAAA/kVgDAAAAIS4kgTDZ9qn\nYRhyOp2yLCtPaOxyubztKgrDrsUekOfxeOR2u+n5DgAA/MIwDRkh1AYilGrxNwJjAAAAIAQUFAoX\ndFsgjm8PtLOP6XK5vO0ritLnWCqdAXkAAADwPwJjAAAAlCt2m4VQFYjVwsVhD687lX2bw+Hwtqtw\nu93FCo7tY4SHh9PnGAAAoIwgMAYAAAACIFSC4eKwQ2H7w25XceqAvKK2q2BAHgAAKC7DMGQUY0Bv\noBTlL6/KGgJjAAAAoJiC3UYiEE5deWyHxqZpensc2x8lDY7tdhUAAAAIHQTGAAAAwBmU5dXC/mQH\nxLkH5JUkOGZAHgAAQOghMAYAAAD+RjBcOPaAvFNXHdsrkc0i/rkoA/IAAMAZmYYMM4TaQIRSLX5G\nYAwAAIAKpTy2kQgWX8Gx2+32rjpmQB4AAEDZQ2AMAACAconVwqUnd3B86oA8h8NR5OBYYkAeAABA\nsBAYAwAAoEwjGA4ddjBsGEa+4LgkA/Jyr1wGAAAVk2kaMh2h0wbCpCUFAAAAEDy0kTizUHod7NDY\nXnFsh73FHZBnfzAgDwAAIPAIjAEAABAyWC1c/hiGIafT6V1xnHtAnt2uorAsy2JAHgAAKPPWrl2r\nlStX6tixY2rYsKFuueUWNWnSpMDtV61apY8//lhHjhxRVFSUrrjiCg0dOlRhYWEBqY/AGAAAAKWO\nYLji8TUgz+VyeVciF3dAHu0qAACoGAzTkBFCbSCKW8uWLVs0b948jRkzRk2aNNGqVas0Y8YMPffc\nc4qOjs63/aZNm7RgwQLdcccdatq0qQ4dOqSXXnpJhmHopptuKunT8MkMyF4BAABQ4Z0uALRbDKDi\nsYNjp9Mph8PhbVlhD7crynlhB8dOp1Ph4eEyTX69AQAAoW3VqlXq1q2bOnXqpPr162v06NGqVKmS\n1q9f73P7X3/9Veeff77atWunmjVrqlWrVmrfvr12794dsBr5iQoAAAAlUlAwTCiM07FXFtvBsWEY\n3uC4uD2Kw8LCFB4eLofDEYCKAQAASsblcmnPnj1q2bKl9zbDMNSyZUv9+uuvPh/TtGlT7dmzR7t2\n7ZIkJSQkaPv27br44osDVictKQAAAFAotJEo/+yvZVH6CpdUYQbkFZZdv72C2Q6gAQBA2WcYhowQ\n+mui4vy8lJqaKo/Ho5iYmDy3x8TE6NChQz4f06FDB6WmpmrKlCnemRDdu3dX3759i1V3YRAYAwAA\nwCv3D74EwyhtpxuQV1T2gLzcfY45hwEAgL/NnTtXCQkJeW5r3769OnToUKT9FPTzzs6dO/X+++9r\n9OjRatKkiQ4fPqw5c+Zo6dKluuGGG4pd9+kQGAMAAFRArBau2EpzBXFx+BqQJ0lut7vIA/IkyTRN\nORwOBuQBAAC/GzFiRKG3jYqKkmmaOn78eJ7bjx8/nm/VsW3x4sXq2LGjunTpIklq0KCBMjMz9dpr\nrwUsMA6dddwAAADwO/oLoyyzg+Pcb3C43W5v6MuAPAAAKhDTkBFCHzKL/ga80+lUo0aN9MMPP3hv\nsyxLP/74o5o1a+bzMVlZWfl+bilo8Ye/sMIYAACgjKONBMo7wzBkWVaedhVut1uSvH2Oi7pqOiws\nLE8ADQAAUBp69eqll156SY0aNVKTJk20atUqZWVlqXPnzpKkF198UdWrV9fQoUMlSW3atNGqVavU\nsGFDb0uKxYsX69JLLw3YX40RGAMAAJQRtJGAP4V6Wwpfcg/Iy93jOPeAvMI+LwbkAQCAYGjXrp1S\nU1O1ePFiHTt2TA0bNtRDDz2k6OhoSVJSUlKeFcU33HCDDMPQO++8o+TkZEVHR6tNmzYaPHhwwGo0\nrEL+hlHQpD4AAAD4V6D/xKy8s1ejomCmacrpzL92xLIsuVwub//gUGG3oAgLC8t3X+4BeVLxVxzb\nGJAHAAikevXqBbuEMuv3B+9Q1t5dwS7Dq9K5TdTw3y8Fu4yAYIUxAAAot0I5OKSNBOAfvgbkeTwe\n70rkogbfuQfkuVwuvh8BAECFQ2AMAAAQQLSRAEqHr+DYXplsrzguSrsKwzC8K5pdLpd3BTMAAEB5\nR2AMAADgBwTDQGjIHRyfOiDP4XAUKTi2MSAPAIDgMwxDhhk6MxjK4jyIwiIwBgAAKCTaSADBU9Rf\nyuxg2G5Nkzs4Lu6APKfTyYA8AABQ7hEYAwAAnILVwmUbX6e8TFeOHNlZsgxDltMpq1KVMwalobZi\npiRfUzs0tlcc220q7FYVxQmOTdNUeHg4A/IAAEC5RGAMAAAqLIJhVASO9BNKffJBGVWqyqxTV466\nDeQ4p7GMs+rLqBwhIyxccjikKhGSUbQBcWWNYRhyOp3eFce5B+TZ7SqKw151zLUDAIDAMUxDRhGH\n2QZSKLXH8DcCYwAAUK7RRgIVmSMnWxkr35EsS1b6Cbn3/ib33t+kLetybeSUWauOHHXqyXF2Yzni\nGkrRMVJ4Zck0pUqVTv67HPE1IM/lcnlXIhe1zzED8gAAQHlCYAwAAMoFVgsD+RnHkuXa+e3pN3K7\n5Dl8UJ7DB5Xz/da8j4+pdjJIjmsoZ4NGMmvVkRFeWQoLk5xOqXKEFGLtK4ri1AF5uYfaMSAPAABU\nVATGAACgTCEYBgrHmZWpjKVzS7QP6/hRuY4flevXncrKfUelynLUrivHWXFynNNEjnoNZERUlRFe\n6eSq5MpVJGdYiY5dmhiQBwBA6DNMQ6YjdN6opiUFAABAKaKNBFAyhmHIOvyH3Pv3BOYAWZly/7FX\n7j/2Sls//7/bTVNm9Vp/t7doJEeDc2XGVj+5KtnhkMLCT4bJIYoBeQAAAATGAACUa/ZquVDFamEg\nMBwZaUp/d27pH9jjkedIgjxHEpSzc3ueu4zIaDlq15VZ/2xV6txTkiFVqiyjcmj2Ry7MgDz7WlWY\nENk0TTkcDm+/ZK5zAAAgVBEYAwCAgCMYBkqPIcm96yd5kv4Kdil5WCdS5DqRoojufZT54w4lz3pB\nER06Keb6QTKiY2WEhwe7RJ9ONyCvqCzLYkAeAADFZJhGSLWBCKVa/I3AGAAA+AVtJIDQ4MxI04ll\nC4JdRn7OcEX+c4rStmxSyvuLJUnpG9crffPnirrmWkX26CkzJvZk6wofihPQ+pOvAXk2e+UxA/IA\nAEB5QGAMAACKhNXCQOgyLY+yv9kiK/1EsEvJKzpWkeMm6fiSBUrfvDHvfW6XUj94Xyc+XqOY/oNV\n5R9XyoiJzfcmVLADY1vuAXl2T2IG5AEAgPKEwBgAgHKsJCEuwTBQ9jgy0pT+8bJgl5GHWf9sVb1p\nvI688pyyf/mpwO2srEwdmz9XKSveU+zwkarcopWMmNjSK7SI7NDYsixvb2IG5AEAEDiGYcgwzWCX\n4RUqb2YHAoExAAAVGG0kgPLD4XYpa90qKScn2KV4OS68RFV6DlTiE4/KdfhQoR7jSU1R8sv/laNm\nbVUbeZvCz20sVa4S4EpLxg6ITx2QV9TgOPf+GJAHAACChcAYAIAKgNXCQPlnpqUq+4v1wS7DK7xz\nT4VddLkSHpkoT8rxIj/efSRRR2ZOU9jZ56jayLEyzqorRUYFoFL/8TUgz+5v7HA4ihQc2204wsPD\nZVkWA/IAAECpITAGAKAcIRgGKiZHTpYyViyUQuR7vXL/W6SYGkqYOkFWdnaJ9pWzf58SH3lQlZpf\nqGo3j5ZZvYaMKhF+qrRkCuqt7Cs4drlcMgzDu+K4qO0q7AF5BMcAgIrKMA0ZZui0gQilWvwtdBp/\nAACAQisobLAsi3AYqICMY8ly/fRdsMuQJEWMuV9ut6G/Hn+kxGFxblk//ajDE/6po2++Kk9igpSd\n5bd9B4odHDudTjkcDknyDrbzeDxFul7nDo7Dw8PldLL2BwAABAY/ZQAAEKIKWi1c0G0AKiZnVqYy\n3p0b7DIkZ7gi75qitC83K+W9dwJ2mIwvNynj6y2K7H6Nonr2kRkTK4V4eGq/yWcPybOH2kliQB4A\nAAg5of2TFQAAFQBtJAKH1xDlnWEYsv78Q+4/9ga3kKgYRd7xkI6/u0jpmzYE/ngej058uEpp6z5W\nVL8Biryyi4yYWCmEJqf7YofG9oA8t9vNgDwAAAqJlhSlh8AYAIBSQjAMwN+cGWlKC/LqYrP+2Yq4\nabySXnle2b/sLNVjWznZSlk8X6mrlil26AhVad3mZHBcBhiGIafT6V1xzIA8AAAQKgiMAQDwI9pI\nACgthiTXrzvlSf4raDU4L7xYlXsO0l9PPCrX4UNBq8NKS9PR119SSvUaqjZijMKbNJURHRO0eooi\nUAPyWHEMAACKi8AYAIBiYLUwgGDIfe1xZqTpxIqFQaslvNP/U9jF7ZTwyER5Uo4HrY7c3MlJOvLM\nE3LWi1P1W8fKWT9ORmRUsMsqlNzBsd2uwu12Fzs4th8bFhbm3RcAAGWaYcoIpfZTRgjV4mcExgAA\nnAbBMIBgONO1x/R4lL1tk6z0E6VemyRVvuFmGdVqK2HqBFlZWUGp4XRchw4ocdpDCj+vmardcpsc\nNWvLiIjw+3Esy5Lp519c/Tkgz+ZwOOR0Or3BMf+HAQCA0ym/UTgAAIV0ulVblmXxizWAgCjJtceR\nkaasT1YEqrTTihh9v9xyKnHG1JAMi3PL/u1/Spj0LyXPel7uw3/KysoMdkmFZq8sdjqdcjqdMgzD\n22aiOKGvHW6Hh4crLCysyKEzAACoOFhhDAAo1+wVWva/JVYLAyhd/r72OFwuZa37QMrJKXFtReJ0\nKvKuqUr76kulLA1eK4ziyPzmax3+dpuqdu6m6L43yIiOlREWFuyyCq2gAXnFWXGce0CevXqZAXkA\ngLLAMCXDDJ03PMtxRwoCYwBA+UMwDCAYSuvaY6alKPvLDX7d5xlFRivqzsk6tvQdpX++vnSP7S+W\nR2nrP1La5+sU3bufqna9WmZMrBRKvRDPwNeAPI/H412NnLs9xpnaZdjBcVhYmCzLksvlIjgGAACS\nCIwBAGVUQcFMQbcBgL8E800pR062MlYslErxOmfWa6CqN9+lpFefV9bPO0vtuAHjcill2RKlfviB\nYgbeqIi2V0gxsWWqRUNBA/LsVcdFXXEsyRscMyAPAAAQGAMAQhqrhQEESyhef4yjR+T6+ftSO56z\neWtV7j1Eif9+TK4/D5bacUuDlZGhY/GvK2XZYlW7abQqnd9cRkxssMsqktMNyJOKdq7a2zIgDwAA\nEBgDAEJCKAYzAMq/svTXCs7sTGUsmVNqxwvv2ENhl3RQwqMPynP8eKkdt7R5jh9X0gtPyVnnLNWe\n+rhkOGRERQW7rCKxQ+PcrSrswYlut7tYq47tFhd2CB1q3w8AgIrHMEwZIdRKyijHTYwJjAEApaYs\nBTMAypey/qaUYRiyDu6T++C+Ujle5etvllG9jhKmTpCVlVUqxwwmZ/041bp3kk58vFZZO3eo2sjb\nZdaoJaNKlQIfE6rnjh305vw9FLEkA/Ls/TkcDgbkAQBQgRAYAwD8rqwHMwDKrvJ6/XFmpCnt3bml\ncqyI0ffJdfSYkmZMkazyHw5G9Ruoqu06Kun5p5Sz/3dJUsKkfymiXUdF9x8iIzZWhjOswMeHcu/j\n3KuEcw/IczgcRV5xbBiGnM6Tvz4yIA8AgPKNwBgAUGzlNZgBEPoq0vXHkOT634/yHE0K7IGcTkWO\nn6K0bV8rZcmCwB4rFEREqM6kk72ZEyffJysnO8/d6Vs2Kv3rLYru019VO3UtU4Px7O8Du1WFPSDP\nDo1dLpe3hYW9TVEwIA8AEBSGcfIjVIRSLX5GYAwAOK3cv0RWhGAGQGipSMFwQZwZaTqxcmFgDxIZ\nrag7J+vYe4uVvnFdYI8VAqq0vUKxw0bo2Py5yvzm64I3dLmUsnSRTny0WrE3j1Kl81vIiI4ptTr9\nKXdwfOqAPHvFcWGDY18D8lwuV8BqBwAApYvAGAAgiVAGQPDwxlTBTMuj7K2fy0pPC9wx6sap6oi7\nlTTreWX99GPAjhMqatz9oBwx0Up8ZKI8KYUb5udJTVHyi8/IWS9O1UaPk/Os+lJERIArDQw7GDYM\nI19wXJw+x/aAvPDwcAbkAQBQThAYA0AFQzAMIFi4/hSdIz1N6Z+sDNj+nRe0VuXrhijxycfkOnQg\nYMcJBc4GZ6vWPROV9tmnSl75XrH24Tp0QH89OkmVWl2i2OG3yIyOlRwOP1daOuzQ2F5xbA+0Y0Ae\nACBUGaYhwwydNhChVIu/ERgDQDnEaj0AwUQw7B8Od46yPlkhuXICsv/wK7srrM2VSnhkojzHjwXk\nGKEiqv9QVb38H0r675PKObC/xPvL2vGtEh74TlW7X6OontdJMWU3OJbkHWhnrzhmQB4AABUbgTEA\nlGFnCmXKynAeAGUTwXBgmSdSlf31xoDsu3K/4TJq1lPC1AmysrICcoxQYEZEqtZDjyrnwH4lTL5P\nyvFj+G55lPbRKqVv/FQxg29SlTaXyYiJ9d/+g4ABeQAAQCIwBoAygVAGQDBxDSp9jpwsZSyfLwXg\nNY4Y9S+5jqcqacbDUjle+Vn58naqNuQmHZs3W5nbtwXsOFZmpo7NfU2pK99TtVvHKqxhIxmRUQE7\nXqFqKuEbx6cOyLPD3uIExwzIAwD4y8mWFGawy/CiJQUAlDH2IJeyhDYSgcFrBxQO16DQYiQfkeuX\nH/y7U6dTVe+covRvvlbKkgX+3XcoMU3VuOdBOSKqKnHqg/KkppTKYd1JR3Rk5jSFNz5P1W4dK7NG\nLRlVqpTKsQMlUAPywsLCvMPxuL4AABB6CIwBoJSxUg9AMHENCn3OrAxlLJnj351WjVLUnZN1fPm7\nStvwqX/3HUKc55yrWnc/oLRPP1Ly6uVBqSF7929KmPQvRbTrqOj+Q2TExspwhgWlFn/x94A8j8cj\ny7IUHh7ubXvBNQgAgNBBYAwAAUIoAyCYuAaVTYZhyHNwn9yHSj6YzWbWjVPVEf9U0msvKmunn1ct\nh5DowcMVcUlbHXn6CbkOHQh2OUrfslHpX29RdJ/+qtqpqxQTWy5mCxQ0IK+4K44Nw1BY2MlAnQF5\nAIDTMozQagNRDv5fLwiBMQCUAH/CDSDYCIbLF2dGmk68G++//Z3fSpX7DFPik9NCIkQNBDMyUrUm\nTVPOvj0nB9uFUn9cl0spSxfpxEerFXvzKFU6v4WM6JhgV+UXvgbkeTwe70pksxg9JhmQBwBAaCAw\nBoBCIJABEGxch8o/Q5Lrlx2yjiX5ZX/hHborrG1HJTwyUZ7jx/yyz1BT5R9XKnbgMB2Nf11ZO7YH\nu5wCeVJTlPziM3LWi1O10XfIWbeejIiqwS7LLwoakGevOi7OgDyn08mAPAAAgojAGAByIZABEEz8\n1ULF5sxI04mVi/yyr8r9bpRRq74SpkyQlZXpl32GFNNUzXsnyaxUSYlTJ8hzIjXYFRWK69AB/fXo\nRFVqdYmqDb9FZrXqUnglvx/Hvl6UZguM0w3IczgceYJjuxVFQez6TdP09jm2h+QBACouwzRlFOMv\nWAIllGrxNwJjABUOgQwqGvuXd4QO3pzCqUzLo+yvP5OVkV7ifVW59R65U9OUNP1hqRz2gw1r2Eg1\n//mATny8WifWfhDscoola8e3OvzAd6ra4xpF9+wjIyZGMh3BLssvcg/Is9tU2MFxcVtVmKYph8PB\ngDwAAEoJgTGAcotABkCwcR1CYTnSTyj9kxKGnw6Hqo6foozt3+j4O2/7p7AQEzN0hKpcdLGOPDVd\nrj8PBbuckrE8SvtwldI/+1Qxg29SlTaXyYiJDXZVfmUHxHarCjtALg4G5AEAUHoIjAGUeQQyBeM1\nAEoH1yGUhMPlUubHyyV3CXq1Vo1S1J2TdXz5UqVt+MR/xYUIMzpatSc9pqzdvyph8v0le61CjJWZ\nqWNzX1PqyvdU7daxCmvYSEZkVLDL8ivDMOR0Or2tKuyg1+VyedtVFBUD8gCg4jEMyTBLr93SmZRi\n56dSR2AMoEwoahsJQhoAgUAwjEAwTxxXztZNxX98nfqqOvJuJb3+krJ+3OHHykJDRIdOirlhsI7O\neU1ZP34f7HICxp10REdmTlN44/NU7daxMmvUklGlSrDL8it7QJ4dGFuWJZfL5W1hwYA8AABCA4Ex\ngJBCGAMgFHAtQmlxZGcpY/l8qZjnlrNZS1Xuc6MSZ06X6+Affq4uyEynat7/kAyHqcQpE+RJOxHs\nikpF9u7flDDpX4po11HR/YfIiI2V4QwLdll+lbvP8amrhE8dkHcmDMgDAMD/CIwBBAVhDIBgK+g6\nVNBtgL8ZhiHj6BG5/vdjsR4f1r6rwi/rrITHJslz7Kifqwuu8Ebnqcb4e5W6dqXSPl4T7HKCIn3L\nRqV/vUXRffqraqeuUkxskULU4rR5KC12fbk/7HYVpw7IK+rzYEAeAJRfhmmEVkuKEKrF3wiMAQRM\nUdtIAEAg8AYVQpUjM0MZi2cX67GV+9wo46w4JUyZICsr08+VBVfM8FtVpUVLHZk5Ta6EP4NdTnC5\nXErf9Jki/tFB1r49MmvWLnf9jSXfK47tXsfFCY4ZkAcAQMkQGAMoMcIYAKGAaxHKEsMw5DmwV+4/\ni95GosrIe+ROS1fStMlSOQrBzNhqqj3xEWX98rMSHr5fYpCZovrcoKodOuvY68/L9ftuhTW9QDEj\nxsqsVkNyls9f5XwNyPN4PN7+xyUZkEdwDABA4ZTPnzIABARhDIBQwLUI5YEzI00nlsYX7UGmqci7\npir9u291fNG8wBQWJBGduiqmT38dnf2qsn76IdjlBJ0RUVW1Jj4id8KfOvLYA1J2tiQp59efdWTK\nvxR1wzBVvryDjKjoIFcaOHZAbJqmNzQu6YA8e8UxA/IAoIwyzZMfoSKUavEzAmMAedBGAkCoIBhG\neWVIcv38vaxjyYV/UESkIsc/rJQV7ylt/ccBq63UmU7VmjBZsiwlTHlAVnpasCsKuspXdFC1QcOU\nsiheWd9tzb+By6XUd+KV/tnHih1ztxy160iVKpd+oaUkd3Cce0BeSYJjBuQBAHB6BMZABUUQAyAU\n8CYVKiJnRppOrHyn0NubdeopYuQ9Sn79ZWX9+H0AKytd4ec1U4077lHqquVK+/TDYJcTfKZTNe59\nUI7wcCXNmCRPasppN3cfPqSkaRMUcdU1qnrNdTJiqpVSoSVjX9uL2lqCAXkAAJQeAmOgnLF/iM79\nuUQQAyC4uBYBJ5kej7K/3CArM71Q2zubtVTlvjfqyH+mK+dA0fsdh6rYm0er8vnN9de/H5M78XCw\nywm68Kbnq/rtdyn941U6vm5t4R9oWUr/dLUyt25RzOjxcp59royIqpKKHsiWFbkH5OXucVzSAXnh\n4eH0OQaAEGeo8H9VUhoMhU4t/kZgDJQTBV00CWMAlCaCYeD0HOknlL5uVaG2DWt3lcIvv0qJjz0k\n99EitK8IYWa16icH2+38QQlTHmCwnaTYkWNVqUkTHX1mutx/JRRrH56UYzr69DRVat1W0UNGSJHR\nksPh30JDkB0Qnzogr7jBsXSyzzErjgEAFR2BMVCGFBTE5L4tlN5tA1B+EQwDRedw5Sjz4+WS+8zD\ntipfN0RG3XOUMHWCrMyMUqgu8CKu6qGY3v2U/ObLyv55Z7DLCTpHrTqq9cBDytrxrZKmPSj5YVVr\n1ndbdeTnHxQ17FaFX9haKsdD8XLzNSDP4/F4VyKbRRhKZIfPkrx9jhmQBwCoaAiMgRBEEAMgVHA9\nAvzHPJGinG2bzrhdxC13y5WeqaTpD5ePFbhOp2pNmCLl5Cjh4ftlZRSuHUd5Ftm7nyI7d9XxN19U\nzp7f/LpvKytTKbNfkrNhY8WOulNGtZoywsP9eoxQ5Ss4drvd3lXHRRmQZzNNU5UqVfIO2+P/PwAI\nItOQUYQ3AQPOLL8L9giMgSAiiAEQChg8BwSeIztLGe/Pk073PWWaihw/Rek7vtfxhfGlV1wAhTdr\nrhpj71LKiveUvuGTYJcTfBERqj3xEXn+SlDSow/Iys4K2KFcv+/WkSn3KvLa/qrSsZuM6JiAHaso\nSuOv4nIHx6cOyHM4HGcMju2+xrk/Z0AeAKAiITBGSNm7d68++OADjR8/Ptil+E1h2kggME4dAIiK\ni3PhJN6kAoLDMAwZyYly/fZTwRtFRCryzslKWbVMaZ9+VHrFBVC1W8eqUuPz9NcTj8j9V2Kwywm6\nype1U7Uhw5Wy+C1lfft16RzU49GJ5YuVsXmDYm+7W2adejKqVCmdY4cAOxi2fw7IHRyXdEBe7tXL\nAACUNyG0jhuQUlNTtXjx4mCXUSwFrVSwLIswBkCp4noEhBZHZrrSF88p8H6zTj1F/XOqkue+Vi7C\nYrN6DZ018wVZ2VlKmPIAYbHpUI37HlJ0j2uUNGNS6YXFubiPJCppxiSdWBwv61hyhfu/wO5l7HQ6\n5XQ6ZRiGd6VwcdpM2MFxWFiYwsPDi9QjGQBQfIZphNxHecUKY4SU6OhoHT9+PNhlnBYr9ACECq5H\nQOgzDEOeP/bKc/iAz/udzS5U5b436a+nHlfOH/tKuTr/q9r9GkVfc52SX3tB2b/+Euxygi68SVPV\nGLjcxPcAACAASURBVHe30j5do+OfrA52OcrYtF6Z27cq5pZxCm/STKoaGeySSp1hGHI6nd4Vx7kH\n5NntKgrL/v82LCxMlmV5+xwDAFDWERgjpERHRyslJSVf37DSVpbbSIR6fSh9tGMo+wiGgbLLmZGm\nE0t99yM2qtVUxJDb5D6arEoXXChPRrrcR/4q5Qr9JCxMtR+cKk9mphIevk9WRkawKwq62BG3qVKz\nZkp+dobciYeDXY6XlXZCx16cqfDmrRQzfLSMatUlR8X7tdDXgDyXy+X9uak4wbHD4ZDT6WRAHgCg\nzKt4PxkgpEVHRysnJ0cZGRmKiIgI+PEIYQCECgbPAeWPIcn103ZZx4/6vD9qzL0yt34ox+H9im19\nuaJ7XC1LprL3/a70LRuVufMHWZmhH7xWuuBCVR9zp1KWv6v0jeuCXU7QmTVrqfYDDyvrx++U9NgE\nKUR73Gb/tEN/PfwvRQ+6SZXaXCEjKjrYJQXFqQPy7BXC9grkMw3IO5U9IM/eHwPyAMCPDFMKpTZA\nRvFrWbt2rVauXKljx46pYcOGuuWWW9SkSZMCt09PT9eCBQu0detWnThxQrVq1dKIESPUunXrYtdw\nOgTGCClOp1ORkZE6fvx4kQLjM62gJBgGECq4HgEVhzMjTakf+J7NEN7xapmVwmXs+kGSJfOrD73D\nRcLqN1aV63rLc+MIeTIzlbHje2V8tVnZe/dIVmiFj9VG36Hwho301xNTy+7qaD+q2rOvorp21/E3\nXlDOnt+CXc6ZuXKUMv9NOTd8pNjR/5RZs5ZUqXJAD2n/fxfMvyb0JfeAPJfLJUklGpBn75MBeQCA\nU23ZskXz5s3TmDFj1KRJE61atUozZszQc889p+jo/G/gulwuTZs2TTExMbr33ntVvXp1/fXXX6pa\ntWrAaiQwRsiJiYlRSkqK6tatW6THleU2EgDKn1AKhrkGAqXP9HiUvWWd5GOFsFm9lqp07SV9/ZEk\nH9+fB3fLPLj7ZIDscCr8/DaKHHObPJUi5U5OUvqXm5XxzVa5k48E+mkUyKxVW7UnTFHmt9uUOGVC\nyAXZpa5yZdWe+Kg8R48o6dEHZGVlBruiInEd/ENHHr1fEVdfq6rdesqIqRbskoLG/v/bDontoNfj\n8RQrOLbbWzidJ3/1drlcBMcAUMGtWrVK3bp1U6dOnSRJo0eP1rfffqv169erT58++bZft26d0tLS\nNGPGDO+g1Zo1awa0RgLjCmr37t1at26dDhw4oJSUFN1666268MILT/uY3/4/e/cZH1WdNXD8d8u0\nlJmEAJFepShYQIoUwQJSFKyI2Ltud9eyj7uru6vr7urq2pC1966oWFAsYKGJdKSLdAgkpE+m3vu8\niBMSSEibyb2TnO/nkxfcueUkJP+5c+bMOZs28f7777N3714yMzMZM2YMgwcPjntsXq+X/Px89uzZ\nQ05ODjk5Oezbt4/evXszePBgWyVhhBBC1iQhRHU0fwn+edUMOVMUUq/6DYoR+bm6uBbRCPywGO2H\nxWiAIz0T54AheMdNwDQVQlu3UDr/a4Jr1zRZkjJ13Fl4x0zgwBOPENq8sUmuaWeek4aQccmVFL31\nMsGli6wOp+FME/8nswgs+paM636N1rELSkriKpfsqvLzdzwH5MXIgDwhhGg4RQVFtc8nVBrSkSIS\nibBlyxbOPffcg+dRFPr378/GjdXfVy1dupRevXrx9NNPs2TJErxeLyNGjGDy5MkVCeR4k4RxCxUK\nhejQoQNDhgzhueeeq3X/vLw8nnrqKUaMGMFll13Gxo0bef311/H5fPTu3bvBcfj9/ipJ4ZycHPr3\n788bb7xRcbOWmppKdnZ2xb8lCSOEsIIkhoUQdaVFwgTmvAvVJIPcZ56LmtEKFn9KtdXFtSnOR130\nycH2FR2Pxn3OZMzLr8YoC1C2ajn+xQsIb90C8V6fnE7a/vGvGCXF5YPtAslVRRt3qkrr392OmpZK\n3r1/wigqtDqiuDAKDnDg/r/hGjQM74WXllcb26lfZBOpnAw+0oC8WMVxXZPH1Q3Ii7XAEEII0fwV\nFxdjGAY+n6/Kdp/Px+7du6s9Zt++faxZs4aRI0dyxx13sGfPHp555hkMw+D8889PSJySMG6h+vbt\nS9++feu8//z588nKymLSpEkAZGdns2XLFubNm1enhHE0GmXjxo0VSeFYgri4uBgovwlr1aoV2dnZ\nRKNRhg0bxuDBg2nbti1paWkN+yZFiyeJPNEQkhgWQjSWWlJIeOmCw7cf1RHX4JEo4SDKppXxudjO\nTWg7f+6Vqztx9h1I2g2/wHClENm/H//ibwks+55o/oFGXcbV73haXXsTRe+8gX/+V3EIPLk5uvek\n9a9+T+ncOfjnfGB1OAkRXLKA3NXL8V5+Pc6+/VHS0q0OyXLVDciLRqMNShzDwQF5lfscy/2GEEK0\nXDU9hxiGQUZGBjfccAOKotCtWzcOHDjABx98IAljYa1t27bRq1evKtv69OnDe++9V6fjTdPkqaee\nQlVV2rZtS3Z2Nj179iQ7O5vs7GzatGmD0+kE4Oabb8bv99O9e/e4fx9CCAHS81wIEX+xdUULBih7\n9+XDq3s1jbQrfolKBHP5NzSourg2kRCsXoi2emF5+wpfFq5BQzAmTMI0IbhlM/75XxNctwYzFKrz\naVvd+FscHTqy/56/ED2QF/+4k4zvimvx9DmWAw/dSzRnj9XhJJQZKKPwyYdx9OiF7+pfomRmoTgc\njTvnzz19k1nlAXmxdhWNHZCnqiqapsmAPCGEOAJFUVEa0gciQWKxPP/88+Tk5FR5bPjw4YwYMeKw\nY9LT01FVlcLCqp9MKiwsPKzqOCYzMxNd16s8t3Ts2JGCggKi0SiapjX2WzmMJIxFnRQVFZGeXrWq\nID09nUAgQCQSqRjiUBNd1/nLX/6Cz+ertb+K1+s97A+nNpLgEUJUp6YXa7JmCCEa6kifQlAUBfJy\niGxae9hxnnMvRTfKMJSM+FUX16YwD3XhxwfbV3Tuhef88zDTryPqLyWw8uf2Fdu3Vtu+QmubTZvb\n/kLZ94vJf2p6ix9sp2a1pu1tfyG4bjW5d99ebcuR5ir840Zy7/w9aedOxTNsFEp69S9oW5pY0rhy\nqwoZkCeEEC3PlVdeWed9dV2ne/furF69mpNOOgkoX//XrFnD+PHjqz2md+/ezJ8/v8q23bt3k5mZ\nmZBkMUjCWDShzMy6TVvOyMiod8JYCNGy1dZGItkrmYQQTa8h7Wm0gB//W4fPhtC698LZ9ziIBmHZ\nV/HvLVxX2zeibS8fpqLrTlzHDCLtF7/GdHoI79+Hf+G3lC3/HqMgn7QJ55B++hjyZjxMeMtma+K1\nkdRxZ5M+ZhyFz04nvHmD1eFYIxql5O1XKPv6CzJu+B1q23YobrfVUSVMQyqEY60qGps4jpEBeUII\n0TxNnDiR6dOn0717d3r27MlHH31EMBhk9OjRADz22GO0atWKadOmATB27Fg++eQTnnvuOcaNG8ee\nPXt49913mThxYsJilISxqBOv11vRbzimuLgYt9tda3VxQ661ffv2uJ5TCNE8SH9hIUS8xWtdURQF\nY/uPGHt3VX3A6SJ16nW4d60m2Ok4lE0rGhVv3ERCsGo+2qryahU9ow2uk4fgO3syuNwoisKe22/G\nLC2xOFCLud20/eNdGEUF5P39Nhn0B0T37SXv7j/iGT2WtInngS+jWb0x29h7iuoG5BmGUVGJXJ9p\n9jIgTwghDqEooNroOaeBz3/Dhg2juLiYN998k4KCArp27cqf/vQnvF4vAHl5eVWeL7Kysvjzn//M\nCy+8wK233kqrVq2YOHEikydPjsu3UR1JGIs66dq1K+vWrauybcOGDXTt2jXu1/L5fBQVFcX9vC1J\nrJ+aEJCcyVRJDAsh4i3R64peVkrJzJcO254y9Voc/n2Ej+plbXVxbQr2oy6dC5OuIbJkHpgm7f/5\nAMXzvqRo5htWR2cJz8AhZFx6JUXvvEpwyeFDDFu6snlzCC5dRKs7/oHi8UCqDMWrrLrEcaw3sQzI\nE0IIceaZZ3LmmWdW+9hdd9112Lajjz6ae+65J9FhVbBPp2jRpILBILt27WLnzp0A5ObmsmvXLvLz\n8wH44IMPeOWVVyr2HzZsGLm5ucyaNYucnBy+/fZbVq5cWVEuH08N6WEshEg+R3qhZJpmXF8EyQsq\nIVqOmtaWeK8rVa6JSeSHZZiF+VW26/0G4OzcFUfudgyH2z7VxdXJ7owx+XrCH75KdPFcot/NI/jU\nP0npeBTtH5qBZ9BQqyNsOqpK1s23k37WZPL++WdJFtfE6SLzN7dj5uyg7NkHiSz5GgoOQPDIVdjN\nYehdfcQSx7quV/SZjFUKG4bRoHUplnDWNK1F/SyFEEI0HakwbqF27NjB9OnTK/79/vvvAzBo0CCm\nTZtGcXExBQUFFY9nZWVx/fXX89577/HNN9/g8/mYOnUqvXv3jntsPp9PEsZCNCNSLSyESAQ7rS26\nv5TiD9+ssk1JSSPlnEtwb/2OUOfjYPnXtq0uNo4ZDH0HE3rpYcyCvIMPhIJEPn0TxZtJxoSpZFww\nlf3T/0tk+zbrgk0wR9fuZP3mFvxffUbhJ7OsDse2nH37kXHVTQQ/f5/I8oUABN99kaCioB19LM5T\nxqG2OQpS0iFBw3iSTezNrNgnAWNVwkC9+xzH3gBTVRWHwwHIgDwhRMugqApKPVr7JJpip/YYcaaY\ndbyr3r17d6JjEQKA9evXc8UVV7B48WKrQ0la0pJCWKGm5I0QVpM1MbnZfW1RjSjRLz8i+OWHVban\n3XAbbj2AXphDoMcQlDcetmXC2Bh1LqbHS+j1GRAKHnFfpV1nHBOmEi4sJu+R+zEOmW+R7HyXXo2n\n33Hkz3iA6F557VMT75U34urWg7KXH8MsOFDzjm4PjgHDcQ4+pbxdxc8tK8LhMKqqJmyqe2PF2kbo\nup7w6t3YQLvY+lbXxLFpmkQikYqWF3DwuU4G5Alhf+3bt7c6hKRV/Py/iObstDqMClp2R9Kv/KPV\nYSSEVBgL25GWFELYm52q+oQQzUeyri2avwT/V7OrbHMOGYUjqxXOrUsIdB1gz+piVcU4+1qMXdsI\nv/1cneIz92wn9Mx9qH1P5Ki776Ns7Rryn54BSV7VqLbKos3tdxLe8AO5f78dojJUrDpqRiuybr2T\n6KY1+KffXfvvTKCM8ILPCS/4HDUrG8eIsei9+6O5XJhOd9ME3QhN0epBURR0Xa+oOK48IK++7SZi\na6Wu6zIgTwghRKNJwljYTkZGBsXFxUSjUdtWHgjREiRr8kYIYW/NaW3RImECn7wLlar5FF8mnrHn\n4PpxAYaqYzg8KBuXWxhlNVK9GBOvJrJgDtFl8+t9uLFuOaENq3CefDrtH5pB0ScfUfJxcrZvSBkz\nAe+4iRQ+P4PwxnW1H9BCeUaeTvpZ5xJ45zmiP22s9/FGXg7B918qb1nRoy/OU8ZB2/aQmgaavCSt\nbkBeJBJBUZR6D8irXK0sA/KEEM2OotirDUQz7iMvz87CdjweD7quU1RURGZmptXhCNGsVX7x0RyS\nN0II+2hOieGaqMWFhJdVGoimKKRe+Rtc+zeiYhDofAKs+MZe1cXtu2OMnETo3Rcwd25p+HmMKNH5\nc4gu/Ya0MefjHfM4ec89RXCVzZLjNXG6afPHOzFLi8j7++2YgTKrI7InVSPz5jvQdJXSx/4Ojf05\nmSbRzWsp27wWXG4cJ56Mc8hoSPVCWnpcQk5mlRPHh7aXiFUcV753q0sSOdb+I5aEbk5rsBBCiMSR\nhLGwHUVR8Pl89U4YS4/Kg+TnIA7VEhI3Qoim15LfdNJDQcrefanKNtdpZ+H0OND37/u5ujgFZYN9\nEqhG/2HQ8wRCLzyEWZQfn5MGyoh88DJKRhZZF1yMMfVSch95gIiNewC7TzyJzMuvofi91wks+tbq\ncGxL79KdVr/4A6H5n1G28Iv4XyAYILxoLuFFc1FatcE5Ygx6n+PBnQJuT/yvl0RqG5BXX6ZpoiiK\nDMgTQghRZ5IwFrYkfYyFaBhJDAshEqGmxHBLXVsURcHM3Utk88EWBmrbdriHn4Zzc3kCMtT5uJ+r\ni+2RlDFOuxBTcxJ69n4Ih+J+frMgj9Arj6F26k7bW/5IaN9+ch97APz+uF+rMbJ+ext6ZiZ5//oL\nRkGckubNUNoFl5Jy4kDKnn8II3dvwq9nHthPcNarBD94Da17H5ynjEM9qgOkNH3Lilhy1Q5iSePK\nFceVE70NjdXhcMiAPCFEclLU8i+7sFMscSYJY2FLGRkZkjAWcdFcK88lMSyESARZW+pGC/jxv/Xc\nwQ2qRuoVv8KzezUqVKouXmZZjBU0HWPStUR/2khkzkwgsf+Xxo4thJ76J9pxQ+jwzwcpXfY9BS88\nndBr1oXepRutf3MLZd9+SeH/3rM6HPtKSaX1bX/FzNlJ6aN/b/oBgKZJ9Md1lP24DpxuHCcOLW9Z\nkebFTE23TSLXCpUH5MV6EscSyDIgTwghRLxJwljYklQYC1FOkjdCiESQtaVxjG0/YuQcbLngPvsi\nHARR/eX3LqFOx8FKG1QXp2VgTLySyFcfE121uEkvHV21mOgPS3GPHEf7h5+g8L23KZ37WZPGEOOb\ndiWe40+k4LH7iOzeaUkMycA1YAi+qZcT+PA1outWWh0OhAKEF88jvHgeSmZrnMPHoPc9HjypLbpl\nRaziOBqNVgzJqzwgrz4tK2RAnhBCiJpIwljYUqyHsRAtQUvuASqESCxJDMefI+CnZOaLFf/WOnXH\ndfxJODfPB36uLnamoKy3uLq409EYJ08g9PYzmHu2WxNDNEJk3ofw3Ty8Yy/Ae9Zk8p6aQWj9D01y\neTWzFW1uv5Pw5vXk/u22pq+WTSIZN/0BR+ss/DPuxSyx3z24mZ9L8MPXCH70Olq3XjhPGY96VMfy\nlhV6y3tJG1vDqxuQZxgGqqoeNiCvLmRAnhDC7hRVQVHt82kTO8USby3v2VUkBakwFs2RJG6EEIki\n60vTUEyTyOrvMYsKyjc4HKReeiOe7cuJ1fSFOvWHld9aWl1snDgKOvcl+PyDYIfkn7+E8HvPo2Rl\n0/qyK4gasP+h+zDychN2yZQzxuEbfzaFLzxJaMOahF0n2anZ7Wh98x2EVyzC/85TYPc1wzSJbtlA\n2ZYN4HDiOGEojqGnoqT7IM0bp0uYDRosZ5XaBuTFksd1JQPyhBBCgCSMhU01pIexvCgWdiGJGyFE\nosj6Yi29rJTij9+u+HfKhVfjCBxADZUPdiuvLk5FWb/UqhAxxlyMGTUJPfcfiIQti6M6Zl4OoRf/\ni9qtN+3+9FcC27eT9/hDEIrjED6nmza3/wXKSsm9+4+YZfYaumcnKeMmkTZ6DGWvP4Gxa5vV4dRf\nOER4ydeEl3yNkpGFc9jp6McOBI8H3ClWR9fkKg/IMwyjyldDEscxsQF5kjgWQoiWRRLGwpa8Xi87\nd0qPOWFvkrgRQiSKrC/2oxpRQvM/h2AAAL1Pf5w9jsb148KKfUKd+sOq+dZUF+vO8uF2G1cT+XJW\n01+/HoyfNhB84l70AcPp8J9HKZ7/DUVvvNzo87qOG0CrK6+jeNabBBZ+HYdImymnk6xb7kQpK6H0\nkb9COI4Je4uYBXkEP36T4MdvonU9Gueo8ajtOkNKKugOq8NrcrEEcaziuDGJ49jzTqziWAbkCSEs\npShgp0+BNONhrJIwFrbk9Xqlh7GwDUncCCESRdaX5KH5S/B/9SkAijuFlAuuxL3t+4rHDVXHcKWi\nrPu+plMkji8LY9zlhL94F2Pt8qa/foOYRJd9S3T1d6SMPou0h/9H/puvUja/YYneVr/+A47Wbcj7\n950YBQfiHGvz4ex9LBnX/ILgF7OILFtgdTgJEd26ibKtm8DhRD9+MO4x52JqWnm/42Yi1jaiNoqi\nVPQ5rpw4jm1vSOJYBuQJIUTLIAljYUsy9E40NRk8J4RIJEkMJzctEiYw+x0wyvuCplx6I86CnaiR\ng5WZoc79YGXTVxcbXfrA4DMJvfEE5r5dTXrtuAiHiHw2ExZ+Tsb4i8g45wJyZzxCeMvmOh2ud+pM\n69/dTtmCryh66mH79+C1kPfy63H17IX/qfsxC/KsDifxwiHMcAgjfz/BT97BPekSyMgCt8fqyJpc\ndYnjSCRS0cJCBuQJIYQ4lCSMhS35fD4KCgqsDkM0Qy01aRMbhCKESKyWusY0d2pRAeHliwBwDByG\nIzsb50/fVTxuqCqGM63Jq4uNQadjtutR3q/YX9Kk1467kiLCbz2F0rYDba6/iUhZgNyH78c4wv2g\n9+LLSTlhIAXT7yeya0cTBptc1IxMsm65k+iPa/FPvxtaSh9aXybusefhn/EP8Jfif+zvaH1PwDX+\nAkjzgdN12CGxtbohvX6TQeXE8aED8mIVxw0ZkOd0OqXPsRCiSTTkDa5EslMs8SYJY2FLUmEsGkuS\nNkKIRJI1puXQQ0H8M18EQPH6SJlwAe6fFlbZJ9TpuCavLjbGXYZRVkb4uQcg2nz6iZr7dhF67j+o\nvfrT7q/3UrZxIweeeKzK96hmZNDm9rsI/7SJ3L/fBtJPtUae4aeSPul8AjOfJ7plg9XhNKnUa28h\n8Paz4C+t2BZdtwL/+pXog07BOWo8pHlBa3kviWMJl1hBQeXEcWP7HEviWAghmoeW9+wokoLX66Ww\nsNDqMJJaS6kolaSNECKRZI1JPvF8/lMUBXP/nopEW+oVv8aV+yNqpUSIoaoYrjSU9U1UXex0Y0y6\nlsjq74l+M7tprmkBY+Nqgpt+wDF4NO3/O53iLz6j+P23STl1DL6zz6XwxScIrVtjdZj2papk/u7/\n0JwOSqffDWV+qyNqUu5pNxFZtQRjWzWtTUyTyHdfEVk2H+foiegDR0Bqur2GKNXCNE3UOMQbSxrH\nKo6j0WjcBuTFEtEyIE8IIZKTJIyFLWVkZEiFsahCkjZCiESSNUZURwv48b/1HADOUWfiSPOgb1tX\nZZ/y6uIFTfMx/8xsjLHTCH/6FsbG1Ym/ntVMg+jiL4kuX0DqmReQPuNZInv3kPv32zFbWAK0PvTO\nXWn1y1sILfyCsvmfWx1Ok9NOPBk1zUvZ608cecdIhNDn7xP69jNc4y9A690fUr1NE6QNKYqCrusV\nid54DMirnHiWAXlCiLhQVXu9wWenWOJMEsbCltLT0wkGg5SVleHxtLzBFC2ZJG2EEIkka4yoK0VR\nMLZuwti3BzWrLZ5R43Bt/rbKPuXVxeko65ckPB6jez8YcBqh1x7HzN2b8OvZiTZwBFrXozFWLkDr\nMxDPsNH4v/jY6rBsKe28aaQMHEzZCw9j7G9ZvycAZLTCfcZk/I//o+7HBPwE330RxZeJa9Il6B26\nYqakJS5Gm5MBeUIIIUASxsKmHA4HqampFBUVScK4GaopYVPTNiGEqC9JDIvG0stKKZn5EigKqVf+\nGveetRxaQxLq2B9WzU94dbExdBxm647lw+3KSms/oLnIysZ14XWY+3cRffEBCAdhyZeknnUl7pOG\nUjDjQYwiGZIMQEoqrW+9C3P/bkof/Vuz6mtdH6nX3ELgzacb9HdiFuYTeOkx1LbtcZ1zGbQ5Cjyp\nCYgyORw6IC8ajRKNRhucOJYBeUIIkVwkYSxsK9bHODs7u87HtJS+vclCEjbCTmR9aH4qv1CVdUbE\nk2KaRFYtwSwuxD3xQhxaFK30QJV9DFXFdHtR1iW2utiYcBVGYT7h5x9omrYXNuE4+1LUjl2JfvI6\n5Ow4+IBhYM56FrVnP7L+dA/F775BYNE31gVqA64TTsI37SqCH71BZO1yq8OxjPvSXxFesQhjx5ZG\nncfYt5uyJ/+N1q0XrrOnga8VuNxxirLxYs9v9a3ybajGDMirrteyDMgTQjSGoigoatOsf3XRVGux\nFSRhLGwrIyNDBt8lCUkMC2F/yZwwlzVGNDW9rJTi2e+gdeiCa+AwnIe0ooDy6mJz9QKURCU6XCkY\nk64hsmwB0YUtpw+t2uMYHOMvwly7pLyq2Kzh57t5DebWjaSfcw2eoSMoePIRTH8Lqr7+WcaNN+No\n2xb/jHsxS1ru/A9t4HBUTwrBuR/G7ZzRnzbif+Sv6P1Pwjn2PEjzgsMZt/Mnm0QPyItVMAshhLAH\nSRgL24pVGAv7kKSNECKRZI0RdqBGowS//QwiEVIvuwnPzhWHtaKoqC5em6Dq4tbtMU6/iPDHr2H8\nuK72/ZsDpxvn1BtRNJXoW49DcR1aTURCmG/PQO83hNZ3/pvCV58ltGpZ4mO1AbVNNlm//xORVd/h\nn/kMtOR1MjML96ln1a9vcT1EVn9P5IdlOIaehmPEmPLBeJqWkGsli5oG5DUmcaxpGrquVySO5blf\nCCGsJQljYVs+n4+iopZbKdFYjbnJkqSNECKRZI0Rdqb5S/B/PYeUC67AES5CDZQctk95dfFCFCP+\n1XBGrxPhuBGEXnkU88D+uJ/fjrQhp6EPHoXx7WyMDfVvqWCuWQybV+O74DrCJ59C4XMzMEPBBERq\nDyljzybttLGUvfEkxs6tVodjudRr/lDetzjgT9xFDIPwgs8JL/ka52ln4zhhKEaat1l/FLkuqhuQ\nZxhGRSVybJ+6irWwkAF5QogaKQooh76Vb6Fm/Dxgo5+yEFVJhXFiVe5HdijTNOXmTAjRaLLGiGSj\nhUMEZr+N3r0Xjt7H4tqz4bB9DlYXfxf36xvDz8bseQLBZ+5vGcniVm1w3fgntE5dib70AGYDksUV\nAn7M1x/GEThA67/ej+PoPvGL0y6cTrL+725Sju1H6aN/k2Qx4Ln8N4SXLsDY+VPTXDAcIvTpO/gf\n/RvGysVgQRuQpu5hXBexxLGu62iaVtFiAhr2nF95QJ7D4TisD7IQQojEkwpjYVs+n08SxnEglXwC\n5P9bJJasM6K5UIoLCK9bifeWe/Bsq761Qahjf8w18a4uVjHOvgpjfw7hFx6quW9vM+I4+xLUWGUX\n/AAAIABJREFUjt0x5ryBuWdb3M5rLpkL65eTcdWNBNf+QNHrz0EkErfzW8XRuy+Z1/yK4JcfEFk6\n3+pwbEEfdAqK00n4q4+b/Nqmv4Tg28+iZGThOucy1PadISWtyeOwm8oFKZUrjg3DQNO0Gt9Irkks\ncazr5WkLGZAnhBBNRxLGwrYa0pKiJScnJGEjhEg0WWdEc6aHApS98wKpF1+Ps2QvaiRw2D4V1cU/\nxLG62J1aPtxu8VdEl8yL33ltSu3RF8f4qZjrlhJ98T+JSY4XF2C+/ACukWfR+q77KHjiYSI745eU\nbmrey67DdXRv/E/fj5mfZ3U4tqC0aotr1Hj80++xNA6zII/A8w+htuuE65zLUFq1BU+KpTHZQawl\nRayvceWK44b0OY6RAXlCCFSl/Msu7BRLnEnCWNiW1+tl9+7dVodhO5KwEUIkmqwzoqVRFAVz3x7U\n9AwcHTvh3LKo2v3Kq4sXxa+6OLszxujzCc96CWPbpvic066cbpwXXY+iO4i+PQOK8hN+SfObDyEr\nm8xf3ULZdwsoefeNpKreVn0ZtLr1ToyfNuCffjdIZWU5VSXl6psJvPEkBMusjgYAY88Oymbci9bz\nGFxnTQVvBjjdVodlqdg9Qyw5nKgBeZFm8AkCIYSwI0kYC9tq6UPvJGETP7GbVCFEVbLOCFFOK/NT\nNvsdUi+9EfdPi6vdxyBWXVz94/VlHDMY+g4i9OJDmIUH4nJOu9IGj0YfcirG/NkY6xvRp7gh8nIw\nX7wPz5gpuO/8F/kzHiS6b2/TxtAAnmGjSD9nCoF3nie6Zb3V4diK5/LfEF7yDcYu+1WNRzevxf/w\nXejHD8F5xmRI84LuiOs1kvU5+kgD8mLtKuojNiDP6XRiGAbRaDRpfzZCCGFHkjAWtuXz+SgoKLA6\njISqfGMkCRshRKLU9CJM1hkhyv8+jK0b8Yw7D1feT6hG9dVqoc79MH9YHJfqYmPUuZjudELP3A+h\nYKPPZ1uZbXBNuR4zby/Rlx6E0OFtPpqK+dmb0L4rWX/4MyVffIp/zgeWxXJEqkrmb/6I5nFR+tjd\nUFZqdUS2og89FUVTCX/zidWh1Mw0iaxYRGT1EhzDx+AYelp54jjOg9vsNPSuPqpLHEcikYo2FvXt\ncwzlVcyaplUkjqXPsRDNl6KoKIp9BmHaKZZ4k4SxsK3mVGEsVXxCiKZwpLUmWV9YCpFour+E0N5d\nuE4agmNr9W0hyquLfShrqm9VUWeqinH2tUR3bSPy1nNA870PcEychtqlB8an8R1q1yi7t2K8cD+p\nZ12OZ+AQ8mc8iFFgn+puvVMXWv3qVkKL5lL27Ryrw7EdNSsb14ix5e05kkE0SvjrTwgvmodzzGT0\n/idhpnrl+fhnlRPHsXYVsb7EMiBPCCGsJwljYVter5fCwkKrw6gXSQwLIZqCrDVCxIdimkS3bcY9\ndBSuzQtq3C/cKQ7VxalejIlXE5k/h+jy+Q0/j82p3XrjmDgNc/2y8qF2tkvYGJgfPo/S/Riy/u9u\nSma9Rdn8eVYHRdq5F5Ey6GTKXnwUY5/M8DiMquK5+ncEXnsCgtZVqjdIKEDoozcIfzUb51lT0br1\nhtR0q6NKuNg9SW1J31hiuHKfYxmQJ4QQ1pOEsbAtO1cYS7JGCNEUZK0RIrH0QClkt8edsx6V6hOb\nBiqGp5HVxe27Y4ycROjdFzB3bmn4eezM6cY55XoUp7PJhto1ypa1mNs3kjbpGtyDh1Pw5MOYpSVN\nH4c7hda334WZu5fSR/8GMsCrWp4rf0t40TyM3dutDqXBzJIigq8/iZLVFvfky1CO6ggpqfU/TzP9\n1FAsaRyrOI61lpABeUKIKhRAtdEaaKNQ4k0SxsK2Ygnj2E2CFSRZI4RoCrLWCNH0VCOKYproahSt\nOLfG/cKd+mGu/a7B1cVG/2HQ8wRCLzyEafckagNpg0ahDz0NY8GnGOuWWh1O3UUimDOfQO87kNZ/\n+RdFr79AcMWSJru86/iB+C65huDHbxD5YVmTXTfZ6CefDqZJeP5nVocSF2bePsqefQC1Y1dcky9D\nycwCd4rVYdlKrLVErOJYBuQJIUTTk4SxsK3U1FRUVaW4uBifz5ew6zTnwXOxj3YJIexBEsNC2IdD\nUUDTcG5ZWeM+FdXFqxc26BrGaRdiak5Cz94P4VBDQ7WvjCxcF92AeSDH8qF2jWGuWwpb1uI97zrC\nJ4+k8LnHMQOJ/V4ybvgdjuyj8P/vXszi5GrB1pTUNu1wDTsd//R7rA4l7oydWymbfjcpv74LgkFw\ne8DltjqsuIlHJXSiB+RFIhG5BxNCiBpIwljYlqIoFX2M45EwlkSNEC1bU/2tN+c3oYRoLrRICMXp\nwr1zJUf6DFODq4s1HWPStUS3bCTy2Uya43A7x4SLUbsejTHnDczdW60Op/GCZZivP4Jj4Cha33U/\nBc//j/CGH+J+GbVNNlm//xOR1Uvwv/ssyPNCzVQVz5W/JfDqjKR9M6I2rmk3Ed6ygbKZL+E48WQ8\n486FdB84nFaHZiuHDsiL9SVuaOI4lsx2OByADMgTIqmoKopFn0Cvlp1iiTNJGAtba0gfY0kMCyGa\ngqw1QiQvhzsFNWcLalnN9xgN7l2cloEx8UrC8z7CWP1dIyO1n/KhdhdjblhB9MX7bTjUrnHMpV/B\nhhVkXn4tgU0bKHr5GYiE43LulDETSDt9PGVvPIWx86e4nLM581x1M+GFX2Ds2WF1KAnhnHwpZiRK\n2TsvAhBetoDwikU4Tz4V96kTIM0LuuOw42LtFVqiRA3IiyWkARmQJ4QQP5OEsajwzTffMHfuXIqL\ni2nfvj3nn38+nTt3rnH/efPmsWDBAvLz80lNTeX444/n7LPPRtfj92vVqlUr9u3bx4oVK8jJySEn\nJ4e9e/cyaNAgTjvtNKD65IwkbERl8vsgGkMSw0I0L3o4iBksxb1n3RH3C3c6FnPtEpRoPYYkdToa\n4+QJhN5+BnNP8g7nqpbuxHnRDSguF9F3noDCA1ZHlDglhRivPIhr+ARa//U+Cp56hMi2RiR4dZ1W\nt9yJGg5Q+ujfm221bDw5RoyFSITwgi+sDiUhHKdNQs1sTckT91d9wDAIzf+C0KKvcI0ej2vYaZDu\nBVWzJlCbStSAPIfDIQPyhBDiZ5IwFgAsW7aM999/nylTptClSxfmzZvH//73P+644w7S0tIO23/p\n0qV89NFHXHzxxXTr1o19+/bx6quvoqoqkydPrvf1Q6FQRUK4cmK4R48ezJo1CyjvaZydnU2XLl04\n6qijJFkjhIgrSQwL0fwpioLu9uBeN++I+xmA4clAWVP33sXGiaOhSx+Czz8IJfX7dJTdaQNPQR92\nOsbCORhrv7c6nCZjzv8Y1i6h1U2/p2zpYorfebXeFdWOnr3JvP43BOd+SPD7bxMUafOiZnfAOWRU\ns+xbDKANGYXW8xhKpt9bc0uSaITgFx8Q/GYO7rHn4Bxwcnmrikb2BG5KTVUJXdOAvPokjmOxxu75\nZECeEDamKPZaC+0US5xJwlgA8NVXXzFs2DAGDx4MwJQpU1i7di2LFy/m9NNPP2z/rVu30q1bNwYM\nGABAZmYmAwYMYNu2bbVeyzAMvvvuu4qkcE5ODgcOHKxS8fl8ZGdn06dPH/bu3cvw4cOZOHFitYlr\nIYSoL0kMC9FyORUTbe8m1FDZEfcLd+qPue77OlcXG2dcjGmYhJ65H+pTkWx3GVk4L7oB8vcTfflB\nCLbAytj8/Rgv3of79Atw3flv8v/3X6J7d9fpUO8l1+Dqcwz+p/+DmZ+b4ECbCVXFc/mvCbw8HUJB\nq6OJO+3YATgHjaL40XvqtlaEggQ+fIPgFx/gnjgFxzEngCc18YEmoeoG5BmGUVGJ3JDktQzIE0K0\nZJIwFkSjUXbs2MGYMWMqtimKQq9evdi6dWu1x3Tt2pWlS5eyfft2OnfuTG5uLmvXrmXQoEG1Xk9V\nVT7++GOcTifZ2dmccMIJZGdnV3x5PJ6KfRctWkR+fr4ki4UQ9SaJYREj/+cCQFUU1JAfV86PR9zP\nAIyUDJTVC2o/qe4sH263cTWRL2fFJ1CbcEyYitq1F8Znb2Lukn675hdvw1GdaXXzHfjnfU7pJ+/X\nWB2qen20uvUujK0b8T92N9R3aGIL5rnmFkLfzsHI2WV1KHGndu2Fa8y5FD96d73ffDHL/JS9/TyB\ndB+eydNQe/QprzgWh6lpQF6s6lgG5AkhRN1IwlhQUlKCaZqHJWXT09PZt29ftccMHDiQ0tJSHn74\nYaC8anj48OGcccYZdbrmXXfdVTFY4EgaMvROCNGySGJYCFEXTqK4N9c+wC7csT/m2jpUF/uyMMZd\nRviL9zDWLo9TlNZTuxyN4+xLMDesbJZD7Rpl73bMF+4nZeIluAcMJv/xBzDy86rs4h46Eu95Uwm8\n+yLRzWstCjQ5OU4ZD8EAkcXzrA4l7tTsDrjPu4Lix/+FWVrS4POYxYX4X56Bmtkaz/mXo3XsBmnp\ncYw0PuxwD3akAXmaplV5LLZ/XTgcjiqJaCFEE1MUsNPgT2lJIVqqmp44N23axOeff86UKVMqKoxn\nzpyJ1+tl7NixtZ63LsliAK/Xy969e+sVsx1uUOxCfhaisso3xclIEsONIz8n0RLF1g0VEz1vB0ok\ndMT9DcBIzay9urhrX4zBYwm98STmvmZSCak7cU65DsXtIfrOk1CYV/sxLZKB+dFLKF16k/XHv1Py\n0buUff05qCqZv74dLcWDf/rdmP5SqwNNKmp2B5yDRpRXZDc3vla4L/sVJU8+gFkQn78rIz+X0qcf\nRG3bnpQLrkA9qgOk2O8TmfWp5E1kDLG2FLE2FbFEb32H48HB+yld12VAnhCiWZOEsSAtLQ1FUSgp\nqfpud3FxMenp1b9jPXv2bE466SSGDBkCQLt27QgGg7z55pt1ShjXlc/nY+PGjXE7nxDC/iQxLISo\nr9rWDSdRHHtqv58Id+yHuW7JEauLjUGnY7brQejZ/4C/4ZWCdqINGIk+/AyMRZ9h/LDE6nCSw7YN\nmC/9h7TJV+MZMRrdl0Fo8TzKvvnU6siSj6riufw3BF56BMJHflMn6bhTSLn2VkpfehxjX916X9eH\nsW83JY//E61jVzznX4Hauq30OD6CWC/jWIVw5dYS9b3PlAF5QojmzkZ13MIqmqbRqVOnKolZ0zTZ\ntGkT3bp1q/aYcDh82LuxNb1Yawyfz0dBQUHczieEsI+aesiZpik320KIajVk3dAVE8fu9SjmkVsr\nlFcXt0JZVXN1sTHuMoyUzOaTLPa1wnn9HWg9+xB9+b+YkiyuHyOC+cNiXEcdhVlSRFiSxQ2Scu2t\nhL6ejbFvj9WhxJfuIOUXd+Cf+QLRbUfund5Y0Z1bKXn4b/hfeAxz704I+BN6vWSnKEpFhXDsOaWx\ng+1iiWOHw2GLymohmi1Fsd9XMyUVxgKA0aNH8+qrr9KxY0e6dOnCvHnzCIVCDB48GICXX36ZjIwM\nzjrrLACOPfZY5s2bR4cOHejSpQu5ubnMnj2bfv36xfUJUnoYC5H8pGJYCFFf8Vw3HNEQ2oGdte4X\n7ngs5rql1VcXO90Yk64lsvp7ot/MrncMduQYPwW1ex+MOW9h7tpidThJSR05Aa3HsWgfP4d58tno\ng0YSWfKN1WElFcepEzH9JUSWfG11KPGlqnh+8WfKPn2PyLpVTXbZyOZ1FP/nz+jHDsBz9kUo6T5w\nuZvs+jH17QtslVirimg0WtGyIhKJVGyXAXlCiJZMEsYCgBNPPJHS0lJmz55NSUkJ7du358Ybb6wY\nhFdYWIhaqbH42LFjURSF2bNnU1hYSGpqKv369WPixIlxjcvr9VJYWBjXcwohEkMSw0KI+kr0uuFU\nTJzbVlLby/3y6uIslFUvH/5gZjbG2GmEP30LY+PquMRlJbVzTxyTLsXctIroC/8BQ4Y2NYR63nVo\nuoo250UUw0D75m3c466hdP1qzGL5dFxdqO064zzxZPzT77E6lLjz3PBHQou/Iry0ln7oCRL5YRnF\na5fjGHAynjPPhXQfOJyWxJIsDm1XUd2AvPqSAXlCiGSmmHW8I9+9O/49l4SozY8//siFF17IsmXL\nrA5FiKQXr6F3iWg/I4Ro3ppy3YitdYqi4AkU4t60sNZjgh2OJbpzK8qyeVW2G937wYDTCL35BGZu\n/Ybw2o6u45xyA4onhejHr8hQu4bSneiX/Q5112bUVd9UeTPC6HA0oa7H4f/fvywLL2noOqk330Pg\nxUcw9if539Yh3Ff+jsjOrQQ+fNPqUMqpKs6TT8N96nhI84Ge+JqxWD/fyi0f7Kq6WGNtjgzDqNKr\nuCFD8uDgc6AMyBMx7du3tzqEpBX46GnMA/Z53lBaHYV74rVWh5EQUmEsbM3n80mFsRAWkYphIUR9\n2WndcBLFuW1lrfsZgJGWhbKyanWxMWISZqujCD17f9L3A9UGDEcfPhZj8ecYa76zOpzkldEafcpN\naCvmoW5ff9jD6q5NaD2OxzH0NMKLvrQgwOSRcs2thL76uNkli11TrsEoyLdPshjAMAjN/5zQ4q9w\njR6P6+RTId0LqmZ1ZLZQ3fNTrKL40AF5hmE0KHEsA/KEEMlIEsbC1rxeL4FAgEAggNvd9P23hGgJ\n7JTgEUIkB7uvG6oCetE+1FDtid5wh2Mx1x/SuzirHUrXPiihIK4rf4+xfw/GptUY2zZjFh5IXODx\n5s3EOfVGKDxA9JWHkj7xbalufXCMuRD165moBftq3E37diaucVcTWbccszC/CQNMHo7TzsYsKSDy\n/bdWhxJXzvFTwOHG/9JDVodSvUiY4OezCH79Ke4zz8V54tDyVhUJqABOlh7GldUUa2xAXqziOPal\nKEpFu4r6UlUVTdMaPWhPCCESSRLGwtacTicej4eioiJJGAvRCLGb2eraUshNqhCiJnZPDNfEiYFz\n5w+17ldRXbzqlaoPnDEF54/foeftwgDMjKOIDBxCdMSZmIqKWVJEdMt6jC3rMPfssGUfYMeZF6L2\n7Ev0s7dh549Wh5PU1CFnoB07EG3OSyiB0iPvaxjoK77EM+0m/DPubaIIk4fasSvO4wc3u77F+sgz\nUdt3pmTGv8Hm6yOhIIEPXif4xQe4J07BccwJmGnepEruWiGWII4Nx4vXgDyn04lpmjIgT4i6UhRQ\n1Nr3ayrNeO2UhLGwPZ/PR1FREW3btrU6FJHE4tW/1+4q36hW/n5jN6Ut4WcghKi/ZE0MV0dTwJGz\npWrFcA3Kq4uXoUTCFdvMTr1QHU60vF0AqAAFe9EKDn503nCmEO3cg8ixJ2DobggFMHZvJ7ppNcaO\nLeAvife3VWdq5x44Jl2GuXm1DLWLA+3sK1DT0tA+fbFOv1MA6u4taD1OwDF8DOH5nyU4wiSiO/BM\nu4my5x+CSn9zyU478WQc/U+i+NF7kurvzfSXUvbWcwS8PjyTL0Hv3ru84rgFqk+it3LiuPJAu8Yk\njuHggDxJHAsh7EISxsL2GtLHWBJjorlrTskdIUTTaQlrh8MIo+dsqXW/aquLFRVOmYy+4weO9HJf\nDflRt63GUXEuFaNNJyIjz8Bwp2NGDYyCPIxNP2Bs24i5fy+Q4J+xruO88HqU1DSiM5+CgtzEXq+5\nU3X0S3+LmrsLde7HR/x9qI42/73y1hQ/LMMskAGDACnX3Uroiw8wc3OsDiVutF79cI4cR8kjd0M4\nOZPgZlEh/pceR81sjef8y9E6doO0dKvDajINff6LJYZjrztjfYmhYQPyDk0cxyqYhRDCKpIwFrbn\n9Xpl8F0jSPI8ucUzuSO/B0K0HC0hMVwdh2Li2LEGpQ7J2XCHYzA3LK9aXXz8CBRNRdtbvxYOKgbq\n/m3o+7dVbDNSMwkfcwzRQSMwNQdmWSnGts0Ym3/A2LUNwsF6XeOI1z9xGI4RZ2Is/gJjzeK4nbfF\nSvOhX/xr1DUL0H5a3aBTqIaBvvRzPJfchP/xf9i/TUGCOcecg1FwgMjyBVaHEjdqx664Jk6l+LF/\nYDaD/uBGfi6lTz+Imt2elPOvQD2qA6SkNehcsU+2tQSVB+RV7nEsA/KESBBFKR9WYRfNeK2ThLGw\nvVhLCiGas5aa3BFCNI6sHVXp4QBaQe3Vi+XVxa1RVr56cKPLA8cNQ9u7GcVs/MeB1dJ8XFuWHrym\n7iDathuRsedgOlMwIxGM/bsxNq7B2N7AYXreDJwX3QhF+TLULl469sAxYRrqglmoubsbdSo1Zyta\nz+NxjDyT8NefxCnA5KN27o6j30D80++2OpS4UbKycV90AyX/+xdmcfMqbDFydlPy+D/ROnXHc/5l\nqFltwZNqdVhJIZYgPnRAXkMSx5XPKQPyhBBWkISxsD2v10tBQYHVYQgRF5LcEUI0hKwdtXMpJq5t\nK+rUOiDc4RjMjYdUF4+chKIoOHasTUh8aiSMunsjjt0bAQ4O0ztpKNGR4zAVBbO4kOiWdRhb1mPu\n3QFH6GOpj70A7ehjfx5qtzkhMbc0yokj0AeMRPv8FRR/cVzOqc1/H9eEa4msWYp5YH9czplUdCee\nqTdQ9uyD0Fw+Xp/mw3PV7yh99mGMvOb7fxrdsYWSh/6G3rMvKdNuxNQ0lNSGVRzbmWmaqGp8B2hV\nNyDPMIyKSuT6Xq/ygLxYz2R5/hdCJJokjIXtZWRkSIWxSDqS3BFCNISsHQ2jKAq6/wBqWe33CxXV\nxSsqVRe3ykZp3w31wC6UaNP0Ia12mJ4rlWiX7kT6DcDQXRAKYuzeRnTjz8P0ykpRO3bHMflyzB/X\nEH3xfogmz5AtO1PPnIrWOhvt0xeqvJHQ6PMC+vdzSJl2E6XT725xrSlSbriN0GfvN59kudNNyg23\nU/raU0R3b7c6mibhHDyS6J7tBGe/g2PAyeh9j0fxpJS3q6gh8ZmIJGwyqi5xHI1GK6qO6zsgL3Z8\n7JwyIE+0RIqils+csAnFRrHEmySMhe1JD2NhZ5LcSS7S01vYhawd8eUiinPbqjrtG25/DObGFVWr\ni0efh6KA86cViQqxTtRgadVheoqK0bozkVPGYLjSMXUnCiaRt/8H+c0kAWc5FX3ar1FLDqB+8Wq9\nh9vV6Qr7tqP2OB7n6AmE5n6UgCvYk/PM8zHy9hFZucjqUOJDVUn55Z/wz3qd6I/rrY6mSaT94v8w\n8vbhf/YhMM3yJPmHb6D4MnGcMBTniUNQ0r3lyWNNUgs1qZw4PnRAnqZp9U4cx8QG5MWqjoUQIp5k\nVRe25/P52LdvX72OkRfcIt4kuSOEaAhZOxJPVUDP34USqX2InAEY6a1RZh+sLjZ7HoeSmo5aUoAS\nKktgpPWnmgbq/q3o+7cS6ngM0VbtMTUntO0gCeN4SElDn/Zb1I3fo21cltBLaQs/wDnhOiKrvsfI\nq73PdrJTO/fE0fd4/NPvsTqUuPH84s8E5s4msvp7q0NJPN1B+u/+SnjldwS/+OCwh83CfEJfzSb0\n1WwUdwr6sSfiHDwStVUb8KSgqJoFQddf7Lm4qQb0xRLDsQKGyonjxgzI0zQNXdelXYUQIq4kYSxs\nz+fzSQ9j0Wh1vXGS5I4QoiEqv8CrvF7I2pF4TqI4dm+o077hDn0xN1WqLtZ0GHQGKOCoNKDObsJZ\nnTGyu+GaPxMTYOT5RAJlsK1u37eoxlGdcUy6EnXxx6g52xJ+ORXQv/sYzyU3Uvro35t3awqnE8/U\n6yh75gGINo++xZ7rbiO04jtCi+ZZHUripXnx/uYvBD59j/DyhbXubgb8hJfOJ7x0Pmg6+tHH4Bgy\nCrV9Z3C7ZWBeNWJJ41jFcaxNRW0D8o6U4I61AVFVlVAolPDvQQjLqEr5l13YKZY4k4SxsD2fzyc9\njBtBkhXVk8SwEKKhqls/ZO2whq6YOPdsQjFq/yhuee/iNijLX6/YZg46A8WMoISiqKX2fHM6mt6a\nSI8Tcc2fiWJEUADXovdh7IVEPngR9raMPqrxpBw7CP3ksWhfvoZS0nT/72ruLtSyQpynTyL0+ftN\ndt2mlnLd7YTmzMTMz7U6lLhwXfJLIju3Efx8ltWhJJya3YG0a3+P/81nGtZ2Ixohsn4VkfWrQFHQ\nOnbDOeQU9B59wOWBtPT4B53kFEVB1/WKiuPKA/Ji7SrqQ3oaCyHiRRLGwvakh7FoDEkMCyEaStYP\n+3MYYbTculWHllcXr0SJ/Fx5leqDHv1AAX3j4gRG2XCGO43QMSNxLnofJRyo2K5GAriWfAhnX07k\nnSfhQP1ad7Vk6mnnonXsVj7cLlx7G5N40xZ9hHPCtURWLsbYv7f2A5KMc8KFGPv3EFm1xOpQ4sJ5\nzuWYoSBl775kdSgJpx99DCkXXkXpMw9i7NvT+BOaJtEdWyjbsQUAtXU2joHDcPQbiJKSCqnpNQ7N\na2pN1ZKithgOHZAXiUQqKpHtEKMQomWxxwotxBFIhbGoi5qGRZimKckdIcQRyfqRnJyKiXPb6joN\nKTtYXfx1xTZz9LkoZQdQAK3Afok7Q3cQPH4MjqWfopYVH/a4GijBtfxT9POuA2+mBREmH23KL9Ay\nWqF99rIlyWL4uTXF4g/xTLvJNsmyeFG7Ho3j6H4E333B6lDiwjHmHFRvJv6XZ1gdSsI5Bp9CyrmX\nUfL4P+OTLK6GkZtD8NN3KXngz5T89y6Cs17D3PETFBVAOFz7CRLAjs/xscSxrutomlbRsiISiUj1\nsBCiSUmFsbA9n88nFcaiglT8CSEaStaP5kNRFPRgCVpJ3T7yHm7fp0p1sdmuK0pGFpgG+rZVdUo6\nNyUDleCACTh++AatqObhdmpJPs4fvsK84Eairz8K/pImjDKJuDzol/wOdesatLWLrI4GNW8vWnEu\nrjHnEPx0ptXhxIfTTcqUa/E//R+I1t4ixu70oaehdetDyfR7m3e/acA1/gIcvY6h+NG7IRio/YA4\nMEuKCC34gtCCL8DpwtH3OJxDRqG2aQcuN7g9TRKHnVU3IC92vxLrdXykPsdCNF8KKHZGCZz9AAAg\nAElEQVR6w9Vud5HxIwljYXuxCuPYE6NoGSSxI4RoKFk/mj8nBs5tK+q0rwEY6W1RZr9RvkFRYOQk\nyN8Brbuh5WxNWJwNYQChgePRt6xA21d7uw0tfy+uzd8RvPAXRF97BEJNk/BJGlntcJx/Ler3n6Hu\n/tHqaCqoSz7BMeFawisWYeTstjqcRku5/jYCn7yNWZBndSiNpvUbiOOkERQ/cnezGdpXk5RLb0Jx\neyh9/J/WJfpDQcIrlxBeuQRUFb1bLxxDR6N37l6ePE5JsyYum6g8IC8SiVTpdXykAXlCCNFYkjAW\ntpeaWj5Zt7S0lPR0GZTQ3EhiRwjRULJ+tEyqoqAX70cNltZp/3D7PpibVx2sLj52aHlhirct+q4N\nKNjr9yXU/3TUfdvQt6+t8zHavm04HG6YchPR1x+DiDUf77ado4/DMXoS2ry3UIrslchUAX3hB3im\n3Ujpw3+DOgxutCvnxKkYe3cQXbPU6lAaTe3eG9cZ55RX24asaVvSVNJ+eQfRfXsoe+1J+1RRGwaR\nH9cT+XngntquE87BI9F790dxe8r7HscxORq7X0imhGusZUVNA/LkHkiI5PHJJ5/wwQcfUFBQQNeu\nXbnqqqvo2bNnrcfNnz+fRx55hEGDBnHLLbckLD4p1xS2p6qqDL5LcpU/UnUo6REqhKiNrB+iMidR\nnDtW12nfiuri5V/9fLAbjh8OB7aBKw191/rEBdoAwV5DUYKl6BvqP4TPsWsDet52tPOvb3a9cRtC\nHTEBx/Az0ea8aLtkcYyan4NWkINr3PlWh9JgavfeOHr2Ifjey1aH0mjqUR1xn3M5xTP+hVnajNu7\n6E7Sb72XyIY1BN55wT7J4moYe3YQeP9VSu77P0oevZvQpzMx9+yE4sJmX/19JJX7HKuqimmaRCKR\nigpkIZo1RbHfVwMsWLCAl156iSlTpnDffffRpUsX/vGPf9Q6v2v//v28/PLL9O3bt0HXrQ+5mxRJ\nQfoYJ4fakjpyAyOEOBJJDIvaaAroudtQonWroA23K68uJvxzdfGwiSiBAvBmo+VuR7FRVWeocz9M\nVwqOFV82uBue86eV6IECtMlX05x76tVGO+9atPadypPFwTKrwzki9fs5OPoNQG3XyepQ6s/tJuWC\nqyl78bGkrpAGIKMV7kt+SclTD2AW5lsdTeKkefHe9g+CX35I8MsPrY6mXsz8PIJffkTJf++k5D9/\nJjDzJYxtm6EoHywaYmm16gbkCSGSw0cffcQZZ5zBqFGj6NChA9dddx0ul4u5c+fWeIxhGDz66KNM\nmTKFtm3bJjxGSRiLpNCQCuNk+mhRspGkjhCiMWQNEQ3lNCM49m6q074GYHgrVRdntIEO3dCK90Nq\nKxxbVyUu0HoKt+mG0bozziUfN7pFhnPDYjQN1PHT4hRdEtGd6Ffehlaaj/b1TBTDsDqiWqmAvmAW\nnqk3gKZZHU69pFx/O4GP38QsPGB1KI2TkkrKNbdS+uJ0jP17rY4mYdSjOuL97V3433yW8HLrhz82\nhllWSnjJN5ROv5fif/2RsleeILp+FRQX1asXs2maSfWasaZ4Yz2OdV06jgqRDCKRCFu2bKF///4V\n2xRFoX///mzcuLHG495++228Xi+nnnpqU4QpCWORHGKD70TTkqSOEKIxZA0R8eRQTBy71qHU8Xcn\n3K435o+rD1YXjz4PtWgPRmomalEuik0q0qK+tkS79se5eFbcKp5da+ahZ2SinnpOXM6XFDKy0K++\nHW3tQrSVXydVfbVauB/twC5cE6ZYHUqdOSddgrFzK9G1y60OpXF0Jyk33oH/7eeJ7thidTQJo/fu\nT9rVv6X0mQeJbtlgdTjxFQkTWbuCwOez0FQDLWzvTxUkkvQwFi2Cqtrvq56Ki4sxDAOfz1dlu8/n\no6CgoNpj1q9fz9y5c7nxxhsb9GNrCHkLSiQF6WGcWDI4quWQG0mRCLKGiKagR4Jo+bvrtG95dXE2\nyqdvAWB2OwYlJQU1dy/RzD44V36WwEjrzvCkE+ozHNfC9+KewHYt/wRz8CQYcgbG4s/jem7b6dYH\nx5gLUb+ZiZq/z+poGkRf9jnGuKsJL1uAsWub1eEckdbzGBxdeuKfca/VoTSOqpLyyz9RNnsmkQ11\n64uejBxDR+M5ZSwl0+/FLG6er6fUrLZ4r/oNjm/fJtr5GKK9B0svdyFEk3r++efJycmpsm348OGM\nGDGiXueprtgmEAjw2GOPccMNN5CWltaoOOtDEsYiKWRkZEjCOA4kqSOEaAxZQ4RVnIqJc9vKOleN\nllcXrylPwqoaDD0T5cB2DGcqSrAUtaw4ofHWhaE7CR53Bo7vZ6MEEjNgy/ndLIInn49ZVoq5amFC\nrmE1dfBpaP0Goc15CSVQanU4jaLPfx/P1Ospfegu+w70cqfgPu8Kyp68L+n7Fntu+D+CC+cSXt48\n/zYA3BMvRO/Rl+JH74FgwOpwEiM1jfRf3YG++AMUfxHapqUY3foTdafWeqhpmqhJkliO3WslUwsN\nIVqSK6+8ss77pqeno6rqYTmuwsLCw6qOAfbu3cv+/fv597//XbHN+Lnt1sUXX8zDDz+ckJ7GkjAW\nSUEqjOvn0KROrKpUkjqipZO/gbqRxLCwE0VR0MsK0PzVf0TvUIdVFw88FSUaRDUiRDO741z3bQKj\nrRtDVQkOnIBj9Ty04ryEXUcFXAvfJTjiAiIBP2xcmbBrWUE763LUdC/apy+i2DXBWg9qcR7avm24\nzppK8P2XrQ6nWqk33Ebwo9cxi5J7MJz7qpsJb1xD8Os5VoeSMCmX/gLF5aJ0xj/r1dc3qehOfL//\nG/ryz1AL9wOgRMMoe7ZAt/61HCyESEqKAoqN3uhpwJs4uq7TvXt3Vq9ezUknnQSUv85as2YN48eP\nP2z/jh078sADD1TZ9tprrxEIBLjqqqvIyspqWOy1sNFPWYiaSQ/j6kl/0PqRd+SFqErWEJEMnERx\nbq/7gLpwu96YW9ZAOAgp6dDrRJSCXRi6EyUaRSvKTWC0tTOA0IAJ6Bu/R8vdmfDrqRi4FsxEH3U2\ndO6V8Os1CVVHv+wPaGYYbe4bzSJZHKOvmIuj17GonbpZHcphnJMvJbrtR6LrkvuNB9eU6zAO5BL4\n6C2rQ/l/9u48PI7qzvf/+5yq6m6tLe+yLS8Yr9gGjLHxih32fTPYwCRkICEhMJBkQpIZMsnkZsLc\nSebO7z73TjKTZe4kkxAmQNghBMJmGy8yYPC+ypbkTYstW9baS536/SFkvEiylu6u7tb39Tx6eOiu\n5StZOip96tT3JE3+X30Hr7WZ5l/9n+wNi4HCR3+AvaMUq3bfKa/b29agI80+VeUfuXYTInNcf/31\nvPnmmyxfvpwDBw7wy1/+kkgkwuLFiwH4yU9+wpNPPgm0BcwlJSWnfOTl5ZGTk0NJSQlWkhbNlcBY\nZITCwsJOm393Jpt+YUqoI4ToCxlDRKbSSmHXV6Gj3VvE6MTs4vXvAuBdejOqqRYNeANGYZf7H3RF\nL7gSfbAM+0DqFp7SJk5w7QvYVy+FYaNSdt6kyA9jf+Fv0GUfYX34ZkYtbtdd9nvPk7P0i2Cnz8Og\n1oRpOKPGEXnlv/0upU8C1y0D26b5qf/ndynJYQco+NY/Et+6gdbnfuN3NUlV8LXvYR/cgbVv+xnv\nqUgz+mhVl/tn2vWPtKQQIrvMmzePe+65h6effppvfetbVFRU8J3vfIfCwkIAjhw50uMMLNHS5ypE\niC4UFRX1ixnG8hi4EKIvzjaGyKKHItMEcAns39bt7WPDJ+Lt2YKKRvCGlqAGDcOq3Y1Bg7axDlcm\nsdqzi0yej2qqx9n9QcrPrWOtBD94FW76PPFnfwF1Gbg4XMm5ONfdjV79MvrwAb+rSRrdeBSrag+h\nmz9L67O/9rscyM0jdOvnaPn5j+CTnomZyF50Lbq4hMaf/ejsG2eigjCFD3+X1j89S+zjUr+rSaq8\nL3wdp+Uo9q4PO93G3rQSM3A4JpDT5bGyKYCVazzRLygFOo1+bvswhlx99dVcffXVHb7393//913u\n++CDD/b6vN0lM4xFRsi2HsYy208I0Rcyhoj+wFbgVO1Gme61G2ibXVyMWv8OoGDRLaijbS0fvEGj\nsPdv83U2anTsBXhWAGfjO77VoFsaCH78Z+zb7oeCIt/q6A01YwHO1Uux3nwyq8PidvbGFdjjJmKN\nGe93KeTd/y0iLz+J1+DvTKe+sGbOxznvIhp/+b8yOvTujB4+isJHvkfzU/+R9WFx6I57CeQHsTYu\n73I71XgU3Zi537NCCOE3CYxFRsjUHsYS6ggh+kLGENGfOSaGXbu329u3zS7eCtEI3pSLUbaFjrdi\nAJxc7IM7k1brWWsbNh4zYDiBD//oewsF3XCEwJYV2Hd8BXLyfK6me/TVy7CnXoz1+n+hmjPverC3\n7BXPErrjC+AEfKsheOvncct34u7Y5FsNfWVNOp/AvCtp+Pd/gljM73ISzp48nfx7H6HpP/4Fd69/\n41wqBK+4kZyxY7HXvdatsVRvWoGORZJeVyp057pPrg2FEIkkgbHICOFwOK1nGEuoI4Toq47GERlD\nRH/lKA9n/+Zuh6unzC52AjDjUtSRtvYTXng4Vs1elOfPrEJ3QDHu6CkESl9GpcnMRuvoIZyyD7CW\nPgiBoN/ldEFj3f0IdiiI9daTqHj2hX1d0c3HsQ7sIHTL53w5vzX5fOwRo4m88ntfzp8IetQ4gtct\npfHf/wlau9cLPZM4cz9D7o130fjTf8TUdt2zN9M5sxaQM3s+9uoXUHTv2siqO4RqbezwvUztCZxp\n9QqRcEqn30eWyt7PTGSVdAmMMzUYTufahOhvMnUcESJVlFLYsRbs+u732I0VT8DbuxWirXhzrkFF\nG9B8Es7mFuFUbk5StV0zuWGiE+a0hcXxqC81dMau3otzcDvWHQ+C7fhdzply8rC/8DdYB3Zilb6G\n6qfjo715FfaYcVjjJqf2xLn5hG76C1qe+Alk6NdeDRlGaOkXafz5P+M1Zt/M9NANywjOWkDDT36I\n1+D/30nJZE+cSt41t+Cs/APKuD3a19qyut/dbBJCiESQwFhkhIKCAlpaWohGU/PHlgQ6Qoi+knFE\niN4JYAiWf9zt7Q1gwsNRH74DhQNh9ESs49Vt7+UPQh+r9iWsNU6IyPTLCHzwR1RrU8rP3x3Ovm3Y\nxw5g3XZ/es2QGTYK53PfwPrgDawdnS9q1V/YK58ldNvnU9qaIu9L3yLy4u8yN4jMD5Pz+a/R+Kv/\ng6mr9buahMu95yGsYcNp+rd/gkir3+UklR4xmvy7vojz3rO9Gsv1oTJ0NPNnl3ued9bZxXJ9KYRI\npDS6MhSic6FQiFAolNA+xu1hjgQ6oj+R7+vEk3FEiMRRSmE31aFbG7q9T2zYBLzybW2zixffhm74\n9LFsL38Izp6PklFql4zWRC66BmfDW+jGoyk/f08EytZjRxuxbv5L8L3DMqipF+PceA/W2/+Nrq7w\nu5y0oJsbsCq3Elrylyk5X/D2e3HLtuHu8mdmfp8FQ+R++ds0PfkLzMF9fleTcPkP/x1eUwPNv/q/\n0MPZtplGFQ2i8Et/jbP6hV7feFOAtevDMxZQlWs0ITKUApRKow+/vyDJI4GxyBi9bUvRWTDcHubI\nxUL/If/Woi8kGBYi+YK4BCo3dnt7A5ii4agP3sYbNQGVX3gibDahAlTLcXQktbN7DRC56HrsHeuw\n6g6l9Ny9Fdi+Gsux0Nfc6Wsd+jO3Ys9c1La4XeMxX2tJN/a2tdgjR2NNOC+55znvQqxhI4n88emk\nnidptCb3wb+j+YUncffs8LuaxAoEKPjW/yS+eT2tzz/hdzXJF8qh8Kt/h7Xuj6g+3njT5ZvR0Y5n\nYmdTT2C5HhVCJJIExiJjFBYWUl9fT1NTE2VlZaxatYrnnnuO7du3A2cPc+QXqBCiOyQYFsIfWoF9\nZH+PHjmODRuPV74d4lGYfz2q7tMZqV54BIGy1LcziFx4NdaBHdgHd6X83H0R3PQO9sDB6EU3+XJ+\na+mDWAMHYv35t6hYxJca0p294g+EbrkHAqHknCC/kOANd9H628ztW5zz0HdpeftV4lvW+11KYhUW\nUfjoP9L6xgtElv/J72qST2sKH/0H7I3vYh3t+2J+yrjoiq3g0+KnidCdlhRCCJFItt8FiPS0cuVK\n3nnnHRoaGhgxYgRLlixh9OjRnW7f0tLCq6++ysaNG2lpaWHAgAHceuutTJkypdc1NDY2UlVVdeJj\nxowZPPfcczQ2tq10q7Vm8ODBlJSUAB3fUVVKScAjhOhQ+0X36WOEjBlC+COAi3Oo+zMC22YXj0C9\n+TzeBZeivDj6k0eOjR1ExaMpbwcRmbIQffwwTlnq22AkQnD9a3iX3Awtl2PWvZWakwZC2J/9Grp8\nK9bWNak5Z4bSrU1YezeSc/tf0vLkzxJ+/Lz7v0nkhd9k7AJxOV/6NtH1a4mVLve7lITSI0eT/5df\npfn3v8Ddm1k3onqr8NF/wClbj1W1N2HHtHZ+gBk7FTeYl7Bjphu5hhX9gtKg02juazqtAZFgEhiL\nM6xfv54XX3yRpUuXMmbMGN59911+9rOf8dhjj5Gfn3/G9q7r8m//9m8UFhZy3333EQ6HqaurIycn\np1vnO378OFVVVVRXV58SEDc1tT1CqrVm6NChuK7L5MmTmTp1KsXFxQwZMgTblm9hIUTXJBgWIv3Z\nCgIHd6B6MPsrNmw8XsV2lNZw3sWo2k+DFG/gKJxdHySj1E5Fx12EpxSBzStSet5EC5S+SHTeEryW\nJrxNa5N7skHDcJZ8Cf3hm+gDu5N7rixh73gfc+U9WJOm4+7YlLDjBpd+kfjOTbi7tyXsmKkU+txf\nEa/cQ+Stl/0uJaHsKReQe8tnafrl/8Icrva7nJTI/6vHsGsrsMoT20NbxaOo6goYNQU+mVQkM3aF\nEKJzkraJMyxfvpx58+Yxe/ZsAJYuXcrWrVspLS3l8ssvP2P7tWvX0tLSwte//nX0J3d6BgwY0O3z\n/ehHP6KpqQnLshg6dCjFxcVMmDCB4uLiE8GwZVk8/PDDGGOYMWNGYj5RIURWkWBYiMzluFGsI91f\nnOrE7OK3XsC79GZUS92JPmtG2yg01tGDSam1I7EREzGFQwiseSHj1z7RQGD183gL7sBtacLbnbhQ\n8hQTzsdZfBPW8j+g6g8n5xxZyl7+B0I3/QVN//o/oLWlz8ezps3EGjyMlmd/lYDqUi9w6+cxLS20\nvPA7v0tJKGfe5eTMv5zGnz6esbO+eyrvnodwTCv29uTcrLK3rMYMG4sJ5ibl+MnSfi0rAbcQIpUk\nMBancF2Xffv2ceWVV554TSnFxIkTKS8v73CfLVu2MHbsWJ555hk2b95Mfn4+F110EZdffvmJALkr\n9957LwUFBQwePBjLsjrdrr2HsRCif5NgWIjsElAegcoNPQpa22YX70DlF6GGlmCdPLt4QAlO5eaU\nBbfxgSW4IyYSWPVcj2ZIpzONIbj2WSKfuR030oy3ryyxx19wHdb4qVhv/AYV6Xvg2d/oaDNW2Ufk\n3H4fLU/8tG8Hyw8TuvYOWn72PzOyb7Fz5a2ogjBNv/wXv0tJqNCNd2KPHU/Dv/4DRPtHT+/QTXcT\nGDQAe+1LSTuHam1EH6vBDBubtHMIIZJMqbaPdJFOtSRY9jbbEL3S2NiI53lntJ4oKCjg+PGO72wf\nPnyYDRs24HkeX/7yl7nqqqt49913efPNN7t1zvHjxzNs2LAuw2KAcDjcaQ1CiOwji88Jkf2UUtiR\nRqzGum7vc2J28Qdv4S2+FVW//5T3cEJY1YkNODutJX8AsfEXEyh9GeXGUnLOVNHxOMHSF7GuuRuG\nliTsuNatX8QaORrrjd9KWNwH9q71WEOGYZ93YZ+Ok/elbxJ5/r/wmhoSVFnq2POuwBo7kab/978z\nMuzuTO7nH8YaMoymf/9RvwmLg4uuJjTlPOzSV5J+s8/etAIdbcm6lhRybSyESDQJjEW3dfYLtT1g\nXrZsGSUlJcyYMYMrr7ySVatWJfT8MsNYiOwkwbAQ/VcAl0D5xz3aJzb0XLyKHXhjJqECAXT009DR\nG1CCdWg3KgVjhwnkEJm6mMD7r6AizUk/nx90tIXgh69i3/yXMGBI3w5mB7A//y2s5qNYy59FGTch\nNfZn9opnCN1wF+T07vH64LL7iW/7GHdP9xebTBfW+bNwZsyl8ec/BjdLvpe0Jv+R7+I1HKP51/8K\n/eRnxL5gNjkLLsNJ0VMaqqEO1ZSZf1NmU8AthEh/EhiLU+Tn56OUorGx8ZTXGxoaKCgo6HCfwsJC\nhg4desovsGHDhnH8+HHcBF7AFRUV9TgwlrDpU3KBIfwmwbAQ4mRagX28Fh3tfthqADNgJOrjFXDx\n5agjFae8R6gAZ//WhNd6Rh3aJjLjGpyP30RnaPDQXbr5OMENf8a+7X7ID/fuIEWDsO/9Nta2tVgb\nVmR8n+d0oaOt6F0fkLP0iz3e1zp/FtaAwUTfeD4JlSWXPncKwc/cSOO//1P2zMANBCh49HFiGz+g\nNct6MXfFOmci+Tcvw3nvWZQbT915N7+HY1J3vr7qznWyXEuLfkMpUDqNPrL3qkYCY3EKy7IYNWoU\nO3fuPPGa53ns2rWLc845p8N9xo0bx+HDpy5WUlNTQ2Fh4VnbTPREYWGhtKQQIgNIMCyEOF1H40IA\nQ2D/lh4dJzb0XLzKHXgXLETFmtB8OhvNKxiKdeRA0kMHA0RmXoe9bTXW0aqknitd6ONHCGx/D/uO\nr0BOXs92HjsZZ+mDWO89j67clpwC+zF798dYAwdiT7+4+zsVFhG6egmtT/w041o56OGjCd38WRp+\n9iO85ia/y0mMwgEUfvMfaX3jeaIrXve7mpTRw4ZT8PkHcd57DhVtTe25D+/H+qQthVybCiFExyQw\nFmdYvHgxa9asYd26dVRXV/P0008TjUaZPXs2AE888QSvvPLKie3nz59PU1MTzz33HLW1tWzZsoU3\n33yThQsXJrSucDgsLSmESCMSDAshTtfdccFW4NTs7VHf3xOzi7d9COdMQdUfOnWD/IE45Rt6W3q3\nRWZci1W5BbtqT9LPlU6sIwex96zHWvoVcILd2kfPvgznsluw/vxb9NHqJFfYf9nvPkPo2jtQud0L\n8/O++CiRZ3+F19x49o3TSdEgQnd/hcZf/gte/VG/q0kIPXI0hQ9/h+Ynf0F84wd+l5M6BWEKvvJt\n7DUvolpS3z9bAdb2tVgmTjwex3XdjL92zfT6hRDpx/a7AJF+ZsyYQVNTE6+99hqNjY2MGDGCBx54\n4MRCePX19Wj96b2GoqIiHnjgAV544QV+/OMfEw6HWbRoEZdffnlC65LAWAh/tIc/p1+IyoWpEP1X\nX8cFx4tj1/RsYbq22cU7Yc416MaaU2Y9mJwwuuEoKprcRdRapy5GHz2Es3djUs+TrpzqPRAMwR0P\n4D71U+hiNrd1w+fQBWGs1/8rpY+a90c6HkVvLyW07Eu0/Op/d7lt6K4HiG9Zj1u+K0XVJUhuPrlf\neJSm3/wEU5sdM/vt8y4k9+a7afrlv2AO96MbKoEA4a//PfaHb6CPH/GtDL1/F/bUBZhgHsYYjDEo\npbAsK+1a+bX/bk23uoTwhdZtH+kinWpJMAmMRYcWLFjAggULOnzvoYceOuO1sWPH8rWvfS2pNcmi\nd0IkV38JhpVSWfc5CZEsyRgXHOXh7Nvao4Xp2mcXs+NPqNET0Yd3n1pPuJjAxrd7XVN3RMdfDG4c\nZ2tiF/XNNE7lVjwnBLfdj/uHn8Ppi1RpG/svHkHXHUK/85T0K04Re+8mvHOmYV9wCfENpR1uY11w\nCbqwiJanf5ni6vrIDpD7wN/S/Mx/4u7b63c1CRFYcCXBuYtp/Ok/4jX2o5Z7WlP46A+xt67COrzf\n11IUHlbZx9hT52Ns+0RoHI/HUUqhte70qRkhhOgPsjcKF1knHA5z/PhxCXpEn8hFn7SSEEJ0rKOx\nIRnjgh1vxTp26OwbniQ29Fy8/WUw6wrUscpT3jOBHFS0Fd2cvJvKsZIpmLwBBD56QwJQIFC2Hive\njHXj5+Hkr0h+GPsLf4Mu24D14ZvytUoxa8WzhK5egsrrYKHq8ABCV91Ky+8yrG+x1uQ+9He0/PEP\nxHf2rOd5ugrdfDeBGZfQ+K8/7F9hMVDw9e/jVG7GOpAeM9z1ng3oaMuJmcW2bWNZFp7n4bouruti\njMmI6+NMqFEIkVkkMBYZo6CgAGMMTU1ZssCFECmQqgBIZAb5dxfg702joPIIlm/ocZBoBoyE1iaU\nMuh49JT3vAElOGXrE1bj6WKDSnCHjSOw7pUezYrOdsFtq7BCAfTVS9teKBmHc/dXsda8grWnf7bs\n8JuOR9Fb3yPnri+f8V7eFx+l9Q//CRm2UFzOA48RWfUWsY87njWdaXLvfQRrwGCa/v1HEI34XU5K\n5X35UZz6Kqyyj/0u5QRlXPS+HSduorTPLG4PjgFc1yUej/saHHueJ5NehDhBgUqjjyy+PS6BscgY\nWmtpSyFEJ2TWsBDidOk2LiilsJqPoVt69ns8MnQcXvU+mHABqu602cXaRhkPXZ+c/p9uwSDi584k\nUPqS9OHtQHDjW9iDh6GXPoBzzZ1Yb/4OffiA32X1a3bFNqzcHOyL5p14LfQXDxLf+D6mYncXe6af\n0H1/TWzbBiLv/dnvUvpOa/K/+j28Y3U0/+YnYFy/K0qp3DvvJxDQWFve87uUM1g71mFFm0957fTg\nWCl1IjhO1wXy0rEmIURmk8BYZBQJjHtPLiKyQyICIJmhIER2SbdguDNBXIKVPZ95agaUQCCIaj16\nxoWrN2g0dsXGpMztMKE8oudd2jazONqahDNkiWgL1uCRqO3rUM396/H6dGWt/AOhK29B5RdizZiL\nzisg+tZLfpfVI8E7v4ypraH1tWf9LqXvAiEKvvmPxD4upfXFJ/2uJuWC19xGsGon1JUAACAASURB\nVGQE9gd/Sst5eCoWQdV23k+5PTi2bRul1Ik+x+kaHAshRKLIoncio7T3MRYi2/WXBeiESJVsWOww\nk8cFrRT20UOoWM+C18jQcXgNR2HgMKzaU3teGjRYDlZNRSJLbTu27RC54Cqc9W+gJQTtkAGi82/D\nikewt71DZNpCvJp9qON1fpfW7+l4HL1pBTn3PIzKK6D5pz/0u6QeCdx4N6BofuY//S6lz1TRQAoe\neoyWl39PfHPyWuekK2fuYnJmXIy94pm0DIvb2ZvfwwwZhQnmdrqNUgrbtvE878QCecYYtNYnFshL\nFs/z0LrruX6ZcC0gREIo3faRLtKplgSTwFhklN7MMM6GkEBkr0wOgIQQyZGN40KAOM7BbT3ezwwo\nATeOrj94xnvewBLsAztQJPbrYoDIRdfhbHkPq74mocfOFsYOEV14O/bhCpyaPQAEyz8isuh2rNd+\nhYrH/C1QoKOtWEOG4bW2kvvFRzHHjmAOVuIerMQcrsI7ehjc9GuL4Cy+Hj24mMaf/9jvUvpMjzqH\n/Hv+iuYnf4ZbUeZ3OSlnn3cheVdcj/PuU6g0b8GhWhrQxw9jhow++7afLJCntT4lOG5/XZ7kE0Jk\nCwmMRUYpKiqSlhQiI2VjACSE6LuOxoZsGxdsBc6h3T0ODKJDz8EDlNbo6KkLdRmAYC72gR0Jq7P9\nuJGZ12Pv2YBVU57QY2cLUzCI6OwbcCo3Yh//NFDXLfVYRw/gLrwN652n0no2YTYzgLnkWtTgEQTf\newbV0tj28xIeihk+EjNpEiaYj2cH8FwXohHM4RrcgxWYqv2Y2iq8+roTi4ClkjVrIfbkC2j4yeNg\nTMrPn0j2tBnk3ngXTb/4Z8yR/nfjSY8eR/4d9+Asfxp12kKl6cratBKzYAkmEOrW9h0Fx/F4/ET/\n487aRfVUtl0TCCEyhwTGIqMUFhZKSwqR1iQYFkJ0pD+PDY6JYR8u7/F+7sDRYNmoml1nvOeFi7Fq\nKxM+ay06/TKsmkrsyi0JPW62iA8fj3vePIK71qAjTWe8H6jdS+u4WZhp87A2r/ahwv7N5BZgLr8L\nXVOBvfJp1Cfjiwaor0F3MGPeaA0DRuCeMwoz/QJMMA8sBy8eh9YW3NoqzP5yTM0BzOFqvIbkTNyw\nplxIYM5lNPzff4AMn6EeuPQqgrMvpfGnj+M1NvhdTsrpgUMovO8RnPeeRUWaz75DmtD1taiW49DN\nwLjdycGx53m4rovrugkPjmXmshCfUEA6/TykUSmJJoGxyCjhcFhmGIu00J/DHyFE52RsOJWjPJyK\nTT2+lo4OOQcKBqCOHECb+Jkb5BThbF2VkBrbRSbMQUVbsXesTehxs0V08hy84nMIbluBcjsP9AJ7\n3icyeRGmphJd0/lCUiKx3PEX4k2bi/3Rm1h1h7q9nzYGjuxHHznz38rYAbxBIzFTJmEuntPW31Vb\neLEYXnMTpvoA7oFyTM0hzOFqaDnzJkK3ahg9nuA1t9Pwrz+ESGYvMBm65bPYI8fQ+JMfQjQzZtYm\nVG4+BQ8/hl36Cqop8/5msza/hzf7eowT7PG+7cFweztEYwzuJ21f2ltVJCv07a/XGEKI5JLAWGSU\nwsJC6upkMRXRez29oJLwRwjRERkbzk4phRNtxG6o7fG+7uAxeE3H0fUHznjP5A1EHz+MikUSUSYA\n0dHT8EJ5BEpfzuaJIr1igNjsG1C2TXD7yhOzVjujgeDuUiJzb0S9/htUa+9CRNE9RmvMZ5ahtCKw\n/KmEPv6v41Go3otVvffM8wZz8QaX4M6YgZezGBPIAVRbmNxQj1u1H3OgHFNbjTlSDdGOf171kOGE\n7riPxn/7J7zGzH6KMO++r4Hr0vTzH2V8S41esW3C3/gf2BveRh/LzDYcuqaybVZ0LwLjdu3B8Mmt\nKtqD41QskCeEEIkigbHIKEVFRezde+ZFqxB9JeGPEKIjMjb0XgCXQMWGHu8XHTIWLy+MrtlNR+tO\newVDCXz8Rt8L/ERsyBjM4NEEVj+X8AX0Mp3RNtGFd2Adr8Gp2N7tMF3HW3EO7SC2+A6s1//rrCGz\n6B0zaARm4S1Yu9djVWxO6c0OHWmGAzuxDuw8s67cMGbwSMyc+ZhQIV4gCMZrC5Pr63AP7msLk5ub\nCN1+H43/7//DHD2cwuoTTGvyH/ku7t5dtL78e7+r8U3hN/4Be8c6rOoKv0vpNQVYO9ZhLrwcz+p7\nVNIeELe3qmgPkHsSHLdfb3S1rVyTiH5F67aPdJFOtSSYBMYio/Smh7H8AhUnk/BHCNERGRsSSyuF\n3XgE3drY433dweegGo+im46e8Z4J5qFaG3t13A7PFR6KO/ZCAqufTXg/5ExncguJzr0F58A27KMH\ne7y/XV+FWzAIM/tarNI/JqHC/s296HK8kefirH0RnWaP/uvmenRlPVRuPeX1tsX3hmCGjMCdeBUM\nHolpbib/cw/iVh8ktmMz8YrdmOqDviy61yuBEAV//X2iq98h+t6f/a7GN/lf/S521S6sfdv8LqXP\n9L7t6PPm4eYUJOyYSils2z7RqqI3wbEQQqSaBMYio0gPY9FdEv4IITrT0fggY0NiBXAJVG7q8X7R\nwWPwQrnog1s7fN8rGklw68q+lgeACRUQnTyf4NoXE9reIhu4g0cRv/AyArvfx2rp/XVXcP8WWibO\nx4ydii6XhQQTwQRyMVfejT5WjbP89ygvc1oftC2+VwsNR3HHnY+94S2smsq2IHnQCOIXz8Qsvqqt\nd3IkgntoH7Htm3AryjBHatIuRNZFg8h/6G9peen3xLes97sc3+Td91WcSD32zg/8LiUhlOeh92zA\nTJmLp63EHvukBfJODo7bX+9tcCzXMEKIZJDAWGSU3swwFtlNgmEhRGdkfPCHpcA+UtnlwmidcYeN\nRzXVoeNnBrjGDqDcOLrhSJ9rNHaAyAVX4HzwGqqloc/HyyaxcRdixkxt61ecgCA9uHsNkQsXoeoO\noY7LOhR94Y6egnfRZ7A3vYtVU+l3Ob1i0MQWLcPeXnric9AARw4SOHLwpO3AGzoGd9583CtvxNM2\nXmsL8QMVxHdswq3Y42sbCz3qHPLv+Suan/g33H39t11eaMk9BArzsEtf8buUhLLKPsacOwM3lJeU\n43cUHMfj8RO9j09eIK87LSmE6E88FF5a/TykUy2JJYGxyCjhcJhjx475XUbGal+1NxNJ8JM4/f1r\n1t8//2wk40N6CXguzqFdPd4vOvQcsB1UVcf9L70Bo3H2ftTX8jBaE7noOpxNy7ESED5nk+iMKyEv\n3BYWJ6hFhzaGYPlHRBbdjvXar1Dxnt9I6O8MYBbdjgrlElj5NCra6ndJvWKA2KJlWHs3YB3seozQ\nADUVWDWfjgdGa7yhY3EXXYabc2tbiNzSRLxyD/Edm4lX7sE7nvy/E6xpF5F34500/eKf22Y+91PB\ny64n59zx2O89m3VxiXLjqIO7YNwFJDMMOjk4bu9z7LruKcGxEEL4RQJjkVHC4bDMMM5yEvwIIToj\n40P6sxU4+7f16jF5t3giHK/ucKE7ozVKa6zD+/pUnwGiM67D3v1hn4+VTQya2IIl6EgDzq41CY9H\ndEs91tEDuAtvw3rnqawLl5LJhIdgFt+OVbkFa/36jP7axRbcjnVwF3ZF79qTaGOgag9W1Z4TrxnL\nxiseR/yKazA5hXhovOZG4uW7iO3YgrtvL15T4p4iCCy6huCsBTT+5PGEHjfTOBfNJWfuQux3n87a\nRS3tbaV4IyfgBpMzy/hk7TOK2yf3GGNwXffEe2cj10FCiGSQwFhklMLCQpqamojFYjiO43c5og8k\n+BFCdEbGh8zluBGsowd6vF90+EQArOPVHb7vDRiNvW9rn8Oy6PlXoKv3Yu/f3scjZQ8TyCW6YAlO\ndRn24Y5ndydCoHYvreNmYabNw9q8OmnnySbu9AV450zFef9VdENmt/OIzrkJXVeFvfvDhB5Xu3E4\nsBPrwM4Trxk7gBkxHve6mzChQjzjYRobiO/dQXznFtx95XitzT0+V+i2z2EPH0Xjv/4QYtFEfhoZ\nxZ4wlbwbbsd59/coE/e7nKRR0RbUkYMwYkLqzvlJaHxyq4r2ax/XdWXWsRAASoHqaHqBT7L4Z1IC\nY5FRcnJyCAaDNDQ0MHDgQL/LEd0gwY8QojMyPmSXgPIIVHzc41DXKI1bPAFVvbvj9wGcEPahjt/v\nrsikeajmBuyd6/p0nGxiioqJXnw1gb0fYTUmvz1HYM/7RCYvwtRUomv2J/18mcrYAcyVf4Furm9b\n2C5B7UH8Ep15Naq1CXvbqpScT8ej6Mqt2JWfLp5pArltIfLk2zGhfDzXYI4fI757G/Hd24nvL4do\n5z27877wdYjHaPr5j8FkzkKDiaaHl5B/9xdxVjzTLxYLtTe/hxlUggnmpPzcWmu01sRibW182gPk\n9tdPDo7lukkIkQwSGIuMU1hYyLFjxyQwTjMS/AghutLRGCHjQ/ZQSmG31mM19ax/qBvMIzppHtRV\noiMdP97tFY3Aqt7bqzYX7aJjzsdzggQ+eiWjH+lPpNioKZgJFxPcsRod7flsy97QQHB3KZG5N6Je\n/w2qtSkl580kZsS5mNlXY29dhXWozO9y+ix6/mKU0tgb3vb1Z09Hm9HlG7HLN554zeQUYEZPIH7+\nDLxgHl48jjlWR2zXVuJl23EPVoLrUvDV7xEv20HrK0/5+BmkgfAACr/8KM7qF1CtjX5XkxKqqR7d\neAQTLPGvhk+unyzLOhEadxYcCyFEIklgLDKO9DH2V2d3syX4EUKA3DzqrwK4BCo29Gif2MAS4iMm\noqt2odwuHu/OCeNsWt7r2mLDxmEGjiSw5nkJiz8RnXop3uDhBLevQLmpfaRcx1txDu0gtvgOrNf/\nK2v7n/aUAcy8m1BFgwisejYrwvTY5DmQF8Ze+3Ja/uzplgZ02fpT/iA2+QNwJ4zHnTkbL5ALoVzi\nOzZJWBzKIfzV72G9/xoqw9uj9JS1aSVm/q0YJ+TL+T3POxEMty+Qd3JwnMmLmgvRK0qnWUuKNKol\nwbL3MxNZq7CwUALjFGnvo3Uyz/NOfAgh+q+OxgdAxod+SCuFXV+NjrZ0a3tPW0TGzsAdNBJ9aEeX\nYbGbPwh9tArlxnpVm1tUjDt6GoHSlzL+sf5EMEBk7i2ogjDB7atSHha3s+ur0LFmzOxrfTl/ujH5\nAzA3fRkr2oSz8pnsCIvPvRBv8Eic0ldQZM7vBN14FGfn+4Q+eBWrvgrrcCXOqDHk3P1lsPvp+ila\nU/iNH2BvXo5Vd8jvalJOH61GNafPAoftwbFt22it5ZpLCJE0EhiLjFNUVMSxYz175FUe1WnT2QWF\nBD9CiM7I+CDOJoBLYP/Ws28ImFABkUnzIN6Crtt39iApfwjO3o96VZfJDROdNKctLI733wWq2hk7\nQPQzf4HVfJTA3vW+h3jB/Zvxho7EjJ3qax1+cyfPwly+DPujN7F3lKblTNyeio+agjdyEs7al/rU\nSsZPJjwMBo/E+fhtQqv+QE4ICv76B+hi/1oT+KXwGz/A2bsB69Aev0vxjbV1tS+/R7q6zjo5OBZC\niGSQ0UVkHJlh3HvyqLgQojMyPojesBU4NWUo0/VMVQ+IDx6DO3gU6khlt2YMm1ABurkeHel5f13j\nhIhMv5zA+69mxWzNvjL5A4hechPOvk3Y9dV+l3NCcPcaIhcuQtUdQh3vX4+5G21jrrgLFY8QWP5U\nr2fRp5v4sHMw587AWfWsbzPY+8qgiM28CmfNCycCb2fnOqxDZaj7vkqkdDmRt17xucrUyH/wb7CP\n7MPau/HsG2cxXbUXHWnGtQO+nL+ryU8yMUr0N55SeOn0fZ9OtSSYzDAWGSccDlNfX+93GWlNZgR2\nTS6sRH8m44NIJMfEsav3drmNp22i4y7GLRiIqt3T7WDMKxqBU7a+xzUZrYlcdA3OxrfRjf0rhOxI\nvPgcYpfcSHB3aVqFxQDaGILlH+Euuh2vHz3ub4aOxtz0Jaz92wm8/8esCYvNoBLcqfNx1jyf0bP6\n47Ovx9rzMbrx1CcadcMRgiueJGfadPL/6juo/EKfKkyN3M9+BUfFsLet8bsU3ynA2vlBWt4EkWs3\nIUSySGAsMo7MMP6UBD9CiK501YdciL5ylIezf3OXrQ3c3HBbC4rW4+jj1d1+3N7YIVQsgm462qOa\nDBCZeT32znVYRw70aN9sFJ04G3fyXILbV6Jb06cH58l0Sz3W0QO4C2/LoE63vReffQ3erKsIrH4e\ne982v8tJGBMeQuzCywiseQEVbfW7nF6LDx8Plo1V9nGH72sg+OGfCFXvoOCR72JPuyi1BaZI6MZl\nBIcOxl7/Z79LSRu6Ymu3e/ULIUQ2kMBYZJze9DDOdBIMCyG6ImOESDWlFHaspdMZqx4QG3YusVHT\nUIfLUZHGHh3fGzgKe0/PexdHLrwKa/9O7AO7erxvNjFA5OJrYchIgttXpv1sz0DtXlQohJk2z+9S\nksbk5BO/4X60pXBWPoVqSc8AvzdMXpjYrOtwSl/K6BYwxgnhTpmLs+6PZ725ZR0qI7j6D+RdvyTr\nFsQLLLiK0NTzsde+nBU9tRNFeQZdvgVM6vpyt1/DdfV0pFzniX5HKVA6jT6yd6SUwFhknGyeYSyh\njxCiKzJGiHQRwCVY0fEMPM9yiI6fjZtTgDq896z9jU9ntN32+O/RQz3aLzJlAbqhrldtLLKJ0TbR\nS+9EmxiB3esyZtGxwJ738c69ADM0+xYVc8edj7n6c9ibV+BsXonKovHahPKIzb0F+/1X0U2Z3TIu\nNucm7E3LUd2cRarj0axbEM+efjG5i6/Aee+5jBk7Usna9SGWzDIWQvQTEhiLjFNYWNjjHsbpFqRI\n6COE6IqMESKdKaWwm46iO5gh6eYNIDJxLjTVoRtqejU7zRs4Crtyc4/2jZ4zA09ZOJuW9+KM2cPk\nFBD9zN04tXsJHNiWUbMDNRDcXYqZeyNeKM/vchLCaE38sjvh3OkEVjyddW1SjB0gtmAJ9vo3sI4f\n8bucPolNnIVuOIJV1XVP9o44O9cR2vQW+fd9leDlNyShutSwxk4g/9a7cFY+mzV9tRNNuTHUoT2Q\nRtdicl0ohEgWCYxFxgmHwxkzw1hCHyFEV2SMEJkoiEugctMpr3lAbPhEYiVTULV7UdHmXh3bANhB\nrOo93d4nVjwBEx5K4MM/ZVRAmmjuoJHE5t1KYM+H2HX7/S6nV3S8FefQDtzFd6TXCui9YAYVY276\nMtbhSpy1L6JiEb9LSiijbWKLlmFvWoF1tMrvcvrE5A/AjJiA/dFbvT5Gpi+Ip4cMo+AvH8JZ9Vy3\nZ1j3V/a2NSnrZdydlhRC9D/qk7YUafKRxVefEhiLjBMOh9Ouh7GEPkKIrsgYIbKFVmDXHUDFPw2/\nPDtAdMIc3ECwrV+x5/b6+N6AEqxDu7r9yH584AjckskE1r3crx+fjp1zPvHzP0Nwxyqs5vS6Ruop\nu74KHWvGnX2N36X0mjvjMsy8m3DWvoy9d2PW/Slp0MQW3Ym9vRSrpsLvcvrEALFZ12O//xrK9H7s\nggxeEC+/kIIH/xZ7zUuo5uzprZ0sKtKMrkufmyRyHSmESBYJjEXG8XOGsYQ+IlvITIXk6WickDFC\nZIuA5+Ic3H7i/+MFg4lMmAMNNejGI30KxgxAqABn/7bubZ83gNiE2QRKX0LF++/j05ELLsMrmURw\n+wpUrNXvchIiuH8zDC3BjD3P71J6xARCxK//Aiovn8Dy36ObMju874gBYouWYe3dgHUw8xeXjF90\nFXr/dqzjhxN2zIxaEM8OEP7697HX/xmdwK9BtrM3r0zZLGMhhPCLBMYi47T3ME5m+CLBsBDibGSc\nEP2NrSBQtRPlGTwU0ZHnES8ej6rdk5Cg0isYinV4P8o9+yJ5JpBDZNpiAuteRUV61/4i0xkgMn8J\nOhgisHN1n2dHppvg7jWYCxfjFQ70u5RucUdNwlx3H/b2tTgb3s7aGe+xBbdjHdyFXbHZ71L6zB06\nBnILsbeXJvzYGbEgntYUfvMfsLevwTq8z+9qMopqPIpuPJr083ieJ5M8hDid1un3kaWy9zMTWauw\nsJB4PE5LS9/v6krg0z/Jv63oCRknhGjjuFGsw5V4TojopLkYS6PrKhMXjOUNwinfcNbNjLaJzLgG\n5+M3s3IGZ3eYQIjYZZ/Fqq8mULEh61oeAGhjCFZ8hLvodrw0nqFpgPiiJTD1EgIrn8n4Fg1dic65\nCV1Xhb37Q79L6TOjbeLTLsUpfTWpPz/pvCBewVe/h7N/G9b+HX6XkpH0ppXoNOhNLteiQohkkcBY\nZBzLsigoKKC+vv6M9zr7hSmBz6fkLrUQHesv44SMAaI3AsojULkBNzyMyPjZUF+FbqpL2PFNThG6\nse6sM5UNEJl5Hfa2NRm/0FZvmfAQoguX4VRuwqnd63c5SaWb69HHDuAuvI10HIVN4SDMzQ9gHT+c\n9YuFRWdejWptwt62yu9SEiI292bsbWtQrY1JP1c6LoiXd/83cJpqsXav97uUjGXVHUrJ948QQvjF\n9rsAkf5WrlzJO++8Q0NDAyNGjGDJkiWMHj36rPutX7+e3/72t0yfPp377rsvoTUNHz6c8vJyysvL\nOXToEFVVVVRVVXHbbbcxY8aMM8KdbAp7hBB90x6YyjghRPcopbAiDbgDRmJCuajasm4vStddXriY\nwMY3z7pd5IIrwQ6gms+8adwfxEZOwkyeTXDXGnSkye9yUiJYs5fWcbMw0+ZhbV7tdzknuNPm442b\nhv3Ba1jHj/hdTlJFz1+MUhp7w9tZMZs9fs4FqEhzSmfWti+IZw8/F/3Id2l+6b+Jb/YnrM1Z9gUC\nOTbW+yt9OX82sbauwcy8Cs8OJOX4nuehz/K4u1y/in5HKbw0mgCTzZNxJDAWXVq/fj0vvvgiS5cu\nZcyYMbz77rv87Gc/47HHHiM/P7/T/erq6njppZcYN25cn87f2tp6Igw+ORi+4IILeOqpp1BKMXjw\nYIYPH86cOXMYOnSo/NIUQgASDAuRKAHloSwbDxddl/g+lyaQ27bqfHPXC9q6OQVQMAhqy4ldfC1u\n3UGczSv6zYJ30Snz8YaOIrhtJcrtH59zu8Ce94lMXoSpqUTX7Pe1FmMHMFfcjW5twFn++6zrHX26\n2OQ5kBfGXvtyVoTFJpSPO3Y6gXef9OX81qEygrX7UNcvIXr+LFqe/k9I4RgWvPpWQqNHY7/3XFb8\ne/pNH9yNnn4pbpICYyGE8JMExqJLy5cvZ968ecyePRuApUuXsnXrVkpLS7n88ss73McYwxNPPMG1\n115LWVkZra3dXwhn3bp1HDp06EQ4fOxYW29CpRSDBg2iuLiY2bNn89xzz7FkyRKuvPJKHCd9+9oJ\nIVKjo3C4q2BYKSXBsRDdYFsa7UZRdZWoeDQp5/AGlBA4y2PuHhA9/wo4XoOKtUBNGSZ/MJGFy7B3\nlGId3JW14YcBYpfchNIQ3PFewmd3ZwINBHeXEpl7I+r136Ba/ZldbUaMw8y+BnvbaqyDu32pIZVi\n4y7EGzwSZ/ULqLRsCtJzsTk3Ya9/w9cbTe0L4lkTZ2P/9Q9o+s1PMVXJvxHiXHIpOTNnYy9/Omv+\nPf2mAGvXh5jpC/F0YqMVuU4VQvhNAmPRKdd12bdvH1deeeWJ15RSTJw4kfLy8k73e/3118nPz+eS\nSy6hrKysR+d8/fXXASguLmbmzJkUFxdTXFzMsGHDCAQ+vXP73HPP0dzc3KOwWAIiITKfzBoWInWC\ntka31KMPVyQtXDDaRhmDdby2y+3ixeeCE4TaTx/9V42H8RrriE+4GHfs9LZF8M4ySznTGNsmumAp\n1rFDOId2Zm0o3h063opzaAexxXdgvf5fKQ3ODWDm3YgqGkxg1bO+BdapFC+ZglcyCWf1c4lb2NJn\n0emL0DUVWHWH/C4FaFsQzzpUhvrCV4msXU7krVeSdi578vnkXXUTzrvZPys+1XT5ZvSkWbihzp++\n7YuuHneX61/RLykFKo2WY5OWFKI/amxsxPO8M1pPFBQUUFNT0+E+e/bsobS0lG9961u9Ouff/u3f\nYttn/7YMh8MdLnonhMgOEgwL4R+lFEFLoQ9XoFuOJfVc3qDROOUbut7GDhA/dybUV50RmCoMHKnA\nC+YTnXMz1qHd2DtKUSbzAy6TGyY692ac/Vuwj/XPBf5OZ9dX4RYMxsy+Bqv0tZSc0+SFMZffia7a\ng73yD/1iZqZbfA5m/AycVc+i3Ljf5SSEGTAcBhRjv/vffpdyCt1whODyJ9Ezr8GZcj5Nv/4JXmNi\nb3zpkrHkL7sXZ/lTSXtSpD9TxkVXbMWdeHF6hVhCCNFHMqKJXunoTmckEuF3v/sdy5YtIzc3t1fH\n7U5YDFBYWMjx49k1i0iI/kgp1eF44nmehMNC+MC2NCHiWAe3Jj0sNmjQDlZtRZfbRSYvwFMamo52\nuo2KNEJNGe6gEUQuvRN38MhEl5tS7tAxROfeTKDsfQmLTxPcvxlvaAlm7HlJP5c7cSbmiruxP3oL\nZ9uafhEWm0ElxM+bj7Pm+awJFw2a2IwrcNa9mpYtXdoXxAtU7aDgke9hT7soYcdWAwZT+MWv4ax6\nHhVpTthxxamsnR9gRVr8LkMIIRJKZhiLTuXn56OUorGx8ZTXGxoaKCgoOGP7w4cPU1dXx3/8x3+c\nCHra//uNb3yDxx57jEGDBiWktnA4fKK/segZCeGEH2TGsBDpL2hrdFMdum5fSlofeANHYR/Y3uW5\n3AHFeAOGoY4dOmtNCuDYITxtE5u+mHjjMQIb3kZFM+uP+Nj4mZhRkwltfw8Vj/hdTloK7l5D5MLF\nqLoq1PG6hB/faI25/G6UiRNY8ft+s7CiCQ8hduFlBFY/h4p2fw2SdBe/5AassvWopvR+OtGp2oN1\neH/iFsQL5VL4yHewS19FNcnfTcmk4lFUTQWMmpKwx9Pbr5GlJYUQp/LQf1KAOgAAIABJREFUbRMJ\n0oSXxfNwJTAWnbIsi1GjRrFz506mTZsGtP1S2rVrF5deeukZ2w8bNoxvf/vbp7z26quvEolEuO22\n2ygqKkpYbeFwmMrKyoQdTwiRGBIMC5F5FBC0Fbp2D7q1ISXnNACBHOyDOzvdxtMW0Ulz8TwP1cXs\n4tMpE4favXi5RUQW3I61dyP23g0Z0f83ctHVqNx8gttWZE3f2GTQxhCs+IjIotux/vgrlJu4QNcM\nKcHMvwlr1wfYlVsTdtx0Z/LCxGZdh7P2xazq0RwfOQkUWHs2+l1KtyRsQTzbpvDRH2BveAd9rDrx\nhYoz2FtWY4aNxQR796StEEKkGwmMRZcWL17Mk08+SUlJCWPGjOHdd98lGo0ye/ZsAJ544gmKioq4\n4YYbsG2b4uLiU/bPyclBKXXG631VWFgoPYyF6INEBLgdhcMSDAuRWRxLY7tR9IFdbUFrinjh4Vi1\nFV0uvhQ7dybGCaGPHuxV2Kuaj+E1H8MtmYwpmYy94S2s44d7X3QSGa2Jzb8d3XwMZ9fajAi3/aab\n69HHDuBeeivWO08n5GsWv/hqVPEoAmteQGXZAopdMaE8YnNvwX7/VXSaz8LtCRMI4U6aTWD57zPu\nZ+qUBfHWLCfyds8WxCv8xg9wdn+IVV2enALFGVRrI/pYDWbY2JSdU667hRDJJIGx6NKMGTNoamri\ntddeo7GxkREjRvDAAw+cWAivvr4erVM/BT8cDve4h7H8QhWid2TWsBDZKWhrdMNh9LEDqQ9Tcotw\ntqzs9G2TV0R8yJi2WbbNvX+UWgEc3YfnhIhdfC3ukQM4m1cmdEZqX5lQHtF5t+FU7cY+Ik9P9USw\nZi+t42Zhps7F2rKm18cxoTzMFXejjxzAXvFUWva5TRZjB4gtWIK9/g2s40f8LiehYnNuxt74Tsa2\n1zhlQbzzur8gXv7D38Gu3oNVsSUFVYqT2ZtWYAYMwwRy+nys7rSkEKJfUiphrV8SIp1qSTAJjMVZ\nLViwgAULFnT43kMPPdTlvnfffXcySpIexkIkgQTDIpvJ9/GntFIELNA1ZehI49l3SDCTNxBdX9Pp\ngloeED1vIa62sHs5u/h0KtYKNWWY/CFELl2GvaMU6+Au32cdugOGE7voKgJ712M1Jb4Xb38Q2PM+\nkcmLMDX70LU9f3TfPWc63gULsT9+G+tILx79z2BG28QWLcPetALraHYtrhibPAd9rBaruutFNdNd\n+4J4ungc+pHv0fzik8S3rO90+7x7H8GJN2HvWJe6IsUJqqGurVd2AgLj7pBrGyFEMmVvd2aR1Xoz\nw1iIk/Xnu/VKqQ4/f8/z5MJTiCzV/nPvWJqgiWAd2OJLWAzgFQzF2fNRp+/HR00lagWx8KAlsY/H\nq8ZaOFxOfMLFROfdhsktTOjxeyI2ZirxGZcT3LlKwuI+0EBwdylm3o14obxu72eA+GXLYML5BFY8\n1f/CYjSxRXdiby/FqsnsUPV0pmAQXvE47A1v+11KwjhVewiufoa8G5aQc/eXwXbO2CZ062cJFBVg\nf5w9n3cmsja/h47JgqVCiMwngbHISIWFhRIYC3EWEgwL0T919LPveR5BS2E3VKOrdnTZOziZTDAf\n1dKAbu04rPYCucRLpqAsC5Wg2cWnU55BHanAizYRnXMzsclz8VLcXisyfTFmzFSC21aioy0pPXc2\n0vFWnEM7cBffgdeNG8JmwDDMLV/BOrwfZ82LqH4W7hggtmgZ1t4NWAd3+V1OQhkgNuta7HV/9G2c\nS5b2BfFyQlDw1z9AF5eceC/4mesITZyEXfqK709O9Hf68H5UpO8LR3qe168nuAjRGU8pPKXT6CN7\nf06lJYXISEVFRTQ0NOC6LpZl+V2OEL6SVhJC9E/d/dnXWhNQBl29Cx1tTll9HfEGjCC4eUWn70fO\nW0hz3CUnqKEluTeGVWsDXmsD7uARuMV34mxajnXkQFLPaYDovNvQbpTAjlUS7CSQXV+FWzAYM/sa\nrNLXOt3OvXAx3qiJOKWvoBuPprDC9BFbcDvWwV3YFZv9LiXh4jOvwarcim7Irn7MJztlQby1yzF1\nh8mZtwhnef/qv52uFGBtW4uZcSVeBzPBE0mu9YXIbH/60594+eWXOXbsGGPHjuXee+9l/PjxHW77\n1ltvsWLFCior29a7GDduHHfddVen2yeCzDAWGSkUCuE4jswyFv1OZzMH5YJRiOzVl6cFApYmGG9u\na0Hhc1hs7CAqHkc3dtx+IT50LPGcAoKBAKouObOLT6cAdewQ1O0ndv5iIrOuw0tS70ljh4h+5rPY\nDbUEyz+SsDgJgvs34w0twYw974z3TCBE/Lr7UAVhAiue6rdhcXTOTei6KuzdH/pdSsK5Q8dCKA9r\nx/t+l5J07Qvi5Vw4k7xb7sZZ+SzKjftdlviE3r9Lnh4RQnRp9erV/Pa3v2Xp0qX8+Mc/ZsyYMTz+\n+OOdZlxbt25l/vz5fP/73+fxxx9n0KBBPP744xw9mrzrGQmMRUZSShEOh6mvT2xvQyHShbSTEKL/\nSfTPfcjW2PUH0dW7UJ5JRIl94g0swd7bce9iz3KInTuT5tYIGg9aG1JamzJxVO1ePAWRBbcTO+cC\nEjnSmoJBRBctw9m/GadmTwKPLE4X3L0Gc+FivMKBJ14zoyZirrsPe+c6nI/fyrpWBd0VnXk1qrUJ\ne9sqv0tJOGPbxKctxOlHLRlUKA8KBwIexFr9LkecROFhlX3cp7GmOy0p5G8C0S8plX4fvfDqq69y\nxRVXsGjRIkaOHMn9999PMBjknXfe6XD7hx9+mKuuuooxY8YwYsQIHnjgAYwxbNq0qS9fzS5JYCwy\nlvQxFtlAgmF/yNdW+C2ZTwtYWpFjeVhVO9ANtWkRnhitUcrqtOVDdNJcWuKG3Nxc1NEDvtWsmo9B\nTRnuqMlEFy7DFA7u8zHjw8cTm309wd1rsY/XJqBK0RVtDMGKj3AX3Y6xHeILb8ObOpfAymewqsv9\nLs830fMXo5TG3vB2WowJiRabcwv21lWoiL9PUqSKFwgRXbAE1VANnsErGup3SeI0es8GmWUshOhQ\nPB5nz549TJ8+/cRrSimmT5/Ozp07u3WMSCSC67rk5+cnq0wJjEXmCofDHDt2zO8yMpIsoJB6EgwL\n0T+l+mc/aGkCsSb0ga2oNJpx5g0chV25pcOgyi0cSrxoGG48hsZAJwvipYoCVN0+vMZaohdfS/SC\ny/Cs3vWhjE6agztpFsFtKzpd6E8knm6uRx2vwtzyIFbrcZxVz6L6cXATmzwH8sLYH76enWHxuAvR\nLY1YB7JrAb/OeLZDbMESaD6Cbm1ARZswI5LXw1L0jjIuev9OkOt8IcRpGhoaMMYQDodPeb0nGdfv\nfvc7Bg4cyPnnn5+MEgEJjLOOMac+cnrw4EFeeeUVn6pJrnA4LDOMRdqRYFiI/snvn32lFCFbYx3b\nj1VTlhYtKNoZADsHq6rsjPc8pYlOmU9jSws5ufkp613cHSrWiqopw+QWELl0GfER47vdpsIAkdk3\nwKBigttXotxYMksVpzFovCFj0c3HMMPHQW747Dtlqdi4C/EGj/ykVUP2XYeY3ELMmGnYH77hdykp\n4WmL2Pzb8KINWM2ftOZrrMUUn+NvYaJD1vZ1WL1YP6D9uqGrST7yd4Xot5ROv49EfnrdmNz3wgsv\nsGbNGr75zW9i23ZCz3+y5B1ZpIwxBq3bvkm11jQ3N7N161bWrVvHvn37aG1t5aqrriIQCPhcaWL1\npoexUkp+uYqEaB/IT/9+ku8vIbJfR79L/PzZt7XGwUUf2oWKR3yrozNe0Uisqo5D7Ni4GUTQaEDj\noiLpNwtXNdTiNR4hPmEW7pjpOB+/iW7pvMey0TbRhXdgHa/BqdieNgF4f2GAyNTFWIfLsY9VYXIK\nic6/FXvLe1gHd/tdXkrFS6bglUzCWf1cWt1EShQDxGbf0DZzuh/clPGUIjbvFjwvjnXS4qHaGNxg\nCE9b/bY/d7pSsVZU7X4omeR3KUKIJPv1r39NdXX1Ka/Nnz+fBQsWnLFtQUEBWusz8qz6+vozZh2f\n7qWXXuLFF1/ke9/7HqNGjep74V2QwDgLtIfFBw8eZOvWrWzZsoXy8nLy8vKYNWsWc+bMybqwGKSH\nseibnoQ7HYXDEgwLkf0y4cZQ0NboluPow+XpO3swpxBn07tnvGxyCokPG0ekqYm8gkLUkcrU19ZN\nyjNwpAIvVEB07i1YB3Zh71x3RghncguJzr0F58B27KMd92sWyRWdvBCrvgr7WBUAuuU4TvkHxCbP\nwfz/7N1XcFznff//9/OcsovFYheFDYW9ipQoiRRVKFKy4si2bP9cJLdJcTLji7SZeDK5ySQzyUWu\nk6tkkkkucpP4l7//SfyXE0WOrOIS2ZJYZRVLrAArQAIgsH1PeZ7/xRJgA4m2u2fL85rBjATunvMF\nsOXs53zP91m5DvsXbyAa6DlcK+GajagtD1fGcYRB1OXURLD7V5CjZ7GujUZdSs1pwH/0c2jLwpq+\ndOcNVIju7UeMX6h7bca92e//L2rlWlQsEXUphmHU0G//9m8v+La2bbNp0ybee+89HnnkEaDy+eL9\n99/nueeeu+v9vv/97/O9732PP/uzP2PjxtpfWWIC4yZXKpW4fPkyH374IceOHWNiYoK1a9fy5S9/\nmW3bttHb29uSYTGYGcZG9TVDOGQYy2Uez3dqxue+EIKYJZAT55CFa1GXc1dhcgVy8tId3X8a8HY9\nRb5cRkoLqUJEOR9NkYsgSll0KUu4coiwfxPOez+eXcgvXDFE8OAncc8cunGpuFFXpa2PIco57NtO\nPlQWwjuKt2oL3sGv4b7zX4hS4z/elkr1DRHsfBL3zX9HBF7U5dSE6huA9ArsH/1r1KXUnAaCPZ9C\nd3ZhTZ6f8zailCEc2oo0gXHDEcUsMjOOWrluwfcxIykM4+40lSsuGsVSn4mf+9zn+Nu//Vs2bdrE\nli1beOmllyiXy3ziE58A4G/+5m/o7e3l137t1wB48cUX+e53v8u3v/1tVqxYMZuFxeNx4vF4FX6S\nO5nAuAkppSiVSly8eJF33nmHd999FyEEW7du5VOf+hSbNm1ixYrlr+rd6NLpNOfPz33QZBj30ozh\nkGEYy9cqz33bkjg6QF46iQgbPAxKrsA9+vId3w4Gt+M7cVShcL27eCSC4pZGAExdQksbf/czBNlJ\nmL4KQ9uIffy/DbXYYDvxNjyEAOzRuy985l45RdjZh3fgK9jHX8cab73jSJVeif/Qr+D+7D8QXms+\nFpWU+A9+EvfN/2jcKyuqKLj/IKpnFdbE8N1vlJtAr1h4IGnUl/XeT1EHXkC5tQl1DMNoPvv37yeb\nzfLd736XqakpNmzYwJ/92Z+RSqUAmJiYmJ0mAPDKK68QBAF/9Vd/dct2vvrVr/KVr3ylJjWawLgJ\nffDBB7z++uuMjIzQ29vLY489xo4dO9i2bRuOc2MVb631ggZmN6tUKrXoGcZGe2mVcMgwjMVr1VEy\nMVsiC9eQE+cafjau6kgj81MIr3jL97UTJ1j3AIVCAWnZSBUgyotfFChqQgVw9Qx65SZYOUTsF682\n5AzpduANbEfFkzgjx+d9Xlj5CcTINP5Dz6AunsL+5c8a/rm0UKozjb/vszhvvdjSHdTBY1/AOnkY\nUWj90XT+tkdQ/Zuwrt65aOjNJBDaLtp2EEHrz3NuNiLwQKuqfj5vhWMaw2h3n/70p/n0pz8957/9\nxV/8xS3//7d/+7f1KOkW1V3Oz6iLI0eOMDw8TG9vL8888wzPPfccu3btmg2LtdYo1XoLW9wunU6b\nGcYGUAmG5jr40lqbg6l7aOUTSkb7aJfnvxCCuC2xxoexmiAsBtDpNThnjt7x/fLOgxT8ykzVjkQC\nMdm8l1DrnkFAIyYv4G18OOpy2pK/Yj2qux/n3LsLfl5IFeCcPYzuW4P/5AtoJ1bTGutBxTvxn/gS\n9qGXkPnWbagI1t4HYYg1/H7UpdRcsP5+1Ib7EfOExbOUj1pR2wWQjKXx9zyLZ7mEYYhSat7jk4WM\npDCMtiVk4321KNNh3IS2b9/OxYsXKRaLfO973+P1119n8+bNrF27loGBATZs2IBlWbO3V0rd0sre\nKtLp9KI7jFspPFiOZv49CCFMx7BhtKF2vmLAtiSO8pCXTt0xC7hRKacD4ZWR+VvXGgj61hIk0gQ3\ndxff1oHcLHTvWpASOXYCADW4CxVPIku5iCtrH373asI1W3DOHl70QnYScC9/RJBahffU17GP/A/W\n1Ni892tEynbxD7yAffQVrMxE1OXUjHIThFsfwf3R/22Kk2bLEQ5sIdzxKGLsxII7vERhCrV2O9bo\nmZrWZixO2DeISq1ASolSijAMAbAs664nvReiHY5/DMOIlgmMm9ATTzzBE088waVLl3j//fc5d+4c\np0+f5tChQ8TjcdasWcPg4CBbtmxhx44dNRuAHbWlBMZG82jncMgw2pl57t8qZktkbgJ57UJTBSS6\nZwjnxNu3fs+y8bc9Sr5QGT/RkUggxocjqG75dN86ECCvnJr9u4jxYcqbH6Xjg9cjra1dhJ09BGsf\nwD17BKHCJW/HzlxBFjL4j3wGPfwe1qmjTfVcU9LGf/rr2O/9BOvaaNTl1JT/xBewj7+G8Ft79Eu4\nci3BA08hxk4t7nLg4jS6b2OtyjKWQAPhnl9Fu3EkzIbGNwfHUkqklKab2DCMhmMC4yaltWZgYICB\ngQGgMhB7ZGSEixcvMjIywjvvvMObb75JKpWir6+PZ555hgceeKCl5hqnUikzkqIFmHDIMNpXq84Z\nrgYBxGyBvHIaWW6ujlUlbQRgTd0aXnnbHqcYVv6+0raRod+U3cV6xXrQCnnlzC3BoiznCXVI0L0G\ne6q1g7uoqXgSb/M+3OGjVem6l0EJ5+whgqEHUCuGcA693BTd/AqJ//Q3sD96G+tK8ywcuRT+ffuR\nk6NYV1tvocKbhd2rCfY8i7h6GsniRgxKIJQWOtaBKDffa2srUhsqV57cbCYg1lrPjqiYuSJ4Jjhe\nyGd2c7xktCuNQDfQqd1GqqXaTGDcpG5/A+nr66Ovr489e/aQyWQYHx9ncnKSEydOcOTIEd5+++2W\nC4xnOoxb6WdqZSYYNoz2ZZ7/i+NYEjssIy+eqiys1mR07zqckV/ccvgcdvUR9vTj5ysLcSU6OhFX\nm++yabVyIyL0kOPDc348kOMj+Ot2I6dGzUIhNaLsGOVt+7HPvYvwS1XbrgTcC+8R9AzhPf11nEP/\njcxOVm371aYA/+mvY519F+vSyajLqSmVWoletR7nje9EXUpNqa4egsc+hxgfRi61az4so1ZvwDr3\ny+oWZyyaljbhjifQtjPnvwshsG17zuDYfL41DKMRmMC4iQVBQC6XQwhBOp0GIAxDUqkUqVSKTZs2\nsXv3bj772c/O3qeVZhmnUik8z6NUKtHR0RF1OcZ1JhgyjPZlnv/LF3MsZHa86UZQzFAAtoM1Njz7\nPS0E3n0HyRcrHW/SdhBhuaphXz2oVZsQfgk5MXLXv40IPShO4w/uIHbxo7rW1w6UtCnvfAr74odY\n5XxN9mFfu4AsXMN//AtYH7+Dfe7DmuxnufwDX8G6dBJ7pLUXf1OA/8hncN56EaFbd1Fv3dGF/8SX\nEBPnkKG35O2I3CRqaJsJjBtAuPNxwlhi3tvdHBzPhMZwY/FeExwbhhEVExg3qWw2y09+8hM++ugj\nbNtm9+7dHDx4ENu2GRsb45VXXuGpp55i/fr1uK4bdbk1Yds2yWSS6elpExhHwARDhtHezDiJ6rJt\nGyEEZc8jnuiG7FUImm9Op+5di33xBIIbjwV/w4N4wkKpSgiSSCQQV5qnu1gDevVmRDmPnDw/b5Av\nr12sLIB3+QRStW7AVW8K8HY9jT16CqswNe/tl0OW8zhnDuNvfgi9ch32sR8ua05ytXmPfwE5OYp9\n6kjUpdRc8MhnsYbfQ2avRV1KzehYAu/A84jpS8hgeSfSpJcnXLGpSpUZS6VjCdS6nbCIZi0hBJZl\nIaUkCCpXFwVBcNcZx+aYy2hbQqBFAzVCtvBJnQb6LRsL5XkeL7/8Mq+++irJZGUm0ksvvcRbb70F\nVFZcvXjxIm+/XVlsRrXwhxUzx7g+5lrBd+ast2EYre1uK3ib14DqEELgui6e55HNZCiVSmRKPmH/\nDlRHOuryFkUBxJLYN3XWqniSoH8rpVKlu9iyHYTfPN3FGtBrtiBLOawFhMUAQivE9Cjepn21Lq9t\nKKC88xPIiQtY2at12adEETt3HGyJ/9TX0R1dddnvfLy9n0aU8ti/fDPqUmouXLMJ3BjWydYNxrUT\nq4TFuXFktbrmhUAnUtXZlrEkwcOfJHSX1tA0c8wlhJhdJC8IAoIgMMddhmHUlQmMm5DneRw9epSD\nBw/yO7/zO3z729/mgQce4Cc/+QkA3d3d7N69m1OnTgF3zjtuJTNzjI3qMMGQYbQv8/yvP8dxEUKQ\nzWTwyje6iVUYMpXNE/ZtIOwepFl++zq1GuvqudlOTA14u54iX75xeXVHRwdi8kJEFS5OJSzeiixM\nI68trmaRvYpOpFDu/JcjG/Pztj+JlZ/Avnax7vt2xoexx8/iHXiecM3muu//Zt7uTyCExH739aYc\nWbMYynYJdj6J885/t+zPqi0b/8nnoTiNLFbx84xfQvWbLuOoqFQfqm9g2V2HMx3Htm3PzjWeCY5b\nuSHMMIzGYQLjJuQ4Dr7vs2nTptk3jl27djE5OYlSCiEEXV1ds0GqCYyN25lgyDDal3n+R8+2bRzH\noVDIU8jn7/p7n87l8Tp6UWu2Ndald3eT6MUZ+cXs/4b9W/HdBCrwAbAcFxGUEU0wakMDun8bMj+J\nnLq06PsLQIyPUN76aNVrazflTY8g/DL2lbOR1SCL0zjDRwh27cff/Ql0BMfW/o7HoTONfeR/WjZA\nvZn/+Bex3/8polyIupSa0ELi7/8yOihhFao7bkPkrqIGt1Z1m8bCBXs/hVpidzHcGDVxc6fxTHBs\nWdbsInmG0baEbLyvFtW6P1kLi8VirFu3jlOnTs0Oye/t7SUMQ4IgwLIszp07R0dHR8uffUylUiYw\nvgcTDN1bK59MMQww42QajRAS13XxPZ9sJkN4fUbhveSLRQqhRTi4E23H6lDl0qjOHmR2HOFXwmDt\nxPA3PEihcCPs6Yh3ICbr3yG6WJWweDsyexU5Pbrk7chyDoAgvapKlbUfb+gBtO1iX45+AUGpFLHh\nI+hkGv/gV9ELWMyqWvxND6FXDOK8/V+3zAdvVf6Wvcj8NNbl01GXUhMagf/4F9ASrFz1R6zIwEMn\nUm3wSGk8Yf8mdGd3TbY9M6JiJjg2DMOoNRMYN6lnn32W48ePc/jwYcbGxshms0gp+eCDD3jjjTc4\nfPgw+/a1/uy8dDq96BnGrRgSmmDYMNqbeQ1ofI7jIGVl/ES5vLj5vWXfJ1Ns7LnGums1zpmjs/9f\n3vEkBf9GIG47LsIvNnx3sQb0wHZkZgyZubLs7cnxYfz1Dy2/sDbkrd6C6urBOf9eQ3XUumMnsTKj\neAe/SrhiqOb7C4buQw9tx3nr+wjd2o0gAKozjVq7A/vYD6MupSY0EOz7DDoex5q+XMM9KXR6RQ23\nb9xOC0G4+2mUU9uTu634WdYwjMZkR12AsTRnzpyhs7OTf/mXfyGRSBCLxVBK8W//9m8APPLIIxw4\ncAC5iJVZm1EqlWJqqrYrZTeSmQOE2wOgpQRCQggTJLU58/dvPtV8DTDqo7LiuUWxWCDw/SVvZ2au\ncbpvAzo/gbx2oWFCNOV2Isp5ZDELQNA7SJDsIyjcWMAp3tGBGDsVVYkLUgmLdyCnLiFzE1XZpgg8\nKGUp928ndvnjqmyzHfg9Q6gVa3HOHmnIjlorO44oZPAf+hX0xZNYv/x5TZ6P4ZqNqC0P47z574hw\n/isSmp0C/Ec/j334By378wYPPoNK9WJNjNR2R14e1b8ZOT1e2/0Ys8LNe1CxzmVv5/aRFIZh3EoL\nEcloqLtppFqqzQTGTer06dOUy2W2bNmC4zjEYjEefPBBOjs7Wb16NZs3byaRaP2FVrq7uxfdYdws\n5gqGTChkGO1jrpM65jWgeUgpsWwb3/PI53JV2+50Lk9nogc31okcO9kQHYe6ZxD3l29W/lta+Nse\nI18ozv677cYQXqESnjYojYSBbchrF5H5yapuW06eRw3uQl0+iST6v1ejC7pWEgztwD1zuCEe33cj\nQw/n7GGCgZ2oJ5/Heeel2ZEs1aD6Bgl2Pon75r839HOnmoKHPom8dApravnd/Y0ouO8J1MohrPHa\nz+MW2YnKwncfvV3zfRmgbRe1dQ/aMvGKYRitw7yiNalvf/vb894mDMNbhuW34lnKVCrFxYuNPw/x\nXkzHoGFEo1G67M1rQOuxbQfQ5LPZmqwlkC8UCVyXjsFdWKMnIh3zoCwHEYZYmcocTm/rPkoKuCkY\njcfjiNGT0RS4AFpI6N+GmDyPLFT/qiWhFWJ6FG/TXuJnDlV9+61EdaTxNz6Ee/YIQjV+h6kE3Esf\nEqTX4D31dezDP8CaXn7YqdIr8R/6JO7P/gPhLW6ETbMKVwxBVy/2kdYcRRFseohw7XbElfpcaSFV\nQBhLoIVs6BMvrSJ44CBhbOkL3S2WOUY0DKMeWnteQZurXAYrkVK2ZFgMlRnGzbLonZkxahjtzbwG\ntD4pLRzXpVwukatRWDyj7HlkCuXrc41rs8DOQujeddjD7wKVhe/CvrV4N81ott0YolxAhEsfx1FL\nWkro346YOFeTsHiGyF5FJ3tQbv0ChWaj3A7KWx/DGTnedB219vQozsUPCB59jmDTw8saoqE60/j7\nnsN5+/uIUn7+O7QAJW2CB5/BefulhhxBslzh0A7CrQ8jrpyq74dvFaJ719Rzj21JJ7pQ/ZtBVOev\na44JDePeKiMpZAN9tWbWBqbDuGmVy2UymQzlcplCoUCxWKRQKMz0Gb8nAAAgAElEQVR+FYvF2a9y\nuYzv+1y5coWvf/3r7N27N+ryq6YRA2PTLWgY7c28BrQnx3EJAp/sdPXGT8xHKcVUNk+qbz1WPln3\nucYKCdLGGj+HRlDe9RT54q3dkI3cXaylDWu2IsaHkaXajrcSgJgYobzlMTo+/FFN99WMlOVS3nEQ\n+/x7SK84/x0akPSLOGcOEax9ALVyCOfwy4uew6vinfhPfAn70EvIfGMd39aS/9j/wfroHcT1Oeit\nJFy9gWDXfsTYibp3agkvhxrcipy4VOc9t5dgz7OoWPVHQd6r4cscUxqGUQ8mMG5Sr732Gj/84Q9J\nJBKEYTjbSWxZFrZtY9s2juPgui6u65JOp0mn0y0313gpgXG13mBNKGQYhpk1bti2jRCCQj5HGIaR\n1JCJaK6x7luLfeGXCMBbfz++tFHqRtjnuDFEOd+Q3cVa2tC/FXH1DLJUn5BflrKE3QMEXSuxs1fr\nss9moKSkvPNp7MsfYZWaOzCUgHvhPfzetXhPfwPnnZeQuWsLuq+yXfwnX8A++gpWpjqLLjaDYP0u\nROBhn/sw6lKqLuztJ3jok4irJ6O5rDc7jlq1Loo9t42wZw2qe1XUZRiGYdSECYyb1NatWwnDkO7u\nbmzbxnVdYrHYLSHxzJfjOLMhspStNYUklUrVfNE7Ewy3LvM3NBbKvA4YtxNC4DgOpVIJrxzdDOEZ\n+UIR33VJDO7CGjuJ8Gs791QBOB3Yl06gYp2Eg9sp5gu33CYWjyNGT9S0jqXQlgtrNiOunEaW63vJ\nvxwfxt/4EPYvWnNO62IpwNv5DPaVs1i56i42GCVn8jwqfw3/iS9iffQ29vlf3vP2Str4T38d+/2f\nYF0brVOV0VPxBOHmh3Hf+L9Rl1J1YWoFwb7nEFdPI2s4nuheJJrQdtGWvehud2N+Ggj3Poty4tXd\nrtbzjpM0x5+GYdSDCYyb1NatW9m6dWvUZUSu2iMp2qlbsFV/LsNYDhMMGwvhOC5hGJLNZBrqseF5\nHkEgSa3ZjpwYqelMXp3ux7oyDFrh7XyKfPnWLmLHjSFKuYYLKbTtwurNiLFTSK8w/x2qTARlKOcp\nr9lKrEFHddSTd99TyKnLWJnWC0llOYdz5jD+lofRK9diH38Noe68CkEh8Z/+BvZHb2NdGYmg0uj4\nj30R+9hrTTezej66M03w+P9BTIwgo168UQWoFYNYY+312KoHtXYHqiMVdRmG0YYENNTc4EaqpbpM\nYNwCisUipVJpdlax7/sEQUCxWCSZTLJ582aUUi3XXQyVwDh7fWGhxfx8JhQyjPZ2t84N8xpg3Itl\n2UgpKBTyhEFjBaEzZuca967HiieRkzWaa5zoxvngp4SrN+HHOwkLt4av8Xgccbmxuou1HYPVmxBj\nJyOdkysnzqMGd6JGTyOJpvOwEZS2Po4oZrAnzkVdSs1IFLFzx/FXbsR/6ms4b//XLXN6FeA//TWs\ns+9iXWqvEwjeroPI8YtY4xeiLqWqdLwTb/+XEVMXkUH0V5+I4hRqaLsJjKtMS4tw15No24m6FMMw\njJoxgXETC8OQEydOcOzYMa5evUoul8PzPHzfJwxDPM9j48aN/OEf/mHVQpCf/vSnvPHGG2SzWQYG\nBnjhhRdYt27u2Vg///nPOXToEKOjla6RoaEhPv/5z9/19kuRSCSwLIsLFy6Qy+UYHR3l8uXLXL58\nmV//9V9nYGAAMMGwYbSzu105MN/lfoYxQwiJ49iUy2XKpdqOeqiW2bnGa6o/11gl+5BTYwD4mx6m\ncFtY7LhxRCmLiLqz7ibaicOqjcjREzUf1zEfoUNE5grepj3EzxyOtJaolDc8jNAae+xU1KXUhXP1\nLCrRjffkC9jv/xhr9CwA/oGvYF06hT3yfsQV1pdKr4IVg9gtNopCu3G8Ay8gsmORXMEwp8IUundD\n1FW0nHDHozVZ6A4qx6jzNUKZz7KGYdSDCYyblFKKn/70p7z44ousXLmSFStWsHr1ajo7O+ns7CSZ\nTBKPx+nt7QXAsqxl7/Po0aO8+OKLfO1rX2P9+vX86Ec/4u///u/50z/9U5LJ5B23P336NHv37mXj\nxo3Yts2rr77K3/3d3/Enf/InpNPpJdWQzWa5fPnybDA8OjrK888/z1//9V8D4Loua9asYXBwECml\neTM1jDZirhwwasF2HLTSZLNZdERzKJeqVnONdXIl7vH/wduxn2Jw5/MrHo/B5cbpZtNuAlasb4iw\neIbIjKEG70c5cWSD1FQv3uB96FgnzrnjLXwR551kYQpn5Cj+rgOolevQnWnk5Cj2qSNRl1ZXCvD3\nfhrn5/9f3RborAdtO/gHXoDCJLKBFm+UQGjZaCfeMK9/zU67HagND6Dl8j9fG4axeFpItGicq+cb\nqZZqM4Fxk8rlcrzyyivs2LGD3/qt3yIer+6w/bn8+Mc/Zv/+/Tz66KMAfO1rX+PDDz/k7bff5pOf\n/OQdt/+N3/iNW/7/G9/4Br/4xS84efIkjzzyyD33pbVmeHh4tlt4JiDO5SormVuWxerVq+nv7+fY\nsWN885vf5MEHH6Snp6clR28YhnGDCYaNerAsCyktisUCge/Pf4cGVe25xirWhShm0PEugtRK/Pyt\ni8Y5sTiimGmY7mId64S+dcjRjyvzgxuEAMTEOcpbH6Pjwx9HXU7d+Cs2oNKrcYaPtFVYPEOqAGf4\nCP6WJwBwjvwg4orqL3j081hn3kXmajdjvd60tPCffB7t5bBqODt+yUIPtWod1sXGGhPUrIKHniF0\nO6IuwzAMo+ZMYNykLMuiWCzy+OOPE4/HCa7PUrz9EmshRFUC1DAMOX/+PM8+++wt2962bRvDw8ML\n2obneYRhSCKxsMt3/vEf/5FiscjKlSvp7+/nySefpL+/n/7+flasWDHbNf2v//qvuK5LX1/fon8u\nwzAaWzstRGk0BiEEtu3g+z756ycpm92tc427kJPnlxzW6e4BYh/+lPKug+SKd84BjsdicHl4OeVW\njY4loW8IOfpRQy6qJUsZwp4Bwq4+rOxE1OXUnN/dT7hmM87Zw4g2fR1X0sbftA9r+jIEHv7Br+G8\n9SKi2BqvNfMJ+jeDZWOdPhZ1KVWjhcB/4otoAqxcYz6PRX4StXa7CYyrQHf1olaurdmCWzPHuPca\nm2aOgw3DqBcTGDepeDxOf38/Fy5c4MEHH8S2a/unzOVyaK3vGD3R1dXFlStXFrSN//zP/ySdTrNt\n27Z5byuE4I/+6I/o7u7Gce69mEAqlWJ6enpBNRiG0ZhM17DRCOzri9fkc5XFVFtNJpcnkUgTW5NA\njp1C6HBR91d2DBF4BKs3UtYSbvsdOfE4ojCNUIvbbi3oji7oGURe/ggRNm6HuLx6Fm/DHjre+2HU\npdRUmOwjWLsL9+yRhnh8REHFu/DX7ca+/BFW/lrle14Rb//z2If/B2t6LOIKa0vZLuF9+3F//P+0\nTHe5BvxHP4e2bazpS1GXc1eynCNcsSnqMlqCv/dZlOkuNoxIaQS6gd5JGqmWajPX7jcpy7L41V/9\nVY4fP87HH3/M5OQk2WyWqakpJiYmGB0d5cKFC5w9e7bmgctCFo569dVXOX78ON/61rcWHG6vXLly\n3rAYIJ1Ok8lkFrRNwzCiJYSY8zVDa23CYSMyUkpc16VcLpHLZloyLJ5RKJTIK4twcGdlIbhF0L1r\nscbOEqzaQLk0R3exG4Pp0WqVumQ6noKegYYPi4HKmAyvQHn15qhLqRkVT+Jt2oM7cqzh/x61EqTX\n4K+9H+fc8dmwGEAWp3Euvk+w7zOEA1sirLD2/Ce+hP3ejxHena8dzUgDwZ5n0Z1dDR0WzxKyciLN\nWLJw1Xp0sjfqMszxsmEYdWM6jJtYJpPBsiz+/u//njVr1pBIJPA8j1KpdH1mYUChUOAv//Iv51yU\nbjGSySRCiNkZwjOy2SxdXfc++Hj99dd5/fXX+f3f/336+/uXVcdcltJhLIQwb7aGUUOmY9hoFo7j\nEAQh2UymbR6ft8w1njyPzE/Oex8lbQSSsH8z+dKds4DdeAeiMBV596hOpCG9Bnnpo4aZozwfOXkO\nNbATNXa65To5lB2nvG0/zrl3EX7jzJCuJ2/NNuhIXe+uvvMxKYMyzvBR/PueQCd7sU6803K9Sv62\nR5HZSazRs1GXUjXBroOontVYE8NRl7IwQQm1ZgPW2feirqQpaQThQ8+gnFht97OAkRSGYRj1YgLj\nJnbo0CFKpRIbN27Esiwcx6G7u5tEIkEymaSzsxPHcXBdd9n7siyLtWvXcuLECe6//36g8oZ28uRJ\nnnrqqbve7/XXX+eHP/whv/d7v8fQ0NCy65hLd3e3GUlhGEtUjZMnZs6w0Yxs20YIQT6fR4Xtd4n8\n7FzjniGsWBI5ee6eIZXuGUKEPp7TgfIKd/x7zHVh/EztCl4AneiG9Crk5V9GHlwvhlAhInsVb8Me\n4sNHoy6napS0Ke88iH3hA2Q5P/8dWowC/A17kEEZe+QYgru/L0oUzshRgsH70cke7GOvtMycZ5Xs\nQQ1swX3jO1GXUjXB1kdQA5uwrp6OupQFE9lx1OA2Exgvkdq8GxVfXgOWYRhVIgRaNNAp9hY+wWMC\n4yb2x3/8x1VZ0G6hPvGJT/Cd73yHoaEh1q9fz49+9CM8z+PRRx8F4J//+Z/p7u7m85//PACvvfYa\nL7/8Mt/85jfp6ekhm80C4LousVj1zs6mUikuX75cte0ZhjG3Vusabta6q6G9r7IQuK5TuRqn3J4d\njzfL5AokEili/TuQoyfnnGusADq60EpRzN8Z/MXiCUR+CqGjG+WhO3uha0VlDEUThcUzxPQoavB+\nlB1HBqWoy1k2BXi7PoE9ehKr2H4n9ZW08Tfvw7p2EXvywoLuIwH34vt4qzbj738e5+3vI4LmHuGh\nAH/f57AP/XdTPi/nEqzfRbjxAcSVk1GXsigyKBF2D6Kh5TrYa01bDuG2fWirMaKT9j1+Mwyj3hrj\nVc9YkpvD4jAMmZqaolQq4bouK1eurPr+Hn74YfL5PC+//DK5XI6BgQF+93d/d3bcxfT09C01vfnm\nm4RhyD/90z/dsp1Pf/rTfOYzn6laXel0mo8++qhq2zOMdtdqwXCzklIihERr1dIzdevJcRzCULXV\n+ImFKBRKBI5DYnAn1thJhH9rYKl7hsCJUcjOvV6A6zowHt3sYp3sg2RvpbM4wtB6OQQgJs9T3voo\nHb/8SdTlLIsCyruewZo4h5Udj7qculMdXfhrH8C+9DFW4dr8d7iNe+V0Zebxwa/i/Pz7iFJu/js1\nqODhT2Fd+Bhr+mrUpVRFOLCFcMdjiLETTTo+RkNXL2TnH0Nk3BDc/yShm6jLvsyxiWEYjcQExk1O\na82lS5c4dOjQ7AJ3YRgSj8d57LHH2Lt3L5ZlVW1/Bw4c4MCBA3P+2x/8wR/c8v9//ud/XrX93stS\nZhgbFe3dZWjcjXlMRM+2byz4aVn29b+JQKPRqhIgm7/TwlmWjZSSQiFPGDTHXNt683yfIAzvmGus\nABI9hKUCge/dcb9YRwciNxlZUKu7VkJnd6WzuEnD4hmyOE3Y3U+Y7MPKTURdzpJ52w9gZa5iX2uC\nhcCqLEj3E67cgDvyLsJf+uJu9vQoyiviPfk89uEfYE1fqWKV9RGuXAedKazDL0ddSlWEK9YSPPA0\nYuxkk4bFgF8k7N+MbQLjBdPxJHpwG9Txql649wxjc/xntD0hGmsMRCPVUmVN+35nVJw8eZJ/+Id/\n4K233iIWizE6OorWmkQiwX/8x3/wyiuvtHxnmplhbCxHuywqIYRom5+1mUkpcVyXUtljOltgOlvA\n98PrXbHTFPN5fN9HCIHjODiOi+M4VT0x2EqEqPw+g8Anm5k2YfE8ZuYaBz1DhL3r0IBOrQbLIl+4\nc24xgOu4kIkmzNKp1ZBIN3Vn8e3k+DDexj1Rl7Fk5c37EH4Re3w46lLqzuvfjuodxB0+vKyweIYs\nTuNc/IBg33OE/VuqUGH9KGkT7H4a5+2XWmL8Qdi9mmDvs4irp5E072uNyF5FD2yOuoymEuz5VcJY\nfbqLDcMwGo3pMG5i2WyWH/zgBziOw7e+9S36+/v5x3/8R1atWsVXvvIVvv/973Po0CGGhoZ44IEH\nUErVdeZxvaRSKTKZuS+TvRtzZtZoVWacRPNyHJcgVExn8tz858oVSsTjLslUilwmQxiGt8zetSwL\ny7axbQfLklQubtcopQnD9g1IbccBrclnsy1/4rTaMrkCiY7KXGNtxymV5g6/Yh2JyLqLdXoNxDuR\nox+1zOJgQGUciF/EW7kB9+pw1OUsirduN1g29vn2WlRLAf7GvUiviD1ytKoBqQxKOMNH8e97At3V\ng3XiUFMEsP7jX8D+8OdNPU5jhkr2EDz2OcTEMFI193uqVAFhvBONuOcijEaF6l6F6lld131qredt\n8DDH9IZh1EvrpYdt5PTp04yNjfHZz36WdevWoZTCdd3Zbtv9+/fT2dnJxx9/DLTum0s6nTYdxkbb\nuVvHsNa6ZZ/rrcqybBzHJVsokcsXmevPVyp5FIseXan0HSf+ZgLkQj5HNpMhm5mmkM8T+B5SWrOd\nyJVAufXPE0tpVbq0i0VyJixeEttxEJZF6CQINfh3WRzQdRzIjNW5OtA9AxBLIC9/3FJh8Qw5cY5w\nYHtT9TF6a7aiOruxz7/fFIFmtSjLxd/6BFbmKs7lj2rys0sUzrmjqP5NBHs+hW7wq4WCDbsRXgnr\nwsdRl7JsuqMLf/+XEJPnkMGdI3maklboOoegzUgDwd5nUW5H1KUYhnEbjWy4r1bVuj9ZGyiVSti2\nPbvAnWVZJBIJCtcvG3Vdl2QySTabjbLMmkun04vuMDaMZjJXOGyC4eYnhMRxXPxAMZXJEwb3XkHe\n8wOy+SKdXV3Y9r2D3zAMKd8WIheLBQLfx7IsbNu+KURujXEWlTEdLkopstPTBL4fdUlNxY3F6Orq\nIpVKEXNs/GKB/PQkhezUnLevdBeP1z2w1T2DYMeQox+3bIecUAEiexVv/cNRl7Igft8Qqm8IZ+R4\ny/5N5qI6UvibH8G+/DH2tQs13ZcE3Ivvo+Nx/P1fRlvOvPeJgoonCTfuxjnyP1GXsmza7cB78nnE\n9CXkbQuBNjPh5VCDzTXiJApqcCsqkY66DMMwjEiZwLiJdXZ2EobhbPeUEIKOjg48r3IGPJfLcfHi\nRXp7e2f/vRWlUilKpRKlUusczBntyXQNtw/bdpCWRSZXoFiau3tzLmGoyGQLxBOdOE5sUfsMg4By\nuUQ+d3uIHGBZM53ITlOGyDPd0/lcllJx7lm7xq2klHQkEqRSKbq6urCEppTLkJ+epJTPEgb3Dtwr\n3cVX61Rthe5dC5aNHDvR8l2sYnoMnV6Jst2oS7mnIL2KsH87zvDRlpkjvRBB9wD+4E7ckeNYhblP\nqtSCe+U0spTBP/hVdLyzbvtdKP/xL2Af+yGiybtxte3iHXwBkR9HlvNRl1NdmXHU6vVRV9HQtJCE\n9x9ER/D6a0ZSGIbRSFr/2tQW1tPTg+M4DA8Ps3595Y0/mUxy7do1fv7zn/PjH/8YIQQHDhwAaMn5\nxQCO45BIJMhkMsTj8ajLMYx5mTnD7UtKiWXbFIplPG9psxCV0mQyebq6EliWvOt82YUIg+B6kHzj\ne7Ztz85ElnLmsQpaK8Lw3l3Q9SalxLZtisUivtfcAUU9OI6DG4thSYlSIX65RL6QZc45KPcQT3Qi\nsvXtLtZ960CAvHKq5cNiAIFGTF6gvOUxOj76adTlzEkl0vjrH8Q9ewShGuu1oZa8gR0Q68Qdjubn\ntqcvo/wi3pMvYB/+AdZ0NItO3s574GnklRGsiUtRl7Is2rLxD7wAxQyy2Hoj7ySK0ImjpdVWz9vF\nCLc9QuB2wALCW8Mw6k8LGmo8k26cUqquNRPENtHT08OGDRs4ceLE7Pfi8Tj5fJ6XXnqJRCLBb/7m\nb9LX1xdhlfWxlIXvDBNQ1prpGDZu5jguWgumM/klh8UzNJDJFkBaJJLJ6hR4XRAElEsl8rks2UyG\nXLbStRsEwfV5y9c7kZ1oO5Edx0FryGYyJiy+h1g8TleqMmrCsS38Yp789CTF7DSBV150WAzg2FZd\nu4v1ivWARl453RZh8QxRnALbJezsibqUOyg3QXnLY7gjx5u+m3ShFFDe+EhlnNDIsUjDNlmYwrn4\nAcG+5wj7N0dWxwzVswZ61mC/35gnNxZKC1kZ+RGUsQqTUZdTOzpA9w1EXUVD0k6McNODBLpyPBSG\noTlmNwyjbZkO4ybW2dnJpz71KUZGRvB9H8dx2LJlC1/4whfo6+tjcHCwLcJigO7ubqam6ndJoGHc\nbq6u4WY4wGyGGpudZdkIKcnmS1Xv0M0XSsRjDsmuFLlsbU6aaa0JgqASJFMZ/SOEwLKs2zqRBRqN\nVrXtRLZtGyEE+Xwe1WAdz43AsixisRi2baO1wi+XKWYL6Cot/hdPJBGZ8brNqlUrNyJCDzk+3FZh\nMYAA5Pgw3qa9dLz3atTlzFK2S3nHAezz7yG8pV/h0EyU7eJv3Is1eQH72sWoywFABiWc4aP4O/ej\nkz3YJw9HUodC4j/8LO7PvtfUi1BqBP7jX0BLGqZru1ZEMUM4tB159XzUpTSc4MFPoOIJbM3s6Eel\nFFJKpJQ17TieOSa/1z7McbthGPVkAuMm19/fT39//13/v12YDmOjXsw4CWOhhJDYjk257FNcxtiI\n+ZTKPqHSdKXT5LPZ2bn2tXTXENm2sa9/CSGBSjf9zAeu5Zj9fZbKeGUzs/5mbiyG6zhIKVFhgF8u\nUc77UINQ17ElZMervt25qFWbEH4ROXGu7cLiGcIvgl/CW7Eed3wk6nJQUlK+72nsS7/EKrX2osoz\nVEcaf2gX9qWPsIqN1ZwgUTgjRwkG78dP9mAff7XuoW3w2OewTh9D5Jt3fIMGgn2fQcfjWDVewLAh\n5CevX7lh3Ex3dqNWrQcEQnD9xOuNY5h6BceGYdybRqJF4wxL0C08uMEExi3g9uH4Czk72WrS6TTT\n0817oGpE525BrwmGjeVwHAelK2MjlKr9Y8b3AzI5RSrZRbGQJwiWN/JiKbTWBL5P4N9YLK1aIbLj\nOCilyGUy5jkIIATxWAzHdRBA4PuUCzlUWNu/ezzRichcrXl3sQb06s3Ich4xeb5tw+IZcuIc4eB9\nqPGRSD+SKMDb+Qz2ldNY+WsRVlI/Qc8gqm8t7rnjCL8xT1RJwL34Pt6qLfj7v4zz1n8iwnsvWlkt\nweA2EBLrzLt12V+tBA8+g0r1Yk1Ef1KmHiQQWg7aiSH8hS+82+qCvc+iYolbvjdzRZWUsiGCY3MM\nZBhGPZnAuAXc/kbVTkHxjFQqZQJjY8madZyE0XiktLBsi0KhjOfXN7RVoSKTLdDV1Um5WMJvgA+B\n9w6RnXlDZMuykVJSKOQJIwjBG4lt27ixGLZlXR81UaKYKaB17TvKZzi2VfPuYg3oNVuQxSyyHTr9\nFkCoAJGbwF//ILGRaII5BXj3PY2cuoSVae3L9Wd4A/dBLIET0eJ2i+VeOUWQHsA/+FWct15ElPI1\n3Z9y44TbH8P98b829Umd4L7HUavWYl09E3Up9RX6qJVrsS6dirqShhCuGEKl7j7KsR7BcTs2fRmG\n0dhMYGy0hO7u7kUHxkIIEwq2GdM1bNSS47j4QUhuOl+n6a53UlozncnTlezAsiWlYuPNF11oiCxE\n5bb5XH3GbDSiWCyG47pIIQjDgKBcpOxHs8BYPNGJmL5S02CoEhZvQxauIacu1XBPzUdMXUYN3Y+6\n+EtkBIvMedueQBSmsCdaf+apAvxN+5ClLPbIsaYKQ+3pSyi/iPfkC9iH/hsrU7sTPP7jX8T+xRsI\nrzE7rxci2PQQ4dodiCvtF5qK4iRqaLsJjKm894QPfxLlxOe9baN1HBtGO9IIdAO9OzdSLdVmAmOj\nJZgZxsbNTDBs1JNt2yAk2XyRMGyMYDObK9KZiNOZ7CKfa/w5o3OFyFJKbMehI9GJlAKlNL7v4ZWj\n75yuFSllZcE6x0Gg8b0y5XymIRb3c2wLchM1274GdP82ZG4COT1as/00K4FGTF7A27yP+Mdv1nXf\n5Y17ECrAvnK6rvuNwuzidhPnsZv0pIUsXMO5+AH+o5+D9/8Xa7T6fzd/+2PI6atYY807wiEc2k64\ndQ9i7EQLT5+8h2IW3bcx6ioagtpwPyqeXNR97hYcz3y/VsGx+SxjGEY9mcDYaAnpdJrRUfMBs92Y\nYNiI0swibKWST6nceJ28+UKJeMyhK5Umm2m+kT1KKbxyeTYgtiwL23ZIdnUhhESFIX7gN32AbDsO\nsZiLJS2UUgTlEsVirqFex+KdScT0WM36Jyph8XZk9gqyTcYdLIUoXEN1r0El0shCfZ7T3uBOtNuB\nc+7dFu6fqVCJbvzBndiXPsQqNncTggxKOMNH8XftRye7sU8dqdq2VVcvun8zzhvfqdo26y1cvYFg\n15OIsZPtGRZzfY6xtNDxzpqPL2lk2rIJ73scbTtLuv9cwXEQBDUPjg3DMOrBBMZGSzAzjFufmTNs\nNBLHcQiVJpMpoBr4cVgq+wShoiuVbvrRDmEYEoYh5XLl8mfLtnFsh2RXCiEq/+57Hr5fn8WelsON\nxYi5DkJIwsDHLxYoBY1btyMl5CZrsm0N6IHtyOkxZPZqTfbRKgQgx4cpb36Ejvdeq/n+/JUbUemV\nOMNHWz4sDnqGUH1DDb243WJJFM7IUYLBB/C7erGPvbrsBSsV4O/7LM5b/9UUc53nEvb2Ezz0K4ir\np5CRDZBqEGEZtXo91siHUVcSmXDnfkI3Mf8N51GN4HghM4zNZx/DAIREiwY63ddItVSZCYyNlrCU\nGcZGYzJdw0Yjm1nULl8o49d5UbulCoKQbL5IV7KLYiFP0CKLx4VBUFkIr1Tp7rZsG9d1iXV0IIAg\nCPA9ryF+XiklsXgc27ZAQ+CXKeWyqCYIXDo6uxDTozUJDMkYj2gAACAASURBVCth8Q7k1CVkDcdd\ntBLhFSHw8PrW4tZwnrDf00+4eiPOmcOIFn//9QZ2ghvHOXsEoRv/ObkYEnAvvoe3agv+/i/hvP1f\niHDpJ6eCvZ/GOvchMtucz9cw1Uew7znE1TPIJj6BWi0iN16ZY9ymgbGOJVBrd4CsXthzc3CstSYM\nQ9NxbBhG02rdKNxoK2aGcXXZVu1fGoQQcx40aa1NOGw0JMdxURqmpvNNExbPCEPFdLZAPJHAcWNR\nl1MTYRBQLBTIZTLkslkC38eNxehKpehKpYh3dCCr+KFwPo7j0JlMkkqlSCQ6UH6ZQmaKQuYaXrHQ\nFGExgC0F5K9VfbsaCQM7kNcumrB4keTECOHQTmoVd4VdfQRDu3CGjyF064ZqCihv2odA4Zw73nJh\n8c3cK6eQ5Rz+wa+g451L2ka4agPEk1gfH6pucXWiO9MEj38RMTGCVM31Hl4r0iuikz1t22cd7PlV\nQrejJtsWQlTWYrBtLMuqrNUQBARBcM+rveYLlM1nJMMw6sl0GBstIZ1OL7rD2LzhVgghZn8XUgoc\nAQKF7ViU/Op8eLp5HzPM799oFjOL2mXyRVSDLGq3FFprpjMFupId2LZFsVCIuqSa0Vrj+/7seAoh\nBI7j0JFIzHb9+L5PuVyGKr4WxeNxHNdBIAh8D7+Yb+hRE/Pp6OxCTF2uenexFhL6tyMmzyELU1Xe\neusTYYDITeKve4DYufequm0V78LbuAd3+OiyOlEbXWVxu0ewJkawpy5HXU5d2NOXUH4R78kXsA/9\nN1ZmfMH3VbZNcP9B3J/+v005nkTHO/H2fxkxdQEZNPfc+6oTQLIbcu31WqxSK1C9/VDjjt+ZBpmZ\nz0Iz47WUUkgpb2mgMZ+NDGNhtADdQN36unFKqTrTYWy0hHQ6bTqMl8mxJI6XR1w5A+MjIARxx1rU\ni8S9uoYNo9kIIXFcl7IXMJ3JN3VYfLNsrojSgs5kV9Sl1I3WGs/zyOdyZDMZCvk8aE0ymaQrlSKZ\nTBKLLb7z2rIsEokEqVSKrq4kqJBidpr89CTlQo6wicNiENgSqHKgq+X1sHhixITFyyCmLqF6BlCy\ner0fyolT3vYEzsi7CL91QzXV2YO/8RHsSx+2TVg8Qxau4Vz6gODRzxGu2bTg+/mPfwn7w5815eJo\n2onhHXgBkR1Deq17onTJ/OKiHgutQAPB3k+hatRdPJe5Oo5vDo8X+lnJfKYyDKOeTIex0RJmRlLM\nnK01Fk4IgWsJuHYJMXMgrUK4dhk6unBtl1DY+DeFZWbOcG1JKStdB1R+16HShKG5fLIW5up+B7Ad\nB9UEi9otVaFYJuY6dKXSZDPtN/9dKUW5XK50GFMJfh3HIdnVhRACpRSe5+F73h33dWMxXMepLGwT\nBvjlEuW8Dy12UW9HMomYqu7sYi1tWLMVMT6MLJmTvMsh0IhrF/G2PEb8xJvL3p6SNuX7DmJfeL+l\nQzW/dy26dxB35BiiTTtNpV/COXcUf9eT6GQ39qmj97y9v+khRDGHdfFEnSqsHm05+Ae+AoVJZCkb\ndTkNSeTG0YNbYJ7HQStR/ZvRnelI9n17x7FSijAM73o8ahiGESUTGBstIZlMApDL5UilUhFX0zwc\nSyKDEmL8Etw+p7CUhY4umB7DiqeQHV2Ug8ptzAFNdcwsfiEECASg0UqjwoDQK6LCELRC2i6OG0NY\nFnD9APN6Z4JRXVJaWJZFvtg8i9otVdnzCZWiK5Umn8vec6Zeq5vp8qFUAipjSBzHoSOdhuvPt8rz\nFALfp1zIoVr5JI6Q2IKqdhdraUP/VsSVM8hyrmrbbWciP4lKr0F1pJDFpQfwCijv+gT25RNYy9hO\no/MGd4Hj4pw93NKzmRdCKoUzcpRg6AH8ZC/28dcQc5z0Uh1dqPX3477xnQiqXB4tLfwDz6P9HJa5\nmuGuZOARdiTR0JTjRhZLC0G4+ymUE+16DnMFxzOUUne9atMwDNCV0+ZRlzGrkWqpNhMYGy1BCDHb\nZWwC4/kJwLUlTF9B3KvLa2oU+tbCxDko54l39+OFqiU7LmulMp9MIgTImQM/rVEqRPllwtBHhQH6\nHuGvCgOC8vWOLyGwbAfLieE6LgiB1qCuLxbYzqHfcjmOix8EZDPNd8ntUgVBSDZXpCvZRSFfIGzh\nmaVzkVJiWZWTBNKykPLGCuYaTRAqwlAhANsSFLLtETokOjsR16o3u1hbLqzZjLhyGllun+dXrQlA\njA9T3ryPjvdfW9I2KmHxM1jjI1gtuvigQuJvegRZnMY+98sW/li3OBJwL7yHt2or/v4v4bz9X7fM\nrVaA/9gXsI/8T9PNs9ZC4D/xRTRByz6uq0prdPcqxNSVqCupuXDLHlRsaQs/1sJMMCylnF13YaYh\n5EZjiXnVMgwjGiYwNlpGd3c3U1NTDA0NRV1KQ7OlxNY+XB2pjJ64F60gOw6p1YjpURgfxu1bS4Ak\naJF5rnD3sQSLuf/swhWI6+tnXO8WDnzC0EeHQaUjcblhu9aEvkfo37hUXkgLy3Gx3Bi2Y2O6kBfH\nsm2EkGTzRcIWelwvVKgU09kCqWQHnifxyq11mbbtONizgXDlBA5UBkhoVQmEg1ChgoBQeSg193O0\nq7MDJ9aBXy7WsfoISIklgGJ1RpVo24XVmxFjp1p61EFUpFc50eP3DuJMXlz0/b0dB5GZqy07y1fZ\ncfyNe7DGh7GnR6MupyG5V04S9AzgH/wKzs+/j7h+Uid48Bnk2Bmsa831e9OA/+jn0LaDNb3450Q7\nEl4eNbAF2eKBsbZd1JY9aKvxIpAbC5DL2dFYtwfHhmEY9dZ4r5aGsUQzHcZGxe1zhoUQOJZA5CYh\nP7nwDZXz0JGCWALKBbhyFru7HyuWmB1R0S5mwiYpZn6/utItHIaEXmk2FNbzBfFVplVIUC4S3BRk\nyetdyI7jIq7P9Va6cpmb6UKeIbAdl1KpTKncXN1T1aa1ZjpboCvZgWXZFAvN0wVq2zaWbVdmf1vW\n9U7+ysXVWlcC4TBUeOWAUC29Cz+bL9Kd6iT0yy39HEokOhHXLlalC1PbcVi9ETF2Eum1eNAeITkx\nQrB2F9bkxUUtVFva8iiinMcZH65VaZEKO3sIBu/DvvABlplfe0/2tUuEXhHvwAvYh15C2DFIr8L+\n0f+NurRF0UCw51l0Zwpr8lzU5TSP3FXUmg3w4c+irqSmgt1PEcYSUZdxTzNNKFLKWxbGA8w6PYZx\nnRYSLRrn+dBItVSbCYyNlpFOp5mebr/FmxayAJ0lBTYaMX4ewjsXcZrX9FhlNIV3vtJ1PHUJOlLE\n06sp+a3VwXpjhISYnVmK1pXgKfAJA58gDCrzhRt4kSsV+KjAx7+e0QgpkbaL7bjYtnPLKIvFrM7c\nKhzHrXTWZvJt97PfSzZXJNERI9mVIpdtjBNwlm1XOoSlxLJshKwEwsD1D1OVLhzPV4TloKZd4tl8\nka5UD4WpFr3EWUosNBSXH65pJw6rNiJHTyD8UhWKM+5GhD4iP4U/tIvYhQ8WdB9v3W6EkNijzbeQ\n2UL4fevQPQO4w0cRwRKOe9qQlb+G8D7Af/TzlTnmx18HaUETzWsPdh1A9a7BGj8bdSlNRSpF6Hag\nhWzZ+d46kUKt2QRN1KkrhMC27dnguJVPVhuG0ZhMYGy0jHboMJ4rHJ4v7HJtiShmEJllXGamFWSu\nQno1XL9sVRQz4JeI963FV4KwyQ5iZueCcaNbWOvKgnPKv7lbuLl+rrvRShF6JULvRnAjbQfLdrCd\nGMKuvB20eheydX1Oba5QIgha62RHtRSKZWKuQ1cqTTZT+5NwUkpsx8Ga6RCWklsC4etjI7xAEXrl\n6yc4al7WnMJQUSx6xLvSlLKtd4Kys0rdxdpNwIr1JiyuIzF1ETV4P+rSx0h174DPW7MNlUjjjBxr\nyXm+3tD9YDlmcbtFUkDQMwS2jShkCHY9CdZToBQiDKCYQ2TGkVNjiNw0Ij9V+X6D8LfuRQ1sRlw9\nHXUpzUmH6L5+xHhrjvEI9jyLavDu4ruZCY5v7jY2DMOoBxMYGy1jZoZxK1hI1/B8pBA4EsTkBajG\nB3avcH00RRJmVrgPPLhyFqd3EMuK4zXYQczsXGEh7rLgXHBjtnAbmu1CLlXmigohkY6DZbvXF9ST\nLTMLuXKw7eD5AYVc84xbiErZ8wmVoiudJp/NLusEgpRydmSEZdmzz0uo9OjPzOnzQoXyfcKwsTve\ny56P67TePGMhJRINy7x0X8c6oW8dcvRjRNBa87AbmdAaMXUJb8s+4id+ftfb+X3rUH2DOGePtFxY\nrJD4mx9B5qewL33Ycj9fLYXJFQRrtiKz41gjxxC3vQYrADeB7ukjXLUWbTkgbdC3h8lXELmpuofJ\nwfpdqI27EVdOLmosi3GDKGUIB7chWzAwDnsHUN0roy7jnm4eIWgYxr1pBLqB3uUbqZZqM4Gx0TKW\n0mG83MXOlqsawfBcHEsi/QJi/DJVHZuQGYP/n703i5EkO8i2n3NOROS+1NZLdfU23R7PjGfs8YAX\nFhnzYZDMIoQQIAFC7Ns9N9wgkLiBK7YLrn4kEAJukAAbkBA2i1j0g8F4fvA3xjO9d9eSlfsSEWf5\nL6KqeqvqqqzKrFwqHqlkT3Zkxsmsysw473nP+y5dScTjXdeOs1C7iygukiksEU7Atflki/CuDOWs\nw5okQmJkhXNzjnOJg9NEj0UeFWTIFsr4SmId6BncEqeUjxBJ3MKsOeEnidaGVrtHuVii3+uh9cE5\nz08Wywn5RDmLA/tkjrCOdxzss/1enMc843y+iKjfPdElr8sUYWkN+ejLaQzABBCdGrZ8HpstIfcR\n/nXlHObi++bSebtbbudtvYdqrk96ODOD9QLiyx9EWIN370sHvm8lJNd++xRX7onJ1UXM+R0xWXjg\nHMLEMOgimptPiMnN5PYRYS7exLzyMcT6O6lYfBI6NdzKlUmPYuQ4wLz1LVg/O+mhpKSkpMwcqWCc\nMjdUKhXW16d3knCcOInjnCNQAhoP91quR4pz0NqA6gWoP3j63J1tXNgju7hGpC2jnoo+LpwTj+PH\nnMXayRbOzTNBoYTn+ejthyAEqnoedESQSS66Y22m2gkqhMDzffr9iDA626V2x8VaR7PdpVTMk7EZ\nrLNIqRBSIHaL5azDWIM2FhtqjI3nRkR9EfOUZyylRGIQg86xH8PlSrBwCfnwyyMVg1KOjgBE7Tbh\nzY+Qe/tvn/o3U1ggvvIhgvf+HTFn35OmsIhefQXvflpud1QsoC+8H1dYQG68i+off4fe4WJyLhGT\nd53JygPrEgfyYMeZXN9IXMndBuIFi5PPYpbX0B/8pqRY89jPIAWS36PxApzy5+oz3F55FZsrTXoY\nKSkpKTNJKhinzA3lcpl33pl8ecu4XMOH4SuJNBFi4z64MU4Goz5kS8nPMxMzEQ9g8z2CxcsY6REP\nWT4l5WNBWEJSzEaSv2u1xpjdwrnULTxOpPTIlCq4QRfTeLwI47pNVLZAv7GF8gO8bAHp+TsZs9Ml\nQPh+gDGWZqs31aL2tBP4HrmMj8RhrSbwA2Jjabf7Z/4tOE95xrlCEVG7c+TjHUCQg2wJF+STgjvl\noZoP50pomEVk2E0WcBZW8XYWdm2QJ7rxkaQA7jjFt1NMvHQVV71AcDsttzsqpnwOfe4GsrWRxE+M\nscA3EZP7yc8z7InJ5Spm5RJOBU/EXJgdMbmGaGwgO/XnxGRTOYf+mm9DbH4VOXKbwhnFauzKGurR\nfJQGOqnQr34dVvlTv2H8KJEU6fVsSsoOQuDEFC0TznGUTCoYp8wN1WqVZvP0Ju2TEoafGwdJsR2t\nTUT/lJ5/axOWd6IpnnUqWQNbt5DlFTK56nMRFXuiMG4nV9gl4q81EPchGiS5lzrCBTlE5Tz91vbp\nPK8UgnwZz/cSofiZybftNZGZHH42TzzoYeIIhMDP5AmyOZwDM+HCvLTU7uRIKSnkAjwlMXFI3NnG\nPZFFqbIFqsUCxjo6/XDm4yVOQhjFBEEOL8ih9xFEZgEpFdKafXelONhZICxggzx4SbY5zuGMxsUh\nLhrg+snioStUEZULyG4dUbubijgTQm7dIr7yOrL+ALyA8JVvxLvzX3NVQGgBvfY6KA//1r/PXcTG\nOLBelvjyGwgT4d39r4kv7hwqJvs5XLmCXVnF7IrJOITWMOjiyouI2q1DSx5Tjo7o1bGX3z83grF5\n5WPEfhY1x2JOSkpKyjhJBeOUueE4GcZHYVqE4f1QUuBhEFt34FSL2xw015Noiu39yzFEaxPCHtmF\n1aQ8zWgwMYQhxIPE5aSj5wXnJx9j0MapgGypyqA9H4WG08pTruLmowOPs411/KU1TBRhbeL0jgdd\n4kEX6fkEuSKef/qu491itTDS9Oe81C4X+GSyHnFsiGJDPCJhPJcJyAQKnCXutxlE+4tLZtDFDLoo\nP0M5X8YJQa8fjWwcs0a7k+QZWz2beca5QgFRu4fLVXHZfJJFqvzELeEsTseJMNzv4kwdXvAcTWsL\nhEDmSsjLb2B1hKzdQs6omD6rCBMjek2itddwi5fw7v8PKjx+3Mi08bjcrp6W2x0BC+jVV3G5Mmr9\nq8jB6K+VR42ExEQQ7yMmqwB9+YMIEyFTV/lo6TdxS9cmPYqR4IIc5uoHsAjUpAczIqZh7pmSknK2\nSAXjlLmhUqmcWDA+jZzhURF4Mtme19k6/ZMrH6TCIRC5ChzkbA67sPkeLF6BsINobQx9KtGtIf2A\nIF8m6k3/JGcWeZGr+Dmcw9QfkV28SK/x9N+e1TGDdh2EwMvkyGTzOMTYi/I8zweg3Z7vUjspoFLK\nJ4sv1qLQ5AOBzGVBCKx1xNoSaY3WR3sdfE+RywYoATrsEza7jwstD8HEIaa5iVAe+XwZmc/QH8QM\nzmBedLvbp1RaoNec8jxjKfH9AKU8pJJIIQGHXbyE0xE2DqHbxJn4+LE/zmF7LWyvBUEWtXIjyZ9v\nPkS0N9Oc0TFjkVBYwHkZ3PISqlObCYHwqNggR3z1w3ib76KOcU1x1tCVC5iV68jGI9TGV2deXHdS\nYdZeZ7OnWCkEOJj55zRNSMBID5fJIcLZXujTb/4ftJ994SLnNOGce2EcRUpKymMcAjdFn/7TNJZR\nkwrGKXNDuVx+LpKi0+nw8OFDbt68+dSX8DS7hg9DSoEvHKJ2F3Q4prOIZOux5+O8LPhZhBfghAAh\nsc5hrCC2llwpiwy7cNCWQKNh812oXsQtX0Ns3Rp+NI2HeEtXsdk8evB8qUrK8Tiqq/g5TIxtb5Mt\nLTJo7xMX4hx60EMPekjl4eeL+EGAdY44Hp2YuOsqPguldoVcQCbwiDpNTDRASEWmskT/CdFeKA/l\n+RSCAJkLnhCRDZE2eyKylJDPZvA9hdERplsnHqJk6Fmc0UTtbRCSIF8kV84RxZZuf1yfT9OHMZZ+\nOD15xlJ6KN9HeQolJUJKsA5nDU6HuEEfqyPsuLekRwNM9AikhyosIqqXcP0GYvsOckYm8dPMnjic\nr+CCfLKQ6yxEA2zUh34Hmy9jb3wMoUPk9gNUe2NmS+9McQl98f14999GnaCg8Sxggzzx2uuIeIB3\n94tJwdyM44REr73B1sAn1KCdRPm5fV3IKSfARNhzV1F3vzzpkRwbV1rErqxh50yEnYV5akpKynyR\nCsYpx+Yf/uEf+NznPke73WZ1dZXv/d7v5cqVKwce/5//+Z989rOfpV6vs7Kywnd+53fy2muvjWQs\nYRjS6XR48803+dM//VMePnzIw4cPabeTXMVf+ZVfoVwu7x0/q1+4vpLIsINorsNJS0qEBC/AeQH4\nO6Kw8nYC5AXGgbagjSM2Bh3uP8EMlaS6cAlZu/3i0zUe4nJlOH8TavdAD5elKGp3yJy7jjUaG6db\nEE/KUK7ifXCDDjKTx88ViPsHR0BYownbDSBxHQe5PEJIrAOt42O/F30/QJ+BUjvPk5QKWZyO6dc3\n9lyfzhpM2CfIF4l6iXDijEYbjX7CFSSVh9wVkfMZhBAIIdBxTNTZxo5QwMdZ4m6LuNvCyxZYKO/k\nHPeimYxqGJYwjAn8080zlp6P7/tIpZBSItiJkTA6EYZ7EcbEpxxZtA9WY9o1QCBzReSlN7A6RtZu\nI6P5jpAZFYk4XMXlq/uLw732vgu3rrWJBpAecnEVc+56Ih7XH6BasyMexyvXcOXzabndIVgk+tJr\nuEwetf6/yDmJInGAvvQadZ2hv/O11YskQb6KaKaC8SgR3VqSYzzDgnH8Nd+GDXKgZ3+hJCUlJWWS\nCHfEmfaDBw/GPZaUGeILX/gCf/iHf8j3f//3c/XqVT7/+c/zxS9+kV/8xV+kWCw+d/x7773Hb/3W\nb/Fd3/VdvPbaa3zhC1/gb/7mb/iFX/gFLly4cOTzGmPY3NzcE4R3f2q12p5otLKywsWLF5/6WVlZ\nQcrZ3QgrhcBXAur3EcMIEVLticLCz+H8DEJ6OJFs47AWtHXE1hHH9tjb+QsZRS6sI3r1ww/2Ali8\njOvWEd0hy+ykgpXr9Dst7KQFkBnlSVex65y8TFAtrzHotrFDOFSl8vBzBZQfJOKxtRjrcLiddZAd\nUdS558Rg5XlIIc9EqV2pkMX3JFGngYn2d+tmF84lpZBDvHcTETlA+QFSeSDAGZtETET9pwruTory\nM/j5Ek5Iumck57haLjBo10cqknt+gPI8lPIQUiJ2ykKdjrHxAHSMM9HMbL0FwM+gigtJXEVrHdFa\nT+MqdjhUHI7Dg3f1HAXpIYtVpJdB6AhZvz+14rEF9OU3QCr8e2+n5XYvQC9cwixdQdYfJBEwkx7Q\niHCAufgKbVmh/sQlsAQuFUPUw9kVNqcVs/wSwV//P5MexrEw56+hP/JprJ9B7wjGnjf9Hrk4jpOd\nc+rgxOUoShfL5onV1dVJD2FmuX/3LtEBc6NJEAQZLl2+fKz7/tVf/RV//ud/TqPR4Nq1a/zYj/0Y\nN2/ePPD4f/7nf+ZP/uRP2NjYYHV1lR/8wR/kwx/+8HGHfijT/+mZMpX83d/9HV//9V/PRz/6UQC+\n//u/n//+7//mX//1X/mWb/mW547/+7//e1599VW++Zu/GYBPf/rTfPnLX+Yf/uEf+L7v+74XniuK\nIv7kT/6EBw8esL6+vlekVS6XuXjxIq+//vqeMPypT32Kf/qnf2J5eXnEz3hy+Eoi4wGi9mD/bFHl\nJaKwyiCCLM7LIKTCCYnDYSxoK4iNJR7opKhsxHRDQ1BYwgs7SbHdi9BRElGxsIbLFhG1O0c/kTVQ\nu0N26Sq9Vv3IWaspCUG+lBTSHdNVvB9m+xHZpdXn8oxfhDWasJNs21eZLEG2gOcpQOyIxwJtHBaB\nFEmEgq8EQiQuzl5/vt1EGV9RyGcwcUS/3nhhlmzUaZAtVhm0ji7+W6OxRqPDx/Eu0vMTt2qhgpQK\nBFiTxBfEg8Gxxakk5zhEKI9CvoTI5+gP9FznHB87z1hKfN9HKf+pfGGsTfKFo14iEJvo+PnC00Qc\nYuqPQCpUoYKoXMQNWojaHeQYvqemlYPF4RAb9Q50Dp/spBrb2sICSA9RvYRaub4jHu86jyf/O7BS\nEl//CLJTw9t8d24E0FFjM0XiS68hwh7enf+cSuH/JJiVl+h5ZerPbEawgFN+mmM8DoTA5cuIGesO\ncQjMhz6J9TPJfzs304ahlJSU+eaf/umf+P3f/31++qd/mps3b/KZz3yGX/3VX+U3fuM3ntodv8s7\n77zDb/7mb/JDP/RDvPXWW/zjP/4jv/7rv86v/dqvsba2NpYxpoJxytAYY7h79y7f+q3funebEIKX\nX36ZW7du7XufW7du8clPfvKp21555RXefvvtQ8/n+z7tdpurV6/y8Y9/fE8c3s/JXCqVaDQacyEY\nC5JiO1obiHiQNNf7mb08YYRMRGHn0A60gdhYop4BTn+i1+rrJJriKBnFziG27+IKi0lExeZtsEcU\nkHQE9XvkF9amv2BqSng6q3h9tA9uNaa1Sa6yRL85vGPZhAP6YZLJ6+cKeH4GIQxSWaT0sA6sA2cd\nrW4fa+dAKDsAKaFcyCGFIGw3kgKyQ7BxBM6igsyBLuSjYHWM1fFTGeHS81F+QKZUQcjE8eJ2ImHi\nsJ8s4BwRZzRhu57kHOfmO+f4sDxjKRXKD16QL9zGmQh9glzpmcIaTHsb2EZmi8hLH8CaGFm7Mzfb\n6XeZiDh86KA0rv1EbEV1FbNyDXSEmqB4vFdut/Euqp2W2+2HRaLXPoALsniPvoKYw3gXs7BGmF9m\nq72/JBxbicoUkpLllNERD7AXr6O++sVJj2Qo7I0PYbOFSQ9jaI6y4Xue49dSUs4qn/nMZ/jUpz7F\nN33TNwHwUz/1U3zhC1/gc5/7HN/93d/93PGf/exnefPNN/nO7/xOIDFtfvGLX+Sv/uqv+Mmf/Mmx\njDEVjFOGptPp4Jx7TrAtlUpsbOx/Ud9qtSiVSs8dv5sx/CKEEPzcz/3ckcZWLpdptWZrNXw/lFL4\nvgdG48rnMc6hrUjiI7QhDi2Jt2J6MNbRM4pCYQnRPZqQK7rbuKiHWLmKa24gjtjkLqI+rrVBrryS\nbMdPOZBxuIqfI+xD0CcolIi6h7+n98NZQ9RtEQEqyOLnCuAcJkqyrmUmRz6Xodcb8KRmXC5kiGI7\n847VpNTOx0QD+p0mw2SUR+0G2eoKvWhzpGPaFZEfZ1QLpOftiMjVRER24KzGxGGSnXxYJIKzxL0W\nca+Fl8mzUC7OZc7xbp5xtlhBYKc7X3iKsIMOdtBB+Bnk0lWk8hCtdUTz0czFVeyJw7kKLlOYDnH4\nMKzGtjf3nMd2Vzw2Ear+ENVaP5XyNFNaRl94Ge/e26g5WzQYFfHiFeziJeT2XdSjjbl02JryOaLK\nKuutg59dJxJk8guIVDAeKaKzhb30vpkSjJ3yMS9/LU75kx7KsZmngr6UlHHiSHYUTAvHWc7RWvPu\nu+/yPd/zPXu3CSF44403eOedd/a9zzvvvLMnFu/ycDiNtwAAIABJREFUoQ99iH/7t387xgiORioY\np4yUSX/RVSoVms3JN9SfHMlmvXf4YVNGPzRkCwt4gzaYo4mTIh7AxruIpcu4XAlRv3+0+/Wb4GXI\nFCt78QYpjxmrq3gfbLuGt7SK8QPMCUsJTTTARDuu42weFWSxJoY4plzMAoJOPyTjKwJh8bOJEBPG\ns7cN11OSUjGLcI6wvX2sQkfnLPGgQ1CoEHXH+V5wz4vIQqB24iwypYU9J7LVGhuHSfnbAUKwDnvo\nsIf0M5TzJZjBnGMpBb6n8JTCU0nevIBEGNYDXBzidISZtXzhCePiENNYx0iFzFeQVy7g+m1E7fZU\nxlUcKA7HO+Jwc332FgaeEo8VtnIBs3wVTIxqPEA1NxCHRVAdg3jlOq68QnDr38fy+LOOzZaS+Il+\ney7jJ3Yx+Sp68RoPXyAWA3RDx2KpDEeo0Eg5OlKHmIXyTMV96De+EZPJ7/33riN30nPTUZE6jFNS\n5ot2u421lkql8tTtlUrlwP64RqNBtVp96rZqtUqj0RjbOFPBOGVoisUiQgg6naddH+12+zkX8S7l\ncvk5N/GLjj8u8+Aw9v2ARnsw6WEcm2Zfs7BwCbn13tHv5Cxs3YbSOdy5G4it944kroj2Bspbw88V\nifvz6UKSyiOTLyYZbFKio5DwEBfvqbiK98HUH5FZWqM3ZAnbQThriHpt6LWT8rRcASEEJo4oZH18\nAfLB/2AvvI9iJvEfzpJoXC5k8TyFifpEnRbHW59O0P0u2Wo+ybU4TWHSOUwcYeJoHxE5IFNaTCIX\n2HEsRyE6HPDkDgkbh4Q7Ocf5fAmZzzEINf1wOsQiT0k8T+EpiZICKUQyg3YOa3fdw4AA3dg41ffc\n3GMNtrON7ezGVbyWvOa128jB8XYznHhI8ygOH4Y12PbWY/G4fAGztCseP0Q1108s7lpAX/kgIPBv\n/TsiFUeewkoviZ9QPt7DLw9XgDxj2EwBff59PGgeTeizUiGFTAsRR41zuPIyonX0jopJ4XJF7Or7\nQMzaXpSUlJR54vd+7/dYX3/aqPUN3/ANfOM3fuNQjzPMQpdzbqwLY6lgnDI0SikuX77MO++8w+uv\nvw4kf6hf+cpX+MQnPrHvfa5du8ZXvvKVvXwWSCz1165dG+nYKpXKWFdYxo3yPDr9CDvDEyVrHT0t\nyJdWkO3htsiL9gYu6sDKS9B4eKRMOlG/R7B8DRtk9+IL5gHlB2RyBYQzuNY6LupDvoJXXkF5iYs3\n7D0tmAjpkT1FV/FzWItprJOvnBt5vrSJQ0wcIqTEyxYIpEA+egdhNaK9hc2WKAZZQE29aJwNPHK5\nDMJZwtY2dkQCY9RpkCst0J90tvdTIvLObbsisp8hk80nIrJzWK0x8QAT9nFGE+3kHPu5Itlyjlhb\nOr3x5xz7ntoRhQVKCIQUO0/FJbnNJsZGmtgYrDHsivvZYgWMIW5uIZSHVz2H3ro39vGeRfbiKrwA\nuXgliatobyIaD8YWV5GIwxVcrrqPONyfT3H4MJ4SjyW2fA6zdOVE4vFuuZ3qbKE235sZR+NpES9f\nw1YvImt3UO3NuX59nJ9FX3yVh2155CXUyEi8TBGOGGuWckTiHnb1BnIGBGP91rdin3AXzxpHcUOn\nDuOUlMc4IXBTtHtgdyw/+qM/euT7lEolpJTP7Y5vNpvPuY532c9N/KLjR0EqGKcci09+8pP84R/+\nIWtra1y9epXPf/7zRFHERz/6UQD+4A/+gGq1upex8olPfILf/u3f5nOf+xyvvfYaX/jCF7h79y4/\n8AM/MNJxVSqVmXUYSykxxhFNudh1FPqRJVOoIPst0MOJPSLsweYtWLqMC3uI1hGEz9odsivX6Vs7\nMvFtUviZHEE2B/EAV7+Pe/L59Jo46UG2gIx75CtLe8LxpFzFzxGH2H6bbLHCYAxRIc5aMpkMYvN2\nEmcCiE4NWT6H3bpDcekyUvj0pzDTWEpJuZBBSokO+8Qjjo+wOsYajZrGxZMnReSdm4SQSM9H7i6O\niF0ROcbEIXGjjRfkdnKOodMLj51zLAHP9xJBWCmU3JmYObcnChsTYmONNgZ3hG3eucoSLuyid36P\nzsSYdh1vcRW9vf9WspST43SUxFUIuRNX8SY27CI3byGPWp66D6k4fAysxbZr+4jHekc8fnSoeGyD\nPPG1N/HWv4oacpF53rH5KvHFVxC9Bt7t/5h7B61TPvrSB1jvqqE2ynQiyBYWUKlgPFJEewt78SX4\n8r9OeigvxFbPYSrnxu6yS0lJSRklnufx0ksv8aUvfYmv/dqvBZKFobfffptPf/rT+97n5Zdf5u23\n3+bbv/3b92770pe+xMsvvzy+cY7tkVPmmg9/+MN0u13+8i//kk6nw+rqKj/7sz+7V4TXbDaTLfQ7\nXL9+nR/5kR/hM5/5DJ/97GdZXl7mJ37iJ7hw4cJIx3WcDGMhxFSs2krp0WzPzxbDVm8nmmLz3eHv\nbDVsvgfVi7iV64jNW7xwu/5OpEV25Tq9dn0ms0KDfBHfD2DQxm3dhoMEq04NlEL4WezWHWS+TKG6\njAPM5l1OEmswKly3gQpyeEEWPWLhsrCwgmhtIPuPV1eFsxAPwM/ganfIL10hk83R68dE8XQIPMV8\nhiDwcNYStmpYPR5BO+o0yFXP0Zs2wXgfnLN7zvE9EVkmIrLyA7xccU/UlTiqpRzWWrqDmHif36uU\nEt9XeFKilEDK3Txhh3M2yVU2GhNptDW444rPyiNbqqLbNdwzr7ON+klhW3kZOwOurJnGWWy3ju3W\nEZk8avVVrLPI7bvI/ouvAyxiJ1aiisvkQXqpOHxSnhGPTWkZtXQZrEY1HqEajxDPdBvo4jLm4st4\nd7+ESkvL9rDSI778BgiJ9+C/9xZH5xknFXrtDTb6HsP6JvoRuHLx8ANThkJajcnkcUJMbUSMI3EX\nR9IDrZFSJjFRT8ztUhE5JSVlWvmO7/gOfud3foeXXnqJmzdv8pnPfIYwDPnkJz8JwG//9m+zuLjI\nD/7gDwLw7d/+7fzSL/0Sf/EXf8Fbb73FP/7jP/Luu+/yMz/zM2Mbo3BHVMoOCl5OSZkmfvd3f5ev\nfvWr/Nqv/dqR7zMNgrHn+7Q6IcZO5wXZccn6koLrIlsbx34Mly0jKudg+z6P97cfcKyfgcUrI49D\nGBtSks2XUErhunXoNeCof4vVi4DbEaUEIl9B5CvYqI+bBqFKCNTSGr1242Dxe0hy5UWU7iPX//e5\nLbk2U8QurOK274MQiKUrWBUAEEaa3mAyrmvfU5QKGUCgB13i3vhzV71MHpnJErZnN57nSRIROUD5\nAcrzETLZquysY28e6BzWGKzROxESGmvN0d9PR8QLsgS5Arq5gXuBoOhVz2H6HRjMZ7b6tCI8H1lY\nQHo+olND1HfiQQoLuGwFly08IQ5HSeZwPEjF4XEiJSJfRQW5x+Jx8xF6YQ1XXsa/88W03G4HC5hz\nN7DlFeTWLVRne9JDOhWcEOi1D1KLc/Si431mXypbvIf/PbclgJPCLF/H+3//Grn9cNJD2Rdz6WXi\ntz6FVT7W2r1dSLumJWstnudNvWhsjMFai+/7Bx6jtT72LquU6WR1dXXSQ5hZ7t69TxhNz67iTBBw\n+fKlY933r//6r/mzP/szGo0G165d48d//Me5ceMGAL/8y7/MysoKP//zP793/L/8y7/wR3/0R2xu\nbnLx4kV++Id/mDfffHMkz2M/UsE4Za744z/+Y/72b/+W3/3d3z3yfSYtGCulGMSWQTifE9Zq3sNr\n3j+ZQ0b5sHQF12sgOi8Wg12mCNUL9JrTO9GSnkcmV0QKcO0aHLe8aekyNgqhu/tcp0w49gLUwgV6\njZOPI8iXCZRAPvyffZ0uDrCXXsNu3k5u2BONPYRUgCCONd3+4NQM6OViFs/zcEYTtusvFBhHTba6\nQthpYudUCBNSgRCn+ppmCmWkFOjm5uFCtBD4CxeSY2c8JmcmERJVWQE/i7QaFw1ScXgqkIjiAjJb\nRCiFd/dLyO72XGfyHhVTWERffBnZ2UbWbk+to3PUOEBfep2GK3CSvuelgqDQuoXszcdC6bRgSyuI\n2ibel/5u0kN5Dick8bf9KCZffnzbThntk8KqUuqpXa/TSCoYn01Swfj4zJNgPO1M96dnSsqQlMvl\noSMpJokQAoeYW7EYoNnXuOolOMmU0MSw8S4EOdzy1RceKsIOdLbJlhaOf74xofwM+fICuWwO0VrH\nbd46vlgMULuLzOQgt3ux7HC9BnbrDsJEqJXLyPLyKIZ+PHSE7dTJlU/2u1B+hsD3kOtfOXASLQDR\nrUNh51zO4Wp3kCZG93uYOMT3FNVykWoph+epE43pReQyPovVAp5S6H6HQWPzVIVNgLBdJ1Oqnuo5\nTxNnzam+prnyIsJqdGPjaK5l54ibm3jV86SXWqeNRFYvYGRAp9vDCoVtbSRu71QsnixSInJFdL9N\nuP2Q6Pz7iF76KKa8MgVhSpPBegHhtbcwK9fw7v1/qK1bZ0osNhfeT1fkTyQWA3RChyssjmRcKU/Q\nrmHPXZ70KPbFvP8j2EzhqduESHoKPO9x6qYxBmPMxHeTnpRZH39KSspsks5iUuaKcrk8U6V3yvNp\ndYcrhZs1nIO+dlA9f9JHQmzfg34Lzt8ELzjwSNHdRkZdgkL5wGNOEz+Tp1BZJOtJ2L6Hq92FaER5\n1bUkx9g9ddHscN0GdvMOmAhv5TKyNBnh2PXbYGL8bOHwg/dDSnKFEmLjq4duWxbtLWTuid+5c7ja\nXXxPYqOQXmOTqNtGCCgXsiyU82SDg90cww9VUi3nyGUDnDEMmlunEkGxH85oTBzhZWe3NXwaEFKR\nry5jek1Mpz7cnY1Gt2t4ixfHM7iU5ylUUMuX6UWadqeL1obeIEQsTqfgcZZwmQJyaQ3d3ML222A0\ncXOTsL1NtHSN6MbH0QuXpqr1fJxYIDr/MvHVt5Db9/HuvY0YsiR41jHL1+kHFbZ7J/+dhxpckBvB\nqFKeRGKTfgg1XbVHzs9iX/ogTu2/+C+E2HMVSymx1qK1nlrhOC3sS0kZjqTdZLp+5pX5fWYpZ5Lj\nlN5NCs/z6fSiUUdsTh1CQDYbEHrFkVzMi24dt303iajIVw4+rvkID4t3XKFyBAT5EoXKIr6LcVu3\ncY2HiVt6lDgHW3dQxUV47vVNhGOzeQfsrnC8NNrzHwHb2MTPZJDe8BOOYmUJsX0XGfUOPVaYOClM\nVE+IwM7htu4SBD5+kEWHPfqNLQatbayOyOd8Fqt5ivnM0GN7apz5gGopixSCeEKu4meJO02CVDA+\nNsrPkCtX0Y0NXHj4399+7EYhqMrKiEeX8hSej1q+jPWLNFrtp0oRoygmRkBx+nadnBVE5RyquEi8\n/RD3bESLtcStGmFjg7BygejGx4lXruPk+HaBTBpTWia++XUILN7t/0D1hlyMmgNMdZWwsMJmZ3Qi\nmUXh1OgWgVN2sBq3PF1brfWbn8QER7u+2XUcz4Jw/CJmbbwpKSnzQSoYp8wVxxGMJ/EFLKUk0pZY\nz385R7mQpd2NafQstrLKiaIpdhBxmERU5Ku4hbWDj6vdJeN7KP9kYuBQSEm2WKFQWcAL27iN96C9\nObLit31xFmp3UeUV8PZ7rk8Kx3oCwrHD1B+RLQ4XkVBYOAedLWT36HnUorUBz7mpd0Rj38fPJBMM\nazRhp0mvvknU7eArwWKlQKWUQw2RdRd4koVyHt9TOJu4inV/WorOHFG3RWYK41mmnSBfIsjliLcf\n4U64yGO6TRAScqURjS7lSWTlHLJ6kXZ3QLe3/86NbrcPueoBn48p40Mil6/gHMT1R8l31Qsw7Tph\nfZ0wVyV66aPEF96PUwfvJpo1rJchvP61mMU1vHtfQtXuIM5gGIcpLRNX11hvj9ZR2TMSl5uOnWXz\nhBg0MWvvn/Qw9nDFKnblChziyH3StftkVMWsC8cpKSkpp0kqGKfMFZVKhVarNdVf/kIIhFR0+9MT\n1D4u8lkf62AQO5yD1gBsdUTbs51FbN0GHeLO3QC5n3s1cd9m8oVjuVuHQXo+uVKVQqGE7NYSobjX\nSMZwGliNq91FLVw44LWARDiuT0Y4NhrTqpErHy1jMFdZQkRd5Pa9oU4jek3kvgsESTxF4Hv4maed\n2Drs02/W6Le2wcRUSklcRRC8+G+mUspRyGcQAvSgy6CxhTPTtQhkogFSCOQLIlxSniZbXkRi0fX1\nQwWuo6KbW3j5Mpzm4tWcIzJ51PIVQitptjqYQ9577U4PsZAWzJwafg65cgXdqQ8d52K7zUQ4Vhmi\n619DvPY6dobjBiwQXXyF+OqbqK1bePf/G3FGyzBNroJefokHrdFvv+8M0hzjsdCt46YoWkm/9W3Y\nzPF2T02rcJxGUqSkDIcDHGKKfuaXVDBOmSuKxSLWWrrd7qSHciCe5819bjGApyTZwKfReezOC7Uj\nUnncEbeRHQXR3oTGQ1i5Btl9HHzOILZuky1UYAwtyV6QJV9ZIJfJIJqPcFu3k3KlSWBibO0eaunS\nIc91QsJx2IV4QJB/sdMyKJRRziDX/3doP7rAQb/9RBHgkxwsGkOS+7vrOo57bQoZj8XK83EV+YzP\nQjmPxIGzDBpb6P70fuaE7TqZYuq6OhQpyVeXsP0Wpn10V/vRcMSNTbzKCuml10mRyIVVXGGJVrvL\nIDya8GatTfOMTwlRWERWVojrj3DR8dvM7KCTCMfWEV35ENGVN7H7fc9PMbp8Pomf0CHe7f9A9men\nZ2PU2EwBc+FlHjbHI4xpA87LzPXEfRJIAOXh/Oykh4JZuYwtn/x6dVqF45SUlJRpI521pMwVUsqp\nzjFWyqM70Fg73xcjAigXMtTazwvjzf5ONIUY3cePiHqw+R6UlnGVfVwQJob6PfIj3JrvZ5Miu4wC\navdw2/cgPmHN9wgQOsTWH6AWL3N4/MfpC8e2tZVcoPv7O149P0Pge8hHX0Ec090p2xuI/EHxFw5X\nu5eIxi/I99XRIHEdN7eRTrNYyVMp5hPnsS8RgB70ElfxOONGRoCzBhMO8HPFSQ9lalF+hnx5kbi5\nhRuMSfy3Gt3axluaHqfWzPFMqZ0dcmKf5hmPH7l4CfwM8fbDkUUx2bBPVN9gEIZElz6QxDoUFqda\nGLRBjvD6R7CV83h3/wtVvz+CQK7ZxfkZ9OqrPGpLRrNvY38MMo2eGQcmwp67MtEhOMC8+X+wI9yp\nMw3C8VHOk4rYKSkpkyIVjFPmjnK5PJWCsRAC6yCMJluEdRqUihk6fY3dZ1bgHDQHDrufsHsSrElE\nYxxu5SWe/XgTUR9a6+QqJ9uumCnsFNnZELd5C9d4NPoiuxMioj62uY5cPqqT7nnhWBTHt63T1B+R\nLZR49nckpUe2WEJsvIswx9+uK+LBTrTdQV9xO6Kxp14oGkMitkbdFr36JnrQwlmLAAbNTfS4hMUx\nEPdaeJks6df+8/i5Apl8gbj+EMa8TdzFA2y/i6qcG+t55g7PRy3tX2o3LGme8ZhQPnLlKibsoVtb\n4zmHDokaG4SdFtH5m0QvfRRdPo+bIinWAtGlDxCvfRC1+S7ewy8nhaxnGKd89KXXWe8q9DjVYqAX\nS9yBC8Ypx0V069jLk80xttffwA6x8D1MzMNBwrHW+tTE2jSSIiXl6Ew+guL5n3klnTmmzB3T6jBW\nnk/7DERRZAMPEPSjg2cFkXaEMofLFEZ+ftF8lJTMnX8Jnsk8FP0WotcgUxpyMiEl2VKFQnkBNWjt\nFNnVRpZvOg5E2IFODbk0zPbrx8KxcGZ8wrE1mOYm+crTLr98ZQFRv48MTy7EitYmlJ8tv3uSJ0Xj\nw/8OVZAhky9hBl0GzS3cfqshU07caZItpxPpJ8mWqnhS7rghT+d3avotQEC+cirnm3VkeSUptesd\nXGo3LGme8YjJlpCLq+jmJvY0Sj+tJm5uETa3iRavEN34GHrxMm6EO5eOg65cTOInwi7enf9ADtoT\nHc804IRCr73OZt/jBOs8R6YdulQwHgdhG1d50TXVeHHKw7zyMZzyx3qeZ4Vj59ypC8f7kTqMU1JS\nJkUqGKfMHeVymVZrujLivB2xeN6/7pUU5LMB9fbhbppm32LLF0caTbGLGLRh6zZUL+JKK0//W3sL\npUOC/OEuBekF5MoLSZFdewu3+R70mpxakd1J6TWh10q2CA/FjnC8tSMcL49BOI4G2EGXTCHJ1i0u\nnoNuHdkejTNNdLeRh5ai7GQae/KFonGmtICfzSdZxWFvJOObBCYOwTlUWrwGQpKrLOHC/vjckC9A\nt7bwckWYgkzIaWWv1M6pI5XaDUOaZzw6RPUCIl8h3n6I06ftpLXodo2wsUFYOkd042PE527gDix+\nHdMoggLhSx/Flpbx7nwR1Xgwx16jo+OEQK99gO0ww+CU/jSsBecFs3KVNjNIACFxE4q2Mq99PWaE\n/SeHMa3CcUpKSsppkwrGKXNHpVKh0WhMehh7SCkJY4M2s+dIHJZKMUt9n9zig2j0HbY6rJh5REwM\nG++BF+CWrz71T6J+H19KvH2KzyDJ0c1XFsllAkTjQVJkNwLX60To1iDqI6sXhr+v2xGOa3cQzo5c\nOHadbZSU5KvLEPWQtdsje2zhLET951zm+46jdpdACfzc06Kx8jPkq8uYsEfYrOGm2FF+VKJOnaAw\nW6VRo0Z6AfnKIrpdw06sgMoRNzbwKstjKeOcbSRycfhSu2FJ84xPiJTI5as4rdGN9SRvaoKYTp2w\nvk6UKRG99BHii6/gxhw7YpFEa68Tr30Atf4VvEf/F2HnP3bsKDhAX3yVpsnRjU73b0NbAacoLp4Z\n9AB74fqpn9ZlC9jLrwz1Xbkr6p405uE0hOOjjDUVqVNSnmbS8RNpJEVKygxTqVSmymEspKJ3WtaK\nCVIuZOiGZqh8utg4BiKDG1vruUPU7ydO2/M3wXuiaK12m0wm+1T5mp8rPFFkd2enyG4OYkRaG2AN\n4ri5qc7hutvPCMejEVmc1UhnkI/+d+RftbK1cWSB223fI5CPReNseRE/m6ff2MKEo9kGPw04a9GD\nHsGOs/us4WfzZAsl4vqjyb+3rSFu1fAW0hK8PXZL7cLjldoNS5pnfEyCPHLpMrpdw/SmKwLM9FqE\n9XVC6RNefYto7Q3sGMTDeGGN+ObHEL0W3p3/HEmU0rzgAHP+fXRVkdYEuoA7scKlkT8jR7S3sJde\nPvXz6g9/C+YIi//jJHUcp6SknFVSwThl7pgmwdjzA1qdORAcDyETeAgh6A2G3zLc6lts6TxINYaR\nJYheA2p3YenK42w752DrNtl8kUyhnBTZ6UFSZNdcBzNnLqHGQ4SQiOLS8R/jSeEYlwjHheMLxzJb\nQvkZ5KOvINzotpvvEXYQ6uhbk3dF43xlGd3vELamO6f6uOh+B+UHZ87Zmi1W8TxvJ694DH9vxyEO\nMf0Oqnp+0iOZLGp0pXbDkuYZD4coLSPLy8T1ddykF11egB10iRrrhMYSXf4g0dUPY3InXyiz2RLh\njY/h8hW8O/+Jaj2aY1/R8TBLVxlkFtjuTuaV6Q4sLpfmGI8aqQe4QuVU4z5sZQW7eBGmpBBuUsJx\nKkqnpKRMirM1W0w5E5TL5aFL78bRTKs8j24/GrtDatIoKSjkAraPkFt8EPWeHV80xS46hI13IVfG\nLV5O3MaLqwjnUP0GbuNd6MynQLjH9j2kn0Gc1HnjHK6zIxyLYwrHfhZVXkBsvofQ4xEdBCA6dSgc\nPUbDbd9D6AHSO90MzNMmajfIlc7OVvxcZQniAbq5ybRlkNt+G5xDFM6mwCHLK8iF0ZbaDYO1ll4/\nRAxVEHo2kUuXcXLKFl0OwUV9osYGg0Gf6OKrhNc/gikuD/0pYKUkuvwh4ouv4D38v3jrX0HMyGtw\nmpjqRaLieTY6kxP4HElJ2jxvEZ4cDkpjKEPe/0zot74Vewx38agiKQ5ilMLxuMeakjKfTD6C4uk4\nivl9/6aCccrcUalUhhaMR40QAmMcUTz/k4lyMUujc7KMSW1h4AJcbsxbCJ1FdGqITB4W1nCd7aTI\nbmI5pqeP276LzJUgO4LikmeF45XLRxO9pMRfOI9oPBx7i7zobCbPdwjc9j18KfCPUIw4q1gd4YxB\nBfNduiY9L8mh7mxP3db5J9GtLVSmcKTM7XlhnKV2wxLFMbETMOpyz3nBC5Ar1zC9NqZdm/RojoeO\niJubhJ0G0bmXiG58DF25eCRRMV66SvzSxxCdLby7X0REs1t+Ok5McYm4eplH7clPnCMr4QVltinH\nJO5jLr50KqeyF2/gitO9kJpGVaSkpMw7qWCcMndMg2CsPJ92bzxFPdNEKZ+hHxpiffKLotbAYoor\nMKZ2c5dfwJ2/iSsu4bbu4LZuQXgGJ33O4bbuoAoLkBlRruOucLx1ByHEocKxv3IFek1ka2M0538B\nwmiwGpQ/1P3c9v25F43DToPMHD8/L8iSLVaIG+u4aAJBmkMSNzfwyktj+wycHk6n1G5YkjzjSppn\n/AwiX0EuXEQ31rHzkNNrDXFzi7BRI1q4lAjHS1dx4vkpkc1VCG98HJfJ493+D1R7c449RCfD5iro\n5Rs8aE3HK9SNBDZ/dnbRnBaivYlbvTH28zghMR/8BPbJ7pEpZtzCcSo+p6SkTIp5n5WknEHK5fJE\nM4w936fVnd5cv1ER+AqlJI3u6Cb7jb5jceESsnZ7ZI/pyucgV07E4a07iXh41nEWV7uDWr6KMetJ\nXMdIHtfhOjVMt44oLKBWLmN6bVy3sXeIt3wZ4gFy69ZoznkERGsDSsvQeDjU/dz2ffzFS5AvEffG\n64SeCM4S97tkihXCzvS6b49DplhGCplsnZ+VidaOkOUvXEDX7k16NOMhX0Hlq3T7feJ4+kT8dqdH\neWE12XmSgqheBCGJaw+ZtiiXk2PR7W00oApLeAuryPYW3tatROxZewMnFd6D/0HE81N8Og5skEdf\neJmHzekQiwG6oWOhNK5C5bOLtBqTLeIQiDFri05EAAAgAElEQVR+Jpj3vYWdQYf4rnC8KxobY9Ba\n792+X+yEcy6No0hJGRKHwLnped/McwRS6jBOmTsm6TCWSjEIDcbMcQ4uIIWgmM9Qa43WGaaNo2d9\n3EldIVLiFtYSR7GzsHkLmo9SsfhJrMHV7qIWLgztvj0UZxPheOsuQj52HKvqeQQW+eidsU40nkX0\nmkj/eK5Bt30fX4Cfn8+Jpx50kcpDzpGrNVdZRBiNbqzPjli8i44wvVbyvpwndkvtgtMvtRuGNM94\nB+khl69idYRubjB/YvHTmG6DsL5O6OcJr3+E6ObHEa11vHv/lYrFh+C8DHr1NR61JdN25euk2tc5\nnnJCnMEtnBvfw3sB9saHcSe4Lpl0LrAQAiklnuehlDqx4zh1GKekpEyK9Fs0Ze6YbCSFpB8ev/xt\nVigXszTb49lG3BlYTGEJ1DEuFL0Mbvkqbvk6hB3YeBfR2Z7vIruTYGJc7R5qcRWkGv3jO4tr7wjH\nQQbpB8j1/z31oiCBS3Kqc+Vj3d/V7+MLR1CYT9E46jTIlMacH34KCJnkFdtuE9OpT3o4x8YOOjhj\nhi+SnFImXWo3LGc9z9gFeeTSGrq1he2dnXx/AJSHBUw0wFYvzLFfaDQ46aEvvc5GT6Gn8DIrNAqX\nnc/v7Ukioi529ebYHl9/8Jswo4pMmzCjFo5TUlJSTptUME6ZO44jGI/iS9v3A1rd6dtiO2qKuYA4\ntkRmfBc69Z7DVteOfodMEXfuJVz1Iq5dgzNWZHcidIjdvo9aWmNsXwl+FuVnEFu3ERPahi7bm4jC\n8UVRV3+Ax3yKxlbHWB3jZWd3guYFWXLlCrqxgZ2DbHLTrqEyOQhm93ciMrmpKbUblr08Y3++SyGf\no3wOVVoi3n6I09ORLX1aeJVzSKmI6+vodh2NJF59bdLDmlqckOi1N9gaeETTuWGAbuhwc7LwNlV0\ntrAXro3loV2+jL1wHeYsouEw4TiNpEhJGQ7HTizF1PzML6lgnDJ3lMtler0ecXx6Tl/P8+j0opnb\n/TwsvqfwfUWrP97ZgbGOrlG4wosdXq6wAOdvJv9bfwC1O2l7+TEQ8QDbeIRcHkKkPyrKx6ueRzQ3\nkP3J5eSKeIBAgDz+1948i8ZRt0kwg3mBAEGhjJ/NEW8/wpn52eERN2a1BG+31G55qkrthqXd6SUZ\nvmcCiVy+AkBcf3TmduX4i6tYHRK3anu36XYdrQLiC++f4MimE4dAr73OdpyhP8Ufuf0YXGY2v9em\nGWkt+FncGHam6a/5NuwI3MXTKsLuJxxDEoeUOo5TUlKmkVQwTpk7stks2Wz21IrvpJTExhHp2XFP\nHQchBKVCwHbzdCb/3dCh84v75uu68gXc+ZtJBMXWbajfhzPmhho1IuxCews50uxOgbd8BQZtRHO4\nwrlxIFobUFw+0WO4+gM8ZwkKx4u3mFqcI+q1yZRmy42VLS8inUXPo8jlLLq5ibc4Q6JlvoJavkwv\n1LQ7XewMT4DPTJ6xn0WuXMF0GjMd5XIspIe/dAndbWK6z18z6lYNE+SJz41v+/2s4QCz+iotm6Mb\nTv/72yFPlIWbcgBW45ZWR/qQZnEVWz7ZNdqs8KRwvMuu49jap69lUiE5JSVlkqSCccpcUi6XTy3H\nWMrEXTzvVIoZmp34VEtNGj2HXdhxvUoPt3gZzt9Myus234PWBpxyHu5c029Br4FcHI3TWK1cgXiA\n3Hh3KrIgRbeOHIVzpfEQz5m5E41N2ENKgfRGXII4DqQkX13G9duYdu3w42cUpyNMt4GadtFYeTNR\najcs855nLAoLyMp54vo6Npr+fOmR4mfwF84TN7deGGMTN7cw2TLxyvVTHNx04gBz/n10VInmjPy5\n9IzEHbO/IOVgxKCFWXt5ZI/nAPPWp7DBGYsB2kFKuec4NsbsKxynpKQ8ZvIRFM//zCupYJwyl5xW\n8Z13RnKL81kfbRyRPt1VbmMdnVjizt1Iyuz6bdzGu9DdZu7zPyZFtw5hB7lwMueIXFhFOId89M7U\nJDsJZyHqjyQXdl5F47DdIFOc7gI85WfIlxeJm5vYQWf/gzIF8DOnO7AxYQddnI6RpaVJD2VfklK7\n1ZkptRuWec0zlouXwM8Rbz9MFmHPECJbxC8tEdXXj5TVHDc3MflF4sUrpzC66cUsXWGQWWC7O+mR\nHJ1uSJpjPA6627jlSyN7OHvlNWxudHFf0xpJcRD7RVWkwnFKSso0kArGKXNJuVweeySFUor+IMbY\n6RDDRoUQAs+TZAOPQi6gUsyQy/pYCxlfouTpXoD1IkdfC6yOEeEB4lDKaGlvgYmQlfPHurssLiE9\nH7Hxv4gpEyJkax0xIrfgrmicOUGZ3rThjMZEIX5uOnMf/VyRTL6QiFz7CT3Sx19axS+U8SsrqKU1\nvKVLqPIyMsid/oBHhGlvI/0MTFFzvAhmt9RuWOYqz1h6yJWrmLCHbm3ClCzonRaquIjKFYnqj4ba\noRQ3NrDlc8TV0Ylks4SpXCAuXWCjMzsiHECkwfmz+9k/rUgA5eO84MSP5aTCvPZ1uFnY3TRm9hOO\nU8E4JSVlkqShTilzybgdxkIkWw8G01oNfQhSCpSUKCXwlMSTMikkFjvGXZeULzhnscYRGY2vJIEn\nAIWQAucSB3CsLbF2xMaOzfTb6lsC5VNZuoZoPkDE8+/qnjiNR7B4CVFaxrW3jn6/TAGZLyG2biOn\ncYtz2EUoNTKJxDUeoqoXyBQqhN3JlfqNkrjbIruwQtyfLhtZtryAMDoRi/fBq55HSIVu1XBxiFc9\nTz/SaGPwPA8/X8ErLSEAp0PcoIcNp+s5voi4sYG/eAGtIzCT/O6RyMULWBSt9mznFB+V3Tzj/NJl\nXO3upIdzfLJFZGkJ3dw6krN23vCq53HWENfXj3X/qL5BsHARbQ1e69GIRze9mMIS8cIVHrZmSyze\nxSJxXoA4g3/zY8XG2JXLqIdfPdHDmFc/jpmixdDTZDef+Fk3tBBi78cYk+YYp6Q8w7TFQEzTWEZN\nKhinzCXjFoyV59NoT6EY9gRKJW5gT8m9/7/7Ueacw+Fw1gL2SM6wxyvcBp44PFCSrC9BKAQCC+gn\nRGRtRnORExnHVtexUF7FGzQR3fnNLZ0atu8nhU/5Kq7XOPx4L5OIdq0N5FGOnwACEJ3tJJO0sz2S\nx3SNR3MmGrtENC4vMGhNQQmWlORKC9h+G9NvP//PxQVUJo/pNp+KqNCtLQoLF2i0O8RxTBzHe//m\neR5BtoRXXEgEZBPhwh62P8W7GJwlbm7iL1xEb01ItMxXUPkq3X6f+Iwt3EVxjB94eMUl6Mze94+o\nnEd4PvH2HBZEHorAX7yIGXQxvZPsPnNEjQ2CpcvgDF57c2QjnFZstow+9xIPmrM7Ge5qSSVXQZyB\n39dpInp17OX3n0gwdpkc9uprINXIxjVP4uquaJySkpIyKVLBOGUuqVQqY4uk8DyfTi+ceISuEOy4\nhCWeEiglkeIZUXhnUuicwRwhf1gIMfSFlnMWY56efCoh8AJJXiiEkDjAOofWjkgnIvJxojycg+2u\npZCpUFgsIBr309K7cVO7h1y+grEGBs+LdXtIiVq6BIM2on7/9MZ3DER7C3nhZeyIBGN4UjQuE3bH\nG4dzGphogJcrIP0AG0/OlSW9gGyxTNzagjh86t9EUMArVbFhj7j2gOe21luD7XfI57L0+k+Lm7tt\n5Lt4nofvF/ByFaQQOKsTB3K/A6da9XkIOsZ0GqjFVcz2g9M7r/JQ1YvExtJuveBzYM7pdvtUymUI\nuzArgrmQyKU1bNhHH9NZO9NID3/hPLq9jY1G8Dtzjqi+TrB8HazB647ue2TacEEOffH9PGzNdoJh\nd+AoFxYgFYxHS7+FWzpZGaR+81swI+iV2I95EVrnSQBPSUmZPVLBOGUuKZfLY3EYSymJtCHWpyMg\nPBsdoeTuFiXAJWJtIgy7JHv0VEZ1OMmYnh+NryS+EghxsliLbmgJpWRh8Sqi9QgRHdxwnnJSHK52\nF7V8FW3Nga+1WrqC0BFy492p35QjrAYTgxfsn4N7TFzjEapyfm6cxlG7QaayRL8xRCTJCPGzefxM\nlvjZrFEvwC8v40z8/L89g+k1CZYKDKR8YQ7gswKyUgrfz+HnSo8F5LCPHbRhwnmCNuwi/P+fvTeL\nsSSr73W/tWLY85A7p8qsqatn3DTNbV/flt1cyXA4V5YtC9MyWJZoi8GWkAceGhuZF2QJkOwnS5aA\ndiMjy9ZBAqk9iAckdIXsF98HH45HDu6moQe6MivnPQ8Rsda6D5E7K7Mq59xD7J3rk5KmMiN2rBx2\nRKwv/uv395HFOXR9+L8bWZwHP0Oj1ZnqnOLT0mi2KZaXMBuvj3soJ+NnkKXF3ZiWCRHcA0T4GdxC\n3CDTROHJO5yWvjReeBi59gqyPfnn+3sxrk+4/AR3GpJJb9URaTCOj4HE359MEhJQUmJSWUTv7Pfh\nplBBz12NK2AuKUdFUlgslhMwAmMS9L5J0lgGjBXGlqmkVCrx+utnm8ydprpWSIdWa7CTLkfG1cHu\nrhh2HHlfdAT78oQnmUHGWkQaNpuaUv4KftRE1NdH8j1cSozGbL2FO3cTVVu/r7LOqVxFoJF3XkVM\nyFJnUV+H4gLsDLZK09TWYmmcL9FrTrZEMFqheh38bIGgPdqq0nS+jEDHy+f3KoclbnkeISVR/fQC\nKKxvUSjMUWuePm5CKYVSiv5fuuM4uG4Kv5zfFcgKE3TiCuQxNHZUzR288iI6nYfukGI0/AxOcZ5u\nL6BbT3BUx4iZmDzj/CwynTvxocq0IjIF3L3mdkO4LhlNUF2HK4/hrfwAedwKnAnDSJfo6jtZbzuM\nqD5i6ERIHC89OSsDJoUoQC8+gPPW/z7zruFP/z/oCW5Ga7FYLJcBK4wtU8lFM4z7T3r3C2TP86g2\ne0ftcsxr3RMdISWynycswGgDmL0ICRVdvondRWItqm1Nxs9RmH0AUX17zM2gphitMFtv4czeQFXv\n7FXmyuI8wvEQ668h1AAruIaM6NSQlWtDCRuYJmkcthukZxag2xpZZW2mNIvptoj2Ve05hQrSz6Aa\n2+izNlMMe6ACfM8jCM/3N9oXyL1efA2QUuK5Hl5pASll/FAl6MYZyiNqrBTW1vEqS3ETvIEe8/I1\ntTsrSc8zlrPX0Mc0iJx2nMIswnEJtte4L6pmkGhFsLMOy+/Au/195AQ10TwKIyTRtXey2fWY0L7O\nh9IOJH62jKhdnmaFo0C0NuMc4zMKY7X4ACY/M5QxTVvVro2ksFgs48QKY8tUUiwW78swbjQa1Go1\nrl27duDzh91Q3HtxdhyXVjdCH7EuTwpxT5O5w6MjALSOUFNSsTFMzhpr0VaaTOUmorGOmKJKn0Sh\nIsz22ziV66jt24hUFpnOI7Z/MnETZQFxJnOmBJ3BS11TW8MpLpApztJrN9AT3J09aFZJ52fo1ocr\nxqTrks6XiRpbmN2sUZEu4OaKqE6DcOv82dhRfYtsZencwvhetNb0goBesPvgRMq4kV5hblcgGwi7\nqG7zvuzlgWEMUXUDt7w4uCZ42RJOtkSr0710Te3OSiLzjF0fObOMalbRE3ZOHhTuzBVMFBBWR7Tq\nSCvC6gZcfQLv7f9AnvWBVoIwCKKrT7ATpuhMzvPfU9HsGUq5MlhhPFBk0EHNLZ0p7sMgUE/9PNpL\nDXNoE4ExZmrEtsUySjQCnaCQoSSNZdBYYWwZCu12m5dffpnvf//7CCF46qmn+OAHP0gqdfjNQbvd\n5tvf/javvPIK1WqVXC7Hk08+yS/+4i+STqfPdOwgCNBac+vWLf72b/+W1dVVVlZWaDablMtl/uiP\n/ujA9ic9iRZCoA1EkcL3HBx5t8ncXpWw6cdGTEd0RJI5KtbCkxKNxCkuQqYAO6sMtbLoshIF6J3b\nOLNXEdKBxhYygRV2p0HW1jALD2GGIIwBTH0dUVwglStipAAj0FGICrqoCZIKOgzAKBwvhRqS/HRT\nGfx0lrC6BipCeGncQgUT9eIqyYtGnRiNatXIZQu02oPPPNdaEwQBwa5AFkLguS5eroLjOAgMJuyh\nuy3MAH/3RoWoxjbu7FWiCwj1g03tbPzEaUlUnnGmiMzPEFXXMRO02mNwSLzZK6h2A9UZ7d+wURFh\ndROuPYn31r8hoyE9JBoiBlDLj9MwWc6xmC7xaAPG8WyO8TAQAnJlaFVPtbl+6N2odG7Ig5oebIWx\nxWIZJ1YYW4bCX/3VX9FsNvnt3/5tlFJ8/etf55vf/CbPP//8odvXajXq9Tq/8iu/wuLiItvb23zz\nm9+kXq/z0Y9+9NB9tNbs7OywsrLCysrKnhje2NjYu7h+//vfZ3l5mWeffZbl5WWWl5fP/L04jgMI\nCjn/gBS+jNERSaYfa6EAKVN48w/Azu2RLQ+fetJ5SBcRXgqBieMJwi5y+61xj+zciKgXizwphxa3\nEEtj0F6aXrOG4/k4fhovmwch4maVQY+om+xqwKBRJV2ep10dfJf5VL6IFIJwZxWEg1u+AsIQ1tYH\nGjGju028bAF5QgO8QWCMIQjDvYpmIQSu6+JlS7iFWQRgoh6m20Kfo1nQfnTQQQRpZGkeXTv778c2\ntTs/SckzFuUlkHL34collAuOh1deIKpvo8dU7W1USFjbRNx4Cvetf0VO2L2HWniIllOkmuxL0YUI\ntcBJ5eJVAZbBEXZQS7dwX/uXEzc1rkf0yE8TapAopJQDr7C1gtVisVgGhxXGloGztrbGK6+8wqc/\n/em9+IfnnnuOr371q3zgAx+gWCzet8/S0hIf+9jH9v49OzvLL/3SL/E//sf/QGsdL/EljpX49re/\nvSeI+3mS2WyW5eVlHnvsMd773vcipeSTn/wk//IvJ9+8nEQUTVGI2yVBG0NPgV+5hmjtQGtn3EOa\nPNIFyBQQbgowcQfs1lacZeulYPY6cu21ia/UEY0NKMxDbW1oxzD1dZzifJxp3KiigrvlW9Lzcbw0\n6fIcCBk3mgt6RL32yDKDT4MxmrDbxM+VCFqDq8jOlGYxQYeouYNTnEO6fhxJMaRK5qi+SaG0QK0x\n4gpEYwjDkHBfJIbnunjpIm6+EgtkFWB67biR3hlRzR3c8mL8vj1tJI9tajcQgjDE88aUZyydOIu9\n20S16ydvP4UIP41bmCWsrmPG3MPARCFBbQtuvBv3jf+FHENDzPOgKtfppmfZmvLTQCuQpLJlhBXG\nA0U0NzFXH4FTCOPonf83OpVBmviBW3+ONwxxPAlRD8aYvTmuxWI5PXEHqOS8x6f5MZUVxpaB88Yb\nb5DJZA5kBT/22GMAvPnmmzz55JOnep1Op0MqlTpwIfU8jzfeeIPl5WWefPJJlpeXWVpaolQqHbgx\nqFarbG9vnykbyj6Rnr6fQRBp3MxMXFGys3LxZe3TTKYA6XsEcWMLei3Evp+bkS5m7gbOnVcPfH5S\nEc1tRPHK0C/0pr4RS+NCmV7j7rJNHQboMCDcLTKVrof0UqQKM3Hkh9GoMEB12+gxy5Co0yJdzoJ0\nQF+sElVKl3SxjGpug+vjzV5FtWuE9c0BjfZwTBRigi4p39/LHx4XYRQR7nsg6bounpfDzZSQQmBU\niOl1dgXyye+1qLaON7NEFPVOWFlhm9oNmlZ7DHnGqRyyOE9U3xzaA5akI7NFnHSOYPtOYq7vJgoI\n6ttw8//AffN7yAQ9+DsMVVwkKC6xXk/OxHtYNHuGcqGI3LlAfI/lPmQUoDL5E+M+TKaAXn4YpIMD\ne6t9hi2OJ5lpm5dZLJbJwwpjy8Cp1+vk8/kDn5NSks1m72tEdxTNZpPvfOc7/NzP/dyBz6fTaT7z\nmc+cuH+xWEQpRbvdJpezOVmXmUhrtPDw5h/A7KwgktKcaNxkirEg9nzQBhG0DhXE+zGAWXwQufkm\nYsKW2x6FMBoRdjB+FoLBZ9vux9Q3kIW5+6TxfnQUoqOQaLfKVEgHx0vh5YoIxwVj4q/3OugxSKKg\nWSVTKNOpnb+S0vXT+Nkcqt3Ayc+gex3CrRVG9XxeNbfJVJbHLozvJYqiAytaHMfB8zJ4mUIskHUU\nC+Ru4/DqcxPHeHjlRaLN2xwqmW1Tu6HRaLYpzixh1keQZ1ycQ3qZweR7TyhOcQ4hZCyLE4YJe4TN\nKtz8adzXv4c8xQOfcaByM4SzN1mtXR5BZ6SLEQJhRdxgMQZTmkccE4sUPf3f0ans3r+FEDiOY8Wx\nxWKxJBgrjC2n5lvf+hbf/e53j93ms5/97LFfP83Fv9vt8tJLL7G0tMQv/MIvnGmMfaSUFAoFarWa\nFcaWOKIiMvgzy9CuQ3O4VYyJpC+IXR+MRgRtaGxAr33qSmGz8CCysYE87ZL3CUHW7mAq1zFbwxXG\nADQ2kYU50sUZuvWTo1KMVkS9dhxRAQghkZ6Pm84gc3G8j1YKFXRQveE30tNRiFYRjp9GBWcXjn6u\ngOO4oA3STxHurF24WvnMGINq7lDIl2k0k7s0WSmFUor+T9lxHFw3hV/O7wpkhQl2K5D7S99VRNTY\nwp1dOtgEzza1Gzpaa1rtHrnZG5itYWW7C+TsNXQUEO4kT5SOCndmaVfKJrfhqg66hK063Hoa9/X/\nSdIWnet0nmjhEVYukSwGCJTETeVPH91jORUiaKOXH0YeIYzVzCK6vHD4vgMWx2dZXTpOTmq6brFY\njsYYgTHJee8kaSyDxgpjy6l53/vexzPPPHPsNrOzsxSLRZrNgxNSrTXtdptCoXDs/r1ejxdffJFM\nJsPHP/7xC+U6lUol6vX6uRrdWaaTINI46QJuOgvbt0cvqkbJvYK4d3ZBvB8zs4SIesj6+hAGO2Z6\nLYR0Rpc/1dhEwKml8X6M0aige1fWCoHj+kg/jZfpN9JTqLBL1OlwmiiDsxI0q2TKC7TPKIzTxTir\nV2DinOIxVqnrXhs3W8R13YnJqe8L5H52v5QSz/XwSgvxtdJoTK+L7jXQvTZOeQFVXbdN7UZIGIaE\nw8ozdlPImSVUc+fCjRInFinj2JVWDZ3wRqEQn2dCIeCBn8Z943uJkcbGyxAtvYPVxvROcI+iGUA6\nV8GxwniwNDfQS7fgB//ffV8ygHr6v6P99LEvYSuO78dGUlgslnFjhbHl1ORyuVNV6z7wwAN0Oh3e\nfvvtvRzjV199FYCbN28euV+32+XFF1/E8zx+67d+C9e92J9nsVikVhtccybLdKC0QQsHf+4mprY2\nPc1PMqW4SZ3j7Qri1q4gbl146aXJlsDPIFdfGdBgk4UgzjI2+VnEqCrWLiCND2AMKuyhwh79dmrS\n9XH8FOlSBaTEaL2vkd4AhKExhO3GsdEaB5CSbLECmF3ZNfxK6NMQ1TfJlxepjrgB3qDQWtMLgr1o\nDSklruviF+ZBShACOXeDXmCb2o2SoeQZZ8vIbImwugZjzjIfG66PV5onrA+vKeYw0N0WkRBw82nc\nN//X2KWxcX3Cq09wpyGT1Fd1ZHQCMIX8yRtazoTUCuVnMELeV5Sgrz+Gzt7f8PworDi2WCyW5GCF\nsWXgLC4u8vjjj/ONb3yDD33oQ0RRxMsvv8zTTz9NsRjfMNRqNb70pS/xkY98hBs3btDr9fjKV75C\nGIY8//zzdDp3hUIulztXpXGpVLLC2HIoZjeiwisuxtm9tbVxD+nsZMuQziNcD/SuIK6tQdAeaDaf\n8dJQuoJc/UGCetEOHtHYQC49hhnlEufGJsIY0oUZuo0LSON70FEQL1knrqASjovjpUjlywjHuSuZ\nex10FJ7waocT9dqkMzmk4x7bjM/L5PFSaXSngWqfLsN+ZKgI3W2RTadpdyc/z1drTRAEBLsCWQhB\noVAgCC6pYBwjd/OM3+Ci2dxiJl4lFW6vXvi1JhXhZ3ELZcLqOmYChbnqNEFIuPFu3Lf+dWzS2EiH\n6Oo72Wg7RJdQFvcxQmKkg5jmVWbjwCjM7BJi824ckhES9VPPYlz/zC93XnFsjLnQCtVRcZpIClth\nbLEcjkFgEjQzTdJYBo0Vxpah8Pzzz/Pyyy/z5S9/GSEETz31FM8999ze15VSbGxsEIaxrPjJT37C\nW2/FmX9f+MIXDrzW5z73OWZmZs48BiuMLScRKo3jZXHnH4gjKtT55NlIuFcQd5uxIO61EUOSCEZI\nmL+JXHtt6idWQkcIFcaTmlFGJTS34krjAUvj/RgVEamIaHcJt5BOnIOcKSDdfY30gg46OH3lXq+x\nQ6pYoVO9PxNcuj7pfAEd9BLdmEu1qvizy7Qn3xffhzGGVqtFIZ+lZiuMR0qcZ9wlN3v9/HnG0kXO\nXkW1G+jO5V0+L3NlpJ8h2F5L7HnkNKh2HXIluP4u/J/8+8iPb4QkuvYkm12P3uQ594HSVZJcuoBo\nn2KFjOXUiG4DdfVR5D5hrB7/v9Dpi/WSsRXHFovFMj6EOeWjq5WVlWGPxWIZKC+88AJPPPEEn/jE\nJ8Y9FEvCEULgOxJT30B0k1AFKSFXhFR+N2JCIbot6NSHKoj3YwCz9Chy+21k53I8eNGZEqq4CDu3\nT9540ORnMekC3fr26I8tJI7n4/hppOvt5iCHRL0e6oSsVC9fQitF1N3dTkrS+RLCGFRjayKqAYWX\nRhYq1BPcAO8iZNJpDIJOZwqteMLJZTO4YfvMjVZNKo9TnCWqbY4163vcuKV5AMLa9DSqdfMzuGi8\n2/85smMaILr2TnZ0jqY9DZByYcGt42y8Pu6hTBUaMDM38P/fvwbA+GnC//Y86oLC+F6MMXvSGLhP\nHIdhiJQSx3EGetxBo7VGKYXrukdK7yiK9r5Py/Rh+yydn//9Zo1OLznFTJmUw0/dLI17GEPBVhhb\nphZbYWw5LXFEhcIrzCEyeaiuwkiXgcm48iiVRzhuLIg7TajdgV5nJIL4Xsz8A8jm9qWRxQCiU0NW\nrg+hTdwpaG4hMKSLldFL43sb6SGQnofjp/Ey87FA1iquFu612R98GTbrZGbmibpt0vkS0nGIGtsT\nlTFqwi5CRXiuSzghDfDOQqfbpVgoEOlIC68AACAASURBVAQSpezEc5TEecYF6DVPn2dcWsRxPcLt\nOxNdUXtRvMoSqtdFtaarCjRq7kChAsvvwFv5wdCPZwC19DhNY2Vxn14EJpsd9zCmDgko18c4HkKF\nRE+9F5Ua/M95f8WxUupAxXFfvE5C1bGNm7BYLoARGJOg93mSxjJgrDC2TC3FYpFqdbomGpbhEiqN\n46Rx5x6AnRWIhiW9YkEs0nlwXNC7gri6CsF4BPF+TGkRoRWitjrWcYwaAdCtx/Ef41iq2txGGMYj\njQ9g0GGADoN9jfQ8pJciVZhBSCdupBf2iLodVBCQLc+hWlXC7mRW6UaNLXIzVya2Ad5JNFstCvk8\n1drljTYYF41m65R5xhI5dw0ddAl3JjBXf1BIiTezRNSqorvHr3CYVKLGNqI4C4uP4q29OtRjqYUH\nabsldibz1Dw0NM6e2LQMEB2h568imzX0wg0YorgVQuC6LsaYPXHcZ5JkrM0wtlgsScYKY8vUUi6X\nefPNN8+0jxDCXpwvOdoYetrgV64hWtvQGkCurJCQLSPSuVgQKxVnEO+sxk3qLn6EgWEyRcgUkCv/\nlahxjQpZW8MsPIQZV7ZhaxsBZIoVOmOVxgfRURjnHHdioSocFy9bJFvOorotwq1NJrohl1aoToNc\nJktrX9PVaUFrTbfbJZ/L0mxNp4RLKlqbk/OM/QyytEjU2MIEl7gM1E3hleYIL0EUR1jfgtIcLDyM\nt/7aUI6hZq7Ry8yxaZ8T3UdHSdx0EdEaYaPbS4Bo76CvPY7OFdF+ZjTHvEcc9yMrAJtxbLFYLBfE\nCmPL1FIsFqnXk5BHO1lYaR4TRAo3O4OTyseZtmdZGix3BXEqB3K3grjbiKuWg05iRaxxfSgvIVf/\na+xVzuNCRD0EBiPlgeiFkdLaBkzipHEf4bikCjOgQoLtVZiShoi6Xcer5Pca60wbvSDA9308zyUM\npy96I8mEYUToebj5ufvzjHMVZCZPuHNnat5L50Gkc7i5EuHOGuaS/BzC2iaU52HuAbzNNwb62qq4\nQFBaZq2e1DuO8dLoGvL5GbDCeLB0apgr78CMQdL2oyqiKEIIsRdV4TgOQojEiWNjTOLGZLFMCgaB\nSdCMOkljGTRWGFumFpthbLkokdIo4eLPP4CpriKCIyoPpYTsDCKV3VdBXE+8IN6PQcLCLcT6jxD6\ncsskUd+AwkKcIT0udivbkyWNJaliGSkkYX0TprACMKxvUijOUZvSBnjNVotioWCjKcbAYXnGsnIV\nozXh9uWK/7kXJzeD8FME23dG3D9g/ITVDSgvgFZ42z8ZyGuqbJmo8gCrVhYfSaTAeGkMTMQ92qQg\npIt2HLTjjeX4/YKXfsM7pRRKqb3PJVEcH4ct4LFYLOPGCmPL1GKFsWUQxA3xDH5pCboNaGzsE8Q5\ncBxQEaKT/AriozCAufIQcuc28igpfokQrW1E6cr4a6z70rhUoVMbrzT2c0UcP4Vq7hD2pvhvJAog\nCkj5Pr1g+oS4MYZ2p0OhkKPRmE4pnmT28oy3biMrS6hWHd2dztzs0+KUFsDoS53bHFbXETOLhFrh\nVVcu9Fo6lSdafISV2qTdiYweZSSOmxpiv4rLhRESfeXRscnie7k3qmKSxbHFYrGMCyuMLVNLqVSy\nkRSWgREojZPK42VLccREawe234awO3GC+D7mbiA7NeQg8pqnAGE0IuxgUjnojVmqtXbAGDKlWTq1\nCyyddTwyhRKd6ubJ2+7DTWfxMnl0p0m4dTmW7kaNbTKVpakUxgBhGOL7Pr7vEQS24dMo0doQhIrU\n7DWi+sblzisGvMoSqttGte29WrCzjj+zTKQVbv188tx4aaLld7DakON/4DkBtEOJlykhGuvjHsrE\nYxDoK4+iHH/cQ7mPpIpjYwxSyrEc22KZdIwBY5IzA5/mxQD2LGWZWorFoq0wtgwMIQTaQC9SaARG\nuhD2Jl4Wm8I8CIHYuT3uoSQKWbuDyM+Oexgx7Sq0dsiUzjce6XpkCyWEVmQrC0j35AmddH0y5Xkc\nxyXcXkW1L9G51GhUq0Y+mx33SIZGq9Uik0lP/Plr0igW80gBzXYbWZjDSco5ZtRIF2/2KlGrbmXx\nHoaguo6avUFUmDv73o5HdPUJ7jTl2OL3J41Gz2ByM+MexsRjAL34MMpNw5irdvsRDodJ4L44dl0X\nIQRKKaIoQmudyOiHJI7JYrFcPqwwtkwtpVKJZrNJFJ0+j9VenC1w+I2mMWbvI1CaIF1ALz2KSefH\nMMLBYNJ5yJWR6z+y4uheei1Ekio/zimNHS9FOl+EjdfjyBSlSOUKeNnC4TtISbo0i5/NE9XWUY2t\nszV8nBJ0t4kr7uYgTiPtdpticXLPX5OE73uUSwU6nQ6tdpsoiqg3GgTSxZ27hvDS4x7iyBBeCm9m\nkbC2ie61xz2cZGEMwc46au5BdK5y+t2kQ3TtSdbbLtHl6Bc4ELSOm/3aO//zYwA9fwvtZ8cui09L\nXxz3K4yTLo4tFotlnCRoNmyxDJZ0Oo3v+zaWwnIkhy1F60vhk9Da0FOGaOYqev5WXHE8QRjpQuUq\ncu2HCHuDfB8CEM1tzDkqvYbGGaWxm8qSymRh7cegorjRlo5QtQ0co0nf8zp+oUymOItqVomqaxh1\nuZsfRvVN8tnMuIcxNKIoIooiMunUuIcy1RQLeXzfo16v3/cAu9vt0mi2EIVZnPLimEY4OkQ6j1uY\nJdhZw0xh08yBYDTBzhrhwsPoTOnkzYUguvZOtno+vct9yj4XkRbgT+95ftiYmWvodBEjJk8pSCnH\nJo6Pq4S+dxuLxWIZJ5N3drdYTokQwuYYn4NpvEE5KqPstHL4OEJl6EkfvfgwpjA/EZUqBuDKQ4j1\n1xHKZpgehWhsIJNWQX5KaexlC/i+F1cW768Qrq/jFiroxhamuU2mPI+XLZGZWYBeh3B7BRNe7lzV\nPkaFmF6bdGp6hWq708H3PZujOAR8z6VcKtDtdmi1WkdeG7TWNJpNOqGKq40zxZGOc1Q4hQpOOkew\ncwe0LYM9FqMJquuES4+h00esBiG+lkfLT1CN0rSDSbj7SB7NyMFkyuMexkSii4uofCVRstgYc+ZM\n4nGKY4vFcj50Aj+mleSc4S2WIWBzjC8fF6kaPi/GGHpKE+QqmCuPYBJerWIWH0JUV5HBmBu6JRyh\no1ionyLzd6ScII1T+RIeCjbeuL8LQ68dLxuVLqbXQW2v4HouuttCd5vDH/uEoZpV0n4yOr4Pi1ar\nRaGQG/cwpopCPkcq5VNvNAhPGYsVhiH1egOTyuHMXoUJW7VyHG55EYQkrK5Pd2eYQaIVwc464fI7\n0P79708DqCuP0RRZGvYZ37lpdzQme3Ilt+UgOldBlxYxYnpim6w4tlgslvuxwtgy1ZRKJSuMp5Rh\nVg2fF6UNXS1QszfRleuJqrroYyrXEL0Wsrk17qFMBKK2BoX5cQ/jfvrSuHxQGqeLMzhBG7aPaWJY\nX8cp7u6nFWrrNgLwZhbBplnfg0E1tinkpleoKq0JgoDcFMdvjArPcyiXCvSCHs1W68zXIgO02m1a\n7Q7OzBWcwqQ3xRN4lWV00CGq22vOmdGKsLpBeO0JtHfw/anmb9H2S+y07Tn7ImjihoHGXvtOjU4X\n0JVr6CmSxfs5TBwrpQYqjm0khcVimRSSZzMslgFihfF0MI6q4YsQKE3gZdFXHsFky4mJqTC5Crge\ncvsn4x7KxCA6NWTSKoz7tKvQvCuNM6VZZKsK9fXj9+s2EY7D/lsA3dhCN3fwKkvgTm8Ew3nQQQdp\nFK47PRWf99LtdnFcZ6q/x2FTyGdJp9NxVXF4sagfpVTcFE/sNsVL+KqVQ5Eu3uwyUXMH1W6MezQT\ni1ERYXWT8PqT6N1zsypfpZedZ7NpJecgCLSA1PQ+FBwk2s+g525NrSzej5QSx3H2mt8OQxxbLJbz\nIjAmOR/TXHBjhbFlqikWizbDeIJIYtXwedEmbooXFhcxiw9hxiwdjZ+F4ixy7bUpvqQNHgHQrUE2\noRmH7So0q2Rn5hH1NWhtn26/+ibOPZEW/YgKrzCDk0vo9zsmovom+Ux63MMYKq1mk3xuAsXkmPFc\nl1KpQBCGNJvNgV6rut0ujUYTka/gzlxhUm7bhZ/Bm1kkrG6gA5uXcFGMCglrW0Q3niIqLRGUr7LW\nsFfyQdEKJCap1/gEYdwUeuFhtEyuLD5PhvFxCCHGJo4nbd5jsVimk8m487RYzomtME4u0yKGTyLS\nhh4ueuFBdOnKWJY9GunC3PVYFptpjuUfDrK2jsglNOPQ8RD5Gdh+GzpnqOLr1BDeIQ8xbETF4WiF\n7jTJTrE01sbQ6XTI57PjHsrEUMhnyWRSNBoNgiAYyjG0MTSaTdpBhDu3jMgmuymeyBRw82WCnTsY\n21R1YBgVoZVCzd5ktW7Py4Ok1TPoKW02OSiM46EWH0ZPUbb6WThJHJ+VaZvrWCyW6cUKY8tUY5ve\njZ/jqoYvC8YYepEmTJfQS49iRrj00QAsPoTYfAMRDUdoTDsi6iGMgaRV1XhpxOw12HorbmZ3Vhpb\nyCMyUm1Exf2odg3fdZByem+dgjBEAL433Y3+LorrxlnFQRjSGHBV8VGEYUit3kD7OdzKVXCSJ26c\nwixOKkuwvQbnkCiWQxASt1DBqyxxu5Wnp+17cxgY6SSy70QSMMJBLT5CYJL98xnFefhecWyMOdAc\n7zyvdxSXaZ5ksZwVg0jcx7SS7DO/xXJByuWyFcYj5LJUDZ8XtRtTEVWuo+duYkYgIM3Cg4j6GrLb\nHPqxphlR30hW87tUDlFego03Ieyd7zVaVWTq6IrZOKJiNY6osF3kAYjqWxSy012B22q3yWant5L6\nouRzGbLZDI1mc2hVxcfRbrdpdjo45Ss4hbmRH/8o4sgMQ1hdh8Qk908wQuLmZ/AqS6x0y/zrapGN\nVop2KElZZzxwekpi0vlxDyNxGCHQVx4lEu7E3MsPMpLiuGPsb453UXFssVgsScUKY8tUc54M41Hc\naEw6tmr4YoRK03PScVO8/OzQptZmZhkR9ZCNjSEd4fIgWtuIVELyXbNlRGEONl6HCy35NtCsIvMz\nR2+ioziiQggbUQGYsAcqmOoKXGMMrXabYtHKk/24jqRUKqCUotFojFUK7DXFQ+LOXUOOtSmexJtd\nRndbRI2dMY5jStgnileDGf51Nc9G6+4qj52OQzZlp2+DptXbbQxs2cMAevERlJdCDzgbeFo4rzge\ndNayxWKxDIvkrWezWAZIqVSyTe8uiBDiPhFsxfDFiWMqDE5+Di9fga2fIMLBNQcy2TJ4aeSdVwb2\nmpcZYTSi147jRHqt8Q2kMIfw07EsHsT7sLWNXHwQ3Txe9OjGFiKVxZ9djpeaCzCYeAzGYLTGaAW7\n/zUqwqgIdHTxMSaMqL5FtrJEEE5vPmsUxXmp6bRPt2ujbHLZDI7r0Gw2E1U91u316AUBuXwFxyjU\nzjowwvG5Hl5pgai+hT7vSgdLjBA4uRIylWO1kWJt2+Owup5m18EtOYz093wJ6IRgiqOLC0s6BtDz\nD6K9LJf9QfFp6BfS9OdM+/ONpZTnirKycy2L5WiMERiTnHNTksYyaKwwtkw1xWKRarU67mFMBP0n\n3VYOjxalDQqBP/cAstdA7KxeuDGd8dJQWkCu/pe9zR8gsnYHM3sTMy5hXFpCCOIYikFhNHQayGwR\n3T7+4ZrptYm2VnBmFjGdBqK5DVKCdBFSIqQD0gPHATcdf97PgFYgQIUhql2FaMJFq9Godp18Nk+z\nfY7s6Amh1W5TLBQJgsu7xFZKSaGQIwgC2o1k/q6NMTSbTTzXJTu3jG430O3hR3EJP41bmCWsrscP\nhyznY58oXmumWD1CFPdRCZskTxNGSIx0EFqNeyhjR1euo9MFzARVwfbnK+Os3D1OHDuOY6uKLRbL\nxGHXNFmmmnK5bCuM7+G4OAkrh8dHoDSBl49jKi7QrdtICfM3kWuv2UnPoAnaiDE1PBOz1xAmgu23\nB//ijU1kpnC6bXcjKnA8xOx1UBrCbtx0r9OA1jbUN0BrhOsimpvIlR8g6hs4roeTn8WdvYpbmgfX\nH/z3MiJ0p4ErmeoGeACtdotCfrozm48im0lTyGdpNpt0u4Nb/TEswiiiVm+gvAzu7FVwhxebIrNF\nnPwMwfYdK4vPixA4+TJeZZmNsMK/ruZZbaQ4zdSsFUgy05uKMzY6kcSkz3//NS3o0hV0rnKgCaCN\nUDgbh0VVRFFEFEV78y3787RYLJOArTC2TDXFYvFSN707rGr4NFL4sBgKy/DRxtBT4JWWcApziK2f\nIM6QUWsAs/gwcvMtRGSXBw8aAYjmFqYwh2hsju64czehXYPm1nAOoBX0WohUHtM7XXNEXd/CpDI4\nC7cwOysQdkC6UF5EOh7UNxFbbyD6FT+1O2hA5Ct0d9Zx/DRufgbpuOgoRLVqMGF/s1F9k0JpgVpj\nehtKKqUIw5BsJk27k3xpOgiklBTyWcIwpN5ojHs4Z6bd6eA4DrnSIibsoeuDzbB3inMIIQm37wz0\ndS8NQuBkS8h0jvVmipUTKooPY6fjsFRw6IT2ofAgafYgl69A+/Jmcev8LKqwcEAWW87PYRXHURQ/\nZDtunmXnYBbL8RgEJkHraJM0lkFjhbFlqulnGE/7k1wbJzFYxi3MQ22IhIe/8BCivY2orZ/qMmTm\nH0A2t5BdW1U/LERjE7n0GGYUwlhIxPxNqK1DZ8i/08YGztwDRKcUxgCm1yGKVnHKV8BxkVEPdu4g\nOtVD/15lLRZM6VKFbm0bFcQC0vFTuPky0nExKiBq1WEC8lBNFGLCLinfpxdMb85vp9ulUCjghg5R\nNN2CKpNO4fserXYbpSb3e+03xUv5Pum5a+jGNrp38UgNd2YJHXaJhvXwaqoROLkiMp1nvemzsupz\n3oWezZ6DW5bA5P6NJpEgApNNSHPbMaAzJaLSMqHSSBM/PJvmudMoOUwc96uObVSFxWI5Dc1mk699\n7Wt873vfQ0rJM888w0c/+lHS6fSR23/zm9/k3//939na2qJQKPAzP/Mz/Nqv/RrZ7OlXD1phbJlq\nisUiQRDQ7XbJZE53E5h0yXreqmHLZGGMoacMTmYGN1tGbr2NCI6e8JvSFYRWiJqt+homQkeIKMS4\nqeFWxEoXMXcDdlZG02RPRXG0xFmb+qk4okKW5jCOh+jUj324ca80BlBBDxXEP0vppXCzJRzXw0Qh\nql3HDLAZ5KBRjW0yleWpFsYArVaLQj5PtTZ5FbenYa+qOIomsqr4KHpBQBCG5LJlnGwJVVuLm1ae\nFSnxZpaIWjV0d4xNPycSgZMtIjN5Nlo+ty8givtoI9A2x3goaGR8LTvD6q5pQPs59NxNDBKJRmt9\noYZt4yAJGcYnsX9sfXkcRRFCiAPi2M7rLBbLvfzZn/0ZtVqNz33uc0RRxJe//GVeeuklPvWpTx26\n/c7ODtVqld/4jd/g2rVrbGxs8NJLL7Gzs8MLL7xw6uNOxhXAYjknjuNQKBQmMpbCZg1bIG6K11Og\nZm+gZ29gpHPfNiZdhHQOufH6FC+ISQ6ifgeKc8M7gJeOZfHWT0Yji/vU13HzpXPtqmubRK065voT\n6FT+2G1l7Q5Oc5tMqXL/64Q9gsYOnZ11gm4LkS3izV7FLS/GDfSShjGoZpVCbrpzfrXWcaXxFOYZ\np9M+hXyOVrtNp9MZ93AGjjGGZqtFuxfgVpaRuTO+x10fb2aJsL5lZfGZiEWxN7vMtokzim/X0wxq\n6tUKJJnJjYFPLK1IYrLnuw5OKsZLoxceRAtnT1y6rouUEq31XoSCZbD0M46llPdlHFssluMxBnSC\nPob9tr19+zb/9m//xic/+UkeeughHnvsMT72sY/xT//0T1Sr1UP3uX79Oi+88AJPP/00CwsLPPHE\nE/z6r/863/ve987UzNoKY8vUMwk5xofJYSuGLfsJlCZwM+jFRzC5Cv2/DOP6MLMUN7nD/r2MAtGp\nI50hzdT9LKK8BBtvxBW/oyQKQIXgH7606UR6LaLtVfTcTdTM1WM3lbU7yCOkcR8dBnflcaeJyBT2\n5LFMJUce614LicF1p3vRVhAEIASeNx3fpxRQKuZxHId6oz7RERSnIeo3xXP7TfFOPocJP4tXmiOs\nrmMmICYmKfRF8c6uKH67NjhR3Kfaccn49z9AtlyMVtdgskdfl6YN43ioxUfQ8uB5fb847s9PtNZ7\nUQqWwXCvoO+L47PIHIvFMv28+uqr5HI5bt26tfe5d73rXQgh+OEPf3jq12m1WmSz2TOtHJmOu36L\n5RiKxSL1ejIyXW3WsOUi6N2YCrcwj5uvwPZtmL+BWPsRQp9cAXL3r0xA/wGFELv/vufzh21z2Hb9\nv2lkbGBaO1P/JFIAdGqQLUP78Ke65yJTROQrsPF63IhuHNTWcGeuEW2vnG//vYiKeVh+B2LlFSSH\nT3z68RSZUoXObjzFUegwIAjj2Afp+rjpPF6+glEK3akPJJ/1IkT1TfLlRapT3AAP4hvNYqEw8dEU\nKd8nnUnRbrcvXfVcu9NBSkm+tABRgKqtH7qdzJWRfoZgew2MlRenIY6eKLDd9nhrNcUw63KaPYlj\nc4wHTqTjB/EGpn7FlpEO+sqj98ni/fSFZj824d6oiiTFP0xKv5rDojP6P+d+VbedF1oslv1Uq1VK\npYOrX6SU5PP5IyuM76Ver/M3f/M3vP/97z/Tsa0wtkw95XL51G+kQWKzhi3DItIGJRy8+QeQAvT8\nLdS9Uxtx3/85gOn/j2B3HY2IP3Fkw7/D65f3f85zXajcwEQ9RHMLGhtTK49lfQ2z+AhmUMI4P4tI\nZWH99fHKmbAHRsXVh9H5c3l1bQOTyuFcfwK9/mPkEdEad6XxLJ3a6Rpp6SggaPblsYebzuHlZzBa\nodsN9ChjPPqoCN1rk0mn6XSTm7l8UYwxtDsdioUc9cZkxhMUi3m0Uol5kDwOtNbUGw183yczdx3V\n2Mbse9+4xXmMgHDHZuKfhr4o3ul4vDlkUdzH5hgPD4XE8dKjX+UzQoyQ6CuPoaR36n36glgplWhx\nPMn0xfG0r3ixWC6KAUyCHuud1/B8/etf5+///u+P3eZP//RPjz7uKR+UdTod/viP/5jr16/zoQ99\n6ExjtMLYMvUMu8J4GquGJ3nslwVjDEHUv6E87kJx2t9lv+LhqBymk19HBSEpRyLq65hUHnP1CYzR\n0NhCNDaQUxSZIaIAYXScKX3RauDSIkI6sPEm57/lGCC1NZzyEmp79UIvY3otou0ezvwtTGsHZ+f2\nodudRxr30VFI0IylfSyPs3j5ciyPO010d3QVv6q5Q2r2Kp3pdQwAhGGI73mkfH+imv35vkc2k6Z1\nCauKjyIIAsIwJJct4WSLqNo6XnkB1eugWsmO8koCTqaAzBapdjzeGJEo3k+zJ8mmBO1eAq4bU0Qr\nkHjZEqI2nSdzg0AvPoJyU6fbfl9FrBAC13UxxhwQx/2GbVYcDwY7D7NYJpO//Mu/ZG1t7cDnnn32\nWd7znvccuv0v//Iv8/M///PHvubi4iLlcvm+iFWtNa1W677K43vpdrt88YtfJJfL8fu///tnbmRq\nhbFl6imVSgPLMLZVw5Zp56J/z4E2+OVl5PqPkNXVOGM5V0Ff/an4tVs7iNrakTEFk4Sor0NxHqoX\nqMKrXENEQdzgLikEnThpRLpwiqiTY1ERavPtEyMqLiKN+8TyOD7XS8eNK49zJYzW6G4D3Rm+PI4a\nWxTzFerNyay+PS2tdptisUgYhWid/GtgsZBDG0O9Xk/CI5mRI6XEkRK5T+pIIRBSIhAIEd/fuJVl\njFaozmRHjgwbJ5OPRXHXH4so7lPtuFwthbR7thpxkDQDQylThtrayRtPGAbQCw+ivHP2KtjlXnHc\nr4gdpzg2xpxZhIwDO2+0WKaXj370o2favlAoUCgUTtzu0UcfpdVq8frrr+/lGP/Hf/wHxhgeeeSR\nI/frdDp88YtfxPd9PvOZz5yr34oVxpap5zxN76axathiGQXGGAIt8BceRK79KJahtTuI2h2M40Fu\nBr38OFoIRKuKqN9Bjiuv94KI1jaivHRuASXmbkCnAY3NgY5rEJjaOrI0jx7QkvTTRFQMQhrvHU9F\nBK0atEA4blx5PNuXx030kISYCboIHeF5HmEYDuUYSaHdalHI56jVk5vb7HkuuWyGdrtNOOFVxf18\nSyklMja8d/8/u4Km72h2M4cM8YoRow3aaLQGpTVmNyNT7zbXjT/iXX3fJTuzBFoR1TdBTfbPbZDc\nFcUeb6wOvpHdWWkG8YMAm2M8WPQU5xjr2ZvoVB7EYP52kyiOJ4XjfjZ2vmmxHI8x4ogVsePBDDki\n6urVq7z73e/mz//8z/nN3/xNoijia1/7Gs8++yzlchmA7e1tPv/5z/O7v/u7PPTQQ3S7Xb7whS8Q\nBAGf+tSnaLXuzr2KxeKpH7BZYWwZOu12m5dffpnvf//7CCF46qmn+OAHP0gqdbqlUC+++CKvvPIK\nn/jEJ3jnO9955uOXy2Xq9TqNRoOVlRVWVla4ffs2y8vLvO9977NVw5b7sL//i2GMIVCQWngI1l5D\n7GbyChVCfT2OrHBcyJbjhitCIjp1RHUVedGK1hEijEH02phUDs6UmysR8zegsTXYpnkDRHSbiNIV\ntJTx7HkAnCaiIpbGhkxp7sLSeO+4KiJs1Qlb9Vgep7K4s1fBaFSniRmwPI7qW+RmrlCdcmEcKUUY\nRWQyaToJzOEo5HOAod5oJOKc3pe7Yp/0Ff0qXyF2K33FXpw89JVvfE7V2uz9N1IGYzTGqLvSV5uB\nVE8HQUQQRHiuQ7a0iMCgm9voIHm/41HRF8W1nsebq2l0QtL5jREom2M8FEItcPwsBONtqDpIdHkZ\nnS1jBiSL99MXx/2ICqVUvJKhf36z4thisVguxKc+9Sn+4i/+gs9//vNIKXnmmWf42Mc+tvd1pRQr\nKyv0ej0AfvzjH/Paa68B8Hu/+PwcbgAAIABJREFU93sHXutLX/oSc3NzpzquMKe8i15ZOWfHdMul\n58UXX6TZbPLhD38YpRRf//rXuXHjBs8///yJ+/7DP/wDr776Kj/4wQ9OLYyjKGJtbW1PDv/zP/8z\nxpi9pyq+77O0tMS73/1u3vve9174+7NYLIcjgJQ0iLUf7UnjwzDSgWwZk5sBKWNZWVtFXqDp2qgw\nfhY1dxOz+dbpdpBuXFlcXYUR5uueB5MpYXJldHV94K8tS/NIxz0yokIXF9GFwUnjwxCOg5vK4qTS\nCANRt4FpD0Yey2wR7WVpdToDeb0kUywUaDbbqAE9WLgonuuQy2VpdzoDr/LeX+G7X4RIKeOK391q\n34PSF9iVun25q/U+AbxP+OoEiO17cRxJNpPCkQLVrmFGEO2SFGQ6j5MrUu95vLGdHFG8n2ulHp7o\n0bI5xgMln5ZUonXkEdn7k4YuzKPLS2jhnHnfflax552+QV5fHPcbMvXPncPCGEMURXsrMZJMP0P/\nuKXhYRgm4kGnZbgsLy+PewgTy/98rUuzm5z3SD4t+D8fvljUT1KxFcaWobK2tsYrr7zCpz/9aa5d\nuwbAc889x1e/+lU+8IEPUCwWj9z39u3b/OM//iMvvPACn/vc5+77utnNI1xZWWF1dZXbt2+zsrLC\n2toaenfiOjs7i+/7+L7Phz/8YZaXl5mdnT3xZkIIYS/UFssFMUBgJP7Cg7D+I8QR7ymhFTS3EM2t\nuPIlW0LPPYh2XETQQuysIqOEVrcFbYQ4ZTs/N4WoXIXttyFIvkgUnRqiND+UtOmTIipkPc6OHGSl\n8b0YpQjbDcJ2AyEd3HQWtxLfvOtuC90+f/a9btfxKnmklHvXo2ml2WqRL+Sp1cafe1vIZ0GII6uK\n9wtfsRfn0P+8A/S7TR8uffU+sWu0RmnQu5W+e5+fsnsHpTSNZgcpBJlMHj9bRncbU90UT6ZzOLlS\nLIoTVFF8GNWOy7VySMvmGA+UZlczky8Cky+MdbZ8bll8XvoP1OIHZHpPOg9bHE8L03YdsVgGjTFH\nNWkfD0kay6CxwtgyVN544w0ymcyeLAZ47LHHAHjzzTd58sknD90vCAL++q//ml/91V89Mgj8lVde\n4cUXXwTiquHl5WVu3brFs88+y/LyMsvLy6TTab7zne/w0ksv8Yd/+IcD/u4sFstJaGMIhMSffxDW\nf4w4Qa0Ko+PGeK2dWB5niui5m2jHQwSduPI4QUtEBcSiuzCPaGwcvaGfRZQXYfNNmIDK6T6msRVn\nGdeO+d7O+9onRFTsSePiHJ368CqNAYw+KI+dVOauPO610K06nHHBf1TfpFCap9aY7opMrTVBr0c+\nl6HZGs6DkD3RKwVCSBzZF72x3JUyFsD9X1EhX+BAQIPZjXXY+28sfkNtdicdCik12bRPrdG2k/VD\n0MbQavdo0yOdTpOaK0DQJapvwxQ0MYW7orjR83g94aK4T8vmGA8N7bjxw6UJPh/oVB49e2OksrhP\nfwVGXxz3M4611nsZxxaLxWJJNlYYW4ZKvV4nn88f+JyUkmw2S71eP3K/v/u7v+PWrVs88cQTR25z\n48YNPv7xj7O8vEylUjnyiXU/w9hisYyO/dng2kAgXfz5B2Dj9VM3kRFGQ7uKaFcxCMgU0DPX0J6P\nCLqI2hqyN/6qRtHYQC69A3OUME4XEIVZWH8DJiijGYgbExbmhqeDVITafBtZmoflxxErrx6IqBil\nNO5jtCLqNIk6TYSUOKksbmUJBOhue1cen/wTMVEAYY+U59Gb8jzjbq9HIZ/Hc90DzeXuq+iVAsFd\n0buXbSlA7OvYtqdnjhK9qi969V7Dtv7XL4KUgnw2RaOV0BUNCcAAnW5ApxuQ8j0ys8ugQqL61sSd\n3/rIVA4nX6IZePz4Thptki+K+xgESlvxNgwCJXFTeeiO/z7jPGgvjZ6/dWFZ3I+VOC+HieMoihBC\nXEpxbIyxVdYWi2VisMLYci6+9a1v8d3vfvfYbT772c8e+/WjbhD+8z//kx/+8If8wR/8wbH7Z7NZ\n3vWudx0/UOIukLXa9C6dtFjGzWERLvf+W2tDKH28uZuw+eaZO48LDHTqiE49lknpPKa0SOTfRIQ9\nRH0d2RnP+1xohVA9jJuCqHfwi7kKIpOH9dfhmBzn5GIwrR1kYRbdGJ6wPS6iIpbGhkxxfmTSuI/R\n+h55nMGtXInH3Guj2/Vjf69RY5tMZWnihHFf9O5lT+6LbrgreoHdZm19stlMrHuPEL1K9T93UPQa\nM5iGbRel0w3x8xl83yUIJlN+jpJeENILQjzPIVteRKBR9W3MvefBhCJTWZxcmWY4eaJ4P/WeQz4l\naNoc44HSDAXp7AzOBApj4/roxUfQMjlT/WGK4/495zTIZ7vCxWI5GYNI1Nomc+aZ7eSQnKuIZaJ4\n3/vexzPPPHPsNrOzsxSLRZrNg8txtda02+0joyZ++MMfsrW1dV+ExNe+9jUeeughfud3fudMYy2V\nSrbC2GIZAPurhvdz2ptbZQy4abzZ67D1k3NfWgXEDeP6TeNSOUx+jmj2OiIK4miI1s5IFxOL2hoU\nr8D+WIXiAsL14sriROiw8yGaW4jFh9FDnjMfjKjYxtm522xX1uPGe5nS/FAb4R07Pq2JOi2iTguE\nxE1lcGcWAYEJOqh2De7NKzYa1aqRz+ZptseXWy2lxHEcHBkvX49FsEDIuPLMGO6KXthryNaXvZGK\nIwkMardBW7JE7yCptzqUC1miSE99/vSgCENFLWzjOJJcYRZXClSzir4nmzwpyFQGJzdDK/L40Xoa\nrSdTFPepdRxKMw7Nnn3IMUg6PYMp5E/eMGEY6aIXH02ULN7PfnHcb453mSuOLRaLJckk80piSTy5\nXI5cLnfidg888ACdToe33357L8f41VdfBeDmzZuH7vP+97+fn/3Znz3wuT/5kz/hgx/84LERFUfR\nF8ZKKRxn9BleFsskcpgcHkTVg9IGvCxe5Sps377w81gB0GvFHwB+FpOvYMpXMTqK5XFza+jyWHTq\nyMqNu0+7Z5YRWsPmW0M+8ggwBtOuI3JlTKs63GMdE1GRBGm8h9FE3RZRty+P07ilRYSUqF4nbpin\n40xR3W3iZQo4UqIGLCD3RLDj4OxOwOOc3913ljGxCFYarRXSkQjHBSGIlCLodIjCkHw+T7MTEEVW\nkBoD9VaXYiFDtZZM4ZlUlNLUmx2kFGQzRbz8DLpTR7WT8dB+vyh+fT1NNOGiuE8ruLsKwDJYjHAw\nQsYRWROAERJ95VFUQmXxfvqCuN8cti+O969wmTZOUwltK4wtFkuSSP7VxDLRLC4u8vjjj/ONb3yD\nD33oQ0RRxMsvv8zTTz9NsVgEoFar8aUvfYmPfOQj3Lhxg0KhcGj18czMDJVK5cxjyGQyuK5Lo9Gg\nXC6fah97sT48ZsAyfVy0avg8KG0Qfh63fAVRvTOw1xUAQRu2d5vieWlMfhZKV1BaI1pbUF8fijwW\nAJ0aJltCZkqxwN4VnNOAaGzgLD5ENGxhvMtRERWJksZ9jCbqtom6bRAC18/glhYQUqKDDqpVJ6pv\nki8vnLoBnpQSd3ci7exNnrlPBBulMFphoi5Gx1I40grTF9NS4mUKSD+FxCMMQ4JeG6UONshqtdsU\n8nl2aslpKDlOlNJ0OiGFfIZGc3yV4ZOK1oZmq4sQkElnSc0VMb0O0RBjbY5D+hmcfJl25PPjKRLF\ndxFEOg6HsXdtg6WrJLl0ATGmyKuzYBCxLHZ8GKBsHXbm7mHiWGt9JnFs5ysWy+XCIEjS295GUlgs\nF+D555/n5Zdf5stf/jJCCJ566imee+65va8rpdjY2CAcUsajEIJSqUStVju1MLZYppFhVQ2fh0gb\nRKaM0BoxBLEqAMLuXkSEcVOY/Azm6hPx99zcinOPBzm9rm8grzwKu5EYU4XR0G0iMgVMZzR5jgci\nKprbONU4oiKR0riPMUS9NlEvlseOn8YtzSF3ox8y6fTeahe5mw98nwg2JpbASmFUDxPqXQm8TwQf\ng/TTeOkswvHQRhMEIWGzdex7XWtNr9cjn03RbE9G/uyw6QYhvueQ9j26wWRlUCcFY6Dd6dHu9Eil\nPDKz10AFRPXN++NbhkBfFHcijx+tZ6ZQFN+l3nXIpSXN7mRUwk4KzQCyuQokXBgbQC8+jHLTA5XF\no2QQ4ngaq5ItFotlnAhzSluwsrJy8kYWS0J5z3vew1e+8hWefPLJcQ9lYrAVxpPLUVXD42R/Zt1+\nHAFOfQPR3BzZWIzrQ24GMqXYLLe2EfU15AUEhpYueulxqK1NbEf1E5EuLNwi2nx79IcuzSMd50BE\nhS4uoAsJlMaHsSuPHT+9K4OjPQFslMJcaLmzxM3kcFJpEJIoigjCkCg6e55pIV+g2enZaIp9/P/s\nvXmQJFd5xXvuzax963V6pnukkUY7kowNCLABjcz+JCMhMBDhsEBsNrKxwjKBlgjA7NgsQSDbGLBN\nEGDAIiw/ISxZxoAwtgE7FLzAQshCYjQaqVszvddelZn33vdHddZU19TeWZXb94uomJ7a8tatrKov\nT557vulsEsVS1fE4kbASjehIJKJgUsAqbgKW4fg2eDQOLT3dEIo3gy0U2yQjAoema9goUo6x0xzM\nWtCXH3J7GF1RAOTc2ZCJLBRzfl83TbMZfzRJlFJN0RhAT+FYCAEpJSKRyETHOCxKKViWBV3Xu4rb\nQojTVgERwWRxcdHtIfiW/37UQLHqnePcTILheedF3R7GWCCHMREKpqamsL09maXUBDFJvOQaBk6N\nZ9Dli0IBLDsPrgTYhFy5zDIawm7+JJQWAVJTUPsvhAADKts74vHgB91Sj0PuPxfYXAaMAC9fl1bj\n9cVTQG2y2a4yvwYV3x1R4WmncTtKQdSrEHVn9g+uR6HHU2CRKJRSMEwT1Up1z03aypUyMqk0tgoU\nTWFTKFUpz9hBDNOCYVrQNQ3J7Bw4AFnegnTgs2ELxTURwSOrCRghEIptKiYH5+SuHAcSGhTXwYao\nCyaJml4am1g8SObuuBjWcewHd7GXjBwE4WeUgrciKTw0FqchwZgIBdlsFoWCN5quEMSodHJ9uy0O\nd3IND4ulAD23H1xJsMpkl30yYQKFNaCwBsX1HfH4PAjGgWoRbPtpcNl9ObqIZaDmDwHrTwJWCJby\n509Cmz8EMWHBGABUrQzL3B1R4SvReI/o8RS0WBLYOXiuGQZMh93sUkrUDYqmaEVIhXLFQC6TRL5I\nQrpTWEKgUKyCc45kYgqR9AxEpQhZHb5WC7NQfIpGjjEHQF54Z6kKDj2RBStvuj2U05DZBYj07FjE\nYq8wiHAcJCE2SK+FIAj/Q4IxEQpyuRwJxsRQuFmAutGIrhf2eJwQh9uxX5OpgMjUAXApwVyKdGDS\nAorrQHEdimtAMge5cA4k18HqpYZ43CIKi+QU1PQSsHYMEN50HjmOMBsO7WjCHTe1sCDWnwLPzQOL\nFzYiKgqrABQSuX3BEo25jkgiBR6NA2gsC67VamNfqlqv15FJR6FrHJYg6QlouGKjUQ2JeBTVmvMR\nCmFGSrnTII8hGU8imspC1soQpf4rTngk1hCKZRSPrCVgiOCKZoOQr2pIxRmKNRKcnKRUU0inZgCP\nCcYyNQOZW4Bik42KcItW4diOoJBSQtM0KKV84TAmCILwGyQYE6Egm80in/d2wwoinHg1UmKc4nD7\n3zaGAKIzS+AbT4LV3V3+zaQASptgpc2GcyeRg5o7C5YWATPKUJYJJDINsViGLGtu+yS02TMgNpZd\nG4LMr0HGU9DtiIrCGgD4XjTWoglo8SS4rkNKhbphwCyVJv6dQNEUp1Mq1zGVTcK0LMp4HgNKKZSr\ndZSrdcTjEcRnDwJWHVZx47QGeSwSg56eRl1G8MhGEoYVbqHYJl/TMTOtoVgLyQnMCWEKQEXibg9j\nFzKegZw5CBkSsbgVxhh0XYdSalfer9ppGutl4XiQiA9yGBNEfxSYk23L94yCd7939goJxkQomJqa\nIsGYcBWvuYaBzpESdsHdWngPW3wPI363PjdjrBFPMXsm+PoxMI/kATMlgcpW48IY1L7DgB5riMVh\nLOytesONrUfH0rBqYDpEVAB+E43thnUJgDFYQqBqGLAq7u77UkoYpkHRFG0USlXkKM947NRqJmo1\nE9GIjuT0AUAKWIV1MK5BT0/DUBE8vJFEnYTiXVRNBkY5xmNBgEPTo40VNi4jownI+bMnIha7mWHc\nj1bh2G7yalkWNE0bi+mBIAgijJBgTISCbDaL5WX33HB+hM5wj45XXcODNKLrJB53uq31Pp3+7vfc\nvQp5SwH63Fnga4+DmbW+Y54kavZMQAhg80m3h+IqKn8S2tQixOaKuwNpjag4cCHY04+Aw9uiMdej\n0BMpML21YV1lzw3rnKZWqyGbyVA0RQtSKpQqdcoznhB2g7xkPIro9BKklPi/9SRqJBR3gcEUDJyf\nZsom9kjF5IgkcmDFNVfHofQY5L5zQ+ks7kbryjgATcexX4VjOv4iCMJLUMVFhIJRMoz9VmAQk6db\nIdoutE4Sxhg459A0rXmxG4MM8xytr81+PVLK5r/2pdWN3Onx9rbtxiSDFu+WAuT8WVB6dPhJGAMK\ngNx3DiCt0IvFAMCMamMxmOaN884yvwZRLUGdcQlQr4AXV5HIzbo9rCZ6IoXY1DziM/vBk1nULIlC\nsYhiqYR6ve45sdimVC4jk/bWUmy3MU0B05JIJWJuDyXwcM6RzaQheBwnChx1oZFY3Id8VUcqSvWr\n05TqCio17eoYlKZD7j8Pknvjd9dr2I5jWyi24yrsWtULeD0ygyD8glKA9NDFI18xY4F+cYhQQBnG\nxF7xqmt43FnD/W4b1DU8CpZiiOw7DJz8JZgwHX3uYVAA1IHzocrbYCVvulbdQOVPQssuQGw97fZQ\nAACqVoJl1qDNnw1W2gQvrLrnNOY6ook0WLQhKpqmiVp1/A3rnEZKCcMwkE5EUaq6vxTbK5SrdUxl\nEojoGkzLX++pX0ink1DQsZznOyKxwnzamydWvES+pmE2RTnGTiNkw92rAFeSKhXTIPdfAMEjLmzd\n27THZtgmBdvYIITYZWAgCIIgBocEYyIUUIYxMQz9ohcmzTDO3GHYS55y+xLAcWHaovHqL8HE5A+A\nFdeg9p8HVTgJVhlulULQYfUywBk8tf65GVGxr3FwWFxDIjc/EdFYiyWgxZLgmg4hpWsN65ymGU1R\n57C88j57gHyphqlsAoV8GTQrzpFMxKFFolgrchTrDKfkOQZDAMmIQMWk5fjdqFn+W4LvFyzFoEUS\ngDnZjHnFGOT+81wRi72cYdwPe2Vba3M8KSUJxwRBEENAgjERCshhTHSiVyM6t4rjSYjDe8ka7hZB\nMU5McET2nQOcfAxMTs7Np/Qo1L7DUFvLYHXKK+2EKqyBZ+cht0+6PZRdyPwqVDwNLbcAXt5CIjfr\nvGjMOfR4GlosDoDBsixU6wYs4Y1mjU5SKpeRyaSxlafPgY1SCsVyHdlsCtsFaoK3V+KxCKKxOLYq\nHJt5jk4+znyVY1/GxLFNEoy7w2AIDo03XLGEc5QNjmhyCiw/ue94BUAunAcRafzOEMPRWlO3C8d2\ndMUkUUr1FKv9foKZICaFUoBS3vlODPJHlwRjIhSMkmFMBAuvRkqMw+UwjkZ0rfexC+/Wy7gbi1jg\n0BfOAU48BqbGfxSsokmouTOhNo6DmfWxb8+vsGoBLLcPEhzwmM+yGVExvR9MmI6IxjwShR5Pg+mR\nRsM6w0C1VIYMcqWIU9EUqUQUZYqmaGJZAjXDRCYVR7HsrQadfiES0ZFIJFCuMyxvaJA9DgDLBsNc\nmiJA+rFd1ZCLMRSqwf5emjTlmsJUOgfkJxPDpADI+cMwtRiYAnxo8p0ovWrQTsKxZVlgjLkiHBME\nQfgFEoyJUJDL5chhHBJ6uYaHwUkx2euu4VFofY5JOI4VAAtaQzQ++cuxisYyngVmDgBrx1zNTvYL\nqrgOnpuDzK+6PZTTaY2o0LWRRGMtnoYeT4BxDZZloWYYMKvhEwftaIpanUNQNEWTas1ENJ1ANKrD\nMCg3dlA450ilkjAExxObGizZ/3dDKgalAM4kpKIl5d0o1DTMpzSgSvujk0gASotAgTWavo57ezNn\nQMTSEEIC0trVQHiSeF1MHTZOzS3heJBxksOYIAivQYIxEQoymQzq9TpqtRricer4HhTINdx7HO1/\nO40rojHToduZxmN4r2V6BsjMAauPAxOMv/AzrLwNlpn3mL94N82IiswsElOzqG53F40Z1xFJpsEi\njYZ1hmmiXKmSSAqgXC4jm05jq0DRFK0UylVMZZKwrEaTJaI3dkO7lWZDu8Ep1hnm0yZOFmNjGp3/\nqVOO8dgwJEMilgTq442hkbn9kKkZgHHo7FQDt9YM3km8x0EVMFuFY3teJyUc02eTIPZOI5LC7VGc\nwktjcRoSjIlQEIlEkEqlkM/nBxaMg1ok+RGnXMNO4oVGdJMShvsxyZgKBcDiEejzh4HVo466fOTU\nfiCWBlaPBvuXfwyo0iZ4dhayMP7mcqPSGlERn5pHbXuteZsWS0CPp8C4dqphXd3/DeucRkgJ06Ro\ninaUAgrlGrKZBLbzlGfcje4N7QanWOM4kBM4WXR+fMGBoS44dA5YdP7CUcoGRzw53Wj6OiZkehYy\nuw+KNU6m2CIm5+4Jx0Gl09xalntubqo5CILwGiQYE6Ehm82iUChgYWHB7aEQPfCqa7hV/LTF0b3i\nRdfwXpiU41gBsPQo9PmzgLXHHWkDI2fPALgGrD3uwLOFD1beBEuf62mXMYCdiIpl8Nw8EtP7dsbb\naFhXqdVhCXKV96O6E01RrXNy07YghES1aiKTTqBYCl7jw70Qj0YQjfduaDcohmDgDGgEBFAsRTe2\nKxqm4xz5Cn1GnaRcV5jOZMG3xvP8MpGFnF6CZKc3diThuDN2rbmX109zSxAE0RkSjInQMDU1RTnG\nHqOT8OoFcdguGFuvaxdDhyVo4nA3JiYaK0DocWhzh4D1J0aWHxQAtXAOYNaBjScdG1/oUAqqkgdL\nTUOVx3Qk7RgKMr8KlsyCJXPIl0puD8h3lMtl5NIpbBVIGG2lZpiIRjTEoxHUDMo/bza0M/o3tBuG\nmgnk4hL5GgnG3SjUNOzLeK8ZaRBQXINi3PFeCjKagpw7q6NY3Eo3cdOOUnC21lJjiVnzKuMUjp0Q\ntgmCaCDBPPXr5qWxOE14fgGI0JPNZkkwHhKnippuBazb4jDnHJqmNS92QWjfbo+xXfjsRev9W4vN\nTs/TGmthb9suSMcR5zBJ7NfUOo/2PDiFBCCiCajZM0cKplAA1P7zoWolYHsyXc+DDCuuQ0uk3B7G\nwKhKAaiVkEn5Z8xeoRFNYSKViLo9FM9RKNcQj0ehhUhkaYdzjkwmBS2SxPEtHScKumNiMQDkaxzz\naWro1gtDcDBH1t8Q7RiCQ8XTjj6nisQg9x3uKxa3Youbuq6DMdZs4OZ0rRVGWufWFo8ty4IQYqxz\nS+8bQRBeI7zVLBE6crkcCoWC28MIPJ2EzmEE13GNp1UYtsXhXoLsIIJnJ3G4m8DcSRwOgjDcj/bX\nOKwA3w+pGEQ0BTWzNJRorDiHOnABVGkDrLjuyFhCj5JArQSWzLo9koGRpS1wo4J0Mun2UHxHtVZD\nNKKFyn02KIVSFZlMwu1huEI6nUQilcZKPoInt3WYwvnft4rBENeD7OdxhrrFoQ+uPxIDUjIAlZp2\n7PmUFoFYOB+Sj7bwlzEGXddDKxzbPTPGgVvCMUEQhFegKp8IDeQwdpZermE3xeFeruFRn7OVfuJw\nkF3DozJ20RiAiGeBqQMD3V9pkYazePsEWGXbkTEQOxRWoflIMAYAWdyAJgwkE4M1RCVOUa5UkEvH\n3B6G5xBSoVwxkMuE50REMhFHJpvFejmKYxsaatY4DzEYLAlENRKNe7FV0ZCO0aGe01QNQEWdcRg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GXYnzZwfDvu9lA8iVQsZCcRxk9UZ8ilhheLgywOknB8OuSEJghnUapx8QpeGovTkGBM7Jlv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\noNh1cXtCxggJxgQxAqZp4n//939x9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WwVZOyUOIlmcMeKkUL/xjtWFEVB07SMCMfSkkIi\nkeQKUjCWSCQp4fjjj2fDhg2cc845nHfeedx99934fPEVYsr00sBowWq2i3qxsmYkYyNeIT5fxeFo\nhCZQdmdBSvYf8PuocuenDUM09vfaaCjzZ7oZGaHHp1Hlzj+xPBkUOXWqiwIowkzp+eiY67xpgOGn\nvkQ/kiGe2etTn1+RmepR6PVpuBP0MVaAqmINYepZdV0bSTiWJM5IwnEisbaiKGHRdzzCcSaTJ+QY\nkkjGhxBgWuiRyz9pKRhLJJKUYbPZ+PKXv8xLL73Etm3bOPPMM3njjTfi3j4dtgq55BM7GjLbOPlE\nE+Jz5SZDKjCFwFngQhnHsuaY+zZ0HFp+FnuLRl9AxW4jLwvAHeizU52nHs7jQVMFsyoHUEUwbeej\n4dcl0whQ7AgwpdzArmXu2tTjU6kolDcdhtM1oOFM0Me4wqOCyF7xXcZOySEyFhqp7+KJkULfQSKF\n7SKF45DonGnhWCKRSKyOFIwlEknKmTx5Mr/85S+54YYb+NKXvsQ3v/lNuru749o2maJxrvvExks6\nhPhcZvg4Gu/EJ98wTJPCouLU7FsPUCJtKY6gcLhPo740/4p3ef0qTps8nyWGoLGqH43MCO2RopwQ\nBqrwM7k0SKkrM9nGPp2MCtZWpS9BH+PSQhWbKsh0xngyyPTqt2wikXg7FvHGSKH9x7uNoijhwngh\n4dgwjPA+xkvo8+VrjCeRSHILKRhLJFmKaZr89re/5Y477uDGG2/kzjvvZOPGjZluVkwUReGCCy5g\n8+bN9Pf3s3jxYp577rmEMgMSWRoorQBGRorG8ZHoOIpFLo+lsSAAh6sw6fsN+n1US1uKMAf7Ncrz\n0ppBYSCg4nHm42cfG7XFAVz2IJkW9iLPpaYRoNwVZFKZgaaku10KASM/fcBHQqAMejzHgdupUGBn\niBd2tjOSrUK+xk/jjbcj/x6+v0TaEBJ+Q8cajVBhPE3TwtsnkrE8HvJ1rEgkyUII6z1yFVumGyCR\nSMbGK6+8whtvvMHll1/OhAkT2LNnD4899hgul4vFixdnunkxqaio4MEHH2Tz5s3cdNNNrFu3jrvv\nvpuJEyfGtf3wgDL092BW0shn68gAUgp4g4SC9+FFXXJdMI9G5PgZLWs42t+R/080kybfEELgcDjR\nA35MI3lLlQ1dpyBsSyHviZtCwRdUKXXpdA3kV8i332ujtjjA+5359bnHQqFDZ2KxH8UitgFDbH5M\nHRsGDeUO9vdqeAPp+113DahUF+l8fEiOoUh6fBpFTgWvP/Z10mkHj0sZ9KbOQYZbUUX+O/z1XCIZ\ncdJI24w3Hg0JvyEhOh6Ormw4KhqH7Cty9XuUSCSSeJGzKYkkS/nkk084/vjjOe644ygrK6O5uZnG\nxkZ2796d6abFxbJly/jd737HpEmTOP3003n00UdHXQ42WkZxJPmaOTxWonnx5nIGRKoz0KXn4egY\npomryJP8/epBPM7cyWgbL/t6bdSX5l/xu26fDbdDjoPRUBXB7CofqrBeZv7R8ygIw0+1J0htiY6S\npmzjvoBCoRxDx9DtG9nH2KZCmVvLWbE4kpFWv2X79T5TK/WSkcEdEn9DgnM8xwxlHKuqihACXdfD\n+xgLIx0328eGRCLJH6RgLJFkKVOmTGHXrl10dnYC0N7ezscff8ycOXMy3LL4cbvdfO973+P//u//\n+MUvfsG5557Lzp07AQgEAnzyySf88Y9/5PHHH+fpp5+OKwiPDN6lOJwYuSpyZtKeRFp/jIwQUFDo\nTuo+g/4BqousJ35lCp+uoipgU/NL+BJCIWgoOGz59bkTZVbVADas7XMdzhY0dQrUAFPLdQrsqf9e\nhVAwRf79dkajL6DGzN5UFags1hBmfll5JGqbZjWsZuOWLOuP8RTGU1U1vH0iwnG2fOcSSTYjhECY\nFnrk8O9errGSSLKU5cuX4/P5uOuuu8J3w1evXs1JJ52U6aYlzKxZs/iv//ov1q1bx80338wJJ5zA\n4cOHMU0TVVWprq6msbFxxCVu0lIheYyUaWzlPo13qSSk154kW/szHQgh0OwONM2PYSRHYDB0Hbdb\nCjyRHPBqNJT5+fCgK9NNSSsH+mzUFfv5+FB+fe54qSnyU+QIZo35Xth6ygxQV2yjN6Cxv1cDUnce\n7fUpVLmD7O11puwY2YeCbiooHOt4PSgW535mcSyyweYrlbYSyWakWD+RPjVNc4jQHU+MqGlaWDQO\nPYb7LY+VXBaXJBJJbiEFY4kkS9myZQt/+9vf+Kd/+idqampob2/n6aefpqSkhJNPPjnTzYuKEILu\n7m7a29tpa2sL/3vo0CEA7HY7J554IoFAgJKSEs444wxOPfVUnM7RJ2pSlEsuVu9Pq4rDo7XD6hPJ\nTGAesabwdh9O3j71IB6nQa9fS9o+s5muAY1qj59883Y+1G9nzoT+TDfDkrjsJvWlARSRXZmg4WxD\nU8djNymsEHR02QgYqTmH9vpVaksM9vamZPdZS49Pw12g4vUdvTlX4VFRMRAZLpxoBaLFUJm43meT\nODwSyfCMjsygDgm/iQjHhmGEheOQv7EV+0oikUiSiRSMJZIsZcOGDaxYsYKWlhYAJk6cyKFDh3j5\n5ZctKRjv2rWLn//853i9XgAKCwupq6ujubmZuro66uvrqaqqClcr/vWvf811113Heeedxy233ILH\nM7rXqdVFzmzECiJnrkx4QshxeiymEBS4i/D1eZOyv4DfR1VRgRSMjyBQ8PpVqooMOr35IxjrpoIp\nFFRMzDwSykdDUQSzq/tRhbWtKEZi8FwpUM0Ak0oFhwc0DvWrJDvbOGgoqArk282W0ege0CgtOyoY\nlxQq2NTcXpabKMnKjo2XXIuVohEtJh3+ejxECr/xHtdms4W9kY0jxXqjCcehNmVb30ok2YQQYFro\ncpPLlz4pGEskWUoweKxHZ3i5pgWprKxk4cKFYXG4rKwsZjClKAqXXHIJZ5xxBv/v//0/li5dyj33\n3MPKlSvjOtZIAaUM4MZGLJFTTnjGhhSNhyKEQLPZ0Wx2DH38/sOGHsTtzt9l0dHo9NpoKA/Q6bVn\nuilp5XC/xsSSAO3dBZluimWYUTmAXQkc6yeQhSiKAkKntMCkyGmjo1tDN5N7Dh0IQqnLpGtACsYh\n+oMq6qCSTqFDweVQ8tqKYiSSkR07nHyJlWKRLOHYMIwh/tPxZBzbbLaw4By5fSxf72hYda4mkUgk\nw5GCsUSSpcydO5dNmzZRVlbGhAkTaGtr47XXXuOUU07JdNOiUlZWxqpVqxLaprq6mocffpiNGzdy\n0003sW7dOu68806qq6vj2n548ZHhz0sSI9lLLPN9wgPWyOC2CqZp4nIX4e3pSsqtetPQcTsM+gIy\nyxggYKgIAU6biV/PH+HrQJ+dmVU+2rsz3RJrUOkOUuLMHt/ieFEwsSsBGsrtdPZq9PiTN8Z7BjSq\n3DpdA3LadJRBH+MCOxQXqnlX5G4sjFXklLFSbJKRIBLKGk5EOI58XyjjOJGMZYlEIskW8mfGIJHk\nGBdccAHNzc2sW7eOe+65hw0bNrBw4cKERdlsYOXKlbz66quUl5ezdOlSHnvssYQqHg+vXJ1olWXJ\nUKJVrh6pGvhwMdQKFbitRrQljfk4Ro0jonEyCPoHqCkaf7ZyLrHfqzGl3JfpZqQVv66iKoJBS4H8\nxmkzaSjzZZ1vcUKYQaqKgtSX6Ee+9/HTHxzsO8lRFEXgC6qUF2lSLE6QaHFptIeMleInWlyaaKwf\nKf7G04ehvrfZbGE7PV3XMU15rpBIUo0Q1nvkKoqI80za0dGR6rZIJBLJqPzlL3/hW9/6FlVVVdx3\n331MmzYt7m2HLwXM18A6mYyUJZMtxeisRqyJYb6gqgr+/n704Pj9Vd0l5byzz52EVuUKgtnVfra2\nF5JPOQNTyn30+DQ6+xyZbkrGUBA01fbhUPyZbkqaUBCKjb09GgPB8Y/1SWU6nxwqyKvs/KEICu2C\nUpdOcYGOpggUDBDxiWuSY4lmTxENGSslRizbj0T7LlLUj/e4IZE/tH20fQghotoKSvKP2traTDch\na3n0ZZN9XZluxVFqSuGq5bkZH+Tmp5JIJDnLySefzIsvvsipp57KWWedxYMPPhh34JWMDATJUWL5\nGMcSkWU2THwkmsGdC0R+Rl03cBa6kzIuTEOn0C6zfY6i0D2gMdGTX5PVA312qvPsMw9nWoVv0Lc4\nbxAoIkhtic4Ej44yTsPmngGFmqJ86j+wayaV7iAzKweYO2GAaRX9VBYO+l8rIoAwdRlHxclImcOx\niMxElrFS/ET213ji/ZBHccgmLJ7jRlpSmKaJruvhfUgkEkk2IgVjiUSSdTidTv71X/+VDRs2sHHj\nRs466yy2bt0a9/b5KMiNl3iXSUYixeGxk8tjNJ6xpOs6riLPuI8V9Puo8eSXyDMaB/psVHvyawm5\n16/i1LL/tzNWylxBylyBcYumWYmp43YEmVKh47SN/fN7/Sqegty++aQqgpICnanlPuZM6Gd2VT+1\nngEKND+qCKAI/ZgVWvIG/LEkw1YitB/Zp2MnWtw5llgqUeEYCFtVqKp6jHAsv0+JZPyYprDcI1eR\n1RskEknWMnPmTJ566il++ctfcskll3DxxRfzrW99C7d79CXoI/nF5ruoOd4CK7KAW/LI9qJ44xpL\nioLd4SQYGPsSej0YwF1ojHn7XEQ3FYImuB06fYF8CQMVBoIqHqdOrz9fPvMgDs1kWkWO+xaPhhCo\nBKkvFXQPaBzoU4HEzp+GCMntJrmTbyNwO0zKXDqeAuOIzYQJwiB0b2GkKfBoq4uy4Ro1XpJdkG54\nLDV8v/nQp8lmrMUGI4m8ARCvVUUo41hV1XBRPNM05XcokUiyilyJeCQSSZ6iqipXXnklL7/8Mp98\n8gnLli3j1VdfjXv7kYqP5AOpKLCSy9mxmSIb+jTZY0kIgdNViKKOL1QRpkGBLFg1hP29NhrK8sXL\ndpD9Xhu1xfmWbS5orO5HFfn2uY8y5LykByh2BGgoN7CpiZ87+/wKVUXZfANK4LSZ1BQFmFU1aDMx\ntayfcpcPGwEUERwUixNkpOuTla5R4yUV8VIsRiuMJ0mcZPVppE9xtJsm0Y5rs9mw2WxSLJZIJFlH\nfqVZSCSSnGXixIn85Cc/4be//S033HADS5Ys4fbbb6eioiKu7YcHkMOfz3bizYKB5BRYkRncycdK\nfZrsrKpYGKaJq8hDf093wtuGCPgGqPE42H24YMz7yDX6Aip2G6iYmHmSO9Dts9FQll/C6ZQyPw4t\nOHKaaA4Rz3lJCAMNk4ZyBwf7VLoG4s827vGp1Hh0Or32ZDQ3LWiqoNipU15oUGA3UTBROeKpGsoi\nTtL4GCmTM9uu++m6xo1GLvWpVUhGnwohMAxjiAg92n5CwrGu5/FqD4kkSQiSd+1KBhZqStLJj1mC\nRCLJCxRFYc2aNbz66qs4nU6WLFnCunXr4s4ciJZ9kI3+cfFmwUDqi9HlSp9aiXT3aTqzqqKj4Chw\njXlrPRigyJHNWYGpQOFwn0Z9af4IqEIoBA0FR55km5cU6FS6AygiNz/veM9LmEEqCnUmlQ5aMcSD\nX2dMmcnpREHgcRpMLvVzXE0/jdX91JcMUGjzHeNDnLI2ZNl1P/PXuNHJtj7NBqJlxifapyHh2DDi\njzGk0C+RSLIJKRhLJJKco7S0lHvvvZcf//jHPPDAA1x22WXs3r077u1jLa+0IuOZ6KR7spMtfZot\nJNumItpYssLEWQiBw1mAqmqjvznWPkwDZ54IhfFysF+j3J1fmU4H+mzUFee+FYdNNZleOTBoMZAD\npEzQEwYOdbAgXpEjnvODgl+HIoeVfjeCArvJxOIAjdUDzJ3Qz5SyfkoLBm0mVBGEDN00SIYglwoS\nvaFupeK9Vu3TbCWyP8fSp5FjKRgMhrOHRxoj8ruSSCTZhBSMJRJJznLqqafy8ssvc/zxx7NixQoe\nfvjhuJeCWTEoz4YsmJGwYp9mO2MVja2UhR4PIWuKsRLwD1BTlBviWbIwhYIvqFLqspL4lVoO9dsp\ndeV6trmgsXoALUvF4vRf5wSYQWo8QSYW6yijLCzt9qlUeTL7m7GrJhWFQWZUDvoQz6jop9I1gF3x\nh32IrXJdjSXIpeum8UjjKVY7h48nq5HpPs1F4hGOEzk3ye9BIkkxYtCSwiqPXPakkIKxRCLJaQoK\nCvj2t7/Nk08+yTPPPMOaNWt455134t4+U0F5tovDIyEnOslnpOWq2ZKFPhoCcLoKx7StHgjgceaP\nMBov+3pt1JfmfsZtCN1UMIWCSu5mm08q9eNU/YgssKKw1HVOGBTagkyt0EcsktnnV3Db09u3qiIo\nLtCZUuZjTk0/s6sHqC0ewKX5wzYTVp+tjnTdT9a131LjKQ2ko0/zjWhjIFo8Nfz9w8dS5DbR9ieR\nSCTZgix6J5FI8oI5c+bwzDPP8LOf/YwvfvGLXHnllfzbv/0bLtfo3qiRwV60YHE8DA8cRwokI4+V\nTZOaaKSyTyVHiTWesnEsCSGwOZwEAwFMI3HxV5gGDs0kYMh75SF8uoqqDFoY6GZ+9Mvhfo2JJQHa\nu3OvCGKRU6e6KACGcaQgjLCMCBZ5LrLudU6giCB1pYJen8Z+77EF8QQKhkj1b0ZQ6DApLTAoLtDR\nVDF4k0MYoZezltB4jCW+xUt2jKf0MFKfhl6XxCbesRSJqo7+24+8eRESk6VYLJFIsg1FxHnm6ujo\nSHVbJBJJlvHv//7vHD58+JjnFy1axAUXXJCBFsVHW1sbN998Mx9//DH33XcfixYtSmj7yIA8FIjH\nE5AnEpTmwyQnklhZG5LYpGqSY3VUVaWv+9jzzmjYHU58agmfdjlT0Krspcyl47ILPjw49sKC2YTT\nZjKz0se2ve5MNyWpaKqgeaIXjcCQJdSQ2HUqGeSEmKeoGMJGe7dG0BjatvJCA0Oo7O1J3rnEoZmU\nuAxKXToOTaBgomIda4lUEc+1PyfGUxqR8VRsxjqWIvtUCMEHH3zAzJkz44qpvF4vra2ttLa2sm3b\nNiZPnsy3vvWtcXwKSa5QW1ub6SZkLY+8qPNZ4lOBlDGhDP7lrNzMxc3NTyWRSNLCN77xDUzz6NLM\nvXv38tBDD9HS0pLBVo1OfX09v/jFL1i/fj1f+cpXWLlyJbfddhulpaVxbT98+d/w50OvRft7pP0N\n/zufkBnHIzOeSU7kNsMFpGxECEGBuwhfnzeh7YKBAJ4SHZCCcSRdAxrVHj9gkg9OZX5dRVUFufV5\nBbOr+tEY9C1O5/k0Z8U8YaIRYHKZnUP9Kof7j2Yb9/hU6ksN9vaMffeaIvAUGJQX6hTYTdRIgfhI\nN+a2VDxItLEaj0iedeMpjcTq03wTjpN5borM4v7444/54Q9/yJQpUzj77LOZNWtW+H0DAwO88847\nbN26la1bt7Jt2zY++eQTZsyYQVNTE/PmzWPBggVJ+HQSiUSSHqRgLJFIxozbPTRDa9OmTVRWVjJ9\n+vQMtSh+FEXh/PPPZ+nSpdx+++0sXryYO++8k3POOSeugDraEkApDI8PKRoPkuxJTuS+cqFPhRBo\nNjuazYYRZxHLI1uCMLGrJsE8sV+IB4GC169SVWTQ6c2PfunxaVS5dTr7HJluSlKoLQ5QaA8eqbxy\nlFjXqbEKRzkrDo+EGaTcpeJxDmYbG6aCbioMfqxEbjoIihwmpYU6HqeBqghUDBBmXgnEkeTleEoT\n0bK0c1k4TtdYUhSF6dOn87WvfY0NGzbw4IMP0tDQQHt7O62trezcuZPJkyfT3NxMU1MTV1xxBXPn\nzqWwcGz1FyQSiSTTSMFYIpEkBcMwePvttzn99NMz3ZSEKC8v5wc/+AG///3vuemmm3jyySe5++67\nqauri/r+YDCIzTZ46pQTnNSQiyJnLNI5yUmmcJRpTNPE5fbgTdCaIugboMbjoK1bZhlH0um10VAe\noNNrz3RT0sKBPjv1pf6cEIwLHTq1xX6UkL9tFMaScSjFvAiEiUMNMqVcsL9Xo9evMhBUKHeZHBqI\nJRgLnDZBaYFOqcvApgkUDFTMIVnE+UIi4yke731JfMQSjmO9ni3EM56SeW7SdZ1du3axZcsWtm3b\nxrZt23jvvfe48MIL6ejoQNd1Vq1axSOPPEJDQ8O4jiWRSEZHmIMPq2CltiQbKRhLJJKk0Nrais/n\nY/78+ZluyphYsmQJr7zyCg888ABnnHEGN910ExdeeCFtbW3hx6efforD4eCmm24asm2kRUWIbBbj\nrECuicZWsSjJpX41jojGA329cW8TDAYoLtZBCsZDCBgqQgz6+/r13M8y9vpVnLbsV+xURTC7yoci\ngqO+dyThKHS9Cv0dzz6y7XwxXgZF3iDVRSbFBRpdAyoV7iCHBo5OpWyqoLhAp8ylU2ATKEp+2kyM\n92bDcNuvbL5OWYWRrv3DX7ca6b55ZZomH330UdhSYuvWrWzfvp3S0lKamppoaWlh5cqVNDU1UV5e\njmEYvPnmm7z00kv813/9F4sWLWLFihUUFRWNuQ0SiURiFaRgLJFIksJbb73FcccdR3FxcaabkjBC\nCLq6umhra2PevHkUFhby5z//mS1btgDgdDqpr69nzpw5NDQ0xCwgNNwzVk5yxk82ZsZaRRyO55jZ\nPk5Vmw2b3YEeDMS3gRi0pbCpJrq0pRjCfq/GlHIf7+/Ph6WzCgMBFY9Tp9efvaHwrKoBbMQ59mMw\n3N88RD6LwyMiDAo0kwnFNhRFweM0KCvUKXIYg4XqFAPTMAbdKhTlyD+523+pFPOy8fpvdaL16fDX\nM0m6xWEhBHv27BkiDre2tuJ0OmlpaaGpqYmvfvWrNDc3U1NTE3UfmqaxaNEiTj75ZF577TVeeeUV\n3nrrLc444wyWLl2K0ylvUEskkuwle6NkiURiGQ4fPszOnTu55pprMt2UUTFNkwMHDgzJHG5vb6ev\nrw8Y9GWur69n5cqVdHR08Pzzz3P66adz3XXXxRX05ZIYZyWs2q9jmdxE+38myIXJuGmaFBS66esJ\nxlUoCSDoH6CmyEF7j5zERdLj05hYrJNbxeBis99ro7Y4wPud2RkK1xT5KXIc61scSSI3r0Jk0+8/\ncwgwg6DamFLWjxKymTjyUuR5NZJs79dM2ZTIIm7JZyThOF19mglxeO/evWFLiZBIbJomTU1NNDc3\nc9VVV9HS0kJtbW3Cx3I6naxcuZLTTjuNTZs28dJLL+F2u1m4cOGY2yyRSKIjECOFP2nHQk1JOtkZ\nJUskkqRgmiaqOn5h4E9/+hMej4fjjjsuCa1KLdu3b+enP/0pAGVlZdTV1bF48WLq6+upr6+npKRk\nSJC4atUqbrrpJpYvX84DDzwQt+WGFYLxXCPTonGuenpmul/Hi2GauIo89Pf2xPV+PRCgxKNLwfgY\nFLoHNCZ6guztzf2+6fbZaCgbX3ZupnDZTepLAyjiaNHH8Zyfhq+MkWJcfAhzsP+H93YuXP+tdr0b\nfp1RshX7AAAgAElEQVQa3q5s6VerkS4xPhPjqbOzc4gwvG3bNrxeb1gcvuiii7jzzjuZMmVKUj9r\nUVFRuKh2Nq66lEgkkkikYCyR5CG9vb04nU4cjsGCP5H+hYkihODPf/4z8+fPT4r4nGqmT5/Odddd\nR11dXVz+YlOmTOHxxx/nN7/5Df/0T//EOeecw3e+8524g8DhXnzDn5ckTjom41abLKeabBeNFVXD\n7nASDPhHfe/gZ5O2FNE40GdjWmUgLwRjIRSChoLDZhLIIt9mRRHMrupHMf0IknN+kmJcasiWzNhs\nut5Z3VIhG4nlbz7WsZqJ8dTV1RUWhUMi8cGDB5k7dy7Nzc2sWbOGW265henTp6Np2riOFS/l5eVp\nOY5EIpGkEikYSyR5RiAQ4E9/+hObN29m1apVLFq0CEVRxpxtvHPnTrq6urKm2J3b7Wb27NkJbaMo\nChdffDFnnHEGt912G0uXLuXuu+/mrLPOinv7bF/6b0WSJcZn02Q51WTrWDVNE6erED0YRMRRqjjo\n91Fd5KSjx5GG1mUPuqkQNMDt0OkL5H6IeKDPRl2xn48PuTLdlLiZUTGAhj8lnsO5kBlrNWKJcbFe\nTzW5cr2TYzX5jGWsZmI8eb1eWltbh4jD7e3tNDY20tLSwrJly1i7di2zZ8/GbreP61gSicSaCBPM\n0cP9tBHH1CNrUUScpmYdHR2pbotEIkkDH330Eb/61a/o6+sjGAxSW1vLP/7jP1JZWQmML9s4X3j5\n5Ze5+eabOfHEE7nrrrtiFsKIxvDsLasLcdlCtImNVSY32Uq8fWoVFEUBIejv7Y7rvS5PGe/ud6eh\nZdmF22FQXaTz7r7c7xubKpgzoZ8t7dlRzb7SHaShtG+IFUUqf5PZdg7IBmKJcKno13y53sXKis/G\nz2IlomVww9ACz9FI5ngaGBjgnXfeYevWrWFriU8++YQZM2bQ1NREU1MTLS0tNDY2UlBQMK5jSSTp\npra2NtNNyFoefi7I3kOZbsVRJpbDtV/IzRtUuZ8+IpFIwnR3d/P666/j9Xr50pe+hM/n45VXXuH+\n++/n7LPPZuHChTLAjoPly5dzyimncO+997Js2TK+853vcNlll8WVoZ0tS1SzjVj9GnotlyfLqSLb\nso2FEKiKgt1ZQNDvG/W9CiaqamJKW4oh9AVU7DZQMTFzvPidbiqYQsmKz+q0mTSU+VCFAWn6/WW7\nVY0VSVVmbL6Iw9FItqWChBHjp1hjdrz9HAgE2LFjxxDf4Z07dzJ58mSam5tpamriiiuuYO7cuRQW\nFo7rWBKJRJJteL1efvrTn/L222+jqioLFizgqquuGvVm2c6dO3n88cfZtWsXqqoydepUvvOd78S9\nAkNmGEskeYJhGPzlL3/hN7/5DatXr2b58uUAfPbZZ/z+979n165dfP7zn2fevHlJK4aXD7z99tt8\n61vfoqysjPvvv5/p06fHvW20bOPIfyXxEe9EGXJzspwOsmmsaqpKX283YpS1ag6nix5RzGfSluIY\nqtyDGax7unI/Y6uuxI8hoL3bup9VQdBU24dDGd2jO1WkMzM2nxhLFnc+i8PxIMdqYiQSQ0UyHjFe\n13V27drFli1bwtYSO3bsoKamhubm5rBAfMIJJ8jCcZKcRWYYj52Hng2y91D856tUM7Fc4bqzU5th\nfNddd9Hd3c2Xv/xldF3nRz/6EdOnT2ft2rUxt9m5cyd33XUXX/ziF/nc5z6Hqqrs3r2befPmYbPF\nlzssM4wlkjyho6ODzZs3M2XKFE499dTw8xMmTODMM8+ko6ODTZs2MXfuXFyu7PFzzDSf+9zneOGF\nF/jRj37EqlWr+NrXvsZXv/rVuO7ayeytxBnPxCbyX0liZNNYNUyTwqJi+nq6RnxfMOCnzBOUgnEU\nDvZrzKzys2fkLswJDvbZmVHpo310J5OMMa3Ch10JZLQN0jM2NYy26kiKw4kjC+ONTDxjKtZ4irx5\n7PP52LBhA8uXL6eioiLm8UzT5MMPPxziObx9+3ZKS0vDlhIrV66kqalJFoqTSCSSKLS3t7Nt2zbu\nuecepk6dCsDVV1/NPffcw5VXXklpaWnU7X7+85+zevVqzjnnnPBzEydOTOjYUjCWSPKAvr4+3nrr\nLQ4ePMiFF16I2z3oTRkK/CoqKlizZg0/+tGPeP/992lpaclkc7MOh8PB17/+db7whS9w0003sX79\neh544AFOOumkuLaXE/HoJCNzWPZpcskWmwoBOFyFBAb6Y79HmKiYWWFHkG5MoeALqpS6dLoGcjtU\n9OkqqioAEyw4DspcQcpcAZQEbpClEmmrlHwiC7iGiOUfG/n+4X9LhiJjq+Rno0e+tn//frZt28ab\nb77J0qVLWblyJYWFhezZsydsKbF161ZaW1txOp20tLTQ1NTEV7/6VZqbmxOq/yGRSCT5zM6dO3G7\n3WGxGKCpqQlFUdi1axcnn3zyMdv09PTwwQcfsHjxYm699VY+++wz6urquOSSS2hsbIz72Lk9C5BI\nJJimya5du3jrrbdYvHgx06ZNC78WGfiFLCg+/vhjKRiPkRkzZvDEE0/w2GOPcdlll/EP//APfPvb\n3w4L9KMROWnMt4lNqrKoskXgzDasnnFsmiZ2hxM94Mc0jJjvCwb8VHmc7OuVWcbD2ee1UV/qz3nB\nGKDHp1Hl1unss9Y4cGgm0yp8Q4rcWYFYnrGxXpccS6KrZeQ1a+zky02OdGajK4rC5MmT+cpXvsIf\n/vAH/vjHP/LHP/6R7du3s2fPHo4//niam5u5+uqraW5upra2Nqf6WiKRZBZTDD6sQqrb0tXVRUlJ\nyZDnVFWlqKiIrq7oywH37dsHwBNPPMGVV15JQ0MDr732GnfccQcPPPAAEyZMiOvYuT8LkEjynP37\n9/Pqq69SWVnJ0qVLj/EmjvQrVhQlvKxM+hiPDVVVueKKK1i+fDm33norS5Ys4d577+XMM8+Ma/t8\nEDgzscTW6gJnNmKVPo01nnRdx+X2jGhNEQz4KS/SpWAcBV9QRVXApproOV4Y8ECfnfpSv8UEY0Fj\ndT+qyKwVxUhY5RxgdcZ6zYvs0+H+8ZLEyLXCeJmIozo7O4cUpNu2bRter5empiYWLFiAzWYjGAyy\nYMEC1qxZw0knnSTnERKJRDICjz32GM8888yI7/nP//zPmK+FrmGxXgNYsWIFS5cuBWDKlCls376d\nV199lUsvvTSuNkrBWCLJYXw+H2+//TaffvopV1111TF3puBoZvFbb72FECJcXEIGeeNjwoQJPPLI\nI7zwwgt84xvf4LTTTuP222+nqqoqru1jTcTlpGbsSHEjNaTzJkeiNiWmEBQUuvH190XfnyltKUbi\ngFejoczPhwdz29fe61dxahZKVQGmlPlxaMFBfxWLkw83OuMlmde8fMmMTSex/HijvW4VMhFHdXV1\nhUXhkEh88OBB5s6dS3NzM2vWrOGWW25h+vTpaJoW3m7fvn08//zz/PKXv2Tz5s2cffbZzJ49e1xt\nkUgkkmzh0UcfDWf2hli4cCGLFi2K+v6zzz6bZcuWjbjPmpoaSktL6e4eWmzDNE36+vqi6jtA2Ne4\nvr5+yPN1dXUcOHBgxGNGIgVjiSSHCVlRnHzyyTQ1NQFD70QZhoGmaXz00Uf89a9/ZdKkScyYMSPm\n/kLvl8TPqlWrOO2007jrrrtYunQpt912GxdddFHcdgohsmGyaCVxeCSkr2FqSLYgnwwPawDN7kCz\n+TH06Mv6g0E/lUUO9nutlF1qDboGNKo9fqzq75s8FAaCKh6nTq8/86FxSYFOpTuAIsxMNyUhsu2a\nNV7Scc2T9h+pwaqF8TIRR3m9XlpbW4eIw+3t7TQ2NtLS0sKyZctYu3Yts2fPHrWgc01NDf/8z//M\nRx99xIYNG3jooYdobGzk/PPPl57FEokkeQiBsJInxZGmXHXVVQlt5vF48Hg8o75v1qxZ9PX18fHH\nH4d9jP/+978jhGDmzJlRt6murqasrIyOjo4hz+/du5cTTzwx7jZmPiqWSCQpIRAI8N5779Hb20tF\nRQU9PT0UFxcPZt0dsZvQNA3TNHn66acRQnDaaadRVFREf38/+/bt491330UIQVVVFQsWLEDTtBGX\nPkiiU1JSwve//32++MUvcuONN/Lkk09y33330dDQENf2VsyMzRZxeCTy2TM6VYx1rKZyPJmmicvt\nwdt9OOrrQb+PiiKXFIyjIFDo86tUFRl0enNZMIb9Xhu1xQHe78xsaGxTTWZUDqCIYEbbMVZyWeCM\nlpEajdBnTOZntWIckAtk8gZyJuKogYEB3nnnHbZu3Rq2lvjkk0+YMWMGTU1NnHzyyfzLv/wLjY2N\nFBQUjPk406ZN44YbbqC1tZXnn3+eYDA7z2cSiURiBerq6mhpaeHHP/4xX/rSl9B1nZ/+9KcsXLgw\nnEl86NAh7rjjDq6//nqmT58OwDnnnMMTTzzB5MmTmTJlCps3b6ajo4NvfOMbcR9bCsYSSY7icDi4\n4IILCAaDvPDCC7S1tbFw4ULq6uooKioCBv3IXnzxRdra2liwYAGnnHIKMLicoq2tDU3T8Hg8dHV1\n8Yc//IErr7yS6urqTH6srGbBggVs3LiRBx98kBUrVvD1r3+dL3/5y9hso5+KMzVZTFaWp1WRS6lT\nw0j9Gkm6JsmGaVLgLsLX5z3mNWGaqIpJ7mfRjo39XhsN5QE6vSNnlmU73T4bk8sy7RcsaKweQM1S\nsTiSbBc4rXpTVF6zUkOqs+MzMZ4CgQA7duwY4jm8c+dOJk+eTHNzM01NTVxxxRXMnTuXwsLCcR0r\nGoqi0NzczAknnCBt7iQSiWScrF27lp/85CfccccdqKrKggULuPrqq8OvG4ZBR0cHfr8//Nzq1asJ\nBoP84he/wOv10tDQwK233pqQnqOI0RSAIwxPZZZIJNYmsmjdBx98wK9//WsCgQD19fWUlJSgaRqt\nra309vYyb9481qxZQ0lJCevWreP111/nkksuYcGCBXR3d9Pe3s769euZOHEil19+OQ6HzMQbL++9\n9x433ngjwWCQ//iP/+CEE05IaPtovsbjnVzkujg8GrGKC+XK58sE0Zb6RiMd40lVVXx9Xgz9WDHO\n6SrkUNBDp8wyjsqMSj87O1349dye9M+p6ef9Ay4CGfqck0r81BQNoGBk5PipJJpobJVzq1XF4dGI\n5cFrhbZlM+Pt10yMJ13X2bVrF1u2bAmLwzt27KCmpobm5uawQHzCCSeEa5VIJJLMUltbm+kmZC0/\nXB+g46B1LClqKxSuPy835xAyw1giyVFUVcU0B/0PZ8yYwXe+8x1ee+01PvjgA3bt2kV3dzcTJkxg\n1apVHH/88RQVFdHa2sqbb74JwJYtW5gzZw4lJSWUlJSwb98+nn32WQ4ePMjEiROHHMsKNhXd3d08\n++yz7Nixg0AgQFVVFZdeeimTJk3KaLti0djYyPr16/nFL37BhRdeyBVXXME3vvGNuLM8xmunkO/i\ncDSyPSMu0yQypkKkM+to0JqiCG9PFwxrX9Dvp8LtkoJxDPZ7NRrKfezcn/wsNCtxoM9GXbGfjw+l\nv8hfkUOnxuNHEbknFoN1/I2zVRyORi7bf2SSRArjZWI8mabJhx9+OMRzePv27ZSWltLU1ERLSwsr\nV66kqamJ8vLycR1LkjibNm3i73//O/v27cNutzN16lTOPvvsIRl9b775Jm+//TZtbW34/X7uvvvu\ncVmASCQSSaqQgrFEksOExJhQsbqlS5eyYMECVFXF7/cfY7L+6quvUl1dzeLFi3njjTe44447WL16\nNcuWLaO2thZN0/B6hy7pjhSLI7Oa00l/fz8/+MEPmDVrFtdeey1ut5vOzs6ULLFLJpqmcfXVV7Ny\n5UpuvvlmTj/9dO6//34WL14c1/bxLk3NpQlyOpBF8UZnrGNq+OR7eDZ3qjGO+BkPeHuGPG+aBpoq\nbSli0ePTmFisk+v9c6jfzpwJ/Wk/rqYKZlVlr29xvCQixCWDfLn2jXSzc/jrkviJFQuE4qxYYyqZ\n40kIwZ49e8K2Elu3bqW1tRWn00lLSwtNTU189atfpbm5WRaVswgfffQRixcvZtKkSZimyXPPPcdD\nDz3EzTffHF6hGQgEOO644zjuuON47rnnMtxiiUQiiY0UjCWSPCBUrA4I38Eebitx+PBh9u/fT3Nz\nM/Pnz2fu3Lm8/vrrPP/88/ztb39jwoQJCCEoKysDBpe/bd26lY6ODmbOnMlxxx0XzmpOt2j8yiuv\nUFZWxiWXXBJ+LpuyKurq6vj5z3/Ohg0buO666zjzzDO57bbb4v4MsTK34t1GTiajI4viDZJM0cUK\nWdyqpmKzO9CDQ/1q9UCACncBB/tyVxAdOwrdPo2JniB7e52ZbkzK0E0FUyiomJhpE8YFs6v60cht\nsTiSVNyUyxdxeCTkzc7kMtI4itW34+lnIQR79+4NC8OhDGLTNGlqaqK5uZmrr76a5uZmamtr5Xdq\nUb7yla8M+f9ll13GrbfeSltbG9OmTQNg6dKlwKBloEQiSRzTFJimdSwpjizqzkmkYCyR5AmjBZZu\ntxtVVbHb7dhsNkpKSlixYgWNjY28/PLL/OUvf+HEE0+ksrIS0zQJBoO8++67dHZ28tZbb9HQ0MBl\nl10WLqg3nL6+Ptxudyo+Gu+88w6NjY08+uijfPDBB5SWlrJw4UJOPfXUlBwvFSiKwrnnnsuSJUu4\n4447WLJkCXfeeSfnnntuzO9OTpBTT74VGErXmMpkv5qmoKDQTV9PcMhnDPp9VLoLONgnQ6MQCoJC\nh8DjNCh2GpQWGKDA/l4Hhsi98Q9wuF9jYkmA9u70LA+uLQ5QaA8eY5OSD4z1PCCvfSOTb9etZDAW\nS6VIxtKvnZ2dx4jDXq83LA5fdNFF3HnnnUyZMkV+b1nMwMAAgOVXPUokEkk05KxIIpEAgxnHxx13\nHNu2beOkk06ioaEh7L110UUX8eGHHzJ58mRg0OrC5XJx5ZVXsn//fnbv3s3GjRv5yU9+wjXXXHOM\naBwq7NbS0sKaNWuSnoF88OBBXn/9dU4//XRWrFjB7t27eeqpp7Db7cybNy+px0o1ZWVl/Md//Ad/\n+MMf+Pa3v826deu45557KCsro62tjba2Nj799FPa2tpYuHBhOEshRLzLJCWJYYXM2GRjBdElU/1q\nmCauIg/9vUetKUzTwJbnthSaIih0GpQ4TVwOE00RKJgomIPfjYCJniA1niD9AZX2bid9AS3TzU4q\nB/vszKj00d6d+mMV2nVqi3PXtzheRrNUiHwtnn1k6zk52eTidSsZJOPaF2lP8ctf/pKysjKWL18e\n04e2q6srLAqHROKDBw8yd+5cmpubWbNmDbfccgvTp09H03LrnJrPCCF4+umnmTZtGhMmTMh0cyQS\niSRhpGAskUjCzJs3j507d7Jx40aWLFlCbW0tHo+H0tJSmpubsdls+P1+fD4fPT09TJo0ierqasrL\ny/H7/Tz55JPs2bOHOXPmDNnv5s2b6enpOaZYXrIwTZOGhgZWr14NDFo8fPbZZ7z++utZJxgDeL1e\nKisr+e53v8vmzZu58847sdvtANjtdurq6pg1axZ1dXXhSUy0iY1clppcsnnybQVxOJ5jprVfFRW7\nw0kw4A8/ZQQDlBcWcKg/HwRjgV2DIodBcYFBgU2gKAIFAwURFogH3xm5mUAlQJEdZlbqGEJjX6+d\nA312zBzIOvbpKqoqSPWNA1URzK725bxvcbwMv9lptfNUtpLv/sapGlORtTs8Hg+vvPIKb775JqtX\nr+aEE05g+/btQ7KH29vbaWxspKWlhWXLlrF27Vpmz54dju0kuckTTzzBvn37WLt2baabIpHkFPHY\nL6YTCzUl6UjBWCKRhJk1axaXX345jz/+OL/61a+or6+nvLycz3/+87jdbgYGBnjqqafYsWMHTqcT\nh8PBmjVrOP744zn11FN58skn6e3tBY4WBeno6GDjxo0sWLAg7HMcWSgvGRQXFx9T7KOmpobW1tak\nHSMVCCHo7u4OZw6HHl1dXcCg33R9fT0ul4v29nb27dvHtddey9y5c+Pav/QzTA1WX+6braJLuvtV\nCIHTVYiuBxFHzMcCR2wpDvXnYngkKLAJipwGxQUmdu1o9jDCDL0l8p+jW8YcUwE0oM7jp7bYQa9f\no6PHyUAwuwX3Hp9GlVuns88x+pvHyKyqAWwERn9jjjIWC4BMFNXNFfIhHsjEtS8QCFBfX09zczMH\nDx7k8ccf55FHHuHgwYNMmzaN+fPn8+Uvf5nGxsaY2ceS3GTdunXs2LGDtWvXUlJSkunmSCQSyZjI\nxRmRRCIZA6HgeubMmdx6661s3ryZtrY2+vr6wkHuiy++yLvvvsvcuXOZPHkyH3zwAT/5yU9oaWmh\npqYGt9sd9ugKTUzWr19PWVkZp5xyStjDONmTk2nTprF///4hz+3fvz9coM+qPPzww7z//vvAoId0\nfX09n/vc55g0aVJYrA9NkHVd53/+5384//zzueaaa7jhhhvinnzI4m2pIbJfI/9NZ79mqzg8EunM\nODZMk8IiD309g/4DpmFgT0N2aTqI9B8uchpoqkARgwKxaRpgAIqC4Ng+j/b3MfsfMqZAIUBpgUJx\nQZCgqbG3x8GhPhsCa4+3aBzos1Nf6k+ZYFxT5KfIkT++xWM9Tw0/t5qmaZkbc9mK1W94xkumxOEd\nO3YM8RzeuXMnkydPprm5maamJlavXs0777zD7t27mTVrFitXrqS2tnZcx5VkH+vWrWP79u1cf/31\nlp+LSCQSyUhIwVgikQBHA2nDMNA0jWXLlhEIBFAUBU3T6O7uZs+ePUycOJHLLrsMgObmZk477TR+\n85vf0NraytSpU5k0aVJ4n2+88Qa7du3ikksuCdtRJDu7GAarDf/gBz9g06ZNnHjiiezevZs//elP\nXHzxxUk9TrJZvHgxixYtor6+ntLS0hH7xWazce2117Jq1Sq+/e1vc+aZZ3L//ffHXdgvVyaJViNd\n/Tp8Qpwr4nAs0ikaCxQcBS4CvsHCNEbQT6nLSddAdgnGkf7DhQ4TNYr/cIjIMQujZ3jGO6aEECjo\nOBSdhlKdSaUaXQM29vY48OvZ059ev4pTS42Y67Kb1JcGUISekv1nmmQKedF8Y+VNz+QQ7Rxr1Zgg\nE+Kwruvs2rWLLVu2hMXhHTt2UFNTQ3NzM83NzZx33nmccMIJFBcXD9l2xYoV/P3vf2fDhg3cd999\nLFiwgFWrVsks0zzhiSee4G9/+xtf+tKXcDqd4ZWXBQUFYQuS3t5eenp66OzsBKCjowOn00lZWZks\njieRxIM4ujjOEuTw/X8pGEskkiFomhYOyB2Oo9lVJSUl4cCnt7cXj8cTftTW1hIMBpk/fz6lpaUA\nHDp0iOeff56TTjqJxsbGcBGPyCDeNM2kLDGdPHky11xzDc8++ywbN26koqKC888/n5NOOmnc+04l\nxx9/fMLbNDQ08Nhjj7Fu3TquueYa1qxZw3e/+924JyKxhDirTRCzjWQKnIks1c4FcXgk0iHICyFw\nOAvQAwFM0yDg91PtDtI1YOUQKXH/4bSLLsJAxaC8IEipK0jQUGnvdhzpV6uPVYWBoIrHqdPrT944\nUBTB7Op+VJEbVhTpGlPZ7B9vZaKtLMh032ZCHDZNkw8//HBIQbrt27dTWlpKU1MTLS0trFy5kqam\nJsrLy0fdn6IoNDU1MWfOHN544w1eeukl/va3v7FmzZpjChVLco833ngDgB/+8IdDnr/00kuZP38+\nAK+//jovvfRS+LUHH3zwmPdIJBKJFVBEnMZhHR0dqW6LRCKxKKHTxO9//3uee+455s2bx5w5c3A4\nHLz77rv8/ve/Z968eVx++eXhbR599FE++eQTrrzySqZNmxZ+/vDhw/j9fsrKynA6nUDyhON84+DB\ng3zve9/j9ddf56677mLNmjUJbR9t4i0n3+MnkX6V4nD8pHq8qopCX8+gf7i7pJx39rmwji1FyH/Y\npLjAiO4/HPnuBESXWEWwUuIbrWiYaBzqt/FZj4OgYZX+PZZSV5BKt877ncnL9ppZ2U9pgS8rrSis\nZH8jr12pId39mokxJYRgz549YVuJrVu30traitPppKWlhaamJpqammhubj6mNsZY6e/v5+WXX6a6\nuppTTjklKfuUSCTZj7SrGTs/WOej/YB1Yqm6SoUbLsxNn3orp89IJBKLEArQly5disvl4re//S2H\nDx9m9+7dAFRVVXHGGWeE3x8KxM8991zq6+vDz7399tu88847FBcXo6oqZ5xxBosWLZJi8RipqKjg\nwQcf5He/+x0333wzTz75JHfddRcTJkyIa3uZtZUaYmXFhl4DKQ6PhVRnGwvA6SrEP9CPoQcyaksR\n1X8YgYpxjL3EeEWX4R7nqTwPKMJAw6DKFaSiMIhf12jvdtDj07Ba1nG3z0ZDWfIygSvdQYqdgXCB\nRSv/rq0kDo92XKvbKWQTKT/HxjGuki0O7927NywMhzKITdMMi8JXX301zc3N1NbWpmzsFBYWcs45\n56Rk3xKJRJKPmEJgWujmu2mdpiQdKRhLJJK4CGUBz58/nxNPPBHDMHjiiSd4//33Oe2008IexV6v\nl2eeeYbGxkZaWlpwOBzs2rWL//3f/2X69OlceumlGIZBR0cHzzzzDJ2dnZxzzjlhywpJ4pxxxhn8\n7ne/495772XZsmXccsstXHHFFXEJ8VI0Tg2xJsOxsjmH/y2JTarGrBACm8NJMBAg6PdR5S5Imy2F\npgjcToPiUfyHzRQKeen1OReoIohLCzK9PIiJxoE+O5/1OjBMa/wOhFAIGgoOm0lgnP7LTptJQ5kP\njOAQmzsr/OatLg7HQvobp45kCPKZGFednZ3HiMNerzcsDl900UXceeedTJkyRY4PiUQikUjiQArG\nEokkLkLio2ma2O127HY7ixYtwuFwsHDhwvD7XnzxRfx+P8uWLaO0tJRDhw7x7LPPYpomxx9/PD7k\nHaYAACAASURBVCeffDIAPT09CCFobW1l8eLFVFZWDjmetKlIDLfbze233855553HjTfeyJNPPsn9\n99/PzJkz49o+mlgU+ZokNonYSoSQmXDjI1WisWmauIo89HUfpkAzAZPk21LE5z8shIjqPzycZAt5\n6b6JpDCYdVxTFKSqKMhAUKW924nXr5LprOMDfTbqiv18fMg15n0oCBpDvsWqmiZBPjrZKg6PhLzp\nmRoSEeQzMa66urrConBIJD548CBz586lubmZNWvWcMsttzB9+nSZkJAhNm3axN///nf27duH3W5n\n6tSpnH322VRXV4ffo+s669evZ8uWLei6TmNjIxdeeCEejyeDLZdIJBJJCCkYSySShIgUcadOncrU\nqVPD/9+xYwdvvvkmK1euZNKkSQD8+c9/pq2tjRkzZvD000/z0UcfccEFF1BcXMznPvc5/vjHP3Lo\n0KGwYGwYBpqmSbF4jJx44om88MILPPzww6xZs4Zrr72W66+/fkgBw5EYvjx9+PP5zngmxpFFhWTf\nJodUZMUKIShwF2HoQYqdTnr84z0Xje4/LMxBcVhgDSEvIyKcEKgEcNtgZoWOgcb+Xhv7vQ5MkZnf\nyKF+O3Mm9I9rH9MqfNiVo9YW6erbXBSHRyK9GfL5Q6zxmk5bJa/XS2tr65Ds4fb29vBKtmXLlrF2\n7Vpmz56N3W4f17EkyeOjjz5i8eLFTJo0CdM0ee6553jooYe4+eabwzHpU089xY4dO7j66qspKChg\n3bp1/OxnP2Pt2rUZbr1EIrEyQsSfpJMORIbi1HQgBWOJRDJmQpOx0N9//vOfqaiooKWlhcLCQnRd\n569//SuzZ8/mggsu4LPPPmPDhg3cddddnHPOORQWFqKqKj6fDxjMNHjrrbf+P3v3Hhdlnfd//DUz\nMJzPyBlEQAWRGbJVPKUieUjTsIOdrDyU29Ff3Ztm/qxty+5qt/R+7H23e9/9dqttO5mWh9VNzTyl\n5dqdIioUiiIHwQDlzADDzO8Pl2sZBRwOwwzweT4ePZKZueb6zpcvF1zv63t9vpSVlXHbbbcp+5HZ\nxp3j7OzMU089xaxZs3juuefYsmULa9eu5cYbb7RqeznxvqKnAxeZCWc7Pdm3ZrMZjZMzxgYDQZ6N\nVDV07k+l69UfNv+z0JmjhMMdsdexQIURJ4yEeTUR4t1ETYOGC5Va6pp6d6ag0aTCZFahxoSpCzPN\n/dya8HNrRNVBKZqeOBYMtHC4I1LfuOfYY1zV19dz6tQpMjIylHA4Ly+PuLg4dDodY8aMYenSpcTH\nx+Pq2j8XGOovfvnLX1p8fd999/HCCy9QWFhITEwMBoOBf/zjHzz00EPExcUBcO+99/L6669z/vx5\nBg8ebI9mCyGEaEUCYyFEl119kvDQQw9RWlpqMVtYpVLh6elJYGAg/v7+hIaG8s0337Bp0yaamprw\n8vIiMTERgMLCQg4fPkxVVRXjx4/HZDIRHBwsYXEXxcbGsmHDBj799FMWLFjA7bffzvPPP4+np6dV\n27cXavTHk+7ePDGWQN42ejKEM5lMOLu44qy6/uyF1vWH3bQmNK3qD5tMpitVLYDmPhzk2e9ihwm1\nuREfFxVeg5owmjWUVDtTVuvca7M5LtdpCPVppKiyc+GUVmMiJsCAymxs9zVdORZIOHx9bZVLkAt0\nHevMuLr6+ZZx29nSD42NjWRnZ1vUHM7JySEqKgq9Xo9Op2PBggUkJibi7u7euQ8kHE59fT2A8r0s\nKCjAZDIxbNgw5TXBwcH4+vqSl5cngbEQQjgACYyFED2ipZTEoEGDAJSTB09PT4qLi2lsbESr1RIQ\nEMAtt9xCQkICe/fuZeTIkWg0Gqqrqzl27BiFhYV4e3vz4YcfUldXR0BAAA8++KCcLHSRSqXi3nvv\nZerUqbzwwgtMmjSJ119/nenTp1u9fYv+Em46SuAykAL53tRTgXyzyYRWq8XLpZnqhpYgxIyzxoyn\n1nRN/WHM/1qgrqOVm/tqkGfPGfJmsxkVRpxVRiJ9mgj3caLKoOFCpQuGbi5Idz3ltc7EBRooquzM\nVq3qFluhveNs6+ckHO48qc3ftu7+Drx6TBoMBtauXcvYsWO56aab2iyBZTQaOX36NMeOHVPC4ezs\nbIKDg9Hr9ej1etLT00lKSsLb27vbn1E4FrPZzKZNm4iJiSEkJAS4spaJk5PTNTPFvby8qKqqskcz\nhRB9hNlsxmRypJIUjtOWniaBsRCiR1w9s0SlUuHk5MTkyZPZsGEDBw8eZNSoUfj6+uLi4sLQoUMJ\nDw/H3d0dk8nE6dOn+e6774iOjmby5Ml4e3tTVFTEN998g9HY/gwt+FdpjNYlMoSl4OBg3nnnHXbu\n3Mlzzz3H559/zpo1a5SA/3r6ajkFRwmHrdlvfwnkHUVPjFmj0UiIF7g4mS3rD5ubMZtNVxanM/1r\ncbqO2tBfvp92nyFvNqGmET9XFd4uRppMaoqrtFyuc8Jsg0XyDEY1arWZziyAGO3XgFbTRLsDoxOu\nPm71xzFla3Yfs3Zky9+BrYPjuLg4tm7dysGDB5k7dy4eHh4WdYdPnjyJr68vOp2O5ORkpk+fjk6n\nw9/fv4ufTPQlGzZs4OLFi1bXJu7vP5dCCNFXSGAshLCpxMREcnJy2LZtGwUFBcTFxRETE0NoaKhS\nGqGkpIR9+/bh7+/P3Xffrcw+iIiIYMSIEXh7e19Tx7h1ONz6/yaTaUCcBHbVjBkzGD9+PK+99hqT\nJk3ixRdf5J577rGqvxw9NHb0cLgjjt63fVV3+9VkMuGsNjLIvQmz2YS5+V+L03W0r4HwPbP3mDWb\nzahpwkUNg32NRPlquFzvRHGVlsbmnp11XGXQMMjDSGnt9RcP9XE1EujRiOqfCxq2x9rFw1rI77Xu\ns/eYtTV7/A40m82Ulpbi6upKdHQ0DQ0NvPvuu1y+fBm1Wk1iYiKPP/44er2e4ODgbu1L9E0bN24k\nOzubZcuW4ePjozzu7e2N0WjEYDBYzDKurq7Gy8vLHk0VQghxFQmMhRA2YzabcXZ2Zv78+cTHx/PV\nV19RXFzM7t27eeSRRwgLC6O+vp7vv/+ewsJCFi9erITFpn/eDh4QEACghMUtQXFzczMlJSXs2bMH\njUaDq6sr48aNIywsTNleah+3zcvLi3//939n3rx5LF++nC+++ILf/e53REdHW7W9I9zm25fD4fb0\n9zDDnqydYdj2uLo2+OtL48qWHGXMqmhGRTMBbk34uTXR2KyhqNKZSoMT9MCs47JaZyJ8G64bGDup\nTcQF1qMyN1k83p3j1UCcFWtLHY3Zq593dNZcdOjpcLi4uFiZNdxSWsJkMqHT6ZTSEuPGjeObb77h\n559/JiwsjJSUFIugUAwcGzdu5OTJkzz55JP4+flZPBcZGYlarSYnJwedTgfAzz//TEVFhdV/jwoh\nBiaz+cp/jsKR2tLTJDAWQthMy4xftVqNTqcjKSmJs2fPolarCQsLw2w2c+7cOQ4dOkRKSgoJCQnK\ntu2FvS0nPMePH2fLli2YTCb8/f1Rq9V89913TJo0iblz50pYbIXRo0ezc+dO3n77baZPn86yZct4\n9NFHcXKy7lfD1YHG1Y/3lP4YDndkIN8+bWtX195sKzBqb5ur/y3+xXHG7JVZx66aJmICmjCZNZTX\nOlNSrcVo6no7ahrUuDhd72zATHxQPSpTIy1FSnrieOUooXx/4wgXPjujs78He+IzlJaWXhMO19TU\nKOHw/PnzWbNmDdHR0dfsb9y4cXz33Xd8+eWXZGRkkJaWRmpqapv1jUX/tGHDBo4ePcrDDz+Mi4sL\n1dXVALi6uuLs7Iyrqytjx45l8+bNuLm54erqyhdffMGQIUNkwTshhHAQKrOV98JduHDB1m0RQvRj\nbc34LSkpYcOGDVRVVfHoo48qs4mvp7q6mg8//JDz58+zevVqPDw8+Pnnn8nMzOTbb78lOjqa9PT0\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R2tLTVGYri8dduHDB1m0RQohuycvLw8/PDx8fH4xGI05OTtTW1rJp0yZ+/PFHFi1aRGxs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\n5swZzp07R01NDR4eHiQkJJCcnExgYCB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"text/plain": [ "<matplotlib.figure.Figure at 0x11109ee90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# For plotting: Create value function from action-value function\n", "# by picking the best action at each state\n", "V = defaultdict(float)\n", "for state, action_values in Q.items():\n", " action_value = np.max(action_values)\n", " V[state] = action_value\n", "plotting.plot_value_function(V, title=\"Optimal Value Function\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, <<<<<<< HEAD "nbformat_minor": 0 } ======= "nbformat_minor": 1 } >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6

mit

c4fcm/oped-gender-report

data_acquisition/Opinion Byline Statistics, New York Times LA Times Washington Post.ipynb

1

489579

{ "metadata": { "name": "", "signature": "sha256:940f75de9540a72ccb338bf9a98bf355489253e70514f1688c5afb5dcc350949" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#TODO: words written by men and women\n", "\n", "import csv\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pylab as P\n", "import os\n", "\n", "csv.field_size_limit(sys.maxsize)\n", "lines = []\n", "\n", "#CHOOSE A FOLDER FROM ABOVE\n", "#folder = \"./test_results/30bf9fd5bb824eb49e89c8a828276348c0b1570c/\"\n", "folder = \"./annual_data/\"\n", "\n", "files = [\"[2013-08-01T00:00:00Z TO 2013-09-01T00:00:00Z].csv\",\n", "\"[2013-09-01T00:00:00Z TO 2013-10-01T00:00:00Z].csv\",\n", "\"[2013-10-01T00:00:00Z TO 2013-11-01T00:00:00Z].csv\",\n", "\"[2013-11-01T00:00:00Z TO 2013-12-01T00:00:00Z].csv\",\n", "\"[2013-12-01T00:00:00Z TO 2014-01-01T00:00:00Z].csv\",\n", "\"[2014-01-01T00:00:00Z TO 2014-02-01T00:00:00Z].csv\",\n", "\"[2014-02-01T00:00:00Z TO 2014-03-01T00:00:00Z].csv\",\n", "\"[2014-03-01T00:00:00Z TO 2014-04-01T00:00:00Z].csv\",\n", "\"[2014-04-01T00:00:00Z TO 2014-05-01T00:00:00Z].csv\",\n", "\"[2014-05-01T00:00:00Z TO 2014-06-01T00:00:00Z].csv\",\n", "\"[2014-06-01T00:00:00Z TO 2014-07-01T00:00:00Z].csv\",\n", "\"[2014-07-01T00:00:00Z TO 2014-08-01T00:00:00Z].csv\"]\n", "\n", "top = 0\n", "for filename in files:\n", " #with open (os.path.join(folder,\"month_06_2014.csv\")) as f:\n", " with open (os.path.join(folder,filename)) as f:\n", " reader = csv.DictReader(f)\n", " for i, row in enumerate(reader):\n", " lines.append(row)\n", " lines.pop(top)\n", " top = len(lines)\n", "\n", "lines[0].keys()\n", "len(lines)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "430135" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "import json\n", "\n", "# reduces social media metrics to a single number\n", "# highly reductionist, as you might expect\n", "class SocialMedia:\n", " def facebook(self, url):\n", " #res = requests.get(\"http://graph.facebook.com/\" + url)\n", " res = requests.get(\"https://graph.facebook.com/fql?q=SELECT%20like_count,%20total_count,%20share_count,%20click_count,%20comment_count%20FROM%20link_stat%20WHERE%20url%20=%20%22{0}%22\".format(url.replace(\"http://\",\"\")))\n", " j = json.loads(res.text)\n", " if 'data' in j.keys() and len(j['data'])>0:\n", " return j['data'][0]['total_count']\n", " return None\n", "\n", " def twitter(self, url):\n", " res = requests.get(\"http://urls.api.twitter.com/1/urls/count.json?url=\" + url)\n", " j = json.loads(res.text)\n", " if 'count' in j.keys():\n", " return j['count']\n", " return None\n", "\n", " def reddit(self, url):\n", " reddit_url = \"http://buttons.reddit.com/button_info.json?url={0}\".format(url)\n", " res = requests.get(reddit_url)\n", " #import pdb; pdb.set_trace()\n", " j = json.loads(res.text)\n", " if not \"data\" in j:\n", " print \"REDDIT ERROR WITH {0}\".format(reddit_url)\n", " return None\n", " #return {\"ups\":\"0\", \"num_comments\":\"0\"}\n", " else:\n", " data = j['data']\n", " if \"children\" in data and len(data[\"children\"]) > 0 and \"data\" in data[\"children\"][0]:\n", " child = data[\"children\"][0]\n", " return child['data']['ups'] + child['data']['num_comments']\n", " #return {\"ups\":child[\"data\"][\"ups\"],\"num_comments\":child[\"data\"][\"num_comments\"]}\n", " #return {\"ups\":\"0\", \"num_comments\":\"0\"}\n", " return None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "#SUMMARIZE SECTION IDENTIFICATION\n", "lines[0].keys()\n", "\n", "MEDIA= {\n", " '1': \"new york times\",\n", " '2': \"washington post\",\n", " '6':\"la times\",\n", " '7': \"new york post\",\n", " '1150': \"wall street journal\",\n", " '1757': \"salon\",\n", " '1707': \"daily beast\",\n", " '1750': \"telegraph\",\n", " '314' : \"huffington post\",\n", "\"27502\":\"huffington post\" #assuming these are the same for now\n", "}\n", "\n", "media = {}\n", "\n", "for line in lines:\n", " mediakey = MEDIA[line['media_id']]\n", " section = line['section']\n", " if(not mediakey in media):\n", " media[mediakey] = {}\n", " if(not section in media[mediakey]):\n", " media[mediakey][section] = 0\n", " media[mediakey][section] += 1\n", " \n", " \n", "for key in media.keys():\n", " articles = 0\n", " for section in media[key].keys():\n", " articles += media[key][section]\n", " print \"{0}: {1} sections, {2} articles\".format(key,len(media[key]),articles)\n", " \n", " #for section in media[key].keys():\n", " # if(not section is None):\n", " # if(section.lower().find(\"opinion\")>=0):\n", " # print \" {0}: {1}\".format(section,media[key][section])\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "salon: 1 sections, 4856 articles\n", "huffington post: 2 sections, 152765 articles\n", "washington post: 918 sections, 12135 articles\n", "la times: 67 sections, 51766 articles\n", "new york post: 18 sections, 46237 articles\n", "new york times: 129 sections, 89820 articles\n", "wall street journal: 41 sections, 72556 articles\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# GROUP BYLINES BY MEDIA ORGANISATION\n", "# AND SUMMARIZE\n", "media_people = {}\n", "\n", "from byline_gender import BylineGender\n", "b = BylineGender()\n", " \n", "#for key in media.keys():\n", "# articles = 0\n", "# for section in media[key].keys():\n", "# articles += media[key][section]\n", "# print \"{0}: {1} sections, {2} articles\".format(key,len(media[key]),articles)\n", "# for section in media[key].keys():\n", "# if(section.lower().find(\"opinion\")>=0):\n", "# print \" {0}: {1}\".format(section,media[key][section])\n", "\n", "sections = []\n", "for line in lines:\n", " mediakey = MEDIA[line['media_id']]\n", " byline_text = line['byline']\n", " if(not line['section'] is None and line['section'].lower().find(\"opinion\")>=0):\n", " section = line['section']\n", " if not section in sections:\n", " sections.append(section)\n", " for byline in b.get_full_names(byline_text):\n", " if(not mediakey in media_people):\n", " media_people[mediakey] = {}\n", " if(not byline in media_people[mediakey]):\n", " media_people[mediakey][byline] = 0\n", " media_people[mediakey][byline] += 1\n", " \n", "#print \"---\"\n", "#print sections\n", "#for key in media_people.keys():\n", "# print \"{0}: {1} bylines\".format(key,len(media_people[key]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "for key in sort(media_people.keys()):\n", " print \"{0}: {1} bylines\".format(key,len(media_people[key]))\n", " values = media_people[key].values()\n", " plt.hist(values, max(values))\n", " plt.xlabel(\"Articles Published in {0}\".format(key))\n", " plt.ylabel('Number of Authors', fontsize= 20)\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "la times: 918 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8eb1a850>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york post: 116 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x71582e50>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york times: 1445 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x5c573050>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "wall street journal: 559 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1ecf0e50>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "washington post: 192 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8f12f910>" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "from byline_gender import BylineGender\n", "b = BylineGender()\n", "b.load_name_org_online()\n", "print b.org_name_gender(\"washington post\",\"editorial board\")\n", "unknown = []\n", "known = []\n", "\n", "for org in sort(media_people.keys()):\n", " print \"{0}: {1} bylines\".format(org,len(media_people[org]))\n", " vals = {\"female\":{},\"male\":{},\"unknown\":{}}\n", " for name in media_people[org].keys():\n", " #gender = b.single_name_gender(name)\n", " gender = b.org_name_gender(org,name)\n", " if(not gender == \"ignore\"):\n", " vals[gender][name]=media_people[org][name]\n", " if gender == \"unknown\":\n", " unknown.append(name)\n", " else:\n", " known.append(name) \n", " m = 0\n", " for v in vals.values():\n", " if(len(v) > 0 and max(v)>m):\n", " m = max(v)\n", " \n", " h = []\n", " labels = []\n", " for v in sort(vals.keys()):\n", " labels.append(v)\n", " h.append(vals[v].values())\n", " if(len(h[-1]) == 0):\n", " h[-1]=[0]\n", " plt.figure() \n", " n,bins,patches = plt.hist(h)\n", " plt.xlabel(\"Articles Published in {0}\".format(key))\n", " plt.ylabel('Number of Authors', fontsize= 20)\n", " legend(patches, labels)\n", " plt.show()\n", "\n", "print \"UNKNOWN BYLINES: {0}\".format(len(unknown))\n", "print \"GUESSED BYLINES: {0}\".format(len(known))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ignore\n", "la times: 918 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SMmnIV9jw9KMJtXEkqhLoAnwGHAUEbzvfDPQIO66723aIKVOm1K37/f4kwxER\nOfyUl5dTXl6eknMldKdjHA8D44FWHp9XBrwIfMM9ngF8AdyHJaZi97Mf8DTWTtINS159ObR0kvo7\n4BfBkMXNWHLwYMw3UQAcOHhQd8CLSE5qqjvgG3IE1lD+L4/PmwMMAzq55/4UmA48C1yLNbRf4Y5d\n5bavwibmuhFVc4mIZJyXZDKZ6F/czYGeWE+rDth9IF5cFWP7OTG23+sWERHJEl6TSTzVwD1Y1ZSI\niOQRL8nkrBjba7H7RP6B5oQXEclLXpJJebqCEBGR3KZuRyIikrSGSiaJJpvGTqQlIiKHgYaSyQG8\ndb31kR0zLYqISBNqKJn808O52gEdk4hFRERyVEPJpKwR52gB3ALc5R5XJBOQiIjknmQb4K/AJqma\niVVx3Q4cn2xQIiKSWxIdTmUIlkBOw+YWeQiYxqHzkoiISB7wmkz6Yne4j3KPn8eGT9mQyqBERCS3\nNDaZdMSGU5mAtZG8BfyQ+hNViYhInmoombQCJmLDv3fASiCTgD+lOS4REckhDSWTNdiIwFXAD4Df\noPG3REQkQkPJpKf76cOqtX7YyPP2bPgQERE5XDS2zaTELSIiIodI19hcIiKSR5QsREQkaUomIiKS\nNCUTERFJmpKJiIgkTclERESSpmQiIiJJUzIREZGkKZmIiEjSlExERCRpSiYiIpI0JRMREUlariaT\nC7C559cBP8lwLClRVFyEz+eLuRQVF2U6RBGRmHIxmTQDHsYSSj/gKuDfMhpRCtTsrIEp2DKW0Lpb\nanbWZCawBJSXl2c6hKTkcvy5HDso/lyWi8lkELAe2AjsB/4HuDSTAaXcxkwHkJxc/4fK5fhzOXZQ\n/LksF5NJN+BfYY83uW1ZraioNG41lohILmvs5FjZJJDIk5o3L6B9+/EUFLSLun/33n/SfFlz2m5u\nG3X/vm37+DDwNSOL4rRd1MSuiqqp2UH80OMnlObQYNIpKSykqro67jHRFBUXxa1GK+xQSPWX3s8L\nlkTtvcdQANTG3p3O1y4sLKG6uiqhc0viGvq83Tt1KvvjPD/Rz3ljXjvRz1v4Z23q1KnRD0rjZz0b\n5OIl8WCsJeEC9/gO7E90X9gx64E+TRuWiEjO2wD0zXQQTaU59obLgJbACg6DBngREWl6I4A1WAnk\njgzHIiIiIiIicqhcuqGxB7AQ+Bj4CLjVbS8FXgfWAq8BxRmJrvGaAcuBF93jXIq/GHge+AewCjiN\n3Ir/DuythbgEAAAJeElEQVTz8yHwNNCK7I7/90AlFm9QvHjvwP6XVwPnNVGMsUSL/RfYZ2cl8ALQ\nIWxfNsUO0eMP+iHW9lwati3b4m9SzbCqrzKgBdnfntIF6O/W22NVd/8GzABud9t/Akxv+tA8uQ14\nCpjnHudS/LOBa9x6c+zLIFfiLwM+wRIIwDPYLa/ZHP9Q4GTqf6HFircf9j/cAnuv68ns7QzRYj+X\nUEzTyd7YIXr8YBe1rwCfEkom2Rh/kzod+6UETXJLrpgLnINdCXR227q4x9mqO/BXYDihkkmuxN8B\n+zKOlCvxl2IXICVYInwR+3LL9vjLqP+FFiveO6hfu/AK1pszk8qIfmUPMAr4b7eejbFD9PifA06k\nfjLxHP/hlmly8oZGpwy7angH+8eqdNsrCf2jZaMHgB9Tvwd9rsTfG9gO/AF4H5gFtCN34q8C7gf+\nCWwBvsSqi3Il/qBY8XbF/oeDsv3/+RrgZbeeK7FfisX2QcR2z/EfbskkoRsas0B74E/A94HIO6oC\nZO/7uhjYhrWXxLpnKZvjbw6cAjzifu7m0JJsNsffB5iIXYh0xT5H3404Jpvjj6aheLP1vdwFfI21\nW8WSbbG3Be4EJodti3fvYdz4D7dkshmr/wvqQf3smo1aYInkSayaC+zqrItbPwr7ws5GZwCXYMXj\nOcBZ2PvIlfg3ueVd9/h5LKl8Rm7EfyrwJvAFcABrAD6d3Ik/KNbnJfL/ubvblm3GARcCY8K25ULs\nfbALkZXY/3B3YBlWMsyF+NMq125o9AF/xKqKws0gVF85iexqQI1lGKE2k1yKfzFwrFufgsWeK/Gf\nhPUCbIN9lmYDN5H98ZdxaAN8tHiDjcAtsSrJDWR+1I4y6sd+AdabrlPEcdkYO8Rv84nWAJ9t8Tep\nXLqh8ZtYW8MKrKpoOfbhLMUatbOxa2cswwj15sql+E/CSibhXTtzKf7bCXUNno2VdLM5/jlY+87X\nWPvm94gf753Y//Jq4PwmjfRQkbFfg3WdrSD0//tI2PHZFDuE4t9H6Hcf7hPqdw3OtvhFRERERERE\nREREREREREREREREREREpGlcht3vclycYzoAN4Q97ooNEBdPOTAgqcjs7uLtWB/+j4HrGjjeT+jm\nyUgbCfWj/3sD59nVqOhiC3+tcC8BRUmeO1FlxL5hbSpwdoLnjfxsZINewFWZDkIk3zyD3XA4Jcb+\n5sT/IoplITY8STLGAr9y60dgQ20cEed4P7GTSfgdvg2JHCPNKy+v1VTK8P43zOR5k+En9udA0uBw\nG5tLvGuPTQh1M3Bl2HY/8AbwZ6xE8HNsLJ/lwH3Yld9H7thmwEzsC2UlNqRHpPOwcaSWAc9io/OC\nDZ3xsXveL2LEGBzGYTuh4XKeAL4Vdkx4SaIImI/dufso0YeBCB5/FDakynIX/5CwY/4fNjrBW8CR\nbtsR2BheS91yhtveEbt7+yNs9OFYQ09sxJJMGTap0mPuOa8CrSOObUZoiPxi4CA2agIu5j7AIOz3\n+j5W2goODXMCNgL1cux32yfsnNFe8wlCv8+N2IXFMmw02WCJ9QhsVOLge9zo3vd06n82wP6WH7rn\nX+G2+bHS6nPuvQeHa49UDjxI6G8y0G0vxcavW4n9Tb7htg8jdAf6MuwzPR2bv2M5NoCqiKTZGOD/\nu/XFhEoSfuwLt5d73Iv6V59lYY9vwBJE8OKkxP0Mlkw6AYuwMaTAxmG6G/tyCJ9rI1r1z1jg1279\naGxQwBJs2PjwZBIsSfiBr1x8BdgXfPC48NJC8PgfYsNG4I5v79ZrgYvc+n3YqLBgo8IGE05PbHZG\nsNLTf7n1Czl01rqgYAxlwH5sHgmw0uGYKMf/BRsn6WIsed2JTYYVTDKFWIIAmwvnebf+a+A/3Hpz\nLGnEe80/AJeHxRi8ILgBSxwADxMaQ+v8sPcY+dn4FvZ792FJuAIbyNGPDZPf1e17k/rJO2gh8Fu3\nPjTs3L/GPjdg8+csd+vzsAEuwUbCbUb9seKkCTTPdACScVcRGmjyOff4ffd4KfZFAPEHeTsbKwEE\n5zTZEbbPh02q0w/78gAbPO5NYCewF3gcK0nMj3JuH1Zi+iY2ptD1EeePZil21Qw2HtE3sZGZYx37\ne2xMq+BVL9j4Sy+59WXYpFNgX9jhg4cWYqWsodjkSGBzWjQUI9iXdnAeiWXYl32kN4AzscH2fg6M\nxxJzcKTjYmyw0L7YEOHB/+k3sQTYHRtzbL2H18Q9B+yzEEwyQ7D2NbBSTfA9Rn42hmBJN4BVSy7C\nShfV2O97iztuhXv9aO1Xc9zPN7CLjA7uvMFYFmKlokL3/Aew2T5fwEa3zbtBCTNN1Vz5rRS7wnsc\n+5L5MaEqCbD5PRqroX/e17HJv07GqmDGY9U2g7Cr6YupP0tmUAD4H/e8wVi1G9iQ68HPbwGWoMKf\nEx5X+MRdkd7AEsFmrKrnP932/WHH1BL6kvZh1YLB99KD0O/J6xfYvrD1g0S/uFuMJZNBWJIqxq7w\nF7v99wALsCqfkYRKf3Pc46/c84Z7eM3w4yKPaex7jDwu+Ddp7OtHCj4/2nnvA67F3vvfid+RRNJE\nySS/fRu7qi3Drnx7YkllaJRja7CrwGheByYQqm4pCdsXAN7GriqD9fbtgGPcz2KsKuc2bATfSD6i\nf4FtJNRT7BKsZBE0iFA115XAkhhxg73n7cDvsKR6cpxjwapvbg17HIx5MaFqpRHU/x0kI9gucxD7\nIl6J/a6DyaSI0JV++CiwR2N/y19jCfgbJD85098JXWycR+g9Rn423sB+7wVYO8uZ7n14SbbB9rtv\nYlVj1e68wWo5P/Z324V9rj7GhrJ/F0sm1cT+vEoaKJnkt9HA/0Zs+xNW1RU5490X2JfJh9iVYPj+\n32FTx36AVV1Edsn8HOviOwf7MnwT+4cvxOq1V2JfFD+IEmOsmfdmYfXiK7ASy66w49/F6vdXYQ32\nke8xeBzYFfsKrDrnO8BDEfsjY7gVm5RqJfYFNsFtn4p9aX6EVXdVEF3keWPtC/oa+92+7R4vxtp1\ngu0IM7Dqr/exZB48xxUuluVYSfCP2Jd5Y14zcn/wmKlYEvkQuxD5DEskkZ+N/8U+CyuxUtOPsequ\naH/LWK+/172nR7BSB1ingAHuvPdi7WlgDezBzh9fYxcnH2AJeAVqgBcRySotCZU+TyfUtpZqqehS\nLk1MDfAi0lg9CfXa+xpr9xIRERERERERERERERERERERERERERGRw8f/AWyu5lXJ1FG+AAAAAElF\nTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x8c2281d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york post: 116 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8f12cd10>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york times: 1445 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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5apg2w5vvvsrqeW/FTFKwZw+7Skpo3bp1PWZMRCS1vASTScAPsIby+7AxtUIP\nFPqBe7GeVZMSm8UGpAyu9fmYVloaM0n2YYdx6NChesyUiEjqVRdMyqjaUyo0Te/z7jVIxal7j8Ha\nTpomJHciIpIWqgsmi+M8prrqiog0MtUFE399ZUJERNKbl67BIiIiUSmYiIhInXkd6NEHDAVOxB5O\njPXcyQ11yZSIiKQXL8GkJzAXOL4WaRVMREQaES/B5PdYIHkGGyF4KzYul4iINHJegsnZwDvAjUnK\ni4iIpCkvDfAHgdXJyoiIiKQvL8HkQ+D7ycqIiIikLy/B5F7sQcark5MVERFJV17aTD7BJqx6AxgD\nLCf66MHgbbDH7liD/pHYUCzTscb+dsAsrBfZRuAqYKf7zHisx9gh4HasLUdERFLESzBpCzyIzfl+\nplti8RJMDgC/wKbubYMFqfnYcPfzgSnAXdh0wOOAvsAw99oVeBfogw1MKSIiKeAlmDwCDMJu3s9j\nk2UlomvwdrcA7Ab+DwsSlwKD3fYZ2NTA44DLgJlYENoIFAADsbnpRUQkBbwEk6HAEuACkjcycB5w\nEjZnSkdsvhQIz5sC0IWKgWMzFnxERCRFvASTw4F/kLxA0gZ4Ffg5UHn2qWAN562yLz8/v3zd7/fX\nOXMiIpkmEAgQCAQSciwvwWQlcFRCzlpVcyyQPA/MdtsKgU5YFVhn4Gu3fQvWaB/SzW2rIDKYABQU\nFCQyvyIiac/v91f4sT1x4sS4j+Wla/AkrKprUNxni84HPA2sAR6N2D4HGOHWRxAOMnOA4cBhQC9s\ndsdlCc6TiIh44KVk0gUb6HEB1gD+T2J3Df6Lh+OeAVyLPV2/wm0bD0wGXgJGEe4aDBZ0XnKvB4Gb\n0eyOIiIp5SWYPBux/t9uiSaIt2DyAbFLSOfG2P6AW0REpAHwEkxqO6y8SgkiIo2Ml2DyXLIyISIi\n6U3T9oqISJ0pmIiISJ15qeb6iprbQ3wuTbKeRxERkQbISzDxuaWyHGzwR7CpfA/UNVMiIpJevAST\nvGr29caGjW8NXFiXDImISPpJVJtJAfBjbMDFCQk6poiIpIlENsD/BxuefngCjykiImkg0b25DmKD\nMoqISCOSyGByBHA58O8EHlNERNKAlwb4CUTvGtwM6IHNgNgWG6RRREQaEa/BpDolwH3AQ/FnR0RE\n0pGXYHJ2jO1lQDE2d3si5oQXEZE04yWYBJKVCRERSW8am0tEROqspmDSJM7Fi2ew+d4/jdjWDpgP\nrAfewYaTNU4VAAAMoElEQVRsCRkPfAGsBc73eC4REUmCmm78B7Gxtmq7hNJ78SxVh2AZhwWTPtg0\nwePc9r7AMPd6IfBELa5BRESSrKY2k395OFZroH0ceXifquN+XQoMduszsPaacVj345lYwNqIDeMy\nEFgax3lFRCRBagomebU4RnPgNuAe935TXTLkdMSqvnCvHd16FyoGjs3YeGAiIpJCXnpzRXMV8CDQ\nC9gJ3ImNHpxIQaqfRyXqvvz8/PJ1v9+f0AyJiGSCQCBAIBBIyLHiDSZnAFOBH2JVTo8Bk7DnTRKh\nEOgEbMfG+vrabd8CdI9I181tqyIymAAUFBQkKGsiIpnB7/dX+LE9ceLEuI/ltfG6N/Aq1s7xQ+AV\nrDH8FyQukADMAUa49RHA7Ijtw4HDsNLQMcCyBJ5XRETiUNuSSXtsOJUxWBvJEuCXJKbheybW2N4B\nGyTyN8Bk4CVgFNbQfpVLu8ZtX4P1HLuZmqcSFhGRJKspmLQAxmI9qdoCG9z6qwnMw9Uxtp8bY/sD\nbhERkQaipmCyDhsRuAiryvojGn9LREQqqSmY9HCvPqxa65e1PG6PmpOIiEimqG2bSa5bREREqqgp\nmGioEhERqZGChYiI1JmCiYiI1JmCiYiI1JmCiYiI1JmCiYiI1JmCiYiI1JmCiYiI1JmCiYiI1JmC\niYiI1JmCiYiI1JmCiYiI1Fm6BpMLgbXAF8BdKc6LiEijl47BpCnwOBZQ+mKTa30vpTmqZ4FAINVZ\nSCpdX/rK5GuDzL++ukjHYDIQKMCm8z0A/BW4LJUZqm+Z/oXW9aWvTL42yPzrq4t0DCZdsbniQza7\nbVJH2TnZ+Hy+apfsnOxUZ1NEGqDaTo7VkATj+VCTJk0oK3uH7OyhMdOU7A3SZm4bmjSPHmP3bt3L\n6wTZnB37hrp39258Pl/UfdnZ7SgtLa4mk0BZ7N0AWW2zuGPsHdUnilPprlLIryFNfmncx/dy/RMn\nTqyyO6ttFiU7S+I+f02yc7LtbxBDOp8/Ud+9ZF6/pLfod72G7VTslnehez8e+2/wUESaAuDo+s2W\niEja2wD0TnUm6ksz7ILzgMOAlTSyBngREUmMIcA6rAQyPsV5ERERERERqSqTHmjsDiwEPgc+A253\n29sB84H1wDtATkpylzhNgRXA6+59Jl1fDvAK8H/AGuCHZNb1jce+n58CLwItSO/rewYoxK4npLrr\nGY/da9YC59dTHuMV7dp+i303VwGvAW0j9qXTtSVcU6zqKw9oTvq3p3QC+rn1NljV3veAKcCdbvtd\nwOT6z1pC3QG8AMxx7zPp+mYAN7j1Zth/1ky5vjzgSyyAAMwCRpDe1zcIOImKN9xY19MXu8c0x/4W\nBTTsxy2iXdt5hPM8mfS9toQ7DXgr4v04t2SK2cC52C+Fjm5bJ/c+XXUD3gXOIlwyyZTra4vdbCvL\nlOtrh/3AycUC5evYzSndry+PijfcWNcznoq1H29hvU0bsjwqXlukK4D/deuery3TIk0mP9CYh/2q\n+Aj7Yhe67YWEv+jp6BHg11R8yiFTrq8XsAN4FvgEeBJoTeZcXxHwMPAvYCuwE6sOypTrC4l1PV2w\ne0xIut9vbgDecOuery3TgklcDzSmgTbAq8DPgcpPtQVJ3+u+BPgaay+J9cxTOl9fM+Bk4An3uoeq\nJeV0vr6jgbHYD50u2Pf02kpp0vn6oqnpetL1Wu8BvsPavWKp9toyLZhswRqtQ7pTMbqmo+ZYIHke\nq+YC+3XUya13xm7I6eh04FLgK2AmcDZ2nZlyfZvd8rF7/woWVLaTGdd3CvAh8C1wEGvAPY3Mub6Q\nWN/Hyvebbm5buhkJXARcE7HN87VlWjD5J3AM4QcahxFu1E1HPuBprBfQoxHb52ANnbjX2aSnu7Ev\nbC9gOPAe8N9kzvVtx6pd+7j352I9n14nM65vLVaP3hL7rp6LfVcz5fpCYn0f52Df28Ow7/AxwLJ6\nz13dXIhVM18G7IvYngnXVmeZ9EDjj7C2hJVYVdAK7B+/HdZonY5dL2MZTDjwZ9L1nYiVTCK7XmbS\n9d1JuGvwDKwknc7XNxNr//kO+yFwPdVfz93YvWYtcEG95tS7ytd2A9b1dxPh+8sTEenT6dpERERE\nREREREREREREREREREREREREJHEux55nObaaNG2BmyLedwFeruG4AaB/nXJmT+fuwPrAfw7cWEN6\nP+EBIyvbiD0zAPCPGo6zu1a5iy3yXJHmAdl1PHa88og9yN9E4Jw4j1v5u9EQ9ASuTnUmRBqbWdgD\ng/kx9jej+htRLAux4UPqYgTwe7d+BDaUxRHVpPcTO5h8RfQbfDSVx0Dzysu56kse3v8NU3ncuvAT\n+3sgSZBpw6mId22wCZtuxYafCfED7wN/x0oED2ID+60AHsJ++X3m0jYFpmI3lFXALVHOcz42jtNy\n4CVs9Fyw+RM+d5/7bYw8hgaB3AFswG5ezwE/jkgTWZLIBuZiT+5OI/ogkqH0nYHF7ro+Bc6ISPP/\nsNEHlgBHum1HYGNsLXPL6W57e+zp6M+w0YFjDVy5EQsyedikRNPdZ94GDq+UtinhIexzgEPYqAi4\nPB8NDMT+rp9gpa3Q0C3HYyNMr8D+tkdHHDPaOZ8j/PfciP2wWA6sJlxiPQIbFTh0jRvddU+m4ncD\n7N/yU/f5q9w2P1Zafdlde2i488oC2PBBoX+TAW57O2wok1XYv8kP3PbBhJ/gXo59pydj83eswAZI\nFZEkuwb4k1tfTLgk4cduuD3d+55U/PWZF/H+JixAhH6c5LrXUMmkA7AIG8MJbJ6Ee7GbQ+RcF9Gq\nf0YAf3DrR2GD7uViw7pHBpNQScIP/Mflrwl2gw+liywthNL/Ehs2Ape+jVsvAy526w9ho6qCjaoa\nCjg9sLGowEpP/+PWL3Kfj1YyCeUhDzgAnOC2z6LiQHshb2ITFV2CBa+7scmoQkEmCwsQYGNjveLW\n/wD8l1tvhgWN6s75LHBlRB5DPwhuwgIHwOOE57i4IOIaK383foz93X1YEN6EDZTox4ap7+L2fUjF\n4B2yEPizWx8Ucew/YN8bsPlvVrj1OdgAkwCtsL/HYFQyqVfNUp0BSbmrsTlFwH4xXo39ygW7eW1y\n67F+aYPVtU8jPCdJccQ+HzYYYF/s5gE2eNyHwC5scLmnsZLE3CjH9mElph8B+4GfVTp+NMuwX81g\n4xH9CBt5OVbaZ7AxpUK/esHGL5rn1pdjkz6B3bAjZ+/MwkpZg7DJhcDmhKgpj2A37dUR58iLkuZ9\n4ExssL0HgdFYYA6NRJwD/AXojQ0RHvo//SEWALthY4IVeDgn7jNg34VQkDkDa18DK9WErrHyd+MM\nLOgGsWrJRVjpogT7e2916Va680drv5rpXt/HfmS0dccN5WUhVirKcp9/BJut8zVsdNvqvq+SBKrm\natzaYb/wnsZuMr8mXCUBNv9GbdX0n3c+NrnXSVgVzGis2mYg9mv6EirOkhkSBP7qPncqVu0GNuR5\n6PvbBAtQkZ+JzFfkxFuVvY8Fgi1YVc9/u+0HItKUEb5J+7BqwdC1dCf8d/J6A9sfsX6I6D/uFmPB\nZCAWpHKwX/iL3f77gAVYlc9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"text": [ "<matplotlib.figure.Figure at 0x35b5850>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "wall street journal: 559 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8e32c550>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "washington post: 192 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x3926cc90>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "UNKNOWN BYLINES: 522\n", "GUESSED BYLINES: 2701\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "#OUTPUT TO BYLINE FILE\n", "\n", "#f = open('org_people_upload.csv', 'w')\n", "#b.export_org_names(media_people,f)\n", "#f.close()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "def pct(a,b):\n", " return 100*(float(a)/float(b))\n", "\n", "pct_table = []\n", "pct_table.append([\"org\",\"type\",\"female\",\"male\",\"unknown\"])\n", "\n", "for org in sort(media_people.keys()):\n", " print \"{0}: {1} bylines\".format(org,len(media_people[org]))\n", " article_count = {\"female\":0,\"male\":0,\"unknown\":0}\n", " people_count = {\"female\":0,\"male\":0,\"unknown\":0}\n", " \n", " total = 0\n", " nontotal = 0\n", " people_total = 0\n", " for name in media_people[org].keys():\n", " #gender = b.single_name_gender(name)\n", " gender = b.org_name_gender(org,name)\n", " if(not gender == \"ignore\"):\n", " article_count[gender]+= media_people[org][name]\n", " people_count[gender] += 1\n", " people_total += 1\n", " total += media_people[org][name] \n", " else:\n", " nontotal += media_people[org][name]\n", "\n", " #ARTICLE COUNT CHART\n", " colors= '#78E678','#E8CA53',\"#CCCCCC\"\n", " #print \"NONTOTAL: {0}\".format(nontotal)\n", " pct_table.append([\"people\",org,people_count['female'],people_count['male'],people_count['unknown']])\n", " pct_table.append([\"article\",org,article_count['female'],article_count['male'],article_count['unknown']])\n", " #print \"{0},{1},{2},{3},{4}\".format(\"people\",org,people_count['female'],people_count['male'],people_count['unknown'])\n", " #print \"{0},{1},{2},{3},{4}\".format(\"article\",org,article_count['female'],article_count['male'],article_count['unknown'])\n", " \n", " P.figure(1, figsize=(6,6))\n", " labels = 'female', 'male', 'unknown'\n", " fracs = [pct(article_count['female'],total), pct(article_count['male'],total), pct(article_count['unknown'],total)]\n", " explode=(0.06, 0, 0)\n", " P.pie(fracs, explode=explode, colors=colors, labels=labels,\n", " autopct='%1.1f%%')\n", " P.title('Author Gender per Article in {0} across {1} authors and {2} articles'.format(org,len(media_people[org]), total), bbox={'facecolor':'0.8', 'pad':5})\n", " P.show()\n", " \n", " #PEOPLE COUNT CHART\n", " P.figure(1, figsize=(6,6))\n", " labels = 'female', 'male', 'unknown'\n", " fracs = [pct(people_count['female'],people_total), pct(people_count['male'],people_total), pct(people_count['unknown'],people_total)]\n", " explode=(0.06, 0, 0)\n", " P.pie(fracs, explode=explode, colors=colors, labels=labels,\n", " autopct='%1.1f%%')\n", " P.title('Unique Author Gender in {0} across {1} authors and {2} articles'.format(org,len(media_people[org]), total), bbox={'facecolor':'0.8', 'pad':5})\n", " P.show()\n", " \n", "for l in pct_table:\n", " print l\n", " \n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "la times: 918 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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QDBkyBCtWrLAMIkpOTsbu3buxefNmy3wGg8FqOXd3d8v/y2Qyy0CI+/fvo3HjxlbxFUhN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\nnYfI7+XketDnuY2u/ALvK5qOn4zOQ8Zhq4POSW6174jXd7o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"text": [ "<matplotlib.figure.Figure at 0x8b80d5d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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hOi3H1nnNR46o6WusEOyOCsHuKO6nA8MwalsFIQgyYKU0yLKsmi3YIE1FeJX1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\nMwDkAtgK4BUAbU/xGtcCeBxAAYCVAHwAmpxFlhK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"text": [ "<matplotlib.figure.Figure at 0x8eb00810>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york post: 116 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0Mv7c/qfxrx1/Wa1Ga57NZvvWordsAnAIlXChQYg7oqRNiHQ4AOEAmgBoqtXw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\nzkOhgUWb/pGAoi87lOkc3rh+rOOKxjy/TVQSBc4Aeo5Y7yl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"text": [ "<matplotlib.figure.Figure at 0x620837d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JbBFR3NKxpb+ibg13SCR0COSHs5eSsGzttfRVm28ILMtcTE41/gBgFYBUsWMj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\nGdLT0yvt56gMRUVFUBTFD4A3Ogc5lqwJCCIvfqK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"text": [ "<matplotlib.figure.Figure at 0x1ece8a10>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "new york times: 1445 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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kAVgDQPLjZMReiG0aeyG2ycMTD5NjL8by2lQtI6iEM+kJ6ZsBRAC4J2l4Qswc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\nnYtrrrnG6jghRdd13HHHHd6CgoLhANbV8uH+F8A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"text": [ "<matplotlib.figure.Figure at 0x6209b290>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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LWEED1ulNuJKtQtyTu0wmM6SocDcu85Cl9vmgT0Kz/s1qfL6lN0pxYccFnP7+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\nMFXZh2/1wPHjJ6z5+flmR6FqSkpK0nRdjwXAbjg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"text": [ "<matplotlib.figure.Figure at 0x8c3323d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "wall street journal: 559 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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wXfO2326b7j9MswBYYTRZfwRwFPToJyHFjs5QhBQvFkAbrZp71WIVugb4asU+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\ndwCQXnkpqabh3pwqnKZhQX6BO/VfXxxla4KoHJo3CsfeVb3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4uNhfJyYmYuzYsVnKUS6X4+HDh/bXXl5eL4w/O8/Gkvn3ZdawYUPMnz8fycnJEAQBkZGR\n+Pbbb3Hnzh2kpaWhRo0aAGxn4XPnzsX58+dhMpkgCAICAgLyFBMAjBo1CosXL8abb74JJycn9O3b\nF506dXrh9M+WXU7rKjdllNt7zMnJyXB2doZarba/5+XlhX/++SdX82dM/6xnt+GMfbUg6/fgwYNY\nsmQJbt68CavVCpPJBD8/PwC2Wo2WLVvmKsb79+8/d1zI2JdUKhVmzJiBlStXYtq0aahXrx5Gjx6N\nypUr56lM69evD8B2C2zs2LEICwvDtWvXUK1atSz35atXr45BgwZh5cqV9sQ5e/ZszJo1C8uXL0et\nWrUQGRmZ7Qk7YDveffHFFzh16hT0ej1EUYSzs3O20+ZWRhU0YCs7s9kMq9WK+/fvP1fW5cqVs+9z\nDMNk2f8BYNasWVi2bBkWLFiA6tWrY+TIkahTp06+fsez+3pGo+aCHPNzwnEcevXqhZ9//hlHjx5F\nWFhYls89PDwwbNgwjBw5EmPHjoVarYZer88yTVpaGjQaTbbLz81vzsvxMCkpCc7OztDpdDlOd+fO\nHdy/fz9LnrNarfaTgSlTpmDx4sXo3r07ypcvj7i4uBz3rZcmeG9vbyQmJmZp5PXs69xyd3dHUlKS\n/XXmvwFArVZnOTF48OBBljgaNmyIhQsX5uq7tmzZgrt37yIqKgqA7SD7+PFjHDhwAKGhoVCpVDCZ\nTNl+F5D3hj4eHh5ITExElSpV7L/Nw8Mj18sLCgpCfHw84uLiXjitt7c32rVrh8mTJ+cptoz4Mt/O\nMJlMePz4cZZlf/TRR/Yz/MwSExNz9RtyktO8vr6+UKlUWLduHQIDA6HVauHu7o4NGzbYD8YA8Nln\nn6FmzZqYMWMG1Go1Vq9ejT179uQ5Fnd3d/s6PHnyJEaMGIGGDRuiQoUKuYo9p3X1sjLKyzosW7Ys\nUlNTYTAY7Aeiu3fv2g/UL9uG8/p9+V2/ZrMZ48ePx7Rp0xAaGgq5XI6xY8far0K9vLxw8+bNF86f\nOUZPT0/s3bs3y+dJSUn2GpGmTZuiadOmMJvNWLRoEaZPn44lS5bkuUwzvOyK+dnPqlevjm+//db+\nesCAAejYsWO28y1cuBByuRzr1q2Dk5MT9u3bl6XdxbPll5ycbE8a2ZVbTmXp4eGBu3fvQhRF+3R3\n7tzJ8TgdEBCA2bNnQxAErFu3DhMmTHiuljM3vyMnLzvmF5QgCFlOgDPjeR4qlQoAUKlSJQiCgJs3\nb9qr6S9evIjq1atnO29ufvOz5ZFT+Xh7eyM1NdVeW/IiXl5e8PHxea4NSwZfX1/7Ux+7d+/G+++/\nj927d9t/57NeWkXfpk0bLFu2DPfu3YPVasWRI0dw4MABREREvGxWALadI2MHad26NTZv3oyrV6/C\nZDI911LV398fe/fuhclkws2bN7M0yGjZsiVu3LiBP/74AzzPg+d5nDt3LttbAadPn8bt27exYsUK\n+z3xdevWoW3btvYNuEaNGjh48CBSU1ORnJyMNWvWZFmGm5tbnh6XioqKwrJly5CSkoKUlBQsWbIE\n7dq1y/X8ffr0QWpqKj788EPcunULoihCr9fj33//tW840dHRSEhIwJ9//glBEJCeno5jx47h3r17\n9uW86EDVqlUrHDhwACdPnoTFYsHixYuzVL9369YNCxcutO+Ajx49Qnx8fK7jf5mXVUs3bNgQ69ev\nt1fHBwYGYv369fb77wBgMBig1WqhUqlw7do1/Pzzz/n6vl27dtnbHzg52TpszLgaz02557SuXlZG\nedmuvL29UbduXSxYsABmsxmXLl3Cb7/9Zt+uXrYN51Ve1m9mFosFPM/ba/IOHjxov2cIAJ07d8bm\nzZvx119/wWq14t69e1n228xl1aJFC9y4cQPbtm0Dz/PYsWMHrl27huDgYDx8+BD79u2D0WgEy7JQ\nq9X2csupTDO7cuUK/v33XwiCAIPBgLlz58LT09N+Yn7w4EH7idK1a9ewbNmyLFeHly9fRnp6Okwm\nE1auXIkHDx68MMEbjUao1WpotVrcu3cPK1asyPK5v78/tm3bBkEQcOjQoSxVwO7u7khJScnSsCun\nbbp27dpQqVRYvnw5eJ7HsWPHcODAAbRp0ybbeXmex9atW5GWlga5XA6NRvPCms2X/Y7s5PaY/yyr\n1Yr09HTwPA9RFGE2m+01yGfPnrUfv0wmE3744QeYzWZ7rcPWrVvt++SdO3ewaNEitGrVCoDt4jE8\nPByLFy+GyWTCyZMnsX///hceo/Pzm3Pat8uWLYvmzZvjs88+w5MnT8DzfLa3a2rVqgWNRoPly5fb\na9EuX75sr7X7448/8OjRIwCwnyjk9DTESxP8oEGDULduXQwaNAitWrXCggUL8Mknn+T60YLMDYGa\nN2+O3r17Y+jQoejatSuCgoKyTBsbGwuO4xAVFWVvbZgxr1arxYIFC7Bjxw5ER0cjKioKCxcutLdU\nz2zLli0ICwtDtWrV4ObmBjc3N7i7u6NXr144ePAgnjx5gnbt2sHPzw8dO3bEqFGj7DtChrfeegvL\nli1DeHi4vaFaTmdoAwcOREBAAHr16oVevXohICAAAwcOzNU6Amy3OX744QcoFAoMGjQIoaGh6NOn\nD0wmEyZMmADAdnY3e/ZsfPfdd2jTpg06dOiAVatWZdl5M8eYed1Xq1YN48ePxwcffIC2bdvC2dk5\nS3VZRmvQESNGIDQ0FP3798e5c+eyXW5+vKxBWMOGDWE0Gu1VUYGBgVleA8Do0aOxbds2hIaGYvr0\n6YiKinru9+bm+/755x/0798fISEheO+99zBu3DiUL18egO3+9NSpU+3tO7JbRk7r6mVl1Lt3b+ze\nvRutWrXC7NmzX7repk+fjjt37iA6Ohrjxo3DkCFD7PvNy7bh7ORUBnlZv5lptVqMHTsWEydORKtW\nrbB9+3aEhobaP69Vq5a94V14eDiGDBmS5Uou83JdXFwwd+5crFq1CpGRkfjxxx8xd+5cuLi4wGq1\nYvXq1YiOjkZERAROnDhhby2fU5lm9vDhQ0yaNAlhYWGIiYnB3bt3MW/ePHtyO3bsGGJjYxEcHIx3\n3nkHrVq1Qv/+/e3zb9myBdHR0WjTpg2OHTuGhQsXvvDxw8GDB+PChQsICwvDmDFj7Mkmw9ixY5GQ\nkIBWrVph27ZtWaplK1eujKioKHTu3BmtWrWyt6J/0VUjx3GYO3cuDh06hMjISMyaNQsff/yx/RZl\ndvNu3boVnTp1QmhoKDZu3Jjt0z25+R3PetkxP6dtcMuWLWjZsiVmzpyJEydOoEWLFvYrVrPZjFmz\nZiEyMhIdOnTAiRMn8OWXX9prt65evYqBAwciODgYQ4YMQZ06deyN7ABgwoQJSE9PR+vWrTFlyhRM\nnDjRfmJX0N8MvDxn/N///R9YlkX37t3Rpk2bLK3jM8jlcsybNw8XL15E586d0bp1a3z66af22wuH\nDx9Gz549ERISgjlz5uDTTz/NsQ2A5F3VBgUFYePGjS+tSiOkNLh16xa6deuGI0eOSB0KIaSEeeVd\n1RJCcu+///7L9sqTEELySvKurUpKr3iEFLUff/wRK1euxPjx46UOhRDiACSvoieEEEJIwVEVPSGE\nEFIKUIInhBBCHFC2VfRyuTxVEASnVx0MIYQQQvJHLpc/EQShYN0lEkIIIYQQQgghhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBBSJBipAyCEvBIMAOXTv9MBiBLGQgh5BSjBE1KyaAFUyPzPScv6cazMF4BO\nBDRWq6gRRSgFq6gSBKuSF0Qlz4usXMZYAUCwijK5nBFYOWOWyRiLXMaky2SMWSaDiQFjAgO91Sre\n0hv5yzwv3gRwG0Di0/+TAAjS/HRCSF5Qgiek+NECqA2gnlbNBikVsgDBKvoYTYKn1Spybq5KYzlP\ntdW3nEZeuYJW7eOtkXu6q6BVs1Cr5FCr5FAp5U//tr2nUsghl9t2d6tVRLrZClO6gHSzAFO6AFP6\n/17rjTzuPTDhzl2jePOOwXQjUW9JvGcU7z8wKfRGXqFSyJ8oFLJ7AC6nplmOWq04B+AcgMsALJKt\nNUJIFpTgCZEOA8AXQD2GQX0XJ64FL4h1TemCh285raH+a2XY+gFlNFV8dSjvpUZ5TzXKuCjAMNLt\nthbeirvJJiTdM+LabT0u/PdYOHU+RX/xaqrswaN0lVbD3hRF/JWaZjkI4ASAUwBSJQuYkFKMEjwh\nrw4DIABAK1dnRYzRxDdRKuSymtWczYG13bS1a7iyAX4uqF7JCRwrkzrWPDOaBFy48hhn/03BiXOP\nTMfPPky/cjNNo1HJb6SbrX+Y0oUdAPYDeCx1rISUBpTgCSk6DICqAFq5OnOdTOnWUCcdy4Q38ZKH\nN/NWNw8sC6+yaqljLFIW3oqT/zzC/r/uWXcduJN29uJjlVolv2pKF7akm607ARwAkCZ1nIQ4Ikrw\nhBQuNYBoZx3XUxCsESwrU4c09hRbNffWtgj0QMXyWqnjk5QpXcDf5x7iwF/3hF0Hk9LOX07VaNTy\ni3ojv57nxXUA/pU6RkIcBSV4QgpOBaCts47rbzILberWcDV3bVvROaSxJ6pV1El6z7y4M5h4HDv9\nAL/vuZ2+aectq1UU7xpNwg9Pk/0FqeMjpCSjIw8h+aMC0MZZx72VbhbaBvi58L06VHZqF14eHm4q\nqWMrkQRBxF+nH2DD9hu2ZG8V7xlNwg+8IK4DcF7q+AgpaSjBE5J7MgCRTjo2zmy2tqtZzcXSq0Ml\np/bhPoxnWUrqhclqFXH01ANs3HEz/dcdNwWrVbxvShe+tfDiEgD3pY6PkJKAEjwhL+fJypmBSoX8\nHa+yKs2AHtV0HSN8GEdvIFdcWK22K/sVG64Yf99zW8Zxsi1pen42gMOgHvkIeSFK8IRkjwHQzFnH\nvW+2CG3ah/uIA3tWVzcIKEP31CX06LEZa3+/Zv1m1SWj3sgnpRn4WaKI1aCW+IQ8h45UhGSlBNDD\nWcdNVqvkFYb39Vf36lhJ5uKkkDoukonVKiLh6D0sXn0p7fDf92VyGfOjwSTMB/CP1LERUlxQgifE\nRqfgZKPkcmZ8bX9X+cg3a+gimnvbu3clxdetJANWbLjCf//zfxaIOPpEz48HcFTquAiRGh29SGmn\n5VjmbblcNjm8qZd8/NAAzWvVXKSOieRDulnA6k3XxFnf/mMUBPFoapplPIC/pI6LEKlQgiellYaV\nM8M5TvZBcJAnN3F4LUrsDoISPSE2lOBJaaOSy5mhCk72UbOGZbnJw2tra/m7Sh0TKQKU6ElpRwme\nlBasTIYhSoV8WuN67ooP3q6jrVODEntpkDnR84L1yJM0fiiAi1LHRUhRowRPSoMQrYb94bVqzp6f\nvFdfWz+gjNTxEAmkmwUsW/+f8MW3/5hFYLHRJHwIeryOODBK8MSRldNp2a84VhY98/0Gmo4RPvQM\nO8HdZCM+mnvauH3/HZPRJIwAsBbUYQ5xQHS0I46IZeXMKJaVTevfvSo7dnCAQqthpY6JFDNHTyVj\nzLTj+nsPTBee6PkBAE5LHRMhhYkSPHE0IVoN+0MtPxfP2ZMbav0qO0sdDynGBEHEio1XrJ8sOJsO\nUVypNwoTADySOi5CCgMleOIoPHRadrGCk7Wd+X4DTYdWVB1Pcu9BSjqmfXXG9OuOW2ZTujAAwC9S\nx0RIQdERkDiCaLVKvrpvTBXNhGG1FFo1VceT/Dl+5gEGTvjT8ETPb9Eb+MEAHksdEyH5RQmelGQa\nrVr+pUol7/3tp001LQI9pI6HOAC9kceU2adMG3fcTDOahNcB7JM6JkLyQy51AITkUyOtWr4/vJl3\nk/ULgjV0r50UFgUnQ1RIeba2v4tmz+GkngzDePO8uBcAL3VshOQFXcGTkoZVKmSTWVb2/heTGqq6\ntPGlbZgUmQcp6Rj9f8cMh44n39Mb+S4ATkodEyG5RVfwpCSpqtOwu2v5uXTcsDhE07heWUrupEhp\nVCy6tPHXmGlhAAAgAElEQVTlynupXeKP3H1TJmNEQRAPgp6bJyUAHSBJSdFJpZSvfn9IgGpIrJ9c\nJqNNl7xaN+/oMfD9P/VXbqQdTjPw3QCkSh0TITmhoyQp7hiVUvaRWsmOXzWvhbphbTep4yGlmNli\nxYSZJ0ybdt66qzfybUB92pNijKroSXGm02rYDVUq6GI3Lw3T+FWhhnREWnI5g6iQ8mwZF4XzgWP3\nB/C8eArAJanjIiQ7lOBJcVVZq2YPtgkpV3/lnOYaV2eF1PEQYlfvtTJM84ZlFVvjE7uKIhhBEPdL\nHRMhz6IqelIchamV8k0ThtfSxvWqLqce6UhxdeeeEbGjD+hvJhr2phn4XgD0UsdESAY6cpLihGFZ\n5m2VQj7zu1lN1SGNvaSOh5CXMqULeHf6cdP2hDu39Qa+NYCruZjNC0A6gJSijY6UZpTgSXEh06jl\nCz3cVP3WfdVSW7mCTup4CMk1URSxZO1lYcbX51KNJqEVcn5e3pdTc8fBwGgxWJoDuP2KwiSlDN2D\nJ8UBq9Wwq6pXcnp989IwrbeHWup4CMkThmEQWMddVq2iTrXrYFJfnhcPA7iWzaSenIY7Ejwu2NOn\nkY9T4t+JfawW68+gPu9JEaAET6Sm1GnY3+rWdG3z08JgrZOWkzoeQvKtRlVnplFtN8WWvYk9LLz1\nAoDzmT52UWgVhxoNbOTbYkwL1reJr4xTcZpbR271s/LWDaBhakkhowRPpKTVadidzRp6NFsxu7lG\nraRR4EjJV9FHi7JllNyeQ0mdrCJmArACUCt0in0BXQL8Iz6OUGQ0HPUJ9JGpy6jV1w9e72flrb8B\nSJYyduJYKMETqbjqNOz+qJBydRZ/0lit4GhTJI7h/OXHiJt0xGgyW7sD+BcAp9AptlYNr9qw/bz2\nKuaZXhjL1SvHOJVzUl2Nv9rXylu3ALgnRdzE8dBRlUjBU6thD/doV7HaF5MCVXK5TOp4CCkU126l\noeOgfcYnen4AgI0AZAqd4mefQJ/QLku6qGVs9tu6Vy0vxtnHWXl139UeVt66GsCTVxk3cUyU4Mmr\nVkGjlh8d0tuvwoej6iioT3niKJLuG9FuwF5DapplnGDF9wAYTsMtLetftnOPVT007EtuQXkGeDIA\nFIknEztbLdYfAJhfQdjEgVGCJ6+Sh0YtPzpmwGvl3h30Gkcd2BBH8TAlHe0H7jU8SDF/ZraIswGA\nVbOzXH1d+/fZ0Eer0OauJ8YKTSrIHl19pE25nhImmIUfYbt/T0i+UIInr4qTVsMeGPB6tYrj4gKo\nqTxxGGl6C2KGxusT7xqXmNKtkwFArpS/ry2rHdfvt35atWvuH/tkGAbVI6uz1w9c9zA8NPhZLdZf\niyxw4vAowZNXQaXTsLs7Rvi8Nn1sfSVduRNHYUoX0HPkAcPla2kbDCZhGABRxsoGqVxUn72x+Q2N\nzivvHTYxMgb+7fy5C79dqG4xWDgrb40v/MhJaUAJnhQ1VqdhNzUP9Giy6P8aq+meO3EUPG/FW+MP\nG07982iP3sj3BmAFg+4KJ8W3b2x+Q+NayTXfy2YVLPyj/RVn1p1pzKfzNyHidOFFTkoLar5MihKj\n1bA/1PJzCVkyo4laLqfkThyD1Spi5MfHTEdPPjieZuC7ARAAtFFoFctjf4pVu1V1K/B3OHk7ofdP\nvTWsiv0GQHiBF0hKHUrwpMioVfLZFctrYlbPb6lRKqiyiDgGURQxefYp8879dy6kGfho2Fq7N+c0\n3IYeP/bQeNUuvEGSPGp4oNv33dSsmt0EIKDQFkxKBTrqkiKhVMje93BTvffb0jCtixON5U4cxxdL\nzluW/3Llht7ItwSQCqAuq2b3dF3aVVepZaVC/z7Xiq5wLu+suJpw9XWrxboKQFqhfwlxSJTgSVHo\n5KzlFmz9IVzjVZYGjiGO49u1l4R53124ZzAKTWDrVrY6q2YPtp/b3tm/rX+R3YPyrOXJWHmrIulU\nUifBIiwHPSNPcoESPClsNdVK+fa1X7XU1KjqInUshBSa9Vuuix/NO/3IaBKawjbEqw+n4Y5EfhxZ\nps7rdYr8dqdvU1/5g0sPdI9vPq4jmIV1Rf19pOSje/CkMDlr1ez2ae/VUwfWcZc6FkIKzbb4RLz/\n2YknRpMQDNswsO6cljvQfHRz9/p967+SCyWGYdB2ZluVylXVBgxiX8V3kpKNEjwpLDKdlv2pU+sK\nnn1jqtB2RRzGgWP3MGzKUb0xXYiAbfhXJ4VOkdCgX4Pyzd5u9kqHQOQ0HLou7ap92rK+8G/4E4dC\nVfSkUKiUso+q+up6fT+rmYalwWOIgzjxz0PEjjpgMJiEDgAOAVApdIo9NTvUfK319NaSdNqk89KB\nkTGyOyfvhAlmYRkA8ZUHQUoESvCkMLTXadn5vy0J07g6U4t54hj+vZKKLkPijWkGoReAHQBYhU7x\ne+XgykEdvuqglsmkO5H1aeQju7TjkrPpkUlmFawJkgVCijW61CIF5adWydeunNNC7e1BLeaJY7h+\nW4+YIfFGg1EYAmAzAJlCq1hdrl65Fp2/7qyWSVxLJZPLEPNNjFbGyiYCaCRpMKTYogRPCkKtVbM7\nPhpVRxNUlxrVEcdwL9mEznH7DHoDP0GwiithG/b16zJVy7Trvry7Rl5MOm1yqeCCtp+3VXEabiMA\nrdTxkOKHEjzJN61aPi+0qafXm92q0nZEHEJKqhmd4/YZUlLNX5gt1i8BgFWxnziVc+rTe31vLacp\nXgMhBsQEMNUiqrkrtIqFUsdCih86MJP8aqtUyvvOmRyoptHhiCPQG3l0H56gv5ts+sGUbp0KAHKF\n/F11GfXoPhv6aFUuKokjzF7bz9uqOQ33OoBOUsdCihdK8CQ/PNQq+epvP21CjeqIQ0g3C+gz+qDh\n6i39ZoNJGAnbsK9vKXSKT/pu6qvRehTfGnCVswox38ZoWBW7EoC31PGQ4oMSPMkrRqdlV/TrUkXb\nspGn1LEQUmCCIGLwxCPGs/+mJOgNfD8AVgAxnIZb1HdjX7VLheLfI6NvE180GtRIrdAp1gGgKjUC\ngBI8ySOGQS83F0Xw5BG16dKdlHiiKGL0tGOmQ8fvn0wz8F0A8AAiOC23qvf63mp3v5LTeDR4XDDn\nXN45kJExA6WOhRQPlOBJXnirFPJvvvm0iZaGfyUlnSiK+GjeafPWfYmX0gx8GwAmAI05Nbfp9RWv\na7zrlqzabjknR/t57bVypfxzAE5Sx0OkRwme5BbjpGWXD+hRTdkgwE3qWAgpsPnf/8uv+vXarTQD\nHwbbEKy1WDW7s/PiztqKzSpKHF3+lKtfDn6t/ZSsiv1A6liI9CjBk9x6vYyLssX4IQFUNU9KvO9/\n/s/65fILyXojHwzgIYAqnJpLiP482ql66+pSh1cg4VPC1QBGAiiZZymk0FA9K8kNrUYl37X0s6Yu\nlSvopI6FkALZsP2GOPmLUylPh329AaAcp+GOhH0QVrZebL0Sf9GjdFbCYrTg3rl7VQSzsF7qeIh0\nSvzGTIqeWimfEt7MS920QVmpQyGkQHYeuIP3Pvk7zWgSQgFcAeDGabkDTUc0LRvYP9BhjofNRjbj\nZHJZWwCNpY6FSIeu4MnLVOU42fIf57bQOOmKVy9ehOTF4b/vo/+4w3pjuhAJ4AQAnUKn2F+vd72q\noRNDFY7UYZNcIYfaXc3eOHwjSDAL30odD5GGw5yxkqLhpGW/HvlmDa68l0bqUAjJt9MXHqHvmENG\ng0mIAXAEgFKhU2zza+vnF/FxhEMl9wx1etRhdB666gC6SR0LkQYleJKT1moV23J4X39W6kAIya9L\n11LRffh+o97I9wWwC7ZhXzdWbFaxYfu57VWOmNwB24hzbT5ro+M03AIAxbOfXVKkKMGTF+G0Gnbp\nrAkNNCol3ckhJdOtJANihiQYDEb+bQAbADCcllvuVcsrNObbGMmHfS1qlVtWRoWgCjq5Qj66kBY5\nCsA/AFYW0vKeNRXAe0W07FLHsbdukm+snBlZp4are1RIOalDISRf7j80odPgfYYnaZaPeEH8DrZh\nX78sU7lM59d/fF3DKktHxVTktEgtI2M+AOBRCIsbBiASQL9CWFZ2xCJabqlECZ5kx4NlZf83a0ID\nraNWXxLHlppmQZchCfpHj81fmi3WLwCAVbEf6jx1/WN/itUqtKWnOwf36u6o27Muq9AqZhRwUYsB\nVAWwDcAkAMtga8/wN/43kt1bAH4FsAPAVQBvAxj7dJrDAMo8nW4wgKMATgL4GYA6m++rBmArgGMA\nEgDUKGD8pQ4lePIctVI+sVtbX9a/irPUoRCSZwYTj9dHJBgS7xlWG03CJACQK+QjVS6q8X029tGq\nXEvf7ejgccFKURRjYUua+TUUQCKAMABaAHsANAHQCsDnADJa4tYC0AVAEIDpAFIBNIQtwb/xdJpf\nYHuErz6A8wAy95+fcRX/LWwd9jQCMA7AogLEXipRgifPKmsVxaFjBr6mlDoQQvLKbLHijXcPGS5f\nT/vDYBSGAhAZOdNXoVV81ndTX43Oq3R21KR2UyOwfyDLabkJhbA4BkAUgAmwPW64F4AStp7zxKev\n9QCSAaQA2Px0vjMAKj/9uw6A/QBOA+gDIOCZ79ACaA7gp6ffsRg0FG6eUYInWaiU8vFd2vgyFbzp\nsThSsgiCiKEfHDGe/OfRIb2Bj4Vt2NcOnIb7NnZDrMa1oqvUIUqq0aBGnCiIfQAUVo9VXQE0ePqv\nMoALT99PzzSNNdNrEUBGw4cfAAwHUBfAx3i+il4G4FGm5TeArWaA5AEleJKZGyCOeHfQa6WvDpOU\naKIoYtyMv00JR+6dSTPwHQFYAIRyGm5dr7W91B41CqN9Wcmm89KhZoeakCvkbxfC4rbD1qI+Q4On\n/+e20Y4OQBIADkBf/K9annn67wls9/C7Z3q/bgHiLZUowRM7pUI2tmNEBaZiea3UoRCSJ58sOGve\ntOvWlTQD3xq2YV8DWTW7pdsP3TTlG5SXOrxio+nIpmpGzoxB9o3ackN8+m8abMn5NICzsF2FZ/48\n8/TPzgsAU2BroHcAtnvw2U3TB7Z78yeffkcnkDyhJtIkg6tKKbu1b01rLQ0oQ0qSBSv+5ecsO59o\nMAqBsN33rcmq2T87Lejk4h/tL3V4xc6anmvSrh+4PhYivpE6FlK06AqeAACUCtm70aE+MkrupCRZ\nufGKdc7S8w8NRqEFbMm9Eqfm9kfNiHKm5J69FmNa6BQaxYeg47/DowImAODMMMyYsYNfy2+1HSGv\n3G+7bokfzj392GASWgK4BcCL03AHQ94PKVOnRx2qnXwB3ya+0HnpnGDrsIY4MErwBKycGR7ZwltW\nrZKT1KEQkit7Dyfhnf87lmY0CeEALgFw5bTc/qC4IM+guCDqWzkHDMOg8bDGOqWzcrzUsZCiRQme\nyJQK2Zjh/fzpuThSIvx1+gEGTvjTYDQJ0QBOAdAodIq9dbrXqRg8LpjGNM6FgC4BjNVibQHbs+vE\nQVGCJ629PdTqBgFlXj4lIRI7dzEFvUcdMBqMQlcABwEoFDrF1mqR1Wq2nt5aSV0r545Co0CdnnUY\nVsWOkDoWUnQowZdyzjruvaF9/HR0YCTF3ZUbT9B1WIJRb+TfhO05bLlCp/ipQlCFoI5fdlQxMtqG\n8yJwQKAStu5nqddKB0UJvnSrYOGtwV3a+NKRkRRriXcN6BwXb9Ab+NGiiJ9gGxlumUcNj8iuy7qq\nZSwdyvLKvbo7vGp5MQC6SR0LKRq0V5RiCk4W1zXKFzot3bYkxdeDlHR0jos3pKZZpvGC+C0AcGpu\ntktFl+491vTQsKrSMexrUQgcGOikclENlzoOUjQowZderFzOvD3g9WrULS0ptp6kWdB1aII++VH6\n1+lm62cAwCrZyRoPzZA+P/fRKnVUu1wQ1SKqwWK0BAFwkToWUvgowZde7av46tha/qV7AA5SfBlN\nAnqNOqC/dcew3mgSxgGAjJMNVbooJ/Xd2FejdqNuGwpK6aSET5BPOoB2UsdCCh8l+FLKWceNHRrr\nRw++k2LJwlvx1rhDhgtXUnfpjfxgACIY9FRoFHP6buyrcSpHm25hqdWllpPSWdlP6jhI4aMEXzpV\n4Hlro44RFaSOg5DnWK0iRnz4l/H4mYd/6Q18DwACgLYKreL72F9i1WWq0COdhckvyg+8iQ9H/geg\nIcUUJfhSiGHQtXVwOataRR1+keJFFEVMmHUifc+hpPNpBr4dADOAlpyG+6Xn6p5qzwBPqUN0OBp3\nDTxe8zCDuq51OJTgSyFnHde/W1tf6rmOFDszF/9j+WXrzetpBj4CgAFAfVbNbu26rKvGp5GP1OE5\nrFpdazkpnBSxUsdBChcl+NLHK90svBbS2EvqOAjJYvHqS8K3ay/d1Rv5YAApAPxZNbu3w/wO2iqh\nVaQOz6H5R/szglnoCICeOXQglOBLn5jwZt68SknV86T4WLv5mnXm4nOPng77eg+AL6fmDrSe1tq5\nZoea1BFTEXOp4AKXCi5WAMFSx0IKDyX4UsbFievfrW1FrdRxEJLhj723MfHzk0+MJiEYwA0AHpyG\nO9DyvZZl6sXWo2PUK1KrWy0Np+F6Sh0HKTx0Zly6uCsVstv/7Oyo1KqpJo5IL+HoXbw59nCa0SSE\nAvgbgLNCqzjS8K2GVcMmhymK6nu3jNmC/3b/B21ZLQbuGWiLZVYCLu24BAYM1G5qtJ/bHs4+zrma\nFwD2frIXV/ZdgVctL3SY3wEAcPaXszA+MiJoUFBR/ZRCk3wxGcvbLX9oMVjKAhCljocUHJ0dly6d\nWjbytFByJ8XB32cf4q2xhw1Gk9AetuSuVugUu1+Lea1K6KTQIkvuAFC3V130WNUjy3tNhjfBwF0D\nMWDXAPhF+eHAnAO5nteUasLds3cxcNdAyDk57l+4D4vRgjPrziCwf2CR/Y7C5O7nDpWLSgGgkdSx\nkMJBCb4UcXXi3urW1lcndRyEnP/vMXqO3G8wmIQeABIAcAqdYnOVsCq12s5sW+TDvvo28YXKNWsv\nzZm7vbUYLNC4Zf+gSXbzMjIGVt4KURRhMVogY2U4uvgoGg1sBJm8ZBxmGYZBQJcApVwpf13qWEjh\nKBlbHikMWr1JaNq6ZTmp4yCl3PXbaeg6JMGYZuAHAdgCQKbQKtaWb1C+WaeFndRSDvsa/1k8FjVa\nhDPrz6Dp201zPZ9Sp0S1VtXwfZvvofPSQemkROKJRPhF+RVhtIWveuvqHKtiu0gdBykcVFdbejTz\nr+xkctJxRVr1SUhOku4b0WlwvCHNYBkvilgDgOG03Dfufu5R3X7oppFz0j7dETohFKETQnH4q8PY\n/dFutJ/XPtfzNhneBE2GNwEAbB27FSHjQ3Bq1SlcTbgKzwBPNH+neVGFXWi863jDordUAqACYJI6\nHlIwdAVfSig4WUREC29qPU8k8+ixGZ3j4vUpqeaZFl5cCACsmp3hXN65d8+1PbWcuvgMWxzQJQB3\nTt3J17xJZ5IAAG5V3XBhywXEfBODR9ce4dHVR4UZYpHgNBycfZyNAOpJHQspOErwpYRGLe/QItCD\nHn4nktAbeHQblqC//8C0LN1snQYAcqV8nMZNM7LPhj5albP0oxY/vPLQ/vel7ZfgVTt/nUHt/3w/\ngscHQ7AIEAVbY3RGxsBishRKnEXNt4kvB6Cx1HGQgqMq+tJBozfw/o3quksdBymF0s0Cer9zwHD9\ndtpGg0kYDUCUsbKBSifl1L6/9tVo3F99r8mbhm3CzT9vwvDQgIWBC9FybEtc2XMFD/97CEbGwLWy\nK6I+iwIAPEl6gm3jtuH1la9nmdf4yIiFgQsRPC4YdXvVBQBc3HYR5eqXg87T1pbVs5YnlkUsg2eA\nJzxfKxn96PsE+agvbrsYlp6a/pXUsZCCoefgS4eIgOouG/asjnz+oV5CihDPW/HmuMPGIyeS96QZ\n+M4ABDDopnRWrnzz9zfVbtXcpA6RPOPu2btY1W3VbfMTMw03WcJRFX0pwHGyVq2ae9HgMuSVslpF\njPq/Y6YjJ5KPpxn4brAN+9qa03Are6/vTcm9mCpboywEk+AJwEXqWEjBUIIvBbRqeYeWjTzpdgx5\nZURRxJQ5p9J3JNz5N83AtwWQDqApp+E29vixh9q7jrfUIZIXkHNyuFV3MwAoGT30kBeiBO/41HoD\nXzOI7r+TV2jOsvOWtZuv30oz8OEA9ADqsGp2R8w3MVrfJr5Sh0dewreprxoMin//uiRHlOAdX5Oq\nFZ1MWg1dwJNXY+m6y8LClReT9Ua+JYBHAKqxaja+3ex2umoR1aQOj+SCT6CPQuWiaiV1HKRg6Kjv\n+Bo0qe+ufPlkhBTcT39cF6cvPJtiNAnNASQBKM9puIOtPmzlEhATQI16S4hy9ctBMAtURV/C0RW8\ng3PWcU1q+7tSgidFbsf+RIyfceKJ0SSEALgGwJ3TcgebjWrm3uCNBnSsKUHKVCkDURR1APLXGQAp\nFminc3AyGRq8Vp0aw5KidfD4fQyZfFRvTBciAfwDwEmhVexr0LdB+eajmlNNYQnDMAy8anmZALoP\nX5JRgndsMr2Br1yjKj3+TorOyX8e4Y13DxqMJqEjgL8AqBQ6xY4a7WtUD/8wnMY+KKF8m/jqGBlD\nQ8eWYJTgHVsVJx1ncdYVnz6+iWO5eDUVPd7eb9AbhVgAewGwCp3i10otK9WLnh2tKuphX0nRKVO5\njFyhU9SQOg6Sf5TgHVvtGlWdBamDII7pRqIeMUPiDXoDPxzAJtiGff3Ru653cMziGHVJGQedZE/n\nrYNMLqssdRwk/2gPdGAMg7r1A8pQD3ak0N1/YEKnwfsMT/T8ZMEqLgfAcBpuYZmqZTp0X9FdI1fQ\nuEYlnc5LB6tgLSd1HCT/qPGLA3Nx4prW8nOlMiaF6vETMzoPidenpFrmWSzWeQDAqtj/c/J26td7\nfW+tQkO33R2BUzkn8Ol8WanjIPlHV/AOzGpFnZrVqIEdKTx6I4/uw/frk+4ZV5jShSkAIOfko1Wu\nqnf7bOyjVblIP+wrKRzqMmpYeasKABVqCUUJ3nHJ9Aa+fLWKTlLHQRyE2WJF3zEHDVdupP1uMAlv\nAxAZOfOGwkkxvd9v/TRaD63UIZJCxMgYqFxUJgBUTV9CUYJ3XO5KhcyiVtG9UFJwgiBi8MQ/jWcu\npOzXG/m+AKwAOiu0isV9NvbRuFSgvhYckc5TxwPwkToOkj+U4B1XOfcySrPUQZCSTxRFvDv9uOng\nsfun0gx8DAAeQDin5Vb3WtdLXdaPbtM6KqfyTgyA8lLHQfKHGmA5rvJeZVWi1EGQku/j+WfMv++5\nfVlv4NsAMAEI4tTc5u7Lu2vK1aPaW0fm6uuqBF3Bl1h0Be+4ylfwpiHkSMHM//4Cv3Lj1dt6Ax8G\n4AmAAFbN7ur0dSdtpeaVJI6OFDVnH2elXCmvKHUcJH8owTuu8hXKaaj1K8m35b9csc7//sKDp8O+\nPgBQmVWzCW1ntnXya+MndXjkFdB568CpuapSx0Hyh67wHJROw1bx9lBTCzuSLxt33BSnzj/92GgS\nWgBIBODNabiDoRNDXWt3r039z5YSOm8dwICu4EsoSvAOiuNklb091FKHQUqgXQfv4N1PjqcZTUIo\ngP8AlOG03IEmQ5t4NBrYiE4aSxEnLydYeau31HGQ/KEE76BEUfTxLks19CRv/jyRjMETjxiMJqEN\ngDMAtAqtYm+dnnUqtHi3BY1aVMpoymogpAv0DGQJRffgi4cwAJsLc4EWi9XDkxI8yYMz/6agz+iD\nRqNJiAHwJwClQqfY5hflVyNyWqSSRoYrfWScDKIoUq1NCUUJ3kFZeFHj4kQXXCR3/rv+BN2GJRgN\nJr4vgJ0A5Aqt4hffpr4N289rT8O+llJyVg7RKlKeKKGoir7wVAawDcBhAM0BHAOwHMBHADwA9Hk6\n3XzY+nY2AugP4OIzy9EC+ApALQAcgKkAfstrMDxvVahVxbN4L19/gqGTj9hfX7+tx/ghAXj42Izt\n8XfAMEAZFwXmf9QIPl7PD4a353ASPpxzCoJVRGynKhj5pm3I6mlfncHew3dRy98FX00NAgD8vPUG\nHj1Ox+Be1Or7RW7fNaBzXLzBYORHiSI2AGA4LbfcI8AjvMuSLhoZS8f30krGyiBa6Qq+pKI9t3BV\nA/AFgJoAagDoCaAFgLEAJgE4DyAYQEPYEv+n2SxjMoDdAJoAaAXgcwB5HfJVAQZQcMWzeKtXcsKu\nHyOx68dI7FgRAbWKRbtwH4zo6489qyOxe1Uk2oaWx+wl55+bVxBETPr8JFbPb4mEdW3w646buHg1\nFalpFpy9mII9qyOh4GQ4/99jGE0C1v1+DQNery7BrywZkh+lo9PgfYbUNMtUXhCXwTbs6zzXSq4x\nPVf31LDK4nmSSF4NRs4AIhhQriiRqNAK11UA5wCIT//f9fT9s7Bd4bsC+Bm2xktzYLtKf1YbABMA\nnACwF4ASgG8e49BwrIzP4zySSDh6D5UraOHjpYFO+79bCgYjDzfX54cdPXHuIapU0KFieS04VoaY\nNr7YnnAHchkDCy9CFEUYTQI4VoavV13EoJ7VIZdT9XJ2UtMs6DIkXv/wkfkrs8X6OQCwSvYDrYd2\nYOxPsVqFloZ9Le0YhgEjY6wA6Cq+BKLT88KVnulvKwBzpr9ZANNguzrvAqASgH0vWE5XAJcKEIdG\nqZDzAIr9EfrXnTfRJep/5y8zFp3Fz1tvQK2SY8t34c9Nf+e+EeW9/vf4XzlPNf4++xBaDYuI5l5o\n3W83ght7wknL4sS5h3h34Guv5HeUNEaTgJ5v79ffvmtYY0wXJgKAjJMNtQrWqU2GN5HdOnpL6hBJ\n8cIBsEgdBMkbSvCvDgPAGbZOQwDb/ffsbAcwCsDIp68bwHY1nxccK2eKfT/0ZosVO/bfwQdv17G/\nN3F4bUwcXhtfLf8XH849jfkfNsoyT06NvUb0q4ER/Wz349+bfhzvD62FVb9eRfzRewio7oLRA2oW\nzdxX3iUAACAASURBVA8pYSy8FW+8d8hw8dqTbQajMAS2GifIFXJfVskeSJiZIHGEpDjhNNwDc5qZ\nBq4qgSjBF65nk2rm11bY7qcvB/ABgC3PfJ7x9zQA8wCchu0WyhUAnfIYBysvAQl+z6Ek1KtZBmXL\nKJ/7rEuUL/qMPvjc++U8VEi8a7S/TrxrQDnPrB36nPk3BQBQtaIO0xecxZovW2L0tGO4ejMNVXx1\nhfwrSharVcSwD44aT5x7eFhv4HvDtl0CACx6y2SLni7SCHEUlOALzzUAdTO97v+Cz2pken/K0//3\n4X/V9SYAQwsYS4lI8Bt33ERMm/9Vz1+58QRVKzoBALbFJ6JODdfn5qn3WhlcuZmGG4l6eHuosWnn\nLXz9SeMs08z65hxmTwqExWKFYLWtBhnDwJQuFOGvKf5EUcTbU/9K33Mo6V+DSYiD7ekOQgDgIWzH\nHuJAKME7Jq64J3i9kcf+o/cwe1JD+3vTF53Df9efQC5jULmCFp+93wAAkHTfiPc+/Rur5rYAy8rw\n6bj66D3qwNPH5CrDv4qzfRnb4hNRP8ANGZ381PJ3QXjsTtTyc8Vr1Ut3h1zHzjzEhm03lUqlspZO\npz4jdTykeOB5npXL5X/o9fouUsdCChc1L3ZMdX3Lafb/tSna+eWTktKkx9sHrTfvq7Fy5UqZSkU9\nHRJg165dmDFjxs7Hjx+3kToWUrjoMTnHZDClC1S25Dlrv2wms1oeipMnTxJEsVhX8pBXxGq1AkDp\nvn/loCgJOCa9KV2g51bJc2QyGbb/ECI/efI4s3jxYjqoE0rwDowSvGPSp5ut1L6CZMvVWYGNC5vK\nVq9eJd+5cyddxpdyZrMZoihSAzsHRAneMektvJWlKljyIgH+rpj7QV18/PHHzPnzz3cJTEoPvV4P\nnucfSB0HKXyU4B2TIJMxgrGUPxZGchbT2hcDXq+IkSPfRnJystThEIno9XqYTCbaABwQJXgHxcqZ\ndIOREjzJ2Ycj66Cun1oYPny41WSiWtrS6MmTJxar1fpY6jhI4aME76BYVmb6//buOzqqOm8D+HPv\nlPQQWkIHEUU6rCL40lSKCoqCqyjuuoV1V4qyrorgC4sILoiFXVRWaSJEQFRKEJXeWwhIi4SSAiSB\nBEiZemdue/9I9EUFaZn8JjPP5xxOJsPM5Mk5yTzzu/PN73q8VeJ8MyTY4vfusmi+C+bYsWM5WR+G\nHA6HCoAFH4JY8CHKIkteNwuersIPk/Xf7dsjzZo107jyPSiUOBwOHYBDdA6qeCz4ECXL8Hh5iJ6u\nUvVqdnw5o5O8YMECed26dVzGhxGn02mABR+SWPAhSpakCxdKfFe+IVG51rdWx9tj2uC1116TMjIy\nRMehSlJaWgrwEH1IYsGHKE03My8+6xrR1Xj0/kb406MNzREjhnOyPkxcuHDBCiBXdA6qeCz4EOVw\nqUdPn3HzGD1ds/Ej20qtm0Xqw4cP52R9iNN1HQ6HIwpAvugsVPFY8KHrVPZpN5fwdF2WvP8/Fr9y\nnpP1Ia6oqAh2u90FwC86C1U8FnzoOnUqnyt4uj6yLGPNj5P1s/hzFKIKCgpgt9sLReegwGDBh67T\n+YVennCGrlv1anZ8/n4necH8+ZYNGzaIjkMBUFhYCEmSTovOQYHBgg9duSWl/ihd5+FVun5tb6uO\nt0a3xj//+U9wsj70FBYWQlXVLNE5KDBY8KHLZ7fL7sILHJKiG/Pbvo3x9IAG5ogR3LM+1OTn52se\nj+eE6BwUGCz4EBZht5zJL/CIjkEh4PUX2kktb7brI0aMMHw+7q8QKo4dO+YBcFR0DgoMFnwIk4Cc\nU/kseKoYX3zQxaK4z5njxo3jZH2IyMrKkgF8LzoHBQYLPoQ53Oru9OMl3JCeKoQsy1j9SVfL3rTd\n0pw5c7hnfRXn9XpRWloaCSBTdBYKDBZ8CNN1c++eg0VcwlOFqZkQiSXTO8nz5s2TN23aJDoO3YCc\nnBxERUXlAeAiIESx4EPb/iMnSm2iQ1BoadeyOqaMaoWxY8fi2LFjouPQdcrKyoIsy4dE56DAYcGH\nttM+v24UnuckPVWsJx5sgt/1r28OHz4cFy5cEB2HrsOJEyc0p9O5R3QOChwWfGgzo6OsGYePl4jO\nQSFo0kvtpeZNbAYn66umI0eOeEzTPCw6BwUOCz7EeRV9+6GMEo48U0B88UEX2eMqMMePH8/J+irE\nNE1kZGTYAHwnOgsFDgs+xCk+fc/ew0Uu0TkoNFmtMtZ80s2SunuH9PHHH3Oyvoo4c+YMVFVVAeSI\nzkKBw4IPffsPHCmWRIeg0FUzIRJL3uskz507V968ebPoOHQVDh48iIiIiD0AeNglhLHgQ1/GhWJf\nhMutis5BIax9yxqY8nLZZP3x48dFx6Er2L9/v9/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"text": [ "<matplotlib.figure.Figure at 0x8c3355d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "washington post: 192 bylines\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ibwCXAFy8/X/dgwQuVUuv/yPjH75eEV5VDhcFEdezruPs5rNCdkZ2cf6ZfJlE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\nJZqmW1cjOpGX6JhTZAcgx+mSnuZe9+Dtw3J+f81gLodNtJe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"text": [ "<matplotlib.figure.Figure at 0x8e31f190>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8f9kdErKtdliyRew/chuvTT9wMsMk1M47vyZAsy1uZlxMva71in3D3Yk5Ii7u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\nf+yzyT0tO3b8YNm7d6/ZUX5D0zRUVVVhwYIFGDt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"text": [ "<matplotlib.figure.Figure at 0x8eb311d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "['org', 'type', 'female', 'male', 'unknown']\n", "['people', 'la times', 226, 564, 124]\n", "['article', 'la times', 808, 1931, 281]\n", "['people', 'new york post', 17, 83, 15]\n", "['article', 'new york post', 77, 224, 30]\n", "['people', 'new york times', 329, 841, 274]\n", "['article', 'new york times', 876, 2158, 468]\n", "['people', 'wall street journal', 73, 408, 78]\n", "['article', 'wall street journal', 200, 994, 136]\n", "['people', 'washington post', 46, 114, 31]\n", "['article', 'washington post', 587, 2022, 61]\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "b.org_name_gender(\"washington post\",\"editorial board\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "'ignore'" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 } ], "metadata": {} } ] }

mit

kriukov/interval-methods

ipynb/.ipynb_checkpoints/pi from area-checkpoint.ipynb

1

71642

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$y = x^2$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using PyPlot" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x8dd1dfac>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(0.0,1.0,0.0,1.0)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = 0:0.01:1\n", "y = sqrt(1 - x.^2);\n", "\n", "plot(x, y)\n", "axis(\"image\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PyObject <class 'matplotlib.patches.Rectangle'>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using PyCall\n", "@pyimport matplotlib.patches as patches\n", "rectangle = patches.Rectangle" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "draw_rectangle (generic function with 2 methods)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function draw_rectangle(x, y, xwidth, ywidth, color=\"grey\")\n", " ax = gca()\n", " ax[:add_patch](rectangle((x, y), xwidth, ywidth, facecolor=color, alpha=0.5))\n", "end\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x8dc27cec>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(0.1,0.2,0.1,0.2)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "draw_rectangle(0.1, 0.1, 0.1, 0.1)\n", "axis(\"image\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", 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"execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "step = 0.001\n", "area = 0.0\n", "for i in 0.0:step:1.0\n", " for j in 0.0:step:1.0\n", " \n", " if i^2 + j^2 < 1.0\n", " \n", " area += step^2\n", " \n", " end\n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.1455200000071017" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4*area" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.0", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

tensorflow/docs

site/en/guide/dtensor_overview.ipynb

2

43204

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1ljvLya59ep5" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "VcQIa1uG86Wh" }, "source": [ "# DTensor Concepts" ] }, { "cell_type": "markdown", "metadata": { "id": "6dWNQEum9AfY" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/dtensor_overview\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/dtensor_overview.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/guide/dtensor_overview.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/guide/dtensor_overview.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "MGZuakHVlVQf" }, "source": [ "## Overview\n", "\n", "This colab introduces DTensor, an extension to TensorFlow for synchronous distributed computing.\n", "\n", "DTensor provides a global programming model that allows developers to compose applications that operate on Tensors globally while managing the distribution across devices internally. DTensor distributes the program and tensors according to the sharding directives through a procedure called *[Single program, multiple data (SPMD)](https://en.wikipedia.org/wiki/SPMD) expansion*.\n", "\n", "By decoupling the application from sharding directives, DTensor enables running the same application on a single device, multiple devices, or even multiple clients, while preserving its global semantics.\n", "\n", "This guide introduces DTensor concepts for distributed computing, and how DTensor integrates with TensorFlow. To see a demo of using DTensor in model training, see [Distributed training with DTensor](https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial) tutorial." ] }, { "cell_type": "markdown", "metadata": { "id": "h7ZTDq7KngwA" }, "source": [ "## Setup\n", "\n", "DTensor is part of TensorFlow 2.9.0 release, and also included in the TensorFlow nightly builds since 04/09/2022." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OKaPw8vwwZAC" }, "outputs": [], "source": [ "!pip install --quiet --upgrade --pre tensorflow" ] }, { "cell_type": "markdown", "metadata": { "id": "O3pG29uZIWYO" }, "source": [ "Once installed, import `tensorflow` and `tf.experimental.dtensor`. Then configure TensorFlow to use 6 virtual CPUs.\n", "\n", "Even though this example uses vCPUs, DTensor works the same way on CPU, GPU or TPU devices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q92lo0zjwej8" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.experimental import dtensor\n", "\n", "print('TensorFlow version:', tf.__version__)\n", "\n", "def configure_virtual_cpus(ncpu):\n", " phy_devices = tf.config.list_physical_devices('CPU')\n", " tf.config.set_logical_device_configuration(phy_devices[0], [\n", " tf.config.LogicalDeviceConfiguration(),\n", " ] * ncpu)\n", "\n", "configure_virtual_cpus(6)\n", "DEVICES = [f'CPU:{i}' for i in range(6)]\n", "\n", "tf.config.list_logical_devices('CPU')" ] }, { "cell_type": "markdown", "metadata": { "id": "O-lsrxUnlsCC" }, "source": [ "## DTensor's model of distributed tensors\n", "\n", "DTensor introduces two concepts: `dtensor.Mesh` and `dtensor.Layout`. They are abstractions to model the sharding of tensors across topologically related devices.\n", "\n", "- `Mesh` defines the device list for computation.\n", "- `Layout` defines how to shard the Tensor dimension on a `Mesh`." ] }, { "cell_type": "markdown", "metadata": { "id": "JjiHaH0ql9yo" }, "source": [ "### Mesh\n", "\n", "`Mesh` represents a logical Cartisian topology of a set of devices. Each dimension of the Cartisian grid is called a **Mesh dimension**, and referred to with a name. Names of mesh dimension within the same `Mesh` must be unique.\n", "\n", "Names of mesh dimensions are referenced by `Layout` to describe the sharding behavior of a `tf.Tensor` along each of its axes. This is described in more detail later in the section on `Layout`.\n", "\n", "`Mesh` can be thought of as a multi-dimensional array of devices." ] }, { "cell_type": "markdown", "metadata": { "id": "_J6cOieEbaUw" }, "source": [ "In a 1 dimensional `Mesh`, all devices form a list in a single mesh dimension. The following example uses `dtensor.create_mesh` to create a mesh from 6 CPU devices along a mesh dimension `'x'` with a size of 6 devices:\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_mesh_1d.png\" alt=\"A 1 dimensional mesh with 6 CPUs\" class=\"no-filter\">\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QLH5fgdBmA58" }, "outputs": [], "source": [ "mesh_1d = dtensor.create_mesh([('x', 6)], devices=DEVICES)\n", "print(mesh_1d)" ] }, { "cell_type": "markdown", "metadata": { "id": "hSZwaUwnEgXB" }, "source": [ "A `Mesh` can be multi dimensional as well. In the following example, 6 CPU devices form a `3x2` mesh, where the `'x'` mesh dimension has a size of 3 devices, and the `'y'` mesh dimension has a size of 2 devices:\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_mesh_2d.png\" alt=\"A 2 dimensional mesh with 6 CPUs\"\n", " class=\"no-filter\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "op6TmKUQE-sZ" }, "outputs": [], "source": [ "mesh_2d = dtensor.create_mesh([('x', 3), ('y', 2)], devices=DEVICES)\n", "print(mesh_2d)" ] }, { "cell_type": "markdown", "metadata": { "id": "deAqdrDPFn2f" }, "source": [ "### Layout\n", "\n", "**`Layout`** specifies how a tensor is distributed, or sharded, on a `Mesh`.\n", "\n", "Note: In order to avoid confusions between `Mesh` and `Layout`, the term *dimension* is always associated with `Mesh`, and the term *axis* with `Tensor` and `Layout` in this guide.\n", "\n", "The rank of `Layout` should be the same as the rank of the `Tensor` where the `Layout` is applied. For each of the `Tensor`'s axes the `Layout` may specify a mesh dimension to shard the tensor across, or specify the axis as \"unsharded\".\n", "The tensor is replicated across any mesh dimensions that it is not sharded across.\n", "\n", "The rank of a `Layout` and the number of dimensions of a `Mesh` do not need to match. The `unsharded` axes of a `Layout` do not need to be associated to a mesh dimension, and `unsharded` mesh dimensions do not need to be associated with a `layout` axis.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_components_diag.png\" alt=\"Diagram of dtensor components.\"\n", " class=\"no-filter\">" ] }, { "cell_type": "markdown", "metadata": { "id": "Px_bF1c-bQ7e" }, "source": [ "Let's analyze a few examples of `Layout` for the `Mesh`'s created in the previous section." ] }, { "cell_type": "markdown", "metadata": { "id": "fqzCNlWAbm-c" }, "source": [ "On a 1-dimensional mesh such as `[(\"x\", 6)]` (`mesh_1d` in the previous section), `Layout([\"unsharded\", \"unsharded\"], mesh_1d)` is a layout for a rank-2 tensor replicated across 6 devices.\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_replicated.png\" alt=\"A tensor replicated across a rank-1 mesh\" class=\"no-filter\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-a3EnmZag6x1" }, "outputs": [], "source": [ "layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh_1d)" ] }, { "cell_type": "markdown", "metadata": { "id": "ywRJwuLDt2Qq" }, "source": [ "Using the same tensor and mesh the layout `Layout(['unsharded', 'x'])` would shard the second axis of the tensor across the 6 devices.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_rank1.png\" alt=\"A tensor sharded across a rank-1 mesh\" class=\"no-filter\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7BgqL0jUvV5a" }, "outputs": [], "source": [ "layout = dtensor.Layout([dtensor.UNSHARDED, 'x'], mesh_1d)" ] }, { "cell_type": "markdown", "metadata": { "id": "DgciDNmK76l9" }, "source": [ "Given a 2-dimensional 3x2 mesh such as `[(\"x\", 3), (\"y\", 2)]`, (`mesh_2d` from the previous section), `Layout([\"y\", \"x\"], mesh_2d)` is a layout for a rank-2 `Tensor` whose first axis is sharded across across mesh dimension `\"y\"`, and whose second axis is sharded across mesh dimension `\"x\"`." ] }, { "cell_type": "markdown", "metadata": { "id": "Eyp_qOSyvieo" }, "source": [ "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_rank2.png\" alt=\"A tensorr with it's first axis sharded across mesh dimension 'y' and it's second axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "p8OrehEuhPbS" }, "outputs": [], "source": [ "layout = dtensor.Layout(['y', 'x'], mesh_2d)" ] }, { "cell_type": "markdown", "metadata": { "id": "1Kyg0V3ehMNJ" }, "source": [ "For the same `mesh_2d`, the layout `Layout([\"x\", dtensor.UNSHARDED], mesh_2d)` is a layout for a rank-2 `Tensor` that is replicated across `\"y\"`, and whose first axis is sharded on mesh dimension `x`.\n", "\n", "<img src=\"https://www.tensorflow.org/images/dtensor/dtensor_layout_hybrid.png\" alt=\"A tensor replicated across mesh-dimension y, with it's first axis sharded across mesh dimension 'x'\" class=\"no-filter\">\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IkWe6mVl7uRb" }, "outputs": [], "source": [ "layout = dtensor.Layout([\"x\", dtensor.UNSHARDED], mesh_2d)" ] }, { "cell_type": "markdown", "metadata": { "id": "TTalu6M-ISYb" }, "source": [ "### Single-Client and Multi-Client Applications\n", "\n", "DTensor supports both single-client and multi-client applications. The colab Python kernel is an example of a single client DTensor application, where there is a single Python process.\n", "\n", "In a multi-client DTensor application, multiple Python processes collectively perform as a coherent application. The Cartisian grid of a `Mesh` in a multi-client DTensor application can span across devices regardless of whether they are attached locally to the current client or attached remotely to another client. The set of all devices used by a `Mesh` are called the *global device list*.\n", "\n", "The creation of a `Mesh` in a multi-client DTensor application is a collective operation where the *global device list* is identicial for all of the participating clients, and the creation of the `Mesh` serves as a global barrier.\n", "\n", "During `Mesh` creation, each client provides its *local device list* together with the expected *global device list*. DTensor validates that both lists are consistent. Please refer to the API documentation for `dtensor.create_mesh` and `dtensor.create_distributed_mesh`\n", " for more information on multi-client mesh creation and the *global device list*.\n", "\n", "Single-client can be thought of as a special case of multi-client, with 1 client. In a single-client application, the *global device list* is identical to the *local device list*.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "P_F7DWkXkB4w" }, "source": [ "## DTensor as a sharded tensor\n", "\n", "Now let's start coding with `DTensor`. The helper function, `dtensor_from_array`, demonstrates creating DTensors from something that looks like a `tf.Tensor`. The function performs 2 steps:\n", " - Replicates the tensor to every device on the mesh.\n", " - Shards the copy according to the layout requested in its arguments." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s6aws-b8dN9L" }, "outputs": [], "source": [ "def dtensor_from_array(arr, layout, shape=None, dtype=None):\n", " \"\"\"Convert a DTensor from something that looks like an array or Tensor.\n", "\n", " This function is convenient for quick doodling DTensors from a known,\n", " unsharded data object in a single-client environment. This is not the\n", " most efficient way of creating a DTensor, but it will do for this\n", " tutorial.\n", " \"\"\"\n", " if shape is not None or dtype is not None:\n", " arr = tf.constant(arr, shape=shape, dtype=dtype)\n", "\n", " # replicate the input to the mesh\n", " a = dtensor.copy_to_mesh(arr,\n", " layout=dtensor.Layout.replicated(layout.mesh, rank=layout.rank))\n", " # shard the copy to the desirable layout\n", " return dtensor.relayout(a, layout=layout)" ] }, { "cell_type": "markdown", "metadata": { "id": "r3o6IysrlGMu" }, "source": [ "### Anatomy of a DTensor\n", "\n", "A DTensor is a `tf.Tensor` object, but augumented with the `Layout` annotation that defines its sharding behavior. A DTensor consists of the following:\n", "\n", " - Global tensor meta-data, including the global shape and dtype of the tensor.\n", " - A `Layout`, which defines the `Mesh` the `Tensor` belongs to, and how the `Tensor` is sharded onto the `Mesh`.\n", " - A list of **component tensors**, one item per local device in the `Mesh`.\n", "\n", "With `dtensor_from_array`, you can create your first DTensor, `my_first_dtensor`, and examine its contents." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mQu_nScGUvYH" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 6)], devices=DEVICES)\n", "layout = dtensor.Layout([dtensor.UNSHARDED], mesh)\n", "\n", "my_first_dtensor = dtensor_from_array([0, 1], layout)\n", "\n", "# Examine the dtensor content\n", "print(my_first_dtensor)\n", "print(\"global shape:\", my_first_dtensor.shape)\n", "print(\"dtype:\", my_first_dtensor.dtype)" ] }, { "cell_type": "markdown", "metadata": { "id": "r8LQy1nqmvFy" }, "source": [ "#### Layout and `fetch_layout`\n", "\n", "The layout of a DTensor is not a regular attribute of `tf.Tensor`. Instead, DTensor provides a function, `dtensor.fetch_layout` to access the layout of a DTensor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dCSFyaAjmzGu" }, "outputs": [], "source": [ "print(dtensor.fetch_layout(my_first_dtensor))\n", "assert layout == dtensor.fetch_layout(my_first_dtensor)" ] }, { "cell_type": "markdown", "metadata": { "id": "ed7i3l2lmatm" }, "source": [ "#### Component tensors, `pack` and `unpack`\n", "\n", "A DTensor consists of a list of **component tensors**. The component tensor for a device in the `Mesh` is the `Tensor` object representing the piece of the global DTensor that is stored on this device.\n", "\n", "A DTensor can be unpacked into component tensors through `dtensor.unpack`. You can make use of `dtensor.unpack` to inspect the components of the DTensor, and confirm they are on all devices of the `Mesh`.\n", "\n", "Note that the positions of component tensors in the global view may overlap each other. For example, in the case of a fully replicated layout, all components are identical replicas of the global tensor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BGbjqVAOnXMk" }, "outputs": [], "source": [ "for component_tensor in dtensor.unpack(my_first_dtensor):\n", " print(\"Device:\", component_tensor.device, \",\", component_tensor)" ] }, { "cell_type": "markdown", "metadata": { "id": "-tqIQM52k788" }, "source": [ "As shown, `my_first_dtensor` is a tensor of `[0, 1]` replicated to all 6 devices." ] }, { "cell_type": "markdown", "metadata": { "id": "6By3k-CGn3yv" }, "source": [ "The inverse operation of `dtensor.unpack` is `dtensor.pack`. Component tensors can be packed back into a DTensor.\n", "\n", "The components must have the same rank and dtype, which will be the rank and dtype of the returned DTensor. However there is no strict requirement on the device placement of component tensors as inputs of `dtensor.unpack`: the function will automatically copy the component tensors to their respective corresponding devices.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9lT-6qQwxOgf" }, "outputs": [], "source": [ "packed_dtensor = dtensor.pack(\n", " [[0, 1], [0, 1], [0, 1],\n", " [0, 1], [0, 1], [0, 1]],\n", " layout=layout\n", ")\n", "print(packed_dtensor)" ] }, { "cell_type": "markdown", "metadata": { "id": "zvS3autrpK2U" }, "source": [ "### Sharding a DTensor to a Mesh\n", "\n", "So far you've worked with the `my_first_dtensor`, which is a rank-1 DTensor fully replicated across a dim-1 `Mesh`.\n", "\n", "Next create and inspect DTensors that are sharded across a dim-2 `Mesh`. The next example does this with a 3x2 `Mesh` on 6 CPU devices, where size of mesh dimension `'x'` is 3 devices, and size of mesh dimension`'y'` is 2 devices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KWb9Ae0VJ-Rc" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 3), (\"y\", 2)], devices=DEVICES)" ] }, { "cell_type": "markdown", "metadata": { "id": "ndSeQSFWKQk9" }, "source": [ "#### Fully sharded rank-2 Tensor on a dim-2 Mesh\n", "\n", "Create a 3x2 rank-2 DTensor, sharding its first axis along the `'x'` mesh dimension, and its second axis along the `'y'` mesh dimension.\n", "\n", "- Because the tensor shape equals to the mesh dimension along all of the sharded axes, each device receives a single element of the DTensor.\n", "- The rank of the component tensor is always the same as the rank of the global shape. DTensor adopts this convention as a simple way to preserve information for locating the relation between a component tensor and the global DTensor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ax_ZHouJp1MX" }, "outputs": [], "source": [ "fully_sharded_dtensor = dtensor_from_array(\n", " tf.reshape(tf.range(6), (3, 2)),\n", " layout=dtensor.Layout([\"x\", \"y\"], mesh))\n", "\n", "for raw_component in dtensor.unpack(fully_sharded_dtensor):\n", " print(\"Device:\", raw_component.device, \",\", raw_component)" ] }, { "cell_type": "markdown", "metadata": { "id": "zhsLC-NgrC2p" }, "source": [ "#### Fully replicated rank-2 Tensor on a dim-2 Mesh\n", "\n", "For comparison, create a 3x2 rank-2 DTensor, fully replicated to the same dim-2 Mesh.\n", "\n", " - Because the DTensor is fully replicated, each device receives a full replica of the 3x2 DTensor.\n", " - The rank of the component tensors are the same as the rank of the global shape -- this fact is trivial, because in this case, the shape of the component tensors are the same as the global shape anyway." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xmyC6H6Ec90P" }, "outputs": [], "source": [ "fully_replicated_dtensor = dtensor_from_array(\n", " tf.reshape(tf.range(6), (3, 2)),\n", " layout=dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh))\n", "# Or, layout=tensor.Layout.fully_replicated(mesh, rank=2)\n", "\n", "for component_tensor in dtensor.unpack(fully_replicated_dtensor):\n", " print(\"Device:\", component_tensor.device, \",\", component_tensor)" ] }, { "cell_type": "markdown", "metadata": { "id": "KWoyv_oHMzk1" }, "source": [ "#### Hybrid rank-2 Tensor on a dim-2 Mesh\n", "\n", "What about somewhere between fully sharded and fully replicated?\n", "\n", "DTensor allows a `Layout` to be a hybrid, sharded along some axes, but replicated along others.\n", "\n", "For example, you can shard the same 3x2 rank-2 DTensor in the following way:\n", "\n", " - 1st axis sharded along the `'x'` mesh dimension.\n", " - 2nd axis replicated along the `'y'` mesh dimension.\n", "\n", "To achieve this sharding scheme, you just need to replace the sharding spec of the 2nd axis from `'y'` to `dtensor.UNSHARDED`, to indicate your intention of replicating along the 2nd axis. The layout object will look like `Layout(['x', dtensor.UNSHARDED], mesh)`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DygnbkQ1Lu42" }, "outputs": [], "source": [ "hybrid_sharded_dtensor = dtensor_from_array(\n", " tf.reshape(tf.range(6), (3, 2)),\n", " layout=dtensor.Layout(['x', dtensor.UNSHARDED], mesh))\n", "\n", "for component_tensor in dtensor.unpack(hybrid_sharded_dtensor):\n", " print(\"Device:\", component_tensor.device, \",\", component_tensor)" ] }, { "cell_type": "markdown", "metadata": { "id": "T7FtZ9kQRZgE" }, "source": [ "You can inspect the component tensors of the created DTensor and verify they are indeed sharded according to your scheme. It may be helpful to illustrate the situation with a chart:\n", "\n", " <img src=\"https://www.tensorflow.org/images/dtensor/dtensor_hybrid_mesh.png\" alt=\"A 3x2 hybrid mesh with 6 CPUs\"\n", " class=\"no-filter\" width=75%>\n" ] }, { "cell_type": "markdown", "metadata": { "id": "auAkA38XjL-q" }, "source": [ "#### Tensor.numpy() and sharded DTensor\n", "\n", "Be aware that calling the `.numpy()` method on a sharded DTensor raises an error. The rationale for erroring is to protect against unintended gathering of data from multiple computing devices to the host CPU device backing the returned numpy array." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hNdwmnL0jAXS" }, "outputs": [], "source": [ "print(fully_replicated_dtensor.numpy())\n", "\n", "try:\n", " fully_sharded_dtensor.numpy()\n", "except tf.errors.UnimplementedError:\n", " print(\"got an error as expected for fully_sharded_dtensor\")\n", "\n", "try:\n", " hybrid_sharded_dtensor.numpy()\n", "except tf.errors.UnimplementedError:\n", " print(\"got an error as expected for hybrid_sharded_dtensor\")" ] }, { "cell_type": "markdown", "metadata": { "id": "8WcMkiagPF_6" }, "source": [ "## TensorFlow API on DTensor\n", "\n", "DTensor strives to be a drop-in replacement for tensor in your program. The TensorFlow Python API that consume `tf.Tensor`, such as the Ops library functions, `tf.function`, `tf.GradientTape`, also work with DTensor.\n", "\n", "To accomplish this, for each [TensorFlow Graph](https://www.tensorflow.org/guide/intro_to_graphs), DTensor produces and executes an equivalent [SPMD](https://en.wikipedia.org/wiki/SPMD) graph in a procedure called *SPMD expansion*. A few critical steps in DTensor SPMD expansion are:\n", "\n", " - Propagating the sharding `Layout` of DTensor in the TensorFlow graph\n", " - Rewriting TensorFlow Ops on the global DTensor with equivalent TensorFlow Ops on the component tensors, inserting collective and communication Ops when necessary\n", " - Lowering backend neutral TensorFlow Ops to backend specific TensorFlow Ops.\n", "\n", "The final result is that **DTensor is a drop-in replacement for Tensor**.\n", "\n", "Note: DTensor is still an experimental API which means you will be exploring and pushing the boundaries and limits of the DTensor programming model.\n", "\n", "There are 2 ways of triggering DTensor execution:\n", " - DTensor as operands of a Python function, e.g. `tf.matmul(a, b)` will run through DTensor if `a`, `b`, or both are DTensors.\n", " - Requesting the result of a Python function to be a DTensor, e.g. `dtensor.call_with_layout(tf.ones, layout, shape=(3, 2))` will run through DTensor because we requested the output of tf.ones to be sharded according to a `layout`." ] }, { "cell_type": "markdown", "metadata": { "id": "urKzmqAoPssT" }, "source": [ "### DTensor as Operands\n", "\n", "Many TensorFlow API functions take `tf.Tensor` as their operands, and returns `tf.Tensor` as their results. For these functions, you can express intention to run a function through DTensor by passing in DTensor as operands. This section uses `tf.matmul(a, b)` as an example." ] }, { "cell_type": "markdown", "metadata": { "id": "7LO8ZT7iWVga" }, "source": [ "#### Fully replicated input and output\n", "\n", "In this case, the DTensors are fully replicated. On each of the devices of the `Mesh`,\n", " - the component tensor for operand `a` is `[[1, 2, 3], [4, 5, 6]]` (2x3)\n", " - the component tensor for operand `b` is `[[6, 5], [4, 3], [2, 1]]` (3x2)\n", " - the computation consists of a single `MatMul` of `(2x3, 3x2) -> 2x2`,\n", " - the component tensor for result `c` is `[[20, 14], [56,41]]` (2x2)\n", "\n", "Total number of floating point mul operations is `6 device * 4 result * 3 mul = 72`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TiZf2J9JNd2D" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 6)], devices=DEVICES)\n", "layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)\n", "a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=layout)\n", "b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=layout)\n", "\n", "c = tf.matmul(a, b) # runs 6 identical matmuls in parallel on 6 devices\n", "\n", "# `c` is a DTensor replicated on all devices (same as `a` and `b`)\n", "print('Sharding spec:', dtensor.fetch_layout(c).sharding_specs)\n", "print(\"components:\")\n", "for component_tensor in dtensor.unpack(c):\n", " print(component_tensor.device, component_tensor.numpy())\n" ] }, { "cell_type": "markdown", "metadata": { "id": "QXtR9qgKWgWV" }, "source": [ "#### Sharding operands along the contracted axis\n", "\n", "You can reduce the amount of computation per device by sharding the operands `a` and `b`. A popular sharding scheme for `tf.matmul` is to shard the operands along the axis of the contraction, which means sharding `a` along the second axis, and `b` along the first axis.\n", "\n", "The global matrix product sharded under this scheme can be performed efficiently, by local matmuls that runs concurrently, followed by a collective reduction to aggregate the local results. This is also the [canonical way](https://github.com/open-mpi/ompi/blob/ee87ec391f48512d3718fc7c8b13596403a09056/docs/man-openmpi/man3/MPI_Reduce.3.rst?plain=1#L265) of implementing a distributed matrix dot product.\n", "\n", "Total number of floating point mul operations is `6 devices * 4 result * 1 = 24`, a factor of 3 reduction compared to the fully replicated case (72) above. The factor of 3 is due to the sharing along `x` mesh dimension with a size of `3` devices.\n", "\n", "The reduction of the number of operations run sequentially is the main mechansism with which synchronuous model parallelism accelerates training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EyVAUvMePbms" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 3), (\"y\", 2)], devices=DEVICES)\n", "a_layout = dtensor.Layout([dtensor.UNSHARDED, 'x'], mesh)\n", "a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=a_layout)\n", "b_layout = dtensor.Layout(['x', dtensor.UNSHARDED], mesh)\n", "b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=b_layout)\n", "\n", "c = tf.matmul(a, b)\n", "# `c` is a DTensor replicated on all devices (same as `a` and `b`)\n", "print('Sharding spec:', dtensor.fetch_layout(c).sharding_specs)" ] }, { "cell_type": "markdown", "metadata": { "id": "IhD8yYgJiCEh" }, "source": [ "#### Additional Sharding\n", "\n", "You can perform additional sharding on the inputs, and they are appropriately carried over to the results. For example, you can apply additional sharding of operand `a` along its first axis to the `'y'` mesh dimension. The additional sharding will be carried over to the first axis of the result `c`.\n", "\n", "\n", "Total number of floating point mul operations is `6 devices * 2 result * 1 = 12`, an additional factor of 2 reduction compared to the case (24) above. The factor of 2 is due to the sharing along `y` mesh dimension with a size of `2` devices." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0PYqe0neiOpR" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 3), (\"y\", 2)], devices=DEVICES)\n", "\n", "a_layout = dtensor.Layout(['y', 'x'], mesh)\n", "a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=a_layout)\n", "b_layout = dtensor.Layout(['x', dtensor.UNSHARDED], mesh)\n", "b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=b_layout)\n", "\n", "c = tf.matmul(a, b)\n", "# The sharding of `a` on the first axis is carried to `c'\n", "print('Sharding spec:', dtensor.fetch_layout(c).sharding_specs)\n", "print(\"components:\")\n", "for component_tensor in dtensor.unpack(c):\n", " print(component_tensor.device, component_tensor.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "c-1NazCVmLWZ" }, "source": [ "### DTensor as Output\n", "\n", "What about Python functions that do not take operands, but returns a Tensor result that can be sharded? Examples of such functions are\n", "\n", " - `tf.ones`, `tf.zeros`, `tf.random.stateless_normal`,\n", "\n", "For these Python functions, DTensor provides `dtensor.call_with_layout` which eagelry executes a Python function with DTensor, and ensures that the returned Tensor is a DTensor with the requested `Layout`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J0jo_8NPtJiO" }, "outputs": [], "source": [ "help(dtensor.call_with_layout)" ] }, { "cell_type": "markdown", "metadata": { "id": "V-YdLvfytM7g" }, "source": [ "The eagerly executed Python function usually only contain a single non-trivial TensorFlow Op.\n", "\n", "To use a Python function that emits multiple TensorFlow Ops with `dtensor.call_with_layout`, the function should be converted to a `tf.function`. Calling a `tf.function` is a single TensorFlow Op. When the `tf.function` is called, DTensor can perform layout propagation when it analyzes the computing graph of the `tf.function`, before any of the intermediate tensors are materialized." ] }, { "cell_type": "markdown", "metadata": { "id": "DLrksgFjqRLS" }, "source": [ "#### APIs that emit a single TensorFlow Op\n", "\n", "If a function emits a single TensorFlow Op, you can directly apply `dtensor.call_with_layout` to the function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "G1CuKYSFtFeM" }, "outputs": [], "source": [ "help(tf.ones)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2m_EAwy-ozOh" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 3), (\"y\", 2)], devices=DEVICES)\n", "ones = dtensor.call_with_layout(tf.ones, dtensor.Layout(['x', 'y'], mesh), shape=(6, 4))\n", "print(ones)" ] }, { "cell_type": "markdown", "metadata": { "id": "bx-7Xo8Cpb8S" }, "source": [ "#### APIs that emit multiple TensorFlow Ops\n", "\n", "If the API emits multiple TensorFlow Ops, convert the function into a single Op through `tf.function`. For example `tf.random.stateleess_normal`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "H8BQSTRFtCih" }, "outputs": [], "source": [ "help(tf.random.stateless_normal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TvP81eYopSPm" }, "outputs": [], "source": [ "ones = dtensor.call_with_layout(\n", " tf.function(tf.random.stateless_normal),\n", " dtensor.Layout(['x', 'y'], mesh),\n", " shape=(6, 4),\n", " seed=(1, 1))\n", "print(ones)" ] }, { "cell_type": "markdown", "metadata": { "id": "qKoojp9ZyWzW" }, "source": [ "Wrapping a Python function that emits a single TensorFlow Op with `tf.function` is allowed. The only caveat is paying the associated cost and complexity of creating a `tf.function` from a Python function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LbAtKrSkpOaq" }, "outputs": [], "source": [ "ones = dtensor.call_with_layout(\n", " tf.function(tf.ones),\n", " dtensor.Layout(['x', 'y'], mesh),\n", " shape=(6, 4))\n", "print(ones)" ] }, { "cell_type": "markdown", "metadata": { "id": "D-m1816JP3CE" }, "source": [ "### From `tf.Variable` to `dtensor.DVariable`\n", "\n", "In Tensorflow, `tf.Variable` is the holder for a mutable `Tensor` value.\n", "With DTensor, the corresponding variable semantics is provided by `dtensor.DVariable`.\n", "\n", "The reason a new type `DVariable` was introduced for DTensor variable is because DVariables have an additional requirement that the layout cannot change from its initial value." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "awRPuR26P0Sc" }, "outputs": [], "source": [ "mesh = dtensor.create_mesh([(\"x\", 6)], devices=DEVICES)\n", "layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)\n", "\n", "v = dtensor.DVariable(\n", " initial_value=dtensor.call_with_layout(\n", " tf.function(tf.random.stateless_normal),\n", " layout=layout,\n", " shape=tf.TensorShape([64, 32]),\n", " seed=[1, 1],\n", " dtype=tf.float32))\n", "\n", "print(v.handle)\n", "assert layout == dtensor.fetch_layout(v)" ] }, { "cell_type": "markdown", "metadata": { "id": "Pb9jn473prC_" }, "source": [ "Other than the requirement on matching the `layout`, a `DVariable` behaves the same as a `tf.Variable`. For example, you can add a DVariable to a DTensor,\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "adxFw9wJpqQQ" }, "outputs": [], "source": [ "a = dtensor.call_with_layout(tf.ones, layout=layout, shape=(64, 32))\n", "b = v + a # add DVariable and DTensor\n", "print(b)" ] }, { "cell_type": "markdown", "metadata": { "id": "QxBdNHWSu-kV" }, "source": [ "You can also assign a DTensor to a DVariable.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oYwfiyw5P94U" }, "outputs": [], "source": [ "v.assign(a) # assign a DTensor to a DVariable\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": { "id": "4fvSk_VUvGnj" }, "source": [ "Attempting to mutate the layout of a `DVariable`, by assigning a DTensor with an incompatible layout produces an error." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3pckUugYP_r-" }, "outputs": [], "source": [ "# variable's layout is immutable.\n", "another_mesh = dtensor.create_mesh([(\"x\", 3), (\"y\", 2)], devices=DEVICES)\n", "b = dtensor.call_with_layout(tf.ones,\n", " layout=dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], another_mesh),\n", " shape=(64, 32))\n", "try:\n", " v.assign(b)\n", "except:\n", " print(\"exception raised\")" ] }, { "cell_type": "markdown", "metadata": { "id": "3LadIcwRvR6f" }, "source": [ "## What's next?\n", "\n", "In this colab, you learned about DTensor, an extension to TensorFlow for distributed computing. To try out these concepts in a tutorial, see [Distributed training with DTensor](https://www.tensorflow.org/tutorials/distribute/dtensor_ml_tutorial)." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "dtensor_overview.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

Xilinx/BNN-PYNQ

notebooks/CNV-QNN_Cifar10.ipynb

1

2225893

bsd-3-clause

jbpoline/cnv_analysis

CNV_dangerosite.ipynb

1

36579

{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['datetime']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'cnv_util' from 'cnv_util.pyc'>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import scipy.stats as sst\n", "import matplotlib.pyplot as plt\n", "import os\n", "import os.path as osp\n", "from __future__ import print_function\n", "from __future__ import division\n", "import six\n", "import cnv_util as util\n", "from datetime import datetime\n", "reload(util)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading TSV files " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \\\n", " 'guillaume_huguet_CNV','File_OK')\n", "filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'\n", "fullfname = osp.join(CWD, filename)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr = np.loadtxt(fullfname, dtype='str', comments=None, delimiter='\\Tab', \n", " converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['IID_projet', 'IID_genotype', 'SampleID', 'CHR de Merge_CIA_610_660_QC', 'START', 'STOP', 'TYPE de Merge_CIA_610_660_QC', 'SCORE', 'ConcatSNP', 'Gene', 'Location', '#Genes', '#Exons', \"5'gene\", \"5'dist(kb)\", \"3'gene\", \"3'dist(kb)\", 'dups(DGV)', 'dels(DGV)', 'total_known_CNVs(DGV)', 'num_papers(DGV)', 'papers', 'hg18_DGV_1%_Mar2010.txt %overlap', 'hg18_Chromosome_band.txt', 'hg18_segdups.txt %overlap', 'hg18_genome_features.txt', 'hg18_genome_features.txt %overlap', 'Pvalue_MMAP_V2_sans_intron_and_Intergenic']\n", "27 7\n" ] } ], "source": [ "EXPECTED_LINES = 19542\n", "expected_nb_values = EXPECTED_LINES - 1 \n", "assert arr.shape[0] == EXPECTED_LINES\n", "line0 = arr[0].split('\\t')\n", "print(line0)\n", "\n", "danger = 'Pvalue_MMAP_V2_sans_intron_and_Intergenic'\n", "score = 'SCORE'\n", "i_danger = line0.index(danger)\n", "i_score = line0.index(score)\n", "print(i_danger, i_score)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# check that all lines have the same number of tab separated elements \n", "larr = np.asarray([len(arr[i].split('\\t')) for i in range(arr.shape[0])])\n", "assert not (larr - larr[0]).any() # all element have the same value " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dangers = np.asarray([line.split('\\t')[i_danger] for line in arr[1:]])\n", "scores = np.asarray([line.split('\\t')[i_score] for line in arr[1:]])\n", "# print(np.unique(scores))\n", "\n", "assert len(dangers) == expected_nb_values\n", "assert len(scores) == expected_nb_values\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## transforming the \"Pvalue_MMAP_V2_...\" into danger score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing the function danger_score" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert util._test_danger_score_1()\n", "assert util._test_danger_score()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__ QUESTION pour Guillaume: __\n", "a quoi correspondent les '' dans la colonne \"Pvalue_MMAP_V2_sans_intron_and_Intergenic\" (danger)?\n", "\n", "Ansewer: cnv for which we have no dangerosity information" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "danger_not_empty = dangers != ''\n", "danger_scores = dangers[danger_not_empty]\n", "danger_scores = np.asarray([util.danger_score(pstr, util.pH1_with_apriori) \n", " for pstr in danger_scores])\n", "\"\"\";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## To be or not to be a CNV: p value from the 'SCORE' column " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14903\n" ] } ], "source": [ "reload(util)\n", "#get the scores\n", "scores = np.asarray([line.split('\\t')[i_score] for line in arr[1:]])\n", "assert len(scores) == expected_nb_values\n", "print(len(np.unique(scores)))\n", "#tmp_score = np.asarray([util.str2floats(s, comma2point=True, sep=' ')[0] for s in scores])\n", "assert scores.shape[0] == EXPECTED_LINES - 1 \n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# h = plt.hist(tmp[tmp > sst.scoreatpercentile(tmp, 99)], bins=100)\n", "# h = plt.hist(tmp[tmp < 50], bins=100)\n", "\n", "\"\"\"\n", "print(\"# CNV with score == 0.: \", (tmp==0.).sum())\n", "print(\"# CNV with score >=15 < 17.5 : \", np.logical_and(tmp >= 15., tmp < 17.5).sum())\n", "tmp.max()\n", "\"\"\";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Replace the zero score by the maximum score: cf Guillaume's procedure" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3588.26 0.0 27\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3a8eefa490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scoresf = np.asarray([util.str2floats(s, comma2point=True, sep=' ')[0] \n", " for s in scores])\n", "print(scoresf.max(), scoresf.min(),(scoresf==0).sum())\n", "#clean_score = util.process_scores(scores)\n", "#h = plt.hist(clean_score[clean_score < 60], bins=100)\n", "#h = plt.hist(scoresf[scoresf < 60], bins=100)\n", "h = plt.hist(scoresf, bins=100, range=(0,150))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transforms the scores into P(cnv is real)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Creating a function from score to proba from Guillaume's values\n", "p_cnv = util._build_dict_prob_cnv()\n", "#print(p_cnv.keys())\n", "#print(p_cnv.values())\n", "\n", "#### Definition with a piecewise linear function\n", "#score2prob = util.create_score2prob_lin_piecewise(p_cnv)\n", "#scores = np.arange(15,50,1)\n", "#probs = [score2prob(sc) for sc in scores]\n", "#plt.plot(scores, probs)\n", "\n", "#### Definition with a corrected regression line\n", "score2prob = util.create_score2prob_lin(p_cnv)\n", "#x = np.arange(0,50,1)\n", "#plt.plot(x, [score2prob(_) for _ in x], '-', p_cnv.keys(), p_cnv.values(), '+')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p_scores = [score2prob(sc) for sc in clean_score]\n", "assert len(p_scores) == EXPECTED_LINES -1 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finally, putting things together " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# re-loading \n", "reload(util)\n", "CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \\\n", " 'guillaume_huguet_CNV','File_OK')\n", "filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'\n", "fullfname = osp.join(CWD, filename)\n", "\n", "# in numpy array\n", "arr = np.loadtxt(fullfname, dtype='str', comments=None, delimiter='\\Tab', \n", " converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)\n", "\n", "line0 = arr[0].split('\\t')\n", "\n", "i_DANGER = line0.index('Pvalue_MMAP_V2_sans_intron_and_Intergenic')\n", "i_SCORE = line0.index('SCORE')\n", "i_START = line0.index('START')\n", "i_STOP = line0.index('STOP')\n", "i_5pGENE = line0.index(\"5'gene\")\n", "i_3pGENE = line0.index(\"3'gene\")\n", "i_5pDIST = line0.index(\"5'dist(kb)\")\n", "i_3pDIST = line0.index(\"3'dist(kb)\")\n", "#i_LOC = line0.index('Location')\n", "\n", "scores = np.asarray([line.split('\\t')[i_SCORE] for line in arr[1:]])\n", "clean_score = util.process_scores(scores)\n", "max_score = clean_score.max()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['IID_projet', 'IID_genotype', 'SampleID', 'CHR de Merge_CIA_610_660_QC', 'START', 'STOP', 'TYPE de Merge_CIA_610_660_QC', 'SCORE', 'ConcatSNP', 'Gene', 'Location', '#Genes', '#Exons', \"5'gene\", \"5'dist(kb)\", \"3'gene\", \"3'dist(kb)\", 'dups(DGV)', 'dels(DGV)', 'total_known_CNVs(DGV)', 'num_papers(DGV)', 'papers', 'hg18_DGV_1%_Mar2010.txt %overlap', 'hg18_Chromosome_band.txt', 'hg18_segdups.txt %overlap', 'hg18_genome_features.txt', 'hg18_genome_features.txt %overlap', 'Pvalue_MMAP_V2_sans_intron_and_Intergenic']\n" ] } ], "source": [ "print(line0)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "with names from: ['IID_projet', 'IID_genotype', 'CHR de Merge_CIA_610_660_QC', 'START', 'STOP']\n", "we have 19541 unique elements out of 19541 rows in the tsv\n", "with names from: ['CHR de Merge_CIA_610_660_QC', 'START', 'STOP']\n", "we have 7337 unique elements out of 19541 rows in the tsv\n", "with names from: ['IID_projet']\n", "we have 1712 unique elements out of 19541 rows in the tsv\n" ] } ], "source": [ "#names_from = ['START', 'STOP', \"5'gene\", \"3'gene\", \"5'dist(kb)\", \"3'dist(kb)\"]\n", "\n", "#---------- ligne uniques:\n", "names_from = ['IID_projet', 'IID_genotype', \"CHR de Merge_CIA_610_660_QC\", 'START', 'STOP'] \n", "cnv_names = util.make_uiid(arr, names_from)\n", "print(\"with names from: \", names_from)\n", "print(\"we have {} unique elements out of {} rows in the tsv\".format(\n", " len(np.unique(cnv_names)), len(cnv_names)))\n", "\n", "#---------- CNV uniques ? \n", "names_from = [\"CHR de Merge_CIA_610_660_QC\", 'START', 'STOP'] \n", "cnv_names = util.make_uiid(arr, names_from)\n", "print(\"with names from: \", names_from)\n", "print(\"we have {} unique elements out of {} rows in the tsv\".format(\n", " len(np.unique(cnv_names)), len(cnv_names)))\n", "\n", "#---------- sujets uniques ? \n", "names_from = ['IID_projet'] # , 'IID_genotype'] \n", "cnv_names = util.make_uiid(arr, names_from)\n", "print(\"with names from: \", names_from)\n", "print(\"we have {} unique elements out of {} rows in the tsv\".format(\n", " len(np.unique(cnv_names)), len(cnv_names)))\n", "\n", "dangers = np.asarray([line.split('\\t')[i_DANGER] for line in arr[1:]])\n", "scores = np.asarray([line.split('\\t')[i_SCORE] for line in arr[1:]])\n", "\n", "#danger_not_empty = dangers != ''\n", "#print(danger_not_empty.sum())\n", "#print(len(np.unique(cnv_name)))\n", "#print(cnv_name[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a dict of the cnv" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import OrderedDict\n", "cnv = OrderedDict()\n", "names_from = [\"CHR de Merge_CIA_610_660_QC\", 'START', 'STOP'] \n", " #, \"5'gene\", \"3'gene\", \"5'dist(kb)\", \"3'dist(kb)\"]\n", "blank_dgr = 0\n", "\n", "for line in arr[1:]:\n", " lline = line.split('\\t')\n", " dgr = lline[i_DANGER]\n", " scr = lline[i_SCORE]\n", " cnv_iid = util.make_uiid(line, names_from, arr[0])\n", " \n", " if dgr != '':\n", " add_cnv = (util.danger_score(lline[i_DANGER], util.pH1_with_apriori),\n", " score2prob(util.process_one_score(lline[i_SCORE], max_score)))\n", " if cnv_iid in cnv.keys():\n", " cnv[cnv_iid].append(add_cnv)\n", " else:\n", " cnv[cnv_iid] = [add_cnv]\n", " else:\n", " blank_dgr += 1\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3374 12294\n", "['10_134903403_134996704', '18_2578360_2611407', '17_17683629_17693534', '18_2576907_2611407', '17_17651995_17693534']\n", "[[(5.9939961064914957, 0.4658285714285714)], [(0.99999942000033648, 0.17050257142857136), (0.99999942000033648, 0.13648257142857131)], [(0.99999942000033648, 0.15716457142857138)], [(0.99999942000033648, 0.17914257142857137)], [(3.9999976800013459, 0.2850365714285713), (3.9999976800013459, 0.3167345714285713), (3.9999976800013459, 0.31824657142857143), (3.9999976800013459, 0.1896185714285713)]]\n" ] } ], "source": [ "print(len(cnv), (blank_dgr))\n", "print([k for k in cnv.keys()[:5]])\n", "print([k for k in cnv.values()[:5]])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21_14830513_14852044 : [(0.99999999999989209, 0.9614243714285715), (0.99999999999989209, 0.7371245714285714), (0.99999999999989209, 0.35037657142857137)]\n", "11_127888014_127946707 : [(0.99999999999999989, 0.2014445714285714)]\n", "7_73481452_73512817 : [(0.99999999999998568, 0.16234857142857137)]\n", "22_48960865_49003996 : [(2.9999999979009999, 0.1562465714285714)]\n", "22_48951780_48979320 : [(1.9999999996940001, 0.36306657142857135)]\n", "22_48951780_49000551 : [(3.9999999977480001, 0.16170057142857136), (3.9999999977480001, 0.14296257142857138)]\n", "22_48943190_49003996 : [(3.9999999977480001, 0.3968705714285714)]\n", "22_48943190_49000551 : [(3.9999999977480001, 0.19280457142857138)]\n", "16_29554843_30085308 : [(26.0, 1.0)]\n", "15_29143717_29172089 : [(1.9999999998241, 1.0)]\n" ] } ], "source": [ "for k in cnv.keys()[3340:3350]:\n", " print(k,': ',cnv[k])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a dictionary of the subjects - " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cnv = OrderedDict({})\n", "#names_from = ['START', 'STOP', \"5'gene\", \"3'gene\", \"5'dist(kb)\", \"3'dist(kb)\"]\n", "names_from = ['IID_projet']\n", "\n", "for line in arr[1:]:\n", " lline = line.split('\\t')\n", " dgr = lline[i_DANGER]\n", " scr = lline[i_SCORE]\n", " sub_iid = util.make_uiid(line, names_from, arr[0])\n", " \n", " if dgr != '':\n", " add_cnv = (util.danger_score(lline[i_DANGER], util.pH1_with_apriori),\n", " score2prob(util.process_one_score(lline[i_SCORE], max_score)))\n", " if sub_iid in cnv.keys():\n", " cnv[sub_iid].append(add_cnv)\n", " else:\n", " cnv[sub_iid] = [add_cnv]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Histogram of the number of cnv used to compute dangerosity" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1656\n", "59\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd0d0d3b890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(len(cnv))\n", "nbcnv = [len(cnv[sb]) for sb in cnv]\n", "hist = plt.hist(nbcnv, bins=50)\n", "print(np.max(np.asarray(nbcnv)))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# definition of dangerosity from a list of cnv\n", "def dangerosity(listofcnvs):\n", " \"\"\"\n", " inputs: list tuples (danger_score, proba_cnv)\n", " returns: a dangerosity score \n", " \"\"\"\n", " last = -1 #slicing the last\n", " tmp = [np.asarray(t) for t in zip(*listofcnvs)]\n", " return tmp[0].dot(tmp[1])\n", "\n", "# or: return np.asarray([dgr*prob for (dgr,prob) in listofcnvs]).cumsum()[last]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing dangerosity" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0.99999942000033648, 0.17050257142857136), (0.48377137638161954, 0.1629047714285713), (0.49999999999999989, 0.42222357142857136), (1.1666666666666665, 0.6306365714285714)] yields 1.19616559041\n" ] } ], "source": [ "for k in range(1,30, 30):\n", " print(cnv[cnv.keys()[k]], ' yields ', dangerosity(cnv[cnv.keys()[k]]))\n", " \n", "test_dangerosity_input = [[(1., .5), (1., .5), (1., .5), (1., .5)],\n", " [(2., 1.)],\n", " [(10000., 0.)]]\n", "test_dangerosity_output = [2., 2., 0]\n", "\n", "#print( [dangerosity(icnv) for icnv in test_dangerosity_input]) # == test_dangerosity_output\n", "assert( [dangerosity(icnv) for icnv in test_dangerosity_input] == test_dangerosity_output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Printing out results" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dtime = datetime.now().strftime(\"%y-%m-%d_h%H-%M\")\n", "outfile = dtime+'dangerosity_cnv.txt'\n", "fulloutfile = osp.join(CWD, outfile)\n", "\n", "with open(fulloutfile, 'w') as outf:\n", " for sub in cnv:\n", " outf.write(\"\\t\".join([sub, str(dangerosity(cnv[sub]))]) + \"\\n\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

artistic-2.0