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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
knowledgeanyhow/notebooks	airline/Exploration of Airline On-Time Performance.ipynb	2	375407	{ "metadata": { "name": "", "signature": "sha256:4997000efd69b335ff55f6eed3b9ee20a47bb92f0fd25ec1119af0e633fbe289" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploration of Airline On-Time Performance\n", "\n", "In this notebook, we explore a sample of data from the U.S. Department of Transportation (US-DOT) Research and Innovative Technology Administration (RITA) [Bureau of Transportation Statistics](http://www.rita.dot.gov/bts/about/) (BTS). The data comes from the [On-Time Performance](http://www.transtats.bts.gov/Fields.asp?Table_ID=236) table:\n", "\n", "> This table contains on-time arrival data for non-stop domestic flights by major air carriers, and provides such additional items as departure and arrival delays, origin and destination airports, flight numbers, scheduled and actual departure and arrival times, cancelled or diverted flights, taxi-out and taxi-in times, air time, and non-stop distance.\n", "\n", "## Questions\n", "\n", "For the purposes of this notebook, I have captured a subset of the table in a [Cloudant](https://cloudant.com) database. We will start by connecting to the database and simply looking at the available data. Once we understand the content, we will try to answer the following questions about flights during the month of June, 2014:\n", "\n", "1. What is the distribution of departure delays of at least 15 minutes by state? Arrival delays?\n", "1. Is there a tendency of flights from one state to another to experience a delay of 15 minutes or more on the arriving end?\n", "1. How did arrival delay in minutes vary day-by-day?\n", "\n", "## Connect to Cloudant\n", "\n", "To get to the data, we can use a [Cloudant client for Python](https://cloudant.com/python/). We'll can install the official client by shelling out to `bash` and running a `pip` command right here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!pip install cloudant" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Requirement already satisfied (use --upgrade to upgrade): cloudant in /usr/local/lib/python2.7/dist-packages\r\n", "Requirement already satisfied (use --upgrade to upgrade): requests-futures==0.9.4 in /usr/local/lib/python2.7/dist-packages (from cloudant)\r\n", "Requirement already satisfied (use --upgrade to upgrade): requests>=1.2.0 in /usr/lib/python2.7/dist-packages (from requests-futures==0.9.4->cloudant)\r\n", "Requirement already satisfied (use --upgrade to upgrade): futures>=2.1.3 in /usr/local/lib/python2.7/dist-packages (from requests-futures==0.9.4->cloudant)\r\n", "Cleaning up...\r\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll import the `cloudant` package we just installed and use it to connect to the read-only `rita_transtats_2014_06` database in the `parente` user account." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import cloudant" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "account = cloudant.Account('parente')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "database = account.database('rita_transtats_2014_06')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `cloudant` package builds on the popular Python [requests](http://docs.python-requests.org/en/latest/) package. Almost every object that comes back from the API is a subclass of a `requests` class. This means we can perform a HTTP GET against the database and get the JSON body of the response with a couple method calls." ] }, { "cell_type": "code", "collapsed": false, "input": [ "database.get().json()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "{u'compact_running': False,\n", " u'db_name': u'rita_transtats_2014_06',\n", " u'disk_format_version': 5,\n", " u'disk_size': 160707408,\n", " u'doc_count': 502500,\n", " u'doc_del_count': 0,\n", " u'instance_start_time': u'0',\n", " u'other': {u'data_size': 265247850},\n", " u'purge_seq': 0,\n", " u'update_seq': u'502516-g1AAAADveJzLYWBgYMlgTmGQT0lKzi9KdUhJMtJLykxPyilN1UvOyS9NScwr0ctLLckBKmRKZEiy____f1YSA-PrcKJ1JTkAyaR6qMZX84nWmMcCJBkagBRQ736w5mgSNR-AaIbYvDgLACDLUQs'}" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the above, we can see the database contains roughly 500,000 documents. We can grab a couple from the database to inspect locally." ] }, { "cell_type": "code", "collapsed": false, "input": [ "items = []\n", "for i, item in enumerate(database.all_docs(params={'include_docs' : True})):\n", " if i > 1: break\n", " items.append(item)\n", "print items" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[{u'value': {u'rev': u'1-40fc15f608f95f0428e2e9d6b468e483'}, u'id': u'04da0d01eb0f15d5c56eb1399a000a35', u'key': u'04da0d01eb0f15d5c56eb1399a000a35', u'doc': {u'DISTANCE': 1947.0, u'DEST_AIRPORT_ID': 12892, u'ARR_DEL15': 0.0, u'ORIGIN_STATE_ABR': u'GA', u'_rev': u'1-40fc15f608f95f0428e2e9d6b468e483', u'ARR_DELAY_NEW': 0.0, u'UNIQUE_CARRIER': u'DL', u'ORIGIN_AIRPORT_ID': 10397, u'DISTANCE_GROUP': 8, u'DEP_DEL15': 0.0, u'_id': u'04da0d01eb0f15d5c56eb1399a000a35', u'DEST_STATE_ABR': u'CA', u'DEP_DELAY_NEW': 0.0, u'FL_DATE': u'2014-06-30'}}, {u'value': {u'rev': u'1-4dd24d56dc537210f49fe327c7773178'}, u'id': u'04da0d01eb0f15d5c56eb1399a001198', u'key': u'04da0d01eb0f15d5c56eb1399a001198', u'doc': {u'DISTANCE': 282.0, u'DEST_AIRPORT_ID': 13487, u'ARR_DEL15': 0.0, u'ORIGIN_STATE_ABR': u'NE', u'_rev': u'1-4dd24d56dc537210f49fe327c7773178', u'ARR_DELAY_NEW': 0.0, u'UNIQUE_CARRIER': u'DL', u'ORIGIN_AIRPORT_ID': 13871, u'DISTANCE_GROUP': 2, u'DEP_DEL15': 0.0, u'_id': u'04da0d01eb0f15d5c56eb1399a001198', u'DEST_STATE_ABR': u'MN', u'DEP_DELAY_NEW': 0.0, u'FL_DATE': u'2014-06-30'}}]\n" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dictionary format is hard to read and contains metadata from Cloudant that we don't care about. Let's use the `pandas` package to put the data in a tabular, HTML format instead." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "pandas.DataFrame([item['doc'] for item in items])" ], "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>ARR_DEL15</th>\n", " <th>ARR_DELAY_NEW</th>\n", " <th>DEP_DEL15</th>\n", " <th>DEP_DELAY_NEW</th>\n", " <th>DEST_AIRPORT_ID</th>\n", " <th>DEST_STATE_ABR</th>\n", " <th>DISTANCE</th>\n", " <th>DISTANCE_GROUP</th>\n", " <th>FL_DATE</th>\n", " <th>ORIGIN_AIRPORT_ID</th>\n", " <th>ORIGIN_STATE_ABR</th>\n", " <th>UNIQUE_CARRIER</th>\n", " <th>_id</th>\n", " <th>_rev</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 12892</td>\n", " <td> CA</td>\n", " <td> 1947</td>\n", " <td> 8</td>\n", " <td> 2014-06-30</td>\n", " <td> 10397</td>\n", " <td> GA</td>\n", " <td> DL</td>\n", " <td> 04da0d01eb0f15d5c56eb1399a000a35</td>\n", " <td> 1-40fc15f608f95f0428e2e9d6b468e483</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 0</td>\n", " <td> 13487</td>\n", " <td> MN</td>\n", " <td> 282</td>\n", " <td> 2</td>\n", " <td> 2014-06-30</td>\n", " <td> 13871</td>\n", " <td> NE</td>\n", " <td> DL</td>\n", " <td> 04da0d01eb0f15d5c56eb1399a001198</td>\n", " <td> 1-4dd24d56dc537210f49fe327c7773178</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " ARR_DEL15 ARR_DELAY_NEW DEP_DEL15 DEP_DELAY_NEW DEST_AIRPORT_ID \\\n", "0 0 0 0 0 12892 \n", "1 0 0 0 0 13487 \n", "\n", " DEST_STATE_ABR DISTANCE DISTANCE_GROUP FL_DATE ORIGIN_AIRPORT_ID \\\n", "0 CA 1947 8 2014-06-30 10397 \n", "1 MN 282 2 2014-06-30 13871 \n", "\n", " ORIGIN_STATE_ABR UNIQUE_CARRIER _id \\\n", "0 GA DL 04da0d01eb0f15d5c56eb1399a000a35 \n", "1 NE DL 04da0d01eb0f15d5c56eb1399a001198 \n", "\n", " _rev \n", "0 1-40fc15f608f95f0428e2e9d6b468e483 \n", "1 1-4dd24d56dc537210f49fe327c7773178 " ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Returning to [the source of the data](http://www.transtats.bts.gov/Fields.asp?Table_ID=236), we can get definitions for each of these fields. \n", "\n", "<dl class=\"dl-horizontal\">\n", "<dt>ARR_DEL15</dt>\n", "<dd>Arrival Delay Indicator, 15 Minutes or More (1=Yes)</dd>\n", "<dt>ARR_DEL15</dt>\n", "<dd>Arrival Delay Indicator, 15 Minutes or More (1=Yes)</dd>\n", "<dt>ARR_DELAY_NEW</dt>\n", "<dd>Difference in minutes between scheduled and actual arrival time. Early arrivals set to 0.</dd>\n", "<dt>DEP_DEL15</dt>\n", "<dd>Departure Delay Indicator, 15 Minutes or More (1=Yes)</dd>\n", "<dt>DEP_DELAY_NEW</dt>\n", "<dd>Difference in minutes between scheduled and actual departure time. Early departures set to 0.</dd>\n", "<dt>DEST_AIRPORT_ID</dt>\n", "<dd>Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused.</dd>\n", "<dt>DEST_STATE_ABR</dt>\n", "<dd>Destination Airport, State Code</dd>\n", "<dt>DISTANCE</dt>\n", "<dd>Distance between airports (miles)</dd>\n", "<dt>DISTANCE_GROUP</dt>\n", "<dd>Distance Intervals, every 250 Miles, for Flight Segment</dd>\n", "<dt>FL_DATE</dt>\n", "<dd>Flight Date (yyyymmdd)</dd>\n", "<dt>ORIGIN_AIRPORT_ID</dt>\n", "<dd>Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused.</dd>\n", "<dt>ORIGIN_STATE_ABR</dt>\n", "<dd>Origin Airport, State Code</dd>\n", "<dt>UNIQUE_CARRIER</dt>\n", "<dd>Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years.</dd>\n", "</dl>\n", "\n", "\n", "\n", "For the purposes of the specific questions stated at the top of this notebook, we only need a subset of the available columns, namely delay metrics, origin and destination states, and the flight date. We'll ignore the other fields." ] }, { "cell_type": "code", "collapsed": false, "input": [ "columns = [u'FL_DATE', u'ORIGIN_STATE_ABR', u'DEST_STATE_ABR', u'ARR_DEL15', u'ARR_DELAY_NEW', u'DEP_DEL15', u'DEP_DELAY_NEW', u'DISTANCE', u'DISTANCE_GROUP',]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moving forward, we'll assume we only have 1 GB of RAM total. Since we're only dealing with half a million records here, we can probably pull the entire contents of the database into local memory. If the data proves too large, we can rely on the map/reduce and search capabilities in Cloudant to work with the data instead.\n", "\n", "Being optimistic, we write a little loop that gets up to 20,000 docs at a time from the database. It stores the 20,000 in a simple Python list. Once the buffer reaches the threshold, we create a DataFrame from the buffer which reduces the data to just the fields we want. We do this chunking because appending to a DataFrame one row at a time is much slower." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "dfs = []\n", "buff = []\n", "for i, item in enumerate(database.all_docs(params={'include_docs' : True})):\n", " buff.append(item['doc'])\n", " if i > 0 and i % 20000 == 0:\n", " print 'Processed #{}'.format(i)\n", " df = pandas.DataFrame(buff, columns=columns)\n", " dfs.append(df)\n", " buff = []\n", "# don't forget the leftovers\n", "df = pandas.DataFrame(buff, columns=columns)\n", "dfs.append(df)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Processed #20000\n", "Processed #40000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #60000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #80000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #100000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #120000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #140000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #160000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #180000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #200000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #220000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #240000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #260000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #280000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #300000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #320000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #340000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #360000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #380000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #400000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #420000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #440000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #460000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #480000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Processed #500000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "CPU times: user 29 s, sys: 1.92 s, total: 30.9 s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Wall time: 43.5 s\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can build one DataFrame by quickly concatenating all the subframes we built in the loop above." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pandas.concat(dfs)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we have two copies of all the data in memory, which is undesirable. Before we delete the temporary buffer and subframes to free up some RAM, let's ensure the DataFrame row count matches the document count in the database." ] }, { "cell_type": "code", "collapsed": false, "input": [ "assert len(df) == database.get().json()['doc_count']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "del dfs\n", "del buff" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "!free -m" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " total used free shared buffers cached\r\n", "Mem: 989 700 289 1 73 178\r\n", "-/+ buffers/cache: 449 540\r\n", "Swap: 2047 29 2017\r\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As one last step, we reset the index on the DataFrame so that it is a unique, monotonically increasing integer. As it stands, we have dupes in our index because of our chunking (i.e., each chunk starts at index 0). This reset will prove important in some of our later plots where the index must be unique per row." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = df.reset_index(drop=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of Delay Counts by State\n", "\n", "> What is the distribution of departure delays of at least 15 minutes by state? Arrival delays?\n", "\n", "Let's look at some basic information about delays to start and work up to delays grouped by state. Because the question asks about delays 15 minutes or longer, we'll focus on the `DEP_DEL15` and `ARR_DEL15` columns." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.DEP_DEL15.value_counts() / len(df)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "0 0.731817\n", "1 0.248993\n", "dtype: float64" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "df.ARR_DEL15.value_counts() / len(df)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "0 0.718265\n", "1 0.258054\n", "dtype: float64" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Roughly a quarter of all departures and a quarter of all arrivals have delays. We can look at the distribution more closely once we enable and configure plotting with [matplotlib](http://matplotlib.org/) and [seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "import seaborn as sns\n", "sns.set_palette(\"deep\", desat=.6)\n", "colors = sns.color_palette(\"deep\")\n", "sns.set_context(rc={\"figure.figsize\": (18, 5)})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's group by state now and sum the number of delays. We'll do it for both departure and arrival delays." ] }, { "cell_type": "code", "collapsed": false, "input": [ "by_origin_state = df.groupby('ORIGIN_STATE_ABR')\n", "departure_delay_counts = by_origin_state.DEP_DEL15.sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "by_dest_state = df.groupby('DEST_STATE_ABR')\n", "arrival_delay_counts = by_dest_state.ARR_DEL15.sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To plot, we'll put both series in a DataFrame so we can view the arrival and departure delays for each state side-by-side." ] }, { "cell_type": "code", "collapsed": false, "input": [ "delay_df = pandas.DataFrame([departure_delay_counts, arrival_delay_counts]).T" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "delay_df.sort('DEP_DEL15', ascending=False).plot(kind='bar', title='Number of delayed flights by state')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ac978990>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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AKt8DolbXmiWLHuHME97PpEkbjGDLJEmSJEmrq+nTN2T58qdZuHAhAHfeeTsHH3xAz/wT\nT/zqCpdLLFu2jN/97rdsv/0rGrqNqVM3q7vtnZ1Pccgh72DNNdfkHe84iB122LnuGrWs8gEErPyu\nNZIkSZIk1WPLLbfmK1/pfXlEZ+dTPaHBi1+8DbvvvkfDt1Gvyy67kilTpvDAA/M48sj3s+mmz2Pq\n1BcMu+6oCCAkSZIkSWol8+bdzxprjGHSpEnMmdP3cuPGrTXk8RYGu42BtLWtOEbElCnFFQbTp2/I\n1ltvyz33JFttZQAhSZIkSVIvSxY9MmK1Fi5cyMknf5G99tq3YW1o1ja6urroqhg8YvHixay11lqM\nGzeORYsWceedf+Ltbz9ouM0FDCAkSZIkSaPMjBkzOf4jBw643OTJ41mwYMmga/bnqaeKSykqb5G5\n337vAIoeBtXjM7zrXe9mp51e3av3QX+6L9eoZxt7770HTz31X/bc84090/fb7+1sueVWfPrTH2fx\n4sf57W9v4txzv8UFF/yQe+/9Fyef/EXa2sbQ1fU0Bx74LmbO3GTQbeyPAYQkSZIkaVQZN24cs2bN\nHnC5qVMn8PDDixuyzbvuuqvPWltvvS1XX31DzXnXXvvrQW/j17/+fZ/z+tvGjTfeUnP65Zdf2Wva\nFlu8mPPP/99Bt6keq/xtOCVJkiRJUuuzB4QkSZIkSS3isccW8Z73HMiyZctXmH766Wez7rrtI9Sq\nxjCAkCRJkiSpRbS3T+SKK65o2KUhrcRLMCRJkiRJUtMZQEiSJEmSpKbzEgxJkiRJ0qjS2dlJR8fc\nAZdbuLC+23COGzduuE1brRlASJIkSZJGlY6OuRx/0QlMWK8xgzYufvQxjjvg2EHd2lN9M4CQJEmS\nJI06E9ZrZ+L6k1b6dm+88QaOOebj/OAHl7Lxxpvw4IMP8Pa3v42ZM2eybNkyXvSiLfjEJ45hzJgx\n3HbbHzn66I8yffqGLF26lB13fBXvfe/hfda+6qqfcdZZp/Oc56zPk0/+h+nTN+SQQ97D5ptvCcCJ\nJ36OO+64nfHjnw3A2ms/ix/96BKuuupnZP6No476xAr1zjnnG1xzzVUsXryY6667sdd2pk59DgB7\n7bUvBx/8jmG/NgYQkiRJkiQ1yC9/eQ3/8z+v5LrrruHQQw8DYKONNuJ737uIp59+mqOO+gA33vj/\n2Hnn1wDw4hdvw1e+cipPPfUUhxzydnbc8VVMnfqymrXb2trYZZfX8+EPfxyA2277I8cc83HOOOMc\nZs7chLa2Nj74wSPZaadX91qvlh122Im9996X/fbbs9/tNIqDUEqSJEmS1ABPPvkkd931F4466pP8\n6lfX9Zo/ZswYXvCCFzFv3v295q211lo873nP54EH5vW7ja6urp7H22zzEt785j356U8vrzl/IC98\n4east96Umtuop85gjcoeEE8vX8acOXN6DSbioCGSJEmSpGa5+eZfs912L2fatGlMnDiJzL+z7rrr\n9sx/6qmnuOOO2zjooEN6rfv444/xt7/9lYMOOrSubc6eHT0BRFdXF9/4xhmcf/53Adh001mcccZp\ndYcJbW1t3HDDr7jjjtuYMWMmH/rQR5g6dUJdNWoZlQHEE4sXcfqV56ww4IiDhkiSJEmSmumXv7yG\nffY5AIBXveo1/PKX17DXXvswb979HHzwATz44ANsu+1LefnLX9mzzp133s673nUA999/H3vssReb\nbjqrrm1Whgv1XoLRl1e8Ygde+9pdGTt2LD/5yeWceOLnuOii79dVo5ZRGUBA4wYc6ezs5O6777Y3\nhSRJkiStQhY/+thKrbVo0SJuu+2P/Otf/6StrY3ly5czZswY9tzzbWy4YTEGxGOPLeIDH3gvf//7\nXWy22QsB2HLLrfnKV07lwQcf4EMfeh/77LN/Xb0N7rkn2WSTTYf83GpZd91nTubvvvsenH32GQ2p\nO2oDiEapdfsWe1NIkiRJUuuaMWMmxx1w7IDLTZ48vtfJ5v5q9ueaa65h113fyMc+dnTPtA9+8L3M\nn/9Qz8/t7RN573sP55xzvsGpp35jhfU32GA6b3vbfpx33nc5+eQv1dxG9aUUt99+Kz/72Y/5+tfP\n6XOZvqb159FHH+kZG+Lmm29sWMBhADEII3X7FkmSJElS/caNGzeoE8ZTp07g4YcXN2SbV155Jfvu\ne+AK03be+dV8//vnrXAJxI477sy5536Lu+76C21tbVReHbHHHnux//578tBDD7HGGs/utY22tjau\nv/467rzzDv773/8yffpGnHjiV9l44016lqkcA6KtrY3LL7+MtrY2rrrq59x00697ljvnnO9x6aUX\n88tfXktn51PsuecbedOb3sLBB7+HH/3oh9x8869ZY42xtLe38+lPf7Yhr5EBhCRJkiRJw3TBBRf0\nCjP23ns/9t57v17LnnfeRT2Pt956257Ha621FpdffmWfwchuu+3Obrvt3mcbagUFa665Zp/rHX74\nkRx++JG9ph922Ac47LAP9LmdofI2nJIkSZIkqensASFJkiRJUgu5/PLLOffc760wbcstt+Kooz4x\nQi1qDAMISZIkSZJayJ577skOO7x2pJvRcF6CIUmSJEmSms4AQpIkSZIkNZ0BhCRJkiRJajoDCEmS\nJEmS1HQGEJIkSZIkqekMICRJkiRJUtMZQEiSJEmSpKYzgJAkSZIkSU1nACFJkiRJkprOAEKSJEmS\nJDWdAYQkSZIkSWo6AwhJkiRJktR0BhCSJEmSJKnpDCAkSZIkSVLTGUBIkiRJkqSmM4CQJEmSJElN\nZwAhSZIkSZKabuxIN6CVdHZ20tExd4Vp9903t4+lJUmSJEnSYBlAVOjomMtxp1zI+IlTeqbN77iH\njbZvG8FWSZIkSZK06jOAqDJ+4hTaJ0/r+XnxokeARSPXIEmSJEmSRgHHgJAkSZIkSU1nACFJkiRJ\nkprOAEKSJEmSJDWdAYQkSZIkSWo6AwhJkiRJktR0BhCSJEmSJKnpDCAkSZIkSVLTjR1ogYg4F3gj\n8O/M3KKc9jng3cDD5WKfzsxflPOOBg4BlgMfysxry+nbAucBawNXZeaR5fS1gAuAbYBHgX0zc26D\nnp8kSZIkSWoBg+kB8T1g16ppXcApmbl1+a87fHghsC/wwnKdsyKirVznbODQzJwNzI6I7pqHAo+W\n008FvjysZyRJkiRJklrOgAFEZt4ELKwxq63GtD2AizNzaWbeC/wD2C4iNgAmZOYt5XIXAG8pH78Z\nOL98fBnwmsE3X5IkSZIkrQqGMwbEERHxp4j4bkRMLKdNB+6vWOZ+YMMa0+eV0yn/7wDIzGXAYxEx\neRjtkiRJkiRJLWbAMSD6cDZwfPn4C8DXKC6lWCkmTVqHsWPXAGDhwvGDXm/y5PFMnTqhz/mNrNWf\noa5nLWutqrVasU3Wspa1rGWtxtZqxTZZy1rWspa1GltruHWGFEBk5r+7H0fEd4CflT/OA2ZULLoR\nRc+HeeXj6und62wMPBARY4H2zFzQ3/YXLnyy5/GCBUsG3e4FC5bw8MOL+53fqFp9mTp1wpDWs5a1\nVtVardgma1nLWtayVmNrtWKbrGUta1nLWo2tNdg6/YUUQ7oEoxzTodtbgT+Xj38K7BcR4yLiucBs\n4JbMfAh4PCK2KwelPBD4ScU6B5WP9wauH0qbJEmSJElS6xrMbTgvBnYCpkREB/BZYOeI2Iribhhz\ngMMAMvOuiLgEuAtYBhyemV1lqcMpbsP5LIrbcF5dTv8ucGFE3ENxG879GvTcJEmSJElSixgwgMjM\n/WtMPref5U8CTqox/VZgixrTnwL2GagdkiRJkiRp1TWcu2BIkiRJkiQNigGEJEmSJElqOgMISZIk\nSZLUdAYQkiRJkiSp6QwgJEmSJElS0xlASJIkSZKkpjOAkCRJkiRJTWcAIUmSJEmSms4AQpIkSZIk\nNZ0BhCRJkiRJajoDCEmSJEmS1HQGEJIkSZIkqekMICRJkiRJUtMZQEiSJEmSpKYzgJAkSZIkSU1n\nACFJkiRJkprOAEKSJEmSJDWdAYQkSZIkSWo6AwhJkiRJktR0BhCSJEmSJKnpDCAkSZIkSVLTGUBI\nkiRJkqSmM4CQJEmSJElNZwAhSZIkSZKazgBCkiRJkiQ1nQGEJEmSJElqOgMISZIkSZLUdAYQkiRJ\nkiSp6QwgJEmSJElS0xlASJIkSZKkpjOAkCRJkiRJTWcAIUmSJEmSms4AQpIkSZIkNZ0BhCRJkiRJ\narqxI92A0aqzs5OOjrm9pre3bz4CrZEkSZIkaWQZQDRJR8dcjjvlQsZPnNIzbcmiRzjzhPczadIG\nI9gySZIkSZJWPgOIJho/cQrtk6eNdDMkSZIkSRpxjgEhSZIkSZKazgBCkiRJkiQ1nQGEJEmSJElq\nOgMISZIkSZLUdAYQkiRJkiSp6QwgJEmSJElS03kbzpXo6eXLmDNnDgsWLFlh+owZMxk3btwItUqS\nJEmSpOYzgFiJnli8iNOvPIcJ67X3TFv86GMcd8CxzJo1ewRbJkmSJElScxlArGQT1mtn4vqTRroZ\nkiRJkiStVI4BIUmSJEmSms4AQpIkSZIkNZ0BhCRJkiRJajoDCEmSJEmS1HQGEJIkSZIkqekMICRJ\nkiRJUtMZQEiSJEmSpKYzgJAkSZIkSU1nACFJkiRJkprOAEKSJEmSJDWdAYQkSZIkSWo6AwhJkiRJ\nktR0BhCSJEmSJKnpDCAkSZIkSVLTGUBIkiRJkqSmGzvQAhFxLvBG4N+ZuUU5bTLwQ2AmcC+wT2Yu\nKucdDRwCLAc+lJnXltO3Bc4D1gauyswjy+lrARcA2wCPAvtm5tzGPUVJkiRJkjTSBtMD4nvArlXT\nPgVcl5nPB64vfyYiXgjsC7ywXOesiGgr1zkbODQzZwOzI6K75qHAo+X0U4EvD+P5SJIkSZKkFjRg\nAJGZNwELqya/GTi/fHw+8Jby8R7AxZm5NDPvBf4BbBcRGwATMvOWcrkLKtaprHUZ8JohPA9JkiRJ\nktTChjoGxPqZOb98PB9Yv3w8Hbi/Yrn7gQ1rTJ9XTqf8vwMgM5cBj5WXeEiSJEmSpFFiwDEgBpKZ\nXRHR1YjGDNakSeswduwaACxcOH7Q602ePJ6pUyf0Ob9Va/VnqOtZy1ors1Yrtsla1rKWtazV2Fqt\n2CZrWcta1rJWY2sNt85QA4j5ETEtMx8qL6/4dzl9HjCjYrmNKHo+zCsfV0/vXmdj4IGIGAu0Z+aC\n/ja+cOGTPY8XLFgy6EYvWLCEhx9e3O/8VqzVl6lTJwxpPWtZa2XWasU2Wcta1rKWtRpbqxXbZC1r\nWcta1mpsrcHW6S+kGOolGD8FDiofHwRcUTF9v4gYFxHPBWYDt2TmQ8DjEbFdOSjlgcBPatTam2JQ\nS0mSJEmSNIoM5jacFwM7AVMiogM4DvgScElEHEp5G06AzLwrIi4B7gKWAYdnZvflGYdT3IbzWRS3\n4by6nP5d4MKIuIfiNpz7NeapSZIkSZKkVjFgAJGZ+/cxa5c+lj8JOKnG9FuBLWpMf4oywJAkSZIk\nSaPTUC/BkCRJkiRJGjQDCEmSJEmS1HQGEJIkSZIkqekMICRJkiRJUtMZQEiSJEmSpKYzgJAkSZIk\nSU1nACFJkiRJkprOAEKSJEmSJDWdAYQkSZIkSWo6AwhJkiRJktR0BhCSJEmSJKnpDCAkSZIkSVLT\nGUBIkiRJkqSmM4CQJEmSJElNZwAhSZIkSZKazgBCkiRJkiQ1nQGEJEmSJElqOgMISZIkSZLUdAYQ\nkiRJkiSp6QwgJEmSJElS0xlASJIkSZKkpjOAkCRJkiRJTWcAIUmSJEmSms4AQpIkSZIkNd3YkW6A\nBtbZ2UlHx9xe09vbNx+B1kiSJEmSVD8DiFVAR8dcjjvlQsZPnNIzbcmiRzjzhPczadIGddUyzJAk\nSZIkjQQDiFXE+IlTaJ88bdh1GhlmSJIkSZI0WAYQq6FGhRmSJEmSJA2WAcQq6unly5gzZw4LFixZ\nYfqMGTMZN27cCLVKkiRJkqTaDCBWUU8sXsTpV57DhPXae6YtfvQxjjvgWGbNmj2CLZMkSZIkqTcD\niFXYhPXambj+pJFuhiRJkiRJAxoz0g2QJEmSJEmjnwGEJEmSJElqOgMISZIkSZLUdAYQkiRJkiSp\n6QwgJEkW5hN8AAAgAElEQVSSJElS0xlASJIkSZKkpjOAkCRJkiRJTWcAIUmSJEmSms4AQpIkSZIk\nNZ0BhCRJkiRJajoDCEmSJEmS1HRjR7oBGnlPL1/GnDlzWLBgyQrTZ8yYybhx40aoVZIkSZKk0cQA\nQjyxeBGnX3kOE9Zr75m2+NHHOO6AY5k1a/YItkySJEmSNFoYQAiACeu1M3H9SSPdDEmSJEnSKOUY\nEJIkSZIkqensAaGW1dnZyd13373C2BSOSyFJkiRJqyYDCLWsjo65HH/RCT1jUzguhSRJkiStugwg\n1NIcm0KSJEmSRgcDCA1ZZ2cnHR1ze01vb998BFojSZIkSWplBhAaso6OuRx3yoWMnzilZ9qSRY9w\n5gnvZ9KkDUawZZIkSZKkVmMAoWEZP3EK7ZOnjXQzJEmSJEktzttwSpIkSZKkpjOAkCRJkiRJTWcA\nIUmSJEmSms4AQpIkSZIkNZ0BhCRJkiRJajoDCEmSJEmS1HQGEJIkSZIkqenGjnQDNLo8vXwZc+bM\nYcGCJStMnzFjJuPGjRuhVkmSJEmSRtqwAoiIuBd4HFgOLM3Ml0XEZOCHwEzgXmCfzFxULn80cEi5\n/Icy89py+rbAecDawFWZeeRw2qWR88TiRZx+5TlMWK+9Z9riRx/juAOOZdas2SPYMkmSJEnSSBru\nJRhdwM6ZuXVmvqyc9ingusx8PnB9+TMR8UJgX+CFwK7AWRHRVq5zNnBoZs4GZkfErsNsl0bQhPXa\nmbj+pJ5/lWGEJEmSJGn11IgxINqqfn4zcH75+HzgLeXjPYCLM3NpZt4L/APYLiI2ACZk5i3lchdU\nrCNJkiRJkkaBRvSA+GVE/DEi3lNOWz8z55eP5wPrl4+nA/dXrHs/sGGN6fPK6ZIkSZIkaZQYbgDx\niszcGtgN+EBE7FA5MzO7KEIKSZIkSZK0GhvWIJSZ+WD5/8MR8WPgZcD8iJiWmQ+Vl1f8u1x8HjCj\nYvWNKHo+zCsfV06f1992J01ah7Fj1wBg4cLxg27v5MnjmTp1Qp/zrTVytQZbfyh1Kg1nXWuNfB1r\nWcta1rJW69ZqxTZZy1rWspa1GltruHWGHEBExDrAGpm5OCKeDbwO+DzwU+Ag4Mvl/1eUq/wUuCgi\nTqG4xGI2cEtmdkXE4xGxHXALcCBwRn/bXrjwyZ7H1bd77M+CBUt4+OHF/c631sjUGmz9odTpNnXq\nhCGva62Rr2Mta1nLWtZq3Vqt2CZrWcta1rJWY2sNtk5/IcVwLsFYH7gpIu4Afg/8vLyt5peA10bE\n3cCry5/JzLuAS4C7gF8Ah5eXaAAcDnwHuAf4R2ZePYx2SZIkSZKkFjPkHhCZOQfYqsb0BcAufaxz\nEnBSjem3AlsMtS1a9XV2dtLRMXeFaffdN7ePpSVJkiRJq5phjQEhNUpHx1yOO+VCxk+c0jNtfsc9\nbLR99V1eJUmSJEmrIgMItYzxE6fQPnlaz8+LFz0CLBq5BkmSJEmSGma4t+GUJEmSJEkakAGEJEmS\nJElqOgMISZIkSZLUdAYQkiRJkiSp6QwgJEmSJElS0xlASJIkSZKkpjOAkCRJkiRJTTd2pBsgNVpn\nZycdHXN7TW9v33wEWiNJkiRJAgMIjUIdHXM57pQLGT9xSs+0JYse4cwT3s+kSRvUVcswQ5IkSZIa\nwwBCo9L4iVNonzxt2HUaGWZIkiRJ0urMAEIaQKPCDEmSJElanRlASCOos7OTu+++mwULlvRMmzFj\nJuPGjRvBVkmSJElS4xlASCOoo2Mux190AhPWawdg8aOPcdwBxzJr1uwRbpkkSZIkNZYBhFYLTy9f\nxpw5cxrS06CRtQAmrNfOxPUnDWldSZIkSVpVGEBotfDE4kWcfuU5Delp0MhakiRJkrS6MIDQaqOR\nPQ3stSBJkiRJ9Rkz0g2QJEmSJEmjnz0gpJWks7OTjo65K0y77765fSwtSZIkSaOLAYS0knR0zOW4\nUy5k/MQpPdPmd9zDRtu3jWCrJEmSJGnlMICQVqLxE6fQPnlaz8+LFz0CLKq7Tq3eFADt7ZsPp3mS\nJEmS1DQGENIqqFZviiWLHuHME97PpEkbDLt+Z2cnd999d8NuNSpJkiRJBhDSKqq6N8XTy5cxZ86c\nukODvsamOO/mC73VqCRJkqSGMYCQRoknFi/i9CvPqTs06HtsCm81KkmSJKlxDCCkUWTCekMLDRo1\nNoUkSZIk9WXMSDdAkiRJkiSNfvaAkNQ0tQazBAe0lCRJklZHBhCSGqZ6QMvqwSzBAS0lSZKk1ZUB\nhKSGqR7Q0sEsJUmSJHUzgJDUUJUDWjqYpSRJkqRuBhCSWlL15RwA7e2bN6x2K45N0ch2tepzlCRJ\n0urLAEJSS6q+nGPJokc484T3M2nSBnXXauTYFNW1li5dyvz5z2bJks4VlhvKgX5Hx1yOv+iEhoyZ\n0chajWQwIkmStPoygJDUsiov5xiORo5NUatW++zHG3agP2G9xo2Z0chajdKqwYgkSZKazwBC0mqh\nkWNTVNeasF5byx3ot7JWDEYkSZLUfGNGugGSJEmSJGn0sweEJKlfrTpuQ6u2S5IkSbUZQEjSCKk1\nOGYj6gynVi2tOm5Dq7ZLkiRJtRlASFolPL18GXPmzBlVZ7trD47ZNuw6w6nVV5jRquM2tGq7JEmS\n1JsBhKRVwhOLF3H6leeMurPdjRocs/qOIUOt1cgwQ5IkSapkACFpleHZ7pWjUWFGsy8NkSRJ0qrF\nAEKS1BT2ppAkSVIlAwhJUtOsKr0pvKOGJElS8xlASJJaXrN7U7TqHTUMRiRJ0mhiACFJWiU0uzdF\nK44x0qrBiCRJ0lAYQEiSVivN7k3R6F4LjQpG7E0hSZJGmgGEJGm10+zeFOfdfGHL9VqwN4UkSRpp\nBhCSJA1R370pWu9yDvBWtpIkaWQZQEiSNAyryp0+JEmSRpoBhCRJLaDZY1M0kuNJSJKkoTCAkCSp\nRawqvSkaOZ6EYYYkSasPAwhJkkaZldGbolHjSTQ7zDDIkCSpdRhASJI0Cq0qvSmgeWGGd/mQJKm1\nGEBIkqQ+NbI3RSPDjL5qNSrMaGRvilatJUnSymYAIUmS+tWo3hSNDDNWRjBy3s0X1t2bopG1amlk\nL4/RHmY4vogktR4DCEmStNI0KsxoZK2+w4z6e1M0slZfWu2SlUYe6DeyViPHF5EkNYYBhCRJWu21\nYjCyqlyy0sgD/UaHBo0KaxrJnhmSVmcGEJIkSS1oVblkZThBRiNrNVOr9swwzJC0qjGAkCRJalGt\n2DOjOswYzi1eG1mrVpjRKK3aM8PLTCStagwgJEmSVJfKMGM4oUgjazU7zGhkL49GauadX2B09aZY\nHZ6j1OoMICRJkjQqtGKY0ahajRwTpJZWvTTEy19GzurwHLXytUwAERG7AqcBawDfycwvj3CTJEmS\ntJpqtV4eK+PWs0PpTTGYW8/C4A70V0atVrz8pVUP9L3ER83QEgFERKwBnAnsAswD/hARP83Mv41s\nyyRJkqTW0PxbzzZygNORvY3t6hDY9FW/kWFGK17iU6vWUJ9fI2tpcFoigABeBvwjM+8FiIj/BfYA\nDCAkSZKkBmvFAU5btVarBjYro8fIUKzsXjGDDWuaXWvp0qXMn/9slizp7JlmmNFbqwQQGwIdFT/f\nD2w3Qm2RJEmSpB6rVjAysj1GVr1eMY2r1T778RENM5pZq1adwdaq1CoBRFc9C2+77eY9j5cuXcqi\nx5cwZswavH7/jwHw5OKFrPno4z3L/PzUS+l6uosbv3kNa665Zs/0W2/9S6/aSxY9wjUXn9zz8/Jl\nSxlzdRdtY9rY/ai3AcWbqa/2VLZrix32XWFad7t+fuqlPdMq21WrPQBvfevuPc+xsl2vnva6FZbr\nblet9lQ+3yWLHllh+k0/+3bPc6xs11GvP6Jmne76la89wOv3/1iv1x7gZ1+7pNdrX9mebt3t6n79\nK197gJ3eueLzrW5Pt+527fb2T/ZMq2xXPe8HgF/84Mu9XvsxV3fxpo/uAwzu/QBwySU/7vXaP7l4\nIb/52qU9z3Ew74dtt92812sPsNUr91jhtR/s+wFWfE9cc/HJvV77wbwfoPfncTjvh+529fV+2P2o\nt/V67ft6vtWfx+r3Q/dzrGzXYD+P1e8HWPE9Uc/nsfr9UNmuO+/MmnUG+37obtdb37p7r9e++vkO\n9H6AwX0e/X5+xmj/fh7s53Ew38/dz3Ewn0e/n4f+/dz9HCvb5ffzis+3Ed/P3c+xsl1+P/duT2W7\n/H4u1PN5hBW/n2//TWO+n+GZ16fez+NNV36312t/+2/q/zxecsmPa07/2dcuadj3c2U76vl+rtTz\nefzNit/Pxx1wbJ/t6bZ06VIWLnqctjFjeMVu7wLg0fn3MX7jJTx74ngAbjj/GiZNmDTg+6GjYy4f\n+dxp3H7TFT3Tli9fxpixRbt2OvB1PLFoCV894isrBCN9fR6f++JdWGf8xF5t+vWF1/Y8x8p29fX6\nVGrr6qrr2L8pImJ74HOZuWv589HA0w5EKUmSJEnS6NAqPSD+CMyOiE2AB4B9gf1HtEWSJEmSJKlh\nxox0AwAycxnwQeAa4C7gh94BQ5IkSZKk0aMlLsGQJEmSJEmjW0v0gJAkSZIkSaObAYQkSZIkSWo6\nAwhJkiRJktR0rXIXDA1RRGyXmb8f6Xa0koiYlpkPjXQ71BoiYuP+5mfmfSurLSMhItbMzKUj3Q4J\nICLWyMzlI92OZoqIbcqHbUCvgbYy87aV26LWFRHtmflYH/M2Hu3fz60kIsYBLwLmZea/R7o9GpyI\n2Cwz/14+Xjsz/1sxb/vM/N3Ita71RMTk/uZn5oKV1ZbV2SofQPT3ByoidsjMmxqxDWDfzPzqcGuV\n9fbKzMsaUQv4ETBjuEUi4lnA7pl56RDXHw+QmUuGuH4jvyT/FBF/Bi4GLsvMRUMtFBGRmdnHvFdk\n5m+GWns4IuKjwGOZ+Z2q6YcCEzLztDpr9eUp4B/AtZn59CBqbQF8nGInBuAvwNcy887Btqei1mbA\ne4HNykl3Ad/u6/fRj6uocRAATC3/rVFHm77ez+yuzPxQnW3razvDCg0iog14DcXtjHcH1q9j3c/2\nMasLIDOPH2q7qrbz0sz8Qx3L71W2oa3i/562ZeblDWhT3d+DEbFnI7a9MkXERpl5fx3Lj8vMzj7m\nPTcz59Sx+dsi4v2Z+ds61umrXQf1Mav7vXrBcLcxRH+k+O57tI/5r2rERurdl4iIXSn+PlxaNX1v\nir8n1zWiXXW6Adi6bMf1mfmaink/6Z43GAP8LevKzFOG1MIVt9EG7JOZP2xArYbuWw5h++cAX8/M\nv0REO/A7YBmwXkR8LDMvGkbtKcCOwNzMvHWINbak+PvfBfwtM/8yjLYcwIr7EhdnZl+fz/5qvQv4\nUFWtr2fm+XXWOSkzP13v9vtwMc98Tn4LbFMx72zq+wz9uZ/ZXZm5Zf3Nazm38cw+4XTggYp5XcCm\nK71FDRYRr87MX5WPV/gbXc8+S0Rcm5mva0YbV/kAArih/BI9ufusSkRMA04GXgBsO5SiEfEc4G0U\nO/DTgR83prkAnAY0KoAYsohYA9iV4jm+FrgZqCuAiIjDgU8B48uflwBfzsxv1NmcsyPiFuCTwwkM\nShsCuwD7ASdFxO8ovqB/kpn/qbPW3yLi+8DhNcKVM6nji72WiHgexeu/X2a+aKDlK7wd2L7G9AuB\nWyneY4M1gdoH6AATgVcDh1J8HvoUEXtQfO6+CHytnLwtcFlEfDwzrxhsgyLi5cDlwLeAcyguF9ua\n4vO+Z2b+32BrZebmVbU3oXjP7gKcONg6pVvpffDbbVi3FBpOaFBR4+Xl+m8BJlPc3vjjdZZ5gt7P\n5dkU74EpwJADiIh4Udm+/YDHqO/7+U0V7Xoz8NOq+UMKARrwPfiZoW67RlsauvMXEdtS7EzdlZl/\njYgZFO3dFei3Z1CVn0TEWzLzqar6L6b4Pcyso9Z7ga9HxJ+AT2TmwjrWrfZSer9X2yjeKxsBDQkg\nIuJlmXlLHat8hOL78kngh8CPM3NxI9pSpd59ieMovhuq/Rr4GTCoAGKAXgsvycw/1tGmSv2emRyE\nvv6W1eyJ0p/ypMphwCyKMOmbwB4UfzP+QfF7rdtw9y0bfECwQ2YeVj4+GMjMfEu5D301MOgAIiKu\npNh/+0tEbADcDvwBmBUR387MU+uo1U4RPm0M/Ini97dFRNwH7JGZj9dR6wXAr4BrKQ48xwAvAz5d\nHqT9vY5aBwFHUny+by/btTXw1YjoqjPw3A1oVABRqda+ST3e1M+8ej9D36R4T9T8rqizVsPe95m5\nSUXd2zNzyPvxEfEJijCrY7jtavDf/6/xzPHJ5ax4rFLPPsvUOrZZl9EQQGwLfAm4IyI+DGwBHAV8\nFXhnPYUiYl1gT4o/DM8DrgCem5kbNrTFI6g8yNmJ4jm+Afg9sAPF83yyzlrHAv8D7JyZ/yqnbQqc\nERGTM/MLdZR7CXAE8IeI+MJwzlxl5jKKP55XR8RaFF/0+wKnRcSvMvOAOsr9FbgfuD0i3lnPgW9f\nImLDsj37U7xfv0RxQFaPsbXOSGZmZ/k7HrTM/NxAy0TEYHowfAF4bWbeWzHtTxHxK4qDlEEHEMBn\ngf0z84aKaT+OiOspdqJ3q6MWABHxfIo/+NtTfDkfUW8vg8w8r97tDqJdww4NIuKLwF7Av4BLgM8B\ntw6lvZl5ckXddSnO9hwM/C/PBEv1tO25FO/v/YFOYBPgJVXvk8G0610VNW/PzIPrbUvF+g37Hmyw\ncynOYC2geK1giDuUEXECxXviDuBLEXEFxd+30yl+p/W4FbgqIt7U/fpExM7A9yneG4OWmb+PiO2B\n9wG3RkRlD6W6ehFl5ge7H0fEGIqznJ+kOJNbV7hYrv9WygPOzLwqIl4CnAQ8B9iqjnadRvH3ZhbF\nd/31ETEXODEz76inXQ22Vq2u9Zn5cEQ8u44610fE66q7KkfE6yjewxsNs51DMpi/ZXW4AHgc+D/g\ndcC7gP8CB9T7O2zwvmUjDwgqA8XXUQavmflQRNRba5OKHgoHU/SafGdETKD4Tht0AAGcQNGL6NXd\nPS/LoPiLFJ/rI+qsdWRmXlI5sexRdyLFd+RgHQ7sWdXj61dlrR9SX+C5RvRzKcBIXQbQ19/l7p4/\nwNw6yv2T4jv+s5n5g2E2rWkHwsM0Hfht+f1+EXBpZj48xFodFO/xDvo+0bWytUfEnvRx0m04vT9X\n+QCiPHtyWBk+XEfRleblQ0yj5pc1Ptt9OUD5wo+oiPhZP7PXq7NcB0WXsXOBj2TmExExZ4g73e8E\nXlzZqyAz/xURbwPupDggHZSy98ppEXEdxYf5LFbcKV13CO0jM5+KiLuAv1GEHC+os8SyzPx0RFwN\nfD8iLgC+MJjLEapFxGEUOyDPobh05hDgp0PcaWqLGmNdRMT61J9SD9jlfpDJ69haf7wy896IWLOe\nNgGbVoUP3bV+HRHfqqdQFJeFHENxWchXgENziNegl5/FPntAZOab66jVsNAAeDfFQeLZwC/KIGoI\nZXrath5FkPt2ip2qbYZypjoi/g8YR7Fj+5by+2FOveFDEzTyezD6OXNR71mLjSh21F8A/JmiN8Zv\ngd8OYYd0T2DrzPxvuaPbAbxoKK99Zh5bBs7XRMRuFAcrp1H8Todytnsyxffxvynet08zhLPUUFyu\nBBwEfIwiSNo7s+7LtKDobfVc4Bbg2CguZ9sMOKae3luVMvOfEfETYB3gHUBQBEIjZULUuLyrfA3X\nrqPOOcD/i4jXdgcaEXEARVjzhjrbNDUiPkLx+698DHUedMSKl8jVukyrnuDted2f3Yj4DvAgMHMI\nvSihsfuWjTwgeCwi3gTMoziZdGjZtnrfDwCV76ldgG8DZObiiKh3f2kXYMvK/azMXB4Rx1B8L9Zj\ni8zsFTJk5mXl3+B6TMgal5uV+zgT6qy1GcV3Xy31XgawUUScQfGe2LDiMRQ9ggetkT1/MvOrEXER\ncGpEHEKxf1K5X1/Pe7VpB8LDkZkfLr+zdqQ40fKZ8oTdRcDldfZ8u5ZiH3U6xet8cWbe3ug216md\n/nvFrL4BRERMojiDvD3FWdHdgF9ExJGZeX2d5Y6mOEA8KyIuoc7LEara1d+XZL1dq/s763hyP/Nq\n+RFF9+V9YcBwYyBP1/pjnJn/iYi6D/DKHb6jKQ4WzxrKQX5FrY0pvgz2o7g85GLgTfV0t6uUmTdG\n0Z35m8BNEfGOIZQ5k6JnxpGZ+aeynUNpDhQ9fK6M4prX7j9iLymn13uWulFd7pdGxMzMXCEhj4iZ\nrLhzMhj9jSVS70HiHRS9WH5O0fXyZRWve707pduXtS6mONiBZ/4g1nvw1MjQYAOKywf2B86MiBuA\nZ9U62BhIRJxMcSb4WxQ7gcPpOj4f2JziO+85FGFLK2jk9+Aciktmhn22IjM/WrZnLYrP88spgspv\nR8SizKwnQH0qy8HIMnNBRNwznOAnM0+IiP9QdGMGeE1m3lNvnYh4H0UPn5MpwsAhX7oUER+k6M1x\nPbBbrYODOmxPedATEWsDDwGzcmjXic+i+NuzB3Afxc7kiUM5eG3wvsTlwLci4ogsLyksD5xOp44d\nycz8dkT8l+Ls72spPkfvo+gNeW+dbfoOxaUT1Y/bKA9i61B5idznKXrLDfX7uWcfpjz4nTfE8AEa\nuG9JYw8IDgPOAKYBH87MB8vprwGurLNd90fEERRhxtYU+zpExDrUf6zRWevvVmYujYinaq3QjyeG\nOK+W/w5xXi1/zWF0/a/ycZ5531eHGvUGxA3r+QOQmfOiuDznRIr3beV+fV0BBA1635f7zd2vV3Xo\nWfdYMeWxyg0Ulwh/gCJA+xLFvt06ddTp7jm3CcXfj3PLz89FFGHE3XU0a9OI+CnF83pu1T7Oc+uo\n89Bwepv2Z5UPIHhmB/4DWXS9vyYitqIYU+Ddmbn/YAtVdZvcj6Kb3AYR8UmKazjr+eX390Gp15zq\ng7qhqkjrdqb4g3gyMDEi9gWuzPoGkXwgInbJzF9WToyI11CcLRi0iPgtRdeuV1af1a9XWWsjirPK\n78khDoBULYuxKfaL4jrAm4Bn1VliA4prP8+I4jrQHwH19gzobssFEfEwRTjQPXbEX4HPZOYv6qzV\nqC73nwV+GREnsmIocjRFt+h6zKhK8SvV22310PL/7h3QFc6K1Vmr8kB/f4qdtIsz86911qmuNazQ\noPzu+wVF+Lo2xQHxOhQ7hddnfZcdfYSi+/+xFGeCK+fV1Rspi+uJJ1KcjT8+ijFPJsUQ7t5T/Qe0\n6ue6ep80+Huws1HfzxWeBaxLsdPVTtGzr96BXDeteo02qfi53t46lXWmAvcAp5TvjbpqUXTjfXnW\nuBQgInbPzJ/XUesMil4UrwReWeO9Wk/vk6XdoXfZa2TOUMKH0j0UZ2qvoNiZ3xh4fxTdmOvdwa3e\nl+jecd6YYiybehxL0SX93iiup4diEOtzy3mDlpkXlgeDd1D87d4hh9D1eIg9APuqdV734/IkVF0D\nA1bZMiIqw9dnVfxc7/dgI/ct72vgAcGTmfn66omZeXXU32vxUIr9kV0oBtbs7jG3HfC9OmutFcWd\nZCoHHO7+f606a1UfYK4wr85aL+gnEJxVZ62GGWKvyb40rOdPRGwOnFXWeGlFwDUUjXzfV44VUx16\nDicQ35Li870P8AjFvm/dyhD3SxSXTm5N8fk5jjoGTKcIv7tV78fXc+K65uDTjTAaAoins2oE4cy8\nIyL+B3hPPYUiYjawfmbeTJHWnRhF9+0zKLoWDvqXP5wzTTVcwTOjRF9WqztZPcqdrF9RnL1YE3g9\nxU74NyjOeA/WERSDk91MccDZRjEmxytZ8c0/GMdVBxnD8CngpuGcWavwneoJmXl+RMyhSIbrcTxw\nUWaeHcVgcPsC8yPi7xRdteoakKgMGuoKG/oSDehyn5lXlK/Lx3jmGs27gLd19/ioQ2WiX62uRL+R\nf5yrDvTXovjc/DoiPpeZZ9ZZ7gjgNxQ7bmMoDjSGFBpEcfeG91FcX3wncG5m/qgMlGoNOtefPzXw\nzEx3cHcuRZq/PsUf51MjYkZm1nMHn69RvCfWoeiqCEWX0CGN2VD1PTiOoX8Pzq4+gwI8DNxc7xn5\niP/f3v0Hy1WXdxx/38AgUMeKlGoLE0PVPCWatoBiASs/KtAWZhLEClRFE2sq1DEmYylS2vKjiFUp\nMI5QqpS0DGTaDDZidSwjMChgaUXEGOjT2pKqKBZQoIMO0XL7x/Nd7rmb3c357j7r3dz7ec3cgbu7\n53tOvnvP+T7nOd8f9jFgGfC/xFCAu4C/qD0XixXM1NdLiTobtr4y635/oofVLKWL7nlET6W2Mmcr\n/8Wum4uXNH6vTWZ0eo1NUyZoLqoD3GYsUW7KTicS2duon8z6EKK3w4XE38RRRE+gvYggvNUwn656\n2psYCnprIyHVuq7sJ7TqTi13rwn225b5n8yOLU8n2pOam9el1mMFLjN7DfCdso+2Pmdmv9F9nWqc\ni617hrn7d4keFd2v3wbcVnFMEL2P+j38qL2Jbd5gNg3Tw6Z2CO8gV2QVZIlDQ8nt+XMPMeHhZbUP\nVXp4jpm9ptyfjSQz6Wkxt9hpRDz/DNE79ngv8+INWebuxFC204jeSLcRD/ha88YQZjPbr7w2zNwU\ny4fYppX5kIDo2T243HxWjRUnxrTOyli5+xYzW0skIFqzWA2iX6Ax9JwGjBhwmdlK4IDGzdKdzGSB\n11cWt514Ur6UCJoBPk9c1GsvWEeWpFH3BXSYIOQY4GjbcTLG6rK8sZpHI/h7I9Htujb4+3dituTm\n+K4PNy5grXUFbc2Gp/rfaIld7kui4S3Dbt8oZ8OoZXQkN86UHgYnEt/ZEiKQGGaVnAOIa85BRNLg\nLmAD8B7ql+n7G+J8vINouJYRQ32eJGklgAwlSP0IsQpCzcoJENeqi4khCZ2nt4uJpwMjzSbuMaHr\np2gKOaEAAAxkSURBVIBPmVntU4sPs2OAu4ToPXK+u2+sKGsx8YTvP4iuzA8Bw64KlFlfmWWtA242\nsxM7T35Lnb+JGEfbWnKiv/vmYuieBskBrhHtzqlEYmsTsMjdjx6iuKuJoTM/KD2TziUmvT2YuP6/\noWU5mb0yxrbqzigaSd2XEL1ZrikJ6BTuvqWUW3v+3E3vuPdJoj2p6X2bdi4mt7NnA9/sPDEvvU5P\nIXranF9zXMk9bLZllQWcYoPnNKipr8yhoWk9f4hhxyuB95Wk5Z3lZ5g5jTayY/w81PwIyUnPB4gE\nwek+xHLzXcd1PBFXnkg8gNgIrKnskdkpa4pIWryL8vDcYmj8R9z9goqiHh7Qg2ikpY3nQwJiUPeq\n2sp5Ya8/IHf/qsWYnNbc/bk7/9ScOJvZN7t7EN3kf4q4+am5WbkcOMfdr2m+WLoh1TaEmUFIWll9\ngr+pYYI/38n4rsriMusrpct9ZgCSHMykNc5mdh0x5OUzwIUliByK9x7vv6r893HqzsWD3H15Ke/j\nxPJnw0q7pu7seySevrb1IeJp8oGdJFnp4XEpkQRYW1HWIGcRM1G30i/AtZj48RYqzm13P8FiNYaX\nE38H64nl5x4D/tnd/6RtWeTWV1pZHqtLPE30IlpBzIVyGNGFv6qnR2aiP7OnwRgC3H8ETnD3b5Ty\nax8WdCxqBP6nAle7+43EUsmte6ll1pUnrrrT9ffQvHGC+punnkndmuPpc1zdao/reQPi1Jqx3ann\nIrk3wVcTT34xs9cS3dE7ibKraZ8oSz0Xk7/HzPpKGxqa2fOnT4wz1JxGHqvqXdQvfva6YUyZ8fPl\nxASut1tMPnknw08cfQ7x9/DeIbbttg44khj68iA8u0rhX5rZ+oo4bjd69yAa2XxIQGRWzvMHvFc7\nI3CmZkZy1EZ1j04QU9zhMc71MatbhgsiYbPDDdiQDWFaEJJZFrnBX+f4tjHi+K7k+lpU8/kBMhvU\niWyciSdDTxGB6NphkzVdMsb7P/tkzt1/bCOsgEHuNTXzezwJWOqzZ0Z/0mJSQycvAZHCY+LHYbZ7\nBthiZo8DTxBPNk8ixlLXJCAy6yu17t39FjNbBdxOBGzHepkws7KctER/ck+DzAC3s3zj5y1WY9rE\n8BOe7mYzc8y8DljTeK91PJhcV2mr7iQ/+ElL6iYfV2qcmnUuktvOpiTKirRzMfl7zEwaZA4NHYeM\nGAeYyPg5M8lybM2+d+IM4DhvDLvwWIXsTcSKPG0TEA9X9phobT4kIDIr50tmtsbdZw3dMLN30H+5\nnLHLzEgC+3SV/a7Gr7UT8qQ2hFlBSHJZmcFf59hGHt9VykmrryTjmqBxYhrnxGRN9nj/zG6TmdfU\nzL+JZ7zHyjge41SHXjFnXMzsGKD2if5a4mnK4URS6S7ipuAaYkm0Gpn1lVZW11PEPYlr4CM2M3/A\nsMMTR5WWbE4OcDcDmy2Wx1tBXPP3M7OriAkMbx5YwGwbiWvfo8T8HV8ox/gy6ob6pNWV5a66kykz\nqZspLU7NPBeTb4JTEmXluDIfSKXJThpY3tDQNMkxTqfMSY2f05IsSXb3HnM+uPsjpQ7n3EQcxAR5\nD/APJUPUuZAfSozJPXnOjirX3X0ar3cy84SyrcyGMC0IySwrM/iz3PFdExe0Jd/oz/vGmcTx/slJ\nyjTJ3+MDZvZW75rZ3szeAlQtr7uTrrStl80qZfUahrMPMVnaGTVlEX+bfw+sc/dvV27bLa2+MstK\nfoqYKTXZnB3glnbieuB6i+E9byC67LZug9z9YjO7lVh28eZGUmmKmYmD28isq7RVd5JlJnUzpcWp\n2ediYjublSjrHNekPawB8urLEoeGJkuLcSY1fh5HkiXJoEk/ayYEfd2oB9LP1PR0xkIBc8fM9vXh\nl8rqVd4UMQHcK4gAdau735pV/lyzmIV+M/A0M+u5H0Jkv1d6xRKYZvYi4mK5nR4NoVcsuVOeom2n\n94lR1dhnltWn/E7wd1pNl6kS+G0EbvQRx3eN+984rB4N6k3EigwPzVVZXY3z301Q44zNHu9/BDHj\n8DDj/TOPKfuamvU9HkCs9f1DZl9v9iauN9/KOubK41rS9dI08NgwgVGmzPqa1Lofh0ay+XQiFvhb\n6pPNzQD3yklIEI9DRl1JvUmMU7PbWTM7nJlE2VPltaXAc939ywM3nl3ORJ6LmfVV4sGn+rw9l8my\ntBhnUuNnM/snYhWgrwFfLD9bPGcVvqFZTDjZb5Wqvdx9zjsg7PIJCKlXGq9jiYvCSI3XJDaEMjeS\nG9QF0Th3WCzLegQxadBJwL7u/tNze1SjG0NQ2n3tut/dbxn5QOepzPpaiHU/QrJ5IhPE4zRsXcn8\nMKnt7KSei5NaX+MyX2McmMwHSbsCJSBEJEVmg7o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"text": [ "<matplotlib.figure.Figure at 0x7f67ac9784d0>" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Big states with big airports appear to be in the top five. But we haven't accounted for how many total flights these states service. We should plot the percentage of flights that are delayed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pct_departure_delay = departure_delay_counts / df.ORIGIN_STATE_ABR.value_counts()\n", "pct_arrival_delay = arrival_delay_counts / df.DEST_STATE_ABR.value_counts()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ranking states of origin by their percentage of departures tells a different story than the plot above. For example, here we see Illinois and Arkansas at the top of the list whereas IL was third in total departure delay counts and AR was ranked 25th or so. California, which is #2 in the number of total departure delays is only #17 in percentage of departures delayed. Not bad." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pct_departure_delay.order(ascending=False).plot(kind='bar', title='% flights with departure delays by origin state')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f67abb67e90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SRobjNUgzj4kFSZIkSSNlWOM12PpBGg4TC5IkSZJ2SrZ+kIbDxIIkSZKknZZP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\n/igDnx0J/D4ifkHprjbogEzD3K/PAO4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"text": [ "<matplotlib.figure.Figure at 0x7f67b205be50>" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, when we look at destination states ranked by percentage of arrivals delayed, we see some new states at the head of the list. For instance, Delaware, second to last in the total number of delays overall, has the highest percentage of arrival delays for inbound flights. Iowa and Kansas are also new entries near the top." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pct_arrival_delay.order(ascending=False).plot(kind='bar', color=colors[1], title='% flights with arrival delay by destination state')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f67ad3fec10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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WyvOfT590jSVJO7TqKQ7Lprsemh6Z+dw+xbmRMj6GJEmaYp0SC92MYHwq5Zngo9WjjBpb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\n9LI/dtoemuveXJ/GenXaHqC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"text": [ "<matplotlib.figure.Figure at 0x7f67abf97d50>" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a sense of the difference between the two percentages for each state by plotting them on the same axes. In the plot below, we find that most states see more arrival delays than departure delays. The disparity seems greatest for smaller, less populated states that don't have huge airports (e.g., DE, IA, RI). We can't say for sure without studying more data or perhaps correlating the disparity with the state's ranking in terms of the total number of flights it serviced. We'll leave that as an exercise for the future." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pct_delay_df = pandas.DataFrame([pct_departure_delay, pct_arrival_delay], index=['PCT_DEP_DEL15', 'PCT_ARR_DEL15']).T" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "pct_delay_df.sort('PCT_ARR_DEL15', ascending=False).plot(kind='bar', title='Overlapping % delay plots for comparison')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f67a96854d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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XFew/GrgIeC7375yI+Hlu32LgKWALsCki5tcy+XritApJkiRJ0ng0YGEhpTQB\nuAo4GFgO3J9SujUiHsprdldE/DDX/qXAzcBuuX09wIERsbbmmdchp1VIkiRJksabciMW5gOLImIx\nQErpBuBooK+wEBFP57VvAVYXxGgaepoaad3d3XR2Lum3rbV1z1HKRpIkSZJUr8oVFmYBnXmPlwH7\nFjZKKb0BuBTYCTgkb1cPcFdKaQuwICK+PrR0NVI6O5dwweevo2XaDAA2rlvNVRe/m7a2nUY5M0mS\nJElSPSlXWOipJEhE3ALcklJ6FXAdkHK79o+IlSmlduBnKaWHI+LeUnHa2iYzceKEAY/V1dWy1bbp\n01tob59aSaoNEavXUPoONU5XVwst02bQOn3msORkLGMZy1jGqm2seszJWMYylrGMVb+x6jEnYzVu\nrHKFheVAR97jDrJRC0VFxL0ppYkppe0jYk1ErMxtX5VSuplsakXJwkJX1zNlE86/60L+tlWrNpTt\n2yixIPumVtu3FnGKnQ9Qk5ygdudnLGMZy1jGqs+cjGUsYxnLWPUbqx5zMlZjxCpVfNimTL8HgHkp\npTkppWbgWODW/AYppbkppabc138HEBFrUkqTU0pTc9unkE2R+GPZTCVJkiRJUsMYcMRCRGxOKZ0J\n3EF2u8lrIuKhlNIZuf0LgDcDJ6eUNgEbgbflus8Ebkop9R7n2xFx5/CchiRJkiRJGg3lpkIQEbcD\ntxdsW5D39WeAzxTp9xjwihrkKEmSJEmS6lTZwoJGXnd3NwsXLuy3zkFHx2yam5tHMStJkiRJkrZm\nYaEOdXYu4aLrL2bq9q0AbFiznguOP5+5c+eNcmaSJEmSJPVnYaFOTd2+lWk7to12GpIkSZIkDcjC\nwhhWbEoFOK1CkiRJklQ7FhbGsMIpFeC0CkmSJElSbVlYGONqNaXiuS2befzxxx39IEmSJEnqx8KC\nKvL0hnV88ScLHP0gSZIkSerHwoIq5oKSkiRJkqRCFhY04lxUUpIkSZLGDgsLGnG1XFTSIoUkSZIk\njS4LCxoVtZpWYZFCkiRJkkaXhQU1PIsUkiRJkjR6LCxIeeqxSCFJkiRJ9czCgjRMalWkcPSDJEmS\npHpmYUGqc45+kCRJklTPLCxIDcDRD5IkSZLqlYUFaRxx9IMkSZKkWrOwII0ztRr9IEmSJElgYUFS\nlZxWIUmSJAksLIy67u5uOjuX9Nu2dOmSEq2l+uG0CkmSJElgYWHUdXYu4YLPX0fLtBl9257sfIRd\n9msaxawv6+QaAAAgAElEQVSkyjitYvwqVhQFaG3dcxSykSRJ0miysFAHWqbNoHX6zL7HG9atBtaN\nXkKSVEaxoujGdau56uJ309a20yhmJkmSpJFWtrCQUjoUuAKYAFwdEZcV7D8auAh4LvfvnIj4eSV9\nJQlcr6FRFRZFJUmSND4NWFhIKU0ArgIOBpYD96eUbo2Ih/Ka3RURP8y1fylwM7BbhX0lqabrNVik\nkCSV4jQuSRoe5UYszAcWRcRigJTSDcDRQF9xICKezmvfAqyutK9qq/CHpYtAqpHUar0GF5WUJJXi\nNC5JGh7lCguzgM68x8uAfQsbpZTeAFwK7AQcMpi+qp3CH5YuAqnxqlZFCkc/SNLY4zQuSaq9coWF\nnkqCRMQtwC0ppVcB16WU9qgmmba2yUycOGHANl1dLVttmz69hfb2qYM+Xj3EKtavmEpj5f+wLLUI\n5EjnVWlsYxmr3mItXLiQ91/1ka1GP1xx5qeZNWv3QcXqLVIUmjNnTtVFimo+q2oVa6DPiNHMazzH\nqsecjFVcd3c3ixcv3mp7a+ukMXOO9RrLzy5jGav2cYxlLChfWFgOdOQ97iAbeVBURNybUpoITM+1\nq7gvQFfXM2XSYau/HPZuW7VqQ9m+9RirWL9S7Ro1VqWxjWWseoxVbPRDNbEeffSRmq4j8fTTa2o2\nkqK9fWrNnmegqs/QYqrJa7zGqsecjFXao48+MuzD8Uf7HOs1lp9dxjJWbeMYa/zFKlV8KFdYeACY\nl1KaA6wAjgWOy2+QUpoLPBYRPSmlvwOIiDUppfXl+krSeOI6EpJ6ORxfkjSWDFhYiIjNKaUzgTvI\nbhl5TUQ8lFI6I7d/AfBm4OSU0iZgI/C2gfoO36lIagTFVuR2odHBcx0JSZIk1YtyIxaIiNuB2wu2\nLcj7+jPAZyrtK2l8K7YitwuNjh5HP0iSJGmoyhYWJKnWCocAl1poVCOjVqMfJEmSND5ZWJAk1cRz\nWzbz+OOP95tWUe2UimJTNJyeIUmSVJ8sLEiSauLpDev44k8W9E2rGMqUisIpGkOJZZFCkiRpeFlY\nkCTVTC2nVQzXXTRcQ0KSJKm2LCxIQ+RdDqT6N5x30XD0gyRJGu8sLEhD5F0OpPHD0Q+SJElbs7Ag\n1YB3OZDGD0c/SJIk9WdhQZKkUVCr0Q/FChRgkUJjR7EphwCtrXuOQjaSpGIsLEiSRtV4XqekFqMf\nCgsUYJFCY0uxKYcb163mqovfTVvbTqOYmSSpl4UFSdKocp2SoRuuO2hAfRQpLHiocMqhJKm+WFiQ\nJI061ympH/VYpKjXgockScpYWJAkScOiVkWKWsaqZZFCkiRlLCxIkqRxpZYFD0mSBNuMdgKSJEmS\nJKlxOWJBkiSpCq7XIElSxsKCJElSFWq5XsNzWzbz+OOPexcNSVJDsrAgSRozuru76exc0m/b0qVL\nSrSWhq5W6zU8vWEdX/zJgrq7i4YkSZWwsCBJGjM6O5dwweevo2XajL5tT3Y+wi77NY1iVlJl6vEu\nGpIkVcLCgiRpTGmZNoPW6TP7Hm9YtxpYN3oJSZIkjXEWFiRJklSU6zVIkiphYUGSJElFuV6DJKkS\nFhYkSZJUkus1SJLKKVtYSCkdClwBTACujojLCvafAHwYaAI2AO+OiD/k9i0GngK2AJsiYn4tk9f4\nU7ji+1hb7d0V7SVJY5XTKiRp7BqwsJBSmgBcBRwMLAfuTyndGhEP5TV7DDggItbnihBfA/bL7esB\nDoyItbVPfXC8YBsbCld8H2urvbuivSRprHJaxcgo9jsvQGvrnqOQjaTxotyIhfnAoohYDJBSugE4\nGugrLETEfXntfwvsUhCjLq6IvGAbO/JXfB+Lq727or0kaaxyWsXwK/Y778Z1q7nq4nfT1rbTKGYm\naSwrV1iYBXTmPV4G7DtA+3cCt+U97gHuSiltARZExNcHk1ytRxl4wSZJkqSxrvB3XkkabuUKCz2V\nBkopvQY4Fdg/b/P+EbEypdQO/Cyl9HBE3FsqRlvbZCZOnND3eOHChRWNMpg+vYX29qkD5tfV1VLR\neVQSq9L4I51XvcaqNHaj5lVpTiMdazDxG/X5qmVexhpcrHr9vKnXz/rhjFUPr4dax+ru7mbx4sV9\nj9evX1UXedUqlp+Dg4tVrN9zWzbz+OOPb7V9zpw5A67XMNBzX837uB5jjYdzNFZtYtVjTsZq3Fjl\nCgvLgY68xx1koxb6SSm9DPg6cGhEdPVuj4iVuf9XpZRuJptaUbKw0NX1TL/Ha9durGiUwdq1G1m1\nasOAJ1K4UNBA7crFqnQkxUjnVa+xKo3dqHlVmtNIxxpM/EZ9vmqZl7EGF6teP2/q9bO+VA61iFUP\nr4dax3r00UcqWk+nUc/Rz8HBxSrW7+kN6/jiTxYMer2GgZ77at7HxbS3Tx3VWOPhHI019Fj1mJOx\nGiNWqeJDucLCA8C8lNIcYAVwLHBcfoOU0q7ATcCJEbEob/tkYEJEbEgpTQEOAS4sm2kDcL0GSRr7\n/KwfXWN9PZ1aGc+LU7tegyTVjwELCxGxOaV0JnAH2e0mr4mIh1JKZ+T2LwAuANqAr6SU4PnbSs4E\nbsptmwh8OyLuHLYzGWGu1yCplPH8i/5Y42e96p0FMElSPSg3YoGIuB24vWDbgryv3wW8q0i/x4BX\n1CBHSWoo/qIvaSRZAJMkjbayhQVJ0uD5i74kNYbehSAL1ybo6Jg94EKQUqMoNpKytXXPUcpGY5WF\nBUnC6QuSNF5VuxCk1CgKR1JuXLeaqy5+N21tO41yZhpLLCxIEk5fkKTxrFYLQXZ3d7Nw4UJHP6ju\nFI6klGrNwoIk5Th9QZI0FJ2dS7jo+osd/SBp3LGwIEmSJNWIox8kjUcWFiRJkqQ64+gHSY3EwoIk\nSZJcxLYO1Wr0gyQNNwsLkjROeNEgaSAuYjt21fKWmk7RkFSMhQUNu8KLmXq5kKnXvFQ5L5QHx4sG\nSeW4iO3YVMtbatbDFI1iP/8BWlv3HJHjS9qahQUNu8KLmXq5kKnXvFQ5L5QHz4sG5bPAKo0ftZxW\nMdpTNIr9/N+4bjVXXfxu2tp2GlQsixRSbVhY0IjIv5ippwuZes1LlfNCWaqeBVZJjarw53+1almk\nkMYzCwuSJI1jFlgllTLcUw6LrdcwGms11KpIIY1nFhYkSdKQOa1CGnuGe8ph4XoN3k5TalwWFqQx\nyoUNJY0kp1VIY9NwTzkc7fUaJNWGhQVpjHJhQ0kjzWkVkkZLsVtqegtMaeRYWJDGMBc2lCRJ40Hh\nLTWdViGNLAsLkiRJkhqe0yqk0WNhQZIkSZJyajmtol7ufCENNwsLkiRJqikXEFYjq+W0Cu98ofHC\nwoIkSQ3E2zoOjs/X6HABYTW6Wk6rcIqGxgMLC5IkNRBv6zg4Pl+jxwWEJWn8sLAgSVKD8baOg+Pz\nJWkscL0G1bOyhYWU0qHAFcAE4OqIuKxg/wnAh4EmYAPw7oj4QyV9JUmSJEnluV6D6tk2A+1MKU0A\nrgIOBV4MHJdSelFBs8eAAyLiZcC/Al8bRF9JkiRJUgV612uYtmNbX4FBqgflRizMBxZFxGKAlNIN\nwNHAQ70NIuK+vPa/BXaptK8kSZIkaeQUu50mOK1CQ1OusDAL6Mx7vAzYd4D27wRuq7KvJEmSJGkY\nFd5OE5xWoaErV1joqTRQSuk1wKnA/oPt26utbTITJ07oe9zV1VJRv+nTW2hvnzpgG2MZa7hiVRqn\nkWMNJn6jnmO9xhpM/Ho8x0Z9X9c6VqXxG/kcx0OsSmPX43vRWLWPNZj4vr4qjz+WPiNKxa+HnxvF\nboFZSazu7m4WL17c7/GTT7LVSIc5c+ZUPfqhmufGWKMfq1xhYTnQkfe4g2zkQT8ppZcBXwcOjYiu\nwfTN19X1TL/HhcNzSlm7diOrVm0o28ZYxhqOWJXGaeRYg4nfqOdYr7EGE78ez7FR39e1jlVp/EY+\nx/EQq9LY9fheNFbtYw0mvq+vyuOPpc+IUvEb+efGo48+stUtfFvnPVWz0Q/t7VOrem6MNXKxShUf\nyhUWHgDmpZTmACuAY4Hj8huklHYFbgJOjIhFg+krSZIkSWochbfwnbp901ajH6pR7Haa4NoPjWLA\nwkJEbE4pnQncQXbLyGsi4qGU0hm5/QuAC4A24CspJYBNETG/VN9hPBdJkiRJUgMqvJ0mVD/6wSLF\nyCs3YoGIuB24vWDbgryv3wW8q9K+kiRJkiQVKrb2QzVqWaRQZcoWFiRJkiRJaiS1KlKoMtuMdgKS\nJEmSJKlxWViQJEmSJElVs7AgSZIkSZKqZmFBkiRJkiRVzcUbJUmSpFHU3d1NZ+eSftuWLl1SorUk\n1R8LC5IkSdIo6uxcwgWfv46WaTP6tj3Z+Qi77Nc0illJUuUsLEiSJEmjrGXaDFqnz+x7vGHdamDd\n6CUkSYNgYUGSJEkaJKcvqJCvCY1nFhYkSZKkQXL6ggr5mtB4ZmFBkiRJqoLTF1TI14TGK283KUmS\nJEmSqmZhQZIkSZIkVc2pEJIkSZKGlQsbqlF1d3ezcOFC1q7d2G97R8dsmpubRymr+mNhQZIkqQKF\nF0ZeFEmVc2FDNarOziVcdP3FTN2+tW/bhjXrueD485k7d94oZlZfLCxIkiRVoPDCyIsiaXBc2FCN\naur2rUzbsW2006hrFhYkleXwRUnK5F8YeVEkSVLGwoKkshy+KEmSJKkUCwuSKuLwRUmSJEnFeLtJ\nSZIkSZJUNQsLkiRJkiSpak6FkCRpmLkAqiRJ6u7uZuHChaxdu7Hf9o6O2TQ3N49SVrVRtrCQUjoU\nuAKYAFwdEZcV7N8DuBbYC/hYRFyet28x8BSwBdgUEfNrlrkkSQ3CBVAlSVJn5xIuuv5ipm7f2rdt\nw5r1XHD8+cydO28UMxu6AQsLKaUJwFXAwcBy4P6U0q0R8VBeszXAWcAbioToAQ6MiLU1yleSpIbk\nAqiSJGnq9q1M27FttNOouXJrLMwHFkXE4ojYBNwAHJ3fICJWRcQDwKYSMfxzjCRJkiRJY1S5wsIs\noDPv8bLctkr1AHellB5IKZ022OQkSZIkSVJ9K7fGQs8Q4+8fEStTSu3Az1JKD0fEvaUat7VNZuLE\nCX2Pu7paKjrI9OkttLdPHbCNsYw1XLEqjWMsY1UTazDx6/EcG/V9bSxjDWesev28MZax6iFWo76v\nax2r0vhj6Ryr/f1mvMbqVW2/WscqV1hYDnTkPe4gG7VQkYhYmft/VUrpZrKpFSULC11dz/R7XLha\nZilr125k1aoNZdsYy1jDEavSOMYyVjWxBhO/Hs+xUd/XxjLWcMaq188bYxmrHmI16vu61rEqjT+W\nzrHa32/GayzICgHV9BtKrFLFh3KFhQeAeSmlOcAK4FjguBJt+62lkFKaDEyIiA0ppSnAIcCFZTOV\nJEmSJEkNY8DCQkRsTimdCdxBdrvJayLioZTSGbn9C1JKM4H7ge2A51JK7wNeDOwA3JRS6j3OtyPi\nzuE7FUmSJElSI+ju7qazc0nf46VLlwzQWoW6u7tZuHDhVqMgOjpm09zcPOL5lBuxQETcDtxesG1B\n3tdP0H+6RK+NwCuGmqAkjWeFP3TBH7ySJKnxdXYu4YLPX0fLtBkAPNn5CLvs5w0FK9XZuYSLrr+Y\nqdu39m3bsGY9Fxx/PnPnzhvxfMoWFiRJo6fwhy74g1eSJI0NLdNm0Dp9JgAb1q0G1o1uQg1m6vat\nTNuxbbTTACwsSFLdy/+hC/7glSRJUn2xsCBJkiRJdcSpkGo0FhYkSZIkqY44FVKNxsKCJEmSJNUZ\np0KqkVhYkCRJkiSpTjXC1BgLC5IkSZIk1alGmBpjYUGSJEmSpDpW71NjLCxIkiRJkhpW4VSBepsm\nMB5YWJAkSZIkNazCqQL1Nk1gPLCwIEmSJElqaPlTBeptmsB4sM1oJyBJkiRJkhqXhQVJkiRJklQ1\nCwuSJEmSJKlqFhYkSZIkSVLVLCxIkiRJkqSqWViQJEmSJElVs7AgSZIkSZKqZmFBkiRJkiRVzcKC\nJEmSJEmqmoUFSZIkSZJUtYmjnYAkSZIkSfWgu7ubzs4lfY+XLl0yQGv1KltYSCkdClwBTACujojL\nCvbvAVwL7AV8LCIur7SvJEmSJEn1orNzCRd8/jpaps0A4MnOR9hlv6ZRzqr+DTgVIqU0AbgKOBR4\nMXBcSulFBc3WAGcBn6uiryRJkiRJdaNl2gxap8+kdfpMJk9tG+10GkK5NRbmA4siYnFEbAJuAI7O\nbxARqyLiAWDTYPtKkiRJkqTGVq6wMAvozHu8LLetEkPpK0mSJEmSGkC5NRZ6hhB70H3b2iYzceKE\nvsddXS0V9Zs+vYX29qkDtjGWsYYrVqVxjGWs8RirUd/XxjLWcMZq9Pe1sYw1nLEa9X1tLGNVolTs\nkXwvDqTaflC+sLAc6Mh73EE28qASg+7b1fVMv8dr126s6EBr125k1aoNZdsYy1jDEavSOMYy1niM\n1ajva2MZazhjNfr72ljGGs5Yjfq+NpaxKu1TTaxavhdLaW+fWlG/UsWHcoWFB4B5KaU5wArgWOC4\nEm0Ll8ocTF9JkiRJksaEwttWwti+deWAhYWI2JxSOhO4g+yWkddExEMppTNy+xeklGYC9wPbAc+l\nlN4HvDgiNhbrO5wnI0mSJEnSaCu8bSWM7VtXlhuxQETcDtxesG1B3tdP0H/Kw4B9JUmSJEka63pv\nW9lrw7rVwLrRS2gYlbsrhCRJkiRJUkkWFiRJkiRJUtXKToWQJEmSJEljU3d3NwsXLtzq7hMdHbNp\nbm6uKIaFBUmSJEmSxqnOziVcdP3FTN2+tW/bhjXrueD485k7d15FMSwsSJIkSZI0jk3dvpVpO7ZV\n3d81FiRJkiRJUtUsLEiSJEmSpKpZWJAkSZIkSVWzsCBJkiRJkqpmYUGSJEmSJFXNwoIkSZIkSaqa\nhQVJkiRJklQ1CwuSJEmSJKlqFhYkSZIkSVLVLCxIkiRJkqSqWViQJEmSJElVs7AgSZIkSZKqZmFB\nkiRJkiRVzcKCJEmSJEmqmoUFSZIkSZJUtYmjnYAkSZIkSRp+3d3ddHYu6bdt6dIlJVpXzsKCJEmS\nJEnjQGfnEi74/HW0TJvRt+3JzkfYZb+mIcUtW1hIKR0KXAFMAK6OiMuKtLkSOAx4BnhHRPxPbvti\n4ClgC7ApIuYPKVtJkiRJklS1lmkzaJ0+s+/xhnWrgXVDijngGgsppQnAVcChwIuB41JKLypocziw\nW0TMA04HvpK3uwc4MCL2sqggSZIkSdLYU27xxvnAoohYHBGbgBuAowvaHAV8EyAifgtMSyntmLd/\naGMqJEmSJElS3SpXWJgFdOY9XpbbVmmbHuCulNIDKaXThpKoJEmSJEmqP+XWWOipME6pUQn/GBEr\nUkrtwM9SSg9HxL2lgrS1TWbixAl9j7u6Wio6+PTpLbS3Tx2wjbGMNVyxKo1jLGONx1iN+r42lrGG\nM1ajv6+NZazhjNWo72tjGavaOI0cK1+5wsJyoCPvcQfZiISB2uyS20ZErMj9vyqldDPZ1IqShYWu\nrmf6PV67dmOZ9J5vt2rVhrJtjGWs4YhVaRxjGWs8xmrU97WxjDWcsRr9fW0sYw1nrEZ9XxvLWNXG\nabRYpQoN5aZCPADMSynNSSk1A8cCtxa0uRU4GSCltB+wLiKeTClNTilNzW2fAhwC/LHis5AkSZIk\nSXVvwMJCRGwGzgTuAP4MfDciHkopnZFSOiPX5jbgsZTSImAB8J5c95nAvSml3wG/BX4cEXcO03lI\nkiRJkqRRUG4qBBFxO3B7wbYFBY/PLNLvMeAVQ01QkiRJkiTVr3JTISRJkiRJkkqysCBJkiRJkqpm\nYUGSJEmSJFXNwoIkSZIkSaqahQVJkiRJklQ1CwuSJEmSJKlqFhYkSZIkSVLVLCxIkiRJkqSqWViQ\nJEmSJElVs7AgSZIkSZKqZmFBkiRJkiRVzcKCJEmSJEmqmoUFSZIkSZJUNQsLkiRJkiSpahYWJEmS\nJElS1SwsSJIkSZKkqllYkCRJkiRJVbOwIEmSJEmSqmZhQZIkSZIkVc3CgiRJkiRJqpqFBUmSJEmS\nVDULC5IkSZIkqWoTyzVIKR0KXAFMAK6OiMuKtLkSOAx4BnhHRPxPpX0lSZIkSVLjGnDEQkppAnAV\ncCjwYuC4lNKLCtocDuwWEfOA04GvVNpXkiRJkiQ1tnJTIeYDiyJicURsAm4Aji5ocxTwTYCI+C0w\nLaU0s8K+kiRJkiSpgZWbCjEL6Mx7vAzYt4I2s4CdK+jbz95779nv8aZNm1j31EYOO+Hcvm3PbOhi\n2zVPAfDjL3yfnud6uOerd7Dtttv2tfnv//5T0fi3f/syttlmQt/jLZs3sc1Pe3j9h94KwIY16wfM\np9f3vnczG9et7rftmQ1d/Pry79O0TRNAv7xK5bP33nv2nWN+Xq/4x6P7zjE/r1L55MfPz+veH32d\nbX7a05dTb14feN1ZJfPplZ/X6447u9/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"text": [ "<matplotlib.figure.Figure at 0x7f67abdaa850>" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delay Tendency\n", "\n", "> Is there a tendency of flights from one state to another to experience a delay of 15 minutes or more on the arriving end?\n", "\n", "While there are many ways to answer this question, we'll look at visualizations of two metrics:\n", "\n", "1. How many times does a delay occur for an (origin → destination) state pair over all flights during the time period? (This is the [support of a simple association rule](http://en.wikipedia.org/wiki/Association_rule_learning#Useful_Concepts).)\n", "2. What percentage of total flights from an origin to a destination are delayed during the time period?\n", "\n", "First, we'll compute the support. We do so by grouping all of the arrival delay counts by the origin and destination." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "delay_counts_df = df[['ORIGIN_STATE_ABR', 'DEST_STATE_ABR', 'ARR_DEL15']].groupby(['ORIGIN_STATE_ABR', 'DEST_STATE_ABR']).sum()\n", "delay_counts_df.head()" ], "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></th>\n", " <th>ARR_DEL15</th>\n", " </tr>\n", " <tr>\n", " <th>ORIGIN_STATE_ABR</th>\n", " <th>DEST_STATE_ABR</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">AK</th>\n", " <th>AK</th>\n", " <td> 351</td>\n", " </tr>\n", " <tr>\n", " <th>AZ</th>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>CO</th>\n", " <td> 21</td>\n", " </tr>\n", " <tr>\n", " <th>GA</th>\n", " <td> 3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ " ARR_DEL15\n", "ORIGIN_STATE_ABR DEST_STATE_ABR \n", "AK AK 351\n", " AZ 5\n", " CA 11\n", " CO 21\n", " GA 3" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We divide each (origin → destination) delay count by the total number of flights during the period." ] }, { "cell_type": "code", "collapsed": false, "input": [ "support = (delay_counts_df / len(df))\n", "support.head()" ], "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></th>\n", " <th>ARR_DEL15</th>\n", " </tr>\n", " <tr>\n", " <th>ORIGIN_STATE_ABR</th>\n", " <th>DEST_STATE_ABR</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">AK</th>\n", " <th>AK</th>\n", " <td> 0.000699</td>\n", " </tr>\n", " <tr>\n", " <th>AZ</th>\n", " <td> 0.000010</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td> 0.000022</td>\n", " </tr>\n", " <tr>\n", " <th>CO</th>\n", " <td> 0.000042</td>\n", " </tr>\n", " <tr>\n", " <th>GA</th>\n", " <td> 0.000006</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ " ARR_DEL15\n", "ORIGIN_STATE_ABR DEST_STATE_ABR \n", "AK AK 0.000699\n", " AZ 0.000010\n", " CA 0.000022\n", " CO 0.000042\n", " GA 0.000006" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We unstack the multiple indices so that we have a N x N matrix with origins as rows and destinations as columns." ] }, { "cell_type": "code", "collapsed": false, "input": [ "support = support.unstack()\n", "support.head()" ], "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>\n", " <th></th>\n", " <th colspan=\"21\" halign=\"left\">ARR_DEL15</th>\n", " </tr>\n", " <tr>\n", " <th>DEST_STATE_ABR</th>\n", " <th>AK</th>\n", " <th>AL</th>\n", " <th>AR</th>\n", " <th>AZ</th>\n", " <th>CA</th>\n", " <th>CO</th>\n", " <th>CT</th>\n", " <th>DE</th>\n", " <th>FL</th>\n", " <th>GA</th>\n", " <th>...</th>\n", " <th>TT</th>\n", " <th>TX</th>\n", " <th>UT</th>\n", " <th>VA</th>\n", " <th>VI</th>\n", " <th>VT</th>\n", " <th>WA</th>\n", " <th>WI</th>\n", " <th>WV</th>\n", " <th>WY</th>\n", " </tr>\n", " <tr>\n", " <th>ORIGIN_STATE_ABR</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AK</th>\n", " <td> 0.000699</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000010</td>\n", " <td> 0.000022</td>\n", " <td> 0.000042</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000006</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000050</td>\n", " <td> 0.000004</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000209</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AL</th>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000064</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000080</td>\n", " <td> 0.000364</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000631</td>\n", " <td> NaN</td>\n", " <td> 0.000018</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AR</th>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.000036</td>\n", " <td> 0.000098</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000163</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000826</td>\n", " <td> 0.000002</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AZ</th>\n", " <td> 0.000026</td>\n", " <td>NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.000129</td>\n", " <td> 0.002559</td>\n", " <td> 0.000352</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000086</td>\n", " <td> 0.000113</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000722</td>\n", " <td> 0.000239</td>\n", " <td> 0.000072</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000291</td>\n", " <td> 0.000062</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td> 0.000056</td>\n", " <td>NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.001847</td>\n", " <td> 0.011355</td>\n", " <td> 0.001409</td>\n", " <td> 0.000014</td>\n", " <td>NaN</td>\n", " <td> 0.000302</td>\n", " <td> 0.000344</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.002113</td>\n", " <td> 0.000557</td>\n", " <td> 0.000406</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.001423</td>\n", " <td> 0.000068</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 53 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ " ARR_DEL15 \\\n", "DEST_STATE_ABR AK AL AR AZ CA CO \n", "ORIGIN_STATE_ABR \n", "AK 0.000699 NaN NaN 0.000010 0.000022 0.000042 \n", "AL NaN NaN NaN NaN NaN 0.000064 \n", "AR NaN NaN NaN 0.000008 0.000036 0.000098 \n", "AZ 0.000026 NaN 0.000008 0.000129 0.002559 0.000352 \n", "CA 0.000056 NaN 0.000008 0.001847 0.011355 0.001409 \n", "\n", " ... \\\n", "DEST_STATE_ABR CT DE FL GA ... TT TX \n", "ORIGIN_STATE_ABR ... \n", "AK NaN NaN NaN 0.000006 ... NaN 0.000050 \n", "AL NaN NaN 0.000080 0.000364 ... NaN 0.000631 \n", "AR NaN NaN NaN 0.000163 ... NaN 0.000826 \n", "AZ NaN NaN 0.000086 0.000113 ... NaN 0.000722 \n", "CA 0.000014 NaN 0.000302 0.000344 ... NaN 0.002113 \n", "\n", " \n", "DEST_STATE_ABR UT VA VI VT WA WI WV WY \n", "ORIGIN_STATE_ABR \n", "AK 0.000004 NaN NaN NaN 0.000209 NaN NaN NaN \n", "AL NaN 0.000018 NaN NaN NaN NaN NaN NaN \n", "AR 0.000002 NaN NaN NaN NaN NaN NaN NaN \n", "AZ 0.000239 0.000072 NaN NaN 0.000291 0.000062 NaN NaN \n", "CA 0.000557 0.000406 NaN NaN 0.001423 0.000068 NaN 0 \n", "\n", "[5 rows x 53 columns]" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, we now have a multilevel set of columns. One way to remove the outer level is to rotate the matrix, reset the outer index to drop it, and then rotate it back.\n", "\n", "In the resulting matrix, each cell represents the proportion of total, system-wide flights that were delayed between an (origin → destination) pair." ] }, { "cell_type": "code", "collapsed": false, "input": [ "support = support.T.reset_index(level=0, drop=True).T\n", "support.head()" ], "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>DEST_STATE_ABR</th>\n", " <th>AK</th>\n", " <th>AL</th>\n", " <th>AR</th>\n", " <th>AZ</th>\n", " <th>CA</th>\n", " <th>CO</th>\n", " <th>CT</th>\n", " <th>DE</th>\n", " <th>FL</th>\n", " <th>GA</th>\n", " <th>...</th>\n", " <th>TT</th>\n", " <th>TX</th>\n", " <th>UT</th>\n", " <th>VA</th>\n", " <th>VI</th>\n", " <th>VT</th>\n", " <th>WA</th>\n", " <th>WI</th>\n", " <th>WV</th>\n", " <th>WY</th>\n", " </tr>\n", " <tr>\n", " <th>ORIGIN_STATE_ABR</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AK</th>\n", " <td> 0.000699</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000010</td>\n", " <td> 0.000022</td>\n", " <td> 0.000042</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000006</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000050</td>\n", " <td> 0.000004</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000209</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AL</th>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000064</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000080</td>\n", " <td> 0.000364</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000631</td>\n", " <td> NaN</td>\n", " <td> 0.000018</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AR</th>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.000036</td>\n", " <td> 0.000098</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> 0.000163</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000826</td>\n", " <td> 0.000002</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AZ</th>\n", " <td> 0.000026</td>\n", " <td>NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.000129</td>\n", " <td> 0.002559</td>\n", " <td> 0.000352</td>\n", " <td> NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000086</td>\n", " <td> 0.000113</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.000722</td>\n", " <td> 0.000239</td>\n", " <td> 0.000072</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.000291</td>\n", " <td> 0.000062</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>CA</th>\n", " <td> 0.000056</td>\n", " <td>NaN</td>\n", " <td> 0.000008</td>\n", " <td> 0.001847</td>\n", " <td> 0.011355</td>\n", " <td> 0.001409</td>\n", " <td> 0.000014</td>\n", " <td>NaN</td>\n", " <td> 0.000302</td>\n", " <td> 0.000344</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td> 0.002113</td>\n", " <td> 0.000557</td>\n", " <td> 0.000406</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td> 0.001423</td>\n", " <td> 0.000068</td>\n", " <td>NaN</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 53 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "DEST_STATE_ABR AK AL AR AZ CA CO \\\n", "ORIGIN_STATE_ABR \n", "AK 0.000699 NaN NaN 0.000010 0.000022 0.000042 \n", "AL NaN NaN NaN NaN NaN 0.000064 \n", "AR NaN NaN NaN 0.000008 0.000036 0.000098 \n", "AZ 0.000026 NaN 0.000008 0.000129 0.002559 0.000352 \n", "CA 0.000056 NaN 0.000008 0.001847 0.011355 0.001409 \n", "\n", "DEST_STATE_ABR CT DE FL GA ... TT TX \\\n", "ORIGIN_STATE_ABR ... \n", "AK NaN NaN NaN 0.000006 ... NaN 0.000050 \n", "AL NaN NaN 0.000080 0.000364 ... NaN 0.000631 \n", "AR NaN NaN NaN 0.000163 ... NaN 0.000826 \n", "AZ NaN NaN 0.000086 0.000113 ... NaN 0.000722 \n", "CA 0.000014 NaN 0.000302 0.000344 ... NaN 0.002113 \n", "\n", "DEST_STATE_ABR UT VA VI VT WA WI WV WY \n", "ORIGIN_STATE_ABR \n", "AK 0.000004 NaN NaN NaN 0.000209 NaN NaN NaN \n", "AL NaN 0.000018 NaN NaN NaN NaN NaN NaN \n", "AR 0.000002 NaN NaN NaN NaN NaN NaN NaN \n", "AZ 0.000239 0.000072 NaN NaN 0.000291 0.000062 NaN NaN \n", "CA 0.000557 0.000406 NaN NaN 0.001423 0.000068 NaN 0 \n", "\n", "[5 rows x 53 columns]" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we have a DataFrame that we can query but no clear idea of where to start looking. A visualization of the entire DataFrame can help us find interesting pairs. We borrow and slightly modify some code from `seaborn` to plot our asymmetric matrix as a heatmap." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "def asymmatplot(plotmat, names=None, cmap=\"Greys\", cmap_range=None, ax=None, *kwargs):\n", " '''\n", " Plot an asymmetric matrix with colormap and statistic values. A modification of the\n", " symmatplot() function in Seaborn to show the upper-half of the matrix.\n", " \n", " See https://github.com/mwaskom/seaborn/blob/master/seaborn/linearmodels.py for the original.\n", " '''\n", " if ax is None:\n", " ax = plt.gca()\n", "\n", " nvars = len(plotmat)\n", "\n", " if cmap_range is None:\n", " vmax = np.nanmax(plotmat) 1.15\n", " vmin = np.nanmin(plotmat) * 1.15\n", " elif len(cmap_range) == 2:\n", " vmin, vmax = cmap_range\n", " else:\n", " raise ValueError(\"cmap_range argument not understood\")\n", "\n", " mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, *kwargs)\n", "\n", " plt.colorbar(mat_img, shrink=.75)\n", " \n", " ax.xaxis.set_ticks_position(\"bottom\")\n", " ax.set_xticklabels(names, rotation=90)\n", " ax.set_yticklabels(names)\n", "\n", " minor_ticks = np.linspace(-.5, nvars - 1.5, nvars)\n", " ax.set_xticks(minor_ticks, True)\n", " ax.set_yticks(minor_ticks, True)\n", " major_ticks = np.linspace(0, nvars - 1, nvars)\n", " ax.set_xticks(major_ticks)\n", " ax.set_yticks(major_ticks)\n", " ax.grid(False, which=\"major\")\n", " ax.grid(True, which=\"minor\", linestyle=\"-\")\n", "\n", " return ax" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the plot, gray boxes represent cases where there are no flights between the origin (row) and destination (column) states. We see mostly light yellowish boxes representing state pairs where delays occur, but in tiny numbers compared to the total number of system-wide flights. \n", "\n", "We do see a couple very hot spots, namely in the (CA → CA) and (TX → TX) cells. We also see a few moderately hot spots, for example (TX → CA) and (FL → NY). We can interpret these cells as indicators of where delays tend to occur most across the entire set of flights." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(18,18))\n", "asymmatplot(support, names=support.columns, ax=ax, cmap='OrRd')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 39, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f679b621890>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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DP+goatp1zT7ee6CszoNJ2Dv70LLsVPn6fqfsg9vKsls73F4V+3jvwamy7Nb6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\naokAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x7f67a9448550>" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand what the percentage of flights for a particular (origin → destination) state pair that are delayed, we can look at a second metric using the same visualization. Here we compute the total number of flights for each (origin → destination) pair." ] }, { "cell_type": "code", "collapsed": false, "input": [ "trip_counts_df = df[['ORIGIN_STATE_ABR', 'DEST_STATE_ABR', 'FL_DATE']].groupby(['ORIGIN_STATE_ABR', 'DEST_STATE_ABR']).count()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 40 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To put trip counts DataFrame and our earlier `delay_counts` DataFrame on the same axes, we rename the columns to `COUNTS` in both cases." ] }, { "cell_type": "code", "collapsed": false, "input": [ "delay_counts_df = delay_counts_df.rename_axis({'ARR_DEL15' : 'COUNTS'}, axis=1)\n", "trip_counts_df = trip_counts_df.rename_axis({'FL_DATE' : 'COUNTS'}, axis=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we divide the delay counts by the total trip counts and perform the same transforms we did previous to produce the N by N matrix. In this case, each cell represents the proportion of flights between each (origin → destination) that were delayed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mat = (delay_counts_df / trip_counts_df).unstack().T.reset_index(level=0, drop=True).T" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this second heatmap plot, the (CA → CA) and (TX → TX) hotspots from the firt visualization no longer stand out. Though there are many in-state delays for these two states, there are even more flights, keeping the percentage of delayed flights for these in-state trips lower than other routes.\n", "\n", "To the contrary, we see some cases where all flights from one state to another had arrival delays:\n", "\n", " (AR → UT)\n", "* (MT → NY)\n", "* (CO → RI) and (RI → CO)\n", "\n", "We can also see some other moderately hot spots, such as (AK → NJ) and (OK → MN), which seem to have a higher percentage of delays than other state pairs.\n", "\n", "One \"crosshair\" jumps out in the visualization: the row and column representing Illinois are nearly both filled with non-gray cells. On closer inspection, we see Illinois sends flights to and receives flights from every other state except one: TT, the state code abbreviation for U.S. Pacific Trust Territories and Possessions. And though it is difficult to make accurate relative value judgments from this visualization, it appears the run of cells in the row and column for Illinois are darker than most other row or column runs (e.g., GA)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(18,18))\n", "asymmatplot(mat, names=mat.columns, ax=ax, cmap='OrRd', cmap_range=(0., 1.0))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f679b051950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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E3vmfg1zZvt/+g5Lnfu8rd6NDlNi1vmyYqmT+DF92zAFKnrrUl938WDU0LHdl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\nR7v7AwAtNHy8LdkeT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"text": [ "<matplotlib.figure.Figure at 0x7f679b307f50>" ] } ], "prompt_number": 43 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, this plot only shows proportions of delayed flights between two states and doesn't depict the number of flights between the two nor the magnitude of the delay. So while all flights between Rhode Island and Colorado were delayed, we need to keep in mind ..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print delay_counts_df.loc['RI', 'CO']\n", "print trip_counts_df.loc['RI', 'CO']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "COUNTS 3\n", "Name: (RI, CO), dtype: float64\n", "COUNTS 3\n", "Name: (RI, CO), dtype: int64\n" ] } ], "prompt_number": 44 }, { "cell_type": "markdown", "metadata": {}, "source": [ "there were only three of them!\n", "\n", "A visualization that captures both the proportion of (origin → destination) flights delayed as well as the proportion of total flights represented by that state pair is yet another exercise for the future." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delay Distribution by Date\n", "\n", "> How did arrival delay in minutes vary day-by-day?\n", "\n", "To address this question, we can group the `ARR_DELAY_NEW` column by date and look at their descriptive stats. A Tukey box plot by day is a reasonable way for us to start." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(18,10))\n", "sns.boxplot(df.ARR_DELAY_NEW, df.FL_DATE, ax=ax)\n", "fig.autofmt_xdate()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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8rPXz08Bv5Zx/Z9lKKOmY+NClqCItFhlh5MdAiSQNrgjZcKXvI96/VFLXIEVK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\nD+SU3IL0p6i2IF0J/APVFqQ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jxFA9jNZqZetFhABaBFH+/RFGnyKskg/VubjvvqrtvPXW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8BzQwjdMcYtMeUpTibsbzL3yKQWoJJJ9MVpWV+sDCF8cjLjok1OzfPb8LCayOLnaSHVK9kWY/xu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\niTGeLDScA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"text": [ "<matplotlib.figure.Figure at 0x7f679ad6e190>" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oh my, outliers! If the data are to be trusted, there's at least one flight every day that is over 500 minutes (8 hours) late in arriving. In the most extreme case, a flight scheduled on 2014-06-19 appears to have arrived 1800 minutes (30 hours) late. \n", "\n", "Whether this information is accurate or not requires some fact checking against other historical sources. For now, we can turn off the fliers to get a better view of the interquartile range." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = plt.subplots(figsize=(18,10))\n", "sns.boxplot(df.ARR_DELAY_NEW, df.FL_DATE, ax=ax, showfliers=False)\n", "fig.autofmt_xdate()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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AlmOPCAAAAKA1iggAAACgNYoIAAAAoDWKCAAAAKA1iggAAACgNYoIAAAAoDWK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\nkiaOD1NOek8GPmypQ7828DnSi/7DpEH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"text": [ "<matplotlib.figure.Figure at 0x7f679a66ab90>" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the median arrival delay on most days is zero (or better than zero: this data column counts early arrivals as zeros). Some days see greater skew toward longer delays than others, particularly over the five day periods of Sunday, 2014-06-08 through Friday, 2014-06-13 and Monday, 2014-06-23 through Friday, 2014-06-27. There's also another period of elongation from Wednesday, 2014-06-18 through Thursday, 2014-06-19. These might relate to weather patterns during those weeks or an increase in number of passengers (e.g., summer vacations). Further study and sources of data are required to test these hypotheses." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!cal 6 2014" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " June 2014 \r\n", "Su Mo Tu We Th Fr Sa \r\n", " 1 2 3 4 5 6 7 \r\n", " 8 9 10 11 12 13 14 \r\n", "15 16 17 18 19 20 21 \r\n", "22 23 24 25 26 27 28 \r\n", "29 30 \r\n", " \r\n" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Going Further\n", "\n", "If you wish to take this exploration further, here are some questions you might consider addressing with additional thinking and data.\n", "\n", "* How accurately can a model predict if a flight will be delayed or not using simple features like origin, destination, day of the week, etc.?\n", "* What factors (e.g., U.S. weather) help explain the greater median arrival delay and dispersion from 2014-06-08 to 2014-06-13?\n", "* How do the results above contrast with the results from applying the same analyses to data from June, 2001? June, 2002?" ] } ], "metadata": {} } ] }	mit
Ledoux/ShareYourSystem	Ouvaton/Concluder.ipynb	1	7082	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Concluder\n\n##Doc\n----\n\n\n> \n> A Concluder\n> \n> \n\n----\n\n<small>\nView the Concluder notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Concluder.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\nA Concluder\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Objects.Conditioner\"\nDecorationModuleStr=\"ShareYourSystem.Classors.Tester\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass ConcluderClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'ConcludingTestVariable',\n\t\t\t\t\t\t\t\t\t'ConcludingConditionTuplesList',\n\t\t\t\t\t\t\t\t\t'ConcludedConditionIsBoolsList',\n\t\t\t\t\t\t\t\t\t'ConcludedIsBool'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t_ConcludingTestVariable=None,\n\t\t\t\t_ConcludingConditionTuplesList=None,\n\t\t\t\t_ConcludedConditionIsBoolsList=None,\n\t\t\t\t_ConcludedIsBool=True,\n\t\t\t\t_KwargVariablesDict\n\t\t\t\t):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,_KwargVariablesDict)\n\n\tdef do_conclude(self):\n\t\t\"\"\" \"\"\"\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['ConcludingConditionTuplesList']))\n\t\t'''\n\t\t\n\t\t#Apply __getitem__\n\t\tself.ConcludedConditionIsBoolsList=map(\n\t\t\t\tlambda __ConcludingConditionTuple:\n\t\t\t\tself.condition(\n\t\t\t\t\t\tself.ConcludingTestVariable[\n\t\t\t\t\t\t\t__ConcludingConditionTuple[0]\n\t\t\t\t\t\t] if type(\n\t\t\t\t\t\t\t__ConcludingConditionTuple[0])\n\t\t\t\t\t\tin SYS.StrTypesList else __ConcludingConditionTuple[0],\n\t\t\t\t\t\t__ConcludingConditionTuple[1],\n\t\t\t\t\t\t__ConcludingConditionTuple[2]\n\t\t\t\t\t).ConditionedIsBool,\n\t\t\t\tself.ConcludingConditionTuplesList\n\t\t\t)\n\n\t\t#all\n\t\tself.ConcludedIsBool=all(self.ConcludedConditionIsBoolsList)\n\n\t\t#Return self\n\t\t#return self\n#</DefineClass>\n\n```\n\n<small>\nView the Concluder sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Objects/Concluder\" 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 do a simple conclude call", "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.Objects import Concluder\n", "import operator\n", "\n", "#Definition of an instance Concluder and make it print hello\n", "MyConcluder=Concluder.ConcluderClass().conclude(\n", " {'MyColorStr':'Black','MySuperInt':6},\n", " [\n", " ('MyColorStr',operator.eq,\"Black\"),\n", " ('MySuperInt',operator.gt,3),\n", " (1,operator.eq,1)\n", " ]\n", ")\n", " \n", "#Definition the AttestedStr\n", "SYS._attest(\n", " [\n", " 'MyConcluder is '+SYS._str(\n", " MyConcluder,\n", " {\n", " 'RepresentingBaseKeyStrsListBool':False,\n", " 'RepresentingAlineaIsBool':False\n", " }\n", " ),\n", " ]\n", ") \n", "\n", "#Print\n", "\n", "\n", "\n", "\n", "\n" ], "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "*Start of the Attest *\n", "\n", "MyConcluder is < (ConcluderClass), 4537219024>\n", " /{ \n", " / '<New><Instance>IdInt' : 4537219024\n", " / '<Spe><Instance>ConcludedConditionIsBoolsList' : \n", " / /[\n", " / / 0 : True\n", " / / 1 : True\n", " / / 2 : True\n", " / /]\n", " / '<Spe><Instance>ConcludedIsBool' : True\n", " / '<Spe><Instance>ConcludingConditionTuplesList' : \n", " / /[\n", " / / 0 : \n", " / / /(\n", " / / / 0 : MyColorStr\n", " / / / 1 : <built-in function eq>\n", " / / / 2 : Black\n", " / / /)\n", " / / 1 : \n", " / / /(\n", " / / / 0 : MySuperInt\n", " / / / 1 : <built-in function gt>\n", " / / / 2 : 3\n", " / / /)\n", " / / 2 : \n", " / / /(\n", " / / / 0 : 1\n", " / / / 1 : {...}< (builtin_function_or_method), 4522748384>\n", " / / / 2 : 1\n", " / / /)\n", " / /]\n", " / '<Spe><Instance>ConcludingTestVariable' : \n", " / /{ \n", " / / 'MyColorStr' : Black\n", " / / 'MySuperInt' : 6\n", " / /}\n", " /}\n", "\n", "*End of the Attest ***\n", "\n", "\n" ] } ], "collapsed": false, "metadata": { "slideshow": { "slide_type": "-" } } } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }	mit
NYUDataBootcamp/Projects	UG_F16/RodriguezBallve-Spain's_Labor_Market.ipynb	1	292917	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring Spain's Broken Labor Market" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author Bosco Rodríguez Ballvé\n", "Date Fall 2016\n", "Class Data Bootcamp @ NYU Stern \n", "Instructors Coleman, Lyon\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abstract" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A successful economy in the 21st century, in which the mix of products and services is changing constantly, requires a dynamic labor market as a mechanism to shift capital and labor. The ability of an economy to reallocate jobs across firms, industries, and geographical areas is, perhaps, even more important than capital. \n", "\n", "Decades of persistently high unemployment in Spain, regardless of business cycle fluctuations, suggest that lack labor market dynamism has hindered Spain’s economy. Currently, almost a decade after the Great Recession, with economic recovery underway, Spain’s unemployment rate remains stubbornly high. Particularly amongst the youth. Chronic unemployment suggests deep rooted, structural causes that go beyond demand-deficient or cyclical unemployment. \n", "\n", "The aim of this project is to compile and process data to shed ight on the relationship between education levels, age and structural unemployment in Spain." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the modules" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas_datareader import data, wb # we will be working with World Bank Data \n", "import wbdata\n", "import pandas\n", "import matplotlib.pyplot as plt\n", "import sys \n", "import matplotlib as mpl \n", "import matplotlib.pyplot as plt \n", "import datetime as dt \n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data extraction and clean up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My first data source is the World Bank. We will access World Bank data by using 'Wbdata', Wbdata is a simple python interface to find and request information from the World Bank's various databases, either as a dictionary containing full metadata or as a pandas DataFrame. Currently, wbdata wraps most of the World Bank API, and also adds some convenience functions for searching and retrieving information.\n", "\n", "Documentation is available at http://wbdata.readthedocs.org/ \n", "\n", "We install it with 'pip install wbdata'\n", "\n", "Credits go to:\n", "\n", "Sherouse, Oliver (2014). Wbdata. Arlington, VA. Available from http://github.com/OliverSherouse/wbdata.\n", "\n", "Let's get to it." ] }, { "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>id</th>\n", " <th>name</th>\n", " <th>source</th>\n", " <th>sourceNote</th>\n", " <th>sourceOrganization</th>\n", " <th>topics</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>685</th>\n", " <td>6.0.GDPpc_constant</td>\n", " <td>GDP per capita, PPP (constant 2011 internation...</td>\n", " <td>LAC Equity Lab</td>\n", " <td>GDP per capita based on purchasing power parit...</td>\n", " <td>b'World Development Indicators (World Bank)'</td>\n", " <td>Economy & Growth</td>\n", " </tr>\n", " <tr>\n", " <th>7437</th>\n", " <td>NY.GDP.PCAP.KD</td>\n", " <td>GDP per capita (constant 2010 US$)</td>\n", " <td>World Development Indicators</td>\n", " <td>GDP per capita is gross domestic product divid...</td>\n", " <td>b'World Bank national accounts data, and OECD ...</td>\n", " <td>Economy & Growth</td>\n", " </tr>\n", " <tr>\n", " <th>7439</th>\n", " <td>NY.GDP.PCAP.KN</td>\n", " <td>GDP per capita (constant LCU)</td>\n", " <td>World Development Indicators</td>\n", " <td>GDP per capita is gross domestic product divid...</td>\n", " <td>b'World Bank national accounts data, and OECD ...</td>\n", " <td>Economy & Growth</td>\n", " </tr>\n", " <tr>\n", " <th>7441</th>\n", " <td>NY.GDP.PCAP.PP.KD</td>\n", " <td>GDP per capita, PPP (constant 2011 internation...</td>\n", " <td>World Development Indicators</td>\n", " <td>GDP per capita based on purchasing power parit...</td>\n", " <td>b'World Bank, International Comparison Program...</td>\n", " <td>Economy & Growth</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id name \\\n", "685 6.0.GDPpc_constant GDP per capita, PPP (constant 2011 internation... \n", "7437 NY.GDP.PCAP.KD GDP per capita (constant 2010 US$) \n", "7439 NY.GDP.PCAP.KN GDP per capita (constant LCU) \n", "7441 NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2011 internation... \n", "\n", " source \\\n", "685 LAC Equity Lab \n", "7437 World Development Indicators \n", "7439 World Development Indicators \n", "7441 World Development Indicators \n", "\n", " sourceNote \\\n", "685 GDP per capita based on purchasing power parit... \n", "7437 GDP per capita is gross domestic product divid... \n", "7439 GDP per capita is gross domestic product divid... \n", "7441 GDP per capita based on purchasing power parit... \n", "\n", " sourceOrganization topics \n", "685 b'World Development Indicators (World Bank)' Economy & Growth \n", "7437 b'World Bank national accounts data, and OECD ... Economy & Growth \n", "7439 b'World Bank national accounts data, and OECD ... Economy & Growth \n", "7441 b'World Bank, International Comparison Program... Economy & Growth " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wb.search('gdp.capita.const') # we use this function to search for GDP related indicators" ] }, { "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>id</th>\n", " <th>name</th>\n", " <th>source</th>\n", " <th>sourceNote</th>\n", " <th>sourceOrganization</th>\n", " <th>topics</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>9238</th>\n", " <td>SL.AGR.0714.FE.ZS</td>\n", " <td>Child employment in agriculture, female (% of ...</td>\n", " <td>World Development Indicators</td>\n", " <td>Employment by economic activity refers to the ...</td>\n", " <td>b\"Understanding Children's Work project based ...</td>\n", " <td>Social Protection & Labor ; Gender</td>\n", " 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Indicators</td>\n", " <td>Unemployment refers to the share of the labor ...</td>\n", " <td>b'International Labour Organization, Key Indic...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>9419</th>\n", " <td>SL.UEM.TOTL.MA.ZS</td>\n", " <td>Unemployment, male (% of male labor force) (mo...</td>\n", " <td>World Development Indicators</td>\n", " <td>Unemployment refers to the share of the labor ...</td>\n", " <td>b'International Labour Organization, Key Indic...</td>\n", " <td>Education ; Social Protection & Labor ; Gende...</td>\n", " </tr>\n", " <tr>\n", " <th>9420</th>\n", " <td>SL.UEM.TOTL.NE.ZS</td>\n", " <td>Unemployment, total (% of total labor force) (...</td>\n", " <td>World Development Indicators</td>\n", " <td>Unemployment refers to the share of the labor ...</td>\n", " <td>b'International Labour Organization, Key Indic...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>9421</th>\n", " <td>SL.UEM.TOTL.ZS</td>\n", " <td>Unemployment, total (% of total labor force) (...</td>\n", " <td>World Development Indicators</td>\n", " <td>Unemployment refers to the share of the labor ...</td>\n", " <td>b'International Labour Organization, Key Indic...</td>\n", " <td>Education ; Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>9422</th>\n", " <td>SL.WAG.0714.FE.ZS</td>\n", " <td>Children in employment, wage workers, female (...</td>\n", " <td>World Development Indicators</td>\n", " <td>Wage workers (also known as employees) are peo...</td>\n", " <td>b\"Understanding Children's Work project based ...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>9423</th>\n", " <td>SL.WAG.0714.MA.ZS</td>\n", " <td>Children in employment, wage workers, male (% ...</td>\n", " <td>World Development Indicators</td>\n", " <td>Wage workers (also known as employees) are peo...</td>\n", " <td>b\"Understanding Children's Work project based ...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>9424</th>\n", " <td>SL.WAG.0714.ZS</td>\n", " <td>Children in employment, wage workers (% of chi...</td>\n", " <td>World Development Indicators</td>\n", " <td>Wage workers (also known as employees) are peo...</td>\n", " <td>b\"Understanding Children's Work project based ...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12130</th>\n", " <td>ccx_unempr_pop_eld</td>\n", " <td>Unemployment rate - elderly</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12131</th>\n", " <td>ccx_unempr_pop_fem</td>\n", " <td>Unemployment rate - female</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12132</th>\n", " <td>ccx_unempr_pop_mal</td>\n", " <td>Unemployment rate - male</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12133</th>\n", " <td>ccx_unempr_pop_rur</td>\n", " <td>Unemployment rate - rural</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12134</th>\n", " <td>ccx_unempr_pop_tot</td>\n", " <td>Unemployment rate in total population</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12135</th>\n", " <td>ccx_unempr_pop_urb</td>\n", " <td>Unemployment rate - urban</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12136</th>\n", " <td>ccx_unempr_pop_wrk</td>\n", " <td>Unemployment rate - working age</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12137</th>\n", " <td>ccx_unempr_pop_you</td>\n", " <td>Unemployment rate - youth</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12159</th>\n", " <td>ccx_yaurr_pop_fem</td>\n", " <td>Youth to adult unemployment rate - female</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12160</th>\n", " <td>ccx_yaurr_pop_mal</td>\n", " <td>Youth to adult unemployment rate - male</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12161</th>\n", " <td>ccx_yaurr_pop_rur</td>\n", " <td>Youth to adult unemployment rate - rural</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12162</th>\n", " <td>ccx_yaurr_pop_tot</td>\n", " <td>Youth to adult unemployment rate in total popu...</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12163</th>\n", " <td>ccx_yaurr_pop_urb</td>\n", " <td>Youth to adult unemployment rate - urban</td>\n", " <td>Global Social Protection</td>\n", " <td>NULL</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " <tr>\n", " <th>12582</th>\n", " <td>per_lm_alllm.adq_pop_tot</td>\n", " <td>Adequacy of unemployment benefits and ALMP (% ...</td>\n", " <td>World Development Indicators</td>\n", " <td>Adequacy of unemployment benefits and active l...</td>\n", " <td>b'The Atlas of Social Protection: Indicators o...</td>\n", " <td>Social Protection & Labor</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>121 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " id \\\n", "9238 SL.AGR.0714.FE.ZS \n", "9239 SL.AGR.0714.MA.ZS \n", "9240 SL.AGR.0714.ZS \n", "9241 SL.AGR.EMPL.FE.ZS \n", "9242 SL.AGR.EMPL.MA.ZS \n", "9243 SL.AGR.EMPL.ZS \n", "9244 SL.EMP.1524.SP.FE.NE.ZS \n", "9245 SL.EMP.1524.SP.FE.ZS \n", "9246 SL.EMP.1524.SP.MA.NE.ZS \n", "9247 SL.EMP.1524.SP.MA.ZS \n", "9248 SL.EMP.1524.SP.NE.ZS \n", "9249 SL.EMP.1524.SP.ZS \n", "9255 SL.EMP.INSV.FE.ZS \n", "9257 SL.EMP.MPYR.FE.ZS \n", "9258 SL.EMP.MPYR.MA.ZS \n", "9259 SL.EMP.MPYR.ZS \n", "9260 SL.EMP.OWAC.FE.ZS \n", "9261 SL.EMP.OWAC.MA.ZS \n", "9262 SL.EMP.SELF.FE.ZS \n", "9263 SL.EMP.SELF.MA.ZS \n", "9264 SL.EMP.SELF.ZS \n", "9267 SL.EMP.TOTL.FE \n", "9268 SL.EMP.TOTL.MA \n", "9269 SL.EMP.TOTL.SP.FE.NE.ZS \n", "9270 SL.EMP.TOTL.SP.FE.ZS \n", "9271 SL.EMP.TOTL.SP.MA.NE.ZS \n", "9272 SL.EMP.TOTL.SP.MA.ZS \n", "9273 SL.EMP.TOTL.SP.NE.ZS \n", "9274 SL.EMP.TOTL.SP.ZS \n", "9278 SL.EMP.UNDR.FE.ZS \n", "... ... \n", "9408 SL.UEM.PRIM.ZS \n", "9409 SL.UEM.SECO.FE.ZS \n", "9410 SL.UEM.SECO.MA.ZS \n", "9411 SL.UEM.SECO.ZS \n", "9412 SL.UEM.TERT.FE.ZS \n", "9413 SL.UEM.TERT.MA.ZS \n", "9414 SL.UEM.TERT.ZS \n", "9416 SL.UEM.TOTL.FE.NE.ZS \n", "9417 SL.UEM.TOTL.FE.ZS \n", "9418 SL.UEM.TOTL.MA.NE.ZS \n", "9419 SL.UEM.TOTL.MA.ZS \n", "9420 SL.UEM.TOTL.NE.ZS \n", "9421 SL.UEM.TOTL.ZS \n", "9422 SL.WAG.0714.FE.ZS \n", "9423 SL.WAG.0714.MA.ZS \n", "9424 SL.WAG.0714.ZS \n", "12130 ccx_unempr_pop_eld \n", "12131 ccx_unempr_pop_fem \n", "12132 ccx_unempr_pop_mal \n", "12133 ccx_unempr_pop_rur \n", "12134 ccx_unempr_pop_tot \n", "12135 ccx_unempr_pop_urb \n", "12136 ccx_unempr_pop_wrk \n", "12137 ccx_unempr_pop_you \n", "12159 ccx_yaurr_pop_fem \n", "12160 ccx_yaurr_pop_mal \n", "12161 ccx_yaurr_pop_rur \n", "12162 ccx_yaurr_pop_tot \n", "12163 ccx_yaurr_pop_urb \n", "12582 per_lm_alllm.adq_pop_tot \n", "\n", " name \\\n", "9238 Child employment in agriculture, female (% of ... \n", "9239 Child employment in agriculture, male (% of ma... \n", "9240 Child employment in agriculture (% of economic... \n", "9241 Employment in agriculture, female (% of female... \n", "9242 Employment in agriculture, male (% of male emp... \n", "9243 Employment in agriculture (% of total employment) \n", "9244 Employment to population ratio, ages 15-24, fe... \n", "9245 Employment to population ratio, ages 15-24, fe... \n", "9246 Employment to population ratio, ages 15-24, ma... \n", "9247 Employment to population ratio, ages 15-24, ma... \n", "9248 Employment to population ratio, ages 15-24, to... \n", "9249 Employment to population ratio, ages 15-24, to... \n", "9255 Share of women in wage employment in the nonag... \n", "9257 Employers, female (% of female employment)\\r\\n \n", "9258 Employers, male (% of male employment)\\r\\n \n", "9259 Employers, total (% of total employment)\\r\\n \n", "9260 Own-account workers, female (% of female emplo... \n", "9261 Own-account workers, male (% of male employmen... \n", "9262 Self-employed, female (% of female employment)... \n", "9263 Self-employed, male (% of male employment)\\r\\n \n", "9264 Self-employed, total (% of total employment)\\r\\n \n", "9267 Total employment, female (ages 15+) \n", "9268 Total employment, male (ages 15+) \n", "9269 Employment to population ratio, 15+, female (%... \n", "9270 Employment to population ratio, 15+, female (%... \n", "9271 Employment to population ratio, 15+, male (%) ... \n", "9272 Employment to population ratio, 15+, male (%) ... \n", "9273 Employment to population ratio, 15+, total (%)... \n", "9274 Employment to population ratio, 15+, total (%)... \n", "9278 Time-related underemployment, female (% of emp... \n", "... ... \n", "9408 Unemployment with primary education (% of tota... \n", "9409 Unemployment with secondary education, female ... \n", "9410 Unemployment with secondary education, male (%... \n", "9411 Unemployment with secondary education (% of to... \n", "9412 Unemployment with tertiary education, female (... \n", "9413 Unemployment with tertiary education, male (% ... \n", "9414 Unemployment with tertiary education (% of tot... \n", "9416 Unemployment, female (% of female labor force)... \n", "9417 Unemployment, female (% of female labor force)... \n", "9418 Unemployment, male (% of male labor force) (na... \n", "9419 Unemployment, male (% of male labor force) (mo... \n", "9420 Unemployment, total (% of total labor force) (... \n", "9421 Unemployment, total (% of total labor force) (... \n", "9422 Children in employment, wage workers, female (... \n", "9423 Children in employment, wage workers, male (% ... \n", "9424 Children in employment, wage workers (% of chi... \n", "12130 Unemployment rate - elderly \n", "12131 Unemployment rate - female \n", "12132 Unemployment rate - male \n", "12133 Unemployment rate - rural \n", "12134 Unemployment rate in total population \n", "12135 Unemployment rate - urban \n", "12136 Unemployment rate - working age \n", "12137 Unemployment rate - youth \n", "12159 Youth to adult unemployment rate - female \n", "12160 Youth to adult unemployment rate - male \n", "12161 Youth to adult unemployment rate - rural \n", "12162 Youth to adult unemployment rate in total popu... \n", "12163 Youth to adult unemployment rate - urban \n", "12582 Adequacy of unemployment benefits and ALMP (% ... \n", "\n", " source \\\n", "9238 World Development Indicators \n", "9239 World Development Indicators \n", "9240 World Development Indicators \n", "9241 World Development Indicators \n", "9242 World Development Indicators \n", "9243 World Development Indicators \n", "9244 World Development Indicators \n", "9245 World Development Indicators \n", "9246 World Development Indicators \n", "9247 World Development Indicators \n", "9248 World Development Indicators \n", "9249 World Development Indicators \n", "9255 World Development Indicators \n", "9257 World Development Indicators \n", "9258 World Development Indicators \n", "9259 World Development Indicators \n", "9260 Gender Statistics \n", "9261 Gender Statistics \n", "9262 World Development Indicators \n", "9263 World Development Indicators \n", "9264 World Development Indicators \n", "9267 Africa Development Indicators \n", "9268 Africa Development Indicators \n", "9269 World Development Indicators \n", "9270 World Development Indicators \n", "9271 World Development Indicators \n", "9272 World Development Indicators \n", "9273 World Development Indicators \n", "9274 World Development Indicators \n", "9278 Gender Statistics \n", "... ... \n", "9408 World Development Indicators \n", "9409 World Development Indicators \n", "9410 World Development Indicators \n", "9411 World Development Indicators \n", "9412 World Development Indicators \n", "9413 World Development Indicators \n", "9414 World Development Indicators \n", "9416 World Development Indicators \n", "9417 World Development Indicators \n", "9418 World Development Indicators \n", "9419 World Development Indicators \n", "9420 World Development Indicators \n", "9421 World Development Indicators \n", "9422 World Development Indicators \n", "9423 World Development Indicators \n", "9424 World Development Indicators \n", "12130 Global Social Protection \n", "12131 Global Social Protection \n", "12132 Global Social Protection \n", "12133 Global Social Protection \n", "12134 Global Social Protection \n", "12135 Global Social Protection \n", "12136 Global Social Protection \n", "12137 Global Social Protection \n", "12159 Global Social Protection \n", "12160 Global Social Protection \n", "12161 Global Social Protection \n", "12162 Global Social Protection \n", "12163 Global Social Protection \n", "12582 World Development Indicators \n", "\n", " sourceNote \\\n", "9238 Employment by economic activity refers to the ... \n", "9239 Employment by economic activity refers to the ... \n", "9240 Employment by economic activity refers to the ... \n", "9241 Employment is defined as persons of working ag... \n", "9242 Employment is defined as persons of working ag... \n", "9243 Employment is defined as persons of working ag... \n", "9244 Employment to population ratio is the proporti... \n", "9245 Employment to population ratio is the proporti... \n", "9246 Employment to population ratio is the proporti... \n", "9247 Employment to population ratio is the proporti... \n", "9248 Employment to population ratio is the proporti... \n", "9249 Employment to population ratio is the proporti... \n", "9255 Share of women in wage employment in the nonag... \n", "9257 Employers refers are those workers who, workin... \n", "9258 Employers refers are those workers who, workin... \n", "9259 Employers refers are those workers who, workin... \n", "9260 Own-account workers are workers who, working o... \n", "9261 Own-account workers are workers who, working o... \n", "9262 Self-employed workers are those workers who, w... \n", "9263 Self-employed workers are those workers who, w... \n", "9264 Self-employed workers are those workers who, w... \n", "9267 Total employment shows the total number employ... \n", "9268 Total employment shows the total number employ... \n", "9269 Employment to population ratio is the proporti... \n", "9270 Employment to population ratio is the proporti... \n", "9271 Employment to population ratio is the proporti... \n", "9272 Employment to population ratio is the proporti... \n", "9273 Employment to population ratio is the proporti... \n", "9274 Employment to population ratio is the proporti... \n", "9278 Time-related underemployment refers to all per... \n", "... ... \n", "9408 Unemployment with primary education is the sha... \n", "9409 Female unemployment with secondary education i... \n", "9410 Male unemployment with secondary education is ... \n", "9411 Unemployment with secondary education is the s... \n", "9412 Female unemployment with tertiary education is... \n", "9413 Male unemployment with tertiary education is t... \n", "9414 Unemployment with tertiary education is the sh... \n", "9416 Unemployment refers to the share of the labor ... \n", "9417 Unemployment refers to the share of the labor ... \n", "9418 Unemployment refers to the share of the labor ... \n", "9419 Unemployment refers to the share of the labor ... \n", "9420 Unemployment refers to the share of the labor ... \n", "9421 Unemployment refers to the share of the labor ... \n", "9422 Wage workers (also known as employees) are peo... \n", "9423 Wage workers (also known as employees) are peo... \n", "9424 Wage workers (also known as employees) are peo... \n", "12130 NULL \n", "12131 NULL \n", "12132 NULL \n", "12133 NULL \n", "12134 NULL \n", "12135 NULL \n", "12136 NULL \n", "12137 NULL \n", "12159 NULL \n", "12160 NULL \n", "12161 NULL \n", "12162 NULL \n", "12163 NULL \n", "12582 Adequacy of unemployment benefits and active l... \n", "\n", " sourceOrganization \\\n", "9238 b\"Understanding Children's Work project based ... \n", "9239 b\"Understanding Children's Work project based ... \n", "9240 b\"Understanding Children's Work project based ... \n", "9241 b'International Labour Organization, Key Indic... \n", "9242 b'International Labour Organization, Key Indic... \n", "9243 b'International Labour Organization, Key Indic... \n", "9244 b'International Labour Organization, Key Indic... \n", "9245 b'International Labour Organization, Key Indic... \n", "9246 b'International Labour Organization, Key Indic... \n", "9247 b'International Labour Organization, Key Indic... \n", "9248 b'International Labour Organization, Key Indic... \n", "9249 b'International Labour Organization, Key Indic... \n", "9255 b'International Labour Organization, Key Indic... \n", "9257 b'ILO Key Indicators of the Labour Market (KIL... \n", "9258 b'ILO Key Indicators of the Labour Market (KIL... \n", "9259 b'ILO Key Indicators of the Labour Market (KIL... \n", "9260 b'International Labour Organization, Key Indic... \n", "9261 b'International Labour Organization, Key Indic... \n", "9262 b'International Labour Organization, Key Indic... \n", "9263 b'International Labour Organization, Key Indic... \n", "9264 b'International Labour Organization, Key Indic... \n", "9267 b'International Labour Organization, Key Indic... \n", "9268 b'International Labour Organization, Key Indic... \n", "9269 b'International Labour Organization, Key Indic... \n", "9270 b'International Labour Organization, Key Indic... \n", "9271 b'International Labour Organization, Key Indic... \n", "9272 b'International Labour Organization, Key Indic... \n", "9273 b'International Labour Organization, Key Indic... \n", "9274 b'International Labour Organization, Key Indic... \n", "9278 b'ILO Key Indicators of the Labour Market (KIL... \n", "... ... \n", "9408 b'International Labour Organization, Key Indic... \n", "9409 b'International Labour Organization, Key Indic... \n", "9410 b'International Labour Organization, Key Indic... \n", "9411 b'International Labour Organization, Key Indic... \n", "9412 b'International Labour Organization, Key Indic... \n", "9413 b'International Labour Organization, Key Indic... \n", "9414 b'International Labour Organization, Key Indic... \n", "9416 b'International Labour Organization, Key Indic... \n", "9417 b'International Labour Organization, Key Indic... \n", "9418 b'International Labour Organization, Key Indic... \n", "9419 b'International Labour Organization, Key Indic... \n", "9420 b'International Labour Organization, Key Indic... \n", "9421 b'International Labour Organization, Key Indic... \n", "9422 b\"Understanding Children's Work project based ... \n", "9423 b\"Understanding Children's Work project based ... \n", "9424 b\"Understanding Children's Work project based ... \n", "12130 b'The Atlas of Social Protection: Indicators o... \n", "12131 b'The Atlas of Social Protection: Indicators o... \n", "12132 b'The Atlas of Social Protection: Indicators o... \n", "12133 b'The Atlas of Social Protection: Indicators o... \n", "12134 b'The Atlas of Social Protection: Indicators o... \n", "12135 b'The Atlas of Social Protection: Indicators o... \n", "12136 b'The Atlas of Social Protection: Indicators o... \n", "12137 b'The Atlas of Social Protection: Indicators o... \n", "12159 b'The Atlas of Social Protection: Indicators o... \n", "12160 b'The Atlas of Social Protection: Indicators o... \n", "12161 b'The Atlas of Social Protection: Indicators o... \n", "12162 b'The Atlas of Social Protection: Indicators o... \n", "12163 b'The Atlas of Social Protection: Indicators o... \n", "12582 b'The Atlas of Social Protection: Indicators o... \n", "\n", " topics \n", "9238 Social Protection & Labor ; Gender \n", "9239 Social Protection & Labor ; Gender \n", "9240 Social Protection & Labor \n", "9241 Agriculture & Rural Development ; Social Pro... \n", "9242 Agriculture & Rural Development ; Social Pro... \n", "9243 Agriculture & Rural Development ; Social Pro... \n", "9244 Social Protection & Labor \n", "9245 Social Protection & Labor ; Gender \n", "9246 Social Protection & Labor \n", "9247 Social Protection & Labor ; Gender \n", "9248 Social Protection & Labor \n", "9249 Social Protection & Labor \n", "9255 Social Protection & Labor ; Gender ; Social De... \n", "9257 Social Protection & Labor ; Gender \n", "9258 Social Protection & Labor ; Gender \n", "9259 Social Protection & Labor \n", "9260 Gender \n", "9261 Gender \n", "9262 Social Protection & Labor ; Gender \n", "9263 Social Protection & Labor ; Gender \n", "9264 Social Protection & Labor \n", "9267 \n", "9268 \n", "9269 Social Protection & Labor \n", "9270 Social Protection & Labor ; Gender \n", "9271 Social Protection & Labor \n", "9272 Social Protection & Labor ; Gender \n", "9273 Social Protection & Labor \n", "9274 Social Protection & Labor \n", "9278 Gender \n", "... ... \n", "9408 Social Protection & Labor \n", "9409 Social Protection & Labor ; Gender \n", "9410 Social Protection & Labor ; Gender \n", "9411 Social Protection & Labor \n", "9412 Social Protection & Labor ; Gender \n", "9413 Social Protection & Labor ; Gender \n", "9414 Social Protection & Labor \n", "9416 Social Protection & Labor \n", "9417 Education ; Social Protection & Labor ; Gende... \n", "9418 Social Protection & Labor \n", "9419 Education ; Social Protection & Labor ; Gende... \n", "9420 Social Protection & Labor \n", "9421 Education ; Social Protection & Labor \n", "9422 Social Protection & Labor \n", "9423 Social Protection & Labor \n", "9424 Social Protection & Labor \n", "12130 Social Protection & Labor \n", "12131 Social Protection & Labor \n", "12132 Social Protection & Labor \n", "12133 Social Protection & Labor \n", "12134 Social Protection & Labor \n", "12135 Social Protection & Labor \n", "12136 Social Protection & Labor \n", "12137 Social Protection & Labor \n", "12159 Social Protection & Labor \n", "12160 Social Protection & Labor \n", "12161 Social Protection & Labor \n", "12162 Social Protection & Labor \n", "12163 Social Protection & Labor \n", "12582 Social Protection & Labor \n", "\n", "[121 rows x 6 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wb.search('employment') # we use this function to search for employment related indicators" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", 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\n", "12136 Unemployment rate - working age \n", "12137 Unemployment rate - youth \n", "12159 Youth to adult unemployment rate - female \n", "12160 Youth to adult unemployment rate - male \n", "12161 Youth to adult unemployment rate - rural \n", "12162 Youth to adult unemployment rate in total popu... \n", "12163 Youth to adult unemployment rate - urban \n", "12582 Adequacy of unemployment benefits and ALMP (% ... \n", "\n", " source \\\n", "9392 World Development Indicators \n", "9393 World Development Indicators \n", "9394 Gender Statistics \n", "9395 Gender Statistics \n", "9396 World Development Indicators \n", "9397 World Development Indicators \n", "9398 World Development Indicators \n", "9399 World Development Indicators \n", "9400 World Development Indicators \n", "9401 World Development Indicators \n", "9402 World Development Indicators \n", "9406 World Development Indicators \n", "9407 World Development Indicators \n", "9408 World Development Indicators \n", "9409 World Development Indicators \n", "9410 World Development Indicators \n", "9411 World Development Indicators \n", "9412 World Development Indicators \n", "9413 World Development Indicators \n", "9414 World Development Indicators \n", "9416 World Development Indicators \n", "9417 World Development Indicators \n", "9418 World Development Indicators \n", "9419 World Development Indicators \n", "9420 World Development Indicators \n", "9421 World Development Indicators \n", "12130 Global Social Protection \n", "12131 Global Social Protection \n", "12132 Global Social Protection \n", "12133 Global Social Protection \n", "12134 Global Social Protection \n", "12135 Global Social Protection \n", "12136 Global Social Protection \n", "12137 Global Social Protection \n", "12159 Global Social Protection \n", "12160 Global Social Protection \n", "12161 Global Social Protection \n", "12162 Global Social Protection \n", "12163 Global Social Protection \n", "12582 World Development Indicators \n", "\n", " sourceNote \\\n", "9392 Youth unemployment refers to the share of the ... \n", "9393 Youth unemployment refers to the share of the ... \n", "9394 Ratio of female to male youth unemployment is ... \n", "9395 Ratio of female to male youth unemployment is ... \n", "9396 Youth unemployment refers to the share of the ... \n", "9397 Youth unemployment refers to the share of the ... \n", "9398 Youth unemployment refers to the share of the ... \n", "9399 Youth unemployment refers to the share of the ... \n", "9400 Long-term unemployment refers to the number of... \n", "9401 Long-term unemployment refers to the number of... \n", "9402 Long-term unemployment refers to the number of... \n", "9406 Female unemployment with primary education is ... \n", "9407 Male unemployment with primary education is th... \n", "9408 Unemployment with primary education is the sha... \n", "9409 Female unemployment with secondary education i... \n", "9410 Male unemployment with secondary education is ... \n", "9411 Unemployment with secondary education is the s... \n", "9412 Female unemployment with tertiary education is... \n", "9413 Male unemployment with tertiary education is t... \n", "9414 Unemployment with tertiary education is the sh... \n", "9416 Unemployment refers to the share of the labor ... \n", "9417 Unemployment refers to the share of the labor ... \n", "9418 Unemployment refers to the share of the labor ... \n", "9419 Unemployment refers to the share of the labor ... \n", "9420 Unemployment refers to the share of the labor ... \n", "9421 Unemployment refers to the share of the labor ... \n", "12130 NULL \n", "12131 NULL \n", "12132 NULL \n", "12133 NULL \n", "12134 NULL \n", "12135 NULL \n", "12136 NULL \n", "12137 NULL \n", "12159 NULL \n", "12160 NULL \n", "12161 NULL \n", "12162 NULL \n", "12163 NULL \n", "12582 Adequacy of unemployment benefits and active l... \n", "\n", " sourceOrganization \\\n", "9392 b'International Labour Organization, Key Indic... \n", "9393 b'International Labour Organization, Key Indic... \n", "9394 b'International Labour Organization, Key Indic... \n", "9395 b'International Labour Organization, Key Indic... \n", "9396 b'International Labour Organization, Key Indic... \n", "9397 b'International Labour Organization, Key Indic... \n", "9398 b'International Labour Organization, Key Indic... \n", "9399 b'International Labour Organization, Key Indic... \n", "9400 b'International Labour Organization, Key Indic... \n", "9401 b'International Labour Organization, Key Indic... \n", "9402 b'International Labour Organization, Key Indic... \n", "9406 b'International Labour Organization, Key Indic... \n", "9407 b'International Labour Organization, Key Indic... \n", "9408 b'International Labour Organization, Key Indic... \n", "9409 b'International Labour Organization, Key Indic... \n", "9410 b'International Labour Organization, Key Indic... \n", "9411 b'International Labour Organization, Key Indic... \n", "9412 b'International Labour Organization, Key Indic... \n", "9413 b'International Labour Organization, Key Indic... \n", "9414 b'International Labour Organization, Key Indic... \n", "9416 b'International Labour Organization, Key Indic... \n", "9417 b'International Labour Organization, Key Indic... \n", "9418 b'International Labour Organization, Key Indic... \n", "9419 b'International Labour Organization, Key Indic... \n", "9420 b'International Labour Organization, Key Indic... \n", "9421 b'International Labour Organization, Key Indic... \n", "12130 b'The Atlas of Social Protection: Indicators o... \n", "12131 b'The Atlas of Social Protection: Indicators o... \n", "12132 b'The Atlas of Social Protection: Indicators o... \n", "12133 b'The Atlas of Social Protection: Indicators o... \n", "12134 b'The Atlas of Social Protection: Indicators o... \n", "12135 b'The Atlas of Social Protection: Indicators o... \n", "12136 b'The Atlas of Social Protection: Indicators o... \n", "12137 b'The Atlas of Social Protection: Indicators o... \n", "12159 b'The Atlas of Social Protection: Indicators o... \n", "12160 b'The Atlas of Social Protection: Indicators o... \n", "12161 b'The Atlas of Social Protection: Indicators o... \n", "12162 b'The Atlas of Social Protection: Indicators o... \n", "12163 b'The Atlas of Social Protection: Indicators o... \n", "12582 b'The Atlas of Social Protection: Indicators o... \n", "\n", " topics \n", "9392 Social Protection & Labor \n", "9393 Social Protection & Labor ; Gender \n", "9394 Gender \n", "9395 Gender \n", "9396 Social Protection & Labor \n", "9397 Social Protection & Labor ; Gender \n", "9398 Social Protection & Labor \n", "9399 Social Protection & Labor \n", "9400 Social Protection & Labor ; Gender \n", "9401 Social Protection & Labor ; Gender \n", "9402 Social Protection & Labor \n", "9406 Social Protection & Labor ; Gender \n", "9407 Social Protection & Labor ; Gender \n", "9408 Social Protection & Labor \n", "9409 Social Protection & Labor ; Gender \n", "9410 Social Protection & Labor ; Gender \n", "9411 Social Protection & Labor \n", "9412 Social Protection & Labor ; Gender \n", "9413 Social Protection & Labor ; Gender \n", "9414 Social Protection & Labor \n", "9416 Social Protection & Labor \n", "9417 Education ; Social Protection & Labor ; Gende... \n", "9418 Social Protection & Labor \n", "9419 Education ; Social Protection & Labor ; Gende... \n", "9420 Social Protection & Labor \n", "9421 Education ; Social Protection & Labor \n", "12130 Social Protection & Labor \n", "12131 Social Protection & Labor \n", "12132 Social Protection & Labor \n", "12133 Social Protection & Labor \n", "12134 Social Protection & Labor \n", "12135 Social Protection & Labor \n", "12136 Social Protection & Labor \n", "12137 Social Protection & Labor \n", "12159 Social Protection & Labor \n", "12160 Social Protection & Labor \n", "12161 Social Protection & Labor \n", "12162 Social Protection & Labor \n", "12163 Social Protection & Labor \n", "12582 Social Protection & Labor " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wb.search('unemployment') # we use this function to search for unemployment related indicators" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>GDP/capita(US$ 2016)</th>\n", " <th>UnemploymentRate</th>\n", " <th>YouthUnempRate</th>\n", " <th>UnempW/PrimEd.</th>\n", " <th>UnempW/SecEd</th>\n", " <th>UnempW/TertEd</th>\n", " <th>Ni-nis</th>\n", " </tr>\n", " <tr>\n", " <th>country</th>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"26\" valign=\"top\">Spain</th>\n", " <th>2015</th>\n", " <td>25831.582305</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td>29718.500216</td>\n", " <td>24.700001</td>\n", " <td>57.900002</td>\n", " <td>54.500000</td>\n", " <td>23.100000</td>\n", " <td>22.500000</td>\n", " <td>18.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2013</th>\n", " <td>29370.663867</td>\n", " <td>26.299999</td>\n", " <td>57.099998</td>\n", " <td>53.799999</td>\n", " <td>23.299999</td>\n", " <td>22.200001</td>\n", " <td>19.440001</td>\n", " </tr>\n", " <tr>\n", " <th>2012</th>\n", " <td>28647.835243</td>\n", " <td>25.200001</td>\n", " <td>54.299999</td>\n", " <td>54.700001</td>\n", " <td>23.299999</td>\n", " <td>21.100000</td>\n", " <td>19.570000</td>\n", " </tr>\n", " <tr>\n", " <th>2011</th>\n", " <td>31832.238081</td>\n", " <td>21.700001</td>\n", " <td>47.099998</td>\n", " <td>55.099998</td>\n", " <td>23.799999</td>\n", " <td>20.100000</td>\n", " <td>19.200001</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>30737.832271</td>\n", " <td>20.200001</td>\n", " <td>42.500000</td>\n", " <td>57.200001</td>\n", " <td>22.900000</td>\n", " <td>18.799999</td>\n", " <td>18.799999</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <td>32333.466104</td>\n", " <td>18.100000</td>\n", " <td>38.500000</td>\n", " <td>58.599998</td>\n", " <td>22.700001</td>\n", " <td>17.600000</td>\n", " <td>19.400000</td>\n", " </tr>\n", " <tr>\n", " <th>2008</th>\n", " <td>35578.736190</td>\n", " <td>11.500000</td>\n", " <td>25.400000</td>\n", " <td>58.299999</td>\n", " <td>22.400000</td>\n", " <td>18.000000</td>\n", " <td>13.920000</td>\n", " </tr>\n", " <tr>\n", " <th>2007</th>\n", " <td>32709.401038</td>\n", " <td>8.400000</td>\n", " <td>18.900000</td>\n", " <td>54.599998</td>\n", " <td>23.700001</td>\n", " <td>20.500000</td>\n", " <td>10.440000</td>\n", " </tr>\n", " <tr>\n", " <th>2006</th>\n", " <td>28482.609483</td>\n", " <td>8.600000</td>\n", " <td>18.500000</td>\n", " <td>53.799999</td>\n", " <td>22.799999</td>\n", " <td>22.500000</td>\n", " <td>10.280000</td>\n", " </tr>\n", " <tr>\n", " <th>2005</th>\n", " <td>26510.717453</td>\n", " <td>9.300000</td>\n", " <td>20.400000</td>\n", " <td>53.900002</td>\n", " <td>21.900000</td>\n", " <td>23.299999</td>\n", " <td>11.120000</td>\n", " </tr>\n", " <tr>\n", " <th>2004</th>\n", " <td>24918.645842</td>\n", " <td>11.200000</td>\n", " <td>22.799999</td>\n", " <td>54.799999</td>\n", " <td>21.500000</td>\n", " <td>22.500000</td>\n", " <td>10.310000</td>\n", " </tr>\n", " <tr>\n", " <th>2003</th>\n", " <td>21495.707408</td>\n", " <td>11.500000</td>\n", " <td>23.400000</td>\n", " <td>56.400002</td>\n", " <td>21.400000</td>\n", " <td>21.200001</td>\n", " <td>9.960000</td>\n", " </tr>\n", " <tr>\n", " <th>2002</th>\n", " <td>17019.535414</td>\n", " <td>11.600000</td>\n", " <td>23.000000</td>\n", " <td>56.799999</td>\n", " <td>20.700001</td>\n", " <td>22.500000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2001</th>\n", " <td>15359.108440</td>\n", " <td>10.700000</td>\n", " <td>21.299999</td>\n", " <td>57.799999</td>\n", " <td>20.200001</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <td>14787.756064</td>\n", " <td>14.200000</td>\n", " <td>26.299999</td>\n", " <td>59.599998</td>\n", " <td>19.400000</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1999</th>\n", " <td>15859.086026</td>\n", " <td>15.900000</td>\n", " <td>29.299999</td>\n", " <td>58.900002</td>\n", " <td>20.000000</td>\n", " <td>21.100000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1998</th>\n", " <td>15534.359889</td>\n", " <td>19.000000</td>\n", " <td>35.299999</td>\n", " <td>61.500000</td>\n", " <td>18.500000</td>\n", " <td>20.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1997</th>\n", " <td>14872.565891</td>\n", " <td>21.100000</td>\n", " <td>38.700001</td>\n", " <td>62.200001</td>\n", " <td>19.400000</td>\n", " <td>18.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>16236.771679</td>\n", " <td>22.500000</td>\n", " <td>41.500000</td>\n", " <td>63.799999</td>\n", " <td>18.799999</td>\n", " <td>17.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1995</th>\n", " <td>15561.972746</td>\n", " <td>23.100000</td>\n", " <td>41.700001</td>\n", " <td>65.300003</td>\n", " <td>18.700001</td>\n", " <td>16.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1994</th>\n", " <td>13465.377826</td>\n", " <td>24.299999</td>\n", " <td>44.200001</td>\n", " <td>68.000000</td>\n", " <td>17.700001</td>\n", " <td>14.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1993</th>\n", " <td>13362.018346</td>\n", " <td>22.799999</td>\n", " <td>42.099998</td>\n", " <td>70.699997</td>\n", " <td>16.400000</td>\n", " <td>12.900000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1992</th>\n", " <td>16105.418729</td>\n", " <td>18.400000</td>\n", " <td>33.200001</td>\n", " <td>72.099998</td>\n", " <td>16.900000</td>\n", " <td>11.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1991</th>\n", " <td>14782.038901</td>\n", " <td>16.400000</td>\n", " <td>29.799999</td>\n", " <td>62.000000</td>\n", " <td>15.000000</td>\n", " <td>12.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <td>13773.365698</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>61.200001</td>\n", " <td>16.000000</td>\n", " <td>12.300000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " GDP/capita(US$ 2016) UnemploymentRate YouthUnempRate \\\n", "country year \n", "Spain 2015 25831.582305 NaN NaN \n", " 2014 29718.500216 24.700001 57.900002 \n", " 2013 29370.663867 26.299999 57.099998 \n", " 2012 28647.835243 25.200001 54.299999 \n", " 2011 31832.238081 21.700001 47.099998 \n", " 2010 30737.832271 20.200001 42.500000 \n", " 2009 32333.466104 18.100000 38.500000 \n", " 2008 35578.736190 11.500000 25.400000 \n", " 2007 32709.401038 8.400000 18.900000 \n", " 2006 28482.609483 8.600000 18.500000 \n", " 2005 26510.717453 9.300000 20.400000 \n", " 2004 24918.645842 11.200000 22.799999 \n", " 2003 21495.707408 11.500000 23.400000 \n", " 2002 17019.535414 11.600000 23.000000 \n", " 2001 15359.108440 10.700000 21.299999 \n", " 2000 14787.756064 14.200000 26.299999 \n", " 1999 15859.086026 15.900000 29.299999 \n", " 1998 15534.359889 19.000000 35.299999 \n", " 1997 14872.565891 21.100000 38.700001 \n", " 1996 16236.771679 22.500000 41.500000 \n", " 1995 15561.972746 23.100000 41.700001 \n", " 1994 13465.377826 24.299999 44.200001 \n", " 1993 13362.018346 22.799999 42.099998 \n", " 1992 16105.418729 18.400000 33.200001 \n", " 1991 14782.038901 16.400000 29.799999 \n", " 1990 13773.365698 NaN NaN \n", "\n", " UnempW/PrimEd. UnempW/SecEd UnempW/TertEd Ni-nis \n", "country year \n", "Spain 2015 NaN NaN NaN NaN \n", " 2014 54.500000 23.100000 22.500000 18.000000 \n", " 2013 53.799999 23.299999 22.200001 19.440001 \n", " 2012 54.700001 23.299999 21.100000 19.570000 \n", " 2011 55.099998 23.799999 20.100000 19.200001 \n", " 2010 57.200001 22.900000 18.799999 18.799999 \n", " 2009 58.599998 22.700001 17.600000 19.400000 \n", " 2008 58.299999 22.400000 18.000000 13.920000 \n", " 2007 54.599998 23.700001 20.500000 10.440000 \n", " 2006 53.799999 22.799999 22.500000 10.280000 \n", " 2005 53.900002 21.900000 23.299999 11.120000 \n", " 2004 54.799999 21.500000 22.500000 10.310000 \n", " 2003 56.400002 21.400000 21.200001 9.960000 \n", " 2002 56.799999 20.700001 22.500000 NaN \n", " 2001 57.799999 20.200001 21.000000 NaN \n", " 2000 59.599998 19.400000 21.000000 NaN \n", " 1999 58.900002 20.000000 21.100000 NaN \n", " 1998 61.500000 18.500000 20.000000 NaN \n", " 1997 62.200001 19.400000 18.299999 NaN \n", " 1996 63.799999 18.799999 17.299999 NaN \n", " 1995 65.300003 18.700001 16.000000 NaN \n", " 1994 68.000000 17.700001 14.400000 NaN \n", " 1993 70.699997 16.400000 12.900000 NaN \n", " 1992 72.099998 16.900000 11.000000 NaN \n", " 1991 62.000000 15.000000 12.400000 NaN \n", " 1990 61.200001 16.000000 12.300000 NaN " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#I have identified the relevant variables in the three fields\n", "#To download data for multiple indicators, I specify them as a list \n", "#ESP is the ISO code for Spain\n", "#I equalize the start and end dates\n", "wb.download( indicator=['NY.GDP.PCAP.CD','SL.UEM.TOTL.ZS','SL.UEM.1524.ZS',\n", " 'SL.UEM.PRIM.ZS', 'SL.UEM.SECO.ZS','SL.UEM.TERT.ZS','SL.UEM.NEET.MA.ZS','SL.UEM.NEET.MA.ZS'], \n", " country=['ESP'], start=1990, end=2015)\n", "#Construct the dataframe\n", "data = wb.download(indicator=['NY.GDP.PCAP.CD','SL.UEM.TOTL.ZS','SL.UEM.1524.ZS',\n", " 'SL.UEM.PRIM.ZS', 'SL.UEM.SECO.ZS','SL.UEM.TERT.ZS','SL.UEM.NEET.MA.ZS','SL.UEM.NEET.MA.ZS'], \n", " country=['ESP'], start=1990, end=2015)\n", "esplbr = pd.DataFrame(data)\n", "#Rename the columns for clarity \n", "esplbr.columns = [\"GDP/capita(US$ 2016)\", \"UnemploymentRate\", \"YouthUnempRate\", \"UnempW/PrimEd.\", \"UnempW/SecEd\",\"UnempW/TertEd\", \"Ni-nis\"]\n", "esplbr\n", "#What on earth are Ni-nis? A Spanish neologism for \"ni estudia, ni trabaja\": percentage of youth \"not working, not studying\"\n", "#A cultural and socioeconomic phenomenon" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>country</th>\n", " <th>year</th>\n", " <th>GDP/capita(US$ 2016)</th>\n", " <th>UnemploymentRate</th>\n", " <th>YouthUnempRate</th>\n", " <th>UnempW/PrimEd.</th>\n", " <th>UnempW/SecEd</th>\n", " <th>UnempW/TertEd</th>\n", " <th>Ni-nis</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Spain</td>\n", " <td>2015</td>\n", " <td>25831.582305</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Spain</td>\n", " <td>2014</td>\n", " <td>29718.500216</td>\n", " <td>24.700001</td>\n", " <td>57.900002</td>\n", " <td>54.500000</td>\n", " <td>23.100000</td>\n", " <td>22.500000</td>\n", " <td>18.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Spain</td>\n", " <td>2013</td>\n", " <td>29370.663867</td>\n", " <td>26.299999</td>\n", " <td>57.099998</td>\n", " <td>53.799999</td>\n", " <td>23.299999</td>\n", " <td>22.200001</td>\n", " <td>19.440001</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Spain</td>\n", " <td>2012</td>\n", " <td>28647.835243</td>\n", " <td>25.200001</td>\n", " <td>54.299999</td>\n", " <td>54.700001</td>\n", " <td>23.299999</td>\n", " <td>21.100000</td>\n", " <td>19.570000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Spain</td>\n", " <td>2011</td>\n", " <td>31832.238081</td>\n", " <td>21.700001</td>\n", " <td>47.099998</td>\n", " <td>55.099998</td>\n", " <td>23.799999</td>\n", " <td>20.100000</td>\n", " <td>19.200001</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Spain</td>\n", " <td>2010</td>\n", " <td>30737.832271</td>\n", " <td>20.200001</td>\n", " <td>42.500000</td>\n", " <td>57.200001</td>\n", " <td>22.900000</td>\n", " <td>18.799999</td>\n", " <td>18.799999</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Spain</td>\n", " <td>2009</td>\n", " <td>32333.466104</td>\n", " <td>18.100000</td>\n", " <td>38.500000</td>\n", " <td>58.599998</td>\n", " <td>22.700001</td>\n", " <td>17.600000</td>\n", " <td>19.400000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Spain</td>\n", " <td>2008</td>\n", " <td>35578.736190</td>\n", " <td>11.500000</td>\n", " <td>25.400000</td>\n", " <td>58.299999</td>\n", " <td>22.400000</td>\n", " <td>18.000000</td>\n", " <td>13.920000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Spain</td>\n", " <td>2007</td>\n", " <td>32709.401038</td>\n", " <td>8.400000</td>\n", " <td>18.900000</td>\n", " <td>54.599998</td>\n", " <td>23.700001</td>\n", " <td>20.500000</td>\n", " <td>10.440000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Spain</td>\n", " <td>2006</td>\n", " <td>28482.609483</td>\n", " <td>8.600000</td>\n", " <td>18.500000</td>\n", " <td>53.799999</td>\n", " <td>22.799999</td>\n", " <td>22.500000</td>\n", " <td>10.280000</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Spain</td>\n", " <td>2005</td>\n", " <td>26510.717453</td>\n", " <td>9.300000</td>\n", " <td>20.400000</td>\n", " <td>53.900002</td>\n", " <td>21.900000</td>\n", " <td>23.299999</td>\n", " <td>11.120000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Spain</td>\n", " <td>2004</td>\n", " <td>24918.645842</td>\n", " <td>11.200000</td>\n", " <td>22.799999</td>\n", " <td>54.799999</td>\n", " <td>21.500000</td>\n", " <td>22.500000</td>\n", " <td>10.310000</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Spain</td>\n", " <td>2003</td>\n", " <td>21495.707408</td>\n", " <td>11.500000</td>\n", " <td>23.400000</td>\n", " <td>56.400002</td>\n", " <td>21.400000</td>\n", " <td>21.200001</td>\n", " <td>9.960000</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Spain</td>\n", " <td>2002</td>\n", " <td>17019.535414</td>\n", " <td>11.600000</td>\n", " <td>23.000000</td>\n", " <td>56.799999</td>\n", " <td>20.700001</td>\n", " <td>22.500000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Spain</td>\n", " <td>2001</td>\n", " <td>15359.108440</td>\n", " <td>10.700000</td>\n", " <td>21.299999</td>\n", " <td>57.799999</td>\n", " <td>20.200001</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Spain</td>\n", " <td>2000</td>\n", " <td>14787.756064</td>\n", " <td>14.200000</td>\n", " <td>26.299999</td>\n", " <td>59.599998</td>\n", " <td>19.400000</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Spain</td>\n", " <td>1999</td>\n", " <td>15859.086026</td>\n", " <td>15.900000</td>\n", " <td>29.299999</td>\n", " <td>58.900002</td>\n", " <td>20.000000</td>\n", " <td>21.100000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Spain</td>\n", " <td>1998</td>\n", " <td>15534.359889</td>\n", " <td>19.000000</td>\n", " <td>35.299999</td>\n", " <td>61.500000</td>\n", " <td>18.500000</td>\n", " <td>20.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Spain</td>\n", " <td>1997</td>\n", " <td>14872.565891</td>\n", " <td>21.100000</td>\n", " <td>38.700001</td>\n", " <td>62.200001</td>\n", " <td>19.400000</td>\n", " <td>18.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Spain</td>\n", " <td>1996</td>\n", " <td>16236.771679</td>\n", " <td>22.500000</td>\n", " <td>41.500000</td>\n", " <td>63.799999</td>\n", " <td>18.799999</td>\n", " <td>17.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Spain</td>\n", " <td>1995</td>\n", " <td>15561.972746</td>\n", " <td>23.100000</td>\n", " <td>41.700001</td>\n", " <td>65.300003</td>\n", " <td>18.700001</td>\n", " <td>16.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Spain</td>\n", " <td>1994</td>\n", " <td>13465.377826</td>\n", " <td>24.299999</td>\n", " <td>44.200001</td>\n", " <td>68.000000</td>\n", " <td>17.700001</td>\n", " <td>14.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Spain</td>\n", " <td>1993</td>\n", " <td>13362.018346</td>\n", " <td>22.799999</td>\n", " <td>42.099998</td>\n", " <td>70.699997</td>\n", " <td>16.400000</td>\n", " <td>12.900000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Spain</td>\n", " <td>1992</td>\n", " <td>16105.418729</td>\n", " <td>18.400000</td>\n", " <td>33.200001</td>\n", " <td>72.099998</td>\n", " <td>16.900000</td>\n", " <td>11.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Spain</td>\n", " <td>1991</td>\n", " <td>14782.038901</td>\n", " <td>16.400000</td>\n", " <td>29.799999</td>\n", " <td>62.000000</td>\n", " <td>15.000000</td>\n", " <td>12.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Spain</td>\n", " <td>1990</td>\n", " <td>13773.365698</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>61.200001</td>\n", " <td>16.000000</td>\n", " <td>12.300000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country year GDP/capita(US$ 2016) UnemploymentRate YouthUnempRate \\\n", "0 Spain 2015 25831.582305 NaN NaN \n", "1 Spain 2014 29718.500216 24.700001 57.900002 \n", "2 Spain 2013 29370.663867 26.299999 57.099998 \n", "3 Spain 2012 28647.835243 25.200001 54.299999 \n", "4 Spain 2011 31832.238081 21.700001 47.099998 \n", "5 Spain 2010 30737.832271 20.200001 42.500000 \n", "6 Spain 2009 32333.466104 18.100000 38.500000 \n", "7 Spain 2008 35578.736190 11.500000 25.400000 \n", "8 Spain 2007 32709.401038 8.400000 18.900000 \n", "9 Spain 2006 28482.609483 8.600000 18.500000 \n", "10 Spain 2005 26510.717453 9.300000 20.400000 \n", "11 Spain 2004 24918.645842 11.200000 22.799999 \n", "12 Spain 2003 21495.707408 11.500000 23.400000 \n", "13 Spain 2002 17019.535414 11.600000 23.000000 \n", "14 Spain 2001 15359.108440 10.700000 21.299999 \n", "15 Spain 2000 14787.756064 14.200000 26.299999 \n", "16 Spain 1999 15859.086026 15.900000 29.299999 \n", "17 Spain 1998 15534.359889 19.000000 35.299999 \n", "18 Spain 1997 14872.565891 21.100000 38.700001 \n", "19 Spain 1996 16236.771679 22.500000 41.500000 \n", "20 Spain 1995 15561.972746 23.100000 41.700001 \n", "21 Spain 1994 13465.377826 24.299999 44.200001 \n", "22 Spain 1993 13362.018346 22.799999 42.099998 \n", "23 Spain 1992 16105.418729 18.400000 33.200001 \n", "24 Spain 1991 14782.038901 16.400000 29.799999 \n", "25 Spain 1990 13773.365698 NaN NaN \n", "\n", " UnempW/PrimEd. UnempW/SecEd UnempW/TertEd Ni-nis \n", "0 NaN NaN NaN NaN \n", "1 54.500000 23.100000 22.500000 18.000000 \n", "2 53.799999 23.299999 22.200001 19.440001 \n", "3 54.700001 23.299999 21.100000 19.570000 \n", "4 55.099998 23.799999 20.100000 19.200001 \n", "5 57.200001 22.900000 18.799999 18.799999 \n", "6 58.599998 22.700001 17.600000 19.400000 \n", "7 58.299999 22.400000 18.000000 13.920000 \n", "8 54.599998 23.700001 20.500000 10.440000 \n", "9 53.799999 22.799999 22.500000 10.280000 \n", "10 53.900002 21.900000 23.299999 11.120000 \n", "11 54.799999 21.500000 22.500000 10.310000 \n", "12 56.400002 21.400000 21.200001 9.960000 \n", "13 56.799999 20.700001 22.500000 NaN \n", "14 57.799999 20.200001 21.000000 NaN \n", "15 59.599998 19.400000 21.000000 NaN \n", "16 58.900002 20.000000 21.100000 NaN \n", "17 61.500000 18.500000 20.000000 NaN \n", "18 62.200001 19.400000 18.299999 NaN \n", "19 63.799999 18.799999 17.299999 NaN \n", "20 65.300003 18.700001 16.000000 NaN \n", "21 68.000000 17.700001 14.400000 NaN \n", "22 70.699997 16.400000 12.900000 NaN \n", "23 72.099998 16.900000 11.000000 NaN \n", "24 62.000000 15.000000 12.400000 NaN \n", "25 61.200001 16.000000 12.300000 NaN " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Wbata renders a complex multi-index, which I convert to old-school columns that are easier to work with\n", "esplbr.reset_index(inplace=True) \n", "esplbr" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['country', 'year', 'GDP/capita(US$ 2016)', 'UnemploymentRate',\n", " 'YouthUnempRate', 'UnempW/PrimEd.', 'UnempW/SecEd', 'UnempW/TertEd',\n", " 'Ni-nis'],\n", " dtype='object')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "esplbr.columns" ] }, { "cell_type": "code", "execution_count": 10, "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>Country</th>\n", " <th>Year</th>\n", " <th>GDP/capita(US$ 2016)</th>\n", " <th>UnemploymentRate</th>\n", " <th>YouthUnempRate</th>\n", " <th>UnempW/PrimEd.</th>\n", " <th>UnempW/SecEd</th>\n", " <th>UnempW/TertEd</th>\n", " <th>Ni-nis</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Spain</td>\n", " <td>2015</td>\n", " <td>25831.582305</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Spain</td>\n", " <td>2014</td>\n", " <td>29718.500216</td>\n", " <td>24.700001</td>\n", " <td>57.900002</td>\n", " <td>54.500000</td>\n", " <td>23.100000</td>\n", " <td>22.500000</td>\n", " <td>18.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Spain</td>\n", " <td>2013</td>\n", " <td>29370.663867</td>\n", " <td>26.299999</td>\n", " <td>57.099998</td>\n", " <td>53.799999</td>\n", " <td>23.299999</td>\n", " <td>22.200001</td>\n", " <td>19.440001</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Spain</td>\n", " <td>2012</td>\n", " <td>28647.835243</td>\n", " <td>25.200001</td>\n", " <td>54.299999</td>\n", " <td>54.700001</td>\n", " <td>23.299999</td>\n", " <td>21.100000</td>\n", " <td>19.570000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Spain</td>\n", " <td>2011</td>\n", " <td>31832.238081</td>\n", " <td>21.700001</td>\n", " <td>47.099998</td>\n", " <td>55.099998</td>\n", " <td>23.799999</td>\n", " <td>20.100000</td>\n", " <td>19.200001</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Spain</td>\n", " <td>2010</td>\n", " <td>30737.832271</td>\n", " <td>20.200001</td>\n", " <td>42.500000</td>\n", " <td>57.200001</td>\n", " <td>22.900000</td>\n", " <td>18.799999</td>\n", " <td>18.799999</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Spain</td>\n", " <td>2009</td>\n", " <td>32333.466104</td>\n", " <td>18.100000</td>\n", " <td>38.500000</td>\n", " <td>58.599998</td>\n", " <td>22.700001</td>\n", " <td>17.600000</td>\n", " <td>19.400000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Spain</td>\n", " <td>2008</td>\n", " <td>35578.736190</td>\n", " <td>11.500000</td>\n", " <td>25.400000</td>\n", " <td>58.299999</td>\n", " <td>22.400000</td>\n", " <td>18.000000</td>\n", " <td>13.920000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Spain</td>\n", " <td>2007</td>\n", " <td>32709.401038</td>\n", " <td>8.400000</td>\n", " <td>18.900000</td>\n", " <td>54.599998</td>\n", " <td>23.700001</td>\n", " <td>20.500000</td>\n", " <td>10.440000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Spain</td>\n", " <td>2006</td>\n", " <td>28482.609483</td>\n", " <td>8.600000</td>\n", " <td>18.500000</td>\n", " <td>53.799999</td>\n", " <td>22.799999</td>\n", " <td>22.500000</td>\n", " <td>10.280000</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Spain</td>\n", " <td>2005</td>\n", " <td>26510.717453</td>\n", " <td>9.300000</td>\n", " <td>20.400000</td>\n", " <td>53.900002</td>\n", " <td>21.900000</td>\n", " <td>23.299999</td>\n", " <td>11.120000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Spain</td>\n", " <td>2004</td>\n", " <td>24918.645842</td>\n", " <td>11.200000</td>\n", " <td>22.799999</td>\n", " <td>54.799999</td>\n", " <td>21.500000</td>\n", " <td>22.500000</td>\n", " <td>10.310000</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Spain</td>\n", " <td>2003</td>\n", " <td>21495.707408</td>\n", " <td>11.500000</td>\n", " <td>23.400000</td>\n", " <td>56.400002</td>\n", " <td>21.400000</td>\n", " <td>21.200001</td>\n", " <td>9.960000</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Spain</td>\n", " <td>2002</td>\n", " <td>17019.535414</td>\n", " <td>11.600000</td>\n", " <td>23.000000</td>\n", " <td>56.799999</td>\n", " <td>20.700001</td>\n", " <td>22.500000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Spain</td>\n", " <td>2001</td>\n", " <td>15359.108440</td>\n", " <td>10.700000</td>\n", " <td>21.299999</td>\n", " <td>57.799999</td>\n", " <td>20.200001</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Spain</td>\n", " <td>2000</td>\n", " <td>14787.756064</td>\n", " <td>14.200000</td>\n", " <td>26.299999</td>\n", " <td>59.599998</td>\n", " <td>19.400000</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Spain</td>\n", " <td>1999</td>\n", " <td>15859.086026</td>\n", " <td>15.900000</td>\n", " <td>29.299999</td>\n", " <td>58.900002</td>\n", " <td>20.000000</td>\n", " <td>21.100000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Spain</td>\n", " <td>1998</td>\n", " <td>15534.359889</td>\n", " <td>19.000000</td>\n", " <td>35.299999</td>\n", " <td>61.500000</td>\n", " <td>18.500000</td>\n", " <td>20.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Spain</td>\n", " <td>1997</td>\n", " <td>14872.565891</td>\n", " <td>21.100000</td>\n", " <td>38.700001</td>\n", " <td>62.200001</td>\n", " <td>19.400000</td>\n", " <td>18.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Spain</td>\n", " <td>1996</td>\n", " <td>16236.771679</td>\n", " <td>22.500000</td>\n", " <td>41.500000</td>\n", " <td>63.799999</td>\n", " <td>18.799999</td>\n", " <td>17.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Spain</td>\n", " <td>1995</td>\n", " <td>15561.972746</td>\n", " <td>23.100000</td>\n", " <td>41.700001</td>\n", " <td>65.300003</td>\n", " <td>18.700001</td>\n", " <td>16.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Spain</td>\n", " <td>1994</td>\n", " <td>13465.377826</td>\n", " <td>24.299999</td>\n", " <td>44.200001</td>\n", " <td>68.000000</td>\n", " <td>17.700001</td>\n", " <td>14.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Spain</td>\n", " <td>1993</td>\n", " <td>13362.018346</td>\n", " <td>22.799999</td>\n", " <td>42.099998</td>\n", " <td>70.699997</td>\n", " <td>16.400000</td>\n", " <td>12.900000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Spain</td>\n", " <td>1992</td>\n", " <td>16105.418729</td>\n", " <td>18.400000</td>\n", " <td>33.200001</td>\n", " <td>72.099998</td>\n", " <td>16.900000</td>\n", " <td>11.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Spain</td>\n", " <td>1991</td>\n", " <td>14782.038901</td>\n", " <td>16.400000</td>\n", " <td>29.799999</td>\n", " <td>62.000000</td>\n", " <td>15.000000</td>\n", " <td>12.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Spain</td>\n", " <td>1990</td>\n", " <td>13773.365698</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>61.200001</td>\n", " <td>16.000000</td>\n", " <td>12.300000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Country Year GDP/capita(US$ 2016) UnemploymentRate YouthUnempRate \\\n", "0 Spain 2015 25831.582305 NaN NaN \n", "1 Spain 2014 29718.500216 24.700001 57.900002 \n", "2 Spain 2013 29370.663867 26.299999 57.099998 \n", "3 Spain 2012 28647.835243 25.200001 54.299999 \n", "4 Spain 2011 31832.238081 21.700001 47.099998 \n", "5 Spain 2010 30737.832271 20.200001 42.500000 \n", "6 Spain 2009 32333.466104 18.100000 38.500000 \n", "7 Spain 2008 35578.736190 11.500000 25.400000 \n", "8 Spain 2007 32709.401038 8.400000 18.900000 \n", "9 Spain 2006 28482.609483 8.600000 18.500000 \n", "10 Spain 2005 26510.717453 9.300000 20.400000 \n", "11 Spain 2004 24918.645842 11.200000 22.799999 \n", "12 Spain 2003 21495.707408 11.500000 23.400000 \n", "13 Spain 2002 17019.535414 11.600000 23.000000 \n", "14 Spain 2001 15359.108440 10.700000 21.299999 \n", "15 Spain 2000 14787.756064 14.200000 26.299999 \n", "16 Spain 1999 15859.086026 15.900000 29.299999 \n", "17 Spain 1998 15534.359889 19.000000 35.299999 \n", "18 Spain 1997 14872.565891 21.100000 38.700001 \n", "19 Spain 1996 16236.771679 22.500000 41.500000 \n", "20 Spain 1995 15561.972746 23.100000 41.700001 \n", "21 Spain 1994 13465.377826 24.299999 44.200001 \n", "22 Spain 1993 13362.018346 22.799999 42.099998 \n", "23 Spain 1992 16105.418729 18.400000 33.200001 \n", "24 Spain 1991 14782.038901 16.400000 29.799999 \n", "25 Spain 1990 13773.365698 NaN NaN \n", "\n", " UnempW/PrimEd. UnempW/SecEd UnempW/TertEd Ni-nis \n", "0 NaN NaN NaN NaN \n", "1 54.500000 23.100000 22.500000 18.000000 \n", "2 53.799999 23.299999 22.200001 19.440001 \n", "3 54.700001 23.299999 21.100000 19.570000 \n", "4 55.099998 23.799999 20.100000 19.200001 \n", "5 57.200001 22.900000 18.799999 18.799999 \n", "6 58.599998 22.700001 17.600000 19.400000 \n", "7 58.299999 22.400000 18.000000 13.920000 \n", "8 54.599998 23.700001 20.500000 10.440000 \n", "9 53.799999 22.799999 22.500000 10.280000 \n", "10 53.900002 21.900000 23.299999 11.120000 \n", "11 54.799999 21.500000 22.500000 10.310000 \n", "12 56.400002 21.400000 21.200001 9.960000 \n", "13 56.799999 20.700001 22.500000 NaN \n", "14 57.799999 20.200001 21.000000 NaN \n", "15 59.599998 19.400000 21.000000 NaN \n", "16 58.900002 20.000000 21.100000 NaN \n", "17 61.500000 18.500000 20.000000 NaN \n", "18 62.200001 19.400000 18.299999 NaN \n", "19 63.799999 18.799999 17.299999 NaN \n", "20 65.300003 18.700001 16.000000 NaN \n", "21 68.000000 17.700001 14.400000 NaN \n", "22 70.699997 16.400000 12.900000 NaN \n", "23 72.099998 16.900000 11.000000 NaN \n", "24 62.000000 15.000000 12.400000 NaN \n", "25 61.200001 16.000000 12.300000 NaN " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# housekeeping for column names \n", "esplbr.columns = [\"Country\", \"Year\", \"GDP/capita(US$ 2016)\", \"UnemploymentRate\", \"YouthUnempRate\", \"UnempW/PrimEd.\", \"UnempW/SecEd\",\"UnempW/TertEd\", \"Ni-nis\"]\n", "esplbr\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Year</th>\n", " <th>GDP/capita(US$ 2016)</th>\n", " <th>UnemploymentRate</th>\n", " <th>YouthUnempRate</th>\n", " <th>UnempW/PrimEd.</th>\n", " <th>UnempW/SecEd</th>\n", " <th>UnempW/TertEd</th>\n", " <th>Ni-nis</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>25831.582305</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2014</td>\n", " <td>29718.500216</td>\n", " <td>24.700001</td>\n", " <td>57.900002</td>\n", " <td>54.500000</td>\n", " <td>23.100000</td>\n", " <td>22.500000</td>\n", " <td>18.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2013</td>\n", " <td>29370.663867</td>\n", " <td>26.299999</td>\n", " <td>57.099998</td>\n", " <td>53.799999</td>\n", " <td>23.299999</td>\n", " <td>22.200001</td>\n", " <td>19.440001</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2012</td>\n", " <td>28647.835243</td>\n", " <td>25.200001</td>\n", " <td>54.299999</td>\n", " <td>54.700001</td>\n", " <td>23.299999</td>\n", " <td>21.100000</td>\n", " <td>19.570000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011</td>\n", " <td>31832.238081</td>\n", " <td>21.700001</td>\n", " <td>47.099998</td>\n", " <td>55.099998</td>\n", " <td>23.799999</td>\n", " <td>20.100000</td>\n", " <td>19.200001</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2010</td>\n", " <td>30737.832271</td>\n", " <td>20.200001</td>\n", " <td>42.500000</td>\n", " <td>57.200001</td>\n", " <td>22.900000</td>\n", " <td>18.799999</td>\n", " <td>18.799999</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2009</td>\n", " <td>32333.466104</td>\n", " <td>18.100000</td>\n", " <td>38.500000</td>\n", " <td>58.599998</td>\n", " <td>22.700001</td>\n", " <td>17.600000</td>\n", " <td>19.400000</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2008</td>\n", " <td>35578.736190</td>\n", " <td>11.500000</td>\n", " <td>25.400000</td>\n", " <td>58.299999</td>\n", " <td>22.400000</td>\n", " <td>18.000000</td>\n", " <td>13.920000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2007</td>\n", " <td>32709.401038</td>\n", " <td>8.400000</td>\n", " <td>18.900000</td>\n", " <td>54.599998</td>\n", " <td>23.700001</td>\n", " <td>20.500000</td>\n", " <td>10.440000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2006</td>\n", " <td>28482.609483</td>\n", " <td>8.600000</td>\n", " <td>18.500000</td>\n", " <td>53.799999</td>\n", " <td>22.799999</td>\n", " <td>22.500000</td>\n", " <td>10.280000</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2005</td>\n", " <td>26510.717453</td>\n", " <td>9.300000</td>\n", " <td>20.400000</td>\n", " <td>53.900002</td>\n", " <td>21.900000</td>\n", " <td>23.299999</td>\n", " <td>11.120000</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2004</td>\n", " <td>24918.645842</td>\n", " <td>11.200000</td>\n", " <td>22.799999</td>\n", " <td>54.799999</td>\n", " <td>21.500000</td>\n", " <td>22.500000</td>\n", " <td>10.310000</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2003</td>\n", " <td>21495.707408</td>\n", " <td>11.500000</td>\n", " <td>23.400000</td>\n", " <td>56.400002</td>\n", " <td>21.400000</td>\n", " <td>21.200001</td>\n", " <td>9.960000</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2002</td>\n", " <td>17019.535414</td>\n", " <td>11.600000</td>\n", " <td>23.000000</td>\n", " <td>56.799999</td>\n", " <td>20.700001</td>\n", " <td>22.500000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2001</td>\n", " <td>15359.108440</td>\n", " <td>10.700000</td>\n", " <td>21.299999</td>\n", " <td>57.799999</td>\n", " <td>20.200001</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2000</td>\n", " <td>14787.756064</td>\n", " <td>14.200000</td>\n", " <td>26.299999</td>\n", " <td>59.599998</td>\n", " <td>19.400000</td>\n", " <td>21.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1999</td>\n", " <td>15859.086026</td>\n", " <td>15.900000</td>\n", " <td>29.299999</td>\n", " <td>58.900002</td>\n", " <td>20.000000</td>\n", " <td>21.100000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1998</td>\n", " <td>15534.359889</td>\n", " <td>19.000000</td>\n", " <td>35.299999</td>\n", " <td>61.500000</td>\n", " <td>18.500000</td>\n", " <td>20.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1997</td>\n", " <td>14872.565891</td>\n", " <td>21.100000</td>\n", " <td>38.700001</td>\n", " <td>62.200001</td>\n", " <td>19.400000</td>\n", " <td>18.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1996</td>\n", " <td>16236.771679</td>\n", " <td>22.500000</td>\n", " <td>41.500000</td>\n", " <td>63.799999</td>\n", " <td>18.799999</td>\n", " <td>17.299999</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1995</td>\n", " <td>15561.972746</td>\n", " <td>23.100000</td>\n", " <td>41.700001</td>\n", " <td>65.300003</td>\n", " <td>18.700001</td>\n", " <td>16.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1994</td>\n", " <td>13465.377826</td>\n", " <td>24.299999</td>\n", " <td>44.200001</td>\n", " <td>68.000000</td>\n", " <td>17.700001</td>\n", " <td>14.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1993</td>\n", " <td>13362.018346</td>\n", " <td>22.799999</td>\n", " <td>42.099998</td>\n", " <td>70.699997</td>\n", " <td>16.400000</td>\n", " <td>12.900000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1992</td>\n", " <td>16105.418729</td>\n", " <td>18.400000</td>\n", " <td>33.200001</td>\n", " <td>72.099998</td>\n", " <td>16.900000</td>\n", " <td>11.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>1991</td>\n", " <td>14782.038901</td>\n", " <td>16.400000</td>\n", " <td>29.799999</td>\n", " <td>62.000000</td>\n", " <td>15.000000</td>\n", " <td>12.400000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>1990</td>\n", " <td>13773.365698</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>61.200001</td>\n", " <td>16.000000</td>\n", " <td>12.300000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year GDP/capita(US$ 2016) UnemploymentRate YouthUnempRate \\\n", "0 2015 25831.582305 NaN NaN \n", "1 2014 29718.500216 24.700001 57.900002 \n", "2 2013 29370.663867 26.299999 57.099998 \n", "3 2012 28647.835243 25.200001 54.299999 \n", "4 2011 31832.238081 21.700001 47.099998 \n", "5 2010 30737.832271 20.200001 42.500000 \n", "6 2009 32333.466104 18.100000 38.500000 \n", "7 2008 35578.736190 11.500000 25.400000 \n", "8 2007 32709.401038 8.400000 18.900000 \n", "9 2006 28482.609483 8.600000 18.500000 \n", "10 2005 26510.717453 9.300000 20.400000 \n", "11 2004 24918.645842 11.200000 22.799999 \n", "12 2003 21495.707408 11.500000 23.400000 \n", "13 2002 17019.535414 11.600000 23.000000 \n", "14 2001 15359.108440 10.700000 21.299999 \n", "15 2000 14787.756064 14.200000 26.299999 \n", "16 1999 15859.086026 15.900000 29.299999 \n", "17 1998 15534.359889 19.000000 35.299999 \n", "18 1997 14872.565891 21.100000 38.700001 \n", "19 1996 16236.771679 22.500000 41.500000 \n", "20 1995 15561.972746 23.100000 41.700001 \n", "21 1994 13465.377826 24.299999 44.200001 \n", "22 1993 13362.018346 22.799999 42.099998 \n", "23 1992 16105.418729 18.400000 33.200001 \n", "24 1991 14782.038901 16.400000 29.799999 \n", "25 1990 13773.365698 NaN NaN \n", "\n", " UnempW/PrimEd. UnempW/SecEd UnempW/TertEd Ni-nis \n", "0 NaN NaN NaN NaN \n", "1 54.500000 23.100000 22.500000 18.000000 \n", "2 53.799999 23.299999 22.200001 19.440001 \n", "3 54.700001 23.299999 21.100000 19.570000 \n", "4 55.099998 23.799999 20.100000 19.200001 \n", "5 57.200001 22.900000 18.799999 18.799999 \n", "6 58.599998 22.700001 17.600000 19.400000 \n", "7 58.299999 22.400000 18.000000 13.920000 \n", "8 54.599998 23.700001 20.500000 10.440000 \n", "9 53.799999 22.799999 22.500000 10.280000 \n", "10 53.900002 21.900000 23.299999 11.120000 \n", "11 54.799999 21.500000 22.500000 10.310000 \n", "12 56.400002 21.400000 21.200001 9.960000 \n", "13 56.799999 20.700001 22.500000 NaN \n", "14 57.799999 20.200001 21.000000 NaN \n", "15 59.599998 19.400000 21.000000 NaN \n", "16 58.900002 20.000000 21.100000 NaN \n", "17 61.500000 18.500000 20.000000 NaN \n", "18 62.200001 19.400000 18.299999 NaN \n", "19 63.799999 18.799999 17.299999 NaN \n", "20 65.300003 18.700001 16.000000 NaN \n", "21 68.000000 17.700001 14.400000 NaN \n", "22 70.699997 16.400000 12.900000 NaN \n", "23 72.099998 16.900000 11.000000 NaN \n", "24 62.000000 15.000000 12.400000 NaN \n", "25 61.200001 16.000000 12.300000 NaN " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we know we are dealing exclusively with Spain, so we drop the reduntdant 'Country' column\n", "esplbr.drop('Country', axis=1, inplace=True)\n", "esplbr" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Year object\n", "GDP/capita(US$ 2016) float64\n", "UnemploymentRate float64\n", "YouthUnempRate float64\n", "UnempW/PrimEd. float64\n", "UnempW/SecEd float64\n", "UnempW/TertEd float64\n", "Ni-nis float64\n", "dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# what do I have in my hands?\n", "esplbr.dtypes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=26, step=1)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "esplbr.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x11bf333c8>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UKKWUUonQUork0ZIKpZRSygUtpUg+Lamw2X333ceDDz4YP4gUQK1atVi/fj0j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\niKSkiHTrprtuvVXkl188ji8Sbd4sMmyYSL16+kR37y4ye7b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QqZNOQDn7bK8jMmVFSNRU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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x114ebf2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# with a clean and orthodox Dataframe, I can start to do some graphics\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "# we invert the x axis. Never managed to make 'Year' the X axis, lost a lot of hair in the process :(\n", "plt.gca().invert_xaxis() # Came up with this solution\n", "# and add the indicators \n", "plt.plot(esplbr.index, esplbr['UnemploymentRate'])\n", "plt.plot(esplbr.index, esplbr['YouthUnempRate'])\n", "plt.plot(esplbr.index, esplbr['Ni-nis'])\n", "# and modify the plot\n", "plt.title('Labor Market in Spain', fontsize=14, loc='left') # add title\n", "plt.ylabel('Percentage Unemployed') # y axis label \n", "plt.legend(['UnemploymentRate', 'YouthUnempRate','Ni-nis'], fontsize=8, loc=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observations\n", "\n", "* Spain has recently lived through a depression without precedent, yet unemployment rates above 20% are nothing new: there is a large structural component in addition to the demand-deficient factor.\n", "\n", "* Youth unemployment is particuarly bad, which is the norm elsewhere too, but the spread is accentuated in Spain. Deductively, this hints at labor market duality between bullet-proof contracts and part-time or 'indefinite' contracts. \n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x11b7c35c0>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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C77/vGnZOnw5nnQXTprnGncccA+edB089BZs2FX/sxhhTHvXs2TO7+6mJTMyT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\n4mUSX4UKJ/AeUOex+Wbg30BDST2t9W0daUBXkFDRMZX1lWS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"text/plain": [ "<matplotlib.figure.Figure at 0x11c579da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let's take a look at unemployment by education level\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "# we invert the x axis\n", "plt.gca().invert_xaxis()\n", "#we add the variables \n", "plt.plot(esplbr.index, esplbr['UnempW/PrimEd.'])\n", "plt.plot(esplbr.index, esplbr['UnempW/SecEd'])\n", "plt.plot(esplbr.index, esplbr['UnempW/TertEd'])\n", "plt.plot(esplbr.index, esplbr['Ni-nis'])\n", "# we modify the plot\n", "plt.title('Education and Employment Outcomes', fontsize=14, loc='left')\n", "plt.ylabel('Percentage Unemployed') \n", "plt.legend(['UnempW/PrimEd.', 'UnempW/SecEd','UnempW/TertEd', 'Ni-nis'], fontsize=7, loc=0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observations\n", "\n", "* Those unemployed with only primary education completed and ni-nis start to rise hand in hand ten years ago, when the crisis hits. This suggests overlap between the two groups.\n", "* The elephant in the room a massive construction bubble that made Spain's variant of the crisis particularly brutal. For decades, a debt-fueled bubble in real estate signaled youngsters to drop the books and pick up the bricks.\n", "* The labor market now faces the painful readjustment of the economy's productive model, from \"deuda y ladrillo\" (debt and brick) to exports, that account for Spain's recent growth\n", "\n", "P.S.: if you ever need to investigate (how not to execute) a Keynesian stimulus plan, check out how the government's Plan E added fuel to malinvestments http://www.economist.com/node/13611650 \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Digging for more" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm interested in measuring structural unemployment. Ideally, I would build an unemployment model myself based on separation and accesion rates to arrive at the Natural Rate of Unemployment, as we see in one of my three bibles:\n", "\n", "http://www.stern.nyu.edu/sites/default/files/assets/documents/The_Global_Economy_Amazon_Digital%20%282%29.pdf\n", "\n", "In the interest of time, I sought an indicator that acts as a proxy for structural unemployment. The NAIRU and NAWRU come to mind, but they are not reported by the World Bank. \n", "\n", "And so I became acquainted with Quandl's API and proceeded to dig through several economic databases, and landed at the notorious OECD database: I suspect Quandl and I are going to become good friends moving forward. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the modules" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Don't forget the the DMV paperwork\n", "import quandl # Quandl package\n", "quandl.ApiConfig.api_key = '3w_GYBRfX3ZxG7my_vhs' # register for a key and unlimited number of requests \n", "# Playing it safe\n", "import sys # system module\n", "import pandas as pd # data package\n", "import matplotlib.pyplot as plt # graphics module \n", "import datetime as dt # date and time module\n", "import numpy as np\n", "%matplotlib inline \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data extraction and clean up " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to be comparing Spain's NAIRU to that of Denmark. Don't tell Sanders, but Denmark is well known for having one of the most 'flexible' labor markets in Europe." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>OECD/EO91_INTERNET_ESP_NAIRU_A - Value</th>\n", " <th>OECD/EO91_INTERNET_DNK_NAIRU_A - Value</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>1990-12-31</th>\n", " <td>14.353311</td>\n", " <td>6.724285</td>\n", " </tr>\n", " <tr>\n", " <th>1991-12-31</th>\n", " <td>14.571544</td>\n", " <td>6.860554</td>\n", " </tr>\n", " <tr>\n", " <th>1992-12-31</th>\n", " <td>14.953883</td>\n", " <td>6.932394</td>\n", " </tr>\n", " <tr>\n", " <th>1993-12-31</th>\n", " <td>15.478153</td>\n", " <td>6.912441</td>\n", " </tr>\n", " <tr>\n", " <th>1994-12-31</th>\n", " <td>15.824915</td>\n", " <td>6.700012</td>\n", " </tr>\n", " <tr>\n", " <th>1995-12-31</th>\n", " <td>15.807487</td>\n", " <td>6.448332</td>\n", " </tr>\n", " <tr>\n", " <th>1996-12-31</th>\n", " <td>15.465041</td>\n", " <td>6.182695</td>\n", " </tr>\n", " <tr>\n", " <th>1997-12-31</th>\n", " <td>15.006013</td>\n", " <td>5.900041</td>\n", " </tr>\n", " <tr>\n", " <th>1998-12-31</th>\n", " <td>14.366738</td>\n", " <td>5.663739</td>\n", " </tr>\n", " <tr>\n", " <th>1999-12-31</th>\n", " <td>13.586143</td>\n", " <td>5.476128</td>\n", " </tr>\n", " <tr>\n", " <th>2000-12-31</th>\n", " <td>12.956937</td>\n", " <td>5.294450</td>\n", " </tr>\n", " <tr>\n", " <th>2001-12-31</th>\n", " <td>12.448287</td>\n", " <td>5.209656</td>\n", " </tr>\n", " <tr>\n", " <th>2002-12-31</th>\n", " <td>12.150120</td>\n", " <td>5.148396</td>\n", " </tr>\n", " <tr>\n", " <th>2003-12-31</th>\n", " <td>11.908023</td>\n", " <td>5.129223</td>\n", " </tr>\n", " <tr>\n", " <th>2004-12-31</th>\n", " <td>11.784227</td>\n", " <td>5.087734</td>\n", " </tr>\n", " <tr>\n", " <th>2005-12-31</th>\n", " <td>11.764572</td>\n", " <td>5.023641</td>\n", " </tr>\n", " <tr>\n", " <th>2006-12-31</th>\n", " <td>12.025529</td>\n", " <td>4.976982</td>\n", " </tr>\n", " <tr>\n", " <th>2007-12-31</th>\n", " <td>12.599862</td>\n", " <td>5.008570</td>\n", " </tr>\n", " <tr>\n", " <th>2008-12-31</th>\n", " <td>13.485426</td>\n", " <td>5.104536</td>\n", " </tr>\n", " <tr>\n", " <th>2009-12-31</th>\n", " <td>14.771787</td>\n", " <td>5.339819</td>\n", " </tr>\n", " <tr>\n", " <th>2010-12-31</th>\n", " <td>15.646397</td>\n", " <td>5.507396</td>\n", " </tr>\n", " <tr>\n", " <th>2011-12-31</th>\n", " <td>16.207122</td>\n", " <td>5.608726</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-31</th>\n", " <td>16.465432</td>\n", " <td>5.662474</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-31</th>\n", " <td>16.531184</td>\n", " <td>5.680385</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " OECD/EO91_INTERNET_ESP_NAIRU_A - Value \\\n", "Date \n", "1990-12-31 14.353311 \n", "1991-12-31 14.571544 \n", "1992-12-31 14.953883 \n", "1993-12-31 15.478153 \n", "1994-12-31 15.824915 \n", "1995-12-31 15.807487 \n", "1996-12-31 15.465041 \n", "1997-12-31 15.006013 \n", "1998-12-31 14.366738 \n", "1999-12-31 13.586143 \n", "2000-12-31 12.956937 \n", "2001-12-31 12.448287 \n", "2002-12-31 12.150120 \n", "2003-12-31 11.908023 \n", "2004-12-31 11.784227 \n", "2005-12-31 11.764572 \n", "2006-12-31 12.025529 \n", "2007-12-31 12.599862 \n", "2008-12-31 13.485426 \n", "2009-12-31 14.771787 \n", "2010-12-31 15.646397 \n", "2011-12-31 16.207122 \n", "2012-12-31 16.465432 \n", "2013-12-31 16.531184 \n", "\n", " OECD/EO91_INTERNET_DNK_NAIRU_A - Value \n", "Date \n", "1990-12-31 6.724285 \n", "1991-12-31 6.860554 \n", "1992-12-31 6.932394 \n", "1993-12-31 6.912441 \n", "1994-12-31 6.700012 \n", "1995-12-31 6.448332 \n", "1996-12-31 6.182695 \n", "1997-12-31 5.900041 \n", "1998-12-31 5.663739 \n", "1999-12-31 5.476128 \n", "2000-12-31 5.294450 \n", "2001-12-31 5.209656 \n", "2002-12-31 5.148396 \n", "2003-12-31 5.129223 \n", "2004-12-31 5.087734 \n", "2005-12-31 5.023641 \n", "2006-12-31 4.976982 \n", "2007-12-31 5.008570 \n", "2008-12-31 5.104536 \n", "2009-12-31 5.339819 \n", "2010-12-31 5.507396 \n", "2011-12-31 5.608726 \n", "2012-12-31 5.662474 \n", "2013-12-31 5.680385 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We extract the indicators and print the dataframe\n", "NAIRU = quandl.get((['OECD/EO91_INTERNET_ESP_NAIRU_A','OECD/EO91_INTERNET_DNK_NAIRU_A']), #We call for both\n", " start_date = \"1990-12-31\", end_date = \"2013-12-31\") # And limit the time horizon \n", "NAIRU" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What do we have here?\n", "type(NAIRU)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['OECD/EO91_INTERNET_ESP_NAIRU_A - Value', 'OECD/EO91_INTERNET_DNK_NAIRU_A - Value'], dtype='object')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NAIRU.columns " ] }, { "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>NAIRU Spain</th>\n", " <th>NAIRU Denmark</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>1990-12-31</th>\n", " <td>14.353311</td>\n", " <td>6.724285</td>\n", " </tr>\n", " <tr>\n", " <th>1991-12-31</th>\n", " <td>14.571544</td>\n", " <td>6.860554</td>\n", " </tr>\n", " <tr>\n", " <th>1992-12-31</th>\n", " <td>14.953883</td>\n", " <td>6.932394</td>\n", " </tr>\n", " <tr>\n", " <th>1993-12-31</th>\n", " <td>15.478153</td>\n", " <td>6.912441</td>\n", " </tr>\n", " <tr>\n", " <th>1994-12-31</th>\n", " <td>15.824915</td>\n", " <td>6.700012</td>\n", " </tr>\n", " <tr>\n", " <th>1995-12-31</th>\n", " <td>15.807487</td>\n", " <td>6.448332</td>\n", " </tr>\n", " <tr>\n", " <th>1996-12-31</th>\n", " <td>15.465041</td>\n", " <td>6.182695</td>\n", " </tr>\n", " <tr>\n", " <th>1997-12-31</th>\n", " <td>15.006013</td>\n", " <td>5.900041</td>\n", " </tr>\n", " <tr>\n", " <th>1998-12-31</th>\n", " <td>14.366738</td>\n", " <td>5.663739</td>\n", " </tr>\n", " <tr>\n", " <th>1999-12-31</th>\n", " <td>13.586143</td>\n", " <td>5.476128</td>\n", " </tr>\n", " <tr>\n", " <th>2000-12-31</th>\n", " <td>12.956937</td>\n", " <td>5.294450</td>\n", " </tr>\n", " <tr>\n", " <th>2001-12-31</th>\n", " <td>12.448287</td>\n", " <td>5.209656</td>\n", " </tr>\n", " <tr>\n", " <th>2002-12-31</th>\n", " <td>12.150120</td>\n", " <td>5.148396</td>\n", " </tr>\n", " <tr>\n", " <th>2003-12-31</th>\n", " <td>11.908023</td>\n", " <td>5.129223</td>\n", " </tr>\n", " <tr>\n", " <th>2004-12-31</th>\n", " <td>11.784227</td>\n", " <td>5.087734</td>\n", " </tr>\n", " <tr>\n", " <th>2005-12-31</th>\n", " <td>11.764572</td>\n", " <td>5.023641</td>\n", " </tr>\n", " <tr>\n", " <th>2006-12-31</th>\n", " <td>12.025529</td>\n", " <td>4.976982</td>\n", " </tr>\n", " <tr>\n", " <th>2007-12-31</th>\n", " <td>12.599862</td>\n", " <td>5.008570</td>\n", " </tr>\n", " <tr>\n", " <th>2008-12-31</th>\n", " <td>13.485426</td>\n", " <td>5.104536</td>\n", " </tr>\n", " <tr>\n", " <th>2009-12-31</th>\n", " <td>14.771787</td>\n", " <td>5.339819</td>\n", " </tr>\n", " <tr>\n", " <th>2010-12-31</th>\n", " <td>15.646397</td>\n", " <td>5.507396</td>\n", " </tr>\n", " <tr>\n", " <th>2011-12-31</th>\n", " <td>16.207122</td>\n", " <td>5.608726</td>\n", " </tr>\n", " <tr>\n", " <th>2012-12-31</th>\n", " <td>16.465432</td>\n", " <td>5.662474</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-31</th>\n", " <td>16.531184</td>\n", " <td>5.680385</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NAIRU Spain NAIRU Denmark\n", "Date \n", "1990-12-31 14.353311 6.724285\n", "1991-12-31 14.571544 6.860554\n", "1992-12-31 14.953883 6.932394\n", "1993-12-31 15.478153 6.912441\n", "1994-12-31 15.824915 6.700012\n", "1995-12-31 15.807487 6.448332\n", "1996-12-31 15.465041 6.182695\n", "1997-12-31 15.006013 5.900041\n", "1998-12-31 14.366738 5.663739\n", "1999-12-31 13.586143 5.476128\n", "2000-12-31 12.956937 5.294450\n", "2001-12-31 12.448287 5.209656\n", "2002-12-31 12.150120 5.148396\n", "2003-12-31 11.908023 5.129223\n", "2004-12-31 11.784227 5.087734\n", "2005-12-31 11.764572 5.023641\n", "2006-12-31 12.025529 4.976982\n", "2007-12-31 12.599862 5.008570\n", "2008-12-31 13.485426 5.104536\n", "2009-12-31 14.771787 5.339819\n", "2010-12-31 15.646397 5.507396\n", "2011-12-31 16.207122 5.608726\n", "2012-12-31 16.465432 5.662474\n", "2013-12-31 16.531184 5.680385" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dataframe housekeeping \n", "NAIRU.columns = ['NAIRU Spain', 'NAIRU Denmark']\n", "NAIRU" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['NAIRU Spain', 'NAIRU Denmark'], dtype='object')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Nice and polished\n", "NAIRU.columns" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['seaborn-colorblind',\n", " 'seaborn-dark-palette',\n", " 'fivethirtyeight',\n", " 'seaborn-bright',\n", " 'seaborn-muted',\n", " 'grayscale',\n", " 'bmh',\n", " 'seaborn-dark',\n", " 'seaborn-poster',\n", " 'seaborn-darkgrid',\n", " 'classic',\n", " 'seaborn-white',\n", " 'seaborn-whitegrid',\n", " 'seaborn-pastel',\n", " 'seaborn-ticks',\n", " 'ggplot',\n", " 'dark_background',\n", " 'seaborn-deep',\n", " 'seaborn-paper',\n", " 'seaborn-talk',\n", " 'seaborn-notebook']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.style.available #Take a look at the menu" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eeustGjRokCOhAejTp092FVTw/qHVSL169aJ58+Y89dRTAIwZM4Zff/2V1q1b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\nGGOMMaZQ+enS7afKyhhjjDGmQOUrQRGRcvEKxBh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"text/plain": [ "<matplotlib.figure.Figure at 0x11c7d48d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We are ready to plot\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "#we add the variables \n", "plt.plot(NAIRU.index, NAIRU['NAIRU Spain'])\n", "plt.plot(NAIRU.index, NAIRU['NAIRU Denmark'])\n", "#We modify the plot\n", "plt.title('Measuring Structural Unemployment ESP v DEN', fontsize=15, loc='left') # add title\n", "plt.ylabel('Percentage Unemployed') # y axis label \n", "plt.legend(['NAIRU Spain', 'NAIRU Denmark'], fontsize=8, loc=2) # more descriptive variable namesDescribe what each of these arguments/parameters does\n", "plt.style.use(\"bmh\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Observations\n", "\n", "* Although the NAIRU is not a perfect proxy for structural unemployment, it's a good place to start\n", "* Again, we witness how Spain's unemployment problem is almost ingrained in its 'production function'\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "*****\n", "\n", "This project has left me at the doors of great questions,\n", "\n", "that this course has given me the tools to answer,\n", "\n", "and for that I thank you,\n", "\n", "Bosco Rodríguez Ballvé" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tensorflow/probability	tensorflow_probability/python/experimental/nn/examples/vib_dose.ipynb	1	185056	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XXDeo-aGOAXF" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "9XRGdjHNOE9D" }, "outputs": [], "source": [ "#@title ##### Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\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": { "colab_type": "text", "id": "KJihamFwOLUT" }, "source": [ "# VIB + DoSE\n", "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/main/tensorflow_probability/python/experimental/nn/examples/vib_dose.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/probability/blob/main/tensorflow_probability/python/experimental/nn/examples/vib_dose.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "IaFh9M5MRAd0" }, "source": [ "In this example, we train a deep variational information bottleneck model (VIB) on the MNIST dataset. We then use density of states estimation to turn our VIB model into an Out-of-distribution (OOD) detector. Our current implementation achieves near-SOTA performance on both OOD detection and classification simultaneously without any exposure to OOD data during training.\n", "\n", "## References\n", "\n", "The VIB paper (Alemi et al. 2016) can be found [Here](https://arxiv.org/abs/1612.00410)\n", "\n", "The DoSE paper (Morningstar et al. 2020) can be found [Here](https://arxiv.org/abs/2006.09273)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "B0HrNKbJw2bA" }, "source": [ "### 1 Imports" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": {}, "colab_type": "code", "id": "cttwhYKYGhPj" }, "outputs": [], "source": [ "import functools\n", "import sys\n", "import time\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import tensorflow.compat.v2 as tf\n", "tf.enable_v2_behavior()\n", "\n", "import tensorflow_datasets as tfds\n", "import tensorflow_probability as tfp\n", "\n", "# Globally Enable XLA.\n", "# tf.config.optimizer.set_jit(True)\n", "\n", "try:\n", " physical_devices = tf.config.list_physical_devices('GPU')\n", " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n", "except:\n", " # Invalid device or cannot modify virtual devices once initialized.\n", " pass\n", "\n", "tfb = tfp.bijectors\n", "tfd = tfp.distributions\n", "tfn = tfp.experimental.nn" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "nbQ3rcTowypZ" }, "source": [ "### 2 Load Dataset" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "height": 150 }, "colab_type": "code", "id": "rjgnFMxvG9Ab", "outputId": "7751f329-299e-4c0c-9960-be487e996ba0" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1000x200 with 1 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "text/plain": [ "(<Figure size 1000x200 with 1 Axes>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7f32fd30ce48>)" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "[train_dataset, eval_dataset], datasets_info = tfds.load(\n", " name='mnist',\n", " split=['train', 'test'],\n", " with_info=True,\n", " shuffle_files=True)\n", "\n", "def _preprocess(sample):\n", " return (tf.cast(sample['image'], tf.float32) * 2 / 255. - 1.,\n", " tf.cast(sample['label'], tf.int32))\n", "\n", "train_size = datasets_info.splits['train'].num_examples\n", "batch_size = 32\n", "\n", "train_dataset = tfn.util.tune_dataset(\n", " train_dataset,\n", " batch_size=batch_size,\n", " shuffle_size=int(train_size / 7),\n", " preprocess_fn=_preprocess)\n", "\n", "eval_dataset = tfn.util.tune_dataset(\n", " eval_dataset,\n", " repeat_count=1,\n", " preprocess_fn=_preprocess)\n", "\n", "x = next(iter(eval_dataset.batch(10)))[0]\n", "tfn.util.display_imgs(x)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sbaPm7ABwvde" }, "source": [ "### 3 Define Model" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "iK6_hK6RyunW" }, "outputs": [], "source": [ "input_shape = datasets_info.features['image'].shape\n", "encoded_size = 16\n", "base_depth = 32" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "N3Q78OptyyOx" }, "outputs": [], "source": [ "prior = tfd.MultivariateNormalDiag(\n", " loc=tf.zeros(encoded_size),\n", " scale_diag=tf.ones(encoded_size))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": { "height": 287 }, "colab_type": "code", "id": "96g5flbdy6lK", "outputId": "8d52909d-5a7f-4fd9-82e6-1bab750a4683" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== encoder ==================================================\n", " SIZE SHAPE TRAIN NAME \n", " 32 [32] True bias:0 \n", " 800 [5, 5, 1, 32] True kernel:0 \n", " 32 [32] True bias:0 \n", " 25600 [5, 5, 32, 32] True kernel:0 \n", " 64 [64] True bias:0 \n", " 51200 [5, 5, 32, 64] True kernel:0 \n", " 64 [64] True bias:0 \n", "102400 [5, 5, 64, 64] True kernel:0 \n", " 64 [64] True bias:0 \n", "200704 [7, 7, 64, 64] True kernel:0 \n", " 152 [152] True bias:0 \n", " 9728 [64, 152] True kernel:0 \n", "trainable size: 390840 / 1.491 MiB / {float32: 390840}\n" ] } ], "source": [ "Conv = functools.partial(\n", " tfn.Convolution,\n", " init_bias_fn=tf.zeros_initializer(),\n", " init_kernel_fn=tf.initializers.he_uniform()) # Better for leaky_relu.\n", "\n", "encoder = tfn.Sequential([\n", " lambda x: 2. * tf.cast(x, tf.float32) - 1., # Center.\n", " Conv(1, 1 * base_depth, 5, strides=1, padding='same'),\n", " tf.nn.leaky_relu,\n", " Conv(1 * base_depth, 1 * base_depth, 5, strides=2, padding='same'),\n", " tf.nn.leaky_relu,\n", " Conv(1 * base_depth, 2 * base_depth, 5, strides=1, padding='same'),\n", " tf.nn.leaky_relu,\n", " Conv(2 * base_depth, 2 * base_depth, 5, strides=2, padding='same'),\n", " tf.nn.elu,\n", " Conv(2 * base_depth, 4 * encoded_size, 7, strides=1, padding='valid'),\n", " tf.nn.leaky_relu,\n", " tfn.util.flatten_rightmost(ndims=3),\n", " tfn.Affine(4encoded_size, encoded_size + encoded_size (encoded_size + 1) // 2),\n", " lambda x: tfd.MultivariateNormalTriL(\n", " loc=x[..., :encoded_size],\n", " scale_tril=tfb.FillScaleTriL()(x[..., encoded_size:]))\n", "], name='encoder')\n", "\n", "print(encoder.summary())" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": { "height": 107 }, "colab_type": "code", "id": "P6cn2Qhh0lkz", "outputId": "7fca6506-f16c-44ee-cce8-7a421b1704ca" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Affine__lambda ==================================================\n", " SIZE SHAPE TRAIN NAME \n", " 10 [10] True bias:0 \n", " 160 [16, 10] True kernel:0 \n", "trainable size: 170 / 0.001 MiB / {float32: 170}\n" ] } ], "source": [ "DeConv = functools.partial(\n", " tfn.ConvolutionTranspose,\n", " init_kernel_fn=tf.initializers.he_uniform()) # Better for leaky_relu.\n", " \n", "Affine = functools.partial(\n", " tfn.Affine,\n", " init_kernel_fn=tf.initializers.he_uniform())\n", "\n", "decoder = tfn.Sequential([\n", " Affine(encoded_size, 10),\n", " lambda x: tfd.Categorical(logits=x)])\n", "\n", "print(decoder.summary())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J9XuHd6Iw7_a" }, "source": [ "### 4 Loss / Eval" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "z90ohRKUs0xL" }, "outputs": [], "source": [ "def compute_loss(x, y, beta=1.):\n", " q = encoder(x)\n", " z = q.sample()\n", " p = decoder(z)\n", " kl = tf.reduce_mean(q.log_prob(z) - prior.log_prob(z), axis=-1)\n", " # Note: we could use exact KL divergence, eg:\n", " # kl = tf.reduce_mean(tfd.kl_divergence(q, prior))\n", " # however we generally find that using the Monte Carlo approximation has\n", " # lower variance.\n", " nll = -tf.reduce_mean(p.log_prob(y), axis=-1)\n", " loss = nll + beta * kl\n", " return loss, (nll, kl), (q, z, p)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "6PO6SxWm_5RA" }, "outputs": [], "source": [ "train_iter = iter(train_dataset)\n", "\n", "def loss():\n", " x, y = next(train_iter)\n", " loss, (nll, kl), _ = compute_loss(x, y, beta=0.075)\n", " return loss, (nll, kl)\n", "\n", "opt = tf.optimizers.Adam(learning_rate=1e-3, decay=0.00005)\n", "\n", "fit = tfn.util.make_fit_op(\n", " loss,\n", " opt,\n", " decoder.trainable_variables + encoder.trainable_variables,\n", " grad_summary_fn=lambda gs: tf.nest.map_structure(tf.norm, gs))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "code", "colab": {}, "colab_type": "code", "id": "iUmS-7IATIcI" }, "outputs": [], "source": [ "eval_iter = iter(eval_dataset.batch(5000).repeat())\n", "\n", "@tfn.util.tfcompile\n", "def eval():\n", " x, y = next(eval_iter)\n", " loss, (nll, kl), _ = compute_loss(x, y, beta=0.05)\n", " return loss, (nll, kl)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QuCSidxVABmA" }, "source": [ "### 5 Train" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "xKbiQZcu-z5l" }, "outputs": [], "source": [ "DEBUG_MODE = False\n", "tf.config.experimental_run_functions_eagerly(DEBUG_MODE)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "height": 1000 }, "colab_type": "code", "id": "ba5W_N6oTNbo", "outputId": "c8705f83-d522-4db3-87e2-852c649ffbc6" }, "outputs": [ { "name": "stdout", "output_type": "stream", 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sum_norm_grad:8.3901\n", "it:42355 ms/it:2.0599 trn_loss:0.3690 tst_loss:0.3099 tst_nll:0.1064 tst_kl:4.0709 sum_norm_grad:5.9114\n", "it:42589 ms/it:2.0279 trn_loss:0.3238 tst_loss:0.2942 tst_nll:0.0702 tst_kl:4.4802 sum_norm_grad:3.2264\n", "it:42823 ms/it:2.0444 trn_loss:0.3442 tst_loss:0.3001 tst_nll:0.1081 tst_kl:3.8386 sum_norm_grad:5.5690\n", "it:43057 ms/it:2.0192 trn_loss:0.3318 tst_loss:0.3118 tst_nll:0.1116 tst_kl:4.0026 sum_norm_grad:3.2110\n", "it:43291 ms/it:1.9677 trn_loss:0.3935 tst_loss:0.3002 tst_nll:0.1001 tst_kl:4.0032 sum_norm_grad:11.5776\n", "it:43525 ms/it:1.9658 trn_loss:0.3071 tst_loss:0.3102 tst_nll:0.1131 tst_kl:3.9408 sum_norm_grad:4.3626\n", "it:43759 ms/it:1.9620 trn_loss:0.3644 tst_loss:0.2980 tst_nll:0.0992 tst_kl:3.9754 sum_norm_grad:5.5277\n", "it:43993 ms/it:1.9328 trn_loss:0.3614 tst_loss:0.3072 tst_nll:0.1156 tst_kl:3.8331 sum_norm_grad:7.3897\n", "it:44227 ms/it:1.9292 trn_loss:0.3308 tst_loss:0.2909 tst_nll:0.0970 tst_kl:3.8780 sum_norm_grad:2.4891\n", "it:44461 ms/it:1.9642 trn_loss:0.4045 tst_loss:0.2893 tst_nll:0.0951 tst_kl:3.8827 sum_norm_grad:7.6299\n", "it:44695 ms/it:1.9854 trn_loss:0.3186 tst_loss:0.3050 tst_nll:0.1065 tst_kl:3.9710 sum_norm_grad:3.9649\n", "it:44929 ms/it:2.0799 trn_loss:0.3767 tst_loss:0.3039 tst_nll:0.1033 tst_kl:4.0110 sum_norm_grad:8.9531\n", "it:45163 ms/it:2.0373 trn_loss:0.3146 tst_loss:0.2952 tst_nll:0.0945 tst_kl:4.0136 sum_norm_grad:2.7310\n", "it:45397 ms/it:2.0647 trn_loss:0.3301 tst_loss:0.2869 tst_nll:0.0841 tst_kl:4.0547 sum_norm_grad:4.1736\n", "it:45631 ms/it:2.1135 trn_loss:0.3127 tst_loss:0.3072 tst_nll:0.1161 tst_kl:3.8221 sum_norm_grad:2.9071\n", "it:45865 ms/it:2.0044 trn_loss:0.3673 tst_loss:0.3104 tst_nll:0.1286 tst_kl:3.6354 sum_norm_grad:10.2989\n", "it:46099 ms/it:2.0737 trn_loss:0.3188 tst_loss:0.3074 tst_nll:0.1103 tst_kl:3.9426 sum_norm_grad:2.2955\n", "it:46333 ms/it:1.9900 trn_loss:0.2997 tst_loss:0.3047 tst_nll:0.1083 tst_kl:3.9283 sum_norm_grad:3.3990\n", "it:46567 ms/it:1.9837 trn_loss:0.3438 tst_loss:0.3015 tst_nll:0.0958 tst_kl:4.1142 sum_norm_grad:8.2752\n", "it:46801 ms/it:1.9785 trn_loss:0.3467 tst_loss:0.3049 tst_nll:0.1095 tst_kl:3.9077 sum_norm_grad:2.6538\n", "it:46875 ms/it:2.0126 trn_loss:0.3597 tst_loss:0.2929 tst_nll:0.0980 tst_kl:3.8981 sum_norm_grad:4.1507\n" ] } ], "source": [ "num_train_epochs = 25. # @param { isTemplate: true}\n", "num_evals = 200 # @param { isTemplate: true}\n", "\n", "dur_sec = dur_num = 0\n", "num_train_steps = int(num_train_epochs * train_size // batch_size)\n", "for i in range(num_train_steps):\n", " start = time.time()\n", " trn_loss, (trn_nll, trn_kl), g = fit()\n", " stop = time.time()\n", " dur_sec += stop - start\n", " dur_num += 1\n", " if i % int(num_train_steps / num_evals) == 0 or i == num_train_steps - 1:\n", " tst_loss, (tst_nll, tst_kl) = eval()\n", " f, x = zip([\n", " ('it:{:5}', opt.iterations),\n", " ('ms/it:{:6.4f}', dur_sec / max(1., dur_num) 1000.),\n", " ('trn_loss:{:6.4f}', trn_loss),\n", " ('tst_loss:{:6.4f}', tst_loss),\n", " ('tst_nll:{:6.4f}', tst_nll),\n", " ('tst_kl:{:6.4f}', tst_kl),\n", " ('sum_norm_grad:{:6.4f}', sum(g)),\n", "\n", " ])\n", " print(' '.join(f).format([getattr(x_, 'numpy', lambda: x_)()\n", " for x_ in x]))\n", " sys.stdout.flush()\n", " dur_sec = dur_num = 0\n", " # if i % 1000 == 0 or i == maxiter - 1:\n", " # encoder.save('/tmp/encoder.npz')\n", " # decoder.save('/tmp/decoder.npz')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "G1ImqkK7xAv1" }, "source": [ "### 6 Evaluate Classification Accuracy" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Fg2mxVJPsSOQ" }, "outputs": [], "source": [ "def evaluate_accuracy(dataset, encoder, decoder):\n", " \"\"\"Evaluate the accuracy of your model on a dataset.\n", " \"\"\"\n", " this_it = iter(dataset)\n", " num_correct = 0\n", " num_total = 0\n", " attempts = 0\n", " for xin, xout in this_it:\n", " xin, xout = next(this_it)\n", " e = encoder(xin)\n", " z = e.sample(10000) # 10K samples should have low variance.\n", " d = decoder(z)\n", " yhat = d.sample()\n", " confidence = tf.reduce_mean(d.probs_parameter(), axis=0)\n", " most_likely = tf.cast(tf.math.argmax(confidence, axis=-1), tf.int32)\n", " num_correct += np.sum(most_likely == xout, axis=0)\n", " num_total += xout.shape[0]\n", " attempts +=1\n", " return num_correct, num_total" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": { "height": 35 }, "colab_type": "code", "id": "8fZiEgQJuoMW", "outputId": "f9624596-07f9-4f35-a63f-9178ec36bf8b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.9930\n" ] } ], "source": [ "nc, nt = evaluate_accuracy(eval_dataset.batch(100), encoder, decoder)\n", "print(\"Accuracy: %.4f\"%(nc/nt))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lyyh37u80SNT" }, "source": [ "The accuracy of one training run with this particular model and training setup was 99.15%, which is within a half of a percent of the state of the art, and comparable to the mnist accuracy reported in Alemi et al. (2016)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Y0A3I1IymUWT" }, "source": [ "# OOD detection using DoSE\n", "\n", "From the previous section, we have trained a variational classifier. However, this classifier was trained assuming that all of the inputs are from the distribution which generated the training set. In general, we may not always receive images drawn from this distribution. In these situations, our model prediction is unreliable. We want to be able to identify when this may be the case to avoid serving these flawed predictions. \n", "\n", "In this section, we turn the VIB classifier into an OOD detector using DoSE." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "T6eHmdEjIeuL" }, "source": [ "### 1 Get statistics" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Z3QMQ-L3mTQJ" }, "outputs": [], "source": [ "def get_statistics(encoder, decoder, prior):\n", " \"\"\"Setup a function to evaluate statistics given model components.\n", " \n", " Args:\n", " encoder: Callable neural network which takes in an image and \n", " returns a tfp.distributions.Distribution object.\n", " decoder: Callable neural network which takes in a vector and \n", " returns a tfp.distributions.Distribution object.\n", " prior: A tfp.distributions.Distribution object which operates \n", " on the same spaces as the encoder.\n", " Returns:\n", " T: A function, which takes in a tensor containing an image (or \n", " batch of images) and evaluates statistics on the model. \n", " Optionally it also returns the prediction, under the assumption \n", " that the DoSE model will only dress an actual classifier.\n", " \"\"\"\n", " def T(x, return_pred=False):\n", " \"\"\"Evaluate statistics on an input image or batch of images.\n", " \n", " Given an input tensor `x` containing either an image or a batch of \n", " images, this function evaluates 4 statistics on a VIB model; the\n", " kl-divergence between the posterior and prior, the expected entropy\n", " of the decoder computed using samples from the posterior, the \n", " posterior entopy, and the cross-entropy between the posterior and\n", " the prior. We also allow for the prediction to be optionally\n", " returned.\n", "\n", " Args: \n", " x: rank 4 tensor containing a batch of images\n", " return_pred: Bool indicating whether to return the model\n", " prediction.\n", " Returns:\n", " tf.tensor containing the 4 statistics evaluated on the input.\n", " pred (optional): The prediction of the model.\n", " \"\"\"\n", " pzgx = encoder(x)\n", " z = pzgx.sample(100, seed=42) # Seed is fixed for determinism.\n", " pxgz = decoder(z)\n", "\n", " kl = pzgx.kl_divergence(prior)[tf.newaxis,...]\n", " dent = tf.reduce_mean(pxgz.entropy(), axis=0)[tf.newaxis,...]\n", " eent = pzgx.entropy()[tf.newaxis,...]\n", " xent = pzgx.cross_entropy(prior)[tf.newaxis,...]\n", " if return_pred:\n", " pred = tf.math.argmax(\n", " tf.reduce_mean(pxgz.probs_parameter, axis=0), \n", " axis=-1)\n", " return tf.concat([kl, dent, eent, xent], axis=0), pred\n", " else:\n", " return tf.concat([kl, dent, eent, xent], axis=0)\n", " return T" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "nwVSy6J3JK46" }, "outputs": [], "source": [ "T = get_statistics(encoder, decoder, prior)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "n2szpmf2IymI" }, "source": [ "### 2 Define DoSE helper classes and functions" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "KK0cmEgwoEGH" }, "outputs": [], "source": [ "def get_DoSE_KDE(T, dataset):\n", " \"\"\"Get a distribution and decision rule for OOD detection using DoSE.\n", "\n", " Given a tensor of statistics tx, compute a Kernel Density Estimate (KDE) of\n", " the statistics. This uses a quantiles trick to cut down the number of \n", " samples used in the KDE (to lower the cost of evaluating a trial point).\n", "\n", " Args:\n", " T: A function which takes an input image and returns a vector of\n", " statistics evaluated using the model.\n", " dataset: A tensorflow_datasets `Dataset` which will be used to evaluate\n", " statistics to construct the estimator.\n", " Returns:\n", " is_ood: A function which takes a new point `x` and `threshold`, and \n", " computes the decision rule KDE.log_prob(T(x)) < threshold\n", " dose_kde: A tfd.MixtureSameFamily object. The distribution used as the KDE\n", " from which the log_prob of a batch of statistics can be computed.\n", " \"\"\"\n", "\n", " # First we should evaluate the statistics on the training set.\n", " it = iter(dataset)\n", " for x, y in it:\n", " if not \"tx\" in locals():\n", " tx = T(x)\n", " else:\n", " tx = tf.concat([tx, T(x)], axis=-1)\n", "\n", " n = tf.cast(tf.shape(tx)[-1], tx.dtype)\n", " num_quantiles = int(25)\n", " q = tfp.stats.quantiles(tx, num_quantiles, axis=-1)\n", " q = tf.transpose(q, tf.roll(tf.range(tf.rank(q)), shift=-1, axis=0))\n", " # Scott's Rule:\n", " h = 3.49 tf.math.reduce_std(tx, axis=-1, keepdims=True) * (n)*(-1./3.)\n", " h = n / num_quantiles\n", " \n", " dose_kde = tfd.Independent(\n", " tfd.MixtureSameFamily(\n", " mixture_distribution=tfd.Categorical(logits=tf.zeros(num_quantiles + 1)),\n", " components_distribution=tfd.Normal(loc=q, scale=h)),\n", " reinterpreted_batch_ndims=1)\n", " is_ood = lambda x, threshold: dose_kde.log_prob(tf.transpose(T(x), [1, 0])) < tf.math.log(threshold)\n", " dose_log_prob = lambda x: dose_kde.log_prob(tf.transpose(T(x), [1, 0]))\n", "\n", " # T(x) returns shape [T, N], but dose_kde works on shape [N, T]\n", " return is_ood, dose_kde, dose_log_prob, tx\n", "\n", "\n", "class DoSE_administrator(object):\n", " def __init__(self, T, train_dataset, eval_dataset):\n", " \"\"\"Administrate DoSE for model evaluation in a more efficient way.\n", "\n", " This high level object just calls the lower level DoSE methods, but\n", " also evaluates the DoSE log-probabilities on the evaluation dataset.\n", " Using these, we can do things like compute auroc much more efficiently.\n", " \"\"\"\n", " dose_build = get_DoSE_KDE(T, train_dataset)\n", " \n", " # Call DoSE on an image/batch x for lp threshold `threshold`\n", " self.is_ood = dose_build[0]\n", "\n", " # Actual dose distribution\n", " self.dose_dist = dose_build[1]\n", " self.dose_lp = lambda t: self.dose_dist.log_prob(tf.transpose(t, [1, 0]))\n", "\n", " # Get the log-probability of a batch from dose\n", " self.dose_log_prob = dose_build[2] \n", "\n", " # This helps us evaluate auroc more reliably.\n", " self.training_stats = dose_build[3]\n", " \n", " # Get training_log probs efficiently\n", " train_size = self.training_stats.shape[-1]\n", " bs = train_size // 1000\n", " for i in range(1000):\n", " tlp = self.dose_lp(self.training_stats[..., bsi:bs(i+1)])\n", " if not hasattr(self, 'training_lp'):\n", " self.training_lp = tlp\n", " else:\n", " self.training_lp = tf.concat([self.training_lp, tlp], axis=0)\n", "\n", " # Get log_probs, images, labels, and statistics \n", " # on the evaluation dataset.\n", " eval_it = iter(eval_dataset)\n", " for x, y in eval_it:\n", " if not hasattr(self, 'eval_lp'):\n", " self.eval_stats = T(x)\n", " self.eval_lp = self.dose_lp(self.eval_stats)\n", " self.eval_label = y\n", " self.eval_ims = x\n", " else:\n", " tx = T(x)\n", " self.eval_stats = tf.concat([self.eval_stats,\n", " tx], \n", " axis=0)\n", " self.eval_lp = tf.concat([self.eval_lp,\n", " self.dose_lp(tx)],\n", " axis=0)\n", " self.eval_label = tf.concat([self.eval_label, y], axis=0)\n", " self.eval_ims = tf.concat([self.eval_ims, x], axis=0)\n", "\n", " \n", " def get_acc(self, threshold):\n", " \"\"\"Evaluate the OOD accuracy for a certain threshold probability.\n", " \n", " This computes the decision rule: `log q(x) < tf.math.log(thresh)`\n", " on the eval dataset. It uses this decision rule to evaluate the\n", " number of correct predictions, along with the 4 components of the\n", " confusion matrix.\n", "\n", " Args:\n", " threshold: A threshold on the DoSE probability density.\n", " Returns:\n", " nc: Number of correct predictions\n", " nt: Number of total predictions\n", " tp: Number of true positives\n", " tn: Number of true negatives\n", " fp: Number of false positives\n", " fn: Number of false negatives\n", " \"\"\"\n", "\n", " yhat = self.eval_lp < tf.math.log(threshold)\n", "\n", " fp = tf.reduce_sum(tf.cast(\n", " tf.logical_and(tf.math.not_equal(yhat, self.eval_label), \n", " tf.equal(self.eval_label, False)),\n", " tf.int32),axis=0)\n", " fn = tf.reduce_sum(tf.cast(\n", " tf.logical_and(tf.math.not_equal(yhat, self.eval_label), \n", " tf.equal(self.eval_label, True)),\n", " tf.int32),axis=0)\n", " tp = tf.reduce_sum(tf.cast(\n", " tf.logical_and(tf.equal(yhat, self.eval_label), \n", " tf.equal(self.eval_label, True)),\n", " tf.int32),axis=0)\n", " tn = tf.reduce_sum(tf.cast(\n", " tf.logical_and(tf.equal(yhat, self.eval_label), \n", " tf.equal(self.eval_label, False)),\n", " tf.int32),axis=0)\n", " nc = tp+tn\n", " nt = tf.cast(tf.size(self.eval_label), tf.float32) \n", " return nc, nt, tp, tn, fp, fn\n", " \n", " def roc_curve(self, nbins):\n", " \"\"\"Get the roc curve for the model.\"\"\"\n", "\n", " nc, nt, tp, tn, fp, fn = self.get_acc(\n", " np.float32(np.exp(np.percentile(self.eval_lp, 0.))))\n", " fpr = [fp.numpy() / (fp.numpy()+tn.numpy())]\n", " tpr = [tp.numpy()/ (tp.numpy() + fn.numpy())]\n", " for i in range(1, nbins+1):\n", " nc, nt, tp, tn, fp, fn = self.get_acc(\n", " np.float32(np.exp(np.percentile(self.eval_lp, i/float(nbins)100.))))\n", " fpr.append(fp.numpy()/ (fp.numpy() + tn.numpy()))\n", " tpr.append(tp.numpy()/ (tp.numpy() + fn.numpy()))\n", " return fpr, tpr\n", "\n", " def precision_recall_curve(self, nbins):\n", " \"\"\"Get the precision-recall curve for the model.\"\"\"\n", "\n", " nc, nt, tp, tn, fp, fn = self.get_acc(\n", " np.float32(np.exp(np.percentile(self.eval_lp, 0.))))\n", " precision = [tp.numpy()/ (tp.numpy() + fp.numpy())]\n", " recall = [tp.numpy() / (tp.numpy() + fn.numpy())]\n", " for i in range(1, nbins+1):\n", " nc, nt, tp, tn, fp, fn = self.get_acc(\n", " np.float32(np.exp(np.percentile(self.eval_lp, i/float(nbins)100.))))\n", " precision.append(tp.numpy()/ (tp.numpy() + fp.numpy()))\n", " recall.append(tp.numpy() / (tp.numpy() + fn.numpy()))\n", " return precision, recall" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jXozW6F4I9hy" }, "source": [ "### 3 Setup OOD dataset" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "8ivxmHF4txD4" }, "outputs": [], "source": [ "# For evaluating statistics on the training set, we need to perform a\n", "# pass through the dataset.\n", "train_one_pass = tfds.load('mnist')['train']\n", "train_one_pass = tfn.util.tune_dataset(train_one_pass, \n", " batch_size=1000,\n", " repeat_count=None,\n", " preprocess_fn=_preprocess)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Xhcfu7UX8eC4" }, "outputs": [], "source": [ "# OOD dataset is Fashion_MNIST\n", "ood_data = tfds.load('fashion_mnist')['test'].map(_preprocess).map(\n", " lambda x, y: (x, tf.ones_like(y, dtype=tf.bool)))\n", "\n", "# In-distribution data is the MNIST test set.\n", "ind_data = tfds.load('mnist')['test'].map(_preprocess).map(\n", " lambda x, y: (x, tf.zeros_like(y, dtype=tf.bool)))\n", "\n", "# Our trial dataset is a 50-50 split of the two.\n", "hybrid_data = ind_data.concatenate(ood_data)\n", "hybrid_data = tfn.util.tune_dataset(hybrid_data, batch_size=100,\n", " shuffle_size=20000,repeat_count=None)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oj7j-Z-SJXWv" }, "source": [ "### 4 Administer DoSE" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Rp7837jIwvTV" }, "outputs": [], "source": [ "DoSE_admin = DoSE_administrator(T, train_one_pass, hybrid_data)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "K9HhI88EJd8N" }, "source": [ "### 5 Evaluate OOD performance" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": { "height": 409 }, "colab_type": "code", "id": "MjWjlZOBok5z", "outputId": "9a502caf-992f-42ab-a90e-3c7f2b2fcd22" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/export/hda3/borglet/remote_hdd_fs_dirs/0.colab_kernel_brain_frameworks_gpu_wmorning.kernel.wmorning.579822584826.14b334fb3717c109/mount/server/ml_notebook:162: RuntimeWarning: invalid value encountered in int_scalars\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'AUPRC: 0.9896')" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" }, { "data": { "image/png": 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06aLU1FRNnz69ynVWrlyp7t27Ky0tTf369at1m8ZIERH1XSkAuPNG/wIQOuoj\nsPkk7jidTk2aNEnLly9XcnKyevbsqaFDh+qKK65wrXP8+HFNnDhRS5cuVdu2bXXo0CGPts0MGwBv\n8mb/AhAaAmaX6Nq1a5WamqqOHTuqYcOGyszM1OLFi93WWbhwoYYPH662bdtKklq0aFHrdvngXADe\n5q3+BSB0BMwu0YKCAqWkpLgeJycnq6CgwG2dbdu26dixY+rfv7969OihuXPn1rpddokC8DZv9S8A\noSNgdokaYyo953A43B6fO3dOX3/9tT7++GOdPn1affr0Ue/evdW5c2e39bKzs5Wdne16DYENgDfV\nZ/+S3HvY4cOHvVM0AFsJmBm25ORk7du3z/U4Pz9fSUlJldYZOHCgoqOj1axZM91www3asGFDpW1l\nZWUpNzdXubm5Cg+PYJcoAK+qz/4lufew5s2be7V2APYQFiZd8HfexW+jfkqpWc+ePbV9+3bl5eWp\nrKxMixYt0tChQ93W+eUvf6lPP/1U586dU0lJidasWaOuXbvWuF12iQLwNm/1LwChpa55xSdxJyIi\nQjNnztSAAQPkdDo1btw4paWladasWZKkCRMmqGvXrho4cKDS09MVFhame++9V1deeWWN2yWwAbgU\n5eXlbo/DajiFy1v9C0BoqWtecZiqDtAIEA0aZOjee3P1r3/5uxIAvpKRkaHc3NyLft26des0adIk\nbdy4UWfOnJFkHZ/mcDjkdDrru0yPXOpYAASe+Hjpsssu/T0f0PNTxkiNGvm7CgCB4K677tKQIUP0\nyiuvKCoqyt/lAAgxAbFL1FuMkRo29HcVAALBnj179NRTT1U6wxMAfKGugS2gryVaXs6VDgB45le/\n+pU+/PBDf5cBIESF9AybxAwbAM+cOXNGv/rVr3TdddepVatWbsv4oFsA3lbXCaaAD2zMsAHwxBVX\nXOF2/U8A8KWQn2HjpAMAnvjTn/7k7xIAhLCQD2w1fHwSALhZsWKF5s2bp4KCArVp00ZjxozRTTfd\n5O+yAISAkD7pQCKwAfDMyy+/rNtvv12tWrXS8OHD1bp1a91xxx166aWX/F0agBAQ8jNsXEsUgCee\neeYZLV++XFdddZXrudtvv10jRozQ+PHj/VgZgFDQoIH1cWSXKuDnp5hhA+CJI0eOVDrpoEuXLjp6\n9KifKgIQStglGvAjAOAL1113nR555BGVlJRIkoqLi/Wb3/xGffv29XNlAEJBXfcI1inubNq0qW7f\nvR6wSxSAJ2bNmqWNGzcqPj5eLVu2VJMmTbRhwwb9+9//9ndpAEKA149hO3nypLZv36527dqpWbNm\nkqQNGzZo6tSpysnJ0enTp+sgB/VWAAAXCElEQVRWQR0xwwbAE61bt9aqVau0b98+HThwQElJSUpO\nTvZ3WQBChFcD2/vvv6/MzEwVFxerYcOGmj9/vlavXq158+Zp/Pjx2rFjR92+ez0gsAGojjHGde3Q\n8vJySVKbNm3Upk0bt+fCaCQAvKyuewRrDGx//OMf9eyzz+rXv/61XnnlFd11110aOnSodu7cqaZN\nm9btO9cTdokCqE58fLxOnjwpSYqIiKh04feKQOd0Ov1RHoAQ4tUZtry8PGVlZUmSJkyYoMmTJ2v2\n7NmKioqq23etR/xhDKA6mzdvdn2dl5fnx0oAhDqvBraK3QWSFB4erpiYGFuFNUmKjPR3BQDsKiUl\nxfV1u3bt3JadPn1a4eHhatiwoa/LAhCCvLpLtKSkRDfccIPr8alTp9weS9Lq1avrVkEd0WsBeGLK\nlCkaOXKkevXqpffff1+33nqrHA6HXn/9dQ0ZMsTf5QEIcl6dYZs9e7bb43vuuadu380LLjgkBQCq\ntGDBAk2bNk2SNG3aNM2fP1/x8fGaPHkygQ2A13k1sN11112SpKNHj9rmJIMLEdgAeKKkpERRUVE6\ncuSIdu3apREjRkiS9uzZ4+fKAIQCr35w7n//+1+1adNGzZs3V7t27bR+/fq6fTcvILAB8ETnzp21\nYMECzZw5U7fccoskqbCwUJEcCAvAB7x6aaopU6ZozJgx+vbbbzVy5EhNmTKlbt/NCwhsADzxz3/+\nUy+++KJWrFihv/zlL5KkZcuW6Wc/+5mfKwMQCup6zH2NeW/Lli1atWqVwsPD9eSTT1Y6y8oOCGwA\nPNGzZ0998cUXbs+NHj1ao0eP9lNFAEJJ//7S119f+utrDGznzp1T+P92ujZq1EhlZWWX/p28hMAG\noDqrV692ndn+ySefVLveTTfd5KuSAISoUaOkZ5+99NfXGNjOnDmjO++80/W4uLjY7bEkzZ0799K/\nez0gsAGozsSJE7Vp0yZJ1Z/l7nA4tGvXLl+WBQAXrcbA9oc//MHt8e9//3uvFnMpCGwAqlMR1iSu\ndAAgsNUY2Dp37qxRo0b5qpZLQmAD4In169crMTHR7eoH+/bt09GjR3XVVVf5sTIAqF2NZ4ned999\nvqrjkhHYAHhizJgxOnv2rNtzZWVl+vWvf+2nigDAczUGNmOMr+q4ZAQ2AJ7Yu3evOnbs6PZcp06d\ntHv3bv8UBAAXocZdok6nUytWrKgxuPn77CoCGwBPJCcna926dbrmmmtcz61bt05JSUl+rAoAPFNj\nYCstLdU999xTbWCzw9lVBDYAnpg8ebJ++ctf6re//a06deqknTt36v/+7/8qnVwFAHZUY2CLjo72\neyCrDYENgCfGjx+vJk2aaPbs2dq3b59SUlL07LPP6tZbb/V3aQBQqzpe2cr/CGwAPHXbbbfptttu\n83cZAHDROOkAQEgwxuill17ST3/6U6Wnp0uyroTwxhtv+LkyAKhdjYHt1KlTvqrjkhHYAHjiiSee\n0OzZszV+/Hjt3btXknUiwowZM/xcGQDUrsbAFggIbAA88Z///EdLlixRZmamHP9rHB06dLD9cboA\nIBHYAIQIp9OpmJgYSXIFtqKiItdzAGBnBDYAIWHQoEF65JFHVFpaKsk6pu3xxx/XkCFD/FwZANSO\nwAYgJDz33HPav3+/4uPjdeLECcXExGjPnj0cwwYgIPCxHgCCnjFGhYWFeuutt3T06FHt2bNHKSkp\natWqlb9LAwCPENgABD2Hw6Fu3brp1KlTatGihVq0aOHvkgDgorBLFEBIuPrqq7Vt2zZ/lwEAl4QZ\nNgAhoX///ho4cKDGjh2rlJQU15mikjRu3Dg/VgYAtQv4wBYR8CMA4Auff/65OnTooFWrVrk973A4\nCGwAbC/g4054uL8rAGBnJSUlevLJJxUTE6NrrrlGv//979WoUSN/lwUAF8Vnx7AtXbpUXbp0UWpq\nqqZPn17tel999ZXCw8P11ltv+ao0AEHsgQce0HvvvaeuXbvq7bff1pQpUy56G/QvAP7mk8DmdDo1\nadIk5eTkaMuWLXrttde0ZcuWKtd79NFHNWDAAI+3zTFsAGqSk5OjDz/8UM8884xycnK0ZMmSi3q9\nN/sXAHjKJ4Ft7dq1Sk1NVceOHdWwYUNlZmZq8eLFldZ74YUXNGLECE65B1BviouL1bp1a0lSSkqK\nTpw4cVGvp38BsAOfHMNWUFCglJQU1+Pk5GStWbOm0jrvvPOOPvnkE3311Vceb5sZNgA1OXfunFas\nWCFjTJWPJemmm26q9vXe7F8A4CmfBLbzG2MFxwVJ6+GHH9aMGTMUXstZBNnZ2crOzj5vO/VTI4Dg\n1KJFC7ezQBMTE90eOxwO7dq1q9rX12f/ktx72OHDh2tdHwAkHwW25ORk7du3z/U4Pz9fSUlJbuvk\n5uYqMzNTklRYWKgPPvhAERERGjZsmNt6WVlZysrKkiQ5HBlerhxAoNu9e3edXl+f/Uty72EZGfQw\nAJ7xSWDr2bOntm/frry8PLVp00aLFi3SwoUL3dbJy8tzfT127Fj94he/qLLZXYgZNgDe5M3+BQCe\n8klgi4iI0MyZMzVgwAA5nU6NGzdOaWlpmjVrliRpwoQJvigDAC4a/QuAHThMVQdoBAiHI0PffZer\nyy/3dyUAfCUjI0O5ubn+LqNeBNNYANSuLu95Lv4OAABgcwEf2AAAAIJdwAc2ZtgAAECwC/jABgAA\nEOwCPrAxwwYAAIIdgQ0AAMDmAj6wAQAABLuAD2zMsAEAgGBHYAMAALC5gA9sAAAAwS7gAxszbAAA\nINgFfGADAAAIdgEf2JhhAwAAwY7ABgAAYHMBH9gAAACCXcAHNmbYAABAsAv4wAYAABDsAj6wMcMG\nAACCHYENAADA5gI+sAEAAAS7gA9szLABAIBgF/CBDQAAINgFfGBjhg0AAAQ7AhsAAIDNBXxgAwAA\nCHYBH9iYYQMAAMEu4AMbAABAsAv4wMYMGwAACHYENgAAAJsL+MAGAAAQ7AI+sDHDBgAAgh2BDQAA\nwOYCPrABAAAEu4APbA0b+rsCAAAA7wr4wBYW8CMAAACoGXEHAADA5ghsAAAANhfwgY2zRAEAQLAL\n+MAGAAAQ7AI+sDHDBgAAgl3ABzYAAIBgR2ADAACwuYAPbOwSBQAAwS7gAxsAAECwI7ABAADYXMAH\nNnaJAgCAYOezwLZ06VJ16dJFqampmj59eqXlCxYsUHp6utLT09W3b19t2LDBV6UBQI3oXwD8LcIX\n38TpdGrSpElavny5kpOT1bNnTw0dOlRXXHGFa50OHTpo1apVSkhIUE5OjrKysrRmzRpflAcA1aJ/\nAbADn8ywrV27VqmpqerYsaMaNmyozMxMLV682G2dvn37KiEhQZLUu3dv5efne7RtdokC8CZv9i8A\n8JRPAltBQYFSUlJcj5OTk1VQUFDt+rNnz9agQYOqXJadna2MjAxlZGTUe50AcKH67F+Sew87fPhw\nvdYKIHj5ZJeoMabSc45qpsZWrFih2bNn67PPPqtyeVZWlrKysv63jQxm2AB4VX32L8m9h/GHJwBP\n+SSwJScna9++fa7H+fn5SkpKqrTexo0bde+99yonJ0eJiYm+KA0AakT/AmAHPtkl2rNnT23fvl15\neXkqKyvTokWLNHToULd19u7dq+HDh2vevHnq3LmzL8oCgFrRvwDYgU9m2CIiIjRz5kwNGDBATqdT\n48aNU1pammbNmiVJmjBhgqZNm6YjR45o4sSJrtfk5ubWum12iQLwJm/2LwDwlMNUdYBGgHA4MlRW\nlqsGDfxdCQBfycjICJowFExjAVC7urznA/5KBwAAAMEu4AMbu0QBAECwC/jABgAAEOwCPrAxwwYA\nAIJdwAc2AACAYEdgAwAAsLmAD2zsEgUAAMEu4AMbAABAsCOwAQAA2FzABzZ2iQIAgGAX8IENAAAg\n2BHYAAAAbC7gAxu7RAEAQLAL+MAGAAAQ7AhsAAAANkdgAwAAsDkCGwAAgM0R2AAAAGyOwAYAAGBz\nBDYAAACbI7ABAADYHIENAADA5ghsAAAANkdgAwAAsDkCGwAAgM0R2AAAAGyOwAYAAGBzBDYAAACb\nC+jA5nD4uwIAAADvC+jABgAAEAoIbAAAADZHYAMAALA5AhsAAIDNEdgAAABsjsAGAABgcwQ2AAAA\nmyOwAQAA2ByBDQAAwOYIbAAAADZHYAMAALA5AhsAAIDNBXRg4+LvAAAgFAR0YAMAAAgFBDYAAACb\nI7ABAADYnM8C29KlS9WlSxelpqZq+vTplZYbY/Tggw8qNTVV6enpWrduna9KA4Aa0b8A+JtPApvT\n6dSkSZOUk5OjLVu26LXXXtOWLVvc1snJydH27du1fft2ZWdn6/777/dFaQBQI/oXADvwSWBbu3at\nUlNT1bFjRzVs2FCZmZlavHix2zqLFy/WnXfeKYfDod69e+v48eM6cOCAL8oDgGrRvwDYgU8CW0FB\ngVJSUlyPk5OTVVBQcNHrAICv0b8A2EGEL76JMabSc44LPkTNk3UkKTs7W9nZ2ZKkBg02KSMjo56q\n9K/Dhw+refPm/i6jXgTLWIJlHFJwjWXr1q0+/X712b8k9x62aVNw9LBg+vliLPYTLOOQ6ta/fBLY\nkpOTtW/fPtfj/Px8JSUlXfQ6kpSVlaWsrCxJUkZGhnJzc71UtW8xFvsJlnFIwTcWX6rP/iUFZw8L\nlnFIjMWOgmUcUt36l092ifbs2VPbt29XXl6eysrKtGjRIg0dOtRtnaFDh2ru3Lkyxui///2v4uPj\n1bp1a1+UBwDVon8BsAOfzLBFRERo5syZGjBggJxOp8aNG6e0tDTNmjVLkjRhwgQNHjxYH3zwgVJT\nUxUVFaU5c+b4ojQAqBH9C4Ad+CSwSdLgwYM1ePBgt+cmTJjg+trhcOjFF1+8qG1W7FYIBozFfoJl\nHBJjqStv9C8peP5fgmUcEmOxo2AZh1S3sThMVUfLAgAAwDa4NBUAAIDNBURgC6bLwtQ2lgULFig9\nPV3p6enq27evNmzY4Icqa1fbOCp89dVXCg8P11tvveXD6i6OJ2NZuXKlunfvrrS0NPXr18/HFXqu\ntrGcOHFCQ4YM0VVXXaW0tDTbHms1btw4tWjRQldeeWWVy4PpPR9MYwmU/iUFTw+jf9mP1/qXsblz\n586Zjh07mp07d5rS0lKTnp5uNm/e7LbO+++/bwYOHGjKy8vNl19+aXr16uWnamvmyVg+//xzc/To\nUWOMMR988IEtx+LJOCrWu/HGG82gQYPMm2++6YdKa+fJWI4dO2a6du1q9uzZY4wx5uDBg/4otVae\njOWpp54yv/3tb40xxhw6dMgkJCSY0tJSf5Rbo1WrVpmvv/7apKWlVbk8mN7zwTSWQOhfxgRPD6N/\nhVb/sv0MWzBdFsaTsfTt21cJCQmSpN69eys/P98fpdbIk3FI0gsvvKARI0aoRYsWfqjSM56MZeHC\nhRo+fLjatm0rSbYdjydjcTgcOnXqlIwxKioqUtOmTRUR4bNzjzx2ww03qGnTptUuD6b3fDCNJRD6\nlxQ8PYz+FVr9y/aBLZguC3Oxdc6ePVuDBg3yRWkXxdP/k3feecftTDo78mQs27Zt07Fjx9S/f3/1\n6NFDc+fO9XWZHvFkLA888IC+++47JSUlqVu3bnr++ecVFmb7NlBJML3ng2ks57Nr/5KCp4fRv0Kr\nf9kvml7A1PNlYfzpYupcsWKFZs+erc8++8zbZV00T8bx8MMPa8aMGQoPD/dVWZfEk7GcO3dOX3/9\ntT7++GOdPn1affr0Ue/evdW5c2dflekRT8aybNkyde/eXZ988ol27typW265Rddff73i4uJ8VWa9\nCKb3fDCNpYKd+5cUPD2M/hVa/cv2ga2+LwvjT57WuXHjRt17773KyclRYmKiL0v0iCfjyM3NVWZm\npiSpsLBQH3zwgSIiIjRs2DCf1lobT3++mjVrpujoaEVHR+uGG27Qhg0bbNfwPBnLnDlz9Nhjj8nh\ncCg1NVUdOnTQ1q1b1atXL1+XWyfB9J4PprFI9u9fUvD0MPpXiPWvuhxY5wtnz541HTp0MLt27XId\niLhp0ya3dZYsWeJ2AF/Pnj39VG3NPBnLnj17TKdOncznn3/upypr58k4znfXXXfZ8oBdYzwby5Yt\nW8xNN91kzp49a4qLi01aWpr59ttv/VRx9TwZy4QJE8yf/vQnY4wxP/zwg0lKSjKHDx/2Q7W1y8vL\nq/ag3WB6zwfTWAKhfxkTPD2M/hVa/cv2M2zBdFkYT8Yybdo0HTlyRBMnTnS9xm4XvfVkHIHCk7F0\n7dpVAwcOVHp6usLCwnTvvfdWe7q2P3kylscff1xjx45Vt27dZIzRjBkz1KxZMz9XXtmoUaO0cuVK\nFRYWKjk5WVOnTtXZs2clBd97PpjGEgj9SwqeHkb/Cq3+xZUOAAAAbC7wTq8AAAAIMQQ2AAAAmyOw\nAQAA2ByBDQAAwOYIbAAAADZHYAMAwKZWrlyp5ORk1+P27dvro48+8mNF8BcCG2ylffv2ioyMVExM\njOv2xRdfyOFwuB63b99e06dPd73G4XAoOjpaMTExatOmjR555BE5nU4/jgJAsDq/R7Vq1Upjx45V\nUVGRv8tCCCCwwXbee+89FRUVuW4Vl+w4fvy4ioqK9Nprr2natGlaunSp6zUbNmxQUVGRVq1apddf\nf12vvPKKv8oHEOQqetT69ev1zTff6Omnn/Z3SQgBBDYEnD59+igtLU2bNm2qtCw1NVXXXnut1q9f\n74fKAISSVq1aacCAAa5+U1paqilTpqht27Zq2bKlJkyYoNOnT7vWX7x4sbp37664uDh16tTJ9Ufn\nnDlz1LVrV8XGxqpjx47697//7ZfxwN4IbAgoxhh9/vnn2rx5s66++upKy7du3apPP/1UqampfqgO\nQCjJz89XTk6Oq988+uij2rZtm9avX68dO3aooKBA06ZNkyStXbtWd955p/7617/q+PHjWr16tdq3\nby9JatGihZYsWaKTJ09qzpw5mjx5statW+evYcGmuDQVbKV9+/YqLCxURIR1mdv+/fvr73//uzp0\n6KD4+Hg5HA61atVK999/vx588EFJ1jFssbGxcjqdKikpUWZmpv7zn/+oUaNG/hwKgCBU0aMcDoeK\niop000036e2331Z8fLxiYmK0ceNGderUSZL05Zdf6o477lBeXp7uu+8+RUVF6bnnnqv1ewwbNkw3\n3nijHnroIa1cuVJjxoxRfn6+6/u//PLLuvnmm706TtiP7S/+jtDz7rvvujWj3bt3S5JbkLvQunXr\n1KlTJ7355pt67LHHVFxcTGAD4BUVPWrVqlW64447VFhYqLKyMpWUlKhHjx6u9YwxrhOg9u3bp8GD\nB1e5vZycHE2dOlXbtm1TeXm5SkpK1K1bN5+MBYGDXaIIGg6HQyNHjlSfPn1cuyEAwFv69eunsWPH\nasqUKWrWrJkiIyO1efNmHT9+XMePH9eJEydcZ5CmpKRo586dlbZRWlqqESNGaMqUKTp48KCOHz+u\nwYMHi51fuBCBDUHnscceU3Z2tn744Qd/lwIgyD388MNavny5Nm7cqPHjx2vy5Mk6dOiQJKmgoEDL\nli2TJN1zzz2aM2eOPv74Y5WXl6ugoEBbt25VWVmZSktL1bx5c0VERCgnJ0cffvihP4cEmyKwIeh0\n69ZN/fr101//+ld/lwIgyDVv3lx33nmn/vKXv2jGjBlKTU1V7969FRcXp5tvvlnff/+9JKlXr16u\nEwri4+PVr18/7dmzR7GxsfrHP/6hkSNHKiEhQQsXLtTQoUP9PCrYEScdAAAA2BwzbAAAADZHYAMA\nALA5AhsAAIDNEdgAAABsjsAGAABgcwQ2AAAAmyOwAQAA2ByBDQAAwOYIbAAAADb3/wF4BetbUZe9\n5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1000x500 with 2 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "fp, tp = DoSE_admin.roc_curve(10000)\n", "precision, recall = DoSE_admin.precision_recall_curve(10000)\n", "plt.figure(figsize=[10,5])\n", "plt.subplot(121)\n", "plt.plot(fp, tp, 'b-')\n", "plt.xlim(0, 1.)\n", "plt.ylim(0., 1.)\n", "plt.xlabel('FPR', fontsize=12)\n", "plt.ylabel('TPR', fontsize=12)\n", "plt.title(\"AUROC: %.4f\"%np.trapz(tp, fp), fontsize=12)\n", "plt.subplot(122)\n", "plt.plot(recall, precision, 'b-')\n", "plt.xlim(0, 1.)\n", "plt.ylim(0., 1.)\n", "plt.xlabel('Recall', fontsize=12)\n", "plt.ylabel('Precision', fontsize=12)\n", "plt.title(\"AUPRC: %.4f\"%np.trapz(precision[1:], recall[1:]), fontsize=12)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": { "height": 321 }, "colab_type": "code", "id": "NXftuKHAjcib", "outputId": "32136743-138e-4696-8e26-25a9d582ee3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most False Positive\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x200 with 1 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Most True Negative\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 2000x200 with 1 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Most False Negative\n" ] }, { "data": { "image/png": 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86C9vfetbm23jjTeWJH35y19uNs7dhYwRt/JMiWFzaPW/aYjcqrLMZhpFPeo8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\n+++/X5J03333NRtZLIi2Sp3AoZ8j4oe+BvJjWJh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"text/plain": [ "<Figure size 2000x200 with 1 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Most True Positive\n" ] }, { "data": { "image/png": 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FQnVexkcVflyl6xiWuoE2J6xdkjbaaCNJT68UGVCFsbpoL2I73ONMjw3DwlxZ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\n", "text/plain": [ "<Figure size 2000x200 with 1 Axes>" ] }, "metadata": { "tags": [] }, "output_type": "display_data" }, { "data": { "text/plain": [ "(<Figure size 2000x200 with 1 Axes>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7f3051175128>)" ] }, "execution_count": 0, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "Sorted_ims = tf.gather(DoSE_admin.eval_ims, tf.argsort(DoSE_admin.eval_lp))\n", "Sorted_labels = tf.gather(DoSE_admin.eval_label, tf.argsort(DoSE_admin.eval_lp))\n", "sorted_ind = tf.gather(Sorted_ims, tf.where(Sorted_labels == False))[:,0]\n", "sorted_ood = tf.gather(Sorted_ims, tf.where(Sorted_labels == True))[:,0]\n", "\n", "print(\"Most False Positive\")\n", "tfn.util.display_imgs(sorted_ind[:20])\n", "print(\"Most True Negative\")\n", "tfn.util.display_imgs(sorted_ind[-20:])\n", "print(\"Most False Negative\")\n", "tfn.util.display_imgs(sorted_ood[-20:])\n", "print(\"Most True Positive\")\n", "tfn.util.display_imgs(sorted_ood[:20])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VLDpJs-u1N3j" }, "source": [ "DoSE on this particular classifier appears to get 0.982-0.993 AUROC and 0.982-0.993 AUPRC. This is comparable to the accuracy of the classifier itself, but was trained without access to the OOD dataset in question." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "VIB + DoSE", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
mne-tools/mne-tools.github.io	dev/_downloads/c6baf7c1a2f53fda44e93271b91f45b8/50_beamformer_lcmv.ipynb	1	15968	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Source reconstruction using an LCMV beamformer\n\nThis tutorial gives an overview of the beamformer method and shows how to\nreconstruct source activity using an LCMV beamformer.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Britta Westner <[email protected]>\n# Eric Larson <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\nimport mne\nfrom mne.datasets import sample, fetch_fsaverage\nfrom mne.beamformer import make_lcmv, apply_lcmv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to beamformers\nA beamformer is a spatial filter that reconstructs source activity by\nscanning through a grid of pre-defined source points and estimating activity\nat each of those source points independently. A set of weights is\nconstructed for each defined source location which defines the contribution\nof each sensor to this source.\nBeamformers are often used for their focal reconstructions and their ability\nto reconstruct deeper sources. They can also suppress external noise sources.\nThe beamforming method applied in this tutorial is the linearly constrained\nminimum variance (LCMV) beamformer :footcite:`VanVeenEtAl1997` operates on\ntime series.\nFrequency-resolved data can be reconstructed with the dynamic imaging of\ncoherent sources (DICS) beamforming method :footcite:`GrossEtAl2001`.\nAs we will see in the following, the spatial filter is computed from two\ningredients: the forward model solution and the covariance matrix of the\ndata.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data processing\nWe will use the sample data set for this tutorial and reconstruct source\nactivity on the trials with left auditory stimulation.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nsubjects_dir = data_path / 'subjects'\nmeg_path = data_path / 'MEG' / 'sample'\nraw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'\n\n# Read the raw data\nraw = mne.io.read_raw_fif(raw_fname)\nraw.info['bads'] = ['MEG 2443'] # bad MEG channel\n\n# Set up the epoching\nevent_id = 1 # those are the trials with left-ear auditory stimuli\ntmin, tmax = -0.2, 0.5\nevents = mne.find_events(raw)\n\n# pick relevant channels\nraw.pick(['meg', 'eog']) # pick channels of interest\n\n# Create epochs\nproj = False # already applied\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax,\n baseline=(None, 0), preload=True, proj=proj,\n reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))\n\n# for speed purposes, cut to a window of interest\nevoked = epochs.average().crop(0.05, 0.15)\n\n# Visualize averaged sensor space data\nevoked.plot_joint()\n\ndel raw # save memory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing the covariance matrices\nSpatial filters use the data covariance to estimate the filter\nweights. The data covariance matrix will be `inverted`_ during the spatial\nfilter computation, so it is valuable to plot the covariance matrix and its\neigenvalues to gauge whether matrix inversion will be possible.\nAlso, because we want to combine different channel types (magnetometers and\ngradiometers), we need to account for the different amplitude scales of these\nchannel types. To do this we will supply a noise covariance matrix to the\nbeamformer, which will be used for whitening.\nThe data covariance matrix should be estimated from a time window that\nincludes the brain signal of interest,\nand incorporate enough samples for a stable estimate. A rule of thumb is to\nuse more samples than there are channels in the data set; see\n:footcite:`BrookesEtAl2008` for more detailed advice on covariance estimation\nfor beamformers. Here, we use a time\nwindow incorporating the expected auditory response at around 100 ms post\nstimulus and extend the period to account for a low number of trials (72) and\nlow sampling rate of 150 Hz.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_cov = mne.compute_covariance(epochs, tmin=0.01, tmax=0.25,\n method='empirical')\nnoise_cov = mne.compute_covariance(epochs, tmin=tmin, tmax=0,\n method='empirical')\ndata_cov.plot(epochs.info)\ndel epochs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When looking at the covariance matrix plots, we can see that our data is\nslightly rank-deficient as the rank is not equal to the number of channels.\nThus, we will have to regularize the covariance matrix before inverting it\nin the beamformer calculation. This can be achieved by setting the parameter\n``reg=0.05`` when calculating the spatial filter with\n:func:`~mne.beamformer.make_lcmv`. This corresponds to loading the diagonal\nof the covariance matrix with 5% of the sensor power.\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The forward model\nThe forward model is the other important ingredient for the computation of a\nspatial filter. Here, we will load the forward model from disk; more\ninformation on how to create a forward model can be found in this tutorial:\n`tut-forward`.\nNote that beamformers are usually computed in a :class:`volume source space\n<mne.VolSourceEstimate>`, because estimating only cortical surface\nactivation can misrepresent the data.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read forward model\n\nfwd_fname = meg_path / 'sample_audvis-meg-vol-7-fwd.fif'\nforward = mne.read_forward_solution(fwd_fname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling depth bias\n\nThe forward model solution is inherently biased toward superficial sources.\nWhen analyzing single conditions it is best to mitigate the depth bias\nsomehow. There are several ways to do this:\n\n- :func:`mne.beamformer.make_lcmv` has a ``depth`` parameter that normalizes\n the forward model prior to computing the spatial filters. See the docstring\n for details.\n- Unit-noise gain beamformers handle depth bias by normalizing the\n weights of the spatial filter. Choose this by setting\n ``weight_norm='unit-noise-gain'``.\n- When computing the Neural activity index, the depth bias is handled by\n normalizing both the weights and the estimated noise (see\n :footcite:`VanVeenEtAl1997`). Choose this by setting ``weight_norm='nai'``.\n\nNote that when comparing conditions, the depth bias will cancel out and it is\npossible to set both parameters to ``None``.\n\n\n## Compute the spatial filter\nNow we can compute the spatial filter. We'll use a unit-noise gain beamformer\nto deal with depth bias, and will also optimize the orientation of the\nsources such that output power is maximized.\nThis is achieved by setting ``pick_ori='max-power'``.\nThis gives us one source estimate per source (i.e., voxel), which is known\nas a scalar beamformer.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filters = make_lcmv(evoked.info, forward, data_cov, reg=0.05,\n noise_cov=noise_cov, pick_ori='max-power',\n weight_norm='unit-noise-gain', rank=None)\n\n# You can save the filter for later use with:\n# filters.save('filters-lcmv.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to compute a vector beamformer, which gives back three\nestimates per voxel, corresponding to the three direction components of the\nsource. This can be achieved by setting\n``pick_ori='vector'`` and will yield a :class:`volume vector source estimate\n<mne.VolVectorSourceEstimate>`. So we will compute another set of filters\nusing the vector beamformer approach:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filters_vec = make_lcmv(evoked.info, forward, data_cov, reg=0.05,\n noise_cov=noise_cov, pick_ori='vector',\n weight_norm='unit-noise-gain', rank=None)\n# save a bit of memory\nsrc = forward['src']\ndel forward" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply the spatial filter\nThe spatial filter can be applied to different data types: raw, epochs,\nevoked data or the data covariance matrix to gain a static image of power.\nThe function to apply the spatial filter to :class:`~mne.Evoked` data is\n:func:`~mne.beamformer.apply_lcmv` which is\nwhat we will use here. The other functions are\n:func:`~mne.beamformer.apply_lcmv_raw`,\n:func:`~mne.beamformer.apply_lcmv_epochs`, and\n:func:`~mne.beamformer.apply_lcmv_cov`.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stc = apply_lcmv(evoked, filters)\nstc_vec = apply_lcmv(evoked, filters_vec)\ndel filters, filters_vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the reconstructed source activity\nWe can visualize the source estimate in different ways, e.g. as a volume\nrendering, an overlay onto the MRI, or as an overlay onto a glass brain.\n\nThe plots for the scalar beamformer show brain activity in the right temporal\nlobe around 100 ms post stimulus. This is expected given the left-ear\nauditory stimulation of the experiment.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lims = [0.3, 0.45, 0.6]\nkwargs = dict(src=src, subject='sample', subjects_dir=subjects_dir,\n initial_time=0.087, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### On MRI slices (orthoview; 2D)\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stc.plot(mode='stat_map', clim=dict(kind='value', pos_lims=lims), kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### On MNI glass brain (orthoview; 2D)\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stc.plot(mode='glass_brain', clim=dict(kind='value', lims=lims), kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Volumetric rendering (3D) with vectors\nThese plots can also be shown using a volumetric rendering via\n:meth:`~mne.VolVectorSourceEstimate.plot_3d`. Let's try visualizing the\nvector beamformer case. Here we get three source time courses out per voxel\n(one for each component of the dipole moment: x, y, and z), which appear\nas small vectors in the visualization (in the 2D plotters, only the\nmagnitude can be shown):\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "brain = stc_vec.plot_3d(\n clim=dict(kind='value', lims=lims), hemi='both', size=(600, 600),\n views=['sagittal'],\n # Could do this for a 3-panel figure:\n # view_layout='horizontal', views=['coronal', 'sagittal', 'axial'],\n brain_kwargs=dict(silhouette=True),\n **kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the activity of the maximum voxel with all three components\nWe can also visualize all three components in the peak voxel. For this, we\nwill first find the peak voxel and then plot the time courses of this voxel.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "peak_vox, _ = stc_vec.get_peak(tmin=0.08, tmax=0.1, vert_as_index=True)\n\nori_labels = ['x', 'y', 'z']\nfig, ax = plt.subplots(1)\nfor ori, label in zip(stc_vec.data[peak_vox, :, :], ori_labels):\n ax.plot(stc_vec.times, ori, label='%s component' % label)\nax.legend(loc='lower right')\nax.set(title='Activity per orientation in the peak voxel', xlabel='Time (s)',\n ylabel='Amplitude (a. u.)')\nmne.viz.utils.plt_show()\ndel stc_vec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Morph the output to fsaverage\n\nWe can also use volumetric morphing to get the data to fsaverage space. This\nis for example necessary when comparing activity across subjects. Here, we\nwill use the scalar beamformer example.\nWe pass a :class:`mne.SourceMorph` as the ``src`` argument to\n`mne.VolSourceEstimate.plot`. To save some computational load when applying\nthe morph, we will crop the ``stc``:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fetch_fsaverage(subjects_dir) # ensure fsaverage src exists\nfname_fs_src = subjects_dir / 'fsaverage' / 'bem' / 'fsaverage-vol-5-src.fif'\n\nsrc_fs = mne.read_source_spaces(fname_fs_src)\nmorph = mne.compute_source_morph(\n src, subject_from='sample', src_to=src_fs, subjects_dir=subjects_dir,\n niter_sdr=[5, 5, 2], niter_affine=[5, 5, 2], zooms=7, # just for speed\n verbose=True)\nstc_fs = morph.apply(stc)\ndel stc\n\nstc_fs.plot(\n src=src_fs, mode='stat_map', initial_time=0.085, subjects_dir=subjects_dir,\n clim=dict(kind='value', pos_lims=lims), verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
AaltoML/kalman-jax	kalmanjax/notebooks/2d_log_gaussian_cox_process.ipynb	1	157430	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2D Log-Gaussian Cox Process via Spatio-Temporal Kalman Smoothing\n", "\n", "## Import and load data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rainforest data loaded\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import sys\n", "sys.path.insert(0, '../')\n", "import numpy as np\n", "from jax.experimental import optimizers\n", "import matplotlib.pyplot as plt\n", "import time\n", "from sde_gp import SDEGP\n", "import approximate_inference as approx_inf\n", "import priors\n", "import likelihoods\n", "from utils import softplus_list, discretegrid\n", "\n", "plot_intermediate = False\n", "\n", "data = np.loadtxt('../../data/TRI2TU-data.csv', delimiter=',')\n", "\n", "nr = 100 # spatial grid point (y-aixs)\n", "nt = 200 # temporal grid points (x-axis)\n", "scale = 1000 / nt\n", "\n", "t, r, Y = discretegrid(data, [0, 1000, 0, 500], [nt, nr])\n", "\n", "np.random.seed(99)\n", "N = nr * nt # number of data points\n", "print('rainforest data loaded')\n", "\n", "plt.figure(1, figsize=(10, 5))\n", "plt.plot(data[:, 0], data[:, 1], 'k.', markersize=2)\n", "plt.title('Tree locations')\n", "plt.xlim(0, 1000)\n", "plt.ylim(0, 500);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the GP model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/wilkinw1/Library/Python/3.7/lib/python/site-packages/jax/lib/xla_bridge.py:116: UserWarning: No GPU/TPU found, falling back to CPU.\n", " warnings.warn('No GPU/TPU found, falling back to CPU.')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "building SDE-GP with Spatial Matern-3/2 prior and Poisson likelihood ...\n", "inference method is Extended Kalman Smoother (EKS)\n" ] } ], "source": [ "var_f = 1 # GP variance\n", "len_f = 10 # lengthscale\n", "\n", "prior = priors.SpatialMatern32(variance=var_f, lengthscale=len_f, z=r[0, ...], fixed_grid=True)\n", "lik = likelihoods.Poisson()\n", "inf_method = approx_inf.ExtendedKalmanSmoother(damping=0.5)\n", "# inf_method = approx_inf.ExtendedEP()\n", "\n", "model = SDEGP(prior=prior, likelihood=lik, t=t, y=Y, r=r, approx_inf=inf_method)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up the optimiser" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "opt_init, opt_update, get_params = optimizers.adam(step_size=2e-1)\n", "# parameters should be a 2-element list [param_prior, param_likelihood]\n", "opt_state = opt_init([model.prior.hyp, model.likelihood.hyp])\n", "\n", "\n", "def gradient_step(i, state, mod):\n", " params = get_params(state)\n", " mod.prior.hyp = params[0]\n", " mod.likelihood.hyp = params[1]\n", "\n", " # grad(Filter) + Smoother:\n", " neg_log_marg_lik, gradients = mod.run()\n", " # neg_log_marg_lik, gradients = mod.run_two_stage() # <-- less elegant but reduces compile time\n", "\n", " prior_params = softplus_list(params[0])\n", " if (i % 5) == 0:\n", " print('iter %2d: var=%1.2f len=%1.2f, nlml=%2.2f' %\n", " (i, prior_params[0], prior_params[1], neg_log_marg_lik))\n", "\n", " return opt_update(i, gradients, state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimise the hyperparameters and site parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "optimising the hyperparameters ...\n", "iter 0: var=1.00 len=10.00, nlml=25473.41\n", "iter 5: var=0.98 len=10.99, nlml=24866.37\n", "iter 10: var=1.42 len=11.95, nlml=24804.02\n", "iter 15: var=1.58 len=12.86, nlml=24779.10\n", "iter 20: var=1.42 len=13.67, nlml=24692.80\n", "iter 25: var=1.23 len=14.40, nlml=24617.45\n", "iter 30: var=1.21 len=15.02, nlml=24576.84\n", "iter 35: var=1.31 len=15.56, nlml=24566.41\n", "iter 40: var=1.36 len=16.01, nlml=24567.53\n", "iter 45: var=1.32 len=16.39, nlml=24558.20\n", "optimisation time: 261.67 secs\n" ] } ], "source": [ "print('optimising the hyperparameters ...')\n", "t0 = time.time()\n", "for j in range(50):\n", " opt_state = gradient_step(j, opt_state, model)\n", "t1 = time.time()\n", "print('optimisation time: %2.2f secs' % (t1-t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make predictions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "calculating the posterior predictive distribution ...\n", "prediction time: 5.02 secs\n" ] } ], "source": [ "print('calculating the posterior predictive distribution ...')\n", "t0 = time.time()\n", "# nlpd = model.negative_log_predictive_density(t=t, r=r, y=Y)\n", "mu, var = model.predict(t=t, r=r)\n", "t1 = time.time()\n", "print('prediction time: %2.2f secs' % (t1-t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the posterior mean" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "link_fn = model.likelihood.link_fn\n", "plt.figure(2, figsize=(10, 5))\n", "# im = plt.imshow(mu.T, extent=[0, 1000, 0, 500], origin='lower')\n", "im = plt.imshow(link_fn(mu).T / scale, extent=[0, 1000, 0, 500], origin='lower')\n", "plt.colorbar(im, fraction=0.0235, pad=0.04)\n", "plt.xlim(0, 1000)\n", "plt.ylim(0, 500)\n", "# plt.title('2D log-Gaussian Cox process (rainforest tree data). Log-intensity shown.')\n", "plt.title('2D log-Gaussian Cox process (rainforest tree data). Tree intensity per $m^2$.')\n", "plt.xlabel('first spatial dimension, $t$ (metres)')\n", "plt.ylabel('second spatial dimension, $r$ (metres)');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.4 64-bit", "language": "python", "name": "python37464bit113ddd5dfc1a43b9ae5272bebdfca0b2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
4dsolutions/Python5	Comparing JavaScript with Python.ipynb	1	11516	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Python for Everyone!<br/>[Oregon Curriculum Network](http://4dsolutions.net/ocn/)\n", "\n", "## Python and JavaScript\n", "\n", "JavaScript has been moving [a lot closer to Python](http://worldgame.blogspot.com/2016/10/javascript-for-pythonistas.html), nowadays supporting classes, with a constructor like \\_\\_init\\_\\_ and this for self within that construct (ES has had this for awhile). \n", "\n", "Here in a Jupyter Notebook, we have a way to run both JavaScript and Python together.\n", "\n", "I'm using source code from [this article](https://medium.freecodecamp.com/a-gentle-introduction-to-data-structures-how-queues-work-f8b871938e64#.l2p002vqt), A Gentle Introduction to Data Structures: How Queues Work by Michael Olorunnisola, to show off the similarities between the two languages." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "\n", "class Queue {\n", " constructor(){\n", " this._storage = {};\n", " this._start = -1; //replicating 0 index used for arrays\n", " this._end = -1; //replicating 0 index used for arrays\n", " }\n", "\n", " enqueue(val){\n", " this._storage[++this._end] = val; \n", " }\n", "\n", " dequeue(){\n", " if(this.size()){ \n", " let nextUp = this._storage[++this._start];\n", " delete this._storage[this._start];\n", "\n", " if(!this.size()){ \n", " this._start = -1;\n", " this._end = -1; \n", " }\n", "\n", " return nextUp;\n", " }\n", " }\n", " \n", " size(){\n", " return this._end - this._start;\n", " }\n", "} //end Queue\n", "\n", "var microsoftQueue = new Queue();\n", "\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "\n", "var sendTo = function(s){\n", " element.append(s + \" gets a Surface Studio<br />\");\n", "}\n", "\n", "//Function to send everyone their Surface Studio!\n", "let sendSurface = recepient => {\n", " sendTo(recepient);\n", "}\n", "\n", "//When your server is ready to handle this queue, execute this:\n", "while(microsoftQueue.size() > 0){\n", " sendSurface(microsoftQueue.dequeue());\n", "}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "\n", "class Queue {\n", " constructor(){\n", " this._storage = {};\n", " this._start = -1; //replicating 0 index used for arrays\n", " this._end = -1; //replicating 0 index used for arrays\n", " }\n", "\n", " enqueue(val){\n", " this._storage[++this._end] = val; \n", " }\n", "\n", " dequeue(){\n", " if(this.size()){ \n", " let nextUp = this._storage[++this._start];\n", " delete this._storage[this._start];\n", "\n", " if(!this.size()){ \n", " this._start = -1;\n", " this._end = -1; \n", " }\n", "\n", " return nextUp;\n", " }\n", " }\n", " \n", " size(){\n", " return this._end - this._start;\n", " }\n", "} //end Queue\n", "\n", "var microsoftQueue = new Queue();\n", "\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "microsoftQueue.enqueue(\"{user: [email protected]}\");\n", "\n", "var sendTo = function(s){\n", " element.append(s + \" gets a Surface Studio<br />\");\n", "}\n", "\n", "//Function to send everyone their Surface Studio!\n", "let sendSurface = recepient => {\n", " sendTo(recepient);\n", "}\n", "\n", "//When your server is ready to handle this queue, execute this:\n", "while(microsoftQueue.size() > 0){\n", " sendSurface(microsoftQueue.dequeue());\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't see four lines of output above, you might be rendering this on Github. If you want to see the output, same as the Python output below, cut and paste the Github URL to nbviewer.jupyter.org, which will do a more thorough rendering job.\n", "\n", "Now lets do the same thing in Python. Yes, Python has it's own collections.deque or we could use a list object as a queue, but the point here is to show off similarities, so lets stick with a dict-based implementation, mirroring the JavaScript." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{user: [email protected]} gets a Surface Studio\n", "{user: [email protected]} gets a Surface Studio\n", "{user: [email protected]} gets a Surface Studio\n", "{user: [email protected]} gets a Surface Studio\n" ] } ], "source": [ "class Queue:\n", " \n", " def __init__(self):\n", " self._storage = {}\n", " self._start = -1 # replicating 0 index used for arrays\n", " self._end = -1 # replicating 0 index used for arrays\n", " \n", " def size(self):\n", " return self._end - self._start\n", "\n", " def enqueue(self, val):\n", " self._end += 1\n", " self._storage[self._end] = val\n", "\n", " def dequeue(self):\n", " if self.size():\n", " self._start += 1\n", " nextUp = self._storage[self._start]\n", " del self._storage[self._start]\n", " \n", " if not self.size(): \n", " self._start = -1\n", " self._end = -1\n", " return nextUp\n", " \n", "microsoftQueue = Queue()\n", "\n", "microsoftQueue.enqueue(\"{user: [email protected]}\")\n", "microsoftQueue.enqueue(\"{user: [email protected]}\")\n", "microsoftQueue.enqueue(\"{user: [email protected]}\")\n", "microsoftQueue.enqueue(\"{user: [email protected]}\") \n", "\n", "def sendTo(recipient):\n", " print(recipient, \"gets a Surface Studio\")\n", "\n", "# Function to send everyone their Surface Studio!\n", "def sendSurface(recepient):\n", " sendTo(recepient)\n", "\n", "# When your server is ready to handle this queue, execute this:\n", "\n", "while microsoftQueue.size() > 0:\n", " sendSurface(microsoftQueue.dequeue())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example of features JavaScript is acquiring with ES6 (Sixth Edition we might call it), are rest and default parameters. A \"rest\" parameter has nothing to do with RESTful, and everything to do with \"the rest\" as in \"whatever is left over.\"\n", "\n", "For example, in the function below, we pass in more ingredients than some recipe requires, yet because of the rest argument, which has to be the last, the extra ingredients are kept. Pre ES6, JavaScript had no simple mechanism for allowing parameters to \"rise to the occasion.\" Instead they would match up, or stay undefined." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "var sendTo = function(s){\n", " element.append(s + \"<br />\");\n", "}\n", "\n", "//Function to send everyone their Surface Studio!\n", "let sendSurface = recepient => {\n", " sendTo(recepient);\n", "}\n", "\n", "function recipe(ingredient0, ingre1, ing2, ...more){\n", " sendSurface(ingredient0 + \" is one ingredient.\");\n", " sendSurface(more[1] + \" is another.\");\n", "}\n", "recipe(\"shrimp\", \"avocado\", \"tomato\", \"potato\", \"squash\", \"peanuts\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "var sendTo = function(s){\n", " element.append(s + \"<br />\");\n", "}\n", "\n", "//Function to send everyone their Surface Studio!\n", "let sendSurface = recepient => {\n", " sendTo(recepient);\n", "}\n", "\n", "function recipe(ingredient0, ingre1, ing2, ...more){\n", " sendSurface(ingredient0 + \" is one ingredient.\");\n", " sendSurface(more[1] + \" is another.\");\n", "}\n", "recipe(\"shrimp\", \"avocado\", \"tomato\", \"potato\", \"squash\", \"peanuts\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Python we have both sequence and dictionary parameters, which we could say are both rest parameters, one for scooping up positionals, the other for gathering the named. Here's how that looks:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('tomato', 'potato')\n", "{'dessert': 'peanuts'}\n" ] } ], "source": [ "def recipe(ingr0, more, ingr1, meat=\"turkey\", others):\n", " print(more)\n", " print(others)\n", " \n", "recipe(\"avocado\", \"tomato\", \"potato\", ingr1=\"squash\", dessert=\"peanuts\", meat = \"shrimp\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thanks to \\more being a sequence parameter, the parameter ingr1 may only be reached with a named argument, yet need have no default value. \\\\others scoops up anything named that doesn't match anything explicitly required as such.\n", "\n", "You'll see the sequence parameter \\more* begets a tuple with its contents, whereas the dict parameter *\\\\others* begets a dict." ] } ], "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
WOnder93/pbkdf2-gpu	data/metacentrum/graphs.ipynb	1	680583	{ "metadata": { "name": "", "signature": "sha256:c97123e1d02797e16b0ef122721699efffa69f1c66333f77596b14d6dc9db5a8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "from itertools import \n", "from collections import defaultdict\n", "import numpy as np\n", "\n", "def groupby(seq, key):\n", " groups = defaultdict(list)\n", " for item in seq:\n", " groups[key(item)].append(item)\n", " return groups.iteritems()\n", "\n", "def read_means(fname, header_cols):\n", " with file(fname) as f:\n", " for l in f:\n", " values = map(int, l.split(','))\n", " yield values[:header_cols] + [np.mean(values[header_cols:], dtype=np.float64)]\n", "\n", "def get_dk_blocks(dk_length):\n", " return dk_length / 20 + (1 if dk_length % 20 != 0 else 0)\n", "\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from matplotlib import cm\n", "\n", "def fig_init(size=10):\n", " fig = plt.figure()\n", " fig.set_size_inches(size, size)\n", " return fig\n", "\n", "def plot_3d(fig, data, label, xlabel, ylabel, zlabel, bot, top, azim, elev=10):\n", " ax = fig.gca(projection='3d')\n", " ax.plot_trisurf(zip(data), cmap=cm.coolwarm, edgecolor='none', label=label)\n", " \n", " ax.set_xlabel(xlabel)\n", " ax.set_ylabel(ylabel)\n", " ax.set_zlabel(zlabel)\n", " \n", " ax.azim = azim\n", " ax.elev = elev\n", " ax.set_zlim(bot, top)\n", "\n", "def plot_2d(fig, data, label, xlabel, ylabel, bot, top):\n", " ax = fig.gca()\n", " ax.plot(zip(sorted(data, key=lambda (x, y): x)), marker='o', markersize=4, markeredgewidth=1, label=label)\n", " \n", " ax.set_xlabel(xlabel)\n", " ax.set_ylabel(ylabel)\n", " \n", " ax.set_ylim(bot, top)\n", "\n", "def plot_bs_dl_to_ips(fig, data, label=None, azim=-120, elev=15):\n", " plot_3d(fig, data, label, 'Batch size', 'DK length', 'PBKDF2 iteration-blocks per second', 0, 500000000, azim, elev)\n", "\n", "def plot_dl_it_to_ips(fig, data, label=None, azim=190, elev=10):\n", " plot_3d(fig, data, label, 'DK length', 'Iterations', 'PBKDF2 iteration-blocks per second', 0, 500000000, azim, elev)\n", " \n", "def plot_dl_it_to_bestbs(fig, data, label=None, azim=110, elev=10):\n", " plot_3d(fig, data, label, 'DK length', 'Iterations', 'Best batch size', 0, 70000, azim, elev)\n", "\n", "def plot_it_to_ips(fig, data, label=None):\n", " plot_2d(fig, data, label, 'Iterations', 'PBKDF2 iteration-blocks per second', 0, 500000000)\n", "\n", "def plot_sl_to_ips(fig, data, label=None):\n", " plot_2d(fig, data, label, 'Salt length', 'PBKDF2 iteration-blocks per second', 0, 500000000)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def read_dl_iter_bs(fname):\n", " return [(dl, iter, bs, get_dk_blocks(dl) val) for (dl, iter, bs, val) in read_means(fname, 3)]\n", "\n", "def get_bs_dl_to_max_from_dl_iter_bs(data):\n", " return [(bs, dl, max(val for (_, _, _, val) in rows)) for (bs, dl), rows in groupby(data, lambda (dl, it, bs, val): (bs, dl))]\n", "\n", "def get_dl_it_to_max_from_dl_iter_bs(data):\n", " return [(dl, it, max(val for (_, _, _, val) in rows))\n", " for (dl, it), rows in groupby(data, lambda (dl, it, bs, val): (dl, it))]\n", " \n", "def get_dl_it_to_bestbs_from_dl_iter_bs(data):\n", " return [(dl, it, max(rows, key=lambda (dl, it, bs, val): val)[2])\n", " for (dl, it), rows in groupby(data, lambda (dl, it, bs, val): (dl, it))]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def read_dl_bs(fname):\n", " return [(dl, bs, get_dk_blocks(dl) * val) for (dl, bs, val) in read_means(fname, 2)]\n", "\n", "def read_2d(fname):\n", " return list(read_means(fname, 1))\n", "\n", "def read_dl(fname):\n", " return [(dl, get_dk_blocks(dl) * val) for (dl, val) in list(read_means(fname, 1))]\n", "\n", "def get_bs_dl_to_ips_from_dl_bs(data):\n", " return [(bs, dl, val) for (dl, bs, val) in data]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "data_doom = read_dl_iter_bs('data-v1/benchmark-dl-iter-bs-gpu-doom.csv')\n", "data_konos = read_dl_iter_bs('data-v1/benchmark-dl-iter-bs-gpu-konos.csv')\n", "data_gram = read_dl_iter_bs('data-v1/benchmark-dl-iter-bs-gpu-gram.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#fig = fig_init()\n", "#plot_dl_it_to_ips(get_dl_it_to_max_from_dl_iter_bs(data_doom))\n", "#fig.show()\n", "\n", "#fig = fig_init()\n", "#plot_dl_it_to_ips(get_dl_it_to_max_from_dl_iter_bs(data_konos))\n", "#fig.show()\n", "\n", "#fig = fig_init()\n", "#plot_dl_it_to_ips(get_dl_it_to_max_from_dl_iter_bs(data_gram))\n", "#fig.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_max_from_dl_iter_bs(data_doom))\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_max_from_dl_iter_bs(data_konos))\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_max_from_dl_iter_bs(data_gram))\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/python2.7/dist-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", " \"matplotlib is currently using a non-GUI backend, \"\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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RbaXryQfpPvkobSfuLKlzeS22sVgolUoVV6BVss8PbMrwI4GnWh544AG+9a1v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9DjA9Ciceh8BDo4RLQyxLGt4Buej+4vhoYYPSBsklVhQ/5F4u3PFwo5vXDT6ebN0xxJcWCCs6Io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"text": [ "<matplotlib.figure.Figure at 0x7f2cc97d7910>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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MJgNFUZBMJq0p7m6r9/ELRwKPrusIBoP4/e9/j6eeegptbW1Wrw8AhMNhvPfe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DHhqTOORcuNgxAC0TUS63pm76OkAXXUo1SjZCSwoxDhF9FCl3DgNNrjm4h0BlXIbq3n6ehHhHv7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\nevfuTR05coSiKIoaNmwY9c0331AURVHvvPMONXv2bIqiKGr79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"text": [ "<matplotlib.figure.Figure at 0x7f2cafcf8750>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jxzJls1kwDINoNAoAVq98gEz5Yne9h+fss89GNpu1Ctmy2Sw0TcPh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\ncPhF5ai7XJcAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x7f2cafc87c10>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "data_doom = read_dl_bs('data-v2/benchmark-dl-bs-gpu-doom.csv')\n", "data_konos = read_dl_bs('data-v2/benchmark-dl-bs-gpu-konos.csv')\n", "data_gram = read_dl_bs('data-v2/benchmark-dl-bs-gpu-gram.csv')\n", "\n", "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_ips_from_dl_bs(data_doom))\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_ips_from_dl_bs(data_konos))\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_bs_dl_to_ips(fig, get_bs_dl_to_ips_from_dl_bs(data_gram))\n", "fig.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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lrdty4adUKuF5HvPz85tuda+o1Pzslsc+sTkSeDZB1/UlPzybsdGuJlVVq69YtnuraS8L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\n5rF9+3ZmzpyJj49PoftNRYCpM8jSN72aTvPOzs7WZmaKWpIqWNxA/pLUt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"text": [ "<matplotlib.figure.Figure at 0x7f2cafb28190>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ElDeBh6Xa42rgWbduHWbMmIGPf/zj6O3thSzLsCwL06ZNw6233ootW7a4cTg1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\nFxYtWoSsrCxMmzYNH330EQDA2dkZixYtgrOzMwwNDZGZmSkNd2VmZiI6O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"text": [ "<matplotlib.figure.Figure at 0x7f2cafb8d350>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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33XUXmpub/TickqPAQwipRX5uv+AXrx9rYr1POp2GrutIJBLuEvdKq/cJCl8C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ks0XM5qTb3Bbe7a4CuWfk6u5kfIZoo8LdfSEov8PBv6h7rETr2ubfa3vzipPW9k2BA4Vxf34nW7\nZlfPp556asm/n3nmGR577DEWFhZoatpkOOM2xna64K1Wrck/Dl6LUZble6Zasyn4w7iqDyGbXv+5\nsCJi3dV8Kye04C7hKVbNqUKFZzudq/WI9UI2G5/vUuSNDPM6n8VJ7TuZ+Oz0Ftp2RM0Iz+zsLOl0\nmmw2i23bjI+Pk81u1Lm2PlAr0XIxTc3iNtRqouFMJoOiKI1pihLgRjoQpq6X+OxlFR7VB04RG4OC\nhqfIxb4hWK5bLF5sKrnw1OvClk9q9/l8GIZRMDKE6rynev3cGigdVV/RbNtGkiR+9md/ljNnztDc\n3IwoigQCAT796U/T19dX7UOqa2xENNxoQ5UfbqQTSiU8yz5yR9PBtlduM/fdOA3CU7do/L42j8VJ\n7XkH52g0uq0Ezg3UJ6pOePIlyldffbXau64KKubsW0bvmgbKh43peJZ+F46ig1UkikT0fiNOMQ1P\nhT14GudLA9sFeZ2PIAgEg0HS6XTBwVnX9S07ONcStar43+u/75qOpf/P//k/efnll0kmkwwMDPCL\nv/iLjI2N1eqQtgU26jTcqNbUVhPhhttwBRHB3bjhpKNoYBYLEM19n2KjwtPA9kU1F1BZlgmFQksE\nzuU2MqzFdeRev3ZXG1WnyPmKxB/8wR/wH//jf+TQoUP8/b//95mdneVXf/VXuXTpErCzhH3LNTx5\nUmNZFtlslkwmQyqVIplMkkwmMQwD27YLdzi6rhMIBArCPk3TUBQFSZLu+R9Mzd+/JOOGW0t6qrvs\nWB21SKwE3PXZEcWVXjyNsfQG7mHkBc5NTU1IkkQ8HicWi2GaZlnWjJpfTxqoKGriwwPwgx/8gM9+\n9rM8+OCDgDel9dxzzzE9Pc2ePXt2xImXJza2beM4DplMpqhoeHGS93ZoQ92rztCbhRvphOj0hl/n\n5MiLKykI9t3W1hJiJKtgLxI2Nyo8DTSAKIpLjAxTqRRAI6l9DTRaWjWo8OSxZ88eTp48yezsLFNT\nU5w7dw5N05ifn2d8fLxwApcbH/nIR+js7OTQoUNl2Z7rukxOTvLDH/6Q//pf/yvz8/O8//3v58CB\nA/zZn/0ZhmEUWlKSJKFp2opqjaqqSwhPA/WF0nU8y75bQQDdD6q27PFFP0tlWdRKI0ergXsI6914\n5Y0Mw+FwYbprYWGBdDq9rXMNtwP5qPX+a4GaEZ6hoSH++T//57zvfe/jE5/4BM888wxzc3N84xvf\n4KMf/ShXrlypyH7/0T/6R/zv//2/y7Kt73//+7S0tLB//35++7d/m9dffx1BEPjH//gf881vfpMX\nXngBv99faD812lA7E1tKTtcDXmL6YojLKjyL0ajwNHCPoZTrZT6pPRwOF7Q+0WiUZDKJXWQSshga\nVe2dj6rfLuaV9c888wxHjhxB13Ucx+FjH/tYgZFbllWx8fQnnniCa9eulWVbDz/8MBcvXqStra3w\n2DPPPMOzzz67IgS18WPawdADXkxEJrnm09wi121XDyAYS6uZ7qL7EFdRl9aFGoSngXWwHaoHtUQ+\nqT0vI9hIUvtO/tzu9fMCahgt8cADD1R712WH3+/H7/cveUxRFEzTXEJ47vWT7F6AG+lEmFivKlkk\nN0vzIVjLJrWWaHiWEZyGaLmBbYB6uIFbbmS4nZLa6+Hz24moXyODbQpZljHNIt4qdYSGaHnjcCMb\nzdXKvU7zrUhMXyxadle0tBoannrHTvpt1cPN3P/P3nmHSVXe7f8+U3d2ZxsL7MJSAyht2V16jCIG\nWBAENBAVpUQhIq9oCCtVaeICvmqMaLD8BEUTijEqaCxIFBL1DShIF+nSRdru9Hp+fwzPs2fOnunt\nzJnnc11eyDROmXnOfb7l/hIjw/z8fOj1ethsNtTW1sJut6f0XKTDsVMabPWMMzqdLuycMSN25CLO\n+MIw6ngkFjivzgC1+HEW4VE87GIXHvFMwwSa1E6MDJWe8lH6/oUDEzxxRgkRHkbk8LlF4NUacB6J\n2VhB8OqyAIgEm6BLq4EPD6vhYTBiItCkdlL4zFAuGZfSGjNmDG644QYcOnQILVu2xOuvvx7Xz5cS\nPHKJQjASiEoFPq9JxG/zavWAuH02WISHCR4GI24IjQzJ4GSTyRQ3I8NAsGhLasi4CM/atWsT+vmk\naJmRefAFxcCVc0Fe0XCB8+ikBI8wwiNOaWXcT5bBSDjE/JWYFpJJ7UoyMhSLLCXsU6RkXIQn0ZD8\nMCPzCFnHI1XDo9U3GCDqX7TMUlqMyEhG9ECJEQqe52kXV35+Pu3ukkOBMyM+sNvFOKPVatNe8LAU\nXHREZUCoUqNhDU/9hcSrYkXLjMwlVcKK1PMEKnCOdVJ7sveLrec+WIQnzrCi5QxGlwU+Oz/ICwIs\ncCr/+w7/WVoswsNgpBJiZJiXlwee51FbWwuz2ZyWN7ZKi8pFChM8cYYVLWc20UR5eI3GX+QIani8\nDaals6BsOsJ+/+lPoEntTqeTnd80ga2ecYbV8CQfOS02fEFT4Owh6ecC3Fzx+mxwWj3gtF97naCG\nR8UiPOmG1+sFx3FwuVzwer3wer3weDzweDwxp0IYiSPcNJN4UrvNZoPNZouowFlOa1YmwQRPnGFd\nWslFbtEzb2EJ1AGflV4IeX02oMuigkf4Ol4t+jQmeGQDz/NU0Aj/E144VSoVHWOgUqlgNpvB8zzz\newkTOf22xUgZGVqtVmRlZUGv14cUt8mu4cn0dBbABE/c0Wg0LMKTyRgLwWt04NzOsN/C67P8hYww\nwtMgpcUET7IJJmxUKhX9T6vVQqVSweFwQKPRQKvV+n2GVqtFTk4OTCYTnE4nPB6PLOY6RUMyL6By\nPzZCI0MifGpra6HT6ZCVlQW1+KaFkTKY4IkzUhEeuUUhQsFxHJ1cz4gQjgOf3xTcpdOSz0nh1Rn8\na3P8urTqF0tepQJYSiRhRCpsOI6TvBgHu0CTSA9JfZB0iMFgSEvhw/BHalK7RqOBwWAIOak9kUhd\nfzLxu8YET5xRQls6Izb4gqaAlOAJgFenv9aefu39woVI7Sto5nieRXfiRLyETSwI0yEulyuqOpBA\nsPRFdMTzuIWa1J6Kc8S+E0zwxB1Ww8PgCyObnO7V6v0iNzwniuJodIDLwep3IkQOwoZESnmeD3iX\nrdPp6I2SUPjo9Xp2kUpziFuzXq+nwtZqtUKj0aRV1F8pMMETZ1iEh8EXNAUPDlwQQ0EhXq0eAT16\nACZ4QiBHYUNmMZF/h6SJA7UwC+tAXC4XNbozGAwZLXyUEq0SC1ur1QqPx0OLnBPdvcfElQ8meOJM\nMONBpfx4GSHQ6ABjIWC+HNbLvRod/NyWxREerRawIeNTWkTYEEHhdDpTLmxICzqB4zio1Wq4XC64\n3W7odDqo1Wramk6KlYXbKUZYACuO+LC29vSGCFsS8SFGhskocM70OVoAEzxxR6fTwWaz+T2WqV+u\nZCHHuxdvQVOoGwieAN8DjgOv1tQ/qxK9TnOthTmFRY/JJFTEhpzvVAsbjuOg0Wj8RIjH46Gix+Px\nwG73WQ2o1Wqo1Wq/+hyHw0GHVkpd6DQaDXJzc+HxeGCz2VBbWxu30QaxosSbt2Tvk0qlQk5ODq3z\nIQXOWVlZ0Gg0iju+ciAzVtAkotVqUVdXl+rNiIl06iqT66LAF5YApw+G/wZB9MYrEka8Rud7JEkR\nnmSd+2hTUVarFVqtNi53w2JhQ/adXPwCCRuhmSDZbrK9arUaWq0WWVlZ4DgOHo8HDocDbrcbarWa\nXszI+5xOZ1Dho1arYTQaqYAiEQGDwZBy4cOIHaGRocPhSMik9nRZzxMNEzxxhhUtM4AAIyaCrFu8\nWtqHB4AvpQUkpYYnEQJSjjU24gsA+TfJtgjfR0QN+VMsbNRqNd1uKYiYIYLF4XBAr9dDq9VCo9FI\nCh+yLULIaAODwUAjPszrJT0JVMclLHAWDyyN9Tch15vDZMIET5zRarXweDyp3gxGqsnJB6/LAkfd\nk4Fgiof3S1eJIjxqktKSdw2PkoWNRqOhNTTRbDOJEhmNRrjdbjgcDtjtdtqaLhQ+5IZJ6NAsRJgK\nIV4vWq0WBoMBarVaUXfzSkydEQLtl9Sk9qtXr0Kv10Ov1zNxGwNM8MQZVrTMIPD5TcH9fLL+78Fe\nG8BpGUC90JHJ4FA5CBthAXOswobU3MRD2ISDRqOhjuwOh4NGfAIJH5JOkxI+2dnZNBVCakBY/Ud0\nyHF9JiKZpEWJuCV1PuEix31LBfJYQRVEoJQW+7JlHnxhCSAQPEFTWlq9r3DZ424geKgYSnKERy7C\nRuo/l8vll0qKRNiQtBHpoErVb5OIE3IxM5lM0Ol00Ov1VPjwPE9tLoIJH2ENCGmacLlcfuMtGOmL\nWq32E7diI0N2fQkPJnjiDPPhYRAa1vEEXpS8uixAlwXebgkseBJUtCwUNkQcEL+YZAkbAEEjNgDo\ndpAp1U6nk26TnIVNKMjFTCx8hNstFD4kGiUWPqQGhOM42O12WCwWKobiHfVhEYPYiPb4xWNSO5C5\nN+BM8MQZJRQtp1OXlpzh85uA51Tg+NBzybzaLECrAxy2Bs/xqms/0xgjPOFEbMSznlIpbMgFXdga\n7na7qbjhOA5utxs8zyuinVcsfMxmM/VsISJH2B0WTPioVCoYjUY4nU7a9cPmdSmHSCe1M4Hqgwme\nOENC1IzkIGtxptaAzy0CV/dzyJd6tTpfBEdsOghhhCe8n2ssqSi73S55EY2GeAobYcSGFBKTbSbd\nT6SjJV6tvKmCCB+v19tA+JDjJRQ+wuMiJJHzupJFMi/U6SgKhA7dbFJ7aJjgiTPBanhke2FmJAy+\nsBiggifwYurR6n2CRkJo8GrpCI8camyA0MJGKFjIdgcSNkTMEJO+cFJRpF1b3PadLhf1QJD0hV6v\np8KHRLLEwsfhcNCiazHCsQbCdmc2ryt1kN9oPAk0qT0rKyuu/046wwRPnFFCSosRP/iCYuDHfb6/\nBLuuqDWBIzzX7tI8nApuhyOthA3gi8C4XK6gwkaqGDdSMkH4OJ1OKnxIqkssfEhrvhhhuzOJ+Njt\ndiZ8FAbp3iMOzhaLhT6ejlGseMIET5xhRcsMIZIGhIFeG6C+wgOfEPByahrCTqSw4XkeHo8npLAh\n/75Q2Ljdblo8DCAhwiYUShY+RJwIIz7Ee4eIHlLT5HQ6JVNdQOB5XeEa3LFotfwRGhlaLBa4XC7U\n1tYiKysLBoMh1ZuXEpjgiTNM8DD8MBjBZ+WAs1vABw3xAG5ODY1EhIfT+YwHtQYDcO3/44FUxEZ8\n0RQWMhMRE0zYCCNNqR57oBThIxQzwj+B+nNBjjtJd5H3CFNdUueDzOsSG9yFM68rGccwmcIq2fVC\nyYKYXpIIn91uh9vtRlFRUdK2QS4wwRNnQhkPpgOs3ig+0KGTeU2gsVtCvl6VZQCnbniR8ZIanijb\n0sMtHibRI9LyTTqBhCk0YWdQoiNN8SJdhI+UPYCU67MwUkZ+q06nEw6HAx6Ph/r4EJEarvARz+uS\ny6BSOZ2jeJLM/SJijkT2lHpMQ8EET5xhxoOZR6jiYeQ2hubCidCfo9FJ1/CE2ZYuFDbi6d6Av7Ah\nUQCSvhIOwiTCRmjwl52dLWsvm3CQi/ARHnfxENJoXJ+lurFIOoN8jlD4kK6uQINKc3JykJWVxTp+\nFEyqRWyqYIInzrCUlnKJtiuKa9ICOPptw5ER4s/X6KS7tFTXLjTXBE84wkY4gFLcFSUWNsJ5UcLa\nHLLPDoeDTiiX8vhIN5IlfITHXShqpFrt42GOKO7GIsKHRHwCDSoNJnwCzetiMNIRJnjiDOvSSj7x\nTr/Fu92bz2t8TbSEIXgkIjxQqcCr1HDzgNfppP9WKGEjrMcJJWwCQSIFOp1O0hMmnYmn8AmUigKS\nX7gtFD6kNgcANWaMZEJ7sHldSoKsIUr1/Mn07iyCsr61MoBFeJJLLD/ipPnYqFTg8xoj+PhQwK3V\nQRtIFGl1UOuzoNXrEyJsQu9CQ08Y0uKcScJHnIoKdOzlUN9EajaEg0rJhHbyuIq1llwAACAASURB\nVHhQabAJ7WSkgd1upyIq0fO6EuFXw8hcmOCJM4GKltOpEDidtjUc5GDQ52tPD/GZGh0QIF3Aa3Rw\neHh4zGa63cJaj3gJm1AEEj5K8HERCh/iUUMEgFDYpOrYR4uU8CGiLlLhQ0ZUcBxHPV4SNa+LwYg3\nTPDEGRbhSR1yEDaAdI2NKrcxYDMFf59W50trSaHRQWfIhiorO2QRazIQCh+73e436TvV2xYJgVq+\nSWcUmdWl1WqRk5Mji2MfLeIxBMKIj06nkxQ+gSa0kxqknJwcNq8rClKR0mKRMiZ44g65i2IkDqGw\nISkdi8UiC2EjRDjzSdW4FJ4zh4N/nlYHXquXfI7T6aDOMoCTWcEoqfEQTvpO55ZvrVZLU1PiWV0W\ni0WW+xYNpA5HHPERCp9wJ7Sn+7wugNW4ZApM8MQZpaWDUkm4072FYfZUCBuh87DwguB/YeVD++hw\nKqgMOdLPabPAhTk8NBUIJ33LveWbFOmG0/INyKedPREQ4SPcNxKpIyJQeByJ8BETaF6XwWBQxHFi\nKAP5rqCMjCGWVJTwLj1W4iNs6tMh5OJKCnuduYVwO+zBt0EnbfnO67Nj3r9kQMSBMHKQlZUV9zRH\nslu+AWULH+G+kUidsChdOK+L/CeFuENMGPGJJt2p1MhLsm+KxcdRicc0HJjgSQBSXyYW+ZFXjQ35\nMx7CJlg6RMgFV2Po+CvI4gK7Lnt10pON+az0mn0jTJkIxUE0wkdOLd+A8oWPOEVJBKP4PASb1yWu\nF4pmXpfSYccg+TDBwwhItHdXyRQ2wYQkuSiSFEe4wkYcNYhU2ASCV+nx0mcl+EVTJ27pdBV5moZF\nzK4ARct8gMiP3NFoNA3EAfGDSeeWb0B5wkcoaISi0ul0AgCNVpJ0FzlfiZzXlSyUGkkiZPrNNoEJ\nHkYDwv3hyyViA9Sno8giLSSUsImkgDUWigt9fx67oMOxC03RNK8Rqspq0cRQB/DXIhYGo+R7eX16\nCh6gfnghSXXZbDYAoP4t6dzyDaSf8AlU4wQ0jJiR4+/1emmK0uv1UtEjTHVFMq/LZrPJal5XJiDH\n72KyYYKHERK5CBthxEb4/2R4olCcRCJsIilgjQWDHsjLBuqsvr9fqNPgr18VwZhViMFltWiVXwtv\nAGHj1UqnuuSMuDNKONKC4zg4r7lGk1lN6dzyDchP+Ei13EdrUCm0IXA6nTCbzfR3Qz6LnOtQwket\nVjcYVJpp87qUHlGSK0zwJBG5hxWFFygAsNlsVFCkWtgIIYKG/KlWq+mkaI1G47cfJLqTTGETjKaF\n9YKHYLar8I9vCqFVF2BAFxPaNTFDB5vfa1wqDbhrnilyWyijbfkmw0kdDgfcbjdNdaU7yRY+4iGw\ngY6/eMp6NKhUKlqATIwHYxE+oeZ1MWEQH+R+7UkW6b+6yBC5Fy2HE7EBQC9QchE2wveJF3ayjS6X\ny88bRG6LZXEBcOSM9HMuD4fdP2bhkz15MGZ50aedFe2aWJCjsUKVY4TX602pz41Uy3egzqhwp3wL\nW5mtVivUajUddpnuJEL4SEXMoj3+scBxHBU+xHhQrVbTKE0kwod4OQnndWm1WmRlJS+qmQnCSun7\nFw7pv6owAhJLKoqErOPxI4lG2HAcRxd04YTvUC3HJGpgt9vh8XhkVx9A6ngC4XG6AOhgtqvwr/1G\n/AtGaNU8+nd14qZm+qQZ/ElFbMiFVZgKEZorRotQ+DidTip8lJLiiEb4RFNnkwqENxdC4UNEq1j4\nkN+t1HklaTMyr8tk8hX1K0H8iskEgSVHlPdNykASUWNDIlKR/CjFwkbK+yGUsInVS0V48ZTjdO9Q\ngsdpcwDwNx90eTj8ZPI5MJO2YeJz43Q6o273BhAwFQLUX1iF351EHkOpi6cwXZLuSAkf8UiHWOps\nUonw3JFoHUl/ke0n+yic0B7IxJAIn9raWtjtdrhcLjavKwbE2YVMPYZM8KQRciweljLpi0XYxCNi\nQP49Ym9PhI8c0lxN8gEVB3gDZDfdbg+0eh4uj/82akXXBSmfm0Dt3kD4Ld9yuLAKL57COhG5Resi\nQVxnQ44v6Xwiv9t41NmkEnGa0maz0fNJvrNSwkectiafpVKpoNf7xD4ZVJooI0ulk47fp3jDBE+S\nIK2d4ZBOwoa0HIcjbOKdCgkHEibX6XR+wiBVAw41aqAoD/i5Vvp5r5eHQcfDZRMJngC/VGG7t93u\nc3Emok4cMRBGbIQRM7kuhMI6ETlG6wIRaZ2N1+uF3W6n0TolRDHEwod8N4koj2RCu1AMOp3OhM3r\nSuYxzwSBJUeY4EkQ4aSD5CxsAMDlcvl11YiFDVnIAwmbZLrfhkI48oAIHxIiTzbFhYEFj8fNw6Bu\nuBiKIzxSLd8A6MWT4zhFRAwA/2gdaYmWg/CJV52N1FgHOUQj44FQ+IgntGu12ogntEsNKk3XeV3J\nFljpdnwSARM8CUCtVtMWacD/4kSMu+QibIS1OsKIjVqtht1uh9frMxkT13oAqbH1jxWxAZ6wziBZ\nNC0I/BzPe6HmPAD8t0fFeWC3u8Jq+QZ87rgOhwM8zyvGyp+cK2GaMhnCJ55+NsGQGuugJOFDBI54\nQrtQ+AgntBOLCanPiue8LkbmkJGC55NPPsG0adPg8XgwadIkzJo1Ky6fe/HiRRw4cAB2ux2PPvoo\nfvjhBzz44IMYMGAA/RGSH34yhY1UwZowYkO2QbiYk4gBz/N+EQNSRyF3YRMM4eIrLI5NVo1IsMJl\nr9cL3u3r1BKi4tzgeT7slmNhDYxcIiLxQmiCR/aPjDyI5feUTD+bYGSK8CHF2yTiI0zFer1eWK1W\nvyntUhGfeM3rSmaKiaWzUkfGCR6Px4OpU6di8+bNKC0tRa9evTBixAh06tQpqs87ePAgpkyZQoVO\nly5dcOnSJbRs2RK33norevbsSSMKbrcbOp30rKRoCFfYiBdnsbAhd1YkWiOONgGgHUEAkuqPkUjE\nxbHxunCGokm+F4C08PB4vPC6XQ0ezzHoYYhwuoRU4XYy9i9ZCIUPaWMOd//k4mcTDCULHwC0IFmY\n6gLqR8GQqJnQSV1K+ADS87pIxCdckZ/sY8pSWskn4wTP9u3b0b59e7Rp0wYAcPfdd2PDhg1RC56S\nkhLMmTMHXbp0QfPmzcFxHG6//Xbcd999aNSoEX1dLF+2RAgbYSoknGgTuXCSC4uSFl6hMIjn/gWq\n8dDwPLSaPLjcDT9bxanASxS3BypaDgcpYaCk80eM66SEAQDJc0DeJxc/m2Cku/AJp9aJ3EQ5nU7f\nb+SaFYXUWibs6BTD5nUxgpFxgufMmTNo2bIl/XuLFi2wbdu2qD+voKAAVVVVfo+REGukxCJsxAZ9\niWg3Fl5YbDYbnE4n7XhSAsL9I10zwVq9CdG0fBcXcDh9UfqznHYnIDqksQieQPuX6o61eEJ+K6Ru\nThgxEAobObTdR4vchU+g2WkkJRjOOdDpdH77J5zOTjrawhlUyuZ1BUcO35dUkHGCJxknWqPR0I6D\nQBBRIiVsAPgVM5PCYrKgCLuiUuGjIu54IsJAKQsJ2T/STksiQGq1uoGoES/o4bZ8FxcioOBx2F0N\nBU8cD61Ux1o4wk4OhFtnYzAYaCqERAyUIOwAeQifUClBqdlp4SK1fzqdzq92KhLhI57XJSV8kpny\nSUV6iaW0fGSc4CktLcWpU6fo30+dOoUWLVrE9d8QRniIsCGLApkQTZCzsAmGVOGvUgpjha355Fxa\nLBYA/vUFsRSvBipc5r08HFY3VLn+j8cjwiNG7OFDhJ1crPzjUWej0+n8hF0srtRyIxnCR9ihJhW9\nTGRKkOwf6W4VF99HInxIdJOMrRDO65LL952ReDLuTPfs2ROHDx/GiRMn0Lx5c6xfvx5r166N679x\n5swZHDx4EM2bN6ePkQXZ5XLRu4tAwiadQvCk8Fc4ykFOYfZgRNqV43a7qTsseSxaArWm8zwPm9UB\nI8eD5+uPnyZBwTOhcBUO8ExmxC7Rc6NIXUc6RrTCIR7CJ9RvIZWGlVJdeVLChwijcIQPifiYTKaA\nIy4YyiPjBI9Go8GLL76IwYMHw+PxYOLEiVEXLAdi6tSpeOGFF/D111/joYcewunTp3HgwAHceuut\nMBgMsNlsANBgIUlngzixo3G49S/JIJIp38EuquT5eHQ8BYzwXEtvZmkBm7P+cV2Cf6lCb5NEzbFK\nlp9NIKRcqeXyHY0H4QqfcNJRqe5Qk0IofIgBpfA7Gonw4bj6eV0Oh4OuyS6XSzHfBwJZU5S0T9HC\nhfAEYIYBEXDq1Cls2bIF+/fvx/79+7F9+3ZcvnwZ7dq1Q5cuXbBw4UK0atUKHMfRaIGS2oSFCOtf\nDAZD0u6ghIu5cFEnxd7CC2ssIXhyUXG73VEX/v7v24DZJvrcCyfgsNpR2uU6XLHUL9R/uMM3kiJZ\n8DxPrQgiTVWGGzkj5yIVF1VicCeu0VLS75C0e7vdbvr7k7KgiPW3kCpIiYDT6WwgzoXO9cLUWyBs\nNhtcLhf9jiaymN/j8cBkMqGgIIgDaRzheR5Xrlzx6xpOhwh8DATcsYyL8CSS7777Dv/85z/RpUsX\n3HfffXjmmWdQUlKCp556Ctu2baOhWKA+WkDCqkrpliEk2tgvUBokkJ9QvOuKyN10LGmS4sKGgsfr\n5QEe0Kg8EHr1xLNoORzC9fBJBz8bKcSpPDLkMh1rOkIJTOH4BmHXU7pDhAlJdVksFr90rDDiE86E\ndrVajdzcXL95XQaDQVHrcqbDIjxJ4siRI6iurkZJSQnmzZvnp7aJS2iyoyHJgrg1u1yuiCNaoYom\nxVGbVNQ6iaMF4Z7DT74Bvj7g/5jz3DE4bQ78oqwtLljqDR5n3w1k6+O95eFDrAiI6y1Qb6MQz8hZ\nquB5nkYl1Wo1HeIpN6SEjVhgCgW/8DwIo5LpUmcXCaQrj6SzhOdQGPEhwodEfQHAbrfD4/EgJyeH\nfhYRwjzPx3VeV7IjPF6vF7W1tSgsrM+jK+3ci2ARnlTTvn17bNiwAf/85z8xevRo3HPPPbjvvvto\nntloNMLlcimq24lARIAwokUGd5IfnVR9R7Qt38lGqmMtnIiWVB0P7+Xh9fLwiHycEtGlJUWoOhth\ntIBcNJXwPRXXMKWieFtIuPVOkUQw5dDOnkhIAwUZMmu1WmkUSPzdJd2ygaI3wu9DvOd1yaFFPNX/\nfqpggifJDBs2DAMHDsSf/vQnDB06FAsXLsQvf/lLvx+Y3W5Pq26ncCELLrlzAnzRAbK4J3NeUSIQ\nLrjCbpJAc32kBI/Xy8PjAVyOeh8njot/SivWuVGkPsRisSiq1Vt80UxE8bYQqYL6SM5DNGSS8BGm\nK0nER2pCO4lWSn0WmddFIoDRzutipB6W0kohZ86cwYwZM8BxHJ544gk0a9aMPkdSCCScKsfwejCC\nLeQk/A74LpwkGqK0VB7pFnG5XJIXFJcHqPkb4BX8yqwnD8NscqB56yZwG4sB+Dq0Hr83tu0IVWcT\nKA0SCpLKI1PZldjhQtIksQqfWNJRiSQTUl0ulwsOh4PeWJL6HqE7vVarhcFgCBkxIxEf0rAQaTSe\n+Hrl5+fHY/dCIpVC0+tTmB9PPCylJUdKS0uxZs0abN26FRMmTMCwYcMwZcoUmrYhLbRWqzWp07wj\nIdaWb9INZLFYFNexJm7VF49y0KqBRrnAxbr693i9gNPFw2x2IMvoeyzcdFai/WykkLt5YayIo3bh\npCsTkY5KJEqN+IjXJpVKRW8kAdA0lzA6SQxjhcJTDBlUSj4r0nldbFp66mARHpngdrvx8ssvY82a\nNZg9ezYGDhxInxO2CKdSFEi1fMercNXr9cJut8fU5i13pPxf1m8B9v9Y/5q6Y4dw6ZIVBqMBJZ2u\nBwDk5wDVo+tfE+yCKo4SJLPeSVz4q9SonbAVWqfTSRbWy6X9PhrSMeITTOxLrUtkH3me90vJkk5P\n4XsDCR+Cx1M/e0+n09EoUSBImi0vLzk+E+IID4lyKZiAX1YmeGTGxYsXMXfuXPz888+oqalBm2tT\n3QHfYmuz2eD1ehOaPgh3GKYw9B6v7ZASBUpCLAq2HTJgy576xfHqkR9w/rwVbi+Hrv0qAABFeTwm\n3+qQpZ+NFOI0kBwjk5EidUH1eDwAfBcQUhsiFJnpjhyFTziO0OLuzWCfRcQKWVOFN1rkswHQKHWw\nzyM3bQ6HI+ig0mQLHnEKLZMFj7KuJgqgcePGePXVV7Fjxw5MnToVffv2xfTp05GdnQ2VSuU31FLY\ngRAN4bZ8J3O8BUmRkDEHSrlgEsTdQPkGO4Bs+rzH44HV5obTBWhUPNxeDhqV765Tjn42UgQq3k6H\nzsNIJ34Tcedyuajvk5zPTSRIpbqSGWEOVfMUqyM0Eapk7IjD4YDdbvfrPiS1PuQYhDO2ItS8LpbS\nSh1M8MiUHj16YPPmzXjrrbcwbNgw/OEPf8DIkSMlW6CDdQIBkbV8yyFSIBQF8RjjICeEkQKv14um\nBf6Ln8fDw+kCVCrAoOdhsnHQ63zTv9ONcM0LU4VY2IiL6kmdTagiYjLugFguyGkf40GihU8wkZms\nmicSoSPChwyajVX4kOOl0Wj8mk+S+d1gAqseltJKA+rq6rBo0SLs378fNTU1frO/hLUvpINEKiUl\nTIEI/0yHRTkd63vCrbPhOBWeelsDl8e3P+f37cOJkzZkG1S4ZXgFLpnVaNcMmFCV4h2KA+LvarJS\nJOEWc8cjHSXHNFC8Ee5jNEaioaI2clifgu2jOBIeKtVFajBJGpsIq9zc3KTsiziFxlJaDFmTl5eH\nZ599Ft9//z2qq6vRrl07jB8/HidOnMCBAwdw//33004goL77IF1SIKEgd0zCMQ5yadWP1c8G8E1O\nP3OJ/M33nF7HQcW7AaiTZjqYaMh5JHUT4q61WIk0HZWIFK0wGkL2UWnCJ5yIT6hONeHoFznWPAXa\nR3HEh1hPBBM+JNJJRmCQLjEyp05u+65kFLKUKps9e/bg888/x/79+3HlyhWsXLkSb775Jjp37ozy\n8nK43W7k5+dDpVJRUUDm5si9ZiIShC3QqXDCTdTcqOLCesFD/M+y9Gp43E4AemhUXgjnaqU7QsuF\naOeQhToX4aajEgXZx0SJOzlARIHwPJL1RglGokD9PhJhI65Hi0b4AKDCJxnzulhKqx4meNKAXbt2\n4fDhw+jevTvGjRuHLl26IDs7G0899RS2bNmCM2fOoLj4mkmdwt2ao6lhipRwBpPG08+mqcBx2XvN\nhVCnU4F3+9QPBzcsFpfi2rzD8fAJVVifCG+heCIl7tLVmTpU1Ear1dLnSJdSuu1jIIinFonSxCp8\nyPeCpJuIe3Oi1mqlnIdYYTU8ac6JEyfw6KOPIi8vDwsWLECTJk3oc+nu1hwOsdSFyMXP5ug5YPUm\n3/+f3LkXZ8/b0b5tNlq1L4ZV2xi9r+cxsMJBQ+Dp0O0UKcTfhkQJhBeQQB5P6biIp4szdaCCbrEd\ngtS5iKXGJ10QuqiLHbjJsSPHSzyhnbzPaPQ5iwqtKkh7fDyPGfldkZohkkpUMKyGR6m0adMG77zz\nDjZt2oQxY8Zg9OjRmDRpEv2RiVNAoUyx0g1hXYjNZoPT6aSpA0I86mwSSbFgaDKJ8GjUHCxmB7hC\nQKeVd7dTpARLR2k0Gjp9XtzZogSEUS1SyJpK4RMoggbUF3QHckkPRKrb2ZOBMOIjNXNNKNidTqef\n8BEPDyVFxGT2F5nXRT4/1mPGUlr1sAiPgnC5XFi+fDneffddzJ8/HzfddBN9Ti5uzYlEeKfEcRxU\nKhUVOqmeVxSKp9YDFjtwdPtu/HzRiS7XG+FR61DQph1uKQduqah/LakL8Xg8sq4LCScdFciZW/h9\nVWpUiwg78n0lwy0TZSYabEhpIiNomRDxEX5fxbWF4ogPOe4kwiNFrPO6hIgjSpkc4WGCR4GcP38e\ns2fPhtVqRU1NDUpLS+lzxK3Z4/HQu+d0XHxC1dkAvoU2XQaTvrEJOHYOOPx/u3DpigtlHY24cNWL\ntt27oKoHcGPXhu8Rp0dStYiF434b7cVUmDpQ8sVSONwyVodxsdAM1IafbNGfKcKHpJCEtWUej8dv\nUCmJvpMbs0CQyDUZQByNCSsTPPUoJ1bMoJSUlOCNN97A119/jYkTJ2LAgAF45JFH6F0CCanbbLaY\n3ZoTTbh1NlKtxuRiabFYZF+8XVzgEzyea11anAq4fNGOX4CHViO9zeKiX5LOS+S5DGfidzztEISp\nA2LsJ/dzGSlCo03iMK5Wq2nEJxDhjllIplN6MJSe6iJrFWmsIKUEQP1oCuH31uVyAQAtbpYSMmq1\nGkajkUZ1a2trw5rXJd4uJRzfeKCsGHEMXL16FaNHj0anTp3QuXNnbNu2DZcvX8agQYNw3XXXoaqq\nClevXqWvX7p0KTp06ICOHTti06ZNKdzywNxwww344osv0KxZM9x66634+OOP6XPEUl2j0cBisdDi\n5lRBFguSkrJarTCZTKirq4PVaqWLg06nQ05ODvLy8pCbm4vs7Gza9SK+wJKLJREFZrMZLpdLljnt\n4mudWqSGBzzgcnuRpQO0QfQLWVyF59JqtdI7+mghF1Kn0wm73Q6LxYK6ujrU1dXRdBoRy7m5ucjL\ny0NOTg6dDh/vjilSq0XOpclkgtPplOW5jBYifHJzc6HVamG1WmGxWKjAd7vdcDqdsNlsMJvNqKur\ng9lspsdBo9EgOzs7rN9GKiHCJycnB16vFyaTiUYq0wWv10vrsMRrldvtppE6o9FIW9GFdWpCywRy\nXt1ud8DfLYkIkXlYtbW19LvBCB+W0rrGhAkTcPPNN+P++++nw9ZqamrQuHFjzJw5E0899RSuXLmC\nZcuW4cCBA7jnnnvwzTff4MyZMxg4cCAOHTok6xqDK1euYN68efjxxx9RU1OD9u3b0+eS7WQcjp9N\nIkLuwvoeg8Egq6jWmYvAK/8E9m7ZCYvVg64djdh30Iwhv+2BQT1UKGsb3ueIa19CtQaHqu0Qnwu5\nXDiF6bx0bfOWQhy1cblcQdNRcl5zwkXOqa5wDBTDcekWpy2F9VokFU/Os/AzAyEcVKrVaoOuZ+QG\nJScnB0Bmp7SY4IFPLVdWVuLYsWN+j3fs2BFbt25FcXExzp8/j/79++PgwYNYunQpVCoVZs2aBQAY\nMmQIFi5ciL59+6Zi8yNiz549qK6uRkVFBWbMmOFXOBfvSeXh+NkEKlpNFHKd5O10AzVrgO8274Dd\n4UWX64zYf8iM4XdVYFBPLTq2iuzzpNr1AYRMR8mxoDsY6dLmLUW4YxaEUQBxC7SSSLXwEZ4P4TkJ\npxU/kn9Dap0Vj60Awhc+pNsvUFejzWaD1+tlggeshgcAcPz4cTRp0gT33Xcfdu/ejR49euDPf/4z\nfvrpJ2roV1xcjJ9++gkAcPbsWT9x06JFC5w5cyYl2x4p3bp1w6ZNm7Bu3ToMHz4cU6ZMwW9/+1sa\nao1mUnksdTbJhtxdkVEccmnx1mmAQiPguZbScl+72+O8Hmg1kS1OJEpAFj6yIAL+UYJEDmNMFuGY\nF6YaqZEXwigBOSfBxiwQoUNq0uQk1uNFsmp8Qp2PRJpZCo1TxRPahb9H4baR+h+pc03S9uJBpeIm\nBnEbfKYin1UhhbjdbuzcuRMvvvgievXqhWnTpmHZsmV+rwl1kU6nLxHHcRgzZgyGDx+OmpoajBw5\nEjU1NSgrK/MroBS7NQPSd6Vy8bOJBJLWIsLHZDKlvMW7pNA3LR0A3G7fn163G4Gu3eGmowwGA3ie\np4JAp9PJShDEivAiIiz6TUUxfjgjL6L9fRAxp9Pp4HQ6G7j9KoV4Cp9womipWK+khI/QhVssfCKd\n12WxWKgYSqfaqESjnFUvBlq0aIEWLVqgV69eAIDRo0dj6dKlKCkpwfnz51FSUoJz586hadOmAIDS\n0lKcOnWKvv/06dN+rd/pgtFoxNKlS3H48GFUV1ejefPmePzxx9GoUSNcuHABdXV1aNGihV+EIFHd\nOKlCaM4oNC5MhSBoWlg/S8vl8i1STocLOnV4E79DGcSJu4Dk3J0XDUKxLjSDS0QkJNzajkQVcIuN\nKDNd+Ihrn8h5EXesyS2qSYQP6eoSRnzEg0ojFT7kN8DzPLRaLevWAuvSAuBr427ZsiUOHToEANi8\neTO6dOmC4cOHY/Xq1QCA1atX4/bbbwcAjBgxAuvWrYPT6cTx48dx+PBh9O7dO2XbHwsmkwmXLl3C\niBEjcPbsWZSXl6NVq1aorKzEa6+9BgDQ6/V0kSERg0R146QK0rWm0+lgtVrj0ukUKU3z6+/EHE5f\nHt9cZ4PT4evIIUZkKpUKer0eRqMRubm5MBqN9HwEq2ERdgEJu/OSvZ+JhqQtc3NzoVKpYDabY9rP\nYB05wu5Bo9GIvLw8ej4SaSQI1KczSB2e2WxOu26ncBB3dZFjTwZwms1mmEwmv441rVYr2bEWSCjI\nAZKeJQNZTSYTHA4HLQ0gESFSt+N0OgN+p8lvID8/n0Y+6+rq6OdlKizCc40XXngB9957L5xOJ9q1\na4fXX38dHo8Hd955J1auXIk2bdrg7bffBgB07twZd955Jzp37gyNRoMVK1ak5UW/rq4OzZo1Q8eO\nHVFWVoZ+/fph0qRJ2LFjB/71r3/hN7/5DQwGA329cHCeHOpe4o0wQpCM/RSnP4y6+hZTh8O3kFks\nTuQaDcjLjV8UjSyGydrPVCFMAYWzn+E4Q0c6ZiEZSHkVKeV8SkXRAF/Hpcvlon5FoYp70wmNRgON\nRiMZ2Yp2UCl5DZnXVVBQIPEvKx/WpZXheDweybTGqVOnMGPGDGi1WixatAglJSX0uWS3saeKeO1n\nOOko3zngMGriHjicXhiyONjsPNq2zcefFl6HnKw475yAWAawphPkAkJca9VqddLHLCQDYbdTupzP\ncGZ6iTsIU93VlSzE+0lSXYD/2IpAwsdisdAUtnCyvYJhbemM6Pjiiy/wM9SxPwAAIABJREFU+OOP\n044u4Q+F1L3I0dcmnoTbri/V/SFVRyD07BAv0A/N2Y/Dx3zurBwHGHN1WPtSOXRJiMWmc4t3IKTG\nLAjN2sjiTy4U6b6/Qog7r8fjkZXwCTaJPRqxmYnCR1yzJRY+9TdR/oIHABVGCoYJHkb0uN1urFix\nAmvXrsXcuXMxYMAA+pzQ10aJhZME4WBSEkYHGvraiD1UIvW0eXrFMXy29RIAICtLBYfDi0/X9UrY\nfokRD7SUW4t3IMIdsyAUm0QQKEngSSEUPsmMyMbLtC9cMkX4COfLBRM+pLGErFlM8DDBw4iAn3/+\nGXPmzMGlS5ewZMkStG7dmj4n/BEqKc0VLEJAvIvEhmSx8M4H5/HqX30dgIX5GlhtXnzwVo+Y9yNS\nhAJPbp4vwVKEkRoopqvAiwZSeO31euPuTh1O+3ey3LqZ8KmvfXK73TS1SZ4na5aCYYKHET++/fZb\nzJgxAzfccAOmT5/uV9js8XjoXC4p10+5EunEbzLCId51TN/ursXcJb5uwZKmOlisHvxjZfeYPzda\nxKMqkhnBC7fVOB5jFsQRPKW17AuJZSxHsHMi/o2kurA7U4SPx+Obeed0OqmYJI72Ypdo0gLPanik\nYYInBtq0aYO8vDz6Rdu+fTsuX76Mu+66Cz/++CPt/CIV80uXLsWqVaugVquxfPlyVFVVpXgPAuP1\nerF69Wq8/PLLmDZtGkaMGEEXEzlHB4DwJn6HGyGIt8C7dMWJMQ/uBgC0Ks2C1e7BmhUVMX1mPBDe\nTSaiHiTcMQuJHnshHj2i1DEOJLJF2pSlUnpSkbRUnJNYUZLwCRRxJueEnLNPPvkEHTt2RHl5Obxe\nL44fP47du3dj7969AIAlS5Yo8nt9DSZ4UkHbtm2xY8cONGrUiD42c+ZMxQwkBXxzyBYuXIjvv/8e\nS5YsQceOHelzwuhAKoomw2kzFi7Y0R5rYVqEmMLFspiMnvQd6kxutGuTDYfDi1V/Lov6s+JNrPUg\nwQq7pcRmqr7/qYxsJRPhzYnwopmoSFoqSTfhE6q4W3xOiFg/ePAg/vrXv+Lvf/879Ho9CgsL0bVr\nV1RUVND/mjRpIut9jxE2SytViAXlxo0bsXXrVgC+Ce39+/fHsmXLsGHDBowZMwZarRZt2rRB+/bt\nsX37dtkPJM3Pz8dzzz2HAwcOoLq6Gtdddx3mzJmDvLw8WhNBxlQQF+N4h1PDTUcJ/VMSNR+HuJuG\nM6k8EG1bGrD7gAkaDQe5eYMKnanJtOZAka1AwxhTbesfDpF6+KQLUhECoXmdy+Xy+92m876KiefI\ningilSYM5f/E8zyuXLmCvXv3Ys+ePdizZw9OnDgBnU6HTp06oaKiAnfddRe++eYb/PnPf8bly5cx\nYMAA9OiR/HpAOcEETwLhOA4DBw6EWq3G5MmT8fvf/16RA0kBnxnjRx99hH/84x8YOXIkJk6ciHvu\nuYcKDjKUVCh8oomChJOOStXIC7Ghn8lkiiqy1aaVT/Co1YBaJc8LjnBop9VqpccdgOSCnagxC4lG\nytRPTi3ewQin/Zuk7MhNAIn4kOiWEou4Uyl8QqWkpG4CvF4vTp48SYXNnj17cPnyZRQUFKC8vBwV\nFRW444470K5duwZrar9+/TB16lS8+eabuHTpUkL3LR1Q1jdZZnz11Vdo1qwZfv75ZwwaNMgv3QMo\nayAp4Nve0aNHY+jQoVi2bBmGDx+OJ598EpWVlQDQIAoSruutVEurcMK03MLs5CJJBpMKLxzhnNO2\nrbJ9n8Nx0Grls1+A9HBMcq4cDgeNpJG6rXT7DgdCpVLRiySJbMklChJO+3e4DtFCt3Elz10DEi98\nwklJCdcvkpI6cOAAdu/ejX379uHAgQOw2+103E///v0xbdq0iFJSer0ev//972PeHyXABE8Cadas\nGQCgSZMmuOOOO7B9+3YUFxcreiApAGRnZ+OJJ57A8ePH8eijj6KwsBDz589H48aN/aIgdrsddXV1\nNPUjjt6k2wR2MWRBjbS+p21LX9ebSsVBr09dDUs4YxaEHiqk7oUUwhKvIiUhldJLpodPoDSh+LcS\na+pWahArEz7SRJuSqq2txZ49e2ha6tixY9BoNDQlNXbsWJSVlSE7Oztt1jy5w4qWE4TVaoXH40Fu\nbi4sFguqqqqwYMECbN68GUVFRZg1axaWLVuGq1ev+hUtb9++nRYtHzlyRBFf9E8//RQLFy7EiBEj\n0L17d3z//feoq6vDgw8+SD1tAPg53qZb6iMUkRg02uwe3P67nSjrZERhvhaPTWuf0G2Tig7E4nyb\nKaMqwul0iuWzpSIEUgX3yfitZEr3GhC8uDmclJTYb8jr9eLUqVM0HbV3715cvHgR+fn56NatGyor\nK1FZWYn27dsr9pgmGVa0nGx++ukn3HHHHQB8vhf33nsvqqqq0LNnT0UPJCXwPI+///3v9Ad+/vx5\nLFiwAK1bt0bXrl3Rr18/v7SHsFNEiY63UgM7A4kBQ5YaxU308HoBvS5+C2A484riMRxTnP6JNKWX\nLgiL1WMxLwynJT/VEU7y/SVF3BaLRZaWE/FAHJklqVoAIVNSLpcLBw8epC3g+/fvh8PhQIsWLVBR\nUYGbbroJU6dORUlJiaJ+C+kCi/AwEsakSZNQWlqKsrIydOvWDe3atcOFCxcwa9YsOJ1OPPnkk2je\nvDl9Pc/zsNvtCfN6kRNEDHi9XkkxsODpw6gzufGL1tl4eGLrIJ/UkGjGLCTyOAvFrBKLYAmhzAsj\nMVKUe4RTSW37oVJSZDp5bW0t3nzzTTz44IMoKChAXV2dX9Tm6NGjUKvV6NixIyoqKlBZWYmysjLk\n5OTI+lwqEObDw5AXX331FebMmYNBgwZh6tSpfvUe8TbzkzOBBrC+vu40vttXh64dc/HA2JYB3x/P\nMQuJJJOcjIW1TMILZrTmlnIm2HgDORJtSurMmTPYtm0b1qxZg//7v/9Dfn4+OnTogB49elBx06FD\nB0WvVWkEEzwM+eHxePD//t//w+rVqzFjxgwMGTKEPkfqI2w2m2JD5wRxfURWVhb+/d8reHvjefTp\nXoAJd5ZGHLWR67FS2rBZ4XmRqoECfKKA1L0o9YIoFD46nY6m4FK9TZEa97ndbvzwww80JbVv3z7Y\n7XaUlpZSYZOXl4fXXnsNH374IR5++GHMmjWLDuZkyAImeBjy5fLly3j88cdx+vRp1NTUoF27dvQ5\nYehcDiZhiYSk9JxOJ85d8OCZl07j5l8W4I5bGykuOiC+QKbDeQ2nBkrqvKTjvkaLsGA9md42obqk\nxOeF53mYTCbaIbV3714cOXIEKpUK119/PXUk7tatG4xGo+Q+HDlyBCtXrkRNTU3KxR3DDyZ4GPJn\n9+7dqK6uRvfu3TFjxgzk5OTQ57xeL2w2W8Cal3Qk2JgFj4dH9aITuPXXRbhjaImsozaxIOyIkYuv\nDRCeaV8knWuA/75mQo1aIvY12pTU2bNn/eptzp8/j9zcXHTr1o1Gbq6//nrFRuAyDCZ4GOkBz/NY\ns2YNli9fjoceegijRo3yWyhJHUg8ZlYlk2iGY8544nvc0MOIQTcXKrrYF/Cf4J1sX5tQpn3iAu9Y\nIQXrHk9088jSiViET7QpqcOHD/ulpGw2G5o3b47y8nJUVlaioqICzZs3V+wxZzDBo0g8Hg969uyJ\nFi1a4IMPPlDMJHYAMJlMePLJJ7Fz507U1NSga9eu9DlxHUi0M6sSQSj/FPFiHWy7n3vlOLp1zsVN\nffJkO3k+ngiHsIqLuOP1+VKmfeLoQDI614DUibxUIBR5YuETbUrKbDb7paQOHz4MjuNw3XXX0ZRU\neXk5cnNzFXtcGZIwwaNE/vSnP2HHjh0wmUzYuHGj4iaxA8ChQ4dQXV2Nli1b4rHHHkNhYSF9Tlgv\nkIo7Zal0lNj1Vhxej4S3N5xDafMs/KpXYcbVMomLuCP5robTZiyX9m+xyFN6JI/M6fJ4PFTMhpOS\nOn/+vF9K6ty5czAajSgrK2uQklLq74IRNkzwKI3Tp0/jd7/7HR577DH86U9/wgcffICOHTti69at\ndHxF//79cfDgQSxduhQqlQqzZs0CAAwZMgQLFy6U/SR2As/z+OCDD7BkyRKMHz8e48eP97sAkgsG\ngIRcMMJJeyTC9Xb7d1ehVnPo0S2fPpZqkZdMwvF6keqQCpUqlCNKbNsPlpIiQuby5cv49ttvcccd\nd0ClUsHj8eDQoUNU2OzduxdWqxXNmjWjgzIrKytRWlqaFjdsjJTAnJaVxh//+Ec8/fTTqKuro48p\ndRI7x3EYMWIEqqqq8Mwzz2Do0KFYvHgxevXqBaB+cjcZdhhL6ifYIh3PWUXh0KalAT9fcvo9RlyM\nhS6wSvUqIhEPMoTVZDJRZ9tAbflyHCYbDlKzq9JlhEO4s6SIYBWmpHbt2oVnn30W8+fPR3Z2Nho1\nakRTUnfccQcWLlyIvLw82QpVRnqhvFUyA/jwww/RtGlTVFZWYsuWLZKvUdokdsAXvXn88ccxYcIE\nzJgxAytXrsSiRYtQXFzsd8Gw2+1BRzcA4Q/HjHXMQiw0bayH2y0dZBWLPLVaDYPBkHYXeimkOnG8\nXi84joPb7QbP89BqtbTGJx2/y4GQGuEgJ7+icLqkxGMweJ7HTz/9hN27d9PIzZkzZ5CTk4OysjL8\n8Y9/hNlsxmuvvQaHw4E777zTz5OLwYgXTPCkIV9//TU2btyIjz76iE4cHzduXEZMYgeAli1bYt26\ndfj8888xduxYjBw5EpMnT6bpHYPBAJ1OB5vNRqdZkxB6oBbjZEVtIqV5SWBDM6HII/O55FbEHYpQ\ng0uJ8BS25ZPolt1uV2zNizC6Rc5tsmu3wumSEkfU3G43jhw54peSMpvNKC4upoXE9913H1q0aNFA\nwP3P//wP3nvvPWzcuJEJHkZCYDU8ac7WrVvxzDPP4IMPPsDMmTMzbhK72+3GX/7yF6xfvx6PPvoo\nGjdujL1790Kv12PEiBHUGI4MJRVePJWw/0LkPKU8nIhaJGaKSqx5CUYiz220XVJWqxX79u2j4uaH\nH34Az/No3749FTcVFRXIz8+XzfeQkRGwGh4lQxaT2bNnZ8QkdgCwWCz44osvaOfGpUuXcOedd6Jt\n27YoKyvDkCFD/OofnE4nnE4nVCqVYgt9hVPKbTYbnVKu1WqTuh1SF89410EFqnlRatu+eAI9iVxG\n+l0ONyUlPDc8z+PChQt+XVKnT5+GwWBA165dUVlZiYcffhgdO3aUlchmMMSwCA8jLTl9+jQmTpyI\nbt26oby8HN26dUPHjh2xe/duzJgxAzfddBOmTZsGg8FA30Pcmj0eDy30VeriLGx3TpRJYyjPoUR1\nrwXaFrvdnhHjG4DwPHwiNe4DfC3i4pSUyWRC06ZN/bqkWrVqpUhhyVAErC2dkTl4vV688cYbePXV\nV/HHP/4Rt912m9/FgAwlTTe35miI17DOcJyiY/EcihdyTuvFG+GAXZKyBRBWupDnedhsNuzfv5+K\nm4MHD8Lj8aBdu3aoqKhA9+7dUV5ejoKCAsUeQ4YiYYKHkXlcvXoVCxcuxKFDh1BTU4Prr7+ePidn\nt+ZEIBxgGap7TcqR2Ov1ys60Lxgkrac0F+NgKSmyll+4cAHt2rXzS0n9/PPPNCW1Z88enD59GllZ\nWejatSuN2nTq1EnxkTElYbfbcfPNN1OvqpEjR2Lp0qVROe7v2LEDv/vd72C32zF06FA8//zzAACH\nw4Hx48dj586dKCoqwvr169G6dWsAwOrVq1FTUwMAePzxxzF+/PgUHAVJmOBhZC779+9HdXU1OnXq\nhFmzZiEvL48+l0lGfkC9xb/X66XRnnQ37QtEursYRzNL6tixY6iqqkKnTp3Qpk0bnD17FnV1dWjc\nuDFNSXXv3h2tW7dmKSkFYLVaqS/XjTfeiGeeeQYbN24M23GfjOPo3bs3XnzxRfTu3RtDhw7FI488\ngiFDhmDFihXYt28fVqxYgfXr1+O9997DunXrcPnyZfTq1Qs7duwAAPTo0QM7duygwirFsKJlRubS\npUsXfPzxx3jnnXcwcuRI/P73v8eYMWPAcVwDIz9S6JtOF8ZQiKM2wpQGANq9lq6mfYHgOA5arRYa\njcbPr0huacxwaqGkjPvsdjv27dtHB2UePHgQbrcbAwcOhNPpxLvvvovbbrsNr776Klq1apXq3WQk\ngOzsbAC+pgyPx4PCwkJs3LgRW7duBQBMmDAB/fv3x7Jly7BhwwaMGTMGWq0Wbdq0Qfv27bFt2za0\nbt0aJpMJvXv3BgCMHz8e77//PoYMGYKNGzdi0aJFAIBRo0Zh6tSpAIBPP/0UVVVVVOAMGjQIn3zy\nCe6+++5kH4KIUM6qzmAEgeM4/Pa3v8WwYcOwdOlSDB8+HIsXL0ZlZSWA+Lo1p5JApn0AaGSAXDw5\njqOzjUj3WrrtbziI/YpSaeYXbZfUpUuX/FJSJ0+ehF6vR5cuXVBRUYHJkyejc+fOfimpq1ev4umn\nn8agQYOwf/9+RYl4hg+v14vu3bvj6NGjmDJlCrp06RKx475Wq0WLFi3o46WlpdSJ/8yZM2jZsiUA\n3xqZn5+PS5cu4ezZs37vSRf3fvYLYPgRz7ywHMnOzsbixYtx7NgxPProoygqKsL8+fNRVFQUsVtz\nqgmV8lCpVLQ1P1D7N9m/dNjfWEm2mV80xn0ejwfHjx/3awGvra1FUVERTUndddddaNu2bUixVlBQ\ngJqaGixYsICJHYWiUqmwa9cu1NbWYvDgwfjiiy/8npebkWqqYb8Chh9ZWVn44osv/PLCX375JTZu\n3IhBgwbRvPCyZctoXnj9+vU4cOBAWk1i/8UvfoF3330XH3/8Me68807cddddmDhxIhUHxK1ZmOZK\ntp8NIZzhpbGMwQi0v0op9BWjUqn89tdkMsVUvxVtSsrhcPgJm++//x4ulwtt2rRBRUUFqqqqqJlo\nLOdBp9NF/V5GepCfn49hw4Zhx44dETnut2jRAqWlpTh9+nSDx8l7Tp48iebNm8PtdlPxXVpa6jfW\n6NSpU/j1r3+dnJ2NAXlflRgpIVBeeMKECQB8eeH3338fACTzwtu3b0/ZtkfKrbfeiq1bt8LpdGLo\n0KH46quv6HNqtRrZ2dnIysqC3W6HxWKh6aFEQQptHQ4HrFYrzGYz6urqYLVa4XK5APguYEajEXl5\neTAajfTiHatAUavVyMnJgcFgoPtLnKqVCNnf7OxsOJ1OmM1muFwuBGvkILVQTqcTNpuNnh+LxQKn\n0zfoVXx+srKyYDKZ8O9//xvLly/HxIkTMWjQIIwaNQpr166FVqvF/fffj48//hhffvkl/va3v2HG\njBkYOHAgGjdurEjRyYidixcv4urVqwAAm82Gzz77DJWVlRgxYgRWr14NwNdJdfvttwMARowYgXXr\n1sHpdOL48eM4fPgwevfujZKSEuTl5WHbtm3geR5vvfUWRo4cSd9DPuudd97BgAEDAABVVVXYtGkT\nrl69iitXruCzzz7D4MGDk30IIoZFeBgNiEdeOJ3Q6XSYOXMmxo4di1mzZmHVqlVYvHgxmjdv7lf4\nGs80SDhRAXE9R7LQaDQwGo1wuVyKdzAG6uu3hB1dBoOhwfy1cFJSXq8XJ06c8IvcXLlyBYWFhTQl\nNXr0aLRr106xx5ORHM6dO4cJEybQdWTcuHEYMGAAKisrI3bcX7FiBX73u9/BZrNh6NChdJbZxIkT\nMW7cOHTo0AFFRUVYt24dAKBRo0aYN28eevXqBQBYsGCBXDq0gsLa0hkBIXnhpUuX4je/+Q2uXLlC\nn2vUqBEuX76Mhx9+GH379sW9994LAJg0aRKGDh2K3/zmN6na7Jj58ssvMWfOHAwePBgPPfQQ9Ho9\nfS6aNvZ0Me2TgqRenE6nYh2MheLT7XbD7Xb7FXqT+Wti4z6n04nvv/+edkkdOHAALpcLrVu3pt42\nFRUVLEqTZpw6dQrjx4/HhQsXwHEcHnjgATzyyCNYuHAhXnvtNTRp0gQAsGTJEtx6660AMsbfJl1g\nbemMyIk2L5zOk9gB4MYbb8SWLVvw6quvYujQoZg5cyYN1wrb2Mm8KoPBALVa3aD9W1h3EywqIGeE\nhb7xqHdJNeF0SZFoltPpxEMPPYSmTZti8uTJOHnyJI3cnDhxAlqtFp06dUJFRQUmTJiAsrIyGhli\npC9arRbPPfccKioqYDab0aNHDwwaNAgcx2H69OmYPn263+ul6hiJv82UKVOwcuVK6m/zySefYMiQ\nIVi5ciWKiopw+PBhrF+/HrNmzaL+Nk888YSfv82IESPSInqSDsh/xWUklXjlhdMdtVqNKVOm4MMP\nP8SmTZtw991349ixY/R5l8tFa2ZIHUddXR3MZjOcTid4nodGo0F2djby8vKQm5uL7Oxs6PV6aDSa\ntBA7QojQI/UuFosFbrc71ZsVFBKxIfVQJpOJ1kO53W46WiQ3Nxd5eXnIycmBTqfDmTNn8OGHH+KZ\nZ56B0+nE7t270bNnTzz77LNo0aIFFixYgH//+9/YsmULXnrpJUyePBl9+vRBdnY2EzsKoKSkBBUV\nFQAAo9GITp060TS9VEYkkL/NuXPnJP1tAPjVRI4aNQr/+te/APj72xQUFFB/G0Z8YBEehh/xzAsr\ngaKiIixYsAD/+Mc/cPvtt6O4uBiXLl3Cjz/+iHfffRe9evWCXq+Hx+PJCLdmOfoVRdsl5XQ6sX//\nfuzevRv79u3DgQMHYLfb0apVK1RUVKB///74wx/+gOLiYuzfvx+zZ8/G/PnzsX79elkZFzISx4kT\nJ/Ddd9+hb9+++Oqrr/DCCy/gzTffpAK4oKCA+dukEUzwMPwoKyvDzp07GzzeqFEjbN68WfI9c+fO\nxdy5cxO9aUln9uzZeP311+FyuVBeXo5hw4bRadJz5sxBv379/IQNmd/kcDjoNHYlIjbyS7SfjRCS\nMgw2DkPKuK+2ttbPuO/YsWN+KamxY8eirKwsYJSma9eu+PDDD/HFF1/QWguGsjGbzRg9ejSef/55\nGI1GTJkyBfPnzwcAzJs3D9XV1Vi5cmWKt5IRCcpckRXCLbfcgvvuu48VraWICRMm4KGHHkKLFi38\nLoImkwmLFy/GmjVrUFNTg86dOwOob3OWU/QjkSS6vica4z6v14tTp075dUldvHgR+fn56NatGyoq\nKjBixAh06NAhqijNLbfcEvN+MeSPy+XCqFGjMHbsWJq+J3WLgK85Y/jw4QCYv006wbq0ZMqSJUuw\nfPly/PKXv4TD4cCSJUtoXpkhD3744QdUV1ejTZs2mDt3rl9hobC7ScnuxUJIWzfP8xFFuMJt0Rd3\nSblcLvzwww/YtWsX7ZKy2Wxo2bIlKisrUVlZifLycpSUlCj+2DPiB8/zmDBhAoqKivDcc8/Rx8+d\nO4dmzZoBAJ577jl88803WLNmDR3KuX37dlq0fOTIEXAchz59+mD58uXo3bs3hg0b5jeUc+/evXjp\npZewbt06vP/++7RouWfPnti5cyd4nkePHj2wc+dOVrQcGWxaejpRV1eHli1bYvfu3WjTpg1efvll\nvPvuu3jllVfQtm3bVG8eQwDP89i4cSOWLl2KCRMmYNy4cX4RHeF08lS6NScLIkTsdrvkoM5wUlLk\nP2FKqq6uzi9qc/ToUajVanTs2BEVFRWoqKhAt27dkJOTw8QNIya+/PJL9OvXD926daPfpSVLlmDt\n2rXYtWsXOI5D27Zt8corr1BvsiVLlmDVqlXQaDR4/vnnaVcnaUsn/jbLly8H4GtLHzduHL777jvq\nb9OmTRsAwOuvv44lS5YA8LWlk+JmRtgwwZNO/Pa3v4XBYMCbb74JnufBcRxuu+02PPDAAxgxYgQc\nDoefNwwj9djtdjz99NPYvHkzFi9ejJ49e/o9T0QA6QxSetErmebtdDppZEYqJSUUN4AvJXXmzBns\n2bOH+ttcuHABeXl56NatG43cdOjQQbE1UkokkLdNNDP6mLcNIwRM8KQLX3/9NYYNG4azZ89Si/+s\nrCxMmTIFJSUlWLBgAT7//HNs2LABixcvhtFoVGyNSDpy8uRJzJgxAwaDAQsXLvTL+5POIIfDoSgT\nv1ApKfL8N998g379+kGn09ERGj/88AMVNvv27YPdbkdpaSk17isvL6eO14z05fz58zh//ryft837\n77+P119/HY0bN6Yz+q5cuUJn9N1zzz345ptvGnjb9O7dGy+++CL1thGmifbt24cVK1Zg/fr1eO+9\n92iaqFevXn7eNjt27GBpIuUScLFgV0qZ8T//8z9YsmQJDAYDbDYbneO0bt06VFVVweFw4P3336f+\nLsQgLR04deoUbrnlFnTp0gVdu3al4d3Lly9j0KBBuO6661BVVUV9gADfXV6HDh3QsWNHbNq0KVWb\nHjatWrXC+vXrMW7cONx7771YsWIFnYHFcRz0ej2MRiO8Xi9MJhP17EkXiFCJZJaU0WiE1WrF0qVL\n0bNnT4wcORKDBw/GbbfdhldffRUOhwN33XUXNmzYgK+++gp///vf8dhjj2Ho0KEoLS1lYkcBBPK2\niWRGH/O2YcQKiwnLiLVr12LPnj2YMmUKAMBgMADwiaB+/frhl7/8Jd5++20cP34cCxcuxObNm/HB\nBx/g6NGjePDBB3HbbbelcvNDEsjB9PXXX1fUJHYAGDBgAPr164e//OUvGDp0KB5//HHcfPPNAIK7\nNcuJcLukxCmps2fP+tXbnD9/Hrm5ubjhhhug0Wiwbt06dO7cGc8++yztcGNkDsTbpk+fPhHP6GPe\nNoxYYIJHRtx+++2YMmUKfvWrX2Hs2LHo1KkTPv30U2zYsAG7du3CiRMn8Le//Q0fffQRSkpKcPLk\nSdx4442YNGkSGjduDAC05keOlJSUoKSkBEDDu7ytW7cC8N3l9e/fH8uWLQs4iV24EMoZrVaLadOm\nYcyYMZg9ezZWrVqFJ5980m9RFg7p1Gq11BwvmURr3Od2u3Ho0CGDTE75AAAWwUlEQVTs3r0be/bs\nwb59+2C1WlFaWory8nL06dMHkydPbpCSWrx4MV588UWMGTMGO3bsYLU4GYTZbMaoUaPw/PPPIzc3\n1+85UqTOYCQKttLIBK/XC4PBgL/85S/Ys2cPFixYgG3btqFly5bYsGEDWrZsieeeew7nzp1DXl4e\nli5dSkUOAHqBIgtGbW0t8vPzU7U7IYnlLi/dKC4uxuuvv45t27bhgQceoA6+WVlZ1MRPOI09kW3s\n0Rr3mc1m7N27l0ZtSD3FddddRyeAL168GLm5uSG3W6fTYfr06Zg2bVpaROsY8YF424wbN45620Qy\no4952zBihQkemaBSqeiE5m7duuG9996D2WyG0WgEAPz3v//Fli1bsHz5crz44os4deoUioqK6MVF\nrVbD6/WC4zj885//xLp16/DEE0/Iso09lru8dL4D7NOnD7744gusWrUKQ4cORXV1NYYOHQqO46BS\nqWAwGKDT6fzSXLFEP4TDSyNJSZ0/f97Plfjs2bPIzc1FWVkZKioqUF1djeuvv57OEosWJnYyB57n\nMXHiRHTu3BnTpk2jj5MZfbNmzWowo++ee+7B9OnTcebMGTqjj+M45OXlYdu2bejduzfeeustPPLI\nI36f1bdvX7zzzjsYMGAAAKCqqgpz587F1atXwfM8PvvsMzz11FPJPwiMlMMEj4wgFwCPxwO1Wk3F\njslkwjvvvIOmTZuib9++ePrpp3H27FlUVlbS9/I8D5VKBYfDgY8++giVlZX0/XJKc8V6l5fuk9hV\nKhUmTZqE0aNHY/78+XjjjTewZMkSdOjQAUB0bs3RpqQ8Hg8OHTpEozZ79+6F1WpFs2bNUF5eju7d\nu2PixIkoLS1l4oQRE1999RX++te/UmsBwNeQMHv27Ihn9K1YscLP22bIkCEAgIkTJ2LcuHHo0KED\n9bYBfGNx5s2bh169egEAFixYwDq0MhTWlp4m/PTTT+A4Dk2bNsW8efPw008/4dVXX6XPE1GzatUq\nbNu2DZMnT0b37t3p8x6Ph17sUkUgB9OZM2eiqKgIs2bNwrJly3D16lW/1lQpB1OlsHfvXlRXV6Os\nrAwzZ870i3gJ3ZpJGzuAqIz7LBYL9u3bR8XNoUOHwPM8OnToQI37KioqkJeXp6jjy2AwMo7ACxjP\n88H+Y8gAj8fD8zzPe71enud5fvv27XzPnj15s9ns9/yJEyf4sWPH8m+99RbP8zx/7tw5/r333uNr\na2tTsNUN+c9//sNzHMeXl5fzFRUVfEVFBf/xxx/zly5d4gcMGMB36NCBHzRoEH/lyhX6npqaGr5d\nu3b89ddfz3/yyScp3PrE4fV6+XXr1vG9e/fmV65cyZvNZt5isfDHjh3jf/jhB/7SpUv8uXPn+LNn\nz/Jnz57lz58/z//888/85cuX+draWvp6i8XCm81m/ujRo/y7777LL1y4kB81ahTft29ffsCAAfy0\nadP4N954g9+9ezfvcDjo94mRftx3331806ZN+a5du9LHFixYwJeWltLf1kcffUSfW7JkCd++fXv+\n+uuv5z/99FP6+Lfffst37dqVb9++Pf/II4/Qx+12O3/nnXfy7du35/v06cOfOHGCPvfGG2/wHTp0\n4Dt06MCvXr06wXvKYERMQE3DIjxpyltvvYV77rnHL2qzaNEiOBwOTJw4ETt37sRrr72GQYMGYe3a\ntXjooYdw//330xoO8icj9Xi9Xhw9ehT//e9/8fLLL+Ps2bOw2Wyw2+147LHHcP/991MDvwsXLmD2\n7NmYN28e2rdvjyNHjvilpEwmE0pKSlBeXk7N+1q2bMnOtcL4z3/+A6PRiPHjx2Pv3r0AfL//3Nxc\nTJ8+3e+1zMSPkWEEjPCwGp40g7+Wuho3bhz9OwBs2bIFR48exbhx42C32/HUU0/h6tWrWLVqFaqq\nqvDKK6/g/vvvpxc+InpYK2hqeemllzBz5kw0btwYFRUVqKqqQnFxMT7//HMUFBTg7rvvRlZWFqxW\nK/bt24ddu3ZBp9Ohf//+yMvLQ79+/dC7d28MHz4c8+bNQ35+PjufGcBNN92EEydONHhc6gY2kIlf\n69atJU38hgwZgo0bN2LRokUAfCZ+U6dOBeBv4geAmvjdfffdCdpTBiN+MMGTZogvZuTvX3/9NVq3\nbo2BAwfiiSeeQEVFBcaPH49Ro0ahadOmcDqdMJvNeOutt1BXV4eHHnpIlkXNmcbo0aMxZsyYBnfI\nDz74ID766CP8+te/RnZ2Nho3boyuXbuioqICc+bMwf/+7/9i4cKF2LRpE2699Vb0798/NTvAkBUv\nvPAC3nzzTfTs2RPPPvssCgoKmIkfg3ENJngUwty5c2lhMwDceOON6NevH/773//i2Wefhd1uh1ar\nxdtvv43CwkJs27YN5eXlWLBgARM7KaRJkyYBnxs6dCh+9atfITs7W3LKOvH2IaaNjMxmypQpmD9/\nPgBg3rx5qK6uxsqVK1O8VQyGfGCCRwGQehxi3nfjjTdi+vTp8Hq9uP/++1FdXQ3AJ4ratm2Lhx9+\nGEVFRRgzZgxGjhyJiooKGuWRQzcXo55Q5pF9+vRBnz59krQ1DDkjHFQ7adIkDB8+HAAz8WMwCKyS\nUQGIC1IHDBiAlStX4s0338QDDzyAy5cvY/v27di+fTumTZuGsrIytGrVCk2aNKELocViwYULF2g7\ns8fjScWuMBiMKDl37hz9//feew9lZWUAfIZ869atg9PpxPHjx6mJX0lJCTXx43keb731FkaOHEnf\ns3r1agBoYOK3adMmXL16FVeuXMFnn32GwYMHJ3lPGYzoYBEehUHa73r27EkLmYnx1uDBg9GhQwdo\nNBp88MEHOHHiBG677TasWbMGH3/8MXbs2IFx48Zhzpw5shtkyWAw6hkzZgy2bt2KixcvomXLlli0\naBG2bNmCXbt2geM4tG3bFq+88goAZuLHYFCC9awnsW+eEWfcbjf9f6/Xy69cuZI/fvw4z/M873K5\n+F69evEffvgh/8knn/B33XUXv27dOv7y5cv8H/7wB37w4MH0tZ9//jm/bt26FOxBdEj5k1y6dIkf\nOHCgpM9PIH8SRuYQr+8M87RhMGRBQE3DBE8GMn/+fL5v3748z/P8vffey2/YsIE3mUw8z/P8zJkz\n+TZt2vC7d+/mn376aT47O5vfu3cvfW9dXV1Ktjlc/v3vf/M7d+70u3jNmDGDf+qpp3ie5/lly5bx\ns2bN4nme5/fv38+Xl5fzTqeTP378ON+uXbv/397dx1RZxn8cfx8PKmCG6BSRJBUIIkFRfIhpNRUI\nzdbJJ6YZqFnKDJfzafkw/UOT1K0S2dJAm2tBQxO1SS6d01LRKYrToWRplMSaKKh4FM+5f3+wc35q\n+Mt+AUdvPq9/cDeHw3Wxe/jhvq7v93I3cZSW47/eM64GjgMGDDCKiooMwzCMpKQkY/fu3YZhGMb6\n9euNmTNnGoZhGLm5ucaECRMMw6gPVb169TKuXr1qXL161f1vEflPHppptIenBTAe6M0RFxdHVlYW\nUH92U4cOHXjqqae4desWP/74I0uWLCE6Opr9+/djGAYFBQXcuXOH8vJyhg4dyrlz5zwxjUcydOhQ\n/P3977u2Y8cOUlJSAEhJSWH79u1Aw/1Jjh492uxjFs/6r/dMUVERFRUVDfa0efC9xowZw969e4H7\ne9p06NDB3dNGRJqGAk8L8GDFVWJiIjExMTgcDm7cuMF3330HwLvvvktwcDBTp07l/PnzHDx4kJKS\nEhwOB8XFxWzZsoWoqCjCw8Pd5zk9CSorK90VbAEBAVRWVgKop4g81L+9Zx68rp42Io8fBZ4WrLi4\nmM2bN2O1WklKSuL7779nxowZAEyfPp0ZM2YQGhrK0qVLsVgsbNy4kS5dunDu3DmsVusTeVzBP3WW\nVjm+PEjdyEXM4cn7H0saxZUrV0hPTyc9PZ1Jkybx+++/s3jxYl5++WV27NjBL7/8QkZGhvvE7gUL\nFtCvXz8SEhIYPXq0e0nsSRAQEMCff/4J1JfuuvqVNNSfJCgoyCNjlMfLv7lnHrWnDfC3njb3vld5\nefl9T3xEpHEp8LRQnTp14tChQ0RFRbFmzRpSU1NJT08H6n9Zx8XFAfV/3ebn52O329m6dSuJiYnY\nbDZu3rzpyeH/K/f2FPnyyy9544033Ncb6k8i8m/vGfW0EXkC/F87mpt9b7V4hKvKxKWmpsaYPHmy\nkZaWZvzxxx9GXFyckZ+fbxiGYZw+fdqYM2eOkZ2d7Ymh/qPk5GQjMDDQaN26tfHMM88YOTk5xpUr\nV4zhw4c3WGK8YsUKIyQkxAgPDzcKCws9OHLxlMa6Z1xl6SEhIcb777/vvm63241x48a5y9JdLR8M\nwzBycnKM0NBQIzQ01Ni8eXOzzFfE5B6aaSxGA6fr3puHmit4yePBdUwFwLVr1ygsLGT9+vUcPHgQ\ngA0bNlBaWkpKSgp9+vTx5FBFREQe9NANd1rSkvu0atXKXX3VoUMHkpOT2bFjBwB79+7l7NmzREdH\nK+zII+nRowfR0dHExMS4lwurqqqIj4/nueeeIyEhgWvXrrlf/9FHHxEWFkZERAR79uxxXz9+/DhR\nUVGEhYUxe/Zs9/Xbt28zYcIEwsLCGDx4MJcuXWq+yYnIE0WBR/7G9YTHFXxcB1g6nU6sVisjRozw\n2NjkyWKxWNi/fz/FxcXuHkerVq0iPj6e8+fPM3z4cFatWgXA2bNnycvL4+zZsxQWFpKWlubuITVz\n5kyys7MpKyujrKzM3a8mOzubTp06UVZWxgcffMCCBQs8M1EReewp8MhDuYKP62N8fDzLli1TJYn8\nKw8umzdHUz95MjidThwOx9/uEZGmoMAjj8S16at9+/aeHoo8QSwWCyNGjCA2NpaNGzcCTd/Ur6qq\nqlnmJg/nCjCuj06nk7t37+JwOO57TatWrbBarVgsFurq6jwyVmk5FHjkkaj5mvx//PTTTxQXF7N7\n9+77Nr+76L4yF8MwuHv3LhaLhZKSEt566y2g/imxl5cXVqsVqC+IsFgsXLp0ieTkZOLi4khNTVWn\naWlSCjwi0mQCAwMB6Ny5MzabjaNHjzZ5U7+OHTs2y9zk7ywWC15eXgBER0fz1VdfAXDmzBkWLlzI\nqFGj6Nu3L4mJidTV1ZGbm0tqaioHDhxgyJAhLFq0iL/++suTUxATU+ARaUSFhYVEREQQFhZGRkaG\np4fjUbW1tVy/fh2AmzdvsmfPHqKiopqlqZ80nosXL1JdXY3D4bhvSepBt2/f5tixY2RmZnLs2DGg\nPvScOHGCmpoaunTpwurVq3n22WdJTU3FMAzy8/NZt24dY8eOZd26dTidTm7fvt1cU5MWxsvTAxAx\nC4fDwaxZs/jhhx8ICgpiwIABvP766zz//POeHppHVFZWYrPZgPqnL5MmTSIhIYHY2FjGjx9PdnY2\nPXr04JtvvgEgMjKS8ePHExkZiZeXF1lZWe7lrqysLFJTU7l16xYjR47k1VdfBWDatGlMnjyZsLAw\nOnXqRG5urmcmayKuUGOxWGjVqhXjxo1j9erVvPLKKw2+3jAMLBYL06dPp6Kigp49exIZGQlAcHAw\nJ0+eZOrUqbz44otcuHABPz8/4uPjqaqqIjw8nICAAGbPnk1gYCCtW7durmlKC6TGgyKN5PDhwyxf\nvtxdMu0qt164cKEnhyXSIIfDQWlpKb169cLHx+ehr5syZQp9+vRxHzOTkpLCxIkT8fX1dTcqPXjw\nINu2bWPMmDEMGTLE/bWLFy+mqqqKrKwsDMNg3rx5WK1WMjIyqK2tZe3atVy6dIkvvvgCqO+31LZt\nW3r37t3k8xfTUuNBkaZ2b8UQ/G+VkSdoaU3u5Sr/vndJymq18vnnn7v3RzkcDurq6ti6dStTpkwh\nJSWFiooKYmNj2blzJ3a7nfT0dA4cOOAOKK736927N97e3sybN4958+axbNkybty4wUsvvcTp06cB\nOHXqFJcvXyYjIwO73U5tbS1Tp07F6XQydOhQoqOjmTt3rqq1pMloSUukkTwu1UZaWmuZamtrOXHi\nBCUlJRQVFWGz2Rg5ciRt2rRx99K6V2VlJYcPH2bfvn1EREQwf/586urq2LRpE2PHjiU0NJTAwEBC\nQkKw2+2MHj2ayMhIampq2L59O+np6e573t/fnxUrVuB0Otm3bx85OTmsW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"text": [ "<matplotlib.figure.Figure at 0x7f2cafad8ed0>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "data_doom = read_2d('data-v2/benchmark-iterations-gpu-doom.csv')\n", "data_konos = read_2d('data-v2/benchmark-iterations-gpu-konos.csv')\n", "data_gram = read_2d('data-v2/benchmark-iterations-gpu-gram.csv')\n", "fig = fig_init()\n", "plot_it_to_ips(fig, data_doom, 'doom')\n", "plot_it_to_ips(fig, data_konos, 'konos')\n", "plot_it_to_ips(fig, data_gram, 'gram')\n", "plt.legend(loc='lower right')\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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teJpSkiQ1hUadpuzvyFjvKUlPT0qSJDVGrVdTAowFflt9npk5YcCNe2RMkiQ1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\nJkmSVND/A05dcjkBgdRoAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7f2cadf2c450>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "data_doom = read_2d('data-v2/benchmark-salt-len-gpu-doom.csv')\n", "data_konos = read_2d('data-v2/benchmark-salt-len-gpu-konos.csv')\n", "data_gram = read_2d('data-v2/benchmark-salt-len-gpu-gram.csv')\n", "fig = fig_init()\n", "plot_sl_to_ips(fig, data_doom, 'doom')\n", "plot_sl_to_ips(fig, data_konos, 'konos')\n", "plot_sl_to_ips(fig, data_gram, 'gram')\n", "plt.legend(loc='lower right')\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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JiMUR8auIuGBQtZQkSdpC1RLGro2Ii4FtIuLdwPeB/6jl4pn5InBYZs4C9gcOi4g/HXRt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\n", "text": [ "<matplotlib.figure.Figure at 0x7f2cade7a210>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "data = read_2d('data-v2/benchmark-iterations-cpu-manegrot.csv')\n", "\n", "fig = fig_init()\n", "plot_2d(fig, data, 'manegrot', 'Iterations', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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2fWAF6gIAwCpabIzh/t19xU62/Yfu/tiK1mwVGWMIAKwXsxpj+N7tX4M3Ln5U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\nb3Kh7offkvMDrDTBEGDHXpz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"text": [ "<matplotlib.figure.Figure at 0x7f2cadf35090>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "data = read_2d('data-v2/benchmark-batch-size-cpu-manegrot.csv')\n", "\n", "fig = fig_init()\n", "plot_2d(fig, [(x, y) for (x, y) in data if 0 <= x <= 1024], 'manegrot', 'Batch size', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_2d(fig, data, 'manegrot', 'Batch size', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f2cadd47dd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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GSV6d5PtJ7kxyV3vd2Y+TS5IkaeHo5VLy/wu8vKq+O5wqjZ49hpIkaVwMtccQuHmaQqEk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\nJe3+t9buK4H9h/Xz9gbXkiR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"text": [ "<matplotlib.figure.Figure at 0x7f2cadce3b90>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "data = read_2d('data-v2/benchmark-salt-len-cpu-manegrot.csv')\n", "\n", "fig = fig_init()\n", "plot_2d(fig, [(x, y) for (x, y) in data if 0 <= x <= 4096], 'manegrot', 'Salt length', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()\n", "\n", "fig = fig_init()\n", "plot_2d(fig, data, 'manegrot', 'Salt length', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ZzT2Ghye5dGr+siT3nE11AABYLytdSl7y7iTvqqqzM7nf8ElJzp1prQAAmLvqvtm4kpuu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\nYAgAwCAYAgCQRDAESJJU1W9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"text": [ "<matplotlib.figure.Figure at 0x7f2cadbaa890>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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sCgCA+VrK5ebf6e6/XqF6VgWXmwGAtWLF70msqp/u7g9U1S9l2+9ufstyF7Na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\nT/L2bZzrl5O8fyX+gJUy/L2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"text": [ "<matplotlib.figure.Figure at 0x7f2cadc6f350>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "data = read_dl('data-v2/benchmark-dk-length-cpu-manegrot.csv')\n", "\n", "fig = fig_init()\n", "plot_2d(fig, data, 'manegrot', 'DK length', 'PBKDF2 iteration-blocks per second', 0, 2000000)\n", "fig.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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alepoydes/introduction-to-numerical-simulation	practice/What does mean mean mean.ipynb	1	365364	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Вычисление сумм" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В статистике часто приходится считать выборочное среднее, т.е. по данной выборке значений $x_k$, $k=1..N$, нужно вычислить\n", "$$\\bar x=\\frac1N\\sum_{k=1}^N x_k.$$\n", "С точки зрения математики не имеет значения, как считать указанную сумму, так как результат сложения всегда будет один и тот же.\n", "Однако при вычислениях с плавающей запятой ответ будет зависеть от порядка выполнения операций, хотя бы потому, что сложения чисел с плавающей запятой не ассоциативно.\n", "Но будет ли зависеть точность вычислений от порядка операций?\n", "Давайте это проверим.\n", "\n", "Сконструируем выборку таким образом, что сумма всех элементов равна $1$, и порядок элементов меняется в широком диапазоне.\n", "Для этого разобьем единицу на $K$ частей, и $k$-ую часть разобьем на $7^k$ равных значений.\n", "Полученные элементы перемешаем." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "base=10 # параметр, может принимать любые целые значения > 1\n", "\n", "def exact_sum(K):\n", " \"\"\"Точное значение суммы всех элементов.\"\"\"\n", " return 1.\n", "\n", "def samples(K):\n", " \"\"\"\"Элементы выборки\".\"\"\"\n", " # создаем K частей из base^k одинаковых значений\n", " parts=[np.full((basek,), float(base)(-k)/K) for k in range(0, K)] \n", " # создаем выборку объединяя части\n", " samples=np.concatenate(parts) \n", " # перемешиваем элементы выборки и возвращаем\n", " return np.random.permutation(samples)\n", "\n", "def direct_sum(x):\n", " \"\"\"Последовательная сумма всех элементов вектора x\"\"\"\n", " s=0.\n", " for e in x: \n", " s+=e\n", " return s" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def number_of_samples(K):\n", " \"\"\"Число элементов в выборке\"\"\"\n", " return np.sum([basek for k in range(0, K)])\n", "\n", "def exact_mean(K):\n", " \"\"\"Значение среднего арифметического по выборке с близкой к машинной точностью.\"\"\"\n", " return 1./number_of_samples(K)\n", "\n", "def exact_variance(K):\n", " \"\"\"Значение оценки дисперсии с близкой к машинной точностью.\"\"\"\n", " # разные значения элементов выборки\n", " values=np.asarray([float(base)(-k)/K for k in range(0, K)], dtype=np.double)\n", " # сколько раз значение встречается в выборке\n", " count=np.asarray([base*k for k in range(0, K)])\n", " return np.sum(count(values-exact_mean(K))*2)/number_of_samples(K)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим выборку из значений, отличающихся на 6 порядков, и просуммируем элементы выборки." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Число элементов: 1111111\n", "Самое маленькое и большое значения: 1.4285714285714285e-07 0.14285714285714285\n", "Погрешность прямого суммирования: 1.7459145240345464e-11\n" ] } ], "source": [ "K=7 # число слагаемых\n", "x=samples(K) # сохраняем выборку в массив\n", "print(\"Число элементов:\", len(x))\n", "print(\"Самое маленькое и большое значения:\", np.min(x), np.max(x))\n", "\n", "exact_sum_for_x=exact_sum(K) # значение суммы с близкой к машинной погрешностью\n", "direct_sum_for_x=direct_sum(x) # сумма всех элементов по порядку\n", "\n", "def relative_error(x0, x):\n", " \"\"\"Погрешность x при точном значении x0\"\"\"\n", " return np.abs(x0-x)/np.abs(x)\n", "\n", "print(\"Погрешность прямого суммирования:\", relative_error(exact_sum_for_x, direct_sum_for_x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Попробуем теперь просуммировать элементы в порядке возрастания." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Погрешность суммирования по возрастанию: 1.0016432128178195e-12\n" ] } ], "source": [ "sorted_x=x[np.argsort(x)]\n", "sorted_sum_for_x=direct_sum(sorted_x)\n", "print(\"Погрешность суммирования по возрастанию:\", relative_error(exact_sum_for_x, sorted_sum_for_x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Попробуем просуммировать в порядке убывания." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Погрешность суммирования по убыванию: 3.864975006557192e-11\n" ] } ], "source": [ "sorted_x=x[np.argsort(x)[::-1]]\n", "sorted_sum_for_x=direct_sum(sorted_x)\n", "print(\"Погрешность суммирования по убыванию:\", relative_error(exact_sum_for_x, sorted_sum_for_x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Таким образом погрешность результата зависит от порядка суммирования. \n", "Как можно объяснить этот эффект?\n", "\n", "На практике суммирование предпочтительно проводить не наивным способом, а компенсационным суммированием (см. [алгоритм Кэхэна](https://ru.wikipedia.org/wiki/%D0%90%D0%BB%D0%B3%D0%BE%D1%80%D0%B8%D1%82%D0%BC_%D0%9A%D1%8D%D1%85%D1%8D%D0%BD%D0%B0)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Погрешность суммирования по Кэхэну: 0.0\n" ] } ], "source": [ "def Kahan_sum(x):\n", " s=0.0 # частичная сумма\n", " c=0.0 # сумма погрешностей\n", " for i in x:\n", " y=i-c # первоначально y равно следующему элементу последовательности\n", " t=s+y # сумма s может быть велика, поэтому младшие биты y будут потеряны\n", " c=(t-s)-y # (t-s) отбрасывает старшие биты, вычитание y восстанавливает младшие биты\n", " s=t # новое значение старших битов суммы\n", " return s\n", "\n", "Kahan_sum_for_x=Kahan_sum(x) # сумма всех элементов по порядку\n", "print(\"Погрешность суммирования по Кэхэну:\", relative_error(exact_sum_for_x, Kahan_sum_for_x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Задания\n", "\n", "1. Объясните различие в погрешностях при различных порядках суммирования.\n", "3. Почему алгорит Кэхэна имеет значительно лучшую точность, чем последовательное суммирование?\n", "3. Получим ли мы те же значения погрешностей, если будем суммировать последовательность со слагаемыми разных знаков? Проверьте на следующей последовательности: \n", "$$x_k=\\sin k.$$\n", "4. Что произойдет с погрешностью, если элементы выборки с разными знаками упорядочить по возрастанию? По возрастанию абсолютной величины? Проверьте экспериментально." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Подсказка" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Сумма первых $N$ элементов последовательности из задания 4 может быть найдена явна:\n", "$$\\sum_{k=1}^N\\sin k=\\frac{1}{2}\\bigg(\\sin n-\\mathrm{ctg}\\frac{1}{2}\\cos n+\\mathrm{ctg}\\frac{1}{2}\\bigg).$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Вычисление дисперсии" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Кроме вычисления оценки математического ожидания, часто требуется вычислить оценку среднеквадратического отклонения или его квадрата - дисперсии.\n", "Дисперсия $D[X]$ случайной величины $X$ определена через математическое ожидание $E[X]$ следующим образом:\n", "$$D[X]=E[(X-E[X])^2].$$\n", "Для оценки дисперсии мы можем воспользоваться формулой для оценки математического ожидания через выборочное среднее:\n", "$$E[X]\\approx\\frac1N\\sum_{n=1}^N x_n,$$\n", "т.е. можно предложить следующую формулу для оценки дисперсии (первая формула):\n", "$$D[X]\\approx\\frac1N\\sum_{n=1}^N\\left(x_n-\\frac1N\\sum_{n=1}^Nx_n\\right)^2.$$\n", "Полученная оценка является [смещенной](https://ru.wikipedia.org/wiki/%D0%9D%D0%B5%D1%81%D0%BC%D0%B5%D1%89%D1%91%D0%BD%D0%BD%D0%B0%D1%8F_%D0%BE%D1%86%D0%B5%D0%BD%D0%BA%D0%B0), т.е. ее мат. ожидание не совпадает с верным значением дисперсии, поэтому на практике нужно использовать следующую несмещенную оценку:\n", "$$D[X]\\approx\\frac1{N-1}\\sum_{n=1}^N\\left(x_n-\\frac1N\\sum_{n=1}^Nx_n\\right)^2,$$\n", "однако в этой работе мы удовлетворимся смещенной оценкой.\n", "К сожалению, наша формула не позволяет обновлять значения дисперсии при добавлении значения в выборку, так как требует двух проходов по выборке: сначала считается среднее, затем считается дисперсия.\n", "Однако в учебниках теории вероятности можно встретить и другую эквивалентную формулу для дисперсии, получим ее, опираясь на свойства мат. ожидания:\n", "$$D[X]=E[(X-E[X])^2]=E[X^2-2E[X]X+E[X]^2]=E[X^2]-2E[X]E[X]+E[E[X]^2]=E[X^2]-E[X]^2.$$\n", "Снова заменяя мат. ожидание на выборочное среднее, получаем новую оценку для дисперсии (вторая формула):\n", "$$D[X]\\approx \\frac1N\\sum_{n=1}^N x_n^2-\\left(\\frac1N\\sum_{n=1}^Nx_n\\right)^2.$$\n", "Вторая формулы для вычисления дисперсии более привлекательна, так как обе суммы могут вычисляться одновременно, а значения мат. ожидания и дисперсии вычислить, последовательно добавляя значения.\n", "Действительно, введем обозначения для оценок мат. ожидания и дисперсии по первым $n$ членам выборки:\n", "$$E_n=\\frac1n\\sum_{k=1}^n x_n,\\quad D_n=\\frac1n\\sum_{k=1}^n x_n^2-E_n^2.$$\n", "Отсюда легко вывести рекуррентные формулы:\n", "$$E_{n}=\\frac{x_{n}+(n-1)E_{n-1}}{n},\\quad D_{n}=\\frac{x_{n}^2+(n-1)D_{n-1}}{n}-E_{n}^2.$$\n", "Хотя эти формулы и просты, погрешность вычислений по второй формуле может быть значительно выше, чем по первой. Проверим это." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Рассмотрим выборку, среднее для которой на порядки больше среднеквадратического отклонения. Пусть ровно половина значений больше среднего на $delta$, а половина меньше на $delta$.\n", "Оценка дисперсии и мат. ожидания в этом случае легко вычисляются явно." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# параметры выборки\n", "mean=1e6 # среднее\n", "delta=1e-5 # величина отклонения от среднего\n", "\n", "def samples(N_over_two):\n", " \"\"\"Генерирует выборку из 2N_over_two значений с данным средним и среднеквадратическим \n", " отклонением.\"\"\"\n", " x=np.full((2N_over_two,), mean, dtype=np.double)\n", " x[:N_over_two]+=delta\n", " x[N_over_two:]-=delta\n", " return np.random.permutation(x)\n", "\n", "def exact_mean():\n", " \"\"\"Значение среднего арифметического по выборке с близкой к машинной точностью.\"\"\"\n", " return mean\n", "\n", "def exact_variance():\n", " \"\"\"Значение оценки дисперсии с близкой к машинной точностью.\"\"\"\n", " return delta2\n", "\n", "x=samples(1000000)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размер выборки: 2000000\n", "Среднее значение: 1000000.0\n", "Оценка дисперсии: 1.0000000000000002e-10\n", "Ошибка среднего для встроенной функции: 2.3283064365386957e-16\n", "Ошибка дисперсии для встроенной функции: 8.053584167008077e-06\n" ] } ], "source": [ "print(\"Размер выборки:\", len(x))\n", "print(\"Среднее значение:\", exact_mean())\n", "print(\"Оценка дисперсии:\", exact_variance())\n", "print(\"Ошибка среднего для встроенной функции:\",relative_error(exact_mean(),np.mean(x)))\n", "print(\"Ошибка дисперсии для встроенной функции:\",relative_error(exact_variance(),np.var(x)))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ошибка среднего для последовательного суммирования: 1.164153218269348e-16\n" ] } ], "source": [ "def direct_mean(x):\n", " \"\"\"Среднее через последовательное суммирование.\"\"\"\n", " return direct_sum(x)/len(x)\n", "\n", "print(\"Ошибка среднего для последовательного суммирования:\",relative_error(exact_mean(),direct_mean(x)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ошибка второй оценки дисперсии для последовательного суммирования: 1.000000029257143\n", "Ошибка второй оценки дисперсии для однопроходного суммирования: 1.0000000199804877\n" ] } ], "source": [ "def direct_second_var(x):\n", " \"\"\"Вторая оценка дисперсии через последовательное суммирование.\"\"\"\n", " return direct_mean(x2)-direct_mean(x)2\n", "\n", "def online_second_var(x):\n", " \"\"\"Вторая оценка дисперсии через один проход по выборке\"\"\"\n", " m=x[0] # накопленное среднее \n", " m2=x[0]2 # накопленное среднее квадратов\n", " for n in range(1,len(x)):\n", " m=(m(n-1)+x[n])/n\n", " m2=(m2(n-1)+x[n]2)/n\n", " return m2-m2\n", "\n", "print(\"Ошибка второй оценки дисперсии для последовательного суммирования:\",relative_error(exact_variance(),direct_second_var(x)))\n", "print(\"Ошибка второй оценки дисперсии для однопроходного суммирования:\",relative_error(exact_variance(),online_second_var(x)))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ошибка первой оценки дисперсии для последовательного суммирования: 8.053990749371736e-06\n" ] } ], "source": [ "def direct_first_var(x):\n", " \"\"\"Первая оценка дисперсии через последовательное суммирование.\"\"\"\n", " return direct_mean((x-direct_mean(x))2)\n", "\n", "print(\"Ошибка первой оценки дисперсии для последовательного суммирования:\",relative_error(exact_variance(),direct_first_var(x)))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как мы видим, суммирование по первой формуле дает наиболее точный результат, суммирование по второй формуле менее точно, а однопроходная формула наименее точна." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Задания\n", "\n", "5. Обьясните, почему формулы оценки дисперсии имеют разные погрешности, хотя чтобы их применить, нужно выполнить одни и те же действия, но в разном порядке? Оцените погрешности обоих формул.\n", "6. Предложите однопроходную формулу для оценки мат. ожидания и дисперсии, основанную на первой формуле для дисперсии. Воспользуйтесь компенсационным суммированием, чтобы увеличить точность. Попробуйте увеличить точность вычисления по сравнению со второй формулой хотя бы на два порядка." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Суммирование ряда для экспоненты\n", "\n", "Показательная функция имеет одно из самых простых разложений в ряд Тейлора:\n", "\n", "$$e^x = \\sum_{k=0}^\\infty \\frac{x^k}{k!}.$$\n", "\n", "Естественным желанием при решении задачи вычисления показательной функции является воспользоваться этим рядом.\n", "В данном разделе мы рассмотрим результативность этого подхода.\n", "\n", "Так как на практике мы не можем суммировать бесконечное число слагаемых, то будем приближать ряд его частичной суммой:\n", "\n", "$$e^x \\approx \\sum_{k=0}^N \\frac{x^k}{k!}.$$\n", "\n", "Так как частичная сумма является многочленом, то для практического счета удобно воспользоваться (схемой Горнера)[ru.wikipedia.org/wiki/Схема_Горнера]:\n", "\n", "$$e^x \\approx 1+x\\bigg(1+\\frac{x}{2}\\bigg(1+\\frac{x}{3}\\bigg(1+\\frac{x}{4}\\bigg(\\ldots+\\frac{x}{N}\\bigg(1\\bigg)\\ldots\\bigg)\\bigg)\\bigg)\\bigg).$$\n", "\n", "Проведем эксперимент по оценки точности этого разложения.\n", "Сравнивать будем с библиотечной функцией numpy.exp, которая не дает совершенно точный ответ.\n", "Оценим погрешность библитечной функции, предполагая, что она вычисляется с максимальной возможной точностью.\n", "Число обусловленности показательной функции для относительной погрешности равно $\\kappa_{exp}(x)=\|x\|$,\n", "тогда учитывая погрешности округления до числа с плавающей запятой, мы ожидаем предельную погрешность результата не менее $\|x\|\\epsilon/2+\\epsilon$." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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ePXtwOBy0a9euwf4OhwPtb/i/acWKFSQmJhIe7rpDuHTp0otu684772Tx4sU89dRTF93Gla7F9IBeCCnlOinlQ1LKe6WUrzeyzxtSyj5Syj4eV/HbdRdCaDTEdO7DgKlP4x8YAkCrnjeyNXI6OqeFpCNL6Pj5WEqe64DdZgXAUnt1PlurKIrSEjmdDpa/+V/69OpJt8Qu3H3H7dhzd1Cw+yd69OpDTWEWTqeDwbfdzecp+9lfoaPzyCnMevzv9Bl+M3fN+T1oDeh0DTtuFi5cSF5eHsOHD2f4cFcnxX333UefPn1ISEjgmWeeAWDt2rXceuut9cd9//33jBs3rkF7r7zyComJiSQmJvLqq68CkJ2dTXx8PNOnTycxMZGjR4+ecsx7773HLbfcUr/s4+PDo48+Svfu3UlJSWHbtm0MHTqU3r17M2bMGPLz8xuc929/+xt9+/YlMTGR2bNnI6Vk1apVpKamMm3aNHr06EFtbS3Dhg0jNTWVJUuW1M9VD66g+uCDDwLw7rvv0q9fP3r06MG9996Lw+EAYOzYsaxcufL8/tCuVid+wmgJX0AMkH7SchLw3UnLfwL+dKHtenl5SeW3Kyk4Kreu/o/8ZcWf69dlzB8gDz/bTab85z6566fPZK3Z5MYKFUVR3Gvv3r2nLN++5JcGX2//kiWllNJcZz/j9g+35kgppSw11TXYdjK73SbNpkpZXZonTQWHZO2xdJn+4yp507WDpTV7s7QfS5P3zJgs/2/Ry9JUXiQXL35dTpgwQS5YsEDOnj1bSillVlaWBOSGDRuklFLOnDlT/vOf/2z0+qKjo2VxcXH9cmlp6fFa7HLo0KFy586d0ul0yvj4eFlUVCSllHLKlCny888/P+X41NRUmZiYKE0mk6yurpZdunSR27dvl1lZWVIIIVNSUs54/iFDhshdu3bVLwMyOTlZSiml1WqVSUlJ9ef94IMP5MyZM6WUUs6YMUN+9NFHp9QspZR33HFHfW1Dhw6VW7durd92YrmoqEi2b9++fv11110n169fL/fu3StvuukmabVapZRS3nffffKtt96q369Dhw6ypKSk0c/yhNO/Z45fV41sAbnsUn619K7BrUCcECIWOAZMBqa6t6SrV3DrSILHzjllXUX0GHyO/kiv/GT0Be9Ru0ZPStvpJP3uZTdVqSiKcuWRUiKdDkyleQibGQ9nHXppxfP4y0F2tFiFgW837SE1/QB9xt6NEBpqa2sJj4nHOyCU++67n88+W82SJUtIS0urbzsqKoqBAwcCcMcdd7Bw4UIee+yx86rrww8/5I033sBut5Ofn8/evXvp1q0bd955J++++y4zZ84kJSWFt99++5TjNmzYwLhx4/D2dr3oPX78eNavX8/YsWOJjo5mwIABZzxffn4+oaG/jmmt1Wq57bbbANi/fz/p6emMGjUKcN2Sb9OmTYM2fvzxRxYsWIDZbKasrIyEhARuvvnmRq8xNDSUdu3asWnTJuLi4ti3bx8DBw7k9ddfZ9u2bfTt2xeA2tpaWrVqVX9cq1atyMvLIzg4+Hw+yqtOiwmgQoiVwDAgRAiRCzwjpXxTCPEg8B2uYZiWSSn3uLFM5TQD7pgHzMNsqiRjyzdYMr5HF+p6g760MBfrf4ZxNLA/Hh2vpf2Am+tv7SuKolwNku9NanSbp157xu12mxVzdTn6uhqW3RKCzmlBj921sa4QGx7YNAZqPPzQ6L3QGX3w0OnxEgK9dyB33TWTf/zjHw3aNZvN5ObmAmAymfD19QVAnDYu5+nLjcnKyuKll15i69atBAYGctddd2GxWACYOXMmN998M0ajkYkTJ3Ihj8KdCKVn4unpWX8OAKPRWP/cp5SShIQEUlJSGj3eYrFw//33k5qaSlRUFPPmzTulvcZMnjyZDz/8kE6dOjFu3DiEEEgpmTFjxhk/6xPn8vT0PGfbV6sW8wyolHKKlLKNlFInpYyUUr55fP3XUsqOUsr2Usrn3V2ncmZePv50HzGZ/g+8SZ/jvaS11RUUeMfTuWwNvTY/jM+rHdg3fwAH09a7uVpFUZSWwW63Ya4ux1SSi7ngoOtN9OI9eFVn42MtRuesw6YxYtK3wuwbgz00AV14V7zCOuITEoWXXzA6vaE+NI4cOZJVq1ZRVFQEQFlZGUeOHAHgiSeeYNq0afztb3/jnnvuqa8hJyenPrS9//77DBo0qNF6fX19qa6uBqCqqgpvb2/8/f0pLCzkm2++qd8vPDyc8PBw5s+fz8yZMxu0M3jwYD777DPMZjM1NTV8+umnDB48+JyfV+fOncnMzDzjtvj4eIqLi+uvxWazsWfPqX1WJ8JmSEgIJpPplDfjT762040bN47Vq1ezcuVKJk+eDJz9s5ZSUlBQQExMzDmv6WrVYnpAlStPZIdEIh//Cpu1jowd66jY/S3BBRvw9nfdPtn29XLkvq+g42g6DBhLQEiYmytWFEW5dBwOO9baGhx1JoStFp2zFv1JMwdZ0bneRNd5oTV4ozd6o/fw4ELGcenSpQvz589n9OjROJ1OdDodr7/+OtnZ2WzdupWNGzei1Wr5+OOPWb58OcOHDyc+Pp7XX3+dWbNm0aVLF+67775G2589ezbXXXcd4eHh/Pjjj/Ts2ZNOnTqdchv/hGnTplFcXHzGgfl79erFXXfdRb9+/QDXkEc9e/YkOzv7rNd34403sm7dOq699toG2/R6PatWreKhhx6isrISu93Oww8/TEJCQv0+AQEB3HPPPSQmJhIWFlZ/+xxcQzXNmTMHT0/PBr2ogYGBdO7cmb1799bX3NhnHR0dzbZt2xgwYMAF9fxebVrMTEiX0tU8E1JLtvmDf9Bx32ICqcIhBQd1nSiPGEr/6X9XwzwpinJZOjGrjdPhoM5Sg8NSg7Cb8XDUopc2TtzdtuKBTWNEerjCps7TGw+P0wc9uvSys7O56aabSE9Pb/K2H3zwQXr27Mnvfve7JmuztraW4cOH1wfplur3v/89Y8eOZeTIkefcV82EpCjNrP/kP+GwP86BXRso3fElwfk/0frot2i0LwKugOrhF0bcNWPxC1APcSuK0jJZ6ywcydhK2cHNGNr2x5K3B8NJLwjZ0GLTGKnxCECj90Lv6YNep7+gns3LTe/evfH29ubll5v2hVRPT0+effZZjh07Rtu2bZu07aaUmJh4XuHzaqZ6QJuAtc6C+R/tqRHe1Gp8sHj4YvXwxa7zxWnwRxr9EUZ/tF4B6LwD0fsE4ukbiKdfMD7+wXj7+CM0LeZxXLey1NZg9PTG6XBQ9Fw8YRRjlxr2GxKojhxORNIEouK6u7tMRVGuUtLppDD3EMfSf8Z2ZAsBZbuItR7EIGwA7BmzitiYtjg9PNHovdF5+qDTX36TVI4bN46srKxT1r344ouMGTPGTRVduVQPqHLRHHYb+0NGo7VWobNVo7dXE1R7BK8aEz6yBi9Rd9bj7VKDSXhdVID1CwzF08v3igmwRk/X3zeNVkvIU3vJ2P4jFTu/olXhzyQcXkhKXRVRca9hqa0hY/2ndEy6CW/fADdXrSjKlaqmuoLsXRupyvwFY+EOosx7CKOCMMAidWTp49gRNgFddF/adB6IptaJV5t4d5f9m3366afuLkFpYkIIb+AnYJ6U8ku316N6QC89m7UOU2UZNZWl1FaXYakux1ZTht1cgbO2Allbiaau8pQA6+kw4eU8vwBrlR5UCR9MGl/MWn/qdH7Y9AE4Df7gGYTwDsLDOxiDbxBGvxC8A0LwC2qNl7ffZRVci45loREaQsKj2f3TJ3T9cSZW6cF+Y1dq2o4gKuk2ItolnLshRVGUM3A6HBw9kEZRxgacuamEVuwi2nEErXD9P3lUhFPom4Ajog9BHa8huks/9AbjKW2cqTdLUc7mYntAhRDLgJuAIill4knrrwNewzV85VIp5QvH1/8NMAF7VQBtJu4OoL9VowG2phynuRxZW4G2rhyPukoMtgo87VV4O6vxlaazhler1FIlfKnR+GLW+lKn88emD8BhCEB6BqDxCsLDOwi9byhega3xCw7DPziswT+47mCts3Ag9XtMu7+hTdHPRDtd07VljvuKDt0HUV1Zhpe3H1r1BqKiKI0wVZWTlbYO08GN+BSlEmPZh6+oBaAKb7KNnagJ7YlXbH/adh1MYGjDQc1PpwKocqF+QwAdgitQvn0igAohtMABYBSQi2tCnylABBAMGIGSlhBA1f/OlwGd3kBgaJvz+sfvdJbaGqrLizFVFFNbWUJddSm26hKc5jJkbTkaSwU6awUGayW+dQV4mw/iJ6vPGlyrpSeVGn9qtAHU6gKwGoJweAYhvEPQeIdg8A/FMyAM74DW+IeEXZJnXPUGI4kDb4aBrtkrjh3OIHfravomugZ13vvOY3Qs+Z5M/yS0na6n48Bb8fELbNIaFEW5vBQdy+Jo2hrs2SkEl+0g1n6YrkLikIJsjxj2hoxBRPahdZfBRHXoSrcW/Ja1okgpfxZCxJy2uh+QKaU8DCCE+AC4BfABvIEuQK0Q4msppbMZy21ABdArnNHTG6OnN6HhMRd0XJ3FTHVZMdUVRdRWlmCpLMRWVYyzpgRhLsXDUobBWoavtQjf2gMElFehF/YztyV1VAg/TFp/zB4B1BmCsBsCkd4haH1bo/cPwysoHL+QcAJbRWAwel3wdUa060xEu19/gjQmXM+hHVV0rNyA/+bvsG56lFT/4fR55OMLbltRlMuP0+HgyL5Uivb8jDZ3E+HVuwiXRbQCzNLAYWNntraZhXfcIGJ6DKO9fxDt3V20ovzKQwiRetLyG1LKN87juAjg6EnLuUB/KeWDAEKIu3D1gLo1fIIKoEojDEYvDOHRhIRHn9f+0unEZKqksqQAU1k+lqoi6iqLcZqKkeZStLVl6OvK8LRVEFidh39lZf2trtNV4k2FJhCTRxAWQzA2z1CkdyhavzAM/mF4B4fjFxJBYGh4o2+Xdh8+EYZPxG6zsjd1DVU7vwAPY32tu/45BnNQAsG9b6VD98Fq3FFFuczZrHUc3rWRsr0/4pW/idjaPcRSQyxQQgA5Pt3ICZ9OcOehxCT0J/EyfDO9JYiJiSE1NZWQkJYzrfKrr75KUFAQ06dPP+9jVqxYQWpqKv/+978b3WfdunXo9XquueYaAJYsWYKXl9cFneeExx57jBtuuIERI0ac7yF2KWWfCz7ROUgpVzR1mxdLBVClSQiNBh+/QNdt7nbn9/xTncVMeXEeVcXHMJflYa0swFFVgKamGJ2lGGNdGa1M+wmo2tRoWC3Hl0pNICZdEBZDCHav1uAXjj4wAq/gSPxbt6VDr+Hok66vP6aqsgwPRy19c9/C49hySj4P4HDgQIKG3U+H7o1PQacoSstxcuD0zkuhfe1u4oVrmsUjmigygkaiaTuA8G4jCI+JJ+QyeuHySmW323/zzEAOh+OUAejtdjvLli1j+/btTX6+devW4ePjUx9A58yZc9FtzZ07l3vuuedCAujFOgZEnbQceXxdi6MCqOI2BqMXYVEdCIvqcM59LWYTZUXHqCrJpbYsH2tlAc7qQjQ1RegtJXhaywiv3k1I5Tr0BQ0fBSjDj3JtCCZ9KHWerXCEJfGL8UbMlaUEVuyhS9laDuaNhu6DyMvax7Fda+k4eCL+QaGX4tIVRblAZwuc2Zq2pIfegK79EKJ7jSK6dSTnd++mGSy/seG6hFuh3z1gNcN7Extu7zEVek6DmlL48LTetplfnfOU7777LgsXLsRqtdK/f38WL15Mbm4u1157LSkpKQQFBTF06FCefvppOnbsyHXXXUfv3r3Zvn07CQkJvP3223h5Nf4o1KJFi/jiiy+w2Wx89NFHdOrUibKyMmbNmsXhw4fx8vLijTfeoFu3bsybN49Dhw5x+PBh2rZty5gxY/j8888xm80cOnSIcePGsWDBAgD+97//8cwzz1BXV0f79u1Zvnw5Pj4+xMTEMGnSJL7//nv++Mc/1s/FDrB27Vp69epVHzSHDRtGjx492LBhA1OmTGH69OnMmTOHnJwcwNVbevqUoV988QXz58/HarUSHBzMe++9R21tLUuWLEGr1fLuu++yaNEi1qxZg4+PDzfddBPTp09ny5YtgGs2qZtvvpndu3ezbeeewHEAACAASURBVNs2HnnkEUwmEyEhIaxYsYI2bdoQHR1NaWkpBQUFhIVd0mmntwJxQohYXMFzMjD1Up7wYqkAqlwWjF4+hMfEEx5z9vH1pNNJeWkh5YU5VBfnUFeWi6MyH40pH2NtIT7WYiJr9xFcVnnKcVY0xKc8Tt6W59njjGGU2IJt+1/YoYmnKKgPbXrfQIdu1+Dl438pL1NRlOOk00nW3q0UpX2DV+6G0wJn1CmBM6Z1JDHuLbfFyMjIIDk5mY0bN6LT6bj//vt57733mD59Ok888QT33Xcf/fr1o0uXLowePZrs7Gz279/Pm2++ycCBA5k1axaLFy/msccea/QcISEhbN++ncWLF/PSSy+xdOlSnnnmGXr27Mlnn33G2rVrmT59OmlpaQDs3buXDRs24OnpyYoVK0hLS2PHjh0YDAbi4+OZO3cunp6ezJ8/nx9++AFvb29efPFFXnnlFf76178CEBwcfMZezo0bN9K7d+9T1lmtVlJTXY9PTp06lT/84Q8MGjSInJwcxowZQ0ZGxin7Dxo0iE2bNiGEYOnSpSxYsICXX36ZOXPm4OPjU/9ZrFmzBoBOnTphtVrJysoiNjaW5ORkJk2ahM1mY+7cuaxevZrQ0FCSk5N56qmnWLZsGQC9evVi48aN3HbbbRfzR9uAEGIlMAwIEULkAs9IKd8UQjwIfIdrGKZlUso9TXLCJqYCqHJFERrNSSMG9G90P2udhdKCI1QWHcVcchRr+TGoysOjpoBwcz5bbJ2wS0GULKZn6dvUffc+zu8EFcJAnginzjMEq3c40j8SXVBbfFrFEhzenqDWkep5UkW5SCUFOWRt/hIO/0hs5RbaUUE74IgmkvTQG/BoN4ToXtcSExZ1+QTOs/VY6r3Ovt07+Lx6PE+2Zs0atm3bRt++fQHX3OmtWrUC4O677+ajjz5iyZIl9eEQICoqqr5X8I477mDhwoVnDaDjx48HXNNtfvLJJwBs2LCBjz92veQ5YsQISktLqaqqAmDs2LF4enrWHz9y5Ej8/V0/zHfp0oUjR45QUVHB3r176+uwWq0kJSXVHzNp0qQz1pKfn99gCKOT9/3hhx/Yu3dv/XJVVRUmk+mU/XNzc5k0aRL5+flYrVZiY2MbvfYTbr/9dpKTk3nyySdJTk4mOTmZ/fv3k56ezqhRowDX4wJt2vw6ek2rVq3Iy8s7Z9vnS0o5pZH1XwNfN9mJLhEVQJWrkt5gpE10PG2iG+9RtdusFOdlsz7tZ0w5uwjw80VTlYtv8Q4CTIfJq66kdeFG2ot8xPE5ny1SR6E2jHJDBBafKAiMwdi6A4HhcbSOjsfo5dNMV6goLV9tTTUHt/4Pc8b3tC5OIdaZTQhQjh+HfftwuN1wovveSHRk+5ZzS72Fk1IyY8YM/vGPfzTYZjabyc3NBcBkMuHr6wuAOPEP2HGnL5/OYHC9wKXVarHbzzz6ycm8vU8dzvLE8Se3IaVk1KhRrFy58rzaOMHT0xOLxdLovk6nk02bNmE0Nj5+9dy5c3nkkUcYO3Ys69atY968eee6JCZNmsTEiRMZP348Qgji4uLYvXs3CQkJpKSknPEYi8VyShC/2qkAqiiN8NDpaRPdkTbRHevXSaeTzckvILO+ZkBdOlohySOEXcE3ENgqAlmWjcF0FP/aXOLMaXgXW1xDAh9XRBClujaYvCKx+8egC4nFp00cYbGJBIRc0ueCFKVFOHY4g6ObP8XryBria3fSTdiwSg8OGBNJiXiI0O7X0S5xAL3VnYSLMnLkSG655Rb+8Ic/0KpVK8rKyqiuriY6OponnniCadOmER0dzT333MOXX7rGIs/JySElJYWkpCTef/99Bg268JcxBw8ezHvvvcfTTz/NunXrCAkJwc/P77yPHzBgAA888ACZmZl06NCBmpoajh07RseOHc96XOfOncnMzGx0++jRo1m0aBGPP/44AGlpafTo0eOUfSorK4mIiADgrbfeql/v6+tb34t7uvbt26PVannuuefqe1zj4+MpLi6u/yxtNhsHDhwgIcE1Q9+BAweYOPEMz/xepVQAVZQLIDQaBkz5M/BnyoqOcWjDKnSZ3xAU3p5+tz1MeXE++z98mrohD2HsNYKy8iKKcvZhys/EVnIYj8ojeJtzia7cSqvK7yDn17bL8aVQF0WVdwyOoA4Yw+IJiupMm3YJLWL2KUW5GDZrHQdS11C968v6WcsigBxNBDvCbsOr82ji+o4m0dvX3aVeEbp06cL8+fMZPXo0TqcTnU7H66+/TnZ2Nlu3bmXjxo1otVo+/vhjli9fzvDhw4mPj+f1119n1qxZdOnShfvuu++Czztv3jxmzZpFt27d8PLyOiXInY/Q0FBWrFjBlClTqKtzTYQyf/78cwbQ66+/njvvvLPR7QsXLuSBBx6gW7du2O12hgwZwpIlSxrUPnHiRAIDAxkxYgRZWVkA3HzzzUyYMIHVq1ezaNGiBm1PmjSJxx9/vH5/vV7PqlWreOihh6isrMRut/Pwww+TkJCAzWYjMzOTPn2afGSly5aailNRmtDunz6h49rZGISNIoI4HDoS/z4Tie9zbYNnQy21NRTmHKQ8dx+W/H1oyjLxMWXTynqUECrq93NIQb6mNaXGttT6tUOExOHXtivhHXvjH9hyxuJTlBMqy0s4uH4V4uC3xFVvwY8arFLLfmM3aqKvJbLfrUR2SDx3Q5ehy20qzuzsbG666SbS09PdXcpFO/EmfVxcnLtLadSnn37K9u3bee655xpsu9ipOC93qgdUUZpQ16HjMfUczu6fP0KbsZqeRZ9h+OYj8lptJjy2ExUlBfgFhqLRajF6ehMd34Po+B4N2qmqKKXgcDpVuXuxFR3AUHGIAPMROhak4VlohT3AN1BIMIXGdpgDOqIN60JATHci43rgqXqTlGZWWpjLofUfYsz8is61O+gjHJQQwL6Aoeg6X0dc0li6qulwlUvghRdeID8/v0UHULvdzqOPPuruMloU1QOqKJeQqaqcQ6nf033E7QDsWHADEeYMskJH4Nt7Ip36jrqgt+adDgeFuZkUHdpJbe5uPEoyCKw5RKT9KAZhc+0jBcc0bSjxaoclqBPGqJ606dSf1pHtEWowbqUJFRzNJHtDMr5Z39Dp+DPRx0Rrjra+loA+t9Gx57CrblSIy60HtDHjxo2rv7V8wosvvsiYMWPcVNGV62rtAVUBVFGa0fZvVyB2f0Rn02aMJ27Td5jOgDue/U3t2m1W8rL2UnI4jbq8PRjK9hFiPkSEIw+tcP0dL8eXXEMcpqBE9FE9aN2xP+Gxna+6gKD8NqWFuWT++A7+mavpZHeNp5itaUt+xGha9ZtIu4R+V/UPOldKAFWajwqgVzAVQJWWxlRVzr71q9Du+QRL654kzfg7dRYz21c+S+SgqUTFdW+S89TWVHMkYwuVh1IRBTsJqtpHW3s2euEAoFp6kmPoQHVQN/Qx/YjqNozQ8JgmObdy5TBVlZPx40oMGR/TpXY7HsLJYU0MhdE3EpF0O207NnyM5GqVkZFBp06dzjmUkaKAa9isffv2qQB6uRBCeAOLASuwTkr53tn2VwFUuRzs3fQtnb6ZjEZIDnrEURo7lvbDpzd5IKyzmDm6fztlmVuReWkEVGYQazuEXrjG8ysghGM+idja9CYgfiCxiUkYjI1Py6dcmew2K+k/f4Ij7QO6VG3EU1jJJ5Ts8BsIG3QnsV36urvEFikrKwtfX1+Cg4NVCFXOSkpJaWkp1dXVDQa/VwG0GQkhlgE3AUVSysST1l8HvIZrSqmlUsoXhBB3AhVSyi+EEMlSyjNPkXCcCqDK5aLoWBaH171DcNbnxNkP4pSCI5O+v+T/2ddZzGSnb6L8wEZ0+duIqE4njGIArNKDLF0HykL74d1xKO16j8RHvUxyxTp2eA85a96g/bHPaUUZ5fhxIORa/PtNJb7PyKv69vr5sNls5ObmNhgcXVHOxGg0EhkZiU6nO2W9CqDNSAgxBDABb58IoEIILa5hvEcBucBWYApwC/CNlDJNCPG+lHLq2dpWAVS5HOUcSCNv8yf0nzYPodGwafFsDKZcZPcpJA6beMnHBi3OyyZ390/UZW0moHQH7az70QsHdqnhkC6OstD+eMcPo33vkXj7BlzSWpRLy2I2kf7Du3imv0+CdScOKdjt1R/Z804Sh01EpzecuxFFUZqMCqDNTAgRA3x5UgBNAuZJKcccX/7T8V1zgXIp5ZdCiA+klJPP1q4KoMqVIGXZH4nLSSaECsrx5UDoGEIG/4723a5plvObTZUc2r4O0/4fCSjaTAfrfnTHA2mmLp7y8CEE97iRDt0HqRebLhMFOQfJ+mYhnfM/IQCT6w326Am0G3UPrSLOPR+2oiiXhgqgzewMAXQCcJ2U8u7jy3cC/YEngH8DFmDDmZ4BFULMBmYD6PX63idmVlCUy5ndZmXPhtXYt71L1+oN7Ai+gf4PvYN0OiktOkZIWFSz1eIKpGsx7V9HcOEvdLAdRCMk5fhxyK8fdLiWdgPGEtQqotlqUs5NOp3sTfmGul/+Q3fTBgB2+gxGn3QPXZJuVD88KEoLoAJoMzvfACqlfPBC2lU9oMqVqLKsGKulhtDwGPalriHui9tI9+qLvetkEoZPxujZvP92lRUd4/DmL5GZP9C+chNBVOGUggO6TlTEjCHqmtuJaJfQrDUpv7JZ60j7+r+E7Povsc5sKvAhI3w8sdc9RFjbljuAt6JcjVQAbWbnewteSvmPC2lXBVDlSldwNJOsbxbSLu9LWlNKFd5kBI+i4+QXCAxt0+z1OB0ODu3+hZIdXxKa+z0dHIcAOKSNpShiFGH9JxDTua96oaUZWMwmdn7xb6IzlhJGMVmaGEoSZ9H1ut9h9PJxd3mKopyBCqDN7AwB1APXS0gjgWO4XkKaKqXccyHtqgCqXC0cdjt7f/mCutR3aVu1Hb8n0jF6enM4fTOt2nZ029vredn7ydmYjH/2t8Rb96IRkiOaSPLb3kL08Bm0iY53S11XsurKMtJX/4uOh98imEr26bpQl/R7ug27XQV/RWnhVABtRkKIlcAwIAQoBJ6RUr4phLgBeBXXMEzLpJTPX2jbKoAqVyObtQ6d3oB0Osl9LoFgZynpwaMJGHQPcT0Guy2ElBTkcOjnZPwOfkpnm+tnyT36rtTE30ankdPxCwh2S11XCkttDWmfvEz8wTcIpJpdxj54DH2Mzv3HqOCpKJcJFUCvECqAKlcz6XSyf/uPVG1YSmL5GrxEHYe07TANfILuI846gMQll5e1jyPrlhOZ8zlRMg+L1LE7YCR+g++lY69hKjBdALvNyo4v/kPUroWEUcJuQy8MY+bRsddQd5emKMoFUgH0CqECqKK4VFWUkvG/NwnZv5Kqfg/Tc8wMSgtzqSjKpX3XAW6rSzqdHExbT/nGZSSWfIu3sJCpbU9ZlztJHDMLLx9/t9V2Odi17mP8f/4r0c5cDnh0xDrsaRIHjXV3WYqiXCQVQK8QKoAqyqmk0wmA0GhIWfY4STlvkKFLwNxjFt1G3enWgcdNVeXs+fa/tNr3LrHOI1ThxZ7wCcSNfZyQsLZuq6slysvaR+FHj9DTvJGjIpziAX+m56hpqudYUS5zKoBeIVQAVZTGVZYVk/HNYqIy3yNCFlJMIJkxkxkw/e9uDTLS6WTf1u+pXf9velSvx4YHaSE3EnnjH6/64ZwstTXseP8ZeuaswImGne3vpdftf8Jg9HJ3aYqiNAEVQK8QKoAqyrk5HQ52//QRYstS7FojvR7/EoAj+7bTtmMPt4bRo5m7yf/6RXqUfoMWBzv8R9Lm1vlEtOvstprcZX/qWoxfP0S08yjbfEcQOeklWke2d3dZiqI0IRVArxAqgCrKhTnxBv3RgzuJem8I+z06UdPnPrpfewdaDw+31VWcl82hzxfQPf8jVxANvZX2E55t1hmg3MViNpH29h/pm/8+JSKIwqEL6DZ8grvLUhTlElAB9AqhAqiiXJya6grSv/oPkfuXEyELyRVhHOs0i+43P+DWQcyL87I5vOppepd+iRUdO6Nn0HPyM1fswOpZe7ciVs0kxnmUzUFj6TLjNXz9g9xdlqIol0hTB1AhRDvgKcBfStkifnJVAVRRlHNy2O3s/P4dfLYtpq0tC/MDOwlqFYHT4XDr3OFHD+6kePVf6GX6mVwRRumQ+XQfPtFt9TQ16XSy9ZNX6bb775iEN/nD/0XXoePdXZaiKJfY+QRQIcQy4Cag6MQEPsfXXwe8hmv89KVSyhdO2rZKBdBmpAKoojQN6XSSl72//tnLPX8fgsk3hqibnyI8xn2zGaVv+By/tU/S1nmM7d5DiJq2iNDwGLfV0xQsZhPpS2bQp+oHdht60Wbm21fFowaKopx3AB0CmIC3T5pBUotrBslRQC6uGSSnSCn3Ht/eYgKoGqtDUZTzJjSa+vBZZzFj8ommZ8lXhC5PYsurU8jNTHdLXYmDxtL6j6lsinmALqYU9G9cw7avl7ullqZQnJdNzivD6VW5hpToOST88QcVPhVFOYWU8meg7LTV/YBMKeVhKaUV+AC4pdmLOw8qgCqKclEMRi/6P/QO5fdsYVur8XQr/5427wxi14+r3FbPgLv+TvEdayj0iKD3lodJfeU2KstL3FLPxTqYth75xnAibUfYOfDfJM180a2POSiK4hYeQojUk75mn+dxEcDRk5ZzgQghRLAQYgnQUwjxpyav9iKoW/CKojSJkoIcDn75L3pM+Rue3r6kb/gcr4DWtEvs3+y12Kx1pL77F/oeWUqBpjW2CW8Rm9D8dVyo9PWraffDPVQJP8wT3nPLZ6coivud70tIQogY4MuTbsFPAK6TUt59fPlOoL+U8sFLWO5FUT2giqI0iZCwtiTd/S88vX2RTieePz5Du1WjSX15fLPfmtfpDSTN+ieZNyRjkBZaf3gzqV++0aw1XKgd/3uXjj/MolAbhsfsNSp8KopyMY4BJz+vE3l8XYujAqiiKE1OaDSE3P8tKeEzSKhaT+t3hrB50QyK87KbtY5O/UcjZv/EEX0H+qQ+TsryJ+qnIW1Jtn+znK4b55Kla0/QA98TEh7t7pIURbk8bQXihBCxQgg9MBn43M01nZEKoIqiXBL+wa1Jmr2QmntT2R56C71KvuDozh+bvY6Q8GjaP7aWrf6jSTqyhC3/noHdZm32Ohqz68dVJG56lIP6TkQ89B3+wa3dXZKiKJcBIcRKIAWIF0LkCiF+J6W0Aw8C3wEZwIdSyj3urLMx6hlQRVGaRf6R/bSO7IBGq2XTyr+DdNB7wh/R6Q3Ncn7pdLJp6cMk5b3Fdp+hdPv9Kjx0+mY5d2MyNn9HzNd3cMwjilZzv8cvINit9SiK0jJcDTMhqR5QRVGaRZvoeDRaLdLpRH8shQEHXiL/hZ7sXPtBs9wWFxoNSbMXsinuEXqZfiJt0RQcdvslP29jjh7cScQ3MynWhhI4+3MVPhVFuaqoAKooSrMSGg09H1nNziH/h5CS7j/fy+4Fozh6cGeznH/AtGdIiX2QPlU/sH3RNJwOR7Oc92RVFaXIlVNwoMVj+qcEt45s9hoURVHcSQVQRVGandBo6D5iMq2f3MGmjo8RbcnAXFnabOdPmvE8KW1n07fyWzYvf6zZzgvgdDjI+r/JtHEUkD/mDbfOIKUoiuIu6hlQRVHczmyqxMvHH4CUNx/DGNWDnqPvuKTnlE4nWxfdQb/yr0jt9QJ9xt53Sc93wqb3nmXAwVfY3PnP9J/0RLOcU1GUy8vV8AyoCqCKorQYltoajr00kPaOLHZ4XUObKYsIi+pwyc5nrbNw8OVRxNXtJWf8ajp0H3TJzgWQuXMjbT+5mT0+SfR49AuERt2EUlq2E4+onHh+21Jbg5ROpJQ4na5f9QYjRk9vHHY7FaUFSCnB6UQikVLi5RuAj18g1joLJXnZSCmRTodru9NBQKso/AKCqa2ppiB7L1Li2i4lUjpp1bYT/kGhVFWUkpeZBie1DxAe1wv/oFDKi/PJz0yjPtcc/zUqYQB+AcGU5B2h4FAav8Ye17PnsT2G4eMXSEHOQYoOuR4Fksj64zv2vx5Pb19yM9MpzUmn+4jJl/xzvxoCqIe7C1AURTnB6OlN2yc2s+mD+XQ/tAS59Bo2dfo9/W5/8pJMR6k3GImY/REV/07C8Nk91LRLwds3oMnPA1BnMaNbPZsK4U/szDdV+LxC1dZUYzGbsNaZsVlqsdXVIDRaYjr3AVwjH1gqCnDabUiHDWm34uETXN/jv+XjV3FU5SMcru3CaUMExdB/0pMAbPrPvejMhWicNoTTjkY6qGnVk6RZ/wQg7cUxGG2VaHCgkQ4ETorChpJ0z2sAZP8tEb2sQysdaHCiwcmBNjeTdO/r2Kx12J+PRHt8vYdwogFS2kwn6d5FVJUX47+oY4NrTomeQ9LMFynOyyJsWZ8G2zd1fJwBU/9C3uF0YpJHNti+tdvf6Dv+9xzJ2EKnL8c32L6t3yv0vuF3HNn1M13X3tVg+y7zm3QbPoGs7T/QK6XhhD8ZHh/i138MWVu+pG/anxtsPxT4HT5dB3Ak5WP6Z/yjwfZjMZuIaNeZoxtX0vfwYmiGAHo1uCwDqBCiM/B7IARYI6X8j5tLUhSliej0BgZMf468rGmUJD9It32vUZg7gTbRl+ZZyYCQMPaMfp3O301l25tz6PvwB5fkPNtXPkuSM5ddw5bRLSTskpxDObfammqqK0qw1FRSZ67Gaq7GbjHRfcTtAOz+6RNqslKRVhMaWw3CZkYg678vUt54iNYFP6GTVnTSih4r1Ro/ov/qGmrx4KJxdLNsPeWcRzSRcHw7PzxLT9upwzIe9IiD4wE0eO9btHccBsAqPbCj5UB1L8AVQH3LM/CzFWMXOhzCA6fQYrb+eofPqdFh03oihRYpNDiFFuEdUr+9yK8rQtqRwgOEBqnRoovoDoBW68G2NhORQgsaDUJokRotPh1cdwYMXj6ktHsIAaDRAAKEhqB413bfwFA2d/4TiBPbBEJoaN1lMACBYTFs6T4fITTHt7mOD09wHd8mNpHt/V9FaDRINIjjx0d1SQIgsvMAdjqWIoTm+LEC0BDVuR8AMT1HkO79jutChXD9gobIjr0BiB0wlr1h7Y9vPrFd0Da2MwDth05lX7s+Jx0rEBoNbdu0BSBu1GyyCscQd6ZvLOWCNfsteCHEMuAmoOjE3KXH118HvAZogaVSyhfOoy0N8LaU8qwPi6lb8IpyeZJOJzkHdxEd3wPpdJK+YTWJg265JL2HKUv/QFLuMtIG/oceo6Y2advHDmcQ/NZg9vgOpPdjq5u07auR2VRJRXE+pvICaiuKsJnK6DJiCl4+/uz6cRWWXZ/gYa1Gb6/CaK/G02ki4JEtePsGsOk/cxhQuLJBm/anivHQ6dm8aAb9Sz/DJrWYhZFaPKnR+tL+6TQANr3zVwz5qTi1hl+/PINIuvtfAGz/7h2sZUfR6Ixo9J5odEb0vsEkDhoLQM6BNGx1Fjw8dGh1BrQ6HQZPH4JaRQCunnKt1gOt1kP1kl/FroZb8O4IoEMAE67gmHh8nRY4AIwCcnFNJTUFVxg9vT98lpSySAgxFrgPeEdK+f7ZzqkCqKJc/nau/YDuP99LmlcSbe96s/4/7KZis9aR+0JfPJ01+D66rUlvxe98cTQdzDupmb2JVhGxTdbuleLEOLBCo6HgaCZHt32LvaoQUVOM1lKGvq6M4AmvEtkhkc3JL9I/4+8N2jh6xwaiOnRl08rnabd/KWaND7VaH6wevth0vsTN/D/8A0PYl7qGquydaAzeeBh98fD0Qe/lR4fug9FotVjMJjRaD/QGY3N/DIpSTwXQS3VSIWKAL08KoEnAPCnlmOPLfwKQUjZ8GKNhW19JKW/8//buO0zOutz/+PveXpJslvTeSSGU9AQQAalKU1AIIEpADgoeGwoeLHjUH3hEPaB4NAKCjSICIqJIByGBVAIhpCekQHrZlO3374/vTGa2z252Z3ZnP6/req6dedrcz06751sb20cJqEjHV11VxRsP/pAJK+5kj3Vl+1n/x7jpZ7XqY7z7xjOMeeoi5va9jOnX/rJVzvn2q39j/DOXM3fkl5l++fda5ZwdTenB/eBOXkEXNq1ZynvP/Irs/e+TX7qVbhXb6FG9g9Wn/oqjP/wJFj79+0Pt+A56DnusiJKs7mRecBfDx09j9ZLX2Lr4H2R16Ul2UR/yinpRWNSTPoNHK2mUtKEEtK0etG4CehFwlrtfHbn/aWCau9dtTRy2nwx8AsgFlrj7XfXscw1wDUBOTs6ksrKy1r8QEUm61UteI/exWfSt3sL8MTcwfebNrXr+N/53Jsft+hfbr3ztsMfo9Opqlt96AsUVWyi68S3y8tP3+6S6qoqMzEx2bXufd5+4new96yk8uImeFe/Ti12HOpqsXPwKQx47nx12BHuye3EgtxflBX3pc/LnGDZuCvv27mLP9s0U9x54aGgukc6mMySgHbITkru/CLzYxD6zgdkQSkDbPioRSYYRxxxPyZDXePPuWeT1Gt7q5x9y0Q+o/s0zbHrsW/T/yp8P61xLXvoLx1a8w+tHfYdpaZJ8VlaU8+7r/2T/xqWwfQWFJWvoU7aeVYM/xYxZ/0N1dRVT37uHbdaTHTn9WNt9BquKBtN75BQARhx9PBy9jX6ZmfSr5/xduhXTpVtxci9KRJKuvSSgm4BBcfcHRtaJiNTRtegIJn3t8UP35z1+F73HzGDImImHfe4+A0cwp/9Mpm3+PeuWzT80fE5LZM25gw/oxcTz663Made8upotG1fz/ruvU7phMRnd+jHtk18DYOQzs8izCko8n83Zg1nXfTr5gycAcESv/lT81wf0zc2jvr7+bTGcloh0PO0lAZ0HjDKzYYTE8xKgdbuhikhaOrBvD0MW307+ooMsOfkXHHPKRYd9zrEX3kzZzx9k19mp6wAAIABJREFU69M/YejYuj2mE7HqzVc5qvwt5o78Cn1zcg87prYWrUIHeOPOyxm58yX6spe+QLUbi7qdAnyNrOwcVp/7MD36j6BH30GMrtVT2zIy1BZTRJqU9ATUzB4ATgZ6mtlG4Lvufo+ZXQ88Tej5fq+7L23kNCIiABR0KWLvrH+x9f5PctSLVzP3/XeZfum3Duuc3Xv25fVe5zBh21/Zvnk9PfsPafY5dj1/Bwc8l7Efa5+ln2WlB1i54DlK3nmW4i2vk19dcmgsy+qsfFYXHc/K/hMpGj6JwWOnMCmuPeboyaemKmwRSROailNE0sL+kt2s+NVlTNj/b+b0u5wZ/1Gnb2KzbFqzlH73n8DrA65gxjV3NuvY3ds/oODn41jU63ymXf/bw4qjLcz94/c4dsUvyLdyKj2DVTlj2NVzIlNm/Yys7JxUhyfS6akTkohIB1HYtTvHfOWvvP5/V2H5Rxz2+QYMP4pFhTM4cvPjVJT/mOxmVKMvf/53TLMqenzo6sOO43BUV1WxYtGL7FrwOAO2PId/6g8MGTOR/P7jWLL7PHJHn8aIKWcypujw/18iIs2hBFRE0kZmVhZTr/vtoRlk1i9bQN9hY8nNK2jR+WziFfT497UsfukvzZodqdvKx1ifMYgRR89o0eMerp1bN7Hi0R8y/IN/MIadVHgm7+YdQ15pqAk69pRPwimfTElsIiIAmudLRNJKNPncte19ih86h3fuvJDystIWnWv8hy9kO93xRX9I+JjN65YztuId3h98XlKnUty9/QNWL3kNgMzsXMa//yibC0Yzf+KPOPCl5Rz9zRcZddyHkhaPiEhjVAIqImmpuFc/Xh/7JaYtu5WFd17E0V/6S7Oq0QGysnNY1et0Jmx9nH17dyU0PuX6l+6nPzD45M+0MPLmWfXmv9n5wl0cs+sZqrKHwTHzKCruycFvrOS4wq5JiUFEpLlUAioiaWvaxTcx98gbmLj/FZb8/GKqKiubfY5uEy8k1ypY/spfEtq/eMPzrMwaddizKDXl7X8/wbs/mM7Ixz7G+F3P8WbPj5H9idj0oflKPkWkHVMCKiJpbfql32buiC8xqeQF5j/602YfP3rK6eygCJb9rcl9d217nyMr3mV7v5NbEGnTqquqqCgP0wof3LqWblU7mTv6G1R+ZRnTvng/w8ZNaZPHFRFpbY1WwZtZBjDd3V9LUjwiIq1u+qf/mwV/H8zE0z/d7GMzs7JY3f0Exux+iarKSjKzGv7YXD33CSab02PCOYcTbr3emfMPcp69me0jPs70S7/NhHM/j59zLf07wCD3IiK1NVoC6u7VwOENpici0g5M+tjVZOfksmPLRt56+a/NOjZj5Cl0Yz+rl7za6H6++gV20ZWRx7ZeZ5/Na99l4Y/PZdzTl9Clag+5PQYDoX1qc9u0ioi0FjPLMLNPtfT4RKrgnzOzC83MWvogIiLtxbo/fJERz32O9e8uTPiYoZPPBmDHW083ul+/PYtZV3BMq813Pu+vv6T4vg8xZt/rzBlyLUVfX8yEM5PTuUlE0p+ZXWBmvzGzh8zsjOYcGymk/EZLHzuRBPQ/gD8D5Wa218xKzGxvSx9QRCSVBl58O6WWCw9/mn17dyV0TM++g1iTMZSumxsuAd3+wXsM9Pcp6z+1tUKlsM9wlhdMpORzc5hx5Y/UsUhEDjGze81sq5m9XWv9WWa23MxWmdlNjZ3D3R93988B1wIXtyCMZ83sBjMbZGZHRJdEDmwyAXX3ru6e4e7Z7t4tcr9bC4IUEUm5PgNHsPm0uxhYtYkVsz+DV1cndNy2IyYytHQ51VVV9W5/b/HzAHQfc3jV7/P/9mvm3PM1AMZNP4vjbnyaPgNHHNY5RSQt3QecFb/CzDIJTSfPBsYBM81snJkdbWZP1lp6xx36LVrW5PJi4DrgZWBBZJmfyIEJ9YI3s/PM7PbI0vqt60VEkmj8iefxxogvMnHfS8z7a2KfuTZgIl3sIBtWvlnv9vK1cyj1bIYffUKLYiorPcDrP/8Mkxd8g6Itr7d48HwR6Rzc/WVgZ63VU4FV7r7G3cuBB4Hz3f0tdz+n1rLVgh8B/3D3xNslxWIYVs8yPJFjm0xAzew24EvAO5HlS2Z2a3ODFBFpT6ZddgtzBn2OEcd/PKH9+4w5HoCt79Y/KEjXXctYnz2cnNy8ZseyZ9d2Vv30TKbteJw5/S7nyK8/36LziEjayDKz+XHLNQkeNwDYEHd/Y2RdQ74InAZcZGbXNjdIM8s2s/80s0ciy/Vmlp3IsYnMhPRR4LhIY1PM7H5gEfDN5gYqItJeZGRmMuOq2wEOVas31nlo4Khj2ef5VG9cUGebV1fTv3wNK4s/3Ow4qior2faLMxhVuY75k37EjPOa/R0gIumn0t0nt/WDuPudwJ2HcYr/A7KB6CwYn46su7qpAxOdirM7sWLeouZGJyLSXu3ZsYXNv7qAktGfZOpFX21wv8ysLNbnjqL77qV1tu34YAM9KaG691HNfvzMrCz2TPwCK4p6M/nE85p9vIhInE3AoLj7AyPr2soUdz827v7zZlZ/O6VaEmkDeiuwyMzui5R+LgB+2IIgRUTanW7FvXDL5Mi3f8Lu7R80uu++biPpX/FenY5Lm1eENvddhxyX8ON+sGEVbz7/MBDGKB2v5FNEDt88YJSZDTOzHOAS4Ik2fLwqMzvUS9LMhgP199SspdEENDL257+B6cCjwF+AGe7+UMtjFRFpPywjg/wLfkYXP8CKP93Q6L7eYxRd7SA7tm6ssf7AhvCDf+DoSQk95p4dW6i491wGv/xVSvbU7kMgItI0M3sAmAOMNrONZnaVu1cC1wNPA8uAh929brVN6/k68IKZvWhmLwHPA19L5MBGq+Dd3c3sKXc/mrbNoEVEUmbYuCnM7XsxUz94kFVv/puRx55Y736F/cfActiy5m169h18aH3mjhVso5hePfo0+VgV5WVsmP0pjqzeyqqz/8i4ooSGzBMRqcHdZzaw/ingqbZ+/Mh07QeBUcDoyOrl7l6WyPGJVMEvNLMpLYxPRKRDGDfzh5RYAbuf+98G9+k5NLTx3LdpWY31BQc2sj27X0KPs/DX1zC+bDFvTvhvxk0/q+kDRETaoeh07e5e5u5LIktCySck1glpGnCZma0H9gMWHtePaVnIIiLtT7fuPVhx3gOMH9NwNXqfgSM56Dn49pU11hdXbGFz16Y/Et+Z8w+m7XicuX0vY/oF1x12zCIiKfacmV0IPOru3pwDE0lAz2xZTCIiHcuRE8MwStVVVfUOyZSRmcn7mf3J37vm0LrKinJ6Ve9gfdeBTZ5/7LQzWbDrp0w67fLWC1pEJHX+A/gqUGlmpcQKKZucMbOpTkiZwNPuvr720iphi4i0M6vfmsv7PxjLioUv1bt9d/5gistinZC2v7+ebKsio/vgeveHME7oji0bsYwMJn30KrJzcls9bhGRZIp0VD8qMl17TnOna280AXX3KmC5mTX8yZoCZpZhZj80s5+b2WdSHY+IpI8+Q0bT1UvY9/xP6t1eXtiPnlXbDw3FtHPzagAKeg9r8JwLnvw1eb+cxNp35rV+wCIiKRCpcv97S49PpBNSMbDUzJ4zsyeiS0sf0MzuNbOtZvZ2rfVnmdlyM1tlZjc1cZrzCYOrVhCmmRIRaRVduhWztP8nObbkZTatqWf0kqIBFFgZeyPDJ+3fEqrji/rVP/1xyZ6dDF14G5uyBzNk9MQ2i1tEJAVa3FE9kTag327JiRtxH/AL4HfRFZGq/ruA0wkJ5bxIkptJGAg/3ixCd//X3P3XZvYI8FwrxygindiIj30FZv+O9579NQOuqTlLXXZxaOu5c/Nqiop7UrkztEjqPXBkved6+6HvMs33sOtjv290qk8RkQ6oxR3Vm0xA3f0lMxsCjHL3Z82sgJAYtoi7v2xmQ2utngqscvc1AGb2IHC+u98KnFP7HGa2ESiP3E1oxH0RkUT1HjCMxYXTGbX5r1RW3E5Wds6hbYW9hgCwd8t6OGoamXs3sp3u9CzoUuc8O7Zs5NhND7Gw6CNMnnBS0uIXEUmSFndUb7IK3sw+BzwC/DqyagDweEsfsAEDgA1x9zdG1jXkUeBMM/s58HJ9O5jZNWY238zmV1ZWtl6kItIp5J1yA+/N+AFhrOWYbj1DCWjZnjBtZ07ZDvZkFtd7jjVz/koOFfQ+p7UrkkREUi/SKX0QcGrk9gESa96ZUBX8dYQSytcjD7bSzHq3MNZW4e4HgKua2Gc2MBugsLCwWWNTiYiMmXJaveuLevYFoLpkGwD55bs4kNW93n2nXHAdH0w6k8GD6q+eFxHpyMzsu8BkQtPI3wLZwB+AE5o6NpEstczdo9XdmFkW0NoJ3SZCBh01MLJORCRltmxczZzf3lhjvvaCwm6UejYc2AFAYdVuynLqTqdZVnoAgL5KPkUkfX0cOI/Q/hN33wx0TeTARBLQl8zsv4B8Mzsd+DPwtxYG2pB5wCgzG2ZmOcAlaO55EUmxHRtXMmP9r1jx70cPrbOMDHZbdzIPbgegqHoPlXk1E9Dqqiq2/M9k5t791aTGKyKSZOWR4ZgcwMwKEz0wkQT0JmAb8BZhxPungG+1IEgAzOwBYA4w2sw2mtlV7l4JXA88DSwDHnb3esY/ERFJntGTT2MHRbCs5m/ukswicsp2UlZ6gK52EC/oUWP72688zuDqTWT1HZPMcEVEku1hM/s10D3SZ+hZ4DeJHJhIL/jqyMkSOmEC55vZwPqnCMmtiEi7kJmVxaoeJ3P09n9SemAfeZGe7geziymo2MWeHVvoDWR06VXjuIr5v2MX3ThaU26KSBpz99sjteN7Ce1Av+PuzyRybEI9lUREOqv8o8+nwMpYPvfJQ+vKc4rIr97Hgb2hHWhWYawEdH/JbsaVvMaKHqeSm1eQ9HhFRJLJ3Z9x96+7+w2JJp+gBFREpFFHTj2TvRRQum3doXVV2V0o8AOU7tsNQHZBbOrjZS//mXwrp+vkS5IdqohIh5HIMEwAmFlBZPgjEZFOI6+gC5n/tY5pObmH1lXndKHQD1K+fw8AOYVFh7b1P+ok5u69kalTz0h6rCIiHUUiA9Efb2bvAO9G7h9rZr9s88hERNqJ7LjkE4DcruRaBeV7twKQ1yU2Dmj/oaOZPvO/NO2miHQKZpZvZqObe1wiVfA/I0y1tAPA3d8ENKeciHQaWzetZfkPprLoX38AwHJDlXvF7s1ALAFdv3wx8//+G0oP7EtNoCIiSWRm5wKLgX9G7h9nZgkNo5lQG1B331BrleZfF5FOo6hHH4ZVrKZszWsAZOaHBDRjb5gvo6BrmIpz88v3cdwb36CsrDQ1gYqIJNcthNkydwO4+2JgWCIHJpKAbjCz4wE3s2wzu4EwVqeISKeQm1fAuuzhdNu5BIDM/NDmM+dAmA++sEu4X7R1HmuyR1JU3DM1gYqIJFeFu++ptS6h2TITSUCvJcwHP4AwPeZxwBeaFZ6ISAe3q/gYhpatoLKinJxIr/cu5Vs54LlkZmVRXlbK8PLl7OwxMcWRiogkzVIzuxTINLNRZvZz4LVEDkwkAR3t7pe5ex937+3ulwNjDydaEZGOJnPQFAqsjPeWLyKnMLT5LK7cxn4LY32ufes18qyCnKHTUxmmiEgyfRE4CigD/gTsAb6cyIGJJKA/T3CdiEja6jf+wywsPAmvriI3P8yI1JPdlFo+ALtXzwNg0LGnpCxGEZHmMLOTzewVM/uVmZ3cglOMcfeb3X1KZPmWuyfUCL7BcUDNbAZwPNDLzL4at6kboPFFRKRTGTB8LAO+HuaE37Rm6aH1lZYNwNRPfp0PNl1I3/5DUxGeiHQyZnYvcA6w1d3Hx60/C7iDkKvd7e63NXIaB/YBecDGFoTxEzPrCzwCPOTubyd6YGMD0ecAXSL7dI1bvxe4qAVBioh0eKUH9pGVk3fofpWFj1HLyKDvoJGpCktEOp/7gF8Av4uuMLNM4C7gdEJCOS8yLFImcGut42cBr7j7S2bWB/gpcFlzAnD3UyIJ6KeAX5tZN0Ii+oOmjm0wAXX3l4CXzOw+d1/fnIBERNLRG3deTv9d88i/9rlD6yoth4ryMhbf9WkKpn2Wo47/aAojFJHOwt1fNrOhtVZPBVa5+xoAM3sQON/dbyWUljZkF5DbyPbG4vgAuNPMXgC+AXwHaHkCGuc+M6vTpd7dT212lCIiHVhVYV/679jC9urYR2JVRhabVr/FlD1PM3+7PhZFpFVkmdn8uPuz3X12AscNAOLHbt8ITGtoZzP7BGGyoe6E0tRmMbOxwMXAhYQJix4CvpbIsYkkoDfE3c6LPEhlM2MUEenwcvqNJWODs3PTKnpH1lVl5LBjzWKGAsXDjkthdCKSRirdfXJbP4i7Pwo8ehinuJeQdJ7p7pubc2CTCai7L6i16lUze6M5DyIikg6KB4+HN6Dkg5WH1lVbNuXvL6XSMxg46pgURiciwiZgUNz9gZF1bcLdZ7T02CYTUDM7Iu5uBjAJKGrpA4qIdFR9howGoHLHeio9gyyrpjojh5zda/ggow8D8wpSHKGIdHLzgFFmNoyQeF4CXNraD2JmD7v7p8zsLWrOfGSAu3uTv8YTqYJfEDm5Eare1wJXtSBeEZEOrbBrd+YM+CxdR06nfM1vyKKM6oxszKvYkTeYgakOUEQ6DTN7ADgZ6GlmG4Hvuvs9ZnY98DSh5/u97r60kdO01Jcifxvr2NQoc09oys4OrbCw0Pfv35/qMEQkjey9pT/d2M+87mcz5csPpjocEUkjZnbA3QtTHUdTzOxH7n5jU+vq09hA9J9o7MBIw1URkU7lwL497N7+ATmEAeirM7JTHJGISMqcDtRONs+uZ10djVXBn9vINufwek2JiHRIS+7/GkdtfZL9Fgon9lVm8vatHybjlG8ybvpZKY5ORKTtmdnngS8Aw81sSdymrsCriZyjsYHorzy88NqOmY0DbiGMOfWcuz+S2ohEpNPo2o+u2w7yAT3pC5RWOePLFvN2RVmqIxMRSZY/Af8gzK50U9z6EnffmcgJMprawcyKzOynZjY/svzEzFrcC97M7jWzrWb2dq31Z5nZcjNbZWY3NXR8xNnAz93988AVLY1FRKS5srr3B2APXQDwqjAsclG/4SmLSUQkmdx9j7uvc/eZkdkyDxJqx7uY2eBEztFkAkoYZLSEMM/npwhzwf+2hTFDmLu0Rj1V3NylZwPjgJlmNs7MjjazJ2stvYHfA5eY2Y+BHocRi4hIs+QdMQCA/YT54LOqSgHo1X9YymISEUkFMzvXzFYSRkh6CVhHKBltUiLDMI1w9wvj7n/PzBY3O8qIVpy79LpI4qq2qCKSNF17hAS00jPAIL96HzvpxhEFXVIcmYhI0v0AmA486+4TzOwU4PJEDkykBPSgmZ0YvWNmJxCKWltTfXOXDmhoZzMbamazgd8BP25gn2uizQYqKzVzqIi0jh4DhjN39I10sdDmsyyzkPUFR6c4KhGRlKhw9x1AhplluPsLQEJTiCZSAvp54P5Iu08DdgKfbWmkrcHd1wHXNLHPbGA2hHFAkxCWiHQCXboVM33mf7H6+w9DFRQNn8KES76Z6rBERFJht5l1AV4G/mhmW4GEBl5PZC74xcCxZtYtcn/v4UTagKTOXSoicjjWL1/MjqoCRgCWlZvqcEREUuV8oBT4CnAZYar2/07kwER6wX8pknyWAD81s4VmdsZhBFufQ3OXmlkOYe7SJ1r5MUREWkXmg5dQXV0NQOHbf2DuH7+X4ohERJLP3fe7e5W7V7r7/e5+Z6RKvkmJtAGdFSn1PIPQ4/zTwG0tDTYyd+kcYLSZbTSzq9y9EojOXboMeLiN5i4VETls+zOLyM1wDlZnMtbX4KVtUTEkItI+mVmJme2NW0ri/yZyjkTagFrk70eB37n7UjOzxg5ojLvPbGD9U8BTLT2viEiyHMzuTjffSul1S8m/awxWoNHgRKTzcPeuh3uOREpAF5jZvwgJ6NNm1hWoPtwHFhHpqCqzu5BXvZ99u7cCkFV4RIojEhFJDTM70cyujNzuaWYJDYqcSAnoVcBxwBp3P2BmPYB2O02niEhbq8ruQr4fZMe+PQBk5XdLcUQiIslnZt8lDLs0mjBJUQ7wB+CEpo5NpBd8dWTg+MvNzIF/u/tjhxOwiEhHVnzCLNZtO43i3Hzeyp1IYa9BTR8kIpJ+Pg5MABYCuPvmSE15k5pMQM3sl8BI4IHIqv8ws9Pc/boWBisi0qEdOfHDsTvjXkhdICIiqVXu7h4poMTMChM9MJEq+FOBse4ePfn9wDstClNEJA1s37ye91ct5MipZ5KbV5DqcEREUuVhM/s10N3MPgfMAu5O5MBEEtBVwGBgfeT+IGBlS6IUEUkHa+Y8ytS3bmHO6msZ8t6j5H/hZYp79Ut1WCIiSeXut5vZ6cBeQjvQ77j7M4kc22ACamZ/AxzoCiwzszci96cBbxx21CIiHVRmXmjilLV7Hf19KwfyVQoqIp1TJOF8BsDMMszsMnf/Y1PHNVYCentjj9fM+ERE0ka013te6Vaq3MgvOOwh8UREOozIDJnXAQMIM1c+E7l/A/Am0PIE1N1fauBBTwRmEiaeFxHpdLILQgLatWIbB8ija0YiQyqLiKSN3wO7CDNbXg38F2HiogvcfXEiJ0ikDShmNgG4FPgksBb4S0uiFRFJB7mRBPSI6h0ctHxU/imS5qqrYc0aWLgQcnLgggtSHVGqDXf3owHM7G7gfWCwu5cmeoLG2oAeSSjpnAlsBx4CzN1POayQRUQ6uD5Dx/LmSb/hwOpXySjbS+9UByQiraeqCpYvD8lmdFm0CPZGpjg/4QQloFARveHuVWa2sTnJJ4SEsv4NZtXAK8BV7r4qsm6Nuw8/jIBTorCw0Pfv35/qMERERKQ9KS+HpUtrJpqLF8PBg2F7fj4ceyxMnBhbjjoqlIK2ITM74O4Jj6mZbGZWBUQTKwPygQOR2+7uTU4P11gV/CeAS4AXzOyfwIORE4uIdGqlB/ez7JXH6D1yAgOGH5XqcEQkEaWlsGRJLNlcsADeegsqIoV5XbvChAnwH/8RSzZHj4ashFordkhmNhi4E9gJrHD32xI5zt0zD/uxGyoBjQuuEDifUBV/KvA74DF3/9fhPniyqARURFrTrm3vU3zXGACW5E3mmJueS3FEIlLDgQPw5pshyYwmm0uXhup1gOLikGBOmhRLNkeMgHbSoTCRElAzuxc4B9jq7uPj1p8F3AFkAnc3llSa2ceAYnf/g5k95O4Xt84VNC2RueD3A38C/mRmxYSOSDcCHSYBFRFpTVk5ubHb1WUpjEREKCkJ1ebxyea774aOQwC9eoVE85xzYgnnkCFgHb5S9z7gF4SCQQDMLBO4Czgd2AjMM7MnCMnorbWOnwXMBR4xs1mEnu1J06xyZXffBcyOLCIinVJ2XAJaZelbPSfS7uzeHdppxiebK1dCtDa3X7+QZF50USzZHDAgHZLNOtz9ZTMbWmv1VGCVu68BMLMHgfPd/VZCaWkNZnYD8N3IuR4Bftu2Ucfok1NEpJmys2MJaHVGdgojEUljO3bEksxou83Vq2PbBw0KCebll8eq0fulzZS4WWY2P+7+bHdPpPBvALAh7v5GwgyWDfkncIuZXQqsa3aUh0EJqIhIM2VmZVHpGWRZNdWmBFTksG3ZUjfZXL8+tn3YsJBgXnVVLNns1St18ba9Snef3NYP4u5vAxe19ePURwmoiEgLrPzog+x77R6q+09KdSgiHYc7bN5csyf6woWwaVNsn1GjYPp0uO66kGhOmABHHJG6mDuWTcCguPsDI+vanSZ7wacD9YIXERFJMnfYsKFme82FC0NpJ4R2mWPG1OyNftxxUFSU2rjbgUTHAY20AX0y2gvezLKAFcBHCInnPOBSd1/adtG2jEpARURaYOE/fktBj4GMmXp6qkMRST13WLu2brK5Y0fYnpkJ48bBWWfFEs5jj4UuXVIbdwdmZg8AJwM9zWwjoTPRPWZ2PfA0oef7ve0x+QSVgIqItMiWW4bThx283usipl13T6rDEUme6mpYtapmsrloUeihDmHg9vHjY6WakybB0UdDQUFq4+5A2vtMSK2hQ5SAmtlw4GagyN0viqy7APgY0A24pyMNjC8iHV+lZYGDW/sYuFqkTcTPix5NOBctCmNvQpiS8phj4OKLY8nm+PGQm9v4eaXTa/MEtDVG6o+MZ3VVZIyq6LrHgccjg+PfjgbGF5EkqrTskIBmtu2c0CJJU1UVBnBfsADmzw9/Fy8OswpBbF70K66oOS96tkaCkOZLRgnofRzmSP3uvrWR838rci4RkaSpin58KgGVjqiqClasiCWa8+eHks1osllQEHqfX311rCp9zJi0nhddkqvNX0mtMVJ/fczMgNuAf7j7wtaLWESkadWWGW4oAZX2rro6JJvxJZsLF0K0b0R8sjl5ckg4R48OHYdE2kiqfso0a6R+M+sB/BCYYGbfjCSqXwROA4rMbKS7/6rWMdcA1wDk5OgLQkRaV/ZFv2HR375Nl+GNTTIikmTRDkK1SzajbTbz8kKyOWtWSDQnTw4lm0o2JcmS0gu+nnGqLgLOcverI/c/DUxz9+vb4vHVC15ERNJOdXWYmrJ2yebevWF7Xl4YVzOaaE6aBGPHqhq9A1Av+LbTYUbqFxGpz+JnH6Bi71YmnncdmfpCl7bmDmvW1CzZXLgQ9uwJ23NzQwehyy+PJZxjx6qDkLRbqfrUnAeMMrNhhMTzEuDSFMUiItJsXeb8mJFVq3mjuoqpF3011eFIOokf1D2acC5YEBtnMycnJJszZ8ZKNtUbXTqYZAzD1KFH6hcRqY8Rmi9ZltqYy2Fwh/Xra5ZsLlgAu3aF7dnZsXE2oyWbRx0VklCRDiwZveBnNrD+KeCptn58EZG2sK3XDEZ8sIaC3sNTHYrLTTiFAAAgAElEQVR0FO7w3ns1E83582HnzrA9OzvMGHTRRbGSTQ3qLmlKU3GKiLRQWekBcvM0vaA0YNOmkGDOmxdLOLdvD9ui01VGE83Jk0PyqWRTUCckERFphJJPOWTHjliyGV3efz9sy8wMyeZ558USzmOOCb3URTopJaAiIiLNUVISeqDHJ5tr18a2jx4NH/kITJkSluOOC9NYisghqoIXERFpSGkpvPlmLNGcPx+WLQvtOQGGDIklmlOmhCkri4pSG7N0eKqCFxER6SwqK2Hp0liiOW8eLFkS1gP06ROSzIsvDlXpkydD796pjVmkg1IJqIiIdD7V1bByZc12m4sWwcGDYXtRUUgw40s3Bw4Es9TGLZ1CZygBVQIqIiLpzR02bKjZZnPBgtgsQvn5oeo8PtkcMQIyMlIbt3RanSEBVRW8iIikl61bayab8+eHdRAb2H3mzJBoTp4M48ZpfnSRJNM7TkREOq79+0OP9DfegNdfD3/Xrw/bzEJyefbZsZJNDX8k0i6oCl5ERDqGqip4552ayebbb4f1AEOHwtSpYYn2SO/SJaUhi7SEquBFRERSwR02bgxJZjThnD8/lHgCdO8eEs1zz4Vp00LC2adPamMWkYQpARURkdTbuze014wv3YzOJJSdHQZzv/LKkHROmwYjR6qTkEgHpgRURESSq6IC3norlmi+/jq8+25scPdRo+DUU0OiOXVqSD41R7pIWlECKiIibcc9TFMZn2wuWhRmGALo2TMkmpdcEqtKP+KI1MYsIm1OnZBERKT17N0bEs05c2Du3HB7+/awLS8PJk2KVaNPnRo6Dmlwd5EaktUJycyGAzcDRe5+UUPr2uSxlYCKiEiLVFeHqvO5c2MJ59Klsar0sWNh+vRYwjl+fGjPKSKNSiQBNbN7gXOAre4+Pm79WcAdQCZwt7vflsDjPVI72axvXWtSFbyIiCRm165QhR5NOF9/PTabUPfuIdm86CKYMSMknd27pzZekfR2H/AL4HfRFWaWCdwFnA5sBOaZ2ROEZPTWWsfPcvetyQm1LiWgIiJSV3TMzWjJ5pw5obQTQpX5+PFw8cUh6ZwxA448Ur3SRVpPlpnNj7s/291nx+/g7i+b2dBax00FVrn7GgAzexA4391vJZSWthtKQEVEBHbsqFmV/sYbUFIStvXoERLNyy8Pf6dMgW7dUhuvSHqrdPfJLThuALAh7v5GYFpDO5tZD+CHwAQz+6a731rfuhbE0SQloCIinU1VVWir+dprIeGcMwdWrgzbMjPDdJWXXx5KNqdPD2NuqqOQSNpx9x3AtU2tawtKQEVE0t3+/aFE89VX4d//Dgnn3r1hW+/eIdGcNSv8nTwZCtN6BkCRdLYJGBR3f2BkXbvT7hPQBoYIOBn4PrAUeNDdX0xZgCIi7c3mzSHZjC6LFsXmSz/qqDDm5gknhGX4cJVuiqSPecAoMxtGSDwvAS5NbUj1a9MEtDWGCIg0pL3KzB6JXw3sA/II7RtERDqnaHV6fMK5bl3Ylp8feqPfeGNINmfMgOLilIYrIq3DzB4ATgZ6mtlG4Lvufo+ZXQ88Tcix7nX3pSkMs0FtOg6omZ1ESBR/F01AI0MErCBuiABgJk0MERA/HpWZZbh7tZn1AX7q7pc1FofGARWRtLF/fxj+KJpsxlen9+0bK9k84YQwhWVOTmrjFZFmS9ZA9KnUpiWgbTVEgLtXR27uAjRBsIikr127QqL58sthWbAAKivDNlWni0gHlYo2oK0xRMAngDOB7oRBWOs77hrgGoAclQCISEexdSu88gq89FJIOJcsCTML5eSE6vSvfx1OPFHV6SLSobX7TkgNDBHwKPBoE8fNBmZDqIJvswBFRA7Hxo0h0YwmnNHB3vPz4fjj4ZZb4KSTwlSW+fkpDVVEpLWkIgHtMEMEiIi0KndYs6Zmwrl2bdjWrVso2fzsZ0PCOWmS2m+KSNpKRQLaYYYIEBE5bJs3w/PPh+W55+C998L6Hj1Covmf/xn+HntsGAReRKQTaOthmDr0EAEiIs22Ywe8+GIs6YxWqRcXwymnhDacp5wCY8dq7nQR6bTadBim9kLDMIlImykpCZ2Gognn4sWhqr2wMJRsfuQjcOqpoYRTCaeIJKAzDMOkBFREpCHusHNn6CjU0LJqVRgWKScnDIV06qlhmTIFsrNTfQUi0gEpAU0TSkBFpA73MMbmxo2wYUPNJbpu40YoLa15XEYG9O8PAweG5cgjQ8J5/PHqpS4irUIJaJpQAirSCZWU1E0sayeZtT8XMjNDcjloUFiiSWb80rcvZLX7EexEpANTApomlICKpKGSkjCE0bp1YYnejv7ds6fm/mbQr18ssYwmmfFL377qiS4iKdcZElD9jBeR9mn/fli/vm5iGb29c2fN/QsKYNgwGDo0jKc5eHDN5LJ/f7XJFBFpJ5SAikhqVFaGqvDVq8Oydm3NZHPbtpr75+aG5HLYsNDBJ5psRv/27Kl50EVEOggloCLSdg4eDDP/RJPM+GXt2pCERmVnw5AhIZm84IKayeWwYdC7t4YxEhFJE2oDKiKHZ9euWFK5alXNJHNTrVl2u3WDkSNhxIi6y4ABan8pIoLagIqIhOGKNm+uW4IZTTZ37aq5f9++IaE87bS6SWaPHqomFxERlYCKCCHJ3LYNVq6EFStq/l21Cg4ciO2bmRmqyqNJZXyJ5vDhYQYgERFpMZWAikh62b27/iRzxQrYuze2X1ZWSCZHjQpTSUaTzJEjQ+9y9SYXEZHDoBJQkXSzf39IKqNLfJK5fXtsP7NQknnkkSHRHDUqdnvoUA22LiKSIioBFZH2qawstL+MTzCjtzdvrrlv//4hsfz4x2smmcOHQ15eauIXEZFOTQmoSHtVVQXvvQfLl4fEMr4k8733oLo6tm/PniGxPP30mknmyJHQpUvqrkFERKQeqoIXSbW9e0OS+e674W90WbkSSktj+3XrVn91+ahRUFycuvhFRKRVqQpeRFpHVVWY4ae+RPODD2L7ZWaGqvHRo+GMM8Lf6NK7t4YwEhGRtKAEVKQ17dpVM7mMJpurVkF5eWy/I46AMWPg7LNrJpkjRkBOTuriFxGRTsPMhgM3A0XuflHc+kLgJeAWd3+yLR5bCahIc1VWhukl4xPNaLIZP395VlZIKMeMgXPOqZlo9uyZuvhFRKTDM7N7gXOAre4+Pm79WcAdQCZwt7vf1tA53H0NcJWZPVJr043Aw60fdYwSUJGG7NhRt7o8WpoZP4d5r14hqTz//JpJ5rBhGi9TRETayn3AL4DfRVeYWSZwF3A6sBGYZ2ZPEJLRW2sdP8vdt9Y+qZmdDrwDtOkwKUpApXOLlmYuW1Y32dyxI7ZfTk7oUT52bBjOKD7RVAcgERFJMnd/2cyG1lo9FVgVKdnEzB4Eznf3WwmlpYk4GSgExgEHzewpd69u/JDmUwIqncO+fbFq8mXLYgnnypVQURHbr2/fkFReeGGoOo8mmUOHhg5CIiIibS/LzObH3Z/t7rMTOG4AsCHu/kZgWkM7m1kP4IfABDP7prvf6u43R7Z9FtjeFsknKAGVdOIOW7fWTTKXLYMNce/HzMxQmjlmDJx3Xvg7dmxINIuKUhe/iIhIUOnuk9v6Qdx9B3BtA9vua8vHbvcJaH09tMxsMHAnsBNY0VgDW0lD0SGN4hPM6O1du2L7FRaG5PLDH44lmWPGhORTPc1FRCT9bAIGxd0fGFnX7rRpAtqGPbSOBh5x9z+Y2UNtE72k3MGDNavNo39XrAhTUUb16ROSy4svjiWZY8fCgAGQkZG6+EVERJJrHjDKzIYREs9LgEtTG1L92roE9D7aoIcWMBd4xMxmAb9vg7glmbZvr5tkLlsG69eHanUIieSwYSGxPOusmiWa6gQkIiKdjJk9QOgw1NPMNgLfdfd7zOx64GlCXnWvuy9NYZgNavOpOCM9tJ6MloCa2QzCwKZnRu5/EyDSQ6ux8zwSVwV/A/BGpAfYofUN0VSc7UB1dZi/PD7JjP7dvj22X35+aIsZTTCjSeaoUZDXpiNCiIiItAuairNtHHYPLeCfwC1mdimwroHjrgGuAchRe7/kKSsLPctrdwJavjxUqUf17BkSy49/vGayOXiwqs1FRETSXLvvhFRfDy13fxtotNQzMlzBbAgloG0WYGe1d29ILN95p2ayuWZNKO2EMG/5kCEhsTzllJrtMzUTkIiISKeVigS0w/TQEsJg7O+8E0s0o7c3xT1lOTlw5JEwYQJcemksyTzySCgoSF3sIiIi0i6lIgHtMD20muWf/wxTMWZlhekXo8vDD8OJJ4btN9wQ2x79+6tfwbhx8MwzcNddNbdlZcEPfgD9+8NLL8Hf/lZ3+/XXQ7duMH8+LFhQ9/znnx8SxBUrwliY8duys+HYY0P8S5fCkiWhBHP16lCNvnx5zfaZhYUhsTz11BDzuHHh/rBh4ZwiIiIQOpBWVISlvDx2u3//UDv2/vuwZUtsfUVFGGLv1FPD8fPmhWmPKyrCjHWVleF7ZtassP2xx8L3VnRbRUX4Lrz55rD9Zz8L32nRbZWVYWSUO+4I27/4xbA9/vxHHQW/j/RrPvPMcP747R/+MDzxRHL/j2msrYdh6tA9tJpl6FD4yldiL/boizZa1dy1a+hcE/9mqKiIza5TUgJr19bdHn0zLVkC//d/sXNHffaz4U335JPwve/VjWvPnpCAzp4NP/lJ3e3Tp4eSzT176m7LygrHjBsXjn/yyVD6uXIlPPVU+CBZsiTs+8UvwiuvhKQ2JycsQ4bAffeF7T/4QUhoc3Ji+wwdCl/9atj+29/Ctm2xY7OzYeBAOPvssP2FF0L70vjz9+gRxvSEkFxnZMS2RRfNXiQiHZV7SMrKykISV14ePu/z88PsbitXxtaXl4f9Jk+G3r1Dp8/nnqubAM6cCYMGwcKF8OCDNbdVVITvkcGDw2f8L39Zc1t5OTz6aEjkZs+GH/2obpK5ejX06gXf+hb8v/9X95oOHAjx33Yb3HlnzW2ZmeG7D0LhzL331txeVBRLQB94AP7859hxWVnhOyf6nblgQfhOysqKFbpUVcXOVV0dvjMKC2MFM/37x7ZPmxb+T/GFOmPGtPy5lDravBd8e5CWveCrqsIbNScn/Jrcty8ksRUV4UNozZpQ6rl3b0j8Fi0KH1bx42cWFYVq87FjQw/zjIzwwZWfH85vBl/6Utj34YfDOeIT5MLC8AEE4YPmjTdiH0Tl5eHN/MADYftll8GcOTW3H3NMKNmFEMfixTWv8ZRT4Pnnw+1Ro8Kv4Xjnnhv7Ndq3b/g1He/SS+GPf4xtr6iomQBfdln4sHWHk08O6/LyIDc3LOeeC5dcAqWl8N3vxtZH95kxAyZNCp2rnn46tj26DBkSPogrK0NThvht6mgl0r5UVob3uln4bHMPP7hLS8PnZmlpWAYPhvHjw2fYfffVTA7Ly+Gkk8Jn1+7dcOONdRPEK68MnS/XrYNPfjJ2fPTvj34El18earWmTo0NRRf1wAPhc+mFF2KlhfH+9jc455zw2Xj++XW3v/hiKMl78MGQzMXX2GVnh8Rz/Hh45JGQJNbefs89IQF98kl46KG627/3vZAkv/givPZa3e1XXhn+vvlm+J6qvf1DHwpxbtoUvtNq19r16hW2l5eH5yorK/xNM52hF7wS0I6svDwklbXbZ9YeqH3gwFh1eXzVeY8eqYu9tvjENHo7KyskjgBvvQX799fcp0eP8AEN4UO5pKTm8WPHhqk2Ab7xjZAoxm8/9dTwAVxRAaefHv5n8cvnPhe+QHbsCB+48f9TCKW6N98cxisdOrTuNf3v/4YEfunS8IEeLysL7r4bPvOZkNh/4hM1k9u8vPBBfvLJ4fjbbw8/DOKXmTNh+PBQ0vHaa3W3jxkDXbqE6y4tDetyc9Pyw1rSRLS0L9p2fM2aMLvZgQPhdXzgQEhuoonX/ffDBx/EksPS0vBj9QtfCNuvuipU9Ua3lZWFY3/847B96NDQzKi0NFY6dvXV8JvfhMQvM7NuAvjlL4fq3f37w/urtm9/G/77v8O0wMccE37Y5ubGamW+8hW44grYvDl8xsRvy82FT386JLGbN4dSwPhtOTlw2mnhGrdtg1dfrXv8kUdC9+7hf7VtW90ELz9fP4A7ACWgaaLDJ6AHDoRSzNqJ5qpVsQ9Ns9hA7fFJ5tix4QNbDp97rKSirCwkiV27htvvvBMrJYluP+ooGDEifME9/HDdBPfCC0MJ6sqV8P3v1zy+tDR8iZ10UihJuOKK8AUcXaqrQ7vh004L57744rrxzpkTmljce2/4Io7KywtfQq+9FpLUP/0pfKFGE9eCgrDccUdoQvLKK+GLrrCw5nLGGeFLb+vW8GUcXV9QoCQ3HVVUhOd5//7wgwzC637t2rAumiACfP7z4e8994SakfgEsnv38JqD8Lp+9tnY9vJyOProWNOe6dPh9ddrxjFjRnjtQtj37bfD7WgNxhlnxKpmzz47/ICM/2F30knw9a+H7TfdFEo+8/JiyzHHhHMA/OUvscQuur1///Cj3j0kt/HJX3a2kjtpFUpA00SHSUBrD20UTTTXrYv9Cs/MDL9+ayeao0erx3ln4R6+NDMywuuhpAQ2bqyZoB48GDq/FReHEtRnnomtLy0Nf7/1rTCN6WOPhfZc8cfu3w9z54bqru98JyTItZWUhBKgr341JLDxCgvD6zkjIxz797/XTF6POCJ0uoPQQW/jxvBDqWvX8Le4OLy2IdZWS5pv795QkrZnT7i9d29orvOpT4UfG88+G6pc9+0Ly/794e/f/x6SrVtugV/8IqyL1gBkZITXn1koLbznnpqP2bVreBwIP3z+/vfYj5qCgpC8Pfpo2P7Tn4bh26I/fPLzQ3Ib/cH0wgvhdRbdVlAQEthhw8L2kpJQm6BmLZJmlICmiXaXgCY6tFF0OKP4RHPUqLBNJFncQ/IRLf2KLhMnhi/9+fNjTSSiS1lZrH3wHXeEJCd+e25uOAZCe7jHH6/5mEOGhB9eEHqjvvJKLDnt1i2UfEU7uP30p6Gqsbg4tgweHGuesX9/x6t2dA8J3q5d4QfErl0hiYwuV1wRErnnnw/Xv3dvLMncsydWuv2zn8U6+sVbvz78j267LbTfLiwMPya6dAm3n3oq/J8feSQkgdFt0WXWrPDjZ+XK0NYxmlxGS9GLipL/PxNJI0pA00RKElD30DapdpL5zjvhyzIWXKyqXEMbSWe0b19IYqIldCUlYf3pp4e/v/1teA/Fbx8wILSPg9Dh49VXa44OcfLJIXGC0CZu9epQchZNUM88M7ThhZCEZWaG0t7evcPfwYND6fDhqq4O17ZlS2iqsH077NwZOoEceWRILr/znZBg7twZWx5+GD76UfjrX+GCC+qe9/nnw3X//e+hzWFRUUgYi4rC8o1vhB68774b2hhHt3frFhLIwYP1+SLSjikBTRNtnoCWlYUvhPhEc9my8MUTVVQUSzDjE81BgzpWyYxIe+Qe2hHu2hWWjIzQBhfg178OVfzRbTt3htLR6LBlRxwR1se78srQdtY9NG/p3r1mgnr66WHZty/0BN66teZyzTWhh/OSJbGxduPdfXeoZl68OPR4Li4OcUSXK68MHde2bg29hYuLY8llt27qTCaS5pSApok2T0D37QvVgxC+oGqXZo4bF3pz6wtDpP1xD+/hbdtCwrdtW3i/TpkSflxefXXNbdu2hU4s3/9+aE4THeu3S5fw/u/dG/7zP8MoBXv2hKYC0fU9e4bRG3r1CkmkiEg9lICmiaRUwc+dGwZF1xznIuktOjh4Vla4vWFDSCjz81MdmYikCSWgaaLddUISERERaUBnSEDV+FBEREREkkoJqIiIiIgklRJQEREREUkqJaAiIiIiklRKQEVEREQkqZSAioiIiEhSKQEVERERkaRSAioiIiIiSaUEVERERESSSgmoiIiIiCRVp5iK08yqgYNJeKgsoDIJj9MedeZrh859/br2zqszX39nvnbo3NefjGvPd/e0LiTsFAlospjZfHefnOo4UqEzXzt07uvXtXfOa4fOff2d+dqhc19/Z7721pTW2bWIiIiItD9KQEVEREQkqZSAtq7ZqQ4ghTrztUPnvn5de+fVma+/M187dO7r78zX3mrUBlREREREkkoloCIiIiKSVEpAm8nMPmlmS82s2swm19r2TTNbZWbLzezMBo4fZmavR/Z7yMxykhN564rEvjiyrDOzxQ3st87M3orsNz/ZcbYVM7vFzDbF/Q8+2sB+Z0VeD6vM7KZkx9kWzOzHZvaumS0xs8fMrHsD+6XNc9/U82hmuZH3xKrI+3to8qNsG2Y2yMxeMLN3Ip99X6pnn5PNbE/c++E7qYi1LTT1Orbgzshzv8TMJqYiztZmZqPjns/FZrbXzL5ca5+0et7N7F4z22pmb8etO8LMnjGzlZG/xQ0c+5nIPivN7DPJi7oDc3ctzViAscBo4EVgctz6ccCbQC4wDFgNZNZz/MPAJZHbvwI+n+praoX/yU+A7zSwbR3QM9UxtsE13wLc0MQ+mZHXwXAgJ/L6GJfq2Fvh2s8AsiK3fwT8KJ2f+0SeR+ALwK8ity8BHkp13K14/f2AiZHbXYEV9Vz/ycCTqY61ja6/0dcx8FHgH4AB04HXUx1zG/wPMoEPgCHp/LwDJwETgbfj1v0PcFPk9k31fd4BRwBrIn+LI7eLU3097X1RCWgzufsyd19ez6bzgQfdvczd1wKrgKnxO5iZAacCj0RW3Q9c0JbxtrXINX0KeCDVsbRDU4FV7r7G3cuBBwmvkw7N3f/l7tFBmOcCA1MZTxIk8jyeT3g/Q3h/fyTy3ujw3P19d18YuV0CLAMGpDaqduV84HcezAW6m1m/VAfVyj4CrHb39akOpC25+8vAzlqr49/bDX1nnwk84+473X0X8AxwVpsFmiaUgLaeAcCGuPsbqfsh3QPYHfflXd8+Hc2HgC3uvrKB7Q78y8wWmNk1SYwrGa6PVLnd20C1TCKviY5uFqH0pz7p8twn8jwe2ify/t5DeL+nlUjTggnA6/VsnmFmb5rZP8zsqKQG1raaeh13hvf5JTRcyJCuz3tUH3d/P3L7A6BPPft0htdAq8tKdQDtkZk9C/StZ9PN7v7XZMeTKgn+H2bSeOnnie6+ycx6A8+Y2buRX5ntXmPXD/wf8H3Cl9P3Cc0QZiUvuraVyHNvZjcTpqP7YwOn6bDPvdRlZl2AvwBfdve9tTYvJFTP7ou0h34cGJXsGNtIp34dR/opnAd8s57N6fy81+HubmYaOqiVKAGth7uf1oLDNgGD4u4PjKyLt4NQPZMVKSWpb592o6n/g5llAZ8AJjVyjk2Rv1vN7DFCdWaH+PBO9HVgZr8BnqxnUyKviXYpgef+s8A5wEc80giqnnN02Oe+lkSex+g+GyPviyLC+z0tmFk2Ifn8o7s/Wnt7fELq7k+Z2S/NrKe7b09mnG0hgddxh32fJ+hsYKG7b6m9IZ2f9zhbzKyfu78faVqxtZ59NhHaw0YNJPQTkUaoCr71PAFcEukNO4zwK/CN+B0iX9QvABdFVn0G6MglqqcB77r7xvo2mlmhmXWN3iZ0Xnm7vn07mlptvD5O/dc1DxhlYeSDHEI11hPJiK8tmdlZwDeA89z9QAP7pNNzn8jz+ATh/Qzh/f18Q4l5RxNpy3oPsMzdf9rAPn2jbV7NbCrhu6XDJ+AJvo6fAK6I9IafDuyJq7JNBw3WcqXr815L/Hu7oe/sp4EzzKw40hzrjMg6aUyqe0F1tIWQbGwEyoAtwNNx224m9JZdDpwdt/4poH/k9nBCYroK+DOQm+prOoz/xX3AtbXW9QeeirvWNyPLUkL1bcrjbqVr/z3wFrCE8AHVr/b1R+5/lNBreHW6XH/ktbsBWBxZor2/0/a5r+95BP6bkIQD5EXez6si7+/hqY65Fa/9REJTkyVxz/lHgWuj73/g+sjz/CahY9rxqY67la693tdxrWs34K7Ia+Mt4kZH6egLUEhIKIvi1qXt805ItN8HKiLf81cR2nI/B6wEngWOiOw7Gbg77thZkff/KuDKVF9LR1g0E5KIiIiIJJWq4EVEREQkqZSAioiIiEhSKQEVERERkaRSAioiIiIiSaUEVERERESSSgmoiKQ9M7vAzNzMxqQ6lqaY2VAzuzTVcYiItCUloCLSGcwE/h35W0Nk5qL2ZCigBFRE0poSUBFJa5E5zE8kDCp9SWTdyWb2ipk9AbwTWfdtM1tuZv82swfM7IbI+hfNbHLkdk8zWxe5/Vkze9zMnjGzdWZ2vZl91cwWmdlcMzsist8IM/unmS2IPOaYyPr7zOxOM3vNzNaYWXSGtNuAD5nZYjP7StL+USIiSdTefvmLiLS284F/uvsKM9thZpMi6ycC4919rZlNAS4EjgWygYXAggTOPR6YQJgJaRVwo7tPMLOfAVcA/wvMJswcs9LMpgG/BE6NHN+PkByPIcyo9QhwE3CDu59zuBcuItJeKQEVkXQ3E7gjcvvByP0ngTfcfW1k/QnAX929FCg1s78leO4X3L0EKDGzPUD0uLeAYyKlr8cDf45MmQ2QG3f84+5eDbxjZn1acG0iIh2SElARSVuRavBTgaPNzIFMwrzmfwf2J3iaSmLNlfJqbSuLu10dd7+a8PmaAex29+MaOHf88dbAPiIiaUdtQEUknV0E/N7dh7j7UHcfBKwFPlRrv1eBc80sL1JqGV/9vQ6IVttfRDO4+15grZl9EsCCY5s4rATo2pzHERHpaJSAikg6mwk8VmvdX6jVG97d5xHaYC4B/kGoQt8T2Xw78HkzWwT0bEEMlwFXmdmbwFJCm9TGLAGqzOxNdUISkXRl7p7qGEREUs7Murj7PjMrAF4GrnH3hamOS0QkHakNqIhIMNvMxhHaed6v5FNEpO2oBFREREREkkptQEVEREQkqQVVjMoAAAAvSURBVJSAioiIiEhSKQEVERERkaRSAioiIiIiSaUEVERERESSSgmoiIiIiCTV/wdhII2tOqKV2gAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def exp_taylor(x, N=None):\n", " \"\"\"N-ая частичная сумма ряда Тейлора для экспоненты.\"\"\"\n", " acc = 1 # k-ая частичная сумму. Начинаем с k=0.\n", " xk = 1 # Степени x^k.\n", " inv_fact = 1 # 1/k!.\n", " for k in range(1, N+1):\n", " xk = xkx\n", " inv_fact /= k\n", " acc += xkinv_fact\n", " return acc\n", "\n", "def exp_horner(x, N=None):\n", " \"\"\"N-ая частичная сумма ряда Тейлора для экспоненты методом Горнера.\"\"\"\n", " if N<=0: return 1 # Избегаем деления на ноль.\n", " acc = 1 # Выражение во вложенных скобках в схеме Горнера\n", " for k in range(N, 0, -1):\n", " acc = acc/kx + 1\n", " return acc\n", "\n", "def make_exp_test(fns, args={}, xmin=-1, xmax=1):\n", " \"\"\"Проводит тест приближения fn показательной функции.\"\"\"\n", " x = np.linspace(xmin, xmax, 1000)\n", " standard = np.exp(x)\n", " \n", " theoretical_relative_error = (np.abs(x)/2+1)np.finfo(float).eps\n", " theoretical_absolute_error = theoretical_relative_error standard\n", " \n", " fig, ax1 = plt.subplots(1,1,figsize=(10,5))\n", " ax2 = plt.twinx(ax1)\n", " ax1.set_xlabel(\"Argument\")\n", " ax1.set_ylabel(\"Absolute error\")\n", " ax2.set_ylabel(\"Relative error\")\n", "\n", " ax1.semilogy(x, theoretical_absolute_error, '-r')\n", " \n", " line, = ax2.semilogy(x, theoretical_relative_error, '--r')\n", " line.set_label(\"theory (relative)\")\n", " \n", " for fn in fns:\n", " subject = fn(x, *args)\n", " absolute_error = np.abs(standard-subject)\n", " relative_error = absolute_error/standard\n", " \n", " ax1.semilogy(x, absolute_error, '-')\n", " \n", " line, = ax2.semilogy(x, relative_error, '--')\n", " line.set_label(\"{} (relative)\".format(fn.__name__))\n", " \n", " \n", " plt.legend()\n", " plt.show()\n", " \n", " \n", "make_exp_test([exp_taylor, exp_horner], args={\"N\": 3}, xmin=-0.001, xmax=0.001) \n", "make_exp_test([exp_taylor, exp_horner], args={\"N\": 3}, xmin=-1, xmax=1)\n", "make_exp_test([exp_taylor, exp_horner], args={\"N\": 3}, xmin=-10, xmax=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ясно, что 4-x слагаемых слишком мало, чтобы хорошо приблизить ряд. Попробуем взять больше." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "make_exp_test([exp_taylor, exp_horner], args={\"N\": 15}, xmin=-0.001, xmax=0.001) \n", "make_exp_test([exp_taylor, exp_horner], args={\"N\": 15}, xmin=-1, xmax=1)\n", "make_exp_test([exp_taylor, exp_horner], args={\"N\": 15}, xmin=-10, xmax=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Точность приближения растет с увеличением числа слагаемых, однако даже для умеренно больших аргументов ни одного верного знака в ответе не получается. Посмотрим, как погрешность изменяется в зависимости от числа слагаемых." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def cum_exp_taylor(x, N=None):\n", " \"\"\"Вычисляет все частичные суммы ряда Тейлора для экспоненты по N-ую включительно.\"\"\"\n", " acc = np.empty(N+1, dtype=float)\n", " acc[0] = 1 # k-ая частичная сумму. Начинаем с k=0.\n", " xk = 1 # Степени x^k.\n", " inv_fact = 1 # 1/k!.\n", " for k in range(1, N+1):\n", " xk = xkx\n", " inv_fact /= k\n", " acc[k] = acc[k-1]+xkinv_fact\n", " return acc\n", "\n", "x = -10\n", "standard = np.exp(x)\n", "theoretical_relative_error = (np.abs(x)/2+1)np.finfo(float).eps\n", "theoretical_absolute_error = theoretical_relative_error * standard\n", "Ns = np.arange(100)\n", "\n", "partial_sums = cum_exp_taylor(x, N=Ns[-1])\n", "absolute_error = np.abs(partial_sums-standard)\n", "relative_error = absolute_error/standard\n", "\n", "fig, ax1 = plt.subplots(1,1,figsize=(10,5))\n", "ax2 = plt.twinx(ax1)\n", "ax1.set_xlabel(\"Argument\")\n", "ax1.set_ylabel(\"Absolute error\")\n", "ax2.set_ylabel(\"Relative error\")\n", "\n", "ax1.semilogy(Ns, Ns0+theoretical_absolute_error, '-r')\n", "\n", "line, = ax2.semilogy(Ns, Ns0+theoretical_relative_error, '--r')\n", "line.set_label(\"theory (relative)\")\n", "\n", "ax1.semilogy(Ns, absolute_error, '-')\n", " \n", "line, = ax2.semilogy(Ns, relative_error, '--')\n", "line.set_label(\"experiment (relative)\")\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Оказывается, что даже суммируя очень большое число слагаемых, мы не можем достигнуть максимальной точности. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Задания\n", "\n", "7. Относительная ошибка приближения частичной суммой ряда Тейлора показательной функцией много больше для отрицательных аргументов. Объясните причину этого. Воспользуйтесь свойствами показательной функции, чтобы выравнить точность вычислений при положительных и отрицательных аргументах.\n", "8. Почему абсолютная погрешность мала при аргументах близких к нулю? Как именно погрешность зависит от аргумента? \n", "9. Абсолютная погрешность приближения функции частичной суммой ряда равна остатку этого ряда. Оцените остаток ряда Тейлора для экспоненты и найдите число слагаемых, необходимое для вычисления экспоненты с наперед заданной точностью. Проведите эксперимент и убедитесь, что предсказанная вами точность отличается от фактической не более чем на порядок.\n", "10. Ошибка вычисления через частичную сумму складывается из ошибки отбрасывание остатка ряда и ошибки вычисления умножений и сложений. При увеличинии числа слагаемых первая ошибка уменьшается, но вторая растет. Для произвольного x оцените число слагаемых, при которых точность вычисления показательной функции максимальна.\n", "11. Схема Горнера дает несколько меньшую погрешность, чем суммирование одночленов. Почему?\n", "12. Можете предложить лучший способ вычисления показательной функции?" ] } ], "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
yuvrajsingh86/DeepLearning_Udacity	sentiment-network/Sentiment_Classification_Projects.ipynb	1	99598	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- Twitter: @iamtrask\n", "- Blog: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (Check inside your classroom for a discount code)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\" (this lesson)\n", "\n", "- [Curate a Dataset](#lesson_1)\n", "- [Developing a \"Predictive Theory\"](#lesson_2)\n", "- [PROJECT 1: Quick Theory Validation](#project_1)\n", "\n", "\n", "- [Transforming Text to Numbers](#lesson_3)\n", "- [PROJECT 2: Creating the Input/Output Data](#project_2)\n", "\n", "\n", "- Putting it all together in a Neural Network (video only - nothing in notebook)\n", "- [PROJECT 3: Building our Neural Network](#project_3)\n", "\n", "\n", "- [Understanding Neural Noise](#lesson_4)\n", "- [PROJECT 4: Making Learning Faster by Reducing Noise](#project_4)\n", "\n", "\n", "- [Analyzing Inefficiencies in our Network](#lesson_5)\n", "- [PROJECT 5: Making our Network Train and Run Faster](#project_5)\n", "\n", "\n", "- [Further Noise Reduction](#lesson_6)\n", "- [PROJECT 6: Reducing Noise by Strategically Reducing the Vocabulary](#project_6)\n", "\n", "\n", "- [Analysis: What's going on in the weights?](#lesson_7)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset<a id='lesson_1'></a>\n", "The cells from here until Project 1 include code Andrew shows in the videos leading up to mini project 1. We've included them so you can run the code along with the videos without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: The data in `reviews.txt` we're using has already been preprocessed a bit and contains only lower case characters. If we were working from raw data, where we didn't know it was all lower case, we would want to add a step here to convert it. That's so we treat different variations of the same word, like `The`, `the`, and `THE`, all the same way." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson: Develop a Predictive Theory<a id='lesson_2'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 1: Quick Theory Validation<a id='project_1'></a>\n", "\n", "There are multiple ways to implement these projects, but in order to get your code closer to what Andrew shows in his solutions, we've provided some hints and starter code throughout this notebook.\n", "\n", "You'll find the [Counter](https://docs.python.org/2/library/collections.html#collections.Counter) class to be useful in this exercise, as well as the [numpy](https://docs.scipy.org/doc/numpy/reference/) library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll create three `Counter` objects, one for words from postive reviews, one for words from negative reviews, and one for all the words." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create three Counter objects to store positive, negative and total counts\n", "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Examine all the reviews. For each word in a positive review, increase the count for that word in both your positive counter and the total words counter; likewise, for each word in a negative review, increase the count for that word in both your negative counter and the total words counter.\n", "\n", "Note: Throughout these projects, you should use `split(' ')` to divide a piece of text (such as a review) into individual words. If you use `split()` instead, you'll get slightly different results than what the videos and solutions show." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Loop over all the words in all the reviews and increment the counts in the appropriate counter objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells to list the words used in positive reviews and negative reviews, respectively, ordered from most to least commonly used. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Examine the counts of the most common words in positive reviews\n", "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Examine the counts of the most common words in negative reviews\n", "negative_counts.most_common()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, common words like \"the\" appear very often in both positive and negative reviews. Instead of finding the most common words in positive or negative reviews, what you really want are the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, you'll need to calculate the ratios of word usage between positive and negative reviews.\n", "\n", "TODO: Check all the words you've seen and calculate the ratio of postive to negative uses and store that ratio in `pos_neg_ratios`. \n", ">Hint: the positive-to-negative ratio for a given word can be calculated with `positive_counts[word] / float(negative_counts[word]+1)`. Notice the `+1` in the denominator – that ensures we don't divide by zero for words that are only seen in positive reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create Counter object to store positive/negative ratios\n", "pos_neg_ratios = Counter()\n", "\n", "# TODO: Calculate the ratios of positive and negative uses of the most common words\n", "# Consider words to be \"common\" if they've been used at least 100 times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examine the ratios you've calculated for a few words:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking closely at the values you just calculated, we see the following:\n", "\n", "* Words that you would expect to see more often in positive reviews – like \"amazing\" – have a ratio greater than 1. The more skewed a word is toward postive, the farther from 1 its positive-to-negative ratio will be.\n", "* Words that you would expect to see more often in negative reviews – like \"terrible\" – have positive values that are less than 1. The more skewed a word is toward negative, the closer to zero its positive-to-negative ratio will be.\n", "* Neutral words, which don't really convey any sentiment because you would expect to see them in all sorts of reviews – like \"the\" – have values very close to 1. A perfectly neutral word – one that was used in exactly the same number of positive reviews as negative reviews – would be almost exactly 1. The `+1` we suggested you add to the denominator slightly biases words toward negative, but it won't matter because it will be a tiny bias and later we'll be ignoring words that are too close to neutral anyway.\n", "\n", "Ok, the ratios tell us which words are used more often in postive or negative reviews, but the specific values we've calculated are a bit difficult to work with. A very positive word like \"amazing\" has a value above 4, whereas a very negative word like \"terrible\" has a value around 0.18. Those values aren't easy to compare for a couple of reasons:\n", "\n", "* Right now, 1 is considered neutral, but the absolute value of the postive-to-negative rations of very postive words is larger than the absolute value of the ratios for the very negative words. So there is no way to directly compare two numbers and see if one word conveys the same magnitude of positive sentiment as another word conveys negative sentiment. So we should center all the values around netural so the absolute value fro neutral of the postive-to-negative ratio for a word would indicate how much sentiment (positive or negative) that word conveys.\n", "* When comparing absolute values it's easier to do that around zero than one. \n", "\n", "To fix these issues, we'll convert all of our ratios to new values using logarithms.\n", "\n", "TODO: Go through all the ratios you calculated and convert them to logarithms. (i.e. use `np.log(ratio)`)\n", "\n", "In the end, extremely positive and extremely negative words will have positive-to-negative ratios with similar magnitudes but opposite signs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Convert ratios to logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examine the new ratios you've calculated for the same words from before:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything worked, now you should see neutral words with values close to zero. In this case, \"the\" is near zero but slightly positive, so it was probably used in more positive reviews than negative reviews. But look at \"amazing\"'s ratio - it's above `1`, showing it is clearly a word with positive sentiment. And \"terrible\" has a similar score, but in the opposite direction, so it's below `-1`. It's now clear that both of these words are associated with specific, opposing sentiments.\n", "\n", "Now run the following cells to see more ratios. \n", "\n", "The first cell displays all the words, ordered by how associated they are with postive reviews. (Your notebook will most likely truncate the output so you won't actually see all the words in the list.)\n", "\n", "The second cell displays the 30 words most associated with negative reviews by reversing the order of the first list and then looking at the first 30 words. (If you want the second cell to display all the words, ordered by how associated they are with negative reviews, you could just write `reversed(pos_neg_ratios.most_common())`.)\n", "\n", "You should continue to see values similar to the earlier ones we checked – neutral words will be close to `0`, words will get more positive as their ratios approach and go above `1`, and words will get more negative as their ratios approach and go below `-1`. That's why we decided to use the logs instead of the raw ratios." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]\n", "\n", "# Note: Above is the code Andrew uses in his solution video, \n", "# so we've included it here to avoid confusion.\n", "# If you explore the documentation for the Counter class, \n", "# you will see you could also find the 30 least common\n", "# words like this: pos_neg_ratios.most_common()[:-31:-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 1. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", "\n", "# Transforming Text into Numbers<a id='lesson_3'></a>\n", "The cells here include code Andrew shows in the next video. We've included it so you can run the code along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 2: Creating the Input/Output Data<a id='project_2'></a>\n", "\n", "TODO: Create a [set](https://docs.python.org/3/tutorial/datastructures.html#sets) named `vocab` that contains every word in the vocabulary." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Create set named \"vocab\" containing all of the words from all of the reviews\n", "vocab = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to check your vocabulary size. If everything worked correctly, it should print 74074" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the following image. It represents the layers of the neural network you'll be building throughout this notebook. `layer_0` is the input layer, `layer_1` is a hidden layer, and `layer_2` is the output layer." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9M+3mYEl15p1MumgWi24VOcyStAwavngEdCikeIwbmwM96LvuusuarSETc+fO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\nOHz4cLNgwYLQxbKSWgAIL4teQUqwqOTdyPeqPvJFklhleqV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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network_2.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Create a numpy array called `layer_0` and initialize it to all zeros. You will find the [zeros](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html) function particularly helpful here. Be sure you create `layer_0` as a 2-dimensional matrix with 1 row and `vocab_size` columns. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Create layer_0 matrix with dimensions 1 by vocab_size, initially filled with zeros\n", "layer_0 = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell. It should display `(1, 74074)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`layer_0` contains one entry for every word in the vocabulary, as shown in the above image. We need to make sure we know the index of each word, so run the following cell to create a lookup table that stores the index of every word." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a dictionary of words in the vocabulary mapped to index positions\n", "# (to be used in layer_0)\n", "word2index = {}\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", " \n", "# display the map of words to indices\n", "word2index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Complete the implementation of `update_input_layer`. It should count \n", " how many times each word is used in the given review, and then store\n", " those counts at the appropriate indices inside `layer_0`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \"\"\" Modify the global layer_0 to represent the vector form of review.\n", " The element at a given index of layer_0 should represent\n", " how many times the given word occurs in the review.\n", " Args:\n", " review(string) - the string of the review\n", " Returns:\n", " None\n", " \"\"\"\n", " global layer_0\n", " # clear out previous state by resetting the layer to be all 0s\n", " layer_0 = 0\n", " \n", " # TODO: count how many times each word is used in the given review and store the results in layer_0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to test updating the input layer with the first review. The indices assigned may not be the same as in the solution, but hopefully you'll see some non-zero values in `layer_0`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "update_input_layer(reviews[0])\n", "layer_0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO:* Complete the implementation of `get_target_for_labels`. It should return `0` or `1`, \n", " depending on whether the given label is `NEGATIVE` or `POSITIVE`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " \"\"\"Convert a label to `0` or `1`.\n", " Args:\n", " label(string) - Either \"POSITIVE\" or \"NEGATIVE\".\n", " Returns:\n", " `0` or `1`.\n", " \"\"\"\n", " # TODO: Your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells. They should print out`'POSITIVE'` and `1`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells. They should print out `'NEGATIVE'` and `0`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 2. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 3: Building a Neural Network<a id='project_3'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: We've included the framework of a class called `SentimentNetork`. Implement all of the items marked `TODO` in the code. These include doing the following:\n", "- Create a basic neural network much like the networks you've seen in earlier lessons and in Project 1, with an input layer, a hidden layer, and an output layer. \n", "- Do not add a non-linearity in the hidden layer. That is, do not use an activation function when calculating the hidden layer outputs.\n", "- Re-use the code from earlier in this notebook to create the training data (see `TODO`s in the code)\n", "- Implement the `pre_process_data` function to create the vocabulary for our training data generating functions\n", "- Ensure `train` trains over the entire corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch earlier Udacity lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (Check inside your classroom for a discount code)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Encapsulate our neural network in a class\n", "class SentimentNetwork:\n", " def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1):\n", " \"\"\"Create a SentimenNetwork with the given settings\n", " Args:\n", " reviews(list) - List of reviews used for training\n", " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", " learning_rate(float) - Learning rate to use while training\n", " \n", " \"\"\"\n", " # Assign a seed to our random number generator to ensure we get\n", " # reproducable results during development \n", " np.random.seed(1)\n", "\n", " # process the reviews and their associated labels so that everything\n", " # is ready for training\n", " self.pre_process_data(reviews, labels)\n", " \n", " # Build the network to have the number of hidden nodes and the learning rate that\n", " # were passed into this initializer. Make the same number of input nodes as\n", " # there are vocabulary words and create a single output node.\n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", "\n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " # TODO: populate review_vocab with all of the words in the given reviews\n", " # Remember to split reviews into individual words \n", " # using \"split(' ')\" instead of \"split()\".\n", " \n", " # Convert the vocabulary set to a list so we can access words via indices\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " # TODO: populate label_vocab with all of the words in the given labels.\n", " # There is no need to split the labels because each one is a single word.\n", " \n", " # Convert the label vocabulary set to a list so we can access labels via indices\n", " self.label_vocab = list(label_vocab)\n", " \n", " # Store the sizes of the review and label vocabularies.\n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " # Create a dictionary of words in the vocabulary mapped to index positions\n", " self.word2index = {}\n", " # TODO: populate self.word2index with indices for all the words in self.review_vocab\n", " # like you saw earlier in the notebook\n", " \n", " # Create a dictionary of labels mapped to index positions\n", " self.label2index = {}\n", " # TODO: do the same thing you did for self.word2index and self.review_vocab, \n", " # but for self.label2index and self.label_vocab instead\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Store the number of nodes in input, hidden, and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Store the learning rate\n", " self.learning_rate = learning_rate\n", "\n", " # Initialize weights\n", " \n", " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", " # the input layer and the hidden layer.\n", " self.weights_0_1 = None\n", " \n", " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", " # These are the weights between the hidden layer and the output layer.\n", " self.weights_1_2 = None\n", " \n", " # TODO: Create the input layer, a two-dimensional matrix with shape \n", " # 1 x input_nodes, with all values initialized to zero\n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", " # TODO: You can copy most of the code you wrote for update_input_layer \n", " # earlier in this notebook. \n", " #\n", " # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", " # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", " # For example, replace \"layer_0 = 0\" with \"self.layer_0 = 0\"\n", " pass\n", " \n", " def get_target_for_label(self,label):\n", " # TODO: Copy the code you wrote for get_target_for_label \n", " # earlier in this notebook. \n", " pass\n", " \n", " def sigmoid(self,x):\n", " # TODO: Return the result of calculating the sigmoid activation function\n", " # shown in the lectures\n", " pass\n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " # TODO: Return the derivative of the sigmoid activation function, \n", " # where \"output\" is the original output from the sigmoid fucntion \n", " pass\n", "\n", " def train(self, training_reviews, training_labels):\n", " \n", " # make sure out we have a matching number of reviews and labels\n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " # Keep track of correct predictions to display accuracy during training \n", " correct_so_far = 0\n", " \n", " # Remember when we started for printing time statistics\n", " start = time.time()\n", "\n", " # loop through all the given reviews and run a forward and backward pass,\n", " # updating weights for every item\n", " for i in range(len(training_reviews)):\n", " \n", " # TODO: Get the next review and its correct label\n", " \n", " # TODO: Implement the forward pass through the network. \n", " # That means use the given review to update the input layer, \n", " # then calculate values for the hidden layer,\n", " # and finally calculate the output layer.\n", " # \n", " # Do not use an activation function for the hidden layer,\n", " # but use the sigmoid activation function for the output layer.\n", " \n", " # TODO: Implement the back propagation pass here. \n", " # That means calculate the error for the forward pass's prediction\n", " # and update the weights in the network according to their\n", " # contributions toward the error, as calculated via the\n", " # gradient descent and back propagation algorithms you \n", " # learned in class.\n", " \n", " # TODO: Keep track of correct predictions. To determine if the prediction was\n", " # correct, check that the absolute value of the output error \n", " # is less than 0.5. If so, add one to the correct_so_far count.\n", " \n", " # For debug purposes, print out our prediction accuracy and speed \n", " # throughout the training process. \n", "\n", " elapsed_time = float(time.time() - start)\n", " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \"\"\"\n", " Attempts to predict the labels for the given testing_reviews,\n", " and uses the test_labels to calculate the accuracy of those predictions.\n", " \"\"\"\n", " \n", " # keep track of how many correct predictions we make\n", " correct = 0\n", "\n", " # we'll time how many predictions per second we make\n", " start = time.time()\n", "\n", " # Loop through each of the given reviews and call run to predict\n", " # its label. \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " # For debug purposes, print out our prediction accuracy and speed \n", " # throughout the prediction process. \n", "\n", " elapsed_time = float(time.time() - start)\n", " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \"\"\"\n", " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", " \"\"\"\n", " # TODO: Run a forward pass through the network, like you did in the\n", " # \"train\" function. That means use the given review to \n", " # update the input layer, then calculate values for the hidden layer,\n", " # and finally calculate the output layer.\n", " #\n", " # Note: The review passed into this function for prediction \n", " # might come from anywhere, so you should convert it \n", " # to lower case prior to using it.\n", " \n", " # TODO: The output layer should now contain a prediction. \n", " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", " # and `NEGATIVE` otherwise.\n", " pass\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to create a `SentimentNetwork` that will train on all but the last 1000 reviews (we're saving those for testing). Here we use a learning rate of `0.1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to test the network's performance against the last 1000 reviews (the ones we held out from our training set). \n", "\n", "We have not trained the model yet, so the results should be about 50% as it will just be guessing and there are only two possible values to choose from." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to actually train the network. During training, it will display the model's accuracy repeatedly as it trains so you can see how well it's doing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That most likely didn't train very well. Part of the reason may be because the learning rate is too high. Run the following cell to recreate the network with a smaller learning rate, `0.01`, and then train the new network." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That probably wasn't much different. Run the following cell to recreate the network one more time with an even smaller learning rate, `0.001`, and then train the new network." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a learning rate of `0.001`, the network should finall have started to improve during training. It's still not very good, but it shows that this solution has potential. We will improve it in the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 3. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Neural Noise<a id='lesson_4'></a>\n", "\n", "The following cells include includes the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 = 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 4: Reducing Noise in Our Input Data<a id='project_4'></a>\n", "\n", "TODO:* Attempt to reduce the noise in the input data like Andrew did in the previous video. Specifically, do the following:\n", "* Copy the `SentimentNetwork` class you created earlier into the following cell.\n", "* Modify `update_input_layer` so it does not count how many times each word is used, but rather just stores whether or not a word was used. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Projet 3 lesson\n", "# -Modify it to reduce noise, like in the video " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to recreate the network and train it. Notice we've gone back to the higher learning rate of `0.1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should have trained much better than the earlier attempts. It's still not wonderful, but it should have improved dramatically. Run the following cell to test your model with 1000 predictions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 4. \n", "## Andrew's solution was actually in the previous video, so rewatch that video if you had any problems with that project. Then continue on to the next lesson.\n", "# Analyzing Inefficiencies in our Network<a id='lesson_5'></a>\n", "The following cells include the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0 = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0[4] = 1\n", "layer_0[9] = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "weights_0_1 = np.random.randn(10,5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0.dot(weights_0_1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "indices = [4,9]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (1 * weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 5: Making our Network More Efficient<a id='project_5'></a>\n", "TODO: Make the `SentimentNetwork` class more efficient by eliminating unnecessary multiplications and additions that occur during forward and backward propagation. To do that, you can do the following:\n", "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", "* Remove the `update_input_layer` function - you will not need it in this version.\n", "* Modify `init_network`:\n", ">* You no longer need a separate input layer, so remove any mention of `self.layer_0`\n", ">* You will be dealing with the old hidden layer more directly, so create `self.layer_1`, a two-dimensional matrix with shape 1 x hidden_nodes, with all values initialized to zero\n", "* Modify `train`:\n", ">* Change the name of the input parameter `training_reviews` to `training_reviews_raw`. This will help with the next step.\n", ">* At the beginning of the function, you'll want to preprocess your reviews to convert them to a list of indices (from `word2index`) that are actually used in the review. This is equivalent to what you saw in the video when Andrew set specific indices to 1. Your code should create a local `list` variable named `training_reviews` that should contain a `list` for each review in `training_reviews_raw`. Those lists should contain the indices for words found in the review.\n", ">* Remove call to `update_input_layer`\n", ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", ">* In the forward pass, replace the code that updates `layer_1` with new logic that only adds the weights for the indices used in the review.\n", ">* When updating `weights_0_1`, only update the individual weights that were used in the forward pass.\n", "* Modify `run`:\n", ">* Remove call to `update_input_layer` \n", ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", ">* Much like you did in `train`, you will need to pre-process the `review` so you can work with word indices, then update `layer_1` by adding weights for the indices used in the review." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Project 4 lesson\n", "# -Modify it according to the above instructions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to recreate the network and train it once again." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should have trained much better than the earlier attempts. Run the following cell to test your model with 1000 predictions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 5. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", "# Further Noise Reduction<a id='lesson_6'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.models import ColumnDataSource, LabelSet\n", "from bokeh.plotting import figure, show, output_file\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"Word Positive/Negative Affinity Distribution\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frequency_frequency = Counter()\n", "\n", "for word, cnt in total_counts.most_common():\n", " frequency_frequency[cnt] += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"The frequency distribution of the words in our corpus\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 6: Reducing Noise by Strategically Reducing the Vocabulary<a id='project_6'></a>\n", "\n", "TODO: Improve `SentimentNetwork`'s performance by reducing more noise in the vocabulary. Specifically, do the following:\n", "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", "* Modify `pre_process_data`:\n", ">* Add two additional parameters: `min_count` and `polarity_cutoff`\n", ">* Calculate the positive-to-negative ratios of words used in the reviews. (You can use code you've written elsewhere in the notebook, but we are moving it into the class like we did with other helper code earlier.)\n", ">* Andrew's solution only calculates a postive-to-negative ratio for words that occur at least 50 times. This keeps the network from attributing too much sentiment to rarer words. You can choose to add this to your solution if you would like. \n", ">* Change so words are only added to the vocabulary if they occur in the vocabulary more than `min_count` times.\n", ">* Change so words are only added to the vocabulary if the absolute value of their postive-to-negative ratio is at least `polarity_cutoff`\n", "* Modify `__init__`:\n", ">* Add the same two parameters (`min_count` and `polarity_cutoff`) and use them when you call `pre_process_data`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Project 5 lesson\n", "# -Modify it according to the above instructions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to train your network with a small polarity cutoff." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the following cell to test it's performance. It should be " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to train your network with a much larger polarity cutoff." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the following cell to test it's performance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 6. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Analysis: What's Going on in the Weights?<a id='lesson_7'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp_full.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_most_similar_words(focus = \"horrible\"):\n", " most_similar = Counter()\n", "\n", " for word in mlp_full.word2index.keys():\n", " most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],mlp_full.weights_0_1[mlp_full.word2index[focus]])\n", " \n", " return most_similar.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_most_similar_words(\"excellent\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_most_similar_words(\"terrible\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.colors as colors\n", "\n", "words_to_visualize = list()\n", "for word, ratio in pos_neg_ratios.most_common(500):\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)\n", " \n", "for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]:\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos = 0\n", "neg = 0\n", "\n", "colors_list = list()\n", "vectors_list = list()\n", "for word in words_to_visualize:\n", " if word in pos_neg_ratios.keys():\n", " vectors_list.append(mlp_full.weights_0_1[mlp_full.word2index[word]])\n", " if(pos_neg_ratios[word] > 0):\n", " pos+=1\n", " colors_list.append(\"#00ff00\")\n", " else:\n", " neg+=1\n", " colors_list.append(\"#000000\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.manifold import TSNE\n", "tsne = TSNE(n_components=2, random_state=0)\n", "words_top_ted_tsne = tsne.fit_transform(vectors_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"vector T-SNE for most polarized words\")\n", "\n", "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n", " x2=words_top_ted_tsne[:,1],\n", " names=words_to_visualize,\n", " color=colors_list))\n", "\n", "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n", "\n", "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n", " text_font_size=\"8pt\", text_color=\"#555555\",\n", " source=source, text_align='center')\n", "p.add_layout(word_labels)\n", "\n", "show(p)\n", "\n", "# green indicates positive words, black indicates negative words" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
mne-tools/mne-tools.github.io	0.15/_downloads/plot_brainstorm_data.ipynb	1	3144	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Brainstorm tutorial datasets\n\n\nHere we compute the evoked from raw for the Brainstorm\ntutorial dataset. For comparison, see [1]_ and:\n\n http://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf\n\nReferences\n----------\n.. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM.\n Brainstorm: A User-Friendly Application for MEG/EEG Analysis.\n Computational Intelligence and Neuroscience, vol. 2011, Article ID\n 879716, 13 pages, 2011. doi:10.1155/2011/879716\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Authors: Mainak Jas <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\nimport mne\nfrom mne.datasets.brainstorm import bst_raw\n\nprint(__doc__)\n\ntmin, tmax, event_id = -0.1, 0.3, 2 # take right-hand somato\nreject = dict(mag=4e-12, eog=250e-6)\n\ndata_path = bst_raw.data_path()\n\nraw_fname = data_path + '/MEG/bst_raw/' + \\\n 'subj001_somatosensory_20111109_01_AUX-f_raw.fif'\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.plot()\n\n# set EOG channel\nraw.set_channel_types({'EEG058': 'eog'})\nraw.set_eeg_reference('average', projection=True)\n\n# show power line interference and remove it\nraw.plot_psd(tmax=60., average=False)\nraw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')\n\nevents = mne.find_events(raw, stim_channel='UPPT001')\n\n# pick MEG channels\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,\n exclude='bads')\n\n# Compute epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=reject, preload=False)\n\n# compute evoked\nevoked = epochs.average()\n\n# remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline\nevoked.add_proj(mne.compute_proj_evoked(evoked.copy().crop(tmax=0)))\nevoked.apply_proj()\n\n# fix stim artifact\nmne.preprocessing.fix_stim_artifact(evoked)\n\n# correct delays due to hardware (stim artifact is at 4 ms)\nevoked.shift_time(-0.004)\n\n# plot the result\nevoked.plot()\n\n# show topomaps\nevoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]))" ], "outputs": [], "metadata": { "collapsed": false } } ], "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": "2.7.14", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	bsd-3-clause
hmenke/pairinteraction	doc/sphinx/examples_python/vdw_near_surface.ipynb	3	25578	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dispersion Coefficients Near Surfaces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we reproduce the results depicted in Figure 5 from J. Block and S. Scheel \"van der Waals interaction potential between Rydberg atoms near surfaces\" [Phys. Rev. A 96, 062509 (2017)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.062509). We calculate the van der Waals $C_6$-coefficient between two Rubidium Rydberg atoms that are equidistantly placed in front of a perfect mirror (i.e. in horizontal alignment in front of a perfectly conducting plate). One finds that the relevant length scale is interatomic distance devided by distance from surface and that for decreasing surface distance the $C_6$ coefficient is significantly reduced." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As described in the [introduction](https://pairinteraction.github.io/pairinteraction/sphinx/html/introduction.html), we start our code with some preparations and load the necessary modules. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Arrays\n", "import numpy as np\n", "\n", "# Plotting\n", "import matplotlib.pyplot as plt\n", "\n", "# Operating system interfaces\n", "import os\n", "\n", "# pairinteraction :-)\n", "from pairinteraction import pireal as pi\n", "\n", "# Create cache for matrix elements\n", "if not os.path.exists(\"./cache\"):\n", " os.makedirs(\"./cache\")\n", "cache = pi.MatrixElementCache(\"./cache\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plate lies in the $xy$-plane with the surface at $z = 0$. The atoms lie in the $xz$-plane with $z>0$.\n", "\n", "We can set the angle between the interatomic axis and the z-axis `theta` and the center of mass distance from the surface `distance_surface`. `distance_atom` defines the interatomic distances for which the pair potential is plotted. The units of the respective quantities are given as comments.\n", "\n", "Be careful: `theta = np.pi/2` corresponds to horizontal alignment of the two atoms with respect to the surface. For different angles, large interatomic distances `distance_atom` might lead to one of the atoms being placed inside the plate. Make sure that `distance_surface` is larger than `distance_atomnp.cos(theta)/2`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "theta = np.pi/2 # rad\n", "distance_atoms = 10 # µm\n", "distance_surface = np.linspace(distance_atomsnp.abs(np.cos(theta))/2, 2distance_atoms,30) # µm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define the state that we are interested in using pairinteraction's `StateOne` class . As shown in Figures 4 and 5 of [Phys. Rev. A 96, 062509 (2017)](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.062509) we expect changes of about 50% for the $C_6$ coefficient of the $\|69s_{1/2},m_j=1/2;72s_{1/2},m_j=1/2\\rangle$ pair state of Rubidium, so this provides a good example. \n", "\n", "We set up the one-atom system using restrictions of energy, main quantum number n and angular momentum l. This is done by means of the `restrict...` functions in `SystemOne`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "state_one1 = pi.StateOne(\"Rb\", 69, 0, 0.5, 0.5)\n", "state_one2 = pi.StateOne(\"Rb\", 72, 0, 0.5, 0.5)\n", "\n", "# Set up one-atom system\n", "system_one = pi.SystemOne(state_one1.getSpecies(), cache)\n", "system_one.restrictEnergy(min(state_one1.getEnergy(),state_one2.getEnergy()) - 30, \\\n", " max(state_one1.getEnergy(),state_one2.getEnergy()) + 30)\n", "system_one.restrictN(min(state_one1.getN(),state_one2.getN()) - 3, \\\n", " max(state_one1.getN(),state_one2.getN()) + 3)\n", "system_one.restrictL(min(state_one1.getL(),state_one2.getL()) - 1, \\\n", " max(state_one1.getL(),state_one2.getL()) + 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pair state `state_two` is created from the one atom states `state_one1` and `state_one2` using the `StateTwo` class.\n", "\n", "From the previously set up `system_one` we define `system_two` using `SystemTwo` class. This class also contains methods `set..` to set angle, distance, surface distance and to `enableGreenTensor` in order implement a surface." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Set up pair state\n", "state_two = pi.StateTwo(state_one1, state_one2)\n", " \n", "# Set up two-atom system\n", "system_two = pi.SystemTwo(system_one, system_one, cache)\n", "system_two.restrictEnergy(state_two.getEnergy() - 3, state_two.getEnergy() + 3)\n", "\n", "system_two.setAngle(theta)\n", "system_two.setDistance(distance_atoms)\n", "system_two.setSurfaceDistance(distance_surface[0])\n", "system_two.enableGreenTensor(True)\n", "system_two.buildInteraction()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We calculate the $C_6$ coefficients. The `energyshift` is given by the difference between the interaction energy at given `surface_distance` and the unperturbed energy of the two atom state `state_two.getEnergy()`. The $C_6$ coefficient is then given by the product of `energyshift` and `distance_atoms6`.\n", "\n", "`idx` is the index of the two atom state. The command `getOverlap(state_two, 0, -theta, 0)` rotates the quantisation axis of `state_two` by `theta` around the y-axis. The rotation is given by the Euler angles `(0, -theta, 0)` in zyz convention. The negative sign of theta is needed because the Euler angles used by pairinteraction represent a rotation of the coordinate system. Thus, the quantisation axis has to be rotated by the inverse angle." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Calculate C6 coefficients\n", "C6 = []\n", "for d in distance_surface:\n", " system_two.setSurfaceDistance(d)\n", " system_two.diagonalize()\n", " idx = np.argmax(system_two.getOverlap(state_two, 0, -theta, 0))\n", " energyshift = system_two.getHamiltonian().diagonal()[idx]-state_two.getEnergy()\n", " C6.append(energyshiftdistance_atoms**6)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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DAMzscuAM4GJ397BOmbtvCodnEFzj+cJj5O4+wd3z3D2ve/fW2Sz8hHeWsaOi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\nWOdyghuHYudL9vY8huCazuy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"text/plain": [ "<matplotlib.figure.Figure at 0x10b7945c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results\n", "plt.plot(distance_surface/distance_atoms, np.abs(C6))\n", "plt.xlim(min(distance_surface/distance_atoms), max(distance_surface/distance_atoms))\n", "plt.xlabel(\"distance to surface / interatomic distance\")\n", "plt.ylabel(\"\|C$_6$\| (GHz $\\mu m^6$)\");" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
xbsd/CS109	content_raj/HW1-Copy0.ipynb	1	37311	{ "metadata": { "name": "", "signature": "sha256:3eabce1a0b9c3e1a9ee432ec43b13290cab7e68011597e38d675bc10224aa3ef" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 1. Which of two things is larger?\n", "\n", "Due: Thursday, September 19, 11:59 PM\n", "\n", "<a href=https://raw.github.com/cs109/content/master/HW1.ipynb download=HW1.ipynb> Download this assignment</a>\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Useful libraries for this assignment\n", "\n", "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n", "* [pandas](http://pandas.pydata.org/), for data frames\n", "* [matplotlib](http://matplotlib.org/), for plotting\n", "* [requests](http://docs.python-requests.org/en/latest/), for downloading web content\n", "* [pattern](http://www.clips.ua.ac.be/pages/pattern), for parsing html and xml pages\n", "* [fnmatch](http://docs.python.org/2/library/fnmatch.html) (optional), for Unix-style string matching" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# special IPython command to prepare the notebook for matplotlib\n", "%matplotlib inline \n", "\n", "from fnmatch import fnmatch\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import requests\n", "from pattern import web\n", "\n", "\n", "# set some nicer defaults for matplotlib\n", "from matplotlib import rcParams\n", "\n", "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", " (0.4, 0.6509803921568628, 0.11764705882352941),\n", " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", " (0.4, 0.4, 0.4)]\n", "\n", "rcParams['figure.figsize'] = (10, 6)\n", "rcParams['figure.dpi'] = 150\n", "rcParams['axes.color_cycle'] = dark2_colors\n", "rcParams['lines.linewidth'] = 2\n", "rcParams['axes.grid'] = True\n", "rcParams['axes.facecolor'] = '#eeeeee'\n", "rcParams['font.size'] = 14\n", "rcParams['patch.edgecolor'] = 'none'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This was the [XKCD comic](http://xkcd.com/1131/) after the 2012 Presidential election:\n", "\n", "<img src=\"http://imgs.xkcd.com/comics/math.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The comic refers to the fact that Nate Silver's statistical model (which is based mostly on combining information from pre-election polls) correctly predicted the outcome of the 2012 presidential race in all 50 states. \n", "\n", "Polling data isn't a perfect predictor for the future, and some polls are more accurate than others. This means that election forecastors must consider prediction uncertainty when building models.\n", "\n", "In this first assignment, you will perform a simple analysis of polling data about the upcoming <a href=\"http://en.wikipedia.org/wiki/Governor_(United_States)\">Governor races</a>. The assignment has three main parts:\n", "\n", "First you will build some tools to download historical polling data from the web, and parse it into a more convenient format. \n", "\n", "Next you will use these tools to aggregate and visualize several past Governor races\n", "\n", "Finally you will run a bootstrap analysis to estimate the probable outcome of current Governor races, given the level of precision of historical polls.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Part 1: Collect and Clean\n", "\n", "The [Real Clear Politics](http://www.realclearpolitics.com) website archives many political polls. In addition, they combine related polls to form an \"RCP average\" estimate of public opinion over time. For example, the chart on [this page](http://www.realclearpolitics.com/epolls/2012/president/us/general_election_romney_vs_obama-1171.html) shows historical polling data for the Obama-Romney presidential race. The chart is an average of the polling data table below the chart.\n", "\n", "The data used to generate plots like this are stored as XML pages, with URLs like:\n", "\n", "http://charts.realclearpolitics.com/charts/[id].xml\n", "\n", "Here, [id] is a unique integer, found at the end of the URL of the page that displays the graph. The id for the Obama-Romney race is 1171:\n", "\n", "http://charts.realclearpolitics.com/charts/1171.xml\n", "\n", "Opening this page in Google Chrome or Firefox will show you the XML content in an easy-to-read format. Notice that XML tags are nested inside each other, hierarchically (the jargony term for this is the \"Document Object Model\", or \"DOM\"). The first step of webscraping is almost always exploring the HTML/XML source in a browser, and getting a sense of this hierarchy.\n", "\n", "---\n", "\n", "#### Problem 0\n", "\n", "The above XML page includes 5 distinct tags (one, for example, is `chart`). List these tags, and depict how they nest inside each other using an indented list. For example:\n", "\n", "* Page\n", " * Section\n", " * Paragraph\n", " * Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "#### Answer: Problem 0\n", "\n", "* Chart\n", " - Series\n", " - Graphs\n", " - Graph\n", " - Graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "#### Problem 1\n", "\n", "We want to download and work with poll data like this. Like most programming tasks, we will break this into many smaller, easier pieces\n", "\n", "Fill in the code for the `get_poll_xml` function, that finds and downloads an XML page discussed above\n", "\n", "Hint \n", "\n", "`requests.get(\"http://www.google.com\").text` downloads the text from Google's homepage" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Setup\n", "\n", "import pattern.web as web\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from cs109style import customize_mpl, customize_css\n", "customize_mpl()\n", "customize_css()\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "No module named cs109style", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-b861867ac100>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcs109style\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcustomize_mpl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustomize_css\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mcustomize_mpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mcustomize_css\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: No module named cs109style" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Function\n", "--------\n", "get_poll_xml\n", "\n", "Given a poll_id, return the XML data as a text string\n", "\n", "Inputs\n", "------\n", "poll_id : int\n", " The ID of the poll to fetch\n", "\n", "Returns\n", "-------\n", "xml : str\n", " The text of the XML page for that poll_id\n", "\n", "Example\n", "-------\n", ">>> get_poll_xml(1044)\n", "u'<?xml version=\"1.0\" encoding=\"UTF-8\"?><chart><series><value xid=\\'0\\'>1/27/2009</value>\n", "...etc...\n", "\"\"\" \n", "#your code here \n", "\n", "def get_poll_xml(poll_id):\n", " url = 'http://charts.realclearpolitics.com/charts/' + str(poll_id) + '.xml'\n", " website_html = requests.get(url).text\n", " return website_html\n", "\n", "get_poll_xml(1044)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "global name 'requests' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-7b74d0c7d67d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwebsite_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mget_poll_xml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1044\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-2-7b74d0c7d67d>\u001b[0m in \u001b[0;36mget_poll_xml\u001b[0;34m(poll_id)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_poll_xml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://charts.realclearpolitics.com/charts/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoll_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.xml'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mwebsite_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwebsite_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: global name 'requests' is not defined" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some other functions we'll use later. `plot_colors` contains hints about parsing XML data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# \"r\"egular \"e\"xpressions is kind of a mini-language to\n", "# do pattern matching on text\n", "import re\n", "\n", "def _strip(s):\n", " \"\"\"This function removes non-letter characters from a word\n", " \n", " for example _strip('Hi there!') == 'Hi there'\n", " \"\"\"\n", " return re.sub(r'[\\W_]+', '', s)\n", "\n", "def plot_colors(xml):\n", " \"\"\"\n", " Given an XML document like the link above, returns a python dictionary\n", " that maps a graph title to a graph color.\n", " \n", " Both the title and color are parsed from attributes of the <graph> tag:\n", " <graph title=\"the title\", color=\"#ff0000\"> -> {'the title': '#ff0000'}\n", " \n", " These colors are in \"hex string\" format. This page explains them:\n", " http://coding.smashingmagazine.com/2012/10/04/the-code-side-of-color/\n", " \n", " Example\n", " -------\n", " >>> plot_colors(get_poll_xml(1044))\n", " {u'Approve': u'#000000', u'Disapprove': u'#FF0000'}\n", " \"\"\"\n", " dom = web.Element(xml)\n", " result = {}\n", " for graph in dom.by_tag('graph'):\n", " title = _strip(graph.attributes['title'])\n", " result[title] = graph.attributes['color']\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "#### Problem 2\n", "\n", "Even though `get_poll_xml` pulls data from the web into Python, it does so as a block of text. This still isn't very useful. Use the `web` module in `pattern` to parse this text, and extract data into a pandas DataFrame.\n", "\n", "Hints\n", "\n", "* You might want create python lists for each column in the XML. Then, to turn these lists into a DataFrame, run\n", "\n", "`pd.DataFrame({'column_label_1': list_1, 'column_label_2':list_2, ...})`\n", "\n", "* use the pandas function `pd.to_datetime` to convert strings into dates" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", " Function\n", " ---------\n", " rcp_poll_data\n", "\n", " Extract poll information from an XML string, and convert to a DataFrame\n", "\n", " Parameters\n", " ----------\n", " xml : str\n", " A string, containing the XML data from a page like \n", " get_poll_xml(1044)\n", " \n", " Returns\n", " -------\n", " A pandas DataFrame with the following columns:\n", " date: The date for each entry\n", " title_n: The data value for the gid=n graph (take the column name from the `title` tag)\n", " \n", " This DataFrame should be sorted by date\n", " \n", " Example\n", " -------\n", " Consider the following simple xml page:\n", " \n", " <chart>\n", " <series>\n", " <value xid=\"0\">1/27/2009</value>\n", " <value xid=\"1\">1/28/2009</value>\n", " </series>\n", " <graphs>\n", " <graph gid=\"1\" color=\"#000000\" balloon_color=\"#000000\" title=\"Approve\">\n", " <value xid=\"0\">63.3</value>\n", " <value xid=\"1\">63.3</value>\n", " </graph>\n", " <graph gid=\"2\" color=\"#FF0000\" balloon_color=\"#FF0000\" title=\"Disapprove\">\n", " <value xid=\"0\">20.0</value>\n", " <value xid=\"1\">20.0</value>\n", " </graph>\n", " </graphs>\n", " </chart>\n", " \n", " Given this string, rcp_poll_data should return\n", " result = pd.DataFrame({'date': pd.to_datetime(['1/27/2009', '1/28/2009']), \n", " 'Approve': [63.3, 63.3], 'Disapprove': [20.0, 20.0]})\n", "\"\"\"\n", "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output from `rcp_poll_data` is much more useful for analysis. For example, we can plot with it:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def poll_plot(poll_id):\n", " \"\"\"\n", " Make a plot of an RCP Poll over time\n", " \n", " Parameters\n", " ----------\n", " poll_id : int\n", " An RCP poll identifier\n", " \"\"\"\n", "\n", " # hey, you wrote two of these functions. Thanks for that!\n", " xml = get_poll_xml(poll_id)\n", " data = rcp_poll_data(xml)\n", " colors = plot_colors(xml)\n", "\n", " #remove characters like apostrophes\n", " data = data.rename(columns = {c: _strip(c) for c in data.columns})\n", "\n", " #normalize poll numbers so they add to 100% \n", " norm = data[colors.keys()].sum(axis=1) / 100 \n", " for c in colors.keys():\n", " data[c] /= norm\n", " \n", " for label, color in colors.items():\n", " plt.plot(data.date, data[label], color=color, label=label) \n", " \n", " plt.xticks(rotation=70)\n", " plt.legend(loc='best')\n", " plt.xlabel(\"Date\")\n", " plt.ylabel(\"Normalized Poll Percentage\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you've done everything right so far, the following code should reproduce the graph on [this page](http://www.realclearpolitics.com/epolls/other/president_obama_job_approval-1044.html)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "poll_plot(1044)\n", "plt.title(\"Obama Job Approval\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Part 2: Aggregate and Visualize\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 3\n", "\n", "Unfortunately, these data don't have any error bars. If a candidate leads by 10% in the RCP average, is she a shoo-in to win? Or is this number too close to call? Does a 10% poll lead mean more 1 day before a race than it does 1 week before? Without error estimates, these questions are impossible to answer.\n", "\n", "To get a sense of how accurate the RCP polls are, you will gather data from many previous Governor races, where the outcome is known.\n", "\n", "This url has links to many governer races. \n", "\n", "http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", "\n", "Notice that each link to a governor race has the following URL pattern:\n", "\n", "http://www.realclearpolitics.com/epolls/[YEAR]/governor/[STATE]/[TITLE]-[ID].html\n", "\n", "\n", "Write a function that scans html for links to URLs like this\n", "\n", "Hint The [fnmatch](http://docs.python.org/2/library/fnmatch.html) function is useful for simple string matching tasks." ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", " Function\n", " --------\n", " find_governor_races\n", "\n", " Find and return links to RCP races on a page like\n", " http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html\n", " \n", " Parameters\n", " ----------\n", " html : str\n", " The HTML content of a page to scan\n", " \n", " Returns\n", " -------\n", " A list of urls for Governer race pages\n", " \n", " Example\n", " -------\n", " For a page like\n", " \n", " <html>\n", " <body>\n", " <a href=\"http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html\"></a>\n", " <a href=\"http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html\"></a>\n", " </body>\n", " </html>\n", " \n", " find_governor_races would return\n", " ['http://www.realclearpolitics.com/epolls/2010/governor/ma/massachusetts_governor_baker_vs_patrick_vs_cahill-1154.html',\n", " 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html']\n", "\"\"\"\n", "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Problem 4\n", "\n", "At this point, you have functions to find a collection of governor races, download historical polling data from each one,\n", "parse them into a numerical DataFrame, and plot this data.\n", "\n", "The main question we have about these data are how accurately they predict election outcomes. To answer this question, we\n", "need to grab the election outcome data.\n", "\n", "Write a function that looks up and returns the election result on a page like [this one](http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html). \n", "\n", "Remember to look at the HTML source!\n", "\n", "You can do this by selection `view->developer->view source` in Chrome, or `Tools -> web developer -> page source` in Firefox. Altenatively, you can right-click on a part of the page, and select \"inspect element\"" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", " Function\n", " --------\n", " race_result\n", "\n", " Return the actual voting results on a race page\n", " \n", " Parameters\n", " ----------\n", " url : string\n", " The website to search through\n", " \n", " Returns\n", " -------\n", " A dictionary whose keys are candidate names,\n", " and whose values is the percentage of votes they received.\n", " \n", " If necessary, normalize these numbers so that they add up to 100%.\n", " \n", " Example\n", " --------\n", " >>> url = 'http://www.realclearpolitics.com/epolls/2010/governor/ca/california_governor_whitman_vs_brown-1113.html'\n", " >>> race_result(url)\n", " {'Brown': 56.0126582278481, 'Whitman': 43.9873417721519}\n", "\"\"\"\n", "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some more utility functions that take advantage of what you've done so far." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def id_from_url(url):\n", " \"\"\"Given a URL, look up the RCP identifier number\"\"\"\n", " return url.split('-')[-1].split('.html')[0]\n", "\n", "\n", "def plot_race(url):\n", " \"\"\"Make a plot summarizing a senate race\n", " \n", " Overplots the actual race results as dashed horizontal lines\n", " \"\"\"\n", " #hey, thanks again for these functions!\n", " id = id_from_url(url)\n", " xml = get_poll_xml(id) \n", " colors = plot_colors(xml)\n", "\n", " if len(colors) == 0:\n", " return\n", " \n", " #really, you shouldn't have\n", " result = race_result(url)\n", " \n", " poll_plot(id)\n", " plt.xlabel(\"Date\")\n", " plt.ylabel(\"Polling Percentage\")\n", " for r in result:\n", " plt.axhline(result[r], color=colors[_strip(r)], alpha=0.6, ls='--')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that this is done, we can easily visualize many historical Governer races. The solid line plots the poll history, the dotted line reports the actual result.\n", "\n", "If this code block fails, you probably have a bug in one of your functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "page = requests.get('http://www.realclearpolitics.com/epolls/2010/governor/2010_elections_governor_map.html').text.encode('ascii', 'ignore')\n", "\n", "for race in find_governor_races(page):\n", " plot_race(race)\n", " plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Briefly summarize these graphs -- how accurate is the typical poll a day before the election? How often does a prediction one month before the election mispredict the actual winner?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your summary here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Part 3: Analysis\n", "\n", "#### Problem 5\n", "\n", "You are (finally!) in a position to do some quantitative analysis.\n", "\n", "We have provided an `error_data` function that builds upon the functions you have written. It computes a new DataFrame with information about polling errors.\n", "\n", "Use `error_data`, `find_governer_races`, and `pd.concat` to construct a Data Frame summarizing the forecast errors\n", "from all the Governor races\n", "\n", "Hint \n", "\n", "It's best to set `ignore_index=True` in `pd.concat`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def party_from_color(color):\n", " if color in ['#0000CC', '#3B5998']:\n", " return 'democrat'\n", " if color in ['#FF0000', '#D30015']:\n", " return 'republican'\n", " return 'other'\n", "\n", "\n", "def error_data(url):\n", " \"\"\"\n", " Given a Governor race URL, download the poll data and race result,\n", " and construct a DataFrame with the following columns:\n", " \n", " candidate: Name of the candidate\n", " forecast_length: Number of days before the election\n", " percentage: The percent of poll votes a candidate has.\n", " Normalized to that the canddidate percentages add to 100%\n", " error: Difference between percentage and actual race reulst\n", " party: Political party of the candidate\n", " \n", " The data are resampled as necessary, to provide one data point per day\n", " \"\"\"\n", " \n", " id = id_from_url(url)\n", " xml = get_poll_xml(id)\n", " \n", " colors = plot_colors(xml)\n", " if len(colors) == 0:\n", " return pd.DataFrame()\n", " \n", " df = rcp_poll_data(xml)\n", " result = race_result(url)\n", " \n", " #remove non-letter characters from columns\n", " df = df.rename(columns={c: _strip(c) for c in df.columns})\n", " for k, v in result.items():\n", " result[_strip(k)] = v \n", " \n", " candidates = [c for c in df.columns if c is not 'date']\n", " \n", " #turn into a timeseries...\n", " df.index = df.date\n", " \n", " #...so that we can resample at regular, daily intervals\n", " df = df.resample('D')\n", " df = df.dropna()\n", " \n", " #compute forecast length in days\n", " #(assuming that last forecast happens on the day of the election, for simplicity)\n", " forecast_length = (df.date.max() - df.date).values\n", " forecast_length = forecast_length / np.timedelta64(1, 'D') # convert to number of days\n", " \n", " #compute forecast error\n", " errors = {}\n", " normalized = {}\n", " poll_lead = {}\n", " \n", " for c in candidates:\n", " #turn raw percentage into percentage of poll votes\n", " corr = df[c].values / df[candidates].sum(axis=1).values * 100.\n", " err = corr - result[_strip(c)]\n", " \n", " normalized[c] = corr\n", " errors[c] = err\n", " \n", " n = forecast_length.size\n", " \n", " result = {}\n", " result['percentage'] = np.hstack(normalized[c] for c in candidates)\n", " result['error'] = np.hstack(errors[c] for c in candidates)\n", " result['candidate'] = np.hstack(np.repeat(c, n) for c in candidates)\n", " result['party'] = np.hstack(np.repeat(party_from_color(colors[_strip(c)]), n) for c in candidates)\n", " result['forecast_length'] = np.hstack(forecast_length for _ in candidates)\n", " \n", " result = pd.DataFrame(result)\n", " return result" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "function\n", "---------\n", "all_error_data\n", "\n", "Calls error_data on all races from find_governer_races(page),\n", "and concatenates into a single DataFrame\n", "\n", "Parameters\n", "-----------\n", "None\n", "\n", "Examples\n", "--------\n", "df = all_error_data()\n", "\"\"\"\n", "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "errors = all_error_data()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a histogram of the error of every polling measurement in the data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "errors.error.hist(bins=50)\n", "plt.xlabel(\"Polling Error\")\n", "plt.ylabel('N')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 6\n", "\n", "Compute the standard deviation of the polling errors. How much uncertainty is there in the typical RCP poll?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 7\n", "\n", "Repeat this calculation for the data where `errors.forecast_length < 7` (i.e. the polls within a week of an election). How much more/less accurate are they? How about the data where `errors.forecast_length > 30`? \n", "\n", "Comment on this in 1 or 2 sentences. Does this make sense?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Problem 8\n", "\n", "Bootstrap resampling is a general purpose way to use empirical data like the `errors` DataFrame to estimate uncertainties. For example, consider the [Viriginia Governor Race](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html). If we wanted to estimate how likey it is that McAuliffe will win given the current RCP data, the approch would be:\n", "\n", "1. Pick a large number N of experiments to run (say N=1000).\n", "2. For each experiment, randomly select a value from `errors.error`. We are assuming that these numbers represent a reasonable error distribution for the current poll data.\n", "3. Assume that the error on McAullife's current polling score is given by this number (and, by extension, the error on Cuccinelli's poll score is the opposite). Calculate who actually wins the election in this simulation.\n", "4. Repeat N times, and calculate the percentage of simulations where either candidate wins.\n", "\n", "Bootstrapping isn't foolproof: it makes the assumption that the previous Governor race errors are representative of the Virginia race, and it does a bad job at estimating very rare events (with only ~30 races in the errors DataFrame, it would be hard to accurately predict probabilities for 1-in-a-million scenarios). Nevertheless, it's a versatile technique.\n", "\n", "Use bootstrap resampling to estimate how likely it is that each candidate could win the following races.\n", "\n", " * [Virginia Governor](http://www.realclearpolitics.com/epolls/2013/governor/va/virginia_governor_cuccinelli_vs_mcauliffe-3033.html)\n", " * [New Jersey Governor](http://www.realclearpolitics.com/epolls/2013/governor/nj/new_jersey_governor_christie_vs_buono-3411.html)\n", " \n", "Summarize your results in a paragraph. What conclusions do you draw from the bootstrap analysis, and what assumptions did you make in reaching this conclusion. What are some limitations of this analysis?\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#your code here\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your summary here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parting Thoughts\n", "\n", "For comparison, most of the predictions in Nate Silver's [presidental forecast](http://fivethirtyeight.blogs.nytimes.com/fivethirtyeights-2012-forecast/) had confidences of >95%. This is more precise than what we can estimate from the RCP poll alone. His approach, however, is the same basic idea (albeit he used many more polls, and carefully calibrated each based on demographic and other information). Homework 2 will dive into some of his techniques further.\n", "\n", "\n", "## How to submit\n", "\n", "To submit your homework, create a folder named lastname_firstinitial_hw0 and place this notebook file in the folder. If your notebook requires any additional data files to run (it shouldn't), add them to this directory as well. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "css tweaks in this cell\n", "<style>\n", "div.text_cell_render {\n", " line-height: 150%;\n", " font-size: 110%;\n", " width: 800px;\n", " margin-left:50px;\n", " margin-right:auto;\n", " }\n", "</style>" ] } ], "metadata": {} } ] }	mit
metpy/MetPy	v0.9/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb	1	4765	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nSimple Sounding\n===============\n\nUse MetPy as straightforward as possible to make a Skew-T LogP plot.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport metpy.calc as mpcalc\nfrom metpy.cbook import get_test_data\nfrom metpy.plots import add_metpy_logo, SkewT\nfrom metpy.units import units" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Change default to be better for skew-T\nplt.rcParams['figure.figsize'] = (9, 9)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Upper air data can be obtained using the siphon package, but for this example we will use\n# some of MetPy's sample data.\n\ncol_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n\ndf = pd.read_fwf(get_test_data('jan20_sounding.txt', as_file_obj=False),\n skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n\ndf['u_wind'], df['v_wind'] = mpcalc.wind_components(df['speed'],\n np.deg2rad(df['direction']))\n\n# Drop any rows with all NaN values for T, Td, winds\ndf = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',\n 'u_wind', 'v_wind'), how='all').reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will pull the data out of the example dataset into individual variables and\nassign units.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = df['pressure'].values * units.hPa\nT = df['temperature'].values * units.degC\nTd = df['dewpoint'].values * units.degC\nwind_speed = df['speed'].values * units.knots\nwind_dir = df['direction'].values * units.degrees\nu, v = mpcalc.wind_components(wind_speed, wind_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "skew = SkewT()\n\n# Plot the data using normal plotting functions, in this case using\n# log scaling in Y, as dictated by the typical meteorological plot\nskew.plot(p, T, 'r')\nskew.plot(p, Td, 'g')\nskew.plot_barbs(p, u, v)\n\n# Add the relevant special lines\nskew.plot_dry_adiabats()\nskew.plot_moist_adiabats()\nskew.plot_mixing_lines()\nskew.ax.set_ylim(1000, 100)\n\n# Add the MetPy logo!\nfig = plt.gcf()\nadd_metpy_logo(fig, 115, 100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Example of defining your own vertical barb spacing\nskew = SkewT()\n\n# Plot the data using normal plotting functions, in this case using\n# log scaling in Y, as dictated by the typical meteorological plot\nskew.plot(p, T, 'r')\nskew.plot(p, Td, 'g')\n\n# Set spacing interval--Every 50 mb from 1000 to 100 mb\nmy_interval = np.arange(100, 1000, 50) * units('mbar')\n\n# Get indexes of values closest to defined interval\nix = mpcalc.resample_nn_1d(p, my_interval)\n\n# Plot only values nearest to defined interval values\nskew.plot_barbs(p[ix], u[ix], v[ix])\n\n# Add the relevant special lines\nskew.plot_dry_adiabats()\nskew.plot_moist_adiabats()\nskew.plot_mixing_lines()\nskew.ax.set_ylim(1000, 100)\n\n# Add the MetPy logo!\nfig = plt.gcf()\nadd_metpy_logo(fig, 115, 100)\n\n# Show the plot\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
intel-analytics/BigDL	apps/ray/parameter_server/sharded_parameter_server.ipynb	1	15424	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# This notebook is adapted from: \n", "https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb\n", "\n", "# Sharded Parameter Servers\n", "\n", "GOAL: The goal of this exercise is to use actor handles to implement a sharded parameter server example for distributed asynchronous stochastic gradient descent.\n", "\n", "Before doing this exercise, make sure you understand the concepts from the exercise on Actor Handles.\n", "\n", "### Parameter Servers\n", "\n", "A parameter server is simply an object that stores the parameters (or \"weights\") of a machine learning model (this could be a neural network, a linear model, or something else). It exposes two methods: one for getting the parameters and one for updating the parameters.\n", "\n", "In a typical machine learning training application, worker processes will run in an infinite loop that does the following:\n", "1. Get the latest parameters from the parameter server.\n", "2. Compute an update to the parameters (using the current parameters and some data).\n", "3. Send the update to the parameter server.\n", "\n", "The workers can operate synchronously (that is, in lock step), in which case distributed training with multiple workers is algorithmically equivalent to serial training with a larger batch of data. Alternatively, workers can operate independently and apply their updates asynchronously. The main benefit of asynchronous training is that a single slow worker will not slow down the other workers. The benefit of synchronous training is that the algorithm behavior is more predictable and reproducible." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import numpy as np\n", "import ray\n", "import time" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Init SparkContext" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current pyspark location is : /root/anaconda2/envs/ray_train/lib/python3.6/site-packages/pyspark/__init__.py\n", "Start to pack current python env\n", "Collecting packages...\n", "Packing environment at '/root/anaconda2/envs/ray_train' to '/tmp/tmp7qvxc3o2/python_env.tar.gz'\n", "[########################################] \| 100% Completed \| 34.4s\n", "Packing has been completed: /tmp/tmp7qvxc3o2/python_env.tar.gz\n", "pyspark_submit_args is: --master yarn --deploy-mode client --archives /tmp/tmp7qvxc3o2/python_env.tar.gz#python_env --num-executors 2 --executor-cores 4 --executor-memory 2g pyspark-shell \n" ] } ], "source": [ "from bigdl.dllib.nncontext import init_spark_on_local, init_spark_on_yarn\n", "import numpy as np\n", "import os\n", "hadoop_conf_dir = os.environ.get('HADOOP_CONF_DIR')\n", "\n", "if hadoop_conf_dir:\n", " sc = init_spark_on_yarn(\n", " hadoop_conf=hadoop_conf_dir,\n", " conda_name=os.environ.get(\"ZOO_CONDA_NAME\", \"zoo\"), # The name of the created conda-env\n", " num_executors=2,\n", " executor_cores=4,\n", " executor_memory=\"2g\",\n", " driver_memory=\"2g\",\n", " driver_cores=1,\n", " extra_executor_memory_for_ray=\"3g\")\n", "else:\n", " sc = init_spark_on_local(cores = 8, conf = {\"spark.driver.memory\": \"2g\"})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start to launch the JVM guarding process\n", "JVM guarding process has been successfully launched\n", "Start to launch ray on cluster\n", "Start to launch ray on local\n", "Executing command: ray start --redis-address 172.16.0.158:34046 --redis-password 123456 --num-cpus 0 --object-store-memory 400000000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2019-07-18 07:09:19,971\tWARNING worker.py:1341 -- WARNING: Not updating worker name since `setproctitle` is not installed. Install this with `pip install setproctitle` (or ray[debug]) to enable monitoring of worker processes.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "2019-07-18 07:09:19,855\tINFO services.py:409 -- Waiting for redis server at 172.16.0.158:34046 to respond...\n", "2019-07-18 07:09:19,858\tINFO scripts.py:363 -- Using IP address 172.16.0.102 for this node.\n", "2019-07-18 07:09:19,861\tINFO node.py:511 -- Process STDOUT and STDERR is being redirected to /tmp/ray/session_2019-07-18_15-09-10_137772_188428/logs.\n", "2019-07-18 07:09:19,862\tINFO services.py:1441 -- Starting the Plasma object store with 0.4 GB memory using /dev/shm.\n", "2019-07-18 07:09:19,887\tINFO scripts.py:371 -- \n", "Started Ray on this node. If you wish to terminate the processes that have been started, run\n", "\n", " ray stop\n", "\n", "\n" ] } ], "source": [ "# It may take a while to ditribute the local environment including python and java to cluster\n", "import ray\n", "from bigdl.orca.ray import OrcaRayContext\n", "ray_ctx = OrcaRayContext(sc=sc, object_store_memory=\"4g\")\n", "ray_ctx.init()\n", "#ray.init(num_cpus=30, include_webui=False, ignore_reinit_error=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple parameter server can be implemented as a Python class in a few lines of code.\n", "\n", "EXERCISE: Make the `ParameterServer` class an actor." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dim = 10\n", "@ray.remote\n", "class ParameterServer(object):\n", " def __init__(self, dim):\n", " self.parameters = np.zeros(dim)\n", " \n", " def get_parameters(self):\n", " return self.parameters\n", " \n", " def update_parameters(self, update):\n", " self.parameters += update\n", "\n", "\n", "ps = ParameterServer.remote(dim)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A worker can be implemented as a simple Python function that repeatedly gets the latest parameters, computes an update to the parameters, and sends the update to the parameter server." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "@ray.remote\n", "def worker(ps, dim, num_iters):\n", " for _ in range(num_iters):\n", " # Get the latest parameters.\n", " parameters = ray.get(ps.get_parameters.remote())\n", " # Compute an update.\n", " update = 1e-3 * parameters + np.ones(dim)\n", " # Update the parameters.\n", " ps.update_parameters.remote(update)\n", " # Sleep a little to simulate a real workload.\n", " time.sleep(0.5)\n", "\n", "# Test that worker is implemented correctly. You do not need to change this line.\n", "ray.get(worker.remote(ps, dim, 1))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Start two workers.\n", "worker_results = [worker.remote(ps, dim, 100) for _ in range(2)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the worker tasks are executing, you can query the parameter server from the driver and see the parameters changing in the background." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[19.16281869 19.16281869 19.16281869 19.16281869 19.16281869 19.16281869\n", " 19.16281869 19.16281869 19.16281869 19.16281869]\n" ] } ], "source": [ "print(ray.get(ps.get_parameters.remote()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sharding a Parameter Server\n", "\n", "As the number of workers increases, the volume of updates being sent to the parameter server will increase. At some point, the network bandwidth into the parameter server machine or the computation down by the parameter server may be a bottleneck.\n", "\n", "Suppose you have $N$ workers and $1$ parameter server, and suppose each of these is an actor that lives on its own machine. Furthermore, suppose the model size is $M$ bytes. Then sending all of the parameters from the workers to the parameter server will mean that $N * M$ bytes in total are sent to the parameter server. If $N = 100$ and $M = 10^8$, then the parameter server must receive ten gigabytes, which, assuming a network bandwidth of 10 gigabits per second, would take 8 seconds. This would be prohibitive.\n", "\n", "On the other hand, if the parameters are sharded (that is, split) across `K` parameter servers, `K` is `100`, and each parameter server lives on a separate machine, then each parameter server needs to receive only 100 megabytes, which can be done in 80 milliseconds. This is much better.\n", "\n", "EXERCISE: The code below defines a parameter server shard class. Modify this class to make `ParameterServerShard` an actor. We will need to revisit this code soon and increase `num_shards`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "@ray.remote\n", "class ParameterServerShard(object):\n", " def __init__(self, sharded_dim):\n", " self.parameters = np.zeros(sharded_dim)\n", " \n", " def get_parameters(self):\n", " return self.parameters\n", " \n", " def update_parameters(self, update):\n", " self.parameters += update\n", "\n", "\n", "total_dim = (10 ** 8) // 8 # This works out to 100MB (we have 25 million\n", " # float64 values, which are each 8 bytes).\n", "num_shards = 2 # The number of parameter server shards.\n", "\n", "assert total_dim % num_shards == 0, ('In this exercise, the number of shards must '\n", " 'perfectly divide the total dimension.')\n", "\n", "# Start some parameter servers.\n", "ps_shards = [ParameterServerShard.remote(total_dim // num_shards) for _ in range(num_shards)]\n", "\n", "assert hasattr(ParameterServerShard, 'remote'), ('You need to turn ParameterServerShard into an '\n", " 'actor (by using the ray.remote keyword).')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below implements a worker that does the following.\n", "1. Gets the latest parameters from all of the parameter server shards.\n", "2. Concatenates the parameters together to form the full parameter vector.\n", "3. Computes an update to the parameters.\n", "4. Partitions the update into one piece for each parameter server.\n", "5. Applies the right update to each parameter server shard." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "@ray.remote\n", "def worker_task(total_dim, num_iters, ps_shards):\n", " # Note that ps_shards are passed in using Python's variable number\n", " # of arguments feature. We do this because currently actor handles\n", " # cannot be passed to tasks inside of lists or other objects.\n", " for _ in range(num_iters):\n", " # Get the current parameters from each parameter server.\n", " parameter_shards = [ray.get(ps.get_parameters.remote()) for ps in ps_shards]\n", " assert all([isinstance(shard, np.ndarray) for shard in parameter_shards]), (\n", " 'The parameter shards must be numpy arrays. Did you forget to call ray.get?')\n", " # Concatenate them to form the full parameter vector.\n", " parameters = np.concatenate(parameter_shards)\n", " assert parameters.shape == (total_dim,)\n", "\n", " # Compute an update.\n", " update = np.ones(total_dim)\n", " # Shard the update.\n", " update_shards = np.split(update, len(ps_shards))\n", " \n", " # Apply the updates to the relevant parameter server shards.\n", " for ps, update_shard in zip(ps_shards, update_shards):\n", " ps.update_parameters.remote(update_shard)\n", "\n", "\n", "# Test that worker_task is implemented correctly. You do not need to change this line.\n", "ray.get(worker_task.remote(total_dim, 1, ps_shards))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Experiment by changing the number of parameter server shards, the number of workers, and the size of the data.\n", "\n", "NOTE: Because these processes are all running on the same machine, network bandwidth will not be a limitation and sharding the parameter server will not help. To see the difference, you would need to run the application on multiple machines. There are still regimes where sharding a parameter server can help speed up computation on the same machine (by parallelizing the computation that the parameter server processes have to do). If you want to see this effect, you should implement a synchronous training application. In the asynchronous setting, the computation is staggered and so speeding up the parameter server usually does not matter." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This took 4.21185827255249 seconds.\n" ] } ], "source": [ "num_workers = 4\n", "\n", "# Start some workers. Try changing various quantities and see how the\n", "# duration changes.\n", "start = time.time()\n", "ray.get([worker_task.remote(total_dim, 5, *ps_shards) for _ in range(num_workers)])\n", "print('This took {} seconds.'.format(time.time() - start))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (ray_train)", "language": "python", "name": "ray_train" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
Zhang-O/small	tensor__cpu/skimages/get_started.ipynb	2	336126	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import skimage\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# %matplotlib\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage import data\n", "camera = data.camera()\n", "\n", "type(camera)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((512, 512),\n", " 262144,\n", " 0,\n", " 255,\n", " 118.31400299072266,\n", " 61.996043576099197,\n", " 3843.5094190895907)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "camera.shape, camera.size, camera.min(), camera.max(), camera.mean(), camera.std(), camera.var()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x19383128320>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jLMwhzWA0DB5nz1KELmjKLRdqcE+FTelZCgEhuQO4Tmm0WTIRCuR5XQTg4ilG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9CjmsE7d1MsHWDPqYPzHstVP/gax3/ycn78PocRbXzhq9n2mrXc+KlSSOYl77mM73z6n+L/z/3g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\nIR7MkToGvQ9czx0f/01kp4e20iAo2/gljd5EDlnIMvm1GtnzTaKSTXcqh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udoTIM1V6d0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"text/plain": [ "<matplotlib.figure.Figure at 0x19383071208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask = camera < 87\n", "camera[mask] = 255\n", "plt.imshow(camera)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x193831c2e48>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QsDFHqdTMXs87LLwQu19oO3uPKaQSFmxIpluJcwCyoPa1cIyaj8SM0BlNZoSc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\ntA6UTvHwqbeWj5SWM1tuvEWleaZTUWo+bZ8VdNrolv0oYGO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rUwWf4vb7/r3KKksiWx9d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VyFr4O7XkM9pmhGjG+j7VhUp6Fz9NnlGH7oO7/EK8bu9sIh+LCwq6Afvrau8vLdBqiA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"text/plain": [ "<matplotlib.figure.Figure at 0x1938315a128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inds_r = np.arange(len(camera))\n", "inds_c = 4 * inds_r % len(camera)\n", "camera[inds_r, inds_c] = 0\n", "plt.imshow(camera)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x19383254e48>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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skUsDWalIHpdzkhRFuAwiGjtlWr78xXO54T/vX3xhd3bjr8b32fAj7+Xue4/O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1r4Mm2DNazm4J4LO60cw8Vv+SqiEMiBRPYlyVGNGCrkMY1aUIhSIgqJWNLI3StBtReC6YkSpkrk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\nHOMYxxnx/wPSE1pcg54v5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x193ff4072e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrows, ncols = camera.shape\n", "row, col = np.ogrid[:nrows, :ncols]\n", "\n", "cnt_row, cnt_col = nrows / 2, ncols / 2\n", "outer_disk_mask = (row - cnt_row)2 + (col - cnt_col)2 > (nrows / 2)**2\n", "camera[outer_disk_mask] = 0\n", "plt.imshow(camera)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[1],\n", " [2]]), array([[1, 2]]), array([[2, 3],\n", " [3, 4]]))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array([[1],[2,]]), np.array([[1,2]]), np.array([[1],[2,]]) + np.array([[1,2]])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x193832f3048>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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skUsDWalIHpdzkhRFuAwiGjtlWr78xXO54T/vX3xhd3bjr8b32fAj7+Xue4/O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1r4Mm2DNazm4J4LO60cw8Vv+SqiEMiBRPYlyVGNGCrkMY1aUIhSIgqJWNLI3StBtReC6YkSpkrk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\nI+TzwqDMcS1P64EE1yAeAVBU6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"text/plain": [ "<matplotlib.figure.Figure at 0x193ff407278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lower_half = row > cnt_row\n", "lower_half_disk = np.logical_and(lower_half, outer_disk_mask)\n", "camera[lower_half_disk] = 0\n", "plt.imshow(camera)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jeroenjanssens/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers	Chapter5_LossFunctions/LossFunctions.ipynb	4	1358823	null	mit
roatienza/Deep-Learning-Experiments	versions/2020/keras/cnn/cnn-siamese.ipynb	1	14604	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Implements a Siamese/Y-Network using Functional API\n", "\n", "~99.4% test accuracy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) (None, 28, 28, 1) 0 \n", "__________________________________________________________________________________________________\n", "input_2 (InputLayer) (None, 28, 28, 1) 0 \n", "__________________________________________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 28, 28, 32) 320 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 28, 28, 32) 320 input_2[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 28, 28, 32) 0 conv2d_1[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_4 (Dropout) (None, 28, 28, 32) 0 conv2d_4[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2D) (None, 14, 14, 32) 0 dropout_1[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_4 (MaxPooling2D) (None, 14, 14, 32) 0 dropout_4[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 14, 14, 64) 18496 max_pooling2d_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 14, 14, 64) 18496 max_pooling2d_4[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 14, 14, 64) 0 conv2d_2[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_5 (Dropout) (None, 14, 14, 64) 0 conv2d_5[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_2 (MaxPooling2D) (None, 7, 7, 64) 0 dropout_2[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_5 (MaxPooling2D) (None, 7, 7, 64) 0 dropout_5[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 7, 7, 128) 73856 max_pooling2d_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_6 (Conv2D) (None, 7, 7, 128) 73856 max_pooling2d_5[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_3 (Dropout) (None, 7, 7, 128) 0 conv2d_3[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_6 (Dropout) (None, 7, 7, 128) 0 conv2d_6[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_3 (MaxPooling2D) (None, 3, 3, 128) 0 dropout_3[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling2d_6 (MaxPooling2D) (None, 3, 3, 128) 0 dropout_6[0][0] \n", "__________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 3, 3, 256) 0 max_pooling2d_3[0][0] \n", " max_pooling2d_6[0][0] \n", "__________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 2304) 0 concatenate_1[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_7 (Dropout) (None, 2304) 0 flatten_1[0][0] \n", "__________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 10) 23050 dropout_7[0][0] \n", "==================================================================================================\n", "Total params: 208,394\n", "Trainable params: 208,394\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n", "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/20\n", "60000/60000 [==============================] - 351s 6ms/step - loss: 0.1769 - acc: 0.9435 - val_loss: 0.1412 - val_acc: 0.9904\n", "Epoch 2/20\n", "60000/60000 [==============================] - 361s 6ms/step - loss: 0.0664 - acc: 0.9795 - val_loss: 0.0923 - val_acc: 0.9903\n", "Epoch 3/20\n", "60000/60000 [==============================] - 359s 6ms/step - loss: 0.0528 - acc: 0.9835 - val_loss: 0.0772 - val_acc: 0.9908\n", "Epoch 4/20\n", "60000/60000 [==============================] - 286s 5ms/step - loss: 0.0471 - acc: 0.9854 - val_loss: 0.0836 - val_acc: 0.9930\n", "Epoch 5/20\n", "60000/60000 [==============================] - 244s 4ms/step - loss: 0.0429 - acc: 0.9861 - val_loss: 0.0736 - val_acc: 0.9931\n", "Epoch 6/20\n", "60000/60000 [==============================] - 242s 4ms/step - loss: 0.0393 - acc: 0.9878 - val_loss: 0.0507 - val_acc: 0.9931\n", "Epoch 7/20\n", "60000/60000 [==============================] - 183s 3ms/step - loss: 0.0364 - acc: 0.9883 - val_loss: 0.0434 - val_acc: 0.9934\n", "Epoch 8/20\n", "60000/60000 [==============================] - 141s 2ms/step - loss: 0.0364 - acc: 0.9890 - val_loss: 0.0471 - val_acc: 0.9931\n", "Epoch 9/20\n", "60000/60000 [==============================] - 139s 2ms/step - loss: 0.0358 - acc: 0.9892 - val_loss: 0.0384 - val_acc: 0.9938\n", "Epoch 10/20\n", "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0342 - acc: 0.9891 - val_loss: 0.0500 - val_acc: 0.9915\n", "Epoch 11/20\n", "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0327 - acc: 0.9895 - val_loss: 0.0328 - val_acc: 0.9919\n", "Epoch 12/20\n", "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0328 - acc: 0.9896 - val_loss: 0.0364 - val_acc: 0.9928\n", "Epoch 13/20\n", "60000/60000 [==============================] - 139s 2ms/step - loss: 0.0334 - acc: 0.9898 - val_loss: 0.0322 - val_acc: 0.9938\n", "Epoch 14/20\n", "60000/60000 [==============================] - 143s 2ms/step - loss: 0.0306 - acc: 0.9908 - val_loss: 0.0346 - val_acc: 0.9942\n", "Epoch 15/20\n", "60000/60000 [==============================] - 180s 3ms/step - loss: 0.0319 - acc: 0.9903 - val_loss: 0.0417 - val_acc: 0.9942\n", "Epoch 16/20\n", "60000/60000 [==============================] - 148s 2ms/step - loss: 0.0306 - acc: 0.9906 - val_loss: 0.0308 - val_acc: 0.9930\n", "Epoch 17/20\n", "60000/60000 [==============================] - 149s 2ms/step - loss: 0.0299 - acc: 0.9906 - val_loss: 0.0300 - val_acc: 0.9945\n", "Epoch 18/20\n", "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0316 - acc: 0.9904 - val_loss: 0.0333 - val_acc: 0.9923\n", "Epoch 19/20\n", "60000/60000 [==============================] - 141s 2ms/step - loss: 0.0279 - acc: 0.9917 - val_loss: 0.0255 - val_acc: 0.9942\n", "Epoch 20/20\n", "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0297 - acc: 0.9910 - val_loss: 0.0249 - val_acc: 0.9938\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "10000/10000 [==============================] - 4s 377us/step\n", "\n", "Test accuracy: 99.4%\n" ] } ], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import numpy as np\n", "\n", "from keras.layers import Dense, Dropout, Input\n", "from keras.layers import Conv2D, MaxPooling2D, Flatten\n", "from keras.models import Model\n", "from keras.layers.merge import concatenate\n", "from keras.datasets import mnist\n", "from keras.utils import to_categorical\n", "from keras.utils import plot_model\n", "\n", "# load MNIST dataset\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "\n", "# from sparse label to categorical\n", "num_labels = len(np.unique(y_train))\n", "y_train = to_categorical(y_train)\n", "y_test = to_categorical(y_test)\n", "\n", "# reshape and normalize input images\n", "image_size = x_train.shape[1]\n", "x_train = np.reshape(x_train,[-1, image_size, image_size, 1])\n", "x_test = np.reshape(x_test,[-1, image_size, image_size, 1])\n", "x_train = x_train.astype('float32') / 255\n", "x_test = x_test.astype('float32') / 255\n", "\n", "# network parameters\n", "input_shape = (image_size, image_size, 1)\n", "batch_size = 32\n", "kernel_size = 3\n", "dropout = 0.4\n", "n_filters = 32\n", "\n", "# left branch of Y network\n", "left_inputs = Input(shape=input_shape)\n", "x = left_inputs\n", "filters = n_filters\n", "# 3 layers of Conv2D-Dropout-MaxPooling2D\n", "# number of filters doubles after each layer (32-64-128)\n", "for i in range(3):\n", " x = Conv2D(filters=filters,\n", " kernel_size=kernel_size,\n", " padding='same',\n", " activation='relu')(x)\n", " x = Dropout(dropout)(x)\n", " x = MaxPooling2D()(x)\n", " filters = 2\n", "\n", "# right branch of Y network\n", "right_inputs = Input(shape=input_shape)\n", "y = right_inputs\n", "filters = n_filters\n", "# 3 layers of Conv2D-Dropout-MaxPooling2D\n", "# number of filters doubles after each layer (32-64-128)\n", "for i in range(3):\n", " y = Conv2D(filters=filters,\n", " kernel_size=kernel_size,\n", " padding='same',\n", " activation='relu',\n", " dilation_rate=2)(y)\n", " y = Dropout(dropout)(y)\n", " y = MaxPooling2D()(y)\n", " filters = 2\n", "\n", "# merge left and right branches outputs\n", "y = concatenate([x, y])\n", "# feature maps to vector in preparation to connecting to Dense layer\n", "y = Flatten()(y)\n", "y = Dropout(dropout)(y)\n", "outputs = Dense(num_labels, activation='softmax')(y)\n", "\n", "# build the model in functional API\n", "model = Model([left_inputs, right_inputs], outputs)\n", "# verify the model using graph\n", "plot_model(model, to_file='cnn-y-network.png', show_shapes=True)\n", "# verify the model using layer text description\n", "model.summary()\n", "\n", "# classifier loss, Adam optimizer, classifier accuracy\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "\n", "# train the model with input images and labels\n", "model.fit([x_train, x_train],\n", " y_train, \n", " validation_data=([x_test, x_test], y_test),\n", " epochs=20,\n", " batch_size=batch_size)\n", "\n", "# model accuracy on test dataset\n", "score = model.evaluate([x_test, x_test], y_test, batch_size=batch_size)\n", "print(\"\\nTest accuracy: %.1f%%\" % (100.0 * score[1]))\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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
root-mirror/training	SummerStudentCourse/2019/Exercises/ROOTBooks/CentralLimitTheorem_Solution.ipynb	5	50466	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Central Limit Theorem\n", "\n", "In this exercise we will show what is the Central Limit Theorem and how it applies " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 1: Generate random number according to a uniform distriobution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first goal is to generate n random numbers according to a uniform distribution between [-1,1], fill an histogram and compute the average of the generated numbers.\n", "Display also the obtained histogram.\n", "\n", "Let's start with n = 10" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "int n = 10; \n", "TRandom3 r(0); // initialize with zero to have a random seed " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Create and book the histogram" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "auto h1 = new TH1D(\"h1\",\"Distribution of generated values\",100,-1,1);" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Generate the numbers and fill the histogram.\n", "You can compute the average directly or let the histogram computing it for you" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for (int i = 0; i < n; ++i) {\n", " double x = r.Uniform(-1,1);\n", " h1->Fill(x); \n", "}" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "scrolled": true }, "source": [ "Display the histogram and print out the average result" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fAgAedcGgIBAAwF50PQAAWYICAJAlKAAAWYICAJAlKAAAWYICAJAlKAAAWRec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\nA8XQyPVr5oXjAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "sample mean = 0.265254\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Info in <TCanvas::MakeDefCanvas>: created default TCanvas with name c1\n" ] } ], "source": [ "h1->Draw();\n", "gPad->Draw();\n", "std::cout << \"sample mean = \" << h1->GetMean() << std::endl;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 2: Study the distribution of the sample mean composed of n numbers uniformly distributed\n", "\n", "Now repeat many times what has been done before to study the distribution of the average, $\\mu$. The exercise will show that this distribution will converge very quickly to a Gaussian distribution. It is enough to have a very small \n", "$n$ to get already a pretty good Gaussian. \n", "For having the sigma of the distribution indipendent on the number of generated events $n$, we will \n", "make an histogram of $\\sqrt{n} \\times \\mu$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do then as following: \n", "* Make a loop where for each time $n$ uniform numbers are generated and their average $\\mu$ is computed. \n", "* Make an histogram now of $\\sqrt{n} \\times \\mu$.\n", "\n", "Start using a very small $n$ (e.g. $n=2$) but use for the loop, which performs the generation of $n$ numbers, a large value (e.g. $n_{experiments} = 10000$. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "auto hout = new TH1D(\"h\",\"Sample Mean Distribution\",50,-2,2);\n", "int nexp = 10000; " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 2;\n", "hout->Reset(); // for running this cell a second time\n", "for (int iexp = 0; iexp < nexp; ++iexp){ \n", " h1->Reset();\n", " for (int i = 0; i < n; ++i) {\n", " h1->Fill(r.Uniform(-1,1));\n", " }\n", " double average = sqrt(n)h1->GetMean();\n", " hout->Fill(average);\n", "}\n", "hout->Draw();\n", "gPad->Draw();" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Part 3: Fit the obtained histogram with a Gaussian function\n", "\n", "we perform now a fit with a Gaussian distribution and see how the obtained data agree with the function" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hEJV6hbaHQnXYcUWAKg6qUbDZgQMY864HqQmweyPKaym56XkA/3AtCyA4YwaF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\notEB8BFZAdtyvY8CDkfrF1u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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " FCN=226.458 FROM MIGRAD STATUS=CONVERGED 59 CALLS 60 TOTAL\n", " EDM=9.76666e-11 STRATEGY= 1 ERROR MATRIX ACCURATE \n", " EXT PARAMETER STEP FIRST \n", " NO. NAME VALUE ERROR SIZE DERIVATIVE \n", " 1 Constant 5.32296e+02 6.54072e+00 3.96502e-02 5.18912e-07\n", " 2 Mean 3.63931e-03 6.37389e-03 4.68616e-05 -6.85231e-04\n", " 3 Sigma 5.95212e-01 4.63812e-03 1.58913e-05 5.77603e-03\n" ] } ], "source": [ "hout->Fit(\"gaus\");\n", "gPad->Draw();\n", "gStyle->SetOptFit(1111);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Repeat the operation above by increasing $n$ to a larger value (e.g. $n=10$). For the Central Limit Theorem as $n$ is increased the obtained distribution will converge to a Gaussian. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question* : What is the computed standard deviation of the distribution when we generate $n$ uniform number between [-1,1] ? \n", "What will be then the $\\sigma$ if I generate the number between $[-\\sqrt{3},\\sqrt{3}]$ ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer : $\\sigma = 2/\\sqrt{12}$ if we generate between [-1,1] and $\\sigma = 1$ if $x \\ \\epsilon \\ [-\\sqrt{3},\\sqrt{3}]$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "ROOT C++", "language": "c++", "name": "root" }, "language_info": { "codemirror_mode": "text/x-c++src", "file_extension": ".C", "mimetype": " text/x-c++src", "name": "c++" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
llooker/public-datasets-pipelines	samples/tutorial.ipynb	3	6210	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "7i8lsRFe2lu5" }, "source": [ "# Overview\n", "\n", "Add a brief description of this tutorial here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cFeLh7K7KI6B" }, "outputs": [], "source": [ "%%capture\n", "\n", "# Installing the required libraries:\n", "!pip install matplotlib pandas scikit-learn tensorflow pyarrow tqdm\n", "!pip install google-cloud-bigquery google-cloud-bigquery-storage\n", "!pip install flake8 pycodestyle pycodestyle_magic" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6_jTxerkMtkg" }, "outputs": [], "source": [ "# Python Builtin Libraries\n", "from datetime import datetime\n", "\n", "# Third Party Libraries\n", "from google.cloud import bigquery\n", "\n", "# Configurations\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "id": "2x4wG61omjBQ" }, "source": [ "### Authentication\n", "In order to run this tutorial successfully, we need to be authenticated first. \n", "\n", "Depending on where we are running this notebook, the authentication steps may vary:\n", "\n", "\| Runner \| Authentiction Steps \|\n", "\| ----------- \| ----------- \|\n", "\| Local Computer \| Use a service account, or run the following command: <br><br>`gcloud auth login` \|\n", "\| Colab \| Run the following python code and follow the instructions: <br><br>`from google.colab import auth` <br> `auth.authenticate_user() ` \|\n", "\| Vertext AI (Workbench) \| Authentication is provided by Workbench \|" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i7aszhgnkxuv" }, "outputs": [], "source": [ "try:\n", " from google.colab import auth\n", "\n", " print(\"Authenticating in Colab\")\n", " auth.authenticate_user()\n", " print(\"Authenticated\")\n", "except: # noqa\n", " print(\"This notebook is not running on Colab.\")\n", " print(\"Please make sure to follow the authentication steps.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "N7qEYK98Nx89" }, "source": [ "### Configurations\n", "\n", "Let's make sure we enter the name of our GCP project in the next cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "So_ed4wf0lKu" }, "outputs": [], "source": [ "# ENTER THE GCP PROJECT HERE\n", "gcp_project = \"YOUR-GCP-PROJECT\"\n", "print(f\"gcp_project is set to {gcp_project}\")" ] }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "xhyBJNts3MHK" } }, { "cell_type": "code", "source": [ "def helper_function():\n", " \"\"\"\n", " Add a description about what this function does.\n", " \"\"\"\n", " return None" ], "metadata": { "id": "eo0IVOdr3MSU" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UXIitTXv7IEu" }, "source": [ "## Data Preparation" ] }, { "cell_type": "markdown", "metadata": { "id": "LBLKdccfZzKu" }, "source": [ "### Query the Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P1VfAL04kwF7" }, "outputs": [], "source": [ "query = \"\"\"\n", " SELECT\n", " created_date, category, complaint_type, neighborhood, latitude, longitude\n", " FROM\n", " `bigquery-public-data.san_francisco_311.311_service_requests`\n", " LIMIT 1000;\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eoJgVS5KkwF7" }, "outputs": [], "source": [ "bqclient = bigquery.Client(project=gcp_project)\n", "dataframe = bqclient.query(query).result().to_dataframe()" ] }, { "cell_type": "markdown", "metadata": { "id": "_I8mnYhOBsnr" }, "source": [ "### Check the Dataframe\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "84lvVNg8odvS" }, "outputs": [], "source": [ "print(dataframe.shape)\n", "dataframe.head()" ] }, { "cell_type": "markdown", "source": [ "### Process the Dataframe" ], "metadata": { "id": "K3-yckkMKX3g" } }, { "cell_type": "code", "source": [ "# Convert the datetime to date\n", "dataframe['created_date'] = dataframe['created_date'].apply(datetime.date)" ], "metadata": { "id": "fFMgzswOG0Wz" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Cloud Datasets - Tutorial Template", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
stereoboy/Study	Issues/algorithms/Linked Lists.ipynb	1	6428	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Add two numbers represented by linked lists" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Node():\n", " def __init__(self, value, Next=None):\n", " self.value = value\n", " self.next = Next\n", " def __str__(self):\n", " if self.next == None:\n", " return str(self.value)\n", " return str(self.value) + '-' + self.next.__str__()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 0\n", "0 1\n", "4 1\n", "3 0\n", "1 0\n", "1-3-4-0-5\n" ] } ], "source": [ "def getSize(node):\n", " count = 0\n", " while node != None:\n", " count += 1\n", " node = node.next\n", " return count\n", " \n", "def _add(node0, size0, node1, size1):\n", " \n", " if node0 == None:\n", " return None, 0\n", " \n", " if size0 > size1:\n", " node, carry = _add(node0.next, size0 - 1, node1, size1)\n", " \n", " new = node0.value + carry\n", " else:\n", " node, carry = _add(node0.next, size0 - 1, node1.next, size1 -1)\n", " \n", " new = node0.value + node1.value + carry\n", " \n", " carry = new//10 \n", " new = new%10\n", " print(new, carry)\n", " return Node(new, node), carry\n", "\n", "def add(node0, size0, node1, size1):\n", " \n", " node, carry = _add(node0, size0, node1, size1)\n", " if carry > 0:\n", " node = Node(1, node)\n", " \n", " return node\n", "\n", "a = Node(1, Node(2, Node(5, Node(6, Node(3, None)))))\n", "b = Node(8, Node(4, Node(2, None)))\n", "\n", "print(add(a, getSize(a), b, getSize(b)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 Remove Dups: \n", "Write code to remove duplicates from an unsorted linked list.\n", "FOLLOW UP\n", "How would you solve this problem if a temporary buffer is not allowed?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input:1-2-3-4-4-4-3-2-1\n", "output:1-2-3-4\n" ] } ], "source": [ " \n", "List = Node(1, Node(2, Node(3, Node(4, Node(4, Node(4, Node(3, Node(2, Node(1)))))))))\n", "\n", "def remove_dups(List):\n", " marks = {}\n", " cur = List\n", " prev = None\n", " while cur != None:\n", " if marks.get(cur.value, 0) == 0: # not duplicated\n", " marks[cur.value] = 1\n", " else: # duplicated\n", " prev.next = cur.next\n", " cur = prev\n", " \n", " prev = cur\n", " cur = cur.next\n", "\n", "print('input:' + str(List))\n", "remove_dups(List)\n", "print('output:' + str(List))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input:1-2-3-4-4-4-3-2-1-3-2\n", "output:1-2-3-4\n" ] } ], "source": [ "def remove_dups_wo_buffer(List):\n", " cur0 = List\n", " while cur0 != None:\n", " prev = cur0\n", " cur1 = cur0.next\n", " while cur1 != None:\n", " if cur1.value == cur0.value:\n", " prev.next = cur1.next\n", " cur1 = prev\n", " prev = cur1\n", " cur1 = cur1.next\n", " \n", " cur0 = cur0.next\n", " \n", "List = Node(1, Node(2, Node(3, Node(4, Node(4, Node(4, Node(3, Node(2, Node(1, Node(3, Node(2)))))))))))\n", "\n", "print('input:' + str(List))\n", "remove_dups_wo_buffer(List)\n", "print('output:' + str(List))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Return Kth to Last: \n", "Implement an algorithm to find the kth to last element of a singly linked list." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "List = Node(1, Node(2, Node(3, Node(4, Node(4, Node(4, Node(3, Node(2, Node(1, Node(3, Node(2)))))))))))\n", "\n", "def kth_to_last(List, k):\n", " cur = List\n", " size = 0\n", " while cur != None:\n", " size += 1\n", " cur = cur.next\n", " if size < k:\n", " return None\n", " \n", " cur = List\n", " for _ in range(size - k):\n", " cur = cur.next\n", " return cur.value\n", "\n", "print(kth_to_last(List, 4))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2-1-3-2\n" ] } ], "source": [ "def kth_to_last(head, k, i):\n", "\n", " if head == None:\n", " return None\n", " \n", " node = kth_to_last(head.next, k, i)\n", " i[0] = i[0] + 1\n", " if i[0] == k:\n", " return head\n", " else:\n", " return node\n", " \n", "print(kth_to_last(List, 4, [0]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7f7bea452dc0>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "from folium import plugins\n", "\n", "\n", "m = folium.Map([40.0, -105.0], zoom_start=6)\n", "\n", "url = \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data/us-states.json\"\n", "stripes = plugins.pattern.StripePattern(angle=-45)\n", "stripes.add_to(m)\n", "\n", "circles = plugins.pattern.CirclePattern(\n", " width=20, height=20, radius=5, fill_opacity=0.5, opacity=1\n", ")\n", "circles.add_to(m)\n", "\n", "\n", "def style_function(feature):\n", " default_style = {\n", " \"opacity\": 1.0,\n", " \"fillColor\": \"#ffff00\",\n", " \"color\": \"black\",\n", " \"weight\": 2,\n", " }\n", "\n", " if feature[\"properties\"][\"name\"] == \"Colorado\":\n", " default_style[\"fillPattern\"] = stripes\n", " default_style[\"fillOpacity\"] = 1.0\n", "\n", " if feature[\"properties\"][\"name\"] == \"Utah\":\n", " default_style[\"fillPattern\"] = circles\n", " default_style[\"fillOpacity\"] = 1.0\n", "\n", " return default_style\n", "\n", "\n", "# Adding remote GeoJSON as additional layer.\n", "folium.GeoJson(url, smooth_factor=0.5, style_function=style_function).add_to(m)\n", "\n", "m" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
TimothyADavis/KinMSpy	kinms/docs/KinMSpy_tutorial.ipynb	1	227468	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# KinMS galaxy fitting tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial aims at getting you up and running with galaxy kinematic modelling using _KinMS_! To start you will need to download the KinMSpy code and have it in your python path. \n", "\n", "To do this you can simply call `pip install kinms`\n", "\n", "To get started with kinematic modelling we will complete the following steps:\n", "1. Generate a model to fit (can be skipped if you have your own observed data cube)\n", "2. Read in that cube, and extract the important information from the header\n", "3. Fit the data using an MCMC code\n", "\n", "We will start by importing a variety of modules we will need to work with KinMS, and plot its output." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from kinms import KinMS\n", "import numpy as np\n", "from astropy.io import fits\n", "from kinms.utils.KinMS_figures import KinMS_plotter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate a model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will generate a simple galaxy model using KinMS itself, that we can attempt to determine the parameters of later. If you have your own observed galaxy to fit then of course this step can be skipped!\n", "\n", "The `make_model` function below creates a simple exponential disc:\n", "\n", "$\n", "\\begin{align}\n", "\\large \\Sigma_{H2}(r) \\propto e^{\\frac{-r}{d_{scale}}}\n", "\\end{align}\n", "$\n", "\n", "with a circular velocity profile which is parameterized using an arctan function:\n", "\n", "$\n", "\\begin{align}\n", "\\large V(r) = \\frac{2V_{flat}}{\\pi} \\arctan\\left(\\frac{r}{r_{turn}}\\right)\n", "\\end{align}\n", "$\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def make_model(param,obspars,rad,filename=None,plot=False):\n", " '''\n", " This function takes in the `param` array (along with obspars; the observational setup,\n", " and a radius vector `rad`) and uses it to create a KinMS model.\n", " '''\n", " \n", " total_flux=param[0]\n", " posAng=param[1]\n", " inc=param[2]\n", " v_flat=param[3]\n", " r_turn=param[4]\n", " scalerad=param[5]\n", " \n", " ### Here we use an exponential disk model for the surface brightness of the gas ###\n", " sbprof = np.exp((-1)rad/scalerad)\n", "\n", " ### We use a very simple arctan rotation curve model with two free parameters. ###\n", " vel=(v_flat2/np.pi)np.arctan(rad/r_turn)\n", "\n", " ### This returns the model\n", " return KinMS(obspars['xsize'],obspars['ysize'],obspars['vsize'],obspars['cellsize'],obspars['dv'],\\\n", " obspars['beamsize'],inc,sbProf=sbprof,sbRad=rad,velRad=rad,velProf=vel,\\\n", " intFlux=total_flux,posAng=posAng,fixSeed=True,fileName=filename).model_cube(toplot=plot)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we have set `fixSeed=True` in the KinMS call - this is crucial if you are fitting with KinMS. It ensures if you generate two models with the same input parameters you will get an identical output model! \n", "\n", "Now we have our model function, lets use it to generate a model which we will later fit. The first thing we need is to define the setup of our desired datacube (typically if you are fitting real data this will all be determined from the header keywords- see below). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "### Setup cube parameters ###\n", "obspars={}\n", "obspars['xsize']=64.0 # arcseconds\n", "obspars['ysize']=64.0 # arcseconds\n", "obspars['vsize']=500.0 # km/s\n", "obspars['cellsize']=1.0 # arcseconds/pixel\n", "obspars['dv']=20.0 # km/s/channel\n", "obspars['beamsize']=np.array([4.0,4.0,0]) # [bmaj,bmin,bpa] in (arcsec, arcsec, degrees)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need to create a radius vector- you ideally want this to oversample your pixel grid somewhat to avoid interpolation errors!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "rad=np.arange(0,100,0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have all the ingredients we can create our data to fit. Here we will also output the model to disc, so we can demonstrate how to read in the header keywords from real ALMA/VLA etc data. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "'''\n", "True values for the flux, posang, inc etc, as defined in the model function\n", "'''\n", "\n", "guesses=np.array([30.,270.,45.,200.,2.,5.]) \n", "\n", "'''\n", "RMS of data. Here we are making our own model so this is arbitary. \n", "When fitting real data this should be the observational RMS\n", "'''\n", "error=np.array(1e-3)\n", "\n", "\n", "fdata=make_model(guesses,obspars,rad, filename=\"Test\",plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we already have our data in memory. But if you are fitting a real datacube this wont be the case! Here we read in the model we just created from a FITS file to make it clear how to do this." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "### Load in your observational data ###\n", "hdulist = fits.open('Test_simcube.fits',ignore_blank=True)\n", "fdata = hdulist[0].data.T \n", "\n", "\n", "### Setup cube parameters ###\n", "obspars={}\n", "obspars['cellsize']=np.abs(hdulist[0].header['cdelt1']3600.) # arcseconds/pixel\n", "obspars['dv']=np.abs(hdulist[0].header['cdelt3']/1e3) # km/s/channel\n", "obspars['xsize']=hdulist[0].header['naxis1']obspars['cellsize'] # arcseconds\n", "obspars['ysize']=hdulist[0].header['naxis2']obspars['cellsize'] # arcseconds\n", "obspars['vsize']=hdulist[0].header['naxis3']obspars['dv'] # km/s\n", "obspars['beamsize']=np.array([hdulist[0].header['bmaj']3600.,hdulist[0].header['bmin']3600.,hdulist[0].header['bpa']])# [bmaj,bmin,bpa] in (arcsec, arcsec, degrees)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit the model\n", "\n", "Now we have our 'observational' data read into memory, and a model function defined, we can fit one to the other! As our fake model is currently noiseless, lets add some gaussian noise (obviously dont do this if your data is from a real telecope!):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "fdata+=(np.random.normal(size=fdata.shape)error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we will proceed using the MCMC code GAStimator which was specifically designed to work with KinMS, however any minimiser should work in principle. For full details of how this code works, and a tutorial, see https://github.com/TimothyADavis/GAStimator ." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from gastimator import gastimator,corner_plot\n", "\n", "mcmc = gastimator(make_model,obspars,rad)\n", "\n", "mcmc.labels=np.array(['Flux','posAng',\"Inc\",\"VFlat\",\"R_turn\",\"scalerad\"])\n", "mcmc.min=np.array([30.,1.,10,50,0.1,0.1])\n", "mcmc.max=np.array([30.,360.,80,400,20,10])\n", "mcmc.fixed=np.array([True,False,False,False,False,False])\n", "mcmc.precision=np.array([1.,1.,1.,10,0.1,0.1])\n", "mcmc.guesses=np.array([30.,275.,55.,210.,2.5,4.5]) #starting guesses, purposefully off!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting good priors on the flux of your source is crucial to ensure the model outputs are physical. Luckily the integrated flux of your source should be easy to measure from your datacube! If you have a good measurement of this, then I would recommend forcing the total flux to that value by fixing it in the model (set `mcmc.fixed=True` for that parameter). If you can only get a guess then set as tight a prior as you can. This stops the model hiding bad fitting components below the noise level. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Its always a good idea to plot your model over your data before you start a fitting processes. That allows you to check that the model is reasonable, and tweak the parameters by hand to get good starting guesses. Firs you should generate a cube from your model function, then you can overplot it on your data using the simple plotting tool included with KinMS:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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iMCiMBoVSipi4pEyn83JyciCwrD/BNQOpVaMs1auUoVaNclSqEJB1n9103Q7SCwoqw5xZE3lj/CDemzyHL2d+x5Lv1jHlveE8179L5teVUGs3pM6K/JI+yB5fZVtXA7j37gaZ9Z29U6AtVCTQ5ps8nc1Aax2CdblFlFLdgLbAWKwFFEBn8g/qzQ0bsRbXB7XW+/PyPoUdyEGQTU5OZuas75nw9pfcuBFF1y5tmDT6cWpWT7UoUuogmE2L7JmzYfz4yx7W/riF3X9f4EZkPACOjkYqlvUhuJIfTz5WheqV/ChdwhM/H3f8fNzx9nTJEBi11sQnJBMVk0h4RDzn4rw5feYqp8+GceLUJfbsP8WyVTtuHV+iuDetHqpFq4dq0qblA5QPLJ7xBrNopb2pXLmSzJoxgaEv9mboy+8xcPAEFn3zI7NnTKBy5cDMT5LBYnZD6qzIbbndCnunQHs3rbP5Jif9ZZdn8claZtM0SsDNdfk2NVfKR1a3PrZKmdTbh9sFNxyISFnVRoi7su6X3xkxahr//htK64cb8uHUUQRXusPH91kE2YuXwpn79UaWr9nJwUNnAKgU6Eu3R6rzYK1S1KtZkpqV/HF2ztmvkFIKN1cn3FydKOFXhOoAwaWAUtyciivOrSL/Hr/Avr9D2PLHITZv+4clP1jnj3moSXUG9G1N906NcXdP1TKSTaAFeOCBKmzdvID5C1YyauyH1Knfk9kz3uLppzpkfoK00todqbMiv+SkdTY7dwq0eTqjQVbTcuV00FdWQTYr0mKb6wpsntmUSb0jASmq4s5saJUNC7vO4CFvs2r1ZipWLMvqFZ/RsUNLVPxJm59Ga83OP4/x+ayf+WH1TsxmC80eLMvUse3o+HBVqgQVs/k6UTGJhF2PTdOlQClFsaJu+Hi73nG6L7e4k9QtDXVLB/D8s23RWnPsxAVWr/2TeYs28eyLn/PS6Ln06dmc0a90IbBcymwFNgRag8HAwAHdePSRZvR+ejR9+o3jj+37+fjD0Rmn8rpJWmntltRZkZfys7tBriuoIHuTBNpcJYsmCLv3409bGDBoAlFRMUx+71WGv/KMdWCXjQO6LBYLq5b8wOSZv/PXwQt4ejgztE8DhvZpQMVA3yzPO38pkkPHw/h3317+PRvD0TMxnL8az+XwROITM1/mFsDBwUBxXw8C/D2oGuRH5SBfqpQvRrWKflSv6JdmIJi+ag3yVYrCmOFdGf1qF37fcYR5izYxb9Em5izcyIC+rXlrTE9KBvhk/pozCbilShVn84a5vPHW50z9YAF//vUPP676Iu00XqlJK60QIhO5FWjztbtBQQfZm4xGlCU2zft3bOW9XfJ+JWFW2K2kpGTGjv+Yjz9dRO3gqvy2YR41alS07rQhyCYnm/h22e9M+WApR09dJahMUaZP6kDfzsF4uGdspbx+I45NO0LYtDOEzTtCOHU2/Na+okUcqV7OgyY1ilLcx5kAXxf8vZ1wdLhd7MxmzbXIJK7cSORyeCIXriWwZXcoi1f/fesYH29XWjYsT8uG5WnbrEKa1mB99RDKrybNm9agedMavPvmU7z/0QrmLNzI10u3MPKlTrwxqnvaGRpuvheZBFpHR0emvD+Cpk3q8PQzY2nRuj/bNn91eyUyIYSwQU4DLWQeavMi0ObZTAb3GGTj+vQBIJbB6fYsvKfr3q8kzAq7dO3aDbp0f5U/tu/jpaFPMW3Kazg7O9l8/obNBxjy2hxOhlwiuFoJvv2kB90fqY6DQ9rO+haLhc07Q5m9ZA+rNhzBZNZ4ujvQItiHYR2rU7eyF1XLulPMyylD14GkZAtaa4zG27MZZCY23sTJC3EcDIliy4HrbD54kRW/HgGgUe3SDH6qPj0fq4mri+OtQAtQulQxpn84iBFDn+CNd77lf1OXsfbXvXw7bzhVKqWbn/EOXRCe6NiK9T/Pot1jg2ndfiBbNs7H3z/rFmkhhEjv+CrroLHc7naQbyuB5bRV9h7cDLI3uS1efPubhRJm74aEWVG4ZdJf9sSJMzz2xBDOnbvMksVTby3vaosrYRGMGL+Ab5f9TqUKAayZ/TSPt6qcIWjGxiUx89u/mPntX5w6G46PpyMvdQ2kR8sAHqzihYPR+r9yi0Vz5EwMK7ZdZvfRCM6FxXPpeiIXrycQEWNKc01HB0WpYi6UK+5KYAk3yge40ai6N01qFiW4oifBFT3p28463UzopThWHXJm9tI99B+9khHv/sKz3eowelAzSqRrOK0QVIIl80fQq2tTBr70JXWbj+ST959jYL82aV/XHQJt48a1WbtmOo92GEKb9s+zecM8ihUrmvYg6WIghMhGbg4MyxNZdTHIiXtolU0fZNOQPrR3TcKsKFyy6Zv555//8GjHFzEYDGxeP48mTWrbfOlvvt/KsJFziY1L5K0xPRn7TBVcnNN+JJ+QmMyMb/5i8pebuRqRRPMHfJjUtzZdm5fAxcnaaht2I5HV26/w444r/H4wnMhYa2j1K2KkQjEnqvo40DKwCMU9jRgNCrNFY7ZAgklzMSKZ09fj2fRnFBciTGgNDgaoV8Wb5sE+9GgZQL3KXpQPcGN4ALzapgFbLxZn1pK/+GzhLmYv3cPYFx5i9LhBGboTdO7QkAb1KtHvhc8Y9MoMNvz2N4vnvJKx20EWmj/0IGtWfkaHTsNo9+hgtm/7GlfXQvyPkhAiTyg1D63vfnG5whJo86SLwV0E2TsG2JskyN4TCbOi8EjdCptJi+zBg8do//hgihb1ZMO6OVSoUCbz66TrL5uUlMyI8V8xfc46mjaqytzPh1ClaESaY7TWLFr1N29+tIlzlyJpXdeXt5+rQuMa1tbJuAQz02ft4fs9UWw/FY9FQ/lijvSs60HTCq40CXKjvK8D12MtXIw0cSEimStRZpQCJ6PC2UHh4qgIKuZFRX8nnBwU0QlmdpyKZ9uJOLadiOOTH0KZtjSEqmXd6dO2NH3alqJscVdalgqj5ac9mRR6jXHTNvDmR5v4dvVBZk9/lWaNq6V5HSUDfPh15VtM+3QVYycuRmvNkvkjMnSfyErrhxuxbOmHdOw8jAmTvmTq5BHWHdIqK8R/hsEwHQCLZWiW+5Sad2vb3QTbnHY7uCmzfrO51tXgXgd+SZAttCTMCrsQEnKO9o+/gLu7K5vXzyMwMIs1u9MF2YuXwunRbxo7dh9j5EudeH9iH4w3jqY55sq1GAaNX82Pm4/RoKo3C0Y25OG61oFXsfEmZv54lg++Oc6VKDO1SjnzxmPF6FKnCNVKOLH3bAJbjscxdMlltp+KIy5JZ/tajAYIKuZE9QAn2tfwYFirorzb2Z8bsWaWXXBm8YbzvDHvGBMWHOfFTuWY1L8yRc/tpnL5hiz/8knW/naMYRPX0uKxN5gyuh2vjR2UpjuBwWBgzPCuODk5MmL8AtyGTWfBl8NsXi63w+MteH5ANz78eCE9urWjfn0JskL8FxkM09ME2ptBNrV7aaGFwtNKm6WCDrIiV0iYFYXDHeaSDQu7TvvHXyApKZnff1uYeZDNZPaC7buO0v2ZaUTHJPDdgtfo2bXpramublq5/giDX19DdGwSH45/hFdaGzAYFMkmC5+vOM2Uxce5Gm2mTVV3lg0qRrOKbhy/ksiMrREs3BVBRJx1Cq6aJZ15trE3VUo44R0fTwkPIz4uCqMCM5BogjiTJszZlaOXEzl2OYl9ZxNY/XcMQ5dAo/KudK1ThP5NvBn0WRNCL8Uxbekpvlx9mqWbL/LuwCo896jGGNiIx1tVoUWDQJ4bs4pRk3/l0PErzJwxFheXtAPghg/tSExsPG+9uxR3N2emfziIrGe3TWvalNf4+Zff6f/8m+zd/R05XBtCCGEnbgba9EH2XkOsLbIaBHY3sxqEBKT9PkMXg8xaZfMoyEqIzX/yT5Qo1BITk3is4xAuXAhj069zqF69QtoDspiC69eN++n01GTKli7GxtUTqVGtbJogazZbeGnSWmZ++xd1qgfw9QfdqOFqnVf+UGg0T/1vP4dCo2lbzZ0JHYrRpIIbW4/H8uhnZ/n1SCwOBuhYyZlOVZxpUNKR0Agzm08n8tuBOM5FmTkbaeZqnLWV1sUB3BwVHo6KasViqRvgSK/yDsx4KpBLkSZWHohm1YFoRi0PY+JPV3nl4euMfelBvhxei0Edy/LK50cY/OE/zPnpLGveS6SEjwseZRqy9LMevDPdn4mf/sbJM6NZ/9MU3NzSTin2xqgexMQkMPXTVRT392bC22/Y9L57eRVh1pcT6NBpKNM+XMAbb9W19UcmhLAzeR1kC7x1VoLsf56EWVGovfv+bPbuO8LKHz6hceN0g72yCLJH/j1Hz/4fUq1yaTaumYivT5EMQfa5MStZtOpvXhvYlHdHtMbpyl4AFqw7x7BPD+HprFj1YmmeCC7C9RgT/RZcZNHuSIp7GhnX1J0+tZzZe8nMj8cTGL4+mhsJGgcDlPU04udkoXlxRQlXAxqIN2kMbs5EJFg4dNXE+pAkNKCWR/JIBScG1XXjjfHlOXQxkffWXee9ddf5ascmpnT156m+tdnySSOWbr7IoA/+4eHhu9j8cSNKsBtDmYa89VIrqgb58eSry3hu4Lss+WZSmi4HSikmT+rLhUvhvDPtB3p1bUrV2q1seu8ff6w5jz7SjJmzv2fcmIEYnYNz9LMTQojckm9TdKV2r4si2CiuXz/cZEque5I/PykbKaU8lVKBSinPgr4XUfAOHPiX96fM45k+T9C5U2ubzrkeHk3HXu/h6uLEmqXj8PUpkma/yWTm2VErWLTqb94e/jDTxrbH6cpekk0WBk47yICpB2kU6ML+14Po+IAH3++JovqkEJb8FclrjdzY178owf4O9FwexTOrI9l8Oon2Qc7MfbwInzY20jsQKnkqrsZrfj1v4beLFg7f0JwOS4DEZPqUh93P+bC6pzevNHDjz4vJdFkWQfU3TrBp1zW+7l+SP0aVo6S3A30XXGTQmztINmmebF2KtZPrcy4sgdYjdnM5PAHO7Qag5+M1eW9kG75be4jpH8/P8J4opfjovWdxc3Pm1bHz0bHHbf4ZPPtMJy5cCGPrtj02nyMKN6mzIjtKzcv0kReO5sf8rg9lsi0Plta9l1bZuH79cvFO7j/5HmaVUu/fYXcFoC3QSyk1OZtjxX9YYmIS/Z57HV9fLz7+cHTGAzJplU1ONtH9mWlcuBTOqm/HUKa0dRDXzVZZk8nMMyNX8M2ag7wzojVvDG0J53YTE2/iifF7mP/zOcb3qcj6V8ri6qToMfsCvedeoJSb4re+PrQp70y3HyLpvTKSuGTNnA6ezGpmxNWUxLhN0Qzdbua9A2aWh1q4Eg9lPMDPBZItcCYGNl2w8OYeMw3mhzPq10iiIhP4qrmRLx4pgouDYsSGaBq+fRLCotk1JpDxj/oyb3sE7YZu5VpkEs2DfVk7uT5nrsTTesRuroQn3nrto55vyuOtKvPae7+w97f1Gd4bfz9vJo3rxa+bDvDTL7YH044dWlKkiDuLv/npjv2aReEidVbkhbwOtoVKPrXKpiaB9u4VRMtsm6x2aK33a63npDzGAlJo71OTp87j4D/HmTtrEj4+XjadM3biIrb8foi5nw+hUf0qGfYPfuNHlv70D++Pasv4IS0A6ypdj4z6kw17rzJ7ZC3eaeZIZLyFJlNOs+bvaCY2d2f900X55VQijy65wcnrybxZx8inDRXTd0bTfYOJRScs1CiqGF7VwMd1FaOrKRr6AGZwN0D1IjCwqoFPGxv5rrWR4bWMeDvB18ctdFlv4kBoLOuf8mZ+R0/OR5lptSic+Wsv8k4nfxb1L8nu0HgaP7+FG9HJNA/25eeUQNvmtV0khewErDMYfDW1K/6+7vR6+XuSLv6dYbDb0OcfpVqV0gwftwBLzLHbO+JO3H6k4+rqQveubflhxQYSE5Mk0NoPqbMiT+U00N6cqiszmbXOnt6SwxvKTTJzgd0piDBr64BqtNaReXkjonCKjo7l408X0bVLGzo83sKmc46duMCnM9YyuH87+vS6fc7NQPfrthMs+GEfYwY/xJjBKZ85ndvN21+fYMfhGyx+vQ4DK5owWzRPz7vAqWvJ/PJyWV5u4M6MvfG8+0csPZLbXRgAACAASURBVKs7s/YRR0q7K/psNnEqSjM62MiGxxyo7aVYec7CiH2aSYc0X4dq9oVrfrui+ea0ZuyfZvptNfPydjOuRviiqQPrHnXk8bIG5h6z8MQ313mojBM7+vvSoKQjQ9dFsfa3Szzd0Iv1r5Tl9PVkRs20TinWPNiXb16vzeHTMSz89fyt1+pb1I1P33qMU2fDWf/HyQzvkaOjA2Ne7cKp0MscPHTG5p9H2zaNiY6O5dSpczafIwqc1FmR5/6TrbQSZO1SQYTZ7CfiTJHSpyvLFoaCppSqq5QapJRqc/NrQd/Tf8H8BSuJjIxm9Gv9bT5n3MTFuLk58/brvTPsi4tPYsiEn6hc3peJL98e/LTryA0mf3uSZx8pTe+HSwIw4cer/Hokls97FaeuQwILDsQzYWsMXao480X7Iiw5aeHFP0wUd1N819qRyl6KgVtNfPKvBZOG7mUVY6obmNXAwOyGRhY2NrKgkYEP6xp4uYrC0xHeO2Cm9U/JrAg180ZdI28/aGT/NU2rReHEJGkWd/aivLeRvqsjOX0tiYcquTG8tQ/zfz7HzsM3AHiiaXHqV/Vi8rcnMZ3eees1Pd6yMj7ernyz+mCm71PbVtZBXBt++/v2RrdKtx+ZqBBkXZzi5GkliyfYD6mzotC5U+usQAaB3YM8m81AKdUaa7+s9EU1SCn1Htm3HAQB3YExeXB790wpFQRM0Vq3TbVtmVIqRGsdUoC3ZtdMJhMff7aIZk3r0rDhAzads33XUVb+tJv/vfEk/n7et7bfbJV9Z/pWQs/dYPPi/jinTJgad2IH/d7/mzJ+rnwyrDoAqw5E89666wxo6k2PAAvfH0lg5MZo2gc58Xn7IvT7IZx15yy0L21geC0D43Yls/8G+DrDkEoKR4OBi/GwLczaGnslwYKbEcq5Q4CroqQrjKqmuBSv+PGChRlHLfx+WfNpEwe+bqUY/LuJHssjWPdkUZZ29ab51+E8PfMs28ZX4K3H/fj2zyhe/eIwO6c3xWBQvNG3Ep1e38O3Gy/yzEDra3ZycqDnYzVZuOIA0TGJePqlfa9KBvhQo1oZNm45yKhxtv1Mbq60durUKdtOEPlG6qz4r8hqztk8k0sDz3KrVVaC7L3JszCrtd6klArBWix7AIOwFlwFjLXhEiHAC1rrOXl1j/doMDAr3bZZwBSsr1fchTU/buHMmYt8+pHt/7a+8c4SAkoUZfiQjre23QyyIWfD+WDudp7tXoeWjcrf2v/OohOcOB/Lpo8a4unuyLW9x3n2q4vUL+fC572Ls3fPVYb9EkWzMo7M7+jFhK0xrDtn4ZWaRp6qoOj0q4nrSdAnUNG+pGL5WcWa82YsgFFBgIsi2NtAvFlzNRGORWkSLbDkDLxeQzGqupG/rms+P2bh6c3JrG7vyBdNHHj+DxOD1kayokdRprb2YMi6aGZtu8GQlj5M7urPMwsusnzbZXq0DKBDY3+CK3gybekpnmm/G8o0BKBP52BmfvsXazb9S5+B9TK8X21bBTNz/nqSkpJxcnLM9v318fHCy8tLwmwhJHVW2JsCn3P2Tgpg0BdIkM0NefqT01qHaq03aa1fACoCkcBeoOidHlprg9a6YiEusGBtzdiXbtuelO3iLq3fsANPTw+b+8pGRsaybfsRnu/XFnf3jAVy7ZbjmEwW3hza8tY2rTVLNl2kQ2N/WtWxzniwYn80UQkWZj4dgIujgUUH4zEqWPCEFwkmzaJ/4ulW3sDAqkZWntZcToBR1Qx0LG0gPBF+vmimpJuiX5CRUdUceLaCA4+UNNKljAMDKjgwspoDgyo64GSAOSctmLWmvq9iRDUDl+Nh2yULtYsZ6FvRwLazyUQnWehV3YVALwMb90cA8FR9T5wcFHuOWb9XSvF4Y3+Ono3BYrndMFevhrXLxOkLEZm+Z0GBxUlISCI6Otam91gphZubG0lJSTYdL/KX1FlxP3op4Hz2B+Wx3GqVlVkM7l2+/Tck5SOhQSl/jrzTI7/u6W4ppbyxtoSEp96utY5I2R9UEPf1X/DHjv00aRyM0Wi07fhdR7FYLLRsViPT/b/tDCWoTFHKlyl6a9vhE2GcuRJPxybFb21bvi+aSv5O1C7jzLXj11h5LIH2FZzxcTWw7GgCiWboXcFAskUz518zVT0huKjCrDXTjmgU0KW0kTJuBhwMGT/ZVUrh76JoV8LI2Tj49aI1fD7gDV6OsPyUdVncOr4GLBoOXDahlKKyrwMnw80AGAyK0t4OXLh6u9+Zr6cjFgtExppubXNyMmI0GoiNyzx8OjlaP5BJijyW6f7MJCTE4uJSSFtTxC1SZ8V/SYHOaJDPJNDem/xuU98AbMrn58wLPnC7qGZCiuxduHEjksOHT9KsqQ1Lp6YMVtr6xxGcnBxoVL9yhkPMZgtbdofSqnHaH8dPm60h7rGG/gBcj0xi87FYutUtglKKLWeSuBqn6VHNBa018/bGUr2ooqq3gZ/OWrieCF3KWH91Vp3TnI/XPFbSiJdT9gPIq3gqKngolp7R3EjSGJSiga9if7gmwayp5WO9xp5LyQBULGrk1A3TrZbX0kUdOX/29l87X08n62twq3Zrm1IKd1dHYuOTM70Hx5thNsmU6f7MJCQkWcOs6VDahyiMpM4KkcfyYgYDCbR3L1/DbEqLwK1+XEqp2kqpgUqprqmPS/99IeSd/SG3bd26FaVUto/73a7d1hH4TZvUsfmcP3YdpUG9Sri6OmfY98+xK0REJdCyYWCa7b9u2EedSp6U8rO2NK5bfRizBbrWsa4W9uPxRDydFW3KO3H0mpkTkZqugdZflQVHzZRzh2BvuJqgWXZWU91TUcPbtl8lpRRtA4wkWmBuyuxZdX0UiRZYcdyMt7OilBvsCokDINDbSLwJjhy4CkDxIkYuRtwOqV7u1mB6IyrtKGFXF0fisgizRqP1XpOTzTbds8ViISEhERenqIw7JdAWOvdrnQWptcL+xfXrl+YhbFMgvZ1TiutfWPt1zcbayT+1/UqpmUqp2vl/dyLfpQSiS5euAVA+sJTNp0ZExhJQvGim++ITrGGuWFG3NNuTTBZ8itwe+GTR1lZPb1dr1waDAgcDODsoUiY/wDHlN8XfTWHW1lBaxBE8HSHKZO2Ha6ujkdYuBaXcrP+oHorQGIAAV7gUp7kUB4FFrPv+upiMl7PCz82AxaLZFRpP1RK3g/tfxyIxGhRBqbpRhF2P4cq1GCqW88n0+f89fgEHByOlSma+P73jx0+jtSaofGmbX6MoeFJnhchCfiyhK/JVQSxn6wVsBm4APYF6wMbUx6QMaHgBaKuUCszve7RVSp+ubLVo0QKtdbaP+1Kqlr2oqBgAPD3dbTs3patBVu+dm6v1I/i4hLQtlL6eTlyPur2tdK2yAJy/Yd1WoagD4fGa8HgL5b2NeDorDt+wPkfzAAPn4yAsQeNiVPQqpzgfpzkWZdvP73ychW1hFmp4KZ4sp4lK1my8rGnqp2gV6MDKUDMaGNKsKDFJFtaeSKRzFWeKVy3Gn6cTOHfDRPd6RW5d76cdV2hasyi+qQL7ll2nATK0SN+0869j1K4VmGlrdmb27D0MwIP1MumXLPPOFkr3Y50FqbW2sliG5ur1Cv3CCb8X9A3cHZnlwHYF0TI7GXhea91Oa71ca72fLCb41lpPI2NrQmFwc37DNE1bqYquzH9oi3QfUd+IiEIphZdXkSxOyCirjwyVX03cXK2tr+k/bvf1dOJa5O3BUaVTuhucj7D2Ia3kY22hPXXDjEEpahd3uBVmW5a0/srsDU/5vriitBv8cdWM2XLnfyQTzZpV58wUc4aR1awfd667aJ2yq3MZ62CyFactNC6uKOdtZN3JJOJM0KOa9f6W7YvC0QhPPGB9f85cjuNgSDQdmvjDud23nue33aEUcXemXs2SGe7BZDLz176TNG6QcbnfrOzddwRXVxeqVi2f/cGisJA6K+4otwNtYVI+rKDv4N5JkM2ZggizkVrr5Tk4vtB1cEoZkBBCxj5dPkCETOZtg0z6WkZGxuDh4YbBYPtfS6Ug2ZR530/3lDAbHhmfZnsxbyeuRiQRl2A9r7SfKwCHLyYCUKGoNcz+edEagmsXd+R4pOZsjKash6J8EdhwSRORpDEqRd/yBi4nwHdnTVxPNHEhzkKi2Zob4k2aU9EWtoWZ+TrURGQyDKtiwM1BcThCs+6ipoEvtAtyYOkpC1fioXt5I4kmzax9cZQuYqBRaUci4sws/SuKdtU98K5jbZFetOECAB0b356VITHRxC9bT/BQ/bI4BgRneE92/XWcuLjEHIXZHTv/pnZwFRwc0k1LLa2yhZnUWZGt/3KgtWcSZHOuIMLstRwe75Und3HvNgIPpttWl3Qf5Qnb+fv7EB0dS0xMnM3n1KxWlj/3nsj0o8OSNRpTvkxRftqUdgqqjo39SUy2MO/nswC4uRjpFOzBl1tvcCXKRL36/jQr48iUHbGcjzLTP9gVDyfFsO3JRCVpxgY7EJ4EYw9YOHhDE+xtXTwh1gQzTmgWhJiZdtTEh0eT+fBfE0vOmPk9zIKTAV6qovBzts41+84hCz5OMLG+A7OPmpl8wEzzEopeDb15fm0U+y6bmNjCA+8KvvRdcJGr0SbeeMw6L+6h0GjeWXSSzs2KU6Wsx63XNmX275y5EMGwvo0yfb8mTf4OX58iPN4u42IKmfnnn+P8+dc/dOnc2qbjRaEhdVYUSlktmJDZ6l+BLfP2Xu6F2+LFeXZtGfiVcwURZn3z+Pj8MoaMK9AMppAuC1noZNKqVzFl2dSQkHM2X6ZNywe4dPkGR49lnEBbKcXTnR5g084QLl65PRK/eYfHaFarKNOWhpCUbB2MNXlEPeKTLLy55ipKKRYOLovWMPCnKAKKGPi6kxfnY2HANhNBnor5LRwo6gzvHrbwv0MWAj0UH9VVfFzPwMhqBnqXUzQupniynOLNmgbmNzLwZk0Dp6LhlT0WfruiaROgmN/SgckHzHx+2MyjZQws7unLsF+i+elEIu8/7EHXqi689eNV1v4Twyc9i9OwvCuJSWb6vLMfbw8HZr1Wy/qiyjTkWMg13vtyG7071OKRFpUyvB+bthxk45aDvD6yO56ebhn2Z+bLmd/h4uLMc892ybhTZjIozKTOimwZDNNz5TpaD8iV6+TE55eyHpAa6n+XF+1mydHhEmgLj4IIs0op1Sr9tiwOnAH8mfe3lHMpH4GNUUpNUUp1V0qNxrqGuHz0dZfKB1qLU+jpCzaf06al9aP0jVsOZrq/T6dgLBbNkh//SbN9fJ+KnL+awNfrrSG4SlkPhrXyYd72CP4Mjad8MSc+a1+EPy8mM35zDE3LOLG4sxfnYjRPbU5GAUtaOzLqASNhifDOIQsT/tFsuaIJT9SUdlO0LK7wdoI/r2veP2xh6B4LP1+0Dvb6pJ6BAdWNPLfVxI4rmvG1jSzs7sPIjdGsPJbI2y08GFzXjY2Rjry37joDmnrzYouiUCWIN+cf52BINHNHPYCftzOUaYjWmhffXIObqyMfv/FohvdBa834t7+hTOlivDigvU3vbWRkNIu++ZHePR/B1zfHsySJgiV1VtxRbgXZnCg0y9hm0gp8tyTQFg4O2R+Su7TWY5VSJ5VS3wPva62jSTcwIWWqmCmAj9b6xfy+R1tprfeRcalFYSuHmmla94KCrGH26L8hdHriYZsuEVjOn4pBASxbtYOXBj+WYUBY5fLFaBBcipnf/sULT9XH3c06w0H7rh2oN+84E786Ttt6xShXwo2Jwx/kuz2b6TP/Ar+8XJb+HUrx9xUTn/1l7fbwdksPfn3ah57Lwum7xUSncgaermigZ5ADa85ovj1l4acLGmt32dt/pV2MEOgO7UooWhYHRxcjq8+a2bLXhK8LfNXSgSpBnjy5MopNp5N48yF3htV3Y9WxBIb+cpXGQa580bs4SinmrT3Lh9+H8HyHsnRI1Vf2nelb2bL7NLPeeYLixTxIb+7Cjfy59wTzvhiKi4uTTe/t9BlLiY2NZ8gLvW06XhQeUmfFnRREkL1XlhK2N3LYpBeZT9HVzQLLc9bOdzPQ5sVCCsI2BTLPLNAO63QxEUqp68BgpdRfKY/rWOdF9AHaFND9iQLg6+tN3TrVWL4iB93h3CoxfEgH/th5lO9XbM+wW/nV5L2RbQk5d4OhE3661bdWKcWcD54iLsHMwyN2cf5qPJ7ujnz/fCmux5ppMvU0f4bG82H/sgx90JX5f8fTalE4l2PNbO7nS4eyBn48Y6HLBhOd15s4HqUZXtPI962NvF9b8VZNxahqitdrKEZUUTTxUySY4Z3Dmld3mth3TfNkRQNLuhVl3VUHGs0PZ+f5JD5o48GzD7jy8q9RPPdjFHXKuLDyhdI4OSj+t9vM8x/8Q7sH/fh4aHUAdOkGTPhkMxM+2UyfTsEM6Jlx9bStfxxm6Mg5tG0VTL+nWtr0tv77bwhvvzOTLp1bU7++DPSyU1Jnhd3K1f6yD+Xw+Bx2N7gpL1tpxZ0VSJjVWodorSsC44AIoALWeRDrYZ0X8QWtdX17WD9c3KN0fWf7PNWBPXsPc+xYqM2XGPxcO+oGBzHi9a+Ijo7PsP/hxkG89VJLvl55gAU/7L+1vXb1AH5Z+BzXIpNpPWI3F68l0PTxmuwYHYi7s4FWH51h8e5IPhtQjlU9vYlL1nT/IZKuyyJoXdWd3QN8eLOukUpeijWnLQzbYaLbRjPjDmjePqSZdlTz7mHNe0c0809pdl7XNClu4IumRj5uZMTRzZn2397gh6MJPF/Xlb0DffF0NtBowXWWHE5gVDtfNo8oh6uTou8PUUxYcJy+bUux5r0HcXMxoks34PUPN/K/L7bwbPc6LJja5dZMEMrP+r6eCrlMt75TCQoszvdfjcRoNN5+Y9wy9qsF64pfzz3/Fu7urnz5+RtZv/Eym0GhJnVW5Adb+8sWmi4GqeVid4ObcjPQyqwGtsv3bgapaa2nAlMBlFLlgXAprPe33r0eZeSYD/lmyVrenjgs+xPiTmA0Gvnyo0E0bjOOiZOX8uG7/dMcovxq8sZQC9v3nmHohJ+oWdmfBsHWLg0Ngkuz7qtneaTfAtq8tovfPm5MlRbV2OlmpPvs8/RfeIlFuyL58qkS7CnpyPJ/E/hyTxzDfommhLuBtkFOdK7lyAvmOGKTITwRYpKtjziTxt9V4e8KDgoikzR/XIGJ+yxci9c4GOLpW8uVEY3cuBprYcgv0fx2OokGgS6s7xNAcGkX9p9NoNfCK4RciuN/Ayoz/umKKKXQWjNq8q98NG8Hz/d+kBlvd8gwpVlkZCwde7+HxaL56bvxeHunWowiiyALMGPmd+zc9TcL579LiRLFMj9IgqzdkDorBNbW2ZwsnnAX3Q1ykwTZnCnQMJua1tr2pjjxnxUQ4Efrhxsyb8FKRo54Fk/PjP0/M9PwwcoMfKYNn85YS4umNXjisQZp9juUeIBvP+5Bg66z6DBwMcu/7M1D9QMBaFK3LD/N68djzy2k3uDf+WhIdXq0rMQWDyOz/4hg3Mowak4K4ekGXgxp6cvBJ0qyevMV5u6P48cTiSz6J+H2/XsYcNAWnI0KRwNcibcQnnj7PrxdFG3LO9GugjNNSzuy9UwS/ddEsueSCU8XA5/1Ks6LLYoSl2Th9d+T+OC70/h7O7P5o0Y0D7YOOL8akcgLMy6wcv1RhvZtyGdvpe0rrPxqcu16FE/0fp8Tpy6xYdUEKlYIuH0Tdwiy+/cfZcz4j2nbpjF9+3S06b0X9kPqrMgtuTmDQWbTcuWJnAbau+S2ePE99Z+VIJtz+R5mM1kHPERrHZWybyTWaVd8sM4jOEZrfTp/71Dku3QDwSa9NYSmLZ5hzLiPmTH9zTuf61YJ4k4A8ME7/TjwTyg9+n3A6iVjeaRN2v6jxXzc+fWrfnR8fjFtnlnI9IkdGNjLOt9q8waBbFv6PINGfUvvt/cz/+dzfP5KTV5orugcXIR3111j0a5IFu6KJKiYI93qejKhe0nqlHHm731X2XspmVM3zFyOtZBg0iSYIMmsqe9hINDLSDkvI4HeBlwcFLvOm1h7IpHRG6O5kaCpXNyJj3oUp38TL8wWeOfna3y2NZIb0cn0a1+aaS9Wo5iXddDWqj8uM/jDf4iMNTN1bDteG9A0w6C3vftP0e2ZqVwJi+S7Ba/R8qFUrah3CLIhIed4tOOL+Ph4sWDu/7JcXU1aZQs/qbMit+RGaC2UXQxscQ+ts3cbaCXI3p2CaENvi3Vk6jhSTYatlJqMdWTtJqwDEuYAswrzmuEibzRuXJvhr/Rl5uzv2fzb7uxPSAlonp5u/LriLWpULUPnp6awKd10XcqvJpUCfdn5wyAeblSeQa+v5vnxq4mMtras1q1Zkt0zmvHZyzXYdTSCWs9tY8jPsVzwKs5nbzUm5N2KzOkbQOXiTny88TqNppzG45VjPLsuhvWXINkC5byM1CjmQP2SjjQr44Snk4F/wkzM2hfPkyuiaPrVDV7bGM3uC8l0CPZkw6tlOTKhPO2ru/P+L9cp//pJJv10jYdq+bDry6YsGBtMMS8nrkcm0X/y33R9cy+lS/qyZ/ULjBzYLG3gLFaDuWsv0aTdOLTWbPv5f3R9IvPFE9ILC7tO+8dfICkpmV/XzqJUqeKZHyhB1l5InRX3LD+CbKFfLOEuB4OBNdDmpA+tBNm7pzJbOSlPn1Cp57G2EmxKta0O1pG1s1JPEaOU8gLGaq3H5etN5iKl1JYWLVq02LJlS0HfSuGXqnU2Li6e4HrdMZvNHNy3Ag8PGyb5T2mhvXY9ioc7TuBkyCXW/fAmLZrVyHBo8qW/eeOjTXwwdzsBfkWYPqkDT7SpCues4flyeAKvzz3Gkk0XSUiyEFzBk+ceK02Xh0pQOvwSN2LN/HwohoMXEjl+JZF/Lydx6moSpnR1z9kIZXwcKePjSDkfRxoFudKqsjuBvg78dSaB1X/HsPrvaI5fSUIp6NkygHFPV+SBCp4ARMYk88nyUD5eFkpsvJmxLzbnzaEtcHJK+6FKgkclhrw2m6+++Y12D9fmm7mvUszXM+3NZNEqGxkZTet2Azl85BSbfp1LkybpG/VSSJDNVMuWLdm6detWrXXLgr6Xm+63OgtSa3Miu6m5cqsLwd0EWcg8zKafmiv9ogkhAWm+pXxYJhfOqotBZlN0pZaLfWezaq2VIHtn2dXZgugz65W6wKbohXUOxCmpN2qtI5VS4fl2Z6LQcHNzZcGc/9H84Wd5+pkxLFv6EU5OjtmcZO1yUMzXk42rJ9Dy8bdo12USUyb15eUXHk8zQMoxIJjJo410bV+d58evpvML39KuWQXeHt6aBj4XKOHjwrzRwXw4pDpLNl1k/s/neOXzI7zy+REqlXanVR1fmhWDvg09KefrSBEXIxaLxmQBs0VjsmgsGjxdDCSZNOcjTIRcTWJHSDxDllxiV2g8sYkaBwO0quLOy70r0alpCUr5WYt/2I1EZqw5w6c/hBIRY6LrQyWYOK4bNStnbDHdfNjCiyNGcOLUJd4a05O3xvRIO2vBzfcmE+fPX+aJLi/zz6ETrFr+aeZBVkKsPZI6K3IsN/vB2m3Xgszk4mCwzLofSJC9dwURZjMbRdsGiMii31b+Nh2LgpOu72yzZnX5/JNxDHvlPZ7qO4Yli6fg6GhboPX382bbuncYMGw6w8ct4Of1+/hqxkuUDPC5dajyq0mDYNiz6gW+WLSbd6ZvpVG32TSq7s1LXQPp1jwAbw9HXuxUjhc7leNQaDQb9lzlt/3XWbLpIrPjTLeu5e3hQBl/V5wdDZBgHfFltmguRZq4HGW+/ZwKgks782xjbx6q6Ea7TjXw9rC+JpPZwtqdV5i/7hw/7gjDZNZ0alOVN4a1pF7Nkhle6jVVhtde/4pFS7dSMSiATWsm8XCLWpm/J5nYu/cwT3R9mejoWNas/IxHH8lkMkYJsvZK6qywWW4vR2tLkL2XVtm7ltUAsKwWUMgjaboemM1ZHyhsVhBhNv0qNF5AXWBDFscX1jXDRV5IF2iHDnmS5GQTw0dOpe+zRhYvfB8Hh2z+2qZqoV317VhmL1jP8PELeKDJcD6fNpDe3W73NVV+NXG8eojhzzVhQI+6LFx5gC++3s3T7xxgpO9RerUqSbv6xWj+gC81yxehZvkiDO8RhMls4XBoDP+ejeHMlXjOhsVzPiwBk9kCWAdrGQyKug84U8bPlbLFXSjr70q9yl54edwO5JfDE1iyKYxN+67zy59hXLyWiJ+3Ey8/25gBPepRraJfhpcXFZ3AnJ/O8/5HU4mOSeDN0T0Y/1q3jCt73WGw1/IVG3im/+sUK+bN9q1fU6tW5cx/FsJeSZ0VWbJYhubZKmD3EmQzk+srf91toM2LqbokyOaaggiz6Rd5v7n6zLL0B6YUYPn4636TLtC++kpfTGYzo8Z8iNFg4Kv579jcQquUYvBz7Wn5UE36PP8JTw34mI+n/8jkiX1vtWIqv5roq4fwLOLCS880YmifBvz6+0m+XPwnM9aE8MkPoTg7OfBQLW/qV/WiejlrqK1WzoPgip53vo8UiUlmzlyJZ9O+axw5E8PR0zEcOBXF0TMxgLVlt1UdX55uU4oOPTpk6BMLcCksmk+/2snMpfuIioqjTcsH+GzqQKpVKZ3h2KwkJCQycvQHTJ+xlIYNHmDV8k+znktW2DOps+KOcjvQ2tqt4E5BNjcHfoX6Z9Fv9k7yM9BKkM1VBRFmQ5VSXbXWK5RSnlj7b93QWs9NfVDK1DKDC/Oa4SIPpQu0I0c8i9lsZuz4Tzh77jJLv5ma9Yj7m262TMadoEqlUuzaNJnF323jzXeX0PqJCbR8qCavv9aNh1vUwpASaAEMBgOPtqjMoy0qE5+QzLY/T7P+j1Ns3H6KqUtDMZtvj/IqW9wVf28n/L2d8PN2RikwmTXJJguJyRYuXkvkbFg8koK7PgAAIABJREFUl1NPNgsElnClRmAR+rUvzcOPtqBO9QCMxoxFUmvN9r1nmbN0D9//fJhkk4VuTzRi9CudebBuxRy9pXv2HKbfgNc5cuQUI159hvfffTXrfsjSKmvvpM6KbOVWoM2NIHuvgi5lHASWpTvNN2tLoL3pboOtBNlcl+9hVmu9XCk1Uyk1l9utB3XhVgvBIKx/neoCWim1N30BFvenMaMGUKZ0CQa9OInaD/Zg8cL3ad+uafYnprTSGo1G+j3Vil5dmzJrwXomf7yCtp0nUalCAIP7t6PfU60o5ut5K9QCuLo40r55Jdo3twbjpCQTJ06H88/xK/x76iqnz0cQduECl8MTOXQ6Bq01jg4GHIwKJwcDASX9eayWF2VLelG2pDc1K/tTNagYHu7Od7zlsxcjWP7LEeZ+t5ejp67i6eHMgGfaMHxIx7QLINzpNaeIjo7l3fdn88FHCylRwpdf1s607X0TdkvqrLCVxTIUpRKyPzAL/6mBXjfZ2oe2gFcJE7flSZhVSnXFOtBgc2b7tdYvKKXGAEFa6/3pdu9LedwkH3+JW5568nHq1qlGj96v8WiHFxkz6jkmvPkiLi53DoepF1dwcXHilRc7MLh/O5av2cnM/7N33/FRVfn/x18nCQmQQAoQShAwFEFBQ8cKShEpa6MIYldw/anrKgK6fl12V1cD6iquIgEFGxawobvqUhYUdVVKEEFpoYO0kNBCSXJ+f8wdGMIkmSQzSSbzfj4e8wi599x7D5fwyeeee8pr/2H0Y6/zp7/NpG+v9vTt2Z4re6bQLHrPGaeJjIzgvFaJnNcq0a9/r4OHjvH9im188dU6vli0jtXrXdfu1rkVrz04hCHXXkx0dMl+aeTl5THj9U/40+OT2LVrH7fdcg3PPfMwcXG+dY2Qyk1xVipaSRLZclvly0OpuhqUlBLaSiFQLbOP4hpocEaQNcakWGvTnbXBTwuwzraC08mInKZ162S+/3Ym9z/wNE9PeJVZH/yHlyb9qfjWRo+EFlxJ7Y1DunPjkO78vHozaTPmMufzH/nkXz8A0KpFIy7u1pqUdmfT4YJkOjQ+So3qxfTVLYa1lm07D7By7S5+Wb+H5at3svTnHazduA9rLZHVwrmsS1Nuv7Uf/a/sSOtWvveHPXmNGi2YP/87Hh77HOkrfuWiC1OY8+GLdOniZaYDCWaKs1Jh/N0iG6iFEkqd0JbzDAdSNoFKZucBTxeybyrQ2dcTGWOuKKzlQUJXzZo1mJb2F4bdcBX33PckffvfzcABPXgm9SFatWpWxIGnJ7Rubc9tyqQJd/JC6h2sXb+DL+en8+X85Xz2xRKmv3Xqx691qyRaJEWT3DSB5k0SqBtfk1rRUdSKjiQmOoq8vHyOHsvl6LFcjhw9wc7dB9n22wF27DrA5u1Z/LRmF5lZOSfPl1S/Nh3bNmLYwHZ0ufRiLr2wDTExNUp9X777YQ2PPvEUCxf9SJMmDXn37YkMGXxl4UvTeqP+ssFCcVYqREkT2YpolS1Xap2tcIFKZts7T//elOC3KuAauOBzUJYqpMAgMG96XtGNn5Z9wPOT3uSJv6fRpt3VDBl8JeMevoMLLjjH+0GFJLQAxhjOaZnEOS2TuP/u/lhr+W3Xfn5ctp6l6RmsXLWZ9Rt/Y8H3yzly5JjXcxQUHh5Go4YJNG5Uh+uvvoQL2jXjgrbNaBV/gMTWXX06R1GstXz7/a88/Y+P+OyLJdSvX4cXn3+Eu+4cRFRUZPEn8KRENpgozopfWFt0clqWPrW+JLIVsnxtUYPA3ErSOutrQqvBXwERqGS2tzHmXeAua+3BAvtKOjl3sp/qJMGosOTKI8mNiopk7MN3cOvNV/Pc828wecr7vPve5/S76lIe+uMt9Oje+bTVv4DTZjooijGGhg0S+F2/LvyuX5eT26217Nqdxf6swxzctoqDh49x6PBxIiLCqB4VQfXEltSoHkmD+vHUT4w9c0UuP9hzuA5vvvUp06Z/yC+/ZBAXV4u/P/EH7r93ONHRPiz/W5AS2WCjOCvlwtrqpUpoA9UiW3Ap26KUuc+sPxNaJbIBE8jZDAYDg40xWZw+uCDZGLPPx3MUnCtRxMWdeHkktfXr1yX1qQcZN+YOXn7lPZ6f9BY9+9xJkyYNuenGAdx68zW0aNHk9PP4mNQWZIyhQf14GtSPhxLM81pWO3Zm8vFn3/PZvF+YN/9/nDiRy4XdLuDVtL8yZPCVxMSUIomVYKY4K+WipAmtr4lshbTKlpQ/ElolsgEVyGQ2G0jjzEBZBy8TdxeiDnCdPyslVYyXpDY+PpY/PTKSP/7hJj6Z81/eeGsOT6W+ypNPTeXSSzowbGg/Bl3fm3r1Ti1te9pqWSVMbAPJWsuva7fz5fzlzPn3jyxcvAprLS1aNOH+e2/ktluu4bzzSjbfrFdqlQ1WirNSbnxNaP2VyJZ19S+/zmRQloRWiWzABSqZHWWtnepthzGmk7X2bl9PZIxZ779qSSipWbMGw27ox7Ab+rFjx25mvPEJb838jHvue4L7HniKnld05ZrfXUHPK7rSsmXTU4OkKjCxzc/PZ826Hfyw8gCLv1nOl3O/YevW3wDX4LP/GzOYG268kTZt/PhWWIlssFKclXJnbXXOubboMr+8V3xCWylaZH3pN+tpKHB9SXvwSHkIVDIbW8S+kv4kTClLRSREFDNYrFGjRB4ddxePjL2Tn39ex7vvf8G773/OPfc9AUDjxvW5okdXLux2ARec34p27Vq5Xtn7O7H1ON+BA4dYvXoDq9K/ZdWvW1m5ajM/Ll9PdvYRAGJja9Hziq489shI+vS+iGbNksp+/YKUyAYzxVmpdNZ85Pp6zbtlO09ZW2UDQolspRWoZLaOv05krZ3or3OJGGNo186VrD7x1/tYv34LC/77PQsW/sC/v/iaN96ac7Jc8+Zn0aplU5qc1ZAmTRpyVuP6NGnSkLp14qlXL56EhFjCj2Wcdn5rLYcPH+XAwRyyDxxh+459bNuxj11ZEezencmWrTvZtGkHmzbvYO/e/SePq1GjOue2SWbY0AF06dyWrl3Op3Xrs88cuOar4maCUBJbFSjOSqXiTmQBPr6h8IQ20N0L3Py6aIIS2UotUMlsvL9OZIy5U8ssSiAYY2jZsiktWzZl1MghWGvZvHkHK35aw4qf1vDTynVkZGzj+x9Wsm9fltdzVKsWQVhYGOHh4RgDOTnHyM/P91q2Ro3qNGnSgGZNk+jY4VyaNWvEuW2ac965LWjWrFHZZzwomKB6fu9ObJXEViWKs1JpeCaybt4S2rJ2LyjJTAY+8aWrgRLZSi9QyewsY8x71lp/TMwxFlCQlbLxMlCsIGMMzZol0axZElf/7orT9h05ksPmzTvZvmMXe/dmsWdPJnv3ZXHiRC55eXnk51vy8/OpWbM6tWvHUCsmmtq1o2nUKJGzGjegQYM6pZsuq6R/P7eCyauS2KpIcVYqhLfE1ZvSdDUIVPeCjc4K5F5baotKaJXIBoWAJLPW2vnGmHHGmHW4RtR6votNNsY8hG+TetdB8x+Kr4pL6MqgZs0atGmT7N+BV/5S2N/b/WclslWS4qxUZoUlspsWBm7wV/JOyGh4+jZ3ElusggntpUCiEtlgEbCpuay1vY0xs4BxXnaXpH+WfpqkdIpK8qoKb10JJGQozkplVNrBX4Ee9FVs/9lLPf6sRDaoBHQxYWvtYGttmOcHWFZwW2EfIAHw3llRJNSpxVVQnJXQ4Ut/2eSdfriQEtmgE8hFE8rMWptljNlY0fWQKsKXEf7B0rrpLZH1oV+wSEGKs+JvH99Q2iO9Tz94P64W2/sabis2obUNLWanOaO7gTcbE+FsvZSoEioimX2qJIWttZ0CVREJQcW1ZhaV0FaWbgu+/B1A/WVDm+KsVBmTSDqZ0BbFNrQnv2700l28YP9ZJbJVR0C7GXhjrf2gvK8pUiIRbU9PAgt+77m9pOctq5KcQ4lsyFKclapmUiGttm7uRNatuERViWzVUqm7GYhUKF+SwUC/2ldCKiICuBLaF71sL5jIup2N9xZaJbJVT7kls8aY2sBIXKsbdyiwexnwLjDVWnugvOokUi4KtvL62o1BpIQUZ6WqKyxx9ZUS2aqpXJJZY8xTwBj3t16KdMQVeCcYY1KttY+WR71E/Kakr/8LJrRKZKWMFGdFiqZEtuoKaDLrtBLMxxVEM4B5wFxc08BkOsWSnU8foCcwzhjTC+il1gMJCUpkpQwUZ0WKp0S2agt0y+wCIA4YXMSAhOXO14nGmFhgFPA0roDcJcD1E6kY7tZZJbJSdoqzIkVQIlv1BWw2A+eVV6a1toWvI2uttdnW2glACyDBGPNyoOonUuGUyEoZKc6KFE2JbGgISDJrjDkb6G2t7VOa4621GUAnoI8xppkfqyYiUiUozoqIuASqZfZ64K6ynMBam4XrVdgov9RIRKRqUZwVESFwyWxna+3y4osVzVo7H2juh/qIiFQ1irMiIgQumfXnOt8ZfjyXiEhVoTgrIkLgkll/9rhW720RkTMpzoqIELhkNtmP54rz47lERKoKxVkREQKXzHpbfabkJ3HNh1jHH+cSEaliFGdFRAhcMvueM/9hWT2Nay1xERE5neKsiAgBSmadybsHG2MuL+05jDF34lpq8UP/1UxEpGpQnBURcQnYCmDA3cA8Y8wdJT3QGPM0MAXNfSgiUhTFWREJeQFLZq2184DfA1ONMT8YY641xtQurLwxppkxZrQxZh/wMHC3tXZBoOonIhLsFGdFRCAikCe31qYZYzKB94HZAMaYDFxzGmbhGkGbgGtUbhynBjQM9nWdcRGRUKY4KyKhLqDJLIC1drYxJh6YgGvpxeYUvtpMGjDWWpsd6HqJiFQVirMiEsoCnswCOEFzFDDKGNMT6MCpQLsByFALgYhI6SnOikioKpdk1pOzDvj88r6uiEioUJwVkVASyNkMREREREQCSsmsiIiIiAQtJbMiIiIiErSUzIqIiIhI0FIyKyIiIiJBS8msiIiIiAQtJbMiIiIiErSUzIqIiIhI0FIyKyIiIiJBS8msiIiIiAQtJbMiIiIiErSUzIqIiIhI0FIyKyIiIiJBS8msiIiIiAStiIquQGVjjBkEJAPNna9TrLWzC5TpAHQCMpwyGdbaeeVdVxGRYKQ4KyL+pGTWgxNgM9xB1RgTByw1xiRYa9OcbclAqrW2t8dxs4wxGdbajAqpuIhIkFCcFRF/UzeD0yVba5e5v7HWZgGpwBSPMqMKfI/zfWrgqyciEvQUZ0XEr5TMOpzWgaHOV0/znP3JzveDgGUFyixxtouISCEUZ0UkEJTMOpzWgWTn45UTgJOBTC/Hegbi0yxatAhjTLEfEZGqLJBxFhRrRUKVklkP1tp4z9dfjl5AltNPK8Epl1XIKQoNsiIiojgrIv6nZLZ4o4CnnD8XfDXmk+7du2OtLfYjIhKiyhxnQbFWJFQpmS2CMWYkkGmtnVDRdRERqYoUZ0WkrKrk1FxF9anyItPb6yznHKOstR297Isr4hWYiEiVpzgrIpVFlUtm3fMTluCQHwFvLQKpQM8C29zzGyYAJ4Osx8hczX8oIlWe4qyIVCZVLpl1BhAMLss5jDFTgLEFWwWstVnGmAzO7NOVwKnBCyIiVZrirIhUJuozW4DTfyvVM2AaY3p5vFKbh2uJRU8dnO0iIlIMxVkR8Sclsx6cZRYB4owxHZxPL2CwR9Ady5ktEqOc7SIiUgTFWRHxtyrXzaC0nP5YswrZfbL1wHkFNtYYk4qrH5h7DXG9+hIRKYLirIgEgpJZh9Nvy6elYZwJvwtO+i0iIkVQnBWRQFA3AxEREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFaKZIzBGFPR1QhKunelo/smoUY/86Wne1d6VeneKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoKZkVERERkaClZFZEREREgpaSWREREREJWkpmRURERCRoRVR0BUJAi/T0dHr06FHR9SiTYK9/RdK9K51guW/p6ekALSq6HhL8sTaY617RdO9KLxjuXXFx1lhry682IcgYsxyoB6yv6LqISEC0APZYa9tXdEVCmWKtSJVWZJxVMisiIiIiQUt9ZkVEREQkaCmZFREREZGgpQFgIn5mjOkAdAIygGQgw1o7r2JrVfkYYwbhuj/Nna9TrLWzC5TRvRSRMyg2+CZU4qySWRE/MsYkA6nW2t4e22YZYzKstRkVWLVKxQmwGe6gaoyJA5YaYxKstWnONt1LETmDYoNvQinOqpuBFMkYM8gYM8YYM8UYM9f5z1GwTAdjzEhjTC/314qoayUxCphSYNsUILUC6lKZJVtrl7m/sdZm4bpHnvdO91JCguJsiSk2+CZk4qxmM5BCeTzVLXO+jwOW4nqK83yqm1LwqQ4YG0xPdf5ijNkA9Pb8uzv3bb+11lRczSoP537MB3o6wdW9PRnYADS31mboXkooUJwtOcWG4oVanFXLrBQlZJ7q/MEJAMlApud2dyBxgkjIc+5HsvPxSvdSQojibAkoNvgm1OKsklnxyvkhH+p89TTP2e/+IR8ELCtQZomzPdQkwKlA4EXQBIZAs9bGe/4Cd/QCspwWAt1LqfIUZ0tFscFHoRRnlcyKV6H2VOcnBX8hScmMAp5y/qx7KVWe4mypKDaUTZWMs0pmpVCh9FQnFcsYMxLItNZOqOi6iJQnxVkpL1U5ziqZlZKqkk91/uTllaEUwWlZGuU5uMVjn+6lhCLF2WIoNpRMVY+zSmbFZ1X5qc5P3KNBEzw3egSKkBt17KNUoGeBbbqXEpIUZ4ul2FA6VTrOatGEEFDCPlWZ3l5neTzVdfSyL66IV2Ahw1qbZYzJ4MyWlAROvTIUD8aYKbimFzrt50f3UoKN4mz5UGwouVCIs0pmqzj36h4lOORHwFuLQHFPdZ7z2AXdU50fzcO1LKBnH7gOznbx4LRApRaY37AXrjk3M9C9lCChOFvuFBt8FCpxVosmSLGcp7pUb09pzoTLgz0HMDiBfam1Nr4cq1kpOL9gZhWY3HwurtaWUPyl45UzUXwCrumF3BJw/SyNcsroXkrIUJz1nWKDb0IpziqZlSI5T3XzCnuqcwLwUvdKNc7+QcBQa+3g8q9xxTPGdACG4mp9SQaWWWuD6ik3kNyryxSyO8Na29yjrO6lVHmKsyWn2FC0UIuzSmalUKH0VCciUhEUZ0XKTsmseBVqT3UiIuVNcVbEP5TMioiIiEjQ0jyzIiIiIhK0lMyKiIiISNBSMisiIiIiQUvJrIiIiIgELSWzIiIiIhK0lMyKVCElXB/eb8eKiIQKxdnKR8msSBXhrBjUq7B9xphUj/XcvW3PdFYaEhERLxRnKyfNMytSBTgTqo9yrxjkZf9+IM4pk1bYdqfVYIrnSkMiIqI4W5kpmZVKw73WuPOte4nGZFwr3swu6bKNzhP0YCAL2Od89Xwingcsw/WU/ZS1dnbpa19xnKf9pUBHa22Wl/3JwAbn23h3mSK2jwHqWGvHBrzyIlKuFGdLR3G2couo6AqIOMFwCq5AOtjL/g7ALGPMPF//4zvrlmcVPJ8xJhXXE/Jsa+1gj9dBZwSnQDHG9PLzMpSpQKq3AOsY5HydXaCM1+3W2gnGmA3GmCla912kalCcLTPF2UpMfWalQhljRgJzcb1+8RpArbXLrLUdgQ7GmKUF+yN5OecsINlbwAbcr37ucs6dZa0dW15rnDtP6X57teScr5fnKy0v3Ncr2E+rsO0AYwvZLiJBRnHWL+dTnK3ElMxKhXFed03BFWCLDXJO/6I4YH4xRQcBhb3KisPVklBuLQQF+Hska6rzKUovIMPLPS5sO86rwE4aeSsS3BRn/UJxtpJTMisVwnnqnwUsK+Zpt6CxuFoOigssG4rYl1mC6/mbt1aMshgEvF/YTufVIhQIxIVtL+B9wOtABxGp/BRn/UZxtpJTMisVxf2f+6mSHOQ8yWYAY4p7DVbZOE/fI/14vkG4fkkV1frRG1cLScFfZIVt9zQLP9ZXRMqd4mzZz6c4GwSUzEpFcf/nLU0fKvcxQREAjDFxTkBc6udT9waWFFOmF6f6r/my/STntVicXoGJBC3F2bJTnA0CmppLyp3z6mUurn5EzUtx/CBOvTrr6LHtEadIB1yjZr2NEE3G1Z9rmce2u6y1yzzOk8ypUbdxzp/jgHnucgXq42792AfUccqO9ZiCJZVTk2x7q5vPo4cLXHcDrtG1XoOl06KyH2juOVq2sO2luYaIVE6Ks4qzoURTc0lF6OB8Le10JO4A6D6P+7XYbABjjMU1n+GEggc6K68McQdnL/vmFjzO45dCxwLbkzk1Qniex/ZBwEZjTE9nhPBYZ7s7uKX5aW7BZIpuMeiE93kjC9vuTQZQ4l+EIlLhFGcVZ0OGuhlIRXD/py3tSFe/DyxwAuAQbxN6OwHU26jdubiC1bwC5WfjCn5T/V1PN1/mbbTWzvM2bU5h2wuRgf9HBotI4CnOlpHibPBQMisVqTINLEjG1W+psDrN9fzGWb0lmcIHVkzBNRq4QyH7yyrB+RroEcPuV38iEpwq0/9fxVnvFGfLSMmsVAT3dC4JRZYqnN+fYD36aC01xgzyEmzf5/TXdaMoeh5Fd9lOfqymp2RwTUYeoPO77aP0/04iUnEUZ8tOcTZIqM+sVATP9cBLw33cGYMEyqgjronCZwEYY5bhGtH7npcBCclAhtNyUJh5VOxci/6iFgOR4KM4G1wUZ8tAyayUO2vtbGMMONORlGJdavfygO/5uV7LgHhn6cfBuJ72x+Caa3GCxwADd5DP8jb4wUNR+8qqvIJ3HcpxPXUR8Q/FWb9QnA0S6mYgFcU9BcmgkhzkvJZyT7/it2lMnDkK4wCstWnW2t7W2nhcgyjScAVad+uAXwOcMaZDKSYmd09HE+in+ThKPxpaRCqW4uypayvOVmFKZqWiuKdMeaTIUmdyT+A9wc/9mDrhZXJwa22GtXYUrkA7ytnmnr/QX33KOlHC/lIerSyB7meVQNV4hScSihRnT7+24mwVpWRWKoQTqAbjegVW3PrfwMnXTqm4JvH2x/yBBQ0tYt8UTg9oqbjqXugoWmNMsjMXIlDsIILSBrJAjeJ1S6bo9ddFpJJSnD2D4mwVpWRWKowzT+AoXK+Wilwy0XnNMxfXYISeAapSUVO8JOCxJKSzUssyTq197s1YL/MpemtpaF7K1o95Xs7lbx3w/wAQESknirMnKc5WYUpmpUI5wao3MNYYM8Vb3yRnZZiluJYj7FhUQPI4vk4hRRIoetToqIJ1cL4fy6lXdm49gQRnRZuC9RiDq5WhoFTg5JQ0ZeyLtQzoXIbji+QegFFwsnIRCS6Ks4qzVZ2x1lZ0HUSAk8sTDsX1KsgdSJNxPWVP8WF966WcWhMc57hUa22aE/RGceoJ290fa6y1dp7TUtDJo2wdXHP/4fy50Os75TsXqHdx5YfitECU9lWeU+el1lpTmuN9OP9IYLC1tnexhUUkKCjOlozibHBQMisSxJxfLHd5mZ/RH+eei+uXhbclJkVEQoLibOWnZFYkiDlP9b1LsAa4r+eNAzY60+aIiIQsxdnKT8msSJAzxmwAiuzjVopzpgIbnL52IiIhTXG2ctMAMJHgNxZnaUh/cAYk9FKAFRE5SXG2ElMyKxLknL5WGc5oZH+YgmtuShERQXG2slM3A5EqwhgzC9cghVK/BnNGAGdoMIKIyJkUZysnJbMiVYgxZoy1dkIpj+0Fmu9QRKQoirOVj5JZEREREQla6jMrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBC0lsyIiIiIStJTMioiIiEjQUjIrIiIiIkFLyayIiIiIBK2Iiq5AVWeMWQ7UA9ZXdF1EJCBaAHuste0ruiKhTLFWpEorMs4qmQ28erGxsUkpKSlJFV0REfG/9PR0srOzK7oaolgrUmUVF2eVzAbe+pSUlKSFCxdWdD1EJAB69OjBokWL1BpY8RRrRaqo4uKs+syKiIiISNBSMisiIiIiQUvJrIiIiIgELSWzIiIiIhK0lMyKiIiISNBSMisiIiIiQUvJrIiIiIgELSWzIiIiIhK0lMyKiIiISNAKuRXAjDGDgGSgufN1irV2doEyHYBOQIZTJsNaO6+kZUREREQksEIqmXUS2Qx38mqMiQOWGmMSrLVpzrZkINVa29vjuFnGmAxrbYavZURERETOkJYGM2cWX274cBg5MvD1qQJCrZtBsrV2mfsba20WkApM8SgzqsD3ON+nlrCMiIiIyOlmzoT09KLLpKf7lvAKEEIts04r7FBjTJqTxLrNc/YnO62qgzgzUV0CzPX43pcyIiIiImdKSYGFCwvf36NHedWkSgiZllkngU12Pl45CW8ykOnlWIwxyb6U8W/NRURERKQwIdMyC2DP6M4yAAAgAElEQVStjfeyuReQZa3NcCeiBVpuPZ1MVIspc1q/2UWLFmGM8aV+xZYRESkvZR3o6oxTyPJ2THGDcZ2Gg5HAbFyNBwm4unjN1WBbEfEUUslsIUYBTzl/jvOhvC9lRESCWlkHuhpjegFTgcFe9hU7GBdX8prKqbEIWcBdSmRFpKCQ6WbgjTFmJJBprZ0QyOt0794da22xHxGRSqRUA12d7lhT8NIdy4Mvg3EBegPxQHNrbXzBaRRFRCCEk1mn1WGUZ6uDx75iW199KSMiEsQGAcsKbFvibC+UtTbDWjvKo4X1NB6DcQvG0JODcQucL0tTHopIUUI2mcXVCtCzwDZ3wEzw3OgRdDN8LCMiErQCOdDVl8G4IiIlEZJ9Zp1XYGMLDuKy1mYZYzI4s19sAs4gMef4YsuIiASxBCjZQNeSKG4wrud1nP617jplFtXVQINtRUJTyCWzTj/ZVM+A6QxUcA9qmIdr9K7n67UOznY3X8qIiASriuhG5TkYF5xW4QIzHMwyxqC+syLiKaSSWY8n/DhnyhlwPe0PttaOcr4fC8wCPPt7jXI+lKCMiIj4wNtgXKdVuGC/2ynOx2sy2717dxYWNRF9qPB1uVTQkqlSJYRMMuv0AZtVyO6TrbROV4OxxphU4Edcr9NOa8n1pYyISLAzxsQV0dXAX9dwD8bt6EPxDFxdDwJer6DmXi41JaXocu4lVZXMSpALmWTWCXzFd6ZylV3GmaN4S1xGRCRIeQ50PZk0Bmigq7fBuBhjxniZNtE9IC0Zxd+iFbdcKmjJVKkyQnk2AxER8cJ5+A/4QNfCBuO6F2zwMmuCexYZvQUTkZOUzIqIiDfuga6e/DbQtbDBuMaYZGfbKC9Jcy9gmboYiIgnJbMiIuLNWM5cinaUsx1wdTswxmxwElNvEvAyM0LBwbjOpxeuwbjuBDbTs2XW6eIwCrirdH8dEamqQqbPrIiI+K4EA129LSDziFM+Dld3gd7AXGvt7BIMxp1tjBnkJL51nHMN1kBbESlIyayIiHhV3EBX53V/vJdtY70fUeLBuJpPVkSKpW4GIiIiIhK0lMyKiIiISNBSMisiIiIiQctvfWaNMc1wTZvSnFMd/90jWd1zFmYCG4Cl1tr/+uvaIiIiIhKaypTMOgns3cBIIBbYiCtpzXD+vM8p6h6JWgfoDEwwxlhc8xVOsdZ+WJZ6iIiIiEhoKlUy6ySxaUBHXFOsDLbWzi/hOZJxLWF4tzFmKjDGWvtqaeojweHEiRNs2bKFbdu2sW3bNrZv387OnTvJyspi//79ZGVlcfDgQfLz88nLyyM/P5/w8HASEhKoU6cOCQkJNG7cmG7dutGtWzdq165d0X8lERERqWAlTmaNMU8D1wPjrLUflPbCzlyBGcBUZ97BccaYH4FB1trNpT2vVLzc3FzWrFnD8uXLSU9PZ82aNaxdu5aMjAxyc3NPKxsdHU1CQgJxcXHEx8fToEEDIiIiCAsLIzw8nBMnTpCZmcnq1avJzMxk9+7dWGsxxtCuXTuuuuoqxo4dS3x8fCG1ERERKQdpaTBzZvHl0tMhJSXw9QkhJUpmjTH/Af5jrW3pz0o48w6Oc1prpxpjXlHXg+CRnZ3N4sWLWbRoEYsXLyY9PZ2cnBwAoqKiaNWqFe3atWPQoEG0aNGCs846i8aNG5OUlEStWrVOnsday4EDBzh+/Di5ubnk5uZSrVo16tevjzGuaSkPHDjA999/z7fffsvixYuZOHEir776Kn//+9+5/fbbCQ8Pr5B7ICIiIW7mTN8S1ZQUGD68fOoUInxOZo0xr+BaK3tjoCrjtNb2McY8bIy5wlq7IFDXktKz1pKens6cOXP47LPPWLZsGfn5+VSrVo3OnTszatQoOnToQPv27WndujUREaf/mOXl5bF69Wree+89li9fzqZNm9i4cSObNm06mQR7io6OpmXLlrRq1YqOHTty++2307t3bwDS09O5//77GTlyJJMnT2by5Ml07dq1XO6DiIjIaVJSYOHCiq5FyPEpmTXGnA2MtdZmB7g+AFhrJxpjepbHtcR3y5Yt4/XXX+fjjz9my5YtGGPo1q0bjz32GN27d6dbt27UrFnzjOOstaxevZpPP/2U//znP/z4448cOnQIgNq1a9O8eXPatGnDVVddRaNGjYiKiqJatWpERESQk5PD+vXrWbt2LcuWLeP9999n/Pjx3HbbbTz44IOkpKSwaNEi3n//fUaPHk2vXr1YsWIFycnJZ9RDREREqh6fktlAtsYWcc0SDSiTwMjMzOTtt9/mtddeIz09naioKPr27cuf//xnBgwYQGJiotfjrLUsXbqUN998k08//ZSNG10/QikpKdxyyy107dqVrl270rJlS4wxnDhxgl9//ZV169Zx9OhRjh8/zrFjx6hVqxZ33XUXbdq0ISwsjFWrVvHcc88xbdo0Jk+ezK233sqUKVMYOnQoF154Ie3ateOWW25h4cKF6nIgIiISAvw2z6xULTt37mTixIm88sor5OTk0LFjR1566SWGDRtW5GArd/L76quvsmLFCqpXr06vXr0YN24c/fv3JykpCYDDhw/z+eefM3HiRJYvX87PP//MsWPHCj1v7dq16dq1K1deeSWTJ0/miSee4JlnnuG5557j6NGjvPnmmzRp0oQXX3yRW265hWeffZYxY8b4/b6IiIhI5aJkVk6zbds2JkyYQFpaGrm5uYwYMYIHHniAlGI6tG/dupUJEyYwbdo0jh49SseOHZk8eTLDhg0jNjYWgCNHjvDWW2/xwQcf8MUXX3D06FHi4uLo2LEj9913HykpKbRp04aaNWsSFRVFVFQU2dnZ/PDDD3z33Xd8++23jB49mhkzZjB9+nSeffZZEhMTGTduHLVr12by5MncdNNNfPLJJ/zf//0fffv25fzzzy+P2yYiIiIVxO/JrDHmKWvtI/4+rwRWTk4Of//733nmmWfIzc3llltu4ZFHHqF58+ZFHrd582aeeOIJXn/9day13HzzzScTU7etW7fywgsv8Oqrr5KVlUVSUhJ33nkn119/PZdccgkRERHs2LGD2bNn89prr3HkyBFycnI4evQoUVFRXH755fz1r38lMTGRzz77jFGjRtGtWzcefvhh/vznP5OVlcXTTz9N7dq1SU1N5ZVXXqFdu3aMGDGCr7/++mQyLSIiIlVPIFpmNfImCM2YMYMnnniCmJgYvv76azp16uTTcSNGjGDx4sUkJSWxYMECWrVq5bXMV199RWxsLJ9//jl9+vQhLCzstDLXXXcd33///cnvW7RoQc2aNcnMzOS9997jL3/5C4sWLWLAgAGsWrWKhx56iKeffpp33nmHe+65h1tvvZWJEyfy0Ucfcc011/DHP/6RcePGkZCQwPnnn0/nzp3p0qULnTt35rzzzjtjhgURkZCUng49ehRdZvhwGDmyXKojUhphRe10psh6qiQfoFc51V38aMSIEdx3333k5ORw+eWX8/jjj5OVlVXscWlpafTr14/t27fTq1cvXn/9dfLy8k4r88orr9C/f3+ys7O54447SEtL4/jx46eVmTFjBhdffDHVqlUDXKuF9enThy+++ILvvvuOEydOcMUVV7Bx40bi4uJ49dVXmTt3Ls2aNWPs2LG8++67dO/enTp16vDCCy+c7HrQunVrjh49yjvvvMNdd91FSkoKsbGxXHrppTz00EPMnTuX/Px8/91IEZFgMXx48XOipqf7thCASEWy1hb6Ac4G1gNfAu/7+Mkr6pyh9gEWdu/e3QaL1atX28GDB1vAxsXF2SeeeMJmZWUVe9yCBQts586dLWBbtmxpX3vtNXv8+PHTyixatMhedNFFFrDNmjWz06dPtydOnDitzP79++2MGTNsv379bEREhI2JibEHDhyw6enpNj4+3taqVcv27t3bjhs3zs6ePduuW7fOLlmyxI4cOdLWqFHDArZ69eq2TZs2tm3btjYhIcECFrDR0dG2U6dOtm/fvrZz5842KirKArZ58+Z2woQJdvfu3X69lxIaunfvboGFthLEm1D+BFusDaju3V2fynauqk73PWCKi7O+BIizgdHFlfMo/76vZUPhE6wBdvny5XbgwIEnk8C7777brly5sshj8vPz7QcffGDbt29vAdu0aVP78ssv2yNHjlh7YqW1J1ba/OM/2c8/m2w7duxoAXvOOefYL/895eR+z88nH06ygF20YLq1J1balcs/tHfefr1t3769jYiIOJmkhoeH27PPPtt2797dXnnllbZHjx62VatWNjw83AI2IiLCtmzZ0rZv397WrVv35N9pyJAh9i9/+Yu99NJLLWAjIyPtk08+WT43WKoMJbOV4xOssTYglFRVDN33gCkuzhbbcdBau9EY06IEjb0ZJSgrlVRKSgpz5sxh6dKl/POf/2T69Om88sordO/enTvuuINrr72WmJiY044xxnDddddx7cCWfP7F1zzx9zTuuecexo9/jPvvvZHfjxpKQkIsfa+8hCv7XMwncxbw8LjnuLLfKG4YehXPTXyYhg3rnTxfl87tAFi6dDWXXdqJtm1bMnXKeACOHj3GypXr+HnVOjZsOsaGDRvYsGED6enpZGefWtsjLi6OpKQkjh07xooVK8jPz6dz5840bNiQ+fPn8+GHH/LMM8/w8ssvM378eP70pz8RHx/P73//+8DfZBERES/Sduxg5u7dxfdnBoYPH87IEO/TXGSfWQ9jfT2htXZcKesilVDHjh2ZPn0627ZtIzU1lS1btnDzzTdTv359RowYwZdffklubi7k/nzyY4yh31WX8c1Xb/Lfea/RqeN5PPb4izRJ7s0DD6ayY8dujDFcc3VPVi7/kPGP38OHH82jddsBTPnnP+HIOgAaNKhL48b1WbJ01Rn1ql49isaN67MvM5tdO39l+7a1bN68mePHj9O4cWNat25N27ZtadiwIdu2bWP9+vVYaznnnHPYsGEDc+bMoVWrVlx00UU88MADPPnkk0ybNo0BAwZw77338vHHH5f3rRYREQFg5u7dpDsrZRYlPT2dmerT7PMKYOWyjK1UXnXr1mXMmDGMHj2ab7/9ljfffJP333+Ht99+mzp14rjmd1cweFAfrri8y8lBXMYYenTvTI/unVm5ci3PPPc6L01+lylTZ3HP3UMZ+/DtJMZk8ueHejHs6nO558E07v7jFBo1TGDgVa7rdkppypIl6ScTXABqtuTYseNc2W8UK39eR716CZzTqhmXXXIBOUePcfAQHDhwgOzsbLZu3Upubi61a9emadOmbNiwgby8PK666ipWrFjBwYMH+cMf/sCLL77IypUrmTlzJrt372bYsGHMnz+fiy66qALutoiId2lpacUnL+npDE9MJLTb6oJfSkwMCxcuLLJMDx9abkOBry2zIgCEhYVxSbc4prx0H79tW8hHs5/nyt4X8f7sL+nb/27qJ/Xgtjse47N/LeLYsVMzFrRrbnj9pVtZs2QSQ6+7iOcnvUVyq7489reZHDt2glYtGvH5B4/R9twm3PvwVA4dygGgU0oL1q7fQXb24VOVOLKOPz/2BCt/Xsecj17k7TeeonbtaGZ/OJd//fsrvvrqK9LT09m4cSO5ubk0aNCAJk2asHnzZo4cOUKLFi34/PPPiYyMJDIykg8//JC3336bnTt3cuONN/LRRx/RuHFjBg4cyJo1a8r7FouIFGrmzJmkp6cXWSb90CHXK2qREFGmyTaNMVdYaxf4qzJSCeX+XOiuqKhIrrm6J9dc3ZOjR48xd953zJr9Hz76ZAEz3viEWrVqMLBvJ4YPvpQ+V6RQrVoEyWc3YMbk+xj3x2sZ/9R7PPnMbBZ9s4oP3nyYemzjlfF9uGTINB7/v5d49tG+dEwOB2DZigwuv8zVh3bxd78w4YWPGXhVJx7900R+Xr2FxMQE+ve7jP37D3Dw4GEOHc7lwIED1KxZk+rVq7N69Wry8/OpX78+q1at4vzzzycrK4tjx46Rk5PDhAkTTi7X+/bbb/Pll1/StWtXRo4cycKFCzHGlMvtFhEpTkpKSpEtdj3i4sqvMiKVQFlnjp8CtPRHRSS4Va8excABPRg4oAfHs1az4KuVfPDJ//jos++ZOetrEuvFMmzQJdwy7HLaX5BM61aNeWfClVzTvRG3jfmILt0fZE7ajVzUoQkjh3XihRnfMeLqC+jYthEAPy7+hh5tDAcPHeOWu6fRpHFdvvthLbViavBC6h38e/6v/OvfX9GqVTNia8dQvXokcXFxZGZmsm6dq4tCcnIyW7duJTExkbVr1xIdHU1YWBh16tQhPT2dt99+m4EDBzJ+/HiGDBnC+PHjuffee5k7dy59+vSpyNsrIiJVhC9dRdIPHSKlwCBrKVxZuxmouaoqK6JV1qsj6+DIOiIjq9G3VwemvngPO9ZM45N3xnHphW2Y/OqXdLhsNFf2f5jv530BwND+7Vj0zh0cP5HHxUOmMmferzw1ujf1EqIZ9dgnxMfWoFnjOJau3AHA6Ke/JGPTLpo0qMnBQzmMfeAannxmNv9d+APJyY1Zu3YTPy75mR+XrGL58uVs3ryZ8PBwzjnnHHJycjhx4gSHDx+mWrVqREdHk5uby5YtW2jfvj2fffYZSUlJANx///3cddddNGvWjEcffdQ99Y+IiEiZ+NJVJCUmhuGJieVUo+BX1mRWv+HlZBLrTWRkNX7XrwuznuvHzu8eJnVsH5au2km369MY/ZQroe18fhI//LMLrRvX4Lp7ZrL6m4X84/ctWbJyB//4x7t0apvE4qWbWfT9Rqa+u4T+l7fi6x83c23v1tz9xynExUYTFRnOrl17mfD0gyen73Lr378/Bw8eZOfOnSQmJnL48GEiIyPZsWMHERER1KtXj2XLltGlSxemTp3KTTfdxJw5c3jnnXcYP348S5cuZdKkSYG+iyIiEiLcXUUK/aSkMLJRo4quZtDQADApN/GxNXj4rkvI+O8fueuGTjz36re8NmsZAEkdujP3ma40a1CTG/66nCvaJ3DtpfV5JG0NXS5IYufuQ0x6/X/UrxtD+uqd1IqOZMnK7cTWqs6B7AM0bVSbo0ePY07s5borzyapUcLJ6z42bhhbMv7NgP7dOX48h1tu+h3Z2VnUr1+H6Oho9uzZQ+PGjVmyZAmNGzdm5syZdOrUiTvuuIPY2FgGDhzIAw88wN/+9je10IpIUEg/dIgePXoU+0lLS6voqoqUmZJZKbuaLV2fIph6bU9+asVE8dL4/vS6OJl7Hv+Ur37YBEBsm0uYNfkW9h7I5aZnNzHt+dtodXZdnp7yNQ/cdiEf/ecXhg9sx/4DR2lQrxabtmfRslkCBti6M5vLOjfl4f97gwHXPsZHb40la8ub5O2fTefzogg/lkFSYhRRkeE89eQDREZG0vqcZuzbt5fmyY3Ztm0bdevGkZ2dSa1atdi4cSOtW7dm+PDhjBkzhptvvpnHH3+cp556KvD3U0SkDIYnJvrU31JzlEpVUdYBYFKVRbQtWb9Zd0JbSJcDN1OvLdWAd184xiVDp3H1qJl89e4dtDunPu3Pa8iLf+7PyD99wgsz/scnU4bT9fo0vli0ln49WvHC6//jD7deyAszvuOC1g1YvnonbZrXo0bOCb5bvpUHbruQdz5dSZcrxnLr9e35++heNKhXC4BjB/cQGRlBw9gsRt83kL+mvs+fRg/iyWdmk5zcmK1bfyMmpiaGXIwxZGZmUq9ePa655hoWL17M8ePHefzxx+nduzedO3cu5U0VEQmskY0auV5Ra45SCRFqmRX/c7fUFtNaW6dlF774+Amia1aj3+1vsmVHFgB3DOnALdel8Ld/LmLDlkxm/XMoazdlkp+fz2Wdm/L89O8YcfX5LF+9k94d67Jh817yjh+lecPqTHr9O/5wbRJjbkhm5pwVdPndi+xYvgiA48fziAzLx+75mYeGtSSxTjSzP/mOW4dfTkbGNuJiaxIWFsb+rAMkxEdz5MhB4uLiXCua9evH+PHjadiwISNGjODIkSMBv40iIiJSPCWzUrSItt4/viomqW3aJJEvPvorh47m0ve2N9i3/wjGGF76ywDatkpk+B9nUyMqgucfu4ovvlpPu9b1ubxbM978eAX9uyXyxQ97uPDceA7l5PJb5jEuO78Oj05dw+6s4yz841nsP3iC3z36I4fXfsPx7N1EmuOw9Xtq7U9n1j+Hsn3HHr5anE7nji05eDCHo0eP0qBBXTZkbCMpqT4//fQTQ4cOZdeuXQwfPpzJkyezdu1aHn74YT/cXBERESkrJbNSOiVJaKHI1tp25zVlzjuPsHFbNgPufIvd+w5Rs0YkH00eRp24GlwxYgaJdaK5e3hnXnz9exol1ubylDr863+7GdKjIQuW76NJ7TCiIyxLfsnkrkvimPHFNh6avZvJN9Qnff0BRjzyDTmZh4jKPwFrMgC4qN5vfJnakX37c/htx27iakURXSOKjRu3065tS1av3kBycmOmTp3KM888w4oVK/jXv/7Fgw8+yMsvv8znn3/ujzspIiIiZaBkVkqvNC214DWxvezi83j3tQdJ/3UXKb9LY/7PeSQ3SeDbWXfRsW1Dht7/Ps3OiuP/7u3OW5+s4PDRPLpfkMD7C3cyuGMtVv92HJOXT+1qhg+WZvNEjxhWbMnh6c928USPGD5ZcYgvfj5E2Ik8ALKWr6PTrfO594VVvPJAGw4dOU6+tezPPkTL5Aakr/iVlAvOYePG7dSoEcn06dO58847efXVVxk5ciRt27bl9ttvZ+/evf68oyKVijGmgzFmpDGml/trCY8fVNgxvpy7rNcXkdCgZFb8oyzdEBzXDOjKDwtSiY+Loc+1f+XRl9KJrVWdeW/eypB+bRmXOpffNm1kxrgLWLHhABlbshnQLoZZSw9yUVIEB45ZjuVZwg288L9D3H9eOKv35mEP5TC8RRh5FiLDDXvW7OXaFzayascxtv12mFufXsHtVzYk78RxEmKrs3bDTi5o24z0FWuIialJYmIdfvzxRxISEggLC2PixIm89dZb7N27l7/85S/+v5cilYAxJhlItdamWWvnWWvTgFHOdl+O7wVMLe25y3p9EQkdWgFMAqMkya1HK+35bZvx438ncMdNPXn6Hx9yydBp/PjTdmY+P4ixoy5l6mdb+Nsb63j+3nPJt4b5vx5iUJso/rv5BIlRlqMnLNFhluP5MO2XPM6qCc//lMdV9SwDmoRxXkwev/9oH4s2n+D5PrVYPCKeK9vU5Nn3M2jaoAZ7Mw/TrHEcK37exDktk8jPy2Xdus106ngezz77DIMGDWLGjBnExMQwYsQIXnvtNfbt21cON1Sk3I3CtWS5pylAalEHGWOSjTFTgGQgswznLtX1RST0lDWZ7eiXWkjV50ti6yS00dHVmfriPbw3/SG27T5C92Gv0f+Ot7i+77ks+Ec3LHD3cz/Tq3U05yREMPuXY1zVOIxNByEqDLYcggZRcCAXDufCnmOwYBfc1MSQc8zy4aZ8bmtleDf9EMNm7WNochiv3FifNVsOUzc2kk3bsmjWOI6Nm3dx9NgJEuslsGnzDmrWrMHOnTupVq0af/vb3xg9ejRHjhzh5ZdfLp97KFK+BgHLCmxb4mwvlLU2w1o7ymlJLcu5S3V9EQk9ZZpn1lqb7a+KlCdjzCAgy1o7z8u+DkAnIANXy0JGwXK+lJEieCa0Beexrdny5Dy1Q667mAF9O/HSC9NJnbKYLtdOYXD3hnz3xyT+/vk+Ji3I5KzahssbGT7fls/5sbD6AMRWgzUHITnGkHHIUjcK3t9iyTpu+Wib5aK6hg1ZlsW7IbEG3PjxAVrXOcyEa+ty/3u7aVq/Bpu3Z1EnriaEhbN7j6tx6eKL27NgwQKGDRvGm2++yaOPPkr//v2ZNGkSo0ePpkaNGuV1B0UCyhgTh5eWVWttljEGY0yytTYjUOd29gXk+iKlkpYGxS0wkZ4OKSnlUx85Tcj1mVU/rkrGW2utR7eDmjWjGH3nJWxY8ACP3dudWYt28uJ/9/OPIfX51w1xZB6F3DzLqDZh/JQNPRJh/wk4qyZkHLIkRMIRp4V2SaZrKdrE6mFsPgwRBi5vGIYBft2XR83IMB7pW4fNu3JIqhtFg3ox7N13kOiaUSQ3q8+yZauJi6vF4YOu1ll3Ert3716toiNVTQK4ksdC9pcl3vly7kBeX6TkZs50JatFSUmB4cPLpz5yGr8ms8aYlAKf2h77Rhtj1hlj9hlj3jPGNPPntX2om/pxBRsnoTX12lK7VnX++kBPhl3RiIlzM8nYc5y+PRry8IXRfL0LLkgwtIkzLM2Ec2rB3qNQIxzCDRzJg8TqsO2IJTEKvtyZxw1NDbkW3svIp3tDw+xeEVxdL4+busYC0OqsGNZk7CE+rgYN6sezbcc+cnKO0blTW/79xWIG9L+MN954g44dO3L++eczadIkrLUVebdE/Cmugs9dqusvWrQIp+W2yI9IqaSkuFZVK+ozcmTF1S+E+btltjeuPk6P4HoND4Ax5mlcyd58wN0yOqU8E1r146rkihkkZuq59k/463Aiwg2jvzwEwNjBjUmOC+f5X2DMBWHsP+FKXI/mQ6tarv6yLZ3ktno41I50Jbgfb4fvro7g332r8eLF1TgnzvVfoWX9SFrVj+TgvsOcyLV0u6AxW7bu5vjxXBrWj2f3bzvJzc0lIb42Bw8e5J133uH+++/np59+4quvvgrsPRIRkUorLS2NHj16FPtJL66FV0rM38lsFtDbWjvUWjvNWnvAGNMeGAOkWWvvttYud/qXDsHVylkpFNWPy9mf7EuZ8qltFeUtoS2wyEJSg9o8+v8u5+PFu/jPgVpERhievaEhazPz2B1eg+vPDuPbvXB5I8PKLGgeA9uOQGQ4JETC+oMwoEkYa7ItE1fkc1bM6a00Wev3MaBdDCu2HyOhVgS5h/dzIjefhokxREZGsOLnTeXoqsEAACAASURBVHTrej6ff7mY885tzqvT/snw4cNJSEhg0qRJgbw7IuXOiXkVdu6SXr979+5Ya4v9SAmlp0OPHsV/0opqK6r6Zs6c6VOimpKSwnB1R/CrMg0A8yLWWju/wLahgKXAa3hrbbYxprDX/RXB5z5axZTRoISyiGhb6KAwU68tds/PPHj7RUyfvYw/vLiKFa9exu/sRnq2rsnT3x5m7o3x/HfmfnbnWKpHQHx12HwYzokzrNpvaVoTPtmczw3Nw3h3Qz4d6uZxdbNwALZvyiKpWRyX18nnuVxL1yZRzF+RSduzY8jKCWPz1j1UqxZOQmwE//t+F31uvYjXZnzEhg0bGDlyJBMmTGDz5s00bdq0Am6ciF+541gCrkYK4LTksixxzpdzZ/pQRvwgPT2dHj16FF5g506GJyZS7MtzdxIX4q/ZU1JSWLhwYUVXI+T4u2XW2+wGvXDNHLDJy77K9IisflyVRREttKZeW6o3TuEfj13Fmq2HmfThRkzr5jz3cEeyj1mmb7CMvyyGn/fDlc2jWLIXLmvoSmQbR8PhPDAGMrLz6VDXMPGnPA4cP/3HsGtSNeKqGyIjDMdP5NO8UTTbfjtAeLiheZN4vv1+DY0axrNx03aqVYvgtWkT+P3vfw/AtGnTAn57RALNeWDP4MyYl4Arnpc6mfTl3IG8vpwyfPhwUooZfZ++axczGzUqvq+oRvFLBfJ3MntaVmCMiQU64OpT6k0dP19fqopi+tAOGHI9Ay5vxePT1/PN7ga0S67N769pyksL95PUrDYXN6/Bos3HaN8ggqX7LEk14Xg+7D0GHeoYftgLHeoaDhyHab/mnTzv9k1ZRIQZep0dyTdrD9Pt7OqsXpdJRDic3aAGv+0+RFb2Ybp2asV/F/5Azyu68ubbn9GgQQP69OnD66+/Tn5+fqDvjkh5mIfH2AdHB2d7eZw7kNcXYOTIkSxcuLDIT3HJrkhl4O9ktuBTtHsd7VkFCzqJbmXqZgCoH1elUnDaLo8puwBem/YITRrFMvCut/k5pxkT725Dp3NiufXNXTzcO4Hs49A0NoxjedAwNoLdOdAqFr7bZUmpY5i5Pp8rGhneXp/PjsOn7v/2TVlc2TyKvTmWixLDWLf7BBe3TWBP1nGyDh6lQd0YduzMJDw8jFox0ezdu59PP36JW2+9la1bt7JgwYLyvEsigTIWGFxg2yhnO+CKhcaYDcaYwt4tJ+D9jVax5/axjIiI35PZjcaY6wCcablSgf3W2tPevRpjUoCnrbUT/Xz9svDsx3VSgT5avpQRf/M2Dy1Qr24s/5nzJDWja9L3tjfYVf1cZk+9g6hqYTzyeTb/r0c8H685zs3d4liyM5fODSPIOABRERBmXMnrgROury+uyjvtEueG5RBuICfXUrt6GOFHj5J9OJe6sZHEREfy/ZJ1XHbxuXz9zTIaNarHq9M/5Or+LYiLi2PGjBkBvyUigea86h9rjEk1xgwyxozBNcd2wTh3Rjx0jpmFK5FNNcZMcRar8fncJbi+iIQ4vw4As9Z+YIx5xRgzjVNP4x3gZEvsSFwDwjoA1hiztGCiW1GclWWK7aPlSxkJgIIDw5yEtmkT+OKD/+PSq/5E3zvf4+u3R/DeP4fT55bXadU4kXObxvDpTwfpdnYNft6eQ1SEoX51y7K9MLBJGJ9uyadvY8NnW/K5OCGX5BjDuQ3CqR1puKSB4aNfj3Ftq0je/yWH+rXDia1VjbWbMwkPN9SuVZPfftvL4EF9+ODDeezdu59hw4Yxffp0XnrpJWJjYyvoZon4h7V2GWdORei5PwuI97Kt2NbT4s7taxkREb+vAGatvRs4G+horQ2z1nrOU7EMV5DrDfQBlvr7+mWkflyVmbvbQYGuB+069+LTj19m89Y9DLznQy7scBapY/vwyTe7uLx9HfYcziexVjh51nBW7TAyDkKjmvDjnnwa1oCMg5aYCHhrYz7WWlb/lsfq3/K47uxwfjv8/9m78+iojjPx+9/qvaXWvu8LEohdFosNBsS+GcfGMU6CncRObHAymWQmkzG2Z+bN753fO+NgZzLJJBPHOImdOMYrBtvsi43A7AIEYhcIISGhfVd3q6Xuev+4LYwxIAk3NJLqc849gtt1697qY9qPqp96ykNGuB5Hh2R0goWz5W0IARkpEezae5LoqBAaaivxeDz85Y2PePyxyTidTt59913/vU+KoiiKMoD4ujQXoJXdAg5f49zVZbv85UZ5XO8BVxbLW8oX6+H2pI1yq10Z0HYeY/KkMbz51+V8/ZF/5DfvnmfZsiXsP1LOinUn+fHXU/nVu+f5YW4ov89rJCVEh8vlodIB85ME68sk06IFn1ZLTjbDMO+E6pRYQaABjpY6GBVtoLq1E4+ErKRA6hod1Na3sXDB3Xy04QD3TszmzbfW8fyzT5GVlcXbb7/NU0895Z/3RlEURfGpFRUVrKyu1urpXkdBQYFaMOcntySYvRN581qfQ6sF25XHNQvYIqV8Hy6nGiwTQiwHDnjbfimPq7s2ym3mTUF4aOFMZs64h9++upmf/uh+/vM/fsh7G/4OY2gCIYFl1GAh2KIjOkDHgSYPGcFQWC+JtUKVUxJqhDVlHoaF6BkWq6fTI3G6IdwMs6JN/HqfnaxYExKoqW/DajEiAbfbQ0ZGCn/564dUVFQzd+5c/vCHP9De3o7ZbPb3u6MoivKVdFuLVmvUs3q0d6IVK2Dlyhs2WVlcTAFwo1BVbYbgPwMmmFV5XP2cN6D96T98h/n3/5B3V+/msW/kMn92Dq9/UMB3Zify8kcX+M7dwbyxr4kQsyAkQM/Byk4eTNGx5oKHefGCDRWSdoNW87fSAW4Jw5MDSQ7R8197tWB2faG2lW56UhhHCkuwWk20t9UCsGXrHnInpfLrXzs5cOAAkyZN8ttboiiK8lX1NDgraG2F1laWdBf0Ll58522ssHKltunDjWZVbTayY2LUhgh3qAETzCoDw9w5kxg2bBD/9b+bePSRKfz9kvnM/fpBUrLuxb2mBHNUKB3uJkalWdh9zkGEVVDt8GDUgc0MURb470I3f5kqLpfrSg7RMy7OiFkPeiFwuSEhUptxPX+hiikTh1FQWEJsbCQbNn3Gy7/7N4QQbN++XQWziqL0aUuWLGFJD4LPqUOGQFXVjRvdybuEZWdrmz9cT3dBuuJXPl8Apih+YxiBEIJ//PG3KThyiu07jzFr+mgGZ8Tz3vrjzJ86mA92VJI7Opzi2g7cEoZEGNhbo+XHriuTfG+InsN1ku2XJOV2LZhNCtZhNQrGxBk5X9eBxSiIDDFRXKrNxoaHB3HqTDm5k8eyafNugoICGDVqFHl5ef58NxRFUW6fuLjPA0K1S5hym6lgVulfDCN47NEFREWF86s/fIJOp+NHS+ax78hF5kzOpKqhneFpQZQ3dnJXkpmzDZ0A2Ixg7wSBJNUGvznmpqxVIoCEID0AExONFJQ5uTfdSl2dA0e7h8TYYGprtV2cI0MFTU0t7N5TQG5uLrt27cLlcvnrnVAURVGUAUEFs0q/Y7GY+bsffJO16/I4Varnu9+ahs1mYf+RMoZlRLH7WD2x4WZ0QlDdJsmOMbC7BoaFCt4t9vD3I/Sca5a8V+wh2gomvZZDOzHJhEdCWqSRi42dGA2CqIhADhacIyoymOqaJoxGA+vW72Tq5HQcDgcHDhzw87uhKIqiKP2bCmaV/scwgh8sfQSz2cR//+YNgoMDeHzxNN7dcILvLcqh4GwLc8ZFcbDUSUyQHmHWU2OXjE82U9wCIUYYGSZodEFCgLjc7bg4IwYdeLw73w6KD6ChrhGHs4PhQ5PJ23WcSffmsH7jTnKnjEWn07F582Y/vQmKoiiKMjCoYFbpl6Ljc3n8Ow/w+l8/5GJ9ED9aMh+Xq5OmVicRYQFUOa3odYJB0SYOXmgnKVjH8dpOwq2Ct4s9/OMoLbUgPvDzYDbQJLgr1sCJS+0khBowGnSUVDowm/QYDXqqa5oYMzKO48fP0tLSxsSJE1m7dq2/3gJFURRFGRBUNQOl33r2+Rf502ur+cWLf+J3//MvzJ6ezZ/eL+T7i3L45R93MXNMBAdONaHXQUachU9P2/nWcDPvnmhn2WjBP43UM3NE8Bf6vDfRxO/y7XxzXDAfHtFKdGWkRHC+uAwAodOC33Xrd3DfvDE89y+/oaKigvj4+Ns7eEVRlNus23q0d2gtWrUhQt/n15lZIUSqP++v9G+pqak88cQTvPqnVVy8WMlP/uEpKi7Vk5SZhU4nsIVH09DSQXZGCEcuOrEYBe1uiUfCxnojjw/Rc3eC8Qt9Tkwy0umB5DAjLU4PkSEm9B4HZy/UMzgjngMHixg0KIl1G3awYH4uAOvXr/fH8BVFUW6bxYsXdxvsFbS2akHjHWZldbVWJ/cG1IYIdzZ/z8y+B4zz8zMo/djzzz/Pa6+9xi9/9Rd+9ct/ZnBGPH97J49FD97L2g37SE8Ko72jk/o2D7mZVjYWO5meauSNo06emRBxuR9Hh2TxmkZmppnQC2jvlOgEJEVbKLrYBkBqcjSf7Cjk+9+ezl/e2sGgQUmkpKSwdu1annzySX+9BYqiKLdcT+rRTg0NpaC19cazt5cusVhKlvTk2ywfbsCQbbOpDRH6sFs2MyuESBVCZN/oQNsKVlFumdTUVB5++GH++rePcLk6WLJkMfvyi5g36y5a2lyMGRHPsfMtxEeY6fCA3SVJDzNQbfewreTzslpWo+CP94XwwzEBDI00UFjRzrA4Mx6PpNXhJjIsgA5HE52dbqKjQnE629l/oJCZ08eQl5eH2+3247ugKIrif4ujo8m22W7YpuDcOVYW92B3+IKCbregVQYOn8/MCiFeAJ7xdb+KcrOeeOIJ3n77bT76+FMWf3M+zzz7K06dKSc5KZKqWm/ea0IguwrrGRRl5GQLRFoFbx1zMneQ+XI/EQHa7353xRpYV+zk/pGBrCvUZmUT40IoOl+HyWSgobEVIQQ7dh4kd8pY/vTaBxQWFqp8K0VRBrQl8fHajOsNZkAvz972oL/FFRV3XP6t4h8+DWaFEP8MLAVeAs510zwMeMGX91eUa5kxYwaJiYm89pc1PLJoLjNn3MOb7+1g8cOTeel/PmTcqAQq6hpwSxgaa2JtYRuPjrDw3kknDQ4PYdYvfoFxV6yRNwqdpEQYqW1zkxBpAVcbFyubGT8mgz37zzBq5GDyduTz3VcfACAvL08Fs4qiKN1YHB3do3YFra3Q2sqSG6QsrKioYKUQ2u5k3fTV3Yyxcmfz9czsLCBNStnUk8ZCCPVLlXLL6fV6vvvd7/LCCy9w8WIl3350Ad9+/HmyBifgdntISQjhwNFyMhICqGjqRAiwhVtwuZ18cMrJ9+8K+EJ/k8dGwJYWDN7NFDISAjh2vgWA+LgIPlp/gCe/M4M33tlJbGwkqamp5OXl8ZOf/OS2j11RFKUv6cnsLcDUIUMoOHeOqQUF122T16SFIrndBLPZNluPg2jlzuTrYPZQTwNZr6U+vr+iXNPjjz/Of/zHf/DXv33MT/7+UQIDrXy25xSjR6ZSXNqAXq8jIcpCXkE996RZ2XKilVHRBv58xMH3sq0I8Xm92ZEJFkwGQV2rG70OAix66po7CAky42yux+PxEB5mw+FwcvDgcaZOGc3H63bg8XjQ6VRpZ0VRBrCCghuWwKKgAHrwLdbif/qnbnNmcwsKWOxy9SwVQZVP7NN8HczW9qaxlHKbj++vKNeUkZHB5MmTef2vH/Lcsid56MGZvPfhNpb9w0Ke/3/fJHd8KkXnLqHTQXSQnr3nHTw3N4IXNtaxo7SD3BTT5b5MBsHoBDNHLmqLwFrsndo9UiI4dqYKk8lAU7MdIQRbtu0hd8pYXv/rh5w4cYIRI0b46y1QFEXxr56UtsrO7lG7nlRPYMWKni0S6+E9lTuXr4PZJiFEsJSyuSeNhRDTpZSf+PgZFOWannjiCb73ve+xZ88Rvv3YAt5482PCQ20IIYgICyCvrp1xQ0IoqGwn0CyobO4kOkjPHw7avxDMAoxNtfK3fU08dJeN9SfbCA8yYDTqtbzZsZnsPXCG8eNGsn7DZ7z1t+WAljergllFUQasJUt8Vkrrjryf4jc+/c5TSvkq8HwvNkNY7sv7K8qNLFq0iMDAQF77yxqmT7ub+Pho1m0+yNRJwyk4cYnAABM2q4HSaieTRkWw6lALT00KZVOxi3MN2uxraIZWe3ZsioUWp4fEMCM1jS7GDA6l8pL2xUR0ZAgFhSVMuzeD/QcKCQy0kpSURF5ent/GriiKoij9lc8T+KSUzwLLhBCbhBAvCCGevM7xMyDH1/dXlOux2Ww8/PDDvPPeRtrbXSz+5nw2bDnMgwvuprisgSnjUjh0rhWbVY8Qgmanh8QwAyaD4PVz8nIgC1owC6D35tLGRZopqXQQHx1Ec30tUkoiI4KQUrJp825yJ48mLy8PKaVfxq4oiqIo/ZVPg1khRIgQ4gDawq5ZwDJgxXWOF315b0XpiSeeeIKWljZWr9nGo9+6j85ON+5ON2azUct1bWln7JAQPiusJznawjv5LSweF8zrexpptH++8cHQWDNWo6C21Y1eJ9B5g9rBaREcO12F2Wyk9GItMTERrN+4k9wpY6murub06dP+GrqiKIqi9Eu+npldDjQAi4Ax3RyzfXxvRenW5MmTSUtL47W/rGH06CEMGzaIVR/vZcGcMezKv0BCTBDtIpBWh5tJo8LZfsbOnGGBtLVL/rSr8XI/Br0gO8nC0XInw1NtlNc6sVn16HQ66pscjBqewvbPjjF/1mg2bvqMKZPHALBx40Z/DV1RFEVR+iVfB7PpUsrZUspVUsrD3RxbgcM+vr+i3JBOp+Oxxx7j0+0HqK9v4pGH57B732nmzcqhtsHOhJxkDp+4RFSoCbdbSwkorutgdKKZzSfavtDX8HgzZ6pc5AwOobC4hfFZodQ12gGIigjm+MkyJt49hMZGrQZtamoq+/btu70DVhRFUZR+ztfB7JZetl/k4/srSre+9rWv4fF4WL9hJ7NnTkBKiRDici1ZZ3snQ1NsHCpqJjMxkIMXnGQnWjhW0f6FfhJCDVS3uBka0E5lfTsJURYuXmrCYNBBRwtut4cAq5Zbe6G0gmHDhnHq1KnbPl5FURRF6c98Hcz2anWLlPK8j++vKN3KyckhLi6Oj9cfZty4EQQH29h74AzZI1O5VK3NooZERlF0sY0RaTbyKzoZHm/mUlMnda2dl/tJCNUq2yWGGgGwmHTUNTpIig2mzdEBgMet5dmWlFSQlZXF6dOn8Xg8t3O4iqIoitKv+TqYPS+E6PEG9N6KBopyW+l0OhYsWMDGjRvxeCTTpo5jy6dHuGf8EI6eqSE5PoQ2uwuAUJuR0ioHSWFa4Hrl7Gx8iBbEhli1f0ad3rSEmEgb9Q0OAOob29Dr9ZRcqCArMwiHw0FZWdltG6uiKIqi9He+rjO7CpglhHioh5eo7WwVv7j//vtpaWlhx858Zs2YQElpNekpMbS0OBiWGc3p4lpMRh2Odm1m1eUNVAvLPw9mE7wBrt0liQ03U9+szcbaAs2UXmoiMNDM2eJLJCXFUlJSTlZWGoBKNVAURVEUH/LpDmBCiBe8f5wphHgVyAeKr9M8HEj35f0VpadmzJiBxWLh47V5/N0PvgmA3a4FqqFBFsqrWrhrWBwXqpwIAedrXcQE69l1zsGPpml9dKUZlDd2Mio9iAtV2mysXidoanGSPTKVU2fKSUmO40LpJbKGfB7Mzpkz5zaPWFEURVH6J19vZ7sUCAGE9++zummvKsgrfhEQEEBubi6f5h3l179aRlJSLEeOnSc8zIbdqc2wxkXb+GR3FUOSAjlUb2D+CBsfHG6hwy0x6gURgXrMBkFFUycjB4Xwuw9KSIg00+7S8mpjY8I4cbqMaVMnsvWTvURGhhEeHq5mZhVFURTFh3ydM1sPLJVS6npyAE0+vr+i9Ni9997LsWPHaGpqYdaMCXz62UnuHpvJ2YutBFiNeCQ4XR7S4gLIP9PI/aOCaHJ42Fmkld8SQhAfaqCisYNR6UG0d3iIj7TQ2OwEIMhmpbSslvj4aCoqanC5OsjKylLBrKIoiqL4kK+D2WJgay/bK4pfTJw4ESkle/cdZeaMe2hoaCY5MYqTp8vJGR5HeWUzoFUpqKhtZ0S8CbNB8PHR1st9xIcYvGkGwQDYrAbKq7SKCHq99s/LrGtBSklZWaUKZhW/6MU6hquvyxFCLBFCzOz66YvrhBDLva+F3qCPUCHEM0KIdO+f07uuu5mxKIrSf/l6AdhsKWVJLy552Jf3V5TeuPvuu9Hr9ezeU8CM6XcD4HJ1IqUkKS6EE2drSIgJorlNSxs4qQ9nRlYAawu14BS0vNnyxk6GJAciBOh0gpr6NmIibTidWkWEzk5vea4L5WRlZVFZWUljY+M1nkhRekYIUdTLS5bfxD3SgeVSyhVSyq1SyhXAUu/5r3pdDlpd8gYhhLzqOOdtE+597nNoO0seBA54N9xRFEW5zKfB7E2U2urtJguK4jM2m43Ro0eza08R0dERjByRSUlZDUII9AFhuN0eMlIiOH2xDZ0O8k83sWBUEOdqOjh5SQtU40ONlDd2YC4uISnaSkeHVkM2LtpGTV0zOp2OpmYtLeHChUtkZWUBcPr0af8MWukvBgkhgnrSUAiRxs0ttl0KvHLVuVfoPjDuyXXFaGsqxgCDrjiW8sXNdGYBYcAgKWWYlPL93gxAUZSBwddpBs/1tKEQYgaqmoHiZ5MnT2bPnj3Y7Q7unXgX+YeLGZaVSGlZLTqdwGzWc7HayeDEQD4rrGfBSBsAHx3VUgmsRoGjQ9LpgSFJgVTWaxURbAFmTp66QEZ6LMUlVZjNJo4dL2L4EG3N5aFDh/wzYKU/ebW7Bt663/k32f/DwNX/oebT/TdqPbnuoHfW9pCUsrjrAJBSfuFaKWVj12uKoijX4utgNkwI8f3uGnlncDf7+N6K0msLFizA4XCwZese5s2dREtLGyOHJbN7/2nGj0qgrEJbo5gQaWHHkXoCh6czJtnCR0e0vNn9JQ5GJ5ox6gW5oyM4c7GN6DATnZ1u6hsdjBiaxPbPjjPp3rvYtGU3aWmJJCcns3mz+s9f+coeudG3Yd7XDqLNbPaKN5c1HW1R72VSykbv69eciOjpdd7Ug6uvXXKt84qiKN3xdTAL8OL1vv4SQqQKIQ4AL6IqGSh3gNzcXMLCwli9ZhszZ9yDxWLG7ZZ0drpJzUjl5LlaMlMjaGjtwO2RrN9bzQPZQewrcVBW38GeYgeTMgIAmDU2EoCsZBsnztYgBBgMetranGRmpHDyZDGlpZeYM2cOn3zyCR0dHf4cutK3veitCHNYCPHylS8IIYKFEO+gfc6uQAtmV/Wy/3D4PAi9hut9q3ZT13kXdV1rBjldCPGw91gihFDrLBRF+RJfB7ONwC+AP179gneW4BxaXtQiKWU4PfiaTFFuJaPRyIIFC/h43WeYTEZmzZzA/sMlxMWG0VCvzb6mJoZScLaFmDATq3dW8sBoG1LCv6+rxe6STPYGszmZIYQFGdEJaG5tZ8TgaE4XVWAyGWi31wKwdl0ec2YOpbm5mX379vlt3ErfJqV81vtzG/CsEOIP3smCbLSv+Behfc7+QErZJKV8pJe3uG6VgVt0Xc7V6QV4Z3ellO97jxXAN24U0Obl5SGE6PZQFKV/8fWmCWlSyiZvGZWXpZQ/EEKkAu+hJfq/DzwlpWwCkFI+7eP7K0qvLVy4kDfeeIMdOw9y/325fLx2O4sWTmT95kMMzoinqcWJxyMZmR7E+n3VvPbsTNIjL/KnXdrkU1cwq9cLZuZEsutYPXodhIcGkLevhEkThrL/YBGDB6fy4cef8u5bv0Sv17Np0yYmTZrkz6Er/YD38/RpIUQ+cBdaMDtISnnev0/WM97g9Es5sd7Z3avTDl7xHv1uIdiKFStYuXJlt+0KCgrIzs6+DU+kKH2Hr0tzdQWpjWizBZv5fDZ2lpTyka42inKnmDNnDlarldVrtrHgvlwArLZI2tqcjBiWzKHjl4iNtNHRKXG6PKzfW8PXRmuZNLHBemJDPv+dcObYSCrq2hkzJIRL1doisbiYMI6fLGNa7ji25x1ACMHdd9/Npk2bbv9glX7Hm1awCa3c1Spgq68C2RvVgfXhdc/R8/rkxWipB9fsPzc3Fyllt8edaOXKlRQUFHTbLjs7m8WLF9+GJ1L6ioKCAqZOnXrDY8WK/p2O7uuZ2cu8M7RPoy30ypFSNl/dRgjxkJTyg1v1DIrSEwEBAcyZM4c1H33C//z6OcaPG8nJk8UEBVlxtXfS2elh+OBo9haUERNm4v28S/xorI1fb6v/Ykeni5k1NhaA2HAz+3dVEx8TRF29FtQGB9vo6Ohkw8bPmDMrm//z7y9TW1tLZGTk7R6y0scJIaZLKT/xphVsQ8uLXSSlXCWESPPm0S671uduD3XNlIajpY913Tf0qte/0nXe8znXyrEVQjwjpXzxqtNd/+jS+XLFhD4vOzub7du3+/sxlD6kJ7/YdP2StGTJklv9OH5zKxaAXeYtpzKL65fs6nUhb0W5FRYuXMjFi1Xk5x/n/gW5HMg/xrRJIzhwuIiI8CDcbg9t9g7GDgll/b5qsqcN4fjP0yn418/Xs1xq6iA52kpGQgCNrdpGCxkp4ew7WERSYgRnTh0jKiqcDz/+lLmzJyGlZMOGDf4astK3bfEu8jqItqHAICnlKgAp5Xkp5Q+A54UQ06H3NcC9wWUxX86BDQeuWyrrJq6byRVBb5eujReuUTUh3PtTlepSFLQAdfv27Tc8BkJaSq9mZr0fjDk3cZ8c7wfvgSvORaDqzCp3iAULFqDX61n94Ta++chc/u3nvyM6Lpmq6gPMn53Drj3HsQWYkEgc7R42HajlkXjz5esLypyMf+E8v/+pY2iTxAAAIABJREFUlZljInljczkpMRba2920tTnJvXcY2/IK+frCOaz+cBuv/fH/Eh8fz+rVq/n2t7/tx5ErfZRAq9v66vXWHkgpnxVC/LMQYisggV/28h5bgbF8cQY0h+5TAnpz3TiunS9bLIRYep3g99ANqiUoijIA9TbNYDbwzFe436Kr/n5nJi8pA054eDhTpkxh3Yb9/Mf//THJyXFcLK9Er9dhsZhoamln2j1p7DtVeTnV4JH/MwZOFyOl5GfvV9HpgU37a1g8M4E/fFTKrLGRbD5YhdmkRwhBa6uTjIwkmptb2fnZQR588EFee+017HY7AQEB/n4LlL5nmZTyhgGqlPIlIUQk0NvdGQGWoS3evTLZbqn3AC6nCRzEu31tT6+7wpdq0l6hXgiR3hXQeu+1FHiq90NRFKU/622awTm037bDpJS6r3JwVU6VovjbvHnzOHr0KOXlVXxtwVTydhzk3ruzOHnmImazkUCrkbpGB+OzQlm3t5o2RycMSWdtYSufnLYTEagn70g9U7PD0esEFpMOu6ODUUNiOHGqDJPJQGVVLVarhQ8/+pSHHrgLh8OhNlBQbkZjd4FsFynlMrSZ3F7xzn4uE0Is99Z5fQYtaL16tjT8Jq8DLZC9XsrC+2jf6j0jhFiOlpa26BolvBRFGeB6G8zWo62U/coVCbwfeH2idMz1CCFyvIW8Z3b99PczKTdv3rx5AGzZuof7F0zF4XAyeOhQTp66yITxQzhVXEtggAmdXuBo97D1oFY79v/7pIXBMSb+88EoaptcVNa3M35oCOcvObCadQQGmDl/oZqJ44eweVMes2bew/qNO5kyeQxhYWF8/PHH/hy20jf1dPV/l5sKAL3bzS7z1nl9UUq59arXG6WUYVfv3NXddVe0WyqlvNaMbdfrXdcv87ZVubKKonxJr4JZKeWqrmLdviClHOurvm63rgUKUsoV3j3GVwBLr7fNo3LnGzp0KEajkTPn7Nxz9yjvWS0TJj42jPMXGxmSFkGrJxCAkkoHAA0tHYxJtpCdZAHgTFkb9wwL48i5ZoYkBeJ0aTt9xcaGce58FSOGZ1BSUgHA6NGjOXXq1G0cpdIf3MQmCH+4JQ+iKIpyB7il1Qz6uaVoxbuv9AqqQkOfpdfrSU5OpqSkhOBgG7GxkTQ1tQFgNOpxuz2EhViprmtFrxNUN7YDYDXrcbg8pEcaAThf6eCuzGCcLg8RwSbqGrSg16DX4fF4sFosSCmprKwjNTWVkpISv4xXGVCu/qxSFEXpN25ZndkB4GG+/D+IfGCLH55F8ZErg8vEhBiampoRQuDxaDO0VouRS9WtRIWaqG50aedMehwdHYQH6gmy6CiusDNzTDIARoOO8qpGzCY9LpdWrqtr1WN5eRVpaWlUVFTQ3t6O2WxGUXpCCPFkL5qPuWUPoiiKcgfoUTArhEgDZkgp/3iLn6frfsHAkp4ucLjdvKtqv7QKV0rZ6N37O13ldvVNqamprFu3DoCEhGiKiy8SHxdGm12bhdXrBLUNdkalB1HT4A1mzTocdokQgrQIIyWVdtLjtOoEOgF2RwcZqeG0tGoztO3t2nXlFdWkpqYCUFpaSmZm5u0cqtK3rUD7vai7hV1dbVTlGEVR+q0epRl4t0Y8L4R4WQgRdCsfSAgxA6124h0ZyHqFw+VFbNfyhbzZvLw8vEHuDQ/F/1JTU6msrMThcJIQH83F8ipSkqKorW9Br9fR6fYAEBxo+FKaAUBapJHzl+wEWPREhpjodGsxRFRYIJVVjZhMBpqatB3Byss/D2bPn+/TayGV268ReBZt1vVaxyzgabTdwWYCGf55TEVRlFuvxzmzUsptwKvAYSHE9339IN79xTcDD0spv+Hr/n3spvYrV+58l2dKK4JISIihoaGZxKRUyspqiY8Lx9mupQpYzfrP0wzMOhwdWtCaFmnifKUDKSUpMVZaHW4AbIEmSkprSE2OprKyFpPJyEVvmgGg8maV3qqXUr4kpTx8nWObd3HqbLTgVs3MKorSb/W2msEhtJ1dxgkhioQQ3/emBNw0IUS2EOJdtMLbr3i3YexXcnNzkVJ2eyj+1xXMlpSUkJgQA0BYWDBl5bUkxkfQ3KrNxpoMOqrqvTOzJj2Ojs9nZu1ON9UNLlJirNQ2awGv0aCnobGVpMRIis+dIyEhmvLyKuLj4zEYDCqYVXprVk8bSilfQsvxVxRF6Zd6Xc3AW1fwaWCO92gUQhzwpiA86Q1OU68Ocr0zr6lCiIeEED8TQrwjhHCjzfZukVJmdu0t3ld4c2eVfuTKYDYhIRqAgAALnZ1uoiKDqalru9y2zemmzdGJxazH4fLOzEZ0VTSwkxxj5WKNk2CbGbc3PSEizEZxSRUJ8TGUV1RfrqCg0gyU3vCmfvWGymNSFKXfuulqBt4FTo8ACCG+jjZT8Cyf54vKa+SBdp3YilbEe6yU8vDNPoMfdS3u+sIuZlcEt2rxVx8VFxeH0WikpKSE3HsnAGDQ6wGwBVqoqG7BbDJczp2taXJ50wy6ZmZNAJy/pAWzdqeb4ZkRtDm0WrNWi4nGpjaio8M5cvQ0gCrPpdwO4d03URRF6Zt8UprLO6N6eVZVCBGC9uHZFdjWo22/2C+mn7xVC4r5cu5sONo4VTDbR11ZazYh4UEA3B4tUDUa9bg63CTHh+D0LviqbnB50wy6cma1mdniS3aGpWhrJSMDOqlutAPg8aaTBFgtlJdXI6UkLS3tcgUFRfE177dkqjyXoij91i2pM+vd7raJPr5dbTe2ouUPX7lNZA6932ZSucOkpqZy/vx5goICCQoKxG53AlxOFQgLsdJs14LTqoZ2rGY9zg6JxyMJMOmICdZTXGFn3nhvmoJFT9nZJsJDrdi9Jb50Oh1OZzv11buuqKDgwGq1+mHESl8jhHi5h03D0fJlF93Cx1EURfErtWnCzVsGvIdW77HLUu+h9GHZ2dn89re/pbm5lZEjMtm3v5BBabGUXawDIMBq5OgpB3qdYO+JBgbFa9vbnrjUzogEC+NTrWzaXcmvfjgUi0lHR6eH1jYX40cncOxkKRaLiebmVgA+23WIUaO0rXP37NnD9OnT/TNopa/pyedMI9q3Yk9LKT+4xc+jKIriN2o725vkrTG7TAixXAjxsBDiGWC5SjHo+xYuXIjL5WLdpvM8+MB0DhecZOrkEew5cJqhgxNoa3PRZu9gWIqNj3dXM2tsJACbTmiLw56YGEpFUyc7CxuYOz6K4yWt6PWCYJuF00UVTBg3mIMHjxAcbGP1mm1MnxKL2Wxm7dq1/hy20rc0opUx1N3gCJdSZkgpX/X3wyqKotxKKpj9CqSUh6SUy6SU70spX5RSqhSDfmDChAnExsayatUqFj4wA4CAoGhcrk4GZyZwrKiaQKsRW4CeY+db6OiUDE2xseWkFszeN9JGTLCeP71ziq9PieNSXTtjMoM5W6LN7EaEB1FWXsekiXfx8bo8LBYT06ZNU8Gs0hvFarZVURRFo4JZRbmKTqdj4cKFbNiwgfj4KEaOyORwwUmio0Kw29vxeCRDM6I4V6lVKPhwVyWzxkayo8iOw+XBqBd8555Q1ha2MnZICCajjuBAIyXljWQNiqSktBohBDZbAPX1TezYeZAF83IoKiri9OnTfh690hdIKcf6+xkURVHuFCqYVZRreOihh7Db7WzavJuFD85g1+4CpueOZN/BM8TFhCGEoLqujUHxAXy0q4o546Jwdkh2ntUWhn1vYghuD2zcX8PssZGcKm1FCIiNtHGwoJhxORmcPHEKq9XC6jXb+Nr90wBYs2aNP4et9FNCiJ/5+xkURVFuFRXMKso15ObmEh4ezpqPD7HwgRlIKQmLSKC52cGY7HROnK1GrxfEhJvZWVjPiLQgrEbBx0e1hV1DYs0MizOxdlsJX58Sx8UaJ6MHBVNR3YKUkuTECApPXGDSpLtY/eE2EhKiGTd2BKtW9al9Q5S+Qy1MVRSl31LVDBTlGoxGI3PnzmXDhg38ecVPSU1N4FxxGSaTAb1BT5u9g5FDYqiqb8Hjge0FdcwdbmNNQQu/eSQGnU5w/6gg/mtLHa+ODsegF4QFGSk4XEdCbBC1dVrQGxURRnl5Nfn5x3lo4Qye+5ffUFpaSnJysp/fAcWfvBvRzPRhl+ndN1EURembVDCrKNcxf/58Vq5cycGDJ1j4wHT+9+W3mTJxGIXHSwkMtGALNFF42k50qImPdlWxcH46q//zCPkXnIxPs3L/KBvLN9Wxd9tpZo6J5ERJCwCDkiLYs/8UQ7MSKSk+h8FgYPWH23jiuw/y3L/8hjVr1vDjH//Yz6NX/Cwd386mSh/2pSiKckdRaQaKch1z585Fp9OxbsMOHnpwJi5XB0kp6RSXVDJx/BCKLzQAMCghkI37a5iRE4lBBx8UaEHr3WlWooL0rD3aykNTYimtdpKREEBji4N2VycjspLZc+AMEyeM5oM128jMTGHE8Aw++EAtUldoBLZ0U3qrxwdXbLutKIrS3/g0mBVCpPqyP0Xxp4iICO655x7Wb8xnwoTRREWFU1evxQRhoTaq6lpJTwqj1dFBm9PN4aJmpmcF8sGhZqSU6HWC+0baWH+8lfmRDnQ6iIuwcOxMNaHBFuyOdqSUJMYGcOZMCSdPFvPQwpns3LmT6upqP49e8bNi4LAP++vPuzEqijLA+Xpm9qCP+1MUv7rvvvvIz8+npqaBBfOnsGPnQUaNSKH0Yg06nY7Y6CCOl7QRaNHz4a4qFmYHcbamg+MV2ra1XxsVRKPdw+kqF5NGhHOpzonHIxmWEcXuvSdITY6msloLkFd/uI2HHpyJx+Pho48+8uewFT+TUm6TUj7rw/5UKS9FUfotXwezYUKI7/u4T0Xxm9mzZwOw87ODzJl9L42NLYwZcxcHDp1l/JgMWlu1urPD04LIO1LHfQ8OA+DTM1qJrllDAzEbBGsLW1mQYeBsuZ24CDNSQkOTg/FjMti19xRjcoazafMuRo0aTEpKPBs3bvTbmBVFURSlL7kVObMvCSHeEUKoTeaVPm/o0KEAnCm6QGaGVmEgOMSG2+0hLjaM6npt169Ai57zl+zEhpkxGQQXGzq182YdkzMC2HyijbEpFgAy4gO5WNkMgNlsor29g8y0MA4dPonH42HCPaM5cODA7R6qcocRQkwXQjwphHhBCPGy93P1ZVUzVlEU5Yt8Xc2gWEqZASCEmCGE+ANwFlghpWz28b0U5ZYLDAwkISGBM2ebWPpUHAB6nR4Aq9lEZU0rIUFmpJR0dEou1jpJDDVQ3tBxuY85wwP551XVRNq062wBesou1RMfHUR9VQUAAQEm2tocnDp1nrFjhvP2Oxuorq4mOjr6No9YuYNsRatC8D7wCymlL3NoFUW50xUUwNSp3bdbvBiWLLnlj3Mn8+nMbFcg6/3zNinl08CrwFLv7IKarVX6nMGDB1NUVERERChWq4X2di0f1uPxAJAQE0yb0w3AuXI7SWFGyq4MZocFArCvxElKuBFXh3ZdWlIYx4uqCQkJwOnU2uftyGfc2OEA5Ofn354BKneyFVLKb6hAVlEGmMWLITu7+3YFBbBy5a1/njvcLa8zK6VsAl4CNVur9E2ZmZmsWrUKIQTJybFUVdcRGxNKS5sTgLAQK+Xl2iYIReVtJIYZ2F3suHz98HgzCaEGNh1vJTvJzKkaJzodBFiMlFxsZNI9QzlztoKkxEg+zdvPtx+7HyEE+fn5zJ8/3y9jVu4Yy/39AIqi+MGSJT2bbe3JzO0AcFvrzEoptwGvALOBBpVbq/QFgwcPpq6ujvr6JlKS4yktrWRIZgJVNVoVAovZwMUaBzarnuMlLSSEGSlv7MTj0erUCyGYPSyQLSfbGBFvpqi8jawkG81t2gxvdFQwR49fYMrEYWzPy8dmC2Do0HSVN6sgpSy5+pwQIlgIkXq947Y/pKIoip/dtmDWu5ChCMhH26ZxFfALtAoI7wohfiaECL5dz6MoPTV48GAAiooukJwUx4XSCoZkxFN8vorAABMAnW4YnBhI4bkWEkMNuDolta3uy33MHW6jyeEh0CTweCA9PoCzxVXodAKPsxmXq5OM9Fhqaxs4fvwsY3OGk5+fj5Rq46YBrPg65wcBi4BnvW3OoZVFXIpvt8BVFEXpE3y9acKTV/091bv61g2sAATaB3CYlPIRKeVhKeUqKeUjwDbgxav7UBR/y8zMBLSKBsnJsVRV1ZGeFkt9QyspSdHY7S5A2xCh8HwLCaFa9s7Fxs/zZiekWwHwpssSEmigrrmDrPRIquu0FAWTyQjAp9v3M27scCorKyktLb0tY1TuSA3XOun93HzJuyZhNtrn6iNSyueklH+8rU+oKIpyB/D1zOxyIUSKEOIhIcQBtBmDpWizsLOklBneD+Gmqy/0fkA/DZwXQjzk4+dSlJuWnp6OTqfjzDk7KcnxAISFaIu6IsKDqG3U8mODAgw0tHRgMggAyuo7L/eREGog2KKjqrmTYIsOV6c24xoXHUTh6WpCQwK5UFpNanI0n+YdYNrU8QBs3rz5to1T6XuklFu9P7f5+1kURVH8xeebJqB97fW+989XzsL26MPW226cj59LUW6ayWQiLS2NoqIikpO18lxmszaLarWaKLvUhNGoR2gxLI12bfr1yplZIQTD4sycrHQxOtHMxRoHVrMOvauZlrZ2stLDyD98jmlTRpC3I5+srDSSkmLZsGHD7R2s0hcd6q6BEOKd2/EgiqIo/nArcmYP080s7PV40xKCgbpb8FyKctMyMzM5c+YMyUmxAHSIcPR6HVKCs72TpNhg7A4tR7asoQOjnssbJ3QZGmfixKV2RidZKCxuISczmKoGbRFYWLCVwhOlTLoni/r6Jo4dO8u8OZPYunUrLpfr9g5W6Wt6klidczMdCyFyhBBLhBAzu3764johRKgQ4hkhRLr3z+lCiOXXaHdT91cUZWDxdTDbKKUc+xW+8ipGW8gQ4cNnUpSvbPDgwZw+fZqYmAh0Oh3nSypITY7G7tCC0ajwAM5X2kmMsnCsvJ2EUCOl9R1f6GN4vJmqZjfJYUZaHW4GJQRysrSNkCAzzvZOOjo6CQ2xAbBl2x7mzplES0sLO3fuvO3jVe4I6f7sSwiRDiyXUq6QUm6VUq5Aqxl+w756eF04Wtmxc2i5wQeBA11pE1/l/oqiDDy+Dmbf/YrXP4tWf/Y5XzyMovjKtGnTsNvt7N13lPHjRrBl6x7Gj8mk6FwFQggCAkwUnm9hZFoQ+aVOhsebOVre/oU+poe6EQIuNXViNgia2zpxdXgYkRLAwWMVmIx68nYdJ2d0Ou++t4k5sydiswXw9ttv+2nUip+FCSHcQoi6Gx1ATg/a3IylaKUUr/QK3de+7el1s9DS0QZJKcOklO/76P6Kogwwvt4B7OmveP2LUsqXfPU8iuIrs2fPxmKxsG79Du6bP4UD+ccYk51OdU0zwzKiaGh0ICWE2AycqXIxLM7EyUvt2F2ey32khuqZm27ib/ubWDQmiC0HqkmKsuBwuWlubeeuYXG8u3oX33hoIgfyj3HpUi0LH5jB+++/f3nXMWXAEWgB342OnrS5GQ/z5XzcfO95n1wnpWyUUl6vBNnN3l9RlAHmdtaZ7Sr0rWrJKn1OQEAAEydOZPuOY9w3bwoAHZ1ajmxCTBDHiqoJtOgvb2trM+vwSDh68YtB6FM5AdS0uBkUZaKtXTI8LYiCs81Eh5lwezxUVjWSlBgFwNvvbmDxt+bT2NioFoINXDl0H6h2d4zt7U2FEKFoqQn1V56XUjZ6X7/mV/03e92t6kdRlIHB13VmX7jBy4PQvlb6hhDiF920VZQ7ztSpUykoKCA5OY74+Gjyj1YSHxdOu7TQ2ekhMz2K4gqtTFdbuzYjm39B+3vjWe2b3txkI4PD9aw92srQWCPVl5rxeGBoso0jJysJDDDySV4hkyYM5a231jBzxj1ERYWzUu29PRAVSykLpJRNX/E4BJzv5b3D4fPg8RquF0z25rp0IcTD3mOJEOLKGdebvb+iKAOQr2dmr7vS1FtH9lXv8SygAlqlT5k6dSpSSj7bdYj5cyezZetecu8dxskzFzGbDFgtRk5caCEtzsqZKhfRQXoOlTq/0IcQgqU5ARwsdZI7OJBDZe2MTA+isr6djk4PwzOiWfXhLh5+YALHT5Zx4sQ5vrFoDh9//BHNzc1+GrniJ77MDV3Wy/ahN3mfnl5XDyClfN97rECb6OgKaG/q/nl5eQghuj0URelffB3M9vhTojcluxTlTjB+/HgsFgvb8w5w3/zJNDe3Eh8bTnVNE9nDYqmubUVKSIqykl/qZGyKhYNXBbMAi4aZCTYLKho7EQISrB5Ol7WRGmPF7uygoclBRLgNvV7HW+9s4NFv3YfT2c7q1av9MGrFX6SUr/qwr1W+6ssXvLmyK646rRZ39cSKFTB16o2PggL/PZ+i+IGvg9kebyTvzZ1VNQOVPsNsNl/Om505YwIGg4GWVi2NICbCxrnSBowGgV4vuNjQyeAYM8crvrgIDMBm0vHYCAvrj7UyId3C6UoXOgEJURaOF1UTGmxhw9rtzJo2mrfe/ojx40eSnp7Im3+7emG3otxa3tzV23VdMVrqweVre9tPbm4uUspujz5t5crug9XsbFi8+PY8j6LcAQw3c5EQYgZa/uvVnwrpQoj/pPsZ2nS0Fam9/epLUfxq6tSp/PznP8fl6mD8uBEcOVZCYkIEbZ3ajmCpsQFUezdCCDAJPBIKypwMA6SUl7/i/F62ld8fdBAfYmT3OScTB1k5V2FHShiSHsmHW0/x6+Xf56m/f5m9e4+y+Jvz+c9f/JFLly4RFxfnr+ErA0dXhYFw4HLe6hXB5fUqEPToOiHEM1LKF6+6tmuxV/pXuP/AkJ0N27f7+ykU5Y5xUzOz3k0RXgG2oq2WXQY8w+db2C7r5hgDPC2l/OVXfH5Fua268mZ3fnaQ3CljyT9czKS7szh6/AJBgWYCg4M4XdaKTget3kVgB0udlDW5SftdLatPaWkH6WEGxsUbKLxgx2yAAKOgsr6dzIQAGpudtNldGOyVWCwmVv5tJY9+6z48Hg9vv/nf/hy+MkB4F14V8+Xc1XC0zXGuGUz25LquzRCuUZEg3Puz+GbvryjKwHTTaQZSyvNSym3e2rIZQBPaLi43LBMjpdR5t7r1WT6YotwuV+bNTs0dh9vtJi42nJraZkZlxdDQ6KDTDakxAZy61E5ssJ49hU0UVHXQ3C75+43NHK/Rtrn9xjALp+vcTB0cyKFSJwEmCA82cbq4lqjwQD7ceoqvzRvLu6t3MWhQEmPHDOeNN9f6+R1QBpCtfLmsV473/E1f5w1El14jIJ0JHLqigsHN3l9RlAHGJzmz3g+lJd4/37BMjC/upyj+0pU3+2leIRMnZKPX63E4XQCEBVu4UNGEEBBsM5Bf2k5OsoVDlR2cb9Tqz5oNgu9+2ITbI3lwiAWjDgyuDurtHsYkWzld1grAoJQw1m8/w9fmjaemtpktW/fw7Ufv53DBSQoLC/02fmVAWQYsuurcUq5IDxNChAohzgkhlvTmOqD+yplZb/rAUuCpXvajKIri0wVgW4BtPuxPUe5IM2fO5MiRI7S0tHHP3aPYf7CI1ORoGhzaP6fUWCt2h5v6NjcpEUaK6t2Xr21wSiICBBIIt+qYl2Fm98UO4oL12F2SxtZOhqXaKC2twdXhpvzsCaKjQvjDy6+x+FvzsVot/NdL/+qnkSsDiXeGdJkQYrm3FuwzwPJrzKiG9/Y679a1OUKIZ4QQy9GqGCzy1sTt7f0VRRngbmoB2LV4Z12f7c01QojpUspPfPUMinI7zJ8/n+eff56Nmz5j3txJ/Ov/81u+u3ga763ZTbDNTHBoCMdOVwLQ6dZWSUYGCHJiDcQH6VlxXzBuD7x+xM53Rlr46Ew78xP1rD/rJDHMgHS6qKhzMTzVxitvHeDJRaN54eWdNFUeY+lTD/Pb/32Ln//bRtIy5/r3jVD6PW9wefWWsle+3sg1tsvt7jpvm/e/6v0VRVHgNm5nex2q1pDS54waNYqEhATWbdjJvDmTAAgPDcRub2fkkGgqa1pweyAxysL5Wq3sVmmTh62PhfPXB0KwGASbi9t5ZlsrR6o6GRap53yjG4OAlHAjJytdRATqMJt0FJc1kJEcjl6v43//uIGf/SAXvV7H8pf+DJ3H/PxOKIqiKIr/3Wxprp+hJep/ctX5F+jdzi1qS0KlzxFCMH/+fN5++23e/OsLxMZGUnqxFqPRQEBwOFW1ZVhNOsKCjOwubmVEvJn9FR1f6KNrEdj/7G9j2b02nv+0ldxkI4fKnBh0kBZp4mBRM3ERZt786CiPzB/On97Yxr8//02+9/hC/vz6av71+SUkJgKGEX54FxRFURTlztDrmVlvjdkXgfeu8fIitAT9nh6K0ifdd999tLS0sHvPEebOvpdPdp7g7rGZVFY1AJCeEkldswu7S5IaaeRQlRu35/OyzCdq3QQaoMkFZZV2gs1aHm2LSzIq2sDpKhdSQlayjW27i5mXm0lzs52V7+1k2Y+m43Z7+OWv/qJ1pmZoFUVRlAHsZtIM8oH3uXZ+bFfJFV1PDq4ohq0ofcmMGTMwGAxs3lbEvLmTaGhoZvCgOI6dLCMlIRQhoKK2HZ13+5Bmp4fTdZ8vBDtZ28nEGMHkWMGbZz3MS4DdZR1khutodEpanB5Gxps4ca4Ri0mwY38Jo4fG8vs/bSQlOYpvf3MKK/74PnV16p+QoiiKMrD1Opj1lth65Dp1YovRqhr01Pne3l9R7gQ2m42xY8eyc+dOZs2cgF6vp5NApJRkZaVSXKbN0KbEWimt01IMulIN2lyS4gY3mSE6fjhMT3MHmPSCTgnxJklxo5uUYB1tdjdVzW7uHRHO39Yc5jsLszlSWMLeA2f46d99DYfDyZ9fX609kJqdVRRFUQYony4WpvsFAAAgAElEQVQAk1I+LaUs6UX7qwtiK0qfMWXKFPbv34/FYmbCPaM5WliE1WpCr9dhd3QQFR6AxaTnyMV2Im16DniD2dN1nUggM0QwIlxHbpzgwxIPd0fBiUZJgF4r21Xc6CY6SI+90Y6j3YO9spigQDMv/+/bjByeQu6k4fz+D+/gdntnfFVAqyiKogxAt6yagRAi+Drnp1/vNUXpSyZPnkxHRwf7D7Uxb+4kCo6cYvyYTIpLqtDpdMRFBVFe40AC6ZFG8mu07W2P12qLvwaHaDkIP/DOzkZadNQ4ISdScKymE6se4qyCPcUO7hkWyqvrynj0gVG8u/44dfUt/OipeZSUlLN+w05/vQWKoiiK4nc+D2aFENlCiHeBBiHEmWs0OQg8L4R4yNf3VpTb6d5770UIwY4dOy6X6IqOSeDUmXLuGpVGm8NFs92NxaRDrxMUVbuotXs44Q1UEwO1foaH6RgTDtsrPESYtEVhHR5IDxKcqe9EJyDO5Ka0ysHQiDbaXZ38+ZU3eeC+8STEh/O737/1+UOp2VlFURRlgPFpMCuESENbAPYIUAIUXN3Gm3P7rNZcZPvy/opyO4WFhTFq1Ch27tzJ6DFfJyoqnLY2BwApSZGX82aTY6yUN36eN3uippNBwQKdEJyodHOi0s1DSTra3JASCIUNkpFhUOWUODphaKSez87aSQjVs25PNZPHpfDKynz0eh1Pf28Om7fs5tQptSmSoiiKMjD5emZ2iZTyBwBSykHeoPaapJSrgG/4+P6KcltNmzaNHTt20NzczMwZd7N3/1EiIkJpbLIjJSTGBdPu8lBa34nVKNh0rp1Wl7xc5aBLRpBgcBAUt4JRgF4nqHVCXABUtnqoaXUzMsHC5vxaJmToOVdaz69f+iNPfXcWgYEWvvv4z3A4nFpnncfUDK2iKIoyYPg6mBXdN/lK7X3Cu8/3zOu8liOEWCKEmNn182baKAPDY489Rnt7O++88w7fffwn1Nc3MX7cSHbuOcGgtFgMeh0XqhwY9ILUSCNrilzMzTBztF5yoUUyLFZ/ua9H03Q0dsDoCEFBnWRoqJZyUOeQpIbo2FFkJyvWxGsfX2DOuEiee2kzl07l89eXHmT/wSKe/P5PkW1XZPZ0BbUquFUURVH6MV8Hs7L7Jl8Q4uP7d8sbeF6rrBhCiHRguZRyhZRyq5RyBbDUe77HbZSBIycnhxEjRvD6668zc+ZMEhNjaG5uoaPDzYihSZRcbMRg0JEaa6W82UOL00OoWaAT8OEFrQpBV0CbFSyYl6SjsF4SZYZ2N7jckBQIpc0e8EiCzDqaHG48rXbCgww8+qO/MmdyBv/+j9NZ+d5O/vzGNrAXacfVVFCrKIqi9EO+DmbDetpQCBECRPj4/je6X7oQ4hW0LXTrr9NsKfDKVedeAZb3so0yQAgheOKJJ9i7dy9nzpzh8cefYs/eo4wckUnR+UqMRgNpiWFUNnbS3NZJVKiJzRUepqeaWHsR3FL7/W9YrJ5hsXr+8f9v777jo6ryxo9/TiYhBQKTgCBVmQC6oggBRbGEEhA7YiKCqwJKgruWdUViWfdRH1cIoiIusgnq64HFQnFRV10VEHzk8SctBKSokES6lJAECKSR8/vj3gnDZGoyyaR836/XfUHunFvmzsy533PuKZdZUAo6tVTknoArzlPsLYaWodAuKoT1u0sYGRfO8h2nSL68Jdt3n2TKswt45pYIhl7dnUenzuPnnfuNk/MU1AohhBBNRKCD2Y1KqUU+pl0MfB3g47ultc7VWqeaNanuJAFZTus2mOv9SSOakXvuuQeLxcL8+fOZMGEClZWVXHhhJ7bv2Mv1gy7ht/xTnCwuIyY6jJjoML75+RS3D4zh4MlKdltanrOvjlGK8b1C2HxM068tbDyq6RgFYSFG7Wyv2BBW5pVydecw3l5TSMp1Vv7x6R4++3+Hmf+4jYjwUO4ZP43S/Q59L10FtRLQCiGEaCICPWnCPCBWKZWvlHpAKXWh4+tKqQuVUlOUUvlm+rcDefzaUEpZcVFrq7UuNF+3+ZLG1b6//fZblFJeF9E4dejQgZtuuokFCxbQrVs3hgwZwpYfdxIREU54i1BOnDjNebFRtI6OImd/MVrDgcJyQkNg1a9ldL7Qes7+JlxkoX0knKyASAuEh8CxUugUBQdPGGPVntGaijOaE8dKiO8WwQPTNqEUvP3EJWzceoDnZn2DPuIUsEpAK4QQogmqi0kTkoBvMNql5iilzpjB7RkgB+Nx/ErA7UgHQRILZwNTF2w+phHN0IQJEzh48CBff/01EydOZPfuAyRcP4Bvv99Bty7tiG4dze4DRZyphJ5doli0pYSrbJGs2l0GcE5AGxWqmNLHws4iGNghhF9PGhMsHD4Np8qhV2vFugMVDOoSxqLtJUzp34LT5ZVM+OsP3N6umJRbu/FK5hpWfp+DPrL13KBWAlohhBBNTMCDWXMc2WRgBPAvIA+jLW0RZhCrtb5La10U6GPXktV7Ep/SVJOQkIDW2usiGq+bb76ZDh068NZbb3HnnXfSpk00AMXFp7m8b29yfz2EUnD+ea2ooAW5B07Rs30LNh+q4OipSorLNJ/mt+CHQ5WUntGM7BLCLd1CWLGvkivaKXKPaywKOraETfma3jGwbn85kRb4n80lPH9dS5bvKOazH0/y2vBILu7WkglPLOJ0iTG+rQS0Qgghmqo6m87W7OmfrLXuobUO0VrHaq1HmOPLCtGktGjRgtTUVL744gv279/P2LH3sPrb9fTu3YOcnH2EhIRwQdf2lJefIW9fIS0jLByubAHAhtIW/CenlGdXnWTSdxUM+qScSf9bQY/W0LUV5J7QRFogKhT2FUOrUCgqMUY7iGut+Dq3jDZlp4mLsfDU4oOEhyr+kdyOfUdKeHP2kqpzrNbswJEEtEIIIRqpOgtm64rZdtXXxe+aVF+2qcl+RdM3efJkLBYLc+bM4YEHHqC0tIxLL+nB9h05DLr6cgoKT5JfeBpLiCLuwnas2pSPtVUoX+8oZr9qgQLeGBTKmLgQjpXBrK2VXN0+hOPlENsCCsqgdRiEKth3Gnq1hq3HNNYwmL21ktResOPoGeb9ez/X94zixt4tmf7+Lgq2r6k6x6qAVkY5EEII0USEBvsE/GEf49WPTdYDM3xMa58PNBaoahPrELjmcrbjl6c0opnq2LEjycnJvPvuu7z44ov07duXrdv30rJlJBHh4RQdP0V4eBjtz2vDwcMnOFVayWW21qzYcZKEnlF0ig7h3kGxDP21EK01T68/w6LcSm7uBJ8dMALaE+VQriEmDPJOgkUZ67cVaPYVVHJJjGLa/xVzx0VHefmO9sT/LY8ZH+YwbVIYdB147gmf2glRPYNzsYQQQogAqbNgVinVGkgBrgDiMQK9jUCm1vrXmuxTa50LJAfqHJ32XaiUyqV6u9hYoNA8Nr6kEc3XI488wgcffMCCBQuYPHkykydP5pabr2fFyrW0axdDaKiFI0eLKCkpp2VkGEVloewrqGDbwVL2n6hkT9EZLBjj1yZ3gm/2w/bj0LuNYnuR0a66dSicqIAKDW1bQG4xtA2H+XmaBy+28OqPZ3jgsyL+PlIz7orWzPwwhxOnKvjr071p37YV+shW1HmXGifsHNBWbIXQS+v/wgkhhBA1VCfNDJRSU4ACjFrRZCAOGA48hTHCwct1cdwAWAEMcFoXb673J41opq666iquuuoqXnvtNcaNG8f555/P4SMllJWVc1nvHvz221HKyiqwtmlJRGQEP+UcpUNMC3YeKadjm1AGvJPPzF9COFCs2VOuOH0Grj1P8effKSItRnvZ4xWgNbQPh/wysIYZQ3eFKHj9xzNcf77iq5wyrp9/jDv6RTPplm5kfLqHnkPfYNrc/63qFFbFVYcwaXIghBCikQh4MKuUmg5MNpc4jJEM7Et/YCYwWSn1VqCP7YdYXI9MkEb1mt9Uc70/aUQzpZTiySefJDc3ly+//JKnn36adevWkZiYyPc/bObii7rTqlVLCouKyT92grhusRwqKEMphdUawfir2/DB9lJu+aqcaZvPMKCd4on4UFqFKu63KU5UQEsLRIfB4VLo0QoKyyE2HI6Xwe9iFP/7m6bf+aGER1hIythPaXkl3/99EEOv7s6zr67gdyNm894771NZWXn2xD1NfyuBrRBCiAYsoMGsUqofEG+OYDBPa51nDtVlXzZprdO01rFAD6XU0EAe38u5WZVS6UqpJRiBbLpSKkMpVTVzlzl+bJqZLkkpNRVId2w+4Esa0bzdfvvt9OjRgxkzZjBp0iQ6depEfn4+ANaY1hw/fpJWraLo3DGWnD3H6HJ+ayoJYee+YtbsrWDFn7rxwLVWurWxMG9ULF27x3DJ+RYm9wnl8liFUkYAe0U7xa6T0KM15JdC55ZG29mrOoex+XAFpRWaCTd2YcFX+xn1/BYm3T2AbxZOoF1MFL//81ISb3uewsLisyfuKqC1cwxsJbgVQgjRgAS6ZjZFaz3Cl4Rmujpp/+rmeIVmIJ2stVZa6zhzetulTumyzHRLtdYztNbVmg/4kkY0XxaLhSlTprBhwwZWr17Ns88+y8aNG5k48QF++GEzA/r3prS0jP0Hj3Fxz84cOHwCS0gIXTtZOVRQxh2ZBxh7RRt2/K0Xlw9oDxiTKnTtHsMbN1s5dQbOj4TthZpRvcLZdRwutIawpxj6dgjlh/3lXHx+OBVnNEtWH2TmQ7+jXUwUt6W8x77fjrNuWSoZL93Gmh9+YvAtz/HboYKzJ+9q6ltXnINbCXCFEEIESaCDWX/nZG1oEycIERD3338/F198MRMnTuTWW2+la9euZGVlMWzYMLZtz8FiCaFbt478tHM/nc6PBaXYvb+Q+Mu60rZ1CxLf2MP/5IUBYO3Rtmrp0yGMB/pGcrgEOrSy8OnOUgZfEMavhZVc1KEF2Ycq6Nc1nJwjpaiwUC7oEMkTc3fw+9sv5/orLuD+J//F/H9lM+nuAfw7cxy7cg5wzYhnyMn97dw3YA9qfQls7SSoFUIIEQSBDmZ3+Zlepr0STVJERASLFi2ioKCAlJQUnnnmGdauXcuIESMID4+g/Xmx7NlzkF69LuTwUaNM16G9lZXf5zJ8yKUkDOzOxPQtbCAWLrIZC0Zgm35vV9q2shDTOoybL2vF6t3l9O0azs+HyrikYws27yulY7soikvOUFxyhuHXxJE242su7dWBxEE2HnjqY+Z9uIER1/VgxYL7KSw8zjXDp5K9Jc/1m/E3sJWgVgghRD0KdDAbF+D9CdFo9enTh9dff50vv/ySoqIiRowYwdNPP83EiRPZs/c3bN27kJOzl7ZtYwixhHDwtwIuuagLc/65ltuGXYy1dQTTlxWcHR/WDGqt/Xoy54nL2by/lLV7y7h7aEey95bS/fxIfvqtDFunKA4XlBIeGsLR42fI21fA+KR+/P2fa7nr5t7cNLgnqX/5lMwPNzCwb1e++/BBwkJDuCXpBfJ3rvP8pvytrRVCCCHqWKDHmc1SSj2otX7bW0Kl1DQgP8DHF6JBmTx5MitXruQvf/kLX3/9NUVFRcyZM4dhw4axevVqunXryOHDxwhRcF67GLb/vI9+fbrz55e/Yszoa3h/yXf8lHOEi+POnfDg1uQKPo8K5el5P/HhNwe5ureVrF+K6NQugn1HS2hrbUXB8dN07tCavQeLWL9lP4P6deWx//4P/7foQQAefv4z4nt3ZMBlnVn2j3FcnZTJ5L/8m0VvRqKUOjsWrSuOAa27iRdkzFohhBD1IKA1s1rrecBdSqmXlVIXukqjlOqrlJoLJGqtZwby+EI0NEop5s2bR+fOnZk4cSILFy7EZrOxbt06unbtSn7+cVq1iqZFeBhHjhbQrp2VvD1Hsdm68u//bCAiPIwZC4xJDuzLus376HfbW9wwdR0dOnXgoXuuYOMvJ4iKDOfYiTPEtI7idEk5LcIs7NqdT/9LO7MzL59TpeVERYTx+yc+IuOl2+l4XjT3PL6Uk8Wl9L+0Ey/+aShLv9zGgmXZgDH1rX3xSGprhRBCBFFdTJqQDIzAmBxhp1JqvVLqK/PffIxZwAYAd9XBsYVocGJiYvjggw84cOAAd955JwsXLsRqtXLy5EnCw8PRWlNaeobzzjuPo0cLOX26lDZtWnHi5Gku6d2DhYu+Y29+NKWWC3j2xfcYlPw2J0s0Ux8bxboff2Pue+sZ2LcLLVqEYrEoikvKUSGKiopKzm/XijUbdnPzkF5kb/+Na/p3Y/uuI/zXG9+wYOZodu0+xuN/+xKAJyddy/VXXMAjL3xO7p5j57wHn4NaIYQQop4FPJg1x5MdADyEMVpBf4zZv/pjzAr2lNb6Cq21m94mQjQ9V199NZ999hm7du1i3LhxfPzxx1RWVhIZGcnp06dp164dR48epV27doSEWFi/fiuXXHIJP/+8B4ApT83j+iHjefnVj4jv9zu2bPqE9Jn/Td6Wf/Dnh2/lu/W7OXT0JK2iW1FeoTlZXIbFojiUX0yPC2JZ9vUObht2MZ+s+Im7hpzPu0uyOLpzM2ljbbyzeCMf/M+/sBxYz/w/27BQyZ1//JD/l7UHrc/to+k1qHWupZWOYEIIIepYoNvMVtFaZwKZAEqp7hK8iuZu+PDhLFu2jBtuuIEVK1bw0UcfMWTIEO644w6WLVvG4MGDWb16NREREcTExLBt2zaio6OxWCwsXrwYq9VKeHg4GzZuo++AcVjbRFB86jQ5OXsBsFhCOFZwAmublhzNP05YiyhCSypoFxPFrt3HSB07gE3bD1JU0ZKeXVry/P/8wqpZV/F/WwuYkL6F86zhJPZvx8Jn+3L/jG1cc9fb9LgglrG39mHcbX24yNbOtzfqrg2taHSUUvEYT9JyARuQ68u42r5sZ05YY8PoOGwDMhzH/VZKWYEUYClwDGPmxlRguYztLYRwVBfNDKpxF8gqpabUx/GFaChGjBjBjTfeyPTp0+nXrx8PPvggn376KSNHjuT777/nmmuuoby8nIKCArp27cqJEyeIiIhAKUVISAilpaW0bNmSyspKyisstGgRxoD+velzWS/OnKmkQ3srh48U0atHJwYNvIiQkBB+yjvKsEE2Rib05N47LufrNTn89U8j+XnfKUa/9AsL54wnrltbRkxZy/jZ++mXMJicVY/z9rRRdOvUhpfmfMvvRszmslvn8cTr6/l6czmnTpW6foPOgax0AGu0lFI2jNkNM7XWK8wKilRzfa22MwPZXHPSmVSM5mnpSqkUh13FAulADsZTvY3AeglkhRDO6iWY9SA1yMcXot699NJLFBQU8OqrrzJ9+nRiYmI4evQonTt3Ztu2bXTs2JHo6Gj27t1LXFwcRUVFREdHU1hYSGRkZFWThB07drBjRy6/7j6AUopePS9gz94jJN05nIzXJ/Pv/2ygb5/uFBaVMH3qCJRS3D+6H5WVmv2Hj/Pea0l8n7WXB5/6hNXvT2BqyrV8+NmPXDR8Nm8u+IGxt17Gin9OYN+Oebz28gQ6dojhrbe/ZOSd/03shfeReNvzTHv1I9Zt3MmZM2ekRrbpSQUynNZlYASYtd3OprXOsv9hThOe7mK74UAMEKe1jnGesVEIIcDPZgZKqTuBxAAe32MJX4imKD4+nqSkJF5//XVSUlJ49dVXuf/++3n55Zd58803OX36NGVlZcTFxfHbb78RFRVFeXk5LVq04Pzzz+fIkSOUlJRU7e/o0QKOHi3AYrHw0OQxHDlyjKG3PU+MtSXZP+YxLvk6BgwxZpnudR4MGvgl8z/Zwba1b1B8upyJactIefZTFr85hkljBpA26wf++vo3vDF/PePHDSF14gge/+OtPP7HWzl1qpTvvt/OV99ks+Lbn3jmxffgxfewWqNJHHYVN428jpE3XEvHjucF6/KKwEmienC5AVhem+3M5gNjlFKZZhBrt8J83aa1zrWvNNM4phNCiHP422bWRmBrU2UGMNEs/e1vf+Orr74iMTGRVatWkZCQwCuvvEJmZiaTJ08mOjqaX3/9lcjISNq3b09eXh5dunQhPz+fkydPYrFY6Ny5M1FRUURGRhIZoTl0OJ+5/1hEbGwb/vTo79mz5yAff/oNL/1l3DnHHj9uKCmPzWVD1i4mTL6X4pC2PPLk29z80DJmvHgfHy2cynffb+eNuZ8xa+5nvPr3Txl6/WWkThjB7cnjuOG2S7nhNmMwksOH8/lm1TpWrPyBL79ew9KPjDgnvt/vuGP0OMaMCadnT6mxbWzMgNOG0Va1ita6UClVLeD0dzuzyYENyHLehxBC+MvfZgaFGI3vQwKxIKVt0Uz16tWLL774gr179zJ8+HDS09OJjIxk3Lhx3HvvvZSWltKlSxcuuugi8vLyiI2NpbCwkOPHjxMSEkLHjh0JCQmhoqKCkydPcvjwMc5rF8Mr6U/w9RcZJFw/gKX/Ws4fJo+h+yXXnnPsu+4YREREC2bN/YyKijM8nHITc19LZcOmHOKvn8Id90yndXQkS/85lb3bM3npxUfI2Z3PmAmv0vmCYTz+RDpbtxojFrRv35a7x9zI25kvsDdvBZs3LmXa3x4jIiKc5557jl69ehEfH8/06dM5cOBAMC61qJlYqKoVdcXdUzWftjObDDgHsolAoVOQbFNKJZlLitnWVgghzqW19nkBhgHT/dnGy/42BGpfDXUBVickJGghXFm5cqWOiIjQffv21Tt37tSjRo3SgL7iiit0586dNaA7deqkO3bsqAFtsVj0BRdcUPW3pyUmprU+cvB/tS7/sdry6MP3aEBfckmc/vzTObqy8CNdsPuf+vm//kG3aROtAX37bUP0mtULdGXZFl1Rkq2/+iJDJyeN0GFhoRrQ114Tr5cuek2Xn9507v5Ne/fu1a+99poeOHCgBnRoaKhOTk7Wq1ev1pWVlUG86oGVkJCggdW6AeQ3gVqAeOP24PI1jTHpTcC2M1/fCEx1+NsKpDilWQIkudl+tbffhH1piBISErRP94qEBGMRQmufvg8+f7caMG/5rF81s1rrlVrrp/zZxsv+BgRqX0I0RkOHDuXjjz9m+/bt3H333Tz66KPMnTuXrVu3UlxczLBhw4iOjubgwYNERERwwQUXUF5ezsGDBwGwWCzExcVx3XXXMWbMGF599VUWLVrE559/ztbsZbRrF+PyuLNeS2PZ0lmUlZVz821/ZMSdr5BzMIz/eu4h8nb+h/967iG+W5PFtYPvY9B1v+fjT75h2NCBLP7gVfbvP8DMmTPZf6CApDF/psfFo5g560uOn+p2zugFXbp04fHHH+eHH35g586dPPbYY6xYsYLBgwdz2WWX8eGHH1JZWVkv11k0bOYoBse01jPs67TWhdoYCcGRLx3QhBDNTJ2NZmBOW/ugUmq00/rR7rYRojm64YYb+Oijj9i5cydDhw5lxowZTJw4kWuuuYa1a9fy888/Ex0dTdeuXSkuLq56XN+qVSsuuugiIiMj2bp1K4sWLWLKlCnMnj2bHTt2cEb1NIJLF8NjKaUYdfswtm3+mFmvprExazsDrrqbhKHjWf3tBp57NpU9uV8zZ84cjhwtJmnMn+l9+RjeX7yF2NhYnnjiCXbu3MmyZcu48MILefLJJ+nevTszZszg1KlT1Y7Xo0cPZs6cyb59+3jnnXdQSjF27FiuvPJKVq5cWefXWNSM2Qa2Trcz28+maq2H+5A8F6Ppgcv9JyQk+FrzLIRoQgIezJpB7HqMR0aZVO8wtkkp9Q+lVN9AH1uIxuqWW27h4MGDLFy4kLi4ON566y0+//xzWrZsyYABA7DZbBw6dIhDhw4B0KZNG9q1a8eBAwfYunUrBQUF9OnTh3vuuYfi4mKmTJnCxRdfzIcffmgcwB7UOi0tovrx2J+nk5u7m5kzZ7J7z1FGJ/+Jnr+7g3cXrGXChAn8/PPPLFmyhBYtWnDPPfdU1aoqpRg1ahSrV69mw4YNDBw4kLS0NOLi4vj73/9OWVlZtfcZFRXFxIkTyc7OZsGCBRw5coTExERGjBjBjh076vOSC8/s7VZjHVc6BJHVOn/VYrt0jCZs51BKTXWR1t6xTEbCEUJUCWgwq5RqA3yDMcD1XRhT2J4zwLXWOk9rPRkYrpS6MJDHF6Ixi4qK4p577mH58uX8+uuvzJ49mxtuuIHi4mK2bNnC8ePHAWjfvj1t27alqKiIwsJCWrVqxYABAygsLGThwoW0atWKzz//nPj4eMaOHcvjjz9+zlBerlitVp544gl27drF0qVL6dSpE48++ijdu3dnwYIFJCUlkZ2dzeLFiwkJCWHs2LGEh4eTlWX04enfvz9ffPEF3333Hb169eKRRx4hPDycH374weXxLBYL9957Lz///DOvvfYaGzZsoG/fvrz22muBvaiiRrTRgSsXo92qo1iqd9Kq8XZKqQwgTTt1GLNPvOBiggZ7kOwumBZCNEOBrpmdDkzSWo/QWn+ktd6Em+G3tNavIJMmCOFSt27deOSRR5g/fz7bt2+nqKiIVatW8fLLLzNw4ED27NlDWFgYy5YtY/To0fz444+0a9eO2bNn88svvzB+/HjeeustHnnkEWbNmkV8fDyLFi3i9OnTHo97+vRpoqKiuOqqq2jZsiWHDh3inXfeASAkJIQhQ4Zw9913A1BRUcFXX311zvaDBg3i4Ycfrvr7888/93i8iIgIJk6cyB133EFZWRnvvvuuPAZuOFZgTEnrKB6nCoqabme2k013DHCVUokOw36lugiaE4Es5+BXCNG8+TvOrDdFWuuP/EivAnx8IZqk6OhoBg8ezODBgwFYt24dgwYN4uOPP2b+/Pnceuut3HXXXXTp0oWVK1cyfPhw+vTpQ2ZmJp999hlTp07l7rvvJiwsjP79+3PttdfSrVs3jh8/TlFREUVFRWzZsoX169dz5swZWrRoQWJiIpMmTeKmm25i06ZNzJ07l3/+85+UlArwjuIAAB24SURBVJQwYsQInnzySYYNM54OnzlzhsWLF/PSSy+xfft2Lr74Yl544QWSk5PdvqfTp0/z1ltvMW3aNPLz83nwwQeZMWMGSkm20ECkYYwe4NgJKxWHSgiz+cBGzOlr/djOPsSWVSkVb/4/FkjWxvS2AMccx7M1j5UKTArAexNCNCGBDmaP+pm+TYCPL0SzcOWVV/Lss8/y4osvkpiYyO9//3vefPNNHn74YbTWfP/999x3332kpKRw+eWXM3v2bEpKSlizZg1r1qxh9uzZVW1aIyIiaN26NTabjbS0NIYMGcLAgQPZvHkzy5Yt44knniA3N5eIiAjuu+8+Hn30UXr37g3AgQMHWLBgAe+88w67du2id+/efPjhhyQlJWGxWFye++nTp1m4cCEvvvgi+/btY8SIEbz88sv079+/3q6f8E4bEx2kKaXSgfUY7VTTXdSWxvqznRmULnFzWMeZv5bax5gF2mI0XUh218RBCNF8BTqYbVvH6YUQpueee45Vq1bx0EMPMXDgQP74xz8SEhLCH/7wByorK1m+fDmffvopU6dOJTExkeuvv57+/fszceJEpk+fTlRUFCEhIZSXl3Py5Eny8vLYtGkTL7zwAps3b+bEiRNVNbRPPfUUo0ePrmqru3jxYhYsWMB//vMfKisrue6665g2bRqjR48mJMR166UdO3aQmZnJ/PnzKSgoYODAgSxYsIAhQ4bU85UTvtLGxAZuZ+kyH/dXG//N03bmNj5Vv2utl/p2pkKI5izQwaxSSg3RWq9yXOcm4VxgXYCPL0SzERoaynvvvcfll1/O2LFj+f7773nooYcICQlh8uTJJCYm8uSTT7JlyxbmzJnDRx99xNy5cz12BmvZsiWXX3459913H9dddx033ngjUVFRbNu2jXfffZcvvviCNWvWUFFRQadOnXjqqacYP3682ylrd+/ezSeffMKSJUtYs2YNYWFhjB49mpSUFIYMGSJNCoQQQtRaQINZrfVTSqldSqnFwDSt9QmcOoCZQ3KlA7Fa64cCeXwhmpuuXbvy7rvvcscdd/D4448za9YsUlNTiYiIIC0tjVGjRtG+fXvuu+8+3njjDbp168apU6fYtm0bBQUFtGzZsmrp2LEjUVFR7Nmzh7y8PNatW8ecOXPYuHFj1dixffr0YcqUKdx0001cffXVhIaem4WUlJSwdu1aVq1axaeffsqmTZsA6N27N+np6YwfP5727dvX+3USQgjRdPkdzCqlhmqtv/GQZATwNZCmlCrEaMQ/xnzNhtHuKQujV6oQopZGjRrFo48+yuzZs/niiy945plnuP/++xk3bhxffvkl7777LrNmzWLmzJkAhIWF0aVLF1q3bk1paSmlpaWUlJRw9OhRysvLq/YbHh5OfHw8Dz74IFdeeSXXX389Xbt2rXpda01ubi6bNm0iKyuLNWvWsHbtWkpLS1FKcfXVV/PKK69w++23u625FUIIIWqrJjWzS/DQ1tVsnN/DHPA6FYhzeDkXeEprPa8GxxVCuDFr1ixGjhzJ888/T0pKCi+99BIPP/wwAwcOZP78+ZSUlLBu3Tr27t3Lnj172L17N8XFxURERBAeHk54eDixsbF07979nMVisZCfn8/+/ftZt24d7733Hjt37uSXX37hxx9/pKioCDDGje3Xrx8PP/wwCQkJXHvttcTEuJ5KVwghhAikmgSzMUqpB7TW73hKZM6xPQNAKdUdY97tohocTwjhhVKKG2+8kZEjR/LVV1/xwgsvMHXq2QmU4uLi6NWrF1arlTZt2tCtWzfCw8OpqKioWoqKili9ejUff/wxBQUFHDp0iAMHDpxTWwvQoUMHevbsydixY+nXrx/9+vXj0ksvJTIysr7fthBCCFHjNrOZSqkRGO1is70l1lrn1fA4Qgg/KKUYOXIkI0eO5ODBg2RnZ7Np0yY2bdpEXl4eO3furBpXtqysjNDQUEJDQ7FYLERFRRETE4PVasVqtdKzZ086d+5ctXTv3p0ePXrQunXrYL9NIYQQokpNg9kYjFEK7lJKPYMxKkGm1vp4wM5MCFErHTt2pGPHjtx4440uX9day2gCQgghGr2aBLPJDkHrPGCeUqofMEMppYElXjqICSEaAAlkhRBCNAWuRzf3wNV0tVrrTVrrycBTQJxSarFSaopSSp5HCiGEEEKIOhPocWaLkNpaIYQQQghRT/yumfWV1NYKIYQQQoi6FujpbKtxUVubbrbVW+w07a0QQgghhBB+qbOaWTdiMCZcSAFWKKV21vPxhRBCCCFEE1LnNbNms4IUjNnAbBhDegFkAhl1fXwhhBBCCNF01Vkwq5QaihHAJtlXAVlAhkxnK4QQQgghAiGgway3Wlit9aZAHk8IIYQQQjRvfgezSqkHtdZvO62TWlghhBBCCFHvalIzmwa8LbWwQgghhBAi2GoSzMaZoxDYzL+lFlYIIYQQQgRFTdvMxpn/Si2sEEIIIYQImpoGs1O11jMDeib1QCmVhFGjHGf+m6G1XuqUJh4YAOSaaXK11iv8TSOEEEIIIepeTYLZ3EYcyObag1ellBXYqJSK1VpnmutsQLrWerjDdkuUUrla61xf0wghhBBCiPpRkxnAlnpP0iDZtNZZ9j+01oVAOudO3JBK9YkcMsx0/qQRQgghhBD1wO9gVmv9VF2cSF0ya2HHmP86WmG+bu/MloTRmc3RBs4OOeZrGiGEEEIIUQ9qUjPb6Ji1sDbOjsBQjRno2oBjLrZFKWXzJY2rfX/77bcopbwuQgghhBDCP3U2nW1Do7WOcbE6ESjUWufaA1F7YOpCVaDqJY20mxVCCCGEqCfNombWg1Rgmvl/5yYIrviSppqEhAS01l4XIYQQQgjhn2YbzCqlUoBjWusZwT4XIYQQQghRM42umYG7dqluHHPVJMDcR6rWur+L16wemhH4nEYIIYQQQtS9RhXM2sd49WOT9YCrmtd0YJjTOntb11igKlB1GAEhl7MdvzylEUIIIYQQ9aRRBbPmpATJtdmHUioDSHOuWdVaFyqlcqneLjYWs5OYub3XNEIIIYQQon40qzazZjvZdMegUymV6NB0YQXGNLWO4s31+JFGCCGEEELUg2YTzJrT2QJYlVLx5pIIJDsEt2lUr/lNNdfjRxohhBBCCFEPGlUzg5oy27QucfNyVS2t2dQgTSmVjtHe1oZTTa4vaYQQoilQSsVjPInKxRxHW2vt9SmUL9sFKo0QQjSLYNZsH+vTFFta6yyqT1frdxohhGjM7B1utdbDHdYtUUrleiq8+7JdoNI0FpmZmbz//vse02RnZ9O3b996OiMhmpZmEcwKIYTwWyqQ4bQuA2M0GE8dcX3ZLlBpGoX333/fa7Dat29fxo0bV49nJZqT7OxsBg8e7DHNuHHjSElJqZ8TCjAJZoUQQriSRPVgcgOwPADbBSpNo9G3b19Wr14d7NMQzZAvhaTs7GwACWaFEEI0DWY/Axtnx9YGqvoMoJSyuXrU78t25mu1TtPYmhoIESwpKSleg1RvtbYNnQSzQgghnMVCVX8DV2y4niTGl+0IUJpqx//2229Rynv3iMf69iU7L89rukDJPnmSvq1agbeAYdw4aKQ1YyKIsrNr/906eJDsnBwGW52H0a9bfbt3Z9amTbXejwSzQgghnNX0jubLdoFK02j0bdWKce3be0707bfG4qWjGNnZIB3FhJ0v7ax9+G6N++WXAJ5U/ZNgVgghRJOQkJDQeNulZmZ6D2TBCGSlo5iwS0nxXpvvw3crJSGBlEb8ZECCWSGEEC4ppaweHvXXartApWkyfAlKhKiJZvDdajYzgAkhhPCZvT1qrONKs4OX4+s12S5QaYQQApCaWeGFvTOF1jrIZ9L4yLWrGbluwWeOGpBL9barsUChu5EEfN0uUGkaIvn+Bodc9+BpCNdeamaFEEK4sgJjKllH8eb62m4XqDRCCCHBrBBCCJfSqD7TVqq5HjAe+yulcpRSKf5sF8A0QgghzQyEEEJUZzYZSFNKpQPrMcZ2TXfxiD/W3+0ClUYIIUCCWSGEEG5orbOALA+vFwIx/m4XyDRCCCHNDIQQQgghRKMlwawQQgghhGi0JJgVQgghhBCNlgSzQgghhBCi0ZIOYHWvR3Z2NoMHDw72edRKYz//YJJrVzON5bplZ2cD9Aj2eYiGldc2lPNobuS6B09dXntv+ayS2TLqllJqE3AesCvY5yKEqBM9gCNa637BPpHmTPJaIZo0j/msBLNCCCGEEKLRkjazQgghhBCi0ZJgVgghhBBCNFrSAUyIAFNKxQMDgFyMKThztdYrgntWDY9SKgnj+sSZ/2ZorZc6pZFrKYTwSPKJwAtU/lxfn40Es8IjCTj8o5Syzx8/3GHdEqVUrswpf5b5vcq1f5eUUlZgo1IqVmudaa6TaykavMZ2029qJJ8IvEDlz/X62WitZZHF5QIkAfEOf1uBHCDFYZ0NWO603RLAFuzzD9I1SweSnNYlAkuCfW4NaQGmuliXYmRJci1laRxLoPJIyUdr9RlIPhH4axqQ/Lk+PxtpMys8sWmts+x/aK0LMb6cGQ5pUp3+xvw7ve5Pr0FKArKc1m0w1wuqSvljzH8drTBft5l/y7UUDV2g8kjJR2tO8okACnD+XG+fjQSzwiUJOPxnXisbcMxxvXmDc7xmzZp5PWzm4pJcS9HQNdabflMi+UTgBSp/ru/PRoJZ4ZIEHDUSC2ffvwvN7Xq4pbWOcazRMiUChdpoSyXXUjRojfWm38RIPlEHApQ/1+tnIx3AhFta6xgXq6u+0PZM1suXtTk1wHeuoRH+SQWmmf+XaykavADlkfiQpjnlo/6QfKL++Js/1+tnIzWzwl8ScIiAU0qlAMe01jOCfS5C1FKDvukL4a/GkD9LMCt81hi+0A2BizZ0wgOz9ipVOwzf4vCaXEvRaEgeGRyST9Sd2ubP9fXZSDODZsDPNlfHXD3ucvhC93fxmtXDI7LmxP4oMBaouh4OP2Z5VOhaOjDMaZ1cS1FvGkIeKflojUg+Ufdqmj8f8yFNwEgw28TZBy32Y5P1gKtaBQk4vNBaFyqlcqn+2DCWsw3nhQOlVAaQ5nwTl2sp6ksDyCPr9abflEg+Ubdqmz/X52cjwWwTZ35hkmuzDwk4/LICYxYfx56g8eZ64cB8JJvu+D1RSiVizDyTi1xLUQ8aQh4p+WitSD5RBwKUP9fbZyNtZoVH7r7QDo/l7F9WR805I0mj+o0x1VwvTOZ0iQBWpVS8uSQCyQ7fNbmWosELUB4p+WjNST4RYAHMn+vts1Hm9GJCVGN+oWMxBu+2i8X4QqeaaawYU9M5zr28HKPtWLOsUTDnWB+D8TjSBmRpmWO9ivmdKXDzcq7WOs4hrVxL0WAFKo+UfLR2JJ8InEDnz/X12UgwK1ySgEMIIdxrrDd9IZoiCWaFEEIIIUSjJW1mhRBCCCFEoyXBrBBCCCGEaLQkmBVCCCGEEI2WBLNCCCGEEKLRkmBWCCGEEEI0WhLMCiGEEEKIapRSNqXU1GCfhzcSzArRhDjMOlSv2wohhGhazHvCcqBtsM/FGwlmhWgizOkGE929ppRKNwd6d7f+mDnHvBBCNHpSQK8ZpZTVvBckAW5noGtI11eCWSGaAHP2oGStdaabJEuAqcBd7tZrrQuBdHMaTSGEaLQ8Fe6FZ1rrQq11qtZ6BlDoIWmDqQAJDfYJCGFnznM+xvzTXhq0YUzvuNTfOcrNzCwZ48eYb/7r+MNbAWRhZHjTtNZLa372wWOf1x3o7+Z1G2CvkV3sab3WOlcptVwpla61Tqu7sxZCuGPmXfbfZ25jzZuCxaFwn+q0PgmIBeKA5TJdcO1orQvNJ3vLtdbDg3kuEsyKoDMz7gyMgDXZxevxwBKl1ApfAyyzdrHQeX9KqXSMG8RSrXWyw2N3T6XPgFJKJQY4E00H0s2aVVeSzH+XOqVxuV5rPUMplaOUyvC3ACFEU2HmFUkYQaVdLsZvzd0TEPu2SRgFTLtCjODK19/9cCAeo6CdCQQtmDXzyAGNJfDzUri/AuOaxmNUcDSK99SQNZQKEGlmIIJKKZWC0cA81d0PQWudpbXuD8QrpTY6t/t0sc8lgM1VYIxxYwCYZO67UGudVl8ZtVkbGrASrLm/RC83V/vxnB8HuVsPkOZmvRDNgpkvxGH8FsCoIY3zFsia2y4FYsw/U7XWMf7kMeaxg1rT5WAesNxTj3azQqKhcFu4N+8xw+r/lJo2szlCUjDb0EowK4LGrL3IwMjsvWb0ZuZuBVZ6SZqE+5oMK0aNbb3VxDoJ9I893Vw8ScS4ETtfY3fr7TfjAQ2pgb8QwWDeqAFs3grSLiz1Jfht4JZj1CxnuXox0AX02vClcB/EvL+pC2oFiASzIigcHgVl+ZnZp2HU0HoL4HI8vHbMj+MFmqva4tpIwqEdrDOHGpN0X9Y7WQykenhdiObCnkel+LHNXcC0OjiXeqW1zvRSs9yQCry+FO6bPHM0ghw/l1rVrge7AkSCWREs9gzHr8ze/MHkAlNrUEsSVOaP3J+bobf9JWEUBjzVNAzHqIl2LjC4W+9oCQE8XyEaMXuNkz+Fu+Faa5e1mU1MoAvoteGxcN9cmM3n4vxcAtHULmgVIBLMimCxB0k1+QHZt2kUgZZZSk4CNgZ418OBDV7S2DuQ+Lq+ipm5WaWpgWjuzKA0C6OpgdcaLLPT6vo6P7EgC3QBvTZ8LNyLuhW0ChAZzUDUO4ebQW4NM57lGD+YMcAMc59JwNMOadKVUq5KiDaMAM0xsJxkr0Ex92Pj7OgGVvP/VmCFq5oWhyYP+RgzpViBNIf39jRm+1SMXrQpTjdEn0dpcJKIh0dqZs11PE41J+7Wu5GLD4GvEM1Ahrmk4r0QnsrZjmNNjpmHJGJ0DmsofCncizqktV5hVt7Y6nskHAlmRTDEm//W9MtuDxLt+7E3P1gKoJTSGOPGznDe0Bzg+S5zdARXry133s4MPJfjNNSLw1R/53RgMwPiPKXUMHMkhjRzvRUoADIDNISJDc+Z9wBcj8/rbr0ruRhjMgrRrGmtM+2zIimlrO4K4vbmTx5e91b49Zk5Gkx/jD4C9t9phi/NGxy2dTzuOcPxme/XZi5LHfItnwvo9jyXs2NaF2K833MKyEqpHM62vy0E+vsZEHks3Htjfm55Tuc5zRyqcLl5brGY+bdDxQcYgXQuDp+jeX2tGJ9xPC7uLTU4R78rW4IgOBUgWmtZZKnXBaN2QwNLarh9vLm9dvO6BqZ6OHaBi/VWV+sdXl8CxDuty8EYAsZV+uXARhfH0O628fMa2Pdlq4fPqkafkyyyNLXFIe9ymb+YaVIwetQ7r7eZeUai0/okjEJuvIttNEaA6bzeauYxKS5eW+4pj3HYttrv2nx/8U7nnOLlPHzK0zCaWXnMs8zrUO09+fjZaFfX0EPaap8hxmyI1c7B6Tqkm+mc7wdVeb6Zxup0nQpqk/ebn02Si/WJ/rx3P46Xbt73tHnuS3z8nD1+/+pqkTazIpgaUgcue/MDd+d0zhSv5piLNtx3YMvAGHUh3s3rtRVr/lvXIzPYS/1CCN86gg3XrjvTLMeo3TznNW08VdqAf4/sV+JmJBhtDGGY5GFc2HkYkyBUa2akjRmznnb4O9fVMWrIXqvrKU9cUZPjBWLyG3MfVwDdnc/B6TrEYzSRc64Ftef5GRiBv+NENIUYNZVux+r14dzu0i5mgjO/TwGfVEMbYx0na62VNkazSNa+PVHMJQgjXEgwK4LBPmxWrMdU7gX8h+KQMW1USiW5CGoXc26ziFQ8j1drTzsggKfpyAb1MmZiPjX/nIRoUrSXjmDuOn4FsvBrPr6Ox/OYnukY/QbOycfMc07CTXte81G/q/yv1sygK5dz+zY4n1tNxymtVeHefL/pZsDmLU8d4CqoxGEKdu26eUSOeaya3L/8qmwJsqBUgEibWREMVT/6Gm5v3y7Q7YP6Y9R4LAFQSmVhdPRY5KIUbgNyPc2KY24bzDFtA0VqZoU4y1NHMHcdv/wp/HrL11IxagY9tSd1HPHFsZ1mqtPrzrKgTgvJGRhBtquAL9msGa6JGhfuzeAyQ/s+45q3TmbePj+/81OtdZZSCozKljSMGmzH99qQhiMLSgWIBLOi3mmtl5o/zJr2erRnOosCfF5ZQIxZ85GMcWOZijGm7Qx9tkNDVScF7blBf60a+3tRX0FyW2rx6E6Ipka76QjmpeNXIAu/8XgPmOz7ucLFtrjLc101PQiwTIxa4zQcmmqY187TRDd1wqETr82Pe5G3NHX1PvypbAm2eq8AkWYGIljs7Y+S/NnIYUgYx33UmjmciP1mlKm1Hq61jsHoIZyJEdDab0QBDSSVUvE1eKx3zg20Dlmp+agTQjRVrmYEuwsz0HDkXPj1sAx38/ja1b58zYOcmy049oSvd2agv5TqY5GmULv8vCZ58nCMgNo+okO1z64h0cbIODGcfSJgw6hs2ejDjJj1KSgVIBLMimBxHOLFH/ZMcEaAH4UNwMVgz2bD/1SMjDbVXFdIYBu5D8DPxzIONQh1/TgnlqbRVEKIQHLVEcxdx6+A/X5q8Lt3LojWS3tGLwX0DDONY37btpb5eU0K94VmJ6dCjCdx8V5qzoPGz8qWYAtKBYgEsyIoHDIQq6+lSrNWIh2jF29dDEg+xsNrGZx7A0nHOHe3HTaUUjZzXEDAa3uumt7w6mq0BDv7cEJCCJNzRzBPM37VQeHXl32561ewAWrcCckfbgvoDh3B7M227ON411gNC/dVn5fDiADpDXTGQ58rWxqAoFSASDArgsZ8pJaKUar0OAWeWSpdjpE5D6ujU/LUmzgWh04T5jAtWXgepDvNxWNDVzeiuBrWStgfNdUlX9rnCdEcOdbO2gMKd/wu/Ho5rsd9cbZg7jw6gP1vt8cxawG9TtkLtSqgZ2AUBOIxOn7VZFpzV2pTuJ9Ew25u4E9lSzAFpQJEglkRVGZQOBxIU0pluHpMZGasGzF6cPb3lIE6bN/WTZJYPD9mS3UxnI0VoxbBuTZ4GBBrdgZxPo+puB5mJh2HoW9q2eY1i+odPALGXkMRwBuNEE2Gw7ijSebfbvOlWhR+Xe1rhrkvTzVxKRhNsc553KvPzpToqXnX0/g3LWxNCuj2a5dO4AKfWhXuzfOdRMNtbuBzZUuQBaUCREYzEEFnBktxZq3EPKXUMc42ILdhZJbDvfU0VUpt5GxmNtXcX7rZ+3gqRuZvM9MWcHb6QXsv4lR7WqVUW4whRsAIjFNd3BgKgf5m+iXmPuznneHqfM39W4GVSqkV5rqaNplYhBHk15VEGk4GKURDlIkROPpSmzcM43ef4TwElYfCrzvJwBKlVLpz/mFOvbrYQ74yydx2OUataKHDtknAej+fFKUDGfaRHXwpoJvplmIUBAI1goJPhXtPFR7mSDuFGM0NstwU5N3VgNrXu3v/gWirnKqUSnP6zOyVLUFvZhDMChCljenHhBCNkBnAT6qLoVnMm12GL7VFQjRHZk3ZEq11nB/bTMUIutwWfh0K5vYAKNdMM8PFvhyDMqt5Pl6DCbNp13Cn8zhnmCc355HqvH/zPMZgFn59KaCb1+7pQA0HZu5vo9ZaeUjj6v1U1YibT9kcm7zlYlyjJS62y9BazzADuCWc28Qhy9zvCofJIKpGtTC3T/Z1WErzvQ1wqJhxrmxxWXlS3+zDWvoxZm/gji3BrBCNl/2GFOjxIc3Sfp7ZY1YIIQLKrAUuDGQtXl0W7oV3wawAkWBWiEbOnILSY1viGuwzHcjRgZuXXQghqiilltRBIbxOCvfCu2BXgEgHMCEavzQC2APXfGyWKIGsEKIueBrKrDbMPKsmk9CI2nsa11M51wsJZoVo5MxHOrm+DqfjgwwC1ylDCNGMmR1ktdP4rd6GMquNgBbuhXcNoQJEglkhmgCzd3S1YcX8Ze9V3RA6EwghmoxczHFn7UMtBngGxyp1ULgX3gW9AkTazArRhCilpjr3ePZj20SQcWWFEIFltsHPx+h5v74+OgiZwyVOqqugWRjMCpDcYI96I8GsEEIIIZqc2hTuhXcNqQJEglkhhBBCCNFoSZtZIYQQQgjRaEkwK4QQQgghGi0JZoUQQgghRKMlwawQQgghhGi0JJgVQgghhBCNlgSzQgghhBCi0fr/AdAcieQz5VYAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model=make_model(mcmc.guesses,obspars,rad) # make a model from your guesses\n", "\n", "KinMS_plotter(fdata, obspars['xsize'], obspars['ysize'], obspars['vsize'], obspars['cellsize'],\\\n", " obspars['dv'], obspars['beamsize'], posang=guesses[1],overcube=model,rms=error).makeplots()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the black contours of the model arent a perfect match to the moment zero, spectrum and position-velocity diagram extracted from our \"observed\" datacube. One could tweak by hand, but as these are already close we can go on to do a fit! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you are experimenting then running until convergence should be good enough to get an idea if the model is physical (setting a low number of iterations, ~3000 works for me)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing chain 1\n", " Chain has not converged - Accept rate: 0.08666666666666667\n", " Chain has not converged - Accept rate: 0.1\n", " Still varying: ['posAng' 'Inc' 'R_turn' 'scalerad']\n", " Chain has not converged - Accept rate: 0.2866666666666667\n", " Still varying: ['Inc']\n", " Chain has not converged - Accept rate: 0.45666666666666667\n", " Still varying: ['Inc']\n", "Chain converged: LL: -5417.6915586532605 - Accept rate:0.43\n", "Best chain so far!\n", "Best fit:\n", " Flux: 30.0\n", " posAng: 269.90007276088943\n", " Inc: 47.854655719347996\n", " VFlat: 190.0066572057255\n", " R_turn: 1.9306907362857078\n", " scalerad: 4.931147911070421\n", "Starting final chain\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Final best fit values and 1sigma errors:\n", " Flux: 30.0 (Fixed)\n", " posAng: 269.8973861297394 + 0.046268424097263505 - 0.017317013075341947\n", " Inc: 46.69701598839461 + 0.10982841217780503 - 0.20773477781962413\n", " VFlat: 193.9123324657629 ± 0.34437214188913856\n", " R_turn: 1.9498716985274913 + 0.013209686719802827 - 0.010186707854953791\n", " scalerad: 4.8752275278090975 ± 0.013733314328538526\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=3)]: Done 3 out of 3 \| elapsed: 6.6min remaining: 0.0s\n", "[Parallel(n_jobs=3)]: Done 3 out of 3 \| elapsed: 6.6min finished\n" ] } ], "source": [ "outputvalue, outputll= mcmc.run(fdata,error,3000,plot=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the final parameters (listed in the output with their 1sigma errors) are pretty close to those we input! One could use the cornor_plot routine shipped with GAStimator to visualize our results, but with only 3000 steps (and a $\\approx$30% acceptance rate) these wont be very pretty. If you need good error estimates/nice looking cornor plots for publication then I recommend at least 30,000 iterations, which may take several hours/days depending on your system, and the size of your datacube. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can visualize the best-fit model again to check how we did - turns out pretty well! (Note the flux in the integrated spectrum isnt perfect, this is because of the masking of the noisy data). " ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bestmodel=make_model(np.median(outputvalue,1),obspars,rad) # make a model from your guesses\n", "\n", "KinMS_plotter(fdata, obspars['xsize'], obspars['ysize'], obspars['vsize'], obspars['cellsize'],\\\n", " obspars['dv'], obspars['beamsize'], posang=guesses[1],overcube=bestmodel,rms=error).makeplots()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tiny error problem\n", "\n", "I have found that fitting whole datacubes with kinematic modelling tools such as KinMS can yield unphysically small uncertanties, for instance constraining inclination to $\\pm\\approx0.1^{\\circ}$ in the fit example performed above. This is essentially a form of model mismatch - you are finding the very best model _of a given type_ that fits the data - and as you have a large number of free-parameters in a data cube you can find the best model (no matter how bad it is at actually fitting the data!) really well. \n", "\n", "In works such as [Smith et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.4359S/abstract) we have attempted to get around by taking into account the variance of the $\\chi^2$ statistic.\n", "\n", "As observed data are noisy, the $\\chi^2$ statistic has an additional uncertainty associated with it, following the chi-squared distribution ([Andrae 2010](https://ui.adsabs.harvard.edu/abs/2010arXiv1009.2755A/abstract)). This distribution has a variance of $2(N - P)$, where $N$ is the number of constraints and $P$ the number of inferred parameters. For fitting datacubes $N$ is very large, so the variance becomes $\\approx2N$. \n", "Systematic effects can produce variations of $\\chi^2$ of the order of this variance, and ignoring this effect yields unrealistically small uncertainty estimates. In order to mitigate this effect [van\n", "den Bosch & van de Ven (2009)](https://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1117V/abstract) proposed to increase the $1\\sigma$ confidence interval to $\\Delta\\chi^2=\\sqrt{2N}$. To achieve the same effect within the Bayesian MCMC approach discussed above we need to scale the log-likelihood, by increasing the RMS estimate provided to GAStimator by $(2N)^{1/4}$. This approach appears to yield physically credible formal uncertainties in the inferred parameters, whereas otherwise these uncertainties are unphysically small. \n", "\n", "Lets try that with the example above:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing chain 1\n", " Chain has not converged - Accept rate: 0.11\n", "Chain converged: LL: -112.97430961201346 - Accept rate:0.2966666666666667\n", "Best chain so far!\n", "Best fit:\n", " Flux: 30.0\n", " posAng: 270.0\n", " Inc: 44.957062230395515\n", " VFlat: 201.01285763281135\n", " R_turn: 2.03929515740805\n", " scalerad: 5.013047475148751\n", "Starting final chain\n", "Final best fit values and 1sigma errors:\n", " Flux: 30.0 (Fixed)\n", " posAng: 269.8723305066128 + 0.946321323228176 - 1.1107270271547236\n", " Inc: 45.781525045651954 + 2.1113884290902405 - 0.9361234945474166\n", " VFlat: 199.09454514602965 + 3.294369458631081 - 5.327156135062893\n", " R_turn: 2.110158720751331 + 0.15726926408759345 - 0.18741126338718428\n", " scalerad: 5.029909360644904 + 0.20293482085984138 - 0.2480078138126771\n" ] } ], "source": [ "error=((2.0fdata.size)**(0.25))\n", "outputvalue, outputll= mcmc.run(fdata,error,3000,plot=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see we now get a much more reasonable error estimates, for instance a 1$\\sigma$ inclination error of $\\pm2^{\\circ}$. \n", "\n", "If you want to implement this fix yourself there is a wrinkle to consider. You need to be careful choosing $N$. Formally this should be the number of constraints- i.e. the total number of pixels in your cube. But consider a large datacube with signal only in a small section (although fitting such a datacube would be inefficient anyway; see speed tips below), all of the actual constraints are coming from a small number of pixels. If you find yourself in this situation I would recommend setting $N$ to the number of pixels with actual detected flux in your datacube. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Speed tips\n", "\n", "There are some common ways to make sure you don't spend a lot of time MCMCing rather than doing science. \n", "\n", "- Cut down your observed data to only include spaxels and frequency channels near signal. Ideally you want some padding around your observed signal so the model knows it must not include flux in those positions, but not so much as to drastically increase runtime! On a similar note...<p>\n", "\n", "- Make sure your observed data (and thus models) have spatial dimensions that are $2^n$. If this is impossible then $3^n$ and $6^n$ are pretty good too. This is because convolving the KinMS model with the beam takes the majority of the computation time, and FFTs are faster when working with such dimensions.<p>\n", "\n", "- Don't provide a radius vector that is very oversampled/overlong well beyond the projected dimensions of the cube. This can slow down internal interpolation routines." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
napsternxg/ipython-notebooks	Monte Carlo Integration.ipynb	1	232991	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Monte Carlo Integration\n", "\n", "Inspired from the following posts:\n", "\n", "* http://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab7/GibbsSampler.ipynb\n", "* http://twiecki.github.io/blog/2015/11/10/mcmc-sampling/\n", "* https://en.wikipedia.org/wiki/Monte_Carlo_integration" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from numba import jit # Use it for speed\n", "\n", "from scipy import stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is Monte Carlo (MC) Integration?\n", "\n", "Let us say that we want to approximate the area between the curve defined by $f(x) = x^2 + 3x + \\ln{x}$ between $x\\in (0,5]$ and the x-axis." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0 5.0\n", "4.0 41.6094379124\n" ] }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fe4f4f43cd0>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe550234a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def f(x):\n", " return x*2 + 3x + np.log(x)\n", "\n", "step= 0.001\n", "x = np.arange(1,5+step0.1,step)\n", "y = f(x)\n", "print x.min(), x.max()\n", "print y.min(), y.max()\n", "plt.plot(x, y, lw=2., color=\"r\")\n", "plt.fill_between(x, 0, y, color=\"r\", alpha=0.5)\n", "plt.axhline(y=0, lw=1., color=\"k\", linestyle=\"--\")\n", "plt.axhline(y=y.max(), lw=1., color=\"k\", linestyle=\"--\")\n", "plt.axvline(x=x.min(), lw=1., color=\"k\", linestyle=\"--\")\n", "plt.axvline(x=x.max(), lw=1., color=\"k\", linestyle=\"--\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.title(\"$f(x) = x^2 + 3x + \\ln{x}, x\\in[1,5]$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Concretely, we are interested in knowing the area of the red-shaded region in the above figure. Furthermore, I have also provided a rectangular bounding box for the range of values of $x$ and $y$. The true value of the area under the curve is $\\sim{81.381}$ using its analytic integral formula (see http://www.wolframalpha.com/input/?i=integrate+x%5E2+%2B+3x+%2B+ln(x),+x+in+%5B1,5%5D).\n", "\n", "The most accurate way to get the value of the area is to find the value of the definite integral $\\int_{1}^{5} f(x) dx$. However, in many cases analytically finding this integral is very tough, especially if the function is not easily integrable. This is where numerical methods for approximating the integral come handy. Monte Carlo (MC) techniques are one of the most popular form of numerical solution used for definite integral calculation.\n", "\n", "A basic intuition of the Monte Carlo Integration is as follows:\n", " Define the input domain $[a, b]$ of the integral $\\int_{a}^{b} f(x) dx$.\n", "* Uniformly, sample $N$ points from rectangular region between $[a, b)$ and $[\\min(f(x)), \\max(f(x)))$\n", "* Find the proportion of points that lie in the region included in the area of $f(x)$, call it $p$\n", "* Multiply the area of the rectangular region ($A$) by $p$ to get the area under the curve $A^=pA$\n", "* As $N \\to \\infty$, the area of the shaded region $A^* \\to \\int_{a}^{b} f(x) dx$\n", "* Usually, a much smaller value of $N$ will give approximate value within a reasonable error span.\n", "\n", "\n", "Below, we will try to approximate the area of the curve using the MC integration method described above. We will use $N = 10^5$, and plot the points which fall in the region of the area in red and the other points in grey." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit\n", "def get_MC_area(x, y, f, N=10*5, plot=False):\n", " x_rands = x.min() + np.random.rand(N) (x.max() - x.min())\n", " y_rands = np.random.rand(N) * y.max()\n", " y_true = f(x_rands)\n", " integral_idx = (y_rands <= y_true)\n", " if plot:\n", " plt.plot(x_rands[integral_idx], y_rands[integral_idx],\n", " alpha=0.3, color=\"r\", linestyle='none',\n", " marker='.', markersize=0.5)\n", " plt.plot(x_rands[~integral_idx], y_rands[~integral_idx],\n", " alpha=0.3, color=\"0.5\", linestyle='none',\n", " marker='.', markersize=0.5)\n", " plt.axhline(y=0, lw=1., color=\"k\", linestyle=\"--\")\n", " plt.axhline(y=y.max(), lw=1., color=\"k\", linestyle=\"--\")\n", " plt.axvline(x=x.min(), lw=1., color=\"k\", linestyle=\"--\")\n", " plt.axvline(x=x.max(), lw=1., color=\"k\", linestyle=\"--\")\n", " plt.xlabel(\"x\")\n", " plt.ylabel(\"y\")\n", " plt.title(\"$f(x) = x^2 + 3x + \\ln{x}, x\\in[1,5]; N=%s$\" % N)\n", " print \"Proportion points in space: %.3f\" % (integral_idx).mean()\n", " area = (integral_idx).mean() * (\n", " (x_rands.max() - x_rands.min()) * (y_rands.max() - y_rands.min())\n", " )\n", " return area\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/entity/anaconda2/lib/python2.7/site-packages/numba/dataflow.py:346: RuntimeWarning: Python2 style print partially supported. Please use Python3 style print.\n", " \"Python3 style print.\", RuntimeWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Proportion points in space: 0.490\n", "\n", "Area is: 81.533\n" ] }, { "data": { "image/png": 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e2O3bt2m326ysrNDtdun3+5IXX1xcZGRkBL9/0K49lUrRarVkLmlmZgaDwUAk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rO3WFiXHTt0fIC6DFQeWSK6foLKAqpiB4qK0HRLSgCswUEEVEGBroebmgrIx8fTh8pFv3Yt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\nPAIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe4f45b5ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "area = get_MC_area(x, y, f, N=105, plot=True)\n", "print \"Area is: %.3f\" % area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can observe, the number of points which fall inside the region of interest, are proportional to the area of the region. The area however, marginally close to the true area of $81.38$. Let us also try with a higher value of $N=10^7$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Proportion points in space: 0.489\n", "\n", "Area is: 81.398\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe4e41c4950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "area = get_MC_area(x, y, f, N=107, plot=True)\n", "print \"Area is: %.3f\" % area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above figure, shows that for $N=10^7$, the region covered by the sampled points is almost as smooth as the shaded region. Furthermore, the area is closer to the true value of $81.38$.\n", "\n", "Now, let us also analyze, how the value of the calculated area changes with the order of number of sampled points. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 88.0738917212\n", "3 74.327056357\n", "4 80.2320526499\n", "5 81.0771326803\n", "6 81.2459063119\n", "7 81.4131110967\n" ] } ], "source": [ "for i in xrange(2,8):\n", " area = get_MC_area(x, y, f, N=10*i, plot=False)\n", " print i, area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, as the number of points increase, the area becomres closer to the true value.\n", "\n", "Let us further examine this change by starting with $10^3$ points and then going all the way till $10^6$ points. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 36.4 s, sys: 3.63 s, total: 40 s\n", "Wall time: 40 s\n" ] } ], "source": [ "%%time\n", "N_vals = 1000 + np.arange(1000)1000\n", "areas = np.zeros_like(N_vals, dtype=\"float\")\n", "for i, N in enumerate(N_vals):\n", " area = get_MC_area(x, y, f, N=N, plot=False)\n", " areas[i] = area" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean area of last 100 points: 81.380\n", "Areas of last 10 points: [ 81.41165876 81.31764473 81.57499032 81.35316251 81.40445249\n", " 81.41623796 81.46210595 81.4105668 81.4002441 81.34190896]\n" ] } ], "source": [ "print \"Mean area of last 100 points: %.3f\" % np.mean(areas[-100:])\n", "print \"Areas of last 10 points: \", areas[-10:]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fe4e41d6c50>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0aVPvjszfaMA9rl69ekWt4z/eQw45pNK0BzX97dChQ+CyI0aM4NRTT+Wxxx5j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\n/saw0mCMMSY91VV81BNYpqr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe4e41cedd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(N_vals, areas, color=\"0.1\", alpha=0.7)\n", "plt.axhline(y=np.mean(areas[100:]), linestyle=\"--\", lw=1., color=\"k\")\n", "plt.ylabel(\"Area\")\n", "plt.xlabel(\"Number of samples\")\n", "#plt.xscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can observe from the figure above, for lower number of sampled points, the estimates of MC integration are quite noisy. However, for larger number of points this value converges to the true estimates. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
mackst/myPyTutorialNotebook	21 类实战(1).ipynb	1	30736	{ "metadata": { "name": "21 \u7c7b\u5b9e\u6218(1)" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "point = Point(2, 3, 4)\n", "point.x # 2\n", "point.y # 3\n", "point.z # 4\n", "\n", "uv = Point(.2, .3)\n", "uv.u # .2\n", "uv.v # .3\n", "point = uv\n", "point.x # .2\n", "point.y # .3\n", "\n", "point * 1.0\n", "point = 1.0\n", "point + 1.0\n", "point += 1.0\n", "point + uv # Point(2.2, 3.3, 4)\n", "point += uv" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point.x, point.y, point.z" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point.One()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point.x, point.y, point.z" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattr__(self, name):\n", " print(\"get: \" + name)\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(1, 2, 3)\n", "point.u\n", "point.x\n", "point.y\n", "point.z" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(.2, .3)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print(point.x, point.y)\n", "print(point.u, point.v)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point.u = 1" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print(point.x, point.y)\n", "print(point.u, point.v)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(.2, .3)\n", "\n", "print(point.x, point.y)\n", "print(point.u, point.v)\n", "\n", "point.u = 1\n", "point.v = .5\n", "\n", "print(point.x, point.y)\n", "print(point.u, point.v)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 * 2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self other\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 = 2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self * other\n", " \n", " def __div__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x /= other\n", " self.y /= other\n", " self.z /= other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rdiv__(self, other):\n", " return self / other\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(8, 8, 8)\n", "point1 = point1 / 2\n", "print(point1.x, point1.y, point1.z)\n", "point1 /= 2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self other\n", " \n", " def __div__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x /= other\n", " self.y /= other\n", " self.z /= other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rdiv__(self, other):\n", " return self / other\n", " \n", " def __add__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x += other\n", " self.y += other\n", " self.z += other\n", " elif isinstance(other, Point):\n", " self.x += other.x\n", " self.y += other.y\n", " self.z += other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __radd__(self, other):\n", " return self + other\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 + 2.\n", "print(point1.x, point1.y, point1.z)\n", "point2 = Point(3, 2, 1)\n", "point1 = point1 + point2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 += 2.\n", "print(point1.x, point1.y, point1.z)\n", "point2 = Point(3, 2, 1)\n", "point1 += point2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self other\n", " \n", " def __div__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x /= other\n", " self.y /= other\n", " self.z /= other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rdiv__(self, other):\n", " return self / other\n", " \n", " def __add__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x += other\n", " self.y += other\n", " self.z += other\n", " elif isinstance(other, Point):\n", " self.x += other.x\n", " self.y += other.y\n", " self.z += other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __radd__(self, other):\n", " return self + other\n", " \n", " def __sub__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x -= other\n", " self.y -= other\n", " self.z -= other\n", " elif isinstance(other, Point):\n", " self.x -= other.x\n", " self.y -= other.y\n", " self.z -= other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rsub__(self, other):\n", " return self - other\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 - 2.\n", "print(point1.x, point1.y, point1.z)\n", "point2 = Point(3, 2, 1)\n", "point1 = point1 - point2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point1 = Point(1, 2, 3)\n", "point1 -= 2.\n", "print(point1.x, point1.y, point1.z)\n", "point2 = Point(3, 2, 1)\n", "point1 -= point2\n", "print(point1.x, point1.y, point1.z)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print(point1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "str(point)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self other\n", " \n", " def __div__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x /= other\n", " self.y /= other\n", " self.z /= other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rdiv__(self, other):\n", " return self / other\n", " \n", " def __add__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x += other\n", " self.y += other\n", " self.z += other\n", " elif isinstance(other, Point):\n", " self.x += other.x\n", " self.y += other.y\n", " self.z += other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __radd__(self, other):\n", " return self + other\n", " \n", " def __sub__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x -= other\n", " self.y -= other\n", " self.z -= other\n", " elif isinstance(other, Point):\n", " self.x -= other.x\n", " self.y -= other.y\n", " self.z -= other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rsub__(self, other):\n", " return self - other\n", " \n", " def __str__(self):\n", " return \"(%d, %d, %d)\" % (self.x, self.y, self.z)\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(1, 2, 3)\n", "print(point)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "class Point(object):\n", " '''This is a Point in 2/3D space.\n", " You can use .u ro .v for a uv point'''\n", " \n", " def __init__(self, x=0, y=0, z=0):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __getattribute__(self, name):\n", " if name == 'u':\n", " return self.x\n", " elif name == 'v':\n", " return self.y\n", " \n", " return super(Point, self).__getattribute__(name)\n", " \n", " def __setattr__(self, name, value):\n", " if name == 'u':\n", " self.x = value\n", " elif name == 'v':\n", " self.y = value\n", " \n", " super(Point, self).__setattr__(name, value)\n", " \n", " def __mul__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x = other\n", " self.y = other\n", " self.z = other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rmul__(self, other):\n", " return self other\n", " \n", " def __div__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x /= other\n", " self.y /= other\n", " self.z /= other\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rdiv__(self, other):\n", " return self / other\n", " \n", " def __add__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x += other\n", " self.y += other\n", " self.z += other\n", " elif isinstance(other, Point):\n", " self.x += other.x\n", " self.y += other.y\n", " self.z += other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __radd__(self, other):\n", " return self + other\n", " \n", " def __sub__(self, other):\n", " if isinstance(other, int) or isinstance(other, float):\n", " self.x -= other\n", " self.y -= other\n", " self.z -= other\n", " elif isinstance(other, Point):\n", " self.x -= other.x\n", " self.y -= other.y\n", " self.z -= other.z\n", " else:\n", " raise TypeError(\"must mul with int or float type\")\n", " \n", " return self\n", " \n", " def __rsub__(self, other):\n", " return self - other\n", " \n", " def __str__(self):\n", " return \"(%d, %d, %d)\" % (self.x, self.y, self.z)\n", " \n", " def __repr__(self):\n", " return \"Point(%d, %d, %d)\" % (self.x, self.y, self.z)\n", " \n", " @staticmethod\n", " def One():\n", " return Point(1, 1, 1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "point = Point(1, 2, 3)\n", "print(point)\n", "point" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# -------------------------------------------------------------------------------- \n", "# Copyright (c) 2013 - 2014 Mack Stone. All rights reserved. \n", "# \n", "# Permission is hereby granted, free of charge, to any person obtaining a copy \n", "# of this software and associated documentation files (the \"Software\"), to deal \n", "# in the Software without restriction, including without limitation the rights \n", "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell \n", "# copies of the Software, and to permit persons to whom the Software is \n", "# furnished to do so, subject to the following conditions: \n", "# \n", "# The above copyright notice and this permission notice shall be included in \n", "# all copies or substantial portions of the Software. \n", "# \n", "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR \n", "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, \n", "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE \n", "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER \n", "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, \n", "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN \n", "# THE SOFTWARE. \n", "# -------------------------------------------------------------------------------- " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\u89c6\u9891\u4e0b\u8f7d\u5730\u5740\n", "\n", "http://schi.iteye.com/blog/1938511\n", "\n", "Notebook\u4e0b\u8f7d\u5730\u5740\n", "\n", "https://github.com/mackst/myPyTutorialNotebook" ] } ], "metadata": {} } ] }	mit
Neuroglycerin/neukrill-net-work	notebooks/Checking resumed 40aug and 16aug models.ipynb	1	981797	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 2: Tesla K40c\n", ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Welcome to the HoloViews IPython extension! (http://ioam.github.io/holoviews/)\n", "Available magics: %compositor, %opts, %params, %view, %%labels, %%opts, %%view\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc692f8d910>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc692fac2d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc692fac0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pylearn2.utils\n", "import pylearn2.config\n", "import theano\n", "import neukrill_net.dense_dataset\n", "import neukrill_net.utils\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import holoviews as hl\n", "%load_ext holoviews.ipython\n", "import sklearn.metrics" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = pylearn2.utils.serial.load(\n", " \"/disk/scratch/neuroglycerin/models/resume_40aug.pkl\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<center><img 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' style='max-width:100%'/><center/>" ], "text/plain": [ "Curve.Valid_y_nll (y)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import neukrill_net.plotting as pl\n", "pl.monitor_channels(m, [\"valid_y_nll\"], x_axis = \"epoch\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = pylearn2.utils.serial.load(\n", " \"/disk/scratch/neuroglycerin/models/resume_40aug_recent.pkl\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<center><img 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' style='max-width:100%'/><center/>" ], "text/plain": [ " Valid_y_nll\n", " I : Curve.Valid_y_nll (y)\n", " Train_y_nll\n", " I : Curve.Train_y_nll (y)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import neukrill_net.plotting as pl\n", "pl.monitor_channels(m, [\"valid_y_nll\"], x_axis = \"epoch\") + pl.monitor_channels(m, [\"train_y_nll\"], x_axis = \"epoch\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = pylearn2.utils.serial.load(\n", " \"/disk/scratch/neuroglycerin/models/resume_16aug.pkl\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<center><img 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' style='max-width:100%'/><center/>" ], "text/plain": [ "Curve.Valid_y_nll (y)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import neukrill_net.plotting as pl\n", "pl.monitor_channels(m, [\"valid_y_nll\"], x_axis = \"epoch\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = pylearn2.utils.serial.load(\n", " \"/disk/scratch/neuroglycerin/models/resume_16aug_recent.pkl\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<center><img 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fDIjhKSogz87Uey76MQIHufuW5LA0chWkYGRdrW0kFRtU1l0mdnsTng3+PjiQwKqASBEA2S9Fktf2ngKL0lvEviFh2VyaAwIMaICuwt9H0KzV/PWYnbu6RPkZf4UwNHIYRwh7q6ogzpVyREkwIuffbBMTMvZ1YwqaeJh4dE+jAyIbRN0mfa1tL0GcC+QgsPbCuhV4SeldfGeTgyIbRP0me1OpmcM0Vnq2SmSAgRGPrHGDHpIbfcTpHMkgvRqIAbFPlLA0fJA3uXxH2Oxa7yRV61259X+I5RpzA4tjaFVujbFJp81rxL4m6dgBsUdaqrKdJ4obUQ3na0zMYr+8u5ZUMBT39T5utwhJul1e6DJnVFQjQu4GqKHKrKuLVnsTpg3Q0JhBgUH0YnhG+ZbQ425dWw9kQV3xXbXD+/LNLA+ze2bRNS4R2tqSkCOFhs5Vf/K6ZrqJ5/Xid1RSKwNVZTFHDdC3WKQoJJR77ZQUG1XRo4ioCjqir7i6z850Q1X+RXU107aRpmULiuu4kbephIipLPRUfTJ8pAmEEh32znB7OdS0L1vg5JCM0JuPQZnLcsX8PF1pIH9q5AiLu4xsH/5Zi544sifrOthHXfOwdEQ+KM/C4tgjVj45mbEkFytBFFkRnUjsagU0jVQAotED5rWiJxt05Afh30lwaOQrSXXVXZfcbC2hPVbDtdg702WR4brGNconNWSGZLA0dafBDbTlvIKLAyvkeIr8MRQnMCrqYIYFlWBe/kmJnZL4zpSWE+ikwIzzlVaec/31fx2Ylq10pLHTD8kiBu6BHC8EuCMOiang2SPkXa1tqaInAW08/cXESCSce718fJjKAIWFJTdB7XsnyZKRIdSI1dZevpGtYer6q3I3rXUD0Te5oYl2gi3iR1JIGsV4Se6CCFs9UO8irtdJdZQiHqCciaIn9o4Ch5YO/y57iPlNr4a2Y5t/y3gGe+KePbAitBOriuWzBLRkbzjx/HcnvfMBkQCXSKQmrtlh/nD5y9yZ8/a/5I4m6dgPya4C8NHIVoylc/1PDp8SrWbyly/axPpIGJPU1c181EhOyGLhqQFh/E5vwaMgosTO4ldUVCnC8ga4qKaxzctL6ACKPCJxMSfBSZEG1zymznlf3lbDvtXEEUZlC4vm4pfbTRbb9Haoq0rS01RQDfV9j45aYiooMUPhwXL3VFIiBJTdF5ooIUjDoot6pU2VRp4Cj8gsWu8q8jZv6RXUmNHUL0Cncmh3FjrxBMcg6LFuoepifepKOg2sGxcjuXRgbkPwNCNCgg59frGjgCFFRrs9ha8sDepfW4d52pYebmIt446BwQ/bhbMKt/HMvR916SAZFoFUVRSK+tK/JFvyKtf9YaI3F7l9QUeVlCiJ58s4MzVQ4Sw30djRAN+8Fs59UDFXx5qgaAnuF65gyOID0hyMeRCX+WFm/kvyer+bbAws2Xhvo6HCE0IyBrigCe/aaUjXk1PJ4aIU3MhOZYHSrvHjGz+nAl1XYw6RXuTA7l5ktDMTbTX8hdpKZI29paUwRw2mzn1o2FhBkUPp4Qj17qikSAkZqiC7i2+pAVaEJjdp+18FJmOd9XOFO713YN5tcDw+kUIkvqhXt0DtXTNdS5B2R2qY1+bizQF8KfBWRNEWi/gaPkgb1LC3GfqbLz9O5SHtlRwvcVdhLD9LwwPJqnhkY1OiDSQtzCP6XV1RWd9W5dkb+esxK3d0lNkZf5QwNHERisDpU1R82sPGSm2q5i0sMvk8L42aWhBOklrSE8Iz0+iLUnqskotHJbX19HI4Q2BGxN0aESK/d+WcxlkQbeuCbWB5EJrVBVlQqbSrhB8XrPlowCCy/uK+d4bars6i7B3DcwnEtCfZ8qk5oibWtPTRFAYbWdm/9biEmv8MmEeK/VqgmhBVJTdIG6dMQZjabPhPf8I9vMGwcriTAq9Iky0CfSUPtfIz0i9B75x6Kg2s7rByr4PM+5qqxbmJ4HBoczrFOw23+XEA2JM+npFaEnt9zOwWIrg+NkRaMQAVtTdGEDR62RPLB32FWVD49VceqDlym3qmQUWHnvaBXzM8q5a0sRE9ae5e7NRSzIKOP9o2b2FFgot7Y95WqrXVX2y8+L+DyvhmA93NUvjDeviW3TgMjf3m+hLa66Ii/ug+av56zE7V0dsqbo8OHD3HnnnZjNZsLCwnjrrbfo06dPvWNyc3OZOXMmp0+fxmq1cs011/Dqq68SFBTU5H12u50HHniAL774AoCHH36Yu+66q8Wx1TVwzDc7KKi2kyi7RQekjLNWimocRAbpePf6OI6U2cgpdf45UmbjZKWdnDIbOWU2+P7c4y4J0Z03q2SkT5SBziG6JtNvewudqbJj5c7ZyVGdg7hvUARdNJAqE4EpPT6ID49V8W2BhenJYb4ORwif82hN0fDhw3nssceYMmUK77//PosXL2b79u31jrn77ruJjY1l4cKF2Gw2Ro0axZw5c7jtttsavO+BBx5g6tSpLF26lM8++4yPPvqIkpISUlNTWbduHf3796//ApvIu8/ZVszeQiuLRkRzuTTDC0jzvy1j/clq7kwO484G/lEw2xwcLbOTU2p1Do5KbRwts2FpYLIozHBB+i3KQM9wA+VWB0uzKthw0pkq6xqq4zeDIxhxibZTZVJTpG3trSkCKLM4uPGzAgw6+HRCAsFS2C8ChNdrivLz88nOzmbKlCkA3Hzzzdx7772cPn2azp07u47r3bs333//PaqqUlVVhcVioVevXo3e17t3bwA+/PBD7r//fgCio6O56aab+Oijjy4aFDWlbqsPrS7LF55VZVNdnaKv69bwACXUoGNQrI5Bsef6uNgcKnmVdueMkmtmyUqxRWVvoZW9hedSEXoFDDqosYNRB7f3DeO2PqHyj4/QhMgg54xndqmN/UVW+XIoAp7HaoqOHz9Ojx49XLcVRSExMZHjx4/XO+7RRx8lKyuLLl260LVrV8aPH8+IESOave/48eP07NnT9Tw9e/a86Lmbo+UGjpIH9rztp2uosqsMiDGw+q/Pt/hxBp1CzwgDP+5u4t4B4Tw/IpoPxyewZmwcfxkexT39w7i2azCJ4XocqnNANPySIN66No47k8PcOiDyp/dbaFNavHPA76190Pz1nJW4vctXcfu80Pqhhx4iLS2N06dPk5eXx5YtW1izZk2z97XG+W/u/PnzXbcTQnSc+uBl3n/t+Qbvl9utv71161ZNxdPU7f+erObUBy9T+ekrbnm+OJOeTW8s5sT7L/PU0ChWj4ljTOYbjNq7nAXDoukapve791t0fL7cHFYIrfFYTVFeXh4pKSkUFhYCzl4w8fHxHDhwoF76rH///qxevZqhQ4cCsHDhQnJzc3nttdeavG/s2LHcf//9TJ48GYAHH3yQzp078/jjj9d/gU3k3beequEPX5cyvFMQC4ZHu/09ENpVVO3glg0FKMCasfFEB/v8+4HmSE2RtrmjpgicdXM/WVcAwKcT4gk1yGdBdHyNXd88dvZ369aNPn36uGZ21qxZQ1JSUr0BEUBSUhLr1q0DwGKxsGHDBpKTk5u976abbuKNN95AVVVKSkr46KOPuPHGG1sVo2urDw2mz4RnfZFfjUOFYZ2CZEAkAlqoQUf/aAMOFfYVem9pvhBa5NF/DVatWsWiRYsYMmQIS5YsYdWqVQBMnDiRb7/9FoCXX36ZLVu20L9/fwYMGEDv3r2ZPXt2s/fde++9dOvWjYEDBzJy5Ej+8Ic/tKrIGrTdwNFfUxj+EveGk9UAXN/dBPhP3Bfy17iFttT1K/rWCyk0fz1nJW7v6pB9ipKTky9agg+wdu1a19979uzJxo0bG3x8U/fpdDpee+21dsV3YQPHEIOsCAoEJypsHCyxEWpQGNlZ28vihfCG9Pgg/pFt9moTRyG0KGD3PqszdWMB+WYHq8fESgPHALHiYAWrDpuZkGjit2mRvg5Hs6SmSNvcVVMEUGNX+cm6s9gc8NH4eKKCJKUsOjav1xT5C9ey/CqpKwoEqqpelDoTwp0OHz7MyJEjSU1N5aqrriInJ+eiY1asWMGQIUNITU0lOTmZZ555pt797777Lunp6aSlpTF48GDOnDnj0ZiD9QoDY4yowF5ZhSYCmAyKNNrAUfLAnrG/yMops4MEk47U+HMNGbUed2P8Ne6ObPr06TzyyCPs2bOHuXPnMn369IuO+fnPf87evXvZs2cP33zzDcuXL2fXrl0AbNmyheeee47PP/+cjIwMdu7c6ZVZO9fSfA8XW/vrOStxe1fA9inyNS03cBTuV7fVxnXdTeia2KdMiLZoqJP/oUOHOH36dL3jwsPDXX+vqKgAICEhAYBXXnmFxx57jJiYGABCQ0MJDvZ87Zu3mzgKoUUBX1P0wTEzL2dWMKmniYeHSH1JR2Z1qExZX0C5VWXFNbFcGik1ZE2RmqLW27FjB7NnzyYjI8P1s9TUVJYtW8awYcPqHfvJJ58wb948cnJyWLBgAQ8++CAAAwYM4Kc//SkbN26kqqqKm266iaeffvqizYbdWVMEzs/HpHUFVNtV1oyNI84kGxWLjktqihrRqfaDf1Zqijq8nT9YKLeqXBZpkAGR8LlJkyaxf/9+srOzWbp0qWulrt1uJysri23btrFjxw42bdrEG2+84fF4jDqFlDjnbNEeWYUmAlTAD4q02sDRH/PAxTUOfvOHZ9Hq5OO5AuuLUxH++H6D/8bdUfXo0YMTJ064bquqyvfff19vn8YLJSYmMm7cOLZu3Qo4N8K+5ZZbMBqNhIeHc+ONN/L11183+Fh3b/GSt+ZlwNmvyFNb7NT9TEtb/rTk9sSJEzUVj7zfntnCKODTZ8U1Dm5aX0CkUeHjCQlejKxp8+fPZ968eb4Oo1We/LqUf736PH995veMTQzxdTj1lFsdTFlfgM0B/7o+ztW4s44/vt/g2bglfdY2w4YN47HHHuPmm2/m/fffZ9GiRezYsaPeMTk5OfTp0weAkpISRo0axeLFixk7dizLly9ny5YtrF69GqvVyk9+8hMmT57MfffdV+853J0+AzhYYuVXXxbTNVTHP6+Ld+tz15HPmndJ3A1r7PoW8IMih6oybu1ZrA747IYETNLAsU3sqsrkdQVU2lS6hel569pYDDrtvJefHq/ihb3lpMcbWTwyxtfh+AUZFLXNoUOHmDFjBpWVlYSHh7Ny5Ur69u3LxIkTeeaZZ0hPT+fBBx9k06ZN6PV6bDYb99xzDw888ADgnF165JFHWLduHTabjXHjxvHSSy+h09Wf2PfEoOj8z/Hfro4hKdrY/IOE8EMyKGqCNHBsv5xSK3dvKXbd/l1ahKZmi+ZsK2ZvoZXfpkYwoYd24tIyGRRpmycGRQCL95bz8fEq4k06Xhsdc9GsqhAdgRRaN0GLDRz9rVZkX5GzMLP4338FYPVhMzaHNsbbP5jt7C20EqSDq7s0vLTZ397vOv4at9Cu+waFMzjWSEG1g99+VUK51b3XRX89ZyVu75I+RT6k1QaO/iSztuFbaryRrqE6vq+0symvxsdROW3McxZYX9U5mDCjnPJCNCVYr/DnK6PoGa7nWLmdJ3aVYrFr4wuOEJ4m6TNgWVYF7+SYmdkvjOlJYV6KrONQVZVb/ltIYY2Dt66NJavYyl/2lJMYpmflmFj0PmySqKoqMzYXkVtu57kro2QD2FaQ9Jm2eSp9Vue02c59/yumsMbBmG7B/CE9Uhqeig5D0mdNcC3Ll5miNjlldlBY4yAySKFHuJ7ru5voopHZopwyG7nldqKCFK7sFOTTWITwJ51D9SwYHkWoQWFTXg3Lsip9HZIQHieDIrTZwNGf8sD7Cp3bAgyONbJgwQIMOoVpfZ0zbqsOV2L34WTkhu+dqbMx3UxNrobzp/f7fP4at/APfaOM/GloFHoF/nXEzJqj5nY/p7+esxK3d0lNkQ9ptYGjv6grsk6JPTcTMy7RROdQHd9X2PnCR7NFdlVlY+3vvr67yScxCOHvhnYK4repEQC8sr+CLfnVPo5ICM+RmiK028DRX/xyUyHfV9h5fXQM/WPO9TWp6w3UI1zPm9d6v7bWsJs+AAAgAElEQVRo9xkLj3xVQrcwPf8YE3vR3lGiaVJTpG2erim60D8OV7L8YCVGHSwaEU1KnKSjhf+SmqImRAUpGHVQZlWptnXoMaLbFdc4+L7CjkkPfaPq93iqmy06UWFnc773Z4vObethkgGREO10e99QJvcMweqA3+8q5Xi5zdchCeF2MigCdIpybll+tTaKrf0lD5xZW080IMaIQafUi9t4fm3RIe/WFlXZVL485RyIXdet+RVn/vJ+X8hf4xb+R1EU5qSEc1XnIMqtKo99VUJhG66X/nrOStzeJTVFPqbFBo7+ILO2nmhwbMPbAYxLNHFJiI7jFXa2eHG2aPvpGqrsKgNiDHSXLuVCuIVeUXgiPYoBMQZ+qHLw269KqXRzc0chfElqimo9+00pG/NqeDw1gvGyDUSL3ftlEYdKbLwwIpqhCQ3XGHySW8WifeX0itCz4ppYr/Q6efyrEr46Y+HBweH8tHeox39fRyQ1Rdrm7Zqi85XUOLh/azEnK+0MTTCyYFi0pvY6FKI5ba4pqq4OjJUGrpkiWYHWYmabg+xSGzoFBsQ0Phszvodztii33DuzRUXVDnadtaBX4JqusupMtF6gXPfaKjpYx8Lh0cQEKew+a+X5PeV08O/XIkA0Oyjq1asXDz/8MEeOHPFGPD6jtQaO/pAHziq24VAhKcpAqMH5/jUUt1GncHttbdFbhytxePji+UV+NQ4VhnUKIjq4ZRlif3i/G+KvcWtdoFz32qNrmJ75w6Ix6WH9yWreONiy5o7+es5K3N6l2ZqivXv3Eh0dzZgxY5gwYQKffvqpN+LyOi02cNS6zPOaNjZnQg8TnWpni+oKoD3l/FVnQrRFoFz32qtfjJE/Do1Cp8A/ss18nFvl65CEaJcW1xTZbDb+/e9/M3fuXPR6PbNnz+b+++8nJETb9TctzbsfKrFy75fFXBZp4I1rYr0Qmf+bu72YjAIrz1wRxehGdp8/379zq1iyr5zeEXre8FBt0YkKG9M3FRFqUPhwXDzBeqlzaCupKdL2dc+XNUUXWnu8iuf3lqMDnrkyiqtkj0Ghce3qU2Q2m1m+fDl/+tOf6NOnD88++yzZ2dmMHz/erUH6UqeQupkibaTPtM7mUMkqbnrl2YUmJJpIMOk45sHZoo21s0Q/6hIsAyLRLoFw3XOXiT1DuCMpFAfwp29KOVC7KlUIf9PsoOj+++/nsssuIyMjg7fffptNmzZx++2387e//Y2ioiJvxOgVWmvgqPU88OFSGzV2SAzX16vbaSruIL3CtL7OlWCrDrm/tkhVVVfqbGxi61JnWn+/G+OvcWtdoFz33OnO5DBu6GGixg6/21XCyYqGmzv66zkrcXuXr+JutoFLz549ycrKIiYm5qL71q9f75GgfKGugWO+2cHZajuJ0tumSfsK6/Y7a9ksUZ0JPUL4R7aZo+V2/neqhh+5cXXY/iIrp8wOEkw6hsS1Li4hzhco1z13UhSFh1IiKKh2sOuMhce+KuHV0bHEtHCxgxBaIH2KzjNnWzF7C60sGhHN5Y303BFOv99VwrbTFualRTAusXX1FR8eM/NSZgWXRRr4+49i3FZbtHhvOR8fr+K2PqHcOyDcLc8ZyKSmSNu0VFN0PrPNwYPbSjhcaqNftIElI2MIMUgqW2iL7H3WAq6tPqSuqEkOVT2vk3XrB4839Agh3qTjSJmNrW6qLbI6VL7Il1VnQvhaqEHHgmHRdAnVcbDExtO7S7E5OvR3b9GByKDoPFpq4KjlPPCJCjtlFpV4k44uofVPoZbEHaxXmFpbW/TWYbNbaot2/mCh3KpyWaSBSyNbn/rU8vvdFH+NW3RssSZnc8fIIIWvzlhYsu9cc0d/PWclbu/SbJ+iQKK1Bo5alVl4btVZW3efn3jebNG205Z2x3SuN5EsBRZCCxLDDcy/MpogHaw9Uc2qw2ZfhyREs6Sm6DxbT9Xwh69LGd4piAXDoz0cmf/687elbDhZwwODw5nSjn3FPjhq5uX9FfSprS1q6wCr3OpgyvoCbA549/o414yfaB+pKdI2rdYUXeh/p2p46utSHMBjqRHcIHtLCg2QmqIWcM0UaSB9pmVtXXl2oYk9Q4gL1pHTztmiLfk1WB2QFm+UAZEQGjO6SzAPDHYufHhhbzk7z3h+/0Mh2koGRefRUgNHreaBz1TZ+aHKQZhBoXcDtTutibt+bVFlmzeUdMe2Hlp9v5vjr3GLwPLT3qFM7ROKQ4WZjz/L9430MNIyf/2sSdytI4Oi82itgaMW1dUTDYo1onfDUvqf9AwhNlhHdqmN7T+0frboB7OdvYVWgnRwdQu2GhFC+MY9/cO4tmswNodar/BaCC2RmqILTN1YQL7ZweoxsdLAsQFL9pXz79wq7ukf5tr5vr3eP2rmlf0V9I0y8LerW1db9HZ2JX//rpJruwbz1FCpgXEnqSnSNn+pKTpfSY2D6ZsKKbOqPJEeyY+lfYbwEZ/UFB0+fJiRI0eSmprKVVddRU5OzkXH5ObmMmbMGAYMGEDfvn255557sFicMwZ33HEHaWlprj96vb7ebtXvvvsu6enppKWlMXjwYM6cOdPumF3L8qukrqghmYXO/zct3e+sJSadN1u0oxWzRedv6yG9iYTQvuhgHbNqG6u+eqCCCqtcZ4W2eHRQNH36dB555BH27NnD3LlzmT59+kXHPPvsswwdOpSsrCy+++47MjMzef/99wF46623yMjIICMjg7feeouYmBjGjRsHwJYtW3juuef4/PPPycjIYOfOnW75ZquVBo5azAOXWxwcK7dj1EFydMODorbEHaxXuK2Ps7Zo5aGW1xbllNnILbcTFaRwZaf2dSDX4vvdEv4atwhc+/75IgNjDBTVOFhxsNLX4bSYv37WJO7W8digKD8/n+zsbKZMmQLAzTffzKFDhzh9+nS943r37k1ZWRmqqlJVVYXFYqF3794XPd/y5cuZNm0aRqPzH+NXXnmFxx57zLU3UWhoKMHB7a8p0VIDR63JLLKiAv2ijW7fgX5yrxBignUcbsVsUd0s0ZhuJgw62UZACH+gAA+lRKJT4KNjVRwusfo6JCFcPDYoOn78OD169HDdVhSFxMREjh8/Xu+4Rx99lKysLLp06ULXrl0ZP348I0aMqHeMxWLhnXfeYebMma6fHThwgP3793PllVcyePBgnnzySbcU7mmlgeO8efN8+vsbUre1R0oTm622Ne5gvcLUPi1fiWZXVT4/6Vza647UmRbf75bw17hF4Jo3bx6XRRm4pXcIDmDxvnLsflDa6q+fNYm7dXy++uyhhx4iLS2N06dPk5eXx5YtW1izZk29Yz766CN69uxJSkqK62cOh4OsrCy2bdvGjh072LRpE2+88UaDv+P8abj58+c3eXvD8sWc+uBlztbWFDV3fCDdziy0cuqDl9n79oseef5JPUMo+fivbF6xmK/OWJo8PuOslcIaB5WfvsK/l76gifenI94WwlPu7BdGvMm5P9qnuVW+DkcIwIOrz/Ly8khJSaGwsBBwFsXGx8dz4MABOnfu7Dquf//+rF69mqFDhwKwcOFCcnNzee2111zHjB8/nsmTJzN79ux6P5s2bRrTpk0D4PnnnycnJ4dly5bVf4GtXKFxqMTKvV8Wc1mkgTeuiW39C3eT+fPna2qEX2NXmfifs9hV+HhCPBHGhsfT7Y37XzlmXs+qoF+0gddHN74Sbf63Zaw/Wc2dyWHcmdz+VXBae79bypNxy+ozbfPH1WdQ/5zdkl/NU7vLCDMorB4TR6zJ59/TGyXXCO/ydNxeX33WrVs3+vTp45r1WbNmDUlJSfUGRABJSUmsW7cOcKbJNmzYQHJysuv+kydPsnXrVm6//fZ6j7vllltYv349AFarlY0bN9abSWorLTVw1JLviq3YVLg00tDogMgdJvcKISZI4WCJjZ1nGq4tqrKpfHnKmTq7rpv0JhLCX13dJZgrOwVRaVN5Pavc1+EI4dk+RYcOHWLGjBlUVlYSHh7OypUr6du3LxMnTuSZZ54hPT2d48ePc9ddd5GXl4fVamXMmDG8+uqrroLq5557jv379/PPf/6z3nOrqsojjzzCunXrsNlsjBs3jpdeegmdrv4/2K39NuVQVcatPYvVAZ/dkIDJIAW8AKsPV/LGwUpu6h3CnMERHv1d/5djZmlWBf2jDbzWwGzR5yereebbMgbGGHh1tO9m8zo6mSnSNn+dKbpQXqWNGV8UYXHAkpHRpMW3byWpEC3R2PVNmjc2QBo4XuzRHSV8fdbCk5dHMqabZ3sCVdlUbttYQIlF5S/Dohh2Sf3ZoMe/KuGrMxYeHBzOT9uxIa1omgyKtK2jDIoAVh2qZMWhSnqG61l+TSxGWU0qPEw2hG0FLTRw1FKxq11VOVDc/MozcE/cIQaFW+v6Fl2wEq2o2sGusxb0ClzT1X2DMy29363hr3F3ZC1pWrtixQqGDBlCamoqycnJPPPMMxcdc+jQIUJDQ3n00Ue9EbbXNHTO3tonlMQwPccr7Lx7xOyDqJrnr581ibt1ZFDUAK00cNSKo2U2zDaVrqE64k3e2YX+xl6hRAUpfFds4+uz52qLvsivxqHCsE5BRAfL6Su0pyVNa3/+85+zd+9e9uzZwzfffMPy5cvZtWuX63673c69997r6vPW0QXpFeakONPyqw5XcqpSrr3CNyR91oBlWRW8k2NmZr8wpie5Z38vf7bmqJm/7q9gXHcT89IjvfZ738muZNl3lQyIMfDqKGdt0a++LOJgiY2nLo/kWg+n8QKdpM9aLz8/n8GDBze76vZ8p0+fZtiwYWzevNnVuPbPf/4zISEhlJeXU1FRwfPPP3/R4zpS+qzOn74pZVNeDSMuCeK5K6NatQ+iEK0h6bNW0EoDR63YV+hMnQ1uJnXmbjf2DiEqSCGr2MbusxZOVNg4WGIjzKAwsrOsOhPa09KmtQCffPIJgwYNolevXsydO9c1INq7dy8bNmxg7ty5XotbK+4bGE6YQWHHDxa2nm75PohCuIsMihrQqTZFdNaHW31oJQ+sqmqLOlnXcWfcoQYdv7js3J5oddt6/KhrsNu3GdHK+91a/hq3gEmTJrF//36ys7NZunQp27dvx2q1MmvWLF5//XUURWm2s7uWGn229Hbdzxq6/29LFnJXP+fs/Jwnn+WZP/s+3joTJ07UxPvX2ttNvd9avu3p97sxkj5rgBYaOGql4dbJChvTNhURHaTw4bj4Zqez3R232ebg1o2FlFlUTHqFarvqkWW7Wnm/W0uaN2pLS5vWXmjOnDl07dqVqVOnkp6eTni4cyf5kpISVFXl1ltvZenSpfUe46/ps+bOWbuq8usvizlcauO2PqHcOyDci9E1Tq4R3uWr5o0yKGpAcY2Dm9YXEGlU+HhCgoci8w/rTlTxlz3ljO4czDNX+uYfybezK/n7d87dtBNMOv51fRw6qTXwOBkUtc2wYcN47LHHuPnmm3n//fdZtGgRO3bsqHdMTk4Offr0AZwDn1GjRrF48WLGjh1b77inn36ayspKFi5ceNHv8ddBUUscLLby6/8Vo1Pg7z+K5dJIaY0i3EtqilohKkjBqIMyq0q1rUOPGZvVmtSZp9zUO4RIo3MQdF13kwyIhKatWrWKRYsWMWTIEJYsWcKqVasAZzrg22+/BeCVV14hJSWFtLQ0Ro8ezaxZsy4aEAWyfjFGJvcKwa7Ci/vK3bLZtxAtIYOiBugU5dyy/GrfFFtrpVbEVWQd27JBkSfiDjXomDM4gkExRm7qHeL25wftvN+t5a9xd2TJycls376dvXv3sm3bNvr27QvA2rVrSU9PB+DFF19k3759ZGRkkJmZyQMPPNDgcz311FMNzhL5s5aes3f3CyMmSGFfkZXPvq/2cFTN89fPmsTdOjIoaoQWGjj6WmG1nZOVdkx6hT5Rvp2+/nF3E6+MjnHtTSeE6NgignT8eqCznmhpVgWllsC9FgvvkZqiRjz7TSkb82p4PDWC8T08MzuhdXU7WF8eb2TRyBhfhyO8TGqKtK0j1xTVUVWVudtL2FNoZVJPEw8P8V6fNNGxSU1RK7lminy4LN/X9hXV9SeSDRqFEN6nKApzUyIwKPDJ8WoO1F6ThPAUGRQ1wtcNHLWQB86srSdKaWE9EWgj7raQuIXwjtaesz0jDPyidi/ExfvKsTl8k9zw18+axN06MihqhBYaOPqS2eYgp9SGXoH+Mb5beSaEEL/sG8YlITqOlNn48FiVr8MRHZjUFDVCCw0cfenrMzU8+lUp/aMNvH514L1+ITVFWhcINUXn2366ht/tKiVEr7BqTKyrxEGItpCaolaqW+UUqPufnasnklkiIYTvjewczFWdg6iyq7x6oMLX4YgOSgZFjfB1A0df54HP1RO1rsja13G3lcQthHe055x9YFAEJj1szq9h15kaN0bVPH/9rEncrSODokZooYGjr1gdKlnFzkHRoFYUWQshhCddEqrnjiTnhrEvZVZQY+/Q1R/CB6SmqAlzthWzt9DKohHRXJ4QOMvSDxRZuW9rMT3D9bw1Js7X4QgfkZoibQu0mqI6NofK3VuKyC23c0dSKDP6aWPDWOFfpKaoDVwzRQFWV5RZZAF8u9+ZEEI0xKBz9i4C+GeOmZMVNh9HJDoSGRQ1wZcNHH2ZBz6331nrZ8ckf+1d/hq3CFzuOGeHxAUxLtGE1QEvZnpnw1h//axJ3K0jg6Im+LqBoy84VJVMWXkmhNC4Xw8IJ8KosPuslS/yvVt0LTouqSlqwtZTNfzh61KGXxLEgmHRbo5Mm46V2ZixuYgEk453r49DURRfhyR8RGqKtC1Qa4rO90luFYv2lRMXrGPVmFjCjPI9X7SM1BS1wbmZosDpal3XnyglzigDIiGEpk3saWJAjIHCGgcrDlX6OhzRAcigqAm+bODoq3xqZqGzyHpwG5fiS/7au/w1bhG43HnO6mo3jNUBHx6tIrvUcxvG+utnTeJuHRkUNcHXDRx94dxMUeC0IBBC+K++UUZuujQEB77dMFZ0DFJT1IypGwvINztYPSaWxHCDGyPTnh/Mdn6xsZBwo8LH4+PRSfosoElNkbZJTdE5lVYHd3xRREG1g+5hemb1D2N0l2ApARCNkpqiNnItyw+AuqK6WaJBsUYZEAkh/EaYUcfTQ6PoHqbnZKWdJ3eXcf/WYvYXeS6dJjomGRQ1w1cNHH2RT91XW0+U0o6tPSR/7V3+GrcIXJ46ZwfGGll5bSwPDg4nOkjhQLGN+7cW8+TXpXzvhgaP/vpZk7hbRwZFzfBlA0dvy5R6IiGEHzPoFH7aO5S3fxzHL5NCCdbDl6dquOOLIl7cV05xTce/jov2kZqiZnxwzMzLmRVM6mni4SGRboxMW0otDm78rACjDtZOSCBIL+mzQCc1RdomNUXNK6i28+bBStadqMYBhOgVbusbys8uDSXEINe4QCY1RW3UyVS7LL+DzxTV5d4HxBhlQCSE6BDiTXoeTY1k+TWxDL8kiCq7yoqDlfxyUyFrj1dh79hzAqINZFDUDF81cPR2PjXTtd9Z+7b2kPy1d/lr3CJw+eKcvTTSwIJh0SwZGU1SlIGCagfP7y3nrs1F7PihpkV7p/nrZ03ibh0ZFDXDlw0cvWlfUW3TRtnvTAjRQaXFB7H06hieSI+kc6iO3HI783aW8tCOEg6VyEo1ITVFzXKoKuPWnsXqgM9uSMDUAfPQ1TaVievOoqrwyYR42T9IAFJTpHVSU9Q+FrvKR7lVrD5cSbnV+c/gdd2Cuat/OF1C9T6OTnia1BS1kU5Rzi3Lr+6Ys0XflVixq3BZlEEGREKIgBCkV/j5ZaH888dx3HpZKEYdbMyrYfqmQl47UE6ZpWPXkYqGyb+ALeCLBo7ezKe6q54IJH/tbf4atwhcWjtnI4J0/GpgOKvHxDG2uwmrA949UsXUzwv5vxwzNXbnLJLW4m4pibt1PD4oOnz4MCNHjiQ1NZWrrrqKnJyci47Jzc1lzJgxDBgwgL59+3LPPfdgsThrXO644w7S0tJcf/R6PZ9++ikAf/zjHxk4cCApKSmkpKTwySefeOQ1+KqBo7fU1ROlSD2RECJAdQ7V87v0SP7+oxjS441UWFWWZlUwfVMhG05W06HrTISLx2uKhg8fzmOPPcaUKVN4//33Wbx4Mdu3b693zN13301sbCwLFy7EZrMxatQoHnjgAaZOnVrvuH379jFmzBhOnTqF0Wjkiy++YOTIkQQHB7Nv3z5GjRpFfn4+4eHh516gG/Luy7IqeCfHzF39wvhlUli7nktrbA6VSesKqLKrrBkbR5xJcunCSWqKtE1qijxHVVW+Pmth6YEKjpY7vwwnRRn4XXokvSI69h6YgcInNUX5+flkZ2czZcoUAG6++WYOHTrE6dOn6x3Xu3dvysrKUFWVqqoqLBYLvXv3vuj5li9fzrRp0zAanTMa1157LcHBwQCkpKQQFBREQUGB219H3bL8Mx1wpuhImY0qu0rXUL0MiIQQAueA88pOwfz9mlh+mxpBvEnH4VIbv/6ymP+dqvF1eMKDPDooOn78OD169HDdVhSFxMREjh8/Xu+4Rx99lKysLLp06ULXrl0ZP348I0aMqHeMxWLhnXfeYebMmQ3+rrfeeotevXrRq1cvt78OXzRw9FY+dV9h3dYe7kmdSf7au/w1bhG4/Omc1SsKE3qEsHpMHKaNr1FlV3ni61LeOFiBw08WbvvT+32+DltT1BIPPfQQaWlpnD59mry8PLZs2cKaNWvqHfPRRx/Rs2dPUlJSLnr8l19+yZNPPsk777zT4POf/+bOnz+/1bc/WvoC4Gzg2JbHa/n2319cyKkPXnYNitr7fFu3btXU6+votz39fgshIMSg8KMuwfxqQDg6YPVhM7/bWUq5VVaodTQerSnKy8sjJSWFwsJCwJmnjY+P58CBA3Tu3Nl1XP/+/Vm9ejVDhw4FYOHCheTm5vLaa6+5jhk/fjyTJ09m9uzZ9X7Hjh07+MUvfsHHH39MamrqRTG4I+9eXOPgpvUFRBoVPp6Q0K7n0hJVVZmyvoBii8o/xsTSPVxy5eIcqSnSNqkp8o3dZyz86ZtSyqwq3cP0PHtllNQZ+SGf1BR169aNPn36uGZ91qxZQ1JSUr0BEUBSUhLr1q0DnGmyDRs2kJyc7Lr/5MmTbN26ldtvv73e477++mt+8YtfsGbNmgYHRO4SFaRg1EGZVaXa5h9Tpi1xstJOsUUlJkihW5jUEwkhRHOGdgpi2dWxXBZp4GSlnV9/WcyX+dW+Dku4icfTZ6tWrWLRokUMGTKEJUuWsGrVKgAmTpzIt99+C8DLL7/Mli1b6N+/PwMGDKB37971ZoRWrVrF5MmTLxrZ3XfffdTU1DBr1izXkv0DBw64/TX4ooGjN1IYmbWbwA6OC0JR3NOp219TLxK3EN7hr+fs+XF3CdPz6qgYftwtmCq7ypO7y1j+XYUmN5jtCO+3N3l8zi85OfmiJfgAa9eudf29Z8+ebNy4sdHn+N3vftfgz3ft2tX+AFsoIURPvtnBmSoHieHNH98eVodKtd3zHy53F1kLIZwOHz7MnXfeidlsJiwsjLfeeos+ffrUO2bFihW89NJLKIpCVVUV06ZN44knngCcPdjee+899HrnDO6f//xnJk2a5PXXIRpnMij8IT2SpKgqlmVV8I9sMzmlNn5/eSQRsjOA35K9z1ro2W9K2ZhXw+OpEYzvEeKGyBpmV1Ue2VHCngIrY7oFMyM5zGO1PlM3FpJvtrPs6hiSo2VgJOqTmqK2a0l/toqKCldPtYqKCgYOHMh7773HlVde6bUebMI9dp+trTOyqHQL0/PsFVH0jpQ6Iy2Tvc/aqW6rD08vy3/vSBUZBVZU4PO8GqZ/UcTze8r4wezetF1htZ18s50QvcJl8uEVwm1a2p/t/AFORUUFAAkJzoUc3urBJtxjaIKzzqhPpIG8Sju//l8xW6TOyC/JoKiFvNHA8ViZjTcOOi+Ol25fxg09TACsPVHNtE2F/DWznCI3Dcrq6okGxRox6NxTTwSSv/Y2f427I2tpfzaATz75hEGDBtGrVy/mzp3bYNNaT/Zg8wV/PWebi7tLqJ5XauuMqu0qT2mkzqijvt+eIoOiFvJ0A0ebQ2V+RhlWB0zsYaJvlIHHUiN569pYftwtGJsD1hyrYurnBfwtq6LdOzjvc+MmsEKItpk0aRL79+8nOzubpUuXXpRi83QPNrnt3p5gS55fwB/SI5k9MJzTH7zM839Z4OxnZOl4Pe608H57ogeb1BS10KESK/d+WcxlkQbeuCbWDZHVt+pQJSsOVXJJiI4V18QSdkGh3tHaWaRtp52bt4YZFH5xWSi3XBZCqKH1Y9t7thSRXWpjycho0uKD3PIaRMciNUVt09L+bBeaM2cOXbt25be//S3gnR5swnO+PWvhj7V1Rl1Dnf2MLpVSBc2QmqJ26lRXU+SB9Fl2qZW3DlcC8Hha5EUDIoBLIw38+cpoXh8dw9AEI5U2lRWHKrltYyH/yjFT04rVapVWB0dKbRgU6C8F1kK4VUv7s+Xk5Lj+XlJSwueff05aWhrgvR5swnPSz6szyjfbmS11Rn5BBkUt5KkGjha7yvxvy7CrMKV3iGvWprFpvv4xRl4YEcOSkdEMijFSalF5PauC2z8v5N/HzFgdzce2v9iKA0iKNmAyuK+eqKm4tU7iFu7Ukv5sr7zyCikpKaSlpTF69GhmzZrF2LFjAe/1YPMFfz1n2xJ3XZ3RdefVGf3dy3VGgfR+u4PM5bVQXQPHfLODs9V2Et20TP6tw5UcLbfTLUzPPf1b3gApLT6Iv44ysvOMhTcOVjpTYZkV/N8RM3ckhXF9ogl9Iw0ZM+v6E8VK2kwIT2hJf7YXX3yx0cd7sweb8CyTQeH36ZEkR1fxelYFb2ebyS618UR6JBFBMi+hNVJT1ApzthWzt9DKohHRXKuMwz0AAA55SURBVJ7Q/gHFgSIrv9laDMDLo2IY1MaiZ4eq8uWpGt48WMnxCmd6r0e4npn9wri6SzC6CwZHda/juSujGNk5uH0vQnRYUlOkbVJT5H+kzkg7pKbIDVxbfbihrqjaprIgowwH8PPLQts8IALnLNY1XU2suDaWeWkRdAnVcaLCzh93l3Hvl8Xs+KGGurGvxa6SVeycKRooK8+EEMJr0hOC+NvVsfSNOldntFnqjDRFBkWt4M4GjssPVvB9pZ1eEXpm9Au76P625FP1isK4xBBWjYljbkoE8SYd2aU25u0s5TdbS8gosHCoxIrVAb0i9ER5YOpW8tfe5a9xi8Dlr+esu+LuXFtndH13Z53RH3eXsSyrolWLZVoj0N/v1pJ5u1ZwVwPHPQUW1hytQqfAvLRIgvXuLXY26hRu7BXC+EQT/86t4u3sSvYXW5m7vYS4YOdrkHoiIYTwjWC9wu/SnPumvZ5VwTs5ZtaeqOInPUK4sVcIl4TqfR1iwJKaolbYeqqGP3xdyvBLglgwLLpNz2G2ObhrcxGnzA7uSAplRj8P7y5b+zvfP1LFv46YqaxdOff79Eiu727y+O8W/ktqirRNaoo6hj0FFl47UMHhUhvgTN+M6hLMTb1DSI0zojSyYEa0T2PXN5kpaoW6maKzVW1Pn71+oIJTZgd9owxMS7o4beYJoQYd05PD+GnvEN49YuZMlYPRUmAthBA+lxofxLKrY8gqtvHBMTOb82v48pTzz6URem7qHcr13U1ub58iGiY1Ra3Q3gaOu87U8Mnxagy1aTNjE3uOeSKfGhmk4+7+4fwuPdJjHzDJX3uXv8YtApe/nrOejFtRFAbGGnni8ijevT6OO5JCiQnWcbTczqJ95fxsQ4HzC3Vl6//tkfe7dWSmqBUubODYmoFFudXBwj3lAMzoFybLMIUQQlwkzqRnRr9wpiWFsTm/hg+Omfmu2Ma/jph594iZkZ2DmNI7lPR4Sa15gtQUtdLUjQXkmx2sHhPbqgaO8zPKWP99Nf1jDPz1qhi37kwvhCdITZG2SU1R4DhYbOWDY1VsyqumbkOFXhF6buoVwvWJpjbtfxnopE+Rm9Qtyz/TirqibadrWP99NUE6Z9pMBkRCCCFaql+Mkd+lR/Lu9fHMTA4j3qQjt9zOkswKfvbfQl7ZX87JCpuvw+wQZFDUSq1t4FhS4+CFvc602T39w+nRwtklyQN7l8QthHf46zmrhbhjTc5FM/93XRxPXh7JoFjn5uDvH63il5uKeHxnCbvO1OA4LwGkhbjbQmqK/ERrGzi+lFlOcY2DIXFGbr40xJOhCSGECAAGncKYbibGdDNxuMSZWvs8r5qvfrDw1Q8WEsP03NQ7hHGJ0naltaSmqJU+OGbm5cwKJvU08fCQyCaP3ZRXzZ++KcOkV3jzmli6hElDLuE/pKZI26SmSJyvpMbB2hNVfHSsyvWlPdSgMLa7iRGXBDEo1kiYUZJDdaRPkZt0MrVspqiw2s6L+5xps9kDw2VAJIQQwmOig3Xc3jeMX1wWytbTNXx4rIq9hVY+yq3io9wqdECfKAND4oykxAWREmf0yFZP/k7ekVZqSQNHVVVZtLecMqvKFQlBTOrZ+ilMyQN7l8QthHf46znrL3EbdM4Nwl+6KoblP4ohfvPrDIgxoChwuNTGe0ereOLrUm78rIAZXxTy4r5yNuVVU1jd/o3O3UlqivxESxo4rv++mu0/WAgzKDyaGiG9JIQQQnhdnygjlycEMW90LFU2lQPFVvYVWthbaCWr2MqxcjvHyp0zSQDdw/SkxBlJrZ1N6hyAe7BJTVErOVSVcWvPYnXAZzckXNTA8UyVnRlfFFFpU5mXFsG4RCmuFv5Jaoq0TWqKRHtY7CoHS6zsLbSyr9BKZpGVanv94cAlITpS4owMiQtiSJyR7mH6DvMlX2qK3ESnKCSYdOSbHZytttdr4KiqKgv3lFFpU7mqcxBjZcNVIYQQGhSkV2pri4IAsDlUsktt7C20srfQwr5CKz9UOdhwsoYNJ2sAiAnWMSTOWPsniF4RenQdZJBUR2qK2qCxBo4fH69m91krkUEKD6W0L23mL/nrC0nc3uWvcYvA5a/nbEeP26BT6B9j5NY+ocwfFs3HE+JZ/qMYHhgUztVdgokOUiiucbA5v4aXMiuYubmIGz8rYO72Yv66v5z/nKjiYImVapt7kk9SU+RHGmrgmF9p5/UDFQA8lBJBnCnwcrFCCCE6Br2i0CfKSJ8oI1MudWZCTlTY2Vc7k7Sn0EpBtYOMAisZBVbX43RAtzA9l0YauCzS4PrvJaE6v5hVkpqiNliWVcE7OWbu6hfGL5PCcKgqD24rYV+RlWu7BvPUUKnFEP5Paoq0TWqKhC+pqsrZagdHymwcLbO5/nuiwo6jgVFFqEHh0oi6QZJz0NQ70kC4j3onSU2RG9Utyz9TO1O05mgV+4qsxATreDAlwpehCSGEEB6nKAqdQvR0CtEz4pJg189r7ConKuoPlI6U2SmucbC/2Mr+Ymu957kkRFdvRunSSAPdwvQ+2yNUaora4PwGjsfLbfztO2fa7NEhEW5rhtXR89daI3EL4R3+es5K3C0TrFfoG2VkXGIIswdG8MKIGD4cF88HY+N5YUQ0sweGMz7RRFKUAaMOfqhysP0HC//INvP0N2Xc8UURN/znLCN/9SR//raUVYcr2ZxfzdEyGzV2zye2ZKaoDepmik6b7SzIKMPqgPGJJkZ2Dm7mkUIIIUTgiTXpiDUFMTQhyPUzm0Mlr9J+UQruhyoHhdWO2lVvNa7jdUDnUD09wmv/RBhq/24gKkhxS7sAqSlqg+IaBzetL3DdTjDpePPaWJ/lRoXwBKkp0japKRIdVbnVwbEy2/+3d38hUed7GMcfx5LcHI96apEFp5modRuYsgNaoyR0c6QLgzj2R9qgvIi8KDqQQRClFMHSRdDNuQ2hDmVCkd1IUEkhXQgHacypIP/kNpDkRSr+WeezF7VyglpH0/nuTO/XVb8h5cHi8ZHvz/lpcHRGA6MzGhj98Odfx2b0pWdJeJdnzA4kX06minKWyefN1A/fff4ojnuKFtHfsjK03CNNf/zXOVniZRABALAIvMs9H99D6dPXp2ZMv47PaOD9hxu6Bz8OpoHRGb2fNkVGflNk5LdPPiYz48NvwxX932D6KW+5Sr7wM19afyefnJyc+y8twB9v4ChJO9dkq/T7xT02m5yc1Llz5xb1cyYDuZMrVXPj6y1Vty21VP0/S+7k+lLurMwM+b3LVPnDCv3840qd+keu/lNZoLYdq9T6z7/rUnme/r3Rq3+tzVbp6iwVfudR3KSB0Rk9jk3pvy/H9cv/3s8+1uRzvonjMwALk+b1kNLoNuDrfK7f0n4UAQAAJCKtj88AAAASxSgCAAAQowgAAEBSGo+i58+fq7y8XCUlJaqoqNDLly9dR0rIu3fvVFVVpWAwqA0bNqi6ulqxWMx1rIQ1NTXJ4/Gop6fHdZSETUxMqL6+XqFQSJs2bdLhw4ddR0rI1atXFQwGFQqFFAwGdf36ddeRkAR0mxt0W/I47TZLU1u2bLHW1lYzM2tpabFwOOw4UWJGRkass7Nz9rqhocH279/vMFHiurq6bMeOHRYIBCwSibiOk7C6ujo7ffr07PXw8LDDNImZnp627Ozs2a9zd3e3ZWdnWzwed5wMS41uSz66LXlcd1tajqKhoSErKCiYvY7H41ZQUGBv3rxxmGphbt++bZWVla5jzGliYsLC4bD19fWZ3+9PmeIYGRmxvLw8m5qach1l3tatW2ePHz82M7OHDx9acXGx40RYanRb8tFtyeey29Ly+Ky/v18+n2/2OiMjQ0VFRerv73eYav7i8bguX76smpoa11HmdObMGR04cEBr1qxxHWVeotGoCgsLdfz4cW3cuFEVFRW6f/++61gJaW5uVnV1tfx+v3bt2qXm5mbXkbDE6Lbko9uSz2W3peUoShfHjh2T1+vV0aNHXUf5U52dnerq6lJ9ff3sa5Yib38Vj8cVjUa1bds2dXd369KlS6qpqfnLP1NqdHRUe/fuVVtbm/r6+nTnzh3t3r1bY2NjrqMBc6Lblh7dtjBpOYp8Pp8GBgZmr81Mg4ODKbX0T5w4oRcvXujGjRuuo8ypo6NDz549UyAQUCAQ0OvXr1VVVaV79+65jjYnv9+vzMxM7du3T5JUVlamwsJC9fb2Ok72554+fSqv16twOCxJKi8v18qVK1PqJlDMH92WXHRb8jnvtqQd1CVZWVmZ3bx508w+3Iy4detWx4kSd+rUKdu+fbuNj4+7jrIgqXTubmZWUVFh7e3tZmYWjUYtPz//L39DYiwWs9zcXItGo2Zm1tPTY7m5ufb27VvHybDU6DZ36Lal57rb0vYxH9FoVIcOHdLY2JhycnJ05coVrV+/3nWsOUUiEYVCIRUXF2vFihWSpLVr16q1tdVxssQFAgHdvXtXwWDQdZSEvHr1SnV1dRoeHpaZ6cKFC9q5c6frWHO6du2azp8/L4/Ho3g8rsbGRu3Zs8d1LCwxus0dui05XHZb2o4iAACA+UjLe4oAAADmi1EEAAAgRhEAAIAkRhEAAIAkRhEAAIAkRhEAAIAkRhEAAIAkRhEAAIAkRhEc6O3t/eQZTk1NTaqtrXWcCgC+Dt2W+jIbGxsbXYfAt2XVqlVavXq1Tp48KZ/Pp4sXL6qtrU1ZWVmuowHAgtFtqY/HfMCZgwcPqqWlRY8ePdLmzZtdxwGARUG3pS6Oz+DE1NSUIpGI8vPzFYvFXMcBgEVBt6U2RhGcaGhoUGlpqdrb23XkyBENDQ25jgQAX41uS23LXAfAt+fWrVvq6OjQkydPlJWVpbNnz6q2tlYPHjyQx8NOB5Ca6LbUxz1FAAAA4vgMAABAEqMIAABAEqMIAABAEqMIAABAEqMIAABAEqMIAABAEqMIAABAkvQ7bflFTBU0jM0AAAAASUVORK5CYII=' style='max-width:100%'/><center/>" ], "text/plain": [ " Valid_y_nll\n", " I : Curve.Valid_y_nll (y)\n", " Train_y_nll\n", " I : Curve.Train_y_nll (y)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import neukrill_net.plotting as pl\n", "pl.monitor_channels(m, [\"valid_y_nll\"], x_axis = \"epoch\") + pl.monitor_channels(m, [\"train_y_nll\"], x_axis = \"epoch\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_curves(model, args):\n", " curves = None\n", " for c in args:\n", " channel = m.monitor.channels[c]\n", " c = c[0].upper() + c[1:]\n", " if not curves:\n", " curves = hl.Curve(zip(channel.example_record,channel.val_record),group=c)\n", " else:\n", " curves += hl.Curve(zip(channel.example_record,channel.val_record),group=c)\n", " return curves" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ 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' style='max-width:100%'/><center/>" ], "text/plain": [ " Mean_update_h1_W_kernel_norm_max \n", " I : Curve.Mean_update_h1_W_kernel_norm_max (y)\n", " Mean_update_h1_W_kernel_norm_mean \n", " I : Curve.Mean_update_h1_W_kernel_norm_mean (y)\n", " Mean_update_h1_W_kernel_norm_min \n", " I : Curve.Mean_update_h1_W_kernel_norm_min (y)\n", " Mean_update_h1_b_norm_max \n", " I : Curve.Mean_update_h1_b_norm_max (y)\n", " Mean_update_h1_b_norm_mean \n", " I : Curve.Mean_update_h1_b_norm_mean (y)\n", " Mean_update_h1_b_norm_min \n", " I : Curve.Mean_update_h1_b_norm_min (y)\n", " Mean_update_h2_W_kernel_norm_max \n", " I : Curve.Mean_update_h2_W_kernel_norm_max (y)\n", " Mean_update_h2_W_kernel_norm_mean \n", " I : Curve.Mean_update_h2_W_kernel_norm_mean (y)\n", " Mean_update_h2_W_kernel_norm_min \n", " I : Curve.Mean_update_h2_W_kernel_norm_min (y)\n", " Mean_update_h2_b_norm_max \n", " I : Curve.Mean_update_h2_b_norm_max (y)\n", " Mean_update_h2_b_norm_mean \n", " I : Curve.Mean_update_h2_b_norm_mean (y)\n", " Mean_update_h2_b_norm_min \n", " I : Curve.Mean_update_h2_b_norm_min (y)\n", " Mean_update_h3_W_kernel_norm_max \n", " I : Curve.Mean_update_h3_W_kernel_norm_max (y)\n", " Mean_update_h3_W_kernel_norm_mean \n", " I : Curve.Mean_update_h3_W_kernel_norm_mean (y)\n", " Mean_update_h3_W_kernel_norm_min \n", " I : Curve.Mean_update_h3_W_kernel_norm_min (y)\n", " Mean_update_h3_b_norm_max \n", " I : Curve.Mean_update_h3_b_norm_max (y)\n", " Mean_update_h3_b_norm_mean \n", " I : Curve.Mean_update_h3_b_norm_mean (y)\n", " Mean_update_h3_b_norm_min \n", " I : Curve.Mean_update_h3_b_norm_min (y)\n", " Mean_update_h4_W_kernel_norm_max \n", " I : Curve.Mean_update_h4_W_kernel_norm_max (y)\n", " Mean_update_h4_W_kernel_norm_mean \n", " I : Curve.Mean_update_h4_W_kernel_norm_mean (y)\n", " Mean_update_h4_W_kernel_norm_min \n", " I : Curve.Mean_update_h4_W_kernel_norm_min (y)\n", " Mean_update_h4_b_norm_max \n", " I : Curve.Mean_update_h4_b_norm_max (y)\n", " Mean_update_h4_b_norm_mean \n", " I : Curve.Mean_update_h4_b_norm_mean (y)\n", " Mean_update_h4_b_norm_min \n", " I : Curve.Mean_update_h4_b_norm_min (y)\n", " Mean_update_h5_W_kernel_norm_max \n", " I : Curve.Mean_update_h5_W_kernel_norm_max (y)\n", " Mean_update_h5_W_kernel_norm_mean \n", " I : Curve.Mean_update_h5_W_kernel_norm_mean (y)\n", " Mean_update_h5_W_kernel_norm_min \n", " I : Curve.Mean_update_h5_W_kernel_norm_min (y)\n", " Mean_update_h5_b_norm_max \n", " I : Curve.Mean_update_h5_b_norm_max (y)\n", " Mean_update_h5_b_norm_mean \n", " I : Curve.Mean_update_h5_b_norm_mean (y)\n", " Mean_update_h5_b_norm_min \n", " I : Curve.Mean_update_h5_b_norm_min (y)\n", " Mean_update_h6_W_col_norm_max \n", " I : Curve.Mean_update_h6_W_col_norm_max (y)\n", " Mean_update_h6_W_col_norm_mean \n", " I : Curve.Mean_update_h6_W_col_norm_mean (y)\n", " Mean_update_h6_W_col_norm_min \n", " I : Curve.Mean_update_h6_W_col_norm_min (y)\n", " Mean_update_h6_W_norm \n", " I : Curve.Mean_update_h6_W_norm (y)\n", " Mean_update_h6_W_row_norm_max \n", " I : Curve.Mean_update_h6_W_row_norm_max (y)\n", " Mean_update_h6_W_row_norm_mean \n", " I : Curve.Mean_update_h6_W_row_norm_mean (y)\n", " Mean_update_h6_W_row_norm_min \n", " I : Curve.Mean_update_h6_W_row_norm_min (y)\n", " Mean_update_h6_b_norm \n", " I : Curve.Mean_update_h6_b_norm (y)\n", " Mean_update_softmax_W_col_norm_max \n", " I : Curve.Mean_update_softmax_W_col_norm_max (y)\n", " Mean_update_softmax_W_col_norm_mean\n", " I : Curve.Mean_update_softmax_W_col_norm_mean (y)\n", " Mean_update_softmax_W_col_norm_min \n", " I : Curve.Mean_update_softmax_W_col_norm_min (y)\n", " Mean_update_softmax_W_norm \n", " I : Curve.Mean_update_softmax_W_norm (y)\n", " Mean_update_softmax_W_row_norm_max \n", " I : Curve.Mean_update_softmax_W_row_norm_max (y)\n", " Mean_update_softmax_W_row_norm_mean\n", " I : Curve.Mean_update_softmax_W_row_norm_mean (y)\n", " Mean_update_softmax_W_row_norm_min \n", " I : Curve.Mean_update_softmax_W_row_norm_min (y)\n", " Mean_update_softmax_b_norm \n", " I : Curve.Mean_update_softmax_b_norm (y)\n", " Train_h1_kernel_norms_mean \n", " I : Curve.Train_h1_kernel_norms_mean (y)\n", " Train_h2_kernel_norms_mean \n", " I : Curve.Train_h2_kernel_norms_mean (y)\n", " Train_h3_kernel_norms_mean \n", " I : Curve.Train_h3_kernel_norms_mean (y)\n", " Train_h4_kernel_norms_mean \n", " I : Curve.Train_h4_kernel_norms_mean (y)\n", " Train_h5_kernel_norms_mean \n", " I : Curve.Train_h5_kernel_norms_mean (y)\n", " Train_h6_col_norms_mean \n", " I : Curve.Train_h6_col_norms_mean (y)\n", " Train_h6_row_norms_mean \n", " I : Curve.Train_h6_row_norms_mean (y)\n", " Train_y_col_norms_mean \n", " I : Curve.Train_y_col_norms_mean (y)\n", " Train_y_row_norms_mean \n", " I : Curve.Train_y_row_norms_mean (y)\n", " Valid_h1_kernel_norms_mean \n", " I : Curve.Valid_h1_kernel_norms_mean (y)\n", " Valid_h2_kernel_norms_mean \n", " I : Curve.Valid_h2_kernel_norms_mean (y)\n", " Valid_h3_kernel_norms_mean \n", " I : Curve.Valid_h3_kernel_norms_mean (y)\n", " Valid_h4_kernel_norms_mean \n", " I : Curve.Valid_h4_kernel_norms_mean (y)\n", " Valid_h5_kernel_norms_mean \n", " I : Curve.Valid_h5_kernel_norms_mean (y)\n", " Valid_h6_col_norms_mean \n", " I : Curve.Valid_h6_col_norms_mean (y)\n", " Valid_h6_row_norms_mean \n", " I : Curve.Valid_h6_row_norms_mean (y)\n", " Valid_y_col_norms_mean \n", " I : Curve.Valid_y_col_norms_mean (y)\n", " Valid_y_row_norms_mean \n", " I : Curve.Valid_y_row_norms_mean (y)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means = [c for c in sorted(m.monitor.channels.keys()) if \"mean\" in c and \"norm\" in c]\n", "make_curves(m,means)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
linncy/Tester-Automation	cm22c.ipynb	1	2489	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Device Found.\n", "Error\n" ] } ], "source": [ "import visa\n", "rm = visa.ResourceManager()\n", "if len(rm.list_resources())==0:\n", "\tprint('No Device Found.')\n", "else:\n", "\tprint('Device Found:', rm.list_resources())\n", "try:\n", " cm22c = rm.open_resource(\"COM3\")\n", " print(\"COM3 Successfully Connected\")\n", "except:\n", "\tprint('Error')\n", "\texit()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'cm22c' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-6-0013e21ad0ee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstrInsId\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcm22c\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"idn?\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'cm22c' is not defined" ] } ], "source": [ "strInsId=cm22c.query(\"idn?\")\n", "strTempInputA=cm22c.query(\"input? a\")\n", "strTemUnitA=cm22c.query(\"input a:units?\")\n", "strLoopOnOff=cm22c.query(\"control?\")\n", "if(cm22c.query(\"input a:units k;:OPC?\")):\n", "\tprint(\"Input A Unit set to K\")\n", "if(cm22c.query(\"LOOP 1:SETPOINT 273;:OPC?\")):\n", "\tprint('LOOP 1 Setpoint set to 273')\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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jimthompson5802/kaggle-BNP-Paribas	src/sandbox/test_model_parms_retrieval.ipynb	1	2616	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/jim/Desktop/Kaggle/BNPParibasCardif/src/sandbox\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "\n", "work_dir = \".\"\n", "print os.getcwd()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "parmdf = pd.read_csv(work_dir+'/py_parms.tsv',header=0,sep='\\t')" ] }, { "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>n.tree</th>\n", " <th>max.depth</th>\n", " <th>alpaha</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1000</td>\n", " <td>20</td>\n", " <td>0.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " n.tree max.depth alpaha\n", "0 1000 20 0.5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parmdf" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'alpaha': {0: 0.5}, 'max.depth': {0: 20}, 'n.tree': {0: 1000}}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parm_dict = parmdf.to_dict()\n", "parm_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jdvelasq/machine-learning	old/ML-00-scraping.ipynb	1	46289	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aprendizaje de Máquinas -- 0 -- Scraping\n", "Notas de clase sobre aprendizaje de máquinas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Juan David Velásquez Henao \n", "[email protected] \n", "Universidad Nacional de Colombia, Sede Medellín \n", "Facultad de Minas \n", "Medellín, Colombia \n", "\n", "[Licencia](https://github.com/jdvelasq/machine-learning/blob/master/LICENCIA.txt) \n", "[Readme](https://github.com/jdvelasq/machine-learning/blob/master/Readme.md)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Software utilizado\n", "\n", "> Este es un documento interactivo escrito como un notebook de [Jupyter](http://jupyter.org), en el cual se presenta un tutorial sobre la extracción, transformación, visualización y carga de datos usando Python en el contexto de la ciencia de los datos. Los notebooks de Jupyter permiten incoporar simultáneamente código, texto, gráficos y ecuaciones. El código presentado en este notebook puede ejecutarse en los sistemas operativos Linux y OS X.\n", "\n", "> Haga click [aquí](https://github.com/jdvelasq/guias-de-instalacion) para obtener instrucciones detalladas sobre como instalar Jupyter en Windows y Mac OS X.\n", "\n", "> Haga clic [aquí](http://nbviewer.jupyter.org/github/jdvelasq/ETVL-IPython/blob/master/ETVL-IPy-1-uso-interactivo.ipynb) para ver la última versión de este documento en nbviewer.\n", "\n", "> Descargue la última versión de este documento a su disco duro; luego, carguelo y ejecutelo en línea en [Try Jupyter!](https://try.jupyter.org)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Contenido" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bibliografía.\n", "\n", "> [The Python Tutorial](https://docs.python.org/3/tutorial/index.html) by Python Software Fundation\n", "\n", "> [IPython in deep](https://github.com/ipython/ipython-in-depth/blob/master/examples/Index.ipynb) at GitHub\n", "\n", "> [IPython wiki](https://github.com/ipython/ipython/wiki) at GitHub" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Web Scraping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ejemplo 1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# importa las librerias\n", "import urllib.request\n", "import urllib.parse\n", "# import urllib2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'<!doctype html><html itemscope=\"\" itemtype=\"http://schema.org/WebPage\" lang=\"es-419\"><head><meta content=\"text/html; charset=UTF-8\" http-equiv=\"Content-Type\"><meta content=\"/images/branding/googleg/1x'\n" ] } ], "source": [ "google = urllib.request.urlopen('http://google.com') \n", "google = google.read()\n", "print(google[:200])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'<!doctype html><html itemscope=\"\" itemtype=\"http://schema.org/WebPage\" lang=\"es-419\"><head><meta content=\"text/html; charset=UTF-8\" http-equiv=\"Content-Type\"><meta content=\"/images/branding/googleg/1x/googleg_standard_color_128dp.png\" itemprop=\"image\"><title>Google</title><script>(function(){window.google={kEI:\\'xkK4WJSRG8yymwGYzZ24Dw\\',kEXPI:\\'18167,1351828,1351903,1352240,1352623,1352995,3700284,37'\n" ] } ], "source": [ "url = 'http://google.com?q='\n", "url_with_query = url + urllib.parse.quote('python web scraping')\n", "web_search = urllib.request.urlopen(url_with_query)\n", "web_search = web_search.read() \n", "print(web_search[:400])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n", "b'<!doctype html><html itemscope=\"\" itemtype=\"http://schema.org/WebPage\" lang=\"es-419\"><head><meta content=\"text/html; charset=UTF-8\" http-equiv=\"Content-Type\"><meta content=\"/images/branding/googleg/1x'\n", "{'Date': 'Thu, 02 Mar 2017 16:06:18 GMT', 'Expires': '-1', 'Cache-Control': 'private, max-age=0', 'Content-Type': 'text/html; charset=ISO-8859-1', 'P3P': 'CP=\"This is not a P3P policy! See https://www.google.com/support/accounts/answer/151657?hl=en for more info.\"', 'Content-Encoding': 'gzip', 'Server': 'gws', 'Content-Length': '4579', 'X-XSS-Protection': '1; mode=block', 'X-Frame-Options': 'SAMEORIGIN', 'Set-Cookie': 'NID=98=s6YBZCZgKy3lIWCYYXCzoa0rxmueIipaGfKsAuvBly3OKk7Sq0ncGjmi7yzZv_uvQGj0zVxvYTUTNFbAitFkjahnfytwxL3T7xW12Kf-W5g0zB8yvKQPUHaeFOkAwMxl; expires=Fri, 01-Sep-2017 16:06:18 GMT; path=/; domain=.google.com.co; HttpOnly'}\n", "[('NID', '98=s6YBZCZgKy3lIWCYYXCzoa0rxmueIipaGfKsAuvBly3OKk7Sq0ncGjmi7yzZv_uvQGj0zVxvYTUTNFbAitFkjahnfytwxL3T7xW12Kf-W5g0zB8yvKQPUHaeFOkAwMxl')]\n" ] } ], "source": [ "import requests\n", "google = requests.get('http://google.com') \n", "print(google.status_code)\n", "print(google.content[:200])\n", "print(google.headers)\n", "print(google.cookies.items())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ejemplo 2" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from urllib.request import urlopen\n", "from bs4 import BeautifulSoup \n", "import requests" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<a href=\"http://www.allitebooks.com/code-generation-with-roslyn/\" rel=\"bookmark\">\n", "<img alt=\"Code Generation with Roslyn\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Code-Generation-with-Roslyn.jpg\" width=\"351\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/code-generation-with-roslyn/\" rel=\"bookmark\">Code Generation with Roslyn</a>\n", " \n", "<a href=\"http://www.allitebooks.com/beginning-power-bi-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Beginning Power BI\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Beginning-Power-BI.jpg\" width=\"350\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/beginning-power-bi-2nd-edition/\" rel=\"bookmark\">Beginning Power BI, 2nd Edition</a>\n", " \n", "<a href=\"http://www.allitebooks.com/cisco-lan-switching-configuration-handbook-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Cisco LAN Switching Configuration Handbook, 2nd Edition\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Cisco-LAN-Switching-Configuration-Handbook-2nd-Edition.jpg\" width=\"392\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/cisco-lan-switching-configuration-handbook-2nd-edition/\" rel=\"bookmark\">Cisco LAN Switching Configuration Handbook, 2nd Edition</a>\n", " \n", "<a href=\"http://www.allitebooks.com/data-visualisation-with-r/\" rel=\"bookmark\">\n", "<img alt=\"Data Visualisation with R\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Data-Visualisation-with-R.jpg\" width=\"332\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/data-visualisation-with-r/\" rel=\"bookmark\">Data Visualisation with R</a>\n", " \n", "<a href=\"http://www.allitebooks.com/pro-mongodb-development/\" rel=\"bookmark\">\n", "<img alt=\"Pro MongoDB Development\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Pro-MongoDB-Development.jpg\" width=\"350\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/pro-mongodb-development/\" rel=\"bookmark\">Pro MongoDB Development</a>\n", " \n", "<a href=\"http://www.allitebooks.com/html5-for-flash-developers/\" rel=\"bookmark\">\n", "<img alt=\"HTML5 for Flash Developers\" class=\"attachment-post-thumbnail wp-post-image\" height=\"493\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/HTML5-for-Flash-Developers-400x493.jpg\" width=\"400\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/html5-for-flash-developers/\" rel=\"bookmark\">HTML5 for Flash Developers</a>\n", " \n", "<a href=\"http://www.allitebooks.com/microsoft-windows-server-2012-administration-instant-reference/\" rel=\"bookmark\">\n", "<img alt=\"Microsoft Windows Server 2012 Administration Instant Reference\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Microsoft-Windows-Server-2012-Administration-Instant-Reference.jpg\" width=\"332\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/microsoft-windows-server-2012-administration-instant-reference/\" rel=\"bookmark\">Microsoft Windows Server 2012 Administration Instant Reference</a>\n", " \n", "<a href=\"http://www.allitebooks.com/learning-concurrent-programming-in-scala-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Learning Concurrent Programming in Scala, 2nd Edition\" class=\"attachment-post-thumbnail wp-post-image\" height=\"475\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Learning-Concurrent-Programming-in-Scala-2nd-Edition-400x475.jpg\" width=\"400\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/learning-concurrent-programming-in-scala-2nd-edition/\" rel=\"bookmark\">Learning Concurrent Programming in Scala, 2nd Edition</a>\n", " \n", "<a href=\"http://www.allitebooks.com/ebay-commerce-cookbook/\" rel=\"bookmark\">\n", "<img alt=\"eBay Commerce Cookbook\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/eBay-Commerce-Cookbook.jpg\" width=\"389\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/ebay-commerce-cookbook/\" rel=\"bookmark\">eBay Commerce Cookbook</a>\n", " \n", "<a href=\"http://www.allitebooks.com/beginning-c-2008-objects/\" rel=\"bookmark\">\n", "<img alt=\"Beginning C 2008 Objects\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Beginning-C-2008-Objects.jpg\" width=\"378\"/> </a>\n", " \n", "<a href=\"http://www.allitebooks.com/beginning-c-2008-objects/\" rel=\"bookmark\">Beginning C# 2008 Objects</a>\n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jdvelasq/anaconda/lib/python3.6/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n", "\n", "The code that caused this warning is on line 193 of the file /Users/jdvelasq/anaconda/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n", "\n", " BeautifulSoup([your markup])\n", "\n", "to this:\n", "\n", " BeautifulSoup([your markup], \"lxml\")\n", "\n", " markup_type=markup_type))\n" ] } ], "source": [ "html = urlopen(\"http://www.allitebooks.com\") \n", "bsObj = BeautifulSoup(html.read())\n", "titles = bsObj.findAll(\"a\", {'rel':'bookmark'})\n", "for x in titles:\n", " print(x)\n", " print(' ')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<title>All IT eBooks - Free IT eBooks Download</title>\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jdvelasq/anaconda/lib/python3.6/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n", "\n", "The code that caused this warning is on line 193 of the file /Users/jdvelasq/anaconda/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n", "\n", " BeautifulSoup([your markup])\n", "\n", "to this:\n", "\n", " BeautifulSoup([your markup], \"lxml\")\n", "\n", " markup_type=markup_type))\n" ] } ], "source": [ "page = requests.get('http://www.allitebooks.com') \n", "bs = BeautifulSoup(page.content)\n", "print(bs.title)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<a href=\"/\" title=\"All IT eBooks\">All IT eBooks</a>, <a title=\"All Categories\">Categories</a>, <a href=\"http://www.allitebooks.com/web-development/\">Web Development</a>, <a href=\"http://www.allitebooks.com/web-development/asp-net/\">ASP.NET</a>, <a href=\"http://www.allitebooks.com/web-development/cms/\">CMS</a>, <a href=\"http://www.allitebooks.com/web-development/html-html5-css/\">HTML, HTML5 & CSS</a>, <a href=\"http://www.allitebooks.com/web-development/javascript/\">JavaScript</a>, <a href=\"http://www.allitebooks.com/web-development/jsp/\">JSP</a>, <a href=\"http://www.allitebooks.com/web-development/php/\">PHP</a>, <a href=\"http://www.allitebooks.com/web-development/python/\">Python</a>, <a href=\"http://www.allitebooks.com/web-development/ruby/\">Ruby</a>, <a href=\"http://www.allitebooks.com/web-development/rails/\">Rails</a>, <a href=\"http://www.allitebooks.com/web-development/xml/\">XML</a>, <a href=\"http://www.allitebooks.com/web-development/services-apis/\">Services & APIs</a>, <a href=\"http://www.allitebooks.com/web-development/other-web-development/\">Other</a>, <a href=\"http://www.allitebooks.com/programming/\">Programming</a>, <a href=\"http://www.allitebooks.com/programming/c/\">C & C++</a>, <a href=\"http://www.allitebooks.com/programming/c-programming/\">C#</a>, <a href=\"http://www.allitebooks.com/programming/delphi/\">Delphi</a>, <a href=\"http://www.allitebooks.com/programming/java/\">Java</a>, <a href=\"http://www.allitebooks.com/programming/net/\">.NET</a>, <a href=\"http://www.allitebooks.com/programming/objective-c/\">Objective-C</a>, <a href=\"http://www.allitebooks.com/programming/opencl/\">OpenCL</a>, <a href=\"http://www.allitebooks.com/programming/perl/\">Perl</a>, <a href=\"http://www.allitebooks.com/programming/powershell/\">PowerShell</a>, <a href=\"http://www.allitebooks.com/programming/scala/\">Scala</a>, <a href=\"http://www.allitebooks.com/programming/swift/\">Swift</a>, <a href=\"http://www.allitebooks.com/programming/visual-basic/\">Visual Basic</a>, <a href=\"http://www.allitebooks.com/datebases/\">Datebases</a>, <a href=\"http://www.allitebooks.com/datebases/big-data/\">Big Data</a>, <a href=\"http://www.allitebooks.com/datebases/data-analysis/\">Data Analysis</a>, <a href=\"http://www.allitebooks.com/datebases/mongodb/\">MongoDB</a>, <a href=\"http://www.allitebooks.com/datebases/mysql/\">MySQL</a>, <a href=\"http://www.allitebooks.com/datebases/nosql/\">NoSQL</a>, <a href=\"http://www.allitebooks.com/datebases/postgresql/\">PostgreSQL</a>, <a href=\"http://www.allitebooks.com/datebases/oracle/\">Oracle</a>, <a href=\"http://www.allitebooks.com/datebases/sql/\">SQL</a>, <a href=\"http://www.allitebooks.com/game-programming/\">Game Programming</a>, <a href=\"http://www.allitebooks.com/graphics-design/\">Graphics & Design</a>, <a href=\"http://www.allitebooks.com/graphics-design/3d-max/\">3D MAX</a>, <a href=\"http://www.allitebooks.com/graphics-design/cad/\">CAD</a>, <a href=\"http://www.allitebooks.com/graphics-design/coreldraw/\">Coreldraw</a>, <a href=\"http://www.allitebooks.com/graphics-design/dreamweaver/\">Dreamweaver</a>, <a href=\"http://www.allitebooks.com/graphics-design/flash/\">Flash</a>, <a href=\"http://www.allitebooks.com/graphics-design/illustrator/\">Illustrator</a>, <a href=\"http://www.allitebooks.com/graphics-design/maya/\">Maya</a>, <a href=\"http://www.allitebooks.com/graphics-design/photoshop/\">Photoshop</a>, <a href=\"http://www.allitebooks.com/graphics-design/premiere/\">Premiere</a>, <a href=\"http://www.allitebooks.com/operating-systems/\">Operating Systems</a>, <a href=\"http://www.allitebooks.com/operating-systems/windows/\">Windows</a>, <a href=\"http://www.allitebooks.com/operating-systems/linux-unix/\">Linux & Unix</a>, <a href=\"http://www.allitebooks.com/operating-systems/macintosh/\">Macintosh</a>, <a href=\"http://www.allitebooks.com/operating-systems/android/\">Android</a>, <a href=\"http://www.allitebooks.com/operating-systems/ios/\">iOS</a>, <a href=\"http://www.allitebooks.com/operating-systems/windows-phone/\">Windows Phone</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/\">Networking & Cloud Computing</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/cloud-computing/\">Cloud Computing</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/network-administration/\">Network Administration</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/network-security/\">Network Security</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/networks-protocols-apis/\">Networks, Protocols & APIs</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/wireless-networks/\">Wireless Networks</a>, <a href=\"http://www.allitebooks.com/administration/\">Administration</a>, <a href=\"http://www.allitebooks.com/administration/cloud-virtualization/\">Cloud & Virtualization</a>, <a href=\"http://www.allitebooks.com/administration/infrastructure/\">Infrastructure</a>, <a href=\"http://www.allitebooks.com/administration/mail-servers/\">Mail Servers</a>, <a href=\"http://www.allitebooks.com/administration/microsoft-platform/\">Microsoft Platform</a>, <a href=\"http://www.allitebooks.com/administration/monitoring/\">Monitoring</a>, <a href=\"http://www.allitebooks.com/administration/task-automation/\">Task Automation</a>, <a href=\"http://www.allitebooks.com/administration/web-servers/\">Web Servers</a>, <a href=\"http://www.allitebooks.com/administration/other/\">Other</a>, <a href=\"http://www.allitebooks.com/computers-technology/\">Computers & Technology</a>, <a href=\"http://www.allitebooks.com/computers-technology/computer-science/\">Computer Science</a>, <a href=\"http://www.allitebooks.com/certification/\">Certification</a>, <a href=\"http://www.allitebooks.com/enterprise/\">Enterprise</a>, <a href=\"http://www.allitebooks.com/enterprise/business-applications/\">Business Applications</a>, <a href=\"http://www.allitebooks.com/enterprise/communications/\">Communications</a>, <a href=\"http://www.allitebooks.com/enterprise/erp-crm/\">ERP & CRM</a>, <a href=\"http://www.allitebooks.com/marketing-seo/\">Marketing & SEO</a>, <a href=\"http://www.allitebooks.com/hardware/\">Hardware & DIY</a>, <a href=\"http://www.allitebooks.com/security/\">Security</a>, <a href=\"http://www.allitebooks.com/software/\">Software</a>, <a href=\"http://www.allitebooks.com/software/mac/\">Mac</a>, <a href=\"http://www.allitebooks.com/software/office/\">Office</a>, <a href=\"http://www.allitebooks.com/software/windows-pc/\">Windows & PC</a>, <a href=\"http://www.allitebooks.com/web-development/\">Web Development</a>, <a href=\"http://www.allitebooks.com/programming/\">Programming</a>, <a href=\"http://www.allitebooks.com/datebases/\">Datebases</a>, <a href=\"http://www.allitebooks.com/graphics-design/\">Graphics & Design</a>, <a href=\"http://www.allitebooks.com/operating-systems/\">Operating Systems</a>, <a href=\"http://www.allitebooks.com/networking-cloud-computing/\">Networking & Cloud Computing</a>, <a href=\"http://www.allitebooks.com/administration/\">Administration</a>, <a href=\"http://www.allitebooks.com/certification/\">Certification</a>, <a href=\"http://www.allitebooks.com/computers-technology/\">Computers & Technology</a>, <a href=\"http://www.allitebooks.com/enterprise/\">Enterprise</a>, <a href=\"http://www.allitebooks.com/game-programming/\">Game Programming</a>, <a href=\"http://www.allitebooks.com/hardware/\">Hardware & DIY</a>, <a href=\"http://www.allitebooks.com/marketing-seo/\">Marketing & SEO</a>, <a href=\"http://www.allitebooks.com/security/\">Security</a>, <a href=\"http://www.allitebooks.com/software/\">Software</a>, <a href=\"http://www.allitebooks.com/code-generation-with-roslyn/\" rel=\"bookmark\">\n", "<img alt=\"Code Generation with Roslyn\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Code-Generation-with-Roslyn.jpg\" width=\"351\"/> </a>, <a href=\"http://www.allitebooks.com/code-generation-with-roslyn/\" rel=\"bookmark\">Code Generation with Roslyn</a>, <a href=\"http://www.allitebooks.com/author/nick-harrison/\" rel=\"tag\">Nick Harrison</a>, <a href=\"http://www.allitebooks.com/beginning-power-bi-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Beginning Power BI\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Beginning-Power-BI.jpg\" width=\"350\"/> </a>, <a href=\"http://www.allitebooks.com/beginning-power-bi-2nd-edition/\" rel=\"bookmark\">Beginning Power BI, 2nd Edition</a>, <a href=\"http://www.allitebooks.com/author/dan-clark/\" rel=\"tag\">Dan Clark</a>, <a href=\"http://www.allitebooks.com/cisco-lan-switching-configuration-handbook-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Cisco LAN Switching Configuration Handbook, 2nd Edition\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Cisco-LAN-Switching-Configuration-Handbook-2nd-Edition.jpg\" width=\"392\"/> </a>, <a href=\"http://www.allitebooks.com/cisco-lan-switching-configuration-handbook-2nd-edition/\" rel=\"bookmark\">Cisco LAN Switching Configuration Handbook, 2nd Edition</a>, <a href=\"http://www.allitebooks.com/author/david-hucaby/\" rel=\"tag\">David Hucaby</a>, <a href=\"http://www.allitebooks.com/author/david-jansen/\" rel=\"tag\">David Jansen</a>, <a href=\"http://www.allitebooks.com/author/steve-mcquerry/\" rel=\"tag\">Steve McQuerry</a>, <a href=\"http://www.allitebooks.com/data-visualisation-with-r/\" rel=\"bookmark\">\n", "<img alt=\"Data Visualisation with R\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Data-Visualisation-with-R.jpg\" width=\"332\"/> </a>, <a href=\"http://www.allitebooks.com/data-visualisation-with-r/\" rel=\"bookmark\">Data Visualisation with R</a>, <a href=\"http://www.allitebooks.com/author/thomas-rahlf/\" rel=\"tag\">Thomas Rahlf</a>, <a href=\"http://www.allitebooks.com/pro-mongodb-development/\" rel=\"bookmark\">\n", "<img alt=\"Pro MongoDB Development\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/03/Pro-MongoDB-Development.jpg\" width=\"350\"/> </a>, <a href=\"http://www.allitebooks.com/pro-mongodb-development/\" rel=\"bookmark\">Pro MongoDB Development</a>, <a href=\"http://www.allitebooks.com/author/deepak-vohra/\" rel=\"tag\">Deepak Vohra</a>, <a href=\"http://www.allitebooks.com/html5-for-flash-developers/\" rel=\"bookmark\">\n", "<img alt=\"HTML5 for Flash Developers\" class=\"attachment-post-thumbnail wp-post-image\" height=\"493\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/HTML5-for-Flash-Developers-400x493.jpg\" width=\"400\"/> </a>, <a href=\"http://www.allitebooks.com/html5-for-flash-developers/\" rel=\"bookmark\">HTML5 for Flash Developers</a>, <a href=\"http://www.allitebooks.com/author/matt-fisher/\" rel=\"tag\">Matt Fisher</a>, <a href=\"http://www.allitebooks.com/microsoft-windows-server-2012-administration-instant-reference/\" rel=\"bookmark\">\n", "<img alt=\"Microsoft Windows Server 2012 Administration Instant Reference\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Microsoft-Windows-Server-2012-Administration-Instant-Reference.jpg\" width=\"332\"/> </a>, <a href=\"http://www.allitebooks.com/microsoft-windows-server-2012-administration-instant-reference/\" rel=\"bookmark\">Microsoft Windows Server 2012 Administration Instant Reference</a>, <a href=\"http://www.allitebooks.com/author/chris-henley/\" rel=\"tag\">Chris Henley</a>, <a href=\"http://www.allitebooks.com/author/matthew-hester/\" rel=\"tag\">Matthew Hester</a>, <a href=\"http://www.allitebooks.com/learning-concurrent-programming-in-scala-2nd-edition/\" rel=\"bookmark\">\n", "<img alt=\"Learning Concurrent Programming in Scala, 2nd Edition\" class=\"attachment-post-thumbnail wp-post-image\" height=\"475\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Learning-Concurrent-Programming-in-Scala-2nd-Edition-400x475.jpg\" width=\"400\"/> </a>, <a href=\"http://www.allitebooks.com/learning-concurrent-programming-in-scala-2nd-edition/\" rel=\"bookmark\">Learning Concurrent Programming in Scala, 2nd Edition</a>, <a href=\"http://www.allitebooks.com/author/aleksandar-prokopec/\" rel=\"tag\">Aleksandar Prokopec</a>, <a href=\"http://www.allitebooks.com/ebay-commerce-cookbook/\" rel=\"bookmark\">\n", "<img alt=\"eBay Commerce Cookbook\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/eBay-Commerce-Cookbook.jpg\" width=\"389\"/> </a>, <a href=\"http://www.allitebooks.com/ebay-commerce-cookbook/\" rel=\"bookmark\">eBay Commerce Cookbook</a>, <a href=\"http://www.allitebooks.com/author/chuck-hudson/\" rel=\"tag\">Chuck Hudson</a>, <a href=\"http://www.allitebooks.com/beginning-c-2008-objects/\" rel=\"bookmark\">\n", "<img alt=\"Beginning C 2008 Objects\" class=\"attachment-post-thumbnail wp-post-image\" height=\"499\" src=\"http://www.allitebooks.com/wp-content/uploads/2017/02/Beginning-C-2008-Objects.jpg\" width=\"378\"/> </a>, <a href=\"http://www.allitebooks.com/beginning-c-2008-objects/\" rel=\"bookmark\">Beginning C# 2008 Objects</a>, <a href=\"http://www.allitebooks.com/author/grant-palmer/\" rel=\"tag\">Grant Palmer</a>, <a href=\"http://www.allitebooks.com/author/william-barker/\" rel=\"tag\">William Barker</a>, <a href=\"http://www.allitebooks.com/page/2/\" title=\"2\">2</a>, <a href=\"http://www.allitebooks.com/page/3/\" title=\"3\">3</a>, <a href=\"http://www.allitebooks.com/page/4/\" title=\"4\">4</a>, <a href=\"http://www.allitebooks.com/page/5/\" title=\"5\">5</a>, <a href=\"http://www.allitebooks.com/page/692/\" title=\"Last Page →\">692</a>]\n" ] } ], "source": [ "print(bs.find_all('a'))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<p class=\"rteright\"><input name=\"organization_KEY\" type=\"hidden\" value=\"51595\"/> <input name=\"chapter_KEY\" type=\"hidden\" value=\"314\"/> <input name=\"email_trigger_KEYS\" type=\"hidden\" value=\"28321\"/> <input name=\"object\" type=\"hidden\" value=\"supporter\"/> <input name=\"Receive_Email\" type=\"hidden\" value=\"1\"/> <input name=\"link\" type=\"hidden\" value=\"groups\"/> <input name=\"linkKey\" type=\"hidden\" value=\"152258\"/> <input name=\"redirect\" type=\"hidden\" value=\"http://www.enoughproject.org/eloqua/thank-you-signing\"/> <input class=\"salsainput\" id=\"Email_4\" name=\"Email\" placeholder=\"email address\" title=\"enter value\" type=\"text\" value=\"\"/> <input type=\"Submit\" value=\"Sign up\"/></p>, <p>Thank you for your committment to ending genocide and mass atrocities. We will be updating this page shortly with additional actions.</p>, <p style=\"padding:0\"><a href=\"http://www.facebook.com/enoughproj\"><img name=\"Facebook\" src=\"http://www.enoughproject.org/files/icons/facebook.png\" style=\"margin:0 10px\" title=\"Facebook\"/></a><a href=\"http://www.twitter.com/enoughproject\"><img name=\"Twitter\" src=\"http://www.enoughproject.org/files/icons/twitter.png\" style=\"margin:0 10px\" title=\"Twitter\"/></a><a href=\"http://www.youtube.com/EnoughProject\"><img name=\"YouTube\" src=\"http://www.enoughproject.org/files/icons/youtube.png\" style=\"margin:0 10px\" title=\"YouTube\"/></a><a href=\"http://www.flickr.com/photos/enoughproject/\"><img name=\"Flickr\" src=\"http://www.enoughproject.org/files/icons/flickr.png\" style=\"margin:0 10px\" title=\"Flickr\"/></a><a href=\"http://instagram.com/enoughproject\"><img name=\"Instagram\" src=\"http://www.enoughproject.org/files/icons/instagram.png\" style=\"margin:0 10px\" title=\"Instagram\"/></a></p>, <p><strong>Enough Project</strong><br/>\n", "1420 K St. NW, Suite 200, Washington, DC 20005<br/>\n", "Phone: (<span style=\"color: rgb(38, 50, 56); font-family: arial, sans-serif; line-height: 16px;\">202) 580-7690</span></p>]\n" ] } ], "source": [ "print(bs.find_all('p'))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['\\n', <meta charset=\"utf-8\"/>, '\\n', <title>All IT eBooks - Free IT eBooks Download</title>, '\\n', <link href=\"http://gmpg.org/xfn/11\" rel=\"profile\"/>, '\\n', <link href=\"http://www.allitebooks.com/xmlrpc.php\" rel=\"pingback\"/>, '\\n', <meta content=\"width=device-width, initial-scale=1.0\" name=\"viewport\"/>, '\\n', ' This site is optimized with the Yoast WordPress SEO plugin v2.1.1 - https://yoast.com/wordpress/plugins/seo/ ', '\\n', <meta content=\"Free IT eBooks Download\" name=\"description\"/>, '\\n', <link href=\"http://www.allitebooks.com\" rel=\"canonical\"/>, '\\n', <link href=\"http://www.allitebooks.com/page/2/\" rel=\"next\"/>, '\\n', <script type=\"application/ld+json\">{\"@context\":\"http:\\/\\/schema.org\",\"@type\":\"WebSite\",\"url\":\"http:\\/\\/www.allitebooks.com\\/\",\"name\":\"All IT eBooks\",\"potentialAction\":{\"@type\":\"SearchAction\",\"target\":\"http:\\/\\/www.allitebooks.com\\/?s={search_term}\",\"query-input\":\"required name=search_term\"}}</script>, '\\n', ' / Yoast WordPress SEO plugin. ', '\\n', <link href=\"http://www.allitebooks.com/feed/\" rel=\"alternate\" title=\"All IT eBooks » Feed\" type=\"application/rss+xml\"/>, '\\n', <link href=\"http://www.allitebooks.com/comments/feed/\" rel=\"alternate\" title=\"All IT eBooks » Comments Feed\" type=\"application/rss+xml\"/>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/plugins/wp-to-twitter/css/twitter-feed.css?ver=4.1.1\" id=\"wpt-twitter-feed-css\" media=\"all\" rel=\"stylesheet\" type=\"text/css\"/>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/themes/allitebooks/css/bootstrap.css?ver=4.1.1\" id=\"bootstrap-style-css\" media=\"all\" rel=\"stylesheet\" type=\"text/css\"/>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/themes/allitebooks/css/font-awesome.min.css?ver=4.1.1\" id=\"fontawesome-style-css\" media=\"all\" rel=\"stylesheet\" type=\"text/css\"/>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/themes/allitebooks/style.css?ver=4.1.1\" id=\"classPlus-style-css\" media=\"all\" rel=\"stylesheet\" type=\"text/css\"/>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/themes/allitebooks/css/custom.css.php?ver=4.1.1\" id=\"custom-css-css\" media=\"all\" rel=\"stylesheet\" type=\"text/css\"/>, '\\n', <script src=\"http://www.allitebooks.com/wp-includes/js/jquery/jquery.js?ver=1.11.1\" type=\"text/javascript\"></script>, '\\n', <script src=\"http://www.allitebooks.com/wp-includes/js/jquery/jquery-migrate.min.js?ver=1.2.1\" type=\"text/javascript\"></script>, '\\n', <script src=\"http://www.allitebooks.com/wp-content/themes/allitebooks/js/superfish.js?ver=4.1.1\" type=\"text/javascript\"></script>, '\\n', <script src=\"http://www.allitebooks.com/wp-content/themes/allitebooks/js/bootstrap.min.js?ver=4.1.1\" type=\"text/javascript\"></script>, '\\n', <script src=\"http://www.allitebooks.com/wp-content/themes/allitebooks/js/jquery.autosize.js?ver=4.1.1\" type=\"text/javascript\"></script>, '\\n', <script type=\"text/javascript\">\n", "\twindow._wp_rp_static_base_url = 'https://wprp.zemanta.com/static/';\n", "\twindow._wp_rp_wp_ajax_url = \"http://www.allitebooks.com/wp-admin/admin-ajax.php\";\n", "\twindow._wp_rp_plugin_version = '3.5.4';\n", "\twindow._wp_rp_post_id = '26687';\n", "\twindow._wp_rp_num_rel_posts = '4';\n", "\twindow._wp_rp_thumbnails = true;\n", "\twindow._wp_rp_post_title = 'Code+Generation+with+Roslyn';\n", "\twindow._wp_rp_post_tags = ['.net', 'c+%26amp%3B+c%2B%2B', 'system', 'write', 'busi', 'learn', 'comput', 'code', 'innov', 'gener', 'softwar', 'logic', 'tree', 'tabl', 'data', 'design', 'book'];\n", "\twindow._wp_rp_promoted_content = true;\n", "</script>, '\\n', <script async=\"\" src=\"https://wprp.zemanta.com/static/js/loader.js?version=3.5.4\" type=\"text/javascript\"></script>, '\\n', <link href=\"http://www.allitebooks.com/wp-content/themes/allitebooks/images/favicon.ico\" id=\"site-favicon\" rel=\"shortcut icon\" type=\"image/x-icon\"/>, ' ', ' Mobile Specific Meta ', '\\n', <meta content=\"yes\" name=\"apple-mobile-web-app-capable\"/>, '\\n', <meta content=\"black\" name=\"apple-mobile-web-app-status-bar-style\"/>, '\\n']\n" ] } ], "source": [ "header_children = [c for c in bs.head.children] \n", "print(header_children)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'NoneType' object has no attribute 'descendants'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-31-569213ddd6da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mnavigation_bar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"globalNavigation\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnavigation_bar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescendants\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'descendants'" ] } ], "source": [ "navigation_bar = bs.find(id=\"globalNavigation\") \n", "for d in navigation_bar.descendants:\n", " print(d)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<ul>\n", "<li id=\"navAbout\"><a href=\"/about\" title=\"About\"><span></span>About</a></li>\n", "<li id=\"navBlog\"><a href=\"/blog\" title=\"Blog\"><span></span>Blog</a></li>\n", "<li id=\"navConflicts\"><a href=\"/conflicts\" title=\"Conflicts\"><span></span>Conflicts</a></li>\n", "<li id=\"navReports\"><a href=\"/reports\" title=\"Reports\"><span></span>Reports</a></li>\n", "<li id=\"navTakeAction\"><a class=\"selected\" href=\"/take_action\" title=\"Take Action\"><span></span>Take Action</a></li>\n", "<!--<li id=\"navShop\"><a href=\"/shop\" title=\"Shop\"><span></span>Shop</a></li>-->\n", "<li id=\"navDonate\"><a href=\"/donate\" title=\"Donate\"><span></span>Donate</a></li>\n", "</ul>\n", "\n", "\n" ] } ], "source": [ "for s in d.previous_siblings:\n", " print(s)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "<h2><a href=\"\">Please Check Back Soon For Our Latest Actions!</a></h2> <a href=\"\">Please Check Back Soon For Our Latest Actions!</a> [<p>Thank you for your committment to ending genocide and mass atrocities. We will be updating this page shortly with additional actions.</p>]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/jdvelasq/anaconda/lib/python3.6/site-packages/bs4/__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n", "\n", "The code that caused this warning is on line 193 of the file /Users/jdvelasq/anaconda/lib/python3.6/runpy.py. To get rid of this warning, change code that looks like this:\n", "\n", " BeautifulSoup([your markup])\n", "\n", "to this:\n", "\n", " BeautifulSoup([your markup], \"lxml\")\n", "\n", " markup_type=markup_type))\n" ] } ], "source": [ "from bs4 import BeautifulSoup \n", "import requests\n", "page = requests.get('http://www.allitebooks.com') \n", "bs = BeautifulSoup(page.content)\n", "ta_divs = bs.find_all(\"div\", class_=\"views-row\")\n", "print(len(ta_divs))\n", "for ta in ta_divs: title = ta.h2\n", "link = ta.a\n", "about = ta.find_all('p') \n", "print(title, link, about)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lxml import html" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "cssselect does not seem to be installed. See http://packages.python.org/cssselect/", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/Users/jdvelasq/anaconda/lib/python3.6/site-packages/lxml/cssselect.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcssselect\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexternal_cssselect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cssselect'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-37-0b427fa23b52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.enoughproject.org/take_action'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mta_divs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcssselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'div.views-row'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/jdvelasq/anaconda/lib/python3.6/site-packages/lxml/html/__init__.py\u001b[0m in \u001b[0;36mcssselect\u001b[0;34m(self, expr, translator)\u001b[0m\n\u001b[1;32m 430\u001b[0m \"\"\"\n\u001b[1;32m 431\u001b[0m \u001b[0;31m# Do the import here to make the dependency optional.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlxml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcssselect\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCSSSelector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 433\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCSSSelector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtranslator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/jdvelasq/anaconda/lib/python3.6/site-packages/lxml/cssselect.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m raise ImportError(\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;34m'cssselect does not seem to be installed. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m 'See http://packages.python.org/cssselect/')\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cssselect does not seem to be installed. See http://packages.python.org/cssselect/" ] } ], "source": [ "page = html.parse('http://www.enoughproject.org/take_action')\n", "root = page.getroot()\n", "ta_divs = root.cssselect('div.views-row')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "Missing parentheses in call to 'print' (<ipython-input-38-4cc9c52e6889>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-38-4cc9c52e6889>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print ta_divs\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" ] } ], "source": [ "print ta_divs" ] }, { "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 }	mit
ES-DOC/esdoc-jupyterhub	notebooks/nasa-giss/cmip6/models/giss-e2-1h/aerosol.ipynb	1	84302	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: NASA-GISS \n", "Source ID: GISS-E2-1H \n", "Topic: Aerosol \n", "Sub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "Properties: 69 (37 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:20" ], "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', 'nasa-giss', 'giss-e2-1h', 'aerosol')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n", "[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n", "[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n", "[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n", "[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n", "[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n", "[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n", "[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Key properties of the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of aerosol model code" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Scheme Scope\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Atmospheric domains covered by the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Basic approximations made in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Prognostic variables in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of tracers in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are aerosol calculations generalized into families of species?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Software Properties \n", "Software properties of aerosol code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Timestep Framework \n", "Physical properties of seawater in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Mathematical method deployed to solve the time evolution of the prognostic variables" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (integrated)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol advection (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol physics (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Integrated Timestep\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Timestep for the aerosol model (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Integrated Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the type of timestep scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Meteorological Forcings \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "Is Required: FALSE    Type: STRING    Cardinality:** 0.1\n", "### Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Variables 2D\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Two dimensionsal forcing variables, e.g. land-sea mask definition" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Frequency\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Frequency with which meteological forcings are applied (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Resolution \n", "Resolution in the aersosol model grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Canonical Horizontal Resolution\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Number Of Horizontal Gridpoints\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Number Of Vertical Levels\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Number of vertical levels resolved on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Is Adaptive Grid\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Default is False. Set true if grid resolution changes during execution." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Tuning Applied \n", "Tuning methodology for aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Global Mean Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List set of metrics of the global mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Regional Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List of regional metrics of mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Trend Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List observed trend metrics used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Transport \n", "Aerosol transport" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of transport in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method for aerosol transport modeling" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to ensure mass conservation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Transport by convention" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "Atmospheric aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of emissions in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to define aerosol species (several methods allowed because the different species may not use the same method)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Sources of the aerosol species are taken into account in the emissions scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### Specify the climatology type for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed via a climatology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed as spatially uniform" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.7. Interactive Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an interactive method" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.8. Other Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an "other method"" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.9. Other Method Characteristics\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Characteristics of the "other method" used for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Concentrations \n", "Atmospheric aerosol concentrations" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of concentrations in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Prescribed Lower Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the lower boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Prescribed Upper Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the upper boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as mass mixing ratios." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as AOD plus CCNs." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Optical Radiative Properties \n", "Aerosol optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Optical Radiative Properties --> Absorption \n", "Absortion properties in aerosol scheme" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Dust\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Organics\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Optical Radiative Properties --> Mixtures \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Is there external mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there internal mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If there is internal mixing with respect to chemical composition then indicate the mixinrg rule" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Optical Radiative Properties --> Impact Of H2o \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Does H2O impact size?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Internal Mixture\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does H2O impact internal mixture?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Optical Radiative Properties --> Radiative Scheme \n", "Radiative scheme for aerosol" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of radiative scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Shortwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of shortwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Optical Radiative Properties --> Cloud Interactions \n", "Aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Twomey\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the Twomey effect included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Twomey Minimum Ccn\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If the Twomey effect is included, then what is the minimum CCN number?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Drizzle\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect drizzle?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Cloud Lifetime\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect cloud lifetime?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Model \n", "Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Processes included in the Aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Other model components coupled to the Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of gas phase aerosol precursors." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of species covered by the bulk scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon / soot\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
ES-DOC/esdoc-jupyterhub	notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/seaice.ipynb	1	99821	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: CNRM-CERFACS \n", "Source ID: CNRM-CM6-1 \n", "Topic: Seaice \n", "Sub-Topics: Dynamics, Thermodynamics, Radiative Processes. \n", "Properties: 80 (63 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:53:52" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-cm6-1', 'seaice')" ], "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 --> Model](#1.-Key-Properties--->-Model) \n", "[2. Key Properties --> Variables](#2.-Key-Properties--->-Variables) \n", "[3. Key Properties --> Seawater Properties](#3.-Key-Properties--->-Seawater-Properties) \n", "[4. Key Properties --> Resolution](#4.-Key-Properties--->-Resolution) \n", "[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n", "[6. Key Properties --> Key Parameter Values](#6.-Key-Properties--->-Key-Parameter-Values) \n", "[7. Key Properties --> Assumptions](#7.-Key-Properties--->-Assumptions) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid --> Discretisation --> Horizontal](#9.-Grid--->-Discretisation--->-Horizontal) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Seaice Categories](#11.-Grid--->-Seaice-Categories) \n", "[12. Grid --> Snow On Seaice](#12.-Grid--->-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --> Energy](#14.-Thermodynamics--->-Energy) \n", "[15. Thermodynamics --> Mass](#15.-Thermodynamics--->-Mass) \n", "[16. Thermodynamics --> Salt](#16.-Thermodynamics--->-Salt) \n", "[17. Thermodynamics --> Salt --> Mass Transport](#17.-Thermodynamics--->-Salt--->-Mass-Transport) \n", "[18. Thermodynamics --> Salt --> Thermodynamics](#18.-Thermodynamics--->-Salt--->-Thermodynamics) \n", "[19. Thermodynamics --> Ice Thickness Distribution](#19.-Thermodynamics--->-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --> Ice Floe Size Distribution](#20.-Thermodynamics--->-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --> Melt Ponds](#21.-Thermodynamics--->-Melt-Ponds) \n", "[22. Thermodynamics --> Snow Processes](#22.-Thermodynamics--->-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --> Model \n", "Name of seaice model used." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.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": [ "# 2. Key Properties --> Variables \n", "List of prognostic variable in the sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the sea ice component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Seawater Properties \n", "Properties of seawater relevant to sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant seawater freezing point, specify this value." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \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 --> Resolution \n", "Resolution of the sea ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.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": [ "### 4.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.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": [ "### 4.3. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Tuning Applied \n", "Tuning applied to sea ice model component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.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": [ "### 5.2. Target\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \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. Simulations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which simulations had tuning applied, e.g. all, not historical, only pi-control? " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any observed metrics used in tuning model/parameters" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Which variables were changed during the tuning process?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.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": [ "# 6. Key Properties --> Key Parameter Values \n", "Values of key parameters" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### What values were specificed for the following parameters if used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "Is Required:* FALSE    Type: STRING    Cardinality: 0.N\n", "### If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \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. Key Properties --> Assumptions \n", "Assumptions made in the sea ice model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### General overview description of any key* assumptions made in this model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \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": [ "### 7.2. On Diagnostic Variables\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.N\n", "### Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### List any key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the sea ice component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.1\n", "### Provide a general 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.seaice.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. Properties\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in sea ice 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.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.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": [ "### 8.4. Was Flux Correction Used\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does conservation involved flux correction?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.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": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any 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.seaice.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": [ "# 9. Grid --> Discretisation --> Horizontal \n", "Sea ice discretisation in the horizontal" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Grid on which sea ice is horizontal discretised?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the type of sea ice grid?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the advection scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model thermodynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_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": [ "### 9.5. Dynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model dynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.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": [ "### 9.6. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional horizontal discretisation details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Sea ice vertical properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using multi-layers specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_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": [ "### 10.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional vertical grid details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Seaice Categories \n", "What method is used to represent sea ice categories ?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Set to true if the sea ice model has multiple sea ice categories." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \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": [ "### 11.2. Number Of Categories\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using sea ice categories specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \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. Category Limits\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### If using sea ice categories specify each of the category limits." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \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. Ice Thickness Distribution Scheme\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the sea ice thickness distribution scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \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. Grid --> Snow On Seaice \n", "Snow on sea ice details" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow on ice represented in this model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels of snow on ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_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": [ "### 12.3. Snow Fraction\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how the snow fraction on sea ice is determined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional details related to snow on ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "Sea Ice Dynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of horizontal advection of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice transport in thickness space (i.e. in thickness categories)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Which method of sea ice strength formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which processes can redistribute sea ice (including thickness)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Rheology, what is the ice deformation formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --> Energy \n", "Processes related to energy in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the energy formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of thermal conductivity is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of heat diffusion?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method by which basal ocean heat flux is handled?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which the heat content of precipitation is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \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. Thermodynamics --> Mass \n", "Processes related to mass in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which new sea ice is formed in open water." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \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. Ice Vertical Growth And Melt\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs the vertical growth and melt of sea ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \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. Ice Lateral Melting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice lateral melting?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs sea ice surface sublimation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of frazil ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \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. Thermodynamics --> Salt \n", "Processes related to salt in sea ice thermodynamics." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \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": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does sea ice salinity impact the thermal properties of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \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. Thermodynamics --> Salt --> Mass Transport \n", "Mass transport of salt" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the mass transport of salt calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \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. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --> Salt --> Thermodynamics \n", "Salt thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the thermodynamic calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \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. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --> Ice Thickness Distribution \n", "Ice thickness distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice thickness distribution represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --> Ice Floe Size Distribution \n", "Ice floe-size distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice floe-size represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Please provide further details on any parameterisation of floe-size." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --> Melt Ponds \n", "Characteristics of melt ponds." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are melt ponds included in the sea ice model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_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": [ "### 21.2. Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What method of melt pond formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What do melt ponds have an impact on?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --> Snow Processes \n", "Thermodynamic processes in snow on sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has a snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \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": [ "### 22.2. Snow Aging Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has snow ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \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": [ "### 22.4. Snow Ice Formation Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow ice formation scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What is the impact of ridging on snow cover?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the heat diffusion through snow methodology in sea ice thermodynamics?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "Sea Ice Radiative Processes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method used to handle surface albedo." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method by which solar radiation through sea ice is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
ledeprogram/algorithms	class9/homework/Kandrach_Sasha_9_3.ipynb	1	4467	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn import cross_validation\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.feature_extraction.text import CountVectorizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "########## STEP 1: DATA IMPORT AND PREPROCESSING ##########\n", "\n", "# Here we're taking in the training data and splitting it into two lists: One with the text of\n", "# each bill title, and the second with each bill title's corresponding category. Order is important.\n", "# The first bill in list 1 should also be the first category in list 2.\n", "training = [line.strip().split('\|') for line in open('../data/bills_training.txt', 'r').readlines()]\n", "text = [t[0] for t in training if len(t) > 1]\n", "labels = [t[1] for t in training if len(t) > 1]\n", "\n", "# A little bit of cleanup for scikit-learn's benefit. Scikit-learn models wants our categories to\n", "# be numbers, not strings. The LabelEncoder performs this transformation.\n", "encoder = preprocessing.LabelEncoder()\n", "correct_labels = encoder.fit_transform(labels)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "########## STEP 2: FEATURE EXTRACTION ##########\n", "vectorizer = CountVectorizer(stop_words='english')\n", "data = vectorizer.fit_transform(text)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "########## STEP 3: MODEL BUILDING ##########\n", "model = DecisionTreeClassifier()\n", "fit_model = model.fit(data, correct_labels)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ########## STEP 4: EVALUATION ##########\n", "# Evaluate our model with 10-fold cross-validation\n", "scores = cross_validation.cross_val_score(model, data, correct_labels, cv=5)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ########## STEP 5: APPLYING THE MODEL ##########\n", "docs_new = [\"Public postsecondary education: executive officer compensation.\",\n", " \"An act to add Section 236.3 to the Education code, related to the pricing of college textbooks.\",\n", " \"Political Reform Act of 1974: campaign disclosures.\",\n", " \"An act to add Section 236.3 to the Penal Code, relating to human trafficking.\"\n", " ]\n", "\n", "test_data = vectorizer.transform(docs_new)\n", "\n", "for i in range(len(docs_new)):\n", " print('%s -> %s' % (docs_new[i], encoder.classes_[model.predict(test_data.toarray()[i])]))\n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Overall, this model make a good amount of sense to me. The concept of keeping the information in order while separating it by title and corresponding category is something that I think could be useful in a variety of ways. \n", "\n", "The code seems pretty straight forward, however, the last step gets a bit confusing. I understand everything prior to the for loop and then the for loop I understand to an extent. If we could maybe go over the last step in more detail I think that would be helpful.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
google/starthinker	colabs/cm360_segmentology.ipynb	1	15783	{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "CM360 Segmentology", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "8b181d27-001" }, "source": [ "#CM360 Segmentology\n", "CM360 funnel analysis using Census data.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-002" }, "source": [ "#License\n", "\n", "Copyright 2020 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.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-003" }, "source": [ "#Disclaimer\n", "This is not an officially supported Google product. It is a reference implementation. There is absolutely NO WARRANTY provided for using this code. The code is Apache Licensed and CAN BE fully modified, white labeled, and disassembled by your team.\n", "\n", "This code generated (see starthinker/scripts for possible source):\n", " - Command: \"python starthinker_ui/manage.py colab\"\n", " - Command: \"python starthinker/tools/colab.py [JSON RECIPE]\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-004" }, "source": [ "#1. Install Dependencies\n", "First install the libraries needed to execute recipes, this only needs to be done once, then click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "8b181d27-005" }, "source": [ "!pip install git+https://github.com/google/starthinker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-006" }, "source": [ "#2. Set Configuration\n", "\n", "This code is required to initialize the project. Fill in required fields and press play.\n", "\n", "1. If the recipe uses a Google Cloud Project:\n", " - Set the configuration project value to the project identifier from [these instructions](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md).\n", "\n", "1. If the recipe has auth set to user:\n", " - If you have user credentials:\n", " - Set the configuration user value to your user credentials JSON.\n", " - If you DO NOT have user credentials:\n", " - Set the configuration client value to [downloaded client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md).\n", "\n", "1. If the recipe has auth set to service:\n", " - Set the configuration service value to [downloaded service credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_service.md).\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "8b181d27-007" }, "source": [ "from starthinker.util.configuration import Configuration\n", "\n", "\n", "CONFIG = Configuration(\n", " project=\"\",\n", " client={},\n", " service={},\n", " user=\"/content/user.json\",\n", " verbose=True\n", ")\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-008" }, "source": [ "#3. Enter CM360 Segmentology Recipe Parameters\n", " 1. Wait for BigQuery->->->Census_Join to be created.\n", " 1. Join the [StarThinker Assets Group](https://groups.google.com/d/forum/starthinker-assets) to access the following assets\n", " 1. Copy [CM360 Segmentology Sample](https://datastudio.google.com/c/u/0/reporting/3673497b-f36f-4448-8fb9-3e05ea51842f/). Leave the Data Source as is, you will change it in the next step.\n", " 1. Click Edit Connection, and change to BigQuery->->->Census_Join.\n", " 1. Or give these intructions to the client.\n", "Modify the values below for your use case, can be done multiple times, then click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "8b181d27-009" }, "source": [ "FIELDS = {\n", " 'account':'',\n", " 'auth_read':'user', # Credentials used for reading data.\n", " 'auth_write':'service', # Authorization used for writing data.\n", " 'recipe_name':'', # Name of report, not needed if ID used.\n", " 'date_range':'LAST_365_DAYS', # Timeframe to run report for.\n", " 'recipe_slug':'', # Name of Google BigQuery dataset to create.\n", " 'advertisers':[], # Comma delimited list of CM360 advertiser ids.\n", "}\n", "\n", "print(\"Parameters Set To: %s\" % FIELDS)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8b181d27-010" }, "source": [ "#4. Execute CM360 Segmentology\n", "This does NOT need to be modified unless you are changing the recipe, click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "8b181d27-011" }, "source": [ "from starthinker.util.configuration import execute\n", "from starthinker.util.recipe import json_set_fields\n", "\n", "TASKS = [\n", " {\n", " 'dataset':{\n", " 'description':'Create a dataset for bigquery tables.',\n", " 'hour':[\n", " 4\n", " ],\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Credentials used for writing data.'}},\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','description':'Place where tables will be created in BigQuery.'}}\n", " }\n", " },\n", " {\n", " 'bigquery':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Credentials used for writing function.'}},\n", " 'function':'Pearson Significance Test',\n", " 'to':{\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}}\n", " }\n", " }\n", " },\n", " {\n", " 'google_api':{\n", " 'auth':'user',\n", " 'api':'dfareporting',\n", " 'version':'v3.4',\n", " 'function':'accounts.get',\n", " 'kwargs':{\n", " 'id':{'field':{'name':'account','kind':'integer','order':5,'default':'','description':'Campaign Manager Account ID'}},\n", " 'fields':'id,name'\n", " },\n", " 'results':{\n", " 'bigquery':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Credentials used for writing function.'}},\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}},\n", " 'table':'CM360_Account'\n", " }\n", " }\n", " }\n", " },\n", " {\n", " 'dcm':{\n", " 'auth':{'field':{'name':'auth_read','kind':'authentication','order':0,'default':'user','description':'Credentials used for reading data.'}},\n", " 'report':{\n", " 'filters':{\n", " 'advertiser':{\n", " 'values':{'field':{'name':'advertisers','kind':'integer_list','order':6,'default':[],'description':'Comma delimited list of CM360 advertiser ids.'}}\n", " }\n", " },\n", " 'account':{'field':{'name':'account','kind':'string','order':5,'default':'','description':'Campaign Manager Account ID'}},\n", " 'body':{\n", " 'name':{'field':{'name':'recipe_name','kind':'string','suffix':' Segmentology','description':'The report name.','default':''}},\n", " 'criteria':{\n", " 'dateRange':{\n", " 'kind':'dfareporting#dateRange',\n", " 'relativeDateRange':{'field':{'name':'date_range','kind':'choice','order':3,'default':'LAST_365_DAYS','choices':['LAST_7_DAYS','LAST_14_DAYS','LAST_30_DAYS','LAST_365_DAYS','LAST_60_DAYS','LAST_7_DAYS','LAST_90_DAYS','LAST_24_MONTHS','MONTH_TO_DATE','PREVIOUS_MONTH','PREVIOUS_QUARTER','PREVIOUS_WEEK','PREVIOUS_YEAR','QUARTER_TO_DATE','WEEK_TO_DATE','YEAR_TO_DATE'],'description':'Timeframe to run report for.'}}\n", " },\n", " 'dimensions':[\n", " {\n", " 'kind':'dfareporting#sortedDimension',\n", " 'name':'advertiserId'\n", " },\n", " {\n", " 'kind':'dfareporting#sortedDimension',\n", " 'name':'advertiser'\n", " },\n", " {\n", " 'kind':'dfareporting#sortedDimension',\n", " 'name':'zipCode'\n", " }\n", " ],\n", " 'metricNames':[\n", " 'impressions',\n", " 'clicks',\n", " 'totalConversions'\n", " ]\n", " },\n", " 'type':'STANDARD',\n", " 'delivery':{\n", " 'emailOwner':False\n", " },\n", " 'format':'CSV'\n", " }\n", " }\n", " }\n", " },\n", " {\n", " 'dcm':{\n", " 'auth':{'field':{'name':'auth_read','kind':'authentication','order':0,'default':'user','description':'Credentials used for reading data.'}},\n", " 'report':{\n", " 'account':{'field':{'name':'account','kind':'string','default':''}},\n", " 'name':{'field':{'name':'recipe_name','kind':'string','order':3,'suffix':' Segmentology','default':'','description':'Name of report, not needed if ID used.'}}\n", " },\n", " 'out':{\n", " 'bigquery':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Authorization used for writing data.'}},\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}},\n", " 'table':'CM360_KPI',\n", " 'header':True\n", " }\n", " }\n", " }\n", " },\n", " {\n", " 'bigquery':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Authorization used for writing data.'}},\n", " 'from':{\n", " 'query':'SELECT Id AS Partner_Id, Name AS Partner, Advertiser_Id, Advertiser, Zip_Postal_Code AS Zip, SAFE_DIVIDE(Impressions, SUM(Impressions) OVER(PARTITION BY Advertiser_Id)) AS Impression, SAFE_DIVIDE(Clicks, Impressions) AS Click, SAFE_DIVIDE(Total_Conversions, Impressions) AS Conversion, Impressions AS Impressions FROM `{dataset}.CM360_KPI` CROSS JOIN `{dataset}.CM360_Account` ',\n", " 'parameters':{\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','description':'Place where tables will be created in BigQuery.'}}\n", " },\n", " 'legacy':False\n", " },\n", " 'to':{\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','description':'Place where tables will be written in BigQuery.'}},\n", " 'view':'CM360_KPI_Normalized'\n", " }\n", " }\n", " },\n", " {\n", " 'census':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Authorization used for writing data.'}},\n", " 'normalize':{\n", " 'census_geography':'zip_codes',\n", " 'census_year':'2018',\n", " 'census_span':'5yr'\n", " },\n", " 'to':{\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}},\n", " 'type':'view'\n", " }\n", " }\n", " },\n", " {\n", " 'census':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Authorization used for writing data.'}},\n", " 'correlate':{\n", " 'join':'Zip',\n", " 'pass':[\n", " 'Partner_Id',\n", " 'Partner',\n", " 'Advertiser_Id',\n", " 'Advertiser'\n", " ],\n", " 'sum':[\n", " 'Impressions'\n", " ],\n", " 'correlate':[\n", " 'Impression',\n", " 'Click',\n", " 'Conversion'\n", " ],\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}},\n", " 'table':'CM360_KPI_Normalized',\n", " 'significance':80\n", " },\n", " 'to':{\n", " 'dataset':{'field':{'name':'recipe_slug','kind':'string','suffix':'_Segmentology','order':4,'default':'','description':'Name of Google BigQuery dataset to create.'}},\n", " 'type':'view'\n", " }\n", " }\n", " }\n", "]\n", "\n", "json_set_fields(TASKS, FIELDS)\n", "\n", "execute(CONFIG, TASKS, force=True)\n" ] } ] }	apache-2.0
aborgher/Main-useful-functions-for-ML	.ipynb_checkpoints/NLP-checkpoint.ipynb	1	67248	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Correction-with-enchant\" data-toc-modified-id=\"Correction-with-enchant-1\"><span class=\"toc-item-num\">1  </span>Correction with enchant</a></div><div class=\"lev2 toc-item\"><a href=\"#Add-your-own-dictionary\" data-toc-modified-id=\"Add-your-own-dictionary-11\"><span class=\"toc-item-num\">1.1  </span>Add your own dictionary</a></div><div class=\"lev2 toc-item\"><a href=\"#check-entire-phrase\" data-toc-modified-id=\"check-entire-phrase-12\"><span class=\"toc-item-num\">1.2  </span>check entire phrase</a></div><div class=\"lev2 toc-item\"><a href=\"#tokenization\" data-toc-modified-id=\"tokenization-13\"><span class=\"toc-item-num\">1.3  </span>tokenization</a></div><div class=\"lev1 toc-item\"><a href=\"#Word2vec\" data-toc-modified-id=\"Word2vec-2\"><span class=\"toc-item-num\">2  </span>Word2vec</a></div><div class=\"lev1 toc-item\"><a href=\"#Translate-using-google-translate\" data-toc-modified-id=\"Translate-using-google-translate-3\"><span class=\"toc-item-num\">3  </span>Translate using google translate</a></div><div class=\"lev1 toc-item\"><a href=\"#TreeTagger-usage-to-tag-an-italian-(or-other-languages)-sentence\" data-toc-modified-id=\"TreeTagger-usage-to-tag-an-italian-(or-other-languages)-sentence-4\"><span class=\"toc-item-num\">4  </span>TreeTagger usage to tag an italian (or other languages) sentence</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Correction with enchant\n", "- install via pip install pyenchant\n", "- add ita dictionary: sudo apt-get install myspell-it myspell-es\n", "- Tutorial at: http://pythonhosted.org/pyenchant/tutorial.html" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:47:39.947813Z", "start_time": "2017-09-10T12:47:39.941138Z" }, "collapsed": true }, "outputs": [], "source": [ "import enchant" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:47:44.713145Z", "start_time": "2017-09-10T12:47:44.692481Z" }, "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<Enchant: Aspell Provider>, <Enchant: Ispell Provider>, <Enchant: Hspell Provider>, <Enchant: Myspell Provider>]\n" ] }, { "data": { "text/plain": [ "[('en', <Enchant: Aspell Provider>),\n", " ('en_CA', <Enchant: Aspell Provider>),\n", " ('en_GB', <Enchant: Aspell Provider>),\n", " ('en_US', <Enchant: Aspell Provider>),\n", " ('es_MX', <Enchant: Myspell Provider>),\n", " ('es_BO', <Enchant: Myspell Provider>),\n", " ('es_CO', <Enchant: Myspell Provider>),\n", " ('es_VE', <Enchant: Myspell Provider>),\n", " ('es_UY', <Enchant: Myspell Provider>),\n", " ('es_PR', <Enchant: Myspell Provider>),\n", " ('es_EC', <Enchant: Myspell Provider>),\n", " ('es_CU', <Enchant: Myspell Provider>),\n", " ('es_ES', <Enchant: Myspell Provider>),\n", " ('es_PA', <Enchant: Myspell Provider>),\n", " ('es_NI', <Enchant: Myspell Provider>),\n", " ('es_CR', <Enchant: Myspell Provider>),\n", " ('es_PE', <Enchant: Myspell Provider>),\n", " ('it_CH', <Enchant: Myspell Provider>),\n", " ('es_GT', <Enchant: Myspell Provider>),\n", " ('es_PY', <Enchant: Myspell Provider>),\n", " ('es_SV', <Enchant: Myspell Provider>),\n", " ('it_IT', <Enchant: Myspell Provider>),\n", " ('es_HN', <Enchant: Myspell Provider>),\n", " ('es_CL', <Enchant: Myspell Provider>),\n", " ('es', <Enchant: Myspell Provider>),\n", " ('es_DO', <Enchant: Myspell Provider>),\n", " ('es_AR', <Enchant: Myspell Provider>)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The underlying programming model provided by the Enchant library is based on the notion of Providers. \n", "# A provider is a piece of code that provides spell-checking services which Enchant can use to perform its work. \n", "# Different providers exist for performing spellchecking using different frameworks - \n", "# for example there is an aspell provider and a MySpell provider.\n", "## no need to check brokers while running enchant, this is just a simple check if all is installed\n", "b = enchant.Broker()\n", "print(b.describe())\n", "b.list_dicts()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:47:54.862556Z", "start_time": "2017-09-10T12:47:54.851131Z" }, "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "['en',\n", " 'en_CA',\n", " 'en_GB',\n", " 'en_US',\n", " 'es_MX',\n", " 'es_BO',\n", " 'es_CO',\n", " 'es_VE',\n", " 'es_UY',\n", " 'es_PR',\n", " 'es_EC',\n", " 'es_CU',\n", " 'es_ES',\n", " 'es_PA',\n", " 'es_NI',\n", " 'es_CR',\n", " 'es_PE',\n", " 'it_CH',\n", " 'es_GT',\n", " 'es_PY',\n", " 'es_SV',\n", " 'it_IT',\n", " 'es_HN',\n", " 'es_CL',\n", " 'es',\n", " 'es_DO',\n", " 'es_AR']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enchant.list_languages()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:48:32.886297Z", "start_time": "2017-09-10T12:48:32.848584Z" }, "collapsed": true }, "outputs": [], "source": [ "d = enchant.Dict(\"it_IT\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:48:33.036465Z", "start_time": "2017-09-10T12:48:33.025864Z" } }, "outputs": [ { "data": { "text/plain": [ "(True, False)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.check('Giulia'), d.check('pappapero')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:48:37.997355Z", "start_time": "2017-09-10T12:48:37.218697Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['potrei ma', 'potrei-ma', 'potrei', 'impomatare']\n", "['marame', 'marea', 'maremma', 'Carema', 'ma rema', 'ma-rema', 'mare ma', 'mare-ma', 'Maremma', 'remare', 'remar', 'mare']\n", "[]\n", "['vanno', 'vano']\n", "['duellatole']\n" ] } ], "source": [ "print( d.suggest(\"potreima\") )\n", "print( d.suggest(\"marema\") )\n", "print( d.suggest(\"se metto troppe parole lo impallo\") )\n", "print( d.suggest(\"van no\") )\n", "print( d.suggest(\"due parole\") )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add your own dictionary" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:49:12.131328Z", "start_time": "2017-09-10T12:49:12.128663Z" }, "collapsed": true }, "outputs": [], "source": [ "# Dict objects can also be used to check words against a custom list of correctly-spelled words \n", "# known as a Personal Word List. This is simply a file listing the words to be considered, one word per line. \n", "# The following example creates a Dict object for the personal word list stored in “mywords.txt”:\n", "pwl = enchant.request_pwl_dict(\"../Data_nlp/mywords.txt\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:49:12.583454Z", "start_time": "2017-09-10T12:49:12.575641Z" } }, "outputs": [ { "data": { "text/plain": [ "(False, [], False)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwl.check('pappapero'), pwl.suggest('cittin'), pwl.check('altro')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:49:19.107158Z", "start_time": "2017-09-10T12:49:19.102634Z" }, "collapsed": true }, "outputs": [], "source": [ "# PyEnchant also provides the class DictWithPWL which can be used to combine a language dictionary \n", "# and a personal word list file:\n", "d2 = enchant.DictWithPWL(\"it_IT\", \"../Data_nlp/mywords.txt\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:49:34.145022Z", "start_time": "2017-09-10T12:49:34.097786Z" } }, "outputs": [ { "data": { "text/plain": [ "(False, ['cittadino'])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d2.check('altro') & d2.check('pappapero'), d2.suggest('cittin')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:49:42.086244Z", "start_time": "2017-09-10T12:49:41.074963Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 24.1 ms per loop\n" ] } ], "source": [ "%%timeit\n", "d2.suggest('poliza')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## check entire phrase" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:25.871039Z", "start_time": "2017-09-10T12:50:25.868833Z" }, "collapsed": true }, "outputs": [], "source": [ "from enchant.checker import SpellChecker\n", "chkr = SpellChecker(\"it_IT\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:26.105785Z", "start_time": "2017-09-10T12:50:26.030779Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "picclo\n", "['picco', 'piccolo', 'picciolo', 'epiciclo', 'ciclopico']\n", "esmpio\n", "['espio', 'empio', 'esempio']\n" ] } ], "source": [ "chkr.set_text(\"questo è un picclo esmpio per dire cm funziona\")\n", "for err in chkr:\n", " print(err.word)\n", " print(chkr.suggest(err.word))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:26.224793Z", "start_time": "2017-09-10T12:50:26.220167Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "esmpio 19\n" ] } ], "source": [ "print(chkr.word, chkr.wordpos)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:27.683652Z", "start_time": "2017-09-10T12:50:27.678472Z" } }, "outputs": [ { "data": { "text/plain": [ "'questo è un picclo pippo per dire cm funziona'" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chkr.replace('pippo')\n", "chkr.get_text()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## tokenization\n", "As explained above, the module enchant.tokenize provides the ability to split text into its component words. The current implementation is based only on the rules for the English language, and so might not be completely suitable for your language of choice. Fortunately, it is straightforward to extend the functionality of this module.\n", "\n", "To implement a new tokenization routine for the language TAG, simply create a class/function “tokenize” within the module “enchant.tokenize.TAG”. This function will automatically be detected by the module’s get_tokenizer function and used when appropriate. The easiest way to accomplish this is to copy the module “enchant.tokenize.en” and modify it to suit your needs." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:35.437831Z", "start_time": "2017-09-10T12:50:35.426129Z" } }, "outputs": [ { "data": { "text/plain": [ "[('this', 0), ('is', 5), ('some', 8), ('simple', 13), ('text', 20)]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from enchant.tokenize import get_tokenizer\n", "tknzr = get_tokenizer(\"en_US\") # not tak for it_IT up to now\n", "[w for w in tknzr(\"this is some simple text\")]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:40.168971Z", "start_time": "2017-09-10T12:50:40.161116Z" } }, "outputs": [ { "data": { "text/plain": [ "[('this', 0),\n", " ('is', 5),\n", " ('span', 9),\n", " ('class', 14),\n", " ('important', 21),\n", " ('really', 32),\n", " ('important', 39),\n", " ('span', 50),\n", " ('text', 56)]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from enchant.tokenize import get_tokenizer, HTMLChunker\n", "tknzr = get_tokenizer(\"en_US\")\n", "[w for w in tknzr(\"this is <span class='important'>really important</span> text\")]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:57.403831Z", "start_time": "2017-09-10T12:50:57.399961Z" } }, "outputs": [ { "data": { "text/plain": [ "[('this', 0), ('is', 5), ('really', 32), ('important', 39), ('text', 56)]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tknzr = get_tokenizer(\"en_US\",chunkers=(HTMLChunker,))\n", "[w for w in tknzr(\"this is <span class='important'>really important</span> text\")]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:57.625939Z", "start_time": "2017-09-10T12:50:57.614942Z" } }, "outputs": [ { "data": { "text/plain": [ "[('send', 0),\n", " ('an', 5),\n", " ('email', 8),\n", " ('to', 14),\n", " ('fake', 17),\n", " ('example', 22),\n", " ('com', 30),\n", " ('please', 34)]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from enchant.tokenize import get_tokenizer, EmailFilter\n", "tknzr = get_tokenizer(\"en_US\")\n", "[w for w in tknzr(\"send an email to [email protected] please\")]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:50:57.814020Z", "start_time": "2017-09-10T12:50:57.804621Z" } }, "outputs": [ { "data": { "text/plain": [ "[('send', 0), ('an', 5), ('email', 8), ('to', 14), ('please', 34)]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tknzr = get_tokenizer(\"en_US\", filters = [EmailFilter])\n", "[w for w in tknzr(\"send an email to [email protected] please\")]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other modules:\n", "- CmdLineChecker\n", "\n", "The module enchant.checker.CmdLineChecker provides the class CmdLineChecker which can be used to interactively check the spelling of some text. It uses standard input and standard output to interact with the user through a command-line interface. The code below shows how to create and use this class from within a python application, along with a short sample checking session:\n", "\n", "- wxSpellCheckerDialog\n", "\n", "The module enchant.checker.wxSpellCheckerDialog provides the class wxSpellCheckerDialog which can be used to interactively check the spelling of some text. The code below shows how to create and use such a dialog from within a wxPython application." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Word2vec\n", "- pip install gensim\n", "- pip install pyemd\n", "- https://radimrehurek.com/gensim/models/word2vec.html" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:52:27.414784Z", "start_time": "2017-09-10T12:52:26.990904Z" }, "collapsed": true }, "outputs": [], "source": [ "import gensim, logging\n", "from gensim.models import Word2Vec" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:54:12.165301Z", "start_time": "2017-09-10T12:52:30.863143Z" }, "collapsed": true }, "outputs": [], "source": [ "model = gensim.models.KeyedVectors.load_word2vec_format(\n", " '../Data_nlp/GoogleNews-vectors-negative300.bin.gz', binary=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:54:14.052138Z", "start_time": "2017-09-10T12:54:12.166233Z" } }, "outputs": [ { "data": { "text/plain": [ "'brian'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.doesnt_match(\"breakfast brian dinner lunch\".split())" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:56:19.105300Z", "start_time": "2017-09-10T12:56:19.098142Z" }, "collapsed": true }, "outputs": [ { "ename": "TypeError", "evalue": "evaluate_word_pairs() missing 1 required positional argument: 'pairs'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-35-8fd5a98fc876>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# give text with w1 w2 your_distance to check if model and w1-w2 have give the same distance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_word_pairs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: evaluate_word_pairs() missing 1 required positional argument: 'pairs'" ] } ], "source": [ "# give text with w1 w2 your_distance to check if model and w1-w2 have give the same distance\n", "model.evaluate_word_pairs() " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:56:27.887161Z", "start_time": "2017-09-10T12:56:27.884606Z" } }, "outputs": [ { "data": { "text/plain": [ "3000000" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(model.index2word)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.190063Z", "start_time": "2017-09-10T12:56:28.138790Z" }, "collapsed": true }, "outputs": [], "source": [ "# check accuracy against a premade grouped words\n", "questions_words = model.accuracy('../Data_nlp/word2vec/trunk/questions-words.txt')\n", "phrases_words = model.accuracy('../Data_nlp/word2vec/trunk/questions-phrases.txt')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.200556Z", "start_time": "2017-09-10T12:57:34.191292Z" } }, "outputs": [ { "data": { "text/plain": [ "[('BOY', 'GIRL', 'STEPFATHER', 'STEPMOTHER'),\n", " ('BROTHER', 'SISTER', 'STEPFATHER', 'STEPMOTHER'),\n", " ('BROTHERS', 'SISTERS', 'STEPFATHER', 'STEPMOTHER'),\n", " ('DAD', 'MOM', 'HUSBAND', 'WIFE'),\n", " ('DAD', 'MOM', 'STEPFATHER', 'STEPMOTHER'),\n", " ('GRANDFATHER', 'GRANDMOTHER', 'HUSBAND', 'WIFE'),\n", " ('GRANDFATHER', 'GRANDMOTHER', 'STEPFATHER', 'STEPMOTHER'),\n", " ('GRANDSON', 'GRANDDAUGHTER', 'STEPFATHER', 'STEPMOTHER'),\n", " ('GROOM', 'BRIDE', 'NEPHEW', 'NIECE'),\n", " ('GROOM', 'BRIDE', 'STEPFATHER', 'STEPMOTHER'),\n", " ('GROOM', 'BRIDE', 'UNCLE', 'AUNT'),\n", " ('GROOM', 'BRIDE', 'BROTHER', 'SISTER'),\n", " ('GROOM', 'BRIDE', 'BROTHERS', 'SISTERS'),\n", " ('HE', 'SHE', 'HUSBAND', 'WIFE'),\n", " ('HE', 'SHE', 'STEPFATHER', 'STEPMOTHER'),\n", " ('HIS', 'HER', 'HUSBAND', 'WIFE'),\n", " ('HIS', 'HER', 'STEPFATHER', 'STEPMOTHER'),\n", " ('HUSBAND', 'WIFE', 'BROTHER', 'SISTER'),\n", " ('HUSBAND', 'WIFE', 'DAD', 'MOM'),\n", " ('HUSBAND', 'WIFE', 'FATHER', 'MOTHER'),\n", " ('HUSBAND', 'WIFE', 'GRANDFATHER', 'GRANDMOTHER'),\n", " ('HUSBAND', 'WIFE', 'HE', 'SHE'),\n", " ('HUSBAND', 'WIFE', 'HIS', 'HER'),\n", " ('KING', 'QUEEN', 'STEPFATHER', 'STEPMOTHER'),\n", " ('MAN', 'WOMAN', 'STEPFATHER', 'STEPMOTHER'),\n", " ('MAN', 'WOMAN', 'HUSBAND', 'WIFE'),\n", " ('NEPHEW', 'NIECE', 'STEPFATHER', 'STEPMOTHER'),\n", " ('PRINCE', 'PRINCESS', 'STEPFATHER', 'STEPMOTHER'),\n", " ('PRINCE', 'PRINCESS', 'DAD', 'MOM'),\n", " ('PRINCE', 'PRINCESS', 'HUSBAND', 'WIFE'),\n", " ('SON', 'DAUGHTER', 'STEPFATHER', 'STEPMOTHER'),\n", " ('SONS', 'DAUGHTERS', 'STEPFATHER', 'STEPMOTHER'),\n", " ('STEPFATHER', 'STEPMOTHER', 'GRANDFATHER', 'GRANDMOTHER'),\n", " ('UNCLE', 'AUNT', 'STEPFATHER', 'STEPMOTHER')]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "questions_words[4]['incorrect']" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.209759Z", "start_time": "2017-09-10T12:57:34.204123Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.660332138317\n", "0.458195260447\n", "0.177056094484\n", "0.760945708978\n" ] } ], "source": [ "print( model.n_similarity(['pasta'], ['spaghetti']) )\n", "print( model.n_similarity(['pasta'], ['tomato']) )\n", "print( model.n_similarity(['pasta'], ['car']) )\n", "print( model.n_similarity(['cat'], ['dog']) )" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.481775Z", "start_time": "2017-09-10T12:57:34.213291Z" } }, "outputs": [ { "data": { "text/plain": [ "[('welcome', 1.0),\n", " ('welcomed', 0.7077772617340088),\n", " ('welcoming', 0.7071465849876404),\n", " ('welcomes', 0.6647579669952393),\n", " ('warmly_welcomed', 0.6219103336334229),\n", " ('warmly_welcome', 0.5892778038978577),\n", " ('Welcoming', 0.5658251047134399),\n", " ('greatly_appreciated', 0.5299198627471924),\n", " ('warmly_welcomes', 0.521955132484436),\n", " ('invite', 0.5170012712478638)]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.similar_by_vector( model.word_vec('welcome') )" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.739154Z", "start_time": "2017-09-10T12:57:34.485640Z" } }, "outputs": [ { "data": { "text/plain": [ "[('welcomed', 0.7077772617340088),\n", " ('welcoming', 0.7071465849876404),\n", " ('welcomes', 0.6647579669952393),\n", " ('warmly_welcomed', 0.6219103336334229),\n", " ('warmly_welcome', 0.5892777442932129),\n", " ('Welcoming', 0.5658251047134399),\n", " ('greatly_appreciated', 0.5299198627471924),\n", " ('warmly_welcomes', 0.521955132484436),\n", " ('invite', 0.517001211643219),\n", " ('delighted', 0.5136862397193909)]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.similar_by_word('welcome')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.762615Z", "start_time": "2017-09-10T12:57:34.744872Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00704956, -0.07324219, 0.171875 , 0.02258301, -0.1328125 ,\n", " 0.19824219, 0.11279297, -0.10791016, 0.07177734, 0.02087402,\n", " -0.12304688, -0.05908203, 0.10107422, 0.01074219, 0.14355469,\n", " 0.25976562, -0.03637695, 0.18554688, -0.07861328, -0.02270508,\n", " -0.12060547, 0.17773438, 0.04956055, 0.01721191, 0.07958984,\n", " -0.0456543 , -0.18847656, 0.18945312, -0.02319336, 0.06298828,\n", " 0.09765625, -0.01904297, -0.07910156, 0.15234375, 0.17382812,\n", " 0.1015625 , -0.16308594, 0.11474609, 0.10058594, -0.09277344,\n", " 0.109375 , 0.05883789, -0.02160645, 0.06347656, 0.04199219,\n", " -0.0088501 , 0.03222656, 0.10644531, 0.06445312, -0.11865234,\n", " 0.03051758, 0.06689453, 0.12207031, -0.08300781, 0.171875 ,\n", " 0.07861328, 0.09521484, -0.00778198, 0.02319336, 0.0234375 ,\n", " -0.0168457 , 0.15527344, -0.10986328, -0.17675781, -0.11621094,\n", " 0.0234375 , -0.01062012, 0.05273438, -0.13378906, 0.07958984,\n", " 0.07373047, 0.04394531, 0.11523438, -0.02062988, 0.07470703,\n", " -0.01153564, 0.08056641, 0.04174805, 0.08007812, 0.3515625 ,\n", " 0.09667969, -0.21289062, 0.16503906, -0.078125 , 0.06982422,\n", " -0.00139618, -0.09130859, 0.12988281, 0.25195312, -0.01611328,\n", " 0.09326172, -0.14648438, -0.00151062, -0.15136719, -0.02685547,\n", " -0.15722656, 0.02636719, 0.0859375 , 0.07177734, 0.07714844,\n", " -0.0390625 , 0.05444336, -0.12792969, 0.09130859, -0.18457031,\n", " -0.03759766, -0.0279541 , -0.08984375, -0.11669922, -0.09863281,\n", " 0.0480957 , -0.16210938, -0.10888672, 0.08496094, -0.0456543 ,\n", " 0.15820312, -0.03808594, -0.08203125, 0.203125 , 0.08642578,\n", " 0.06933594, 0.03222656, -0.16015625, 0.09472656, -0.0246582 ,\n", " 0.05419922, 0.0279541 , 0.04492188, 0.16992188, 0.07275391,\n", " -0.03637695, -0.01025391, -0.01708984, -0.10742188, -0.0007019 ,\n", " -0.07373047, 0.25390625, 0.05664062, 0.03515625, -0.00860596,\n", " 0.18554688, 0.02148438, 0.26367188, -0.02380371, -0.09912109,\n", " -0.04125977, -0.06933594, -0.11376953, 0.05004883, -0.05883789,\n", " 0.04614258, 0.08740234, 0.10546875, 0.10644531, 0.0279541 ,\n", " 0.09472656, 0.11621094, -0.17285156, -0.03491211, -0.20800781,\n", " 0.05957031, 0.10400391, -0.00179291, 0.05859375, -0.02978516,\n", " -0.03759766, 0.04858398, -0.06396484, 0.07958984, 0.06933594,\n", " -0.10498047, -0.14453125, 0.04345703, -0.06884766, -0.03564453,\n", " -0.01171875, 0.01367188, -0.06591797, 0.11914062, 0.03125 ,\n", " -0.04638672, -0.00196838, 0.00735474, -0.05664062, 0.02783203,\n", " 0.08251953, -0.01348877, 0.07177734, 0.14453125, 0.12792969,\n", " 0.04223633, 0.14160156, -0.01806641, 0.02160645, -0.09179688,\n", " 0.13378906, -0.1953125 , -0.05029297, -0.0378418 , -0.09619141,\n", " 0.10302734, -0.10693359, -0.14746094, 0.09960938, -0.23046875,\n", " 0.22753906, -0.07519531, 0.06494141, 0.09179688, 0.046875 ,\n", " 0.06298828, 0.06982422, 0.04614258, 0.09716797, -0.20214844,\n", " 0.19921875, 0.18652344, -0.11962891, -0.14257812, 0.15039062,\n", " -0.03369141, -0.14550781, -0.00069046, -0.07324219, 0.13378906,\n", " 0.03564453, -0.02294922, 0.02770996, -0.07910156, 0.20703125,\n", " -0.08349609, -0.04956055, 0.03149414, 0.1484375 , 0.05566406,\n", " -0.04492188, -0.07958984, 0.00476074, -0.02075195, 0.06005859,\n", " 0.00476074, 0.01116943, 0.17285156, -0.13476562, 0.03076172,\n", " -0.07958984, 0.09033203, 0.06103516, 0.07714844, -0.05029297,\n", " -0.09228516, -0.26757812, 0.10791016, 0.0859375 , 0.06298828,\n", " 0.10791016, -0.0267334 , 0.10205078, -0.12060547, 0.05297852,\n", " 0.09472656, -0.16503906, 0.04418945, 0.07226562, 0.04125977,\n", " 0.42578125, -0.10302734, -0.16015625, -0.09033203, -0.06396484,\n", " -0.0480957 , 0.14453125, 0.06542969, 0.04931641, 0.05419922,\n", " 0.13574219, -0.01928711, -0.21582031, -0.07421875, -0.14648438,\n", " 0.01147461, -0.16503906, -0.10498047, 0.00320435, 0.13476562,\n", " -0.00396729, -0.10351562, -0.13964844, 0.10449219, -0.01257324,\n", " -0.23339844, -0.03637695, -0.09375 , 0.18261719, 0.02709961,\n", " 0.12792969, -0.02478027, 0.01123047, 0.1640625 , 0.10693359], dtype=float32)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.syn0[4,]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.775518Z", "start_time": "2017-09-10T12:57:34.766603Z" } }, "outputs": [ { "data": { "text/plain": [ "'is'" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.index2word[4]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.803979Z", "start_time": "2017-09-10T12:57:34.781263Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00704956, -0.07324219, 0.171875 , 0.02258301, -0.1328125 ,\n", " 0.19824219, 0.11279297, -0.10791016, 0.07177734, 0.02087402,\n", " -0.12304688, -0.05908203, 0.10107422, 0.01074219, 0.14355469,\n", " 0.25976562, -0.03637695, 0.18554688, -0.07861328, -0.02270508,\n", " -0.12060547, 0.17773438, 0.04956055, 0.01721191, 0.07958984,\n", " -0.0456543 , -0.18847656, 0.18945312, -0.02319336, 0.06298828,\n", " 0.09765625, -0.01904297, -0.07910156, 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"array([ 0.00374603, -0.03891977, 0.09133173, 0.01200026, -0.07057451,\n", " 0.10534285, 0.05993645, -0.0573418 , 0.03814138, 0.01109213,\n", " -0.06538521, -0.03139528, 0.05370928, 0.00570823, 0.07628275,\n", " 0.13803546, -0.01933015, 0.09859675, -0.04177389, -0.01206513,\n", " -0.06408789, 0.09444531, 0.02633571, 0.00914615, 0.04229282,\n", " -0.02425999, -0.10015354, 0.10067248, -0.01232459, 0.033471 ,\n", " 0.05189303, -0.01011914, -0.04203335, 0.08095312, 0.09236959,\n", " 0.05396875, -0.08666135, 0.06097431, 0.05344982, -0.04929838,\n", " 0.05812019, 0.03126555, -0.01148133, 0.03373047, 0.022314 ,\n", " -0.00470281, 0.0171247 , 0.0565634 , 0.0342494 , -0.06305003,\n", " 0.01621657, 0.03554672, 0.06486628, -0.04410907, 0.09133173,\n", " 0.04177389, 0.0505957 , -0.00413523, 0.01232459, 0.01245433,\n", " -0.00895155, 0.08250991, -0.05837966, -0.09392638, -0.0617527 ,\n", " 0.01245433, -0.00564337, 0.02802224, -0.07109345, 0.04229282,\n", " 0.03917924, 0.02335186, 0.06123377, 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, -0.03684405, -0.06045538, 0.02659518, -0.03126555,\n", " 0.02451946, 0.04644426, 0.05604447, 0.0565634 , 0.01485438,\n", " 0.05033624, 0.0617527 , -0.09185066, -0.01855176, -0.11053215,\n", " 0.03165475, 0.05526607, -0.00095272, 0.03113582, -0.01582737,\n", " -0.01997882, 0.02581678, -0.03398993, 0.04229282, 0.03684405,\n", " -0.055785 , -0.07680168, 0.0230924 , -0.03658459, -0.01894096,\n", " -0.00622716, 0.00726502, -0.03502779, 0.06330949, 0.01660577,\n", " -0.02464919, -0.00104597, 0.00390819, -0.03009796, 0.01478951,\n", " 0.04384961, -0.00716772, 0.03814138, 0.07680168, 0.06797986,\n", " 0.02244373, 0.07524489, -0.00960021, 0.01148133, -0.04877945,\n", " 0.07109345, -0.10378606, -0.02672491, -0.02010855, -0.05111463,\n", " 0.05474715, -0.05682287, -0.07835847, 0.05293089, -0.12246755,\n", " 0.12091076, -0.03995763, 0.03450887, 0.04877945, 0.02490865,\n", " 0.033471 , 0.03710352, 0.02451946, 0.05163356, -0.10741857,\n", " 0.10586178, 0.09911568, -0.06356896, -0.07576382, 0.07991526,\n", " -0.0179031 , -0.07732061, -0.0003669 , -0.03891977, 0.07109345,\n", " 0.01894096, -0.01219486, 0.01472465, -0.04203335, 0.11001322,\n", " -0.04436854, -0.02633571, 0.0167355 , 0.0788774 , 0.02957903,\n", " -0.02387079, -0.04229282, 0.00252979, -0.01102727, 0.03191421,\n", " 0.00252979, 0.00593527, 0.09185066, -0.07161238, 0.0163463 ,\n", " -0.04229282, 0.04800105, 0.03243314, 0.04099549, -0.02672491,\n", " -0.04903891, -0.1421869 , 0.0573418 , 0.04566586, 0.033471 ,\n", " 0.0573418 , -0.01420572, 0.05422821, -0.06408789, 0.02815197,\n", " 0.05033624, -0.08769922, 0.02348159, 0.03840084, 0.0219248 ,\n", " 0.2262536 , -0.05474715, -0.08510457, -0.04800105, -0.03398993,\n", " -0.02555732, 0.07680168, 0.03476833, 0.02620598, 0.02880063,\n", " 0.07213131, -0.01024887, -0.11468359, -0.0394387 , -0.07783955,\n", " 0.00609743, -0.08769922, -0.055785 , 0.00170274, 0.07161238,\n", " -0.00210815, -0.05500661, -0.07420703, 0.05552554, -0.00668123,\n", " -0.12402434, -0.01933015, -0.04981731, 0.09703996, 0.01440032,\n", " 0.06797986, -0.01316786, 0.0059677 , 0.08718029, 0.05682287], dtype=float32)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.syn0norm[4,]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:34.831845Z", "start_time": "2017-09-10T12:57:34.826363Z" } }, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.vector_size" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.079426Z", "start_time": "2017-09-10T12:57:34.836351Z" } }, "outputs": [ { "data": { "text/plain": [ "[('Goofy', 0.796820342540741),\n", " ('Minni', 0.7049012184143066),\n", " ('Mickey_Minnie_Goofy', 0.5468583703041077),\n", " ('Mickey_Goofy', 0.5395780205726624),\n", " ('Pluto_Goofy', 0.5347572565078735),\n", " ('Mickey_Minnie', 0.5343326330184937),\n", " ('Daisy_Duck', 0.5236194729804993),\n", " ('Sora_Donald', 0.5230178236961365),\n", " ('Mickey_Mouse_Goofy', 0.5048299431800842),\n", " ('nephews_Huey_Dewey', 0.5034050345420837)]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "model.similar_by_vector( (model.word_vec('Goofy') + model.word_vec('Minni'))/2 )" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.402374Z", "start_time": "2017-09-10T12:57:35.083676Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.3741233214730024\n" ] } ], "source": [ "import pyemd\n", "# This method only works if `pyemd` is installed (can be installed via pip, but requires a C compiler).\n", "\n", "sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()\n", "sentence_president = 'The president greets the press in Chicago'.lower().split()\n", "\n", "# Remove their stopwords.\n", "import nltk\n", "stopwords = nltk.corpus.stopwords.words('english')\n", "sentence_obama = [w for w in sentence_obama if w not in stopwords]\n", "sentence_president = [w for w in sentence_president if w not in stopwords]\n", "\n", "# Compute WMD.\n", "distance = model.wmdistance(sentence_obama, sentence_president)\n", "print(distance)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.411055Z", "start_time": "2017-09-10T12:57:35.403743Z" }, "collapsed": true }, "outputs": [], "source": [ "import nltk\n", "stopwords = nltk.corpus.stopwords.words('english')\n", "\n", "def sentence_distance(s1, s2):\n", " sentence_obama = [w for w in s1.split() if w not in stopwords]\n", " sentence_president = [w for w in s2.split() if w not in stopwords]\n", " print(sentence_obama, sentence_president, sep='\\t')\n", " print(model.wmdistance(sentence_obama, sentence_president), end='\\n\\n')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.425796Z", "start_time": "2017-09-10T12:57:35.412421Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['I', 'run', 'every', 'day', 'morning']\t['I', 'like', 'football']\n", "2.3376889762187165\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'run', 'since', 'I', 'born']\n", "1.820895138922882\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['idiot']\n", "3.3750919594666007\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Are', 'idiot?']\n", "3.976704031329918\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Is', 'possible', 'die?']\n", "3.1081644990045545\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Is', 'possible', 'die']\n", "3.1858849239788394\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'run', 'every', 'day']\n", "0.5563409008898735\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'eat', 'every', 'day']\n", "1.2005782773353697\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'breakfast', 'morning']\n", "1.711929159530717\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'breakfast', 'every', 'day', 'morning']\n", "0.6631782969713211\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Each', 'day', 'I', 'run']\n", "1.1626442679190636\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['I', 'run', 'every', 'day', 'morning']\n", "0.0\n", "\n" ] } ], "source": [ "sentence_distance('I run every day in the morning', 'I like football')\n", "sentence_distance('I run every day in the morning', 'I run since I was born')\n", "sentence_distance('I run every day in the morning', 'you are idiot')\n", "sentence_distance('I run every day in the morning', 'Are you idiot?')\n", "sentence_distance('I run every day in the morning', 'Is it possible to die?')\n", "sentence_distance('I run every day in the morning', 'Is it possible to die')\n", "sentence_distance('I run every day in the morning', 'I run every day')\n", "sentence_distance('I run every day in the morning', 'I eat every day')\n", "sentence_distance('I run every day in the morning', 'I have breakfast in the morning')\n", "sentence_distance('I run every day in the morning', 'I have breakfast every day in the morning')\n", "sentence_distance('I run every day in the morning', 'Each day I run')\n", "sentence_distance('I run every day in the morning', 'I run every day in the morning')" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.435685Z", "start_time": "2017-09-10T12:57:35.426717Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['I', 'run', 'every', 'day', 'morning']\t['Each', 'day', 'I', 'run']\n", "1.1626442679190636\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Each', 'I', 'run']\n", "1.804157856150221\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Each', 'day', 'run']\n", "1.7262934209024152\n", "\n", "['I', 'run', 'every', 'day', 'morning']\t['Each', 'day', 'I']\n", "1.716070718874115\n", "\n", "['I', 'every', 'day', 'morning']\t['Each', 'day', 'I', 'run']\n", "1.4545022893238069\n", "\n", "['I', 'run', 'day', 'morning']\t['Each', 'day', 'I', 'run']\n", "1.0145831108093262\n", "\n", "['I', 'run', 'every', 'morning']\t['Each', 'day', 'I', 'run']\n", "1.1818685887026787\n", "\n", "['I', 'run', 'every']\t['Each', 'day', 'I', 'run']\n", "1.3422707755860321\n", "\n" ] } ], "source": [ "sentence_distance('I run every day in the morning', 'Each day I run')\n", "sentence_distance('I run every day in the morning', 'Each I run')\n", "sentence_distance('I run every day in the morning', 'Each day run')\n", "sentence_distance('I run every day in the morning', 'Each day I')\n", "sentence_distance('I every day in the morning', 'Each day I run')\n", "sentence_distance('I run day in the morning', 'Each day I run')\n", "sentence_distance('I run every in morning', 'Each day I run')\n", "sentence_distance('I run every in', 'Each day I run')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.448516Z", "start_time": "2017-09-10T12:57:35.436914Z" }, "collapsed": true }, "outputs": [], "source": [ "def get_vect(w):\n", " try:\n", " return model.word_vec(w)\n", " except KeyError:\n", " return np.zeros(model.vector_size)\n", " \n", "def calc_avg(s):\n", " ws = [get_vect(w) for w in s.split() if w not in stopwords]\n", " avg_vect = sum(ws)/len(ws)\n", " return avg_vect\n", "\n", "\n", "from scipy.spatial import distance\n", "def get_euclidean(s1, s2):\n", " return distance.euclidean(calc_avg(s1), calc_avg(s2))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.455703Z", "start_time": "2017-09-10T12:57:35.450671Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Astrology:', 'I', 'Capricorn', 'Sun', 'Cap', 'moon', 'cap', 'rising...what', 'say', 'me?']\t[\"I'm\", 'triple', 'Capricorn', '(Sun,', 'Moon', 'ascendant', 'Capricorn)', 'What', 'say', 'me?']\n", "2.49434997539555\n", "\n", "0.8109230127174104\n" ] } ], "source": [ "# same questions\n", "s1 = 'Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?'\n", "s2 = \"I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?\"\n", "sentence_distance(s1, s2)\n", "print(get_euclidean(s1, s2))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.466581Z", "start_time": "2017-09-10T12:57:35.456745Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Astrology', 'I', 'Capricorn', 'Sun', 'Cap', 'moon', 'cap', 'rising', 'say']\t['I', 'triple', 'Capricorn', 'Sun', 'Moon', 'ascendant', 'Capricorn', 'What', 'say']\n", "2.0696045677228887\n", "\n", "0.9102963209152222\n" ] } ], "source": [ "# same questions as above without punctuations\n", "s1 = 'Astrology I am a Capricorn Sun Cap moon and cap rising what does that say about me'\n", "s2 = \"I am a triple Capricorn Sun Moon and ascendant in Capricorn What does this say about me\"\n", "sentence_distance(s1, s2)\n", "print(get_euclidean(s1, s2))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.473424Z", "start_time": "2017-09-10T12:57:35.467803Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['What', 'best', 'way', 'make', 'money', 'online']\t['What', 'best', 'way', 'ask', 'money', 'online?']\n", "0.9525941046914722\n", "\n", "0.5846541179497374\n" ] } ], "source": [ "# same questions\n", "s1 = 'What is best way to make money online'\n", "s2 = 'What is best way to ask for money online?'\n", "sentence_distance(s1,s2)\n", "print(get_euclidean(s1, s2))" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.480354Z", "start_time": "2017-09-10T12:57:35.474342Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['How', 'Darth', 'Vader', 'fought', 'Darth', 'Maul', 'Star', 'Wars', 'Legends?']\t['Does', 'Quora', 'character', 'limit', 'profile', 'descriptions?']\n", "4.066483587027646\n", "\n", "1.7708145158159805\n" ] } ], "source": [ "# different questions\n", "s1 = 'How did Darth Vader fought Darth Maul in Star Wars Legends?'\n", "s2 = 'Does Quora have a character limit for profile descriptions?'\n", "sentence_distance(s1,s2)\n", "print(get_euclidean(s1, s2))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T12:57:35.490536Z", "start_time": "2017-09-10T12:57:35.481434Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.066483587027646\n", "4.066483587027646\n", "4.066483587027646\n", "4.066483587027646\n" ] } ], "source": [ "# the order of the words doesn't change the distanace bewteeen the two phrases\n", "s1ws = [w for w in s1.split() if w not in stopwords]\n", "s2ws = [w for w in s2.split() if w not in stopwords]\n", "print(model.wmdistance(s1ws, s2ws) )\n", "print(model.wmdistance(s1ws[::-1], s2ws) )\n", "print(model.wmdistance(s1ws, s2ws[::-1]) )\n", "print(model.wmdistance(s1ws[3:]+s1ws[0:3], s2ws[::-1]) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "conclusion:\n", "- distance work well\n", "- the order of the words is not taken into account" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Translate using google translate\n", "- https://github.com/ssut/py-googletrans\n", "- should be free and unlimted, interned connection required\n", "- pip install googletrans" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:04.307396Z", "start_time": "2017-09-10T13:24:04.300177Z" }, "collapsed": true }, "outputs": [], "source": [ "from googletrans import Translator" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:20.243136Z", "start_time": "2017-09-10T13:24:20.236702Z" }, "collapsed": true }, "outputs": [], "source": [ "o = open(\"../AliceNelPaeseDelleMeraviglie.txt\")\n", "all = ''\n", "for l in o: all += l" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:28.331327Z", "start_time": "2017-09-10T13:24:28.328841Z" }, "collapsed": true }, "outputs": [], "source": [ "translator = Translator()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:30.379798Z", "start_time": "2017-09-10T13:24:28.968707Z" }, "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ranuncolo e facendosi vento con\n", "una delle sue foglie. - Oh, avrei voluto insegnargli dei giuochi se... se fossi stata d'una statura adatta!\n", "Poveretta me! avevo dimenticato che avevo bisogno di crescere ancora! Vediamo, come debbo fare?\n", "Forse dovrei mangiare o bere qualche cosa; ma che cosa?\n", "Il problema era questo: che cosa? Alice guardò intorno fra i fiori e i fili d'erba; ma non poté veder\n", "nulla che le sembrasse adatto a mangiare o a bere per l'occasione. C'era però un grosso fungo vicino\n", "a lei, press'a poco alto quanto lei; e dopo che l'ebbe esaminato di sotto, ai lati e di dietro, le parve\n", "cosa naturale di vedere che ci fosse di sopra. \n", "Alzandosi in punta dei piedi, si affacciò all'orlo del fungo, e gli occhi suoi s'incontrarono con quelli\n", "d'un grosso Bruco turchino che se ne stava seduto nel centro con le braccia conserte, fumando\n", "tranquillamente una lunga pipa, e non facendo la minima attenzione ne a lei, né ad altro.\n", "\n", "\n", "\n", "\n", "CONSIGLI DEL BRUCO\n", "Il Bruco e Alice si guardarono a vicend\n", "\n", "ranch and getting wind with\n", "one of her leaves. \"Oh, I wanted to teach him some jokes if ... if I had been of a suitable stature!\n", "Poverty me! I had forgotten that I needed to grow again! Let's see how do I do it?\n", "Maybe I should eat or drink something; but what?\n", "The problem was this: what? Alice looked around in the flowers and grass roots; but could not see it\n", "nothing that seems fit to eat or drink for the occasion. There was, however, a big fungus near\n", "to her, she pressed as high as her; and after looking at it below, at the sides and behind, it seemed to her\n", "natural thing to see that it was above.\n", "Standing to the tip of his feet, he sprang to the brim of the fungus, and his eyes met with those\n", "a big turquoise Bruco sitting in the center with his arms folded, smoking\n", "quietly a long pipe, and not paying the least attention to her, or to anything else.\n", "\n", "\n", "\n", "\n", "BRUCO ADVICE\n", "Bruco and Alice looked at each other\n" ] } ], "source": [ "for i in range(42, 43, 1):\n", " print(all[i * 1000:i * 1000 + 1000], end='\\n\\n')\n", " print(translator.translate(all[i * 1000:i * 1000 + 1000], dest='en').text)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:45.955157Z", "start_time": "2017-09-10T13:24:45.593413Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Languge: it with confidence: 0.21400356\n" ] } ], "source": [ "## if language is not passed it is guessed, so it can detect a language\n", "frase = \"Ciao Giulia, ti va un gelato?\"\n", "det = translator.detect(frase)\n", "print(\"Languge:\", det.lang, \" with confidence:\", det.confidence)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:49.682076Z", "start_time": "2017-09-10T13:24:48.959670Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[la] veritas lux mea\r\n", " ->\r\n", "[en] The truth is my light\r\n", "[pron.] The truth is my light\r\n" ] } ], "source": [ "# command line usage, but it seems to don't work to me\n", "!translate \"veritas lux mea\" -s la -d en" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:55.594612Z", "start_time": "2017-09-10T13:24:54.604695Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The quick brown fox -> 빠른 갈색 여우\n", "jumps over -> 점프하다\n", "the lazy dog -> 게으른 개\n" ] } ], "source": [ "translations = translator.translate(\n", " ['The quick brown fox', 'jumps over', 'the lazy dog'], dest='ko')\n", "for translation in translations:\n", " print(translation.origin, ' -> ', translation.text)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:24:58.701988Z", "start_time": "2017-09-10T13:24:58.612950Z" } }, "outputs": [ { "data": { "text/plain": [ "('Ciao Giulia, ti va un gelato?',\n", " 'Hi Giulia, do you go ice cream?',\n", " 'it',\n", " 'Hi Giulia, do you go ice cream?',\n", " 'en')" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phrase = translator.translate(frase, 'en')\n", "phrase.origin, phrase.text, phrase.src, phrase.pronunciation, phrase.dest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TreeTagger usage to tag an italian (or other languages) sentence\n", "How To install:\n", "- nltk need to be already installed and working\n", "- follow the instruction from http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ \n", "- run TreeTagger on terminal (echo 'Ciao Giulia come stai?' \| tree-tagger-italian) to see if everything is working\n", "- download the github to get the python support from: https://github.com/miotto/treetagger-python\n", "- run /home/ale/anaconda3/bin/python setup.py install and everything should work (note that you need to specify which python you want, the default is python2)\n", "\n", "Infos:\n", "- The maximum character limit on a single text is 15k.\n", "- this API does not guarantee that the library would work properly at all times\n", "- for a more stability API use the non-free https://cloud.google.com/translate/docs/\n", "- If you get HTTP 5xx error or errors like #6, it's probably because Google has banned your client IP address" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:25:29.915440Z", "start_time": "2017-09-10T13:25:29.675724Z" } }, "outputs": [ { "data": { "text/plain": [ "[['What', 'WP', 'what'],\n", " ['is', 'VBZ', 'be'],\n", " ['the', 'DT', 'the'],\n", " ['airspeed', 'NN', 'airspeed'],\n", " ['of', 'IN', 'of'],\n", " ['an', 'DT', 'an'],\n", " ['unladen', 'JJ', '<unknown>'],\n", " ['swallow', 'NN', 'swallow'],\n", " ['?', 'SENT', '?']]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from treetagger import TreeTagger\n", "tt = TreeTagger(language='english')\n", "tt.tag('What is the airspeed of an unladen swallow?')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2017-09-10T13:25:30.963426Z", "start_time": "2017-09-10T13:25:30.420851Z" } }, "outputs": [ { "data": { "text/plain": [ "[['Proviamo', 'VER:pres', 'provare'],\n", " ['a', 'PRE', 'a'],\n", " ['vedere', 'VER:infi', 'vedere'],\n", " ['un', 'DET:indef', 'un'],\n", " ['pò', 'ADV', 'pò'],\n", " ['se', 'PRO:refl', 'se'],\n", " ['funziona', 'VER:pres', 'funzionare'],\n", " ['bene', 'ADV', 'bene'],\n", " ['questo', 'PRO:demo', 'questo'],\n", " ['tagger', 'VER:infi', '<unknown>']]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tt = TreeTagger(language='italian')\n", "tt.tag('Proviamo a vedere un pò se funziona bene questo tagger')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "153px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
ES-DOC/esdoc-jupyterhub	notebooks/nerc/cmip6/models/ukesm1-0-ll/aerosol.ipynb	1	84294	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: NERC \n", "Source ID: UKESM1-0-LL \n", "Topic: Aerosol \n", "Sub-Topics: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "Properties: 69 (37 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:26" ], "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', 'nerc', 'ukesm1-0-ll', 'aerosol')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n", "[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n", "[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n", "[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n", "[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n", "[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n", "[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n", "[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Key properties of the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of aerosol model code" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Scheme Scope\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Atmospheric domains covered by the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Basic approximations made in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Prognostic variables in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of tracers in the aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are aerosol calculations generalized into families of species?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Software Properties \n", "Software properties of aerosol code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Timestep Framework \n", "Physical properties of seawater in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Mathematical method deployed to solve the time evolution of the prognostic variables" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (integrated)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol advection (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Timestep for aerosol physics (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Integrated Timestep\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Timestep for the aerosol model (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Integrated Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Specify the type of timestep scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Meteorological Forcings \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "Is Required: FALSE    Type: STRING    Cardinality:** 0.1\n", "### Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Variables 2D\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Two dimensionsal forcing variables, e.g. land-sea mask definition" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Frequency\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Frequency with which meteological forcings are applied (in seconds)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Resolution \n", "Resolution in the aersosol model grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Canonical Horizontal Resolution\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Number Of Horizontal Gridpoints\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Number Of Vertical Levels\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Number of vertical levels resolved on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Is Adaptive Grid\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Default is False. Set true if grid resolution changes during execution." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Tuning Applied \n", "Tuning methodology for aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Global Mean Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List set of metrics of the global mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Regional Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List of regional metrics of mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Trend Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List observed trend metrics used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Transport \n", "Aerosol transport" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of transport in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method for aerosol transport modeling" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to ensure mass conservation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Transport by convention" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "Atmospheric aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of emissions in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Method\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method used to define aerosol species (several methods allowed because the different species may not use the same method)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Sources of the aerosol species are taken into account in the emissions scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### Specify the climatology type for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed via a climatology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and prescribed as spatially uniform" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.7. Interactive Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an interactive method" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.8. Other Emitted Species\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of aerosol species emitted and specified via an "other method"" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.9. Other Method Characteristics\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Characteristics of the "other method" used for aerosol emissions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Concentrations \n", "Atmospheric aerosol concentrations" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of concentrations in atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Prescribed Lower Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the lower boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Prescribed Upper Boundary\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed at the upper boundary." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as mass mixing ratios." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Prescribed Fields Mmr\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### List of species prescribed as AOD plus CCNs." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Optical Radiative Properties \n", "Aerosol optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of optical and radiative properties" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Optical Radiative Properties --> Absorption \n", "Absortion properties in aerosol scheme" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Dust\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Organics\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Optical Radiative Properties --> Mixtures \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Is there external mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there internal mixing with respect to chemical composition?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If there is internal mixing with respect to chemical composition then indicate the mixinrg rule" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Optical Radiative Properties --> Impact Of H2o \n", "" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Does H2O impact size?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Internal Mixture\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does H2O impact internal mixture?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Optical Radiative Properties --> Radiative Scheme \n", "Radiative scheme for aerosol" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of radiative scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Shortwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of shortwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Optical Radiative Properties --> Cloud Interactions \n", "Aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of aerosol-cloud interactions" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Twomey\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the Twomey effect included?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Twomey Minimum Ccn\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If the Twomey effect is included, then what is the minimum CCN number?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Drizzle\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect drizzle?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Cloud Lifetime\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the scheme affect cloud lifetime?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Longwave Bands\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of longwave bands" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Model \n", "Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of atmosperic aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Processes\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Processes included in the Aerosol model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Other model components coupled to the Aerosol model" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of gas phase aerosol precursors." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of species covered by the bulk scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon / soot\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
qinwf-nuan/keras-js	notebooks/pipeline/pipeline_10.ipynb	1	27682	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import json\n", "from keras.models import Model\n", "from keras.layers import Input\n", "from keras.layers.convolutional import Conv2D\n", "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n", "from keras.layers.normalization import BatchNormalization\n", "from keras import backend as K" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def format_decimal(arr, places=8):\n", " return [round(x * 10places) / 10places for x in arr]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### pipeline 10" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": 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0.6814779, -0.87846374, -0.67810693, 0.51097247, 0.85375437, -0.47049519, -0.3370149, 0.03683555, 0.20874076, -0.46373424, -0.35484567, -0.68557246, -0.23030547, -0.22318676, -0.26829927, -0.08639185, 0.25801868, -0.12501729, 0.37867492, 0.04590355, -0.46139934, 0.63055799, -0.84571702, 0.65960319, 0.31657999, -0.18741285, 0.11709874, -0.48004723]}, 'expected': {'shape': [1, 1, 2], 'data': [-1.23473299, 0.05060276]}}\n" ] } ], "source": [ "data_in_shape = (24, 24, 2)\n", "\n", "conv_0 = Conv2D(5, 3, 3, activation='relu', border_mode='valid', subsample=(2, 2), dim_ordering='tf', bias=True)\n", "bn_0 = BatchNormalization(mode=0, axis=-1, epsilon=1e-3)\n", "conv_1 = Conv2D(4, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)\n", "bn_1 = BatchNormalization(mode=0, axis=-1, epsilon=1e-3)\n", "conv_2 = Conv2D(3, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)\n", "bn_2 = BatchNormalization(mode=0, axis=-1, epsilon=1e-3)\n", "pool_0 = AveragePooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='tf')\n", "conv_3 = Conv2D(4, 3, 3, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=True)\n", "bn_3 = BatchNormalization(mode=0, axis=-1, epsilon=1e-3)\n", "conv_4 = Conv2D(2, 3, 3, activation='relu', border_mode='same', subsample=(1, 1), dim_ordering='tf', bias=True)\n", "bn_4 = BatchNormalization(mode=0, axis=-1, epsilon=1e-3)\n", "pool_1 = AveragePooling2D(pool_size=(2, 2), strides=None, border_mode='valid', dim_ordering='tf')\n", "\n", "input_layer = Input(shape=data_in_shape)\n", "x = conv_0(input_layer)\n", "x = bn_0(x)\n", "x = conv_1(x)\n", "x = bn_1(x)\n", "x = conv_2(x)\n", "x = bn_2(x)\n", "x = pool_0(x)\n", "x = conv_3(x)\n", "x = bn_3(x)\n", "x = conv_4(x)\n", "x = bn_4(x)\n", "output_layer = pool_1(x)\n", "model = Model(input=input_layer, output=output_layer)\n", "\n", "np.random.seed(11000)\n", "data_in = 2 * np.random.random(data_in_shape) - 1\n", "\n", "# set weights to random (use seed for reproducibility)\n", "weights = []\n", "for i, w in enumerate(model.get_weights()):\n", " np.random.seed(11000 + i)\n", " if i % 6 == 5:\n", " # std should be positive\n", " weights.append(0.5 * np.random.random(w.shape))\n", " else:\n", " weights.append(np.random.random(w.shape) - 0.5)\n", "model.set_weights(weights)\n", "\n", "result = model.predict(np.array([data_in]))\n", "\n", "print({\n", " 'input': {'data': format_decimal(data_in.ravel().tolist()), 'shape': list(data_in_shape)},\n", " 'weights': [{'data': format_decimal(weights[i].ravel().tolist()), 'shape': list(weights[i].shape)} for i in range(len(weights))],\n", " 'expected': {'data': format_decimal(result[0].ravel().tolist()), 'shape': list(result[0].shape)}\n", "})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
google-research/google-research	prime/prime_colab.ipynb	1	109093	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "PA8SSKskdEyX" }, "source": [ "\n", "Copyright 2022 Google LLC.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "WQAPD6-tdHXF" }, "outputs": [], "source": [ "#@title License\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": "JXTGE2CLeZ5H" }, "source": [ "# Instructions to use this colab\n", "\n", "This colab implements the [PRIME](https://arxiv.org/abs/2110.11346) model for learning conservative models for offline model-based optimization. PRIME trains a surrogate model (in this case a transformer model) to predict the objective value for a given accelerator using feasible and infeasible data. The objective to train PRIME is based on a supervised regression objective on the feasible data, and then PRIME applies an objective to push down the predicted value on infeasible points.\n", "\n", "In this colab, we implement the PRIME model, and show how it can be trained on dummy data. If you are interested in using the model and training procedure for PRIME or modifying it and want to dig into the code, please check out the section in the colab titled ``PRIME: Model Definition and Loss computation''. \n", "\n", "Running this notebook in google colab does not require installation of any dependencies since tensorflow and tensorflow_probability are installed by default, but you might need to install them if you run this notebook locally. The latest versions should work." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MrTtvT9Nc6aD" }, "outputs": [], "source": [ "from absl import app\n", "from absl import flags\n", "from absl import logging\n", "\n", "# These tensorflow installs are automatically provided by the\n", "# Google colab runtime. If you want to run this code locally,\n", "# make sure to install tensorflow and tensorflow_probability.\n", "import tensorflow.compat.v2 as tf\n", "import tensorflow_probability as tfp\n", "import numpy as np\n", "import os\n", "import pickle\n", "import csv\n", "from typing import Optional, Dict, List\n", "from copy import deepcopy\n", "\n", "gfile = tf.io.gfile.GFile\n", "\n", "# Default area constraint for the models we train\n", "AREA_THRESHOLD = 27.0" ] }, { "cell_type": "markdown", "metadata": { "id": "cfZA-5GDE4wn" }, "source": [ "# PRIME: Model Definition and Loss computation" ] }, { "cell_type": "markdown", "metadata": { "id": "Z6W-wFO7FK0O" }, "source": [ "## Transformer Utils" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "2vu05EjgE8QW" }, "outputs": [], "source": [ "#@title Basic utility functions for training transformers\n", "\"\"\"\n", "Code largely taken from https://www.tensorflow.org/text/tutorials/transformer\n", "\"\"\"\n", "\n", "def get_angles(pos, i, d_model):\n", " \"\"\"Get angles for using tansformer.\"\"\"\n", " angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n", " return pos * angle_rates\n", "\n", "\n", "def positional_encoding(position, d_model):\n", " \"\"\"Obtain positional encdoing for training the PRIME Transformer.\"\"\"\n", " angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n", " np.arange(d_model)[np.newaxis, :],\n", " d_model)\n", " \n", " # apply sin to even indices in the array; 2i\n", " angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n", "\n", " # apply cos to odd indices in the array; 2i+1\n", " angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n", "\n", " pos_encoding = angle_rads[np.newaxis, ...]\n", "\n", " return tf.cast(pos_encoding, dtype=tf.float32)\n", "\n", "\n", "class SplitEmbeddingLayer(tf.keras.layers.Layer):\n", " \"\"\"Layer for embedding individual components in a split way\"\"\"\n", " def __init__(self, softmax_splits=None, output_size=32):\n", " \"\"\"\n", " Initialize the layer to split the input and generate embeddings for\n", " each field.\n", " \"\"\"\n", " super(SplitEmbeddingLayer, self).__init__(trainable=True)\n", " self.softmax_splits = softmax_splits\n", " self.output_size = output_size\n", "\n", " # create layers\n", " self.dense_layers = []\n", " print (self.softmax_splits)\n", " for idx, val in enumerate(self.softmax_splits):\n", " self.dense_layers.append(\n", " tf.keras.layers.Dense(\n", " self.output_size, name='insidelayer_' + str(idx)))\n", "\n", " # Add position embeddings\n", " self.pos_encoding = positional_encoding(position=200, d_model=output_size)\n", "\n", " def call(self, x):\n", " \"\"\"Call the Split embedding function.\"\"\"\n", " split_x = tf.split(x, num_or_size_splits=self.softmax_splits, axis=-1)\n", " modified_splits = []\n", " idx = 0\n", " for param in split_x:\n", " out = self.dense_layers[int(idx)](param)\n", " modified_splits.append(tf.expand_dims(out, axis=1))\n", " idx += 1\n", " out = tf.concat(modified_splits, axis=1)\n", " # print ('Out shape before: ', out)\n", " out = out + self.pos_encoding[:, :len(modified_splits), :]\n", " # print ('Out shape after: ', out)\n", " return out\n", "\n", "\n", "def scaled_dot_product_attention(q, k, v, mask):\n", " \"\"\"Scaled dot product attention in transformer.\"\"\"\n", " matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k)\n", "\n", " # scale matmul_qk\n", " dk = tf.cast(tf.shape(k)[-1], tf.float32)\n", " scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n", "\n", " # add the mask to the scaled tensor.\n", " if mask is not None:\n", " scaled_attention_logits += (mask * -1e9)\n", "\n", " # softmax is normalized on the last axis (seq_len_k) so that the scores\n", " # add up to 1.\n", " attention_weights = tf.nn.softmax(\n", " scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k)\n", " output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v)\n", " return output, attention_weights\n", "\n", "\n", "def point_wise_feed_forward_network(d_model, dff):\n", " return tf.keras.Sequential([\n", " tf.keras.layers.Dense(dff, activation='relu'),\n", " tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model)\n", " ])\n", "\n", "\n", "class MultiHeadAttention(tf.keras.layers.Layer):\n", " \"\"\"Multi Head Attention for the model.\"\"\"\n", "\n", " def __init__(self, d_model, num_heads):\n", " \"\"\"Initialize the multi-head attention model.\"\"\"\n", " super(MultiHeadAttention, self).__init__()\n", " self.num_heads = num_heads\n", " self.d_model = d_model\n", "\n", " assert d_model % self.num_heads == 0\n", "\n", " self.depth = d_model // self.num_heads\n", "\n", " self.wq = tf.keras.layers.Dense(d_model)\n", " self.wk = tf.keras.layers.Dense(d_model)\n", " self.wv = tf.keras.layers.Dense(d_model)\n", "\n", " self.dense = tf.keras.layers.Dense(d_model)\n", "\n", " def split_heads(self, x, batch_size):\n", " \"\"\"Split the last dimension into (num_heads, depth).\n", " Transpose the result such that the\n", " shape is (batch_size, num_heads, seq_len, depth)\n", " \"\"\"\n", " x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n", " return tf.transpose(x, perm=[0, 2, 1, 3])\n", "\n", " def call(self, v, k, q, mask):\n", " batch_size = tf.shape(q)[0]\n", "\n", " q = self.wq(q)\n", " k = self.wk(k)\n", " v = self.wv(v)\n", "\n", " q = self.split_heads(q, batch_size) \n", " k = self.split_heads(k, batch_size) \n", " v = self.split_heads(v, batch_size)\n", "\n", " scaled_attention, attention_weights = scaled_dot_product_attention(\n", " q, k, v, mask)\n", "\n", " scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) \n", "\n", " concat_attention = tf.reshape(scaled_attention,\n", " (batch_size, -1, self.d_model)) \n", "\n", " output = self.dense(concat_attention) \n", "\n", " return output, attention_weights\n", "\n", "\n", "class TransformerLayer(tf.keras.layers.Layer):\n", " \"\"\"Define the transformer layer to be used in the PRIME Transformer model.\"\"\"\n", "\n", " def __init__(self, d_model, num_heads, dff, rate=0.1):\n", " \"\"\"Initialize the transformer layer.\"\"\"\n", " super(TransformerLayer, self).__init__()\n", "\n", " self.mha = MultiHeadAttention(d_model, num_heads)\n", " self.ffn = point_wise_feed_forward_network(d_model, dff)\n", "\n", " self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n", " self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n", "\n", " self.dropout1 = tf.keras.layers.Dropout(rate)\n", " self.dropout2 = tf.keras.layers.Dropout(rate)\n", "\n", " def call(self, x, training=True, mask=None):\n", " attn_output, _ = self.mha(x, x, x, mask) \n", " # (batch_size, input_seq_len, d_model)\n", " attn_output = self.dropout1(attn_output, training=training)\n", " out1 = self.layernorm1(x + attn_output) \n", " # (batch_size, input_seq_len, d_model)\n", "\n", " ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)\n", " ffn_output = self.dropout2(ffn_output, training=training)\n", " out2 = self.layernorm2(out1 + ffn_output) \n", " # (batch_size, input_seq_len, d_model)\n", " return out2" ] }, { "cell_type": "markdown", "metadata": { "id": "ll-Ud813QwND" }, "source": [ "## Utility and Helper Functions for Loss Computation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "PCzXIHNoQtoM" }, "outputs": [], "source": [ "#@title Helper functions for MSE/Huber Loss computation\n", "\n", "def weighted_mse_loss(input, target, weight):\n", " \"\"\"Compute weighted MSE Loss\"\"\"\n", " mse_loss_val = (tf.squeeze(input) - tf.squeeze(target))*2\n", " return tf.reduce_mean(mse_loss_val tf.squeeze(weight))\n", "\n", "\n", "def weighted_huber_loss(input, target, weight):\n", " \"\"\"Compute weighted Huber Loss\"\"\"\n", " mse_loss = tf.keras.losses.Huber(\n", " reduction=tf.keras.losses.Reduction.NONE)\n", " return tf.reduce_mean(mse_loss(\n", " y_pred=tf.squeeze(input),\n", " y_true=tf.squeeze(target)) * tf.squeeze(weight))\n", "\n", "\n", "def weighted_approx_loss(input, target, weight):\n", " \"\"\"Compute weighted Approximation Loss\"\"\"\n", " abs_diff = tf.abs(tf.squeeze(input) - tf.squeeze(target))\n", " ratio_diff = abs_diff / (tf.abs(tf.squeeze(target)) + 1e-6)\n", " return tf.reduce_mean(ratio_diff * tf.squeeze(weight))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "b5aLcKscR0T-" }, "outputs": [], "source": [ "#@title Helper functions for ranking loss computation\n", "\n", "def ranking_loss(input, target, context=None):\n", " \"\"\"Compute measures of ranking for the PRIMETransformerModel.\"\"\"\n", " if context is not None:\n", " # Compute ranking loss per context, and then average it.\n", " unique_contexts, indices = tf.unique(\n", " tf.squeeze(tf.cast(context, tf.int32)), name='None')\n", " all_corr = []\n", " for idx in range(unique_contexts.shape[0]):\n", " curr_context = unique_contexts[idx]\n", " locations_idx = tf.squeeze(tf.where(tf.equal(indices, curr_context)))\n", " input_tmp = tf.gather(\n", " tf.squeeze(input), indices=locations_idx)\n", " target_tmp = tf.gather(\n", " tf.squeeze(target), indices=locations_idx)\n", " input_ranks = tf.argsort(input_tmp, axis=-1)\n", " target_ranks = tf.argsort(target_tmp, axis=-1)\n", " input_ranks = tf.cast(tf.argsort(input_ranks, axis=-1), dtype=tf.float32)\n", " target_ranks = tf.cast(tf.argsort(target_ranks, axis=-1),\n", " dtype=tf.float32)\n", " std_input = tf.math.reduce_std(input_ranks)\n", " std_target = tf.math.reduce_std(target_ranks)\n", " cov = tf.reduce_mean((target_ranks - tf.reduce_mean(target_ranks)) \\\n", " (input_ranks - tf.reduce_mean(input_ranks)))\n", " pearson_corr = cov/ (std_target std_input)\n", " all_corr.append(pearson_corr)\n", " print (all_corr)\n", " pearson_corr = tf.reduce_mean(pearson_corr)\n", " else:\n", " input = tf.squeeze(input)\n", " target = tf.squeeze(target)\n", " input_ranks = tf.argsort(input, axis=-1)\n", " target_ranks = tf.argsort(target, axis=-1)\n", " input_ranks = tf.cast(tf.argsort(input_ranks, axis=-1), dtype=tf.float32)\n", " target_ranks = tf.cast(tf.argsort(target_ranks, axis=-1), dtype=tf.float32)\n", " std_input = tf.math.reduce_std(input_ranks)\n", " std_target = tf.math.reduce_std(target_ranks)\n", " cov = tf.reduce_mean((target_ranks - tf.reduce_mean(target_ranks)) \\\n", " (input_ranks - tf.reduce_mean(input_ranks)))\n", " pearson_corr = cov/ (std_target std_input)\n", " return pearson_corr\n", "\n", "\n", "def ranking_trainable_loss(input, target, context=None):\n", " \"\"\"Compute a differentiable ranking loss, that can be used for training.\"\"\"\n", " if context is not None:\n", " unique_contexts, indices = tf.unique(\n", " tf.squeeze(tf.cast(context, tf.int32)), name='None')\n", " all_corr = []\n", " for idx in range(unique_contexts.shape[0]):\n", " curr_context = unique_contexts[idx]\n", " locations_idx = tf.squeeze(tf.where(tf.equal(indices, curr_context)))\n", " input_tmp = tf.expand_dims(tf.gather(\n", " tf.squeeze(input), indices=locations_idx), 1)\n", " target_tmp = tf.expand_dims(tf.gather(\n", " tf.squeeze(target), indices=locations_idx), 1)\n", " input_transpose = tf.transpose(input_tmp, [1, 0]) # 1 x B\n", " target_transpose = tf.transpose(target_tmp, [1, 0]) # 1 x B\n", " diff_true = input_tmp - input_transpose # B x 1 - 1 x B = B x B = y_i - y_j\n", " diff_pred = target_tmp - target_transpose # fx_i - fx_j\n", " product = tf.sign(diff_true) * diff_pred # sign(y_i = y_j) * (fx_i - fxj)\n", " bce_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n", " labels=tf.ones_like(product), logits=product))\n", " all_corr.append(bce_loss)\n", " bce_loss = tf.reduce_mean(all_corr)\n", " else:\n", " input_transpose = tf.transpose(input, [1, 0]) # 1 x B\n", " target_transpose = tf.transpose(target, [1, 0]) # 1 x B\n", " diff_true = input - input_transpose # B x 1 - 1 x B = B x B = y_i - y_j\n", " diff_pred = target - target_transpose # fx_i - fx_j\n", " product = tf.sign(diff_true) * diff_pred # sign(y_i = y_j) * (fx_i - fxj)\n", " bce_loss = tf.nn.sigmoid_cross_entropy_with_logits(\n", " labels=tf.ones_like(product), logits=product)\n", " return tf.reduce_mean(bce_loss)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "2GVLIu49SUo2" }, "outputs": [], "source": [ "#@title Helper function for Kendall correlation\n", "\n", "def kendall_correlation(input, target, context=None):\n", " \"\"\"Compute Kendall's correlation over the input, target and context.\"\"\"\n", " if context is not None:\n", " unique_contexts, indices = tf.unique(\n", " tf.squeeze(tf.cast(context, tf.int32)), name='None')\n", " all_corr = []\n", " for idx in range(unique_contexts.shape[0]):\n", " curr_context = unique_contexts[idx]\n", " locations_idx = tf.squeeze(tf.where(tf.equal(indices, curr_context)))\n", " input_tmp = tf.expand_dims(tf.gather(\n", " tf.squeeze(input), indices=locations_idx), 1)\n", " target_tmp = tf.expand_dims(tf.gather(\n", " tf.squeeze(target), indices=locations_idx), 1)\n", " input_transpose = tf.transpose(input_tmp, [1, 0])\n", " target_transpose = tf.transpose(target_tmp, [1, 0])\n", " diff_true = input_tmp - input_transpose\n", " diff_pred = target_tmp - target_transpose\n", " product = tf.sign(diff_true) * tf.sign(diff_pred)\n", " positive_pairs = tf.where(tf.greater_equal(product, tf.zeros_like(product)),\n", " tf.ones_like(product), tf.zeros_like(product))\n", " n = tf.cast(tf.shape(input_tmp)[0], dtype=tf.float32)\n", " total_positive = tf.reduce_sum(positive_pairs) - n\n", " ratio = total_positive/ (n * (n-1))\n", " all_corr.append(ratio)\n", " ratio = tf.reduce_mean(all_corr)\n", " else:\n", " input_transpose = tf.transpose(input, [1, 0])\n", " target_transpose = tf.transpose(target, [1, 0])\n", " diff_true = input - input_transpose\n", " diff_pred = target - target_transpose\n", " product = tf.sign(diff_true) * tf.sign(diff_pred)\n", " positive_pairs = tf.where(tf.greater_equal(product, tf.zeros_like(product)),\n", " tf.ones_like(product), tf.zeros_like(product))\n", " n = tf.cast(tf.shape(input)[0], dtype=tf.float32)\n", " total_positive = tf.reduce_sum(positive_pairs) - n\n", " ratio = total_positive/ (n * (n-1))\n", " return 2 * ratio - 1.0" ] }, { "cell_type": "markdown", "metadata": { "id": "CyYBvUsEGPln" }, "source": [ "## Code for the PRIME surrogate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "ziT-hyEYFthV" }, "outputs": [], "source": [ "#@title Definition of the PRIME surrogate model, training procedure\n", "\n", "class PRIMETransformerModel(tf.keras.Model):\n", " \"\"\"\n", " The transformer model used by PRIME. This class implements ability to \n", " instantiate a transformer model, and train it via the PRIME training objective\n", " (Equation 3 in https://arxiv.org/abs/2110.11346). \n", " \n", " Additionally it also implements the ability to train a contextual model,\n", " conditioned on the context. \n", " \"\"\"\n", "\n", " def __init__(self,\n", " num_outputs,\n", " num_inputs,\n", " optimizer,\n", " layers=(256, 256, 256),\n", " penalty_weight=10.0,\n", " negative_sampler=None,\n", " contextual=False,\n", " params_dict=None):\n", " \"\"\"Initializes the PRIMETransformer model.\n", "\n", " Args:\n", " num_outputs: the dimensionality of the output of the PRIME surrogate. \n", " Typically set to 1, but you can increase it to model multiple cost\n", " functions together.\n", " num_inputs: the dimensionality of the total number of inputs to the model.\n", " optimizer: the optimizer to optimize the trainable model.\n", " layers: hidden layer sizes for the feed-forward layers after extracting\n", " the transformer embedding.\n", " penalty_weight: the value of alpha in Equation 2 in PRIME.\n", " negative_sampler: an instance of a negative sampler. A negative sampler\n", " is basically an optimizer that can take in the current snapshot of the\n", " this PRIMETransformerModel, and optimize the predictions of the current\n", " model snapshot w.r.t its input. In the paper, we utilize an evolutionary\n", " optimizer to optimize the predictions. For this code release, we present\n", " a simple gradient-descent based optimizer for optimization as a\n", " demonstration. Users are encouraged to pass in their relevant\n", " negative sampler here.\n", " contextual: bool, indicates whether we are training a contextual model\n", " or a non-contextual model. Contextual is used for multi-model and\n", " zero-shot experiments. \n", " params_dict: dictionary. Can store additional parameters and their values.\n", " This dictionary provides an easy and convenient way to add new hyper-\n", " parameters, via keys of this dictionary. \n", " \"\"\"\n", " super().__init__()\n", " self.num_inputs = num_inputs\n", " self.num_outputs = num_outputs\n", " self.optimizer = optimizer\n", " self.params_dict = params_dict\n", " self.penalty_weight = penalty_weight\n", " self.contextual = contextual\n", "\n", " # Setting the following variable to True shouldn't cause issues since\n", " # it is not passed into the GradientTape, but better to be safe, and set\n", " # it to false if the variable is not used.\n", "\n", " # This variable determines the alpha multiplier in Equation 2.\n", " self.log_cql_alpha = tf.Variable(tf.math.log(self.penalty_weight + 1e-6),\n", " trainable=False)\n", " self.cql_alpha_value = tf.Variable(self.penalty_weight, trainable=False)\n", "\n", " self.negative_sampler = negative_sampler\n", "\n", " # In the paper, we use an evolutionary optimizer for obtaining adversarial\n", " # examples. However, unfortunately, this optimizer is proprietary, and so\n", " # we provide the example negative sampler that uses gradient ascent, similar\n", " # to conservative objective mocels https://arxiv.org/abs/2107.06882.\n", " self.num_gradient_infer_steps = 0\n", " if 'num_gradient_steps' in params_dict:\n", " self.num_gradient_infer_steps = params_dict['num_gradient_steps']\n", "\n", " self.opt_lr = 1e-3\n", " if 'opt_lr' in params_dict:\n", " self.opt_lr = params_dict['opt_lr']\n", "\n", " # the multiplier beta in Equation 3 in the paper.\n", " self.infeasible_alpha = 0.01\n", " if 'infeasible_alpha' in params_dict:\n", " self.infeasible_alpha = params_dict['infeasible_alpha']\n", "\n", " # Since the input to the model is a concatenation of one-hot values\n", " # representing each field, using the input_splits parameter, we partition\n", " # this big input vector into a list of one-hot vectors, one corresponding \n", " # to each discrete parameter. \n", " self.input_splits = None\n", " if 'input_splits' in params_dict:\n", " self.input_splits = params_dict['input_splits']\n", "\n", " # We use an architecture which resembles a mixture of experts, and so the\n", " # following parameter decides how many parameters we wish to have.\n", " self.num_votes = 1\n", " if 'num_votes' in params_dict:\n", " self.num_votes = params_dict['num_votes']\n", "\n", " # Whether to add dropout or not, in intermediate layers of the model, as \n", " # a means to prevent overfitting.\n", " use_dropout = False\n", " if 'use_dropout' in params_dict:\n", " use_dropout = params_dict['use_dropout']\n", "\n", " if self.contextual:\n", " \"\"\"For contextual version of PRIME\"\"\"\n", " self.num_contexts = 0\n", " if 'num_contexts' in params_dict:\n", " self.num_contexts = params_dict['num_contexts']\n", "\n", " print ('Infeasible alpha: ', self.infeasible_alpha)\n", " print ('CQL Alpha: ', self.log_cql_alpha)\n", " print ('Num votes: ', self.num_votes)\n", "\n", " self.input_layer = tf.keras.Input(num_inputs)\n", " temp_num_inputs = num_inputs\n", "\n", " # The following layer splits the input into a list of embeddings for \n", " # each parameter. Check the SplitEmbeddingLayer class for details.\n", " x = SplitEmbeddingLayer(softmax_splits=self.input_splits,\n", " output_size=64)(self.input_layer)\n", " if use_dropout:\n", " x = tf.keras.layers.Dropout(rate=0.1)(x)\n", " \n", " # Now feed the split embedding layer output into TransformerLayer\n", " x = TransformerLayer(d_model=64, num_heads=8, dff=256)(x)\n", " x = TransformerLayer(d_model=64, num_heads=8, dff=256)(x)\n", " \n", " x = tf.keras.layers.Reshape(target_shape=(640,))(x)\n", " \n", " if self.contextual:\n", " context_input = tf.keras.Input(self.num_contexts)\n", " out_context = tf.keras.layers.Dense(640, use_bias=False)(context_input)\n", "\n", " # Pointwise multiply the contexts to make sure that the context\n", " # conditioning is done properly. From https://arxiv.org/abs/1912.13465.\n", " x = x * out_context\n", " self._base_network = tf.keras.Model(\n", " inputs=[self.input_layer, context_input], outputs=x)\n", " else:\n", " self._base_network = tf.keras.Model(\n", " inputs=self.input_layer, outputs=x)\n", "\n", " self.optimize_networks = [self._base_network,]\n", "\n", " # Now feedforward layers to finish the model\n", " layers = list(layers)\n", " layers[0] = 64 * len(self.input_splits)\n", "\n", " \"\"\"Voting based routing\"\"\"\n", " num_networks = self.num_votes\n", " self._all_networks = []\n", " for jdx in range(num_networks):\n", " # Make each of the networks used in routing\n", " new_network = tf.keras.Sequential()\n", " for idx in range(len(layers) - 1):\n", " new_network.add(\n", " tf.keras.layers.Dense(layers[idx+1], input_shape=(layers[idx],)))\n", " new_network.add(tf.keras.layers.LeakyReLU(0.1))\n", " if use_dropout:\n", " new_network.add(tf.keras.layers.Dropout(rate=0.1))\n", "\n", " new_network.add(tf.keras.layers.Dense(\n", " num_outputs, input_shape=(layers[idx],)))\n", " self._all_networks.append(new_network)\n", "\n", " self.optimize_networks.extend(self._all_networks)\n", "\n", " # Now make the network that decides the contribution of these\n", " self.voting_network = tf.keras.Sequential()\n", " if self.contextual:\n", " self.voting_network.add(\n", " tf.keras.layers.Dense(layers[1], input_shape=(2layers[0],)))\n", " else:\n", " self.voting_network.add(\n", " tf.keras.layers.Dense(layers[1], input_shape=(layers[0],)))\n", " self.voting_network.add(tf.keras.layers.LeakyReLU(0.1))\n", " if use_dropout:\n", " self.voting_network.add(tf.keras.layers.Dropout(rate=0.1))\n", "\n", " self.voting_network.add(\n", " tf.keras.layers.Dense(self.num_votes, input_shape=(layers[1],)))\n", "\n", " if self.contextual:\n", " # Add the vote generation network input again\n", " self.embedding_network = tf.keras.Sequential()\n", " self.embedding_network.add(\n", " tf.keras.layers.Dense(256))\n", " self.embedding_network.add(tf.keras.layers.LeakyReLU(0.1))\n", " self.embedding_network.add(\n", " tf.keras.layers.Dense(layers[0]))\n", "\n", " self.optimize_networks.append(self.embedding_network)\n", " self.optimize_networks.append(self.voting_network)\n", "\n", " print ('All networks: ', len(self.optimize_networks))\n", "\n", " @tf.function\n", " def call(self, inputs, training=True, with_logging=False):\n", " \"\"\"Function to call one forward pass on the PRIME Transformer.\"\"\"\n", " extra_dict = dict()\n", " if not self.contextual:\n", " transformer_embedding = self._base_network(inputs, training=training)\n", " else:\n", " # TODO(aviralkumar): Fix the hardcoded 77 input dimensionality in code\n", " if not isinstance(inputs, list) and not isinstance(inputs, tuple):\n", " inputs = (inputs[:, :77], inputs[:, 77:])\n", "\n", " transformer_embedding = self._base_network(inputs, training=training)\n", " \n", " # Get all outputs from each expert\n", " all_outputs = []\n", " for idx in range(self.num_votes):\n", " all_outputs.append(\n", " self._all_networks[idx](transformer_embedding, training=training))\n", " \n", " # Get the voting probabilities\n", " if self.contextual:\n", " vote_input = self.embedding_network(inputs[1])\n", " vote_input = tf.concat([transformer_embedding, vote_input], axis=-1)\n", " vote_logit = self.voting_network(vote_input, training=training)\n", " else:\n", " vote_logit = self.voting_network(transformer_embedding,\n", " training=training)\n", "\n", " # Append all_outputs in a list and compute average score\n", " all_outputs = tf.concat(all_outputs, axis=-1) # [B x num_votes]\n", " vote_prob = tf.nn.softmax(vote_logit, axis=-1) # [B x num_votes]\n", " vote_entropy = tf.reduce_sum(\n", " tf.nn.log_softmax(vote_logit, axis=-1) vote_prob, axis=-1)\n", " extra_dict['vote_entropy'] = tf.reduce_mean(vote_entropy)\n", " fwd_model_pred = tf.reduce_sum(vote_prob * all_outputs, axis=-1)\n", " fwd_model_pred = tf.expand_dims(fwd_model_pred, axis=-1)\n", " \n", " if with_logging:\n", " return fwd_model_pred, extra_dict\n", "\n", " return fwd_model_pred\n", "\n", " def compute_loss(self, data_batch, loss_type='mse', training=True,\n", " ranking_penalty_weight=0.0, inp_batch_type=None):\n", " \"\"\"\n", " Compute the loss function and additional logging metrics for training.\n", "\n", " Args:\n", " data_batch: A dictionary of various input fields, and their corresponding\n", " tensor values. The keys for this dictionary are:\n", " - design --\u003e denotes the input (accelerator config in this case)\n", " - objective --\u003e denotes the objective value for the given input\n", " - context_id --\u003e denotes the context vector for the case of contextual\n", "\n", " loss_type: string, either mse or mse+rank. It essentially computes the\n", " training loss used to train the PRIME model. We can optionally add some\n", " ranking regularization for training if needed. Though, we did not find\n", " this to be essential. \n", "\n", " inp_batch_type: string, either 'valid' or 'mixed'. Mixed indicates that\n", " the batch consists of both valid and invalid samples, whereas valid\n", " indicates the samples are only valid samples.\n", "\n", " ranking_penalty_weight: float, the weight on the ranking loss function\n", " in addition to the PRIME objectives. This is not needed for PRIME, but\n", " can help in some cases. So, leaving the facility here.\n", " \"\"\"\n", " loss_dict = dict()\n", " if loss_type == 'mse':\n", " fwd_loss = weighted_mse_loss\n", " elif loss_type == 'mse+rank':\n", " fwd_loss = weighted_mse_loss\n", " ranking_loss_fn = ranking_trainable_loss\n", "\n", " loss_dict['y_values_max'] = tf.reduce_max(data_batch['objective'])\n", " loss_dict['y_values_mean'] = tf.reduce_mean(data_batch['objective'])\n", "\n", " data_batch = data_batch.copy()\n", " weights = tf.ones_like(data_batch['objective'])\n", " \n", " if self.contextual:\n", " model_pred, extra_dict = self(\n", " inputs=[data_batch['design'], data_batch['context_id']],\n", " training=training, with_logging=True)\n", " else:\n", " model_pred, extra_dict = self(\n", " data_batch['design'], training=training, with_logging=True)\n", "\n", " loss_dict.update(extra_dict)\n", "\n", " if self.negative_sampler is not None:\n", " # This branch of the code will not run off-the-shelf, since it assumes \n", " # access to a negative_sampler. A negative sampler is simply any kind of\n", " # optimizer that can take in the current PRIMETransformerModel and\n", " # optimize its predictions.\n", " negatives_batch = self.negative_sampler.run_inference(\n", " num_iters=2, model=self)\n", " negatives_pred = self(inputs=negatives_batch, training=training)\n", " else:\n", " negatives_batch = self.infer_negatives(data_batch)\n", " if self.contextual:\n", " negatives_pred = self(\n", " (negatives_batch['design'], negatives_batch['context_id']),\n", " training=training)\n", " else:\n", " negatives_pred = self(negatives_batch['design'], training=True)\n", " \n", " negatives_pred = tf.clip_by_value(negatives_pred, clip_value_min=-4000.0,\n", " clip_value_max=4000.0)\n", " \n", " cql_loss = tf.reduce_mean(negatives_pred)\n", " cql_loss = tf.clip_by_value(cql_loss, \n", " clip_value_min=-4000, \n", " clip_value_max=1e6)\n", " loss_dict['negatives_dist'] = tf.reduce_mean(negatives_pred)\n", "\n", " mse_loss = weighted_mse_loss(\n", " model_pred, data_batch['objective'], weights)\n", "\n", " if loss_type == 'mse+rank':\n", " if self.contextual:\n", " avg_ranking_train_loss = ranking_loss_fn(\n", " model_pred, data_batch['objective'],\n", " context=data_batch['raw_context'])\n", " else:\n", " avg_ranking_train_loss = ranking_loss_fn(\n", " model_pred, data_batch['objective'])\n", " else:\n", " avg_ranking_train_loss = 0.0\n", "\n", " # Only used for logging, measures how big the MSE error is relative to\n", " # the output of the model. \n", " avg_approx_loss = weighted_approx_loss(\n", " model_pred, data_batch['objective'], weights)\n", " passed_context = None\n", "\n", " if self.contextual:\n", " passed_context = data_batch['raw_context']\n", " \n", " avg_ranking_loss = ranking_loss(\n", " model_pred, data_batch['objective'], context=passed_context)\n", " avg_kendall_loss = kendall_correlation(\n", " model_pred, data_batch['objective'], context=passed_context)\n", "\n", " train_loss = mse_loss\n", " loss_dict['mse_loss'] = mse_loss\n", " loss_dict['avg_approx_loss'] = avg_approx_loss\n", " loss_dict['avg_ranking_loss'] = avg_ranking_loss\n", " loss_dict['avg_ranking_train_loss'] = avg_ranking_train_loss\n", " loss_dict['avg_kendall_loss'] = avg_kendall_loss\n", " loss_dict['cql_loss'] = cql_loss\n", " loss_dict['negatives_pred'] = tf.reduce_mean(negatives_pred)\n", " loss_dict['model_pred_average'] = tf.reduce_mean(model_pred)\n", " train_loss = train_loss + ranking_penalty_weight * avg_ranking_train_loss\n", " train_loss = train_loss + self.cql_alpha_value * cql_loss\n", " \n", " if inp_batch_type is not 'valid':\n", " weights_negatives = tf.ones_like(data_batch['objective'])\n", " if self.contextual:\n", " model_pred_invalid, invalid_dict = self(\n", " inputs=(data_batch['invalid/design'], data_batch['context_id']),\n", " training=training, with_logging=True)\n", " else:\n", " model_pred_invalid, invalid_dict = self(\n", " data_batch['invalid/design'], training=training, with_logging=True)\n", "\n", " for key in invalid_dict:\n", " loss_dict['invalid/'+key] = invalid_dict[key]\n", "\n", " ## Conservatism training\n", " loss_dict['y_value_infeasible'] = tf.reduce_mean(model_pred_invalid)\n", " loss_dict['y_value_infeasible'] = tf.clip_by_value(\n", " loss_dict['y_value_infeasible'], \n", " clip_value_min=-1000, clip_value_max=1e6)\n", " train_loss = train_loss + self.infeasible_alpha \\\n", " loss_dict['y_value_infeasible']\n", "\n", " mse_loss_invalid = weighted_mse_loss(\n", " model_pred_invalid, data_batch['invalid/objective'], \n", " weights_negatives)\n", " avg_approx_loss_invalid = weighted_approx_loss(\n", " model_pred_invalid, data_batch['invalid/objective'], \n", " weights_negatives)\n", " mse_loss = mse_loss + mse_loss_invalid\n", " loss_dict['mse_loss_invalid'] = mse_loss_invalid\n", " loss_dict['mse_loss_overall'] = mse_loss\n", " loss_dict['avg_approx_loss_invalid'] = avg_approx_loss_invalid\n", " return loss_dict, train_loss\n", "\n", " def perform_training(self, batch, loss_type,\n", " ranking_penalty_weight=0.0, kwargs):\n", " \"\"\"\n", " Actually perform training by computing loss, and then taking gradients\n", " through it. Makes sure to backpropagate through all networks.\n", " \"\"\"\n", " with tf.GradientTape(\n", " watch_accessed_variables=False, persistent=True) as tape:\n", " tape.watch(\n", " [v for net in self.optimize_networks\\\n", " for v in net.trainable_variables])\n", " loss_dict, loss_train = self.compute_loss(\n", " batch, loss_type, training=True,\n", " ranking_penalty_weight=ranking_penalty_weight)\n", "\n", " grads = tape.gradient(loss_train,\n", " [v for net in self.optimize_networks\\\n", " for v in net.trainable_variables])\n", " gen_grads_op = self.optimizer.apply_gradients(\n", " zip(grads, [v for net in self.optimize_networks\\\n", " for v in net.trainable_variables]))\n", " return loss_dict\n", "\n", " def measure_stats(self, batch, batch_type=None, kwargs):\n", " \"\"\"Simply make a forward pass through compute_loss to measure losses.\"\"\"\n", " loss_dict, _ = self.compute_loss(batch, loss_type='mse+rank',\n", " training=False,\n", " inp_batch_type=batch_type)\n", " return loss_dict\n", "\n", " def infer_negatives(self, batch):\n", " \"\"\"Run gradient descent to obtain negative examples\"\"\"\n", " temp_batch = dict()\n", " log_probs = batch['design']\n", " if self.contextual:\n", " contexts = batch['context_id']\n", " for _ in range(self.num_gradient_infer_steps):\n", " with tf.GradientTape(\n", " watch_accessed_variables=False, persistent=False) as tape:\n", " tape.watch(log_probs)\n", " if self.contextual:\n", " model_pred = self((log_probs, contexts), training=False)\n", " else:\n", " model_pred = self(log_probs, training=False)\n", " grad = tape.gradient(model_pred, log_probs)\n", " log_probs = log_probs + self.opt_lr grad[0]\n", " temp_batch['design'] = tf.stop_gradient(log_probs)\n", " if 'context_id' in batch and self.contextual:\n", " temp_batch['context_id'] = batch['context_id']\n", " return temp_batch" ] }, { "cell_type": "markdown", "metadata": { "id": "1RPktU1SVbIK" }, "source": [ "# Data Loading and Problem Definition" ] }, { "cell_type": "markdown", "metadata": { "id": "uvF6OMaCVlaf" }, "source": [ "## Hardware Optimization Problem \u0026 Offline Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "mg6CWNYlVkqO" }, "outputs": [], "source": [ "#@title Define the hardware optimization problem\n", "\n", "class HardwareOptProblem:\n", " \"\"\"\n", " Problem for loading the task dataset and training\n", " \"\"\"\n", " def __init__(self,\n", " config: dict,\n", " data_file: dict, \n", " params_dict: Optional[dict] = None):\n", " \"\"\"Initialize a hardware optimization problem.\n", "\n", " config: a dictionary of various input fields and their corresponding\n", " possible valid number of discrete values. \n", " data_file: a dictionary of a list of various input fields.\n", " params_dict: a dictionary of additional inputs to the HardwareOptProblem.\n", " \"\"\"\n", "\n", " # Batch size for the batch sampling\n", " self.batch_size = 256\n", " if 'batch_size' in params_dict:\n", " self._batch_size = params_dict['batch_size']\n", "\n", " # Whether to train on infeasible points or not\n", " # use 'valid' for feasible points, and 'mixed' for both infeasible and\n", " # feasible points\n", " self._batch_type = 'valid'\n", " if 'batch_type' in params_dict:\n", " self._batch_type = params_dict['batch_type']\n", "\n", " # Add any area constraints or not: this flag enables filtering the data\n", " # basedn on whether the area constraint is not satisfied\n", " self._add_area_constraints = False\n", " if 'add_area_constraints' in params_dict:\n", " self._add_area_constraints = params_dict['add_area_constraints']\n", "\n", " self.dataset = PRIMEDataset(config=config,\n", " data_dict=data_file)\n", " self.feasible_probs,\\\n", " self.infeasible_probs = self.dataset.get_feasible_probs(\n", " add_area_constraints=self._add_area_constraints)\n", "\n", " # Choose what kind of batch to provide while training the model\n", " self.get_training_batch = None\n", " if self._batch_type == 'valid':\n", " self.get_training_batch = self.get_valid_only_batch\n", " elif self._batch_type == 'mixed':\n", " self.get_training_batch = self.get_mixed_batch\n", " else:\n", " self.get_training_batch = self.get_all_batch\n", "\n", " def get_all_batch(self,):\n", " \"\"\"Sample i.i.d. from the entire dataset.\"\"\"\n", " indices = np.random.randint(1, \n", " self.dataset._top, self._batch_size)\n", " batch_x, batch_y = self.dataset._get_batch(indices)\n", " batch_dict = dict()\n", " batch_dict['design'] = batch_x\n", " batch_dict['objective'] = batch_y\n", " return batch_dict\n", "\n", " def get_valid_only_batch(self,):\n", " \"\"\"Get only valid samples in the batch.\"\"\"\n", " indices = np.random.choice(np.arange(0, self.dataset._top),\n", " size=self._batch_size, p=self.feasible_probs)\n", " batch_x, batch_y = self.dataset._get_batch(indices)\n", " batch_dict = dict()\n", " batch_dict['design'] = batch_x\n", " batch_dict['objective'] = batch_y\n", " return batch_dict\n", "\n", " def get_top_batch(self,):\n", " \"\"\"Get only the top scoring batch for eval\"\"\"\n", " indices = self.dataset._tf_dataset['argsort'][-self.batch_size:]\n", " batch_x, batch_y = self.dataset._get_batch(indices)\n", " batch_dict = dict()\n", " batch_dict['design'] = batch_x\n", " batch_dict['objective'] = batch_y\n", " return batch_dict\n", "\n", " def get_mixed_batch(self,):\n", " \"\"\"Get both valid and invalid samples to train in a batch\"\"\"\n", " # Should be called when training with invalid samples as negatives\n", " valid_indices = np.random.choice(np.arange(0, self.dataset._top),\n", " size=self._batch_size,\n", " p=self.feasible_probs)\n", " invalid_indices = np.random.choice(np.arange(0, self.dataset._top),\n", " size=self._batch_size,\n", " p=self.infeasible_probs)\n", " batch_x, batch_y = self.dataset._get_batch(valid_indices)\n", " batch_x_in, batch_y_in = self.dataset._get_batch(invalid_indices)\n", " batch_dict = dict()\n", " batch_dict['design'] = batch_x\n", " batch_dict['objective'] = batch_y\n", " batch_dict['invalid/design'] = batch_x_in\n", " batch_dict['invalid/objective'] = batch_y_in\n", " return batch_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "b1WllMw1K-Ad" }, "outputs": [], "source": [ "#@title Define the dataset\n", "\n", "class PRIMEDataset(tf.Module):\n", " \"\"\"\n", " Load the dataset to be able to train the PRIMETransformerModel. \n", " \"\"\"\n", " def __init__(self,\n", " config,\n", " data_dict: dict,\n", " **kwargs):\n", " \"\"\"Create a dataset for training PRIME.\"\"\"\n", " self._config = config\n", "\n", " self.data_dict = data_dict\n", " self._design_space_dict = {}\n", " self._segment_lengths = {}\n", " self._max_ctr = 0\n", " self._eval_metric_keys = ['area', 'runtime', 'score']\n", " self._validity_keys = ['infeasible',]\n", "\n", " self._active_training_keys = ['param_1', 'param_2', 'param_3',\n", " 'param_4', 'param_5', 'param_6',\n", " 'param_7', 'param_8', 'param_9',\n", " 'param_10']\n", "\n", " self._tf_dataset = {}\n", " self._top = 0\n", " if self.data_dict is not None:\n", " self._setup_dataset()\n", "\n", " def _setup_dataset(self,):\n", " \"\"\"Main function to setup the dataset\"\"\"\n", " self.load_or_refresh_config()\n", " logging.info('Loading dataset..')\n", " self._convert_to_tf_dataset()\n", " self.get_score_function()\n", " print ('Loaded dataset....', self.size)\n", "\n", " def get_input_splits(self,):\n", " \"\"\"Get the splits of input of the dataset.\"\"\"\n", " lengths = []\n", " for key in self._active_training_keys:\n", " ctr_idx = self._design_space_dict[key]['ctr']\n", " lengths.append(self._segment_lengths[ctr_idx])\n", " self._active_lengths = lengths\n", " return lengths\n", "\n", " def get_score_function(self,):\n", " \"\"\"Get the objective function which is being maximized\"\"\"\n", " runtime = self._tf_dataset['runtime'].numpy()\n", " area = self._tf_dataset['area'].numpy()\n", " scores = -runtime\n", " self._tf_dataset['score'] = tf.convert_to_tensor(\n", " scores, dtype=tf.float32)\n", " print ('Score stats: ')\n", " print ('--------------------------------------------')\n", " print ('Max: ', scores.max())\n", " print ('Mean: ', scores.mean())\n", " print ('Min: ', scores.min())\n", " print ('--------------------------------------------')\n", "\n", " # Since we need top batch for eval, store top scores\n", " self._tf_dataset['argsort'] = np.argsort(\n", " self._tf_dataset['score'].numpy())\n", " return scores\n", "\n", " def _convert_to_tf_dataset(self,):\n", " \"\"\"Convert the dataset to a tensorflow dataset, easy to read from.\"\"\"\n", " tf_dataset = {}\n", " for key in self._active_training_keys +\\\n", " self._eval_metric_keys + self._validity_keys:\n", " tf_dataset[key] = []\n", "\n", " # Load the data from the data file. Note that most of the fields are\n", " # actually not one-hots, and essentially corresponds to the original data\n", " # with field-value pairs for each field, and the value is a discrete value.\n", " tf_actual_dataset = {}\n", " parsed_dataset = self.data_dict\n", " for p in parsed_dataset:\n", " tf_dataset[p] = parsed_dataset[p]\n", " tf_actual_dataset[p] = tf.convert_to_tensor(tf_dataset[p])\n", "\n", " if key in self._active_training_keys:\n", " tf_actual_dataset[p] = tf.cast(tf_actual_dataset[p], tf.int32)\n", "\n", " self._design_space_dict_copy = deepcopy(self._design_space_dict)\n", "\n", " # Now convert the dataset to actually use one-hot representations. This is\n", " # used for training, and so it is important to use this.\n", " tf_actual_temp_dataset = {}\n", " for key in self._active_training_keys:\n", " design_space_map = dict(\n", " self._design_space_dict[key]['mapping_one_hot_to_value'])\n", " data_val = tf_actual_dataset[key].numpy().astype(np.int32).tolist()\n", " out_vals = []\n", " for x in data_val:\n", " out_vals.append(design_space_map[x])\n", "\n", " tf_actual_temp_dataset[key] = tf.constant(out_vals, dtype=tf.int32)\n", "\n", " ## Finally load the tf_actual_temp_dataset into the tf_dataset\n", " for key in tf_actual_temp_dataset:\n", " tf_actual_dataset[key] = tf_actual_temp_dataset[key]\n", "\n", " self._tf_dataset = tf_actual_dataset\n", " self._infeasible_np = self._tf_dataset['infeasible'].numpy().astype(\n", " np.float32)\n", " self._top = self._infeasible_np.shape[0]\n", "\n", " def load_or_refresh_config(self):\n", " \"\"\"Load config file with specifications.\"\"\"\n", " self._design_space_dict = {}\n", " self._segment_lengths = {}\n", " \n", " try:\n", " # The case when the config is a file to open\n", " with gfile.Open(self._config, 'r') as f:\n", " line = f.readline()\n", " line = line.replace('\\n', '')\n", " # print ('Line: ', line)\n", " ctr = 0\n", " while line:\n", " ind_field = dict()\n", " split_line = line.split(':')\n", " ind_field['data_type'] = split_line[0]\n", " ind_field['value_range'] = [int(x) for x in split_line[-1].split(',')]\n", " index_vals = np.arange(len(ind_field['value_range']))\n", " ind_field['mapping_one_hot_to_value'] = zip(\n", " ind_field['value_range'], index_vals)\n", " ind_field['ctr'] = ctr\n", " self._design_space_dict[split_line[1]] = ind_field\n", " self._segment_lengths[ctr] = len(ind_field['value_range'])\n", " self._max_ctr += 1\n", " line = f.readline()\n", " ctr += 1\n", " except:\n", " # When config is a string of the contents of the file\n", " lines = self._config.split(\"\\n\")\n", " lines = [line.replace('\\n', '') for line in lines]\n", " ctr = 0\n", " for line in lines:\n", " ind_field = dict()\n", " split_line = line.split(':')\n", " ind_field['data_type'] = split_line[0]\n", " ind_field['value_range'] = [int(x) for x in split_line[-1].split(',')]\n", " index_vals = np.arange(len(ind_field['value_range']))\n", " ind_field['mapping_one_hot_to_value'] = zip(\n", " ind_field['value_range'], index_vals)\n", " ind_field['ctr'] = ctr\n", " self._design_space_dict[split_line[1]] = ind_field\n", " self._segment_lengths[ctr] = len(ind_field['value_range'])\n", " self._max_ctr += 1\n", " ctr += 1\n", "\n", " split_lengths = []\n", " for key in self._active_training_keys:\n", " split_lengths.append(\n", " self._segment_lengths[self._design_space_dict[key]['ctr']])\n", " total_length_split = 0\n", " \n", " if total_length_split \u003e 0:\n", " split_lengths.append(total_length_split)\n", " self.split_lengths = split_lengths # later used to split input when needed\n", " self.continuous_or_not = (total_length_split \u003e 0)\n", "\n", " @property\n", " def size(self,):\n", " return self._top\n", "\n", " @property\n", " def input_properties(self):\n", " \"\"\"Get the total length of the vector to be fed as input to the model.\"\"\"\n", " length = 0\n", " for val in self._active_lengths:\n", " length += val\n", " return length\n", "\n", " def get_feasible_probs(self, add_area_constraints=False):\n", " \"\"\"\n", " Get the probability of points that are feasible, meaning they don't\n", " violate the area constraint and also obtain the feasibility result. \n", " \"\"\"\n", " feasible = (1.0 - self._infeasible_np)\n", " print ('Number of feasible points: ', np.sum(feasible))\n", " if add_area_constraints:\n", " print ('Min area: ', tf.reduce_min(self._tf_dataset['area']))\n", " feasible_area = (\n", " self._tf_dataset['area'] \u003c= AREA_THRESHOLD).numpy().astype(np.float32)\n", " feasible = np.clip(feasible + feasible_area - 1.0,\n", " a_min=0.0, a_max=1.0)\n", " print ('Number of feasible points due to area constraint: ',\n", " np.sum(feasible_area))\n", " print ('NUmber of feasible points after area constraint: ',\n", " np.sum(feasible))\n", " probs = feasible / np.sum(feasible)\n", " infeasible_probs = (1.0 - feasible)/ np.sum(1.0 - feasible)\n", " return probs, infeasible_probs\n", "\n", " def valid_invalid_data_size(self, add_area_constraints=True):\n", " \"\"\"Get the size of the valid and invalid dataset compositions.\"\"\"\n", " feasible = (1.0 - self._infeasible_np)\n", " if add_area_constraints:\n", " feasible_area = (\n", " self._tf_dataset['area'] \u003c= AREA_THRESHOLD).numpy().astype(np.float32)\n", " feasible = np.clip(feasible + feasible_area - 1.0,\n", " a_min=0.0, a_max=1.0)\n", " return np.sum(feasible), np.shape(feasible)[0] - np.sum(feasible)\n", "\n", " def _get_batch(self, indices):\n", " \"\"\"Sample a batch from the dataset.\"\"\"\n", " all_train_elements = [] # this is the training elements in one-hot form\n", " all_test_elements = [] # this is the evaluation fields (area, runtime, score, etc)\n", "\n", " # Discrete training input keys\n", " for key in self._active_training_keys:\n", " all_train_elements.append(\n", " tf.one_hot(tf.gather(self._tf_dataset[key], indices),\n", " depth=self._segment_lengths[\n", " self._design_space_dict[key]['ctr']]))\n", "\n", " # Eval keys\n", " all_test_elements = tf.expand_dims(\n", " tf.gather(self._tf_dataset['score'], indices), 1)\n", " return tf.concat(all_train_elements, 1), all_test_elements" ] }, { "cell_type": "markdown", "metadata": { "id": "X3XTzmx2XEmu" }, "source": [ "# Training loop and training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "KZt31sS_Xcuz" }, "outputs": [], "source": [ "#@title Defining the function that runs training\n", "\n", "def train_eval_offline(\n", " # Data flags\n", " config=None,\n", " training_dataset=None,\n", " validation_dataset=None, \n", " # Train flags\n", " train_steps=int(1e6),\n", " summary_freq=1000,\n", " eval_freq=1000,\n", " # Train hparams\n", " add_summary=True,\n", " save_dir=None,\n", " loss_type='mse',\n", " layers=(512, 512, 512),\n", " opt_lr=1e-4,\n", " opt_betas=(0.9, 0.999),\n", " with_ranking_penalty=False,\n", " ranking_penalty_weight=0.1,\n", " batch_size=256,\n", " batch_type='mixed',\n", " # params of the model\n", " use_dropout=False,\n", " num_votes=1,\n", " # PRIME parameters:\n", " cql_alpha=1.0,\n", " infeasible_alpha=1.0):\n", " \"\"\"Training loop for the PRIME model. \n", " \n", " Most of the input arguments are primarily hyperparameters for training the\n", " PRIME model, and self explanatory. Other arguments explained below. \n", " \n", " save_dir: the directory where the store the saved model, and the training\n", " summaries. Can be a string or None.\n", " training_dataset: a dictionary of fields in the training dataset, and their\n", " corresponding values used to train.\n", " validation_dataset: a dictionary of fields in the validation dataset, and\n", " their corresponding values to measure cross-validation. \n", " \"\"\"\n", "\n", " # First create the training dataset, note that the dataset below is a\n", " # dummy dataset, that is only well-suited for training as a representative\n", " # example. You can plug in the dataset from the other colab that provides\n", " # the data for training, or you can add your own dataset here. \n", " params_dict = dict()\n", " params_dict['batch_size'] = batch_size\n", " params_dict['batch_type'] = batch_type\n", " params_dict['add_area_constraints'] = True\n", " # Defining the problem automatically does dataset loading\n", " train_problem = HardwareOptProblem(config,\n", " training_dataset, params_dict)\n", " \n", " # Now define the validation dataset (or val_problem)\n", " val_params_dict = dict()\n", " val_params_dict['batch_size'] = batch_size\n", " val_params_dict['add_area_constraints'] = True\n", " # Only validate on the valid samples in the validation dataset\n", " val_params_dict['batch_type'] = 'valid'\n", " val_problem = HardwareOptProblem(config, validation_dataset,\n", " val_params_dict)\n", "\n", "\n", " # The dimensionality of each parameter. this input_splits parameter goes\n", " # into the PRIMETransformer, as it enables us to pass in inputd as a big\n", " # vector of concatenated one-hot vectors for each discrete parameter, and\n", " # then unpack it in the model training. This gives the flexibility of actually\n", " # being able to use the input one-hot vectors in any way as needed. \n", " input_splits = train_problem.dataset.get_input_splits()\n", " print ('Input splits: ', input_splits)\n", "\n", " # Number of inputs in all: the total dimensionality of the input is given by\n", " # the sum of number of possible values each discrete parameter can take\n", " input_properties = train_problem.dataset.input_properties\n", " print ('Loaded validation dataset..', train_problem.dataset.size, \n", " val_problem.dataset.size, input_properties)\n", "\n", " feasible_size,\\\n", " infeasible_size = train_problem.dataset.valid_invalid_data_size()\n", " print ('Feasible/Infeasible size: ', feasible_size, infeasible_size)\n", "\n", " fwd_optimizer = tf.keras.optimizers.Adam(learning_rate=opt_lr,\n", " beta_1=opt_betas[0],\n", " beta_2=opt_betas[1], name='opt')\n", "\n", " training_dict = dict()\n", " training_dict['training_type'] = batch_type\n", " training_dict['use_dropout'] = use_dropout\n", " training_dict['infeasible_alpha'] = infeasible_alpha\n", " training_dict['input_splits'] = input_splits\n", " training_dict['num_votes'] = num_votes\n", " training_dict['infeasbile_multiplier'] = float(feasible_size)/(\n", " float(infeasible_size) + 1)\n", " training_dict['num_gradient_steps'] = 20\n", "\n", " model = PRIMETransformerModel(\n", " num_outputs=1,\n", " num_inputs=input_properties,\n", " optimizer=fwd_optimizer,\n", " layers=layers,\n", " penalty_weight=cql_alpha,\n", " params_dict=training_dict)\n", " \n", " rand_num = np.random.randint(10000)\n", " \n", " # summary writer\n", " if save_dir is not None:\n", " save_dir = os.path.join(save_dir, str(rand_num))\n", " summary_writer = tf.summary.create_file_writer(logdir=save_dir)\n", " summary_writer.set_as_default()\n", " else:\n", " tf.summary.create_noop_writer()\n", "\n", " print ('save dir : ', save_dir)\n", "\n", " # Now start the training\n", " for step in range(train_steps):\n", " batch = train_problem.get_training_batch()\n", " # This is just to build the models.\n", " if step == 0:\n", " _ = model.measure_stats(batch)\n", " loss_dict = model.perform_training(\n", " batch, loss_type=loss_type,\n", " ranking_penalty_weight=ranking_penalty_weight)\n", "\n", " if step % summary_freq == 0:\n", " # regular logging\n", " print ('-------------------------------------------------------')\n", " for key in loss_dict:\n", " tf.summary.scalar('train/' + key, loss_dict[key], step=step)\n", " print ('Step: ', step, 'train/' + key, ':', loss_dict[key])\n", " print ('-------------------------------------------------------')\n", "\n", " if save_dir is not None:\n", " if step == 0:\n", " model.save(save_dir)\n", " if step % 5000 == 0:\n", " model.save_weights(os.path.join(save_dir, \"ckpt-\"+str(step)))\n", "\n", " if step % eval_freq == 0:\n", " val_batch = val_problem.get_training_batch()\n", " # validation batches are only valid batches\n", " val_loss_dict = model.measure_stats(val_batch, batch_type='valid')\n", " print ('-------------------------------------------------------')\n", " for key in val_loss_dict:\n", " tf.summary.scalar('val/' + key, val_loss_dict[key], step=step)\n", " print ('Step: ', step, 'val/' + key, ':', val_loss_dict[key])\n", " print ('-------------------------------------------------------')\n", "\n", " print ('Finished Training')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "sjN3ZcR3kLnt", "outputId": "b92be4a6-c5e2-42a1-c5c2-e5b1ba31dc51" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Keys in the dummy dataset: dict_keys(['param_1', 'param_2', 'param_3', 'param_7', 'param_8', 'param_9', 'param_6', 'param_5', 'param_4', 'param_10', 'area', 'runtime', 'infeasible'])\n" ] } ], "source": [ "#@title Creating dummy data\n", "\n", "\n", "def create_dummy_data(dataset_size, config_dict,\n", " remaining_fields=('area', 'runtime', 'infeasible')):\n", " \"\"\"\n", " Create dummy data for training PRIME.\n", "\n", " dataset_size: int, the number of samples in the training data.\n", " config_dict: dictionary, specifying the design space to sample the dataset.\n", " remaining_fields: tuple, a tuple of float values specifying additional fields\n", " to sample in the dataset. Default includes float parameters 'area' and\n", " 'runtime', and also includes a boolean field 'infeasible'\n", " \"\"\"\n", " data_dict = {}\n", " for key in config_dict:\n", " allowed_values = config_dict[key]['value_range']\n", " sampled_values = np.random.choice(allowed_values, size=dataset_size)\n", " data_dict[key] = sampled_values\n", "\n", " for key in remaining_fields:\n", " if 'infeasible' in key:\n", " sampled_values = np.random.choice([True, False], size=dataset_size)\n", " elif 'runtime' or 'area' in key:\n", " sampled_values = np.random.uniform(low=0.0, high=100.0,\n", " size=dataset_size)\n", " data_dict[key] = sampled_values\n", "\n", " return data_dict\n", "\n", "\n", "config_str = \"\"\"discrete:param_1:float64:true:1,2,4,6,8,10,12,14,16,32\n", "discrete:param_2:float64:true:1,2,4,6,8,10,12,14,16,32\n", "discrete:param_3:float64:true:4,8,16,32,64,128,256\n", "discrete:param_7:float64:true:256,512,1024,2048,4096,8192,16384\n", "discrete:param_8:float64:true:8192,16384,32768,65536\n", "discrete:param_9:float64:true:2048,4096,8192,16384,32768\n", "discrete:param_6:float64:true:4096,8192,16384,32768,65536,131072,262144,524288,1048576,2097152,4194304\n", "discrete:param_5:float64:true:262144,524288,1048576,2097152,4194304,8388608,16777216\n", "discrete:param_4:float64:true:1,2,4,6,8,10,12,14,16,32\n", "discrete:param_10:float64:true:5,10,16,20,25,30\"\"\"\n", "\n", "# Generating dummy data\n", "temp_dataset = PRIMEDataset(config=config_str, data_dict=None, params_dict={})\n", "temp_dataset.load_or_refresh_config()\n", "dictionary_to_generate_data = temp_dataset._design_space_dict\n", "\n", "training_data = create_dummy_data(1000, dictionary_to_generate_data)\n", "validation_data = create_dummy_data(1000, dictionary_to_generate_data)\n", "\n", "print ('Keys in the dummy dataset: ', training_data.keys())\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_V1oRa25sRYV", "outputId": "2dfc8315-1abd-47f1-f12e-0a6072bc99f7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score stats: \n", "--------------------------------------------\n", "Max: -0.6129692603849213\n", "Mean: -51.45989737306953\n", "Min: -99.96909843043453\n", "--------------------------------------------\n", "Loaded dataset.... 1000\n", "Number of feasible points: 511.0\n", "Min area: tf.Tensor(0.06833098587071262, shape=(), dtype=float64)\n", "Number of feasible points due to area constraint: 295.0\n", "NUmber of feasible points after area constraint: 153.0\n", "Score stats: \n", "--------------------------------------------\n", "Max: -0.04601063577058806\n", "Mean: -49.507973451263794\n", "Min: -99.947081272807\n", "--------------------------------------------\n", "Loaded dataset.... 1000\n", "Number of feasible points: 500.0\n", "Min area: tf.Tensor(0.037564213198348906, shape=(), dtype=float64)\n", "Number of feasible points due to area constraint: 255.0\n", "NUmber of feasible points after area constraint: 123.0\n", "Input splits: [10, 10, 7, 10, 7, 11, 7, 4, 5, 6]\n", "Loaded validation dataset.. 1000 1000 77\n", "Feasible/Infeasible size: 153.0 847.0\n", "Infeasible alpha: 0.05\n", "CQL Alpha: \u003ctf.Variable 'Variable:0' shape=() dtype=float32, numpy=-2.302575\u003e\n", "Num votes: 1\n", "ListWrapper([10, 10, 7, 10, 7, 11, 7, 4, 5, 6])\n", "All networks: 3\n", "save dir : None\n", "-------------------------------------------------------\n", "Step: 0 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 0 train/y_values_mean : tf.Tensor(-51.291016, shape=(), dtype=float32)\n", "Step: 0 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 0 train/negatives_dist : tf.Tensor(0.731879, shape=(), dtype=float32)\n", "Step: 0 train/mse_loss : tf.Tensor(3536.3372, shape=(), dtype=float32)\n", "Step: 0 train/avg_approx_loss : tf.Tensor(1.0531151, shape=(), dtype=float32)\n", "Step: 0 train/avg_ranking_loss : tf.Tensor(0.030993937, shape=(), dtype=float32)\n", "Step: 0 train/avg_ranking_train_loss : tf.Tensor(16.001637, shape=(), dtype=float32)\n", "Step: 0 train/avg_kendall_loss : tf.Tensor(0.027144551, shape=(), dtype=float32)\n", "Step: 0 train/cql_loss : tf.Tensor(0.731879, shape=(), dtype=float32)\n", "Step: 0 train/negatives_pred : tf.Tensor(0.731879, shape=(), dtype=float32)\n", "Step: 0 train/model_pred_average : tf.Tensor(0.70406383, shape=(), dtype=float32)\n", "Step: 0 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 0 train/y_value_infeasible : tf.Tensor(0.7113452, shape=(), dtype=float32)\n", "Step: 0 train/mse_loss_invalid : tf.Tensor(3873.5989, shape=(), dtype=float32)\n", "Step: 0 train/mse_loss_overall : tf.Tensor(7409.936, shape=(), dtype=float32)\n", "Step: 0 train/avg_approx_loss_invalid : tf.Tensor(1.0299487, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 0 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 0 val/y_values_mean : tf.Tensor(-50.549976, shape=(), dtype=float32)\n", "Step: 0 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 0 val/negatives_dist : tf.Tensor(0.089412, shape=(), dtype=float32)\n", "Step: 0 val/mse_loss : tf.Tensor(3385.7322, shape=(), dtype=float32)\n", "Step: 0 val/avg_approx_loss : tf.Tensor(1.0002624, shape=(), dtype=float32)\n", "Step: 0 val/avg_ranking_loss : tf.Tensor(-0.014705883, shape=(), dtype=float32)\n", "Step: 0 val/avg_ranking_train_loss : tf.Tensor(16.795597, shape=(), dtype=float32)\n", "Step: 0 val/avg_kendall_loss : tf.Tensor(0.0042892694, shape=(), dtype=float32)\n", "Step: 0 val/cql_loss : tf.Tensor(0.089412, shape=(), dtype=float32)\n", "Step: 0 val/negatives_pred : tf.Tensor(0.089412, shape=(), dtype=float32)\n", "Step: 0 val/model_pred_average : tf.Tensor(0.017796617, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 10 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 10 train/y_values_mean : tf.Tensor(-49.595383, shape=(), dtype=float32)\n", "Step: 10 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 10 train/negatives_dist : tf.Tensor(-5.4266176, shape=(), dtype=float32)\n", "Step: 10 train/mse_loss : tf.Tensor(2678.9875, shape=(), dtype=float32)\n", "Step: 10 train/avg_approx_loss : tf.Tensor(0.9219241, shape=(), dtype=float32)\n", "Step: 10 train/avg_ranking_loss : tf.Tensor(0.041989733, shape=(), dtype=float32)\n", "Step: 10 train/avg_ranking_train_loss : tf.Tensor(14.9530525, shape=(), dtype=float32)\n", "Step: 10 train/avg_kendall_loss : tf.Tensor(0.03296566, shape=(), dtype=float32)\n", "Step: 10 train/cql_loss : tf.Tensor(-5.4266176, shape=(), dtype=float32)\n", "Step: 10 train/negatives_pred : tf.Tensor(-5.4266176, shape=(), dtype=float32)\n", "Step: 10 train/model_pred_average : tf.Tensor(-5.489807, shape=(), dtype=float32)\n", "Step: 10 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 10 train/y_value_infeasible : tf.Tensor(-5.4988065, shape=(), dtype=float32)\n", "Step: 10 train/mse_loss_invalid : tf.Tensor(2921.0833, shape=(), dtype=float32)\n", "Step: 10 train/mse_loss_overall : tf.Tensor(5600.071, shape=(), dtype=float32)\n", "Step: 10 train/avg_approx_loss_invalid : tf.Tensor(0.8700904, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 10 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 10 val/y_values_mean : tf.Tensor(-48.882214, shape=(), dtype=float32)\n", "Step: 10 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 10 val/negatives_dist : tf.Tensor(-6.0133524, shape=(), dtype=float32)\n", "Step: 10 val/mse_loss : tf.Tensor(2728.4995, shape=(), dtype=float32)\n", "Step: 10 val/avg_approx_loss : tf.Tensor(0.8313758, shape=(), dtype=float32)\n", "Step: 10 val/avg_ranking_loss : tf.Tensor(-0.25672352, shape=(), dtype=float32)\n", "Step: 10 val/avg_ranking_train_loss : tf.Tensor(21.984526, shape=(), dtype=float32)\n", "Step: 10 val/avg_kendall_loss : tf.Tensor(-0.1670956, shape=(), dtype=float32)\n", "Step: 10 val/cql_loss : tf.Tensor(-6.0133524, shape=(), dtype=float32)\n", "Step: 10 val/negatives_pred : tf.Tensor(-6.0133524, shape=(), dtype=float32)\n", "Step: 10 val/model_pred_average : tf.Tensor(-6.4133196, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 20 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 20 train/y_values_mean : tf.Tensor(-52.60445, shape=(), dtype=float32)\n", "Step: 20 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 20 train/negatives_dist : tf.Tensor(-11.829678, shape=(), dtype=float32)\n", "Step: 20 train/mse_loss : tf.Tensor(2489.3354, shape=(), dtype=float32)\n", "Step: 20 train/avg_approx_loss : tf.Tensor(0.967807, shape=(), dtype=float32)\n", "Step: 20 train/avg_ranking_loss : tf.Tensor(0.009140394, shape=(), dtype=float32)\n", "Step: 20 train/avg_ranking_train_loss : tf.Tensor(16.383146, shape=(), dtype=float32)\n", "Step: 20 train/avg_kendall_loss : tf.Tensor(0.013235331, shape=(), dtype=float32)\n", "Step: 20 train/cql_loss : tf.Tensor(-11.829678, shape=(), dtype=float32)\n", "Step: 20 train/negatives_pred : tf.Tensor(-11.829678, shape=(), dtype=float32)\n", "Step: 20 train/model_pred_average : tf.Tensor(-11.771761, shape=(), dtype=float32)\n", "Step: 20 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 20 train/y_value_infeasible : tf.Tensor(-11.7452755, shape=(), dtype=float32)\n", "Step: 20 train/mse_loss_invalid : tf.Tensor(2442.1533, shape=(), dtype=float32)\n", "Step: 20 train/mse_loss_overall : tf.Tensor(4931.489, shape=(), dtype=float32)\n", "Step: 20 train/avg_approx_loss_invalid : tf.Tensor(0.91133654, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 20 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 20 val/y_values_mean : tf.Tensor(-51.03371, shape=(), dtype=float32)\n", "Step: 20 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 20 val/negatives_dist : tf.Tensor(-12.448181, shape=(), dtype=float32)\n", "Step: 20 val/mse_loss : tf.Tensor(2303.6575, shape=(), dtype=float32)\n", "Step: 20 val/avg_approx_loss : tf.Tensor(0.93867505, shape=(), dtype=float32)\n", "Step: 20 val/avg_ranking_loss : tf.Tensor(-0.40264937, shape=(), dtype=float32)\n", "Step: 20 val/avg_ranking_train_loss : tf.Tensor(23.87465, shape=(), dtype=float32)\n", "Step: 20 val/avg_kendall_loss : tf.Tensor(-0.2642157, shape=(), dtype=float32)\n", "Step: 20 val/cql_loss : tf.Tensor(-12.448181, shape=(), dtype=float32)\n", "Step: 20 val/negatives_pred : tf.Tensor(-12.448181, shape=(), dtype=float32)\n", "Step: 20 val/model_pred_average : tf.Tensor(-13.260455, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 30 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 30 train/y_values_mean : tf.Tensor(-50.5356, shape=(), dtype=float32)\n", "Step: 30 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 30 train/negatives_dist : tf.Tensor(-20.171307, shape=(), dtype=float32)\n", "Step: 30 train/mse_loss : tf.Tensor(1624.793, shape=(), dtype=float32)\n", "Step: 30 train/avg_approx_loss : tf.Tensor(1.0920005, shape=(), dtype=float32)\n", "Step: 30 train/avg_ranking_loss : tf.Tensor(-0.12022346, shape=(), dtype=float32)\n", "Step: 30 train/avg_ranking_train_loss : tf.Tensor(16.979572, shape=(), dtype=float32)\n", "Step: 30 train/avg_kendall_loss : tf.Tensor(-0.07285541, shape=(), dtype=float32)\n", "Step: 30 train/cql_loss : tf.Tensor(-20.171307, shape=(), dtype=float32)\n", "Step: 30 train/negatives_pred : tf.Tensor(-20.171307, shape=(), dtype=float32)\n", "Step: 30 train/model_pred_average : tf.Tensor(-20.278915, shape=(), dtype=float32)\n", "Step: 30 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 30 train/y_value_infeasible : tf.Tensor(-20.135735, shape=(), dtype=float32)\n", "Step: 30 train/mse_loss_invalid : tf.Tensor(1957.1533, shape=(), dtype=float32)\n", "Step: 30 train/mse_loss_overall : tf.Tensor(3581.9463, shape=(), dtype=float32)\n", "Step: 30 train/avg_approx_loss_invalid : tf.Tensor(1.0983152, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 30 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 30 val/y_values_mean : tf.Tensor(-55.31862, shape=(), dtype=float32)\n", "Step: 30 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 30 val/negatives_dist : tf.Tensor(-21.205795, shape=(), dtype=float32)\n", "Step: 30 val/mse_loss : tf.Tensor(1850.8827, shape=(), dtype=float32)\n", "Step: 30 val/avg_approx_loss : tf.Tensor(1.027076, shape=(), dtype=float32)\n", "Step: 30 val/avg_ranking_loss : tf.Tensor(-0.32453153, shape=(), dtype=float32)\n", "Step: 30 val/avg_ranking_train_loss : tf.Tensor(21.168787, shape=(), dtype=float32)\n", "Step: 30 val/avg_kendall_loss : tf.Tensor(-0.20484066, shape=(), dtype=float32)\n", "Step: 30 val/cql_loss : tf.Tensor(-21.205795, shape=(), dtype=float32)\n", "Step: 30 val/negatives_pred : tf.Tensor(-21.205795, shape=(), dtype=float32)\n", "Step: 30 val/model_pred_average : tf.Tensor(-22.451515, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 40 train/y_values_max : tf.Tensor(-2.018335, shape=(), dtype=float32)\n", "Step: 40 train/y_values_mean : tf.Tensor(-49.35659, shape=(), dtype=float32)\n", "Step: 40 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 40 train/negatives_dist : tf.Tensor(-31.2863, shape=(), dtype=float32)\n", "Step: 40 train/mse_loss : tf.Tensor(1105.0841, shape=(), dtype=float32)\n", "Step: 40 train/avg_approx_loss : tf.Tensor(1.0003604, shape=(), dtype=float32)\n", "Step: 40 train/avg_ranking_loss : tf.Tensor(-0.05272517, shape=(), dtype=float32)\n", "Step: 40 train/avg_ranking_train_loss : tf.Tensor(16.813736, shape=(), dtype=float32)\n", "Step: 40 train/avg_kendall_loss : tf.Tensor(-0.029595613, shape=(), dtype=float32)\n", "Step: 40 train/cql_loss : tf.Tensor(-31.2863, shape=(), dtype=float32)\n", "Step: 40 train/negatives_pred : tf.Tensor(-31.2863, shape=(), dtype=float32)\n", "Step: 40 train/model_pred_average : tf.Tensor(-31.335064, shape=(), dtype=float32)\n", "Step: 40 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 40 train/y_value_infeasible : tf.Tensor(-31.414925, shape=(), dtype=float32)\n", "Step: 40 train/mse_loss_invalid : tf.Tensor(1292.1438, shape=(), dtype=float32)\n", "Step: 40 train/mse_loss_overall : tf.Tensor(2397.228, shape=(), dtype=float32)\n", "Step: 40 train/avg_approx_loss_invalid : tf.Tensor(1.2672777, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 40 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 40 val/y_values_mean : tf.Tensor(-47.77986, shape=(), dtype=float32)\n", "Step: 40 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 40 val/negatives_dist : tf.Tensor(-32.540966, shape=(), dtype=float32)\n", "Step: 40 val/mse_loss : tf.Tensor(1008.05023, shape=(), dtype=float32)\n", "Step: 40 val/avg_approx_loss : tf.Tensor(1.0481693, shape=(), dtype=float32)\n", "Step: 40 val/avg_ranking_loss : tf.Tensor(-0.2252382, shape=(), dtype=float32)\n", "Step: 40 val/avg_ranking_train_loss : tf.Tensor(20.3955, shape=(), dtype=float32)\n", "Step: 40 val/avg_kendall_loss : tf.Tensor(-0.1397059, shape=(), dtype=float32)\n", "Step: 40 val/cql_loss : tf.Tensor(-32.540966, shape=(), dtype=float32)\n", "Step: 40 val/negatives_pred : tf.Tensor(-32.540966, shape=(), dtype=float32)\n", "Step: 40 val/model_pred_average : tf.Tensor(-34.131866, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 50 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 50 train/y_values_mean : tf.Tensor(-48.05492, shape=(), dtype=float32)\n", "Step: 50 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 50 train/negatives_dist : tf.Tensor(-43.863987, shape=(), dtype=float32)\n", "Step: 50 train/mse_loss : tf.Tensor(712.9634, shape=(), dtype=float32)\n", "Step: 50 train/avg_approx_loss : tf.Tensor(1.8901846, shape=(), dtype=float32)\n", "Step: 50 train/avg_ranking_loss : tf.Tensor(0.08773533, shape=(), dtype=float32)\n", "Step: 50 train/avg_ranking_train_loss : tf.Tensor(13.866482, shape=(), dtype=float32)\n", "Step: 50 train/avg_kendall_loss : tf.Tensor(0.067892194, shape=(), dtype=float32)\n", "Step: 50 train/cql_loss : tf.Tensor(-43.863987, shape=(), dtype=float32)\n", "Step: 50 train/negatives_pred : tf.Tensor(-43.863987, shape=(), dtype=float32)\n", "Step: 50 train/model_pred_average : tf.Tensor(-43.911087, shape=(), dtype=float32)\n", "Step: 50 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 50 train/y_value_infeasible : tf.Tensor(-43.836414, shape=(), dtype=float32)\n", "Step: 50 train/mse_loss_invalid : tf.Tensor(924.3006, shape=(), dtype=float32)\n", "Step: 50 train/mse_loss_overall : tf.Tensor(1637.2639, shape=(), dtype=float32)\n", "Step: 50 train/avg_approx_loss_invalid : tf.Tensor(1.7017825, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 50 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 50 val/y_values_mean : tf.Tensor(-47.65081, shape=(), dtype=float32)\n", "Step: 50 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 50 val/negatives_dist : tf.Tensor(-44.91657, shape=(), dtype=float32)\n", "Step: 50 val/mse_loss : tf.Tensor(808.4212, shape=(), dtype=float32)\n", "Step: 50 val/avg_approx_loss : tf.Tensor(1.5485595, shape=(), dtype=float32)\n", "Step: 50 val/avg_ranking_loss : tf.Tensor(-0.17357378, shape=(), dtype=float32)\n", "Step: 50 val/avg_ranking_train_loss : tf.Tensor(19.457308, shape=(), dtype=float32)\n", "Step: 50 val/avg_kendall_loss : tf.Tensor(-0.113051474, shape=(), dtype=float32)\n", "Step: 50 val/cql_loss : tf.Tensor(-44.91657, shape=(), dtype=float32)\n", "Step: 50 val/negatives_pred : tf.Tensor(-44.91657, shape=(), dtype=float32)\n", "Step: 50 val/model_pred_average : tf.Tensor(-47.15584, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 60 train/y_values_max : tf.Tensor(-0.93947476, shape=(), dtype=float32)\n", "Step: 60 train/y_values_mean : tf.Tensor(-49.415802, shape=(), dtype=float32)\n", "Step: 60 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 60 train/negatives_dist : tf.Tensor(-52.542496, shape=(), dtype=float32)\n", "Step: 60 train/mse_loss : tf.Tensor(816.1859, shape=(), dtype=float32)\n", "Step: 60 train/avg_approx_loss : tf.Tensor(2.075222, shape=(), dtype=float32)\n", "Step: 60 train/avg_ranking_loss : tf.Tensor(0.04479072, shape=(), dtype=float32)\n", "Step: 60 train/avg_ranking_train_loss : tf.Tensor(15.618368, shape=(), dtype=float32)\n", "Step: 60 train/avg_kendall_loss : tf.Tensor(0.0363971, shape=(), dtype=float32)\n", "Step: 60 train/cql_loss : tf.Tensor(-52.542496, shape=(), dtype=float32)\n", "Step: 60 train/negatives_pred : tf.Tensor(-52.542496, shape=(), dtype=float32)\n", "Step: 60 train/model_pred_average : tf.Tensor(-52.71419, shape=(), dtype=float32)\n", "Step: 60 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 60 train/y_value_infeasible : tf.Tensor(-52.58152, shape=(), dtype=float32)\n", "Step: 60 train/mse_loss_invalid : tf.Tensor(929.5502, shape=(), dtype=float32)\n", "Step: 60 train/mse_loss_overall : tf.Tensor(1745.7361, shape=(), dtype=float32)\n", "Step: 60 train/avg_approx_loss_invalid : tf.Tensor(2.5227823, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 60 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 60 val/y_values_mean : tf.Tensor(-50.25917, shape=(), dtype=float32)\n", "Step: 60 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 60 val/negatives_dist : tf.Tensor(-53.560272, shape=(), dtype=float32)\n", "Step: 60 val/mse_loss : tf.Tensor(931.7635, shape=(), dtype=float32)\n", "Step: 60 val/avg_approx_loss : tf.Tensor(2.0073552, shape=(), dtype=float32)\n", "Step: 60 val/avg_ranking_loss : tf.Tensor(-0.1795484, shape=(), dtype=float32)\n", "Step: 60 val/avg_ranking_train_loss : tf.Tensor(20.243542, shape=(), dtype=float32)\n", "Step: 60 val/avg_kendall_loss : tf.Tensor(-0.11832106, shape=(), dtype=float32)\n", "Step: 60 val/cql_loss : tf.Tensor(-53.560272, shape=(), dtype=float32)\n", "Step: 60 val/negatives_pred : tf.Tensor(-53.560272, shape=(), dtype=float32)\n", "Step: 60 val/model_pred_average : tf.Tensor(-55.736107, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 70 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 70 train/y_values_mean : tf.Tensor(-51.05237, shape=(), dtype=float32)\n", "Step: 70 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 70 train/negatives_dist : tf.Tensor(-53.52229, shape=(), dtype=float32)\n", "Step: 70 train/mse_loss : tf.Tensor(747.3913, shape=(), dtype=float32)\n", "Step: 70 train/avg_approx_loss : tf.Tensor(1.7664852, shape=(), dtype=float32)\n", "Step: 70 train/avg_ranking_loss : tf.Tensor(0.004579853, shape=(), dtype=float32)\n", "Step: 70 train/avg_ranking_train_loss : tf.Tensor(15.55942, shape=(), dtype=float32)\n", "Step: 70 train/avg_kendall_loss : tf.Tensor(0.009129882, shape=(), dtype=float32)\n", "Step: 70 train/cql_loss : tf.Tensor(-53.52229, shape=(), dtype=float32)\n", "Step: 70 train/negatives_pred : tf.Tensor(-53.52229, shape=(), dtype=float32)\n", "Step: 70 train/model_pred_average : tf.Tensor(-53.431484, shape=(), dtype=float32)\n", "Step: 70 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 70 train/y_value_infeasible : tf.Tensor(-53.6995, shape=(), dtype=float32)\n", "Step: 70 train/mse_loss_invalid : tf.Tensor(960.54016, shape=(), dtype=float32)\n", "Step: 70 train/mse_loss_overall : tf.Tensor(1707.9314, shape=(), dtype=float32)\n", "Step: 70 train/avg_approx_loss_invalid : tf.Tensor(1.8934901, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 70 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 70 val/y_values_mean : tf.Tensor(-48.484962, shape=(), dtype=float32)\n", "Step: 70 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 70 val/negatives_dist : tf.Tensor(-53.476532, shape=(), dtype=float32)\n", "Step: 70 val/mse_loss : tf.Tensor(930.3341, shape=(), dtype=float32)\n", "Step: 70 val/avg_approx_loss : tf.Tensor(2.1871474, shape=(), dtype=float32)\n", "Step: 70 val/avg_ranking_loss : tf.Tensor(-0.29353473, shape=(), dtype=float32)\n", "Step: 70 val/avg_ranking_train_loss : tf.Tensor(21.765175, shape=(), dtype=float32)\n", "Step: 70 val/avg_kendall_loss : tf.Tensor(-0.19185048, shape=(), dtype=float32)\n", "Step: 70 val/cql_loss : tf.Tensor(-53.476532, shape=(), dtype=float32)\n", "Step: 70 val/negatives_pred : tf.Tensor(-53.476532, shape=(), dtype=float32)\n", "Step: 70 val/model_pred_average : tf.Tensor(-55.997845, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 80 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 80 train/y_values_mean : tf.Tensor(-47.936115, shape=(), dtype=float32)\n", "Step: 80 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 80 train/negatives_dist : tf.Tensor(-50.614216, shape=(), dtype=float32)\n", "Step: 80 train/mse_loss : tf.Tensor(785.9317, shape=(), dtype=float32)\n", "Step: 80 train/avg_approx_loss : tf.Tensor(2.087608, shape=(), dtype=float32)\n", "Step: 80 train/avg_ranking_loss : tf.Tensor(-0.057850055, shape=(), dtype=float32)\n", "Step: 80 train/avg_ranking_train_loss : tf.Tensor(16.756374, shape=(), dtype=float32)\n", "Step: 80 train/avg_kendall_loss : tf.Tensor(-0.032169104, shape=(), dtype=float32)\n", "Step: 80 train/cql_loss : tf.Tensor(-50.614216, shape=(), dtype=float32)\n", "Step: 80 train/negatives_pred : tf.Tensor(-50.614216, shape=(), dtype=float32)\n", "Step: 80 train/model_pred_average : tf.Tensor(-50.539318, shape=(), dtype=float32)\n", "Step: 80 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 80 train/y_value_infeasible : tf.Tensor(-50.425545, shape=(), dtype=float32)\n", "Step: 80 train/mse_loss_invalid : tf.Tensor(987.44763, shape=(), dtype=float32)\n", "Step: 80 train/mse_loss_overall : tf.Tensor(1773.3794, shape=(), dtype=float32)\n", "Step: 80 train/avg_approx_loss_invalid : tf.Tensor(2.67552, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 80 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 80 val/y_values_mean : tf.Tensor(-50.924248, shape=(), dtype=float32)\n", "Step: 80 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 80 val/negatives_dist : tf.Tensor(-50.346638, shape=(), dtype=float32)\n", "Step: 80 val/mse_loss : tf.Tensor(828.5458, shape=(), dtype=float32)\n", "Step: 80 val/avg_approx_loss : tf.Tensor(2.064958, shape=(), dtype=float32)\n", "Step: 80 val/avg_ranking_loss : tf.Tensor(-0.11538826, shape=(), dtype=float32)\n", "Step: 80 val/avg_ranking_train_loss : tf.Tensor(18.36464, shape=(), dtype=float32)\n", "Step: 80 val/avg_kendall_loss : tf.Tensor(-0.064828455, shape=(), dtype=float32)\n", "Step: 80 val/cql_loss : tf.Tensor(-50.346638, shape=(), dtype=float32)\n", "Step: 80 val/negatives_pred : tf.Tensor(-50.346638, shape=(), dtype=float32)\n", "Step: 80 val/model_pred_average : tf.Tensor(-52.72599, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 90 train/y_values_max : tf.Tensor(-0.9038583, shape=(), dtype=float32)\n", "Step: 90 train/y_values_mean : tf.Tensor(-51.69957, shape=(), dtype=float32)\n", "Step: 90 train/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 90 train/negatives_dist : tf.Tensor(-48.031395, shape=(), dtype=float32)\n", "Step: 90 train/mse_loss : tf.Tensor(794.3457, shape=(), dtype=float32)\n", "Step: 90 train/avg_approx_loss : tf.Tensor(1.991349, shape=(), dtype=float32)\n", "Step: 90 train/avg_ranking_loss : tf.Tensor(-0.05236754, shape=(), dtype=float32)\n", "Step: 90 train/avg_ranking_train_loss : tf.Tensor(16.758024, shape=(), dtype=float32)\n", "Step: 90 train/avg_kendall_loss : tf.Tensor(-0.03039217, shape=(), dtype=float32)\n", "Step: 90 train/cql_loss : tf.Tensor(-48.031395, shape=(), dtype=float32)\n", "Step: 90 train/negatives_pred : tf.Tensor(-48.031395, shape=(), dtype=float32)\n", "Step: 90 train/model_pred_average : tf.Tensor(-48.295383, shape=(), dtype=float32)\n", "Step: 90 train/invalid/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 90 train/y_value_infeasible : tf.Tensor(-48.175266, shape=(), dtype=float32)\n", "Step: 90 train/mse_loss_invalid : tf.Tensor(927.43634, shape=(), dtype=float32)\n", "Step: 90 train/mse_loss_overall : tf.Tensor(1721.782, shape=(), dtype=float32)\n", "Step: 90 train/avg_approx_loss_invalid : tf.Tensor(1.9503968, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "-------------------------------------------------------\n", "Step: 90 val/y_values_max : tf.Tensor(-0.80804974, shape=(), dtype=float32)\n", "Step: 90 val/y_values_mean : tf.Tensor(-49.71262, shape=(), dtype=float32)\n", "Step: 90 val/vote_entropy : tf.Tensor(0.0, shape=(), dtype=float32)\n", "Step: 90 val/negatives_dist : tf.Tensor(-48.144753, shape=(), dtype=float32)\n", "Step: 90 val/mse_loss : tf.Tensor(898.52563, shape=(), dtype=float32)\n", "Step: 90 val/avg_approx_loss : tf.Tensor(1.6679305, shape=(), dtype=float32)\n", "Step: 90 val/avg_ranking_loss : tf.Tensor(-0.162691, shape=(), dtype=float32)\n", "Step: 90 val/avg_ranking_train_loss : tf.Tensor(19.78121, shape=(), dtype=float32)\n", "Step: 90 val/avg_kendall_loss : tf.Tensor(-0.102757335, shape=(), dtype=float32)\n", "Step: 90 val/cql_loss : tf.Tensor(-48.144753, shape=(), dtype=float32)\n", "Step: 90 val/negatives_pred : tf.Tensor(-48.144753, shape=(), dtype=float32)\n", "Step: 90 val/model_pred_average : tf.Tensor(-50.337814, shape=(), dtype=float32)\n", "-------------------------------------------------------\n", "Finished Training\n" ] } ], "source": [ "#@title Running training\n", "\n", "# A toy example of running training PRIME. \n", "train_eval_offline(\n", " config=config_str,\n", " training_dataset=training_data,\n", " validation_dataset=validation_data,\n", " train_steps=100,\n", " summary_freq=10,\n", " eval_freq=10,\n", " add_summary=True,\n", " save_dir=None,\n", " loss_type='mse+rank',\n", " layers=(256, 256, 256),\n", " with_ranking_penalty=True,\n", " ranking_penalty_weight=0.01,\n", " use_dropout=True,\n", " cql_alpha=0.1,\n", " infeasible_alpha=0.05\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m5KUS8rq3SQf" }, "outputs": [], "source": [ "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "Z6W-wFO7FK0O", "ll-Ud813QwND" ], "name": "[PRIME, ICLR 2022] Colab Demonstration", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
UCBerkeleySETI/blml	gbt_cluster/simple_feature_clustering.ipynb	1	9712776	null	mit
FourthCohortAwesome/NightThree	Night3_DTK.ipynb	1	4446	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Similar type issue from last weeks problem\n", "3 File protocols now\n", "\n", "Protocol1 = 7Col x 3Row --> Alt1\n", "Protocol2 = 2Col x 9Row --> Alt2\n", "Protocol3 = 4Col x 7Row --> Alt3\n", "\n", "File rows might not be in the same order, need to retain the order from the original to the destination\n", "\n", "Output files naming schema - Dest_DTK_alt<#>.csv\n", "\n", "Pseudo Code -\n", "Identify file type\n", "write out appropriate file in the correct order" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/xianarchangel/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:18: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n" ] } ], "source": [ "import csv\n", "import sys\n", "import pandas as pd \n", "\n", "if sys.argv[1] == None:\n", " print(\"Export and run this: python3 <filename>.py <file to process>\")\n", " quit()\n", "\n", "# Need to open file and peek at structure to make decision\n", "argfile = sys.argv[1]\n", "\n", "#debug code\n", "#argfile = 'source.csv'\n", "#argfile = 'alt2.csv'\n", "#argfile = 'alt3.csv'\n", "\n", "with open(argfile, 'r') as infile:\n", " df = pd.read_csv(infile, sep=None) \n", " # extract number of cols\n", " #print(df)\n", " colNum = len(df.columns)\n", " if colNum == 2:\n", " print('logic for fixing the alt2 type')\n", " # read data from each row, if present, replace data in second column\n", " # if data is not present in first col then read second col and replace col 1\n", " \n", " # dictionary of all possible outcomes\n", " type1 = ['1', '3']\n", " type2 = ['2', '4']\n", " type3 = ['3', '6']\n", " \n", " for i, row in df.iterrows():\n", " if row['ONE'] == 1 or row['TWO'] == 3:\n", " df.loc[i] = type1\n", " elif row['ONE'] == 2 or row['TWO'] == 4:\n", " df.loc[i] = type2\n", " elif row['ONE'] == 3 or row['TWO'] == 6:\n", " df.loc[i] = type3 \n", " else:\n", " # What have you given me?\n", " print(\"The world is on FIRE!\")\n", "\n", " df.to_csv('Dest_DTK_alt2.csv', sep='\\t', index = False)\n", " \n", " \n", " elif colNum == 4:\n", " #print('logic for fixing the source.csv')\n", " data = ['a', 'b', 'c', 'd']\n", " rowdata=[data]7\n", " df = pd.DataFrame(rowdata, columns=['One', 'Two', 'Three', 'Four'])\n", " df.to_csv(r'Dest_DTK_source.csv', sep='\\t', index = False)\n", " elif colNum == 7:\n", " #print('logic for fixing the alt3 type')\n", " data = ['1', '2', '3', '4', '5', '6', '7']\n", " rowdata=[data]3\n", " df = pd.DataFrame(rowdata, columns=['One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven'])\n", " df.to_csv(r'Dest_DTK_alt3', sep='\\t', index = False)\n", " else:\n", " print(\"System Error Deleting File System.....Standby...\")\n", " quit()\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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
asimihsan/pydata-ldn2014-writeup	00 - Summary, Highlights, Readings and Order.ipynb	1	20180	{ "metadata": { "name": "", "signature": "sha256:2c00f0ddb7215d7c4cea19200a00bf8d1c743b2cbb0d2fadb582c83fab517cbf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Intro\n", "\n", "Below is:\n", "\n", "- A summary of key points that cropped up again and again.\n", "- A set of proposed readings by topic\n", "\n", "Note that these notes haven't been edited or extended with audio recordings I've made, and some need updating with significantly more material. See the \"Proposed Readings by Topic* section or ask me for more details.\n", "\n", "## Summary\n", "\n", "- Both the financial and academic worlds are increasingly adopting Python for the same reasons.\n", " - They're often encumbered with extremely large, heterogenous, legacy systems.\n", " - Old work isn't discarded. Incremental additions, re-use over interfaces.\n", " - Think COBOL/Excel/VBA for finance, FORTRAN/C in academia.\n", " - They're both seeking one paradigm as an end-to-end high-level solution for all users.\n", " - Neither can sacrifice performance, yet are finding the time-to-market and development lifecycles too long with legacy homogenous systems.\n", " - Python has long had a reputation for being unable to deliver performance, but this is now commonly acknowledged to no longer be true.\n", " - Python easily serves as a glue to high performance wrappers such as NumPy and SciPy, file formats such as HDF5, and heterogenous computation backends such as shared-memory parallelism (SMP) (OpenMP via Cython), GPUs (CUDA) and FPGAs (OpenCL).\n", " - Achieving C/C++ performance can be done with significantly simpler code and designs, yet requires sophisticated knowledge of memory cache hierarchies, disk I/O patterns, and SMP issues.\n", " - The financial industry has long relied on Python as an interface to core, high-performance components, but are paranoid and extremely closed and the only way knowledge gets shared is by stealing employees from other financial companies.\n", " - More information:\n", " - [\"06 - Python in the Financial Industry (KEYNOTE)\"](06%20-%20Python%20in%20the%20Financial%20Industry%20%28KEYNOTE%29.ipynb)\n", " - [\"15 - Building a Cutting-Edge Data Processing Environment on a Budget\"](15%20-%20Building%20a%20Cutting-Edge%20Data%20Processing%20Environment%20on%20a%20Budget.ipynb)\n", " - [\"20 - Python for High Throughput Science\"](20%20-%20Python%20for%20High%20Throughput%20Science.ipynb)\n", "\n", "- IPython Notebook is universally used and loved.\n", " - Most technical talks used IPython Notebook either for all the content or to host demos/examples.\n", " - Of those talks around half provided direct links to a pre-initialised IPython Notebook, so attendees could either download and follow in real-time, or download later.\n", " - Deep and expanding integration with entire scientific Python ecosystem, e.g. `matplotlib`, `pandas`, `numpy`, `bokeh`, `sympy`, ...\n", " - Good examples are:\n", " - [\"04 - Visualisations Using Bokeh\"](04%20-%20Visualisations%20Using%20Bokeh.ipynb)\n", " - [\"16 - presenter notes\" (for \"Generator Showcase Showdown\")](16%20-%20presenter%20notes.ipynb)\n", " - [\"18 - presenter notes\" (for \"Measuring Similarity and Clustering Data\")](18%20-%20presenter%20notes.ipynb)\n", "\n", "- No clear future for visualisations in Python.\n", " - `matplotlib` is universally used for publication-quality charts. API is difficult to use but powerful and well engineered.\n", " - It's clear that web-based visualisations are the future, and very important even for publications.\n", " - In order to reach the browser it's also acknowledged that JavaScript is the ideal interface, rather than static images.\n", " - But how to reach browser? Many different perspectives:\n", " - IPython Notebook - use `matplotlib` magic incantation to draw charts, no interactivity, no JavaScript.\n", " - Other libraries build on top of `matplotlib` of course work just as well: `ggplot`, `seaborn`, `prettyplotlib`\n", " - [\"04 - Visualisations in Bokeh\"](04%20-%20Visualisations%20Using%20Bokeh.ipynb): people love `ggplot` in R because of the Grammar of Graphics, and people love Python because it's a one-stop stop\n", " - So use Python with a ggplot-like grammar to auto-generate HTML5-canvas backed web visualisations using JavaScript.\n", " - HTML5-canvas is an investment and should reap rewards over SVG-based libraries like d3.js for very complex visualisations.\n", " - [\"12 - Getting it out there - Python-JS-web-viz\"](12%20-%20Getting%20it%20out%20there%20-%20Python-JS-web-viz.ipynb): forget Python, just code front-end in JavaScript and defer back-end and data cleaning to Python.\n", " - d3.js, nvd3, crossfilter, rickshaw, ...\n", " - Lightning talks\n", " - One presenter uses Python over a websocket bridge to Angular.js to create an RShiny-type interactive chart environment.\n", " - Another presenter showed off IPython version 2 (coming end of April 2014) functionality with interactive widgets, dynamically recreating charts based on user input.\n", " - There is no clear conclusion, except `matplotlib` is fantastic work and stood the test of time.\n", " - Bokeh seems very exciting but rough around the edges with a large and difficult to install set of dependencies, worth exploring the tutorials in full (!!AI which I will, and post a new article).\n", " \n", "- Cython is almost universally used, but more agile methods are being sought\n", " - Cython is a strict superset of Python that, with annotations, allow it to reach C-like speeds.\n", " - These annotations no longer allow Python compatibility. Can the Python community do better? Perhaps, with Shedskin, Pythran, or Numba. PyPy may eventually reach the Holy Grail of numpy compatibility.\n", " - See:\n", " - [\"10 - The High Performance Python Landscape\"](10%20-%20The%20High%20Performance%20Python%20Landscape.ipynb)\n", " - [\"15 - Building a Cutting-Edge Data Processing Environment on a Budget\"](15%20-%20Building%20a%20Cutting-Edge%20Data%20Processing%20Environment%20on%20a%20Budget.ipynb)\n", " - [\"03 - Faster Python Programs through Optimization\"](03%20-%20Faster%20Python%20Programs%20through%20Optimization.ipynb) (!!AI there is a significant quantity of missing information, I will type up soon).\n", " - [\"11 - Shared Memory Parallelism with Python\"](11%20-%20Shared%20Memory%20Parallelism%20with%20Python.ipynb)\n", "\n", "- Everyone uses `scikit-learn`\n", " - Well thought out, very opinionated design, strong and diverse set of core contributors.\n", " - Stands out amongst Python packages as having > 10 contributors who equally make the same volume of contributions.\n", " - At the very least prototype in `scikit-learn` and `nltk`.\n", " - If you hit scalability issues people usually scale vertically (bigger boxes) or use Cython.\n", " - See:\n", " - [\"15 - Building a Cutting-Edge Data Processing Environment on a Budget\"](15%20-%20Building%20a%20Cutting-Edge%20Data%20Processing%20Environment%20on%20a%20Budget.ipynb)\n", " - [\"22 - Correcting 10 years of messy CRM data\"](22%20-%20Correcting%2010%20years%20of%20messy%20CRM%20data.ipynb)\n", " - [\"19 - Gradient Boosted Regression Trees in scikit-learn\"](19%20-%20Gradient%20Boosted%20Regression%20Trees%20in%20scikit-learn.ipynb)\n", "\n", "- MapReduce/clusters have less hype and traction than you'd expect\n", " - Certainly there are some who use it for their data processing pipeline, e.g. \"07 - Hierarchical Text Clustering in Python and Hive\".\n", " - Given a large data set that cannot fit onto one disk, prefer to create large RDBMS clusters. See:\n", " - [\"05 - Databases for Scientists\"](05%20-%20Databases%20for%20Scientists.ipynb)\n", " - [\"08 - Massively Parallel Processing with Procedural Python\"](08%20-%20Massively%20Parallel%20Processing%20with%20Procedural%20Python.ipynb)\n", " - [Presenter Notes](08%20-%20presenter%20notes.ipynb)\n", " - Given a large data set that cannot fit into memory prefer to use e.g. HDF5 to disk-back it, or create additional abstractions on top of NumPy/HDF5, a la \"23 - Manipulating massive disk-backed arrays\".\n", " - `scikit-learn` core contributors strongly prefer shared-memory parallelism to clusters, and are actively creating OpenMP-style abstractions (with better debugging and NumPy array performance).\n", " - [\"15 - Building a Cutting Edge Data Processing Environment on a Budget\"](15%20-%20Building%20a%20Cutting-Edge%20Data%20Processing%20Environment%20on%20a%20Budget.ipynb)\n", " - Fantastic lightning talk on end used [FireDrake](http://firedrakeproject.org/index.html) to easily switch computing backend from SMP to GPU, but again no mention of clusters.\n", "\n", "## Proposed readings by group\n", "\n", "### Culture, industry background\n", "\n", "- [\"06 - Python in the Financial Industry (KEYNOTE)\"](06%20-%20Python%20in%20the%20Financial%20Industry%20%28KEYNOTE%29.ipynb)\n", "- [\"14 - Panel Discussion - Shouldn't companies be doing more data science?\"](14%20-%20Panel%20Discussion%20-%20Shouldn%27t%20companies%20be%20doing%20more%20data%20science%3F.ipynb)\n", "\n", "### Case studies\n", "\n", "- [\"07 - Hierarchical Text Clustering in Python and Hive\"](07%20-%20Hierarchical%20Text%20Clustering%20in%20Python%20and%20Hive.ipynb)\n", "- [\"09 - Measuring the digital economy using big data\"](09%20-%20Measuring%20the%20digital%20economy%20using%20big%20data.ipynb)\n", "- [\"17 - Adaptive Filtering of Tweets with Machine Learning\"](17%20-%20Adaptive%20Filtering%20of%20Tweets%20with%20Machine%20Learning.ipynb)\n", "- [\"20 - Python for High Throughput Science\"](20%20-%20Python%20for%20High%20Throughput%20Science.ipynb)\n", "- [\"22 - Correcting 10 years of messy CRM data\"](22%20-%20Correcting%2010%20years%20of%20messy%20CRM%20data.ipynb)\n", "\n", "### Technical - software engineering \n", "\n", "- [\"03 - Faster Python Programs through Optimization\"](03%20-%20Faster%20Python%20Programs%20through%20Optimization.ipynb)\n", " - Needs updating with significant amount of presenter material we didn't cover.\n", "- [\"10 - The High Performance Python Landscape\"](10%20-%20The%20High%20Performance%20Python%20Landscape.ipynb)\n", "- [\"11 - Shared Memory Parallelism with Python\"](11%20-%20Shared%20Memory%20Parallelism%20with%20Python.ipynb)\n", "- [\"15 - Building a Cutting-Edge Data Processing Environment on a Budget\"](15%20-%20Building%20a%20Cutting-Edge%20Data%20Processing%20Environment%20on%20a%20Budget.ipynb)\n", "\n", "### Technical - mathematical\n", "\n", "- [\"02 - Introduction to Action Recognition\"](02%20-%20Introduction%20to%20Action%20Recognition.ipynb)\n", "- [\"13 - Python for Optimization\"](13%20-%20Python%20for%20Optimization.ipynb)\n", "- [\"18 - Measuring Similarity and Clustering Data\"](18%20-%20Measuring%20Similarity%20and%20Clustering%20Data.ipynb)\n", " - And presenter notes: [\"18 - presenter notes\"](18%20-%20presenter%20notes.ipynb)\n", "- [\"19 - Gradient Boosted Regression Trees in scikit-learn\"](19%20-%20Gradient%20Boosted%20Regression%20Trees%20in%20scikit-learn.ipynb)\n", "\n", "### Technical - other\n", "\n", "- [\"01 - Interactive Financial Analytics with Python and IPython\"](01%20-%20Interactive%20Financial%20Analytics%20with%20Python%20and%20IPython.ipynb)\n", " - Presenter's tutorial where I followed along with exercises are here: [\"01 - YH_PyData_Eurex_Tutorial\"](01%20-%20YH_PyData_Eurex_Tutorial.ipynb)\n", "- [\"05 - Databases for Scientists\"](05%20-%20Databases%20for%20Scientists.ipynb)\n", " - Needs updating with presenter's material.\n", "- [\"08 - Massively Parallel Processing with Procedural Python\"](08%20-%20Massively%20Parallel%20Processing%20with%20Procedural%20Python.ipynb)\n", " - And presenter notes: [\"08 - presenter notes\"](08%20-%20presenter%20notes.ipynb)\n", "- [\"16 - Generator Showcase Showdown\"](16%20-%20Generator%20Showcase%20Showdown.ipynb)\n", " - And presenter notes: [\"16 - presenter notes\"](16%20-%20presenter%20notes.ipynb)\n", "- [\"23 - Manipulating massive disk-backed arrays\"](23%20-%20Manipulating%20massive%20disk-backed%20arrays.ipynb)\n", "\n", "### Visualisations\n", "\n", "- [\"04 - Visualisations Using Bokeh\"](04%20-%20Visualisations%20Using%20Bokeh.ipynb)\n", " - I need to significantly update with the rest of their tutorial examples.\n", "- [\"12 - Getting it out there - Python-JS-web-viz\"](12%20-%20Getting%20it%20out%20there%20-%20Python-JS-web-viz.ipynb)\n", "- [\"21 - Winning Ways for Your Visualization Plays\"](21%20-%20Winning%20Ways%20for%20Your%20Visualization%20Plays.ipynb)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: 'Noticia Text';\n", " font-style: normal;\n", " font-weight: 400;\n", " src: local('Noticia Text'), local('NoticiaText-Regular'), url(http://themes.googleusercontent.com/static/fonts/noticiatext/v4/wdyV6x3eKpdeUPQ7BJ5uUBa1RVmPjeKy21_GQJaLlJI.woff) format('woff');\n", " }\n", " @font-face {\n", " font-family: 'Noticia Text';\n", " font-style: normal;\n", " font-weight: 700;\n", " src: local('Noticia Text Bold'), local('NoticiaText-Bold'), url(http://themes.googleusercontent.com/static/fonts/noticiatext/v4/pEko-RqEtp45bE2P80AAKRGYIVA-f1n-gQW6IKoy_-M.woff) format('woff');\n", " 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minh5/cpsc	reports/neiss.ipynb	1	37331	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev2 toc-item\"><a href=\"#Are-there-products-we-should-be-aware-of?\" data-toc-modified-id=\"Are-there-products-we-should-be-aware-of?-01\"><span class=\"toc-item-num\">0.1  </span>Are there products we should be aware of?</a></div><div class=\"lev2 toc-item\"><a href=\"#Could-be-useful-to-compare-stratum-types---Do-large-hospitals-see-different-rates-of-injury-than-small-hospitals?\" data-toc-modified-id=\"Could-be-useful-to-compare-stratum-types---Do-large-hospitals-see-different-rates-of-injury-than-small-hospitals?-02\"><span class=\"toc-item-num\">0.2  </span>Could be useful to compare stratum types - Do large hospitals see different rates of injury than small hospitals?</a></div><div class=\"lev2 toc-item\"><a href=\"#Do-we-see-meaningful-trends-when-race-is-reported?\" data-toc-modified-id=\"Do-we-see-meaningful-trends-when-race-is-reported?-03\"><span class=\"toc-item-num\">0.3  </span>Do we see meaningful trends when race is reported?</a></div><div class=\"lev2 toc-item\"><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-04\"><span class=\"toc-item-num\">0.4  </span>Conclusion</a></div>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-12-10T20:21:04.527279", "start_time": "2016-12-10T20:21:04.442399" }, "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'statsmodels'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-190991a72cdd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformula\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msmf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: No module named 'statsmodels'" ] } ], "source": [ "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "import statsmodels.api as sm\n", "import numpy as np\n", "\n", "import neiss\n", "import plotly.offline\n", "\n", "plotly.offline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:32.601254", "start_time": "2016-10-22T17:48:51.892930" }, "collapsed": false }, "outputs": [], "source": [ "#loading in data and preparations\n", "raw = pd.read_csv('/home/datauser/cpsc/data/processed/neiss/neiss-2015.csv')\n", "cleaned = neiss.cleaner(raw)\n", "data = neiss.query(cleaned.processed_data, cleaned.crosstab)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This analysis was done by DataKind DC on behalf of the Consumer Product Safety Commission. This serves as a preliminary study of the NEISS dataset. We have been been contact with the CPSC and figuring out what questions of importance that we can offer insight to. The questions that were analyzed were:\n", "\n", " * Are there products we should be aware of?\n", " * Are there differences between the sizes of hospitals?\n", " * Are there differences where race was reported or between different races?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Are there products we should be aware of?\n", "\n", "To answer this question, I approached it two ways. One way is to tabulate the total number of producted queried by hospitals and another is to look at the top items reported by each item.\n", "\n", "The top ten producted reported by hospitals are listed below. It appears that 1842 and 1807 are the top products that most hospital report." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:32.645880", "start_time": "2016-10-22T17:49:32.602508" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "product_1842 28712\n", "product_1807 28351\n", "product_4076 16784\n", "product_1205 14147\n", "product_5040 12787\n", "product_1211 11664\n", "product_4074 8271\n", "product_1884 7783\n", "product_1893 7723\n", "Name: product, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.data['product'].value_counts()[0:9]" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2016-10-20T20:38:37.995745", "start_time": "2016-10-20T20:38:37.993333" } }, "source": [ " Looking further, I examine what hospitals report this the most, so we can examine hospitals that report these products the most." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:32.684787", "start_time": "2016-10-22T17:49:32.647058" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "hosp_21 2132\n", "hosp_95 1762\n", "hosp_38 1171\n", "hosp_3 1104\n", "hosp_51 920\n", "hosp_61 920\n", "hosp_31 914\n", "hosp_17 777\n", "hosp_42 721\n", "Name: hospital, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.get_hospitals_by_product('product_1842')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:32.718774", "start_time": "2016-10-22T17:49:32.685822" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "hosp_21 2281\n", "hosp_95 2043\n", "hosp_17 1679\n", "hosp_89 1598\n", "hosp_14 1495\n", "hosp_63 1109\n", "hosp_73 1071\n", "hosp_2 946\n", "hosp_42 785\n", "Name: hospital, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.get_hospitals_by_product('product_1807')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also view these as plots and compare the incident rates of these products through different hospitals" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:33.825124", "start_time": "2016-10-22T17:49:32.720670" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/138.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_product('product_1842')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:35.044897", "start_time": "2016-10-22T17:49:33.826736" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/140.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_product('product_1807')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at these, it appears that there are some overlap between the hospitals. Hospital 17, 21, 42, and 95 are the 4 common hospital that are in the top ten of both these products. We will turn to a hospital examination down the road." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:35.137659", "start_time": "2016-10-22T17:49:35.046585" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'hosp_17', 'hosp_21', 'hosp_42', 'hosp_95'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(data.get_hospitals_by_product('product_1842').index.tolist()) & set(data.get_hospitals_by_product('product_1807').index.tolist())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Could be useful to compare stratum types - Do large hospitals see different rates of injury than small hospitals?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of examining product harm would not only to count the total numbers of products but also to see what is the top product that is reported for each hosptial. Here we can look at not only the sheer number which could be due to over reporting or awareness but also to see if there are geographic differences for product harm. However, after examining this, we see that 70 out of the 82 hospitals surveyed have product 1842 and 1807 as the top product.\n", "\n", "However an interesting finding is that product_1267, product_3299, and product_3283 are in the top ten list of top products by hospital but not in the top ten overall. However, the number is small as it only affects 5 hospitals and 14,844 reported cases. It would be interesting to see where these five hospital are at and why these products are the top of their product harm.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:37.166861", "start_time": "2016-10-22T17:49:35.138905" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "product_1842 42\n", "product_1807 28\n", "product_4076 3\n", "product_1267 2\n", "product_1211 2\n", "product_5040 2\n", "product_3299 2\n", "product_3283 1\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.top_product_for_hospital()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way of approaching would be to fit a Negative Binomial Regression to see if there are any meaningful differences between the sizes of the hospitals. I use a negative binomial regression rather than a poisson regression because there is strong evidence of overdispersion, that is, the variance of the data is much higher than the mean, as shown below. This also occurs across all stratum (only shown for small, medium, and large)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:37.255777", "start_time": "2016-10-22T17:49:37.168012" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "variance of product 1842 counts: 139451.173706\n", "mean of product 1842 counts: 350.146341463\n" ] } ], "source": [ "counts = data.data.ix[data.data['product'] == 'product_1842',:]['hospital'].value_counts()\n", "print('variance of product 1842 counts:', np.var(counts.values))\n", "print('mean of product 1842 counts:', np.mean(counts.values))\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:39.591013", "start_time": "2016-10-22T17:49:37.256951" }, "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 5805.36033951\n", "Mean: 90.0277777778\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/142.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1842', 'S')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:40.932873", "start_time": "2016-10-22T17:49:39.592717" }, "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 18568.6666667\n", "Mean: 423.666666667\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/144.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1842', 'M')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:42.644230", "start_time": "2016-10-22T17:49:40.934589" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 68488.9795918\n", "Mean: 645.142857143\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/146.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1842', 'L')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:42.837925", "start_time": "2016-10-22T17:49:42.645911" }, "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>counts</th>\n", " <th>hospital</th>\n", " <th>stratum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>38</th>\n", " <td>2132</td>\n", " <td>hosp_21</td>\n", " <td>V</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1762</td>\n", " <td>hosp_95</td>\n", " <td>V</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>1171</td>\n", " <td>hosp_38</td>\n", " <td>V</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>1104</td>\n", " <td>hosp_3</td>\n", " <td>L</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>920</td>\n", " <td>hosp_51</td>\n", " <td>L</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " counts hospital stratum\n", "38 2132 hosp_21 V\n", "13 1762 hosp_95 V\n", "0 1171 hosp_38 V\n", "42 1104 hosp_3 L\n", "2 920 hosp_51 L" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = data.prepare_stratum_modeling('product_1842')\n", "df.head()\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:42.868479", "start_time": "2016-10-22T17:49:42.839212" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Generalized Linear Model Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>counts</td> <th> No. Observations: </th> <td> 82</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 77</td> \n", "</tr>\n", "<tr>\n", " <th>Model Family:</th> <td>NegativeBinomial</td> <th> Df Model: </th> <td> 4</td> \n", "</tr>\n", "<tr>\n", " <th>Link Function:</th> <td>log</td> <th> Scale: </th> <td>0.572368368102</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -534.00</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Sat, 22 Oct 2016</td> <th> Deviance: </th> <td> 43.333</td> \n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>17:49:42</td> <th> Pearson chi2: </th> <td> 44.1</td> \n", "</tr>\n", "<tr>\n", " <th>No. Iterations:</th> <td>7</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 6.0488</td> <td> 0.268</td> <td> 22.587</td> <td> 0.000</td> <td> 5.524 6.574</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.L]</th> <td> 0.4206</td> <td> 0.392</td> <td> 1.073</td> <td> 0.283</td> <td> -0.348 1.189</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.M]</th> <td> 9.835e-05</td> <td> 0.368</td> <td> 0.000</td> <td> 1.000</td> <td> -0.721 0.721</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.S]</th> <td> -1.5487</td> <td> 0.296</td> <td> -5.227</td> <td> 0.000</td> <td> -2.129 -0.968</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.V]</th> <td> 0.3891</td> <td> 0.313</td> <td> 1.244</td> <td> 0.213</td> <td> -0.224 1.002</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: counts No. Observations: 82\n", "Model: GLM Df Residuals: 77\n", "Model Family: NegativeBinomial Df Model: 4\n", "Link Function: log Scale: 0.572368368102\n", "Method: IRLS Log-Likelihood: -534.00\n", "Date: Sat, 22 Oct 2016 Deviance: 43.333\n", "Time: 17:49:42 Pearson chi2: 44.1\n", "No. Iterations: 7 \n", "================================================================================\n", " coef std err z P>\|z\| [95.0% Conf. Int.]\n", "--------------------------------------------------------------------------------\n", "Intercept 6.0488 0.268 22.587 0.000 5.524 6.574\n", "stratum[T.L] 0.4206 0.392 1.073 0.283 -0.348 1.189\n", "stratum[T.M] 9.835e-05 0.368 0.000 1.000 -0.721 0.721\n", "stratum[T.S] -1.5487 0.296 -5.227 0.000 -2.129 -0.968\n", "stratum[T.V] 0.3891 0.313 1.244 0.213 -0.224 1.002\n", "================================================================================\n", "\"\"\"" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = smf.glm(\"counts ~ stratum\", data=df,\n", " family=sm.families.NegativeBinomial()).fit()\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the model, we see that there are only significant differences between Medium and Small hospital. Given the coefficients, the log count difference between Medium and Small hospitals is -1.55. Other than that there doesn't seem to be any other signficant differences between hospital sizes for Product 1842. \n", "\n", "We can do the same to examine the 2nd most reported product, Product 1807. Below I check the assumption to fit a negative binomial regression, that the variance is far greater than the mean. In this case we see that it is the case." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:44.292443", "start_time": "2016-10-22T17:49:42.870454" }, "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 36427.9183673\n", "Mean: 112.285714286\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/148.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1807', 'S')\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:45.471198", "start_time": "2016-10-22T17:49:44.294273" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 193238.469136\n", "Mean: 522.555555556\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/150.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1807', 'M')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:46.754253", "start_time": "2016-10-22T17:49:45.473527" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance: 177492.77551\n", "Mean: 658.714285714\n" ] }, { "data": { "text/html": [ "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~minh5/152.embed\" height=\"525px\" width=\"100%\"></iframe>" ], "text/plain": [ "<plotly.tools.PlotlyDisplay object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.plot_stratum_dist('product_1807', 'L')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The assumptions have been met and after building the model, we see very similar results as the previous model, that there are only significant differences between the small and large hospitals. For future research, we can use similar techniques to see significant differences between hospital sizes for all products." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:49:46.957282", "start_time": "2016-10-22T17:49:46.756045" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Generalized Linear Model Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>counts</td> <th> No. Observations: </th> <td> 82</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 77</td> \n", "</tr>\n", "<tr>\n", " <th>Model Family:</th> <td>NegativeBinomial</td> <th> Df Model: </th> <td> 4</td> \n", "</tr>\n", "<tr>\n", " <th>Link Function:</th> <td>log</td> <th> Scale: </th> <td>0.572368368102</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -534.00</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Sat, 22 Oct 2016</td> <th> Deviance: </th> <td> 43.333</td> \n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>17:49:46</td> <th> Pearson chi2: </th> <td> 44.1</td> \n", "</tr>\n", "<tr>\n", " <th>No. Iterations:</th> <td>7</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 6.0488</td> <td> 0.268</td> <td> 22.587</td> <td> 0.000</td> <td> 5.524 6.574</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.L]</th> <td> 0.4206</td> <td> 0.392</td> <td> 1.073</td> <td> 0.283</td> <td> -0.348 1.189</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.M]</th> <td> 9.835e-05</td> <td> 0.368</td> <td> 0.000</td> <td> 1.000</td> <td> -0.721 0.721</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.S]</th> <td> -1.5487</td> <td> 0.296</td> <td> -5.227</td> <td> 0.000</td> <td> -2.129 -0.968</td>\n", "</tr>\n", "<tr>\n", " <th>stratum[T.V]</th> <td> 0.3891</td> <td> 0.313</td> <td> 1.244</td> <td> 0.213</td> <td> -0.224 1.002</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: counts No. Observations: 82\n", "Model: GLM Df Residuals: 77\n", "Model Family: NegativeBinomial Df Model: 4\n", "Link Function: log Scale: 0.572368368102\n", "Method: IRLS Log-Likelihood: -534.00\n", "Date: Sat, 22 Oct 2016 Deviance: 43.333\n", "Time: 17:49:46 Pearson chi2: 44.1\n", "No. Iterations: 7 \n", "================================================================================\n", " coef std err z P>\|z\| [95.0% Conf. Int.]\n", "--------------------------------------------------------------------------------\n", "Intercept 6.0488 0.268 22.587 0.000 5.524 6.574\n", "stratum[T.L] 0.4206 0.392 1.073 0.283 -0.348 1.189\n", "stratum[T.M] 9.835e-05 0.368 0.000 1.000 -0.721 0.721\n", "stratum[T.S] -1.5487 0.296 -5.227 0.000 -2.129 -0.968\n", "stratum[T.V] 0.3891 0.313 1.244 0.213 -0.224 1.002\n", "================================================================================\n", "\"\"\"" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = data.prepare_stratum_modeling('product_1807')\n", "model = smf.glm(\"counts ~ stratum\", data=df,\n", " family=sm.families.NegativeBinomial()).fit()\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do we see meaningful trends when race is reported?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the top items, we don't see any meaningful differences between the top ten items for people who have race reported and race not reported. Even among the data where we do have race reported, there doesn't seem to be much variation when it comes to the top ten products causes most harm." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:55:26.068807", "start_time": "2016-10-22T17:55:25.961121" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "product_1842 17393\n", "product_1807 15691\n", "product_4076 10108\n", "product_1205 9108\n", "product_5040 7939\n", "product_1211 7872\n", "product_4074 5142\n", "product_1884 4877\n", "product_1893 4874\n", "Name: product, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.retrieve_query('race_reported', 'reported', 'product')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T17:55:28.333203", "start_time": "2016-10-22T17:55:28.229390" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "product_1807 12660\n", "product_1842 11319\n", "product_4076 6676\n", "product_1205 5039\n", "product_5040 4848\n", "product_1211 3792\n", "product_3299 3715\n", "product_4074 3129\n", "product_611 3018\n", "Name: product, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.retrieve_query('race_reported', 'not reported', 'product')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2016-10-22T18:10:37.784074", "start_time": "2016-10-22T18:10:37.546588" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "white\n", "product_1807 11719\n", "product_1842 11388\n", "product_4076 6523\n", "product_5040 5026\n", "product_1211 4125\n", "product_1205 3867\n", "product_4074 3560\n", "product_464 3104\n", "product_4057 3008\n", "Name: product, dtype: int64\n", "black\n", "product_1842 4389\n", "product_1205 4267\n", "product_1211 3048\n", "product_1807 2615\n", "product_4076 2302\n", "product_5040 1882\n", "product_1893 1462\n", "product_1884 1332\n", "product_4057 1039\n", "Name: product, dtype: int64\n", "hispanic\n", "product_1842 936\n", "product_1267 798\n", "product_4076 730\n", "product_1807 639\n", "product_5040 555\n", "product_1205 484\n", "product_1211 389\n", "product_1884 352\n", "product_4074 310\n", "Name: product, dtype: int64\n", "other\n", "product_1807 13378\n", "product_1842 11999\n", "product_4076 7229\n", "product_1205 5529\n", "product_5040 5324\n", "product_1211 4102\n", "product_3299 3887\n", "product_4074 3381\n", "product_611 3199\n", "Name: product, dtype: int64\n" ] } ], "source": [ "races = ['white', 'black', 'hispanic', 'other']\n", "for race in races:\n", " print(race)\n", " print(data.retrieve_query('new_race', race, 'product'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analysis here is still preliminary and exploratory. Most of the analysis revolved around two product, Product 1842 and 1807, because they vastly outnumbered all the other reported products. Future analysis could include running more negative binomial or Poisson (if the mean and variance are similar) regression and more standard hypothesis tests to see evaluate statistical significant differences. One question that I could not answer is to figure out any regional differences because we do not know the exact location of the hospital. \n", "\n", "We have also attached a document that conducts a much more comprehensive break down of product harm by various segments. This document serves as a starting point for all the analysis done here and will be a value reference for any future research on the NEISS dataset." ] } ], "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" }, "nav_menu": {}, "toc": { "nav_menu": { "height": "99px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": false, "threshold": 6, "toc_cell": true, "toc_section_display": "none", "toc_window_display": false }, "toc_position": { "height": "40px", "left": "119px", "right": "20px", "top": "118px", "width": "263px" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
JAmarel/Phys202	Integration/IntegrationEx02.ipynb	2	11908	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "from scipy import integrate" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Indefinite integrals" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Here is a [table of definite integrals](http://en.wikipedia.org/wiki/List_of_definite_integrals). Many of these integrals has a number of parameters $a$, $b$, etc.\n", "\n", "Find five of these integrals and perform the following steps:\n", "\n", "1. Typeset the integral using LateX in a Markdown cell.\n", "1. Define an `integrand` function that computes the value of the integrand.\n", "2. Define an `integral_approx` funciton that uses `scipy.integrate.quad` to peform the integral.\n", "3. Define an `integral_exact` function that computes the exact value of the integral.\n", "4. Call and print the return value of `integral_approx` and `integral_exact` for one set of parameters.\n", "\n", "Here is an example to show what your solutions should look like:" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Example" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "nbgrader": {} }, "source": [ "Here is the integral I am performing:\n", "\n", "$$ I_1 = \\int_0^\\infty \\frac{dx}{x^2 + a^2} = \\frac{\\pi}{2a} $$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 1.5707963267948966\n", "Exact : 1.5707963267948966\n" ] } ], "source": [ "def integrand(x, a):\n", " return 1.0/(x2 + a2)\n", "\n", "def integral_approx(a):\n", " # Use the args keyword argument to feed extra arguments to your integrand\n", " I, e = integrate.quad(integrand, 0, np.inf, args=(a,))\n", " return I\n", "\n", "def integral_exact(a):\n", " return 0.5np.pi/a\n", "\n", "print(\"Numerical: \", integral_approx(1.0))\n", "print(\"Exact : \", integral_exact(1.0))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Integral 1" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "e034fc7ac9c38bbb9c7c87db4b6c8e4e", "grade": true, "grade_id": "integrationex03a", "points": 1, "solution": true } }, "source": [ "$$ I_1 = \\int_0^a {\\sqrt{a^2-x^2} dx} = \\frac{\\pi a^2}{4} $$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 0.7853981633974481\n", "Exact : 0.7853981633974483\n" ] } ], "source": [ "def integrand(x, a):\n", " return np.sqrt(a2 - x2)\n", "\n", "def integral_approx(a):\n", " # Use the args keyword argument to feed extra arguments to your integrand\n", " I, e = integrate.quad(integrand, 0, a, args=(a,))\n", " return I\n", "\n", "def integral_exact(a):\n", " return 0.25np.pi\n", "\n", "print(\"Numerical: \", integral_approx(1.0))\n", "print(\"Exact : \", integral_exact(1.0))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "b998cb1faa45ae86f0728d51dfa0e45c", "grade": true, "grade_id": "integrationex03b", "points": 1 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Integral 2" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "c3191d99083f6d7cf804f95876e8a624", "grade": true, "grade_id": "integrationex03c", "points": 1, "solution": true } }, "source": [ "$$ I_2 = \\int_0^{\\frac{\\pi}{2}} {\\sin^2{x}}{ } {dx} = \\frac{\\pi}{4} $$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 0.7853981633974483\n", "Exact : 0.7853981633974483\n" ] } ], "source": [ "def integrand(x):\n", " return np.sin(x)*2\n", "\n", "def integral_approx():\n", " I, e = integrate.quad(integrand, 0, np.pi/2)\n", " return I\n", "\n", "def integral_exact():\n", " return 0.25np.pi\n", "\n", "print(\"Numerical: \", integral_approx())\n", "print(\"Exact : \", integral_exact())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "4e20de120f0c45ba666f10ba9a6c82d8", "grade": true, "grade_id": "integrationex03d", "points": 1 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Integral 3" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "c65f5242f7fa5525523b89899f6ca251", "grade": true, "grade_id": "integrationex03e", "points": 1, "solution": true } }, "source": [ "$$ I_3 = \\int_0^{2\\pi} \\frac{dx}{a+b\\sin{x}} = {\\frac{2\\pi}{\\sqrt{a^2-b^2}}} $$" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 0.6283185307179587\n", "Exact : 0.628318530718\n" ] } ], "source": [ "def integrand(x,a,b):\n", " return 1/(a+ bnp.sin(x))\n", "\n", "def integral_approx(a,b):\n", " I, e = integrate.quad(integrand, 0, 2np.pi,args=(a,b))\n", " return I\n", "\n", "def integral_exact(a,b):\n", " return 2np.pi/np.sqrt(a2-b2)\n", "\n", "print(\"Numerical: \", integral_approx(10,0))\n", "print(\"Exact : \", integral_exact(10,0))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "8c60d256fe8559e423cf8946ae70ba8d", "grade": true, "grade_id": "integrationex03f", "points": 1 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Integral 4" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "3a5d3b2070c78b64152c96681e8e6585", "grade": true, "grade_id": "integrationex03g", "points": 1, "solution": true } }, "source": [ "$$ I_4 = \\int_0^{\\infty} \\frac{x}{e^{x}+1} = {\\frac{\\pi^2}{12}} $$" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 0.822467033424113\n", "Exact : 0.8224670334241131\n" ] } ], "source": [ "def integrand(x):\n", " return x/(np.exp(x)+1)\n", "\n", "def integral_approx():\n", " I, e = integrate.quad(integrand, 0, np.inf)\n", " return I\n", "\n", "def integral_exact():\n", " return (1/12)np.pi*2\n", "\n", "print(\"Numerical: \", integral_approx())\n", "print(\"Exact : \", integral_exact())" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "88acfb75979c6551c8b3af758cd86acc", "grade": true, "grade_id": "integrationex03h", "points": 1 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "### Integral 5" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "9dbb9f1159b3c089e60dd167d973cc59", "grade": true, "grade_id": "integrationex03i", "points": 1, "solution": true } }, "source": [ "$$ I_5 = \\int_0^{\\infty} \\frac{x}{e^{x}-1} = {\\frac{\\pi^2}{6}} $$" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical: 1.6449340668482264\n", "Exact : 1.6449340668482262\n" ] } ], "source": [ "def integrand(x):\n", " return x/(np.exp(x)-1)\n", "\n", "def integral_approx():\n", " I, e = integrate.quad(integrand, 0, np.inf)\n", " return I\n", "\n", "def integral_exact():\n", " return (1/6)np.pi**2\n", "\n", "print(\"Numerical: \", integral_approx())\n", "print(\"Exact : \", integral_exact())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "34f6cf778698f4b90fdadc09c2a0f120", "grade": true, "grade_id": "integrationex03j", "points": 1 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the above integral" ] } ], "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
ES-DOC/esdoc-jupyterhub	notebooks/inm/cmip6/models/sandbox-2/ocean.ipynb	1	164407	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: INM \n", "Source ID: SANDBOX-2 \n", "Topic: Ocean \n", "Sub-Topics: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n", "Properties: 133 (101 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:05" ], "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', 'inm', 'sandbox-2', 'ocean')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --> Seawater Properties](#2.-Key-Properties--->-Seawater-Properties) \n", "[3. Key Properties --> Bathymetry](#3.-Key-Properties--->-Bathymetry) \n", "[4. Key Properties --> Nonoceanic Waters](#4.-Key-Properties--->-Nonoceanic-Waters) \n", "[5. Key Properties --> Software Properties](#5.-Key-Properties--->-Software-Properties) \n", "[6. Key Properties --> Resolution](#6.-Key-Properties--->-Resolution) \n", "[7. Key Properties --> Tuning Applied](#7.-Key-Properties--->-Tuning-Applied) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid](#9.-Grid) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Discretisation --> Horizontal](#11.-Grid--->-Discretisation--->-Horizontal) \n", "[12. Timestepping Framework](#12.-Timestepping-Framework) \n", "[13. Timestepping Framework --> Tracers](#13.-Timestepping-Framework--->-Tracers) \n", "[14. Timestepping Framework --> Baroclinic Dynamics](#14.-Timestepping-Framework--->-Baroclinic-Dynamics) \n", "[15. Timestepping Framework --> Barotropic](#15.-Timestepping-Framework--->-Barotropic) \n", "[16. Timestepping Framework --> Vertical Physics](#16.-Timestepping-Framework--->-Vertical-Physics) \n", "[17. Advection](#17.-Advection) \n", "[18. Advection --> Momentum](#18.-Advection--->-Momentum) \n", "[19. Advection --> Lateral Tracers](#19.-Advection--->-Lateral-Tracers) \n", "[20. Advection --> Vertical Tracers](#20.-Advection--->-Vertical-Tracers) \n", "[21. Lateral Physics](#21.-Lateral-Physics) \n", "[22. Lateral Physics --> Momentum --> Operator](#22.-Lateral-Physics--->-Momentum--->-Operator) \n", "[23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff](#23.-Lateral-Physics--->-Momentum--->-Eddy-Viscosity-Coeff) \n", "[24. Lateral Physics --> Tracers](#24.-Lateral-Physics--->-Tracers) \n", "[25. Lateral Physics --> Tracers --> Operator](#25.-Lateral-Physics--->-Tracers--->-Operator) \n", "[26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff](#26.-Lateral-Physics--->-Tracers--->-Eddy-Diffusity-Coeff) \n", "[27. Lateral Physics --> Tracers --> Eddy Induced Velocity](#27.-Lateral-Physics--->-Tracers--->-Eddy-Induced-Velocity) \n", "[28. Vertical Physics](#28.-Vertical-Physics) \n", "[29. Vertical Physics --> Boundary Layer Mixing --> Details](#29.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Details) \n", "[30. Vertical Physics --> Boundary Layer Mixing --> Tracers](#30.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Tracers) \n", "[31. Vertical Physics --> Boundary Layer Mixing --> Momentum](#31.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Momentum) \n", "[32. Vertical Physics --> Interior Mixing --> Details](#32.-Vertical-Physics--->-Interior-Mixing--->-Details) \n", "[33. Vertical Physics --> Interior Mixing --> Tracers](#33.-Vertical-Physics--->-Interior-Mixing--->-Tracers) \n", "[34. Vertical Physics --> Interior Mixing --> Momentum](#34.-Vertical-Physics--->-Interior-Mixing--->-Momentum) \n", "[35. Uplow Boundaries --> Free Surface](#35.-Uplow-Boundaries--->-Free-Surface) \n", "[36. Uplow Boundaries --> Bottom Boundary Layer](#36.-Uplow-Boundaries--->-Bottom-Boundary-Layer) \n", "[37. Boundary Forcing](#37.-Boundary-Forcing) \n", "[38. Boundary Forcing --> Momentum --> Bottom Friction](#38.-Boundary-Forcing--->-Momentum--->-Bottom-Friction) \n", "[39. Boundary Forcing --> Momentum --> Lateral Friction](#39.-Boundary-Forcing--->-Momentum--->-Lateral-Friction) \n", "[40. Boundary Forcing --> Tracers --> Sunlight Penetration](#40.-Boundary-Forcing--->-Tracers--->-Sunlight-Penetration) \n", "[41. Boundary Forcing --> Tracers --> Fresh Water Forcing](#41.-Boundary-Forcing--->-Tracers--->-Fresh-Water-Forcing) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "Ocean key properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of ocean model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of ocean model code (NEMO 3.6, MOM 5.0,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of ocean model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OGCM\" \n", "# \"slab ocean\" \n", "# \"mixed layer ocean\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Basic approximations made in the ocean." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Primitive equations\" \n", "# \"Non-hydrostatic\" \n", "# \"Boussinesq\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the ocean component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# \"Salinity\" \n", "# \"U-velocity\" \n", "# \"V-velocity\" \n", "# \"W-velocity\" \n", "# \"SSH\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Seawater Properties \n", "Physical properties of seawater in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Eos Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Wright, 1997\" \n", "# \"Mc Dougall et al.\" \n", "# \"Jackett et al. 2006\" \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Eos Functional Temp\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Temperature used in EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Eos Functional Salt\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Salinity used in EOS for sea water" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Practical salinity Sp\" \n", "# \"Absolute salinity Sa\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Eos Functional Depth\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Depth or pressure used in EOS for sea water ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pressure (dbars)\" \n", "# \"Depth (meters)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.6. Ocean Specific Heat\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Specific heat in ocean (cpocean) in J/(kg K)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.7. Ocean Reference Density\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Boussinesq reference density (rhozero) in kg / m3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Bathymetry \n", "Properties of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Reference Dates\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Reference date of bathymetry" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Present day\" \n", "# \"21000 years BP\" \n", "# \"6000 years BP\" \n", "# \"LGM\" \n", "# \"Pliocene\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Type\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the bathymetry fixed in time in the ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Ocean Smoothing\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe any smoothing or hand editing of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Source\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe source of bathymetry in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Nonoceanic Waters \n", "Non oceanic waters treatement in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Isolated Seas\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how isolated seas is performed" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. River Mouth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe if/how river mouth mixing or estuaries specific treatment is performed" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Software Properties \n", "Software properties of ocean code" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Repository\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Location of code for this component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Code Version\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Code version identifier." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Code Languages\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### Code language(s)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Resolution \n", "Resolution in the ocean grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Range Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Number Of Vertical Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels resolved on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Is Adaptive Grid\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Default is False. Set true if grid resolution changes during execution." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. Thickness Level 1\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Thickness of first surface ocean level (in meters)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Tuning Applied \n", "Tuning methodology for ocean component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Global Mean Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List set of metrics of the global mean state used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Regional Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Trend Metrics Used\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.N\n", "### List observed trend metrics used in tuning model/component" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the ocean component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Brief description of conservation methodology" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in the ocean by the numerical schemes" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Enstrophy\" \n", "# \"Salt\" \n", "# \"Volume of ocean\" \n", "# \"Momentum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Consistency Properties\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Corrected Conserved Prognostic Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Set of variables which are conserved by more* than the numerical scheme alone." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Was Flux Correction Used\n", "Is Required:* FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Does conservation involve flux correction ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid \n", "Ocean grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of grid in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Properties of vertical discretisation in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Coordinates\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of vertical coordinates in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Z-coordinate\" \n", "# \"Z-coordinate\" \n", "# \"S-coordinate\" \n", "# \"Isopycnic - sigma 0\" \n", "# \"Isopycnic - sigma 2\" \n", "# \"Isopycnic - sigma 4\" \n", "# \"Isopycnic - other\" \n", "# \"Hybrid / Z+S\" \n", "# \"Hybrid / Z+isopycnic\" \n", "# \"Hybrid / other\" \n", "# \"Pressure referenced (P)\" \n", "# \"P\" \n", "# \"Z\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Partial Steps\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality:** 1.1\n", "### Using partial steps with Z or Z vertical coordinate in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Discretisation --> Horizontal \n", "Type of horizontal discretisation scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Horizontal grid type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Lat-lon\" \n", "# \"Rotated north pole\" \n", "# \"Two north poles (ORCA-style)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Staggering\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.1\n", "### Horizontal grid staggering type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"N/a\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Horizontal discretisation scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite difference\" \n", "# \"Finite volumes\" \n", "# \"Finite elements\" \n", "# \"Unstructured grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Timestepping Framework \n", "Ocean Timestepping Framework" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Diurnal Cycle\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Diurnal cycle type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Via coupling\" \n", "# \"Specific treatment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Timestepping Framework --> Tracers \n", "Properties of tracers time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Tracers time stepping scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Tracers time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Timestepping Framework --> Baroclinic Dynamics \n", "Baroclinic dynamics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Baroclinic dynamics type" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Preconditioned conjugate gradient\" \n", "# \"Sub cyling\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Baroclinic dynamics scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Time Step\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Baroclinic time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Timestepping Framework --> Barotropic \n", "Barotropic time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Splitting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Time splitting method" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"split explicit\" \n", "# \"implicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Time Step\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### Barotropic time step (in seconds)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Timestepping Framework --> Vertical Physics \n", "Vertical physics time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Method\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Details of vertical time stepping in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Advection \n", "Ocean advection" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of advection in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Advection --> Momentum \n", "Properties of lateral momemtum advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of lateral momemtum advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flux form\" \n", "# \"Vector form\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Scheme Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of ocean momemtum advection scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. ALE\n", "Is Required: FALSE    Type: BOOLEAN    Cardinality: 0.1\n", "### Using ALE for vertical advection ? (if vertical coordinates are sigma)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Advection --> Lateral Tracers \n", "Properties of lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Order\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Order of lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Flux Limiter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Monotonic flux limiter for lateral tracer advection scheme in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Effective Order\n", "Is Required: TRUE    Type: FLOAT    Cardinality: 1.1\n", "### Effective order of limited lateral tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Passive Tracers\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### Passive tracers advected" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ideal age\" \n", "# \"CFC 11\" \n", "# \"CFC 12\" \n", "# \"SF6\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.6. Passive Tracers Advection\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Is advection of passive tracers different than active ? if so, describe." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Advection --> Vertical Tracers \n", "Properties of vertical tracer advection scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Flux Limiter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Monotonic flux limiter for vertical tracer advection scheme in ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Lateral Physics \n", "Ocean lateral physics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of lateral physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of transient eddy representation in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Eddy active\" \n", "# \"Eddy admitting\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Lateral Physics --> Momentum --> Operator \n", "Properties of lateral physics operator for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Direction\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Direction of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Order\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Order of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Discretisation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Discretisation of lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff \n", "Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Lateral physics momemtum eddy viscosity coeff type in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Constant Coefficient\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Variable Coefficient\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.4. Coeff Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.5. Coeff Backscatter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Lateral Physics --> Tracers \n", "Properties of lateral physics for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Mesoscale Closure\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there a mesoscale closure in the lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Submesoscale Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Lateral Physics --> Tracers --> Operator \n", "Properties of lateral physics operator for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Direction\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Direction of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Order\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Order of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Discretisation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Discretisation of lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff \n", "Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Lateral physics tracers eddy diffusity coeff type in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Constant Coefficient\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Variable Coefficient\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Coeff Background\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.5. Coeff Backscatter\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Lateral Physics --> Tracers --> Eddy Induced Velocity \n", "Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of EIV in lateral physics tracers in the ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"GM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Constant Val\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If EIV scheme for tracers is constant, specify coefficient value (M2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Flux Type\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of EIV flux (advective or skew)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Added Diffusivity\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of EIV added diffusivity (constant, flow dependent or none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Vertical Physics \n", "Ocean Vertical Physics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of vertical physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Vertical Physics --> Boundary Layer Mixing --> Details \n", "Properties of vertical physics in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Langmuir Cells Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there Langmuir cells mixing in upper ocean ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Vertical Physics --> Boundary Layer Mixing --> Tracers \n", "Properties of boundary layer (BL) mixing on tracers in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of boundary layer mixing for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Closure Order\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant BL mixing of tracers, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Vertical Physics --> Boundary Layer Mixing --> Momentum \n", "Properties of boundary layer (BL) mixing on momentum in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of boundary layer mixing for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Closure Order\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant BL mixing of momentum, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Vertical Physics --> Interior Mixing --> Details \n", "Properties of interior mixing in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Convection Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of vertical convection in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Non-penetrative convective adjustment\" \n", "# \"Enhanced vertical diffusion\" \n", "# \"Included in turbulence closure\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Tide Induced Mixing\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how tide induced mixing is modelled (barotropic, baroclinic, none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Double Diffusion\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there double diffusion" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Shear Mixing\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is there interior shear mixing" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Vertical Physics --> Interior Mixing --> Tracers \n", "Properties of interior mixing on tracers in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of interior mixing for tracers in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant interior mixing of tracers, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Profile\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Vertical Physics --> Interior Mixing --> Momentum \n", "Properties of interior mixing on momentum in the ocean " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of interior mixing for momentum in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Constant\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If constant interior mixing of momentum, specific coefficient (m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.3. Profile\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.4. Background\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Uplow Boundaries --> Free Surface \n", "Properties of free surface in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of free surface in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Free surface scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear implicit\" \n", "# \"Linear filtered\" \n", "# \"Linear semi-explicit\" \n", "# \"Non-linear implicit\" \n", "# \"Non-linear filtered\" \n", "# \"Non-linear semi-explicit\" \n", "# \"Fully explicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.3. Embeded Seaice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the sea-ice embeded in the ocean model (instead of levitating) ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Uplow Boundaries --> Bottom Boundary Layer \n", "Properties of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Type Of Bbl\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of bottom boundary layer in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diffusive\" \n", "# \"Acvective\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Lateral Mixing Coef\n", "Is Required: FALSE    Type: INTEGER    Cardinality: 0.1\n", "### If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Sill Overflow\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe any specific treatment of sill overflows" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Boundary Forcing \n", "Ocean boundary forcing" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of boundary forcing in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Surface Pressure\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.3. Momentum Flux Correction\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.4. Tracers Flux Correction\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.5. Wave Effects\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe if/how wave effects are modelled at ocean surface." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.6. River Runoff Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how river runoff from land surface is routed to ocean and any global adjustment done." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.7. Geothermal Heating\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe if/how geothermal heating is present at ocean bottom." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Boundary Forcing --> Momentum --> Bottom Friction \n", "Properties of momentum bottom friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of momentum bottom friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Non-linear\" \n", "# \"Non-linear (drag function of speed of tides)\" \n", "# \"Constant drag coefficient\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Boundary Forcing --> Momentum --> Lateral Friction \n", "Properties of momentum lateral friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of momentum lateral friction in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Free-slip\" \n", "# \"No-slip\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Boundary Forcing --> Tracers --> Sunlight Penetration \n", "Properties of sunlight penetration scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of sunlight penetration scheme in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"1 extinction depth\" \n", "# \"2 extinction depth\" \n", "# \"3 extinction depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.2. Ocean Colour\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is the ocean sunlight penetration scheme ocean colour dependent ?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.3. Extinction Depth\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe and list extinctions depths for sunlight penetration scheme (if applicable)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Boundary Forcing --> Tracers --> Fresh Water Forcing \n", "Properties of surface fresh water forcing in ocean" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. From Atmopshere\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of surface fresh water forcing from atmos in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. From Sea Ice\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Type of surface fresh water forcing from sea-ice in ocean" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Real salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.3. Forced Mode Restoring\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Type of surface salinity restoring in forced mode (OMIP)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
mmckerns/tutmom	solutions.ipynb	2	16237	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions to exercises" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "EXERCISE: Solve the constrained programming problem by any of the means above.\n", "\n", "Minimize: f = -1x[0] + 4x[1]\n", "\n", "Subject to: <br>\n", "-3x[0] + 1x[1] <= 6 <br>\n", "1x[0] + 2x[1] <= 4 <br>\n", "x[1] >= -3 <br>\n", "\n", "where: -inf <= x[0] <= inf" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " pcost dcost gap pres dres\n", " 0: 5.0678e+00 -9.4861e+00 1e+01 5e-17 3e+00\n", " 1: -2.0709e+00 -5.9266e+00 4e+00 3e-16 1e+00\n", " 2: 9.5642e+00 4.5499e-01 9e+00 4e-16 1e+00\n", " 3: -9.7286e+00 -1.7466e+01 8e+00 1e-16 1e+00\n", " 4: -1.0434e+01 -1.2174e+01 2e+00 1e-16 3e-01\n", " 5: -2.1723e+01 -2.3939e+01 2e+00 5e-16 3e-01\n", " 6: -2.0193e+01 -2.2545e+01 2e+00 3e-16 5e-16\n", " 7: -2.1980e+01 -2.2014e+01 3e-02 1e-16 1e-15\n", " 8: -2.2000e+01 -2.2000e+01 3e-04 6e-16 1e-16\n", " 9: -2.2000e+01 -2.2000e+01 3e-06 5e-16 2e-16\n", "Optimal solution found.\n", "[ 1.00e+01]\n", "[-3.00e+00]\n", "\n" ] } ], "source": [ "import cvxopt as cvx\n", "from cvxopt import solvers as cvx_solvers\n", "Q = cvx.matrix([[0.,0.],[0.,0.]])\n", "p = cvx.matrix([-1., 4.])\n", "G = cvx.matrix([[-3., 1., 0.],[1., 2., -1.]])\n", "h = cvx.matrix([6., 4., 3.])\n", "sol = cvx_solvers.qp(Q, p, G, h)\n", "print(sol['x'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Use any of the solvers we've seen thus far to find the minimum of the `zimmermann` function (i.e. use `mystic.models.zimmermann` as the objective). Use the bounds suggested below, if your choice of solver allows it." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 7. 2.]\n" ] } ], "source": [ "import scipy.optimize as opt\n", "import mystic.models\n", "result = opt.minimize(mystic.models.zimmermann, [10., 1.], method='powell')\n", "print(result.x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Do the same for the `fosc3d` function found at `mystic.models.fosc3d`, using the bounds suggested by the documentation, if your chosen solver accepts bounds or constraints." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.21501755 0.24035588]\n" ] } ], "source": [ "import scipy.optimize as opt\n", "import mystic.models\n", "result = opt.minimize(mystic.models.fosc3d, [-5., 0.5], method='powell')\n", "print(result.x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Use `mystic` to find the minimum for the `peaks` test function, with the bound specified by the `mystic.models.peaks` documentation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: -6.551133\n", " Iterations: 3\n", " Function evaluations: 77\n", "[ 0.22827892 -1.62553492]\n" ] } ], "source": [ "import mystic\n", "import mystic.models\n", "result = mystic.solvers.fmin_powell(mystic.models.peaks, [0., -2.], bounds=[(-5.,5.)]2)\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE:* Use `mystic` to do a fit to the noisy data in the `scipy.optimize.curve_fit` example (the least squares fit)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "target parameters: [3, 2, 1, 0.7853981633974483]\n", "Optimization terminated successfully.\n", " Current function value: 4.698514\n", " Iterations: 9\n", " Function evaluations: 908\n", "solved parameters: [ 3.34271079 1.89338122 1.02510143 0.69305712]\n" ] } ], "source": [ "import numpy as np\n", "import scipy.stats as stats\n", "from mystic.solvers import fmin_powell\n", "from mystic import reduced\n", "\n", "# Define the function to fit.\n", "def function(coeffs, x):\n", " a,b,f,phi = coeffs\n", " return a * np.exp(-b * np.sin(f * x + phi))\n", "\n", "# Create a noisy data set around the actual parameters\n", "true_params = [3, 2, 1, np.pi/4]\n", "print(\"target parameters: {}\".format(true_params))\n", "x = np.linspace(0, 2np.pi, 25)\n", "exact = function(true_params, x)\n", "noisy = exact + 0.3stats.norm.rvs(size=len(x))\n", "\n", "# Define an objective that fits against the noisy data\n", "@reduced(lambda x,y: abs(x)+abs(y))\n", "def objective(coeffs, x, y):\n", " return function(coeffs, x) - y\n", "\n", "# Use curve_fit to estimate the function parameters from the noisy data.\n", "initial_guess = [1,1,1,1]\n", "args = (x, noisy)\n", "estimated_params = fmin_powell(objective, initial_guess, args=args)\n", "print(\"solved parameters: {}\".format(estimated_params))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Solve the `chebyshev8.cost` example exactly, by applying the knowledge that the last term in the chebyshev polynomial will always be be one. Use `numpy.round` or `mystic.constraints.integers` or to constrain solutions to the set of integers. Does using `mystic.suppressed` to supress small numbers accelerate the solution?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Differential Evolution\n", "======================\n", "Generation 0 has Chi-Squared: 18689.689184\n", "Generation 0 has fit parameters:\n", " [5.0, -54.0, 25.0, 5.0, -33.0, 88.0, 26.0, -17.0, 1.0]\n", "Generation 50 has Chi-Squared: 264.494065\n", "Generation 50 has fit parameters:\n", " [247.0, -9.0, -419.0, 28.0, 203.0, -23.0, -23.0, 6.0, 1.0]\n", "STOP(\"CollapseAt with {'tolerance': 0.0001, 'mask': None, 'generations': 2, 'target': 0.0} at {5}\")\n", "Generation 100 has Chi-Squared: 37.659874\n", "Generation 100 has fit parameters:\n", " [150.0, 15.0, -296.0, -15.0, 179.0, -0.0, -32.0, 1.0, 1.0]\n", "STOP(\"CollapseAt with {'tolerance': 0.0001, 'mask': {5}, 'generations': 2, 'target': 0.0} at {7}\")\n", "STOP(\"CollapseAt with {'tolerance': 0.0001, 'mask': {5, 7}, 'generations': 2, 'target': 0.0} at {1, 3}\")\n", "STOP(\"VTR with {'tolerance': 0.0001, 'target': 0.0}\")\n", "Generation 143 has best Chi-Squared: 0.000000\n", " 8 6 4 2\n", "128 x - 256 x + 160 x - 32 x + 1\n", "\n", "Actual Coefficients:\n", " 8 6 4 2\n", "128 x - 256 x + 160 x - 32 x + 1\n", "\n" ] } ], "source": [ "# Differential Evolution solver\n", "from mystic.solvers import DifferentialEvolutionSolver2\n", "\n", "# Chebyshev polynomial and cost function\n", "from mystic.models.poly import chebyshev8, chebyshev8cost\n", "from mystic.models.poly import chebyshev8coeffs\n", "\n", "# tools\n", "from mystic.termination import VTR, CollapseAt, Or\n", "from mystic.strategy import Best1Exp\n", "from mystic.monitors import VerboseMonitor\n", "from mystic.tools import random_seed\n", "from mystic.math import poly1d\n", "import numpy as np\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " print(\"Differential Evolution\")\n", " print(\"======================\")\n", " ndim = 9\n", " random_seed(123)\n", "\n", " # configure monitor\n", " stepmon = VerboseMonitor(50,50)\n", "\n", " # build a constraints function\n", " def constraints(x):\n", " x[-1] = 1.\n", " return np.round(x)\n", "\n", " stop = Or(VTR(0.0001), CollapseAt(0.0, generations=2))\n", "\n", " # use DE to solve 8th-order Chebyshev coefficients\n", " npop = 10ndim\n", " solver = DifferentialEvolutionSolver2(ndim,npop)\n", " solver.SetRandomInitialPoints(min=[-100]ndim, max=[100]ndim)\n", " solver.SetGenerationMonitor(stepmon)\n", " solver.SetConstraints(constraints)\n", " solver.enable_signal_handler()\n", " solver.Solve(chebyshev8cost, termination=stop, strategy=Best1Exp, \\\n", " CrossProbability=1.0, ScalingFactor=0.9)\n", " solution = solver.Solution()\n", "\n", " # use monitor to retrieve results information\n", " iterations = len(stepmon)\n", " cost = stepmon.y[-1]\n", " print(\"Generation %d has best Chi-Squared: %f\" % (iterations, cost))\n", "\n", " # use pretty print for polynomials\n", " print(poly1d(solution))\n", "\n", " # compare solution with actual 8th-order Chebyshev coefficients\n", " print(\"\\nActual Coefficients:\\n %s\\n\" % poly1d(chebyshev8coeffs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE:* Replace the symbolic constraints in the following \"Pressure Vessel Design\" code with explicit penalty functions (i.e. use a compound penalty built with `mystic.penalty.quadratic_inequality`)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 5804.376208\n", " Iterations: 839\n", " Function evaluations: 33600\n", "[ 0.72759093 0.35964857 37.69901188 240. ]\n" ] } ], "source": [ "\"Pressure Vessel Design\"\n", "\n", "def objective(x):\n", " x0,x1,x2,x3 = x\n", " return 0.6224x0x2x3 + 1.7781x1x22 + 3.1661x0*2x3 + 19.84x02x2\n", "\n", "bounds = [(0,1e6)]4\n", "# with penalty='penalty' applied, solution is:\n", "xs = [0.72759093, 0.35964857, 37.69901188, 240.0]\n", "ys = 5804.3762083\n", "\n", "from mystic.constraints import as_constraint\n", "from mystic.penalty import quadratic_inequality\n", "\n", "def penalty1(x): # <= 0.0\n", " return -x[0] + 0.0193x[2]\n", "\n", "def penalty2(x): # <= 0.0\n", " return -x[1] + 0.00954x[2]\n", "\n", "def penalty3(x): # <= 0.0\n", " from math import pi\n", " return -pix[2]*2x[3] - (4/3.)pix[2]3 + 1296000.0\n", "\n", "def penalty4(x): # <= 0.0\n", " return x[3] - 240.0\n", "\n", "@quadratic_inequality(penalty1, k=1e12)\n", "@quadratic_inequality(penalty2, k=1e12)\n", "@quadratic_inequality(penalty3, k=1e12)\n", "@quadratic_inequality(penalty4, k=1e12)\n", "def penalty(x):\n", " return 0.0\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2\n", " from mystic.math import almostEqual\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, penalty=penalty,\n", " npop=40, gtol=500, disp=True, full_output=True)\n", " print(result[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE:** Solve the `cvxopt` \"qp\" example with `mystic`. Use symbolic constaints, penalty functions, or constraints operators. If you get it quickly, do all three methods." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.25000001 0.74999999]\n" ] } ], "source": [ "def objective(x):\n", " x0,x1 = x\n", " return 2x02 + x12 + x0x1 + x0 + x1\n", "\n", "bounds = [(0.0, None),(0.0, None)]\n", "\n", "# with penalty='penalty' applied, solution is:\n", "xs = [0.25, 0.75]\n", "ys = 1.875\n", "\n", "from mystic.math.measures import normalize\n", "\n", "def constraint(x): # impose exactly\n", " return normalize(x, 1.0)\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " from mystic.solvers import diffev2, fmin_powell\n", "\n", " result = diffev2(objective, x0=bounds, bounds=bounds, npop=40,\n", " constraints=constraint, disp=False, full_output=True)\n", " print(result[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXERCISE: Convert one of our previous `mystic` examples to use parallel computing. Note that if the solver has a `SetMapper` method, it can take a parallel map." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generation 0 has Chi-Squared: 1613.748959\n", "Generation 10 has Chi-Squared: 9.454376\n", "Generation 20 has Chi-Squared: 0.303547\n", "Generation 30 has Chi-Squared: 0.008984\n", "Generation 40 has Chi-Squared: 0.002842\n", "Generation 50 has Chi-Squared: 0.000096\n", "Generation 60 has Chi-Squared: 0.000083\n", "Generation 70 has Chi-Squared: 0.000002\n", "Generation 80 has Chi-Squared: 0.000000\n", "Generation 90 has Chi-Squared: 0.000000\n", "Generation 100 has Chi-Squared: 0.000000\n", "STOP(\"ChangeOverGeneration with {'tolerance': 1e-06, 'generations': 30}; VTR with {'tolerance': 0.005, 'target': 0.0}\")\n", "[ 0.99994559 0.99988963 0.99978792]\n" ] } ], "source": [ "from mystic.termination import VTR, ChangeOverGeneration, And, Or\n", "stop = Or(And(VTR(), ChangeOverGeneration()), VTR(1e-8))\n", "\n", "from mystic.models import rosen\n", "from mystic.monitors import VerboseMonitor\n", "from mystic.solvers import DifferentialEvolutionSolver2\n", "\n", "from pathos.pools import ThreadPool\n", "\n", "\n", "if __name__ == '__main__':\n", "\n", " solver = DifferentialEvolutionSolver2(3,40)\n", " solver.SetRandomInitialPoints([-10,-10,-10],[10,10,10])\n", " solver.SetGenerationMonitor(VerboseMonitor(10))\n", " solver.SetMapper(ThreadPool().map) #NOTE: evaluation of objective in parallel\n", " solver.SetTermination(stop)\n", " solver.SetObjective(rosen)\n", " solver.SetStrictRanges([-10,-10,-10],[10,10,10])\n", " solver.SetEvaluationLimits(generations=600)\n", " solver.Solve()\n", "\n", " print(solver.bestSolution)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
williamdjones/protein_binding	notebooks/Neural Network (In Progress).ipynb	1	17477	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import time\n", "import glob\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import keras\n", "import keras.backend as K\n", "from keras import initializers\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Input\n", "from keras.layers.advanced_activations import LeakyReLU, PReLU\n", "from keras import optimizers\n", "from keras.utils.np_utils import to_categorical\n", "from utils.input_pipeline import \n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import Imputer, Normalizer\n", "from keras.objectives import kullback_leibler_divergence\n", "\n", "from sklearn.pipeline import Pipeline\n", "\n", "imputer = Imputer()\n", "normalizer = Normalizer()\n", "pre_processing_pipeline = Pipeline([('imputer', imputer), ('normalizer', normalizer)])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#load_data_t0 = time.clock()\n", "#df = pd.concat([pd.read_csv(filename, index_col=[1,0], na_values=['na'], engine='c', header=0) for filename in glob.glob(\"data/parser_output/csv/.csv\")],axis=0)\n", "#df = pd.read_csv(\"data/parser_output/csv/new_mol2_full_feature_-017.csv\", index_col=[1,0], na_values=['na'], engine='c',header=0)\n", "#load_data_t1 = time.clock()\n", "#print (\"data loaded in ~\", ((load_data_t1 - load_data_t0)/60), \"minutes.\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from utils.input_pipeline import load_protein" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "890\n" ] } ], "source": [ "with open(\"data/preprocessed_features.csv\", \"r\") as input_file:\n", " feature_list = []\n", " for line in input_file:\n", " line = line.strip('\\n')\n", " feature_list.append(line)\n", " \n", "print(len(feature_list))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# the generator is probably the only valuable thing in this notebook, put this in the input pipeline module\n", "def data_gen(file_path, batch_steps,categorical=False, sample_size=None, features_list=None, mode=None, conformation=None):\n", " #decide upon receptor versus protein for naming conventions\n", " receptor_list = list(h5py.File(file_path,'r'))\n", " while(1):\n", " random.shuffle(receptor_list)\n", " \n", " X,y = load_protein(file_path, protein_name=receptor_list[0], sample_size=None,\n", " features_list=features_list,mode=mode, conformation=conformation)\n", " X = Normalizer().fit_transform(Imputer(strategy=\"median\").fit_transform(np.nan_to_num(X)))\n", " y = y.flatten()\n", " \n", " positives = X[y==1,:]\n", " negatives = X[y==0,:]\n", " for step in range(batch_steps):\n", " negatives_to_keep = np.random.choice(negatives.shape[0],sample_size,replace = True)\n", "\n", " X_batch = np.vstack((negatives[negatives_to_keep],positives))\n", " X_batch = np.vstack((X_batch,positives))\n", " y_batch = np.hstack((y[y==0][negatives_to_keep],y[y==1]))\n", " y_batch = np.hstack((y_batch,y[y==1]))\n", " if categorical is True:\n", " yield X_batch, to_categorical(y_batch)\n", " else:\n", " yield X_batch, y_batch\n", " \n", "#using for debugging purposes\n", "#next(data_gen(\"data/full_26_kinase_data.h5\", 10))\n", "\n", "def precision(y_true, y_pred):\n", " \"\"\"Precision metric.\n", " Only computes a batch-wise average of precision.\n", " Computes the precision, a metric for multi-label classification of\n", " how many selected items are relevant.\n", " \"\"\"\n", " y_true = K.cast(K.argmax(y_true),'float32')\n", " y_pred = K.cast(K.argmax(y_pred), 'float32')\n", " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n", " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n", " precision = true_positives / (predicted_positives + K.epsilon())\n", " return precision\n", "\n", "\n", "def recall(y_true, y_pred):\n", " \"\"\"Recall metric.\n", " Only computes a batch-wise average of recall.\n", " Computes the recall, a metric for multi-label classification of\n", " how many relevant items are selected.\n", " \"\"\"\n", " y_true = K.cast(K.argmax(y_true),'float32')\n", " y_pred = K.cast(K.argmax(y_pred), 'float32')\n", " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n", " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n", " recall = true_positives / (possible_positives + K.epsilon())\n", " return K.cast(recall,'float32')\n", "\n", "def f1(y_true,y_pred):\n", " y_true = K.cast(K.argmax(y_true),'float32')\n", " y_pred = K.cast(K.argmax(y_pred), 'float32')\n", " return K.cast(2((precision(y_true,y_pred)recall(y_true,y_pred))/\n", " (precision(y_true,y_pred)+recall(y_true,y_pred))),'float32')\n", "\n", "\n", "def load_myloss(weights=None):\n", " if weights is None:\n", " class_weights = [0.25, 1]\n", " else:\n", " class_weights = weights\n", "\n", " def balanced_loss(y_true, y_pred):\n", "\n", " loss_prelim = K.categorical_crossentropy(y_true, y_pred)\n", "\n", " weight = K.cast(K.sum(y_true * class_weights), 'float32')\n", "\n", " # apply weight and average \n", " loss_final = K.cast(K.mean(loss_prelim * weight), 'float32')\n", "\n", " return loss_final\n", "\n", " return balanced_loss\n", "\n", "\n", "def my_loss():\n", " \n", " def custom_loss(y_true,y_pred):\n", " #kl_loss = kullback_leibler_divergence(y_true,y_pred)\n", " #total_loss = kullback_leibler_divergence(y_pred,y_true) + kl_loss\n", " #return total_loss\n", " return K.log(-K.dot(y_true,K.transpose(y_pred)))\n", " \n", " \n", " return custom_loss\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train = np.loadtxt(\"data/random_forest_features_x_train.txt\",delimiter=\",\",dtype=np.float32)\n", "X_test = np.loadtxt(\"data/random_forest_features_x_test.txt\",delimiter=\",\", dtype=np.float32)\n", "y_train = np.loadtxt(\"data/random_forest_features_y_train.txt\",delimiter=\",\",dtype=np.float32)\n", "y_test = np.loadtxt(\"data/random_forest_features_y_test.txt\",delimiter=\",\",dtype=np.float32)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(14884, 1260) (14884,)\n" ] } ], "source": [ "X_train_pos = X_train[y_train == 1]\n", "X_train_neg = X_train[y_train == 0]\n", "balanced_X_train = np.random.choice(np.arange(int(np.floor(X_train_neg.shape[0]/2))),size=X_train_pos.shape[0])\n", "X_train_neg = X_train_neg[balanced_X_train]\n", "X_train_prime = np.vstack((X_train_pos,X_train_neg))\n", "y_train_prime = np.hstack((y_train[y_train==1],y_train[y_train==0][balanced_X_train]))\n", "print(X_train_prime.shape,y_train_prime.shape)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(400, input_dim=1260, kernel_regularizer=keras.regularizers.l2(0.))) \n", "model.add(PReLU())\n", "model.add(Dense(50))\n", "model.add(PReLU())\n", "model.add(Dense(1,activation='sigmoid'))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model.compile(loss='binary_crossentropy', optimizer=optimizers.adam(lr=1e-5), metrics=[\"accuracy\",f1])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<keras.callbacks.History at 0x2abc53666400>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X_train_prime,y_train_prime, shuffle=True,epochs=1000,verbose=0)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "Can't handle mix of binary and continuous", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-64-307d8d4e5ffa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\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[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"accuracy:\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\\tf1-score:\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/global/common/cori/software/python/3.5-anaconda/envs/deeplearning/lib/python3.5/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mf1_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight)\u001b[0m\n\u001b[1;32m 690\u001b[0m return fbeta_score(y_true, y_pred, 1, labels=labels,\n\u001b[1;32m 691\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/global/common/cori/software/python/3.5-anaconda/envs/deeplearning/lib/python3.5/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mfbeta_score\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, sample_weight)\u001b[0m\n\u001b[1;32m 804\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[0mwarn_for\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'f-score'\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[0;32m--> 806\u001b[0;31m sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 807\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 808\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/global/common/cori/software/python/3.5-anaconda/envs/deeplearning/lib/python3.5/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight)\u001b[0m\n\u001b[1;32m 1001\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1004\u001b[0m \u001b[0mpresent_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/global/common/cori/software/python/3.5-anaconda/envs/deeplearning/lib/python3.5/site-packages/sklearn/metrics/classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m raise ValueError(\"Can't handle mix of {0} and {1}\"\n\u001b[0;32m---> 82\u001b[0;31m \"\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Can't handle mix of binary and continuous" ] } ], "source": [ "from sklearn.metrics import accuracy_score, f1_score\n", "f1 = f1_score(y_test,model.predict(X_test))\n", "acc = accuracy_score(y_test,model.predict(X_test),y_test)\n", "print(\"accuracy:\",acc,\"\\tf1-score:\",f1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#model.fit_generator(data_gen('data/full_26_kinase_data.h5',categorical=True,\n", "# sample_size=1000, batch_steps=20000, features_list=feature_list),epochs=10,steps_per_epoch=20000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#from sklearn.metrics import accuracy_score, f1_score\n", "#preds = model.predict(X_test)\n", "\n", "#print(\"accuracy:\",accuracy_score(preds,y_test), \"\\t\",\"f1-score:\",f1_score(preds,y_test))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "deeplearning (3.5)", "language": "python", "name": "deeplearning3.5" }, "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
Krastanov/cutiepy	examples/Lindblad_Master_Equation_Solver_Examples.ipynb	1	108040	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Table of Contents\n", "\n", "1. [Rabi Oscillations](#Rabi-Oscillations)\n", " 1. [Simulating the Full Hamiltonian](#Simulating-the-Full-Hamiltonian)\n", " 2. [With Rotating Wave Approximation](#With-Rotating-Wave-Approximation)\n", " 1. [With $\\gamma_1$ collapse](#With-$\\gamma_1$-collapse)\n", " 2. [With $\\gamma_2$ collapse](#With-$\\gamma_2$-collapse)\n", "2. [Coherent State in a Harmonic Oscillator](#Coherent-State-in-a-Harmonic-Oscillator)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cutiepy import \n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# TODO: implement sparse Lindblad operator\n", "cutiepy.operators.SPARSITY_N_CUTOFF = 60" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Rabi Oscillations\n", "\n", "$\\hat{H} = \\hat{H}_0 + \\Omega \\sin((\\omega_0+\\Delta)t) \\hat{\\sigma}_x$\n", "\n", "$\\hat{H}_0 = \\frac{\\omega_0}{2}\\hat{\\sigma}_z$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\text{Ket }{\| {{0}}_{{\\tiny N\\normalsize 2}} \\rangle} \\text{ on the space }\\mathbb{C}^{2}\\text{ with numerical content: }$$\n", "$$\\begin{equation}\\left(\\begin{array}{{11}c}1.0\\\\0.0\\\\\\end{array}\\right)\\end{equation}$$" ], "text/plain": [ "'{0}_{\\\\tiny N\\\\normalsize 2}'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_state = basis(2, 0)\n", "initial_state" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$${\\left( {{0.5}\\tiny\\times\\normalsize{\\hat{σ}_{z}}}+{{0.005}\\tiny\\times\\normalsize{\\operatorname{sin}\\left( {{{1.002}\\tiny\\times\\normalsize{t}}} \\right)}\\tiny\\times\\normalsize{\\hat{σ}_{x}}} \\right)}$$" ], "text/plain": [ "Add(Mul(0.5, 'σ_z'), Mul(0.005, sin(Mul(1.002, 't')), 'σ_x'))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ω0 = 1\n", "Δ = 0.002\n", "Ω = 0.005\n", "ts = 6np.pi/Ωnp.linspace(0,1,120)\n", "H = ω0/2 * sigmaz() + Ω * sigmax() * sin((ω0+Δ)t)\n", "H" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cython code...\n", "Compiling cython code...\n", "Running cython code...\n", "Starting at 10/25 15:52:02.\n", "Finishing at 10/25 15:52:03.\n", "Total time: 0 seconds.\n", "Formatting the output...\n" ] } ], "source": [ "res = mesolve(H, [], initial_state, ts)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "σz_expect = expect(sigmaz(), res)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\text{Anonymous }\\text{Operator }{\\hat{\\tiny\\boxed{{O}_{30e67746...}}\\normalsize}_{}} \\text{ on the space }\\mathbb{C}^{2}\\text{ with numerical content: }$$\n", "$$\\begin{equation}\\left(\\begin{array}{{11}c}0.151 & (0.031-0.356j)\\\\(0.031+0.356j) & 0.849\\\\\\end{array}\\right)\\end{equation}$$" ], "text/plain": [ "'30e67746-c19b-469d-85c5-b359cd71330e'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res[20]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Q37dNrKf5u1MYvoAEalxhFvpMDA8ERUggw17EG+S8/9TgYMOTZD3rV28xgqVA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\n8rbvfApwYuuTgFsSOc5pO/n2NhregO3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a66159b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(tsΩ/np.pi, σz_expect, 'r.', label='numerical result')\n", "Ωp = (Ω2+Δ2)0.5\n", "plt.plot(tsΩ/np.pi, 1-(Ω/Ωp)*22np.sin(Ωpts/2)*2, 'b-',\n", " label=r'$1-2(\\Omega^\\prime/\\Omega)^2\\sin^2(\\Omega^\\prime t/2)$')\n", "plt.title(r'$\\langle\\sigma_z\\rangle$-vs-$t\\Omega/\\pi$ at '\n", " r'$\\Delta/\\Omega=%.2f$, $\\omega_0/\\Omega=%.2f$'%(Δ/Ω, ω0/Ω))\n", "plt.ylim(-1,1)\n", "plt.legend(loc=3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## With Rotating Wave Approximation\n", "\n", "$\\hat{H}^\\prime = e^{i\\hat{H}_0 t}\\hat{H} e^{-i\\hat{H}_0 t} \\approx \\frac{\\Delta}{2} \\hat{\\sigma}_z + \\frac{\\Omega}{2} \\hat{\\sigma}_x$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$${\\left( {{0.0025}\\tiny\\times\\normalsize{\\hat{σ}_{x}}}+{{0.001}\\tiny\\times\\normalsize{\\hat{σ}_{z}}} \\right)}$$" ], "text/plain": [ "Add(Mul(0.0025, 'σ_x'), Mul(0.001, 'σ_z'))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Hp = Δ/2 sigmaz() + Ω/2 * sigmax()\n", "Hp" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cython code...\n", "Compiling cython code...\n", "Running cython code...\n", "Starting at 10/25 15:52:10.\n", "Finishing at 10/25 15:52:10.\n", "Total time: 0 seconds.\n", "Formatting the output...\n" ] } ], "source": [ "res = mesolve(Hp, [], initial_state, ts)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "σz_expect = expect(sigmaz(), res)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1IenE6RbXEX97jOyh6qwnuHNuhok8BOAlrIq2Zcm+pbQ5k8wLrfn+/mCF/jiq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\nz8Oxl01xx0aOX43pevhrYCumUbeTk+4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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a320e8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(tsΩ/np.pi, σz_expect, 'r.', label='numerical result')\n", "Ωp = (Ω2+Δ2)0.5\n", "plt.plot(tsΩ/np.pi, 1-(Ω/Ωp)*22np.sin(Ωpts/2)*2, 'b-',\n", " label=r'$1-2(\\Omega^\\prime/\\Omega)^2\\sin^2(\\Omega^\\prime t/2)$')\n", "plt.title(r'$\\langle\\sigma_z\\rangle$-vs-$t\\Omega/\\pi$ at '\n", " r'$\\Delta/\\Omega=%.2f$ in RWA'%(Δ/Ω))\n", "plt.ylim(-1,1)\n", "plt.legend(loc=3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With $\\gamma_1$ collapse" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$${{0.03162277660168379}\\tiny\\times\\normalsize{\\hat{σ}_{-}}}$$" ], "text/plain": [ "Mul(0.03162277660168379, 'σ_-')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "γ1 = 0.2Ω\n", "c1 = γ1*0.5 sigmam()\n", "c1" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cython code...\n", "Compiling cython code...\n", "Running cython code...\n", "Starting at 10/25 15:52:17.\n", "Finishing at 10/25 15:52:17.\n", "Total time: 0 seconds.\n", "Formatting the output...\n" ] } ], "source": [ "res = mesolve(Hp, [c1], initial_state, ts)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "σz_expect = expect(sigmaz(), res)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a6e315f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(tsΩ/np.pi, σz_expect, 'r.', label='numerical result')\n", "plt.ylim(-1,1)\n", "plt.title(r'$\\langle\\sigma_z\\rangle$-vs-$t\\Omega/\\pi$ at '\n", " r'$\\Delta/\\Omega=%.2f$ in RWA'%(Δ/Ω) + '\\n' +\n", " r'with $\\gamma_1 \\hat{\\sigma}_-$ at $\\gamma_1=0.2\\Omega$')\n", "plt.hlines(0,0,ts[-1]Ω/np.pi)\n", "plt.legend(loc=3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With $\\gamma_2$ collapse" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$${{0.03162277660168379}\\tiny\\times\\normalsize{\\hat{σ}_{z}}}$$" ], "text/plain": [ "Mul(0.03162277660168379, 'σ_z')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "γ2 = 0.2Ω\n", "c2 = γ20.5 sigmaz()\n", "c2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cython code...\n", "Compiling cython code...\n", "Running cython code...\n", "Starting at 10/25 15:52:24.\n", "Finishing at 10/25 15:52:24.\n", "Total time: 0 seconds.\n", "Formatting the output...\n" ] } ], "source": [ "res = mesolve(Hp, [c2], initial_state, ts)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "σz_expect = expect(sigmaz(), res)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ra0bEOi2U02zSGu5AL52CNII0D2lan47xnYh4NCKWkoLx2xvtkAVwfkTMj4i/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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a69bb898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(tsΩ/np.pi, σz_expect, 'r.', label='numerical result')\n", "plt.ylim(-1,1)\n", "plt.title(r'$\\langle\\sigma_z\\rangle$-vs-$t\\Omega/\\pi$ at '\n", " r'$\\Delta/\\Omega=%.2f$ in RWA'%(Δ/Ω) + '\\n' +\n", " r'with $\\gamma_2 \\hat{\\sigma}_z$ at $\\gamma_2=0.2\\Omega$')\n", "plt.hlines(0,0,ts[-1]Ω/np.pi)\n", "plt.legend(loc=3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Coherent State in a Harmonic Oscillator\n", "\n", "\n", "$\|\\alpha\\rangle$ evolving under $\\hat{H} = \\hat{n}$ coupled to a zero temperature heat bath $\\kappa = 0.5$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\text{Ket }{\| {{\\tiny\\alpha\\normalsize 2.50}}_{{\\tiny N\\normalsize 40}} \\rangle} \\text{ on the space }\\mathbb{C}^{40}\\text{ with numerical content: }$$\n", "$$\\begin{equation}\\left(\\begin{array}{{11}c}0.044\\\\0.110\\\\0.194\\\\0.280\\\\0.350\\\\\\vdots\\\\3.661\\times10^{-08}\\\\1.525\\times10^{-08}\\\\6.270\\times10^{-09}\\\\2.543\\times10^{-09}\\\\1.018\\times10^{-09}\\\\\\end{array}\\right)\\end{equation}$$" ], "text/plain": [ "'{\\\\tiny\\\\alpha\\\\normalsize 2.50}_{\\\\tiny N\\\\normalsize 40}'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N_cutoff = 40\n", "α = 2.5\n", "initial_state = coherent(N_cutoff, α)\n", "initial_state" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\text{Operator }{\\hat{{n}}_{{40}}} \\text{ on the space }\\mathbb{C}^{40}\\text{ with numerical content: }$$\n", "$$\\begin{equation}\\left(\\begin{array}{{11}c}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 2.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 3.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 4.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 35.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 36.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 37.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 38.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 39.0\\\\\\end{array}\\right)\\end{equation}$$" ], "text/plain": [ "'{n}_{40}'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H = num(N_cutoff)\n", "H" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$${{0.25}\\tiny\\times\\normalsize{\\hat{{a}}_{{40}}}}$$" ], "text/plain": [ "Mul(0.25, '{a}_{40}')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "κ = 0.5\n", "n_th = 0\n", "\n", "c_down = (κ * (1 + n_th))*2 destroy(N_cutoff)\n", "c_down" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating cython code...\n", "Compiling cython code...\n", "Running cython code...\n", "Starting at 10/25 15:52:31.\n", "Finishing at 10/25 15:52:32.\n", "Total time: 0 seconds.\n", "Formatting the output...\n" ] } ], "source": [ "ts = 2np.pinp.linspace(0,1,41)\n", "res = mesolve(H, [c_down], initial_state, ts)\n", "a = destroy(N_cutoff)\n", "a_expect = expect(a, res, keep_complex=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd5a2b22278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(4,4))\n", "plt.plot(np.real(a_expect), np.imag(a_expect), 'b-')\n", "for t, alpha in list(zip(ts,a_expect))[:40:4]:\n", " plt.plot(np.real(alpha), np.imag(alpha), 'r.')\n", " plt.text(np.real(alpha), np.imag(alpha), r'$t=%.1f\\pi$'%(t/np.pi), fontsize=14)\n", "plt.title(r'$\\langle\\hat{a}\\rangle$-vs-$t$')\n", "plt.ylabel(r'$\\mathcal{I}(\\alpha)$')\n", "plt.xlabel(r'$\\mathcal{R}(\\alpha)$')\n", "l = abs(a_expect[0])\n", "plt.xlim(-l,l)\n", "plt.ylim(-l,l);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
Gorgel/minkpy	analysis/notebooks/V3_analyser_xibox.ipynb	1	270297	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from scipy.signal import argrelmax, argrelmin\n", "import pickle\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load all data into memory" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html.widgets import interact #import interactive tools\n", "filenames = np.loadtxt('../datafiles/xiboxnames.txt', dtype='str')\n", "\n", "def data_selector(filename):\n", " \n", " global plotname\n", " plotname = filename\n", " global data_dict\n", " #open pickled object\n", " with open('../datafiles/'+ str(filename), 'rb') as infile:\n", " data_dict = pickle.load(infile)\n", " \n", " return data_dict\n", " \n", "interact(data_selector, filename=filenames.tolist())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open ('/home/gorgel/Dropbox/simon/plugg/masterarbete/redshift_files/xion_redshifts.txt', \"r\") as myfile:\n", " redshifts=myfile.read().splitlines()\n", "\n", "#set upper value and lover value of redshifts\n", "z_upper_limit = 17\n", "z_lower_limit = 5\n", "\n", "# selects redshift indexes between the limits\n", "indexes = sp.where( (np.array([float(z) for z in redshifts]) <= z_upper_limit) & \\\n", " (np.array([float(z) for z in redshifts]) >= z_lower_limit))\n", "\n", "#use the above indexes to select the desired redhifts\n", "redshifts = redshifts[indexes[0][0]:indexes[0][-1] +1]\n", "\n", "thresholds = data_dict[str(redshifts[8])]['thresholds']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### V3 as function of threshold $\\theta_{th}$" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<function __main__.plot_explorer>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ItR3Su5n84NgxOsBPbdmy5PFavt27d7N79+61vgytkp/f+PKzG29+fuvXoeOHuOLIFWye\n2s6xqW1sjuDMK6/kG9+5iKve/tc5Y/+VI30+R3+SxkgvsnRiZF8sSJKkCRRV68ns7wrU4v2DoUIa\nI72aECOFJElaTMwFiOz7f+hEO7f/hgppjHSrtaFi9Hbt2rXWl6AG/PzGl5/dePPzW/+yFiqAVm4k\n7KgtjUhEZNt/T0e7XX58/DhbOx3O2LSp1eeSJEnj6+jMUX58+MdsmtrC8amdTEdwtX37+NZ3vsPp\nt78DZ+y7cqQdta2pkMaIzZ8kSdJy9Jo/9e4dsmxsrVuFoUIaIzZ/kiRJyxHM71PRNkOFNEYc/UmS\nJC3HgpqKeU20R38fYaiQxojNnyRJ0spYUyGpxuZPkiRpOaJ2t9DrU9EWQ4U0Rmz+JEmSliOG3CsE\nwejGfDrBUCGNEZs/SZKk5ajPqN12IyhDhTRGbP4kSZKWo7+j9sm4bzBUSGPE5k+SJGklkjwpg8sa\nKqQxYvMnSZK0XPXO2v17Rs1QIY0Rmz9JkqTlqnfWTtobAMpQIY2JzGS2qqmw+ZMkSVrKoM7aw0aF\naspQIY2JY1n+SdgcYaiQJElLmgsQvdm0IwwV0kZ3pFsaP23t+GcrSZKWrz6sbBsdtr07kcaEoUKS\nJK1Er/nT8A7bo+PdiTQGjne7zGYyHcG0oUKSJC1D/1wV0KuhaCdieHcijYHD1lJIkqQVmosPcaJP\nBUC20K/COxRpDNj0SZIkrdSCmopsb/o771CkdW6m22Umkw6w2VAhSZJWrM25tAvvUKR1zloKSZK0\nGvWO2kl7nba9S5HWOUOFJElajXrzJyJoayAo71KkdaybyfFMAthiqJAkSStQ76jdZiMo71KkdexI\nt0tSAkVbM2BKkqTJ1mYH7R5DhbSO2fRJkiStVu8Lyd7XknMzarfwPaV3KtI6lZkcNVRIkqRVOtFB\nu2r+1GKrB+9UpHXqaNX0aXMEHZs+SZKkFZprOt1+6ydDhbRe2fRJkiQ1saCmIpOIdu4rvFuR1qmj\nVacqQ4UkSWpkQUft0beA8G5FWoeOdbvMZjIdwbShQpIkrcLcPBX2qZA2Jps+SZKkpuZm1D4JXTO9\nY5HWIUOFJElqqj6jtpPfaUOLiO0R8a2I6EbEcwfsPyci3hYRl0XEgYj4cETcbkhZnYh4dER8NSIO\nR8TFEfHMiNg+5Phllz0qM90uM5l0gM2GCkmStEpRn6EiotRaOE+FNqinAGdWj+eF7Ii4DvBx4GbA\n04HHATuB90TEHQaU9Szg74EvA+cDbwQeAbwjalNWr6LskbCWQpIkjVTOW7ViusWypcYi4n8Aj6Tc\n0P/DgEOeCpwK3Dgzv1id8wrgP4DnA9frK+sGwMOBN2fmvfq2fxt4DnBv4LWrKXuUDBWSJGkU6h21\ngdbmvvKuRetWREwBFwDvBt46YP8O4G7Ant5NP0BmHgReBFw3Is7tO+U+1frZtaIuAA4B5zUoeyS6\nmRzLJIAthgpJktTAXEft6ucTfSscUlYby6OBcyjNlAb99t8Q2AxcOGDfJ6v1Tfq2nQvMAp/qPzAz\njwJfqPavtuyR6NVSbOl0CGfRliRJDdQ7arc5DJShQutSRPws8GTgyZl58ZDDzqrW3xuwr7ft7Nrx\nezPz+JDjz4yI6b5jV1L2SNj0SZIkjcpcR+1w9CdtXP8EfIPB/Sh6eiM2HR2w70jtmN7jQccOOn6l\nZTeWmRztdgkMFZIkaXROxpCydtTWuhMR5wG/Btw6M2cXOfRQtd4yYN/W2jG9x2cOOLZ3fPYdv9Ky\nAXjMX//13ONb3uY23PK2t11wcr3isb+ZUwKbI1rrRCVJkjaO3j1GAB//0If49Ac/yOyVVxKHDo78\nuQwVWlciYgulduJdwA8j4ueqXb2mRqdVQ73uBS6p7evX29bffOkS4HoRsWlAE6izKU2jZvqOXUnZ\nADzmCU+Y9/OgbwQWbMv5W7ZPTQ04S5IkaWV6zZ+S5Ja3vS23v8UtOHbxxUy9/o0888jhkT6XoULr\nzTZKbcJdqqXuvGp5LPBCSvOkWw447ubV+jN92z4F/Dpl3omP9jZGxFbgRsCevmO/tMKyAThry6CK\njROyFiAGhQ5rKSRJ0ijMtYbINhs+FYYKrTcHgHux8H77asALKMPLvhj4YmYejIh3AL8bETfsm0ti\nJ/Bg4OuZ+em+Ml4P/CXwKPpCBfAQSph5dW9DZh5YYdnLUh/RyfggSZLaFARJGQgmM5nqdMgWvsA0\nVGhdqZofvbm+PSKuVT38Zma+pW/XXwB3AN4bEc8C9lNCwtWB36qV/eWIeD5wfkS8mRJQrk+ZEG9P\nZr6m9rTLLluSJGm9CmJBa4lRM1RorGXmNyPiVsDTgMdT5pb4LHDHzPzAgFMeBVwE/DElGFxKmU37\niSMoW5IkaV2JiLnpKdqooZh7nrZTi7RRRET69yRJktaTHx74IUe6M0xtPoOtx2fI736XvPVt+ckf\nX0pmjixlOBi+JEmSNKEiyhhQmenkd5IkSZJWrn9Y2TYZKiRJkqQJNldT0WKfCkOFJEmSNKEWDmff\nTrAwVEiSJEkTKqoYkZyYhHd03bNPMFRIkiRJE6pXU2FHbUmSJEmrcqKmImFuzorRV1UYKiRJkqQJ\nZ02FJEmSpFUpM2q3N+pTj6FCkiRJmlAnRnuypkKSJEnSKpyopcjaz6NlqJAkSZI2iLZqKwwVkiRJ\n0oTqNX/qzVFRbRw5Q4UkSZI0oXrNneY1gmqhusJQIUmSJE2ouZqKap4KgGyhX4WhQpIkSZpQ9Y7a\n9qmQJEmStCo5N/pTO+UbKiRJkqQJNTdPRZ7Y0gZDhSRJkjSh5s1L0eLM2oYKSZIkaUIF9qmQJEmS\n1ECvpqI3T4WhQpIkSdIqtRUnCkOFJEmSNKHq81QkzlMhSZIkaQXqM2q3xVAhSZIkTaiox4mWRoAy\nVEiSJEkTqt5ROwwVkiRJklan9Kkg2+mwbaiQJEmSJtSC5k8tMVRIkiRJE+pEcyfnqZAkSZK0SkGU\nIWVbZKiQJEmSJlhEaQSVWFMhSZIkqYE2aysMFZIkSdIEi+p/Pc6oLUmSJGlF+ueqsPmTJEmSpBXr\n1VNkb66KFhgqJEmSpAlmTYUkSZKkxnqjP7VUUWGokCRJkiZZEHNhoo1O2mCokCRJkiZaf/Onthgq\nJEmSpAnWG07WeSokSZIkrcrCmgrnqZAkSZK0Qr26iq59KiRJkiStVLRQM1FnqJAkSZImWMSJye+y\nbBj5cxgqJEmSpAlmR21JkiRJjQVRZtS2T4UkSZKklZob/anMqd3KcxgqJEmSpAnWP6N2BGQLucJQ\nIUmSJE2wE/NU0FqvCkOFJEmSNMGCE6M/2fxJkiRJ0qoEZUbttkaAMlRIkiRJE2x+R21oo7bCUCFJ\nkiRNsOibU9s+FZIkSZJW7ERH7WxlNm0wVEiSJEkTLfrm1CbCIWUlSZIkrU5mW42fDBWSJEnSRIsI\nIoL2xn4yVEiSJEkTLfpHe7JPhSRJkqSV6nXUJh1SVpIkSdIq9HXTnrceJUOFJEmStCFkC3UUhaFC\nkiRJmmDzZtSOaKP1k6FCkiRJmnQxv7v2yBkqJEmSpAkXUUJFW3NVGCokSZKkCVfvrD1q0y2VK21I\nV17ZvIzp6bJs2gQdY78kSRqRoISKNrprGyqkETp4cLTlTU2VcNELGb3HkiRJKxEtTXrX4+2JNEKn\nndbs/EyYmYHjx8t6drYs/aan4cwzrcWQJEnLF0QVLNppAGWokEZo+/bRljc7WwJGL2QcO1bW+/fD\nVa4y2ueSJEmTa66moqUKC0OFtI5NTZVl69by88wMXHopHDoEO3bYFEqSJC3P3ICygfNUSBvd9HSp\nDcmEffvW+mokSdK4cUhZSQCcckrpT3HkCBw9utZXI0mSxkFvnoq2GCqkMdPpwM6d5bG1FZIkaTlO\nzFORZAsjQRkqpDG0c2dpCnX8eOlfIUmStJi5GbVbKt9QIY2pU08t6/37Sx8LSZKkYfprKtpgqNC6\nEhHXjYinRMQnIuJHEbEvIj4fEX8ZEQsGbI2IcyLibRFxWUQciIgPR8TthpTdiYhHR8RXI+JwRFwc\nEc8cVO5Ky14LW7fC5s1l2Nn9+9f6aiRJ0noX0FpVhaFC682DgEcB/wU8GXgs8DXgb4GPR8TW3oER\ncR3g48DNgKcDjwN2Au+JiDsMKPtZwN8DXwbOB94IPAJ4R9SmmVxF2WuiN1fFwYMLJ8mTJEnqiQh6\ndzvZQpftaGtYKWk1IuLGwNczc39t+98AfwU8PDOfX217A/A7wI0z84vVth3AfwBHMvN6feffAPgS\n8ObMvFff9vOB5wD3y8zX9m1fdtl95+Ra/D1dcUXpV7FtG5x++kl/ekmSNAb2H93P9w5fAQdn2HqL\n2/Oz/30RmTmydGFNhdaVzPxsPVBU3lCtbwBzN/h3A/b0bvqr8w8CLwKuGxHn9p1/n2r97Fq5FwCH\ngPN6G1ZR9po69VSIgMOHy4zbkiRJdXMdtTOd/E4b2jWq9Q+r9Q2BzcCFA479ZLW+Sd+2c4FZ4FP9\nB2bmUeAL1f6elZa9phxiVpIkLcWO2trwImIKeAJwHHhNtfmsav29Aaf0tp3dt+0sYG9mHh9y/JkR\nMb3Kstfczp0wNVVqKg4fXuurkSRJ61EQzqitDe3ZwM2BJ2bmf1XbeiM2DZpT+kjtmN7jYfNP149f\nadlrLuLEELP79jnErCRJmm9uTJqIVuoqppc+RFo7VQfthwEvzMyn9+3qTfm2ZcBpW2vH9B6fOeRp\ntlIGWDvUd+xKyp6ze/fuuce7du1i165dQ55y9LZtK6NAHTtW1r0mUZIkSR/50Ed45/vew+yxGTZd\nccXIyzdUaN2KiN2UEZ9ekpkPre2+pFoPaobU29bffOkS4HoRsWlAE6izKU2jZlZZ9pz+ULEWTj0V\n9u41VEiSpPluu+u2XPsmN+DogSPseMs7ec6+0QYLmz9pXaoCxROBl2Xmgwcc8iVK86RbDth382r9\nmb5tnwKmKPNO9D/PVuBGtWNXWva6sXkzTE+XOSscCUqSJPVrYdCnOYYKrTsR8URKoHhFZj5o0DGZ\neQB4B7ArIm7Yd+5O4MGUuS4+3XfK6ylNnB5VK+ohwDbg1Q3KXle2Vg207LAtSZJ62h79yeZPWlci\n4mHAbuBi4P0RcV7tkB9k5vuqx38B3AF4b0Q8C9hPCQlXB36r/6TM/HJEPB84PyLeDLwbuD7wcMp8\nFK9hvmWXvd5s3w4HDsCRIydm3JYkSRtb/4zabTBUaL25CaVG4aeBlw/Yvwd4H0BmfjMibgU8DXg8\nZW6JzwJ3zMwPDDj3UcBFwB9TgsGllNm0n1g/cBVlrxvT07BpExw/DkePwpZB3c0lSdKGEn2Nn7KF\nhlDR1li10kYTEble/p4OHChDy27fDqedttZXI0mS1tpMd4bv7P8BB/Yf4pRfvRPX+c63yMyRpQv7\nVEgTaNu2sj5yxDkrJElSYUdtSSsyNVVGgup2SxMoSZK0sc01f0poo3OFoUKaUL3aCkeBkiRJEWFN\nhaSV27atfBFhEyhJktSLFN2WkoWhQppQnU5pApVZgoUkSdq4Sk1FQNLKTBWGCmmC2QRKkiT1S7KV\nHtuGCmmCbd1amkAdPVo6bUuSpI2r0+Lsd4YKaYJ1OmXyO5tASZKkIBjdzBTzGSqkCWcTKEmSBKWm\nIiJamVHbUCFNuP4mULOza301kiRprUSv+VOMvqu2oUKacBElWIBNoCRJ2sicp0JSIzaBkiRJQVTJ\nwuZPklZh69bSafvYMZtASZK0UTn6k6TGek2gDh1a2+uQJElro1PVUDj5naRVswmUJEkbW1hTIamp\nLVtgagpmZuD48bW+GkmSdLJFRBnBpQWGCmkD6TWBsrZCkqSNJ3odtFsIFoYKaQOxCZQkSRtXRIkV\n9qmQ1MjmzaUJ1OxsGQlKkiRtHNHiTBWGCmmD2b69rA8cWNvrkCRJJ1dEtNZZ21AhbTA7dpSmlEeO\nlE7bkiRJTRkqpA2m0zlRW3Hw4NpeiyRJOnmi+l8bDBXSBrRzZ6mtOHQIut21vhpJknQyOE+FpJGa\nmirDy2ZaWyFJ0kYRBNEJsoXaCkOFtEHt3FnWBw+WcCFJkiabNRWSRm7TpjLLdrdbmkFJkqTJ11as\nMFRIG9h3U39vAAAgAElEQVSOHWVtEyhJkibfXDftFpKFoULawLZuhenpMrSss2xLkjTZIqKM1NIC\nQ4W0wfX6VjgZniRJk603nGwbXSkNFdIGt317GQ3q+HE4dmytr0aSJLWtjboKQ4Wkub4V1lZIkjS5\nIkpdRbbQBMpQIYkdO8pM20eOlP4VkiRp8gT2qZDUoojSDAqsrZAkaVL1airamKDKUCEJKLUVEWUU\nqNnZtb4aSZI0atHaLBWGCkmVqakyxGym81ZIkjSp7FMhqXW94WUPHWqlZlSSJK0h56mQdFJs2gRb\ntkC3a22FJEmTJoi2MoWhQtJ8/ZPh2bdCkqTJEb1E0UKwMFRImmfLltK3otuFyy6zGZQkSZOk01Jn\nbUOFpAVOPx2mp8ss21deudZXI0mSRiWindt/Q4WkBSLgjDPK+tChskiSpPEXdtSWdDJNT8Npp5XH\nV15Zai0kSdJ46xBkC02gDBWShtq2rXTcziz9K7rdtb4iSZLURHRs/iRpDZx6KmzeXEaCuvzytb4a\nSZLURFuzahsqJC3pjDPKjNtHj8K+fWt9NZIkab0xVEhaUqdTRoSKKPNXHDmy1lckSZJWoxNBGxNV\nGCokLcvmzaUpFMAVV8DMzNpejyRJWjmbP0laczt2wPbtTownSdK46nTaCRXTrZQqaWJd5SpleNnj\nx2Hv3hP9LVYjuwkJ2UsnvZDSvw6ITdHauNqjlpkLXseC1weL1zx3q/emvs5qX1XevPekv7yqZjs6\nAZ3quM6Jn4mybe5aq2Xez7Xrzf4EWXsdEXHi+WPAtkHn9Zfbe12915rzX/vAcgf8PPd6O9W26nG0\n9B9QSRpHQZAt/LNoqJC0Ir2J8S67DI4dS370w9LfYssWFt5E924MZyFnc25htu8mebnPOx1l2VSW\nzqYOMdXezWJ2k5yZvyy48R50I34S5Vo86Rpb9WuOvrDRH0g68x9HJ2BqfkAxlEiaJO30qDBUSFqm\n7vEueSzn1qccT/ZdWTptX/pD2HlKaRq1EjFV+4a7b937Bjq7SR7vu7Hv7yTegc6mTjlnsRt+Fn57\nXV/XQwSrmZOj71/quVqE+utbTjH9NQoDrrX3eufU7rMHfvvfqxUa8O3/3PUOqgGov75B+t/vvp+H\n3v/Xy+l7XYNqG+rPM/CzHlbLkX2vfXb+BS07oAz7HAas59673uvs/1mS1oEIayoknSQ5m3SP9YWI\n4wtrFaIDVzkDpg/AgYPB/oMw04VTr1JqM+ZuqKZibqFT+3kFujPdE+HieLXMJt2jy7v777+hXNbN\nZKevdqS39H3TXW/y443j+tYfQuaFrv7ts7UmZ7M57zGzzWpK5gJc/+/RkMf1ZmgLAlu9uRtLNIcb\ndtxyrrt+/YMCaMe/AWlcdFrqqG2okASUG6nu4S6zh2fJY/UEwYlmR5s7xOagM12+Qt4CbD9SRoQ6\n3oX9U6U51Gr7WQzTme4s+BerV4vRu8bFbvizmwv6Kcz75j5YGCA0Mfp/J1Yz8sm8gFGr9VnQ76Uv\nENRrVKA6rlfuJDVhq4WjuZDUU6s9nBeclqixnFf+Uv1qBj028Ehz2hpS1lAhbWCZSfdIl+7hbvnG\nv/cf+A50tnROhIglOkpv3QpnntnrZ1E6cJ9+ehmGtk3RCWLL8v5htG28mlhN7VrdgtqS7pDHfTUR\ng26q5xc64PGgDvaLNJcbfsEDyqjXnvTX+PQHdUYTmEYWupYRSGpPvODxvMEKlmuRJoDLupbFPrfF\nglS92WD9cf+25dRqGc4mSltDyhoqpA1o9shsCRJH+oJEQGdbpyxbOiv+j8f0NPzET8Dll5d+Fj/+\ncRkpaqX9LKRJ1bS2ZBzM69PSFzAW3DAPuEFdbBS4QSOTDexXs9Tj2p35WtYUjW0t1WIhaFAY6bfY\nS16sqd2w513iOuZ+HNQ0cAOHpCAWfjYjYKiQNpDMZOaymXn9EDpbOnNhouk/sL2RofbtKzNvX3EF\nHD4Mp502+uZQktafiCijZ62iVqftoDWwX0395/57zwaDLSww6OZ6sWvpP3VYX5kh4WngyHqL3ewP\nqompNVFb0IxvQECb//LGLDANChqDgkv/sfXHnDh3yYDMEr9fg34PBz1nh6HN/wjmmikveLmx/ArL\nlTBUSBvIzOUlUMRUMLVzis7WdoZlPfXU0vTpyivh6FH40Y/glFNg586RP5UkLctGqCk6Ger9Yobe\nQNcf1w0IWks2tauHHGrbBj3nYk3ZBt30D7joNkLSyQhenW0dNp2+aeH2aGfua0OFtEEcv+J4ae7U\ngemrTg/9BmNUtm4twWLfPjh0qKx7tRabFv4bJ0kaA/XhpicloA0KNIvWIi0WXmB47ceQc5bV/6l+\n/LDmf1kGMeke6ZKZC2o72mr2ZaiQNoCZK2foHiqBYtNVN7UeKHo6nRIitm0rtRa9Wbh37Cg1Fxu0\nOaskaZ0ZdOM/zoHp+I+P0z1a+k5ObZvf/rgzcHSC5k7OnYWkNTOzf4bZg7MQsOmMTWWyuJNsy5bS\nibvX/OnAAbj00tI0SpIkjVZnW/lvfffwkLmcWshLhgppgs0enGV2fwkU06dP09m8dn/yEaWvxZln\nluZPMzNlhKjeMLSSJGk0Ols7ENA92l3Q+b/TUjMBQ4U0oWYPzTJz5QwA06dNM7V1fQy/tGlTqbU4\n9dQSNI4cKU2i9u4tjyVJUjPRKfNMkZT+lP37nKdC0nLNHukLFFeZXtCecj3YubPMYXHwYOnIfexY\nqbWYni59LrZvt8+FJEmr1dnWKf0qDs/vV9FWTYWhQpow3aNdZi6fgYSpU6eY2rH+AkVPp3NiqNnD\nh0tfi5mZ0ql7//4SLnbsKMdJkqTlqzeB6nVGD4JsIVgYKqQJMntklpkrqkCxc4rpnePxJx5Raia2\nby9NoA4eLJ249+8vQWPz5jKC1NatBgxJkpaj1wSqPgpURDsNoMbjjkPSkmb2z5RO2cDUjimmTx3P\nP++tW8ty/HgJFEeOlIBx9GgJHwYMSZKWZ1ATKOepkDRQZpaZso90IWDqlPGpoVjMpk1w+umQWYLF\n4cMnwkU9YGzZAlPrt5WXJElrYlATqGhpnorxv/OQNrDuTJeZy2bImSwT252+ic6Wyfr6PqIEh23b\nhgcMKLUWmzeXMNJbDBqSpI1sUBMoayokzTPXf6ILsSnYdMYmYmqyh0saFDCOHCkjR83Onvi5Z2rq\nRMDYvLksjiglSdpIho0CNWqGCmkM9fef6GzrMH3adGvfPKxX/QEDSqg4fvzE0gsavbDRO6c/YGze\nbL8MSdJkW9AEynkqJGU3mbli8vpPjMLUVFm2bj2xrRc0jh0rS//jnl7I2LSpzJExPW3QkCRNjkFN\noLKF/855NyKN0MyBmeaFdEt46F+T1eOsjpnQ/hOjVg8amSVYHD06P2gcPz7/vE6nhIv+oDE9bR8N\nSdJ4qjeBirSjtrSuze6bbfcJOtDZVDV3mvD+E23ojRi1efOJbb3ai+PHy8R7MzPQ7S6s0eiZmiqh\noxdY+pfe9g3WEk2StM7Vm0C1wVAhjdDUKc2/yo5OlJHeOtXjah0d71Tb0OvI3a/bPREw+pdeH41e\ns6phIhYGjfra8CFJOlnqTaDa+A+QoUIDRcTVgZtSGt98LDMvW+NLGgvTp/gnNQl6w9P212j0dLvz\nw8Xs7Pxt3e78ULKYXvgYFDwGLZIkrVZ/Eyhn1NbIRcSfAY8AjgFPycxXRcRDgOcAW6rDDkfE4zLz\nBWt1nWshIjrAI4E/Aa4JXAq8AXhiZh5ay2vT2und4NdrN/plzg8b9XU9fKz0uYctEQt/7i2SpI2t\nvwlUGwwVG1hE3AV4JvAj4BDw0og4ArwAuBB4DbAN+CPguRHx5cz88Fpd7xp4FvBw4C3A/wF+gRLA\nfiUifi3bapSosRdROnYvJXNw8FhqWc319JZ6+KgHkUGhpFeGJGl89TeBihZyhaFiY3s48BXgppl5\nMCKeB7wY+BywKzO7ABHx/4CvUm6oN0SoiIgbUN6fN2fmvfq2f5tSi3Nv4LVrdHmaEP19L5ajP1j0\nAsmwJfPEMb3HUIJLk+ut137Ua0TqP690kSS1p7O1hIo2xpQ1VGxsNwCen5kHq58vAP4n8OJeoACo\nAscrgfuvwTWulftU62fXtl8APA04D0OFTrImfSv6A8ZioaR/ey+M1Je2LDd0LPa4fz1o23LWSx0j\nSeOqs60D+3BIWY3cTwDf7/u59/iiAcd+G7ha2xe0jpwLzAKf6t+YmUcj4gvVfmls9GpFmhoWNOq1\nIv0/L3XeoOPXu3rAWEngWG5wqpe7nMfDrmXY9Y0qiEkaD70mUG0wVGxsVwCn9/3cq50Y1HX0FEq/\ni43iLGBvZg4aOPR7wC0iYjozRzDbnTQ+2m6mNCyk9O9b7HHv5/51fVt9/3LW9bLq4WdcwlCblhNQ\nltq2nJ9HEegWezzsvCbWQ/Bay9fX9utv8vzr4bM52TpbO0QL4z8ZKja2bwI/3/shM/dGxKkMDg/X\nodxMbxTbgaND9h3pO2bfybkcaWPov7kblxnMVxIolltzM+j4Qc837PFyrm2x52kSvAxY0uitJvwM\nHYSDDi300zZUbHAfBm7WvyEzD9QPiojNwD2Bt5+k61oPDgFnDtm3FUg2Vs2NpCFW+k35pFuqpmix\nbcOOWe7Py72u5YahpgFpPQSs5QbLlewbxXOPwlLlt/36ToYmYb03hPlCQbeFN8BQsbHtzswjSx/G\ndspcDV9s+XrWk0uA60XEpgFNoM6mNI1a0PRp9+7dc4937drFrl272rxGSVp37Gshrb3+ZqS9Zc+e\nPXz4w3vIhEu7x0b+nOFQ+xtXRFxOGcHoxZn52bW+nvUkIv4G+CvgNpn50b7tW4EfA3sy87dq5zh1\nhSRJGgsRQebohoFqp/u3xsUVwJ8Cn46IL0TEIyLijLW+qHXi9ZQmTo+qbX8IZULAV5/0K5IkSVqn\nrKnYwCIigNsBfwjcg9JX4Cil78RLMvM9a3h5ay4ingOcD7wVeDdwfcqEeB/NzNsPON6aCkmSNBZG\nXVNhqBAA1ahP96YEjF7n7f8GXk4JGBet0aWtmYjoUGoq/hi4FnAppQbjiZm5oJO2oUKSJI0LQ4Va\nFxHXAx4E/AHwk5RmQHso4cJmP0MYKiRJ0rgwVOikiYgp4E7Aw4DfBDIzx2Tk+JPPUCFJksaFHbV1\nMt0UuBtwy+rnYZPBSZIkaQNzngrNExE/Bdyf0rfinGrzvwMvxhGPJEmSNIChQkTEJkqNxB9SmjlN\nUYabfQFlDovPr+HlSZIkaZ0zVGxgEfHLlCBxP+CqlA7ZH6TUSrwlM23uJEmSpCXZUXsDi4hu9fC/\ngZcBL92IQ8eOih21JUnSuBh1R21rKja2NwMvAt7r3bAkSZJWy5oKaUSsqZAkSePCIWUlSZIkrSuG\nCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIj\nhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElS\nI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJ\nUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIk\nSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKS\nJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFC\nkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1Iih\nQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSI\noUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQutKRJwdEX8RER+KiEsi4kBEfDkinhERZww5\n56yIeEVEXBoRhyLi0xFxz0We4/4R8fnq2B9ExAURceYoypYkSdqIIjPX+hqkORHxp8CzgXcCHwX2\nAzcDHgj8ADg3M3/Yd/wZwGeAM4F/AL4L3A+4LfCgzHxZrfxHA38P7AFeA/w08GfAd4CbZuahBmWn\nf0+SJGkcRASZGSMrz5sgrScR8QvA3sz8UW37HwEXAH+fmY/r2/4M4LHAXTPzXdW2DnAhcB3gmpl5\nsNp+JiU8fAm4RS8BRMRdgLcDf5WZT11N2dU+Q4UkSRoLow4VNn/SupKZ/1kPFJU3VOsb1LbfF/hG\n76a/KqMLPBc4A7hz37F3B7YBz+2/+8/MdwLfAs5rULYkSdKGZajQuLhGte5v+nR14CzgEwOO/2S1\nvknftnOr9YVDjr9eRGxfZdmSJEkblqFC4+LJ1frlfdvOqtbfG3B8b9vZteNzkeOjr8yVli1JkrRh\nTa/1BWgyRcRVgEev4JR/zMzLh5T1GOCewAszc0/fru3V+uiA047Ujpl7nJnLOX6lZUuSJG1Yhgq1\n5XTgiZSagaU6ASXwCmBBqIiIBwPPoIwGdX5td2+kpi0DytxaO2bucURsGRAs6sevtGwAdu/ePfd4\n165d7Nq1a8DpkiRJJ9eePXvYs2dPa+UbKtSKzLyIhs3rIuJBwP8D/hW4R2bO1g65pFoPaobU29bf\nfOkSSsA5m9Ixu358t6/MlZYNzA8VkiRJ60X9y84nP/nJww9eBftUaF2qAsWLgPcCd8/M4/VjMvP7\nlBv7Wwwo4ubV+jN92z5VrW855Piv9eapWEXZkiRJG5ahQutORDyQMifF+4Dfzsxjixz+WuA61VwT\nvfOngIdTmlP9S9+x/wwcBs6v5pvoHX9X4GeBVzcoW5IkacNy8jutKxFxN+CtwJXAn3OiU3TP/sz8\n577jzwA+C1yVMuv1JcB9gNsAD87Ml9bK/zPgmZQZtV9Hacr0GMqkeOcOmFF7JWU7+Z0kSRoLzqit\niRYRTwKexPAO3hdl5rVr55wFPA24E7AT+A/g6Zn5xiHP8QDKyFTnUMLLO4HHZ+beAccuu2xDhSRJ\nGheGCmmdMlRIkqRxMepQYZ8KSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmS\nJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJ\nkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCok\nSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgq\nJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0Y\nKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmN\nGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJ\njRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmSJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIk\nSY0YKiRJkiQ1YqiQJEmS1IihQpIkSVIjhgpJkiRJjRgqJEmSJDViqJAkSZLUiKFCkiRJUiOGCkmS\nJEmNGCokSZIkNWKokCRJktSIoUKSJElSI4YKSZIkSY0YKiRJkiQ1YqiQJEmS1IihQutaRHQi4sKI\n6EbEO4Ycc1ZEvCIiLo2IQxHx6Yi45yJl3j8iPl8d+4OIuCAizhxF2ZIkSRuRoULr3f8EblA9zvrO\niDgD+Chwd+D5wCOAA8AbIuKBA45/NPAy4PLq2BcC9wb2RMT2JmVLkiRtVJG54D5NWhci4hrAfwBP\nAv4BeGdm3q12zDOAxwJ3zcx3Vds6wIXAdYBrZubBavuZwHeALwG3yOqXPyLuArwd+KvMfOpqyq72\npX9PkiRpHEQEmRmjKs+aCq1nzwe+CTxnkWPuC3yjd9MPkJld4LnAGcCd+469O7ANeG7/3X9mvhP4\nFnBeg7IlSZI2LEOF1qWq38JdgD+tbuQHHXN14CzgEwN2f7Ja36Rv27nV+sIhx1+v1wRqFWVLkiRt\nWIYKrTsRcRVK7cQ/ZeanFjn0rGr9vQH7etvOrh2fixwffWWutGxJkqQNa3qtL0CTqQoGj17BKf+Y\nmZdXj59Rrf9iiXN6HauPDth3pHbM3OPMXM7xKy1bkiRpwzJUqC2nA0+k1Aws1QkogVcAl0fErYEH\nA+dl5r4lzjtUrbcM2Le1dszc44jYMiB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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa2f55b07d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.html.widgets import interact #import interactive tools\n", "\n", "#setting plotting visual parameters\n", "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\n", "pylab.rcParams.update({'font.size': 18})\n", "\n", "#define function to use in ipyton interact widget\n", "def plot_explorer(plotnr, ylim_pos=5000, ylim_neg=-5000, local_extrema='True'):\n", " \n", " # plots all data\n", " for z in redshifts:\n", " \n", " V3 = data_dict[z]['CV3']\n", " thresholds = data_dict[z]['thresholds']\n", " plt.plot(thresholds, V3, linewidth=2, alpha=0.1)\n", " plt.xlabel('Threshold value')\n", " plt.ylabel('V3')\n", " plt.ylim(ylim_neg, ylim_pos)\n", " plt.xlim(0,1)\n", " \n", " #selects redshift to highligt from plotnr parameter\n", " z = redshifts[plotnr]\n", " V3 = data_dict[z]['CV3']\n", " thresholds = data_dict[z]['thresholds']\n", " #plots highlighted redshift curve and annotates it\n", " \n", " plt.plot(thresholds, V3, linewidth=2, alpha=1, color='red')\n", " plt.title(str(plotname))\n", " plt.xlabel('Threshold value')\n", " plt.ylabel('V3')\n", " plt.annotate('$z = ' + str(z) + '$', xy=(thresholds[20], V3[20]),\n", " xytext=(0.5, 0.1), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth =10),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", " \n", " if local_extrema == 'True':\n", " \n", " V3_maximums = argrelmax(V3)[0]\n", " V3_minimums = argrelmin(V3)[0]\n", "\n", " for maximum in V3_maximums:\n", " plt.annotate(str(thresholds[maximum]) + ' , ' + str(round(V3[maximum])), xy=(thresholds[maximum],V3[maximum]), xytext=(-20,20), \n", " textcoords='offset points', ha='center', va='bottom',\n", " bbox=dict(boxstyle='round,pad=0.2', fc='white', alpha=0.3),\n", " arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', \n", " color='black'))\n", "\n", " for minimum in V3_minimums:\n", " plt.annotate(str(thresholds[minimum]) + ' , ' + str(round(V3[minimum])), xy=(thresholds[minimum],V3[minimum]), xytext=(20,-40), \n", " textcoords='offset points', ha='center', va='bottom',\n", " bbox=dict(boxstyle='round,pad=0.2', fc='white', alpha=0.3),\n", " arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', \n", " color='black'))\n", " \n", " plt.savefig('V3_threshold.png')\n", " \n", "interact(plot_explorer, plotnr=(0,len(redshifts) - 1,1), ylim_pos = (0,5106,1000), ylim_neg = (-510*6,0,1000), local_extrema=['True', 'False'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### V3 as function of global ionization fraction $x_{glob}$" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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+ZVle/+Nuv/32E2+55ZavDT7H97///XOGGnf/7aCDDvpfx3FK++yzzw2Amjlz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g7Hgv2+/uWnR48ePXo8E+iJ6TMO78I/5MKDQIDwYf/da5IkQf7IjwNhhlRUVaiJUr6t\niXKuBCoGgwR0hBAh9S2EwNXl7vdaU9Q+0nkUcZskDBof6fHY9ZEOh4Fc1uQj1zl3hzv21R6PZxSP\nuB3fcju+ZRSfzrx3h7sQ/FEqnJWvr0/y3lm91Ec770eTMIPaFMSfyOl1XXdUYMzj5HSz2TxGTk0V\ncTW6QgrJ0RzZlbuu7WC9ZhpFDKXEUddIfc1fwn3Fnzsl9X/rpc5KUxyrV1yG2u+hLCSXw0sEgn21\n52iKU/n+8QiHQ+s3LZzjYG3wmzan78Kdhb/2CGHaCqksywDTjjFtNjCMxq1Kuy22p5R+nhNXVdsI\n0JDgxtPrK9GZK53NZrzujc9jjeZD73s/04lAiIQ8d1hTMU3D52BbbMPnr/ExbLeosWI+iBHak+/1\nYyd9e7C4qnvSt/bARDiiKILhEOMc2+2W0QiSgUSOL7HlALHbsat2vys/ez169OjR42OPnpi+SiBF\n1E6SCiFQMQhjUD9WF+t/wQvEVRVmRmPV1kRJWeF9RRzFCJOAAJEI9nWw6VCThOacPm4IaeMHXa+D\nwhbHgUwuFqCCP/P+cM+m2GC9JVUpN6MbLgYXrWJ6MbjgenTdEqNNseH+cB9UtSQJ5/35/FQzdH8f\nCLD3H/m8r8QpuV+f9oVXRNFV28Wp9ZIkSbi6ukJKSZ7nLTk992KaKjqtNFU7clt0C/jrsFBypuLK\nb/tm3Od8LuL+Zfw7vxh19J3Z0tFoyGg0OgtDuU4YChczH4Q/2BQbjOQUwMoylNYsao/rzlrMY35T\n1SHDaZq0a03B3+rbVajNhvZrcjRHjl6fZkS3W2ZStgtUe287qfnzuVJrDzz/hucZzWfsdzvuPvxB\nJpNxXcmaM0kmbePBoTqE3wCkDFJxURDPImaRwu0sm7OJ2kdP+lLG7VypcBkjKRmPxxyFoKoq8jwP\nn8vxGOkusLkmz5Z9fVSPHj16PCPoiekzDleHOzweIQBnEXgi5eEnfhax22He/Mm4N35CWxPlByne\nhJqocMb3+MojxAAdee61JrMWBwyk5CaOg0IpxInN1An6jo80SVof6UP+gHaaSEZcDi9DT6iK26D+\n3V0QAGMVczW64nJ42RKXh/yB9XEdTrDjcVBgx+PwghvLwPH4Ec/7ThB8p0ldK3+jPe8AACAASURB\nVPWgwclWOfW+QutlCO7U3s88z1mv18xmXXLqTdoSxW2xpfSmk5oXec5l3ddaec8GQlL/TW9G/uLP\n47/0TxPJaWe29OLigiRJMMY8MQw1UGHeswlD+TTtfFApdAJKYlj7TV3wmyo1bpsBjFkzm83aCqnd\nbteuQhUFlIU6tQKUW+xw0NkzbU76e2txQ9E5sZ/PlSZJxPNvfD3ee977i7/KfD5AKUlZVuS5aZ9j\nV+1w+BMBzjLUQDIcRgy8wO4d2/OTfl2NZXfNXOm0JcRDgs0jnkww3pNlGUo5RmMB0wW+GiN3e7bF\n5nf3B7FHjx49enxM0BPTZx5BJfXO4wDvg8IkFe0MafG2t0NVhZoo6TBOEjHAmBKlDGka4yuB9YpN\n5DDeEwvBVRxzGcdEjY90t+v6SBtP6Cv4SGfpjNvxLYNogDHdoH4T+Hl4CPx2EA24Gd0wS2etetf6\nT4UIyun1dXc66eEBtH7F837pHfFV3C4K6QeNN6Imp1FNTh+I46iz0vQkcqrciGkybSdETSQ7Kqas\nKq7OJkT3s1lI6s8vkP/X9+K//q8+Nlt6eXmJUoqyLNlut4+FoebpvK1bWhfrQOQawljbCJrC/3Xj\nN60DYA1pbJ7P2kOnkcCYsrMKlchhG1LaFBvO90wfO+mfndjRUatgWrvlLZ/2yURJTLZac/fiy8xm\nwa6x2RxIVNouXWVlFoJdzQvebolmEdMogoPlaCz5K5z0hRBtAwFuz0hCmqboOMY517YtyPEQJS8w\nR9DZpq+P6tGjR49nAD0xfcbR1EU1fab4ENOXSiF/pJ4hfcc7kFVFLARlJCjLUBMF5akmSscUziFS\nyagmemlTkN/4SHe78PiNj7RO0Z/7SIHWRzpJJp3Bp7IMXOfiotsh3zgCnBNMkknrP/X4rv/0EbsA\nVRUeuK6XevS8vzaGo3NEl1Eb3NFLjdeCOL5qVbegnEbtWf+VyOlQTVvytsyXuEHaMaVG1nZO7OWn\nvOWU1P/r34j/n/5OZ7bU+6Ljc83zvBOG2u1O5fuFKYLHtXnt4R1YRBFKCErn2FtLdHlG4grfUTSV\nolMhlabuPPTPfDBvg1cHW3BuRp0K0Q4LHHCnE/vWIOW0JcBSad7w5jcC8Ou//GuMx8Oaex7Zbj3z\ndN42AWirO55W6SqikWImw0k/q1fEZCxRE9XOozZTtMEe4Rn4HRJIJhM0kOc5xlRhEWp2AdUE9gey\n46avj+rRo0ePpxw9MX0VwHuPkw48uDoApe6XqJ//OXw6oHrrZ7dnfJfGOBNjtCGOLVHkEFYg/QCt\nQCjBqPGRnrNGa4O69VF8pNej69az2Iw6nQ8+PffcqQXpjNt2MlSC4D+9Gd2QqORx/+kjxLitlzoc\n2vP+7EzhO1gbeliHZ+S0apTTuCanD0SR6pDT1WrFbHYqCVguYZ4u2o9pmS9DOX3D7lYrUu+Z1TVS\nG2Mwn/92/Lu+CQDxF/4s/JOf7syWKuU7+/bW6k4YqipV63HdV3tKp0+do7sdsixZ1Kf2nbXoR/ym\nwiedJarxeNypkJrPz1ah9rK1LGRlhhkk7QCCOCve31uLH8n2xO723bnSN3/qJ6GimOVL92w3e+bz\nBCEch8MRZxWTJCxNbYpNCEKdeVqjiSSNFGkB1vhTSn/6+Ek/LF4p8BVDUaGUwg+H4bE3G0YjiMcJ\nKl1gjhEu2/b1UT169OjxlKMnps84gsc0nPPDahN4YRj+458CoPo3/xAiTRkY8wo1USG97kiwqQjK\nUyMRNnf286R87SNdHVcf1Ue63T4++OScZb1es16vsdYwnZ7IatM69fLLgWvGKuZ6dM1iECqUOv5T\n705WgqYX80yanURRS6QyG9S3eBG3O/N6pXGlr5XTUEEUyKlsyWlRFKxWKy4u/HlLFIvByQ+7Luoa\nqebEvloxlrKzb++/+i/ivvLPI6oK8SVfgvytl+twkq+7VRMmkwne+yeGoRQp0/RkI7CR6qiZifdM\n618m1lrDUJ5I+ErXBK7xt25YLBbt68vzQ4cIKz9o1epHT/pJWb7iSV+4pJ0rTQaa177+dXhreO8v\n/SpSpjW5z9luYZJM2q9nrvNgrq0/f2K3RY0UU6UQO0tZNw/AIyd9HaZ3lQqfh9TtkXiS0QgjJcYY\n9vs98zn46QxhZrj9kX2+6eujevTo0eMpRk9Mn3XUl0nhBcI5EA4pFMN//BMAFG9/AZ5QE+V9ifcF\ng0GCLyMq7xEDQdqoj8dj10c6HHZ8pIUpPqqP9HzwSakQTLm7u+N4PHI8Hrm/v2e73QLuvIe/7em/\nuwsX62E85HZ8+5j/dFfu8FKGJ7i6OtU4LZewXjMSolUT9zakvaN5dDoLrw3u6GvlNKnJ6ZIoklxf\nX6OUasnpYuHbh99uZLsOVZgiVCA9UiM1jyIGtf9zpTXiW/8W7q0vIJb3+C96Jyr3LZHTesV0Om2V\nzNVqxXDoW7K+WsEomrT+zNVxhR+NTlLuasVEKQZ1bdVaa6KLk9/UbE3dpxrK/r0vOhVSUppO8GqW\nzNtZ1p3JO4pm56QvXOfEfj5X+vybXkeSDrj/0B1ZVjCdSoSoKEvN8Sg6yqzzjvM0llIlUgkmWuBK\n1z3pj0/PB2F+VsoBQniGPkjzchIU2d1uh1KW0SxCjea4YgBZFvytPXr06NHjqURPTJ9xOGfBgyes\nPgnnEd4y/ImamH7eO4i17tREKRRSasAQyQjhUkrhEd4wOBwCQ3nk1p7rnJcPL7en0HE8/og+0vn8\nRDTzPOfu7o59s+Q0HjOpycPhcODu7o7dbkcU+c5CaUN0l0uw9nH/6a7anfynaXrqT206P5dLBtDO\nh+bOBdI2i0IRf01wXO5q5TRplVMp4erqqg0nZdmm0xK136m2b/SgD+z1oVsjtd2yiKK2amkNiO/+\ne/g3vwX5y7+A/xNfSiRmrZXAmDWLxYIoiqiqiu122/b5N2GoxWDRKrXbsu4cbdlySM83zQC7s35T\ne7C4ks5kaZJEnQqp6dS3tbG7nWhXofbVHp3GrSotHknp+7E8Ba72vp0rvXouZb64QJclL33oRaqq\nUczzOmw1IFUpzrvgnVWnCVax36GGglQpBnvfDhgAqKk6Ee5dfeavCfFAlEhXIeMYn6b4eq50NgMx\nmyLdDLOrOB42wRLSo0ePHj2eOvTE9BlHHXlCoergk+fqAx9ErZbYj3s99tM/pZ0h9WmCNylVVZGm\nZzVRJJgYRFkyaFTSOp1UmrL1kTrvWh/pfDB/RR9p0+6ktebh4YHNZoO1liRJuL6+Zj6fM5vNuL29\nZTgc4pxjt9txd3dHnucMBuExmgrTsjxlnPBP9p8+5A+BbEwmgRE3a0kPD6TOcVWX0h+dY6k1aqw6\nXky7t8TxFVKmdcXSsiWnjed0v99ydXVaTqqKmMVwAQTlr/D6VCN1OHRqpArnyKZT+P7vx18skD/4\nA/iv/XriuJktLXBu36muapaanhSGynXOQeen58tz5PHI5RlprKQnmtWvcWOQDNrAkDFrptMpcRyj\ntSbLss4qlNMJ42Tc2gf8WZ9sUhSPn/QBu7cIP2znSm9eO2M4GPLSB15kvzfEsSNJjljrybIQtmqI\nfWnKoADXKrDSGUIJJl4iCkflPXtjQiK/Oenvm5O+agnxSOxDOGo8RghBURRUVcF0LpGzC1wR6qN6\n1bRHjx49nk70xPSZRyCjErCEk/7zv/RLAJSf+3Yw5lQT5RURA7QukFLXNVES7SUMJElZIoSol3QM\nq+OK5XH5RB9pWb6yjxQcm82G+/t7qiqEUhaLBdfX18RxjLUHrD0gpWz/PEmSNpBzf39PWZaMx8F/\nWourbcZpv4dIdv2nla1O/lNB8MM2vs+HBxJrua5JYlmTUzGSJ1VxZ7E7SxRdtv2fQTn1neR8We5Z\nBC7KdguYQdvPuT6uqRTdMvyybMniwVryN78J/53fhY8i5Df/Dfy3/881OW2WmnTnzG5M2fGAmiri\nYlC/vcyohDs933ZLbG0nfMXozG+61p3JUud2XFxctK/NmOL8as8knrV1VZnec94vNYXTSV+6zom9\nWWd63RsvSQYx1S6nOGoOh4rp1AFH8hycidpFqOYXnyaNJaoKJQuEEExyga9VYOMcMnnSSX+MEAmp\n8Ch/wAvRnvTDIpRHzcYk8Zwyc+jdNijtPXr06NHjqUJPTJ9xeF+n8sP/wwt4/ld+EYD8hRdCTRRQ\n1qpbqlKEOK+JSii9R0jDwHu8lGQ25/5w/4o+0tUqnNeNCZfk8/P7fr9vlU8hBNPptFVGrS2oqjuM\n2WLMFq3vsDZvldTmlK21ZrlcslqtsDZMaNY213p//WSDbfyn53vsd4c7dtU+fFBNMOrhgUhrruOY\nqD53L7VGDM7I6d5iM0scX575P5dEkWBRs9Esy/D+2HK09RoSMWYcj9syfJsmgaHXps3YWhY1WcyM\nQb/tj+D/1rcCIL76L8CP/sTZmX1Dkiim02l7ZlfKdmZEIwZMkklQM4/rUFt1ZhIdS3maST33m1a+\nJd9NZZWUtp1I3W63DIeuswq1GC5OqmYs2y9Cc9KH+qQ/Ee1zuByUmhAnMfOrAYNhUE2LwmNtyWh0\nqJ8vBKE6k6hNnxigzAEhHIkXDAvRKrTwyEl/35z0AyEeU+KcxicJURxjrWW329VBqDlCT/DZnl2R\n9fVRPXr06PGUoSemzzh8nTB2BLU0Lkqee99v4JWiesfnkpQlla3amihdaeLYoZQFA5KUKvKIqiLG\nc293H9VHWhQnH+ntbRAmi6Lg7u6OLMtwzjEcDrm9va0JlkXrJcas8N4gRNyee43ZUFX3OFe2f2c2\nm7Wp8SYgJYQ7LwZ4pGNfME2nj/lPV8Uav1hwniJSRdEuRWnvedAaUkl8Gbd+TL3WNTkd0pDTJFHM\naza62WyI46rlgsslTOJ5G05aHpe48ajzvANoK6zWWuP+g38f9xe/GqE14k/8ceRvfLgtqjdmxWQy\nam0Oq9WK0cift1IxTWYtoVsdVzzazn8RRa2imZ37TfcWXwmiKJBRYzaMRgMGg0GrWC8WJ6tsVXRV\nTT+btd6C5Hg8nfStba0RdmdRIsyVvuYNV0DJYZ0hxIDd7sBwWKGURuugAp9PorZ+4fqTG7lwch8f\nQXkeP+nXz+eMQ8oIpSYkEmK/xxGCUI0iLKVmuBiQDOZUe4XbZX19VI8ePXo8ZeiJ6TOOtl5fOBzw\niR94H9I5in/198NsxsDaUBOVpEg7QOuKJHGkqcKXYInxA0lUVRhTYtKIWMbcjG9e0Ud6vhJqjGnV\nTWMMcRy36qeUolZGA/EESRRdkCQ3JMk1UbTolNxrvcQ5zWQy4fb29okBqTj2j3XsN1WreMXF4ILr\n0XWbmF8el7j5rFOEL/Oc6zhu9+0ftMbFYcIUCe7o6pqliw45HQ7TTq3TeGxaQXa5hHmyaE/fq+Pq\nsRqpiVKMaiVzaQx807twL3wBYr2Ed74Ttfetx1XrNfP5vPWArtfrx5ahFsOTjWFbbDlnlOJwaBsJ\nDtZSPuY3HZ2pwmEitWkhOB4P5+4ABnLS+nm3enc66e92zDid9HPlwizq2Un/6vaSdCiRwPp+hbUx\nh8OBySRvHgL8aRI1K7NQ5zSbQRQhhUVWB/AwPYrwdz7CST+KwlzpRFqsPaClJK2XyZoglJ/NiewU\nsz6wL7K+PqpHjx49niL0xPQZh7e07NQD1+sHAI6f8Rmnmijp2poo5wq8LxkMUnwVUTqHiDwDYyhc\nBWnKJJkQyagNHTU+0jQNPtL5HIQI/9A3flApJRcXF9zc3NR+0QNVdYe1gc0qNSFJnkOpEfZosUeL\nUkOS5PasZqhE63u0XiOE/4gBqaZj/zyE3xT0xzLhenTdVh495A/Y8ehEqLZbxH7PVRy3k54PWmOi\nQE6FErjCYVamJqeBwBmzYjqdtB/PcrlkNrNtmn29FlyNrlqyuD6ug52gCWKt11zUhNh6z8o5+M53\n4z7l0xC/9iv4f/tPEtHswFdYu23DUEVRsN/vWkJeFKEQ/7wZ4OiqTvl+rDXz+ty+NQY/FKcFrLVu\nJ0u9r3Du0CrCYW9et4LvZhMaAZrQVRHRjgqIzaY96e+shakMn7/S4QuFUmNe84bnsDYn3+yQckie\nHxBix2Dg8T58fw3jIaN4hPMudMMK0b4WpQrQGlV4Rv606gWgZgqhgoXg/KQfCUHqc6wzMByilKKq\nKsoyZ3oZo0YzzDHUR22Kze/yT2mPHj169PjtoiemrxYIwHtkXUbuhyOisgTvMEmErkJNlFIG0KEy\nyg+opEc4TSoEVSQQCCIGrY9U6+AdPa8KzfOcl19+mUMtoY7HY5577jlGoxHOlVTVPcaEflIpB8Tx\nDVE0w2tP9VBh1gazNlR3FbawKDWuSesUEDh3rL2oGVKKVwxIVVXZaQGAU0F/cVRcj65bBfMhf8AM\n0w5xE9stV3Hc+jGXWqMVRFdRS670UqPkRafn9OLiou0cXa9XXF76Vr3dbiRXo6v2NJ1VO9qd0bpG\n6vLM57odjxHf9334y2vkj/4Q/qv+Su0BbTpHj+3G/W63Q+vi/CVgddx2gm6KDSZWHXV4BK1KuzYG\nNT8RObtzRNGCJniVJLKtkNpsNsxmobu1qiA/KGbprH0eN5u2kvX5SX9rLWpez5VmBsmU177hdSjp\n2K1XCB8BMVm2YTo9tpaBPId5etafWu4CoZ9MkAqU3oHzjA6+VWh3r3jST1BqzFQJrM0ovGdUT7Fm\nWcZw6FCLGamcUa0rymLfB6F69OjR4ynBU0VMhRAjIcQ/F0I4IcQ3P+HtnyyE+AdCiJUQYi+E+DEh\nxFtf4bGkEOJrhBC/IoQ4CiHeL4R4lxBi9Arv/1Q89u8U3taxJ+/wApQP/58kZlDPkPo0AZtSliVp\n6kmSWtEiwaQCWZY4W+IGCWWe8PAgHvORDgZQVRX39/dsNhucc6Rpys3NTa20haJ4rZd4rxEiIoou\nieNLBAq90egHHeqpVB2UMR6zMlQPFV6HDswkua0rjcDafU1Q9x2LwKMBKedMpze18cNuN5Lr0XXr\nxXzIH6gS1alYYrViEUUtsVppTSk88XXchnnMyhCpxVnn6IrFYtGe2TebVVthejzC8RB1ZkRzV3Zq\npOThwGUUtfVV+094A/67vhufJMhv/xb4lr9dF+IHYhVFvg0obTYbpDSdMFQiRq23dnVc4cfjU+ir\nLvtvPLWZs0SXJ78pOupMlk6nk/Z17XZZO2W/30PMqPXRbpoeVYAsY+Z9S7aPkW+bAGxmGQyvuXrt\nNdbmbO6XRNGEqqrI8/tzERtru/2pla0CyY5j1MDBYY8vPTMn20os7RwylR0LAYBSM5SMGEuLtUeq\nOGYwGNTKe8ZsoRDjKbKawjo7Ff336NGjR4/fUzxVxBT4z4Hr+n934rJCiE8EfhL414FvBL4OmAA/\nKIR42xMe628Cfx34BeAvAX8f+Crg+4QQ4il+7N8RXJ3KV16Bg6ghqlFE6j2lsGgXaqKMKZGyJE0j\nqBSVF4hEkGpNaSsKn2KKAdD1kQZlcM3DwwNaa6Io4vLysi2gNyajqu5wriD4SOc1wUwx+6CMutyB\nCGnq+DYmuU1CWrxW7/SDRq803gri+II4vmlP6NZmdYL/+BEDUlK6tiGgIYmrlWAxuGQYDUMwKV9S\nKkKKqpHrVivmSjE56+Y84gI5jevk98rWnliFc2V7Zm8K+He7dVfJrNJWydwWW0rhaHumsoyoqljU\nxf87ayk/57Pw3/Lt4Xvma78KfvjHOwGl4TCtFWlXL0O5ThhqlsxJVIJx5jSTWsudIstY1EQ4d45C\netQ0qJp6rVFi0npbrd22Cu3hcMC5gsnktAo1T0NQqTAFubRtwEtst61toDnpI8EVDqqY59/0RsDz\n0vvfx3x+CyiybE0cl61lYL2GRCVt48Cm2ITU/GKBUBIlCihK5N51elQBovnpe8keLEIIlJozkQJn\n9xRWM5hOEUKQ5zlSVqTXU+JkgskUfr/vT/o9evTo8RTgqSGmQojfD3w18Nde4V2+AZgBn+e9/0bv\n/bcBnw18GPjWRx7r04G/DHy39/7f8d7/j977vwJ8LfBW4E89jY/9LwaP9x7hwQuQNvxDLZQKNVGR\noKogkQlClHivSaIEYVMqPFKcek5LLUnVgOn05CNtfJ3H4xEpZev7DCnuvCaMIdks5YgkuUWpMbaw\n6HuNzSw4kEMZyOg0QntP6RxqFEiqmqmWxOh7jd5ohFfE8eXZlr3FmHWb4G8CUuP6hn8ekErTEJBq\niumXS8E8XXRUxSOGzjs9PDCTsk3Ob4zh4GzwnDbLRmtPFF1xOrPvOwX8ZZl1FEDlRkyT08a9js82\n7tdrUmtbMrcxBvMVX477mq9DWIv4k38c+WsfrNXj4G+dz2ckSYIxpg1DNf7WzUa0PtDCFOz0gfPG\n/KgsO35TRvIxv2lT9C9E2VFox2N3WoXKZKdH1U4n7Uk/zfPTSd/ZzoDB4up5JvMpRh+5//CHGAzm\neO/YbF5iPj8NWGUZTNNpS7K35Ta8cTZDjQQi3+ELy6jipALXJ/1zC4G3HqUGKDViqsCYHQfvmdYn\n/c1mw/xCwHxO5Gboh5yiPHDUx3/5H8kePXr06PEvjKeCmAohFPAdwP8NfM8T3j4Gvhh4j/f+55s/\n994fgP8BeIsQ4g+c/ZUvrf/7mx55qO8AcuBPP6WP/TuGr4VlJwAPqvaYKiXRToeaKJ08VhMFCbpe\ne8JWuDTBmRglFYMBHI/Hluh57xmNRm1S3rmq9pFu8N4iREIcXxPHF3gLeqkxK4M3HhEL4uuYeBFj\nRdiNf9CapdY8VBXae6JJRHKbhN11wOWO6q7CZAZBQpLcPDHBD5b5fM7Nzc1jAamqyrm+PhGehweY\nRBcdonhwJY++00SIdnIzs5ZdQ05rz6nd+rNC/ANw8oDu93u8P5xbPBmqaRvqWR1X2NHw1Dm6WjGC\nVqldaY3/b78B94XvRGw38EVfRLRzZ/7WVUelzbJtx76aH1RrIdhVu6DSNkx5s2Ho/cmy8KjfdO9r\ncgrGZAyHSXv6Xq9PanCeA2ZwCiqVm9NJf7dj5n07i3qMfUt+beZ4/k2fBMCHfvPXWSxeW6uXK6qq\nOyJQlt2w1VEfYTxGDAaooYdsh9u79ut0sJbKOdRAPXbSj6I5YxUhKUMv73BIFEUYYzge98xuUsRw\njD+O8dtdf9Lv0aNHj99jPBXEFPga4JMJZ3HxhLf/PiABfuoJb/vp+r8/8+zP/gBhCOlnzt/Re18C\nP1e//Wl87N85nK2nSME5h3LhH1WlFJXT+DhBurSuibKkaYQvwfgIUkFSVWhbUcqUmAHea7bbJev1\nujMjGhaCPFqv0fqh9pEqomhBklwjiDBbg77XuNKB/P/Ze9NY2db8vOv3DmuoedjD6dvXdtvd7niK\nGhTAQKLIDUEiDB6TQCzHQIwtxZYSIiAEgj+0hQwiwYSYIGyEgUR2sJ146iRKBBI05EMiHMCKg6fu\ndtSDu/vsvatWrbVqWMM78GGteqvq3HN7cre7b7se6dx97j511qpatffZz/r/nwH0XBPfxYhIUBjD\nY9tSOYeEQF6e2pZ122Lo4ozi+7gjF3QayOahwWwNUqav4+DfoNTLDVJluQm5p8Z05DSVkxBNlNc5\nhdnx4oOGzoWopa215M6gb3SIkrKFOCOnJVqbi7amKKrCmn21gmk8v8gc9dNppwHtY6SmSpEeDVjW\nwv/8o7iveQfi19+H/+Y/RMQkuOetLV5ootpfSAi8SULuaFZl2DS5yFOdSknUx2Tlzl40XwkTn2Wp\ndnFVRxJcVdsLXeskmoX0ga1oA9l+mUv/eN1eedOXEsUp201BmT8wmSwBT5Z9BK09/TCTzQYEJ7NV\nXuddpNN8jhpphK3w5QGxdxfyC++7r6FwE7G3CCHRespESqwrKIwJ6QPddN2S3M9IohHtymEPu2td\n6RVXXHHF5xCfc2IqhPgy4PuA7/Pef/B1Hvbm/uNvvOTPjp979YXHP3nv29d5/K0QQn8eHvtThu8a\nSVFKAQJ1zGSMIirlqRtxERMVx1HX9uQcQtmgQ62tRqHZ7VbUdY1Sivl8HmpEjSl7HemhO4+aEEX3\nKDXA7joCaXf9tHasiJ/FqKFiby3Pm4ZtP8kdKcV9HHMfRUyVQgKVczy2LVnbYoUnmkdEd9HZtM3S\nPrTY/bmDf0zn4N8HB38U6bMM1a5vfrPpXPPneaPajy7MSZum6CIHjmRxtSK1lmUUBV1m4bvJKaKb\n6LqdupgwxrFgOp2GtqbhsAkRpqsVLNIlkYxoXdtlnC4Wpxip3oB1JIzZYID46+/G3z1D/p//G/67\n/wRaLcLrlbIJRDjPc6C5II2pHAdNbSDC/S5e9KkAR+PVQZ30pmZjkGLSSydMqCyFjsTFcRuuY56L\nsNIv6xIzHobJc7LbMZRdrFPuLXrSfTv4reeVL3krAB947/sYjxdoHdE0W/I8ZzzmonVqGA3D69hU\nm240PJuhxxLKApu3jKUK1620FiFfttIfMooGaBx1W9AqxfAs23S2kIjphMjNaB+27Jsdtal/M9+W\nV1xxxRVXfJr4nBNT4IeA9wH/5cd5zNHt/rKfFtULjzn+/vV+srz4+M+nY3/q8N0yv6tWFKje/EQU\nYSJF20ikl0SRRYgWjUaQ0EQC0TZob/s4KYVtLHHsSNOU+/t7hsMh1h56HWkJeKQ8Zo9O8I2neWww\nuel0pKkkuovQ005H+tg0bIzBAYmU3EURMymRRYEoCsZCcB/HjJVC0BGlx7Zl07Z4JYiWUWdAigXe\nesymN1LVDq2nr+vgT9M0GLOqqmK1emI+vzQLYQYsB8uwLr5oieoZbNI0F+R0K9ypIaq0UMUoNeU4\nYRwO4xC3tF6vmU5NUAlsNoLlYIkSitrWnav9uIev60AYlRDUzlF88av4n/5pfJIg/4f/Dv78f9tH\nO3VEOElkCPvPsow0tRevb5bM0VLTuvZ0rt4Rpvb7MNUsjMEPJTKReOuxSfd1jAAAIABJREFUGxsi\npJzbo7W7OM9s5oNnzNRJqGLNqn6l3+/jZ2cr/SolvIdvvnsLSg1YP6xp6i3z+Q1CWLbbFfv9/qJ1\nareDWToL12zbbGEwQM5HyAjIcuzWhpX+9nyl36cCmPzo0p8xVQrnDxSmYjyZBPNc0xyYvGmESgew\nH+Lz7cl4dcUVV1xxxW8p9Cd+yGcPQog/AvwLwO/13n+8+pVjyGDykj9LX3jM8fe3L3ns8fH+7PGf\nT8cOeNe73hV+/853vpN3vvOdLz2hdd0PTw/gQLnuB3GrBT5NoOhiouLYEUUCX3usj/CxIGoarG1o\n4wTdpjRN58DuyJWhbXO8bwAQIkbrKVLGeOtp87ZzXANCC/RMI5MurD5rWw69pEAL0a2qleqYRll2\n7Algv0eORkzHY8ZKUVrL3lr2znFoGkZKMY4U8W2Mrboe+2PElI0teqqJ4jnOjbG2wLmq/7hDqSm3\nt7esVivatuXp6anXZ2rKstN/TqcJN4MbVodVaIm6md0gpOzEjllGPJuxSFPWbcvWWqSCwVx3Way5\nQS8GqMhh7Za2XTOZ3GCtpaoqsmzNYnHLei2pKlClYjlesjqs2Ld7lFBMlstupLrfo7RmORzy1Lbs\nrEX/s/80gx/67xF/9NsRf/rfha94O+pf+r1YW9K2GePxLW3bUtc1WZaxXN7QtuJEhOdLnvZPHMyB\nWMWM5vOOtRYFaRQxVoqttWTGcDvX+CePqx1ip9DDGcZsMCZnMrmjrmvatmW7LZjPZ8fDcHs7pbY1\nrWspfMV0NILtFpHnzG9uWLUthTHcTjV+ZUhIWCxeYbX6IL/xgQ/zli//MqbTCXm+ZbPZcH8fM5/r\ncPw47sxWq8OKsi5JVEI0m6H2Ne6hwT6WxKN5+PrZGMNdFKFnmqZucFW30ldDzTCasbMZtcnZqYTp\ndMpmsyHPc+7uYuK7GXyk5fC0QowqCl2EZIUrrrjiiitOeM973sN73vOez8qxP2cTUyFEQjcl/ZvA\ncyHElwshvhx4S/+QuRDibUKIGZ2DHV6+9j5+7nxd/hG6lXr0Oo9/8t6bs8d+vhw74F3velf49Xqk\nFI6ZWh5c91GZjvS1kQ4xUdZ2MVFpGuNrReNBRC60PdU+RhOhVIsQIOWh15E2vY503ulIRYQp+qll\n1etIZ50uVMSdjvShaTj0OtJpv7ZPjelqmY4VUmnaTSahI4APD8jtlln/+KHsviy31vLQNJTGIBP5\nuhFTWPlSB79zZdCdGmN4enoiSZo+caAjPoddFCpMjy1RbjLu3PN97VFyVu9ZWEsVc3KcbwzSji/c\n8/P5yT2/2axYLPzRHE99iC4MSnva06SxKIjqmsXZNLP59m/F/Qf/McI5xLd9K/KXPhBqUrtzzdFa\n0zRNMEMdJ46Hnb5w0DeR5Dz7aSplqGXdnOtNtxZh07M61uwiQgqqs1YoEV7PrtnRDJPTSn+7Pa30\nsUEycLd4E1IO+NgHPob3hjQdMxjEWFuSZRlJ4oM/rIuQSkKEVFZleCGQtwtkKqDYYtY1E63Dayn6\nlX54jwqDdx6tx0x1F4tVtAXJYBAMXpvNhtlthBiPiO2U5qFk1+66LNUrrrjiiisu8M53vvOCp3wm\n8blc5Q/oJo//KvBe4Nf6X/97/+d/pP/8vw38A7p1+O9+yXH+mf7j3z/73P8FKLrs0AAhRAr84y88\n9hc/j479KcE5h6fjNJ1lzIeJqU3js5ioBu8bIhUhfEIjuranCGi0oKklvvXEsUGIAue6Ia5S415H\n2tWItg9tF8ruQQ47oqhGnY70oZ8oerqmofs4ZgyECiljOp3jMWh0Pu8S8Y+ixbKE589Rux1zrbmL\nomAIKo86VWOQA/lxIqZODv7OILXH2ozlchkIyGq1QqnqwgVe5jq0RLWu7VqihumJMJYl6X5/EbfU\npF0mKx7adYvy05AF2gXwz0MRwHZ7crUXBbj2hYxTLTgXiabWXsRW2e9/F+4bvgVRFohv+AbUuj3T\ngZ5qS/f7PVW1C1n+ZQmY9ETqDllHuo9Czixj0UsVKufYS9clI3gwmUHJWahHhf1FhNR4bINModpH\np+zROscfr9tux8y5sNKvBwIRCe7v7onFkKbyPD1/xHvHZDJDqZamOVAUBWeyWIoCpsk0tHgVdQFJ\ngn7TBITHfWyNay5d+rVzqIEKOuWjS38QLxhKibF78rZiPp+fGbx2TN48QUUJvozx5f660r/iiiuu\n+C3G55KYboE/BPzBF359T//nf6v//3f38Up/HXinEOIdxwMIIcbAdwK/5r3/+bNj/wTdMPFPvnDO\n76IjxD92/IT3fvt5dOxPAx6PwOPxQqD7NblL44uYqCjy+MbjSULbE67BxgnSJzRNjdYVaRohRNzr\nSKd4c6oR9dYjYkF0FxHNI1o8T72O1HpPLAR3UcRca2RZwuMjFxVSd3fYSLOpNl19pqQjqeeVTUUB\nDw/oqmIZRdz1ffaOblr50MsELiKmxGXElBRpPz3twvCNWbFYzIL+s0sc2F20NW0yxXJwE/IzV/sV\nbRJdVJgOD4fL3NGhQI1O5FRyqi49EuKjzrWqNufJTa/NOE3jixipsRChSnTtHP7H/hLuH/tdiA/8\nI/jmP0jkx/3rq4D9RSqA9/WFGWqophepACwWQduqypJF1A3/S2txo5frTa3dMhjoQPDzfHPRCpWI\nyYk4ukOYzIo8Z666SWlhDGLWvV+3iztwCR/94CNCeLroryneb9ntdlTV4TyGlaqCxaCfzLY7KlMh\n5jPUNILWYD6So6Vk0p8ruPTnOtzA2INFyoh5PEHgKZsNDoJLvygK4sQS380YqDH18wpjmk7besUV\nV1xxxW8JPmfE1HtvvPc/5b3/6fNfwN/uH/L+/nPv6///PwJy4H8RQvxpIcT3AH8HeIUu8P782P+Q\nLrz+W4QQPyWE+E4hxA/QtTW9x3v/V154Op8Xx/5UYc2ZLNd3LVCyd+V71cVENU1NFLUkSYRvBK2X\nXduTMVS2phYpmm6qCjVpmnaOdy+7GtHHU42oXmji2xivBVmfR9r0JpeF1tzGMVFVdWX12/6HeV8h\n5YdDyrrkYffAvt2zb/c87h7ZVBuskt0k9eami2062rIfHojqmpso4iaKiIXAes+mlwxU3r1uxJTb\nCaLoNtSItu0Tk8kwOOfzPKeqiouM/fVKskhuSHWK9ZbVYdVNM89W7aOqYtJPMzNjcBN5qt9cW7Q8\n5a16fznNtLa8yDhN5QsZp5Mx5/EBc6VIZK/bjWN498/i3/Rm5N/9O/jv/B60mnMkjVHkmEwmr2uG\nmqeLIFfIm5LzkXHSNBevSc5ViFxy+y6BAboGqtlsGiaMTbMNyoDNpjvHkTjWgziMPM9d+oXoprJv\nfvWLELWgXBnqqgEEUjrG4wTnavI8Rwh7QeaF16cIqSrH4VGvLrsChGyLLSrG/Urfek9uTLfSn56C\n/r3zxHrKWEU435I1JWmaBpf+ZrNh9ixFDAfEdkLzULJttrT2ZSEcV1xxxRVXfKbx+eDK/6TgvX8/\n8HuAvwf8h8CfA0rg93vv/9eX/JU/Cfz7wNcAfxH414AfpJMOfD4f+9OD9xcB+42IiGSC9zXeN8RR\nhDAJtfcI0XYxUdJTNxIMxLEljjtNgNurl9aIylRSnulIBTBRivsoYmBtNyHdbDo2lCTdJHQ242Br\nHnYPlE2JxzOMhoyirrFp3+552D2QVzk20l2m6HJ52uNmGTw+kjQNt3HMUmv0MVbJGB6bhuZlEVOl\nxawdWt+EFXvbrhgOo4tA/O12c5Gxv1oJZvHygjBWkeC80mnStqeg+raFmQpTRrO2aLUM01rYhvOV\nZYlS+wvCOIleyDidz0+vvY+ROnbQ56+8Cf8zP4NPB8gf/Z/gz/5gn+sKxuSMRkkoGuhSAXw4VL6R\nLAfLQBwPwl7IByZCBBK8cbabNNJdR+mG4Ro6l19ESKVpGyJgd+WJOG6qDW42Pbn0+5V+7RzVAAbT\nAZPpHHfwfPSDBUJIvHekaUySmPAahsMTV99sYBSPwvXaVBtEkqCedXpg+6E1eB9W+nvnqKxFDc9W\n+nnXEjVPFkhg2xZUtmU2mwWt7n5fMvmiGVonuI3EHqprXekVV1xxxW8RxFU/9fkHIYT/ZN6Xtm74\nsb/wQ3z4g7+BH0/YbRv+xE/8MG9+euDXfvJnSb72X6Ztn0jTDcvJGIoJa61R+sBsX7CJPetqjjxo\nRqMVk7EnacZo38USyYEMgeUHaymsxfbPayAlU627QP+i6PbhEOojSVMa23Smm95AEquYWTIjUt3q\n2DpL2ZQc2kMvSBCM4hHjeIwU/Y69LDvWA900dTqFOGZvLeXZ84mFYKo1sZS4xp2kB1oQ3UQYl/fa\nWYHWM4xRZFmGc44kSZjPl2RZV9+qVMeND65g22wRdJmdg8Z1Bi4hYLFgoxT73uh1ozV+bfFt13al\nb0TfTuVQakTTRGw2G4ToygD2+5Sq6i7XzY1nXT3RupZEJdyki47k266L3kynPLUtjq4lavQTP4X6\nI38YLwT+p34W9/XvxNptb1S7YbXKaNuWNE2ZzZY8PnbEbjIBlXa6SYHodLXFtrvOUYS7ueGxl2VM\nlGJw6IP3lUDfKox9wnuLUlP2e8d2u0VrzWJxx9OTwPvuuu3citrWDPSAhY26rw+tqW5uWBvTXS80\nj7/ynH/4C7/A4HbIP/nPfQ1StbRtjtZTNpsG5xJGoxGTySxcjskERmPHw+4B5x2zZMYoHtH88kfx\nhxb9pjHqzUt21pIbg+rlJcJB89iAA73UqFSxqVZk7Y5IxLw6ekbbtqxWKwBub28pP9ZQP2zY6w2D\nL5szTWeM4/Fn4lv8iiuuuOILCkIIvPcvK0j6lPGGmZhe8XJ41+WYCi/AmzAx9WpIXddEkSWOJb72\ntD6CRBLVNa1tqERCLLqpKlTERAibdGSurxE1otORZmc60tsoYqE1qnfUczh0Ys3ptNORxhHZIeNp\n/0RjG5ToqjJvh7e0dcTz5/Cxj8Fhr5glc+5Gdwz0AI9n22x5vn3eVUOmCdzfd9PKvo+dp6euytM5\nnsUxM61f0yJlNV3+aSTwpnPvK6b9StpjzAalmpB1Wtc16/UTi4U7z9gnYco0mXamnmrDIZYnt36W\nMXcuGLTW1iKXulsrtx6bcVFdGsfuIoB/NGrCpDHLBIv0LOP0GPgvJez36N2OZRSFrM76D/9B7Pe+\nC+E94tu/DfmL/wgp05BGcCwYqKqK/b68aIaSdniWPZp1U81+XCyL4qLxyo5kyB+1uQuFAtaWjMeD\nvnjBsNvlFyv3aTxHCsnBHDgkKrRqpb1L3wGFdNx/6T2T8YRqveeD73tCCI1SaS950IBnt9vRNJdm\nNdPKi7QB4wz6S25ACMzDFl9VjM5kEIUxHbnuV/o2t3jnmSVLYqlpfcOmzonj+EKHPH1liEwTomZI\n9bjtigSc4Yorrrjiis8ersT0DQx7JKEehOgmjsfmJxEPLmOi2ojGe4SypNC1PRmFsF34vtYe0Qqk\nSDu3eSTYtC2PZzrSea8jjeu6I6Rl2Z18OOx0pKMRZbPlYffAwRwQCCbxhPvRPcoPwqbf2gufE/VB\nM08X3I/uLwjqw+6Bsi7x/fGZTk9ZSI+PkGWMvH9pi9TGGdSNRqbdir19ahFm2JMr0RcGbLm9vQ3u\n+aenR6ZTc7FqV3Z8QU6rVJ8il9ZrFs4FArS2BrU8q8TMxZl5qGAwkGfE5xTA3zRQ5Ooi8L+0hwvj\nVVzXF6kA9l3fi/sD/zpit0V84zeinpozbWsZakvLssT76sIMNdKzYPK6CN/f74nPNLQbY1Bn5iG3\nV33jVlco0NXUCvb7PVJWp4CFQl3UvtrpJLiYptaGlX49grd99VeAg4/88gepD3Eg2EK0jEaif84b\nlLIXetZEpSeCfciQoxh5M+1kHB9eg+tc+sdyhLDSP0ouim6lf5t08oa8LWlszXQ6PSPcBZMvmhHr\nFLf22KYlr/Lfou/uK6644orfnrgS0zc8PFIKHF3707H5yeoYKVu8b9BCd857LRBtjXQtNo6wRmNb\nSxRVJHGMNxohJXvteGga9i/oSIfOdRPLLOvYZRx3OtL5nL2tXqMjfTZ+xlBP2GwET0+dhlPrjm+d\n+5zyvCOoTaVZDBbcDe9IdYrzjrIpeb57zrbd4XsjFeNxR3QOB3h8ROT5S1ukVsYg5io4583a4A9x\nP8mUOHfAuQ03NzfEcYy1lqenJ4bD5jzuE9GOTw76Q0Y1PDnoRZax7CfJxnvWzqCXOvTD+62+0IGO\nxycdaJatmM9t4Nr77QsZp9JyPoocWhu64dfG4P7Sj+D+ia9FfOgD8I3fgjbD8LqkrEO8U5cNahgM\nzsxQSWeGqkzVkeAzDe3Ye9Kj6coZokUvvSgt0o1D8gBsg6N9s9kwmViU6l6LawanOlGz7XbwgMxz\nZr1zvrSWxdtuuH/lFdqD5Vf+719F6xlaT7C2JI4tSaL7a5UxmYThazeZTaah3aqoC9QrE0hibGnw\n6w2ql3cA5Nbizl36e4etLIlOmUTde7uqnvDeX2S2qtQRL0aM5ITqI1tqW7NvX9qJccUVV1xxxWcA\nV2L6Rob3OO+Ov0V4HyamLb5ve6Jve4pxMei+7amWCREpxlRIWRHLLuN0haF0DkenI72PYyZSIvK8\nm1IeRZiLBdze0kh/ctd7S6xi7oZ3zJI521K+ZtN/e+v7+tCS5dJxc9N5fc6M+LR1xHKw5HZ4S6IS\nnHcUdcHD7oGdOXQHevasI4cA+30X0l+WTPvnfCSKT22LncgubF2ALSy2kL1jvzMoWbtiuZwHwrha\nrYjj6pyrQTO5yAOtR2moLxXrNUshiISg9Z61N6ew+p3F7y+rS2ezEUmSYK3tA/hdiEVqDi9knMbq\nYkI7hZN8IIrwP/cz+Fe/GPnzfxe+448RnU2E0/TUCd+ZodwpGzRXIX6pbMrO4HWWaj+XMkgktuoy\n37RLA+gIcJIQrltRnCKxiqKbzB7lCbtYhDuRtCwZHFf6wvEVX/tV6EiTfWjFw2/kKDVByhRrN4zH\nAqVUXyBQhMrSwwGq6hTuv222tKJFvmnRr/R3cDgwPFvp58eV/uRspe89y2SGFjGVs5T1Gq01k55I\nbzYbpq+OULEmqlKq1YGiLrDu4xXVXXHFFVdc8eniSky/gGDxnRkJaARo3fQxUZLGC6R2JNZS+5ba\nx2GNL6VDWkntY1wq0EcdaRShdruOLe733ZRyMoH7e2wSBx1p69qX6kiPiVHHTbxSBx4fHyiKgrIs\neXh4oGlKbm/9hRH/SFBtE3MzvOGmzxe13pLXOc+3z9nbqpvy3d+/pkVKbbfcaB3Iz7ptqVICWXR7\nh808Wh/jpAzGrJjNxhcaQ9hd6jPbaSCn68OaejzgOIaU6zU3QgQH/UY6omXUkdPSIuoBSo0AT9uu\nmc8nYWVcluvQDlWWQPtCxunodB7WaxZSEh0bm+7v8D/3c/jhCPkTP4b/T34ArbtJqTEZ0+norIUq\nu2iGqvfxhYveTEZhJCnz/EJvakbiTG/qz6bABZPJKGh1jdkGfptvZCDZRV1gZv1Kf79n1huTaudw\nk4gv/eq3gYf3/r1fQcopWs/7JIUn5vNhSFEwpr64YZBETJJJeA1y1pnvXOVxT51u5LjSPzjHwVrU\nSIXXcnTp36SdHjhrd7Rmz3g8DjcP5bZg/OqMRKXYxxbTGvL6utK/4oorrvhs4EpM38C4zDHtWqCO\n5ici8L4miWKETajxCAwaRxsp2kbije/W+ImGWtF4gUi7kPK4aTp2WBQdIRoMOh3peEzRlEFHKoVk\nmky5H90j3eA1iVG3tzAatazXT32wvSWO4xDUXpYlz58/x5gtt7eexaJb9x+Toh4euqak2+EtN4Mb\nIhmFqKCH3QMH17y0RUo8PLBwLuglc2PYat+ZonoNqFlZtLwJusa2fWI8Ti6yTtu2OI8xRbbToG1c\nH9Y00xFHx5Rcr7npJ421c+TyFLtkcoNox6Hmswvgn59FFGUXhCvmhYzT6WmPLbKMm970VTtH8Y7f\nifsf/zJeCNT3fS/81b8VSPCxhepIHLfb/IJsKzcKK/fskHVxVf0+Pt7twip8YwzyTG9KFYca1hcj\npAaDNsRvNfuUYTTsNLptGSKqZFEw66tnc2P44nd8KZPFhGp/4Nd+/leJohuUmuD9AefWYYLZyRJc\n0AFnGYzjcbhxKdoCdde9Jybv7nIuVvrGnFb64rTSH+qYUTTrbmTqDO8t8/kcKSWHwwEx8CSzlJEc\ns/vwlspUHNrDZ+k7+4orrrjity+uxPQNDO891nqc84C/WOXLQYSUFmEEXiTYuGt7Eral1QnKp7Rt\njdY1sYyAhFaD9I40yzoh4rFG9PYWFgv2rssj3TbboCO9H92TyjFZJlitTjrSrnXUsttteHx8pGka\nlFIsFgtub29ZLpfc3t6SJEm/Bi54/vw51m65u3stQX18BG8S7kZ3LAdLtNQYZ8iqjMfdIxXmtS1S\n6zWTvnv+WFWZYdE3unPstx6zMijmPcnqVu2DgbjIOq2q7IKcajsLZGt9WNNOTzWfar3mpjdiHZyj\n1O7U2b4xKDcLmaDWZiwW8+Cgb5pNCOBfr2GkzjJOqwx/vChNg8xzlmfmnsO3fAPuXd/fvfff8W8i\n/5/3nZ1nE8xQu90Oa/dHySebDYz1PNSxbtqS80qnkTFh8rxx5kS0C4P0k2C4UqpiPB73IfUZ87kP\n8oSBmKGlprENpXbhWqVlGYL3M2t5+z/1VQgJH3nfhzisa5LkzQgRYcyaJDHhZibLOhIfjGMFLNJF\nSAKokxqxmOKtwOYH2O0YKhUkEBtjkFqegvc3XbTYTTJByZS9s+zqFUqpoNPN85zxq2N0pEmqiMOm\nJq/zIKW54oorrrjiM4MrMX0jo8/wFHjwArwPE1MRKZJE4WswXiNiTm1PJCivUaoFWrTXGB8jtCNZ\nrxFN0+17+0lko3ipjnQazykL+ZLmUU/bdqv6/X6PEILJZML9/T1xDE3znKZ5jlLtSwnqw8MDzu24\nu/McB3ht25G1x0cQNuV+dM8iXQTzy/qw5nH3SC1c56w6xjrlOYOy5DaKwoTxyRnkC459aSdnOtCc\nKGpDa9PhcOBwWNMPBclziOw8TDRX1Zp2dppo6jNyuneOXeK7pINjdak7VZe+2A4FJcPhiZzO4mUg\njet6c3LQHw5E220Iky+spf0zfwr3rd+OOOwR3/xNqOeHs677XZhq5nlOHDdBHbDZCOZnxG4nDOcV\nVfMzicJW+6A3tRuLCu1TO0ajOMgT9vv8zEUvmCXzkxZ0MgqvYWZMkCXIuxnP3vZmrLH8f3/vFxE2\nIYruAU9df5jpdHI2+S1fiJA6JQGUTQkTCdMZdtvHPxgTVvrV2Ur/GLzfZm2XPBEvEEKyMRXGlAyH\nw0CIi13J+JUJqRpgnzcYY68u/SuuuOKKzzCuxPQLAL7/r/QeCTgh8NIRxxGYKLQ9xd7RKELbUxQ1\nJInG14LaKwQNKXSr6WfPsGnC+rAOOlItdTAlNVXEw0M3EYPQPIqUBx4eHvqYIs9gMOD+/p7RKKVt\nVxjTrUm7zM2ctn1AqZabm5sLd3ye5zw8POD9jmfPuCCoq1UXDiDdgPvRPfN0jhKK1rWsDiue9k/U\nadSRuF7TGK3X3CoVSNBT2+JfcOxTpRfd8FLuA2msquql5PS4Bl9Va8xi1k2Y25Yoyy40mtWA07my\nbkp7nDYKUYbopbIsiaJdyFNdrwXz5Czj1Gwv6kTTug798Jm12B/5YdzX/m7ERz4M3/DN6DalMyrt\niSIbppqdy90eny7bQl9kgzaDOEgjxGZzMXUOelPjcYU407RumM9ngWRrfQjJC4dtzCju453aAt+P\nbEWeszyL+nrzO95ONNZsntZ87Fc+RqTv+pV+Q9t+5GKS7X1zMflN1CkJoJAFYpTg4yF2ZyHLkEJc\nRm71K32hBL7xmNIwjSJSPaf1grwpcK5hPj9JIfwQknHEiCG7jx44mAOVqT6b395XXHHFFb+tcCWm\nb2Acc0zB4z3IPirKKY2UhkhE4GMa5ZFtA7bBxgnSJTRNTRTVxCpCuJRGgbQNqRD44TDoSCtTBR3p\n3fAOTMrDQ0fMzptHh8NLHWkURdze3jKfT3GupG0f8b7p24kWRNFNbzzqCGrTPEdry+3t7WsI6vPn\nz4H9a7L2V6vul/ZdNNUsmYU++NVhRSGaTobQ/wX19MStEGE1vWpbmrEIq3ZbWPw2IopuOLrOoWC5\nPAXWV9UlOU38IpChp8OqI6e9wDI5I6eFtdRjgRz0lamZQ8tTdalShzDRLIqCwaAK0Uj5RrEc3Jwy\nTmku452sDSvxtVL4n/tp/Be/Bfn//n349u9Eq1n/9VIyHidniQAZi4UPLndTpRexWPYYvt80RGUZ\nSN3GGMRMhUgsqhQpU8AB5cX6ezq1x4hUIjclkhHGGUptA/FVec6iLxBoYs0rX/NWpBa8/5d/FZMZ\nkuRLAIUxK4Q4XJDr4dAdlQFsNoSblMY2HJIDTMaYSuKbBsqSgVLh/c+NQUhxSlDYWlzjWMZDlBxS\nWkvdZgghQixWURSMXh0TRZp4KziUhrzKuTboXXHFFVd8ZnAlpm9gHH8UStkZfHTfSmOVIoroDD4+\nRqSSqGlC21MkYoRogIbIRxhiROSJrKVyNQ8mZ9t0lvpzHel6LYL0tKvShMXCsd1e6kjn8zm3t7dI\nWdM0D6EKVKkJWt/h9xFuq4j0LVovzwjqhqZ5CAR1uVwSRVFPojY8Pj4g5eEia7+uu+npagWxGHE/\numeaTMPaeNUWuNtTaKpYrVhYexEiv0t8lz3axzvZjeiJ83ENnrNczl9KTjebjpwec1dXVXYip01D\nutmE3M7cGMxUBgmBWTu0OmWqRlETjFebTcZ43AQdZZnry4zTiNes20PQ/3KB/7l348cT5M/8VXjX\nfx5kCm2bMZ9Pg+nqRTNUzCToWrN6c5rO7nYMm+akN/X2pJ0tDNJ+iMwrAAAgAElEQVRPA8mOY3sW\nIZWFcP88h0l0WunXozREBCS7XZj8zt/6xQzuR1T1gV//5ffjd4o4fhMAdf0hRqM0kOss6/S/IQt2\nL0IM1l7uMZGF8RS7p9v5ty2zs5X+3lpkLC/isCIE03gKImLTNhizIU3TEL2V77dMng0ZqAHtRw+0\n1lLUxWfr2/yKK6644rcVrsT0DQzXR0MJ70B4pDuG62uiKEI0ES0CIU3X9iQdVavwLcSxIY4FNILW\nawQNyrXk1FhvSVTnhD/Xkdb1SUd6fw9tu73QkY7HY+7v70kSQds+9O1KHikHxPE9ohnSPrTY0mK3\nluZ5g9/pFwiqCQQ1ijx3d3eBoBpjyLKMx8cHlDrw7NklQe3KoASJGHM7vA3r76dqjVnOOa90mlTV\nxao9V64zRSmBqxxm5dHy5ixM/rXk9LyGM/XLQOhW9Qa76LUHdc2wKC6mjWZ6WoWbte/JaafTTFN/\nZiJaM522IbT+sH0h43QQn2KkViuWUqL7LNXN7/wq/I/+FbyUqO//PuSP/42LRICj6Wq/39O223OO\nyzReBLNSbvcXIf9zCOcotQvyBLtxZ3rTLZPJIOSPdpmq3dPclWfxTm3ZpQD0rHh8ZrS6+Yq3IYeS\n3/jAB9k/7VHuJqz06/ojF+v1w2EbbhSKAqSPQ6zXNt7iI431Kd50Nn4JzPv3oziu9Cf61Aq1MUy1\nJonmVB72Zoe1B2azWWgJM0NBMlRM/IDy+YFdu6OxzWfvm/2KK6644rcJrsT0DQ4POE8fFXVc5Suw\nIHxMLT3CNgjbYOMYbyLapu1ionSMcAm18AjXgKnxacI4HnMzvKE+XOpIx+NjHmnFw0OXR+qcCzrS\n8fhSRypETBTdodyM9sliNgYcyER2K23PSwjqop9UGozJLgjqYrFAax0I6tPTI1pXPHvWDQ/P20q3\nRcTN4C5Ubz7tn6jGaUeyenv9oChC7FLlHGss8sKxb1EsenJqeZGc1vX6IuJpwImcPtVZR077JzUq\nitOU1lrcQoXz2A1n2taS0UiFiWOer5nNTutwW72QcTodn6bBfYzUcRq4/Vd+P/4//bMAiO/6o8if\n/9Uzol1eRDxFUX0yQ2WSedJNHXftrpvO9o4skWUse0K/d452LLrXYTyukKGy9BghddSDDofNkacj\n2lO8U07FuVB0DkRCMH3TLembbiD2vPeXfxWbWyL1Su/S32DtZUSVlM15PwDjeNKdQ1m2agvjMaZW\nfbtAQXq20l+3LdDn3PZxWH7vmOkYrSbkxtG2G8CdXtNux/CVIZHWJDkcdo5Ntbmu9K+44oorfpO4\nEtM3MKyxwZmPB+W6H7BWKTAe42NcLFB921O3xk/wvkaImogI42N8DFHbYnwLSYIwg4sI0zQ96kgN\nWbZivV5jjHlBR1rQtk8XOlItl9gNtKsW33qEFt3KfKFgpojuo0uC+tDgdxGRvnspQY1juL+/DwS1\nbVvW6zVPT49EUfWattLVk2Sibi7yQEvtLpztcZYFU1TrPU+2xS/POtVXBmnnn5CcdgEAggHLQLpW\nbX4ip4cDk+02VIpmxuAXCqE7443bKLTuiJYxOdPpaV1dFCtmM3fKHrUvZJzOT9IBtdmw7PWaW2up\n/r1/B/dvfAeiqpDf8i3oj+7Cyl3rOkgHsixjPDbBDLUro2CGyqucdjwMDQg6z8PEcdPXvh71pqIZ\nhmsl5T5Mf7tWqO5rtShgohdBM1ul+tSi1RNfCbzyNb8Dm0iy/InsMcMX3Y1Ol5zwhNaGyWRy9vxd\neP5F0elNBYI6raltjYsnOEN3p1XXzLUO09+j3vRYv2oKQ+oEAz3Ei5TCGto2I47jUMKQ1wfGtwlD\nNaB+XtEY0yUCXHHFFVdc8WnjSkzfwPAXHx26zzB1SiHRtF4htCO1lhpD7SKwgji2RJFAtILGRwga\npDe0kabII8o8ChGmNzcwnzt2u5zHx0fqukZK+RId6YELHekuon1suzB2CWqqUHcRhXI8ti2PbUvm\nLX6miO6iENsTCOr+9QjqI3EsuL+/D+vcI0Fdr59Ikppnz0JUJuu1IHZzZsks6DMzt8ff3JzI3NMT\nt5xVfRqDmUvkUIZYJNnOQy7oy8jpKZ1KMOQmTGpXpsAdyel+z3S3O8vuNLBQIfDfFfqsUWnDfD4K\nEobdbs102r3TeQ4pZxmn9abLOO2ns3EfIwVdR7z54f8G93u+DvH8o/D134SqYo7SgcFAhulslq0v\nzFC2HoQygazenF7H4cDgcDi9Dm9R004fanKDEjOOutnhUIXmqcNhE6aaRa4uWqfsZByIr9psmGtN\nOkyZvuVV7EDw/ve/F9962HWxXtbuaNuM0Sg9M8ptziWxmEYzS2cIJShViZUC6/uWsM0G4f1F2sDB\nWmRyqTedaU2kpxy8oLE1xnTmruP70g4FSSqYthHlY8Ou2dHa9rP4XX/FFVdc8YWNKzF9I8N78L4j\nph6E6cxPTimkSbqYKN+gvMVECtsqXOuIoopYR2BjGgGSFmkbKhKETc8jTDFmx8PDA7t+nz8ej3n2\n7NlLdKTDSx3ptiPJatQRz0MKD03DwTkE3Rde7RxPbUuGxc9fIKjlGUGN7tD6FK9kzJqmeSRJJM+e\nPWM2mwU942q1Yr1+Yjyuz3I0od2PWA6WIavzqdlgl4vAYMVqxdKYi4nmYSw6wuW7XnXRzF6XnDbN\nJTkdiZvgQF+ZEjefBcY0r6qwRs68RSxOE0e/TVBqwjHsf7E4GZXqOgvnyDKY6LOM07Y4xWNttwyq\nKkgH1kJgf+on8V/6VuQv/gLi274DLY8RTznT6TAQraLILsxQqZgFkp01RWeGAihLZs6F+K0y8ie9\naXZeWZozm01CHmwUHUIrlDkML+K2/GIRWqfS3gz1Rb/jrZg4pnJ7PviBD0ClkWaKlEOsLTBmHSKq\nupuEXTBbbTaQqiGpTmEMRVPgogGOqLtryXMiKS8ipIxz6KkOGmByy1hrlJqzsQ5rtzjXhNiqXVWR\n3sXEOiLaWPb7bqV/xRVXXHHFp4crMX2Dwx/npk6gfUcGrdIIKbExqKaLiWpkghYp1tZIWRGLCOtj\nXOTRbYv1LQdSUj1gOgWlah4eHsjzHOccaZpyd3fX60ifXqIjnb5GRxrdRbRjwaM1lNbigYGU3EcR\nz6KIaZ9f+QkJ6vMGv4/PCKq6IKhpqri/v38NQfX+5Djf76HcJCyTu0DmHqsVzXzCuThxejiE0Pqt\ntRSJD9NAm1tEfUlOF4vZBTk9mog2G8FI3oRzrez2RE7LkkVdkx5d9Fjk4pQKwH4Qmqg6o1L3uqqq\nwpjT1HG9FkyjU8ZpZnecu4Am1p4I8GKOe/dfx09nyL/xs4g/8/1BD9oR4PPXUV6YoWbx6RwlTUgD\nEJsNi7OWq3rESW9a6lCL6n0RKkWLoouQOgbjD+U8EPisLS4yWidtyzDSfMlXv52tt3z4ox+gbVrY\nDpGM8d5h7QHvy4uorThug9kqy7qVvpYaMzTsmh1GjsPkl8OB4ZneNDMG73230u9vFoY1xCrGiWGX\n42o2F61QhTOM5oqRSKkfamrThlSLK6644oorPjVciekbGNZa8C6s9JXpl/pSYYTo2p6spXbdNBQj\n0NqglEcaSeNjhGhRztAoiTUa6QX7/ZrVahV0pDc3NywWM7wvex1p+0npSNfOkPWu51gIbqOIRdOg\nHh4QDw+MD4dPTFCTFwjqISaK7l9DUNt2xWCgub+/ZzqdhvrN3e6J5dKF2KVsrZjq21O8037FbqBP\npqiyZNiboo4moix2yFlPTotLcipEcUHq2vZETvONvCCna7/Hn5mvFnUdIp4yYVFneZqiHodsUO83\n4RxdAkIRiFe+USzSmzAJLuULrU39tTfes/nKt+N/7MfxSiH/i/8M+Zd/NpzDuTxMAcuyROvq0gx1\nFlVVDaIwadZZdnK4WwvHfNO9g3rUJy20JIkJkoHtNjvzOwkWaUd8K1NR+OoiBWDhPc9efcZouWCP\n4dc/8H4ECvIUJaf9BLNCa3ORbzqb+WC22u8k83SOHEl2bkfTWqwedefIc7D2Qm9aWItQl/m2EyRa\nj9k6hXUtxuSMRqOgA26GgnQgmdSKfGUo6xLTx7ddccUVV1zxyeNKTN/A8N7jvUcgcEKg7XFiqmic\nR0iDdpZGC9pW9W1PFUnfCNUgOn2pbahIkV5TFE80TYWUktlsxu3tLUo1n5SOVM806i6i7HWkjfco\nIVhoza33xE9Pp2R+56AsPy5BXXuDWyii2zOCWrxIUGc9QW1o2xVtu2I47ExZxxX4ZvPIdNpc6E4T\ntwzu9rzO2aj20hS1XnOnVCArWeS6UHm650A1/ZTI6TF+acUe30/aRFGwbJpAHDNpUTPVkdPCIprp\n2XS2YD6fBZd7HO9OAfyZDi76sinZp+rSTCRlqGMt/sXfh/9zf747/3d/F/Lv/lIgj1LuwhQwyzJG\nozaYifZlfKEJNbNJKC5IdztGx8QBTnpTW9heb9rpWSeTNEy0vS9DK1RZqJA9um22HCIRptgiy1gq\nxVvf8ZW0QvLR7GMUuwLhR7hSotQIaw99eUAa9KxFsQnD47IE6RNG0QgxEeR1jjEx/mysKoQIaQNH\nvakaqKAzVrkjFQKppuQWnNtjbVeKIKXkYAzxXJDoiCi37A/+utK/4oorrvg0cCWmb2SEaBoHCFSf\nY+q0xmqFaLuYKKNTpE9o286JfVrj06/xO8c+rSBJPGmacn9/T5q+TEf6DFG/XEe6TzwPTcO+15FO\nlOJeCAZ53iXgnyfz392F5p/XI6iN96zaljUfj6AmaH33GoLaddDPz4LYV6TpLuhO8xzsYcIiXXZh\n7O2eJ1t2Yfy9CFKtVh2hPk4cj+RUgCvdJySn4VwbyfiMnK5FdUlO+7741ns22l0QO2FmId9Vyt1Z\nA1HOYHDSa+6K5MJFX48HYaope3In6SKe9n/8u3Hf+ccQTYP8A38A/aGCzqxUkSQuBMln2ZrZzIWt\nt29GQROa1XmnCe3X7jNjwnUqIn9mHKMP96ePeJoGcj0aNeHYzSEOGa2bakMzHoQYLJ1lvHk+5f4t\nb+ZgLO/70PuQWiLaMW6v+m8FhzFZ0JseDges3Yf3IMtgEk9JhglOO8p9idWTYIAjz9Ev05vOTvFh\nox1oqWnFiNo5jMn7XN/ueZfCM5rCxMUcHmuqtjNDXXHFFVdc8cnjSkzfwLDW9rmJAuEdql8dOqlQ\nA03cNLSuoSJGOYXWBiEMyilaHyNUN1FtlaA1Gtc60tQzGEQYs8KYzUt0pAaTfxI6Uq2Z7HaIp6cu\nXLRP5m9vFqxs18hUT0efNkEVsQgE1TyaC4IKEu8brF0zn49CpFCe571m04dc0F2eskhuQ43lY7Wm\nXc45ltXL9Zqbtj1NNV8kp4fXJ6fGnJNTxVjenPSgquHoZJJ5zo1zaCFovCeP/cl0tbEod0wmaIii\n6oycbhiP67CyrneDy4zT2SSQ7KiPeDrWozb/9Z/Hfd3vQzw9wNd/E/qgOeaoTiZJcLqXZXbud2Ig\n5kGasLE7zp1GCyGC/KE+5pu2Hr+NQ7i/ELuzCKmM+dwflQ1IOwzB+Nkh62KwjlPZ7Zav/pqvQA8S\nnmcZH9l8BKUG+L1C2ATvW7y3eF+E65PnOcOhOZr9KYquFUpNFXuz55BX+Nn8ZOPv9abDM70pECpL\nOXiGrUCpAaXrIrHaNmMwGHQyBe+pYkeaCqaVJM8cZVNi3bE6+Iorrrjiik+EKzF9A+Poxsd3Pzel\nOzM/tS2J9zTSUxuNNxBFNUmioVE0SISvEa5f4zuNlAbvC6Qsf/M60sfHUzL/aIS7u2WjWh73j9S2\ny5VcHVY8tfmnRVD9UhPddATVWx8IKlVKFN0HImTMmsHAs1wuwyRtu31isbBhWLZZR8yiu1M4/mHF\nYTLgyCpFnnNzrgfVDqayI6fbIzlNPzlyqm6CnjJTbdCDyizjxrnTyj3xp9iizTHoX+FcRRw3F+Ru\nMmkD0aY5yzitslMDVVWRbrdM+4lgJgTmr/04/st/B/KX/yHiW/8ttBgDx6iqSWhWqqr8wtQ1i0/p\nBtvIh/apY8wT0N2kHAn83iHaSZhoDwYE4ns4bAK3zTIYyOlrY7COaQbW8Navejvewy+993341KPk\nrLtR8t15nauJIhOyRtfrNfP56UakrTWz8QyRii6fdc+FphXTR0Sd5ZtKLYPeNNl6tAPUhK0TeN9g\nzDYY72op0WNLqiJUbtjtHXmdfxb/Fbjiiiuu+MLClZi+0dG3Pnnn0X3zk9EKZQ24BhPFCBdj2q7t\nKRYRngQbgTIt3tYcSBBGEEV7kgRAotT0dXWk+pPVkaYp/vaW7UDxcHhi3+4RCCbxhFkyC1PKI0Gt\npsNPSFBVP1VctS1r8VqCanKDeTTIZtpPT7uKTKV23Nx01aZt25Jlj0yn9cm9vZak/uaU21llFAmd\ny10IRFmyPBwCOd1E/jQ53TrYTz4uOQ35nRvNRJ/MSpk2QU+pNhtuvD81Nw36lbgDs3ZI5pzyQT3D\n4RDnHGW5Dk73soTInjJOV80luRtVVdCDZtMp9t3vxs8XyL/9NxF/6vuCk965DYvFPJjIlNpfGK5m\nSd+6VJc0kz58v21JyzJEbm2wyDNZguT0fsxmw3CjIOXhLGmgSwHQUp+mskexaJ7zla8+Y7KYUVcN\nv/jr70UPEqQfYHND989Zd/zxODnLgM0D+c1zSNWIwWKAw5FlGS5KOX8CAkK+6d65Tm86VF0ZhIPR\n1iOFpBZTjKeXutgwqd0qwXBkmRrNftWybyoO7eGz++/AFVdcccUXCK7E9I2Ms/rD80pSqzXaGFzf\n9qR9hBANQrRERDQuQkhDZA2NEhgX9RPVA2maoPUEUQ9enkf6ejrSzeY1OtLDZMBDvaaoC5x3DPSA\nu+E9NBNsNeImvb8gqOvDmsdm83EJ6r3WzPoa0U9EUN0mRqub0HTkfcZyOQ3u8PV6RRSdopHyHNxh\nxiyZByPOWlShz11styz3+1PMk+pd6ALcziE+zuTU2hfIqTqR003ig1lJZxk3cNKDjkUgRC4TaHms\nLt0xHmvSNMVay3a7Yjrt3v+igKFYhhimtSlP+aNFwcyY0/T3bV+G+/GfxGuN/MEfQP7IXzszXJUX\na/HRqAma1mqbnskGNtjZ9FQi0Bu6rPeU8Ylcu/xUWep9yXTaWfM3mw3DYRvMaVnWOfUvkgb6sbPc\nbPhd7/gqhJJ86AMf5smV6HiGbwWmbBAipovZyoPetEsyOISUgSyD5XiJGilqU5M/5J0k4bjzzzK0\nlBftVsY59Fx324JWEO89QkaUPuUYuZUkSTeplZKDNgxSyeTQfV3ldY7z7rP5r8EVV1xxxRcErsT0\nDQzXa+A6lSlIfwzYl6Q4agyVjcAI4rgliiS+ETReI2gQtu1D9TXQoJQhEjFmpV5XR1pYi+MlOtK6\nvtCRPtmSrMqw3hLJiNvhLbFb8PSoKMsuw/LxUWAOlwS1de0nJKij/f7jElS91KFNya5A+ZtAtoxZ\nM53GIVKqLEva9rLxaJ8PmSenlfujKzviJQRitwvk1AHZGTm1W4vYn8jpizmn1q6P/JMyj5joZajl\nzFMRVuJRlrEU4uQQn4hQkWozgVZzOnJaMJ0mwYm+36+YTHxYuU/0mabVHzhP5196f6rj/Oe/Dv9f\n/UUAxB//buT/8Q/ONK11kA1sNhmzmQ3XSbQTUp1ivSUz5cVkcwkoIaicoxrRVa+2HnaD8F5EUX2x\ncp/NbCC+Za5ZnEdUDeOg+11i+bK3vIpwnl/8hV+CuUapMW5vsVV7dv0vifV4bIJ8Y7eVLO+W/z97\n7xYrW5ee5T1jjHmoWTVrVtVaa+9N027ajgPGJoAwCYqQcpcLLkiQHWJCAkQQkUSRQiTLIgIigpJY\nCpGSG3IgkMhSHEQA0QGUmyQKUpASLiwbFA6ddmNM2+1D77Wqap7PY4xcjFmjav3/3+CA2/Ru13dl\nX/Tcc9Vee/3v+r73fV4IoG5q2qK7Uhn63pUULH7TS+ECXP2mmxbEaDFyQ2fU0kxWkGWuEGGKY9Rq\nYC1DZD65k35/P+nf5z73uc8/aO7C9AMee/t/Gwj0IlRliBWaOQyxc8g0ujN+rCIwK+YQ5DxiTU8v\nYoQWhGFHpAJ0IWAWX9VHGn41H2maop8evY901CNKKParPVv1hvwY+Qt/HLsFIbj/+fOzcM1M8c+P\nQD1LjXm86bs/OfbotVGpII4nDoeDF41V9cx+P3thVJwiturJbx1fTM28z/xJ/KFprlB25birF0D+\nRZw6WsJrcWrt2W/uqjxiGzgqQDM1lGvlv8ZowTz5zvsM30akz+om6V6y262dGJomxvG6mc3Pkl10\n3cyWoXmFYXqU8mob+Lf+Dcy/8/sQ04T8nn+Z4O+dudoGpN/MluXJd95XFazFwdMGCobrZvN8Zn95\nf2Nefz5TxoUCkKbBDTnhxMOD9dpwaGOPqDp3Z6Ys9WGu7/j0O9JkRZOXfOEnv0y43yEImIsOq5Xf\nkofh5C0PTlhbD/dXNmH3tMNiOb2cmI191WzFOH7cbxpK1wwlBOvKYo2lFVuMdQgpY3r2+8UCEUiS\nZCKbFE1uaMaOfu6/Vj8O7nOf+9znG2LuwvQDHmst5kadqoVjaqRknCd6EaMIgREhRkJCJiKE0kRG\nMykYdYgZLVHUE4sIyQq5lp/oI90HAW+M+UQfaRWLj/lID9FbunLN6eQupGEIDw+W1aohCGrevLFe\noLYtvLwIxmbD4+rdP7JAPemZJgOZLtiiUmPLFYE6cBFcUpY8Ph78xvF8fiZNu1deypV5uoaidMW0\nv56sD3XtxWkuX4tTmu1HxGnmPZXwWpxm4YO3DlTrwGOe4vOZ/bI5rYxh3EmfdDd56IW2g+Nfw0rW\n5v75xTlgF10Zoe36CsdX57Nnd9Za0/8X/xnmn/9NiPMR/sXvImgE181s4sVv3+evwlBZePDiuk0C\nj3mKi+Kr+E0NgbxUlpbs91vv/S2K022zKmLesA7XTjz2Z8xhD1IS6pnv+OZPowT8xBd+jJOYCNM9\nWBhPBVJmXPym2+3q5t3L1wipdEeyddaO5595xkbRq4ICYS2HpWyhNYZWa9RGIVeSFZKgNCAUrQ+O\nFYShcpvgMKRXE0ks2HbWnfT7YiFp3Oc+97nPfT5p7sL0gx7rK0mtsKjlP3hGSWahGWwEsyAMJ6JI\nICbpz/jMzn8qbQhMKDUR2RDB6mM+0nTxka7znFcq88ZHWo0VFksSJDwlb9HdluNR+Av/fg9p2lEU\nrua0LEteXr6CUhVPT4bNUsTTdfD+PYzNhof4HfvV/h9KoF6ERL6y1y763qCPikA8ejaoMScOh40/\nKef5GaWuKfSqEsjhkVgtTVG6cjWmyz37UFVXvJDUmMM1if5anJYfE6eX16+LmO0i7qqppk4jLvT8\nJM/ZSffPtDSaeScRSmBHiy1X3rNpTMF+v/WeSilLD7Cvi9iHlYp+CZktYaXoJklfAOOf+VOYb/t2\n5Bc+j/iefx1lE+DKIL28vxD19eMvwlfPny7w/WFgexMYKyPr/bI6V/7dtXZBq4uw7rrCB+WLwtWW\nRipySf2p9JaBTz9kvNumzP3A3/n836HfrpDhCjtrprxZhLujDBwOOx/kCoLOfzZ5Do9vH51w7SfO\n+dkJ08U2wOn0yXzTvbOLbGeJbjQDMTMxjgSRk2WZE9tJggxbNlZBbahbfU/p3+c+97nP32fuwvQD\nHjNf+IjOZRrM1+YnGyn0HGBn69L4KsLqiEmCnAcwvaspnfBnfGMCzhhqaV/5SLNP8pE+7F/5SCMV\n8Zg8EczOR9q27s3SFA6HkbZ94Xw+o7UmDEPiOF4S5RXH43uEKHnzxpCmblvWdfD8DH21/ocSqE9h\n6E+wR6nRD+oajDoa5HjwffTzfGaz4XqCbRqG4YX9/gqXp3tgpRxc/jiVDLvU+xH3VcVm8SLmYhGn\nl1rOeuvRVR8Vp1LmXv80xYpt6EJX5VjRbFc+jLO+EaeF1ei9E6emN9gqWb4OgxCVB9g3TU0U1d6a\n0FUJWZz5sNKteEyqiu1ls7nZoP/iX8Q+PCH/j/8V+b1/2D8fSnY7d1qvqor1evDPH5rEEw1OQ47Z\nXyteD1p7DFaX3vpN1wgRLb8gXCtRm6bBmNpvNi9J/UtILqf3W81v+/RbUil5/omf4n1RwcMBpGBu\na+jDm1rX137TLNPeMtD1ksd3jw78f6yp+9qJ34WhSlmSKOVpBqd5BuH8pkoK1h2YyVCz8RaCea7d\n1yMlbSCJ44FdD3VhqYeWYR6+1j8e7nOf+9zng5y7MP2AZ0GY4qJPIJfmJ60UMwHSRhgzIuVARMhs\nI1Ca0GhmYRlNtASjeiJCRhujY0Fw8ZEOw8d9pG+eOMvxlY/0sDqwEU/kx4iqcmIiSeDxUaP1mePx\nhXEcUUqx3+85HBJ2u4inp0dWq9XSn17z/PwVrC14etKkV93H8zN05ZpD9MkCtdsmHxOowfv3PM2z\nD6/kVtPuBHJzPe1Tb1DqWpkZBC2Pjwdfm1lVrsr0ArA37YGVWs7Kc0W/Tz0jdFeWr8Sp3kknTjsD\ndfpKnN5uNoXIfRq9KRLScBFPY0mbJX6zuSkKtpfn48Tp5fmiTX3gR8rai8e6LlmvO//+c5tez+Jj\n4QD2QkDbsu37q2f2Wz6L/rN/DhuGyP/mj6H++J/2AjIIOrIs89vlLJu9eA/0zm82z7px4hGQC3z/\n4jfVO+ktD1LvfNBKiMaL07IsiaL+Fc7rktRvp5YmlpAk7A8Zn9qlJMCX/sb/S4lAZc7i0OdHpLk+\nP4pmT2RwcH/3bV2WQBBz2B3AQP4+Z7QzrzwFXUemFOGlBWyekZFEpYpUKERhmA30wn3NWldIaV3F\na5Iw0LOOIL2c9If7Sf8+97nPfT5p7sL0Ax73HzYL1mLNtflJK8UwSaSRBIHrOxeTYLAhUria0l7F\nCBNgzEgQuDP+aGJkIsnm2flIy/LjPtL2mW7uvI90H76lzimSA0cAACAASURBVBPOZyeuoggeHy1K\nlRyP75fNoGS73fL4mBIEFVqXaF1h7ZksC3h6eiRJEqy1NE3Dy8t7rC14fNRsl6v5MMDLixOo+9AJ\n1Avr8tyfXwvUOHY98XnOvmk4KOWZlHliEXvlRZ05hSjx4MWLtTmPj5kP5BTFkSRpfJpbN3vWwdJO\nNFV02dpvHndFQbqIx0KaV+LxVpy6zeZ22Zy2KFV4cdqVa9LgIk6X5y9/+LYsr89Hw62ntdveiMfW\nd95XVc5mM3jAvBz3Pkl/nMvrZrMs2d80XOX/3G/E/pd/HADxvb8P9Zf/mt8Grlb6RuBdw1BlCRv5\n4EkAZWiuYa48Z6ucx7RAI7aL37QwCwLL+X6DYPTvfj6fSdPJU5yqIvS1q+VQutrVMORX/BOfIZ0m\nhnPB+5/+ClW8QW4CrBmYTg1KXigGV7/pOI6MY3lrJ2W137CJN5jecMyPaCW59RQIrb3ftFv8psE2\nQMaSDMlczjRGgXQs2Hk+s16vieOYOU1BVGyNxDaWqpmpxupr98PhPve5z30+0LkL0w95lo2LXU75\nl/CTVhK9hJrcGT8EHbozvhlBD3Q29tvSUCqMDZmVQDUl8fn8D/SRPq7eMjXORzpNTpsdDpAkDefz\nV6jrGoD1es3T04447tG68BWnlxOr1jXWnthuJW/ePLJe0lAXgap1zuPjTJZdBerx6JBOmXr7yQI1\nW3swPm1LcjrxRgh/2j+p5bQfLin3I8jp4eadcna7yGOS6rogisrL8pKxylgrx/DM55p2dxWPWZ6z\nFcKJR2GYb8Vp9XFxKoSgbRuUKi62UvpqwzZ0afF8LOl3Gy9+s1tPq9BOZC+QfznuFu/sRBT1N+/v\nBN5l+Rfpw5VxaltfKyqKgocl6DZaS/m7fyfm3/s+xDwjf/tvQ33xmct2ebsNPcC+685XFmwuyW7C\nVv028Un6tGk8ZquMjPebzmdLEFz4rM67evH8ns8OI3WtXb3hpw458z4jXMV85pvesR5HfubzP8Zs\nodtsEZFkngpMIQiCC8Wg8HaHuq4Jw/7KNy0Eu0fnZ51yt5FnvfaMWU4nAiFe+U2nxW8aKclqFMyt\npmbtPczzXLDf75FhyBAFhGHHboC6ElR9w6SnX4ifFPe5z33u88HMXZh+wKON9c1PcG1+MkohidB6\nIghc29NsIwgMgdbMwjDaGLv4S2MRMdkYwUg8jgilYL9nfNh93Ee6eoOaDhxfFH3vxGKWQZb1VJUL\nNhljiOOYx8cDm41B6/NScRqg5B7Z7xFdRiCfFqEGWjcY41BHT08PXqC2bcvz83um6czDw0SWXa1/\npxM0+ScL1Pe2ZnzYeZ9mcDzyNI5e1OVW0+6Fg79b0IVL0l8CM1pXJMns24/atkap3G/vxnrLJnCe\nzXxaPKGLANsWBdmy2SyFYbqI0/4iTp0AFqLyoZy2bQiCqzjtyg2bYBFg4+JpXWwD+xsawFkt9ai4\ntLucdzfVn7NHJTXNie3W/eJSVYKNfPSezbMcPUZK3mCkWmNo/+h/gvlNvxmRn5G/5btRpfGfz36/\n8RgsqG7CXBHbyAnBfCheYbYO40iwCN/2xm9qCkkQuG3oPJekaewRVUXhOLOXSns5b0kC5/c9jQV2\nv+eXfctn2ASKsG74yc//HUaRMGxDkJqpK7FNfGOnqNhur3D/7Vb7XzrKUXHIDiir6PPesUd31+8j\n8vyV3/Q8z64RbR+QSYWpNd0wMwlHBTCmBSZ2ux12s2G0HWtlWPeQF5a8z782Pxzuc5/73OcDnbsw\n/YBnyeAjEGANyntMQ6wRKDUhpUFpxWijBao/0MsItMKa6eaMHyHlRCIEOt1wFgMv7csrH2linzgf\nQ5ZlKJsNHA4Tw3DkfD4xzzNBEPDwcGC3C7D2jDE9l4pTOT6gjwpda3StmV8sNCmBfFwCNhcW5InN\nxvL09MBms0EIQdd1vLw8M44nDoeJ3c7ptGm6CtStvArU2cwcx4I6u9ZNirJkX9fsb0/7a3vdOrYG\nc45RYu976ZWq/dm971uUOl83m2VKuoipYq6ptrH3hKbn8ytxOt+QAai2N+K09Bu8tm0Iw/KV+L3Y\nBk63gauu47BsH3096gXFVFikvlSX9mw2eIHXtkfS1PjN5m01arkS/uwenE7e/lACw5/+Qcx3/GrE\nj30R+Vt/F8q4tiNjSi+sq6pitep8GGruHObJWMNpKrHLSVyUJQdrXXmAubE79AZTBQuf1Z3Bd7vU\nY6Sq6vTKE5qI/XXraxrIMr71l38zQd/R/MRPMXQ9nUyZM4G2NXM1IebtYtmYWK209zefz9eChb6H\nIVgsAy3UbU0738D3uw6a5mN+U7VShNuATCjmQlMZPGt2nnNWq4gkSdBpChRkk0X3grKZqIb7Sf8+\n97nPfS5zF6Yf8txCTBEo4zZis1CM40wYDsShO+OPQiCZEHpkIAYtljP/csaXAmUnpqnlvale+Ugz\n9ZbqnLwC5D88aKzNOR6fGYbBB5seHtYIUaC1C0wptUHpR8wpcoEjA3Il3aYSJwbnFwv1hkA+LV3t\nYEyHMUfWa82bNw+kaeq3cy8vz/T9kcNhZL/3i0rOZ6jPa1LxljRygq4cK47h7PmXdB3r45E34JuP\n/Gl/2d7pk0TOB+/ZlLLy3e593yHEiSiyaO0sBalaUE9zQ5mGfruWns/shAumFWimg/Jp+ltxKuXr\nNP1FnE4TTHVGojZenHpUVdtyaBpf/ZlHBnFhtuag7MW32bLdKs9qHYYj67X11ai3DNVmE/l3j4uC\n7FLJmSTMf+kvYp/eIv/KX0b+u38QKa5b38v2sShyttvJ+1mDeefFYy4G/wtCWBRkF7+p1ciH4IrY\n6lbL94Blnk8cDjuPkRqGj/JTb/ysgebxs9/E05sHVFHxs3/zC0gZ08gI1qBNiS40Slz8pg1ZtvLC\ntyzPHA5L1qmTEMdkUYYutENgYbhVxmKaeAhD7zdttCbIAjZxQGRgKCYaYv93PM85u90OlSSMgSIM\nWva94/iXfc28+MPvc5/73OcX+9yF6Yc81mINaAsGS7AIUx1E6Nm1PV3O+Da0BNPEzLxgogRR1BER\nMZkIISbsPFDLCStgHa45RG8Z6y3ns3gFyI+iitPp/ZIqF6RpyuPjliCo0brEia0VAY+YPEHnBjtb\nRCiQDwFtJlwH/FOA2jiBYjrD/Gyw5ZpAvFkYlwJjerQ+kiQTT097tlu3vRyGgZeXF9r2hd1u4HBw\nAnW5ttIXGVt1reR8r0v6w9bD34PTiTfDcAXko2kP4oazaZHd3oskKRv2+2QRST1CnIhjizHQlgmb\nRZzWc0uRhp5Dulkg+QAlmmEnXolTIWI+Kk7btiaKKi+4dbtjHWw8DWDabV09atvy2HV+c1es7NWa\nkINit3yGDbtd7CHzWp9et0+FS6BorF75WTdVdT1Zf+bT6D//57FxjPzv/1vkH/tB76OM45H1eo21\nlrI8kWVuc19VglRdO+/rRF0/l7J89dkHeydOdaUR49XuYIxjnF4pBrW3fBa5Yh9fhXW7Dvkn/6lf\niRQw/L0v0zyfINhRxWCjHqMn97nctGZd2Kx93zOO1cVuSzkrwjBhzRpda07dCRNH3NL5lbWeAVsu\nftPwIWQXBNjeUtYjVmY+NGZMy36/x263TKZlzUQyyftJ/z73uc99buYuTD/g0XrGWgNYJCD15ZQv\nUaFFypnAKCbrhKewE4OKQAdYMxMEE/GCiXJp/QGbrEjDDPo9p6NiGJxO2e9hs2kpivdUVYW1liRJ\neHo6sFqNaJ1j7YwQIYF8gHrLfLTY0SKUQO0Uw0HyImYarWm05tnMlGuLeBOg0uWc3hvmF40tEgLe\nLJ5PiTEDWp+I44E3b/ZkmRMU4zhyPB5pmheyrOdwuNoB6yJmZd6wClbej1ikIXYRF6KqOJSlr85s\njSHfWMTumnY3eYISDj8khBN414alI3FsMMZxSDdyqRedW/KN8g1L6/MZt6eDSphX4lRU2Stxuttt\nl81pRRxXngYwNzuSYO0h/16cNg2PfX/tvV9fIfYmV0ic0jKmYr9f37z7+UoCqBK20cI4HYpX7Va7\nrvOWgfM/+xsw//WfAED+/u9F/W8/5C0PaSqJogit9aswVFkostDVfFZDxbBdX/mvN++dB4Zg50Te\nXMwLRsptrC+d91eMVOftFLdJ/WIoEb/kkV/2rZ+FeeLlr/0NQqHQYk2zsRhZYieLrSPPZrW29Iiq\nqqqQsmO9BmsFlQlIoy1BGzBPswtDZZn/e+V8ZvUJftPVPiSVirnSnMfZe2e1rghDyWa7RW82WFuw\nH2HuBUU90ozN1/6Hxn3uc5/7fJ3PXZh+A4wALMZvTGcpCYKRKFQwBwxIhB2QlzP+LFz7zc0ZX9oJ\nyUyjV5THhLZ1Z800hd1uoGmeyfMcrTVRFPH09ECagjGnhUGpUHKH7A/ML8Kl0AWorWJ8lBxDQ6U1\nFgfuTxcPY28ML3qmSCz2TYDaLl7MwTAfNSaPCeybZcslsXZknk9EUcfT086dRxfu6Ol0oq6f2W57\nv/lqaolpHkiDna/NfFED82HnN4Pr04knY66n/UCjD9Kd9keLPkVI48IsQrTs95FHDhlzZLVyvs2m\njNlI59ts545zIl6L08VbWQlDfxGnw2txqlT9SpxeNqfjCKbdXyH/pmZexKmsa55uQkVl6uwSVlts\nHqKE+zCsrTgcUm9JUKq4kgbq9ArIn0r0LrsC8qfp2hf/O3875vv+AMIY5L/625Gf/xkuafrdLvHC\n15jiWh5QxqThEuS6PBsQdc3jQgEYjKEMjfv7tzDnM4q9x3gFQe8xUkWRk6aj/1yGJvHWjdOQ85lf\n/6tJ1muG45nyb3+RQG0ZrKTfTGg6TGsQY+oJBlI2/tl5nrNeT26xLiXlqNitdlDCqEcXhjoc/PcO\nZckuCK6YrXlGJYpsExBYQXeeac215Wqez2y3W4Lt1lEwKNlNiqqCc1uizaU04z73uc99fnHOXZh+\nyGPthRiFtOIafhLSIYNEiLYRNrCE84y2E92Sxn91xsexTTsbMHQxUiiSBB4eZub5xOl0ZJomgiDg\ncDiw30dLsKkDBEptkeMj+higa/cfVrmWmCfFKTaUWqOtJZaSN9ZyqCqyouCdMWyVQgKDMZz0TB4b\nzJNCZU6g2tEyn2bMOUKZJ4Lgkjqf0PpMGLY8Pm69QJ2midPpRNu+J8uu4qUtNmzEk0/uP88l7X7j\nhWN4PvOm667nZWHcaX912T6GSO3EKXTsdoH3J87zC0miF3EasWYJFemeUwJ24aomee7Faf1zFKdt\nWxHHtddBtjtc61FNg84WiH1Z8jhNV5GXWmTsxKnJI6Rwwsjakv0+9WGrKLo+23S7K+PU1Jjl2bcY\nqd4Y6u//I5h/4bsQVYn6Ld+NOjvkkTHXZzdNQxy33o5g+q3fXJ9t67fWKs95XL4HOmNoE/e9gwF9\nfs04jWN9Ux3rMFKezzpl1834XPEt3/mrAfiZv/WjbIYRqba0wjCta6y16FKjuIbE4th8BFFlXLgu\nUrSdYid32NbSTA2dHrgaUmvoew4f8ZuG+5B95JrX8nxEqK23PjgLwR52O7Rp2cwd8RxQlPeT/n3u\nc5/73IXphzyLMDVYrDU+/KSVQqiZkNCl8cWEMBODCrE6AKMJQ3fGH2yMlJNjm7JipRLS1CBlwen0\nTN/3SCnZ7XY8Pm6QskDrGue7XKPME+YUX4NNsUQ8BpQbOBvNbC2BEDwIwWNdE55OS/R5QJ7PbE8n\n3s0zmVKen3nWmnNk0E+KYOc6ye1k0WeNPoYo/UQQXLZpE1rnhGHD09OW/X5PEATM80xZHlmtKu+n\nrMuQcHxDEqw9I/S8ltjt9Sx+KAp2S0tRawxFamHxbZoiQE473D+bnt3uGioaxxeSZHbitApZ84QS\nil4PTpwuifckz30qvRaG7quI0yBoyLKLyCtZra4CUvQPxCpGW82LvYpTVRQ8zrMT+tZSb61ntdpy\n5StYhbjdypYkSesD52q8YZyKHrsEltT5jCONQm0t3Q/+AObX/DrEl34c+S/9TpSOXj0boCwL0nT0\n4jHSBwIZuM1jZHwffXA+8xAE7tlaM6TCb3znkyVQV8ZpmgY+TV8UR3Y7c+kHIOHgf/EIP73j4Zs+\njZ4nvvRXf5hdsMGKkFJNmKR1Ptzz7bMr0jS6KVY4sd9bpBR0UqHHkM24wRonHiclPP+VPEdp/cpv\nOlvL5ikikZKp1ZyawbNajWlRypDu95j1ejnpG+ZRkdfDPaV/n/vc5xf13IXpBzzaWCwWay3Gylep\n/CRSMElGAgSDa3uyLo0fBB2hlBgboqVw0H0zMYgEM1qq6j3NUkO62Wx4fMwIw4Z5LnBeyBhlH7HF\nGn1+HWyqM3ixM4MxKCHYS8nbrmN1PDrlI6WrqrzE6ecZURSkxyNvx5G9UlffodYcQ830pFC7JTU/\nW+bzjD4GqPmRIDgsAnVmnnOCoOLxcUOapgDUdYXWL2y3ehFfgqnasw2uoZz3onVp9wWQujmdeNLa\nn8bPicFshBOnpUIMTpxa6ywDF2/lMLyQJBPWQl0GrOw1fHVcGUzixOnqRpw2X0WcWqsJw/Yj4rS5\noEyhu4rTo+j8djMoCh61dts7a2l3wlsSKDeIJU1/Eb4ATVOw2QwLJ1T49x71SB4ZLso+ynP20v3I\nKFcrxr/0F7DvPoX8v/8K8t/+/UgRYa0mCDq/fayq8yt+aqqcD7edWtpN5IsJoqLgsIjTUmumTCCi\npQAhF0t1rGOcZlni4f5te77Rh4LsBoH1qV//bcg45viz75l//Eusw4OrdI0qCIzbJpeSILg8O2e3\n23ibRtcV7HYgV4pqkAQmYdWunGWgO2HWCVc6/5nVYlG5+E2FEjweYiRQnycGLV79WWm6JjgcMBKU\nztnPIXUlOLcV/dx/bX5o3Oc+97nP1/nchek3wEghsFajLoD9SKGEQpsIE1gCPWPsSC9WMAmHkRIx\nowkRjKBHehGhJxiHGiEMq9WKx8c96/WMMZdgU+BOq3WGPvEq2NQvwabOGASwlZK3w8D6eHRUdIDN\nhma/4WdFw8+aknK3cpWYYQhaI8qS9csLb4eBg5TXtPk8cwwN44NE7W8Eaj6jXxRqugjUEGs181yw\nWnXs9xvvP22aZ9br9hqMylck5o3vdj/OFdXuKjTCPOdN27ISwp32V5Z5K0CAraUrCVhA9tutJY4j\njDELjmkEoKkCVubJbwmPkb6K0/OZh+WzaoSh3X91cbrdOoRW0xSsVs3SgCUQ/QORipjNzIvoMOkV\nx3R5dms/Yhmot0h5eXZHmm6WdqgTm407yVelZC1uGKeJ9Gn6pCjYLnzW/Je8Y/7c57CrFfIHfwD5\nn/+A32Kv1+aGn3oiTd12vyoCTwEoxiXEtajtVVV5RFWhNebydz1abBl6xqnWOYdD5v2s05RfSFQU\nuQtbCQRTMPH07d8KUvHFv/EFHtuRUK2ZraHclD6AZpvIe0CNKTgcdp4CYEzNZgMiCyhKWOuUYArQ\nVnPuzu4XrItRd0FsXfym53km3ATs1iFYeDl2SJm8QkgdDgfsbocxLeuxZCM25Dmcu/zuN73Pfe7z\ni3LuwvRDHuMS+ca6+qeLx9QqgRlhIkaICaknRqHQs3Kbr2hY0vgrl8Y37ozPLFitLGm6YbuVWJtj\nzABIlMyQ/QPzi/xYsOkl0NRLsGktJW/nme3phKgq946rFd0+5X0wUEwVxhqMNdRjzVd0SbGNXBgp\nihaGUUXy8sKbvudBSs/qLLXmRWmGB4k6BO5MrS1zMTM/S+R4IAgeljS3RsqawyHyp9+6zgmCM5uN\nE0lNrRDdE5vAbRurqeYl1u40vuCYHoqC1LrS1zKyjJl09IBGQLNDiACY2G4tq5UTp113ZL1eNpC1\nIpqfCGXIZCZeohmdrMBa4jznQWsnIHktTim3XpxGUfdqu3k5vfe9QPaP/vR+VCNmswZrifKch4tl\nAEcCuIDsbX31O65WE0mSYK2lbY9sNo6nWZeB327WU0OTxn67ub1tnvqnvxPzJ38AAPmH/n3U//J/\ncfGEbrfBDaIq92Govk7YhNfiAM+YbVs2TcN22Trmeob9TTFBc2Wcan1+JSClrHxYvilj3zy1++YH\n1OOOtmn4ib/9RZ70ComgMy1dOjn6Qq0R4wYpV1irsbb0FABXHDAQrwR2HZDnkI0ZEun4qWPl/KYX\nv0LTeL9pv/hNd48xcSCZR8s5HxYbilr+bfVkT0/YJMGagmwYCEg454ZTd8JeTOT3uc997vOLZO7C\n9AMeuwgmKwRC4FP5RkqUFkz2shEd6EWC0BKl3BlfG4UWIO0EVtMTI7QgCGqCoFqqFEGpFDU9oY/h\nVw02GXDBJmPY5zmqKJxCiCKG/ZbnWHOeK2YzE8qQx+SRN+s3JIGrI22mhue55LxRTIed8x5aC3XN\n6uWFp7blUQjiRQxVWvMsZ7qDRD4EiEi4sEypmZ8FctijpGN4WtuRpjO7navP7LqOvn/Pdjt6z2ZX\nbEnV9Xz9TOOYp0tyJzufyeYZC1SBoc+cyLOdgGoHuC1hmhrW6xXWWrruRJL0CAFtIwmmGwEZzeh1\n4sRpUbwSp80O56kd7d9HnOavxKkaH73wPYUz1vGOiG/CVtVNNartLKLb+cR7mhov3vv+yHrtKAN1\nEflmq3Kq6bO1LynYd911M/g9343+Q/8hwlrk7/pdqL/9ZZyXsvbFBF3XEYa1D6PZPvNWhPNcY/d7\nHyba9r2vjj3Z2QP4daOh3/iNI5Ts91cBmSTX5qmx2bAJNwglePjln8Ika37ix38STgUH6TbQpc0x\nS53rFVHlkvpB0LHdbpcw1JksmwlTxYygzgXZlHl+amenV/B9Nc8f85s+PsbOC1tNTIN9hZBKkojo\n6QkjINDPPEySeQwo6oliKL4WPzruc5/73Ofrdu7C9AMerZ1QtMYC9hp+kgFSrpmVRekJ7ETHJY3f\nuzS+ddtU9EhnQ8wESvaE4YBSEikTlHnEnFavgk08BhRr64NNoRA8Ao9lSXg+O1UQBMy7LaeNdMxN\nM6GEYr/a82bzhjiICVXIITnwdvOWTeiEQjd3PM8Fx5VleNjB2tWU0rbELy88VhVvcLgpCzRa8yJm\n56N8UO79jIO062OAmi7b05kgaDkcVt4PWtdH4rj0/e5VHhHNb3zi/TS5Ta5NnIBMy5LDNLnTe2C9\nL9QOQLnzG8j1emK9jhdxeiZJOoSArpUE49P19B6MzJurOL2EljphnTgNPlmcXs/6N+K0c8L3ahmY\n3XsvYavd8v1SCsPsN74WMez85i5Nr17ZaTqyWt2UBwQL43SqmLLUbZPrmodp8j7c6g//Qcx3fw+i\nqVG/5beiXi4eyYosc+9c1xWbzXANQ5mD9+CebXtNupcl+2liddnKolEXAH+pEWN208rVfKR5avTV\nomLcEauYw6cekI9r5iDgRz//Y6zLmQ3OhpHLFpbGrDmfl2Yol9RfrQzr9RpjDHl+Yr83qF1A28F4\nVmyl+3PzPmeOgit8/3RiJYT3m57mmTBSZFmIBV6OPYLoFUJqdzjA4cA4DUTjew4moakledPSTu3X\n4KfHfe5zn/t8fc5dmH7I4yL5SCROmF4A+wETEilnlJ4YhUTrAGHsx874mJ6OlUdIJckKaVNssUGf\n7ceCTUc7My7ooL0QvGlb4uPRrR6lRG9T8izi2dT0c48UkizOeLt5yzpcf+xLUNJxIt+l79hGW6Rw\nJ9LjVPAcTnSHrauyFAL6nvB45FCWvLWW9RLEaY3hKDR1Bjwqj3jSJYh6t6CSAFqyDNLUvUfb1hjz\nwmYze/HomKd7D8p/iWbm1G0gk7Lkoe/ddlNaqgxQwAQ2zxA4AZkkE2m6Ws7jZ+8L7Tp3evehJXUV\np1FZ8jBNXpzW2SeL0zjuvTht2+Ijwve69T3FxpMA1rfiVBn0RZxWICcnxFyQS3kElrUnfxof6/Ta\nPGUa9NZ9nrIseTAGCbTWUv/An8R85z+D+MkvIb/rX0NOCrAEQct2m/owVJoudoFKsl3CSv3ck9PD\nbnnTouAwz9etrNIOIYYTp8pcGadxPJGm7vlFcSLLZk9yiq1L6n/6238ZXSx4PuVU54J9C5EQTHNB\nERvEatm6nz9KAQg9eaGuzxyeJHKjqErgHLEO1z4MZbfXZjHOZ+831Qvf9JDFhLFk0IbyOBAEmf+F\nBhr2b99is4xpqlkPJ1LWFAWc24JJTz8fPzHuc5/73Ofrfu7C9AMfZy81aCsIFmFqZIi2IOQEZgk9\nzQKlegIhMTZACxB2BGMYRIzUkigaCCaFPgY+2CQ/EmySLMGmpXP+QuK3mw3lPuE9jd/wpFHK243r\nrRdLLedXGykk23jLu807dvEOJRSTmThPJV9RPc0hdexLl/whOJ3Y5zlvtWaz9K53xnBEU27B3ngT\nzSlG6lsh03M4rH36um1dMOpyBm7yNRvxxp/Hn0VHt4lBCOKm4anrHJJJWqq9gACYgWIL1uGe4nhg\nu1259+oK4rj2p3eXqHe80Bc1MN2I08dFnPY/B3HqhG/uxWnfOT+r30ImeCTTJs9x/VVQBAa9df/0\ndQFyvtoesiz0oSIhzj7Xo9sbxulN0CrIcw4LXquOY/r/+c9jf+k3IX/or6J+7/chCJd3HkmSBGMM\nbXvyPt+qCNhHSynB1FKqmUttlFh8sr7VKrI3AP5bDqlra7p6iU9st0stailJ1QPbbMv+lz7QR5If\n/eKXUDrgoZ1QwjDMBVWKpwCY4prUd8zR1H8m41iQ/ZIAGwjyoyXpU78FP/dneHi4wver6mN+08fH\nFUIK8m5irCaPkNK6IQwhffsWs1phdE7WN8QknHPLuT/f/ab3uc99flHMXZh+wGMup3zkEn5a/n+p\n0KFE6nHhk8YwQxh2xCJitDGCEWkmehFhJgjDnlBI5BgjpEJtFcOD4LgEmwA2SvF2mtgej4imcap4\nvabZb/iK6qinBotlHa55u3lLFmdI8f/vW0wIwSbaZdQzRAAAIABJREFU8C59x2HlmJraaoqp4iuy\npdqvMdvU/cd/mlB5zm5hoaa3sH6laQ8CEYsFkC+h2SNlAhiEaNjvFUniNptNk6PUybc41WWAGp5I\nlNsUnuVAs4lACMK25alpUMCIpdgBCy9UFFuESQC3nc6yFUIIuq4kispFswhs+3BtcVIDY+r8tmFZ\n8jiOPydxmqbrxTKQ32xOFeHsNqf93HNe4UsEtkVBKoQLcoUGu4hTU0qUceIUWvb71at2qIvOorth\nnAaT38jGeU62bK+Ld2+ZPvc5bLJG/k//I+o//ROeXrDZ4DFPWl8rUesyZB8fvGezjgWXmL08n3kU\nwhcHVCt7A+BnAfC77WaWRX672fcnf1kvc0cC+Oy3fQs6hNM48uWf+grBFPMwTmAaOt3TZtfwmalC\nf2p3FICdLw4Igpb1mwCj4fRlzU7t/eddTc3VklBVqGHgEIaA80YrKUj3IUbAuRhhlgSBC2o5hNSG\n+N07xyLWRx4GAzriXCzC9z73uc99vsHnLkw/4LnsTwQuCBUsuKg5UKgAAj0xScmsQ9D4M/60nPGt\n7mnNtQkqJkKKhCkVnGJDZQwGWEnJG63ZnU7IsnSmzDim26d8JRgo5hpjDbGKebN+w361R0n1j/z1\nJWHCm80bHhN3/jbWUE01X6Gh2K1cen5hocqiIDseeTeOZEvdaYflvMUFXIQL/djzBmkuHNKBzWby\nAZ1h6JmmZ++D7HvBWO1IhNueFWKg2AQgJUHf81RVhLhl6TmzmAhXA1qsEdqlx8OwJ8sihBD0fU0Q\nFBdcKqY9sFJrJ05Fz3ARp1XF4zB4cVrdiFObp16crlbDR8SpC1v1bUCkr+1T50R43FNWFKwvCKzI\nYBc+q84V0myXT75lt0uWd25ebXtvvaz5Cv/cTVGwWby/51/3a9E/8IMAqD/yH6A+938un3fHbhcv\nordHqdKHoZoyZr9y4rQcSpok8OgudTrxKKVvVupuAPz6LJagmwsS7XZrjwjT+uw9xE2xIkt2fPpX\nfJbWdPzdn36P0YKokxy0wc45jdFXekFroF/fBK0qdovNoCgKtoeZMFNMIxRfNhwS9+7VWNFL47a+\nAHlObO0rvuluHRJsFK0xNMcRKdY3CKkzh8MB+fTEbDWBfuYwKrpWktc99Vj/I/+7us997nOfr+e5\nC9MPeawLPQlrEOIafhqVQuoZ5pFOrGCGIOgXqH6AxiKY3BlfxkgtCIOBWEbks6IIzDXYZC0PRUGQ\n5w4AGoYMu5TnleGsa7TVPmn/uH4kVOHP+5cZBzGP62uS3+KqId/bmvM2dCzMCwu1qkhfXngzDN7f\ndw4N/UH6FiSTB8j+4HmeLhgV+fBP07hg1MUu2FcbYuPQSY2YOG8UVkrUOPJUFEQ46VJswSxeRVus\nELPzYgbBQJaFCCEYhgalzl6Q6Wbvt7In0dNvE7eVrWueFnE6COvC/wqYceKUqzjdbJKbsJUTp10T\nEN+I03yjPG9zXxQkizgtEm6arUIkbksoZctut16qS0tWq9Z7WSP94J9bbgKPkdrVNSsp3Wf+Xb8Z\n/Ue+HwD5e3436q//3eVbtmG/3yzPrVmvuysdoVq5XnqgGAq6dOW3vcHpxMPyC0etNf2W699nEaCU\nMyo4DmnmSQBBUHl27VinfPO3fDPJbstpqPjCT/4MSiREVUdmZ4wuqIXBZOoatJovPtCJMOxvkvon\nDt8EMhS0haU/KrLYbT7zPmder67w/dOJTCn//VhpzSGLEKEgn2am8/QKIaV1xeHxEbvfM04dq+nE\n3q6oSsG5qRjm4ef939h97nOf+3y9zF2YfsDjcFEWRzO9SeUHimDWjk9qY5gFYdgREbozvpgQeqQj\nwo444L4MGG2EjiWBkhyAN3VNfDo5BaUU03bDcSM5mton7Q+rg0/af63nNsl/CVJ1c8ezqThuJMMu\ndULGGIK65inPyW4g9sUO1+AEmAZsvkWyxe2ce7LMkqbJcnqvsfbZ14wOzYpwXoQeE8eNwAQKMc88\nns+slu1yvrHMa7eFtGWMGN3zg2BktwuQUjKOHVKeCALLNMFU70iU43qe6b2fNahrnvrenbFZ/KwX\ncVqkN2Gr8ZU4Xa0u4jQkNk5EtnPntr2LkfZQlqwuwZy1xV4Edb5C4ra9SnVst8vn3BUkiUvat3VA\nwsI4nVvHOF0wUoe2Jbx4Qv/A92H+ld+B6FrUd/825M+6KlshGh9Aq6qcLJt8o9VQr9nFTpzmfc6Q\nbfxWNjqffTtUZQzzXt4A+K+MU2sL9vsMIQR17TBS3o7Q7/gVv+ZXYoXlx3/2p6hmQyh3rMqatWnR\npqdQGpsuHtzFy+pEY0+SWO9lraozh29yl4HiZ2bUvCYJnD3j1J2wu53/vCkKHm78pgDrfcgsLGU3\nY1p74zetkXL2Yah5Lkn7krVdkReWU3e+w/fvc5/7fMPOXZh+wKNn54cEsMb4VL4JAgI7MwqYrTvj\nh2HPavGXfhSqH0UtMRGjiRGxIGsakkunvZTodOOS9jQMekAKyS52SfokTH7Bv+5ABuxXe96l70ij\n9JrkNzXPK0O3T7msydI852kYvFjKV5Zhv9R0ThZzihaslNuKxXHPfr/yYPi+fyaKGoSAsYuQ/RMS\nxYjmZe1+CRDG8FAUrI1x/s3EMi0ncltFiN6JU6UmdrtgOTX3CHEkDK3b5lUZidxiseRioE0Xcdo0\nPLYtSgjGTxSnkRenF4Zq359JErdV6+qISLszc6M7yjT0G85DWV7LC1IgFosVIUEK58UNw+4jG1nX\natVWEYlYGKe6dYxTIRBNw8M0oYSgt5bqv/vjmN/wGxE//WXUd/8ORG+5hMPWa2dDKMsT+/2lMham\ndsM2cp/FqT8z7lIv8FZ5fm2HMq4d6grgvzJOpazJlprWqspJ09ExZVvBU/ZZ3n36U0zTyI984UcR\nyYaANWlZEZsKbQ1FbBHLJlmf7YKRunhZY08vGGzB9kliNRy/NLMN996Hex5yF4ZyfzCy6175Tdeh\nItgpaqPpiwl08KqyNI4D1k9PmCTB6DP7vkfOEefc3P2m97nPfb5h5y5MvwHGIpHG9aNrIRAWhJ3p\nRezT+JczvsGl8bXW9DZCaEMUjCgRMtsA1ZxYtS5Vb9YJ5W7Fe9HSzh0CQRqlvNu8YxNt/rF+zYBH\nUb3bvCOLs2uSX9e8X1uGJAJwZ/E89w1OtbQUO+tP2LoEyp3fFErZsd9LL/TGsUCpHClhHgNk/wZF\nyGw1L4llCl2j1j7P2S4g/nJlGS4Vpk2IaJ2vVcqR3U4ipWSaRqx9IQzNgmXakkjHDM3p3SZSCIK2\nfSVOy9uzfrH14tQxVG8B/06c9k3Myi4bTt1RpREohRhHHqvqWv2aWn8et8UGgSMMJMnEeu3E6TCc\nWK3cFrmvE/++Z90wLRgrVZa+NKAJArrP/TnsZz6L/JEfQv2e70UQYO3MZqOJ4xitNUVx9OK0bcH0\nWzbhYnEYcqZ95tPum7K8tkPZpR3KA/i3nnEahj2bzWbBVJ3Ybq+Yqm//ju8kiEJOx2d+9Pk9Kt4h\njSSrckJboa2lWFuX1NcWnQuUep3Uv3hlZdayWoPuDMefNhxWDx6BVZv+Ct8vCmKt/bs3xpAmIXIt\nKfTMfJ4dP3jZ/M7ziSzbEj49YQIF+sjDYJnHgLwaKfo7fP8+97nPN97chekHPRasxRqQF4apVAhr\nmOz8ik/q0vgRQow3CCl3xo9kwGQjpJhIAKKIepfwPhioZydSb5P2/yD00y/0CLEI5vQd+9WeQAau\nYSkYKTYBVrmTe3Y+8zSOKCGYgHPi2pAuTUvmvELOe58iT5KR3S5BSsk8twhxRCnLPElon1A2RmM4\nJpYhDsBatmXJbnRbxTqydBdx2iloMkAixMR+L1FKMc8TxrwQhhqtYajSa9iKnjqNvDh9ahr37gIn\nThdM1UfFaZLEy+b0xGp12ZzGrFgCOrql3rrzuxgGHuuaQAhHvMosBGAniy3Tm+KA2Z+wp+lIFGmM\ncb7NlXQC8kiLXipRw6JgkWOUT4+Mf+EvYDcp6nN/FvUf/1feT5llyifpy/LIfm9clWvjAPn+ND4W\nrrZ2Watum8a3Q+XSIBcAv6kMcr62Wq3X5hWmKk3dlWHo13zbr/q1AHz+b/4tjsIShA8wzmyrF6Tp\nmexN8Gyy2PKa1Hde1iWp3zYkTxNBCMNZU+aSwxLkqoaKIZSeMsD5zFZK4sWLO1tLnIWMCtpxZs5n\ngmDna3Xn+czDwwPi8RGNJjJn9qOirgXnpqGbul+4f2j3uc997vMLMHdh+gGPtRd3Kch5SeRLCQi0\nMEw2QmhBGPZXqD4jQjvv6UW0rmzEQIJgAD3wXvWUusVYwypY8Wbz85e0/1rPKwG9hJWeNzCtIscK\nrSreliVrllJLZaj2wAUrVSio9wjhTsJB0JFlbsOp9QC8oJRGa4FtHwmsS9WfIk27UmAtm6riMAwO\nvhTaix6FXiFqtzkVYma3gzAM0HpexOmMMTDUGxLhIP8lg9twSonqOp4WETkJKDM+UZxuNrMXp8Nw\n8uf3vl4R2+W5uqXZrkBKZN/z2DTuuUC1c1YHJm5arSY2G+0DYtaeiCKL1jA3OyLpGrOOwYhJVr51\narvgqc6/6tuZ/4c/hRUC9f3/EfLP/O+42tKW/T65EacvXpzWNQTz4cpPHQv0fsdFue77/hq2Uhq5\nXQD8hUGZK+M0TaV//jieSBLXarU/fCuf+sxnMdPMD//wjzAd9gRBhux7du17hDUM1rV8ebtAe03Q\nC1GRZS7wVE8V2weNsJbm/czYxWxjZ0c492dXSrB4ZclzDkHgfsmwFmEtwU5RotGtZq5nwvDBi3dj\nljDU4cA0N6yngkxHFAWc2pzZzL9Q/7zuc5/73OdrPndh+gGPMQYsCCyBvdSRSgSGSbltqT/jE+Kk\n64g2lt6GSGOJghEpI6yVWDHS6wYdKp+0f0geCGTwj/PL/IeaNEp5s3GQ/NlqXsLp1Ql7fz7zsNSA\nDljOqWXeOgFie+CcIrVTlEo5ERkEEq0nrH0hCCaMcan6QC/e0FBTJQqEIKlrHtrW6dEA10olwQ7S\nrTutQghNllmiKEBrjTFHwtA9t6/WrHAismJw3lApUX3vN5yTgCID+3MQp6vVuIS4EuKlB6owLW2W\neNF7axeoM/wmmTpDiACYyDJx0w51JAicR9a2B0LpQPOn2GAXpMG2KK6997/5N2G+/48CoP7N34v6\noS8Cl9P42jNO6/rIbmcuKFDC+UCkIidOdYU57D0n9LDQF2ZrySOD9PWidgHwC4xp2O1iX6gAudeI\nn/2WX8823dGcC/763/p/sId3SLlCNhVZ/+zsCBimnUOO6fpSieo2yVE0eLtAQ0mWGegNxYshMOnr\nMNR+z6UvVdY1+yXIpfn/2Hv3YFmzs7zv967v3l/f9t7nSBpd0UigK5hr4TixI+KEOGCDY2OCEzAF\ngUrKlWCHJOWkKn9QqUq5XInLqSQunAokhRNctjAgkAFpRtjCshGSwJIYgW4WujEaSXN2d399+e5r\nvfljff31PqMjkIRm5ozUT9XUmb1Pn6U+vXdvPfOu9/k9EIQGmQcUarE7i2uUMLzkCN8Pgp751RU6\nn+PcllldEvcR683QOnWG75911llfIjob02e4FEAc4TA1sSbAqtLZALGGOK7vusYX11EPwP3x9/DA\nfWyNpulo6p6KpP2TqdCE3M5vM4uHEIy0PD6BPonAOdKi4FmHA0dSZREr5VL8nqVV3CbC1BdAgDGO\nxQLiOMA5i7V3CMMaVejLGUE3mMjQssnEt0RVFVfD9XsTjH4UOgPFAtEIsMxmbjSn1l4Tx+24w3m8\nft/riaEaDAzVEOiHyeloTjd3m9M0jXHO0bYrkmQwp/sJiQ7m1JZUA6IqKEuu6nrkpx4nvVqDlPNh\nxcFfvwdBQNe1GLMeug4E01yeGKeZjIGl5W53Qnf96I/gvu8HkLom+K7vIXhsh4fYFyyX+Wh6D4dr\nFgsd/KeQ6dUJ7u8OPvEOyHbLZd+P7VDbjBHA7zaGYFiLcG4/8mqrqiKKdoQhKBEvesk3EAcJj37o\n43z4+hOY5fMBQ7i7Ztbu/PeOGapcuVmJ6pP6eQ5JkuDEUZst+VRxu571WpmGy7FBbNPtTvD9/Z6k\n60a+qQNMbGgmcLCWftODCwhDvxDR9wVZFpNeXmLTFHTDZd2jbcC66NnUmyf77XTWWWed9ZTobEyf\nwdLhGl8RjD3umBoQR2eDgV9anaD6x2t8UujEX+MT02qK0GBci04y8ujpDzZ9MTVLZlxNPBi+w/J4\n3LPPo/Ea+3KzYTlMTytR1guw06FP/gAUS3AR4JjNlDQNUFWsXRNFPrFv6wmm8QGjMnCsJoKKENU1\nt7ZbQkY/igsBK+hmfuNcR5KEN3Y4G1Q92/MYXDrQspmGEASYpjkB/m+aU3u3OZ1O7WhOu25Fmnao\nQnvISRiCVu6Uqg/3+xHuXxmlnPsdWVcKVONOAotFNKCvasKw8E2xtSFoB8apO015qWsuy5JQhBYo\nfvx/x/2JP4V86jHMd/5lTCUc9zaXy3wkIpTlNfO5nwQWhTCRE9x/RYUO1+imKLhybmyH2udgEjP8\nx0VIYI5lrDsWi+nQ4LQjTUuMgXxxm2c95ysJNOCD73wf19QEs+cMu7KfYOqGQFt4KiToNw7DMal/\nYD73E1kbWyw7skjpt5b1WlimF/416Sv20o2Vq6zXzERIjPElGUCYh+wTpe4d/arHSDrutfb9isVi\nTnh1RR8IRtdcNUJVGtb7ikN7eKreUmedddZZT5rOxvSZLNUh/6QjKqo3AVYFcWa4xpchja9AS2+V\n2gUEOOKwBxOjTrHUBFFInEyeEbukn6/iIOb25PaY9N6ajju5YKMQrGVSFNwuy3Gyt0kc1Q00E8Uc\n6f1sNc8dee7NadcVBIE3ZrZNkMrjpGrjPOvUyAjij1SxMphT70f9Xbzz6ffp1JFl4XDuijj2wZZq\nn5C4oU9eW9YT483pMVWPN6fF52BO2/aaJBnM6X5KrMMepCtpZpMR7n/Ztr49K1Dq+ZEuYJDGo69E\nahaLZCwNiKIdItBUIbE9EQCOZAFTllwOVatVGHL4x/8I/YoHMb/9ToLv/c8JXMopVDQdr92r6mRO\nt4UhF1+32tiGddBy7B0NNhuuYGyHKuc3AfwJxkzwxIU987kvPjgcCvK8QQQe+IpXME1v01UtH3jk\nfRRJhMkuwPVkq4+NbVlFBqTHmttT65RzO5bL3Jv1uCUIDwSdpascu8LjzQAfhkojxkqqgc0aiGBE\nECCYBRSBpe0s/br3e69DGcQYhrq8xNIR2YJFLWy3sDpsaW37pL+XzjrrrLOeTJ2N6TNYvXU49YY0\ntKcdU3UB2nt26c2rerEtNQnS+2mpB+7HnmtqGzTLRnD9l6JEhEW64Crz5qbF8njmKCeRv8quqrug\n/KVRNsvRN6LbHGk9yidNLfN5gIjQ9wdEVhijuD6C8haiIa047kzw062+51ZRkBxB/DOlHyujpqPp\nnUwsk0k0mNMNcewbl+pDTHwD8L/Kb5jT7ZYIvOl9ojnVe01Or4ljb067w4wYD/dfaUmTpwDEux0X\nXTcgn5Rmijen+xDTzQHBmIr5/GhOdyTJ6bkmOjBOtaae+jPD3Y6LASO1u7ygft3r0PkC80u/iPme\nHyZwCTfN6bFatGlWTKfqe++LgGlwNeKYNrGDycSXKqxWXA7m7uAczRDi8nuyJ8ZpFFVMp8Ne6GHN\nfN4ThIbnvPirScyM699/nMc+9Ri7fI7EKc5WzNaPnZivud6zdUp1x2Ixw8SGgzuQpQ0ceuoa2jId\n2azreo1dzEeerNlux+IABYwIwTJkjaWrLX3RE4YXI2ngGIZyyyVOD0xtRd4GHr5frsefCWedddZZ\nz0SdjekzWUPewWgwXuX3QeALR60ljo9Q/RQjHdwF1T9d4ysNofaQZaRh+jT+hZ4aJWHCs/JnjcGU\nTdCxmga4KByh/LfrmkgEC2ym0PiBG7pLkcobsyiyzOeCiGBtjU/sO5wNvDl1Mb0odzKlDUCs5aoo\nyJxDRdjOoLthemkzQMmynjwPB4bqhjDce8NXRoTtYMq043oiaBBguo6rJ0xkNRJvTot7m9O+P5nT\n/jAn0lMtajv1pQnpbsey730NaAztdGjN2oV31a1Op34XuWkK4ti3Q/mQ1Xxss2rzFFRJioLFENTZ\nvPyraH/pl705/cWfw/xHd5vTy8sZQRDQNA19vz4OR9luQmaDOS27km1mxglkvF5zMVyN79TRL4xP\n1FcODifGaZp2I/5qv79mOrUsr5Ysbz9IojkffeT32NcHytklBCF9s+ZiuxnJBTfDYbpPMcYXEgTB\ngel0ipkaDvWWPOnQynrCgJuRhp5esKrXPgw1EAbiphmLAwCMEWQRsHI9/f7uZijnSsLQMru8xM5m\nqBYs2g5pDKuNZV2d4ftnnXXWM1dnY/oMlsNf5TvnkCPHVAyKRaQlFHAa+l1Ubel7aDQglJ7IOJzE\n4ByqDRJHpPEEI18e3xIiwkV2wcWw/1fT8+nMUWe+mSc8HLhdFCcofwbl0F6qZQQD9ikMHculEASC\ncx2qjxMEPeoMlFeITXEC15lP5+McF5sNedf5rcfp6EdhN0Ga40S2ZzoNERG6bksQbH37VB0Rtrf8\ndTY91xO8Oe17bm23xPiUdzHXz9GcekSVLRdETLw5NQ1dnoEq2XbLoh/A9PGNRqttjFj/XOO4YTo9\nlhGsx/BWd5gSMxjeoMWmCagyKQqmw195/U3fgP2VN3hz+vqfx3z3D91zclrXNX2/GnGguyJiGgzB\nsHbvd4aTBKz17VDGfx8XDO1QAq50SDMfJ4+zmYyA/6q6Zjp1vPArX4oES0wd8nuPfIjSOer50ify\nq0+fSAvcwEiVDuoT8zVNW/I8hxwOhw2ZtqhVNhuYBhd+19l1FK6EIcTFZkOuSj6EoVAliAy6MKxd\nT7/tobs7DJXnCenFxfC6brjVKF0lrHcN22b7pL+HzjrrrLOeDD2tLkREXiYiPy0i7xWRjYgcROQD\nIvJ3ReTFn+XxrxORlYjsReSfi8i3fJazjYj8VyLyPhGpRORjIvK/iMg976rvl7M/L6kbp6bRkMrv\njUFVCaP27mt813iofgdheLriN6YF26CT7GmpF326lUUZz8qfdZpkhR2bqYfy03XM1+uRSVrFAztU\ngCYYk0zHxH4UGZyzqN4hCBpUBapL6HJUYJ0JhwhQZbHbMW/box+lHvJmuk+h9vfmSdIzmx3XBfbj\nLmtbhwTNrXEd4ToXXOhLBK42m9Gcbv4Ac5okEc45rL0e+an2sCRUP0W+Dhr6wZxOtlvm1vrnmird\nYKR1myLOf5Ak7Yin6vvV3YZXBsZpYnFxBNYy325Jh73N1Td+PfYNb0QXS8w/eR3mu/9TAhvhr8e3\nXFycWpacWx9v7tkXCbPIm9Ntu/P7rFEEXUdeFMyMQYFCLCwGc7pTTO//o8K5ivk8HBmnTXNNnisP\nvvqr6bqU8lMHHn/00xw0pJlPUTpkf2fcvz2Io5ubE9jfLkb2aJ5DOktxkaOtC1LbD3knYZlcjtPe\nQ6gn+P5qxSIISI1BhxKLMAnoJkLR93TrDtHkRhhqzXI5J7i8xIYQaMFFpez3wmq/p+7rp+6NdNZZ\nZ531RdLTPR57HvAc4GeB/w74a8AbgL8C/Kub5lREXgL8OvDNwN8C/ltgCrxRRP70Pc7+O8DfBt4D\n/BfAzwA/ArxenlBddJ+d/bnLDoB9cYTdccc0wDnF0N0F1TeupRLPNk2SkpiI3iU4KkJ6TDb5srjG\nv5eMGC6zS5bp0hsGOl9pOrQ5Zfs9t/Z7AqAL8XunAdAPMXsbIaLM50qSGJxzOLciDEtUgXoBjb/W\nLlLYxYAq092OZV17k5OeJrIcEmT4IIp6ZjMz7rIas/bmtAkw9S0CCWm15zrDm1NrvTlV9buscx0o\nAHebU08BiG7wU72RdOUFEUdz2tJP/BX8dLtl5txopO0QANJiAur3EW7iqay9Jgx9m5U7XBDKwDjN\nQAeM1MV2O9ahrr7h6+hHc/oLfnJ6lzmdjagn2JBl3pweipR86Jcv2i3VfDLubs52u1M7VOiQEcDP\nAOD3jNP5PB5JAH2/4ur2hAde/BLaOuZT7/8kTdOwI6SbTujdjnC7ZjnsdO8C5wkOgN0oRo/nliwW\nCdEyou1btC4IrX89tptTGGrbbP2aQxR5sOoQhoqGYgIBomlIlSiH3tKveoJghjHpGIZaLpdwcYGl\nIdWKeakjfN86+2S/fc4666yzvqh6Wo2pqv5TVf3Tqvo/qOrfU9WfUNUfAX4AWADff+PhfxOYA/++\nqv4tVf1x4E8CnwD+7s1zReRVwH8J/Kyqfpeq/qSq/tfAjwLfAnzPE57KfXH25yunPhCixmGG/wPq\njd95DAODEg1AqY62F5oeImOJRVCTItqjrkXSlOxLOPT0uWoSTbg9ue1h7jiu495XmooQNY1P1juH\nNbBZKP3NZH3rU+U+WS/D5HAzXsHTTqG+ABV2CWxSQITJ4cBFWY4T2f0xZFRFsJ8B5q5d1r6vMMYH\nrbrWm9NQIjosdzLFHs1pUZAM5vSEqLo7EPVEcxqGJ3MaMrQthZ2/KnaOWVGQD+Z0myt2CG/JXdNY\nRxxHQzvUNUHgsNZPjgMJaV3HeiJj0cGtgYTQq7L++q/15nR54QNRf+k0OYWTOS3LEpENaQrWQrWd\nMB3M6abd0iymEARQ1ywPh7EdahM7JPcAflsIRv34W3XPcpmN+6yw5sGXvYjp4oqyMDz6nkcRE1MI\n9JOU3m3JimI06tvYoZlfcXAbuYudenE5JZgHlGVJ2OwworQtNAfPC/ZhqI1vtBrQWrLdchVFBMNE\nWYBwHrIzjqr15vQUhuoQKVleXuKWS5QdU9sR18pq7c7w/bPOOusZp6d7YvrZ9LHh1xZARHLgO4A3\nq+pvHx+kqgfgJ4CvEpFvuvHn//Lw6//6hHPyIiWHAAAgAElEQVT/L6AEvvf4ifvs7C9IAoTuFH4K\njBIH4Qmc75oBqi8jVL9xyQDc//K9xr+XAhNwa3LrVGlqeh7PfbI+6HtubTakfe/DS3NovB+FXQ5V\nDgiTiTKdHkNRe4xZI6LQZWh1CWooI7hOvLFJq4qr3c63UN21LhDBzn8Qho7FQjDG0Pc1cD2YUwPV\nFQERPY7rCaM5vdxsRgrAaE7dZ6b1j3B/1Wui6MaUkwSrljtxj038PvKiKJgMhnc789grtTq0Tvkd\ny9lMPdPT9oisCAKl7wxSX47BrWLiG7KkLLm60d60+vqvpf+VN3hz+suvx/zFH8T04fFFZrn0HNKy\nLDGmOK6VUu9y8tCn3ldtQbuYeqNXllzcML9FppjMDNPeiEA8C9Wb08m4MmDMhpd/3aswQczq0ZLr\nj12DmVKEFpsaertntt2SHV+LHIiHYoYixJjjFu2e5bPnSCwcdjsyVx3zToR2PtatrrstXF6OYShz\nOHAZhhgG+L4I4TJkg6Wpe+zODs1QBudK4tiRL5dDGGrDRaO4SlkVHUVTPHVvoLPOOuusP6LuC2Mq\nIomI3BKR54vItwL/J96c/uTwkK8BYuCt9/jjbxt+/cYbn/sm/Jrd228+UFUb4N3D7x91P539eUpB\nFYsieuSYGnAW0wutJiNUvySB3hCFJbGJ6W2AlYrIQJjlxEH8hT+NL0HdVWkqyp2JDy+JKpfbLdOm\n8bYjh9L7UShTOBz3Q5XZTAdzWiFyjYhDbIIersAFNJFwJ1OcQNy23Npux3WBYgFqgPZYGWUIAsd8\nrhgjONcCdzDGedNX3fLX5eonp30UIM5xWRSkR0TV8Vr/hjk9wv1PzVN3CEOLc4I7XBIwVIGmDjeY\n02VRjIasmN9NADiCXxcLIQxD+r5FxK8f9G1I0J7KAg557M3pbsdV05AMk83VcXJ6cYl5wz8h+K4f\nxHQBvp9+f8OcHgjD7Vgv2uxmZMEQtup3dIuZP/9w4LJpTu1Q05sA/ugGgH8/7rNWVcl03vLgK1+G\n7eFjjzyOrS1OctahpQ9bXN+wLApioFdlO1MIQTuFXTYm9aOoYv48b5q3j1+Txx0ARQETsxxLAwpX\nnZqhtluipuEiik4YqUAIlj6p3+56tJYbYagts1lKvFjgJinChqsaqj2sdiVlVz5Vb52zzjrrrD+S\n7gtjCvww8Gm8GX0D0AF/UlU/Nfz+c4dfH73Hnz1+7nk3Pvdc4I6qdp/l8bfEl3/fb2d/Xup63/uk\nCOFwle8w+DvFYGiFaml7Q+eEOOhIg4CeGCMWXAtpShaep6X30rHSdBJNcCir7LQfOt/vT1fwKeyO\n+6F1jG7noIYo8qFrn9hvEbmDSO+rSMtbqA3pQvGsU1HCruNWURA6R3/MVgVAd/ogCPTGmR3enFr6\nXtDDlZ9y4rhOn2BOrUVF2MwVO6wg3Muc+vDW9WhOKa8I8Puh16mO4aWL7fa0KjDTYed2qKDCAB2z\n2WnCa8wGEejqmLD3ZqqQhjpPRnN6WVXjtfvq6/4Y/RsfGszpLxH8xZM5NebAcjkbGpz2xPFuXNHs\nDwvSYOint3vPCxXB7HZcdd3YDrWb3WCRbhKM8YQB3w6VD8Z3zwsenHH1wHNo654PveNR4jDHSsQm\nUXpz8JPp3W7ESB3mQ1K/dlBORzzVZGqZ3JqgTjl86g6TiUMVio1hPgS4Dt2BMnAwNFqx2ZB03V0Y\nqSAyyDxgZXu6TYfYeOSodt2ai4sFZrnERRDqnkVpB/h+QWfv9SPrrLPOOuv+0v1iTH8e+HeBPw/8\nj8BLgF8TkQeH3z8uQDb3+LP1Ex5z/Pd7PfZej7+fzv6CZFw4AvZ7E2AI6M1wjW+PaXzPLk3EB6LQ\neqwg/VKG6n8xtEyXLJIFgt8PXafewmRVxdV2S6BKG52MpAwjT71hJMNQcK4H7iDSggZIdQvtY3oD\nd3KhM/h1gaIgHnZZiwXYQDl+oC7AmJvmtEf1Dsb0WOunnCEpFsedTOmiAJzjcrsd+anF7G5zerMW\nNYoCrO2H/VCLtYKWlwT4vvfrVNHBBV4O00J7rEQ1QGeQAaVlTMdiEQ7mtCQMfTtUV2VE1u9hrqWh\nHRqnZL+/y5xef82r6R96GL24wjz0y4M59ZfbxuzHetH9fkeS7BkyVbjygiQYdmT1gJ155mqw3XJl\nrcc9qcc9SShop2iR3WiHOjCf54Px3fLKr38RcZaweXzHo7+zIQqWtOooMkdPhWlbLvd7f64o1bHG\n9eAw3Smpv3hOSJRFdHVHt7ke1xD224hFMpj1uvChuyO0db0md47pDYxUmBjcRFjbnm7VEch0LA+w\ndjOGoTRomGhHfrCsN8p1uTrD988666z7XveFMVXVR4cg1C+q6o8Br8FPG//O8JDjPVRyjz+ePuEx\nx3+/12OPj9cbj7+fzh71Yz/2Y+M/b37zm+/9v6bOh5vcKfxkA4MoWAmGa/yWSvx+aRTWBBJjbUBv\naqIwIE7zL8kK0i+28jjnMht64CO4M/GGLB6mnNEw5dzMlT6EISEFXTwm9qNIUXX4/dAa1CDVFa71\nrNM7E6gDxTjH1bDL6gZgfhcBTvyZvacAHBFVqnY4s8M5byRH7FOmtLE3pxdFQTZMTm+aU1+L6j/w\nz9ObU7gmDB3OGrS8whDS6cBODcMxZBWpYs1pWKqNgYMfIRvTMpsdeaw7wtD3ufdVzn7V8cEPfJAV\nFe0891fY+z2XZUk2pOmvv/pV9A89dDKnf+EHMa1whNmfzOl2NKdtC1peEJmBBCA1buaNXrjZcKnq\n4QfqaJfm1A61zRE5tUPNZp7h1fUlr/yGlyIifOR9H6dZRUThksq1bLMWJ5awrrkYCAtloHQzD+ew\nW4exp6T+xQsnmMBQrSuMK8bn25bZ2Ay1qlY+qX/kYq1WzEVOGCkRollIG0PRDeY0WI5hKGNK5ssl\nbrEA2THrFXPoWReWTb15yt4zZ5111peu3vzmN9/lU76Yui+M6ROlqo8A7wL+1PCpTwy/3uva+/i5\nm9fln8BfqUef5fF3VLW/D88edfML/prXvOZeDwG8r8ApwXFiKgFOBMThaOisobNCEnUkEmDFt0AZ\n16Jpep6Wfh5KwoTbE7932gXweA6N8a/9rc2GtOtQIxRzpY0VUfF3/HWKCMznfvdUVXFuhTEHQDDN\nJa4ZWKcTQxnquMs66TpUhN2c8UwtZqPhnc2UOJbxCt6Y1oeXyouTOU0cTWS8Od1u7zKnd5EFhgXU\nm+bUM1m9OZXqFgafrB/Nad9ztdsRAn2A37MUoA6R8hjcapnN/Nul77dEUY0q/N8//v/xXd/2l/xz\ndAeaozk9HLg4HO4yp93DD6OXtzAP/wrBf/gDd5nTm9PNND0QBNC2gqmv/NfKdayCFp1MQJV4s8F3\nKMFWLd3SIIHgGofsTyimOK6YTieoKmlueMFLno2o5Xfe9j5SnoUxOXtXsUtrFEgOBxat76rfRYod\nyghswZjUl7Bk+cDUT98/WZBl1TGjheln5FE+mtN+Ph1LA7i+5sKYESOFKtEipAyUfdNjNzfDUBVJ\nomTz+TAt3nDZGrqtZbWt2TW7p+w9c9ZZZ31p6jWvec2XlzEd5JMDXo/gr8P/xD0e98eHX3/zxufe\njk9hfPPNB4ofh3ztEx57P539eanvrb+ORU+p/AEqHgQOGa7xtVWiqCKVmEYToCFwHUy+fNmlX6iO\nqf009FPOVW443DCS06oCEXYzocx8MxeH3P+DMJ0K+QDTd65ApEAETLvAVZ51usmEXehAleVuNwat\ndjOhTkEYDG+bDOYU4lhQdYM5bUbsU+CGNqdUqWMzTk4nR7LAXeZ0cZc5DUNzlzm1/dBmpQGt9qxu\nVqIO5rQL5QgSQKtwKAuAKGrJ82jAaK0Jw5bXvvanePzTn6bedv45ugP1Ij+l6ff7kUN6/epX0h7N\n6a++8S5zGoblON3c7wuyrCQIoGkE01yN4aJVbDkCUNPNhvmAHN6qpT+a09qhuynGJKha0rRlMslQ\nVR548S1my4SmPPCet3+IPHg+SMTW7Tlk3pBO9numfe8xUqkeEa+4TYgRn9QP5xXT5QQ62H5qRZ77\n3c/tFlJZjFW51+W1x0gNqxOyXnMVhgQiqAhGhGgZssVSlj3uAFHkLbe1O+bzjGg+R/MEw5ZlDfut\nsi73NP1n20Y666yzznp69XQ3Pz37s3z+W4BXA78KoKp74PXAa0Tka248bgr8EPABVX3HjSP+Ed4S\n/PUnHP3DeMP708dP3Gdnf2EKlMB5VmEfBCCGoO8R23LQGOOEOKwJggTtwUpNmMQkyZdPBekXUyLC\nZXY5Xr0WmbBJFESYlyXL/R5RpZoIu6n6dYs6he0UVV/rPpv5szw5bAUopp/iyqVnnWaGTezGoNWi\nqgDvb6uj4d1NofHNS7MZpKnnp6quEKkGc7okcH4Kt06UOgm84b0xjS1mOqwKcE9z6ndjrzHG4axn\npxoCGu1Y5waCgKBtudrvCURowxOPlUOMNN6cpmnPZOLN6Uc+8i4ee+zjTKdz3v32D5MFA9PTHjwk\n3xioKpYDJF+B1ateQfumN6FXt705/c7vR2qHv3q/aU434ySyqQ1Bc+UrXG3DKh2+ANaSbzYsju1Q\nWLqhYlRrRfezU3Bp0pOmKQi88OXPJUnh8Ucf49EPlUyj5/tJt25oUuO/XrvdiVowBYaQlW5TjMkQ\nUbJbHVmWYneWw+6aycQeV0qZhhckgUd1Xdcr3OUFwxgYUxRc3cRIBR4jtdbeG/wmHMNQfb/h4mKB\nLBa4yBFTM9t3rNfKdbk+w/fPOuus+1JPtyv5eyLyVhH5n0TkPxORvyYifx+fzP8U8DduPPa/Bwrg\nIRH5GyLyV4G3AA/ggfejVPU9eHj9XxCRnxWRHxKRv41va3qzqv6DJzyP++Lsz1/KsGVKOFSSWjF+\ncoaltSG9E+KwIzUxncYY03l2aXYOPf1RNUtmXGYef1TGwp3U4QQmTcNVUWCco02E7QKcAbqY4wdx\nDPO5IqKo1iNOytgJrhxYp4lhlfqvb16WXBwOfodxIuwnAzR9P4HKh3byXElTBqD6BmN885QrF4TW\nm+h17KiO5nS3I+86GCan3QDMP5pTvxvLMDntEPHm1PYBVFcInkm6ngzmtGm4GkJATQyH3D9H3SfQ\negOdZZYkCXn961+PqtK2De9617+i3c3JzPAc7Z5yMTlB8rdb8qM5feXLaR56yJvTf/Yw4Z//gdGc\nJkk9mtPDYcNkcjSnAWF35Rmqfc0mE46MqXyzYT4Ei7bisEvjzWmlSDlHJEK1Yzp1JElCPsu59bwF\ncWT56Pt/l3IzIw1uo+p4XO7QJZFHat2oht3O1J/ZKrrPEYmR2DG9BXEU0606+n5NmirOwfU1zKNL\njypzPatmg15ejmY93O3uwkiFkSGY+aR+s+4wOhlRVc4VLJdLdLlEoppcId42rNaOdb1+it4pZ511\n1lmfu55uY/oPgDvA9+GB9X8T+HrgfwP+mKp++PhAVf0Q8G8Cv4GvL/2fgR3wZ1T14Xuc/deB/wZ4\nFfB/AN89nPtnn/jA++zsz1nqFJzf6TPDVb41BkQx2lFLivRCHJckGtFqgqMm0B7JsvM1/hdBaZhy\nazL01ofC45nSiRJby+2iIOw6+lDGdL30Q2LfBkSRsFiAMYqq55JCj3Gedao2oI6E64F1mtU1l0cQ\nf3ZjGltm6LAqkOc+M+P3WDeI7FEFW80I7Xwwp5Yy8dO9xW5H3rbD+gGjOdXNfDCn7i5zOk5O+xAZ\nzGlFxyYPwBjCuuaqLH1CPRG/wQDeQHf+Xns6dfzMz7yWpmlo24Z3vOMtWOs5pBMzNDj1e/azhOFO\nnkVRMB3M6fpVr6B5+GH01rMwb37TaE5VLUlSj3uh3pzWiEBThUSDOS37im0e+ivyrmO62TA7Tk7F\n4ZaBT9WXilSLIVTUDisTMc9/yQvIpoLtaj7w27+L1s8nMlOca7kTFrg4GjFdIdAJHI7T2EqReo5I\ngEw7pguDcYa6qMc2K593EpbJjTWEJwD4k7JkMWCkFAizACaGVd/TXXcEshhNdRCUzBYL7GIBUrC0\nAbptWBUtRX2G75911ln3l+RcV3f/SUT0c/m6/NxP/L+8++3vZNX1fMfb3sK/99538XNf8828/1u/\nnT/znX8Ke/FCXGW4ml+zCJeU/RRnrkkix+TZz2eRLp6Cv82Xh5z6+sfWtojCslIy64MqqzynSY+d\n85D04g3lbIvEvgZ0t1P6XvD/rXgJxCgWza4xYU9olatKCBS6KOJ6NsOJELbKbAcGQZMG8j0iUNdQ\nljJMT6f4VlwIswN96M3IojXkjfMT0+mUfRwjwGyrRJ2goshyC6ZH1VAUirWKMRFwC+cEE3Zodo3i\nyIlZHHpwjjbLuJ5MUGBSKlnlEUrMd/z+Jz/Cq171bw31n/C85z2Pd73rY7StwRhIZyWVK1CUWTBh\ntm89pDSK2C0W7IZU/fK97yf91m9FHv8U7t/+d+hf9/+gWYhIQFXFHA4VIkKeX3I4JKhCMmlogxWK\nMo+mTLe1PzuO2S4W7J3zZ7sAs7GgYKag6WagHyRst47NasP7f+s9WJvzole8mgdecBsbv5/etaTR\nbW5XMWItfZJwZzrFAXkvpFu/hmEWigvX2Mpi1ynFrkYuhdl8TtfNaBoIQ7i4tKzqO1i1ZGHGhWSw\nWvlvuosLtlHE3lpk+HnRbnriDq7SmPDK0HV38CGxGdttR71eY4qKnjl3UmV+O+KB5cW5+e2ss876\nI0lEUFX5Ypz1dE9Mz/ojSNU3PxmEwJ04pipKLzHWGdK4JTEhrSQY6cCeK0ifDBkx3Jrc8qlqgfVE\n2Ebe5FwdDuSHA4iwXwhVogiC7BZQJxjjE/tR5K+k4RrVCsGzTl0X0wcexN+JEg2IqsA5+ljYLTxJ\nSpoE2c3HPdY8981TsEfEm9G+ygm6gZkZO3aJjHuR07b1q6tzoYsGAsBmDtZPThcLGcH+/lpfcX2E\n1Kc2p2PVaFxVXFTVuHpQDyA13U553c+9EWNOP78++clPUpa/T5JYnINqOyFl6ZPrtqTIQ46Q0llR\nMBuS6ZtXvIz64YfR28/G/No/JfyO70eqHlVLlrXkeTZMTldMJo2fnJYJsfNA+223p5xnHJlN8xtT\n2Y2x6GKYnO5BGs9mhYb5PGBxseDZX/E8RPY8+qEPUB4cpnkhRkLqfsVqIhAEhE0zFjEcQqXL/d/b\nbYWABUFmkLRmlqfoTgcm62Fss9qsAy5SP+mt+oqCxrc2AGw2zJ27GyO1CGkMbOoOW+hnhKHC2QzN\nI0JKlpVju7ZcHzb0ruess846637Q2Zg+g6XOoerrSI+pfBsIvSiNRp5dGpWkxPQ2opeS2Oi5gvRJ\n1CJdsEy9qdqnpx3RRV2z2G4RVcqpsM+Gifhhih6yASdlSBILKCJrVPcIBlNfYZsUazzrtDFKOKwK\nRNaeVgVEPfC0mIMKSQKz2bDnqQdgjQjYekLQeXO2i5Vt6s3SfLdjdiQAzIX2aE6LzzSn1rb4a33F\ndTEczal0/ppchLQsWQ5sz0MOTQKiwt//qddSVfX4mk0mEx555BH6/g5J0qMK9T4jZWhEcjWb6enq\nfTYk6hVYv/yrqI7m9C1v/gxzOpmkqCpluWIy8cn55pASuWFloNt9hjk97rNuAoubG29Od4Jpl/gf\nmTWLRcQLX/IiJrMc12/48O++B+fmBO2zMUDZX7OZGBAhqSoWtf/77hLFZh4j5TYhgcwIF4IJayZh\niqsd260nCwx/XYp1yEV6ObZD7SM9AfhXKy6AeHg9RCC6CDjg2B063CEgDP203NqCxWIG8zmaWBIc\n+b5jvXZclyvOt2dnnXXW/aCzMX0Gy/UOUNQpgR2MqQSoOroexDrioEeDFHEK2iJpSnYOPT2pmkQT\nriZD2CYSHp8ovSh513G52WCco5kIxdSH16SeoDuf2J9Og6GuUhHZoloAQtBe0lcT1AiriVAGDuOc\nb4nqOtzQWtobReyxhsrXos59KydQoeoJALbOkMYbv32kFIM5ne33zGrP5dzP5QY79WRO5/NTzSpc\n+wBXl4yT07107CbenGaHA4vhyv4whY+vHuP9H3z/Xa+XtT3vfe97sdbS99ekaYcqVLuUVIe9UFv7\nkNUwSpxuNiwG3NPm5V9F+aY3oc96jjenf+77kbJD1TKZdKM5raoVk4lHM3XlhPizmNNFUYyYqiJ0\n6Mz/mHQ7wXQDC4uK+TzipV/9MhTHfvP7PPbRj+DsFaa9AFezd/vxdZgcDky7zoesJoqLQa3iioQg\nmhDMII4apuSoVYrCh7eObVa7TcwyHSa9zZYyC0f0laxWXBpzwkgFxmOk1HIoOujSMQwlsmOxWAxh\nqJLcGcymYrXpz/D9s846677Q2Zg+g6XDP6gQHpufTOC7IVXIkpZUYlo8u1S0PV/jP0WKg3iE8R+v\n4RtxJM5xa7Mh6Dr6IbFvRZE2GSD3QpaZYdKpiBxGMxn2S/pyhgpsJoZd4HcLr7Zb0rZFAz857ULA\nBkPI6mhOFWPAN+H681ybnq7hI2WTAiLMDgfmVeXN6UxonmBOjdG7zKnIChHFdQnUwyTWdOyPpmy/\nZz6sCfzjf/Z6gvDuprGyrHj3u99BksRYa2nba7KBC1rtYxI7XGW7xrNTB65nvl6zHM4oXvaVHB5+\nGH32A5h/cTSn7WBOe7IswTlHXV+TZd74dmVOovc2p8uiIBPBAZvopjkNML03p8bUPPuBBS962Uuw\nfc2nPvZe6rLF9VeEdonaHVtpvYnET6Qza3HAdgYagnYKuynhNIXEEZqaST9BnbLdenM6kKKodum4\nF17UBfUsGwH8ZmCcGvzPhCA2BNOAteupVi1Gj4SBniiqyadT3HKBBDuWNqJb1ax2Fft2/2S8Hc46\n66yzPmedjekzXQoOHVP5vRFUQa2/xo8lou8DWqmIjRBnU0ITPs1P+stDRxh/FmY4Ix7GbyyhKre3\nW+K6xg7X8H1wmnRqb0gSw3yu+ClXDVwDjtDOsIeBdToJKEKLAJe7HXldgxG2c6UJ1TOqigXaB4Sh\njHgqaFD1oRhvJi8RDGUE64HHOi1L5mX5h5pTY8C5ZjSn2qXQ+FWGrek4DKZsutsx6zr+4U/9FGX5\nmS28b3/7bzGbKWka3zCQfi+0OkTEvScf1LbxrVNxDNYyWa9ZDmGo7cu+ksNDD3lz+i9/jfDPHs1p\nT57b8eymuSbL/MpAe8hJOZnTwywdmaEXu93JnMYOpsa/37YhYj2I1piKl77ihVw8+zZNteWj73s7\nQZDTNzMCm+PsliLoaZLQI7qKglgVi+9IOEL9pZwTXcYQ9UTizalzjt1uQ57XR3IWzX7CPBnoCtWa\ndjEdVxzC9foujFQ0CZBMWPU97aonDC7wzVA1eW6IJxN0kSPsWLaG3Z2W1WFHa9sn9X1x1llnnfUH\n6WxMn8HyF8GA6DgxPTY/QUssDg1SDA5DC1lGFp6npU+lRISL7MLD+AWK3JtJA1zt92SHwzjprEPl\neCevbUAUGZZLwRgHeJyUak/gJtjDBc4KhyxgFVtUlcXhwKwsTyGrSBE143lBIMN5ikg3mFOLdglU\n3pxWER5CL8K0qlgMJnI/E+p7mNPFQj7TnLYZWg+TvaCjnPg60uYjH+G9jzxyz9fpwx/+GF1XMZ1a\nsixBVanrFWlaecpAGRK2A5bLdZ4Zm8TgHJPN5m5z+qaH0ec8F/Pr/5zw2/8KcmhQ7ZlOHWnqJ6dt\ne02aenPa7HPSYfZa9HsO82zEVF3sdqRHc5oqOtSMahFhBnMahjVf+8e/mihN2K4+xSc++h6MucDW\nOaYPsK5inTi6KPANYUVBoEpnYDfVEU0VdBdEVzEEHRE1WefDW7vdmum0OWJM6csp09gXEqzqNd1i\nNj7fpCjuwkjF8wgXCddtR7eyRNElxzDUYjEhyHNkHhPSMDv0rO/0XB/WOHX3/DqdddZZZz3ZOhvT\nZ7CsHYypQjBMTDtjUKsEYUtqYlpNUeqxgvR8jf/06CaM/5AFXMcWFbioa6bbLeBZl2WiCAZ2C7SO\nCAIZAkcW6PHmtCXQFMpbOGuok4Dr1OFQZlXFYrfzafj58Tzx5zURxjCcp4j4ulHVHu1jKK9ADXUI\n10ewf1WxOBz88/ss5vTuyakPWNFNoDma054qDfmFN76RKLz3tD6JEz74gY+Oe6F5ftwLXZMkB5+o\nrwOC5hahCem09+Y0TcA5ss2G5YB62n7lS9m/6WH0gedh3vqWJ5hTO64MdN01WWYHczoh0Xub08vB\nnFpVigzIh8lpESMuB5Rs0vOqb3wlAB//4Pto6xUic1w9Rboe63pWqWLDAGMtV0ce7am1FbtTQi6J\nbiWodMQ0pL1/Hbbb1WhOyxK0njOJJh5T1hbYi+UI4J8cDkyH0gBUiZcBvYFV3WG3MoahnNv6MNRs\nhmQ9mRjiTcn1qmdVrb64b4CzzjrrrM9RZ2P6TNbgSoMgGHFRzoQ4VQyWkBDbGTpq4jgiSfNzBenT\nqJsw/iYJuJMpPY5517EsCsQ5qqmwGypH5TBHDynGCIuFIYosIg6Ra5yrMERIeRvbhbSxP8+i5G3L\nxUAAqKbCLnUeT7Wfo1V8w5w6RCwi16h2qI2Q6haiAU0Iq8x/i+V1zfKGOa1upvX7kCDghjn1O6wi\nQJtD46+dN5HlJ3/mtRzucY1/1Dt//V8jQ7l8mrbMZr4Aoq4L4niHMdA2vhI1FN+KdCex2Bvm9GIw\np7uXvuRkTn/jXxB+2/fBrh7NaRxHwz7rnbEOtD3cbU738/Q0Od1uSQZzuskUJmaYnKaI881bz33B\nnOc9+ALUWt7/zreRpiGQ4Kop0rdYnC9LMELYdVzs98fWVjp/BP3aEcpgTumIXUNik2FyumI6bY+M\nfYJuSRqmvrq0K3AXS59y2++ZNw3ZgJESY4gvQmqU9a6FOsWYCeAwZs9sNkWXS0xcMpcEXR1YFS3b\nZvtkvRXOOuussz6rzi7lGS3PMVXVEWMzhswAACAASURBVBfVi0HFEQIuzDBiCWjPFaT3iaIg4nZ+\nmziI6UPDnVxosEys9TWmfU87EbYzhsR+ju4mgDCfByRJDyjGrLF2j+CNmm2H8ybQ4Ui7zp+nSpsb\nthPfEiXlDC0TRBjMrgJHc9qiNkTLK3DenF5PQEWY1DXLwUiVc6E6Tk63n2lOVW+a0ynazNhsNvzW\nO9/1WV+X/WHPO37rHVDMEJcCjjhumM8zRISm2RFFhV//bAxS3SIysTencU+fJaBKutlwaf3e7e4l\nD7J708Poc5+Pedu/JPr274VdDVhmMx3NadPcYTLpR3Mau2FHtj+M7VPStlxut8THyelEITO+JavI\nxuf86m/4CmbLOdX+wPve/TbSdIqqwR4SxFl6nA9wiZA0DYuqAmCXMWKk+rUjCi6JriKUjsR1JOrN\n6X6/YjrtEIHdDqL+wn8vuZ7rfoceGadFwbLvTxipAKJ5yEEdxaYhcHNEYlR7kqRlkue45Rwxey5c\nTPV4xZ3tnrqv7/0FO+uss856knQ2ps9kqYI6VCGwx1Q+GHFkQUTrEqxWvoJ0MiEJkqf5CZ8FHsZ/\nlV2dQlHTgIP0xAP+KWxb+mQIRYkibQbFFHUwnYZMJv5rHQRbrC1G1mlfp9hAuB7MbjyY3cBaukzY\n5gOeqpri9unAThXi+CbYvwEXQnkLXEgb4K/MBSZNczKnM6H8LOZURAdz6q/1pZ3x0OvfQhRHf+Dr\n8tbfeitqFS1ypPcjxCiqmc+TwZweCMPNgFAS9HBFJAlWLXeijm4wp0lRcNn3CLB/yYNsH34Ifd4L\nMG9/K9G3/SewrYD+Ceb0lNa/y5za8i5zerXbEYvQq1LkCqn4l247BU0wAXztv/FS4jjm07//GI8/\n9q9JkhxVwe4DRA2tONYZnlhQlmOxwXZymsT2a0cU3SJcRqhrSW1HYvx+7OFwTZ4fzamQ6qVfb3Ad\nKyp05ndfZbPhUpVwwEgFqSGYGgpr2V03BLJEJMC5hjwXoiyD5QTRknkDu8ebM3z/rLPOesp1NqbP\nYLnh+l6Rcce0NwZxIdYJ2gkaNIRxTBpPhhags+4HfUYoahpSBD0hcGu7JakqXOhxUl2giE2GhL2Q\nZQHTqcVXTR6w1k8nw+6SrpyMBIBSeiJrvdnte/rUUMx8S5Rp/CRWVZnNDEniEFFEVt5Uqm+dwoV0\noXAnU5xA1jQsdzu/JvCHmtOKozl97U//Qw77wx/4mrzvg++n8zf56DZDer+/GYY1i0WCMYamKRFZ\nEQS+wtUdLokkxanjOupoJ4M53W656joMcLhpTt/xG59hTo87p01zPXJOm0NGZE/mdDeNvTltGq52\nO6LBnG6nQCKoVaSYIRozW+Y8+MoHCIOAD/3OB+i7HVEU4yzYvUEw1MGJHTvf70m7znNTJzrusPZr\nR5RcEc5DnG1Ju44k8Oa0LK/Jc28Yd1vDhCu/ImIb1mEHee5b4dZrLkVGjFSUh5hUWPc99aonMEtA\ncO7AYpFhJhPMIiTGke4bVnc6VuX6DN8/66yznjJ9wcZURB4Qke8UkT8nIpdfzCd11ucm30iqKHa8\nyu8IwUBPjDE94jy79HyNf39qlsy4GMDph0nIddSDwFVZMtnt0MCb0zpUxIVQLHCtIUmCoZnSEgQ1\nzl2j6ojsku4ww4mwyQP2pidQ5VZREHUdLhaKud9FlTZDt0ewvyFNT61TzpWoM35yak8sVouSDSil\nozk9xHfvnHo01cmc7nYf4zd/89f/0NdCgN95/MO0GUN9aYo0M0AIAt+2ZIyh62qMWRFFirWDOSXz\n5jRoaQZzGm+3XLatN6cPvpji4YfQ578Q85tve4I5PaX16/p6bIhqy4ywH6pRXXVPc9qpUkwVYhmm\nvTNEIx58xQu5/cAU2/W8/92/izHOc1/73jd9IRxCx36ohL3Y7YitpR92WHXir/XtRokmtwjyENd7\ncxqHHntVVdfjCsJuG5CbodShr9kkOgL4w9WKiyA4YaTmIUTCddPRbYQw9Nf/qjsWiyk6nWImHVOJ\nCa73XK9biqZ4Er77zzrrrLM+U3+oMRWRHxWRj4jIB0Tke4fP/TDwe8DPA78AfFxE/uqT+1TPeqKc\nG0IyKIEecVEQOsVJRK8lIZZwMj1XkN7HyqLsFIpKQ+6kjh7Hsm2ZbzagymEh7GNFCJDdEq1DwtCw\nXBqMsRjjcVLO9UTOs05VDdvcT2INwyS2rnEDO7UTxfQpFDPUQZ4HZJk3p8ZssHYP6vc5sTH9UIna\ni5J23WhO66M5ZTCn3d3m9Fd/9ZfG6f4fpDAMeeTd72Y3gXoIA+k+RmpvTo1pWCxCgiCg6zyLNYoc\n1oI9XBCTe4RS0FLnfm0l3u24ahpfE/rgi9k8/BD6ghd5c/of/MdQlGMg6gjhr6oTQ7Wrsv+fvXcN\nti07y/Oeb4wx51xzXeZa+3JaEt3o1kJGyDJgAjFJOY4IYEzFBHEJRVGYpAhxoGIqCS6IU7m4IBRO\n7EqIgyEJ5dgBYsoOIbaDIRhLJo5TiQ0BDALLQkLdtKS+nL33WnNd5n2MLz/G3GufI0AtoKXmSOv9\n0XX26dOz59p7z7Of9Y3vfV8Sf/Yb4NQ8ODkFtgtFEgFP/Byo4/d9+utZLBzb6zXPPvNBrPUYA0NX\no3URp7GpUidyLErIVMcdVgizOzhN55eY3BH6jrz3pMmDU95o3tqVjrm5QBCqvmKX29iU5T3Zes3S\nxmIDESFdOYIRrqqOsEuwNk6nrT0wn8/Q1RJJKhYyYXhhx/W24tB9+In3SSeddNJLoQ8LpiLyrwJ/\nAZgQs2r+ioh8BfC9wM8A3wR8C/A+4L8VkX/po3u7Jz0sfaD5aTzKtw4Vgw0DmB57qiB9JHRrikpM\nwpDY2BSlA3PvOS9LxHvahbA9OvaX6D4d46QM1g6IxDipEDqcPpB1OnXcuB5UuTgcyOsaHdcEOhMQ\nn8I2tk5Np5bZLH4vxR3WHaoSo6R8FndYp3IHp6P7v1kIhzGaSrcPw+krXvEY3/It38I3fMM38ra3\nfQ1/5I/80ePrvvfYPWazGcYYdrsd7/m5n4tO9RyqyKPoIUGqJWAwphtfr2UYekK4Ikk8IcBwWJIS\n8z3XpqOapSBCst8f4bR+3WsjnD7xasz/949JvvhrkM1hDOEfPiRDtUEEumqCG+7gdDtPwJgjnDoR\neqAs7uCUbcFsXvC6T/tk8lx45t2/Rl31ONciEvBtc4zT2kygtSAhcF6WTCDC6UTv4LRU0sUFJrOE\nriXvB5IkeaApK8LpfpuwcDGWbNfvOcwzbntNp2V5jJESgWRl6UW5v2uRdj6aoTx5PjDJczgvMKai\nCCn7Zw9cH7b0vv8YPhUnnXTSJ6Lkw+0OichPAk8An6OqBxH5HuBrgXcBn6saU5hFZDb+3j9S1a/4\n6N/2x7dERD+Sna7/8b/4i/zqr7yLUg3f8SN/lYv6wLd90VcxedOb+JK3fRHnj6VkFyseO//kU9vT\nIyJVZdNsqIcaCUpxGJiR0ItwM5/j0xTbKsUeDEJIDshidHbvBvo+QVUIYYW1OYN2yPQG6wJpO3De\nWQxCOZlwmM0gKLMtTLyA8eiiRJzSNJ7DwQDCMMzG415FJ2skaTBBuaggUaF1jpvFAjWGyV6ZtRIT\nAIotJAN9H9jtzLh6MkVkxeOPC+cXF/zS+34RK5azztLfbOj7nsnjj7POMhTIepjviGfQEw+zLRBQ\ndWy3MAwD1lqMuaDvHSKQzve0xKijZUiZVRHKh9mM6zzHqzJ56mnOvuALkWeeJnzaWxh+/IfRV8Zm\npLpOqKoWEWEyOaNpJqiCmzT4ZI2izM2EYtdBCPjJhOv5nEGVFGFRanwb7wIsS37x//0n3Dy/w+UL\nPvUPvhljaoZhhsgSOw1ossMgXByUJADOsS4K6nE3dNkazD6AgJlDW16hvcfkKZW19H2Pcw7nLmka\ngzEwW9bshjUAZ0lBXh7Ae5hOWc9m1GOslm88femZGcPFZYY316h6jJmyXncM+z3hqqXBUi0S7j0+\n5RWLe6fYuZNOOukhiQiq+pIYWV7sb5c3Az+kqrdnON8PLIC/fAulAOO//0HgD70UN3XSR6r4JXDm\nbmIajIAoIXRkVkinixOUPkJ6yBRlJJqipCdR5XK3I6lr/AOOfdPP0HIOKhRFQpb1xHzKNcOwx0kK\n1SVD5+gyF3M/NbBsGordDgT2S6hciK1T2yXaGyYTy2KhRPPRgWFYA4JpzwndhGCE6yl0EsiGgfPt\nFgmBZi7s0/DQ5DRJDItFiA59qYC4r/hJr3o12scczpt0wN07Z7VcMtnvuajrGECfwG5JXEBtLLJb\nAhaRgeWSo6ve+yvSdHTV7+ekYQz3Nx37WQIiuMOBi6rCitC89jWs3/52whveiPmVXyL5l78Eed9z\nQCDPe2az28np+tg+NTQTbB8np/vQHCentmm42O+xInQou6WAJe7VbAs+9TM/lSQzDPWWZ59+Fu8t\n1u5R3RHqKfRTAsrNzOCdhWHgrCyZqsbGqSzgb+tQd5AVF4i1hLpjRiBJEoZhYBiuyLJACFBtc2b2\nrmq1LWbcpvOvquoYI2Uzg50ZDiGwuemwckY0Q1UslxMkz7ErSy6WdFtxc91xXV2fmqFOOumkj5pe\nDEzvAc8+8PHtr5/6Tf7s+4DHXoJ7OukjVYwxhWCw4w+K2sRObsQjpwrSR1ZHU5QYDvOEa9thVLk8\nHJjs98c90dYEjJ9AWaADzOcJ0+mAiOLclmEoseKw7SV9mzCkjqtc6dUz7zpW22084l4a9okiGuE0\ndIY0NRSFjteqGYYbQtARTvOj+7+VQOY9FyOctgvzW8JpHH3G97mvec2TmPYc48cGI9NSLSYgQlpV\nXB4OEfZiSytqQDszfuC4NS5lWUIIgWG4Jss6VKGvZneRT9KxnbkIpw9ct3n1E9y84+2EP/CZyFO/\nRvLWP4788vu4Dfifz7Nj+9RkUt3BaRePyvfaUs7cEU4vH4DT/VIQJ9Bb0u4eb3jLGxATWD/3DG2t\neO+xdk8Ia6RdoUMWA/in4BMH3rPabJiF2O5WZoFhHocRYS+ki3PEGPy+ZW4CzjmGYSCEa9I07t02\nuxm5HatLhx19MQcR5HDgvOuOMVLJ3GEnhu3gOVwH7Ai0cKAoZuh8jkw7FmaK3N9zdRPh9OTUP+mk\nkz4aejEw3QBnD3x8+zb5Nwu2WwC/da3LSS+5wjglVQnHiak3BhMUS4A8P1WQPsJ6yBQ1TbmfDXgC\n523LbLsFA7uVUNuAhGR07AvTacJ87lH147TzJu4ht5d0dYZPbMw61YHpOJ2TEGgLYZcGUBkNVpYk\niXAKHucaQrhBVTHtGb6ZEgRupkIrIeambreYEU53D8Cp9vYIp7dGqNe85ol4vF+vMMMiNkTRRIOR\nMbim4XK7xQHDCKfBAYNAWSCaEMPyA5NJhNOuuyZNm2Me6a1xaS/9HUTWNZf7PU6E7rF7XP+9nyR8\n7h9Gnvsgyed/CeYf/QqgZFmEU4C63hyrUYc2w7RjvSzdQ3B6OzltRdkVIFags7yieJLHHn8FGno+\n8J5fxZiCYThgTIn3B6Q+jwYz9bERLEsgBJZlydz7mHOaKf0iwqnWhnR2gRjDsG2ZJ4pzjr7vRzhV\nvId+XzCxUxTlOuwZith/anY7zr0/xki5wiJOuOl6utIezVBJUjOd5rAqIKlY2Rnh+R3X647r+gSn\nJ5100kuvFwPT9wKfcvuBql4BBfDTv8mffRL4wEt2Zye9qELQeKSmHCtJ1ViUgKQJWT4/7YI94kps\nwuX0MpqisuRoilr2Pcux2alaGXZJQHARKGtLlrkxTmrAuQbvrwAlHS5i1qk13MwtNQOT29Yp7+kW\nhu3oiGe/JNTR/b9cCiIea9tjNJXtV/hmhhrhZio0JsLp+Qin3cKwyyKc3k1hLSI7AC4uFsAWVQj1\nAtON0Uy0bOYOrMV2HZdlSaqKN1AW4B3gBd0U4KNhZzbzTKcpqkrfr0nT6s641I8QKT3rqbmbcO52\nJEBfFNz/8b+N/8IvRtY3uC9+G+btP8stnC4WEU6bpiTL9oiA735zOHUjnBqgFWV/hFPHG1//mWT5\nhOZQ8sL7n8GYghC2iNxH1SP1BVbHwoDM049wWpQlxTBEOE2VbiHRFNYZ3DhV92XLYsLRFKZ6TZIo\nwwD+sCI1Y9arVvh5zDh1mw1nqgjEKfXKgRGuDh3+MMWYDFXPdOpJswy5KLDJgXMzxz+/5+oEpyed\ndNJHQS9GLf8AeNODv6Gq+wf3SwFEJAW+AnjxsMKTXmIJGpTkmGMqgGDy7HSM/3EiayyX08vYFOVs\nDM+nZ+Y95+s1pu/pCkOZa3TQH5aEvSNJLKuVQaTHuR7v78c4Kb+i288JIqznlr30x9Yp2/cMU6Gc\nKSpgqiV6SI5waozH2g7vrwjBY/slvp6jRlhPDbXcNU4Z7+nmd3AquxWhM1RVPMpfLgtgj7VbRCC0\nU6Q5RzBU2nE9FdQ5zDBwsdkwCbGBqiyUPmVsXFogQ6wDzfOe6TRBVem6DUkSJ5x9k+H6mPFZyxAr\nQR9w1aci+Dzn6kf/BsNXfDVy2OPe9pWY/+3vAzpWo6aICE2zJU13GBPhNN5vhNPNzN7B6eGAARqj\n7BcKBqyf8IYn/wAiws0Hn2HoOiAnhDXwQvza1Rc4jZmsV+lAm8XVnPl2y7KPjvhdqrRzQEC8wyZn\nCIZh3bCYyhin1QE3OKf0PWh1Rmoi9N7YjjDNYxHBZsPtwb1YIVlZvMD9soV2gYhFtaMoLDZN8asZ\nNjlwxhT/3I7rTcdNfXOC05NOOukl04uB6Z9V1bd+BNeZAn8S+K7f/S2d9JFKx3/eTku9CGoVNZAm\nCRM3eTlv76SXUA+ZoqxhM3ds6ciI+aS2bSNQLiCgmHZJ2GYYI6xWFmM6nPOoXuF9R6oF/X5JCIbt\nPKGUHqcaj867Dp8byjl4DUhTEPYp1hqWS4MxA84NqF5HOB0Khio2WK1nhsp4khDisf4Ip9sH4HR9\nFSemjz02BxTv9xizRkQJfQbVBaKWliGG+ifuGKU09R4VYbeA7tgSNYNuAih5PjCfx+rTritJkt0I\npymmGQPoxd/BaddxcRvR5BxXP/CX6b/hG5Guw33Nn8D+lb8FQJJ0LBbJWI26I0m2GAOhzzBtvG5F\nf4TTpGm4qKoIpxaqAjBwWXwSr3rFk3gfeObd7yZL56haQnhhzI6FUJ3hNB6/36SeehLhdLbdsuq6\nWLWaQjPCqRGHNStEDX7dUMzlmPV625LV9wL1OU6SWF2aejTLIASmZcliNEMZJyRzS49yf9NhQmyG\nUq1ZLieYNGVY5rgRTvtnd1xtWtbN+mP7QJx00kkft3oxMH1WRL5XRD7rw/0hVd2o6t9U1V97Ce/t\npA+jEMJY/QRO7+pI0RhqPpmcKkg/HvWgKWq/SLkxHVaVe7sdaVURsgfC8/s5Wk4RDKtVgnMd1gZE\nrhmGmpQZ/nCGH4TDPGFt+miw2u1I6zpeq4ABxbQLwnaCCKxWFmt7rB1QjaH+zi/w1RJF2EwNBxke\ngtN+hFMUts/GN1L37p0f3fre14hcY0xAfQLVJRISeh2iWSt1EAKrsmTe9yiwm0M7tkSxmyFt3IvM\nsv4IkV23w7lyDLZPMM0lBksrnuuZoNYifc95WZKrEozh6i/+V3Tf+h8hIeC+8Zuw//UPAEKSdBTF\nLZzuj9f1XQr1+W+E07rmfJyc1haqIh7BP/maT2OWnVPvK5799V8jz1eoBobh10nTlhDi8bsL0bi0\nSTzVNML2dLdj1bYx6zWFejzWNy7BsEKCwd80FIs7ODVmjbXQdYJpL3DG0fmOdU4M4B8GFmVJPpqh\n7NTipoY2KOsbj5U4UxWpODtbfAic5nTP7bnaNNzUNx/z5+Gkk076+NNHYn76d4CfEZF/IiLffKof\n/b0hP8SGHgXsrfFJDEYFMY7UJS/r/Z300dODpqhmlnI/7VGUi6oi326P4fmNCRg/hc0c9bBcpmRZ\nh0jA2jV9vyeRCVpdMPSGepZwnfSggYvDgUlVoalhWyjdbTTVdgoKy6XDuR5r4xR2GDqsnxGqFYpQ\njnWoye2KwDBEOJ0omzLGRRWzFWkaq1WNgRBie5UxAxpshFMfj5+vJ+F4rF3sdizbFoD9FKpZ/Lzo\nfnIcI6ZpT1E4RIS+P2DMeoRTh2kuseLo8FxNISTuGNE0CwEV4frb/1Oa7/rzALg/82ew/8l3xzeB\nLh5rR+g94NxmnJxGOBUinK5zAWNIm4bzwwEBaqvUhWCc4Y1PfgZmmPD808/SHDak6YwQBrruvWRZ\njL3yVYHzRYRT27OfxmSBfL/nrK4RoEqU6hZOE4cJS/AGv25ZLBjhtDm+/q41mOYifu/4ls3UHAP4\nV9vtMUbKzS0mFfaDZ3sjGBOhX2TP+fkKk2UMxQTnKs51QjPC6bo+TU5POumk351eDExfD3w+8D8T\nTVDfDXxARP66iPzRD/tfnvRRl6KICMaPdaTWxh9QVrEnMP241kOmqEnK/Ymnx3PW98w3GxBlvxIO\nNiCaRcd+C/N5ynTaIxJIki19X+IkRepLutbS5SlXmSegnNc1090OTQy7JbQSMMMU3cbc1OUyIUn6\nB6awLTZMCVUMqt/OHFsTVwQuttu4vzo3PFuNwe/uCbSzWAvLZeSjEOIU1pjuuHMpwxgnlXqqfDzW\nfgDO6gkc5hqzTg8ZHCKcOtdTFBZjDMNQY8wNxihDH6HXktAzuuATC96zLEsWY0TTzb//p6i/7/tR\na3F//r/EffO3Q1CcG47X7boKa+NEMvQpUl8gGGrjf1M4rZxSL4TirOCJx9+I9gnvfed7mU1znEsZ\nhoameS95HuF0qOa4YYy9sgPbqQURJlXFeVXF158ohwWIEUyaIP0SeiGUHUUhGGPo+/oBOLXYbpzw\n+jGP1Vqk6zg/HO5ipFYOY4WyG2i2E4yZoOqBkrOzJWYywS8nOHvgImQ0zx24Kms2zeZleipOOumk\njwd9WDDVqHeo6tcCryROT38B+ErgJ0TkaRH5dhF57Uf9Tk/6ECkaAvqAI38QQwAEwZqTG//jXQ+Z\notKE66lQaUcRAqvNBuM9za1jXx2yXxFqw3SajnFSA0lyoOtuMFhce4+2Ht3/k0CvnlXXMX8wmuo2\nN3UzJ3ilKBKSpMNaxZgbhqHBhhx/OEODsJ/d7a9e7HbYvmfdjK1MixXslmiXYAwURTxZVg2oXmNt\nfYyTkn6Mk3IDu9vJYVUdj8qbLO6dKgpNCrvYZxohUkY4bRC5xlrFDwatLrDcRTTdrgssNhtWw4AA\n63/za6l+8IfRNMV+/3+P+xPfAr0fr2uO0CcSoTcMSdyR/RA4zZrmCJJVojRzeN0bXk+RPUa7V979\ni+/m7GxFkqR4X1FVTx8rUYd6iunG2CszxCmnCFldHxMAmgT2i2hgspMEaQu0gbBrH3j9NcZsYlpB\nk2Bvr+lr9mNEl6lrzpsm/mAQITlzIHB96Oir2dGpL7J9CE6tqbgIKfVzFfc31QlOTzrppN+xPmJ6\nUdWtqv4Pqvq5wKcBfwFIgf8YeK+IvF1EvuajdJ8nfaiUoxPW+LsMU1HBGIt1p7anTwQ9ZIpyNpqi\ntGWqGp3xbfuAY98ghxVhb8myZIyAGkjThr6/QoC0v6Q9ZPjUcT1GUxV9z3K7RVAOK+FgbqewMdS/\nKD50RaDC6oRQnRN83F8tzZ25qrx/HwC9t4g70bsCbeL+6mIBk4miqni/xphoXtJmgbRjnJQd4WwE\nvovdDgt0Y0uUSvxAtwUgWOtZLuPOpfdxXcDaQPAGqgscMUrpKvM0o9Fout2y6mPGRfllX8LuR/8W\nOptjf+Sv477s34a6wdphNINF6L2dyMYd2Q+B0xEkz0Y4PaTQzOBNb/n9uGHG1ftLnn//FWdnBZNJ\nivc76vo5sizGXvl2gjTjlNP4Y+xV2rZ38VTJyOMWzCSJprUDUPUsFoz3WWFteZdWMEQ43Yaaap7F\nEoL9nvPxtWOFdOlQgauyI3TFh0xOC0yeE5YZ1lScD47q+YqrsqJsyo/9A3HSSSc98vodjdVU9V2q\n+q3AE8CXAH8XeCvwAy/hvZ30YeS9j6aPAM7HvgMvBiuCdYI51ZB+QuloijKWfZGxlvZYY+qqCj8z\nbI6O/RVhexsBZRDpSNOeYbhPCJ4sXNDuc7yN0VS19szGHUyjSr0SdtZjNI3h+Z0wn6fkeaxDtXZD\n1x2wmt3B6Syaqyzw7LvfDYCsUrZTjSsphxl6mAHCbCbMZvFNVwg7ROKUT7sp3MZJGc91DmoMyZh1\n6lTpx0asYEB6d6yMMsaP6wIW73sinHq8F8LhnERnKMo6uVsXyLfb4/Rw//lvZfvjP4GenWN/6v8g\n+eKvg3KPMQNFcQt97XEiewunaITTm2mE00lds7qF0wzMK6a87sk3QDPhvb/0FF3rmc9zptME7zfU\n9dUxnuqYWDAC7/H1ty2XD8G5YFKDzVKkKfA7RbqBxSK+kRmGwzGiq68nOD/Wt2pDM4uZrelux2pc\nETKZIZlbPHD/psWEJcbEiC7YRjidTglFijU1Z53l8HzN/fLAtt1+zJ6Bk0466eNDv9vz3s8hgum/\nMH7c/i6vd9JvS4pKwI4T08EaVDROTBP7Mt/bSR9rPWiKqucZV65DUC6rinS3Q0fHfjQyFehmgkFY\nrRzGtCTJnZEp0zO6/RwvJmadakseAuebDdZ7upVlmwQkONgVhE6YzVJms2HcXy3puj2ODK0uCD6a\nq9a2531PPw2A6zqGaYylCgLSTNAy7q9OJsJioTHfNFSIXCMS0D5DD2OclAkxTsoIdhi4LEuSEPC3\nLVFWY2XUZokGi4inKJQksXgfd1mdGwgBhsOSJDywLpDHXc7scOCirjHA4Z//bDZ/7x3oKz8J8//8\nQ5Iv+Cp44QZrA8slWGvGieyD6QIRThsTuMmJKwgPwOk+Uy4/7ZM5v7jA71L+6T/+NazNyXPLbOYI\noaRptiRJGfdYhwSpL4+v/3oKornU3gAAIABJREFUwQiu6+Ieryq9g3KhSG7izmlV4Esw/kE43WNt\njO3qqynWx9e+lpZumkUwf2Df1uYGlxt6VZ57oUaGAmNy7uB0gZnNYJmSmJqz1rB/oeZ+uWfX7l6W\n5+Gkk056NPXbBlMReaWIfKuI/FPg/wb+LWJD1J8CPuklvr+TfkvFqKhAwN7GRUncPbMSd8pO+sTT\ng6aofppxPxsY8Fx2HdOyRG087q4lYMI8gmAQVqsU51qs9Yhc0/c1GWPWqVq2i5SttmS3Rqauoy8M\nZRZQtchuSWgMeR7rUG/NVW27xZKihwv8YKinCU99MBbEXex2uLbFZ0JZKIMoMqRQLlFvSFNhuYzx\nZyG0wBUiHkKCHsY4KYlwOjiD8Z7LzYbMe4KBzRIGRxyfbmJllEgY4dQQgieEK5KkQxX6w4LEj+sC\nzlPm8XlKqorLwwEL1G/+VK7//jsIr3sS84u/QPJ5Xw5PP4sxgaK4g9Pb6CtCBEnU0FjlJo8HHXld\ns6xig/N+orz+c96Mc47ygy3v++UXsLYgy5TZDFRL2vaAMTGTNAwuJhaoo5M7OHd9H3NoVRkSYVso\nZmoxSQKHOcMaLIH5/BZOdxgToTHUC2yIU+Mb1zPkEU4XY4yWiuAKR5IbOlWev2qQfvEAnO44O1sg\nI5xaKs4aw+6FhhfKHftu/zI8DSeddNKjqI+IXkQkEZEvF5EfA54B/hzwCuB7gc9S1T+oqn9JVU8b\n7x8jDd1wm7CPuzU/GYtBsMZi7Wli+omqW1PUxE0IWcp1DlVoWQ0DRVmCeg5nwt4GjE5gW6AdLJcZ\nWdaN7U5r2nZPJjHrdPCGfZGxkQ4XQlwRaFv8wlLmSlCD7JeEyjCZRDgFT5bt6boSKxHQ/GB57rnn\nATCq3NvvyZqG4IRyBa1VjiPP3mEtrFaCtQHVAbiPSAcanfX4DC/KVa60TpBxtzbve1Qi8PaJImrQ\nsuC2MqooIMssIQSG4ZokqYE4PTxWmNoxiF8EN1aYOqB73Wu5fsfbCZ/2Fsx73k361i+Fd70PY8K4\nLmAeWBcIqL8FSUtjlfU0RjJNH4DTdpXy+n/uzQjwgXc+R/lBj3NnJIkfjWoVfX9r3gp3cVohYRCN\ncG4fnhwPLr5+WViMSWA3Z7hREnsHpyHsMGYfa2GrJSbE1qnrZMCnsRL1bLslJf51kxSOdBbh9IWb\nBukWGDPlbnI6R+ZzzCrFUXFWC/urluc3Ww7d4WP6HJx00kmPpj4smIrIp4vIdwMfBP4X4I8B/yfw\nNcCrVPXfVdWf/+jf5km/pYLgwl3AvhjFpQ6R08T0E1kiwnl+zjydo4ljs0jYacs8hLgr2ve0KzMe\nxyejY19YLLIxTsqTplvatiSRCVQX9J2hmqes7V0Qf1LXhJmhnGlMhKjuzFVFocBAmh5o2zWiNh5t\nA/ceuzdmpioXhwOzqgIj7JbRtS5q4v5qk2IMLJeGJAnENuRrRGpUBakv0D4nEKeRdWpAlbPdjlnb\nggi7QmgzEBXYLdAuwul8Hshzh6rS92uSZH80Bdn2EiEewV/PhGAEO+6ypkD/qldy/x0/hf/sP4R8\n8P2kn/elyM/+ynEia608EH3lITi0uoBgH5qczh6A0+y1l1x+yhN4DbzrZ96D38xx7gxrB+bzAyI9\nwxDNW855NJgRzlO8KNdTobcxPu7idq3BjZPTlUPEwXbOcB1IXGA20xFOt4gcYgJCdfZQduxtxuv5\ndosDPODmjmRuacMIp+0ca2POKew4P18giwVmmeCoWB6U3f2W5zblCU5POumkF9WL0cvPA98MVMB3\nAE+q6uer6g+r6mmf9GWWKhgE84D5KYSAte50lH8SAEVWHE1Ru0XKmobJCJW2aegLwyZXgtrRsW+Y\nTrNx4tmTZREqLbE1qW1iqP9V0qMauDwcyA4HNDds5rctUSvC1pGmjqJgvE5N160xxEn+4088ETNT\n04GAsqxrVrtdbEkqhN0ktkTJYYEeckSgKAxZ5lFVVNeIxEmfNGdoG1uS1llgnwmostzvKeoaBfZz\nqCc6pgAsoIkVptOpZz5PxiD+LdZuji1RUl9ieOC43BrMMHCx2ZCFgF+tuP8TP8bweV+I3FyRfNGX\nIT/9s4joODmNcHpbGkBwUEc4bR0PwWlRVSjwis/6faT35rRNwz/7ufcg+0usWWKMZzbbYm14YD+2\nj1mv1QUMEzxx57RzggmBy82G1Hv8aAiTM4vg0HLBcBNIU2U6jW9qVUtEKlSJRQEhxmhdTxS1FtP3\nXOz3OBE8YGcWV1haVe6vG6hnWBsrZlXHyWlRYApHQs2qCuyuOp4vS6q++lg/BieddNIjpBejl/+V\nOCV9rar+Z6r61Ef/lk76SKRj61NAMUM8yvcm7sQZK6c60pOOypOci+kF1jjqxYRr22GC53K/J9nt\n4sRzrnhAmhW+vIuTgm6EymtEDa69pK0S+jGI3xO4aBry7RYmhm0BrQZMvySUKc5ZlkszXqeh62Jt\n5aufeCNd4+gnSQy4JzDtOs7LEhMC3XhPAUWaKbqbowrzuWU6jd/vqlsgOvalK6AZg+hTZTMBRJhX\nFavbcPuZcBhbojjM0Cr2mcYKUzvuXVbH2KfbXU6j43F5rvRWkHEimYdAmM+5/zd/hOFf+wpkvyP5\n0q/C/O8/jYhSFJAkQgieaIgaHpqctg5uJvE5no+TUwEe/5zfz1BYrq9e4IPvew67fxWGBSI9s9lm\nXBXwhHCNcy0gSHMOQ5wcX+fQJHdrDdkwECxslwJnFgkOXS8Y1spkIkc4hfI4idbqHIKjZ0wUsBbb\ntlweDiRjO5TJLbYwNCj3Nw1U0yOcxp3TGbJcYhYWpw3LQ2B7P8Jp3dcfi2/9k0466RHUiwXsf6Wq\n/qTeBmae9HtGfvAjmvLQUb7FIMacjvJPekipTY+mqG4WTVFeBy7blklZRsd+AS2KHVaETYYzltXK\nAi1Z1tH390GVdLik2Wf4ScpVrnQ6cNb3zMoSEmG/hIaAGRaEMsMaw2rlEOmOu5yvec2TuO6SrkkY\nnOFqJrQSyLzn3maD63v8xFAWRFNUF3NTCUKe29FgBVCheo2IQj+2TqlQJXcTyWnTcLbbIao0E9jP\nx4iqeoqOLVFJ8nAmqUg8go9Zp5eYkI3H5dA6ibuXZclsGNA05f5f+5/ovu7rkabBffXXIT/4txFR\nFgslTSOcxmP9HtEIvOotbSJcT8JxcnpW18wXM1755jdQT+F9730PVdlgdk8gYYpqx3S6ZjKJ+7He\n3+BcPU46z9BujL2aQD3C6XlZMul71Am7laDnFgZLuJoxlIE8N0ynYcxF3iDSgMaMV4KlZThmsZq6\njikPIghgJhazsDQoV2WL7nOsXRA/8/sIp6sVdmFItKY4eDb3O54rNzRD83I8CieddNLvcZ3o5RHW\nEUzHuKheDKCkziGno/yTPkQPmaImGdc51NpxPgzMNhuwyv5MqPDYsEDLKaKGs7MEkZosiy72YeiY\n6AXNLsc7x/VUqEPHchhYbDZgYb8ar+MXaJkfY6n2+2sAlssJGpSku2RocoIRbmaGvRmwqtzbbqMp\nKonH0K0JiE/QzRLtDVlmKYqASCCm1F0BHvET9HB53OW8msYoqskYp2RUabO4d4qANBk8FMT/oVmn\nPSHEalQbpne7rEmE0+V2y6LvUWu5+u++h/bf+9PIMJB+wzdivueHjqUBSXLXaHVr3pI6wmmXmCOc\nTsYEgMdf8wSLVz3GLvH8yrveiajgDq9GhwmqNVlWkucWVWUY1iTJIU6O2yXajtFPE+WQxKbW892O\nvOtQJ+zPI5zK4PDPz/A7Jc8Nee5HOF2j2kSDWX2BBkMtA+VYhypVxcVux0QEI4LJBApLTeB616G7\nCdYW3E1Op5jzc+xcSELDcj+wuep5rlyf4PSkk076DTrRyyMuBazeuvINWMEmQpxnnHTSw3rIFJUm\nbOaOXWhYhsCyLDFhoDo37IzH6BTKOfRwfp7jXI1zHpEbuq5mwhntbo43lvXccggtixBYlSWGQHVm\n2EnAhBlazkAFkTgxPT+f4/0VIXhcf4aviujrnjk2ib8zRR0OYOOkr3IhGqi2K7R1Y0GAYIxHtUfk\nCugRTcaJpKO3cH8Kg4F0dKzbEOgTKAtFDdA72KxQb0Zn/V2cVKxGbQkBQrXCDPMj9N3usi62W5aj\n0er6z/3n1H/2OwFI/vS3Yb7je0ADRSHHulW4wZi7ZAH1LsJpHmOZkqbhcrvlTZ/+JpJ8wv1+x7uf\neg+CJak/GbqcEPZk2Z7ZLO7s9n15DM2XboE2Y2j+BHYpR0PYtGlQK+wvDXoW4XR4doqvlOnUMpkM\nqCoia1TbcfXgHA3CwQzsFhkYg7Qt57sdU2OwxmBTQQtDTeBm36HbbIRTgD2r1QRzcYGbQxIail3P\n+n6E03Y42RVOOumkO53A9BFVUEWDgio23O2YKoY0SU47pid9WB1NUdaxKzLWWjMdj6dt29KdWcok\ngGawXxFqWC5zsqzF2mGMkzowkZh1OqilHJ3/0xA422wwwdOeCVvjMZrDZs76JjYBrVY5aRonsN73\nWD8nHM7RYKgmNjrCUZZNw2qcdNZLwzYbTVH7JVqlWCusVnYsB/DEyWlzhL7Qp3gDV1PoLDjvY5yS\n9zFOaTUG8XsD5Qr6hLs4qWgmDOEGa6MxKNQFpl8ed1m3k/iczfZ7zuoaAdb/4bdw+G/+UoTM7/wu\n7H/wnRA8iwVk2YOT0xYhJhWod3RO4oTXGmzf84r9jjd9+ptQY3jv88/w3P4aQ4qtXwH1DO93ZFnL\nYmGOofkia4wB6WdoHdcadhmUsdCJ1eHArK7BCvvHDKEQpHcM758SGmU2c2RZDygiN6h2GE2PKxI7\nbSKcWgtty2qzYS6CMwaXGsLSUBFYHzq0zLB2OX7HHVguM8zlJW4OaWhZ7DrWVwPPbW/ofPexfQBO\nOumk37M6gekjqqGPOaaq4B6YmFoMziUv892d9CjoIVNUkXMtDYkGLvZ77OHAUJiYURpGx/5OWCwm\nzGYDxgwkSUldl8es095H5/9GGybARVli+55uZdhYj2jG5pkIII8/folzDc5FmOz7Gqv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Iqd0TTt5CfQdYYUis0mQqlh/h569//1e7ZG0knDZS5wUhKMIw83DYUQjAoezTSjdDBdk9MI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\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D2WeoYdA7IPIPcJqSNlEsmphtscrCaGyxQLRTpJgiTE7ahpTuOp9x+5P5tHPniMKSenYf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\nRx999JCtsKZp8oY3vIHdu3cfkN7X10dra+tYC+tHPvIR7r7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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa2f56c5a10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.html.widgets import interact #import interactive tools\n", "\n", "#setting plotting visual parameters\n", "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\n", "pylab.rcParams.update({'font.size': 18})\n", " \n", "#define function to use in ipyton interact widget\n", "def plot_explorer(threshold_value, ylim_pos=5000, ylim_neg=-5000, local_extrema='True'):\n", " \n", " i = 0\n", " for th in thresholds:\n", " V3 = np.array([])\n", " ion_frac_vol = np.array([])\n", " for z in redshifts:\n", "\n", " V3 = np.append(V3,data_dict[z]['CV3'][i])\n", " ion_frac_vol = np.append(ion_frac_vol,data_dict[z]['ion_frac_vol'])\n", "\n", " plt.plot(ion_frac_vol, V3, linewidth=2, alpha=0.1)\n", " plt.xlabel('redshift')\n", " plt.ylabel('V3')\n", " plt.ylim(ylim_neg, ylim_pos)\n", " i +=1\n", " \n", " #selects redshift to highligt from plotnr parameter\n", " th = thresholds[threshold_value]\n", " V3 = np.array([])\n", " ion_frac_vol = np.array([])\n", " for z in redshifts:\n", "\n", " V3 = np.append(V3,data_dict[z]['CV3'][threshold_value])\n", " ion_frac_vol = np.append(ion_frac_vol,data_dict[z]['ion_frac_vol'])\n", " \n", " #plots highlighted redshift curve and annotates it\n", " \n", "\n", " plt.plot(ion_frac_vol, V3, linewidth=2, alpha=1, color='red')\n", " plt.title(str(plotname))\n", " plt.xlabel('global ion fraction')\n", " plt.ylabel('V3')\n", " plt.annotate('$th = ' + str(th) + '$', xy=(ion_frac_vol[8], V3[8]),\n", " xytext=(0.5, 0.1), textcoords='axes fraction',\n", " arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth =10),\n", " horizontalalignment='right', verticalalignment='top',\n", " )\n", " \n", " if local_extrema == 'True':\n", " \n", " V3_maximums = argrelmax(V3)[0]\n", " V3_minimums = argrelmin(V3)[0]\n", "\n", " for maximum in V3_maximums:\n", " #print V3[maximum], ion_frac_vol[maximum]\n", " plt.annotate(str(ion_frac_vol[maximum]) + ' , ' + str(round(V3[maximum])), xy=(ion_frac_vol[maximum],V3[maximum]), xytext=(-20,20), \n", " textcoords='offset points', ha='center', va='bottom',\n", " bbox=dict(boxstyle='round,pad=0.2', fc='white', alpha=0.3),\n", " arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', \n", " color='black'))\n", "\n", " for minimum in V3_minimums:\n", " #print V3[minimum], ion_frac_vol[minimum]\n", " plt.annotate(str(ion_frac_vol[minimum]) + ' , ' + str(round(V3[minimum])), xy=(ion_frac_vol[minimum],V3[minimum]), xytext=(20,-40), \n", " textcoords='offset points', ha='center', va='bottom',\n", " bbox=dict(boxstyle='round,pad=0.2', fc='white', alpha=0.3),\n", " arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5', \n", " color='black'))\n", " \n", " plt.savefig('V3_global.png')\n", " \n", "interact(plot_explorer, threshold_value=(0,len(thresholds)-1,1), ylim_pos = (0,510*7,1000), ylim_neg = (-510**7,0,1000), local_extrema=['True', 'False'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
zzsza/Datascience_School	도커 Tip - 컨테이너의 파일 백업.ipynb	1	6913	{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "da230397189449d98d3f2f9b8d3da5ae" }, "source": [ "# 도커 Tip - 컨테이너의 파일 백업" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "ba6f3bd3fa4e40b3846186c8a6019abf" }, "source": [ "도커 이미지나 컨테이너를 업데이트 하려고 컨테너를 삭제하면 기존에 컨테이너 안에 만들어 놓은 폴더나 파일은 사라진다. \n", "\n", "폴더와 파일을 호스트 컴퓨터에 백업해 놓으려면 다음 두 가지 방법 중 하나를 사용하면 된다.\n", "\n", "1. `docker cp` 명령을 사용하여 복사한다.\n", "2. 도커 실행시에 미리 shared data volume으로 지정하여 실행시킨다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f15fe9fc2c8d476c922066db42340014" }, "source": [ "## `docker cp` 명령을 사용하여 복사하기" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "a4b5e765b1d74f319213dc8b2d654b44" }, "source": [ "이 방법은 미리 shared data volume을 지정하지 않은 상태에서도 쓸 수 있다.\n", "\n", "호스트 컴퓨터로 나온 다음 Docker Quickstart 셸에서 다음과 같이 명령한다.\n", "\n", "```\n", "$ docker cp 컨테이너_이름:컨테이너_폴더나_파일_이름 호스트컴퓨터의_폴더\n", "```" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "d713e22d44c54dd6874d625bc07879cd" }, "source": [ "예를 들어 rpython 이란 이름의 컨테이너 내에 있는 /home/dockeruser/notebook 폴더를 통째로 호스트 컴퓨터의 d:/ 라는 폴더 아래로 복사하고 싶으면 다음과 같이 명령한다.\n", "\n", "```\n", "$ docker cp rpython:/home/dockeruser/notebook d:/\n", "```\n", "\n", "만약 data 폴더 안에 untitled.ipynb 라는 파일만 있었으면 다음과 같이 notebook 폴더가 만들어지고 그 아래에 untitled.ipynb 파일이 있는 것을 볼 수 있다.\n", "\n", "```\n", "$ dir d:/notebook/\n", "untitled.ipynb\n", "```" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "20d5b0865afd439a86addf8243039f8f" }, "source": [ "만약 datascienceschool/rpython 이미지를 사용하고 있을 경우에는 /home/dockeruser 폴더를 통째로 복사하지 않도록 주의한다. 이 폴더 아래에는 Anaconda 설치 파일도 같이 있기 때문에 필요없이 많은 파이썬 실행 파일과 패키지 파일까지 복사된다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "3f269013f6db4ff7ac8546c7b4dc5876" }, "source": [ "이 방법의 단점은 아직까지 * 등의 wild card를 지원하지 않는다는 점이다. 따라서 정확한 파일이름이나 폴더 이름을 지정해야 한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c514369a52c04c03b0d2b5d660667958" }, "source": [ "반대로 호스트 컴퓨터에 백업해 놓은 파일이나 폴더를 컨테이너 안으로 복사하려면 다음과 같이 파일 인수를 바꾸면 된다.\n", "\n", "```\n", "$ docker cp d:/notebook rpython:/home/dockeruser/\n", "```" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "6a8cac3532dc446ba298b84b01cbbfef" }, "source": [ "다만 이 경우에는 호스트 컴퓨터의 사용자와 컨테이너의 사용자가 다른 경우 permission 오류가 발생할 수 있으므로 도커 컨테이너 안에서 다음과 같이 폴더 소유자를 변경해 주어야 한다.\n", "\n", "\n", "```\n", "$ docker attach rpython\n", "\n", "dockeruser@bbbd63bfa054:~$ sudo chown dockeruser:dockeruser -R /home/dockeruser/notebook\n", "```" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "7eeaff59c5b04f29ae13eed46083b65c" }, "source": [ "## shared data volume 지정하여 실행하기" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "19edf5ec216541068b808af916d688c3" }, "source": [ "이 방법은 호스트 컴퓨터와 도커 컨테이간의 공유 폴더를 설정하는 방법이다. 다만 이 방법은 리눅스 호스트에서는 편리하나 윈도우즈/맥 호스트에서 Docker Toolbox와 가상 머신을 사용하는 경우에는 사용하기 힘들다. 공유 폴더가 boot2docker가 설치된 가상 머신과 컨테이너 사이에서만 공유되고 그것을 다시 윈도우즈/맥 호스트와 공유하려면 또 VirtualBox와 호스트 컴퓨터 간의 폴더 공유를 설정해야 하기 때문이다. 또한 이 방식은 최초로 docker run 명령을 사용하여 컨테이너를 실행하는 시점에서만 사용할 수 있고 이미 실행되어 있는 컨테이너에 공유 폴더를 추가할 수는 없다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "cf57292234fb459e8fa66f795beb54f2" }, "source": [ "사용 방법은 다음과 같다.\n", "\n", "`docker run` 명령을 사용할 때 `-v` 라는 옵션을 사용하면 호스트 컴퓨터의 특정 폴더를 컨테이너 이미지 안에 링크시켜서 컨테이너를 실행한다. 링크된 폴더안의 내용은 실제로는 호스트 컴퓨터에 바로 적용되며 해당 컨테이너를 지운 다음에도 변하지 않는다. 사용 방법은 다음과 같다.\n", "\n", "```\n", "docker run -v 호스트_폴더:컨테이너_폴더\n", "```\n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "9e26a28c63e24467a33331dc55ee3b68" }, "source": [ "만약 리눅스 호스트에서 datascienceschool/rpython 이미지를 rpython 이란 이름으로 실행시키면서 ~/rpython/notebook 이라는 폴더를 rpython 컨테이너 안의 /home/dockeruser/notebook 라는 폴더로 공유하고 싶다면 다음과 같이 도커 컨테이너를 생성한다. \n", "\n", "```\n", "docker run -Pit --name rpython -p 8888:8888 -p 8787:8787 -p 6006:6006 -v ~/rpython/notebook:/home/dockeruser/notebook datascienceschool/rpython\n", "```" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
TANAV/predictorsAndDB	predictorNotebooks/SGDClassifier_Jewelry.ipynb	1	12531	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle\n", "from scipy.io import loadmat\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.cross_validation import StratifiedKFold, train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.grid_search import GridSearchCV" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load processed data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "productCategory='Jewelry'\n", "tfIdfArr=loadmat('/home/hencrice/Downloads/AsterixDBClassData/processedData/TfIdf_{0}.mat'.format(productCategory))['data']\n", "scores=load('/home/hencrice/Downloads/AsterixDBClassData/processedData/score_{0}.npy'.format(productCategory))\n", "scores.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "(20442,)" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split data into training+validation (used gridSearch to pick hyper-parameters), and test set (evaluate model performance)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tfIdfArr_trVaSet, tfIdfArr_teSet, scores_trVaSet, scores_teSet = train_test_split(tfIdfArr, scores, test_size=0.1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "hist(scores_teSet)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "(array([ 446., 0., 292., 0., 0., 452., 0., 409., 0., 446.]),\n", " array([ 1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x2a86290>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pick hyper-parameters for SGDClassifier using grid search:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hyperParam={'n_iter':range(5, 20, 5),\n", " # strength of regularization\n", " 'alpha': logspace(-5, -3, 10)\n", " }\n", "clf = GridSearchCV(SGDClassifier(loss='log', class_weight={1:0.45, 2:0.6, 3:0.05, 4:0.1, 5:0.25}), hyperParam, n_jobs=8, verbose=1)\n", "clf.fit(tfIdfArr_trVaSet, scores_trVaSet)\n", "bestClf=clf.best_estimator_\n", "clf.best_params_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=8)]: Done 1 jobs \| elapsed: 0.6s\n", "[Parallel(n_jobs=8)]: Done 50 jobs \| elapsed: 8.0s\n", "[Parallel(n_jobs=8)]: Done 76 out of 90 \| elapsed: 11.5s remaining: 2.1s\n", "[Parallel(n_jobs=8)]: Done 90 out of 90 \| elapsed: 13.2s finished\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "{'alpha': 1.0000000000000001e-05, 'n_iter': 15}" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction accuracy of each class:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "te_cm=confusion_matrix(scores_teSet, bestClf.predict(tfIdfArr_teSet))\n", "te_cm.diagonal()/sum(te_cm,1,dtype=float32)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "array([ 0.83632287, 0.63013699, 0.18584071, 0.43031785, 0.90358744])" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the resulting model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('/home/hencrice/Downloads/AsterixDBClassData/models/clf_{0}.pkl'.format(productCategory),'wb') as fp:\n", " cPickle.dump(bestClf, fp, -1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 } ], "metadata": {} } ] }	apache-2.0
albahnsen/ML_SecurityInformatics	notebooks/14-KaggleCompetition.ipynb	1	31809	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 14 - Kaggle Competition\n", "# Fraud Detection\n", "\n", "\n", "## https://inclass.kaggle.com/c/easy-ml-class\n", "\n", "by [Alejandro Correa Bahnsen](albahnsen.com/)\n", "\n", "version 0.1, May 2016\n", "\n", "## Part of the class [Machine Learning for Security Informatics](https://github.com/albahnsen/ML_SecurityInformatics)\n", "\n", "\n", "This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```pip install tqdm```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fraud Detection" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import zipfile\n", "with zipfile.ZipFile('../datasets/fraud_transactions_kaggle.csv.zip', 'r') as z:\n", " f = z.open('fraud_transactions_kaggle.csv')\n", " data = pd.read_csv(f, index_col=0)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>card_number</th>\n", " <th>type</th>\n", " <th>merchant</th>\n", " <th>amount</th>\n", " <th>fraud</th>\n", " </tr>\n", " <tr>\n", " <th>ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2011-01-01 08:00:06</td>\n", " <td>1942</td>\n", " <td>2</td>\n", " <td>8328</td>\n", " <td>65.16</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2011-01-01 08:00:16</td>\n", " <td>5629</td>\n", " <td>2</td>\n", " <td>42588</td>\n", " <td>260.84</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2011-01-01 08:01:28</td>\n", " <td>408</td>\n", " <td>2</td>\n", " <td>15622</td>\n", " <td>6010.05</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2011-01-01 08:01:43</td>\n", " <td>859</td>\n", " <td>2</td>\n", " <td>45192</td>\n", " <td>348.46</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2011-01-01 08:01:48</td>\n", " <td>3786</td>\n", " <td>2</td>\n", " <td>35549</td>\n", " <td>1160.35</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date card_number type merchant amount fraud\n", "ID \n", "0 2011-01-01 08:00:06 1942 2 8328 65.16 0.0\n", "1 2011-01-01 08:00:16 5629 2 42588 260.84 0.0\n", "2 2011-01-01 08:01:28 408 2 15622 6010.05 0.0\n", "3 2011-01-01 08:01:43 859 2 45192 348.46 0.0\n", "4 2011-01-01 08:01:48 3786 2 35549 1160.35 0.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>card_number</th>\n", " <th>type</th>\n", " <th>merchant</th>\n", " <th>amount</th>\n", " <th>fraud</th>\n", " </tr>\n", " <tr>\n", " <th>ID</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>199995</th>\n", " <td>2012-12-31 17:04:18</td>\n", " <td>4069</td>\n", " <td>2</td>\n", " <td>35828</td>\n", " <td>91.22</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>199996</th>\n", " <td>2012-12-31 17:04:51</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>46923</td>\n", " <td>390.95</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>199997</th>\n", " <td>2012-12-31 17:05:38</td>\n", " <td>1481</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>0.65</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>199998</th>\n", " <td>2012-12-31 17:05:55</td>\n", " <td>1481</td>\n", " <td>1</td>\n", " <td>4535</td>\n", " <td>390.04</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>199999</th>\n", " <td>2012-12-31 17:25:02</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>8322</td>\n", " <td>308.44</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date card_number type merchant amount fraud\n", "ID \n", "199995 2012-12-31 17:04:18 4069 2 35828 91.22 NaN\n", "199996 2012-12-31 17:04:51 9 2 46923 390.95 NaN\n", "199997 2012-12-31 17:05:38 1481 1 -1 0.65 NaN\n", "199998 2012-12-31 17:05:55 1481 1 4535 390.04 NaN\n", "199999 2012-12-31 17:25:02 0 1 8322 308.44 NaN" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " 0.0 171048\n", "NaN 27909\n", " 1.0 1043\n", "Name: fraud, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.fraud.value_counts(dropna=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate aggregated features" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "from tqdm import tqdm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split for each account and create the date as index" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 8087/8087 [00:20<00:00, 390.15it/s]\n" ] } ], "source": [ "card_numbers = data['card_number'].unique()\n", "data['trx_id'] = data.index\n", "data.index = pd.DatetimeIndex(data['date'])\n", "\n", "data_ = []\n", "for card_number in tqdm(card_numbers):\n", " data_.append(data.query('card_number == ' + str(card_number)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create Aggregated Features for one account\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_agg = pd.DataFrame(index=data['trx_id'].values, \n", " columns=['Trx_sum_7D', 'Trx_count_1D'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trx = data_[0]\n", "\n", "for i in range(trx.shape[0]):\n", " date = trx.index[i]\n", " trx_id = int(trx.ix[i, 'trx_id'])\n", " # Sum 7 D\n", " agg_ = trx[date-pd.datetools.to_offset('7D').delta:date-timedelta(0,0,1)]\n", " res_agg.loc[trx_id, 'Trx_sum_7D'] = agg_['amount'].sum()\n", " # Count 1D\n", " agg_ = trx[date-pd.datetools.to_offset('1D').delta:date-timedelta(0,0,1)]\n", " res_agg.loc[trx_id, 'Trx_count_1D'] = agg_['amount'].shape[0]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Trx_sum_7D 1054.881429\n", "Trx_count_1D 0.640693\n", "dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_agg.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All accounts" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 8087/8087 [04:26<00:00, 30.33it/s] \n" ] } ], "source": [ "for trx in tqdm(data_):\n", " for i in range(trx.shape[0]):\n", " date = trx.index[i]\n", " trx_id = int(trx.ix[i, 'trx_id'])\n", " # Sum 7 D\n", " agg_ = trx[date-pd.datetools.to_offset('7D').delta:date-timedelta(0,0,1)]\n", " res_agg.loc[trx_id, 'Trx_sum_7D'] = agg_['amount'].sum()\n", " # Count 1D\n", " agg_ = trx[date-pd.datetools.to_offset('1D').delta:date-timedelta(0,0,1)]\n", " res_agg.loc[trx_id, 'Trx_count_1D'] = agg_['amount'].shape[0]" ] }, { "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>Trx_sum_7D</th>\n", " <th>Trx_count_1D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Trx_sum_7D Trx_count_1D\n", "0 0 0\n", "1 0 0\n", "2 0 0\n", "3 0 0\n", "4 0 0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_agg.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.index = data.trx_id" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = data.join(res_agg)" ] }, { "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>date</th>\n", " <th>card_number</th>\n", " <th>type</th>\n", " <th>merchant</th>\n", " <th>amount</th>\n", " <th>fraud</th>\n", " <th>trx_id</th>\n", " <th>Trx_sum_7D</th>\n", " <th>Trx_count_1D</th>\n", " </tr>\n", " <tr>\n", " <th>trx_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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4082</th>\n", " <td>2011-01-16 16:26:53</td>\n", " <td>3558</td>\n", " <td>2</td>\n", " <td>13505</td>\n", " <td>528.82</td>\n", " <td>0.0</td>\n", " <td>4082</td>\n", " <td>307.85</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23677</th>\n", " <td>2011-04-04 08:13:41</td>\n", " <td>1162</td>\n", " <td>2</td>\n", " <td>9417</td>\n", " <td>117.29</td>\n", " <td>0.0</td>\n", " <td>23677</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>30074</th>\n", " <td>2011-04-29 13:09:07</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>56997</td>\n", " <td>21.29</td>\n", " <td>0.0</td>\n", " <td>30074</td>\n", " <td>14171.9</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>65426</th>\n", " <td>2011-09-09 10:11:24</td>\n", " <td>4420</td>\n", " <td>2</td>\n", " <td>57849</td>\n", " <td>29.70</td>\n", " <td>0.0</td>\n", " <td>65426</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>72272</th>\n", " <td>2011-10-04 10:43:00</td>\n", " <td>2114</td>\n", " <td>2</td>\n", " <td>5109</td>\n", " <td>2170.65</td>\n", " <td>0.0</td>\n", " <td>72272</td>\n", " <td>131020</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>74456</th>\n", " <td>2011-10-11 17:17:22</td>\n", " <td>2148</td>\n", " <td>2</td>\n", " <td>1341</td>\n", " <td>2150.19</td>\n", " <td>0.0</td>\n", " <td>74456</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>84660</th>\n", " <td>2011-11-19 17:06:58</td>\n", " <td>1521</td>\n", " <td>1</td>\n", " <td>35294</td>\n", " <td>651.59</td>\n", " <td>0.0</td>\n", " <td>84660</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>117167</th>\n", " <td>2012-04-01 12:33:33</td>\n", " <td>1471</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " <td>650.94</td>\n", " <td>0.0</td>\n", " <td>117167</td>\n", " <td>4381.21</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>119737</th>\n", " <td>2012-04-09 14:27:12</td>\n", " <td>2723</td>\n", " <td>1</td>\n", " <td>38616</td>\n", " <td>13.03</td>\n", " <td>0.0</td>\n", " <td>119737</td>\n", " <td>13614.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>132467</th>\n", " <td>2012-05-27 16:43:11</td>\n", " <td>4857</td>\n", " <td>2</td>\n", " <td>45373</td>\n", " <td>41.70</td>\n", " <td>0.0</td>\n", " <td>132467</td>\n", " <td>634.13</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>134858</th>\n", " <td>2012-06-03 17:05:21</td>\n", " <td>2114</td>\n", " <td>1</td>\n", " <td>18692</td>\n", " <td>26.06</td>\n", " <td>0.0</td>\n", " <td>134858</td>\n", " <td>175202</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>142133</th>\n", " <td>2012-06-29 16:21:37</td>\n", " <td>7588</td>\n", " <td>2</td>\n", " <td>35991</td>\n", " <td>92.53</td>\n", " <td>0.0</td>\n", " <td>142133</td>\n", " <td>1151.21</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>158154</th>\n", " <td>2012-08-20 10:55:23</td>\n", " <td>4420</td>\n", " <td>2</td>\n", " <td>53353</td>\n", " <td>182.65</td>\n", " <td>0.0</td>\n", " <td>158154</td>\n", " <td>121.77</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>176418</th>\n", " <td>2012-10-16 14:23:04</td>\n", " <td>1595</td>\n", " <td>2</td>\n", " <td>25985</td>\n", " <td>15397.58</td>\n", " <td>NaN</td>\n", " <td>176418</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>186433</th>\n", " <td>2012-11-20 11:04:00</td>\n", " <td>4923</td>\n", " <td>2</td>\n", " <td>36010</td>\n", " <td>217.89</td>\n", " <td>NaN</td>\n", " <td>186433</td>\n", " <td>573.4</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date card_number type merchant amount fraud \\\n", "trx_id \n", "4082 2011-01-16 16:26:53 3558 2 13505 528.82 0.0 \n", "23677 2011-04-04 08:13:41 1162 2 9417 117.29 0.0 \n", "30074 2011-04-29 13:09:07 0 1 56997 21.29 0.0 \n", "65426 2011-09-09 10:11:24 4420 2 57849 29.70 0.0 \n", "72272 2011-10-04 10:43:00 2114 2 5109 2170.65 0.0 \n", "74456 2011-10-11 17:17:22 2148 2 1341 2150.19 0.0 \n", "84660 2011-11-19 17:06:58 1521 1 35294 651.59 0.0 \n", "117167 2012-04-01 12:33:33 1471 1 -1 650.94 0.0 \n", "119737 2012-04-09 14:27:12 2723 1 38616 13.03 0.0 \n", "132467 2012-05-27 16:43:11 4857 2 45373 41.70 0.0 \n", "134858 2012-06-03 17:05:21 2114 1 18692 26.06 0.0 \n", "142133 2012-06-29 16:21:37 7588 2 35991 92.53 0.0 \n", "158154 2012-08-20 10:55:23 4420 2 53353 182.65 0.0 \n", "176418 2012-10-16 14:23:04 1595 2 25985 15397.58 NaN \n", "186433 2012-11-20 11:04:00 4923 2 36010 217.89 NaN \n", "\n", " trx_id Trx_sum_7D Trx_count_1D \n", "trx_id \n", "4082 4082 307.85 0 \n", "23677 23677 0 0 \n", "30074 30074 14171.9 2 \n", "65426 65426 0 0 \n", "72272 72272 131020 7 \n", "74456 74456 0 0 \n", "84660 84660 0 0 \n", "117167 117167 4381.21 1 \n", "119737 119737 13614.2 0 \n", "132467 132467 634.13 10 \n", "134858 134858 175202 7 \n", "142133 142133 1151.21 4 \n", "158154 158154 121.77 0 \n", "176418 176418 0 0 \n", "186433 186433 573.4 0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.sample(15, random_state=42).sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Split train and test" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = data.loc[~data.fraud.isnull()]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = X.fraud" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = X.drop(['fraud', 'date', 'card_number'], axis=1)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_kaggle = data.loc[data.fraud.isnull()]\n", "X_kaggle = X_kaggle.drop(['fraud', 'date', 'card_number'], axis=1)" ] }, { "cell_type": "code", "execution_count": 29, "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>type</th>\n", " <th>merchant</th>\n", " <th>amount</th>\n", " <th>trx_id</th>\n", " <th>Trx_sum_7D</th>\n", " <th>Trx_count_1D</th>\n", " </tr>\n", " <tr>\n", " <th>trx_id</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>172091</th>\n", " <td>2</td>\n", " <td>13273</td>\n", " <td>208.51</td>\n", " <td>172091</td>\n", " <td>120165</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>172092</th>\n", " <td>2</td>\n", " <td>34472</td>\n", " <td>525.05</td>\n", " <td>172092</td>\n", " <td>71042.4</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>172093</th>\n", " <td>2</td>\n", " <td>37909</td>\n", " <td>802.24</td>\n", " <td>172093</td>\n", " <td>120374</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>172094</th>\n", " <td>2</td>\n", " <td>35167</td>\n", " <td>130.32</td>\n", " <td>172094</td>\n", " <td>90638.1</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>172095</th>\n", " <td>2</td>\n", " <td>35073</td>\n", " <td>9696.96</td>\n", " <td>172095</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " type merchant amount trx_id Trx_sum_7D Trx_count_1D\n", "trx_id \n", "172091 2 13273 208.51 172091 120165 14\n", "172092 2 34472 525.05 172092 71042.4 0\n", "172093 2 37909 802.24 172093 120374 15\n", "172094 2 35167 130.32 172094 90638.1 9\n", "172095 2 35073 9696.96 172095 0 0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_kaggle.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Random Forest" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf = RandomForestClassifier(n_estimators=100, n_jobs=-1, class_weight='balanced')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import fbeta_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "KFold cross-validation" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import KFold" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kf = KFold(X.shape[0], n_folds=5)\n", "res = []\n", "for train, test in kf:\n", " X_train, X_test, y_train, y_test = X.iloc[train], X.iloc[test], y.iloc[train], y.iloc[test]\n", " clf.fit(X_train, y_train)\n", " y_pred_proba = clf.predict_proba(X_test)[:, 1]\n", " y_pred = (y_pred_proba>0.05).astype(int)\n", " res.append(fbeta_score(y_test, y_pred, beta=2))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 5.000000\n", "mean 0.078145\n", "std 0.032472\n", "min 0.054945\n", "25% 0.057692\n", "50% 0.062500\n", "75% 0.082713\n", "max 0.132877\n", "dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(res).describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train with all" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict and send to Kaggle" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred = clf.predict_proba(X_kaggle)[:, 1]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_pred = (y_pred>0.05).astype(int)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred = pd.Series(y_pred,name='fraud', index=X_kaggle.index)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "trx_id\n", "172091 0\n", "172092 1\n", "172093 1\n", "172094 0\n", "172095 1\n", "172096 1\n", "172097 1\n", "172098 0\n", "172099 1\n", "172100 0\n", "Name: fraud, dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred.head(10)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred.to_csv('fraud_transactions_kaggle_1.csv', header=True, index_label='ID')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Main Issues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Class imbalance\n", "* Feature creation\n", "* Model selection\n", "* Threshold selection" ] } ], "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
GoogleCloudPlatform/vertex-ai-samples	notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb	1	33506	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 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": "title:generic" }, "source": [ "# Vertex AI Pipelines: AutoML text classification pipelines using google-cloud-pipeline-components\n", "\n", "<table align=\"left\">\n", " <td>\n", " <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.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/pipelines/google_cloud_pipeline_components_automl_text.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/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb\">\n", " Open in Vertex AI Workbench\n", " </a>\n", " </td>\n", "</table>\n", "<br/><br/><br/>" ] }, { "cell_type": "markdown", "metadata": { "id": "overview:pipelines,automl" }, "source": [ "## Overview\n", "\n", "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build an AutoML text classification workflow on [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines)." ] }, { "cell_type": "markdown", "metadata": { "id": "dataset:happydb,tcn" }, "source": [ "### Dataset\n", "\n", "The dataset used for this tutorial is the [Happy Moments dataset](https://www.kaggle.com/ritresearch/happydb) from [Kaggle Datasets](https://www.kaggle.com/ritresearch/happydb). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket." ] }, { "cell_type": "markdown", "metadata": { "id": "objective:pipelines,automl" }, "source": [ "### Objective\n", "\n", "In this tutorial, you create an AutoML text classification using a pipeline with components from `google_cloud_pipeline_components`.\n", "\n", "The steps performed include:\n", "\n", "- Create a `Dataset` resource.\n", "- Train an AutoML `Model` resource.\n", "- Creates an `Endpoint` resource.\n", "- Deploys the `Model` resource to the `Endpoint` resource.\n", "\n", "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform)." ] }, { "cell_type": "markdown", "metadata": { "id": "costs" }, "source": [ "### Costs\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": "setup_local" }, "source": [ "### Set up your local development environment\n", "\n", "If you are using Colab or Google Cloud Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.\n", "\n", "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n", "\n", "- The Cloud Storage SDK\n", "- Git\n", "- Python 3\n", "- virtualenv\n", "- Jupyter notebook running in a virtual environment with Python 3\n", "\n", "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n", "\n", "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n", "\n", "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n", "\n", "3. [Install virtualenv](Ihttps://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3.\n", "\n", "4. Activate that environment and run `pip3 install Jupyter` in a terminal shell to install Jupyter.\n", "\n", "5. Run `jupyter notebook` on the command line in a terminal shell to launch Jupyter.\n", "\n", "6. Open this notebook in the Jupyter Notebook Dashboard.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "install_aip:mbsdk" }, "source": [ "## Installation\n", "\n", "Install the latest version of Vertex AI SDK for Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_aip:mbsdk" }, "outputs": [], "source": [ "import os\n", "\n", "# Google Cloud Notebook\n", "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n", " USER_FLAG = \"--user\"\n", "else:\n", " USER_FLAG = \"\"\n", "\n", "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "install_storage" }, "source": [ "Install the latest GA version of google-cloud-storage library as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_storage" }, "outputs": [], "source": [ "! pip3 install -U google-cloud-storage $USER_FLAG" ] }, { "cell_type": "markdown", "metadata": { "id": "install_gcpc" }, "source": [ "Install the latest GA version of google-cloud-pipeline-components library as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "install_gcpc" }, "outputs": [], "source": [ "! pip3 install $USER kfp google-cloud-pipeline-components --upgrade" ] }, { "cell_type": "markdown", "metadata": { "id": "restart" }, "source": [ "### Restart the kernel\n", "\n", "Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "restart" }, "outputs": [], "source": [ "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": "check_versions" }, "source": [ "Check the versions of the packages you installed. The KFP SDK version should be >=1.6." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "check_versions:kfp,gcpc" }, "outputs": [], "source": [ "! python3 -c \"import kfp; print('KFP SDK version: {}'.format(kfp.__version__))\"\n", "! python3 -c \"import google_cloud_pipeline_components; print('google_cloud_pipeline_components version: {}'.format(google_cloud_pipeline_components.__version__))\"" ] }, { "cell_type": "markdown", "metadata": { "id": "before_you_begin:nogpu" }, "source": [ "## Before you begin\n", "\n", "### GPU runtime\n", "\n", "This tutorial does not require a GPU runtime.\n", "\n", "### 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 $300 free credit towards your compute/storage costs.\n", "\n", "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n", "\n", "3. [Enable the Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n", "\n", "4. [The Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebook.\n", "\n", "5. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "Note: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_project_id" }, "outputs": [], "source": [ "PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_project_id" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n", " # Get your GCP project id from gcloud\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": "code", "execution_count": null, "metadata": { "id": "set_gcloud_project_id" }, "outputs": [], "source": [ "! gcloud config set project $PROJECT_ID" ] }, { "cell_type": "markdown", "metadata": { "id": "region" }, "source": [ "#### Region\n", "\n", "You can also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n", "\n", "- Americas: `us-central1`\n", "- Europe: `europe-west4`\n", "- Asia Pacific: `asia-east1`\n", "\n", "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n", "\n", "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "region" }, "outputs": [], "source": [ "REGION = \"us-central1\" # @param {type: \"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "timestamp" }, "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 the timestamp onto the name of resources you create in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "timestamp" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gcp_authenticate" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "If you are using Google Cloud Notebook, your environment is already authenticated. Skip this step.\n", "\n", "If you are using Colab, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n", "\n", "Otherwise, follow these steps:\n", "\n", "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n", "\n", "Click Create service account.\n", "\n", "In the Service account name field, enter a name, and click Create.\n", "\n", "In the Grant this service account access to project section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select Vertex Administrator. Type \"Storage Object Admin\" into the filter box, and select Storage Object Admin.\n", "\n", "Click Create. A JSON file that contains your key downloads to your local environment.\n", "\n", "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gcp_authenticate" }, "outputs": [], "source": [ "# 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", "import os\n", "import sys\n", "\n", "# If on Google Cloud Notebook, 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": "bucket:mbsdk" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "The following steps are required, regardless of your notebook environment.\n", "\n", "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n", "\n", "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bucket" }, "outputs": [], "source": [ "BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_bucket" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n", " BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "create_bucket" }, "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": "create_bucket" }, "outputs": [], "source": [ "! gsutil mb -l $REGION $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "validate_bucket" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "validate_bucket" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "set_service_account" }, "source": [ "#### Service Account\n", "\n", "If you don't know your service account, try to get your service account using `gcloud` command by executing the second cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_service_account" }, "outputs": [], "source": [ "SERVICE_ACCOUNT = \"[your-service-account]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "autoset_service_account" }, "outputs": [], "source": [ "if (\n", " SERVICE_ACCOUNT == \"\"\n", " or SERVICE_ACCOUNT is None\n", " or SERVICE_ACCOUNT == \"[your-service-account]\"\n", "):\n", " # Get your GCP project id from gcloud\n", " shell_output = !gcloud auth list 2>/dev/null\n", " SERVICE_ACCOUNT = shell_output[2].strip()\n", " print(\"Service Account:\", SERVICE_ACCOUNT)" ] }, { "cell_type": "markdown", "metadata": { "id": "set_service_account:pipelines" }, "source": [ "#### Set service account access for Vertex AI Pipelines\n", "\n", "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step -- you only need to run these once per service account." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "set_service_account:pipelines" }, "outputs": [], "source": [ "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_NAME\n", "\n", "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "setup_vars" }, "source": [ "### Set up variables\n", "\n", "Next, set up some variables used throughout the tutorial.\n", "### Import libraries and define constants" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_aip:mbsdk" }, "outputs": [], "source": [ "import google.cloud.aiplatform as aip" ] }, { "cell_type": "markdown", "metadata": { "id": "pipeline_constants" }, "source": [ "#### Vertex AI Pipelines constants\n", "\n", "Setup up the following constants for Vertex AI Pipelines:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pipeline_constants" }, "outputs": [], "source": [ "PIPELINE_ROOT = \"{}/pipeline_root/happydb\".format(BUCKET_NAME)" ] }, { "cell_type": "markdown", "metadata": { "id": "additional_imports" }, "source": [ "Additional imports." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "import_pipelines:gcpc" }, "outputs": [], "source": [ "import kfp" ] }, { "cell_type": "markdown", "metadata": { "id": "init_aip:mbsdk" }, "source": [ "## Initialize Vertex AI SDK for Python\n", "\n", "Initialize the Vertex AI SDK for Python for your project and corresponding bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "init_aip:mbsdk" }, "outputs": [], "source": [ "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)" ] }, { "cell_type": "markdown", "metadata": { "id": "define_pipeline:gcpc,automl,happydb,tcn" }, "source": [ "## Define AutoML text classification model pipeline that uses components from `google_cloud_pipeline_components`\n", "\n", "Next, you define the pipeline.\n", "\n", "Create and deploy an AutoML text classification `Model` resource using a `Dataset` resource." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "define_pipeline:gcpc,automl,happydb,tcn" }, "outputs": [], "source": [ "IMPORT_FILE = \"gs://cloud-ml-data/NL-classification/happiness.csv\"\n", "\n", "\n", "@kfp.dsl.pipeline(name=\"automl-text-classification\" + TIMESTAMP)\n", "def pipeline(\n", " project: str = PROJECT_ID, region: str = REGION, import_file: str = IMPORT_FILE\n", "):\n", " from google_cloud_pipeline_components import aiplatform as gcc_aip\n", " from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n", " ModelDeployOp)\n", "\n", " dataset_create_task = gcc_aip.TextDatasetCreateOp(\n", " display_name=\"train-automl-happydb\",\n", " gcs_source=import_file,\n", " import_schema_uri=aip.schema.dataset.ioformat.text.multi_label_classification,\n", " project=project,\n", " )\n", "\n", " training_run_task = gcc_aip.AutoMLTextTrainingJobRunOp(\n", " dataset=dataset_create_task.outputs[\"dataset\"],\n", " display_name=\"train-automl-happydb\",\n", " prediction_type=\"classification\",\n", " multi_label=True,\n", " training_fraction_split=0.6,\n", " validation_fraction_split=0.2,\n", " test_fraction_split=0.2,\n", " model_display_name=\"train-automl-happydb\",\n", " project=project,\n", " )\n", "\n", " endpoint_op = EndpointCreateOp(\n", " project=project,\n", " location=region,\n", " display_name=\"train-automl-flowers\",\n", " )\n", "\n", " ModelDeployOp(\n", " model=training_run_task.outputs[\"model\"],\n", " endpoint=endpoint_op.outputs[\"endpoint\"],\n", " automatic_resources_min_replica_count=1,\n", " automatic_resources_max_replica_count=1,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "compile_pipeline" }, "source": [ "## Compile the pipeline\n", "\n", "Next, compile the pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "compile_pipeline" }, "outputs": [], "source": [ "from kfp.v2 import compiler # noqa: F811\n", "\n", "compiler.Compiler().compile(\n", " pipeline_func=pipeline,\n", " package_path=\"text classification_pipeline.json\".replace(\" \", \"_\"),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "run_pipeline:automl,text" }, "source": [ "## Run the pipeline\n", "\n", "Next, run the pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "run_pipeline:automl,text" }, "outputs": [], "source": [ "DISPLAY_NAME = \"happydb_\" + TIMESTAMP\n", "\n", "job = aip.PipelineJob(\n", " display_name=DISPLAY_NAME,\n", " template_path=\"text classification_pipeline.json\".replace(\" \", \"_\"),\n", " pipeline_root=PIPELINE_ROOT,\n", " enable_caching=False,\n", ")\n", "\n", "job.run()\n", "\n", "! rm text_classification_pipeline.json" ] }, { "cell_type": "markdown", "metadata": { "id": "view_pipeline_run:automl,text" }, "source": [ "Click on the generated link to see your run in the Cloud Console.\n", "\n", "<!-- It should look something like this as it is running:\n", "\n", "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" width=\"40%\"/></a> -->\n", "\n", "In the UI, many of the pipeline DAG nodes will expand or collapse when you click on them. Here is a partially-expanded view of the DAG (click image to see larger version).\n", "\n", "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/automl_text_classif.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/automl_text_classif.png\" width=\"40%\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "cleanup:pipelines" }, "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 -- Note: this is auto-generated and not all resources may be applicable for this tutorial:\n", "\n", "- Dataset\n", "- Pipeline\n", "- Model\n", "- Endpoint\n", "- Batch Job\n", "- Custom Job\n", "- Hyperparameter Tuning Job\n", "- Cloud Storage Bucket" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cleanup:pipelines" }, "outputs": [], "source": [ "delete_dataset = True\n", "delete_pipeline = True\n", "delete_model = True\n", "delete_endpoint = True\n", "delete_batchjob = True\n", "delete_customjob = True\n", "delete_hptjob = True\n", "delete_bucket = True\n", "\n", "try:\n", " if delete_model and \"DISPLAY_NAME\" in globals():\n", " models = aip.Model.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " model = models[0]\n", " aip.Model.delete(model)\n", " print(\"Deleted model:\", model)\n", "except Exception as e:\n", " print(e)\n", "\n", "try:\n", " if delete_endpoint and \"DISPLAY_NAME\" in globals():\n", " endpoints = aip.Endpoint.list(\n", " filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n", " )\n", " endpoint = endpoints[0]\n", " endpoint.undeploy_all()\n", " aip.Endpoint.delete(endpoint.resource_name)\n", " print(\"Deleted endpoint:\", endpoint)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_dataset and \"DISPLAY_NAME\" in globals():\n", " if \"text\" == \"tabular\":\n", " try:\n", " datasets = aip.TabularDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.TabularDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"text\" == \"image\":\n", " try:\n", " datasets = aip.ImageDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.ImageDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"text\" == \"text\":\n", " try:\n", " datasets = aip.TextDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.TextDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", " if \"text\" == \"video\":\n", " try:\n", " datasets = aip.VideoDataset.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " dataset = datasets[0]\n", " aip.VideoDataset.delete(dataset.resource_name)\n", " print(\"Deleted dataset:\", dataset)\n", " except Exception as e:\n", " print(e)\n", "\n", "try:\n", " if delete_pipeline and \"DISPLAY_NAME\" in globals():\n", " pipelines = aip.PipelineJob.list(\n", " filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n", " )\n", " pipeline = pipelines[0]\n", " aip.PipelineJob.delete(pipeline.resource_name)\n", " print(\"Deleted pipeline:\", pipeline)\n", "except Exception as e:\n", " print(e)\n", "\n", "if delete_bucket and \"BUCKET_NAME\" in globals():\n", " ! gsutil rm -r $BUCKET_NAME" ] } ], "metadata": { "colab": { "name": "google_cloud_pipeline_components_automl_text.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
lasersonlab/pepsyn	workflows/metagenomic_shotgun_single_end.ipynb	1	231979	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Shotgun metagenomic single end library" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from os.path import join as pjoin\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy as sp\n", "import pandas as pd\n", "import skbio\n", "import skbio.diversity\n", "import seaborn as sns\n", "import random" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.style.use('seaborn')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CONFIGURATION (edit the following cell)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "raw_reads_path = '/Users/laserson/Downloads/BaseSpace/Metagenomiclibraries-39688650/2-48080086/mms500_S2_L001_R1_001.fastq.gz'\n", "analysis_dir_path = '/Users/laserson/tmp/phip_analysis/phip-7/phip-7-meta'\n", "sample_prefix = 'mms500_S2_L001_R1_001'" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vector_depleted_reads_path = pjoin(analysis_dir_path, 'vector_depleted', sample_prefix + '.fastq')\n", "centrifuge_aln_path = pjoin(analysis_dir_path, 'centrifuge', sample_prefix + '.centrifuge_aln.tsv')\n", "kraken_report_path = pjoin(analysis_dir_path, 'centrifuge', sample_prefix + '.centrifuge_kreport.tsv')\n", "clustered_read_counts_path = pjoin(analysis_dir_path, 'clustered_reads', sample_prefix + '.clustered.counts.tsv')\n", "aligned_clustered_read_counts_path = pjoin(analysis_dir_path, 'clustered_reads', sample_prefix + '.aligned_clustered.counts.tsv')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of raw reads" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2784264" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ = !cat {raw_reads_path} \| gunzip \| wc -l\n", "raw_read_count = int(_[0]) // 4\n", "raw_read_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of non-vector reads" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2774446" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ = !cat {vector_depleted_reads_path} \| wc -l\n", "non_vector_read_count = int(_[0]) // 4\n", "non_vector_read_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fraction non-vector reads" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9964737539256335" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "non_vector_read_count / raw_read_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number non-vector reads successfully aligned by centrifuge" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1464878" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ = !cat {centrifuge_aln_path} \| wc -l\n", "aligned_read_count = int(_[0]) // 2\n", "aligned_read_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fraction non-vector reads aligned by centrifuge" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5279893715718381" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aligned_read_count / non_vector_read_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Diversity" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "clustered_read_counts = pd.read_csv(clustered_read_counts_path, sep='\\t', header=None, usecols=[0]).values.ravel()\n", "aligned_clustered_read_counts = pd.read_csv(aligned_clustered_read_counts_path, sep='\\t', header=None, usecols=[0]).values.ravel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observed" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14853" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(clustered_read_counts)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7907" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(aligned_clustered_read_counts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Chao1" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "68222.442748091606" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "skbio.diversity.alpha_diversity('chao1', clustered_read_counts)[0]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29560.926174496642" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "skbio.diversity.alpha_diversity('chao1', aligned_clustered_read_counts)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shannon entropy effective population size" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "416.16611571375267" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 skbio.diversity.alpha_diversity('shannon', clustered_read_counts)[0]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "329.18536381797173" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 skbio.diversity.alpha_diversity('shannon', aligned_clustered_read_counts)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Distribution of \"clone\" counts" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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OemwIuDEiLgVuA64DtgD3RcRKYAdwuJ77KLBzabshSeq3poXxV8A99eUhqtXDVcCz9dh+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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11da347f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "_ = ax.hist(clustered_read_counts, bins=50)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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jyrMHuDgiZiPiR+m/kPqfUcNFxOX0ZwCbMvPfm9VTz5eZD2XmTze/F5cCX8nM67qQrXH/\nwP4uBP55mtlW3LuDmPylLd9B/yz+uyLixLmBtwE3R8RzgH8B7sjMoxFxM/0nYxb4rcx8IiI+AtwW\nEfcDT9I/mQNPHx5ZRf843t8Xyns98PEuZGve3XAh/R/wWeDNwH90JN8HgZ0RcR/9GcA7gC93JNsJ\nY3sum//33/H08zKS5pDLzfQPZ3w6IgC+lJnv7kK+5WTm1zuS7Xrg1oh4E/0XIpdl5tK0svlV0pJU\nsZV4OEiSVIglIEkVswQkqWKWgCRVzBKQpIpZApJUMUtAkir2f+wo/5aoCK1lAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11dc85c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "_ = ax.hist(clustered_read_counts, bins=50, log=True)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 1),\n", " None,\n", " (1.0, 154701.99999999991),\n", " <matplotlib.text.Text at 0x11d93b9b0>,\n", " <matplotlib.text.Text at 0x11da5e4a8>]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OIJekwi3otEJEDALnAcuB7cCazNzSsvxVwFuBYWAz8MbM3NWbciVJ7boZka8GFmbm4cDp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\nYiHq7QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d8f37f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist, bin_edges = np.histogram(clustered_read_counts, bins=np.logspace(0, np.log10(clustered_read_counts.max()), 100))\n", "cdf = np.cumsum(hist) / hist.sum()\n", "fig, ax = plt.subplots()\n", "ax.hlines(cdf, bin_edges[:-1], bin_edges[1:])\n", "ax.set(title='clustered read count distrib', xlabel='count', xlim=[bin_edges[0], bin_edges[-1]], ylim=[0, 1], xscale='log')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11dcab978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "_ = ax.hist(aligned_clustered_read_counts, bins=50)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11d5e2cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "_ = ax.hist(aligned_clustered_read_counts, bins=50, log=True)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 1),\n", " None,\n", " (1.0, 154701.99999999991),\n", " <matplotlib.text.Text at 0x118c98eb8>,\n", " <matplotlib.text.Text at 0x118cb6d30>]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x118c5b438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist, bin_edges = np.histogram(aligned_clustered_read_counts, bins=np.logspace(0, np.log10(clustered_read_counts.max()), 100))\n", "cdf = np.cumsum(hist) / hist.sum()\n", "fig, ax = plt.subplots()\n", "ax.hlines(cdf, bin_edges[:-1], bin_edges[1:])\n", "ax.set(title='clustered ALIGNED read count distrib', xlabel='count', xlim=[bin_edges[0], bin_edges[-1]], ylim=[0, 1], xscale='log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Taxonomic distribution" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = ['pct_reads_clade', 'num_reads_clade', 'num_reads_taxon', 'rank', 'tax_id', 'tax_name']\n", "converters = {'tax_name': lambda x: x.strip()}\n", "td = pd.read_csv(kraken_report_path, sep='\\t', header=None, names=names, converters=converters)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "all_colors = sns.husl_palette(1000)\n", "state = random.getstate()\n", "random.seed(0)\n", "random.shuffle(all_colors)\n", "random.setstate(state)\n", "\n", "def top_hits(df):\n", " return df.sort_values('pct_reads_clade', ascending=False, inplace=False)[:20]\n", "\n", "def plot_distribution(df):\n", " fig, ax = plt.subplots()\n", " left = 0.2\n", " heights = df['pct_reads_clade'].values\n", " bottoms = [0] + list(np.cumsum(heights))[:-1]\n", " colors = all_colors[:len(heights)]\n", " labels = df['tax_name'].values\n", " for h, b, c, l in zip(heights, bottoms, colors, labels):\n", " if h > 0:\n", " ax.bar(left, h, width=0.4, bottom=b, color=c, label=l)\n", " ax.set(xlim=(0, 1))\n", " ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Species" ] }, { "cell_type": "code", "execution_count": 57, "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>pct_reads_clade</th>\n", " <th>num_reads_clade</th>\n", " <th>num_reads_taxon</th>\n", " <th>rank</th>\n", " <th>tax_id</th>\n", " <th>tax_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>11</th>\n", " <td>49.81</td>\n", " <td>1459206</td>\n", " <td>1459206</td>\n", " <td>S</td>\n", " <td>28116</td>\n", " <td>Bacteroides ovatus</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>4.94</td>\n", " <td>144700</td>\n", " <td>144700</td>\n", " <td>S</td>\n", " <td>818</td>\n", " <td>Bacteroides thetaiotaomicron</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>4.27</td>\n", " <td>124995</td>\n", " <td>124995</td>\n", " <td>S</td>\n", " <td>821</td>\n", " <td>Bacteroides vulgatus</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2.47</td>\n", " <td>72277</td>\n", " <td>72277</td>\n", " <td>S</td>\n", " <td>1796613</td>\n", " <td>Bacteroides sp. I48</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1.56</td>\n", " <td>45653</td>\n", " <td>45653</td>\n", " <td>S</td>\n", " <td>246787</td>\n", " <td>Bacteroides cellulosilyticus</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1.31</td>\n", " <td>38427</td>\n", " <td>38427</td>\n", " <td>S</td>\n", " <td>823</td>\n", " <td>Parabacteroides distasonis</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1.12</td>\n", " <td>32889</td>\n", " <td>32889</td>\n", " <td>S</td>\n", " <td>357276</td>\n", " <td>Bacteroides dorei</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>0.81</td>\n", " <td>23662</td>\n", " <td>23662</td>\n", " <td>S</td>\n", " <td>817</td>\n", " <td>Bacteroides fragilis</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>0.53</td>\n", " <td>15634</td>\n", " <td>15634</td>\n", " <td>S</td>\n", " <td>28118</td>\n", " <td>Odoribacter splanchnicus</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>0.29</td>\n", " <td>8537</td>\n", " <td>8537</td>\n", " <td>S</td>\n", " <td>1834196</td>\n", " <td>Lachnoclostridium sp. YL32</td>\n", " </tr>\n", " <tr>\n", " <th>101</th>\n", " <td>0.26</td>\n", " <td>7751</td>\n", " <td>7751</td>\n", " <td>S</td>\n", " <td>301301</td>\n", " <td>Roseburia hominis</td>\n", " </tr>\n", " <tr>\n", " <th>103</th>\n", " <td>0.20</td>\n", " <td>5786</td>\n", " <td>5786</td>\n", " <td>S</td>\n", " <td>39491</td>\n", " <td>[Eubacterium] rectale</td>\n", " </tr>\n", " <tr>\n", " <th>108</th>\n", " <td>0.10</td>\n", " <td>3057</td>\n", " <td>3057</td>\n", " <td>S</td>\n", " <td>292800</td>\n", " <td>Flavonifractor plautii</td>\n", " </tr>\n", " <tr>\n", " <th>141</th>\n", " <td>0.10</td>\n", " <td>2814</td>\n", " <td>2814</td>\n", " <td>S</td>\n", " <td>97478</td>\n", " <td>Lactobacillus mucosae</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>0.08</td>\n", " <td>2204</td>\n", " <td>2204</td>\n", " <td>S</td>\n", " <td>376805</td>\n", " <td>Bacteroides salanitronis</td>\n", " </tr>\n", " <tr>\n", " <th>303</th>\n", " <td>0.08</td>\n", " <td>2457</td>\n", " <td>2457</td>\n", " <td>S</td>\n", " <td>1834205</td>\n", " <td>Burkholderiales bacterium YL45</td>\n", " </tr>\n", " <tr>\n", " <th>589</th>\n", " <td>0.03</td>\n", " <td>959</td>\n", " <td>959</td>\n", " <td>S</td>\n", " <td>10710</td>\n", " <td>Escherichia virus Lambda</td>\n", " </tr>\n", " <tr>\n", " <th>583</th>\n", " <td>0.03</td>\n", " <td>977</td>\n", " <td>977</td>\n", " <td>S</td>\n", " <td>32630</td>\n", " <td>synthetic construct</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>0.02</td>\n", " <td>658</td>\n", " <td>658</td>\n", " <td>S</td>\n", " <td>214856</td>\n", " <td>Alistipes finegoldii</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>0.01</td>\n", " <td>310</td>\n", " <td>310</td>\n", " <td>S</td>\n", " <td>397865</td>\n", " <td>Barnesiella viscericola</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "11 49.81 1459206 1459206 S 28116 \n", "12 4.94 144700 144700 S 818 \n", "13 4.27 124995 124995 S 821 \n", "14 2.47 72277 72277 S 1796613 \n", "15 1.56 45653 45653 S 246787 \n", "22 1.31 38427 38427 S 823 \n", "16 1.12 32889 32889 S 357276 \n", "17 0.81 23662 23662 S 817 \n", "32 0.53 15634 15634 S 28118 \n", "98 0.29 8537 8537 S 1834196 \n", "101 0.26 7751 7751 S 301301 \n", "103 0.20 5786 5786 S 39491 \n", "108 0.10 3057 3057 S 292800 \n", "141 0.10 2814 2814 S 97478 \n", "18 0.08 2204 2204 S 376805 \n", "303 0.08 2457 2457 S 1834205 \n", "589 0.03 959 959 S 10710 \n", "583 0.03 977 977 S 32630 \n", "35 0.02 658 658 S 214856 \n", "25 0.01 310 310 S 397865 \n", "\n", " tax_name \n", "11 Bacteroides ovatus \n", "12 Bacteroides thetaiotaomicron \n", "13 Bacteroides vulgatus \n", "14 Bacteroides sp. I48 \n", "15 Bacteroides cellulosilyticus \n", "22 Parabacteroides distasonis \n", "16 Bacteroides dorei \n", "17 Bacteroides fragilis \n", "32 Odoribacter splanchnicus \n", "98 Lachnoclostridium sp. YL32 \n", "101 Roseburia hominis \n", "103 [Eubacterium] rectale \n", "108 Flavonifractor plautii \n", "141 Lactobacillus mucosae \n", "18 Bacteroides salanitronis \n", "303 Burkholderiales bacterium YL45 \n", "589 Escherichia virus Lambda \n", "583 synthetic construct \n", "35 Alistipes finegoldii \n", "25 Barnesiella viscericola " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "species = td[td['rank'] == 'S']\n", "top_hits(species)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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v3iE5OYm2bdvh6jqW+Pg45s3zQ0dHh6pVq5GQcIugoFB69erK7t0/q2sJDw/B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\nY8k4syGZaJvjP0m3XbYhvYpfGGyXqQDBHDkEuFRVT8WUDVVj2l0cjt8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"text/plain": [ "<matplotlib.figure.Figure at 0x11dcc37b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distribution(species)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Genus" ] }, { "cell_type": "code", "execution_count": 59, "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>pct_reads_clade</th>\n", " <th>num_reads_clade</th>\n", " <th>num_reads_taxon</th>\n", " <th>rank</th>\n", " <th>tax_id</th>\n", " <th>tax_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10</th>\n", " <td>65.06</td>\n", " <td>1906011</td>\n", " <td>271</td>\n", " <td>G</td>\n", " <td>816</td>\n", " <td>Bacteroides</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1.31</td>\n", " <td>38427</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>375288</td>\n", " <td>Parabacteroides</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>0.53</td>\n", " <td>15634</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>283168</td>\n", " <td>Odoribacter</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>0.29</td>\n", " <td>8541</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>1506553</td>\n", " <td>Lachnoclostridium</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>0.26</td>\n", " <td>7751</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>841</td>\n", " <td>Roseburia</td>\n", " </tr>\n", " <tr>\n", " <th>107</th>\n", " <td>0.10</td>\n", " <td>3057</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>946234</td>\n", " <td>Flavonifractor</td>\n", " </tr>\n", " <tr>\n", " <th>140</th>\n", " <td>0.10</td>\n", " <td>2827</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>1578</td>\n", " <td>Lactobacillus</td>\n", " </tr>\n", " <tr>\n", " <th>588</th>\n", " <td>0.03</td>\n", " <td>966</td>\n", " <td>6</td>\n", " <td>G</td>\n", " <td>186765</td>\n", " <td>Lambdavirus</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>0.02</td>\n", " <td>658</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>239759</td>\n", " <td>Alistipes</td>\n", " </tr>\n", " <tr>\n", " <th>197</th>\n", " <td>0.01</td>\n", " <td>256</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>1716</td>\n", " <td>Corynebacterium</td>\n", " </tr>\n", " <tr>\n", " <th>109</th>\n", " <td>0.01</td>\n", " <td>252</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>1392389</td>\n", " <td>Intestinimonas</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>0.01</td>\n", " <td>415</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>28250</td>\n", " <td>Ornithobacterium</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>0.01</td>\n", " <td>186</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>346096</td>\n", " <td>Paludibacter</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>0.01</td>\n", " <td>310</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>397864</td>\n", " <td>Barnesiella</td>\n", " </tr>\n", " <tr>\n", " <th>413</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>497</td>\n", " <td>Psychrobacter</td>\n", " </tr>\n", " <tr>\n", " <th>416</th>\n", " <td>0.00</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>286</td>\n", " <td>Pseudomonas</td>\n", " </tr>\n", " <tr>\n", " <th>376</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>629</td>\n", " <td>Yersinia</td>\n", " </tr>\n", " <tr>\n", " <th>406</th>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>338</td>\n", " <td>Xanthomonas</td>\n", " </tr>\n", " <tr>\n", " <th>402</th>\n", " <td>0.00</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>231454</td>\n", " <td>Dyella</td>\n", " </tr>\n", " <tr>\n", " <th>397</th>\n", " <td>0.00</td>\n", " <td>22</td>\n", " <td>0</td>\n", " <td>G</td>\n", " <td>662</td>\n", " <td>Vibrio</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "10 65.06 1906011 271 G 816 \n", "21 1.31 38427 0 G 375288 \n", "31 0.53 15634 0 G 283168 \n", "97 0.29 8541 0 G 1506553 \n", "100 0.26 7751 0 G 841 \n", "107 0.10 3057 0 G 946234 \n", "140 0.10 2827 0 G 1578 \n", "588 0.03 966 6 G 186765 \n", "34 0.02 658 0 G 239759 \n", "197 0.01 256 0 G 1716 \n", "109 0.01 252 0 G 1392389 \n", "47 0.01 415 0 G 28250 \n", "26 0.01 186 0 G 346096 \n", "24 0.01 310 0 G 397864 \n", "413 0.00 3 0 G 497 \n", "416 0.00 2 0 G 286 \n", "376 0.00 3 0 G 629 \n", "406 0.00 0 0 G 338 \n", "402 0.00 12 0 G 231454 \n", "397 0.00 22 0 G 662 \n", "\n", " tax_name \n", "10 Bacteroides \n", "21 Parabacteroides \n", "31 Odoribacter \n", "97 Lachnoclostridium \n", "100 Roseburia \n", "107 Flavonifractor \n", "140 Lactobacillus \n", "588 Lambdavirus \n", "34 Alistipes \n", "197 Corynebacterium \n", "109 Intestinimonas \n", "47 Ornithobacterium \n", "26 Paludibacter \n", "24 Barnesiella \n", "413 Psychrobacter \n", "416 Pseudomonas \n", "376 Yersinia \n", "406 Xanthomonas \n", "402 Dyella \n", "397 Vibrio " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genuses = td[td['rank'] == 'G']\n", "top_hits(genuses)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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CFmNldZukpHS02qzc31uvNxAfn8rt21k4O1diyJCvCA0N4ezZaEJCviYsbDFb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\nQoJcsQthFJ612xOdnatRpkw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<td>0</td>\n", " <td>F</td>\n", " <td>171550</td>\n", " <td>Rikenellaceae</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>0.01</td>\n", " <td>423</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>49546</td>\n", " <td>Flavobacteriaceae</td>\n", " </tr>\n", " <tr>\n", " <th>196</th>\n", " <td>0.01</td>\n", " <td>256</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>1653</td>\n", " <td>Corynebacteriaceae</td>\n", " </tr>\n", " <tr>\n", " <th>396</th>\n", " <td>0.00</td>\n", " <td>22</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>641</td>\n", " <td>Vibrionaceae</td>\n", " </tr>\n", " <tr>\n", " <th>432</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>444</td>\n", " <td>Legionellaceae</td>\n", " </tr>\n", " <tr>\n", " <th>424</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>72275</td>\n", " <td>Alteromonadaceae</td>\n", " </tr>\n", " <tr>\n", " <th>415</th>\n", " <td>0.00</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>135621</td>\n", " <td>Pseudomonadaceae</td>\n", " </tr>\n", " <tr>\n", " <th>412</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>468</td>\n", " <td>Moraxellaceae</td>\n", " </tr>\n", " <tr>\n", " <th>405</th>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>32033</td>\n", " <td>Xanthomonadaceae</td>\n", " </tr>\n", " <tr>\n", " <th>401</th>\n", " <td>0.00</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>1775411</td>\n", " <td>Rhodanobacteraceae</td>\n", " </tr>\n", " <tr>\n", " <th>436</th>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>72276</td>\n", " <td>Ectothiorhodospiraceae</td>\n", " </tr>\n", " <tr>\n", " <th>375</th>\n", " <td>0.00</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>1903411</td>\n", " <td>Yersiniaceae</td>\n", " </tr>\n", " <tr>\n", " <th>392</th>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>1903412</td>\n", " <td>Hafniaceae</td>\n", " </tr>\n", " <tr>\n", " <th>388</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>F</td>\n", " <td>1903414</td>\n", " <td>Morganellaceae</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "9 65.06 1906011 0 F 815 \n", "20 1.33 38948 0 F 171551 \n", "96 0.75 22080 0 F 186803 \n", "30 0.53 15634 0 F 1853231 \n", "139 0.10 2827 0 F 33958 \n", "587 0.03 966 0 F 10699 \n", "33 0.02 658 0 F 171550 \n", "46 0.01 423 0 F 49546 \n", "196 0.01 256 0 F 1653 \n", "396 0.00 22 0 F 641 \n", "432 0.00 1 0 F 444 \n", "424 0.00 3 0 F 72275 \n", "415 0.00 2 0 F 135621 \n", "412 0.00 3 0 F 468 \n", "405 0.00 0 0 F 32033 \n", "401 0.00 12 0 F 1775411 \n", "436 0.00 0 0 F 72276 \n", "375 0.00 5 0 F 1903411 \n", "392 0.00 0 0 F 1903412 \n", "388 0.00 3 0 F 1903414 \n", "\n", " tax_name \n", "9 Bacteroidaceae \n", "20 Porphyromonadaceae \n", "96 Lachnospiraceae \n", "30 Odoribacteraceae \n", "139 Lactobacillaceae \n", "587 Siphoviridae \n", "33 Rikenellaceae \n", "46 Flavobacteriaceae \n", "196 Corynebacteriaceae \n", "396 Vibrionaceae \n", "432 Legionellaceae \n", "424 Alteromonadaceae \n", "415 Pseudomonadaceae \n", "412 Moraxellaceae \n", "405 Xanthomonadaceae \n", "401 Rhodanobacteraceae \n", "436 Ectothiorhodospiraceae \n", "375 Yersiniaceae \n", "392 Hafniaceae \n", "388 Morganellaceae " ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "families = td[td['rank'] == 'F']\n", "top_hits(families)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11c60b2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distribution(families)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Order" ] }, { "cell_type": "code", "execution_count": 63, "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>pct_reads_clade</th>\n", " <th>num_reads_clade</th>\n", " <th>num_reads_taxon</th>\n", " <th>rank</th>\n", " <th>tax_id</th>\n", " <th>tax_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8</th>\n", " <td>66.94</td>\n", " <td>1961272</td>\n", " <td>0</td>\n", " 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" <td>O</td>\n", " <td>85007</td>\n", " <td>Corynebacteriales</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>0.00</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>191411</td>\n", " <td>Chlorobiales</td>\n", " </tr>\n", " <tr>\n", " <th>477</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>204441</td>\n", " <td>Rhodospirillales</td>\n", " </tr>\n", " <tr>\n", " <th>357</th>\n", " <td>0.00</td>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>91347</td>\n", " <td>Enterobacterales</td>\n", " </tr>\n", " <tr>\n", " <th>395</th>\n", " <td>0.00</td>\n", " <td>22</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>135623</td>\n", " <td>Vibrionales</td>\n", " </tr>\n", " <tr>\n", " <th>400</th>\n", " <td>0.00</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>135614</td>\n", " <td>Xanthomonadales</td>\n", " </tr>\n", " <tr>\n", " <th>411</th>\n", " <td>0.00</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>72274</td>\n", " <td>Pseudomonadales</td>\n", " </tr>\n", " <tr>\n", " <th>423</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>135622</td>\n", " <td>Alteromonadales</td>\n", " </tr>\n", " <tr>\n", " <th>431</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>118969</td>\n", " <td>Legionellales</td>\n", " </tr>\n", " <tr>\n", " <th>435</th>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>135613</td>\n", " <td>Chromatiales</td>\n", " </tr>\n", " <tr>\n", " <th>440</th>\n", " <td>0.00</td>\n", " <td>26</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>356</td>\n", " <td>Rhizobiales</td>\n", " </tr>\n", " <tr>\n", " <th>470</th>\n", " <td>0.00</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>204455</td>\n", " <td>Rhodobacterales</td>\n", " </tr>\n", " <tr>\n", " <th>481</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>204458</td>\n", " <td>Caulobacterales</td>\n", " </tr>\n", " <tr>\n", " <th>338</th>\n", " <td>0.00</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>O</td>\n", " <td>206351</td>\n", " <td>Neisseriales</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "8 66.94 1961272 0 O 171549 \n", "95 0.87 25405 0 O 186802 \n", "138 0.10 2858 0 O 186826 \n", "300 0.09 2600 0 O 80840 \n", "586 0.03 969 0 O 28883 \n", "45 0.01 423 0 O 200644 \n", "195 0.01 260 0 O 85007 \n", "88 0.00 13 0 O 191411 \n", "477 0.00 1 0 O 204441 \n", "357 0.00 55 0 O 91347 \n", "395 0.00 22 0 O 135623 \n", "400 0.00 12 0 O 135614 \n", "411 0.00 5 0 O 72274 \n", "423 0.00 3 0 O 135622 \n", "431 0.00 1 0 O 118969 \n", "435 0.00 0 0 O 135613 \n", "440 0.00 26 0 O 356 \n", "470 0.00 11 0 O 204455 \n", "481 0.00 1 0 O 204458 \n", "338 0.00 2 0 O 206351 \n", "\n", " tax_name \n", "8 Bacteroidales \n", "95 Clostridiales \n", "138 Lactobacillales \n", "300 Burkholderiales \n", "586 Caudovirales \n", "45 Flavobacteriales \n", "195 Corynebacteriales \n", "88 Chlorobiales \n", "477 Rhodospirillales \n", "357 Enterobacterales \n", "395 Vibrionales \n", "400 Xanthomonadales \n", "411 Pseudomonadales \n", "423 Alteromonadales \n", "431 Legionellales \n", "435 Chromatiales \n", "440 Rhizobiales \n", "470 Rhodobacterales \n", "481 Caulobacterales \n", "338 Neisseriales " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orders = td[td['rank'] == 'O']\n", "top_hits(orders)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11daddc88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distribution(orders)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Class" ] }, { "cell_type": "code", "execution_count": 65, "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>pct_reads_clade</th>\n", " <th>num_reads_clade</th>\n", " <th>num_reads_taxon</th>\n", " <th>rank</th>\n", " <th>tax_id</th>\n", " <th>tax_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>7</th>\n", " <td>66.94</td>\n", " <td>1961272</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>200643</td>\n", " <td>Bacteroidia</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>0.87</td>\n", " <td>25406</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>186801</td>\n", " <td>Clostridia</td>\n", " </tr>\n", " <tr>\n", " <th>137</th>\n", " <td>0.10</td>\n", " <td>2896</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>91061</td>\n", " <td>Bacilli</td>\n", " </tr>\n", " <tr>\n", " <th>299</th>\n", " <td>0.09</td>\n", " <td>2604</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>28216</td>\n", " <td>Betaproteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>194</th>\n", " <td>0.01</td>\n", " <td>266</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>1760</td>\n", " <td>Actinobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>347</th>\n", " <td>0.01</td>\n", " <td>227</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>1236</td>\n", " <td>Gammaproteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>0.01</td>\n", " <td>423</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>117743</td>\n", " <td>Flavobacteriia</td>\n", " </tr>\n", " <tr>\n", " <th>293</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>475962</td>\n", " <td>Caldilineae</td>\n", " </tr>\n", " <tr>\n", " <th>546</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>204429</td>\n", " <td>Chlamydiia</td>\n", " </tr>\n", " <tr>\n", " <th>540</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>203683</td>\n", " <td>Planctomycetia</td>\n", " </tr>\n", " <tr>\n", " <th>534</th>\n", " <td>0.00</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>414999</td>\n", " <td>Opitutae</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>0.00</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>203692</td>\n", " <td>Spirochaetia</td>\n", " </tr>\n", " <tr>\n", " <th>502</th>\n", " <td>0.00</td>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>28221</td>\n", " <td>Deltaproteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>486</th>\n", " <td>0.00</td>\n", " <td>28</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>29547</td>\n", " <td>Epsilonproteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>439</th>\n", " <td>0.00</td>\n", " <td>39</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>28211</td>\n", " <td>Alphaproteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>257</th>\n", " <td>0.00</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>31969</td>\n", " <td>Mollicutes</td>\n", " </tr>\n", " <tr>\n", " <th>263</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>188787</td>\n", " <td>Deinococci</td>\n", " </tr>\n", " <tr>\n", " <th>251</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>1497346</td>\n", " <td>Thermoleophilia</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>84995</td>\n", " <td>Rubrobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>237</th>\n", " <td>0.00</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>84998</td>\n", " <td>Coriobacteriia</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "7 66.94 1961272 0 C 200643 \n", "94 0.87 25406 0 C 186801 \n", "137 0.10 2896 0 C 91061 \n", "299 0.09 2604 0 C 28216 \n", "194 0.01 266 0 C 1760 \n", "347 0.01 227 0 C 1236 \n", "44 0.01 423 0 C 117743 \n", "293 0.00 3 0 C 475962 \n", "546 0.00 1 0 C 204429 \n", "540 0.00 1 0 C 203683 \n", "534 0.00 2 0 C 414999 \n", "524 0.00 13 0 C 203692 \n", "502 0.00 8 0 C 28221 \n", "486 0.00 28 0 C 29547 \n", "439 0.00 39 0 C 28211 \n", "257 0.00 10 0 C 31969 \n", "263 0.00 3 0 C 188787 \n", "251 0.00 1 0 C 1497346 \n", "246 0.00 3 0 C 84995 \n", "237 0.00 14 0 C 84998 \n", "\n", " tax_name \n", "7 Bacteroidia \n", "94 Clostridia \n", "137 Bacilli \n", "299 Betaproteobacteria \n", "194 Actinobacteria \n", "347 Gammaproteobacteria \n", "44 Flavobacteriia \n", "293 Caldilineae \n", "546 Chlamydiia \n", "540 Planctomycetia \n", "534 Opitutae \n", "524 Spirochaetia \n", "502 Deltaproteobacteria \n", "486 Epsilonproteobacteria \n", "439 Alphaproteobacteria \n", "257 Mollicutes \n", "263 Deinococci \n", "251 Thermoleophilia \n", "246 Rubrobacteria \n", "237 Coriobacteriia " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classes = td[td['rank'] == 'C']\n", "top_hits(classes)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x118f69208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distribution(classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Phylum" ] }, { "cell_type": "code", "execution_count": 67, "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>pct_reads_clade</th>\n", " <th>num_reads_clade</th>\n", " <th>num_reads_taxon</th>\n", " <th>rank</th>\n", " <th>tax_id</th>\n", " <th>tax_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>66.96</td>\n", " <td>1961757</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>976</td>\n", " <td>Bacteroidetes</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>0.97</td>\n", " <td>28375</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>1239</td>\n", " <td>Firmicutes</td>\n", " </tr>\n", " <tr>\n", " <th>298</th>\n", " <td>0.10</td>\n", " <td>2907</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>1224</td>\n", " <td>Proteobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>193</th>\n", " <td>0.01</td>\n", " <td>285</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>201174</td>\n", " <td>Actinobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>0.00</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>1090</td>\n", " <td>Chlorobi</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>0.00</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>544448</td>\n", " <td>Tenericutes</td>\n", " </tr>\n", " <tr>\n", " <th>262</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>1297</td>\n", " <td>Deinococcus-Thermus</td>\n", " </tr>\n", " <tr>\n", " <th>275</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>1117</td>\n", " <td>Cyanobacteria</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td>0.00</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>200795</td>\n", " <td>Chloroflexi</td>\n", " </tr>\n", " <tr>\n", " <th>523</th>\n", " <td>0.00</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>203691</td>\n", " <td>Spirochaetes</td>\n", " </tr>\n", " <tr>\n", " <th>533</th>\n", " <td>0.00</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>74201</td>\n", " <td>Verrucomicrobia</td>\n", " </tr>\n", " <tr>\n", " <th>539</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>203682</td>\n", " <td>Planctomycetes</td>\n", " </tr>\n", " <tr>\n", " <th>545</th>\n", " <td>0.00</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>204428</td>\n", " <td>Chlamydiae</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>0.00</td>\n", " <td>90</td>\n", " <td>0</td>\n", " <td>P</td>\n", " <td>7711</td>\n", " <td>Chordata</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pct_reads_clade num_reads_clade num_reads_taxon rank tax_id \\\n", "6 66.96 1961757 0 P 976 \n", "93 0.97 28375 0 P 1239 \n", "298 0.10 2907 0 P 1224 \n", "193 0.01 285 0 P 201174 \n", "86 0.00 13 0 P 1090 \n", "256 0.00 10 0 P 544448 \n", "262 0.00 3 0 P 1297 \n", "275 0.00 3 0 P 1117 \n", "292 0.00 3 0 P 200795 \n", "523 0.00 13 0 P 203691 \n", "533 0.00 2 0 P 74201 \n", "539 0.00 1 0 P 203682 \n", "545 0.00 1 0 P 204428 \n", "557 0.00 90 0 P 7711 \n", "\n", " tax_name \n", "6 Bacteroidetes \n", "93 Firmicutes \n", "298 Proteobacteria \n", "193 Actinobacteria \n", "86 Chlorobi \n", "256 Tenericutes \n", "262 Deinococcus-Thermus \n", "275 Cyanobacteria \n", "292 Chloroflexi \n", "523 Spirochaetes \n", "533 Verrucomicrobia \n", "539 Planctomycetes \n", "545 Chlamydiae \n", "557 Chordata " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phyla = td[td['rank'] == 'P']\n", "top_hits(phyla)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11923d4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distribution(phyla)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
zzsza/Datascience_School	16. 과최적화와 정규화/02. 교차 검증.ipynb	1	23090	{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "055ab3178a6042cd8c1b1bed67bfde5b" }, "source": [ "# 교차 검증" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5cf508ad9cfd4b0cb2e234c79e3cd000" }, "source": [ "## 모형 검증" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "8a837fee3c654352998622ac3153f539" }, "source": [ "예측 모형의 최종 성능을 객관적으로 측정하려면 모수 추정(parameter fitting) 즉 트레이닝(training)에 사용되지 않은 새로운 데이터, 즉 테스트 데이터를 사용해야 한다. 모형의 모수 갯수를 증가시킨다든가 커널 모형, 신경망 모형과 같은 비선형 모형을 사용하게 되면 트레이닝 데이터에 대한 예측 성능을 얼마든지 높일 수 있기 때문이다. 이러한 방법에 의해 과최적화(overfitting)가 일어나면 트레이닝 데이터에 대해서는 예측이 잘되지만 테스트 데이터에 대해서는 예측 성능이 급격히 떨어지는 현상이 발생한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5177fe7d5a7b4b88bd3155e6b69c4e54" }, "source": [ "## 교차 검증" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "32072beaf81e49579d871d9dc88edf0a" }, "source": [ "위에서 지적한 바와 같이 모형 성능을 정상적으로 검사하려면 테스트 데이터가 별도로 있어야 하기 때문에 현실에서는 확보한 데이터 중 일부를 떼어내어 테스트 데이터로 사용한다. 그런데 테스트 데이터를 어떻게 골라내느냐에 따라 모형의 성능이 달라지므로 한 개의 테스트 데이터만 사용하는 것이 아니라 각기 다른 방법으로 서로 다른 테스트 데이터를 여러번 골라내서 복수의 테스트를 실시하는 것이 일반적이다.\n", "\n", "이러한 테스트 방법을 교차 검증(cross validation)이라고 한다. 교차 검증을 통한 모형 성능은 보통 다음과 같은 두 가지 값으로 나타난다.\n", "\n", "* 오차 평균(mean performance): 트레이닝에 사용되지 않은 테스트 데이터(test data)에 대해서 평균 오차의 크기가 얼마나 작은가?\n", "* 오차 분산(variance): 트레이닝에 사용되지 않은 테스트 데이터(test data)에 대해 오차의 크기가 얼마나 달라지는가?\n", "\n", "이 중에서 오차 분산을 계산하려면 테스트 데이터 셋이 최소한 세 개 세트가 있어야 한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "46326e3027fc4652870b2aeec56375b1" }, "source": [ "## Scikit-Learn의 교차 검증 기능" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "c4083e1d7f8e462f999da9bcf7fc65d1" }, "source": [ "Scikit-Learn에서는 교차 검증을 위해 전체 데이터 셋에서 트레이닝용 데이터나 테스트용 데이터를 분리해 내는 여러가지 방법을 제공한다." ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "60dfe864a00d4ba58f9ff0cddd4cb7bc" }, "source": [ "* data를 train set과 test set으로 단순 분리\n", " * data splitter\n", " * `train_test_split()` 명령\n", "\n", "\n", "* 복수의 test set 준비\n", " * cross validation iterator\n", " * `KFold`\n", " * `StratifiedKFold`\n", " * `LabelKFold`\n", " * `LeaveOneOut`\n", " * `LeavePOut`\n", " * `LeaveOneLabelOut`\n", " * `LeavePLabelOut`\n", " * `ShuffleSplit`\n", " * `LabelShuffleSplit`\n", " \n", " \n", "* 복수의 test set 사용하여 평가 과정 반복\n", " * cross validation calculator\n", " * `cross_val_score()` \n" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "f734e1ca3289467890c30c34b3a5b22c" }, "source": [ "### 단순 데이터 분리" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "0b0203e7ddc9457994e7e6b859109b75" }, "source": [ "`train_test_split()` 명령은 데이터를 단순히 트레이닝 데이터와 테스트 데이터로 분리한다.\n", "\n", "\n", "* 인수\n", " * arrays : 데이터\n", " * test_size : 테스트 데이터 사이즈\n", " * train_size : 사이즈\n", " * random_state : 난수 시드\n", "\n", "* 반환값\n", " * 배열 리스트" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "school_cell_uuid": "4c3832e557984aee8852ee4abbb8fa2e" }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1],\n", " [2, 3],\n", " [4, 5],\n", " [6, 7],\n", " [8, 9]])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.arange(10).reshape((5, 2))\n", "X" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "school_cell_uuid": "070f95ebcf5d4879b71441a044ed9509" }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.arange(5)\n", "y" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "school_cell_uuid": "90e72c84142a4fc290d08e8989af57da" }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "school_cell_uuid": "338f2ee3049747cdaa037b26852a29fd" }, "outputs": [ { "data": { "text/plain": [ "array([[4, 5],\n", " [0, 1],\n", " [6, 7]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "school_cell_uuid": "028786d2784b4a68ab1fd7994e0d1e66" }, "outputs": [ { "data": { "text/plain": [ "array([2, 0, 3])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "school_cell_uuid": "474efafa7f624560ba21016b9acb15fb" }, "outputs": [ { "data": { "text/plain": [ "array([[2, 3],\n", " [8, 9]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "school_cell_uuid": "89629adfb6b24c51baa49a4dd2690747" }, "outputs": [ { "data": { "text/plain": [ "array([1, 4])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "443b4419f68e4706a530e5682a5884b0" }, "source": [ "### K-fold CV" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "22487434c26f4088a9a7f156e8c447d5" }, "source": [ "K-fold CV(cross-validation) 방법은 데이터 셋을 K개의 sub-set로 분리하는 방법이다. 분리된 K개의 sub-set 중 하나만 제외한 K-1개의 sub-sets를 training set으로 이용하여 K개의 모형 추정한다. \n", " \n", "<img src=\"https://docs.google.com/drawings/d/1JdgUDzuE75LBxqT5sKOhlPgP6umEkvD3Sm-gKnu-jqA/pub?w=762&h=651\" style=\"margin: 0 auto 0 auto;\">" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "4b72b8e8fe234b1aab94d86459d72c62" }, "source": [ "Scikit-Learn 의 cross_validation 서브 패키지는 K-Fold를 위한 `KFold` 클래스를 제공한다." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "school_cell_uuid": "92a46e5d49074eeca371b639dd7296b8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X:\n", "[[ 0 10]\n", " [ 20 30]\n", " [ 40 50]\n", " [ 60 70]\n", " [ 80 90]\n", " [100 110]\n", " [120 130]\n", " [140 150]\n", " [160 170]\n", " [180 190]\n", " [200 210]\n", " [220 230]\n", " [240 250]\n", " [260 270]\n", " [280 290]\n", " [300 310]\n", " [320 330]\n", " [340 350]\n", " [360 370]\n", " [380 390]]\n", "y:\n", "[ 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 3. 3. 3. 3. 3. 4. 4. 4.\n", " 4. 4.]\n" ] } ], "source": [ "N = 5\n", "X = np.arange(8 * N).reshape(-1, 2) * 10\n", "y = np.hstack([np.ones(N), np.ones(N) * 2, np.ones(N) * 3, np.ones(N) * 4])\n", "print(\"X:\\n\", X, sep=\"\")\n", "print(\"y:\\n\", y, sep=\"\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "school_cell_uuid": "556dab2a12f4433088c4a56d7cade7c1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test y: [ 1. 1. 1. 1. 1. 2. 2.]\n", "................................................................................\n", "train y: [ 2. 2. 2. 3. 3. 3. 3. 3. 4. 4. 4. 4. 4.]\n", "================================================================================\n", "test y: [ 2. 2. 2. 3. 3. 3. 3.]\n", "................................................................................\n", "train y: [ 1. 1. 1. 1. 1. 2. 2. 3. 4. 4. 4. 4. 4.]\n", "================================================================================\n", "test y: [ 3. 4. 4. 4. 4. 4.]\n", "................................................................................\n", "train y: [ 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 3. 3. 3. 3.]\n", "================================================================================\n" ] } ], "source": [ "from sklearn.cross_validation import KFold\n", "cv = KFold(len(X), n_folds=3, random_state=0)\n", "for train_index, test_index in cv:\n", " print(\"test y:\", y[test_index])\n", " print(\".\" * 80 ) \n", " print(\"train y:\", y[train_index])\n", " print(\"=\" * 80 )" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "dbc25e3141ff45df8d288b3a0f5f143a" }, "source": [ "### Stratified K-Fold\n", "\n", "* target class가 어느 한 data set에 몰리지 않도록 한다\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "school_cell_uuid": "e98009c7f37b40cd9e4bb8aa1a25869d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test X:\n", " [[ 0 10]\n", " [ 20 30]\n", " [100 110]\n", " [120 130]\n", " [200 210]\n", " [220 230]\n", " [300 310]\n", " [320 330]]\n", "................................................................................\n", "test y: [ 1. 1. 2. 2. 3. 3. 4. 4.]\n", "================================================================================\n", "test X:\n", " [[ 40 50]\n", " [ 60 70]\n", " [140 150]\n", " [160 170]\n", " [240 250]\n", " [260 270]\n", " [340 350]\n", " [360 370]]\n", "................................................................................\n", "test y: [ 1. 1. 2. 2. 3. 3. 4. 4.]\n", "================================================================================\n", "test X:\n", " [[ 80 90]\n", " [180 190]\n", " [280 290]\n", " [380 390]]\n", "................................................................................\n", "test y: [ 1. 2. 3. 4.]\n", "================================================================================\n" ] } ], "source": [ "from sklearn.cross_validation import StratifiedKFold\n", "cv = StratifiedKFold(y, n_folds=3, random_state=0)\n", "for train_index, test_index in cv:\n", " print(\"test X:\\n\", X[test_index])\n", " print(\".\" * 80 ) \n", " print(\"test y:\", y[test_index])\n", " print(\"=\" * 80 )" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "e389cca9f65a4c6a95c5936d83d26e43" }, "source": [ "### Leave-One-Out (LOO)\n", "\n", "* 하나의 sample만을 test set으로 남긴다." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "school_cell_uuid": "0325efaf66ad4b478286b903af2fbd18" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test X: [[ 0 10]]\n", "................................................................................\n", "test y: [ 1.]\n", "================================================================================\n", "test X: [[20 30]]\n", "................................................................................\n", "test y: [ 1.]\n", "================================================================================\n", "test X: [[40 50]]\n", "................................................................................\n", "test y: [ 1.]\n", "================================================================================\n", "test X: [[60 70]]\n", "................................................................................\n", "test y: [ 1.]\n", "================================================================================\n", "test X: [[80 90]]\n", "................................................................................\n", "test y: [ 1.]\n", "================================================================================\n" ] } ], "source": [ "from sklearn.cross_validation import LeaveOneOut\n", "cv = LeaveOneOut(5)\n", "for train_index, test_index in cv:\n", " print(\"test X:\", X[test_index])\n", " print(\".\" * 80 ) \n", " print(\"test y:\", y[test_index])\n", " print(\"=\" * 80 )" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "7e06bb87256c4d2fb47e49b495e02347" }, "source": [ "### Label K-Fold\n", "\n", "* 같은 label이 test와 train에 동시에 들어가지 않게 조절\n", "* label에 의한 영향을 최소화\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "school_cell_uuid": "8be593f50b424978b911ba57ea98cb82" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test y: [ 1. 1. 1. 1. 1. 4. 4. 4. 4. 4.]\n", "................................................................................\n", "train y: [ 2. 2. 2. 2. 2. 3. 3. 3. 3. 3.]\n", "================================================================================\n", "test y: [ 3. 3. 3. 3. 3.]\n", "................................................................................\n", "train y: [ 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 4. 4. 4. 4. 4.]\n", "================================================================================\n", "test y: [ 2. 2. 2. 2. 2.]\n", "................................................................................\n", "train y: [ 1. 1. 1. 1. 1. 3. 3. 3. 3. 3. 4. 4. 4. 4. 4.]\n", "================================================================================\n" ] } ], "source": [ "from sklearn.cross_validation import LabelKFold\n", "cv = LabelKFold(y, n_folds=3)\n", "for train_index, test_index in cv:\n", " print(\"test y:\", y[test_index])\n", " print(\".\" * 80 ) \n", " print(\"train y:\", y[train_index])\n", " print(\"=\" * 80 )" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "a7f299b590f64ab6a6ebb210030e6172" }, "source": [ "### ShuffleSplit\n", "\n", "* 중복된 데이터를 허용" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "school_cell_uuid": "7643b1bcf3b749c2b8103ae803bdf9cc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test X: [[80 90]]\n", "====================\n", "test X: [[ 0 10]]\n", "====================\n", "test X: [[ 0 10]]\n", "====================\n", "test X: [[60 70]]\n", "====================\n", "test X: [[60 70]]\n", "====================\n", "test X: [[80 90]]\n", "====================\n", "test X: [[40 50]]\n", "====================\n", "test X: [[20 30]]\n", "====================\n", "test X: [[ 0 10]]\n", "====================\n", "test X: [[40 50]]\n", "====================\n" ] } ], "source": [ "from sklearn.cross_validation import ShuffleSplit\n", "cv = ShuffleSplit(5)\n", "for train_index, test_index in cv:\n", " print(\"test X:\", X[test_index])\n", " print(\"=\" * 20 ) " ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "a4974220558345148bbc4fcc897a5c7e" }, "source": [ "## 교차 평가 시행 \n", "\n", "CV는 단순히 데이터 셋을 나누는 역할을 수행할 뿐이다. 실제로 모형의 성능(편향 오차 및 분산)을 구하려면 이렇게 나누어진 데이터셋을 사용하여 평가를 반복하여야 한다. 이 과정을 자동화하는 명령이 `cross_val_score()` 이다.\n", "\n", "\n", "* `cross_val_score(estimator, X, y=None, scoring=None, cv=None)` \n", " * cross validation iterator `cv`를 이용하여 `X`, `y` data 를 분할하고 `estimator`에 넣어서 `scoring` metric을 구하는 과정을 반복\n", "\n", "\n", "* 인수\n", " * estimator : ‘fit’메서드가 제공되는 모형\n", " * X : 배열\n", " * 독립 변수 데이터\n", " * y : 배열\n", " * 종속 변수 데이터\n", " * scoring : 문자열\n", " * 성능 검증에 사용할 함수\n", " * cv : Cross Validator\n", " * None 이면 디폴트인 3-폴드 CV\n", " * 숫자 K 이면 K-폴드 CV\n", " * Cross Validator 클래스 객체\n", "\n", "* 반환값\n", " * scores \n", " * 계산된 성능 값의 리스트" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "school_cell_uuid": "baa8622e9ef042e5bf0088551b72fab8" }, "outputs": [ { "data": { "text/plain": [ "array([ 301.58271911, 341.91498985, 410.58098438, 499.68109613,\n", " 461.00979825, 384.106544 , 434.90159273, 377.65506997,\n", " 366.60959935, 371.14031438])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import make_regression\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error\n", "X, y, coef = make_regression(n_samples=1000, n_features=1, noise=20, coef=True, random_state=0)\n", "model = LinearRegression()\n", "cv = KFold(1000, 10)\n", "\n", "scores = np.zeros(10)\n", "for i, (train_index, test_index) in enumerate(cv):\n", " X_train = X[train_index]\n", " y_train = y[train_index]\n", " X_test = X[test_index]\n", " y_test = y[test_index]\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " scores[i] = mean_squared_error(y_test, y_pred)\n", "\n", "scores" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "school_cell_uuid": "3b69e154c0fa4e42a800c9b799bf6aa5" }, "outputs": [ { "data": { "text/plain": [ "array([-301.58271911, -341.91498985, -410.58098438, -499.68109613,\n", " -461.00979825, -384.106544 , -434.90159273, -377.65506997,\n", " -366.60959935, -371.14031438])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.cross_validation import cross_val_score\n", "cross_val_score(model, X, y, \"mean_squared_error\", cv)" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "9902d917f85242f1949582aa8daa5c04" }, "source": [ "### 회귀 분석에 사용되는 성능 함수들" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "609ee1e16a88478681dc0754f979da46" }, "source": [ "* `r2_score(y_true, y_pred[, ...])`:\tR^2 (coefficient of determination) regression score function.\n", "* `explained_variance_score(y_true, y_pred)`:\tExplained variance regression score function\n", "* `mean_squared_error(y_true, y_pred[, ...])`:\tMean squared error regression loss\n", "* `mean_absolute_error(y_true, y_pred)`:\tMean absolute error regression loss\n", "* `median_absolute_error(y_true, y_pred)`:\tMedian absolute error regression loss" ] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
brschneidE3/LegalNetworks	python_code/ipynb/case_class.ipynb	2	8641	{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, sys, io\n", "import json\n", "#import simplejson as json\n", "from pprint import pprint\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_path = '../../data'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "# Given the juristidction, file type and root path to data\n", "# Returns a list of case ids in that jurisdiction\n", "def get_cases_in_jurisdiction( juris_abv = 'nced', file_type = 'opinions', data_path = '../../data'):\n", " \n", " # path leading to the jurisdiction files\n", " path = data_path + '/'+ file_type + '/' + juris_abv + '/'\n", " \n", " # TODO: throw an exception\n", " # Check that the directory exists\n", " if not os.path.isdir(path):\n", " print 'not a legal path'\n", " return []\n", " else:\n", " return [int(f.split('.json')[0]) for f in os.listdir(path)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nced_case_ids = get_cases_in_jurisdiction('nced')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cl_file = data_path + '/clusters/nced/1361899.json'\n", "op_file = data_path + '/opinions/nced/1361899.json'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "# Open the cluster and opinion json files\n", "with open(cl_file) as data_file: \n", " cl_data_temp = json.load(data_file)\n", " \n", "with open(op_file) as data_file: \n", " op_data_temp = json.load(data_file)\n", "\n", "# TODO: do this more succinctly\n", "# Convert to utf8 from unicode\n", "cl_data = {}\n", "for k in cl_data_temp.keys():\n", " value = cl_data_temp[k]\n", " if k == 'opinions_cited':\n", " cl_data['opinions_cited'] = [v.encode('utf8') for v in value]\n", " elif type(value) == unicode:\n", " cl_data[k.encode('utf8')] = value.encode('utf8')\n", " else:\n", " cl_data[k.encode('utf8')] = value\n", " \n", " \n", "op_data = {}\n", "for k in op_data_temp.keys():\n", " value = op_data_temp[k]\n", " if k == 'opinions_cited':\n", " op_data['opinions_cited'] = [v.encode('utf8') for v in value]\n", " elif type(value) == unicode:\n", " op_data[k.encode('utf8')] = value.encode('utf8')\n", " else:\n", " op_data[k.encode('utf8')] = value\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "case_data = {}\n", "\n", "for k in cl_data.keys():\n", " if k == 'case_name':\n", " case_data[k] = cl_data[k]\n", " \n", " if k == 'citation_id':\n", " case_data[k] = cl_data[k]\n", "\n", " if k == 'date_filed' \n", " date_explode = cl_data['date_filed'].split('-') # make sure date is always in this format\n", " file_date = datetime.date(date_explode[0], date_explode[1], date_explode[2])\n", " case_data[k] = file_date\n", "\n", "# Get the case text \n", "text = op_data['html']\n", "if len(text) == 0:\n", " text = op_data['html_with_citations']\n", "elif len(text) == 0:\n", " text = op_data['plain_text']\n", "elif len(text) == 0:\n", " text = op_data['html_lawbox']\n", "elif len(text) == 0:\n", " text = ''\n", " print('case ' + str(i) + ' has no text')\n", " \n", " \n", "case_data['case_text'] = text" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Case:\n", " def __init__(self, op_file, cl_file):\n", " \n", " # Open the cluster and opinion json files\n", " with open(cl_file) as data_file: \n", " cl_data_temp = json.load(data_file)\n", "\n", " with open(op_file) as data_file: \n", " op_data_temp = json.load(data_file)\n", "\n", " # TODO: do this more succinctly\n", " # Convert to utf8 from unicode\n", " cl_data = {}\n", " for k in cl_data_temp.keys():\n", " value = cl_data_temp[k]\n", " if k == 'opinions_cited':\n", " cl_data['opinions_cited'] = [v.encode('utf8') for v in value]\n", " elif type(value) == unicode:\n", " cl_data[k.encode('utf8')] = value.encode('utf8')\n", " else:\n", " cl_data[k.encode('utf8')] = value\n", "\n", " op_data = {}\n", " for k in op_data_temp.keys():\n", " value = op_data_temp[k]\n", " if k == 'opinions_cited':\n", " op_data['opinions_cited'] = [v.encode('utf8') for v in value]\n", " elif type(value) == unicode:\n", " op_data[k.encode('utf8')] = value.encode('utf8')\n", " else:\n", " op_data[k.encode('utf8')] = value\n", "\n", "\n", "\n", "\n", " for k in cl_data.keys():\n", " if k == 'case_name':\n", " self.case_name = cl_data[k]\n", "\n", " if k == 'citation_id':\n", " self.case_id = cl_data[k]\n", "\n", " if k == 'date_filed': \n", " date_explode = cl_data['date_filed'].split('-') # make sure date is always in this format\n", " file_date = datetime.date(int(date_explode[0]), int(date_explode[1]), int(date_explode[2]))\n", " self.date = file_date\n", "\n", " # Get the case text \n", " text = op_data['html']\n", " if len(text) == 0:\n", " text = op_data['html_with_citations']\n", " elif len(text) == 0:\n", " text = op_data['plain_text']\n", " elif len(text) == 0:\n", " text = op_data['html_lawbox']\n", " elif len(text) == 0:\n", " text = ''\n", " print('case ' + str(i) + ' has no text')\n", " \n", " self.text = text\n", " \n", " def __repr__(self):\n", " return \"Name: \\t %s \\n\"\\\n", " \"Id \\t %s \\n\"\\\n", " \"Date \\t %s \\n\"\\\n", " % (self.case_name, self.case_id, self.date )\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "case = Case(op_file, cl_file)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method Case.__repr__ of Name: \t Stott v. Martin \n", "Id \t 1334670 \n", "Date \t 1992-02-12 \n", ">" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case.__repr__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ehsteve/ipython-notebooks	Reading HSI Obssum fits .ipynb	2	345801	{ "metadata": { "name": "", "signature": "sha256:812f6911db3213f05b4e1efc3eccc85dc59f4e59303959ea9c767ff1ffdf7fd4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING: pylab import has clobbered these variables: ['datetime']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "from astropy.io import fits as pyfits" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "file = '/Users/schriste/Downloads/hsi_obssumm_20131217_012.fits'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "fits = pyfits.open(fpyfits)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[0].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "SIMPLE = T /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 0 / \n", "EXTEND = T /File contains extensions \n", "DATE = '2013-12-17' / \n", "ORIGIN = 'sundog ' /Usually the CPU used to generate the file \n", "OBSERVER= 'hessiops' /Usually the name of the user who generated the f\n", "TELESCOP= 'HESSI ' /The High Energy Solar Spectroscopic Imager \n", "OBJECT = 'Sun ' / \n", "DATE_OBS= '2013-12-17T00:00:00.000' / \n", "DATE_END= '2013-12-17T11:37:40.000' / " ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[1].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 84 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 3 /Number of columns \n", "EXTNAME = 'HESSI OBS SUMMARY ID TABLE' /Extension name \n", "TFORM1 = '1I ' /Integer2 (short integer) \n", "TTYPE1 = 'VERSION NUMBER' /Label for column 1 \n", "TFORM2 = '80A ' /Character string \n", "TTYPE2 = 'OBS SUMMARY ID' /Label for column 2 \n", "TFORM3 = '1I ' /Integer2 (short integer) \n", "TTYPE3 = 'INFO VERSION NUMBER' /Label for column 3 " ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[2].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 164 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 4 /Number of columns \n", "EXTNAME = 'HSI_OBSSUMMINFO' /Extension name \n", "TFORM1 = '80A ' /Character string \n", "TTYPE1 = 'SUMMARY_START_TIME' /Label for column 1 \n", "TFORM2 = '80A ' /Character string \n", "TTYPE2 = 'SUMMARY_END_TIME' /Label for column 2 \n", "TFORM3 = '1I ' /Integer2 (short integer) \n", "TTYPE3 = 'SIMULATED_DATA' /Label for column 3 \n", "TFORM4 = '1I ' /Integer2 (short integer) \n", "TTYPE4 = 'CONCAT_FLAG' /Label for column 4 " ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[3].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 200 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 11 /Number of columns \n", "EXTNAME = 'HSI_FILEDB' /Extension name \n", "TFORM1 = '1I ' /Integer2 (short integer) \n", "TTYPE1 = 'VERSION ' /Label for column 1 \n", "TFORM2 = '80A ' /Character string \n", "TTYPE2 = 'FILE_ID ' /Label for column 2 \n", "TFORM3 = '1J ' /Integer4 (long integer) \n", "TTYPE3 = 'ORBIT_START' /Label for column 3 \n", "TFORM4 = '1J ' /Integer4 (long integer) \n", "TTYPE4 = 'ORBIT_END' /Label for column 4 \n", "TFORM5 = '1D ' /Real8 (double precision) \n", "TTYPE5 = 'START_TIME' /Label for column 5 \n", "TFORM6 = '1D ' /Real8 (double precision) \n", "TTYPE6 = 'END_TIME' /Label for column 6 \n", "TFORM7 = '1I ' /Integer2 (short integer) \n", "TTYPE7 = 'STATUS_FLAG' /Label for column 7 \n", "TFORM8 = '1J ' /Integer4 (long integer) \n", "TTYPE8 = 'NPACKETS' /Label for column 8 \n", "TFORM9 = '1E ' /Real4 (floating point) \n", "TTYPE9 = 'CLOCK_DRIFT_START' /Label for column 9 \n", "TFORM10 = '1E ' /Real4 (floating point) \n", "TTYPE10 = 'CLOCK_DRIFT_END' /Label for column 10 \n", "TFORM11 = '80A ' /Character string \n", "TTYPE11 = 'DATA_SOURCE' /Label for column 11 " ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "parse_time(fits[3].data['START_TIME'][0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "datetime.datetime(2013, 12, 17, 0, 0)" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "parse_time(fits[3].data['END_TIME'][0])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 59, "text": [ "datetime.datetime(2013, 12, 17, 11, 37, 40)" ] } ], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[4].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 86 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 4 /Number of columns \n", "EXTNAME = 'HESSI OBS SUMMARY RATE ID TABLE' /Extension name \n", "TFORM1 = '1I ' /Integer2 (short integer) \n", "TTYPE1 = 'VERSION NUMBER' /Label for column 1 \n", "TFORM2 = '80A ' /Character string \n", "TTYPE2 = 'ID STRING' /Label for column 2 \n", "TFORM3 = '1I ' /Integer2 (short integer) \n", "TTYPE3 = 'INFO VERSION NUMBER' /Label for column 3 \n", "TFORM4 = '1I ' /Integer2 (short integer) \n", "TTYPE4 = 'DATA VERSION NUMBER' /Label for column 4 " ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[4].data['ID STRING']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 93, "text": [ "chararray(['HSI_OBS_SUMM_RATE: 2013-12-17T00:00:00.000 TO 2013-12-17T11:37:40.000'], \n", " dtype='\|S80')" ] } ], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[5].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:40 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 876 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 8 /Number of columns \n", "EXTNAME = 'HSI_OBSSUMMRATEINFO' /Extension name \n", "TFORM1 = '1D ' /Real8 (double precision) \n", "TTYPE1 = 'UT_REF ' /Label for column 1 \n", "TFORM2 = '18B ' /Integer1 (byte) \n", "TTYPE2 = 'SEG_INDEX_MASK' /Label for column 2 \n", "TFORM3 = '1J ' /Integer4 (long integer) \n", "TTYPE3 = 'N_TIME_INTV' /Label for column 3 \n", "TFORM4 = '1E ' /Real4 (floating point) \n", "TTYPE4 = 'TIME_INTV' /Label for column 4 \n", "TFORM5 = '1I ' /Integer2 (short integer) \n", "TTYPE5 = 'N_ENERGY_BANDS' /Label for column 5 \n", "TFORM6 = '10E ' /Real4 (floating point) \n", "TTYPE6 = 'ENERGY_EDGES' /Label for column 6 \n", "TFORM7 = '80A ' /Character string \n", "TTYPE7 = 'DIM1_UNIT' /Label for column 7 \n", "TFORM8 = '720A ' /Character string \n", "TTYPE8 = 'DIM1_IDS' /Label for column 8 \n", "TDIM8 = '(80,9) ' /Array dimensions for column 8 " ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[5].data.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "ColDefs(\n", " name = 'UT_REF'; format = '1D'\n", " name = 'SEG_INDEX_MASK'; format = '18B'\n", " name = 'N_TIME_INTV'; format = '1J'\n", " name = 'TIME_INTV'; format = '1E'\n", " name = 'N_ENERGY_BANDS'; format = '1I'\n", " name = 'ENERGY_EDGES'; format = '10E'\n", " name = 'DIM1_UNIT'; format = '80A'\n", " name = 'DIM1_IDS'; format = '720A'; dim = '(80,9)'\n", ")" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[5].data['UT_REF'][0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "1103241600.0" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[5].data['TIME_INTV'][0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "4.0" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[5].data['N_TIME_INTV'][0]" ], 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QsrO9t0ooSto81dLx95/S3CLcERDFLZOGli578803MW/ePGOkaUvCZhu3qk5yMTGVNAat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"text": [ "<matplotlib.figure.Figure at 0x10843b710>" ] } ], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[7].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 41, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:41 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 86 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 4 /Number of columns \n", "EXTNAME = 'HESSI MOD VARIANCE ID TABLE' /Extension name \n", "TFORM1 = '1I ' /Integer2 (short integer) \n", "TTYPE1 = 'VERSION NUMBER' /Label for column 1 \n", "TFORM2 = '80A ' /Character string \n", "TTYPE2 = 'ID STRING' /Label for column 2 \n", "TFORM3 = '1I ' /Integer2 (short integer) \n", "TTYPE3 = 'INFO VERSION NUMBER' /Label for column 3 \n", "TFORM4 = '1I ' /Integer2 (short integer) \n", "TTYPE4 = 'DATA VERSION NUMBER' /Label for column 4 " ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[8].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 42, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:41 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 266 /Number of bytes per row \n", "NAXIS2 = 1 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 7 /Number of columns \n", "EXTNAME = 'HSI_MODVARIANCEINFO' /Extension name \n", "TFORM1 = '1D ' /Real8 (double precision) \n", "TTYPE1 = 'UT_REF ' /Label for column 1 \n", "TFORM2 = '1J ' /Integer4 (long integer) \n", "TTYPE2 = 'N_TIME_INTV' /Label for column 2 \n", "TFORM3 = '1E ' /Real4 (floating point) \n", "TTYPE3 = 'TIME_INTV' /Label for column 3 \n", "TFORM4 = '1I ' /Integer2 (short integer) \n", "TTYPE4 = 'VARIANCE_NBIN' /Label for column 4 \n", "TFORM5 = '2E ' /Real4 (floating point) \n", "TTYPE5 = 'ENERGY_EDGES' /Label for column 5 \n", "TFORM6 = '80A ' /Character string \n", "TTYPE6 = 'DIM1_UNIT' /Label for column 6 \n", "TFORM7 = '160A ' /Character string \n", "TTYPE7 = 'DIM1_IDS' /Label for column 7 \n", "TDIM7 = '(80,2) ' /Array dimensions for column 7 " ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "fits[9].header" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "XTENSION= 'BINTABLE' /Written by IDL: Tue Dec 17 10:28:41 2013 \n", "BITPIX = 8 / \n", "NAXIS = 2 /Binary table \n", "NAXIS1 = 2 /Number of bytes per row \n", "NAXIS2 = 10465 /Number of rows \n", "PCOUNT = 0 /Random parameter count \n", "GCOUNT = 1 /Group count \n", "TFIELDS = 1 /Number of columns \n", "EXTNAME = 'HSI_MODVARIANCEDATA' /Extension name \n", "TFORM1 = '2B ' /Integer*1 (byte) \n", "TTYPE1 = 'MOD_VARIANCE' /Label for column 1 " ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "from sunpy.instr import rhessi" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from sunpy.time import TimeRange\n", "f = rhessi.get_obssum_filename(TimeRange('2003/03/02', '2003/03/03'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Downloading file: http://hesperia.gsfc.nasa.gov/hessidata/dbase/hsi_obssumm_filedb_200303.txt\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "f" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "'http://hesperia.gsfc.nasa.gov/hessidata/hsi_obssumm_20030302_146.fit'" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "r = rhessi.parse_obssumm_file(file)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "r" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "{'data': array([[24, 17, 10, ..., 30, 26, 1],\n", " [26, 17, 12, ..., 29, 26, 1],\n", " [24, 16, 9, ..., 30, 25, 0],\n", " ..., \n", " [ 5, 11, 8, ..., 31, 28, 0],\n", " [ 6, 10, 8, ..., 31, 27, 1],\n", " [ 4, 11, 8, ..., 31, 28, 1]], dtype=uint8),\n", " 'labels': ['3 - 6 keV',\n", " '6 - 12 keV',\n", " '12 - 25 keV',\n", " '25 - 50 keV',\n", " '50 - 100 keV',\n", " '100 - 300 keV',\n", " '300 - 800 keV',\n", " '800 - 7000 keV',\n", " '7000 - 20000 keV'],\n", " 'time': [datetime.datetime(2013, 12, 17, 0, 0),\n", " datetime.datetime(2013, 12, 17, 0, 0, 4),\n", " datetime.datetime(2013, 12, 17, 0, 0, 8),\n", " datetime.datetime(2013, 12, 17, 0, 0, 12),\n", " datetime.datetime(2013, 12, 17, 0, 0, 16),\n", " datetime.datetime(2013, 12, 17, 0, 0, 20),\n", " 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"collapsed": false, "input": [ "pd.DataFrame(r['data'], columns=r['labels'], index = r['time'])" ], "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>3 - 6 keV</th>\n", " <th>6 - 12 keV</th>\n", " <th>12 - 25 keV</th>\n", " <th>25 - 50 keV</th>\n", " <th>50 - 100 keV</th>\n", " <th>100 - 300 keV</th>\n", " <th>300 - 800 keV</th>\n", " <th>800 - 7000 keV</th>\n", " <th>7000 - 20000 keV</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013-12-17 00:00:00</th>\n", " <td> 24</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 13</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:04</th>\n", " <td> 26</td>\n", " <td> 17</td>\n", " <td> 12</td>\n", " <td> 12</td>\n", " <td> 13</td>\n", " <td> 25</td>\n", " <td> 29</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:08</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:12</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:16</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 24</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:20</th>\n", " <td> 26</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 13</td>\n", " <td> 24</td>\n", " <td> 30</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:24</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:28</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:32</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:36</th>\n", " <td> 24</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 25</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:40</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:44</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:48</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:52</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 11</td>\n", " <td> 10</td>\n", " <td> 13</td>\n", " <td> 24</td>\n", " <td> 29</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:00:56</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 11</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:00</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 26</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:04</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:08</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:12</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:16</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 11</td>\n", " <td> 10</td>\n", " <td> 13</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:20</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 13</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:24</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 13</td>\n", " <td> 22</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:28</th>\n", " <td> 26</td>\n", " <td> 17</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 13</td>\n", " <td> 24</td>\n", " <td> 30</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:32</th>\n", " <td> 26</td>\n", " <td> 17</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 13</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:36</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:40</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:44</th>\n", " <td> 22</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 8</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 27</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:48</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:52</th>\n", " <td> 24</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:01:56</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:00</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:04</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:08</th>\n", " <td> 24</td>\n", " <td> 15</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 30</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:12</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:16</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:20</th>\n", " <td> 24</td>\n", " <td> 15</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:24</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:28</th>\n", " <td> 24</td>\n", " <td> 15</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:32</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:36</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:40</th>\n", " <td> 24</td>\n", " <td> 15</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:44</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:48</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:52</th>\n", " <td> 24</td>\n", " <td> 15</td>\n", " <td> 10</td>\n", " <td> 8</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:02:56</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 30</td>\n", " <td> 24</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:00</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 11</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:04</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:08</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 27</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:12</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:16</th>\n", " <td> 25</td>\n", " <td> 17</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:20</th>\n", " <td> 26</td>\n", " <td> 17</td>\n", " <td> 10</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 23</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:24</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 10</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 24</td>\n", " <td> 28</td>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:28</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 23</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:32</th>\n", " <td> 23</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 27</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:36</th>\n", " <td> 24</td>\n", " <td> 17</td>\n", " <td> 11</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:40</th>\n", " <td> 23</td>\n", " <td> 15</td>\n", " <td> 8</td>\n", " <td> 9</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:44</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 12</td>\n", " <td> 22</td>\n", " <td> 30</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:48</th>\n", " <td> 24</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 11</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 28</td>\n", " <td> 24</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:52</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 8</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 27</td>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-17 00:03:56</th>\n", " <td> 25</td>\n", " <td> 16</td>\n", " <td> 9</td>\n", " <td> 10</td>\n", " <td> 11</td>\n", " <td> 22</td>\n", " <td> 29</td>\n", " <td> 25</td>\n", " <td> 1</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", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10465 rows \u00d7 9 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " 3 - 6 keV 6 - 12 keV 12 - 25 keV 25 - 50 keV \\\n", "2013-12-17 00:00:00 24 17 10 11 \n", "2013-12-17 00:00:04 26 17 12 12 \n", "2013-12-17 00:00:08 24 16 9 9 \n", "2013-12-17 00:00:12 24 16 10 12 \n", "2013-12-17 00:00:16 25 16 11 11 \n", "2013-12-17 00:00:20 26 17 10 10 \n", "2013-12-17 00:00:24 23 16 9 10 \n", "2013-12-17 00:00:28 24 16 9 10 \n", "2013-12-17 00:00:32 25 16 9 10 \n", "2013-12-17 00:00:36 24 17 10 10 \n", "2013-12-17 00:00:40 25 16 9 11 \n", "2013-12-17 00:00:44 23 16 8 10 \n", "2013-12-17 00:00:48 23 16 9 9 \n", "2013-12-17 00:00:52 24 16 11 10 \n", "2013-12-17 00:00:56 25 17 11 9 \n", "2013-12-17 00:01:00 24 16 10 11 \n", "2013-12-17 00:01:04 25 17 10 10 \n", "2013-12-17 00:01:08 24 16 9 10 \n", "2013-12-17 00:01:12 25 17 10 10 \n", "2013-12-17 00:01:16 23 16 11 10 \n", "2013-12-17 00:01:20 25 16 10 10 \n", "2013-12-17 00:01:24 23 16 10 11 \n", "2013-12-17 00:01:28 26 17 9 11 \n", "2013-12-17 00:01:32 26 17 9 10 \n", "2013-12-17 00:01:36 24 16 8 10 \n", "2013-12-17 00:01:40 24 16 9 10 \n", "2013-12-17 00:01:44 22 16 9 8 \n", "2013-12-17 00:01:48 24 16 9 11 \n", "2013-12-17 00:01:52 24 17 10 11 \n", "2013-12-17 00:01:56 23 16 9 10 \n", "2013-12-17 00:02:00 24 16 8 9 \n", "2013-12-17 00:02:04 24 16 9 11 \n", "2013-12-17 00:02:08 24 15 10 10 \n", "2013-12-17 00:02:12 25 16 9 10 \n", "2013-12-17 00:02:16 24 16 9 10 \n", "2013-12-17 00:02:20 24 15 9 10 \n", "2013-12-17 00:02:24 25 16 9 9 \n", "2013-12-17 00:02:28 24 15 10 11 \n", "2013-12-17 00:02:32 23 16 8 9 \n", "2013-12-17 00:02:36 25 17 10 11 \n", "2013-12-17 00:02:40 24 15 9 11 \n", "2013-12-17 00:02:44 25 17 10 10 \n", "2013-12-17 00:02:48 25 17 9 10 \n", "2013-12-17 00:02:52 24 15 10 8 \n", "2013-12-17 00:02:56 24 16 9 10 \n", "2013-12-17 00:03:00 23 16 11 9 \n", "2013-12-17 00:03:04 25 16 9 10 \n", "2013-12-17 00:03:08 24 16 9 9 \n", "2013-12-17 00:03:12 24 16 9 11 \n", "2013-12-17 00:03:16 25 17 9 11 \n", "2013-12-17 00:03:20 26 17 10 9 \n", "2013-12-17 00:03:24 23 16 10 10 \n", "2013-12-17 00:03:28 24 16 9 9 \n", "2013-12-17 00:03:32 23 16 9 9 \n", "2013-12-17 00:03:36 24 17 11 9 \n", "2013-12-17 00:03:40 23 15 8 9 \n", "2013-12-17 00:03:44 25 16 9 10 \n", "2013-12-17 00:03:48 24 16 9 11 \n", "2013-12-17 00:03:52 25 16 8 10 \n", "2013-12-17 00:03:56 25 16 9 10 \n", " ... ... ... ... \n", "\n", " 50 - 100 keV 100 - 300 keV 300 - 800 keV \\\n", "2013-12-17 00:00:00 13 23 30 \n", "2013-12-17 00:00:04 13 25 29 \n", "2013-12-17 00:00:08 12 23 30 \n", "2013-12-17 00:00:12 11 23 29 \n", "2013-12-17 00:00:16 12 24 30 \n", "2013-12-17 00:00:20 13 24 30 \n", "2013-12-17 00:00:24 12 22 30 \n", "2013-12-17 00:00:28 12 22 29 \n", "2013-12-17 00:00:32 11 23 30 \n", "2013-12-17 00:00:36 12 25 28 \n", "2013-12-17 00:00:40 12 23 29 \n", "2013-12-17 00:00:44 12 23 30 \n", "2013-12-17 00:00:48 11 22 29 \n", "2013-12-17 00:00:52 13 24 29 \n", "2013-12-17 00:00:56 12 23 30 \n", "2013-12-17 00:01:00 11 23 29 \n", "2013-12-17 00:01:04 12 23 28 \n", "2013-12-17 00:01:08 12 22 29 \n", "2013-12-17 00:01:12 12 23 28 \n", "2013-12-17 00:01:16 13 22 28 \n", "2013-12-17 00:01:20 13 22 29 \n", "2013-12-17 00:01:24 13 22 30 \n", "2013-12-17 00:01:28 13 24 30 \n", "2013-12-17 00:01:32 13 23 29 \n", "2013-12-17 00:01:36 10 23 29 \n", "2013-12-17 00:01:40 12 23 30 \n", "2013-12-17 00:01:44 11 22 27 \n", "2013-12-17 00:01:48 12 22 28 \n", "2013-12-17 00:01:52 12 23 28 \n", "2013-12-17 00:01:56 12 23 29 \n", "2013-12-17 00:02:00 11 23 29 \n", "2013-12-17 00:02:04 11 22 29 \n", "2013-12-17 00:02:08 12 23 30 \n", "2013-12-17 00:02:12 12 23 29 \n", "2013-12-17 00:02:16 12 23 29 \n", "2013-12-17 00:02:20 12 23 28 \n", "2013-12-17 00:02:24 12 22 29 \n", "2013-12-17 00:02:28 11 23 28 \n", "2013-12-17 00:02:32 12 23 29 \n", "2013-12-17 00:02:36 12 23 29 \n", "2013-12-17 00:02:40 12 23 28 \n", "2013-12-17 00:02:44 12 23 29 \n", "2013-12-17 00:02:48 11 23 29 \n", "2013-12-17 00:02:52 11 22 28 \n", "2013-12-17 00:02:56 12 22 30 \n", "2013-12-17 00:03:00 11 23 28 \n", "2013-12-17 00:03:04 12 22 29 \n", "2013-12-17 00:03:08 11 22 27 \n", "2013-12-17 00:03:12 12 22 29 \n", "2013-12-17 00:03:16 12 23 29 \n", "2013-12-17 00:03:20 11 23 29 \n", "2013-12-17 00:03:24 12 24 28 \n", "2013-12-17 00:03:28 12 23 28 \n", "2013-12-17 00:03:32 11 22 27 \n", "2013-12-17 00:03:36 12 22 28 \n", "2013-12-17 00:03:40 12 22 28 \n", "2013-12-17 00:03:44 12 22 30 \n", "2013-12-17 00:03:48 11 22 28 \n", "2013-12-17 00:03:52 11 22 27 \n", "2013-12-17 00:03:56 11 22 29 \n", " ... ... ... \n", "\n", " 800 - 7000 keV 7000 - 20000 keV \n", "2013-12-17 00:00:00 26 1 \n", "2013-12-17 00:00:04 26 1 \n", "2013-12-17 00:00:08 25 0 \n", "2013-12-17 00:00:12 25 1 \n", "2013-12-17 00:00:16 25 1 \n", "2013-12-17 00:00:20 26 1 \n", "2013-12-17 00:00:24 25 1 \n", "2013-12-17 00:00:28 26 1 \n", "2013-12-17 00:00:32 25 0 \n", "2013-12-17 00:00:36 25 1 \n", "2013-12-17 00:00:40 25 1 \n", "2013-12-17 00:00:44 25 1 \n", "2013-12-17 00:00:48 24 1 \n", "2013-12-17 00:00:52 24 1 \n", "2013-12-17 00:00:56 25 1 \n", "2013-12-17 00:01:00 26 0 \n", "2013-12-17 00:01:04 25 1 \n", "2013-12-17 00:01:08 25 1 \n", "2013-12-17 00:01:12 25 1 \n", "2013-12-17 00:01:16 25 1 \n", "2013-12-17 00:01:20 25 1 \n", "2013-12-17 00:01:24 25 1 \n", "2013-12-17 00:01:28 25 1 \n", "2013-12-17 00:01:32 25 0 \n", "2013-12-17 00:01:36 25 1 \n", "2013-12-17 00:01:40 26 1 \n", "2013-12-17 00:01:44 25 1 \n", "2013-12-17 00:01:48 25 1 \n", "2013-12-17 00:01:52 26 1 \n", "2013-12-17 00:01:56 26 1 \n", "2013-12-17 00:02:00 25 1 \n", "2013-12-17 00:02:04 25 0 \n", "2013-12-17 00:02:08 24 1 \n", "2013-12-17 00:02:12 25 1 \n", "2013-12-17 00:02:16 25 1 \n", "2013-12-17 00:02:20 25 1 \n", "2013-12-17 00:02:24 25 1 \n", "2013-12-17 00:02:28 25 1 \n", "2013-12-17 00:02:32 25 1 \n", "2013-12-17 00:02:36 25 1 \n", "2013-12-17 00:02:40 24 1 \n", "2013-12-17 00:02:44 25 1 \n", "2013-12-17 00:02:48 25 0 \n", "2013-12-17 00:02:52 25 1 \n", "2013-12-17 00:02:56 24 0 \n", "2013-12-17 00:03:00 25 1 \n", "2013-12-17 00:03:04 25 1 \n", "2013-12-17 00:03:08 25 1 \n", "2013-12-17 00:03:12 24 1 \n", "2013-12-17 00:03:16 25 1 \n", "2013-12-17 00:03:20 25 1 \n", "2013-12-17 00:03:24 26 1 \n", "2013-12-17 00:03:28 25 0 \n", "2013-12-17 00:03:32 25 1 \n", "2013-12-17 00:03:36 25 0 \n", "2013-12-17 00:03:40 25 1 \n", "2013-12-17 00:03:44 24 1 \n", "2013-12-17 00:03:48 24 1 \n", "2013-12-17 00:03:52 25 1 \n", "2013-12-17 00:03:56 25 1 \n", " ... ... \n", "\n", "[10465 rows x 9 columns]" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "from sunpy.lightcurve import RHESSISummaryLightCurve\n", "from sunpy.time import TimeRange" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/schriste/anaconda/lib/python2.7/site-packages/pytz/__init__.py:29: UserWarning: Module _imaging was already imported from /Users/schriste/anaconda/lib/python2.7/site-packages/PIL/_imaging.so, but /Users/schriste/.local/lib/python2.7/site-packages is being added to sys.path\n", " from pkg_resources import resource_stream\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from sunpy.instr import rhessi" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "lc = RHESSISummaryLightCurve.create(TimeRange('2003/03/02', '2003/03/03'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://hesperia.gsfc.nasa.gov/hessidata/metadata/catalog/hsi_obssumm_20030302_146.fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/schriste/Dropbox/Developer/python/sunpy/sunpy/lightcurve/lightcurve.py:228: RuntimeWarning: Using existing file rather than downloading, use overwrite=True to override.\n", " warnings.warn(\"Using existing file rather than downloading, use overwrite=True to override.\", RuntimeWarning)\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot as plt\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure()\n", "lc.data.plot()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x10799e250>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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hzJkzsLe3x4wZM2BpaYn79+/j+vXrOH36NP73v/8BAKZOnYrz588jLY2bf2NZFnv27Hku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tYhJFMY3Og6GJdfZ/7sZg9RkDmQ+gR/f+QSaAzbKGtb3lvLfRCHHWx+3mP7IzdeFPsGnpWQAPs/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\nXhY4jz4Kc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4ga4tQkJC0LZt2wrT+fv7Y9q0aTh+/Dg6d+4MX19frF69GqdPn0aLFi2wevVq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\nBVG8XaMG6xf8xS9Vv4bQaNhZl9sLqmF7dMtZK/Kp1fURhPJ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sErM5H4QsGajTcFiERSUx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"text": [ "<matplotlib.figure.Figure at 0x107f3da10>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for item, frame in lc.data.iteritems():\n", " print(frame.values)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 97 97 100 ..., 22 21 20]\n", "[115 116 119 ..., 20 19 20]\n", "[38 39 42 ..., 9 9 9]\n", "[24 25 26 ..., 26 25 25]\n", "[37 37 38 ..., 38 38 39]\n", "[45 46 45 ..., 48 46 48]\n", "[39 40 39 ..., 42 41 41]\n", "[38 38 38 ..., 40 41 39]\n", "[4 5 4 ..., 4 4 4]\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "lc.meta" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "OrderedDict([('SIMPLE', True), ('BITPIX', 8), ('NAXIS', 0), ('EXTEND', True), ('DATE', '2014-04-14'), ('ORIGIN', 'shade'), ('OBSERVER', 'hessiops'), ('TELESCOP', 'HESSI'), ('OBJECT', 'Sun'), ('DATE_OBS', '2003-03-02T00:00:00.000'), ('DATE_END', '2003-03-03T00:00:00.000')])" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "timerange_a = TimeRange('2008/06/01', '2008/06/02')\n", "timerange_b = TimeRange('2004/06/03', '2004/06/04')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "lc._get_url_for_date_range(timerange_a)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://hesperia.gsfc.nasa.gov/hessidata/metadata/catalog/hsi_obssumm_20080601_068.fits\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "'http://hesperia.gsfc.nasa.gov/hessidata/metadata/catalog/hsi_obssumm_20080601_068.fits'" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "lc._get_url_for_date_range(timerange_b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://hesperia.gsfc.nasa.gov/hessidata/metadata/catalog/hsi_obssumm_20040603_110.fits\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "'http://hesperia.gsfc.nasa.gov/hessidata/metadata/catalog/hsi_obssumm_20040603_110.fits'" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-3-clause
phuongxuanpham/SelfDrivingCar	CarND-Camera-Calibration/camera_calibration.ipynb	1	875797	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style> code {background-color : pink !important;} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%HTML\n", "<style> code {background-color : pink !important;} </style>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Camera Calibration with OpenCV\n", "===\n", "\n", "### Run the code in the cell below to extract object points and image points for camera calibration. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "import glob\n", "import matplotlib.pyplot as plt\n", "%matplotlib qt\n", "\n", "# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n", "objp = np.zeros((68,3), np.float32)\n", "objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)\n", "\n", "# Arrays to store object points and image points from all the images.\n", "objpoints = [] # 3d points in real world space\n", "imgpoints = [] # 2d points in image plane.\n", "\n", "# Make a list of calibration images\n", "images = glob.glob('calibration_wide/GO.jpg')\n", "\n", "# Step through the list and search for chessboard corners\n", "for idx, fname in enumerate(images):\n", " img = cv2.imread(fname)\n", " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", " # Find the chessboard corners\n", " ret, corners = cv2.findChessboardCorners(gray, (8,6), None)\n", "\n", " # If found, add object points, image points\n", " if ret == True:\n", " objpoints.append(objp)\n", " imgpoints.append(corners)\n", "\n", " # Draw and display the corners\n", " cv2.drawChessboardCorners(img, (8,6), corners, ret)\n", " #write_name = 'corners_found'+str(idx)+'.jpg'\n", " #cv2.imwrite(write_name, img)\n", " cv2.imshow('img', img)\n", " cv2.waitKey(500)\n", "\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### If the above cell ran sucessfully, you should now have `objpoints` and `imgpoints` needed for camera calibration. Run the cell below to calibrate, calculate distortion coefficients, and test undistortion on an image!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1180908d0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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odYzNN1UdisivAf9AXSfPAd4pIjfiFMccp0jHO4q+jTNs3j8pjiiuG/xC17t8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\nbvh9a+Szwnom6F7uhXnpIxMQcntvUS7aUZR9CsCHPz7qfWPz0olONHu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BIZxQT/hvnar3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74WaOAVPBS8buR4u1dA5my7lt9b/B4ouK19tHupp+2b+0KRvdp3/tRLX5fotte0vZXlLfx+be6t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SIEAEwwFy+OA+cGsFKqQXxLcOXcP+TxAIuAWpOBLxNutyse010El5AQCh6CJWe3CrQxmsWg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Qof8JfecQEg+2QhUw4oIz2pjpbsW4OM7MQOHAp23ejmPMaG4r06qvBa6QXRhOhWTjQ3te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5jHWIihmwv6345veJxMjDIiW5C3f8d2xXvG7HeedwdPUaf1t71c+SW9JEkRp1qpkGOJxou3sNTg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\nc/SHjU1d+zxhFWnB5IlhpnXPjLqMVP4c0yQyFkOYpuwP2j02Qv1gvPr+1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4pz1u0KmHZJ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bOp/YWp7vK+fOKroxfFEPhaw6jUayV9z/VJemurJ9zd9/Nsl4GaqKz58/4/Pnz9smOq43vPsO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EKBPMhYReZjbk55sroGX1pbzPGwQNh+uaDc3U0pAr9cX66f9LrmIGfMsNI1BgIE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dj+ia4qYOHA1kqVhYdl0aD/IO/D1wsf4Z8BtyNjpoyneMjewgiFCi93yE3tkkVikAtFkR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\nkiZdh9Dnp1NcVlIe+bBxn2wzEQ3lDeMs0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DoZ/i+i7HtM6nIIwRaa5RueNbimETS2rWDf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cccejgPEu6V/KidJ0nYwViM6qtWLfdzDv+PjhI969e9eO9zbNq/gRli/yLegjQ4sE7b+1zhjR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hvdZIT4OrF+ad9uJJvRxNqLGRllXC/GBxGagMW/6vhfXl25cE/Ywd3acmqjblUOFBpP10Re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\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2z2wSP3Cf8vRnI/tmcg3pFv9X/22LsOPc5q82osqNyGkQBTbAM61q6/tX+tTr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obcY6e+i8uNTGc8lRTjIM/zjO12i4P1IW7fuo3XXns9nvS4+wpu3byF9Xqd8QC/G+kDxtUBAkJc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FUs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\nejzyBRHBdcF5CuMmaKtG1eN/9EcY0yDBBalEexd698a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P6dqHEU8PMBxwQ9JKa7ItvD1r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XRhn2eJtFGb1LjQz3S7M2dKHcUhHChwMmV6hiGVFMXKlcvjI4DZ6f9aXlXKubamzDQRSYic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lk5WJnM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\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knBbazMMSy0wBjqbR1/kiJnIxSOR1gziA/365ia997Wv4yz//c3Qe+PTqVVy7cgXf+/bf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\nuHAhPN9sQINLfKuEwthwIKWTlzLF4kDjyo6x5Yxs4aCUXZSnkIMbsUsmZ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"text/plain": [ "<matplotlib.figure.Figure at 0x11053dd30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pickle\n", "%matplotlib inline\n", "\n", "# Test undistortion on an image\n", "img = cv2.imread('calibration_wide/test_image.jpg')\n", "img_size = (img.shape[1], img.shape[0])\n", "\n", "# Do camera calibration given object points and image points\n", "ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)\n", "\n", "\n", "dst = cv2.undistort(img, mtx, dist, None, mtx)\n", "cv2.imwrite('calibration_wide/test_undist.jpg',dst)\n", "\n", "# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)\n", "dist_pickle = {}\n", "dist_pickle[\"mtx\"] = mtx\n", "dist_pickle[\"dist\"] = dist\n", "pickle.dump( dist_pickle, open( \"calibration_wide/wide_dist_pickle.p\", \"wb\" ) )\n", "#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)\n", "# Visualize undistortion\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))\n", "ax1.imshow(img)\n", "ax1.set_title('Original Image', fontsize=30)\n", "ax2.imshow(dst)\n", "ax2.set_title('Undistorted Image', fontsize=30)" ] }, { "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
Roger-luo/QuDynamics.jl	examples/notebooks/JC_Model_QuTiP_in_QuDynamics_MCWF_method.ipynb	1	37255	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "require(\"/home/amit/Downloads/SemVII/QuBase.jl/src/QuBase.jl\")\n", "using QuBase\n", "require(\"/home/amit/Downloads/SemVII/QuDynamics.jl/src/QuDynamics.jl\")\n", "using QuDynamics" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO: Loading help data...\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "wc = 1.0 * 2 * pi # cavity frequency\n", "wa = 1.0 * 2 * pi # atom frequency\n", "g = 0.25 * 2 * pi # coupling strength\n", "kappa = 0.05 # cavity dissipation rate\n", "gamma = 0.15 # atom dissipation rate\n", "use_rwa = true" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "true" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Hamiltonian\n", "idc = QuArray(eye(10))\n", "ida = QuArray(eye(2))\n", "a = tensor(ida, lowerop(10))\n", "sm = tensor(lowerop(2), idc)\n", "if use_rwa\n", " # use the rotating wave approxiation\n", " hamiltonian = wc * a' * a + wa * sm' * sm + g * (a' * sm + a * sm')\n", "else\n", " hamiltonian = wc * a' * a + wa * sm' * sm + g * (a' + a) * (sm + sm')\n", "end\n", "init_state = complex(tensor(statevec(1, FiniteBasis(2)), statevec(6, FiniteBasis(10))))\n", "init_state_dm = complex(init_stateinit_state')\n", "tlist = linspace(0., 10., 200)\n", "c_ops = [sqrt(0.1) a]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "1-element Array{QuArray{FiniteBasis{Orthonormal},Float64,2,SparseMatrixCSC{Float64,Int64}},1}:\n", " 20x20 QuMatrix in FiniteBasis{Orthonormal}:\n", "...coefficients: SparseMatrixCSC{Float64,Int64}\n", "\n", "\t[1 , 2] = 0.316228\n", "\t[2 , 3] = 0.447214\n", "\t[3 , 4] = 0.547723\n", "\t[4 , 5] = 0.632456\n", "\t[5 , 6] = 0.707107\n", "\t[6 , 7] = 0.774597\n", "\t[7 , 8] = 0.83666\n", "\t[8 , 9] = 0.894427\n", "\t[9 , 10] = 0.948683\n", "\t[11, 12] = 0.316228\n", "\t[12, 13] = 0.447214\n", "\t[13, 14] = 0.547723\n", "\t[14, 15] = 0.632456\n", "\t[15, 16] = 0.707107\n", "\t[16, 17] = 0.774597\n", "\t[17, 18] = 0.83666\n", "\t[18, 19] = 0.894427\n", "\t[19, 20] = 0.948683" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "rho = Array(typeof(init_state_dm), length(tlist)-1)\n", "qumcwfen = QuMCWFEnsemble(complex(init_state), 1000)\n", "for i=1:length(tlist)-1\n", " rho[i] = complex(zeros(init_state_dm))\n", "end" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "for psi0 in qumcwfen\n", " i = 1\n", " mcwf = QuMCWF()\n", " for (t,psi) in QuPropagator(hamiltonian, c_ops, psi0, tlist, mcwf)\n", " rho[i] = rho[i] + (psipsi')/length(qumcwfen)/norm(psi)^2\n", " i = i + 1\n", " end\n", "end" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in 1:length(tlist)-1\n", " plot(tlist[i], real(trace(rho[i]a'* a)), \"ro\")\n", " plot(tlist[i], real(trace(rho[i]sm' sm)), \"go\")\n", "end" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x7f0403b850d0>)" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
Ledoux/ShareYourSystem	Pythonlogy/draft/Directer/Readme.ipynb	1	8380	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nView the Directer notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Directer.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -- coding: utf-8 --\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nThe Directer is a walker through the folders of the harddrive, \nassuring a call of _DirectingCallbackFunction at each level.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Interfacers.Killer\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport os\nfrom ShareYourSystem.Functers import Argumenter\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass DirecterClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'DirectingCallbackFunction',\n\t\t\t\t\t\t\t\t\t'DirectingLiargVariablesList',\n\t\t\t\t\t\t\t\t\t'DirectingFilterFunctionPointer'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_DirectingCallbackFunction=None,\n\t\t\t\t\t\t_DirectingLiargVariablesList=None,\n\t\t\t\t\t\t_DirectingFilterFunctionPointer=None,\n\t\t\t\t\t\t_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,_KwargVariablesDict)\n\n\t#@Argumenter.ArgumenterClass()\n\tdef do_direct(self):\n\n\t\t#Call the folder method before\n\t\tself.folder()\n\n\t\t#debug\n\t\t'''\n\t\tprint('Directer l.62')\n\t\tprint('self.FolderingPathStr is ',self.FolderingPathStr)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Definition the call back function if not already\n\t\tif self.DirectingCallbackFunction==None:\n\n\t\t\t#Definition a test function\n\t\t\tdef test(_LiargVariablesList,_FolderPathStr,_DirKeyStrsList):\n\t\t\t\tpass\n\t\t\t\tprint(_LiargVariablesList,_FolderPathStr,_DirKeyStrsList)\n\n\t\t\t#set\n\t\t\tself.DirectingCallbackFunction=test\n\n\t\t'''\n\t\t#Call the function\n\t\ttry:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList,\n\t\t\t\t\t\t\t\t_KwargVariablesDict\n\t\t\t\t\t\t\t\t)\n\t\texcept:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t\t\t\t\t\t)\n\t\t'''\n\n\t\t#Walk with os\n\t\tos.path.walk(\n\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\tself.DirectingCallbackFunction,\n\t\t\t\t\t\tself.DirectingLiargVariablesList\n\t\t\t\t\t)\n\n\t\t\"\"\"\n\t\t#Do it Manually\n\n\t\t#Call the function\n\t\ttry:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList,\n\t\t\t\t\t\t\t\t_KwargVariablesDict\n\t\t\t\t\t\t\t\t)\n\t\texcept:\n\t\t\tself.DirectingCallbackFunction(\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\tself.FolderingPathStr,\n\t\t\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t\t\t\t\t\t)\n\n\t\t#Filter the folders to walk\n\t\tDirectedFolderKeyStrsList=SYS._filter(\n\t\t\t\t\t\tlambda __FolderedDirKeyStr:\n\t\t\t\t\t\tos.path.isdir(self.FolderingPathStr+__FolderedDirKeyStr),\n\t\t\t\t\t\tself.FolderedDirKeyStrsList\n\t\t\t)\n\n\t\t#debug\n\t\t'''\n\t\tprint('After first filter DirectedFolderKeyStrsList is ',DirectedFolderKeyStrsList)\n\t\tprint('')\n\t\t'''\n\n\t\t#Filter again maybe\n\t\tif self.DirectingFilterFunctionPointer!=None:\n\t\t\tDirectedFolderKeyStrsList=SYS._filter(\n\t\t\t\t\t\tlambda __DirectedFolderKeyStr:\n\t\t\t\t\t\tself.DirectingFilterFunctionPointer(\n\t\t\t\t\t\t\tself,__DirectedFolderKeyStr),\n\t\t\t\t\t\tDirectedFolderKeyStrsList\n\t\t\t)\n\n\t\t#debug\n\t\t'''\n\t\tprint('After second DirectedFolderKeyStrsList is ',DirectedFolderKeyStrsList)\n\t\tprint('')\n\t\t'''\n\n\t\t#Map a recursive direct\n\t\t'''\n\t\tmap(\t\n\t\t\t\tlambda __DirectedFolderKeyStr:\n\t\t\t\tself.__class__().direct(\n\t\t\t\t\t\t\t\tself.DirectingCallbackFunction,\n\t\t\t\t\t\t\t\tself.DirectingLiargVariablesList,\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t'FolderingPathStr':\n\t\t\t\t\t\t\t\t\tself.FolderingPathStr+__DirectedFolderKeyStr+'/'\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t),\n\t\t\t\tDirectedFolderKeyStrsList\n\t\t\t)\n\t\t'''\n\n\t\t\"\"\"\n\n\t\t#Return self\n\t\t#return self\t\n\n#</DefineClass>\n\n\n```\n\n<small>\nView the Directer sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Interfacers/Directer\" 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" } } }, { "source": "```python\n\n#ImportModules\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Interfacers import Directer\nimport os\n\n#Definition an instance \nMyDirecter=Directer.DirecterClass()\n\n#Direct for displaying folders\n'''\nMyDirecter.direct(\n lambda _LiargVariablesList,_FolderPathStr,_FileKeyStrsList:\n Representer._print(_LiargVariablesList[0]+_FolderPathStr),\n [\"_FolderPathStr is \"],\n {'FolderingPathStr':'/'.join(SYS.__file__.split('/')[:-1])}\n )\n'''\n\n#Delete things\ndef delete(_LiargVariablesList,_FolderPathStr,_FileKeyStrsList):\n #os.popen('rm -r '+_FolderPathStr+'/Attests/')\n os.popen('rm '+_FolderPathStr+'/02_ClassCell.md')\n os.popen('rm '+_FolderPathStr+'/03_ClassCell.py')\n os.popen('rm '+_FolderPathStr+'/04_InstanceCell.md')\n os.popen('rm '+_FolderPathStr+'/05_InstanceCell.py')\ndef move(_LiargVariablesList,_FolderPathStr,_FileKeyStrsList):\n os.popen('mv '+_FolderPathStr+'/00_ExampleCell.md '+_FolderPathStr+'/00_ExampleDoc.md')\n os.popen('mv '+_FolderPathStr+'/01_ExampleCell.py '+_FolderPathStr+'/01_ExampleDoc.py')\n\nMyDirecter=Directer.DirecterClass().direct(\n delete,\n [],\n {\n 'FolderingPathStr':\n SYS.ShareYourSystemLocalFolderPathStr+'/ShareYourSystem/Guiders/'\n }\n )\n\n#Definition the AttestedStr\nSYS._attest(\n [\n 'MyDirecter is '+SYS._str(\n MyDirecter,\n {\n 'RepresentingBaseKeyStrsListBool':False\n }\n )\n ]\n) \n\n#Print\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "```console\n>>>\n\n\n***Start of the Attest *\n\nMyDirecter is < (DirecterClass), 4554248848>\n /{ \n / '<New><Instance>IdInt' : 4554248848\n / '<Spe><Class>DirectingFilterFunctionPointer' : None\n / '<Spe><Instance>DirectingCallbackFunction' : <function delete at 0x10f733c08>\n / '<Spe><Instance>DirectingLiargVariablesList' : []\n /}\n\n*End of the Attest ***\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }	mit
paris-saclay-cds/python-workshop	Day_2_Software_engineering_best_practices/solutions/03_code_style.ipynb	1	473748	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "\n", "import six\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# preprocessing\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA\n", "\n", "# classifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import LinearSVC\n", "\n", "# regressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.linear_model import RidgeCV\n", "\n", "# meta-estimator\n", "from sklearn.pipeline import make_pipeline\n", "\n", "# metric\n", "from sklearn.metrics import (r2_score, median_absolute_error,\n", " confusion_matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IO: Reading and preprocess the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can define a function which will read the data and process them." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def read_spectra(path_csv):\n", " \"\"\"Read and parse data in pandas DataFrames.\n", " \n", " Parameters\n", " ----------\n", " path_csv : str\n", " Path to the CSV file to read.\n", " \n", " Returns\n", " -------\n", " spectra : pandas DataFrame, shape (n_spectra, n_freq_point)\n", " DataFrame containing all Raman spectra.\n", " \n", " concentration : pandas Series, shape (n_spectra,)\n", " Series containing the concentration of the molecule.\n", " \n", " molecule : pandas Series, shape (n_spectra,)\n", " Series containing the type of chemotherapeutic agent.\n", " \n", " \"\"\"\n", " if not isinstance(path_csv, six.string_types):\n", " raise TypeError(\"'path_csv' needs to be string. Got {}\"\n", " \" instead.\".format(type(path_csv)))\n", " else:\n", " if not path_csv.endswith('.csv'):\n", " raise ValueError('Wrong file format. Expecting csv file')\n", " \n", " data = pd.read_csv(path_csv)\n", " concentration = data['concentration']\n", " molecule = data['molecule']\n", " spectra_string = data['spectra']\n", " spectra = []\n", " for spec in spectra_string:\n", " # remove the first and last bracket and convert to a numpy array\n", " spectra.append(np.fromstring(spec[1:-1], sep=','))\n", " spectra = pd.DataFrame(spectra)\n", " \n", " return spectra, concentration, molecule" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read the frequency and get a pandas serie\n", "frequency = pd.read_csv('data/freq.csv')['freqs']\n", "\n", "# read all data for training\n", "filenames = ['data/spectra_{}.csv'.format(i)\n", " for i in range(4)]\n", "\n", "spectra, concentration, molecule = [], [], []\n", "for filename in filenames:\n", " spectra_file, concentration_file, molecule_file = read_spectra(filename)\n", " spectra.append(spectra_file)\n", " concentration.append(concentration_file)\n", " molecule.append(molecule_file)\n", "\n", "# Concatenate in single DataFrame and Serie\n", "spectra = pd.concat(spectra)\n", "concentration = pd.concat(concentration)\n", "molecule = pd.concat(molecule)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot helper functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create two functions: (i) to plot all spectra and (ii) plot the mean spectra with the std intervals.\n", "We will make a \"private\" function which will be used by both plot types." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def _apply_axis_layout(ax, title):\n", " \"\"\"Apply despine style and add labels to axis.\"\"\"\n", " ax.set_xlabel('Frequency')\n", " ax.set_ylabel('Intensity')\n", " ax.set_title(title)\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", " ax.get_xaxis().tick_bottom()\n", " ax.get_yaxis().tick_left()\n", " ax.spines['left'].set_position(('outward', 10))\n", " ax.spines['bottom'].set_position(('outward', 10))\n", "\n", "def plot_spectra(frequency, spectra, title=''):\n", " \"\"\"Plot a bunch of Raman spectra.\n", " \n", " Parameters\n", " ----------\n", " frequency : pandas Series, shape (n_freq_points,)\n", " Frequencies for which the Raman spectra were acquired.\n", " \n", " spectra : pandas DataFrame, shape (n_spectra, n_freq_points)\n", " DataFrame containing all Raman spectra.\n", " \n", " title : str\n", " Title added to the plot.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " \"\"\"\n", " fig, ax = plt.subplots()\n", " ax.plot(frequency, spectra.T)\n", " _apply_axis_layout(ax, title)\n", " return fig, ax\n", " \n", "def plot_spectra_by_type(frequency, spectra, classes, title=''):\n", " \"\"\"Plot mean spectrum with its variance for a given class.\n", " \n", " Parameters\n", " ----------\n", " frequency : pandas Series, shape (n_freq_points,)\n", " Frequencies for which the Raman spectra were acquired.\n", " \n", " spectra : pandas DataFrame, shape (n_spectra, n_freq_points)\n", " DataFrame containing all Raman spectra.\n", " \n", " classes : array-like, shape (n_classes,)\n", " Array contining the different spectra class which will be plotted.\n", " \n", " title : str\n", " Title added to the plot.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " \"\"\"\n", " fig, ax = plt.subplots()\n", " for label in np.unique(classes):\n", " label_index = np.flatnonzero(classes == label)\n", " spectra_mean = np.mean(spectra.iloc[label_index], axis=0)\n", " spectra_std = np.std(spectra.iloc[label_index], axis=0)\n", " ax.plot(frequency, spectra_mean, label=label)\n", " ax.fill_between(frequency,\n", " spectra_mean + spectra_std,\n", " spectra_mean - spectra_std,\n", " alpha=0.2)\n", " _apply_axis_layout(ax, title)\n", " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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SvD2XvwOIeQIEOzqO780NQvqJRdO0QS8SqifiM2nOVAIwvqWeovYOkr4y+3iw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\nUsWkmIHMbucygIIYhQKn5JEhDvRvzZqzG6q58P3nmFSvolc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G12jUhCRTo18jPyq8yP2V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\n+0KI9TFHhT/rlvZ+I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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0525dfd6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plot_spectra(frequency, spectra, 'All training spectra')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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C25EmXbIKZ+gAwWVVlAynSY71A9DcZz1KFyu7Z2iMyRstfTFKC7tZ0+6uIBFc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\nGdFieJaMZgpc95mnSBdOTZM0N27ynhbF0iqXsOWSKaTxuVlCFPBTRFA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05211000f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plot_spectra_by_type(frequency, spectra, molecule)\n", "ax.set_title('Mean spectra in function of the molecules')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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y7/g7OGP7aURS/+SFkosgBkvmb6bnxaPAOPBsKCZ/WpTmOi+hcBsWk+nPzyO3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\n2PO1vYkkNeEoG4MRuhPJYcvYg6QcdmJ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jpRmjfp10Ued9K5r4wYY+RKKMRxFsTuf5eGtV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\nzgHbtv91V6m466xK7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05213010f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plot_spectra_by_type(frequency, spectra, concentration,\n", " 'Mean spectra in function of the concentrations')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reusability for new data:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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rfOz/Uq/zbw2bK1B2RB3MD5htN8scT5FtpOENm9+b07IJOi0QHtpvLX1tvPco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\n9NPPp6LZu5a1685iy9YrWP5xIUuXDaG29o0+y6a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VWZnf1o5HE/ydK7mBF/EoNay3PUgS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ZfhsdhYPqZjO9ZRrm9+hfSlCwGNYWPuYWZjDrafVyLMnQdg1AN+mh8agDc+wBFnJF7hILO2kihX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\nQqFQKHoUpVgUCoVC0aP8f2/Vhre5jAyEAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0525dba2b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pLWFZX5Y0KcQ35g34NwuRBUNjTNHoHIoRCvdTKqVk93ZQevYmd0dqnPATXwQg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\nw0xoxjn8T75QBUQH3Oe1qyFcRUnYnT5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qOpRNOc1f7LV0Mfze9WeGvFblhb5XRhO+MKS7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\n+hk5aWGg4SCRwy6BEi1MtVGCJnQEBu+suBVfrBHLKGHzAck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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0525dba320>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4m/L2V/jXv+bTeF4Rjx91ct9qV2IbOe334kw0cVbt+13JL5e08wFPhIWJAKW+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\nN93kYs1Q6IlnqK+qGdbzU4c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4s90E4y3kZrIoSHSzKavoQE5PDUJ1dbX7hBNOmDF16tS5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\nt/+QhBLitr0mflVwbb1ClceJFimKB7+/ESEU0qbJd3b1oQzl6RvMcG5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oPmw7T9vkrxKN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\nEk9kPTxOMVxXkrddR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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05215e68d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectra_test, concentration_test, molecule_test = read_spectra('data/spectra_4.csv')\n", "\n", "plot_spectra(frequency, spectra_test,\n", " 'All training spectra')\n", "plot_spectra_by_type(frequency, spectra_test, molecule_test,\n", " 'Mean spectra in function of the molecules')\n", "plot_spectra_by_type(frequency, spectra_test, concentration_test,\n", " 'Mean spectra in function of the concentrations');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training and testing a machine learning model for classification" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_cm(cm, classes, title):\n", " \"\"\"Plot a confusion matrix.\n", " \n", " Parameters\n", " ----------\n", " cm : ndarray, shape (n_classes, n_classes)\n", " Confusion matrix.\n", " \n", " classes : array-like, shape (n_classes,)\n", " Array contining the different spectra classes used in the\n", " classification problem.\n", " \n", " title : str\n", " Title added to the plot.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " \"\"\"\n", " fig, ax = plt.subplots()\n", " plt.imshow(cm, interpolation='nearest', cmap='bwr')\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 = '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')\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score: 0.83\n", "Accuracy score: 0.93\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05212a23c8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0521301588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for clf in [RandomForestClassifier(random_state=0),\n", " LinearSVC(random_state=0)]:\n", " \n", " pipeline = make_pipeline(StandardScaler(),\n", " PCA(n_components=100, random_state=0),\n", " clf)\n", " y_pred = pipeline.fit(spectra, molecule).predict(spectra_test)\n", " plot_cm(confusion_matrix(molecule_test, y_pred),\n", " pipeline.classes_,\n", " 'Confusion matrix using {}'.format(clf.__class__.__name__))\n", " print('Accuracy score: {0:.2f}'.format(pipeline.score(spectra_test,\n", " molecule_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training and testing a machine learning model for regression" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_regression(y_true, y_pred, title):\n", " \"\"\"Plot actual vs. predicted scatter plot.\n", " \n", " Parameters\n", " ----------\n", " y_true : array-like, shape (n_samples,)\n", " Ground truth (correct) target values.\n", "\n", " y_pred : array-like, shape (n_samples,)\n", " Estimated targets as returned by a regressor.\n", "\n", " title : str\n", " Title added to the plot.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " \"\"\" \n", " fig, ax = plt.subplots()\n", " ax.scatter(y_true, y_pred)\n", " ax.plot([0, 25000], [0, 25000], '--k')\n", " ax.set_ylabel('Target predicted')\n", " ax.set_xlabel('True Target')\n", " ax.set_title(title)\n", " ax.text(1000, 20000, r'$R^2$=%.2f, MAE=%.2f' % (\n", " r2_score(y_true, y_pred), median_absolute_error(y_true, y_pred)))\n", " ax.set_xlim([0, 25000])\n", " ax.set_ylim([0, 25000])\n", " return fig, ax" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def regression_experiment(X_train, X_test, y_train, y_test):\n", " \"\"\"Perform regression experiment.\n", " \n", " Build a pipeline using PCA and either a Ridge\n", " or a RandomForestRegressor model.\n", " \n", " Parameters\n", " ----------\n", " X_train : pandas DataFrame, shape (n_spectra, n_freq_points)\n", " DataFrame containing training Raman spectra.\n", " \n", " X_test : pandas DataFrame, shape (n_spectra, n_freq_points)\n", " DataFrame containing testing Raman spectra.\n", " \n", " y_training : pandas Serie, shape (n_spectra,)\n", " Serie containing the training concentrations acting as targets.\n", " \n", " y_testing : pandas Serie, shape (n_spectra,)\n", " Serie containing the testing concentrations acting as targets.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " \"\"\"\n", " for reg in [RidgeCV(), RandomForestRegressor(random_state=0)]:\n", " pipeline = make_pipeline(PCA(n_components=100), reg)\n", " y_pred = pipeline.fit(X_train, y_train).predict(X_test)\n", " plot_regression(y_test, y_pred,\n", " 'Regression using {}'.format(reg.__class__.__name__))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZwayUMSZy3HLLLQwcOJCGDRuyfv16Hn74YeLiIvduppZZIfQCGaxwXFVVRBRA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\nYxljwi/qr4j89d34cjQrhxiBAOMXkD9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05212c7dd8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GQ/nSEV6rovLyNSLPTkqjoqKoXLkyTzzxBEWKFLmg5fraFznNrhEZY4yHx9pXI6JweJqy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\nvXv3ZsuWLVxU7fpgh2XyoFc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f05254b8438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "regression_experiment(spectra, spectra_test,\n", " concentration, concentration_test)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fit_params(data):\n", " \"\"\"Compute statistics for robustly scale data.\n", " \n", " Compute the median and the variance, i.e. the difference\n", " between the 75th and 25th percentiles.\n", " These statistics are used later to scale data.\n", " \n", " Parameters\n", " ----------\n", " data : pandas DataFrame, shape (n_spectra, n_freq_point)\n", " DataFrame containing all Raman spectra.\n", " \n", " Returns\n", " -------\n", " median : ndarray, shape (n_freq_point,)\n", " Median for each wavelength.\n", " \n", " variance : ndarray, shape (n_freq_point,)\n", " Variance (difference between the 75th and 25th\n", " percentiles) for each wavelength.\n", " \n", " \"\"\"\n", " median = np.median(data, axis=0)\n", " percentile_25 = np.percentile(data, 25, axis=0)\n", " percentile_75 = np.percentile(data, 75, axis=0)\n", " return median, (percentile_75 - percentile_25)\n", "\n", "def transform(data, median, var_25_75):\n", " \"\"\"Scale data using robust estimators.\n", " \n", " Scale the data by subtracting the median and dividing by the\n", " variance, i.e. the difference between the 75th and 25th percentiles.\n", " \n", " Parameters\n", " ----------\n", " data : pandas DataFrame, shape (n_spectra, n_freq_point)\n", " DataFrame containing all Raman spectra.\n", " \n", " median : ndarray, shape (n_freq_point,)\n", " Median for each wavelength.\n", " \n", " var_25_75 : ndarray, shape (n_freq_point,)\n", " Variance (difference between the 75th and 25th\n", " percentiles) for each wavelength.\n", " \n", " Returns\n", " -------\n", " data_scaled : pandas DataFrame, shape (n_spectra, n_freq_point)\n", " DataFrame containing all scaled Raman spectra.\n", " \n", " \"\"\"\n", " return (data - median) / var_25_75" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# compute the statistics on the training data\n", "med, var = fit_params(spectra)\n", "# transform the training and testing data\n", "spectra_scaled = transform(spectra, med, var)\n", "spectra_test_scaled = transform(spectra_test, med, var)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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Hx8fTr18/6tatS7169fjxxx+z3aeIcOutt6b/nJycTKVKlejVq1eG5ebMmYOI8Ouvv6a3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\natpwq7txRuBF4QxSWOi2TwY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f052549e0f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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bAs+uERljQmrLli107tyZDz/8EHAGKf3HP/5hSSiMWCIyxoREcnIyL7/8Mo0a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\nET0C/Cwiv+L8j14K3CsiJYF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dsavoiu/kafe2	examples/jupyter_tutorial_de.ipynb	1	76203	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Jupyter-Notebook-Tutorial: \n", "# Anpassung von Modellen an Daten mit kafe2 \n", "\n", " Johannes Gäßler, März 2021\n", " Günter Quast, April 2020\n", "---\n", "## Grundsätzliches zu Jupyter Notebooks\n", "\n", "Diese Datei vom Typ `.ipynb` enthält ein Tutorial als `Jupyter notebook`.\n", "Jupyter bietet eine Browser-Schnittstelle mit einer (einfachen) Entwicklungsumgebung für\n", "Python-Code und erklärende Texte im intuitiven Markdown-Format.\n", "Die Eingabe von Formeln im LaTeX-Format wird ebenfalls unterstützt.\n", "\n", "Eine Zusammenstellung der wichtigsten Befehle zur Verwendung von Jupyter als Arbeitsumgebung\n", "findet sich im Notebook\n", "[JupyterCheatsheet.ipynb](https://git.scc.kit.edu/yh5078/datenanalyse/-/blob/master/jupyter/JupyterCheatsheet.ipynb).\n", "Grundlagen zur statistischen Datenauswertung finden sich in den Notebooks \n", "[IntroStatistik.ipynb](https://git.scc.kit.edu/yh5078/datenanalyse/-/blob/master/jupyter/IntroStatistik.ipynb)\n", "und\n", "[Fehlerrechnung.ipynb](https://git.scc.kit.edu/yh5078/datenanalyse/-/blob/master/jupyter/Fehlerrechnung.ipynb).\n", "\n", "In Jupyter werden Code und Text in jeweils einzelne Zellen eingegeben. \n", "Aktive Zellen werden durch einen blauen Balken am Rand angezeigt.\n", "Sie können sich in zwei Zuständen befinden: im Edit-Mode ist das Eingabefeld weiß, im\n", "Command-Mode ist es ausgegraut.\n", "Durch Klicken in den Randbereich wird der Command-Mode gewählt, ein Klick in das Textfeld einer\n", "Code-Zelle schaltet in den Edit-Mode.\n", "Die Taste `esc` kann ebenfalls verwendet werden, um den Edit-Mode zu verlassen.\n", "\n", "Die Eingabe von `a` im Command-Mode erzeugt eine neue leere Zelle oberhalb der aktiven Zelle, `b`\n", "eine unterhalb. Eingabe von `dd` löscht die betreffende Zelle.\n", "\n", "Zellen können entweder den Typ `Markdown` oder `Code` haben.\n", "Die Eingabe von `m` im Command-Mode setzt den Typ Markdown, Eingabe von `y` wählt den Typ Code.\n", "\n", "Prozessiert - also Text gesetzt oder Code ausgeführt - wird der Zelleninhalt durch Eingabe von\n", "`shift+return`, oder auch `alt+return` wenn zusätzlich eine neue, leere Zelle erzeugt werden soll.\n", "\n", "Die hier genannten Einstellungen sowie das Einfügen, Löschen oder Ausführen von Zellen sind\n", "auch über das PullDown-Menü am oberen Rand verfügbar.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Übersicht: kafe2\n", "**\n", "\n", "\n", "kafe2* ist ist eine erweiterte Version des seit 2012 entwickelten Pakets kafe zur Anpassung\n", "von Modellfunktionen an Daten.\n", "\n", "Unterstützt werden verschiedene Datentypen wie einfache indizierte Daten, zweidimensionale\n", "Datenpunkte (eine Größe x und eine abhängige Größe y) sowie Häufigkeitsverteilungen\n", "(Histogramme).\n", "Unsicherheiten sowohl der abhängigen als auch der unabhängigen Größen und gegebenenfalls deren\n", "Korrelationen werden unterstützt.\n", "Dazu wird aus verschiedenen Arten von spezifizierten Unsicherheiten die globale Kovarianzmatrix\n", "erstellt und in der Anpassung berücksichtigt.\n", "Im Vergleich zu vielen anderen Anpassungswerkzeugen ist diese Möglichkeit ein\n", "Alleinstellungsmerkmal von kafe(2).\n", "\n", "Unterstützt wird auch die gleichzeitige Anpassung mehrerer Modelle mit jeweils eigenen und\n", "zusätzlich allen oder mehreren Modellen zugehörigen Parametern an verschiedene Datensätze\n", "(\"Multi-Fit\").\n", "\n", "Zur Minimierung des Abstandsmaßes zwischen Daten und Modellfunktion(en) werden numerische\n", "Verfahren angewandt, die aus der quelloffenen, Python-basierten Softwareumgebung SciPy oder\n", "dem am CERN entwickelten Paket MINUIT stammen.\n", "Das jeweils minimierte Abstandsmaß (oder auch die \"Kostenfunktion\") entspricht dem mit einem\n", "Faktor Zwei multiplizierten negativen natürlichen Logarithmus der Likelihood-Funktion\n", "$-2\\,\\ln{\\cal L}$ der Daten für das gegebene Modell.\n", "Für Gauß-förmige Unsicherheiten der Datenpunkte entspricht dies der Methode der kleinsten\n", "Quadrate (auch \"$\\chi^2$-Methode\").\n", "Andere, auf dem Likelihood-Prinzip beruhende Kostenfunktionen für die Anpassung von\n", "Wahrscheinlichkeitsdichten an Histogramme oder an indizierte Daten sind ebenfalls verfügbar.\n", "\n", "Zur Bestimmung der Unsicherheiten auf die Parameter des angepassten Modells wird die Methode der\n", "Profil-Likelihood bereit gestellt, mit deren Hilfe Konfidenzintervalle für die einzelnen\n", "Parameter sowie zweidimensionale Konfidenz-Konturen für Paare von Parametern bestimmt werden\n", "können.\n", "\n", "kafe2 enthält eine stand-alone Anwendung kafe2go, die Anpassungen ohne die Erstellung von\n", "eigenem Code ermöglicht;\n", "Daten, Modellfunktion und Optionen werden dazu in einer Konfigurationsdatei im YAML-Format angegeben.\n", "Mit dem folgenden Konsolenbefehl kann daraus ein Fit erstellt werden:\n", "\n", "`kafe2go <name>.yaml`\n", "\n", "kafe2 kann aber ebenfalls und sehr viel flexibler über ein Python-Interface verwendet werden.\n", "Eine Einführung in die Möglichkeiten gibt dieses Tutorial.\n", "Generell ist die Vorgehensweise folgende:\n", "\n", " - Definition und Initialisierung eines Daten-Containers für die jeweilige Anpassung \n", " (Klassen IndexedContainer, XYContainer, HistContainer, UnbinnedContainer).\n", " - Erzeugung eines geeigneten Objekts zur Durchführung der Anpassung, die den Datencontainer mit\n", " einem Modell verbindet (die allgemeine Klasse Fit bzw. spezialisierte Klassen\n", " IndexedFit, XYFit, HistFit, UnbinnedFit).\n", " - Durchführung der Anpassung mittels $<$Fit_Objekt$>$.do_fit() und Ausgabe der Ergebnisse auf der\n", " Konsole mittels $<$Fit_Objekt$>$.report() oder durch direkten Zugriff auf die\n", " Ergebnis-Variablen des Fit-Objekts.\n", " - Gegebenenfalls Erzeugung und Anzeige von Ergebnisgrafiken mit der generischen Klasse Plot.\n", " \n", "Die folgenden Beispiele zeigen die konkrete Vorgehensweise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Allgemeine Einstellungen und nützliche Pakete" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import division, print_function # Python2-Kompatibilität\n", "import sys, os\n", "\n", "# Zeilen mit % oder %% am Anfang sind sogenannte \"magische Kommandos\",\n", "# die den Zellentyp oder Optionen für die Anzeige von Grafiken festlegen.\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Imports und Voreinstellungen für kafe2:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from kafe2 import config, XYContainer, Fit, Plot\n", "\n", "import numpy as np, matplotlib.pyplot as plt\n", "\n", "# set better default figure size for kafe2\n", "# plt.rcParams['figure.figsize']=[12., 5.] \n", "# !!! must be done after importing kafe2 (will else be overwritten)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "## 1. Einfaches Beispiel zur Funktionsanpassung mit kafe2\n", "*\n", "\n", "Der folgende Code illustriert die Anpassung von Funktionen mit dem Anpassungswerkzeug kafe2:\n", "```\n", "# Create an XYContainer object to hold the xy data for the fit:\n", "xy_data = XYContainer(x_data=[1.0, 2.0, 3.0, 4.0],\n", " y_data=[2.3, 4.2, 7.5, 9.4])\n", "# x_data and y_data are combined depending on their order.\n", "# The above translates to the points (1.0, 2.3), (2.0, 4.2), (3.0, 7.5), and (4.0, 9.4).\n", "\n", "# Important: Specify uncertainties for the data:\n", "xy_data.add_error(axis='x', err_val=0.1)\n", "xy_data.add_error(axis='y', err_val=0.4)\n", "\n", "xy_data.label = 'Data' # How the data is called in plots\n", "\n", "# Create an XYFit object from the xy data container.\n", "# By default, a linear function f=ax+b will be used as the model function.\n", "line_fit = Fit(data=xy_data)\n", "\n", "# Perform the fit: Find values for a and b that minimize the\n", "# difference between the model function and the data.\n", "line_fit.do_fit() # This will throw a warning if no errors were specified.\n", "\n", "# Optional: Print out a report on the fit results on the console.\n", "line_fit.report()\n", "\n", "# Optional: Create a plot of the fit results using Plot.\n", "plot = Plot(fit_objects=line_fit) # Create a kafe2 plot object.\n", "plot.x_label = 'x' # Set x axis label.\n", "plot.y_label = 'y' # Set y axis label.\n", "plot.plot() # Do the plot.\n", "\n", "plot.save() # Saves the plot to file 'fit.png' .\n", "# plot.save('my_fit.pdf') # Saves the plot to a different file / with a different file extension.\n", "\n", "# Show the fit result.\n", "plot.show() # Just a convenience wrapper for matplotlib.pyplot.show() .\n", "# NOTE: Calling matplotlib.pyplot.show() closes all figures by default so call this AFTER saving.\n", "```\n", "\n", "Fügen Sie den Code in die leere Zelle unten ein und führen Sie sie durch Eingabe von `shift+return`\n", "aus." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# einfaches Beispiel: Geradenanpassung mit kafe2\n", "# -> Code hier einfügen \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Korrelierte Unsicherheiten\n", "**\n", "\n", "Zur Illustration der Möglichkeiten zur Behandlung von Unsicherheiten fügen wir eine weitere\n", "korrelierte Unsicherheit der abhängigen Größen y* ein:\n", "```\n", "xy_data.add_error(axis='y', err_val=0.3, correlation=1.)\n", "```\n", "\n", "Fügen Sie diese Zeile in den obigen Code ein.\n", "Die Ausgabe können Sie etwas übersichtlicher gestalten, indem Sie auf die Ausgabe der Daten und\n", "des Modells verzichten.\n", "Modifizieren Sie dazu die Parameter der report()-Methode:\n", "```\n", "line_fit.report(show_data=False, show_model=False)\n", "```\n", "Wiederholen Sie nun die Anpassung.\n", "\n", "Wie erwartet wirkt sich eine solche allen Datenpunkten gemeinsame Unsicherheit nicht auf die\n", "Steigung der Geraden, sondern nur auf den Parameter b aus.\n", "Dessen Unsicherheit wird nun größer - entsprechend der Wurzel aus der\n", "quadratischen Summe der Unsicherheiten von +/-0.58 aus der\n", "ursprünglichen Anpassung und der zusätzlichen korrelierten Unsicherheit von +/-0.40." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "##Code aus dem vorigen Beispiel kopieren und ergänzen\n", "# ->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "## 2. Vergleich von zwei verschiedenen Modellen \n", "\n", "\n", "Einfache, in den Parametern lineare Modelle reichen in der Praxis nicht aus. \n", "Das folgende Beispiel zeigt die Anpassung eines linearen und eines exponentiellen Modells an die\n", "gleichen Daten.\n", "\n", "Um eine Modellfunktion für kafe2* zu definieren, genügt es, eine Python-Funktion\n", "zu schreiben.\n", "Wichtig:\n", "das erste Argument der Python-Funktion wird als unabhängige Variable interpretiert.\n", "Das erste Argument wird während der Anpassung also nicht modifiziert und es ist die Größe,\n", "die vom Fit als x-Achse interpretiert wird.\n", "\n", "Definition von zwei Modellfunktionen:\n", "```\n", "# Our first model is a simple linear function:\n", "def linear_model(x, a, b):\n", " return a * x + b\n", "\n", "\n", "# Our second model is a simple exponential function.\n", "# The kwargs in the function header specify parameter defaults.\n", "def exponential_model(x, A0=1., x0=5.):\n", " return A0 * np.exp(x/x0)\n", "```\n", "\n", "Hier die Definition der Daten als kafe2 `XYContainer`:\n", "```\n", "# The data for this exercise:\n", "x = [19.8, 3.0, 5.1, 16.1, 8.2, 11.7, 6.2, 10.1]\n", "y = [23.2, 3.2, 4.5, 19.9, 7.1, 12.5, 4.5, 7.2]\n", "data2 = XYContainer(x_data=x, y_data=y)\n", "data2.add_error(axis='x', err_val=0.3)\n", "data2.add_error(axis='y', err_val=0.15, relative=True)\n", "```\n", "\n", "Damit die Daten in der grafischen Ausgabe später klar gekennzeichnet werden,\n", "werden noch Namen für den Datensatz und die Achsenbeschriftungen gesetzt:\n", "```\n", "data2.label = 'Datenpunkte'\n", "data2.axis_labels=['x-Wert', 'y-Wert']\n", "```\n", "\n", "Dann wird je ein Fit-Objekt mit den beiden Modell-Funktionen und jeweils\n", "den gleichen Daten erzeugt:\n", "```\n", "# Create 2 Fit objects with the same data but with different model functions:\n", "linear_fit = Fit(data2, model_function=linear_model)\n", "exponential_fit = Fit(data2, model_function=exponential_model)\n", "```\n", "\n", "Damit alles schöner aussieht, definieren wir noch LaTeX-Ausdrücke für die Funktionen,\n", "die Namen der Parameter und die Legende in der grafischen Ausgabe:\n", "```\n", "# Optional: Assign LaTeX strings to parameters and model functions.\n", "linear_fit.assign_parameter_latex_names(a='a', b='b')\n", "linear_fit.assign_model_function_latex_expression(\"{a}{x} + {b}\")\n", "linear_fit.model_label = 'lineares Modell'\n", "exponential_fit.assign_parameter_latex_names(A0='A_0', x0='x_0')\n", "exponential_fit.assign_model_function_latex_expression(\"{A0} e^{{{x}/{x0}}}\")\n", "exponential_fit.model_label = 'exponentielles Modell'\n", "```\n", "\n", "Der Code zur Ausführung der Anpassungen sieht wie folgt aus:\n", "```\n", "# Perform the fits:\n", "linear_fit.do_fit()\n", "exponential_fit.do_fit()\n", "\n", "# Optional: Print out a report on the result of each fit.\n", "linear_fit.report()\n", "exponential_fit.report()\n", "\n", "# Optional: Create a plot of the fit results using Plot.\n", "p = Plot(fit_objects=[linear_fit, exponential_fit], separate_figures=False)\n", "p.plot(fit_info=True)\n", "\n", "# Show the fit results:\n", "plt.show()\n", "```\n", "\n", "Fügen Sie den Code in die leere Zelle unten ein und führen Sie sie durch Eingabe von\n", "`shift+return` aus." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Vergleich von zwei Modellen mit kafe2\n", "# -> code hier einfügen \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Hypothesentest zur Bewertung der Modelle\n", "**\n", "\n", "Die grafische Ausgabe lässt nicht klar erkennen, welches der Modelle akzeptabel ist. \n", "Dazu kann ein Hypothesentest ausgeführt werden, der die sogenannte $\\chi^2$-Wahrscheinlichkeit\n", "angibt - also die Wahrscheinlichkeit dafür, einen schlechteren Wert von\n", "$\\chi^2$ am Minimum zu erhalten als den beobachteten.\n", "Ein höherer Wert entspricht einem besseren Fit.\n", " \n", "Berechnet wird er aus der kumulativen Verteilungsdichte der Chi2-Funktion:\n", "```\n", "from scipy import stats\n", "\n", "def chi2prob(chi2, ndf):\n", " \"\"\" chi2-probability\n", " \n", " Args:\n", " chi2: chi2 value\n", " * ndf: number of degrees of freedom\n", "\n", " Returns:\n", " * float: chi2 probability\n", " \"\"\"\n", "\n", " return 1.- stats.chi2.cdf(chi2, ndf)\n", "```\n", "\n", "Geben Sie den Code für die $\\chi^2$-Wahrscheinlichkeit in die leere Zelle unten ein und\n", "überprüfen Sie die beiden Ergebnisse, die Sie oben erhalten haben.\n", "\n", "Hinweis: Sie können die Werte für $\\chi^2$ und die Zahl der Freiheitsgrade entweder\n", "aus der Ausgabe der vorigen Zelle abtippen oder Sie können die Werte über die Properties `goodness_of_fit` und `ndf` der Fits beziehen.\n", "Das Property `cost_function_value` ist nicht geeignet, da es neben $\\chi^2$\n", "auch Korrekturterme enthält." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Überprüfung der Qualität der Anpassungen\n", "# -> code hier eingeben\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bei der Anwendung von kafe2 außerhalb dieses Notebooks empfiehlt es sich, die\n", "$\\chi^2$-Wahrscheinlichkeit einfach über das entsprechende Property der Fit-Objekte\n", "zu beziehen. \n", "Für die obigen Fits könnte dies z.B. über `chi2_prob = linear_fit.chi2_probability` erfolgen." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Untersuchung des nichtlinearen Modells\n", "*\n", "\n", "Bei Modellfunktionen, die nichtlinear in den Parametern sind, ist die $\\chi^2$-Verteilung\n", "um das Minimum nur näherungsweise eine Parabel, bisweilen weicht sie sogar stark davon ab.\n", "Ob die Abweichungen vernachlässigbar klein sind, kann mit Hilfe der Profil-Likelihood und\n", "durch die Darstellung von Konfidenz-Konturen überprüft werden.\n", "```\n", "from kafe2 import ContoursProfiler\n", "\n", "# Create contour plot and profiles for the exponential fit:\n", "cpf = ContoursProfiler(exponential_fit)\n", "cpf.plot_profiles_contours_matrix(show_grid_for='contours')\n", "plt.show()\n", "```\n", "\n", "Geben Sie das Code-Beispiel in der Zelle unten ein und überprüfen Sie damit die Anpassung des\n", "exponentiellen Modells." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Überprüfung der nichtlinearen Anpassung\n", "# -> code hier eingeben\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Asymmetrische Parameterunsicherheiten\n", "\n", "\n", "Wenn die Abweichungen groß sind, müssen asymmetrische Unsicherheiten angegeben werden.\n", "Dazu wird in der report-Funktion der Fit-Klasse die Option asymmetric_parameter_errors=True\n", "gesetzt.\n", "```\n", "exponential_fit.report(asymmetric_parameter_errors=True)\n", "```\n", "\n", "Es empfiehlt sich, in solchen Fällen mit stark asymmetrischen Parameterunsicherheiten die\n", "Konturen zu dokumentieren, wenn mehr als ein Parameter von Interesse ist." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# hier Code zur Ausgabe asymmetrischer Parameter eingeben\n", "# ->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hinweis: Sie können über `Plot.plot(asymmetric_parameter_errors=True)` auch in der\n", "grafischen Ausgabe die asymmetrischen Parameterfehler einblenden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Beeinflussung der grafischen Ausgabe\n", "*\n", "\n", "Die grafische Ausgabe war noch nicht in allen Belangen optimal. \n", "Es wurden in der Legende z.B. zwei Datensätze angegeben, obwohl es für beide Modelle nur den\n", "gleichen gab.\n", "Außerdem lassen sich Markereigenschaften und Farben anpassen.\n", "\n", "Zur Beeinflussung der Grafik enhält kafe2* eine Methode `Plot.customize()`, mit deren Hilfe für\n", "die verschiedenen Grafikelemente (plot_types: 'data', 'model_line', 'model_error_band',\n", "'ratio', 'ratio_error_band') Werte für matplotlib-Parameter angegeben werden können.\n", "\n", "Die für einen plot_type relevanten Parameter und deren momentane Werte lassen sich über eine\n", "Funktion der Plot-Klasse anzeigen:\n", "```\n", "p.get_keywords('model_error_band')\n", "```\n", "\n", "Die verwendeten Namen für Objekte und mögliche Werte entstprechen den Bezeichnungen in der\n", "Konfigurationsdatei matplotlibrc für matplotlib.\n", "\n", "Zur Änderung des Namens für den Datensatz und die Unterdrückung der zweiten Ausgabe dient\n", "folgender Aufruf:\n", "```\n", "p.customize('data', 'label', [\"test data\", None])\n", "```\n", "Das erste Argument spezifiziert den Teil des Plots, der modifiziert werden soll.\n", "Das zweite Argument spezifiziert welches Keyword gesetzt werden soll.\n", "Das dritte Argument ist eine Liste mit Werten, die jeweils für das Keyword bei den vom\n", "Plot-Objekt verwalteten Fit-Objekten gesetzt werden sollen.\n", "\n", "Alternativ kann für das dritte Argument auch eine Liste an Tupeln aus Fit-Indices und\n", "Werten übergeben werden:\n", "```\n", "p.customize('data', 'label', [(0, \"test data\"), (1, None)])\n", "```\n", "Mit dieser Syntax genügt es, nur für einen Teil der Fits Werte anzugeben.\n", "\n", "Auch Marker-Typ, Größe und Farbe des Markers und der Fehlerbalken lassen sich anpassen:\n", "```\n", "# data\n", "p.customize('data', 'marker', ['o', 'o'])\n", "p.customize('data', 'markersize', [5, 5])\n", "p.customize('data', 'color', [(0, 'blue'), (1,'blue')]) # note: although 2nd label is suppressed\n", "p.customize('data', 'ecolor', [(0, 'blue'), (1, 'blue')]) # note: although 2nd label is suppressed\n", "```\n", "\n", "Ebenso können die entsprechenden Werte für die Modellfunktion angepasst werden:\n", "```\n", "# model\n", "p.customize('model_line', 'color', ['orange', 'lightgreen'])\n", "p.customize('model_error_band', 'label', [(0, r'$\\pm 1 \\sigma$'), (1, r'$\\pm 1 \\sigma$')])\n", "p.customize('model_error_band', 'color', [(0, 'orange')])\n", "p.customize('model_error_band', 'color', [(1, 'lightgreen')])\n", "```\n", "\n", "Es ist auch möglich, Parameter über die matplotlib-Funktionen zu verändern. \n", "Um die Größe der Achsenbeschriftungen zu ändern, verwendet man z.B. folgende Aufrufe:\n", "```\n", "# Größe der Achsenbeschriftungen\n", "import matplotlib as mpl\n", "mpl.rc('axes', labelsize=20, titlesize=25)\n", "```\n", "Achtung: der obige Aufruf führt zu einer globalen Änderung der matplotlib-Parameter.\n", "Plots außerhalb von kafe2 werden also auch beeinflusst.\n", "\n", "Natürlich muss nach diesen Änderungen die Ausgabegrafik neu erzeugt und angezeigt werden:\n", "```\n", "p.plot()\n", "plt.show()\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# code zum Testen hier eingeben:\n", "# --> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 Ausgabe der Anpassungsergebnisse als Variable \n", "**\n", "\n", "In vielen Anwendungen ist es nötig, die Ausgabe einer Anpassung im Programmcode weiter zu verwenden.\n", "Dazu dient die kafe2-Funktion Fit.get_result_dict(), die ein Python-Dictionary mit den\n", "Fit-Ergebnissen zurückgibt.\n", "\n", "Zum Beispiel:\n", "```\n", "result_0 = <Fit_object>.get_result_dict()\n", "```\n", "\n", "Eine formatierte Ausgabe erhält man mit folgender Zeile:\n", "```\n", "print(\"\\n\".join(\"{}\\t{}\".format(k, v) for k, v in result_0.items()))\n", "```\n", "Die im Dictionary enthaltenen Werte können auch über Properties des Fit-Objektes\n", "bezogen werden.\n", "Achtung: die Namen der Properties sind zum Teil etwas unterschiedlich,\n", "mehr dazu weiter unten." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Ausgabe hier testen \n", "# --> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6 Nicht-Linearität durch Fehler in x* - Richtung\n", "*\n", "\n", "Wenn die Fehler in x-Richtung vergrößert werden, wird auch die Anpassung einer Geraden\n", "zu einem nicht-linearen Problem.\n", "Zur Illustration wiederholen wir die gleiche Anpassung wie oben mit vergrößerten\n", "Unsicherheiten auf die x-Werte:\n", "```\n", "# The data for this exercise:\n", "data3 = XYContainer(x_data=x, y_data=y)\n", "data3.add_error(axis='x', err_val=1.0) # Was 0.3 before.\n", "data3.add_error(axis='y', err_val=0.15, relative=True)\n", "\n", "# Create 2 Fit objects with the same data but with different model functions:\n", "linear_fit2 = Fit(data3, model_function=linear_model)\n", "\n", "# Optional: Assign LaTeX strings to parameters and model functions.\n", "linear_fit2.assign_parameter_latex_names(x='x', a='a', b='b')\n", "linear_fit2.assign_model_function_latex_expression(\"{a}{x} + {b}\")\n", "\n", "# Perform the fits.\n", "linear_fit2.do_fit()\n", "\n", "# Optional: Print out a report on the result of each fit.\n", "#linear_fit2.report()\n", "\n", "# Optional: Create a plot of the fit results using Plot.\n", "p2 = Plot(fit_objects = linear_fit2)\n", "\n", "p2.plot(fit_info=True)\n", "\n", "# Create a contour plot and profiles for the linear fit:\n", "cpf2 = ContoursProfiler(linear_fit2)\n", "cpf2.plot_profiles_contours_matrix(show_grid_for='contours')\n", "\n", "# Show the fit results.\n", "plt.show()\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# obigen Code hier eingeben:\n", "# --> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kleine Übung: Hinzufügen eines quadratischen Modells\n", "\n", "Als kleine Übung soll ein weiteres, quadratisches Modell, $y(x) = ax^2 + b x + c$, hinzugefügt\n", "werden und zusammen mit dem linearen und dem exponentiellen Modell in einer Grafik dargestellt\n", "werden.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# eingenen Code hier eingeben:\n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.7 Relative Unsicherheiten\n", "\n", "\n", "Wir hatten schon gesehen, dass kafe2* die Angabe von relativen Unsicherheiten \n", "erlaubt, die wir in diesem Beispiel genauer untersuchen wollen. \n", "\n", "Anpassungen mit relativen Unsicherheiten leiden darunter, dass die Schätzung\n", "der Parameterwerte verzerrt ist.\n", "Denn Messwerte, die zu kleineren Werten fluktuieren, bekommen dadurch kleinere\n", "Unsicherheiten;\n", "für nach oben fluktierende Messwerte sind die Unsicherheiten entsprechend größer.\n", "Wären die zufälligen Fluktuationen genau umgekehrt, würden andere Unsicherheiten zugewiesen.\n", "Richtig wäre es, die relativen Unsicherheiten auf die wahren Werte zu beziehen,\n", "die wir aber nicht kennen.\n", "Stattdessen ermöglicht es die Option ``reference='model'``,\n", "die Unsicherheiten auf den Modellwert zu beziehen - immer noch nicht völlig korrekt,\n", "aber deutlich besser.\n", "\n", "Der Effekt lässt sich illustrieren, wenn das Beispiel der linearen Regeression aus\n", "2.1 wiederholt wird,\n", "aber dieses Mal die relativen Unsicherheiten auf die Modellwerte bezogen werden.\n", "Die Unsicherheiten werden nun statt über eine Methode des Datencontainers mittels\n", "der Methode ``add_error()`` des Fit-Objekts angegeben;\n", "die entsprchende Stelle im Code ist mit `-->` markiert.\n", "Im Code unten wird das ursprüngliche Ergebnis aus 2.1 verwendet und ebenfalls in der\n", "Ausgabegrafik angezeigt, um den Unterschied darzustellen.\n", "Auch Profil-Likelihood und Konturen werden angezeigt, um die Nichlinearität zu demonstrieren.\n", "\n", "``` \n", "data1_4 = XYContainer(x_data=x, y_data=y)\n", "data1_4.add_error(axis='x', err_val = 0.3)\n", "data1_4.label = 'Daten, modellbezogene rel. Unsicherheiten'\n", "\n", "# Create Fit:\n", "linear_fit4 = Fit(data1_4, model_function=linear_model)\n", "# --> Relative uncertainties with reference to model specified here:\n", "linear_fit4.add_error(axis='y', err_val=0.15, relative=True, reference='model')\n", "\n", "# Optional: Assign LaTeX strings to parameters and model functions.\n", "linear_fit4.assign_parameter_latex_names(x='x', a='a', b='b')\n", "linear_fit4.assign_model_function_latex_expression(\"{a}{x} + {b}\")\n", "\n", "# Optional: Assign LaTeX strings to parameters and model functions.\n", "linear_fit4.assign_parameter_latex_names(a='a', b='b')\n", "linear_fit4.assign_model_function_latex_expression(\"{a}{x} + {b}\")\n", "linear_fit4.model_label = 'linear m. rel. Unsicherheiten'\n", "\n", "# Perform the fit:\n", "linear_fit4.do_fit()\n", "\n", "# Optional: print report.\n", "#linear_fit4.report(asymmetric_parameter_errors=True)\n", "\n", "# Optional: Create a plot of the fit results using Plot.\n", "p4 = Plot([linear_fit, linear_fit4])\n", "# Assign colors to data ...\n", "p4.customize('data', 'marker', [(0, 'o'), (1,'o')])\n", "p4.customize('data', 'markersize', [(0, 5), (1, 5)])\n", "p4.customize('data', 'color', [(0, 'grey'), (1,'red')]) # note: although 2nd label is suppressed\n", "p4.customize('data', 'ecolor', [(0, 'grey'), (1, 'red')]) # note: although 2nd label is suppressed\n", "# ... and model:\n", "p4.customize('model_line', 'color', [(0, 'mistyrose'),(1, 'orange')])\n", "p4.customize('model_error_band', 'label', [(0, r'$\\pm 1 \\sigma$'),(1, r'$\\pm 1 \\sigma$')])\n", "p4.customize('model_error_band', 'color', [(0, 'mistyrose'),(1, 'orange')])\n", "\n", "p4.plot(asymmetric_parameter_errors=True)\n", "\n", "# Create contour plot and profiles for the linear fit\n", "cpf4 = ContoursProfiler(linear_fit4)\n", "cpf4.plot_profiles_contours_matrix(show_grid_for='contours')\n", "\n", "# Show the fit results.\n", "plt.show() \n", "``` " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# obigen Code hier einfügen:\n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Im Vergleich zum Ergebnis aus 2.1 ändern sich die Zentralwerte und die Unschicherheiten werden \n", "asymmetrisch. Einen großen Effekt haben die beiden Datenpunkte ganz rechts, die nun bezogen auf das Modell kleinere Unsicherheiten und damit einen größeren Einfluss auf die Anpassung bekommen. Als Konsequenz liegt die angepasste Gerade oberhalb von der aus Beispiel 2.1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 3. Besonderheiten komplexer (nichtlinearer) Modelle\n", "---\n", "\n", "Als weiteres Beispiel für eine nicht-lineare Anpassung dient eine gedämpfte Schwingung eines\n", "Fadenpendels.\n", "Die zugehörigen Messdaten sind in der folgenden Code-Zelle enthalten:\n", "```\n", "# The data:\n", "t = [ ... ]\n", "t_errors = 0.05\n", "\n", "a = [ ... ]\n", "a_errors = 0.05\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The data:\n", "t = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0,\n", " 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0,\n", " 19.5, 20.0, 20.5, 21.0, 21.5,22.0, 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0,\n", " 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0,\n", " 37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 40.5, 41.0, 41.5, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0,\n", " 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 49.5, 50.0, 50.5, 51.0, 51.5, 52.0,52.5, 53.0, 53.5, 54.0, 54.5, 55.0,\n", " 55.5, 56.0, 56.5, 57.0, 57.5, 58.0, 58.5, 59.0, 59.5, 60.0]\n", "t_errors = 0.05\n", "\n", "a = [ 6.06, 5.17, 3.29, 0.64, -2.26, -4.56, -5.74, -5.58, -4.12, -1.62,\n", " 1.11, 3.56, 5.12, 5.43, 4.41, 2.53, -0.18, -2.78, -4.65, -5.5,\n", " -5.04, -3.25, -0.75, 1.79, 3.88, 5.31, 5.2, 3.92, 1.74, -0.85,\n", " -3.13, -4.71, -5.06, -4.26, -2.48, -0.13, 2.19, 4.07, 4.9, 4.64,\n", " 3.16, 1.17, -1.54, -3.26, -4.59, -4.64, -3.69, -1.83, 0.38, 2.76,\n", " 4.16, 4.58, 4.13, 2.45, 0.28, -1.8, -3.53, -4.43, -4.31, -3.03,\n", " -1.05, 1.06, 2.79, 3.97, 4.4, 3.37, 1.92, -0.14, -2.29, -3.7,\n", " -4.28, -3.84, -2.44, -0.59, 1.27, 3.11, 3.9, 4.02, 2.85, 1.21,\n", " -0.64, -2.51, -3.41, -3.84, -3.34, -1.75, -0.17, 1.85, 3.23, 3.72,\n", " 3.4, 2.54, 0.67, -1.13, -2.8, -3.77, -3.65, -2.89, -1.43, 0.42,\n", " 2.2, 3.26, 3.42, 3.25, 1.88, 0.33, -1.35, -3.02, -3.41, -3.32,\n", " -2.2, -0.77, 0.92, 2.44, 3.31, 3.44, 2.77, 1.25, -0.13, -1.69, -2.78 ]\n", "a_errors = 0.05" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Die Amplitude in Abhängigkeit von der Zeit ist durch folgende Modellfunktion gegeben:\n", "```\n", "# Model function for a pendulum as a one-dimensional,\n", "# damped harmonic oscillator with zero initial speed:\n", "# x = time, y_0 = initial_amplitude, l = length of the string,\n", "# r = radius of the steel ball, g = gravitational acceleration, c = damping coefficient.\n", "def damped_harmonic_oscillator(s, a0, l, r, g, c):\n", " # Effective length of the pendulum = length of the string + radius of the steel ball:\n", " l_total = l + r\n", " omega_0 = np.sqrt(g / l_total) # Phase speed of an undamped pendulum.\n", " omega_d = np.sqrt(omega_0 2 - c 2) # Phase speed of a damped pendulum.\n", " return a0 * np.exp(-c * s) * (np.cos(omega_d * s) + c / omega_d * np.sin(omega_d * s))\n", "```\n", "\n", "Daten-Container und Fit-Objekt werden wie üblich erzeugt:\n", "```\n", "# Create data container:\n", "data3 = XYContainer(t, a)\n", "data3.add_error(axis='x', err_val=t_errors)\n", "data3.add_error(axis='y', err_val=a_errors)\n", "data3.axis_labels = ('Time t (s)', 'Amplitude A (°)') \n", "\n", "# Create fit object from data and model function:\n", "fit = Fit(data3, damped_harmonic_oscillator)\n", "```\n", "\n", "Das Modell enthält eine Anzahl an Parametern, die durch \"Hilfsmessungen\" festgelegt sind. \n", "```\n", "# Relevant physical magnitudes and their uncertainties:\n", "lm, delta_lm = 10.000, 0.002 # length of the string, l = 10.0 +- 0.002 m\n", "rm, delta_rm = 0.052, 0.001 # radius of the steel ball, r = 0.052 +- 0.001 m\n", "# Amplitude of the steel ball at x=0 in degrees, a0m = 6 +- 1% degrees:\n", "a0m, delta_a0m = 6.0, 0.01 # Note that the uncertainty on a0m is relative to a0m.\n", "```\n", "\n", "In der Anpassung wird dies berücksichtigt, indem die entsprechenden Parameter sowohl als\n", "Parameter der Anpassung als auch als zusätzliche Datenpunkte berücksichtigt werden.\n", "In kafe2 werden solche durch Messungen eingeschränkte Parameter mit Hifle der Methode\n", "Fit.add_parameter_constraint() berücksichtigt und deren Unsicherheiten in das Ergebnis der\n", "Anpassung propagiert:\n", "```\n", "# Constrain model parameters to measurements:\n", "fit.add_parameter_constraint(name='l', value=lm, uncertainty=delta_lm)\n", "fit.add_parameter_constraint(name='r', value=rm, uncertainty=delta_rm)\n", "fit.add_parameter_constraint(name='a0', value=a0m, uncertainty=delta_a0m, relative=True)\n", "```\n", "Als Alternative könnte man die Parameter mit der Methode Fit.fix_parameter() auf feste Werte\n", "fixieren;\n", "die Unsicherheiten auf das Endergebnis der Anpassung müssten dann allerdings mit Hilfe der\n", "klassischen Fehlerfortpflanzung berechnet werden.\n", "\n", "Als weitere Besonderheit bei nichtlinearen Anpassungen ist zu beachten, dass es häufig\n", "Nebenminima der Kostenfunktion gibt - die Konvergenz zum globalen Minimum kann also nicht\n", "garantiert werden.\n", "Es ist daher notwendig, \"vernünftige\" Start-Parameter für die Anpassung zu wählen.\n", "Dies geschieht mit Hilfe der Funktion Fit.set_parameter_values():\n", "```\n", "g_initial = 9.81 # Initial guess for g.\n", "fit.set_parameter_values(g=g_initial, a0=a0m, l=lm, r=rm)\n", "```\n", "Wenn die Startwerte gänzlich unbekannt sind, sollten Werte in einem großen Bereich ausprobiert\n", "werden, um zu überprüfen, ob die Anpassungen jeweils zum gleichen Minimum konvergieren. \n", "\n", "Ein weiteres Mittel zur Verbesserung der Konvergenz liegt in der Beschränkung der Parameter\n", "auf \"vernünftige\" Intervalle.\n", "Die Parameter a0, l, und r sind zum Beispiel per Definition positiv, können während\n", "der Anpassung jedoch auch negative Werte annehmen.\n", "Der folgende Code beschränkt die erwähnten Parameter auf positive Werte:\n", "```\n", "fit.limit_parameter(\"a0\", lower=1e-6)\n", "fit.limit_parameter(\"l\", lower=1e-6)\n", "fit.limit_parameter(\"r\", lower=1e-6)\n", "```\n", "Aus technischen Gründen können Parameter nur auf geschlossene Intervalle beschränkt werden.\n", "Als untere Grenze wird hier deshalb ein kleiner Wert nahe null angegeben.\n", "Da kein oberer Wert angegeben wird, sind die Parameter nur einseitig beschränkt.\n", "Es ist auch möglich, Parameter durch die Angabe von zwei Intervallgrenzen enger einzugrenzen:\n", "```\n", "fit.limit_parameter(\"g\", lower=9.71, upper=9.91)\n", "```\n", "Im obigen Falle beruht die Beschränkung auf der Einschätzung, dass Ergebnisse außerhalb\n", "dieser Grenzen sehr unwahrscheinlich sind.\n", "Es macht auch Sinn, Parameter basierend auf den physikalischen Gegebenheiten des Systems\n", "zu beschränken.\n", "Zum Beispiel liefert die Modellfunktion nur für $c < \\frac{g}{l + r}$ reelle Lösungen.\n", "Man kann dies folgendermaßen berücksichtigen:\n", "```\n", "c_max = 0.9 * g_initial / (lm + rm) # A little lower than our best guess for the limit.\n", "fit.limit_parameter(\"c\", lower=1e-6, upper=c_max)\n", "```\n", "\n", "Nach diesen Vorbereitungen kann die Anpassung wie üblich vorgenommen werden. \n", "Im folgenden Code-Beispiel wird auch gezeigt, wie man über Properties\n", "auf die Fit-Ergebnisse zugreift, fall sie im Programm weiter verarbeitet werden sollen\n", "oder eine spezifische eigene Ausgabe erfolgen soll.\n", "```\n", "# Perform the fit\n", "fit.do_fit()\n", "# Optional: Print out a report on the fit results on the console.\n", "#fit.report(show_data=False, show_model=False, show_fit_results=True)\n", "\n", "# Custom printout of results:\n", "print(\"cost function at minimum: %.4g \" % fit.cost_function_value,\n", " \" number of degrees of freedom:\", fit.ndf)\n", "print(\" --> probability: %.1f%%\" % (fit.chi2_probability * 100))\n", "print(\"parameter names:\\n\", fit.parameter_names)\n", "np.set_printoptions(precision=5, suppress=False)\n", "print(\"prameter values:\\n\", fit.parameter_values)\n", "print(\"parameter uncertainties:\\n\",fit.parameter_errors)\n", "np.set_printoptions(precision=3, suppress=True)\n", "print(\"correlation matrix:\\n\", fit.parameter_cor_mat )\n", " \n", "# Optional: plot the fit results.\n", "plot = Plot(fit)\n", "plot.plot(fit_info=True)\n", "plt.show()\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Code hier eingeben\n", "# --> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 4. Aufbau einer Kovarianz-Matrix aus einzelnen Unsicherheiten\n", "---\n", "\n", "Behandelt werden: \n", " - der Umgang mit komplexen Unsicherheiten\n", " \n", "Eine der besonderen Stärken von _kafe2_ ist die Unterstützung von korrelierten Unsicherheiten.\n", "Damit sind Beiträge zur Unsicherheit gemeint, die einige oder alle Werte in gleicher Weise\n", "beeinflussen - z.B. weil sie mit dem gleichen, mit einer systematischen Unsicherheit behafteten\n", "Messgerät aufgezeichnet wurden.\n", "Häufig handelt es sich also um gemeinsame Unsicherheiten von Gruppen von Messwerten.\n", "\n", "Zur Angabe von Unsicherheiten dient die Funktion:\n", "> `add_error( [axis], err_val, name=None, correlation=0, relative=False)` \n", " Add an uncertainty source to the data container. Returns an error id which\n", " uniquely identifies the created error source. \n", " Parameters \n", " • axis (str or int) – 'x'/0 or 'y'/1 \n", " • err_val (float or iterable of float) – pointwise uncertainty/uncertainties for all data points \n", " • name (str or None) – unique name for this uncertainty source. If None, the name\n", " of the error source will be set to a random alphanumeric string. \n", " • correlation (float) – correlation coefficient between any two distinct data points \n", " • relative (bool) – if True, err_val will be interpreted as a relative uncertainty \n", " Returns error name \n", " Return type** str \n", "\n", "Sie gehört zur Container-Klasse, kann aber auch über eine Fit-Klasse aufgerufen werden.\n", "Mit diesem recht einfachen Interface lassen sich sowohl unabhängige Unsicherheiten als auch\n", "gemeinsame absolute oder relative Unsicherheiten von Datenpunkten angeben.\n", "Die angegebenen Unsicherheiten werden in eine Kovarianzmatrix der Datenpunkte umgewandelt.\n", "Bei mehrfachem Aufruf werden die sich ergebenden Kovarianzmatrizen addiert (wie es den Regeln der\n", "elementaren Fehlerfortpflanzung entspricht).\n", "\n", "Ein sehr einfach gehaltenes Beispiel soll das illustrieren.\n", "Wir betrachten die Mittelung von vier Werten, die von zwei Gruppen mit unterschiedlichen\n", "Messverfahren durchgeführt wurden.\n", "Jede der beiden Gruppen gibt zwei Messungen an; in der ersten Gruppe gibt es eine absolute, den\n", "beiden Messungen gemeinsame Unsicherheit; die zweite Gruppe gibt eine zwischen ihren beiden\n", "Messungen korrelierte relative - also z.B. durch einen Skalierungsfeher verursachte -\n", "Unsicherheit an.\n", "Den Messungen liegt weiterhin eine gemeinsame (theoretische) Annahme zu Grunde, die zu einer\n", "allen Messungen gemeinsamen, absoluten Unsicherheit führt.\n", "\n", "Bei diesem einfachen Problem nutzen wir die einfachste Datenstruktur von kafe2,\n", "den _IndexedContainer_, zur Bereitstellung der Daten: \n", "```\n", "from kafe2 import IndexedContainer\n", "idx_data = IndexedContainer([5.3, 5.2, 4.7, 4.8]) \n", "```\n", "Als Modell wählen wir eine konstante Funktion:\n", "```\n", "# The very simple \"model\":\n", "def average (a):\n", " return a\n", "```\n", "\n", "Die Unsicherheiten werden dann folgendermaßen angegeben \n", " (Anm.: Für _IndexedContainer_ entfällt die Angabe des '_axis_'-Parameters!):\n", " 1. jeder Messung eigene, unabhängige Unsicherheit \n", " `err_stat = idx_data.add_error([.2, .2, .2, .2])`\n", " 2. die den ersten beiden Werten gemeinsame Unsicherheit \n", " `err_syst12 = idx_data.add_error([.175, .175, 0., 0.], correlation = 1.)`\n", " 3. die den letzten beiden Werten gemeinsame, relative Unsicherheit \n", " `err_syst34 = idx_data.add_error([0., 0., .05, .05], correlation = 1., relative=True)`\n", " 4. die allen Werten gemeinsame Unsicherheit \n", " `err_syst = idx_data.add_error(0.15, correlation = 1.)`\n", "\n", "Wir sollten noch passende Namen für die Daten angeben:\n", "```\n", "idx_data.label = 'Testdaten'\n", "idx_data.axis_labels = [None, 'Messwert (a.u.)']\n", "```\n", "\n", "Das Ausführen der Anpassung ist mittlerweile ja gut bekannt:\n", "```\n", "# Set up the fit:\n", "ifit = Fit(idx_data, average)\n", "ifit.model_label = 'Mittelwert'\n", "\n", "# Perform the fit:\n", "ifit.do_fit()\n", "```\n", "\n", "Die Ergebisse erhält man natürlich mit der _report()_-Funktion, ggf. auch als grafische Darstellung:\n", "```\n", "# Report and plot results:\n", "ifit.report()\n", "p=Plot(ifit)\n", "p.plot()\n", "plt.show()\n", "```\n", "\n", "Die Beschriftung der x-Achse ist noch nicht passend - hier sollten nur die Indizes\n", "der Messungen stehen.\n", "Mit ein wenig Hilfe von matplotlib lässt sich das erreichen.\n", "Dazu muss auf das axis-Objekts der erzeugten Grafik zugegriffen und die entsprechende Anpassung\n", "durchgeführt werden.\n", "Dazu folgenden Code nach der Zeile p.plot() vor plt.show() eingefügen:\n", "```\n", "# illustrate some a-posteriory fixes to plot layout by accessing the axis object\n", "_ax = p.axes[0]['main']\n", "_ax.set_xticks(range(4)) # Integer axis ticks\n", "```\n", "\n", "Wenn ein Problem mehrere Beiträge zur Gesamtunsicherheit enthält, möchte man in der Regel\n", "gerne studieren, welchen Einfluss einzelne Komponenten haben.\n", "Dazu kann man komfortabel mit den Funktionen disable_error() und enable_error() arbeiten\n", "und entsprechende Anpassungen durchführen:\n", "```\n", "print(\"disabling common sysytematic error\")\n", "idx_data.disable_error(err_syst)\n", "_ifit = Fit(idx_data, average) \n", "_ifit.do_fit()\n", "_ifit.report()\n", "# do not forget to switch on again \n", "idx_data.enable_error(err_syst)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Code hier eingeben\n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## 5. Anwendung aus der Praxis: Anpassung einer Breit-Wigner-Resonanz \n", "---\n", "\n", "Behandelt werden: \n", " - der Umgang mit komplexen Unsicherheiten\n", " - das Erzeugen einer ansprechenden grafischen Ausgabe\n", " - das Studium des Einflusses einzelner Fehlerkomponenten\n", "\n", "Typischerweise sind die Unsicherheiten der Messdaten deutlich komplexer als in den bisher\n", "behandelten Beispielen.\n", "Meist sind Unsicherheiten in Ordinate und Abszisse vorhanden, und zusätzlich zu den unabhängigen\n", "Unsicherheiten eines jeden Datenpunktes gibt es allen gemeinsame, korrelierte Unsicherheiten.\n", "\n", "Mit der Methode add_error() bzw. add_matrix_error() können Unsicherheiten auf die 'x'- und\n", "'y'-Daten spezifiziert werden, entweder in Form von unabhängigen bzw. korrelierten, relativen oder\n", "absoluten Unsicherheiten aller oder Gruppen von Messwerten.\n", "Oder durch die Angabe der vollständigen Kovarianz- oder Korrelations-Matrix.\n", "Alle so spezifizierten Unsicherheiten gehen in die globale Kovarianzmatrix für die Anpassung ein.\n", "\n", "Als Beispiel betrachten wir Messungen eines Wirkungsquerschnitts als Funktion der Energie in der\n", "Nähe einer Resonanz.\n", "Es handelt sich dabei um kombinierte Messdaten der vier Experimente am Beschleuniger LEP des\n", "CERN, die auf Effekte durch Photon-Abstrahlung korrigiert wurden:\n", "Messungen des hadronischen Wirkungsquerschnitts $\\sigma_{e^+e^- \\to {\\rm hadrons}}$ als Funktion\n", "der Schwerpunktsenergie $E$.\n", "```\n", "## Data:\n", "# Center-of-mass energy E (GeV)\n", "E = [ 88.387, 89.437, 90.223, 91.238, 92.059, 93.004, 93.916 ] \n", "E_errors = [ 0.005, 0.0015, 0.005, 0.003, 0.005, 0.0015, 0.005 ]\n", "ECor_abs = 0.0017 # correlated absolute errors\n", "\n", "# hadronic cross section with photonic corrections applied (nb)\n", "sig = [6.803, 13.965, 26.113, 41.364, 27.535, 13.362, 7.302 ] \n", "sig_errors = [ 0.036, 0.013, 0.075, 0.010, 0.088, 0.015, 0.045 ]\n", "sigCor_rel = 0.0007 \n", "```\n", "\n", "Als Modell verwenden wir eine modifizierte Breit-Wigner-Resonanz mit von der Schwerpunktsenergie\n", "abhängiger Breite (\"$s$-dependent width\", mit $s = E_{CM}^2$):\n", "```\n", "## Model:\n", "# Breit-Wigner with s-dependent width\n", "def BreitWigner(E, s0 = 41.0, M = 91.2, G = 2.5):\n", " s = EE\n", " Msq = MM\n", " Gsq = GG\n", " return s0sGsq/((s-Msq)(s-Msq)+(ssGsq/Msq))\n", "```\n", "\n", "Der Daten-Container mit den Unsicherheiten wird wie folgt erzeugt:\n", "```\n", "BWdata= XYContainer(ECM, sig)\n", "# Add independent errors:\n", "error_name_sig = BWdata.add_error(axis='x', name = 'deltaE', err_val = E_errors ) \n", "error_name_E = BWdata.add_error(axis='y', name = 'deltaSig', err_val = sig_errors )\n", "# Add fully correlated, absolute Energy errors:\n", "error_name_ECor = BWdata.add_error(axis='x', name='Ecor',err_val = ECor_abs, correlation = 1.) \n", "# Add fully correlated, relative cross section errors:\n", "error_name_sigCor = BWdata.add_error(axis='y', name='sigCor', \n", " err_val = sigCor_rel, correlation = 1., relative=True) \n", "```\n", "\n", "Ob es sich um unabhängige oder korrelierte Unsicherheiten handelt, wird durch den Parameter\n", "correlation bestimmt;\n", "für unabhängige Unsicherheiten ist er Null, für allen Dateneinträgen gemeinsame Unsicherheiten\n", "ist er Eins.\n", "Werte zwischen 0. und 1. sind ebenfalls zulässig;\n", "allerdings wird in der Praxis die Kovarianzmatreix zur Beschreibung der Gesamtunsicherheit meist\n", "aus unkorrelierten und vollständig korrelierten Komponenten zusammengesetzt.\n", "Die in der Fuktion add_error angegebenen Namen erlauben es, später auf die einzelnen\n", "Fehlerkomponenten zuzugreifen.\n", "\n", "Anpassung und Ergebnisausgabe folgen der üblichen Vorgehensweise:\n", "```\n", "BWfit = Fit(BWdata, BreitWigner)\n", "BWfit.do_fit()\n", "BWfit.report()\n", "# Optional: plot the fit results\n", "BWplot = Plot(BWfit)\n", "BWplot.plot(fit_info=True)\n", "plt.show()\n", "```\n", "\n", "Verschönerung der grafischen Ausgabe \n", "Damit die Art der Daten klar beschrieben ist, sollten noch passende Namen vergeben werden.\n", "Die Zeilen unten müssen dazu vor der Erzeugung des Fit-Objekts eingefügt werden.\n", "```\n", "BWdata.label = 'QED-corrected hadronic cross-sections'\n", "BWdata.axis_labels = ('CM Energy (GeV)', '$\\sigma_h$ (nb)' )\n", "```\n", "Alternativ können die folgenden Zeilen nach der Erstellung des Fit-Objektes eingefügt werden:\n", "```\n", "BWfit.data_container.label = 'QED-corrected hadronic cross-sections'\n", "BWfit.data_container.axis_labels = ('CM Energy (GeV)', r'$\\sigma_h$ (nb)')\n", "```\n", "\n", "Es sollte auch noch ein passender Name für das Modell in der Legende für die grafische Ausgabe\n", "gesetzt werden.\n", "Dazu wird die Zeile unten nach der Erzeugung des Fit-Objekts eingesetzt:\n", "```\n", "BWfit.model_label = 'Beit-Wigner with s-dependent width'\n", "```\n", "\n", "Falls ein schön gesetzter Ausdruck für die Modellfunktion gewünscht wird, können LaTeX-Namen für\n", "das Modell, die Parameter und die Modellfunkton gesetzt werden:\n", "```\n", "# Set LaTeX names for printout in info-box:\n", "BWfit.assign_parameter_latex_names(E='E', s0=r'{\\sigma^0}', M=r'{M_Z}', G=r'{\\Gamma_Z}')\n", "BWfit.assign_model_function_latex_name(r'\\sigma^{\\rm ew}_{e^+e^-\\to{\\rm hadrons}}')\n", "BWfit.assign_model_function_latex_expression(\n", " r'{s0}\\frac{{ {E}^2{G}^2}}{{({E}^2-{M}^2)^2+({E}^4{G}^2/{M}^2)}}')\n", "```\n", "\n", "Anmerkung: Die Verdopplung der Klammern \"{\" und \"}\" ist notwendig, weil sie in kafe2,\n", "ähnlich wie in der Python format-Funktion, auch zur Übergabe von Parametern genutzt werden.\n", "\n", "Wir haben bereits gesehen, wie man die Bezeichnung für das Band für die Anzeige der\n", "Modellunsicherheit modifizieren kann:\n", "```\n", "BWplot.customize('model_error_band', 'label', [r'$\\pm 1\\sigma$'])\n", "```\n", "\n", "In diesem Beispiel ist allerdings die Modellunsicherheit extrem klein (weit unter 0.1%) und daher\n", "in der Grafik nicht sichtbar.\n", "Unterdrücken kann man die Ausgabe in der Legende mit folgender Angabe:\n", "```\n", "BWplot.customize('model_error_band', 'label', [None])\n", "```\n", "\n", "Manchmal wird das Unsicherheitsband von der Line überdeckt; in solchen Fällen solle eine\n", "gestrichelte oder gepunktete Linie für das Modell verwendet werden:\n", "```\n", "BWplot.customize('model_line', 'linestyle', [':'])\n", "```\n", "\n", "Nun können noch die Ränder des Plot-Bereiches angepasst werden.\n", "Dies gelingt über die Properties x_range und y_range der Plot-Klasse:\n", "```\n", "BWplot.x_range = (88, 94)\n", "BWplot.y_range = (0, 45)\n", "```\n", "\n", "Da es sich um eine nichtlineare Anpassung handelt, sollten noch Profile-Likelihood \n", "und Konfidenz-Konturen angezeigt werden.\n", "Die folgende Zeile muss dazu vor plt.show() eingefügt werden:\n", "```\n", "ContoursProfiler(BWfit).plot_profiles_contours_matrix(show_grid_for='contours')\n", "``` \n", "!!! Geduld: die Berechnung der Konturen ist rechenaufwändig und dauert eine gewisse Zeit!\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "''' the data for the Breit-Wigner example'''\n", "# Center-of-mass energies E (GeV):\n", "ECM = [ 88.387, 89.437, 90.223, 91.238, 92.059, 93.004, 93.916 ] \n", "E_errors = [ 0.005, 0.0015, 0.005, 0.003, 0.005, 0.0015, 0.005 ]\n", "ECor_abs = 0.0017 # Correlated absolute errors.\n", "\n", "# Hadronic cross sections with photonic corrections applied (nb):\n", "sig = [6.803, 13.965, 26.113, 41.364, 27.535, 13.362, 7.302 ] \n", "sig_errors = [ 0.036, 0.013, 0.075, 0.010, 0.088, 0.015, 0.045 ]\n", "sigCor_rel = 0.0007 " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# obigen Code hier eingeben \n", "# --> \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Studium des Einflusses einzelner Fehlerkomponenten \n", "Um den Einfluss einzelner Fehlerkomponenten auf das Ergebnis zu untersuchen, kann man einzelne\n", "Quellen von Unsicherheiten mit der Methode disable_error() abschalten und eine neue Anpassung\n", "ausführen, hier gezeigt für die korrelierte Unsicherheit der Schwerpunktsenergien:\n", "```\n", "print('!!! disabling error component ', error_name_ECor)\n", "BWfit.disable_error(error_name_ECor)\n", "BWfit.do_fit()\n", "BWfit.report(show_data=False, show_model=False)\n", "\n", "# do not forget to switch on again !\n", "print('!!! re-enabling error component ', error_name_ECor)\n", "BWfit.enable_error(error_name_ECor)\n", "\n", "#### fallback option with new fit object\n", "#print('!!! disabling error component ', error_name_ECor)\n", "#BWdata.disable_error(error_name_ECor)\n", "#_fit = Fit(BWdata, BreitWigner)\n", "#_fit.do_fit()\n", "#_fit.report(show_data=False, show_model=False)\n", "#BWdata.enable_error(error_name_ECor)\n", "```\n", "\n", "Das Ergebnis ist fast identisch zum vorherigen, lediglich die Unsicherheit der Masse ist nun\n", "kleiner.\n", "Dies war auch so zu erwarten, denn eine korrelierte Änderung aller Energien sollte die Breite\n", "oder Höhe der Resonanz nicht beeinflussen.\n", "\n", "Mit der Methode enable_error(error_name_ECor) wird die Fehlerquelle wieder aktiviert." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# hier ausprobieren\n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "## 6. Anpassung an Histogramm-Daten\n", "\n", "\n", "Im Prinzip lässt sich auch die Anpassung einer Verteilungsdichte an eine Häufigkeitsverteilung\n", "als Funktionsanpassung auffassen.\n", "Allerdings gibt es einige Besonderheiten, die berücksichtigt werden müssen:\n", "\n", "- Der dem Wert einer Verteilungsdichte (PDF=Particle Density Function) entsprechende\n", " Funktionswert für ein Bin entspricht dem Integral der PDF über das Bin\n", "- Die Unsicherheit eines Bin-Eintrages ergibt sich aus der Poisson-Verteilung, die nur\n", " bei sehr großen Zahlen an Einträgen pro Bin durch eine Gauß-Verteilung angenähert werden kann.\n", " \n", "kafe2* bietet daher eine spezielle Methode zur Anpassung einer Vereilungsdichte an Histogramme,\n", "die Klassen HistContainer zur Abspeicherung der Histogrammdaten und HistFit zur\n", "Durchführung der Anpassungen:\n", "```\n", "from kafe2 import HistContainer, HistFit\n", "```\n", "\n", "Als Kostenfunktion zur Bewertung der Übereinstimmung der angepassten PDF mit den Bin-Einträgen\n", "in der Häufigkeitsverteilung wird das Doppelte des negativen Logarithmus der Poisson-Likelihood\n", "verwendet, andere Optionen sind konfigurierbar. \n", "\n", "In diesem einfachen Beispiel verwenden wir die Häufigkeitsverteilung von Gauß-verteilten\n", "Zufallszahlen, an die eine Gaußverteilung angepasst wird.\n", "```\n", "def normal_distribution_pdf(x, mu, sigma):\n", " return np.exp(-0.5 * ((x - mu) / sigma) ** 2) / np.sqrt(2.0 * np.pi * sigma** 2)\n", "```\n", "\n", "Die Daten werden zufällig aus der Standardnormalverteilung erzeugt:\n", "```\n", "# create a random dataset of 100 random values, \n", "# following a standard normal distribution with mu=0 and sigma=1\n", "data = np.random.normal(loc=0, scale=1, size=100)\n", "```\n", "\n", "Der Datencontainer und das Fit-Objekt werden analog zu den früheren Beispielen erstellt:\n", "```\n", "# Create a histogram from the dataset by specifying the bin range and the number of bins.\n", "# Alternatively the bin edges can be set.\n", "histogram = HistContainer(n_bins=10, bin_range=(-5, 5), fill_data=data)\n", "\n", "# create the Fit object by specifying a density function\n", "fit = HistFit(data=histogram, model_function=normal_distribution_pdf)\n", "```\n", "\n", "Durchführung der Anpassung und Ausgabe der Ergebnisse unterscheiden sich nicht von der\n", "Vorgehensweise bei den früheren Beispielen:\n", "```\n", "# do the fit\n", "fit.do_fit()\n", "\n", "# Optional: print a report to the terminal\n", "fit.report()\n", "\n", "# Optional: create a plot and show it\n", "phist = Plot(fit)\n", "phist.plot()\n", "plt.show()\n", "```\n", "\n", "An dieser Stelle sollten wir noch einmal die Möglichkeiten zur Anpassung der grafischen Ausgabe\n", "anschauen.\n", "Der Plot-Adapter für Histogramme kennt als plot_type die Werte data, model und\n", "model_density.\n", "Über den Aufruf von `print(phist.get_keywords(<plot_type>))` können die möglichen Parameter zur\n", "Einstellung ausgebeben werden.\n", "Hier ein Vorschlag für Code zur Anpassung der Grafikausgabe, der vor dem Befehl phist.plot()\n", "stehen muss:\n", "```\n", "## reprise: plot customization\n", "# data\n", "phist.customize('data', 'label', [\"random Gaussian data\"] ) \n", "phist.customize('data', 'marker', ['o'])\n", "phist.customize('data', 'markersize', [5])\n", "phist.customize('data', 'color', ['blue']) \n", "phist.customize('data', 'ecolor', ['blue']) \n", "# model\n", "phist.customize('model_density', 'label', [\"Gaussian PDF\"])\n", "phist.customize('model_density', 'color', [\"black\"])\n", "phist.customize('model', 'label', [\"entries per bin\"])\n", "phist.customize('model', 'facecolor', [\"lightgrey\"])\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# hier ausprobieren \n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "## 7. Likelihood-Anpassungen\n", "\n", "\n", "Wenn nur wenige Messungen vorhanden sind, ist es nicht möglich, eine sinnvolle\n", "Häufigkeitsverteilung zu erhalten, denn eine grobe Einteilung in Bins würde die Messungen\n", "verfälschen, während eine zu feine Einteilung zu Bins mit sehr wenigen oder gar null Einträgen\n", "führen würde.\n", "Das oben schon angewendete Verfahren zur Anpassung einer Verteilungsdichte an eine\n", "Häufigkeitsverteilung ist dann nicht anwendbar.\n", "In solchen Fällen verwendet man eine direkte Anpassung mit Hilfe des\n", "Maximum-Likelihood-Verfahrens and die ungebinnten Daten.\n", "Auch dieses Verfahren ist in kafe2* implementiert.\n", "Dazu müssen nur die passenden Klassen importiert werden:\n", "```\n", "from kafe2.fit import UnbinnedContainer, UnbinnedFit\n", "```\n", "\n", "In diesem Beispiel verwenden wir zur Illustration 160 einzelne Messungen der Lebensdauer von in\n", "einem Detektor gestoppten Myonen aus der kosmischen Strahlung.\n", "Die Häufigkeitsverteilung ist eine Exponentialverteilung über flachem Untergrund:\n", "```\n", "def pdf(t, tau=2.2, fbg=0.1, a=1., b=9.75):\n", " \"\"\"\n", " Probability density function for the decay time of a myon. \n", " The pdf is normalized to an integral of one for the interval (a, b).\n", " :param t: decay time\n", " :param fbg: background\n", " :param tau: expected mean of the decay time\n", " :param a: the minimum decay time which can be measured\n", " :param b: the maximum decay time which can be measured\n", " :return: probability for decay time x\n", " \"\"\"\n", " pdf1 = np.exp(-t / tau) / tau / (np.exp(-a / tau) - np.exp(-b / tau))\n", " pdf2 = 1. / (b - a)\n", " return (1 - fbg) * pdf1 + fbg * pdf2\n", "```\n", "\n", "Zu beachten ist, dass die Häufigkeitsverteilung für alle möglichen Parameterwerte auf Eins\n", "normiert sein muss!\n", "\n", "Zur Vorgehensweise bei der Anpassung gibt es nur eine kleine Besonderheit: \n", "der Untergrundanteil ist auf Grund der geringen Anzahl an Beobachtungen mit einer großen\n", "Unsicherheit behaftet und kann daher bei der Variation im Verlauf des Anpassungsalgorithmus sogar\n", "negativ werden.\n", "Um diesen \"unphysikalischen\" Bereich des Parameters zu vermeiden, gibt es die Option\n", "`fit.limit_parameter(<name>, lower=<min>, upper=<max> )`.\n", "\n", "Alle weiteren Schritte im folgenden Beispielcode sind bereits bekannt:\n", "```\n", "data = UnbinnedContainer(dT) # create the kafe data object\n", "data.label = 'lifetime measurements'\n", "data.axis_labels = ('Myon Life Time ' r'$\\tau$' ' (µs)','Density' )\n", "\n", "# create the fit object and set the pdf for the fit\n", "LLfit = UnbinnedFit(data=data, model_density_function = pdf)\n", "\n", "# assign latex names for model and parameters for nicer display\n", "LLfit.model_label = 'Exponential decay + flat background'\n", "LLfit.assign_parameter_latex_names(t='t', tau=r'\\tau', fbg='f', a='a', b='b')\n", "LLfit.assign_model_function_latex_expression(\"\\\\frac{{ (1-{fbg}) \\, e^{{-{0}/{tau}}}}}\"\n", " \"{{{tau}(e^{{-{a}/{tau}}}-e^{{-{b}/{tau}}})}} + \\\\frac{{ {fbg} }} {{ {b}-{a} }}\")\n", "\n", "# Fix the parameters a and b ...\n", "a = 1.0\n", "b = 11.5\n", "LLfit.fix_parameter(\"a\", a)\n", "LLfit.fix_parameter(\"b\", b)\n", "# ... and limit parameter fbg\n", "LLfit.limit_parameter(\"fbg\", lower=0., upper=1.)\n", "\n", "LLfit.do_fit() # perform the fit\n", "LLfit.report(asymmetric_parameter_errors=True)\n", "\n", "pLL = Plot(LLfit) # create a plot object\n", "pLL.x_range = [a, b]\n", "pLL.plot(fit_info=True, asymmetric_parameter_errors=True) # plot the data and the fit\n", "#pLL.axes[0]['main'].set_xlabel('Life time '+r'$\\tau$'+' (µs)', size='large') # overwrite the x-axis label\n", "\n", "cpfLL = ContoursProfiler(LLfit, profile_subtract_min=False) # Optional: create a contours profile\n", "cpfLL.plot_profiles_contours_matrix(parameters=['tau', 'fbg']) # Optional: plot the contour matrix for tau and fbg\n", "\n", "plt.show() # show the plot(s)\n", "```\n", "\n", "Interessant ist die spezielle Form der grafischen Darstellung der Daten, bei der in diesem Fall\n", "jeder Messwert durch einen Strich dargestellt wird.\n", "Die Dichte der Striche pro Längeneinheit entspricht der Verteilungsdichte." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "''' the data for the myon life time example'''\n", "# real data from measurement with a Water Cherenkov detector (\"Kamiokanne\")\n", "dT = [7.42, 3.773, 5.968, 4.924, 1.468, 4.664, 1.745, 2.144, 3.836, 3.132,\n", " 1.568, 2.352, 2.132, 9.381, 1.484, 1.181, 5.004, 3.06, 4.582, 2.076,\n", " 1.88, 1.337, 3.092, 2.265, 1.208, 2.753, 4.457, 3.499, 8.192, 5.101,\n", " 1.572, 5.152, 4.181, 3.52, 1.344, 10.29, 1.152, 2.348, 2.228, 2.172,\n", " 7.448, 1.108, 4.344, 2.042, 5.088, 1.02, 1.051, 1.987, 1.935, 3.773,\n", " 4.092, 1.628, 1.688, 4.502, 4.687, 6.755, 2.56, 1.208, 2.649, 1.012,\n", " 1.73, 2.164, 1.728, 4.646, 2.916, 1.101, 2.54, 1.02, 1.176, 4.716,\n", " 9.671, 1.692, 9.292, 10.72, 2.164, 2.084, 2.616, 1.584, 5.236, 3.663,\n", " 3.624, 1.051, 1.544, 1.496, 1.883, 1.92, 5.968, 5.89, 2.896, 2.76,\n", " 1.475, 2.644, 3.6, 5.324, 8.361, 3.052, 7.703, 3.83, 1.444, 1.343,\n", " 4.736, 8.7, 6.192, 5.796, 1.4, 3.392, 7.808, 6.344, 1.884, 2.332, \n", " 1.76, 4.344, 2.988, 7.44, 5.804, 9.5, 9.904, 3.196, 3.012, 6.056, \n", " 6.328, 9.064, 3.068, 9.352, 1.936, 1.08, 1.984, 1.792, 9.384, 10.15, \n", " 4.756, 1.52, 3.912, 1.712, 10.57, 5.304, 2.968, 9.632, 7.116, 1.212,\n", " 8.532, 3.000, 4.792, 2.512, 1.352, 2.168, 4.344, 1.316, 1.468, 1.152,\n", " 6.024, 3.272, 4.96, 10.16, 2.14, 2.856, 10.01, 1.232, 2.668, 9.176 ]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Likelihood-Anpassung hier ausprobieren\n", "# -->\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "## 8. Multi-Fits:\n", "### simultane Anpassung von Modellfunktionen an verschiedene Datensätze\n", "\n", "\n", "Sehr oft sind die Modelle zu komplex, um alle Parameter in einer Anpassung an ein\n", "einziges Modell zu bestimmen.\n", "Modellparameter sind oftmals das Ergebniss mehrerer Modellanpassungen, oder der\n", "selbe Parameter kommt in verschiedenen Messreihen vor.\n", "\n", "Für solche Fälle bietet kafe2* die Möglichkeit, mehrere Anpassungen unterschiedlicher Modelle\n", "mit gemeinsamen Parametern an verschiedene Datensätze durchzuführen.\n", "\n", "Dazu muss zusätzlich das Paket MultiFit importiert werden:\n", "```\n", "from kafe2 import MultiFit\n", "```\n", "\n", "Wir betrachten als einfaches Beispiel die Bestimmung eines ohmschen Widerstands bei\n", "Zimmertemperatur, der sich bei höherem Stromfluss erwärmt und so seinen Widerstand gemäß seines\n", "Temperaturkoeffizienten ändert.\n", "Zusätzlich zum Strom durch den Widerstand wird daher noch die Temperatur für jeden vorgegebenen\n", "Spannungswert gemessen.\n", "Es müssen also Triplets von Messwerten ausgewertet werden.\n", "\n", "Die Temperaturabhängigkeit wird empirisch durch ein einfaches quadratisches Modell beschreiben:\n", "```\n", "# empirical model for T(U): a parabola\n", "def empirical_T_U_model(U, p2=1.0, p1=1.0, p0=0.0):\n", " # use quadratic model as empirical temperature dependence T(U)\n", " return p2 * U*2 + p1 U + p0\n", "```\n", "\n", "Der Widerstand als Funktion der Temperatur ist durch den Temperaturkoeffizienten $\\alpha$ gegeben\n", "und wird folgendermaßen modelliert:\n", "```\n", "# model of current-voltage dependence I(U) for a heating resistor\n", "def I_U_model(U, R0=1., alph=0.004, p2=1.0, p1=1.0, p0=0.0):\n", " # use quadratic model as empirical temperature dependence T(U)\n", " t_ref = 0.\n", " _delta_t = empirical_T_U_model(U, p2, p1, p0) - t_ref\n", " # plug the temperature into the model\n", " return U / (R0 * (1.0 + _delta_t * alph))\n", "```\n", "Das Modell für den Widerstand enthält also in diesm Fall das erste Modell für die Abhängigkeit\n", "der Temperatur von dem durch die angelegte Spannung bestimmten Strom.\n", "\n", "Hier die Daten für dieses Beispiel:\n", "```\n", "# the data \n", "U = [ 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, \n", " 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0 ] \n", "I = [ 0.5, 0.89, 1.41, 1.67, 2.3, 2.59, 2.77, 3.57, 3.94, 4.24, 4.73,\n", " 4.87, 5.35, 5.74, 5.77, 6.17, 6.32, 6.83, 6.87, 7.17 ]\n", "T = [ 20.35, 20.65, 22.25, 23.65, 26.25, 27.85, 29.85, 34.25, 37.75, 41.95,\n", " 44.85, 50.05, 54.25, 60.55, 65.05, 69.95, 76.85, 81.55, 85.45, 94.75 ]\n", "sigU, sigI, sigT = 0.2, 0.1, 0.5 # uncertainties\n", "```\n", "\n", "Die Fit-Prozedur unterscheidet sich kaum von der bisher vorgestellten Vorgehensweise. \n", "Zunächst werden die Daten-Container und Anpassungen für die beiden Modelle definiert:\n", "```\n", "# Step 1: construct the singular data containers and fit objects\n", "TU_data = XYContainer(U,T)\n", "TU_data.label = 'Temperaturen'\n", "TU_data.axis_labels = ['Spannung (V)','Temperatur (°C)']\n", "fit_1 = Fit(TU_data, model_function=empirical_T_U_model)\n", "fit_1.model_label = 'Parametrisierung'\n", "\n", "IU_data = XYContainer(U,I)\n", "IU_data.label = 'Ströme'\n", "IU_data.axis_labels = ['Spannung (V)','Strom (A)']\n", "fit_2 = Fit(IU_data, model_function=I_U_model)\n", "fit_2.model_label = 'Temperaturabhängiger Leitwert'\n", "\n", "```\n", "\n", "Dann werden beide Anpassungen zu einem MultiFit zusammengefasst.\n", "```\n", "# Step 2: construct a MultiFit object\n", "multi_fit = MultiFit(fit_list=[fit_1, fit_2], minimizer='iminuit')\n", "```\n", "Erst jetzt werden die Unsicherheiten - dieses Mal zu den\n", "Fit-Objekten, hinzugefügt. Dadurch können auch die in beiden\n", "Datensätzen gemeinsamen Unsicherheiten auf der x-Achse berücksichtigt \n", "werden. \n", "```\n", "# Step 3: Add errors (to the fit object in this case)\n", "multi_fit.add_error(sigT, 0, axis='y') # declare errors on T\n", "multi_fit.add_error(sigI, 1, axis='y') # declare errors on I\n", "multi_fit.add_error(sigU, 'all', axis='x') # shared error on x axis\n", "```\n", "\n", "Es folgt noch die Definition aussagekräftiger Namen für die Ausgabe:\n", "```\n", "# (Optional): assign names for models and parameters\n", "multi_fit.assign_parameter_latex_names(\n", " U='U', p2='p_2', p1='p_1', p0='p_0', R0='R_0', alph=r'\\alpha_\\mathrm{T}')\n", "multi_fit.assign_model_function_expression('{p2}{U}^2 + {p1}{U} + {p0}', fit_index=0)\n", "multi_fit.assign_model_function_latex_expression(r'{p2}\\,{U}^2 + {p1}\\,{U} + {p0}', fit_index=0)\n", "multi_fit.assign_model_function_expression('{U} / ({R0} * (1 + ({p2}{U}^2 + {p1}{U} + {p0}) * {alph}))', fit_index=1)\n", "multi_fit.assign_model_function_latex_expression(r'\\frac{{{U}}}{{{R0} \\cdot (1 + ({p2}{U}^2 + {p1}{U} + {p0}) \\cdot {alph})}}', fit_index=1)\n", "```\n", "\n", "Der Rest läuft dann genau so wie schon oft gezeigt:\n", "```\n", "# Step 4: do the fit\n", "multi_fit.do_fit()\n", "\n", "# (Optional): print the results\n", "multi_fit.report()\n", "\n", "# (Optional): plot the results\n", "plot = Plot(multi_fit, separate_figures=True)\n", "plot.customize('data', 'marker', ['.','.'])\n", "plot.customize('data', 'markersize', [6,6])\n", "\n", "plot.plot()\n", "\n", "plt.show()\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# eigenen Code hier eingeben\n", "# -->\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.2" } }, "nbformat": 4, "nbformat_minor": 4 }	gpl-3.0
probml/pyprobml	notebooks/book1/20/fig_20_37.ipynb	1	553	{ "cells": [ { "cell_type": "markdown", "id": "010480a7", "metadata": {}, "source": [ "LLE applied to (a) Swiss roll. Generated by [manifold_swiss_sklearn.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/20/manifold_swiss_sklearn.ipynb) . (b) UCI digits. Generated by [manifold_digits_sklearn.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/20/manifold_digits_sklearn.ipynb) ." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }	mit
NAU-CFL/Python_Learning_Source	02_Setting_Up.ipynb	1	84564	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Up Python Environment\n", "\n", "Setting up Python environment consists of 4 main elements\n", "\n", "1. Install Python Environment and Necessary Tools\n", "2. Execute Python Commands\n", "3. Run Sample Python Program\n", "4. Install Python Packages\n", "\n", "We will go with Python Anaconda distribution for our course because it has really comprehensive in terms of 3rd party packages and it is really powerful with it's own package manager; ```conda```. \n", "\n", "Installing Anaconda is straightforward: [download](https://www.continuum.io/downloads) the binary and follow the instructions. But careful to install Python 3.5 version.\n", "\n", "> If you are asked during the installation process whether you’d like to make Anaconda your default Python installation, say __yes__\n", "\n", "the ```conda``` command is a tool that keep your packages organized and up to date.\n", "\n", "### Jupyter Notebook\n", "\n", "Jupyter notebooks provide a browser-based interface to Python with:\n", "\n", "- The ability to write and execute Python commands directly in your browser\n", "- Formatted output also in the browser, including tables, figures, animation, etc.\n", "- The ability to mix in formatted text and mathematical expressions between cells\n", "\n", "### Let's Start Jupyter Notebook\n", "\n", "- Notebook is running at http://localhost:8888/ in default\n", "\n", "\n", "> Demonstration Starts\n", "\n", "\n", "#### Dashboard\n", "\n", "It's the main page when you hit http://localhost:8888/ . \n", "\n", "#### Open a new File\n", "\n", "\n", "#### Running Cells\n", "\n", "#### Edit Mode\n", "\n", "#### Shortcuts" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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EhUR0m4h2EVHXatfsL7+34qUioqXVrmlPRHHl84zWU9YhPM8nCYIwedmyZZLt30hU7AQm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\nsH8/rgEDmPz001x//fUlDgCJiYl06dLF+uqrr06q6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"text/plain": [ "<matplotlib.figure.Figure at 0x1043bc4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "N = 20\n", "theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)\n", "radii = 10 * np.random.rand(N)\n", "width = np.pi / 4 * np.random.rand(N)\n", "\n", "ax = plt.subplot(111, polar=True)\n", "bars = ax.bar(theta, radii, width=width, bottom=0.0)\n", "\n", "# Use custom colors and opacity\n", "for r, bar in zip(radii, bars):\n", " bar.set_facecolor(plt.cm.jet(r / 10.))\n", " bar.set_alpha(0.5)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional Software \n", "\n", "We will be needing the QuantEcon.py package from QuantEcon organization." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
luctrudeau/Teaching	IntroductionIOAsync/IntroductionIOAsync.ipynb	1	213534	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction à l'I/O Asynchrone" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Le Socket de Berkeley" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Il est difficile d'imaginer le nombre d'instanciations d'objets de type Socket depuis leur introduction en 1983 à l'université Berkeley.\n", "\n", "Le Socket est l'interface de programmation la plus populaire pour faire de la réseautique.\n", "\n", "Elle est si populaire, que tous les systèmes d'exploitation l'offrent et tous les étudiants sont introduits à la programmation réseau avec les Sockets." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Exemple d'un Socket client qui se connecte " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "from IPython.display import Image\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import socket\n", "\n", "sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", "sock.connect((\"etsmtl.ca\" , 80))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Exemple d'un Socket client qui envoi des données" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "msg = b'GET /ETS/media/Prive/logo/ETS-rouge-devise-ecran.jpg HTTP/1.1\\r\\nHost:etsmtl.ca\\r\\n\\r\\n'\n", "sock.sendall(msg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Exemple d'un Socket qui reçoit des données" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/jpeg": 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rQ2JvSnp3bmxDZnNLRVFvbkNqMEtWQXBxQ29FS21BcXVDc1VLM0FyekN3c0wmI3hBO0ln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\nU1hHdXdSeXlJanp2TzNGRlpnQzdmSDBYcmloOWR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FvcL9FdH0WNi8ltI39KSW2HoK9XN9f9j7C7W2xQ70623ft7fG1c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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recvd = b''\n", "while True:\n", " data = sock.recv(1024)\n", " if not data: \n", " break\n", " recvd += data\n", "\n", "sock.shutdown(1)\n", "sock.close()\n", "\n", "response = recvd.split(b'\\r\\n\\r\\n', 1)\n", "Image(data=response[1])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "source": [ "Quoique les versions de l'interface Socket ont évolué avec les années, surtout sur les plateformes orientées-objet, l'essence de l'interface de 1983 reste très présente dans les implémentations modernes." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "```2.3.1.5. Making connections\n", " connect(s, name, namelen);```\n", "\n", "```2.3.1.6. Sending and receiving data\n", " cc = sendto(s, buf, len, flags, to, tolen);\n", " msglen = recvfrom(s, buf, len, flags, from, fromlenaddr);\n", "```\n", "\n", "Extrait du [manuel system de BSD 4.2](http://www.cilinder.be/docs/bsd/4.2BSD_Unix_system_manual.pdf) [1983]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Socket Synchrone\n", "Le Socket Berkeley de 1983 est synchrone\n", "\n", "Ceci implique que lorsqu'une fonction comme ```connect```, ```sendto```, ```recvfrom```... est invoquée, le processus bloque jusqu'à l'obtention de la réponse. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Notons la présence dans le même document d'une fonction d'I/O asynchrone. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Fail Whale\n", "\n", "Le Socket synchrone n'est pas efficace en conditions de charge élevée\n", "\n", "Le déploiement de réseaux haute vitesse combiné à l'explosion de la popularité des réseaux sociaux le démontre bien" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![FailWhale](FailWhale.jpg)\n", "\n", "Les sites de réseau sociaux ne savent pas comment gérer cette impasse. \n", "\n", "La situation est telle, que les pages d'erreurs de Twitter deviennent célèbres." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "#Le Problème:\n", "\n", "Lors d'un appel bloquant, le processus et ses ressources sont suspendus. Lorsque la charge augmente, la quantité de ressources suspendue devient ingérable pour le système d'exploitation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "# La Solution:\n", "\n", "Il ne faut pas bloquer" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Le Socket Asynchrone\n", "\n", "Dès 1983, le socket de Berkley offre un mode asynchrones. Cependant, il n'est pas très utilisé, car ils sont beaucoup plus complexes et prône à l'erreur." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Le Patron Reactor\n", "\n", "En 1995, le Patron Reactor est découvert\n", "\n", "ce patron simplifie grandement l'I/O Asynchrone\n", "\n", "http://www.dre.vanderbilt.edu/~schmidt/PDF/reactor-siemens.pdf" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Une influence est du patron Reactor est la fonction Select" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Select est la fonction asynchrone présentée dans le même document que le Socket" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Exemple d'un Socket client asynchrone qui se connecte\n", "# (Avec le Patron Reactor)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import selectors\n", "import socket\n", "import errno \n", "\n", "sel = selectors.DefaultSelector()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def connector(sock, mask):\n", " msg = b'GET /ETS/media/Prive/logo/ETS-rouge-devise-ecran.jpg HTTP/1.1\\r\\nHost:etsmtl.ca\\r\\n\\r\\n'\n", " sock.sendall(msg)\n", " # Le connector a pour responsabilité \n", " # d'instancier un nouveau Handler\n", " # et de l'ajouter au Selector\n", " h = HTTPHandler()\n", " sel.modify(sock, selectors.EVENT_READ, h.handle)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "class HTTPHandler:\n", " \n", " recvd = b''\n", " \n", " def handle(self, sock, mask):\n", " data = sock.recv(1024)\n", " if not data:\n", " # Le Handler se retire \n", " # lorsqu'il a terminé.\n", " sel.unregister(sock)\n", " response = self.recvd.split(b'\\r\\n\\r\\n', 1)\n", " display(Image(data=response[1]))\n", " \n", " else:\n", " self.recvd += data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Création d'un Socket Asynchrone" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", "\n", "sock.setblocking(False)\n", "try:\n", " sock.connect((\"etsmtl.ca\" , 80))\n", "except socket.error:\n", " pass # L'exception est toujours lancé!\n", " # C'est normal, l'OS veut nous avertir que \n", " # nous ne sommes pas encore connecté\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Enregistrement du Connector" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "SelectorKey(fileobj=<socket.socket fd=63, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('127.0.0.1', 52631), raddr=('127.0.0.1', 5001)>, fd=63, events=2, data=<function connector at 0x10a29d840>)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# L'application enregistre le Connector\n", "sel.register(sock, selectors.EVENT_WRITE, connector)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/jpeg": 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rQ2JvSnp3bmxDZnNLRVFvbkNqMEtWQXBxQ29FS21BcXVDc1VLM0FyekN3c0wmI3hBO0ln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\nU1hHdXdSeXlJanp2TzNGRlpnQzdmSDBYcmloOWR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FvcL9FdH0WNi8ltI39KSW2HoK9XN9f9j7C7W2xQ70623ft7fG1c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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Le Reactor\n", "while len(sel.get_map()):\n", " events = sel.select()\n", " for key, mask in events:\n", " handleEvent = key.data\n", " handleEvent(key.fileobj, mask)" ] } ], "metadata": { "celltoolbar": "Slideshow", "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 }	lgpl-3.0
ThunderShiviah/code_guild	interactive-coding-challenges/graphs_trees/check_balance/check_balance_challenge.ipynb	2	4276	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Check if a binary tree is balanced.\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Is a balanced tree one where the heights of two sub trees of any node doesn't differ by more than 1?\n", " * Yes\n", "* Can we assume we already have a Node class with an insert method?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* 5, 3, 8, 1, 4 -> Yes\n", "* 5, 3, 8, 9, 10 -> No" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Refer to the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/graphs_trees/check_balance/check_balance_solution.ipynb). If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%run ../bst/bst.py\n", "%load ../bst/bst.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def check_balance(root):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following unit test is expected to fail until you solve the challenge." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load test_check_balance.py\n", "from nose.tools import assert_equal\n", "\n", "\n", "class TestCheckBalance(object):\n", "\n", " def test_check_balance(self):\n", " node = Node(5)\n", " insert(node, 3)\n", " insert(node, 8)\n", " insert(node, 1)\n", " insert(node, 4)\n", " assert_equal(check_balance(node), True)\n", "\n", " node = Node(5)\n", " insert(node, 3)\n", " insert(node, 8)\n", " insert(node, 9)\n", " insert(node, 10)\n", " assert_equal(check_balance(node), False)\n", "\n", " print('Success: test_check_balance')\n", "\n", "\n", "def main():\n", " test = TestCheckBalance()\n", " test.test_check_balance()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook](http://nbviewer.ipython.org/github/donnemartin/interactive-coding-challenges/blob/master/graphs_trees/check_balance/check_balance_solution.ipynb) for a discussion on algorithms and code solutions." ] } ], "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
NeuPhysics/codebase	ipynb/matter/.ipynb_checkpoints/stimulated-checkpoint.ipynb	1	331449	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Stimulated Oscillation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define functions and classes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import sys\n", "sys.path.insert(0, '../../module')\n", "import neuosc as ns\n", "import numpy as np\n", "import scipy as sp\n", "from scipy.linalg import expm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'neuosc' from '../../module/neuosc.pyc'>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reload(ns)" ] }, { "cell_type": "code", "execution_count": 244, "metadata": { "collapsed": false }, "outputs": [], "source": [ "thetav = 0.537#sp.pi/180\n", "fraction = 2\n", "lambda0 = np.cos(2thetav)/fraction\n", "alpha = 0.1\n", "omegam = np.sqrt(lambda0*2+1-2lambda0np.cos(2thetav))\n", "beta_r = 1-1/fraction" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def lambda1(beta,x):\n", " \"\"\"perturbation of matter\"\"\"\n", "\n", " return alphalambda0np.sin(betax);" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def hamil(beta,x):\n", " \"\"\"\"Hamiltonian of the system with perturbation\"\"\"\n", " \n", " return (-omegam/2 + (lambda1(beta,x)/2 )((np.cos(2thetav)-lambda0)/(omegam)) )ns.pauli_matrices(3) + (lambda1(beta,x)/2) * (np.sin(2thetav) /omegam)ns.pauli_matrices(1)" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sliced_evo(beta,x,deltax):\n", " \n", " return expm(-1jhamil(beta,x)deltax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the evolution matrix at anytime" ] }, { "cell_type": "code", "execution_count": 248, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def evo(beta,x,deltax):\n", " \n", " evo_mat = np.array([[1,0],[0,1]])\n", " \n", " for xi in np.linspace(0, x, np.floor(x/deltax)):\n", " evo_mat = np.dot(sliced_evo(beta,xi,deltax), evo_mat)\n", " \n", " return evo_mat" ] }, { "cell_type": "code", "execution_count": 249, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evo_00(beta,x,deltax):\n", " \n", " evo_mat = np.array([[1,0],[0,1]])\n", " evo_00_list = np.array([])\n", " \n", " for xi in np.linspace(0, x, np.floor(x/deltax)):\n", " evo_mat = np.dot(sliced_evo(beta,xi,deltax), evo_mat)\n", " evo_00_list = np.append(evo_00_list, np.absolute(evo_mat[0,0])2)\n", " #print xi\n", " #print np.absolute(evo_mat[0,0])2\n", " #print \"\"\n", " \n", " return evo_00_list" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.99992305093046641" ] }, "execution_count": 279, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "np.absolute(sliced_evo(beta_r(1-0.09),1,1)[0,0])2" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1.11803398875" ] }, { "ename": "NameError", "evalue": "name 'norm' 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-278-5c0bbf795ab6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabsolute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.5j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.5j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'norm' is not defined" ] } ], "source": [ "print np.absolute(1+0.5j), norm(1+0.5j)\n" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11357e6d0>]" ] }, "execution_count": 292, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OAAAgIYQ7AACAhBDuAAAAEkK4AwAASAjhDgAAICGEOwAAgIQQ7gAAABJCuAMAAEgI4Q4A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\ncsklzePlz50/+UnH+003XTX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JGqPwI7TpCl+m34D2WzdfCk835kt6ReG7cSPWZFtJeruk\n8ZJmSbpb0lZ1y8QfMQYAAEhIKo9lAQAAIMIdAABAUgh3AAAACSHcAQAAJIRwBwAAkBDCHQAAQEII\ndwAAAAkh3AEAACTk/wNezxTFEL8RHQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11867b190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "endtemp = 10000\n", "step = 1\n", "\n", "sliced_evo_lt = np.ones(endtemp)\n", "\n", "\n", "for temp in range(0,endtemp):\n", " sliced_evo_lt[temp] = np.absolute(sliced_evo(beta_r(1-0.09),temp,step)[0,0])*2\n", "# print np.absolute(sliced_evo(beta_r(1-0.09),temp,step)[0,0])*2\n", " \n", "plt.figure(figsize=(10,10))\n", "plt.plot(sliced_evo_lt)\n" ] }, { "cell_type": "code", "execution_count": 298, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1181eb710>]" ] }, "execution_count": 298, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OnDGFLRS7u74A4pZb/NdqhO0Vrzj8WqmBeq2wgRZh+/CH4W/+Tb9mgoagcOnS4eazxsqK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\nOYctlpIVO7ZluGYpCFuMuFIt8F1jU03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rbC5D83TQdTNU4+oKI2wsx4BQJZMpBoTyYI1B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"text/plain": [ "<matplotlib.figure.Figure at 0x11e3d8ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "endtemp = 100\n", "step = 1\n", "\n", "sliced_evo_lt = np.ones(endtemp)\n", "\n", "\n", "for temp in range(0,endtemp):\n", " sliced_evo_lt[temp] = np.real(hamil(beta_r(1-0.09),temp)[0,1])\n", "\n", " \n", "plt.figure(figsize=(10,10))\n", "plt.plot(sliced_evo_lt)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x135fbcd90>" ] }, "execution_count": 261, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LAW3duhk3iDjdkt8Lk2ljnWlgOKCNGQMkv8VuOzOZNuk7U7ttLf3cqdKCNqJzlPywukEb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PXbcm9py+NiUOhmuzy0nDFcpbC0USy6XEps0vd+8023UgNcp3ugNAoyRcswW2KRSzC/\nIY/rh816B9DM6WOOsX83roKv5TzOAJ8F6JxzgM03j1/ndo+W61hzeswAXdswClfnCUDco6ksWWLv\nD8o9QSmCUG2Lb14tbeWstHEpsUnlEQAe9zmny59TQXb93s2a2RfHvfdObgVULHl0Jd1wg/06xfxI\nGmsuqwTnOFPInjvXbuV1ld2smT0LnMLSVoo5/dln7oaMr76KX6c4qF52mX2doDqIJIVAcJF7pS2p\n1Qz3YuAyQZXiS0PmdgfmzdLGbXm0yd56a/eOCLZSBeWsSAB8lrZmzeyWNs57WM7KxN5761ZWUSg2\nuCSrJlchUs5xHjUKePRRN9nduule0VFcFeQkC14eLG1J1iWXrhbffWfvQEJxEEma0xTdh9IyubnI\nvdJ2wQX2wozlvsHZZFNUw66o0JXAo/w3W9o43aNJliWX8Zg/397Ng3NOz5jhtuimyabY4GyLLsV4\nvPoq8I9/8Mi2jcfChbqRvGtHFU5remOxtFVVuccW9erFUzA6bZzzGNMGuGVy77+/vS7iypVaoXOB\na04D6WPNRe6VtqZNdTZnFM74nw0beJQJ81C5ZFL99a+64GuUco9ps8mdPdueJVcfOC2PkybZi766\njnXfvvbrnIvuCy8At9/uLpsje5TLZQfoDeGpp+LXucZ6+XL9Wq4KEJcy0aQJsGxZvOsG1TrNFfid\npCDPmMHTa5PC0lZdrQ8HtusU8yNJIenVK7vcpHE+91y9D7jAqSDbxiMp9ImK3CttnH34khbHv/3N\nfjqvDz/9pCu9B6HYhJLKY5SzpS2px9/f/55dpoEzpg0Arrgifs117m22WbwCu5HLeVJ2JUlBXr6c\nz9Lmeg/POQc4/3y7bI6xNvfV5WCWtMGddRYwZkx2uQCfMmH+39E1j3NOn3aavc9ufUiLW7UV1i6W\ntIOIqyLx+98D99wTv869ftjcyMWSNM6uVjaAV0H+8UdgxYrwNarkiSRyr7QlxYZxT9C5c91kA8DT\nT4ffUygS550H9OkTv85paaPo42mz0ri0azLMng08/HD8+h/+ALz9trv8pPo/rkpsKeb0pZe6ya6o\niMs2iy6Xpc11PLp3t/cI5RrrZs2Azp3d5KYpEq5wuZKaN9dNtKMHM6qYJducbqv7JKcAACAASURB\nVNsW6NTJTXbaWLso3macoxYZCkXiZz+zX+ea0+aechxEXGKDDZyWegB47rnwe4pxTiP3Sluapc11\n4JYtA2bOtH920EFusoH4qfONN9yadgPJ7UTK2dIG2BeDgw/W1eNdiCrGhh9+AP71LzfZp58O7LNP\n/LrrRsQ5p5OUtuOOS17si+Wzz7QynPR7s8JpaWvosabo4lBRAbz7rpuMJJKSPiiKvtrGmjv22HWs\n05S2nXfOLtfU8Yt+b4pxvuACYNCg+HVOpY0iick2zhdfDJx9tptsrkQEw267hd9zK22MohsGTkvb\ngw/q12gG0i67AL/4hZtsQPfGC0JRdyqtz5pLOyggPabNdfKvXavjMLbbzr9WWwv07Okmt3Nn4Ouv\n3WQkYZt7nuduATIZSZ4XPr1SWJYqK+1JDrW17hscAEyfHn5vZJarpY3bUh/93hQbXJKF+Oyz7c9+\nfeAM2raNNafSRjHWY8dq99cpp4SvN28ObLWVm2wz1sFxpXCPJilAS5fytH2jUo7XrYtfr63lTa5x\nHeu9944/c3PnAqtWuclNo1FY2r75Jn6dU9ulWAz69o0rUa4TCEjfhMrZ0gbErQcU4/zb3wJdu9o/\ns51G64NtcTQLmMsippT+w2GVuP9+4C9/iV+nWHiBeBFLzwO22MJNdl4tbbNm+YkHBopxto0FoMe5\nRw832ZwbnO15WbTInjhVHzgtbQDw8svxaxRrk22sKdyjSXP62WeB0aPdZHNZ2ioqtGcr6i6muIec\nMW22Of3WW24yC5F7pS3JdM2ptFG5OKLffa+9+B5YCqvEO+8A48fHr1ONdfR7U4xz8+Z6M4ty8snu\nhVltNXqoglBt93HFinjQa31JavtEsfAOGgQccED4GuWiG13QOS1tr79uL/ZZX268MfyeYpwHDrRf\n5+wAwKVMXHedm0wAmDfP3nydYqyT4FImKJTjpDkNuCVPANoaFrXUU4yF+b7R+cGlHAN0BdZt+xYn\nuXePBm92cOJQLOhjxvCdsmw3u7YW2H13N7lcRXsB4IMP7NfnzqU50QZdowDfOFPKjo41xbwDtPL3\nxRdA797+tdNPd5fbsaM9hZ7rIEKhSFRU+Cf8qCvJdU6vWWMvZAzY6x3WFw5XUqtWdtdcuW9wSfPD\nFVtRVsC97iCgY6pscbUUY22LH+Sy/hiia2x9eeMNYNo04IQT/GsLF9pDLuqDaekVXS8onhfORATb\nnHbdwwv+Tl7x/JgNMnpTLrsMuOsud9m2m821wVEpErbYOKqgyyjG+uFaJ2nPPePfj2ucqWQnBVZT\njbOt1pIrd99tdxdzHkQorB02FynFWF91lbaq2aA4iPTrF36/YEFyclOxcM5pzkSEOXPiMY8HH+wm\nE0jOWhw7Fhg50k12y5Zx+Z4XjzfNApelrbJSl6KI0rMncMstbrKBeLxW0qGnPmy8sS4Lw2Vp40yu\nsT2L/fu7yU0j90qbSZ/nqD3FGeCaZFZ1XXSXLLFf51LaKNK9AftYL13qXlqF09J2zz3A9deHr1FZ\n2gCeAo2bbGK30rz2GvDRR26ykywpFMqP7bRMOdY2zjrL7d8fe2xcabPVz6ovSRsFl6XN1FCkUL4n\nTgy/32UXYPBgN5lp98nVWpp0uK6o4FHaKCxtr7yiC39HadmSpoxSVNFp1cpdJmAf6y+/1HGPLnBa\nj19/PW4k4V6Xcu8eBYBNN+VR2pIC7+fMcbcs1dTwBHQmbfQUE/SBB3RcW1Qu4J5JZVPazjjDTSbA\na5UAdPBsEErleMstw+9/+9t4YHt9SavT5hoQnkdLW4cOwLffxq+3aaPXFRdsc49iziX1O1y+nCd7\n1Fh/XJUUgCcusU0bYJtt7J+dd56bbJuCTDHOAJ+lzZaYZ2S7jvXZZwM77BC+1qGDe2kmwL5+mEPO\nE09kl8uptNXWAhdeCAwb5l+T4rpFYLsphx8OXH21m9y0Dc61vdLkycA114SvUSgSRnmyLY4c2aPV\n1foE51ING+Cr1O95dmWEK0iZKiljp53smVS2lm31IW2ck7Jsi8WmpHz/vT4tu8JlaXvwQXvbMK7M\n1NNOc5Np5NoOIo8/Hl9T6guX9ccQLXPEmQF88MHASSe5ybYpyBQJKgDfWNu6qQA0a5PtIFNTA7Rr\n5yYXSE+gcIFTabPBbWlrFEqbbSPq2BHYfnt6uQaKE3M0sH/ZMvc+a9262SvTc3VEoJqgtrE+9lh3\nuUm17/7xD7sLoT5st128bRjVQpCkIHO1ZuvcOV6Lqr48/LCOJQ1iKy+SBS5LW4sWdpcRV2Zqs2bA\n0Ue7ya2sBFavtn8WLdhdX5KsPxRz+phj4msyZwZwTQ1NXcOo7KRWgfXFFj9IYWmztdYD+BRkimSj\nJNmXXALccYebXM44TSA+p6W4bhHYNiLODQ7Q5TlciZqZL7zQ3YIH+FaJ4P+fq1kwlSnYNtaHHOLe\n8ift/5yUdVYsv/oV8PHH4WuU42HbPFu0cJdrm9OVlTQLb1SZoGhDA/BZ2jgLRiclqlBk0wJ6rQha\nOPbYQ29yLnApEoB9rLlr7bmO9Ysv6pCQm2/2r3meu1UaSLa0uVrTN98c2Hbb+HUuBZlKaVu1iqf4\nsm2cPY9m7l14YbwAvLhHiyDpdMhVadv0ynThoovijapN2rMrtu+dB0tbVDbFSTlNub7oIjfZXPMO\naPiDCNXCG6VXL+DAA93l2Cxt5d6ajbMEChCfe+3bu1uBON2jSYc+Tkub61hHY3iNXIpnPGmsXRXk\nhj6IUI1HdbVOiKKWbRtno9C7xmlutJF9rxX3aAEacoMz7YUo2oFEF5pf/9rddQLwuTiSxplTSXFd\ndE2AbDQ+bIcd3GsWJS0GXBsc50GEYoP75S+BM88MX6OaHzYLEFerKfNcuj7jTzwRHw+KcTb/nqNj\nBldwPMB36KustHsoFiwo77hVrrFOOohwWdrGjwemTHGTa4iWOcrjnBZLWxE0pKWN0n9ve7AosnC4\nlDZO96hNNpVVwtYSiuIEl2TR5LI8clraKBbHLl3igcoUijeQ7B4t5zkNxDe4114DXn3VTWaTJjps\nwGbF49jguC1tVGMdrU02b5573+HLL4+HJFDNaa6xTrO0uc6PUaOA4cPD11xbYwWJtnbkUto457SU\n/CiChrS0USptHIsuwGtpa0h34Kef0rWEit43zhNcObuSbOO8ejWweLH7vLZZjynnNEciAuecBoCt\ntw6/f+ABGrm2Qx+FMsE5p7nG2sy5devi8WCuiRn77RdPZlq2zL08DpB8EOGytFE8L7bOB0nZqvWl\nS5d4DOxf/qKf+6jFuj4kxWlyzWnuRIRGYWnjsnjY5M6aldywuT6sXx836XOe4MrdKmEb68ce0xmJ\nFLJtAa4cGxylu7ihrMfr1ulXCrddVPaKFcAPP7jJBfgSEbiUY0BXn6co8WHDdh8nTdIFqV3gTETg\nWj9Mb2FbXFufPm6ybYfraIZ0VmwHEU5LG5cFaPhwe9mc+tKli13ZjHbRqC+lOFxzukcbhaWNK7bI\nNvmnTXOTaRg1Sr/efrt/bf16mlIijcXSRsXatbogpint4Hm61lIeLW0c1mMz51wrm9uUY4p+qUDD\nWtqoFl3bhn/KKe4FkgH7WAPJJW6KhdOVlHS4jrrE6otSuj5lVHarVjoRxoUkyzQFDR3TRjGvH388\n3tWieXNdV9KVpDntuifmMU4zDbG0pVBZGe+rxqlBX3EF8Kc/ucvhUmIb2tIGAAMHussGgLff9n82\nv4fL0lbOrqQkl/8mm7gvYqNH62K1HNgsbVOn0txDrpgUm9K23Xba5eZKVZVd+Svng0hDK8hU/YWj\n88O1+4uBM6atupqn+0STJnG5VIXKk+K8r7rKTW6e4zRtNAqljWvg1qyJX+vdO95eKAtdurjLSMI2\nSefN48m0o9rgHngAGDo0fO1//gc47jh32UD4O1L1S7WN8xNPAC+95CbXyOaytK1eHV54a2vdrR2A\ne927NGyWtiVL4iUC6kseFQnDiBHxa/vv7yaTqwyFkc3lik4aa1dlYtaseOLI0KHAYYe5yQX4LEBK\nxcfDKHEUSizXnF68WIdTBNljD73nutDQB5HLLgPuvNNddhKNQml74w3gzTfD16hOFVFqapL73NWH\na68FzjnHXY4N2yRdtcqPXaKUy3mqoGw11b27/7NZdFx7S9rG47HH3GQaOC1tAPDhh/41qkX35z+P\nX7viCvdOC0ByVXNb7E59sCkSP/2U3L+xPrz2GnDXXeFrlPXwoptn27a6I4oLDb3BjRoFPP+8u+zF\ni+OyKdaPjz6yX6c4uNusx5Su6KDsyZP1q+tBlTM5b/p0fVCnls2ZiJDkiuakUShtAPB//xd+Txng\nGrRKUE1QW0+7/v11lX1XuCbSypXxrKlVq+KnIyqolIkddwyPdW2tzjJzjeGybXBUTJ8edukCwIQJ\nwPz5bnKN0hbc8KmU44su0v0eg2y6qXs7OSB5rCkyXqNyXdvmGGzKCOVBhKMGXEMnIgDAv/7lLnvd\nunjSEsX6YYuJo1qXbNZjyrEOzmvXA7vhu+/iMW2LFtHEadqgeF4aOqaNm0ajtEWZMcO9+WyHDvEa\nX5T+++j369XLPdsJSJ5IrguNLX3+7LNp0t8POyy+QFKNdbQgJOWiGx3ntm3d5QK6PZatPMRbb7nJ\nNSft4P+fajySqqVTyE6ytLlaq5s00QeP4KbDtQEBetOjWOR//vO4S5trg6Oy/ixdSleE1UYwG3/V\nKhp34LHHxotwU61LDZ30QcGECfFrt9yiw0Jc2XhjYNiw8DWK9cOMc9D4whnTBgC77eYuO4lGo7TZ\nrCaLF7vLjSpXlBscV2xAdDEwG2nQRZhVbpTvvnOTaTjmmLiV5vvveZQJzkX3mmtorKVpv9OV1q11\n4oHhq69o3IGcfQltrqSOHcP/jyyY8QyWJXE97BmibeoAnX137bXusrnWD0736D33AC+8EL/+i1+4\nywbCe8BXX+lXV3cg50GEM35wxQrggw/89xRF24F4EgIl55wTj19bvZqmwHp0baKa07W14XEGgCFD\ndHcYLhqF0nbmmcB118WvcyhXM2YA//63u9yKivgDS7XBRRcDI9c12NykdXM8uLYN/403gKefppfN\nuehSBprb2GUXdxnt2oU3oqi7Iys2RYJSQY5a2iieF+MuDt6zgQPdN3tAH0SOOspdjg3bWK9fz2dp\no1AkbOy/v3uTe0A3SD/ySHc5UWzr0qRJem1yhdNtBwDvvuv/XF2tg/pdOeYYdxlJ2Mb666/jra2y\nED30USlt992nvSJBqGQn0SiUti23tCsSFK6q6Enr+uvdZQLAk08CY8eGr3FZgKjktm0bT8s+91ya\nDc62CQHAt9+6y/7gA2D2bP89lXLMGZR79dXxoNxOnYBBg9xlRxdHKiXcZpV4+WX32mGA3dJG2Xw9\nqLRVVtJYf2zjQUVU9sqVenzKuSOCDSrZPXqE53G09VRWbOvSE0/QzGlO9ygQnh9UWf4HHBAP4Tn3\nXOCmm9xlJ+0Bq1a5y44e+qgOIraQIVHaiiDJr0xhlbBp/xTMmRO/9vzzevF1xWZpo7L+RGX36EHj\nDuTc4IDwovLOO7pchCtJljYKpa1z57jrj7IURXCsKeIoAeDzz/XYBpk6FXj0UXfZNksb1WGkY8fw\nhk+16NrWjp13pqll98Yb4ULfJtDc9TnnTEQYNUr//6OyOcZaKZqySkmKBAWcYw0AXbuG5XKsHYBu\nY7X55jyyAfewHiC+VlOORxRR2orAtnmaWjWurF4dTvumMDED9lPPDz8Af/iDu+wk9ygFXJN/+XLg\nlVfi12+80V02EP7OX35JI9M27z7/nO5kaCv6yrHBde0KHHigu1yKGNIkopa2Vat0D0iOEAjODa5z\nZ5pyEUuWhOu0GWs3laWNI2i7U6d4UD9XnTaqnrdK6ezI4HgMGQKceqq7bE5L2777hsu/cB5EqPaX\nqiq/NIlho43icyYLXPuWrVaiKG1FEL0hnkeTOWSYOdP/efBg4Kyz3GUOH+4uI4noeMyeDfz4I49s\nysk/b1742g476PgrCjp29H+mOBUCyXXauLpaUI31J5+EXfOU7uIobdsCl17qLjtqaTNuCY5EFSpF\noro6XvyX8gAVlbvVVu7jwRm0bVNiuWRTjbORGXwW27enadvEGdPGeQ+jStusWTRekuefB557LnyN\nSvnm2re23DKeKSoN44sgWuLCuAMpYq0Are0HZVMsBj17xl0FgK515Up0glKm2TekmZlqrI89Vpcm\nMfTpw2Nyp4Q7tihYG6u6mmacbVXiDzoI2Gcfd9lJJT84SsJQjfPLL8evffEFz4JOuVHYnnEORcLI\nphjr554L1+r85z+1MuGKUUaCdc64FAmAztLGaT2Oeipefz1eRJoCz6Nbm6KWeqqYNpsSK5a2IohO\n/qlT6eKjttwybJ7lLK67117arO3K3Lnh2KLNNnOXaeBS2mxV8+fPp0ugCLo3qqv9wskuJCltxx7L\nI5tyMQguWI88QtN6q3PneJcJrkW3thZo08a9QDKgN6Gvv/bfU3YAiPLFFzSHqKFDdbcJA2cIBKUi\nweXyB8JdPp58kkamSWYLrtVz5tDEuXFa2qKWR6pxNqVxoslLwZI5VCxYoF85ChlzWh5FaSuC6OSn\nSBE29OwZvikjRsSzPrNQUaFjJYJQbXBvvBEOdu7aVafEUxAda6oT/hlnALvv7r/3PO3SpbCWRhVk\nypPyjz+GT56DBsWzPrPKjrr8qRIRunfX423429/cZQL2g8jnn/OU/KitpS1DEXRpUy26wfkcZPVq\nd9kdO+pipIZ3342HF2QlatWkUiTef19bwIJwbXBB74gLLVrocIrgvJ4wAbj1VnfZNusxlYL81lvh\nsB6qddqsx1FlkyLb+ne/Aw491H8/daq7TEN0PV27lq5IPpfLP4lGo7QFT3BUMUtA/HQ4bx5NxfRV\nq8IVvAEdiMnhOqmtDcd0ucDpHo22Vgq+uvDss9qaZHjqqXiv2iyY/3dQ+a6t5akBZxR6CiV27715\naslVVMQVkk8/tSeY1BebpY3y/xCURTWnDz1UWwOj7L23u+yogvzUU+4yDcuWAS++6L9ftYpmg7Nl\nzFON9YEHAhdf7L8/6yxt+aXAdhihat0XDFP48Ufd95aqFEWw/h3VOLdurV+Da3W/fjo5w5VOncJF\ngCk7OkTX04svtnecqS8//hhWjgFR2ooiekPatKGzLE2aBDzzDI2sILZecGvW0JiZe/QI1ymi3OC4\nlLYmTcK1j8yiQOHGXLYs3Mfzz392lwn4G1lwrJcvp1GsuMYZ0GMdXHQvv5ymCKz5f0djXiiSYKKW\ntjlz6CzqTZvqgroGKoumrY7fVlvpTc6V998HRo7031PHVgaflzvvtGfJ1Zc994xfo5rX++8frsu5\n0UY0oSaAfqajLjAKS9748eFOJKYdIMeGTzXOnTvrkITgvOYKGaI0vkyfrjtyUGOzmk+fLkpbQZo0\n8f3fgB/vQkXwZh99tC586kqwhk4QCsvS5ZeHXXSvvebes9LApUwsWBDe3Gtq9MJI1cuTi5Ytwyfj\n116zNwqvL5xK2yOPhN07bdrYk2LqizkYRAtOBt14WYla2ih6HRqOOy5s4aYqRFpZGT+EUW1wEyeG\nxyPa/seVli3D7ynK2Jx4Ytji73laIeKIl6OM8fO8sCWzWTPgoYfc5a5ZE35vFEOKQ98WWwAXXui/\nv+QSYMwYd7kA31jPnw/89a/++3btaNYlQ1A2FVttpV+j+3bUi0ZJo1DannginKlF7ToJ0r59ssJV\nH4zpPhrQSbHBRV2NFPEXhhkzwiZ9KmUianmkXHQvuAA46ST/PUW6vqFLl/gpnKIfa9Om4ey31atp\nNk5D1KpJWfIjaoWgyogOWtooXSdRy+ONN9JYY814BN3nXCU/+vfXyicFHTrQ1O2LEl2X5s7VrxQK\n8pNPAjff7L+n3gOCNQj79dOuPFeiY0xZxPfMM4FttvHfU64dK1eGn0WqOf3ss+H31M8KVZeMIH36\naCWbqwCzjUahtEW12nnz4k1cXRgwwP+ZaiIppf8YDd0obzYXQn2JLo6UGxyg3SWGMWNoMg+jcRxU\n1e4BoFevcKzEWWfRNfS1ZQ9RKLFVVeEWXsGYPGoolbZttw1vlsad4krU0kbZ//app+KuE4pCwWZM\ng9/1xx9pxvqWW7RyZaB8XnbfPW45oFDiKivDBxozLlTPS5ApU4D33nOXawhaCKnGetCgcGUCiqLL\nBs5ODgAwbpz/80cfxZPqssDVEhDQczroIRs6FLj3XhrZzZrFx1rqtBUgeqKiaKtkGDo03O+RcnEM\nPli1tVqJozgdPvFE2BRO7V+PTtBoIGYW9t8//juoToe2aulU9/Cjj4DPPgtfO/hgd7lRy6OtRpkL\nwf//Aw/Quc+5xrpp0/Dh7PjjabNHqf7/QaLN6FevpmnqDujC08HuLJSt6qL38PDDgSuvpJEL6GD7\nIBwb3L336sxlCvbeO+x+phprW/JVr17ucgHdyPw3v6GRZSMap7psmbvMa68Nv6+poVM8+/cPe7Eo\n94B16+Lrc8+eNLJtNAql7eijw+8pLUvRB+svf6Gz4lVXh5U2qkkUVaJuuokmY80QNTNTLLpNmoSV\nS1th0qxwKm1AuPVYjx56k3MlOqaUp+YRI3Tco2Hhwnjl/qxwjfWHH4aD4ysqaJRjTkxskrEomYWd\nY8NftYqmlAig4+Weftp/T6WkRGWYOU4Rw3XQQe4ykmjRIjzW774bVzyzUFkJfPWV/55yXYr2sD79\ndBrF2xCNeaSYHz17Ajvu6L8fOVIH9VPAvQeYUjbmWae0mkZpFErbsceGAxYpNzhbHZagadiVN97Q\nr9Qn5SAtWsRbbWSlXz/g178OX6OI74g+VEuXuss0PP88MHq0/37uXJqToYFjMdhrr/B7qvsH2CvT\nUzF7drgA53ff0YxHtMQC5SEnyj776MMZBR06xAPMqepDBe/huefGa6C5EAy8p5rTZkMz39vzwnFX\nLpx/fjhulRKbq3HGDHe50X1l3To6xTvKZpvRlX3q2DFeg5Aizju6LplsWgruvDPsgXvkkXBBbVdM\n6IbxmFF1Y7LRKJS26EMVLBzqyty54YbxAG2Aq9ngvvySzgUWNc1Snir69AlbgTp0oDHDG+XYLOyU\nMUvRmLvRo2myvwwcStt224W7C7RtC/zsZ+5yAf54F5P4EtycXYm6+H/4gc59PmxYuLtA27Z05QaC\nY21eKebH1Km01ug0Xn+dZqw32US/GmWFsvVWdE6fdlo4rMWFV14Jd1ugIhrPfNNNtIpEkMWL6RSJ\nLl3CCmfz5jTJXZWV4cM0VVejJKgOZoD/Xb/9lnbvstFolLbgDe7RQ2fPUPD88/EN3iw+FBhX7uzZ\ndDJvvDGsuEXLabgQXRzbtqWpWWQWFFOAc+ut3WUaOINCAeDkk/Vrba12d3C4vygV788+Cwfh7rkn\nTR0ugyllEHT9uxLtFXvaab6V2pX27fXGY5g1i26sv/vOf8arq7XbhCLGNOhWawgo3FRNm2pl2MyL\nyZPjNf2yEn1eOnaktU5fdVX4PUWJlajlcf58d5mG++4LWx6ffhq4+24a2VGL2Nq1NGvsypXhRJWL\nLuKtd0Z5cN1+e/1KtSal0SiUtpqacBwXV1o9oM3MwexJV0ydF0rtvFmzcNzZyJF02YdLloQrm3/y\nCe1Ym2KTVEVIAb+KNwenneYXcjYmclvh5PoS3YTuvDNcasWFhx4KW3W7ddOnZyqCSgpAkz16+OG0\ntReDRMd69mytUFCwYQPw8MP+z1Ru/+hmds45dGU6bIdeKqtHcKwp62bNmqXbSxlGjqR1rwVp3Vq7\n0F0x1lwzHhTPiSFaPxKgs+LNnKn3AQD4+GP9SrEHRGPRW7cGBg92l5vE8cfTyNltN3+sOZVMQ6NQ\n2ozp3ig+559P6/4K0r07nRKw556+C4yyiKxSdAGcUSZMCFdiB2iUFIPZHL7/nm6RefDBcMmP/v3p\ngnKDVbyjbjAXKiv1CdZkTFIU7E1i7Fhay41RHqqr9fymsExTtfCysWyZ7jAQJBrI7YKxoPzpT3Sn\n+3btwu87daJzn//iF3HLZv/+NLKDShvlBhdMUjFwFFOtrdUFgSksS+3aaQuvGY+zz6YrJmuLxaZK\n0Fu6VJdNAvzSOBTeBTOmZh+/8EK6gsC//GX4ULP77v7/wZXgnOY0EBgahdJGuVlGuemmcJba1Kk0\nmUNGllnQN9mE7oGlqJlTDObhomyhY9KyBw+m+3+0bh22JG2+OV0F+TFjfKuEWSSD9bOyYk6uHPfS\nVipjyhQa2QMH+hv+11/TtGUD9HgsXepbCK++Wls5KfjjH4FXXw1fo65tCITr7rly3nnh9zfeCNxx\nB5384H1r0wbo25dG7uLFvsW4Rw8amQB/CISxypjYNirvQvTQ16cPjdwVK3TB4SCUtR6NocSMA8WB\nylgezbMX7RjhQp8+4Zp406bRyV61yt8DW7cGdtmFTraNRqG0mYnOsdB27BjuDwfQZviYxXfcuHjC\nQ1aiCQ0nngj8/vc0soMYpY1q3PfYwz99U8XgATpO4t13/fcvvBCvvk1Bba1WvilOW9HF0MRMUPDU\nU/FkFSr317hx/thSWjrMeMybp18XL+ZtcXbppXSyTMFsyjACm5WKyjo4YgRw223657VrtSWSMvnK\nZF7ut1+456sLtsxIqppn113nZ0ua55FKSfzpJ7+e3LnnhrN2XRg7Nvx+n33CSgsVlM+gWZOoa1Ia\nonX7zFriyqxZfkWF2lra+pE2GoXSZiYOh6XtvvvC7YQA2qwWswjcfz+dzCgvvkgXM3frrX6cgRkH\nqhpwH3ygC70CtK4TW3kPE5NBybRpdBmNZl6YzfL888MZji40bx6f05StvcwJnzJO0yg8ZkF8+GG6\n2NJoIk2HDnRujkMO8S2vxxxDIxPw58Xy5XQybRhLL6XSZubF8uV03/+SHrU8MwAAGqlJREFUS8Ju\n+AED6ALvmzWLKxKUlj2qGolBot/3nXdoLVfGu1BTQ9sfdPPNeZS2Bx8E3nwzfI0y3MIohLfdFg+1\noKZRKG0Gc1OGDYu3pcmKbQJR1RYC/Kw1SkXwqKP0q1kcf/qJrmXH/Pn+pmy+M6UFwbipbriBbjGw\nWQIpH1ijSFC2TjMYJfDtt/1eja5EXQPduunim9RQBlZz1j0aP96P2Vq3TrsxqZSUV1/1s/gqK3VN\nSQrM9wuuGyecQCPb9nsolTaTYDR0KJ3C0rSpflbMeFDW8QsqbWZOU655HElztsxZqg4RgwfrhDxA\nr62UHq7gWF94oY4DpcDWwJ2i6oHB7LW22EpqGpXSZk6F991H13cuWv2/ooLOKtGjh1+ZnipmBPCt\nVEHLI9WmF3R5/fvfNDKDmEW3VSu6wOpdd41fC/ahc+HOO/XmA/DUFbr+ev36979r5YKCqAWMq1Dt\nPvvQxQ6aCuwc1vS5c/3Fdu1a/UqppBgWLqRL+IhulEceqTNIKTjySP9ns24EWwC5QlGYNgkzLm++\nSWfFCyoSNTXxJA1XOBqZX3ppvGsBlXXwySd9K/ftt8fb+LnQtGl4rKnWJZvVn2of793b94JwhGhF\naVRKW3DTDLZhceGWW8KNn2tr6Rb0Aw/0LW3HH69jGigJmoOp3I3BhyhYU4cKY4l4+GHt1qVg553D\np6pevejiO/78Z/80yFFUkaNCeufO4feUmaNnnOErsTU18d/lStBaQJHwAYRL2FDHLAX53/+li1s1\nlo5gEhbVBnf++f7zwTEev/sdnSyDLRnNlKNwJaq0UR4cunb1EzKGDQP+7/9o5G7YEI8L5pjTn35K\nKy841pMm0cVpXndd+L1SdOvHzJl+31TuwrpAI1Pagu4YqgnaurWvkU+dql+prFajR2sTMMBTWy5Y\niuP882lkBr8jZdYooLMBTRPsDz8EvviCRm6TJnoszHisW8fT53XvvemDUDmsd8FevdTyu3XzF91v\nv43Hzrny6KP69YQT6FwnwblgNmSqg9n22+tEIGpatNBlbMz3ffnleMJUViZM8BVZDsWbA2OtCipU\nVM940Ppz//10BYEBbeU1SWLNmtF14rApDzaPgyvU60ezZv6+MmsWMHw4jVyzrwB6bDyPx5p+7bW8\nzeKBRqS0de+uNwwD1eZZWemb86nKF9i46y7athqAv4Dtuitd4c277orLp6Kigse8bBZvo0DMnctj\nwaqtpUvKMER7kFJQWenXB6S+h9df75cWOPts+rY8HJalYAackU/V9WTw4HATbEoWLQqHKFAFQAcD\n1leupK3UH+TSS4HLLqOR1bGjPpwF5zPVprx6tV/3s6qKRmYQ0zP2zjvpSu+Y/7tR3ior6ZSJYcP8\ncB7qYrIrV4aTuagMJMHSSWaMOeJkZ8+m7eZjo9Eoba1bhx/YYcNo5AYXLE5/dVWVH09DhVFc582j\n2+C23dYPJN5hBxqZhjFj6OJygpgFLHj/qK2EgC5oHM1QcuGkk/zK64MHA48/TiO3slIfQNat4+1B\nyoG5hxMn0sVGBeu9mRqMVM/LsmV0bjobwcBnSsuSYdQoGpmG5s39xIxRo2iVoBYtwvP5oINo5D7x\nhK/8cPfDpCr5EayoUFVFe8jp29cvpzJwIG1B2UWLdCawgeo7B+VwZlz/+c90nWuSaDRK25QpfgPl\nNm2An/+cRm5wAeMwp3LRpYvucwjojcNW9iIrJsnjnXfoZDYEQaWNajG47z7/51/9ikamobrad8s8\n+WS41pwL5v++cKEvnzIjmpNg4Uqqfr1Nmvjt5IKWZAruuQd45hn9c4cOdMk1huDhg2p92mIL/2dq\ni/Qll/g1zwC6uFVAH0QmTfJjbakSBoJ9abmtKFSYHrc1Nb6HiCpk6MMP/U4Fw4fTK0HBGNOLL6aR\nGXw2qOPOrr6ariVWMeRIDSmMiQ1YtkxvSBQEbzZV30ADVW9NG1995dc8A+ishME4M6oMzCTOOINW\nXtOm/kmZKhuOsuhtlGefDZfioAr6NUpbba0fI8aRPUot86CDeAqENmmiN/o1a/gKewI6xo+6oXR1\ntW+hp4pb/d//pZFjY84c3qbaVVX+AZVq/gWVNvO8UFFZCVxwgf/ehC1QsGGDVtqMskY1Hv/4B42c\nJILueaoOEcFDKbW1dNttfQNJQ9ColLagBk3VasoE4W7Y4GeIUBGNkaMOYqc8xRqCSix36xiT+EHF\nhAm+6ZoqzojT9RWFsmk3oO8ldZxm0PJI7Xr917+AIUP855yqnIgZjx9+4K0Hx0H37r6SErSQuWDW\nofXr6ayZhvHj/RguDoJuQKr1iUpxsHHYYeFC3zfcQCv/n//0x4FqblPWN7MRtB5TfefgYY8qucHw\n0ku8xfGjNCqljbL1kcEEb770Ev2CHq3KH62r44pSfkFW6g3OyOeE+kT00kt0rUsMDRkTRmXWN/fw\nX/+id/lTlg9JwliWhgyhkWfGtaaGPrCamyuu8O8hlZJinrt163hrqgHAccfRyvvpJ1/ppJrbJsOf\no5zDiy+G6y9Sx9ouW+a756ngXveDB2rKHq8AMHkyXdcaQzSOmdP7AjArbUqpI5RSVUqp2Uqpqyyf\nD1BKrVRKTa/7c51NTn0wbkCq7C8DRwHS6CJAvYGuWgV88on+uVMnGpmHHaZfP/yQfvJHoVaIlOIP\nJOaEqoaTWXSffz7s+qGgIcbXPDdUm4eRM3s28Ic/0Mg0nHJK+D1lk3SDWTeoxsOsoQ1xINl2W1p5\n997rH96pLEJmHBpiblMrRJ4HvP46rcxovDh1PFews0ywVAcFy5fT38foPeNokRiETWlTSlUCuBvA\nEQD6ABislLLZe97wPG+3uj83u/5ec8OpEhEMnkfblsfITHvvypIl9DLbtNGvnOVPDL/8Ja28igp6\n076pfWRrk0KFuYdU7i+DUnTJDYaG2NjMJko1t82cfuwxGnlBoiEPhx5K/zvMmFNl8ZnxpQoxSYP6\noPrDD35sM5VsY/2qrtaFo6nXpSDUh6jbbqNXvqNz+O9/p5UP+M82tdXK8+gMGIaoMYcjPjgIp6Wt\nL4AvPM/7yvO8DQCeAmAzhrMYW6kHrlUrXdKBkpsjKmowIJUKrtNyQ7TrCNbdo+Ctt+gVnwMO0K/U\nSSpBzKbM4Z75299o5ZmAXDPvqArgBjGyTVcAarmccCj3Jpubas0zcbyPPKLLO1DG2h5ySPg9hzua\nWtk0SltNjS6IPno0rfwggwfTyvv2W/rkmoaI+zStsaj38SefBH7xC9o+vdE4RGrFOwqn0rY1gGBZ\nxm/qrgXxAOyjlJqhlHpBKZU55DNaTJD6BMdReyW66QRT4ano2JFeJsCThBDMdgV4SqxQK8ZmHDxP\nu47/+Eda+YCvTFBvcBzlC0wrNqPEjh1L/ztMZwuq6vEGjoNI+/bh9xxKCrX7y2RWr1mjLVeUHR2i\nG37XrnSyDdRxYeY5mTxZv1LW0zSN3c3zwhGuQH0Y4TxEGoyiSb0HvPWWLur87LN0MqP7uOkXzQWn\n0lbMLZ0GYBvP83YF8CcAmQ2tN96oF3GTLkx9GqAqbBok6qrjSKSYNIleJsDT5HjFivB76mxagLZe\nHeAvKrW1uk6gWdgpePRRbeE1GXzUtdTuvptWHuArJWZh51BSqK2DBmM1peSii/SrUTQ5xoOjULSR\nW1WlrRNU9O+vX01CEEeLL2p3nTn4UravMpg1zlTsp7YsNW8OTJtGK9N4QBYv1q+U39korVwJTRwH\ns6hiSX2YjMJZtGEBgGDXus7Q1rb/4HneqsDPLyql7lVKtfE8L7a13hCwQQ4YMAADBgwIff7gg3rT\np67gbeBoe2Qmuwno57BemZZC1JgabV260Mk0D6yp8k5VSy0IV/KE6UEa7EXqyrJlet699pp+Tx1T\nyYGZ02ZxpFRSWrTQri+uBd0UDKXELOjffqtfqWNtAR18zwFHzbrBg/UB2xyqueN/KOFQjs3/39S/\npNwDTJF56lhNYx018bC33konu1073UOXqwYoh9JmWnpNmjQJwCSMHeu7dzngVNqmAuiulOoCYCGA\nkwGEPPZKqa0ALPY8z1NK9QWgbAobEFbabBiT9YIFbl86CWoLDeDX/xk5Ur9SPrBNm+pFhrpht8Fk\npVIu7GYBM/XZzMOQB4xrl3JRMPFPnAVfqTGbsalbZDoNUDBkiI4nos7wNAT7eFJjDn15mtPUvZAB\n//kwVe/zpLQZNzTlPbzwQn1INXFylOOxbBlPco3B7LmUyV3mkMdlreKIKTWGi6VLBwAYgEGDgEGD\ngBtvvJH+l4FRafM8r1opdRGAlwBUAnjI87yZSqmhdZ+PBjAQwDClVDWAnwCckiiwAKZ1STBdmAPK\nhyraZoUyvZnLZWIw5nxKZdZY1ozrJE+FTs3JilJpM1aaX/+aTiY35h5+/rl+vfRSOtmcMTTcmPU7\nTyVnOHo0mnvI3U2FA+N2jSZTuGBKnpi1Lk+tEk1/b8rn0riL89QT2dwzkxBEXW4s9vs4hXue96Ln\neT09z9ve87xb666NrlPY4HnePZ7n7eh53q6e5/X3PC9zAYKdd6b61ulQTibzoE6cqF8pXY0NBWVQ\n7sCB+pXLksLJ/LqUG8qK5nmyQkShzmgE+DtwcGCClMeN06+USltD9Tukao8F+Bu8mR8cIRDcUCop\nRhZ13FlDYJR6Smv6b3+rX7mNLxzx0qbUzOGH08sOkiO9Ph3ujA1OPvpIv+bplGW4zrkcsk/eqtHb\noOyNGS3MmidM3Bml5bGhlLajj6aTFY1DpNzg8mh5jGbaUbrW9tqLTlYalAYCcw+N5yKPHHssnSwO\nZcrGfvvxyebex3OoJthpKFfaWWfxyab8P3DWEgqy5550svKotEahKnAKAB060MlKw5Qd4ICqxyvg\nW2K54Tw8UGZdN5SrlbKBPHUGdJCVK/lkBzniCDpZ1K0LS0EeD9t5PhA3gm1Ss+++DfN7+vVrmN/j\nSrt2DfN7KC1LnJaUYIYTZ/HDHXagk9VQSizn76E8Oe+yC52sNM45p2F+jyuXXNIwv4fSOshJQylA\nlEoKdZumIMH19KpYE0k6KMcjWteQizyHnjQapc1UYm8sv8eVI49smN9DaUnhtJYGlYe8nAw5A1ov\nu8z/+cAD+X4PJZyb8t57+z9THkQ4aajvmRc37O23+z/vvz/f78lLbGUw5pE7OJ4KTqNI0BWfZ69O\njr96aTjqqFJ/g+LIi2LSUBxzjP8zpaKZV4InzYYKaHeF83QcdNvlRUmhdMVHMUVw80QwXo7TKttQ\ncVeumO4kQL4tS1QEE9wayhPFgShtRRAs2JsXZSj4kFI3yM0jwXZe11xTuu9RLnAUec0zQUUtLxtc\nsJYVZTA4EM6Sp+7Xy0Xwe1JbUoKy81KKKPg9e/cu3fcoF4KHHMq4xIamUSpt1IX52rb1f86LWTX4\nwN50E63syy+nldfQ5GXR5aRHD//nvLj8g+y6K628YAPpaP3EPGBaC1ERVNryosQGraUnnUQrOy/W\ntSDB3sIHH1y675EVjl7chrzMaRs5UUHqx4QJtPI4g0UbAuokjYZI+qBWJIL1oKjr6FDWZkvCdBig\nIhjfkUclxXQRoSKoBOZRqadOShg+nFaeDU5rL3XniYawVP3qV7TygklReWiBF+XMM2nlBeNW8/iM\nGxql0kYdj8H5wJqCfNtvz/c7glYVCo47jlaejbvuopUXtJBSu7gvvphWng3qoPOg9Ziaqio+2Qbq\nLLNevWjlBTF136itg0Gox6MhEpkoy59EobaMvfACrTwbebJ4P/ww/+848URaeaZnKgcNWVWiUSpt\neTJ9Gj/7lVeW9nvUB04X8YgR+pWr9xwHlAVCoxgXB2d9K2qMUkwdZxWEOpmE8+RtClBTb0JBqDsL\ncMbumrZsp57K9zuo4RwPc4AKJg6UO8YKxlHvzDyL1LGUnM+4qYv6pz/x/Q5DTpKXi2PBAmDmTJ6b\n86c/8WRUDRkCfPGFfqVm0SK+diATJ9L24DMMHapfDzuMXvZ77wErVtDLbdlS9yXkGI9p03Sjag5L\n7F138cjddlu9MXO42BYvBiZP5nnGJ0ygLRZt2HlnbY299lp62dOnA998Qy8XAMaP54mFGj5cl83g\nsNjfdx9fa6xhw8L1Hqn48ku9fnBYv595hqf2mVL6UPb44/SyZ8wAPv2UR1EeNw7YZx96uTvvrOfz\nRRfRy46ivBzktyulvDx8T0EQBEEQBKUUPM8jP142SveoIAiCIAhCY0OUNkEQBEEQhBwgSpsgCIIg\nCEIOEKVNEARBEAQhB4jSJgiCIAiCkANEaRMEQRAEQcgBorQJgiAIgiDkAFHaBEEQBEEQcoAobYIg\nCIIgCDlAlDZBEARBEIQcIEqbIAiCIAhCDhClTRAEQRAEIQeI0iYIgiAIgpADRGkTBEEQBEHIAaK0\nCYIgCIIg5ABR2gRBEARBEHKAKG2CIAiCIAg5QJQ2QRAEQRCEHCBKmyAIgiAIQg4QpU0QBEEQBCEH\niNImCIIgCIKQA0RpEwRBEARByAGitAmCIAiCIOQAUdoEQRAEQRBygChtgiAIgiAIOUCUNkEQBEEQ\nhBwgSpsgCIIgCEIOEKVNEARBEAQhB4jSJgiCIAiCkANEaRMEQRAEQcgBorQJgiAIgiDkAFHaBEEQ\nBEEQcoAobYIgCIIgCDlAlDZBEARBEIQcIEqbIAiCIAhCDhClTRAEQRAEIQeI0iYIgiAIgpADRGkT\nBEEQBEHIAaK0CYIgCIIg5ABR2gRBEARBEHKAKG2CIAiCIAg5QJQ2QRAEQRCEHCBKmyAIgiAIQg4Q\npU0QBEEQBCEHiNImCIIgCIKQA0RpEwRBEARByAGitAmCIAiCIOQAUdoEQRAEQRBygChtgiAIgiAI\nOUCUNkEQBEEQhBwgSpsgCIIgCEIOEKVNEARBEAQhB4jSJgiCIAiCkANEaRMEQRAEQcgBorQJgiAI\ngiDkAFHaBEEQBEEQcoAobYIgCIIgCDlAlDZBEARBEIQcIEqbIAiCIAhCDhClTRAEQRAEIQeI0iYI\ngiAIgpADRGkTBEEQBEHIAaK0CYIgCIIg5ABR2gRBEARBEHKAKG2CIAiCIAg5QJQ2QRAEQRCEHCBK\nmyAIgiAIQg4QpU0QBEEQBCEHiNImCIIgCIKQA0RpEwRBEARByAGitAmCIAiCIOQAUdoEQRAEQRBy\nAKvSppQ6QilVpZSarZS6KuXv7aWUqlZK/YLz+wiCIAiCIOQVNqVNKVUJ4G4ARwDoA2CwUqp3wt+7\nDcA/ASiu7yOUjkmTJpX6KwgZkXuXb+T+5Re5d4INTktbXwBfeJ73led5GwA8BeA4y9+7GMA4AEsY\nv4tQQmTxyS9y7/KN3L/8IvdOsMGptG0NYH7g/Td11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"text/plain": [ "<matplotlib.figure.Figure at 0x115281490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.plot(evo_00(beta_r(1-0.1),10000,1))\n", "plt.ylabel(\"1,1 element\")" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1133ca890>" ] }, "execution_count": 264, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bDjJ4sH1mzpUq4ojHo/GXm0vRhniiaEO3CgrsFwcQJ3v3Shs3umtKiczIzZUa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\nRCQGlLSJiIiIxICSNhEREZEY+H+OS3ywOMMzcwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10abc8a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.plot(evo_00(beta_r(1-0.09),1000,0.1))\n", "plt.ylabel(\"1,1 element\")" ] }, { "cell_type": "code", "execution_count": 252, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.+0.j, 1.+0.j],\n", " [ 1.+0.j, 0.+0.j]])" ] }, "execution_count": 252, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ns.pauli_matrices(1.0)" ] }, { "cell_type": "code", "execution_count": 253, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.+0.j, 1.+0.j],\n", " [ 1.+0.j, 0.+0.j],\n", " [ 0.+0.j, 0.-1.j],\n", " [ 0.+1.j, 0.+0.j]])" ] }, "execution_count": 253, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.append(np.array(ns.pauli_matrices(1)),np.array(ns.pauli_matrices(2)),axis=0)" ] }, { "cell_type": "code", "execution_count": 254, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evo_01(beta,x,deltax):\n", " \n", " evo_mat = np.array([[1,0],[0,1]])\n", " evo_01_list = np.array([])\n", " \n", " for xi in np.linspace(0, x, np.floor(x/deltax)):\n", " evo_mat = np.dot(sliced_evo(beta,xi,deltax), evo_mat)\n", " evo_01_list = np.append(evo_01_list, np.absolute(evo_mat[0,1])2)\n", " #print xi\n", " #print np.absolute(evo_mat[0,0])2\n", " #print \"\"\n", " \n", " return evo_01_list" ] }, { "cell_type": "code", "execution_count": 255, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x13539ea90>" ] }, "execution_count": 255, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bKUYJTS+McnvAOBkp0opEH7oq9mk03C8+DjPt55/Pfy978jJiHKbAMW28e2kU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+zHqhyEY7MYMo7LD6xkWLL+CMsDHjC9J7Lit55mts3Gq/jnx0V6UFhYPFB7HGH5/nvyNtBUkcb6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1P69ajxxNN7Dh3FVCgLKxnhk3Yst4jBxJB2uEPcKm8tjdwzGWOWhfHwkDrjsy9g6PyBvWz57l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\nTK9cKxeW5LERqZbDtdacPMt5FKNZwDhdTd+ic/LYqM+YY2Ac85jMLMljt1B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"text/plain": [ "<matplotlib.figure.Figure at 0x1353179d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.plot(evo_01(beta_r,10000,1))\n", "plt.ylabel(\"1,2 element\")" ] }, { "cell_type": "code", "execution_count": 256, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.56206046+0.81906661j, -0.09061432-0.07075996j])" ] }, "execution_count": 256, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(evo(beta_r,5000,1),np.array([1,0]))" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.56206046+0.81906661j, 0.09061432-0.07075996j],\n", " [-0.09061432-0.07075996j, 0.56206046-0.81906661j]])" ] }, "execution_count": 257, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evo(beta_r,5000,1)" ] }, { "cell_type": "code", "execution_count": 258, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.45279719+0.j, 0.00967713+0.j],\n", " [ 0.00967713+0.j, 0.45279719+0.j]])" ] }, "execution_count": 258, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hamil(beta_r,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
trangel/Data-Science	deep_learning_ai/Tensorflow+Tutorial+dropout.ipynb	1	96963	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial\n", "\n", "Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up your machine learning development significantly. All of these frameworks also have a lot of documentation, which you should feel free to read. In this assignment, you will learn to do the following in TensorFlow: \n", "\n", "- Initialize variables\n", "- Start your own session\n", "- Train algorithms \n", "- Implement a Neural Network\n", "\n", "Programing frameworks can not only shorten your coding time, but sometimes also perform optimizations that speed up your code. \n", "\n", "## 1 - Exploring the Tensorflow Library\n", "\n", "To start, you will import the library:\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "from tensorflow.python.framework import ops\n", "from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict\n", "\n", "%matplotlib inline\n", "np.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have imported the library, we will walk you through its different applications. You will start with an example, where we compute for you the loss of one training example. \n", "$$loss = \\mathcal{L}(\\hat{y}, y) = (\\hat y^{(i)} - y^{(i)})^2 \\tag{1}$$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "y_hat = tf.constant(36, name='y_hat') # Define y_hat constant. Set to 36.\n", "y = tf.constant(39, name='y') # Define y. Set to 39\n", "\n", "loss = tf.Variable((y - y_hat)2, name='loss') # Create a variable for the loss\n", "\n", "init = tf.global_variables_initializer() # When init is run later (session.run(init)),\n", " # the loss variable will be initialized and ready to be computed\n", "with tf.Session() as session: # Create a session and print the output\n", " session.run(init) # Initializes the variables\n", " print(session.run(loss)) # Prints the loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Writing and running programs in TensorFlow has the following steps:\n", "\n", "1. Create Tensors (variables) that are not yet executed/evaluated. \n", "2. Write operations between those Tensors.\n", "3. Initialize your Tensors. \n", "4. Create a Session. \n", "5. Run the Session. This will run the operations you'd written above. \n", "\n", "Therefore, when we created a variable for the loss, we simply defined the loss as a function of other quantities, but did not evaluate its value. To evaluate it, we had to run `init=tf.global_variables_initializer()`. That initialized the loss variable, and in the last line we were finally able to evaluate the value of `loss` and print its value.\n", "\n", "Now let us look at an easy example. Run the cell below:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"Mul:0\", shape=(), dtype=int32)\n" ] } ], "source": [ "a = tf.constant(2)\n", "b = tf.constant(10)\n", "c = tf.multiply(a,b)\n", "print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, you will not see 20! You got a tensor saying that the result is a tensor that does not have the shape attribute, and is of type \"int32\". All you did was put in the 'computation graph', but you have not run this computation yet. In order to actually multiply the two numbers, you will have to create a session and run it." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "sess = tf.Session()\n", "print(sess.run(c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! To summarize, remember to initialize your variables, create a session and run the operations inside the session*. \n", "\n", "Next, you'll also have to know about placeholders. A placeholder is an object whose value you can specify only later. \n", "To specify values for a placeholder, you can pass in values by using a \"feed dictionary\" (`feed_dict` variable). Below, we created a placeholder for x. This allows us to pass in a number later when we run the session. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "# Change the value of x in the feed_dict\n", "\n", "x = tf.placeholder(tf.int64, name = 'x')\n", "print(sess.run(2 x, feed_dict = {x: 3}))\n", "sess.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you first defined `x` you did not have to specify a value for it. A placeholder is simply a variable that you will assign data to only later, when running the session. We say that you feed data to these placeholders when running the session. \n", "\n", "Here's what's happening: When you specify the operations needed for a computation, you are telling TensorFlow how to construct a computation graph. The computation graph can have some placeholders whose values you will specify only later. Finally, when you run the session, you are telling TensorFlow to execute the computation graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 - Linear function\n", "\n", "Lets start this programming exercise by computing the following equation: $Y = WX + b$, where $W$ and $X$ are random matrices and b is a random vector. \n", "\n", "Exercise: Compute $WX + b$ where $W, X$, and $b$ are drawn from a random normal distribution. W is of shape (4, 3), X is (3,1) and b is (4,1). As an example, here is how you would define a constant X that has shape (3,1):\n", "```python\n", "X = tf.constant(np.random.randn(3,1), name = \"X\")\n", "\n", "```\n", "You might find the following functions helpful: \n", "- tf.matmul(..., ...) to do a matrix multiplication\n", "- tf.add(..., ...) to do an addition\n", "- np.random.randn(...) to initialize randomly\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: linear_function\n", "\n", "def linear_function():\n", " \"\"\"\n", " Implements a linear function: \n", " Initializes W to be a random tensor of shape (4,3)\n", " Initializes X to be a random tensor of shape (3,1)\n", " Initializes b to be a random tensor of shape (4,1)\n", " Returns: \n", " result -- runs the session for Y = WX + b \n", " \"\"\"\n", " \n", " np.random.seed(1)\n", " \n", " ### START CODE HERE ### (4 lines of code)\n", " X = tf.constant(np.random.randn(3,1), name = 'X')\n", " W = tf.constant(np.random.randn(4,3), name = 'W')\n", " b = tf.constant(np.random.randn(4,1), name = 'b')\n", " Y = tf.constant(np.random.randn(4,1), name = 'Y')\n", " ### END CODE HERE ### \n", " \n", " # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate\n", " \n", " ### START CODE HERE ###\n", " sess = tf.Session()\n", " result = sess.run(tf.add(tf.matmul(W,X),b))\n", " ### END CODE HERE ### \n", " \n", " # close the session \n", " sess.close()\n", "\n", " return result" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "result = [[-2.15657382]\n", " [ 2.95891446]\n", " [-1.08926781]\n", " [-0.84538042]]\n" ] } ], "source": [ "print( \"result = \" + str(linear_function()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Expected Output : \n", "\n", "<table> \n", "<tr> \n", "<td>\n", "result\n", "</td>\n", "<td>\n", "[[-2.15657382]\n", " [ 2.95891446]\n", " [-1.08926781]\n", " [-0.84538042]]\n", "</td>\n", "</tr> \n", "\n", "</table> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 - Computing the sigmoid \n", "Great! You just implemented a linear function. Tensorflow offers a variety of commonly used neural network functions like `tf.sigmoid` and `tf.softmax`. For this exercise lets compute the sigmoid function of an input. \n", "\n", "You will do this exercise using a placeholder variable `x`. When running the session, you should use the feed dictionary to pass in the input `z`. In this exercise, you will have to (i) create a placeholder `x`, (ii) define the operations needed to compute the sigmoid using `tf.sigmoid`, and then (iii) run the session. \n", "\n", "* Exercise : Implement the sigmoid function below. You should use the following: \n", "\n", "- `tf.placeholder(tf.float32, name = \"...\")`\n", "- `tf.sigmoid(...)`\n", "- `sess.run(..., feed_dict = {x: z})`\n", "\n", "\n", "Note that there are two typical ways to create and use sessions in tensorflow: \n", "\n", "Method 1:\n", "```python\n", "sess = tf.Session()\n", "# Run the variables initialization (if needed), run the operations\n", "result = sess.run(..., feed_dict = {...})\n", "sess.close() # Close the session\n", "```\n", "Method 2:\n", "```python\n", "with tf.Session() as sess: \n", " # run the variables initialization (if needed), run the operations\n", " result = sess.run(..., feed_dict = {...})\n", " # This takes care of closing the session for you :)\n", "```\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: sigmoid\n", "\n", "def sigmoid(z):\n", " \"\"\"\n", " Computes the sigmoid of z\n", " \n", " Arguments:\n", " z -- input value, scalar or vector\n", " \n", " Returns: \n", " results -- the sigmoid of z\n", " \"\"\"\n", " \n", " ### START CODE HERE ### ( approx. 4 lines of code)\n", " # Create a placeholder for x. Name it 'x'.\n", " x = tf.placeholder(tf.float32, name='x')\n", "\n", " # compute sigmoid(x)\n", " sigmoid = tf.sigmoid(x)\n", "\n", " # Create a session, and run it. Please use the method 2 explained above. \n", " # You should use a feed_dict to pass z's value to x. \n", " with tf.Session() as sess:\n", " # Run session and call the output \"result\"\n", " result = sess.run(sigmoid, feed_dict = {x: z})\n", " \n", " ### END CODE HERE ###\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sigmoid(0) = 0.5\n", "sigmoid(12) = 0.999994\n" ] } ], "source": [ "print (\"sigmoid(0) = \" + str(sigmoid(0)))\n", "print (\"sigmoid(12) = \" + str(sigmoid(12)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Expected Output *: \n", "\n", "<table> \n", "<tr> \n", "<td>\n", "sigmoid(0)\n", "</td>\n", "<td>\n", "0.5\n", "</td>\n", "</tr>\n", "<tr> \n", "<td>\n", "sigmoid(12)\n", "</td>\n", "<td>\n", "0.999994\n", "</td>\n", "</tr> \n", "\n", "</table> " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<font color='blue'>\n", "To summarize, you how know how to:\n", "1. Create placeholders\n", "2. Specify the computation graph corresponding to operations you want to compute\n", "3. Create the session\n", "4. Run the session, using a feed dictionary if necessary to specify placeholder variables' values. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 - Computing the Cost\n", "\n", "You can also use a built-in function to compute the cost of your neural network. So instead of needing to write code to compute this as a function of $a^{[2](i)}$ and $y^{(i)}$ for i=1...m: \n", "$$ J = - \\frac{1}{m} \\sum_{i = 1}^m \\large ( \\small y^{(i)} \\log a^{ [2] (i)} + (1-y^{(i)})\\log (1-a^{ [2] (i)} )\\large )\\small\\tag{2}$$\n", "\n", "you can do it in one line of code in tensorflow!\n", "\n", "Exercise: Implement the cross entropy loss. The function you will use is: \n", "\n", "\n", "- `tf.nn.sigmoid_cross_entropy_with_logits(logits = ..., labels = ...)`\n", "\n", "Your code should input `z`, compute the sigmoid (to get `a`) and then compute the cross entropy cost $J$. All this can be done using one call to `tf.nn.sigmoid_cross_entropy_with_logits`, which computes\n", "\n", "$$- \\frac{1}{m} \\sum_{i = 1}^m \\large ( \\small y^{(i)} \\log \\sigma(z^{[2](i)}) + (1-y^{(i)})\\log (1-\\sigma(z^{[2](i)})\\large )\\small\\tag{2}$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: cost\n", "\n", "def cost(logits, labels):\n", " \"\"\"\n", " Computes the cost using the sigmoid cross entropy\n", " \n", " Arguments:\n", " logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)\n", " labels -- vector of labels y (1 or 0) \n", " \n", " Note: What we've been calling \"z\" and \"y\" in this class are respectively called \"logits\" and \"labels\" \n", " in the TensorFlow documentation. So logits will feed into z, and labels into y. \n", " \n", " Returns:\n", " cost -- runs the session of the cost (formula (2))\n", " \"\"\"\n", " \n", " ### START CODE HERE ### \n", " \n", " # Create the placeholders for \"logits\" (z) and \"labels\" (y) (approx. 2 lines)\n", " z = tf.placeholder(tf.float32, name='logits')\n", " y = tf.placeholder(tf.float32, name='labels')\n", " \n", " # Use the loss function (approx. 1 line)\n", " cost = tf.nn.sigmoid_cross_entropy_with_logits(logits = z, labels = y)\n", " \n", " # Create a session (approx. 1 line). See method 1 above.\n", " sess = tf.Session()\n", " \n", " # Run the session (approx. 1 line).\n", " cost = sess.run(cost, feed_dict = {z:logits, y:labels})\n", " \n", " # Close the session (approx. 1 line). See method 1 above.\n", " sess.close()\n", " \n", " ### END CODE HERE ###\n", " \n", " return cost" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cost = [ 1.00538719 1.03664088 0.41385433 0.39956614]\n" ] } ], "source": [ "logits = sigmoid(np.array([0.2,0.4,0.7,0.9]))\n", "cost = cost(logits, np.array([0,0,1,1]))\n", "print (\"cost = \" + str(cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Expected Output : \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " cost\n", " </td>\n", " <td>\n", " [ 1.00538719 1.03664088 0.41385433 0.39956614]\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.4 - Using One Hot encodings\n", "\n", "Many times in deep learning you will have a y vector with numbers ranging from 0 to C-1, where C is the number of classes. If C is for example 4, then you might have the following y vector which you will need to convert as follows:\n", "\n", "\n", "<img src=\"images/onehot.png\" style=\"width:600px;height:150px;\">\n", "\n", "This is called a \"one hot\" encoding, because in the converted representation exactly one element of each column is \"hot\" (meaning set to 1). To do this conversion in numpy, you might have to write a few lines of code. In tensorflow, you can use one line of code: \n", "\n", "- tf.one_hot(labels, depth, axis) \n", "\n", "Exercise: Implement the function below to take one vector of labels and the total number of classes $C$, and return the one hot encoding. Use `tf.one_hot()` to do this. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: one_hot_matrix\n", "\n", "def one_hot_matrix(labels, C):\n", " \"\"\"\n", " Creates a matrix where the i-th row corresponds to the ith class number and the jth column\n", " corresponds to the jth training example. So if example j had a label i. Then entry (i,j) \n", " will be 1. \n", " \n", " Arguments:\n", " labels -- vector containing the labels \n", " C -- number of classes, the depth of the one hot dimension\n", " \n", " Returns: \n", " one_hot -- one hot matrix\n", " \"\"\"\n", " \n", " ### START CODE HERE ###\n", " \n", " # Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)\n", " C = tf.constant(C, name='C')\n", " \n", " # Use tf.one_hot, be careful with the axis (approx. 1 line)\n", " one_hot_matrix = tf.one_hot(labels, C, axis=0)\n", " \n", " # Create the session (approx. 1 line)\n", " sess = tf.Session()\n", " \n", " # Run the session (approx. 1 line)\n", " one_hot = sess.run(one_hot_matrix)\n", " \n", " # Close the session (approx. 1 line). See method 1 above.\n", " sess.close()\n", " \n", " ### END CODE HERE ###\n", " \n", " return one_hot" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "one_hot = [[ 0. 0. 0. 1. 0. 0.]\n", " [ 1. 0. 0. 0. 0. 1.]\n", " [ 0. 1. 0. 0. 1. 0.]\n", " [ 0. 0. 1. 0. 0. 0.]]\n" ] } ], "source": [ "labels = np.array([1,2,3,0,2,1])\n", "one_hot = one_hot_matrix(labels, C = 4)\n", "print (\"one_hot = \" + str(one_hot))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output: \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " one_hot\n", " </td>\n", " <td>\n", " [[ 0. 0. 0. 1. 0. 0.]\n", " [ 1. 0. 0. 0. 0. 1.]\n", " [ 0. 1. 0. 0. 1. 0.]\n", " [ 0. 0. 1. 0. 0. 0.]]\n", " </td>\n", " </tr>\n", "\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.5 - Initialize with zeros and ones\n", "\n", "Now you will learn how to initialize a vector of zeros and ones. The function you will be calling is `tf.ones()`. To initialize with zeros you could use tf.zeros() instead. These functions take in a shape and return an array of dimension shape full of zeros and ones respectively. \n", "\n", "Exercise: Implement the function below to take in a shape and to return an array (of the shape's dimension of ones). \n", "\n", " - tf.ones(shape)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: ones\n", "\n", "def ones(shape):\n", " \"\"\"\n", " Creates an array of ones of dimension shape\n", " \n", " Arguments:\n", " shape -- shape of the array you want to create\n", " \n", " Returns: \n", " ones -- array containing only ones\n", " \"\"\"\n", " \n", " ### START CODE HERE ###\n", " \n", " # Create \"ones\" tensor using tf.ones(...). (approx. 1 line)\n", " ones = tf.ones(shape)\n", " \n", " # Create the session (approx. 1 line)\n", " sess = tf.Session()\n", " \n", " # Run the session to compute 'ones' (approx. 1 line)\n", " ones = sess.run(ones)\n", " \n", " # Close the session (approx. 1 line). See method 1 above.\n", " sess.close()\n", " \n", " ### END CODE HERE ###\n", " return ones" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ones = [ 1. 1. 1.]\n" ] } ], "source": [ "print (\"ones = \" + str(ones([3])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output:\n", "\n", "<table> \n", " <tr> \n", " <td>\n", " ones\n", " </td>\n", " <td>\n", " [ 1. 1. 1.]\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2 - Building your first neural network in tensorflow\n", "\n", "In this part of the assignment you will build a neural network using tensorflow. Remember that there are two parts to implement a tensorflow model:\n", "\n", "- Create the computation graph\n", "- Run the graph\n", "\n", "Let's delve into the problem you'd like to solve!\n", "\n", "### 2.0 - Problem statement: SIGNS Dataset\n", "\n", "One afternoon, with some friends we decided to teach our computers to decipher sign language. We spent a few hours taking pictures in front of a white wall and came up with the following dataset. It's now your job to build an algorithm that would facilitate communications from a speech-impaired person to someone who doesn't understand sign language.\n", "\n", "- Training set: 1080 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (180 pictures per number).\n", "- Test set: 120 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (20 pictures per number).\n", "\n", "Note that this is a subset of the SIGNS dataset. The complete dataset contains many more signs.\n", "\n", "Here are examples for each number, and how an explanation of how we represent the labels. These are the original pictures, before we lowered the image resolutoion to 64 by 64 pixels.\n", "<img src=\"images/hands.png\" style=\"width:800px;height:350px;\"><caption><center> <u><font color='purple'> Figure 1</u><font color='purple'>: SIGNS dataset <br> <font color='black'> </center>\n", "\n", "\n", "Run the following code to load the dataset." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Loading the dataset\n", "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change the index below and run the cell to visualize some examples in the dataset." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y = 5\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc55956c358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Example of a picture\n", "index = 0\n", "plt.imshow(X_train_orig[index])\n", "print (\"y = \" + str(np.squeeze(Y_train_orig[:, index])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual you flatten the image dataset, then normalize it by dividing by 255. On top of that, you will convert each label to a one-hot vector as shown in Figure 1. Run the cell below to do so." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of training examples = 1080\n", "number of test examples = 120\n", "X_train shape: (12288, 1080)\n", "Y_train shape: (6, 1080)\n", "X_test shape: (12288, 120)\n", "Y_test shape: (6, 120)\n" ] } ], "source": [ "# Flatten the training and test images\n", "X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T\n", "X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T\n", "# Normalize image vectors\n", "X_train = X_train_flatten/255.\n", "X_test = X_test_flatten/255.\n", "# Convert training and test labels to one hot matrices\n", "Y_train = convert_to_one_hot(Y_train_orig, 6)\n", "Y_test = convert_to_one_hot(Y_test_orig, 6)\n", "\n", "print (\"number of training examples = \" + str(X_train.shape[1]))\n", "print (\"number of test examples = \" + str(X_test.shape[1]))\n", "print (\"X_train shape: \" + str(X_train.shape))\n", "print (\"Y_train shape: \" + str(Y_train.shape))\n", "print (\"X_test shape: \" + str(X_test.shape))\n", "print (\"Y_test shape: \" + str(Y_test.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that 12288 comes from $64 \\times 64 \\times 3$. Each image is square, 64 by 64 pixels, and 3 is for the RGB colors. Please make sure all these shapes make sense to you before continuing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your goal is to build an algorithm capable of recognizing a sign with high accuracy. To do so, you are going to build a tensorflow model that is almost the same as one you have previously built in numpy for cat recognition (but now using a softmax output). It is a great occasion to compare your numpy implementation to the tensorflow one. \n", "\n", "The model** is LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX. The SIGMOID output layer has been converted to a SOFTMAX. A SOFTMAX layer generalizes SIGMOID to when there are more than two classes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 - Create placeholders\n", "\n", "Your first task is to create placeholders for `X` and `Y`. This will allow you to later pass your training data in when you run your session. \n", "\n", "Exercise: Implement the function below to create the placeholders in tensorflow." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: create_placeholders\n", "\n", "def create_placeholders(n_x, n_y):\n", " \"\"\"\n", " Creates the placeholders for the tensorflow session.\n", " \n", " Arguments:\n", " n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)\n", " n_y -- scalar, number of classes (from 0 to 5, so -> 6)\n", " \n", " Returns:\n", " X -- placeholder for the data input, of shape [n_x, None] and dtype \"float\"\n", " Y -- placeholder for the input labels, of shape [n_y, None] and dtype \"float\"\n", " \n", " Tips:\n", " - You will use None because it let's us be flexible on the number of examples you will for the placeholders.\n", " In fact, the number of examples during test/train is different.\n", " \"\"\"\n", "\n", " ### START CODE HERE ### (approx. 2 lines)\n", " X = tf.placeholder(dtype=tf.float32, shape=[n_x, None], name='X')\n", " Y = tf.placeholder(dtype=tf.float32, shape=[n_y, None], name='Y')\n", " keep_prob = tf.placeholder(dtype=tf.float32, shape=[2], name='keep_prob')\n", " ### END CODE HERE ###\n", " \n", " return X, Y, keep_prob" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X = Tensor(\"X_1:0\", shape=(12288, ?), dtype=float32)\n", "Y = Tensor(\"Y_1:0\", shape=(6, ?), dtype=float32)\n" ] } ], "source": [ "X, Y, keep_prob = create_placeholders(12288, 6)\n", "print (\"X = \" + str(X))\n", "print (\"Y = \" + str(Y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output: \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " X\n", " </td>\n", " <td>\n", " Tensor(\"Placeholder_1:0\", shape=(12288, ?), dtype=float32) (not necessarily Placeholder_1)\n", " </td>\n", " </tr>\n", " <tr> \n", " <td>\n", " Y\n", " </td>\n", " <td>\n", " Tensor(\"Placeholder_2:0\", shape=(10, ?), dtype=float32) (not necessarily Placeholder_2)\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 - Initializing the parameters\n", "\n", "Your second task is to initialize the parameters in tensorflow.\n", "\n", "Exercise: Implement the function below to initialize the parameters in tensorflow. You are going use Xavier Initialization for weights and Zero Initialization for biases. The shapes are given below. As an example, to help you, for W1 and b1 you could use: \n", "\n", "```python\n", "W1 = tf.get_variable(\"W1\", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))\n", "b1 = tf.get_variable(\"b1\", [25,1], initializer = tf.zeros_initializer())\n", "```\n", "Please use `seed = 1` to make sure your results match ours." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: initialize_parameters\n", "\n", "def initialize_parameters():\n", " \"\"\"\n", " Initializes parameters to build a neural network with tensorflow. The shapes are:\n", " W1 : [25, 12288]\n", " b1 : [25, 1]\n", " W2 : [12, 25]\n", " b2 : [12, 1]\n", " W3 : [6, 12]\n", " b3 : [6, 1]\n", " \n", " Returns:\n", " parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3\n", " \"\"\"\n", " \n", " tf.set_random_seed(1) # so that your \"random\" numbers match ours\n", " \n", " ### START CODE HERE ### (approx. 6 lines of code)\n", " W1 = tf.get_variable(\"W1\", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", " b1 = tf.get_variable(\"b1\", [25,1], initializer = tf.zeros_initializer())\n", " W2 = tf.get_variable('W2', [12,25], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", " b2 = tf.get_variable('b2', [12,1], initializer = tf.zeros_initializer())\n", " W3 = tf.get_variable('W3', [6,12], initializer = tf.contrib.layers.xavier_initializer(seed=1))\n", " b3 = tf.get_variable('b3', [6,1], initializer = tf.zeros_initializer())\n", " ### END CODE HERE ###\n", "\n", " parameters = {\"W1\": W1,\n", " \"b1\": b1,\n", " \"W2\": W2,\n", " \"b2\": b2,\n", " \"W3\": W3,\n", " \"b3\": b3}\n", " \n", " return parameters" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = <tf.Variable 'W1:0' shape=(25, 12288) dtype=float32_ref>\n", "b1 = <tf.Variable 'b1:0' shape=(25, 1) dtype=float32_ref>\n", "W2 = <tf.Variable 'W2:0' shape=(12, 25) dtype=float32_ref>\n", "b2 = <tf.Variable 'b2:0' shape=(12, 1) dtype=float32_ref>\n" ] } ], "source": [ "tf.reset_default_graph()\n", "with tf.Session() as sess:\n", " parameters = initialize_parameters()\n", " print(\"W1 = \" + str(parameters[\"W1\"]))\n", " print(\"b1 = \" + str(parameters[\"b1\"]))\n", " print(\"W2 = \" + str(parameters[\"W2\"]))\n", " print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output: \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " W1\n", " </td>\n", " <td>\n", " < tf.Variable 'W1:0' shape=(25, 12288) dtype=float32_ref >\n", " </td>\n", " </tr>\n", " <tr> \n", " <td>\n", " b1\n", " </td>\n", " <td>\n", " < tf.Variable 'b1:0' shape=(25, 1) dtype=float32_ref >\n", " </td>\n", " </tr>\n", " <tr> \n", " <td>\n", " W2\n", " </td>\n", " <td>\n", " < tf.Variable 'W2:0' shape=(12, 25) dtype=float32_ref >\n", " </td>\n", " </tr>\n", " <tr> \n", " <td>\n", " b2\n", " </td>\n", " <td>\n", " < tf.Variable 'b2:0' shape=(12, 1) dtype=float32_ref >\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the parameters haven't been evaluated yet." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 - Forward propagation in tensorflow \n", "\n", "You will now implement the forward propagation module in tensorflow. The function will take in a dictionary of parameters and it will complete the forward pass. The functions you will be using are: \n", "\n", "- `tf.add(...,...)` to do an addition\n", "- `tf.matmul(...,...)` to do a matrix multiplication\n", "- `tf.nn.relu(...)` to apply the ReLU activation\n", "\n", "Question: Implement the forward pass of the neural network. We commented for you the numpy equivalents so that you can compare the tensorflow implementation to numpy. It is important to note that the forward propagation stops at `z3`. The reason is that in tensorflow the last linear layer output is given as input to the function computing the loss. Therefore, you don't need `a3`!\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: forward_propagation\n", "\n", "def forward_propagation(X, parameters, keep_prob):\n", " \"\"\"\n", " Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX\n", " \n", " Arguments:\n", " X -- input dataset placeholder, of shape (input size, number of examples)\n", " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\"\n", " the shapes are given in initialize_parameters\n", "\n", " Returns:\n", " Z3 -- the output of the last LINEAR unit\n", " \"\"\"\n", " \n", " # Retrieve the parameters from the dictionary \"parameters\" \n", " W1 = parameters['W1']\n", " b1 = parameters['b1']\n", " W2 = parameters['W2']\n", " b2 = parameters['b2']\n", " W3 = parameters['W3']\n", " b3 = parameters['b3']\n", " \n", " \n", " ### START CODE HERE ### (approx. 5 lines) # Numpy Equivalents:\n", " Z1 = tf.add(tf.matmul(W1, X), b1) # Z1 = np.dot(W1, X) + b1\n", " A1 = tf.nn.relu(Z1) # A1 = relu(Z1)\n", " A1_dropout = tf.nn.dropout(A1, keep_prob[0]) # apply dropout ()\n", " Z2 = tf.add(tf.matmul(W2, A1_dropout), b2) # Z2 = np.dot(W2, a1) + b2\n", " A2 = tf.nn.relu(Z2) # A2 = relu(Z2)\n", " A2_dropout = tf.nn.dropout(A2, keep_prob[1]) # apply dropout ()\n", " Z3 = tf.add(tf.matmul(W3, A2_dropout), b3) # Z3 = np.dot(W3,Z2) + b3\n", " ### END CODE HERE ###\n", " \n", " return Z3" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Z3 = Tensor(\"Add_2:0\", shape=(6, ?), dtype=float32)\n" ] } ], "source": [ "tf.reset_default_graph()\n", "\n", "with tf.Session() as sess:\n", " X, Y, keep_prob = create_placeholders(12288, 6)\n", " parameters = initialize_parameters()\n", " Z3 = forward_propagation(X, parameters, keep_prob)\n", " print(\"Z3 = \" + str(Z3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output: \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " Z3\n", " </td>\n", " <td>\n", " Tensor(\"Add_2:0\", shape=(6, ?), dtype=float32)\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may have noticed that the forward propagation doesn't output any cache. You will understand why below, when we get to brackpropagation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Compute cost\n", "\n", "As seen before, it is very easy to compute the cost using:\n", "```python\n", "tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = ..., labels = ...))\n", "```\n", "Question: Implement the cost function below. \n", "- It is important to know that the \"`logits`\" and \"`labels`\" inputs of `tf.nn.softmax_cross_entropy_with_logits` are expected to be of shape (number of examples, num_classes). We have thus transposed Z3 and Y for you.\n", "- Besides, `tf.reduce_mean` basically does the summation over the examples." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: compute_cost \n", "\n", "def compute_cost(Z3, Y):\n", " \"\"\"\n", " Computes the cost\n", " \n", " Arguments:\n", " Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)\n", " Y -- \"true\" labels vector placeholder, same shape as Z3\n", " \n", " Returns:\n", " cost - Tensor of the cost function\n", " \"\"\"\n", " \n", " # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)\n", " logits = tf.transpose(Z3)\n", " labels = tf.transpose(Y)\n", " \n", " ### START CODE HERE ### (1 line of code)\n", " cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))\n", " ### END CODE HERE ###\n", " \n", " return cost" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cost = Tensor(\"Mean:0\", shape=(), dtype=float32)\n" ] } ], "source": [ "tf.reset_default_graph()\n", "\n", "with tf.Session() as sess:\n", " X, Y, keep_prob = create_placeholders(12288, 6)\n", " parameters = initialize_parameters()\n", " Z3 = forward_propagation(X, parameters, keep_prob)\n", " cost = compute_cost(Z3, Y)\n", " print(\"cost = \" + str(cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output: \n", "\n", "<table> \n", " <tr> \n", " <td>\n", " cost\n", " </td>\n", " <td>\n", " Tensor(\"Mean:0\", shape=(), dtype=float32)\n", " </td>\n", " </tr>\n", "\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 - Backward propagation & parameter updates\n", "\n", "This is where you become grateful to programming frameworks. All the backpropagation and the parameters update is taken care of in 1 line of code. It is very easy to incorporate this line in the model.\n", "\n", "After you compute the cost function. You will create an \"`optimizer`\" object. You have to call this object along with the cost when running the tf.session. When called, it will perform an optimization on the given cost with the chosen method and learning rate.\n", "\n", "For instance, for gradient descent the optimizer would be:\n", "```python\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost)\n", "```\n", "\n", "To make the optimization you would do:\n", "```python\n", "_ , c = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n", "```\n", "\n", "This computes the backpropagation by passing through the tensorflow graph in the reverse order. From cost to inputs.\n", "\n", "Note When coding, we often use `_` as a \"throwaway\" variable to store values that we won't need to use later. Here, `_` takes on the evaluated value of `optimizer`, which we don't need (and `c` takes the value of the `cost` variable). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6 - Building the model\n", "\n", "Now, you will bring it all together! \n", "\n", "Exercise: Implement the model. You will be calling the functions you had previously implemented." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,\n", " num_epochs = 3000, minibatch_size = 32, print_cost = True):\n", " \"\"\"\n", " Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.\n", " \n", " Arguments:\n", " X_train -- training set, of shape (input size = 12288, number of training examples = 1080)\n", " Y_train -- test set, of shape (output size = 6, number of training examples = 1080)\n", " X_test -- training set, of shape (input size = 12288, number of training examples = 120)\n", " Y_test -- test set, of shape (output size = 6, number of test examples = 120)\n", " learning_rate -- learning rate of the optimization\n", " num_epochs -- number of epochs of the optimization loop\n", " minibatch_size -- size of a minibatch\n", " print_cost -- True to print the cost every 100 epochs\n", " \n", " Returns:\n", " parameters -- parameters learnt by the model. They can then be used to predict.\n", " \"\"\"\n", " \n", " ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables\n", " tf.set_random_seed(1) # to keep consistent results\n", " seed = 3 # to keep consistent results\n", " (n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)\n", " n_y = Y_train.shape[0] # n_y : output size\n", " costs = [] # To keep track of the cost\n", " \n", " # Create Placeholders of shape (n_x, n_y)\n", " ### START CODE HERE ### (1 line)\n", " X, Y, keep_prob = create_placeholders(n_x, n_y)\n", " ### END CODE HERE ###\n", "\n", " # Initialize parameters\n", " ### START CODE HERE ### (1 line)\n", " parameters = initialize_parameters()\n", " ### END CODE HERE ###\n", " \n", " # Forward propagation: Build the forward propagation in the tensorflow graph\n", " ### START CODE HERE ### (1 line)\n", " Z3 = forward_propagation(X, parameters, keep_prob)\n", " ### END CODE HERE ###\n", " \n", " # Cost function: Add cost function to tensorflow graph\n", " ### START CODE HERE ### (1 line)\n", " cost = compute_cost(Z3, Y)\n", " ### END CODE HERE ###\n", " \n", " # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.\n", " ### START CODE HERE ### (1 line)\n", " optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)\n", " ### END CODE HERE ###\n", " \n", " # Initialize all the variables\n", " init = tf.global_variables_initializer()\n", "\n", " keep_prob_train = [0.9, 1.0]\n", " \n", " # Start the session to compute the tensorflow graph\n", " with tf.Session() as sess:\n", " \n", " # Run the initialization\n", " sess.run(init)\n", " \n", " # Do the training loop\n", " for epoch in range(num_epochs):\n", "\n", " epoch_cost = 0. # Defines a cost related to an epoch\n", " num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\n", " seed = seed + 1\n", " minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n", "\n", " for minibatch in minibatches:\n", "\n", " # Select a minibatch\n", " (minibatch_X, minibatch_Y) = minibatch\n", " \n", " # IMPORTANT: The line that runs the graph on a minibatch.\n", " # Run the session to execute the \"optimizer\" and the \"cost\", the feedict should contain a minibatch for (X,Y).\n", " ### START CODE HERE ### (1 line)\n", " _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y, keep_prob:keep_prob_train})\n", " ### END CODE HERE ###\n", " \n", " epoch_cost += minibatch_cost / num_minibatches\n", "\n", " # Print the cost every epoch\n", " if print_cost == True and epoch % 100 == 0:\n", " print (\"Cost after epoch %i: %f\" % (epoch, epoch_cost))\n", " if print_cost == True and epoch % 5 == 0:\n", " costs.append(epoch_cost)\n", " \n", " # plot the cost\n", " plt.plot(np.squeeze(costs))\n", " plt.ylabel('cost')\n", " plt.xlabel('iterations (per tens)')\n", " plt.title(\"Learning rate =\" + str(learning_rate))\n", " plt.show()\n", "\n", " # lets save the parameters in a variable\n", " parameters = sess.run(parameters)\n", " print (\"Parameters have been trained!\")\n", "\n", " # Calculate the correct predictions\n", " correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))\n", "\n", " # Calculate accuracy on the test set\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", "\n", " print (\"Train Accuracy:\", accuracy.eval({X: X_train, Y: Y_train, keep_prob : [1.0, 1.0]}))\n", " print (\"Test Accuracy:\", accuracy.eval({X: X_test, Y: Y_test, keep_prob : [1.0, 1.0]}))\n", " \n", " return parameters" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Run the following cell to train your model! On our machine it takes about 5 minutes. Your \"Cost after epoch 100\" should be 1.016458. If it's not, don't waste time; interrupt the training by clicking on the square (⬛) in the upper bar of the notebook, and try to correct your code. If it is the correct cost, take a break and come back in 5 minutes!" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cost after epoch 0: 1.855791\n", "Cost after epoch 100: 1.338880\n", "Cost after epoch 200: 1.070437\n", "Cost after epoch 300: 0.961697\n", "Cost after epoch 400: 0.848927\n", "Cost after epoch 500: 0.733514\n", "Cost after epoch 600: 0.657072\n", "Cost after epoch 700: 0.650445\n", "Cost after epoch 800: 0.551813\n", "Cost after epoch 900: 0.490129\n", "Cost after epoch 1000: 0.495716\n", "Cost after epoch 1100: 0.460395\n", "Cost after epoch 1200: 0.470017\n", "Cost after epoch 1300: 0.433413\n", "Cost after epoch 1400: 0.385584\n", "Cost after epoch 1500: 0.386923\n", "Cost after epoch 1600: 0.343508\n", "Cost after epoch 1700: 0.351981\n", "Cost after epoch 1800: 0.331312\n", "Cost after epoch 1900: 0.399522\n", "Cost after epoch 2000: 0.287658\n", "Cost after epoch 2100: 0.303283\n", "Cost after epoch 2200: 0.258900\n", "Cost after epoch 2300: 0.270582\n", "Cost after epoch 2400: 0.286239\n", "Cost after epoch 2500: 0.223930\n", "Cost after epoch 2600: 0.285352\n", "Cost after epoch 2700: 0.254146\n", "Cost after epoch 2800: 0.247626\n", "Cost after epoch 2900: 0.256016\n" ] }, { "data": { "image/png": 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9NfVaHtP4rdp9lBmrqg64aCgtKF6Er4YhItHAk8CFwG5giYi8p6rrKl06T1Uv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\nCxjGGGNC8v8BjEfu4v46ofwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc4fde59e80>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Parameters have been trained!\n", "Train Accuracy: 0.986111\n", "Test Accuracy: 0.8\n" ] } ], "source": [ "parameters = model(X_train, Y_train, X_test, Y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expected Output:\n", "\n", "<table> \n", " <tr> \n", " <td>\n", " Train Accuracy\n", " </td>\n", " <td>\n", " 0.999074\n", " </td>\n", " </tr>\n", " <tr> \n", " <td>\n", " Test Accuracy\n", " </td>\n", " <td>\n", " 0.716667\n", " </td>\n", " </tr>\n", "\n", "</table>\n", "\n", "Amazing, your algorithm can recognize a sign representing a figure between 0 and 5 with 71.7% accuracy.\n", "\n", "Insights:\n", "- Your model seems big enough to fit the training set well. However, given the difference between train and test accuracy, you could try to add L2 or dropout regularization to reduce overfitting. \n", "- Think about the session as a block of code to train the model. Each time you run the session on a minibatch, it trains the parameters. In total you have run the session a large number of times (1500 epochs) until you obtained well trained parameters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.7 - Test with your own image (optional / ungraded exercise)\n", "\n", "Congratulations on finishing this assignment. You can now take a picture of your hand and see the output of your model. To do that:\n", " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", " 3. Write your image's name in the following code\n", " 4. Run the code and check if the algorithm is right!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "import scipy\n", "from PIL import Image\n", "from scipy import ndimage\n", "\n", "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n", "my_image = \"thumbs_up.jpg\"\n", "## END CODE HERE ##\n", "\n", "# We preprocess your image to fit your algorithm.\n", "fname = \"images/\" + my_image\n", "image = np.array(ndimage.imread(fname, flatten=False))\n", "my_image = scipy.misc.imresize(image, size=(64,64)).reshape((1, 64643)).T\n", "my_image_prediction = predict(my_image, parameters)\n", "\n", "plt.imshow(image)\n", "print(\"Your algorithm predicts: y = \" + str(np.squeeze(my_image_prediction)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You indeed deserved a \"thumbs-up\" although as you can see the algorithm seems to classify it incorrectly. The reason is that the training set doesn't contain any \"thumbs-up\", so the model doesn't know how to deal with it! We call that a \"mismatched data distribution\" and it is one of the various of the next course on \"Structuring Machine Learning Projects\"." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<font color='blue'>\n", "What you should remember:\n", "- Tensorflow is a programming framework used in deep learning\n", "- The two main object classes in tensorflow are Tensors and Operators. \n", "- When you code in tensorflow you have to take the following steps:\n", " - Create a graph containing Tensors (Variables, Placeholders ...) and Operations (tf.matmul, tf.add, ...)\n", " - Create a session\n", " - Initialize the session\n", " - Run the session to execute the graph\n", "- You can execute the graph multiple times as you've seen in model()\n", "- The backpropagation and optimization is automatically done when running the session on the \"optimizer\" object." ] } ], "metadata": { "coursera": { "course_slug": "deep-neural-network", "graded_item_id": "BFd89", "launcher_item_id": "AH2rK" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
anthonyng2/FX-Trading-with-Python-and-Oanda	Oanda v1 REST-oandapy/04.00 Order Management.ipynb	1	11756	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Account Information](03.00 Account Information.ipynb) \| [Contents](Index.ipynb) \| [Trade Management](05.00 Trade Management.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Order Management" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Orders](http://developer.oanda.com/rest-live/orders/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating Orders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`create_order(self, account_id, params)`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "import pandas as pd\n", "import oandapy\n", "import configparser\n", "\n", "config = configparser.ConfigParser()\n", "config.read('../config/config_v1.ini')\n", "account_id = config['oanda']['account_id']\n", "api_key = config['oanda']['api_key']\n", "\n", "oanda = oandapy.API(environment=\"practice\", \n", " access_token=api_key)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'2017-01-28T21:54:17.120062Z'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trade_expire = datetime.now() + timedelta(days=1)\n", "trade_expire = trade_expire.isoformat(\"T\") + \"Z\"\n", "trade_expire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a detailed explanation of the above, please refer to [Rates Information](02.00 Rates Information.ipynb)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'orderOpened': {'takeProfit': 0, 'side': 'buy', 'upperBound': 0, 'expiry': '2017-01-28T21:54:17.000000Z', 'trailingStop': 0, 'stopLoss': 0, 'id': 10618881403, 'lowerBound': 0, 'units': 1000}, 'price': 0.742, 'instrument': 'AUD_USD', 'time': '2017-01-27T13:54:18.000000Z'}\n" ] } ], "source": [ "response = oanda.create_order(account_id,\n", " instrument = \"AUD_USD\",\n", " units=1000,\n", " side=\"buy\",\n", " type=\"limit\",\n", " price=0.7420,\n", " expiry=trade_expire)\n", "print(response)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "expiry 2017-01-28T21:54:17.000000Z\n", "id 10618881403\n", "lowerBound 0\n", "side buy\n", "stopLoss 0\n", "takeProfit 0\n", "trailingStop 0\n", "units 1000\n", "upperBound 0\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(response[\"orderOpened\"])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "order_id = response[\"orderOpened\"]['id']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Open Orders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`get_orders(self, account_id, params)`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'orders': [{'takeProfit': 0, 'type': 'limit', 'side': 'buy', 'trailingStop': 0, 'price': 0.742, 'upperBound': 0, 'instrument': 'AUD_USD', 'id': 10618881403, 'units': 1000, 'expiry': '2017-01-28T21:54:17.000000Z', 'stopLoss': 0, 'lowerBound': 0, 'time': '2017-01-27T13:54:18.000000Z'}]}\n" ] } ], "source": [ "response = oanda.get_orders(account_id)\n", "print(response)" ] }, { "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>expiry</th>\n", " <th>id</th>\n", " <th>instrument</th>\n", " <th>lowerBound</th>\n", " <th>price</th>\n", " <th>side</th>\n", " <th>stopLoss</th>\n", " <th>takeProfit</th>\n", " <th>time</th>\n", " <th>trailingStop</th>\n", " <th>type</th>\n", " <th>units</th>\n", " <th>upperBound</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2017-01-28T21:54:17.000000Z</td>\n", " <td>10618881403</td>\n", " <td>AUD_USD</td>\n", " <td>0</td>\n", " <td>0.742</td>\n", " <td>buy</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2017-01-27T13:54:18.000000Z</td>\n", " <td>0</td>\n", " <td>limit</td>\n", " <td>1000</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " expiry id instrument lowerBound price \\\n", "0 2017-01-28T21:54:17.000000Z 10618881403 AUD_USD 0 0.742 \n", "\n", " side stopLoss takeProfit time trailingStop \\\n", "0 buy 0 0 2017-01-27T13:54:18.000000Z 0 \n", "\n", " type units upperBound \n", "0 limit 1000 0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(response['orders'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Specific Order Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`get_order(self, account_id, order_id, params)`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = oanda.get_orders(account_id)\n", "id = response['orders'][0]['id']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'expiry': '2017-01-28T21:54:17.000000Z',\n", " 'id': 10618881403,\n", " 'instrument': 'AUD_USD',\n", " 'lowerBound': 0,\n", " 'price': 0.742,\n", " 'side': 'buy',\n", " 'stopLoss': 0,\n", " 'takeProfit': 0,\n", " 'time': '2017-01-27T13:54:18.000000Z',\n", " 'trailingStop': 0,\n", " 'type': 'limit',\n", " 'units': 1000,\n", " 'upperBound': 0}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oanda.get_order(account_id, order_id=id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modify Order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`modify_order(self, account_id, order_id, params)`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = oanda.get_orders(account_id)\n", "id = response['orders'][0]['id']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'expiry': '2017-01-28T21:54:17.000000Z',\n", " 'id': 10618881403,\n", " 'instrument': 'AUD_USD',\n", " 'lowerBound': 0,\n", " 'price': 0.704,\n", " 'side': 'buy',\n", " 'stopLoss': 0,\n", " 'takeProfit': 0,\n", " 'time': '2017-01-27T13:54:20.000000Z',\n", " 'trailingStop': 0,\n", " 'type': 'limit',\n", " 'units': 1000,\n", " 'upperBound': 0}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oanda.modify_order(account_id, order_id=id, price=0.7040)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Close Order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`close_order(self, account_id, order_id, **params)`" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = oanda.get_orders(account_id)\n", "id = response['orders'][0]['id']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'id': 10618881403,\n", " 'instrument': 'AUD_USD',\n", " 'price': 0.704,\n", " 'side': 'buy',\n", " 'time': '2017-01-27T13:54:20.000000Z',\n", " 'type': 'BuyLimit',\n", " 'units': 1000}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oanda.close_order(account_id, order_id=id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now when we check the orders. The above order has been closed and removed without being filled. There is only one outstanding order now." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'orders': []}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "oanda.get_orders(account_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Account Information](03.00 Account Information.ipynb) \| [Contents](Index.ipynb) \| [Trade Management](05.00 Trade Management.ipynb) >" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
alexandrejaguar/strata-sv-2015-tutorial	resources/Running the Notebook Server.ipynb	3	14625	{ "metadata": { "name": "", "signature": "sha256:ee4b22b4c949fe21b3e5cda24f0916ba59d8c09443f4a897d98b96d4a73ac335" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Running the Notebook Server" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython notebook server is a custom web server that runs the notebook web application. Most of the time, users run the notebook server on their local computer using IPython's command line interface." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Starting the notebook server using the command line" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can start the notebook server from the command line (Terminal on Mac/Linux, CMD prompt on Windows) by running the following command: \n", "\n", " ipython notebook\n", "\n", "This will print some information about the notebook server in your terminal, including the URL of the web application (by default, `http://127.0.0.1:8888`). It will then open your default web browser to this URL.\n", "\n", "When the notebook opens, you will see the notebook dashboard, which will show a list of the notebooks and subdirectories in the directory where the notebook server was started. As of IPython 2.0, the dashboard allows you to navigate to different subdirectories. Because of this, it is no longer necessary to start a separate notebook server for each subdirectory. Most of the time, you will want to start a notebook server in the highest directory in your filesystem where notebooks can be found. Often this will be your home directory.\n", "\n", "You can start more than one notebook server at the same time. By default, the first notebook server starts on port 8888 and later notebook servers search for open ports near that one.\n", "\n", "You can also specify the port manually:\n", "\n", " ipython notebook --port 9999\n", "\n", "Or start notebook server without opening a web browser.\n", "\n", " ipython notebook --no-browser\n", "\n", "The notebook server has a number of other command line arguments that can be displayed with the `--help` flag: \n", "\n", " ipython notebook --help\n", "\n", "<div class=\"alert alert-failure\">\n", "It used to be possible to specify kernel options, such as <code>--pylab inline</code> from the command line. This is deprecated in IPython 2.0 and will be removed in IPython 3.0. To enable matplotlib based plotting for the Python kernel use the <code>%matplotlib</code> magic command.\n", "</div>\n", "\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Configuring the IPython Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook web server can also be configured using IPython profiles and configuration files. The Notebook web server configuration options are set in a file named `ipython_notebook_config.py` in your IPython profile directory. The profile directory is a subfolder of your IPython directory, which itself is usually `.ipython` in your home directory.\n", "\n", "You can display the location of your default profile directory by running the command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipython profile locate default" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/Users/bgranger/.ipython/profile_default\r\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default version of `ipython_notebook_config.py` lists all of the options available along with documentation for each. Changes made to that file will affect all notebook servers run under that profile. Command line options always override those set in configuration files.\n", "\n", "You can create a new profile:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipython profile create my_profile" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_config.py'\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_qtconsole_config.py'\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_notebook_config.py'\r\n", "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_nbconvert_config.py'\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then view its location:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!ipython profile locate my_profile" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/Users/bgranger/.ipython/profile_my_profile\r\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start the notebook server using a given profile, run the following:\n", "\n", " ipython notebook --profile=my_profile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More details about IPython configuration files and profiles can be found [here](http://ipython.org/ipython-doc/dev/config/intro.html)." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Securing the notebook server" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IPython Notebook allows arbitrary code execution on the computer running it. Thus, the notebook web server should never be run on the open internet without first securing it. By default, the notebook server only listens on local network interface (`127.0.0.1`) There are two steps required to secure the notebook server:\n", "\n", "1. Setting a password\n", "2. Encrypt network traffic using SSL" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Setting a password" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can protect your notebook server with a simple single password by setting the `NotebookApp.password` configurable. You can prepare a hashed password using the function `IPython.lib.passwd`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.lib import passwd\n", "password = passwd(\"secret\")\n", "password" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can then add this to your `ipython_notebook_config.py`:\n", "\n", "```python\n", "# Password to use for web authentication\n", "c = get_config()\n", "c.NotebookApp.password = \n", "u'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'\n", "```" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Using SSL/HTTPS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using a password, it is a good idea to also use SSL, so that your \n", "password is not sent unencrypted by your browser to the web server. When running the notebook on the public internet this is absolutely required.\n", "\n", "The first step is to generate an SSL certificate. A self-signed certificate can be generated with ``openssl``. For example, the following command will create a certificate valid for 365 days with both the key and certificate data written to the same file:\n", "\n", " openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mycert.pem -out mycert.pem\n", "\n", "In most cases, you should run this command in your profile directory, which will make it easy to use the generated key and certificate.\n", "\n", "When you connect to a notebook server over HTTPS using a self-signed certificate, your browser will warn you of a dangerous certificate because it is self-signed. If you want to have a fully compliant certificate that will not raise warnings, it is possible (but rather involved) to obtain one,\n", "as explained in detail in [this tutorial](http://arstechnica.com/security/news/2009/12/how-to-get-set-with-a-secure-sertificate-for-free.ars)\n", "\t\n", "When you enable SSL support, you will need to access the notebook server over ``https://``, rather than plain ``http://``. The startup message from the notebook server prints the correct URL, but it is easy to overlook and think the server is for some reason non-responsive.\n", "\n", "Once you have generated the key and certificate, you can configure the notebook server to use them, by adding the following to `ipython_notebook_config.py`:\n", "\n", "```python\n", "# The full path to an SSL/TLS certificate file.\n", "c.NotebookApp.certfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.crt'\n", "\n", "# The full path to a private key file for usage with SSL/TLS.\n", "c.NotebookApp.keyfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.key'\n", "```" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Running a public notebook server" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-error\">\n", "Don't run a public notebook server unless you first secure it with a password and SSL/HTTPS as described above\n", "</div>\n", "\n", "By default the notebook server only listens on the `localhost/127.0.0.1` network interface. If you want to connect to the notebook from another computers, or over the internet, you need to configure the notebook server to listen on all network interfaces and not open the browser. You will often also want to disable the automatic launching of the web browser.\n", "\n", "This can be accomplished by passing a command line options.\n", "\n", " ipython notebook --ip=* --no-browser\n", "\n", "You can also add the following to your`ipython_notebook_config.py` file:\n", "\n", "```python\n", "c.NotebookApp.ip = '*'\n", "c.NotebookApp.open_browser = False\n", "```" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Running with a different URL prefix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The notebook dashboard typically lives at the URL `http://localhost:8888/tree`. If you prefer that it lives, together with the \n", "rest of the notebook web application, under a base URL prefix, such as `http://localhost:8888/ipython/tree`, you can do so by adding the following lines to your `ipython_notebook_config.py` file.\n", "\n", "```python\n", "c.NotebookApp.base_url = '/ipython/'\n", "c.NotebookApp.webapp_settings = {'static_url_prefix':'/ipython/static/'}\n", "```" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Using a different notebook store" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the notebook server stores the notebook documents that it saves as files in the working directory of the notebook server, also known as the\n", "`notebook_dir`. This logic is implemented in the `FileNotebookManager` class. However, the server can be configured to use a different notebook manager class, which can store the notebooks in a different format. \n", "\n", "The [bookstore](https://github.com/rgbkrk/bookstore) package currently allows users to store notebooks on Rackspace CloudFiles or OpenStack Swift based object stores.\n", "\n", "Writing a notebook manager is as simple as extending the base class `NotebookManager`. The [simple_notebook_manager](https://github.com/khinsen/simple_notebook_manager) provides a great example\n", "of an in memory notebook manager, created solely for the purpose of\n", "illustrating the notebook manager API." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Known issues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When behind a proxy, especially if your system or browser is set to autodetect the proxy, the notebook web application might fail to connect to the server's websockets, and present you with a warning at startup. In this case, you need to configure your system not to use the proxy for the server's address.\n", "\n", "For example, in Firefox, go to the Preferences panel, Advanced section,\n", "Network tab, click 'Settings...', and add the address of the notebook server\n", "to the 'No proxy for' field." ] } ], "metadata": {} } ] }	bsd-3-clause
justanr/notebooks	hexagonal/refactoring_and_interfaces.ipynb	1	25224	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I've been thinking a lot about software achitecure lately. Not just thinking, because I wouldn't come up with these ideas on my own, but consuming a lot about it -- books, talks, slide decks, blog posts. And while thinking about all this, I've been hacking away at some projects in my spare time. And I noticed something, there's a lot of things in these projects that look a lot like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@app.route('/register', methods=['GET', 'POST'])\n", "def register():\n", " form = RegisterUserForm()\n", " \n", " if form.validate_on_submit():\n", " user = User()\n", " form.populate_obj(user)\n", " db.session.add(user)\n", " db.session.commit()\n", " return redirect('homepage')\n", " \n", " return render_template('register.html', form=form)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a bog standard user registration endpoint. We create a form, check if it's valid, shove that information on a user model and then into the database and redirect off. If it's not valid or if it wasn't submitted (the user just navigated to the page), we render out some HTML.\n", "\n", "It's all very basic, well trodden code. Besides, who wants to do registration again? It's boring. We want to do the interesting stuff. But there's some very real consequences to this code: \n", "\n", "## It's not testable\n", "\n", "Everything is wrapped up together, form validation, database stuff, rendering. Honestly, I'm not interested in testing if SQLAlchemy, WTForms of Jinja2 work -- they have their own tests. So testing this ends up looking like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@mock.patch('myapp.views.RegisterUserForm')\n", "@mock.patch('myapp.views.db')\n", "@mock.patch('myapp.views.redirect')\n", "@mock.patch('myapp.views.url_for')\n", "@mock.patch('myapp.views.render_template')\n", "def test_register_new_user(render, url_for, redirect, db, form):\n", " # TODO: Write test\n", " assert True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's even the point of this? We're just testing if Mock works at this point. There's actual things we can do to make it more testable, but before delving into that, \n", "\n", "## It hides logic\n", "\n", "If registering a user was solely about, \"Fill this form out and we'll shove it into a database\" there wouldn't be a blog post here. However, there is some logic hiding out here in the form:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RegisterUserForm(Form):\n", " def validate_username(self, field):\n", " if User.query.filter(User.username == field.data).count():\n", " raise ValidationError(\"Username in use already\")\n", " \n", " def validate_email(self, field):\n", " if User.query.filter(User.email == field.data).count():\n", " raise ValidationError(\"Email in use already\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we call `RegisterUserForm.validate_on_submit` it also runs these two methods. However, I'm not of the opinion that the form should talk to the database at all, let alone run validation against database contents. So, let's write a little test harness that can prove that an existing user with a given username and email causes us to not register:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from myapp.forms import RegisterUserForm\n", "from myapp.models import User\n", "\n", "from collections import namedtuple\n", "\n", "from unittest import mock\n", "\n", "FakeData = namedtuple('User', ['username', 'email', 'password', 'confirm_password'])\n", "\n", "def test_existing_username_fails_validation():\n", " test_data = FakeData('fred', '[email protected]', 'a', 'a')\n", " UserModel = mock.Mock()\n", " UserModel.query.filter.count.return_value = 1\n", " form = RegisterUserForm(obj=test_data)\n", " \n", " with mock.patch('myapp.forms.User', UserModel):\n", " form.validate()\n", " \n", " assert form.errors['username'] == \"Username in use already\"\n", " \n", "def test_existing_email_fails_validation():\n", " test_user = FakeUser('fred', '[email protected]', 'a', 'a')\n", " UserModel = mock.Mock()\n", " UserModel.query.filter.first.return_value = True\n", " form = RegisterUserForm(obj=test_user)\n", " \n", " with mock.patch('myapp.forms.User', UserModel):\n", " form.validate()\n", " \n", " assert form.errors['username'] == \"Email in use already\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If these pass -- which they should, but you may have to install `mock` if you're not on Python 3 -- I think we should move the username and email validation into their own callables that are independently testable:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def is_username_free(username):\n", " return User.query.filter(User.username == username).count() == 0\n", "\n", "def is_email_free(email):\n", " return User.query.filter(User.email == email).count() == 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then use these in the endpoint itself:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@app.route('/register', methods=['GET', 'POST'])\n", "def register():\n", " form = RegisterUserForm()\n", " \n", " if form.validate_on_submit():\n", " if not is_username_free(form.username.data):\n", " form.errors['username'] = ['Username in use already']\n", " return render_template('register.html', form=form)\n", " \n", " if not is_email_free(form.email.data):\n", " form.errors['email'] = ['Email in use already']\n", " return render_template('register.html', form=form)\n", " \n", " user = User()\n", " form.populate_obj(user)\n", " db.session.add(user)\n", " db.session.commit()\n", " return redirect('homepage')\n", " \n", " return render_template('register.html', form=form)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is really hard to test, so instead of even attempting that -- being honest, I spent the better part of an hour attempting to test the actual endpoint and it was just a complete mess -- let's extract out the actual logic and place it into it's own callable:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class OurValidationError(Exception):\n", " def __init__(self, msg, field):\n", " self.msg = msg\n", " self.field = field\n", "\n", "def register_user(username, email, password):\n", " if not is_username_free(username):\n", " raise OurValidationError('Username in use already', 'username')\n", " \n", " if not is_email_free(email):\n", " raise OurValidationError('Email in use already', 'email')\n", " \n", " user = User(username=username, email=email, password=password)\n", " db.session.add(user)\n", " db.session.commit()\n", " \n", " \n", "@app.route('/register', methods=['GET', 'POST'])\n", "def register_user_view():\n", " form = RegisterUserForm()\n", " \n", " if form.validate_on_submit():\n", " try:\n", " register_user(form.username.data, form.email.data, form.password.data)\n", " except OurValidationError as e:\n", " form.errors[e.field] = [e.msg]\n", " return render_template('register.html', form=form)\n", " else:\n", " return redirect('homepage')\n", " \n", " return render_template('register.html', form=form)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're beginning to see the fruits of our labors. These aren't the easiest functions to test, but there's less we need to mock out in order to test the actual logic we're after." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test_duplicated_user_raises_error():\n", " ChasteValidator = mock.Mock(return_value=False)\n", " \n", " with mock.patch('myapp.logic.is_username_free', ChasteValidator):\n", " with pytest.raises(OurValidationError) as excinfo:\n", " register_user('fred', '[email protected]', 'fredpassword')\n", " \n", " assert excinfo.value.msg == 'Username in use already'\n", " assert excinfo.value.field == 'username'\n", "\n", "def test_duplicated_user_raises_error():\n", " ChasteValidator = mock.Mock(return_value=False)\n", " PromisciousValidator = mock.Mock(return_value=True)\n", " \n", " with mock.patch('myapp.logic.is_username_free', PromisciousValidator),\n", " mock.patch('myapp.logic.is_email_free', ChasteValidator):\n", " with pytest.raises(OurValidationError) as excinfo:\n", " register_user('fred', '[email protected]', 'fredpassword')\n", " \n", " assert excinfo.value.msg == 'Email in use already'\n", " assert excinfo.value.field == 'email'\n", "\n", "def test_register_user_happy_path():\n", " PromisciousValidator = mock.Mock(return_value=True)\n", " MockDB = mock.Mock()\n", " \n", " with mock.patch('myapp.logic.is_username_free', PromisciousValidator),\n", " mock.patch('myapp.logic.is_email_free', ChasteValidator), \n", " mock.patch('myapp.logic.db', MockDB):\n", " \n", " register_user('fred', '[email protected]', 'freddpassword')\n", " \n", " assert MockDB.commit.call_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, we should also write tests for the controller. I'll leave that as an exercise. However, there's something very important we're learning from these tests. We have to `mock.patch` everything still. Our validators lean directly on the database, our user creation leans directly on the database, everything leans directly on the database. And I don't want to do that, we've found that it makes testing hard. We're also seeing if we need to add another registration restriction -- say we don't like people named Fred so we won't let anyone register with a username or email containing Fred in it -- we need to crack open the `register_user` function and add it directly. We can solve both of these problems.\n", "\n", "## The Database Problem\n", "\n", "To address the database problem we need to realize something. We're not actually interested in the database, we're interested in the data it stores. And since we're interested in finding data rather than where it's stored at, why not stuff an interface in the way?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from abc import ABC, abstractmethod\n", "\n", "class AbstractUserRepository(ABC):\n", " \n", " @abstractmethod\n", " def find_by_username(self, username):\n", " pass\n", " \n", " @abstractmethod\n", " def find_by_email(self, email):\n", " pass\n", " \n", " @abstractmethod\n", " def persist(self, user):\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm...that's interesting. Since we'll end up depending on this instead of a concrete implementation, we can run our tests completely in memory and production on top of SQLAlchemy, Mongo, a foreign API, whatever.\n", "\n", "But we need to inject it into our validators instead of reaching out into the global namespace like we currently are." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def is_username_free(user_repository):\n", " def is_username_free(username):\n", " return not user_repository.find_by_username(username)\n", " return is_username_free\n", "\n", "def is_email_free(user_repository):\n", " def is_email_free(email):\n", " return not user_repository.find_by_email(email)\n", " return is_email_free" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These validators are simple enough that closures work instead of full-fledged objects. The important part here is to maintain a consistent interface -- if we need to use classes all of a sudden, we need to define a `__call__` on them to maintain this interface.\n", "\n", "We can also change our register callable to accept the repository as well:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def register_user(user_repository):\n", " email_checker = is_email_free(user_repository)\n", " username_checker = is_username_free(user_repository)\n", " \n", " def register_user(username, email, password):\n", " \n", " if not username_checker(username):\n", " raise OurValidationError('Username in use already', 'username')\n", "\n", " if not email_checker(email):\n", " raise OurValidationError('Email in use already', 'email')\n", "\n", " user = User(username=username, email=email, password=password)\n", " user_repository.persist(user)\n", " \n", " return register_user" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course the tests break now, and that's okay. We made a very sweeping change to the architecture here. We need to go back through and alter the tests one by one, but instead of patching everything out we can do something better: Dependency Injection." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def test_duplicated_email_causes_false():\n", " fake_user_repository = mock.create_autospec(AbstractUserRepository)\n", " fake_user_repository.find_by_email.return_value = True\n", " checker = is_email_free(fake_user_repository)\n", " \n", " assert not checker('[email protected]')\n", " \n", "def test_duplicated_username_causes_false():\n", " fake_user_repository = mock.create_autospec(AbstractUserRepository)\n", " fake_user_repository.find_by_username.return_value = True\n", " checker = is_username_free(fake_user_repository)\n", " \n", " assert not checker('fred')\n", "\n", "\n", "def test_register_user_happy_path():\n", " fake_user_repository = mock.create_autospec(AbstractUserRepository)\n", " fake_user_repository.find_by_email.return_value = False\n", " fake_user_repository.find_by_username.return_value = False\n", " registrar = register_user(fake_user_repository)\n", " \n", " registrar('fred', '[email protected]', 'fredpassword')\n", " \n", " assert fake_user_repository.persist.call_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But to test that our validators function correctly in this context, we need to fake out `find_by_email` and `find_by_username` indpendently. This is a symptom of our code not being Open-Closed.\n", "\n", "## The Open-Closed Problem\n", "\n", "Revisiting the other major issue from how the code is laid out right now is that it's not Open-Closed. If you're not familiar with the principle, Wikipedia says this:\n", "\n", "> \"software entities (classes, modules, functions, etc.) should be open for extension, but closed for modification\"\n", "\n", "Or in a different way, \"You should be able to change functionality without editing existing code.\" -- I believe I need to credit Sandi Metz with this, but I'm not sure. We've actually already used this idea by injecting the User Repository. In tests, we inject a fake or in memory repository, but in production it can be a SQLAlchemy implementation, or maybe wrap that up into a caching repository. We can do the same thing with the validators." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def register_user(user_repository, validator):\n", " def registrar(username, email, password):\n", " user = User(username, email, password)\n", " validator(user)\n", " user_repository.persist(user)\n", " return registrar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, our tests break again, so let's revisit the currently breaking one first:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def test_register_user_happy_path():\n", " fake_user_repository = mock.create_autospec(AbstractUserRepository)\n", " registrar = register_user(fake_user_repository, lambda user: None)\n", " \n", " registrar('fred', '[email protected]', 'fredpassword')\n", " \n", " assert fake_user_repository.persist.call_count\n", " \n", "def test_register_user_fails_validation():\n", " fake_user_repository = mock.create_autospec(AbstractUserRepository)\n", " fake_validator = mock.Mock(side_effect=OurValidationError('username in use already', 'username'))\n", " registrar = register_user(fake_user_repository, fake_validator)\n", " \n", " try:\n", " registrar('fred', '[email protected]', 'fredpassword')\n", " except OurValidationError as e:\n", " assert e.msg == 'username in use already'\n", " assert e.field == 'username'\n", " else:\n", " assert False, \"Did not Raise\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll need to tweak the validation logic some to make up for the fact that we're passing the whole user object now:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def validate_username(user_repoistory):\n", " def validator(user):\n", " if not user_repoistory.find_by_username(user.username):\n", " raise OurValidationError('Username in use already', 'username')\n", " return True\n", " return validator\n", "\n", "def validate_email(user_repoistory):\n", " def validator(user):\n", " if not user_repoistory.find_by_email(user.email):\n", " raise OurValidationError(\"Email in use already\", 'email')\n", " return True\n", " return validator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The tests for these are pretty straight forward as well, so I'll omit them. But we need a way to stitch them together..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def validate_many(validators):\n", " def checker(input):\n", " return all(validator(input) for validator in validators)\n", " return checker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then hook it all up like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "validator = validate_username(validate_email(user_repository), validate_username(user_repository))\n", "registrar = register_user(user_repository, validator)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Our neglected Controller\n", "\n", "We've spent a lot of time looking at how to compartmentalize the registration logic and portion out its concerns. However, the controller itself needs some attention as well. When we last left, it looked like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@app.route('/register', methods=['GET', 'POST'])\n", "def register_user_view():\n", " form = RegisterUserForm()\n", " \n", " if form.validate_on_submit():\n", " try:\n", " register_user(form.username.data, form.email.data, form.password.data)\n", " except OurValidationError as e:\n", " form.errors[e.field] = [e.msg]\n", " return render_template('register.html', form=form)\n", " else:\n", " return redirect('homepage')\n", " \n", " return render_template('register.html', form=form)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But we can do beter than that. The problem here is that the logic is set in stone, nested flows of control. But mostly, I really like any excuse to use class based views." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class RegisterUser(MethodView):\n", " def __init__(self, form, registrar, template, redirect):\n", " self.form = form\n", " self.registrar = registrar\n", " self.template = template\n", " self.redirect = redirect\n", " \n", " def get(self):\n", " return self._render()\n", " \n", " def post(self):\n", " if self.form.validate_on_submit():\n", " return self._register()\n", " else:\n", " return self._render()\n", " \n", " def _register(self):\n", " try:\n", " self.registrar(self.form.username.data, self.form.email.data, self.form.password.data)\n", " except OurValidationError as e:\n", " self._handle_error(e)\n", " self._render()\n", " else:\n", " return self._redirect()\n", " \n", " def _render(self):\n", " return render_template(self.template, self.form=form)\n", "\n", " def _redirect(self):\n", " return redirect(url_for(self.redirect))\n", " \n", " def _handle_error(self, e):\n", " self.form.error[e.field] = [e.msg]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that* looks like a lot of code. However, each piece is much simpler than the original function. This speaks to handing out actions, as if it were controlling things. We can test the main logic of it as well. Even though we should test it, I might just leave it alone. Maybe run it through an acceptance test.\n", "\n", "## What did we gain?\n", "\n", "Everything is much more high level. The controller, validation, registration, even the form. Nothing's concerned with more than it needs to be. Sure, there's still some plumbing to do with the SQLAlchemy implementation of the `UserRepository`" ] } ], "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
nicolas998/Analisis_Datos	02_Medidas_Localizacion.ipynb	1	338351	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Medidas de Localización " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Medidas típicas, que son paramétricas:\n", "- Media: $\\mu = \\frac{\\sum_{i=1}^{N} x_i}{N} $\n", "- Desviación $\\sigma = \\sqrt{ \\frac{1}{N} \\sum_{i=1}^{N} (x_i -\\mu)^2}$\n", "- Asimetría.\n", "- etc...\n", "\n", "Existen otras medidas que son no paramétricas, estas pueden ser:\n", "- Mediana.\n", "- Quintiles.\n", "- Deciles.\n", "- ..\n", "- etc.\n", "- Cuantiles.\n", "\n", "Dependiendo de la medida esta puede ser o no robusta, esto significa que no se vea afectada fácilmente por aspectos tales como:\n", "\n", "- Cantidad de datos.\n", "- Calidad de los datos.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "___" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Ejemplo de medidas paramétricas:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.0108811294739\n", "1.04385100251\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pylab as pl\n", "\n", "Serie = np.random.normal(0,1,1000)\n", "# Impresión de la media y la desviación\n", "print Serie.mean()\n", "print Serie.std()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Pero miren lo que pasa con la mediana " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.054493753954\n", "-0.054493753954\n" ] } ], "source": [ "print np.percentile(Serie,50)\n", "print np.median(Serie)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Que ocurre " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- La media esperada es 0, sin embargo esta presenta una diferencia.\n", "- Igualmente ocurre conla desviación estandar.\n", "___\n", "- Pero no ocurre lo mismo con la mediana, en 1000 datos se presenta dos ordenes de magnitud más cerca de 0 \n", "\n", "La siguiente figura ejemplifica esto:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Definición de función para graficar " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def GraficaHistogramaParam(Values,bins=15):\n", " # Genera el histograma de valores\n", " h,b = np.histogram(Values,bins=bins)\n", " h = h.astype(float); h = h / h.sum()\n", " b = (b[1:]+b[:-1])/2.0\n", " # Obtiene la figura \n", " fig=pl.figure(figsize=(10,8))\n", " ax=fig.add_subplot(111)\n", " ax.plot(b,h,'b',lw=2)\n", " ax.fill_between(b,h,color='b',alpha=0.2)\n", " ax.set_xlabel('$X$',size=15)\n", " ax.set_ylabel('$f(x)$',size=15)\n", " ax.set_xlim(-3,3)\n", " ax.set_ylim(0,h.max()+0.05)\n", " ax.grid(True)\n", " ax.legend(loc=0)\n", " # Grafica las localizaciones\n", " ax.vlines(Values.mean(),0,h.max()+0.05,lw=2,color='r')\n", " ax.vlines([Values.mean()+Values.std(),Values.mean()-Values.std()],0,h.max()+0.05,lw=1,color='r')\n", " pl.show()\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def GraficaHistogramaNoParam(Values,bins=15):\n", " # Genera el histograma de valores\n", " h,b = np.histogram(Values,bins=bins)\n", " h = h.astype(float); h = h / h.sum()\n", " b = (b[1:]+b[:-1])/2.0\n", " # Obtiene la figura \n", " fig=pl.figure(figsize=(10,8))\n", " ax=fig.add_subplot(111)\n", " ax.plot(b,h,'b',lw=2)\n", " ax.fill_between(b,h,color='b',alpha=0.2)\n", " ax.set_xlabel('$X$',size=15)\n", " ax.set_ylabel('$f(x)$',size=15)\n", " ax.set_xlim(-3,3)\n", " ax.set_ylim(0,h.max()+0.05)\n", " ax.grid(True)\n", " ax.legend(loc=0)\n", " # Grafica las localizaciones\n", " ax.vlines(np.percentile(Values,50),0,h.max()+0.05,lw=2,color='r')\n", " ax.vlines([np.percentile(Values,10),np.percentile(Values,90)],0,h.max()+0.05,lw=1,color='r')\n", " pl.show()\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Grafica de las medidas de localización paramétricas" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n", " warnings.warn(\"No labeled objects found. \"\n", "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": 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OnJ/qYiU6lJkIRt7p0/PY9tJT5bHcU893rnJl6NbNth94oPTfT68t7qnn4VSU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\nmu6FKrH16KP2r8YTTkje8CYiUlq1asH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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31591d8890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "GraficaHistogramaParam(Serie)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Gráfica de medidas no paramétricas " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iyVDaU6itgUnAdsB5hY87AicCfYEuwIHAMcAcYBhwZtDFSnwoMxGOotOnJV28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\nKaf4rkg2pHuhSmI98ID91XjEEdEd3kR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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31588ee690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "GraficaHistogramaNoParam(Serie)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Caso con menos datos \n", "\n", "En un caso con menor cantida dde datos se espera tener una mayor diferencia entre ambas medidas, de ahí si inestabilidad:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.24550468821\n", "6.24046491251\n", "0.00197201571583\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " if __name__ == '__main__':\n" ] } ], "source": [ "Serie = np.random.uniform(2.5,10,2e5)\n", "print Serie.mean()\n", "print np.median(Serie)\n", "from scipy import stats as st\n", "print st.skew(Serie)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Ejercicio para observar robustez en ambas medidas\n", "\n", "En el siguiente ejercicio generamos 200 veces series aleatorias cada una con 25 entradas, \n", "luego vamos a comprar como son las diferencias entre las medias y las medianas encontradas para cada uno de los casos." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "medianas = np.zeros(20000)\n", "medias=np.zeros(20000)\n", "for i in range(20000):\n", " Serie = np.random.normal(0,1,25)\n", " medias[i] = Serie.mean()\n", " medianas[i]=np.median(Serie)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def ComparaHistogramas(Vec1,Vec2,bins=15):\n", " # Genera el histograma de valores\n", " h1,b1 = np.histogram(Vec1,bins=bins)\n", " h1 = h1.astype(float); h1 = h1 / h1.sum()\n", " b1 = (b1[1:]+b1[:-1])/2.0\n", " h2,b2 = np.histogram(Vec2,bins=bins)\n", " h2 = h2.astype(float); h2 = h2 / h2.sum()\n", " b2 = (b2[1:]+b2[:-1])/2.0\n", " #Genera la figura \n", " fig=pl.figure(figsize=(10,8))\n", " ax=fig.add_subplot(111)\n", " ax.plot(b1,h1,'b',lw=2,label='Vec 1')\n", " ax.plot(b2,h2,'r',lw=2,label='Vec 2')\n", " ax.fill_between(b1,h1,color='b',alpha=0.2)\n", " ax.fill_between(b2,h2,color='r',alpha=0.2)\n", " ax.set_xlabel('$X$',size=15)\n", " ax.set_ylabel('$f(x)$',size=15)\n", " ax.set_xlim(-1,1)\n", " ax.set_ylim(0,h1.max()+0.05)\n", " ax.grid(True)\n", " ax.legend(loc=0)\n", " # Grafica las localizaciones\n", " pl.show()\n", " return h1,h2" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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oKfYG14Ro1tQBlA4eiS8pmax3t5BcfOKix/h71lVUwMKFEQ5QPkdji9hNSZ2I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\nL6S4ay9Wv7KQQGxcMLvntdfgn/8En8/sef3229C9e9CGIBbSQglpUn7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31589f0fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "HistMedianas, HistMedias = ComparaHistogramas(medianas,medias)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Suceptibilidad a Datos Atípicos \n", "\n", "Un dato atípico se define como aquel dato que se encuentra fuera del rango de oscilación de los datos, o bien que no es coherente con la física del fenómeno que se está sensando, los siguientes son ejemplos de datos atípicos:\n", "\n", "- Valores exageradamente altos.\n", "- Valores negativos en casos de fenómenos sin valores negativos.\n", "- Valores fuera de un rango definido.\n", "- Secuencia de valores con el mismo valor (no es tanto atípico, pero si es un indicio de problemas)\n", "\n", "Una forma de identificarlos es a partir de la media de los valores y la desviación, o los percentiles sobre los que se ubiquen.\n", "\n", "- $ValAtipico > \\mu + N \\sigma$, donde $N$ oscila de acuerdo a lo fuerte que se quiera hacer la pregunta \n", "- $ValAtipico > P_{99.9}$\n", "___\n", "\n", "Dependiendo de la cantidad de registros en los datos, de la cantidad de valores atípicos y de los valores que estos tengan pueden tener o no consecuencias sobre la serie y sobre posteriores análisis que se realicen sobre la misma.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Ejemplo de robustez ante datos atípicos" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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90i7ZLF4Md98N73vf0GO1GnznO/baL34x9lD3ImnJBrLt1ipB0kAcQQLVK9u0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\nAAAAANbyCh7Zi/9wpQU1AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3150a271d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Serie = np.random.normal(0,1,50)\n", "fig = pl.figure(figsize=(9,7))\n", "pl.plot(Serie)\n", "pl.grid(True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Incertemos un dato loco, que se salga " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f31507b1cd0>]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qgxESEfn5yQCJxcr+WCNEklQZjJCIKO+hGEi+t0h+fmrHI0mSERIRFYmQqlWhqAiKi1M7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\njxrJmF9GsLDWOBq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3150900710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Serie2 = np.copy(Serie)\n", "Serie2[10] = 50.0\n", "fig = pl.figure(figsize=(9,7))\n", "pl.plot(Serie2)\n", "pl.plot(Serie)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Ahora veamos que ocurre con la media:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.121089383472\n", "0.885952982227\n" ] } ], "source": [ "print Serie.mean()\n", "print Serie2.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y que ocurre con la mediana:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.104065434749\n", "-0.0182564049349\n" ] } ], "source": [ "print np.median(Serie)\n", "print np.median(Serie2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Introducción de múltiples Outliers\n", "\n", "Que pasa si se introduce una alta cantidad de datos atípicos?, es decir como es la tasa a \n", "la cual la media puede ir pasando a ser cada ves un estimador con un mayor error?." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def CreaOutliers(vect,NumOut,Mult=10): \n", " # Encuentra el rango de oscilacion \n", " Per = np.array([np.percentile(vect,i) for i in [0.1,99.9]])\n", " # Genera los aleatorios \n", " vectOut = np.copy(vect)\n", " for i in np.random.choice(vect.shape[0],NumOut):\n", " p = np.random.choice(2,1)[0]\n", " vectOut[i] = vectOut[i] + Per[p]Multnp.random.uniform(2,15,1)[0]\n", " return vectOut" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'Serie3' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-41-6af41c11682d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mSerie3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\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[0mSerie\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\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[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'----------'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSerie3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSerie\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'Serie3' is not defined" ] } ], "source": [ "print Serie3.mean()\n", "print Serie.mean()\n", "print '----------'\n", "print np.median(Serie3)\n", "print np.median(Serie)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Definición de variables\n", "N = 1000\n", "S1 = np.random.normal(0,1,N)\n", "Medias = []; Std = []\n", "Medianas = []; R25_75 = []\n", "# Introduccion de outliers\n", "for i in np.arange(5,200):\n", " S2 = CreaOutliers(S1, i)\n", " Medias.append(S2.mean())\n", " Medianas.append(np.median(S2))\n", " Std.append(S2.std())\n", " R25_75.append(np.percentile(S2,75)-np.percentile(S2,25))\n", "Medias = np.array(Medias)\n", "Medianas = np.array(Medianas)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resultados:\n", "\n", "Según lo obtenido la mediana se ve altamente afectada, y la desviación también:\n", "\n", "## Caso de una distribución Normal" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Definición de variables\n", "N = 1000\n", "S1 = np.random.uniform(0,1,N)\n", "Medias = []; Std = []\n", "Medianas = []; R25_75 = []\n", "# Introduccion de outliers\n", "for i in np.arange(5,200):\n", " S2 = CreaOutliers(S1, i)\n", " Medias.append(S2.mean())\n", " Medianas.append(np.median(S2))\n", " Std.append(S2.std())\n", " R25_75.append(np.percentile(S2,75)-np.percentile(S2,25))\n", "Medias = np.array(Medias)\n", "Medianas = np.array(Medianas)\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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8y2fSx9e5HWKFJhFwuZ1z++crMNtRnXVbdz3zdSiaN4gvjddplwNuxkPl00aW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\nO3P7fj3PPrc7M7vz2bnd+ez35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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31583ee790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = pl.figure(figsize=(13,5))\n", "ax = fig.add_subplot(121)\n", "ax.scatter(Medianas,Medias,c=np.arange(5,200))\n", "ax.set_xlabel('Mediana',size=14)\n", "ax.set_ylabel('Media $\\mu$',size=14)\n", "ax = fig.add_subplot(122)\n", "ax.scatter(R25_75,Std,c=np.arange(5,200))\n", "#ax.set_xlim(0,1)\n", "ax.set_xlabel('Rango $25%$ - $75\\%$',size=14)\n", "ax.set_ylabel('Desviacion $\\sigma$',size=14)\n", "pl.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Caso de una distribución uniforme" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false, "scrolled": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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MX19fSpYsme04jx07RssmQQQXNXIzRcMN6yLs3HswSxO1h739Flt/mk839wQW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\n2yFacJ4gKgvbEpWJlcAnk+0bEBWDxcAuw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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31508f90d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = pl.figure(figsize=(13,5))\n", "ax = fig.add_subplot(121)\n", "ax.scatter(Medianas,Medias,c=np.arange(5,200))\n", "ax.set_xlabel('Mediana',size=14)\n", "ax.set_ylabel('Media $\\mu$',size=14)\n", "ax = fig.add_subplot(122)\n", "ax.scatter(R25_75,Std,c=np.arange(5,200))\n", "#ax.set_xlim(0,1)\n", "ax.set_xlabel('Rango $25%$ - $75\\%$',size=14)\n", "ax.set_ylabel('Desviacion $\\sigma$',size=14)\n", "pl.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Cuantiles \n", "\n", "Como una medida no paramétrica de la distribución de los datos se encuentran los cuantiles,\n", "el más conocido es la mediana, sin embargo se pueden obtener cuantiles de cualquier medida.\n", "\n", "Que representan : El cuantil del 25% igual a 3.56, indica que el 25% de los datos son iguales o inferiores a 3.56.\n", "\n", "Al ser una medida no paramétrica se ve poco afectada por errores en los datos y por datos atípicos." ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7efe5f667290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S1 = np.random.normal(0,1,100)\n", "a=pl.boxplot(S1)\n", "a=pl.xlabel('Serie')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Caso de Introducción de Outliers\n", "\n", "QQ plot de las series donde se introducen outliers y la serie en donde no " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "S1 = np.random.normal(0,1,100)\n", "S2 = CreaOutliers(S1,10)\n", "Per1 = np.array([np.percentile(S1,i) for i in range(10,91,10)])\n", "Per2 = np.array([np.percentile(S2,i) for i in range(10,91,10)])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f31507c0ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = pl.figure(figsize=(9,7))\n", "ax = fig.add_subplot(111)\n", "ax.scatter(Per1,Per2,s=40)\n", "ax.set_xlim(-2,2)\n", "ax.set_ylim(-2,2)\n", "ax.grid(True)\n", "ax.set_xlabel('Deciles Observados',size =14); ax.set_ylabel('Deciles Alterados',size=14) \n", "ax.plot([-2,2],[-2,2],lw=0.5,c='k')\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
Yuanyuan-Shi/batterydeg	.ipynb_checkpoints/phy2nn_battery-checkpoint.ipynb	1	36414	{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn import datasets, linear_model\n", "from sklearn import cross_validation\n", "import numpy as np\n", "numpy.longfloat\n", "import pandas as pd\n", "from sklearn import preprocessing" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "The 'dtype' option is not supported with the 'python' engine", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-31-a7d594b6a404>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data0505.xlsx\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlongfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# clean up data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'all'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Applications/anaconda/lib/python3.5/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheetname, header, skiprows, skip_footer, index_col, names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, squeeze, kwds)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mconvert_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_float\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mskip_footer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskip_footer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m kwds)\n\u001b[0m\u001b[1;32m 466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0masheetname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Applications/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mTextParser\u001b[0;34m(args, kwds)\u001b[0m\n\u001b[1;32m 1470\u001b[0m \"\"\"\n\u001b[1;32m 1471\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'engine'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Applications/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, kwds)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0moptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_options_with_defaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'chunksize'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Applications/anaconda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_get_options_with_defaults\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 670\u001b[0m raise ValueError(\n\u001b[1;32m 671\u001b[0m \u001b[0;34m'The %r option is not supported with the'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 672\u001b[0;31m ' %r engine' % (argname, engine))\n\u001b[0m\u001b[1;32m 673\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The 'dtype' option is not supported with the 'python' engine" ] } ], "source": [ "df = pd.read_excel(\"data0505.xlsx\",header=0,dtype=np.longfloat)\n", "# clean up data\n", "df = df.dropna(how = 'all')\n", "df = df.fillna(0)\n", "df = df.round(4)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>SOC</th>\n", " <th>SOH</th>\n", " <th>Power</th>\n", " <th>T</th>\n", " <th>SEI_after</th>\n", " <th>SEI_delta</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>80</td>\n", " <td>0</td>\n", " <td>-1.0000</td>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>80</td>\n", " <td>0</td>\n", " <td>-0.8947</td>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>80</td>\n", " <td>0</td>\n", " <td>-0.7895</td>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>80</td>\n", " <td>0</td>\n", " <td>-0.6842</td>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>-0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>80</td>\n", " <td>0</td>\n", " <td>-0.5790</td>\n", " <td>23</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " SOC SOH Power T SEI_after SEI_delta\n", "0 80 0 -1.0000 23 0.0 -0.0\n", "1 80 0 -0.8947 23 0.0 -0.0\n", "2 80 0 -0.7895 23 0.0 0.0\n", "3 80 0 -0.6842 23 0.0 -0.0\n", "4 80 0 -0.5790 23 0.0 0.0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>SOC</th>\n", " <th>SOH</th>\n", " <th>Power</th>\n", " <th>T</th>\n", " <th>SEI_after</th>\n", " <th>SEI_delta</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.703816</td>\n", " <td>NaN</td>\n", " <td>-1.647432</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.703816</td>\n", " <td>NaN</td>\n", " <td>-1.473957</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.703816</td>\n", " <td>NaN</td>\n", " <td>-1.300648</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.703816</td>\n", " <td>NaN</td>\n", " <td>-1.127173</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1.703816</td>\n", " <td>NaN</td>\n", " <td>-0.953863</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " SOC SOH Power T SEI_after SEI_delta\n", "0 1.703816 NaN -1.647432 NaN NaN NaN\n", "1 1.703816 NaN -1.473957 NaN NaN NaN\n", "2 1.703816 NaN -1.300648 NaN NaN NaN\n", "3 1.703816 NaN -1.127173 NaN NaN NaN\n", "4 1.703816 NaN -0.953863 NaN NaN NaN" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_normalized=(df-df.mean())/df.std()\n", "# min_max_scaler = preprocessing.MinMaxScaler()\n", "# np_scaled = min_max_scaler.fit_transform(df)\n", "# df_normalized = pd.DataFrame(np_scaled)\n", "df_normalized.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9760" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.array(df_normalized.ix[:,0:2])#first three column are SoC, SoH, power\n", "y = np.array(df_normalized.ix[:,5])#delta SEI\n", "X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(\n", "x, y, test_size=0.2, random_state=42)\n", "total_len = X_train.shape[0]\n", "total_len" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.000000000000000\n" ] } ], "source": [ "print(str.format('{0:.15f}', y[1]))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Parameters\n", "learning_rate = 0.001\n", "training_epochs = 50\n", "batch_size = 100\n", "display_step = 1\n", "dropout_rate = 0.1\n", "# Network Parameters\n", "n_hidden_1 = 10 # 1st layer number of features\n", "n_hidden_2 = 5 # 2nd layer number of features\n", "n_input = X_train.shape[1]\n", "n_classes = 1\n", "\n", "# tf Graph input\n", "x = tf.placeholder(\"float\", [None, 3])\n", "y = tf.placeholder(\"float\", [None])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create model\n", "def multilayer_perceptron(x, weights, biases):\n", " # Hidden layer with RELU activation\n", " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", " layer_1 = tf.nn.relu(layer_1)\n", "\n", " # Hidden layer with RELU activation\n", " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", " layer_2 = tf.nn.relu(layer_2)\n", "\n", " # Output layer with linear activation\n", " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", " return out_layer" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Store layers weight & bias\n", "weights = {\n", " 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], 0, 0.1)),\n", " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], 0, 0.1)),\n", " 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], 0, 0.1))\n", "}\n", "biases = {\n", " 'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)),\n", " 'b2': tf.Variable(tf.random_normal([n_hidden_2], 0, 0.1)),\n", " 'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1))\n", "}" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Construct model\n", "pred = multilayer_perceptron(x, weights, biases)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num batch: 97\n", "Epoch: 0001 cost= 0.001209991\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00404518]\n", "label value: 0.0 estimated value: [ 0.00695085]\n", "label value: 0.0 estimated value: [-0.00441298]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0002 cost= 0.000012889\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.0029598]\n", "label value: 0.0 estimated value: [ 0.00522447]\n", "label value: 0.0 estimated value: [-0.00387982]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0003 cost= 0.000008064\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00236373]\n", "label value: 0.0 estimated value: [ 0.00403679]\n", "label value: 0.0 estimated value: [-0.0030103]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0004 cost= 0.000004712\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00176073]\n", "label value: 0.0 estimated value: [ 0.00297401]\n", "label value: 0.0 estimated value: [-0.00228744]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0005 cost= 0.000002586\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00125664]\n", "label value: 0.0 estimated value: [ 0.00207181]\n", "label value: 0.0 estimated value: [-0.00167676]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0006 cost= 0.000001322\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00085248]\n", "label value: 0.0 estimated value: [ 0.00133884]\n", "label value: 0.0 estimated value: [-0.00117343]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0007 cost= 0.000000626\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.0005428]\n", "label value: 0.0 estimated value: [ 0.00077347]\n", "label value: 0.0 estimated value: [-0.0007759]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0008 cost= 0.000000277\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00032021]\n", "label value: 0.0 estimated value: [ 0.00036713]\n", "label value: 0.0 estimated value: [-0.00047934]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0009 cost= 0.000000122\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 0.00017164]\n", "label value: 0.0 estimated value: [ 9.82955098e-05]\n", "label value: 0.0 estimated value: [-0.00027397]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0010 cost= 0.000000060\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 7.88420439e-05]\n", "label value: 0.0 estimated value: [ -6.53229654e-05]\n", "label value: 0.0 estimated value: [-0.00014188]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0011 cost= 0.000000038\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ 2.47173011e-05]\n", "label value: 0.0 estimated value: [-0.00015584]\n", "label value: 0.0 estimated value: [ -6.20558858e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0012 cost= 0.000000030\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -4.93600965e-06]\n", "label value: 0.0 estimated value: [-0.00020028]\n", "label value: 0.0 estimated value: [ -1.58995390e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0013 cost= 0.000000027\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.03326344e-05]\n", "label value: 0.0 estimated value: [-0.00021815]\n", "label value: 0.0 estimated value: [ 1.05500221e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0014 cost= 0.000000025\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.77869403e-05]\n", "label value: 0.0 estimated value: [-0.00022158]\n", "label value: 0.0 estimated value: [ 2.61142850e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0015 cost= 0.000000024\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.12067568e-05]\n", "label value: 0.0 estimated value: [-0.00021797]\n", "label value: 0.0 estimated value: [ 3.65599990e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0016 cost= 0.000000023\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.26894224e-05]\n", "label value: 0.0 estimated value: [-0.00021117]\n", "label value: 0.0 estimated value: [ 4.46736813e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0017 cost= 0.000000021\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.33562493e-05]\n", "label value: 0.0 estimated value: [-0.00020317]\n", "label value: 0.0 estimated value: [ 5.17927110e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0018 cost= 0.000000020\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.35872173e-05]\n", "label value: 0.0 estimated value: [-0.00019474]\n", "label value: 0.0 estimated value: [ 5.88148832e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0019 cost= 0.000000019\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.36728990e-05]\n", "label value: 0.0 estimated value: [-0.00018631]\n", "label value: 0.0 estimated value: [ 6.59674406e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0020 cost= 0.000000017\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.37138772e-05]\n", "label value: 0.0 estimated value: [-0.00017807]\n", "label value: 0.0 estimated value: [ 7.33882189e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0021 cost= 0.000000016\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.35760415e-05]\n", "label value: 0.0 estimated value: [-0.00016985]\n", "label value: 0.0 estimated value: [ 8.10213387e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0022 cost= 0.000000015\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.32482159e-05]\n", "label value: 0.0 estimated value: [-0.00016174]\n", "label value: 0.0 estimated value: [ 8.89636576e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0023 cost= 0.000000014\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.26558948e-05]\n", "label value: 0.0 estimated value: [-0.00015367]\n", "label value: 0.0 estimated value: [ 9.74349678e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0024 cost= 0.000000013\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.15904617e-05]\n", "label value: 0.0 estimated value: [-0.00014537]\n", "label value: 0.0 estimated value: [ 0.00010644]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0025 cost= 0.000000012\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.02121043e-05]\n", "label value: 0.0 estimated value: [-0.00013707]\n", "label value: 0.0 estimated value: [ 0.00011584]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0026 cost= 0.000000011\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.85692513e-05]\n", "label value: 0.0 estimated value: [-0.0001288]\n", "label value: 0.0 estimated value: [ 0.00011111]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0027 cost= 0.000000010\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.67848372e-05]\n", "label value: 0.0 estimated value: [-0.00012073]\n", "label value: 0.0 estimated value: [ 0.00010429]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0028 cost= 0.000000009\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.50190496e-05]\n", "label value: 0.0 estimated value: [-0.00011299]\n", "label value: 0.0 estimated value: [ 9.78857279e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0029 cost= 0.000000008\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.33948231e-05]\n", "label value: 0.0 estimated value: [-0.00010575]\n", "label value: 0.0 estimated value: [ 9.17762518e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0030 cost= 0.000000008\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.17221677e-05]\n", "label value: 0.0 estimated value: [ -9.87946987e-05]\n", "label value: 0.0 estimated value: [ 8.61771405e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0031 cost= 0.000000007\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -1.99079514e-05]\n", "label value: 0.0 estimated value: [ -9.19662416e-05]\n", "label value: 0.0 estimated value: [ 8.10883939e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0032 cost= 0.000000006\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -1.79074705e-05]\n", "label value: 0.0 estimated value: [ -8.52420926e-05]\n", "label value: 0.0 estimated value: [ 7.65696168e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0033 cost= 0.000000006\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -1.56797469e-05]\n", "label value: 0.0 estimated value: [ -7.85738230e-05]\n", "label value: 0.0 estimated value: [ 7.26580620e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0034 cost= 0.000000005\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -1.34594738e-05]\n", "label value: 0.0 estimated value: [ -7.22110271e-05]\n", "label value: 0.0 estimated value: [ 6.91190362e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0035 cost= 0.000000005\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -1.12429261e-05]\n", "label value: 0.0 estimated value: [ -6.60941005e-05]\n", "label value: 0.0 estimated value: [ 6.59227371e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0036 cost= 0.000000004\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -9.14186239e-06]\n", "label value: 0.0 estimated value: [ -6.03497028e-05]\n", "label value: 0.0 estimated value: [ 6.29462302e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0037 cost= 0.000000004\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -7.20098615e-06]\n", "label value: 0.0 estimated value: [ -5.49964607e-05]\n", "label value: 0.0 estimated value: [ 6.01261854e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0038 cost= 0.000000004\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -5.56558371e-06]\n", "label value: 0.0 estimated value: [ -5.01796603e-05]\n", "label value: 0.0 estimated value: [ 5.73322177e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0039 cost= 0.000000003\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -4.33251262e-06]\n", "label value: 0.0 estimated value: [ -4.59924340e-05]\n", "label value: 0.0 estimated value: [ 5.44488430e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0040 cost= 0.000000003\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.38628888e-06]\n", "label value: 0.0 estimated value: [ -4.23081219e-05]\n", "label value: 0.0 estimated value: [ 5.15766442e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0041 cost= 0.000000003\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.80141830e-06]\n", "label value: 0.0 estimated value: [ -3.91751528e-05]\n", "label value: 0.0 estimated value: [ 4.86150384e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0042 cost= 0.000000003\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.55554914e-06]\n", "label value: 0.0 estimated value: [ -3.65562737e-05]\n", "label value: 0.0 estimated value: [ 4.55565751e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0043 cost= 0.000000002\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.47731805e-06]\n", "label value: 0.0 estimated value: [ -3.42875719e-05]\n", "label value: 0.0 estimated value: [ 4.25614417e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0044 cost= 0.000000002\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -2.66358256e-06]\n", "label value: 0.0 estimated value: [ -3.24323773e-05]\n", "label value: 0.0 estimated value: [ 3.95327806e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0045 cost= 0.000000002\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.15159559e-06]\n", "label value: 0.0 estimated value: [ -3.10689211e-05]\n", "label value: 0.0 estimated value: [ 3.64482403e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0046 cost= 0.000000002\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -3.83704901e-06]\n", "label value: 0.0 estimated value: [ -3.00332904e-05]\n", "label value: 0.0 estimated value: [ 3.33562493e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0047 cost= 0.000000002\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -4.63053584e-06]\n", "label value: 0.0 estimated value: [ -2.92174518e-05]\n", "label value: 0.0 estimated value: [ 3.03126872e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0048 cost= 0.000000001\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -5.52088022e-06]\n", "label value: 0.0 estimated value: [ -2.86288559e-05]\n", "label value: 0.0 estimated value: [ 2.73361802e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0049 cost= 0.000000001\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -6.49690628e-06]\n", "label value: 0.0 estimated value: [ -2.82227993e-05]\n", "label value: 0.0 estimated value: [ 2.44267285e-05]\n", "[]============================\n", "num batch: 97\n", "Epoch: 0050 cost= 0.000000001\n", "[]----------------------------\n", "label value: 0.0 estimated value: [ -7.39470124e-06]\n", "label value: 0.0 estimated value: [ -2.78316438e-05]\n", "label value: 0.0 estimated value: [ 2.17147171e-05]\n", "[]============================\n", "Optimization Finished!\n", "MSE: 1.16811e-09\n" ] } ], "source": [ "# Define loss and optimizer\n", "cost = tf.reduce_mean((tf.transpose(pred)-y)(tf.transpose(pred)-y)) \n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Launch the graph\n", "with tf.Session() as sess:\n", " sess.run(tf.initialize_all_variables())\n", " tf.initialize_all_variables()\n", "\n", " # Training cycle\n", " for epoch in range(training_epochs):\n", " avg_cost = 0.\n", " total_batch = int(total_len/batch_size)\n", " # Loop over all batches\n", " for i in range(total_batch-1):\n", " batch_x = X_train[ibatch_size:(i+1)batch_size]\n", " batch_y = Y_train[ibatch_size:(i+1)batch_size]\n", " # Run optimization op (backprop) and cost op (to get loss value)\n", " _, c, p = sess.run([optimizer, cost, pred], feed_dict={x: batch_x,\n", " y: batch_y})\n", " # Compute average loss\n", " avg_cost += c / total_batch\n", "\n", " # sample prediction\n", " label_value = batch_y\n", " estimate = p\n", " err = label_value-estimate\n", " print (\"num batch:\", total_batch)\n", "\n", " # Display logs per epoch step\n", " if epoch % display_step == 0:\n", " print (\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", " \"{:.9f}\".format(avg_cost))\n", " print (\"[]----------------------------\")\n", " for i in range(3):\n", " print (\"label value:\", label_value[i], \\\n", " \"estimated value:\", estimate[i])\n", " print (\"[]============================\")\n", "\n", " print (\"Optimization Finished!\")\n", " \n", " # Test model\n", " # correct_prediction = tf.equal(tf.argmax(pred,0), tf.argmax(y,0))\n", " # Calculate accuracy\n", " accuracy = tf.reduce_mean((tf.transpose(pred)-y)(tf.transpose(pred)-y)) \n", " print (\"MSE:\", accuracy.eval({x: X_test, y: Y_test}))" ] }, { "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 }	mit
HUDataScience/StatisticalMethods2016	notebooks/Basic_Python.ipynb	1	60389	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic of Python.\n", "\n", "The library we are going to use are the following:\n", "\n", "\n", "* numpy " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "43\n", "86\n", "131\n", "178\n", "227\n", "278\n", "331\n", "386\n", "443\n" ] } ], "source": [ "for i in range(10):\n", " y = i40 +i2 + 2\n", " print(y)\n", " \n", "i = i+2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range(10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "tt\n" ] } ], "source": [ "for i in [\"a\",\"b\",\"tt\"]:\n", " print i" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 5, 2, 3, 4, 5, 6, 7, 8, 9]]\n" ] } ], "source": [ "x = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],[2, 1, 2, 3, 4, 5, 6, 7, 8, 9],[0,5, 2, 3, 4, 5, 6, 7, 8, 9]]\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4.7000000000000002" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(x)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x= np.linspace(-1,1,100)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { 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" 0.21212121, 0.27272727, 0.33333333, 0.39393939, 0.45454545,\n", " 0.51515152, 0.57575758, 0.63636364, 0.6969697 , 0.75757576,\n", " 0.81818182, 0.87878788, 0.93939394, 1. ])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[::3]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.7979798 , -0.73737374, -0.67676768, -0.61616162, -0.55555556,\n", " -0.49494949, -0.43434343, -0.37373737, -0.31313131, -0.25252525,\n", " -0.19191919, -0.13131313, -0.07070707, -0.01010101, 0.05050505,\n", " 0.11111111, 0.17171717, 0.23232323, 0.29292929, 0.35353535,\n", " 0.41414141, 0.47474747, 0.53535354, 0.5959596 , 0.65656566,\n", " 0.71717172, 0.77777778, 0.83838384, 0.8989899 , 0.95959596])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[10::3]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 0.97979798, 0.95959596, 0.93939394, 0.91919192,\n", " 0.8989899 , 0.87878788, 0.85858586, 0.83838384, 0.81818182,\n", " 0.7979798 , 0.77777778, 0.75757576, 0.73737374, 0.71717172,\n", " 0.6969697 , 0.67676768, 0.65656566, 0.63636364, 0.61616162,\n", " 0.5959596 , 0.57575758, 0.55555556, 0.53535354, 0.51515152,\n", " 0.49494949, 0.47474747, 0.45454545, 0.43434343, 0.41414141,\n", " 0.39393939, 0.37373737, 0.35353535, 0.33333333, 0.31313131,\n", " 0.29292929, 0.27272727, 0.25252525, 0.23232323, 0.21212121,\n", " 0.19191919, 0.17171717, 0.15151515, 0.13131313, 0.11111111,\n", " 0.09090909, 0.07070707, 0.05050505, 0.03030303, 0.01010101,\n", " -0.01010101, -0.03030303, -0.05050505, -0.07070707, -0.09090909,\n", " -0.11111111, -0.13131313, -0.15151515, -0.17171717, -0.19191919,\n", " -0.21212121, -0.23232323, -0.25252525, -0.27272727, -0.29292929,\n", " -0.31313131, -0.33333333, -0.35353535, -0.37373737, -0.39393939,\n", " -0.41414141, -0.43434343, -0.45454545, -0.47474747, -0.49494949,\n", " -0.51515152, -0.53535354, -0.55555556, -0.57575758, -0.5959596 ,\n", " -0.61616162, -0.63636364, -0.65656566, -0.67676768, -0.6969697 ,\n", " -0.71717172, -0.73737374, -0.75757576, -0.77777778, -0.7979798 ,\n", " -0.81818182, -0.83838384, -0.85858586, -0.87878788, -0.8989899 ,\n", " -0.91919192, -0.93939394, -0.95959596, -0.97979798, -1. ])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[::-1]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x= [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[::-1]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = np.linspace(0,10,100)\n", "x= np.linspace(-1,1,100)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-1. , -0.97979798, -0.95959596, -0.93939394, -0.91919192,\n", " -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,\n", " -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,\n", " -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,\n", " -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,\n", " -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,\n", " -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,\n", " -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,\n", " -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,\n", " -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,\n", " 0.01010101, 0.03030303, 0.05050505, 0.07070707, 0.09090909,\n", " 0.11111111, 0.13131313, 0.15151515, 0.17171717, 0.19191919,\n", " 0.21212121, 0.23232323, 0.25252525, 0.27272727, 0.29292929,\n", " 0.31313131, 0.33333333, 0.35353535, 0.37373737, 0.39393939,\n", " 0.41414141, 0.43434343, 0.45454545, 0.47474747, 0.49494949,\n", " 0.51515152, 0.53535354, 0.55555556, 0.57575758, 0.5959596 ,\n", " 0.61616162, 0.63636364, 0.65656566, 0.67676768, 0.6969697 ,\n", " 0.71717172, 0.73737374, 0.75757576, 0.77777778, 0.7979798 ,\n", " 0.81818182, 0.83838384, 0.85858586, 0.87878788, 0.8989899 ,\n", " 0.91919192, 0.93939394, 0.95959596, 0.97979798, 1. ])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "careful with \"=\":\n", "\n", " xx= x means they are the same object\n", "* xx = x .... whatever or x.copy() they are two different objects" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xx = x.copy()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xx+=2" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3. , 3.02020202, 3.04040404, 3.06060606, 3.08080808,\n", " 3.1010101 , 3.12121212, 3.14141414, 3.16161616, 3.18181818,\n", " 3.2020202 , 3.22222222, 3.24242424, 3.26262626, 3.28282828,\n", " 3.3030303 , 3.32323232, 3.34343434, 3.36363636, 3.38383838,\n", " 3.4040404 , 3.42424242, 3.44444444, 3.46464646, 3.48484848,\n", " 3.50505051, 3.52525253, 3.54545455, 3.56565657, 3.58585859,\n", " 3.60606061, 3.62626263, 3.64646465, 3.66666667, 3.68686869,\n", " 3.70707071, 3.72727273, 3.74747475, 3.76767677, 3.78787879,\n", " 3.80808081, 3.82828283, 3.84848485, 3.86868687, 3.88888889,\n", " 3.90909091, 3.92929293, 3.94949495, 3.96969697, 3.98989899,\n", " 4.01010101, 4.03030303, 4.05050505, 4.07070707, 4.09090909,\n", " 4.11111111, 4.13131313, 4.15151515, 4.17171717, 4.19191919,\n", " 4.21212121, 4.23232323, 4.25252525, 4.27272727, 4.29292929,\n", " 4.31313131, 4.33333333, 4.35353535, 4.37373737, 4.39393939,\n", " 4.41414141, 4.43434343, 4.45454545, 4.47474747, 4.49494949,\n", " 4.51515152, 4.53535354, 4.55555556, 4.57575758, 4.5959596 ,\n", " 4.61616162, 4.63636364, 4.65656566, 4.67676768, 4.6969697 ,\n", " 4.71717172, 4.73737374, 4.75757576, 4.77777778, 4.7979798 ,\n", " 4.81818182, 4.83838384, 4.85858586, 4.87878788, 4.8989899 ,\n", " 4.91919192, 4.93939394, 4.95959596, 4.97979798, 5. ])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xx" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.02020202, 1.04040404, 1.06060606, 1.08080808,\n", " 1.1010101 , 1.12121212, 1.14141414, 1.16161616, 1.18181818,\n", " 1.2020202 , 1.22222222, 1.24242424, 1.26262626, 1.28282828,\n", " 1.3030303 , 1.32323232, 1.34343434, 1.36363636, 1.38383838,\n", " 1.4040404 , 1.42424242, 1.44444444, 1.46464646, 1.48484848,\n", " 1.50505051, 1.52525253, 1.54545455, 1.56565657, 1.58585859,\n", " 1.60606061, 1.62626263, 1.64646465, 1.66666667, 1.68686869,\n", " 1.70707071, 1.72727273, 1.74747475, 1.76767677, 1.78787879,\n", " 1.80808081, 1.82828283, 1.84848485, 1.86868687, 1.88888889,\n", " 1.90909091, 1.92929293, 1.94949495, 1.96969697, 1.98989899,\n", " 2.01010101, 2.03030303, 2.05050505, 2.07070707, 2.09090909,\n", " 2.11111111, 2.13131313, 2.15151515, 2.17171717, 2.19191919,\n", " 2.21212121, 2.23232323, 2.25252525, 2.27272727, 2.29292929,\n", " 2.31313131, 2.33333333, 2.35353535, 2.37373737, 2.39393939,\n", " 2.41414141, 2.43434343, 2.45454545, 2.47474747, 2.49494949,\n", " 2.51515152, 2.53535354, 2.55555556, 2.57575758, 2.5959596 ,\n", " 2.61616162, 2.63636364, 2.65656566, 2.67676768, 2.6969697 ,\n", " 2.71717172, 2.73737374, 2.75757576, 2.77777778, 2.7979798 ,\n", " 2.81818182, 2.83838384, 2.85858586, 2.87878788, 2.8989899 ,\n", " 2.91919192, 2.93939394, 2.95959596, 2.97979798, 3. ])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Masking \n", "\n", "This only works with numpy array." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### numpy array vs. list" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xlist = [3,4,5,6,7,8,9]\n", "xarray = np.asarray([3,4,5,6,7,8,9]) # np.asarray(xlist)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3, 4, 5, 6, 7, 8, 9, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xlist2" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6, 8, 10, 12, 14, 16, 18])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xarray2" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "strangelist = [\"toto\",3,{},[]]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for : 'dict' and 'int'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-60-ad3befd67480>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrangelist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for : 'dict' and 'int'" ] } ], "source": [ "np.asarray(strangelist)2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### how to apply masking?\n", "\n", "Use Numpy ARRAY" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.02020202, 1.04040404, 1.06060606, 1.08080808,\n", " 1.1010101 , 1.12121212, 1.14141414, 1.16161616, 1.18181818,\n", " 1.2020202 , 1.22222222, 1.24242424, 1.26262626, 1.28282828,\n", " 1.3030303 , 1.32323232, 1.34343434, 1.36363636, 1.38383838,\n", " 1.4040404 , 1.42424242, 1.44444444, 1.46464646, 1.48484848,\n", " 1.50505051, 1.52525253, 1.54545455, 1.56565657, 1.58585859,\n", " 1.60606061, 1.62626263, 1.64646465, 1.66666667, 1.68686869,\n", " 1.70707071, 1.72727273, 1.74747475, 1.76767677, 1.78787879,\n", " 1.80808081, 1.82828283, 1.84848485, 1.86868687, 1.88888889,\n", " 1.90909091, 1.92929293, 1.94949495, 1.96969697, 1.98989899,\n", " 2.01010101, 2.03030303, 2.05050505, 2.07070707, 2.09090909,\n", " 2.11111111, 2.13131313, 2.15151515, 2.17171717, 2.19191919,\n", " 2.21212121, 2.23232323, 2.25252525, 2.27272727, 2.29292929,\n", " 2.31313131, 2.33333333, 2.35353535, 2.37373737, 2.39393939,\n", " 2.41414141, 2.43434343, 2.45454545, 2.47474747, 2.49494949,\n", " 2.51515152, 2.53535354, 2.55555556, 2.57575758, 2.5959596 ,\n", " 2.61616162, 2.63636364, 2.65656566, 2.67676768, 2.6969697 ,\n", " 2.71717172, 2.73737374, 2.75757576, 2.77777778, 2.7979798 ,\n", " 2.81818182, 2.83838384, 2.85858586, 2.87878788, 2.8989899 ,\n", " 2.91919192, 2.93939394, 2.95959596, 2.97979798, 3. ])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mask = x>2" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True,\n", " True, True, True, True, True, True, True, True, True, True], dtype=bool)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mask" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.01010101, 2.03030303, 2.05050505, 2.07070707, 2.09090909,\n", " 2.11111111, 2.13131313, 2.15151515, 2.17171717, 2.19191919,\n", " 2.21212121, 2.23232323, 2.25252525, 2.27272727, 2.29292929,\n", " 2.31313131, 2.33333333, 2.35353535, 2.37373737, 2.39393939,\n", " 2.41414141, 2.43434343, 2.45454545, 2.47474747, 2.49494949,\n", " 2.51515152, 2.53535354, 2.55555556, 2.57575758, 2.5959596 ,\n", " 2.61616162, 2.63636364, 2.65656566, 2.67676768, 2.6969697 ,\n", " 2.71717172, 2.73737374, 2.75757576, 2.77777778, 2.7979798 ,\n", " 2.81818182, 2.83838384, 2.85858586, 2.87878788, 2.8989899 ,\n", " 2.91919192, 2.93939394, 2.95959596, 2.97979798, 3. ])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[mask] # x[x>2]" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.01010101, 2.03030303, 2.05050505, 2.07070707, 2.09090909,\n", " 2.11111111, 2.13131313, 2.15151515, 2.17171717, 2.19191919,\n", " 2.21212121, 2.23232323, 2.25252525, 2.27272727, 2.29292929,\n", " 2.31313131, 2.33333333, 2.35353535, 2.37373737, 2.39393939,\n", " 2.41414141, 2.43434343, 2.45454545, 2.47474747, 2.49494949])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[ (x>2) & (x<2.5) ] # x[ (x>2) * (x>1.5) ] # both have to be true" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.50505051, 1.52525253, 1.54545455, 1.56565657, 1.58585859,\n", " 1.60606061, 1.62626263, 1.64646465, 1.66666667, 1.68686869,\n", " 1.70707071, 1.72727273, 1.74747475, 1.76767677, 1.78787879,\n", " 1.80808081, 1.82828283, 1.84848485, 1.86868687, 1.88888889,\n", " 1.90909091, 1.92929293, 1.94949495, 1.96969697, 1.98989899,\n", " 2.01010101, 2.03030303, 2.05050505, 2.07070707, 2.09090909,\n", " 2.11111111, 2.13131313, 2.15151515, 2.17171717, 2.19191919,\n", " 2.21212121, 2.23232323, 2.25252525, 2.27272727, 2.29292929,\n", " 2.31313131, 2.33333333, 2.35353535, 2.37373737, 2.39393939,\n", " 2.41414141, 2.43434343, 2.45454545, 2.47474747, 2.49494949,\n", " 2.51515152, 2.53535354, 2.55555556, 2.57575758, 2.5959596 ,\n", " 2.61616162, 2.63636364, 2.65656566, 2.67676768, 2.6969697 ,\n", " 2.71717172, 2.73737374, 2.75757576, 2.77777778, 2.7979798 ,\n", " 2.81818182, 2.83838384, 2.85858586, 2.87878788, 2.8989899 ,\n", " 2.91919192, 2.93939394, 2.95959596, 2.97979798, 3. ])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[ (x>2) \| (x>1.5) ] # x[ (x>2) + (x>1.5) ] # any have to be true" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The case of the NaN Value" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "iamnan = np.NaN" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iamnan" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iamnan==iamnan" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.inf==np.inf" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xwithnan = np.asarray([3,4,5,6,7,2,3,np.NaN,75,75])" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3., 4., 5., 6., 7., 2., 3., nan, 75., 75.])" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xwithnan" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6., 8., 10., 12., 14., 4., 6., nan, 150., 150.])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xwithnan2" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4+np.NaN" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4/np.NaN" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4*np.NaN" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(xwithnan)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20.0" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nanmean(xwithnan)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20.0" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(xwithnan[xwithnan==xwithnan])" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False, True, False, False], dtype=bool)" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "~(xwithnan==xwithnan)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False, True, False, False], dtype=bool)" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xwithnan!=xwithnan" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False, True, False, False], dtype=bool)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.isnan(xwithnan)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xwithnan = [3,4,5,6,7,2,3,np.NaN,75,75]" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xwithnan[xwithnan==xwithnan]" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0 == False" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "1 == True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Your first plot\n", "\n", "For ploting we are going to use matplotlib. let's plot 2 random variable a vs. b" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = np.random.rand(30)\n", "b = np.random.rand(30)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# plot within the notebook\n", "%matplotlib inline\n", "import matplotlib.pyplot as mpl" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x105998590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl = mpl.hist(a)" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x1067e86d0>" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LmM/1FSPpm5mdO7nSk6PRb9PGdXbD+fP9p5MgCAURwx8BmPPZTZvmXnbtWj29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\nj4IggqZBA57iOXCAo6W//BI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afeiguin/comp-phys	14_02_multilayer-networks.ipynb	1	194205	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## How a regression network is traditionally trained\n", "\n", "This network is trained using a data set $D = ({{\\bf x}^{(n)}, {\\bf t}^{(n)}})$ by adjusting ${\\bf w}$ so as to minimize an error function, e.g.,\n", "\n", "$$\n", "E_D({\\bf w}) = \\sum_n\\sum_i (y_i({\\bf x}^{(n)};{\\bf w}) - t_i^{(n)})^2\n", "$$\n", "\n", "This objective function is a sum of terms, one for each input/target pair $\\{ {\\bf x}, {\\bf t} \\}$, measuring how close the output ${\\bf y}({\\bf x}; {\\bf w})$ is to the target ${\\bf t}$:\n", "\n", "$$\n", "E_D({\\bf w}) = \\sum_n E_{\\bf x}^{(n)}, \\quad E_{\\bf x}^{(n)}=\\sum_i (y_i({\\bf x}^{(n)};{\\bf w}) - t_i^{(n)})^2\n", "$$\n", "\n", "\n", "\n", "This minimization is based on repeated evaluation of the gradient of $E_D$. This gradient can be efficiently computed using the backpropagation algorithm which uses the chain rule to find the derivatives, as we discuss below.\n", "\n", "Often, regularization (also known as weight decay) is included, modifying\n", "the objective function to:\n", "\n", "$$\n", "M({\\bf w})=\\alpha E_D({\\bf w}) + \\beta E_W({\\bf w}),\n", "$$\n", "where $E_W = \\frac{1}{2}\\sum_i w_i^2$.\n", "\n", "\n", "\n", "\n", "### Gradient descent\n", "(From Wikipedia)\n", "Cool animations at http://www.benfrederickson.com/numerical-optimization/\n", "\n", "Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point.\n", "\n", "Gradient descent is based on the observation that if the multi-variable function $ F(\\mathbf {x} )$ is defined and differentiable in a neighborhood of a point $ \\mathbf {a}$ , then $ F(\\mathbf {x} )$ decreases fastest if one goes from $ \\mathbf {a}$ in the direction of the negative gradient of $F$ at $ \\mathbf {a}$ , $ -\\nabla F(\\mathbf {a} )$. It follows that, if\n", "\n", "$$\\mathbf {a} _{n+1}=\\mathbf {a} _{n}-\\eta \\nabla F(\\mathbf {a} _{n})$$ \n", "\n", "for $\\eta$ small enough, then $F(\\mathbf {a_{n}} )\\geq F(\\mathbf {a_{n+1}} )$. In other words, the term $\\eta \\nabla F(\\mathbf {a} )$ is subtracted from $ \\mathbf {a}$ because we want to move against the gradient, namely down toward the minimum. With this observation in mind, one starts with a guess $\\mathbf {x} _{0}$ for a local minimum of $F$, and considers the sequence $\\mathbf {x} _{0},\\mathbf {x} _{1},\\mathbf {x} _{2},\\dots$ such that\n", "\n", "$${x} _{n+1}=\\mathbf {x} _{n}-\\gamma _{n}\\nabla F(\\mathbf {x} _{n}),\\ n\\geq 0.$$\n", "\n", "We have\n", "\n", "$F(\\mathbf {x} _{0})\\geq F(\\mathbf {x} _{1})\\geq F(\\mathbf {x} _{2})\\geq \\cdots$ ,\n", "so hopefully the sequence $(\\mathbf {x} _{n})$ converges to the desired local minimum. Note that the value of the step size $\\eta$ is allowed to change at every iteration.\n", "\n", "This process is illustrated in the adjacent picture. Here $F$ is assumed to be defined on the plane, and that its graph has a bowl shape. The blue curves are the contour lines, that is, the regions on which the value of $F$ is constant. A red arrow originating at a point shows the direction of the negative gradient at that point. Note that the (negative) gradient at a point is orthogonal to the contour line going through that point. We see that gradient descent leads us to the bottom of the bowl, that is, to the point where the value of the function $F$ is minimal.\n", "\n", "<img src=\"figures/Gradient_descent.png\" style=\"width: 350px;\"/>\n", "#### Illustration of the gradient descept procedure on a series of iterations down a bowl shaped surface\n", "\n", "The \"Zig-Zagging\" nature of the method is also evident below, where the gradient descent method is applied to $$F(x,y)=\\sin \\left({\\frac {1}{2}}x^{2}-{\\frac {1}{4}}y^{2}+3\\right)\\cos(2x+1-e^{y})$$\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3226478037930326 1.602369170618785\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot\n", "pyplot.rcParams['image.cmap'] = 'jet'\n", "import numpy as np\n", "\n", "x0 = -1.4\n", "y0 = 0.5\n", "x = [x0] # The algorithm starts at x0, y0\n", "y = [y0] \n", "\n", "eta = 0.1 # step size multiplier\n", "precision = 0.00001\n", "\n", "def f(x,y):\n", " f1 = x2/2-y2/4+3\n", " f2 = 2x+1-np.exp(y)\n", " return np.sin(f1)np.cos(f2)\n", "\n", "def gradf(x,y):\n", " f1 = x2/2-y2/4+3\n", " f2 = 2x+1-np.exp(y)\n", " dx = np.cos(f1)np.cos(f2)x-np.sin(f1)np.sin(f2)2.\n", " dy = np.cos(f1)np.cos(f2)(-y/2.)-np.sin(f1)np.sin(f2)(-np.exp(y))\n", " return (dx,dy)\n", "\n", "err = 100.\n", "while err > precision:\n", " (step_x, step_y) = gradf(x0, y0)\n", " x0 -= etastep_x\n", " y0 -= etastep_y\n", " x.append(x0)\n", " y.append(y0)\n", " err = eta(abs(step_x)+abs(step_y))\n", "\n", "\n", "print(x0,y0)\n", "\n", "#### All this below is just to visualize the process\n", "dx = 0.05\n", "dy = 0.05\n", "xx = np.arange(-1.5, 1.+dx, dx)\n", "yy = np.arange(0., 2.+dy, dy)\n", "V = np.zeros(shape=(len(yy),len(xx)))\n", "\n", "for iy in range(0,len(yy)):\n", " for ix in range(0,len(xx)):\n", " V[iy,ix] = f(xx[ix],yy[iy])\n", "\n", "X, Y = np.meshgrid(xx, yy)\n", "pyplot.contour(X, Y, V)\n", "\n", "#pyplot.plot(x,y,linestyle='--', lw=3);\n", "pyplot.scatter(x,y);\n", "\n", "pyplot.ylabel(\"y\")\n", "pyplot.xlabel(\"x\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stochastic gradient descent (SGD)\n", "\n", "Stochastic gradient descent (often shortened to SGD), also known as incremental gradient descent, is a stochastic approximation of the gradient descent optimization and iterative method for minimizing an objective function that is written as a sum of differentiable functions. \n", "\n", "There are a number of challenges in applying the gradient descent rule. To understand what the problem is, let's look back at the quadratic cost $E_D$. Notice that this cost function has the form $E=\\sum_n E_{\\bf x}^{(n)}$\n", "In practice, to compute the gradient $\\nabla E_D$\n", " we need to compute the gradients $\\nabla E_{\\bf x}^{(n)}$\n", " separately for each training input, ${\\bf x^{(n)}}$\n", "and then average them.\n", ". Unfortunately, when the number of training inputs is very large this can take a long time, and learning thus occurs slowly.\n", "\n", "Stochastic gradient descent can be used to speed up learning. The idea is to estimate the gradient $\\nabla E$\n", " by computing $\\nabla E_{\\bf x}$\n", "for a small sample of randomly chosen training inputs. By averaging over this small sample it turns out that we can quickly get a good estimate of the true gradient.\n", "\n", "<!--To make these ideas more precise, stochastic gradient descent works by randomly picking out a small number $m$\n", " of randomly chosen training inputs. We'll label those random training inputs ${\\bf x^{(1)},x^{(2)},…,x^{(m)}}$\n", ", and refer to them as a mini-batch. Provided the sample size m\n", " is large enough we expect that the average value of the $\\nabla E_x$\n", " will be roughly equal to the average over all of them, that is\n", "$$\\frac{1}{m}\\sum _{j=1}^m \\nabla E_{x^{j}} \\approx \\frac{1}{n}\\sum _{j=1}^n \\nabla E_{x^{j}}$$\n", "where the second sum is over the entire set of training data. \n", "!-->\n", "\n", "To connect this explicitly to learning in neural networks, suppose $w_k$\n", " and $b_l$\n", " denote the weights and biases in our neural network. Then stochastic gradient descent works by picking out a randomly chosen mini-batch of training inputs, and training with those,\n", "$$\n", "w_k \\rightarrow w_k - \\eta \\sum_{j=1}^m \\frac{\\partial{E_{\\bf x}^{(j)}}}{\\partial w_k}\n", "$$\n", "\n", "$$\n", "b_l \\rightarrow b_l - \\eta \\sum_{j=1}^m \\frac{\\partial{E_{\\bf x}^{(j)}}}{\\partial b_l}\n", "$$\n", "\n", "where the sums are over all the training examples in the current mini-batch. Then we pick out another randomly chosen mini-batch and train with those. And so on, until we have exhausted the training inputs, which is said to complete an epoch of training. At that point we start over with a new training epoch.\n", "\n", "The pseudocode would look like:\n", "\n", "`Choose an initial vector of parameters` $w$ `and learning rate` $\\eta$.\n", "\n", "`Repeat until an approximate minimum is obtained:`\n", "```\n", " Randomly shuffle examples in the training set.\n", " For i=1,2,...,n , do:\n", "```\n", "$\\quad \\quad \\quad \\quad \\quad w:=w-\\eta \\nabla E_{i}(w).$ \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: linear regression\n", "\n", "As seen previously, the objective function to be minimized is:\n", "\n", "$$\n", "\\begin{aligned}\n", "E(w)=\\sum _{i=1}^{n}E_{i}(w)=\\sum _{i=1}^{n}\\left(w_{1}+w_{2}x_{i}-y_{i}\\right)^{2}.\n", "\\end{aligned}\n", "$$\n", "\n", "And the gradent descent equations can be written in matrix form as:\n", "\n", "$$\n", "\\begin{bmatrix}w_{1}\\\\w_{2}\\end{bmatrix}:={\\begin{bmatrix}w_{1}\\\\w_{2}\\end{bmatrix}}-\\eta {\\begin{bmatrix}2(w_{1}+w_{2}x_{i}-y_{i})\\\\2x_{i}(w_{1}+w_{2}x_{i}-y_{i})\\end{bmatrix}}.\n", "$$\n", "\n", "We'll generate a series of 100 random points aligned more or less along the line $y=a+bx$ with $a=1$ and $b=2$\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.1637760980701564 2.001777141438794\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot\n", "import numpy as np\n", "\n", "a = 1\n", "b = 2\n", "num_points = 100\n", "np.random.seed(637163) # we make sure we always generate the same sequence\n", "x_data = np.random.rand(num_points)20.\n", "y_data = x_datab+a+3(2.np.random.rand(num_points)-1)\n", "\n", "pyplot.scatter(x_data,y_data)\n", "pyplot.plot(x_data, bx_data+a)\n", "\n", "#### Least squares fit\n", "sum_x = np.sum(x_data)\n", "sum_y = np.sum(y_data)\n", "sum_x2 = np.sum(x_data2)\n", "sum_xy = np.sum(x_datay_data)\n", "det = num_pointssum_x2-sum_x2\n", "fit_a = (sum_ysum_x2-sum_xsum_xy)/det\n", "fit_b = (num_pointssum_xy-sum_xsum_y)/det\n", "print(fit_a,fit_b)\n", "\n", "pyplot.xlim(-1,22)\n", "pyplot.ylim(-1,24)\n", "pyplot.plot(x_data, fit_bx_data+fit_a);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now write an SGD code for this problem. The training_data is a list of tuples `(x, y)` representing the training inputs and corresponding desired outputs. The variables `epochs` and `mini_batch_size` are what you'd expect - the number of epochs to train for, and the size of the mini-batches to use when sampling. `eta` is the learning rate, $\\eta$. If the optional argument `test_data` is supplied, then the program will evaluate the network after each epoch of training, and print out partial progress. This is useful for tracking progress, but slows things down substantially.\n", "\n", "The code works as follows. In each epoch, it starts by randomly shuffling the training data, and then partitions it into mini-batches of the appropriate size. This is an easy way of sampling randomly from the training data. Then for each `mini_batch` we apply a single step of gradient descent. This is done by the code `self.update_mini_batch(mini_batch, eta)`, which updates the coefficients according to a single iteration of gradient descent, using just the training data in `mini_batch`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 2.851091506514999 1.7876531781856742\n", "Epoch 1: 2.827178748563592 1.963573397229449\n", "Epoch 2: 2.7801714492150196 1.8619634110879069\n", "Epoch 3: 2.756668947213542 2.0288955459781213\n", "Epoch 4: 2.7076355088367525 1.880872788736252\n", "Epoch 5: 2.6709938639494206 1.9094861291196814\n", "Epoch 6: 2.6459565191117687 2.083968448182042\n", "Epoch 7: 2.592571941252409 1.867303977943171\n", "Epoch 8: 2.5668650012203917 1.9282486257504263\n", "Epoch 9: 2.5260855862259803 1.8254093290530535\n", "Epoch 10: 2.4977119308055653 1.8393082159024072\n", "Epoch 11: 2.461504007375818 1.8187446556619598\n", "Epoch 12: 2.440510503849123 1.9639665902712513\n", "Epoch 13: 2.4192661745417667 2.056408083610859\n", "Epoch 14: 2.3778520184589746 1.9200785689241224\n", "Epoch 15: 2.352636754920605 1.9557702061187\n", "Epoch 16: 2.32668522997869 1.985365726392641\n", "Epoch 17: 2.29093524056995 1.9302755099169442\n", "Epoch 18: 2.264373631096743 1.9014850942257417\n", "Epoch 19: 2.240116842945883 1.935758005663645\n", "Epoch 20: 2.2192529930462856 2.0084713102139746\n", "Epoch 21: 2.1882155215215175 1.954607851080249\n", "Epoch 22: 2.1610945265017794 1.871860736902566\n", "Epoch 23: 2.1460869019987685 2.0191055043901693\n", "Epoch 24: 2.1134743012209274 1.917120770897182\n", "Epoch 25: 2.093876809726992 1.9695147135445203\n", "Epoch 26: 2.0732373408257976 2.009659333749805\n", "Epoch 27: 2.0545056929658063 1.9990817647672674\n", "Epoch 28: 2.0358849885566888 2.0684880037349314\n", "Epoch 29: 2.0127961055519528 1.981549450254667\n", "Epoch 30: 1.9912110003344137 1.9953084240830377\n", "Epoch 31: 1.9702356965968113 1.9337881228020706\n", "Epoch 32: 1.958931435902377 1.9703136538892496\n", "Epoch 33: 1.9320635446309764 1.8826305504717291\n", "Epoch 34: 1.9183934975941435 1.9108781471703922\n", "Epoch 35: 1.9002148717519194 1.933657775282379\n", "Epoch 36: 1.8765188176622247 1.8501242260953568\n", "Epoch 37: 1.8636240317182582 1.8732118340436419\n", "Epoch 38: 1.8580542540860803 2.043103451641688\n", "Epoch 39: 1.8404926684574494 1.9833198887817372\n", "Epoch 40: 1.818471335258018 1.885853165755979\n", "Epoch 41: 1.8104060287044144 1.946463310907274\n", "Epoch 42: 1.7945224315136403 1.955383791204891\n", "Epoch 43: 1.782283463654412 2.0023405102821905\n", "Epoch 44: 1.749520717458214 1.8281773503780525\n", "Epoch 45: 1.7453813582714632 2.003640838074842\n", "Epoch 46: 1.728623006260412 1.9766876117635683\n", "Epoch 47: 1.7084093749222484 1.934436123013543\n", "Epoch 48: 1.6882385559184097 1.8316669036210635\n", "Epoch 49: 1.6872347550750442 1.9027956243651138\n", "Epoch 50: 1.6799350406816318 2.0067968405782595\n", "Epoch 51: 1.6544625765529073 1.8881985255370595\n", "Epoch 52: 1.6437316979583543 1.9255046345948543\n", "Epoch 53: 1.6381622976293797 2.0030543868483233\n", "Epoch 54: 1.619288123392432 1.9054952135108394\n", "Epoch 55: 1.6081107386909623 1.8842249840685168\n", "Epoch 56: 1.606385744453914 1.995354114039655\n", "Epoch 57: 1.590951010569201 1.935539287963154\n", "Epoch 58: 1.5907159977092078 2.0039496119285936\n", "Epoch 59: 1.583681348958318 2.0712979889802097\n", "Epoch 60: 1.5630265005446518 1.9298084540897775\n", "Epoch 61: 1.564720140787769 2.0808310251200517\n", "Epoch 62: 1.5466686232306155 1.9187231079896427\n", "Epoch 63: 1.5388524762367308 1.9621349285397989\n", "Epoch 64: 1.5405490598559062 2.0291358831335318\n", "Epoch 65: 1.5208453821228052 1.8248931026367041\n", "Epoch 66: 1.5213798489903287 1.8930706301200002\n", "Epoch 67: 1.529153914875216 2.1490931535209667\n", "Epoch 68: 1.5022716493694421 1.9262311478789624\n", "Epoch 69: 1.500068778772673 2.0144696446422423\n", "Epoch 70: 1.4895292591761131 1.9988696565984023\n", "Epoch 71: 1.4750010294727158 1.9334703018634691\n", "Epoch 72: 1.468734739081002 1.9509592360886463\n", "Epoch 73: 1.4602541491491496 1.9535588676349709\n", "Epoch 74: 1.4629985193934691 2.087981232637853\n", "Epoch 75: 1.4482903637054756 1.9769627087994208\n", "Epoch 76: 1.4441169372446487 1.9911884358343\n", "Epoch 77: 1.4374914169928699 1.9697513594833118\n", "Epoch 78: 1.4290881203003065 1.9810815298729503\n", "Epoch 79: 1.4246361485906045 2.021522320675344\n", "Epoch 80: 1.416276663539712 1.997969411686069\n", "Epoch 81: 1.4088098511794314 1.9930406863592567\n", "Epoch 82: 1.4068185837259672 2.0164782110987383\n", "Epoch 83: 1.3962324622369873 1.8977840351410191\n", "Epoch 84: 1.401436092664027 1.9950621893919915\n", "Epoch 85: 1.387471145834617 1.8824887521430227\n", "Epoch 86: 1.4014093218694679 2.1053832817280766\n", "Epoch 87: 1.3846125002362668 1.961879809671709\n", "Epoch 88: 1.3803176460734246 1.9693072109387666\n", "Epoch 89: 1.3733167787595022 1.9193370938253795\n", "Epoch 90: 1.3719395040783267 1.9529063749285855\n", "Epoch 91: 1.3657851409032489 1.9519469364018414\n", "Epoch 92: 1.365731695836303 2.0299060306472323\n", "Epoch 93: 1.352704037805701 1.9068562249348497\n", "Epoch 94: 1.358872635570531 1.9505330043142486\n", "Epoch 95: 1.352406342772186 1.924937800945274\n", "Epoch 96: 1.3529921471002395 1.9582904963736072\n", "Epoch 97: 1.3467058766687356 1.964421345479529\n", "Epoch 98: 1.3441056012495736 1.9585772301210544\n", "Epoch 99: 1.338307075761737 1.9641242330382802\n", "Epoch 100: 1.3389750343328595 2.0331089097262622\n", "Epoch 101: 1.3292056062449498 1.970316569830166\n", "Epoch 102: 1.3286221627701793 2.0548925899273556\n", "Epoch 103: 1.3249933581558049 2.0102278112773337\n", "Epoch 104: 1.3141363246433797 1.9167494489192494\n", "Epoch 105: 1.3112904405797088 1.9230166472931127\n", "Epoch 106: 1.3155463891174177 2.0363423473576034\n", "Epoch 107: 1.3066507352411303 2.002921226309002\n", "Epoch 108: 1.3012267385619987 1.992156333232618\n", "Epoch 109: 1.2998811762793303 2.0836475234636054\n", "Epoch 110: 1.2869768488136015 2.0326476069467847\n", "Epoch 111: 1.2782409042449163 1.955308271763259\n", "Epoch 112: 1.2702227407769626 1.843641680619616\n", "Epoch 113: 1.2841364346903865 2.0428258403939537\n", "Epoch 114: 1.2799771181355302 2.027691157552291\n", "Epoch 115: 1.272749198706809 1.9838436304512466\n", "Epoch 116: 1.2751164588056392 2.048106822415394\n", "Epoch 117: 1.2709851254712097 1.9917962774665723\n", "Epoch 118: 1.2762427149034599 2.136723976667836\n", "Epoch 119: 1.2614286952500717 1.9537916421283819\n", "Epoch 120: 1.268051811501334 2.003348786243271\n", "Epoch 121: 1.2705624559612398 2.05644114955335\n", "Epoch 122: 1.2488428579582682 1.824502349600589\n", "Epoch 123: 1.2609746890519609 2.046167589066578\n", "Epoch 124: 1.2649024819516497 2.073455837171682\n", "Epoch 125: 1.2553435081447994 1.9933812628787015\n", "Epoch 126: 1.2598887147760574 2.0313796669882813\n", "Epoch 127: 1.2599147404360962 2.062627907835911\n", "Epoch 128: 1.249692892431034 1.975233671612488\n", "Epoch 129: 1.2442021578463776 1.9552628960704093\n", "Epoch 130: 1.2386105150995876 1.982099666924853\n", "Epoch 131: 1.2355586187412317 1.9757114294223843\n", "Epoch 132: 1.2443294895643744 2.101623933536461\n", "Epoch 133: 1.2299004597831222 1.9768187344562476\n", "Epoch 134: 1.2276038542035699 1.9822626470879492\n", "Epoch 135: 1.2277446415672508 1.9615810105868339\n", "Epoch 136: 1.2286379676808046 2.0355041539229313\n", "Epoch 137: 1.2211714193515004 1.9701469343010707\n", "Epoch 138: 1.2298287656838378 2.024296033663489\n", "Epoch 139: 1.227733776450261 2.035072556742687\n", "Epoch 140: 1.2188039214924324 1.9586362914585522\n", "Epoch 141: 1.221958994511311 2.018941730208499\n", "Epoch 142: 1.2157233735073354 1.9671959386100284\n", "Epoch 143: 1.2124366156263169 1.9592948983993996\n", "Epoch 144: 1.2147369220284092 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"Epoch 405: 1.160096559777111 1.9573745798179463\n", "Epoch 406: 1.1561184001485691 1.9270445802835297\n", "Epoch 407: 1.1564428794892025 1.9534130611724356\n", "Epoch 408: 1.157832333324879 1.9990185814286516\n", "Epoch 409: 1.1542536578440337 1.9603170999631656\n", "Epoch 410: 1.1579641202641253 2.0197871817756243\n", "Epoch 411: 1.1483763991029288 1.9513250981321313\n", "Epoch 412: 1.1404922532824875 1.9209794035824443\n", "Epoch 413: 1.1507849734020454 2.0644281753554914\n", "Epoch 414: 1.1522729146307125 2.014082678488246\n", "Epoch 415: 1.1539226907918823 2.0452288186933716\n", "Epoch 416: 1.1553935535896283 2.0516110385635793\n", "Epoch 417: 1.1522612541246897 2.0132793132417066\n", "Epoch 418: 1.1445911719870643 1.8998976720242748\n", "Epoch 419: 1.15111755242973 1.9942442880638716\n", "Epoch 420: 1.1492918953099753 1.9723618584340097\n", "Epoch 421: 1.1508398588156288 2.0285906972912917\n", "Epoch 422: 1.1457613876618533 1.9442137226381164\n", "Epoch 423: 1.1544611539760208 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"Epoch 461: 1.170713830400304 2.044528597373633\n", "Epoch 462: 1.1771672554199253 2.152225397121842\n", "Epoch 463: 1.165837688657203 2.0185833196372154\n", "Epoch 464: 1.1737217995420748 2.1373669706733183\n", "Epoch 465: 1.1578672638403995 1.9871013310791956\n", "Epoch 466: 1.155718232390874 1.9557080632755361\n", "Epoch 467: 1.1557765390859782 1.9816049106330598\n", "Epoch 468: 1.1618268652269887 2.070405227570197\n", "Epoch 469: 1.161491829599467 2.055096537614472\n", "Epoch 470: 1.1501254958475469 1.9661249599952544\n", "Epoch 471: 1.1572012124584645 2.0097660872796412\n", "Epoch 472: 1.1589604474043982 2.0302610947822126\n", "Epoch 473: 1.1570301975614823 2.023791417809254\n", "Epoch 474: 1.154208144563905 1.9919144916032592\n", "Epoch 475: 1.1519935009767621 1.9600780058834755\n", "Epoch 476: 1.1523555948256758 1.9765881895154538\n", "Epoch 477: 1.157402986756551 1.9904666517287626\n", "Epoch 478: 1.1633983269171395 2.052477744992646\n", "Epoch 479: 1.1599612891715394 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"Epoch 517: 1.1724045019844676 2.091767385080294\n", "Epoch 518: 1.1716510265738609 2.0843143345325106\n", "Epoch 519: 1.1686269961900118 2.073748595118983\n", "Epoch 520: 1.1638694305193875 2.0280956923017217\n", "Epoch 521: 1.1636480533268407 2.0332406500489677\n", "Epoch 522: 1.1603089324484057 1.9955956478220662\n", "Epoch 523: 1.1655246105364943 2.0737222183350674\n", "Epoch 524: 1.1662126905080017 2.076627691101107\n", "Epoch 525: 1.1568865515059628 1.974100652504382\n", "Epoch 526: 1.168518075087314 2.116093664338233\n", "Epoch 527: 1.1739213365871974 2.1025021700328166\n", "Epoch 528: 1.1640961527037137 2.0044410213216626\n", "Epoch 529: 1.1672718428903104 2.0129850523107686\n", "Epoch 530: 1.161172217703352 1.9262876678915872\n", "Epoch 531: 1.1585188388036571 1.9180818748253303\n", "Epoch 532: 1.1653540395516186 2.0225110646631754\n", "Epoch 533: 1.157019824202749 1.9078821673186914\n", "Epoch 534: 1.1661352704968753 2.005397500866252\n", "Epoch 535: 1.1627653941287703 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1.1651457482691459 2.0102017022980583\n", "Epoch 555: 1.1715635029370517 2.0164581257660914\n", "Epoch 556: 1.1764307339401388 2.0724774274837436\n", "Epoch 557: 1.1669823043140815 1.9717705460185897\n", "Epoch 558: 1.1744484834107063 1.997186379174134\n", "Epoch 559: 1.169286604704071 1.9482003074049572\n", "Epoch 560: 1.1708232269684675 1.9670896160116247\n", "Epoch 561: 1.177918040159993 2.0833842383961665\n", "Epoch 562: 1.175450588919051 2.0409732812502694\n", "Epoch 563: 1.1757013259264324 2.036028007875955\n", "Epoch 564: 1.169852341967423 1.9178011529882681\n", "Epoch 565: 1.1839854524569744 2.1097700742101275\n", "Epoch 566: 1.1699901890362303 1.9443972610595948\n", "Epoch 567: 1.1784189477903166 2.0134108575047995\n", "Epoch 568: 1.1724111060404596 1.9501752190008588\n", "Epoch 569: 1.1793644855150411 2.058851187496867\n", "Epoch 570: 1.1710704634361726 1.9844881108378327\n", "Epoch 571: 1.170637349147944 2.0128491214203437\n", "Epoch 572: 1.1674859871552459 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1.1709976070406567 2.029604329245804\n", "Epoch 592: 1.1716528984848746 2.0380568684025455\n", "Epoch 593: 1.1686722556889133 2.0331959031481532\n", "Epoch 594: 1.1632339738919797 1.9820065033475909\n", "Epoch 595: 1.1564164096740799 1.9271097108640185\n", "Epoch 596: 1.1666694828568867 2.0724520045260295\n", "Epoch 597: 1.1537707827222201 1.9181065128036707\n", "Epoch 598: 1.1571075122251715 1.9705058791561187\n", "Epoch 599: 1.1721611168487915 2.1617614972119306\n", "Epoch 600: 1.155183953593385 1.9749704550186957\n", "Epoch 601: 1.1554366688928757 1.9844079087563904\n", "Epoch 602: 1.1647280640469377 2.081337702842052\n", "Epoch 603: 1.1658496108379754 2.1060936098291947\n", "Epoch 604: 1.14758503546801 1.8717290219119311\n", "Epoch 605: 1.1510322758027156 1.9657134666956575\n", "Epoch 606: 1.1535227944197683 1.989369661635029\n", "Epoch 607: 1.1538889452189671 2.0018450944565944\n", "Epoch 608: 1.1635852958354194 2.1179681261409202\n", "Epoch 609: 1.1554217599794048 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1.150385307559757 2.027778902322227\n", "Epoch 629: 1.1530967199002244 2.0626847385825093\n", "Epoch 630: 1.1531172636195939 2.065527734414501\n", "Epoch 631: 1.1490742275494856 1.9638324313626334\n", "Epoch 632: 1.155315310074967 2.102823402912834\n", "Epoch 633: 1.1417601690775259 1.9172810647771976\n", "Epoch 634: 1.1510159534567739 2.010997298421388\n", "Epoch 635: 1.149005741676144 1.9937017899446772\n", "Epoch 636: 1.155222921847631 2.01113908577509\n", "Epoch 637: 1.150820535642392 1.9526606615164677\n", "Epoch 638: 1.1551409338435366 2.000433609996005\n", "Epoch 639: 1.1578834103979951 2.0468988071021372\n", "Epoch 640: 1.1565305856473265 2.0128183635846515\n", "Epoch 641: 1.1591606428192935 2.01835623644454\n", "Epoch 642: 1.1569524851263293 2.045253459625615\n", "Epoch 643: 1.1514787531975645 1.9551687657003172\n", "Epoch 644: 1.163649621435769 2.0838699038768436\n", "Epoch 645: 1.1472411948456802 1.9178902458696894\n", "Epoch 646: 1.1447468750038605 1.8467790295194404\n", "Epoch 647: 1.165686895471076 2.1111351565553234\n", "Epoch 648: 1.1576923239875891 1.9899596857567805\n", "Epoch 649: 1.161485272855309 2.025151149884347\n", "Epoch 650: 1.1597597440083867 1.908835408981916\n", "Epoch 651: 1.1650400581070193 2.0420247761455457\n", "Epoch 652: 1.1675470577075604 2.062979899616677\n", "Epoch 653: 1.159614412837413 1.994903243221295\n", "Epoch 654: 1.1582505049578176 1.9941648150426208\n", "Epoch 655: 1.1634176578590663 2.057592596278545\n", "Epoch 656: 1.1611885297523123 2.007311825230074\n", "Epoch 657: 1.154168506409637 1.9096176697993068\n", "Epoch 658: 1.1658267998253125 2.0735413463909116\n", "Epoch 659: 1.1622960295547577 1.9920497664560168\n", "Epoch 660: 1.1605113725197804 1.9769764654165227\n", "Epoch 661: 1.1688240775030034 2.0720466891643783\n", "Epoch 662: 1.166474635389868 2.043905242100123\n", "Epoch 663: 1.1561023168581162 1.891245447819258\n", "Epoch 664: 1.170902381424022 2.0453216165097943\n", "Epoch 665: 1.1735445476514155 2.0428657951344187\n", "Epoch 666: 1.172054013314705 2.0262399185341877\n", "Epoch 667: 1.1708034841598174 2.0146171031004765\n", "Epoch 668: 1.1671142915453008 1.9348631427426084\n", "Epoch 669: 1.1775445636369115 2.0477901564859926\n", "Epoch 670: 1.1644419422264496 1.919474895221312\n", "Epoch 671: 1.1741559338165275 2.074967660671925\n", "Epoch 672: 1.1731059472314012 2.0687478375680235\n", "Epoch 673: 1.1625465689120127 1.9400170806618209\n", "Epoch 674: 1.1621233434513776 1.9331084122401405\n", "Epoch 675: 1.172690681588269 2.078074424231667\n", "Epoch 676: 1.1673711696972886 2.0179073495059945\n", "Epoch 677: 1.1706102870624606 2.0697200328933634\n", "Epoch 678: 1.1703580494763104 1.9874324564763488\n", "Epoch 679: 1.1619811693165076 1.8821263438324085\n", "Epoch 680: 1.1760639541364406 2.0506844372123743\n", "Epoch 681: 1.1768429181245823 2.0749670618770857\n", "Epoch 682: 1.1636128240680195 1.8879778704396915\n", "Epoch 683: 1.1709499629950786 1.9835964000124204\n", "Epoch 684: 1.176966935674532 2.0451598736206993\n", "Epoch 685: 1.1748268450232888 2.010486718394343\n", "Epoch 686: 1.172105455781458 1.9888604323678445\n", "Epoch 687: 1.1773147388707024 2.0552232315545726\n", "Epoch 688: 1.174981523032786 2.025749691408062\n", "Epoch 689: 1.175802764810576 2.0272108047081865\n", "Epoch 690: 1.1741388731261873 2.0438495359399673\n", "Epoch 691: 1.1645637415696226 1.914852964763105\n", "Epoch 692: 1.1775437201929273 2.0837327430593087\n", "Epoch 693: 1.171021545689541 2.0380823234432075\n", "Epoch 694: 1.1720299111368093 2.077853785353434\n", "Epoch 695: 1.166607405867305 2.0266663943476817\n", "Epoch 696: 1.1616206086636185 2.0041200728231723\n", "Epoch 697: 1.1650256609464475 2.0303823418621296\n", "Epoch 698: 1.164814414701397 1.9838895901176739\n", "Epoch 699: 1.1743731417049934 2.121649344069409\n", "Epoch 700: 1.1729325183302142 2.090510587257664\n", "Epoch 701: 1.167460302805175 1.9853618003596634\n", "Epoch 702: 1.171378151734284 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1.1765205975787607 1.9578502910758795\n", "Epoch 722: 1.1774642175778434 2.019709090157791\n", "Epoch 723: 1.1680699833280164 1.898312445095025\n", "Epoch 724: 1.1761840770108003 2.0462077852453637\n", "Epoch 725: 1.1718479222055256 2.0058071438445033\n", "Epoch 726: 1.1664817877999039 1.9096610999007464\n", "Epoch 727: 1.1724817886067012 1.9513913121667488\n", "Epoch 728: 1.177878687532817 2.0048043751218154\n", "Epoch 729: 1.1805702427200604 2.038253110489647\n", "Epoch 730: 1.1731762805250165 1.9773643250294353\n", "Epoch 731: 1.1719481861256338 1.9681530248838734\n", "Epoch 732: 1.170958734170868 1.931736000361683\n", "Epoch 733: 1.1809837229509716 2.076098698308606\n", "Epoch 734: 1.1814920816011996 2.1008648023647707\n", "Epoch 735: 1.1739589961909729 1.9850473133869209\n", "Epoch 736: 1.175209416452425 2.010759548096611\n", "Epoch 737: 1.17394430015845 1.980847883428966\n", "Epoch 738: 1.1742166094964246 1.9785416003352714\n", "Epoch 739: 1.176104874697144 1.9923927012366787\n", "Epoch 740: 1.1713424931242489 1.8749667813350874\n", "Epoch 741: 1.179252499460744 1.9830882619410506\n", "Epoch 742: 1.1719552117063976 1.8948350185906075\n", "Epoch 743: 1.1823893626823063 2.0678006992277806\n", "Epoch 744: 1.1780966097356345 1.998036497360876\n", "Epoch 745: 1.179165780753876 2.004630972509234\n", "Epoch 746: 1.1835778630200546 2.0950285707921648\n", "Epoch 747: 1.1766786813161292 2.044100782536718\n", "Epoch 748: 1.160163503077738 1.9177065378896447\n", "Epoch 749: 1.1675883035849126 2.014212936737423\n", "Epoch 750: 1.1634300571662926 1.9721611100038532\n", "Epoch 751: 1.1715877858447237 2.043941039719496\n", "Epoch 752: 1.1584171500053826 1.8746409041865049\n", "Epoch 753: 1.1763127461801561 2.0918118518975723\n", "Epoch 754: 1.1659716692903694 1.9742224619202564\n", "Epoch 755: 1.1646604634177344 1.9361122857977011\n", "Epoch 756: 1.160620833248367 1.8662711354233066\n", "Epoch 757: 1.1661982405581681 1.968819352593975\n", "Epoch 758: 1.1629045745496742 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"Epoch 796: 1.1691174033705247 1.9006829597676416\n", "Epoch 797: 1.1729928727329737 1.9640889316484784\n", "Epoch 798: 1.1778463811902438 1.9921573108254367\n", "Epoch 799: 1.1773514612461151 1.9667117251989434\n", "Epoch 800: 1.177124613518903 2.0343926448254734\n", "Epoch 801: 1.1749925961056273 1.985964658981048\n", "Epoch 802: 1.179957250611606 2.047084039569667\n", "Epoch 803: 1.178775628421662 2.0210086054432637\n", "Epoch 804: 1.175220208981832 2.02350131862926\n", "Epoch 805: 1.1673621446963132 1.960305978293802\n", "Epoch 806: 1.1791041023504112 2.1120544016014757\n", "Epoch 807: 1.178017568433982 2.0771402984542813\n", "Epoch 808: 1.165647287412458 1.9602070874952493\n", "Epoch 809: 1.1581522666958617 1.9120867281927372\n", "Epoch 810: 1.1574695893847933 1.9553021737969036\n", "Epoch 811: 1.1532611898613325 1.9119749320993886\n", "Epoch 812: 1.1614700136038754 1.98716206728456\n", "Epoch 813: 1.1671994116082518 2.0275299821752375\n", "Epoch 814: 1.1666677665583243 2.0417365532450047\n", "Epoch 815: 1.1697040029415398 2.0734529891667592\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 816: 1.1686166547942265 2.0558238071783412\n", "Epoch 817: 1.1601948873090822 2.010241210464497\n", "Epoch 818: 1.150618385029723 1.9258408855428137\n", "Epoch 819: 1.1636429174708893 2.0233682903580497\n", "Epoch 820: 1.1622884142555794 2.0014636002126447\n", "Epoch 821: 1.1555748556733847 1.906875567540085\n", "Epoch 822: 1.1664335200140223 2.0571278055304023\n", "Epoch 823: 1.1583224588173926 1.9869757506956676\n", "Epoch 824: 1.1702089173813757 2.1543993462777062\n", "Epoch 825: 1.1616518864819756 2.0773275184740485\n", "Epoch 826: 1.1551967684697095 2.0063270888888423\n", "Epoch 827: 1.152287628406903 1.9779246087420448\n", "Epoch 828: 1.157850677359235 2.057287178586857\n", "Epoch 829: 1.1556715647828006 2.0239885485233216\n", "Epoch 830: 1.1565890096397193 2.0503171254560946\n", "Epoch 831: 1.1595389084512515 1.98795193268577\n", "Epoch 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906: 1.1464585126021198 2.0148125135693653\n", "Epoch 907: 1.1404403740371 1.9297813616045891\n", "Epoch 908: 1.1463488767108843 2.0096770631354377\n", "Epoch 909: 1.1546029622929534 2.0832874132502397\n", "Epoch 910: 1.150169572833268 2.03405456699914\n", "Epoch 911: 1.1461421224307 1.940212083531217\n", "Epoch 912: 1.1461235515941184 1.9879469239195826\n", "Epoch 913: 1.1437277165448931 1.9458425306722948\n", "Epoch 914: 1.1465826613349566 1.963993027082908\n", "Epoch 915: 1.153700095135566 2.061082796293308\n", "Epoch 916: 1.145535162769503 1.9399497199454492\n", "Epoch 917: 1.1514181013196756 2.00126064285679\n", "Epoch 918: 1.145316405324972 1.8983230429796274\n", "Epoch 919: 1.14742180546 1.9248133926280373\n", "Epoch 920: 1.1453945133821228 1.9258806454553379\n", "Epoch 921: 1.1564822331850053 2.0417494917942447\n", "Epoch 922: 1.16532666842355 2.111873507098976\n", "Epoch 923: 1.1540591309061792 1.9727663628126484\n", "Epoch 924: 1.1511551928026063 1.9398995635622858\n", "Epoch 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1.1619161143374852 1.8923934588954494\n", "Epoch 963: 1.1697457741957606 1.9844759110197736\n", "Epoch 964: 1.1697351096822857 1.9741557080092809\n", "Epoch 965: 1.1674910142105261 1.970702981080298\n", "Epoch 966: 1.1708913097774833 2.035510742775975\n", "Epoch 967: 1.163560206727453 1.959034424532293\n", "Epoch 968: 1.1645150122282628 2.0045453242661675\n", "Epoch 969: 1.1679107423551707 2.063905013621921\n", "Epoch 970: 1.1677195713875725 2.0433237073388213\n", "Epoch 971: 1.17002548380566 2.087239453154267\n", "Epoch 972: 1.1669677656803277 2.00776004658578\n", "Epoch 973: 1.1693951222961703 2.0054310660716648\n", "Epoch 974: 1.163591461957682 1.9957651646191144\n", "Epoch 975: 1.1631281436776817 1.996814233291856\n", "Epoch 976: 1.1657782005156223 2.0337180441688014\n", "Epoch 977: 1.1613277185536075 1.9761369889578149\n", "Epoch 978: 1.1715340660463078 2.0889472256757897\n", "Epoch 979: 1.1643274955849614 2.0325757476315434\n", "Epoch 980: 1.169053706321655 2.1097168551532635\n", "Epoch 981: 1.162086973500796 1.9555235042178425\n", "Epoch 982: 1.1659416789106982 2.0106789994605276\n", "Epoch 983: 1.169102413154393 2.0281705562256747\n", "Epoch 984: 1.1554108232659615 1.9228184709910991\n", "Epoch 985: 1.1588930741118912 1.921251824994387\n", "Epoch 986: 1.1550587111137056 1.8840277756684038\n", "Epoch 987: 1.1638976152823144 2.025125792607056\n", "Epoch 988: 1.1671422605177448 2.097307271135845\n", "Epoch 989: 1.1556821951123852 1.9115550838186852\n", "Epoch 990: 1.160829285965992 1.940623122785074\n", "Epoch 991: 1.165446317556802 2.000186893753661\n", "Epoch 992: 1.1718972107106718 2.076132788705562\n", "Epoch 993: 1.163709861587923 1.9829789758500083\n", "Epoch 994: 1.1624906108985786 2.000615035032854\n", "Epoch 995: 1.1613760832770657 1.9794077021416954\n", "Epoch 996: 1.1678270786062839 2.0712825199502247\n", "Epoch 997: 1.1632112053389412 2.0243115044550732\n", "Epoch 998: 1.1598619426016632 1.9867915874642743\n", "Epoch 999: 1.1667041528951323 2.064176860885111\n" ] } ], "source": [ "epochs = 1000\n", "mini_batch_size = 10\n", "eta = 0.01/mini_batch_size\n", "\n", "a = 3.\n", "b = 3.\n", "def update_mini_batch(mini_batch, eta):\n", " global a, b\n", " a0 = a\n", " b0 = b\n", " for x, y, in mini_batch:\n", " e = eta(a0+b0x-y)\n", " a -= e\n", " b -= xe\n", " \n", "training_data = list(zip(x_data,y_data))\n", "for j in range(epochs):\n", " np.random.shuffle(training_data)\n", " mini_batches = [training_data[k:k+mini_batch_size]\n", " for k in range(0, len(training_data), mini_batch_size)]\n", " for mini_batch in mini_batches:\n", " update_mini_batch(mini_batch, eta)\n", " print (\"Epoch {0}: {1} {2}\".format(j,a,b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Challenge 14.2\n", "\n", "Use SGD to train the single neuron in the previous notebook using a linearly separable set of 100 points, divided by the line $-\\frac{5}{2}x+\\frac{3}{2}y+3=0$ " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "### We provide a set of randomly generated training points \n", "num_points = 100\n", "w1 = -2.5\n", "w2 = 1.5\n", "w0 = 3.\n", "np.random.seed(637163) # we make sure we always generate the same sequence\n", "x_data = np.random.rand(num_points)10.\n", "y_data = np.random.rand(num_points)10.\n", "z_data = np.zeros(num_points)\n", "for i in range(len(z_data)):\n", " if (y_data[i] > (-w0-w1x_data[i])/w2):\n", " z_data[i] = 1.\n", "\n", "pyplot.scatter(x_data,y_data,c=z_data,marker='o',linewidth=1.5,edgecolors='black')\n", "pyplot.plot(x_data,(-w1x_data-w0)/w2)\n", "pyplot.gray()\n", "pyplot.xlim(0,10)\n", "pyplot.ylim(0,10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will need the following auxiliary functions:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def sigmoid(z):\n", " \"\"\"The sigmoid function.\"\"\"\n", " return 1.0/(1.0+np.exp(-z))\n", "\n", "def sigmoid_prime(z):\n", " \"\"\"Derivative of the sigmoid function.\"\"\"\n", " return sigmoid(z)(1-sigmoid(z))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## A simple network to classify handwritten digits\n", "Most of this section has been taken from M. Nielsen's free on-line book: \"Neural Networks and Deep Learning\" http://neuralnetworksanddeeplearning.com/\n", "\n", "In this section we discuss a neural network which can solve the more interesting and difficult problem, namely, recognizing individual handwritten digits.\n", "\n", "The input layer of the network contains neurons encoding the values of the input pixels. Our training data for the network will consist of many 28 by 28\n", " pixel images of scanned handwritten digits, and so the input layer contains 784=28×28\n", " neurons. The input pixels are greyscale, with a value of 0.0\n", " representing white, a value of 1.0\n", "representing black, and in between values representing gradually darkening shades of grey.\n", "\n", "The second layer of the network is a hidden layer. We denote the number of neurons in this hidden layer by $n$\n", ", and we'll experiment with different values for $n$\n", ". The example shown illustrates a small hidden layer, containing just $n=15$\n", " neurons.\n", " \n", " The output layer of the network contains 10 neurons. If the first neuron fires, i.e., has an output $\\sim 1$\n", ", then that will indicate that the network thinks the digit is a 0\n", ". If the second neuron fires then that will indicate that the network thinks the digit is a 1\n", ". And so on. A little more precisely, we number the output neurons from 0\n", " through 9\n", ", and figure out which neuron has the highest activation value. If that neuron is, say, neuron number 6\n", ", then our network will guess that the input digit was a 6\n", ". And so on for the other output neurons.\n", "\n", "<img src=\"figures/nnetwork.png\" style=\"width: 500px;\"/>\n", "\n", "#### Network to identify single digits. The output layer has 10 neurons, one for each digit.\n", "\n", "\n", "\n", "The first thing we'll need is a data set to learn from - a so-called training data set. We'll use the MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications. MNIST's name comes from the fact that it is a modified subset of two data sets collected by NIST, the United States' National Institute of Standards and Technology. Here's a few images from MNIST:\n", "\n", "<img src=\"figures/digits_separate.png\" style=\"width: 250px;\"/>\n", "\n", "\n", "\n", "The MNIST data comes in two parts. The first part contains 60,000 images to be used as training data. These images are scanned handwriting samples from 250 people, half of whom were US Census Bureau employees, and half of whom were high school students. The images are greyscale and 28 by 28 pixels in size. The second part of the MNIST data set is 10,000 images to be used as test data. Again, these are 28 by 28 greyscale images. We'll use the test data to evaluate how well our neural network has learned to recognize digits. To make this a good test of performance, the test data was taken from a different set of 250 people than the original training data (albeit still a group split between Census Bureau employees and high school students). This helps give us confidence that our system can recognize digits from people whose writing it didn't see during training.\n", "\n", "In practice, we are going to split the data a little differently. We'll leave the test images as is, but split the 60,000-image MNIST training set into two parts: a set of 50,000 images, which we'll use to train our neural network, and a separate 10,000 image validation set.\n", "\n", "We'll use the notation $x$\n", " to denote a training input. It'll be convenient to regard each training input $x$\n", " as a 28×28=784-dimensional vector. Each entry in the vector represents the grey value for a single pixel in the image. We'll denote the corresponding desired output by y=y(x)\n", ", where y\n", " is a 10\n", "-dimensional vector. For example, if a particular training image, $x$\n", ", depicts a 6\n", ", then $y(x)=(0,0,0,0,0,0,1,0,0,0)^T$\n", " is the desired output from the network. Note that T\n", " here is the transpose operation, turning a row vector into an ordinary (column) vector.\n", " " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "mnist_loader\n", "~~~~~~~~~~~~\n", "\n", "A library to load the MNIST image data. For details of the data\n", "structures that are returned, see the doc strings for ``load_data``\n", "and ``load_data_wrapper``. In practice, ``load_data_wrapper`` is the\n", "function usually called by our neural network code.\n", "\"\"\"\n", "\n", "#### Libraries\n", "# Standard library\n", "import pickle\n", "import gzip\n", "\n", "# Third-party libraries\n", "import numpy as np\n", "\n", "def load_data():\n", " \"\"\"Return the MNIST data as a tuple containing the training data,\n", " the validation data, and the test data.\n", "\n", " The ``training_data`` is returned as a tuple with two entries.\n", " The first entry contains the actual training images. This is a\n", " numpy ndarray with 50,000 entries. Each entry is, in turn, a\n", " numpy ndarray with 784 values, representing the 28 * 28 = 784\n", " pixels in a single MNIST image.\n", "\n", " The second entry in the ``training_data`` tuple is a numpy ndarray\n", " containing 50,000 entries. Those entries are just the digit\n", " values (0...9) for the corresponding images contained in the first\n", " entry of the tuple.\n", "\n", " The ``validation_data`` and ``test_data`` are similar, except\n", " each contains only 10,000 images.\n", "\n", " This is a nice data format, but for use in neural networks it's\n", " helpful to modify the format of the ``training_data`` a little.\n", " That's done in the wrapper function ``load_data_wrapper()``, see\n", " below.\n", " \"\"\"\n", " f = gzip.open('data/mnist.pkl.gz', 'rb')\n", " training_data, validation_data, test_data = pickle.load(f, encoding='latin1')\n", " f.close()\n", " return (training_data, validation_data, test_data)\n", "\n", "def load_data_wrapper():\n", " \"\"\"Return a tuple containing ``(training_data, validation_data,\n", " test_data)``. Based on ``load_data``, but the format is more\n", " convenient for use in our implementation of neural networks.\n", "\n", " In particular, ``training_data`` is a list containing 50,000\n", " 2-tuples ``(x, y)``. ``x`` is a 784-dimensional numpy.ndarray\n", " containing the input image. ``y`` is a 10-dimensional\n", " numpy.ndarray representing the unit vector corresponding to the\n", " correct digit for ``x``.\n", "\n", " ``validation_data`` and ``test_data`` are lists containing 10,000\n", " 2-tuples ``(x, y)``. In each case, ``x`` is a 784-dimensional\n", " numpy.ndarry containing the input image, and ``y`` is the\n", " corresponding classification, i.e., the digit values (integers)\n", " corresponding to ``x``.\n", "\n", " Obviously, this means we're using slightly different formats for\n", " the training data and the validation / test data. These formats\n", " turn out to be the most convenient for use in our neural network\n", " code.\"\"\"\n", " tr_d, va_d, te_d = load_data()\n", " training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]\n", " training_results = [vectorized_result(y) for y in tr_d[1]]\n", " training_data = list(zip(training_inputs, training_results))\n", " validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]\n", " validation_data = list(zip(validation_inputs, va_d[1]))\n", " test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]\n", " test_data = list(zip(test_inputs, te_d[1]))\n", " return (training_data, validation_data, test_data)\n", "\n", "def vectorized_result(j):\n", " \"\"\"Return a 10-dimensional unit vector with a 1.0 in the jth\n", " position and zeroes elsewhere. This is used to convert a digit\n", " (0...9) into a corresponding desired output from the neural\n", " network.\"\"\"\n", " e = np.zeros((10, 1))\n", " e[j] = 1.0\n", " return e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note also that the biases and weights are stored as lists of Numpy matrices. So, for example `net.weights[1]` is a Numpy matrix storing the weights connecting the second and third layers of neurons. (It's not the first and second layers, since Python's list indexing starts at 0.) Since `net.weights[1]` is rather verbose, let's just denote that matrix $w$\n", ". It's a matrix such that $w_{jk}$\n", " is the weight for the connection between the $k^{th}$\n", " neuron in the second layer, and the $j^{th}$\n", " neuron in the third layer. This ordering of the $j$\n", " and $k$\n", " indices may seem strange. The big advantage of using this ordering is that it means that the vector of activations of the third layer of neurons is:\n", "$$a'=\\mathrm {sigmoid}(wa+b)$$\n", "\n", "There's quite a bit going on in this equation, so let's unpack it piece by piece. $a$\n", " is the vector of activations of the second layer of neurons. To obtain $a'$\n", " we multiply $a$\n", " by the weight matrix $w$\n", ", and add the vector $b$\n", " of biases. We then apply the function `sigmoid`\n", "elementwise to every entry in the vector $wa+b$.\n", "\n", "Of course, the main thing we want our Network objects to do is to learn. To that end we'll give them an SGD method which implements stochastic gradient descent. \n", "\n", "<!--\n", "The training_data is a list of tuples `(x, y)` representing the training inputs and corresponding desired outputs. The variables `epochs` and `mini_batch_size` are what you'd expect - the number of epochs to train for, and the size of the mini-batches to use when sampling. `eta` is the learning rate, $\\eta$. If the optional argument `test_data` is supplied, then the program will evaluate the network after each epoch of training, and print out partial progress. This is useful for tracking progress, but slows things down substantially.\n", "\n", "The code works as follows. In each epoch, it starts by randomly shuffling the training data, and then partitions it into mini-batches of the appropriate size. This is an easy way of sampling randomly from the training data. Then for each `mini_batch` we apply a single step of gradient descent. This is done by the code `self.update_mini_batch(mini_batch, eta)`, which updates the network weights and biases according to a single iteration of gradient descent, using just the training data in `mini_batch`.\n", "-->\n", "\n", "Most of the work is done by the line\n", "```\n", " delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n", "```\n", "\n", "This invokes something called the backpropagation algorithm, which is a fast way of computing the gradient of the cost function. So `update_mini_batch` works simply by computing these gradients for every training example in the `mini_batch`, and then updating `self.weights` and `self.biases` appropriately.\n", "\n", "The activation $a_{lj}$\n", " of the $j^{th}$\n", " neuron in the $l^{th}$\n", "layer is related to the activations in the $(l-1)^{th}$\n", " layer by the equation\n", "$$a^l_j=\\mathrm{sigmoid}(\\sum_k w_{jk}^l a^{l-1}_k+b^l_j)$$\n", "where the sum is over all neurons $k$\n", " in the $(l−1)^{th}$\n", " layer. To rewrite this expression in a matrix form we define a weight matrix $w^l$\n", " for each layer, $l$\n", ". The entries of the weight matrix $w^l$\n", " are just the weights connecting to the $l^{th}$\n", " layer of neurons, that is, the entry in the $j^{th}$\n", " row and $k^{th}$\n", " column is $w^l_{jk}$. Similarly, for each layer $l$\n", " we define a bias vector, $b^l$. You can probably guess how this works - the components of the bias vector are just the values $b^l_j$\n", ", one component for each neuron in the $l^{th}$\n", " layer. And finally, we define an activation vector $a^l$\n", "whose components are the activations $a^l_j$.\n", "\n", "With these notations in mind, these equations can be rewritten in the beautiful and compact vectorized form\n", "$$a^l=\\mathrm{sigmoid}(w^la^{l-1}+b^l).$$\n", "This expression gives us a much more global way of thinking about how the activations in one layer relate to activations in the previous layer: we just apply the weight matrix to the activations, then add the bias vector, and finally apply the `sigmoid` function.\n", "\n", "Apart from `self.backprop` the program is self-explanatory - all the heavy lifting is done in `self.SGD` and `self.update_mini_batch`, which we've already discussed. The `self.backprop` method makes use of a few extra functions to help in computing the gradient, namely sigmoid_prime, which computes the derivative of the sigmoid\n", " function, and `self.cost_derivative`. You can get the gist of these (and perhaps the details) just by looking at the code and documentation strings. Note that while the program appears lengthy, much of the code is documentation strings intended to make the code easy to understand. In fact, the program contains just 74 lines of non-whitespace, non-comment code. " ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "network.py\n", "~~~~~~~~~~\n", "\n", "A module to implement the stochastic gradient descent learning\n", "algorithm for a feedforward neural network. Gradients are calculated\n", "using backpropagation. Note that I have focused on making the code\n", "simple, easily readable, and easily modifiable. It is not optimized,\n", "and omits many desirable features.\n", "\"\"\"\n", "\n", "#### Libraries\n", "# Standard library\n", "import random\n", "\n", "# Third-party libraries\n", "import numpy as np\n", "\n", "class Network(object):\n", "\n", " def __init__(self, sizes):\n", " \"\"\"The list ``sizes`` contains the number of neurons in the\n", " respective layers of the network. For example, if the list\n", " was [2, 3, 1] then it would be a three-layer network, with the\n", " first layer containing 2 neurons, the second layer 3 neurons,\n", " and the third layer 1 neuron. The biases and weights for the\n", " network are initialized randomly, using a Gaussian\n", " distribution with mean 0, and variance 1. Note that the first\n", " layer is assumed to be an input layer, and by convention we\n", " won't set any biases for those neurons, since biases are only\n", " ever used in computing the outputs from later layers.\"\"\"\n", " self.num_layers = len(sizes)\n", " self.sizes = sizes\n", " self.biases = [np.random.randn(y, 1) for y in sizes[1:]]\n", " self.weights = [np.random.randn(y, x)\n", " for x, y in zip(sizes[:-1], sizes[1:])]\n", "\n", " def feedforward(self, a):\n", " \"\"\"Return the output of the network if ``a`` is input.\"\"\"\n", " for b, w in zip(self.biases, self.weights):\n", " a = sigmoid(np.dot(w, a)+b)\n", " return a\n", "\n", " def SGD(self, training_data, epochs, mini_batch_size, eta,\n", " test_data=None):\n", " \"\"\"Train the neural network using mini-batch stochastic\n", " gradient descent. The ``training_data`` is a list of tuples\n", " ``(x, y)`` representing the training inputs and the desired\n", " outputs. The other non-optional parameters are\n", " self-explanatory. If ``test_data`` is provided then the\n", " network will be evaluated against the test data after each\n", " epoch, and partial progress printed out. This is useful for\n", " tracking progress, but slows things down substantially.\"\"\"\n", " if test_data: n_test = len(test_data)\n", " n = len(training_data)\n", " for j in range(epochs):\n", " random.shuffle(training_data)\n", " mini_batches = [\n", " training_data[k:k+mini_batch_size]\n", " for k in range(0, n, mini_batch_size)]\n", " for mini_batch in mini_batches:\n", " self.update_mini_batch(mini_batch, eta)\n", " if test_data:\n", " print (\"Epoch {0}: {1} / {2}\".format(\n", " j, self.evaluate(test_data), n_test))\n", " else:\n", " print (\"Epoch {0} complete\".format(j))\n", "\n", " def update_mini_batch(self, mini_batch, eta):\n", " \"\"\"Update the network's weights and biases by applying\n", " gradient descent using backpropagation to a single mini batch.\n", " The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``\n", " is the learning rate.\"\"\"\n", " nabla_b = [np.zeros(b.shape) for b in self.biases]\n", " nabla_w = [np.zeros(w.shape) for w in self.weights]\n", " for x, y in mini_batch:\n", " delta_nabla_b, delta_nabla_w = self.backprop(x, y)\n", " nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]\n", " nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]\n", " self.weights = [w-(eta/len(mini_batch))nw\n", " for w, nw in zip(self.weights, nabla_w)]\n", " self.biases = [b-(eta/len(mini_batch))nb\n", " for b, nb in zip(self.biases, nabla_b)]\n", "\n", " def backprop(self, x, y):\n", " \"\"\"Return a tuple ``(nabla_b, nabla_w)`` representing the\n", " gradient for the cost function C_x. ``nabla_b`` and\n", " ``nabla_w`` are layer-by-layer lists of numpy arrays, similar\n", " to ``self.biases`` and ``self.weights``.\"\"\"\n", " nabla_b = [np.zeros(b.shape) for b in self.biases]\n", " nabla_w = [np.zeros(w.shape) for w in self.weights]\n", " # feedforward\n", " activation = x\n", " activations = [x] # list to store all the activations, layer by layer\n", " zs = [] # list to store all the z vectors, layer by layer\n", " for b, w in zip(self.biases, self.weights):\n", " z = np.dot(w, activation)+b\n", " zs.append(z)\n", " activation = sigmoid(z)\n", " activations.append(activation)\n", " # backward pass\n", " delta = self.cost_derivative(activations[-1], y) * \\\n", " sigmoid_prime(zs[-1])\n", " nabla_b[-1] = delta\n", " nabla_w[-1] = np.dot(delta, activations[-2].transpose())\n", " # Note that the variable l in the loop below is used a little\n", " # differently to the notation in Chapter 2 of the book. Here,\n", " # l = 1 means the last layer of neurons, l = 2 is the\n", " # second-last layer, and so on. It's a renumbering of the\n", " # scheme in the book, used here to take advantage of the fact\n", " # that Python can use negative indices in lists.\n", " for l in range(2, self.num_layers):\n", " z = zs[-l]\n", " sp = sigmoid_prime(z)\n", " delta = np.dot(self.weights[-l+1].transpose(), delta) * sp\n", " nabla_b[-l] = delta\n", " nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())\n", " return (nabla_b, nabla_w)\n", "\n", " def evaluate(self, test_data):\n", " \"\"\"Return the number of test inputs for which the neural\n", " network outputs the correct result. Note that the neural\n", " network's output is assumed to be the index of whichever\n", " neuron in the final layer has the highest activation.\"\"\"\n", " test_results = [(np.argmax(self.feedforward(x)), y)\n", " for (x, y) in test_data]\n", " return sum(int(x == y) for (x, y) in test_results)\n", "\n", " def cost_derivative(self, output_activations, y):\n", " \"\"\"Return the vector of partial derivatives \\partial C_x /\n", " \\partial a for the output activations.\"\"\"\n", " return (output_activations-y)\n", "\n", "#### Miscellaneous functions\n", "def sigmoid(z):\n", " \"\"\"The sigmoid function.\"\"\"\n", " return 1.0/(1.0+np.exp(-z))\n", "\n", "def sigmoid_prime(z):\n", " \"\"\"Derivative of the sigmoid function.\"\"\"\n", " return sigmoid(z)*(1-sigmoid(z))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first load the MNIST data:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "training_data, validation_data, test_data = load_data_wrapper()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After loading the MNIST data, we'll set up a Network with 30 hidden neurons. " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "net = Network([784, 30, 10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll use stochastic gradient descent to learn from the MNIST training_data over 30 epochs, with a mini-batch size of 10, and a learning rate of $\\eta$=3.0:\n" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0: 9125 / 10000\n", "Epoch 1: 9201 / 10000\n", "Epoch 2: 9285 / 10000\n", "Epoch 3: 9317 / 10000\n", "Epoch 4: 9299 / 10000\n", "Epoch 5: 9388 / 10000\n", "Epoch 6: 9394 / 10000\n", "Epoch 7: 9397 / 10000\n", "Epoch 8: 9425 / 10000\n", "Epoch 9: 9395 / 10000\n", "Epoch 10: 9408 / 10000\n", "Epoch 11: 9440 / 10000\n", "Epoch 12: 9448 / 10000\n", "Epoch 13: 9460 / 10000\n", "Epoch 14: 9445 / 10000\n", "Epoch 15: 9459 / 10000\n", "Epoch 16: 9467 / 10000\n", "Epoch 17: 9466 / 10000\n", "Epoch 18: 9434 / 10000\n", "Epoch 19: 9450 / 10000\n", "Epoch 20: 9463 / 10000\n", "Epoch 21: 9472 / 10000\n", "Epoch 22: 9465 / 10000\n", "Epoch 23: 9482 / 10000\n", "Epoch 24: 9487 / 10000\n", "Epoch 25: 9458 / 10000\n", "Epoch 26: 9481 / 10000\n", "Epoch 27: 9479 / 10000\n", "Epoch 28: 9476 / 10000\n", "Epoch 29: 9479 / 10000\n" ] } ], "source": [ "net.SGD(training_data, 30, 10, 3.0, test_data=test_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Challenge 14.3\n", "Try creating a network with just two layers - an input and an output layer, no hidden layer - with 784 and 10 neurons, respectively. Train the network using stochastic gradient descent. What classification accuracy can you achieve?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Number of hidden layers\n", "\n", "Suppose that we want to approximate a set of functions to a given accuracy. How many hidden layers do we need? The answer is: At most two layers, with arbitrary accuracy obtained given enough units per layer. It has been also shown that only one layer is enough to approximate any continuous function. Of course, there is no way to know how many units we would need, and this is not known in general, and this number may grow exponentially with the number of input units. " ] }, { "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
JudoWill/ResearchNotebooks	HIVTransTool.ipynb	1	50009	{ "metadata": { "name": "HIVTransTool" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from Bio import Seq\n", "from Bio import SeqIO\n", "import pandas as pd\n", "import numpy as np\n", "import sys\n", "import os\n", "sys.path.append('/home/will/PySeqUtils/')\n", "from GeneralSeqTools import fasta_reader, fasta_writer\n", "from HIVAlignTools import SeqTransformer, build_aligners" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import HIVAlignTools" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "HIVAlignTools.build_aligners()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gag\n", "Fitting 10 folds for each of 20 candidates, totalling 200 fits" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=1)]: Done 1 jobs \| elapsed: 20.0s\n", "[Parallel(n_jobs=1)]: Done 50 jobs \| elapsed: 16.8min\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-3-52ec053a8004>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mHIVAlignTools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_aligners\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/will/PySeqUtils/HIVAlignTools.py\u001b[0m in \u001b[0;36mbuild_aligners\u001b[1;34m(base_path, verbose)\u001b[0m\n\u001b[0;32m 382\u001b[0m 'gp41', 'v3']\n\u001b[0;32m 383\u001b[0m \u001b[1;32mfor\u001b[0m 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1367\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mselect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1368\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEINTR\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import shlex\n", "from subprocess import check_call\n", "\n", "def score_seq(known, guess, gapopen=10, gapextend=1):\n", " \n", " cmd = 'needle -asequence %(cb)s -bsequence %(seq)s -aformat score -gapopen %(go)f -gapextend %(ge)s -outfile %(out)s'\n", " with NamedTemporaryFile() as conb_handle:\n", " fasta_writer(conb_handle, [('SeqA', known)])\n", " conb_handle.flush()\n", " os.fsync(conb_handle.fileno())\n", " with NamedTemporaryFile() as seq_handle:\n", " fasta_writer(seq_handle, [('Seq1', guess)])\n", " seq_handle.flush()\n", " os.fsync(seq_handle.fileno())\n", " with NamedTemporaryFile() as out_handle:\n", " param_dict = {\n", " 'cb':conb_handle.name,\n", " 'seq':seq_handle.name,\n", " 'out':out_handle.name,\n", " 'go':gapopen,\n", " 'ge':gapextend\n", " }\n", " cmd_list = shlex.split(cmd % param_dict)\n", " check_call(cmd_list)\n", " for line in out_handle:\n", " parts = line.split()\n", " if (len(parts) == 4):\n", " return float(parts[-1][1:-2])\n", " \n", "\n", "\n", "def score_seqs(known_seqs, guess_seqs, gapopen=10, gapextend=1):\n", " \n", " score = 0.0\n", " for ind in range(known_seqs.shape[0]):\n", " score += score_seq(known_seqs[ind], guess_seqs[ind],\n", " gapopen=gapopen, gapextend=gapextend)\n", " return score\n", "\n", "\n", "\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.base import BaseEstimator, ClusterMixin\n", "from tempfile import NamedTemporaryFile\n", "from Bio.SubsMat import MatrixInfo as matlist\n", "from Bio.Blast import NCBIXML\n", "from StringIO import StringIO\n", "from Bio.Blast.Applications import NcbiblastxCommandline, NcbiblastnCommandline\n", "\n", "\n", "class BlastAligner(BaseEstimator, ClusterMixin):\n", " \n", " def __init__(self, evalue=10, word_size=2, gapopen=11, gapextend=1, \n", " max_intron_length = 20, tmp_path = '/tmp/', result_type = 'aa',\n", " db_path=NamedTemporaryFile(suffix='.fasta').name, num_threads=1):\n", " self.evalue = evalue\n", " self.word_size = word_size\n", " self.gapopen = gapopen\n", " self.gapextend = gapextend\n", " self.max_intron_length = max_intron_length\n", " self.tmp_path = tmp_path\n", " self.result_type = result_type\n", " self.db_path = db_path\n", " self.num_threads = num_threads\n", " \n", " def _write_seqs(self, X, handle):\n", " \n", " seqs = []\n", " for row in range(X.shape[0]):\n", " seq = ''.join(X[row])\n", " seqs.append(('Seq-%03i' % row, ''.join(l for l in seq if l.isalpha())))\n", " \n", " fasta_writer(handle, seqs)\n", " handle.flush()\n", " os.fsync(handle.fileno())\n", " \n", " \n", " def fit(self, X, y):\n", " \n", " \n", " empty_mask = y == 'XX'\n", " \n", " with open(self.db_path, 'w') as handle:\n", " self._write_seqs(y[~empty_mask], handle)\n", " cmd = 'makeblastdb -in %s -dbtype ' % self.db_path\n", " if self.result_type == 'aa':\n", " cmd += 'prot'\n", " else:\n", " cmd += 'nucl'\n", " \n", " check_call(shlex.split(cmd))\n", " \n", " \n", " return self\n", " \n", " \n", " def predict(self, X):\n", " \n", " if self.result_type == 'aa':\n", " blast_cmd = NcbiblastxCommandline\n", " else:\n", " blast_cmd = NcbiblastnCommandline\n", " \n", " \n", " with NamedTemporaryFile(dir=self.tmp_path, delete=True) as fasta_handle: \n", " self._write_seqs(X, fasta_handle)\n", " blastx_cline = blast_cmd(query=fasta_handle.name,\n", " db = self.db_path, outfmt=5, \n", " out = '-',\n", " evalue=self.evalue,\n", " word_size=self.word_size,\n", " gapopen=self.gapopen,\n", " gapextend=self.gapextend,\n", " max_intron_length=self.max_intron_length,\n", " num_threads=self.num_threads)\n", " stdout, stderr = blastx_cline()\n", " \n", " blast_records = NCBIXML.parse(StringIO(stdout))\n", " seqs = []\n", " names = []\n", " prots = []\n", " for rec in blast_records:\n", " for align in rec.alignments:\n", " hsp = align.hsps[0]\n", " prots.append({\n", " 'ID':rec.query,\n", " 'Seq':hsp.query\n", " })\n", " blast_out = pd.DataFrame(prots).groupby('ID')['Seq'].first()\n", " wanted_out = pd.DataFrame({\n", " 'ID':['Seq-%03i' % i for i in range(X.shape[0])],\n", " 'want_seq':[True]X.shape[0],\n", " }).groupby('ID')['want_seq'].first()\n", " out, _ = blast_out.align(wanted_out, join='right')\n", " \n", " return SeqTransformer().transform(out.fillna('XX').values)\n", " \n", " def score(self, X, y):\n", " \n", " empty_mask = y == 'XX'\n", " out_aligns = self.predict(X)\n", " \n", " pos_scores = score_seqs(y[~empty_mask], out_aligns[~empty_mask])\n", " bad_scores = score_seqs(out_aligns[empty_mask], out_aligns[empty_mask])\n", " return (pos_scores - bad_scores)/y.shape[0]\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "neg_controls = {\n", " 'env':['gag', 'pol', 'vif', 'vpr', 'ltr'],\n", " 'gag':['ltr', 'vif', 'vpr', 'vpu', 'tat', 'rev', 'env'],\n", " #'ltr':['gag', 'pol', 'vpr', 'vpu', 'env'],\n", " 'nef':['pol', 'gag', 'vpu', 'tat'],\n", " 'pol':['env', 'vpr', 'vpu', 'nef', 'rev', 'ltr'],\n", " 'rev':['ltr', 'gag', 'pol', 'vif', 'nef'],\n", " 'tat':['ltr', 'pol', 'vif', 'nef'],\n", " 'vif':['ltr', 'tat', 'vpu', 'rev', 'env', 'nef'],\n", " 'vpr':['ltr', 'gag', 'pol', 'rev', 'env', 'nef'],\n", " 'vpu':['ltr', 'gag', 'pol', 'vif', 'vpr', 'nef'],\n", " }" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_seq(prot_name, typ):\n", " trans_path = '/home/will/PySeqUtils/TransToolStuff/'\n", " tmp = 'HIV1_ALL_2012_%s_%s.fasta' % (prot_name.lower(), typ.upper())\n", " with open(trans_path + tmp) as handle:\n", " return SeqTransformer.get_from_fasta_handle(handle)\n", "\n", "\n", "pos_names, pos_X = get_seq('genome', 'DNA')\n", "env_names, env_y = get_seq('env', 'pro')\n", "neg_names = []\n", "neg_X = None\n", "for neg_prot in neg_controls['env']:\n", " tnames, tx = get_seq(neg_prot, 'DNA')\n", " neg_names += tnames\n", " if neg_X is None:\n", " neg_X = tx.copy()\n", " else:\n", " neg_X = np.concatenate((neg_X, tx))\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "pos_X_ser = pd.Series(pos_X, index=pos_names)\n", "env_y_ser = pd.Series(env_y, index=env_names)\n", "\n", "X_ser, y_ser = pos_X_ser.align(env_y_ser, join='inner')\n", "X = X_ser.values\n", "y = y_ser.values\n", "in_env = set(env_names)\n", "neg_inds = [num for num, name in enumerate(neg_names) if name not in in_env]\n", "wneg_X = neg_X[neg_inds]\n", "wneg_y = np.array(['XX']wneg_X.shape[0])\n", "\n", "print X.shape, y.shape, wneg_X.shape, wneg_y.shape\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1984,) (1984,) (6782,) (6782,)\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "Xall = np.concatenate((X, wneg_X))\n", "yall = np.concatenate((y, wneg_y))\n", "\n", "yclass = yall == 'XX'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split, StratifiedKFold, cross_val_score, StratifiedShuffleSplit\n", "from sklearn.grid_search import GridSearchCV\n", "\n", "param_dict = {'evalue':np.logspace(-100, 1, 20)}\n", "\n", "cv = StratifiedShuffleSplit(yclass, n_iter=3, test_size=500, train_size=100)\n", "aligner = BlastAligner(num_threads=100)\n", "gd = GridSearchCV(aligner, param_dict, refit=False, cv=cv, verbose=5)\n", "gd.fit(Xall, yall)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n", "[GridSearchCV] evalue=1e-100 ...................................................\n", "[GridSearchCV] ........................ evalue=1e-100, score=928.680000 - 3.4s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] evalue=1e-100 ...................................................\n", "[GridSearchCV] ........................ evalue=1e-100, score=931.140000 - 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"text": [ "\n", "[GridSearchCV] evalue=4.83293023857e-05 ........................................\n", "[GridSearchCV] ............. evalue=4.83293023857e-05, score=901.340000 - 3.5s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] evalue=10.0 .....................................................\n", "[GridSearchCV] .......................... evalue=10.0, score=753.180000 - 3.9s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] evalue=10.0 .....................................................\n", "[GridSearchCV] .......................... evalue=10.0, score=729.080000 - 3.9s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] evalue=10.0 .....................................................\n", "[GridSearchCV] .......................... evalue=10.0, score=731.180000 - 3.8s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=1)]: Done 60 out of 60 \| elapsed: 3.5min finished\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "GridSearchCV(cv=StratifiedShuffleSplit(labels=[False False ..., True True], n_iter=3, test_size=50, indices=True, random_state=None),\n", " estimator=BlastAligner(db_path='/tmp/tmpjDibUz.fasta', evalue=10, gapextend=1,\n", " gapopen=11, max_intron_length=20, num_threads=100, result_type='aa',\n", " tmp_path='/tmp/', word_size=2),\n", " fit_params={}, iid=True, loss_func=None, n_jobs=1,\n", " param_grid={'evalue': array([ 1.00000e-100, 2.06914e-095, 4.28133e-090, 8.85867e-085,\n", " 1.83298e-079, 3.79269e-074, 7.84760e-069, 1.62378e-063,\n", " 3.35982e-058, 6.95193e-053, 1.43845e-047, 2.97635e-042,\n", " 6.15848e-037, 1.27427e-031, 2.63665e-026, 5.45559e-021,\n", " 1.12884e-015, 2.33572e-010, 4.83293e-005, 1.00000e+001])},\n", " pre_dispatch='2n_jobs', refit=False, score_func=None, scoring=None,\n", " verbose=5)" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "import " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 81, "text": [ 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{} } ] }	mit
hoh/Hubbub	notebooks/Analysis-ANN.ipynb	1	28236	{ "metadata": { "name": "", "signature": "sha256:5087d4cc9635b7fad9d8507a6fb11981c40db3a17678c6ccaeacdcb010b90212" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", "HTML('''<h1>Testing with ANN</h1>\n", "\n", "<ol>\n", " <li><a href='#Comparing-real-and-dummy-delays'>Comparison of delays between real and dummies</a></li>\n", "</ol>\n", "<hr/>''')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<h1>Testing with ANN</h1>\n", "\n", "<ol>\n", " <li><a href='#Comparing-real-and-dummy-delays'>Comparison of delays between real and dummies</a></li>\n", "</ol>\n", "<hr/>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x10fe3f210>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Large plots\n", "import matplotlib.pylab as pylab\n", "pylab.rcParams['figure.figsize'] = 16, 9" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "from hubbub.generator.generator import Simulator\n", "from hubbub.generator.heartbeat import HeartBeatSimulator\n", "from hubbub.datasets.simulations import simple_log, SIMPLE_LOG as SIMPLE_LOG_SAMPLE" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Warning: This tool is designed for Python 3.\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#SIMPLE_LOG = SIMPLE_LOG_SAMPLE\n", "#SIMPLE_LOG_SAMPLE\n", "\n", "# Generating \"real\" messages dataset:\n", "SIMPLE_LOG = simple_log(n=200, days=1)\n", "#SIMPLE_LOG[:10]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Running simulator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result_sm = Simulator(SIMPLE_LOG).run()\n", "results_HB = [\n", " HeartBeatSimulator(SIMPLE_LOG).run() for i in xrange(5)\n", "# HeartBeatSimulator(SIMPLE_LOG).run(delay=lambda: 5) for i in xrange(10)\n", " ]\n", "\n", "results_HB[0][:2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[(datetime.datetime(2000, 1, 1, 0, 0), 10),\n", " (datetime.datetime(2000, 1, 1, 0, 0, 0, 483881), 10)]" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Converting results to timestamps for plotting:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "def timestamp(n):\n", " unix_time = time.mktime(n.timetuple()) + n.microsecond/1000000.\n", " return unix_time" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "r_real = pd.DataFrame(\n", " [(0, timestamp(i[0]), 'SIMPLE_LOG', i[1]) for i in SIMPLE_LOG],\n", " columns=('dummy', 'timestamp', 'source', 'length'),\n", " )\n", "r_real.head()" ], "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>dummy</th>\n", " <th>timestamp</th>\n", " <th>source</th>\n", " <th>length</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> 9.466812e+08</td>\n", " <td> SIMPLE_LOG</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 0</td>\n", " <td> 9.466813e+08</td>\n", " <td> SIMPLE_LOG</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 0</td>\n", " <td> 9.466839e+08</td>\n", " <td> SIMPLE_LOG</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 0</td>\n", " <td> 9.466841e+08</td>\n", " <td> SIMPLE_LOG</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 0</td>\n", " <td> 9.466846e+08</td>\n", " <td> SIMPLE_LOG</td>\n", " <td> 10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 4 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ " dummy timestamp source length\n", "0 0 9.466812e+08 SIMPLE_LOG 10\n", "1 0 9.466813e+08 SIMPLE_LOG 10\n", "2 0 9.466839e+08 SIMPLE_LOG 10\n", "3 0 9.466841e+08 SIMPLE_LOG 10\n", "4 0 9.466846e+08 SIMPLE_LOG 10\n", "\n", "[5 rows x 4 columns]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "r_dummyHB = [\n", " pd.DataFrame(\n", " [(1, timestamp(i[0]), 'HB{}'.format(index), i[1]) for i in r],\n", " columns=('dummy', 'timestamp', 'source', 'length'),\n", " )\n", " for index, r in enumerate(results_HB)\n", " ]\n", "r_dummyHB[0].head()" ], "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>dummy</th>\n", " <th>timestamp</th>\n", " <th>source</th>\n", " <th>length</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 4 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " dummy timestamp source length\n", "0 1 9.466812e+08 HB0 10\n", "1 1 9.466812e+08 HB0 10\n", "2 1 9.466812e+08 HB0 10\n", "3 1 9.466812e+08 HB0 10\n", "4 1 9.466812e+08 HB0 10\n", "\n", "[5 rows x 4 columns]" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Analyzing delays" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Computing deltas for n-3 to n+3" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r_mixed = [\n", " pd.concat((r_real, r))\n", " for r in r_dummyHB\n", "]\n", "for r in r_mixed:\n", " r.sort('timestamp', inplace=True)\n", " r['dm3'] = r['timestamp'].diff(periods=+3)\n", " r['dm2'] = r['timestamp'].diff(periods=+2)\n", " r['dm1'] = r['timestamp'].diff(periods=+1)\n", " r['dp1'] = -r['timestamp'].diff(periods=-1)\n", " r['dp2'] = -r['timestamp'].diff(periods=-2)\n", " r['dp3'] = -r['timestamp'].diff(periods=-3)\n", "\n", "r_mixed[0].head(10)" ], "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>dummy</th>\n", " <th>timestamp</th>\n", " <th>source</th>\n", " <th>length</th>\n", " <th>dm3</th>\n", " <th>dm2</th>\n", " <th>dm1</th>\n", " <th>dp1</th>\n", " <th>dp2</th>\n", " <th>dp3</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 0.483881</td>\n", " <td> 3.798227</td>\n", " <td> 5.488650</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> 0.483881</td>\n", " <td> 3.314346</td>\n", " <td> 5.004769</td>\n", " <td> 6.011144</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> NaN</td>\n", " <td> 3.798227</td>\n", " <td> 3.314346</td>\n", " <td> 1.690423</td>\n", " <td> 2.696798</td>\n", " <td> 6.974775</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 5.488650</td>\n", " <td> 5.004769</td>\n", " <td> 1.690423</td>\n", " <td> 1.006375</td>\n", " <td> 5.284352</td>\n", " <td> 5.995023</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 6.011144</td>\n", " <td> 2.696798</td>\n", " <td> 1.006375</td>\n", " <td> 4.277977</td>\n", " <td> 4.988648</td>\n", " <td> 5.536058</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 6.974775</td>\n", " <td> 5.284352</td>\n", " <td> 4.277977</td>\n", " <td> 0.710671</td>\n", " <td> 1.258081</td>\n", " <td> 2.511761</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 5.995023</td>\n", " <td> 4.988648</td>\n", " <td> 0.710671</td>\n", " <td> 0.547410</td>\n", " <td> 1.801090</td>\n", " <td> 2.071607</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 5.536058</td>\n", " <td> 1.258081</td>\n", " <td> 0.547410</td>\n", " <td> 1.253680</td>\n", " <td> 1.524197</td>\n", " <td> 1.737170</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 2.511761</td>\n", " <td> 1.801090</td>\n", " <td> 1.253680</td>\n", " <td> 0.270517</td>\n", " <td> 0.483490</td>\n", " <td> 1.892113</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 1</td>\n", " <td> 9.466812e+08</td>\n", " <td> HB0</td>\n", " <td> 10</td>\n", " <td> 2.071607</td>\n", " <td> 1.524197</td>\n", " <td> 0.270517</td>\n", " <td> 0.212973</td>\n", " <td> 1.621596</td>\n", " <td> 8.812251</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows \u00d7 10 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " dummy timestamp source length dm3 dm2 dm1 dp1 \\\n", "0 1 9.466812e+08 HB0 10 NaN NaN NaN 0.483881 \n", "1 1 9.466812e+08 HB0 10 NaN NaN 0.483881 3.314346 \n", "2 1 9.466812e+08 HB0 10 NaN 3.798227 3.314346 1.690423 \n", "3 1 9.466812e+08 HB0 10 5.488650 5.004769 1.690423 1.006375 \n", "4 1 9.466812e+08 HB0 10 6.011144 2.696798 1.006375 4.277977 \n", "5 1 9.466812e+08 HB0 10 6.974775 5.284352 4.277977 0.710671 \n", "6 1 9.466812e+08 HB0 10 5.995023 4.988648 0.710671 0.547410 \n", "7 1 9.466812e+08 HB0 10 5.536058 1.258081 0.547410 1.253680 \n", "8 1 9.466812e+08 HB0 10 2.511761 1.801090 1.253680 0.270517 \n", "9 1 9.466812e+08 HB0 10 2.071607 1.524197 0.270517 0.212973 \n", "\n", " dp2 dp3 \n", "0 3.798227 5.488650 \n", "1 5.004769 6.011144 \n", "2 2.696798 6.974775 \n", "3 5.284352 5.995023 \n", "4 4.988648 5.536058 \n", "5 1.258081 2.511761 \n", "6 1.801090 2.071607 \n", "7 1.524197 1.737170 \n", "8 0.483490 1.892113 \n", "9 1.621596 8.812251 \n", "\n", "[10 rows x 10 columns]" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Creating the Neural Network" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pybrain.datasets import ClassificationDataSet\n", "from pybrain.utilities import percentError\n", "from pybrain.tools.shortcuts import buildNetwork\n", "from pybrain.supervised.trainers import BackpropTrainer\n", "from pybrain.structure.modules import SoftmaxLayer" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "alldata = ClassificationDataSet(6, 1, nb_classes=3)\n", "\n", "for row in r_mixed[0][:1000].iterrows():\n", " r = row[1]\n", " alldata.addSample((r.dm3, r.dm2, r.dm1, r.dp1, r.dp2, r.dp3), [r.dummy])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Randomly split the dataset into 75% training and 25% test data sets." ] }, { "cell_type": "code", "collapsed": false, "input": [ "tstdata, trndata = alldata.splitWithProportion( 0.25 )\n", "# For neural network classification, it is highly advisable to encode classes with one output neuron per class.\n", "trndata._convertToOneOfMany( )\n", "tstdata._convertToOneOfMany( )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test our dataset by printing a little information about it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Number of training patterns: \", len(trndata)\n", "print \"Input and output dimensions: \", trndata.indim, trndata.outdim\n", "print \"First sample (input, target, class):\"\n", "print trndata['input'][0], trndata['target'][0], trndata['class'][0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Number of training patterns: 750\n", "Input and output dimensions: 6 3\n", "First sample (input, target, class):\n", "[ nan nan 0.483881 3.31434596 5.00476897 6.01114404] [0 1 0] [ 1.]\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Now build a feed-forward network with 5 hidden units._" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(20):\n", " trainer.trainEpochs( 1 )\n", " \n", " trnresult = percentError( trainer.testOnClassData(),\n", " trndata['class'] )\n", " tstresult = percentError( trainer.testOnClassData(\n", " dataset=tstdata ), tstdata['class'] )\n", "\n", " print \"epoch: %4d\" % trainer.totalepochs, \\\n", " \" train error: %5.2f%%\" % trnresult, \\\n", " \" test error: %5.2f%%\" % tstresult" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('Total error:', nan)\n", "epoch: 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 3" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 5" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 6" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 7" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 8" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 10" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 11" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 12" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 13" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 14" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 15" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 16" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 17" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 18" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 19" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n", "('Total error:', nan)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "epoch: 20" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " train error: 99.87% test error: 99.60%\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 } ], "metadata": {} } ] }	agpl-3.0
gengho/Car2know	Car2know/analysis/.ipynb_checkpoints/draw_map-checkpoint.ipynb	1	5812796	null	mit
banbh/little-pythoner	python/Main.ipynb	1	12235	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "On this page you'll find a series of exercises. We'll be using Python for all the code, but not really. You barely need to know any Python at all. In fact here is all you need to know (at least about Python).\n", "\n", "## All You Need to Know\n", "Numbers: 0, 1, 2, 3, ... (i.e., no negative numbers or decimals)\n", "\n", "Strings: things like `'hello'` and `'the cat on the mat'` and the empty string `''`\n", "\n", "Booleans: `True`, `False`\n", "\n", "Lists: `[]`, but you can make lists (see `cons` below)\n", "\n", "#### Functions\n", "`is_eq_str(x, y)`: `x` and `y` must both be strings; returns whether `x` equals `y`\n", "\n", "`is_empty(xx)` : `xx` must be a list; returns whether the `xx` is the empty list\n", "\n", "`head(xx)`: `xx` must be a non-empty list; returns the first item of `xx`\n", "\n", "`tail(xx)`: `xx` must be a non-empty list; returns a list with everything after the head\n", "\n", "`cons(h, tl)`: returns a list whose first item is `h` and whose remaining items are the items of `tl` (i.e. it put backs a list taken apart by `head` and `tail`)\n", "\n", "`add1(n)`: `n` must be a number; returns a number one bigger than `n`\n", "\n", "`sub1(n)`: `n` must be a number greater than zero; returns a number one less than `n`\n", "\n", "`is_zero(n)`: `n` must be a number; returns whether `n` is zero\n", "\n", "`is_str(x)`: returns whether `x` is a string\n", "\n", "`is_num(x)`: returns whether `x` is a number\n", "\n", "## Getting Started\n", "The above functions, simple though they are, are not built into Python, so you must download a file that defins them. Download [basic_functions.py](../../raw/master/basic_functions.py)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from basic_functions import " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_empty([1,2])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_empty([])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head([1,2])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head([1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `head([])` is an error since you can't find the first item in an empty list." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tail([1,2])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tail([1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `tail([])` is an error since the tail of a list is what's left over when you remove the head, and the empty list has no head." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cons(1, [2,3])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cons(1, [])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_num(99)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_num('hello')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_str(99)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_str('hello')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_str_eq('hello', 'hello')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_str_eq('hello', 'goodbye')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "add1(99)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "98" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sub1(99)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `sub1(0)` is an error because you can't subtract 1 from 0. (Actually it is possible if you allow negative numbers, but in these exercises we will not allow such numbers.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# All Strings\n", "Write a function, `is_list_of_strings`, that determines whether a list contains only strings. Below are some examples of how it should behave." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from solutions import is_list_of_strings" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_list_of_strings(['hello', 'goodbye'])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_list_of_strings([1, 'aa'])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_list_of_strings([])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last example, `is_list_of_strings([])`, might seem puzzling at first, but really it's not. Suppose you are flying into a strange island and at customs there is a sign that says \"all food in your suitcase must be cooked\". Then if you have a ham sanswich in your suitcase then you are ok, since ham is cooked. What about if you have no food in your suitcase? Then clearly you are also ok. This because in normal language (and in more mathematical language too) when we say \"every X must be (say) big\", then if there are no X's then the statment is true. In normal language it may be less clear what to do with slightly sillier examples; for example, say someone asks you \"Are all the coins in your pocket quarters?\" and you have no* coins in your pocket. Out of politeness you might say \"I have no coins in my pocket\", but if they forced you to say \"yes\" or \"no\", I think you would say \"yes\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Follow the pattern below:\n", " # fill in the blanks\n", " def is_list_of_strings(l):\n", " if is_empty(l):\n", " pass\n", " else:\n", " # do something with head(l), tail(l)\n", " pass\n", "Before you try it, here is a hint. You can do something magical in a function: you call yourself! For example when filling in the blanks above, a good place to try this is in the `else` block. In fact what you want to think about in the `else` block is this: assuming I've broken the list apart into it's first item (`head(l)`) and a list with all the other items (`tail(l)`) how can I figure out the answer in terms of these pieces? If the first item is not a string then we are done (the answer if false). However if the first item is a list then we know we have a list of strings if the rest of the items are all strings. So maybe we can 'cheat' and just call `is_list_of_strings` on the tail to answer that! And in fact you can, and it's not even cheating. In fact it's called recursion." ] }, { "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": "2.7.4" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
davisincubator/seal_the_deal	notebooks/kaf_amp_4.0_copy_slow_and_steady_wins_the_race.ipynb	1	20062	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import cv2\n", "from PIL import Image\n", "from scipy.misc import imread\n", "import matplotlib.pyplot as plt\n", "import skimage.feature\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelBinarizer\n", "import keras\n", "from keras.models import Sequential, load_model\n", "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Lambda, Cropping2D\n", "from keras.utils import np_utils\n", "\n", "from collections import Counter\n", "\n", "from keras.models import load_model\n", "\n", "from tqdm import tqdm_notebook\n", "\n", "import datetime\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class_names = ['adult_females', 'adult_males', 'juveniles', 'pups', 'subadult_males']\n", "\n", "my_dir = \"/seal_the_data/\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# specify run number, which selects appropriate file name subset and file name suffix\n", "# will need 9 runs total\n", "\n", "# completed runs: \n", "\n", "run_num = 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['3.jpg', '7.jpg', '9.jpg', '21.jpg', '30.jpg']\n", "['3.jpg', '7.jpg', '9.jpg', '21.jpg', '30.jpg', '34.jpg', '71.jpg', '81.jpg', '89.jpg', '97.jpg', '151.jpg', '184.jpg', '215.jpg', '234.jpg', '242.jpg', '268.jpg', '290.jpg', '311.jpg', '331.jpg', '344.jpg', '380.jpg', '384.jpg', '406.jpg', '421.jpg', '469.jpg', '475.jpg', '490.jpg', '499.jpg', '507.jpg', '530.jpg', '531.jpg', '605.jpg', '607.jpg', '614.jpg', '621.jpg', '638.jpg', '644.jpg', '687.jpg', '712.jpg', '721.jpg', '767.jpg', '779.jpg', '781.jpg', '794.jpg', '800.jpg', '811.jpg', '839.jpg', '840.jpg', '869.jpg', '882.jpg', '901.jpg', '903.jpg', '905.jpg', '909.jpg', '913.jpg', '927.jpg', '946.jpg', 'train.csv']\n" ] } ], "source": [ "mismatch_id=[3,7,9,21,30,34,71,81,89,97,151,184,215,234,242,268,290,311,331,344,380,384,406,421,469,475,490,499,507,530,531,605,607,614,621,638,644,687,712,721,767,779,781,794,800,811,839,840,869,882,901,903,905,909,913,927,946]\n", "blacklist = []\n", "for i in mismatch_id:\n", " blacklist.append(str(i) + '.jpg')\n", "print(blacklist[:5])\n", "blacklist.append('train.csv')\n", "print(blacklist)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "file_names = os.listdir(my_dir + \"Train/\")\n", "file_names = sorted(file_names, key=lambda \n", " item: (int(item.partition('.')[0]) if item[0].isdigit() else float('inf'), item)) \n", "\n", "# select a subset of files to run on\n", "file_names = file_names[0:1]\n", "\n", "# dataframe to store results in\n", "coordinates_df = pd.DataFrame(index=file_names, columns=class_names)\n", "\n", "#print(file_names[:])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3991f6452ba648f8a1f4eac62eadabf9" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n", "Wall time: 5.96 µs\n" ] } ], "source": [ "for filename in tqdm_notebook(file_names):\n", " if filename in blacklist:\n", " file_names.remove(filename)\n", " else:\n", " # read the Train and Train Dotted images\n", " image_1 = cv2.imread(my_dir + \"/TrainDotted/\" + filename)\n", " image_2 = cv2.imread(my_dir + \"/Train/\" + filename)\n", "\n", " cut = np.copy(image_2)\n", "\n", " # absolute difference between Train and Train Dotted\n", " image_3 = cv2.absdiff(image_1,image_2)\n", "\n", " # mask out blackened regions from Train Dotted\n", " mask_1 = cv2.cvtColor(image_1, cv2.COLOR_BGR2GRAY)\n", " mask_1[mask_1 < 20] = 0\n", " mask_1[mask_1 > 0] = 255\n", "\n", " mask_2 = cv2.cvtColor(image_2, cv2.COLOR_BGR2GRAY)\n", " mask_2[mask_2 < 20] = 0\n", " mask_2[mask_2 > 0] = 255\n", "\n", " image_3 = cv2.bitwise_or(image_3, image_3, mask=mask_1)\n", " image_3 = cv2.bitwise_or(image_3, image_3, mask=mask_2) \n", "\n", " # convert to grayscale to be accepted by skimage.feature.blob_log\n", " image_3 = cv2.cvtColor(image_3, cv2.COLOR_BGR2GRAY)\n", "\n", " # detect blobs\n", " blobs = skimage.feature.blob_log(image_3, min_sigma=3, max_sigma=4, num_sigma=1, threshold=0.02)\n", "\n", " adult_males = []\n", " subadult_males = []\n", " pups = []\n", " juveniles = []\n", " adult_females = [] \n", "\n", " image_circles = image_1\n", "\n", " for blob in blobs:\n", " # get the coordinates for each blob\n", " y, x, s = blob\n", " # get the color of the pixel from Train Dotted in the center of the blob\n", " g,b,r = image_1[int(y)][int(x)][:]\n", "\n", " # decision tree to pick the class of the blob by looking at the color in Train Dotted\n", " if r > 200 and g < 50 and b < 50: # RED\n", " adult_males.append((int(x),int(y)))\n", " cv2.circle(image_circles, (int(x),int(y)), 20, (0,0,255), 10) \n", " elif r > 200 and g > 200 and b < 50: # MAGENTA\n", " subadult_males.append((int(x),int(y))) \n", " cv2.circle(image_circles, (int(x),int(y)), 20, (250,10,250), 10)\n", " elif r < 100 and g < 100 and 150 < b < 200: # GREEN\n", " pups.append((int(x),int(y)))\n", " cv2.circle(image_circles, (int(x),int(y)), 20, (20,180,35), 10)\n", " elif r < 100 and 100 < g and b < 100: # BLUE\n", " juveniles.append((int(x),int(y))) \n", " cv2.circle(image_circles, (int(x),int(y)), 20, (180,60,30), 10)\n", " elif r < 150 and g < 50 and b < 100: # BROWN\n", " adult_females.append((int(x),int(y)))\n", " cv2.circle(image_circles, (int(x),int(y)), 20, (0,42,84), 10) \n", "\n", " cv2.rectangle(cut, (int(x)-112,int(y)-112),(int(x)+112,int(y)+112), 0,-1)\n", "\n", " coordinates_df[\"adult_males\"][filename] = adult_males\n", " coordinates_df[\"subadult_males\"][filename] = subadult_males\n", " coordinates_df[\"adult_females\"][filename] = adult_females\n", " coordinates_df[\"juveniles\"][filename] = juveniles\n", " coordinates_df[\"pups\"][filename] = pups\n", " \n", "%time" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "804b871581404468b7015b1d79b7d30d" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "x = []\n", "y = []\n", "\n", "for filename in tqdm_notebook(file_names): \n", " image = cv2.imread(my_dir + \"/Train/\" + filename)\n", " for lion_class in class_names:\n", " try:\n", " for coordinates in coordinates_df[lion_class][filename]:\n", " thumb = image[coordinates[1]-32:coordinates[1]+32,coordinates[0]-32:coordinates[0]+32,:]\n", " if np.shape(thumb) == (64, 64, 3):\n", " x.append(thumb)\n", " y.append(lion_class)\n", " except:\n", " pass" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(0,np.shape(cut)[0],224):\n", " for j in range(0,np.shape(cut)[1],224): \n", " thumb = cut[i:i+64,j:j+64,:]\n", " if np.amin(cv2.cvtColor(thumb, cv2.COLOR_BGR2GRAY)) != 0:\n", " if np.shape(thumb) == (64,64,3):\n", " x.append(thumb)\n", " y.append(\"negative\") " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class_names.append(\"negative\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.array(x)\n", "y = np.array(y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "encoder = LabelBinarizer()\n", "encoder.fit(y)\n", "y = encoder.transform(y).astype(float)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#my_model = '2017-06-23_model.h5'#what is the model file named?\n", "\n", "my_model = '2017-06-25_model.h5'#what is the model file named?\n", "\n", "model = load_model(my_dir + my_model)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "run number: 1\n", "['0.jpg', '1.jpg', '2.jpg', '3.jpg', '4.jpg']\n" ] } ], "source": [ "test_file_names = os.listdir(my_dir + \"Test/\")\n", "test_file_names = sorted(test_file_names, key=lambda \n", " item: (int(item.partition('.')[0]) if item[0].isdigit() else float('inf'), item)) \n", "\n", "# select a subset of files to run on\n", "\n", "if run_num == 1:\n", " test_file_names = test_file_names[:2000]\n", "elif run_num == 2:\n", " test_file_names = test_file_names[2000:4000]\n", "elif run_num == 3:\n", " test_file_names = test_file_names[4000:6000]\n", "elif run_num == 4: \n", " test_file_names = test_file_names[6000:8000]\n", "elif run_num == 5:\n", " test_file_names = test_file_names[8000:10000]\n", "elif run_num == 6:\n", " test_file_names = test_file_names[10000:12000]\n", "elif run_num == 7:\n", " test_file_names = test_file_names[12000:14000]\n", "elif run_num == 8:\n", " test_file_names = test_file_names[14000:16000]\n", "elif run_num == 9:\n", " test_file_names = test_file_names[16000:]\n", "\n", "\n", "# dataframe to store results in\n", "test_coordinates_df = pd.DataFrame(0,index=test_file_names, columns=class_names)\n", "\n", "print('run number:', run_num)\n", "print(test_file_names[:5])\n", "#print(test_coordinates_df)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "09517be6ec924b95a17cc13d59f29ee3" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "completed 0 images at 16:10\n" ] } ], "source": [ "for filename in tqdm_notebook(test_file_names):\n", " file_int = int(filename[:-4])\n", " current_time = datetime.datetime.now().time().isoformat()[:5]\n", " if file_int%500 == 0:\n", " print('completed %d images at %s' % (file_int, current_time))\n", " \n", " img = cv2.imread(my_dir + \"Test/\" + filename)\n", "\n", " x_test = []\n", "\n", " for i in range(0,np.shape(img)[0],64):\n", " for j in range(0,np.shape(img)[1],64): \n", " thumb = img[i:i+64,j:j+64,:] \n", " if np.shape(thumb) == (64,64,3):\n", " x_test.append(thumb)\n", "\n", " x_test = np.array(x_test)\n", "\n", " y_predicted = model.predict(x_test, verbose=0)\n", "\n", " y_predicted = encoder.inverse_transform(y_predicted)\n", "\n", " the_counter = Counter(y_predicted)\n", " \n", " #print(the_counter)\n", " \n", " for key in the_counter:\n", " test_coordinates_df.set_value(index = filename, col = key, value = the_counter[key])\n", " \n", "%time" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " adult_males subadult_males adult_females juveniles pups\n", "0.jpg 6 3 62 28 117\n", "1.jpg 38 10 199 95 534\n", "2.jpg 324 27 821 401 543\n", "3.jpg 69 8 201 157 926\n", "4.jpg 215 36 441 235 711\n", "5.jpg 116 11 250 139 1093\n", "6.jpg 46 5 337 110 1429\n", "7.jpg 15 2 102 85 802\n", "8.jpg 24 4 160 96 538\n", "9.jpg 212 15 1164 303 2154\n", "10.jpg 48 5 190 97 840\n", "11.jpg 13 1 188 227 1780\n", "12.jpg 87 20 498 146 628\n", "13.jpg 57 1 45 29 885\n", "14.jpg 456 23 813 364 1144\n", "15.jpg 69 18 263 109 168\n", "16.jpg 106 10 239 132 517\n", "17.jpg 213 38 997 303 1119\n", "18.jpg 59 11 327 91 863\n", "19.jpg 104 8 198 117 488\n", "20.jpg 50 5 124 79 1258\n", "21.jpg 40 5 150 91 216\n", "22.jpg 53 8 255 113 364\n", "23.jpg 77 11 306 166 580\n", "24.jpg 481 60 509 310 237\n", "25.jpg 51 14 113 91 321\n", "26.jpg 49 5 226 129 859\n", "27.jpg 51 8 295 230 1402\n", "28.jpg 41 1 95 133 1024\n", "29.jpg 75 5 190 96 256\n", "... ... ... ... ... ...\n", "18606.jpg 0 0 0 0 0\n", "18607.jpg 0 0 0 0 0\n", "18608.jpg 0 0 0 0 0\n", "18609.jpg 0 0 0 0 0\n", "18610.jpg 0 0 0 0 0\n", "18611.jpg 0 0 0 0 0\n", "18612.jpg 0 0 0 0 0\n", "18613.jpg 0 0 0 0 0\n", "18614.jpg 0 0 0 0 0\n", "18615.jpg 0 0 0 0 0\n", "18616.jpg 0 0 0 0 0\n", "18617.jpg 0 0 0 0 0\n", "18618.jpg 0 0 0 0 0\n", "18619.jpg 0 0 0 0 0\n", "18620.jpg 0 0 0 0 0\n", "18621.jpg 0 0 0 0 0\n", "18622.jpg 0 0 0 0 0\n", "18623.jpg 0 0 0 0 0\n", "18624.jpg 0 0 0 0 0\n", "18625.jpg 0 0 0 0 0\n", "18626.jpg 0 0 0 0 0\n", "18627.jpg 0 0 0 0 0\n", "18628.jpg 0 0 0 0 0\n", "18629.jpg 0 0 0 0 0\n", "18630.jpg 0 0 0 0 0\n", "18631.jpg 0 0 0 0 0\n", "18632.jpg 0 0 0 0 0\n", "18633.jpg 0 0 0 0 0\n", "18634.jpg 0 0 0 0 0\n", "18635.jpg 0 0 0 0 0\n", "\n", "[18636 rows x 5 columns]\n" ] } ], "source": [ "protect_df = test_coordinates_df\n", "#print(test_coordinates_df)\n", "\n", "del test_coordinates_df['negative']\n", "test_coordinates_df = test_coordinates_df[['adult_males', 'subadult_males', 'adult_females', 'juveniles', 'pups']]\n", "print(test_coordinates_df)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_coordinates_df.to_csv(my_dir + datetime.date.today().isoformat() + '_submission_' + str(run_num) + '.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mmagnus/rna-pdb-tools	notes/rp18-191116.ipynb	2	17780	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RNA-Puzzle 18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Init the library and needed functions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import rna_tools.Seq as Seq\n", "import rna_tools.BlastPDB\n", "from rna_tools.BlastPDB import BlastPDB\n", "reload(rna_tools.BlastPDB);\n", "reload(Seq);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a RNASeqence object." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rna_seq\n", "GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\n", "\n" ] } ], "source": [ "seq = Seq.RNASequence(\"GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\")\n", "print(seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Secondary structure prediction" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">rna_seq\n", "GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\n", "(((((((((.(((((...)))))(((((((.....)))))))...)))))..)))).(((((....))))) (-33.10)\n" ] } ], "source": [ "print(seq.predict_ss())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">rna_seq [100]\n", "GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG -33.10 1.00\n", "(((((((((.((((.....))))(((((((.....)))))))...)))))..)))).(((((....))))) -32.40\n", "(((((((((.(((((...)))))(((((((.....)))))))...)))))..)))).(((((....))))) -33.10\n", "(((((((((((((....)))).((((((((.....))))))))..)))))..)))).(((((....))))) -32.30\n", "\n" ] } ], "source": [ "print(seq.predict_ss(method='RNAsubopt'))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\n", "(((((((((((((....)))).((((((((.....))))))))..)))))..)))).(((((....)))))\n", "\n" ] } ], "source": [ "print(seq.predict_ss(method='ipknot'))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "ename": "CalledProcessError", "evalue": "Command 'centroid_fold /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmp4ZRI3P.fa' returned non-zero exit status 127", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-fa5ebd28a883>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_ss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'centroid_fold'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/magnus/work-src/rna-tools/rna_tools/Seq.py\u001b[0m in \u001b[0;36mpredict_ss\u001b[0;34m(self, method, constraints, enforce_constraint, shapefn, explore, verbose)\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"centroid_fold\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'centroid_fold '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 550\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/magnus/miniconda2/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(popenargs, *kwargs)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mCalledProcessError\u001b[0m: Command 'centroid_fold /var/folders/yc/ssr9692s5fzf7k165grnhpk80000gp/T/tmp4ZRI3P.fa' returned non-zero exit status 127" ] } ], "source": [ "print(seq.predict_ss(method='centroid_fold'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PDB Blast search" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<HTML>\n", "<TITLE>BLAST Search Results</TITLE>\n", "<BODY BGCOLOR=\"#FFFFFF\" LINK=\"#0000FF\" VLINK=\"#660099\" ALINK=\"#660099\">\n", "<PRE>\n", "<b>BLASTN 2.2.18 [Mar-02-2008]</b>\n", "\n", "\n", "<b><a href=\"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Retrieve&list_uids\n", "=9254694&dopt=Citation\">Reference</a>:</b>\n", "Altschul, Stephen F., Thomas L. Madden, Alejandro A. Schäffer, \n", "Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), \n", "\"Gapped BLAST and PSI-BLAST: a new generation of protein database search\n", "programs\", Nucleic Acids Res. 25:3389-3402.\n", "\n", "<b>Query=</b> UNKNOWN_SEQUENCE\n", " (71 letters)\n", "\n", "<b>Database:</b> pdb_nucleotide \n", " 19,357 sequences; 3,678,086 total letters\n", "\n", "Searching..................................................done\n", "\n", "<PRE>\n", "\n", "\n", " Score E\n", "Sequences producing significant alignments: (bits) Value\n", "\n", "5TPY:1:A\|pdbid\|entity\|chain(s)\|sequence <a href = #16642>105</a> 3e-24\n", "4PQV:1:A\|pdbid\|entity\|chain(s)\|sequence <a href = #11431> 34</a> 0.010\n", "</PRE>\n", "<PRE>\n", "><a name = 16642></a>5TPY:1:A\|pdbid\|entity\|chain(s)\|sequence\n", " Length = 71\n", "\n", " Score = 105 bits (53), Expect = 3e-24\n", " Identities = 53/53 (100%)\n", " Strand = Plus / Plus\n", "\n", " \n", "Query: 1 gggtcaggccggcgaaagtcgccacagtttggggaaagctgtgcagcctgtaa 53\n", " \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\n", "Sbjct: 1 gggtcaggccggcgaaagtcgccacagtttggggaaagctgtgcagcctgtaa 53\n", "</PRE>\n", "\n", "\n", "<PRE>\n", "><a name = 11431></a>4PQV:1:A\|pdbid\|entity\|chain(s)\|sequence\n", " Length = 68\n", "\n", " Score = 34.2 bits (17), Expect = 0.010\n", " Identities = 23/25 (92%)\n", " Strand = Plus / Plus\n", "\n", " \n", "Query: 1 gggtcaggccggcgaaagtcgccac 25\n", " \|\|\|\|\|\|\| \|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\|\n", "Sbjct: 1 gggtcagatcggcgaaagtcgccac 25\n", "</PRE>\n", "\n", "\n", "<PRE>\n", " Database: pdb_nucleotide\n", " Posted date: Sep 28, 2018 10:56 PM\n", " Number of letters in database: 3,678,086\n", " Number of sequences in database: 19,357\n", " \n", "Lambda K H\n", " 1.37 0.711 1.31 \n", "\n", "Gapped\n", "Lambda K H\n", " 1.37 0.711 1.31 \n", "\n", "\n", "Matrix: blastn matrix:1 -3\n", "Gap Penalties: Existence: 5, Extension: 2\n", "Number of Sequences: 19357\n", "Number of Hits to DB: 1840\n", "Number of extensions: 12\n", "Number of successful extensions: 7\n", "Number of sequences better than 10.0: 2\n", "Number of HSP's gapped: 7\n", "Number of HSP's successfully gapped: 2\n", "Length of query: 71\n", "Length of database: 3,678,086\n", "Length adjustment: 14\n", "Effective length of query: 57\n", "Effective length of database: 3,407,088\n", "Effective search space: 194204016\n", "Effective search space used: 194204016\n", "X1: 10 (19.8 bits)\n", "X2: 15 (29.7 bits)\n", "X3: 50 (99.1 bits)\n", "S1: 10 (20.3 bits)\n", "S2: 12 (24.3 bits)\n", "</PRE>\n", "</BODY>\n", "</HTML>\n" ] } ], "source": [ "p = BlastPDB(seq.seq)\n", "p.search()\n", "print p.result" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "ename": "RfamSearchError", "evalue": "Error: File existence/permissions problem in trying to open CM file /home/magnus/work/db/rfamdb/Rfam.cm.\nCM file /home/magnus/work/db/rfamdb/Rfam.cm not found (nor an .i1m binary of it); also looked in RFAMDB", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRfamSearchError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-30-fc7568030162>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#seq = Seq.Seq(\"GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRfamSearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmscan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/magnus/work-src/rna-pdb-tools/rna_pdb_tools/RfamSearch.pyc\u001b[0m in \u001b[0;36mcmscan\u001b[0;34m(self, seq)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRfamSearchError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mof\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# os.chdir(old_pwd)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mRfamSearchError\u001b[0m: Error: File existence/permissions problem in trying to open CM file /home/magnus/work/db/rfamdb/Rfam.cm.\nCM file /home/magnus/work/db/rfamdb/Rfam.cm not found (nor an .i1m binary of it); also looked in RFAMDB" ] } ], "source": [ "import rna_pdb_tools.RfamSearch as rf\n", "#reload(rf)\n", "\n", "#seq = Seq.Seq(\"GGGUCAGGCCGGCGAAAGUCGCCACAGUUUGGGGAAAGCUGUGCAGCCUGUAACCCCCCCACGAAAGUGGG\")\n", "rs = rf.RfamSearch()\n", "print rs.cmscan(seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3D structure analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from rna_pdb_tools.pdb_parser_lib import RNAStructure\n", "\n", "fn = \"rna_pdb_tools/data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb\"\n", "\n", "s = RNAStructure(fn)\n", "print s.get_report()\n", "print s.get_info_chains()\n", "print s.get_head()\n", "#print s.view() # image paste here :-)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "cd rna_pdb_tools\n", "./rna-pdb-tools.py --no_hr --get_seq data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# RNA 3D structure prediction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# model using SimRNA\n", "#res = SimRNA(ss,seq.get_ss())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# fake import, should be \n", "res = \"rna_pdb_tools/data/260c8ff6-f24e-4eff-9760-1831407fc770_ALL_thrs5.30A_clust01-000001_AA.pdb\"\n", "# view\n", "view = nglview.show_structure_file(res)\n", "view.add_representation(repr_type='cartoon')\n", "view" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# rna_pdb_tools --get_seq" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "cd ~/rna-bench/opt/xxxcx rna_pdb_tools\n", "./rna-pdb-tools.py --no_hr --get_seq ~/rna-bench/examples/5k7c.pdb" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "cd rna_pdb_tools\n", "./rna-pdb-tools.py --no_hr --get_seq input/5k7c.pdb\n", "./rna-pdb-tools.py --no_hr --get_seq input/tetraloop.pdb\n", "./rna-pdb-tools.py --get_seq input/1xjr.pdb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "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": "2.7.16" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "120px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false }, "widgets": { "state": { "6ceeeae558b24cccb2f19b5ce9e7a1bf": { "views": [ { "cell_index": 17 } ] }, "ba29d5caff4446039cffaf8a7429154f": { "views": [ { "cell_index": 17 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
ajhalthor/statistical-learning-with-R	Chapter 2/notebooks/question 8.ipynb	1	1047184	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "8 (a) We placed the _College.csv_ file in the _Datasets_ directory. Let us access this file." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>X</th><th scope=col>Private</th><th scope=col>Apps</th><th scope=col>Accept</th><th scope=col>Enroll</th><th scope=col>Top10perc</th><th scope=col>Top25perc</th><th scope=col>F.Undergrad</th><th scope=col>P.Undergrad</th><th scope=col>Outstate</th><th scope=col>Room.Board</th><th scope=col>Books</th><th scope=col>Personal</th><th scope=col>PhD</th><th scope=col>Terminal</th><th scope=col>S.F.Ratio</th><th scope=col>perc.alumni</th><th scope=col>Expend</th><th scope=col>Grad.Rate</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>Abilene Christian University</td><td>Yes </td><td>1660 </td><td>1232 </td><td>721 </td><td>23 </td><td>52 </td><td>2885 </td><td> 537 </td><td> 7440 </td><td>3300 </td><td>450 </td><td>2200 </td><td>70 </td><td>78 </td><td>18.1 </td><td>12 </td><td> 7041 </td><td>60 </td></tr>\n", "\t<tr><td>Adelphi University </td><td>Yes </td><td>2186 </td><td>1924 </td><td>512 </td><td>16 </td><td>29 </td><td>2683 </td><td>1227 </td><td>12280 </td><td>6450 </td><td>750 </td><td>1500 </td><td>29 </td><td>30 </td><td>12.2 </td><td>16 </td><td>10527 </td><td>56 </td></tr>\n", "\t<tr><td>Adrian College </td><td>Yes </td><td>1428 </td><td>1097 </td><td>336 </td><td>22 </td><td>50 </td><td>1036 </td><td> 99 </td><td>11250 </td><td>3750 </td><td>400 </td><td>1165 </td><td>53 </td><td>66 </td><td>12.9 </td><td>30 </td><td> 8735 </td><td>54 </td></tr>\n", "\t<tr><td>Agnes Scott College </td><td>Yes </td><td> 417 </td><td> 349 </td><td>137 </td><td>60 </td><td>89 </td><td> 510 </td><td> 63 </td><td>12960 </td><td>5450 </td><td>450 </td><td> 875 </td><td>92 </td><td>97 </td><td> 7.7 </td><td>37 </td><td>19016 </td><td>59 </td></tr>\n", "\t<tr><td>Alaska Pacific University </td><td>Yes </td><td> 193 </td><td> 146 </td><td> 55 </td><td>16 </td><td>44 </td><td> 249 </td><td> 869 </td><td> 7560 </td><td>4120 </td><td>800 </td><td>1500 </td><td>76 </td><td>72 </td><td>11.9 </td><td> 2 </td><td>10922 </td><td>15 </td></tr>\n", "\t<tr><td>Albertson College </td><td>Yes </td><td> 587 </td><td> 479 </td><td>158 </td><td>38 </td><td>62 </td><td> 678 </td><td> 41 </td><td>13500 </td><td>3335 </td><td>500 </td><td> 675 </td><td>67 </td><td>73 </td><td> 9.4 </td><td>11 </td><td> 9727 </td><td>55 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|lllllllllllllllllll}\n", " X & Private & Apps & Accept & Enroll & Top10perc & Top25perc & F.Undergrad & P.Undergrad & Outstate & Room.Board & Books & Personal & PhD & Terminal & S.F.Ratio & perc.alumni & Expend & Grad.Rate\\\\\n", "\\hline\n", "\t Abilene Christian University & Yes & 1660 & 1232 & 721 & 23 & 52 & 2885 & 537 & 7440 & 3300 & 450 & 2200 & 70 & 78 & 18.1 & 12 & 7041 & 60 \\\\\n", "\t Adelphi University & Yes & 2186 & 1924 & 512 & 16 & 29 & 2683 & 1227 & 12280 & 6450 & 750 & 1500 & 29 & 30 & 12.2 & 16 & 10527 & 56 \\\\\n", "\t Adrian College & Yes & 1428 & 1097 & 336 & 22 & 50 & 1036 & 99 & 11250 & 3750 & 400 & 1165 & 53 & 66 & 12.9 & 30 & 8735 & 54 \\\\\n", "\t Agnes Scott College & Yes & 417 & 349 & 137 & 60 & 89 & 510 & 63 & 12960 & 5450 & 450 & 875 & 92 & 97 & 7.7 & 37 & 19016 & 59 \\\\\n", "\t Alaska Pacific University & Yes & 193 & 146 & 55 & 16 & 44 & 249 & 869 & 7560 & 4120 & 800 & 1500 & 76 & 72 & 11.9 & 2 & 10922 & 15 \\\\\n", "\t Albertson College & Yes & 587 & 479 & 158 & 38 & 62 & 678 & 41 & 13500 & 3335 & 500 & 675 & 67 & 73 & 9.4 & 11 & 9727 & 55 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "X \| Private \| Apps \| Accept \| Enroll \| Top10perc \| Top25perc \| F.Undergrad \| P.Undergrad \| Outstate \| Room.Board \| Books \| Personal \| PhD \| Terminal \| S.F.Ratio \| perc.alumni \| Expend \| Grad.Rate \| \n", "\|---\|---\|---\|---\|---\|---\|\n", "\| Abilene Christian University \| Yes \| 1660 \| 1232 \| 721 \| 23 \| 52 \| 2885 \| 537 \| 7440 \| 3300 \| 450 \| 2200 \| 70 \| 78 \| 18.1 \| 12 \| 7041 \| 60 \| \n", "\| Adelphi University \| Yes \| 2186 \| 1924 \| 512 \| 16 \| 29 \| 2683 \| 1227 \| 12280 \| 6450 \| 750 \| 1500 \| 29 \| 30 \| 12.2 \| 16 \| 10527 \| 56 \| \n", "\| Adrian College \| Yes \| 1428 \| 1097 \| 336 \| 22 \| 50 \| 1036 \| 99 \| 11250 \| 3750 \| 400 \| 1165 \| 53 \| 66 \| 12.9 \| 30 \| 8735 \| 54 \| \n", "\| Agnes Scott College \| Yes \| 417 \| 349 \| 137 \| 60 \| 89 \| 510 \| 63 \| 12960 \| 5450 \| 450 \| 875 \| 92 \| 97 \| 7.7 \| 37 \| 19016 \| 59 \| \n", "\| Alaska Pacific University \| Yes \| 193 \| 146 \| 55 \| 16 \| 44 \| 249 \| 869 \| 7560 \| 4120 \| 800 \| 1500 \| 76 \| 72 \| 11.9 \| 2 \| 10922 \| 15 \| \n", "\| Albertson College \| Yes \| 587 \| 479 \| 158 \| 38 \| 62 \| 678 \| 41 \| 13500 \| 3335 \| 500 \| 675 \| 67 \| 73 \| 9.4 \| 11 \| 9727 \| 55 \| \n", "\n", "\n" ], "text/plain": [ " X Private Apps Accept Enroll Top10perc Top25perc\n", "1 Abilene Christian University Yes 1660 1232 721 23 52 \n", "2 Adelphi University Yes 2186 1924 512 16 29 \n", "3 Adrian College Yes 1428 1097 336 22 50 \n", "4 Agnes Scott College Yes 417 349 137 60 89 \n", "5 Alaska Pacific University Yes 193 146 55 16 44 \n", "6 Albertson College Yes 587 479 158 38 62 \n", " F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal\n", "1 2885 537 7440 3300 450 2200 70 78 \n", "2 2683 1227 12280 6450 750 1500 29 30 \n", "3 1036 99 11250 3750 400 1165 53 66 \n", "4 510 63 12960 5450 450 875 92 97 \n", "5 249 869 7560 4120 800 1500 76 72 \n", "6 678 41 13500 3335 500 675 67 73 \n", " S.F.Ratio perc.alumni Expend Grad.Rate\n", "1 18.1 12 7041 60 \n", "2 12.2 16 10527 56 \n", "3 12.9 30 8735 54 \n", "4 7.7 37 19016 59 \n", "5 11.9 2 10922 15 \n", "6 9.4 11 9727 55 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "college = read.csv(\"Datasets/College.csv\")\n", "head(college) #Use fix(college) in R-Studio to display in internal editor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I used the _head()_ function to display only the first few tuples of the dataset. In 'R', we would use _fix(college)_ in R-Studio to display in the internal editor. \n", "\n", "_NOTE_: All columns may not be visible on print. So lets see the fields in the dataset (for reference)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'X'</li>\n", "\t<li>'Private'</li>\n", "\t<li>'Apps'</li>\n", "\t<li>'Accept'</li>\n", "\t<li>'Enroll'</li>\n", "\t<li>'Top10perc'</li>\n", "\t<li>'Top25perc'</li>\n", "\t<li>'F.Undergrad'</li>\n", "\t<li>'P.Undergrad'</li>\n", "\t<li>'Outstate'</li>\n", "\t<li>'Room.Board'</li>\n", "\t<li>'Books'</li>\n", "\t<li>'Personal'</li>\n", "\t<li>'PhD'</li>\n", "\t<li>'Terminal'</li>\n", "\t<li>'S.F.Ratio'</li>\n", "\t<li>'perc.alumni'</li>\n", "\t<li>'Expend'</li>\n", "\t<li>'Grad.Rate'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 'X'\n", "\\item 'Private'\n", "\\item 'Apps'\n", "\\item 'Accept'\n", "\\item 'Enroll'\n", "\\item 'Top10perc'\n", "\\item 'Top25perc'\n", "\\item 'F.Undergrad'\n", "\\item 'P.Undergrad'\n", "\\item 'Outstate'\n", "\\item 'Room.Board'\n", "\\item 'Books'\n", "\\item 'Personal'\n", "\\item 'PhD'\n", "\\item 'Terminal'\n", "\\item 'S.F.Ratio'\n", "\\item 'perc.alumni'\n", "\\item 'Expend'\n", "\\item 'Grad.Rate'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 'X'\n", "2. 'Private'\n", "3. 'Apps'\n", "4. 'Accept'\n", "5. 'Enroll'\n", "6. 'Top10perc'\n", "7. 'Top25perc'\n", "8. 'F.Undergrad'\n", "9. 'P.Undergrad'\n", "10. 'Outstate'\n", "11. 'Room.Board'\n", "12. 'Books'\n", "13. 'Personal'\n", "14. 'PhD'\n", "15. 'Terminal'\n", "16. 'S.F.Ratio'\n", "17. 'perc.alumni'\n", "18. 'Expend'\n", "19. 'Grad.Rate'\n", "\n", "\n" ], "text/plain": [ " [1] \"X\" \"Private\" \"Apps\" \"Accept\" \"Enroll\" \n", " [6] \"Top10perc\" \"Top25perc\" \"F.Undergrad\" \"P.Undergrad\" \"Outstate\" \n", "[11] \"Room.Board\" \"Books\" \"Personal\" \"PhD\" \"Terminal\" \n", "[16] \"S.F.Ratio\" \"perc.alumni\" \"Expend\" \"Grad.Rate\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(college)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (b)\n", "In the table, we do not want the college name to appear as a part of the data. However, this information may be useful later on. We can store them as row names. Let us now check the current row names of this table." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'1'</li>\n", "\t<li>'2'</li>\n", "\t<li>'3'</li>\n", "\t<li>'4'</li>\n", "\t<li>'5'</li>\n", "\t<li>'6'</li>\n", "\t<li>'7'</li>\n", "\t<li>'8'</li>\n", "\t<li>'9'</li>\n", "\t<li>'10'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item '1'\n", "\\item '2'\n", "\\item '3'\n", "\\item '4'\n", "\\item '5'\n", "\\item '6'\n", "\\item '7'\n", "\\item '8'\n", "\\item '9'\n", "\\item '10'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. '1'\n", "2. '2'\n", "3. '3'\n", "4. '4'\n", "5. '5'\n", "6. '6'\n", "7. '7'\n", "8. '8'\n", "9. '9'\n", "10. '10'\n", "\n", "\n" ], "text/plain": [ " [1] \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"10\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rownames(college)[1:10] #Display the first 10 row names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_rownames()_ gives us the implicit row names of the table. Notice these numbers are not displayed in the table shown in _8(a)_. Let us change it to the first column enteries. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>X</th><th scope=col>Private</th><th scope=col>Apps</th><th scope=col>Accept</th><th scope=col>Enroll</th><th scope=col>Top10perc</th><th scope=col>Top25perc</th><th scope=col>F.Undergrad</th><th scope=col>P.Undergrad</th><th scope=col>Outstate</th><th scope=col>Room.Board</th><th scope=col>Books</th><th scope=col>Personal</th><th scope=col>PhD</th><th scope=col>Terminal</th><th scope=col>S.F.Ratio</th><th scope=col>perc.alumni</th><th scope=col>Expend</th><th scope=col>Grad.Rate</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>Abilene Christian University</th><td>Abilene Christian University</td><td>Yes </td><td>1660 </td><td>1232 </td><td>721 </td><td>23 </td><td>52 </td><td>2885 </td><td> 537 </td><td> 7440 </td><td>3300 </td><td>450 </td><td>2200 </td><td>70 </td><td>78 </td><td>18.1 </td><td>12 </td><td> 7041 </td><td>60 </td></tr>\n", "\t<tr><th scope=row>Adelphi University</th><td>Adelphi University </td><td>Yes </td><td>2186 </td><td>1924 </td><td>512 </td><td>16 </td><td>29 </td><td>2683 </td><td>1227 </td><td>12280 </td><td>6450 </td><td>750 </td><td>1500 </td><td>29 </td><td>30 </td><td>12.2 </td><td>16 </td><td>10527 </td><td>56 </td></tr>\n", "\t<tr><th scope=row>Adrian College</th><td>Adrian College </td><td>Yes </td><td>1428 </td><td>1097 </td><td>336 </td><td>22 </td><td>50 </td><td>1036 </td><td> 99 </td><td>11250 </td><td>3750 </td><td>400 </td><td>1165 </td><td>53 </td><td>66 </td><td>12.9 </td><td>30 </td><td> 8735 </td><td>54 </td></tr>\n", "\t<tr><th scope=row>Agnes Scott College</th><td>Agnes Scott College </td><td>Yes </td><td> 417 </td><td> 349 </td><td>137 </td><td>60 </td><td>89 </td><td> 510 </td><td> 63 </td><td>12960 </td><td>5450 </td><td>450 </td><td> 875 </td><td>92 </td><td>97 </td><td> 7.7 </td><td>37 </td><td>19016 </td><td>59 </td></tr>\n", "\t<tr><th scope=row>Alaska Pacific University</th><td>Alaska Pacific University </td><td>Yes </td><td> 193 </td><td> 146 </td><td> 55 </td><td>16 </td><td>44 </td><td> 249 </td><td> 869 </td><td> 7560 </td><td>4120 </td><td>800 </td><td>1500 </td><td>76 </td><td>72 </td><td>11.9 </td><td> 2 </td><td>10922 </td><td>15 </td></tr>\n", "\t<tr><th scope=row>Albertson College</th><td>Albertson College </td><td>Yes </td><td> 587 </td><td> 479 </td><td>158 </td><td>38 </td><td>62 </td><td> 678 </td><td> 41 </td><td>13500 </td><td>3335 </td><td>500 </td><td> 675 </td><td>67 </td><td>73 </td><td> 9.4 </td><td>11 </td><td> 9727 </td><td>55 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|lllllllllllllllllll}\n", " & X & Private & Apps & Accept & Enroll & Top10perc & Top25perc & F.Undergrad & P.Undergrad & Outstate & Room.Board & Books & Personal & PhD & Terminal & S.F.Ratio & perc.alumni & Expend & Grad.Rate\\\\\n", "\\hline\n", "\tAbilene Christian University & Abilene Christian University & Yes & 1660 & 1232 & 721 & 23 & 52 & 2885 & 537 & 7440 & 3300 & 450 & 2200 & 70 & 78 & 18.1 & 12 & 7041 & 60 \\\\\n", "\tAdelphi University & Adelphi University & Yes & 2186 & 1924 & 512 & 16 & 29 & 2683 & 1227 & 12280 & 6450 & 750 & 1500 & 29 & 30 & 12.2 & 16 & 10527 & 56 \\\\\n", "\tAdrian College & Adrian College & Yes & 1428 & 1097 & 336 & 22 & 50 & 1036 & 99 & 11250 & 3750 & 400 & 1165 & 53 & 66 & 12.9 & 30 & 8735 & 54 \\\\\n", "\tAgnes Scott College & Agnes Scott College & Yes & 417 & 349 & 137 & 60 & 89 & 510 & 63 & 12960 & 5450 & 450 & 875 & 92 & 97 & 7.7 & 37 & 19016 & 59 \\\\\n", "\tAlaska Pacific University & Alaska Pacific University & Yes & 193 & 146 & 55 & 16 & 44 & 249 & 869 & 7560 & 4120 & 800 & 1500 & 76 & 72 & 11.9 & 2 & 10922 & 15 \\\\\n", "\tAlbertson College & Albertson College & Yes & 587 & 479 & 158 & 38 & 62 & 678 & 41 & 13500 & 3335 & 500 & 675 & 67 & 73 & 9.4 & 11 & 9727 & 55 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "\| <!--/--> \| X \| Private \| Apps \| Accept \| Enroll \| Top10perc \| Top25perc \| F.Undergrad \| P.Undergrad \| Outstate \| Room.Board \| Books \| Personal \| PhD \| Terminal \| S.F.Ratio \| perc.alumni \| Expend \| Grad.Rate \| \n", "\|---\|---\|---\|---\|---\|---\|\n", "\| Abilene Christian University \| Abilene Christian University \| Yes \| 1660 \| 1232 \| 721 \| 23 \| 52 \| 2885 \| 537 \| 7440 \| 3300 \| 450 \| 2200 \| 70 \| 78 \| 18.1 \| 12 \| 7041 \| 60 \| \n", "\| Adelphi University \| Adelphi University \| Yes \| 2186 \| 1924 \| 512 \| 16 \| 29 \| 2683 \| 1227 \| 12280 \| 6450 \| 750 \| 1500 \| 29 \| 30 \| 12.2 \| 16 \| 10527 \| 56 \| \n", "\| Adrian College \| Adrian College \| Yes \| 1428 \| 1097 \| 336 \| 22 \| 50 \| 1036 \| 99 \| 11250 \| 3750 \| 400 \| 1165 \| 53 \| 66 \| 12.9 \| 30 \| 8735 \| 54 \| \n", "\| Agnes Scott College \| Agnes Scott College \| Yes \| 417 \| 349 \| 137 \| 60 \| 89 \| 510 \| 63 \| 12960 \| 5450 \| 450 \| 875 \| 92 \| 97 \| 7.7 \| 37 \| 19016 \| 59 \| \n", "\| Alaska Pacific University \| Alaska Pacific University \| Yes \| 193 \| 146 \| 55 \| 16 \| 44 \| 249 \| 869 \| 7560 \| 4120 \| 800 \| 1500 \| 76 \| 72 \| 11.9 \| 2 \| 10922 \| 15 \| \n", "\| Albertson College \| Albertson College \| Yes \| 587 \| 479 \| 158 \| 38 \| 62 \| 678 \| 41 \| 13500 \| 3335 \| 500 \| 675 \| 67 \| 73 \| 9.4 \| 11 \| 9727 \| 55 \| \n", "\n", "\n" ], "text/plain": [ " X Private Apps Accept\n", "Abilene Christian University Abilene Christian University Yes 1660 1232 \n", "Adelphi University Adelphi University Yes 2186 1924 \n", "Adrian College Adrian College Yes 1428 1097 \n", "Agnes Scott College Agnes Scott College Yes 417 349 \n", "Alaska Pacific University Alaska Pacific University Yes 193 146 \n", "Albertson College Albertson College Yes 587 479 \n", " Enroll Top10perc Top25perc F.Undergrad P.Undergrad\n", "Abilene Christian University 721 23 52 2885 537 \n", "Adelphi University 512 16 29 2683 1227 \n", "Adrian College 336 22 50 1036 99 \n", "Agnes Scott College 137 60 89 510 63 \n", "Alaska Pacific University 55 16 44 249 869 \n", "Albertson College 158 38 62 678 41 \n", " Outstate Room.Board Books Personal PhD Terminal\n", "Abilene Christian University 7440 3300 450 2200 70 78 \n", "Adelphi University 12280 6450 750 1500 29 30 \n", "Adrian College 11250 3750 400 1165 53 66 \n", "Agnes Scott College 12960 5450 450 875 92 97 \n", "Alaska Pacific University 7560 4120 800 1500 76 72 \n", "Albertson College 13500 3335 500 675 67 73 \n", " S.F.Ratio perc.alumni Expend Grad.Rate\n", "Abilene Christian University 18.1 12 7041 60 \n", "Adelphi University 12.2 16 10527 56 \n", "Adrian College 12.9 30 8735 54 \n", "Agnes Scott College 7.7 37 19016 59 \n", "Alaska Pacific University 11.9 2 10922 15 \n", "Albertson College 9.4 11 9727 55 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rownames(college) = college[ , 1]\n", "head(college)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perfect! If the above table is viewed in RStudio, we would see the column name _row.names_ above the bold college names. We now remove the column X from the table." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Private</th><th scope=col>Apps</th><th scope=col>Accept</th><th scope=col>Enroll</th><th scope=col>Top10perc</th><th scope=col>Top25perc</th><th scope=col>F.Undergrad</th><th scope=col>P.Undergrad</th><th scope=col>Outstate</th><th scope=col>Room.Board</th><th scope=col>Books</th><th scope=col>Personal</th><th scope=col>PhD</th><th scope=col>Terminal</th><th scope=col>S.F.Ratio</th><th scope=col>perc.alumni</th><th scope=col>Expend</th><th scope=col>Grad.Rate</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>Abilene Christian University</th><td>Yes </td><td>1660 </td><td>1232 </td><td>721 </td><td>23 </td><td>52 </td><td>2885 </td><td> 537 </td><td> 7440</td><td>3300 </td><td>450 </td><td>2200 </td><td>70 </td><td>78 </td><td>18.1 </td><td>12 </td><td> 7041</td><td>60 </td></tr>\n", "\t<tr><th scope=row>Adelphi University</th><td>Yes </td><td>2186 </td><td>1924 </td><td>512 </td><td>16 </td><td>29 </td><td>2683 </td><td>1227 </td><td>12280</td><td>6450 </td><td>750 </td><td>1500 </td><td>29 </td><td>30 </td><td>12.2 </td><td>16 </td><td>10527</td><td>56 </td></tr>\n", "\t<tr><th scope=row>Adrian College</th><td>Yes </td><td>1428 </td><td>1097 </td><td>336 </td><td>22 </td><td>50 </td><td>1036 </td><td> 99 </td><td>11250</td><td>3750 </td><td>400 </td><td>1165 </td><td>53 </td><td>66 </td><td>12.9 </td><td>30 </td><td> 8735</td><td>54 </td></tr>\n", "\t<tr><th scope=row>Agnes Scott College</th><td>Yes </td><td> 417 </td><td> 349 </td><td>137 </td><td>60 </td><td>89 </td><td> 510 </td><td> 63 </td><td>12960</td><td>5450 </td><td>450 </td><td> 875 </td><td>92 </td><td>97 </td><td> 7.7 </td><td>37 </td><td>19016</td><td>59 </td></tr>\n", "\t<tr><th scope=row>Alaska Pacific University</th><td>Yes </td><td> 193 </td><td> 146 </td><td> 55 </td><td>16 </td><td>44 </td><td> 249 </td><td> 869 </td><td> 7560</td><td>4120 </td><td>800 </td><td>1500 </td><td>76 </td><td>72 </td><td>11.9 </td><td> 2 </td><td>10922</td><td>15 </td></tr>\n", "\t<tr><th scope=row>Albertson College</th><td>Yes </td><td> 587 </td><td> 479 </td><td>158 </td><td>38 </td><td>62 </td><td> 678 </td><td> 41 </td><td>13500</td><td>3335 </td><td>500 </td><td> 675 </td><td>67 </td><td>73 </td><td> 9.4 </td><td>11 </td><td> 9727</td><td>55 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|llllllllllllllllll}\n", " & Private & Apps & Accept & Enroll & Top10perc & Top25perc & F.Undergrad & P.Undergrad & Outstate & Room.Board & Books & Personal & PhD & Terminal & S.F.Ratio & perc.alumni & Expend & Grad.Rate\\\\\n", "\\hline\n", "\tAbilene Christian University & Yes & 1660 & 1232 & 721 & 23 & 52 & 2885 & 537 & 7440 & 3300 & 450 & 2200 & 70 & 78 & 18.1 & 12 & 7041 & 60 \\\\\n", "\tAdelphi University & Yes & 2186 & 1924 & 512 & 16 & 29 & 2683 & 1227 & 12280 & 6450 & 750 & 1500 & 29 & 30 & 12.2 & 16 & 10527 & 56 \\\\\n", "\tAdrian College & Yes & 1428 & 1097 & 336 & 22 & 50 & 1036 & 99 & 11250 & 3750 & 400 & 1165 & 53 & 66 & 12.9 & 30 & 8735 & 54 \\\\\n", "\tAgnes Scott College & Yes & 417 & 349 & 137 & 60 & 89 & 510 & 63 & 12960 & 5450 & 450 & 875 & 92 & 97 & 7.7 & 37 & 19016 & 59 \\\\\n", "\tAlaska Pacific University & Yes & 193 & 146 & 55 & 16 & 44 & 249 & 869 & 7560 & 4120 & 800 & 1500 & 76 & 72 & 11.9 & 2 & 10922 & 15 \\\\\n", "\tAlbertson College & Yes & 587 & 479 & 158 & 38 & 62 & 678 & 41 & 13500 & 3335 & 500 & 675 & 67 & 73 & 9.4 & 11 & 9727 & 55 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "\| <!--/--> \| Private \| Apps \| Accept \| Enroll \| Top10perc \| Top25perc \| F.Undergrad \| P.Undergrad \| Outstate \| Room.Board \| Books \| Personal \| PhD \| Terminal \| S.F.Ratio \| perc.alumni \| Expend \| Grad.Rate \| \n", "\|---\|---\|---\|---\|---\|---\|\n", "\| Abilene Christian University \| Yes \| 1660 \| 1232 \| 721 \| 23 \| 52 \| 2885 \| 537 \| 7440 \| 3300 \| 450 \| 2200 \| 70 \| 78 \| 18.1 \| 12 \| 7041 \| 60 \| \n", "\| Adelphi University \| Yes \| 2186 \| 1924 \| 512 \| 16 \| 29 \| 2683 \| 1227 \| 12280 \| 6450 \| 750 \| 1500 \| 29 \| 30 \| 12.2 \| 16 \| 10527 \| 56 \| \n", "\| Adrian College \| Yes \| 1428 \| 1097 \| 336 \| 22 \| 50 \| 1036 \| 99 \| 11250 \| 3750 \| 400 \| 1165 \| 53 \| 66 \| 12.9 \| 30 \| 8735 \| 54 \| \n", "\| Agnes Scott College \| Yes \| 417 \| 349 \| 137 \| 60 \| 89 \| 510 \| 63 \| 12960 \| 5450 \| 450 \| 875 \| 92 \| 97 \| 7.7 \| 37 \| 19016 \| 59 \| \n", "\| Alaska Pacific University \| Yes \| 193 \| 146 \| 55 \| 16 \| 44 \| 249 \| 869 \| 7560 \| 4120 \| 800 \| 1500 \| 76 \| 72 \| 11.9 \| 2 \| 10922 \| 15 \| \n", "\| Albertson College \| Yes \| 587 \| 479 \| 158 \| 38 \| 62 \| 678 \| 41 \| 13500 \| 3335 \| 500 \| 675 \| 67 \| 73 \| 9.4 \| 11 \| 9727 \| 55 \| \n", "\n", "\n" ], "text/plain": [ " Private Apps Accept Enroll Top10perc Top25perc\n", "Abilene Christian University Yes 1660 1232 721 23 52 \n", "Adelphi University Yes 2186 1924 512 16 29 \n", "Adrian College Yes 1428 1097 336 22 50 \n", "Agnes Scott College Yes 417 349 137 60 89 \n", "Alaska Pacific University Yes 193 146 55 16 44 \n", "Albertson College Yes 587 479 158 38 62 \n", " F.Undergrad P.Undergrad Outstate Room.Board Books\n", "Abilene Christian University 2885 537 7440 3300 450 \n", "Adelphi University 2683 1227 12280 6450 750 \n", "Adrian College 1036 99 11250 3750 400 \n", "Agnes Scott College 510 63 12960 5450 450 \n", "Alaska Pacific University 249 869 7560 4120 800 \n", "Albertson College 678 41 13500 3335 500 \n", " Personal PhD Terminal S.F.Ratio perc.alumni Expend\n", "Abilene Christian University 2200 70 78 18.1 12 7041 \n", "Adelphi University 1500 29 30 12.2 16 10527 \n", "Adrian College 1165 53 66 12.9 30 8735 \n", "Agnes Scott College 875 92 97 7.7 37 19016 \n", "Alaska Pacific University 1500 76 72 11.9 2 10922 \n", "Albertson College 675 67 73 9.4 11 9727 \n", " Grad.Rate\n", "Abilene Christian University 60 \n", "Adelphi University 56 \n", "Adrian College 54 \n", "Agnes Scott College 59 \n", "Alaska Pacific University 15 \n", "Albertson College 55 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "college = college[ , -1] #Exclude first column\n", "head(college)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note the first column in bold, although not explicitly mentioned, is called _row.names_. This is not a data column but rather the name that R is giving to each row." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) i. Let us now produce a numerical summary of the dataset." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Private Apps Accept Enroll Top10perc \n", " No :212 Min. : 81 Min. : 72 Min. : 35 Min. : 1.00 \n", " Yes:565 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242 1st Qu.:15.00 \n", " Median : 1558 Median : 1110 Median : 434 Median :23.00 \n", " Mean : 3002 Mean : 2019 Mean : 780 Mean :27.56 \n", " 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902 3rd Qu.:35.00 \n", " Max. :48094 Max. :26330 Max. :6392 Max. :96.00 \n", " Top25perc F.Undergrad P.Undergrad Outstate \n", " Min. : 9.0 Min. : 139 Min. : 1.0 Min. : 2340 \n", " 1st Qu.: 41.0 1st Qu.: 992 1st Qu.: 95.0 1st Qu.: 7320 \n", " Median : 54.0 Median : 1707 Median : 353.0 Median : 9990 \n", " Mean : 55.8 Mean : 3700 Mean : 855.3 Mean :10441 \n", " 3rd Qu.: 69.0 3rd Qu.: 4005 3rd Qu.: 967.0 3rd Qu.:12925 \n", " Max. :100.0 Max. :31643 Max. :21836.0 Max. :21700 \n", " Room.Board Books Personal PhD \n", " Min. :1780 Min. : 96.0 Min. : 250 Min. : 8.00 \n", " 1st Qu.:3597 1st Qu.: 470.0 1st Qu.: 850 1st Qu.: 62.00 \n", " Median :4200 Median : 500.0 Median :1200 Median : 75.00 \n", " Mean :4358 Mean : 549.4 Mean :1341 Mean : 72.66 \n", " 3rd Qu.:5050 3rd Qu.: 600.0 3rd Qu.:1700 3rd Qu.: 85.00 \n", " Max. :8124 Max. :2340.0 Max. :6800 Max. :103.00 \n", " Terminal S.F.Ratio perc.alumni Expend \n", " Min. : 24.0 Min. : 2.50 Min. : 0.00 Min. : 3186 \n", " 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00 1st Qu.: 6751 \n", " Median : 82.0 Median :13.60 Median :21.00 Median : 8377 \n", " Mean : 79.7 Mean :14.09 Mean :22.74 Mean : 9660 \n", " 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00 3rd Qu.:10830 \n", " Max. :100.0 Max. :39.80 Max. :64.00 Max. :56233 \n", " Grad.Rate \n", " Min. : 10.00 \n", " 1st Qu.: 53.00 \n", " Median : 65.00 \n", " Mean : 65.46 \n", " 3rd Qu.: 78.00 \n", " Max. :118.00 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(college)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the description above, it is easy to observe the qualitative(classification) and quantitative(regression) variables. The first column _Private_ has only 2 values _Yes_ or _No_ and is hence categorical. Every other field is quantitative with a minimum value, maximum, mean, 1st Quartile, median and 3rd Quartile value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) ii. We now display the relationship between the first 10 columns using a scatterplot matrix." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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6/wco33cDoPXgSTaYdR2G/B+afbmvfvjmtz3au8ZljTb/nd13VP+1RTk7Xh0A4P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\n0uU9+Jv2Ki6st+15JVj/HcD55TCYC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ICmQFsgJZgaxAViArkBXICmQFsgJZgaxA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\n71yeHo4Bx+JOYPpnoGa+B13HQBlro1B7mYIdlBlKmuuS7bKtj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MaLY2ssmDddf8wXeR7Dfx443xxbx8C3wXnjHHLNHgnbg3XY9gVA+wYY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\nZRv+UON7PKc9Adr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Mmno5xh+iyNp2Xa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\nJjwG18Hh4HzxoOW7tV/Mu0mJu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U2R5/\n7HUxn2yzttB1/HlGsp2/AvsU+dO+O59PgTPBctH3t/E7Z50jcQbzHBFlXTfnhlKyHi9avotS\nWHd/mCrlTbVQeQs4wJzUdXAJzAWXQq3KCaX8CmHf2qpVqMB6rM+6p8v8af0vEjcHxIZrmgvC\nPWCZP4C/KH0NP4VDoStoJjqhbbqB88wF2gUxFDYzTyyW9+OfEerhEkg1moAbh/QBLyovQa1q\nZho+C9h/7bMQaBNtEdoAj+mDwcVfv5cf5+HS4KJ/PnwFjiW/xK0Hv4Z9YC+w7EBwo9wGjoOV\noFBhgY62gAdXx+vvYPYyD3NMS3tpASqK+lxznFsegJTxj4Nx+m+B5+BScN3ZHNT0cDoMghPB\nQ52HszVhbVgdhsEfYXGoFYVdbG/4vSR+Dn3hWCjUOgu4Dx0Lrq2usQNhQ3D93QhCru2bwAQ4\nBLYG12zPBuY3rR+8Cq7pru/uBc4fzws3gPuncdYlyrEd4dXwfwGnQrVoWxpyOcwEl4Ltd+y5\nn00Lx4HzcTEwfhTsC54bQsaLl+CHYT34GELv4jkLToHtwLnqBcv9b0HYFN6DUrI9F4J7ainm\nI/4VKFRY4EcWGELIG3WqxwjEZpPGV7vfSRATM/y6wypsuBPbCXtb5kadxoU/XCdvTHDjIo+L\no/Y9F+YBv4q8DB+B9U9J+TVtRBsa4ELmoh42KOeGLXS1xwPwOHjgN07beImoJrkh2raZK2yU\nX4HdHM6D6H/YJ8K62mP3zDXdcKR7eFOLwljw65njbAxcCW42Hv4s54Ex1ZMEjk4j2tk/nvrq\nKqzTg6l99ADaFXUqnbq9DR2zrHXUipzHXkA8tMXYdUzqT8NpnGOgEtVR6A3I12vdUb+u8yji\ndL+GkTA3PAKuW/uD2hEeAvvxHSwJqZxvfvHuDLlmuHZUIteq1C7h13U/vwvsowdwD7OLQC3J\ni7h7TqVyr3PPq0RHU+iJEgX/Qdw5SbyHei/afbO4GXEdf6/A2VmcztZgvOPQeaPf9+SFQH9g\nXLzHdG9wX0k1gID5Kj1TDKOs47xSvURB1wAveFeB88m5ZJtfB9urXaJfur8H9/21YTykfTU9\n8ke89Rkf9aZ1acODoJysy8tVoRIWmKZEXBH1Ywv4VdqNw4H8JkwHtSonVHsq/UJkvaXqnzN7\noGkuCiHHnofagaB9u4OXo9lgXqh1dcs64ALUErmQuSD2gyj7c/z3gbbpSnIO+XXLMeFi7gUn\nlfH/B+dnrmkHgBdq0w6EyfAqaCvHzFPgQt8L/gWfg3V4WUpl+UKFBTraAjvwgB7wZfIgx2Mo\nHYfGOw/aKueB9crrucriedGG8aRPANeWQTAJ/Jrv3LwPjoSLwMPvOJgW/Kd1S0GqqC+NqyW/\nF1L77wXpE1gUXD/6QKHmLdDU+0/Tlqeqz+BmGA1DQbmmD4GH4Y9wFKjFYY8GX+N4njXz61iv\n4znGdMwd638Tqkn2w70q2mrbHGuefxYB90H90YfT8Xuh+SmsAAvB2+DZybXkNTCv4ajTPXAs\nfAipzQ8n7JlKux4BhVppAV9MofIW6EnSsuDGsSVsAQ64WpYTKJ1EbemLm6aK+mKSRzie5UT+\nFJzI+v1q6SLhZWANcJOqAyf993A2uDhG/XhrTjG3PHCEPfKdiAVOu/n1Unc8aBsP/KuAi2hs\nJnh/JBfRy+EmOBBmgFqQ79v37CXGMZFeogn+j7SHlx/L/QkcS5PAr40Lgl8h68G0gfA8LAFq\nvkan4e8v+LsS3JHE5b3+4nQK3ArWlz8QElWosECTFnDu+0vXOFgV8vMy5j1JDTJs3rbK57wD\nrjeLZS5OSXnYWghcc5yDHspWBOfkIDgKdgPXlSfBNr4Mx4Oqg0XgH1BKtmF7uAaug13A50xp\n2a5Uhu2bdrgS1gEPm0dCoeYtcDtZXFNdW0MD8WwIps0Py4PjyfPUcrA2+MHZC82a4JnqXtgc\nPG+5nv8KXId9F/4yonxX+vPzx3FlnL+63AnVpHoaY/82Az/orQwzg+21PzEeXTPcEw8GP5b+\nFc6AfeBoMN0PLl6CvoWYS9bzb1gU3Eetz/1SaeONwbLWMQcUaoUFNFyh8hZwEC4EDuh5wME3\nEWpd+QWmLf35mMIx2WPSRn3xHO02axZpfi8/TnYvD4/BvOBYHAAujubxgHEb5OskqmYUi1fY\nJxbDtAMRNxOR5lshS9wF18POaFgli0udowh40Lfcu+DXovthRqh22Wcvv154PJAp4wLDMXb0\ne/B7HMJGbiT22Y3AfMNhGHioWRLciNyIXwcPPaPgQbgejgMPfKXUn8hnYVOYAB4YzetmX6iw\nQEst4KXdg6Hj1jH9HwgZDsV41+0VkW1wrWd28MAUimdEONyl8UwGD1vqaXA+nQkngWvz87AX\nePh1Hni488D1KPwVDgXnWildSuTF8A04x8+B68D2VJvs65dwLGi7G6DUmkt0oZwFHBfHg2ur\na6xr7T3guuu6/BY8A7PB++A42x0ce7OAMs21dmYDqB+sA47NnuBe4bhxre8OKvaHdDxdTbz1\nV5P+QGM2B9vl/uQ5J2Qfoh/GOQ6VfdRu7mUXwKWgnJ8/gdgHR+K3/16oXGOs2/r0G28+ba6s\ne9kGX8f/8d2dApeB4+IM+BUsBDWl9GXVVMM7qbFf8xwP6G54LvIfgIeoWtWnNPw7sF9OovZQ\nTypxUoZ9nJgx6XVdNF3oIs7F7ga4Cz6BRcE4F4UrYQXYDdyg1oLtodYUC1Uc4F2k7H/YIPoz\nLonzMOVY+xdok+dALQLvNfj++8f0Y2E7+CXsCV4IFob9oNr1PQ10nLho22/lGFGOS9McpyHD\nLrivw7YwPTwA2sU6noVdYV94EXYB5+rysBE8BQ/DADgOyul0Ep4Gx6B2NP9fwU3KNhQqLNAS\nC3xEJsf4/HAZjAcVa4BjSSKsOwHaKp/ZAxz71qliDUrHr/7NYC9w/nk5eAbUseA6bB7jfg8n\nwmpwI7hm3wergvOllDYg0nV7XdgJdoE14GewJUxpaZuwj20ZCfPCjAaQ62t+zW1IKP6UtMCx\nxLpWusa61m4Cvm/luHHMuAfOCt+B62kvcIxdA46venCsOYa9pDq+zgXfk3tDvK+x+NUTjU5D\n/MQs7KWi2mRfD4fFwXPOdKANHoeQ809b+BHB/s0A5t0QZgP1Gti/Rw2gO2ElcH6PBm1kHeoN\nMP/bsDd8CKZ19Jiehmf4Pq+HBeAdeAF89mBwbx0ChbqABXyR70IdHA/rw83gQKw1eegsxbAK\nO+LkdcGaVKJe49NnOUldEIz7BrxIuVjq/hFuARfEVyAvJ9sl+chOCJ/MM0a04Tm3UtaFK7VD\n3m+fjdNejqkIa68lYRbQPl9DP0i1K4HJaUTmPxP3rhLx7RnlV2TbPHOFlQ6lnG2PceKGGOND\ne4Qd8vaKsOlvwrrgwvsEPASLQHfYBxxnO0Frpa23yBVyY7Ot1t8SjSdTXUsylsgzPXE+a/US\naV0h6lQ6cXsbOmJZ66gF/YNGOmZfBddJ32uM4dRv3MvghcV4x0AlqqOQa0e+but3zpSLN304\n7JsRa40HMQ80rkXTwlbgmrYHNKeTyDC6RCbf359KxLckykOwa0clcq0q1f+Ic/39AraDncD1\naBuoFZ1BQz2bVCr3Ove8lmp+Mu4Ov4LlShTyEuyhf44s7TNc1+kXQNuOhdfAseeh3T1FeYHy\nnTheR8Lp8HkWjndlmTyHEGfdR0JeA4iwrGO4Eg2j0JhKCubK+PyF4CN4EtaA6GvMz1eIcz80\n3j3MC8an4AVTu5nmWLX/1qPrGdV46zAc7iP4n8nC7pcPQSlZ9qxSCRXE/YwytqdnmbKbEF9f\nJq0qoysdNFXZmQ5o1GzU6eCZAIeDBzIHZq3JCWfbQ/qNa6sWaKYCJ988SZ7p8LvRWW4GOBii\nHXPhz0v7v5+PrIGwfeqetTNsHW40P95H9D/C2utFMN4vP1vB65DKzWdGcP5q45ALk2nVLm0T\n/Z4maWzYIKLM44LfDSLN/rppjIKr4JdwPYyF2BxOxH85tFbaLr+4OwZVLdi1saXF3ylpAcez\n43Jw1ojFcWOsZ1E/OI5p0xzDfhBpq1wT8s+KeRPxMZ88gPWASXAceIgyj/udB07XHefVS2AZ\n07ycXgDNybkS8ybNWy3rU9g9bOLlyPb+Dezr0XAtFPpfC2xJ1BXgGixnwGlwKIQWwvMGuH+p\nZ2FNcO023rPU1aDmhMfhTBgNvpMHwb1tbXgKVgLHtvLdhczrxeEPWcS9kVCFrn337PMobASb\ngxca1wvT7JdrRcizkrYxPZX5HKPOJfs/NziXzfc5+GFmZfACpsw/H0yAmcE8HaXeVHwbfFLm\nAXcTbxvmh8ll8lRVdN74VdW4KmiMg8sX2hecsNrLW36tyX6ocJ1Y7a1SdWsz433eCHBiLwxO\n/nNgA3BjOgjmgPMh6hmCf0OoxY3KPkS/8TZIG0R8FtXguHl4ATgJjmqIafwpXVv59ev2LC51\n7iJgfX8EyyptuT1cY6DK5eL+VtLGeOcRFWHnm7iBKDfDkbAk/AS2gE1hNXAT3Rh6wXFQiRxr\nvoO+WWHHpDZ2w/brfKHCAqUs4Bh1DP4UroRtIMZwzHuiflDE6Srd9HDUEFnBnxkp8z747Hi+\n1YRf1zX5G+gBXmScL/tDH+gLe4KHXg9VzrEVIObVcPwt0Q1kWgoOTDLvit/17LokrrO9YYew\nezx/TObZD3dBODkSCrfBAsvw92rw7OP7c0/aGhw3ur5n1+LQS3j6wc7gPv4RaHPHXh9wj3LO\nPAZfg7KOG8FLw9rghcqDvq7jWvn+bgL3xniHniWMdywfCVNStvd6eBX+CcMgrxeIsL2/zVz7\nEXs43gal+50R5nfOKvMbFm34MXSH72AyTAIvQQ/BL2AF6AfzwinQkfJy5vv2neVlG30/X4Dt\nLFTjFvBFx0R00or/xCAWU7w1o2h/uE4y/aUmcEs6NS2Zog7dPPEcF600n/HGuYn3h1TPE3CS\ne3AeD9q+pRsyWdtVbpAj2lDjHZS1/fY3b5u8PSJPuF9T5nXQBgeAB6cVwY0glYexT+E9cExa\n/izoaLkJ2IeZK3zQUMr5nt0EUlvoTwl76Aamj4aQXy7vj0AL3IXIswqUa/uspFn/t/AiuJi7\n2fWBlmo8GetamjmXb3rC9nH1XHxXCZ5KRzxcVSrLWkc1aTEa8wL43hzX6Vg1TmIsp2HjnOu6\nN4Pz2HTHQCWqo5DjttSz8m0wjzgH34G8HiXipHxkK8PuLbZnErwBPssPYcvCMuABrzWaSGbX\njkrkfM/bIH0XttPwIHB9cB2oJXmhdQxVKvc697xScr13HXQ9fBN8jzHGHO/u50/ClRDaDU+a\nT/uaN96B5WOupHHGO1byeeN5+byGrTv2WvPl1/YBxJnPM0slchyPaUHBdcjjJWY82A7b5HO1\n29zgeL8eTBPTpJTfOM+aad6wl3GuG5b9DZhPv8/TFh9k4fweYj9KzXXLeZk7vgxHE98TWirn\nqG3wHOe4cD99DrzI6a4KNaNKB03NdLCNDY1FXNeB2QMWbGOd1VDcCdVeyteV2iw2ew/zciAY\nNwO8DC4ebpoPgYvQs/AAdIeR8BrUorRB2CG1j3ERTl37+QYsDQuAC5KLzGEQlx4PUHvDDeC8\n1X7LwiCYBVyItF8tSDv41Sy1Qdpu05Xp4lgYDGp8o9Pw1wuiY6k5zUmGy8Ev4cpN/VjIH7at\nb13YEPwCPgHugPh6h7fhl06/aE4yUGiqtoDz8FZwLLi+OU77Q8ix61gW/Xm5zqkNwAPuDgba\nINsTzwrX6uLZ0Z67iBsI7mel5k9L5xXFy+oiUrSHdpkGPoJT4HRQb8BOMNpAJyps4SO1kXJ+\ne7gcZQB5AD0JTjAwlev39N+9qDe4v8wHHqp9p8rxsyL0yvyDcC+AI8GL0sLguNTu8gm4v7n+\nfw+fg/MgfiWaA79pjsEz4BiIsngbFO/NeOu2DseXz9oCroDO1mk80I+8/WATeAm0wc7wN/AS\nuiWUkv0IRV+dm8rwf0B7e8mYDbSX4/WQzD8Q91HYFc4F9TP4Z4Ov8U/MacvvDb6zyY1JDf/k\nbUDmzzu+o0vB99YSaftbYUnoC3PBO1APj0OhLmKBIfTDwfEdOEg/Aweq1Jqi3box4fQPq7Aj\n0+bqiUmd1p36XyO/dhSf6wJ7FTjJzeeGpH87qAadTCNc0CqVh+rUJuFP7Z/ax/i3IWwTaS56\ny8H8cCJoP933wTza7EKYATpLK/Mgn53/UtfS5w8lY2qH8EefdYOwR4wbw7EBzI5/PHiQKaeZ\nsoS7cV+ANcANeHfQdralpVqQjB4so71j8K9forBtqisR35Ko6clk3/Nf/1pSthbyeCG9vQ0N\ntax1VIvWpSGOh3myBsU49R2KaTFe8uFI0z0HfOfmcQxUojoKxbPCjWfqBu5pYh7ngO6qEPLg\n5IVh04hohduDvO4NefUiwgPaxfAH+ByibWfj90DcnCaSoTXzNa3PtSqel7phE23wJqwNzvOd\n4SvYE2pBXiRubkND3evc80rJj0mOF9f9Q0FbmV/bPQCTMr/x2ixsWo//C3gC3O9NvzPzm8ew\nmMcDtHmivOPPtJGZm+aPcqlrumUt5yU81QACppcal2m+cv5hJIwpl5jFO35tj7a4EhxLPlO7\n6Up6fjR+cpIWeWI+WlfE6feCGjZM+63/fRgOjlt1LXwHrxjI5IXKd6X9x8EbcDE8BNZxFrSX\npqOiLbPK/g/XS9s1cAFsATWlSgdNTXWyDY31BYvqzENo4xNr56+TWTuFa8v1h+bC4wUgJrF5\nh4AL49zg1xEn6ipwNdS6XABDYZPUHjGmzOPC58IYh6yP8XuIN975+Sy48N8I78Lh4OXSDW0x\n8IDh4rMj1IrCJqkdIi7tg+naY3ZwfBh2kXUR9gDn5vB7yOsQIn4D2tQxNi+sBE+DuhCWgv3g\nCmhOvgcPBW7m64JfKy3rgd2DwyLg2PVZaZ8Itkn7UrpfiRrOIc6NrhK5Yd0FbuKqFzgXtb/j\nyI28UMss4LrmmPgQvLh5UGrq/ZumnYPIuzVxLRmHZGtW1h3SH8+IONemONA9j995MRpuAPP/\nElxrHNst1TJkPBfWBuu/BZwfzge1M3gg9NB2NBwMD4LP3Q0+h+GQahoCm4Ltc+/I94OoNim1\njXW7xt4DHia1i/awD+fD1Czt8j6sDIPAdzUY1DqgHT8FL/Y9wIvKZ7AwqD6gfc3n+tsf+oJx\n/4EZMvRbh9Jv/vUNIP0q6mkMNf41Tvlsx557aWfLvdp9yvXAtfRs0A6/gMXBNs4ISr/zzz3J\nfkX79bvPqIhrDDXuf+FPXfPNAXvCYbAxjIevwX3jYXgO1oNZ4SH4BNYC1y1l29tLtv8FeAtu\ngrPAc4l++3wRLAfHQ6Eat4AD3cHjZM1Ta13Ltz/CwyrsiBPBCR31lPNHunZ08XJxNc7FI9LC\nfYQ4v3xMBPPdAyvBlNDJPHREGx58G2XL2cT4IPqubWKsjcfvpmT4GvgtaJPIq+sm8BK4IK8P\nxs0PnSE3Sts/c4UPG0o5+2sdtjvctH+p38uIeSaA/R4H98OvYSbIy43C8XMwDICrwfJfghfM\nv0BPcOxbp3Kjsb6x4MbhxrIuzAIe/GyDbbod+kLoITy+G8fzo+Clw/J1UIk8INjW1bPCA3G3\nhuPg+cxveC5orezj/mA/ls8K+7wnQFucCPdCR8pLhDasVJa1jmrRHjREe8bc1e/7020Oy7je\nxVwYjN+ycUjE2yrVkbvcM6NN4XpIGgWuM5bZEa4C54rzcxoIOdZM3wv6R2TiLoDfA7TvxrXo\n5/A0eDCbDpRzyEvT2+C8DD2Gx7K2wTnmIVObOM/DNo7Pt8B5Zdsq0cwUir6nNoq4cH2Oz309\nc22Hh33jTwMP89WoM2jUzW1omHvdi+A78H1cDHOAci3UZo5Tx2zYT7/jpz6L025hR/O41pon\nLRNlw7XO8DfnRt3hlss/jjqnh9AAPJbxzFKJXBvHtKDgH8ljfz4Ex2ravuba3Fx61GW+1J5R\nzuc6d0zzPZh/azgbboITYF5wv9sTUlnWi0x7aCMqGZ1V5DuwLdo/tDge2xnrQsRXrVvpoKna\nDrVzwzxUxKDsSraKfrXVXNYTSutM/aYbFr+iuADPDU5MFxI3r2dhDTDP5eBEHwwPwqrgZaCW\n5AHDcWN/VPh1Q6nf/KHeeMZDX6gHD67aw826O1jOg0Y/+DNcAj5nTfCrZy3IBTzts+23Xyrv\n79kY3fBLh3ksuxZ44fkTKC8y64L2GQ6HwbngJr8OWM7x5mXzSFgPPoAXQP0B3DhOgldhCxgJ\njju/vN0JA8G2eGBYHt4D1wTH8tIwFlz446s53jbr/qwGN103muuysE438EDqe78WbKv93x4+\nhVXAi/qjoNwkF4AnDGTaFPdJuCgLe2BdErSV428pcH5eCeNBLQw7gnlHgbbdAByjuheDB/Ad\nQNtdD69AV9M2dOi8rFOO2ZD2CMVYzqcbDsxrme/0tJPyz0urNc13OjCLfAj3bxlZ1A/OJviu\nBt+tY8L5dCncDWPgKdgbnAvOGfMox8Y42Bx8/8+B9poLHgc1EFYD5+Cc0CPjS1zHrx98xoJz\nbmmI8Ye346kOsgAAQABJREFUzdIG6XuyQts+D5wPO8E0YNyuYPuOAOfGxtDV5Lrlu90X9Lt+\nOrfde/uDcr35FmIP0obmXRiU6do07OqYUelYDP9XxE8P2th8lg39B4/x8Y7CjXTrz8dFmm5v\n+A0cb6AT5fjYA2aHfBvDJtFuXZXPF+mNqf9Nj/jIH+muGe5B2su11nyiTd2XDoBUXmhnSiPa\n2d+H+pzryjb5vGcMZHoN93NwrLXnPplVXzidaYEhPMzJGjg4w9+Z7WiPZ0W70z4YN6zCyp2U\n1pVifVF/xMdz82kR70R+G5ZIyk7E78bkJmn609DZOpkHjmjDQ++gbNggdcMOYaewQ7hvUc5F\nz6+WbiLm0xa6xplvPGib97JwlNU9B1wgO1IrU7nt8ZBViYZSKPpkPSn2IcJ5v33+GhYGDypv\nghcbDywfgouxhyvLDwelWw/3gPH3gva1bnGR3hCM+yWkeoCA468PbADmcZyOgWPBTd3nPQip\n6gnUpRGt8HtosJ2r58qsT9g+pPobgcvhZ2DaTuBFURv9GQbDc7AaqBkbnYaDjxc85UHikAZf\n4x/r3AQ2g89gbxgKL8JssC48Aj+F0+FcUGeCG+GB0Bv+CYeDbXsFLKtOhdsbfJX9sax1TEn1\n4uHnQYwj31fgmAq/boyziEvD+h1funeB79x8joFKVEehqF83nl/OjbzblXmYByzHwFNg3o8z\n1/o85Oj+A26Ai0H1gAGwBnjxjnk4A37HkHP0FvAg6Xz2In0CWP8p4PP0vw6OmxthMuwPk8Cx\nWIlcq2xvKXye8brxPvTLOAgticf0tSKiitwzaMvNbWjPvZQ9LSk/L37fzVGgbS6BL+DbLBz2\n0XUfGp/Fhx3DjXzWFbaNtHDNE2nhRrnUjfy64S+V/j3pH4FnFOV4NH+EjWuNhpHZNb8luo9M\n+TbZp2ivbop582nl4tJ69dvPNC7q/Yp49wBt3g1SnUzAfXOxLNJ5afvOysJtdRangndg86yi\nS3B95kzghelgeBlqRja6UNMWiAOnA7DWFX2IPrVHf9I6w5/Wmz5Lv3nSOMfgEVk8TsMX+tlx\ne8I1sDz8HGpJ3VvY2NQO2uVK8NDtghKH5ZijLnbmWQhmAfMoDxoucBvBLrAvVLuiT+l4Sf22\nP2xjvP594DsYATvAObA9XAeXwqzguHFz8NC1ImwK88N6oAaBm7w2+yvcC1eDm6f1pnqbgJtH\nPZjvMfCAPgHWh5vA9zwKOltubFuB79pD6qGwM4QOwXM3XALaSDmu8tJeaby2s271NPwZroDX\nYSB4mL4TfA+6gyEOHuY7E6zTy5gbo23bDNzMa13OP8dKPewO9ttxGeM2XKKalXljfJt5WLMl\nWpch6s67+VreIGI4+C4vB+fLsWDYA/c04KFnI7gV6uEDcA4tC6YtCquC73kiPASPwgrg/FBf\nwbpgvPn+CMqwY9d55rP+CY4t17YXoD88AstAe6ipd6Stwl6Rbw7iemQPfhlXe+XbsghxZ4Fj\n/TxYEmpN9td3GnoHjxfjlbOIu3B9L9OBeVNbzUm4dxZvmu9SN5UX47BtGh9+09J6fVbkDzfy\nlnPjmY6j2WC1chnbKd527QDXwy3gWqydVLQ5+pSPMz7aG2lNxXkRcg6lsp8qnqXd5RS4ANwP\n14ZUxxN4CZxbvt9J0J56jcr2gz/Bm9APDgP30o/BPdw9pGYURq6ZBk+BhqYDeQo8vt0fGROq\n3SumQuuO+rWb/rBfLACPE6cifm78m0KU2xX/Z1m6eZxoO0MtyXkV/UvbHX00LrWNC5ubwsYw\nCyjjvgQXNOuKsr/PwjPgunCeDr+GkXA21JqtaHKD7F/00Qj7nPb7csL290PYBuyzh/F3M7+b\nsGgf8/0Z1gDt6gHdcvIJaFsPcruBh3m1XKPzw9+Z8fkefR+2wzH6AKwLa4Lp18AQsB0hD3cd\nrel5gP12nqh6WFoPcr44bpR+D3nl5MaVpuufkGUekxSKepYhbkFYCwbAFeBhRL3T6DR82Bif\n+XWs5/MkXKteL4nbgmNi2qQTMWbz4zeyGO/4ySvitK0Xi/ZU1F2uzmhzbzIsCWPBdfhW8KDn\nO3PczwCjM+z7IeAh0PE+HRwPS0Af8IPBSNgfHFfOud9AH1Dvw0DYBRy71u8YOh20p3PX8eWl\n6gFYH6zHOO0T8xRvuyhs9H/Upl9ss3PKOOVc/jvYx3lhQUjf1UqE/ZBgP16G5eFJWBdqSdHf\naLOXQvv8zyziMtxZwUNu2M0ky+XLdjMhp7kIp3mjTD7OsPU7tuI5uhGPtyE+4gyrfD2u7zM3\npHTcHy8hF4Nr22T4HTjebUte+b5Ee0vlzZc1rE2di6mibNTtumSc8/cxcA/cCFJ9RWAw/Byu\nhYMhyuNtF11HLYtCHVwKR4B79VbgmHoGCnUBC3jwcZCJgyj8urWmtO1pX4ZV2BE3NOspRSl7\nxfPj2eFG3m+pS/938BPoBYeDC50b1CjoTJ3Mw0a04YG2N/pWykZpnPnExctDQqTZ96gj8phm\nPu1kXH4BdHH0INeRWpnKbUelG9BQyka/rCf84Ub/I834FA9gPtuDzEfgF+Z0U3ajeAG0X9Rl\nee3mAcj4D8ADmpoET8EYWA8WAO3omHwP7oZloS/8CaxnKVBeKHyWB7krwQOFl5M6qETTU8g2\nr54rvD7he3JxhlfI4rSp49U+WX4RUH8F+5JqFAEPcmpFeBA8kPQEbeBBaDMYB85zeRU8qPq1\nfzioOeEKsOyZsCMo81tPDwPoFvA56lS4vcFX2R/LWkdny7XIMSQxLlNXf1PhKBd5HIOPg+Px\neVC+c9MdA5WojkLxnHCjXeFGvG74Hc+7gWP7LXD8Oq593+ZxHjj2rWNjeA+Mt91Rr+Pfegyb\n5ry8GezfJHBs5bU5Edrh1+AcHgfmfx/OAev5Gnz+02C7HOeVyPUi2hZtTl2fFUQ+wz5b1zjX\n3HehO4QewnMDuOaELsbzUgQ6yT2D52jvSjWKgs9BP1gSboJ3wIuwcz9s4zsI+4T9wj7hlor3\nvUYdUT7yp/FpWhrflD+eF2UdU+bvA2oAmOa6VImGUcj1LNVaBOyTdYdcI/JtiXanbpon/KXS\njTM976ZxUT7ifGfmd5yOhXOhOdmPs5rLNLWmVzpoplZ7dZV++6XBSdXesk7rDqX+mMwXkujm\nuxLEYaAb/r/CruBmqT6EveAwuAY6WovygE3BtszVDg+Lvqc2Kef3cdoibKR7F6wDHkBtkyjz\nxWFkoYaYxj/O5R0g7JcktdnrAeMXsAC4SXaEwl5Rd9hKN2QeF/M/wjdgeA1wkXdz8JB3CSwM\npln2QVgaPNAr4+6Dz2B9sE9bwWlwD1hO+/4W7oArwMODmgAe7OIA5BhdDdxEV4bRYH2doWN4\nyB/AA9ts4PtRH8N18B1MhPOhnDx43gqjQPtY56egPBw+CR7+nJsvgIdjbT8SZoLzwOek8l0c\nD3eDac+Dz6k1rUCDj4SlwC+fqWJspXGl/OZT4Tr2lDb10uhB1MuoY83LQHsonpGvKx8ffdDd\nGXz/Z8IJ8EvYDizjuuL7+wJuhk9AvQ0ewpYFx6B5P4OzQbstCabND6eC9f4cZgTH2y3g+u4Y\nng1CtmO/LOBYfAIce/NmcR3hhC3so+ut78d+S7w7349rr+/K8W2f14QN4T8Q8lC6K9he16Ra\nkPO0F4zJGqu9B4P2nwSLgZqh0Wn4m46n8Os6/7Vbqm5ZIPKFTQ2H3yyRnmVvSIs8unl/5Etd\n6/N9OP7qoSM0kEqfgYezyn+C65hQaR9sS7Q50tJwxOXzGV8qLi0bz7Gvjlc1Dxj/LKwOj0Ch\nwgIdYoEh1OrgK0WHPLADK833wUlk3LAKn+kCaB15rLNUXPr8D8jjRhxxLqj6XaRvBA+nV8Fx\n8Cq8DnNAR8qN2ue74b8CtmckVKp7KJi3g2HrzcdHnO6/szweTg1/A1HOcORxIzOvXApHggeJ\nd6EvtKeWoTI3SS8DT0G0aWb8lWgohexHaod8uKm01A76x4FjxkuLhxgPaV9C5Av/R8SFLV/G\nfw5Y7gJwQ9GW32fot765QfWB/hAbEd6yGk9KXdnUphM8gNnG1ZvO9qPU9D3MQorPd3PV31L5\na08cYiyzGVwCHgJtU14ecn1GUzI9PVCZ14Py7XoqlGWtoy1akMJXgIfXCXAGpDYk2PC/usX4\nybu+H0njIy7iUzefdm9W/m3cj8Gx5rxyzpu3lL2JblZ15Ig2pc9M2xLpeddx7zy5KKnDONeT\nKJ8vE2vB+uTZD2L+2P7DsrBxHrIta/+0t4dvw9pdOcaWhvky9PcHD+XpGJtIeChUIt9v3ial\nwrYr39964lz/Poc9wAui+59rgevH1pBKe9jvWdPIDvZry5vb8IwRlD0ZFodFsnpmw30c8u89\n7BbxYa80PvVHvnDT/MZF3nDzcVEu3CifDxvvHv501ua1cdUAMM13VomGUcj9NtVBBMZmEY6D\neoj2hJv2x7g0HH7dSItyEY60iI8ykR7xjrXwR5pz7gFoSZ8tb19uKcMNxLtmTpXy5RZqmQXS\nxbplJaovV/TBydYRivqj7lLPmZ3E/bMML+OeDY+Ak3ow7AMeTH8Kt4KHxQ+ho7QUFfvV70BY\nFNycXSy6QaWaLlewlB3MEvHhxnx0YfsneOAPaZ+L4W/gAdWvVqavCpuDm9kqMB7aU1dRmZvO\nQrASbAXtpeh3ftxYf6SF3/AHoI20hWXcEBwrvivfXXfQNh7S3galjZ6HnmAdMi/4hXFfsMwi\n8CRYl1+wHwP76yVK1cMr4POqTR7c8rKPn+Ujmwh76f13iXQPtekYjCwepn1GUzLdA2Q1yUOf\nX32XgMPgFNgS7oSYe9fg3wFSlRqfphsv9jWIvGk8yT/Ya4bMPw+uY+8v4LwaAu2teEdpm/RH\nOH3exwR2zSJMd045zxwbyvHh+3RMqOXhLfgDzAGmacPTwMP2R/AIXAjK+jaFZWAjcO79Aqzv\nRXC+in7nmofSaD/edlPUGa4V60/DxmkD59Y/wP4p1wUvPnOBa8G1cAL0BjUfaA/Hk7arNb1G\ng8dljT4V1zU1v46UspVFwn7aLZ8nHW+RL3XDbz2RN1+HaSHTYr2KsroDYQHQ9r6rjtJNVOyl\n4WjYHXpB/ozSXPsp8oOiD0ZE/39IzDxpfU3ld1yOAueYHzlaok/INLYJnNuFCgv8yAJuWA42\nB6NuCsGaUtr2tE9+HalEHuBjwjblps9yIwxb6jp5TwInpxcm7W28B//O1HAe9lzugW7w9+Ti\nWhN8isxN2SWfln8/ht8ANwH9Xgy0l4d/D/rGDQQ3sdHQUVqcim2rbmhlPMb5VbYSDaWQ7U9t\nEOGmXNO+g/FZ+TSvfu1jmvUaDr7O/KZHmnbdGpQHAPOuayDT6rjmtawHu9ZoPJnrWlMgyTs9\nfp/r8yuRm6uXvLaqBxU4ztpbjtfb21CpZa2jUh1OwXpwHoX64PEA4OH9Uohx4xjxXURYf4rx\naTj1R5lyblwwn6eOmUDFmHMMVKI6CsXz8m3JhyNfuKaHP9xYewyn/kjXdZ07Ft4H50rwBf5T\nYAXwsCrmPQZCf8dzZQRa4E4kj2tHJXKtChvY7vCXctP+vkLeSeD7sv3uUx9CrAmz4/fC7brk\n5cLL5NMwH3SmzuBhN7fhgSMo656X6iMCvtdnIWwWbmo34yI+dcOf5tUf+dP0lsSl+V3L0zLh\n9z3p3w1CA/D4XM8slWgYhcaUKLgtcT7Pd+7ZJtqQuqX6ntqgVHqUj3yRx/g0Lg1HGV1tsx/M\nAq5za0B/KCfzn1UucWqPr3TQTG128+Dh4OwKKveFoiP6Fs/SdaylNjTuMBgH64GbqnJim3cX\n2BBcgG6AtmwAFC8rDyTx7LKZWpkQG2hLioVNwlYRXigr7CHqPVgXtIWHVxfC34Ffo9PDHsGy\nWpgUF04vO9r8XBgLTUnbqPa2T2Ot//0bfS/nmtM0x0UfCBvhbZBp2rw3mOaBZTrw8uN4UuYx\nTfevcBp4udSGxu0NHgheBL8cK+uYBjwghnyGdlwMtN+fYDxUg+yfY6WtcpxJV9NydOg+8GAT\nqsfzAjifTFeOh1DqjzjdcvFpnsjne1F+DLoDPJTsDHtAe8+tfLuaC9u2aF+a92XiHePOOeeA\nct35GpwzyguQaV6A5oCXYAmwvoFwEHi4NL9zKtYTXdcjuQSugpHQGUr7mH9erBvmsQ/xYch+\nj4dT4FiI9cBLxFqwAZjXPPVwONg3bXgOTIZak/uMB/9lIOxRqg9pWmrb1J+WKxdvnqhLN83n\nu0jDMR4jv2WV4+pkuNhAB+sa6n8AHoNeEG1J20n0jxRp4f4okUA+Pg2nfsul4fBrJ/dBx5z7\nm+NWm6h/gZe6eijUQgvEQGth9qkym4Ouq6mz+hTP0XUSpxM54j4g/m3YCoybBB4i/FLs4aE7\nXAenQ0fIjXlVWDepfB78bZkbLkwtkf1NFeGwlRuUB4+5wLRDwMOc6ROgPywBB0BT8iDjl8CN\n4C2wr8/AT6ApvUii+X+dZOqR+DvSG7ZIXf0R9tnhz9vbi41pXh5jzOlemMVfibsQPA9fgXZe\nH56En8Kh8Dk8AB6aQivj0Y4bgnYZlIVXwy1U/RbwnS2Va6ZjxDnk5cgxIo4d3VTGxXhL41N/\n5ImyXoQizkPLCTA7bA6OuV9AqL3mVXNtjOdFuyKs64E/yi+J38tR2MN4/XE5wtsg7dYHvDiJ\nlyX74uVgC9ga+sCa4FqrvR+F1WE8aI8R4KViSilsYX+j/2lbPHQOgCPBfUktAsfD+dAP/grf\ngGuIfZsM9t/1YgnoCPkutGdH6B4qdQ8U5buXsE+4kaarjDef0p/mizjdVFF3Gqc/yubri/Ab\n5HFP9MLqHrASHAGdJc8tjpnURumzo/1pXEv8aTn9QZRNw2GLtIxri3ug7A3Lg2PzNnAsFyos\n0GYLDKEGJ504+MKvW2tK2572ZViFHYmNJCZqS928LS33MTh5TXPBcYP5BBaF0GA8pntA7Qid\nR6UuKl7Ers78bhCVyktMa2yS2sVyaVi/aJfwP4JfW3nJ+TN4EDsGekEpPUTkzRCLo4vqFWD5\n5rQxGTz4PAYXwSSwjTNDJRpKoehfczaK/qY2CX+kpXWlcXl/PMvNVFvqjoal4YMs/CWutjTN\ncWlaKg92N8FucFLmOmasLzQeT10EWulOT37b6QGrK+pUOnV7GzpmWeuoVB76HctnwgLQD9J5\nFWMrxkpb3Bh/jq1dwOdan2Prncz18u1cvhjeAtMdA5WojkLpXCjX9jRPtNG84f8i8Ucdkebc\nCL/9iHL6jTf8IdhX+/Z3uBai3B34PdSa9xWINWQ7/ObpC+U0kYSh5RKbifc50Zem3Ohb9CX6\nd3ZW/yy4u4N7hHvW83A9vAuu+ePhLxDygDoS7oyIdnIXzeoNu08g7PpeqUZQ8ORc4aUIp+84\nbBE2irBuSt52af40X/jT/Mal+fP+KKPreHGseiH/Cszrvqbmhv3hRDgSzD8tVKJhFBpTpqB1\nRht18+2PsG5rSOtKy6Xx6XMj3vnmvHNN8zyjfSzvWHUe+j4HQSrteFYaUfj/a4G4+f43pvDl\nLRCTLh9fy+HO7JMTNJWTWdkG02Y1gF6FOWBFuB/GQuhuPC/CehHRzu4+1LctuKC4YPwjc3Eq\nUmsWY+1Q7n1EvBvgnKC9tN+C4EbsoWoXMN4vRdrwJDgINgDLe+BaE9zkXSCV+V0U/bI0FzQl\nN/eVwMuB7+pW6EyFDcK17SrC+o2TiAvXNBXhKOsYM87Dq+PKw8A5oD27gQcgD9OmhWbCswas\nBr8HbephXTtrn2WhUPVawPd0OTg3PTy9CR58HAup8mMlTWupP8bZ1xR4DWYE/evCbDAv7Afm\newk8wLflgEvxFsnnpf0r5Z8hqylNM8qwcyPiwzXNc4RhD2fOIfU5dIfVwTX1S9gQXLv8pWoe\neBmOAdPeB+0zJZX2SVuJGgrOcdeDP8K24OVnMbgX+oPrbB/w8h3SHudCe+5brsH3gc8fDI5r\n5bhuDzkfDoDtwfcaNsDb8I7DRmm8aYYl0o0Lf7jG5WVavq7Irxv+tJz5bZtj1XW5Byht4hrt\nPvgbWAuGQ0fJPSBV9CPanLdHmjf1R7nm4qw36oxnRBnjnU+OgwXAdOffGzAE3Ovd/+eHQoUF\n2mwBB1XczHVT2lx5J1eQtj3t07AK2+EkdEK2hPR55o9w+A1/Bm6iH2bpxu0KqeoJeKjoDPk1\nbUQbHjSOsi2xTVN5tEHgwvYx6Mo3WdgDvguf4d3Bg4dlXCi/hXvBDdX0LSDV+gSsa5Y0sgX+\nlcljuz3UVSIPG7axqb6bls8TYd0g8qXhfFyapj+fviNxIW1p+sIRkbhuNtrrJegJHiI8AGlb\n69Xee8J4qINK5CHL569eSeEaKOOF0l+BKpVlraM18l0eD64vN8C7EO/M95ai7QPjw9+cG3nD\n9cDv3HsejBsJHpTzeoeIuizSd+5zHAOVqI5C8fy0vWlc6o88aZz+PObLx+XDUVc9eZ0j9l/3\nTXgQfgmvgJcm16gr4WkwzyfwFfh+nD/lNJEE145K5FoV/Yi2pq79iXC+bxG2fePBeW7e3WAy\n2Ied4Ldg/GqQyjTX7vbSvlTkc70YhM7B05Z55V53MvwJ7O8YsF/2J/qvP8LhD9c84a/EjWek\n9aR+64xwuI6xm2CvLM32+g7GwRXgZUltCZb3zFKJPCNpDy9k64BjcBvwQ6HPtD1BU303T7n0\nUuUjLty8DSJeN9L02yb3pYi/Eb/aFsy3vYFEjuuzknDhTSzgSy/UtAUcVCp/Y2+Mra2/0Yfo\nU0e3vtRzog0+W78T2sXeSf0ZvADqAlgCzHMkzAed/esFj6xIHqZbq9RWqd96nKdedMZCLICG\n68Ffvzxk7AkeOrTjIeCvIv3Aw6EbyQmwAKi5wQ3RjVGbV6vydohwOoZse4TT9IgL1zRxQ/go\nKXMKftUTToN/wQTIqxsR1uW79TDrl+LfwBugDoPzoNKN2DoKta8FXDecMx6c1GbwAThHQjE+\nIqwb4ygfl8bHeDKPdaRpPQh7aD0c1IIQB7aGCP5YxjHlfO5Ipf2LdqZtTdNLtSPymi/NG/5w\nLWtfHgb775rlF/4N4T5YAjycGTcEnG9eMgxfDsrDbEcqbWv6nDS+nN93tTD8OSt4J65rq/Y5\nCb7K8AI/O6jecBRca6Cd5Jr+NHyR1OeaFu8piW6Vd2lyexnYBNxrfX+l6kztE+ml4ijeKllH\n1BP1phVEmq7pfkjYHHwfn8K3sB30BdfimOPv4m+rtMVjcD94mbgGBoPxoWifbYv2p26kmz/i\no2wpN82f+kvljfrMZ5uiXcbLcPgLfAn5dYioQuUsEIYsl17ENz1pa80+6UTq6Lb7rHRihz8m\nbbTFjUe/m6p5LgW/uDk23WwngQedXWAC1ILsUyUK24Stoo6wVR0RV4KHCrUibAP7w8rgxcgv\nRx7SXwc37m3BX97cQDwsPgf1MCPsDtWs1A7607Dt1i4Rpxv2C3tFHl3TPcBpAw8wfs3WVvPD\n8+A4WwR2gFKKtdIyd4CbzYlgvHVfCNrWcVxoylvAy9Cx8CvwXXvxnQ48qPtBxncmKvWXCjdk\nyvKFP3XT8Wa8l7BLwIPJZPgItoeVIOTlyTk4MiI6yc33Nf/Y1Cb5tOinecKvG35/qRkAzg3n\nmmHzxnqoLZ4F1QdOANcqD7ZHw4Zgmc5W2ofoS6k2ONfngQnggfk8MG5BcC3w4LwATATXlNfA\nA/qh0F5y/VoBHMMh7RvvLeJa6/anwN/A+ZLW3VS9kZbazLg0TPBHKpUWZUxL0yMcblRk+ENw\nvX0avJxah3ZQ3zY67fbX/cJx2hveAPeA7pCX7VK2ReXdxtj/xqfhyBtxpdzIoxuk+cJOkc92\nbg5Dwf3Jc4PngkIttICTu1DzFogB13zO6s/RWX2J54SbXzy0lHHfgxuqi5wb6B5wNbiRPgHH\nw6JgXK0oFuq0vdH/NK6UP+yVphnnpfE3sAusDS5+ai1wM1YeNLTngwbQJ+Ci+D6sBtvAZTAU\nVoS3oBaU2i5vH9MiPZ+W9s08bnL9wHxPgTb0MHsp7AZLw+tQSn6x1a7j4C6YCDvBGDDeQ7j2\nbqoNJBfqJAvsznO8mJwLs8MaoFr6fmJMNZZq+m9ap2PqO+gJHmRd1x6B28BDtWPlJTgWbOPb\n0NmKvoXr8/Wn/WiqTWk580U5+/klTAePZ+5iuO/Bk3AkOP88d+wH2uZFMO4eML4HTAlFH3SD\n6GekGd4avPj0Ag/i9td4+2xfXVfuhxGwBbhWuy60ly6nIi+fjqdBMBA8BLdVtt92Di5Rkf0P\nW4SbZkvtY3yE0zzhL5dmfKm0iHNOKeeXbXDdnQS/gHnBuJvBM8RREOVmwd9WTU8Fv4JVwDn9\nGaiwRTxLN/wNGZr4E2VLZYm0cvVFer5sPPvTLMF2Oqe8pG8Po+FhKFRYoM0WGEINTsaYkOHX\nrTWlbXdyRXhYhR2ZlnLWk8d683ERjmdG2EU+4sJ1w3wBNoLvYRBMCZ3MQ93gKpWH7uhna9yw\nXyk3bHQ6df8dtJ828hB4PXwDxu0Aqjs8ADcaaEetTF32aeYK6xxKuehfa2xTKm/YJJ8W9Yeb\npkcZbfU1HNSKfixF3nfATVjbe0ly8/Fi5ebpgelNqINK5EZsW1evpHANlDmVNt7ehnZa1jqa\n0h4kvg7p+pK+//CXGhuR1lo3xpSuB7eLwbUsnqE7EpybJ8JvwUtBKt+5z3UMVKI6CsXzWtv+\nNH/Ukbp5f2pb0wLr+Q6Ogdg/r8K/RBZ2fogfbPzg4FzRDh7sroBnoJyca64dlci1Ku1jS/zR\np3Luh9RpH1xDboK3QbtcAh5C7aeH946QNrsXom0T8Hs5qFQjKHg/vAWpbaw/DXekP56VutE/\nXddd11z9Mbd0v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lA7blpaojp20vNZHb\n2rrUJqnfen2ek9cvCO/DVXAMOLm7ujxgNKWwle4LsDa4sDwD9TC1KOyQ9jc/Tt2E3FDVATBv\n5sblyPhCU4cFHBvjwYO7h3TdUnJcmbfU+Erzu/mbx39Cd0uakPn91WBVeBDGwtxgmUHghana\nFf0PN9prHzy4ePh1/TkFPNDkNYGIneES2Ae+Bm1+JNwHtajUFqnfNcYD3mPwS/gQPNCdBU1J\nO45uKkONpPkRYHo4BNyjJ8P8UO6MmNquOf/r1ON87A1ehrSzWgWifp/lIV1OhH9AtcrLSfQ5\n1hndVLb/fnBuxYUwyhD1Q3n9Tel2El17XKMWANeiNaAfWK+X11ngp6DcL1XYuDFU/q/59soo\nl2s5ErwUNqeNyODauBh8UiLzJsSdC38vkVaVUTE4q7JxVdqoz2jXqVXatnLNGkPC4uUSOyje\nTdgvvX6Z9KuLOqLRqfq/69BCF8FK5QaTLoZRTz7OBW52eDvLfz1utY8tLyhtlYu4G2VeefuY\n71rwwtkH3ITugN5QrXaag7a1VftTwVZtraQKyw+kTf7TrUrleFgLPNi7d3WHcoqxpDsW/Eiz\nGngg8FDr3JsebgPrlFJyHbgCPJy8Cta1RQZOu8kDYlsVfbae1J+v14ufNgitj0fK6VIS+oJz\n9m5wzers+edX6I6Ua4uX3sfg0exBC+L6Vb7atTYNnNTGRnroHg3+Qqoc70PgXZgT4qzoHGzu\n8F1PnpvBvFuC54CbYCH4CfihwbSZQXmY7gkT4R7wn9i11/jyHbZVc1FBvj1eoF3rbbNzzfWk\nT+b/GHd1+Ck8AMuC41e7Oc6U65Hr2HwQcXgbpG2ca7o/y/zWfy88A09BLxgAzkUvY7uBWh8+\nh23BS1Vz+poMx8OIMhm/J/6FMmn56N5EuJ76PkvJtcN37lrnBbzqlX8xVd/gTmzgkjzrTJgp\ne6aT4Fv4KgvXmuOXhh7gOxc3yN+CE64SXUIhJ4SLn3LyayPt8w04uQ1LLepOGn1ehQ3fnXK/\nBMeOhzgXN6XrGJKwT8zByENS1ctD485QSZuXptzJ4CbomNQ+bgaOHxdrF2RtI24gtSZtciT4\nYaC1cixcCh5IuqrcQM+vsHN7Um4zcKw4Zhw/MXa0u/aLNcex5BgyPZ1jkY/oisav5TpKH1Bx\nHVSyZvqV9yTw0qddPNCK/XUe6Tq3tEstyvYfDi9W0HjHy+WwCGgTx4P1xX6uXQzXsm6m8RdV\n2IF9KLdxibLaKeaPY0o7aiddx6hjyXRtqg1j3cb7I5muLGOdljd/qpi3aVx7+d+jol0qrGxF\nyh0PtjuvWH8i3j44piJvOqaMi3jdsKV+7aMdza8NdbVPWiati6QflI7nyG9Z29ISWe9v4JWW\nZG4mT2/SX4IN4ZFcXvf5I6AOzFeosEBhgcIChQUKCxQWKCxQWKCwQGGBwgJd3gJD6aEfet6C\nJ2E0+LHw48xdFbdm5I2zUGGBwgKFBQoLFBYoLFBYoLBAYYHCAoUF2mKBWSnsv8DqC/7zxHeg\nHh6HQoUFCgsUFigsUFigsEBhgcIChQUKCxQWKCxQWKCwQGGBwgKFBQoLFBYoLFBYoLBAYYHC\nAoUFCgsUFigsUFigsEBhgcIChQUKCxQWKCxQWKCwQGGBwgKFBQoLFBYoLFBYoLBAYYHCAoUF\nCgsUFigsUFigsEBhgcIChQUKCxQWKCxQWKCwQGGBwgKFBQoLFBYoLFBYoLBAYYHCAoUFCgsU\nFigsUFigsEBhgcIChQUKCxQWKCxQWKCwQGGB/8/Oe4BZVlT915/koOSchiBRkRxkkCRJMAG+\nOCphCL5gQkxgABUFQVEQBRFEsqCCCIjkHCVIjhJmBoacMwi+/2+tnvsbao63w9y+3dO35+zn\nWV1Vu8Kp2qdqV9W5A7UFagvUFqgtUFugtkBtgdoCtQVqC9QWqC1QW6C2QG2B2gK1BWoL1Bao\nLVBboLZAbYHaArUFagvUFqgtUFugtkBtgdoCtQVqC9QWqC1QW6C2QG2B2gK1BWoL1BaoLVBb\noLZAbYHaArUFagvUFqgtUFugtkBtgdoCtQVqC9QWqC1QW6C2QG2B2gK1BWoL1BaoLTDVW+Bd\nU70FujfAcmQdDjPC/4Np4G14BTpR5qLTs4LjcDwvwZfhVmhFjqPS/DAtTNcInU/a6P9A8Vnm\n/QfM87mG1kn4FnHLi/mWNW6+JE10UOVcnvabFp+4K/W2gekb9bXDLDADaB/H/Doo5ikZZ+yg\njYxrv7wz7REbEZ1Yx/hgyvM8bEewf5Mr76PCgeC6UjI+47Yn2kdbJU+d9snYtYM60ylnvmgr\n85TYK6G2t8xAis/eB25v4SH283iYu4W6zapov9jKULRtX0VbOg+V0vbqTEvKpP2Z0ZmvndWl\nHtEu3SmER5loQXanzkdbqNcpVZ6lo6NBm02ufIAKB4DrQftre0PFOSmmnRNZA64HcS36HuOL\nfb7l807TVvVdWiZi21l7qedzyjIp20poO9+Bu1qobL/+AMuC81NxrG/Aq6AN0vfM23b2neYH\nXM7kCce0+JQvUm+LPtbVTs6xUrRl5lSpz5xQZ9x5kflgO9axbk/z3T3U5+V9mLYd07aR/tiu\nbb3ZCAkmytPEdpqYmrzIKhT/EfjMdortxT6OqVxzWWeOKWNUZx1tIaYdr+KaNv5vSDlD61tH\nnOPqqmKZveDeakadnmDY2g7NLbAy6g3hYvBAuCIsD07CX0MnyZ501sVYldVRtHJBckGOBhec\nDskN9pFGOB9hKVnkpa4ad3GLl4gH4CxQfAcfBh3/yzBYsh4P+ji0ekH6GHU3A8f0FCwMvUnp\n2HRop8Fi4IVC5+W7ughmA+1yCbipnQhuAIMlXoq3hy9AKx8L3HC0jfPFS/vs8Dh4KXAzyKE+\n9kA1UZrpzPTQ5PxYC2xXuylnwBhYHLYB6/8FxsFAyf/S8KrQygXJdbQDeJjTJv0R5++7wXnk\nPHROebnQ5idAb+IcXgiuhpdA3+dc1IYPwxwwayNtv3uS/yPzQVgarN/qBcn+61+ugOEmCzIg\n3/1u4MF9csU5tzn4rrwEaHPbmRN89xHtX6ajN7SuF4b3gO/Wve56+CCcDH40XA2ugdIv+159\n9h8h83YZ4s6hdvmn3WlL39HKBcl9ZRQ49u5EX7EGaLd/wtZwKjwGQ102pIOuDffJVkRf4Ty5\nspfK85LvHPU8dBnor9eFecC96XaIfJaIfsG5ot9YHJx3J8HTsDGsAJ4fjobqu3E9/A9MD2/D\ntOC7MexNnLe/h9fAvXc0fB5sZ3LF+e4Yj5ncij2Un4u87eAJWAQcu31+ERyjY/fc4EVoNjC/\np3G7D1tWbMM99XzIWhlJfFU4CnxOKe7jnifuLZV1vLbAAphA531KN9yE3sl0MdwHLrhnwQnc\naWKfm7FriwOZjnouWhfxq5C21SXeXWiZ/4Dhc5Xy0aGeKDqRnSamBidyII/RwbQqZ1NRu8Th\nOS7RJgljn7fRaQ/D5D1DXLkFvge/AzeVn4KHH8tuArfBd2AwxQ3Dfr67xYduRz1/PdsabKcc\nd8afUBuNh6rdkp/wGMosAjfCW43ybo6lXE3iITi0VA5AfAxtjm6x3Zmo55i86PVXHL+XwlJW\nJGH7ixZKDzfOK+1zOXwJ1gTLbQ7KrKCvfBMybxNaLu+nWTx1XMPa3wNTq3IOFQ9utfIQr+c7\n137OgVZkNJU8KOVd9BSaF8p3pu77oF92HdneOPgzKB7AfJ9bgBehXUBx/Z3RFZv0z+0kvz2p\nquXUI9TcrsXa+irHWY61mtavbQr64hzsDyDeCaJPO7MfHXWvc8/rSbzcWM55oT2XAQ/W+g/9\n+U0Q0W84TxZrKJxDnp0ehnMbXEeYd+DhvZTpSVh2LKRMQueo8TI0XuI7/BEoI8HynllakV2p\ndH8rFYs62up3oO87Ff4OrrFyHF7KHcPbjTDjSbosa57p6Bxvyhv6jv4AkRmJZN3q+w8H/f3p\n8DSMglqaWKDVSdOkqY5TOalehu5s4KQy78OgLD0h6Pi/OjrFxdVf8UuFzixim2m/mS7PTDhn\no1DSJmcBnchVcCxMCy76ThPnj5cZ7ZHxxTYJHdM0/kGiM9QuHwDHnfF7ONkEFgLL2KZ51vdr\n/gvQSZL1ZP9jn4SOI/ZwvIp50Zk2/gZoZw9OHwPXszZRPGjuDkfDjuDzfB9uEL2Jbc8F2lQ/\n0Uliv3NQji3sv+s0F6b9iN8Bq8PW4Hgtq79bH/IePMy8BB6IyrbKd5GyCSnaZbOUH096CdgM\n/GBQliNZSxst4HtUYmPD6BKan3jKqVPUf78Rmud6cs1sBevAzWAZ55LrNn45fgrVJJI59R60\nttWTeNjeETxwXw+/Bw/e7ZTSHmk3tvglil+BZcS+Z3xEp2pZldGfAss2rPA8Yc5N+gffkz7C\nDylzw/LwEDwMnwT3M+eMdtUP3A2pT/T/2xs8wF8Hx4G+fBHI+7fev8E9UDEd8f2Z9iJhm6Zl\nRRgKsgqduBJuBz8oO/45wD578XJfUrRxxuE4HUO5rsoxk9VVNmM1rZhWnLv/A4/AMaAPdr3O\nB65h9efAcuDarKW2wGRb4DBqOEHFyZm4YadJ2fdyLLu2OBAdUdlOGc+z1IXoDKtlq+mU0SE+\nATrJhWAw5UAe5teyVuVMKmYcZVjGy3EnntByz8JPQBvcAOo81P6rEb+tEfq1yHqmPwQDLavx\nAJ/nhtiKeJlxU/X99mSP0hYpp66qN0+ugR+AbUfnBeoZKOt44dGu3cluZGhz67wIPwY3nL7K\nGAqO7mvhSrmZSPvctSr6viS3otCDYH3XjJvvrTAXzAhu0trceTUWtJFzR3vEXoZJ204uWsZT\nxniILmHK5R2Yfq1R9/eEPs9+tCpu6vUvSM2tNxq1h0zfRfX9JJ33kzJ5b4bODf1LdM6DF2Av\n8L09CI+DbZjngXQVUD4KltnQREO+RZi2rON79+DcTD6L0vauhpPhSXDu+gEu4qFO39GK6Ksy\ndsMynj7GBheT/ykwvQZ0ggzkL0j6Dw/2p8HREHv5vi4C540654qhts38OJW4+Q80wtS9grTz\nyXLqxsIfQF99D6TN7t5T3mHy067104cjiCsjwXKeWVqRXal0fysVG3UuJfwrnA0Zb/qYfjdL\nq8t4yvzqmGMLy1Tb15b6X9+dfv8v4Dos9zN/yap/QcIItUyeBQ6juJOuGZPX0pQv3WwM6lz8\nrYjOprpQ84zoy4WbPMMyv9Qnbr4bdQ5Z/bmo0ExLciC1+vPcM6mfcWZcZRjbJCzzyngOqOqy\nabgxjQV1fsn7KKwNx4HOcAUYSGnHBUmnbP8zfsOMu1m8WTnLvwHaw3jsYzwbS9mW+ofBy4/x\nHO6ITpTRxDxkfhtWBdeHff0Z9FXGUHB0XwtXyrV6QdqIdrSD/VwdPg3jwTXkZcnDq2N2bCc1\n4nc2wuSZ3x1VO1ou7yTxZnXLetm8L6Juq3IOFesLUnPrjUadd1m+m7yXUle+l+QbWj959xO/\nBPQp+pnoHyPuXHsQ7gK/di8G/wLbcI55oDb+AHhpWg/OA9fe3FDKbCScp/sUSss8BOW6a9cF\nyX5lLNXQvMzTHLBR9SojKbEzONZ39Vq6/QUG8oL0Bbr7OMwI3wVt1B1nkPd5+GdR5svE14Vy\nL3P+pA3jY0GZD2J/w7wfyxpPuhpPfhmOoLziu7G8Z5ZWxD3AtdBM5kK5LWwHCzcrgM596eeN\nsLsPGLFFxpf0q9RLvBxzdIbVOupiO89Rrl3TnhMehV2gFN/tqFJRx9+xQKuT5p0Wpo5YnJ6T\nsZOl3eMo20tc+xjXVobRJ42qS5rZ0i+H88KF4Bepr8H7oZMl4+9pDLGXZdwwtM30oHN9CEbA\nNKCDuwrWAUWHa91/NHgv4VdhNxjK8u4mnYudqvMkRavzxUOVNvKAZl3tMwauhs+AX89WAC9R\nbu7fh1+D9tSOm8ItUMp3SHgAP6ihvJnQA+Kx8CPYEbx82P/L4Cfgl7kpLXvTgT/CXo2O3ET4\nMFwLbpD/B9rqPfA5ULSNMi1k/iXsyqj8qeb5PqIzVKppyyjqxUOSc7aWgbGA71Ip7T5B887f\nvKtoynTOA/qgJcA545r4EswOR4Fr53R4BsaC82k/eAwOgNVgPbANP2zUGrcAAEAASURBVEK8\nDMr1cB/sCx4ml4ExoD+bGcqLr2vqd+DBLXOaaFsk4y3DzOW3Gk9w3ewIvwEvgd2Jl7u/wobg\n+BeEG+Fj8DQMB1mIQfjO9YFbFAOK/VRpPzkf9KnfAD9mzAq/Asu6n0f01fokQ1kMPKjf3ki/\nTuiepl2rUj7XvLw74+aZvgHGwUDKJ2n8xMYDnDfuCYfCEqBvfRGOAfePnSFjtX9KxpEw+gm5\nE+w1RyNhmTK/Wif50acN90Lt/m0YA/ZpASjFftXSjQVq43RjmELtLV4nH+dZZHVcNIusupBa\nHUjZXuJlW+qizyJOOuVMP9VIzEN4Juhgfww65hmg0yQHjdi5OmbHU+qM68wU67ourastPPg+\nD1uDG8k2YHnno5cEy3wRFDf293bFhvafHOR66qVjlNgwYerYhnZyo3Uz9iuZF2ztcwjMDm/C\nxeDhayew7L9gFvDyWZWlUFxTUZr2guV7OAhugL/AR0F7+xz7MgK8gAyG+EznQmzSrN8e3rTf\n1+ENsG9XgRu2dqhK2kpYza+mUy6h+Xlnzk3jiofqFcHD42+hloG3QPWd5Il5P6ZTJu/JA73x\n+xp5PyYcB4ZLwLPwXdgf7gUvSJYdBV6GNgQvP1vAyeDa3AAibxKxvS+D8/cksN5PwbKusch8\nRNwLXKMjwMNnO+Rd3TQSvT7Eubs7eKH7HiwCc0Iz0c/Yv2XBckuAvuUoGC7iHrMqLAVjikFp\nJy8yhpEjidwIl4J2ULSt56f4RueY+M7Ni+1952uDYv66jdB0M7FM6qdNQ8X3N5CyKI2fCkeA\nc2Nu+BXsBY5heVgHjgP98BxQHW/6T9ZESf8dl3WaSexV5qWeYeK+lwVgcVBcS665b8AHQXGN\n5T11Keo/k1rAw1inyXp0WCe8IPiF4hEYC2c04gRtFZ+RSZnJ19YHDGJjAzkO29Y+eYbDSrxq\nt+hTRueo6ES+2BWb8H/I0dFc3Eh3UjA9nc2YHWvG25NuZKOcZbSD4Q6NuPUvbOh0aAuDz/gJ\nuPF4yDge3FTugE4Ux1vayrg6UZLn4d6DjOPXTr+GPcANIY7fjcqNXfQV/wA3+OdBn2eb1q/K\nAyg+BOc1MhYn/HsjviXhWDgLrgI3RG39e9Du88Pb8DIMlLih/Q4+Do79YXDs6bcHlIgXOG3m\nF3B9paLvjJ27FMWf6GPvImviO+hOZ53Uy3vzPf0ADigr1fEBt4D2VxIa992Yzruppn1X+pUb\nwIOZhynfnWIdPxLOBv8ED35PwLzgxeB1uLwREnTJzfy1zdXgb12aCf9nRNeJ83YT8JK0M4yD\nr8Iv4DA4BtYCxXU+FlxXr0C7JOOvhjM1HnAPoePSV3wGLHcB2N/HQXEc5u0C94PyMHwTzgUv\nde3sM81NEdF/+i7XBOeCoj20TXyoae1hOeOGppWHYImu2Dvz0KTvdlqwfOqnPeeiOufhDKCY\nVuyD8fSl1Bn3/WwMt5oYINmadl0D34X0az3i7jeLgu8958ayvymbvnc3jpSjmf+S5NlG2rZQ\n4glfQPc0fBKsszr8DJYDP+w9BXPDm1BLNxbIJO4me0ip7euf4HRYCJ6EO8EJsSncAn7NGghx\ngmViDkT7g9XmQIwjdinDatx3VEr6YRinar7v2M1J3IzdGN2AOk106o45zsr+O9bokjaMpKz2\nMO6BP5uxzjg2Nd+N3PSB8By4GbsuVoZfwlCX2MExZFxln6OzXCQ6027OOnk3pV3BzeA62BHc\nVHX6M4PyMOwP1ndTNvTQ9hP4BETclC8CL1deQjzMXQ/LNMJHCC+H82ApeBluBzfLw2F52Aqy\nyRNtq2iLM+G9sBm8D/4Ef4GzYVs4BPyC+Tn4LDiHPgaxY2xoWMUy6lKW6MT5WurUR9KG+b4L\nD72fhJ1gSTgAahl8C+S9GCp5f0mrSxnzboAZwDl8FXiI8lL0V9AH7w6vwVxwNGwB58MYuBHe\nD6U4T/XlXiA2AdeF7arbE9aAZ+BiuBemBefrrbAs+Ez7dx+8ADtDO9ZVxkxzk8x1bWCefkNb\nGNfH2i/7vyHMA659dYr90Wc8ZaIQ05bRJ3eSzEFndwP96SJFx13X2sdL70uFPjbTVpFniZj2\nPbtPOWeWgJSxTkQbmRbLK9rcehHnZFXSlmGJ5Uw/DLOZGECxfedv+uJYnNMzg/uP7z55RLvG\nWKaNV9PaodRZL9JMX23D59qG9jPvdXDdOG+fA9ftjLAjfAC+CR+B8p2SrKVTLeDL1Pm4CTeT\nLVGObZbRos6vWR6oMuEMQ4tNTrFq6Xd1LDrDVmQ6KrkI024Wa9pvljYv9jTuJpjybsYe5soy\nY0ivC1NCDuShHgBalcuomLGVtoiuDI0Hyxp/oxG+QvggaKt/NXTPE2qrw8F849bzoLEODLSs\nxgN8XqsHgO2oWx1/bJTxJ9+0cybzxvRDsBYoHl60jTbJpvhL4jp9DzsnQzZt23RTGwVbwJlw\nNSheNtxYnYduJnm+oXPBg96L4AZ+E/wClKfB91LKeBKjS8VkxG3fMWZ8ZVXfrXlLlkriZ8Fp\n8F3w2ZZxHL+FseBcUudYSqIzbBYvbWC+dq7qnHt/aNRfjrA3OZgC5/RWqId869rGcBTfuXZ2\nDrQio6lUfT9Jd/d+XSeurZPA+XIr+PwPgXW/Ah60/NK8MjwEmQsXEV8AfO+W+TUsBvtCfNTj\nxLN2PSzLz2FOmAWcp8m3DeNHwx1wJHgJeQC+A7al72hF9FXNbKAuep+9N3wK9B2OX1zfa4Jj\n/TdsDpF/EDkVPJhGfkPEOoMph/Iw/VmrciEVHb8+UDvrM3aEFUAbZM2+v5FWp91c/7cXuugN\n9a0ngjYy3YzYPvVsryxX5vemt6w4jo0hMpKI+umimMxwV8rfX6mzPmn7ukahf4G4fXSOG2rL\n9Dl9a5ZOnmGzfHWOybmX/IS+J/uRugkfRWee+8JW4Jy0zl5QFd/3qKqyTneeBXany7/vods6\n02dgwR7KTE7WYRR2UmUCZ1IadpqUfS/H4+JvRXQ2aaenMPZz87Nc0mU8Op2AuCGqc5NykX8N\nBlqm4QG7wNlwLrhhnA+tygVUrNol46yOXb32MRQd3pONsKxzN7rXIO2WB1btZvpnMBCyNo2e\nABfDyWAfPHS0IttRqRxXxtMsjE0SaqfZi4euS9x6/yx0fnW8CfKMhHeiexG0VQ5A2tqD3wPg\nYc223m6EbkhuHoqXL9+Jl4Fj4XTYDWz7EChlDInRpWIy4jNR1j6s1aTOZ9Dp3yLTElke7JPj\nyRxwTNfC5uBGaXslsUepK+PmVzF/MTimked7uAecbz7rOuiL1Bek7q3kO9fOzoFWZDSVyvdW\nvtNmccu+DK6Ly2FB8FDnez0OrOOc+ioos4I+SL1+2fACeB9sBo+AuuR7cPbjhOvIZ+i70j/n\nz5/APNekc9h19ASMBdvaBZS/wFGgbjtoRfRV9svnp48Ju9Opd/w+1366nh6C3SHiO3sFroYD\n4GJwLW4Cgyn9vSBdSmfPKTr8LeL6EUPt5DvyGY4/dtQ+ZbxMR68vlTLPeNIpF130ZboaL+sk\nbhgs/wmIjCRinmeWVmRXKt3fpKL7oO/+SHDuun+krz4vFyZ16Vs1LPPKur2VMz91U68aum4i\nCxOxjmu7Ko+jGFVV1unOs8AIuuwkXKdJ1z0U7QfjmuS1qjqMipmEmZCZhK22OaXqpd/V8bj4\nWxGdTWxi2BPdPTt9KUM3pL/CPOAz7J+6j8BAyqk0rrNzIz4c3MDd7FqV86jYk03KPMdXOlc3\nFPO1SzYXy3hA94vveDBPneXGwiLgpuAh5H+hnbItjdmPc8ED8b/A53roaEW2o1Leeexgupku\ndjC0DwdBKdOTeArczBdrZHjheQ58h2nT8PlG2gPMl+F00KbPgO176DsOLgEPRKnv5qKsBR6W\nbMvnmX8XeNAr5VESo0vFZMRnoqx98VlVWQWFeSuB7/qxRlqdfTHtu7kC7GMVy1VJmVIfXRma\nn7S2SVy9l9NFoS9SX5C6t5LvXHs6B1qR0VTKezEs32k1nnznzQ3wW1DmA9f45eDl6WqYGRQP\nXK7BI2FNGANp927iN4HtXg7uw8adjx7AjLtmloQzwHrpw53EZ4edwXWr/kb4A3gp0999DZ4A\nfUcr8m4qpa/NwvTFsIr9Tn/1uR+GUpYjcTS47o4H/c9gy6E88Mx+PFSfd2Clvu/0OohtYoPu\n7FctZ1p7Ve1Z1k9eM101r0wb764/ztGnwY+eykiw7HQmWpBdqXN/k3q2b97f4SLYB1xPzcZS\n9jXjyBhSPvoynXrJq4bJTx3X7C2NPmiHZWAWcO26L9m/qrg+6wtS1SodmtZBPgseBtyYrwS/\nVHlbN1wD+iozUPDT8LluOB99dUImTVZHSfptmEVl3AXeiuhssijTZtot9ea9BYZxlvc20uqC\nizlxL0el/JHEaaWizfFNaM9D36pFux4CLi3Skxs9hwqxQ2kXx1jqk1eOP3bwIJ/yOZQa6uQ8\nVFj3NrgQIh5urk+iDeH0tOFms1/Rlocjn+2hoxVxDTsXYoeMN7ZImLEndOwjmjzwg+jM04Ze\nYAyfB+sZ98ByFvjMV0D9Q2Depo3QZ34GIocR8TJqWQ8K+okt4ALQ1+wI88JIcH4fBRvD7vAq\njIZWZCYq2Ze1uql8Nno3NPvuxcxNzz5ax/DL4GGzXGPmJb8sazykTMLoU94xar8n4WHQ3keA\nts9BhGivcjAlXButinVtYziK71z7OwdakdFUynurvsdqOuUMfZdLQ1VWQeFcc77fBZa9ARYF\n19ep8DXQH10Hacu14hr4JNj2xeB8Nd+19AnYA9S9AR7gFC9i94Hljgfz3eedc18H29wOWpFc\nkGy7tIVxddHHL0Vn+C/4baPMm4Q/h6Emh9KhM/vRqYupe2Clvpdc3218jD7A9xlbxUZVGzbT\nR5e65TtQp93LMilX1XWXLvtgH02vDoo+2rRnllbEM9L9fah4HGWajSN9Th/L0HiVannTqVPm\nZX+KznH7jpyj0T1F3PXrOjoK3Beq4hofVVXW6c61wGx03c3El+qB4H9gDZhcWZYKY2F8N3gb\nz0SrhmR1lFT7n7SLvxXR2ZQL2/bKRZx4QvPd8AzdcKI3FBe2hz/j80EpB5G4rFS0Ob4/7V1d\nadPNwk2jVfkbFTO2qm3KtGVMV9EhfwP8CJAyhtrQ0IuAznhfsEzki0QeTKIN4cq04fMWKNpa\nraHz0NGKbEel6kZSjtF4qNrlWvI+A++DUpYgcQ24OTiX/Iji12jXsHNVxkOe4+ayGbwL8j6O\nIB5ZiUj6cDJxD2e2eyEsD6VsTOIWsJ1nwAvlaGhFersgeYi8C+yLNhQPkLkYqn8RDKu2y3hi\ng2ZhdGVd59wh4CH6S/AFGAGtiJeb+oLU3HLuadq/3RekvFPDkDnvPHkDvNQ0k/eg3BlOBOtu\nDnuBPsY19UFQfxx4mDZ+AuQdX0Xcg7br5zrQ97tGfaYHN9dn6Uf2JZ0+eeCzjH11Tbmm9R2t\niM+wbyHz23Q1Xurst/n2175+HtTNAENJ+ntBupTBHFUMyLnouH8F34P4k9I2sWV0CWPPnsKy\nrD6sp7LVvLJu8kqd7ZnWTykjwbTztRXZlUr391JxDvLzXJ/1O2jWt+jKMH03TNz8xKv6Mq9s\nxz3AtP46NvWDxJ6QDxnO36o8jmJUVVmnJ1ig1UkzJe23aOPhfyJcENy0d4BLwQXt5OiL3Eeh\nxXsoeBh5XynyPUw5WTtdBmIcthkp46W9/NLsu3kvuJm6Ac4OLuqbYG1Q1ofTumITDgtbEf9r\nIz0QgRvfPE0aLsfRJLtHlevKsdtG2jGMjuh/6dWV4nyOk7+I+CawNfwddMh+FdLheRHw8GG4\nOFwP7RJto2ifJ7pi7flT2iUtxk6mq3Zyg1oKnCNrwrRwF3jReQX+DLvD9HAH3AvPwwfAjf9b\n8Ef4OigHwgWwLvhcDz3WXxqs+0lQ9BEeytJf5+oq8AdwHj8AvwB19sn5PQYGSlw3j8L5sAuc\nAM6RseDG6LzzUBux36Uto09ovtKsXHRnkr83vAW+h1o6xwLV92vPo3NeOGdfBH3sDbAfrABe\n9J3j48C1or9x7nvx0dd48HK+fRZcL5Z3broeN2qkvUQsCfODz9SHXAxzwlLgc9wDXK+2uwR8\nBJzPvwPr+Mysq0eIt0ts2/ErZVwfOje8ATODPnh1mBF+CdfDLDAXtNMf0twUFe28I2jr6WFb\n0C7uw75P9Up8SWyXtGVL0Q+mTqlPPPVTr3wHKRNdykSfukkb2v+UM1+ydxEdUHE+nAGebyK7\nJEJoXzKWqMu+qmuWX9Yzv6xjXlVmRfE4LAiZx8sQ9/LsutwHXFe1TIYF3FA7SU6ns5uBi8+D\nyXrgZNFxefjx0OKFqZ1STsx2tjsl26ouyHb1JbayvTwjOt9TnKabj2Shu6jdmNys/wSbgIex\n7WEm8F0PlDinfgz7wv5gn1aB9JvoZEt3Xxi7azP62MN5fDMkrT3Gg7Y5Ch6BZ2EReDfcAKvB\nfOAhpF3yIA3Z9q9hG9Dx2reBFu3h2GMXn5l45tAK6NwQ3IyPLvKfJv4AuGFZ1svOzuAmqhj6\nfn4GX4B7wLnnPFsSFgMPc5aLL7EvshGcB5eDh7qRcB1sAZfAYMidPOQTMDtsDPYz7yQ2QjVR\nmukmZhIp7Ry9dd6C1+BTUdZhR1igu/et3netpIxp574H4hyKiXb9E1QPU+bfDitCfJpz/m24\nG5YFL+vjwUvUmbAMrAxeMpRfguvmA+D6+he4vhTXnP5qcXDdbgBXgZK+ur7bJbaZsSdM2+Z5\n8fF59kvZckLQ9YuZdtoJnmrQyBoWge/zUnD8xneFH8EeoK/RVton74ToJHHTSmw63YRk19+y\nTvKb6VI/edUwddOX4hFdPtB+zwjTNDKuKQsMYNx9ZCnQV74C84JS7WfSGceEUu/8jT7jfidn\nQkx9yiSvTLv+Mvb3NAq4L/guHgY/RtQyjC3wfsZ2K8wGLmRf+gUQWZyIkyDOLfpWw8Oo6OFD\nnJyJG3aapO+OoxyLjrAVcdGlrTKMrcpnlPH0w1CHZuhG+jdYDzzQ3g9ulnfAEbAADLR8hgfo\n4LwAuAHatwuhVbmaihl3aZ9qvLRHyqurxs9Fp03+DleCh2Tt8xhcAXfBCfAV8GC7CLRLlqSh\ne+FNGAvpnxezVmQ7KqWN2KOaro6/mra8eJiRpLWDcf2A8+o2MN+NS71jOAu0nRvol8HN5GKw\n3KtguRcghyOiE0W7HzUxNSHyWwKfGxlDZHQSkxnquxzrWj3U81KX8VZD64Yyr9Qlbpgy0Zk+\nANYB35N2aKccTGPn9KNB69rGcBTfue+h1f1rNHV9f3mXzcLyfSeeOoYPgu8968A21B8C4+AZ\nKNfbQ6RdY5Z5EVxf+k99qbqX4Vl4HV4C/ZXl/ao9N5wEN0Bf5BEK2bdWRF/VzB7R2deM1XgV\nx+7z1VfXP6opLtrzzH704nzqHlipb5uxQzPbRJcwZcsweQnNS9ywjDerF13KOYcSL9t6G33m\npfknQsQLvTrPLK2IZyTPJN2JfdoBvgNlH3xm2deyv4mnTFmumc7yZZ1m8ZRx73P9egbpizxO\noVF9KTg1lml10kwJW63NQz0cOiEVHestXbEJf8YSjId5QWfWTnHSDgcZqHGk3Xc1MVLyyiwX\ns05sJ/Crj1+qxoDyM/g+rGhikORUnnMp+EvNtLAGvBdaldIO1fEnr6pP2vzEdXQePDys7wI/\nhznB/JvgCPBAG7GuG92a4FpohzxEIx8AbbMweAg6HtohjiP2sL1qupkutjFvGnAu6eQXhKXh\nYvgwnAELgRuGBzQP118CD2xV2RjFh2B5GAe24WZXyuwk3gefL5XEj4fdYA5o94WCJicR++Bh\n1Xcw4yQ5/50o55G5VduWdkyedtwHPKR7EbkIauk8C5TvNvOgus7KUVleX7MEnAApq945/RV4\nGJaEy2DVRnoEof7pGnB/dg0tC64dP0KsDK5PL0WyLtwOkeOIXAL+OuU6HQxxTJGMM2H0zxPR\nHjODF7lFYQycDZ+CPUGfMlzFccfPNfM1VXs1s0NZRpubNiztn3jCZu2oS34uuimnPnl53hPo\ndk6BAQ6no3375MV/JbAP6QfRiX0zXuqTV9WpVzKmCal3/qqvPsPcsrx9ssxfzKilfxbQmJ0i\nHnD2g7/DbOAXN53xb0CHthF4gHgUBkKcdOVEHIhnDHSbjkFp9zjS7oTWJ7QfXRZ1+VwvITuk\nMKGH2zGNtHHf52DLkzzw5MZDl+vnwz0UKLHBhFTvfy0feyX0UuIF5R5wfs8Cbt4eXLwAlDIH\nCTc389opHl5cd8pqE4K2/C3HW22wJ9uVecadM4r+zEOYunlgPeirXEVB6U48EHlYyLNSzrT2\n8av5QMjGNPp1GAGOyYuR799LjHNEKe0xQTPhb6kv42UZ4+bZluvy97ABeGj9LNTSeRbIu67O\nj+gdUfKMq9evGIp5CfUpyuLgR0lD86dp8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PJSznTyEppXFfNC8rqrq+8yTx+jfAOcI+pE+TY4H2upLdAWCxxGKzpFyURNui0PGMRG0u/q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\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WLR1qPJNFs1FuU0a4ASE/fyE9I+sakj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Ca5IXhei/fYy+h0ZETTEvTsZFXiMNh6Xx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\nWqVwWm+U1X0PVsiea9jLkWuiftfH/Hh2Tp8CpSpYYLIKcWXUt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IfrQeqfEOMk4m3vl7AVeKHwstSR8rnR9miDrodV15RQ78zzJu5sEHmN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lzLwm8d4kXR8WV9cYGKPOFGPZbXb3+KqIlC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\npUWfyvEUYyXGU4QtpxZluKyrE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KpAKp\nQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqk\nAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQ\nCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlA\nKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQC\nqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAK\npAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAq\nkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKp\nQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqk\nAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQ\nCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlA\nKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQC\nqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAK\npAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAq\nkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKpAKpAKpQCqQCqQCqUAqkAqkAqlAKtB9Cvx/\nz1LDs0lpc84AAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pairs(college[,1:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) iii. Let us produce side-by-side boxplots of _Outstate_ versus _Private_." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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brbJd0n7nO+YY6Fw+5zjk0s96La9+bJLsn9lmMrNeitE/M6kRym1ACgBi73n+dN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", "text/plain": [ "Plot with title “Boxplot of Outstate Vs. Private”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(college$Private, college$Outstate, xlab=\"Public/Private Indicator\", ylab=\"Out of State Tuition($)\", main=\"Boxplot of Outstate Vs. Private\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) iv. Let us add a new categorical field called _Elite_ which takes 2 values depending on _Top10Perc_:\n", "- Yes: when the proportion of students of a given college coming from the top 10% _exceeds_ 50%. \n", "- No: when the proportion of students of a given college coming from the top 10% is _less than_ 50%. \n", "\n", "This is implemented by initializing this field for every college as _No_, then applying the condition." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Elite = rep(\"No\", length(rownames(college))) #Initialize all entries of Elite to 'No'\n", "Elite[college$Top10perc > 50] = \"Yes\" #If Top10Perc > 50, assign field as 'Yes'\n", "Elite = as.factor(Elite)\n", "college = data.frame(college, Elite)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Private</th><th scope=col>Apps</th><th scope=col>Accept</th><th scope=col>Enroll</th><th scope=col>Top10perc</th><th scope=col>Top25perc</th><th scope=col>F.Undergrad</th><th scope=col>P.Undergrad</th><th scope=col>Outstate</th><th scope=col>Room.Board</th><th scope=col>Books</th><th scope=col>Personal</th><th scope=col>PhD</th><th scope=col>Terminal</th><th scope=col>S.F.Ratio</th><th scope=col>perc.alumni</th><th scope=col>Expend</th><th scope=col>Grad.Rate</th><th scope=col>Elite</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>Abilene Christian University</th><td>Yes </td><td>1660 </td><td>1232 </td><td>721 </td><td>23 </td><td>52 </td><td>2885 </td><td> 537 </td><td> 7440</td><td>3300 </td><td>450 </td><td>2200 </td><td>70 </td><td>78 </td><td>18.1 </td><td>12 </td><td> 7041</td><td>60 </td><td>No </td></tr>\n", "\t<tr><th scope=row>Adelphi University</th><td>Yes </td><td>2186 </td><td>1924 </td><td>512 </td><td>16 </td><td>29 </td><td>2683 </td><td>1227 </td><td>12280</td><td>6450 </td><td>750 </td><td>1500 </td><td>29 </td><td>30 </td><td>12.2 </td><td>16 </td><td>10527</td><td>56 </td><td>No </td></tr>\n", "\t<tr><th scope=row>Adrian College</th><td>Yes </td><td>1428 </td><td>1097 </td><td>336 </td><td>22 </td><td>50 </td><td>1036 </td><td> 99 </td><td>11250</td><td>3750 </td><td>400 </td><td>1165 </td><td>53 </td><td>66 </td><td>12.9 </td><td>30 </td><td> 8735</td><td>54 </td><td>No </td></tr>\n", "\t<tr><th scope=row>Agnes Scott College</th><td>Yes </td><td> 417 </td><td> 349 </td><td>137 </td><td>60 </td><td>89 </td><td> 510 </td><td> 63 </td><td>12960</td><td>5450 </td><td>450 </td><td> 875 </td><td>92 </td><td>97 </td><td> 7.7 </td><td>37 </td><td>19016</td><td>59 </td><td>Yes </td></tr>\n", "\t<tr><th scope=row>Alaska Pacific University</th><td>Yes </td><td> 193 </td><td> 146 </td><td> 55 </td><td>16 </td><td>44 </td><td> 249 </td><td> 869 </td><td> 7560</td><td>4120 </td><td>800 </td><td>1500 </td><td>76 </td><td>72 </td><td>11.9 </td><td> 2 </td><td>10922</td><td>15 </td><td>No </td></tr>\n", "\t<tr><th scope=row>Albertson College</th><td>Yes </td><td> 587 </td><td> 479 </td><td>158 </td><td>38 </td><td>62 </td><td> 678 </td><td> 41 </td><td>13500</td><td>3335 </td><td>500 </td><td> 675 </td><td>67 </td><td>73 </td><td> 9.4 </td><td>11 </td><td> 9727</td><td>55 </td><td>No </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r\|lllllllllllllllllll}\n", " & Private & Apps & Accept & Enroll & Top10perc & Top25perc & F.Undergrad & P.Undergrad & Outstate & Room.Board & Books & Personal & PhD & Terminal & S.F.Ratio & perc.alumni & Expend & Grad.Rate & Elite\\\\\n", "\\hline\n", "\tAbilene Christian University & Yes & 1660 & 1232 & 721 & 23 & 52 & 2885 & 537 & 7440 & 3300 & 450 & 2200 & 70 & 78 & 18.1 & 12 & 7041 & 60 & No \\\\\n", "\tAdelphi University & Yes & 2186 & 1924 & 512 & 16 & 29 & 2683 & 1227 & 12280 & 6450 & 750 & 1500 & 29 & 30 & 12.2 & 16 & 10527 & 56 & No \\\\\n", "\tAdrian College & Yes & 1428 & 1097 & 336 & 22 & 50 & 1036 & 99 & 11250 & 3750 & 400 & 1165 & 53 & 66 & 12.9 & 30 & 8735 & 54 & No \\\\\n", "\tAgnes Scott College & Yes & 417 & 349 & 137 & 60 & 89 & 510 & 63 & 12960 & 5450 & 450 & 875 & 92 & 97 & 7.7 & 37 & 19016 & 59 & Yes \\\\\n", "\tAlaska Pacific University & Yes & 193 & 146 & 55 & 16 & 44 & 249 & 869 & 7560 & 4120 & 800 & 1500 & 76 & 72 & 11.9 & 2 & 10922 & 15 & No \\\\\n", "\tAlbertson College & Yes & 587 & 479 & 158 & 38 & 62 & 678 & 41 & 13500 & 3335 & 500 & 675 & 67 & 73 & 9.4 & 11 & 9727 & 55 & No \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "\| <!--/--> \| Private \| Apps \| Accept \| Enroll \| Top10perc \| Top25perc \| F.Undergrad \| P.Undergrad \| Outstate \| Room.Board \| Books \| Personal \| PhD \| Terminal \| S.F.Ratio \| perc.alumni \| Expend \| Grad.Rate \| Elite \| \n", "\|---\|---\|---\|---\|---\|---\|\n", "\| Abilene Christian University \| Yes \| 1660 \| 1232 \| 721 \| 23 \| 52 \| 2885 \| 537 \| 7440 \| 3300 \| 450 \| 2200 \| 70 \| 78 \| 18.1 \| 12 \| 7041 \| 60 \| No \| \n", "\| Adelphi University \| Yes \| 2186 \| 1924 \| 512 \| 16 \| 29 \| 2683 \| 1227 \| 12280 \| 6450 \| 750 \| 1500 \| 29 \| 30 \| 12.2 \| 16 \| 10527 \| 56 \| No \| \n", "\| Adrian College \| Yes \| 1428 \| 1097 \| 336 \| 22 \| 50 \| 1036 \| 99 \| 11250 \| 3750 \| 400 \| 1165 \| 53 \| 66 \| 12.9 \| 30 \| 8735 \| 54 \| No \| \n", "\| Agnes Scott College \| Yes \| 417 \| 349 \| 137 \| 60 \| 89 \| 510 \| 63 \| 12960 \| 5450 \| 450 \| 875 \| 92 \| 97 \| 7.7 \| 37 \| 19016 \| 59 \| Yes \| \n", "\| Alaska Pacific University \| Yes \| 193 \| 146 \| 55 \| 16 \| 44 \| 249 \| 869 \| 7560 \| 4120 \| 800 \| 1500 \| 76 \| 72 \| 11.9 \| 2 \| 10922 \| 15 \| No \| \n", "\| Albertson College \| Yes \| 587 \| 479 \| 158 \| 38 \| 62 \| 678 \| 41 \| 13500 \| 3335 \| 500 \| 675 \| 67 \| 73 \| 9.4 \| 11 \| 9727 \| 55 \| No \| \n", "\n", "\n" ], "text/plain": [ " Private Apps Accept Enroll Top10perc Top25perc\n", "Abilene Christian University Yes 1660 1232 721 23 52 \n", "Adelphi University Yes 2186 1924 512 16 29 \n", "Adrian College Yes 1428 1097 336 22 50 \n", "Agnes Scott College Yes 417 349 137 60 89 \n", "Alaska Pacific University Yes 193 146 55 16 44 \n", "Albertson College Yes 587 479 158 38 62 \n", " F.Undergrad P.Undergrad Outstate Room.Board Books\n", "Abilene Christian University 2885 537 7440 3300 450 \n", "Adelphi University 2683 1227 12280 6450 750 \n", "Adrian College 1036 99 11250 3750 400 \n", "Agnes Scott College 510 63 12960 5450 450 \n", "Alaska Pacific University 249 869 7560 4120 800 \n", "Albertson College 678 41 13500 3335 500 \n", " Personal PhD Terminal S.F.Ratio perc.alumni Expend\n", "Abilene Christian University 2200 70 78 18.1 12 7041 \n", "Adelphi University 1500 29 30 12.2 16 10527 \n", "Adrian College 1165 53 66 12.9 30 8735 \n", "Agnes Scott College 875 92 97 7.7 37 19016 \n", "Alaska Pacific University 1500 76 72 11.9 2 10922 \n", "Albertson College 675 67 73 9.4 11 9727 \n", " Grad.Rate Elite\n", "Abilene Christian University 60 No \n", "Adelphi University 56 No \n", "Adrian College 54 No \n", "Agnes Scott College 59 Yes \n", "Alaska Pacific University 15 No \n", "Albertson College 55 No " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(college)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_NOTE 2_: In case of multiple execution of the code above, we may end up with multiple instances of the field _Elite_. I once ended up with 4 before. They can be deleted using the command: \n", " `college[,-(ncol(college)-4:ncol(college))]`\n", " \n", "Now that the `Elite` field has been appended as the last column, let us see how many such _Elite_ universities exist." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<dl class=dl-horizontal>\n", "\t<dt>No</dt>\n", "\t\t<dd>699</dd>\n", "\t<dt>Yes</dt>\n", "\t\t<dd>78</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[No] 699\n", "\\item[Yes] 78\n", "\\end{description}\n" ], "text/markdown": [ "No\n", ": 699Yes\n", ": 78\n", "\n" ], "text/plain": [ " No Yes \n", "699 78 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(college$Elite)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So of our 777 colleges, 78 of them are _Elite_. The boxpolot below shows _Out of State tution_ for elite and non-elite universities." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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5plC49PrfeCZFqPXVvbrG0/MlnYnp4Z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\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBA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"text/plain": [ "Plot with title “Elite Vs. Outstate”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(college$Elite, college$Outstate, ylab=\"Out of State Tuition ($)\", xlab=\"Is the University Elite?\",main=\"Elite Vs. Outstate\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) v. We will create histograms of quantitative variables. Each histogram will be have 5, 10, 15 & 20 bins specified by the `breaks` argument. To plot a number of these histograms, we use `par()`. Let us start with _Out of State Tuition_." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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AECBAgQIECAQEcFdJA6umM0iwABAgQIECBAgACB+QvoIM3f3BoJECBAgAABAgQI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\nSUVORK5CYII=", "text/plain": [ "Plot with title “Bins = 4”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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QIECBAgQIAAAQIECBAgQIAAAQIECBAg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\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBA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uh7cnMn5JUo/CNeVjGblWMulzP3tl/qHJZ5KmVCes+bmt7dQXMrwmeXbyoaQ+a/SL\nSZX2ftqvq2WnJvdKav36zFPlp8k/JL+TKAQIECAwDAHt0TDqwVEQIECAQAQemNSXMNTngGr8\nyOTByUlJ3YW5b9Iub85EdTjun9ww+fOk7tRU56T5kobjMv7D5D5JdcCqnJDUOm9Kfj85Oqk7\nS6ckte5tk6acmZH61ru6u1Wlxusxv5smtY97JGcltb0nJk0Zf13dbap16ssg7pjU8b4mqeM/\nLFEIECBAYDgC2qPh1IUjIUCAwLYXqEap7rZUZ6JSd1qqU3HPZLxcPzPqjlCz7n9lvO7G1HR1\nXqrcOvluUvMemVSpO1JPT05LqkNWy6qjUo9V3CJpl+p01VeF1zpXTY5KPpDU6yqfT+pLFj6R\nvCVpyvjrav6Dkm8mta2LkroDVR1AhQABAgSGJ6A9Gl6dOCICBAhsa4HqjNSXIDSPt03DOCgL\nrzZthSyr7VXHaLxUR6r2c/nxBa3pevRu/9Z0jdajfZVpZdLrav2DkwOnvdAyAgQIEBiMgPZo\nMFXhQAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0K/D/CQyi2OLHcF8AAAAASUVORK5C\nYII=", "text/plain": [ "Plot with title “Bins = 8”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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DkuqQ/EvpWMK6/OxHeOmzEy7byR19N+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\ngAABAgQIECBAoF+B/wfncGWTtCov/gAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title “Bins = 12”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+byNp49+0yAAIHFEfhWDqWeUPfSpDpH1TZ9Ijko6S03zosnJl/tnWicAAECBOZP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\nhZmrXwq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iosUK0AAAAASUVORK5CYII=", "text/plain": [ "Plot with title “Bins = 16”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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G5wh1SDCBAgQIAAAQIECBCYVcAAaVY56xEgQIAAAQIECBAgMDgBA6TBHVINIkCA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\nOkiTuVmKAAECBAgQIECAAIEBCuggDfCkOiQCBAg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"text/plain": [ "Plot with title “Bins = 20”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(2,2))\n", "for (numBins in 1:20){\n", " hist(college$Outstate, breaks=numBins, xlab=\"OutState\", ylab=\"Freq\", main=paste(\"Bins = \",numBins))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Interesting Observation_: Even after iterating the number of break points from 1 to 20 for the field `OutState`, we observe the number of bins _numBins_ between 7 and 13 to be the same. They are broken down into 10 bins, generating the same histogram. Similarly, the histograms generated for _numBins_ from 14 to 20 are also the same. Each generating the same 20 bin histogram. Also, the number of breaks from 3 to 6 generate a 5 bin histogram.\n", "\n", "Let us check the same for another quantitative field: Book Cost." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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XV2ptUldh9Ijk4OTHSQgqAQIECgqwJtO0i/lwr8dVJHxioXJHWN9UHJPZI/Tqpj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KaIa9EECBAgQIAAAQIECPRLQAepX/vL1hIgQIAAAQIE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\nqT/q45OkAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “Bins = 4”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4AB0nY869pMgAABAgQIECBAgMBUAQOkqSwWEiBAgAABAgQIECCwHQUMkLbjWddm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QIECAwEIEVuUjdgvBmtNOHpx2rpxTW0Nr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\n8wAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title “Bins = 8”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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f2S+kLs28m48qpMfMe4N0amfWrk9bRf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IAAECW1HAeLQV97o+EyCwlAJDnyA1O+XG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\ngAABAgQIECBAgAABAgQ2KfD/AcrJRXrv00vhAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “Bins = 12”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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QIECBAgQIAAAQIECBAgQIAAAQIECBAg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\nIECAAIE9FPj/VCNFXfmsS3EAAAAASUVORK5CYII=", "text/plain": [ "Plot with title “Bins = 16”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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z0CBAisiEDbAdKt4/FXyUeT30/qc0e3Ss4aPG97d7NUVwgQIECAwKYF9EebprMi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\nZ7uahbZe33L5nsSbFgvdDVa+IAHj0YLgp7xa49G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"text/plain": [ "Plot with title “Bins = 20”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(2,2))\n", "for (numBins in 1:20){\n", " hist(college$Books, breaks=numBins, xlab=\"Book Cost\", ylab=\"Freq\", main=paste(\"Bins = \",numBins))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe similar histograms when the number of _bins_ was set from:\n", "- 4 throguh 8\n", "- 9 through 16\n", "- 17 through 20\n", "\n", "Let us plot the graphs in different ways, varying the _par_ arguments." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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aje4+sLsvowtSFZ7us8D123siF7Xk69p71eZ3lAfTs39WXjiUm9931hCGpDxie9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\nWNmbAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “Bins = 10”" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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7lRMwIECCyVQHVIrx9p8Usy/7WRZVsyuzkDpL/LAb6R1JWeptwpE5ckz28WbOFj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\nris94UbEl/6mto/n2u9TxnzU5EGlR2nWby+O/Me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"text/plain": [ "Plot with title “Bins = 20”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(5,2))\n", "for (numBins in 1:20){\n", " hist(college$Books, breaks=numBins, xlab=\"Book Cost\", ylab=\"Freq\", main=paste(\"Bins = \",numBins))\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Observation_: It seems to be plotting _5 rows and 2 columns_ of histograms in the same space as it was plotting 2 rows and 2 columns of histograms before." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8 (c) vi. SEE OBSERVATIONS AND NOTES IN THE ANSWERS ABOVE \n", "\n", "Additionally, From the scatter plot matrix in 8 (b) ii., we observe nearly linear relationships between different variables like _F. Undergrad_ Vs _Enroll_ and _Top10Prec_ Vs _Top25Prec_. While analyzing our data, we should only retain one covariate (feature) among others which are highly correlated." ] } ], "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": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ChadFulton/statsmodels	examples/notebooks/generic_mle.ipynb	1	13574	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation (Generic models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n", "\n", "1. Probit model for binary dependent variables\n", "2. Negative binomial model for count data\n", "\n", "The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Probit model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = sm.datasets.spector.load_pandas()\n", "exog = data.exog\n", "endog = data.endog\n", "print(sm.datasets.spector.NOTE)\n", "print(data.exog.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Them, we add a constant to the matrix of regressors:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exog = sm.add_constant(exog, prepend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create your own Likelihood Model, you simply need to overwrite the loglike method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class MyProbit(GenericLikelihoodModel):\n", " def loglike(self, params):\n", " exog = self.exog\n", " endog = self.endog\n", " q = 2 * endog - 1\n", " return stats.norm.logcdf(qnp.dot(exog, params)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate the model and print a summary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sm_probit_manual = MyProbit(endog, exog).fit()\n", "print(sm_probit_manual.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare your Probit implementation to ``statsmodels``' \"canned\" implementation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sm_probit_canned = sm.Probit(endog, exog).fit()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm_probit_canned.params)\n", "print(sm_probit_manual.params)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(sm_probit_canned.cov_params())\n", "print(sm_probit_manual.cov_params())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Example 2: Negative Binomial Regression for Count Data\n", "\n", "Consider a negative binomial regression model for count data with\n", "log-likelihood (type NB-2) function expressed as:\n", "\n", "$$\n", " \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n", " \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n", " \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n", "$$\n", "\n", "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n", "and the negative binomial heterogeneity parameter $\\alpha$. \n", "\n", "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n", "simply as:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import nbinom" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def _ll_nb2(y, X, beta, alph):\n", " mu = np.exp(np.dot(X, beta))\n", " size = 1/alph\n", " prob = size/(size+mu)\n", " ll = nbinom.logpmf(y, size, prob)\n", " return ll" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### New Model Class\n", "\n", "We create a new model class which inherits from ``GenericLikelihoodModel``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NBin(GenericLikelihoodModel):\n", " def __init__(self, endog, exog, kwds):\n", " super(NBin, self).__init__(endog, exog, kwds)\n", " \n", " def nloglikeobs(self, params):\n", " alph = params[-1]\n", " beta = params[:-1]\n", " ll = _ll_nb2(self.endog, self.exog, beta, alph)\n", " return -ll \n", " \n", " def fit(self, start_params=None, maxiter=10000, maxfun=5000, kwds):\n", " # we have one additional parameter and we need to add it for summary\n", " self.exog_names.append('alpha')\n", " if start_params == None:\n", " # Reasonable starting values\n", " start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n", " # intercept\n", " start_params[-2] = np.log(self.endog.mean())\n", " return super(NBin, self).fit(start_params=start_params, \n", " maxiter=maxiter, maxfun=maxfun, \n", " *kwds) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two important things to notice: \n", "\n", "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n", "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n", " \n", "That's it! You're done!\n", "\n", "### Usage Example\n", "\n", "The [Medpar](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/doc/COUNT/medpar.html)\n", "dataset is hosted in CSV format at the [Rdatasets repository](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets). We use the ``read_csv``\n", "function from the [Pandas library](http://pandas.pydata.org) to load the data\n", "in memory. We then print the first few columns: \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n", "\n", "medpar.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model we are interested in has a vector of non-negative integers as\n", "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n", "``type3``, ``hmo``, ``white``.\n", "\n", "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = medpar.los\n", "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]].copy()\n", "X[\"constant\"] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we fit the model and extract some information: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mod = NBin(y, X)\n", "res = mod.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Extract parameter estimates, standard errors, p-values, AIC, etc.:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Parameters: ', res.params)\n", "print('Standard errors: ', res.bse)\n", "print('P-values: ', res.pvalues)\n", "print('AIC: ', res.aic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, you can obtain a full list of available information by typing\n", "``dir(res)``.\n", "We can also look at the summary of the estimation results." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n", "print(res_nbin.summary())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res_nbin.params)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(res_nbin.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we could compare them to results obtained using the MASS implementation for R:\n", "\n", " url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/csv/COUNT/medpar.csv'\n", " medpar = read.csv(url)\n", " f = los~factor(type)+hmo+white\n", " \n", " library(MASS)\n", " mod = glm.nb(f, medpar)\n", " coef(summary(mod))\n", " Estimate Std. Error z value Pr(>\|z\|)\n", " (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257\n", " factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05\n", " factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20\n", " hmo -0.06795522 0.05321375 -1.277024 2.015939e-01\n", " white -0.12906544 0.06836272 -1.887951 5.903257e-02\n", "\n", "### Numerical precision \n", "\n", "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class." ] } ], "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 }	bsd-3-clause
enchantner/python-zero	lesson_9/Slides.ipynb	1	23939	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".text_cell_render * {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".reveal code {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".text_cell_render h3 {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".reveal section img {\n", " max-height: 500px;\n", " margin-left: auto;\n", " margin-right: auto;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "<style>\n", ".text_cell_render * {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".reveal code {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".text_cell_render h3 {\n", " font-family: OfficinaSansCTT;\n", "}\n", ".reveal section img {\n", " max-height: 500px;\n", " margin-left: auto;\n", " margin-right: auto;\n", "}\n", "</style>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Вопросы\n", "\n", "* Что такое BLAS?\n", "* Как сделать в Numpy вот такую матрицу?\n", "```python\n", "array([[ 0, 4, 8, 12],\n", " [ 1, 5, 9, 13],\n", " [ 2, 6, 10, 14],\n", " [ 3, 7, 11, 15]])\n", "```\n", "* Какой параметр функции read_csv() в Pandas позволяет не читать весь файл сразу целиком в память? И как посмотреть, сколько места датафрейм занимает в памяти?\n", "* Какой метод колонки является аналогом ```df[column].groupby(column).count()```?\n", "* Опишите своими словами, как решается задача предсказания методом линейной регрессии с использованием ." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Что такое DevOps?\n", "\n", "- Это набор практик" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы (это мы уже умеем)\n", "- Шаг 2: Создаем репозиторий на Github" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Модуль argparse\n", "\n", "- Помните sys.argv?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import argparse\n", "\n", "def build_parser():\n", " parser = argparse.ArgumentParser()\n", " parser.add_argument(\n", " '-c', '--config', dest='config', action='store', type=str,\n", " help='path to custom config',\n", " default=os.path.join(os.path.dirname(__file__), \"config.yaml\")\n", " )\n", " return parser\n", "\n", "def main():\n", " parser = build_parser()\n", " params, other_params = parser.parse_known_args()\n", " conf = load_config(params.config)\n", " ..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Конфигурационные файлы\n", "\n", "- В конфигурационном файле должны быть все настройки программы, которые мы хотим менять без модификации ее кода\n", "- Формат конфига может быть любым, я бы предложил взять YAML, JSON или INI (про INI можно почитать вот тут - https://docs.python.org/3/library/configparser.html)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "- *pip freeze > requirements.txt\n", "- Посмотрим глазами на содержимое. На некоторых системах эта команда создает ненужную запись о несуществующем пакете \"pkg-resources==0.0.0\"* - удалим ее, если она присутствует." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "```\n", "my_package <- это папка с нашим проектом\n", "├── MANIFEST.in <- до этого мы сейчас дойдем\n", "├── my_package <- это папка с именем нашего модуля, то, что будет в \"import my_package\"\n", "│ ├── cli.py <- это базовый файл с нашим кодом\n", "│ ├── config.yaml <- это файл конфигурации\n", "│ └── __init__.py <- это чаще всего пустой файл, который превращает папку в модуль питона\n", "├── requirements.txt <- это наш файл с зависимостями\n", "└── setup.py <- до этого мы сейчас дойдем\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Пакеты Python\n", "\n", "- Помните эту команду? *pip install -U pip wheel setuptools\n", "- Пакеты бывают (в общем случае) двух типов: \n", " - архивы (чаще всего формата .tar.gz)\n", " - бинарные дистрибутивы (формата .whl)\n", "- Главный файл пакета - setup.py\n", "- Интерпретатор по умолчанию ищет пакеты в /usr/lib/python3.6* , либо же в venv/lib/python3.6/site-packages\n", "- Можно передавать дополнительные пути для поиска с помощью переменной окружения PYTHONPATH или добавляя их в sys.path" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# setup.py\n", "\n", "import os\n", "import os.path\n", "\n", "from setuptools import find_packages\n", "from setuptools import setup\n", "\n", "def find_requires():\n", " dir_path = os.path.dirname(os.path.realpath(__file__))\n", " requirements = []\n", " with open('{0}/requirements.txt'.format(dir_path), 'r') as reqs:\n", " requirements = reqs.readlines()\n", " return requirements\n", "\n", "\n", "if __name__ == \"__main__\":\n", " setup(\n", " name=\"my_package\",\n", " version=\"0.0.1\",\n", " description='my cool package',\n", " packages=find_packages(),\n", " install_requires=find_requires(),\n", " include_package_data=True,\n", " entry_points={\n", " 'console_scripts': [\n", " 'my_command = my_package.cli:main',\n", " ],\n", " },\n", " )\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### MANIFEST.in (включить не-питоновые файлы в проект)\n", "\n", "```\n", "include requirements.txt\n", "recursive-include my_package \n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"pip install --editable .\" для отладочного режима" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Пути и import\n", "\n", "- Более старый, но стабильный вариант - *python setup.py develop\n", "- Гораздо меньше ошибок возникает, если всегда использовать путь от имени пакета\n", "- Любые изменения в файлах сразу же станут видны внутри пакета\n", "- Точнее, не сразу, а после перезапуска интерпретатора" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"python setup.py develop\" для отладочного режима\n", "- Шаг 7: Проверяем, что пакет реально собирается" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Сборка в tar.gz\n", "\n", "- python setup.py sdist\n", "- Создает папки dist* и %имя проекта%.egg-info, вторую можно смело удалить (там метаданные)\n", "- Созданный в папке dist архив и будем собранным пакетом Python\n", "- https://packaging.python.org/tutorials/distributing-packages/" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "### Сборка в .whl\n", "\n", "- *python setup.py bdist_wheel* (есть еще опция --universal, когда проект совместим с Python 2)\n", "- Создает те же папки плюс build, где будет примерная структура пакета после инсталляции\n", "- В папке dist появится файл .whl, который будет бинарным пакетом Python\n", "- Во многих случаях собранный бинарный пакет будет устанавливаться только на ту же ОС, где собирался" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"python setup.py develop\" для отладочного режима\n", "- Шаг 7: Проверяем, что пакет реально собирается\n", "- Шаг 8: Отмечаем файлы, которые мы не хотим загружать в систему контроля версий" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Файл .gitignore\n", "\n", "- Файл находится в корне проекта/репозитория. Если его там нет - создайте его\n", "- Просто пишем пути к файлам, которые не хотим отслеживать" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github и клонируем его к себе\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"python setup.py develop\" для отладочного режима\n", "- Шаг 7: Проверяем, что пакет реально собирается\n", "- Шаг 8: Отмечаем файлы, которые мы не хотим загружать в систему контроля версий\n", "- Шаг 9: Заливаем код в Git" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Вспоминаем работу с Git из консоли\n", "\n", "- Все команды набираем внутри нашей папки с проектом\n", "- *git status* # что вообще происходит\n", "- *git add путь/к/файлу * # добавляем файл в git\n", "- **git add * # добавляем все файлы в текущей папке в git\n", "- git commit -m \"Initial commit\" # создаем коммит, то есть точку восстановления\n", "- git push origin master # заливаем код в репозиторий" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github и клонируем его к себе\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"python setup.py develop\" для отладочного режима\n", "- Шаг 7: Проверяем, что пакет реально собирается\n", "- Шаг 8: Отмечаем файлы, которые мы не хотим загружать в систему контроля версий\n", "- Шаг 9: Заливаем код в Git\n", "- Шаг 10: Документация" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Модуль Sphinx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- создадим папку docs\n", "- pip install sphinx sphinx-argparse\n", "- в папке docs: sphinx-quickstart\n", "- лучше задать значения для: \"Project name\", \"Author name(s)\", \"Project version\", \"autodoc: automatically insert docstrings from modules\" (y), \"viewcode: include links to the source code of documented Python objects\" (y)\n", "- в файле conf.py** в extensions добавим 'sphinxarg.ext', а еще наверху раскомментируем и поправим одну точку на две:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.insert(0, os.path.abspath('..'))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- добавим логотип для красоты:\n", " - ```html_logo = '../logo.png'```\n", "- добавим в index.rst после \":caption: Contents:\" строчки \"quickstart\" и \"develop\"\n", "- это значит, нам надо будет создать два файла - quickstart.rst и develop.rst в том же каталоге" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### quickstart.rst\n", "\n", "```rst\n", "Quickstart\n", "==========\n", "\n", ".. contents:: :local:\n", "\n", ".. argparse::\n", " :module: my_package.cli\n", " :func: build_parser\n", " :prog: my_command\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### develop.rst\n", "\n", "```rst\n", "Reference\n", "=========\n", "\n", ".. contents:: :local:\n", "\n", ".. automodule:: my_package.cli\n", " :inherited-members:\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- *make html* или *make.bat html* - в папке _build/html создастся куча файлов, главный - index.html" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Создание полноценного проекта на Python\n", "\n", "- Шаг 1: Создаем virtualenv для работы\n", "- Шаг 2: Создаем репозиторий на Github и клонируем его к себе\n", "- Шаг 3: Описываем аргументы командной строки и конфигурационные файлы (если нужно)\n", "- Шаг 4: Сохраняем зависимости нашего проекта в отдельный файл, который затем включим в пакет\n", "- Шаг 5: Структурируем программу, как пакет\n", "- Шаг 6 (опциональный): Используем \"python setup.py develop\" для отладочного режима\n", "- Шаг 7: Проверяем, что пакет реально собирается\n", "- Шаг 8: Отмечаем файлы, которые мы не хотим загружать в систему контроля версий\n", "- Шаг 9: Заливаем код в Git\n", "- Шаг 10: Документация\n", "- Шаг 11: Тестирование (обсудим в другой раз)" ] } ], "metadata": { "celltoolbar": "Slideshow", "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
aattaran/Machine-Learning-with-Python	titanic/titanic_survival_exploration[1].ipynb	2	97790	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Introduction and Foundations\n", "## Project: Titanic Survival Exploration\n", "\n", "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, you will need to implement several conditional predictions and answer the questions below. Your project submission will be evaluated based on the completion of the code and your responses to the questions.\n", "> Tip: Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Started\n", "To begin working with the RMS Titanic passenger data, we'll first need to `import` the functionality we need, and load our data into a `pandas` DataFrame. \n", "Run the code cell below to load our data and display the first few entries (passengers) for examination using the `.head()` function.\n", "> Tip: You can run a code cell by clicking on the cell and using the keyboard shortcut Shift + Enter or Shift + Return. Alternatively, a code cell can be executed using the Play button in the hotbar after selecting it. Markdown cells (text cells like this one) can be edited by double-clicking, and saved using these same shortcuts. [Markdown](http://daringfireball.net/projects/markdown/syntax) allows you to write easy-to-read plain text that can be converted to HTML." ] }, { "cell_type": "code", "execution_count": 1, "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>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 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import libraries necessary for this project\n", "import numpy as np\n", "import pandas as pd\n", "from IPython.display import display # Allows the use of display() for DataFrames\n", "\n", "# Import supplementary visualizations code visuals.py\n", "import visuals as vs\n", "\n", "# Pretty display for notebooks\n", "%matplotlib inline\n", "\n", "# Load the dataset\n", "in_file = 'titanic_data.csv'\n", "full_data = pd.read_csv(in_file)\n", "\n", "# Print the first few entries of the RMS Titanic data\n", "display(full_data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:\n", "- Survived: Outcome of survival (0 = No; 1 = Yes)\n", "- Pclass: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n", "- Name: Name of passenger\n", "- Sex: Sex of the passenger\n", "- Age: Age of the passenger (Some entries contain `NaN`)\n", "- SibSp: Number of siblings and spouses of the passenger aboard\n", "- Parch: Number of parents and children of the passenger aboard\n", "- Ticket: Ticket number of the passenger\n", "- Fare: Fare paid by the passenger\n", "- Cabin Cabin number of the passenger (Some entries contain `NaN`)\n", "- Embarked: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n", "\n", "Since we're interested in the outcome of survival for each passenger or crew member, we can remove the Survived feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets. \n", "Run the code cell below to remove Survived as a feature of the dataset and store it in `outcomes`." ] }, { "cell_type": "code", "execution_count": 3, "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>PassengerId</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>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>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>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>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>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 Pclass Name \\\n", "0 1 3 Braund, Mr. Owen Harris \n", "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", "2 3 3 Heikkinen, Miss. Laina \n", "3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", "4 5 3 Allen, Mr. William Henry \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n", "1 female 38.0 1 0 PC 17599 71.2833 C85 C \n", "2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n", "3 female 35.0 1 0 113803 53.1000 C123 S \n", "4 male 35.0 0 0 373450 8.0500 NaN S " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Store the 'Survived' feature in a new variable and remove it from the dataset\n", "outcomes = full_data['Survived']\n", "data = full_data.drop('Survived', axis = 1)\n", "\n", "# Show the new dataset with 'Survived' removed\n", "display(data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The very same sample of the RMS Titanic data now shows the Survived feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now paired. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n", "\n", "To measure the performance of our predictions, we need a metric to score our predictions against the true outcomes of survival. Since we are interested in how accurate our predictions are, we will calculate the proportion of passengers where our prediction of their survival is correct. Run the code cell below to create our `accuracy_score` function and test a prediction on the first five passengers. \n", "\n", "Think: Out of the first five passengers, if we predict that all of them survived, what would you expect the accuracy of our predictions to be?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 60.00%.\n" ] } ], "source": [ "def accuracy_score(truth, pred):\n", " \"\"\" Returns accuracy score for input truth and predictions. \"\"\"\n", " \n", " # Ensure that the number of predictions matches number of outcomes\n", " if len(truth) == len(pred): \n", " \n", " # Calculate and return the accuracy as a percent\n", " return \"Predictions have an accuracy of {:.2f}%.\".format((truth == pred).mean()100)\n", " \n", " else:\n", " return \"Number of predictions does not match number of outcomes!\"\n", " \n", "# Test the 'accuracy_score' function\n", "predictions = pd.Series(np.ones(5, dtype = int))\n", "print accuracy_score(outcomes[:5], predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Tip:* If you save an iPython Notebook, the output from running code blocks will also be saved. However, the state of your workspace will be reset once a new session is started. Make sure that you run all of the code blocks from your previous session to reestablish variables and functions before picking up where you last left off.\n", "\n", "# Making Predictions\n", "\n", "If we were asked to make a prediction about any passenger aboard the RMS Titanic whom we knew nothing about, then the best prediction we could make would be that they did not survive. This is because we can assume that a majority of the passengers (more than 50%) did not survive the ship sinking. \n", "The `predictions_0` function below will always predict that a passenger did not survive." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predictions_0(data):\n", " \"\"\" Model with no features. Always predicts a passenger did not survive. \"\"\"\n", "\n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Predict the survival of 'passenger'\n", " predictions.append(0)\n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_0(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1\n", "Using the RMS Titanic data, how accurate would a prediction be that none of the passengers survived? \n", "Hint: Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 61.62%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: Replace this text with the prediction accuracy you found above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predictions have an accuracy of 61.62%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "Let's take a look at whether the feature Sex** has any indication of survival rates among passengers using the `survival_stats` function. This function is defined in the `visuals.py` Python script included with this project. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. The third parameter indicates which feature we want to plot survival statistics across. \n", "Run the code cell below to plot the survival outcomes of passengers based on their sex." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xb011a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examining the survival statistics, a large majority of males did not survive the ship sinking. However, a majority of females did survive the ship sinking. Let's build on our previous prediction: If a passenger was female, then we will predict that they survived. Otherwise, we will predict the passenger did not survive. \n", "Fill in the missing code below so that the function will make this prediction. \n", "Hint: You can access the values of each feature for a passenger like a dictionary. For example, `passenger['Sex']` is the sex of the passenger." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predictions_1(data):\n", " \"\"\" Model with one feature: \n", " - Predict a passenger survived if they are female. \"\"\"\n", " \n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Remove the 'pass' statement below \n", " # and write your prediction conditions here\n", " #pass\n", " if passenger['Sex']==\"female\":\n", " predictions.append(1)\n", " else:\n", " predictions.append(0)\n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_1(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2\n", "How accurate would a prediction be that all female passengers survived and the remaining passengers did not survive? \n", "Hint: Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 78.68%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: Replace this text with the prediction accuracy you found above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predictions have an accuracy of 78.68%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "Using just the Sex feature for each passenger, we are able to increase the accuracy of our predictions by a significant margin. Now, let's consider using an additional feature to see if we can further improve our predictions. For example, consider all of the male passengers aboard the RMS Titanic: Can we find a subset of those passengers that had a higher rate of survival? Let's start by looking at the Age of each male, by again using the `survival_stats` function. This time, we'll use a fourth parameter to filter out the data so that only passengers with the Sex** 'male' will be included. \n", "Run the code cell below to plot the survival outcomes of male passengers based on their age." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xae42eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\"])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Examining the survival statistics, the majority of males younger than 10 survived the ship sinking, whereas most males age 10 or older did not survive the ship sinking. Let's continue to build on our previous prediction: If a passenger was female, then we will predict they survive. If a passenger was male and younger than 10, then we will also predict they survive. Otherwise, we will predict they do not survive. \n", "Fill in the missing code below so that the function will make this prediction. \n", "Hint: You can start your implementation of this function using the prediction code you wrote earlier from `predictions_1`." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predictions_2(data):\n", " \"\"\" Model with two features: \n", " - Predict a passenger survived if they are female.\n", " - Predict a passenger survived if they are male and younger than 10. \"\"\"\n", " \n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Remove the 'pass' statement below \n", " # and write your prediction conditions here\n", " #pass\n", " if passenger[\"Sex\"]==\"female\":\n", " predictions.append(1)\n", " #elif passenger[\"Sex\"]==\"male\":\n", " # predictions.append(0)\n", " \n", " elif passenger[\"Sex\"]==\"male\" and passenger[\"Age\"] < 10:\n", " predictions.append(1)\n", " #elif passenger[\"Sex\"]==\"male\" and passenger[\"Age\"] > 10:\n", " # predictions.append(0)\n", " else:\n", " predictions.append(0)\n", " \n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_2(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3\n", "How accurate would a prediction be that all female passengers and all male passengers younger than 10 survived? \n", "Hint: Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 79.35%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Predictions have an accuracy of 79.35%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: Replace this text with the prediction accuracy you found above." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "*\n", "Adding the feature Age as a condition in conjunction with Sex improves the accuracy by a small margin more than with simply using the feature Sex alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions. \n", "Pclass, Sex, Age, SibSp, and Parch are some suggested features to try.\n", "\n", "Use the `survival_stats` function below to to examine various survival statistics. \n", "Hint: To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: `[\"Sex == 'male'\", \"Age < 18\"]`" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "image/png": 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fUfztLYBTMnNqOfnYRgYnSZLap54WPMADFGeV6wsQEVtl5pMNi0qSJHVIPX+T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\n+VeAiJgJ3JqZGRF/BYYCGwC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"text/plain": [ "<matplotlib.figure.Figure at 0xdfc4e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Sex', [ \"Pclass == 3\" ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\", \"Age < 18\"])" ] }, { "cell_type": "code", "execution_count": 284, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xcea4e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'female'\" , \"Embarked == C\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction. \n", "Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model. \n", "Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_2`." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "def predictions_3(data):\n", " \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n", " \n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Remove the 'pass' statement below \n", " # and write your prediction conditions here\n", " #pass\n", " #if passenger[\"Sex\"] == \"female\" :\n", " if passenger[\"Sex\"] == \"female\":\n", " if passenger[\"Pclass\"] ==3 :\n", " predictions.append(0)\n", " else: \n", " predictions.append(1)\n", " \n", " else:\n", " if passenger['Age'] < 10 and passenger['Pclass'] in (1, 2):\n", " predictions.append(1)\n", " elif passenger['Age'] < 18 and passenger['Pclass'] == 1:\n", " predictions.append(1)\n", " else:\n", " predictions.append(0)\n", " \n", " \n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_3(data)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4\n", "Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions? \n", "Hint: Run the code cell below to see the accuracy of your predictions." ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 80.13%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer: Replace this text with your answer to the question above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I used my intuition, that women and childerens would be saved. So I started narrowing down the women with lower class. Then I divided the data with age and saw with kids younger than 10 in first and second class, there is a higher precentage of survival. Then I saw with kids younger than 18; and I divided classes. I saw with kids in first class, the precentage of survival increased more than 80%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "After several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic. The technique applied in this project is a manual implementation of a simple machine learning model, the decision tree. A decision tree splits a set of data into smaller and smaller groups (called nodes), by one feature at a time. Each time a subset of the data is split, our predictions become more accurate if each of the resulting subgroups are more homogeneous (contain similar labels) than before. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. [This link](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) provides another introduction into machine learning using a decision tree.\n", "\n", "A decision tree is just one of many models that come from supervised learning. In supervised learning, we attempt to use features of the data to predict or model things with objective outcome labels. That is to say, each of our data points has a known outcome value, such as a categorical, discrete label like `'Survived'`, or a numerical, continuous value like predicting the price of a house.\n", "\n", "### Question 5\n", "Think of a real-world scenario where supervised learning could be applied. What would be the outcome variable that you are trying to predict? Name two features about the data used in this scenario that might be helpful for making the predictions. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Answer: Replace this text with your answer to the question above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One application is gesture or speech recognition. For a trained model, we can add a sign language to TVs, where the model by hearing the sounds of the TV contents, could recognize them and translate them in sign language and be displayed in the corner of the TV. Features could be the pitch of the sounds, so it recognized that this is a human voice and not a sound of wind or something else. Another feature could be if the sound is inside the dictionary alphabets or not. \n", "Another example is drug discovery. We could train the model to differeciate between the deseased and healthy cells and test it on new subjects (rats).\n", "One feature could be the behavior of the healthy cells to the test drug. another feature could be the duration it takes for the cells to react to the drug.\n", "another example is behavioral detection, we can train a model and install it in the car and when the driver is drunk or sleepy, it would warn the driver or turn off the ignition until he/she is sober or not sleepy.\n", "One feature could be that is the driver's heart beat usual or not, another could be is the driver's movement is usual or not. Is he/she rocking left to right like a drunk person or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
luwei0917/awsemmd_script	notebook/Optimization/read_gamma.ipynb	1	3433	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import datetime\n", "import imp\n", "import numpy as np\n", "import fileinput\n", "from itertools import product\n", "import pandas as pd\n", "from scipy.interpolate import griddata\n", "from scipy.interpolate import interp2d\n", "import seaborn as sns\n", "from os import listdir\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.interpolate import griddata\n", "import matplotlib as mpl\n", "# sys.path.insert(0,'..')\n", "# from notebookFunctions import \n", "# from .. import notebookFunctions\n", "from Bio.PDB.Polypeptide import one_to_three\n", "from Bio.PDB.Polypeptide import three_to_one\n", "from Bio.PDB.PDBParser import PDBParser\n", "from pyCodeLib import \n", "from small_script.myFunctions import *\n", "from collections import defaultdict\n", "%matplotlib inline\n", "# plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio\n", "# %matplotlib notebook\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = [16.18033, 10] #golden ratio\n", "plt.rcParams['figure.facecolor'] = 'w'\n", "plt.rcParams['figure.dpi'] = 100\n", "plt.rcParams.update({'font.size': 22})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "gamma = np.loadtxt(\"/Users/weilu/openmmawsem/parameters/gamma.dat\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "res_type_map = {'A': 0, 'C': 4,'D': 3,'E': 6,'F': 13,'G': 7,'H': 8,'I': 9,'K': 11,'L': 10,'M': 12, 'N': 2,'P': 14,'Q': 5,'R': 1,'S': 15,'T': 16,'V': 19,'W': 17,'Y': 18}\n", "res_type_map_letters = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G',\n", " 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']\n", "\n", "import sys\n", "sys.stdout = open(\"/Users/weilu/Research/server/nov_2019/cys_1fs3/setups/1fs3/label_gamma\", \"w+\")\n", "inverse_res_type_map = dict(list(zip(list(range(20)), res_type_map_letters)))\n", "for i in range(20):\n", " for j in range(i, 20):\n", " print(inverse_res_type_map[i], inverse_res_type_map[j])\n", "print(\"\")\n", "for i in range(20):\n", " for j in range(i, 20):\n", " print(inverse_res_type_map[i], inverse_res_type_map[j])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
NathanYee/ThinkBayes2	code/report03.ipynb	1	192257	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Report03 - Nathan Yee\n", "\n", "This notebook contains report03 for computational baysian statistics fall 2016\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "% matplotlib inline\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import math\n", "import numpy as np\n", "\n", "from thinkbayes2 import Pmf, Cdf, Suite, Joint\n", "import thinkplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The sock problem\n", "\n", "Created by Yuzhong Huang\n", "\n", "There are two drawers of socks. The first drawer has 40 white socks and 10 black socks; the second drawer has 20 white socks and 30 black socks. We randomly get 2 socks from a drawer, and it turns out to be a pair(same color) but we don't know the color of these socks. What is the chance that we picked the first drawer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make calculating our likelihood easier, we start by defining a multiply function. The function is written in a functional way primarily for fun." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from functools import reduce\n", "import operator\n", "\n", "def multiply(items):\n", " \"\"\"\n", " multiply takes a list of numbers, multiplies all of them, and returns the result\n", " \n", " Args:\n", " items (list): The list of numbers\n", " \n", " Return:\n", " the items multiplied together\n", " \"\"\"\n", " return reduce(operator.mul, items, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define a drawer suite. This suite will allow us to take n socks up to the least number of socks in a drawer. To make our likelihood function simpler, we ignore the case where we take 11 black socks and that only drawer 2 is possible." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Drawers(Suite):\n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " Likelihood returns the likelihood given a bayesian update \n", " consisting of a particular hypothesis and new data. In the\n", " case of our drawer problem, the probabilities change with the\n", " number of pairs we take (without replacement) so we we start\n", " by defining lists for each color sock in each drawer.\n", " \n", " Args:\n", " data (int): The number of socks we take\n", " hypo (str): The hypothesis we are updating\n", " \n", " Return:\n", " the likelihood for a hypothesis\n", " \"\"\"\n", " \n", " drawer1W = []\n", " drawer1B = []\n", " drawer2W = []\n", " drawer2B = []\n", " for i in range(data):\n", " drawer1W.append(40-i)\n", " drawer1B.append(10-i)\n", " drawer2W.append(20-i)\n", " drawer2B.append(30-i)\n", " \n", " if hypo == 'drawer1':\n", " return multiply(drawer1W)+multiply(drawer1B)\n", " if hypo == 'drawer2':\n", " return multiply(drawer2W)+multiply(drawer2B)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, define our hypotheses and create the drawer Suite." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drawer1 0.5\n", "drawer2 0.5\n" ] } ], "source": [ "hypos = ['drawer1','drawer2']\n", "drawers = Drawers(hypos)\n", "drawers.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, update the drawers by taking two matching socks." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drawer1 0.5689655172413792\n", "drawer2 0.43103448275862066\n" ] } ], "source": [ "drawers.Update(2)\n", "drawers.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems that the drawer with many of a single sock (40 white 10 black) is more likely after the update. To confirm this suspicion, let's restart the problem by taking 5 pairs of socks." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drawer1 0.8064243448858833\n", "drawer2 0.19357565511411665\n" ] } ], "source": [ "hypos = ['drawer1','drawer2']\n", "drawers5 = Drawers(hypos)\n", "drawers5.Update(5)\n", "drawers5.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that after we take 5 pairs of socks, the probability of the socks coming from drawer 1 is 80.6%. We can now conclude that the drawer with a more extreme numbers of socks is more likely be chosen if we are updating with matching color socks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chess-playing twins\n", "\n", "Allen Downey\n", "\n", "Two identical twins are members of my chess club, but they never show up on the same day; in fact, they strictly alternate the days they show up. I can't tell them apart except that one is a better player than the other: Avery beats me 60% of the time and I beat Blake 70% of the time. If I play one twin on Monday and win, and the other twin on Tuesday and lose, which twin did I play on which day?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To solve this problem, we first need to create our hypothesis. In this case, we have: \n", "\n", "hypo1: Avery Monday, Blake Tuesday \n", "hypo2: Blake Monday, Avery Tuesday \n", "\n", "We will abreviate Avery to A and Blake to B." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB 0.5\n", "BA 0.5\n" ] } ], "source": [ "twins = Pmf()\n", "twins['AB'] = 1\n", "twins['BA'] = 1\n", "twins.Normalize()\n", "twins.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we update our hypotheses with us winning the first day. We have a 40% chance of winning against Avery and a 70% chance of winning against Blake." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB 0.36363636363636365\n", "BA 0.6363636363636364\n" ] } ], "source": [ "#win day 1\n", "twins['AB'] = .4\n", "twins['BA'] = .7\n", "twins.Normalize()\n", "twins.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point in time, there is only a 36% chance that we play Avery the first day while a 64% chance that we played Blake the first day.\n", "\n", "However, let's see what happens when we update with a loss." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB 0.5333333333333333\n", "BA 0.4666666666666667\n" ] } ], "source": [ "#lose day 2\n", "twins['AB'] = .6\n", "twins['BA'] = .3\n", "twins.Normalize()\n", "twins.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting. Now there is a 53% chance that we played Avery then Blake and a 47% chance that we played Blake then Avery." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Who saw that movie?\n", "\n", "Nathan Yee\n", "\n", "Every year the MPAA (Motion Picture Association of America) publishes a [report](http://www.mpaa.org/wp-content/uploads/2016/04/MPAA-Theatrical-Market-Statistics-2015_Final.pdf) about theatrical market statistics. Included in the report, are both the gender and the ethnicity share of the top 5 most grossing films. If a randomly selected person in the United States went to Pixar's \"Inside Out\", what is the probability that they are both female and Asian?\n", "\n", "Data:\n", "\n", "\| Gender \| Male (%) \| Female (%) \|\n", "\| :-------------------------- \| :------- \| :---------- \|\n", "\| Furious 7 \| 56 \| 44 \|\n", "\| Inside Out \| 46 \| 54 \|\n", "\| Avengers: Age of Ultron \| 58 \| 42 \|\n", "\| Star Wars: The Force Awakens\| 58 \| 42 \|\n", "\| Jurassic World \| 55 \| 45 \|\n", "\n", "\n", "\| Ethnicity \| Caucasian (%) \| African-American (%) \| Hispanic (%) \| Asian (%) \| Other (%) \|\n", "\| :-------------------------- \| :------------ \| :------------------- \| :----------- \| :-------- \| :-------- \|\n", "\| Furious 7 \| 40 \| 22 \| 25 \| 8 \| 5 \|\n", "\| Inside Out \| 54 \| 15 \| 16 \| 9 \| 5 \|\n", "\| Avengers: Age of Ultron \| 50 \| 16 \| 20 \| 10 \| 5 \|\n", "\| Star Wars: The Force Awakens\| 61 \| 12 \| 15 \| 7 \| 5 \|\n", "\| Jurassic World \| 39 \| 16 \| 19 \| 11 \| 6 \|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are picking a random person in the United States, we can use demographics of the United States as an informed prior.\n", "\n", "\| Demographic \| Caucasian (%) \| African-American (%) \| Hispanic (%) \| Asian (%) \| Other (%) \|\n", "\| :-------------------------- \| :------------ \| :------------------- \| :----------- \| :-------- \| :-------- \|\n", "\| Population United States \| 63.7 \| 12.2 \| 16.3 \| 4.7 \| 3.1 \|\n", "\n", "Note:\n", "Demographic data was gathered from the US Census Bureau. There may be errors within 2% due to rounding. Also note that certian races were combined to fit our previous demographic groupings." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make writing code easier, we will encoude data in a numerical structure. The first item in the tuple corresponds to gender, the second item in the tuple corresponds to ethnicity.\n", "\n", "\| Gender \| Male \| Female \|\n", "\| :-------------------------- \| :--- \| :----- \|\n", "\| Encoding number \| 0 \| 1 \|\n", "\n", "\| Ethnicity \| Caucasian \| African-American \| Hispanic \| Asian \| Other \|\n", "\| :-------------------------- \| :-------- \| :--------------- \| :------- \| :---- \| :---- \|\n", "\| Encoding number \| 0 \| 1 \| 2 \| 3 \| 4 \|\n", "\n", "\n", "Such that a (female, asian) = (1, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first piece of code we write will be our Movie class. This version of Suite will have a special likelihood function that takes in a movie, and returns the probability of the gender and the ethnicity." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Movie(Suite):\n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " Likelihood returns the likelihood given a bayesian update consisting of a particular\n", " hypothesis and data. In this case, we need to calculate the probability of seeing a\n", " gender seeing a movie. Then we calculat the probability that an ethnicity saw a\n", " movie. Finally we multiply the two to calculate the a person of a gender and \n", " ethnicity saw a movie.\n", " \n", " Args:\n", " data (str): The title of the movie\n", " hypo (str): The hypothesis we are updating\n", " \n", " Return:\n", " the likelihood for a hypothesis\n", " \"\"\"\n", " \n", " movie = data \n", " gender = hypo[0]\n", " ethnicity = hypo[1]\n", " \n", " # first calculate update based on gender\n", " movies_gender = {'Furious 7' : {0:56, 1:44},\n", " 'Inside Out' : {0:46, 1:54},\n", " 'Avengers: Age of Ultron' : {0:58, 1:42},\n", " 'Star Wars: The Force Awakens' : {0:58, 1:42},\n", " 'Jurassic World' : {0:55, 1:45}\n", " }\n", " \n", " like_gender = movies_gender[movie][gender]\n", " \n", " # second calculate update based on ethnicity\n", " movies_ethnicity = {'Furious 7' : {0:40, 1:22, 2:25, 3:8 , 4:5},\n", " 'Inside Out' : {0:54, 1:15, 2:16, 3:9 , 4:4},\n", " 'Avengers: Age of Ultron' : {0:50, 1:16, 2:20, 3:10, 4:5},\n", " 'Star Wars: The Force Awakens' : {0:61, 1:12, 2:15, 3:7 , 4:5},\n", " 'Jurassic World' : {0:39, 1:16, 2:19, 3:11, 4:6}\n", " }\n", " \n", " like_ethnicity = movies_ethnicity[movie][ethnicity]\n", " \n", " # multiply the two together and return\n", " return like_gender * like_ethnicity\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we make our hypotheses and input them as tuples into the Movie class." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "genders = range(0,2)\n", "ethnicities = range(0,5)\n", "pairs = [(gender, ethnicity) for gender in genders for ethnicity in ethnicities]\n", "\n", "movie = Movie(pairs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We decided that we are picking a random person in the United states. So, we can use population demographics of the United States as an informed prior. We will assume that the United States is 50% male and 50% female. Population percent is defined in the order which we enumerate ethnicities." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 0) 0.3185\n", "(0, 1) 0.061\n", "(0, 2) 0.0815\n", "(0, 3) 0.0235\n", "(0, 4) 0.015500000000000002\n", "(1, 0) 0.3185\n", "(1, 1) 0.061\n", "(1, 2) 0.0815\n", "(1, 3) 0.0235\n", "(1, 4) 0.015500000000000002\n" ] } ], "source": [ "population_percent = [63.7, 12.2, 16.3, 4.7, 3.1, 63.7, 12.2, 16.3, 4.7, 3.1]\n", "\n", "for i in range(len(population_percent)):\n", " movie[pairs[i]] = population_percent[i]\n", "\n", "movie.Normalize()\n", "movie.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next update with the two movies" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1969.1499999999999" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movie.Update('Inside Out')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 0) 0.4017743696518802\n", "(0, 1) 0.021374704821877456\n", "(0, 2) 0.03046187441281771\n", "(0, 3) 0.004940710458827413\n", "(0, 4) 0.0014483406545971614\n", "(1, 0) 0.4716481730695985\n", "(1, 1) 0.02509204479089962\n", "(1, 2) 0.0357595917020034\n", "(1, 3) 0.005799964451666963\n", "(1, 4) 0.0017002259858314502\n" ] } ], "source": [ "movie.Normalize()\n", "movie.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that a random person has seen Inside Out, the probability that the person is both female and Asian is .58%. Interestingly, when we update our hypotheses with our data, the the chance that the randomly selected person is caucasian goes up to 87%. It seems that our model just increases the chance that the randomly selected person is caucasian after seeing a movie." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Validation: \n", "To make ourselves convinced that model is working properly, what happens if we just look at gender data. We know that 54% of people who saw inside out were female. So, if we sum together the female audience, we should get 54%." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5399999999999999\n" ] } ], "source": [ "total = 0\n", "for pair in pairs:\n", " if pair[0] == 1:\n", " total += movie[pair]\n", " \n", "print(total)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Parking meter theft\n", "\n", "From DASL(http://lib.stat.cmu.edu/DASL/Datafiles/brinkdat.html)\n", "\n", ">The variable CON in the datafile Parking Meter Theft represents monthly parking meter collections by the principle contractor in New York City from May 1977 to March 1981. In addition to contractor collections, the city made collections from a number of \"control\" meters close to City Hall. These are recorded under the varia- ble CITY. From May 1978 to April 1980 the contractor was Brink's. In 1983 the city presented evidence in court that Brink's employees has been stealing parking meter moneys - delivering to the city less than the total collections. The court was satisfied that theft has taken place, but the actual amount of shortage was in question. Assume that there was no theft before or after Brink's tenure and estimate the monthly short- age and its 95% confidence limits.\n", "\n", "So we are asking three questions. What is the probability that that money has been stolen? What is the probability that the variance of the Brink collections is higher. And how much money was stolen?\n", "\n", "This problem is very similar to that of \"Improving Reading Ability\" by Allen Downey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To do this, we want to calculate First we load our data from the csv file." ] }, { "cell_type": "code", "execution_count": 16, "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>TIME</th>\n", " <th>CON</th>\n", " <th>CITY</th>\n", " <th>BRINK</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2224277</td>\n", " <td>6729</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1892672</td>\n", " <td>5751</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1468074</td>\n", " <td>6711</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1618966</td>\n", " <td>7069</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>1509195</td>\n", " <td>7134</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " TIME CON CITY BRINK\n", "0 1 2224277 6729 0\n", "1 2 1892672 5751 0\n", "2 3 1468074 6711 0\n", "3 4 1618966 7069 0\n", "4 5 1509195 7134 0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('parking.csv', skiprows=17, delimiter='\\t')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to normalize the CON (contractor) collections by the amount gathered by the CITY. This will give us a ratio of contractor collections to city collections. If we just use the raw contractor collections, fluctuations throughout the months could mislead us." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['RATIO'] = df['CON'] / df['CITY']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, lets see what the means of the RATIO data compare between the general contractors and BRINK." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 244.681143201\n", "1 229.583858011\n" ] } ], "source": [ "grouped = df.groupby('BRINK')\n", "for name, group in grouped:\n", " print(name, group.RATIO.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that for a dollar gathered by the city, general contractors report 244.7 dollars while BRINK only reports 230 dollars." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will fit the data to a Normal class to compute the likelihood of a sameple from the normal distribution. This is a similar process to what we did in the improved reading ability problem." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from scipy.stats import norm\n", "\n", "class Normal(Suite, Joint):\n", " \n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " \n", " data: sequence of test scores\n", " hypo: mu, sigma\n", " \"\"\"\n", " mu, sigma = hypo\n", " likes = norm.pdf(data, mu, sigma)\n", " return np.prod(likes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to calculate a marginal distribution for both brink and general contractors. To get the marginal distribution of the general contractors, start by generating a bunch of prior distributions for `mu` and `sigma`. These will be generated uniformly." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mus = np.linspace(210, 270, 301)\n", "sigmas = np.linspace(10, 65, 301)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, use itertools.product to enumerate all pairs of mu and sigma." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.1085419397107893e-52" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from itertools import product\n", "\n", "general = Normal(product(mus, sigmas))\n", "data = df[df.BRINK==0].RATIO\n", "general.Update(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will plot the probability of each mu-sigma pair on a contour plot." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { 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AMQQKYZyTaYon8wwvrovoaKNQ0XcoI08B+4ARWgZ+/RnsAMO3CvSCb9BAkCWY\npQkKqbCsaizKGiupwMyt3r+99/ZwVu4FgPC8M7dhh/cvVA6W9+8ABLWdrQJtsLBK2oKEjZMQYZ4l\nOMlSnGQpiIClAchK6jsNKSfnUDfnibNSGuWcmAMX5ilUH2RidJKJ6IDnrdMc80mC51eFWQYcnfL8\n+H5620YhOHQAwKWBl6YbHl7bp9VwBAZPjsAQT+Cf9lkCsXw7it87iIFCzLqIgUIvdeQpTy8K3jrJ\nMMkSfHS5RllzN66Xl28xdI69eFbh+zRRCAwOFAxAgKEVX55imiZYVjVuyhrXZe18AqoFNkapm3u0\no4IAz2dhz722U16c2DPqgkT/OxX6FjpzFAJwsKOegLZj2fXgA/CwVoZ1bPuAMRUCZ5MMp3mKXAgs\nTHsVUrUsBXccAYbE0E7NcXNPMSshPPbjWcA4mSR4+2yClzcNFRkDl3F+h5gforEQOuAQ9Pg3WQ3Y\nEGcsOGyik47AsEfZpSn2AQxRZT4CGGJA0gcMu1BIYXgvKATA0ecbINLr5yhmfGIWuIspfptX7JoK\nyrQjeXyKyB77iliZcGbNr5/muhe8qiWuygo3Ze3oIaWaPC0g+PsRWFAIgSAEgQY07HPo7vPcNwlu\nk/hKwgcHN1ooBgxBWNPL94DBU76+ZdEMrdW0U5YInOcZzicpBAjXZY1FVbvnLIggQBCiqa/vj2iA\npAGPlmVAviUT+gC61kSeEN57MsXNusblsu71Zfh+DluvLhD0WA5ezz60GryfQavBDzsCw45yBIZ4\nghAYtvUttKJ4itvlHYTZ8NHA4FM+5joR8N7FFOtK4sV12VH8fjpgGBSsYrZl+kDg00XOYjDX5lmC\nszyFIMJlUeF1UaFWytE/PiA0PoRGydsy9cY1DRC0ACgCAv6ch+Z607Yxf0MIILG1kjpOaGrH9ekT\nq9SbY1/Zt0HC5m3j+L17HyAADR6zNMHFJMdZnmJtgFbPWKbGYiB4+TTDbH2KyFJMQ+BggqNWRSoI\n711MUdQSrxZVFAxaQIFu2FirIQYYTd28a0EeiFxvhwVyBIauHIGhm8CPOpZGiloLfYDRE38IFGLK\nvGUpAHj/yRSLosarRdW61jkOFH4MEHxF64OA8o4tQBCAM9OzLaTCq3WJq7Lu0EStvFRjddgwHwCk\ngpfeWilogYcPAC3rwW+r4Dn6EgID0E8bNdcbZd2hiKgdz4ZZpakn3bXTCavITVw7Ma9NETVxMiJc\nTHM8neZCK81JAAAgAElEQVRQzLgsKqxqTe0kRC0rwrcafLrJlhm1GMJefkTpJwJ4/2KKSiq8uKkG\nHdq2/pvAwbZ16FdohXnhYfxtrIaxwBDmE0Y/AsMe5dMADC6Kl7bPWmAvjyFQaNXDU5a+pbAoarw2\newjHFL4PEK6uzL2gIJ0CbhR8o8Q1MJzlGS4mGZZ1jRerUo8mQmMZ6PgM6Xr2zRaVPiDYMPut1KoL\nBiEwMGsQsOdWgaZCKy89IQ0QZkKaT+c0Sqf93G072ZnNyqurVHZCG/QOamaM/yaw8I9tPQCtPH3A\niAEEoGcnE1FjUZj7OctSvDXLIYjwcq3b3loLTV7NMeBbE/3gAKADCB0Lg4DPXExRStWyHDbNr/B9\nKhYYrLSAoAUgbcDw49pr2wCDX1774G6B4S629jzKljJEI22VT1/aKBDtVpYPPL71YI/fPZ9gVWrT\n3tXJAooFlwAgdDx2cWKg4PfMQ1CYZwmeTDOsaonvXi9RmFFFdpaxTxVJxc534AOCnWDXAgZrpagu\nGFggILCZ8KZBIE8ImdAb3lSS9WQ3qSfCScWoTfmSGUqhNWrKPhKnuGCUsGgUdGJmVqeCkCWEmfnN\nEr0xTiXZlKtQmXKJGqBoaCVACZ+G0ovaKW5fS4igmJEa34EiQM/t01t5QgFMrP02VY3TPMWzaQ6e\nZHi1LvXIIQAJyDnoE6POJBh6m2kCwC6OBiGG21oTugwGmQ1/4JS1bbsPLtf47JMpzmcprla1uwYC\nvP18Ou8yTFF6L+u4UrfPxXZ8NgmPjPeQ5GgxBLJtcxzCYtjFv9BnLdg4obXQRy/5cdlL0GcthA5j\n29NnAM9OcxCAj66K1iiijqXAnrM3sBh857IFhBatww2FlAjC27MckhkfL9ZYmiGVSnlp0YCB9RWY\naI21EFgNFiQcgNj6KCBPgEkqMDV/RMC6VlhXCutaYVUpFJVqbSvaopFaz2v8y+ePTvJ1jrM6SAPS\nNCNMMz2BbZYJTFJCJVnXq2as7Y5qrgff5KMBqKF7UuEDk7EYRGNpWKBya0IRQRikeTrJ8PZcz1l5\nuS5h/Qw+leRbEJZ68kcu+cNNRRjuWwQmPE0In3s6bZbRoDZlZOP5VkOMUgLQCotZDTpmEw4/bhDP\nP+/zGxwthqMcVMaMdNkaejvAYYI9wDmdpMhSwg9erp2VYMvqWArw/BMdiyG0CJpzoOn9P5nmOMkS\nfLIscFlUzjrw40ij3J2iZ9tjb8qsVRwQLKAkApilAjOjbCvFWJYKr1c1bkqJsuam/h4IMBpqzF73\n280Ps2lC57IVsj1oBMrEhFvncC2BoiZcrrRStEp6ngnM8wQnucBb8xwAsKokVhVr6wraOpDKKH6h\nO+uuQ806TNcVYGGVu7lXggMY61R5VVS4Liq8PZ/ic2czvFpp/4MAQQZvoLYU4PKzeSuj54S9QjpO\naDmY1kQtgY8uC7z/ZIoPXq81hUjt7TnZWiKEiNrdLDFroMcgiaYztR+IOCKzA8gRGG4pBBqlfB+M\n9FXV68V3LoXWQidd+zxN9OqoP3i1aoDAyytGJbXCTDV9C8Eqct9yAIBMCDyb5VjXEt+5XGhFz02P\nH2g7le11y8lbC8HGszSRuw79XZ5kAvNcL1K3KBVerSRuikIDieJBIHAT4jz6S4eZdjGaRTFjXUpN\nywhClginzH3xAcO/anvPEl5PmJpRRGTu81oybgoJYXwFk5RwPklwOknwdppiXSssS4VVraCURycZ\nQGDVOMcTYZQ2WUtKU1AQDVDomwMqAj5ernFdJnj/dIq5TPBqXYK9PZ9V623SKlOB9RwMsm3bUEuu\nFIILs6dMjKJWeHVT4p2zCT68XLcopbD9dHKT70bN7vBkQ7QNiv+BypFKCmSX5ugFhpHBh6CSQhrJ\npQ2Uu2OCPGBorAFuxfFpH/bjB2W9fzHB9arG1bpuOZp1Xm3ayOZplaX0lSe4DQaqDQ7neYon0wwf\nLQtceVaCZKv8jcI3VoFvIUgPIGovHjNQm7JOcoGzSYJMEC7XNV6vayxKpXue7DmvFbfuw95n4y+w\nIKD5eWVGPVn9pJXbuA6Gr2TCOQv6uE0nuXMvnpuPIBrQsBROmhDOJgmezlKc5AmWlcJNIbGqtbWU\nkqGLjMVgqaOGSoJb4dU62f14Nm5KhHdPpjjNUnyyLNwkOUsfCdGmk+yvvUfrfO5QSR5F44e/fzFB\nUSvtbwgonhbFFKWTTI4b6KTYqCUbvq0DuhU9QjuFeYTpjlTSnsU29F7w0j6WB4i929xfzFqwvX13\nbCKeTVMoRrPLWsQScOEWXOD/NnkOgcKzWY5JIvCbV9q5HAOFBhiGQaGxGDRN8WSa4DRPsCgVPr4p\ncbmW2k+hmnuwVoIdGeQDgW5fhpR6iWsLACGo+20nlaaAGNonAmjqh708gTYIhMAQKsUWMFATLxHa\nziWllTwRgwWBiMAMvKxrvFrWSBPCW/MMT2cpngngei2xqBorAvCoJAFDG5k7FdCeaf/lt9OqQZAE\nfLwssMol3juZ4sWqxLKujYUAEJOz1ppf0y7GerCWg08luU/OOqdNez+/LvG5t2ZYFtJtG+o9hNa3\nGtJNYyW0DnyaKUY57Vv2mf0RGD4l0ocDHLlorYU+8Ggptrblj4t5hg8CCskCQVNmAwY+lRTOFmb2\nfk0qBuP9kykkM757vXJAEAMF6zuoVBNmfQjSA4maGXlCeGuaYJIKvF7V+KcvVihqblkHzcxnHV7V\nCpVSYLZKmlBLBSKC8ibOWQC0Q0yFIJcvoM9rya53XdU6ZWqI+tqM5PF9C/pc//qzlUM/g9+7trSU\nUuR8Dop1b9zWiwW5spiBT24qvFzWmOcCz+YpPjfLcV1IXJfSPRv7nJSn+NvggKbewvSmlT6+LmtU\nconPns6QloSrsoq/dC5fnVcCagGu5evZ9r5Nu5vHgloxLpcVnpxkZoKlaVNYGqur1Mf6HQJG8I2Q\nIzDsQbb1MwT9qFvntw/pjFyKXId/3bcETOD5LMOyrFFK9pR9AwRAMIvYKf6uX0Axo2bVshQYjPdO\npljXEj9YrHU8b8RR5QGCZHZDI31AqKQdHtr00t+apZimAs8XFZ4v1g5E3LwGFYyEUgwpNSi4drD3\n4bgkQAigqnWYEECWCqwLiUkuUFYS0tRvOklRVRKSCElCWK9rAMAkTyBEcx46mid5AgCoWIFNHX3f\nQodOMukTYx242ceCXJgFJ2GO7bWqVrheS0xTgXdOM3z2LMfrlcRVUTvLIxOAFOxoo1SwA7eUCYkC\nskRbGIoYgjUQrZjxncsFvnA+hyDg1bpyw2Hjor+gBIACQYDNb49VYbT81bLC55/NkCbkwJZNdq1h\nr54F0Sh9PXy1QSIGMfX6GXwj5BByaBA6AkOPEKG3x3yIsoD9lxdzFO+UTycbboXbj+BsluIHr1Yu\nTgtEuHvMXrzm2ChfCyCepfDeyRSrWuKjxdo5bht6KeZL0HFioFArxpNpgrNJgueLCt99Xeg0srE6\nVAAQHACDBQSp9NwAadLrtmFMhEBVK0cr5amAlBJKEVgxamMZcM5QUkECSNPUhU8nCapKuvMQGLJU\nNE7qTIMKG6vEAZq1aIzit/X3gUB4loylpe1xwvqXBUGwVuLfu1T4ZCHw7mmGL1xM8HJVY1mZocH2\nORtroamztiDImlFCX1PGapLM+M2rBb54foInU+CyqEw2ZGghn2a0Phn7pnmUUvtFbVkTCoTLVYWL\nWYYXN6WXums1PBin8T1V4QgMR+mVGI70gdd8kqCoJGprLbg8PNooyDykkPxeue/YZgbemU9QSIWP\nl0VrKCoDDhSsxVErPYmstvMTAlDIBOH90wyLUuKffLJCaZS5tT5qqZxylcwtRWvzsxZCLTVwWMek\nU/bMmGQWCPQ5Zvp6neg4srZDQwl1rVBWCifzDGlCKEqJRBAWZT8wXN+UnbBEAGmaIE0Jaar3VBCC\nNOApfW8AeZRWYxUID7QT1j17Bly4MOcJAwUUvvu6wEme4LPnOU4nCV4uK0gmuKnkAhBuqzqtvMk6\nDbz7sQq4ZuB71yt88XwOqRjLSuqhrIpAotGRvs+hQyk1r1eLXrLh16saT57lDpQc5RRRwHfhF3io\ncgSGAdnGahikfzZxR8MZ7552Z4n4FwZoJGY9b+FyVXXC/V+goY5iFFJrfoLt+bPCk0kOBvDBzcop\n/maCG1BJBakaX0Jlev2WStLnmqt/a55glgl893WB60JqEDB51bJtITiA8MABaIa3JoKwLvS6S4kg\npKlAWdRgaJCZ5AJKAeu1WZvpNEddK1wWazx7OseCK5SlhJQKRSlRFBKffLLE+dkE52cTXF4VuL4u\n3LPwh646pUrUAQeiZt4CmRE+kzxFnieY5gmICGUlUdcSdnav8Ggje29C6H2rfUoptcfGdyDN0Nf3\nzjK8f5bjxaLGqlYGgDW1BOhNmTLWFk2WeFajgB4iCiCFQMES37te4ovnczdbW78Q8JzX7m4B6NnS\nvtVgX1LF5OY82M9IAViWNU4mKa7Xtbvo4piDwDMCfMpA4ggMnzLZ5LvYhc4SBOSpwKqQXjkBnRSp\nR0gh+UNrLYU0TRKcZAm+c7Vwq53aDxxo00fS6+Vrx7KJwwCB8dnzDOta4Z8+X6OS7EChbvkiGmvB\nWgM+rQTYUUMMQUJbDbUCEq0wbVxmxnJZI88E1mudbl3UqGuFopB4fbXG+dkEeZ7g1asVFmZxwapS\nuDY0RyjhsFQb5pzQZoSRDdNOZlMvWWFlfBVZJjCdpJjmKYQwICGDocesLQVAgVnTSJpmEibcikIK\ngY+uK9wUEl94MsGsJrxcSaPHrc8CjqQhnY2jlcj4IRTrvv1aKnx4s8b7p1P84GblnrfFBfb+rFb3\nlb8r1VPk1g4lEBaFxJN55oauOpuC22kek+y72kdguCe5C0NgX07szjBVtP0E8zzBupKOc++yRk0O\nIVhYCsn/hZfP2/McHxqHcDMk1PcfMBSsFWH8CcEw1ISAd09zvFhU+GRRN9aAAYUmLw0KvuVQS+Xx\n9R7lJBXSRECQpmmyVIAVo6r0xLG6VriuCpzMMzx7aw4hgFevC6xWehjv5WWBq6uioZnQ/LqGDSWw\nDOxvCBLO4SwosBzIlVNVCgtByDKB2TTFfJpqa6u28zQ0GAACLOCBgb7vuukDwILDolT49ZdrfP5i\ngvdOMzxfVnZOGmplwMyOiCIN5pqmMsAAvS4SM2NRS1yXNd6aTvByVTbvFhoqyepyq/TZ+6raFkTb\nX7AuJPLzybAFEAWJR4wcW8oRGDbIXdJJflmblPquzvGhfDdlF6OXACDPBFaV7Cj8Dp2EdpjbbQ3+\nWkZG+TLjYpphUUksKtmikCrljTgylFEt7aikZrG6SjKmKeHd0ww/uCrxelW7uQXWIrBgAKB1bsFB\nGjrJggEAd06kRw7lmUCWClxdl6hrhbPTHEVRoygkVqsaL16uUFcKdqJba9azA0JuAwPaQNG2Fqgd\n1gIG8yeaYx8gACBJyIFFVelRT0kiMJ+lmM0yKKmwLvQoKSm1LyJNCFIREiGc81o/Jz0bXApGmggw\nE779co3PXUzw7kmGj24qKAYy89yzpLk/56g29p8S2nOQCoFaKXyyLPAjFyfIEjIz1pu0wnNlENDy\nNdhmpZ5fJqCsFfKUUNbc8jPctdM5nNzWV3LfmkqHkiMwPDIJ8eV+hra2ZZIKLIvaXBuuTUgxhVaC\n49MJOM1SfOdy4W3JqZWBH89SRspYHG61UqVXAH33NMMPLktcFdL5C5ohqOxAQOfFHVAAAKUUpPSc\nz+b45qZCmUlMpykurwrcLEpIycanoNOxaoOBf+43StRq6JEWMFBoPZgwRyNpP4cQoulxG8ezHsVE\nYBZgVlgsNd00naQ4mWeoaoWylAAEald6m+wn5x8AJDUM/wdXJT5zlrn2r0kPA9X+JO2UFmauhAUI\nwc0ChQQ9S/z5qsRb0xwfLYrmvaJmmY7mnRrfl2cARaWQpwJl2/QJ4nVBYleH9E7KfGSaQ+DEERhG\nyKGGro6mk/bFOx2Av2LWE7Eq2c7YH41kf31praRq/ikPMi4mGa6KCrUbpWSd1l3LwlJMbv4B9DP7\n7FmOj64rXJfSm/RmlL933jiVVQsUspSQpQmubpSzHAC0rIhFJXF1XbpzpcyoJAMAysx10MNG2YGA\nDwT22C6z0KvlgvYCPDBAAAwOFBSEGU5kquZAghlIEoBZOf8BM7BYVlgXNU5PcpzMM6yLGkrBgQMR\ne1SSBgoy4YoAwdrX8uFNhc8JbbF9vNDbbWpAaAO585EYWsku2KcAXJcV3pplyFOBStoRX2YIq5kd\n3QBEe4JbTKyyr6QGBkBuhSpjFfymaA/dkX0EhpEyFhz2SSdtI2G6zvnmonfGjcRQDoPiWQWhgrNW\ng/0jACfGWrCzjPWsZt2r96kkOyzVWg6SNU3wmXPNcb9a1ailMkNYlaOLpHE+23CgmYeglMJsojn3\nF69WqGvlwgHd467rxu+gpNIznY2FoO9TWwcWECxQWKVtlXloRbAySp/bYNqszePRRP4CewwoVg3I\niMZisHGF2YDZHttfaz0kifLoJoFLWSDPE1yc5agloygllNJUUmoWS2LoXj8nZMCboBI9oQ0Q+P5V\niR9+OsHZROC6UOiKt/0oAEENUJDSs6lfryuc5SleLLVTXrnJZdyUCT10FR2QiM9z8Okw/xXdp77e\nl/K/axoJOALDg5bHsHKrGAFksescXPNHI51kCdZS+xUUQmuhTSXZoa6+BXAxTaAY+Pim2RzIX+HU\nn6PQshikVs4nswyrdY3FsvKsA+XAT8rG98CKIaVsQMA5qBtAABlFzQSpJGQtoWTbggDG0Ukd6wBw\n+SdJApEIBwDMDFlLDQAsXL72WJiJCr61kDqNoJV4WQIvX69xcTbBfJpiVeihsMpQSIr0Bjva6c0t\ny0EphiTgN18V+GfemWFdle4ZKrbP1Vt3CZZaJOdYVgCuiwrvzCcA9PsgzHVLJ7Gn0p2PboMCVcyO\nyrrv3vsm/8Jg2r3WpJE7AQYiEgB+BcD3mPn3EdEPA/hLAN4C8H8B+APMXPfn8LhkG4W+Sy/dsQ1j\nrZhtHOg7WCyhkod/PgAKViG4kU7QynuWpliUdRsQ0ACC8pWJBxR2tMrTWYr/78W6NSHNH8rqA4Q/\n2ogAzKYpbpYl1mbhPOt01mAQUEmysRJ8cHA3SfpPSaUnuknVTyU5gNzc+JYussdEBEhA1aplHSSp\nBgpdZ6mBwNSNDF/DzEiQQBpKqDZfYZpa/4EOv7wucHaaYz5NsCokJFkqywCB0mAhCUhYzyFQrJe9\nKCXj45sKz+YZPjbDcpX353cOdJihGe1Mbej9IqZZgmUlm3eLvN/YSzagNb3sm3YN2zkSsq0MObIH\nndy7avw9IcVdWQz/IYD/B8C5Of8FAH+Kmf8KEf0ZAP8OgD97R3XZWfbia9iSTnIgYx94JK2NE6vf\nblZHvJKx/GM9rj5AADwQCEBBofE5TFOB5yvZ9PJhlQab/Y49xYq2tfBkmuDVqsaqUq0JaT6FVNbK\n+RNqo/QFaVC4XpRYr2tnFUipHG3knNGWOgp+rXVguf26qptroePZNYAyyKbaYdHHYukb8zBINL8m\nzB+RJCpNIyVpgjRLtQUhpaOjkiQBCb08h/5NHD2llECSMNLU+iMIV9clzk5zTPPEzVlpVna2y6sK\n1B6gWMvj+aLCs3mKTBBqZpCye043s6OJNC1pQV4Di77fZS0xSRIsSmkmxNn3mjzLoa0Xh6wB2nB9\nrBD8Hn88sw4AbVFmH410aCNnNDAQ0T8H4LcDmNowZv6LI9J9HsDvBfAnAPxhE/y7Afyb5vgvAPga\nHgEwAIfxNQzo/FtJX101X+008wjfw+YhrvYDsR/bNgAa+kYEEUrZrE4KoEMj+RYDAEc5nU0S/JPn\nK9h9HwC4CWfMZrlseFt4ml7+/CTDcq2HmPqWhB2BJGWbJoqBgkgElFQo16ULa41MUqoNBPbmWY0D\nBqAFAhoYhKeZBJgIzHp2s97wR4ArDQhJogHCrv4qIY210P4FDI9v6CAiBSIBIsbNosTF2URP3Ctq\nCJGYtaI0NWMnBAoiCG6cwcwaHC6mKZ4v647l556z8zkZJ7TR9oVUOM3TVjxxC/WYmMmIY6VrXVBc\nO/cEb8rXpbmNxt8jWowCBiL6OQBfhQaGvw7gXwLwfwLYCAwA/jSAPwLgwuT1DMArZvf2fw/AZ7eq\n9adZDKhsSye5pDFjgDS10HJ2DuQbZiEVIzVjwnsjjRQ9hr1JaOmkUBwN5YCCcZIJLCvlVndt0TVo\nA4R/PMkT1LVyS1fo66oFEJYuAuAUvgUFQFsJVVFBmuE61hltRyRBSVNR2QCBu4EIWMSECJpYQdta\n8IABJABmBxBg7X8QEJCQkFIizVJtQZh78kFBuenIAJGAlJ7/wIDFzaLEk4spqspbrputJdc9ts/g\n9Vri/TO9lailBuPPsnvrlVJIhXVwwylB7tgJ4yQ18zHsvbomRqipuzKmx78pzrZzJe7aDzLWYvjX\nAPw4gF9j5p8iovcA/A+bEhHRvwzgI2b+e0T0Vf/S2Ar+/Ne/5o6//JWv4stf+Wpv3LsS+5A2KWR/\npcbIxY7i9IO2mezmx4nVzVoIG+trKuBbAbB1coDk1cX0jqpaIUu6Y8K3AS0bz86IjV33e5mOgnJK\nCJhlAldmDkF3B7fGx2BnM1tnc56leHW5dr6Eupbm14xG8n0EaPsMrJVQFEULKFgZC0FJ81cHwGC1\nommzEBz6pGMxUDdMJMb0SqFEAogEyoxCsnWTtUQ+zQGGc1B3HOGO8WooIcBYDssS81lmHPRNuG9d\n+COMBDFIMhaVwjQlFLKxDELrzz5XZoCJzH7byu3gNlaGomcpYV2paFzywvwswvON5SNcx6ovXn/6\nsfG/9a1v4Je/+Y0tajcsY4FhxcyKiGoiOgfwMYAvjEj3kwB+HxH9XgAzAGcA/ksAF0QkjNXweQDf\n78vgj/3s10ZW8e5lL7TSLvlvYTXs4hfpG/raB1xFrTDJBBZFZLLQlpYDoRs/VJXdGcL6d5oKfFgp\nT7m10zQjk5rzSS6wWldu4xz/19JMPi0EoEUdyVqiKqvOqKQOKNhjHxxsJf1NlF14BCDIjLu3QCAC\ni0EkDUUYaCEdqtNZq6Bcl8gnObQTtz3xzj/XVBogRHNelgqzqaZk/PYEqGHEvK69fRTLUmKSCqyt\nZeV3/xG3FsJn2WqSLVQ1mfgEYJImuF7V3QiDqSP5UdvKGFOb2FaeQ+WPwcOw0/wnf/7rI2rSL4Pb\nYXjyK0T0BMB/Cz2K6FcB/N1NiZj5Z5j5i8z8WwD8GwD+D2b+twD8HQD/uon2BwH81a1r/iZI5IEP\n9R42fQRjPhIy/9n8XC+ok3TIlm6frs2IEfvhjaljn0NOcfua2oBqfm8zFUBRez1eI45+MOHNftOM\nLEuwNOsXhcqQGa4X7ZzHBgCICEpqB3PLl6BUPyjE/qT5UzUgKxNW9cT1r9de2p54Qfm2bn59y6KE\nXUCuuYdgDkYLUJv2WK0r5FnSAuI2SDRhtu1LqXfL88WfsGfThGKHlg4J9ZyE76UgDWh2OYyYg9d/\nT0f7F4I8biPbWAv79C1YGWUxMPO/bw7/ayL6GwDOmfnv36Lcnwbwl4jojwP4NQC/dIu87lVubTVs\nQSnFIvVZDS0qiqCdCxvoJOeU5iZPwJZDZi/cxnogU3pZsV73n+DWuCfvLoi8W+TAPIdeEdRGkMxI\nvYlHYmQD+2vnWMrJKic3W9qF62uJIMi6mdEcOpyttaBk259gae2qrBytxIrBsm6ooZhyZ9aKnFWc\nQnIadHhUUtun4FFJItHHSdJYJiIxmys0fg0FgMzQ1QQJyqJEPs2hzLak5sE48NNDYu36UApCJFBK\nL/1xepKDi1q3l7UozPLaGpB9oNY7p9mtSAddKYFkiUDNyvXSfcdzs3JrlxJqKVij5OfTFOtK9sdt\n/7h8bdljJEYjxQEIrYMQEDaORDoAKADbjUr6EoAftmmI6Lcy8/86Nj0zfxPAN83xtwH8zq1q+oDl\n0JTSNnkM+jVcHI0TAPR4dg8MXJyBe+rUgfQ485NpiquVnn/QuCHayOfna4HEl9qMaPHf9zFm7Zjv\nI+yVJmav5aZH2/5tWQteL5gEaUvBhrGhjqwytpmEf9Yy8H0JrTSq3eg+QJAA2DqeLRi0h4S6U8vo\niaSdvwUQJY1z2tBjBAcAztKK3LvvaLaUEStutb1iwFsnL/IMthMyt5QL0Vl2JVS+bQzoAoKVk0mC\nxVpGr3UBJm7WW5DwrYdtgOOhy9hRSf8dgC8B+AdoKF8GMBoY3nS5FTh4PXM/qM9qaM1t6LEaQofz\nKKuBQqCw5rQ54qZAywwzdPTFusbFPMP1qnZWQDtOy/wAcWMtkNlQRS91AJRSYZalkGXlXjbfpLcb\n3gPNOjmSm+Mh8Skmuzx2i3ZqUScBKLgHoh227FkVzgqwSrhD63BD68TAwP1a0AiR2vwnEq9BIhYD\nqyZOKEEXlklbgdYBnU2yxkci2vfdpoSaY8UaKP2wIUlFQw/Gxv+75TGovQbULBUopXLKWA9rNscg\n05lociJtAjTxTTmpIEzSBC/K0oXZpnVpbPzQogh7/R4otJr5LqyFnnd929FOfTLWYvjnmfm376XE\nN1h2cfS2M0Bvl6p3JFQADqOK8SJzBwziZbokrjx9YEFqXSk8FYRJKrCqlGeacyQvMtYCm2O9hAKx\nBop1LXGSJVhUtS6D7Ho67BRH8wE0ACGV3imsluY8eu/btFQ7KrOuC4dmjr0xv4fvD0l1557ijw1X\n9Z3S5i5dJawZJhXcyCNrIpBXBiVentT8wds2zbufxhpQnWvj2qf9fEOh4IJeDM8DZ+96AwomrZfv\nLEvxfFn0lNFVsvqwrSaJgPN5ikVRO4s5Fte3FmIO4hhoNPUYUO4xRd/bbpG4G9LsU8Y6n/8uER2B\nYUbiB9cAACAASURBVE8yFtWHYg3Nsux9GSMvbqzQvqUAOj0dG2b+rlY1Lk6yJixW18hHH9Z7WUmc\nZqlXZy2h4tDHzcdb1ArzPP5K2x6lL5Yaaa071CNtZ7bHmzO69E9L6bNxbAThrfg+KPiNx4iYDXHK\nyQee6A1EQGoPIkQDlLYXHxPbxvNMoKjZxNWR7Qxo+w74z5SIMDH+q1rpSW02nu1gdBV6W/Ha91EQ\ncDbVO7fBTxN5V2ODI0Z/txHQ2ZhmD9p+X9YCMN5i+IvQ4PAhgAKm38jMX9pbTd4Q2bczOuzfhnRR\njFKy8cDdpTJ8SgnQCTpWg/3YPOrExhWkV7Ike4GbNSsX6xoXsymmxmoQxFCmlu7jZW0hSJjNVUjb\nHYp1LyUhMhv46BVWb8oKioCEGJIICQE16b0WAE0hJYKQMLCqFM4mCV4uaj3z1q4UqgiKuKOA9L7M\nDe2iwy1IaOvAAZn3tTfKY4cPsW8SG/vEW29ibS04ICA0jgW0LQcmQInmOe8oDZ3jdQy8NiQz6Uxb\ndbrt7B7RPvgTzB4beYLXq9JtHpQIuFVOBRESAwqJORYAnkwy3JS1AxJ3jTwaiciU0RTYUE3698lJ\nhlUpnaVq62UBxoKIveJ3SixtZO8pPLdx+jpiQ9ZCjGbqxO0N6OaxDxkLDL8E4A8A+L/RHVZ+lEDu\nEhx6I20qY0TdrFJhwyGR8U9YishSS37/9vWywltnOX7wau3YD/vx6bqz8zlY0LJgYekkALgqKjyd\nZlhUtQYWMv6IoEcpTB0FgGWl8Pl5jkSgtfVka2czT4HVUiFJBYRot2fX6moDQWu5a18z6IvmQHa/\n8uEGHwcOsRdrqJzYNX/uA4xSF21w6aze2gIIfW0y0TPGmy1Fw6KbPAQRzqcpCqmfv15iu63Q3NpJ\nXlmJIJzlGT68WbeByVO47hHEFLkJzxLC2TTDB6/X7WbxAS9Q4uGjdfcVAYVOnGi6vpN4mvsCBWA8\nlfQJM/81Zv42M/+G/dt7bd4g2aUzuVM5wUthvoNOHWLmre3hkEkYnrfy9D4We9D6gEyey0KCAJxN\n06YHZXti1MRrjr2enRdvWUlMEoFZKlxcQeQpjyZdanqfDGBRKjybZ7r3KUzv1S+/9UeoKoXZNPOA\nw7aXVSwe1eQ1rkhEcI08UABaQ0vdRDTRDm+l8eG1TwxoEDWzm1tl2sqbckRwHmhvX3EnadLyncSW\n925oN53NbJqirGQAvE0xredEwNsnKS5XtXedzJwC/eeDfmLyezbNsahqtzaS8N5bRyN575V9Ru13\nk/D2WY6rlZ7I2FgR3vfjvZ/wev72OxkCAv9bCsPRkyaMs1HuSJ9YGWsx/BoR/U8A/ndoKgkAsM1w\n1U+j2Bdi09yBXqsBcHoiVBu+1RBSSjY+2zoElFLrRQ0pJO/clWrPdWZ6BJGJJ0BuNzVb5oubEu9d\nTLEsJSoZxCGCMHcsBAF6OSGtIEDOKcgEvFpXeHc+xapeglmBhS435WaYa5Zo2yURhIyBq0Li/dMM\nL5YVVNUotEQRlCAo8ysN3VGWEvN5hqKUZqtLu5qo2fksMU5ns6cBoId2plmql8QwexvodYk8B7Q/\nOohZtyH5YeZG7VwDIugt1rgZsRQTkbZBxYIEAOeUFomey2CPbRxhPncT7jbqSfQfK25v5mP2dmg2\n8tFtJgQhzxM9D0TqFVgtCKdCIE0EUiGQmF8AeDLTK6uuasYkIWSJBnSdRrdFZs8NYEwTgYtJjg9v\n1oYa1NW3VFKLUnKdhkYZ2/CzaYI0EXh+Xbp4jWVkAaGhvkIrxL7bzlKh9nnXUuqCQiuKBzp+nE68\ngcBDWAm+jAWGGTQg/B4vjHEcrjpKfCUevW4e8hjKx9P9/eCgM2vH7QOgTYWhAQt2YOGBDPyXVPfG\nasm4WlV493yCH7xau3Bd54ZKspUktssmN7t5JaTnRpzlKZ5OMjxflyDJblXMxPoYlFYUqTC7u0nG\nTSnxmbMc332t+zCKDRiwUWysFZASepP5ulY4mWe4vilbvV4hCErBUS2uR23qmGYpKlW53jL7PXg7\nh4C4PXxU1UYT2HgwQGC6zayaPEJ+y/6GC+i1JrjZcO/PhZOLpxW+Xj8pm2RuNrcDBmsB2CW8vV8h\nCKfzDKuidsAhBHWsNOEVeTpJ8HxZO5+Cby24apH2IVmF/v7JFJdFBQVGamwF1wRo+xasErdvolX0\nWUJ4djbBx5frxpLwFbeN6+rqAUSgfLelkDqg0AKAOzYBtpSxM59/6tAVOUqP8va1eyRoE+j4cazf\noKNvBq0G+NMXAuBpZkObIFe3q1WNaZbgrdMML27K5oNl+1FrBzSxVgYMmHX1daHKKIjnqwKfOZnh\npjKza9mCg84vMYBA1DihX61qfP5igtNJjUWpnJIT5i+httWwLmqcneQo88SMVNJ7EdhjFuz2LAAA\nwXqNpCRNgmUjvCGhLYtB2S4s3CfnrAKhwcFZG8HwU/egBmY9+3MbRNq2FuxsaJEGcyC08s8nuXuW\nwltoz23/6YGBA4WT3G1vmqYNreMoO8ABhR0A8HxRI08FJomhijyAsIvjJaJR9M+mOQQRrsoKKWk/\nkI3XoiI9QHBUkr1FAt67mOByWaGS7PL2m9G3CFrN64FAK74nMWvBpe0GRuUhWgvA+Alu/1Uk+BLA\nrzDzp3Odoy1lDK0E3A4cWmldb79JsBM4AC0aqY9SAuAoI/v7/LrAZ55MUdQKN2s9RFDTR3q0UgIA\ngqEU6RdRQI+kAWB3b6kV8Gpd4nNnM/zG64WN1Ly5NfSsqdaYCIFPFhV+6OkU3361BsrhNgeAopQ4\nO81ba/RbH4Q9bpSOPlBSIcuNf6IkSJJQ5hqLpJncRhQsopfALEmK9lDTnpnPvX4EAwxJhEoK/5IM\nwsSzm/Tks9zdi90WNEkTd56kAmnwdzLPkOcJVuvaheWpQJYIZJHf3Jh2Og4hTQiTlJAn+jw3tBIA\npIZGOstTvD3L8cFijZRMOhKOJrLx3Mgm77gZAkt49zxHrRg369pRSKGj3M6yd+EmLLQMdqGQQkuh\nY4E8UFAAxlNJUwA/BuCvmPN/FcC3Afw4Ef2LzPwfHaJyb6KM6uH3gQPQO1qpZRUgTku1qKcIUFnQ\naIED4ICkdR2E9oxoXSqZWtlNWz65KvDuxRR1zShqzaHrnltDKTXWQ+MvtbRSQtoRnScCnz2b4zev\nl4YK0vESobf9TAQhUQAbC6KUjNerGl+8mODXX66hFOkN65mgEoLiNqXE0OBwcT7B69drYwGQ8Tvo\nPzdyhwFOzMQwqZCmKYQQKFbNBCy934BHIVlaR3nLWtgHYNdPQhKAQzCDuQUKwnNqWy1nrYM2IGh/\nQmMNpFnqltwGG0e651PQWfm+BX08m6aYz/SGRol5AGkikBi/ghANleT/6eekfQipML4Bw5ylfhwi\n5KnA585m+GRZghlIqXHsOsvCo5BaStxT1k9PMuSpwEeXBVoUkm9dhM0aOW45p72wOGVErfMhORQo\njCl7jIwFhi8B+ElmvViL2Y7zlwH8LughrEfZsww6pUe4CkKHtE1mrQObtwOUDYDVGBW+I1uXYt9X\nn0qylFElteXw/pMJPny9xtqtaNkGBWG+PFsFSytZSunlusS78wk+ezLF965XLl4i9BwIrWT0XVlK\n6aqQyFPCDz814FBR1BHNRslVtUJR1nj6ZIpXl2u3LIZSWjmGS2dYva2kXm9odjJDWZTNZj1IwS2+\nn5o/t6aEbM4tOAztLGbBwD48Cwb+uXU0m2NhtvBMkgRprjfpkbUeTWT3hU6SxDmiAW3ZpalwoHB6\nkuNkrkHBzlWw7Z8IwiQTeDLLUJhF8tJEGAAwYOSBQdICiIZKygThh87neL2uUEpl5kP48xaad5Fs\n7x8NONhRamezFGezFB/aoamwo5CMavUsBrIxHGj4XX0XoQUavsSsBi9Zr7XQKz3R7hIUgPHA8BTA\nKTR9BAAnAN5iZklE8XnqR+mVW9FK+kJ0tJL/YrSopR5w0OmC0UqsP5L2Qsj6tVTMYNJKnMGGvDHg\nYI4VGAnITLvSlFFRKbxaVHj/yRQfvF675Y4hdK8cyhBBAki9+xKsSyJSICXwYlninZMJPnc2ww9u\nViaegCCGIAVBAlQzCOYcAjeFwvk0wY8+m+E7L9dYV+QUmxAEUROEsHQRUEtGUUo8vZjiZlFiudLX\npVSOL9fHAlJKCClaG/dkk8xt3uO2/1SJGfFDmJ3MQIJQLpcobhYdGml6cYF0OgMrheXzj8GGcsrm\nJ8jPz0FEWL16BVlVAAmINMX04gIkBMrVGlVZAySchZB4oGDXQmLFSLO0AQQzKilJNDUEwNFESSJw\nca638rSWgqWKAE0Rfe7JBD/6zhzXa4k0IXy8qMBMjioCgEkqHG2Up4RMaOsgJUJmQOSHzudYVhLL\nSiITog0g1LYstO8Dzm9kaaLzWYqnJxk+uizcyDet1NtUkvA0d+Mb6QKEjdZnKfRZHv5BnyXR0eMP\nBBSA8cDwiwD+HhF9A7r6XwbwJ4noBMDf3m+VPj2yM62kL9zecgAQG8raVyfnZjCWg7UW7IbwZKgk\ntzw3rEeAsColBAGfMeBQ1I0vwh/Caukr65AW5M2QJuDjxRrvnkzxudMZPlysdbg/jNUML7WzqQHg\n9VriLBf4Lc+m+PbLNVSha5Yy0PIXJwCgUNWM5bp2fPrryzUA32JoJoPFlqlOLJdPBFIEoTSIzE5n\nWN2sIKsaJxenqGoFVZYOGLJJDkozLF68QDqdYvr0GVavX4OEwOTiCVavXwNEmD59hsWLFwAJzJ4+\nRbkuoKTE9PQEkjVg+qOOrCXAZhiZPY+Bgg8Mk0mKJ+cTKMVYrqW+bvwEqVH4WUp4Os/wDz9aQirg\nMxc53pplWBnfjPUfWEshS7SSt2CQCsJECHz+bIZlJXFZVsiECCyF5te2qx2a2hhhbVDQAxLa/gOf\nSoKnrPsshT4rAUF4lBZ6xKAAjB+V9EtE9NcB/A4T9DPM/ANz/Ef2X61Pj+wLHEKcaPkTesChL39n\nUYxdNsN7MZ1D2voZ0IDEopRgAJ95OsVHrwusKhmhk8yoJGjntF0yw/obGIQPb9Z452SCz5/N8d3r\nJVhpGsItTWSUkfKmEVyXCpVk/Na3Z/juqwIvl5XzObDbOEa4+DUUFqsaeSbwzttzXF2XWCxs+3Qn\n/5NslJZ9IKwYpMyeBno8rlZqaQJZSeTTKdZ282NWyE5OUa7WYJGiKmtMzy+AdIl0NkVd1ZBMro7J\ndKYB2cwlALSVkk9yV14IDC3/gQcMSSKQJOSAgQg4P5tgPsuwWteaqrOO40zgh5/NsSwVLtc1BAnM\n8wSnE4WbUuGmUHhrLnA6SbCulQOGrAUKeiRaSoR5muDzZ3NcFhWuy1pbCh4QNMtkNL31kEYiAp6c\nZDifpfjoskCt2Cl8Bwa2x4/m3H43Mcdy5/kG1kIsrA8UNsoDAwUAoKFVFInox5j5HxHRT8SuM/Ov\nHqZarnxeVSO6xW+IjFlGIwoQG4JcjxjcuchenGZxuHi6Zs04dsfw0vmb4DDg7ZQG19tWDExSPbb8\n+VWBm0K29m12m+aw2WBHMWpzvVaMmhVqs3/zW7McmSB8/2aFVS1RK0alGJVUKCW7VTyLWqctar3q\n69snGa6KGt+/LFHWClVtRkBJhUrq81qx/pV6V7PJRNNBi1WN9bo2+0Or9t7QZlMfAO64tTQ39PLW\nFkyFEHpbUNOYk9kE6+XatdXsZIblYol8koMVo670yK7JdAIpJUB6xrKsNN2UpEkDQBYQDChYOgmA\nObZWQnOcJISTeYbZLIOsFcpa02dZqumj82mGL33+DOtKIRWESjG+/7rAO2c5TvIEr1Y10oRwlieY\nZwLSDC0GEKWPLvIM759M8WpdYllJRx2lJFo+CEcX2QFr1DijifSs5lme4JOrwlmJljYSBhws3QS0\nqSQLCtY/EQJEzIcQWgEhILh8gThwtDKMBQ6DwhgwmGUEtqb8DrLJYvjDAP4QgD/lhfk653fvWvBR\nurKz9RCaAEGQo4kCy8GPZ62E6AxpDiwK27P3LQkAviMa7MXz6wKtqD++XOOd8wmytMbLRdmilQA0\n1BLpoaw1urTS63WFWSbwQ+dzfLgo8GpdQk9OFlDeHAC3/WeineEfXJd4Mk3x296Z4buvC7wO9/4F\nQAZUiLRTerWWEAI4O81xMktxs6iwWtfQO5qRAQeCtPVXAkpocCBFIKk3uKGMWhPl0izVvL+heRI9\nldud2xFPCnq2NWAUO2klL0iAMnLhbs5GElBJnmPZzmC2PoQ0JZzMc8xnKRQDy2XlwCNNNDCkQuDJ\nPEVRK/z6ixVmWYIvPp3g2Umm/UkEnE6EHpBAZuIaE7LUtxgMKAjC+/MpzvIUnywLByA+KPj0UbMU\nSqNs7WS69y4mICJ8dLkG4DmoPfqo2fipAYVwtJF/vA19FIt6G1C4q+Gom2QQGJj5D5nDPwPgbzDz\nFRH9pwB+AsAfP3TlPo1yKHDopG0pcF9xd/NuUUs9tJItz0OZDq0EwC2NUSnGh5cF3j7LMcmm+Ohy\nDZbUTHCDXlnTznOwtJKeENYo/VWlUMoC784nmGcJPrheAaKhkwB4b3kz3+HlqkaekFZu8wwfXJVY\nwvvgqQEGIkJF2kJYrWsIAs7Ocpyd5liuKiwWpVaGCaOuzX0qhpRaKSulIElC1lIrZ4azIIjIWQxE\nhDTVlbV7P6RZqpW0SNxDsv4Cfx0jf6VXazn4FJIdegrADUGdTFJtIUxT1LXCutAjpISxHn7b+6eY\nZQk+vimwqthZhLNMz6S+KRROJgnWNUMy43ySYFmxU9oCGhAAvTVnKgjzTFNHSjE+WKwgINySGD4Q\npB595DuNAa3oJ5nAZ55MsSwkLpdlCwia59aljwiNZWDf2Rh9NDQk9VCWwn1SR51yxmzIQUR/n5m/\nRES/CxoQ/gsAP8vMB92e89NGJYWyM7WkL8QOu6s9B/QSe3FCaskeM/qpJHjXlYlgFYpOb88buunJ\nPMMsT/DR6wJrQ4vYONKjmOyxpZQAOFpJssLFNPv/2zv3WOvS+q5/fuu29z6X97zvvHNjmMLQ0QLG\nyghRMVWYRK1TjUDUkLTRgP7RxBog1qilNkIJRmy8RKOtTVUCVYSW1lKpLaTCzKQmlNKZoVMYCcqU\ny2Tmhbmc9z2XfVmXxz+e51n7WWuvvc8+l/2ey/w+yclZ61nPujx7n/P7rt/v91zoxTHP7A65MSko\n3E0npc0vFG7KjDC0VFSGzSzmUt+GQq7tTJgUhokLFRWVsSEmVxdsyMkuCWpsIjaJyPOK0ch6EfXz\nllVjDemd63v01/oAjPZHZFnmVj+z1/KhojiO61BUnFhBKMvSehcYiklB2rMD6ybjCf1BHxFhPBqT\n9TLSLKmFoD1qOY6FtUFKv2fr5EVF6XpMJc5DEOA1d18iiYUbo5LbNjKu7UwoDdy+kbIzLindC8Ud\nmymTwvZQ2+q7wXHOkPcSqXsvpZFw21qPq/0e2+MJQxeOsiEjl/COAi+ho8eRDyVdXku5sp7x/O6E\nYV7WoaVwSdhm76Punkf2iCwMHzXq1XX8weax9jlBlY6dsPjkBWHVoSSPH7//l4GfM8b8moi8/6g3\nVZbjyN6DPdCZmJ57zbC+TENPtqB7idB2KMmuvzD1JgTswDAx9bhkw7SnEsb2YtreyxlNKl5ypc/2\nfs72Xl53dbV17dtxDJQY+4bZGiFNFbE9ykmjkpdsDNgqCp7eGVlxiF2X1nLqaUQlQERUGnbzimFR\nsZ5FvOr2NZ7ftwIxzG1IyRoNqc+3b8RWHIqyYpJbL2IwSNnc7FEUFZNJyXhcMp6UdiqPqsJs9tnb\nGQOGNEvoDVJG+2MkikjShDiJGQ/HFIUViKyX1eEfMzZ1edpLyXpZ7c1MxnbKkTiOGaylgRDYN/9e\nL6Hfi8mymEiEvCgZ5yXGUHc/TdwbfeIWxYki4Rsv2EQuItx9ucfXt8d2TEMvZlw6gQfWsohJaYU7\nja0x7yVRPaBtkMTctTGgMoand4eANEQhHLgWho1CbyESKy63XuqRRMIz2yNKY6Y9k2rPYGrAF4nC\nsgPX2t1Rz4MonATLegyfBJ4C/gI2jDQEPm+Mec1KH+5F7jF4TiopPc9z6PIauup0JaVrz8GdWDlP\nwHsOYULaX7tyJ3mvwP/2icRI4NvXx4yLapqENoaqanoRQCMh7RPUpam41EvZzBJeGOV8e29EbrzH\nULkEtWFSmHob7BgGg2E9i7nUi7k+Krh2Y8KNcVl7D7aeE4XKegReIKwAWC/Cx/CjSCjyikleWq9i\nXJDnVixExM21RPBj6gS8HzdhP/Npm+PY9+O3al65DznNYvo928U2y2LS1Br8orDPW1Z+/ieXcI5s\nTyM7ctnNbBpH9BLh3lvXeOr6pO6R9N1X++yOrZhs9qxnMCwq1lI7ZYXBdk3tOa8jEbtO853rAwZp\nzPP7E4ZlWecQQlHwoaRaFKJmcjkS2BwkXN3M2B0W9dTd4XQWs+EmaIsCEuQXOkJJB4mCr38eROG4\nHsOywrAGPAA8boz5qoi8BPheY8ynj3rjpR5OhWGGg76uwwpEeM15AuHrdPVaCu+3qNdSFZ4ThJG6\nBGK9F3N53c6f/8Ju7oRhKhBdvZcKF2oqnUgUlV2x7XI/JYsjnh1OeH40oaysQfeCUATCkJemUb6W\n2G6XRWV4fj/nub2csoKimhraUBxKJ05gxaM2xMHbr3+b9yLi6/hwkxeG6Wc3NUb1dcLQUORyAs6Y\nlpWhKqefJT7eH00HiNmRycLdV/pcXc/Ym5Rc25nUo5UBXnG1z6io2BnbLqcbWcTlQcKNcUkvFgap\nnZ7cG2Tf6yiJ7PxItw16bLmV126McyuU0XTOIz/fURgimgkdiR1Ad+tmRhJHPL9ne5HNE4QosNqL\nBMGLga099TD877l5gsb+bJ1WlVP1Em6KMJwWKgzdHMmDOKI4hJvHFQfo7tLaFgew/8iX11Ky1M6j\nv+f607c9htCTKJxAVGbqRYB987zUS0kj4bnhmOeGkxkPAqwwFIE4eKHIYmE9jVnPIvbzihf2C7b3\nc8bO+BelvWfp8hEwzYl4428M7nfgEUgY2nBhEPFGoWk+jPtwDcHnF3y+VZ2MttcJxSCcDhussFxd\nT3nF1QHf3B7x0q0++3nFzrigcLZkI4u4YyO1U2WLHXuw1YspXA+iaVJ5uqZCP464fa3HVs8urrMz\nyYnwU2zQ6HEUToIXTnwX1c8PV9YzttZSbgxzdkbTpT0XicK0V9JiUej0BjpEoR06Cr+Zw4rCzQwb\nqTC8SDkJcWgXHUccwvPmiYOvc5A4gNvG0EsirqzbWTKfvTFmlFcNAWl7EaUTDu9BgDXIhamIRVyC\nOuK54YTv7I8Zu7d/oPY0QlHInSeQOwO/ntoBXWupXdf6xqhge1hwY1TUIgA0RMFOF+7Hdbjt2uMJ\nQ3TmwO91asSChGtQVodimIqB9zK8N5DEEfdc7VMZ+M5uziCLuDJIiUW4PnZjIiK4dS1BgMIYEpc3\nQGySOBy4tpEm3LbWYzNL2ZnYgWpCcxqL9tQWbe8AqBPOm30bNpoUdiqVKsglgDWw80Sh4QmchCjM\nGP853kSz0uz3dpNzCSoML3JuikAEFRYJRPvceR7Dop5L7VyEcQZ1vZewtZYwHJc8tzupwz9hHe85\neCPshaHtRcQC62nCWpqwmxc8NxwzLEoqQx1m8mKQl/a6V/opSRzxnb2xzTlUpp4LqJ/YN+H9vGJ3\nbOf62Rvb1euqQAiM8ypqMSQQyEDoDqItBtO4ersXjxMGqD0I3PZWP2arn/DcfkEcCYNEWM8i8spQ\nVNOZULO4adx7iVulLbLTY9/Sz8jiiJ1JwX5eEBHVE+N57wCYho2EhqcwHXgGa72YqxsZANt7OZNy\nOoK53eNoKgKzyWVbp1sQZt7yDxCEpfMInQU3XxA8KgzKyru1tgXCtOos263V160TrS1xmG6Hx6b1\nwbDZT9kcJOyOCrb3cvKymu3a2spDeC/CexI+JwGGQRqzmaU8tTtkXJR1ncIZ8bw0vHxrbdqvHvjy\nd3a4MbHiUFZ+oSBDLBFZYoWiF9vV4YZFxSivGOYVo7xk5KYfN6b5mYVe0AGzEQDNvv1hWS+J6CcR\ngzSin8YM0ohvbI/rcBJYYeglwuV+zLiw314SQd9Nh1EZP2uqFQgRyCKbpN7MEq70My5lKaOyZHdS\nMC6qGTGwuQQaeQ2RZkLZ/17LYq5sZMSRsL2fM5qUM6Gi0IDXQlDnGprbcLAodOUY2mGjs5xcPggV\nBgVYvTg0zu8QB398oTjU507LwnBT8825JSIm7L0Em/2EzUHK/rjghb3c9mCqpnXCsJQXjIYwmIqq\ncmVuTIT3GMJktgjcu7XB/3l+h7285I/eeolv7Yz49t7YJZ+tEL3y6gZ3uJDX55+6zvOjnAghjXEJ\nWRuPT113UB+u8qGroqqsyATi5j/DMAfhja2P+yeuZ5Hf9mtR5KX9XVSGUWGXQk1kKgxxBGupffMv\nKps3yOJgojogiSGLIzZSKwZbPTvKeW9SMMzLurtoOH327Khl+32Gs6F6D2C9H3PLul2p7fp+zv6k\n7OxthMx6DP4zaS+qo91QLSoMSoNVCET7usv0XuoSiOk2MwLh98PeS+0QUxhe8gZzoxezOUgZ5yXb\n+wX746IhDvZas4Lh518qTdCTqJrul65uP4lYS2Ke2h1SGbvkJAJP742cx2C4a2NAEglffX6PO9Z7\nZHHE117YY1LCPZf73LUxIBL4wtPXeWFocxGxN5KRHYkSzuUTTW1b4/sIBbQtnGVl3NQU07i7X3tZ\nsGti+7d3kWnMH2w+IUsi8sK4LqfCRpZwuZexkblpMvKCUVFiFyyaLprT8A7mhIv8PX0bLw1SttZS\njDHsDIt6JtaGCEiYS2h9Lq0wUdeKa/aMZmipXdb4jDs8iJBlwkbh+Z3HVigEbVQYlLks+mqXbkDk\nJwAAFi9JREFUEYeuopn8Q4eYdOUfujyLGYHoEBDvRXR5EP6elTGsZTGbgwRBuLGfc2OY4+bGmxGG\nMGEdCkMoCt6zWEvtoLBnhxOMMWz1UiIRru3ZAVYCvOLyBk9u77KXlwjCa+7Y4neefoHNNOHeK+s8\n8vR1LvUS7toc8MjT16kwXB1kvPLqOoLwxLM7XNubtESxm2k+wf9u9t33xtQLgzfIccOYTxfYwfjx\nG7CWxnz35XXW0phhUTLMSyYuVBfLVBAa6zSLdIaHwt9gJ07cWsvYHCSM85Ibo6LR9dS2bdZDCL2D\nqZEPQkhzEsvzwkaNPEJdYXp85tzOL2HOd3NGRAFu3shn5RwiMt/I+D/iGYEQZsQhLPLXtO+5pnHQ\nb4rAdAIlU7/d1yxaX9r/c+LCKd6YYbBL8DiBcOWVses6DCcV++MxaRKx2U+4spGxP7a9hfbHdq1l\nY+zDixF7e+Mew4/cdsIQGUMsViCyKKI00HOd7bM4oqgMWRxRGUM/iYlcuwdpTObmMRokES/Z6PPt\n/TFJLIyrko0sZi2zI4Lvu+MSj167Ti+OeOXVDXYn207kFn+nEMwMGhh6/91Mk8/eiE8nnPOexEaa\nsJ4kDNKYtSQmjoT93M5OuzPJeXZ/XCeGbciqKQi+u6l/hrYQ+JxAHNkeRpcGKWks7I4Lnt4e1YP7\nYmfx291Q/XPCdHveoLRFISPCetAUhRmP4GiicNoho1WhwnDB8X+UBwkEBCLR/kM206La8APN2fNw\nptvVF1c3MObt+9YehlijbQKRMWLcMqLea/DbQTgKiIwEYSabcH5hL2d7f8JalnDbZo/oEuyOCnZG\ndiqI0PsIu4u2pwkvjRWA0hg2ssSuSRwLQ1OymaZUWAEwwKUsxQDrqR0Qd9ugRz+JGJbClUFCL4qY\nlBV3bfboJzF7RcEgtesSRBG8dKvP0C8JusBlCEMzUC8Z5MpcDySxI5CzOHIrp8X0oogssQPYJmXl\n5o+q2B7ldmyCM8hpFJFmUUME/DW7Rhh7Map/A+s9u7TmWhYzykt2RgUjP6cRtrusF4/QmM8Tg7Y3\nsEgM5oaSpoX1se5zWhzBO+i67nljpcIgIj3gYSBz9/q4MeYnReQe4KPALcDvAn/TGDM797FyYizy\nHuo6LDfvEjQFwtTHDl5K1FZseRFdM7UGZTiDYtzTGZFGKMmIUJlgSVE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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735af83828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Contour(general, pcolor=True)\n", "thinkplot.Config(xlabel='mu', ylabel='sigma')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, extract the marginal distribution of `mu` from general." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735a9a1198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf_mu0 = general.Marginal(0)\n", "thinkplot.Pdf(pmf_mu0)\n", "thinkplot.Config(xlabel='mu', ylabel='Pmf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the marginal distribution of `sigma` from the general." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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nqGo9UCkimS1cWxZ07a+A7wDW8hdndu8/GvihDxvS1yZ39NFdN11NY1XBtsJS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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f735a6c81d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf_sigma0 = general.Marginal(1)\n", "thinkplot.Pdf(pmf_sigma0)\n", "thinkplot.Config(xlabel='sigma', ylabel='Pmf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will run this again for BRINK and see what the difference is between the group. This will give us insight into whether or not Brink employee's are stealing parking money from the city." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First use the same range of `mus` and `sigmas` calcualte the marginal distributions of brink." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5.1204633776438457e-49" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brink = Normal(product(mus, sigmas))\n", "data = df[df.BRINK==1].RATIO\n", "brink.Update(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the `mus` and `sigmas` on a contour plot to see what is going on." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6CX/w8oSdPOQhigoKr/EJOTzx16IiAqNpGUZEp2Fw2SBLuDMqGE9LSu9D+KbS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\nDKr6LPBs3N4TkQ8BrwK+Fng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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735a985630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Contour(brink, pcolor=True)\n", "thinkplot.Config(xlabel='mu', ylabel='sigma')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the marginal distributions of `mu` from brink." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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M68SSAxx3bdc6+yIeo6pdwAURyYpwbp2zbw5wRkR+JCK7ROQHIjLGqwokov1H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\niaUfsZJYVJXdh2r551/tCC7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735a6a87b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf_mu1 = brink.Marginal(0)\n", "thinkplot.Pdf(pmf_mu1)\n", "thinkplot.Config(xlabel='mu', ylabel='Pmf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the marginal distributions `sigma` from brink" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3nuWXL2yOOSIRuVBKJlkWbpnMmaFkAonFH++66bLU8eO16+no6IwxIhG5UEom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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f735982b630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf_sigma1 = brink.Marginal(1)\n", "thinkplot.Pdf(pmf_sigma1)\n", "thinkplot.Config(xlabel='sigma', ylabel='Pmf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here, we want to compare the two distributions. To do this, we will start by taking the difference between the distributions." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-15.022318455572226" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf_diff = pmf_mu1 - pmf_mu0\n", "pmf_diff.Mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From here we can calculate the probability that money was stolen from the city." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.93875832542936855" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735984c198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cdf_diff = pmf_diff.MakeCdf()\n", "thinkplot.Cdf(cdf_diff)\n", "cdf_diff[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we can calculate that the probability money was stolen from the city is 93.9%" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we want to calculate how much money was stolen from the city. We first need to calculate how much money the city collected during Brink times. Then we can multiply this times our pmf_diff to get a probability distribution of potential stolen money." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(-3594455.9999999991, -1431125.9999999991)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f735a9ba898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "money_city = np.where(df['BRINK']==1, df['CITY'], 0).sum(0)\n", "print((pmf_diff * money_city).CredibleInterval(50))\n", "thinkplot.Pmf(pmf_diff * money_city)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we see a plot of stolen money in millions. We have also calculated a credible interval that tells us that there is a 50% chance that Brink stole between 1.4 to 3.6 million dollars." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In pursuit of more evidence, we find the probability that the standard deviation in the Brink collections is higher than that of the general contractors." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0050527025817088851" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf_sigma1.ProbGreater(pmf_sigma0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there is an extremely low chance that the standard deviation that the Brink collections is higher than the general collections." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we have seemingly overwhelming evidence that Brink stole money from the city. I solved this problem using tools I learned in class. If I were to do this problem again, I would to it without calcualting the variance because the best evidence is to calculate if and how much money was stolen." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final project ideas\n", "\n", "Over the past few weeks, I have dabbled into potential final projects. One of my ideas was to predict the content of tweets based on other words in the tweet. For example, if we were to update with word \"emails\" how does the probability change for the tweet to contain Hillary, Trump, or Hillary and Trump. I mined quite a bit of data over debate night but I'm not convinved that this is a particularly interesting project." ] }, { "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 }	gpl-2.0
marxav/hello-world	keras_regression.ipynb	1	191207	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Example of Linear Regression with Keras and TensorFlow\n", "# Y = 10 * X +100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.12.0\n" ] } ], "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = keras.Sequential([\n", " keras.layers.Flatten(input_shape=(1,)),\n", " keras.layers.Dense(50, activation=tf.nn.relu),\n", " keras.layers.Dense(50, activation=tf.nn.relu),\n", " keras.layers.Dense(50, activation=tf.nn.relu),\n", " keras.layers.Dense(1) \n", "])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001), \n", " loss='mean_squared_error',\n", " metrics=['mean_absolute_error', 'mean_squared_error'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "flatten (Flatten) (None, 1) 0 \n", "_________________________________________________________________\n", "dense (Dense) (None, 50) 100 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 50) 2550 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 50) 2550 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 1) 51 \n", "=================================================================\n", "Total params: 5,251\n", "Trainable params: 5,251\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "output_actions = np.array([])\n", "input_values = np.array([])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "N = 20000\n", "arrayy = np.arange(0, N) \n", "input_values = arrayy[np.indices((N, 1))[0]]\n", "\n", "output_values_0 = np.multiply(input_values, 10)\n", "# add some noise (deliberately)\n", "output_values_1 = np.add(output_values_0, random.randint(0,1))\n", "output_values = np.add(output_values_1, 100)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0],\n", " [ 1],\n", " [ 2],\n", " ...,\n", " [19997],\n", " [19998],\n", " [19999]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input_values" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 101],\n", " [ 111],\n", " [ 121],\n", " ...,\n", " [200071],\n", " [200081],\n", " [200091]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output_values" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1000\n", "20000/20000 [==============================] - 1s 45us/step - loss: 931314750.0369 - mean_absolute_error: 9895.2112 - mean_squared_error: 931314750.0369\n", "Epoch 2/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 1549212.1943 - mean_absolute_error: 897.3197 - mean_squared_error: 1549212.1943\n", "Epoch 3/1000\n", "20000/20000 [==============================] - 1s 32us/step - loss: 1566370.6091 - mean_absolute_error: 900.4903 - mean_squared_error: 1566370.6091\n", "Epoch 4/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 1546851.4102 - mean_absolute_error: 802.4039 - mean_squared_error: 1546851.4102\n", "Epoch 5/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 1543366.0469 - mean_absolute_error: 824.0517 - mean_squared_error: 1543366.0469\n", "Epoch 6/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 1542465.8952 - mean_absolute_error: 904.3179 - mean_squared_error: 1542465.8952\n", "Epoch 7/1000\n", "20000/20000 [==============================] - 1s 32us/step - loss: 1525593.2885 - mean_absolute_error: 829.4882 - mean_squared_error: 1525593.2885\n", "Epoch 8/1000\n", "20000/20000 [==============================] - 1s 32us/step - loss: 1523187.4035 - mean_absolute_error: 799.1227 - mean_squared_error: 1523187.4035\n", "Epoch 9/1000\n", "20000/20000 [==============================] - 1s 36us/step - loss: 1537970.3433 - mean_absolute_error: 899.4831 - mean_squared_error: 1537970.3433\n", "Epoch 10/1000\n", "20000/20000 [==============================] - 1s 39us/step - loss: 1485208.2267 - mean_absolute_error: 846.7556 - mean_squared_error: 1485208.2267\n", "Epoch 11/1000\n", "20000/20000 [==============================] - 1s 32us/step - loss: 1499856.4886 - mean_absolute_error: 805.0920 - mean_squared_error: 1499856.4886\n", "Epoch 12/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 1511609.8851 - mean_absolute_error: 863.5404 - mean_squared_error: 1511609.8851\n", "Epoch 13/1000\n", "20000/20000 [==============================] - 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1s 34us/step - loss: 204310.7306 - mean_absolute_error: 288.3387 - mean_squared_error: 204310.7306\n", "Epoch 965/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 205344.8608 - mean_absolute_error: 306.2038 - mean_squared_error: 205344.8608\n", "Epoch 966/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 207899.5657 - mean_absolute_error: 299.4371 - mean_squared_error: 207899.5657\n", "Epoch 967/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 206294.5683 - mean_absolute_error: 301.0484 - mean_squared_error: 206294.5683\n", "Epoch 968/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 205246.0065 - mean_absolute_error: 280.1738 - mean_squared_error: 205246.0065\n", "Epoch 969/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 207537.8041 - mean_absolute_error: 305.7606 - mean_squared_error: 207537.8041\n", "Epoch 970/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 205199.7559 - mean_absolute_error: 285.4860 - mean_squared_error: 205199.7559\n", "Epoch 971/1000\n", "20000/20000 [==============================] - 1s 42us/step - loss: 203175.2173 - mean_absolute_error: 286.0423 - mean_squared_error: 203175.2173\n", "Epoch 972/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 207726.8764 - mean_absolute_error: 323.2818 - mean_squared_error: 207726.8764\n", "Epoch 973/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 209941.1758 - mean_absolute_error: 313.4742 - mean_squared_error: 209941.1758\n", "Epoch 974/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 204615.8124 - mean_absolute_error: 306.7550 - mean_squared_error: 204615.8124\n", "Epoch 975/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 206912.6118 - mean_absolute_error: 303.2047 - mean_squared_error: 206912.6118\n", "Epoch 976/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 206406.1108 - mean_absolute_error: 313.6651 - mean_squared_error: 206406.1108\n", "Epoch 977/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 204855.9305 - mean_absolute_error: 309.8996 - mean_squared_error: 204855.9305\n", "Epoch 978/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 207568.8980 - mean_absolute_error: 311.9940 - mean_squared_error: 207568.8980\n", "Epoch 979/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 209142.0422 - mean_absolute_error: 306.8441 - mean_squared_error: 209142.0422\n", "Epoch 980/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 205151.5899 - mean_absolute_error: 306.0218 - mean_squared_error: 205151.5899\n", "Epoch 981/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 203682.8573 - mean_absolute_error: 284.7669 - mean_squared_error: 203682.8573\n", "Epoch 982/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 209937.1951 - mean_absolute_error: 324.6341 - mean_squared_error: 209937.1951\n", "Epoch 983/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 208308.1519 - mean_absolute_error: 306.4831 - mean_squared_error: 208308.1519\n", "Epoch 984/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 200092.0834 - mean_absolute_error: 292.9369 - mean_squared_error: 200092.0834\n", "Epoch 985/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 207965.6163 - mean_absolute_error: 302.4512 - mean_squared_error: 207965.6163\n", "Epoch 986/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 208688.9570 - mean_absolute_error: 321.1417 - mean_squared_error: 208688.9570\n", "Epoch 987/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 201839.3496 - mean_absolute_error: 298.4949 - mean_squared_error: 201839.3496\n", "Epoch 988/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 210605.9873 - mean_absolute_error: 322.6236 - mean_squared_error: 210605.9873\n", "Epoch 989/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 206770.8113 - mean_absolute_error: 304.5674 - mean_squared_error: 206770.8113\n", "Epoch 990/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 203298.3943 - mean_absolute_error: 308.4353 - mean_squared_error: 203298.3943\n", "Epoch 991/1000\n", "20000/20000 [==============================] - 1s 35us/step - loss: 208818.8942 - mean_absolute_error: 311.4374 - mean_squared_error: 208818.8942\n", "Epoch 992/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 206877.4570 - mean_absolute_error: 316.2175 - mean_squared_error: 206877.4570\n", "Epoch 993/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 205993.1413 - mean_absolute_error: 323.7939 - mean_squared_error: 205993.1413\n", "Epoch 994/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 206896.7090 - mean_absolute_error: 301.3150 - mean_squared_error: 206896.7090\n", "Epoch 995/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 206854.9065 - mean_absolute_error: 316.9602 - mean_squared_error: 206854.9065\n", "Epoch 996/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 206707.9470 - mean_absolute_error: 339.3015 - mean_squared_error: 206707.9470\n", "Epoch 997/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 207588.7631 - mean_absolute_error: 315.1385 - mean_squared_error: 207588.7631\n", "Epoch 998/1000\n", "20000/20000 [==============================] - 1s 36us/step - loss: 206030.6768 - mean_absolute_error: 313.4762 - mean_squared_error: 206030.6768\n", "Epoch 999/1000\n", "20000/20000 [==============================] - 1s 34us/step - loss: 207152.9517 - mean_absolute_error: 322.0871 - mean_squared_error: 207152.9517\n", "Epoch 1000/1000\n", "20000/20000 [==============================] - 1s 33us/step - loss: 206155.5049 - mean_absolute_error: 295.8557 - mean_squared_error: 206155.5049\n" ] }, { "data": { "text/plain": [ "<tensorflow.python.keras.callbacks.History at 0x7f3ac959aeb8>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(input_values, output_values, epochs=1000)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[111.56281],\n", " [121.54024],\n", " [131.527 ],\n", " [201.39575]], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if prediction follows something like Y = 10 * X +100\n", "# prediction of 1 -> 110, 2 -> 120, 3 -> 130, 10 -> 200\n", "model.predict(np.array([1,2,3,10]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
swartn/weather-with-pandas	.ipynb_checkpoints/pandas_intro-checkpoint.ipynb	1	114378	{ "metadata": { "name": "", "signature": "sha256:53eb410cc446eb137ff6a3c1f9d06a6eb602d14cbad1fb72f5f1c3b8232bf889" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather with Pandas\n", "[email protected]__ \n", "\n", "\n", "## Introduction\n", "This is a basic introduction to pandas using some Environment Canada weather station data. We will look at one year's\n", "worth of daily data from Victoria, BC and we'll cover three basic topics:\n", "\n", "1. Loading and looking at data\n", "1. Basic filtering of data\n", "1. Basic plotting\n", "\n", "To start with, you need to download the data from [here](http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=118&Year=2012&Month=12&day=1&timeframe=2&submit=Download+Data) and save the file as \n", " yyj_daily_2012.csv \n", "You can get similar data for other stations, all of which can be explored on the [EC website](http://climate.weather.gc.ca/index_e.html#access). If you want to download multiple years worth of data checkout\n", "this [wget script](https://github.com/swartn/weather-with-pandas/blob/master/get_ec_stn_data.sh) on my github for an example.\n", "\n", "Some of the material presented here was taken from a [similar tutorial](http://nbviewer.ipython.org/github/swcarpentry/bc/blob/gh-pages/lessons/misc-pandas/an-introduction-to-pandas.ipynb), which documents even more functionality:\n", "\n", "Of course also check out the official docs at http://pandas.pydata.org/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data and viewing its basic properties\n", "Now lets get started. First off lets load up our required modules, including pandas." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "import pandas as pd\n", "from datetime import datetime\n", "import pylab\n", "#%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 87 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets read in the yyj daily weather data from the CSV formatted file into a Pandas dataframe. We will tell pandas to skip the first 24 rows which are unwanted header info. Pandas will automatically read the column names from row 25, and the data below that." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_csv('yyj_daily_2012.csv',skiprows=24)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas dataframes have much of the functionality of numpy arrays, plus much more. Lets look at some properties of the dataframe, like its shape:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 89, "text": [ "(366, 27)" ] } ], "prompt_number": 89 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The shape tells us that the dataframe has 366 rows (one for each day of 2012) and 27 columns. Lets see what the names of those columns are:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 90, "text": [ "Index([u'Date/Time', u'Year', u'Month', u'Day', u'Data Quality', u'Max Temp (\ufffdC)', u'Max Temp Flag', u'Min Temp (\ufffdC)', u'Min Temp Flag', u'Mean Temp (\ufffdC)', u'Mean Temp Flag', u'Heat Deg Days (\ufffdC)', u'Heat Deg Days Flag', u'Cool Deg Days (\ufffdC)', u'Cool Deg Days Flag', u'Total Rain (mm)', u'Total Rain Flag', u'Total Snow (cm)', u'Total Snow Flag', u'Total Precip (mm)', u'Total Precip Flag', u'Snow on Grnd (cm)', u'Snow on Grnd Flag', u'Dir of Max Gust (10s deg)', u'Dir of Max Gust Flag', u'Spd of Max Gust (km/h)', u'Spd of Max Gust Flag'], dtype='object')" ] } ], "prompt_number": 90 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok so now we know what the columns are. The column names arn't particularly easy to read, but we'll get to fixing that soon. First though, lets look at some mean statistics, starting with the mean for each column.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.mean()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 91, "text": [ "Year 2012.000000\n", "Month 6.513661\n", "Day 15.756831\n", "Data Quality NaN\n", "Max Temp (\ufffdC) 14.179508\n", "Max Temp Flag NaN\n", "Min Temp (\ufffdC) 5.578142\n", "Min Temp Flag NaN\n", "Mean Temp (\ufffdC) 9.902186\n", "Mean Temp Flag NaN\n", "Heat Deg Days (\ufffdC) 8.173224\n", "Heat Deg Days Flag NaN\n", "Cool Deg Days (\ufffdC) 0.075410\n", "Cool Deg Days Flag NaN\n", "Total Rain (mm) 2.597541\n", "Total Snow (cm) 0.037705\n", "Total Precip (mm) 2.633607\n", "Snow on Grnd (cm) 0.040984\n", "Dir of Max Gust (10s deg) 19.315789\n", "dtype: float64" ] } ], "prompt_number": 91 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Max, Min, Std, and others are available too. The flag columns have NaN values for the mean, because there is not valid numeric data in those columns. Now lets access some data, lets say the month field" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['Month']" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 92, "text": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", "5 1\n", "6 1\n", "7 1\n", "8 1\n", "9 1\n", "10 1\n", "11 1\n", "12 1\n", "13 1\n", "14 1\n", "...\n", "351 12\n", "352 12\n", "353 12\n", "354 12\n", "355 12\n", "356 12\n", "357 12\n", "358 12\n", "359 12\n", "360 12\n", "361 12\n", "362 12\n", "363 12\n", "364 12\n", "365 12\n", "Name: Month, Length: 366, dtype: int64" ] } ], "prompt_number": 92 }, { "cell_type": "markdown", "metadata": {}, "source": [ "so we got a printout of the row index (left) and month (right). Pandas truncated the data, and only actually printed the first and last 15 columns, to make it easier to read. Now say we only want the first 10 rows, we can use the head function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.Month.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 93, "text": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", "5 1\n", "6 1\n", "7 1\n", "8 1\n", "9 1\n", "Name: Month, dtype: int64" ] } ], "prompt_number": 93 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that here we also used the \"dot syntax\" (df.Month) to access the \"month\" column, compared to before when we used df[\"Month\"]. Both are valid ways to access a column. However because many of our columns names contain spaces and weird characters, using the dot syntax won't work easily for most of our columns. We'll fix that soon. Firstly, lets get focused statistics for the Total precip field using describe:\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df['Total Precip (mm)'].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 94, "text": [ "count 366.000000\n", "mean 2.633607\n", "std 5.164477\n", "min 0.000000\n", "25% 0.000000\n", "50% 0.000000\n", "75% 2.800000\n", "max 29.800000\n", "Name: Total Precip (mm), dtype: float64" ] } ], "prompt_number": 94 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The columns names are a little unweildy, so lets replace them with something better: For each column name, lets strip out the wierd unit and spaces and replace the spaces with underscores and store the result in a list called dcol. This is just a python trick, not really pandas. You could just write a list of names if you wanted." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# use a list comprehension to strip off weird units, and replace spaces with underscores.\n", "dcol = [ re.sub(r'\$.*?\$', '', col).strip(' ').replace(' ', '_') for col in df.columns ]\n", "# now lets replace our old columns headers with dcol in the pandas dataframe. Remember, dcol\n", "# could be any list of names you make up, as long as it is a list containing the correct number strings = # columns.\n", "\n", "df.columns = dcol\n", "\n", "# check that it worked\n", "df.columns" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 95, "text": [ "Index([u'Date/Time', u'Year', u'Month', u'Day', u'Data_Quality', u'Max_Temp', u'Max_Temp_Flag', u'Min_Temp', u'Min_Temp_Flag', u'Mean_Temp', u'Mean_Temp_Flag', u'Heat_Deg_Days', u'Heat_Deg_Days_Flag', u'Cool_Deg_Days', u'Cool_Deg_Days_Flag', u'Total_Rain', u'Total_Rain_Flag', u'Total_Snow', u'Total_Snow_Flag', u'Total_Precip', u'Total_Precip_Flag', u'Snow_on_Grnd', u'Snow_on_Grnd_Flag', u'Dir_of_Max_Gust', u'Dir_of_Max_Gust_Flag', u'Spd_of_Max_Gust', u'Spd_of_Max_Gust_Flag'], dtype='object')" ] } ], "prompt_number": 95 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have fixed the columns names, we can easily use dot sytax to get some more interesting data. Lets look at the maximum temperature field." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.Max_Temp" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 96, "text": [ "0 6.6\n", "1 10.4\n", "2 9.7\n", "3 10.6\n", "4 8.8\n", "5 6.0\n", "6 6.6\n", "7 8.1\n", "8 10.7\n", "9 6.3\n", "10 5.2\n", "11 5.9\n", "12 3.4\n", "13 5.1\n", "14 2.4\n", "...\n", "351 5.8\n", "352 5.5\n", "353 5.8\n", "354 6.9\n", "355 7.9\n", "356 5.6\n", "357 7.7\n", "358 6.4\n", "359 5.9\n", "360 6.5\n", "361 6.6\n", "362 6.6\n", "363 5.5\n", "364 5.4\n", "365 3.7\n", "Name: Max_Temp, Length: 366, dtype: float64" ] } ], "prompt_number": 96 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, now lets look at the \"index\" field that we heard about before. Currently the index is just an integer." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 97, "text": [ "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')" ] } ], "prompt_number": 97 }, { "cell_type": "markdown", "metadata": {}, "source": [ "thats okay, but it turns out to be useful to use the datetime as the index.\n", "let write a function that converts our first column, the string Date/Time into an actual\n", "python datetime object. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define a function to convert strings to dates\n", "def string_to_date(date_string):\n", " return datetime.strptime(date_string, \"%Y-%m-%d\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 98 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the function on every date string and overwrite the column" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.date = df['Date/Time'].apply(string_to_date)\n", "df.date.head()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 99, "text": [ "0 2012-01-01\n", "1 2012-01-02\n", "2 2012-01-03\n", "3 2012-01-04\n", "4 2012-01-05\n", "Name: Date/Time, dtype: datetime64[ns]" ] } ], "prompt_number": 99 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets replace our dataframes index with the date field" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.index = df.date" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 100 }, { "cell_type": "markdown", "metadata": {}, "source": [ "check that it worked" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 101, "text": [ "<class 'pandas.tseries.index.DatetimeIndex'>\n", "[2012-01-01, ..., 2012-12-31]\n", "Length: 366, Freq: None, Timezone: None" ] } ], "prompt_number": 101 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually, we are being inefficient. When we loaded the dataframe,\n", "we could have told pandas to parse the dates and use them. But\n", "the above example shows how you can define a function and apply\n", "it to every line in a dataframe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic filtering\n", "Okay, now that we have the index as datetimes, it allows us to do some neat filetering and plotting. Lets start by doing a seasonal decomposition. Lets find the max temp in summer (JJA or months 6 to 8):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.jja = df[ ( df.index.month >= 6 ) & ( df.index.month <= 8 )]\n", "df.jja.Max_Temp.max()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 102, "text": [ "31.399999999999999" ] } ], "prompt_number": 102 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets find information on 18th July. To do this we will use ix, and pass it the datetime for 18 July." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.ix[ datetime(2012, 6, 18) ]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 103, "text": [ "Date/Time 2012-06-18\n", "Year 2012\n", "Month 6\n", "Day 18\n", "Data_Quality NaN\n", "Max_Temp 18.2\n", "Max_Temp_Flag NaN\n", "Min_Temp 9.5\n", "Min_Temp_Flag NaN\n", "Mean_Temp 13.9\n", "Mean_Temp_Flag NaN\n", "Heat_Deg_Days 4.1\n", "Heat_Deg_Days_Flag NaN\n", "Cool_Deg_Days 0\n", "Cool_Deg_Days_Flag NaN\n", "Total_Rain 1.5\n", "Total_Rain_Flag NaN\n", "Total_Snow 0\n", "Total_Snow_Flag NaN\n", "Total_Precip 1.5\n", "Total_Precip_Flag NaN\n", "Snow_on_Grnd 0\n", "Snow_on_Grnd_Flag NaN\n", "Dir_of_Max_Gust 25\n", "Dir_of_Max_Gust_Flag E\n", "Spd_of_Max_Gust 39\n", "Spd_of_Max_Gust_Flag E\n", "Name: 2012-06-18 00:00:00, dtype: object" ] } ], "prompt_number": 103 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets look only for days that are nice and warm, lets say Mean_Temp > 18. We'll request the index so we get a print out of the datetimes." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df[ df.Mean_Temp > 18 ].index" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 104, "text": [ "<class 'pandas.tseries.index.DatetimeIndex'>\n", "[2012-07-08, ..., 2012-09-08]\n", "Length: 16, Freq: None, Timezone: None" ] } ], "prompt_number": 104 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic plotting\n", "Lets do some basic plotting. First up, lets look at a historgram of our maximum temperature data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.Max_Temp.hist()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 105, "text": [ "<matplotlib.axes.AxesSubplot at 0x710c8d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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jH9HQ0KA61gSDg4Po7+8HAAwMDOCNN95w1D7eqKGhAbHYyG/OxWKxsYPuNE77\n3DRNE1u2bEFFRQW2bt069n6n7edUOZ22n+fOnRurIy5evIj29nb4/X7H7edUOXt6esZuI7Wftv8I\n9jOefvpps6qqyrz77rvNxx9/3Ozp6cnlcpbt3bvX9Hq95ooVK8wXX3xRdZxJnTx50qyurjarq6vN\nyspKR+XctGmTuXjxYnP27NnmsmXLzN///vfmJ598Yq5fv9688847zWAwaPb19amOOSHnK6+84rjP\nzYMHD5oul8usrq42fT6f6fP5zH379jluPyfLuXfvXsft53vvvWf6/X6zurrarKqqMn/+85+bpmk6\nbj+nyml1P/kLQUREmuM/vkxEpDkOciIizXGQExFpjoOciEhzHORERJrjICci0hwHORGR5jjIiYg0\n9/8bh80JrgCC+gAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x70fdf10>" ] } ], "prompt_number": 105 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, that's not bad, but now lets look at a time-series of the data instead:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.Max_Temp.plot()\n", "plt.ylabel('Max. temp. ($^{\\circ}$C)')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 112, "text": [ "<matplotlib.text.Text at 0x8f23090>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SEqrNHxlJHWFTE/2Fhxuu3Svl889FN4ocShaAGgUgd88aGsSVvizBXECennQ90s79t9+o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\nO3bsAEBZQkOGDMHdd9+N4cOHY9q0aR2djy04cuQItFotRo0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"text": [ "<matplotlib.figure.Figure at 0x8c142d0>" ] } ], "prompt_number": 112 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So Pandas has given us a decent plot, with nice labelling on the time-axis. Because pandas uses matplotlib, we can add-to and alter our pandas plots in the same way we would for any matplotlib plot. Note above how I manually added the y-axis label. Now lets resample to a 5 day interval and then plot the result: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "df.Max_Temp.plot(linewidth=1)\n", "df.Max_Temp.resample('5d').plot(color='r',linewidth=3)\n", "plt.ylabel('Max. temp. ($^{\\circ}$C)')\n", "plt.title('Resampled to 5 day means')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 114, "text": [ "<matplotlib.text.Text at 0x9710450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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dB5A49UV6l1fV73/qKfpNLuWAx2zc3eVbvDkLID8fRKWg7+lvmbL2WSgx9NR/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\nAqBVqyKH69WTfmLjEUB57JD6SNYFU3/5z39g8GDkNLHC2NiQHRiil2b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"text": [ "<matplotlib.figure.Figure at 0x872d410>" ] } ], "prompt_number": 114 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
ledeprogram/algorithms	class5/homework/najmabadi_shannon_5_1.ipynb	1	67057	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Assignment 1\n", "\n", "Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do this, find the parameters (lm.params) and use those in your function (i.e. don't generate a model each time)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('fivethirtyeight')\n", "import statsmodels.formula.api as smf\n", "\n", "df = pd.read_csv('heights_weights_genders.csv')" ] }, { "cell_type": "code", "execution_count": 27, "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>Height</th>\n", " <th>Weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>66.367560</td>\n", " <td>161.440357</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>3.847528</td>\n", " <td>32.108439</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>54.263133</td>\n", " <td>64.700127</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>63.505620</td>\n", " <td>135.818051</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>66.318070</td>\n", " <td>161.212928</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>69.174262</td>\n", " <td>187.169525</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>78.998742</td>\n", " <td>269.989699</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Height Weight\n", "count 10000.000000 10000.000000\n", "mean 66.367560 161.440357\n", "std 3.847528 32.108439\n", "min 54.263133 64.700127\n", "25% 63.505620 135.818051\n", "50% 66.318070 161.212928\n", "75% 69.174262 187.169525\n", "max 78.998742 269.989699" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Height 1.000000\n", "Weight 0.924756\n", "Name: Height, dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()['Height'].sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept -350.737192\n", "Height 7.717288\n", "dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = smf.ols(formula=\"Weight~Height\",data=df).fit()\n", "lm.params" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intercept, slope = lm.params" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10840f3c8>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EAoFAUIAQRYFAIBAIChCiKBAIBAJBAUIUBQKBQCAoQIiiQCAQCAQFCFEUCAQC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\ncePG3ZN3btu2jRdee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"text/plain": [ "<matplotlib.figure.Figure at 0x108241550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind=\"scatter\",x=\"Height\",y=\"Weight\")\n", "plt.plot(df[\"Height\"],slopedf[\"Height\"]+intercept,\"-\",color=\"darkgrey\") \n", "\n", "plt.title('Correlation between height and weight')\n", "plt.xlabel('Height (inches)')\n", "plt.ylabel('Weight (lbs)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Function:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Height (in inches): 57\n", "If a person is 57 inches tall, they probably weigh 89.15 pounds.\n" ] } ], "source": [ "height = int(input('Height (in inches): '))\n", "weight = slope height + intercept\n", "print('If a person is ' + str(height) + ' inches tall, they probably weigh ' + str(round(weight,2)) + ' pounds.')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Height (in inches): 57\n", "If a person is 57 inches tall, they probably weigh 89.15 pounds.\n" ] } ], "source": [ "height = int(input('Height (in inches): '))\n", "weight = 7.717288 * height - 350.737192\n", "print('If a person is ' + str(height) + ' inches tall, they probably weigh ' + str(round(weight,2)) + ' pounds.')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
2php/CodeToolKit	9.caffe-ssd/examples/convert_model.ipynb	1	1730512	null	mit
rohinkumar/AngDiameterTest	Diff_DA_LCDM_LC_z_0.57.ipynb	1	193930	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy as sp\n", "from scipy import integrate\n", "from math import \n", "import matplotlib.pyplot as plt\n", "from astropy.io import ascii\n", "import healpix_util as hu" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 79 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define angular diameter distances for Flat LambdaCDM cosmology with $\\Omega_M=0.3$ and $\\Omega_{\\Lambda}=0.7$ starting with definition of $E(z)=\\sqrt{0.3(1+z)^3+0.7}$. As we know Angular diameter distance ($D_A$) is comoving distance ($D_C$) divided by $1+z$. We write $D_A=\\frac{D_C}{1+z}=\\frac{1}{1+z}\\int_0^z\\frac{dz}{E(z)}$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Ez = lambda x: 1/sqrt(0.3(1+x)*3+0.7)\n", "\n", "np.vectorize(Ez)\n", "#Calculate comoving distance of a data point using the Redshift - This definition is based on the cosmology model we take. Here the distance for E-dS universe is considered. Also note that c/H0 ratio is cancelled in the equations and hence not taken.\n", "\n", "def DC_LCDM(z):\n", " return integrate.quad(Ez, 0, z)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 80 }, { "cell_type": "code", "collapsed": false, "input": [ "z=0.57\n", "DMLCDM=DC_LCDM(z)\n", "DMLC=z(z/2+1)/(z+1)\n", "print DMLCDM[0]\n", "print DMLC" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.493380449608\n", "0.46652866242\n" ] } ], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "print DMLCDM[0]4285.714/(1+z)\n", "print DMLC4285.714/(1+z)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1346.80732497\n", "1273.50854773\n" ] } ], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "th,phi=hu.randsphere(1000,'ang',theta_range=[0,0.25], phi_range=[0,0.25] )\n", "thw,phiw=th,phi\n", "#print th,phi\n", "#print len(thw)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "data = open(\"DAvspsi_z_0.57.dat\",'w')\n", "data.write(\"psi \\t lfproper \\t loproper \\t DAf \\t DAo \\n\")\n", "#for i in range(len(thw)):\n", "for thi,phii in zip(thw,phiw):\n", " for i in range(len(thw)-1):\n", " #print \"i j\"\n", " #print thw[i],phiw[i]\n", " #print thi,phii \n", " #print cos(thw[i])cos(thi)+sin(thw[i])sin(thi)cos(phiw[i]-phii)\n", " psi=acos(round(cos(thw[i])cos(thi)+sin(thw[i])sin(thi)cos(phiw[i]-phii),6))\n", " xf1=DMLCDM[0]sin(thw[i])cos(phiw[i])\n", " yf1=DMLCDM[0]sin(thw[i])sin(phiw[i])\n", " zf1=DMLCDM[0]cos(thw[i])\n", " xf2=DMLCDM[0]sin(thi)cos(phii)\n", " yf2=DMLCDM[0]sin(thi)sin(phii)\n", " zf2=DMLCDM[0]cos(thi)\n", " lf=sqrt((xf1-xf2)2+(yf1-yf2)2+(zf1-zf2)*2)\n", " lfproper=lf/(1+z)\n", " xo1=DMLCsin(thw[i])cos(phiw[i])\n", " yo1=DMLCsin(thw[i])sin(phiw[i])\n", " zo1=DMLCcos(thw[i])\n", " xo2=DMLCsin(thi)cos(phii)\n", " yo2=DMLCsin(thi)sin(phii)\n", " zo2=DMLCcos(thi)\n", " lo=sqrt((xo1-xo2)2+(yo1-yo2)2+(zo1-zo2)2) \n", " loproper=lo/(1+z)\n", " if psi!=0: \n", " DAf=lfproper/psi\n", " DAo=loproper/psi\n", " #print psi,DAf,DAo\n", " data.write(\"%f \\t %f \\t %f \\t %f \\t %f \\n\"%(psi,lfproper,loproper,DAf,DAo))\n", " thw=np.delete(thw,0)\n", " phiw=np.delete(phiw,0)\n", " #print thw\n", " #print phiw\n", " #print \"array length\"\n", " #print len(thw)\n", "data.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "DAvspsi=ascii.read(\"DAvspsi_z_0.57.dat\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "print DAvspsi" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " psi lfproper loproper DAf DAo \n", "-------- -------- -------- -------- --------\n", "0.201572 0.063238 0.059796 0.313724 0.29665\n", "0.049523 0.015558 0.014711 0.314162 0.297064\n", "0.106972 0.033601 0.031773 0.314115 0.29702\n", "0.164783 0.051724 0.048909 0.313895 0.296811\n", "0.187748 0.058914 0.055707 0.313791 0.296713\n", "0.026982 0.008477 0.008015 0.314162 0.297064\n", "0.027569 0.008666 0.008195 0.314346 0.297238\n", "0.149652 0.046985 0.044428 0.31396 0.296873\n", "0.021495 0.006759 0.006392 0.314474 0.297359\n", "0.101414 0.031855 0.030121 0.314107 0.297012\n", " ... ... ... ... ...\n", "0.032682 0.01027 0.009711 0.314253 0.29715\n", "0.069785 0.021924 0.020731 0.314168 0.29707\n", "0.040917 0.012859 0.012159 0.31426 0.297157\n", "0.037605 0.011813 0.01117 0.314135 0.297039\n", "0.047733 0.014996 0.01418 0.314166 0.297068\n", "0.094162 0.029581 0.027971 0.314151 0.297054\n", "0.085887 0.026983 0.025514 0.314169 0.29707\n", "0.113516 0.035653 0.033713 0.314082 0.296988\n", "0.008832 0.002774 0.002623 0.314041 0.296949\n", "0.027786 0.008727 0.008252 0.314068 0.296975\n", "0.035555 0.011171 0.010563 0.314186 0.297087\n", "Length = 498297 rows\n" ] } ], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "DAvspsi.sort('psi')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "xint=DAvspsi['psi']\n", "yint=1408np.ones(len(xint))\n", "#print xint" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "idx = np.argwhere(np.isclose(yint, DAvspsi['DAf']4285.714 , atol=0.05)).reshape(-1)\n", "print idx\n", "print xint[idx]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1699]\n", " psi \n", "--------\n", "0.003162\n" ] } ], "prompt_number": 91 }, { "cell_type": "code", "collapsed": false, "input": [ "print DAvspsi['DAo'][idx]4285.714" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " DAo \n", "-------------\n", "1331.36991124\n" ] } ], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(xint,yint,label=\"DA=1408\")\n", "plt.plot(xint[idx], yint[idx], 'ro')\n", "#plt.plot(xint[idx]np.ones(700),range(800,1500),'c',label=\"theta=inters\")\n", "plt.plot(xint[idx], DAvspsi['DAo'][idx]4285.714, 'ro')\n", "plt.plot(DAvspsi['psi'],DAvspsi['DAf']4285.714,'g',label=\"DAf\")\n", "plt.plot(DAvspsi['psi'],DAvspsi['DAo']4285.714,'r',label=\"DAo\")\n", "plt.legend(frameon=0, loc='upper right')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f6079f51b50>" ] } ], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0
DavidBrear/sklearn-cookbook	Chapter 3/3.8 Using KMeans for Outlier Detection.ipynb	1	61538	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Using KMeans to find outliers in a cluster of points.\n", "# Finding outliers means finding the centroids and then looking\n", "# for elements by their distance from the centroids" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import make_blobs\n", "import matplotlib.pyplot as plt\n", "X, label = make_blobs(100, centers = 1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# a cluster with one center is similar to an SVM with one class." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import KMeans" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "kmeans = KMeans(n_clusters=1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=1, n_init=10,\n", " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kmeans.fit(X)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x108b4cb10>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108ac0790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# looking at the plot\n", "f, ax = plt.subplots(figsize=(7,5))\n", "ax.set_title('Blob')\n", "ax.scatter(X[:, 0], X[:, 1], label='Points')\n", "ax.scatter(kmeans.cluster_centers_[:, 0],\n", " kmeans.cluster_centers_[:, 1], label='Centroid',\n", " color='r')\n", "ax.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# identify the 5 closest points\n", "distances = kmeans.transform(X)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# argsort returns an array of indexes which will sort the array\n", "# in ascending order. Reverse it with [::-1]\n", "sorted_idx = np.argsort(distances.ravel())[::-1][:5]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x108d3da10>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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STCA7VsyhC45VczeEtjOh5/f99/ey9zkB5aysP5ruvqO9i5J6Sj4XrYqFPZAO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\nVnRMnSZp++w7MLA8IenoouP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"text/plain": [ "<matplotlib.figure.Figure at 0x108d11790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(7,5))\n", "ax.set_title('Single Cluster')\n", "ax.scatter(X[:, 0], X[:, 1], label='Points')\n", "ax.scatter(kmeans.cluster_centers_[:, 0],\n", " kmeans.cluster_centers_[:, 1],\n", " label='Centroid', color='r')\n", "ax.scatter(X[sorted_idx][:, 0],\n", " X[sorted_idx][:, 1],\n", " label='Extreme Value', edgecolors='g',\n", " facecolors='none', s=100)\n", "ax.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# simulating removing these outliers\n", "new_X = np.delete(X, sorted_idx, axis=0)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=1, n_init=10,\n", " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this causes the centroids to move slightly\n", "new_kmeans = KMeans(n_clusters=1)\n", "new_kmeans.fit(new_X)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x108fce790>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5TCsB6ADg7FoLIuLYVr7UBtNwDvDYmeCwciBfPmtcRewhHk5ocOWKR6n8WtLn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\nupab4MzMzNphzCMh+AbVFSR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"text/plain": [ "<matplotlib.figure.Figure at 0x108e42210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(7,5))\n", "ax.set_title(\"Extreme Values Removed\")\n", "ax.scatter(new_X[:, 0], new_X[:, 1], label='Pruned Points')\n", "ax.scatter(kmeans.cluster_centers_[:, 0],\n", " kmeans.cluster_centers_[:, 1],\n", " label='Old Centroid',\n", " color='r', s=80, alpha=.5)\n", "ax.scatter(new_kmeans.cluster_centers_[:, 0],\n", " new_kmeans.cluster_centers_[:, 1],\n", " label='New Centroid',\n", " color='m', s=80, alpha=.5)\n", "ax.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# There is a fundamental connection between Gaussian distribution\n", "# and the KMeans clustering. Let's create an empirical Gaussian\n", "# based off the centroid and sample covariance matrix and look\n", "# at the probability of each point that we removed.\n", "# this will show that the points we removed were the least likely\n", "# to occur." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "emp_dist = stats.multivariate_normal(kmeans.cluster_centers_.ravel())\n", "lowest_prob_idx = np.argsort(emp_dist.pdf(X))[:5]\n", "np.all(X[sorted_idx] == X[lowest_prob_idx])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5.92830283 6.32759144]]\n", "[ 5.92830283 6.32759144]\n" ] } ], "source": [ "print kmeans.cluster_centers_\n", "print kmeans.cluster_centers_.ravel()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on multivariate_normal_gen in module scipy.stats._multivariate object:\n", "\n", "class multivariate_normal_gen(__builtin__.object)\n", " \| A multivariate normal random variable.\n", " \| \n", " \| The `mean` keyword specifies the mean. The `cov` keyword specifies the\n", " \| covariance matrix.\n", " \| \n", " \| Methods\n", " \| -------\n", " \| pdf(x, mean=None, cov=1, allow_singular=False)\n", " \| Probability density function.\n", " \| logpdf(x, mean=None, cov=1, allow_singular=False)\n", " \| Log of the probability density function.\n", " \| rvs(mean=None, cov=1, allow_singular=False, size=1)\n", " \| Draw random samples from a multivariate normal distribution.\n", " \| entropy()\n", " \| Compute the differential entropy of the multivariate normal.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| x : array_like\n", " \| Quantiles, with the last axis of `x` denoting the components.\n", " \| %(_doc_default_callparams)s\n", " \| \n", " \| Alternatively, the object may be called (as a function) to fix the mean\n", " \| and covariance parameters, returning a \"frozen\" multivariate normal\n", " \| random variable:\n", " \| \n", " \| rv = multivariate_normal(mean=None, cov=1, allow_singular=False)\n", " \| - Frozen object with the same methods but holding the given\n", " \| mean and covariance fixed.\n", " \| \n", " \| Notes\n", " \| -----\n", " \| %(_doc_callparams_note)s\n", " \| \n", " \| The covariance matrix `cov` must be a (symmetric) positive\n", " \| semi-definite matrix. The determinant and inverse of `cov` are computed\n", " \| as the pseudo-determinant and pseudo-inverse, respectively, so\n", " \| that `cov` does not need to have full rank.\n", " \| \n", " \| The probability density function for `multivariate_normal` is\n", " \| \n", " \| .. math::\n", " \| \n", " \| f(x) = \\frac{1}{\\sqrt{(2 \\pi)^k \\det \\Sigma}} \\exp\\left( -\\frac{1}{2} (x - \\mu)^T \\Sigma^{-1} (x - \\mu) \\right),\n", " \| \n", " \| where :math:`\\mu` is the mean, :math:`\\Sigma` the covariance matrix,\n", " \| and :math:`k` is the dimension of the space where :math:`x` takes values.\n", " \| \n", " \| .. versionadded:: 0.14.0\n", " \| \n", " \| Examples\n", " \| --------\n", " \| >>> import matplotlib.pyplot as plt\n", " \| >>> from scipy.stats import multivariate_normal\n", " \| >>> x = np.linspace(0, 5, 10, endpoint=False)\n", " \| >>> y = multivariate_normal.pdf(x, mean=2.5, cov=0.5); y\n", " \| array([ 0.00108914, 0.01033349, 0.05946514, 0.20755375, 0.43939129,\n", " \| 0.56418958, 0.43939129, 0.20755375, 0.05946514, 0.01033349])\n", " \| >>> plt.plot(x, y)\n", " \| \n", " \| The input quantiles can be any shape of array, as long as the last\n", " \| axis labels the components. This allows us for instance to\n", " \| display the frozen pdf for a non-isotropic random variable in 2D as\n", " \| follows:\n", " \| \n", " \| >>> x, y = np.mgrid[-1:1:.01, -1:1:.01]\n", " \| >>> pos = np.empty(x.shape + (2,))\n", " \| >>> pos[:, :, 0] = x; pos[:, :, 1] = y\n", " \| >>> rv = multivariate_normal([0.5, -0.2], [[2.0, 0.3], [0.3, 0.5]])\n", " \| >>> plt.contourf(x, y, rv.pdf(pos))\n", " \| \n", " \| Methods defined here:\n", " \| \n", " \| __call__(self, mean=None, cov=1, allow_singular=False)\n", " \| Create a frozen multivariate normal distribution.\n", " \| \n", " \| See `multivariate_normal_frozen` for more information.\n", " \| \n", " \| __init__(self)\n", " \| \n", " \| entropy(self, mean=None, cov=1)\n", " \| Compute the differential entropy of the multivariate normal.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| %(_doc_default_callparams)s\n", " \| \n", " \| Notes\n", " \| -----\n", " \| %(_doc_callparams_note)s\n", " \| \n", " \| Returns\n", " \| -------\n", " \| h : scalar\n", " \| Entropy of the multivariate normal distribution\n", " \| \n", " \| logpdf(self, x, mean, cov, allow_singular=False)\n", " \| Log of the multivariate normal probability density function.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| x : array_like\n", " \| Quantiles, with the last axis of `x` denoting the components.\n", " \| mean : array_like, optional\n", " \| Mean of the distribution (default zero)\n", " \| cov : array_like, optional\n", " \| Covariance matrix of the distribution (default one)\n", " \| allow_singular : bool, optional\n", " \| Whether to allow a singular covariance matrix. (Default: False)\n", " \| \n", " \| Notes\n", " \| -----\n", " \| Setting the parameter `mean` to `None` is equivalent to having `mean`\n", " \| be the zero-vector. The parameter `cov` can be a scalar, in which case\n", " \| the covariance matrix is the identity times that value, a vector of\n", " \| diagonal entries for the covariance matrix, or a two-dimensional\n", " \| array_like.\n", " \| \n", " \| \n", " \| Returns\n", " \| -------\n", " \| pdf : ndarray\n", " \| Log of the probability density function evaluated at `x`\n", " \| \n", " \| pdf(self, x, mean, cov, allow_singular=False)\n", " \| Multivariate normal probability density function.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| x : array_like\n", " \| Quantiles, with the last axis of `x` denoting the components.\n", " \| mean : array_like, optional\n", " \| Mean of the distribution (default zero)\n", " \| cov : array_like, optional\n", " \| Covariance matrix of the distribution (default one)\n", " \| allow_singular : bool, optional\n", " \| Whether to allow a singular covariance matrix. (Default: False)\n", " \| \n", " \| Notes\n", " \| -----\n", " \| Setting the parameter `mean` to `None` is equivalent to having `mean`\n", " \| be the zero-vector. The parameter `cov` can be a scalar, in which case\n", " \| the covariance matrix is the identity times that value, a vector of\n", " \| diagonal entries for the covariance matrix, or a two-dimensional\n", " \| array_like.\n", " \| \n", " \| \n", " \| Returns\n", " \| -------\n", " \| pdf : ndarray\n", " \| Probability density function evaluated at `x`\n", " \| \n", " \| rvs(self, mean=None, cov=1, size=1)\n", " \| Draw random samples from a multivariate normal distribution.\n", " \| \n", " \| Parameters\n", " \| ----------\n", " \| mean : array_like, optional\n", " \| Mean of the distribution (default zero)\n", " \| cov : array_like, optional\n", " \| Covariance matrix of the distribution (default one)\n", " \| allow_singular : bool, optional\n", " \| Whether to allow a singular covariance matrix. (Default: False)\n", " \| size : integer, optional\n", " \| Number of samples to draw (default 1).\n", " \| \n", " \| Notes\n", " \| -----\n", " \| Setting the parameter `mean` to `None` is equivalent to having `mean`\n", " \| be the zero-vector. The parameter `cov` can be a scalar, in which case\n", " \| the covariance matrix is the identity times that value, a vector of\n", " \| diagonal entries for the covariance matrix, or a two-dimensional\n", " \| array_like.\n", " \| \n", " \| \n", " \| Returns\n", " \| -------\n", " \| rvs : ndarray or scalar\n", " \| Random variates of size (`size`, `N`), where `N` is the\n", " \| dimension of the random variable.\n", " \| \n", " \| ----------------------------------------------------------------------\n", " \| Data descriptors defined here:\n", " \| \n", " \| __dict__\n", " \| dictionary for instance variables (if defined)\n", " \| \n", " \| __weakref__\n", " \| list of weak references to the object (if defined)\n", "\n" ] } ], "source": [ "help(stats.multivariate_normal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
mtmarsh2/vislab	image_style_experiments/pascal results.ipynb	4	377557	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "import re\n", "import pandas as pd\n", "import aphrodite.results\n", "import vislab\n", "import vislab.results\n", "import vislab.datasets" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "label_df = vislab.datasets.pascal.get_clf_df()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normal VOC 20-class results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "results_df, preds_panel = aphrodite.results.load_pred_results(\n", " 'pascal_oct16', vislab.config['paths']['shared_data'] + '/results', force=False)\n", "preds_panel" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Loaded from cache: 38 records\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<class 'pandas.core.panel.Panel'>\n", "Dimensions: 19 (items) x 17125 (major_axis) x 4 (minor_axis)\n", "Items axis: clf pascal_class_aeroplane to clf pascal_class_tvmonitor\n", "Major_axis axis: 2007_000027 to 2012_004331\n", "Minor_axis axis: decaf_fc6 vw to split" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results for a single class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "selected_score = 'decaf_fc6 vw'\n", "pred_prefix = 'clf pascal'\n", "label = 'class_dog'\n", "\n", "pred_df = preds_panel['{}_{}'.format(pred_prefix, label)]\n", "pred_df['pred'] = pred_df[selected_score]\n", "pred_df\n", "full_binary_metrics = vislab.results.binary_metrics(\n", " pred_df, '{} full'.format(label), False, with_plot=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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jIwMFBQUAyiYjk3Hj3EXtmBdNT3IyZswYLFy4EHfu3MGCBQukDksWKiyu3333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\nXIlI1hQKBV555RUsWbLEoPc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"text": [ "<matplotlib.figure.Figure at 0x118ea0290>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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o6Ki3hZXwD18LK2GX0hNaU6ZMgYeHB+7evYu1a9fCxcWF15Pzi4uLIRAIuA6D\nUzQSkY+LvNy8eZPXhZW+K+xRWlyfPHmC2bNno127dhg6dCh++ukn3o5aAeCvv/6iO70S3ujVqxcO\nHDjAy8JK2KW0uLZr1w4A0LlzZxw/fhxpaWkoKSlhPbDWunPnjnQdBH1Fcxfl4yIvxsbG6N27t8bf\nV1X0XWGP0p7rRx99hNLSUmzZsgWLFy/G8+fP8dVXX2kitlZ5/Pgx+vfvz3UYhBA9p7S4jhkzBgBg\nbW2NpKQkAEBqaiqrQbVFdXU1zMzMuA6DU9RHk08TeWEYRqt6/vRdYY/CtkBdXR1+/fVXfPnll4iP\njwdQf9uUkJAQzJ07V2MBtlReXh46dOjAdRhED4lEIsyaNavNU3iIblBYXOfOnYvt27ejpKQE69ev\nx4QJEzBt2jQsXLgQ6enpmoyxRSoqKvT+hoTUR5OPzbw0TLeaPXu2Vo1c6bvCHoVtgZSUFFy7dg0G\nBgaorq5G586dcefOHV6shtWcnJwcdOrUieswiB6heaxEHoUjV2NjYxgY1L9samqK7t27876wNlzk\noO99JH3//IqwkRdtL6z0XWGPwpHrX3/9BS8vL+njO3fuSB8LBAJcu3aN/ehaqLy8HPb29lr1s4xo\nt/j4eK0trIRdCovrrVu3NBmHWpSWlkpHr/qMrheXj428rF+/Xq3H0zT6rrBHYXHl62ItzampqYGT\nkxPXYRBCiGo3KNQWNTU1MDU15ToMztFIRD7KiyzKCXt0rrg2XK5LiLr9+eefKC0t5ToMoiVUKq6V\nlZVascBvcXEx9VxBcxcVaUteRCIRZsyYgezsbDVGxD36rrBHaXGNjY2FUChEaGgogPrbVIwdO5b1\nwFpDIpHo7U0JCXsaT7fi83KbhF+UFte1a9fi0qVLsLGxAQAIhULcvXuX9cBao6CgQDo3V59RH02+\n1uRF2+exKkPfFfYorUTGxsawtrZuuhNPC1hxcTEcHR25DoPoiMzMTJ0urIRdSqtk7969sXfvXojF\nYmRlZWHx4sW8/aLl5uZKR9j6jPpo8rU0L25ubjh8+DBvv+/qQN8V9igtrt988w1u3rwJExMThIeH\nw9LSEl9//bUmYmuxK1eu8HphYqJdDA0N4e7uznUYREspvftrWloafH19NRVPizS++6tEIkHXrl1x\n69YtWFpachwZIUSbqePur0pHrkuXLoWHhwdWr16NGzdutOnN2HT16lUAoMJKWo3WYSXqpLS4JiUl\nITExEfaFNvewAAAcy0lEQVT29pg3bx68vLzw2WefaSK2FsnOzsbgwYO5DoMXqI8mX3N5EYlEeOut\nt/SuwNJ3hT0qnfZ3cHDAkiVL8N1338HHxwfr1q1jO64We/r0KRwcHLgOg2ihhulWixcvphXViNoo\nLa4ZGRlYu3Yt+vTpg0WLFmHQoEHSPief3Lp1i4rrf9HcRfnk5UXX57EqQ98V9ii9QeHMmTMRFhaG\nkydP8noO6d27d/H6669zHQbRIvpeWAm7lBbXlJQUTcTRZnl5eXTp63/RGp3yvZyXP/74Q+8LK31X\n2KOwLTBp0iQAgJeXl8w/3t7eKh08ISEBHh4e6NmzJzZu3CjzelJSEqysrCAUCiEUCpssPKxs35dV\nVFRQW4C0yKpVq/S6sBJ2KZzn2vA32v3792XOoAoEAqWjRIlEAnd3d5w5cwaOjo7o378/9u/fD09P\nT+k2SUlJiIiIQGxsbIv3bYijoKAA1dXVcHV1RXZ2NszMzFqUAEIIeRmr81wbfips374dLi4uTf7Z\nvn270gOnpqbCzc0NLi4uMDY2RlhYGGJiYmS2k/cBVN23QUFBAczNzamwEkJ4Q+lsgVOnTsk8Fx8f\nr/TABQUFcHZ2lj52cnKSmWUgEAggEong4+ODUaNGISMjQ+V9G3v8+DG1BBqhuYuyLl++LP1+kf+h\n7wp7FJ7Q+vbbb7F9+/Ymd30FgLKyMpUm66syX9DX1xd5eXkwNzfHiRMn8MYbb+D27dsqhl5v7dq1\nKCgoQG1tLY4dO4Z+/fpJR90NXxx9e9yAL/Fw/fjevXuYN28e/vWvf8Ha2przePj0+MmTJ7yKh6vH\nIpFIOpC0sLCAOijsuT579gwlJSVYuXIlNm7cKP35bmFhATs7O6UHTklJwdq1a5GQkAAA+OKLL2Bg\nYIAVK1Yo3Kd79+64cuUKbt++rdK+DT3XQ4cOISEhAVFRUap9aqI3aLoVaQ1We64CgQAuLi7Ytm0b\nLCwsYGlpCUtLSwgEAjx9+lTpgf38/JCVlYV79+6hpqYG0dHRMncwePjwofQDpKamgmEY2NraqrRv\nY3l5eejQoYOqn5noCSqshEsK2wLh4eGIi4tDv3795P7Ez8nJaf7ARkaIjIxEaGgoJBIJZs2aBU9P\nT+zYsQMAMG/ePBw+fBjffvstjIyMYG5ujgMHDjS7ryJisRgmJiYqfWB9QHMX69eaeLmwUl5kUU7Y\no3TJQT5raAu89957cHd3x4IFC7gOiRfoD0z9LJQ7d+7Azc1N+hzlRRblRD6NLDmYnJyM8vJyAMDu\n3buxdOlS3L9/v01vqm41NTU0cm2E/rDU/8XbuLAClBd5KCfsUVpc58+fD3Nzc1y9ehURERHo0aMH\npk6dqonYVJafn8/rdQ8IIfpHaXE1MjKCgYEBjh49infeeQeLFi1CWVmZJmJT2YsXL9C+fXuuw+AN\nfZy7WFdXp3QbfcyLMpQT9igtrhYWFtiwYQP27NmD0aNHQyKRoLa2VhOxqayyshK2trZch0E4IhKJ\nMGHCBJUKLCGaorS4RkdHw8TEBDt37kTnzp1RUFCA5cuXayI2lRUWFsLU1JTrMHhDn/poDdOtli9f\nrvSW7/qUF1VRTtijtLg6ODhgypQpKC0txfHjx2Fqasq7nqupqSmsrKy4DoNoGM1jJXymtLgePHgQ\nAQEBOHToEA4ePAh/f38cOnRIE7GprLS0lEaujehDH601hVUf8tJSlBP2KF0se/369bh8+TI6duwI\nACguLkZQUJB0vVeuNZxco+KqXy5dukQjVsJrSosrwzBNLi21s7Pj1R0yy8rKYGpqqrTfpk/0oY/2\n/vvvt3gffchLS1FO2KO0uI4YMQKhoaHS2w5HR0dj5MiRmohNJdnZ2dRvJYTwjtLiumnTJhw5cgQX\nLlwAUL8mwLhx41gPTFUlJSXo0aMH12HwCl3SKB/lRRblhD0Ki+vt27exfPlyZGdnw9vbG5s2bYKT\nk5MmY1NJYWEhOnXqxHUYhEWXLl2Ck5MTXYVHtIrCRuXMmTMxevRo/Prrr/D19cW7776rybhUVlNT\nQ1dnvUSXRiIikQizZ89Gfn5+m4+lS3lRF8oJexSOXMvLyzFnzhwAgIeHB4RCocaCaonCwkIa0eio\nxtOtAgICuA6HkBZRWFyrq6uRlpYGoH7GQFVVFdLS0sAwDAQCAXx9fTUWZHMqKipgZKS0daxXdKGP\nxsYFArqQF3WjnLBHYVXq3LkzPvjgA4WPExMT2Y1MRRKJhEauOub+/ft05RXRegqLa1JSkgbDaL3a\n2loYGxtzHQavaPtIpGvXrjh+/Di6deum1uNqe17YQDlhj9bPvH/w4AG1BXSMQCBQe2ElRNO0vrhK\nJBK69PUldL24fJQXWZQT9mh9cRUIBLC0tOQ6DNIGEomE6xAIUTulxbWurg67d+/GunXrAAC5ublI\nTU1lPTBV1dbWUlvgJdrURxOJRBgzZoxGCqw25UVTKCfsUVpcFy5ciIsXL2Lfvn0AgPbt22PhwoWs\nB6aqmpoaOqGlpRqmW3388ccwNDTkOhxC1Eppcb106RK2b98OMzMzAICtrS2vbvNy584dukLrJdrQ\nR+NioWttyIumUU7Yo7S4tmvXrslPtuLiYt4t79e5c2euQyAtQHcQIPpAaZVcvHgxxo0bh0ePHuHD\nDz/E4MGDsWrVKk3EphJLS0v6SfkSvvfRrl+/zklh5XteuEA5YY+AUWHl61u3buH3338HAAQFBcHT\n05P1wFQhEAhgY2ODGzducB0KIUSHODo6tvmmAEqLa25uLgBI30ggEACov4qGawKBAGZmZsjOzuY6\nFF6h68Xlo7zIopzIp47iqnQO06hRo6QFtbq6Gjk5OXB3d8fNmzfb9MbqQgtlE0L4SGlxffknd1pa\nGrZt28ZaQC1lYmLCdQi8w6eRyMWLF9GpUyde/CXIp7zwBeWEPS0+7e/r64tLly6xEUur2Nrach0C\nUUAkEmHu3Ll4+PAh16EQonFKR65btmyR/nddXR3S0tJ4tcRfu3btuA6Bd/jQR2s83WrgwIGcxtKA\nD3nhG8oJe5QW1/Ly8v9tbGSE0aNHY8KECawG1RI0DYt/aB4rIUqKq0QiwfPnz5uMXvmGLn2VxeVI\npKCgAPPnz+dlYaURmizKCXsUFlexWAwjIyMkJydLb+3CR/b29lyHQBpxdHTEiRMneNU6IoQLCour\nv78/0tLS0LdvX/zjH//ApEmTYG5uDqB+fun48eM1FmRzaLk6WVz30fhaWLnOCx9RTtijsLg2TKCt\nrq6GnZ0dzp492+R1vhRXmi1ACOEjhcW1uLgYERER8PLy0mQ8LUY9V1maHIlo0z3MaIQmi3LCHoXz\nXCUSCcrKylBeXi73H76g2QLcEYlEGDlyJK+WoCSEL5q9tfaaNWs0GUurUHGVpYk+WuPpVtoycqX+\noizKCXv4tTBrK9AtXjSP5rESopzC4nrmzBlNxtFqNHKVxeZIRJsLK43QZFFO2KOwuNrZ2WkyDqIl\nsrOztbKwEqJpWt8W4OvFDVxi875IU6dO1drCSveLkkU5YY/WF1cLCwuuQyCEEBlaX1z5drNEPqA+\nmnyUF1mUE/ZofWWi2QLsEYl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"text": [ "<matplotlib.figure.Figure at 0x1171b5d90>" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results for multi-class." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mc_pred_df = preds_panel.minor_xs('decaf_fc6 vw').join(label_df)\n", "pred_prefix = 'clf pascal'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "full_mc_metrics = vislab.results.multiclass_metrics(\n", " mc_pred_df, pred_prefix, balanced=False,\n", " with_plot=True, with_print=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "------------------------------------------------------------\n", "Classification metrics on the full dataset\n", "top_k_accuracies: [0.627, 0.791, 0.872, 0.919, 0.948, 0.963, 0.972, 0.981, 0.986, 0.99, 0.992, 0.994, 0.995, 0.997, 0.998, 0.998, 0.999, 1.0, 1.0]\n", " precision recall f1-score support\n", "class_aeroplane 0.845722 0.842179 0.843947 716\n", "class_bicycle 0.684800 0.709784 0.697068 603\n", "class_bird 0.824087 0.753713 0.787330 808\n", "class_boat 0.742215 0.812500 0.775769 528\n", "class_bottle 0.528107 0.463636 0.493776 770\n", "class_bus 0.930131 0.488532 0.640602 436\n", "class_car 0.770690 0.478075 0.590099 935\n", "class_cat 0.834171 0.755232 0.792741 1099\n", "class_chair 0.650485 0.191246 0.295588 1051\n", "class_cow 0.591912 0.501558 0.543002 321\n", "class_diningtable 0.154143 0.615385 0.246533 130\n", "class_dog 0.817500 0.569191 0.671113 1149\n", "class_horse 0.574928 0.827801 0.678571 482\n", "class_motorbike 0.708625 0.706977 0.707800 430\n", "class_pottedplant 0.320080 0.626459 0.423684 257\n", "class_sheep 0.594667 0.688272 0.638054 324\n", "class_sofa 0.280967 0.657244 0.393651 283\n", "class_train 0.537217 0.905455 0.674340 550\n", "class_tvmonitor 0.397338 0.749104 0.519255 279\n", "accuracy: 0.627028966012\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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BAQHIyclxbufk5CAwMNBlny1btiA8PBzNmjXDV199heeeew7Lly8vdazo6GjE\nxsYiNjYWCQkJLteHZ2dnu2xbLBaXVx3c3U5JSUF2se1si8W97RI95W1XSa8bj1cV2+xlL3v16LVY\nLEhJSWGvwm2Or+LeLVuKfvqvjw+ymzVDdrNm7m0Xu4KsOo5v8e1K97r7fMeA5z9ajy97vb7XYrE4\nj8HebCQkJDjXd9HR0SiLqX6FkM1mQ8uWLZGcnIwmTZqgU6dOiI+PR6tWrS66/9ChQ/HAAw/gkUce\ncfl7T/8KoaysLFiaNbv8A3j4VwhlZWW5nJSeUJlm9pbPq3ozMmAJCqpcgNUK+PtXeHdvGl+gCsbY\ng+MLGDPGlZFdYmGkmm7jy+/J6lV6TqvM+AIeHWMtx5e9ZfKmXkDTOdhDvWWt+Uy1yAWANWvWIDo6\nGna7HREREYiJicHs2bMBAJGRkS77mmWRy9+TWz5v+p1m7C0ff0ejWpzTqDjdxpfnr3qcg9Vir1re\n1AtoOgd7iFaL3KrARW55D6fXFzh7y+d1vXyCVSbOaVScbuPL81c9zsFqsVctb+oFNJ2DPaSsNZ+p\n7snVVfHrxXXAXrWyMzMrfxAP/uAT7caXvcrp1sxetdirFnvVYq9a7FVPt2az9PoZHUBU7fj5efxV\nbiIiIiIiKsLLlcFLozzBmy4t4aVc5eP4qsU5jYrTbXx5/qrHOVgt9qrlTb2ApnOwh/ByZSIioipg\nrYJbCariGERERHRpXORWAbNce15R7FWLvWqxVz3dmj3Z6+/vDx8fn0r9yS32e1h1wPNBLfaqxV61\n2Kuebs1m6eUil4iIiIiIiKoN3pML3v/jCd50/wTvVyofx1ctzmlqsVctnr/qcQ5Wi71qeVMvoOkc\n7CG8J5f0VhX3r/EeODIKz1/1Kjs+HF8iIqJqhYvcKmCWa88rSrve3NyiV40r88ff33O9uo0ve5XS\n7fwFvHCMOb5lqvTv/vbwiwjajS97lWKvWuxVT7dms/Ty9+QSERHRpVX2d39Xv7uiiIjI5HhPLnj/\njyd43f0/uvV6mNeNr0a9RQ+n4ZymW68HeeX46tbrYd40p2k5vh7GXrV0myMq3XvhQuWukLJaK/z5\nWt2Tm5SUhJCQEAQHB2PKlCmlPp6YmIjQ0FC0a9cO7du3xw8//GBAJREREREREbnw9zfFLUSmWuTa\n7XZERUUhKSkJaWlpiI+PR3p6uss+vXv3xo4dO7Bt2zbMnz8fI0eONKj2b2a59ryi2KsWe9Vir3q6\nNbNXLfYiE7A6AAAgAElEQVSqxV612KsWe9XTrdksvaZa5KampiIoKAgWiwX+/v4IDw9HYmKiyz5X\nXnml8/+fOXMGjRo18nQmERERERERmZSp7sldunQp1q5dizlz5gAAFixYgM2bN2PGjBku+33zzTeI\niYnB4cOH8e2336JTp04uH+c9ueU9nF73I2h5/49uvR7mdeOrUW/Rw2k4p+nW60FeOb669XqYN81p\nWo6vh7FXLd3mCJ3mYG3uya3ooD700ENIT0/HihUr8OSTTyquIiIiIiIiIl2YapEbEBCAnJwc53ZO\nTg4CAwMvuX+3bt1gs9lw/PjxUh+Ljo5GbGwsYmNjkZCQ4HJ9eHZ2tsu2xWKBxWK57O2UlBRkF9vO\ntljc2y7RU952lfS68XhVsW3o+Fos7OX54LrN3jK3U1JS2OupXpN/vWk/vrr1euB8KL5tyPgW3/Z0\nr27jy16v77UUm5O061UwPyQkJDjXd9HR0SiTmIjVapXmzZtLVlaWnD9/XkJDQyUtLc1ln8zMTCks\nLBQRkS1btkjz5s1LHcfdfxaASv3JysoSKXpz/fL+uKlKej3M0PH18Bhr2ethXje+GvVqO6fp1utB\nXjm+uvV6mDfNadqNb0ZG5QMuXPBcL8dXaa8RY6zTHIwy9jfVPbkAsGbNGkRHR8NutyMiIgIxMTGY\nPXs2ACAyMhL//ve/8fnnn8Pf3x9XXXUV/vOf/6Bjx44ux+A9ueU9nOf/k3vd/T+69XqY142vRr1F\nD6fhnKZbrwd55fjq1uth3jSncXzLx/F1+yBu7a7T750F9JqDy1rzmW6RWxW4yC3v4Tghlcfrej3M\n68ZXo96ih9NwTtOt14O8cnx16q3sE1jAo09idZvT+D2ufBxftw/i1u5eN6eZZJFrqntydVX8enEd\nZGdmVv4gVmvlj1FB2o0ve5Vir3q6NbNXLfaqlZ2bW/SEsDJ/KrtIdqdXt/Flr1LsVU+3ZrP0+hkd\nQAbw8/P4q1hERERERESewMuVodfb8oAJeosO4tbu3nRpiZa9HuZ146tRb9HDedmcxsuVy6Tl+HpT\nb9FB3Nrdm+Y0fo8rH8fX7YO4tbvhc0Q17uXlykRERGZR2ds9PHi7CBERkY64yK0CZrn2vKLYqxZ7\n1WKvero1a9db2XswPXj/JaDh+LJXKfaqxV61dOsF9Gs2Sy8XuURERERE5D6brfLH4NUppADvyYVe\n154DJugtOohbu3vT/RNa9nqY142vRr1FD+dlcxp7y3k49pbF8N6ig7i1uzfNafweVz72un0Qt3Y3\nfI6oxr28J5eIiIiIiIi8Ahe5VcAs155XFHvVYq9a7FVPt2b2qsVetdirFnvVYq96ujWbpZeLXCIi\nIiIiIqo2eE8u9Lr2HDBBb9FB3Nrdm+6f0LLXw7xufDXqLXo4L5vT2FvOw7G3LIb3Fh3Erd29aU7j\n97jysdftg7i1u+FzRDXu1eqe3KSkJISEhCA4OBhTpkwp9fGFCxciNDQUt956K+644w7s3LnTgEoi\nIiIiIiIyI1Mtcu12O6KiopCUlIS0tDTEx8cjPT3dZZ/mzZvjf//7H3bu3IkJEyZg5MiRBtX+zSzX\nnlcUe9Vir1rsVU+3ZvaqxV612KsWe9Vir3q6NZul11SL3NTUVAQFBcFiscDf3x/h4eFITEx02adr\n166oV68eAKBz5844ePCgEalERERERERkQqa6J3fp0qVYu3Yt5syZAwBYsGABNm/ejBkzZlx0/6lT\np2Lfvn34+OOPXf6e9+SW93B63Y/A3vLxfiW3D+LW7t7UW/RwXjansbech2NvWQzvLTqIW7t705zG\n73HlY6/bB3Frd8PniGrcW9aaz+/yC6qeO4P63//+F/PmzcOGDRsUFhEREREREZFOTHW5ckBAAHJy\ncpzbOTk5CAwMLLXfzp07MWLECCxfvhwNGjS46LGio6MRGxuL2NhYJCQkuFwfnp2d7bJtsVhgsVgu\nezslJQXZxbazLRb3tkv0lLdteK/F4lZvdnY2e83e6+bjVXbb68ZXo16LxYKUlBT2spe9uvQW3/bA\n8wmtez3w/c2rx5e9Vd5rKTYnsTcbCQkJzvVddHQ0yiQmYrVapXnz5pKVlSXnz5+X0NBQSUtLc9nn\nwIED0qJFC9m0adMlj+PuPwtApf5kZWWJFL25fnl/3GR4r4eb2euBXg/zuvHVqNcr5zT2slfnXg83\na9nrYV43vuxV1uuVc5qbrZdiqntyAWDNmjWIjo6G3W5HREQEYmJiMHv2bABAZGQkhg8fjmXLluGG\nG24AAPj7+yM1NdXlGLwnt7yHq2Rv0UHc2t2b7p/QstfDvG58Neotejgvm9PYW87DsbcshvcWHcSt\n3b1pTuP3uPKx1+2DuLW74XNENe4ta81nukVuVeAit7yH0+sLnL3l4xMAtw/i1u7e1Fv0cF42p7G3\nnIdjb1kM7y06iFu7e9Ocxu9x5WOv2wdxa3fD54hq3FvWms9U9+Tqqvj14jpgr1ra9WZmVv4gVmvl\nj1FB2o2vZr2Afs3sVYu9arFXLfaqxV71dGs2S6+pfroyERnAz8/jr2oSERF5hM1W+WNYrYC/f+WP\nQ0Qew8uVodfb8oAJeosO4tbu3nRpCXvLx163D+LW7obPEewt5+HYWxb2XtZB3Nrdm+Y09paPvW4f\nxK3dDZ8jqnEvL1cmIiIiIiIir8BFbhUwy7XnFcVetdirFnvV062ZvWqxVy32qsVetdirnm7NZunl\nIpeIiIiIiIiqDd6TC72uPQdM0Ft0ELd296b7J9hbPva6fRC3djd8jmBvOQ/H3rKw97IO4tbu3jSn\nsbd87HX7IG7tbvgcUY17eU8uEREREREReQUucquAWa49ryj2qsVetdirnm7N7FWLvWqxVy32qsVe\n9XRrNksvF7lERERERERUbfCeXOh17Tlggt6ig7i1uzfdP8He8rHX7YO4tbvhcwR7y3k49paFvZd1\nELd296Y5jb3lY6/bB3Frd8PniGrcy3tyiYiIiIiIyCuYbpGblJSEkJAQBAcHY8qUKaU+vnfvXnTt\n2hW1atXCtGnTDCgszSzXnlcUe9Vir1rsVU+3ZvaqxV612KsWe9Vir3q6NZul18/ogOLsdjuioqLw\n/fffIyAgAB07dkT//v3RqlUr5z4NGzbEjBkz8M033xhYSkRERERERGZkqntyN23ahIkTJyIpKQkA\nMHnyZADAq6++WmrfiRMn4qqrrsLYsWNLfYz35Jb3cHrdj8De8rHX7YO4tbs39RY9nJfNaewt5+HY\nWxbDe4sO4tbu3jSnsbd87HX7IG7tbvgcUY17tbknNzc3F02bNnVuBwYGIjc318AiIiIiIiIi0omp\nFrmVfeXAKGa59ryi2KsWe9Vir3q6NbNXLfaqxV612KsWe9XTrdksvaa6JzcgIAA5OTnO7ZycHAQG\nBl7WsaKjo1G/fn0AQEhICLp06QKLxQLg78F3bF/q7yu6feTIEcBigeX/b2c7jlvRbTcf1/BeiwXI\nznbrcS0WC3vZy14Nei0WC44cOYKiv2Uve9lr+l7g78+v4OM6sJe97DV3b3HszUZKSgr27t0LAMjP\nz0dZTHVPrs1mQ8uWLZGcnIwmTZqgU6dOiI+Pd/nBUw6xsbGoW7cu78m9rIfT634E9paPvW4fxK3d\nvam36OG8bE5jbzkPx96yGN5bdBC3dvemOY295WOv2wdxa3fD54hq3FvWms9U7+T6+fkhLi4Offr0\ngd1uR0REBFq1aoXZs2cDACIjI3HkyBF07NgRp06dgq+vL95//32kpaXhqquuMrieiIiIiIiIjGaq\nRS4A9O3bF3379nX5u8jISOf/v+6661wuaTaD7Oxs51vrOmCvWuxVi73q6dbMXrXYqxZ71WKvWuxV\nT7dms/Sa6gdPEREREREREVWGqe7JrSq8J7e8h9PrfgT2lo+9bh/Erd29qbfo4bxsTmNvOQ/H3rIY\n3lt0ELd296Y5jb3lY6/bB3Frd8PniGrcq83vySUiIiIiIiKqDC5yq0DJH7ltduxVi71qsVc93ZrZ\nqxZ71WKvWuxVi73q6dZsll4ucomIiIiIiKja4D250Ovac8AEvUUHcWt3b7p/gr3lY6/bB3Frd8Pn\nCPaW83DsLQt7L+sgbu3uTXMae8vHXrcP4tbuhs8R1biX9+QSERERERGRV+AitwqY5drzimKvWuxV\ni73q6dbMXrXYqxZ71WKvWuxVT7dms/RykUtERERERETVBu/JhV7XngMm6C06iFu7e9P9E+wtH3vd\nPohbuxs+R7C3nIdjb1nYe1kHcWt3b5rT2Fs+9rp9ELd2N3yOqMa9vCeXiIiIiIiIvILpFrlJSUkI\nCQlBcHAwpkyZctF9Ro8ejeDgYISGhmLbtm0eLizNLNeeVxR71WKvWuxVT7dm9qrFXrXYqxZ71WKv\nero1m6XXVItcu92OqKgoJCUlIS0tDfHx8UhPT3fZZ/Xq1cjMzERGRgY+/vhjjBo1yqDav6WkpBid\n4Bb2qsVetdirnm7N7FWLvWqxVy32qsVe9XRrNkuvqRa5qampCAoKgsVigb+/P8LDw5GYmOiyz/Ll\ny/H0008DADp37oz8/HwcPXrUiFynvXv3Gvr47mKvWuxVi73q6dbMXrXYqxZ71WKvWuxVT7dms/Sa\napGbm5uLpk2bOrcDAwORm5tb7j4HDx70WCMRERERERGZl6kWuRX9aV4lf4pWZX8KWGXl5+cb+vju\nYq9a7FWLverp1sxetdirFnvVYq9a7FVPt2az9JrqVwilpKQgNjYWSUlJAIB3330Xvr6+GD9+vHOf\nZ599Fj179kR4eDgAICQkBOvWrUPjxo2d+7Rt2xY7duzwbDwRERERERF5RGhoKLZv337Rj/l5uKVM\nHTp0QEZGBrKzs9GkSRN8+eWXiI+Pd9mnf//+iIuLQ3h4OFJSUlC/fn2XBS6AS/5jiYiIiIiIqHoz\n1SLXz88PcXFx6NOnD+x2OyIiItCqVSvMnj0bABAZGYl+/fph9erVCAoKwpVXXolPP/3U4GoiIiIi\nIiIyC1NdrkxERERERERUGab6wVPk3X7//XecOXPG6AwiIiIiItIYF7nVkIigsLDQ+f91sH//frz9\n9tuYP3++0SkVcuHCBW3GVkS0aS1Op2a73W50ApmMTucvoF+v42vO8b2OqpZu5wOgV7NurTr12u12\nrXoBPodQhYvcCrDZbEYnVJjdboePjw98fX1RWFho+K9XKo/jC7tFixa44447sGfPHvz2228AzPtN\nYP369diyZQt8fHxK/R5ns3GcDz4+Prhw4YLRORXiOCfMfu4Cf3/zr1GjBgDgjz/+cP69WZ04cQI7\nd+50bpt9ftP1m7/j/DXzuQDo9fUG/N3r+Jo7d+4cAHOPs9m/xkqy2Wwu54OZxxYANmzYgOTkZPj4\n+DjnYLMSEef3ZQczv1BjtVqdzyHMzvEGT40aNeDj44MTJ04YnVRhjvlswYIFOHTokME1Fbdy5Ups\n27bN6IxLqhEbGxtrdITZ+foWvRawbds21KlTB7Vq1TK46OIcX9wAMGPGDHz44Yc4c+YMAgICUKdO\nHYPrXDkW4I6xXbZsGdauXYvc3FwUFhaic+fOpptURQQ+Pj44duwYxo0bh6SkJMycORPh4eGmPCds\nNhv8/PwgInjttdfw1VdfoUaNGmjcuDFq1qxpdN4lOc6JxYsX46effkKHDh0MLro0xzf/33//HSNG\njMC+ffvQq1cv0527xaWnp+O7777Drl27sGDBAjRr1gzXXHON0VkXVXxOmzt3Lvbs2YPQ0FCDqy7N\nMUc4LFu2DDt37kSbNm1KfcwMRMT59fbZZ59h586d8PHxQePGjU37Iqmjd+vWrRg/fjxWrVqFhx9+\n2JStDr6+vhARbN++HVdddZWp51/g7zGeP38+/Pz8ULt2bdSsWdN054Tja8rX1xc9evTAqVOnMHv2\nbPTs2RP/+Mc/jM4rpbCwEL6+vvD19UVubi7+97//wWKxwM/PVD8DFgCwevVqBAcHO+ffSZMmYcuW\nLRARNG3a1FTngtVqdS5sfXx8cPLkSTz33HN47733UK9ePTRu3Bh16tQx1RxcsmXHjh0YP348/vzz\nT9x9992oXbu2gXXl27VrF1588UUkJiZi1apVaNy4sSnPZb6TWwbHq5erV6/GzTffjJkzZyIiIgKZ\nmZkGl/2tsLAQ3bp1Q0pKCnx9fXHmzBnExMRg69atePnll/H+++9j5syZOH36tNGpAP5+FdPxTfTC\nhQtISEjAv/71LzzxxBNo0KABNm3ahA0bNjj3N1rJdzoCAgJw/PhxHD58GD/++CPq1atnZJ6LvLw8\ntGrVCufPn4efnx+OHDmCIUOGoGbNmhg8eDBeeeUVLFmyxHTvjjl6RAR5eXno27cvPv74YzRp0sTg\nstJKXia5atUqjBs3Dq1bt8Y777xjZNolFf/vfeONN2LRokV46623cO2116JVq1YGll3c448/jh07\ndsDX1xeHDx/Gc889h++++w4tWrQwOu2iHOeCY45wvFNus9kwefJkl48ZLTc3F/PmzcPhw4fh4+OD\nI0eOoF+/fli1ahVq166Nhx56CPv27XPO0UYr+T0gPz8fvXv3xptvvolatWph8+bNSElJuei+RnF0\nOP7366+/RqtWrfDhhx9ixIgRyM7ONrCutOK3OAHATz/9hNDQUPz000+Ij4/HhAkTXF4QMVphYaFz\nkWC321G7dm00bNgQS5YswapVqxAQEGB04kU5xu+jjz7Cvffei+nTpyMyMhLffvutwWV/O3bsGLp0\n6YLFixdDRLBz50506NABeXl5CAoKwoMPPohDhw6Z5lzYvn07hg8f7tyOj4/HiBEj0LFjR4wdOxZr\n1qzBsmXLAJhjDr7YO/nZ2dl4+eWXkZubi/fffx8NGjQwzVx2MT///DMGDx6Mjh07IiUlBU899RS+\n//57bN261ei0UsxxlpqEiLhcVuS43OGbb75BYmIi3nvvPaxduxZr1qyB1Wo1sLSI1WqFr68vevbs\niTFjxgAA/P39YbfbERUVhRUrVsBms+Guu+5C3bp1DW212WzOV/5q1KiB8+fP46WXXsK6deuwdu1a\nDB8+HHfffTcmTZqE2267DStWrDDFK4XFL0X94Ycf8Msvv6Bhw4b44osvUKdOHWRkZJjicjnHk5RG\njRqhadOmeP755wEABQUFuP766/H444/jiy++QOPGjdGjRw/nv8ksatSogXPnzsHHxwe//fYbQkND\nkZSUhAceeAB5eXlG5wH4+7+vY+wcPyStbt26+OOPP0x5j2DJ5lOnTqFhw4Z4+umncf/996Nz584u\n+xnNMYbvvfceWrduDQD47bffkJCQgH79+uH22283xdxbUvEnfD/99BPatm2Ljz76CPfffz9uvvlm\nfPfddwbWuUpPT8eGDRuwfv16AEWX2IeHhyM+Ph5bt25FzZo1TfOiaMnLZgEgKysLN9xwA1auXImp\nU6di6NChePPNNwEY/yS25BNYHx8fHDp0CGvWrEFycjImTZqE5cuXIzk52RSXMB86dAj5+fnOd0MP\nHDgAANiyZQsWLlyI6dOnY926dcjPz8fJkycNri3ieDfUx8cHu3btwq5du1C/fn0sXrwYeXl5yMjI\nAABT3J5TWFjo8v3gr7/+wptvvon33nsPKSkpSE5Oxm233YYlS5aY5oWP/Px8hIWFISIiAomJiQgI\nCMCMGTMQExODlStX4sKFC86vNyM5vme1bdsWs2bNwrFjxwAUfb9Ys2YNRo0ahcceewzdu3fHnj17\n8Msvv7h8nlEcz4FPnjyJadOmYevWrbjhhhswYsQIXHnlldixY4dzP7NxjPFNN92EJk2aYPfu3QCA\nQYMGAQBSU1Nx/Phxw/ouhovc/8/xjcnPzw9nzpzB8ePHUVhYiNOnT6NmzZqYNGkS7rrrLkyePBkv\nvPAC/P39DW0F4GyYNGkS8vLy8MUXX6BmzZrIzs7GgAEDYLfbsX37dnTr1g25ubmGfVPNycnBCy+8\n4Lw/Ij4+HkOGDEGtWrVw99134/bbb8e2bdtgt9vRvHlz1K5dGxs2bMDy5csN6S3Ox8cH+/fvR79+\n/fDvf/8bcXFxmDVrFtq3b4/u3btj1qxZLvsaYfv27UhMTHTeh71kyRIsWbIEe/fuRc2aNbF582YM\nGDAAd9xxB5KTkxEUFIScnBxDWh1KfqM5e/Yshg0bhk8++QQ1atTA1q1b0bt3b7z44ovo3LkzpkyZ\nYthi19Hq+O+7atUqdO/eHa+//jrmzZuH7t2746GHHoLNZkNmZqbz0kQjXay5W7duzubo6Gjccsst\nWLt2Lfbu3Wv4N9SS91lef/316NSpEyZNmoQ77rgDI0eOxKpVqwDA0Lm3OEez44ecTJgwAUeOHEGn\nTp0wePBgbNq0CePHj8e9995r+D1WNpvNeU707t0bbdq0wZYtW5Cbm4tdu3ZhwoQJCAsLQ2FhIXbv\n3o327dsjPz8fgHFPCkXEeelbQkICNm7cCKvViiNHjjifsNasWRNPP/00jh07hiVLlgAw9kUmxxPY\nP/74A//85z9hs9lw+vRp1KpVC6+++ioefPBBzJgxAxEREYZe1ldYWIj8/HxMmjQJc+bMAVC0OHj8\n8cdx8uRJbN68GcOHD8c999yDgQMH4rPPPkO9evVM8QKTr68vTp06hZdffhmDBg3Ck08+idmzZ6ND\nhw4YN24chgwZAgC44oorDH/h2XFp8q+//oqsrCzUqVMHHTp0wLFjx5yL2t69e6NmzZrYt2+fYa3F\nr/SpU6cOli9fjsGDB+Pw4cNo2LAhAgMD8cQTT6BLly7Izc113l5mhIu9E/rbb7+hZcuWyMvLwyuv\nvIKbbroJn376KQCgW7du+Mc//oFVq1bhwoULhnyvK/kC0erVqxEWFoadO3figw8+wJAhQ/DYY4+h\nQYMG+OWXX5wvoBv9PMJh4cKFaNOmDd566y1ERkaiXr16ePXVV3H06FFs374dDRo0QJ8+fZCamoqN\nGzcaneuCi9z/z/Hkav78+bjpppswevRozJw5E40bN8aePXtQp04d/PDDD3j++efx559/IjEx0RSt\nEydOBFD0zsdrr70GAAgJCcHgwYOd7+Z9+OGHGDdunMd/PY/jC7Rp06aYOnWq8xvklVdeia+++gqP\nP/44AKB58+bw8/PD3LlzARRNsldffTUOHjzo8ScsJV8IEBGsWLECI0aMQFJSEk6fPo1FixZh8eLF\neOmll5CVlYUFCxZg0KBBWLRokUdbHeMbHByMp59+GuPHj0evXr1wxRVX4Pnnn0dUVBSaNGmC4OBg\nREZGYujQoQCA8ePH44MPPjDkRY+Sk7bjh4zVrFkTDz/8MJYsWYLWrVvjgw8+wNtvv42JEydi1qxZ\n2L17tyH3qJR8J2np0qWYMWMGZsyYga5du2LmzJn49ttvMXDgQJw9exbr1q0DYOyrsJdqjouLc2ke\nMGAACgoKkJqaiv3792P9+vWGPIktft/tr7/+6nwl+D//+Q9mzpyJCxcu4Nlnn3W+gAMY+8OoSr47\nfvbsWfj4+ODMmTOYMGECNm3ahBYtWuC1115DVlYW5s2b53xRydNfc9999x3Onj0LPz8/57uKAHDf\nfffhr7/+wtq1a/HII48454wpU6agRo0amDdvHr788ksAnj2XMzIy8Mknn+DUqVPw8fHBvn370Llz\nZ3zxxReYPXs2nnrqKdx+++1o1KgRFixYgJo1a6JOnTq48cYb8dFHH8Fms3n8MsqS36Pi4uLQo0cP\n5xPbwsJC7Ny5Ew0bNsT69esRERGBI0eOOF+08bStW7di9OjRqF+/PsLCwpCRkYGMjAxs27YNnTt3\nRr169dCrVy/4+vpi2bJlGD16NICiS2wd87Unlfxa37t3L8aMGYNDhw5h9+7deO+997By5Ups3boV\nb7zxBnJycrBo0SLExcVh4cKFHu918PHxwalTpxAREYGnn34aMTExmDNnDjp27IjIyEjniwutWrXC\nb7/9hiuuuAKAZxc2JX9woojgxIkT6NWrF+6880706dMHQNEcV69ePQwdOhRXXXUVrrvuOsyaNcvj\n75Y7LlGvUaMGzpw5g927d+PcuXNo06YN+vTpg0mTJsHf3x9vvPEG4uLicP78ebRo0QIhISE4e/Ys\njhw54tFeoOhn+fzwww+wWq3IyMjA2bNnsW/fPkRFReGzzz7DvHnz8PPPP2PFihWIiIjAxo0bnbe7\nGP3iMwB8++23WLx4MVasWIFhw4Zh7ty5+Prrr9GzZ0+0b98eH3/8MQCgX79+CAsLQ9u2bQ0uLkG8\nlN1uF7vd7vz/mZmZMmrUKBk9erRkZGTIli1bJCgoSLKysmTGjBny7LPPysKFCyU5OVk6d+4sI0eO\nlAsXLnis12azOVtPnjwpgwcPlt69e8v69evl3LlzIiLSrVs3efvttyU/P19iYmIkLCxM7rrrLund\nu7ds2LDBY61Wq7XU3x06dEhuueUW+fbbb0VEJCwsTMaNGyciIqdOnZJvv/1WWrVqJffee6+0b99e\nfvnlF4/1Xszy5ctlx44dzu2MjAzp1KmTPPfcczJ16lR55pln5MSJE7JlyxZ5+eWXJTIyUs6ePeuR\ntgsXLsiiRYvk9OnTIiKyf/9+6dmzpzRq1EjOnDnj3O/666+X5ORkSU9Pl6FDh0rfvn2lffv2MmjQ\nIMnOzvZIq8PFzon9+/dL/fr1Zf/+/SIicvz4cYmMjJQ33nhDREQKCwslMzNThg0bJo899picPHlS\nCgsLPdotUjTes2bNkpMnT8rBgwfl7Nmz8vHHH0ubNm0kMjJSevXqJQUFBbJgwQJ55pln5Oeff/Z4\n4+U0nzt3TlasWCHh4eHSqFEjiY+PN6x3586d0rdvX+nXr5/06dNHMjIyRESkf//+MnLkSBER+fTT\nT6Vv374u57gnlTz31q5dK926dZMnnnhC3n33XREReffdd+Wdd96RLl26SGJiouTk5EifPn3E39/f\n+RRAEYgAACAASURBVP3Gk26//XaJiYmRvLw86dmzp9x5553yyiuvyOnTp2Xp0qXy3HPPyd69eyU+\nPl7atGkj06ZNk/vvv1+6du0q27Zt81inY2yXLVsmERERsnLlShERWbp0qUyYMEFERPLy8mTUqFEy\nfvx42bx5swQGBkp8fLw8+OCD8sorr8jDDz8sP/74o8eaRcTlv+kPP/wgP/74o4waNUr++OMPl/3+\n+c9/yujRoyUhIUFWr14t7du3l+joaOf3dU86d+6c5OfnS0FBgZw6dUpiYmLkrbfeknfffVdSUlJE\npOjrceTIkTJw4ED58ssv5fbbb5dHH31UTpw44bHOwsJCl/E9ePCgiIj88ccfEhUVJXfddZfzY2PG\njJHx48eLiMi6devkgQcekPDwcDl06JDHei/233Lu3Lny9ttvi4jIiBEjpH379pKZmSmpqaly0003\nyf/93//J3Llz5dZbb5UtW7Z4rLWkFStWyAMPPCBjx46V5ORkERGJjo6WyZMnS35+vuzZs0eGDx8u\nQ4cOlV69esm//vUv+e233zzWV3JsP/jgAwkKCpKhQ4fKM888IyIiubm5YrFYZOvWrSIiMmjQIHn+\n+edFROTMmTMXfQ6iyrlz52Ty5MmyZ88eSUtLk4EDB0qHDh2kT58+kpeXJ/3795ePP/7YuX98fLzc\nc889IiIyZMgQmTt3rkfXFyWdPHlScnNzpbCwUM6dOydnz56VyZMnS9euXWXMmDFSv359ERFJS0uT\nsLAwWb16tWGt5fHKRW7xk92xUMjPz5euXbtKRESE82MTJ06Uhx9+WESKvtkOGzZM+vbtK4mJiZ4N\n/v9OnDghNptNcnJyZMiQIVJQUCAi4vxi2Ldvn1x99dVy5MgRERH55Zdf5IcffnB+vicWCN98840M\nGzZMREQKCgrks88+cy5ipk2bJk899ZSIiOzdu1fq1q3rbBURycrKkv/+978ux1P9xLDkmKSlpUnb\ntm2lb9++0r9/f4mJiZEzZ87IwoUL5cUXXxSRom+izZo1k0mTJklhYaHL+eSp3smTJ0uPHj1k4MCB\nMnjwYNm9e7dce+218tNPPzn3XbBggbRs2dLZtWHDBtm8ebPHWh0udk5kZmaKiMjYsWNlyJAhIlJ0\nHn///ffSvXt32b9/v2zatEnCwsKcTxKMsGzZMmnVqpWMGjVKjh07JiJF58ijjz4qhw4dkiNHjkho\naKhMmTJF/vrrL1m8eLHzRSezN0+aNElERH7//XePPnkt6ezZszJgwABZunSpiIi0bt1a7r//fhER\nOXr0qFx99dWyd+9eOX36tIwaNcqjiy+Hkk+QsrOzpUuXLrJ8+XLZvXu3y3m6ePFiueGGG+Tuu++W\ngoICycrKknXr1omIZ+Zgm83mfFKYnp4uLVq0kOeff17mzp0rOTk58uSTT8qgQYNERGTcuHEydepU\nERHZsGGDTJs2TebNm6e88VJOnjwpL7/8skyYMEEKCgpkwoQJMnDgQBEpmh/27NkjvXr1EqvVKitW\nrJA33nhDFixYIEePHpUnnnjCeb6r5Phv6PjfAwcOyP/93//JfffdJxkZGdK+fXsZMGCADBs2TO67\n7z7p37+//Prrr/Lll1/K0KFDpV+/frJ27VrlnQ4Xm+d/+eUXufHGG+X8+fOSnJwsDzzwgNSqVUtG\njx7tfIH52LFjEhcXJ8OHD5fly5d7rDcrK8vlv+PWrVulb9++MmDAAHn11Vfl5MmTsmXLFhkxYoR8\n9dVXIlL03OfOO++UZcuWiUjReeTgia+54o/x66+/yp9//ikiIjNnzpSRI0fKXXfdJY899phzYVhQ\nUCCTJk2S0NBQefnll+XAgQPKGx1KzmWbN2+WDh06yOrVq2Xy5Mny2GOPyapVq2TXrl3yxBNPOF84\nOnDggMTExDjHWMQzzyFKvtCxa9cuGTJkiJw5c0Y2btwoV1xxhXz44YciIvLWW29J//79nf8ux5tR\nxd/Q8oSCggLnc5y8vDzp2LGjdO/eXdLT00VEJCEhQZo1a+bcf82aNfLqq6+KSNH3YyNNnz5dbrrp\nJnn22WdlzJgxIlL0Ndm/f3/Jy8sTEZGrr75aXnrpJRER2bhxo8uLEEa8EVEWr1nk2u12yc/Pd24X\nFBRIdHS0PPLII86JctmyZfLAAw9Ibm6uiBS9GnPLLbfI/PnzRURKvYOg8lXYi50offr0kbi4ONm4\ncaOEhYWJiDjfPXR88T7xxBPSp0+fUp/rqVeMMzIy5Oabb5a4uDjp06eP9O7dWx5++GGZNm2a2Gw2\n6dOnj3zxxRciIvLss89Kjx49Lnoc1a+6Xez4hYWFMmvWLJk2bZqIiPz8888SFRUlH3zwgaxfv17q\n168v27dvlyFDhsjYsWMlLS3N5XNVTqAle7/++mupW7euPPfcc86/++ijjyQ4ONhlv5YtWzoXM8Vb\nPfkOwqXOienTp8uFCxekZcuWzlePU1NTpV27dhIZGSkirl9znn7X46+//pJnnnlGNm7c6PL38+bN\nc774tWrVKhkyZIg899xzhr7y6uBuc0FBgXOusVqtSr9Bff/99y5XlKSnpzufjJ44cULWr18v7du3\nl1deeUVat24tc+bMERGRqKgo6dy5s4h47knKxTjeHT9+/LgkJyc7X7gRKVr0XnvttXL8+HERERk+\nfLj06NHDo09YSo6NY8547bXXpEWLFi7vvFx33XWyc+dOWbdunQwePFhWrFhR6niefOdDRGThwoXS\ntm1beeihhyQ4OFhWrlwpR44ckRYtWsju3btFRGT79u0ydOhQ53l67tw5SUpKkttuu02io6OVfw2W\nHBO73S69e/d2eQK7a9cuWblypWzfvl2OHTsm99xzj/MKJseL6iLq5+HCwkJZtmyZ87ELCgpkz549\n8tdff4lI0TtdEydOFJGiF03vu+8+mTJlivTq1Uvuvvtu55Pu4lTPwadPn5YxY8Y4v6+lpaXJPffc\nI6tXr5b9+/fLrbfeKq+//rqIiLz//vvywgsvOBfE06dPd76Y5Kne4n799Ve59957pX///nLffffJ\n3r175f3335cOHTq4vEiwfPlyOXz4sGRkZMiwYcNk0aJFIiJy/vx5pfNv8WNfuHBBNm3aJDabTaZM\nmeJcsJw+fVpWrFghAwcOlMLCQnnzzTflhRdeuOiVXyrn4pLHzs3NlcjISOnevbvY7XY5deqUvP76\n6xIWFiYxMTHSo0cP56LymmuukTVr1ihruxSbzeYyxmfPnpVRo0bJjBkz5PDhw/LGG2/IrFmznHPA\n/fffLxERETJhwgS59dZbZebMmSLy938nI77X/fe//5Vnn31WCgsL5ZtvvhF/f3/ZsmWLJCUlyZAh\nQ2Tv3r2ycuVKGTJkiISHh7s0mm1x6+AVi9w///xTRo4cKfHx8WK1WmXv3r3St29feffdd2XdunUS\nFhbmfEI1cOBAef/9953vkn7xxRfyz3/+U0T+PulUTpzFL6MWKXoFxeGTTz5xLlgCAwNdJs4VK1ZI\nTk6O2Gw2w18J+uijj6RJkybOS87WrVsnjz76qKxbt05Wr14tDzzwgBw9elTOnz8v/fr1kzNnznj0\nC+Ri7yw6ngBGRUXJiBEjRKRoklq1apUMHTpULly4IG+++ab07t1b3nrrLY+1lpSYmCiZmZmSk5Mj\n8+fPd77Ycf78eRERuemmm2T69Okyd+5c+fLLL+X48eMeu4y6LBc7JwYMGCAbN26UhQsXyp133ilx\ncXFy3333yYcffij79u1zfm7Jbx6ecv78eenUqZPz6gLHOJ46dUruuusuueeeeyQoKKjUrQBGTvaX\n2+wJQ4cOlf/X3nnGRXV8ffwAiUaNGmMUNdYo1mggEgEVpSkIIsVHCSBWlKIIYkEFg0HFgoA1YBCN\nvaISeyN2LFixF7ChYAAFAWXZ3d/zgs/Of3fBBMsue8l8X+l6d5ydO/fcc86cEhYWhkOHDkFfXx/m\n5uaws7Nj/+7n58c88tOnT0fDhg2ZY1IWvgxUzstfdjru7e2NgoIC3LhxA40aNVIwqlxcXBSMGXmD\nRpUo77edO3fi559/RkREBIqLi/H27Vu0a9cOhw8fZtf4+/tj9erVKCoqwoYNG5iHXjaeuvdwdnY2\nbG1t2Sl9YGAgJk6ciKdPnyIyMhKGhoZYu3YtrKys4Ofnx97Bjx8/xvz581mYrbrYtGkTTpw4AQA4\ndeoUjI2NcenSJQXdoLCwEFu3boWRkRF73mTrqg7j69WrVwgICMCvv/6K+Ph4tG7dGtbW1hgwYABe\nvnyJa9eu4fvvv8fDhw9x/fp1+Pr6Ytu2bQBKT+3koztU/czJ1kMqleLMmTNwcHBg9zQ7OxsHDx6E\nvr4+AgMD8d133+HKlSt4+PAhPDw8sGTJEpXOrTzKc6b4+flh69atAIA2bdrA398fV65cwcCBAzF7\n9mykpqZiwoQJ6NixI1JSUvD27VvEx8dj8ODBzPGgDlavXg09PT2mSx46dAimpqZM501OToarqyuK\niopw/fp1xMXFKfxeVe+FtLQ07Nq1C0DpOj948AA2NjYsChAodSrKH+Y0bdoUPj4+AMCcTTJU/awp\nH3Ckp6ez9dq4cSMcHBzw+vVrbNu2DePGjWPRdGlpaTh27BgmTpzIQqwrg4yMDHbv169fj9DQULi7\nu8PU1JSFIWdnZ2PGjBno3r07DAwMkJqaWmnzfV+qtJEr/7KeN28eAgMDkZ6ejry8PFy/fh0PHjyA\no6MjunbtihEjRiA1NRUpKSno1q1bpYTEyT+M2dnZePDgAVq2bInExES8ffsWa9asYd7VLVu2wNjY\nGNOmTcOAAQPQrVs35slSHkvd5Ofno0WLFpg/fz6A0tCh5cuXY9q0aQAAS0tL5rWqDN51shgREYHL\nly/Dzs4Od+7cAVDqPJAZvSUlJQqhqOo8yZeFUdvY2MDe3p55s62srBAVFcWuu379OhwcHODo6FjG\nUKxM/m1P7NixAyNGjMDq1avZd1StaP/bmrx69QozZsxAXFwcm8vff/+Np0+foqSkpIyhqI41Ftqc\n5U++Ll26BDc3N4Xwxx49ejCZ5ufnh9mzZ+P27dsYP348bGxskJ6eXqmebeDdp+Nubm5wdXVFbm4u\n/vrrL1hYWDBjUR3GTEFBgULOYUlJCQICAtCvXz9cvnwZlpaWmDhxIoDS3MAff/wRx44dw927d9Gt\nW7cyv6cyKSgoQPfu3Vm6xY0bN+Du7o6VK1cCKD39CgwMxPr169U6L2UZJKvNYGtri6FDh8LFxQXF\nxcUYM2YMwsPDmbKYnZ0Ne3t7ODk5qVUhVFa4t23bhuDgYFhbWzPl39XVFfPmzcOLFy8wY8YMltMY\nHR2NiIgItYZ3lhcBVVBQgHnz5mHYsGEASve1u7s7U7jt7OxYPu7Ro0cVUp5U/c6QGV8yZKfIL1++\nRFBQEH755RcYGRnBz8+PRSGlpKQgLCwMjo6OCp8DpfJanRFAFy9eRM+ePRXCozMzM+Hn58fSsXbt\n2gVnZ2e1R3LIKCwshJGRESZPngxXV1f89ddfmDhxIrp16wbgf7U6rKyssGPHDuzYsQN9+vRBTEyM\nWlPHlHn58iXc3NzQrl07jBw5EtevX4dYLIaPjw/Cw8NRUlKCadOmYdy4cbC1tUVsbKzC95UPuFSN\nVCrF1KlT0bhxY/j4+ODMmTM4duwYdHV12aEfAJw8eZLpwzdu3FAYo7L1yopQZY1c5cXPzMzE0KFD\nER8fj8LCQhQXF8PDwwNbtmyBSCRCnz59WCGksLAwXLt2TeH7qtp8JSUlCmO/fv0aAQEBMDIyglQq\nxf79+xEQEICJEyfi6tWr6NixIxPkV65cwe+//65gGKiDimzsLVu2oEuXLkzoREZGshNx+dPpio73\nqVE+WTx27Bjc3NywatUqREZGwsjICAcOHIClpSVCQ0PLFCpT1cu0omHUvr6+iI2NxbVr19C2bVsc\nOXIEgwcPxqNHjxS8wuo6kfnQPSELl1NG1cJefnx5Z4Ay27Ztg6urK6ZPn46tW7fCxMQEixYtUrhG\nXcqAEOcMlJ4ux8TEoLi4GEuXLoWJiQlOnjwJoNR5U6dOHbx69QpJSUnw8PBA8+bNFYpyVDbKp+My\nJfX169fw8vLCwIED0bVrV2zZskWt80pJScGIESNQUFCAX3/9FYWFhThz5gwKCgqwfPlytGzZEtbW\n1mxeVlZW6Nq1K0aNGoXw8HCFsSrboZSdnY3g4GDExcWxfe7k5ARLS0uFOgMVHe9jedf4CQkJrBCW\nrFBecHAwnjx5AgsLC5w7d47NX97prMp3RnlkZ2djzJgxmD59OhYuXIgOHTqwgnhnz56Fs7MzHj16\nhMzMTDRt2hTJycksGkgdlOfEHTt2LNatW4fi4mI8ePCAFb3KysqCjY0N7ty5g1u3bsHf3x+WlpZq\nNW5lNGrUCJs3b8bNmzfRqVMn2NvbIzo6GgDg4OAAc3NzhagT2ckuUOrolVFZBuTly5fh5uaG8ePH\nY+HChfDz88OiRYvw9OlTGBgYwNnZGR07dsTevXsB/O+do671lUqlyM3Nhb6+Pr755hsF40q+bkNu\nbi42b94MIyMj9OnTh6UzqAv5QrAAsGbNGvj4+GDBggV48+YNwsLC0KdPHwDAiRMnWPh6fn4+FixY\ngJkzZyqMp26DfNeuXdi3bx+io6ORm5uL1atXw9TUFADQt29fRERE4OrVq4iPj0eHDh0QHx+v8P3K\n2r8fQpUzcuUfRqlUimXLluH8+fMAgD179mD48OG4ePEi8vLy0LBhQ5YTZmNjA09PTwUBpWoeP34M\nb29vlsdVUlKCIUOGICAgAFlZWey6t2/fwt7eHpMmTYKxsfE7w5HVsfEqqmhLpVJYW1vDxsYGu3fv\nRocOHRATE6MwRmXm1ymfLL569QoxMTHsVCkmJgZeXl4sH1sdfEgYNVBa3MLFxYU5EWSoy3nwKfZE\neWN9apRf1BcuXICFhQVcXFxYoRDlayUSCa5du4bp06dj4MCBZXK+VI0Q5yyPLMzXy8sLr1+/RkZG\nBoYMGYLNmzczpW/kyJFMIXj16pVC0RhNPR3Pzs5mz6a6827l94Senh4aNWoEX19fFBUVQSKRIC4u\njhVzmzFjBqysrJCVlYXk5GRMmTKFnTYC6lFeKyoftmzZgiFDhmDOnDk4cOAArKyssHLlSoWTL3W8\nM5TlcGxsLLvXISEh5RbCEolE8PX1xYQJE8oUnlOXQS67l4mJiRg1ahQrGpOWloZx48YpnM50796d\nFdBUruqr6jXesWMHU5qLi4uRmJiIXr16ITw8HHZ2dhgxYgSeP3+O9evXw8nJCRKJBEFBQXB2dkaD\nBg1YSLW6kNerdu3ahXbt2mHatGnYt28fkpOT4eDggN27d+PIkSOwsrLC7t27cevWLQwaNAhWVlbI\nzMxUMBZVub7/ttcKCgqwfft2TJo0CZs2bcLixYvh6OiIhw8fIj8/v8zhTmVx6NAhWFhYsHo5L1++\nxB9//IFBgwYppF89efKE/VnVaytD/v+Qvas2bdqEDh06sPmKxWJ07dqVdSyYPHkyk8nvGksdpKSk\nwMPDA927d0fbtm0xfvx4AKVr5+DggJiYGDx//hxhYWEsEkVT9sSHUmWMXOWX9fHjxzF06FBYWlrC\nw8ODvbT8/f1Zuwd/f3+YmpqiY8eOmDhxooKnTV0FAAoKClh+6pMnT6Cvr8/+TSQSMaGVkZGBuLg4\n1KhRgz3Y8lUe1TVf4J8VbXnOnj2LmjVrIiAgQO2hcR96sqjsYXuf8T6WjwmjVnfRI6HtCWUHUFZW\nFnr06MGKfryL8kJl1fUyFeKc5XlXmO/KlSvh6+vLqri+ffuWtQaSPWfqysP+FKfj8oW7VIVykaKH\nDx/i9OnTCA4ORrNmzRSuHTduHKZMmQIAWLRoEQwNDcuEWar6ZPFDnDMlJSW4cOECPD09YWNjw3Je\n1Y1MDsfExGDAgAEwNTWFk5MT/vzzT2RlZaF169YsBFlWCAsodYbI6xDqQH6dZaHySUlJMDExUYiS\niYuLg6OjI5YuXYp9+/bB0NBQoXCi8liqmmthYSF+++03jBo1Cs+ePcOWLVugr6/PTsdfvXoFe3t7\nttYjRoxAREQEgNLCTvJ5wurItZRRXFyM5ORkAKV1WywtLQGU6mg7d+6ElZUVRCIRNm/eDB8fH/Tu\n3ZvpmeqiorJMnq1bt8LOzq7MvlX1gcm77p38mickJMDQ0JDNJS0tDe7u7mw/VGQ8VXH27FkMGDAA\nrq6uzHnk6emJiIgI5kTctWsXWrRoAalUikePHuH27dsA/qevq7vuwfXr1/HVV19h8eLFAIAFCxZg\nwoQJLI3h7Nmz0NPTY5GW8gdt6g6l/pQI3sgtr0JhUlIStLS0sHTpUgClocr29vaIi4vD7du34e7u\njsOHD0MikWDv3r3spBdQrWfln/rHHj58GCKRiHkF34W7u7taQ/k+VNGWIe8FqgxPmyafLJbH+4ZR\nywtLdYXECXFPyIiKisLVq1dZ3mdkZCTWrl2L4OBg/Pnnn//6sq+M0Hohzhl4dxGsN2/eYOTIkZgz\nZ06ZfqLqQMin448ePUJQUBCMjIxYwR1PT094e3uza44cOYKuXbvCxsYGZmZmZYxFVT9vH+ucUU63\nqAzlKjY2Fs2bN2ftU5YuXQp/f3/cv3+fOQ7kC2GVlJSUCWFUFeXtXxsbGzg6OmLGjBkQiUSYO3cu\nQkJCWFXczMxMjBkzBmZmZnB1dVV7P2H5Oe/Zswc+Pj5YsWIFRCIRRo8ejWnTpjGletGiRTAzMwNQ\neio9ZcoUhVBqVVeBV0ZWqEnmNLh+/TpatmzJDhtkYeuTJ08GUHr/5U8b1Vm7oyLO5pKSEly7dg02\nNjawsLBQuzOpovqZWCyGk5MTgoODcfz4cSxZsgSpqan/6ERXBTJ5JlvrrKwsdO/eHevXr8f+/fsx\nePBghIeHIz09Hb1798bly5fZtUOGDFGo71PZFYgHDBjAIlEyMjIwbNgwrFq1ihVJnDhxYhn7Qwh5\nt/+EzsyZM2eSgNHS0iJtbW3KycmhM2fOUOPGjUlPT4+OHj1KIpGIHBwcqGbNmvTNN9/QunXryM/P\nj06ePEm5ublkbGxMHTp0oG+//ZZQavCTtra2SuaZmJhIkZGR5ODgQG/fvqWNGzdS7dq1qXnz5vTm\nzRs6fvw46evrExHRvn37yNLSkr744guaMGEC5ebmUufOnamkpIQ2bNhAAwYMoBYtWqhknsrI1iM6\nOppq1apFJSUllJGRQTo6OnT//n3atm0bFRYWUpMmTah69ersexKJhLS1tUlXV1fh71paWiqZJwA2\ntpaWFqWkpJCHhwedO3eOLC0tqUaNGmW+o6WlRXp6ehQWFkZff/01TZ8+nZycnMpco07atWtHK1as\noJYtW1KPHj2oXr16lJ+fT2lpaTRjxgwSi8V07NgxsrOzo4CAANLS0lL43eqYrxD2hPx+ICJKTk6m\n/v37U2FhIdna2lLz5s0pMzOTkpOTqUmTJnTv3j1KTU2lZs2aUbNmzUgqlRIA0tHRISKi1NRU0tXV\nVZl8EOKcZffvXRQUFNCLFy9IIpGQgYEBVatWjXJycujt27fUqlUrqlGjBnXt2rXC430KxGIxWx8i\nohcvXtCIESPI29ubfv311zJyQv5+6Orqkrm5Obm4uFCLFi3YO0NVz5xUKlUY+9ixY+Tm5kb9+vUj\nsVhMx48fp549e5KdnR1NnTqVbG1tqX79+iQWi8ne3p7q1q1LixcvZu8K2VxVLSM+Vj58/vnnRPS/\ne6VuGUz0PzncvHlzMjExobp169KtW7fo8ePHNGHCBGrSpAmdPXuW+vXrR0FBQaStrc1+tyr3w717\n96ikpIRq165NYrGYioqKaOTIkeTq6kqenp60efNmOnXqFE2dOpXWrl1L33zzDbVu3Zrq1KlDOjo6\n5OTkRH5+ftSyZUsCoNL5yqOlpUX5+fnk5uZGR44coZcvX9Ldu3fJ1NSUmjZtSmfOnKGaNWtS+/bt\niYjo4cOHZG9vT9999x3Z2NgoPLOq1COUuXTpEoWHh1NiYiINGDCAiIgaNmxIDx8+pD///JOcnZ2p\nevXqVLduXbp27RqZmZmRjo4OVa9enSQSCdNPVcH7yjIZ2traJBaLqUOHDhQaGspkmSrX9EP0M21t\nberWrRstXbqUTp48SZ6envTDDz9QjRo1yshGVc5Zdv/evn1Ln3/+OZ05c4Zu3bpFc+bMoTZt2pCJ\niQmFhISQl5cXZWRkUFJSEpmZmdEXX3xBzs7O1KhRIzZmZcgyefT19SkmJoa6d+9Oenp6VFBQQPv2\n7aNWrVpRs2bNqE+fPtSuXTuF76j6naxyKsOy/liUPaW//fYbWrVqBTs7OwwaNAgXLlzA3bt3Ua1a\nNRbCc/DgQYwdOxZAqSdG3f2d/q1/bN++fZGQkICsrCx4enrC3t4eHTt2hI+PDwvR2bNnD8aOHavS\nljDKa3HmzBl06dIFAwcOxP379/H27VvExcXB3d0dsbGx8PT0xNChQ1nVVuWwBnXE8wvpZFGIYdRC\n2hPlRXaIRCLMnDmTFdMAUOb0U1atUb6XKFDajsDJyQkeHh4qW2shzvlDw3yNjY0rpeWHMpp8Oq4s\ng2TPX3x8PEu7kUqlrGonAMyfPx/9+vVDq1atWOinOuYqPz8ZmiwfZFRkTbZt26YQLrl9+3YMGTIE\nBw8e/KDxPpb8/Hz88ccfiI6OxsyZMxEXF4fTp0/DxcWFXSORSNCgQQM8fvwYcXFxGD58eLlFeSqj\ncNeBAwfYXK9cuYIpU6awLgFBQUEwMDCAv78/mjVrVqYWRmWFSsoXaoqMjIS3tzd+//13iEQitGzZ\nkvVjrczTrveVZe/qp60qPlQ/k81TVsFaXSjLo6NHj6JTp04YNmwYTp48idzcXDRr1oyF9RYWFsLD\nwwOXL1/Go0ePEBISolD3QNPCfGfNmgVnZ2cApbI7IiKiTBG3yj5x/pQIzsiV3zDJyclMCMlCdKFF\nWQAAHWBJREFUXZYuXcoqTo4aNQp6enrYtGkTOnfujJCQEIUxKjsUVb5/7N69e+Hg4ICMjAxIJBI8\nefKExfDLULViJTRFW5n3FfbK81L1PIUWRi3kPVFQUIDY2FhWFMjPzw8WFhYYNGgQvLy80KpVK6Sk\npODevXsIDAwss8YFBQUICAiAubm52govaPqcVRHmq448QHmEYIDJOH36NLy9vbFu3ToApT0XJ02a\nhKdPnwIoDeVs3rw5m+vp06fL9FtUdd6tEOVDReWwRCKBq6srcxpkZWVh165dCr9HHVXg5f+P+fPn\no2bNmujZsycKCgrw+vVrNG7cWGEtR48ejd27d+PFixdYvHixQuEudXPgwAGWDrZ7927WBgYoddo7\nOjoiJSUFaWlpcHR0xLx58/D8+XO1ze9DCjU5ODggNzcXixcvLtPZQp2hyUKSZcDHOxbVXVg1LS0N\nFy5cQEhICI4ePYply5Zh1KhRuHHjBubPn8/ysi9fvgxzc3O1FiD8GP7++2+0bt2a2SFVHUEYufIF\nlgDgwYMHiIqKgp2dHW7evMmag8v+bdy4cUhKSkJhYSG+/vpr+Pv7lymyUBlUpH/sihUrFISZuhO+\nNV3RBoQl7IVWoKk8NH1PnDx5UsErHB8fj06dOsHLywvjx4/Hpk2bIBKJsHv3bpw5cwavXr3C5MmT\nMWfOHFa1UfkFGxkZqdKenEKbs9CKYAnJABOJRFizZg1rlZOfn49NmzbB3NwcERER+OmnnxAVFYW0\ntDSMHDkSmzdvBlCaZ2loaIh58+YpjKfuPFZNlw/Ah8vhc+fOQVdXFxkZGf84niqQv4eyfshXr17F\nsGHDsGDBAlbZNSgoCL1798azZ89w7do19O7dmxWTURfK6/HgwQNWsMvX1xdhYWE4fvw4/P39WY/s\nJ0+eoHPnzhgyZAikUinCw8MRGBjIeriqeo0/tFCTrEieuhCSLJPNVx5N1s9kyP9/b968QWhoKFq0\naAEzMzPY2toCKK2HMHfuXEybNg1isRiOjo74+eef0b59e9bPu7J7ulcUdVdUr0w03sgt77StdevW\nsLW1ZUVLQkNDFSrZOTk5sWqSERER+PHHHwGULyxUOdfyeJ/+sapGaIq20IS9kMKoZQhpT8heKAsW\nLICfnx+uX7+Oly9fIiIiAjk5OUhNTUX79u1ha2vLDAhZBVdLS0ssW7ZMYTzlntWqQIhzlkeTw3zL\nQwgGWH5+Pq5evcr+HhUVhZ9++glr164FUKqQ1KtXD0VFRdi6dSvs7e3h7OwMAwMDhISEoH379qxY\nk6oNAyHJBxkfK4dlJ5HKznZ1UFRUBH9/f/zwww8YNmwYDh48iIcPH8LX11chpNfX1xdDhw7F999/\nz/avuhTuy5cvY+fOnQDAUqtWr17NesT27dsX//d//4crV65g1apV6NSpE44fP46xY8fCy8uLOXHv\n3bsHHx8fPHjwQGVz/ZSFmtS9HzRdlglNPyuPu3fvIiAgAP379wcAnDp1Ci1btmT7OikpCaNHj2aO\nmkePHqk0ffBTU96hWVUKTS4PjTdyZWzYsIEJQ1mvsvz8fIjFYiQlJTGv96pVq9C9e3eFismNGzdm\n5d9VhZD6xwpd0dZ0Ya+MpodRA8LbE/JrcuHCBfzyyy9YvHgxxGIxSkpKsHLlShgYGGDVqlWYMWMG\nJk6cCKDUIWZsbIwtW7aU+/tViZDmLERvvJAMMOX1KSgoQFhYGFauXIm8vDy4uLjgt99+Y6d1rq6u\nrFVNWloaNm7ciJcvX+L8+fMYNmyYylvXCE0+lMfHymFVy4jy5Pz8+fPh5uaGoqIibNiwAe3atUNu\nbi6WLVuGKVOmIDU1FWfPnsXTp0+Rk5NTpjK1KpGNX1BQgDp16iAoKAiWlpZ48OAB5s6di+7du8PI\nyAiTJk1iLe4kEglWrlyJUaNGYfz48Wp1en2Ms+Pp06esSjzAnUn/hBD0M/n7J5VKkZmZiXnz5iE5\nORkhISEwMjJiz9LgwYMxbtw4AEBubi4WLlyIpUuXKrS5U1fLuw9F2QFx8eJFDB8+vFJTGdSFxhu5\n165dg6GhIYYMGYLg4GD4+PgAAHr37s36PQGl4Z0hISFwcXFhPexkN1VVN1KIoahCUrQBYQl7IRoG\ngPD2hIySkhJMnz4dgwYNwvz58xEQEMAUkaFDh7Kewj4+PtDX18fhw4dZIToZ6la0NX3OQvTGC8kA\nU47IkOUfikQirFixAqNHj0Z2djbWr18PX19fFlaWl5cHLS0tJhOysrLwf//3fzAxMUFSUpJK5ipD\naPJBaHJYeb7p6enMKJw4cSI2bdrE/m3w4MGYMWMG8vPzMWvWLLRq1QpOTk4K+oaqFW6RSISNGzey\ntiMPHjyAmZkZ6tevzwrurFmzBubm5gqtijZs2MAOAOT7uqs7ukOTCzUJSZYBwtLPgLL3Urb3ioqK\n0KZNG9y+fRv379+Hj48Py7d+/PgxGjVqhLNnzwIo7eUsVF6+fAk/Pz9YW1uXW5CuKqJRRm55wmPV\nqlVMUPbt2xf9+/eHRCLB8ePH0bFjR4XKa/IbWNU9Q4UYiipD0xVtQFjCXoiGgTJC2BPyFBUVYfjw\n4Rg5ciRycnKQl5eH4OBgzJo1C0Bpfru3tzc2bNgAGxsbbN26lSllQOVUwxTSnIXgjQeEY4CJRCKF\n57+4uBgBAQFo1aoVgoKCcOnSJeTk5CAwMBBRUVEQi8Xw8vLC0qVLWVHFlJQU9n2JRFLhd82nQAjy\nQYhyWH7c169fY8CAAejUqRP8/Pzw999/Izg4GFOnTmXXnD59GtbW1igpKUFJSQlu3Lihknm9C9nz\nMW/ePPTu3RuDBw+Gu7s7rl+/joYNG7JQ3uTkZEybNg0ODg44efIkBgwYgB49eiikZKla7xGas0Mo\nskx+bCHoZzLS0tJYGqNIJEJ8fDxWr17NwuOnTJmC+fPnQyqVYuXKlfDx8cH9+/cBADNmzEBCQoLC\neELLY92+fTvatGmjIAv/C2iEkSv/MIpEIly9epXFufv5+eHHH3+EkZERZs+erfC9/v37IyAgoMx4\n6lQGhRCKKo8QFG0hCXt5hGIYKCOEPaHM06dP0bVrV4XPDh48CF9fXxw/fhx///03JkyYABsbG4VU\nhcoMKdLUOQvNG6+Mphtgubm5mDZtGlOmEhISMGLECCxYsAD37t1DREQEa+mwb98+jBw5Ejdv3sSR\nI0fw888/M0VL/veqE6HJByHIYfn9JhKJsGXLFuzZswcrVqzA69evMXnyZHh5eeHVq1cwMDDA2rVr\n8fz5c/j4+GDOnDllxlP1GivvuR07dqB27drw9fVln8XGxqJNmzbs77m5uYiOjsbw4cMVou5UjRCd\nHTI0XZYBwtXPCgsLYWRkhMmTJ8Pb2xthYWEICQmBg4MDACAmJgaLFi0CUOrQ8PX1ZTKuKvD06VMU\nFxdX9jTUjkYYuTLi4+PRtm1bjBgxAj///DOKi4uRkJAAIyMjhcIcMTExSEtLQ15entpCB4TmFXwX\nmqpoK6Ppwl7ohoE8QtkT8rx+/Rpubm44evSowudhYWEYP348a8UlQxN6v2nanIXojVdGkw2wu3fv\nYuXKlSguLoZUKsXTp09RWFiIpKQk1K1blyncGRkZGDx4MFauXAmJRIJp06axHDB194gsD02WD0KX\nwwcOHIClpSXMzMzQqlUrxMbGAiht8/Hjjz/iwoULOHHiBAIDA9GtWzeMGTOGFcGpDBITE3H//n08\nefIEf/zxB3r27AkATHlu27YtoqKisHr1aqxYsQKA4ntYnQ4aITg75NFkWaaMputnykilUuTm5kJf\nXx/169fHvXv32L/169cPCxcuREBAAHM2SiQS/PXXXwqVvjVBh+C8Pxpj5J4/fx5ubm548eIFbt26\nhc8//xzLli3DpUuXMHXqVFhbW2PXrl2wsLCAtbU1K6kP8P6x74OmKdrlocnCvioYBsoIYU8oIxaL\nERYWhqlTp7KX59KlSzFx4kSFAiGyazUBTZqzUL3xymiiASYbe+fOnRg5ciT27t2LN2/eoH///vj9\n998BAO7u7pg8eTKA0nuxb98+mJiYIDs7G6mpqawlHlD5YXGaKB+qghxOSEhArVq1WIivj48P5s+f\nz3SbuLg4GBoasuvl2xipK11Ixs2bN6Gvrw8bGxvY29sjODgYAGBlZYWoqCh23fXr1+Hg4ABHR0eF\nApyqTh8TurMD0ExZVh6arJ/9G4cOHYKFhQV27NjBPnvy5AmWLFkCa2trfPPNN2UiZypb1+F8HBpj\n5AKlnrN58+ahW7dumDZtGszNzXHx4kWIRCIsX76cPTiVNTcheQXfhSYp2u9CU4V9VTEMlBHCnigP\nWfietbU1OnfujOHDh7P8RU1Fk+YsNG98eWiiASYjLy8PkydPRnBwMMRiMTZs2IDRo0cjPT0dDx8+\nRMuWLVk/05ycHPj7++PkyZNqmdv7oGnyoarI4by8PLRu3ZqFSJ46dQpDhgzB3r17IZFIIBKJMGTI\nENy6dYvtYXXU7yivqnRMTAwiIyMBlK65r68vYmNjce3aNbRt2xZHjhzB4MGD8ejRo0qp8CxkZ4cM\nTZZl8miqfga8W/7I/98JCQkwNDQsE1WwaNEi/PrrryqdH0f9qM3IrcjL78WLF+jXrx9rm9ChQwd4\neHiUUawqOt6HUhW8gv+EJina5aHJwr4qGAbloel74l1IpVKkpqbi0qVL7DNVnxp8LJowZyF74+XR\nNANMxoYNG6Cvrw9HR0fo6ekhMTGRFZOSGQvBwcGwtLSslPm9L5omH4QghytyP7dt24auXbsyhTss\nLAxeXl4K4ZTqQjlPeM2aNSwSbdy4cRg9ejSA0tzGvXv3spZWy5cvh4uLC0JDQxXG49Eo74emyjJl\nNFU/q2gbT7FYDCcnJ4SEhODo0aNYvny5yufGqTzUYuRWdPM9e/YMtra2WLt2Lfbv3w8bGxtERkYq\nJEuroxpfVfAK/huaoGi/C00V9lXFMHgXmrwnKkJ5qQWaTmXNWZO98e+Lphlg2dnZsLW1xeXLlwEA\ngYGBCAwMREZGBk6cOAEPDw+cPn0aIpEIbm5uzKkLaEYqwLvQFPkgBDlcUZ1HIpHA1dUVISEhAErb\n8Sxfvpy14lEeSx3s378fffr0gZWVFZycnBAREYHLly/Dzs6OOQ92797NjN6SkhKFlkDqRAjOjvdB\n02RZeWiSfvahbTzT0tLQu3dvWFpasjZtsn2gSfuB8/GozMj9kM1XWFiInTt3wtTUFL169cKFCxdU\nNb0yVDWv4PugicaBJgr7qmQY/BuauCc4nw5N9cZ/KJpigAGlqSndu3dnjtAbN27A3d0dcXFxAICx\nY8di3rx5ap/Xp6Qy5YOmyuEPVbjPnTsHXV1dPH36VKXzqwg7duzAF198gT179gAAjh07Bjc3N6xa\ntQqRkZEwMjJixbJCQ0MV5AKPRvk0aJIsexeaoJ99aBtP2XtNE4r6cVSPFgDQJ0YsFtNnn33G/v7i\nxQtydnamsWPHkqur679+/9mzZ9SkSRMiIkKpIU7a2tqfepplEIvFFBoaSvfu3SNDQ0N6/vw5OTg4\nkJmZGQ0bNoyCg4Opbdu25OvrS8nJyRQREUEGBgZUv359NoZUKlXLXP8LAKAbN25QSUkJGRgYEFHp\n+mppaZGWlpba51NQUEBeXl40atQosrCwYJ/PmjWLsrOzKSgoiBo1asTuv+zRqoy5cjj/hEQiofDw\ncCoqKqJJkyZR/fr1admyZfTw4UPq378/mZmZKVyro6NTeZN9TwCQVCpV2Zz/bT1ycnIoOjqaWrZs\nSSNHjiRtbW1ydnamvLw8WrRoETVt2pTq1atX4fE4imiiHP5YnefChQv0008/EQDS0tKqND0iPz+f\nDAwMyNvbmyZPnkx5eXm0adMmevToEc2dO5diY2PpypUrZGJiQsOGDVP7/OTJyMggBwcHSklJYZ8d\nOnSIEhMTycXFhTp27Ejh4eF069YtCg0NJWNjYyIitsZCQNWy7GPQFP0sOjqaLC0tqXr16vTbb79R\nixYtqEGDBnTnzh0yMjIiMzMzql27Nrte+VlV/junaqEzc+bMmZ96UJlwjo6Oplq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"text": [ "<matplotlib.figure.Figure at 0x1188bb2d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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WrVtj+fLlvCNWWG5uLpYuXYqUlBRIkgQvLy988cUXaivECT+urq6IiYlBQECA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TIbN24U0dHRQqPRiAMHDojw8HBp3d69e8WRI0eEv79/vf/2g0ttiouL67zcuXNHuLu7i7/+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\nAaYcGWPbe5r6BiJqOMbY3wFPX58nx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"text": [ "<matplotlib.figure.Figure at 0x1184ede50>" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "full_mc_metrics['binary_metrics_df']" ], "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>accuracy</th>\n", " <th>ap</th>\n", " <th>auc</th>\n", " <th>mcc</th>\n", " <th>pr_fig</th>\n", " <th>results_df</th>\n", " <th>roc_fig</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>class_aeroplane</th>\n", " <td> 0.8603713</td>\n", " <td> 0.8927225</td>\n", " <td> 0.9859671</td>\n", " <td> 0.51083</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_bicycle</th>\n", " <td> 0.6527666</td>\n", " <td> 0.6507277</td>\n", " <td> 0.9429867</td>\n", " <td> 0.279426</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_bird</th>\n", " <td> 0.687472</td>\n", " <td> 0.8353669</td>\n", " <td> 0.969846</td>\n", " <td> 0.3457068</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_boat</th>\n", " <td> 0.7936508</td>\n", " <td> 0.784394</td>\n", " <td> 0.9732621</td>\n", " <td> 0.3799048</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_bottle</th>\n", " <td> 0.3895615</td>\n", " <td> 0.4901989</td>\n", " <td> 0.9003062</td>\n", " <td> 0.1835326</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_bus</th>\n", " <td> 0.8294323</td>\n", " <td> 0.8513738</td>\n", " <td> 0.9843531</td>\n", " <td> 0.3963753</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_car</th>\n", " <td> 0.6191373</td>\n", " <td> 0.6574816</td>\n", " <td> 0.921111</td>\n", " <td> 0.3464022</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_cat</th>\n", " <td> 0.6938391</td>\n", " <td> 0.8213718</td>\n", " <td> 0.96655</td>\n", " <td> 0.395568</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_chair</th>\n", " <td> 0.5925029</td>\n", " <td> 0.6581116</td>\n", " <td> 0.9283187</td>\n", " <td> 0.343856</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_cow</th>\n", " <td> 0.7318626</td>\n", " <td> 0.3643397</td>\n", " <td> 0.9508406</td>\n", " <td> 0.2656333</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_diningtable</th>\n", " <td> 0.716438</td>\n", " <td> 0.6821692</td>\n", " <td> 0.9559175</td>\n", " <td> 0.3415181</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_dog</th>\n", " <td> 0.6804771</td>\n", " <td> 0.7822894</td>\n", " <td> 0.9506014</td>\n", " <td> 0.4072089</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_horse</th>\n", " <td> 0.6812842</td>\n", " <td> 0.6853829</td>\n", " <td> 0.9634471</td>\n", " <td> 0.2867896</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_motorbike</th>\n", " <td> 0.8576809</td>\n", " <td> 0.7757554</td>\n", " <td> 0.973002</td>\n", " <td> 0.4565035</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_pottedplant</th>\n", " <td> 0.4234598</td>\n", " <td> 0.5003621</td>\n", " <td> 0.9111148</td>\n", " <td> 0.1767972</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_sheep</th>\n", " <td> 0.77437</td>\n", " <td> 0.5758884</td>\n", " <td> 0.9637824</td>\n", " <td> 0.2984801</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_sofa</th>\n", " <td> 0.5528652</td>\n", " <td> 0.4769162</td>\n", " <td> 0.920226</td>\n", " <td> 0.2547741</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_train</th>\n", " <td> 0.6713299</td>\n", " <td> 0.7605917</td>\n", " <td> 0.9719644</td>\n", " <td> 0.2964198</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>class_tvmonitor</th>\n", " <td> 0.6513317</td>\n", " <td> 0.6938669</td>\n", " <td> 0.9572594</td>\n", " <td> 0.2933467</td>\n", " <td> None</td>\n", " <td> [precision, recall]</td>\n", " <td> None</td>\n", " </tr>\n", " <tr>\n", " <th>_mean</th>\n", " <td> 0.6768333</td>\n", " <td> 0.6810164</td>\n", " <td> 0.9521503</td>\n", " <td> 0.3294249</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " <td> NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ " accuracy ap auc mcc pr_fig \\\n", "class_aeroplane 0.8603713 0.8927225 0.9859671 0.51083 None \n", "class_bicycle 0.6527666 0.6507277 0.9429867 0.279426 None \n", "class_bird 0.687472 0.8353669 0.969846 0.3457068 None \n", "class_boat 0.7936508 0.784394 0.9732621 0.3799048 None \n", "class_bottle 0.3895615 0.4901989 0.9003062 0.1835326 None \n", "class_bus 0.8294323 0.8513738 0.9843531 0.3963753 None \n", "class_car 0.6191373 0.6574816 0.921111 0.3464022 None \n", "class_cat 0.6938391 0.8213718 0.96655 0.395568 None \n", "class_chair 0.5925029 0.6581116 0.9283187 0.343856 None \n", "class_cow 0.7318626 0.3643397 0.9508406 0.2656333 None \n", "class_diningtable 0.716438 0.6821692 0.9559175 0.3415181 None \n", "class_dog 0.6804771 0.7822894 0.9506014 0.4072089 None \n", "class_horse 0.6812842 0.6853829 0.9634471 0.2867896 None \n", "class_motorbike 0.8576809 0.7757554 0.973002 0.4565035 None \n", "class_pottedplant 0.4234598 0.5003621 0.9111148 0.1767972 None \n", "class_sheep 0.77437 0.5758884 0.9637824 0.2984801 None \n", "class_sofa 0.5528652 0.4769162 0.920226 0.2547741 None \n", "class_train 0.6713299 0.7605917 0.9719644 0.2964198 None \n", "class_tvmonitor 0.6513317 0.6938669 0.9572594 0.2933467 None \n", "_mean 0.6768333 0.6810164 0.9521503 0.3294249 NaN \n", "\n", " results_df roc_fig \n", "class_aeroplane [precision, recall] None \n", "class_bicycle [precision, recall] None \n", "class_bird [precision, recall] None \n", "class_boat [precision, recall] None \n", "class_bottle [precision, recall] None \n", "class_bus [precision, recall] None \n", "class_car [precision, recall] None \n", "class_cat [precision, recall] None \n", "class_chair [precision, recall] None \n", "class_cow [precision, recall] None \n", "class_diningtable [precision, recall] None \n", "class_dog [precision, recall] None \n", "class_horse [precision, recall] None \n", "class_motorbike [precision, recall] None \n", "class_pottedplant [precision, recall] None \n", "class_sheep [precision, recall] None \n", "class_sofa [precision, recall] None \n", "class_train [precision, recall] None \n", "class_tvmonitor [precision, recall] None \n", "_mean NaN NaN " ] } ], "prompt_number": 53 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results for the 4 aggregate metaclasses" ] }, { "cell_type": "code", "collapsed": false, "input": [ "results_df, preds_panel = aphrodite.results.load_pred_results(\n", " 'pascal_mc_oct16', vislab.config['paths']['shared_data'] + '/results', force=True)\n", "preds_panel" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results in collection pascal_mc_oct16: 8\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "<class 'pandas.core.panel.Panel'>\n", "Dimensions: 4 (items) x 17125 (major_axis) x 4 (minor_axis)\n", "Items axis: clf pascal_metaclass_animal to clf pascal_metaclass_vehicle\n", "Major_axis axis: 2007_000027 to 2012_004331\n", "Minor_axis axis: decaf_fc6 vw to split" ] } ], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "mc_pred_df = preds_panel.minor_xs('decaf_fc6 vw').join(label_df)\n", "pred_prefix = 'clf pascal'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "full_mc_metrics = vislab.results.multiclass_metrics(\n", " mc_pred_df, pred_prefix, balanced=False,\n", " with_plot=True, with_print=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "------------------------------------------------------------\n", "Classification metrics on the full dataset\n", "top_k_accuracies: [0.634, 0.925, 0.989, 1.0]\n", " precision recall f1-score support\n", "metaclass_animal 0.963374 0.529758 0.683603 4419\n", "metaclass_indoor 0.777259 0.179174 0.291217 2785\n", "metaclass_person 0.556489 0.969392 0.707075 7449\n", "metaclass_vehicle 0.740019 0.322411 0.449141 2472\n", "accuracy: 0.63404379562\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ryuet51SdT82inIoJlFPxdFrTKSIiIiIiIlWuwk5namoq4eHhhIaGMmXKlFKPP//880RF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\nXp3JkyfzwAMPEBgY6BzpLCws1FUjqRI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"text": [ "<matplotlib.figure.Figure at 0x1185f4790>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pLi7O6CioRJgLuMJcwBXmApcaNWqUli9frkaNGqlDhw7y8fGRxCptVxNHeAA3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\nAAAUZrfs4fniiy80aNAgbd26VZs2bVLfvn0VHx+vOnXqaPPmzdq6dat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"text": [ "<matplotlib.figure.Figure at 0x118278450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11bf166d0>" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "full_mc_metrics['binary_metrics_df']" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	bsd-2-clause
GoogleCloudPlatform/asl-ml-immersion	notebooks/image_models/solutions/2_mnist_models.ipynb	1	20117	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST Image Classification with TensorFlow on Cloud AI Platform\n", "\n", "This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras).\n", "\n", "## Learning Objectives\n", "1. Understand how to build a Dense Neural Network (DNN) for image classification\n", "2. Understand how to use dropout (DNN) for image classification\n", "3. Understand how to use Convolutional Neural Networks (CNN)\n", "4. Know how to deploy and use an image classifcation model using Google Cloud's [AI Platform](https://cloud.google.com/ai-platform/)\n", "\n", "First things first. Configure the parameters below to match your own Google Cloud project details." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from datetime import datetime\n", "\n", "REGION = \"us-central1\"\n", "PROJECT = !(gcloud config get-value core/project)\n", "PROJECT = PROJECT[0]\n", "BUCKET = PROJECT\n", "MODEL_TYPE = \"cnn\" # \"linear\", \"dnn\", \"dnn_dropout\", or \"cnn\"\n", "\n", "# Do not change these\n", "os.environ[\"PROJECT\"] = PROJECT\n", "os.environ[\"BUCKET\"] = BUCKET\n", "os.environ[\"REGION\"] = REGION\n", "os.environ[\"MODEL_TYPE\"] = MODEL_TYPE\n", "os.environ[\"IMAGE_URI\"] = os.path.join(\"gcr.io\", PROJECT, \"mnist_models\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building a dynamic model\n", "\n", "In the previous notebook, <a href=\"mnist_linear.ipynb\">mnist_linear.ipynb</a>, we ran our code directly from the notebook. In order to run it on the AI Platform, it needs to be packaged as a python module.\n", "\n", "The boilerplate structure for this module has already been set up in the folder `mnist_models`. The module lives in the sub-folder, `trainer`, and is designated as a python package with the empty `__init__.py` (`mnist_models/trainer/__init__.py`) file. It still needs the model and a trainer to run it, so let's make them.\n", "\n", "Let's start with the trainer file first. This file parses command line arguments to feed into the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile mnist_models/trainer/task.py\n", "import argparse\n", "import json\n", "import os\n", "import sys\n", "\n", "from . import model\n", "\n", "\n", "def _parse_arguments(argv):\n", " \"\"\"Parses command-line arguments.\"\"\"\n", " parser = argparse.ArgumentParser()\n", " parser.add_argument(\n", " '--model_type',\n", " help='Which model type to use',\n", " type=str, default='linear')\n", " parser.add_argument(\n", " '--epochs',\n", " help='The number of epochs to train',\n", " type=int, default=10)\n", " parser.add_argument(\n", " '--steps_per_epoch',\n", " help='The number of steps per epoch to train',\n", " type=int, default=100)\n", " parser.add_argument(\n", " '--job-dir',\n", " help='Directory where to save the given model',\n", " type=str, default='mnist_models/')\n", " return parser.parse_known_args(argv)\n", "\n", "\n", "def main():\n", " \"\"\"Parses command line arguments and kicks off model training.\"\"\"\n", " args = _parse_arguments(sys.argv[1:])[0]\n", "\n", " # Configure path for hyperparameter tuning.\n", " trial_id = json.loads(\n", " os.environ.get('TF_CONFIG', '{}')).get('task', {}).get('trial', '')\n", " output_path = args.job_dir if not trial_id else args.job_dir + '/'\n", "\n", " model_layers = model.get_layers(args.model_type)\n", " image_model = model.build_model(model_layers, args.job_dir)\n", " model_history = model.train_and_evaluate(\n", " image_model, args.epochs, args.steps_per_epoch, args.job_dir)\n", "\n", "\n", "if __name__ == '__main__':\n", " main()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's group non-model functions into a util file to keep the model file simple. We'll copy over the `scale` and `load_dataset` functions from the previous lab." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile mnist_models/trainer/util.py\n", "import tensorflow as tf\n", "\n", "\n", "def scale(image, label):\n", " \"\"\"Scales images from a 0-255 int range to a 0-1 float range\"\"\"\n", " image = tf.cast(image, tf.float32)\n", " image /= 255\n", " image = tf.expand_dims(image, -1)\n", " return image, label\n", "\n", "\n", "def load_dataset(\n", " data, training=True, buffer_size=5000, batch_size=100, nclasses=10):\n", " \"\"\"Loads MNIST dataset into a tf.data.Dataset\"\"\"\n", " (x_train, y_train), (x_test, y_test) = data\n", " x = x_train if training else x_test\n", " y = y_train if training else y_test\n", " # One-hot encode the classes\n", " y = tf.keras.utils.to_categorical(y, nclasses)\n", " dataset = tf.data.Dataset.from_tensor_slices((x, y))\n", " dataset = dataset.map(scale).batch(batch_size)\n", " if training:\n", " dataset = dataset.shuffle(buffer_size).repeat()\n", " return dataset\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's code the models! The [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras) accepts an array of [layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers) into a [model object](https://www.tensorflow.org/api_docs/python/tf/keras/Model), so we can create a dictionary of layers based on the different model types we want to use. The below file has two functions: `get_layers` and `create_and_train_model`. We will build the structure of our model in `get_layers`. Last but not least, we'll copy over the training code from the previous lab into `train_and_evaluate`.\n", "\n", "TODO 1: Define the Keras layers for a DNN model \n", "TODO 2: Define the Keras layers for a dropout model \n", "TODO 3: Define the Keras layers for a CNN model \n", "\n", "Hint: These models progressively build on each other. Look at the imported `tensorflow.keras.layers` modules and the default values for the variables defined in `get_layers` for guidance." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile mnist_models/trainer/model.py\n", "import os\n", "import shutil\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras import Sequential\n", "from tensorflow.keras.callbacks import TensorBoard\n", "from tensorflow.keras.layers import (\n", " Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Softmax)\n", "\n", "from . import util\n", "\n", "\n", "# Image Variables\n", "WIDTH = 28\n", "HEIGHT = 28\n", "\n", "\n", "def get_layers(\n", " model_type,\n", " nclasses=10,\n", " hidden_layer_1_neurons=400,\n", " hidden_layer_2_neurons=100,\n", " dropout_rate=0.25,\n", " num_filters_1=64,\n", " kernel_size_1=3,\n", " pooling_size_1=2,\n", " num_filters_2=32,\n", " kernel_size_2=3,\n", " pooling_size_2=2):\n", " \"\"\"Constructs layers for a keras model based on a dict of model types.\"\"\"\n", " model_layers = {\n", " 'linear': [\n", " Flatten(),\n", " Dense(nclasses),\n", " Softmax()\n", " ],\n", " 'dnn': [\n", " Flatten(),\n", " Dense(hidden_layer_1_neurons, activation='relu'),\n", " Dense(hidden_layer_2_neurons, activation='relu'),\n", " Dense(nclasses),\n", " Softmax()\n", " ],\n", " 'dnn_dropout': [\n", " Flatten(),\n", " Dense(hidden_layer_1_neurons, activation='relu'),\n", " Dense(hidden_layer_2_neurons, activation='relu'),\n", " Dropout(dropout_rate),\n", " Dense(nclasses),\n", " Softmax()\n", " ],\n", " 'cnn': [\n", " Conv2D(num_filters_1, kernel_size=kernel_size_1,\n", " activation='relu', input_shape=(WIDTH, HEIGHT, 1)),\n", " MaxPooling2D(pooling_size_1),\n", " Conv2D(num_filters_2, kernel_size=kernel_size_2,\n", " activation='relu'),\n", " MaxPooling2D(pooling_size_2),\n", " Flatten(),\n", " Dense(hidden_layer_1_neurons, activation='relu'),\n", " Dense(hidden_layer_2_neurons, activation='relu'),\n", " Dropout(dropout_rate),\n", " Dense(nclasses),\n", " Softmax()\n", " ]\n", " }\n", " return model_layers[model_type]\n", "\n", "\n", "def build_model(layers, output_dir):\n", " \"\"\"Compiles keras model for image classification.\"\"\"\n", " model = Sequential(layers)\n", " model.compile(optimizer='adam',\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'])\n", " return model\n", "\n", "\n", "def train_and_evaluate(model, num_epochs, steps_per_epoch, output_dir):\n", " \"\"\"Compiles keras model and loads data into it for training.\"\"\"\n", " mnist = tf.keras.datasets.mnist.load_data()\n", " train_data = util.load_dataset(mnist)\n", " validation_data = util.load_dataset(mnist, training=False)\n", "\n", " callbacks = []\n", " if output_dir:\n", " tensorboard_callback = TensorBoard(log_dir=output_dir)\n", " callbacks = [tensorboard_callback]\n", "\n", " history = model.fit(\n", " train_data,\n", " validation_data=validation_data,\n", " epochs=num_epochs,\n", " steps_per_epoch=steps_per_epoch,\n", " verbose=2,\n", " callbacks=callbacks)\n", "\n", " if output_dir:\n", " export_path = os.path.join(output_dir, 'keras_export')\n", " model.save(export_path, save_format='tf')\n", "\n", " return history\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Local Training\n", "\n", "With everything set up, let's run locally to test the code. Some of the previous tests have been copied over into a testing script `mnist_models/trainer/test.py` to make sure the model still passes our previous checks. On `line 13`, you can specify which model types you would like to check. `line 14` and `line 15` has the number of epochs and steps per epoch respectively.\n", "\n", "Moment of truth! Run the code below to check your models against the unit tests. If you see \"OK\" at the end when it's finished running, congrats! You've passed the tests!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python3 -m mnist_models.trainer.test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we know that our models are working as expected, let's run it on the [Google Cloud AI Platform](https://cloud.google.com/ml-engine/docs/). We can run it as a python module locally first using the command line.\n", "\n", "The below cell transfers some of our variables to the command line as well as create a job directory including a timestamp." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "current_time = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", "model_type = \"cnn\"\n", "\n", "os.environ[\"MODEL_TYPE\"] = model_type\n", "os.environ[\"JOB_DIR\"] = \"mnist_models/models/{}_{}/\".format(\n", " model_type, current_time\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell below runs the local version of the code. The epochs and steps_per_epoch flag can be changed to run for longer or shorther, as defined in our `mnist_models/trainer/task.py` file." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "python3 -m mnist_models.trainer.task \\\n", " --job-dir=$JOB_DIR \\\n", " --epochs=5 \\\n", " --steps_per_epoch=50 \\\n", " --model_type=$MODEL_TYPE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training on the cloud\n", "\n", "We will use a [Deep Learning Container](https://cloud.google.com/ai-platform/deep-learning-containers/docs/overview) to train this model on AI Platform. Below is a simple [Dockerlife](https://docs.docker.com/engine/reference/builder/) which copies our code to be used in a TensorFlow 2.3 environment." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile mnist_models/Dockerfile\n", "FROM gcr.io/deeplearning-platform-release/tf2-cpu.2-3\n", "COPY mnist_models/trainer /mnist_models/trainer\n", "ENTRYPOINT [\"python3\", \"-m\", \"mnist_models.trainer.task\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The below command builds the image and ships it off to Google Cloud so it can be used for AI Platform. When built, it will show up [here](http://console.cloud.google.com/gcr) with the name `mnist_models`. ([Click here](https://console.cloud.google.com/cloud-build) to enable Cloud Build)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!docker build -f mnist_models/Dockerfile -t $IMAGE_URI ./" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!docker push $IMAGE_URI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can kickoff the [AI Platform training job](https://cloud.google.com/sdk/gcloud/reference/ai-platform/jobs/submit/training). We can pass in our docker image using the `master-image-uri` flag." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "current_time = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n", "model_type = \"cnn\"\n", "\n", "os.environ[\"MODEL_TYPE\"] = model_type\n", "os.environ[\"JOB_DIR\"] = \"gs://{}/mnist_{}_{}/\".format(\n", " BUCKET, model_type, current_time\n", ")\n", "os.environ[\"JOB_NAME\"] = f\"mnist_{model_type}_{current_time}\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "echo $JOB_DIR $REGION $JOB_NAME\n", "gcloud ai-platform jobs submit training $JOB_NAME \\\n", " --staging-bucket=gs://$BUCKET \\\n", " --region=$REGION \\\n", " --master-image-uri=$IMAGE_URI \\\n", " --scale-tier=BASIC_GPU \\\n", " --job-dir=$JOB_DIR \\\n", " -- \\\n", " --model_type=$MODEL_TYPE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deploying and predicting with model\n", "\n", "Once you have a model you're proud of, let's deploy it! All we need to do is give AI Platform the location of the model. Below uses the keras export path of the previous job, but `${JOB_DIR}keras_export/` can always be changed to a different path." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n", "MODEL_NAME = f\"mnist_{TIMESTAMP}\"\n", "\n", "%env MODEL_NAME = $MODEL_NAME" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "MODEL_VERSION=${MODEL_TYPE}\n", "MODEL_LOCATION=${JOB_DIR}keras_export/\n", "echo \"Deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n", "gcloud ai-platform models create ${MODEL_NAME} --region $REGION\n", "gcloud ai-platform versions create ${MODEL_VERSION} \\\n", " --model ${MODEL_NAME} \\\n", " --origin ${MODEL_LOCATION} \\\n", " --framework tensorflow \\\n", " --runtime-version=2.3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To predict with the model, let's take one of the example images.\n", "\n", "TODO 4: Write a `.json` file with image data to send to an AI Platform deployed model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import codecs\n", "import json\n", "\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "\n", "HEIGHT = 28\n", "WIDTH = 28\n", "IMGNO = 12\n", "\n", "mnist = tf.keras.datasets.mnist.load_data()\n", "(x_train, y_train), (x_test, y_test) = mnist\n", "test_image = x_test[IMGNO]\n", "\n", "jsondata = test_image.reshape(HEIGHT, WIDTH, 1).tolist()\n", "json.dump(jsondata, codecs.open(\"test.json\", \"w\", encoding=\"utf-8\"))\n", "plt.imshow(test_image.reshape(HEIGHT, WIDTH));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can send it to the prediction service. The output will have a 1 in the index of the corresponding digit it is predicting. Congrats! You've completed the lab!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "gcloud ai-platform predict \\\n", " --model=${MODEL_NAME} \\\n", " --version=${MODEL_TYPE} \\\n", " --json-instances=./test.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2021 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "metadata": { "environment": { "kernel": "python3", "name": "tf2-gpu.2-6.m86", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m86" }, "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
FourthCohortAwesome/NightThree	ThreeSoln_jef.ipynb	1	1986	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter the name of the file: alt2.csv\n" ] } ], "source": [ "import pandas as pd \n", "file = input(\"Enter the name of the file: \")\n", "row, col = (pd.read_csv(file).shape)\n", "\n", "for _ in file:\n", " if row == 7:\n", " df = pd.DataFrame([[\"a\",\"b\",\"c\",\"d\"]]7,columns = ['One','Two','Three','Four'])\n", " df.to_csv('Dest_jef_alt1.tsv',index=False, sep = '\\t')\n", " \n", " elif row == 9:\n", " df = pd.read_csv(file, delimiter=':')\n", " df[\"TWO\"][df[\"ONE\"] == 1] = 3\n", " df[\"TWO\"][df[\"ONE\"] == 2] = 4\n", " df[\"TWO\"][df[\"ONE\"] == 3] = 6\n", "\n", " df[\"ONE\"][df[\"TWO\"] == 3] = 1\n", " df[\"ONE\"][df[\"TWO\"] == 4] = 2\n", " df[\"ONE\"][df[\"TWO\"] == 6] = 3\n", " df.to_csv('Dest_jef_alt2.tsv',index=False, sep = '\\t')\n", "\n", " elif row == 3:\n", " df = pd.DataFrame([[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\"]]3,columns = ['One','Two','Three','Four', 'Five','Six','Seven'])\n", " df.to_csv('Dest_jef_alt3.tsv',index=False, sep = '\\t')" ] }, { "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
Wx1ng/Python4DataScience.CH	Series_0_Python_Tutorials/S0EP2_Control_Flow_Data_Structure.ipynb	1	35928	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#1 美观并正确地书写Python语句\n", "\n", "###书写的美观性\n", "\n", "往往问题不仅是美观，还在于程序的可读性：" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True\n" ] } ], "source": [ "testnum = True\n", "print testnum\n", "testnum = True;print testnum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###书写的正确性\n", "\n", "用四空格或一Tab表示缩进。\n", "\n", "错误缩进：" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (<ipython-input-1-ca4e5ad1bc31>, line 2)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-1-ca4e5ad1bc31>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m print \"Value is ,{0}\".format(i)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" ] } ], "source": [ "i = 42\n", " print \"Value is ,{0}\".format(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "正确缩进：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3\n" ] } ], "source": [ "for i in xrange(5):\n", " if i > 3:\n", " break\n", " else:\n", " print i,\n", "else:\n", " print \"Expectedly Finished\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#2 Python控制流语法（if/else/for/while等）\n", "\n", "\n", "###2.1 if和else\n", "\n", "在Python中，标准的if-else或者单if条件语句语法是这样的：" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "do something\n" ] } ], "source": [ "expression = True\n", "\n", "if expression:\n", " print \"do something\"\n", "else:\n", " print \"do something_else\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "程序员笑话：" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "hot = True\n", "watermelon = True\n", "cnt_steamdumpling = 12\n", "if hot & watermelon:\n", " cnt_steamdumpling = 1\n", "\n", "print cnt_steamdumpling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运算符有优先级：" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Damn...\n" ] } ], "source": [ "yourage = 25\n", "had_a_girlfriend = False\n", "\n", "if yourage > 18 and not had_a_girlfriend:\n", " print \"Damn...\"\n", "else:\n", " print \"Ok.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "多重if-else使用elif：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(￣﹏￣) (￣ˇ￣)\n" ] } ], "source": [ "your_salary = 7000\n", "your_location = \"Beijing\"\n", "if your_salary > 100000:\n", " print \"(￣︶￣)> []\"\n", "elif your_salary >= 25000:\n", " print \"<(￣︶￣)/\"\n", "else:\n", " print \"(￣﹏￣) (￣ˇ￣)\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "书写if else 语句时候也是另一个需要注意缩进的地方\n", "\n", "如果你在Ipython中书写if-else语句的话，Ipython将在“:”的下一行自动为你缩进，这点非常方便，但是不推荐以下风格的代码：" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "do something\n" ] } ], "source": [ "if True: print \"do something\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 条件表达式（三元操作符）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "正常的if/else写法，有些臃肿" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "17\n" ] } ], "source": [ "x,y = 111,17\n", "if x < y:\n", " smaller = x\n", "else:\n", " smaller = y\n", "print smaller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "简洁的三元表达式，Ruby风味" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "x , y = 25,10\n", "smaller = x if x < y else y\n", "print smaller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "短路求值写法：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "3\n" ] } ], "source": [ "x , y = 3 , 5\n", "smaller = x < y and x or y # x = a?b:c None,'',0,False\n", "print smaller\n", "\n", "x , y = 5 , 3\n", "smaller = x < y and x or y\n", "print smaller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "装X失败案例：x被判False" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "0\n" ] } ], "source": [ "x , y = None , 0\n", "\n", "print x < y\n", "\n", "smaller = x > y and x or y\n", "print smaller" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###2.3 For循环" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "先介绍range(start,end,step)函数：（生成可迭代序列，Iterable）" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "[1, 2, 3, 4]\n", "[5, 4, 3, 2]\n", "xrange(10)\n", "0 1 4 9 16 25 36 49 64 81\n" ] } ], "source": [ "print range(10)\n", "print range(1,5,1)\n", "print range(5,1,-1)\n", "print xrange(10)\n", "for i in xrange(10):\n", " print i * 2,\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = \"string\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for循环可以方便的迭代字符串" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s\n", "t\n", "r\n", "i\n", "n\n", "g\n" ] } ], "source": [ "for eachletter in s:\n", " print eachletter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python 默认会在每个打印出来的字母后加换行符。\n", "\n", "如果不需要这个特性，则在语句后加逗号“,”：" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s t r i n g\n" ] } ], "source": [ "for eachletter in s:\n", " print eachletter," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####同步取循环索引\n", "\n", "回忆一下基础语法中介绍的len函数和字符串的切片访问方法，不推荐以下写法：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n", "B 0\n", "e 1\n", " 2\n", "E 3\n", "n 4\n", "u 5\n", "m 6\n", "e 7\n", "r 8\n", "a 9\n", "t 10\n", "e 11\n", "d 12\n" ] } ], "source": [ "a = \"Be Enumerated\"\n", "lena = len(a)\n", "print lena\n", "print range(lena)\n", "for eachnum in range(lena):\n", " print \"{0} {1:>2}\".format(a[eachnum],eachnum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用enumerate方法来同步循环索引：" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 B\n", "1 e\n", "2 \n", "4 n\n", "6 m\n", "7 e\n", "8 r\n", "10 t\n", "11 e\n", "12 d\n" ] } ], "source": [ "for idx, element in enumerate(a):\n", " if idx%2==0 or element=='e':\n", " print idx, element " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 while语法和奇特的else\n", "\n", "while语句的形式类似if语句。\n", "\n", "如果while后面的条件为真，冒号下的代码块就会不断循环执行，直到判断条件变为0或者False.\n", "\n", "while语句后可以写else语句，如果没被干涉，最终收尾的时候会执行代码。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "looping 0\n", "1\n", "looping 1\n", "2\n", "looping 2\n", "3\n", "looping 3\n", "4\n", "Finite loop\n", "looping 0\n", "1\n", "looping 1\n", "2\n", "looping 2\n", "3\n", "looping 3\n", "4\n" ] } ], "source": [ "count = 0\n", "while count <= 3:\n", " print \"looping {0}\".format(count)\n", " count += 1\n", " print count\n", "else:\n", " print \"Finite loop\"\n", "\n", "count = 0\n", "while True:\n", " print \"looping {0}\".format(count)\n", " count += 1\n", " print count\n", " if count > 3:\n", " break\n", "else:\n", " print \"Broken loop\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while-else的组合有些奇特，补充一个更奇特的组合：for-else。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "Finished\n", "====================\n", "0\n", "1\n" ] } ], "source": [ "for i in xrange(3):\n", " print i\n", "else:\n", " print \"Finished\"\n", "\n", "print \"=\" * 20\n", "\n", "for i in xrange(3):\n", " if i > 1:\n", " break\n", " print i\n", "else:\n", " print \"Finished\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###2.5 干涉循环行为（break/continue/pass）\n", "\n", "* pass：不做任何事。\n", "* continue：告诉 Python 跳过当前循环块中的剩余语句，继续进行下一轮循环。\n", "* break：结束当前循环来跳转到下个语句。\n", "\n", "这三个语句有时和if语句一块搭配使用。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "0.5\n", "0.25\n" ] } ], "source": [ "def foo():\n", " pass\n", "\n", "a = [1, 0, 2, 4]\n", "for element in a:\n", " if element == 0:\n", " continue\n", " print 1. / element" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1+2j)\n", "(-2+4j)\n", "(-11-16j)\n", "(-134+352j)\n" ] } ], "source": [ "z = 1 + 1j\n", "while True:\n", " if abs(z) > 100:\n", " break\n", " z = z ** 2 + 1\n", " print z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#3 Python基本数据结构详述" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python中内置了四种数据结构—列表，元组，字典和集合，用三种不同的括号就可以表示他们。\n", "\n", "list（列表）是处理一组有序项目的数据结构，列表的元素需要以[](中括号）来包裹，元素的个数和值可以改变。\n", "\n", "tuple的元素以() 来包裹，元组可以看作只读的列表。\n", "\n", "列表和元组都使用切片方法来访问元素，数字索引从0开始计数。\n", "\n", "通过切片（[]和[:]),列表和元组可以得到子集。列表的子集是列表，元组切片后结果还是元组（不可改变)。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.1 List 列表（可变类型）\n", "\n", "列表的定义可以直接使用方括号扩住数据。\n", "\n", "熟悉的切片规则[start,end,step]：" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "red white\n", "['green', 'black']\n", "['red', 'blue', 'green'] ['red', 'green', 'white']\n" ] } ], "source": [ "L = ['red','blue','green','black','white']\n", "print isinstance(L,list)\n", "print L[0],L[-1]\n", "print L[2:4]\n", "print L[:3],L[::2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "作为可变类型中的一种，列表可以直接修改：" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4371170728 4371170728\n", "['red', 'blue', 'Ruby', 'sapphire', 'white', 42, False]\n" ] } ], "source": [ "M = ['red','blue','green','black','white',42,True] # 列表中可以包含不同类型的元素\n", "M[2:4] = ['Ruby','sapphire']\n", "N = M\n", "print id(M),id(N)\n", "M[-1] = False # 可以通过切片修改\n", "print N # 可变类型的特点，不直接操作N，N内容被改变" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9] <type 'numpy.ndarray'> int64\n" ] } ], "source": [ "import numpy as np\n", "a = np.array(range(10))\n", "print a,type(a),a.dtype\n", "import scipy as sp\n", "import statsmodels as smodel\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "对于同一类型的数值数据，推荐使用运行效率更高的numpy来进行处理。\n", "\n", "对于列表，我们可以使用多种方法来进行操纵：" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['red', 'blue', 'green', 'black', 'white']\n", "['red', 'blue', 'green', 'black', 'white', 'pink']\n", "['red', 'blue', 'green', 'black', 'white'] pink\n", "['blue', 'green', 'black', 'white'] red\n", "['blue', 'green', 'black', 'white', 'pink', 'purple', 'purple']\n", "['blue', 'green', 'black', 'white', 'pink', 'purple']\n", "pink\n" ] } ], "source": [ "LM = ['red','blue','green','black','white']\n", "print LM\n", "LM.append('pink')\n", "print LM # 列表尾部添加一个元素\n", "popped = LM.pop()# 删除并返回列表最后一个元素\n", "print LM,popped\n", "#试试LM.pop(0)\n", "popped2 = LM.pop(0)\n", "print LM,popped2\n", "LM.extend(['pink','purple','purple']) # 讲extend后的序列添加到列表中，extend后的内容应该是可迭代的\n", "print LM\n", "LM.remove('purple') # 删除指定值的一个元素\n", "print LM\n", "print popped" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['blue', 'green', 'black', 'white', 'pink', 'purple']\n", "<built-in method reverse of list object at 0x1083ca320>\n", "['blue', 'green', 'black', 'white', 'pink', 'purple']\n", "['blue', 'green', 'black', 'white', 'pink', 'purple', 'blue', 'green', 'black', 'white', 'pink', 'purple']\n", "['blue', 'green', 'black', 'white', 'pink', 'purple', 'blue', 'green', 'black', 'white', 'pink', 'purple']\n" ] } ], "source": [ "print LM[::-1]\n", "LL = LM.reverse #此时已经调用完原地翻转列表方法\n", "LL()\n", "print LL\n", "print LM\n", "print LM2 #也可以像字符串一样用方法\n", "print LM+LM #也可以像字符串一样用+方法，但+方法不能直接增加元素。" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['red', 'blue', 'Ruby', 'sapphire', 'white', 42, False]\n", "[False, 42, 'Ruby', 'blue', 'red', 'sapphire', 'white']\n", "['red', 'blue', 'Ruby', 'sapphire', 'white', 42, False]\n", "[False, 42, 'Ruby', 'blue', 'red', 'sapphire', 'white']\n" ] } ], "source": [ "#LL_law = M.sor\n", "#M.sort()\n", "#print M\n", "#print LL_law\n", "print M\n", "Mnew = sorted(M)\n", "print M\n", "M.sort()\n", "print M" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "判断某个元素是否在列表中，可以使用in 方法" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "Ok\n" ] } ], "source": [ "LL_law = ['red', 'blue', 'Ruby', 'sapphire', 'white', 42, False]\n", "my_precious = \"silmarils\"\n", "print my_precious in LL_law\n", "if \"Ruby\" in LL_law:\n", " print \"Ok\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "不爽字符串很久了？" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['M', 'u', 'k', 'a', 't', 's', 'u', 'k', 'u']\n", "Mukatsuku\n" ] } ], "source": [ "string = 'Mukatsuku'\n", "ls_str = list(string)\n", "print ls_str\n", "print ''.join(ls_str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.2 Tuple 元组（不可变类型）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "元组的元素之间以逗号隔开，可以用小括号包裹（推荐）：" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<type 'tuple'> <type 'tuple'>\n" ] } ], "source": [ "war3 = ('Orc','Humans','Undead','Night Elves')\n", "heros = 'Blade Master','Farseer','Tauren Chieftain','Shadow Hunter'\n", "\n", "print type(war3),type(heros)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果需要明确地删除一个列表或者元组，使用del：" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Orc', 'Humans', 'Undead', 'Night Elves')\n" ] } ], "source": [ "war3copy = war3\n", "print war3copy" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Humans\n" ] }, { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-51-39c69d1512f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mwar3copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mwar3copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Trans_Humans\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "print war3copy[1]\n", "war3copy[1]=\"Trans_Humans\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "和列表类似，元组同样支持+、*、和 in 方法。\n", "\n", "折衷方案使用“可变”元组：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(42, False, [False], (-203+1j))\n", "[42, False, [False], (-203+1j)]\n" ] } ], "source": [ "t = (42,False,[True],-203+1j)\n", "t[2][0] = False\n", "print t\n", "print list(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.3 Set 集合（可变类型）与Frozenset 冻结集合（不可变类型）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用花括号来定义，用集合操作来进行运算，用set或者frozenset转化其他序列。" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True\n", "True\n" ] } ], "source": [ "war3 = ('Orcs','Humans','Undead','Night Elves')\n", "Lord_of_ring = ('Ainur','Dragons','Dwarves','Elves','Ents','Hobbits','Humans','Orcs')\n", "test_set = set(war3)\n", "train = set(Lord_of_ring)\n", "ya_test_set = {'Orcs','Humans','Undead','Night Elves'}\n", "print 'Orcs' in test_set\n", "print 'Orcs' in train\n", "print 'Orcs' in ya_test_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于单个集合内的操作对set而言很方便：" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set(['Xmen', 'Undead', 'Humans', 'Orcs', 'Night Elves'])\n", "set(['Undead', 'No.17', 'No.16', 'No.18', 'Humans', 'Orcs', 'Night Elves', 'Xmen'])\n", "set(['Undead', 'Humans', 'Orcs', 'Night Elves'])\n" ] } ], "source": [ "test_set.add('Xmen')\n", "print test_set\n", "test_set.update(['No.16','No.17','No.18'])\n", "print test_set\n", "for item in ['Xmen','No.16','No.17','No.18']:\n", " test_set.remove(item)\n", "print test_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "不可变类型frozenset：" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'test_set' 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-13-14a6fa50a9f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mftest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mftest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mftest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Xmen'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'test_set' is not defined" ] } ], "source": [ "ftest = frozenset(test_set)\n", "print ftest\n", "ftest.add('Xmen')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "集合之间的所有基本操作，对于set和frozenset都适用。\n", "\n", "我们来验证两个集合论公式：\n", "\n", "$A \\hat{} B = (A \\backslash B) \\cup (B \\backslash A)$\n", "\n", "$A \\hat{} B = (A \\cup B) \\backslash ( A \\cap B)$" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "False\n", "False\n", "set(['Humans', 'Orcs'])\n", "set(['Undead', 'Hobbits', 'Dwarves', 'Humans', 'Orcs', 'Night Elves', 'Dragons', 'Ents', 'Ainur', 'Elves'])\n", "set(['Hobbits', 'Dwarves', 'Dragons', 'Ents', 'Ainur', 'Elves'])\n", "set(['Undead', 'Hobbits', 'Dwarves', 'Night Elves', 'Dragons', 'Ents', 'Ainur', 'Elves'])\n", "True\n", "True\n" ] } ], "source": [ "print test_set==train #判断是否相等\n", "print test_set<train #判断是否是子集\n", "print test_set>train #判断是否是超集\n", "print test_set&train #求交集\n", "print test_set\|train #求并集\n", "print train-test_set #求差集\n", "print test_set^train #求异或\n", "\n", "print test_set^train == ((train-test_set) \| (test_set-train))\n", "print test_set^train == (train \| test_set) - (train & test_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3.4 Dict 字典（可变数据类型）\n", "\n", "花括号扩起来，形如{key1:value1,key2:value2,key3:value3}。\n", "\n", "key是非重复的，value和key一一对应，不需要非重复。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Standard ML', 'C', 'Clojure', 'Scala']\n", "['Robin Milner', 'Dennis Ritchie', 'Richy Hickey', 'Martin Odersky']\n", "[('Standard ML', 'Robin Milner'), ('C', 'Dennis Ritchie'), ('Clojure', 'Richy Hickey'), ('Scala', 'Martin Odersky')]\n", "<dictionary-keyiterator object at 0x104891c58>\n", "<dictionary-valueiterator object at 0x104891c58>\n", "<dictionary-itemiterator object at 0x104891c58>\n" ] } ], "source": [ "language={\"Scala\":\"Martin Odersky\",\"Clojure\":\"Richy Hickey\",\\\n", " \"C\":\"Dennis Ritchie\",\"Standard ML\":\"Robin Milner\"}\n", "print language.keys() #取得键\n", "print language.values() #取得值\n", "print language.items() #取得键-值对\n", "print language.iterkeys() #取得上述内容的iterable\n", "print language.itervalues()\n", "print language.iteritems()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "取得某个键对应的值，或者增加一个键值对：" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Robin Milner\n", "Guido van Rossum\n" ] } ], "source": [ "print language['Standard ML'] \n", "language[\"Python\"]=\"Guido van Rossum\" \n", "print language['Python'] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "试验一下迭代器：" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "key=Python,value=Guido van Rossum\n", "key=Standard ML,value=Robin Milner\n", "key=C,value=Dennis Ritchie\n", "key=Clojure,value=Richy Hickey\n", "key=Scala,value=Martin Odersky\n" ] } ], "source": [ "for key in language:\n", " print 'key={0},value={1}'.format(key,language[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果要访问某个键，而字典中又不存在这个键和对应的值，将会报错：" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyError", "evalue": "'Ruby'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-2c7ef4b9ad9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mlanguage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Ruby\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 'Ruby'" ] } ], "source": [ "print language[\"Ruby\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所以使用键之前可以先判断其是否在字典中然后再取：" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "print language.has_key('Scala')\n", "print 'Ruby' in language" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "或者使用一个非常有用的方法：dict.get(key,default=None)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "They hardly understand IT\n", "Guido van Rossum\n" ] } ], "source": [ "print language.get(\"Haskell\",\"They hardly understand IT\")\n", "print language.get(\"Python\",None)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "如果需要删除字典中的某些键使用del somedict[some_key]；\n", "\n", "需要直接删除字典本身使用 del somedict即可。\n", "\n", "向字典中添加键值对，或者根据键更新值非常方便：" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matz is a short form, renew it.\n", "Yukihiro Matsumoto is the full name of Ruby's Creator.\n" ] } ], "source": [ "language[\"Ruby\"] = \"Matz\"\n", "print language[\"Ruby\"] + \" is a short form, renew it.\"\n", "language[\"Ruby\"] = \"Yukihiro Matsumoto\"\n", "print language[\"Ruby\"] + \" is the full name of Ruby's Creator.\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
lalonica/PhD	vehicles/DataExploration.ipynb	1	166116	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "\n", "\n", "c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']\n", "\n", "data = pd.read_table('D:\\\\zzzLola\\\\PhD\\\\DataSet\\\\US101\\\\test\\\\portion1Set2DT.txt', sep='\\t', header=None, names=c_names)\n", "\n", "# Stast description of the whole dataset.\n", "desc = data.describe()\n", "\n", "##++++++++++++Example\n", "# data.groupby(['col5', 'col2']).size().groupby(level=1).max()\n", "\n", "#Mean of values by vehicle Id and DataTime\n", "mean = data.groupby(['vID', 'dateTime']).mean()\n", "\n", "#Number of vehicles\n", "num_v = data.groupby(['vID']).size()\n", "\n", "#Number of registers by timestamp\n", "ts_match = data.groupby(['Timestamp']).size()\n", "ts_match_max = data.groupby(['Timestamp']).size().max()\n", "ts_match_min = data.groupby(['Timestamp']).size().min()\n", "ts_match_mean = data.groupby(['Timestamp']).size().mean()\n", "\n", "#number of register by dataTime\n", "dt_match = data.groupby(['dateTime']).size()\n", "dt_match_max = data.groupby(['dateTime']).size().max()\n", "dt_match_min = data.groupby(['dateTime']).size().min()\n", "dt_match_mean = data.groupby(['dateTime']).size().mean()\n", "\n", "#print (desc)\n", "#print (mean [:10])\n", "\n", "data.plot(kind='barh', stacked=True)\n", "\n", "#print(num_v)\n", "\n", "#print (ts_match_max, ts_match_min, ts_match_mean)\n", "#print (dt_match_max, dt_match_min, dt_match_mean)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'ts' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-6-03d51c611a78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ABCD'\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[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumsum\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[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mdf\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[1;31mNameError\u001b[0m: name 'ts' is not defined" ] } ], "source": [ "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))\n", "df = df.cumsum()\n", "plt.figure(); df.plot();" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'ts' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-7-c7c03188f057>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ABCD'\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 7\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mdf\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[1;31mNameError\u001b[0m: name 'ts' is not defined" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "\n", "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))\n", "df = df.cumsum()\n", "plt.figure(); df.plot();" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "\n", "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "\n", "ts = ts.cumsum()\n", "\n", "ts.plot()\n", "\n", "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))\n", "df = df.cumsum()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "\n", "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "\n", "ts = ts.cumsum()\n", "\n", "ts.plot()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x2319bf28>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5Z7pUin6zCDsNTqqrI8PLqxh2QCz2QMORLKGQJdBOw48vvJCmLOxmsYxMhX38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\nLfb+/emxx94RWV1NN5FcWew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"text/plain": [ "<matplotlib.figure.Figure at 0x23193748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "\n", "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n", "\n", "ts = ts.cumsum()\n", "\n", "ts.plot()\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xad826be0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x53a50630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "from pandas.tools.plotting import andrews_curves\n", "\n", "\n", "c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']\n", "\n", "data = pd.read_table('D:\\\\zzzLola\\\\PhD\\\\DataSet\\\\US101\\\\test\\\\dataset1DT.txt', sep='\\t', header=None, names=c_names)\n", "\n", "# Stast description of the whole dataset.\n", "desc = data.describe()\n", "\n", "##++++++++++++Example\n", "# data.groupby(['col5', 'col2']).size().groupby(level=1).max()\n", "\n", "#Mean of values by vehicle Id and DataTime\n", "mean = data.groupby(['vID', 'dateTime']).mean()\n", "\n", "#Number of vehicles\n", "num_v = data.groupby(['vID']).size()\n", "\n", "#Number of registers by timestamp\n", "ts_match = data.groupby(['Timestamp']).size()\n", "ts_match_max = data.groupby(['Timestamp']).size().max()\n", "ts_match_min = data.groupby(['Timestamp']).size().min()\n", "ts_match_mean = data.groupby(['Timestamp']).size().mean()\n", "\n", "#number of register by dataTime\n", "dt_match = data.groupby(['dateTime']).size()\n", "dt_match_max = data.groupby(['dateTime']).size().max()\n", "dt_match_min = data.groupby(['dateTime']).size().min()\n", "dt_match_mean = data.groupby(['dateTime']).size().mean()\n", "\n", "#print (desc)\n", "#print (mean [:10])\n", "\n", "num_v.plot(kind='barh', stacked=True)\n", "\n", "#print(num_v)\n", "\n", "#print (ts_match_max, ts_match_min, ts_match_mean)\n", "#print (dt_match_max, dt_match_min, dt_match_mean)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " frID tFr Timestamp localX localY globalX globalY vLenght vWidth \\\n", "vID \n", "2 437 437 437 437 437 437 437 437 437 \n", "4 351 351 351 351 351 351 351 351 351 \n", "5 452 452 452 452 452 452 452 452 452 \n", "6 357 357 357 357 357 357 357 357 357 \n", "\n", " vType veloc accel line pred foll spac headway dateTime \n", "vID \n", "2 437 437 437 437 437 437 437 437 437 \n", "4 351 351 351 351 351 351 351 351 351 \n", "5 452 452 452 452 452 452 452 452 452 \n", "6 357 357 357 357 357 357 357 357 357 \n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x9277780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "from pandas.tools.plotting import andrews_curves\n", "\n", "\n", "c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']\n", "\n", "data = pd.read_table('D:\\\\zzzLola\\\\PhD\\\\DataSet\\\\US101\\\\test\\\\portion1Set2DT.txt', sep='\\t', header=None, names=c_names)\n", "\n", "# Stast description of the whole dataset.\n", "desc = data.describe()\n", "\n", "##++++++++++++Example\n", "# data.groupby(['col5', 'col2']).size().groupby(level=1).max()\n", "\n", "#Mean of values by vehicle Id and DataTime\n", "mean = data.groupby(['vID', 'dateTime']).mean()\n", "\n", "#Number of vehicles\n", "num_v = data.groupby(['vID']).size()\n", "\n", "\n", "#Number of registers by timestamp\n", "ts_match = data.groupby(['Timestamp']).size()\n", "ts_match_max = data.groupby(['Timestamp']).size().max()\n", "ts_match_min = data.groupby(['Timestamp']).size().min()\n", "ts_match_mean = data.groupby(['Timestamp']).size().mean()\n", "\n", "#number of register by dataTime\n", "dt_match = data.groupby(['dateTime']).size()\n", "dt_match_max = data.groupby(['dateTime']).size().max()\n", "dt_match_min = data.groupby(['dateTime']).size().min()\n", "dt_match_mean = data.groupby(['dateTime']).size().mean()\n", "\n", "#print (desc)\n", "#print (mean [:10])\n", "\n", "num_v.plot(kind='barh', stacked=True)\n", "print (data.groupby(['vID']).count())\n", "\n", "#ts_match.plot(kind='barh', stacked=True)\n", "\n", "#dt_match.plot(kind='barh', stacked=True)\n", "\n", "#print(num_v)\n", "\n", "#print (ts_match_max, ts_match_min, ts_match_mean)\n", "#print (dt_match_max, dt_match_min, dt_match_mean)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x933cb00>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mVrGACOO+1WMt8J2UFGHREOL4UclvWpS+Q5ohtxRh0RDiuL9wfl/gA0SIe4UQ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\nFwepAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc11ccf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dt_match.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import csv\n", "import matplotlib as plt\n", "from pandas.tools.plotting import andrews_curves\n", "\n", "\n", "c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']\n", "\n", "data = pd.read_table('D:\\\\zzzLola\\\\PhD\\\\DataSet\\\\US101\\\\test\\\\dataset1DT.txt', sep='\\t', header=None, names=c_names)\n", "\n", "# Stast description of the whole dataset.\n", "desc = data.describe()\n", "\n", "##++++++++++++Example\n", "# data.groupby(['col5', 'col2']).size().groupby(level=1).max()\n", "\n", "#Mean of values by vehicle Id and DataTime\n", "mean = data.groupby(['vID', 'dateTime']).mean()\n", "\n", "#Number of vehicles\n", "num_v = data.groupby(['vID']).size()\n", "\n", "\n", "#Number of registers by timestamp\n", "ts_match = data.groupby(['Timestamp', 'vID']).size()\n", "ts_match_max = data.groupby(['Timestamp']).size().max()\n", "ts_match_min = data.groupby(['Timestamp']).size().min()\n", "ts_match_mean = data.groupby(['Timestamp']).size().mean()\n", "\n", "#number of register by dataTime\n", "dt_match = data.groupby(['dateTime', 'vID']).size()\n", "dt_match_max = data.groupby(['dateTime']).size().max()\n", "dt_match_min = data.groupby(['dateTime']).size().min()\n", "dt_match_mean = data.groupby(['dateTime']).size().mean()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " frID tFr Timestamp localX localY globalX globalY vLenght vWidth \\\n", "vID \n", "2 437 437 437 437 437 437 437 437 437 \n", "4 351 351 351 351 351 351 351 351 351 \n", "5 452 452 452 452 452 452 452 452 452 \n", "6 357 357 357 357 357 357 357 357 357 \n", "8 448 448 448 448 448 448 448 448 448 \n", "9 409 409 409 409 409 409 409 409 409 \n", "10 436 436 436 436 436 436 436 436 436 \n", "12 443 443 443 443 443 443 443 443 443 \n", "13 432 432 432 432 432 432 432 432 432 \n", "14 515 515 515 515 515 515 515 515 515 \n", "18 291 291 291 291 291 291 291 291 291 \n", "20 414 414 414 414 414 414 414 414 414 \n", "21 439 439 439 439 439 439 439 439 439 \n", "22 441 441 441 441 441 441 441 441 441 \n", "23 438 438 438 438 438 438 438 438 438 \n", "25 436 436 436 436 436 436 436 436 436 \n", "26 438 438 438 438 438 438 438 438 438 \n", "27 432 432 432 432 432 432 432 432 432 \n", "31 465 465 465 465 465 465 465 465 465 \n", "32 438 438 438 438 438 438 438 438 438 \n", "33 383 383 383 383 383 383 383 383 383 \n", "34 451 451 451 451 451 451 451 451 451 \n", "35 280 280 280 280 280 280 280 280 280 \n", "37 408 408 408 408 408 408 408 408 408 \n", "39 450 450 450 450 450 450 450 450 450 \n", "40 391 391 391 391 391 391 391 391 391 \n", "42 389 389 389 389 389 389 389 389 389 \n", "43 458 458 458 458 458 458 458 458 458 \n", "44 379 379 379 379 379 379 379 379 379 \n", "47 428 428 428 428 428 428 428 428 428 \n", "... ... ... ... ... ... ... ... ... ... \n", "3004 725 725 725 725 725 725 725 725 725 \n", "3005 726 726 726 726 726 726 726 726 726 \n", "3006 741 741 741 741 741 741 741 741 741 \n", "3007 853 853 853 853 853 853 853 853 853 \n", "3008 724 724 724 724 724 724 724 724 724 \n", "3009 732 732 732 732 732 732 732 732 732 \n", "3011 842 842 842 842 842 842 842 842 842 \n", "3014 676 676 676 676 676 676 676 676 676 \n", "3015 578 578 578 578 578 578 578 578 578 \n", "3018 714 714 714 714 714 714 714 714 714 \n", "3019 704 704 704 704 704 704 704 704 704 \n", "3021 678 678 678 678 678 678 678 678 678 \n", "3022 641 641 641 641 641 641 641 641 641 \n", "3023 403 403 403 403 403 403 403 403 403 \n", "3024 834 834 834 834 834 834 834 834 834 \n", "3025 644 644 644 644 644 644 644 644 644 \n", "3026 326 326 326 326 326 326 326 326 326 \n", "3030 667 667 667 667 667 667 667 667 667 \n", "3032 820 820 820 820 820 820 820 820 820 \n", "3033 633 633 633 633 633 633 633 633 633 \n", "3034 567 567 567 567 567 567 567 567 567 \n", "3101 255 255 255 255 255 255 255 255 255 \n", "3102 443 443 443 443 443 443 443 443 443 \n", "3103 461 461 461 461 461 461 461 461 461 \n", "3104 474 474 474 474 474 474 474 474 474 \n", "3105 534 534 534 534 534 534 534 534 534 \n", "3106 515 515 515 515 515 515 515 515 515 \n", "3107 282 282 282 282 282 282 282 282 282 \n", "3108 359 359 359 359 359 359 359 359 359 \n", "3109 510 510 510 510 510 510 510 510 510 \n", "\n", " vType veloc accel line pred foll spac headway dateTime \n", "vID \n", "2 437 437 437 437 437 437 437 437 437 \n", "4 351 351 351 351 351 351 351 351 351 \n", "5 452 452 452 452 452 452 452 452 452 \n", "6 357 357 357 357 357 357 357 357 357 \n", "8 448 448 448 448 448 448 448 448 448 \n", "9 409 409 409 409 409 409 409 409 409 \n", "10 436 436 436 436 436 436 436 436 436 \n", "12 443 443 443 443 443 443 443 443 443 \n", "13 432 432 432 432 432 432 432 432 432 \n", "14 515 515 515 515 515 515 515 515 515 \n", "18 291 291 291 291 291 291 291 291 291 \n", "20 414 414 414 414 414 414 414 414 414 \n", "21 439 439 439 439 439 439 439 439 439 \n", "22 441 441 441 441 441 441 441 441 441 \n", "23 438 438 438 438 438 438 438 438 438 \n", "25 436 436 436 436 436 436 436 436 436 \n", "26 438 438 438 438 438 438 438 438 438 \n", "27 432 432 432 432 432 432 432 432 432 \n", "31 465 465 465 465 465 465 465 465 465 \n", "32 438 438 438 438 438 438 438 438 438 \n", "33 383 383 383 383 383 383 383 383 383 \n", "34 451 451 451 451 451 451 451 451 451 \n", "35 280 280 280 280 280 280 280 280 280 \n", "37 408 408 408 408 408 408 408 408 408 \n", "39 450 450 450 450 450 450 450 450 450 \n", "40 391 391 391 391 391 391 391 391 391 \n", "42 389 389 389 389 389 389 389 389 389 \n", "43 458 458 458 458 458 458 458 458 458 \n", "44 379 379 379 379 379 379 379 379 379 \n", "47 428 428 428 428 428 428 428 428 428 \n", "... ... ... ... ... ... ... ... ... ... \n", "3004 725 725 725 725 725 725 725 725 725 \n", "3005 726 726 726 726 726 726 726 726 726 \n", "3006 741 741 741 741 741 741 741 741 741 \n", "3007 853 853 853 853 853 853 853 853 853 \n", "3008 724 724 724 724 724 724 724 724 724 \n", "3009 732 732 732 732 732 732 732 732 732 \n", "3011 842 842 842 842 842 842 842 842 842 \n", "3014 676 676 676 676 676 676 676 676 676 \n", "3015 578 578 578 578 578 578 578 578 578 \n", "3018 714 714 714 714 714 714 714 714 714 \n", "3019 704 704 704 704 704 704 704 704 704 \n", "3021 678 678 678 678 678 678 678 678 678 \n", "3022 641 641 641 641 641 641 641 641 641 \n", "3023 403 403 403 403 403 403 403 403 403 \n", "3024 834 834 834 834 834 834 834 834 834 \n", "3025 644 644 644 644 644 644 644 644 644 \n", "3026 326 326 326 326 326 326 326 326 326 \n", "3030 667 667 667 667 667 667 667 667 667 \n", "3032 820 820 820 820 820 820 820 820 820 \n", "3033 633 633 633 633 633 633 633 633 633 \n", "3034 567 567 567 567 567 567 567 567 567 \n", "3101 255 255 255 255 255 255 255 255 255 \n", "3102 443 443 443 443 443 443 443 443 443 \n", "3103 461 461 461 461 461 461 461 461 461 \n", "3104 474 474 474 474 474 474 474 474 474 \n", "3105 534 534 534 534 534 534 534 534 534 \n", "3106 515 515 515 515 515 515 515 515 515 \n", "3107 282 282 282 282 282 282 282 282 282 \n", "3108 359 359 359 359 359 359 359 359 359 \n", "3109 510 510 510 510 510 510 510 510 510 \n", "\n", "[2169 rows x 18 columns]\n" ] } ], "source": [ "print (data.groupby(['vID']).count())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xead6470>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xeadc320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_v.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xddcf550>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xeacc780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ts_match.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x18cfa6d8>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EmBYSs0D5zuGU7/S2u9NZl5Wcc+8igWFtYEIbG+sjM0/RWS8WYs4RYs7+ne9j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\nAwDQM3TOAAD0DJ0zAAA9Q+cMAEDP/H+kqpA2OzapGgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13ef8eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dt_match.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(194, 1, 123.89526707944171)\n", "(1926, 3, 1237.524109014675)\n" ] } ], "source": [ "print (ts_match_max, ts_match_min, ts_match_mean)\n", "print (dt_match_max, dt_match_min, dt_match_mean)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " frID tFr Timestamp localX localY \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 16.5 437 1.118847e+12 16.395125 49.380625 \n", " 2005-06-15 14:49:41 25.5 437 1.118847e+12 16.248700 85.049000 \n", " 2005-06-15 14:49:42 35.5 437 1.118847e+12 16.549500 122.327800 \n", " 2005-06-15 14:49:43 45.5 437 1.118847e+12 16.078200 158.535100 \n", " 2005-06-15 14:49:44 55.5 437 1.118847e+12 14.532900 197.567300 \n", " 2005-06-15 14:49:45 65.5 437 1.118847e+12 15.562200 237.208600 \n", " 2005-06-15 14:49:46 75.5 437 1.118847e+12 16.657900 278.598900 \n", " 2005-06-15 14:49:47 85.5 437 1.118847e+12 16.130900 322.998400 \n", " 2005-06-15 14:49:48 95.5 437 1.118847e+12 16.430600 367.761600 \n", " 2005-06-15 14:49:49 105.5 437 1.118847e+12 17.410300 413.588400 \n", "\n", " globalX globalY vLenght vWidth \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 6451147.05075 1873334.595875 14.5 4.9 \n", " 2005-06-15 14:49:41 6451171.07880 1873308.121600 14.5 4.9 \n", " 2005-06-15 14:49:42 6451196.26890 1873280.366000 14.5 4.9 \n", " 2005-06-15 14:49:43 6451221.32770 1873254.149300 14.5 4.9 \n", " 2005-06-15 14:49:44 6451249.44020 1873226.795300 14.5 4.9 \n", " 2005-06-15 14:49:45 6451276.55360 1873197.369400 14.5 4.9 \n", " 2005-06-15 14:49:46 6451305.62580 1873167.561400 14.5 4.9 \n", " 2005-06-15 14:49:47 6451337.91880 1873137.082800 14.5 4.9 \n", " 2005-06-15 14:49:48 6451370.43160 1873105.807000 14.5 4.9 \n", " 2005-06-15 14:49:49 6451403.48850 1873073.998600 14.5 4.9 \n", "\n", " vType veloc accel line pred foll spac \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 2 40.0025 0.03125 2 0 0.0 0 \n", " 2005-06-15 14:49:41 2 39.0260 -1.85300 2 0 11.7 0 \n", " 2005-06-15 14:49:42 2 35.4440 -2.79500 2 0 13.0 0 \n", " 2005-06-15 14:49:43 2 38.1130 4.21500 2 0 13.0 0 \n", " 2005-06-15 14:49:44 2 39.4610 0.59800 2 0 13.0 0 \n", " 2005-06-15 14:49:45 2 39.8820 0.20500 2 0 13.0 0 \n", " 2005-06-15 14:49:46 2 43.2380 3.94300 2 0 13.0 0 \n", " 2005-06-15 14:49:47 2 44.8580 -0.37600 2 0 13.0 0 \n", " 2005-06-15 14:49:48 2 45.4040 2.91600 2 0 13.0 0 \n", " 2005-06-15 14:49:49 2 45.2830 -1.97200 2 0 13.0 0 \n", "\n", " headway \n", "vID dateTime \n", "2 2005-06-15 14:49:40 0 \n", " 2005-06-15 14:49:41 0 \n", " 2005-06-15 14:49:42 0 \n", " 2005-06-15 14:49:43 0 \n", " 2005-06-15 14:49:44 0 \n", " 2005-06-15 14:49:45 0 \n", " 2005-06-15 14:49:46 0 \n", " 2005-06-15 14:49:47 0 \n", " 2005-06-15 14:49:48 0 \n", " 2005-06-15 14:49:49 0 \n" ] } ], "source": [ "print (mean [:10])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " frID tFr Timestamp localX localY \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 16.5 437 1.118847e+12 16.395125 49.380625 \n", " 2005-06-15 14:49:41 25.5 437 1.118847e+12 16.248700 85.049000 \n", " 2005-06-15 14:49:42 35.5 437 1.118847e+12 16.549500 122.327800 \n", " 2005-06-15 14:49:43 45.5 437 1.118847e+12 16.078200 158.535100 \n", " 2005-06-15 14:49:44 55.5 437 1.118847e+12 14.532900 197.567300 \n", " 2005-06-15 14:49:45 65.5 437 1.118847e+12 15.562200 237.208600 \n", " 2005-06-15 14:49:46 75.5 437 1.118847e+12 16.657900 278.598900 \n", " 2005-06-15 14:49:47 85.5 437 1.118847e+12 16.130900 322.998400 \n", " 2005-06-15 14:49:48 95.5 437 1.118847e+12 16.430600 367.761600 \n", " 2005-06-15 14:49:49 105.5 437 1.118847e+12 17.410300 413.588400 \n", " 2005-06-15 14:49:50 115.5 437 1.118847e+12 18.355500 458.081900 \n", " 2005-06-15 14:49:51 125.5 437 1.118847e+12 17.591300 499.134600 \n", " 2005-06-15 14:49:52 135.5 437 1.118847e+12 17.668100 535.880900 \n", " 2005-06-15 14:49:53 145.5 437 1.118847e+12 18.431600 571.199700 \n", " 2005-06-15 14:49:54 155.5 437 1.118847e+12 18.072300 607.279700 \n", " 2005-06-15 14:49:55 165.5 437 1.118847e+12 17.601300 646.529900 \n", " 2005-06-15 14:49:56 175.5 437 1.118847e+12 17.327600 687.665400 \n", " 2005-06-15 14:49:57 185.5 437 1.118847e+12 17.056600 732.249800 \n", " 2005-06-15 14:49:58 195.5 437 1.118847e+12 16.456900 777.691900 \n", " 2005-06-15 14:49:59 205.5 437 1.118847e+12 12.999600 827.392800 \n", " 2005-06-15 14:50:00 215.5 437 1.118847e+12 11.227900 876.678400 \n", " 2005-06-15 14:50:01 225.5 437 1.118847e+12 11.653000 926.757400 \n", " 2005-06-15 14:50:02 235.5 437 1.118847e+12 11.286600 978.255700 \n", " 2005-06-15 14:50:03 245.5 437 1.118847e+12 10.747200 1034.549600 \n", " 2005-06-15 14:50:04 255.5 437 1.118847e+12 10.852600 1092.173700 \n", " 2005-06-15 14:50:05 265.5 437 1.118847e+12 10.917100 1147.714700 \n", " 2005-06-15 14:50:06 275.5 437 1.118847e+12 9.421900 1198.508000 \n", " 2005-06-15 14:50:07 285.5 437 1.118847e+12 7.389700 1246.675700 \n", " 2005-06-15 14:50:08 295.5 437 1.118847e+12 6.532700 1292.783100 \n", " 2005-06-15 14:50:09 305.5 437 1.118847e+12 7.086300 1334.235300 \n", " 2005-06-15 14:50:10 315.5 437 1.118847e+12 7.533600 1374.400900 \n", " 2005-06-15 14:50:11 325.5 437 1.118847e+12 7.642800 1415.181300 \n", " 2005-06-15 14:50:12 335.5 437 1.118847e+12 7.623300 1459.687400 \n", " 2005-06-15 14:50:13 345.5 437 1.118847e+12 7.526000 1505.457700 \n", " 2005-06-15 14:50:14 355.5 437 1.118847e+12 7.349300 1554.578300 \n", " 2005-06-15 14:50:15 365.5 437 1.118847e+12 8.097900 1605.381200 \n", " 2005-06-15 14:50:16 375.5 437 1.118847e+12 7.969900 1659.391200 \n", " 2005-06-15 14:50:17 385.5 437 1.118847e+12 7.225300 1715.205200 \n", " 2005-06-15 14:50:18 395.5 437 1.118847e+12 7.262800 1774.544200 \n", " 2005-06-15 14:50:19 405.5 437 1.118847e+12 7.155600 1836.173900 \n", " 2005-06-15 14:50:20 415.5 437 1.118847e+12 8.351200 1901.437700 \n", " 2005-06-15 14:50:21 425.5 437 1.118847e+12 8.829100 1967.929400 \n", " 2005-06-15 14:50:22 435.5 437 1.118847e+12 9.098000 2035.809700 \n", " 2005-06-15 14:50:23 445.0 437 1.118847e+12 8.816111 2102.117778 \n", "4 2005-06-15 14:49:51 129.0 351 1.118847e+12 66.250667 508.664667 \n", " 2005-06-15 14:49:52 135.5 351 1.118847e+12 65.464600 535.440900 \n", " 2005-06-15 14:49:53 145.5 351 1.118847e+12 62.737400 578.772400 \n", " 2005-06-15 14:49:54 155.5 351 1.118847e+12 61.667000 618.786200 \n", " 2005-06-15 14:49:55 165.5 351 1.118847e+12 61.184700 658.677800 \n", " 2005-06-15 14:49:56 175.5 351 1.118847e+12 58.948700 702.243200 \n", "\n", " globalX globalY vLenght vWidth \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 6451147.050750 1873334.595875 14.5 4.9 \n", " 2005-06-15 14:49:41 6451171.078800 1873308.121600 14.5 4.9 \n", " 2005-06-15 14:49:42 6451196.268900 1873280.366000 14.5 4.9 \n", " 2005-06-15 14:49:43 6451221.327700 1873254.149300 14.5 4.9 \n", " 2005-06-15 14:49:44 6451249.440200 1873226.795300 14.5 4.9 \n", " 2005-06-15 14:49:45 6451276.553600 1873197.369400 14.5 4.9 \n", " 2005-06-15 14:49:46 6451305.625800 1873167.561400 14.5 4.9 \n", " 2005-06-15 14:49:47 6451337.918800 1873137.082800 14.5 4.9 \n", " 2005-06-15 14:49:48 6451370.431600 1873105.807000 14.5 4.9 \n", " 2005-06-15 14:49:49 6451403.488500 1873073.998600 14.5 4.9 \n", " 2005-06-15 14:49:50 6451435.566900 1873043.150300 14.5 4.9 \n", " 2005-06-15 14:49:51 6451466.487100 1873015.865700 14.5 4.9 \n", " 2005-06-15 14:49:52 6451493.863000 1872991.230200 14.5 4.9 \n", " 2005-06-15 14:49:53 6451519.682800 1872967.114800 14.5 4.9 \n", " 2005-06-15 14:49:54 6451546.814900 1872943.332100 14.5 4.9 \n", " 2005-06-15 14:49:55 6451576.569800 1872917.526900 14.5 4.9 \n", " 2005-06-15 14:49:56 6451607.682900 1872890.569600 14.5 4.9 \n", " 2005-06-15 14:49:57 6451641.366700 1872861.358500 14.5 4.9 \n", " 2005-06-15 14:49:58 6451675.894900 1872831.836900 14.5 4.9 \n", " 2005-06-15 14:49:59 6451715.504000 1872801.608200 14.5 4.9 \n", " 2005-06-15 14:50:00 6451753.675600 1872770.389600 14.5 4.9 \n", " 2005-06-15 14:50:01 6451791.118300 1872737.034000 14.5 4.9 \n", " 2005-06-15 14:50:02 6451830.213800 1872703.530700 14.5 4.9 \n", " 2005-06-15 14:50:03 6451873.033300 1872666.971500 14.5 4.9 \n", " 2005-06-15 14:50:04 6451916.415900 1872629.039300 14.5 4.9 \n", " 2005-06-15 14:50:05 6451958.253000 1872592.503000 14.5 4.9 \n", " 2005-06-15 14:50:06 6451997.439200 1872560.216400 14.5 4.9 \n", " 2005-06-15 14:50:07 6452035.007800 1872529.999700 14.5 4.9 \n", " 2005-06-15 14:50:08 6452070.273700 1872500.239700 14.5 4.9 \n", " 2005-06-15 14:50:09 6452101.140700 1872472.575300 14.5 4.9 \n", " 2005-06-15 14:50:10 6452131.148500 1872445.871800 14.5 4.9 \n", " 2005-06-15 14:50:11 6452161.823100 1872419.020700 14.5 4.9 \n", " 2005-06-15 14:50:12 6452195.272300 1872389.700600 14.5 4.9 \n", " 2005-06-15 14:50:13 6452229.703700 1872359.548500 14.5 4.9 \n", " 2005-06-15 14:50:14 6452266.780300 1872327.273500 14.5 4.9 \n", " 2005-06-15 14:50:15 6452304.676400 1872293.402800 14.5 4.9 \n", " 2005-06-15 14:50:16 6452345.398100 1872258.038100 14.5 4.9 \n", " 2005-06-15 14:50:17 6452387.552500 1872221.462900 14.5 4.9 \n", " 2005-06-15 14:50:18 6452432.435400 1872182.497300 14.5 4.9 \n", " 2005-06-15 14:50:19 6452479.042300 1872142.246200 14.5 4.9 \n", " 2005-06-15 14:50:20 6452527.649900 1872098.558900 14.5 4.9 \n", " 2005-06-15 14:50:21 6452578.143000 1872055.148600 14.5 4.9 \n", " 2005-06-15 14:50:22 6452630.515200 1872011.771400 14.5 4.9 \n", " 2005-06-15 14:50:23 6452682.455667 1871970.417222 14.5 4.9 \n", "4 2005-06-15 14:49:51 6451441.192000 1872973.158000 16.0 4.9 \n", " 2005-06-15 14:49:52 6451461.662500 1872955.905300 16.0 4.9 \n", " 2005-06-15 14:49:53 6451495.791800 1872929.041600 16.0 4.9 \n", " 2005-06-15 14:49:54 6451526.375300 1872903.130900 16.0 4.9 \n", " 2005-06-15 14:49:55 6451556.954900 1872876.745300 16.0 4.9 \n", " 2005-06-15 14:49:56 6451591.181200 1872849.671900 16.0 4.9 \n", "\n", " vType veloc accel line pred foll spac \\\n", "vID dateTime \n", "2 2005-06-15 14:49:40 2 40.002500 0.031250 2.0 0 0.0 0 \n", " 2005-06-15 14:49:41 2 39.026000 -1.853000 2.0 0 11.7 0 \n", " 2005-06-15 14:49:42 2 35.444000 -2.795000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:43 2 38.113000 4.215000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:44 2 39.461000 0.598000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:45 2 39.882000 0.205000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:46 2 43.238000 3.943000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:47 2 44.858000 -0.376000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:48 2 45.404000 2.916000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:49 2 45.283000 -1.972000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:50 2 42.870000 -4.140000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:51 2 39.434000 -4.553000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:52 2 35.005000 0.267000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:53 2 35.510000 -0.446000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:54 2 37.734000 4.918000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:55 2 39.891000 0.670000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:56 2 43.209000 4.440000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:57 2 45.000000 0.000000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:58 2 47.360000 6.275000 2.0 0 13.0 0 \n", " 2005-06-15 14:49:59 2 49.574000 -4.008000 1.6 0 11.8 0 \n", " 2005-06-15 14:50:00 2 50.124000 1.663000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:01 2 50.024000 1.104000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:02 2 53.912000 6.474000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:03 2 57.466000 0.426000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:04 2 57.238000 -3.704000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:05 2 53.398000 -5.018000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:06 2 49.199000 -1.014000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:07 2 47.242000 -0.035000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:08 2 43.434000 -4.889000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:09 2 40.679000 -1.014000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:10 2 39.998000 -0.026000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:11 2 42.791000 4.827000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:12 2 45.042000 0.586000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:13 2 47.215000 4.277000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:14 2 50.521000 0.865000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:15 2 51.835000 4.163000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:16 2 55.129000 -0.070000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:17 2 57.605000 5.065000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:18 2 60.097000 0.877000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:19 2 63.904000 4.823000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:20 2 65.980000 0.967000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:21 2 66.890000 0.866000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:22 2 69.209000 2.350000 1.0 0 10.0 0 \n", " 2005-06-15 14:50:23 2 70.008889 0.112222 1.0 0 10.0 0 \n", "4 2005-06-15 14:49:51 2 40.710000 0.000000 7.0 0 0.0 0 \n", " 2005-06-15 14:49:52 2 42.905000 4.067000 7.0 0 0.0 0 \n", " 2005-06-15 14:49:53 2 41.500000 -5.282000 7.0 0 6.0 0 \n", " 2005-06-15 14:49:54 2 39.524000 -0.677000 6.4 0 2.4 0 \n", " 2005-06-15 14:49:55 2 41.218000 5.117000 6.0 0 1.8 0 \n", " 2005-06-15 14:49:56 2 44.814000 -1.848000 6.0 0 6.0 0 \n", "\n", " headway \n", "vID dateTime \n", "2 2005-06-15 14:49:40 0 \n", " 2005-06-15 14:49:41 0 \n", " 2005-06-15 14:49:42 0 \n", " 2005-06-15 14:49:43 0 \n", " 2005-06-15 14:49:44 0 \n", " 2005-06-15 14:49:45 0 \n", " 2005-06-15 14:49:46 0 \n", " 2005-06-15 14:49:47 0 \n", " 2005-06-15 14:49:48 0 \n", " 2005-06-15 14:49:49 0 \n", " 2005-06-15 14:49:50 0 \n", " 2005-06-15 14:49:51 0 \n", " 2005-06-15 14:49:52 0 \n", " 2005-06-15 14:49:53 0 \n", " 2005-06-15 14:49:54 0 \n", " 2005-06-15 14:49:55 0 \n", " 2005-06-15 14:49:56 0 \n", " 2005-06-15 14:49:57 0 \n", " 2005-06-15 14:49:58 0 \n", " 2005-06-15 14:49:59 0 \n", " 2005-06-15 14:50:00 0 \n", " 2005-06-15 14:50:01 0 \n", " 2005-06-15 14:50:02 0 \n", " 2005-06-15 14:50:03 0 \n", " 2005-06-15 14:50:04 0 \n", " 2005-06-15 14:50:05 0 \n", " 2005-06-15 14:50:06 0 \n", " 2005-06-15 14:50:07 0 \n", " 2005-06-15 14:50:08 0 \n", " 2005-06-15 14:50:09 0 \n", " 2005-06-15 14:50:10 0 \n", " 2005-06-15 14:50:11 0 \n", " 2005-06-15 14:50:12 0 \n", " 2005-06-15 14:50:13 0 \n", " 2005-06-15 14:50:14 0 \n", " 2005-06-15 14:50:15 0 \n", " 2005-06-15 14:50:16 0 \n", " 2005-06-15 14:50:17 0 \n", " 2005-06-15 14:50:18 0 \n", " 2005-06-15 14:50:19 0 \n", " 2005-06-15 14:50:20 0 \n", " 2005-06-15 14:50:21 0 \n", " 2005-06-15 14:50:22 0 \n", " 2005-06-15 14:50:23 0 \n", "4 2005-06-15 14:49:51 0 \n", " 2005-06-15 14:49:52 0 \n", " 2005-06-15 14:49:53 0 \n", " 2005-06-15 14:49:54 0 \n", " 2005-06-15 14:49:55 0 \n", " 2005-06-15 14:49:56 0 \n" ] } ], "source": [ "print (mean [:50])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Number of registers by timestamp\n", "ts_match = data.groupby(['Timestamp', 'vID']).size()\n", "ts_match_max = ts_match.max()\n", "ts_match_min = ts_match.min()\n", "ts_match_mean = ts_match.mean()\n", "\n", "#number of register by dataTime\n", "dt_match = data.groupby(['dateTime', 'vID']).size()\n", "dt_match_max = dt_match.max()\n", "dt_match_min = dt_match.min()\n", "dt_match_mean = dt_match.mean()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Timestamp vID\n", "1118846979700 5 1\n", "1118846979800 5 1\n", "1118846979900 5 1\n", "1118846980000 5 1\n", "1118846980100 5 1\n", "1118846980200 2 1\n", " 5 1\n", "1118846980300 2 1\n", " 5 1\n", "1118846980400 2 1\n", " 5 1\n", "1118846980500 2 1\n", " 5 1\n", "1118846980600 2 1\n", " 5 1\n", "1118846980700 2 1\n", " 5 1\n", "1118846980800 2 1\n", " 5 1\n", "1118846980900 2 1\n", " 5 1\n", "1118846981000 2 1\n", " 5 1\n", "1118846981100 2 1\n", " 5 1\n", " 13 1\n", "1118846981200 2 1\n", " 5 1\n", " 13 1\n", "1118846981300 2 1\n", " 5 1\n", " 13 1\n", "1118846981400 2 1\n", " 5 1\n", " 13 1\n", "1118846981500 2 1\n", " 5 1\n", " 13 1\n", "1118846981600 2 1\n", " 5 1\n", " 13 1\n", "1118846981700 2 1\n", " 5 1\n", " 13 1\n", "1118846981800 2 1\n", " 5 1\n", " 8 1\n", " 13 1\n", "1118846981900 2 1\n", " 5 1\n", "dtype: int64\n" ] } ], "source": [ "print (ts_match [:50])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1180598\n" ] } ], "source": [ "print (ts_match.count())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dateTime vID\n", "2005-06-15 14:49:39 5 3\n", "2005-06-15 14:49:40 2 8\n", " 5 10\n", "2005-06-15 14:49:41 2 10\n", " 5 10\n", " 8 2\n", " 13 9\n", "2005-06-15 14:49:42 2 10\n", " 5 10\n", " 8 10\n", " 10 2\n", " 13 10\n", " 14 9\n", " 22 4\n", "2005-06-15 14:49:43 2 10\n", " 5 10\n", " 8 10\n", " 9 6\n", " 10 10\n", " 13 10\n", " 14 10\n", " 18 5\n", " 22 10\n", "2005-06-15 14:49:44 2 10\n", " 5 10\n", " 8 10\n", " 9 10\n", " 10 10\n", " 12 10\n", " 13 10\n", " 14 10\n", " 18 10\n", " 21 9\n", " 22 10\n", " 26 9\n", "2005-06-15 14:49:45 2 10\n", " 5 10\n", " 8 10\n", " 9 10\n", " 10 10\n", " 12 10\n", " 13 10\n", " 14 10\n", " 18 10\n", " 20 1\n", " 21 10\n", " 22 10\n", " 23 6\n", " 26 10\n", " 31 8\n", "dtype: int64\n" ] } ], "source": [ "print (dt_match [:50])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "120032\n" ] } ], "source": [ "print (dt_match.count())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print (ts_match_max)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dt_match_max' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-2-1f083de4b68f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdt_match_max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'dt_match_max' is not defined" ] } ], "source": [ "print(dt_match_max)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dt_match' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-1-c50c1b714d44>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdt_match\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'dt_match' is not defined" ] } ], "source": [ "dt_match " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
midnighteuler/projecteuler	src/Prob38.ipynb	1	2878	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Take the number 192 and multiply it by each of 1, 2, and 3:\n", "\n", "192 × 1 = 192\n", "\n", "192 × 2 = 384\n", "\n", "192 × 3 = 576\n", "\n", "By concatenating each product we get the 1 to 9 pandigital, 192384576. We will call 192384576 the concatenated product of 192 and (1,2,3)\n", "\n", "The same can be achieved by starting with 9 and multiplying by 1, 2, 3, 4, and 5, giving the pandigital, 918273645, which is the concatenated product of 9 and (1,2,3,4,5).\n", "\n", "What is the largest 1 to 9 pandigital 9-digit number that can be formed as the concatenated product of an integer with (1,2, ... , n) where n > 1?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is pan: 1 n: 9 123456789\n", "is pan: 9 n: 5 918273645\n", "is pan: 192 n: 3 192384576\n", "is pan: 219 n: 3 219438657\n", "is pan: 273 n: 3 273546819\n", "is pan: 327 n: 3 327654981\n", "is pan: 6729 n: 2 672913458\n", "is pan: 6792 n: 2 679213584\n", "is pan: 6927 n: 2 692713854\n", "is pan: 7269 n: 2 726914538\n", "is pan: 7293 n: 2 729314586\n", "is pan: 7329 n: 2 732914658\n", "is pan: 7692 n: 2 769215384\n", "is pan: 7923 n: 2 792315846\n", "is pan: 7932 n: 2 793215864\n", "is pan: 9267 n: 2 926718534\n", "is pan: 9273 n: 2 927318546\n", "is pan: 9327 n: 2 932718654\n", "Max: 932718654\n" ] } ], "source": [ "pans = []\n", "for p in range(1,10*4):\n", " digits = set([str(i) for i in range(1,10)])\n", " n = 1\n", " while len(digits) > 0:\n", " pn = str(pn)\n", " d = set(pn)\n", " if not(d.issubset(digits) and len(d) == len(pn)):break\n", " n += 1\n", " digits = digits.difference(d)\n", " \n", " if len(digits) == 0:\n", " pans.append(int(''.join([str(p*i) for i in range(1,n)])))\n", " print \"is pan:\", p, \"n:\", n-1, pans[-1]\n", "print \"Max:\", max(pans)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
jeiros/Jupyter_notebooks	python/markov_analysis/PyEMMA-API.ipynb	1	141652	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing the pyEMMA API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2.1'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pyemma\n", "pyemma.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we import a few general packages that we need to start with. The following imports basic numerics and algebra routines (numpy) and plotting routines (matplotlib), and makes sure that all plots are shown inside the notebook rather than in a separate window (nicer that way)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: pylab import has clobbered these variables: ['plt']\n", "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], "source": [ "import matplotlib.pylab as plt\n", "import numpy as np\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we import the pyEMMA package that we will be using in the beginning: the coordinates package. This package contains functions and classes for reading and writing trajectory files, extracting order parameters from them (such as distances or angles), as well as various methods for dimensionality reduction and clustering.\n", "\n", "The shortcuts module is a bunch of functions specific to this workshop - they help us to visualize some of our results. Some of them might become part of the pyemma package once they are more mature." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyemma.coordinates as coor\n", "import pyemma.msm as msm\n", "import pyemma.plots as mplt\n", "from pyemma import config" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# some helper funcs\n", "def average_by_state(dtraj, x, nstates):\n", " assert(len(dtraj) == len(x))\n", " N = len(dtraj)\n", " res = np.zeros((nstates))\n", " for i in range(nstates):\n", " I = np.argwhere(dtraj == i)[:,0]\n", " res[i] = np.mean(x[I])\n", " return res\n", "\n", "def avg_by_set(x, sets):\n", " # compute mean positions of sets. This is important because of some technical points the set order\n", " # in the coarse-grained TPT object can be different from the input order.\n", " avg = np.zeros(len(sets))\n", " for i in range(len(sets)):\n", " I = list(sets[i])\n", " avg[i] = np.mean(x[I])\n", " return avg\n", "\n", "shortcuts = {'average_by_state': average_by_state, 'avg_by_set': avg_by_set}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./bertha1-WT-ff14SB/05_Prod_0000-0050ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0050-0100ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0100-0150ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0150-0200ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0200-0250ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0250-0300ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0300-0350ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0350-0400ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0400-0450ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0450-0500ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0500-0550ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0550-0600ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0600-0650ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0650-0700ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0700-0750ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0750-0800ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0800-0850ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0850-0900ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0900-0950ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_0950-1000ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1000-1050ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1050-1100ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1100-1150ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1150-1200ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1200-1250ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1250-1300ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1300-1350ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1350-1400ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1400-1450ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./bertha1-WT-ff14SB/05_Prod_1450-1500ns_WT-ff14SB_25Awatsalt_hmr.nc_stripped-autoimaged.nc\n", "\n", "./guanine1-WT-ff14SB/05_0000-0050ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0050-0100ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0100-0150ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0150-0200ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0200-0250ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0250-0300ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0300-0350ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0350-0400ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0400-0450ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0450-0500ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0500-0550ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0550-0600ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0600-0650ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0650-0700ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0700-0750ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0750-0800ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0800-0850ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0850-0900ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0900-0950ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_0950-1000ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1000-1050ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1050-1100ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1100-1150ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1150-1200ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1200-1250ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1250-1300ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1300-1350ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1350-1400ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1400-1450ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./guanine1-WT-ff14SB/05_1450-1500ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0000-0050ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0050-0100ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0100-0150ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0150-0200ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0200-0250ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0250-0300ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0300-0350ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./parnassus1-WT-ff14SB/05_0350-0400ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", 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"./spinoza2-WT-ff14SB/05_0450-0500ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0500-0550ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0550-0600ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0600-0650ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0650-0700ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0700-0750ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0750-0800ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0800-0850ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0850-0900ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0900-0950ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_0950-1000ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1000-1050ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1050-1100ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1100-1150ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1150-1200ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1200-1250ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1250-1300ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1300-1350ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1350-1400ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1400-1450ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n", "./spinoza2-WT-ff14SB/05_1450-1500ns_WT-ff14SB_25Awatsalt_hmr_nowat.nc\n", "\n" ] } ], "source": [ "import glob\n", "trajfiles = sorted(glob.glob('.//05nc'))\n", "for file in trajfiles:\n", " print(\"%s\\n\" % file)\n", "topfile = \"./test.pdb\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat = coor.featurizer(topfile)\n", "feat.add_backbone_torsions(cossin=True)\n", "feat.add_chi1_torsions(cossin=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining file info: 100% (244/244) [##############################] eta 00:01 \|" ] } ], "source": [ "inp = coor.source(trajfiles, feat)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of trajectories: 244\n", "Aggregate simulation time: 16800.00 ns\n", "Number of dimensions: 2390\n" ] } ], "source": [ "print(\"Number of trajectories: %s\" % inp.number_of_trajectories())\n", "print(\"Aggregate simulation time: %.2f ns\" % (inp.n_frames_total() * 0.02))\n", "print(\"Number of dimensions: %s\" % inp.dimension())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TICA and clustering\n", "So we would like to first reduce our dimension by throwing out the ‘uninteresting’ ones and only keeping the ‘relevant’ ones. But how do we do that?\n", "\n", "It turns out that a really good way to do that if you are interesting in the slow kinetics of the molecule - e.g. for constructing a Markov model, is to use the time-lagged independent component analysis (TICA) [2]. Amongst linear methods, TICA is optimal in its ability to approximate the relevant slow coordinates / reaction coordinates from MD simulation [3], and therefore it’s ideal to construct Markov models." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "calculate mean+cov: 100% (8176/8176) [#############################] eta 00:01 \|" ] } ], "source": [ "tica_obj = coor.tica(inp, lag=100)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting output of TICA: 100% (244/244) [###########################] eta 00:01 \|" ] }, { "ename": "TypeError", "evalue": "not all arguments converted during string formatting", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-91a3728df37e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtica_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Projected data shape: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: not all arguments converted during string formatting" ] } ], "source": [ "Y = tica_obj.get_output()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, TICA will choose a number of output dimensions to cover 95% of the kinetic variance and scale the output to produce a kinetic map. In this case we retain 575 dimensions, which is a lot but note that they are scaled by eigenvalue, so it’s mostly the first dimensions that contribute." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Projected data shape: (2500,575)\n" ] }, { "data": { "text/plain": [ "array([ 4.78546917e-01, 1.15171218e+00, 1.33347392e+00, ...,\n", " 1.72825352e+04, 1.72826328e+04, 1.72827617e+04], dtype=float32)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Projected data shape: (%s,%s)\" % (Y.shape[0], Y.shape[1]))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Retained dimensions: 575\n" ] }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11206cf28>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qo6kSxqhRMHx4vaMwM+ufCk8YkiZLmitpnqRzu9nnPyTNlzRL0oTujuXqKDOz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111d69550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Retained dimensions: %s' % tica_obj.dimension())\n", "plot(tica_obj.cumvar, linewidth=2)\n", "plot([tica_obj.dimension(), tica_obj.dimension()], [0, 1], color='black', linewidth=2)\n", "plot([0, Y.shape[0]], [0.95, 0.95], color='black', linewidth=2)\n", "xlabel('Number of dimensions'); ylabel('Cum. kinetic variance fraction')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The TICA object has a number of properties that we can extract and work with. We have already obtained the projected trajectory and wrote it in a variable Y that is a matrix of size (103125 x 2). The rows are the MD steps, the 2 columns are the independent component coordinates projected onto. So each columns is a trajectory. Let us plot them:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1120efd30>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xQe5+z/qArhqCubZYrjou6fFD6yaKoxsgdmaSGUGPN/8KY6habaUoev7uhZgR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\nOwLnnGtx3hE451yL847AOeda3P8DLb/+K+m/ti0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11202c400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mplt.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1])\n", "xlabel('independent component 1'); ylabel('independent component 2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A particular thing about the IC’s is that they have zero mean and variance one. We can easily check that:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean values: -0.0202599\n", "Variances: 0.405257\n" ] } ], "source": [ "print(\"Mean values: %s\" % np.mean(Y[0], axis = 0))\n", "print(\"Variances: %s\" % np.var(Y[0], axis = 0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The small deviations from 0 and 1 come from statistical and numerical issues. That’s not a problem. Note that if we had set kinetic_map=True when doing TICA, then the variances would not be 1 but rather the square of the corresponding TICA eigenvalue.\n", "\n", "TICA is a special transformation because it will project the data such that the autocorrelation along the independent components is as slow as possible. The eigenvalues of the TICA transform are the values of these autocorrelations at the chosen lag time (here 100). We can even interpret them in terms of relaxation timescales:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 58916.3390982 49176.41514982 34933.41607058 32727.40154084\n", " 29415.61528081]\n" ] } ], "source": [ "print(-100/np.log(tica_obj.eigenvalues[:5]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will see more timescales later when we estimate a Markov model, and there will be some differences. For now you should treat these numbers as a rough guess of your molecule’s timescales, and we will see later that this guess is actually a bit too fast. The timescales are relative to the 10 ns saving interval, so we have " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x113290438>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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k9OsXPf/f+lbxfn79hx9GNhrtLlCYl0dtbLQRnHRSq2uRTN5H7zc4wWEhVV2w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\n3HGHfDcCRVxD9P/+nyzdkaDtY68CRWnCLCM14+pXG5hG2LWWD+vg4jq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YZaZxbQHVjUGy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"text/plain": [ "<matplotlib.figure.Figure at 0x11350a8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "subplot2grid((2,1),(0,0))\n", "plot(Y[:,0])\n", "ylabel('ind. comp. 1')\n", "subplot2grid((2,1),(1,0))\n", "plot(Y[:,1])\n", "ylabel('ind. comp. 2')\n", "xlabel('time (10 ns)')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1000" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tica_obj.chunksize" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'TICA' object has no attribute 'lagtimes'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-56-95e8b3752413>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_implied_timescales\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtica_obj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/je714/anaconda/lib/python3.4/site-packages/pyEMMA-2.1-py3.4-macosx-10.5-x86_64.egg/pyemma/plots/timescales.py\u001b[0m in \u001b[0;36mplot_implied_timescales\u001b[0;34m(ITS, ax, outfile, show_mle, show_mean, xlog, ylog, confidence, refs, nits, process, units, dt, **kwargs)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_plt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m 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mdiaz236/DeepLearningFoundations	gan_mnist/Intro_to_GANs_Solution.ipynb	1	179274	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n", "\n", "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n", "* [CycleGAN](https://github.com/junyanz/CycleGAN)\n", "* [A whole list](https://github.com/wiseodd/generative-models)\n", "\n", "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n", "\n", "![GAN diagram](assets/gan_diagram.png)\n", "\n", "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can foold the discriminator.\n", "\n", "The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pickle as pkl\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Model Inputs\n", "\n", "First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def model_inputs(real_dim, z_dim):\n", " inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') \n", " inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " \n", " return inputs_real, inputs_z" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generator network\n", "\n", "![GAN Network](assets/gan_network.png)\n", "\n", "Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n", "\n", "#### Variable Scope\n", "Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n", "\n", "We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n", "\n", "To use `tf.variable_scope`, you use a `with` statement:\n", "```python\n", "with tf.variable_scope('scope_name', reuse=False):\n", " # code here\n", "```\n", "\n", "Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n", "\n", "#### Leaky ReLU\n", "TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can use take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alphax`, and the output for positive `x` is `x`:\n", "$$\n", "f(x) = max(\\alpha x, x)\n", "$$\n", "\n", "#### Tanh Output\n", "The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('generator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(z, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " # Logits and tanh output\n", " logits = tf.layers.dense(h1, out_dim, activation=None)\n", " out = tf.tanh(logits)\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discriminator\n", "\n", "The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(x, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " logits = tf.layers.dense(h1, 1, activation=None)\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Hyperparameters" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Size of input image to discriminator\n", "input_size = 784\n", "# Size of latent vector to generator\n", "z_size = 100\n", "# Sizes of hidden layers in generator and discriminator\n", "g_hidden_size = 128\n", "d_hidden_size = 128\n", "# Leak factor for leaky ReLU\n", "alpha = 0.01\n", "# Smoothing \n", "smooth = 0.1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build network\n", "\n", "Now we're building the network from the functions defined above.\n", "\n", "First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n", "\n", "Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n", "\n", "Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "# Create our input placeholders\n", "input_real, input_z = model_inputs(input_size, z_size)\n", "\n", "# Build the model\n", "g_model = generator(input_z, input_size)\n", "# g_model is the generator output\n", "\n", "d_model_real, d_logits_real = discriminator(input_real)\n", "d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discriminator and Generator Losses\n", "\n", "Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will by sigmoid cross-entropys, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n", "\n", "```python\n", "tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", "```\n", "\n", "For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example, using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n", "\n", "The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n", "\n", "Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Calculate losses\n", "d_loss_real = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, \n", " labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n", "d_loss_fake = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, \n", " labels=tf.zeros_like(d_logits_real)))\n", "d_loss = d_loss_real + d_loss_fake\n", "\n", "g_loss = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n", " labels=tf.ones_like(d_logits_fake)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Optimizers\n", "\n", "We want to update the generator and discriminator variables separately. So we need to get the variables for each part build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n", "\n", "For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables to start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n", "\n", "We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n", "\n", "Then, in the optimizer we pass the variable lists to `var_list` in the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "# Optimizers\n", "learning_rate = 0.002\n", "\n", "# Get the trainable_variables, split into G and D parts\n", "t_vars = tf.trainable_variables()\n", "g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", "\n", "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n", "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkdir: cannot create directory ‘checkpoints’: File exists\r\n" ] } ], "source": [ "!mkdir checkpoints" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/100... Discriminator Loss: 0.3842... Generator Loss: 3.6655\n", "Epoch 2/100... Discriminator Loss: 0.3964... Generator Loss: 3.1699\n", "Epoch 3/100... Discriminator Loss: 0.4226... Generator Loss: 3.3010\n", "Epoch 4/100... Discriminator Loss: 0.5049... Generator Loss: 5.1167\n", "Epoch 5/100... Discriminator Loss: 0.6197... Generator Loss: 7.5422\n", "Epoch 6/100... Discriminator Loss: 0.6663... Generator Loss: 3.4146\n", "Epoch 7/100... Discriminator Loss: 0.6822... Generator Loss: 3.5781\n", "Epoch 8/100... Discriminator Loss: 1.4410... Generator Loss: 3.1040\n", "Epoch 9/100... Discriminator Loss: 0.9926... Generator Loss: 2.1928\n", "Epoch 10/100... Discriminator Loss: 0.8677... Generator Loss: 3.6301\n", "Epoch 11/100... Discriminator Loss: 1.0338... Generator Loss: 1.7675\n", "Epoch 12/100... Discriminator Loss: 0.7970... Generator Loss: 2.2884\n", "Epoch 13/100... Discriminator Loss: 1.1056... Generator Loss: 2.9634\n", "Epoch 14/100... Discriminator Loss: 1.0781... Generator Loss: 2.0506\n", "Epoch 15/100... Discriminator Loss: 0.9444... Generator Loss: 2.0144\n", "Epoch 16/100... Discriminator Loss: 1.2183... Generator Loss: 1.9047\n", "Epoch 17/100... Discriminator Loss: 1.0420... Generator Loss: 2.7613\n", "Epoch 18/100... Discriminator Loss: 1.4393... Generator Loss: 1.9514\n", "Epoch 19/100... Discriminator Loss: 1.5217... Generator Loss: 2.0713\n", "Epoch 20/100... Discriminator Loss: 0.8593... Generator Loss: 2.2685\n", "Epoch 21/100... Discriminator Loss: 1.2896... Generator Loss: 1.6226\n", "Epoch 22/100... Discriminator Loss: 0.6998... Generator Loss: 2.4725\n", "Epoch 23/100... Discriminator Loss: 0.8220... Generator Loss: 2.4169\n", "Epoch 24/100... Discriminator Loss: 0.8358... Generator Loss: 1.8199\n", "Epoch 25/100... Discriminator Loss: 0.9829... Generator Loss: 2.0847\n", "Epoch 26/100... Discriminator Loss: 0.8602... Generator Loss: 1.7121\n", "Epoch 27/100... Discriminator Loss: 0.9274... Generator Loss: 1.9062\n", "Epoch 28/100... Discriminator Loss: 0.8498... Generator Loss: 3.0855\n", "Epoch 29/100... Discriminator Loss: 0.7240... Generator Loss: 2.6028\n", "Epoch 30/100... Discriminator Loss: 0.8622... Generator Loss: 2.1538\n", "Epoch 31/100... Discriminator Loss: 0.9209... Generator Loss: 1.8890\n", "Epoch 32/100... Discriminator Loss: 0.9319... Generator Loss: 2.5738\n", "Epoch 33/100... Discriminator Loss: 0.9176... Generator Loss: 2.8202\n", "Epoch 34/100... Discriminator Loss: 0.8680... Generator Loss: 2.3803\n", "Epoch 35/100... Discriminator Loss: 0.8950... Generator Loss: 2.1691\n", "Epoch 36/100... Discriminator Loss: 0.9739... Generator Loss: 2.1528\n", "Epoch 37/100... Discriminator Loss: 0.9179... Generator Loss: 1.9091\n", "Epoch 38/100... Discriminator Loss: 0.9683... Generator Loss: 1.6559\n", "Epoch 39/100... Discriminator Loss: 1.0320... Generator Loss: 1.6031\n", "Epoch 40/100... Discriminator Loss: 1.1264... Generator Loss: 1.8091\n", "Epoch 41/100... Discriminator Loss: 1.0980... Generator Loss: 1.6661\n", "Epoch 42/100... Discriminator Loss: 0.9794... Generator Loss: 2.0973\n", "Epoch 43/100... Discriminator Loss: 0.8620... Generator Loss: 2.3119\n", "Epoch 44/100... Discriminator Loss: 0.9756... Generator Loss: 1.9464\n", "Epoch 45/100... Discriminator Loss: 1.0872... Generator Loss: 1.6003\n", "Epoch 46/100... Discriminator Loss: 1.0760... Generator Loss: 1.6861\n", "Epoch 47/100... Discriminator Loss: 1.1995... Generator Loss: 1.5049\n", "Epoch 48/100... Discriminator Loss: 1.1637... Generator Loss: 1.5352\n", "Epoch 49/100... Discriminator Loss: 0.9836... Generator Loss: 2.0852\n", "Epoch 50/100... Discriminator Loss: 1.0149... Generator Loss: 1.3999\n", "Epoch 51/100... Discriminator Loss: 1.0147... Generator Loss: 1.8894\n", "Epoch 52/100... Discriminator Loss: 1.2534... Generator Loss: 1.7711\n", "Epoch 53/100... Discriminator Loss: 0.9352... Generator Loss: 1.8878\n", "Epoch 54/100... Discriminator Loss: 1.1472... Generator Loss: 1.4543\n", "Epoch 55/100... Discriminator Loss: 1.3635... Generator Loss: 1.2173\n", "Epoch 56/100... Discriminator Loss: 1.2202... Generator Loss: 1.4520\n", "Epoch 57/100... Discriminator Loss: 0.9861... Generator Loss: 1.9276\n", "Epoch 58/100... Discriminator Loss: 1.1032... Generator Loss: 1.8915\n", "Epoch 59/100... Discriminator Loss: 0.9440... Generator Loss: 2.5136\n", "Epoch 60/100... Discriminator Loss: 0.9744... Generator Loss: 1.8370\n", "Epoch 61/100... Discriminator Loss: 0.8224... Generator Loss: 1.8648\n", "Epoch 62/100... Discriminator Loss: 1.1327... Generator Loss: 1.8926\n", "Epoch 63/100... Discriminator Loss: 1.1150... Generator Loss: 1.8950\n", "Epoch 64/100... Discriminator Loss: 0.8441... Generator Loss: 1.9946\n", "Epoch 65/100... Discriminator Loss: 0.9912... Generator Loss: 1.9305\n", "Epoch 66/100... Discriminator Loss: 0.9527... Generator Loss: 1.6693\n", "Epoch 67/100... Discriminator Loss: 0.9894... Generator Loss: 2.0002\n", "Epoch 68/100... Discriminator Loss: 0.8654... Generator Loss: 2.0725\n", "Epoch 69/100... Discriminator Loss: 0.9363... Generator Loss: 1.6939\n", "Epoch 70/100... Discriminator Loss: 1.0576... Generator Loss: 1.5916\n", "Epoch 71/100... Discriminator Loss: 0.9556... Generator Loss: 1.8366\n", "Epoch 72/100... Discriminator Loss: 0.9269... Generator Loss: 1.7562\n", "Epoch 73/100... Discriminator Loss: 0.9154... Generator Loss: 1.9768\n", "Epoch 74/100... Discriminator Loss: 0.8379... Generator Loss: 1.6849\n", "Epoch 75/100... Discriminator Loss: 0.9979... Generator Loss: 1.8343\n", "Epoch 76/100... Discriminator Loss: 0.9905... Generator Loss: 1.8342\n", "Epoch 77/100... Discriminator Loss: 0.9491... Generator Loss: 2.0298\n", "Epoch 78/100... Discriminator Loss: 1.3740... Generator Loss: 1.4449\n", "Epoch 79/100... Discriminator Loss: 1.1491... Generator Loss: 1.4414\n", "Epoch 80/100... Discriminator Loss: 1.0098... Generator Loss: 1.8602\n", "Epoch 81/100... Discriminator Loss: 1.0153... Generator Loss: 2.1780\n", "Epoch 82/100... Discriminator Loss: 1.0034... Generator Loss: 1.7471\n", "Epoch 83/100... Discriminator Loss: 0.8928... Generator Loss: 1.9146\n", "Epoch 84/100... Discriminator Loss: 0.7530... Generator Loss: 2.0924\n", "Epoch 85/100... Discriminator Loss: 0.8109... Generator Loss: 2.1248\n", "Epoch 86/100... Discriminator Loss: 0.7669... Generator Loss: 2.5912\n", "Epoch 87/100... Discriminator Loss: 0.7951... Generator Loss: 2.8426\n", "Epoch 88/100... Discriminator Loss: 0.8098... Generator Loss: 2.0355\n", "Epoch 89/100... Discriminator Loss: 0.8654... Generator Loss: 2.3322\n", "Epoch 90/100... Discriminator Loss: 0.9150... Generator Loss: 2.5682\n", "Epoch 91/100... Discriminator Loss: 0.8865... Generator Loss: 1.7872\n", "Epoch 92/100... Discriminator Loss: 0.8151... Generator Loss: 2.3254\n", "Epoch 93/100... Discriminator Loss: 0.7518... Generator Loss: 2.2741\n", "Epoch 94/100... Discriminator Loss: 0.8679... Generator Loss: 1.7687\n", "Epoch 95/100... Discriminator Loss: 0.9860... Generator Loss: 2.2423\n", "Epoch 96/100... Discriminator Loss: 0.9436... Generator Loss: 1.7666\n", "Epoch 97/100... Discriminator Loss: 1.0442... Generator Loss: 1.8173\n", "Epoch 98/100... Discriminator Loss: 0.8048... Generator Loss: 2.3917\n", "Epoch 99/100... Discriminator Loss: 0.9749... Generator Loss: 1.7262\n", "Epoch 100/100... Discriminator Loss: 0.8384... Generator Loss: 2.4169\n" ] } ], "source": [ "batch_size = 100\n", "epochs = 100\n", "samples = []\n", "losses = []\n", "# Only save generator variables\n", "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " \n", " # Get images, reshape and rescale to pass to D\n", " batch_images = batch[0].reshape((batch_size, 784))\n", " batch_images = batch_images*2 - 1\n", " \n", " # Sample random noise for G\n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n", " \n", " # Run optimizers\n", " _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n", " _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n", " \n", " # At the end of each epoch, get the losses and print them out\n", " train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n", " train_loss_g = g_loss.eval({input_z: batch_z})\n", " \n", " print(\"Epoch {}/{}...\".format(e+1, epochs),\n", " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", " \"Generator Loss: {:.4f}\".format(train_loss_g)) \n", " # Save losses to view after training\n", " losses.append((train_loss_d, train_loss_g))\n", " \n", " # Sample from generator as we're training for viewing afterwards\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", " samples.append(gen_samples)\n", " saver.save(sess, './checkpoints/generator.ckpt')\n", "\n", "# Save training generator samples\n", "with open('train_samples.pkl', 'wb') as f:\n", " pkl.dump(samples, f)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training loss\n", "\n", "Here we'll check out the training losses for the generator and discriminator." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fab69cbeac8>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LGq3ak3PK9nHpB6DzEHD3tDhuLBxfq33uhnJ8rboRas6P0gK17mzUKPO2oGg1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\nQQv3sYwCokLaIIRgeEwwZRWSbSft97n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"text/plain": [ "<matplotlib.figure.Figure at 0x7fab6dc48dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "losses = np.array(losses)\n", "plt.plot(losses.T[0], label='Discriminator')\n", "plt.plot(losses.T[1], label='Generator')\n", "plt.title(\"Training Losses\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generator samples from training\n", "\n", "Here we can view samples of images from the generator. First we'll look at images taken while training." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def view_samples(epoch, samples):\n", " fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n", " for ax, img in zip(axes.flatten(), samples[epoch]):\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)\n", " im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " \n", " return fig, axes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Load samples from generator taken while training\n", "with open('train_samples.pkl', 'rb') as f:\n", " samples = pkl.load(f)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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SgsvDSVJ+wcfmm2++2n9/7bXXTE+aNMm073+Y/J+3r8xLIZLvat3Olf8fWtu2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\numvOlY/8pI3mi9LkdzKZlO0y+Fmet68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"text/plain": [ "<matplotlib.figure.Figure at 0x7fab69d33160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = view_samples(-1, samples)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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JJ7jkkktGqlCvv/56oPiP3VVOxeLucLvvvjtQ9lwwh9xqaHPErYq2NsAaBbNL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\nM+FYeHh4eN5WNnyDz0XL8wA5FyORc1GQc1GQc/Ea2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"text/plain": [ "<matplotlib.figure.Figure at 0x7fab5baf3550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rows, cols = 10, 6\n", "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n", "\n", "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n", " for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n", " ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Sampling from the generator\n", "\n", "We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fab5b22d6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", "_ = view_samples(0, [gen_samples])" ] }, { "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ad960009/dist-keras	examples/example_1_analysis.ipynb	3	49727	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Development and Evaluation\n", "\n", "Joeri Hermans (Technical Student, IT-DB-SAS, CERN) \n", "Departement of Knowledge Engineering \n", "Maastricht University, The Netherlands" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is dedicated to the development and evaluation of a Keras model based on a large [preprocessed dataset](https://github.com/JoeriHermans/dist-keras/blob/master/examples/data_preprocessing.ipynb)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline \n", "\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from keras.models import Sequential\n", "from keras.layers.core import Dense, Dropout, Activation\n", "\n", "from pyspark import SparkContext\n", "from pyspark import SparkConf\n", "\n", "from pyspark.ml.feature import StandardScaler\n", "from pyspark.ml.feature import VectorAssembler\n", "from pyspark.ml.feature import StringIndexer\n", "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "\n", "from distkeras.transformers import LabelIndexTransformer\n", "from distkeras.predictors import ModelPredictor\n", "from distkeras.trainers import SingleTrainer\n", "from distkeras.trainers import AEASGD\n", "from distkeras.trainers import DOWNPOUR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spark Configuration and Preparation\n", "\n", "Edit the variables in the cell below. If you are running Spark in local mode, please set the `local` flag to true and adjust the resources you wish to use on your local machine. The same goes for the case when you are running Spark 2.0 and higher." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Modify these variables according to your needs.\n", "application_name = \"Distributed Deep Learning: Analysis\"\n", "using_spark_2 = False\n", "local = False\n", "if local:\n", " # Tell master to use local resources.\n", " master = \"local[]\"\n", " num_cores = 3\n", " num_executors = 1\n", "else:\n", " # Tell master to use YARN.\n", " master = \"yarn-client\"\n", " num_executors = 8\n", " num_cores = 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of desired executors: 8\n", "Number of desired cores / executor: 2\n", "Total number of workers: 16\n" ] } ], "source": [ "# This variable is derived from the number of cores and executors, and will be used to assign the number of model trainers.\n", "num_workers = num_executors num_cores\n", "\n", "print(\"Number of desired executors: \" + `num_executors`)\n", "print(\"Number of desired cores / executor: \" + `num_cores`)\n", "print(\"Total number of workers: \" + `num_workers`)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conf = SparkConf()\n", "conf.set(\"spark.app.name\", application_name)\n", "conf.set(\"spark.master\", master)\n", "conf.set(\"spark.executor.cores\", `num_cores`)\n", "conf.set(\"spark.executor.instances\", `num_executors`)\n", "conf.set(\"spark.executor.memory\",\"2g\")\n", "conf.set(\"spark.serializer\", \"org.apache.spark.serializer.KryoSerializer\");\n", "\n", "# Check if the user is running Spark 2.0 +\n", "if using_spark_2:\n", " sc = SparkSession.builder.config(conf=conf) \\\n", " .appName(application_name) \\\n", " .getOrCreate()\n", "else:\n", " # Create the Spark context.\n", " sc = SparkContext(conf=conf)\n", " # Add the missing imports\n", " from pyspark import SQLContext\n", " sqlContext = SQLContext(sc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preparation\n", "\n", "After the Spark Context (or Spark Session if you are using Spark 2.0) has been set up, we can start reading the preprocessed dataset from storage." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Check if we are using Spark 2.0\n", "if using_spark_2:\n", " reader = sc\n", "else:\n", " reader = sqlContext\n", "# Read the dataset.\n", "raw_dataset = reader.read.parquet(\"data/processed.parquet\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " \|-- features_normalized: vector (nullable = true)\n", " \|-- label_index: double (nullable = true)\n", " \|-- label: vector (nullable = true)\n", "\n" ] } ], "source": [ "# Check the schema.\n", "raw_dataset.printSchema()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After reading the dataset from storage, we will extract several metrics such as `nb_features`, which basically is the number of input neurons, and `nb_classes`, which is the number of classes (signal and background)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of features: 30\n", "Number of classes: 2\n" ] } ], "source": [ "nb_features = len(raw_dataset.select(\"features_normalized\").take(1)[0][\"features_normalized\"])\n", "nb_classes = len(raw_dataset.select(\"label\").take(1)[0][\"label\"])\n", "\n", "print(\"Number of features: \" + str(nb_features))\n", "print(\"Number of classes: \" + str(nb_classes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we split up the dataset for training and testing purposes, and fetch some additional statistics on the number of training and testing instances." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DataFrame[features_normalized: vector, label_index: double, label: vector]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finally, we create a trainingset and a testset.\n", "(training_set, test_set) = raw_dataset.randomSplit([0.7, 0.3])\n", "training_set.cache()\n", "test_set.cache()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of testset instances: 6377863\n", "Number of trainingset instances: 14872137\n", "Total number of instances: 21250000\n" ] } ], "source": [ "# Distribute the training and test set to the workers.\n", "test_set = test_set.repartition(num_workers)\n", "training_set = training_set.repartition(num_workers)\n", "\n", "num_test_set = test_set.count()\n", "num_training_set = training_set.count()\n", "\n", "print(\"Number of testset instances: \" + str(num_test_set))\n", "print(\"Number of trainingset instances: \" + str(num_training_set))\n", "print(\"Total number of instances: \" + str(num_test_set + num_training_set))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model construction" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(Dense(500, input_shape=(nb_features,)))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(0.4))\n", "model.add(Dense(500))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(0.6))\n", "model.add(Dense(500))\n", "model.add(Activation('relu'))\n", "model.add(Dense(nb_classes))\n", "model.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "dense_1 (Dense) (None, 500) 15500 dense_input_1[0][0] \n", "____________________________________________________________________________________________________\n", "activation_1 (Activation) (None, 500) 0 dense_1[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 500) 0 activation_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 500) 250500 dropout_1[0][0] \n", "____________________________________________________________________________________________________\n", "activation_2 (Activation) (None, 500) 0 dense_2[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 500) 0 activation_2[0][0] \n", "____________________________________________________________________________________________________\n", "dense_3 (Dense) (None, 500) 250500 dropout_2[0][0] \n", "____________________________________________________________________________________________________\n", "activation_3 (Activation) (None, 500) 0 dense_3[0][0] \n", "____________________________________________________________________________________________________\n", "dense_4 (Dense) (None, 2) 1002 activation_3[0][0] \n", "____________________________________________________________________________________________________\n", "activation_4 (Activation) (None, 2) 0 dense_4[0][0] \n", "====================================================================================================\n", "Total params: 517502\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "# Summarize the model.\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer = 'adagrad'\n", "loss = 'categorical_crossentropy'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model evaluation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evaluate(model):\n", " global test_set\n", "\n", " metric_name = \"f1\"\n", " evaluator = MulticlassClassificationEvaluator(metricName=metric_name, predictionCol=\"prediction_index\", labelCol=\"label_index\")\n", " # Clear the prediction column from the testset.\n", " test_set = test_set.select(\"features_normalized\", \"label\", \"label_index\")\n", " # Apply a prediction from a trained model.\n", " predictor = ModelPredictor(keras_model=trained_model, features_col=\"features_normalized\")\n", " test_set = predictor.predict(test_set)\n", " # Transform the prediction vector to an indexed label.\n", " index_transformer = LabelIndexTransformer(output_dim=nb_classes)\n", " test_set = index_transformer.transform(test_set)\n", " # Store the F1 score of the SingleTrainer.\n", " score = evaluator.evaluate(test_set)\n", " \n", " return score" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results = {}\n", "time_spent = {}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model training and evaluation\n", "\n", "In the next sections we train and evaluate the models trained by different (distributed) optimizers.\n", "\n", "### Single Trainer" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = SingleTrainer(keras_model=model, loss=loss, worker_optimizer=optimizer, \n", " features_col=\"features_normalized\", num_epoch=1, batch_size=64)\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time spent (SingleTrainer): 5927.329083919525 seconds.\n", "F1 (SingleTrainer): 0.839630118149035\n" ] } ], "source": [ "# Fetch the training time.\n", "dt = trainer.get_training_time()\n", "print(\"Time spent (SingleTrainer): \" + `dt` + \" seconds.\")\n", "\n", "# Evaluate the model.\n", "score = evaluate(trained_model)\n", "print(\"F1 (SingleTrainer): \" + `score`)\n", "\n", "# Store the training metrics.\n", "results['single'] = score\n", "time_spent['single'] = dt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asynchronous EASGD" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = AEASGD(keras_model=model, worker_optimizer=optimizer, loss=loss, num_workers=num_workers, batch_size=64,\n", " features_col=\"features_normalized\", num_epoch=1, communication_window=32, \n", " rho=5.0, learning_rate=0.1)\n", "trainer.set_parallelism_factor(1)\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time spent (AEASGD): 903.8733949661255 seconds.\n", "F1 (AEASGD): 0.8326362659335457\n" ] } ], "source": [ "# Fetch the training time.\n", "dt = trainer.get_training_time()\n", "print(\"Time spent (AEASGD): \" + `dt` + \" seconds.\")\n", "\n", "# Evaluate the model.\n", "score = evaluate(trained_model)\n", "print(\"F1 (AEASGD): \" + `score`)\n", "\n", "# Store the training metrics.\n", "results['aeasgd'] = score\n", "time_spent['aeasgd'] = dt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DOWNPOUR" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = DOWNPOUR(keras_model=model, worker_optimizer=optimizer, loss=loss, num_workers=num_workers,\n", " batch_size=64, communication_window=5, learning_rate=0.1, num_epoch=1,\n", " features_col=\"features_normalized\")\n", "trainer.set_parallelism_factor(1)\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time spent (DOWNPOUR): 774.4893491268158 seconds.\n", "F1 (DOWNPOUR): 0.8345395134754954\n" ] } ], "source": [ "# Fetch the training time.\n", "dt = trainer.get_training_time()\n", "print(\"Time spent (DOWNPOUR): \" + `dt` + \" seconds.\")\n", "\n", "# Evaluate the model.\n", "score = evaluate(trained_model)\n", "print(\"F1 (DOWNPOUR): \" + `score`)\n", "\n", "# Store the training metrics.\n", "results['downpour'] = score\n", "time_spent['downpour'] = dt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the plots below, the distributed optimizers finish a single epoch ~7 times however. However, for this, the distributed optimizers use 16 times the amount of resources. However, a not very descriptive measure since some of jobs are scheduled on the same machines, some machines have a higher load etc. Nevertheless, the statistical performance of the optimizers is within 1% error. Which means that the classifiers would have near-identical performance. Furthermore, it is our guess that the statistical performance of the distributed optimizers can be improved by adding adaptive learning rates." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd9fc0a1410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the time.\n", "fig = plt.figure()\n", "st = fig.suptitle(\"Lower is better.\", fontsize=\"x-small\")\n", "\n", "plt.bar(range(len(time_spent)), time_spent.values(), align='center')\n", "plt.xticks(range(len(time_spent)), time_spent.keys())\n", "plt.xlabel(\"Optimizers\")\n", "plt.ylabel(\"Seconds\")\n", "plt.ylim([0, 7000])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TG6Leju7+Wm/lnuf3DVM4rhpWVd9M8lO6oZ1nAm8DbqK70vg+dP8/jrzU+nSf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\nSAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd9d419ba50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the statistical performanc of the optimizers.\n", "fig = plt.figure()\n", "st = fig.suptitle(\"Higer is better.\", fontsize=\"x-small\")\n", "\n", "plt.bar(range(len(results)), results.values(), align='center')\n", "plt.xticks(range(len(results)), results.keys())\n", "plt.xlabel(\"Optimizers\")\n", "plt.ylabel(\"F1\")\n", "plt.ylim([0.83,0.85])\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0

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